





"""simple docstring"""





import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->float:





  """simple docstring"""


  return np.dot(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )






class    _lowerCAmelCase  :
       """simple docstring"""




       def __init__(       self       ,					*,
           __UpperCAmelCase = np.inf       ,					__UpperCAmelCase = "linear"       ,					__UpperCAmelCase = 0.0       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				regularization
         lowerCAmelCase__     :int											=				gamma
         if kernel == "linear":
           lowerCAmelCase__     :Union[str, 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' )
           lowerCAmelCase__     :Optional[int]											=				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:
           lowerCAmelCase__     :List[str]											=				F"Unknown kernel: {kernel}"
           raise ValueError(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return np.dot(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				observations
         lowerCAmelCase__     :int											=				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

         ((lowerCAmelCase__)				,							)     :Tuple											=				np.shape(__UpperCAmelCase )

         def to_minimize(__UpperCAmelCase ) -> float:
           lowerCAmelCase__     :Any											=				0
           ((lowerCAmelCase__)				,							)     :Optional[int]											=				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 )

         lowerCAmelCase__     :Tuple											=				LinearConstraint(__UpperCAmelCase       ,					0       ,					0 )
         lowerCAmelCase__     :Optional[int]											=				Bounds(0       ,					self.regularization )

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

         # calculating mean offset of separation plane to points
         lowerCAmelCase__     :Union[str, Any]											=				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] )
         lowerCAmelCase__     :Optional[int]											=				s / n






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				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()







"""simple docstring"""





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


__A											=						logging.get_logger(__name__)

__A											=						{
    """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    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   """deberta-v2"""






       def __init__(       self       ,					__UpperCAmelCase=1_2_8_1_0_0       ,					__UpperCAmelCase=1_5_3_6       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=6_1_4_4       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=1E-7       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=-1       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase="gelu"       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )

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

         # Backwards compatibility
         if type(__UpperCAmelCase ) == str:
           lowerCAmelCase__     :Dict											=				[x.strip() for x in pos_att_type.lower().split('|' )]

         lowerCAmelCase__     :str											=				pos_att_type
         lowerCAmelCase__     :Dict											=				vocab_size
         lowerCAmelCase__     :Tuple											=				layer_norm_eps

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('pooler_hidden_size'       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				pooler_dropout
         lowerCAmelCase__     :int											=				pooler_hidden_act






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self.task == "multiple-choice":
           lowerCAmelCase__     :str											=				{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           lowerCAmelCase__     :Tuple											=				{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 	snake_case		(       self ):



         '''simple docstring'''

         return 1_2






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :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
