




import argparse

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


logging.set_verbosity_info()




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Tuple       ,     __UpperCamelCase			:						Any       ,     __UpperCamelCase			:						List[Any]      )  ->							List[str]:
    # Initialise PyTorch model
    UpperCAmelCase_							 =     TaConfig.from_json_file(__UpperCamelCase      )
    print(f'Building PyTorch model from configuration: {config}'      )
    UpperCAmelCase_							 =     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__":
 _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)











def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						float      )  ->							float:
    return a + b


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





