'''simple docstring'''





import os





def      lowerCAmelCase_    ( )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(os.path.realpath(snake_case_      )      )
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   "triangle.txt"      )

     with open(snake_case_      ) as f:
          UpperCAmelCase_	   =       f.readlines()

     UpperCAmelCase_	   =       []
     for line in triangle:
          UpperCAmelCase_	   =       []
          for number in line.strip().split(" "      ):
               numbers_from_line.append(int(snake_case_      )      )
          a.append(snake_case_      )

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


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





'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

         UpperCAmelCase_	   =       (image / 2 + 0.5).clamp(0       ,      1			)
         UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			).numpy()
         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




