




"""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


a							      =     logging.get_logger(__name__)





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      ['''input_features''', '''attention_mask''']

 def __init__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			Any=80       ,				_UpperCAmelCase			:			int=16_000       ,				_UpperCAmelCase			:			Optional[int]=80       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			str=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				**_UpperCAmelCase			:			List[Any]       ,				):
       super().__init__(feature_size=__SCREAMING_SNAKE_CASE       ,				sampling_rate=__SCREAMING_SNAKE_CASE       ,				padding_value=__SCREAMING_SNAKE_CASE       ,				**__SCREAMING_SNAKE_CASE						)
       _A     =  num_mel_bins
       _A     =  do_ceptral_normalize
       _A     =  normalize_means
       _A     =  normalize_vars
       _A     =  True

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			Any       ,				):
       _A     =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       _A     =  torch.from_numpy(__SCREAMING_SNAKE_CASE						).unsqueeze(0						)
       _A     =  ta_kaldi.fbank(__SCREAMING_SNAKE_CASE       ,				num_mel_bins=self.num_mel_bins       ,				sample_frequency=self.sampling_rate						)
       return features.numpy()

 @staticmethod
 def      lowerCAmelCase_				(					_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			str = True       ,				_UpperCAmelCase			:			List[Any] = True       ,				_UpperCAmelCase			:			List[Any] = 0.0       ,				):




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

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

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

       return x

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Tuple = None						):
       _A     =  attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				self.normalize_means       ,				self.normalize_vars       ,				self.padding_value						)
           for x, n in zip(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE						)
       ]



 def __call__(					self			:			Any       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Optional[Any] = False       ,				_UpperCAmelCase			:			Optional[Any] = None       ,				_UpperCAmelCase			:			List[str] = False       ,				_UpperCAmelCase			:			int = None       ,				_UpperCAmelCase			:			Any = None       ,				_UpperCAmelCase			:			Tuple = None       ,				_UpperCAmelCase			:			Tuple = None       ,				**_UpperCAmelCase			:			Any       ,				):




       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(__SCREAMING_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     =  is_batched_numpy or (
           isinstance(__SCREAMING_SNAKE_CASE       ,				(list, tuple)						) and (isinstance(raw_speech[0]       ,				(np.ndarray, tuple, list)						))
       )

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

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

       _A     =  self.pad(
           __SCREAMING_SNAKE_CASE       ,				padding=__SCREAMING_SNAKE_CASE       ,				max_length=__SCREAMING_SNAKE_CASE       ,				truncation=__SCREAMING_SNAKE_CASE       ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE       ,				return_attention_mask=__SCREAMING_SNAKE_CASE       ,				**__SCREAMING_SNAKE_CASE       ,				)

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

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

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

       if return_tensors is not None:
             _A     =  padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE						)

       return padded_inputs







'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	[]
     __snake_case            =	{
         '''^''': 3,
         '''*''': 2,
         '''/''': 2,
         '''%''': 2,
         '''+''': 1,
         '''-''': 1,
     }  # Priority of each operator
     __snake_case            =	len(_lowerCamelCase						) if (len(_lowerCamelCase						) > 7) else 7

     # Print table header for output
     print(
         '''Symbol'''.center(8						) ,						'''Stack'''.center(_lowerCamelCase						) ,						'''Postfix'''.center(_lowerCamelCase						) ,						sep=''' | ''' ,						)
     print('''-''' * (print_width * 3 + 7)						)

     for x in infix:
          if x.isalpha() or x.isdigit():
               post_fix.append(_lowerCamelCase						)  # if x is Alphabet / Digit, add it to Postfix
          elif x == "(":
               stack.append(_lowerCamelCase						)  # if x is "(" push to Stack
          elif x == ")":  # if x is ")" pop stack until "(" is encountered
               while stack[-1] != "(":
                    post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
               stack.pop()
          else:
               if len(_lowerCamelCase						) == 0:
                    stack.append(_lowerCamelCase						)  # If stack is empty, push x to stack
               else:  # while priority of x is not > priority of element in the stack
                    while len(_lowerCamelCase						) > 0 and priority[x] <= priority[stack[-1]]:
                         post_fix.append(stack.pop()						)  # pop stack & add to Postfix
                    stack.append(_lowerCamelCase						)  # push x to stack

          print(
              x.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     while len(_lowerCamelCase						) > 0:  # while stack is not empty
          post_fix.append(stack.pop()						)  # pop stack & add to Postfix
          print(
              ''' '''.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     return "".join(_lowerCamelCase						)  # return Postfix as str







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> str:




     '''simple docstring'''





     __snake_case            =	list(infix[::-1]						)  # reverse the infix equation

     for i in range(len(_lowerCamelCase						)						):
          if infix[i] == "(":
               __snake_case            =	''')'''  # change "(" to ")"
          elif infix[i] == ")":
               __snake_case            =	'''('''  # change ")" to "("

     return (infix_2_postfix(''''''.join(_lowerCamelCase						)						))[
         ::-1
     ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
     UpperCAmelCase_							:	Dict	     =		input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
     UpperCAmelCase_							:	Optional[Any]	     =		''''''.join(Infix.split())  # Remove spaces from the input
     print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')






