

"""simple docstring"""


import sys
from collections import defaultdict





class    snake_case     :

				def __init__(     self							:    Tuple)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      []

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Optional[int])->       List[str]:



						'''simple docstring'''


						return self.node_position[vertex]

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Dict)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      pos

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Tuple)->       Dict:



						'''simple docstring'''


						if start > size // 2 - 1:
								return
						else:
								if 2 * start + 2 >= size:
										__lowerCAmelCase:	List[str]   				=      2 * start + 1
								else:
										if heap[2 * start + 1] < heap[2 * start + 2]:
												__lowerCAmelCase:	Dict   				=      2 * start + 1
										else:
												__lowerCAmelCase:	Optional[Any]   				=      2 * start + 2
								if heap[smallest_child] < heap[start]:
										__lowerCAmelCase				,			__lowerCAmelCase:	int   				=      heap[smallest_child], positions[smallest_child]
										__lowerCAmelCase				,			__lowerCAmelCase:	Dict   				=      (
										    heap[start],
										    positions[start],
										)
										__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      temp, tempa

										__lowerCAmelCase:	int   				=      self.get_position(positions[smallest_child])
										self.set_position(
										    positions[smallest_child]					,  self.get_position(positions[start]))
										self.set_position(positions[start]					,  UpperCamelCase__)

										self.top_to_bottom(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Union[str, Any])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      position[index]

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

								if val < heap[parent]:
										__lowerCAmelCase:	int   				=      heap[parent]
										__lowerCAmelCase:	List[str]   				=      position[parent]
										self.set_position(position[parent]					,  UpperCamelCase__)
								else:
										__lowerCAmelCase:	Any   				=      val
										__lowerCAmelCase:	Tuple   				=      temp
										self.set_position(UpperCamelCase__					,  UpperCamelCase__)
										break
								__lowerCAmelCase:	Union[str, Any]   				=      parent
						else:
								__lowerCAmelCase:	Any   				=      val
								__lowerCAmelCase:	List[str]   				=      temp
								self.set_position(UpperCamelCase__					,  0)

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[str])->       int:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      len(UpperCamelCase__) // 2 - 1
						for i in range(UpperCamelCase__					,  -1					,  -1):
								self.top_to_bottom(UpperCamelCase__					,  UpperCamelCase__					,  len(UpperCamelCase__)					,  UpperCamelCase__)




				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Dict)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      positions[0]
						__lowerCAmelCase:	Any   				=      sys.maxsize
						self.top_to_bottom(UpperCamelCase__					,  0					,  len(UpperCamelCase__)					,  UpperCamelCase__)
						return temp






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Optional[Any]   				=      Heap()

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

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

		__lowerCAmelCase:	str   				=      []
		__lowerCAmelCase:	int   				=      1
		__lowerCAmelCase:	Union[str, Any]   				=      sys.maxsize
		for neighbor, distance in adjacency_list[0]:
				__lowerCAmelCase:	Optional[int]   				=      0
				__lowerCAmelCase:	Tuple   				=      distance
		heap.heapify(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":  # pragma: no cover
			# < --------- Prims Algorithm --------- >
			__A						       =      int(input("Enter number of edges: ").strip())
			__A						       =      defaultdict(list)
			for _ in range(edges_number):
						__A						       =      [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    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:	Optional[int]   				=      model
						__lowerCAmelCase:	Tuple   				=      2
						__lowerCAmelCase:	List[Any]   				=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

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

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# 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(__SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
			__A						       =      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						       =      parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)


