





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        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Any				      = ['''audio_values''', '''audio_mask''']


   def __init__(					self    :       List[Any]    ,  a    :       Tuple=2_048    ,  a    :       List[str]=1    ,  a    :       int=[16, 16]    ,  a    :       Optional[int]=128    ,  a    :       Dict=44_100    ,  a    :       Dict=86    ,  a    :       Union[str, Any]=2_048    ,  a    :       int=0.0    ,  **a    :       Optional[Any]    ,  )->     List[Any]:





     """simple docstring"""







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

     lowercase__			    =						spectrogram_length
     lowercase__			    =						num_channels
     lowercase__			    =						patch_size
     lowercase__			    =						feature_size // self.patch_size[1]
     lowercase__			    =						n_fft
     lowercase__			    =						sampling_rate // hop_length_to_sampling_rate
     lowercase__			    =						sampling_rate
     lowercase__			    =						padding_value
     lowercase__			    =						mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2    ,  num_mel_filters=A__    ,  min_frequency=0.0    ,  max_frequency=22_050.0    ,  sampling_rate=A__    ,  norm='slaney'    ,  mel_scale='slaney'    ,  ).T


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  a    :       int )->     Tuple:





     """simple docstring"""







     lowercase__			    =						spectrogram(
         A__    ,  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    ,  )
     lowercase__			    =						log_spec[:, :-1]
     lowercase__			    =						log_spec - 20.0
     lowercase__			    =						np.clip(log_spec / 40.0    ,  -2.0    ,  0.0 ) + 1.0
     return log_spec


   def __call__(					self    :       Optional[int]    ,  a    :       Any    ,  a    :       str = None    ,  a    :       int = True    ,  a    :       Optional[int] = None    ,  a    :       Union[str, Any] = False    ,  a    :       Dict = False    ,  **a    :       Optional[int]    ,  )->     Any:





     """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.' )

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

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

     # Create audio attention mask
     lowercase__			    =						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:
       lowercase__			    =						[
           (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
       ]
       lowercase__			    =						np.array(A__ ).astype(np.floataa )

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

     # return as BatchFeature
     if return_attention_mask:
       lowercase__			    =						{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
     else:
       lowercase__			    =						{"""audio_values""": padded_audio_features}

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









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE = 50	)       ->     int:
  lowercase__			    =						[1] * (length + 1)

  for row_length in range(3	,		length + 1	):
    for block_length in range(3	,		row_length + 1	):
      for block_start in range(row_length - block_length	):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
      print(f'''{solution() = }''')


