






"""simple docstring"""





from __future__ import annotations

import os
from collections.abc import Mapping

__lowerCamelCase							=  tuple[int, int]



class       __A		:

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

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      EdgeT     ,		__snake_case  :      int					)			->							None:
											self.vertices.add(edge[0]					)
											self.vertices.add(edge[1]					)
											__magic_name__:	int							=       weight






					def   lowerCamelCase__   (			self  :      Tuple					)			->							Graph:
											__magic_name__:	Graph							=       Graph({min(self.vertices					)}     ,		{}					)
											__magic_name__:	EdgeT
											__magic_name__:	int
											__magic_name__:	EdgeT
											__magic_name__:	int

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

																	subgraph.add_edge(__snake_case     ,		__snake_case					)

											return subgraph



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

						with open(__UpperCAmelCase  ) as f:
												__magic_name__:	Optional[int]							=       f.read().strip().split("""\n"""  )

						__magic_name__:	List[str]							=       [line.split(""","""  ) for line in data]

						for edgea in range(1      ,   len(__UpperCAmelCase  )  ):
												for edgea in range(__UpperCAmelCase  ):
																		if adjaceny_matrix[edgea][edgea] != "-":
																								__magic_name__:	List[str]							=       int(adjaceny_matrix[edgea][edgea]  )

						__magic_name__:	Graph							=       Graph(set(range(len(__UpperCAmelCase  )  )  )      ,   __UpperCAmelCase  )

						__magic_name__:	Graph							=       graph.prims_algorithm()

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

						return initial_total - optimal_total


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











"""simple docstring"""





from typing import Union

import fire
import torch
from tqdm import tqdm



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str = "cpu"      ,   __UpperCAmelCase      :    Union[str, None] = None  )    ->							None:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location=__UpperCAmelCase  )
						for k, v in tqdm(state_dict.items()  ):
												if not isinstance(__UpperCAmelCase      ,   torch.Tensor  ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""  )
												__magic_name__:	Any							=       v.half()
						if save_path is None:  # overwrite src_path
												__magic_name__:	List[str]							=       src_path
						torch.save(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							fire.Fire(convert)



