# 传统行为克隆 (Behavior Cloning)
class BehaviorCloning:
    def __init__(self):
        self.policy_network = MLP()  # 单点预测

    def predict(self, observation):
        return self.policy_network(observation)  # 确定性输出

# Diffusion Policy
class DiffusionPolicy:
    def __init__(self):
        self.diffusion_model = TemporalUNet()  # 扩散模型
        self.noise_scheduler = NoiseScheduler()

    def predict(self, observation):
        # 迭代去噪过程
        noise = torch.randn(sequence_length, action_dim)
        for t in reversed(range(self.num_timesteps)):
            noise = self.diffusion_model.denoise(
                noise, t, observation
            )
        return noise  # 最终去噪结果作为动作序列

class DiffusionPolicyArchitecture:
    """
    Diffusion Policy核心架构
    """
    def __init__(self, config):
        # 1. 观察编码器
        self.obs_encoder = ObservationEncoder(
            image_encoder=ResNetEncoder(),  # 图像编码
            state_encoder=MLP()             # 状态编码
        )

        # 2. 条件扩散模型
        self.diffusion_model = ConditionalDiffusionModel(
            backbone=TemporalUNet(),
            condition_dim=self.obs_encoder.output_dim
        )

        # 3. 噪声调度器
        self.noise_scheduler = NoiseScheduler(
            num_timesteps=config.num_timesteps,
            beta_start=config.beta_start,
            beta_end=config.beta_end
        )

        # 4. 动作解码器
        self.action_decoder = ActionDecoder()

class TemporalUNet(nn.Module):
    """时序U-Net用于扩散模型的去噪过程"""

    def __init__(self,
                 action_dim,
                 horizon,
                 condition_dim,
                 hidden_dim=256):
        super().__init__()

        # 时间嵌入
        self.time_mlp = nn.Sequential(
            SinusoidalPositionEmbeddings(hidden_dim),
            nn.SiLU(),
            nn.Linear(hidden_dim, hidden_dim * 2)
        )

        # 编码器部分
        self.down_blocks = nn.ModuleList([
            DownBlock(hidden_dim, hidden_dim * 2),
            DownBlock(hidden_dim * 2, hidden_dim * 4),
            DownBlock(hidden_dim * 4, hidden_dim * 8),
        ])

        # 中间层
        self.mid_block = MidBlock(hidden_dim * 8, hidden_dim * 8)

        # 解码器部分
        self.up_blocks = nn.ModuleList([
            UpBlock(hidden_dim * 8, hidden_dim * 4),
            UpBlock(hidden_dim * 4, hidden_dim * 2),
            UpBlock(hidden_dim * 2, hidden_dim),
        ])

        # 条件融合
        self.condition_fusion = ConditionFusion(
            condition_dim, hidden_dim
        )

        # 输出层
        self.output_layer = nn.Conv1d(hidden_dim, action_dim, 1)

    def forward(self, x, timestep, condition):
        # x: [batch, horizon, action_dim]
        # condition: [batch, condition_dim]

        # 时间嵌入
        t_emb = self.time_mlp(timestep)

        # 条件处理
        condition_emb = self.condition_fusion(condition)

        # 编码路径
        skip_connections = []
        for down_block in self.down_blocks:
            x = down_block(x, t_emb, condition_emb)
            skip_connections.append(x)

        # 中间处理
        x = self.mid_block(x, t_emb, condition_emb)

        # 解码路径
        for up_block, skip in zip(self.up_blocks, reversed(skip_connections)):
            x = up_block(x, skip, t_emb, condition_emb)

        # 输出
        output = self.output_layer(x)
        return output

class ConditionFusion(nn.Module):
    """条件融合机制"""

    def __init__(self, condition_dim, hidden_dim):
        super().__init__()

        # 多层感知机
        self.condition_mlp = nn.Sequential(
            nn.Linear(condition_dim, hidden_dim),
            nn.SiLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # 注意力机制
        self.cross_attention = CrossAttention(
            dim=hidden_dim,
            context_dim=hidden_dim
        )

        # 层归一化
        self.layer_norm = nn.LayerNorm(hidden_dim)

    def forward(self, condition, features):
        # 条件嵌入
        condition_emb = self.condition_mlp(condition)

        # 交叉注意力融合
        attended_features = self.cross_attention(
            query=features,
            context=condition_emb
        )

        # 残差连接和归一化
        output = self.layer_norm(features + attended_features)

        return output

class DiffusionPolicyTrainer:
    """Diffusion Policy训练器"""

    def __init__(self, model, config):
        self.model = model
        self.config = config
        self.noise_scheduler = NoiseScheduler(config)

    def train_step(self, batch):
        """
        训练步骤：
        1. 从真实动作序列添加噪声
        2. 预测噪声
        3. 计算损失
        """
        observations, actions = batch

        # 随机采样时间步
        timesteps = torch.randint(
            0, self.config.num_timesteps,
            (actions.shape[0],), device=actions.device
        )

        # 添加噪声
        noise = torch.randn_like(actions)
        noisy_actions = self.noise_scheduler.add_noise(
            actions, noise, timesteps
        )

        # 编码观察
        condition = self.model.obs_encoder(observations)

        # 预测噪声
        predicted_noise = self.model.diffusion_model(
            noisy_actions, timesteps, condition
        )

        # 计算MSE损失
        loss = F.mse_loss(predicted_noise, noise)

        return loss

class DiffusionPolicyInference:
    """Diffusion Policy推理器"""

    def __init__(self, model, config):
        self.model = model
        self.config = config
        self.noise_scheduler = NoiseScheduler(config)

    @torch.no_grad()
    def sample(self, observation, num_inference_steps=20):
        """
        采样过程：
        1. 从纯噪声开始
        2. 迭代去噪
        3. 生成最终动作序列
        """
        batch_size = observation.shape[0]
        horizon = self.config.horizon
        action_dim = self.config.action_dim

        # 编码观察条件
        condition = self.model.obs_encoder(observation)

        # 从纯噪声开始
        action_sequence = torch.randn(
            batch_size, horizon, action_dim,
            device=observation.device
        )

        # 迭代去噪
        timesteps = self.noise_scheduler.get_timesteps(num_inference_steps)

        for t in timesteps:
            # 预测噪声
            predicted_noise = self.model.diffusion_model(
                action_sequence, t, condition
            )

            # 去噪步骤
            action_sequence = self.noise_scheduler.step(
                predicted_noise, t, action_sequence
            )

        return action_sequence

    def get_first_action(self, observation):
        """获取第一个动作用于执行"""
        action_sequence = self.sample(observation)
        return action_sequence[:, 0]  # 返回第一个时间步的动作

class NoiseScheduler:
    """噪声调度器"""

    def __init__(self, num_timesteps=1000, beta_start=0.0001, beta_end=0.02):
        self.num_timesteps = num_timesteps

        # 线性调度
        self.betas = torch.linspace(beta_start, beta_end, num_timesteps)

        # 预计算常用值
        self.alphas = 1.0 - self.betas
        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
        self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1 - self.alphas_cumprod)

    def add_noise(self, original, noise, timestep):
        """向原始动作添加噪声"""
        sqrt_alpha_cumprod = self.sqrt_alphas_cumprod[timestep]
        sqrt_one_minus_alpha_cumprod = self.sqrt_one_minus_alphas_cumprod[timestep]

        # 调整维度以匹配输入
        while len(sqrt_alpha_cumprod.shape) < len(original.shape):
            sqrt_alpha_cumprod = sqrt_alpha_cumprod.unsqueeze(-1)
            sqrt_one_minus_alpha_cumprod = sqrt_one_minus_alpha_cumprod.unsqueeze(-1)

        noisy = sqrt_alpha_cumprod * original + sqrt_one_minus_alpha_cumprod * noise
        return noisy

    def step(self, model_output, timestep, sample):
        """去噪步骤"""
        t = timestep

        # 预计算系数
        alpha_t = self.alphas[t]
        alpha_cumprod_t = self.alphas_cumprod[t]
        beta_t = self.betas[t]
        sqrt_one_minus_alpha_cumprod_t = self.sqrt_one_minus_alphas_cumprod[t]

        # 计算去噪后的样本
        prev_sample = (sample - beta_t / sqrt_one_minus_alpha_cumprod_t * model_output) / torch.sqrt(alpha_t)

        return prev_sample

class MultiModalObservationEncoder(nn.Module):
    """多模态观察编码器"""

    def __init__(self, config):
        super().__init__()

        # 图像编码器
        self.image_encoder = ImageEncoder(
            backbone='resnet18',
            pretrained=True,
            feature_dim=config.image_feature_dim
        )

        # 关节状态编码器
        self.joint_encoder = nn.Sequential(
            nn.Linear(config.joint_dim, 256),
            nn.ReLU(),
            nn.Linear(256, config.joint_feature_dim)
        )

        # 触觉传感器编码器（如果有）
        self.tactile_encoder = TactileEncoder(
            input_dim=config.tactile_dim,
            feature_dim=config.tactile_feature_dim
        )

        # 特征融合
        self.feature_fusion = FeatureFusion(
            image_dim=config.image_feature_dim,
            joint_dim=config.joint_feature_dim,
            tactile_dim=config.tactile_feature_dim,
            output_dim=config.condition_dim
        )

    def forward(self, observation):
        # 解包观察
        images = observation['images']
        joint_states = observation['joint_states']
        tactile_data = observation.get('tactile', None)

        # 编码各个模态
        image_features = self.image_encoder(images)
        joint_features = self.joint_encoder(joint_states)

        if tactile_data is not None:
            tactile_features = self.tactile_encoder(tactile_data)
        else:
            tactile_features = torch.zeros(
                images.shape[0], self.tactile_encoder.output_dim,
                device=images.device
            )

        # 融合特征
        condition = self.feature_fusion(
            image_features, joint_features, tactile_features
        )

        return condition

class HierarchicalDiffusionPolicy:
    """分层Diffusion Policy"""

    def __init__(self, config):
        # 高层策略（粗粒度动作）
        self.high_level_policy = DiffusionPolicy(
            config.high_level_config
        )

        # 低层策略（细粒度修正）
        self.low_level_policy = DiffusionPolicy(
            config.low_level_config
        )

        # 时间尺度配置
        self.high_horizon = config.high_horizon
        self.low_horizon = config.low_horizon
        self.action_scale = config.action_scale

    def generate_actions(self, observation):
        # 生成高层动作序列
        high_level_actions = self.high_level_policy.sample(
            observation, horizon=self.high_horizon
        )

        # 基于高层动作生成低层修正
        refined_actions = []
        for i in range(len(high_level_actions)):
            # 构建低层条件
            low_level_condition = {
                'observation': observation,
                'high_level_action': high_level_actions[i]
            }

            # 生成低层动作
            low_level_actions = self.low_level_policy.sample(
                low_level_condition, horizon=self.low_horizon
            )

            # 融合高低层动作
            refined_action = self.fuse_actions(
                high_level_actions[i], low_level_actions
            )
            refined_actions.append(refined_action)

        return torch.stack(refined_actions)

    def fuse_actions(self, high_action, low_actions):
        """融合高低层动作"""
        # 高层动作提供主要方向
        # 低层动作提供精细调整
        base_action = high_action.repeat(self.low_horizon, 1)

        # 添加低层修正（缩放到适当范围）
        corrections = low_actions * self.action_scale

        return base_action + corrections

class RealTimeDiffusionPolicy:
    """实时Diffusion Policy优化"""

    def __init__(self, model, config):
        self.model = model
        self.config = config

        # 缓存机制
        self.condition_cache = {}
        self.action_buffer = deque(maxlen=config.buffer_size)

        # 自适应推理步数
        self.adaptive_steps = AdaptiveInferenceSteps(config)

    def real_time_predict(self, observation, time_budget_ms):
        """实时预测，考虑时间预算"""
        start_time = time.time()

        # 检查缓存
        obs_key = self.hash_observation(observation)
        if obs_key in self.condition_cache:
            condition = self.condition_cache[obs_key]
        else:
            condition = self.model.obs_encoder(observation)
            self.condition_cache[obs_key] = condition

        # 自适应选择推理步数
        num_steps = self.adaptive_steps.get_steps(time_budget_ms)

        # 快速采样
        action_sequence = self.fast_sample(condition, num_steps)

        # 更新缓冲区
        self.action_buffer.append(action_sequence)

        # 预测下一个动作
        next_action = self.predict_next_action()

        return next_action

    def fast_sample(self, condition, num_steps):
        """快速采样算法"""
        # 使用更大的步长
        large_steps = self.adaptive_steps.get_large_steps(num_steps)

        batch_size = condition.shape[0]
        action_sequence = torch.randn(
            batch_size, self.config.horizon, self.config.action_dim,
            device=condition.device
        )

        for t in large_steps:
            predicted_noise = self.model.diffusion_model(
                action_sequence, t, condition
            )
            action_sequence = self.noise_scheduler.step(
                predicted_noise, t, action_sequence
            )

        return action_sequence

class RoboticManipulationPolicy:
    """机器人操作任务专用Diffusion Policy"""

    def __init__(self, config):
        self.policy = DiffusionPolicy(config)

        # 特定任务配置
        self.gripper_config = config.gripper_config
        self.workspace_limits = config.workspace_limits

    def pick_and_place(self, observation, target_pose):
        """抓取放置任务"""
        # 构建任务特定条件
        task_condition = {
            'observation': observation,
            'target_pose': target_pose,
            'task_type': 'pick_and_place'
        }

        # 生成动作序列
        action_sequence = self.policy.sample(task_condition)

        # 后处理：确保动作符合物理约束
        constrained_actions = self.apply_constraints(action_sequence)

        return constrained_actions

    def apply_constraints(self, actions):
        """应用物理约束"""
        # 工作空间约束
        actions[:, :, :3] = torch.clamp(
            actions[:, :, :3],
            min=self.workspace_limits['min'],
            max=self.workspace_limits['max']
        )

        # 夹爪约束
        actions[:, :, -1] = torch.clamp(
            actions[:, :, -1],
            min=self.gripper_config['min_opening'],
            max=self.gripper_config['max_opening']
        )

        return actions

class MobileNavigationPolicy:
    """移动机器人导航Diffusion Policy"""

    def __init__(self, config):
        self.policy = DiffusionPolicy(config)

        # 导航特定组件
        self.map_encoder = MapEncoder()
        self.path_planner = PathPlanner()

    def navigate(self, observation, goal):
        """导航任务"""
        # 编码地图信息
        map_features = self.map_encoder(observation['occupancy_map'])

        # 编码目标信息
        goal_features = self.encode_goal(goal)

        # 构建导航条件
        nav_condition = {
            'observation': observation,
            'map_features': map_features,
            'goal_features': goal_features
        }

        # 生成轨迹
        trajectory = self.policy.sample(nav_condition)

        return trajectory

    def encode_goal(self, goal):
        """编码目标信息"""
        goal_position = goal['position']
        goal_orientation = goal['orientation']

        return torch.cat([
            torch.tensor(goal_position),
            torch.tensor([goal_orientation])
        ])

class HumanRobotCollaborationPolicy:
    """人机协作Diffusion Policy"""

    def __init__(self, config):
        self.policy = DiffusionPolicy(config)

        # 人体姿态估计
        self.human_pose_estimator = HumanPoseEstimator()

        # 意图识别
        self.intention_predictor = IntentionPredictor()

    def collaborative_task(self, observation, human_state):
        """协作任务"""
        # 估计人体姿态
        human_pose = self.human_pose_estimator(human_state['image'])

        # 预测人类意图
        human_intention = self.intention_predictor(human_pose)

        # 构建协作条件
        collab_condition = {
            'robot_observation': observation,
            'human_pose': human_pose,
            'human_intention': human_intention
        }

        # 生成协作动作
        collaborative_actions = self.policy.sample(collab_condition)

        return collaborative_actions

class EfficientDiffusionPolicy:
    """高效的Diffusion Policy实现"""

    def __init__(self, model, config):
        self.model = model
        self.config = config

        # 模型量化
        self.quantize_model()

        # 知识蒸馏
        self.distilled_model = self.distill_policy()

    def quantize_model(self):
        """模型量化以提高推理速度"""
        # 动态量化
        self.model.diffusion_model = torch.quantization.quantize_dynamic(
            self.model.diffusion_model,
            {nn.Linear, nn.Conv1d},
            dtype=torch.qint8
        )

    def distill_policy(self):
        """知识蒸馏：从扩散策略蒸馏到单步生成器"""
        # 训练单步生成器
        teacher_model = self.model.diffusion_model
        student_model = SingleStepGenerator()

        # 准备训练数据
        for batch in self.distillation_data:
            # 教师模型输出
            with torch.no_grad():
                teacher_output = teacher_model.generate(batch)

            # 学生模型学习
            student_output = student_model(batch)
            loss = F.mse_loss(student_output, teacher_output)

            # 反向传播
            loss.backward()
            optimizer.step()

        return student_model

    def fast_inference(self, observation):
        """快速推理：使用蒸馏后的模型"""
        return self.distilled_model(observation)

class MemoryEfficientDiffusionPolicy:
    """内存高效的Diffusion Policy"""

    def __init__(self, model, config):
        self.model = model
        self.config = config

        # 梯度检查点
        self.gradient_checkpointing = True

        # 混合精度训练
        self.mixed_precision = True

    @torch.cuda.amp.autocast()
    def train_step(self, batch):
        """使用混合精度的训练步骤"""
        observations, actions = batch

        # 梯度检查点以节省内存
        if self.gradient_checkpointing:
            loss = torch.utils.checkpoint.checkpoint(
                self.compute_loss, observations, actions
            )
        else:
            loss = self.compute_loss(observations, actions)

        return loss

    def compute_loss(self, observations, actions):
        """计算损失"""
        # 扩散训练损失计算
        timesteps = torch.randint(
            0, self.config.num_timesteps,
            (actions.shape[0],), device=actions.device
        )

        noise = torch.randn_like(actions)
        noisy_actions = self.noise_scheduler.add_noise(
            actions, noise, timesteps
        )

        condition = self.model.obs_encoder(observations)
        predicted_noise = self.model.diffusion_model(
            noisy_actions, timesteps, condition
        )

        loss = F.mse_loss(predicted_noise, noise)
        return loss

class DiffusionPolicyEvaluator:
    """Diffusion Policy评估器"""

    def __init__(self, config):
        self.config = config

    def evaluate_policy(self, policy, test_dataset):
        """评估策略性能"""
        results = {}

        # 成功率评估
        results['success_rate'] = self.evaluate_success_rate(
            policy, test_dataset
        )

        # 动作质量评估
        results['action_quality'] = self.evaluate_action_quality(
            policy, test_dataset
        )

        # 推理速度评估
        results['inference_speed'] = self.evaluate_inference_speed(policy)

        # 多样性评估
        results['action_diversity'] = self.evaluate_action_diversity(
            policy, test_dataset
        )

        return results

    def evaluate_success_rate(self, policy, test_dataset):
        """评估任务成功率"""
        success_count = 0
        total_count = len(test_dataset)

        for batch in test_dataset:
            observation, ground_truth_actions = batch

            # 生成动作
            predicted_actions = policy.sample(observation)

            # 评估是否成功（任务特定）
            if self.is_task_success(predicted_actions, ground_truth_actions):
                success_count += 1

        return success_count / total_count

    def evaluate_inference_speed(self, policy):
        """评估推理速度"""
        dummy_obs = torch.randn(1, 3, 224, 224)

        # 预热
        for _ in range(10):
            _ = policy.sample(dummy_obs)

        # 计时
        torch.cuda.synchronize()
        start_time = time.time()

        for _ in range(100):
            _ = policy.sample(dummy_obs)

        torch.cuda.synchronize()
        end_time = time.time()

        avg_time = (end_time - start_time) / 100
        fps = 1.0 / avg_time

        return fps

class PolicyComparison:
    """策略比较框架"""

    def __init__(self):
        self.methods = {
            'behavior_cloning': BehaviorCloningPolicy(),
            'diffusion_policy': DiffusionPolicy(),
            'transformer_policy': TransformerPolicy(),
            'rnn_policy': RNNPolicy()
        }

    def compare_methods(self, test_dataset):
        """比较不同方法"""
        results = {}

        for method_name, policy in self.methods.items():
            print(f"Evaluating {method_name}...")

            # 评估各项指标
            method_results = self.evaluate_method(policy, test_dataset)
            results[method_name] = method_results

        # 生成比较报告
        self.generate_comparison_report(results)

        return results

    def evaluate_method(self, policy, test_dataset):
        """评估单个方法"""
        evaluator = DiffusionPolicyEvaluator()

        return {
            'success_rate': evaluator.evaluate_success_rate(policy, test_dataset),
            'inference_speed': evaluator.evaluate_inference_speed(policy),
            'action_quality': evaluator.evaluate_action_quality(policy, test_dataset),
            'training_time': self.measure_training_time(policy),
            'model_size': self.get_model_size(policy)
        }

class DiffusionPolicyDeployment:
    """Diffusion Policy部署系统"""

    def __init__(self, model_path, config):
        self.model = self.load_model(model_path, config)
        self.robot_interface = RobotInterface(config.robot_config)
        self.vision_system = VisionSystem(config.vision_config)

    def load_model(self, model_path, config):
        """加载训练好的模型"""
        model = DiffusionPolicy(config)
        model.load_state_dict(torch.load(model_path))
        model.eval()
        return model

    def run_policy_loop(self):
        """运行策略主循环"""
        while True:
            try:
                # 获取观察
                observation = self.get_observation()

                # 生成动作
                actions = self.model.sample(observation)

                # 执行第一个动作
                first_action = actions[0]
                self.robot_interface.execute_action(first_action)

                # 记录数据用于后续分析
                self.log_episode_data(observation, first_action)

            except KeyboardInterrupt:
                print("Policy execution stopped by user")
                break
            except Exception as e:
                print(f"Error in policy execution: {e}")
                # 安全停止
                self.robot_interface.emergency_stop()
                break

    def get_observation(self):
        """获取当前观察"""
        # 获取图像
        rgb_image = self.vision_system.get_rgb_image()
        depth_image = self.vision_system.get_depth_image()

        # 获取机器人状态
        joint_positions = self.robot_interface.get_joint_positions()
        end_effector_pose = self.robot_interface.get_end_effector_pose()

        # 组装观察
        observation = {
            'rgb_image': rgb_image,
            'depth_image': depth_image,
            'joint_positions': joint_positions,
            'end_effector_pose': end_effector_pose
        }

        return observation

class SafetyMonitor:
    """安全监控系统"""

    def __init__(self, config):
        self.config = config
        self.safety_limits = config.safety_limits

    def validate_action(self, action):
        """验证动作安全性"""
        # 检查关节限制
        if not self.check_joint_limits(action):
            return False, "Joint limits violated"

        # 检查速度限制
        if not self.check_velocity_limits(action):
            return False, "Velocity limits violated"

        # 检查工作空间限制
        if not self.check_workspace_limits(action):
            return False, "Workspace limits violated"

        return True, "Action is safe"

    def emergency_stop(self, reason):
        """紧急停止"""
        print(f"Emergency stop triggered: {reason}")
        # 停止机器人运动
        # 记录错误信息
        # 发送警报