





'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def __init__(   self							:			Dict   ,       __snake_case							:			List[Any]   ,       __snake_case							:			List[str]						)->	Optional[int]:
        super().__init__()

        # make sure scheduler can always be converted to DDIM
        snake_case 						=   DDIMScheduler.from_config(scheduler.config						)

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



     @torch.no_grad()
     def __call__(   self							:			Any   ,       __snake_case							:			int = 1   ,       __snake_case							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,       __snake_case							:			float = 0.0   ,       __snake_case							:			int = 50   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[str] = "pil"   ,       __snake_case							:			bool = True   ,       )->	Union[ImagePipelineOutput, Tuple]:

        # Sample gaussian noise to begin loop
        if isinstance(self.unet.config.sample_size   ,       __snake_case						):
           snake_case 						=   (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
        else:
           snake_case 						=   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

        if isinstance(__snake_case   ,       __snake_case						) and len(__snake_case						) != batch_size:
           raise ValueError(
               f'''You have passed a list of generators of length {len(__snake_case						)}, but requested an effective batch'''
               f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

        snake_case 						=   randn_tensor(__snake_case   ,       generator=__snake_case   ,       device=self.device   ,       dtype=self.unet.dtype						)

        # set step values
        self.scheduler.set_timesteps(__snake_case						)

        for t in self.progress_bar(self.scheduler.timesteps						):
           # 1. predict noise model_output
           snake_case 						=   self.unet(__snake_case   ,       __snake_case						).sample

           # 2. predict previous mean of image x_t-1 and add variance depending on eta
           # eta corresponds to η in paper and should be between [0, 1]
           # do x_t -> x_t-1
           snake_case 						=   self.scheduler.step(
               __snake_case   ,       __snake_case   ,       __snake_case   ,       eta=__snake_case   ,       use_clipped_model_output=__snake_case   ,       generator=__snake_case						).prev_sample

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

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=__snake_case						)








'''simple docstring'''




from __future__ import annotations

import os
from collections.abc import Mapping

_SCREAMING_SNAKE_CASE							     =			tuple[int, int]
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			set[int]   ,       __snake_case							:			Mapping[EdgeT, int]						)->	None:
        snake_case 						=   vertices
        snake_case 						=   {
            (min(__snake_case						), max(__snake_case						)): weight for edge, weight in edges.items()
        }



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			EdgeT   ,       __snake_case							:			int						)->	None:
        self.vertices.add(edge[0]						)
        self.vertices.add(edge[1]						)
        snake_case 						=   weight



     def      lowerCAmelCase			(   self							:			str						)->	Graph:
        snake_case 						=   Graph({min(self.vertices						)}   ,       {}						)
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42

        while len(subgraph.vertices						) < len(self.vertices						):
           snake_case 						=   max(self.edges.values()						) + 1
           for edge, weight in self.edges.items():
              if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                 if weight < min_weight:
                    snake_case 						=   edge
                    snake_case 						=   weight

           subgraph.add_edge(__snake_case   ,       __snake_case						)

        return subgraph





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "p107_network.txt"		)		->  int:
   snake_case 						=   os.path.abspath(os.path.dirname(__lowerCAmelCase		)		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {}
   snake_case 						=   42
   snake_case 						=   42
   snake_case 						=   42

   with open(__lowerCAmelCase		) as f:
      snake_case 						=   f.read().strip().split("""\n"""		)

   snake_case 						=   [line.split(""","""		) for line in data]

   for edgea in range(1      ,    len(__lowerCAmelCase		)		):
      for edgea in range(__lowerCAmelCase		):
         if adjaceny_matrix[edgea][edgea] != "-":
            snake_case 						=   int(adjaceny_matrix[edgea][edgea]		)

   snake_case 						=   Graph(set(range(len(__lowerCAmelCase		)		)		)      ,    __lowerCAmelCase		)

   snake_case 						=   graph.prims_algorithm()

   snake_case 						=   sum(graph.edges.values()		)
   snake_case 						=   sum(subgraph.edges.values()		)

   return initial_total - optimal_total


if __name__ == "__main__":
 print(F"""{solution() = }""")

