






"""simple docstring"""





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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	float:
    """simple docstring"""

    return np.dot(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
class    __lowerCamelCase    :

   def __init__(					self ,				*,
       snake_case_ = np.inf ,				snake_case_ = "linear" ,				snake_case_ = 0.0 ,				)		->       None:
       UpperCamelCase__         =    regularization
       UpperCamelCase__         =    gamma
       if kernel == "linear":
           UpperCamelCase__         =    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'      )
           UpperCamelCase__         =    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:
           UpperCamelCase__         =    F'Unknown kernel: {kernel}'
           raise ValueError(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       float:
       return np.dot(snake_case_ ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       float:
       return np.exp(-(self.gamma * norm_squared(vectora - vectora      ))      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       UpperCamelCase__         =    observations
       UpperCamelCase__         =    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

       ((UpperCamelCase__)       ,						)         =    np.shape(snake_case_      )

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

       UpperCamelCase__         =    LinearConstraint(snake_case_ ,				0 ,				0      )
       UpperCamelCase__         =    Bounds(0 ,				self.regularization      )

       UpperCamelCase__         =    minimize(
           snake_case_ ,				np.ones(snake_case_      ) ,				bounds=snake_case_ ,				constraints=[ly_contraint]      ).x
       UpperCamelCase__         =    l_star

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







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       UpperCamelCase__         =    sum(
           self.optimum[n]
           * self.classes[n]
           * self.kernel(self.observations[n] ,				snake_case_      )
           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"""





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


A__					: List[Any]=	["""bert-base-uncased""", """bert-base-cased"""]
A__					: Optional[int]=	"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():
  class    __lowerCamelCase    (  tf.keras.Model  ):

     def __init__(					self ,				snake_case_      )		->       Optional[int]:
         super().__init__()
         UpperCamelCase__         =    tokenizer
         UpperCamelCase__         =    AutoConfig.from_pretrained(snake_case_      )
         UpperCamelCase__         =    TFAutoModel.from_config(snake_case_      )







     def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
         UpperCamelCase__         =    self.tokenizer(snake_case_      )
         UpperCamelCase__         =    self.bert(**snake_case_      )
         return out["pooler_output"]
@require_tf
@require_tensorflow_text
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       super().setUp()

       UpperCamelCase__         =    [
           BertTokenizer.from_pretrained(snake_case_      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
       ]  # repeat for when fast_bert_tokenizer=false
       UpperCamelCase__         =    [TFBertTokenizer.from_pretrained(snake_case_      ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
           TFBertTokenizer.from_pretrained(snake_case_ ,				use_fast_bert_tokenizer=snake_case_      )
           for checkpoint in TOKENIZER_CHECKPOINTS
       ]
       assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

       UpperCamelCase__         =    [
           'This is a straightforward English test sentence.',
           'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
           'Now we\'re going to add some Chinese: 一 二 三 一二三',
           'And some much more rare Chinese: 齉 堃 齉堃',
           'Je vais aussi écrire en français pour tester les accents',
           'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
       ]
       UpperCamelCase__         =    list(zip(self.test_sentences ,				self.test_sentences[::-1]      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       for tokenizer, tf_tokenizer in zip(self.tokenizers ,				self.tf_tokenizers      ):
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tokenizer(snake_case_ ,				return_tensors='tf' ,				padding='longest'      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in python_outputs.keys():
                   self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape      )      )
                   self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,				tf.intaa      ) == tf_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf_tokenizer(self.paired_sentences      )
           UpperCamelCase__         =    tf_tokenizer(
               text=[sentence[0] for sentence in self.paired_sentences] ,				text_pair=[sentence[1] for sentence in self.paired_sentences] ,				)
           for key in merged_outputs.keys():
               self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,				tf.intaa      ) == separated_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf.function(snake_case_      )
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tf.constant(snake_case_      )
               UpperCamelCase__         =    compiled_tokenizer(snake_case_      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in eager_outputs.keys():
                   self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    ModelToSave(tokenizer=snake_case_      )
           UpperCamelCase__         =    tf.convert_to_tensor(self.test_sentences      )
           UpperCamelCase__         =    model(snake_case_      )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
               UpperCamelCase__         =    Path(snake_case_      ) / 'saved.model'
               model.save(snake_case_      )
               UpperCamelCase__         =    tf.keras.models.load_model(snake_case_      )
           UpperCamelCase__         =    loaded_model(snake_case_      )
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output      )      ) ,				1E-5      )




