



"""simple docstring"""






import numpy as np
from PIL import Image


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  return (gray > 127) & (gray <= 255)


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			np.zeros_like(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

  # Copy image to padded image
  _SCREAMING_SNAKE_CASE        =			image

  # Iterate over image & apply kernel
  for x in range(image.shape[1]    ):
    for y in range(image.shape[0]    ):
      _SCREAMING_SNAKE_CASE        =			(
          kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
      ).sum()
      _SCREAMING_SNAKE_CASE        =			int(summation == 5    )
  return output


# kernel to be applied
lowerCamelCase				:     str								=  np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
    # read original image
    lowerCamelCase				:     Optional[int]								=  np.array(Image.open(r"""..\image_data\lena.jpg"""))
    # Apply erosion operation to a binary image
    lowerCamelCase				:     Optional[Any]								=  erosion(grayabinary(rgbagray(image)), structuring_element)
    # Save the output image
    lowerCamelCase				:     Dict								=  Image.fromarray(output).convert("""RGB""")
    pil_img.save("""result_erosion.png""")





"""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)
