





'''simple docstring'''

import cva
import numpy as np







def       a__       (       lowercase      :  np.ndarray,      lowercase      :  int,      lowercase      :  int,      lowercase      :  int   )	->	int:





     """simple docstring"""

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







def       a__       (       lowercase      :  np.ndarray,      lowercase      :  int,      lowercase      :  int   )	->	int:





     """simple docstring"""

     _UpperCamelCase       					= image[x_coordinate][y_coordinate]
     _UpperCamelCase       					= [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
     _UpperCamelCase       					= [
         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__		:    Optional[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
   lowerCamelCase__		:    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]):
         lowerCamelCase__		:    Tuple =       local_binary_value(image, i, j)

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










'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__		:    Union[str, Any] =       {
    'configuration_retribert': ['RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RetriBertConfig'],
    'tokenization_retribert': ['RetriBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:    Dict =       ['RetriBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:    Tuple =       [
       'RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RetriBertModel',
       'RetriBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
   from .tokenization_retribert import RetriBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_retribert_fast import RetriBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_retribert import (
          RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RetriBertModel,
          RetriBertPreTrainedModel,
      )

else:
   import sys

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



