



'''simple docstring'''

from __future__ import annotations

from math import pi






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		Any				,						_SCREAMING_SNAKE_CASE	:		Union[str, Any]				,						_SCREAMING_SNAKE_CASE	:		Optional[int]				)     ->		Union[str, Any]:







	"""simple docstring"""

	if (inductance, frequency, reactance).count(0				) != 1:
		raise ValueError("""One and only one argument must be 0"""				)
	if inductance < 0:
		raise ValueError("""Inductance cannot be negative"""				)
	if frequency < 0:
		raise ValueError("""Frequency cannot be negative"""				)
	if reactance < 0:
		raise ValueError("""Inductive reactance cannot be negative"""				)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	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


a_      =     {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Swinv2ForImageClassification',
			    'Swinv2ForMaskedImageModeling',
			    'Swinv2Model',
			    'Swinv2PreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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