




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__              =						{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__              =						['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__              =						['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__              =						[
							    '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

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








"""simple docstring"""







import cva
import numpy as np

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """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(_snake_case      ,       x_coordinate - 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       y_coordinate - 1      ,       _snake_case							),
      ]

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


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()



