import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

_UpperCAmelCase						=		'lm_head.decoder.weight'
_UpperCAmelCase						=		'lm_head.weight'




def 				UpperCamelCase						(       __lowercase						: int						,__lowercase						: str	):
		'''simple docstring'''




		A_		:				Optional[int]     =      torch.load(a_	)
		A_		:				Dict     =      d.pop(a_	)
		os.makedirs(a_						,exist_ok=a_	)
		torch.save(a_						,os.path.join(a_						,a_	)	)


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							:List[Any]							 =				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[str]							 =				['BartphoTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
