



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[Any]      =  {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   str				=	"""roberta-prelayernorm"""



	def __init__(       self			,	UpperCAmelCase__=50_265			,	UpperCAmelCase__=768			,	UpperCAmelCase__=12			,	UpperCAmelCase__=12			,	UpperCAmelCase__=3_072			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=512			,	UpperCAmelCase__=2			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-1_2			,	UpperCAmelCase__=1			,	UpperCAmelCase__=0			,	UpperCAmelCase__=2			,	UpperCAmelCase__="absolute"			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(pad_token_id=UpperCAmelCase__			,	bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)

							A__							=       vocab_size
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       hidden_act
							A__							=       intermediate_size
							A__							=       hidden_dropout_prob
							A__							=       attention_probs_dropout_prob
							A__							=       max_position_embeddings
							A__							=       type_vocab_size
							A__							=       initializer_range
							A__							=       layer_norm_eps
							A__							=       position_embedding_type
							A__							=       use_cache
							A__							=       classifier_dropout






class      UpperCamelCase    (						_UpperCAmelCase     ):



	@property
	def 			__A							(       self		):
							if self.task == "multiple-choice":
													A__							=       {0: "batch", 1: "choice", 2: "sequence"}
							else:
													A__							=       {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]		)










import math
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * power_factor
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





