import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
from peft import PeftModel, PeftConfig
import json
import csv

lora_path = "/root/lanyun-tmp/output/MiniCPM/checkpoint-9000/"
model_path = '/root/lanyun-tmp/OpenBMB/MiniCPM-2B-sft-fp32'
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
)
model.generation_config = GenerationConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(
    model_path, use_fast=False, trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, lora_path
)




# 读取JSONL文件
filename = '/root/lanyun-tmp/Dataset/test.jsonl'
data = []
with open(filename, 'r') as f:
    for line in f:
        item = json.loads(line)
        data.append(item)



    files = 'MiniCPM2B-ZH-_answers.csv'
    with open(files, 'w', newline='') as csvfile:
        writer = csv.writer(csvfile)
        # 提取内容
        for item in data:
            context = item['context']
            question = item['question']
            answer0 = item['answer0']
            answer1 = item['answer1']
            answer2 = item['answer2']
            answer3 = item['answer3']
  
            messages = str([
                {"role": "system", "content": "作为阅读理解专家，你​​将收到上下文，问题和四个选项，请先理解下面给出的上下文，然后根据上下文输出正确选项的标签作为问题的答案}"},
                {"role": "user", "content": str({'context':{context},'question':{question},"answer0":{answer0},"answer1":{answer1},"answer2":{answer2},"answer3":{answer3}})},
            ])
            response = model.chat(tokenizer, messages)
            
            answer = response[0][0]
            print(answer)
            writer.writerow(answer)