




'''simple docstring'''







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 TensorType, logging


lowerCAmelCase_    	=							logging.get_logger(__name__)

class   _snake_case( lowerCAmelCase_			):
 __snake_case:       Tuple									=				['''input_values''', '''attention_mask''']






 def __init__(self					:     Any						,		a					:     Any=1						,		a					:     Optional[Any]=1_60_00						,		a					:     Optional[int]=1_28						,		a					:     Tuple=10_24						,		a					:     Dict=0.0						,		a					:     List[Any]=True						,		a					:     str=-4.267_7393						,		a					:     Optional[Any]=4.568_9974						,		a					:     int=False						,		**a					:     Optional[Any]						,		)					->      Optional[Any]:






    """simple docstring"""


    super().__init__(feature_size=__a						,		sampling_rate=__a						,		padding_value=__a						,		**__a						)
    A__         =    num_mel_bins
    A__         =    max_length
    A__         =    do_normalize
    A__         =    mean
    A__         =    std
    A__         =    return_attention_mask






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Tuple						,		a					:     Optional[Any]						,		)					->      List[Any]:






    """simple docstring"""


    A__         =    torch.from_numpy(__a						).unsqueeze(0						)
    A__         =    ta_kaldi.fbank(
        __a						,		htk_compat=__a						,		sample_frequency=self.sampling_rate						,		use_energy=__a						,		window_type='hanning'						,		num_mel_bins=self.num_mel_bins						,		dither=0.0						,		frame_shift=10						,		)

    A__         =    fbank.shape[0]
    A__         =    max_length - n_frames

    # pad or truncate, depending on difference
    if difference > 0:
       A__         =    torch.nn.ZeroPadad((0, 0, 0, difference)						)
       A__         =    pad_module(__a						)
    elif difference < 0:
       A__         =    fbank[0:max_length, :]

    A__         =    fbank.numpy()

    return fbank






 def 	_UpperCamelCase   (self					:     Optional[int]						,		a					:     Optional[int]						)					->      Optional[int]:






    """simple docstring"""


    return (input_values - (self.mean)) / (self.std * 2)






 def __call__(self					:     Tuple						,		a					:     Any						,		a					:     List[Any] = None						,		a					:     Dict = None						,		**a					:     str						,		)					->      List[str]:






    """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.'						)

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

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

    # always return batch
    if not is_batched:
       A__         =    [raw_speech]

    # extract fbank features and pad/truncate to max_length
    A__         =    [self._extract_fbank_features(__a						,		max_length=self.max_length						) for waveform in raw_speech]

    # convert into BatchFeature
    A__         =    BatchFeature({'input_values': features}						)

    # make sure list is in array format
    A__         =    padded_inputs.get('input_values'						)
    if isinstance(input_values[0]						,		__a						):
       A__         =    [np.asarray(__a						,		dtype=np.floataa						) for feature in input_values]

    # normalization
    if self.do_normalize:
       A__         =    [self.normalize(__a						) for feature in input_values]

    if return_tensors is not None:
       A__         =    padded_inputs.convert_to_tensors(__a						)

    return padded_inputs








'''simple docstring'''







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if not isinstance(UpperCAmelCase			,UpperCAmelCase	) or not all(
       isinstance(UpperCAmelCase			,UpperCAmelCase	) and item >= 0 for item in citations	):
      raise ValueError('The citations should be a list of non negative integers.'	)

   citations.sort()
   A__         =    len(UpperCAmelCase	)

   for i in range(UpperCAmelCase	):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()


