from __future__ import annotations

from math import pi
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    dict[str, float]:



      '''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


snake_case__				= {
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TrajectoryTransformerModel',
         'TrajectoryTransformerPreTrainedModel',
         'load_tf_weights_in_trajectory_transformer',
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

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