




"""simple docstring"""


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A_						=				logging.get_logger(__name__)






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["input_features"]


 def __init__(				self:		List[Any],      a_:		Tuple=80,      a_:		List[str]=16_000,      a_:		Tuple=160,      a_:		Optional[int]=30,      a_:		List[str]=400,      a_:		Union[str, Any]=0.0,      a_:		Union[str, Any]=False,      **a_:		Dict,      ):




   '''simple docstring'''



   super().__init__(
       feature_size=a_,      sampling_rate=a_,      padding_value=a_,      return_attention_mask=a_,      **a_,      )
   _snake_case							:		Union[str, Any]								=  n_fft
   _snake_case							:		Optional[Any]								=  hop_length
   _snake_case							:		Optional[int]								=  chunk_length
   _snake_case							:		str								=  chunk_length * sampling_rate
   _snake_case							:		Dict								=  self.n_samples // hop_length
   _snake_case							:		List[str]								=  sampling_rate
   _snake_case							:		Optional[int]								=  mel_filter_bank(
       num_frequency_bins=1 + n_fft // 2,      num_mel_filters=a_,      min_frequency=0.0,      max_frequency=8_000.0,      sampling_rate=a_,      norm="""slaney""",      mel_scale="""slaney""",      )


 def  UpperCamelCase_   (				self:		List[Any],      a_:		np.array		):




   '''simple docstring'''



   _snake_case							:		str								=  spectrogram(
       a_,      window_function(self.n_fft,      """hann"""		),      frame_length=self.n_fft,      hop_length=self.hop_length,      power=2.0,      mel_filters=self.mel_filters,      log_mel="""log10""",      )
   _snake_case							:		Dict								=  log_spec[:, :-1]
   _snake_case							:		List[Any]								=  np.maximum(a_,      log_spec.max() - 8.0		)
   _snake_case							:		int								=  (log_spec + 4.0) / 4.0
   return log_spec


 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def  UpperCamelCase_   (				a_:		List[np.ndarray],      a_:		List[np.ndarray],      a_:		float = 0.0		):




   '''simple docstring'''



   if attention_mask is not None:
     _snake_case							:		str								=  np.array(a_,      np.intaa		)
     _snake_case							:		str								=  []

     for vector, length in zip(a_,      attention_mask.sum(-1		)		):
       _snake_case							:		List[str]								=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7		)
       if length < normed_slice.shape[0]:
         _snake_case							:		Any								=  padding_value

       normed_input_values.append(a_		)
   else:
     _snake_case							:		Dict								=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

   return normed_input_values


 def __call__(				self:		Optional[int],      a_:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],      a_:		bool = True,      a_:		Optional[int] = None,      a_:		Optional[Union[str, TensorType]] = None,      a_:		Optional[bool] = None,      a_:		Optional[str] = "max_length",      a_:		Optional[int] = None,      a_:		Optional[int] = None,      a_:		Optional[bool] = None,      **a_:		Any,      ):




   '''simple docstring'''



   if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
       raise ValueError(
           f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
           f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
           f" was sampled with {self.sampling_rate} and not {sampling_rate}."		)
   else:
     logger.warning(
         """It is strongly recommended to pass the `sampling_rate` argument to this function. """
         """Failing to do so can result in silent errors that might be hard to debug."""		)

   _snake_case							:		str								=  isinstance(a_,      np.ndarray		) and len(raw_speech.shape		) > 1
   if is_batched_numpy and len(raw_speech.shape		) > 2:
     raise ValueError(f"Only mono-channel audio is supported for input to {self}"		)
   _snake_case							:		Optional[Any]								=  is_batched_numpy or (
       isinstance(a_,      (list, tuple)		) and (isinstance(raw_speech[0],      (np.ndarray, tuple, list)		))
   )

   if is_batched:
     _snake_case							:		Union[str, Any]								=  [np.asarray([speech],      dtype=np.floataa		).T for speech in raw_speech]
   elif not is_batched and not isinstance(a_,      np.ndarray		):
     _snake_case							:		Optional[Any]								=  np.asarray(a_,      dtype=np.floataa		)
   elif isinstance(a_,      np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
     _snake_case							:		Tuple								=  raw_speech.astype(np.floataa		)

   # always return batch
   if not is_batched:
     _snake_case							:		List[str]								=  [np.asarray([raw_speech]		).T]

   _snake_case							:		Optional[int]								=  BatchFeature({"""input_features""": raw_speech}		)

   # convert into correct format for padding

   _snake_case							:		Optional[Any]								=  self.pad(
       a_,      padding=a_,      max_length=max_length if max_length else self.n_samples,      truncation=a_,      pad_to_multiple_of=a_,      return_attention_mask=return_attention_mask or do_normalize,      )

   # zero-mean and unit-variance normalization
   if do_normalize:
     _snake_case							:		Dict								=  self.zero_mean_unit_var_norm(
         padded_inputs["""input_features"""],      attention_mask=padded_inputs["""attention_mask"""],      padding_value=self.padding_value,      )
     _snake_case							:		Tuple								=  np.stack(padded_inputs["""input_features"""],      axis=0		)

   # make sure list is in array format
   _snake_case							:		Optional[int]								=  padded_inputs.get("""input_features"""		).transpose(2,      0,      1		)

   _snake_case							:		Dict								=  [self._np_extract_fbank_features(a_		) for waveform in input_features[0]]

   if isinstance(input_features[0],      a_		):
     _snake_case							:		List[str]								=  [np.asarray(a_,      dtype=np.floataa		) for feature in input_features]
   else:
     _snake_case							:		List[Any]								=  input_features

   if return_attention_mask:
     # rescale from sample (48000) to feature (3000)
     _snake_case							:		Any								=  padded_inputs["""attention_mask"""][:, :: self.hop_length]

   if return_tensors is not None:
     _snake_case							:		List[str]								=  padded_inputs.convert_to_tensors(a_		)

   return padded_inputs


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		Union[str, Any]								=  self.__class__.__name__
   if "mel_filters" in output:
     del output["mel_filters"]
   return output









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



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



   def __init__(					self					,  *,
       lowercase = np.inf					,  lowercase = "linear"					,  lowercase = 0.0					,  )			->						None:







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	Optional[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')
                 a__						:	str =				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:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				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

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[int] =				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)

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

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

          # calculating mean offset of separation plane to points
          a__						:	int =				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])
          a__						:	List[str] =				s / n

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	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()


