code: 
  
  import os
  def 		SCREAMING_SNAKE_CASE__				(							)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase    			=      os.path.dirname(os.path.realpath(_UpperCAmelCase				)				)
      lowerCAmelCase    			=      os.path.join(_UpperCAmelCase,      'triangle.txt'				)
  
      with open(_UpperCAmelCase				) as in_file:
          lowerCAmelCase    			=      [[int(_UpperCAmelCase				) for i in line.split()] for line in in_file]
  
      while len(_UpperCAmelCase				) != 1:
          lowerCAmelCase    			=      triangle.pop()
          lowerCAmelCase    			=      triangle[-1]
          for j in range(len(_UpperCAmelCase				) - 1				):
              curr_row[j] += max(last_row[j],      last_row[j + 1]				)
      return triangle[0][0]
  
  
  if __name__ == "__main__":
   print(solution())
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A   :     Optional[int]      =			{
      '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
      '''tokenization_splinter''': ['''SplinterTokenizer'''],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Optional[int]      =			['''SplinterTokenizerFast''']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Union[str, Any]      =			[
       '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SplinterForQuestionAnswering''',
       '''SplinterForPreTraining''',
       '''SplinterLayer''',
       '''SplinterModel''',
       '''SplinterPreTrainedModel''',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __A   :     Optional[Any]      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  