传统Transformer架构流程:
┌──────────┐    ┌──────────────────┐    ┌──────────┐
│ 输入嵌入  │ -> │ 多层自注意力机制  │ -> │ 输出头    │
│ Embedding│    │ O(n²) 复杂度     │    │ Output   │
└──────────┘    └──────────────────┘    └──────────┘
                       │
                 内存与计算随序列
                 长度二次方增长

Liquid Foundation Model架构流程:
┌──────────┐    ┌──────────────────┐    ┌──────────┐
│ 输入嵌入  │ -> │ 液态状态空间模型  │ -> │ 输出头    │
│ Embedding│    │ O(n) 复杂度      │    │ Output   │
└──────────┘    └──────────────────┘    └──────────┘
                       │
                 内存与计算随序列
                 长度线性增长
                 固定大小状态记忆

'''边缘AI Agent能力评估框架'''

from dataclasses import dataclass, field
from typing import List, Optional, Callable
from enum import Enum


class TaskStatus(Enum):
    '''任务执行状态'''
    PENDING = "待执行"
    IN_PROGRESS = "执行中"
    SUCCESS = "成功"
    FAILED = "失败"


@dataclass
class AgentStep:
    '''Agent执行的单个步骤'''
    step_id: int
    action: str
    tool_called: Optional[str] = None
    result: Optional[str] = None
    status: TaskStatus = TaskStatus.PENDING


@dataclass
class AgentEvaluator:
    '''Agent能力评估器'''

    model_name: str
    param_count: str
    steps: List[AgentStep] = field(default_factory=list)

    def add_step(self, action: str, tool: str = None) -> AgentStep:
        '''添加执行步骤'''
        step = AgentStep(
            step_id=len(self.steps) + 1,
            action=action,
            tool_called=tool
        )
        self.steps.append(step)
        return step

    def mark_result(self, step_id: int, result: str, success: bool):
        '''标记步骤结果'''
        for s in self.steps:
            if s.step_id == step_id:
                s.result = result
                s.status = TaskStatus.SUCCESS if success else TaskStatus.FALED
                break

    def evaluate(self) -> dict:
        '''评估Agent整体表现'''
        total = len(self.steps)
        success = sum(1 for s in self.steps if s.status == TaskStatus.SUCCESS)
        tool_uses = sum(1 for s in self.steps if s.tool_called)

        return {
            "model": self.model_name,
            "params": self.param_count,
            "total_steps": total,
            "success_rate": f"{success}/{total}",
            "tool_calls": tool_uses,
            "autonomy_score": round(success / max(total, 1) * 100, 2)
        }


# 评估LFM2.5-2.6B的Agent能力
if __name__ == "__main__":
    evaluator = AgentEvaluator(
        model_name="LFM2.5-2.6B",
        param_count="2.6B"
    )

    # 模拟多步骤Agent任务
    s1 = evaluator.add_step("分析用户请求并分解任务", None)
    evaluator.mark_result(s1.step_id, "任务已分解为3个子步骤", True)

    s2 = evaluator.add_step("查询本地数据库获取上下文", "local_db_query")
    evaluator.mark_result(s2.step_id, "获取到5条相关记录", True)

    s3 = evaluator.add_step("调用计算工具处理数据", "calculator")
    evaluator.mark_result(s3.step_id, "计算完成", True)

    s4 = evaluator.add_step("生成最终回复", None)
    evaluator.mark_result(s4.step_id, "回复已生成", True)

    result = evaluator.evaluate()
    print("Agent评估结果:")
    for k, v in result.items():
        print(f"  {k}: {v}")

'''LFM2.5-2.6B本地部署与Agent运行示例'''

import time
from dataclasses import dataclass
from typing import List, Optional


@dataclass
class AgentConfig:
    '''Agent运行配置'''
    model_path: str = "liquidai/LFM2.5-2.6B"
    max_tokens: int = 2048
    temperature: float = 0.7
    context_window: int = 32768
    device: str = "auto"  # auto, cpu, metal, cuda


class LocalAgent:
    '''本地运行的AI Agent'''

    def __init__(self, config: AgentConfig):
        '''初始化本地Agent'''
        self.config = config
        self.conversation_history: List[dict] = []
        self.available_tools: List[str] = []
        self.model = None
        self._load_model()

    def _load_model(self):
        '''加载本地模型'''
        print(f"正在加载模型: {self.config.model_path}")
        start = time.time()

        # 实际部署中替换为真实的模型加载逻辑
        # 例如使用 llama.cpp、MLX 或 Liquid AI SDK
        # self.model = load_liquid_model(self.config.model_path)

        load_time = time.time() - start
        print(f"模型加载完成，耗时: {load_time:.2f}秒")
        print(f"设备: {self.config.device}")
        print(f"内存占用: ~2.4GB")

    def register_tool(self, name: str, handler):
        '''注册可用工具'''
        self.available_tools.append(name)
        print(f"已注册工具: {name}")

    def run(self, user_input: str) -> str:
        '''运行Agent处理用户输入'''
        self.conversation_history.append({
            "role": "user",
            "content": user_input
        })

        start = time.time()

        # 模拟模型推理过程
        # 实际部署中调用 self.model.generate(...)
        response = self._generate_response(user_input)

        elapsed = time.time() - start
        token_count = len(response.split())
        tps = token_count / elapsed if elapsed > 0 else 0

        self.conversation_history.append({
            "role": "assistant",
            "content": response
        })

        print(f"
[推理统计] 生成{token_count}tokens, "
              f"耗时{elapsed:.2f}秒, 速度{tps:.1f} tokens/秒")

        return response

    def _generate_response(self, prompt: str) -> str:
        '''生成响应（模拟）'''
        # 模拟多步骤Agent推理
        steps = [
            "正在分析您的请求...",
            "正在检索相关信息...",
            "正在组织回复内容..."
        ]
        for step in steps:
            print(f"  [Agent] {step}")
            time.sleep(0.1)

        return f"已处理您的请求: {prompt[:50]}..."


if __name__ == "__main__":
    config = AgentConfig(
        model_path="liquidai/LFM2.5-2.6B",
        max_tokens=2048,
        device="metal"  # Apple Silicon加速
    )

    agent = LocalAgent(config)

    # 注册本地工具
    agent.register_tool("calendar", lambda: "日历工具")
    agent.register_tool("file_search", lambda: "文件搜索工具")
    agent.register_tool("calculator", lambda: "计算器工具")

    # 运行Agent任务
    result = agent.run(
        "帮我查看明天的日程安排，并计算本周剩余的可用工作时间"
    )
    print(f"
Agent回复: {result}")