






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







def 			A__							(      _a  :					ndarray	):





 '''simple docstring'''







 return np.dot(_a		,  _a	)







class  _lowercase   :
      def __init__(     self				,			*,
          a = np.inf				,			a = "linear"				,			a = 0.0				,			):
       snake_case__  :    str							       =regularization
       snake_case__  :    Optional[Any]							       =gamma
       if kernel == "linear":
        snake_case__  :    Optional[int]							       =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"""   )
        snake_case__  :    List[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:
        snake_case__  :    int							       =F"Unknown kernel: {kernel}"
        raise ValueError(a   )
      def 						lowercase__					(     self				,			a				,			a   ):
       return np.dot(a				,			a   )
      def 						lowercase__					(     self				,			a				,			a   ):
       return np.exp(-(self.gamma * norm_squared(vectora - vectora   ))   )
      def 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Any							       =observations
       snake_case__  :    Optional[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

       ((snake_case__)       ,   )  :    Union[str, Any]							       =np.shape(a   )

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

       snake_case__  :    Any							       =LinearConstraint(a				,			0				,			0   )
       snake_case__  :    Union[str, Any]							       =Bounds(0				,			self.regularization   )

       snake_case__  :    Optional[Any]							       =minimize(
           a				,			np.ones(a   )				,			bounds=a				,			constraints=[ly_contraint]   ).x
       snake_case__  :    Optional[Any]							       =l_star

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



      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =sum(
           self.optimum[n]
           * self.classes[n]
           * self.kernel(self.observations[n]				,			a   )
           for n in range(len(self.classes   )   )   )
       return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()













import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )







class  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=1_3				,			a=7				,			a=True				,			a=True				,			a=True				,			a=True				,			a=9_9				,			a=3_2				,			a=5				,			a=4				,			a=3_7				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_2				,			a=1_6				,			a=2				,			a=0.02				,			a=4				,			):
       snake_case__  :    Any							       =parent
       snake_case__  :    Dict							       =batch_size
       snake_case__  :    List[Any]							       =seq_length
       snake_case__  :    str							       =is_training
       snake_case__  :    Union[str, Any]							       =use_attention_mask
       snake_case__  :    str							       =use_token_type_ids
       snake_case__  :    int							       =use_labels
       snake_case__  :    Tuple							       =vocab_size
       snake_case__  :    List[str]							       =hidden_size
       snake_case__  :    Dict							       =num_hidden_layers
       snake_case__  :    Optional[Any]							       =num_attention_heads
       snake_case__  :    List[str]							       =intermediate_size
       snake_case__  :    str							       =hidden_act
       snake_case__  :    Union[str, Any]							       =hidden_dropout_prob
       snake_case__  :    Tuple							       =attention_probs_dropout_prob
       snake_case__  :    Tuple							       =max_position_embeddings
       snake_case__  :    str							       =type_vocab_size
       snake_case__  :    Optional[Any]							       =type_sequence_label_size
       snake_case__  :    str							       =initializer_range
       snake_case__  :    List[Any]							       =num_choices
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

       snake_case__  :    Tuple							       =None
       if self.use_attention_mask:
        snake_case__  :    Union[str, Any]							       =random_attention_mask([self.batch_size, self.seq_length]   )

       snake_case__  :    Union[str, Any]							       =None
       if self.use_token_type_ids:
        snake_case__  :    Optional[Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size   )

       snake_case__  :    Tuple							       =RoFormerConfig(
           vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			is_decoder=a				,			initializer_range=self.initializer_range				,			)

       return config, input_ids, token_type_ids, attention_mask



      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.prepare_config_and_inputs()
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Optional[Any]							       =config_and_inputs
       snake_case__  :    Dict							       ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict







@require_flax
class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						str										=							True

      _a							:						Optional[Any]										=							(
          (
              FlaxRoFormerModel,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =FlaxRoFormerModelTester(self   )



      @slow
      def 						lowercase__					(     self   ):
       for model_class_name in self.all_model_classes:
        snake_case__  :    Tuple							       =model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,			from_pt=a   )
        snake_case__  :    List[Any]							       =model(np.ones((1, 1)   )   )
        self.assertIsNotNone(a   )







@require_flax
class  _lowercase   (   unittest.TestCase						):
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
       snake_case__  :    Tuple							       =jnp.array([[0, 1, 2, 3, 4, 5]]   )
       snake_case__  :    str							       =model(a   )[0]

       snake_case__  :    List[str]							       =5_0_0_0_0

       snake_case__  :    str							       =(1, 6, vocab_size)
       self.assertEqual(output.shape				,			a   )

       snake_case__  :    Optional[int]							       =jnp.array(
           [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

       self.assertTrue(jnp.allclose(output[:, :3, :3]				,			a				,			atol=1e-4   )   )





