
from __future__ import annotations

from math import pi


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						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()




__lowerCAmelCase					:			Dict			      =		"\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase					:			Tuple			      =		[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase					:			List[Any]			      =		{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


