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


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Dict						=      ["""audio_values""", """audio_mask"""]







    def __init__(	self , __lowercase=2_048 , __lowercase=1 , __lowercase=[16, 16] , __lowercase=128 , __lowercase=44_100 , __lowercase=86 , __lowercase=2_048 , __lowercase=0.0 , **__lowercase , )       ->	Optional[int]:
        super().__init__(
            feature_size=__lowercase , sampling_rate=__lowercase , padding_value=__lowercase , **__lowercase , )

        __UpperCamelCase     :Any			  =		spectrogram_length
        __UpperCamelCase     :Tuple			  =		num_channels
        __UpperCamelCase     :Tuple			  =		patch_size
        __UpperCamelCase     :Any			  =		feature_size // self.patch_size[1]
        __UpperCamelCase     :Union[str, Any]			  =		n_fft
        __UpperCamelCase     :Any			  =		sampling_rate // hop_length_to_sampling_rate
        __UpperCamelCase     :int			  =		sampling_rate
        __UpperCamelCase     :List[str]			  =		padding_value
        __UpperCamelCase     :List[Any]			  =		mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowercase , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=__lowercase , norm='''slaney''' , mel_scale='''slaney''' , ).T







    def 			UpperCamelCase__   (	self , __lowercase)       ->	np.ndarray:
        __UpperCamelCase     :str			  =		spectrogram(
            __lowercase , 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 , )
        __UpperCamelCase     :Union[str, Any]			  =		log_spec[:, :-1]
        __UpperCamelCase     :int			  =		log_spec - 20.0
        __UpperCamelCase     :Optional[int]			  =		np.clip(log_spec / 40.0 , -2.0 , 0.0) + 1.0
        return log_spec







    def __call__(	self , __lowercase , __lowercase = None , __lowercase = True , __lowercase = None , __lowercase = False , __lowercase = False , **__lowercase , )       ->	BatchFeature:

        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.''')

        __UpperCamelCase     :Dict			  =		isinstance(__lowercase , 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     :str			  =		is_batched_numpy or (
            isinstance(__lowercase , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
        )
        if is_batched:
            __UpperCamelCase     :Optional[int]			  =		[np.asarray([speech] , dtype=np.floataa).T for speech in raw_speech]
        elif not is_batched and not isinstance(__lowercase , np.ndarray):
            __UpperCamelCase     :int			  =		np.asarray(__lowercase , dtype=np.floataa)
        elif isinstance(__lowercase , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
            __UpperCamelCase     :Optional[int]			  =		raw_speech.astype(np.floataa)
        # always return batch
        if not is_batched:
            __UpperCamelCase     :Optional[int]			  =		[np.asarray([raw_speech]).T]

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

        # Create audio attention mask
        __UpperCamelCase     :Dict			  =		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:
            __UpperCamelCase     :List[Any]			  =		[
                (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
            ]
            __UpperCamelCase     :Union[str, Any]			  =		np.array(__lowercase).astype(np.floataa)

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

        # return as BatchFeature
        if return_attention_mask:
            __UpperCamelCase     :Optional[Any]			  =		{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
        else:
            __UpperCamelCase     :Optional[Any]			  =		{'''audio_values''': padded_audio_features}

        __UpperCamelCase     :Any			  =		BatchFeature(data=__lowercase , tensor_type=__lowercase)
        return encoded_inputs






import random



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		a[left_index]
    __UpperCamelCase     :Any			  =		left_index + 1
    for j in range(left_index + 1  ,			SCREAMING_SNAKE_CASE	):
        if a[j] < pivot:
            __UpperCamelCase  ,       __UpperCamelCase     :str			  =		a[i], a[j]
            i += 1
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		a[i - 1], a[left_index]
    return i - 1



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if left < right:
        __UpperCamelCase     :int			  =		random.randint(SCREAMING_SNAKE_CASE  ,			right - 1	)
        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        __UpperCamelCase     :Dict			  =		partition(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			pivot_index + 1  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the right of the pivot point



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		input('''Enter numbers separated by a comma:\n'''	).strip()
    __UpperCamelCase     :Union[str, Any]			  =		[int(SCREAMING_SNAKE_CASE	) for item in user_input.split(''','''	)]

    quick_sort_random(SCREAMING_SNAKE_CASE  ,			0  ,			len(SCREAMING_SNAKE_CASE	)	)

    print(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





