

from __future__ import annotations

from math import pi





def      snake_case_				(__A					:						Any ,       __A					:						List[str] ,       __A					:						List[str]					)      ->     dict[str, float]:

   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()





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use GLPNImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )


