#!/usr/bin/env python3
"""
账号人设树节点（_type=class 与 _type=ID）与帖子选题点的语义相似度匹配。

选题点：examples_how/overall_derivation/input/{account_name}/处理后数据/post_topic/{post_id}.json
人设树：examples_how/overall_derivation/input/{account_name}/处理后数据/tree/*_point_tree_how.json

用法:
  python tree_post_point_match.py <account_name> <post_id>

模块方式:
  python -m examples_how.overall_derivation.data_process.tree_post_point_match <account_name> <post_id>
"""

from __future__ import annotations

import argparse
import asyncio
import glob
import json
import logging
import os
import sys
from typing import Any, Dict, List

_ROOT = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..", ".."))
if _ROOT not in sys.path:
    sys.path.insert(0, _ROOT)

from examples_how.overall_derivation.data_process.tree_lib_post_point_match import (  # noqa: E402
    CLASS_NODE_BATCH_SIZE,
    MAX_CONCURRENT_BATCHES,
    _chunk_class_nodes,
    _merge_batch_into_by_topic,
    _similarity_one_batch,
    load_post_topics,
)

logger = logging.getLogger(__name__)


def _overall_derivation_dir() -> str:
    """overall_derivation 根目录（input 的父目录）。"""
    return os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))


def _input_dir() -> str:
    return os.path.join(_overall_derivation_dir(), "input")


def _load_post_id_list_from_exclude_note_ids(account_name: str) -> List[str]:
    """从 input/{account_name}/原始数据/exclude_note_ids.json 读取帖子 ID 列表（字符串数组）。"""
    path = os.path.join(_input_dir(), account_name, "原始数据", "exclude_note_ids.json")
    if not os.path.isfile(path):
        raise FileNotFoundError(f"未找到帖子 ID 列表文件: {path}")
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)
    if not isinstance(data, list):
        raise ValueError(f"exclude_note_ids.json 应为字符串数组: {path}")
    out: List[str] = []
    for x in data:
        if isinstance(x, str) and x.strip():
            out.append(x.strip())
    return out


def _walk_class_and_id_nodes(
    children: Any,
    dimension: str,
    out: List[Dict[str, str]],
    seen: set[tuple[str, str]],
) -> None:
    """与 tree_lib 中仅 class 的遍历分离：账号树需同时收集 class 与 ID（如意图树 root 下直连 ID）。"""
    if not isinstance(children, dict):
        return
    for name, node in children.items():
        if not isinstance(node, dict):
            continue
        ntype = node.get("_type")
        if ntype == "class" or ntype == "ID":
            key = (dimension, name)
            if key not in seen:
                seen.add(key)
                entry: Dict[str, str] = {"name": name, "dimension": dimension}
                if ntype == "ID":
                    entry["tree_type"] = "ID"
                out.append(entry)
        sub = node.get("children")
        if isinstance(sub, dict):
            _walk_class_and_id_nodes(sub, dimension, out, seen)


def collect_match_nodes_from_tree_file(path: str) -> List[Dict[str, str]]:
    """从单棵人设树 JSON 收集 _type=class 与 _type=ID（含任意深度、root 下直连 ID）。"""
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)
    if not isinstance(data, dict):
        raise ValueError(f"树文件格式错误（应为对象）: {path}")
    out: List[Dict[str, str]] = []
    seen: set[tuple[str, str]] = set()
    for dimension, root in data.items():
        if not isinstance(root, dict):
            continue
        ch = root.get("children")
        _walk_class_and_id_nodes(ch, str(dimension), out, seen)
    return out


def load_account_tree_class_nodes(account_name: str) -> List[Dict[str, str]]:
    """从账号「处理后数据/tree」下所有人设树 JSON 收集 class 与 ID 节点（顶层 key 为 dimension）。"""
    tree_dir = os.path.join(_input_dir(), account_name, "处理后数据", "tree")
    if not os.path.isdir(tree_dir):
        raise FileNotFoundError(f"人设树目录不存在: {tree_dir}")
    paths = sorted(glob.glob(os.path.join(tree_dir, "*_point_tree_how.json")))
    if not paths:
        raise FileNotFoundError(
            f"未找到人设树文件（期望 *_point_tree_how.json）: {tree_dir}"
        )
    merged: List[Dict[str, str]] = []
    seen: set[tuple[str, str]] = set()
    for p in paths:
        for node in collect_match_nodes_from_tree_file(p):
            key = (node["dimension"], node["name"])
            if key not in seen:
                seen.add(key)
                merged.append(node)
    return merged


async def run_match(
    account_name: str,
    post_id: str,
    *,
    class_batch_size: int = CLASS_NODE_BATCH_SIZE,
    max_concurrent_batches: int = MAX_CONCURRENT_BATCHES,
) -> List[Dict[str, Any]]:
    topics = load_post_topics(account_name, post_id)
    class_nodes = load_account_tree_class_nodes(account_name)
    if not topics:
        raise ValueError("选题点列表为空")
    if not class_nodes:
        raise ValueError("人设树中未找到任何可匹配节点（_type=class 或 _type=ID）")

    phrases_a = topics
    chunks = _chunk_class_nodes(class_nodes, class_batch_size)
    batch_total = len(chunks)
    semaphore = asyncio.Semaphore(max(1, max_concurrent_batches))

    logger.info(
        "分类节点共 %d 个，分为 %d 批（每批最多 %d 个），最多 %d 批并发",
        len(class_nodes),
        batch_total,
        class_batch_size,
        max_concurrent_batches,
    )

    tasks = [
        _similarity_one_batch(semaphore, phrases_a, chunk, bi + 1, batch_total)
        for bi, chunk in enumerate(chunks)
    ]
    batch_results = await asyncio.gather(*tasks, return_exceptions=True)

    by_topic: List[List[Dict[str, Any]]] = [[] for _ in range(len(phrases_a))]
    for bi, res in enumerate(batch_results):
        if isinstance(res, Exception):
            logger.error(
                "相似度批次协程异常（批次 %d/%d）: %s",
                bi + 1,
                batch_total,
                res,
                exc_info=res,
            )
            continue
        items, chunk_nodes = res
        _merge_batch_into_by_topic(by_topic, items, chunk_nodes)

    output: List[Dict[str, Any]] = []
    for t, matches in zip(topics, by_topic):
        matches_sorted = sorted(matches, key=lambda m: m["match_score"], reverse=True)
        output.append({"name": t, "match_personas": matches_sorted})
    return output


def write_match_result(account_name: str, post_id: str, data: List[Dict[str, Any]]) -> str:
    out_dir = os.path.join(_input_dir(), account_name, "处理后数据", "match_data")
    os.makedirs(out_dir, exist_ok=True)
    out_path = os.path.join(out_dir, f"{post_id}_匹配_all.json")
    with open(out_path, "w", encoding="utf-8") as f:
        json.dump(data, f, ensure_ascii=False, indent=2)
    return out_path


async def main_async(
    account_name: str,
    post_id: str,
    *,
    class_batch_size: int = CLASS_NODE_BATCH_SIZE,
    max_concurrent_batches: int = MAX_CONCURRENT_BATCHES,
) -> str:
    logger.info("account=%s post_id=%s", account_name, post_id)
    result = await run_match(
        account_name,
        post_id,
        class_batch_size=class_batch_size,
        max_concurrent_batches=max_concurrent_batches,
    )
    path = write_match_result(account_name, post_id, result)
    logger.info("已写入: %s（共 %d 个选题点）", path, len(result))
    return path


def main(account_name, post_id) -> None:
    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
    asyncio.run(
        main_async(
            account_name,
            post_id,
            class_batch_size=20,
            max_concurrent_batches=10,
        )
    )


if __name__ == "__main__":
    account_name = "空间点阵设计研究室"
    post_id_list = _load_post_id_list_from_exclude_note_ids(account_name)
    for post_id in post_id_list:
        main(account_name, post_id)