# -*- coding: utf-8 -*-
import json
from typing import Any, Dict, List, Optional

from db.base import BaseDBOperator
from db.connection import DBConnectionManager
from utils.wechat.emoji_semantic_parser import (
    dedupe_emoji_semantic_candidates,
    extract_emoji_meta,
    extract_emoji_semantic_info,
    safe_text,
)


class EmojiAssetDB(BaseDBOperator):
    """表情资产查询。

    说明：
    1. 这里单独抽出查询类，避免自动回复插件为了拿表情库再去依赖后台蓝图；
    2. 查询只关心消息表里的原始表情记录，不负责语义解析和匹配打分；
    3. 后续无论后台页面、自动回复还是其他插件，都可以复用同一份表情资产数据源。
    """

    def __init__(self, db_manager: DBConnectionManager):
        super().__init__(db_manager)
        self.init_tables()

    def init_tables(self) -> bool:
        """初始化表情语义资产表。

        说明：
        1. 这张表专门保存“可发送表情”的结构化资产，避免每次都去 messages 现算语义；
        2. md5 作为主键，同一个表情后续只做增量补充：先有发送参数，后补预览图，再补人工修正都方便；
        3. semantic_aliases 用 JSON 字符串保存，便于后续扩展同义词和人工校准。
        """
        return self.execute_update(
            """
            CREATE TABLE IF NOT EXISTS t_emoji_assets (
                md5 VARCHAR(64) PRIMARY KEY,
                total_length INT NOT NULL DEFAULT 0,
                semantic_text VARCHAR(255) DEFAULT '' COMMENT '主语义文本',
                semantic_aliases LONGTEXT NULL COMMENT '语义别名列表(JSON数组)',
                semantic_source VARCHAR(64) DEFAULT '' COMMENT '语义来源字段，如 desc/emojiattr',
                preview_url VARCHAR(255) DEFAULT '' COMMENT '本地预览图路径',
                sample_message_id VARCHAR(32) DEFAULT '' COMMENT '示例消息ID',
                sample_group_id VARCHAR(100) DEFAULT '' COMMENT '示例群ID',
                sample_sender VARCHAR(100) DEFAULT '' COMMENT '示例发送者',
                first_seen_at DATETIME DEFAULT CURRENT_TIMESTAMP,
                last_seen_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
                created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
                updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
                INDEX idx_emoji_last_seen (last_seen_at),
                INDEX idx_emoji_preview (preview_url)
            )
            """
        )

    def get_recent_emoji_assets(self, limit: int = 500) -> List[Dict]:
        """获取近期表情消息记录。"""
        sql = """
        SELECT message_id, group_id, sender, timestamp, message_type, attachment_url, image_path
        FROM messages
        WHERE message_type IN ('47', '1048625', '1090519089')
          AND attachment_url IS NOT NULL
          AND attachment_url <> ''
        ORDER BY timestamp DESC
        LIMIT %s
        """
        return self.execute_query(sql, (limit,)) or []

    def list_emoji_assets(self, limit: int = 500, require_preview: bool = False) -> List[Dict]:
        """获取持久化后的表情资产列表。"""
        sql = """
        SELECT md5, total_length, semantic_text, semantic_aliases, semantic_source,
               preview_url, sample_message_id, sample_group_id, sample_sender,
               first_seen_at, last_seen_at
        FROM t_emoji_assets
        WHERE total_length > 0
        """
        params: List[Any] = []
        if require_preview:
            sql += " AND preview_url IS NOT NULL AND preview_url <> ''"
        sql += " ORDER BY last_seen_at DESC LIMIT %s"
        params.append(int(limit))
        rows = self.execute_query(sql, tuple(params)) or []
        return [self._normalize_asset_row(row) for row in rows]

    def count_emoji_assets(self, require_preview: bool = False) -> int:
        """统计当前持久化表情资产数量。"""
        sql = "SELECT COUNT(*) AS total FROM t_emoji_assets WHERE total_length > 0"
        params: List[Any] = []
        if require_preview:
            sql += " AND preview_url IS NOT NULL AND preview_url <> ''"
        row = self.execute_query(sql, tuple(params), fetch_one=True) or {}
        return int(row.get("total") or 0)

    def get_emoji_asset_by_md5(self, md5: str) -> Optional[Dict]:
        """根据 md5 获取最近一条表情记录。"""
        sql = """
        SELECT message_id, group_id, sender, timestamp, message_type, attachment_url, image_path
        FROM messages
        WHERE message_type IN ('47', '1048625', '1090519089')
          AND attachment_url IS NOT NULL
          AND attachment_url <> ''
          AND attachment_url LIKE %s
        ORDER BY timestamp DESC
        LIMIT 1
        """
        return self.execute_query(sql, (f'%md5="{md5}"%',), fetch_one=True)

    def get_persisted_emoji_asset_by_md5(self, md5: str) -> Optional[Dict]:
        """按 md5 读取持久化表情资产。"""
        row = self.execute_query(
            """
            SELECT md5, total_length, semantic_text, semantic_aliases, semantic_source,
                   preview_url, sample_message_id, sample_group_id, sample_sender,
                   first_seen_at, last_seen_at
            FROM t_emoji_assets
            WHERE md5 = %s
            LIMIT 1
            """,
            (safe_text(md5).strip().lower(),),
            fetch_one=True,
        )
        return self._normalize_asset_row(row) if row else None

    def sync_recent_emoji_assets(self, limit: int = 500) -> int:
        """从最近历史消息回填表情资产表。

        说明：
        1. 这一步既用于老数据补齐，也用于兜底线上漏网之鱼；
        2. 真正的“自动增量”仍由消息归档和媒体补偿流程触发，这里主要做懒回填；
        3. 返回成功同步的条数，便于上层做日志和观察。
        """
        synced = 0
        for row in self.get_recent_emoji_assets(limit=limit):
            if self.upsert_asset_from_message_record(row):
                synced += 1
        return synced

    def upsert_asset_from_message_record(self, row: Dict[str, Any]) -> bool:
        """把一条 messages 表记录同步到表情资产表。"""
        attachment_url = safe_text((row or {}).get("attachment_url")).strip()
        md5, total_length = extract_emoji_meta(attachment_url)
        if not md5 or total_length <= 0:
            return False

        semantic_info = extract_emoji_semantic_info(attachment_url)
        asset = {
            "md5": md5,
            "total_length": total_length,
            "semantic_text": semantic_info.get("semantic_text", ""),
            "semantic_aliases": semantic_info.get("semantic_aliases", []) or [],
            "semantic_source": semantic_info.get("semantic_source", ""),
            "preview_url": safe_text(row.get("image_path") or row.get("preview_url")).strip(),
            "sample_message_id": safe_text(row.get("message_id")).strip(),
            "sample_group_id": safe_text(row.get("group_id")).strip(),
            "sample_sender": safe_text(row.get("sender")).strip(),
        }
        return self.upsert_asset(asset)

    def upsert_asset(self, asset: Dict[str, Any]) -> bool:
        """写入或更新一条表情资产。"""
        normalized_md5 = safe_text(asset.get("md5")).strip().lower()
        total_length = int(asset.get("total_length") or 0)
        if not normalized_md5 or total_length <= 0:
            return False

        current = self.get_persisted_emoji_asset_by_md5(normalized_md5) or {}
        merged_aliases = dedupe_emoji_semantic_candidates(
            list(current.get("semantic_aliases") or []) + list(asset.get("semantic_aliases") or [])
        )

        # 合并策略：
        # 1. preview_url、semantic_text 这类字段优先保留“新值非空，否则沿用旧值”；
        # 2. 别名列表总是做并集，避免后来的历史样本把早先语义覆盖掉；
        # 3. sample_* 记录最近一条有用样本，方便后续人工回查原消息来源。
        payload = {
            "md5": normalized_md5,
            "total_length": total_length if total_length > 0 else int(current.get("total_length") or 0),
            "semantic_text": safe_text(asset.get("semantic_text")).strip() or safe_text(current.get("semantic_text")).strip(),
            "semantic_aliases": merged_aliases,
            "semantic_source": safe_text(asset.get("semantic_source")).strip() or safe_text(current.get("semantic_source")).strip(),
            "preview_url": safe_text(asset.get("preview_url")).strip() or safe_text(current.get("preview_url")).strip(),
            "sample_message_id": safe_text(asset.get("sample_message_id")).strip() or safe_text(current.get("sample_message_id")).strip(),
            "sample_group_id": safe_text(asset.get("sample_group_id")).strip() or safe_text(current.get("sample_group_id")).strip(),
            "sample_sender": safe_text(asset.get("sample_sender")).strip() or safe_text(current.get("sample_sender")).strip(),
        }

        return self.execute_update(
            """
            INSERT INTO t_emoji_assets (
                md5, total_length, semantic_text, semantic_aliases, semantic_source,
                preview_url, sample_message_id, sample_group_id, sample_sender
            )
            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
            ON DUPLICATE KEY UPDATE
                total_length = VALUES(total_length),
                semantic_text = VALUES(semantic_text),
                semantic_aliases = VALUES(semantic_aliases),
                semantic_source = VALUES(semantic_source),
                preview_url = VALUES(preview_url),
                sample_message_id = VALUES(sample_message_id),
                sample_group_id = VALUES(sample_group_id),
                sample_sender = VALUES(sample_sender),
                last_seen_at = CURRENT_TIMESTAMP
            """,
            (
                payload["md5"],
                payload["total_length"],
                payload["semantic_text"],
                json.dumps(payload["semantic_aliases"], ensure_ascii=False),
                payload["semantic_source"],
                payload["preview_url"],
                payload["sample_message_id"],
                payload["sample_group_id"],
                payload["sample_sender"],
            ),
        )

    @staticmethod
    def _normalize_asset_row(row: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """把数据库行统一整理成业务侧更好消费的结构。"""
        if not row:
            return {}
        normalized = dict(row)
        aliases = normalized.get("semantic_aliases")
        if isinstance(aliases, str):
            try:
                normalized["semantic_aliases"] = json.loads(aliases)
            except Exception:
                normalized["semantic_aliases"] = []
        elif aliases is None:
            normalized["semantic_aliases"] = []
        return normalized