"""
缓存机制测试

测试 Anthropic Prompt Caching 的命中率
"""

import argparse
import os
import sys
import asyncio
from pathlib import Path

# 添加项目根目录到 Python 路径
sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from dotenv import load_dotenv
load_dotenv()

from agent.llm.prompts import SimplePrompt
from agent.core.runner import AgentRunner, RunConfig
from agent.trace import FileSystemTraceStore, Trace, Message
from agent.llm import create_openrouter_llm_call


async def main():
    parser = argparse.ArgumentParser(description="缓存机制测试")
    parser.add_argument(
        "--trace", type=str, default=None,
        help="已有的 Trace ID，用于恢复继续执行",
    )
    args = parser.parse_args()

    # 路径配置
    base_dir = Path(__file__).parent
    prompt_path = base_dir / "test.prompt"
    output_dir = base_dir / "output"
    output_dir.mkdir(exist_ok=True)

    print("=" * 60)
    print("Prompt Caching 测试")
    print("=" * 60)
    print()

    # 加载 prompt
    print("1. 加载 prompt 配置...")
    prompt = SimplePrompt(prompt_path)

    # 构建消息
    print("2. 构建任务消息...")
    messages = prompt.build_messages()

    # 创建 Agent Runner
    print("3. 创建 Agent Runner...")
    print(f"   - 模型: {prompt.config.get('model', 'sonnet-4.6')}")

    store = FileSystemTraceStore(base_path=".trace")
    runner = AgentRunner(
        trace_store=store,
        llm_call=create_openrouter_llm_call(model=f"anthropic/claude-{prompt.config.get('model', 'sonnet-4.6')}"),
        skills_dir=None,
        debug=True
    )

    # 判断是新建还是恢复
    resume_trace_id = args.trace
    if resume_trace_id:
        existing_trace = await store.get_trace(resume_trace_id)
        if not existing_trace:
            print(f"\n错误: Trace 不存在: {resume_trace_id}")
            sys.exit(1)
        print(f"4. 恢复已有 Trace: {resume_trace_id[:8]}...")
        print(f"   - 状态: {existing_trace.status}")
        print(f"   - 消息数: {existing_trace.total_messages}")
    else:
        print(f"4. 启动新 Agent 模式...")

    print()

    current_trace_id = resume_trace_id

    try:
        if resume_trace_id:
            initial_messages = None
            config = RunConfig(
                model=f"anthropic/claude-{prompt.config.get('model', 'sonnet-4.6')}",
                temperature=float(prompt.config.get('temperature', 0.3)),
                max_iterations=50,
                trace_id=resume_trace_id,
            )
        else:
            initial_messages = messages
            config = RunConfig(
                model=f"anthropic/claude-{prompt.config.get('model', 'sonnet-4.6')}",
                temperature=float(prompt.config.get('temperature', 0.3)),
                max_iterations=50,
                name="缓存测试",
            )

        print("▶️ 开始执行...")
        print()

        async for item in runner.run(messages=initial_messages, config=config):
            # 处理 Trace 对象
            if isinstance(item, Trace):
                current_trace_id = item.trace_id
                if item.status == "running":
                    print(f"[Trace] 开始: {item.trace_id[:8]}...")
                elif item.status == "completed":
                    print(f"\n[Trace] ✅ 完成")
                    print(f"  - Total messages: {item.total_messages}")
                    print(f"  - Total tokens: {item.total_tokens:,}")
                    print(f"  - Cache creation: {item.total_cache_creation_tokens:,}")
                    print(f"  - Cache read: {item.total_cache_read_tokens:,}")
                    print(f"  - Cache hit rate: {item.total_cache_read_tokens / item.total_prompt_tokens * 100:.1f}%")
                    print(f"  - Total cost: ${item.total_cost:.4f}")
                elif item.status == "failed":
                    print(f"\n[Trace] ❌ 失败: {item.error_message}")

            # 处理 Message 对象
            elif isinstance(item, Message):
                if item.role == "assistant":
                    content = item.content
                    if isinstance(content, dict):
                        tool_calls = content.get("tool_calls")
                        if tool_calls:
                            print(f"[{item.sequence}] Tool calls: {len(tool_calls)}")

    except KeyboardInterrupt:
        print("\n\n用户中断 (Ctrl+C)")
        if current_trace_id:
            await runner.stop(current_trace_id)

    # 分析缓存情况
    if current_trace_id:
        print()
        print("=" * 60)
        print("缓存分析")
        print("=" * 60)

        trace = await store.get_trace(current_trace_id)
        if trace:
            print(f"\nTrace ID: {current_trace_id}")
            print(f"总消息数: {trace.total_messages}")
            print(f"总 tokens: {trace.total_tokens:,}")
            print(f"Prompt tokens: {trace.total_prompt_tokens:,}")
            print(f"Cache creation: {trace.total_cache_creation_tokens:,} ({trace.total_cache_creation_tokens / trace.total_prompt_tokens * 100:.1f}%)")
            print(f"Cache read: {trace.total_cache_read_tokens:,} ({trace.total_cache_read_tokens / trace.total_prompt_tokens * 100:.1f}%)")
            print(f"总成本: ${trace.total_cost:.4f}")

            # 计算节省的成本
            # cache_read 的价格是 input 的 0.1x
            # 如果没有缓存，这些 tokens 会按 input 价格计费
            saved_tokens = trace.total_cache_read_tokens
            # 假设 input 价格是 $3/MTok，cache_read 是 $0.3/MTok
            saved_cost = saved_tokens / 1_000_000 * 3 * 0.9  # 节省了 90%
            print(f"\n估算节省成本: ${saved_cost:.4f}")

        print()
        print(f"Trace 目录: .trace/{current_trace_id}")


if __name__ == "__main__":
    asyncio.run(main())