#!/usr/bin/env python3
"""
剧本文档解析器
从飞书文档中提取末尾的组件类型表格和内嵌sheet中的组件配置
支持两种数据源：
  1. 文档 markdown 中的 lark-table（备选）
  2. 内嵌 sheet 的二维数组数据（主要）
"""

import re
import json
import html
import logging

logger = logging.getLogger("parse_script")
if not logger.handlers:
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter(
        "%(asctime)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s"
    ))
    logger.addHandler(handler)
    logger.setLevel(logging.INFO)


def parse_lark_table(table_html: str) -> list:
    """
    解析飞书lark-table HTML为二维数组
    """
    rows = []
    # 提取所有行
    tr_pattern = re.compile(r'<lark-tr>(.*?)</lark-tr>', re.DOTALL)
    td_pattern = re.compile(r'<lark-td(?:\s+[^>]*)?>(.*?)</lark-td>', re.DOTALL)
    
    for tr_match in tr_pattern.finditer(table_html):
        tr_content = tr_match.group(1)
        cells = []
        for td_match in td_pattern.finditer(tr_content):
            cell_content = td_match.group(1).strip()
            # 清理HTML标签但保留文本
            cell_text = re.sub(r'<[^>]+>', '', cell_content).strip()
            # 清理多余空白
            cell_text = re.sub(r'\s+', ' ', cell_text).strip()
            cells.append(cell_text)
        rows.append(cells)
    return rows


def extract_component_table(markdown: str) -> list:
    """
    从剧本markdown中提取末尾的组件类型表格。
    
    表格特征：第一行包含"类型"和"知识点"列头
    
    Returns:
        [
            {"index": 1, "type": "对话朗读", "knowledge_points": "school 1"},
            {"index": 2, "type": "对话挖空", "knowledge_points": "school 2"},
            ...
        ]
    """
    # 找到所有lark-table
    table_pattern = re.compile(r'<lark-table[^>]*>(.*?)</lark-table>', re.DOTALL)
    tables = list(table_pattern.finditer(markdown))
    
    if not tables:
        raise ValueError("文档中未找到lark-table表格")
    
    # 从最后一个表格开始往前找，找到包含"类型"列头的表格
    component_table = None
    for table_match in reversed(tables):
        table_html = table_match.group(0)
        rows = parse_lark_table(table_html)
        if rows and len(rows) > 1:
            # 检查第一行是否包含"类型"
            header = rows[0]
            if any("类型" in cell for cell in header):
                component_table = rows
                break
    
    if component_table is None:
        raise ValueError("未找到包含'类型'列头的组件表格")
    
    # 解析表头，确定列索引
    header = component_table[0]
    type_col = None
    kp_col = None
    index_col = None
    component_col = None  # "组件"列（如果有）
    
    for i, cell in enumerate(header):
        cell_lower = cell.strip()
        if cell_lower == "类型":
            type_col = i
        elif "知识点" in cell_lower:
            kp_col = i
        elif cell_lower == "" and i == 0:
            index_col = i  # 第一列通常是序号
        elif "组件" in cell_lower:
            component_col = i
    
    if type_col is None:
        raise ValueError(f"表头中未找到'类型'列: {header}")
    
    # 解析数据行
    components = []
    for row in component_table[1:]:
        if len(row) <= type_col:
            continue
        
        type_text = row[type_col].strip()
        if not type_text:
            continue
        
        entry = {
            "index": len(components) + 1,
            "type": type_text,
        }
        
        # 序号
        if index_col is not None and len(row) > index_col:
            idx_text = row[index_col].strip()
            if idx_text.isdigit():
                entry["index"] = int(idx_text)
        
        # 知识点
        if kp_col is not None and len(row) > kp_col:
            kp_text = row[kp_col].strip()
            entry["knowledge_points"] = kp_text
        
        # 组件配置（如果有）
        if component_col is not None and len(row) > component_col:
            comp_text = row[component_col].strip()
            if comp_text:
                entry["component_config"] = comp_text
        
        components.append(entry)
    
    return components


def extract_sheet_token(markdown: str) -> str:
    """提取内嵌sheet的token"""
    match = re.search(r'<sheet\s+token="([^"]+)"\s*/>', markdown)
    if match:
        return match.group(1)
    return None


def extract_script_metadata(markdown: str) -> dict:
    """提取剧本元信息（标题等）"""
    metadata = {}
    
    # 尝试从标题提取级别和单元信息
    # 标题格式如: L1-S2-U14-L1 到你上学啦
    title_match = re.search(r'(L\d+)-S(\d+)-U(\d+)-L(\d+)\s+(.+)', markdown[:500])
    if title_match:
        metadata["level"] = title_match.group(1)
        metadata["season"] = int(title_match.group(2))
        metadata["unit"] = int(title_match.group(3))
        metadata["lesson"] = int(title_match.group(4))
        metadata["title"] = title_match.group(5).strip()
    
    return metadata


def parse_script_document(markdown: str) -> dict:
    """
    解析完整剧本文档
    
    Returns:
        {
            "metadata": {...},
            "sheet_token": "xxx_yyy",
            "components": [...],
        }
    """
    result = {
        "metadata": extract_script_metadata(markdown),
        "sheet_token": extract_sheet_token(markdown),
        "components": extract_component_table(markdown),
    }
    return result


# ============ Sheet 数据解析（主要入口） ============

def extract_component_id(type_cell):
    """
    从 sheet 类型单元格中提取组件类型名、组件ID、是否配图

    Sheet 中类型列格式:
      - "对话朗读+图片\n1214101"        → type_name="对话朗读", cId="1214101", has_image=True
      - "对话挖空\n1214102"             → type_name="对话挖空", cId="1214102", has_image=False
      - "合作阅读  0000800"             → type_name="合作阅读", cId="0000800" (单行空格分隔)
      - "核心互动- 囗语\n听力选择\n0000810" → type_name="听力选择", cId="0000810" (多行带前缀)
      - "TL"                            → None (非组件行)
      - "场景"                          → None (非组件行)

    Returns:
        dict: {"type_name": str, "cId": str, "has_image": bool} 或 None
    """
    if not type_cell or not isinstance(type_cell, str):
        return None

    text = type_cell.strip()
    lines = [l.strip() for l in text.split("\n") if l.strip()]

    # 检测是否为非组件标记行
    non_component_markers = {
        "TL", "场景", "角色", "AI动画", "场景变换", "画面",
        "BGM", "SE", "类型",
    }

    type_part = None
    cId = None
    has_image = False

    # --- 策略1: 标准格式 "类型名\nID" ---
    if len(lines) >= 2:
        first_line = lines[0]
        # 检查每一行是否为纯数字 ID
        for i in range(1, len(lines)):
            if re.match(r'^\d{5,}$', lines[i]):
                cId = lines[i]
                # 类型名取 ID 所在行的前一行
                type_part = lines[i - 1] if i > 0 else first_line
                break

        # 如果未在后续行找到 ID，尝试从第一行尾部提取
        if cId is None:
            m = re.search(r'\s+(\d{5,})$', first_line)
            if m:
                cId = m.group(1)
                type_part = first_line[:m.start()].strip()

    # --- 策略2: 单行 "类型名  ID" (空格分隔) ---
    if cId is None and len(lines) == 1:
        m = re.search(r'\s+(\d{5,})$', lines[0])
        if m:
            cId = m.group(1)
            type_part = lines[0][:m.start()].strip()

    # --- 策略3: 经典单行格式（只有一行，ID在第二行）---
    if cId is None and len(lines) >= 2:
        id_candidate = lines[1]
        if re.match(r'^\d+$', id_candidate):
            cId = id_candidate
            type_part = lines[0]

    # 如果依然无 type_part，用第一行
    if type_part is None and lines:
        type_part = lines[0]

    # 去掉"核心互动-"前缀（如果 type_part 本身就是前缀而实际类型在后面）
    if type_part and re.match(r'^核心互动', type_part):
        # type_part 是前缀，不是实际类型名
        # 向后找有效的类型名（非前缀、非ID的行）
        for l in lines:
            cleaned = l.strip()
            if cleaned == type_part or re.match(r'^\d+$', cleaned):
                continue
            if re.match(r'^核心互动', cleaned):
                continue
            type_part = cleaned
            break

    # 过滤非组件标记
    if not type_part:
        return None
    base_type = type_part.replace("+图片", "").replace("-配图", "").strip()
    if base_type in non_component_markers or not base_type:
        return None

    # 如果没有 ID，不是组件行（组件行必须有 ID）
    if cId is None:
        if re.match(r'^[A-Z0-9_]+$', type_part):
            return None
        return None

    # 检测配图
    if "+图片" in type_part:
        has_image = True
        type_part = type_part.replace("+图片", "").strip()
    elif "-配图" in type_part:
        has_image = True
        type_part = type_part.replace("-配图", "").strip()

    return {
        "type_name": type_part,
        "cId": cId,
        "has_image": has_image,
    }


# LLM 兜底缓存（避免重复调用）
_llm_fallback_cache = {}


def extract_component_id_with_llm_fallback(type_cell, llm_client=None):
    """
    先用正则提取，失败时用 LLM 兜底。
    仅在 type_cell 看起来可能是组件行（含中文且有数字）时调用 LLM。

    Returns:
        dict: {"type_name": str, "cId": str, "has_image": bool} 或 None
    """
    result = extract_component_id(type_cell)
    if result is not None:
        return result

    # 快速判断是否值得调 LLM：必须同时包含中文和数字
    if not type_cell or not isinstance(type_cell, str):
        return None
    text = type_cell.strip()
    has_chinese = bool(re.search(r'[一-鿿]', text))
    has_digits = bool(re.search(r'\d{5,}', text))
    if not has_chinese or not has_digits:
        return None
    if not llm_client:
        return None

    # 检查缓存
    if text in _llm_fallback_cache:
        return _llm_fallback_cache[text]

    logger.info(f"正则无法解析，尝试 LLM 兜底: {repr(text[:80])}")
    try:
        system_prompt = """你是组件类型解析器。用户给你一个表格单元格的文本内容，你需要提取：
1. type_name: 组件类型名（如 "对话朗读"、"合作阅读"、"听力选择" 等）
2. cId: 组件ID（纯数字字符串，通常5-7位）
3. has_image: 是否配图（文本中含"+图片"或"-配图"则为true）

注意：
- 忽略"核心互动"等前缀
- type_name 只保留最终的类型名
- 如果无法确定，返回 null

返回 JSON 格式：{"type_name": "...", "cId": "...", "has_image": false}
如果不是组件行，返回 null"""

        parsed, _ = llm_client.call_for_json(system_prompt, text, max_tokens=200, temperature=0)
        if parsed and isinstance(parsed, dict) and parsed.get("type_name") and parsed.get("cId"):
            result = {
                "type_name": str(parsed["type_name"]).strip(),
                "cId": str(parsed["cId"]).strip(),
                "has_image": bool(parsed.get("has_image", False)),
            }
            _llm_fallback_cache[text] = result
            logger.info(f"LLM 兜底成功: {result}")
            return result
        else:
            _llm_fallback_cache[text] = None
            return None
    except Exception as e:
        logger.warning(f"LLM 兜底失败: {e}")
        _llm_fallback_cache[text] = None
        return None


def extract_section_character_map(markdown):
    """
    从文档 markdown 中提取 "角色-section对应" 表格。

    格式示例:
        # 角色-section对应
        <lark-table ...>
          <lark-tr><lark-td>S15-S16</lark-td><lark-td>738-eva</lark-td></lark-tr>
          <lark-tr><lark-td>S1-S14</lark-td><lark-td>663-EVA</lark-td></lark-tr>
          <lark-tr><lark-td></lark-td><lark-td>653-peter</lark-td></lark-tr>
          ...

    Returns:
        list[dict]: [
            {
                "sections": ["S15", "S16"],  # or ["S1".."S14"], or [] for global
                "characters": {"Eva": 738}
            },
            ...
        ]
    """
    if not markdown:
        return []

    # Find the "角色-section对应" section
    match = re.search(r'#\s*角色-section对应\s*\n(.*?)(?=\n#|$)', markdown, re.DOTALL)
    if not match:
        logger.info("文档中未找到 '角色-section对应' 章节")
        return []

    table_html = match.group(1)
    # Parse table rows
    rows = re.findall(r'<lark-tr>(.*?)</lark-tr>', table_html, re.DOTALL)

    char_pattern = re.compile(r'(\d{2,})[-_]([A-Za-z]{2,})')
    section_range_pattern = re.compile(r'S(\d+)(?:\s*-\s*S(\d+))?', re.IGNORECASE)

    entries = []
    current_sections = []  # carry forward from previous row if empty

    for row_html in rows:
        cells = re.findall(r'<lark-td>(.*?)</lark-td>', row_html, re.DOTALL)
        if len(cells) < 2:
            continue

        section_cell = cells[0].strip()
        char_cell = cells[1].strip()

        # Parse section range
        if section_cell:
            sections = []
            for sm in section_range_pattern.finditer(section_cell):
                start = int(sm.group(1))
                end = int(sm.group(2)) if sm.group(2) else start
                for s in range(start, end + 1):
                    sections.append(f"S{s}")
            if sections:
                current_sections = sections

        # Parse character
        char_match = char_pattern.search(char_cell)
        if char_match:
            char_id = int(char_match.group(1))
            char_name = char_match.group(2).capitalize()
            entries.append({
                "sections": list(current_sections),
                "characters": {char_name: char_id},
            })

    logger.info(f"提取角色-section对应: {len(entries)} 条记录")
    return entries


def resolve_resource_mapping(section_char_map, config_info, fallback_char_map=None):
    """
    根据组件的 "配置信息" 字段（如 "S1主线"）和角色-section映射，
    解析出该组件应使用的 resourceMapping。

    Args:
        section_char_map: list from extract_section_character_map()
        config_info: str, e.g. "S1主线"
        fallback_char_map: dict, fallback character map from sheet header

    Returns:
        dict: {"Eva": 663, "Peter": 653, "Vicky": 658}
    """
    if not config_info or not section_char_map:
        return fallback_char_map or {}

    # Extract section number from config_info (e.g. "S1主线" → "S1")
    sm = re.search(r'S(\d+)', config_info, re.IGNORECASE)
    if not sm:
        return fallback_char_map or {}

    section_key = f"S{sm.group(1)}"
    result = {}

    for entry in section_char_map:
        # If entry has no sections, it applies globally
        if not entry["sections"] or section_key in entry["sections"]:
            result.update(entry["characters"])

    if not result:
        return fallback_char_map or {}

    logger.debug(f"Section {section_key} → resourceMapping: {result}")
    return result


def extract_character_map(sheet_rows):
    """
    从 sheet 数据中提取角色表：NPC名称 → 角色ID

    角色信息通常在表头几行中，格式如 "653-peter"、"663-EVA"、"658-Vicky"
    出现在 col B（配置信息）或 col C（剧情描述）中

    Returns:
        dict: {"Eva": 663, "Peter": 653, "Vicky": 658, ...}
    """
    char_map = {}
    # 正则匹配 "数字-名字" 或 "数字_名字" 模式
    # 名字至少2个字母，避免匹配 L1_S02 之类的剧本标记
    pattern = re.compile(r'(\d{2,})[-_]([A-Za-z]{2,})')

    for row in sheet_rows[:30]:  # 角色表通常在前 30 行
        for cell in row:
            if not cell or not isinstance(cell, str):
                continue
            for match in pattern.finditer(cell):
                char_id = int(match.group(1))
                char_name = match.group(2)
                # 过滤掉明显不是角色名的（纯大写短标记如 "TA"）
                if len(char_name) <= 2 and char_name.isupper():
                    continue
                # 统一首字母大写
                normalized = char_name.capitalize()
                if normalized not in char_map:
                    char_map[normalized] = char_id

    logger.info(f"提取角色映射: {char_map}")
    return char_map


def parse_sheet_rows(sheet_rows, llm_client=None):
    """
    从 sheet 二维数组中识别并提取所有组件行

    Args:
        sheet_rows: list[list] — 来自 feishu_client.read_sheet_data
        llm_client: 可选 LLM 客户端，用于正则无法提取时兜底

    Returns:
        list[dict]: [
            {
                "row_index": int,       # 原始行号（0-based）
                "type_name": str,       # 中文组件类型名（如 "对话朗读"）
                "cId": str,             # 组件ID（如 "1214101"）
                "has_image": bool,      # 是否配图变体
                "teaching_config": str, # 教研配置文本（col G）
                "knowledge_text": str,  # 知识点文本（col H）
            },
            ...
        ]
    """
    if not sheet_rows or len(sheet_rows) < 2:
        return []

    # 确定列索引 — 按实际表头匹配
    # 实际表头: 类型 | 配置信息 | 剧情描述 | 角色名 | 编剧台词 | 英文台词 | 组件配置 | 知识点
    header = sheet_rows[0]
    col_map = {}
    component_cols = []  # 可能有多个"组件配置"列
    for i, cell in enumerate(header):
        if not cell:
            continue
        cell_str = str(cell).strip()
        if cell_str == "类型":
            col_map["type"] = i
        elif cell_str == "配置信息":
            col_map["config_info"] = i
        elif cell_str == "ID":
            col_map["id"] = i
        elif "组件" in cell_str and "配置" in cell_str:
            # "组件配置" — 教研配置文本列（可能有多个）
            component_cols.append(i)
        elif "知识点" in cell_str:
            col_map["knowledge"] = i

    type_col = col_map.get("type", 0)
    kp_col = col_map.get("knowledge")
    config_info_col = col_map.get("config_info")
    id_col = col_map.get("id")

    # 组件配置列：优先选第一个（通常包含配置文本），如有多个则全保留用于fallback
    comp_col = component_cols[0] if component_cols else None
    alt_comp_cols = component_cols[1:] if len(component_cols) > 1 else []

    # 回退默认值（以防表头格式变化）
    if comp_col is None:
        logger.warning("sheet表头中未找到'组件配置'列，回退到默认列索引6")
        comp_col = 6 if len(header) > 6 else None
    if kp_col is None:
        logger.warning("sheet表头中未找到'知识点'列，回退到默认列索引7")
        kp_col = 7 if len(header) > 7 else None
    if config_info_col is None:
        logger.warning("sheet表头中未找到'配置信息'列，回退到默认列索引1")
        config_info_col = 1 if len(header) > 1 else None

    logger.info(f"列映射: 类型={type_col}, 配置信息={config_info_col}, 组件配置={comp_col}, 知识点={kp_col}, ID={id_col}, alt_comp={alt_comp_cols}")

    components = []
    for row_idx, row in enumerate(sheet_rows[1:], start=1):
        # 获取类型列
        type_cell = row[type_col] if len(row) > type_col else None
        comp_info = extract_component_id_with_llm_fallback(type_cell, llm_client)

        # 如果类型列没有嵌入式ID，但有独立ID列且类型名是纯中文组件名
        if comp_info is None and id_col is not None:
            raw_cell = str(row[id_col]).strip() if len(row) > id_col and row[id_col] else ""
            if raw_cell and re.match(r'^\d{5,}$', raw_cell):
                # 从类型列提取纯类型名（可能是"图片多选"、"对话挖空"等简单名称）
                type_text = str(type_cell).strip() if type_cell else ""
                # 过滤非组件标记
                base_type = type_text.split('\n')[0].strip() if '\n' in type_text else type_text
                non_component_markers = {
                    "TL", "场景", "角色", "AI动画", "场景变换", "画面",
                    "BGM", "SE", "类型", "测试类型",
                }
                if base_type and base_type not in non_component_markers:
                    has_image = "+图片" in type_text or "-配图" in type_text
                    clean_type = type_text.replace("+图片", "").replace("-配图", "").strip()
                    comp_info = {
                        "type_name": clean_type,
                        "cId": raw_cell,
                        "has_image": has_image,
                    }

        if comp_info is None:
            continue

        # 获取教研配置（组件列 + 备选列 fallback）
        teaching_config = ""
        for col in [comp_col] + alt_comp_cols:
            if col is not None and len(row) > col and row[col]:
                teaching_config = str(row[col]).strip()
                break

        # 如果配置信息列有内容且组件配置为空，用配置信息列作为fallback
        if not teaching_config and config_info_col is not None:
            if len(row) > config_info_col and row[config_info_col]:
                cfg = str(row[config_info_col]).strip()
                # 只取包含数字ID的配置信息行（排除纯TL标记）
                if re.search(r'\d{5,}', cfg):
                    teaching_config = cfg

        # 必须有教研配置才算有效组件行
        if not teaching_config:
            continue

        # 获取知识点
        knowledge_text = ""
        if kp_col is not None and len(row) > kp_col and row[kp_col]:
            knowledge_text = str(row[kp_col]).strip()

        # 获取配置信息（如 "S1主线"）
        config_info = ""
        if config_info_col is not None and len(row) > config_info_col and row[config_info_col]:
            config_info = str(row[config_info_col]).strip()

        components.append({
            "row_index": row_idx,
            "type_name": comp_info["type_name"],
            "cId": comp_info["cId"],
            "has_image": comp_info["has_image"],
            "teaching_config": teaching_config,
            "knowledge_text": knowledge_text,
            "config_info": config_info,
        })

    logger.info(f"从 sheet 中识别到 {len(components)} 个组件行")
    return components


def parse_script_from_sheet(sheet_rows, markdown="", llm_client=None):
    """
    从 sheet 数据解析完整剧本信息

    Returns:
        dict: {
            "metadata": {...},
            "character_map": {"Eva": 663, ...},
            "section_char_map": [...],  # from "角色-section对应"
            "components": [...],
        }
    """
    metadata = {}
    section_char_map = []
    if markdown:
        metadata = extract_script_metadata(markdown)
        section_char_map = extract_section_character_map(markdown)

    character_map = extract_character_map(sheet_rows)
    components = parse_sheet_rows(sheet_rows, llm_client=llm_client)

    return {
        "metadata": metadata,
        "character_map": character_map,
        "section_char_map": section_char_map,
        "components": components,
    }


# ============ CLI ============
if __name__ == "__main__":
    import sys
    if len(sys.argv) > 1:
        with open(sys.argv[1], 'r') as f:
            markdown = f.read()
        result = parse_script_document(markdown)
        print(json.dumps(result, ensure_ascii=False, indent=2))
    else:
        # 测试用硬编码的markdown片段
        test_md = """
<sheet token="SlFGsyYkPh33kZtDQtecxc7vn4c_m8vOBk"/>

<lark-table rows="4" cols="3" column-widths="100,100,100">
  <lark-tr>
    <lark-td></lark-td>
    <lark-td>类型</lark-td>
    <lark-td>知识点</lark-td>
  </lark-tr>
  <lark-tr>
    <lark-td>1</lark-td>
    <lark-td>对话朗读</lark-td>
    <lark-td>school 1</lark-td>
  </lark-tr>
  <lark-tr>
    <lark-td>2</lark-td>
    <lark-td>对话挖空</lark-td>
    <lark-td>school 2</lark-td>
  </lark-tr>
  <lark-tr>
    <lark-td>3</lark-td>
    <lark-td>听力选择</lark-td>
    <lark-td>school 3 study 2</lark-td>
  </lark-tr>
</lark-table>
"""
        result = parse_script_document(test_md)
        print(json.dumps(result, ensure_ascii=False, indent=2))