






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:     Optional[Any]   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''input_features''']


  def __init__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			str=80       ,				lowerCamelCase		:			Any=1_6000       ,				lowerCamelCase		:			int=160       ,				lowerCamelCase		:			Optional[Any]=30       ,				lowerCamelCase		:			List[str]=400       ,				lowerCamelCase		:			Any=0.0       ,				lowerCamelCase		:			Any=False       ,				**lowerCamelCase		:			int       ,				)						->	str:


         """simple docstring"""
         super().__init__(
             feature_size=lowerCamelCase       ,				sampling_rate=lowerCamelCase       ,				padding_value=lowerCamelCase       ,				return_attention_mask=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    n_fft
         _UpperCAmelCase							=    hop_length
         _UpperCAmelCase							=    chunk_length
         _UpperCAmelCase							=    chunk_length * sampling_rate
         _UpperCAmelCase							=    self.n_samples // hop_length
         _UpperCAmelCase							=    sampling_rate
         _UpperCAmelCase							=    mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2       ,				num_mel_filters=lowerCamelCase       ,				min_frequency=0.0       ,				max_frequency=8000.0       ,				sampling_rate=lowerCamelCase       ,				norm="""slaney"""       ,				mel_scale="""slaney"""       ,				)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			np.array							)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    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       ,				log_mel="""log10"""       ,				)
         _UpperCAmelCase							=    log_spec[:, :-1]
         _UpperCAmelCase							=    np.maximum(lowerCamelCase       ,				log_spec.max() - 8.0							)
         _UpperCAmelCase							=    (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  lowerCamelCase       (      lowerCamelCase		:			List[np.ndarray]       ,				lowerCamelCase		:			List[np.ndarray]       ,				lowerCamelCase		:			float = 0.0							)						->	List[np.ndarray]:


         """simple docstring"""
         if attention_mask is not None:
                _UpperCAmelCase							=    np.array(lowerCamelCase       ,				np.intaa							)
                _UpperCAmelCase							=    []

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

                       normed_input_values.append(lowerCamelCase							)
         else:
                _UpperCAmelCase							=    [(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

         return normed_input_values


  def __call__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[int] = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[str] = "max_length"       ,				lowerCamelCase		:			Optional[int] = None       ,				lowerCamelCase		:			Optional[int] = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				**lowerCamelCase		:			Union[str, Any]       ,				)						->	BatchFeature:


         """simple docstring"""

         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."""							)

         _UpperCAmelCase							=    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}"""							)
         _UpperCAmelCase							=    is_batched_numpy or (
             isinstance(lowerCamelCase       ,				(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(lowerCamelCase       ,				np.ndarray							):
                _UpperCAmelCase							=    np.asarray(lowerCamelCase       ,				dtype=np.floataa							)
         elif isinstance(lowerCamelCase       ,				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]

         _UpperCAmelCase							=    BatchFeature({"""input_features""": raw_speech}							)

         # convert into correct format for padding

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

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

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

         _UpperCAmelCase							=    [self._np_extract_fbank_features(lowerCamelCase							) for waveform in input_features[0]]

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

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

         if return_tensors is not None:
                _UpperCAmelCase							=    padded_inputs.convert_to_tensors(lowerCamelCase							)

         return padded_inputs





  def  lowerCamelCase       (      self		:			Any							)						->	Dict[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    copy.deepcopy(self.__dict__							)
         _UpperCAmelCase							=    self.__class__.__name__
         if "mel_filters" in output:
                del output["mel_filters"]
         return output







def       _SCREAMING_SNAKE_CASE (							__snake_case = 1_0_0_0 )				->						int:
       _UpperCAmelCase	,			_UpperCAmelCase							=    1, 1
       _UpperCAmelCase							=    []
       for i in range(1   ,      n + 1 ):
              _UpperCAmelCase							=    prev_numerator + 2 * prev_denominator
              _UpperCAmelCase							=    prev_numerator + prev_denominator
              if len(str(__snake_case ) ) > len(str(__snake_case ) ):
                     result.append(__snake_case )
              _UpperCAmelCase							=    numerator
              _UpperCAmelCase							=    denominator

       return len(__snake_case )


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