


'''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


lowercase				=logging.get_logger(__name__)






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["input_features"]






       def __init__(     self    ,				snake_case=8_0    ,				snake_case=1_6_0_0_0    ,				snake_case=1_6_0    ,				snake_case=3_0    ,				snake_case=4_0_0    ,				snake_case=0.0    ,				snake_case=False    ,				**snake_case    ,				)		->    Union[str, Any]:




          '''simple docstring'''
          super().__init__(
              feature_size=snake_case    ,				sampling_rate=snake_case    ,				padding_value=snake_case    ,				return_attention_mask=snake_case    ,				**snake_case    ,				)
          _UpperCAmelCase  :  Any				       =n_fft
          _UpperCAmelCase  :  Union[str, Any]				       =hop_length
          _UpperCAmelCase  :  List[Any]				       =chunk_length
          _UpperCAmelCase  :  Optional[int]				       =chunk_length * sampling_rate
          _UpperCAmelCase  :  Any				       =self.n_samples // hop_length
          _UpperCAmelCase  :  List[Any]				       =sampling_rate
          _UpperCAmelCase  :  Any				       =mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2    ,				num_mel_filters=snake_case    ,				min_frequency=0.0    ,				max_frequency=80_00.0    ,				sampling_rate=snake_case    ,				norm='slaney'    ,				mel_scale='slaney'    ,				)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =spectrogram(
              snake_case    ,				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'    ,				)
          _UpperCAmelCase  :  Dict				       =log_spec[:, :-1]
          _UpperCAmelCase  :  str				       =np.maximum(snake_case    ,				log_spec.max() - 8.0)
          _UpperCAmelCase  :  Optional[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      lowerCAmelCase						(     snake_case    ,				snake_case    ,				snake_case = 0.0)		->    List[np.ndarray]:




          '''simple docstring'''
          if attention_mask is not None:
             _UpperCAmelCase  :  Union[str, Any]				       =np.array(snake_case    ,				np.intaa)
             _UpperCAmelCase  :  Optional[int]				       =[]

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

                normed_input_values.append(snake_case)
          else:
             _UpperCAmelCase  :  Optional[int]				       =[(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

          return normed_input_values






       def __call__(     self    ,				snake_case    ,				snake_case = True    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = "max_length"    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				**snake_case    ,				)		->    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.__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.')

          _UpperCAmelCase  :  Tuple				       =isinstance(snake_case    ,				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  :  int				       =is_batched_numpy or (
              isinstance(snake_case    ,				(list, tuple)) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)))
          )

          if is_batched:
             _UpperCAmelCase  :  List[str]				       =[np.asarray([speech]    ,				dtype=np.floataa).T for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case    ,				np.ndarray):
             _UpperCAmelCase  :  int				       =np.asarray(snake_case    ,				dtype=np.floataa)
          elif isinstance(snake_case    ,				np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
             _UpperCAmelCase  :  Union[str, Any]				       =raw_speech.astype(np.floataa)

          # always return batch
          if not is_batched:
             _UpperCAmelCase  :  Any				       =[np.asarray([raw_speech]).T]

          _UpperCAmelCase  :  Tuple				       =BatchFeature({'input_features': raw_speech})

          # convert into correct format for padding

          _UpperCAmelCase  :  str				       =self.pad(
              snake_case    ,				padding=snake_case    ,				max_length=max_length if max_length else self.n_samples    ,				truncation=snake_case    ,				pad_to_multiple_of=snake_case    ,				return_attention_mask=return_attention_mask or do_normalize    ,				)

          # zero-mean and unit-variance normalization
          if do_normalize:
             _UpperCAmelCase  :  Any				       =self.zero_mean_unit_var_norm(
                 padded_inputs['input_features']    ,				attention_mask=padded_inputs['attention_mask']    ,				padding_value=self.padding_value    ,				)
             _UpperCAmelCase  :  List[str]				       =np.stack(padded_inputs['input_features']    ,				axis=0)

          # make sure list is in array format
          _UpperCAmelCase  :  Dict				       =padded_inputs.get('input_features').transpose(2    ,				0    ,				1)

          _UpperCAmelCase  :  int				       =[self._np_extract_fbank_features(snake_case) for waveform in input_features[0]]

          if isinstance(input_features[0]    ,				snake_case):
             _UpperCAmelCase  :  Any				       =[np.asarray(snake_case    ,				dtype=np.floataa) for feature in input_features]
          else:
             _UpperCAmelCase  :  int				       =input_features

          if return_attention_mask:
             # rescale from sample (48000) to feature (3000)
             _UpperCAmelCase  :  Dict				       =padded_inputs['attention_mask'][:, :: self.hop_length]

          if return_tensors is not None:
             _UpperCAmelCase  :  Dict				       =padded_inputs.convert_to_tensors(snake_case)

          return padded_inputs






       def      lowerCAmelCase						(     self)		->    Dict[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =copy.deepcopy(self.__dict__)
          _UpperCAmelCase  :  Optional[int]				       =self.__class__.__name__
          if "mel_filters" in output:
             del output["mel_filters"]
          return output





'''simple docstring'''






from typing import Any

import numpy as np





def lowerCamelCase__     (		__lowerCamelCase			:				np.ndarray				):







   '''simple docstring'''




   return np.array_equal(__lowerCamelCase			,       matrix.conjugate().T				)





def lowerCamelCase__     (		__lowerCamelCase			:				np.ndarray			,       __lowerCamelCase			:				np.ndarray				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =v.conjugate().T
   _UpperCAmelCase  :  Optional[int]				       =v_star.dot(__lowerCamelCase				)
   assert isinstance(__lowerCamelCase			,       np.ndarray				)
   return (v_star_dot.dot(__lowerCamelCase				)) / (v_star.dot(__lowerCamelCase				))





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]				)
   _UpperCAmelCase  :  List[str]				       =np.array([[1], [2], [3]]				)
   assert is_hermitian(__lowerCamelCase				), f"{a} is not hermitian."
   print(rayleigh_quotient(__lowerCamelCase			,       __lowerCamelCase				)				)

   _UpperCAmelCase  :  List[str]				       =np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]				)
   assert is_hermitian(__lowerCamelCase				), f"{a} is not hermitian."
   assert rayleigh_quotient(__lowerCamelCase			,       __lowerCamelCase				) == float(3				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()

