"""
Jarvis核心：text-to-command分类器

将自然语言指令映射到结构化命令

示例：
"打开车窗" → {"tool": "windows", "action": "open", "params": {"level": "full"}}
"帮我导航到加油站" → {"tool": "navigation", "action": "set_destination", "params": {"type": "gas_station"}}
"我有点冷" → {"tool": "climate", "action": "increase_temp", "params": {"delta": 2}}

Mistral 7B微调：
1. 构建指令-命令对数据集
2. LoRA/QLoRA微调Mistral 7B
3. 量化部署(INT4/INT8)
"""

from dataclasses import dataclass
from typing import Optional
import json

@dataclass
class VehicleCommand:
    """结构化车辆命令"""
    tool: str              # 目标工具
    action: str            # 动作
    params: dict           # 参数
    safety_level: str = "low"  # 安全等级
    
# 指令分类示例
command_examples = [
    # (自然语言, 结构化命令)
    ("打开车窗", 
     VehicleCommand("windows", "open", {"level": "full"})),
    ("把空调调到22度",
     VehicleCommand("climate", "set_temp", {"temp": 22})),
    ("导航到最近的加油站",
     VehicleCommand("navigation", "set_destination", {"type": "gas_station", "nearest": True})),
    ("打开后备箱",
     VehicleCommand("trunk", "open", {}, "medium")),  # 安全等级中
    ("紧急停车",
     VehicleCommand("emergency", "stop", {}, "critical")),  # 安全等级高
    ("帮我看一下后视镜",
     VehicleCommand("mirror", "adjust", {"view": "rear"}, "low")),
]

# 微调数据集格式
def create_finetune_example(utterance, command):
    """创建Mistral微调样本"""
    return {
        "instruction": "将以下语音指令转换为结构化命令",
        "input": utterance,
        "output": json.dumps({
            "tool": command.tool,
            "action": command.action,
            "params": command.params,
            "safety_level": command.safety_level,
        }, ensure_ascii=False)
    }

# 生成微调数据
print("=== Mistral 7B微调数据示例 ===")
for utterance, cmd in command_examples:
    example = create_finetune_example(utterance, cmd)
    print(f"\n输入: {example['input']}")
    print(f"输出: {example['output']}")

# 部署推理
print("\n=== 部署推理参数 ===")
print("模型: Mistral 7B (4-bit量化)")
print("量化: Q4_K_M (GGUF格式)")
print("模型大小: ~4GB")
print("推理框架: llama.cpp / Ollama")
print("硬件: CPU 8核 / GPU可选")
print("延迟目标: <2s")
print("实测延迟: 1.39s 平均")

# 边缘部署硬件需求
hardware = {
    'CPU方案': {
        'CPU': '8核 ARM Cortex-A78',
        'RAM': '16GB',
        '存储': '8GB eMMC(模型+数据)',
        '功耗': '~15W',
        '延迟': '1.39s',
        '成本': '~$200(仅计算)',
    },
    'GPU加速方案': {
        'GPU': 'NVIDIA Orin Nano 8GB',
        'RAM': '8GB LPDDR5',
        '存储': '8GB NVMe',
        '功耗': '~25W',
        '延迟': '~0.5s(预估)',
        '成本': '~$400(仅计算)',
    },
    'NPU方案': {
        'NPU': 'QCS8255 Hexagon 26TOPS',
        'RAM': '8GB',
        '存储': '8GB UFS',
        '功耗': '~10W',
        '延迟': '~1s(预估)',
        '成本': '~$150(仅计算)',
    },
}

print("=== Jarvis边缘部署方案 ===")
for name, spec in hardware.items():
    print(f"\n{name}:")
    for k, v in spec.items():
        print(f"  {k}: {v}")

# 融合架构
class CabinVoiceAssistant:
    def __init__(self):
        self.asr = None  # faster-whisper
        self.llm = None  # Mistral 7B
        self.tts = None  # Piper
        self.safety = SafetyEnforcementLayer()
        self.dms = None  # DMS状态输入
    
    def process(self, audio, dms_state):
        """完整处理链路"""
        # 1. 语音识别
        text = self.asr.transcribe(audio)
        
        # 2. DMS状态检查
        if dms_state.fatigue > 0.7:
            # 疲劳→简短回复+确认
            cmd = self.llm.classify(text, mode='confirm')
        elif dms_state.distraction > 0.7:
            # 分心→推迟执行
            return "请先注视道路"
        else:
            cmd = self.llm.classify(text)
        
        # 3. 安全执行层
        final = self.safety.enforce(cmd, dms_state)
        
        # 4. 执行+语音反馈
        result = self.execute(final)
        speech = self.tts.synthesize(result.message)
        
        return speech, final