






import argparse

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


logging.set_verbosity_info()



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							TaConfig.from_json_file(lowerCAmelCase__	)
		print(F"""Building PyTorch model from configuration: {config}"""	)
		__A    =							TaForConditionalGeneration(lowerCAmelCase__	)

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

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


if __name__ == "__main__":
					snake_case_   :	Optional[int]							      =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.'''
					)
					snake_case_   :	List[str]							      =parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						float:





       '''simple docstring'''


       return a + b


if __name__ == "__main__":
   A_				:					Tuple							=      5
   A_				:					Tuple							=      6
   print(f"""The sum of {a} + {b} is {add(a, b)}""")



