import torch
import torch.nn as nn
import onnxruntime as ort
import numpy as np
from typing import Tuple

class DMSModelQuantizer:
    """
    DMS模型量化部署器
    
    支持的量化方案:
    1. 训练后量化 (PTQ) - INT8
    2. 混合精度量化 - FP16/INT8
    3. 量化感知训练 (QAT)
    """
    
    def __init__(self, model: nn.Module):
        self.model = model
        self.calibration_data = None
    
    def prepare_for_quantization(self):
        """准备模型量化"""
        # fuse layers for better quantization
        self.model = torch.quantization.fuse_modules(
            self.model,
            [['conv', 'bn', 'relu'], ['fc', 'relu']],
            inplace=False
        )
        # set qconfig
        qconfig = torch.quantization.get_default_qconfig('fbgemm')  # x86
        self.model.qconfig = qconfig
        return self.model
    
    def calibrate(
        self, 
        calibration_loader,
        num_batches: int = 100
    ) -> dict:
        """
        校准量化参数
        
        Args:
            calibration_loader: 校准数据加载器
            num_batches: 校准批次数量
        
        Returns:
            stats: 校准统计信息
        """
        self.model.eval()
        self.prepare_for_quantization()
        
        # 插入观察者
        self.model = torch.quantization.prepare(self.model)
        
        # 收集激活统计
        min_vals, max_vals = [], []
        with torch.no_grad():
            for i, (data, _) in enumerate(calibration_loader):
                if i >= num_batches:
                    break
                _ = self.model(data)
                # 收集各层激活范围
        
        # 转换
        self.quantized_model = torch.quantization.convert(self.model)
        
        # 统计
        original_size = sum(
            p.nelement() * p.element_size() 
            for p in self.model.parameters()
        )
        quant_size = sum(
            p.nelement() * p.element_size()
            for p in self.quantized_model.parameters()
        )
        
        return {
            'original_size_mb': original_size / 1024**2,
            'quantized_size_mb': quant_size / 1024**2,
            'compression_ratio': original_size / quant_size
        }
    
    def export_onnx(
        self, 
        output_path: str,
        input_shape: Tuple[int, ...] = (1, 3, 224, 224)
    ) -> str:
        """
        导出ONNX格式（用于NPU部署）
        
        Args:
            output_path: 输出路径
            input_shape: 模型输入形状
        
        Returns:
            output_path: ONNX文件路径
        """
        dummy_input = torch.randn(*input_shape)
        
        torch.onnx.export(
            self.quantized_model,
            dummy_input,
            output_path,
            opset_version=17,
            input_names=['input'],
            output_names=['output'],
            dynamic_axes={
                'input': {0: 'batch'},
                'output': {0: 'batch'}
            }
        )
        return output_path


# 量化测试
if __name__ == "__main__":
    # 使用MobileViT作为示例DMS模型
    model = nn.Sequential(
        nn.Conv2d(3, 32, 3, padding=1),
        nn.BatchNorm2d(32),
        nn.ReLU(),
        nn.MaxPool2d(2),
        nn.Conv2d(32, 64, 3, padding=1),
        nn.BatchNorm2d(64),
        nn.ReLU(),
        nn.AdaptiveAvgPool2d((1, 1)),
        nn.Flatten(),
        nn.Linear(64, 10)
    )
    
    quantizer = DMSModelQuantizer(model)
    
    # 模拟校准数据
    cal_data = [(torch.randn(4, 3, 224, 224), 0) for _ in range(50)]
    stats = quantizer.calibrate(cal_data)
    print(f"原始大小: {stats['original_size_mb']:.2f} MB")
    print(f"量化大小: {stats['quantized_size_mb']:.2f} MB")
    print(f"压缩比: {stats['compression_ratio']:.1f}x")

class QualcommNPUDeployer:
    """
    Qualcomm Hexagon NPU部署指南
    
    支持平台:
    - QCS8255 (26 TOPS) - 中端
    - QCS8295 (30 TOPS) - 高端  
    - QCS8775 (48 TOPS) - 旗舰
    
    工具链:
    1. PyTorch → ONNX
    2. ONNX → Qualcomm AI Hub
    3. Qualcomm AI Hub → Hexagon DSP
    """
    
    # 平台规格
    PLATFORMS = {
        'QCS8255': {'tops': 26, 'memory': '8GB LPDDR4', 'tdp': '5-8W'},
        'QCS8295': {'tops': 30, 'memory': '12GB LPDDR5', 'tdp': '8-12W'},
        'QCS8775': {'tops': 48, 'memory': '16GB LPDDR5', 'tdp': '12-20W'},
    }
    
    def deploy_to_hexagon(
        self,
        onnx_model_path: str,
        platform: str = 'QCS8255',
        input_resolution: tuple = (224, 224)
    ) -> dict:
        """
        部署ONNX模型到Hexagon DSP
        
        Args:
            onnx_model_path: ONNX模型路径
            platform: 目标平台
            input_resolution: 输入分辨率
        
        Returns:
            deployment_info: 部署信息
        """
        if platform not in self.PLATFORMS:
            raise ValueError(f"不支持的平台: {platform}")
        
        specs = self.PLATFORMS[platform]
        
        # Step 1: 通过Qualcomm AI Hub优化
        # qai_hub submit --model model.onnx --device "Snapdragon 8 Gen3"
        optimized_model = self._optimize_for_hexagon(onnx_model_path, platform)
        
        # Step 2: 量化到INT8 (Hexagon原生支持)
        quantized = self._quantize_int8(optimized_model)
        
        # Step 3: 编译为Hexagon上下文
        compiled = self._compile_hexagon(quantized, platform)
        
        # Step 4: 性能预估
        perf = self._estimate_performance(compiled, specs, input_resolution)
        
        return {
            'platform': platform,
            'model_path': compiled,
            'quantization': 'INT8',
            **perf
        }
    
    def _optimize_for_hexagon(self, model_path: str, platform: str) -> str:
        """通过Qualcomm AI Hub优化模型"""
        # 实际命令: qai_hub submit --model model.onnx --device <platform>
        return f"optimized_{platform}_{model_path}"
    
    def _quantize_int8(self, model_path: str) -> str:
        """INT8量化"""
        return f"int8_{model_path}"
    
    def _compile_hexagon(self, model_path: str, platform: str) -> str:
        """编译Hexagon上下文"""
        return f"hexagon_ctx_{platform}"
    
    def _estimate_performance(
        self, 
        model_path: str, 
        specs: dict,
        input_res: tuple
    ) -> dict:
        """性能预估"""
        # 简化估算: 模型FLOPs / NPU算力
        h, w = input_res
        estimated_flops = 2 * 3 * h * w * 64 * 64  # 简化
        tops = specs['tops'] * 1e12
        
        # 考虑NPU利用率 (通常30-60%)
        utilization = 0.4
        effective_tops = tops * utilization
        
        latency_ms = (estimated_flops / effective_tops) * 1000
        fps = 1000 / latency_ms if latency_ms > 0 else 0
        
        return {
            'estimated_latency_ms': round(latency_ms, 1),
            'estimated_fps': round(fps, 1),
            'power_consumption_w': specs['tdp'],
            'meets_30fps': fps >= 30,
            'meets_15fps': fps >= 15
        }


# 部署测试
if __name__ == "__main__":
    deployer = QualcommNPUDeployer()
    
    for platform in ['QCS8255', 'QCS8295', 'QCS8775']:
        result = deployer.deploy_to_hexagon(
            "dms_model.onnx",
            platform=platform,
            input_resolution=(224, 224)
        )
        print(f"\n=== {platform} ===")
        print(f"延迟: {result['estimated_latency_ms']}ms")
        print(f"帧率: {result['estimated_fps']} FPS")
        print(f"30fps达标: {'✅' if result['meets_30fps'] else '❌'}")

class ONNXRuntimeDeployer:
    """
    ONNX Runtime跨平台部署器
    
    支持的Execution Provider:
    - CUDA (NVIDIA GPU)
    - TensorRT (NVIDIA优化)
    - CoreML (Apple)
    - NNAPI (Android)
    - DirectML (Windows)
    - Hexagon (Qualcomm DSP)
    """
    
    PROVIDERS = {
        'nvidia_jetson': ['CUDAExecutionProvider', 'TensorRTExecutionProvider'],
        'qualcomm': ['QNNExecutionProvider', 'HexagonExecutionProvider'],
        'apple': ['CoreMLExecutionProvider'],
        'android': ['NNAPIExecutionProvider'],
        'cpu': ['CPUExecutionProvider'],
    }
    
    def __init__(self, model_path: str):
        self.model_path = model_path
        self.session = None
    
    def load_model(
        self, 
        target: str = 'cpu',
        optimization_level: str = 'all'
    ) -> ort.InferenceSession:
        """
        加载模型到指定平台
        
        Args:
            target: 目标平台
            optimization_level: ORT优化级别
        
        Returns:
            session: ONNX Runtime推理会话
        """
        providers = self.PROVIDERS.get(target, ['CPUExecutionProvider'])
        
        # 会话选项
        sess_options = ort.SessionOptions()
        if optimization_level == 'all':
            sess_options.graph_optimization_level = \
                ort.GraphOptimizationLevel.ORT_ENABLE_ALL
        
        # 内存优化
        if target in ['qualcomm', 'android']:
            # 内存映射模式 (减少内存占用)
            sess_options.enable_mem_pattern = False
            sess_options.enable_mem_reuse = True
        
        # 线程配置
        if target == 'cpu':
            sess_options.intra_op_num_threads = 4
            sess_options.inter_op_num_threads = 1
        
        self.session = ort.InferenceSession(
            self.model_path,
            sess_options=sess_options,
            providers=providers
        )
        
        return self.session
    
    def benchmark(
        self, 
        input_data: np.ndarray,
        num_runs: int = 100
    ) -> dict:
        """性能基准测试"""
        if self.session is None:
            raise RuntimeError("模型未加载")
        
        input_name = self.session.get_inputs()[0].name
        
        # warmup
        for _ in range(10):
            self.session.run(None, {input_name: input_data})
        
        # benchmark
        import time
        latencies = []
        for _ in range(num_runs):
            start = time.time()
            self.session.run(None, {input_name: input_data})
            latencies.append((time.time() - start) * 1000)
        
        return {
            'mean_ms': np.mean(latencies),
            'p50_ms': np.percentile(latencies, 50),
            'p95_ms': np.percentile(latencies, 95),
            'p99_ms': np.percentile(latencies, 99),
            'fps': 1000 / np.mean(latencies)
        }


# 测试
if __name__ == "__main__":
    deployer = ONNXRuntimeDeployer("dms_model.onnx")
    session = deployer.load_model(target='cpu')
    
    dummy_input = np.random.randn(1, 3, 224, 224).astype(np.float32)
    results = deployer.benchmark(dummy_input, 100)
    
    print("=== CPU基准测试 ===")
    for k, v in results.items():
        print(f"  {k}: {v:.2f}")