"""simple docstring"""






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   int:
				if index == number_of_items:
								return 0
				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				knapsack(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					index + 1       )
				if weights[index] <= max_weight:
								SCREAMING_SNAKE_CASE__		  =				values[index] + knapsack(
								    __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					max_weight - weights[index]  ,					index + 1       )
				return max(__UpperCAmelCase  ,					__UpperCAmelCase       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


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




