"""新热事件需求词来源：按 sync_log 定位热点标题并返回单条展示数据。"""

from __future__ import annotations

import hashlib
import json
import math
from datetime import datetime
from typing import Any

from sqlalchemy import text

from app.core.config import settings
from app.db.hot_content_mysql import HotContentSessionLocal

TYPE_FEATURE_POINT = "特征点"
TYPE_PHRASE = "短语"
ITEM_TYPE_ELEMENT = "元素"
ITEM_TYPE_PHRASE = "短语"
TITLE_RETAIN_POINT_CATEGORIES = frozenset({"灵感点", "目的点"})
WEIGHT_DIVISOR = 1_000_000.0
DEFAULT_WXINDEX_THRESHOLD = 1_000_000.0
DEFAULT_HOT_STRATEGY = "新热事件"


def _hot_strategy_name() -> str:
    return str(
        getattr(settings, "hot_demand_pool_strategy", None) or DEFAULT_HOT_STRATEGY
    ).strip() or DEFAULT_HOT_STRATEGY


def _wxindex_threshold() -> float:
    raw = getattr(settings, "hot_content_wxindex_threshold", None)
    if raw is None:
        return DEFAULT_WXINDEX_THRESHOLD
    try:
        return float(raw)
    except (TypeError, ValueError):
        return DEFAULT_WXINDEX_THRESHOLD


def _normalize_date(date_value: str | None) -> str:
    if not date_value:
        return ""
    normalized = str(date_value).replace("-", "").strip()
    return normalized if len(normalized) == 8 and normalized.isdigit() else ""


def _normalize_text(value: str) -> str:
    return "".join(str(value or "").split())


def _load_json(value: Any) -> dict[str, Any]:
    if value is None:
        return {}
    if isinstance(value, dict):
        return value
    if isinstance(value, (bytes, bytearray)):
        value = value.decode("utf-8")
    if isinstance(value, str):
        text_value = value.strip()
        if not text_value:
            return {}
        try:
            parsed = json.loads(text_value)
        except json.JSONDecodeError:
            return {"__parse_error__": text_value}
        return parsed if isinstance(parsed, dict) else {"value": parsed}
    return {"value": value}


def _fmt_datetime(value: Any) -> str:
    if value is None:
        return "-"
    if isinstance(value, datetime):
        return value.strftime("%Y-%m-%d %H:%M:%S")
    return str(value)


def build_demand_id(*, strategy: str, demand_name: str, partition_dt: str) -> str:
    raw = f"{strategy}{demand_name.strip()}{partition_dt}"
    return hashlib.md5(raw.encode("utf-8")).hexdigest()


def _record_wxindex_score(export_rows: list[dict[str, Any]]) -> float:
    scores: list[float] = []
    for row in export_rows:
        try:
            scores.append(float(row.get("wxindex_latest_score") or 0))
        except (TypeError, ValueError):
            continue
    return max(scores) if scores else 0.0


def _has_inspiration_or_goal_demand_match(row: dict[str, Any]) -> bool:
    point_category = str(row.get("point_category") or "").strip()
    if point_category not in TITLE_RETAIN_POINT_CATEGORIES:
        return False
    return bool(str(row.get("matched_demand") or "").strip())


def _export_row_contributes_to_demand(
    row: dict[str, Any],
    *,
    demand_name: str,
    demand_type: str,
) -> bool:
    normalized_name = _normalize_text(demand_name)
    item_type = str(row.get("item_type") or "")
    item_text = str(row.get("item_text") or "")
    matched = str(row.get("matched_demand") or "").strip()
    if not matched:
        return False
    if demand_type == TYPE_PHRASE:
        return item_type == ITEM_TYPE_PHRASE and _normalize_text(item_text) == normalized_name
    if demand_type == TYPE_FEATURE_POINT:
        return item_type == ITEM_TYPE_ELEMENT and _normalize_text(item_text) in normalized_name
    return False


def _hive_weight_for_record(
    export_rows: list[dict[str, Any]],
    *,
    wxindex_threshold: float,
) -> float | None:
    if _record_wxindex_score(export_rows) < wxindex_threshold:
        return None
    if not any(_has_inspiration_or_goal_demand_match(row) for row in export_rows):
        return None
    return _record_wxindex_score(export_rows) / WEIGHT_DIVISOR


def fetch_sync_log_row(
    *,
    demand_name: str,
    demand_type: str,
    partition_dt: str,
    strategy: str | None = None,
) -> dict[str, Any] | None:
    """按需求名称、需求类型、分区 dt 查询 hot_content_odps_sync_log 唯一记录。"""
    name = (demand_name or "").strip()
    dtype = (demand_type or "").strip()
    dt = _normalize_date(partition_dt)
    if not name or not dtype or not dt:
        raise ValueError("demand_name、demand_type、dt 均为必填")
    strategy_value = (strategy or _hot_strategy_name()).strip()
    sql = text(
        """
        SELECT
            id,
            partition_dt,
            strategy,
            demand_id,
            demand_name,
            demand_type,
            record_id,
            synced_at
        FROM hot_content_odps_sync_log
        WHERE demand_name = :demand_name
          AND demand_type = :demand_type
          AND partition_dt = :partition_dt
          AND strategy = :strategy
        LIMIT 2
        """
    )
    params = {
        "demand_name": name,
        "demand_type": dtype,
        "partition_dt": dt,
        "strategy": strategy_value,
    }
    with HotContentSessionLocal() as session:
        rows = session.execute(sql, params).mappings().all()
    if not rows:
        return None
    if len(rows) > 1:
        raise ValueError("匹配到多条同步记录，请检查查询条件")
    return dict(rows[0])


def fetch_hot_content_source_detail(
    *,
    demand_name: str,
    demand_type: str,
    partition_dt: str,
    strategy: str | None = None,
) -> dict[str, Any]:
    sync_row = fetch_sync_log_row(
        demand_name=demand_name,
        demand_type=demand_type,
        partition_dt=partition_dt,
        strategy=strategy,
    )
    if sync_row is None:
        raise LookupError("未找到对应的同步记录")

    record_id = int(sync_row.get("record_id") or 0)
    if record_id <= 0:
        raise LookupError("同步记录未关联热点内容")

    wxindex_threshold = _wxindex_threshold()
    strategy_value = str(sync_row.get("strategy") or _hot_strategy_name())
    partition_dt = str(sync_row.get("partition_dt") or "")
    sync_demand_name = str(sync_row.get("demand_name") or "")
    sync_demand_type = str(sync_row.get("demand_type") or "")
    demand_id = str(sync_row.get("demand_id") or "")

    record_sql = text(
        """
        SELECT
            id,
            source,
            title,
            article_title,
            article_body,
            hot_rank,
            execution_status,
            postprocess_status,
            created_at,
            contribution_demand_match_json,
            wxindex_trend_json
        FROM hot_content_records
        WHERE id = :record_id
        LIMIT 1
        """
    )
    export_sql = text(
        """
        SELECT
            id,
            item_type,
            item_text,
            point_category,
            matched_demand,
            contribution_score,
            wxindex_keyword,
            all_hot_keywords,
            wxindex_latest_score,
            wxindex_trend
        FROM hot_content_demand_exports
        WHERE record_id = :record_id
        ORDER BY id ASC
        """
    )
    with HotContentSessionLocal() as session:
        record_row = session.execute(record_sql, {"record_id": record_id}).mappings().first()
        export_rows_raw = session.execute(export_sql, {"record_id": record_id}).mappings().all()

    if record_row is None:
        raise LookupError("未找到关联热点内容")

    export_dicts: list[dict[str, Any]] = []
    export_items: list[dict[str, Any]] = []
    for row in export_rows_raw:
        item = dict(row)
        contributes = _export_row_contributes_to_demand(
            item,
            demand_name=sync_demand_name,
            demand_type=sync_demand_type,
        )
        export_dicts.append(
            {
                "item_type": item.get("item_type"),
                "item_text": item.get("item_text"),
                "point_category": item.get("point_category"),
                "matched_demand": item.get("matched_demand"),
                "wxindex_latest_score": item.get("wxindex_latest_score"),
            }
        )
        export_items.append(
            {
                "id": int(item["id"]),
                "item_type": str(item.get("item_type") or ""),
                "item_text": str(item.get("item_text") or ""),
                "point_category": str(item.get("point_category") or ""),
                "matched_demand": str(item.get("matched_demand") or ""),
                "contribution_score": float(item["contribution_score"])
                if item.get("contribution_score") is not None
                else None,
                "wxindex_keyword": str(item.get("wxindex_keyword") or ""),
                "all_hot_keywords": str(item.get("all_hot_keywords") or ""),
                "wxindex_latest_score": float(item.get("wxindex_latest_score") or 0),
                "wxindex_trend": str(item.get("wxindex_trend") or ""),
                "contributes_to_sync": contributes,
            }
        )

    max_wxindex = _record_wxindex_score(export_dicts)
    hive_weight = _hive_weight_for_record(
        export_dicts,
        wxindex_threshold=wxindex_threshold,
    )
    expected_id = build_demand_id(
        strategy=strategy_value,
        demand_name=sync_demand_name,
        partition_dt=partition_dt,
    )

    contribution = _load_json(record_row.get("contribution_demand_match_json"))
    wxindex = _load_json(record_row.get("wxindex_trend_json"))

    return {
        "wxindex_threshold": wxindex_threshold,
        "sync_log": {
            "id": int(sync_row["id"]),
            "partition_dt": partition_dt,
            "strategy": strategy_value,
            "demand_id": demand_id,
            "demand_name": sync_demand_name,
            "demand_type": sync_demand_type,
            "record_id": record_id,
            "synced_at": _fmt_datetime(sync_row.get("synced_at")),
            "demand_id_verified": expected_id == demand_id,
            "hive_weight": hive_weight,
        },
        "record": {
            "id": int(record_row["id"]),
            "source": str(record_row.get("source") or ""),
            "title": str(record_row.get("title") or ""),
            "article_title": str(record_row.get("article_title") or ""),
            "article_body": str(record_row.get("article_body") or ""),
            "hot_rank": int(record_row["hot_rank"])
            if record_row.get("hot_rank") is not None
            else None,
            "created_at": _fmt_datetime(record_row.get("created_at")),
            "contribution": contribution,
            "wxindex": wxindex,
            "max_wxindex_score": max_wxindex,
            "passes_wxindex_gate": max_wxindex >= wxindex_threshold,
            "passes_point_gate": any(
                _has_inspiration_or_goal_demand_match(row) for row in export_dicts
            ),
        },
        "export_rows": export_items,
    }


def format_number(value: Any) -> str:
    try:
        number = float(value)
    except (TypeError, ValueError):
        return "-"
    if math.isnan(number):
        return "-"
    if abs(number) >= 10000:
        return f"{number / 10000:.1f}万"
    return f"{number:.0f}"