






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     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	42
				a__       							=	42







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__()
								self.register_modules(unet=__lowerCAmelCase							,       scheduler=__lowerCAmelCase  )







				@torch.no_grad()
				def __call__(    self							,       __lowerCAmelCase = 1							,       __lowerCAmelCase = 2_0_0_0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = "pil"							,       __lowerCAmelCase = True							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   self.unet.config.sample_size
								__magic_name__		:Optional[Any]   =   (batch_size, 3, img_size, img_size)

								__magic_name__		:Tuple   =   self.unet

								__magic_name__		:Dict   =   randn_tensor(__lowerCAmelCase							,       generator=__lowerCAmelCase  ) * self.scheduler.init_noise_sigma
								__magic_name__		:str   =   sample.to(self.device  )

								self.scheduler.set_timesteps(__lowerCAmelCase  )
								self.scheduler.set_sigmas(__lowerCAmelCase  )

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

												# correction step
												for _ in range(self.scheduler.config.correct_steps  ):
																__magic_name__		:List[Any]   =   self.unet(__lowerCAmelCase							,       __lowerCAmelCase  ).sample
																__magic_name__		:Tuple   =   self.scheduler.step_correct(__lowerCAmelCase							,       __lowerCAmelCase							,       generator=__lowerCAmelCase  ).prev_sample

												# prediction step
												__magic_name__		:Union[str, Any]   =   model(__lowerCAmelCase							,       __lowerCAmelCase  ).sample
												__magic_name__		:Dict   =   self.scheduler.step_pred(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       generator=__lowerCAmelCase  )

												__magic_name__  ,					__magic_name__		:List[str]   =   output.prev_sample, output.prev_sample_mean

								__magic_name__		:Tuple   =   sample_mean.clamp(0							,       1  )
								__magic_name__		:List[Any]   =   sample.cpu().permute(0							,       2							,       3							,       1  ).numpy()
								if output_type == "pil":
												__magic_name__		:Union[str, Any]   =   self.numpy_to_pil(__lowerCAmelCase  )

								if not return_dict:
												return (sample,)

								return ImagePipelineOutput(images=__lowerCAmelCase  )







'''simple docstring'''

import os



def snake_case_      (					__snake_case  :							List[Any])			->					str:
   lowerCAmelCase_			=      len(grid[0])
   lowerCAmelCase_			=      len(__snake_case)

   lowerCAmelCase_			=      0
   lowerCAmelCase_			=      0
   lowerCAmelCase_			=      0

   # Check vertically, horizontally, diagonally at the same time (only works
   # for nxn grid)
   for i in range(__snake_case):
      for j in range(n_rows - 3):
         lowerCAmelCase_			=      grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
         lowerCAmelCase_			=      grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

         # Left-to-right diagonal (\) product
         if i < n_columns - 3:
            lowerCAmelCase_			=      (
                grid[i][j]
                * grid[i + 1][j + 1]
                * grid[i + 2][j + 2]
                * grid[i + 3][j + 3]
            )

         # Right-to-left diagonal(/) product
         if i > 2:
            lowerCAmelCase_			=      (
                grid[i][j]
                * grid[i - 1][j + 1]
                * grid[i - 2][j + 2]
                * grid[i - 3][j + 3]
            )

         lowerCAmelCase_			=      max(
             __snake_case	,   __snake_case	,   __snake_case	,   __snake_case)
         if max_product > largest:
            lowerCAmelCase_			=      max_product

   return largest



def snake_case_      (					)			->					List[Any]:
   lowerCAmelCase_			=      []
   with open(os.path.dirname(__snake_case) + '''/grid.txt''') as file:
      for line in file:
         grid.append(line.strip('''\n''').split(''' '''))

   lowerCAmelCase_			=      [[int(__snake_case) for i in grid[j]] for j in range(len(__snake_case))]

   return largest_product(__snake_case)


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





