



from __future__ import annotations

from math import pow, sqrt





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    if (resistance, reactance, impedance).count(0    ) != 1:
        raise ValueError('''One and only one argument must be 0'''    )
    if resistance == 0:
        return {"resistance": sqrt(pow(_UpperCAmelCase       ,			2    ) - pow(_UpperCAmelCase       ,			2    )    )}
    elif reactance == 0:
        return {"reactance": sqrt(pow(_UpperCAmelCase       ,			2    ) - pow(_UpperCAmelCase       ,			2    )    )}
    elif impedance == 0:
        return {"impedance": sqrt(pow(_UpperCAmelCase       ,			2    ) + pow(_UpperCAmelCase       ,			2    )    )}
    else:
        raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						:Tuple						=					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Optional[int]						=					[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 __snake_case						:Dict						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





