


"""simple docstring"""


import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			with open(_UpperCamelCase      ) as metadata_file:
						__lowerCAmelCase				    =			json.load(_UpperCamelCase      )
			__lowerCAmelCase				    =			LukeConfig(use_entity_aware_attention=_UpperCamelCase    , **metadata["model_config"]      )

			# Load in the weights from the checkpoint_path
			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )

			# Load the entity vocab file
			__lowerCAmelCase				    =			load_entity_vocab(_UpperCamelCase      )

			__lowerCAmelCase				    =			RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

			# Add special tokens to the token vocabulary for downstream tasks
			__lowerCAmelCase				    =			AddedToken("<ent>"    , lstrip=_UpperCamelCase    , rstrip=_UpperCamelCase      )
			__lowerCAmelCase				    =			AddedToken("<ent2>"    , lstrip=_UpperCamelCase    , rstrip=_UpperCamelCase      )
			tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}      )
			config.vocab_size += 2

			print(f"Saving tokenizer to {pytorch_dump_folder_path}"      )
			tokenizer.save_pretrained(_UpperCamelCase      )
			with open(os.path.join(_UpperCamelCase    , LukeTokenizer.vocab_files_names["entity_vocab_file"]      )    , "w"      ) as f:
						json.dump(_UpperCamelCase    , _UpperCamelCase      )

			__lowerCAmelCase				    =			LukeTokenizer.from_pretrained(_UpperCamelCase      )

			# Initialize the embeddings of the special tokens
			__lowerCAmelCase				    =			state_dict["embeddings.word_embeddings.weight"]
			__lowerCAmelCase				    =			word_emb[tokenizer.convert_tokens_to_ids(["@"]      )[0]].unsqueeze(0      )
			__lowerCAmelCase				    =			word_emb[tokenizer.convert_tokens_to_ids(["#"]      )[0]].unsqueeze(0      )
			__lowerCAmelCase				    =			torch.cat([word_emb, ent_emb, enta_emb]      )

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers      ):
						for matrix_name in ["query.weight", "query.bias"]:
									__lowerCAmelCase				    =			f"encoder.layer.{layer_index}.attention.self."
									__lowerCAmelCase				    =			state_dict[prefix + matrix_name]
									__lowerCAmelCase				    =			state_dict[prefix + matrix_name]
									__lowerCAmelCase				    =			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			__lowerCAmelCase				    =			state_dict["entity_embeddings.entity_embeddings.weight"]
			__lowerCAmelCase				    =			entity_emb[entity_vocab["[MASK]"]]

			__lowerCAmelCase				    =			LukeModel(config=_UpperCamelCase      ).eval()

			__lowerCAmelCase    ,	__lowerCAmelCase				    =			model.load_state_dict(_UpperCamelCase    , strict=_UpperCamelCase      )
			if not (len(_UpperCamelCase      ) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f"Missing keys {', '.join(_UpperCamelCase      )}. Expected only missing embeddings.position_ids"      )
			if not (all(key.startswith("entity_predictions"      ) or key.startswith("lm_head"      ) for key in unexpected_keys      )):
						raise ValueError(
						    "Unexpected keys"
						    f" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'      ) or key.startswith('lm_head'      ))]      )}"      )

			# Check outputs
			__lowerCAmelCase				    =			LukeTokenizer.from_pretrained(_UpperCamelCase    , task="entity_classification"      )

			__lowerCAmelCase				    =			(
			    "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
			    " new world number one avoid a humiliating second- round exit at Wimbledon ."
			)
			__lowerCAmelCase				    =			(39, 42)
			__lowerCAmelCase				    =			tokenizer(_UpperCamelCase    , entity_spans=[span]    , add_prefix_space=_UpperCamelCase    , return_tensors="pt"      )

			__lowerCAmelCase				    =			model(**_UpperCamelCase      )

			# Verify word hidden states
			if model_size == "large":
						__lowerCAmelCase				    =			torch.Size((1, 42, 1024)      )
						__lowerCAmelCase				    =			torch.tensor(
						    [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]      )
			else:  # base
						__lowerCAmelCase				    =			torch.Size((1, 42, 768)      )
						__lowerCAmelCase				    =			torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]      )

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"      )
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]    , _UpperCamelCase    , atol=1e-4      ):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						__lowerCAmelCase				    =			torch.Size((1, 1, 1024)      )
						__lowerCAmelCase				    =			torch.tensor([[0.04_66, -0.01_06, -0.01_79]]      )
			else:  # base
						__lowerCAmelCase				    =			torch.Size((1, 1, 768)      )
						__lowerCAmelCase				    =			torch.tensor([[0.14_57, 0.10_44, 0.01_74]]      )

			if not (outputs.entity_last_hidden_state.shape != expected_shape):
						raise ValueError(
						    f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
						    f" {expected_shape}"      )
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]    , _UpperCamelCase    , atol=1e-4      ):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print("Saving PyTorch model to {}".format(_UpperCamelCase      )      )
			model.save_pretrained(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			{}
			with open(_UpperCamelCase    , "r"    , encoding="utf-8"      ) as f:
						for index, line in enumerate(_UpperCamelCase      ):
									__lowerCAmelCase    ,	__lowerCAmelCase				    =			line.rstrip().split("\t"      )
									__lowerCAmelCase				    =			index

			return entity_vocab


if __name__ == "__main__":
	A     :   str			    =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
	parser.add_argument(
	    "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
	)
	parser.add_argument(
	    "--entity_vocab_path",
	    default=None,
	    type=str,
	    help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
	)
	parser.add_argument(
	    "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
	)
	A     :   Tuple			    =			parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)








"""simple docstring"""


import sys
from collections import defaultdict
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[]

							def 			snake_case				(     self    ,   __a	):
										return self.node_position[vertex]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			pos

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a	):
										if start > size // 2 - 1:
													return
										else:
													if 2 * start + 2 >= size:
																__lowerCAmelCase				    =			2 * start + 1
													else:
																if heap[2 * start + 1] < heap[2 * start + 2]:
																			__lowerCAmelCase				    =			2 * start + 1
																else:
																			__lowerCAmelCase				    =			2 * start + 2
													if heap[smallest_child] < heap[start]:
																__lowerCAmelCase    ,	__lowerCAmelCase				    =			heap[smallest_child], positions[smallest_child]
																__lowerCAmelCase    ,	__lowerCAmelCase				    =			(
																    heap[start],
																    positions[start],
																)
																__lowerCAmelCase    ,	__lowerCAmelCase				    =			temp, tempa

																__lowerCAmelCase				    =			self.get_position(positions[smallest_child]	)
																self.set_position(
																    positions[smallest_child]    ,   self.get_position(positions[start]	)	)
																self.set_position(positions[start]    ,   __a	)

																self.top_to_bottom(__a    ,   __a    ,   __a    ,   __a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			position[index]

										while index != 0:
													__lowerCAmelCase				    =			int((index - 2) / 2	) if index % 2 == 0 else int((index - 1) / 2	)

													if val < heap[parent]:
																__lowerCAmelCase				    =			heap[parent]
																__lowerCAmelCase				    =			position[parent]
																self.set_position(position[parent]    ,   __a	)
													else:
																__lowerCAmelCase				    =			val
																__lowerCAmelCase				    =			temp
																self.set_position(__a    ,   __a	)
																break
													__lowerCAmelCase				    =			parent
										else:
													__lowerCAmelCase				    =			val
													__lowerCAmelCase				    =			temp
													self.set_position(__a    ,   0	)

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			len(__a	) // 2 - 1
										for i in range(__a    ,   -1    ,   -1	):
													self.top_to_bottom(__a    ,   __a    ,   len(__a	)    ,   __a	)



							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			positions[0]
										__lowerCAmelCase				    =			sys.maxsize
										self.top_to_bottom(__a    ,   0    ,   len(__a	)    ,   __a	)
										return temp


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			Heap()

			__lowerCAmelCase				    =			[0] * len(_UpperCamelCase      )
			__lowerCAmelCase				    =			[-1] * len(_UpperCamelCase      )  # Neighboring Tree Vertex of selected vertex
			# Minimum Distance of explored vertex with neighboring vertex of partial tree
			# formed in graph
			__lowerCAmelCase				    =			[]  # Heap of Distance of vertices from their neighboring vertex
			__lowerCAmelCase				    =			[]

			for vertex in range(len(_UpperCamelCase      )      ):
						distance_tv.append(sys.maxsize      )
						positions.append(_UpperCamelCase      )
						heap.node_position.append(_UpperCamelCase      )

			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			1
			__lowerCAmelCase				    =			sys.maxsize
			for neighbor, distance in adjacency_list[0]:
						__lowerCAmelCase				    =			0
						__lowerCAmelCase				    =			distance
			heap.heapify(_UpperCamelCase    , _UpperCamelCase      )

			for _ in range(1    , len(_UpperCamelCase      )      ):
						__lowerCAmelCase				    =			heap.delete_minimum(_UpperCamelCase    , _UpperCamelCase      )
						if visited[vertex] == 0:
									tree_edges.append((nbr_tv[vertex], vertex)      )
									__lowerCAmelCase				    =			1
									for neighbor, distance in adjacency_list[vertex]:
												if (
												    visited[neighbor] == 0
												    and distance < distance_tv[heap.get_position(_UpperCamelCase      )]
												):
															__lowerCAmelCase				    =			distance
															heap.bottom_to_top(
															    _UpperCamelCase    , heap.get_position(_UpperCamelCase      )    , _UpperCamelCase    , _UpperCamelCase      )
															__lowerCAmelCase				    =			vertex
			return tree_edges


if __name__ == "__main__":  # pragma: no cover
	# < --------- Prims Algorithm --------- >
	A     :   Optional[Any]			    =			int(input("Enter number of edges: ").strip())
	A     :   Dict			    =			defaultdict(list)
	for _ in range(edges_number):
		A     :   str			    =			[int(x) for x in input().strip().split()]
		adjacency_list[edge[0]].append([edge[1], edge[2]])
		adjacency_list[edge[1]].append([edge[0], edge[2]])
	print(prisms_algorithm(adjacency_list))




