


"""simple docstring"""
import numpy as np

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







   return vector * sigmoid(__snake_case   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''ibert'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_0522      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-1_2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE="absolute"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="none"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

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







class 	lowercase__				(					A_				):





   @property
   def 		UpperCamelCase_  (							self)  ->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         _lowerCamelCase	:					Dict   =		{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         _lowerCamelCase	:					Optional[int]   =		{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ])

