



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


snake_case    :						Dict  =						logging.get_logger(__name__)




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    List[Any]           =						['input_features', 'attention_mask']







					def __init__(       self      :     Optional[Any]			,		_A      :     Optional[int]=80			,		_A      :     Tuple=1_6000			,		_A      :     Union[str, Any]=80			,		_A      :     Dict=0.0			,		_A      :     Union[str, Any]=True			,		_A      :     int=True			,		_A      :     Dict=True			,		**_A      :     int			,		):
											super().__init__(feature_size=_A			,		sampling_rate=_A			,		padding_value=_A			,		**_A)
											A__	:							Dict						    = num_mel_bins
											A__	:							str						    = do_ceptral_normalize
											A__	:							Union[str, Any]						    = normalize_means
											A__	:							Optional[Any]						    = normalize_vars
											A__	:							int						    = True







					def  _lowercase			(       self      :     Tuple			,		_A      :     np.ndarray			,		):
											A__	:							str						    = waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
											A__	:							Any						    = torch.from_numpy(_A).unsqueeze(0)
											A__	:							List[Any]						    = ta_kaldi.fbank(_A			,		num_mel_bins=self.num_mel_bins			,		sample_frequency=self.sampling_rate)
											return features.numpy()







					@staticmethod
					def  _lowercase			(       _A      :     np.ndarray			,		_A      :     int			,		_A      :     Optional[bool] = True			,		_A      :     Optional[bool] = True			,		_A      :     float = 0.0			,		):
											# make sure we normalize float32 arrays
											if normalize_means:
																	A__	:							Optional[Any]						    = x[:input_length].mean(axis=0)
																	A__	:							Tuple						    = np.subtract(_A			,		_A)
											if normalize_vars:
																	A__	:							Optional[Any]						    = x[:input_length].std(axis=0)
																	A__	:							Tuple						    = np.divide(_A			,		_A)

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

											# make sure array is in float32
											A__	:							Union[str, Any]						    = x.astype(np.floataa)

											return x







					def  _lowercase			(       self      :     List[Any]			,		_A      :     List[np.ndarray]			,		_A      :     Optional[np.ndarray] = None):
											A__	:							str						    = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
											return [
											    self.utterance_cmvn(_A			,		_A			,		self.normalize_means			,		self.normalize_vars			,		self.padding_value)
											    for x, n in zip(_A			,		_A)
											]







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

											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.")

											A__	:							Dict						    = isinstance(_A			,		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}')
											A__	:							Any						    = is_batched_numpy or (
											    isinstance(_A			,		(list, tuple)) and (isinstance(raw_speech[0]			,		(np.ndarray, tuple, list)))
											)

											if is_batched:
																	A__	:							int						    = [np.asarray(_A			,		dtype=np.floataa) for speech in raw_speech]
											elif not is_batched and not isinstance(_A			,		np.ndarray):
																	A__	:							Tuple						    = np.asarray(_A			,		dtype=np.floataa)
											elif isinstance(_A			,		np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
																	A__	:							Optional[Any]						    = raw_speech.astype(np.floataa)

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

											# extract fbank features
											A__	:							Tuple						    = [self._extract_fbank_features(_A) for waveform in raw_speech]

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

											A__	:							Any						    = self.pad(
											    _A			,		padding=_A			,		max_length=_A			,		truncation=_A			,		pad_to_multiple_of=_A			,		return_attention_mask=_A			,		**_A			,		)

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

											A__	:							Any						    = padded_inputs.get("attention_mask")
											if attention_mask is not None:
																	A__	:							Dict						    = [np.asarray(_A			,		dtype=np.intaa) for array in attention_mask]

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

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

											return padded_inputs




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()