from __future__ import annotations

import re
import shutil
import subprocess
from pathlib import Path

import numpy as np
from llama_index.core.schema import TextNode

from .config import Settings
from .embeddings import embed_documents
from .markdown_chunker import chunk_markdown_file
from .vector_store import db_path, load_nodes_from_db, write_vector_db


def sync_notes(settings: Settings) -> None:
    settings.notes_dir.mkdir(parents=True, exist_ok=True)
    subprocess.run(
        [
            "rsync",
            "-av",
            "--delete",
            "--exclude=.git/",
            "--exclude=.trash/",
            "--exclude=.obsidian/workspace*",
            settings.notes_remote,
            f"{settings.notes_dir}/",
        ],
        check=True,
    )


def markdown_files(notes_dir: Path) -> list[Path]:
    if not notes_dir.exists():
        raise FileNotFoundError(f"笔记目录不存在: {notes_dir}")

    return sorted(notes_dir.rglob("*.md"))


def chinese_tokenizer(text: str) -> list[str]:
    stopwords = {
        "请",
        "根据",
        "笔记",
        "简要",
        "回答",
        "哪些",
        "什么",
        "内容",
        "一下",
        "我的",
        "有哪些",
        "是什么",
    }
    tokens: list[str] = []

    for part in re.findall(r"[A-Za-z0-9_#+.-]+|[\u4e00-\u9fff]+", text.lower()):
        if re.fullmatch(r"[A-Za-z0-9_#+.-]+", part):
            if part in {"md", "markdown"}:
                continue
            if part not in stopwords:
                tokens.append(part)
            continue

        if len(part) == 1:
            if part not in stopwords:
                tokens.append(part)
            continue

        max_n = min(4, len(part))
        for n in range(2, max_n + 1):
            for index in range(0, len(part) - n + 1):
                token = part[index : index + n]
                if token not in stopwords:
                    tokens.append(token)

    return tokens


def build_index(settings: Settings) -> int:
    nodes: list[TextNode] = []
    for path in markdown_files(settings.notes_dir):
        nodes.extend(chunk_markdown_file(path, settings.notes_dir))

    if settings.index_dir.exists():
        shutil.rmtree(settings.index_dir)
    settings.index_dir.mkdir(parents=True, exist_ok=True)

    texts = [node_to_embedding_text(node) for node in nodes]
    vectors = embed_documents(settings, texts)
    write_vector_db(db_path(settings.index_dir), nodes, vectors)
    return len(nodes)


def load_index_nodes(settings: Settings) -> list[TextNode]:
    database = db_path(settings.index_dir)
    if not database.exists():
        raise FileNotFoundError(
            f"索引不存在: {database}，请先运行 mengya-build-index"
        )
    return load_nodes_from_db(database)


def node_to_embedding_text(node: TextNode) -> str:
    title = str(node.metadata.get("title", ""))
    rel_path = str(node.metadata.get("rel_path", ""))
    heading_path = str(node.metadata.get("heading_path", ""))
    tags = node.metadata.get("tags", [])
    return (
        f"文件名: {title}\n"
        f"路径: {rel_path}\n"
        f"标题路径: {heading_path}\n"
        f"标签: {tags}\n"
        f"正文:\n{node.get_content()}"
    )