





'''simple docstring'''






from __future__ import annotations

from typing import Any
class 					__snake_case  :






   '''simple docstring'''





   def __init__(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					List[Any]    =  num_of_nodes
    snake_case__     :					list[list[int]]    =  []
    snake_case__     :					dict[int, int]    =  {}

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    self.m_edges.append([u_node, v_node, weight]       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):

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

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      snake_case__     :					Union[str, Any]    =  self.find_component(__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):

    if component_size[u_node] <= component_size[v_node]:
     snake_case__     :					List[str]    =  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(__SCREAMING_SNAKE_CASE       )

    elif component_size[u_node] >= component_size[v_node]:
     snake_case__     :					Optional[Any]    =  self.find_component(__SCREAMING_SNAKE_CASE       )
     component_size[u_node] += component_size[v_node]
     self.set_component(__SCREAMING_SNAKE_CASE       )


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  []
    snake_case__     :					Tuple    =  0

    snake_case__     :					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       )

    snake_case__     :					Any    =  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      snake_case__  ,							snake_case__  ,							snake_case__     :					List[Any]    =  edge

      snake_case__     :					Dict    =  self.m_component[u]
      snake_case__     :					Any    =  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
        ):
         snake_case__     :					Optional[int]    =  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
       snake_case__  ,							snake_case__  ,							snake_case__     :					int    =  edge

       snake_case__     :					List[Any]    =  self.m_component[u]
       snake_case__     :					Any    =  self.m_component[v]

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

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



def        UpperCamelCase__		(						)				->			None:
 '''simple docstring'''








if __name__ == "__main__":
    import doctest

    doctest.testmod()






import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for shard in shards:
						for i in range(a	):
									yield {"i": i, "shard": shard}


def    a      ( )     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= int(os.environ['''RANK''']	)
			SCREAMING_SNAKE_CASE     						= int(os.environ['''WORLD_SIZE''']	)

			SCREAMING_SNAKE_CASE     						= ArgumentParser()
			parser.add_argument('''--streaming'''		,						type=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						default=0	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= args.streaming
			SCREAMING_SNAKE_CASE     						= args.num_workers

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			SCREAMING_SNAKE_CASE     						= NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE     						= full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)	)

			SCREAMING_SNAKE_CASE     						= sum(1 for _ in dataloader	)
			if local_size != expected_local_size:
						raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()