





from typing import List, Optional, Tuple, Union

import torch

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






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						42
			lowerCAmelCase_												=						42





			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__()

				lowerCamelCase__        =		PNDMScheduler.from_config(scheduler.config							)

				self.register_modules(unet=__lowerCAmelCase	,  scheduler=__lowerCAmelCase							)





			@torch.no_grad()
			def __call__(   self	,  __lowerCAmelCase = 1	,  __lowerCAmelCase = 5_0	,  __lowerCAmelCase = None	,  __lowerCAmelCase = "pil"	,  __lowerCAmelCase = True	,  **__lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		randn_tensor(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,  generator=__lowerCAmelCase	,  device=self.device	,  )

				self.scheduler.set_timesteps(__lowerCAmelCase							)
				for t in self.progress_bar(self.scheduler.timesteps							):
					lowerCamelCase__        =		self.unet(__lowerCAmelCase	,  __lowerCAmelCase							).sample

					lowerCamelCase__        =		self.scheduler.step(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							).prev_sample

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

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__lowerCAmelCase							)










import os





def lowerCAmelCase__()    ->				Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__        =		os.path.dirname(os.path.realpath(__snake_case							)							)
	lowerCamelCase__        =		os.path.join(__snake_case	,'''triangle.txt'''							)

	with open(__snake_case							) as f:
		lowerCamelCase__        =		f.readlines()

	lowerCamelCase__        =		[[int(__snake_case							) for y in x.rstrip('''\r\n'''							).split(''' '''							)] for x in triangle]

	for i in range(1	,len(__snake_case							)							):
		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(__snake_case	,__snake_case							)
	return max(a[-1]							)


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



