

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  lowerCamelCase_			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=['''input_features''', '''attention_mask''']







  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Union[str, Any]=80	,							lowerCAmelCase			:   int=1_60_00	,							lowerCAmelCase			:   Any=80	,							lowerCAmelCase			:   Optional[Any]=0.0	,							lowerCAmelCase			:   int=True	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   Union[str, Any]=True	,							**lowerCAmelCase			:   Union[str, Any]	,							)							->     Union[str, Any]:






     """simple docstring"""







     super().__init__(feature_size=__snake_case	,							sampling_rate=__snake_case	,							padding_value=__snake_case	,							**__snake_case   )
     __lowerCAmelCase  :      Union[str, Any]    =						num_mel_bins
     __lowerCAmelCase  :      int    =						do_ceptral_normalize
     __lowerCAmelCase  :      str    =						normalize_means
     __lowerCAmelCase  :      Union[str, Any]    =						normalize_vars
     __lowerCAmelCase  :      int    =						True







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   np.ndarray	,							)							->     np.ndarray:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     __lowerCAmelCase  :      int    =						torch.from_numpy(__snake_case   ).unsqueeze(0   )
     __lowerCAmelCase  :      Tuple    =						ta_kaldi.fbank(__snake_case	,							num_mel_bins=self.num_mel_bins	,							sample_frequency=self.sampling_rate   )
     return features.numpy()







  @staticmethod
  def 	SCREAMING_SNAKE_CASE   (      lowerCAmelCase			:   np.ndarray	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[bool] = True	,							lowerCAmelCase			:   Optional[bool] = True	,							lowerCAmelCase			:   float = 0.0	,							)							->     np.ndarray:






     """simple docstring"""







     if normalize_means:
        __lowerCAmelCase  :      Dict    =						x[:input_length].mean(axis=0   )
        __lowerCAmelCase  :      Optional[int]    =						np.subtract(__snake_case	,							__snake_case   )
     if normalize_vars:
        __lowerCAmelCase  :      Union[str, Any]    =						x[:input_length].std(axis=0   )
        __lowerCAmelCase  :      Optional[int]    =						np.divide(__snake_case	,							__snake_case   )

     if input_length < x.shape[0]:
        __lowerCAmelCase  :      Tuple    =						padding_value

     # make sure array is in float32
     __lowerCAmelCase  :      str    =						x.astype(np.floataa   )

     return x







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   List[np.ndarray]	,							lowerCAmelCase			:   Optional[np.ndarray] = None   )							->     List[np.ndarray]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [
         self.utterance_cmvn(__snake_case	,							__snake_case	,							self.normalize_means	,							self.normalize_vars	,							self.padding_value   )
         for x, n in zip(__snake_case	,							__snake_case   )
     ]







  def __call__(      self			:   Dict	,							lowerCAmelCase			:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,							lowerCAmelCase			:   Union[bool, str, PaddingStrategy] = False	,							lowerCAmelCase			:   Optional[int] = None	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Optional[int] = None	,							lowerCAmelCase			:   Optional[Union[str, TensorType]] = None	,							lowerCAmelCase			:   Optional[int] = None	,							lowerCAmelCase			:   Optional[bool] = None	,							**lowerCAmelCase			:   Tuple	,							)							->     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} was trained using a sampling rate of'''
               f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
               f''' {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."""   )

     __lowerCAmelCase  :      Any    =						isinstance(__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}'''   )
     __lowerCAmelCase  :      Dict    =						is_batched_numpy or (
         isinstance(__snake_case	,							(list, tuple)   ) and (isinstance(raw_speech[0]	,							(np.ndarray, tuple, list)   ))
     )

     if is_batched:
        __lowerCAmelCase  :      Union[str, Any]    =						[np.asarray(__snake_case	,							dtype=np.floataa   ) for speech in raw_speech]
     elif not is_batched and not isinstance(__snake_case	,							np.ndarray   ):
        __lowerCAmelCase  :      Optional[Any]    =						np.asarray(__snake_case	,							dtype=np.floataa   )
     elif isinstance(__snake_case	,							np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
        __lowerCAmelCase  :      Dict    =						raw_speech.astype(np.floataa   )

     # always return batch
     if not is_batched:
        __lowerCAmelCase  :      Optional[int]    =						[raw_speech]

     # extract fbank features
     __lowerCAmelCase  :      Dict    =						[self._extract_fbank_features(__snake_case   ) for waveform in raw_speech]

     # convert into correct format for padding
     __lowerCAmelCase  :      int    =						BatchFeature({"""input_features""": features}   )

     __lowerCAmelCase  :      int    =						self.pad(
         __snake_case	,							padding=__snake_case	,							max_length=__snake_case	,							truncation=__snake_case	,							pad_to_multiple_of=__snake_case	,							return_attention_mask=__snake_case	,							**__snake_case	,							)

     # make sure list is in array format
     __lowerCAmelCase  :      Dict    =						padded_inputs.get("""input_features"""   )
     if isinstance(input_features[0]	,							__snake_case   ):
        __lowerCAmelCase  :      Tuple    =						[np.asarray(__snake_case	,							dtype=np.floataa   ) for feature in input_features]

     __lowerCAmelCase  :      List[Any]    =						padded_inputs.get("""attention_mask"""   )
     if attention_mask is not None:
        __lowerCAmelCase  :      Optional[int]    =						[np.asarray(__snake_case	,							dtype=np.intaa   ) for array in attention_mask]

     # Utterance-level cepstral mean and variance normalization
     if self.do_ceptral_normalize:
        __lowerCAmelCase  :      int    =						(
            np.array(__snake_case	,							dtype=np.intaa   )
            if self._get_padding_strategies(__snake_case	,							max_length=__snake_case   ) is not PaddingStrategy.DO_NOT_PAD
            else None
        )
        __lowerCAmelCase  :      Tuple    =						self.normalize(
            padded_inputs["""input_features"""]	,							attention_mask=__snake_case   )

     if return_tensors is not None:
        __lowerCAmelCase  :      Optional[int]    =						padded_inputs.convert_to_tensors(__snake_case   )

     return padded_inputs





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use GLPNImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )


