
def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      str       )  ->    float:

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

														return "".join(UpperCAmelCase__       )

							# matching characters
							lowerCamelCase_							  =					get_matched_characters(UpperCAmelCase__			,				UpperCAmelCase__       )
							lowerCamelCase_							  =					get_matched_characters(UpperCAmelCase__			,				UpperCAmelCase__       )
							lowerCamelCase_							  =					len(UpperCAmelCase__       )

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

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

							# common prefix up to 4 characters
							lowerCamelCase_							  =					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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase        =  {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''DeiTFeatureExtractor''']
		lowercase        =  ['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





