

'''simple docstring'''




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   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				*,
     a__		:   float = np.inf			,				a__		:   str = "linear"			,				a__		:   float = 0.0			,				):
      __magic_name__						       =    regularization
      __magic_name__						       =    gamma
      if kernel == "linear":
           __magic_name__						       =    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'''      )
           __magic_name__						       =    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:
           __magic_name__						       =    F'''Unknown kernel: {kernel}'''
           raise ValueError(a__      )



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   ndarray			,				a__		:   ndarray      ):
      return np.dot(a__			,				a__      )



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



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   list[ndarray]			,				a__		:   ndarray      ):
      __magic_name__						       =    observations
      __magic_name__						       =    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

      ((__magic_name__  )       ,    )						       =    np.shape(a__      )

      def to_minimize(a__		:   ndarray      ) -> float:
           __magic_name__						       =    0
           ((__magic_name__  )       ,    )						       =    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__      )

      __magic_name__						       =    LinearConstraint(a__			,				0			,				0      )
      __magic_name__						       =    Bounds(0			,				self.regularization      )

      __magic_name__						       =    minimize(
          a__			,				np.ones(a__      )			,				bounds=a__			,				constraints=[ly_contraint]      ).x
      __magic_name__						       =    l_star

      # calculating mean offset of separation plane to points
      __magic_name__						       =    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]      )
      __magic_name__						       =    s / n



 def 			snake_case__  (   self		:   str			,				a__		:   ndarray      ):
      __magic_name__						       =    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()




'''simple docstring'''




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowerCAmelCase         =						get_tests_dir("fixtures")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy-config.json")






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      with tempfile.TemporaryDirectory() as tmpdirname:
           __magic_name__						       =    WavaVecaConfig()

           # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      ).to_dict()

           config_dict.pop('''feature_extractor_type'''      )
           __magic_name__						       =    WavaVecaFeatureExtractor(**a__      )

           # save in new folder
           model_config.save_pretrained(a__      )
           config.save_pretrained(a__      )

           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )

           # make sure private variable is not incorrectly saved
           __magic_name__						       =    json.loads(config.to_json_string()      )
           self.assertTrue('''_processor_class''' not in dict_as_saved      )

      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				'''bert-base is not a local folder and is not a valid model identifier'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''bert-base'''      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				revision='''aaaaaa'''      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      with self.assertRaisesRegex(
          a__			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''      )



 def 			snake_case__  (   self		:   Dict      ):
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )

      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
          '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
      self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )

      # Test feature extractor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(a__      )
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				trust_remote_code=a__      )
      self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )



 def 			snake_case__  (   self		:   int      ):
      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(a__      ):
                AutoFeatureExtractor.register(a__			,				a__      )

           # Now that the config is registered, it can be used as any other config with the auto-API
           __magic_name__						       =    CustomFeatureExtractor.from_pretrained(a__      )
           with tempfile.TemporaryDirectory() as tmp_dir:
                feature_extractor.save_pretrained(a__      )
                __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
                self.assertIsInstance(a__			,				a__      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



 def 			snake_case__  (   self		:   int      ):






      class   _SCREAMING_SNAKE_CASE   (   __a     ):
       __SCREAMING_SNAKE_CASE					:Optional[int]      	=					True

      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # If remote code is not set, the default is to use local
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote code is disabled, we load the local one.
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote is enabled, we load from the Hub
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(not hasattr(a__			,				'''is_local'''      )      )

      finally:
           if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
           if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






