1094-- The Largest Generation (25)

本文探讨了如何通过树的层次遍历算法,找到家族树中人口最多的代及其人数。通过对输入数据的解析和处理,利用队列进行层次遍历,并记录每一代的人口数量,最终找出人口最多的代。

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1005

题目:

题目描述

A family hierarchy is usually presented by a pedigree tree where all the nodes on the same level belong to the same generation. Your task is to find the generation with the largest population.

输入描述:

Each input file contains one test case. Each case starts with two positive integers N (<100) which is the total number of family members in the tree (and hence assume that all the members are numbered from 01 to N), and M (<N) which is the number of family members who have children. Then M lines follow, each contains the information of a family member in the following format:
ID K ID[1] ID[2] ... ID[K]
where ID is a two-digit number representing a family member, K (>0) is the number of his/her children, followed by a sequence of two-digit ID's of his/her children. For the sake of simplicity, let us fix the root ID to be 01. All the numbers in a line are separated by a space.

输出描述:

For each test case, print in one line the largest population number and the level of the corresponding generation. It is assumed that such a generation is unique, and the root level is defined to be 1.

输入例子:

23 13
21 1 23
01 4 03 02 04 05
03 3 06 07 08
06 2 12 13
13 1 21
08 2 15 16
02 2 09 10
11 2 19 20
17 1 22
05 1 11
07 1 14
09 1 17
10 1 18

 

输出例子:

9 4

算法分析:

树的层次遍历,求出拥有节点最多的层数及其节点数目。

(也可以记录每个节点的父亲,然后通过DFS递归求深度)

代码:

#include <iostream>
#include <vector>
#include <algorithm>
#include <queue>
using namespace std;

const int MAXN = 10001;
int N, M;
struct Tree {
	int level = 0;
	vector<int> kids;
}tree[MAXN];

void Input(void) {
	cin >> N >> M;
	for (int i = 1; i <= M; i++) {
		int index, n;
		cin >> index >> n;
		if (1 == index)
			tree[index].level = 1;
		for (int j = 1; j <= n; j++) {
			int temp;
			cin >> temp;
			tree[index].kids.push_back(temp);
		}
	}
}

//树的层次遍历
void LevelOrder(void) {
	queue<Tree> q;
	q.push(tree[1]);

	while (!q.empty()) {
		Tree temp = q.front();
		q.pop();
		for (int i = 0; i < temp.kids.size(); i++) {
			tree[temp.kids.at(i)].level = temp.level + 1;
			q.push(tree[temp.kids.at(i)]);
		}
	}
	return;
}

int main(void) {
	Input();
	LevelOrder();
	
	int level[101]; fill(level, level + 101, 0);
	int maxlevel = 0, maxcount = 0, levelindex = 0;
	for (int i = 1; i <= N; i++) {
		level[tree[i].level]++;
		if (maxlevel < tree[i].level)
			maxlevel = tree[i].level;
	}
	for (int i = 1; i <= maxlevel; i++)
		if (maxcount < level[i]) {
			maxcount = level[i];
			levelindex = i;
		}

	cout << maxcount << " " << levelindex << endl;

	system("pause");
	return 0;
}

 

import time import torch, torch_npu from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig # 替换成本地的模型权重路径 MODEL_PATH = "/models/z50051264/Qwen2.5-7B-Instruct" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, # Support torch.float16, torch.float32, torch.bfloat16 bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=False, bnb_4bit_quant_storage=torch.uint8 ) torch.npu.synchronize() start_time = time.time() model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, device_map={"":0}, quantization_config=bnb_config, low_cpu_mem_usage=True, torch_dtype=torch.float16 # Support torch.float16, torch.float32, torch.bfloat16 ) torch.npu.synchronize() print(f"[+] load time: {time.time() - start_time:.6}s") tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) model.eval() prompt = "Once upon a time, " inputs = tokenizer([prompt], return_tensors="pt") input_ids = inputs.input_ids.npu() attention_mask = inputs.attention_mask.npu() torch.npu.synchronize() start_time = time.time() generated_ids = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=32, do_sample=False, ) torch.npu.synchronize() print(f"[+] inference time: {time.time() - start_time:.6}s") print(tokenizer.batch_decode(generated_ids)) 我在使用npu版本的bitsandbytes,但是执行以上代码,出现错误: [root@190f3c453709 inference]# python nf4.py /usr/local/python3.10.17/lib/python3.10/site-packages/torch_npu/utils/storage.py:38: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() if self.device.type != 'cpu': Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.26s/it] [+] load time: 14.9728s The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details. [+] inference time: 3.78472s ['Once upon a time, 123456789 was the largest known prime number. If a new prime number, 123456789'] 请分析问题原因,并给出详细解决方法
07-23
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