


"""simple docstring"""







from __future__ import annotations

import os
from collections.abc import Mapping

__lowercase    					=      tuple[int, int]






class 						_A  :







    """simple docstring"""




    def __init__(					self				:    Any				, __UpperCAmelCase				:    set[int]				, __UpperCAmelCase				:    Mapping[EdgeT, int]):
      a						:		set[int]      							=			vertices
      a						:		dict[EdgeT, int]      							=			{
          (min(__UpperCAmelCase), max(__UpperCAmelCase)): weight for edge, weight in edges.items()
      }






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    EdgeT				, __UpperCAmelCase				:    int):
      self.vertices.add(edge[0])
      self.vertices.add(edge[1])
      a						:		List[str]      							=			weight






    def 	__snake_case							(					self				:    List[Any]):
      a						:		Graph      							=			Graph({min(self.vertices)}				, {})
      a						:		EdgeT
      a						:		int
      a						:		EdgeT
      a						:		int

      while len(subgraph.vertices) < len(self.vertices):
        a						:		Tuple      							=			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:
              a						:		Optional[Any]      							=			edge
              a						:		str      							=			weight

        subgraph.add_edge(__UpperCAmelCase				, __UpperCAmelCase)

      return subgraph






def      lowercase			(				A_ = "p107_network.txt"       )->      str:
  '''simple docstring'''







  a						:		str      							=			os.path.abspath(os.path.dirname(__lowerCamelCase       )       )
  a						:		str      							=			os.path.join(__lowerCamelCase  ,   __lowerCamelCase       )
  a						:		dict[EdgeT, int]      							=			{}
  a						:		list[str]
  a						:		int
  a						:		int

  with open(__lowerCamelCase       ) as f:
    a						:		List[str]      							=			f.read().strip().split("\n"       )

  a						:		Optional[Any]      							=			[line.split(","       ) for line in data]

  for edgea in range(1  ,   len(__lowerCamelCase       )       ):
    for edgea in range(__lowerCamelCase       ):
      if adjaceny_matrix[edgea][edgea] != "-":
        a						:		Optional[Any]      							=			int(adjaceny_matrix[edgea][edgea]       )

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

  a						:		Graph      							=			graph.prims_algorithm()

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

  return initial_total - optimal_total


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













'''simple docstring'''


from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase					:     Optional[int]												=logging.get_logger(__name__)

lowerCAmelCase					:     Tuple												={
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class       a_   (				_lowerCAmelCase							):
     __A      =			"autoformer"
     __A      =			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
         "num_hidden_layers": "encoder_layers",
     }





     def __init__( self		:					Any	,      lowercase		:					Optional[int] = None	,      lowercase		:					Optional[int] = None	,      lowercase		:					str = "student_t"	,      lowercase		:					str = "nll"	,      lowercase		:					int = 1	,      lowercase		:					List[int] = [1, 2, 3, 4, 5, 6, 7]	,      lowercase		:					bool = True	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					int = 64	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 32	,      lowercase		:					int = 32	,      lowercase		:					str = "gelu"	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					int = 100	,      lowercase		:					float = 0.02	,      lowercase		:					bool = True	,      lowercase		:					int=True	,      lowercase		:					int = 10	,      lowercase		:					int = 25	,      lowercase		:					int = 3	,      **lowercase		:					Dict	,      ):





        """simple docstring"""




        lowercase_			:str    			= prediction_length
        lowercase_			:Dict    			= context_length if context_length is not None else prediction_length
        lowercase_			:Any    			= distribution_output
        lowercase_			:Tuple    			= loss
        lowercase_			:Dict    			= input_size
        lowercase_			:Tuple    			= num_time_features
        lowercase_			:int    			= lags_sequence
        lowercase_			:Tuple    			= scaling
        lowercase_			:List[Any]    			= num_dynamic_real_features
        lowercase_			:Union[str, Any]    			= num_static_real_features
        lowercase_			:str    			= num_static_categorical_features
        if cardinality is not None and num_static_categorical_features > 0:
           if len(lowercase						) != num_static_categorical_features:
              raise ValueError(
                  "The cardinality should be a list of the same length as `num_static_categorical_features`"						)
           lowercase_			:Optional[int]    			= cardinality
        else:
           lowercase_			:Optional[int]    			= [0]
        if embedding_dimension is not None and num_static_categorical_features > 0:
           if len(lowercase						) != num_static_categorical_features:
              raise ValueError(
                  "The embedding dimension should be a list of the same length as `num_static_categorical_features`"						)
           lowercase_			:Tuple    			= embedding_dimension
        else:
           lowercase_			:Dict    			= [min(50	,      (cat + 1) // 2						) for cat in self.cardinality]
        lowercase_			:Any    			= num_parallel_samples

        # Transformer architecture configuration
        lowercase_			:Tuple    			= input_size * len(self.lags_sequence						) + self._number_of_features
        lowercase_			:Union[str, Any]    			= d_model
        lowercase_			:Optional[Any]    			= encoder_attention_heads
        lowercase_			:Optional[Any]    			= decoder_attention_heads
        lowercase_			:Optional[int]    			= encoder_ffn_dim
        lowercase_			:int    			= decoder_ffn_dim
        lowercase_			:Any    			= encoder_layers
        lowercase_			:Optional[Any]    			= decoder_layers

        lowercase_			:Dict    			= dropout
        lowercase_			:Dict    			= attention_dropout
        lowercase_			:str    			= activation_dropout
        lowercase_			:int    			= encoder_layerdrop
        lowercase_			:Dict    			= decoder_layerdrop

        lowercase_			:List[str]    			= activation_function
        lowercase_			:int    			= init_std

        lowercase_			:Optional[int]    			= use_cache

        # Autoformer
        lowercase_			:List[str]    			= label_length
        lowercase_			:List[str]    			= moving_average
        lowercase_			:Optional[int]    			= autocorrelation_factor

        super().__init__(is_encoder_decoder=lowercase	,      **lowercase						)







     @property
     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        return (
            sum(self.embedding_dimension						)
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )


