from langchain.llms.base import LLM
from typing import Any, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
from peft import PeftModel
import torch
import jsonlines
import json
import csv
class MiniCPM_LLM(LLM):
    # 基于本地 InternLM 自定义 LLM 类
    tokenizer : AutoTokenizer = None
    model: AutoModelForCausalLM = None

    def __init__(self, model_path :str):
        # model_path: InternLM 模型路径
        # 从本地初始化模型
        super().__init__()
        print("正在从本地加载模型...")
        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
        self.model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,torch_dtype=torch.bfloat16,  device_map="auto")
        self.model = PeftModel.from_pretrained(model = self.model, model_id="/root/lanyun-tmp/output/MiniCPM/checkpoint-9000/")
        print("完成本地模型的加载")

    def _call(self, prompt : str, stop: Optional[List[str]] = None,
                run_manager: Optional[CallbackManagerForLLMRun] = None,
                **kwargs: Any):
        # 通过模型获得输出
        responds, history = self.model.chat(self.tokenizer, prompt, temperature=0, top_p=0.8, repetition_penalty=1.02)
        return responds
        
    @property
    def _llm_type(self) -> str:
        return "MiniCPM_LLM"
    

llm = MiniCPM_LLM('/root/lanyun-tmp/OpenBMB/MiniCPM-2B-sft-fp32')


# 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_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']
            message = "<User>As a reading comprehension expert, you will receive context, question and four options. Please understand the context given below first, and then output the label of the correct option as the answer to the question based on the context"+str({'context':{context},'question':{question},"answer0":{answer0},"answer1":{answer1},"answer2":{answer2},"answer3":{answer3}})+"<AI>"
        

            answer=llm._call(message)
            writer.writerow(answer)