






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}




class       snake_case__ (				A_):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			"deberta-v2"



       def __init__(				self      ,    a__=12_81_00      ,    a__=15_36      ,    a__=24      ,    a__=24      ,    a__=61_44      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=5_12      ,    a__=0      ,    a__=0.02      ,    a__=1e-7      ,    a__=False      ,    a__=-1      ,    a__=0      ,    a__=True      ,    a__=None      ,    a__=0      ,    a__="gelu"      ,    **a__      ,    )			->   Tuple:






            '''simple docstring'''





            super().__init__(**a__     )

            __snake_case      :Optional[Any] 							=    hidden_size
            __snake_case      :Dict 							=    num_hidden_layers
            __snake_case      :int 							=    num_attention_heads
            __snake_case      :int 							=    intermediate_size
            __snake_case      :List[str] 							=    hidden_act
            __snake_case      :List[str] 							=    hidden_dropout_prob
            __snake_case      :List[Any] 							=    attention_probs_dropout_prob
            __snake_case      :List[Any] 							=    max_position_embeddings
            __snake_case      :Tuple 							=    type_vocab_size
            __snake_case      :Tuple 							=    initializer_range
            __snake_case      :Optional[int] 							=    relative_attention
            __snake_case      :Optional[int] 							=    max_relative_positions
            __snake_case      :Optional[int] 							=    pad_token_id
            __snake_case      :Any 							=    position_biased_input

            # Backwards compatibility
            if type(a__     ) == str:
                 __snake_case      :Optional[int] 							=    [x.strip() for x in pos_att_type.lower().split("""|"""     )]

            __snake_case      :int 							=    pos_att_type
            __snake_case      :Optional[Any] 							=    vocab_size
            __snake_case      :Optional[Any] 							=    layer_norm_eps

            __snake_case      :Optional[Any] 							=    kwargs.get("""pooler_hidden_size"""      ,    a__     )
            __snake_case      :Any 							=    pooler_dropout
            __snake_case      :Optional[int] 							=    pooler_hidden_act




class       snake_case__ (				A_):

       '''simple docstring'''



       @property
       def        __lowercase    (				self     )			->   Mapping[str, Mapping[int, str]]:






            '''simple docstring'''





            if self.task == "multiple-choice":
                 __snake_case      :List[str] 							=    {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                 __snake_case      :int 							=    {0: """batch""", 1: """sequence"""}
            if self._config.type_vocab_size > 0:
                 return OrderedDict(
                     [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]     )
            else:
                 return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]     )



       @property
       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            return 12



       def        __lowercase    (				self      ,    a__      ,    a__ = -1      ,    a__ = -1      ,    a__ = -1      ,    a__ = False      ,    a__ = None      ,    a__ = 3      ,    a__ = 40      ,    a__ = 40      ,    a__ = None      ,    )			->   Mapping[str, Any]:






            '''simple docstring'''





            __snake_case      :int 							=    super().generate_dummy_inputs(preprocessor=a__      ,    framework=a__     )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                 del dummy_inputs["token_type_ids"]
            return dummy_inputs






"""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()

