


"""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 PaddingStrategy, TensorType, logging


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( SCREAMING_SNAKE_CASE__      ):
   __UpperCAmelCase					:			List[Any]				    =    ['''input_features''', '''attention_mask''']







   def __init__(      self		,      UpperCamelCase__=80		,      UpperCamelCase__=1_6000		,      UpperCamelCase__=80		,      UpperCamelCase__=0.0		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      **UpperCamelCase__		,      )      ->						Any:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      )      ->						Optional[Any]:

       '''simple docstring'''




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







   @staticmethod
   def 					lowerCamelCase						(      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = True		,      UpperCamelCase__ = True		,      UpperCamelCase__ = 0.0		,      )      ->						str:

       '''simple docstring'''




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

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

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

       return x







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						Union[str, Any]:

       '''simple docstring'''




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







   def __call__(      self		,      UpperCamelCase__		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      **UpperCamelCase__		,      )      ->						Union[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."		)

       snake_case       : Optional[int]    =       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}'		)
       snake_case       : List[str]    =       is_batched_numpy or (
           isinstance(__lowerCAmelCase		,      (list, tuple)		) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)		))
       )

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

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

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

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

       snake_case       : Tuple    =       self.pad(
           __lowerCAmelCase		,      padding=__lowerCAmelCase		,      max_length=__lowerCAmelCase		,      truncation=__lowerCAmelCase		,      pad_to_multiple_of=__lowerCAmelCase		,      return_attention_mask=__lowerCAmelCase		,      **__lowerCAmelCase		,      )

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

       snake_case       : Union[str, Any]    =       padded_inputs.get("attention_mask"		)
       if attention_mask is not None:
           snake_case       : Union[str, Any]    =       [np.asarray(__lowerCAmelCase		,      dtype=np.intaa		) for array in attention_mask]

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

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

       return padded_inputs













'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






