"""Cluster-level LLM 호출 + JSON 파싱 + timeout + drop금지 fallback.

핵심 결정:
- AIClient._call_chat 직접 호출 (client.py 수정 회피, fallback 로직 재사용)
- 전역 MLX gate(BACKGROUND) 경유로 동시성 제어 (services.search.llm_gate 단일 게이트)
- Per-call timeout = config.digest_llm_timeout_s (asyncio.wait_for, gate 안쪽)
- JSON 파싱 실패 → 1회 재시도 → 그래도 실패 시 minimal fallback (drop 금지)
- fallback: topic_label="주요 뉴스 묶음", summary = top member ai_summary[:200]
"""

import asyncio
from pathlib import Path
from typing import Any

from ai.client import parse_json_response
from core.config import settings
from core.utils import setup_logger
from services.search.llm_gate import Priority, acquire_mlx_gate

logger = setup_logger("digest_summarizer")

# 2026-06-15: config 단일소스 (구 하드코딩 25s = 빠른 Gemma 기준, Qwen 27B 교체 후 누락).
LLM_CALL_TIMEOUT = settings.digest_llm_timeout_s
FALLBACK_SUMMARY_LIMIT = 200

_PROMPT_PATH = Path(__file__).resolve().parent.parent.parent / "prompts" / "digest_topic.txt"
_PROMPT_TEMPLATE: str | None = None


def _load_prompt() -> str:
    global _PROMPT_TEMPLATE
    if _PROMPT_TEMPLATE is None:
        _PROMPT_TEMPLATE = _PROMPT_PATH.read_text(encoding="utf-8")
    return _PROMPT_TEMPLATE


def build_prompt(selected: list[dict]) -> str:
    """digest_topic.txt 템플릿에 selected article들의 ai_summary_truncated 주입.

    템플릿 placeholder: {articles_block}
    """
    template = _load_prompt()
    lines = []
    for i, m in enumerate(selected, start=1):
        text = (m.get("ai_summary_truncated") or m.get("ai_summary") or m.get("title") or "").strip()
        lines.append(f"[{i}] {text}")
    articles_block = "\n".join(lines)
    return template.replace("{articles_block}", articles_block)


async def _try_call_llm(client: Any, prompt: str) -> str:
    """전역 MLX gate(BACKGROUND) + per-call timeout 으로 감싼 단일 호출.

    영구 룰(llm_gate): Mac mini endpoint 는 단일 게이트 공유, 새 Semaphore 금지.
    동시성 lever = config.mlx_gate_concurrency. timeout 은 gate 안쪽에서만.
    """
    async with acquire_mlx_gate(Priority.BACKGROUND):
        return await asyncio.wait_for(
            client._call_chat(client.ai.primary, prompt),
            timeout=LLM_CALL_TIMEOUT,
        )


def _make_fallback(cluster: dict) -> dict:
    """cluster 의 top member 데이터로 minimal fallback 생성 — 정보 손실 회피."""
    members = cluster["members"]
    if not members:
        return {
            "topic_label": "주요 뉴스 묶음",
            "summary": "",
            "llm_fallback_used": True,
        }
    top = max(members, key=lambda m: m.get("_rel", m.get("weight", 0.0)))
    text = (top.get("ai_summary") or top.get("title") or "").strip()
    return {
        "topic_label": "주요 뉴스 묶음",
        "summary": text[:FALLBACK_SUMMARY_LIMIT],
        "llm_fallback_used": True,
    }


async def summarize_cluster_with_fallback(
    client: Any,
    cluster: dict,
    selected: list[dict],
) -> dict:
    """cluster 1개에 대해 LLM 호출 + JSON 파싱 + fallback.

    Returns:
        {topic_label, summary, llm_fallback_used}
    """
    prompt = build_prompt(selected)

    for attempt in range(settings.digest_llm_attempts):  # config 단일소스 (기본 2 = 1회 재시도)
        try:
            raw = await _try_call_llm(client, prompt)
        except asyncio.TimeoutError:
            logger.warning(
                f"LLM 호출 timeout {LLM_CALL_TIMEOUT}s "
                f"(attempt={attempt + 1}, cluster size={len(cluster['members'])})"
            )
            continue
        except Exception as e:
            logger.warning(
                f"LLM 호출 실패 attempt={attempt + 1} "
                f"(cluster size={len(cluster['members'])}): {e}"
            )
            continue

        parsed = parse_json_response(raw)
        if (
            parsed
            and isinstance(parsed.get("topic_label"), str)
            and isinstance(parsed.get("summary"), str)
            and parsed["topic_label"].strip()
            and parsed["summary"].strip()
        ):
            return {
                "topic_label": parsed["topic_label"].strip(),
                "summary": parsed["summary"].strip(),
                "llm_fallback_used": False,
            }
        logger.warning(
            f"JSON 파싱 실패 attempt={attempt + 1} "
            f"(cluster size={len(cluster['members'])}, raw_len={len(raw) if raw else 0})"
        )

    return _make_fallback(cluster)