



def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str						):







		'''simple docstring'''





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

				return "".join(_UpperCAmelCase						)

		# matching characters
		__UpperCAmelCase    :     Optional[Any]													=	get_matched_characters(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     Any													=	get_matched_characters(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     Dict													=	len(_UpperCAmelCase						)

		# transposition
		__UpperCAmelCase    :     Any													=	(
		    len([(ca, ca) for ca, ca in zip(_UpperCAmelCase ,   _UpperCAmelCase						) if ca != ca]						) // 2
		)

		if not match_count:
				__UpperCAmelCase    :     List[Any]													=	0.0
		else:
				__UpperCAmelCase    :     Dict													=	(
				    1
				    / 3
				    * (
				        match_count / len(_UpperCAmelCase						)
				        + match_count / len(_UpperCAmelCase						)
				        + (match_count - transpositions) / match_count
				    )
				)

		# common prefix up to 4 characters
		__UpperCAmelCase    :     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"))










import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
				import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





