"""
Functor基础模块

该模块定义了Functor的基类,所有功能性的类(如VAD、PUNC、ASR、SPK等)都应继承自这个基类。
基类提供了数据处理的基本框架,包括:
- 回调函数管理
- 模型配置管理
- 线程运行控制

主要类:
    BaseFunctor: Functor抽象类
    FunctorFactory: Functor工厂类
"""

from abc import ABC, abstractmethod
from typing import Callable, List, Dict
from queue import Queue
import threading

from src.utils.logger import get_module_logger

logger = get_module_logger(__name__)

class BaseFunctor(ABC):
    """
    Functor抽象类

    该抽象类规定了所有的Functor类必须实现run()方法启动自身线程

    属性:
        _callback (Callable): 处理完成后的回调函数
        _model (dict): 存储模型相关的配置和实例
    """

    def __init__(self):
        """
        初始化函数器

        参数:
            callback (Callable): 处理完成后的回调函数
            model (dict): 模型相关的配置和实例
        """
        self._callback: List[Callable] = []
        self._model: dict = {}
        # flag
        self._is_running: bool = False
        self._stop_event: bool = False
        # 状态锁
        self._status_lock: threading.Lock = threading.Lock()
        # 线程资源
        self._thread: threading.Thread = None

    def add_callback(self, callback: Callable):
        """
        添加回调函数

        参数:
            callback (Callable): 新的回调函数
        """
        self._callback.append(callback)

    def set_model(self, model: dict):
        """
        设置模型配置

        参数:
            model (dict): 新的模型配置
        """
        self._model = model

    def set_input_queue(self, queue: Queue):
        """
        设置输入队列

        参数:
            queue (Queue): 新的输入队列
        """
        self._input_queue = queue

    @abstractmethod
    def _run(self):
        """
        线程运行逻辑

        返回:
            当达到条件时触发callback
        """

    @abstractmethod
    def run(self):
        """
        启动_run方法线程

        返回:
            线程实例
        """

    @abstractmethod
    def _pre_check(self):
        """
        预检查

        返回:
            预检查结果
        """

    @abstractmethod
    def stop(self):
        """
        停止线程

        返回:
            停止结果
        """


class FunctorFactory:
    """
    Functor工厂类

    该工厂类负责创建和配置Functor实例

    主要方法:
        make_funtor(funtor_name: str, config: dict, models: dict) -> BaseFunctor:
            创建并配置Functor实例
    """
    def _make_vadfunctor(config: dict, models: dict) -> BaseFunctor:
        """
        创建VAD Functor实例
        """
        from src.functor.vad_functor import VADFunctor

        audio_config = config["audio_config"]
        model = {"vad": models["vad"]}

        vad_functor = VADFunctor()
        vad_functor.set_audio_config(audio_config)
        vad_functor.set_model(model)

        return vad_functor

    def _make_asrfunctor(config: dict, models: dict) -> BaseFunctor:
        """
        创建ASR Functor实例
        """
        from src.functor.asr_functor import ASRFunctor

        audio_config = config["audio_config"]
        model = {"asr": models["asr"]}

        asr_functor = ASRFunctor()
        asr_functor.set_audio_config(audio_config)
        asr_functor.set_model(model)

        return asr_functor

    def _make_spkfunctor(config: dict, models: dict) -> BaseFunctor:
        """
        创建SPK Functor实例
        """
        from src.functor.spk_functor import SPKFunctor

        logger.debug(f"创建spk functor[开始]")
        audio_config = config["audio_config"]
        # model = {"spk": models["spk"]}

        spk_functor = SPKFunctor(sv_pipeline=models["spk"])
        spk_functor.set_audio_config(audio_config)
        # spk_functor.set_model(model)

        logger.debug(f"创建spk functor[完成]")
        return spk_functor

    def _make_resultbinderfunctor(config: dict, models: dict) -> BaseFunctor:
        """
        创建ResultBinder Functor实例
        """
        from src.functor.resultbinder_functor import ResultBinderFunctor

        resultbinder_functor = ResultBinderFunctor()

        return resultbinder_functor

    factory_dict: Dict[str, Callable] = {
        "vad": _make_vadfunctor,
        "asr": _make_asrfunctor,
        "spk": _make_spkfunctor,
        "resultbinder": _make_resultbinderfunctor,
    }

    @classmethod
    def make_functor(cls, functor_name: str, config: dict, models: dict) -> BaseFunctor:
        """
        创建并配置Functor实例

        参数:
            funtor_name (str): Functor名称
            config (dict): 配置信息
            models (dict): 模型信息

        返回:
            BaseFunctor: 创建的Functor实例
        """
        if functor_name in cls.factory_dict:
            return cls.factory_dict[functor_name](config=config, models=models)
        else:
            raise ValueError(f"不支持的Functor类型: {functor_name}")