



"""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__   (		SCREAMING_SNAKE_CASE__			:	Union[str, Any]					,     SCREAMING_SNAKE_CASE__			:	Any      ):



					"""simple docstring"""





					snake_case_       :		Optional[Any]			       =       torch.load(SCREAMING_SNAKE_CASE__      )
					snake_case_       :		List[str]			       =       d.pop(SCREAMING_SNAKE_CASE__      )
					os.makedirs(SCREAMING_SNAKE_CASE__					,     exist_ok=SCREAMING_SNAKE_CASE__      )
					torch.save(SCREAMING_SNAKE_CASE__					,     os.path.join(SCREAMING_SNAKE_CASE__					,     SCREAMING_SNAKE_CASE__      )      )


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,
						)








'''simple docstring'''






def    _snake_case  (	A					,       A			)		->       int:
							return x if y == 0 else greatest_common_divisor(A					,       x % y			)


def    _snake_case  (	A					,       A			)		->       int:
							return (x * y) // greatest_common_divisor(A					,       A			)


def    _snake_case  (	A = 20			)		->       int:
							lowerCAmelCase__			  =      1
							for i in range(1					,       n + 1			):
														lowerCAmelCase__			  =      lcm(A					,       A			)
							return g


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