code: import cva
  import numpy as np
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->int:
  
    '''simple docstring'''
  
  
  
  
    try:
      return int(image[x_coordinate][y_coordinate] >= center					)
    except (IndexError, TypeError):
      return 0
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->int:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     image[x_coordinate][y_coordinate]
    a__     		=     [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
    a__     		=     [
        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   						=    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   						=    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   						=    local_binary_value(image, i, j)
  
       cva.imshow("""local binary pattern""", lbp_image)
       cva.waitKey(0)
       cva.destroyAllWindows()
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  