





from typing import TYPE_CHECKING

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


UpperCamelCase__           =			{"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__           =			["GPTNeoXTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__           =			[
					    "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "GPTNeoXForCausalLM",
					    "GPTNeoXForQuestionAnswering",
					    "GPTNeoXForSequenceClassification",
					    "GPTNeoXForTokenClassification",
					    "GPTNeoXLayer",
					    "GPTNeoXModel",
					    "GPTNeoXPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

					UpperCamelCase__           =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











from __future__ import annotations

from math import pi





def      _UpperCamelCase  (a__ :float ,      a__ :float ,      a__ :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()




