



"""simple docstring"""

from __future__ import annotations

import os
from collections.abc import Mapping

lowercase__			   =  tuple[int, int]




class SCREAMING_SNAKE_CASE__					:



      def __init__(self						,     _lowercase						,     _lowercase							):





             '''simple docstring'''

             __a		:		set[int]									=       vertices
             __a		:		dict[EdgeT, int]									=       {
                 (min(_lowercase							), max(_lowercase							)): weight for edge, weight in edges.items()
             }



      def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





             '''simple docstring'''

             self.vertices.add(edge[0]							)
             self.vertices.add(edge[1]							)
             __a		:		Union[str, Any]									=       weight





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Graph									=       Graph({min(self.vertices							)}						,     {}							)
             __a		:		EdgeT
             __a		:		int
             __a		:		EdgeT
             __a		:		int

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

                    subgraph.add_edge(_lowercase						,     _lowercase							)

             return subgraph







def       __magic_name__    (   _lowerCamelCase				: str = "p107_network.txt"					):
       __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		:		Any									=       f.read().strip().split("""\n"""					)

       __a		:		Dict									=       [line.split(""","""					) for line in data]

       for edgea in range(1  ,       len(_lowerCamelCase					)					):
              for edgea in range(_lowerCamelCase					):
                     if adjaceny_matrix[edgea][edgea] != "-":
                            __a		:		Tuple									=       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"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

