





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

import numpy as np
import torch

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


__A							   =							logging.get_logger(__name__)



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     ["input_features", "is_longer"]

 def __init__(self							:  int			,					UpperCAmelCase_							:  List[str]=64			,					UpperCAmelCase_							:  Any=48_000			,					UpperCAmelCase_							:  List[str]=480			,					UpperCAmelCase_							:  List[str]=10			,					UpperCAmelCase_							:  Tuple=1_024			,					UpperCAmelCase_							:  int=0.0			,					UpperCAmelCase_							:  Union[str, Any]=False			,					UpperCAmelCase_							:  float = 0			,					UpperCAmelCase_							:  float = 14_000			,					UpperCAmelCase_							:  int = None			,					UpperCAmelCase_							:  str = "fusion"			,					UpperCAmelCase_							:  str = "repeatpad"			,					**UpperCAmelCase_							:  Union[str, Any]			,					)			->Union[str, Any]:



   '''simple docstring'''



   super().__init__(
       feature_size=UpperCAmelCase_			,					sampling_rate=UpperCAmelCase_			,					padding_value=UpperCAmelCase_			,					return_attention_mask=UpperCAmelCase_			,					**UpperCAmelCase_			,					)
   lowerCamelCase__: int         =top_db
   lowerCamelCase__: List[Any]         =truncation
   lowerCamelCase__: Any         =padding
   lowerCamelCase__: Any         =fft_window_size
   lowerCamelCase__: Optional[Any]         =(fft_window_size >> 1) + 1
   lowerCamelCase__: Union[str, Any]         =hop_length
   lowerCamelCase__: Union[str, Any]         =max_length_s
   lowerCamelCase__: Dict         =max_length_s * sampling_rate
   lowerCamelCase__: int         =sampling_rate
   lowerCamelCase__: Any         =frequency_min
   lowerCamelCase__: Optional[int]         =frequency_max
   lowerCamelCase__: Tuple         =mel_filter_bank(
       num_frequency_bins=self.nb_frequency_bins			,					num_mel_filters=UpperCAmelCase_			,					min_frequency=UpperCAmelCase_			,					max_frequency=UpperCAmelCase_			,					sampling_rate=UpperCAmelCase_			,					norm=UpperCAmelCase_			,					mel_scale="htk"			,					)
   lowerCamelCase__: Optional[int]         =mel_filter_bank(
       num_frequency_bins=self.nb_frequency_bins			,					num_mel_filters=UpperCAmelCase_			,					min_frequency=UpperCAmelCase_			,					max_frequency=UpperCAmelCase_			,					sampling_rate=UpperCAmelCase_			,					norm="slaney"			,					mel_scale="slaney"			,					)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Dict[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: int         =copy.deepcopy(self.__dict__)
   lowerCamelCase__: List[Any]         =self.__class__.__name__
   if "mel_filters" in output:
     del output["mel_filters"]
   if "mel_filters_slaney" in output:
     del output["mel_filters_slaney"]
   return output

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  np.array			,					UpperCAmelCase_							:  Optional[np.array] = None)			->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__: Dict         =spectrogram(
       UpperCAmelCase_			,					window_function(self.fft_window_size			,					"hann")			,					frame_length=self.fft_window_size			,					hop_length=self.hop_length			,					power=2.0			,					mel_filters=UpperCAmelCase_			,					log_mel="dB"			,					)
   return log_mel_spectrogram.T

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  Any			,					UpperCAmelCase_							:  Any)			->List[str]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =np.array_split(list(range(0			,					total_frames - chunk_frames + 1))			,					3)
   if len(ranges[1]) == 0:
     # if the audio is too short, we just use the first chunk
     lowerCamelCase__: Optional[int]         =[0]
   if len(ranges[2]) == 0:
     # if the audio is too short, we just use the first chunk
     lowerCamelCase__: Union[str, Any]         =[0]
   # randomly choose index for each part
   lowerCamelCase__: Any         =np.random.choice(ranges[0])
   lowerCamelCase__: int         =np.random.choice(ranges[1])
   lowerCamelCase__: Optional[int]         =np.random.choice(ranges[2])

   lowerCamelCase__: Optional[int]         =mel[idx_front : idx_front + chunk_frames, :]
   lowerCamelCase__: Any         =mel[idx_middle : idx_middle + chunk_frames, :]
   lowerCamelCase__: Optional[int]         =mel[idx_back : idx_back + chunk_frames, :]

   lowerCamelCase__: List[str]         =torch.tensor(mel[None, None, :])
   lowerCamelCase__: List[Any]         =torch.nn.functional.interpolate(
       UpperCAmelCase_			,					size=[chunk_frames, 64]			,					mode="bilinear"			,					align_corners=UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =mel_shrink[0][0].numpy()
   lowerCamelCase__: Optional[Any]         =np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,					axis=0)
   return mel_fusion

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  np.array			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Tuple)			->np.array:



   '''simple docstring'''



   if waveform.shape[0] > max_length:
     if truncation == "rand_trunc":
       lowerCamelCase__: str         =True
       # random crop to max_length (for compatibility) -> this should be handled by self.pad
       lowerCamelCase__: Optional[int]         =len(UpperCAmelCase_) - max_length
       lowerCamelCase__: int         =np.random.randint(0			,					overflow + 1)
       lowerCamelCase__: Dict         =waveform[idx : idx + max_length]
       lowerCamelCase__: Dict         =self._np_extract_fbank_features(UpperCAmelCase_			,					self.mel_filters_slaney)[None, :]
     elif truncation == "fusion":
       lowerCamelCase__: List[Any]         =self._np_extract_fbank_features(UpperCAmelCase_			,					self.mel_filters)
       lowerCamelCase__: str         =max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
       lowerCamelCase__: Optional[Any]         =mel.shape[0]
       if chunk_frames == total_frames:
         # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
         # In this case, we just use the whole audio.
         lowerCamelCase__: Dict         =np.stack([mel, mel, mel, mel]			,					axis=0)
         lowerCamelCase__: List[Any]         =False
       else:
         lowerCamelCase__: str         =self._random_mel_fusion(UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_)
         lowerCamelCase__: Any         =True
     else:
       raise NotImplementedError(F"""data_truncating {truncation} not implemented""")

   else:
     lowerCamelCase__: List[str]         =False
     # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
     if waveform.shape[0] < max_length:
       if padding == "repeat":
         lowerCamelCase__: str         =int(max_length / len(UpperCAmelCase_))
         lowerCamelCase__: List[Any]         =np.stack(np.tile(UpperCAmelCase_			,					n_repeat + 1))[:max_length]
       if padding == "repeatpad":
         lowerCamelCase__: Tuple         =int(max_length / len(UpperCAmelCase_))
         lowerCamelCase__: List[Any]         =np.stack(np.tile(UpperCAmelCase_			,					UpperCAmelCase_))
       lowerCamelCase__: Tuple         =np.pad(UpperCAmelCase_			,					(0, max_length - waveform.shape[0])			,					mode="constant"			,					constant_values=0)

     if truncation == "fusion":
       lowerCamelCase__: List[str]         =self._np_extract_fbank_features(UpperCAmelCase_			,					self.mel_filters)
       lowerCamelCase__: List[str]         =np.stack([input_mel, input_mel, input_mel, input_mel]			,					axis=0)
     else:
       lowerCamelCase__: Union[str, Any]         =self._np_extract_fbank_features(UpperCAmelCase_			,					self.mel_filters_slaney)[None, :]

   return input_mel, longer







 def __call__(self							:  str			,					UpperCAmelCase_							:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,					UpperCAmelCase_							:  str = None			,					UpperCAmelCase_							:  Optional[str] = None			,					UpperCAmelCase_							:  Optional[int] = None			,					UpperCAmelCase_							:  Optional[int] = None			,					UpperCAmelCase_							:  Optional[Union[str, TensorType]] = None			,					**UpperCAmelCase_							:  List[Any]			,					)			->BatchFeature:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =truncation if truncation is not None else self.truncation
   lowerCamelCase__: List[str]         =padding if padding else self.padding

   if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
       raise ValueError(
           F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
           F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
           F""" was sampled with {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.")

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

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

   # always return batch
   if not is_batched:
     lowerCamelCase__: List[str]         =[np.asarray(UpperCAmelCase_)]

   # convert to mel spectrogram, truncate and pad if needed.
   lowerCamelCase__: List[Any]         =[
       self._get_input_mel(UpperCAmelCase_			,					max_length if max_length else self.nb_max_samples			,					UpperCAmelCase_			,					UpperCAmelCase_)
       for waveform in raw_speech
   ]

   lowerCamelCase__: List[str]         =[]
   lowerCamelCase__: Optional[Any]         =[]
   for mel, longer in padded_inputs:
     input_mel.append(UpperCAmelCase_)
     is_longer.append(UpperCAmelCase_)

   if truncation == "fusion" and sum(UpperCAmelCase_) == 0:
     # if no audio is longer than 10s, then randomly select one audio to be longer
     lowerCamelCase__: Any         =np.random.randint(0			,					len(UpperCAmelCase_))
     lowerCamelCase__: str         =True

   if isinstance(input_mel[0]			,					UpperCAmelCase_):
     lowerCamelCase__: Optional[Any]         =[np.asarray(UpperCAmelCase_			,					dtype=np.floataa) for feature in input_mel]

   # is_longer is a list of bool
   lowerCamelCase__: Tuple         =[[longer] for longer in is_longer]

   lowerCamelCase__: List[str]         ={"input_features": input_mel, "is_longer": is_longer}
   lowerCamelCase__: str         =BatchFeature(UpperCAmelCase_)

   if return_tensors is not None:
     lowerCamelCase__: Any         =input_features.convert_to_tensors(UpperCAmelCase_)

   return input_features







'''simple docstring'''

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : int(x / y      )  # noqa: E731 integer division operation
					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:
															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

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

