






import os



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							os.path.dirname(os.path.realpath(lowerCAmelCase__	)	)
		__A    =							os.path.join(lowerCAmelCase__					,     "triangle.txt"	)

		with open(lowerCAmelCase__	) as f:
				__A    =							f.readlines()

		__A    =							[[int(lowerCAmelCase__	) for y in x.rstrip("\r\n"	).split(" "	)] for x in triangle]

		for i in range(1					,     len(lowerCAmelCase__	)	):
				for j in range(len(a[i]	)	):
						__A    =							a[i - 1][j] if j != len(a[i - 1]	) else 0
						__A    =							a[i - 1][j - 1] if j > 0 else 0
						a[i][j] += max(lowerCAmelCase__					,     lowerCAmelCase__	)
		return max(a[-1]	)


if __name__ == "__main__":
					print(solution())



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase	=    {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''DetrFeatureExtractor''']
							__lowercase	=    ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






