code: 
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
      """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
  }
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       ["""LayoutLMTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]
  
  try:
       if not is_tf_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLayoutLMForMaskedLM""",
           """TFLayoutLMForSequenceClassification""",
           """TFLayoutLMForTokenClassification""",
           """TFLayoutLMForQuestionAnswering""",
           """TFLayoutLMMainLayer""",
           """TFLayoutLMModel""",
           """TFLayoutLMPreTrainedModel""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
       from .tokenization_layoutlm import LayoutLMTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_layoutlm import (
                LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMForMaskedLM,
                LayoutLMForQuestionAnswering,
                LayoutLMForSequenceClassification,
                LayoutLMForTokenClassification,
                LayoutLMModel,
                LayoutLMPreTrainedModel,
            )
       try:
            if not is_tf_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_tf_layoutlm import (
                TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLayoutLMForMaskedLM,
                TFLayoutLMForQuestionAnswering,
                TFLayoutLMForSequenceClassification,
                TFLayoutLMForTokenClassification,
                TFLayoutLMMainLayer,
                TFLayoutLMModel,
                TFLayoutLMPreTrainedModel,
            )
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  '''simple docstring'''
  
  import os
  
  
  def 	_UpperCAmelCase				(  )    ->						int:
    _lowerCAmelCase       :  Optional[int]   				=					os.path.dirname(os.path.realpath(_lowerCamelCase		)		)
    _lowerCAmelCase       :  Optional[Any]   				=					os.path.join(_lowerCamelCase  ,      """triangle.txt"""		)
  
    with open(_lowerCamelCase		) as in_file:
      _lowerCAmelCase       :  List[Any]   				=					[[int(_lowerCamelCase		) for i in line.split()] for line in in_file]
  
    while len(_lowerCamelCase		) != 1:
      _lowerCAmelCase       :  Any   				=					triangle.pop()
      _lowerCAmelCase       :  List[str]   				=					triangle[-1]
      for j in range(len(_lowerCamelCase		) - 1		):
        curr_row[j] += max(last_row[j]  ,      last_row[j + 1]		)
    return triangle[0][0]
  
  
  if __name__ == "__main__":
       print(solution())
  
  
  