
"""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 A_	(_UpperCAmelCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		["""input_features""", """attention_mask"""]







  def __init__(		self				,   lowercase_=80				,   lowercase_=1_6000				,   lowercase_=80				,   lowercase_=0.0				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(feature_size=lowercase_				,   sampling_rate=lowercase_				,   padding_value=lowercase_				,   **lowercase_  )
     UpperCAmelCase_					:					Dict			     =    num_mel_bins
     UpperCAmelCase_					:					Any			     =    do_ceptral_normalize
     UpperCAmelCase_					:					Union[str, Any]			     =    normalize_means
     UpperCAmelCase_					:					int			     =    normalize_vars
     UpperCAmelCase_					:					List[Any]			     =    True







  def        UpperCamelCase__					(		self				,   lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     UpperCAmelCase_					:					Optional[Any]			     =    torch.from_numpy(lowercase_  ).unsqueeze(0  )
     UpperCAmelCase_					:					Optional[Any]			     =    ta_kaldi.fbank(lowercase_				,   num_mel_bins=self.num_mel_bins				,   sample_frequency=self.sampling_rate  )
     return features.numpy()







  @staticmethod
  def        UpperCamelCase__					(		lowercase_				,   lowercase_				,   lowercase_ = True				,   lowercase_ = True				,   lowercase_ = 0.0				,   ):

     """simple docstring"""
     # make sure we normalize float32 arrays
     if normalize_means:
        UpperCAmelCase_					:					Optional[Any]			     =    x[:input_length].mean(axis=0  )
        UpperCAmelCase_					:					Dict			     =    np.subtract(lowercase_				,   lowercase_  )
     if normalize_vars:
        UpperCAmelCase_					:					Dict			     =    x[:input_length].std(axis=0  )
        UpperCAmelCase_					:					Optional[int]			     =    np.divide(lowercase_				,   lowercase_  )

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

     # make sure array is in float32
     UpperCAmelCase_					:					Optional[int]			     =    x.astype(np.floataa  )

     return x







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    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				,   lowercase_				,   lowercase_ = False				,   lowercase_ = None				,   lowercase_ = False				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   **lowercase_				,   ):

     """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."  )

     UpperCAmelCase_					:					Optional[Any]			     =    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}"""  )
     UpperCAmelCase_					:					Dict			     =    is_batched_numpy or (
         isinstance(lowercase_				,   (list, tuple)  ) and (isinstance(raw_speech[0]				,   (np.ndarray, tuple, list)  ))
     )

     if is_batched:
        UpperCAmelCase_					:					Any			     =    [np.asarray(lowercase_				,   dtype=np.floataa  ) for speech in raw_speech]
     elif not is_batched and not isinstance(lowercase_				,   np.ndarray  ):
        UpperCAmelCase_					:					Union[str, Any]			     =    np.asarray(lowercase_				,   dtype=np.floataa  )
     elif isinstance(lowercase_				,   np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
        UpperCAmelCase_					:					Tuple			     =    raw_speech.astype(np.floataa  )

     # always return batch
     if not is_batched:
        UpperCAmelCase_					:					Dict			     =    [raw_speech]

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

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

     UpperCAmelCase_					:					Any			     =    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
     UpperCAmelCase_					:					Optional[int]			     =    padded_inputs.get("input_features"  )
     if isinstance(input_features[0]				,   lowercase_  ):
        UpperCAmelCase_					:					str			     =    [np.asarray(lowercase_				,   dtype=np.floataa  ) for feature in input_features]

     UpperCAmelCase_					:					Dict			     =    padded_inputs.get("attention_mask"  )
     if attention_mask is not None:
        UpperCAmelCase_					:					Optional[int]			     =    [np.asarray(lowercase_				,   dtype=np.intaa  ) for array in attention_mask]

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

     if return_tensors is not None:
        UpperCAmelCase_					:					Optional[int]			     =    padded_inputs.convert_to_tensors(lowercase_  )

     return padded_inputs



'''simple docstring'''




import operator as op




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       []
							lowercase_					:			str										=       lambda UpperCAmelCase__      ,      UpperCAmelCase__ : int(x / y )  # noqa: E731 integer division operation
							lowercase_					:			Optional[Any]										=       {
							    """^""": op.pow,
							    """*""": op.mul,
							    """/""": div,
							    """+""": op.add,
							    """-""": op.sub,
							}  # operators & their respective operation

							# print table header
							print("""Symbol""".center(8 )      ,      """Action""".center(12 )      ,      """Stack"""      ,      sep=""" | """ )
							print("""-""" * (30 + len(UpperCAmelCase__ )) )

							for x in post_fix:
														if x.isdigit():  # if x in digit
																					stack.append(UpperCAmelCase__ )  # append x to stack
																					# output in tabular format
																					print(x.rjust(8 )      ,      ("""push(""" + x + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )
														else:
																					lowercase_					:			str										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					lowercase_					:			Optional[int]										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + a + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(UpperCAmelCase__ )      ,      int(UpperCAmelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
																					# output in tabular format
																					print(
																					    x.rjust(8 )      ,      ("""push(""" + a + x + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """      ,      )

							return int(stack[0] )


if __name__ == "__main__":
						_lowercase						: Tuple						=						input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
						print("\n\tResult = ", solve(Postfix))





