

from typing import List, Optional, Union

import numpy as np

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


_UpperCAmelCase		:   Union[str, Any]										=       logging.get_logger(__name__)







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			['''input_features''', '''attention_mask''']




				def __init__(					self      ,   snake_case=80      ,   snake_case=1_6000      ,   snake_case=0.0      ,   snake_case=10      ,   snake_case=25      ,   snake_case="hamming_window"      ,   snake_case=3_27_68.0      ,   snake_case=0.97      ,   snake_case=1.0      ,   snake_case=True      ,   snake_case=True      ,   snake_case=False      ,   **snake_case      ,   ):
											super().__init__(feature_size=snake_case      ,   sampling_rate=snake_case      ,   padding_value=snake_case      ,   **snake_case       )

											snake_case_							=    feature_size
											snake_case_							=    sampling_rate
											snake_case_							=    padding_value
											snake_case_							=    hop_length
											snake_case_							=    win_length
											snake_case_							=    frame_signal_scale
											snake_case_							=    preemphasis_coeff
											snake_case_							=    mel_floor
											snake_case_							=    normalize_means
											snake_case_							=    normalize_vars
											snake_case_							=    win_function
											snake_case_							=    return_attention_mask

											snake_case_							=    win_length * sampling_rate // 1000
											snake_case_							=    hop_length * sampling_rate // 1000

											snake_case_							=    optimal_fft_length(self.sample_size       )
											snake_case_							=    (self.n_fft // 2) + 1




				def     a							(					self      ,   snake_case       ):
											if self.win_function == "hamming_window":
																		snake_case_							=    window_function(window_length=self.sample_size      ,   name=self.win_function      ,   periodic=snake_case       )
											else:
																		snake_case_							=    window_function(window_length=self.sample_size      ,   name=self.win_function       )

											snake_case_							=    mel_filter_bank(
											    num_frequency_bins=self.n_freqs      ,   num_mel_filters=self.feature_size      ,   min_frequency=0.0      ,   max_frequency=self.sampling_rate / 2.0      ,   sampling_rate=self.sampling_rate      ,   )

											snake_case_							=    spectrogram(
											    one_waveform * self.frame_signal_scale      ,   window=snake_case      ,   frame_length=self.sample_size      ,   hop_length=self.sample_stride      ,   fft_length=self.n_fft      ,   center=snake_case      ,   preemphasis=self.preemphasis_coeff      ,   mel_filters=snake_case      ,   mel_floor=self.mel_floor      ,   log_mel='log'      ,   )
											return msfc_features.T




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											# make sure we normalize float32 arrays
											if self.normalize_means:
																		snake_case_							=    x[:input_length].mean(axis=0       )
																		snake_case_							=    np.subtract(snake_case      ,   snake_case       )
											if self.normalize_vars:
																		snake_case_							=    x[:input_length].std(axis=0       )
																		snake_case_							=    np.divide(snake_case      ,   snake_case       )

											if input_length < x.shape[0]:
																		snake_case_							=    padding_value

											# make sure array is in float32
											snake_case_							=    x.astype(np.floataa       )

											return x




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
											return [self._normalize_one(snake_case      ,   snake_case      ,   self.padding_value       ) for x, n in zip(snake_case      ,   snake_case       )]




				def __call__(					self      ,   snake_case      ,   snake_case = False      ,   snake_case = None      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   **snake_case      ,   ):

											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.'       )

											snake_case_							=    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}'''       )
											snake_case_							=    is_batched_numpy or (
											    isinstance(snake_case      ,   (list, tuple)       ) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list)       ))
											)

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

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

											# extract fbank features
											snake_case_							=    [self._extract_mfsc_features(snake_case       ) for one_waveform in raw_speech]

											# convert into correct format for padding
											snake_case_							=    BatchFeature({'input_features': features}       )

											snake_case_							=    self.pad(
											    snake_case      ,   padding=snake_case      ,   max_length=snake_case      ,   truncation=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_attention_mask=snake_case      ,   **snake_case      ,   )
											# make sure list is in array format
											snake_case_							=    padded_inputs.get('input_features'       )
											if isinstance(input_features[0]      ,   snake_case       ):
																		snake_case_							=    [np.asarray(snake_case      ,   dtype=np.floataa       ) for feature in input_features]

											snake_case_							=    padded_inputs.get('attention_mask'       )
											if attention_mask is not None:
																		snake_case_							=    [np.asarray(snake_case      ,   dtype=np.intaa       ) for array in attention_mask]

											if self.normalize_means or self.normalize_vars:
																		snake_case_							=    (
																		    np.array(snake_case      ,   dtype=np.intaa       )
																		    if self._get_padding_strategies(snake_case      ,   max_length=snake_case       ) is not PaddingStrategy.DO_NOT_PAD
																		    and padding
																		    else None
																		)
																		snake_case_							=    self.normalize(
																		    padded_inputs['input_features']      ,   attention_mask=snake_case       )

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

											return padded_inputs







from __future__ import annotations

import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    np.shape(UpperCamelCase__      )
							if rows != columns:
														snake_case_							=    (
														    '\'table\' has to be of square shaped array but got a '
														    F'''{rows}x{columns} array:\n{table}'''
														)
														raise ValueError(UpperCamelCase__      )

							snake_case_							=    np.zeros((rows, columns)      )
							snake_case_							=    np.zeros((rows, columns)      )
							for i in range(UpperCamelCase__      ):
														for j in range(UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					if upper[j][j] == 0:
																												raise ArithmeticError('No LU decomposition exists'      )
																					snake_case_							=    (table[i][j] - total) / upper[j][j]
														snake_case_							=    1
														for j in range(UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					snake_case_							=    table[i][j] - total
							return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()




