from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   ['''input_values''', '''padding_mask''']



   def __init__(	self					:  int		,  _lowercase					:  int = 1		,  _lowercase					:  int = 24_000		,  _lowercase					:  float = 0.0		,  _lowercase					:  float = None		,  _lowercase					:  float = None		,  **_lowercase					:  List[str]		,  ):



    """simple docstring"""



    super().__init__(feature_size=_lowercase		,  sampling_rate=_lowercase		,  padding_value=_lowercase		,  **_lowercase							)
    _UpperCamelCase:     List[str]							= chunk_length_s
    _UpperCamelCase:     List[Any]							= overlap



   @property
   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    if self.chunk_length_s is None:
     return None
    else:
     return int(self.chunk_length_s * self.sampling_rate							)



   @property
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    if self.chunk_length_s is None or self.overlap is None:
     return None
    else:
     return max(1		,  int((1.0 - self.overlap) * self.chunk_length							)							)



   def __call__(	self					:  List[str]		,  _lowercase					:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,  _lowercase					:  Optional[Union[bool, str, PaddingStrategy]] = None		,  _lowercase					:  Optional[bool] = False		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  Optional[Union[str, TensorType]] = None		,  _lowercase					:  Optional[int] = None		,  ):



    """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} was trained using a sampling rate of"""
          f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
          f""" {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.'''							)

    if padding and truncation:
     raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
    elif padding is None:
     # by default let's pad the inputs
     _UpperCamelCase:     Optional[Any]							= True

    _UpperCamelCase:     List[str]							= bool(
        isinstance(_lowercase		,  (list, tuple)							) and (isinstance(raw_audio[0]		,  (np.ndarray, tuple, list)							))							)

    if is_batched:
     _UpperCamelCase:     Optional[Any]							= [np.asarray(_lowercase		,  dtype=np.floataa							).T for audio in raw_audio]
    elif not is_batched and not isinstance(_lowercase		,  np.ndarray							):
     _UpperCamelCase:     List[str]							= np.asarray(_lowercase		,  dtype=np.floataa							)
    elif isinstance(_lowercase		,  np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
     _UpperCamelCase:     int							= raw_audio.astype(np.floataa							)

    # always return batch
    if not is_batched:
     _UpperCamelCase:     Tuple							= [np.asarray(_lowercase							).T]

    # verify inputs are valid
    for idx, example in enumerate(_lowercase							):
     if example.ndim > 2:
      raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""							)
     if self.feature_size == 1 and example.ndim != 1:
      raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""							)
     if self.feature_size == 2 and example.shape[-1] != 2:
      raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""							)

    _UpperCamelCase:     List[Any]							= None
    _UpperCamelCase:     Dict							= BatchFeature({'''input_values''': raw_audio}							)
    if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
     if truncation:
      _UpperCamelCase:     Optional[int]							= min(array.shape[0] for array in raw_audio							)
      _UpperCamelCase:     Dict							= int(np.floor(max_length / self.chunk_stride							)							)
      _UpperCamelCase:     List[Any]							= (nb_step - 1) * self.chunk_stride + self.chunk_length
     elif padding:
      _UpperCamelCase:     Any							= max(array.shape[0] for array in raw_audio							)
      _UpperCamelCase:     Any							= int(np.ceil(max_length / self.chunk_stride							)							)
      _UpperCamelCase:     Union[str, Any]							= (nb_step - 1) * self.chunk_stride + self.chunk_length
      _UpperCamelCase:     Any							= '''max_length'''
     else:
      _UpperCamelCase:     Dict							= input_values

        # normal padding on batch
    if padded_inputs is None:
     _UpperCamelCase:     int							= self.pad(
         _lowercase		,  max_length=_lowercase		,  truncation=_lowercase		,  padding=_lowercase		,  return_attention_mask=_lowercase		,  )
     if padding:
      _UpperCamelCase:     Dict							= padded_inputs.pop('''attention_mask'''							)

    _UpperCamelCase:     List[Any]							= []
    for example in padded_inputs.pop('''input_values'''							):
     if self.feature_size == 1:
      _UpperCamelCase:     str							= example[..., None]
     input_values.append(example.T							)

    _UpperCamelCase:     Optional[Any]							= input_values
    if return_tensors is not None:
     _UpperCamelCase:     Optional[Any]							= padded_inputs.convert_to_tensors(_lowercase							)

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




def       lowerCAmelCase_     (		lowercase:							ndarray		)    ->				float:







 '''simple docstring'''
 return np.dot(lowercase  ,					lowercase		)




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  int		,  *,
       _lowercase					:  float = np.inf		,  _lowercase					:  str = "linear"		,  _lowercase					:  float = 0.0		,  ):



    """simple docstring"""



    _UpperCamelCase:     int							= regularization
    _UpperCamelCase:     Optional[int]							= gamma
    if kernel == "linear":
     _UpperCamelCase:     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'''							)
     _UpperCamelCase:     Optional[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:
     _UpperCamelCase:     Optional[int]							= f"""Unknown kernel: {kernel}"""
     raise ValueError(_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



    return np.dot(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  list[ndarray]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= observations
    _UpperCamelCase:     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

    ((_UpperCamelCase)	,							):     Any							= np.shape(_lowercase							)

    def to_minimize(_lowercase					:  ndarray							) -> float:
     _UpperCamelCase:     Optional[int]							= 0
     ((_UpperCamelCase)	,							):     str							= 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							)

    _UpperCamelCase:     Optional[Any]							= LinearConstraint(_lowercase		,  0		,  0							)
    _UpperCamelCase:     Optional[int]							= Bounds(0		,  self.regularization							)

    _UpperCamelCase:     Dict							= minimize(
        _lowercase		,  np.ones(_lowercase							)		,  bounds=_lowercase		,  constraints=[ly_contraint]							).x
    _UpperCamelCase:     Union[str, Any]							= l_star

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



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     Optional[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()