import argparse

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


logging.set_verbosity_info()




def 				UpperCamelCase						(       __lowercase						: List[Any]						,__lowercase						: Dict						,__lowercase						: Any	):
		'''simple docstring'''




		A_		:				Any     =      TaConfig.from_json_file(_SCREAMING_SNAKE_CASE	)
		print(f'''Building PyTorch model from configuration: {config}'''	)
		A_		:				str     =      TaForConditionalGeneration(_SCREAMING_SNAKE_CASE	)

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

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


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












import argparse

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


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

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

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   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.'
       )
       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






