

from collections import OrderedDict
from typing import Mapping

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


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

a_    :						int    		=      {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="roberta-prelayernorm"

    def __init__(			self :      List[str]							,    snake_case__ :      str=5_0_2_6_5							,    snake_case__ :      str=7_6_8							,    snake_case__ :      str=1_2							,    snake_case__ :      Any=1_2							,    snake_case__ :      Any=3_0_7_2							,    snake_case__ :      int="gelu"							,    snake_case__ :      Union[str, Any]=0.1							,    snake_case__ :      Any=0.1							,    snake_case__ :      str=5_1_2							,    snake_case__ :      Optional[int]=2							,    snake_case__ :      int=0.02							,    snake_case__ :      str=1E-12							,    snake_case__ :      Any=1							,    snake_case__ :      Tuple=0							,    snake_case__ :      List[str]=2							,    snake_case__ :      Union[str, Any]="absolute"							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      List[Any]=None							,    **snake_case__ :      Tuple							,    ):

     """simple docstring"""






     super().__init__(pad_token_id=snake_case__							,    bos_token_id=snake_case__							,    eos_token_id=snake_case__							,    **snake_case__    )

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




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    @property
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






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




import numpy as np


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return 1 / (1 + np.exp(-vector	))


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return vector * sigmoid(_UpperCamelCase	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

