from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}


class      lowerCamelCase_					(       __SCREAMING_SNAKE_CASE			):






    '''simple docstring'''






    a__     :						Tuple      =				"""megatron-bert"""







    def __init__(     self			,			__lowercase=29_056			,			__lowercase=1_024			,			__lowercase=24			,			__lowercase=16			,			__lowercase=4_096			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=512			,			__lowercase=2			,			__lowercase=0.02			,			__lowercase=1E-1_2			,			__lowercase=0			,			__lowercase="absolute"			,			__lowercase=True			,			**__lowercase			,			)	->     str:
        super().__init__(pad_token_id=UpperCamelCase__			,			**UpperCamelCase__)

        __UpperCamelCase					:Any        =		vocab_size
        __UpperCamelCase					:List[Any]        =		hidden_size
        __UpperCamelCase					:Tuple        =		num_hidden_layers
        __UpperCamelCase					:Union[str, Any]        =		num_attention_heads
        __UpperCamelCase					:Dict        =		hidden_act
        __UpperCamelCase					:List[Any]        =		intermediate_size
        __UpperCamelCase					:Dict        =		hidden_dropout_prob
        __UpperCamelCase					:List[Any]        =		attention_probs_dropout_prob
        __UpperCamelCase					:List[Any]        =		max_position_embeddings
        __UpperCamelCase					:Optional[Any]        =		type_vocab_size
        __UpperCamelCase					:int        =		initializer_range
        __UpperCamelCase					:int        =		layer_norm_eps
        __UpperCamelCase					:Tuple        =		position_embedding_type
        __UpperCamelCase					:List[Any]        =		use_cache






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''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()





