# -*- coding: utf-8 -*-
"""
年报 PDF 文本解析模块
从年报中提取关键风险信息：核心技术人员、技术路线、诉讼、风险提示等
"""
import re
import logging
from pathlib import Path
from typing import Optional

logger = logging.getLogger(__name__)


def parse_annual_report(pdf_path: str) -> dict:
    """
    解析年报 PDF，提取关键风险相关信息
    返回结构化的风险要素字典
    """
    text = _extract_text_from_pdf(pdf_path)
    if not text:
        return {"error": "PDF 解析失败", "raw_text": ""}

    return {
        "core_personnel_info": _extract_core_personnel(text),
        "tech_route_info": _extract_tech_route(text),
        "litigation_info": _extract_litigation(text),
        "risk_factors": _extract_risk_factors(text),
        "rd_capitalization_info": _extract_rd_capitalization(text),
        "customer_concentration": _extract_customer_concentration(text),
        "raw_text_length": len(text),
    }


def _extract_text_from_pdf(pdf_path: str) -> Optional[str]:
    """使用 pdfplumber 提取 PDF 全文"""
    try:
        import pdfplumber
        text_parts = []
        with pdfplumber.open(pdf_path) as pdf:
            for page in pdf.pages:
                page_text = page.extract_text()
                if page_text:
                    text_parts.append(page_text)
        return "\n".join(text_parts)
    except ImportError:
        logger.warning("pdfplumber 未安装，尝试 PyPDF2")
        try:
            from PyPDF2 import PdfReader
            reader = PdfReader(pdf_path)
            return "\n".join(
                page.extract_text() or "" for page in reader.pages
            )
        except Exception as e:
            logger.error(f"PyPDF2 解析失败: {e}")
    except Exception as e:
        logger.error(f"PDF 解析失败: {e}")
    return None


def _extract_core_personnel(text: str) -> dict:
    """提取核心技术人员相关信息"""
    result = {
        "has_departure": False,
        "departure_details": [],
        "personnel_count": 0,
        "key_mentions": [],
    }

    # 匹配离职/辞职相关表述
    departure_patterns = [
        r"(核心技术人员|核心人员|关键技术人员).{0,30}(离职|辞职|离任|不再担任)",
        r"(CTO|首席技术官|技术总监|研发总监).{0,30}(离职|辞职|离任)",
        r"(离职|辞职).{0,30}(核心技术人员|核心人员)",
    ]
    for pattern in departure_patterns:
        matches = re.findall(pattern, text)
        if matches:
            result["has_departure"] = True
            result["departure_details"].extend([str(m) for m in matches])

    # 统计核心技术人员数量
    count_match = re.search(r"核心技术人员\s*(\d+)\s*[名人]", text)
    if count_match:
        result["personnel_count"] = int(count_match.group(1))

    return result


def _extract_tech_route(text: str) -> dict:
    """提取技术路线相关信息"""
    result = {
        "competing_tech_mentioned": False,
        "tech_keywords": [],
        "risk_mentions": [],
    }

    # 技术竞争关键词
    tech_keywords = [
        "技术路线", "技术迭代", "技术替代", "技术颠覆",
        "竞争技术", "替代方案", "新一代技术",
    ]
    for kw in tech_keywords:
        if kw in text:
            result["tech_keywords"].append(kw)
            result["competing_tech_mentioned"] = True

    return result


def _extract_litigation(text: str) -> dict:
    """提取诉讼/仲裁相关信息"""
    result = {
        "has_litigation": False,
        "litigation_count": 0,
        "ip_related": False,
    }

    # 诉讼关键词
    litigation_patterns = [
        r"(诉讼|仲裁|起诉|被告).{0,50}(知识产权|专利|商标|著作权)",
        r"(专利侵权|商标侵权|著作权纠纷)",
    ]
    for pattern in litigation_patterns:
        if re.search(pattern, text):
            result["has_litigation"] = True
            result["ip_related"] = True

    return result


def _extract_risk_factors(text: str) -> list:
    """提取风险因素章节的关键信息"""
    risk_keywords = [
        "地缘政治", "实体清单", "出口管制", "贸易摩擦",
        "数据安全", "数据合规", "算法备案", "数据出境",
        "客户集中", "供应商集中", "单一客户", "单一供应商",
        "研发资本化", "开发支出", "无形资产",
        "人才流失", "核心人员", "竞业限制",
    ]
    found_risks = []
    for kw in risk_keywords:
        if kw in text:
            found_risks.append(kw)
    return found_risks


def _extract_rd_capitalization(text: str) -> dict:
    """提取研发资本化相关信息"""
    result = {
        "has_capitalization": False,
        "capitalization_mentioned": False,
        "amount_keywords": [],
    }

    cap_keywords = ["开发支出", "研发资本化", "资本化研发", "开发阶段支出"]
    for kw in cap_keywords:
        if kw in text:
            result["capitalization_mentioned"] = True
            result["amount_keywords"].append(kw)

    # 检查是否有具体的资本化金额
    cap_amount = re.search(r"开发支出.{0,30}([\d,\.]+)\s*(万元|百万|亿)", text)
    if cap_amount:
        result["has_capitalization"] = True

    return result


def _extract_customer_concentration(text: str) -> dict:
    """提取客户/供应商集中度信息"""
    result = {
        "top5_customer_ratio": None,
        "top5_supplier_ratio": None,
        "single_customer_dependency": False,
    }

    # 前五大客户占比
    customer_match = re.search(
        r"前五[名大]客户.{0,30}([\d\.]+)\s*%", text
    )
    if customer_match:
        result["top5_customer_ratio"] = float(customer_match.group(1)) / 100

    # 单一客户依赖
    if re.search(r"(第一大客户|最大客户).{0,30}([\d\.]+)\s*%", text):
        result["single_customer_dependency"] = True

    return result