
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


UpperCAmelCase__		:     Tuple     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    int 				=    ['''input_features''']


   def __init__(					self     :							int			,			lowerCAmelCase_     :							Optional[int]=8_0			,			lowerCAmelCase_     :							int=1_6_0_0_0			,			lowerCAmelCase_     :							Optional[Any]=1_6_0			,			lowerCAmelCase_     :							List[Any]=3_0			,			lowerCAmelCase_     :							List[Any]=4_0_0			,			lowerCAmelCase_     :							List[str]=0.0			,			lowerCAmelCase_     :							Any=False			,			**lowerCAmelCase_     :							Optional[Any]			,			):

      """simple docstring"""
      super().__init__(
          feature_size=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			padding_value=lowerCAmelCase_			,			return_attention_mask=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
      _A:  Optional[int]					=       n_fft
      _A:  Optional[Any]					=       hop_length
      _A:  Any					=       chunk_length
      _A:  int					=       chunk_length * sampling_rate
      _A:  Optional[Any]					=       self.n_samples // hop_length
      _A:  int					=       sampling_rate
      _A:  Any					=       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  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							np.array     ):

      """simple docstring"""
      _A:  str					=       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'''			,			)
      _A:  Union[str, Any]					=       log_spec[:, :-1]
      _A:  Union[str, Any]					=       np.maximum(lowerCAmelCase_			,			log_spec.max() - 8.0     )
      _A:  Any					=       (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  __magic_name__				(					lowerCAmelCase_     :							List[np.ndarray]			,			lowerCAmelCase_     :							List[np.ndarray]			,			lowerCAmelCase_     :							float = 0.0     ):

      """simple docstring"""
      if attention_mask is not None:
         _A:  int					=       np.array(lowerCAmelCase_			,			np.intaa     )
         _A:  Any					=       []

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

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

      return normed_input_values


   def __call__(					self     :							List[str]			,			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_     :							int			,			):

      """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.'''     )

      _A:  Dict					=       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}"""     )
      _A:  List[Any]					=       is_batched_numpy or (
          isinstance(lowerCAmelCase_			,			(list, tuple)     ) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)     ))
      )

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

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

      _A:  List[str]					=       BatchFeature({'''input_features''': raw_speech}     )

      # convert into correct format for padding

      _A:  Optional[int]					=       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:
         _A:  str					=       self.zero_mean_unit_var_norm(
             padded_inputs['''input_features''']			,			attention_mask=padded_inputs['''attention_mask''']			,			padding_value=self.padding_value			,			)
         _A:  Dict					=       np.stack(padded_inputs['''input_features''']			,			axis=0     )

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

      _A:  Optional[int]					=       [self._np_extract_fbank_features(lowerCAmelCase_     ) for waveform in input_features[0]]

      if isinstance(input_features[0]			,			lowerCAmelCase_     ):
         _A:  Tuple					=       [np.asarray(lowerCAmelCase_			,			dtype=np.floataa     ) for feature in input_features]
      else:
         _A:  Optional[Any]					=       input_features

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

      if return_tensors is not None:
         _A:  Any					=       padded_inputs.convert_to_tensors(lowerCAmelCase_     )

      return padded_inputs


   def  __magic_name__				(					self     :							Optional[Any]     ):

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





from .imports import is_tqdm_available


if is_tqdm_available():
   from tqdm.auto import tqdm as _tqdm

from ..state import PartialState






def   lowerCamelCase__							(					a = True		,		*a		,		**a      )      ->   Optional[Any]:
   if not is_tqdm_available():
      raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''      )
   _A:  Optional[Any]					=       False
   if main_process_only:
      _A:  Union[str, Any]					=       PartialState().local_process_index == 0
   return _tqdm(*a		,		**a		,		disable=a      )



