code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase_   =   {
      "configuration_mobilenet_v1": [
          "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileNetV1Config",
          "MobileNetV1OnnxConfig",
      ],
  }
  
  try:
     if not is_vision_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   ["MobileNetV1FeatureExtractor"]
     lowerCAmelCase_   =   ["MobileNetV1ImageProcessor"]
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     lowerCAmelCase_   =   [
         "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MobileNetV1ForImageClassification",
         "MobileNetV1Model",
         "MobileNetV1PreTrainedModel",
         "load_tf_weights_in_mobilenet_v1",
     ]
  
  
  if TYPE_CHECKING:
     from .configuration_mobilenet_va import (
         MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileNetVaConfig,
         MobileNetVaOnnxConfig,
     )
  
     try:
        if not is_vision_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )
  
  
  else:
     import sys
  
     lowerCAmelCase_   =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import cva
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
  
         try:
                return int(image[x_coordinate][y_coordinate] >= center  )
         except (IndexError, TypeError):
                return 0
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
         _snake_case      :							List[str]     				=						[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
         _snake_case      :							Any     				=						[
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
         ]
  
         # Converting the binary value to decimal.
         return sum(
             binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )
  
  
  if __name__ == "__main__":
     # Reading the image and converting it to grayscale.
     lowerCAmelCase_   =   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
     lowerCAmelCase_   =   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]):
           lowerCAmelCase_   =   local_binary_value(image, i, j)
  
     cva.imshow("local binary pattern", lbp_image)
     cva.waitKey(0)
     cva.destroyAllWindows()
  
  
  
  
  
  
  