




import cva
import numpy as np

def 	lowerCAmelCase_	(     lowerCamelCase:				np.ndarray   , lowerCamelCase:				int   , lowerCamelCase:				int   , lowerCamelCase:				int  )    ->	int:


      '''simple docstring'''




      try:
            return int(image[x_coordinate][y_coordinate] >= center  )
      except (IndexError, TypeError):
            return 0

def 	lowerCAmelCase_	(     lowerCamelCase:				np.ndarray   , lowerCamelCase:				int   , lowerCamelCase:				int  )    ->	int:


      '''simple docstring'''




      lowerCamelCase__					     = image[x_coordinate][y_coordinate]
      lowerCamelCase__					     = [1, 2, 4, 8, 16, 32, 64, 1_28]

      # skip get_neighbors_pixel if center is null
      if center is None:
            return 0

      # Starting from the top right, assigning value to pixels clockwise
      lowerCamelCase__					     = [
          get_neighbors_pixel(lowerCamelCase   , x_coordinate - 1   , y_coordinate + 1   , lowerCamelCase  ),
          get_neighbors_pixel(lowerCamelCase   , lowerCamelCase   , y_coordinate + 1   , lowerCamelCase  ),
          get_neighbors_pixel(lowerCamelCase   , x_coordinate - 1   , lowerCamelCase   , lowerCamelCase  ),
          get_neighbors_pixel(lowerCamelCase   , x_coordinate + 1   , y_coordinate + 1   , lowerCamelCase  ),
          get_neighbors_pixel(lowerCamelCase   , x_coordinate + 1   , lowerCamelCase   , lowerCamelCase  ),
          get_neighbors_pixel(lowerCamelCase   , x_coordinate + 1   , y_coordinate - 1   , lowerCamelCase  ),
          get_neighbors_pixel(lowerCamelCase   , lowerCamelCase   , y_coordinate - 1   , lowerCamelCase  ),
          get_neighbors_pixel(lowerCamelCase   , x_coordinate - 1   , y_coordinate - 1   , lowerCamelCase  ),
      ]

      # Converting the binary value to decimal.
      return sum(
          binary_value * power for binary_value, power in zip(lowerCamelCase   , lowerCamelCase  )  )


if __name__ == "__main__":
      # Reading the image and converting it to grayscale.
      __SCREAMING_SNAKE_CASE			:      Dict      			=cva.imread(
          """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
      )

      # Create a numpy array as the same height and width of read image
      __SCREAMING_SNAKE_CASE			:      str      			=np.zeros((image.shape[0], image.shape[1]))

      # Iterating through the image and calculating the
      # local binary pattern value for each pixel.
      for i in range(0, image.shape[0]):
            for j in range(0, image.shape[1]):
                  __SCREAMING_SNAKE_CASE			:      List[str]      			=local_binary_value(image, i, j)

      cva.imshow("""local binary pattern""", lbp_image)
      cva.waitKey(0)
      cva.destroyAllWindows()





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

           # set step values
           self.scheduler.set_timesteps(A__	)

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 lowerCamelCase__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = image.cpu().permute(0		,     2		,     3		,     1	).numpy()
           if output_type == "pil":
                 lowerCamelCase__					     = self.numpy_to_pil(A__	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)