





from typing import TYPE_CHECKING

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


A__:    Optional[Any]  	=	{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[Any]  	=	[
       '''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:
   A__:    Tuple  	=	[
       '''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:
   A__:    Dict  	=	[
       '''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

   A__:    Union[str, Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








from __future__ import annotations

from collections import namedtuple



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	List[str]					=     namedtuple("result"    ,"name value")
    if (voltage, current, power).count(0) != 1:
        raise ValueError("Only one argument must be 0")
    elif power < 0:
        raise ValueError(
            "Power cannot be negative in any electrical/electronics system")
    elif voltage == 0:
        return result("voltage"    ,power / current)
    elif current == 0:
        return result("current"    ,power / voltage)
    elif power == 0:
        return result("power"    ,float(round(abs(voltage * current)    ,2)))
    else:
        raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
   import doctest

   doctest.testmod()

