D-We Love MOE Girls

D - We Love MOE Girls
Time Limit:500MS     Memory Limit:32768KB     64bit IO Format:%I64d & %I64u

Description

Chikami Nanako is a girl living in many different parallel worlds. In this problem we talk about one of them.  
In this world, Nanako has a special habit. When talking with others, she always ends each sentence with "nanodesu".  
There are two situations:  
If a sentence ends with "desu", she changes "desu" into "nanodesu", e.g. for "iloveyoudesu", she will say "iloveyounanodesu". Otherwise, she just add "nanodesu" to the end of the original sentence.  
Given an original sentence, what will it sound like aften spoken by Nanako?
 

Input

The first line has a number T (T <= 1000) , indicating the number of test cases.  
For each test case, the only line contains a string s, which is the original sentence.  
The length of sentence s will not exceed 100, and the sentence contains lowercase letters from a to z only.
 

Output

For every case, you should output "Case #t: " at first, without quotes. The   t  is the case number starting from 1. Then output which Nanako will say.
 

Sample Input

       
2 ohayougozaimasu daijyoubudesu
 

Sample Output

       
Case #1: ohayougozaimasunanodesu Case #2: daijyoubunanodesu
 
#include<stdio.h>
#include<string.h>
int main()
{
    int T, len, i, j;
    char s[102];
    scanf("%d", &T);
   for(j=1; j<=T;j++)
    {
        scanf("%s", s);
        len = strlen(s);
        if( s[len-4]=='d' && s[len-3]=='e' && s[len-2]=='s' && s[len-1]=='u')
        {
            printf("Case #%d: ", j);
            for(i=0;i<(len-4); i++)
                printf("%c", s[i]);
            printf("nanodesu\n");
        }
        else
        {
            printf("Case #%d: ", j);
            for(i=0; i<len; i++)
                printf("%c", s[i]);
            printf("nanodesu\n");
        }

    }
    return 0;
}


### Mixture-Experts (MoE) 是一种用于提升神经网络性能的技术,尤其适用于大规模分布式训练环境下的模型扩展。该架构的核心理念在于将多个小型专家子网络组合起来,在不同输入条件下激活特定的子集来处理数据[^1]。 #### 专家层设计 在 MoE 中,整个网络被划分为若干个并行工作的“专家”,每个专家都是独立的小型前馈神经网络。这些专家负责学习不同类型的数据特征,并根据当前样本的特点选择最合适的几个来进行计算。这种机制使得模型能够更加灵活地适应复杂多变的任务需求[^3]。 #### 路由器组件 除了众多的专家外,还有一个重要的组成部分叫做路由器(Router),它决定了哪些专家应该参与到具体的预测过程中去。具体来说,给定一个输入向量 \( \mathbf{x} \),路由器会先对其进行编码得到权重分布 \( w_i(\mathbf{x}) \),再依据此概率分配任务给相应的专家们完成最终输出\[ y=\sum_{i=1}^{N}{w_i(\mathbf{x})f_i(\mathbf{x})}\]。 ```python import torch.nn as nn class Router(nn.Module): def __init__(self, input_dim, num_experts): super().__init__() self.linear = nn.Linear(input_dim, num_experts) def forward(self, x): logits = self.linear(x) weights = F.softmax(logits, dim=-1) return weights ``` ### 应用实例分析 Google 开发的 Switch Transformer 就是一个基于 MoE 思想构建的大规模预训练语言模型。在这个例子中,研究人员利用稀疏门控结构实现了参数数量级上的显著增长而不增加太多额外开销;同时保持了良好的泛化能力和推理效率。
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