





'''simple docstring'''
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  str				,  __lowercase						:  Optional[Any]     ) ->      List[Any]:



       """simple docstring"""






       if openai_config_file == "":
              __UpperCamelCase       =  OpenAIGPTConfig()
       else:
              __UpperCamelCase       =  OpenAIGPTConfig.from_json_file(UpperCamelCase_     )
       __UpperCamelCase       =  OpenAIGPTModel(UpperCamelCase_     )

       # Load weights from numpy
       load_tf_weights_in_openai_gpt(UpperCamelCase_				,  UpperCamelCase_				,  UpperCamelCase_     )

       # Save pytorch-model
       __UpperCamelCase       =  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
       __UpperCamelCase       =  pytorch_dump_folder_path + '/' + CONFIG_NAME
       print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''     )
       torch.save(model.state_dict()				,  UpperCamelCase_     )
       print(F'''Save configuration file to {pytorch_config_dump_path}'''     )
       with open(UpperCamelCase_				,  'w'				,  encoding='utf-8'     ) as f:
              f.write(config.to_json_string()     )


if __name__ == "__main__":
    a__							:							Any =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--openai_checkpoint_folder_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(
        '''--openai_config_file''',
        default='''''',
        type=str,
        help=(
            '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    a__							:							Tuple =parser.parse_args()
    convert_openai_checkpoint_to_pytorch(
        args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
    )



"""simple docstring"""





import baseaa




def   _A  (  UpperCamelCase_     :       str)	->						bytes:
   '''simple docstring'''


   return baseaa.baaencode(string.encode("utf-8"))




def   _A  (  UpperCamelCase_     :       bytes)	->						str:
   '''simple docstring'''


   return baseaa.baadecode(UpperCamelCase_).decode("utf-8")


if __name__ == "__main__":
   _a       =							'Hello World!'
   _a       =							baseaa_encode(test)
   print(encoded)

   _a       =							baseaa_decode(encoded)
   print(decoded)




