

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


lowercase_			=  logging.get_logger(__name__)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							['input_features', 'attention_mask']


    def __init__(				self			:	Union[str, Any],lowercase_			:	Dict=8_0,lowercase_			:	Optional[Any]=1_6_0_0_0,lowercase_			:	Optional[int]=8_0,lowercase_			:	List[Any]=0.0,lowercase_			:	Optional[int]=True,lowercase_			:	Optional[Any]=True,lowercase_			:	List[str]=True,**lowercase_			:	Any,)->   Any:





        '''simple docstring'''







        super().__init__(feature_size=lowercase_,sampling_rate=lowercase_,padding_value=lowercase_,**lowercase_		)
        A__   				=       num_mel_bins
        A__   				=       do_ceptral_normalize
        A__   				=       normalize_means
        A__   				=       normalize_vars
        A__   				=       True


    def 						snake_case__					(				self			:	Dict,lowercase_			:	np.ndarray,)->   np.ndarray:





        '''simple docstring'''







        A__   				=       waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        A__   				=       torch.from_numpy(lowercase_		).unsqueeze(0		)
        A__   				=       ta_kaldi.fbank(lowercase_,num_mel_bins=self.num_mel_bins,sample_frequency=self.sampling_rate		)
        return features.numpy()


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





        '''simple docstring'''







        if normalize_means:
            A__   				=       x[:input_length].mean(axis=0		)
            A__   				=       np.subtract(lowercase_,lowercase_		)
        if normalize_vars:
            A__   				=       x[:input_length].std(axis=0		)
            A__   				=       np.divide(lowercase_,lowercase_		)

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

        # make sure array is in float32
        A__   				=       x.astype(np.floataa		)

        return x


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





        '''simple docstring'''







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


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

        A__   				=       isinstance(lowercase_,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(lowercase_,(list, tuple)		) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)		))
        )

        if is_batched:
            A__   				=       [np.asarray(lowercase_,dtype=np.floataa		) for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase_,np.ndarray		):
            A__   				=       np.asarray(lowercase_,dtype=np.floataa		)
        elif isinstance(lowercase_,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
        A__   				=       [self._extract_fbank_features(lowercase_		) for waveform in raw_speech]

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

        A__   				=       self.pad(
            lowercase_,padding=lowercase_,max_length=lowercase_,truncation=lowercase_,pad_to_multiple_of=lowercase_,return_attention_mask=lowercase_,**lowercase_,)

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

        A__   				=       padded_inputs.get('attention_mask'		)
        if attention_mask is not None:
            A__   				=       [np.asarray(lowercase_,dtype=np.intaa		) for array in attention_mask]

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

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

        return padded_inputs




from __future__ import annotations

from typing import Any
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list				)							->				int:





    '''simple docstring'''






    if not postfix_notation:
        return 0

    A__   				=       {'+', '-', '*', '/'}
    A__   				=       []

    for token in postfix_notation:
        if token in operations:
            A__				,						A__   				=       stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(SCREAMING_SNAKE_CASE__				)				)

    return stack.pop()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

