





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     :     Union[str, Any]							=	logging.get_logger(__name__)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : List[str]            =      ["""audio_values""", """audio_mask"""]







      def __init__(   self		,				lowerCAmelCase__=2_0_4_8		,				lowerCAmelCase__=1		,				lowerCAmelCase__=[1_6, 1_6]		,				lowerCAmelCase__=1_2_8		,				lowerCAmelCase__=4_4_1_0_0		,				lowerCAmelCase__=8_6		,				lowerCAmelCase__=2_0_4_8		,				lowerCAmelCase__=0.0		,				**lowerCAmelCase__		,				):



        '''simple docstring'''




        super().__init__(
            feature_size=lowerCAmelCase__		,				sampling_rate=lowerCAmelCase__		,				padding_value=lowerCAmelCase__		,				**lowerCAmelCase__		,				)

        a__     : Any  =spectrogram_length
        a__     : List[str]  =num_channels
        a__     : Dict  =patch_size
        a__     : str  =feature_size // self.patch_size[1]
        a__     : str  =n_fft
        a__     : List[Any]  =sampling_rate // hop_length_to_sampling_rate
        a__     : Optional[Any]  =sampling_rate
        a__     : Dict  =padding_value
        a__     : Optional[Any]  =mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2		,				num_mel_filters=lowerCAmelCase__		,				min_frequency=0.0		,				max_frequency=2_2_0_5_0.0		,				sampling_rate=lowerCAmelCase__		,				norm="slaney"		,				mel_scale="slaney"		,				).T







      def        _lowercase  (   self		,				lowerCAmelCase__							):



        '''simple docstring'''




        a__     : Dict  =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__     : Union[str, Any]  =log_spec[:, :-1]
        a__     : str  =log_spec - 20.0
        a__     : List[Any]  =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__		,				):



        '''simple docstring'''




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

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

        # Create audio attention mask
        a__     : 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:
          a__     : 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
          ]
          a__     : Any  =np.array(lowerCAmelCase__							).astype(np.floataa							)

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

        # return as BatchFeature
        if return_attention_mask:
          a__     : Tuple  ={"audio_values": padded_audio_features, "audio_mask": audio_mask}
        else:
          a__     : Union[str, Any]  ={"audio_values": padded_audio_features}

        a__     : Optional[Any]  =BatchFeature(data=lowerCAmelCase__		,				tensor_type=lowerCAmelCase__							)
        return encoded_inputs













from maths.prime_check import is_prime
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : Dict  =f'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE						)
  if is_prime(SCREAMING_SNAKE_CASE						) and is_prime(number + 2						):
    return number + 2
  else:
    return -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()






