"""
全局配置模块
定义数据库、Redis、MQTT、推理等各项配置参数
"""

import os
from dataclasses import dataclass, field
from typing import List, Optional


@dataclass
class DatabaseConfig:
    """数据库配置类（MySQL - 云端）"""
    host: str = "localhost"
    port: int = 3306
    username: str = "root"
    password: str = ""
    database: str = "edge_inference"
    pool_size: int = 10
    pool_recycle: int = 3600
    echo: bool = False


@dataclass
class SQLiteConfig:
    """SQLite 配置（边缘侧本地存储）"""
    db_path: str = "./data/security_events.db"
    image_dir: str = "./data/captures"
    retention_days: int = 7
    wal_mode: bool = True
    batch_size: int = 100
    flush_interval: float = 5.0


@dataclass
class RedisConfig:
    """Redis配置类"""
    host: str = "localhost"
    port: int = 6379
    db: int = 0
    password: Optional[str] = None
    decode_responses: bool = True
    max_connections: int = 50


@dataclass
class MQTTConfig:
    """MQTT配置类"""
    broker_host: str = "localhost"
    broker_port: int = 1883
    client_id: str = "edge_inference_service"
    username: Optional[str] = None
    password: Optional[str] = None
    keepalive: int = 60
    qos: int = 1
    reconnect_delay: int = 5
    max_reconnect_attempts: int = 10


@dataclass
class VideoStreamConfig:
    """视频流配置类"""
    default_fps: int = 5
    reconnect_max_attempts: int = 5
    reconnect_base_delay: float = 1.0
    reconnect_max_delay: float = 60.0
    frame_buffer_size: int = 30
    connection_timeout: int = 10
    read_timeout: int = 30


@dataclass
class InferenceConfig:
    """推理配置类"""
    model_path: str = "./models/yolo11n.engine"
    input_width: int = 480
    input_height: int = 480
    batch_size: int = 1
    max_batch_size: int = 8
    conf_threshold: float = 0.5
    nms_threshold: float = 0.45
    device_id: int = 0
    fp16_mode: bool = True


@dataclass
class CameraConfig:
    """单个摄像头配置"""
    camera_id: str
    rtsp_url: str
    enabled: bool = True
    roi_ids: List[str] = field(default_factory=list)


@dataclass
class ROIConfig:
    """ROI区域配置"""
    roi_id: str
    camera_id: str
    roi_type: str  # 'polygon' or 'rectangle'
    coordinates: List[List[float]]  # 多边形顶点或矩形坐标
    algorithm_type: str  # 'leave_post', 'intrusion', etc.
    alert_threshold: int = 3  # 连续N帧触发告警
    alert_cooldown: int = 300  # 告警冷却时间(秒)


class Settings:
    """全局设置单例类"""
    
    _instance = None
    _initialized = False
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance
    
    def __init__(self):
        if not self._initialized:
            self._load_env_vars()
            self._initialized = True
    
    def _load_env_vars(self):
        """从环境变量加载配置"""
        self.database = DatabaseConfig(
            host=os.getenv("DB_HOST", "localhost"),
            port=int(os.getenv("DB_PORT", "3306")),
            username=os.getenv("DB_USERNAME", "root"),
            password=os.getenv("DB_PASSWORD", ""),
            database=os.getenv("DB_DATABASE", "edge_inference"),
        )
        
        self.sqlite = SQLiteConfig(
            db_path=os.getenv("SQLITE_DB_PATH", "./data/security_events.db"),
            image_dir=os.getenv("SQLITE_IMAGE_DIR", "./data/captures"),
            retention_days=int(os.getenv("SQLITE_RETENTION_DAYS", "7")),
            wal_mode=os.getenv("SQLITE_WAL_MODE", "1") == "1",
        )
        
        self.redis = RedisConfig(
            host=os.getenv("REDIS_HOST", "localhost"),
            port=int(os.getenv("REDIS_PORT", "6379")),
            password=os.getenv("REDIS_PASSWORD"),
        )
        
        self.mqtt = MQTTConfig(
            broker_host=os.getenv("MQTT_BROKER_HOST", "localhost"),
            broker_port=int(os.getenv("MQTT_BROKER_PORT", "1883")),
            client_id=os.getenv("MQTT_CLIENT_ID", "edge_inference_service"),
            username=os.getenv("MQTT_USERNAME"),
            password=os.getenv("MQTT_PASSWORD"),
        )
        
        self.video_stream = VideoStreamConfig(
            default_fps=int(os.getenv("VIDEO_DEFAULT_FPS", "5")),
            reconnect_max_attempts=int(os.getenv("VIDEO_RECONNECT_ATTEMPTS", "5")),
        )
        
        self.inference = InferenceConfig(
            model_path=os.getenv("MODEL_PATH", "./models/yolo11n.engine"),
            input_width=int(os.getenv("INPUT_WIDTH", "480")),
            input_height=int(os.getenv("INPUT_HEIGHT", "480")),
            batch_size=int(os.getenv("BATCH_SIZE", "1")),
            max_batch_size=int(os.getenv("MAX_BATCH_SIZE", "8")),
            conf_threshold=float(os.getenv("CONF_THRESHOLD", "0.5")),
            nms_threshold=float(os.getenv("NMS_THRESHOLD", "0.45")),
        )
        
        self.log_level = os.getenv("LOG_LEVEL", "INFO")
        self.log_dir = os.getenv("LOG_DIR", "./logs")
        self.log_file_max_size = int(os.getenv("LOG_FILE_MAX_SIZE", "10485760"))
        self.log_file_backup_count = int(os.getenv("LOG_FILE_BACKUP_COUNT", "5"))
        
        self.working_hours = self._parse_working_hours()
    
    def _parse_working_hours(self) -> List[dict]:
        """解析工作时间配置"""
        working_hours_str = os.getenv("WORKING_HOURS", "")
        if not working_hours_str:
            return []
        
        working_hours = []
        periods = working_hours_str.split(";")
        for period in periods:
            try:
                start, end = period.split("-")
                start_h, start_m = map(int, start.split(":"))
                end_h, end_m = map(int, end.split(":"))
                working_hours.append({
                    "start": [start_h, start_m],
                    "end": [end_h, end_m]
                })
            except (ValueError, AttributeError):
                continue
        
        return working_hours
    
    @property
    def config_version(self) -> str:
        """获取配置版本号"""
        return os.getenv("CONFIG_VERSION", "1.0.0")


def get_settings() -> Settings:
    """获取全局配置单例"""
    return Settings()