


'''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 TensorType, logging


a_       :     Optional[int] 							=     logging.get_logger(__name__)




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						["""input_values""", """attention_mask"""]
 def __init__(			self   ,					__magic_name__=1   ,					__magic_name__=1_60_00   ,					__magic_name__=1_28   ,					__magic_name__=10_24   ,					__magic_name__=0.0   ,					__magic_name__=True   ,					__magic_name__=-4.2_6_7_7_3_9_3   ,					__magic_name__=4.5_6_8_9_9_7_4   ,					__magic_name__=False   ,					**__magic_name__   ,					)       ->				int:
      super().__init__(feature_size=__magic_name__   ,					sampling_rate=__magic_name__   ,					padding_value=__magic_name__   ,					**__magic_name__    )
      _a         =				num_mel_bins
      _a         =				max_length
      _a         =				do_normalize
      _a         =				mean
      _a         =				std
      _a         =				return_attention_mask
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					)       ->				np.ndarray:
      _a         =				torch.from_numpy(__magic_name__    ).unsqueeze(0    )
      _a         =				ta_kaldi.fbank(
          __magic_name__   ,					htk_compat=__magic_name__   ,					sample_frequency=self.sampling_rate   ,					use_energy=__magic_name__   ,					window_type='hanning'   ,					num_mel_bins=self.num_mel_bins   ,					dither=0.0   ,					frame_shift=10   ,					)

      _a         =				fbank.shape[0]
      _a         =				max_length - n_frames

      # pad or truncate, depending on difference
      if difference > 0:
           _a         =				torch.nn.ZeroPadad((0, 0, 0, difference)    )
           _a         =				pad_module(__magic_name__    )
      elif difference < 0:
           _a         =				fbank[0:max_length, :]

      _a         =				fbank.numpy()

      return fbank
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				np.ndarray:
      return (input_values - (self.mean)) / (self.std * 2)




 def __call__(			self   ,					__magic_name__   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					**__magic_name__   ,					)       ->				BatchFeature:

      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(__magic_name__   ,					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(__magic_name__   ,					(list, tuple)    ) and (isinstance(raw_speech[0]   ,					(np.ndarray, tuple, list)    ))
      )

      if is_batched:
           _a         =				[np.asarray(__magic_name__   ,					dtype=np.floataa    ) for speech in raw_speech]
      elif not is_batched and not isinstance(__magic_name__   ,					np.ndarray    ):
           _a         =				np.asarray(__magic_name__   ,					dtype=np.floataa    )
      elif isinstance(__magic_name__   ,					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 and pad/truncate to max_length
      _a         =				[self._extract_fbank_features(__magic_name__   ,					max_length=self.max_length    ) for waveform in raw_speech]

      # convert into BatchFeature
      _a         =				BatchFeature({'input_values': features}    )

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

      # normalization
      if self.do_normalize:
           _a         =				[self.normalize(__magic_name__    ) for feature in input_values]

      if return_tensors is not None:
           _a         =				padded_inputs.convert_to_tensors(__magic_name__    )

      return padded_inputs





'''simple docstring'''






import os






def 						_A					() ->	int:




     '''simple docstring'''



     _a         =				os.path.dirname(os.path.realpath(lowerCAmelCase__		)		)
     _a         =				os.path.join(lowerCAmelCase__					,    'triangle.txt'		)

     with open(lowerCAmelCase__		) as in_file:
          _a         =				[[int(lowerCAmelCase__		) for i in line.split()] for line in in_file]

     while len(lowerCAmelCase__		) != 1:
          _a         =				triangle.pop()
          _a         =				triangle[-1]
          for j in range(len(lowerCAmelCase__		) - 1		):
               curr_row[j] += max(last_row[j]					,    last_row[j + 1]		)
     return triangle[0][0]


if __name__ == "__main__":
 print(solution())

