



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




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)