
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE							:Optional[Any]							 =				['small', 'medium', 'large']

SCREAMING_SNAKE_CASE							:Any							 =				'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE							:Any							 =				'lm_head.weight'


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= torch.load(a_						)
				__A   			= d.pop(a_						)
				os.makedirs(a_		,   exist_ok=a_						)
				torch.save(a_		,   os.path.join(a_		,   a_						)						)


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


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return lst

				__A   			= 1

				while i < len(a_						):
								if lst[i - 1] <= lst[i]:
												i += 1
								else:
												__A			,							__A   			= lst[i], lst[i - 1]
												i -= 1
												if i == 0:
																__A   			= 1

				return lst


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:List[Any]							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:List[Any]							 =				[int(item) for item in user_input.split(',')]
					print(gnome_sort(unsorted))
