


'''simple docstring'''



import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A__						:   Optional[int]				=    logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   bool					=     field(default=_UpperCAmelCase   ,						metadata={'help': 'Whether to use SortishSampler or not.'}    )
    lowerCamelCase    :   bool					=     field(
        default=_UpperCAmelCase   ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}    )
    lowerCamelCase    :   Optional[int]					=     field(
        default=_UpperCAmelCase   ,						metadata={
            'help': (
                'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                'to the `max_length` value of the model configuration.'
            )
        }   ,						)
    lowerCamelCase    :   Optional[int]					=     field(
        default=_UpperCAmelCase   ,						metadata={
            'help': (
                'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                'to the `num_beams` value of the model configuration.'
            )
        }   ,						)
    lowerCamelCase    :   Optional[Union[str, Path, GenerationConfig]]					=     field(
        default=_UpperCAmelCase   ,						metadata={
            'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
        }   ,						)







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase  :      int														=						super().to_dict()
         for k, v in d.items():
              if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      Union[str, Any]														=						v.to_dict()
         return d










'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

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





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         __lowerCamelCase  :      set[int]														=						vertices
         __lowerCamelCase  :      dict[EdgeT, int]														=						{
             (min(SCREAMING_SNAKE_CASE_      ), max(SCREAMING_SNAKE_CASE_      )): weight for edge, weight in edges.items()
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      Any														=						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  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

     with open(UpperCAmelCase_				) as f:
          __lowerCamelCase  :      Any														=						f.read().strip().split('\n'				)

     __lowerCamelCase  :      Any														=						[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														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

     __lowerCamelCase  :      int														=						sum(graph.edges.values()				)
     __lowerCamelCase  :      int														=						sum(subgraph.edges.values()				)

     return initial_total - optimal_total


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






