

def a(lowercase__			, lowercase__	):





	'''simple docstring'''

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

		return "".join(lowercase__	)

	# matching characters
	snake_case_      		= get_matched_characters(lowercase__			, lowercase__	)
	snake_case_      		= get_matched_characters(lowercase__			, lowercase__	)
	snake_case_      		= len(lowercase__	)

	# transposition
	snake_case_      		= (
	    len([(ca, ca) for ca, ca in zip(lowercase__			, lowercase__	) if ca != ca]	) // 2
	)

	if not match_count:
		snake_case_      		= 0.0
	else:
		snake_case_      		= (
		    1
		    / 3
		    * (
		        match_count / len(lowercase__	)
		        + match_count / len(lowercase__	)
		        + (match_count - transpositions) / match_count
		    )
		)

	# common prefix up to 4 characters
	snake_case_      		= 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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


A											=     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
