from __future__ import annotations

import os
import shutil
import subprocess
import textwrap
import time
from pathlib import Path
from typing import Dict, Tuple

OUTPUT_DIR = Path("outputs")


def latex_escape(value: str) -> str:
    replacements = {
        "\\": r"\textbackslash{}",
        "&": r"\&",
        "%": r"\%",
        "$": r"\$",
        "#": r"\#",
        "_": r"\_",
        "{": r"\{",
        "}": r"\}",
        "~": r"\textasciitilde{}",
        "^": r"\textasciicircum{}",
    }
    return "".join(replacements.get(ch, ch) for ch in value)


def build_solution_tex(data: Dict[str, str]) -> str:
    original = latex_escape(data.get("original_problem", ""))
    structured = latex_escape(data.get("structured_problem", ""))
    strategy = latex_escape(data.get("strategy", ""))
    result = latex_escape(data.get("tool_result", ""))
    final_answer = latex_escape(data.get("final_answer", ""))
    process = latex_escape(data.get("process_log", ""))
    complexity = latex_escape(data.get("complexity_analysis", "本系统在工具层执行真实计算，再由语言模型或规则化报告模块生成解释，从而降低纯文本推理产生错误答案的风险。"))
    timestamp = latex_escape(time.strftime("%Y-%m-%d %H:%M:%S"))

    return rf"""
\documentclass[11pt,fontset=none]{{ctexart}}
\usepackage[a4paper,margin=2.4cm]{{geometry}}
\usepackage{{booktabs}}
\usepackage{{enumitem}}
\usepackage{{hyperref}}
\usepackage{{xcolor}}
\usepackage{{listings}}
\setCJKmainfont{{Songti SC}}
\setCJKsansfont{{Songti SC}}
\setCJKmonofont{{Songti SC}}
\hypersetup{{colorlinks=true,linkcolor=blue,urlcolor=blue}}
\lstset{{basicstyle=\ttfamily\small,breaklines=true,frame=single}}

\title{{通用问题优化智能体求解报告}}
\author{{Problem Optimization Agent}}
\date{{{timestamp}}}

\begin{{document}}
\maketitle

\section{{原始问题}}
{original}

\section{{优化后的结构化问题}}
\begin{{lstlisting}}
{structured}
\end{{lstlisting}}

\section{{求解策略}}
{strategy}

\section{{工具调用与计算结果}}
\begin{{lstlisting}}
{result}
\end{{lstlisting}}

\section{{智能体求解过程记录}}
\begin{{lstlisting}}
{process}
\end{{lstlisting}}

\section{{最终答案}}
{final_answer}

\section{{复杂度与适用性分析}}
{complexity}

\section{{结论}}
本次求解将自然语言理解、结构化建模和确定性算法工具结合起来。语言模型或规则化模块负责
问题优化、解释和报告化表达，工具层负责关键计算和验证，因此求解过程可以被复查和复现。

\end{{document}}
""".strip()


def _fallback_pdf(text: str, pdf_path: Path) -> None:
    from reportlab.lib.pagesizes import A4
    from reportlab.lib.styles import getSampleStyleSheet
    from reportlab.pdfbase import pdfmetrics
    from reportlab.pdfbase.cidfonts import UnicodeCIDFont
    from reportlab.platypus import Paragraph, SimpleDocTemplate, Spacer

    font_name = "STSong-Light"
    pdfmetrics.registerFont(UnicodeCIDFont(font_name))
    doc = SimpleDocTemplate(str(pdf_path), pagesize=A4, rightMargin=42, leftMargin=42, topMargin=42, bottomMargin=42)
    styles = getSampleStyleSheet()
    styles["BodyText"].fontName = font_name
    styles["BodyText"].leading = 15
    story = []
    for para in text.split("\n\n"):
        clean = para.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;").replace("\n", "<br/>")
        story.append(Paragraph(clean, styles["BodyText"]))
        story.append(Spacer(1, 10))
    doc.build(story)


def write_solution_report(data: Dict[str, str], stem: str = "solution_report") -> Tuple[str, str]:
    OUTPUT_DIR.mkdir(exist_ok=True)
    tex_path = OUTPUT_DIR / f"{stem}.tex"
    pdf_path = OUTPUT_DIR / f"{stem}.pdf"
    tex = build_solution_tex(data)
    tex_path.write_text(tex, encoding="utf-8")

    engine = shutil.which("xelatex") or shutil.which("pdflatex")
    if engine:
        try:
            subprocess.run(
                [engine, "-interaction=nonstopmode", "-halt-on-error", tex_path.name],
                cwd=OUTPUT_DIR,
                check=True,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
                timeout=60,
            )
        except Exception:
            fallback_text = "\n\n".join(
                [
                    "通用问题优化智能体求解报告",
                    data.get("original_problem", ""),
                    data.get("structured_problem", ""),
                    data.get("strategy", ""),
                    data.get("tool_result", ""),
                    data.get("final_answer", ""),
                ]
            )
            _fallback_pdf(fallback_text, pdf_path)
    else:
        fallback_text = textwrap.dedent(
            f"""
            通用问题优化智能体求解报告

            原始问题:
            {data.get('original_problem', '')}

            优化后的结构化问题:
            {data.get('structured_problem', '')}

            求解策略:
            {data.get('strategy', '')}

            工具调用与结果:
            {data.get('tool_result', '')}

            最终答案:
            {data.get('final_answer', '')}
            """
        )
        _fallback_pdf(fallback_text, pdf_path)

    return str(pdf_path), str(tex_path)