




"""simple docstring"""







def 							lowercase					(snake_case__   : str      ,       snake_case__   : str )     ->  float:


				'''simple docstring'''






				def get_matched_characters(snake_case__   : str      ,       snake_case__   : 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(snake_case__ )
																lowerCAmelCase  =				f'''{_stra[0:_stra.index(snake_case__ )]} {_stra[_stra.index(snake_case__ ) + 1:]}'''

								return "".join(snake_case__ )

				# matching characters
				lowerCAmelCase  =				get_matched_characters(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				get_matched_characters(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				len(snake_case__ )

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

				if not match_count:
								lowerCAmelCase  =				0.0
				else:
								lowerCAmelCase  =				(
								    1
								    / 3
								    * (
								        match_count / len(snake_case__ )
								        + match_count / len(snake_case__ )
								        + (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'))











"""simple docstring"""







from typing import TYPE_CHECKING

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


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

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





