





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   A							(						__lowercase    ):
      _snake_case				  =42
      _snake_case				  =42



      def __init__(						self:    Tuple		,	_lowerCAmelCase:    UNetaDModel		,	_lowerCAmelCase:    ScoreSdeVeScheduler							)    ->     Optional[int]:
         '''simple docstring'''

         super().__init__()
         self.register_modules(unet=_lowerCAmelCase		,	scheduler=_lowerCAmelCase							)






      @torch.no_grad()
      def __call__(						self:    int		,	_lowerCAmelCase:    int = 1		,	_lowerCAmelCase:    int = 2000		,	_lowerCAmelCase:    Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	_lowerCAmelCase:    Optional[str] = "pil"		,	_lowerCAmelCase:    bool = True		,	**_lowerCAmelCase:    Tuple		,	)    ->     Union[ImagePipelineOutput, Tuple]:
         '''simple docstring'''

         UpperCAmelCase_           =self.unet.config.sample_size
         UpperCAmelCase_           =(batch_size, 3, img_size, img_size)

         UpperCAmelCase_           =self.unet

         UpperCAmelCase_           =randn_tensor(_lowerCAmelCase		,	generator=_lowerCAmelCase							) * self.scheduler.init_noise_sigma
         UpperCAmelCase_           =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							)							):
            UpperCAmelCase_           =self.scheduler.sigmas[i] * torch.ones(shape[0]		,	device=self.device							)

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

            # prediction step
            UpperCAmelCase_           =model(_lowerCAmelCase		,	_lowerCAmelCase							).sample
            UpperCAmelCase_           =self.scheduler.step_pred(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase		,	generator=_lowerCAmelCase							)

            UpperCAmelCase_    ,							UpperCAmelCase_           =output.prev_sample, output.prev_sample_mean

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

         if not return_dict:
            return (sample,)

         return ImagePipelineOutput(images=_lowerCAmelCase							)





import math
class     lowerCAmelCase_     :







							def __init__(   self      :     Tuple ,			_A      :     int=0		):  # a graph with Node 0,1,...,N-1
												_UpperCamelCase			     =     n
												_UpperCamelCase			     =     [
												    [math.inf for j in range(0 ,			_A		)] for i in range(0 ,			_A		)
												]  # adjacency matrix for weight
												_UpperCamelCase			     =     [
												    [math.inf for j in range(0 ,			_A		)] for i in range(0 ,			_A		)
												]  # dp[i][j] stores minimum distance from i to j







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     str ,			_A      :     List[str] ,			_A      :     Optional[Any]		):
												_UpperCamelCase			     =     w







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												for k in range(0 ,			self.n		):
																	for i in range(0 ,			self.n		):
																						for j in range(0 ,			self.n		):
																											_UpperCamelCase			     =     min(self.dp[i][j] ,			self.dp[i][k] + self.dp[k][j]		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[int] ,			_A      :     Optional[int]		):
												return self.dp[u][v]


if __name__ == "__main__":
					_lowerCAmelCase       =		Graph(5)
					graph.add_edge(0, 2, 9)
					graph.add_edge(0, 4, 10)
					graph.add_edge(1, 3, 5)
					graph.add_edge(2, 3, 7)
					graph.add_edge(3, 0, 10)
					graph.add_edge(3, 1, 2)
					graph.add_edge(3, 2, 1)
					graph.add_edge(3, 4, 6)
					graph.add_edge(4, 1, 3)
					graph.add_edge(4, 2, 4)
					graph.add_edge(4, 3, 9)
					graph.floyd_warshall()
					graph.show_min(1, 4)
					graph.show_min(0, 3)
