
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

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

            return "".join(UpperCamelCase__		)

      # matching characters
      UpperCamelCase__    :   int					=				get_matched_characters(UpperCamelCase__ ,		UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				get_matched_characters(UpperCamelCase__ ,		UpperCamelCase__		)
      UpperCamelCase__    :   List[str]					=				len(UpperCamelCase__		)

      # transposition
      UpperCamelCase__    :   Tuple					=				(
          len([(ca, ca) for ca, ca in zip(UpperCamelCase__ ,		UpperCamelCase__		) if ca != ca]		) // 2
      )

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

      # common prefix up to 4 characters
      UpperCamelCase__    :   Optional[int]					=				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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase      ={
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =["BlenderbotTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BlenderbotForCausalLM",
        "BlenderbotForConditionalGeneration",
        "BlenderbotModel",
        "BlenderbotPreTrainedModel",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TFBlenderbotForConditionalGeneration",
        "TFBlenderbotModel",
        "TFBlenderbotPreTrainedModel",
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "FlaxBlenderbotForConditionalGeneration",
        "FlaxBlenderbotModel",
        "FlaxBlenderbotPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_blenderbot import (
        BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BlenderbotConfig,
        BlenderbotOnnxConfig,
    )
    from .tokenization_blenderbot import BlenderbotTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blenderbot import (
            BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlenderbotForCausalLM,
            BlenderbotForConditionalGeneration,
            BlenderbotModel,
            BlenderbotPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blenderbot import (
            TFBlenderbotForConditionalGeneration,
            TFBlenderbotModel,
            TFBlenderbotPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_blenderbot import (
            FlaxBlenderbotForConditionalGeneration,
            FlaxBlenderbotModel,
            FlaxBlenderbotPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


