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] | list[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 _normalize_space(text: str) -> str:
    return re.sub(r'\s+', ' ', text or '').strip()


def _compact(text: str, max_len: int) -> str:
    normalized = _normalize_space(text)
    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 = _normalize_space(text)
    if not normalized:
        return floor
    return max(floor, min(ceiling, int(len(normalized) * ratio)))


def _normalize_title_key(text: str) -> str:
    return re.sub(r'\s+', '', text or '').lower()


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 = _normalize_space(raw)
        if not line or line.startswith('/*') or line.startswith('[') or 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'[image] \1', text)
    text = re.sub(r'\n{3,}', '\n\n', text)
    return text.strip()


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


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()
    key = _normalize_title_key(clean)
    if any(token in key for token in ['혼용', '실태', '현실']):
        return 'problem', 'flow', 'intro'
    if any(token in key for token in ['정의', '개념', '용어']):
        return 'definition', 'flow', 'core'
    if any(token in key for token in ['상호관계', '관계', '위치']):
        return 'hierarchy', 'flow', 'core'
    if any(token in key for token in ['구분', '비교']):
        return 'comparison', 'reference', 'supporting'
    if any(token in key for token in ['사례', '근거', '대표']):
        return 'evidence', 'reference', 'supporting'
    if any(token in key for token in ['궁극적목표', '시행목표', '목표']):
        return 'goal', 'flow', 'core'
    if any(token in key for token in ['기대효과', '주체별', '효과']):
        return 'stakeholder_effect', 'flow', 'core'
    if any(token in key for token in ['필수요건', '요건']):
        return 'requirements', 'flow', 'core'
    if 'process' in key or '과정' in clean:
        return 'process', 'flow', 'core'
    if 'product' in key or '결과' in clean:
        return 'product', 'flow', 'core'
    if any(token in key for token in ['핵심요약', '요약', '결론']):
        return 'conclusion', 'flow', 'conclusion'
    if layer_hint == 'supporting':
        return 'support', 'reference', 'supporting'
    return 'section', 'flow', 'core'


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 '상세 내용'
        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': '상세 근거 또는 부연 설명',
                'role': 'reference',
                'layer': 'supporting',
                'relation_type': 'evidence',
                'source_hint': summary,
                'summary': _compact(detail_source, _preserve_len(detail_source, floor=220, ceiling=560)),
                'source_data': detail_source,
                'structured_text': detail_source,
                'popup_candidate': True,
            })
            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_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 = _normalize_space(m.group(1)) or '핵심 요약'
    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 _content_family(topics: list[dict[str, Any]]) -> str:
    relation_types = {str(t.get('relation_type', '') or '') for t in topics}
    if ('comparison' in relation_types or 'definition' in relation_types or 'hierarchy' in relation_types) and 'goal' not in relation_types:
        return 'type-a-compare-define-relate'
    if 'goal' in relation_types or 'stakeholder_effect' in relation_types:
        return 'type-b-goal-effect'
    if 'requirements' in relation_types or 'product' in relation_types or 'process' in relation_types:
        return 'type-b-requirements-process-product'
    return 'type-b-section-stack'


def _popup_candidate(topic: dict[str, Any]) -> bool:
    relation = str(topic.get('relation_type', '') or '')
    source = _normalize_space(str(topic.get('source_data', '') or ''))
    return relation in {'comparison', 'evidence'} or len(source) > 520


def extract_topics_from_raw(raw: str, repo_root: Path) -> tuple[str, list[dict[str, Any]], str]:
    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': '문서 도입 또는 문제 제기',
                'role': role,
                'layer': layer,
                'relation_type': relation,
                'source_hint': title,
                'summary': _compact(intro_source, _preserve_len(intro_source, floor=260, ceiling=760)),
                'source_data': intro_source,
                'structured_text': intro_source,
                'popup_candidate': False,
            })
            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}의 핵심 내용',
                    'role': role,
                    'layer': layer,
                    'relation_type': relation,
                    'source_hint': section_title,
                    'summary': _compact(source, _preserve_len(source, floor=240, ceiling=780)),
                    'source_data': source,
                    'structured_text': source,
                    'popup_candidate': False,
                })
                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}의 세부 내용',
                    'role': role,
                    'layer': layer,
                    'relation_type': relation,
                    'source_hint': sub_title,
                    'summary': _compact(source, _preserve_len(source, floor=220, ceiling=760)),
                    'source_data': source,
                    'structured_text': source,
                    'popup_candidate': False,
                })
                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': '핵심 요약',
            'purpose': '결론 또는 핵심 메시지',
            'role': 'flow',
            'layer': 'conclusion',
            'relation_type': 'conclusion',
            'source_hint': '핵심 요약',
            'summary': _compact(conclusion_source, _preserve_len(conclusion_source, floor=140, ceiling=360)),
            'source_data': conclusion_source,
            'structured_text': conclusion_source,
            'popup_candidate': False,
        })

    for topic in topics:
        topic['popup_candidate'] = _popup_candidate(topic)

    return doc_title, topics, _content_family(topics)


def _page_structure(topics: list[dict[str, Any]], family: str) -> 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 family == 'type-a-compare-define-relate':
        if intro_ids:
            structure['background'] = {'topic_ids': intro_ids, 'weight': 0.22}
        if core_ids:
            structure['body'] = {'topic_ids': core_ids, 'weight': 0.50}
        if support_ids:
            structure['support'] = {'topic_ids': support_ids, 'weight': 0.18}
    else:
        top_ids = intro_ids + core_ids[:1]
        body_ids = core_ids[1:] if len(core_ids) > 1 else core_ids[:1]
        support_main = support_ids[:]
        if top_ids:
            structure['body'] = {'topic_ids': top_ids + body_ids, 'weight': 0.58 if support_main else 0.64}
        if support_main:
            structure['support'] = {'topic_ids': support_main, '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, family = 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': _normalize_space(str(core_topic.get('source_data', ''))),
            'total_pages': 1,
            'layout_template': ('A' if family == 'type-a-compare-define-relate' else ('B_GOAL' if family == 'type-b-goal-effect' else ('B_RPP' if family == 'type-b-requirements-process-product' else 'B_STACK'))),
            'content_family': family,
        },
        'page_structure': _page_structure(topics, family),
        'topics': topics,
    }
    stage1b = {
        'concepts': [
            {
                'topic_id': t['id'],
                'relation_type': t['relation_type'],
                'expression_hint': (
                    '원문 제목과 원문 bullet을 우선 유지한다. 긴 세부 설명이나 큰 표는 popup으로 이동하되, 본문에는 핵심 bullet과 진입 요약을 남긴다.'
                    if t.get('popup_candidate') else
                    '원문 제목과 원문 bullet을 visible block으로 유지하고, 임의 재서술을 최소화한다.'
                ),
                'summary': t['summary'],
            }
            for t in topics
        ]
    }

    input_dir = run_dir / '01-input'
    interp_dir = run_dir / '02-kei-interpretation'
    structure_dir = run_dir / '03-structure'
    plan_dir = run_dir / '04-plan'
    for d in (input_dir, interp_dir, structure_dir, plan_dir):
        d.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)
    _write_json(structure_dir / 'source-blocks.json', {
        'title': doc_title,
        'content_family': family,
        'blocks': [
            {
                'id': t['id'],
                'title': t['title'],
                'layer': t['layer'],
                'relation_type': t['relation_type'],
                'popup_candidate': bool(t.get('popup_candidate')),
                'source_data': t['source_data'],
            }
            for t in topics
        ],
    })

    input_lines = [
        '# Input Review',
        '',
        f'- 입력 파일: {input_file.name}',
        f'- 문서 제목: {doc_title}',
        f'- content family 후보: {family}',
        '- 우선 목표: 원문 block과 원문 순서를 최대한 보존한다.',
        '- popup 전략: 큰 표, 긴 사례, 긴 근거는 popup 후보로 분리하고 본문에는 제목과 핵심 bullet을 남긴다.',
        '',
        '## 원문 블록 식별',
    ]
    for topic in topics:
        popup_mark = ' [popup]' if topic.get('popup_candidate') else ''
        input_lines.append(f"- {topic['title']} ({topic['relation_type']}/{topic['layer']}){popup_mark}: {_compact(_normalize_space(topic['source_data']), 180)}")
    _write_text(input_dir / 'input-review.md', '\n'.join(input_lines) + '\n')

    interp_lines = [
        '# Interpretation',
        '',
        f'- content family: {family}',
        '- 해석 원칙: 원문 제목/순서/표현을 우선 보존하고, 임의 재서술은 최소화한다.',
        '- grouping 원칙: 관계가 같은 block만 묶고, 내용이 길다고 해서 본문에서 제거하지 않는다.',
        '- popup 원칙: 상세는 popup으로 보내되 본문에는 핵심 bullet과 진입 문장을 남긴다.',
        '',
        '## Topic Classification',
    ]
    for topic in topics:
        interp_lines.append(
            f"- {topic['title']}: relation={topic['relation_type']} / layer={topic['layer']} / popup_candidate={str(bool(topic.get('popup_candidate'))).lower()}"
        )
    _write_text(interp_dir / 'kei-interpretation.md', '\n'.join(interp_lines) + '\n')

    structure_lines = [
        '# Content Structure',
        '',
        f'- content family: {family}',
        '- visible block 원칙: 각 섹션 제목과 핵심 bullet은 본문에 남긴다.',
        '- popup block 원칙: 큰 표, 긴 사례, 긴 상세 설명만 popup으로 보낸다.',
        '- 결론 원칙: note/결론 문장은 footer 또는 결론 배너에 직접 노출한다.',
        '',
        '## Ordered Blocks',
    ]
    for idx, topic in enumerate(topics, start=1):
        popup_mark = ' popup' if topic.get('popup_candidate') else ' visible'
        structure_lines.append(f"{idx}. {topic['title']} ({topic['relation_type']} / {topic['layer']} /{popup_mark})")
    _write_text(structure_dir / 'content-structure.md', '\n'.join(structure_lines) + '\n')

    plan_lines = [
        '# Execution Plan',
        '',
        f'- content family: {family}',
        '- stage-1a/stage-1b는 raw MDX 기반 block 추출 결과를 그대로 사용한다.',
        '- Type A는 비교/정의/관계형으로, Type B는 본문 중심형으로 렌더한다.',
        '- popup 후보 block은 삭제하지 않고 popup overlay로 이동한다.',
        '- visible 영역에는 섹션 제목과 핵심 bullet을 남겨 원문 85% 보존 목표를 유지한다.',
    ]
    _write_text(plan_dir / 'execution-plan.md', '\n'.join(plan_lines) + '\n')

    return {'title': doc_title, 'topics': topics, 'content_family': family}