



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Optional[Any]:



    """simple docstring"""






    if gpta_config_file == "":
        A :       Any          =   GPTaConfig()
    else:
        A :       Dict          =   GPTaConfig.from_json_file(_lowerCAmelCase		)
    A :       Union[str, Any]          =   GPTaModel(_lowerCAmelCase		)

    # Load weights from numpy
    load_tf_weights_in_gpta(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)

    # Save pytorch-model
    A :       Optional[Any]          =   pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
    A :       int          =   pytorch_dump_folder_path + """/""" + CONFIG_NAME
    print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''		)
    torch.save(model.state_dict()      ,     _lowerCAmelCase		)
    print(f'''Save configuration file to {pytorch_config_dump_path}'''		)
    with open(_lowerCAmelCase      ,     """w"""      ,     encoding="""utf-8"""		) as f:
        f.write(config.to_json_string()		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[Any]             =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--gpt2_config_file""",
         default="""""",
         type=str,
         help=(
             """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
             """This specifies the model architecture."""
         ),
     )
     SCREAMING_SNAKE_CASE_:Any             =						parser.parse_args()
     convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)







def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  int:



    """simple docstring"""






    while second != 0:
        A :       int          =   first & second
        first ^= second
        A :       Tuple          =   c << 1
    return first


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_:int             =						int(input("""Enter the first number: """).strip())
     SCREAMING_SNAKE_CASE_:Optional[int]             =						int(input("""Enter the second number: """).strip())
     print(F"""{add(first, second) = }""")


