from __future__ import annotations

from typing import Any


class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase)	->     None:
        __UpperCamelCase					:List[Any]        =		num_of_nodes
        __UpperCamelCase					:list[list[int]]        =		[]
        __UpperCamelCase					:dict[int, int]        =		{}







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     None:
        self.m_edges.append([u_node, v_node, weight])







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:

        if self.m_component[u_node] == u_node:
            return u_node
        return self.find_component(self.m_component[u_node])







    def       UpperCamelCase__       (     self			,			__lowercase)	->     None:

        if self.m_component[u_node] != u_node:
            for k in self.m_component:
                __UpperCamelCase					:Optional[int]        =		self.find_component(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     None:

        if component_size[u_node] <= component_size[v_node]:
            __UpperCamelCase					:Dict        =		v_node
            component_size[v_node] += component_size[u_node]
            self.set_component(__lowercase)

        elif component_size[u_node] >= component_size[v_node]:
            __UpperCamelCase					:Tuple        =		self.find_component(__lowercase)
            component_size[u_node] += component_size[v_node]
            self.set_component(__lowercase)







    def       UpperCamelCase__       (     self)	->     None:
        __UpperCamelCase					:str        =		[]
        __UpperCamelCase					:Union[str, Any]        =		0

        __UpperCamelCase					:list[Any]        =		[-1] * self.m_num_of_nodes

        # A list of components (initialized to all of the nodes)
        for node in range(self.m_num_of_nodes):
            self.m_component.update({node: node})
            component_size.append(1)

        __UpperCamelCase					:int        =		self.m_num_of_nodes

        while num_of_components > 1:
            for edge in self.m_edges:
                __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Dict        =		edge

                __UpperCamelCase					:Union[str, Any]        =		self.m_component[u]
                __UpperCamelCase					:List[str]        =		self.m_component[v]

                if u_component != v_component:

                    for component in (u_component, v_component):
                        if (
                            minimum_weight_edge[component] == -1
                            or minimum_weight_edge[component][2] > w
                        ):
                            __UpperCamelCase					:Any        =		[u, v, w]

            for edge in minimum_weight_edge:
                if isinstance(__lowercase			,			__lowercase):
                    __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:str        =		edge

                    __UpperCamelCase					:Tuple        =		self.m_component[u]
                    __UpperCamelCase					:List[str]        =		self.m_component[v]

                    if u_component != v_component:
                        mst_weight += w
                        self.union(__lowercase			,			__lowercase			,			__lowercase)
                        print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
                        num_of_components -= 1

            __UpperCamelCase					:Optional[int]        =		[-1] * self.m_num_of_nodes
        print(f"""The total weight of the minimal spanning tree is: {mst_weight}""")



def lowerCamelCase  (       ):
    '''simple docstring'''









if __name__ == "__main__":
    import doctest

    doctest.testmod()






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		ArgumentParser(
        description=(
            '''PyTorch TPU distributed training launch '''
            '''helper utility that will spawn up '''
            '''multiple distributed processes'''
        )     )

    # Optional arguments for the launch helper
    parser.add_argument('''--num_cores'''  ,      type=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_SNAKE_CASE  ,      help=(
            '''The full path to the single TPU training '''
            '''program/script to be launched in parallel, '''
            '''followed by all the arguments for the '''
            '''training script'''
        )  ,      )

    # rest from the training program
    parser.add_argument('''training_script_args'''  ,      nargs=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

    # Import training_script as a module.
    __UpperCamelCase					:Dict        =		Path(args.training_script     )
    sys.path.append(str(script_fpath.parent.resolve()     )     )
    __UpperCamelCase					:Any        =		script_fpath.stem
    __UpperCamelCase					:Union[str, Any]        =		importlib.import_module(SCREAMING_SNAKE_CASE     )

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores     )]
    xmp.spawn(mod._mp_fn  ,      args=()  ,      nprocs=args.num_cores     )


if __name__ == "__main__":
    main()





