





from math import ceil
from typing import List, Optional, Union

import numpy as np

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


a__:      Optional[int]									=		logging.get_logger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	['''audio_values''', '''audio_mask''']



 def __init__(							self,__lowerCamelCase=2048,__lowerCamelCase=1,__lowerCamelCase=[16, 16],__lowerCamelCase=128,__lowerCamelCase=4_4100,__lowerCamelCase=86,__lowerCamelCase=2048,__lowerCamelCase=0.0,**__lowerCamelCase,):
        super().__init__(
            feature_size=__lowerCamelCase,sampling_rate=__lowerCamelCase,padding_value=__lowerCamelCase,**__lowerCamelCase,)

        A__											=						spectrogram_length
        A__											=						num_channels
        A__											=						patch_size
        A__											=						feature_size // self.patch_size[1]
        A__											=						n_fft
        A__											=						sampling_rate // hop_length_to_sampling_rate
        A__											=						sampling_rate
        A__											=						padding_value
        A__											=						mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2,num_mel_filters=__lowerCamelCase,min_frequency=0.0,max_frequency=22050.0,sampling_rate=__lowerCamelCase,norm='''slaney''',mel_scale='''slaney''',).T



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						spectrogram(
            __lowerCamelCase,window_function(self.n_fft,'''hann''' ),frame_length=self.n_fft,hop_length=self.hop_length,power=2.0,mel_filters=self.mel_filters.T,log_mel='''dB''',db_range=80.0,)
        A__											=						log_spec[:, :-1]
        A__											=						log_spec - 20.0
        A__											=						np.clip(log_spec / 40.0,-2.0,0.0 ) + 1.0
        return log_spec




 def __call__(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = False,__lowerCamelCase = False,**__lowerCamelCase,):

        if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          '''This feature extractor is set to support sampling rate'''
                          f" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                          f" 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([speech],dtype=np.floataa ).T 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([raw_speech] ).T]

        # Convert audio signals to log mel spectrograms, truncate by time axis
        A__											=						[
            self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
        ]
        if isinstance(audio_features[0],__lowerCamelCase ):
               A__											=						[np.asarray(__lowerCamelCase,dtype=np.floataa ) for feature in audio_features]

        # Create audio attention mask
        A__											=						max(
            [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
        if return_attention_mask:
               A__											=						[
                   (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
                   + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
                   for feature in audio_features
               ]
               A__											=						np.array(__lowerCamelCase ).astype(np.floataa )

        # convert into correct format for padding
        A__											=						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
        A__											=						np.ones([len(__lowerCamelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
        A__											=						padded_audio_features * self.padding_value
        for i in range(len(__lowerCamelCase ) ):
               A__											=						audio_features[i]
               A__											=						feature

        # return as BatchFeature
        if return_attention_mask:
               A__											=						{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
        else:
               A__											=						{'''audio_values''': padded_audio_features}

        A__											=						BatchFeature(data=__lowerCamelCase,tensor_type=__lowerCamelCase )
        return encoded_inputs












import random


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       List[Any]  )->tuple:
       A__					,	A__					,	A__											=						[], [], []
       for element in data:
              if element < pivot:
                     less.append(UpperCamelCase__  )
              elif element > pivot:
                     greater.append(UpperCamelCase__  )
              else:
                     equal.append(UpperCamelCase__  )
       return less, equal, greater


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       int  )->Optional[int]:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(UpperCamelCase__  ) or index < 0:
              return None

       A__											=						items[random.randint(0 ,     len(UpperCamelCase__  ) - 1  )]
       A__											=						0
       A__					,	A__					,	A__											=						_partition(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(UpperCamelCase__ ,     UpperCamelCase__  )
       # must be in larger
       else:
              return quick_select(UpperCamelCase__ ,     index - (m + count)  )





