

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Any  	=					(VQDiffusionScheduler,)

  def   lowerCAmelCase__       (   self  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  {
          'num_vec_classes': 4097,
          'num_train_timesteps': 100,
      }

      config.update(**_A)
      return config

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  4
      SCREAMING_SNAKE_CASE_													=  8
      SCREAMING_SNAKE_CASE_													=  8

      SCREAMING_SNAKE_CASE_													=  torch.randint(0  ,						_A  ,						(batch_size, height * width))

      return sample

  @property
  def   lowerCAmelCase__       (   self):
      assert False

  def   lowerCAmelCase__       (   self  ,						_A):
      def model(_A  ,						_A  ,						*_A):
          SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  sample.shape
          SCREAMING_SNAKE_CASE_													=  torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))
          SCREAMING_SNAKE_CASE_													=  F.log_softmax(logits.double()  ,						dim=1).float()
          return return_value

      return model

  def   lowerCAmelCase__       (   self):
      for timesteps in [2, 5, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def   lowerCAmelCase__       (   self):
      for num_vec_classes in [5, 100, 1000, 4000]:
          self.check_over_configs(num_vec_classes=_A)

  def   lowerCAmelCase__       (   self):
      for t in [0, 50, 99]:
          self.check_over_forward(time_step=_A)


  def   lowerCAmelCase__       (   self):
      pass





import cva
import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  image[x_coordinate][y_coordinate]
    SCREAMING_SNAKE_CASE_													=  [1, 2, 4, 8, 16, 32, 64, 128]

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

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

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


if __name__ == "__main__":
       # Reading the image and converting it to grayscale.
       UpperCamelCase__					: Optional[int]								=      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
       UpperCamelCase__					: Dict								=      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]):
                     UpperCamelCase__					: Any								=      local_binary_value(image, i, j)

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


