


'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


a_    :			Tuple      				=			logging.get_logger(__name__)




class        a   (   _a				):
 _lowerCAmelCase           =		['''input_features''']
 def __init__(       self					, __magic_name__=80					, __magic_name__=1_60_00					, __magic_name__=1_60					, __magic_name__=30					, __magic_name__=4_00					, __magic_name__=0.0					, __magic_name__=False					, **__magic_name__					, )    ->							Optional[int]:
      super().__init__(
          feature_size=__magic_name__					, sampling_rate=__magic_name__					, padding_value=__magic_name__					, return_attention_mask=__magic_name__					, **__magic_name__					, )
      _a						=							n_fft
      _a						=							hop_length
      _a						=							chunk_length
      _a						=							chunk_length * sampling_rate
      _a						=							self.n_samples // hop_length
      _a						=							sampling_rate
      _a						=							mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2					, num_mel_filters=__magic_name__					, min_frequency=0.0					, max_frequency=80_00.0					, sampling_rate=__magic_name__					, norm='slaney'					, mel_scale='slaney'					, )
 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							np.ndarray:
      _a						=							spectrogram(
          __magic_name__					, window_function(self.n_fft					, 'hann'  )					, frame_length=self.n_fft					, hop_length=self.hop_length					, power=2.0					, mel_filters=self.mel_filters					, log_mel='log10'					, )
      _a						=							log_spec[:, :-1]
      _a						=							np.maximum(__magic_name__					, log_spec.max() - 8.0  )
      _a						=							(log_spec + 4.0) / 4.0
      return log_spec
 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 							__UpperCAmelCase						(       __magic_name__					, __magic_name__					, __magic_name__ = 0.0  )    ->							List[np.ndarray]:



      if attention_mask is not None:
           _a						=							np.array(__magic_name__					, np.intaa  )
           _a						=							[]

           for vector, length in zip(__magic_name__					, attention_mask.sum(-1  )  ):
                _a						=							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7  )
                if length < normed_slice.shape[0]:
                     _a						=							padding_value

                normed_input_values.append(__magic_name__  )
      else:
           _a						=							[(x - x.mean()) / np.sqrt(x.var() + 1e-7  ) for x in input_values]

      return normed_input_values
 def __call__(       self					, __magic_name__					, __magic_name__ = True					, __magic_name__ = None					, __magic_name__ = None					, __magic_name__ = None					, __magic_name__ = "max_length"					, __magic_name__ = None					, __magic_name__ = None					, __magic_name__ = None					, **__magic_name__					, )    ->							BatchFeature:



      if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                    f' was sampled 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(__magic_name__					, 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(__magic_name__					, (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(__magic_name__					, np.ndarray  ):
           _a						=							np.asarray(__magic_name__					, dtype=np.floataa  )
      elif isinstance(__magic_name__					, 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]

      _a						=							BatchFeature({'input_features': raw_speech}  )

      # convert into correct format for padding

      _a						=							self.pad(
          __magic_name__					, padding=__magic_name__					, max_length=max_length if max_length else self.n_samples					, truncation=__magic_name__					, pad_to_multiple_of=__magic_name__					, return_attention_mask=return_attention_mask or do_normalize					, )

      # zero-mean and unit-variance normalization
      if do_normalize:
           _a						=							self.zero_mean_unit_var_norm(
               padded_inputs['input_features']					, attention_mask=padded_inputs['attention_mask']					, padding_value=self.padding_value					, )
           _a						=							np.stack(padded_inputs['input_features']					, axis=0  )

      # make sure list is in array format
      _a						=							padded_inputs.get('input_features'  ).transpose(2					, 0					, 1  )

      _a						=							[self._np_extract_fbank_features(__magic_name__  ) for waveform in input_features[0]]

      if isinstance(input_features[0]					, __magic_name__  ):
           _a						=							[np.asarray(__magic_name__					, dtype=np.floataa  ) for feature in input_features]
      else:
           _a						=							input_features

      if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           _a						=							padded_inputs['''attention_mask'''][:, :: self.hop_length]

      if return_tensors is not None:
           _a						=							padded_inputs.convert_to_tensors(__magic_name__  )

      return padded_inputs




 def 							__UpperCAmelCase						(       self  )    ->							Dict[str, Any]:
      _a						=							copy.deepcopy(self.__dict__  )
      _a						=							self.__class__.__name__
      if "mel_filters" in output:
           del output["mel_filters"]
      return output








def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


				return "\n".join(
				    f'''{number} * {i} = {number * i}''' for i in range(1 ,				number_of_terms + 1 ) )


if __name__ == "__main__":
	print(multiplication_table(number=5, number_of_terms=1_0))
