"""
故事提取器 - v4 五步流水线策略
第一步：代码预处理（识别老师→过滤杂音→时间范围截取→过滤短段落/非主讲述人）
第二步：判断故事 + 提取事实（短段落直接提取，长段落先判断再切片提取）
第三步：切片提取事实（按自然段切分，并行提取）
第四步：重组（纯代码，事件/情感/原话/人物去重）
第五步：生成最终故事（基于素材生成5部分结构化故事）
"""
import json
import asyncio
import logging

from sqlalchemy import select

from app.database import async_session
from app.models.story import Story
from app.models.transcript import Transcript
from app.services.docx_parser import parse_transcript
from app.services.preprocessor import Preprocessor
from app.services.llm_client import llm_client
from app.state import TaskState

logger = logging.getLogger(__name__)

# 并发控制
MAX_CONCURRENT_FILES = 3
MAX_CONCURRENT_SEGMENTS = 5

# 短段落阈值（字数）：<=4000 字直接提取事实，>4000 字先判断是否有故事
SHORT_THRESHOLD = 4000

# 切片参数
CHUNK_MAX_CHARS = 3000
CHUNK_OVERLAP_PARAGRAPHS = 5


# ============================================================
# 主入口
# ============================================================
async def run_extraction(state: TaskState):
    """主提取流程：文件间串行（避免 API 限流），文件内各步骤并行"""
    state.is_running = True
    try:
        async with async_session() as db:
            result = await db.execute(
                select(Transcript).where(Transcript.status == "pending")
            )
            transcripts = result.scalars().all()

        if not transcripts:
            state.update(status="completed", percent=100)
            return

        file_total = len(transcripts)
        state.update(
            status="running", percent=0,
            files_total=file_total, files_completed=0,
            stories_found=0,
            current_file="", action=f"共发现 {file_total} 份文字稿，开始处理...",
            phase="preprocess", phase_percent=0, phase_detail="准备中",
        )

        total_stories = [0]

        # 文件间串行处理（避免 API 限流）
        for file_index, transcript in enumerate(transcripts):
            await _process_file(state, file_index, file_total, transcript, total_stories)

        state.update(
            status="completed", percent=100,
            files_completed=file_total,
            stories_found=total_stories[0],
            current_file="", action="全部处理完成！",
            phase="preprocess", phase_percent=100, phase_detail="全部完成",
        )
        state.is_running = False

    except asyncio.CancelledError:
        state.update(status="stopped", action="提取已停止")
        state.is_running = False
    except Exception as e:
        logger.exception("Extraction failed")
        state.update(status="error", action=str(e))
        state.is_running = False


# ============================================================
# 单文件处理（五步流水线）
# ============================================================
async def _process_file(state: TaskState, file_index: int, file_total: int,
                       transcript: Transcript, total_stories: list):
    """处理单个文件 - 五步流水线"""
    try:
        async with async_session() as db:
            t = await db.get(Transcript, transcript.id)
            if not t:
                _increment_files_completed(state)
                return

            t.status = "processing"
            await db.commit()

            # 计算该文件在总进度中的权重区间
            # 预处理 0-5%, 判断+提取 5-30%, 切片提取 30-55%, 重组 55-60%, 生成 60-95%, 完成 100%
            # 每个文件占 95/file_total 的权重
            file_weight = 95.0 / max(file_total, 1)
            file_start_percent = file_index * file_weight

            def _file_percent(phase_start_ratio, phase_end_ratio, inner_ratio=1.0):
                """计算当前文件在总进度中的百分比"""
                phase_range = phase_end_ratio - phase_start_ratio
                return min(99, int(file_start_percent + file_weight * (phase_start_ratio + phase_range * inner_ratio)))

            state.update(
                current_file=t.filename, action="正在读取文件...",
                phase="preprocess", phase_percent=0, phase_detail="读取文件",
            )

            # 解析 DOCX
            lines = await asyncio.to_thread(parse_transcript, t.file_path)
            if not lines:
                t.status = "completed"
                t.error_message = "文件为空"
                await db.commit()
                _increment_files_completed(state)
                return

            # ========== 第一步：代码预处理（纯代码，不用 LLM）==========
            state.update(
                current_file=t.filename, action="正在分析对话结构...",
                phase="preprocess", phase_percent=10, phase_detail="统计说话人",
            )

            preprocessor = Preprocessor()
            filtered_lines = [p for p in lines if not preprocessor.is_management_line(p)]

            if not filtered_lines:
                t.status = "completed"
                t.error_message = "过滤后无有效对话内容"
                await db.commit()
                _increment_files_completed(state)
                return

            segments, teacher = _preprocess_segments(filtered_lines)
            logger.info(f"预处理完成: 识别老师={teacher}, 有效段落={len(segments)}")

            if not segments:
                t.status = "completed"
                t.error_message = "未发现学员对话段落"
                await db.commit()
                _increment_files_completed(state)
                return

            state.update(
                current_file=t.filename,
                action=f"发现 {len(segments)} 个学员段落，开始识别故事...",
                phase="preprocess", phase_percent=100, phase_detail="预处理完成",
                percent=_file_percent(0, 0.05),
            )

            # ========== 第二步：判断故事 + 提取事实（并行）==========
            state.update(
                current_file=t.filename,
                action=f"正在判断 {len(segments)} 个段落是否包含故事...",
                phase="identify", phase_percent=0,
                phase_detail=f"准备处理 {len(segments)} 个段落",
            )

            seg_semaphore = asyncio.Semaphore(MAX_CONCURRENT_SEGMENTS)
            identify_results = [None] * len(segments)
            identify_errors = []

            async def process_segment_for_identify(seg_idx, speaker, seg):
                """对单个段落进行判断+提取"""
                async with seg_semaphore:
                    text = _segment_to_text(seg)
                    text_len = len(text)

                    # 更新进度
                    inner_pct = int((seg_idx + 1) / len(segments) * 100)
                    state.update(
                        phase="identify", phase_percent=inner_pct,
                        phase_detail=f"正在处理段落 {seg_idx + 1}/{len(segments)}（{speaker}, {text_len}字）",
                        percent=_file_percent(0.05, 0.30, (seg_idx + 1) / len(segments)),
                    )

                    if text_len <= SHORT_THRESHOLD:
                        # 短段落：跳过判断，直接提取事实
                        logger.info(f"  段落 {seg_idx}: {speaker} ({text_len}字) - 短段落，直接提取事实")
                        facts = await _extract_facts_from_chunk(text, "")
                        if facts:
                            hints = facts.get("story_hints", "") or f"{speaker}的故事"
                            return (speaker, seg, hints, [facts])
                        else:
                            logger.info(f"  段落 {seg_idx}: 提取失败，跳过")
                            return None
                    else:
                        # 长段落：先判断是否有故事
                        is_story, hints = await _identify_story(text)
                        logger.info(f"  段落 {seg_idx}: {speaker} ({text_len}字) - is_story={is_story}, hints={hints}")
                        if is_story:
                            return (speaker, seg, hints, None)  # None 表示需要第三步切片提取
                        else:
                            logger.info(f"  段落 {seg_idx}: 无故事，跳过")
                            return None

            tasks = [
                process_segment_for_identify(i, speaker, seg)
                for i, (speaker, seg) in enumerate(segments)
            ]
            results = await asyncio.gather(*tasks, return_exceptions=True)

            # 收集结果：分为两类
            # - 直接提取到事实的（短段落）
            # - 需要切片提取的（长段落，有故事）
            all_facts = []  # 最终的 (speaker, seg, hints, facts_list) 列表
            needs_chunking = []  # 需要进入第三步的 (speaker, seg, hints)

            for i, r in enumerate(results):
                if isinstance(r, Exception):
                    error_str = str(r)
                    logger.warning(f"段落 {i} 处理异常: {error_str}")
                    identify_errors.append(error_str)
                    if "529" in error_str or "overloaded" in error_str.lower() or "timed out" in error_str.lower():
                        raise r
                    continue
                if r is None:
                    continue
                speaker, seg, hints, facts_list = r
                if facts_list is not None:
                    # 短段落已直接提取到事实
                    all_facts.append((speaker, seg, hints, facts_list))
                else:
                    # 长段落有故事，需要切片提取
                    needs_chunking.append((speaker, seg, hints))

            if not all_facts and not needs_chunking:
                t.status = "completed"
                t.error_message = "未发现包含个人故事的段落"
                await db.commit()
                _increment_files_completed(state)
                state.update(
                    percent=_file_percent(0.05, 0.30, 1.0),
                    action="未发现故事",
                )
                return

            logger.info(f"第二步完成: 直接提取={len(all_facts)}, 需切片={len(needs_chunking)}")

            # 第二步完成时已知道发现了多少个故事，立即更新
            discovered = len(all_facts) + len(needs_chunking)
            total_stories[0] += discovered
            state.update(stories_found=total_stories[0])

            # ========== 第三步：切片提取事实（并行）==========
            if needs_chunking:
                state.update(
                    current_file=t.filename,
                    action=f"正在切片提取 {len(needs_chunking)} 个故事...",
                    phase="chunk", phase_percent=0,
                    phase_detail=f"准备切片 {len(needs_chunking)} 个故事段落",
                )

                chunk_semaphore = asyncio.Semaphore(MAX_CONCURRENT_SEGMENTS)

                async def process_chunked_story(story_idx, speaker, seg, hints):
                    """对单个长段落进行切片提取"""
                    async with chunk_semaphore:
                        text = _segment_to_text(seg)
                        chunks = _chunk_text_by_paragraphs(text, max_chars=CHUNK_MAX_CHARS, overlap_paragraphs=CHUNK_OVERLAP_PARAGRAPHS)
                        logger.info(f"  故事 {story_idx}: {speaker} - 切成 {len(chunks)} 块")

                        facts_list = []
                        chunk_facts = await asyncio.gather(
                            *[_extract_facts_from_chunk(chunk, hints) for chunk in chunks],
                            return_exceptions=True,
                        )
                        for j, facts in enumerate(chunk_facts):
                            inner_pct = int((story_idx * 10 + j + 1) / (len(needs_chunking) * 10) * 100)
                            state.update(
                                phase="chunk", phase_percent=inner_pct,
                                phase_detail=f"正在切片提取 故事{story_idx + 1}/{len(needs_chunking)} 块{j + 1}/{len(chunks)}",
                                percent=_file_percent(0.30, 0.55, (story_idx + j / max(len(chunks), 1)) / len(needs_chunking)),
                            )
                            if isinstance(facts, Exception):
                                logger.warning(f"  块{j}提取异常: {facts}")
                                continue
                            if facts:
                                facts_list.append(facts)

                        return (speaker, seg, hints, facts_list)

                chunk_tasks = [
                    process_chunked_story(i, speaker, seg, hints)
                    for i, (speaker, seg, hints) in enumerate(needs_chunking)
                ]
                chunk_results = await asyncio.gather(*chunk_tasks, return_exceptions=True)

                for r in chunk_results:
                    if isinstance(r, Exception):
                        error_str = str(r)
                        logger.warning(f"切片提取异常: {error_str}")
                        if "529" in error_str or "overloaded" in error_str.lower() or "timed out" in error_str.lower():
                            raise r
                        continue
                    if r is not None:
                        all_facts.append(r)

                logger.info(f"第三步完成: 总共 {len(all_facts)} 个故事段落完成事实提取")

            state.update(
                current_file=t.filename,
                action="切片提取完成，正在重组素材...",
                phase="chunk", phase_percent=100,
                phase_detail="切片提取完成",
                percent=_file_percent(0.30, 0.55, 1.0),
            )

            # ========== 第四步：重组（纯代码，无需 LLM）==========
            state.update(
                current_file=t.filename,
                action="正在重组素材...",
                phase="merge", phase_percent=50,
                phase_detail="去重合并素材",
                percent=_file_percent(0.55, 0.60, 0.5),
            )

            merged = _merge_facts(all_facts)
            logger.info(f"第四步完成: 重组出 {len(merged)} 个故事素材")

            if not merged:
                t.status = "completed"
                t.error_message = "素材重组后为空"
                await db.commit()
                _increment_files_completed(state)
                return

            state.update(
                current_file=t.filename,
                action=f"重组完成，正在生成 {len(merged)} 个故事...",
                phase="merge", phase_percent=100,
                phase_detail="重组完成",
                percent=_file_percent(0.55, 0.60, 1.0),
            )

            # ========== 第五步：生成最终故事（并行）==========
            state.update(
                current_file=t.filename,
                action=f"正在生成 {len(merged)} 个故事...",
                phase="generate", phase_percent=0,
                phase_detail=f"准备生成 {len(merged)} 个故事",
                percent=_file_percent(0.60, 0.95, 0),
            )

            gen_semaphore = asyncio.Semaphore(MAX_CONCURRENT_SEGMENTS)

            async def generate_one_story(story_idx, speaker, seg, material):
                """生成单个故事并保存到数据库"""
                async with gen_semaphore:
                    inner_pct = int((story_idx + 1) / len(merged) * 100)
                    state.update(
                        phase="generate", phase_percent=inner_pct,
                        phase_detail=f"正在生成故事 {story_idx + 1}/{len(merged)}（{speaker}）",
                        percent=_file_percent(0.60, 0.95, (story_idx + 1) / len(merged)),
                    )

                    story_data = await _generate_story(material)
                    if not story_data:
                        logger.warning(f"  故事 {story_idx}: 生成失败")
                        return None

                    # 保存到数据库
                    raw_content = story_data.get("content", {})
                    if isinstance(raw_content, dict):
                        content_str = _format_story_content(raw_content)
                    else:
                        content_str = str(raw_content)

                    story = Story(
                        title=story_data.get("title", "未命名故事"),
                        summary=story_data.get("summary", ""),
                        tags=json.dumps(story_data.get("tags", []), ensure_ascii=False),
                        content=content_str,
                        raw_material=json.dumps(material, ensure_ascii=False),
                        speaker_nickname=story_data.get("speaker_nickname", ""),
                        source_transcript_id=t.id,
                        source_lines=json.dumps([l.get("index", 0) for l in seg if isinstance(l, dict)]),
                        duration_minutes=_estimate_duration(seg),
                        confidence=story_data.get("confidence", 0.7),
                        confidence_level=_confidence_level(story_data.get("confidence", 0.7)),
                    )
                    db.add(story)
                    await db.commit()

                    state.update(
                        action=f"生成第 {total_stories[0]} 个故事：{story.title}",
                    )
                    logger.info(f"  故事 {story_idx}: 保存成功 - {story.title}")

                    return story

            gen_tasks = [
                generate_one_story(i, speaker, seg, material)
                for i, (speaker, seg, material) in enumerate(merged)
            ]
            gen_results = await asyncio.gather(*gen_tasks, return_exceptions=True)

            for r in gen_results:
                if isinstance(r, Exception):
                    error_str = str(r)
                    logger.warning(f"故事生成异常: {error_str}")
                    if "529" in error_str or "overloaded" in error_str.lower() or "timed out" in error_str.lower():
                        raise r

            t.status = "completed"
            await db.commit()
            _increment_files_completed(state)
            state.update(
                current_file=t.filename,
                action=f"文件处理完成，共生成 {total_stories[0]} 个故事",
                phase="generate", phase_percent=100,
                phase_detail="文件处理完成",
                percent=_file_percent(0.60, 0.95, 1.0),
            )

    except Exception as e:
        error_str = str(e)
        logger.exception(f"Error processing file {transcript.filename}")
        try:
            async with async_session() as db:
                t = await db.get(Transcript, transcript.id)
                if t:
                    t.status = "error"
                    t.error_message = error_str
                    await db.commit()
        except Exception:
            pass
        _increment_files_completed(state)

        if "529" in error_str or "overloaded" in error_str.lower():
            state.update(status="error", error="AI 服务暂时过载（529），请稍后再试")
        elif "timed out" in error_str.lower():
            state.update(status="error", error="AI 服务请求超时，请稍后再试")
        else:
            state.update(status="error", error=f"处理文件时出错：{error_str[:200]}")


# ============================================================
# 第一步：代码预处理
# ============================================================
def _preprocess_segments(lines: list[dict]) -> tuple[list[tuple[str, list[dict]]], str]:
    """
    代码预处理（v3：时间范围截取，允许重叠）。
    识别老师 → 过滤杂音（<20句）→ 按时间范围截取 → 过滤短段落（<15句/<80字）→ 过滤非主讲述人（占比<10%）

    返回: (segments, teacher)
      segments: [(speaker, segment_lines), ...]
      teacher: 老师名称
    """
    # 1. 统计每个说话人
    speaker_stats = {}
    for i, line in enumerate(lines):
        speaker = line.get("speaker", "")
        if not speaker:
            continue
        if speaker not in speaker_stats:
            speaker_stats[speaker] = {"lines": 0, "chars": 0, "first": i, "last": i}
        speaker_stats[speaker]["lines"] += 1
        speaker_stats[speaker]["chars"] += len(line.get("content", ""))
        speaker_stats[speaker]["last"] = i

    # 2. 识别老师（发言最多的人）
    teacher = max(speaker_stats, key=lambda s: speaker_stats[s]["lines"])
    logger.info(f"识别老师: {teacher} ({speaker_stats[teacher]['lines']}行, {speaker_stats[teacher]['chars']}字)")

    # 3. 过滤杂音（< 20句的说话人）+ 排除老师
    students = []
    noise_speakers = []
    for s, stats in speaker_stats.items():
        if s == teacher:
            continue
        if stats["lines"] < 20:
            noise_speakers.append(f"{s} ({stats['lines']}句)")
            continue
        students.append(s)

    if noise_speakers:
        logger.info(f"杂音过滤: {', '.join(noise_speakers)}")
    logger.info(f"有效学员: {students}")

    # 4. 按时间范围截取：每个学员从第一句到最后一句，中间所有行全部截取
    segments = []
    for speaker in students:
        stats = speaker_stats[speaker]
        first_idx = stats["first"]
        last_idx = stats["last"]
        seg = lines[first_idx:last_idx + 1]
        segments.append((speaker, seg))

    # 5. 过滤：短段落 + 当事人占比过低（非主讲述人）
    final_segments = []
    for speaker, seg in segments:
        student_lines = [l for l in seg if l.get("speaker", "") == speaker]
        if len(student_lines) < 15:
            logger.info(f"过滤: {speaker} ({len(student_lines)}句<15)")
            continue
        student_chars = sum(len(l.get("content", "")) for l in student_lines)
        if student_chars < 80:
            logger.info(f"过滤: {speaker} ({student_chars}字<80)")
            continue
        # 当事人说话占比 < 10% → 非主讲述人，丢弃
        ratio = len(student_lines) / len(seg) if seg else 0
        if ratio < 0.1:
            logger.info(f"过滤: {speaker} (占比{ratio:.1%}<10%)")
            continue
        final_segments.append((speaker, seg))

    logger.info(f"最终: {len(final_segments)} 个学员段落")
    for i, (speaker, seg) in enumerate(final_segments):
        student_lines = [l for l in seg if l.get("speaker", "") == speaker]
        student_chars = sum(len(l.get("content", "")) for l in student_lines)
        ratio = len(student_lines) / len(seg) if seg else 0
        logger.info(f"  段落{i}: {speaker}, 截取{len(seg)}行, 学员{len(student_lines)}句/{student_chars}字, 占比{ratio:.1%}")

    return final_segments, teacher


# ============================================================
# 第二步：判断故事
# ============================================================
async def _identify_story(text: str) -> tuple[bool, str]:
    """判断对话段落是否包含个人故事"""
    prompt = """判断这段对话是否包含学员的个人故事。直接输出JSON，不要分析。

个人故事特征（满足至少2条）：学员讲述具体经历、包含时间地点人物细节、表达深层情感、老师深入引导疗愈、揭示心理或家庭问题。

非故事内容：纯课堂讲解、简单问答寒暄、课堂管理、纯疗愈引导词。

直接输出JSON：
{"is_story": true或false, "confidence": 0.0到1.0, "story_hints": "一句话描述故事主题和讲述者"}

对话内容：
```
""" + text[:4000] + """
```"""

    try:
        result = await llm_client.chat_json([{"role": "user", "content": prompt}], temperature=0.2)
        if result:
            is_story = result.get("is_story", False)
            confidence = result.get("confidence", 0)
            hints = result.get("story_hints", "")
            logger.info(f"Story identification: is_story={is_story}, confidence={confidence}, hints={hints[:80]}")
            return bool(is_story) and confidence >= 0.5, hints
    except Exception as e:
        logger.warning(f"Story identification error: {e}")
        if "529" in str(e) or "overloaded" in str(e).lower() or "timed out" in str(e).lower():
            raise

    return False, ""


# ============================================================
# 第三步：切片提取事实
# ============================================================
def _chunk_text_by_paragraphs(text: str, max_chars: int = 3000, overlap_paragraphs: int = 5) -> list[str]:
    """按自然段切分，在 max_chars 附近找段落结尾切，重叠 overlap_paragraphs 个自然段"""
    paragraphs = [p for p in text.split("\n") if p.strip()]
    if not paragraphs:
        return [text]
    if len(text) <= max_chars:
        return [text]

    chunks = []
    i = 0
    while i < len(paragraphs):
        # 从当前位置开始，累加段落直到超过 max_chars
        current = []
        current_len = 0
        j = i
        while j < len(paragraphs):
            para = paragraphs[j]
            # 超过 max_chars 且已有内容 → 在这个段落前切断（段落结尾切）
            if current_len + len(para) > max_chars and current:
                break
            current.append(para)
            current_len += len(para)
            j += 1

        chunks.append("\n".join(current))

        # 到末尾了就结束
        if j >= len(paragraphs):
            break

        # 下一次起点：当前切到的位置 - 重叠段落数
        next_i = j - overlap_paragraphs
        # 防止死循环：确保至少前进1个段落
        if next_i <= i:
            next_i = i + 1
        i = next_i

    return chunks


async def _extract_facts_from_chunk(text: str, hints: str) -> dict | None:
    """从对话切片中提取关键信息（事件、情感、原话、人物）"""
    prompt = f"""从这段对话中提取关键信息。只输出JSON。

故事主题：{hints}

提取以下内容：
- events: 发生的事件（尽量详细，保留具体细节）
- emotions: 表达的情感
- quotes: 讲述者的原话（保留原话，不要改写，尽量多提取；完全相同的重复语句只保留一条）
- key_people: 提到的人物

{{"events":["事件1","事件2"],"emotions":["情感1","情感2"],"quotes":["原话1","原话2","原话3"],"key_people":["人物1"]}}

对话内容：
```
{text}
```"""

    try:
        result = await llm_client.chat_json([{"role": "user", "content": prompt}], temperature=0.1)
        if not result:
            return None
        # chat_json 可能返回 list（解析错误），需要转为 dict
        if isinstance(result, list):
            for item in result:
                if isinstance(item, dict) and ("events" in item or "quotes" in item):
                    result = item
                    break
            else:
                logger.warning(f"chat_json returned list, cannot convert to dict: {result[:3]}")
                return None
        return result
    except Exception as e:
        logger.warning(f"Extract facts error: {e}")
        if "529" in str(e) or "overloaded" in str(e).lower() or "timed out" in str(e).lower():
            raise

    return None


# ============================================================
# 第四步：重组（纯代码，无需 LLM）
# ============================================================
def _fuzzy_dedup_quotes(quotes: list[str]) -> list[str]:
    """
    模糊去重：对高度相似的语句只保留第一条。
    策略：提取每条语句的核心部分（去掉常见前缀后缀），相似度超过阈值的视为重复。
    """
    import re

    def normalize(s: str) -> str:
        """提取语句核心，去掉语气词和细微差异"""
        s = s.strip()
        # 去掉「」包裹
        if s.startswith("「") and s.endswith("」"):
            s = s[1:-1]
        # 去掉常见前缀
        for prefix in ["嗯", "啊", "呃", "就是", "然后", "那个", "所以"]:
            if s.startswith(prefix):
                s = s[len(prefix):].strip()
        # 去掉常见尾缀
        for suffix in ["啊", "吧", "呢", "嘛", "哦", "呀"]:
            if s.endswith(suffix) and len(s) > 4:
                s = s[:-1]
        # 去掉多余空白和重复字（如"我我我"、"的的的"）
        s = re.sub(r'(.)\1{2,}', r'\1', s)
        return s

    def similarity(a: str, b: str) -> float:
        """简单的字符级相似度（Jaccard on character bigrams）"""
        if not a or not b:
            return 0.0
        a_bigrams = set(a[i:i+2] for i in range(len(a)-1))
        b_bigrams = set(b[i:i+2] for i in range(len(b)-1))
        if not a_bigrams or not b_bigrams:
            return 0.0
        intersection = a_bigrams & b_bigrams
        union = a_bigrams | b_bigrams
        return len(intersection) / len(union)

    kept = []
    kept_normalized = []
    for q in quotes:
        nq = normalize(q)
        is_dup = False
        for kn in kept_normalized:
            # 短语句用高阈值，长语句用低阈值
            threshold = 0.8 if len(nq) < 15 else 0.7
            if similarity(nq, kn) >= threshold:
                is_dup = True
                break
        if not is_dup:
            kept.append(q)
            kept_normalized.append(nq)

    removed = len(quotes) - len(kept)
    if removed > 0:
        logger.info(f"    模糊去重: {len(quotes)} -> {len(kept)} (去除 {removed} 条相似重复)")

    return kept


def _merge_facts(all_facts: list) -> list[tuple[str, list, dict]]:
    """
    重组素材（Reduce）。
    对每个故事段落的所有切片事实进行合并去重：
    - 事件按原文顺序保留
    - 情感去重
    - 原话去重（不限制数量）
    - 人物去重

    输入: [(speaker, seg, hints, facts_list), ...]
    输出: [(speaker, seg, material), ...]
      material: {"events": [...], "emotions": [...], "quotes": [...], "key_people": [...], "story_hints": "..."}
    """
    merged = []
    for i, (speaker, seg, hints, facts_list) in enumerate(all_facts):
        all_events, all_emotions, all_quotes, all_people = [], [], [], []
        for facts in facts_list:
            if isinstance(facts, dict):
                all_events.extend(facts.get("events", []))
                all_emotions.extend(facts.get("emotions", []))
                all_quotes.extend(facts.get("quotes", []))
                all_people.extend(facts.get("key_people", []))

        # 情感去重（保持顺序）
        all_emotions = list(dict.fromkeys(all_emotions))
        # 人物去重（保持顺序）
        all_people = list(dict.fromkeys(all_people))
        # 原话去重（保持顺序，不限制数量）
        # 第一轮：精确去重
        seen_quotes = set()
        unique_quotes = [q for q in all_quotes if q not in seen_quotes and not seen_quotes.add(q)]
        # 第二轮：模糊去重（去除重复模式的语句，如释放语句只保留一条）
        unique_quotes = _fuzzy_dedup_quotes(unique_quotes)

        material = {
            "events": all_events,
            "emotions": all_emotions,
            "quotes": unique_quotes,
            "key_people": all_people,
            "story_hints": hints,
        }

        logger.info(f"  重组故事 {i}: {speaker} - 事件:{len(all_events)}, 情感:{len(all_emotions)}, "
                     f"原话:{len(unique_quotes)}, 人物:{len(all_people)}")

        merged.append((speaker, seg, material))

    return merged


# ============================================================
# 第五步：生成最终故事
# ============================================================
async def _generate_story(material: dict) -> dict | None:
    """基于素材生成第三人称故事（5部分结构化输出）"""
    prompt = f"""基于素材生成第三人称故事。只输出JSON，不要其他内容。

素材：
主题：{material['story_hints']}
事件：{json.dumps(material['events'], ensure_ascii=False)}
情感：{json.dumps(material['emotions'], ensure_ascii=False)}
原话：{json.dumps(material['quotes'], ensure_ascii=False)}
人物：{json.dumps(material['key_people'], ensure_ascii=False)}

写作手法（非常重要）：

【双时空结构】
故事有两个时空层：
- "当下时空"：学员在课堂上与卢慧老师的对话——这是故事的框架，用对话体呈现，保留师生互动的完整脉络
- "过往时空"：学员讲述的过去经历——这是故事的核心，用"场景重建"手法呈现，让读者穿越到那个时空

【场景重建手法（用于"过往的故事"部分）】
当学员讲述过去的经历时，不要写成"她说她小时候…"这种转述体，而是直接把读者拉进那个时空：
- 直接切入场景，用过去时态叙述。比如不要写"文晔回忆说她的儿子15岁去了美国"，而要写"十五岁那年，文晔的儿子独自登上了飞往美国的航班"
- 用五感描写构建画面：她看到了什么、听到了什么、身体感受到了什么（比如"手心出汗"、"脚发凉"这些身体反应要融入场景中）
- 用环境细节暗示情绪：不要直接说"她很痛苦"，而是通过场景让读者感受到痛苦
- 关键情感爆发处用直接引语（「」包裹原话），其他地方用间接引语和叙述自然融合
- 场景之间要有过渡，比如"然而，命运的转折发生在前年冬天——"

【对话体手法（用于"当下的困境"和"转变"部分）】
- 保留师生对话的现场感，写出谁说了什么、对方如何回应
- 在对话中穿插学员的内心活动、身体反应、情绪变化
- 老师的引导语要完整保留，这是疗愈过程的关键

【通用要求】
- 每个事件都要展开写，写出具体过程和细节，不要一句话概括
- 事件之间要有过渡和因果逻辑，不能跳跃
- 情感变化要写出过程：从什么情感转变到什么情感，中间经历了什么
- 不编造素材中没有的内容，但可以根据素材合理补充场景细节（如环境、氛围）
- 每个部分充分展开，不要限制长度，越详细越好
- 原文金句数组中的每条金句必须用英文双引号包裹，如 ["金句1", "金句2"]，不要用「」替代双引号

禁止事项：
- 禁止用一句话概括多个事件（如"她倾诉了挣扎"、"她经历了疗愈过程"）
- 禁止省略素材中的具体细节
- 禁止省略老师的引导和回应
- 禁止事件之间跳跃，要保持时间顺序和因果逻辑
- 禁止过度总结或抽象化描述
- 禁止在"过往的故事"部分全程使用转述体（"她说…她回忆…"），必须用场景重建手法让读者身临其境
- 禁止为了简洁而牺牲内容的丰富性

输出格式：{{"title":"标题","summary":"80字摘要","tags":["标签"],"speaker_nickname":"昵称","content":{{"讲述者":"...","当下的困境":"...","过往的故事":"...","转变":"...","原文金句":["原话1","原话2","原话3"]}}}}"""

    try:
        result = await llm_client.chat_json(
            [{"role": "user", "content": prompt}],
            temperature=0.7,
        )
        if not result:
            return None
        # chat_json 可能返回 list（解析错误），需要转为 dict
        if isinstance(result, list):
            for item in result:
                if isinstance(item, dict) and "title" in item:
                    result = item
                    break
            else:
                logger.warning(f"chat_json returned list, cannot convert to dict: {result[:3]}")
                return None

        # 解析 content 纯文本为结构化字段
        raw_content = result.get("content", "")
        if isinstance(raw_content, str) and "|||" in raw_content:
            parts = raw_content.split("|||")
            content_dict = {}
            keys = ["讲述者", "当下的困境", "过往的故事", "转变", "原文金句"]
            for idx, key in enumerate(keys):
                content_dict[key] = parts[idx].strip() if idx < len(parts) else ""
            result["content"] = content_dict
        elif isinstance(raw_content, dict):
            for key in ["讲述者", "当下的困境", "过往的故事", "转变", "原文金句"]:
                if key not in raw_content:
                    raw_content[key] = ""
            result["content"] = raw_content
        else:
            result["content"] = {"完整故事": str(raw_content)}

        if not result.get("tags"):
            result["tags"] = []

        return result
    except Exception as e:
        logger.warning(f"Generate story error: {e}")
        if "529" in str(e) or "overloaded" in str(e).lower() or "timed out" in str(e).lower():
            raise

    return None


# ============================================================
# 工具函数
# ============================================================
def _segment_to_text(segment_lines: list) -> str:
    """将段落行列表转为对话文本"""
    parts = []
    for line in segment_lines:
        speaker = line.get("speaker", "")
        content = line.get("content", "")
        if speaker:
            parts.append(f"{speaker}: {content}")
        else:
            parts.append(content)
    return "\n".join(parts)


def _increment_files_completed(state: TaskState):
    """增加已完成文件计数"""
    p = state.progress
    files_completed = p.get("files_completed", 0) + 1
    files_total = p.get("files_total", 1)
    state.update(files_completed=files_completed)


def _estimate_duration(segment_lines: list) -> float:
    """根据时间戳估算故事时长（分钟）"""
    timestamps = []
    for line in segment_lines:
        if isinstance(line, dict) and line.get("timestamp"):
            try:
                parts = line["timestamp"].split(":")
                if len(parts) >= 2:
                    minutes = int(parts[-2]) + int(parts[-1]) / 60
                    if len(parts) == 3:
                        minutes += int(parts[0]) * 60
                    timestamps.append(minutes)
            except (ValueError, IndexError):
                continue

    if len(timestamps) >= 2:
        return round(max(timestamps) - min(timestamps), 1)
    return 0


def _confidence_level(confidence: float) -> str:
    if confidence >= 0.8:
        return "high"
    elif confidence >= 0.6:
        return "medium"
    return "low"


def _format_story_content(content_dict: dict) -> str:
    """将 LLM 返回的结构化故事内容转为可读的 Markdown 格式"""
    parts = []

    # 按顺序输出各个章节
    sections = ["讲述者", "当下的困境", "过往的故事", "转变"]
    for section_name in sections:
        if content_dict.get(section_name):
            parts.append(f"## {section_name}\n\n{content_dict[section_name]}")

    # 原文金句
    quotes = content_dict.get("原文金句", [])
    if not quotes:
        quotes = content_dict.get("金句摘录", [])
    if quotes and isinstance(quotes, list):
        parts.append("## 原文金句")
        for q in quotes:
            parts.append(f"\n> {q}")

    # 兼容旧格式：故事梗概
    if content_dict.get("故事梗概") and "讲述者" not in content_dict:
        parts.insert(0, f"## 故事梗概\n\n{content_dict['故事梗概']}")

    # 兼容旧格式：关键要素
    elements = content_dict.get("关键要素", [])
    if elements and isinstance(elements, list) and "讲述者" not in content_dict:
        parts.append("## 关键要素")
        for elem in elements:
            if isinstance(elem, dict):
                title = elem.get("标题", "")
                desc = elem.get("描述", "")
                parts.append(f"\n### {title}\n\n{desc}")

    # 如果上面都没匹配到，按通用 key-value 格式输出
    if not parts:
        for key, value in content_dict.items():
            if isinstance(value, str):
                parts.append(f"## {key}\n\n{value}")
            elif isinstance(value, list):
                parts.append(f"## {key}")
                for item in value:
                    if isinstance(item, dict):
                        for k, v in item.items():
                            parts.append(f"\n**{k}**：{v}")
                    else:
                        parts.append(f"\n- {item}")

    return "\n\n".join(parts) if parts else json.dumps(content_dict, ensure_ascii=False)