




import os





def __UpperCamelCase			(       ):
						lowerCAmelCase_      			=  os.path.dirname(os.path.realpath(_A  )  )
						lowerCAmelCase_      			=  os.path.join(_A ,  '''triangle.txt'''  )

						with open(_A  ) as f:
												lowerCAmelCase_      			=  f.readlines()

						lowerCAmelCase_      			=  []
						for line in triangle:
												lowerCAmelCase_      			=  []
												for number in line.strip().split(''' '''  ):
																		numbers_from_line.append(int(_A  )  )
												a.append(_A  )

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


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










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  A		(  unittest.TestCase     ):



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  UNetaDModel(
								    block_out_channels=(32, 64),	layers_per_block=2,	sample_size=32,	in_channels=3,	out_channels=3,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),	)
								return model







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.dummy_uncond_unet
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy''',	return_dict=UpperCamelCase__   )[0]

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								lowerCAmelCase_      			=  image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCAmelCase_      			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch
class  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''google/ncsnpp-celebahq-256'''
								lowerCAmelCase_      			=  UNetaDModel.from_pretrained(UpperCamelCase__   )
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=20,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								lowerCAmelCase_      			=  np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




