



"""simple docstring"""






import cva
import numpy as np


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			image[x_coordinate][y_coordinate]
  _SCREAMING_SNAKE_CASE        =			[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
  _SCREAMING_SNAKE_CASE        =			[
      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.
    lowerCamelCase				:     List[str]								=  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				:     Any								=  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				:     List[Any]								=  local_binary_value(image, i, j)

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





"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}'''    )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    )

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(UpperCamelCase__    )

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to a folder where the PyTorch model will be placed.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
