1094 The Largest Generation (25分)

本文介绍了一种通过深度优先搜索(DFS)算法遍历家族树结构的方法,旨在找出具有最多成员的一代及其具体代数。通过对输入数据进行解析,构建家族成员间的连接,算法能够有效地确定各代的人口数量,并最终输出人口最多的代数及其人口数。

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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.

Input Specification:

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.

Output Specification:

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.

Sample Input:

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

Sample Output:

9 4

题意:建立一棵祖先树,每一层代表一代人,求最多那代人的人数和是哪一代人,根为第一代。

思路:用DFS遍历的同时使用height来保存第几代,并使用数组记录每代人数(下标为第几代),最后遍历数组,求出最大值并输出值和下标。

#include<iostream>

#include<vector>

using namespace std;
vector<int> vec[100];
bool visit[100] = {false};
int cnt_gen[100];
void dfs(int start, int height){

    cnt_gen[height]++;

    for(int i = 0; i < vec[start].size(); i++){

        if(!visit[vec[start][i]]){
            
            visit[vec[start][i]] = true;
            dfs(vec[start][i], height + 1);
        }
    }

}
int main(){

    int n, m;
    cin >> n >> m;

    for(int i = 0; i < m; i++){
        int id, k;
        scanf("%d %d", &id, &k);
        for(int j = 0; j < k; j++){
            int temp;
            scanf("%d", &temp);
            vec[id].push_back(temp);
        }

    }

    dfs(1, 1);
    int max_cnt = -1;
    int max_level = -1;
    for(int i = 1; i < 100; i++){
        if(cnt_gen[i] == 0){
            break;
        }
        if(max_cnt < cnt_gen[i]){
            max_cnt = cnt_gen[i];
            max_level = i;
        }
    }
    printf("%d %d\n", max_cnt, max_level);
    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'] 请析问题原因,并给出详细解决方法
最新发布
07-23
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