1094. The Largest Generation (25)

本文介绍了一种通过构建家族树并使用层次遍历算法来找出具有最多成员的一代的方法。输入包括家庭成员总数及拥有后代的成员信息,输出为人口最多的代数及其水平。文章详细解释了创建树结构和层级遍历的过程。

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1094. The Largest Generation (25)
时间限制 200 ms 内存限制 65536 kB 代码长度限制 16000 B
判题程序 Standard 作者 CHEN, Yue

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(less than 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

#define _CRT_SECURE_NO_WARNINGS
#include <algorithm>
#include <iostream>
#include <queue>
#include <iomanip>

using namespace std;
const int MaxN = 110;
int Gner = 0, Num =-1;

struct tnode
{
    vector<int>child;
}Tree[MaxN];


void Level(int root)
{
    queue<int> que, backque;
    que.push(root);
    int tNum = 0, G = 0;

    while (que.size())
    {
        ++G; tNum = 0;
        while (que.size())
        {
            ++tNum;
            int id = que.front(); que.pop();

            for (int i = 0; i < Tree[id].child.size(); ++i)
                backque.push(Tree[id].child[i]);
        }

        if (tNum > Num)
        {
            Num = tNum;
            Gner = G;
        }

        while (backque.size())
        {
            que.push(backque.front());
            backque.pop();
        }

    }

}


int  CreateTree(int Nonchild)
{
    for (int i = 0; i < Nonchild; ++i)
    {
        int id,childnum, child;
        cin >> id >> childnum;

        for (int k = 0; k < childnum; ++k)
        {
            cin >> child;
            Tree[id].child.push_back(child);
        }
    }
    return 0;
}


int main()
{
#ifdef _DEBUG
    freopen("data.txt", "r+", stdin);
#endif

    std::ios::sync_with_stdio(false);

    int N, Nonchild;
    cin >> N >> Nonchild;

    CreateTree(Nonchild);
    Level(1);
    cout << Num << " " << Gner;
    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
最新发布
07-23
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