





'''simple docstring'''

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 __a			( _snake_case      ):
   __UpperCamelCase :			List[str]						   =						['input_features', 'is_longer']





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]=64					,lowerCamelCase					:      Any=4_8000					,lowerCamelCase					:      int=480					,lowerCamelCase					:      Any=10					,lowerCamelCase					:      Dict=1024					,lowerCamelCase					:      Union[str, Any]=0.0					,lowerCamelCase					:      int=False					,lowerCamelCase					:      float = 0					,lowerCamelCase					:      float = 1_4000					,lowerCamelCase					:      int = None					,lowerCamelCase					:      str = "fusion"					,lowerCamelCase					:      str = "repeatpad"					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

      super().__init__(
          feature_size=lowerCamelCase					,sampling_rate=lowerCamelCase					,padding_value=lowerCamelCase					,return_attention_mask=lowerCamelCase					,**lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							top_db
      __SCREAMING_SNAKE_CASE   				=							truncation
      __SCREAMING_SNAKE_CASE   				=							padding
      __SCREAMING_SNAKE_CASE   				=							fft_window_size
      __SCREAMING_SNAKE_CASE   				=							(fft_window_size >> 1) + 1
      __SCREAMING_SNAKE_CASE   				=							hop_length
      __SCREAMING_SNAKE_CASE   				=							max_length_s
      __SCREAMING_SNAKE_CASE   				=							max_length_s * sampling_rate
      __SCREAMING_SNAKE_CASE   				=							sampling_rate
      __SCREAMING_SNAKE_CASE   				=							frequency_min
      __SCREAMING_SNAKE_CASE   				=							frequency_max
      __SCREAMING_SNAKE_CASE   				=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase					,min_frequency=lowerCamelCase					,max_frequency=lowerCamelCase					,sampling_rate=lowerCamelCase					,norm=lowerCamelCase					,mel_scale="""htk"""					,)
      __SCREAMING_SNAKE_CASE   				=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase					,min_frequency=lowerCamelCase					,max_frequency=lowerCamelCase					,sampling_rate=lowerCamelCase					,norm="""slaney"""					,mel_scale="""slaney"""					,)





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      np.array					,lowerCamelCase					:      Optional[np.array] = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							spectrogram(
          lowerCamelCase					,window_function(self.fft_window_size					,"""hann"""     )					,frame_length=self.fft_window_size					,hop_length=self.hop_length					,power=2.0					,mel_filters=lowerCamelCase					,log_mel="""dB"""					,)
      return log_mel_spectrogram.T





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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
         __SCREAMING_SNAKE_CASE   				=							[0]
      if len(ranges[2]     ) == 0:
         # if the audio is too short, we just use the first chunk
         __SCREAMING_SNAKE_CASE   				=							[0]
      # randomly choose index for each part
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[0]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[1]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[2]     )

      __SCREAMING_SNAKE_CASE   				=							mel[idx_front : idx_front + chunk_frames, :]
      __SCREAMING_SNAKE_CASE   				=							mel[idx_middle : idx_middle + chunk_frames, :]
      __SCREAMING_SNAKE_CASE   				=							mel[idx_back : idx_back + chunk_frames, :]

      __SCREAMING_SNAKE_CASE   				=							torch.tensor(mel[None, None, :]     )
      __SCREAMING_SNAKE_CASE   				=							torch.nn.functional.interpolate(
          lowerCamelCase					,size=[chunk_frames, 64]					,mode="""bilinear"""					,align_corners=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							mel_shrink[0][0].numpy()
      __SCREAMING_SNAKE_CASE   				=							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,axis=0     )
      return mel_fusion





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.array					,lowerCamelCase					:      str					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      if waveform.shape[0] > max_length:
         if truncation == "rand_trunc":
            __SCREAMING_SNAKE_CASE   				=							True
            # random crop to max_length (for compatibility) -> this should be handled by self.pad
            __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     ) - max_length
            __SCREAMING_SNAKE_CASE   				=							np.random.randint(0					,overflow + 1     )
            __SCREAMING_SNAKE_CASE   				=							waveform[idx : idx + max_length]
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters_slaney     )[None, :]
         elif truncation == "fusion":
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters     )
            __SCREAMING_SNAKE_CASE   				=							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
            __SCREAMING_SNAKE_CASE   				=							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.
               __SCREAMING_SNAKE_CASE   				=							np.stack([mel, mel, mel, mel]					,axis=0     )
               __SCREAMING_SNAKE_CASE   				=							False
            else:
               __SCREAMING_SNAKE_CASE   				=							self._random_mel_fusion(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
               __SCREAMING_SNAKE_CASE   				=							True
         else:
            raise NotImplementedError(f"""data_truncating {truncation} not implemented"""     )

      else:
         __SCREAMING_SNAKE_CASE   				=							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":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,n_repeat + 1     )     )[:max_length]
            if padding == "repeatpad":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,lowerCamelCase     )     )
            __SCREAMING_SNAKE_CASE   				=							np.pad(lowerCamelCase					,(0, max_length - waveform.shape[0])					,mode="""constant"""					,constant_values=0     )

         if truncation == "fusion":
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters     )
            __SCREAMING_SNAKE_CASE   				=							np.stack([input_mel, input_mel, input_mel, input_mel]					,axis=0     )
         else:
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters_slaney     )[None, :]

      return input_mel, longer





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							truncation if truncation is not None else self.truncation
      __SCREAMING_SNAKE_CASE   				=							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."""     )

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

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

      # always return batch
      if not is_batched:
         __SCREAMING_SNAKE_CASE   				=							[np.asarray(lowerCamelCase     )]

      # convert to mel spectrogram, truncate and pad if needed.
      __SCREAMING_SNAKE_CASE   				=							[
          self._get_input_mel(lowerCamelCase					,max_length if max_length else self.nb_max_samples					,lowerCamelCase					,lowerCamelCase     )
          for waveform in raw_speech
      ]

      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							[]
      for mel, longer in padded_inputs:
         input_mel.append(lowerCamelCase     )
         is_longer.append(lowerCamelCase     )

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

      if isinstance(input_mel[0]					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							[np.asarray(lowerCamelCase					,dtype=np.floataa     ) for feature in input_mel]

      # is_longer is a list of bool
      __SCREAMING_SNAKE_CASE   				=							[[longer] for longer in is_longer]

      __SCREAMING_SNAKE_CASE   				=							{"""input_features""": input_mel, """is_longer""": is_longer}
      __SCREAMING_SNAKE_CASE   				=							BatchFeature(lowerCamelCase     )

      if return_tensors is not None:
         __SCREAMING_SNAKE_CASE   				=							input_features.convert_to_tensors(lowerCamelCase     )

      return input_features













'''simple docstring'''



import os



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = "matrix.txt"     )	->      int:

   """simple docstring"""
   with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__     ),       SCREAMING_SNAKE_CASE__     )     ) as in_file:
      __a					  =     in_file.read()

   __a					  =     [[int(SCREAMING_SNAKE_CASE__     ) for cell in row.split(','     )] for row in data.strip().splitlines()]
   __a					  =     [[0 for cell in row] for row in grid]
   __a					  =     len(grid[0]     )

   __a					  =     [[0 for i in range(SCREAMING_SNAKE_CASE__     )] for j in range(SCREAMING_SNAKE_CASE__     )]
   __a					  =     grid[0][0]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[0][i] + dp[0][i - 1]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[i][0] + dp[i - 1][0]

   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      for j in range(1,       SCREAMING_SNAKE_CASE__     ):
         __a					  =     grid[i][j] + min(dp[i - 1][j],       dp[i][j - 1]     )

   return dp[-1][-1]


if __name__ == "__main__":
 print(f"""{solution() = }""")