



"""simple docstring"""

import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )




class  _UpperCAmelCase (    unittest.TestCase):

 def __init__(  self	: Optional[Any]						,  lowercase_	: Any						,  lowercase_	: Union[str, Any]=13						,  lowercase_	: str=7						,  lowercase_	: Dict=True						,  lowercase_	: Union[str, Any]=True						,  lowercase_	: List[str]=True						,  lowercase_	: Optional[Any]=True						,  lowercase_	: Dict=99						,  lowercase_	: List[str]=32						,  lowercase_	: Dict=5						,  lowercase_	: Dict=4						,  lowercase_	: List[Any]=37						,  lowercase_	: str="gelu"						,  lowercase_	: List[str]=0.1						,  lowercase_	: int=0.1						,  lowercase_	: Optional[Any]=512						,  lowercase_	: List[Any]=16						,  lowercase_	: Dict=2						,  lowercase_	: List[Any]=0.02						,  lowercase_	: List[str]=4						,  ):
     snake_case_				:  Any       =			parent
     snake_case_				:  Dict       =			batch_size
     snake_case_				:  str       =			seq_length
     snake_case_				:  Optional[Any]       =			is_training
     snake_case_				:  str       =			use_attention_mask
     snake_case_				:  Optional[Any]       =			use_token_type_ids
     snake_case_				:  Optional[int]       =			use_labels
     snake_case_				:  int       =			vocab_size
     snake_case_				:  Any       =			hidden_size
     snake_case_				:  Tuple       =			num_hidden_layers
     snake_case_				:  int       =			num_attention_heads
     snake_case_				:  Tuple       =			intermediate_size
     snake_case_				:  List[Any]       =			hidden_act
     snake_case_				:  Union[str, Any]       =			hidden_dropout_prob
     snake_case_				:  List[str]       =			attention_probs_dropout_prob
     snake_case_				:  Any       =			max_position_embeddings
     snake_case_				:  int       =			type_vocab_size
     snake_case_				:  Any       =			type_sequence_label_size
     snake_case_				:  Any       =			initializer_range
     snake_case_				:  List[str]       =			num_choices

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  List[str]       =			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size       )

     snake_case_				:  List[Any]       =			None
     if self.use_attention_mask:
         snake_case_				:  List[Any]       =			random_attention_mask([self.batch_size, self.seq_length]       )

     snake_case_				:  Optional[int]       =			DistilBertConfig(
         vocab_size=self.vocab_size						,  dim=self.hidden_size						,  n_layers=self.num_hidden_layers						,  n_heads=self.num_attention_heads						,  hidden_dim=self.intermediate_size						,  hidden_act=self.hidden_act						,  dropout=self.hidden_dropout_prob						,  attention_dropout=self.attention_probs_dropout_prob						,  max_position_embeddings=self.max_position_embeddings						,  initializer_range=self.initializer_range						,  tie_weights_=lowercase_						,  )

     return config, input_ids, attention_mask






 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Optional[int]       =			self.prepare_config_and_inputs()
     snake_case_,			snake_case_,			snake_case_				:  Dict       =			config_and_inputs
     snake_case_				:  str       =			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict





@require_flax
class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Optional[int]	      =	(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[int]       =			FlaxDistilBertModelTester(self       )






 @slow
 def 		_snake_case      (  self	: str       ):
     for model_class_name in self.all_model_classes:
         snake_case_				:  int       =			model_class_name.from_pretrained('''distilbert-base-uncased'''       )
         snake_case_				:  Union[str, Any]       =			model(np.ones((1, 1)       )       )
         self.assertIsNotNone(lowercase_       )





@require_flax
class  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[str]       =			FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''       )
     snake_case_				:  Tuple       =			np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
     snake_case_				:  List[str]       =			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
     snake_case_				:  Dict       =			model(lowercase_						,  attention_mask=lowercase_       )[0]
     snake_case_				:  Any       =			(1, 11, 768)
     self.assertEqual(output.shape						,  lowercase_       )
     snake_case_				:  Tuple       =			np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]       )

     self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,  lowercase_						,  atol=1E-4       )       )










"""simple docstring"""

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




def __lowercase    (							_a					):
    return np.dot(_a	,    _a					)




class  _UpperCAmelCase :

 def __init__(  self	: int						,  *,
     lowercase_	: float = np.inf						,  lowercase_	: str = "linear"						,  lowercase_	: float = 0.0						,  ):
     snake_case_				:  Optional[Any]       =			regularization
     snake_case_				:  Tuple       =			gamma
     if kernel == "linear":
         snake_case_				:  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_				:  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:
         snake_case_				:  List[Any]       =			f"Unknown kernel: {kernel}"
         raise ValueError(lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.dot(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: list[ndarray]						,  lowercase_	: ndarray       ):
     snake_case_				:  Union[str, Any]       =			observations
     snake_case_				:  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_),			)				:  List[str]       =			np.shape(lowercase_       )

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

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

     snake_case_				:  int       =			minimize(
         lowercase_						,  np.ones(lowercase_       )						,  bounds=lowercase_						,  constraints=[ly_contraint]       ).x
     snake_case_				:  Optional[Any]       =			l_star

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






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: ndarray       ):
     snake_case_				:  int       =			sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]						,  lowercase_       )
         for n in range(len(self.classes       )       )       )
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





