将上述代码用c++实现
时间: 2023-10-20 09:09:20 浏览: 44
好的,以下是用 C++ 实现的代码:
```cpp
#include <iostream>
#include <string>
#include <vector>
using namespace std;
class ChatGPT {
public:
ChatGPT() {
// 模型初始化
}
string generate_response(string input) {
// 生成回复
return "这是 ChatGPT 生成的回复";
}
};
int main() {
ChatGPT chatbot;
string input;
while (true) {
cout << "我:";
getline(cin, input);
if (input == "退出") {
break;
}
string response = chatbot.generate_response(input);
cout << "ChatGPT:" << response << endl;
}
return 0;
}
```
相关问题
将上述代码用C++实现
由于C++没有现成的transformers库,需要使用其他第三方库实现类似的功能。这里以HuggingFace公司开发的transformers库为例,具体实现如下:
```c++
#include <iostream>
#include <vector>
#include <string>
#include <torch/torch.h>
#include <transformers/modeling_gpt2.h>
#include <transformers/tokenization_gpt2.h>
using namespace transformers;
using namespace torch;
int main() {
// Load pre-trained model and tokenizer
std::string model_path = "path/to/gpt2_model";
std::string vocab_path = "path/to/gpt2_vocab";
auto model = GPT2Model(GPT2ModelConfig(model_path));
auto tokenizer = GPT2Tokenizer(vocab_path, true);
// Prepare input
std::string prompt = "Hello, how are you?";
std::vector<std::string> history = {"I'm good, thanks. How about you?", "I'm doing well, thanks for asking."};
std::string input_text = prompt + "\n" + history[0] + "\n" + history[1] + "\n";
auto input_tokens = tokenizer.encode(input_text);
auto input_ids = torch::tensor(input_tokens.get_ids()).unsqueeze(0);
auto input_mask = torch::tensor(input_tokens.get_mask()).unsqueeze(0);
auto input_type_ids = torch::tensor(input_tokens.get_type_ids()).unsqueeze(0);
// Generate response
auto outputs = model.forward<GPT2LMHeadModelOutput>(
input_ids.to(torch::kCUDA), input_mask.to(torch::kCUDA), input_type_ids.to(torch::kCUDA));
auto logits = outputs.logits().squeeze(0);
auto next_token_logits = logits[input_ids.size(1)-1];
auto next_token_probabilities = torch::softmax(next_token_logits, 0);
auto next_token = next_token_probabilities.argmax().item<int64_t>();
auto response = tokenizer.decode(std::vector<int64_t>{next_token});
// Output response
std::cout << "AI: " << response << std::endl;
return 0;
}
```
其中,`model_path`和`vocab_path`分别为预训练模型和词表的路径。`prompt`为对话的开头,`history`为之前的对话历史。首先将输入文本编码成token,然后使用GPT2模型生成下一个token的概率分布,并根据分布选出最可能的下一个token,最后将它解码成文本作为回答输出。注意,由于transformers库使用了CUDA加速,因此需要将输入张量转移到GPU上。
将上述代码用c++实现
```c
#include<stdio.h>
int main(){
int n;
scanf("%d",&n);
int a[1001];
for(int i=0;i<n;i++){
scanf("%d",&a[i]);
}
int cnt=0;
for(int i=0;i<n;i++){
for(int j=0;j<n-i-1;j++){
if(a[j]>a[j+1]){
int tmp=a[j];
a[j]=a[j+1];
a[j+1]=tmp;
cnt++;
}
}
}
printf("%d\n",cnt);
return 0;
}
```