


import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

lowerCAmelCase	=							'lm_head.decoder.weight'
lowerCAmelCase	=							'lm_head.weight'
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				torch.load(SCREAMING_SNAKE_CASE					)
		lowercase__						=				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__":
			lowerCAmelCase	=							argparse.ArgumentParser()
			parser.add_argument('--dialogpt_path', default='.', type=str)
			lowerCAmelCase	=							parser.parse_args()
			for MODEL in DIALOGPT_MODELS:
						lowerCAmelCase	=							os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
						lowerCAmelCase	=							f"""./DialoGPT-{MODEL}"""
						convert_dialogpt_checkpoint(
						    checkpoint_path,
						    pytorch_dump_folder_path,
						)






def       _a			(				SCREAMING_SNAKE_CASE = 10_00					):


		"""simple docstring"""




		return sum(e for e in range(3						,    SCREAMING_SNAKE_CASE					) if e % 3 == 0 or e % 5 == 0					)


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


