






from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
		import pyximport

		pyximport.install(setup_args={'''include_dirs''': np.get_include()})
		from . import algos_graphormer  # noqa E402







def 						a_      (__A							,					__A = 512			)							->	List[Any]:



					"""simple docstring"""
					__a				:	Dict			=				x.shape[1] if len(x.shape			) > 1 else 1
					__a				:	str			=				1 + np.arange(0							,					feature_num * offset							,					__A							,					dtype=np.intaa			)
					__a				:	Dict			=				x + feature_offset
					return x







def 						a_      (__A							,					__A=True			)							->	Union[str, Any]:



					"""simple docstring"""
					requires_backends(__A							,					["cython"]			)

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										__a				:	List[str]			=				np.asarray(item["edge_attr"]							,					dtype=np.intaa			)
					else:
										__a				:	Optional[Any]			=				np.ones((len(item["edge_index"][0]			), 1)							,					dtype=np.intaa			)  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										__a				:	str			=				np.asarray(item["node_feat"]							,					dtype=np.intaa			)
					else:
										__a				:	str			=				np.ones((item["num_nodes"], 1)							,					dtype=np.intaa			)  # same embedding for all

					__a				:	Optional[Any]			=				np.asarray(item["edge_index"]							,					dtype=np.intaa			)

					__a				:	Union[str, Any]			=				convert_to_single_emb(__A			) + 1
					__a				:	List[str]			=				item["num_nodes"]

					if len(edge_attr.shape			) == 1:
										__a				:	int			=				edge_attr[:, None]
					__a				:	Optional[int]			=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]							,					dtype=np.intaa			)
					__a				:	Dict			=				convert_to_single_emb(__A			) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					__a				:	Tuple			=				np.zeros([num_nodes, num_nodes]							,					dtype=__A			)
					__a				:	int			=				True

					__a			, __a				:	List[str]			=				algos_graphormer.floyd_warshall(__A			)
					__a				:	Tuple			=				np.amax(__A			)

					__a				:	str			=				algos_graphormer.gen_edge_input(__A							,					__A							,					__A			)
					__a				:	Dict			=				np.zeros([num_nodes + 1, num_nodes + 1]							,					dtype=np.single			)  # with graph token

					# combine
					__a				:	Any			=				input_nodes + 1  # we shift all indices by one for padding
					__a				:	Optional[Any]			=				attn_bias
					__a				:	int			=				attn_edge_type
					__a				:	Any			=				shortest_path_result.astype(np.intaa			) + 1  # we shift all indices by one for padding
					__a				:	List[Any]			=				np.sum(__A							,					axis=1			).reshape(-1			) + 1  # we shift all indices by one for padding
					__a				:	Dict			=				item["in_degree"]  # for undirected graph
					__a				:	Optional[Any]			=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										__a				:	Union[str, Any]			=				item["y"]

					return item




class        snake_case_					:

						"""simple docstring"""



						def __init__(self:				List[Any]   ,				__UpperCAmelCase:				Optional[Any]=20   ,				__UpperCAmelCase:				str=False      )						->    Optional[Any]:




											'''simple docstring'''



											if not is_cython_available():
																raise ImportError("Graphormer preprocessing needs Cython (pyximport)"      )

											__a				:	List[Any]			=				spatial_pos_max
											__a				:	Optional[int]			=				on_the_fly_processing



						def __call__(self:				List[Any]   ,				__UpperCAmelCase:				List[dict]      )						->    Dict[str, Any]:




											'''simple docstring'''



											if self.on_the_fly_processing:
																__a				:	Any			=				[preprocess_item(lowerCAmelCase_      ) for i in features]

											if not isinstance(features[0]   ,				lowerCAmelCase_      ):
																__a				:	Optional[Any]			=				[vars(lowerCAmelCase_      ) for f in features]
											__a				:	Optional[int]			=				{}

											__a				:	Tuple			=				max(len(i["input_nodes"]      ) for i in features      )
											__a				:	int			=				len(features[0]["input_nodes"][0]      )
											__a				:	Optional[Any]			=				len(features[0]["attn_edge_type"][0][0]      )
											__a				:	Optional[Any]			=				max(len(i["input_edges"][0][0]      ) for i in features      )
											__a				:	str			=				len(features[0]["input_edges"][0][0][0]      )
											__a				:	Optional[Any]			=				len(lowerCAmelCase_      )

											__a				:	int			=				torch.zeros(lowerCAmelCase_   ,				max_node_num + 1   ,				max_node_num + 1   ,				dtype=torch.float      )
											__a				:	str			=				torch.zeros(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				dtype=torch.long      )
											__a				:	str			=				torch.zeros(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				dtype=torch.long      )
											__a				:	Any			=				torch.zeros(lowerCAmelCase_   ,				lowerCAmelCase_   ,				dtype=torch.long      )
											__a				:	Union[str, Any]			=				torch.zeros(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				dtype=torch.long      )
											__a				:	Tuple			=				torch.zeros(
											    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				dtype=torch.long      )

											for ix, f in enumerate(lowerCAmelCase_      ):
																for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																					__a				:	Any			=				torch.tensor(f[k]      )

																if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]      ) > 0:
																					__a				:	List[str]			=				float("-inf"      )

																__a				:	List[Any]			=				f["attn_bias"]
																__a				:	Dict			=				f[
																    "attn_edge_type"
																]
																__a				:	Optional[Any]			=				f["spatial_pos"]
																__a				:	List[str]			=				f["in_degree"]
																__a				:	Union[str, Any]			=				f["input_nodes"]
																__a				:	List[str]			=				f["input_edges"]

											__a				:	List[Any]			=				batch["in_degree"]

											__a				:	int			=				features[0]["labels"]
											if len(lowerCAmelCase_      ) == 1:  # one task
																if isinstance(sample[0]   ,				lowerCAmelCase_      ):  # regression
																					__a				:	Any			=				torch.from_numpy(np.concatenate([i["labels"] for i in features]      )      )
																else:  # binary classification
																					__a				:	Optional[Any]			=				torch.from_numpy(np.concatenate([i["labels"] for i in features]      )      )
											else:  # multi task classification, left to float to keep the NaNs
																__a				:	Union[str, Any]			=				torch.from_numpy(np.stack([i["labels"] for i in features]   ,				axis=0      )      )

											return batch













def 						a_      (__A							,					__A			)							->	list[tuple[int, int, int]]:



					"""simple docstring"""
					__a				:	str			=				sorted(__A							,					key=lambda __A			: edge[2]			)

					__a				:	int			=				list(range(__A			)			)

					def find_parent(__A			):
										if i != parent[i]:
															__a				:	Optional[Any]			=				find_parent(parent[i]			)
										return parent[i]

					__a				:	Any			=				0
					__a				:	Optional[int]			=				[]

					for edge in edges:
										__a				:	Any			=				find_parent(edge[0]			)
										__a				:	Optional[Any]			=				find_parent(edge[1]			)
										if parent_a != parent_b:
															minimum_spanning_tree_cost += edge[2]
															minimum_spanning_tree.append(__A			)
															__a				:	List[str]			=				parent_b

					return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
			UpperCAmelCase__ ,       UpperCAmelCase__			 =	list(map(int, input().strip().split()))
			UpperCAmelCase__			 =	[]

			for _ in range(num_edges):
						UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__			 =	(int(x) for x in input().strip().split())
						edges.append((nodea, nodea, cost))

			kruskal(num_nodes, edges)





