





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				:   int						       =						logging.get_logger(__name__)


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  ['''input_features''', '''attention_mask''']



     def __init__(				self							:str      ,   snake_case							:int=80      ,   snake_case							:Tuple=16_000      ,   snake_case							:Any=80      ,   snake_case							:Dict=0.0      ,   snake_case							:List[Any]=True      ,   snake_case							:int=True      ,   snake_case							:Dict=True      ,   **snake_case							:List[Any]      ,   ):





        '''simple docstring'''




        super().__init__(feature_size=snake_case      ,   sampling_rate=snake_case      ,   padding_value=snake_case      ,   **snake_case	)
        A_       :					List[Any]        =      num_mel_bins
        A_       :					Tuple        =      do_ceptral_normalize
        A_       :					int        =      normalize_means
        A_       :					Tuple        =      normalize_vars
        A_       :					List[Any]        =      True



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:np.ndarray      ,   ):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
        A_       :					Optional[int]        =      torch.from_numpy(snake_case	).unsqueeze(0	)
        A_       :					Optional[Any]        =      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		(				snake_case							:np.ndarray      ,   snake_case							:int      ,   snake_case							:Optional[bool] = True      ,   snake_case							:Optional[bool] = True      ,   snake_case							:float = 0.0      ,   ):





        '''simple docstring'''




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

        if input_length < x.shape[0]:
           A_       :					Dict        =      padding_value

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

        return x



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:List[np.ndarray]      ,   snake_case							:Optional[np.ndarray] = None	):





        '''simple docstring'''




        A_       :					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							:Union[str, Any]      ,   snake_case							:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,   snake_case							:Union[bool, str, PaddingStrategy] = False      ,   snake_case							:Optional[int] = None      ,   snake_case							:bool = False      ,   snake_case							:Optional[int] = None      ,   snake_case							:Optional[Union[str, TensorType]] = None      ,   snake_case							:Optional[int] = None      ,   snake_case							:Optional[bool] = None      ,   **snake_case							:List[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."	)

        A_       :					List[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}"	)
        A_       :					str        =      is_batched_numpy or (
            isinstance(snake_case      ,   (list, tuple)	) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list)	))
        )

        if is_batched:
           A_       :					Any        =      [np.asarray(snake_case      ,   dtype=np.floataa	) for speech in raw_speech]
        elif not is_batched and not isinstance(snake_case      ,   np.ndarray	):
           A_       :					Union[str, Any]        =      np.asarray(snake_case      ,   dtype=np.floataa	)
        elif isinstance(snake_case      ,   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_       :					Any        =      [raw_speech]

        # extract fbank features
        A_       :					List[str]        =      [self._extract_fbank_features(snake_case	) for waveform in raw_speech]

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

        A_       :					Dict        =      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
        A_       :					Tuple        =      padded_inputs.get("input_features"	)
        if isinstance(input_features[0]      ,   snake_case	):
           A_       :					List[str]        =      [np.asarray(snake_case      ,   dtype=np.floataa	) for feature in input_features]

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

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
           A_       :					List[Any]        =      (
               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
           )
           A_       :					str        =      self.normalize(
               padded_inputs["input_features"]      ,   attention_mask=snake_case	)

        if return_tensors is not None:
           A_       :					int        =      padded_inputs.convert_to_tensors(snake_case	)

        return padded_inputs







from __future__ import annotations

from cmath import sqrt

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int						)			->    tuple[complex, complex]:

   if a == 0:
      raise ValueError("Coefficient 'a' must not be zero."						)
   A_       :					Tuple        =      b * b - 4 * a * c

   A_       :					Any        =      (-b + sqrt(_lowerCAmelCase						)) / (2 * a)
   A_       :					Union[str, Any]        =      (-b - sqrt(_lowerCAmelCase						)) / (2 * a)

   return (
       root_a.real if not root_a.imag else root_a,
       root_a.real if not root_a.imag else root_a,
   )

def   __snake_case    (       )			->    List[Any]:
   A_   ,    A_       :					Optional[Any]        =      quadratic_roots(a=5					,					b=6					,					c=1						)
   print(f"The solutions are: {solutiona} and {solutiona}"						)


if __name__ == "__main__":
 main()
