from langchain.llms.base import LLM
from typing import Any, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch


class ChatGLM_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)
            .to(torch.bfloat16)
            .cuda(device=1)
        )
        self.model = self.model.eval()
        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
    ):
        # 重写调用函数
        response, history = self.model.chat(
            self.tokenizer, prompt, history=[], do_sample=False
        )
        return response

    @property
    def _llm_type(self) -> str:
        return "ChatGLM3-6B"