1094 The Largest Generation

通过构建节点结构并遍历家族成员的子代数量,本程序旨在找出家族树中人口最多的代,提供了一个有效的算法实现。

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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

解题思路:创建一个节点结构,每个节点中存放该节点的孩子数以及孩子的编号,然后从根节点开始,一层一层的统计每层的孩子的总数,当当前层的孩子总数比之前记录的总数大的时候,更新最大值。

#include<iostream>
#include<stdio.h>
#include<vector>
#include<queue>
using namespace std;
struct node{
    int size;
    vector<int>children;
};
int main(){
    for (int n, m; scanf("%d%d", &n,&m) != EOF;){
        node *arr = new node[n+1];
        for (int i = 0; i < m; i++){
            int num, size, temp;
            scanf("%d", &num);
            scanf("%d", &size);
            arr[num].size = size;
            while (size--){
                scanf("%d", &temp);
                arr[num].children.push_back(temp);
            }
        }
        int count = 1;
        int level = 1;
        int ger_sum = 1;
        int ger_level = 1;
        vector<node>vec;
        vec.push_back(arr[1]);
        while (1){
            level++;
            int sum = 0;
            vector<node>temp;
            for (int i = 0; i < vec.size(); i++){
                sum += vec[i].size;
                for (int j = 0; j < vec[i].children.size(); j++){
                    temp.push_back(arr[vec[i].children[j]]);
                    count++;
                }
            }
            if (sum > ger_sum){
                ger_sum = sum;
                ger_level = level;
            }
            vec = vector<node>(temp);
            if (count == n){
                break;
            }
        }
        printf("%d %d\n", ger_sum, ger_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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