




from __future__ import annotations




def 			__magic_name__				( A		:					list[float]   ):






			'''simple docstring'''







			a    =			0.00
			a    =			0
			for resistor in resistors:
						if resistor <= 0:
									a    =			F"""Resistor at index {index} has a negative or zero value!"""
									raise ValueError(__snake_case   )
						first_sum += 1 / float(__snake_case   )
						index += 1
			return 1 / first_sum




def 			__magic_name__				( A		:					list[float]   ):






			'''simple docstring'''







			a    =			0.00
			a    =			0
			for resistor in resistors:
						sum_r += resistor
						if resistor < 0:
									a    =			F"""Resistor at index {index} has a negative value!"""
									raise ValueError(__snake_case   )
						index += 1
			return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A		:       List[Any]  				=		{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       List[str]  				=		[
						    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MegaForCausalLM''',
						    '''MegaForMaskedLM''',
						    '''MegaForMultipleChoice''',
						    '''MegaForQuestionAnswering''',
						    '''MegaForSequenceClassification''',
						    '''MegaForTokenClassification''',
						    '''MegaModel''',
						    '''MegaPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mega import (
												    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MegaForCausalLM,
												    MegaForMaskedLM,
												    MegaForMultipleChoice,
												    MegaForQuestionAnswering,
												    MegaForSequenceClassification,
												    MegaForTokenClassification,
												    MegaModel,
												    MegaPreTrainedModel,
												)

else:
						import sys

						__A		:       Any  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



