






"""simple docstring"""





from typing import TYPE_CHECKING

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


__lowerCamelCase							=  {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCamelCase							=  [
							    'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'RobertaPreLayerNormForCausalLM',
							    'RobertaPreLayerNormForMaskedLM',
							    'RobertaPreLayerNormForMultipleChoice',
							    'RobertaPreLayerNormForQuestionAnswering',
							    'RobertaPreLayerNormForSequenceClassification',
							    'RobertaPreLayerNormForTokenClassification',
							    'RobertaPreLayerNormModel',
							    'RobertaPreLayerNormPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCamelCase							=  [
							    'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFRobertaPreLayerNormForCausalLM',
							    'TFRobertaPreLayerNormForMaskedLM',
							    'TFRobertaPreLayerNormForMultipleChoice',
							    'TFRobertaPreLayerNormForQuestionAnswering',
							    'TFRobertaPreLayerNormForSequenceClassification',
							    'TFRobertaPreLayerNormForTokenClassification',
							    'TFRobertaPreLayerNormMainLayer',
							    'TFRobertaPreLayerNormModel',
							    'TFRobertaPreLayerNormPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCamelCase							=  [
							    'FlaxRobertaPreLayerNormForCausalLM',
							    'FlaxRobertaPreLayerNormForMaskedLM',
							    'FlaxRobertaPreLayerNormForMultipleChoice',
							    'FlaxRobertaPreLayerNormForQuestionAnswering',
							    'FlaxRobertaPreLayerNormForSequenceClassification',
							    'FlaxRobertaPreLayerNormForTokenClassification',
							    'FlaxRobertaPreLayerNormModel',
							    'FlaxRobertaPreLayerNormPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

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





"""simple docstring"""




from __future__ import annotations

from math import pow, sqrt


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


if __name__ == "__main__":
   import doctest

   doctest.testmod()


