


import requests
from bsa import BeautifulSoup







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       BeautifulSoup(requests.get(__lowerCamelCase							,     params=__lowerCamelCase     ).content							,     "html.parser"     )
							__snake_case		:      Tuple										=       soup.find("div"							,     attrs={"class": "gs_ri"}     )
							__snake_case		:      Optional[Any]										=       div.find("div"							,     attrs={"class": "gs_fl"}     ).find_all("a"     )
							return anchors[2].get_text()


if __name__ == "__main__":
			_snake_case					:		Union[str, Any]      =    {
			    "title": (
			        "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
			        "capacitance, volumetric capacitance, and energy density"
			    ),
			    "journal": "Chem. Mater.",
			    "volume": 30,
			    "pages": "3979-3990",
			    "year": 2_018,
			    "hl": "en",
			}
			print(get_citation("https://scholar.google.com/scholar_lookup", params=params))





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_snake_case					:		Union[str, Any]      =    ["small", "medium", "large"]

_snake_case					:		List[Any]      =    "lm_head.decoder.weight"
_snake_case					:		Optional[Any]      =    "lm_head.weight"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase     )
							__snake_case		:      Dict										=       d.pop(__lowerCamelCase     )
							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							torch.save(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )


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

