



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 		a__			(		_UpperCamelCase			: Optional[int] ,_UpperCamelCase			: Any ,_UpperCamelCase			: str							):
   # Initialise PyTorch model
   __lowerCamelCase													=    TaConfig.from_json_file(_UpperCamelCase							)
   print(F"""Building PyTorch model from configuration: {config}"""							)
   __lowerCamelCase													=    TaForConditionalGeneration(_UpperCamelCase							)

   # Load weights from tf checkpoint
   load_tf_weights_in_ta(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
   model.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
  a_			=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  a_			=					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)











def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str = " "							):
   __lowerCamelCase													=    []

   __lowerCamelCase													=    0
   for index, char in enumerate(_UpperCamelCase							):
      if char == separator:
         split_words.append(string[last_index:index]							)
         __lowerCamelCase													=    index + 1
      elif index + 1 == len(_UpperCamelCase							):
         split_words.append(string[last_index : index + 1]							)
   return split_words


if __name__ == "__main__":
  from doctest import testmod

  testmod()






