



"""simple docstring"""






def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  if len(UpperCamelCase__    ) == 1:
    return a[0], a[0]
  else:
    # split A into half.
    _SCREAMING_SNAKE_CASE        =			a[: len(UpperCamelCase__    ) // 2]
    _SCREAMING_SNAKE_CASE        =			a[len(UpperCamelCase__    ) // 2 :]

    # 2 sub problems, 1/2 of original size.
    _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			max_difference(UpperCamelCase__    )
    _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			max_difference(UpperCamelCase__    )

    # get min of first and max of second
    # linear time
    _SCREAMING_SNAKE_CASE        =			min(UpperCamelCase__    )
    _SCREAMING_SNAKE_CASE        =			max(UpperCamelCase__    )

    # 3 cases, either (small1, big1),
    # (min_first, max_second), (small2, big2)
    # constant comparisons
    if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
      return smalla, biga
    elif biga - smalla > max_second - min_first:
      return smalla, biga
    else:
      return min_first, max_second


if __name__ == "__main__":
    import doctest

    doctest.testmod()





"""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)
