


"""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))








"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class   _UpperCamelCase  (   pl.LightningModule		):
							'''simple docstring'''







							def __init__(     self    ,   __a	):
										super().__init__()
										__lowerCAmelCase				    =			model
										__lowerCAmelCase				    =			2
										__lowerCAmelCase				    =			nn.Linear(self.model.config.hidden_size    ,   self.num_labels	)



							def 			snake_case				(     self	):
										pass


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			LongformerModel.from_pretrained(_UpperCamelCase      )
			__lowerCAmelCase				    =			LightningModel(_UpperCamelCase      )

			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location=torch.device("cpu"      )      )
			lightning_model.load_state_dict(ckpt["state_dict"]      )

			# init longformer question answering model
			__lowerCAmelCase				    =			LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase      )

			# transfer weights
			longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()      )
			longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()      )
			longformer_for_qa.eval()

			# save model
			longformer_for_qa.save_pretrained(_UpperCamelCase      )

			print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}"      )


if __name__ == "__main__":
	A     :   Tuple			    =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--longformer_model",
	    default=None,
	    type=str,
	    required=True,
	    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
	)
	parser.add_argument(
	    "--longformer_question_answering_ckpt_path",
	    default=None,
	    type=str,
	    required=True,
	    help="Path the official PyTorch Lightning Checkpoint.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	A     :   Optional[int]			    =			parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)




