"""
简化版 PDF 结构提取器

基于 PyMuPDF 的规则引擎，将 PDF 按一级/二级标题切分为章节结构。
不依赖 OCR，不依赖任何 core/foundation 代码。
"""

import re
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple

import fitz


@dataclass(frozen=True)
class BodyLine:
    """一条规范化后的正文行，以及它所在的 PDF 页码。"""
    page: int
    text: str


class SimplePdfExtractor:
    """基于规则的 PDF 正文结构提取器。"""

    RULE_LIB = {
        "Rule_1_纯数字派": {
            "l1": re.compile(
                r"^\d{1,2}(?:[\.．。])?\s+(?:(?!\d)[\u4e00-\u9fa5A-Za-z].*|[、,，]\s*[\u4e00-\u9fa5A-Za-z0-9].*)"
            ),
            "l2": re.compile(r"^(\d+)\.(\d+)(?!\.\d)\.?\s*([\u4e00-\u9fa5]+.*)"),
        },
        "Rule_2_混合章派": {
            "l1": re.compile(r"^第\s*(\d+)\s*[章部部分篇]\s*[,、]?\s*(.*)"),
            "l2": re.compile(r"^(\d+)\.(\d+)(?!\.\d)\.?\s*([\u4e00-\u9fa5]+.*)"),
        },
        "Rule_3_中英混血派": {
            "l1": re.compile(r"^第\s*[一二三四五六七八九十百零两]+\s*[章部部分篇]\s*[,、]?\s*(.*)"),
            "l2": re.compile(r"^(\d+)\.(\d+)(?!\.\d)\.?\s*([\u4e00-\u9fa5]+.*)"),
        },
        "Rule_4_传统公文派": {
            "l1": re.compile(r"^第\s*[一二三四五六七八九十百零两]+\s*[章部部分篇]\s*[,、]?\s*(.*)"),
            "l2": re.compile(r"^([一二三四五六七八九十百零两]+)[,、\s]+([\u4e00-\u9fa5]+.*)"),
        },
        "Rule_5_单边括号派": {
            "l1": re.compile(r"^第\s*(?:\d+|[一二三四五六七八九十百零两]+)\s*[章部部分篇]\s*[,、]?\s*(.*)"),
            "l2": re.compile(r"^([一二三四五六七八九十百零两]+)[）)\]]\s*([\u4e00-\u9fa5]+.*)"),
        },
        "Rule_6_小节派": {
            "l1": re.compile(r"^第\s*(?:\d+|[一二三四五六七八九十百零两]+)\s*[章部部分篇]\s*[,、]?\s*(.*)"),
            "l2": re.compile(r"^第\s*(\d+|[一二三四五六七八九十百零两]+)\s*节\s*[,、]?\s*([\u4e00-\u9fa5]+.*)"),
        },
        "Rule_7_粗体括号派": {
            "l1": re.compile(r"^第\s*[一二三四五六七八九十百零两]+\s*[章部部分篇]\s*[,、]?\s*(.*)"),
            "l2": re.compile(r"^[【\[]\s*(\d+)\s*[\]】]\s*([\u4e00-\u9fa5]+.*)"),
        },
        "Rule_8_中文序号章数字小节派": {
            "l1": re.compile(r"^([一二三四五六七八九十百零两]+)[,、）)\]]\s*([\u4e00-\u9fa5A-Za-z].*)"),
            "l2": re.compile(r"^(\d+)\.(\d+)(?!\.\d)\.?\s*([\u4e00-\u9fa5]+.*)"),
        },
    }

    CN_NUM_MAP = {
        "零": 0, "〇": 0, "一": 1, "二": 2, "两": 2, "三": 3, "四": 4,
        "五": 5, "六": 6, "七": 7, "八": 8, "九": 9,
    }

    TOC_PATTERN = re.compile(r"\.{3,}|…{2,}|-{3,}|·{3,}|•{3,}")

    def __init__(self, clip_top: float = 60, clip_bottom: float = 60):
        self.clip_top = clip_top
        self.clip_bottom = clip_bottom

    def extract(self, file_content: bytes) -> Dict[str, Any]:
        """提取章节结构。"""
        result: Dict[str, Any] = {
            "chapters": {},
            "total_pages": 0,
        }
        doc = fitz.open(stream=file_content, filetype="pdf")
        try:
            body_lines = self._extract_body_lines(doc)
            raw_data, winning_rule, coverage_rate, rule_performance = self._extract_body_with_best_rule(body_lines)
            chapters = self._convert_rule_output_to_chapters(raw_data)

            result["chapters"] = chapters
            result["total_pages"] = len(doc)
            result["body_rule"] = winning_rule
            result["body_coverage"] = coverage_rate
            result["rule_performance"] = rule_performance
            return result
        finally:
            doc.close()

    def _extract_body_lines(self, doc: fitz.Document) -> List[BodyLine]:
        """读取裁剪后的页面文本，规范化正文行。"""
        page_lines_by_page: List[Tuple[int, List[str]]] = []
        total_pages = len(doc)

        for page_index in range(total_pages):
            page = doc.load_page(page_index)
            rect = page.rect
            clip_box = fitz.Rect(0, self.clip_top, rect.width, rect.height - self.clip_bottom)
            text = page.get_text("text", clip=clip_box)

            page_lines: List[str] = []
            for line in text.splitlines():
                stripped = line.strip()
                if not stripped or self._is_header_footer(stripped):
                    continue
                page_lines.append(stripped)

            page_lines_by_page.append((page_index + 1, page_lines))

        # 移除跨页重复的非标题噪声（页眉页脚）
        repeated_noise_keys = self._find_repeated_non_heading_lines(page_lines_by_page, total_pages)
        body_lines: List[BodyLine] = []
        for page, lines in page_lines_by_page:
            for line in lines:
                if self._normalize_repeated_line_key(line) in repeated_noise_keys:
                    continue
                body_lines.append(BodyLine(page=page, text=line))
        return body_lines

    def _is_header_footer(self, text: str) -> bool:
        """判断是否为页眉页脚。"""
        # 纯数字页码
        if re.match(r"^\d+$", text):
            return True
        # 常见页眉格式
        if re.match(r"^(四川路桥|专项施工方案|第\s*\d+\s*页|Page\s*\d+)$", text, re.IGNORECASE):
            return True
        return False

    def _normalize_repeated_line_key(self, text: str) -> str:
        """归一化行文本，用于检测重复。"""
        return text.replace(" ", "").replace("\t", "").replace("\u3000", "")

    def _find_repeated_non_heading_lines(self, page_lines_by_page: List[Tuple[int, List[str]]], total_pages: int) -> set:
        """找出跨页重复且不像标题的行。"""
        line_counts: Dict[str, int] = {}
        for _, lines in page_lines_by_page:
            for line in lines:
                key = self._normalize_repeated_line_key(line)
                line_counts[key] = line_counts.get(key, 0) + 1

        repeated = set()
        for key, count in line_counts.items():
            if count >= 2 and count >= total_pages * 0.3:
                # 只移除明显不像标题的重复行
                sample = next((line for _, lines in page_lines_by_page for line in lines
                               if self._normalize_repeated_line_key(line) == key), "")
                if not self._looks_like_heading(sample):
                    repeated.add(key)
        return repeated

    def _looks_like_heading(self, text: str) -> bool:
        """判断文本是否像标题。"""
        for rule_name, rule in self.RULE_LIB.items():
            if rule["l1"].match(text) or rule["l2"].match(text):
                return True
        return False

    def _extract_body_with_best_rule(
        self, body_lines: List[BodyLine]
    ) -> Tuple[Dict[str, Any], str, float, Dict[str, Any]]:
        """用所有规则竞争，选出覆盖率最高的规则。"""
        best_result = None
        best_rule = ""
        best_coverage = 0.0
        rule_performance = {}

        for rule_name, rule in self.RULE_LIB.items():
            try:
                result, coverage = self._apply_rule(body_lines, rule["l1"], rule["l2"])
                rule_performance[rule_name] = {"coverage": coverage}
                if coverage > best_coverage:
                    best_coverage = coverage
                    best_result = result
                    best_rule = rule_name
            except Exception:
                rule_performance[rule_name] = {"coverage": 0.0, "error": True}

        if best_result is None:
            best_result = {}
            best_rule = "none"
            best_coverage = 0.0

        return best_result, best_rule, best_coverage, rule_performance

    def _apply_rule(
        self,
        body_lines: List[BodyLine],
        l1_pattern: re.Pattern,
        l2_pattern: re.Pattern,
    ) -> Tuple[Dict[str, Any], float]:
        """应用一组规则，提取章节结构。"""
        result: Dict[str, Any] = {"chapters": []}
        current_chapter = None
        current_section = None
        current_content_lines: List[str] = []
        current_pages: List[int] = []
        total_lines = len(body_lines)
        heading_lines = 0

        def _flush_section():
            nonlocal current_chapter, current_section, current_content_lines, current_pages
            if current_chapter is None:
                return
            if current_section is None:
                # 章节标题行
                chapter_data = result["chapters"][-1] if result["chapters"] else None
                if chapter_data:
                    chapter_data["sections"]["章节标题"]["content"] = "\n".join(current_content_lines).strip()
                    if current_pages:
                        chapter_data["sections"]["章节标题"]["page_start"] = min(current_pages)
                        chapter_data["sections"]["章节标题"]["page_end"] = max(current_pages)
            else:
                chapter_data = result["chapters"][-1] if result["chapters"] else None
                if chapter_data and current_section in chapter_data["sections"]:
                    chapter_data["sections"][current_section]["content"] = "\n".join(current_content_lines).strip()
                    if current_pages:
                        chapter_data["sections"][current_section]["page_start"] = min(current_pages)
                        chapter_data["sections"][current_section]["page_end"] = max(current_pages)
            current_content_lines = []
            current_pages = []

        for line in body_lines:
            text = line.text
            page = line.page
            l1_match = l1_pattern.match(text)
            l2_match = l2_pattern.match(text)

            if l1_match and not l2_match:
                # 一级标题
                _flush_section()
                current_chapter = text
                current_section = None
                result["chapters"].append({
                    "title": text,
                    "page_start": page,
                    "sections": {
                        "章节标题": {
                            "content": "",
                            "page_start": page,
                            "page_end": page,
                        }
                    }
                })
                current_content_lines = [text]
                current_pages = [page]
                heading_lines += 1
            elif l2_match and current_chapter is not None:
                # 二级标题
                _flush_section()
                current_section = text
                chapter_data = result["chapters"][-1]
                chapter_data["sections"][text] = {
                    "content": "",
                    "page_start": page,
                    "page_end": page,
                }
                current_content_lines = [text]
                current_pages = [page]
                heading_lines += 1
            else:
                # 正文
                current_content_lines.append(text)
                current_pages.append(page)

        _flush_section()

        # 计算覆盖率
        coverage = heading_lines / max(total_lines, 1)
        return result, coverage

    def _convert_rule_output_to_chapters(self, raw_data: Dict[str, Any]) -> Dict[str, Dict[str, Any]]:
        """将规则输出转换为以章节标题为键的字典。"""
        chapters: Dict[str, Dict[str, Any]] = {}
        for chapter in raw_data.get("chapters", []):
            title = chapter.get("title", "未命名章节")
            sections = {}
            for sec_name, sec_data in chapter.get("sections", {}).items():
                sections[sec_name] = {
                    "content": sec_data.get("content", ""),
                    "page_start": sec_data.get("page_start", 1),
                    "page_end": sec_data.get("page_end", 1),
                }
            chapters[title] = sections
        return chapters