






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_a    :   Tuple        =			["""small""", """medium""", """large"""]

_a    :   Union[str, Any]        =			"""lm_head.decoder.weight"""
_a    :   Optional[Any]        =			"""lm_head.weight"""


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  str       ):
    lowerCAmelCase__   :Tuple      							=				torch.load(UpperCAmelCase       )
    lowerCAmelCase__   :Dict      							=				d.pop(UpperCAmelCase       )
    os.makedirs(UpperCAmelCase    ,			exist_ok=UpperCAmelCase       )
    torch.save(UpperCAmelCase    ,			os.path.join(UpperCAmelCase    ,			UpperCAmelCase       )       )


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











def 				snake_case__							(							UpperCAmelCase							:  Tuple       ):
    lowerCAmelCase__   :List[Any]      							=				len(UpperCAmelCase       )
    for i in range(length - 1       ):
        lowerCAmelCase__   :Union[str, Any]      							=				i
        for k in range(i + 1    ,			UpperCAmelCase       ):
            if collection[k] < collection[least]:
                lowerCAmelCase__   :Any      							=				k
        if least != i:
            lowerCAmelCase__							,lowerCAmelCase__   :int      							=				(collection[i], collection[least])
    return collection


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



