




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


lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  lowercase__				):
  _UpperCamelCase       :      List[str]			=      ['''input_features''', '''attention_mask''']






  def __init__(  self		:     str				,						_A		:     Optional[Any]=80				,						_A		:     Tuple=16_000				,						_A		:     Tuple=80				,						_A		:     Optional[Any]=0.0				,						_A		:     Optional[int]=True				,						_A		:     Optional[int]=True				,						_A		:     str=True				,						**_A		:     Optional[Any]				,						)		->   Union[str, Any]:






       """simple docstring"""







       super().__init__(feature_size=_a				,						sampling_rate=_a				,						padding_value=_a				,						**_a				)
       lowercase :       str       =			num_mel_bins
       lowercase :       Dict       =			do_ceptral_normalize
       lowercase :       Dict       =			normalize_means
       lowercase :       List[Any]       =			normalize_vars
       lowercase :       Optional[Any]       =			True






  def  __a      (  self		:     Dict				,						_A		:     np.ndarray				,						)		->   List[Any]:






       """simple docstring"""







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






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






       """simple docstring"""







       if normalize_means:
            lowercase :       Union[str, Any]       =			x[:input_length].mean(axis=0				)
            lowercase :       Any       =			np.subtract(_a				,						_a				)
       if normalize_vars:
            lowercase :       Union[str, Any]       =			x[:input_length].std(axis=0				)
            lowercase :       str       =			np.divide(_a				,						_a				)

       if input_length < x.shape[0]:
            lowercase :       List[Any]       =			padding_value

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

       return x






  def  __a      (  self		:     int				,						_A		:     List[np.ndarray]				,						_A		:     Optional[np.ndarray] = None				)		->   Dict:






       """simple docstring"""







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






  def __call__(  self		:     Optional[Any]				,						_A		:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,						_A		:     Union[bool, str, PaddingStrategy] = False				,						_A		:     Optional[int] = None				,						_A		:     bool = False				,						_A		:     Optional[int] = None				,						_A		:     Optional[Union[str, TensorType]] = None				,						_A		:     Optional[int] = None				,						_A		:     Optional[bool] = None				,						**_A		:     List[str]				,						)		->   Any:






       """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.'''				)

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

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

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

       # extract fbank features
       lowercase :       Union[str, Any]       =			[self._extract_fbank_features(_a				) for waveform in raw_speech]

       # convert into correct format for padding
       lowercase :       Tuple       =			BatchFeature({'''input_features''': features}				)

       lowercase :       int       =			self.pad(
           _a				,						padding=_a				,						max_length=_a				,						truncation=_a				,						pad_to_multiple_of=_a				,						return_attention_mask=_a				,						**_a				,						)

       # make sure list is in array format
       lowercase :       Union[str, Any]       =			padded_inputs.get('''input_features'''				)
       if isinstance(input_features[0]				,						_a				):
            lowercase :       int       =			[np.asarray(_a				,						dtype=np.floataa				) for feature in input_features]

       lowercase :       List[Any]       =			padded_inputs.get('''attention_mask'''				)
       if attention_mask is not None:
            lowercase :       List[Any]       =			[np.asarray(_a				,						dtype=np.intaa				) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
            lowercase :       Optional[Any]       =			(
                np.array(_a				,						dtype=np.intaa				)
                if self._get_padding_strategies(_a				,						max_length=_a				) is not PaddingStrategy.DO_NOT_PAD
                else None
            )
            lowercase :       List[str]       =			self.normalize(
                padded_inputs['''input_features''']				,						attention_mask=_a				)

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

       return padded_inputs





import math






def  snake_case(    __magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

     return exponent == int(__magic_name__   )






def  snake_case(    __magic_name__ = 1 / 1_23_45   )			->							int:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			0
     lowercase :       str       =			0

     lowercase :       Optional[int]       =			3
     while True:
          lowercase :       Any       =			(integer**2 - 1) / 4
          # if candidate is an integer, then there is a partition for k
          if partition_candidate == int(__magic_name__   ):
               lowercase :       Any       =			int(__magic_name__   )
               total_partitions += 1
               if check_partition_perfect(__magic_name__   ):
                    perfect_partitions += 1
          if perfect_partitions > 0:
               if perfect_partitions / total_partitions < max_proportion:
                    return int(__magic_name__   )
          integer += 1


if __name__ == "__main__":
 print(f'''{solution() = }''')