"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_a				:		Dict	   =       ['small', 'medium', 'large']

_a				:		Dict	   =       'lm_head.decoder.weight'
_a				:		Dict	   =       'lm_head.weight'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	str     )	->				Dict:
  _lowerCAmelCase						:	Dict        =					torch.load(snake_case_     )
  _lowerCAmelCase						:	Union[str, Any]        =					d.pop(snake_case_     )
  os.makedirs(snake_case_      ,exist_ok=snake_case_     )
  torch.save(snake_case_      ,os.path.join(snake_case_      ,snake_case_     )     )


if __name__ == "__main__":
     _a				:		Union[str, Any]	   =       argparse.ArgumentParser()
     parser.add_argument('--dialogpt_path', default='.', type=str)
     _a				:		Optional[Any]	   =       parser.parse_args()
     for MODEL in DIALOGPT_MODELS:
          _a				:		Optional[Any]	   =       os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
          _a				:		Dict	   =       F"""./DialoGPT-{MODEL}"""
          convert_dialogpt_checkpoint(
              checkpoint_path,
              pytorch_dump_folder_path,
          )





"""simple docstring"""




_a				:		Any	   =       '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a				:		List[Any]	   =       [{'type': 'code', 'content': INSTALL_CONTENT}]
_a				:		Union[str, Any]	   =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




