'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


UpperCamelCase__     :	List[Any]				=			logging.get_logger(__name__)


class 				_lowercase	(      lowercase__   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Union[str, Any]      							=   ['''input_features''', '''attention_mask''']




      def __init__(				self					,lowerCamelCase_=80					,lowerCamelCase_=16000					,lowerCamelCase_=80					,lowerCamelCase_=0.0					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_=True					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         super().__init__(feature_size=lowerCamelCase_					,sampling_rate=lowerCamelCase_					,padding_value=lowerCamelCase_					,**lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  num_mel_bins
         UpperCAmelCase__							:				Dict   				=  do_ceptral_normalize
         UpperCAmelCase__							:				List[str]   				=  normalize_means
         UpperCAmelCase__							:				List[str]   				=  normalize_vars
         UpperCAmelCase__							:				Any   				=  True




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,)			->			np.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
         UpperCAmelCase__							:				List[Any]   				=  torch.from_numpy(lowerCamelCase_							).unsqueeze(0							)
         UpperCAmelCase__							:				int   				=  ta_kaldi.fbank(lowerCamelCase_					,num_mel_bins=self.num_mel_bins					,sample_frequency=self.sampling_rate							)
         return features.numpy()




      @staticmethod
      def 		lowerCAmelCase__					(				lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = True					,lowerCamelCase_ = True					,lowerCamelCase_ = 0.0					,)			->			np.ndarray:



         '''simple docstring'''



         if normalize_means:
            UpperCAmelCase__							:				List[str]   				=  x[:input_length].mean(axis=0							)
            UpperCAmelCase__							:				Tuple   				=  np.subtract(lowerCamelCase_					,lowerCamelCase_							)
         if normalize_vars:
            UpperCAmelCase__							:				Dict   				=  x[:input_length].std(axis=0							)
            UpperCAmelCase__							:				List[str]   				=  np.divide(lowerCamelCase_					,lowerCamelCase_							)

         if input_length < x.shape[0]:
            UpperCAmelCase__							:				Optional[Any]   				=  padding_value

         # make sure array is in float32
         UpperCAmelCase__							:				Optional[Any]   				=  x.astype(np.floataa							)

         return x




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[np.ndarray]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [
             self.utterance_cmvn(lowerCamelCase_					,lowerCamelCase_					,self.normalize_means					,self.normalize_vars					,self.padding_value							)
             for x, n in zip(lowerCamelCase_					,lowerCamelCase_							)
         ]




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_ = False					,lowerCamelCase_ = None					,lowerCamelCase_ = False					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			BatchFeature:



         '''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 `raw_speech` 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.'''							)

         UpperCAmelCase__							:				List[str]   				=  isinstance(lowerCamelCase_					,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}'''							)
         UpperCAmelCase__							:				Any   				=  is_batched_numpy or (
             isinstance(lowerCamelCase_					,(list, tuple)							) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)							))
         )

         if is_batched:
            UpperCAmelCase__							:				Any   				=  [np.asarray(lowerCamelCase_					,dtype=np.floataa							) for speech in raw_speech]
         elif not is_batched and not isinstance(lowerCamelCase_					,np.ndarray							):
            UpperCAmelCase__							:				Dict   				=  np.asarray(lowerCamelCase_					,dtype=np.floataa							)
         elif isinstance(lowerCamelCase_					,np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
            UpperCAmelCase__							:				List[Any]   				=  raw_speech.astype(np.floataa							)

         # always return batch
         if not is_batched:
            UpperCAmelCase__							:				Any   				=  [raw_speech]

         # extract fbank features
         UpperCAmelCase__							:				List[Any]   				=  [self._extract_fbank_features(lowerCamelCase_							) for waveform in raw_speech]

         # convert into correct format for padding
         UpperCAmelCase__							:				Union[str, Any]   				=  BatchFeature({'''input_features''': features}							)

         UpperCAmelCase__							:				Any   				=  self.pad(
             lowerCamelCase_					,padding=lowerCamelCase_					,max_length=lowerCamelCase_					,truncation=lowerCamelCase_					,pad_to_multiple_of=lowerCamelCase_					,return_attention_mask=lowerCamelCase_					,**lowerCamelCase_					,)

         # make sure list is in array format
         UpperCAmelCase__							:				List[Any]   				=  padded_inputs.get('''input_features'''							)
         if isinstance(input_features[0]					,lowerCamelCase_							):
            UpperCAmelCase__							:				Union[str, Any]   				=  [np.asarray(lowerCamelCase_					,dtype=np.floataa							) for feature in input_features]

         UpperCAmelCase__							:				List[Any]   				=  padded_inputs.get('''attention_mask'''							)
         if attention_mask is not None:
            UpperCAmelCase__							:				List[Any]   				=  [np.asarray(lowerCamelCase_					,dtype=np.intaa							) for array in attention_mask]

         # Utterance-level cepstral mean and variance normalization
         if self.do_ceptral_normalize:
            UpperCAmelCase__							:				Optional[Any]   				=  (
                np.array(lowerCamelCase_					,dtype=np.intaa							)
                if self._get_padding_strategies(lowerCamelCase_					,max_length=lowerCamelCase_							) is not PaddingStrategy.DO_NOT_PAD
                else None
            )
            UpperCAmelCase__							:				Any   				=  self.normalize(
                padded_inputs['''input_features''']					,attention_mask=lowerCamelCase_							)

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

         return padded_inputs





'''simple docstring'''

import unittest

import numpy as np







def __UpperCamelCase(   _A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray | None = None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  np.shape(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.shape(_A    )
   UpperCAmelCase__							:				Tuple   				=  np.shape(_A    )

   if shape_a[0] != shape_b[0]:
      UpperCAmelCase__							:				Any   				=  (
          '''Expected the same number of rows for A and B. '''
          F'''Instead found A of size {shape_a} and B of size {shape_b}'''
      )
      raise ValueError(_A    )

   if shape_b[1] != shape_c[1]:
      UpperCAmelCase__							:				List[str]   				=  (
          '''Expected the same number of columns for B and C. '''
          F'''Instead found B of size {shape_b} and C of size {shape_c}'''
      )
      raise ValueError(_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  pseudo_inv
   if a_inv is None:
      try:
         UpperCAmelCase__							:				str   				=  np.linalg.inv(_A    )
      except np.linalg.LinAlgError:
         raise ValueError(
             '''Input matrix A is not invertible. Cannot compute Schur complement.'''    )

   return mat_c - mat_b.T @ a_inv @ mat_b


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				List[Any]   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[2, 1], [6, 3]]							)

         UpperCAmelCase__							:				Tuple   				=  schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  np.block([[a, b], [b.T, c]]							)

         UpperCAmelCase__							:				int   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.linalg.det(lowerCamelCase_							)

         self.assertAlmostEqual(lowerCamelCase_					,det_a * det_s							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[2, 1], [6, 3]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				str   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Any   				=  np.array([[2, 1, 3], [6, 3, 5]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()




