





'''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 TensorType, logging


A :			int							   =      logging.get_logger(__name__)







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     ["input_values", "attention_mask"]


					def __init__(       self						:	Optional[int]							,  __snake_case						:	Tuple=1							,  __snake_case						:	Optional[int]=1_60_00							,  __snake_case						:	Any=1_28							,  __snake_case						:	Optional[Any]=10_24							,  __snake_case						:	int=0.0							,  __snake_case						:	List[str]=True							,  __snake_case						:	Dict=-4.2_677_393							,  __snake_case						:	Tuple=4.5_689_974							,  __snake_case						:	Optional[int]=False							,  **__snake_case						:	Optional[int]							,  ):





											'''simple docstring'''





											super().__init__(feature_size=lowercase__							,  sampling_rate=lowercase__							,  padding_value=lowercase__							,  **lowercase__							)
											_snake_case: Any	       =		num_mel_bins
											_snake_case: Dict	       =		max_length
											_snake_case: Union[str, Any]	       =		do_normalize
											_snake_case: List[str]	       =		mean
											_snake_case: List[Any]	       =		std
											_snake_case: Union[str, Any]	       =		return_attention_mask


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	np.ndarray							,  __snake_case						:	int							,  ):





											'''simple docstring'''





											_snake_case: int	       =		torch.from_numpy(lowercase__							).unsqueeze(0							)
											_snake_case: List[Any]	       =		ta_kaldi.fbank(
											    lowercase__							,  htk_compat=lowercase__							,  sample_frequency=self.sampling_rate							,  use_energy=lowercase__							,  window_type='hanning'							,  num_mel_bins=self.num_mel_bins							,  dither=0.0							,  frame_shift=10							,  )

											_snake_case: Dict	       =		fbank.shape[0]
											_snake_case: Tuple	       =		max_length - n_frames

											# pad or truncate, depending on difference
											if difference > 0:
																	_snake_case: str	       =		torch.nn.ZeroPadad((0, 0, 0, difference)							)
																	_snake_case: List[str]	       =		pad_module(lowercase__							)
											elif difference < 0:
																	_snake_case: str	       =		fbank[0:max_length, :]

											_snake_case: Union[str, Any]	       =		fbank.numpy()

											return fbank


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	np.ndarray							):





											'''simple docstring'''





											return (input_values - (self.mean)) / (self.std * 2)




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





											'''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.'							)

											_snake_case: int	       =		isinstance(lowercase__							,  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: Optional[Any]	       =		is_batched_numpy or (
											    isinstance(lowercase__							,  (list, tuple)							) and (isinstance(raw_speech[0]							,  (np.ndarray, tuple, list)							))
											)

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

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

											# extract fbank features and pad/truncate to max_length
											_snake_case: Dict	       =		[self._extract_fbank_features(lowercase__							,  max_length=self.max_length							) for waveform in raw_speech]

											# convert into BatchFeature
											_snake_case: Dict	       =		BatchFeature({'input_values': features}							)

											# make sure list is in array format
											_snake_case: List[Any]	       =		padded_inputs.get('input_values'							)
											if isinstance(input_values[0]							,  lowercase__							):
																	_snake_case: List[Any]	       =		[np.asarray(lowercase__							,  dtype=np.floataa							) for feature in input_values]

											# normalization
											if self.do_normalize:
																	_snake_case: str	       =		[self.normalize(lowercase__							) for feature in input_values]

											if return_tensors is not None:
																	_snake_case: List[Any]	       =		padded_inputs.convert_to_tensors(lowercase__							)

											return padded_inputs













'''simple docstring'''







def     _UpperCamelCase				(  snake_case__				):
      if len(snake_case__				) <= 1:
            return data
      else:
            return [
                *quick_sort([e for e in data[1:] if e <= data[0]]				),
                data[0],
                *quick_sort([e for e in data[1:] if e > data[0]]				),
            ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


