

'''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


lowerCamelCase :      Optional[int]					 =     logging.get_logger(__name__)







class 							A__    (       A__  ):
  A__		 =				['input_features']





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





       '''simple docstring'''






       super().__init__(
           feature_size=_a							,					sampling_rate=_a							,					padding_value=_a							,					return_attention_mask=_a							,					**_a							,					)
       _SCREAMING_SNAKE_CASE							    =n_fft
       _SCREAMING_SNAKE_CASE							    =hop_length
       _SCREAMING_SNAKE_CASE							    =chunk_length
       _SCREAMING_SNAKE_CASE							    =chunk_length * sampling_rate
       _SCREAMING_SNAKE_CASE							    =self.n_samples // hop_length
       _SCREAMING_SNAKE_CASE							    =sampling_rate
       _SCREAMING_SNAKE_CASE							    =mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2							,					num_mel_filters=_a							,					min_frequency=0.0							,					max_frequency=80_00.0							,					sampling_rate=_a							,					norm='slaney'							,					mel_scale='slaney'							,					)





  def 						A  (		self      :	str							,					_a      :	np.array )	->    np.ndarray:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =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							,					log_mel='log10'							,					)
       _SCREAMING_SNAKE_CASE							    =log_spec[:, :-1]
       _SCREAMING_SNAKE_CASE							    =np.maximum(_a							,					log_spec.max() - 8.0 )
       _SCREAMING_SNAKE_CASE							    =(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 						A  (		_a      :	List[np.ndarray]							,					_a      :	List[np.ndarray]							,					_a      :	float = 0.0 )	->    List[np.ndarray]:





       '''simple docstring'''






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

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

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

       return normed_input_values





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

       _SCREAMING_SNAKE_CASE							    =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}" )
       _SCREAMING_SNAKE_CASE							    =is_batched_numpy or (
           isinstance(_a							,					(list, tuple) ) and (isinstance(raw_speech[0]							,					(np.ndarray, tuple, list) ))
       )

       if is_batched:
            _SCREAMING_SNAKE_CASE							    =[np.asarray([speech]							,					dtype=np.floataa ).T for speech in raw_speech]
       elif not is_batched and not isinstance(_a							,					np.ndarray ):
            _SCREAMING_SNAKE_CASE							    =np.asarray(_a							,					dtype=np.floataa )
       elif isinstance(_a							,					np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
            _SCREAMING_SNAKE_CASE							    =raw_speech.astype(np.floataa )

       # always return batch
       if not is_batched:
            _SCREAMING_SNAKE_CASE							    =[np.asarray([raw_speech] ).T]

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

       # convert into correct format for padding

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

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

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

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

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

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

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

       return padded_inputs





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





       '''simple docstring'''






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



'''simple docstring'''
lowerCamelCase :      Any					 =     "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase :      int					 =     [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase :      str					 =     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
