



"""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


lowercase__		:    List[Any]				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					["""input_features""", """attention_mask"""]



      def __init__(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Tuple=8_0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=1_6_0_0_0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=0.0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=1_0   ,      SCREAMING_SNAKE_CASE_    :				str=2_5   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="hamming_window"   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=3_2_7_6_8.0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.97   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=1.0   ,      SCREAMING_SNAKE_CASE_    :				Tuple=True   ,      SCREAMING_SNAKE_CASE_    :				Tuple=True   ,      SCREAMING_SNAKE_CASE_    :				Any=False   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(feature_size=SCREAMING_SNAKE_CASE_   ,      sampling_rate=SCREAMING_SNAKE_CASE_   ,      padding_value=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

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

        lowerCAmelCase_       :    Optional[int]								=			win_length * sampling_rate // 1_0_0_0
        lowerCAmelCase_       :    Optional[int]								=			hop_length * sampling_rate // 1_0_0_0

        lowerCAmelCase_       :    List[str]								=			optimal_fft_length(self.sample_size	)
        lowerCAmelCase_       :    str								=			(self.n_fft // 2) + 1



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				np.array	):
        if self.win_function == "hamming_window":
          lowerCAmelCase_       :    List[Any]								=			window_function(window_length=self.sample_size   ,      name=self.win_function   ,      periodic=SCREAMING_SNAKE_CASE_	)
        else:
          lowerCAmelCase_       :    str								=			window_function(window_length=self.sample_size   ,      name=self.win_function	)

        lowerCAmelCase_       :    Dict								=			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   ,      )

        lowerCAmelCase_       :    List[Any]								=			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    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        # make sure we normalize float32 arrays
        if self.normalize_means:
          lowerCAmelCase_       :    int								=			x[:input_length].mean(axis=0	)
          lowerCAmelCase_       :    str								=			np.subtract(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        if self.normalize_vars:
          lowerCAmelCase_       :    int								=			x[:input_length].std(axis=0	)
          lowerCAmelCase_       :    List[str]								=			np.divide(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

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

        # make sure array is in float32
        lowerCAmelCase_       :    List[Any]								=			x.astype(np.floataa	)

        return x



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[np.ndarray]   ,      SCREAMING_SNAKE_CASE_    :				Optional[np.ndarray] = None	):
        lowerCAmelCase_       :    Union[str, 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    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,      SCREAMING_SNAKE_CASE_    :				Union[bool, str, PaddingStrategy] = False   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = None   ,      SCREAMING_SNAKE_CASE_    :				bool = False   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[str, TensorType]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[int] = None   ,      **SCREAMING_SNAKE_CASE_    :				Union[str, 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.'	)

        lowerCAmelCase_       :    List[Any]								=			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}"	)
        lowerCAmelCase_       :    Tuple								=			is_batched_numpy or (
            isinstance(SCREAMING_SNAKE_CASE_   ,      (list, tuple)	) and (isinstance(raw_speech[0]   ,      (np.ndarray, tuple, list)	))
        )

        if is_batched:
          lowerCAmelCase_       :    Optional[Any]								=			[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	):
          lowerCAmelCase_       :    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	):
          lowerCAmelCase_       :    Union[str, Any]								=			raw_speech.astype(np.floataa	)

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

        # extract fbank features
        lowerCAmelCase_       :    Dict								=			[self._extract_mfsc_features(SCREAMING_SNAKE_CASE_	) for one_waveform in raw_speech]

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

        lowerCAmelCase_       :    Union[str, 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
        lowerCAmelCase_       :    Any								=			padded_inputs.get('input_features'	)
        if isinstance(input_features[0]   ,      SCREAMING_SNAKE_CASE_	):
          lowerCAmelCase_       :    Union[str, Any]								=			[np.asarray(SCREAMING_SNAKE_CASE_   ,      dtype=np.floataa	) for feature in input_features]

        lowerCAmelCase_       :    Union[str, Any]								=			padded_inputs.get('attention_mask'	)
        if attention_mask is not None:
          lowerCAmelCase_       :    int								=			[np.asarray(SCREAMING_SNAKE_CASE_   ,      dtype=np.intaa	) for array in attention_mask]

        if self.normalize_means or self.normalize_vars:
          lowerCAmelCase_       :    Optional[int]								=			(
              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
          )
          lowerCAmelCase_       :    Optional[int]								=			self.normalize(
              padded_inputs['input_features']   ,      attention_mask=SCREAMING_SNAKE_CASE_	)

        if return_tensors is not None:
          lowerCAmelCase_       :    Union[str, Any]								=			padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_	)

        return padded_inputs










"""simple docstring"""







from __future__ import annotations

def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       list[int]:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			2
  lowerCAmelCase_       :    List[Any]								=			[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(lowerCAmelCase__						)
  if n > 1:
    factors.append(lowerCAmelCase__						)
  return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()





