



import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase_		 =					logging.get_logger(__name__)


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: str =  ["input_values", "attention_mask"]

 def __init__(    self					:					Tuple			,						__lowerCAmelCase					:					int = 1			,						__lowerCAmelCase					:					int = 1_60_00			,						__lowerCAmelCase					:					float = 0.0			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					int = 80			,						__lowerCAmelCase					:					int = 16			,						__lowerCAmelCase					:					int = 64			,						__lowerCAmelCase					:					str = "hann_window"			,						__lowerCAmelCase					:					float = 1.0			,						__lowerCAmelCase					:					float = 80			,						__lowerCAmelCase					:					float = 76_00			,						__lowerCAmelCase					:					float = 1E-10			,						__lowerCAmelCase					:					int = 2			,						__lowerCAmelCase					:					bool = True			,						**__lowerCAmelCase					:					List[Any]			,						)   ->	Dict:
    super().__init__(feature_size=__lowerCAmelCase			,						sampling_rate=__lowerCAmelCase			,						padding_value=__lowerCAmelCase			,						**__lowerCAmelCase      )
    _A      =       do_normalize
    _A      =       return_attention_mask

    _A      =       num_mel_bins
    _A      =       hop_length
    _A      =       win_length
    _A      =       win_function
    _A      =       frame_signal_scale
    _A      =       fmin
    _A      =       fmax
    _A      =       mel_floor
    _A      =       reduction_factor

    _A      =       win_length * sampling_rate // 10_00
    _A      =       hop_length * sampling_rate // 10_00
    _A      =       optimal_fft_length(self.sample_size      )
    _A      =       (self.n_fft // 2) + 1

    _A      =       window_function(window_length=self.sample_size			,						name=self.win_function			,						periodic=__lowerCAmelCase      )

    _A      =       mel_filter_bank(
        num_frequency_bins=self.n_freqs			,						num_mel_filters=self.num_mel_bins			,						min_frequency=self.fmin			,						max_frequency=self.fmax			,						sampling_rate=self.sampling_rate			,						norm='''slaney'''			,						mel_scale='''slaney'''			,						)

    if frame_signal_scale != 1.0:
       warnings.warn(
           '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''			,						__lowerCAmelCase			,						)
    if reduction_factor != 2.0:
       warnings.warn(
           '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''			,						__lowerCAmelCase			,						)

 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def     snake_case_      (    __lowerCAmelCase					:					List[np.ndarray]			,						__lowerCAmelCase					:					List[np.ndarray]			,						__lowerCAmelCase					:					float = 0.0      )   ->	List[np.ndarray]:
    if attention_mask is not None:
       _A      =       np.array(__lowerCAmelCase			,						np.intaa      )
       _A      =       []

       for vector, length in zip(__lowerCAmelCase			,						attention_mask.sum(-1      )      ):
          _A      =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7      )
          if length < normed_slice.shape[0]:
             _A      =       padding_value

          normed_input_values.append(__lowerCAmelCase      )
    else:
       _A      =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7      ) for x in input_values]

    return normed_input_values

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					np.ndarray			,						)   ->	np.ndarray:
    _A      =       spectrogram(
        __lowerCAmelCase			,						window=self.window			,						frame_length=self.sample_size			,						hop_length=self.sample_stride			,						fft_length=self.n_fft			,						mel_filters=self.mel_filters			,						mel_floor=self.mel_floor			,						log_mel='''log10'''			,						)
    return log_mel_spec.T

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,						__lowerCAmelCase					:					Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,						__lowerCAmelCase					:					Union[bool, str, PaddingStrategy] = False			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[bool] = None			,						__lowerCAmelCase					:					Optional[Union[str, TensorType]] = None			,						__lowerCAmelCase					:					Optional[int] = None			,						**__lowerCAmelCase					:					List[str]			,						)   ->	BatchFeature:
    if audio is None and audio_target is None:
       raise ValueError('''You must provide either `audio` or `audio_target` values.'''      )

    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 audio 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.'''      )

    if audio is not None:
       _A      =       self._process_audio(
           __lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						**__lowerCAmelCase			,						)
    else:
       _A      =       None

    if audio_target is not None:
       _A      =       self._process_audio(
           __lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						**__lowerCAmelCase			,						)

       if inputs is None:
          return inputs_target
       else:
          _A      =       inputs_target['''input_values''']
          _A      =       inputs_target.get('''attention_mask'''      )
          if decoder_attention_mask is not None:
             _A      =       decoder_attention_mask

    return inputs

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					Union[bool, str, PaddingStrategy] = False			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					bool = False			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[bool] = None			,						__lowerCAmelCase					:					Optional[Union[str, TensorType]] = None			,						**__lowerCAmelCase					:					int			,						)   ->	BatchFeature:
    _A      =       isinstance(__lowerCAmelCase			,						np.ndarray      ) and len(speech.shape      ) > 1
    if is_batched_numpy and len(speech.shape      ) > 2:
       raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''      )
    _A      =       is_batched_numpy or (
        isinstance(__lowerCAmelCase			,						(list, tuple)      ) and (isinstance(speech[0]			,						(np.ndarray, tuple, list)      ))
    )

    if is_batched:
       _A      =       [np.asarray(__lowerCAmelCase			,						dtype=np.floataa      ) for speech in speech]
    elif not is_batched and not isinstance(__lowerCAmelCase			,						np.ndarray      ):
       _A      =       np.asarray(__lowerCAmelCase			,						dtype=np.floataa      )
    elif isinstance(__lowerCAmelCase			,						np.ndarray      ) and speech.dtype is np.dtype(np.floataa      ):
       _A      =       speech.astype(np.floataa      )

    # always return batch
    if not is_batched:
       _A      =       [speech]

    # needed to make pad() work on spectrogram inputs
    _A      =       self.feature_size

    # convert into correct format for padding
    if is_target:
       _A      =       [self._extract_mel_features(__lowerCAmelCase      ) for waveform in speech]
       _A      =       BatchFeature({'''input_values''': features}      )
       _A      =       self.num_mel_bins
    else:
       _A      =       BatchFeature({'''input_values''': speech}      )

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

    _A      =       feature_size_hack

    # convert input values to correct format
    _A      =       padded_inputs['''input_values''']
    if not isinstance(input_values[0]			,						np.ndarray      ):
       _A      =       [np.asarray(__lowerCAmelCase			,						dtype=np.floataa      ) for array in input_values]
    elif (
        not isinstance(__lowerCAmelCase			,						np.ndarray      )
        and isinstance(input_values[0]			,						np.ndarray      )
        and input_values[0].dtype is np.dtype(np.floataa      )
    ):
       _A      =       [array.astype(np.floataa      ) for array in input_values]
    elif isinstance(__lowerCAmelCase			,						np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
       _A      =       input_values.astype(np.floataa      )

    # convert attention_mask to correct format
    _A      =       padded_inputs.get('''attention_mask'''      )
    if attention_mask is not None:
       _A      =       [np.asarray(__lowerCAmelCase			,						dtype=np.intaa      ) for array in attention_mask]

    # zero-mean and unit-variance normalization
    if not is_target and self.do_normalize:
       _A      =       (
           attention_mask
           if self._get_padding_strategies(__lowerCAmelCase			,						max_length=__lowerCAmelCase      ) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       _A      =       self.zero_mean_unit_var_norm(
           padded_inputs['''input_values''']			,						attention_mask=__lowerCAmelCase			,						padding_value=self.padding_value      )

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

    return padded_inputs

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict[str, Any]:
    _A      =       super().to_dict()

    # Don't serialize these as they are derived from the other properties.
    _A      =       ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
    for name in names:
       if name in output:
          del output[name]

    return output









import operator as op

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Any:
      SCREAMING_SNAKE_CASE_      		=     []
      SCREAMING_SNAKE_CASE_      		=     lambda __UpperCAmelCase     ,     __UpperCAmelCase				: int(x / y				)  # noqa: E731 integer division operation
      SCREAMING_SNAKE_CASE_      		=     {
          '^': 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:
                  SCREAMING_SNAKE_CASE_      		=     stack.pop()  # pop stack
                  # output in tabular format
                  print(''.rjust(8				)     ,     ('pop(' + b + ')').ljust(12				)     ,     ','.join(__UpperCAmelCase				)     ,     sep=' | '				)

                  SCREAMING_SNAKE_CASE_      		=     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__":
   lowerCamelCase__					:  Tuple      							=				input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
   print('\n\tResult = ', solve(Postfix))