






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A:      Tuple  				=				{
    "configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Dict  				=				["ConvNextFeatureExtractor"]
						A:      Dict  				=				["ConvNextImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Dict  				=				[
						    "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvNextForImageClassification",
						    "ConvNextModel",
						    "ConvNextPreTrainedModel",
						    "ConvNextBackbone",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      List[str]  				=				[
						    "TFConvNextForImageClassification",
						    "TFConvNextModel",
						    "TFConvNextPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_convnext import ConvNextFeatureExtractor
												from .image_processing_convnext import ConvNextImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convnext import (
												    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvNextBackbone,
												    ConvNextForImageClassification,
												    ConvNextModel,
												    ConvNextPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
						import sys

						A:      Any  				=				_LazyModule(__name__, globals()["__file__"], _import_structure)








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			float:





						"""simple docstring"""

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

												return "".join(a							)

						# matching characters
						lowercase_      :   Union[str, Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[Any]        =			get_matched_characters(a						,			a							)
						lowercase_      :   Optional[int]        =			len(a							)

						# transposition
						lowercase_      :   Dict        =			(
						    len([(ca, ca) for ca, ca in zip(a						,			a							) if ca != ca]							) // 2
						)

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

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