



'''simple docstring'''


import os







def 				__lowerCamelCase				(   A__ = "input.txt"    )       ->	int:






   """simple docstring"""

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

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

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

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

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

      for i in range(rows - 2    ,						-1    ,						-1    ):
         UpperCamelCase					=  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() = }''')






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "IBertForMaskedLM",
       "IBertForMultipleChoice",
       "IBertForQuestionAnswering",
       "IBertForSequenceClassification",
       "IBertForTokenClassification",
       "IBertModel",
       "IBertPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase	:       Union[str, Any]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

