


"""simple docstring"""


from __future__ import annotations

from math import pow, sqrt







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		dict[str, float]:







		"""simple docstring"""





		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()









"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase						:				Dict             =      {
    "configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Any             =      [
	    "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MegatronBertForCausalLM",
	    "MegatronBertForMaskedLM",
	    "MegatronBertForMultipleChoice",
	    "MegatronBertForNextSentencePrediction",
	    "MegatronBertForPreTraining",
	    "MegatronBertForQuestionAnswering",
	    "MegatronBertForSequenceClassification",
	    "MegatronBertForTokenClassification",
	    "MegatronBertModel",
	    "MegatronBertPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_megatron_bert import (
		    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MegatronBertForCausalLM,
		    MegatronBertForMaskedLM,
		    MegatronBertForMultipleChoice,
		    MegatronBertForNextSentencePrediction,
		    MegatronBertForPreTraining,
		    MegatronBertForQuestionAnswering,
		    MegatronBertForSequenceClassification,
		    MegatronBertForTokenClassification,
		    MegatronBertModel,
		    MegatronBertPreTrainedModel,
		)

else:
	import sys

	UpperCamelCase						:				Dict             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





