





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


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   ["""audio_values""", """audio_mask"""]





     def __init__(     self				,							_UpperCamelCase=2048				,							_UpperCamelCase=1				,							_UpperCamelCase=[16, 16]				,							_UpperCamelCase=128				,							_UpperCamelCase=44100				,							_UpperCamelCase=86				,							_UpperCamelCase=2048				,							_UpperCamelCase=0.0				,							**_UpperCamelCase				,							):
        super().__init__(
            feature_size=_UpperCamelCase				,							sampling_rate=_UpperCamelCase				,							padding_value=_UpperCamelCase				,							**_UpperCamelCase				,							)

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   spectrogram(
            _UpperCamelCase				,							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           =   log_spec[:, :-1]
        _UpperCAmelCase           =   log_spec - 20.0
        _UpperCAmelCase           =   np.clip(log_spec / 40.0				,							-2.0				,							0.0					) + 1.0
        return log_spec





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

        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           =   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}'''					)
        _UpperCAmelCase           =   is_batched_numpy or (
            isinstance(_UpperCamelCase				,							(list, tuple)					) and (isinstance(raw_speech[0]				,							(np.ndarray, tuple, list)					))
        )
        if is_batched:
           _UpperCAmelCase           =   [np.asarray([speech]				,							dtype=np.floataa					).T for speech in raw_speech]
        elif not is_batched and not isinstance(_UpperCamelCase				,							np.ndarray					):
           _UpperCAmelCase           =   np.asarray(_UpperCamelCase				,							dtype=np.floataa					)
        elif isinstance(_UpperCamelCase				,							np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
           _UpperCAmelCase           =   raw_speech.astype(np.floataa					)
        # always return batch
        if not is_batched:
           _UpperCAmelCase           =   [np.asarray([raw_speech]					).T]

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

        # Create audio attention mask
        _UpperCAmelCase           =   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           =   [
               (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           =   np.array(_UpperCamelCase					).astype(np.floataa					)

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

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

        _UpperCAmelCase           =   BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)
        return encoded_inputs






import baseaa
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  bytes:






   """simple docstring"""







   return baseaa.baaencode(string.encode('''utf-8'''						)						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      bytes						)  ->  str:






   """simple docstring"""







   return baseaa.baadecode(SCREAMING_SNAKE_CASE_						).decode('''utf-8'''						)


if __name__ == "__main__":
   UpperCAmelCase_           =	"Hello World!"
   UpperCAmelCase_           =	baseaa_encode(test)
   print(encoded)

   UpperCAmelCase_           =	baseaa_decode(encoded)
   print(decoded)