



"""simple docstring"""
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


A:   Any								=		logging.get_logger(__name__)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   str						=						['input_features', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=80      ,     _SCREAMING_SNAKE_CASE=16000      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=10      ,     _SCREAMING_SNAKE_CASE=25      ,     _SCREAMING_SNAKE_CASE="hamming_window"      ,     _SCREAMING_SNAKE_CASE=3_2768.0      ,     _SCREAMING_SNAKE_CASE=0.97      ,     _SCREAMING_SNAKE_CASE=1.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=False      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




    super().__init__(feature_size=_SCREAMING_SNAKE_CASE      ,     sampling_rate=_SCREAMING_SNAKE_CASE      ,     padding_value=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	feature_size
    UpperCAmelCase	:					str	=	sampling_rate
    UpperCAmelCase	:					List[str]	=	padding_value
    UpperCAmelCase	:					Dict	=	hop_length
    UpperCAmelCase	:					str	=	win_length
    UpperCAmelCase	:					int	=	frame_signal_scale
    UpperCAmelCase	:					Tuple	=	preemphasis_coeff
    UpperCAmelCase	:					Any	=	mel_floor
    UpperCAmelCase	:					Optional[int]	=	normalize_means
    UpperCAmelCase	:					Union[str, Any]	=	normalize_vars
    UpperCAmelCase	:					Optional[int]	=	win_function
    UpperCAmelCase	:					Optional[int]	=	return_attention_mask

    UpperCAmelCase	:					Optional[Any]	=	win_length * sampling_rate // 1000
    UpperCAmelCase	:					List[Any]	=	hop_length * sampling_rate // 1000

    UpperCAmelCase	:					Optional[int]	=	optimal_fft_length(self.sample_size						)
    UpperCAmelCase	:					List[Any]	=	(self.n_fft // 2) + 1

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      np.ndarray:





    '''simple docstring'''




    if self.win_function == "hamming_window":
     UpperCAmelCase	:					str	=	window_function(window_length=self.sample_size      ,     name=self.win_function      ,     periodic=_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Any	=	window_function(window_length=self.sample_size      ,     name=self.win_function						)

    UpperCAmelCase	:					Optional[int]	=	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      ,     )

    UpperCAmelCase	:					Optional[int]	=	spectrogram(
        one_waveform * self.frame_signal_scale      ,     window=_SCREAMING_SNAKE_CASE      ,     frame_length=self.sample_size      ,     hop_length=self.sample_stride      ,     fft_length=self.n_fft      ,     center=_SCREAMING_SNAKE_CASE      ,     preemphasis=self.preemphasis_coeff      ,     mel_filters=_SCREAMING_SNAKE_CASE      ,     mel_floor=self.mel_floor      ,     log_mel="""log"""      ,     )
    return msfc_features.T

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




    if self.normalize_means:
     UpperCAmelCase	:					str	=	x[:input_length].mean(axis=0						)
     UpperCAmelCase	:					Optional[int]	=	np.subtract(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    if self.normalize_vars:
     UpperCAmelCase	:					Any	=	x[:input_length].std(axis=0						)
     UpperCAmelCase	:					List[str]	=	np.divide(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

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

    # make sure array is in float32
    UpperCAmelCase	:					Dict	=	x.astype(np.floataa						)

    return x

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[np.ndarray]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
    return [self._normalize_one(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     self.padding_value						) for x, n in zip(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)]




   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_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} 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."""						)

    UpperCAmelCase	:					List[str]	=	isinstance(_SCREAMING_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	:					Any	=	is_batched_numpy or (
        isinstance(_SCREAMING_SNAKE_CASE      ,     (list, tuple)						) and (isinstance(raw_speech[0]      ,     (np.ndarray, tuple, list)						))
    )

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

    # always return batch
    if not is_batched:
     UpperCAmelCase	:					Union[str, Any]	=	[raw_speech]

    # extract fbank features
    UpperCAmelCase	:					Optional[int]	=	[self._extract_mfsc_features(_SCREAMING_SNAKE_CASE						) for one_waveform in raw_speech]

    # convert into correct format for padding
    UpperCAmelCase	:					Optional[Any]	=	BatchFeature({"""input_features""": features}						)

    UpperCAmelCase	:					List[Any]	=	self.pad(
        _SCREAMING_SNAKE_CASE      ,     padding=_SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE      ,     truncation=_SCREAMING_SNAKE_CASE      ,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,     return_attention_mask=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )
    # make sure list is in array format
    UpperCAmelCase	:					Union[str, Any]	=	padded_inputs.get("""input_features"""						)
    if isinstance(input_features[0]      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[Any]	=	[np.asarray(_SCREAMING_SNAKE_CASE      ,     dtype=np.floataa						) for feature in input_features]

    UpperCAmelCase	:					Union[str, Any]	=	padded_inputs.get("""attention_mask"""						)
    if attention_mask is not None:
     UpperCAmelCase	:					Optional[Any]	=	[np.asarray(_SCREAMING_SNAKE_CASE      ,     dtype=np.intaa						) for array in attention_mask]

    if self.normalize_means or self.normalize_vars:
     UpperCAmelCase	:					Union[str, Any]	=	(
         np.array(_SCREAMING_SNAKE_CASE      ,     dtype=np.intaa						)
         if self._get_padding_strategies(_SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE						) is not PaddingStrategy.DO_NOT_PAD
         and padding
         else None
     )
     UpperCAmelCase	:					Any	=	self.normalize(
         padded_inputs["""input_features"""]      ,     attention_mask=_SCREAMING_SNAKE_CASE						)

    if return_tensors is not None:
     UpperCAmelCase	:					Optional[Any]	=	padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE						)

    return padded_inputs






class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    n
							__lowerCamelCase		       =    [None] * self.n
							__lowerCamelCase		       =    0  # index of the first element
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    0


			def __len__(      self							)    ->      int:



							'''simple docstring'''
							return self.size


			def       lowercase_    (      self							)    ->      bool:



							'''simple docstring'''
							return self.size == 0


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							return False if self.is_empty() else self.array[self.front]


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Optional[Any]:



							'''simple docstring'''
							if self.size >= self.n:
											raise Exception('QUEUE IS FULL'							)

							__lowerCamelCase		       =    data
							__lowerCamelCase		       =    (self.rear + 1) % self.n
							self.size += 1
							return self






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							if self.size == 0:
											raise Exception('UNDERFLOW'							)

							__lowerCamelCase		       =    self.array[self.front]
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    (self.front + 1) % self.n
							self.size -= 1
							return temp


