import cva
import numpy as np

def      lowercase_							(     _lowerCamelCase				:			np.ndarray						,      _lowerCamelCase				:			int						,      _lowerCamelCase				:			int						,      _lowerCamelCase				:			int):

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

def      lowercase_							(     _lowerCamelCase				:			np.ndarray						,      _lowerCamelCase				:			int						,      _lowerCamelCase				:			int):
    lowercase__					:  Optional[Any]          =							image[x_coordinate][y_coordinate]
    lowercase__					:  Union[str, Any]          =							[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
    lowercase__					:  Dict          =							[
        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.
      UpperCamelCase    				=		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    				=		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    				=		local_binary_value(image, i, j)

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase    				=		{'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase    				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



