


import math







class 		_snake_case  :




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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Tuple 							=       w




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for k in range(0					,  self.n    ):
             for i in range(0					,  self.n    ):
                   for j in range(0					,  self.n    ):
                         a						:Union[str, Any] 							=       min(self.dp[i][j]					,  self.dp[i][k] + self.dp[k][j]    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return self.dp[u][v]


if __name__ == "__main__":
     snake_case				:    str       				=     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)









"""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 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

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

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


