





import argparse

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


logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple )->						Dict:
  # Initialize PyTorch model
  _snake_case      : Optional[int]														=		CanineConfig()
  _snake_case      : str														=		CanineModel(lowerCAmelCase )
  model.eval()

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

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

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

  # Save tokenizer files
  _snake_case      : int														=		CanineTokenizer()
  print(F"""Save tokenizer files to {pytorch_dump_path}""" )
  tokenizer.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
      lowerCAmelCase_        =			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_        =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)











"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
