





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


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   ["""input_features"""]





     def __init__(     self				,							_UpperCamelCase=80				,							_UpperCamelCase=16000				,							_UpperCamelCase=160				,							_UpperCamelCase=30				,							_UpperCamelCase=400				,							_UpperCamelCase=0.0				,							_UpperCamelCase=False				,							**_UpperCamelCase				,							):
        super().__init__(
            feature_size=_UpperCamelCase				,							sampling_rate=_UpperCamelCase				,							padding_value=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							**_UpperCamelCase				,							)
        _UpperCAmelCase           =   n_fft
        _UpperCAmelCase           =   hop_length
        _UpperCAmelCase           =   chunk_length
        _UpperCAmelCase           =   chunk_length * sampling_rate
        _UpperCAmelCase           =   self.n_samples // hop_length
        _UpperCAmelCase           =   sampling_rate
        _UpperCAmelCase           =   mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2				,							num_mel_filters=_UpperCamelCase				,							min_frequency=0.0				,							max_frequency=8000.0				,							sampling_rate=_UpperCamelCase				,							norm='''slaney'''				,							mel_scale='''slaney'''				,							)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   spectrogram(
            _UpperCamelCase				,							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           =   log_spec[:, :-1]
        _UpperCAmelCase           =   np.maximum(_UpperCamelCase				,							log_spec.max() - 8.0					)
        _UpperCAmelCase           =   (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(     _UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = 0.0					):
        if attention_mask is not None:
           _UpperCAmelCase           =   np.array(_UpperCamelCase				,							np.intaa					)
           _UpperCAmelCase           =   []

           for vector, length in zip(_UpperCamelCase				,							attention_mask.sum(-1					)					):
              _UpperCAmelCase           =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
              if length < normed_slice.shape[0]:
                 _UpperCAmelCase           =   padding_value

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

        return normed_input_values





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = "max_length"				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):

        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           =   isinstance(_UpperCamelCase				,							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           =   is_batched_numpy or (
            isinstance(_UpperCamelCase				,							(list, tuple)					) and (isinstance(raw_speech[0]				,							(np.ndarray, tuple, list)					))
        )

        if is_batched:
           _UpperCAmelCase           =   [np.asarray([speech]				,							dtype=np.floataa					).T for speech in raw_speech]
        elif not is_batched and not isinstance(_UpperCamelCase				,							np.ndarray					):
           _UpperCAmelCase           =   np.asarray(_UpperCamelCase				,							dtype=np.floataa					)
        elif isinstance(_UpperCamelCase				,							np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
           _UpperCAmelCase           =   raw_speech.astype(np.floataa					)

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

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

        # convert into correct format for padding

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

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

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

        _UpperCAmelCase           =   [self._np_extract_fbank_features(_UpperCamelCase					) for waveform in input_features[0]]

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

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

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

        return padded_inputs





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   copy.deepcopy(self.__dict__					)
        _UpperCAmelCase           =   self.__class__.__name__
        if "mel_filters" in output:
           del output["mel_filters"]
        return output







"""simple docstring"""
import numpy as np






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ):
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[1]
	# Ensure proper dimensionality.
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[0]
	# Ensure inputs are either both complex or both real
	assert np.iscomplexobj(snake_case__       ) == np.iscomplexobj(snake_case__       )
	A_		:    Union[str, Any]						     = np.iscomplexobj(snake_case__       )
	if is_complex:
		# Ensure complex input_matrix is Hermitian
		assert np.array_equal(snake_case__ , input_matrix.conj().T       )

	# Set convergence to False. Will define convergence when we exceed max_iterations
	# or when we have small changes from one iteration to next.

	A_		:    List[str]						     = False
	A_		:    int						     = 0
	A_		:    List[Any]						     = 0
	A_		:    Tuple						     = 1E12

	while not convergence:
		# Multiple matrix by the vector.
		A_		:    Dict						     = np.dot(snake_case__ , snake_case__       )
		# Normalize the resulting output vector.
		A_		:    str						     = w / np.linalg.norm(snake_case__       )
		# Find rayleigh quotient
		# (faster than usual b/c we know vector is normalized already)
		A_		:    str						     = vector.conj().T if is_complex else vector.T
		A_		:    Optional[Any]						     = np.dot(snake_case__ , np.dot(snake_case__ , snake_case__       )       )

		# Check convergence.
		A_		:    Any						     = np.abs(lambda_ - lambda_previous       ) / lambda_
		iterations += 1

		if error <= error_tol or iterations >= max_iterations:
			A_		:    Optional[Any]						     = True

		A_		:    Union[str, Any]						     = lambda_

	if is_complex:
		A_		:    str						     = np.real(lambda_       )

	return lambda_, vector






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]       )
	A_		:    Tuple						     = np.array([41, 4, 20]       )
	A_		:    Tuple						     = real_input_matrix.astype(np.complexaaa       )
	A_		:    List[Any]						     = np.triu(1J * complex_input_matrix , 1       )
	complex_input_matrix += imag_matrix
	complex_input_matrix += -1 * imag_matrix.T
	A_		:    Any						     = np.array([41, 4, 20]       ).astype(np.complexaaa       )

	for problem_type in ["real", "complex"]:
		if problem_type == "real":
			A_		:    Optional[int]						     = real_input_matrix
			A_		:    List[str]						     = real_vector
		elif problem_type == "complex":
			A_		:    List[Any]						     = complex_input_matrix
			A_		:    str						     = complex_vector

		# Our implementation.
		A_     ,  A_		:    Dict						     = power_iteration(snake_case__ , snake_case__       )

		# Numpy implementation.

		# Get eigenvalues and eigenvectors using built-in numpy
		# eigh (eigh used for symmetric or hermetian matrices).
		A_     ,  A_		:    Optional[Any]						     = np.linalg.eigh(snake_case__       )
		# Last eigenvalue is the maximum one.
		A_		:    List[Any]						     = eigen_values[-1]
		# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
		A_		:    Optional[Any]						     = eigen_vectors[:, -1]

		# Check our implementation and numpy gives close answers.
		assert np.abs(eigen_value - eigen_value_max       ) <= 1E-6
		# Take absolute values element wise of each eigenvector.
		# as they are only unique to a minus sign.
		assert np.linalg.norm(np.abs(snake_case__       ) - np.abs(snake_case__       )       ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()

