


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


SCREAMING_SNAKE_CASE       :		Optional[int]         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		List[str]         =				{
    "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      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	Tuple									="""deberta-v2"""
   def __init__(     self   ,							UpperCamelCase_=12_8100   ,							UpperCamelCase_=1536   ,							UpperCamelCase_=24   ,							UpperCamelCase_=24   ,							UpperCamelCase_=6144   ,							UpperCamelCase_="gelu"   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=512   ,							UpperCamelCase_=0   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=1E-7   ,							UpperCamelCase_=False   ,							UpperCamelCase_=-1   ,							UpperCamelCase_=0   ,							UpperCamelCase_=True   ,							UpperCamelCase_=None   ,							UpperCamelCase_=0   ,							UpperCamelCase_="gelu"   ,							**UpperCamelCase_   ,							):
        super().__init__(**UpperCamelCase_		)

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

        # Backwards compatibility
        if type(UpperCamelCase_		) == str:
             lowercase_       :Optional[int]     =       [x.strip() for x in pos_att_type.lower().split('''|'''		)]

        lowercase_       :Optional[int]     =       pos_att_type
        lowercase_       :Any     =       vocab_size
        lowercase_       :List[str]     =       layer_norm_eps

        lowercase_       :List[Any]     =       kwargs.get('''pooler_hidden_size'''   ,							UpperCamelCase_		)
        lowercase_       :int     =       pooler_dropout
        lowercase_       :str     =       pooler_hidden_act






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @property
   def 		UpperCamelCase  (     self		):
        if self.task == "multiple-choice":
             lowercase_       :int     =       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
             lowercase_       :List[Any]     =       {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 		UpperCamelCase  (     self		):
        return 12




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = -1   ,							UpperCamelCase_ = -1   ,							UpperCamelCase_ = -1   ,							UpperCamelCase_ = False   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = 3   ,							UpperCamelCase_ = 40   ,							UpperCamelCase_ = 40   ,							UpperCamelCase_ = None   ,							):
        lowercase_       :Union[str, Any]     =       super().generate_dummy_inputs(preprocessor=UpperCamelCase_   ,							framework=UpperCamelCase_		)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
        return dummy_inputs





import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return np.dot(_a       ,		_a		)






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							*,
       UpperCamelCase_ = np.inf   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = 0.0   ,							):
        lowercase_       :Dict     =       regularization
        lowercase_       :Optional[Any]     =       gamma
        if kernel == "linear":
             lowercase_       :Optional[Any]     =       self.__linear
        elif kernel == "rbf":
             if self.gamma == 0:
                  raise ValueError('''rbf kernel requires gamma'''		)
             if not isinstance(self.gamma   ,							(float, int)		):
                  raise ValueError('''gamma must be float or int'''		)
             if not self.gamma > 0:
                  raise ValueError('''gamma must be > 0'''		)
             lowercase_       :Union[str, Any]     =       self.__rbf
             # in the future, there could be a default value like in sklearn
             # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
             # previously it was 1/(n_features)
        else:
             lowercase_       :str     =       f"Unknown kernel: {kernel}"
             raise ValueError(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.dot(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       observations
        lowercase_       :Union[str, Any]     =       classes

        # using Wolfe's Dual to calculate w.
        # Primal problem: minimize 1/2*norm_squared(w)
        #   constraint: yn(w . xn + b) >= 1
        #
        # With l a vector
        # Dual problem: maximize sum_n(ln) -
        #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
        #   constraint: self.C >= ln >= 0
        #           and sum_n(ln*yn) = 0
        # Then we get w using w = sum_n(ln*yn*xn)
        # At the end we can get b ~= mean(yn - w . xn)
        #
        # Since we use kernels, we only need l_star to calculate b
        # and to classify observations

        ((lowercase_)    ,      )       :Optional[Any]     =       np.shape(UpperCamelCase_		)

        def to_minimize(UpperCamelCase_		) -> float:
             lowercase_       :Dict     =       0
             ((lowercase_)    ,      )       :Tuple     =       np.shape(UpperCamelCase_		)
             for i in range(UpperCamelCase_		):
                  for j in range(UpperCamelCase_		):
                       s += (
                           candidate[i]
                           * candidate[j]
                           * classes[i]
                           * classes[j]
                           * self.kernel(observations[i]   ,							observations[j]		)
                       )
             return 1 / 2 * s - sum(UpperCamelCase_		)

        lowercase_       :Dict     =       LinearConstraint(UpperCamelCase_   ,							0   ,							0		)
        lowercase_       :Optional[Any]     =       Bounds(0   ,							self.regularization		)

        lowercase_       :Any     =       minimize(
            UpperCamelCase_   ,							np.ones(UpperCamelCase_		)   ,							bounds=UpperCamelCase_   ,							constraints=[ly_contraint]		).x
        lowercase_       :Tuple     =       l_star

        # calculating mean offset of separation plane to points
        lowercase_       :str     =       0
        for i in range(UpperCamelCase_		):
             for j in range(UpperCamelCase_		):
                  s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                      observations[i]   ,							observations[j]		)
        lowercase_       :List[str]     =       s / n




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Union[str, Any]     =       sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]   ,							UpperCamelCase_		)
            for n in range(len(self.classes		)		)		)
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

