code: 
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      load_image,
      load_numpy,
      nightly,
      require_torch_gpu,
      skip_mps,
      torch_device,
  )
  
  from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  class 		_snake_case      (			lowerCamelCase					,unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			RePaintPipeline
   lowerCamelCase_          =			IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
   lowerCamelCase_          =			PipelineTesterMixin.required_optional_params - {
       '''latents''',
       '''num_images_per_prompt''',
       '''callback''',
       '''callback_steps''',
   }
   lowerCamelCase_          =			IMAGE_INPAINTING_BATCH_PARAMS
   lowerCamelCase_          =			False
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     torch.manual_seed(0					)
     torch.manual_seed(0					)
     _A           =    UNetaDModel(
         block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    sample_size=3_2	,    in_channels=3	,    out_channels=3	,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,    )
     _A           =    RePaintScheduler()
     _A           =    {'''unet''': unet, '''scheduler''': scheduler}
     return components
   def lowercase_		(						self	,    a	,    a=0					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if str(a					).startswith('''mps'''					):
       _A           =    torch.manual_seed(a					)
     else:
       _A           =    torch.Generator(device=a					).manual_seed(a					)
     _A           =    np.random.RandomState(a					).standard_normal((1, 3, 3_2, 3_2)					)
     _A           =    torch.from_numpy(a					).to(device=a	,    dtype=torch.floataa					)
     _A           =    (image > 0).to(device=a	,    dtype=torch.floataa					)
     _A           =    {
         '''image''': image,
         '''mask_image''': mask,
         '''generator''': generator,
         '''num_inference_steps''': 5,
         '''eta''': 0.0,
         '''jump_length''': 2,
         '''jump_n_sample''': 2,
         '''output_type''': '''numpy''',
     }
     return inputs
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     _A           =    self.get_dummy_components()
     _A           =    RePaintPipeline(**a					)
     _A           =    sd_pipe.to(a					)
     sd_pipe.set_progress_bar_config(disable=a					)
  
     _A           =    self.get_dummy_inputs(a					)
     _A           =    sd_pipe(**a					).images
     _A           =    image[0, -3:, -3:, -1]
  
     assert image.shape == (1, 3_2, 3_2, 3)
     _A           =    np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]					)
  
     assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
   @skip_mps
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return super().test_save_load_local()
   @unittest.skip('''non-deterministic pipeline'''					)
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return super().test_inference_batch_single_identical()
   @skip_mps
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return super().test_dict_tuple_outputs_equivalent()
   @skip_mps
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return super().test_save_load_optional_components()
  
  
  
   @skip_mps
   def lowercase_		(						self					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return super().test_attention_slicing_forward_pass()
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 		_snake_case      (			unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
  
  
  
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
         '''repaint/celeba_hq_256.png'''					)
     _A           =    load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''					)
     _A           =    load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
         '''repaint/celeba_hq_256_result.npy'''					)
  
     _A           =    '''google/ddpm-ema-celebahq-256'''
     _A           =    UNetaDModel.from_pretrained(a					)
     _A           =    RePaintScheduler.from_pretrained(a					)
  
     _A           =    RePaintPipeline(unet=a	,    scheduler=a					).to(a					)
     repaint.set_progress_bar_config(disable=a					)
     repaint.enable_attention_slicing()
  
     _A           =    torch.manual_seed(0					)
     _A           =    repaint(
         a	,    a	,    num_inference_steps=2_5_0	,    eta=0.0	,    jump_length=1_0	,    jump_n_sample=1_0	,    generator=a	,    output_type='''np'''	,    )
     _A           =    output.images[0]
  
     assert image.shape == (2_5_6, 2_5_6, 3)
     assert np.abs(expected_image - image					).mean() < 1e-2
style_context: 
  
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  __lowerCamelCase  				=					1_8_0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Dict:
    _A           =    k_size // 2
    _A    ,   _A           =    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _A           =    (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(__snake_case							) + np.square(__snake_case							)) / (2 * np.square(__snake_case							))							)
    )
    return g
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _A           =    np.zeros(__snake_case							)
  
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        _A           =    gradient_direction[row, col]
  
        if (
            0 <= direction < PI / 8
            or 15 * PI / 8 <= direction <= 2 * PI
            or 7 * PI / 8 <= direction <= 9 * PI / 8
        ):
          _A           =    sobel_grad[row, col - 1]
          _A           =    sobel_grad[row, col + 1]
          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
            _A           =    sobel_grad[row, col]
  
        elif (
            PI / 8 <= direction < 3 * PI / 8
            or 9 * PI / 8 <= direction < 11 * PI / 8
        ):
          _A           =    sobel_grad[row + 1, col - 1]
          _A           =    sobel_grad[row - 1, col + 1]
          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
            _A           =    sobel_grad[row, col]
  
        elif (
            3 * PI / 8 <= direction < 5 * PI / 8
            or 11 * PI / 8 <= direction < 13 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col]
          _A           =    sobel_grad[row + 1, col]
          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
            _A           =    sobel_grad[row, col]
  
        elif (
            5 * PI / 8 <= direction < 7 * PI / 8
            or 13 * PI / 8 <= direction < 15 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col - 1]
          _A           =    sobel_grad[row + 1, col + 1]
          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
            _A           =    sobel_grad[row, col]
  
    return destination
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Optional[int]:
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        if destination[row, col] >= threshold_high:
          _A           =    strong
        elif destination[row, col] <= threshold_low:
          _A           =    0
        else:
          _A           =    weak
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> List[Any]:
    for row in range(1						,				image_shape[0]							):
      for col in range(1						,				image_shape[1]							):
        if destination[row, col] == weak:
          if 255 in (
              destination[row, col + 1],
              destination[row, col - 1],
              destination[row - 1, col],
              destination[row + 1, col],
              destination[row - 1, col - 1],
              destination[row + 1, col - 1],
              destination[row - 1, col + 1],
              destination[row + 1, col + 1],
          ):
            _A           =    strong
          else:
            _A           =    0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=15						,				__snake_case=30						,				__snake_case=128						,				__snake_case=255							)   -> List[Any]:
    # gaussian_filter
    _A           =    img_convolve(__snake_case						,				gen_gaussian_kernel(9						,				sigma=1.4							)							)
    # get the gradient and degree by sobel_filter
    _A    ,   _A           =    sobel_filter(__snake_case							)
    _A           =    PI + np.radadeg(__snake_case							)
  
    _A           =    suppress_non_maximum(image.shape						,				__snake_case						,				__snake_case							)
  
    detect_high_low_threshold(
        image.shape						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    track_edge(image.shape						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      __lowerCamelCase  				=					cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      __lowerCamelCase  				=					canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)