code: 
  
  import os
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Tuple   = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE_    )    )
       lowercase__					:     int   = os.path.join(SCREAMING_SNAKE_CASE_			,  'triangle.txt'    )
  
       with open(SCREAMING_SNAKE_CASE_    ) as in_file:
            lowercase__					:     Tuple   = [[int(SCREAMING_SNAKE_CASE_    ) for i in line.split()] for line in in_file]
  
       while len(SCREAMING_SNAKE_CASE_    ) != 1:
            lowercase__					:     Tuple   = triangle.pop()
            lowercase__					:     Optional[Any]   = triangle[-1]
            for j in range(len(SCREAMING_SNAKE_CASE_    ) - 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_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  