





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_   :   List[Any]       =					{
    "configuration_squeezebert": [
        "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SqueezeBertConfig",
        "SqueezeBertOnnxConfig",
    ],
    "tokenization_squeezebert": ["SqueezeBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase_   :   Optional[Any]       =					["SqueezeBertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase_   :   List[str]       =					[
		    "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "SqueezeBertForMaskedLM",
		    "SqueezeBertForMultipleChoice",
		    "SqueezeBertForQuestionAnswering",
		    "SqueezeBertForSequenceClassification",
		    "SqueezeBertForTokenClassification",
		    "SqueezeBertModel",
		    "SqueezeBertModule",
		    "SqueezeBertPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_squeezebert import (
		    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SqueezeBertConfig,
		    SqueezeBertOnnxConfig,
		)
		from .tokenization_squeezebert import SqueezeBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_squeezebert import (
				    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SqueezeBertForMaskedLM,
				    SqueezeBertForMultipleChoice,
				    SqueezeBertForQuestionAnswering,
				    SqueezeBertForSequenceClassification,
				    SqueezeBertForTokenClassification,
				    SqueezeBertModel,
				    SqueezeBertModule,
				    SqueezeBertPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase_   :   str       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''


from __future__ import annotations

from math import pi







def    _lowerCamelCase  (__lowerCamelCase  :     float					,  __lowerCamelCase  :     float					,  __lowerCamelCase  :     float     )   ->    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()



