





import argparse

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


logging.set_verbosity_info()



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		List[str]			,       a_					:		str							)      ->				Union[str, Any]:
      # Initialise PyTorch model
      __SCREAMING_SNAKE_CASE       :Dict		    =  TaConfig.from_json_file(a_							)
      print(f'''Building PyTorch model from configuration: {config}'''							)
      __SCREAMING_SNAKE_CASE       :int		    =  TaForConditionalGeneration(a_							)

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

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
      model.save_pretrained(a_							)


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 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."
  )
  lowerCamelCase_       =		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str							)      ->				list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(a_							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
  __import__("doctest").testmod()