1094 The Largest Generation (25 分)

本文介绍了一种算法,用于解决在给定的家族树结构中找出人口最多的世代的问题。通过深度优先搜索(DFS)遍历家族树,并使用哈希表记录每个世代的成员数量,从而确定人口最多的世代及其具体人数。

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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

  思路: 求树中的一层,孩子数最多,用dfs深搜,然后用hashtable 记录每一层的节点数,关键点在这里,每一次dfs就需要进行一次记录,故需要把hasht 放在dfs的入口处

#include<bits/stdc++.h>
#define ll long long
using namespace std;
const int maxn=110;
vector<int> tree[maxn];
int n,m,hasht[maxn]={0},maxc=0;
void dfs(int root, int depth)
{
    hasht[depth]++;
    if(tree[root].size()==0) {
        return ;
    }
    for(int i=0;i<tree[root].size();i++)
    {
        dfs(tree[root][i],depth+1);
    }
}
int main()
{
    cin>>n>>m;
    int id,k,tmp;
    for(int i=0;i<m;i++)
    {
        cin>>id>>k;
        for(int i=0;i<k;i++)
        {
            cin>>tmp;
            tree[id].push_back(tmp);
        }
    }
    dfs(1,1);
    int cnt=0;
    for(int i=0;i<maxn;i++)
    {
        if(hasht[i]>maxc)
        {
            cnt=i;
            maxc=hasht[i];
        }
    }
    //cout<<hasht[1]<<endl;
    cout<<maxc<<" "<<cnt<<endl;
    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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