






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A:      List[Any]  				=				["small", "medium", "large"]

A:      int  				=				"lm_head.decoder.weight"
A:      Optional[Any]  				=				"lm_head.weight"






def 	_UpperCAmelCase    (  a		:	Union[str, Any]						,			a		:	int							)			->			Tuple:





						"""simple docstring"""
						lowercase_      :   List[str]        =			torch.load(lowerCamelCase_							)
						lowercase_      :   Tuple        =			d.pop(lowerCamelCase_							)
						os.makedirs(lowerCamelCase_						,			exist_ok=lowerCamelCase_							)
						torch.save(lowerCamelCase_						,			os.path.join(lowerCamelCase_						,			lowerCamelCase_							)							)


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








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			float:





						"""simple docstring"""

						def get_matched_characters(a		:	str						,			a		:	str							) -> str:
												lowercase_      :   Union[str, Any]        =			[]
												lowercase_      :   Tuple        =			min(len(_stra							)						,			len(_stra							)							) // 2
												for i, l in enumerate(_stra							):
																		lowercase_      :   Optional[int]        =			int(max(0						,			i - limit							)							)
																		lowercase_      :   Union[str, Any]        =			int(min(i + limit + 1						,			len(_stra							)							)							)
																		if l in _stra[left:right]:
																								matched.append(a							)
																								lowercase_      :   Union[str, Any]        =			f"{_stra[0:_stra.index(a							)]} {_stra[_stra.index(a							) + 1:]}"

												return "".join(a							)

						# matching characters
						lowercase_      :   Union[str, Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[int]        =			len(a							)

						# transposition
						lowercase_      :   Dict        =			(
						    len([(ca, ca) for ca, ca in zip(a						,			a							) if ca != ca]							) // 2
						)

						if not match_count:
												lowercase_      :   List[str]        =			0.0
						else:
												lowercase_      :   Any        =			(
												    1
												    / 3
												    * (
												        match_count / len(a							)
												        + match_count / len(a							)
												        + (match_count - transpositions) / match_count
												    )
												)

						# common prefix up to 4 characters
						lowercase_      :   Optional[Any]        =			0
						for ca, ca in zip(stra[:4]						,			stra[:4]							):
												if ca == ca:
																		prefix_len += 1
												else:
																		break

						return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler("hello", "world"))
