

def        lowerCamelCase__       (    _a							,	_a):

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

		return "".join(lowerCAmelCase_)

	# matching characters
	SCREAMING_SNAKE_CASE      :					int    =				get_matched_characters(lowerCAmelCase_							,	lowerCAmelCase_)
	SCREAMING_SNAKE_CASE      :					List[str]    =				get_matched_characters(lowerCAmelCase_							,	lowerCAmelCase_)
	SCREAMING_SNAKE_CASE      :					str    =				len(lowerCAmelCase_)

	# transposition
	SCREAMING_SNAKE_CASE      :					List[Any]    =				(
	    len([(ca, ca) for ca, ca in zip(lowerCAmelCase_							,	lowerCAmelCase_) if ca != ca]) // 2
	)

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

	# common prefix up to 4 characters
	SCREAMING_SNAKE_CASE      :					int    =				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'))



from typing import Union

import fire
import torch
from tqdm import tqdm



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


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