




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_				:    int										=     ['''small''', '''medium''', '''large''']

a_				:    Any										=     '''lm_head.decoder.weight'''
a_				:    List[str]										=     '''lm_head.weight'''





def 						UpperCAmelCase				(			A__:    str							,				A__:    str     )  ->     Tuple:
			__lowerCamelCase					:	Dict	       =				torch.load(A__     )
			__lowerCamelCase					:	int	       =				d.pop(A__     )
			os.makedirs(A__							,				exist_ok=A__     )
			torch.save(A__							,				os.path.join(A__							,				A__     )     )


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









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



