
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


lowercase__          =logging.get_logger(__name__)



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				["input_features", "is_longer"]
   def __init__(self			: str ,    snake_case_			: List[str]=6_4 ,    snake_case_			: Any=4_8_0_0_0 ,    snake_case_			: Dict=4_8_0 ,    snake_case_			: List[Any]=1_0 ,    snake_case_			: Optional[Any]=1_0_2_4 ,    snake_case_			: Tuple=0.0 ,    snake_case_			: Any=False ,    snake_case_			: float = 0 ,    snake_case_			: float = 1_4_0_0_0 ,    snake_case_			: int = None ,    snake_case_			: str = "fusion" ,    snake_case_			: str = "repeatpad" ,    **snake_case_			: int ,    ):
    super().__init__(
        feature_size=snake_case_ ,    sampling_rate=snake_case_ ,    padding_value=snake_case_ ,    return_attention_mask=snake_case_ ,    **snake_case_ ,    )
    __a			:  List[str]		    =  top_db
    __a			:  Optional[int]		    =  truncation
    __a			:  Optional[int]		    =  padding
    __a			:  Optional[Any]		    =  fft_window_size
    __a			:  List[str]		    =  (fft_window_size >> 1) + 1
    __a			:  Tuple		    =  hop_length
    __a			:  Optional[int]		    =  max_length_s
    __a			:  List[str]		    =  max_length_s * sampling_rate
    __a			:  List[str]		    =  sampling_rate
    __a			:  Tuple		    =  frequency_min
    __a			:  str		    =  frequency_max
    __a			:  str		    =  mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins ,    num_mel_filters=snake_case_ ,    min_frequency=snake_case_ ,    max_frequency=snake_case_ ,    sampling_rate=snake_case_ ,    norm=snake_case_ ,    mel_scale='''htk''' ,    )
    __a			:  Tuple		    =  mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins ,    num_mel_filters=snake_case_ ,    min_frequency=snake_case_ ,    max_frequency=snake_case_ ,    sampling_rate=snake_case_ ,    norm='''slaney''' ,    mel_scale='''slaney''' ,    )
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Optional[Any]		    =  copy.deepcopy(self.__dict__       )
    __a			:  str		    =  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			: Tuple ,    snake_case_			: np.array ,    snake_case_			: Optional[np.array] = None       ):
    __a			:  str		    =  spectrogram(
        snake_case_ ,    window_function(self.fft_window_size ,    '''hann'''       ) ,    frame_length=self.fft_window_size ,    hop_length=self.hop_length ,    power=2.0 ,    mel_filters=snake_case_ ,    log_mel='''dB''' ,    )
    return log_mel_spectrogram.T
   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: Any ,    snake_case_			: Union[str, Any] ,    snake_case_			: List[str]       ):
    __a			:  Tuple		    =  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
     __a			:  List[str]		    =  [0]
    if len(ranges[2]       ) == 0:
     # if the audio is too short, we just use the first chunk
     __a			:  Any		    =  [0]
    # randomly choose index for each part
    __a			:  List[Any]		    =  np.random.choice(ranges[0]       )
    __a			:  Dict		    =  np.random.choice(ranges[1]       )
    __a			:  str		    =  np.random.choice(ranges[2]       )

    __a			:  Any		    =  mel[idx_front : idx_front + chunk_frames, :]
    __a			:  Optional[Any]		    =  mel[idx_middle : idx_middle + chunk_frames, :]
    __a			:  Union[str, Any]		    =  mel[idx_back : idx_back + chunk_frames, :]

    __a			:  Union[str, Any]		    =  torch.tensor(mel[None, None, :]       )
    __a			:  Tuple		    =  torch.nn.functional.interpolate(
        snake_case_ ,    size=[chunk_frames, 6_4] ,    mode='''bilinear''' ,    align_corners=snake_case_       )
    __a			:  Tuple		    =  mel_shrink[0][0].numpy()
    __a			:  Dict		    =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,    axis=0       )
    return mel_fusion
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: np.array ,    snake_case_			: Tuple ,    snake_case_			: List[Any] ,    snake_case_			: Optional[int]       ):
    if waveform.shape[0] > max_length:
     if truncation == "rand_trunc":
      __a			:  Union[str, Any]		    =  True
      # random crop to max_length (for compatibility) -> this should be handled by self.pad
      __a			:  str		    =  len(snake_case_       ) - max_length
      __a			:  List[str]		    =  np.random.randint(0 ,    overflow + 1       )
      __a			:  str		    =  waveform[idx : idx + max_length]
      __a			:  Optional[int]		    =  self._np_extract_fbank_features(snake_case_ ,    self.mel_filters_slaney       )[None, :]
     elif truncation == "fusion":
      __a			:  int		    =  self._np_extract_fbank_features(snake_case_ ,    self.mel_filters       )
      __a			:  Union[str, Any]		    =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
      __a			:  Any		    =  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.
       __a			:  Union[str, Any]		    =  np.stack([mel, mel, mel, mel] ,    axis=0       )
       __a			:  Dict		    =  False
      else:
       __a			:  Dict		    =  self._random_mel_fusion(snake_case_ ,    snake_case_ ,    snake_case_       )
       __a			:  int		    =  True
     else:
      raise NotImplementedError(f"data_truncating {truncation} not implemented"       )

    else:
     __a			:  Optional[Any]		    =  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":
       __a			:  int		    =  int(max_length / len(snake_case_       )       )
       __a			:  str		    =  np.stack(np.tile(snake_case_ ,    n_repeat + 1       )       )[:max_length]
      if padding == "repeatpad":
       __a			:  Dict		    =  int(max_length / len(snake_case_       )       )
       __a			:  Dict		    =  np.stack(np.tile(snake_case_ ,    snake_case_       )       )
      __a			:  Tuple		    =  np.pad(snake_case_ ,    (0, max_length - waveform.shape[0]) ,    mode='''constant''' ,    constant_values=0       )

     if truncation == "fusion":
      __a			:  List[Any]		    =  self._np_extract_fbank_features(snake_case_ ,    self.mel_filters       )
      __a			:  List[str]		    =  np.stack([input_mel, input_mel, input_mel, input_mel] ,    axis=0       )
     else:
      __a			:  Any		    =  self._np_extract_fbank_features(snake_case_ ,    self.mel_filters_slaney       )[None, :]

    return input_mel, longer







   def __call__(self			: List[str] ,    snake_case_			: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,    snake_case_			: str = None ,    snake_case_			: Optional[str] = None ,    snake_case_			: Optional[int] = None ,    snake_case_			: Optional[int] = None ,    snake_case_			: Optional[Union[str, TensorType]] = None ,    **snake_case_			: List[str] ,    ):
    __a			:  int		    =  truncation if truncation is not None else self.truncation
    __a			:  List[str]		    =  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.'''       )

    __a			:  str		    =  isinstance(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}"       )
    __a			:  Union[str, Any]		    =  is_batched_numpy or (
        isinstance(snake_case_ ,    (list, tuple)       ) and (isinstance(raw_speech[0] ,    (np.ndarray, tuple, list)       ))
    )

    if is_batched:
     __a			:  Tuple		    =  [np.asarray(snake_case_ ,    dtype=np.floataa       ) for speech in raw_speech]
    elif not is_batched and not isinstance(snake_case_ ,    np.ndarray       ):
     __a			:  Tuple		    =  np.asarray(snake_case_ ,    dtype=np.floataa       )
    elif isinstance(snake_case_ ,    np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
     __a			:  Union[str, Any]		    =  raw_speech.astype(np.floataa       )

    # always return batch
    if not is_batched:
     __a			:  Tuple		    =  [np.asarray(snake_case_       )]

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

    __a			:  Optional[int]		    =  []
    __a			:  Optional[Any]		    =  []
    for mel, longer in padded_inputs:
     input_mel.append(snake_case_       )
     is_longer.append(snake_case_       )

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

    if isinstance(input_mel[0] ,    snake_case_       ):
     __a			:  int		    =  [np.asarray(snake_case_ ,    dtype=np.floataa       ) for feature in input_mel]

    # is_longer is a list of bool
    __a			:  Union[str, Any]		    =  [[longer] for longer in is_longer]

    __a			:  Optional[int]		    =  {'''input_features''': input_mel, '''is_longer''': is_longer}
    __a			:  Tuple		    =  BatchFeature(snake_case_       )

    if return_tensors is not None:
     __a			:  List[Any]		    =  input_features.convert_to_tensors(snake_case_       )

    return input_features


def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):

 def merge(lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ) -> list:

  def _merge():
   while left and right:
    yield (left if left[0] <= right[0] else right).pop(0  )
   yield from left
   yield from right

  return list(_merge()  )

 if len(lowerCAmelCase__  ) <= 1:
  return collection
 __a			:  str		    =  len(lowerCAmelCase__  ) // 2
 return merge(merge_sort(collection[:mid]  )  ,       merge_sort(collection[mid:]  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__          =input('Enter numbers separated by a comma:\n').strip()
      lowercase__          =[int(item) for item in user_input.split(',')]
      print(*merge_sort(unsorted), sep=',')
