from __future__ import annotations

import json
import re
from pathlib import Path
from typing import Any


def _read_text(path: Path) -> str:
    return path.read_text(encoding="utf-8-sig")


def _write_json(path: Path, data: dict[str, Any]) -> None:
    path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")


def _write_text(path: Path, text: str) -> None:
    path.write_text(text, encoding="utf-8")


def _compact(text: str, max_len: int) -> str:
    normalized = re.sub(r"\s+", " ", text).strip()
    if len(normalized) <= max_len:
        return normalized
    cut = normalized[:max_len].rsplit(" ", 1)[0].strip()
    return (cut or normalized[:max_len]).rstrip(" ,.;:") + "..."


def _preserve_len(text: str, ratio: float = 0.85, floor: int = 180, ceiling: int = 900) -> int:
    normalized = re.sub(r"\s+", " ", text).strip()
    if not normalized:
        return floor
    return max(floor, min(ceiling, int(len(normalized) * ratio)))


def _strip_frontmatter_and_imports(raw: str) -> str:
    text = raw.replace("\r\n", "\n")
    if text.startswith("---\n"):
        end = text.find("\n---", 4)
        if end != -1:
            text = text[end + 4 :]
    text = re.sub(r"^import\s+.+?$", "", text, flags=re.M)
    return text.strip()


def _dx_effect_lines(repo_root: Path) -> list[str]:
    path = repo_root / "components" / "dx.astro"
    if not path.exists():
        return []
    text = _read_text(path)
    text = re.sub(r"<style.*?</style>", "", text, flags=re.S)
    text = text.replace("<br />", " ")
    text = re.sub(r"</?(div|table|thead|tbody|tr|td|th|colgroup|col|ul|strong)[^>]*>", "\n", text)
    text = re.sub(r"<li[^>]*>", "- ", text)
    text = re.sub(r"</li>", "\n", text)
    text = re.sub(r"<[^>]+>", " ", text)
    lines: list[str] = []
    for raw in text.splitlines():
        line = re.sub(r"\s+", " ", raw).strip()
        if not line:
            continue
        if line.startswith("/*") or line.startswith("["):
            continue
        if len(line) < 6:
            continue
        lines.append(line)
    deduped: list[str] = []
    for line in lines:
        if line not in deduped:
            deduped.append(line)
    return deduped[:24]


def _normalize_block_for_storage(text: str, repo_root: Path) -> str:
    dx_lines = _dx_effect_lines(repo_root)
    if "<DxEffect" in text and dx_lines:
        replacement = "\n".join(f"* {line}" for line in dx_lines)
        text = re.sub(r"<DxEffect\s*/>", replacement, text)
    text = re.sub(r"<summary[^>]*>(.*?)</summary>", lambda m: f"**{re.sub(r'<[^>]+>', ' ', m.group(1)).strip()}**", text, flags=re.S)
    text = text.replace("<details>", "").replace("</details>", "")
    text = re.sub(r"<br\s*/?>", "\n", text, flags=re.I)
    text = re.sub(r"</?div[^>]*>", "", text)
    text = re.sub(r":::\s*note\[(.*?)\]", r"**\1**", text)
    text = text.replace(":::", "")
    text = re.sub(r"!\[([^\]]+)\]\(([^\)]+)\)", r"[???] \1", text)
    text = re.sub(r"\n{3,}", "\n\n", text)
    return text.strip()


def _first_nonempty_lines(text: str, limit: int = 8) -> list[str]:
    lines: list[str] = []
    for raw in text.splitlines():
        line = raw.strip()
        if not line:
            continue
        if line.startswith("---"):
            continue
        lines.append(line)
        if len(lines) >= limit:
            break
    return lines


def _extract_detail_topics(block: str, start_id: int, repo_root: Path) -> tuple[list[dict[str, Any]], str, int]:
    topics: list[dict[str, Any]] = []
    next_id = start_id

    def repl(match: re.Match[str]) -> str:
        nonlocal next_id
        inner = match.group(1)
        summary_match = re.search(r"<summary[^>]*>(.*?)</summary>", inner, flags=re.S)
        summary = re.sub(r"<[^>]+>", " ", summary_match.group(1)).strip() if summary_match else "?? ??"
        detail_body = re.sub(r"<summary[^>]*>.*?</summary>", "", inner, flags=re.S)
        detail_source = _normalize_block_for_storage(detail_body, repo_root)
        if detail_source:
            topics.append({
                "id": next_id,
                "title": summary,
                "purpose": "?? ?? ??",
                "role": "reference",
                "layer": "supporting",
                "source_hint": summary,
                "summary": _compact(detail_source, _preserve_len(detail_source, floor=220, ceiling=560)),
                "source_data": detail_source,
            })
            next_id += 1
        return f"\n* **{summary}**\n"

    stripped = re.sub(r"<details>(.*?)</details>", repl, block, flags=re.S)
    return topics, stripped, next_id


def _extract_title_from_intro(block: str) -> str:
    m = re.search(r"\*\s+\*\*(.+?)\*\*", block)
    if m:
        return m.group(1).strip()
    return "도입"


def _section_chunks(text: str) -> list[tuple[str, str]]:
    matches = list(re.finditer(r"^##\s+(.+)$", text, flags=re.M))
    chunks: list[tuple[str, str]] = []
    for idx, match in enumerate(matches):
        title = match.group(1).strip()
        start = match.end()
        end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text)
        chunks.append((title, text[start:end].strip()))
    return chunks


def _subsection_chunks(text: str) -> list[tuple[str, str]]:
    matches = list(re.finditer(r"^###\s+(.+)$", text, flags=re.M))
    chunks: list[tuple[str, str]] = []
    for idx, match in enumerate(matches):
        title = match.group(1).strip()
        start = match.end()
        end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text)
        chunks.append((title, text[start:end].strip()))
    return chunks


def _classify(title: str, layer_hint: str = "core") -> tuple[str, str, str]:
    clean = title.strip()
    if "혼용" in clean:
        return "problem", "flow", "intro"
    if "정의" in clean:
        return "definition", "flow", "core"
    if "상호관계" in clean or "관계" in clean:
        return "hierarchy", "flow", "core"
    if "구분" in clean or "비교" in clean:
        return "comparison", "reference", "supporting"
    if "사례" in clean:
        return "evidence", "reference", "supporting"
    if "궁극적 목표" in clean:
        return "goal", "flow", "core"
    if "기대효과" in clean:
        return "stakeholder_effect", "flow", "core"
    if "필수 요건" in clean:
        return "requirements", "flow", "core"
    if "Process" in clean or "과정" in clean:
        return "process", "flow", "core"
    if "Product" in clean or "결과" in clean:
        return "product", "flow", "core"
    if "핵심 요약" in clean or "결론" in clean:
        return "conclusion", "flow", "conclusion"
    if layer_hint == "supporting":
        return "support", "reference", "supporting"
    return "section", "flow", "core"


def _extract_conclusion(text: str, repo_root: Path) -> tuple[str, str]:
    m = re.search(r":::\s*note\[(.*?)\](.*?):::", text, flags=re.S)
    if not m:
        return text, ""
    note_title = re.sub(r"\s+", " ", m.group(1)).strip() or "\ud575\uc2ec \uc694\uc57d"
    note_body = _normalize_block_for_storage(m.group(2), repo_root)
    note_source = f"**{note_title}**\n{note_body}".strip()
    stripped = text[: m.start()] + text[m.end() :]
    return stripped.strip(), note_source


def extract_topics_from_raw(raw: str, repo_root: Path) -> tuple[str, list[dict[str, Any]]]:
    title_match = re.search(r"^title:\s*(.+)$", raw, flags=re.M)
    doc_title = title_match.group(1).strip() if title_match else "Document"
    clean = _strip_frontmatter_and_imports(raw)
    clean, conclusion_source = _extract_conclusion(clean, repo_root)

    topics: list[dict[str, Any]] = []
    next_id = 1

    first_section = re.search(r"^##\s+", clean, flags=re.M)
    intro_block = clean[: first_section.start()].strip() if first_section else clean.strip()
    if intro_block:
        detail_topics, intro_stripped, _ = _extract_detail_topics(intro_block, next_id + 1, repo_root)
        intro_source = _normalize_block_for_storage(intro_stripped, repo_root)
        if intro_source:
            title = _extract_title_from_intro(intro_source)
            relation, role, layer = _classify(title, "intro")
            topics.append({
                "id": next_id,
                "title": title,
                "purpose": "?? ?? ?? ??",
                "role": role,
                "layer": layer,
                "source_hint": title,
                "summary": _compact(intro_source, _preserve_len(intro_source, floor=260, ceiling=760)),
                "source_data": intro_source,
            })
            next_id += 1
        topics.extend(detail_topics)
        next_id = max([t["id"] for t in topics], default=0) + 1

    for section_title, section_body in _section_chunks(clean):
        detail_topics, section_stripped, next_id = _extract_detail_topics(section_body, next_id, repo_root)
        subsections = _subsection_chunks(section_stripped)
        lead = re.split(r"^###\s+.+$", section_stripped, maxsplit=1, flags=re.M)[0].strip() if subsections else section_stripped
        if lead:
            source = _normalize_block_for_storage(lead, repo_root)
            if source:
                relation, role, layer = _classify(section_title)
                topics.append({
                    "id": next_id,
                    "title": section_title,
                    "purpose": f"{section_title} ?? ??",
                    "role": role,
                    "layer": layer,
                    "source_hint": section_title,
                    "summary": _compact(source, _preserve_len(source, floor=240, ceiling=780)),
                    "source_data": source,
                })
                next_id += 1
        for sub_title, sub_body in subsections:
            source = _normalize_block_for_storage(sub_body, repo_root)
            if source:
                relation, role, layer = _classify(sub_title)
                topics.append({
                    "id": next_id,
                    "title": sub_title,
                    "purpose": f"{sub_title} ?? ??",
                    "role": role,
                    "layer": layer,
                    "source_hint": sub_title,
                    "summary": _compact(source, _preserve_len(source, floor=220, ceiling=760)),
                    "source_data": source,
                })
                next_id += 1
        topics.extend(detail_topics)
        next_id = max([t["id"] for t in topics], default=0) + 1

    if conclusion_source:
        topics.append({
            "id": next_id,
            "title": "\ud575\uc2ec \uc694\uc57d",
            "purpose": "?? ?? ??",
            "role": "flow",
            "layer": "conclusion",
            "source_hint": "\ud575\uc2ec \uc694\uc57d",
            "summary": _compact(conclusion_source, _preserve_len(conclusion_source, floor=140, ceiling=360)),
            "source_data": conclusion_source,
        })

    return doc_title, topics


def _page_structure(topics: list[dict[str, Any]]) -> dict[str, Any]:
    intro_ids = [t["id"] for t in topics if t["layer"] == "intro"]
    core_ids = [t["id"] for t in topics if t["layer"] == "core"]
    support_ids = [t["id"] for t in topics if t["layer"] == "supporting"]
    conclusion_ids = [t["id"] for t in topics if t["layer"] == "conclusion"]
    structure: dict[str, Any] = {}
    if intro_ids:
        structure["background"] = {"topic_ids": intro_ids, "weight": 0.24}
    if core_ids:
        structure["body"] = {"topic_ids": core_ids, "weight": 0.48 if support_ids else 0.58}
    if support_ids:
        structure["support"] = {"topic_ids": support_ids, "weight": 0.18}
    if conclusion_ids:
        structure["key_message"] = {"topic_ids": conclusion_ids, "weight": 0.10}
    return structure


def rebuild_run_from_raw(repo_root: Path, run_dir: Path, input_file: Path) -> dict[str, Any]:
    raw = _read_text(input_file)
    doc_title, topics = extract_topics_from_raw(raw, repo_root)
    core_topic = next((t for t in topics if t["layer"] == "conclusion"), topics[-1] if topics else {"source_data": ""})
    stage1a = {
        "analysis": {
            "title": doc_title,
            "core_message": re.sub(r"\s+", " ", str(core_topic.get("source_data", ""))).strip(),
            "total_pages": 1,
        },
        "page_structure": _page_structure(topics),
        "topics": topics,
    }
    stage1b = {
        "concepts": [
            {
                "topic_id": t["id"],
                "relation_type": _classify(t["title"], t["layer"])[0],
                "expression_hint": "?? ??? ??? ???. ??? ? ?? ??? ??? popup?? ???. visible ??? ?? ???? 85% ??? ?? ???.",
                "summary": t["summary"],
            }
            for t in topics
        ]
    }

    plan_dir = run_dir / "04-plan"
    plan_dir.mkdir(parents=True, exist_ok=True)
    _write_json(plan_dir / "stage-1a-topics.json", stage1a)
    _write_json(plan_dir / "stage-1b-refined-concepts.json", stage1b)

    input_dir = run_dir / "01-input"
    input_dir.mkdir(parents=True, exist_ok=True)
    input_lines = [
        "# Input Review",
        "",
        f"- ?? ???: {input_file.name}",
        f"- ?? ??: {doc_title}",
        "- ?? ?? ??: ?? block? ???? ?? ???? ???.",
        "- ?? ??: ???? ?? 85% ?? ????, ? ?/?? ??? popup ??? ???.",
        "",
        "## ?? ??",
    ]
    for topic in topics:
        input_lines.append(f"- {topic['title']}: { _compact(re.sub(r'\s+', ' ', topic['source_data']), 160) }")
    _write_text(input_dir / "input-review.md", "\n".join(input_lines) + "\n")

    interp_dir = run_dir / "02-kei-interpretation"
    interp_dir.mkdir(parents=True, exist_ok=True)
    interp_lines = [
        "# Interpretation",
        "",
        "- ?? ??: ????? ?? ??? ???.",
        "- ?? ??: ?? ??? ????, ??/??/popup ???? ???.",
        "- popup ??: ? ?, ?? ??, ? ??? ??? popup?? ?? ???.",
        "",
        "## Topic Classification",
    ]
    for topic in topics:
        interp_lines.append(f"- {topic['title']}: layer={topic['layer']} / role={topic['role']}")
    _write_text(interp_dir / "kei-interpretation.md", "\n".join(interp_lines) + "\n")

    structure_dir = run_dir / "03-structure"
    structure_dir.mkdir(parents=True, exist_ok=True)
    structure_lines = [
        "# Content Structure",
        "",
        "- ??? ??: ?? ?? ??? ???.",
        "- ??? ??: ?? ? ???? ????, ?? ???? ????.",
        "- popup ??: ??? ? ?? ??? ? ?/? ??? popup?? ???.",
        "",
        "## Ordered Blocks",
    ]
    for idx, topic in enumerate(topics, start=1):
        structure_lines.append(f"{idx}. {topic['title']} ({topic['layer']})")
    _write_text(structure_dir / "content-structure.md", "\n".join(structure_lines) + "\n")

    plan_lines = [
        "# Execution Plan",
        "",
        "- ??? raw mdx?? ?? ???? stage-1a/stage-1b? ???.",
        "- ?? ??? ??? ???.",
        "- ?? ??, ? ?, ??? ?? ??? popup?? ?? ???.",
        "- visible ??? section title + ?? bullet + ?? ?? ???? ???.",
    ]
    _write_text(plan_dir / "execution-plan.md", "\n".join(plan_lines) + "\n")

    return {"title": doc_title, "topics": topics}