






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					=	logging.get_logger(__name__)



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Optional[Any]				= ['input_features']



    def __init__(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=80 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=16000 ,     __SCREAMING_SNAKE_CASE      :     int=160 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=30 ,     __SCREAMING_SNAKE_CASE      :     str=400 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=0.0 ,     __SCREAMING_SNAKE_CASE      :     List[str]=False ,     **__SCREAMING_SNAKE_CASE      :     List[str] ,     )      ->							Union[str, Any]:
           super().__init__(
               feature_size=__SCREAMING_SNAKE_CASE ,     sampling_rate=__SCREAMING_SNAKE_CASE ,     padding_value=__SCREAMING_SNAKE_CASE ,     return_attention_mask=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     )
           __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=__SCREAMING_SNAKE_CASE ,     min_frequency=0.0 ,     max_frequency=8_000.0 ,     sampling_rate=__SCREAMING_SNAKE_CASE ,     norm="""slaney""" ,     mel_scale="""slaney""" ,     )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     np.array      )      ->							np.ndarray:
           __UpperCAmelCase     				=spectrogram(
               __SCREAMING_SNAKE_CASE ,     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(__SCREAMING_SNAKE_CASE ,     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 _a							(				__SCREAMING_SNAKE_CASE      :     List[np.ndarray] ,     __SCREAMING_SNAKE_CASE      :     List[np.ndarray] ,     __SCREAMING_SNAKE_CASE      :     float = 0.0      )      ->							List[np.ndarray]:
           if attention_mask is not None:
                  __UpperCAmelCase     				=np.array(__SCREAMING_SNAKE_CASE ,     np.intaa      )
                  __UpperCAmelCase     				=[]

                  for vector, length in zip(__SCREAMING_SNAKE_CASE ,     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(__SCREAMING_SNAKE_CASE      )
           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] ,     __SCREAMING_SNAKE_CASE      :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Optional[int] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, TensorType]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[str] = "max_length" ,     __SCREAMING_SNAKE_CASE      :     Optional[int] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[int] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     **__SCREAMING_SNAKE_CASE      :     Any ,     )      ->							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."""      )

           __UpperCAmelCase     				=isinstance(__SCREAMING_SNAKE_CASE ,     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(__SCREAMING_SNAKE_CASE ,     (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(__SCREAMING_SNAKE_CASE ,     np.ndarray      ):
                  __UpperCAmelCase     				=np.asarray(__SCREAMING_SNAKE_CASE ,     dtype=np.floataa      )
           elif isinstance(__SCREAMING_SNAKE_CASE ,     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(
               __SCREAMING_SNAKE_CASE ,     padding=__SCREAMING_SNAKE_CASE ,     max_length=max_length if max_length else self.n_samples ,     truncation=__SCREAMING_SNAKE_CASE ,     pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,     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(__SCREAMING_SNAKE_CASE      ) for waveform in input_features[0]]

           if isinstance(input_features[0] ,     __SCREAMING_SNAKE_CASE      ):
                  __UpperCAmelCase     				=[np.asarray(__SCREAMING_SNAKE_CASE ,     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(__SCREAMING_SNAKE_CASE      )

           return padded_inputs



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








'''simple docstring'''






import warnings
from functools import wraps
from typing import Callable





def lowerCamelCase__     (		__lowerCamelCase			:				Callable				):







   '''simple docstring'''




   @wraps(__lowerCamelCase				)
   def _inner_fn(*__lowerCamelCase			:				int			,       **__lowerCamelCase			:				Optional[Any]				):
      warnings.warn(
          (f"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")			,       __lowerCamelCase			,       )
      return fn(*__lowerCamelCase			,       **__lowerCamelCase				)

   return _inner_fn

