

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


lowerCamelCase           =					logging.get_logger(__name__)







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	str    =   ['''input_features''', '''is_longer''']



   def __init__(				self,    lowercase_=64,    lowercase_=48000,    lowercase_=480,    lowercase_=10,    lowercase_=1024,    lowercase_=0.0,    lowercase_=False,    lowercase_ = 0,    lowercase_ = 14000,    lowercase_ = None,    lowercase_ = "fusion",    lowercase_ = "repeatpad",    **lowercase_,    )							->    Optional[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__  =copy.deepcopy(self.__dict__       )
     a__  =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  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =spectrogram(
         lowerCAmelCase__,    window_function(self.fft_window_size,    '''hann'''       ),    frame_length=self.fft_window_size,    hop_length=self.hop_length,    power=2.0,    mel_filters=lowerCAmelCase__,    log_mel='''dB''',    )
     return log_mel_spectrogram.T



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_       )							->    Tuple:






     """simple docstring"""






     a__  =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__  =[0]
     if len(ranges[2]       ) == 0:
       # if the audio is too short, we just use the first chunk
       a__  =[0]
     # randomly choose index for each part
     a__  =np.random.choice(ranges[0]       )
     a__  =np.random.choice(ranges[1]       )
     a__  =np.random.choice(ranges[2]       )

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

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



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    str:






     """simple docstring"""






     if waveform.shape[0] > max_length:
       if truncation == "rand_trunc":
         a__  =True
         # random crop to max_length (for compatibility) -> this should be handled by self.pad
         a__  =len(lowerCAmelCase__       ) - max_length
         a__  =np.random.randint(0,    overflow + 1       )
         a__  =waveform[idx : idx + max_length]
         a__  =self._np_extract_fbank_features(lowerCAmelCase__,    self.mel_filters_slaney       )[None, :]
       elif truncation == "fusion":
         a__  =self._np_extract_fbank_features(lowerCAmelCase__,    self.mel_filters       )
         a__  =max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
         a__  =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__  =np.stack([mel, mel, mel, mel],    axis=0       )
           a__  =False
         else:
           a__  =self._random_mel_fusion(lowerCAmelCase__,    lowerCAmelCase__,    lowerCAmelCase__       )
           a__  =True
       else:
         raise NotImplementedError(F"""data_truncating {truncation} not implemented"""       )

     else:
       a__  =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(max_length / len(lowerCAmelCase__       )       )
           a__  =np.stack(np.tile(lowerCAmelCase__,    n_repeat + 1       )       )[:max_length]
         if padding == "repeatpad":
           a__  =int(max_length / len(lowerCAmelCase__       )       )
           a__  =np.stack(np.tile(lowerCAmelCase__,    lowerCAmelCase__       )       )
         a__  =np.pad(lowerCAmelCase__,    (0, max_length - waveform.shape[0]),    mode='''constant''',    constant_values=0       )

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

     return input_mel, longer



   def __call__(				self,    lowercase_,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    lowercase_ = None,    **lowercase_,    )							->    List[Any]:






     """simple docstring"""






     a__  =truncation if truncation is not None else self.truncation
     a__  =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__  =isinstance(lowerCAmelCase__,    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__  =is_batched_numpy or (
         isinstance(lowerCAmelCase__,    (list, tuple)       ) and (isinstance(raw_speech[0],    (np.ndarray, tuple, list)       ))
     )

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

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

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

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

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

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

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

     a__  ={"""input_features""": input_mel, """is_longer""": is_longer}
     a__  =BatchFeature(lowerCAmelCase__       )

     if return_tensors is not None:
       a__  =input_features.convert_to_tensors(lowerCAmelCase__       )

     return input_features





'''simple docstring'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]					):
		if not nums:
				return 0
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nums[0]
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0
		for num in nums[1:]:
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Any           =		(
				    max_excluding + num,
				    max(_lowerCamelCase       ,      _lowerCamelCase					),
				)
		return max(_lowerCamelCase       ,      _lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()