


"""simple docstring"""
from typing import Union

import fire
import torch
from tqdm import tqdm







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = "cpu"			,	_SCREAMING_SNAKE_CASE = None			)      ->			None:
							snake_case_          =						torch.load(_SCREAMING_SNAKE_CASE			,	map_location=_SCREAMING_SNAKE_CASE			)
							for k, v in tqdm(state_dict.items()			):
														if not isinstance(_SCREAMING_SNAKE_CASE			,	torch.Tensor			):
																					raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""			)
														snake_case_          =						v.half()
							if save_path is None:  # overwrite src_path
														snake_case_          =						src_path
							torch.save(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				fire.Fire(convert)







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





print(fmt.format(*covid_stats()))



