



import os

def      lowerCAmelCase__  (							):
				"""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        =						[]
				for line in triangle:
								__a        =						[]
								for number in line.strip().split(""" """			):
												numbers_from_line.append(int(__lowerCamelCase			)			)
								a.append(__lowerCamelCase			)

				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())








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


