

import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  """encodec"""




  def __init__(				self							,   lowerCAmelCase=[1.5, 3.0, 6.0, 12.0, 24.0]							,   lowerCAmelCase=24_000							,   lowerCAmelCase=1							,   lowerCAmelCase=False							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=128							,   lowerCAmelCase=32							,   lowerCAmelCase=1							,   lowerCAmelCase=[8, 5, 4, 2]							,   lowerCAmelCase="weight_norm"							,   lowerCAmelCase=7							,   lowerCAmelCase=7							,   lowerCAmelCase=3							,   lowerCAmelCase=2							,   lowerCAmelCase=True							,   lowerCAmelCase="reflect"							,   lowerCAmelCase=2							,   lowerCAmelCase=2							,   lowerCAmelCase=1.0							,   lowerCAmelCase=1_024							,   lowerCAmelCase=None							,   lowerCAmelCase=True							,   **lowerCAmelCase							,   )  -> Any:
        '''simple docstring'''






        _lowercase	       =target_bandwidths
        _lowercase	       =sampling_rate
        _lowercase	       =audio_channels
        _lowercase	       =normalize
        _lowercase	       =chunk_length_s
        _lowercase	       =overlap
        _lowercase	       =hidden_size
        _lowercase	       =num_filters
        _lowercase	       =num_residual_layers
        _lowercase	       =upsampling_ratios
        _lowercase	       =norm_type
        _lowercase	       =kernel_size
        _lowercase	       =last_kernel_size
        _lowercase	       =residual_kernel_size
        _lowercase	       =dilation_growth_rate
        _lowercase	       =use_causal_conv
        _lowercase	       =pad_mode
        _lowercase	       =compress
        _lowercase	       =num_lstm_layers
        _lowercase	       =trim_right_ratio
        _lowercase	       =codebook_size
        _lowercase	       =codebook_dim if codebook_dim is not None else hidden_size
        _lowercase	       =use_conv_shortcut

        if self.norm_type not in ["weight_norm", "time_group_norm"]:
              raise ValueError(
                  F'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''					)

        super().__init__(**lowerCAmelCase					)




  @property
  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        if self.chunk_length_s is None:
              return None
        else:
              return int(self.chunk_length_s * self.sampling_rate					)




  @property
  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        if self.chunk_length_s is None or self.overlap is None:
              return None
        else:
              return max(1							,   int((1.0 - self.overlap) * self.chunk_length					)					)




  @property
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =np.prod(self.upsampling_ratios					)
        return math.ceil(self.sampling_rate / hop_length					)




  @property
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)





import os
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


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


