"""M5 批量嵌入入口脚本。

输入策略:
    A. 默认 events 优先 (--input events):
       data/events/{day}/*.json (M4 ExtractedEvent),用 LLM 摘要 + 事件标签 + 正文组装文本;
       同时回查对应 article(M2 输出)以拿正文,如查不到则用 ExtractedEvent.event.summary 作为正文。
    B. articles 模式 (--input articles):
       data/processed/{source}/{day}/*.json (M2 Article 直出),仅用 title + content。
    C. deduped 模式 (--input deduped):
       data/deduped/{day}/uniques/*.json (M3 唯一文章),仅 title + content。

输出:
    data/embeddings/{day}/{url_hash}.json     (含 vector 完整内容)
    data/embeddings/{day}/index.jsonl         (扁平摘要,不含向量,便于检索/调试)
    data/embeddings/{day}/failed.jsonl        (失败列表)
"""

from __future__ import annotations

import argparse
import asyncio
import json
import os
import sys
import time

# 修复树莓派等环境的 ASCII locale 问题(UnicodeEncodeError)
os.environ.setdefault("PYTHONUTF8", "1")
from datetime import date, datetime
from pathlib import Path
from typing import Any

from dotenv import load_dotenv
from loguru import logger
from pydantic import ValidationError

from embedding import (
    EmbeddingError,
    EmbeddingResult,
    compose_text,
    make_async_provider,
)
from embedding.base import _from_event_dict
from extractor import Article


def _setup_logger(level: str) -> None:
    logger.remove()
    logger.add(
        sys.stderr,
        level=level,
        format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {name} | {message}",
    )
    log_path = Path("logs") / "embedding.log"
    log_path.parent.mkdir(parents=True, exist_ok=True)
    logger.add(log_path, level="DEBUG", rotation="10 MB", retention=5, encoding="utf-8")


# --------------------------------------------------------------------------- #
# 输入收集与文本组装
# --------------------------------------------------------------------------- #

def _list_source_dirs(p: Path) -> list[str]:
    if not p.is_dir():
        return []
    return sorted(d.name for d in p.iterdir() if d.is_dir())


def _load_article_by_hash(processed_root: Path, day: str, url_hash: str) -> Article | None:
    """根据 url_hash 在 data/processed/*/{day}/ 中查 Article。"""
    for src_dir in processed_root.iterdir():
        if not src_dir.is_dir():
            continue
        candidate = src_dir / day / f"{url_hash}.json"
        if candidate.is_file():
            try:
                return Article.model_validate(
                    json.loads(candidate.read_text(encoding="utf-8"))
                )
            except (json.JSONDecodeError, ValidationError):
                return None
    return None


def _build_text_from_event(
    event_path: Path,
    processed_root: Path,
    day: str,
) -> tuple[str, Article, str | None] | None:
    """从 ExtractedEvent JSON 构造嵌入文本与文章元数据。"""
    try:
        obj: dict[str, Any] = json.loads(event_path.read_text(encoding="utf-8"))
    except json.JSONDecodeError as e:
        logger.warning("跳过损坏 event 文件 {}: {}", event_path, e)
        return None

    article, head, summary = _from_event_dict(obj)
    # 真正的正文要去 processed/ 找
    real_article = _load_article_by_hash(processed_root, day, article.url_hash)
    if real_article is None:
        # 退化:用 summary 作为正文(质量降级,但仍可嵌入)
        real_article = article
        logger.debug("未找到 Article(url_hash={}),用 summary 作为正文兜底", article.url_hash)
    else:
        # 用 processed 的真实数据,但保留 event 元数据(time/source 等)
        real_article = real_article.model_copy(
            update={"publish_time": article.publish_time or real_article.publish_time}
        )
    text = compose_text(real_article, head=head, summary=summary)
    return text, real_article, summary


def _build_text_from_article(article_path: Path) -> tuple[str, Article, str | None] | None:
    try:
        obj = json.loads(article_path.read_text(encoding="utf-8"))
        article = Article.model_validate(obj)
    except (json.JSONDecodeError, ValidationError) as e:
        logger.warning("跳过损坏 article 文件 {}: {}", article_path, e)
        return None
    return compose_text(article), article, None


def _collect_inputs(args: argparse.Namespace) -> list[tuple[Path, str]]:
    """返回 [(input_file, kind), ...],kind 为 'event'/'article'。"""
    day = args.date
    files: list[tuple[Path, str]] = []
    if args.input == "events":
        d = Path(args.events_root) / day
        files = [(p, "event") for p in sorted(d.glob("*.json"))]
    elif args.input == "deduped":
        d = Path(args.deduped_root) / day / "uniques"
        files = [(p, "article") for p in sorted(d.glob("*.json"))]
    elif args.input == "articles":
        proc = Path(args.processed_root)
        srcs = [args.source] if args.source else _list_source_dirs(proc)
        for src in srcs:
            sd = proc / src / day
            if sd.is_dir():
                files.extend((p, "article") for p in sorted(sd.glob("*.json")))
    else:
        raise ValueError(f"未知 --input 模式: {args.input}")
    return files


# --------------------------------------------------------------------------- #
# 主流程
# --------------------------------------------------------------------------- #

async def _run(args: argparse.Namespace) -> int:
    load_dotenv()

    files = _collect_inputs(args)
    if args.limit:
        files = files[: args.limit]
    if not files:
        logger.error("未发现任何输入文件: {} ({})", args.input, args.date)
        return 2
    logger.info("待嵌入文章数: {} (input={})", len(files), args.input)

    # 准备每篇文本
    prepared: list[tuple[str, Article, str | None]] = []
    for fp, kind in files:
        if kind == "event":
            built = _build_text_from_event(fp, Path(args.processed_root), args.date)
        else:
            built = _build_text_from_article(fp)
        if built is not None:
            prepared.append(built)
    if not prepared:
        logger.error("所有输入文件均无法解析")
        return 2

    out_dir = Path(args.out_root) / args.date
    out_dir.mkdir(parents=True, exist_ok=True)
    index_path = out_dir / "index.jsonl"
    failed_path = out_dir / "failed.jsonl"
    for p in (index_path, failed_path):
        if p.exists():
            p.unlink()

    started = time.time()
    succ_cnt = 0
    fail_cnt = 0

    async with make_async_provider(
        args.provider,
        **({"model": args.model} if args.model else {}),
    ) as provider:
        logger.info(
            "Embedding provider={} model={} dim={}",
            provider.name, provider.model, provider.dim,
        )

        # 异步分批
        batch_size = args.batch_size
        for i in range(0, len(prepared), batch_size):
            batch = prepared[i : i + batch_size]
            texts = [t for t, _, _ in batch]
            try:
                vectors = await provider.embed_batch(texts)
            except EmbeddingError as e:
                logger.warning("批 {} 嵌入失败: {}", i // batch_size, e)
                for _, art, _ in batch:
                    fail_cnt += 1
                    with failed_path.open("a", encoding="utf-8") as f:
                        f.write(
                            json.dumps(
                                {"url_hash": art.url_hash, "url": art.url,
                                 "error": str(e)},
                                ensure_ascii=False,
                            )
                            + "\n"
                        )
                continue

            for (text, article, _summary), vec in zip(batch, vectors, strict=True):
                if len(vec) != provider.dim:
                    logger.warning(
                        "维度不一致 url_hash={} 实际={} 预期={}",
                        article.url_hash, len(vec), provider.dim,
                    )
                result = EmbeddingResult(
                    url_hash=article.url_hash,
                    source_id=article.source_id,
                    title=article.title,
                    text=text,
                    vector=vec,
                    dim=len(vec),
                    provider=provider.name,
                    model=provider.model,
                    embedded_at=datetime.now(),
                    char_count=len(text),
                    publish_time=article.publish_time,
                )

                # 落盘:完整 + index
                (out_dir / f"{result.url_hash}.json").write_text(
                    result.model_dump_json(indent=2), encoding="utf-8"
                )
                meta = result.model_dump(exclude={"vector", "text"}, mode="json")
                meta["text_preview"] = text[:60]
                with index_path.open("a", encoding="utf-8") as f:
                    f.write(json.dumps(meta, ensure_ascii=False) + "\n")
                succ_cnt += 1
            logger.info(
                "已处理批 {}: 成功 +{}/{} (累计成功 {})",
                i // batch_size + 1, len(batch), len(batch), succ_cnt,
            )

    elapsed = time.time() - started
    total = succ_cnt + fail_cnt
    rate = succ_cnt / max(total, 1)
    logger.info(
        "Embedding 完成: 成功 {}/{} 成功率 {:.1%} 用时 {:.1f}s",
        succ_cnt, total, rate, elapsed,
    )
    return 0 if rate >= 0.95 or total == 0 else 1


def main() -> int:
    parser = argparse.ArgumentParser(description="A 股新闻 Embedding 向量化 (M5)")
    parser.add_argument(
        "--input", default="events",
        choices=["events", "deduped", "articles"],
        help="输入来源:events (M4) / deduped (M3) / articles (M2)",
    )
    parser.add_argument("--events-root", default="data/events")
    parser.add_argument("--deduped-root", default="data/deduped")
    parser.add_argument("--processed-root", default="data/processed")
    parser.add_argument("--source", default=None,
                        help="--input articles 时按源过滤")
    parser.add_argument("--out-root", default="data/embeddings")
    parser.add_argument(
        "--date", default=date.today().strftime("%Y%m%d"),
        help="日期 YYYYMMDD,默认今日",
    )
    parser.add_argument("--provider", default=None,
                        help="dashscope (默认) / local-bge,可被 .env 覆盖")
    parser.add_argument("--model", default=None,
                        help="嵌入模型名,覆盖 .env 中的 *_EMBEDDING_MODEL")
    parser.add_argument("--batch-size", type=int, default=10,
                        help="每批送 embed 的条数(DashScope 上限 10)")
    parser.add_argument("--limit", type=int, default=0,
                        help="最多处理 N 篇,0=不限")
    parser.add_argument("--log-level", default="INFO")
    args = parser.parse_args()

    _setup_logger(args.log_level)
    return asyncio.run(_run(args))


if __name__ == "__main__":
    raise SystemExit(main())