



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a_			=					logging.get_logger(__name__)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  ["""input_features""", """is_longer"""]




 def __init__(						self	,				__UpperCAmelCase=64	,				__UpperCAmelCase=48000	,				__UpperCAmelCase=480	,				__UpperCAmelCase=10	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase=False	,				__UpperCAmelCase = 0	,				__UpperCAmelCase = 14000	,				__UpperCAmelCase = None	,				__UpperCAmelCase = "fusion"	,				__UpperCAmelCase = "repeatpad"	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(
        feature_size=__UpperCAmelCase	,				sampling_rate=__UpperCAmelCase	,				padding_value=__UpperCAmelCase	,				return_attention_mask=__UpperCAmelCase	,				**__UpperCAmelCase	,				)
    __lowerCamelCase													=    top_db
    __lowerCamelCase													=    truncation
    __lowerCamelCase													=    padding
    __lowerCamelCase													=    fft_window_size
    __lowerCamelCase													=    (fft_window_size >> 1) + 1
    __lowerCamelCase													=    hop_length
    __lowerCamelCase													=    max_length_s
    __lowerCamelCase													=    max_length_s * sampling_rate
    __lowerCamelCase													=    sampling_rate
    __lowerCamelCase													=    frequency_min
    __lowerCamelCase													=    frequency_max
    __lowerCamelCase													=    mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins	,				num_mel_filters=__UpperCAmelCase	,				min_frequency=__UpperCAmelCase	,				max_frequency=__UpperCAmelCase	,				sampling_rate=__UpperCAmelCase	,				norm=__UpperCAmelCase	,				mel_scale='''htk'''	,				)
    __lowerCamelCase													=    mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins	,				num_mel_filters=__UpperCAmelCase	,				min_frequency=__UpperCAmelCase	,				max_frequency=__UpperCAmelCase	,				sampling_rate=__UpperCAmelCase	,				norm='''slaney'''	,				mel_scale='''slaney'''	,				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    copy.deepcopy(self.__dict__				)
    __lowerCamelCase													=    self.__class__.__name__
    if "mel_filters" in output:
       del output["mel_filters"]
    if "mel_filters_slaney" in output:
       del output["mel_filters_slaney"]
    return output




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



    '''simple docstring'''
    __lowerCamelCase													=    spectrogram(
        __UpperCAmelCase	,				window_function(self.fft_window_size	,				'''hann'''				)	,				frame_length=self.fft_window_size	,				hop_length=self.hop_length	,				power=2.0	,				mel_filters=__UpperCAmelCase	,				log_mel='''dB'''	,				)
    return log_mel_spectrogram.T




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    np.array_split(list(range(0	,				total_frames - chunk_frames + 1				)				)	,				3				)
    if len(ranges[1]				) == 0:
       # if the audio is too short, we just use the first chunk
       __lowerCamelCase													=    [0]
    if len(ranges[2]				) == 0:
       # if the audio is too short, we just use the first chunk
       __lowerCamelCase													=    [0]
    # randomly choose index for each part
    __lowerCamelCase													=    np.random.choice(ranges[0]				)
    __lowerCamelCase													=    np.random.choice(ranges[1]				)
    __lowerCamelCase													=    np.random.choice(ranges[2]				)

    __lowerCamelCase													=    mel[idx_front : idx_front + chunk_frames, :]
    __lowerCamelCase													=    mel[idx_middle : idx_middle + chunk_frames, :]
    __lowerCamelCase													=    mel[idx_back : idx_back + chunk_frames, :]

    __lowerCamelCase													=    torch.tensor(mel[None, None, :]				)
    __lowerCamelCase													=    torch.nn.functional.interpolate(
        __UpperCAmelCase	,				size=[chunk_frames, 64]	,				mode='''bilinear'''	,				align_corners=__UpperCAmelCase				)
    __lowerCamelCase													=    mel_shrink[0][0].numpy()
    __lowerCamelCase													=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,				axis=0				)
    return mel_fusion




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    if waveform.shape[0] > max_length:
       if truncation == "rand_trunc":
          __lowerCamelCase													=    True
          # random crop to max_length (for compatibility) -> this should be handled by self.pad
          __lowerCamelCase													=    len(__UpperCAmelCase				) - max_length
          __lowerCamelCase													=    np.random.randint(0	,				overflow + 1				)
          __lowerCamelCase													=    waveform[idx : idx + max_length]
          __lowerCamelCase													=    self._np_extract_fbank_features(__UpperCAmelCase	,				self.mel_filters_slaney				)[None, :]
       elif truncation == "fusion":
          __lowerCamelCase													=    self._np_extract_fbank_features(__UpperCAmelCase	,				self.mel_filters				)
          __lowerCamelCase													=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
          __lowerCamelCase													=    mel.shape[0]
          if chunk_frames == total_frames:
             # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
             # In this case, we just use the whole audio.
             __lowerCamelCase													=    np.stack([mel, mel, mel, mel]	,				axis=0				)
             __lowerCamelCase													=    False
          else:
             __lowerCamelCase													=    self._random_mel_fusion(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)
             __lowerCamelCase													=    True
       else:
          raise NotImplementedError(F"""data_truncating {truncation} not implemented"""				)

    else:
       __lowerCamelCase													=    False
       # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
       if waveform.shape[0] < max_length:
          if padding == "repeat":
             __lowerCamelCase													=    int(max_length / len(__UpperCAmelCase				)				)
             __lowerCamelCase													=    np.stack(np.tile(__UpperCAmelCase	,				n_repeat + 1				)				)[:max_length]
          if padding == "repeatpad":
             __lowerCamelCase													=    int(max_length / len(__UpperCAmelCase				)				)
             __lowerCamelCase													=    np.stack(np.tile(__UpperCAmelCase	,				__UpperCAmelCase				)				)
          __lowerCamelCase													=    np.pad(__UpperCAmelCase	,				(0, max_length - waveform.shape[0])	,				mode='''constant'''	,				constant_values=0				)

       if truncation == "fusion":
          __lowerCamelCase													=    self._np_extract_fbank_features(__UpperCAmelCase	,				self.mel_filters				)
          __lowerCamelCase													=    np.stack([input_mel, input_mel, input_mel, input_mel]	,				axis=0				)
       else:
          __lowerCamelCase													=    self._np_extract_fbank_features(__UpperCAmelCase	,				self.mel_filters_slaney				)[None, :]

    return input_mel, longer




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



    '''simple docstring'''
    __lowerCamelCase													=    truncation if truncation is not None else self.truncation
    __lowerCamelCase													=    padding if padding else self.padding

    if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
          raise ValueError(
              F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
              F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
              F""" was sampled with {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													=    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}"""				)
    __lowerCamelCase													=    is_batched_numpy or (
        isinstance(__UpperCAmelCase	,				(list, tuple)				) and (isinstance(raw_speech[0]	,				(np.ndarray, tuple, list)				))
    )

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

    # always return batch
    if not is_batched:
       __lowerCamelCase													=    [np.asarray(__UpperCAmelCase				)]

    # convert to mel spectrogram, truncate and pad if needed.
    __lowerCamelCase													=    [
        self._get_input_mel(__UpperCAmelCase	,				max_length if max_length else self.nb_max_samples	,				__UpperCAmelCase	,				__UpperCAmelCase				)
        for waveform in raw_speech
    ]

    __lowerCamelCase													=    []
    __lowerCamelCase													=    []
    for mel, longer in padded_inputs:
       input_mel.append(__UpperCAmelCase				)
       is_longer.append(__UpperCAmelCase				)

    if truncation == "fusion" and sum(__UpperCAmelCase				) == 0:
       # if no audio is longer than 10s, then randomly select one audio to be longer
       __lowerCamelCase													=    np.random.randint(0	,				len(__UpperCAmelCase				)				)
       __lowerCamelCase													=    True

    if isinstance(input_mel[0]	,				__UpperCAmelCase				):
       __lowerCamelCase													=    [np.asarray(__UpperCAmelCase	,				dtype=np.floataa				) for feature in input_mel]

    # is_longer is a list of bool
    __lowerCamelCase													=    [[longer] for longer in is_longer]

    __lowerCamelCase													=    {'''input_features''': input_mel, '''is_longer''': is_longer}
    __lowerCamelCase													=    BatchFeature(__UpperCAmelCase				)

    if return_tensors is not None:
       __lowerCamelCase													=    input_features.convert_to_tensors(__UpperCAmelCase				)

    return input_features









def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str = " "							):
   __lowerCamelCase													=    []

   __lowerCamelCase													=    0
   for index, char in enumerate(_UpperCamelCase							):
      if char == separator:
         split_words.append(string[last_index:index]							)
         __lowerCamelCase													=    index + 1
      elif index + 1 == len(_UpperCamelCase							):
         split_words.append(string[last_index : index + 1]							)
   return split_words


if __name__ == "__main__":
  from doctest import testmod

  testmod()






