






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


__A					=	"scheduler_config.json"



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							str				= 1
    lowerCamelCase	:							int				= 2
    lowerCamelCase	:							Any				= 3
    lowerCamelCase	:							Tuple				= 4
    lowerCamelCase	:							Dict				= 5
    lowerCamelCase	:							Optional[int]				= 6
    lowerCamelCase	:							Optional[Any]				= 7
    lowerCamelCase	:							Union[str, Any]				= 8
    lowerCamelCase	:							str				= 9
    lowerCamelCase	:							Union[str, Any]				= 10
    lowerCamelCase	:							Tuple				= 11
    lowerCamelCase	:							Dict				= 12
    lowerCamelCase	:							int				= 13
    lowerCamelCase	:							List[Any]				= 14




@dataclass
class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							torch.FloatTensor




class 				_A			:
    """simple docstring"""

    lowerCamelCase	:							Optional[Any]				= SCHEDULER_CONFIG_NAME
    lowerCamelCase	:							Dict				= []
    lowerCamelCase	:							Any				= True



    @classmethod
    def _a							(				cls      :     Any ,     __SCREAMING_SNAKE_CASE      :     Dict[str, Any] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[str] = None ,     __SCREAMING_SNAKE_CASE      :     Dict=False ,     **__SCREAMING_SNAKE_CASE      :     Tuple ,     )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=cls.load_config(
               pretrained_model_name_or_path=__SCREAMING_SNAKE_CASE ,     subfolder=__SCREAMING_SNAKE_CASE ,     return_unused_kwargs=__SCREAMING_SNAKE_CASE ,     return_commit_hash=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     )
           return cls.from_config(__SCREAMING_SNAKE_CASE ,     return_unused_kwargs=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, os.PathLike] ,     __SCREAMING_SNAKE_CASE      :     bool = False ,     **__SCREAMING_SNAKE_CASE      :     Tuple      )      ->							Tuple:
           self.save_config(save_directory=__SCREAMING_SNAKE_CASE ,     push_to_hub=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    @property
    def _a							(				self      :     Union[str, Any]      )      ->							Any:
           return self._get_compatibles()



    @classmethod
    def _a							(				cls      :     int      )      ->							Tuple:
           __UpperCAmelCase     				=list(set([cls.__name__] + cls._compatibles      )      )
           __UpperCAmelCase     				=importlib.import_module(__name__.split("""."""      )[0]      )
           __UpperCAmelCase     				=[
               getattr(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) for c in compatible_classes_str if hasattr(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           ]
           return compatible_classes











"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

__UpperCamelCase     :						Optional[Any]						       =  tuple[int, int]







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= vertices
  lowerCAmelCase    				= {
      (min(_snake_case     ), max(_snake_case     )): weight for edge, weight in edges.items()
  }



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.vertices.add(edge[0]     )
  self.vertices.add(edge[1]     )
  lowerCAmelCase    				= weight



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Graph({min(self.vertices     )}				,       {}     )
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42

  while len(subgraph.vertices     ) < len(self.vertices     ):
   lowerCAmelCase    				= 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:
      lowerCAmelCase    				= edge
      lowerCAmelCase    				= weight

   subgraph.add_edge(_snake_case				,       _snake_case     )

  return subgraph


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str = "p107_network.txt"			):
 lowerCAmelCase    				= os.path.abspath(os.path.dirname(_UpperCAmelCase			)			)
 lowerCAmelCase    				= os.path.join(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42

 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read().strip().split('\n'			)

 lowerCAmelCase    				= [line.split(','			) for line in data]

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

 lowerCAmelCase    				= Graph(set(range(len(_UpperCAmelCase			)			)			)  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= graph.prims_algorithm()

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

 return initial_total - optimal_total


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





