




import cva
import numpy as np


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





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


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  _lowercase:			int      =     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:			Optional[Any]      =     [
      get_neighbors_pixel(UpperCamelCase       ,  x_coordinate - 1       ,  y_coordinate + 1       ,  UpperCamelCase				),
      get_neighbors_pixel(UpperCamelCase       ,  UpperCamelCase       ,  y_coordinate + 1       ,  UpperCamelCase				),
      get_neighbors_pixel(UpperCamelCase       ,  x_coordinate - 1       ,  UpperCamelCase       ,  UpperCamelCase				),
      get_neighbors_pixel(UpperCamelCase       ,  x_coordinate + 1       ,  y_coordinate + 1       ,  UpperCamelCase				),
      get_neighbors_pixel(UpperCamelCase       ,  x_coordinate + 1       ,  UpperCamelCase       ,  UpperCamelCase				),
      get_neighbors_pixel(UpperCamelCase       ,  x_coordinate + 1       ,  y_coordinate - 1       ,  UpperCamelCase				),
      get_neighbors_pixel(UpperCamelCase       ,  UpperCamelCase       ,  y_coordinate - 1       ,  UpperCamelCase				),
      get_neighbors_pixel(UpperCamelCase       ,  x_coordinate - 1       ,  y_coordinate - 1       ,  UpperCamelCase				),
  ]

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


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

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






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__    :							List[Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
