import torch
import torch.nn as nn
import numpy as np

class FastKANLayer(nn.Module):
    """
    Fast KAN层：用径向基函数(RBF)替代传统权重矩阵
    
    核心思想（Kolmogorov-Arnold表示定理）：
    任意多变量连续函数可表示为单变量函数的有限组合
    
    优势：
    - 用可学习的非线性激活替代固定激活+权重
    - 参数量极小（RBF只需中心和宽度参数）
    - 天然可解释
    """
    
    def __init__(self, in_features, out_features, num_centers=5):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.num_centers = num_centers
        
        # RBF中心（可学习）
        self.centers = nn.Parameter(
            torch.randn(num_centers, in_features) * 0.1
        )
        # RBF宽度（可学习）
        self.widths = nn.Parameter(
            torch.ones(num_centers) * 0.5
        )
        # 输出投影
        self.weight = nn.Parameter(
            torch.randn(num_centers, out_features) * 0.1
        )
    
    def forward(self, x):
        """
        Args:
            x: shape=(batch, in_features)
        
        Returns:
            output: shape=(batch, out_features)
        """
        # 计算RBF响应
        # x: (B, 1, I), centers: (K, I) -> dist: (B, K)
        dist = torch.cdist(x.unsqueeze(1), self.centers.unsqueeze(0)).squeeze(1)
        
        # RBF核函数
        rbf = torch.exp(-(dist ** 2) / (2 * self.widths ** 2 + 1e-8))
        
        # 投影到输出
        return rbf @ self.weight  # (B, K) @ (K, O) = (B, O)


class FastKANDDD(nn.Module):
    """
    FastKAN-DDD: 驾驶员疲劳检测模型
    
    基于Fast Kolmogorov-Arnold Network
    针对TinyML优化（微控制器部署）
    
    性能：99.94%准确率, 35KB, 0.04ms推理
    """
    
    def __init__(self, input_size=64*64*3, num_classes=3, 
                 hidden_dim=32, num_centers=8):
        super().__init__()
        
        # 特征提取（极简，适合MCU）
        self.features = nn.Sequential(
            nn.Conv2d(3, 8, 3, 2, 1),    # 64→32
            nn.BatchNorm2d(8),
            nn.ReLU(),
            nn.Conv2d(8, 16, 3, 2, 1),   # 32→16
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.Conv2d(16, 32, 3, 2, 1),  # 16→8
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d(1)
        )
        
        # KAN分类头（替代传统MLP）
        self.kan1 = FastKANLayer(32, hidden_dim, num_centers)
        self.kan2 = FastKANLayer(hidden_dim, num_classes, num_centers)
    
    def forward(self, x):
        """
        Args:
            x: shape=(batch, 3, 64, 64) 低分辨率图像
        
        Returns:
            logits: shape=(batch, 3) [alert/drowsy/very_drowsy]
        """
        x = self.features(x)          # (B, 32, 1, 1)
        x = x.flatten(1)               # (B, 32)
        x = self.kan1(x)                # (B, 32)
        x = torch.relu(x)
        x = self.kan2(x)                # (B, 3)
        return x


# 测试
if __name__ == "__main__":
    model = FastKANDDD(input_size=64*64*3, num_classes=3)
    
    # 模拟输入（64x64 RGB）
    x = torch.randn(1, 3, 64, 64)
    output = model(x)
    
    param_count = sum(p.numel() for p in model.parameters())
    print(f"输入: {x.shape}")
    print(f"输出: {output.shape}")
    print(f"参数量: {param_count:,} (~{param_count*4/1024:.1f} KB FP32)")
    print(f"INT8量化后: ~{param_count/1024:.1f} KB")

class QuantizationConfig:
    """
    FastKAN-DDD量化配置
    
    论文评估了三种量化方案
    """
    
    quantization_schemes = {
        "dynamic_range": {
            "description": "动态范围量化（权重INT8，激活FP32）",
            "size": "35 KB",
            "accuracy": 99.94,
            "latency_ms": 0.04,
            "platform": "STM32H7, ESP32-S3"
        },
        "float16": {
            "description": "半精度浮点量化",
            "size": "70 KB",
            "accuracy": 99.92,
            "latency_ms": 0.06,
            "platform": "支持FP16的MCU"
        },
        "weight_only": {
            "description": "仅权重量化（INT8权重，FP32激活）",
            "size": "35 KB",
            "accuracy": 99.94,
            "latency_ms": 0.05,
            "platform": "通用MCU"
        },
        "full_int8": {
            "description": "全INT8量化（权重+激活）",
            "size": "30 KB",
            "accuracy": 99.91,
            "latency_ms": 0.03,
            "platform": "支持INT8加速的MCU"
        }
    }

# TFLite转换代码
"""
import tensorflow as tf

# 动态范围量化
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# 保存
with open('fastkan_ddd_int8.tflite', 'wb') as f:
    f.write(tflite_model)
    
print(f"模型大小: {len(tflite_model)/1024:.1f} KB")
"""

dataset_info = {
    "name": "UTA-RLDD (Real-Life Drowsiness Dataset)",
    "classes": 3,
    "class_names": ["Alert", "Drowsy", "Very Drowsy"],
    "subjects": 60,  # 41男, 19女
    "video_clips": 480,  # 每人8段
    "resolution": "640x480 → 64x64（降采样）",
    "total_frames": "~300,000",
    "split": "70% train / 15% val / 15% test",
    "diversity": "多光照/多姿态/多种族"
}

class IMSTinyMLIntegration:
    """
    将FastKAN-DDD集成到IMS系统
    
    架构：
    - MCU级：FastKAN-DDD快速判断（0.04ms）
    - SoC级：现有DMS精细化分析（30fps）
    - 融合级：两级结果加权输出
    """
    
    def __init__(self):
        self.mcq_model = FastKANDDD()  # 35KB MCU模型
        self.soc_model = None  # 现有高通平台DMS
        
    def two_stage_detection(self, frame):
        """
        两级检测：MCU快速预筛 → SoC精细分析
        """
        # Stage 1: MCU快速判断（0.04ms）
        mcu_pred = self.mcq_model(frame_64x64)
        
        if mcu_pred == "Alert":
            return "Alert", 0.99  # 跳过SoC
        
        # Stage 2: SoC精细分析（仅对非Alert帧）
        soc_result = self.soc_model(frame_full_res)
        
        return soc_result