import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__lowerCamelCase    :				str							     =	['''small''', '''medium''', '''large''']

__lowerCamelCase    :				Optional[int]							     =	'''lm_head.decoder.weight'''
__lowerCamelCase    :				int							     =	'''lm_head.weight'''



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: str			)							->      Dict:







		"""simple docstring"""





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

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list				,  __UpperCamelCase					: list				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  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()
