



from typing import List, Optional, Tuple, Union

import torch

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






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	UNetaDModel
							__lowerCamelCase	:	ScoreSdeVeScheduler





							def __init__(   self		:		Tuple      ,				__lowercase		:		UNetaDModel      ,				__lowercase		:		ScoreSdeVeScheduler	):

											'''simple docstring'''
											super().__init__()
											self.register_modules(unet=__lowercase      ,				scheduler=__lowercase	)







							@torch.no_grad()
							def __call__(   self		:		Any      ,				__lowercase		:		int = 1      ,				__lowercase		:		int = 2000      ,				__lowercase		:		Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,				__lowercase		:		Optional[str] = "pil"      ,				__lowercase		:		bool = True      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											__a       =		self.unet.config.sample_size
											__a       =		(batch_size, 3, img_size, img_size)

											__a       =		self.unet

											__a       =		randn_tensor(__lowercase      ,				generator=__lowercase	) * self.scheduler.init_noise_sigma
											__a       =		sample.to(self.device	)

											self.scheduler.set_timesteps(__lowercase	)
											self.scheduler.set_sigmas(__lowercase	)

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
															__a       =		self.scheduler.sigmas[i] * torch.ones(shape[0]      ,				device=self.device	)

															# correction step
															for _ in range(self.scheduler.config.correct_steps	):
																			__a       =		self.unet(__lowercase      ,				__lowercase	).sample
																			__a       =		self.scheduler.step_correct(__lowercase      ,				__lowercase      ,				generator=__lowercase	).prev_sample

															# prediction step
															__a       =		model(__lowercase      ,				__lowercase	).sample
															__a       =		self.scheduler.step_pred(__lowercase      ,				__lowercase      ,				__lowercase      ,				generator=__lowercase	)

															__a     ,					__a       =		output.prev_sample, output.prev_sample_mean

											__a       =		sample_mean.clamp(0      ,				1	)
											__a       =		sample.cpu().permute(0      ,				2      ,				3      ,				1	).numpy()
											if output_type == "pil":
															__a       =		self.numpy_to_pil(__lowercase	)

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__lowercase	)





import os

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		os.path.dirname(os.path.realpath(_SCREAMING_SNAKE_CASE    )    )
				__a       =		os.path.join(_SCREAMING_SNAKE_CASE				,     """triangle.txt"""    )

				with open(_SCREAMING_SNAKE_CASE    ) as f:
								__a       =		f.readlines()

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

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


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