"""综合报告生成 - 汇总所有测试结果"""
import json
import os
from datetime import datetime


def load_json(path):
    """加载 JSON 文件"""
    if os.path.exists(path):
        with open(path, "r", encoding="utf-8") as f:
            return json.load(f)
    return None


def generate_report():
    """生成综合测试报告"""
    results_dir = "vsp/qwen3.5-9b/results"

    speed = load_json(os.path.join(results_dir, "benchmark_speed.json"))
    accuracy = load_json(os.path.join(results_dir, "accuracy_results.json"))
    concurrency = load_json(os.path.join(results_dir, "concurrency_results.json"))
    gpu_req = load_json(os.path.join(results_dir, "gpu_requirements.json"))

    report_lines = [
        "# Qwen3.5-9B 性能测试报告",
        f"\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
        "\n## 1. 测试环境",
        "",
        "| 项目 | 值 |",
        "|------|-----|",
        "| 模型 | Qwen3.5-9B |",
        "| 加载方式 | 4-bit NF4 量化 (bitsandbytes)，纯 GPU |",
        "| GPU | NVIDIA GeForce RTX 3050 OEM |",
        "| GPU 显存 | 8 GB |",
        "| CUDA | 12.1 |",
        "| Python 环境 | conda yolo |",
    ]

    if speed and "memory" in speed:
        mem = speed["memory"]
        report_lines.extend([
            f"| 模型显存占用 | {mem.get('gpu_allocated_gb', 'N/A')} GB |",
            f"| 系统内存占用 | {mem.get('ram_used_gb', 'N/A')} GB |",
        ])

    # 推理速度
    report_lines.extend(["\n## 2. 推理速度", ""])
    if speed and "speed_benchmark" in speed:
        report_lines.extend([
            "| 测试场景 | 输入tokens | 输出tokens | 耗时(s) | 速度(tokens/s) |",
            "|---------|-----------|-----------|---------|---------------|",
        ])
        for r in speed["speed_benchmark"]:
            report_lines.append(
                f"| {r['test_name']} | {r['input_tokens']} | {r['avg_output_tokens']} | {r['avg_time_s']} | {r['avg_tokens_per_sec']} |"
            )
    else:
        report_lines.append("*未运行速度测试*")

    # 精度
    report_lines.extend(["\n## 3. 精度评估", ""])
    if accuracy and "accuracy" in accuracy:
        acc = accuracy["accuracy"]
        report_lines.append(f"**总准确率: {acc['accuracy']}% ({acc['passed']}/{acc['total']})**\n")
        report_lines.extend([
            "| 分类 | 通过/总数 | 准确率 |",
            "|------|---------|--------|",
        ])
        for cat, stats in acc.get("category_stats", {}).items():
            rate = stats["passed"] / stats["total"] * 100
            report_lines.append(f"| {cat} | {stats['passed']}/{stats['total']} | {rate:.0f}% |")
    else:
        report_lines.append("*未运行精度测试*")

    # 并发
    report_lines.extend(["\n## 4. 并发性能", ""])
    if concurrency and "concurrency_results" in concurrency:
        report_lines.extend([
            "| 并发数 | 总耗时(s) | 吞吐量(tokens/s) | 平均延迟(s) |",
            "|-------|---------|----------------|-----------|",
        ])
        for r in concurrency["concurrency_results"]:
            report_lines.append(
                f"| {r['concurrency']} | {r['total_time_s']} | {r['throughput_tokens_per_sec']} | {r['avg_latency_s']} |"
            )
        report_lines.append(f"\n> 注: {concurrency.get('note', '单GPU串行推理')}")
    else:
        report_lines.append("*未运行并发测试*")

    # GPU 需求
    report_lines.extend(["\n## 5. GPU 算力需求", ""])
    if gpu_req:
        report_lines.extend([
            "| 精度 | 模型大小 | 最低显存 | 推荐显卡 |",
            "|------|---------|---------|---------|",
        ])
        for precision, info in gpu_req.get("precision_requirements", {}).items():
            gpus = ", ".join(info["recommended_gpus"][:2])
            report_lines.append(
                f"| {precision} | {info['model_size_gb']}GB | {info['min_vram_gb']}GB | {gpus} |"
            )

    # 实际测试结论
    report_lines.extend([
        "\n## 6. 实际测试结论",
        "",
        "### RTX 3050 8GB 测试结果 (4-bit NF4 量化，纯 GPU)",
        "",
        "| 指标 | 结果 |",
        "|------|------|",
        "| GPU 显存占用 | 7.13 GB / 8 GB |",
        "| 系统内存占用 | 7.59 GB |",
        "| 推理速度 | 1.0-1.8 tokens/s |",
        "| 精度 | 90% (9/10) |",
        "| 输出质量 | 正常，回答准确 |",
        "",
        "### 注意事项",
        "",
        "1. **必须使用 4-bit NF4 量化**: device_map={\"\":0} 将模型全部放在 GPU 上",
        "2. **必须关闭 thinking 模式**: enable_thinking=False，否则输出中包含思考过程且容易被截断",
        "3. **显存接近上限**: 7.13GB / 8GB，长文本输入可能导致 OOM",
        "4. **并发不可行**: 单 GPU 串行推理，吞吐量恒定 ~2 tokens/s",
        "",
        "### 部署建议",
        "",
        "1. **RTX 3050 8GB 可用于开发测试**，4-bit 量化后勉强可用",
        "2. **推荐**: RTX 3060 12GB (INT8) 或 RTX 4060 Ti 16GB，有更大显存余量",
        "3. **生产部署**: RTX 4090 24GB (FP16) 或 A100 40GB/80GB + vLLM",
        "4. **替代方案**: 使用更小的模型如 Qwen2.5-3B 或 Qwen2.5-7B 在 8GB 显卡上更流畅运行",
    ])

    # 保存报告
    report_text = "\n".join(report_lines)
    report_path = os.path.join(results_dir, "REPORT.md")
    with open(report_path, "w", encoding="utf-8") as f:
        f.write(report_text)

    print(report_text)
    print(f"\n\n报告已保存到 {report_path}")


if __name__ == "__main__":
    os.chdir(os.path.dirname(os.path.abspath(__file__)) + "/../..")
    generate_report()