
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			:       int									=				logging.get_logger(__name__)

class __lowerCAmelCase	(   lowercase_    ):
     lowercase		     =		['''input_features''', '''attention_mask''']





     def __init__(			self			,  __UpperCAmelCase=80			,  __UpperCAmelCase=1_6000			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=10			,  __UpperCAmelCase=25			,  __UpperCAmelCase="hamming_window"			,  __UpperCAmelCase=3_2768.0			,  __UpperCAmelCase=0.9_7			,  __UpperCAmelCase=1.0			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




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

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

      __UpperCamelCase       						= win_length * sampling_rate // 1000
      __UpperCamelCase       						= hop_length * sampling_rate // 1000

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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if self.win_function == "hamming_window":
       __UpperCamelCase       						= window_function(window_length=self.sample_size			,  name=self.win_function			,  periodic=__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= window_function(window_length=self.sample_size			,  name=self.win_function   )

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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if self.normalize_means:
       __UpperCamelCase       						= x[:input_length].mean(axis=0   )
       __UpperCamelCase       						= np.subtract(__UpperCAmelCase			,  __UpperCAmelCase   )
      if self.normalize_vars:
       __UpperCamelCase       						= x[:input_length].std(axis=0   )
       __UpperCamelCase       						= np.divide(__UpperCAmelCase			,  __UpperCAmelCase   )

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

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

      return x





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




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







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

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

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

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

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

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

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

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

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

      if return_tensors is not None:
       __UpperCamelCase       						= padded_inputs.convert_to_tensors(__UpperCAmelCase   )

      return padded_inputs





"""simple docstring"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



