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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							torch.load(UpperCamelCase__				)
							A__	     =							d.pop(UpperCamelCase__				)
							os.makedirs(UpperCamelCase__       ,    exist_ok=UpperCamelCase__				)
							torch.save(UpperCamelCase__       ,    os.path.join(UpperCamelCase__       ,    UpperCamelCase__				)				)


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,
								)






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__     ,  A__	     =							[], []
							while len(UpperCamelCase__				) > 1:
														A__     ,  A__	     =							min(UpperCamelCase__				), max(UpperCamelCase__				)
														start.append(UpperCamelCase__				)
														end.append(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				__lowerCamelCase											=  [int(item) for item in user_input.split(",")]
				print(*merge_sort(unsorted), sep=",")





