"""本地文档处理器（MVP：同步处理）。"""

from pathlib import Path

from app.core.logging import get_logger
from app.core.security import decrypt_token, encrypt_token, generate_token, hash_token
from app.models.document import Document
from app.processors.parsers.docx_parser import parse_docx_with_python_docx
from app.processors.parsers.markitdown_parser import parse_with_markitdown
from app.processors.parsers.pdf_parser import has_text_layer, parse_pdf_with_pymupdf
from app.repositories.doc_repo import DocumentRepository
from app.storage.local_storage import get_storage
from app.storage.object_keys import markdown_object_key
from sqlalchemy.orm import Session

logger = get_logger(__name__)


class LocalDocumentProcessor:
    """本地文档处理器：在同一进程中完成解析。"""

    def __init__(self, session: Session) -> None:
        self._session = session
        self._doc_repo = DocumentRepository(session)

    def process(self, document_id: str) -> None:
        """处理文档：解析 → 提取元数据 → 保存 Markdown → 更新 DB。"""
        doc = self._doc_repo.get_by_id(document_id)
        if doc is None:
            logger.error("Document not found: %s", document_id)
            return

        # 更新状态为 PROCESSING
        doc.status = "PROCESSING"
        self._session.commit()

        try:
            # 读取原始文件
            storage = get_storage()
            file_content = storage.read(doc.storage_path)

            # 解析
            markdown = self._parse_file(file_content, doc.file_ext, doc.original_filename)

            if markdown is None:
                # 解析失败
                if doc.file_ext == ".pdf" and not has_text_layer(file_content):
                    doc.error_code = "SCANNED_PDF_NO_TEXT_LAYER"
                    doc.status = "FAILED"
                else:
                    doc.error_code = "PARSING_FAILED"
                    doc.status = "FAILED"
                self._session.commit()
                logger.warning("Document parsing failed: %s (%s)", doc.id, doc.error_code)
                return

            # 清洗 Markdown
            markdown = self._clean_markdown(markdown)

            # 保存 Markdown 文件
            md_key = markdown_object_key(
                user_id=doc.user_id,
                knowledge_base_id=doc.knowledge_base_id,
                document_id=doc.id,
            )
            storage.save(md_key, markdown.encode("utf-8"))
            doc.markdown_path = md_key

            # 提取元数据
            title = self._extract_title(markdown, doc.original_filename)
            summary = self._extract_summary(markdown)
            keywords = self._extract_keywords(markdown)

            doc.title = title
            doc.content_summary = summary
            doc.keywords = keywords
            doc.status = "READY"
            self._session.commit()

            logger.info("Document processed: %s → READY", doc.id)

        except Exception as e:
            logger.error("Document processing error: %s - %s", doc.id, e, exc_info=True)
            doc.status = "FAILED"
            doc.error_code = "PARSING_FAILED"
            self._session.commit()

    def _parse_file(self, content: bytes, ext: str, filename: str) -> str | None:
        """按扩展名路由到对应解析器。"""
        # 优先 MarkItDown
        markdown = parse_with_markitdown(content, filename)
        if markdown and markdown.strip():
            return markdown

        # Fallback
        if ext == ".pdf":
            return parse_pdf_with_pymupdf(content)
        elif ext == ".docx":
            return parse_docx_with_python_docx(content)

        return None

    def _clean_markdown(self, markdown: str) -> str:
        """清洗 Markdown：压缩空行、规整标题层级。"""
        import re

        # 压缩连续空行为最多 2 个
        markdown = re.sub(r"\n{3,}", "\n\n", markdown)
        # 截断超长行（> 1000 字符的行）
        lines = markdown.split("\n")
        cleaned = []
        for line in lines:
            if len(line) > 1000:
                line = line[:1000] + "..."
            cleaned.append(line)
        return "\n".join(cleaned)

    def _extract_title(self, markdown: str, fallback: str) -> str:
        """提取标题：H1 → 文件名（去扩展名）。"""
        import re

        # 找第一个 H1
        match = re.search(r"^#\s+(.+)$", markdown, re.MULTILINE)
        if match:
            return match.group(1).strip()

        # Fallback：文件名去扩展名
        return Path(fallback).stem

    def _extract_summary(self, markdown: str, max_len: int = 200) -> str:
        """抽取式摘要：取正文前 ~200 字纯文本。"""
        import re

        # 去掉 Markdown 标记
        text = re.sub(r"[#*_`\[\]()>]", "", markdown)
        text = re.sub(r"\s+", " ", text).strip()

        if len(text) <= max_len:
            return text

        # 在 max_len 附近找句号/逗号断句
        cut = text[:max_len]
        for sep in ["。", "！", "？", "；", ".", "!", "?", "；"]:
            idx = cut.rfind(sep)
            if idx > max_len * 0.5:
                return cut[: idx + 1]
        return cut + "..."

    def _extract_keywords(self, markdown: str, top_k: int = 10) -> str:
        """提取关键词（jieba TF-IDF）。"""
        try:
            import jieba.analyse

            # 去掉 Markdown 标记
            import re

            text = re.sub(r"[#*_`\[\]()>]", "", markdown)
            text = re.sub(r"\s+", " ", text).strip()

            keywords = jieba.analyse.extract_tags(text, topK=top_k)
            return ",".join(keywords)
        except Exception as e:
            logger.warning("关键词提取失败: %s", e)
            return ""