class Pi0Architecture:
    """
    Pi0的核心架构设计
    """
    def __init__(self, config):
        # 基础组件
        self.vision_encoder = VisionEncoder()  # 视觉编码器
        self.language_encoder = LanguageEncoder()  # 语言编码器
        self.action_expert = ActionExpert()  # 动作专家网络

        # 流匹配模型
        self.flow_model = FlowMatchingModel()

        # 动作标记化器
        self.action_tokenizer = ActionTokenizer()

class VLA_Model(nn.Module):
    """Pi0的VLA模型架构"""

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

        # 基础视觉语言模型（基于PaliGemma）
        self.vlm_backbone = PaliGemmaModel(config.vlm_config)

        # 动作专家网络
        self.action_expert = ActionExpert(
            input_dim=self.vlm_backbone.hidden_size,
            output_dim=config.action_dim,
            hidden_dim=config.action_hidden_dim
        )

        # 流匹配头
        self.flow_head = FlowMatchingHead(
            input_dim=self.vlm_backbone.hidden_size,
            action_dim=config.action_dim
        )

        # 时间步编码
        self.time_embedding = TimeEmbedding(config.time_dim)

    def forward(self, images, instructions, actions=None, timesteps=None):
        """
        前向传播
        Args:
            images: 视觉观测
            instructions: 语言指令
            actions: 目标动作（训练时使用）
            timesteps: 流匹配时间步
        """
        # 1. 视觉语言编码
        vlm_features = self.vlm_backbone(images, instructions)

        # 2. 时间步编码
        time_emb = self.time_embedding(timesteps)

        # 3. 特征融合
        combined_features = self.fuse_features(vlm_features, time_emb)

        if self.training:
            # 训练模式：流匹配
            noise = torch.randn_like(actions)
            noisy_actions = self.add_noise(actions, noise, timesteps)

            # 预测流
            predicted_flow = self.flow_head(combined_features)

            return predicted_flow, noise, noisy_actions
        else:
            # 推理模式：动作生成
            action_logits = self.action_expert(combined_features)
            return action_logits

class ActionTokenizer:
    """动作标记化器 - Pi0的核心创新"""

    def __init__(self,
                 action_dim=7,  # 机器人动作维度
                 vocab_size=1000,  # 动作词汇表大小
                 sequence_length=16):  # 动作序列长度

        self.action_dim = action_dim
        self.vocab_size = vocab_size
        self.sequence_length = sequence_length

        # 学习动作码本
        self.action_codebook = nn.Parameter(
            torch.randn(vocab_size, action_dim)
        )

        # 编码器网络
        self.encoder = nn.Sequential(
            nn.Linear(action_dim, 256),
            nn.ReLU(),
            nn.Linear(256, vocab_size)
        )

        # 解码器网络
        self.decoder = nn.Sequential(
            nn.Linear(vocab_size, 256),
            nn.ReLU(),
            nn.Linear(256, action_dim)
        )

    def encode(self, continuous_actions):
        """将连续动作编码为离散标记"""
        batch_size, seq_len, action_dim = continuous_actions.shape

        # 计算与码本的相似度
        actions_flat = continuous_actions.view(-1, action_dim)

        # 找到最近的码本条目
        similarities = torch.mm(
            actions_flat, self.action_codebook.t()
        )
        tokens = torch.argmax(similarities, dim=-1)

        return tokens.view(batch_size, seq_len)

    def decode(self, discrete_tokens):
        """将离散标记解码为连续动作"""
        batch_size, seq_len = discrete_tokens.shape

        # 从码本获取动作
        selected_codes = self.action_codebook[discrete_tokens]

        # 通过解码器细化
        actions_flat = selected_codes.view(-1, self.vocab_size)
        refined_actions = self.decoder(actions_flat)

        return refined_actions.view(batch_size, seq_len, self.action_dim)

    def get_token_logits(self, vlm_features):
        """从VLM特征获得动作token预测"""
        # 使用encoder网络获得token分布
        token_logits = self.encoder(vlm_features)
        return token_logits

class FlowMatchingModel(nn.Module):
    """流匹配模型 - 替代传统扩散模型"""

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

        # 流匹配网络
        self.flow_network = nn.Sequential(
            nn.Linear(config.feature_dim + config.action_dim, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, config.action_dim)
        )

        # 时间编码
        self.time_encoder = SinusoidalTimeEmbedding(config.time_dim)

    def sample_time(self, batch_size):
        """采样流匹配时间步"""
        return torch.rand(batch_size, device=self.device)

    def compute_velocity_field(self, x0, x1, t):
        """计算速度场"""
        # 直线路径的速度场
        return x1 - x0

    def forward(self, vlm_features, target_actions):
        """
        流匹配训练
        Args:
            vlm_features: VLM编码的特征
            target_actions: 目标动作
        """
        batch_size = vlm_features.size(0)

        # 1. 采样时间步
        t = self.sample_time(batch_size)

        # 2. 采样起始点（噪声）
        x0 = torch.randn_like(target_actions)

        # 3. 计算插值
        t_expanded = t.unsqueeze(-1).unsqueeze(-1)
        x_t = (1 - t_expanded) * x0 + t_expanded * target_actions

        # 4. 计算目标速度场
        velocity_field = self.compute_velocity_field(x0, target_actions, t)

        # 5. 预测速度场
        time_emb = self.time_encoder(t)
        combined_features = torch.cat([vlm_features, x_t], dim=-1)
        predicted_velocity = self.flow_network(combined_features)

        return predicted_velocity, velocity_field, x_t, t

class Pi0TrainingPipeline:
    """Pi0多阶段训练流程"""

    def __init__(self, config):
        self.config = config
        self.model = VLA_Model(config)
        self.action_tokenizer = ActionTokenizer()

    def stage1_pretraining(self, dataset):
        """阶段1：大规模预训练"""
        print("Stage 1: Large-scale pretraining")

        # 使用多样化的机器人数据集
        for batch in dataset:
            images, instructions, actions = batch

            # 动作标记化
            action_tokens = self.action_tokenizer.encode(actions)

            # VLM预训练
            vlm_loss = self.train_vlm(images, instructions, action_tokens)

            # 流匹配训练
            flow_loss = self.train_flow_matching(images, instructions, actions)

            # 总损失
            total_loss = vlm_loss + flow_loss

            self.backward_pass(total_loss)

    def stage2_task_specific_finetuning(self, task_datasets):
        """阶段2：任务特定微调"""
        print("Stage 2: Task-specific finetuning")

        for task_name, dataset in task_datasets.items():
            print(f"Finetuning on {task_name}")

            for batch in dataset:
                loss = self.compute_task_loss(batch, task_name)
                self.backward_pass(loss)

    def stage3_multi_task_co_training(self, multi_task_data):
        """阶段3：多任务协同训练"""
        print("Stage 3: Multi-task co-training")

        for batch in multi_task_data:
            # 混合不同任务的数据
            mixed_loss = 0

            for task_batch in batch:
                task_loss = self.compute_task_loss(task_batch)
                mixed_loss += task_loss

            # 梯度累积
            mixed_loss = mixed_loss / len(batch)
            self.backward_pass(mixed_loss)

class Pi0DataPipeline:
    """Pi0数据处理流程"""

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

        # 支持的数据类型
        self.supported_modalities = [
            'rgb_images',      # RGB图像
            'depth_images',    # 深度图像
            'joint_states',    # 关节状态
            'end_effector_pose', # 末端执行器位姿
            'force_torque',    # 力扭矩传感器
            'language_instructions' # 语言指令
        ]

    def collect_data(self, robot_tasks):
        """收集机器人任务数据"""
        dataset = []

        for task in robot_tasks:
            task_data = self.execute_and_record(task)
            dataset.extend(task_data)

        return dataset

    def process_episode(self, raw_episode):
        """处理单个episode数据"""
        processed_data = {
            'observations': [],
            'actions': [],
            'instructions': [],
            'task_metadata': {}
        }

        # 1. 观测预处理
        for obs in raw_episode['observations']:
            processed_obs = self.preprocess_observation(obs)
            processed_data['observations'].append(processed_obs)

        # 2. 动作序列处理
        actions = raw_episode['actions']
        processed_data['actions'] = self.normalize_actions(actions)

        # 3. 指令处理
        instruction = raw_episode['instruction']
        processed_data['instructions'] = self.process_instruction(instruction)

        return processed_data

    def create_training_batch(self, episode_data_list):
        """创建训练批次"""
        batch = {
            'images': [],
            'instructions': [],
            'actions': [],
            'attention_masks': []
        }

        for episode_data in episode_data_list:
            # 随机采样时间步
            sample_indices = self.sample_temporal_indices(episode_data)

            for idx in sample_indices:
                batch['images'].append(episode_data['observations'][idx])
                batch['actions'].append(episode_data['actions'][idx])
                batch['instructions'].append(episode_data['instructions'][idx])

        # 批次后处理
        return self.postprocess_batch(batch)

class Pi0Evaluator:
    """Pi0性能评估器"""

    def __init__(self):
        self.metrics = {
            'success_rate': SuccessRateMetric(),
            'action_quality': ActionQualityMetric(),
            'instruction_following': InstructionFollowingMetric(),
            'generalization': GeneralizationMetric()
        }

    def evaluate_on_tasks(self, model, task_suite):
        """在任务套件上评估模型"""
        results = {}

        for task in task_suite:
            task_results = self.evaluate_single_task(model, task)
            results[task.name] = task_results

        return self.aggregate_results(results)

    def evaluate_generalization(self, model, novel_tasks):
        """评估泛化能力"""
        generalization_scores = {}

        for novel_task in novel_tasks:
            # 零样本评估
            zero_shot_score = self.zero_shot_eval(model, novel_task)

            # 少样本适应
            few_shot_score = self.few_shot_adapt(model, novel_task, k=5)

            generalization_scores[novel_task.name] = {
                'zero_shot': zero_shot_score,
                'few_shot': few_shot_score
            }

        return generalization_scores

class HomeServiceRobot:
    """基于Pi0的家庭服务机器人"""

    def __init__(self, pi0_model):
        self.pi0 = pi0_model
        self.robot_interface = RobotInterface()
        self.perception_system = PerceptionSystem()

    def execute_instruction(self, instruction):
        """执行自然语言指令"""
        # 获取环境观测
        observations = self.perception_system.get_observations()

        # 生成动作序列
        actions = self.pi0.generate_actions(
            observations, instruction
        )

        # 执行动作
        for action in actions:
            self.robot_interface.execute(action)

            # 实时反馈
            current_obs = self.perception_system.get_observations()
            if self.should_replan(current_obs, instruction):
                actions = self.pi0.generate_actions(
                    current_obs, instruction
                )

    def interactive_task_execution(self):
        """交互式任务执行"""
        print("家庭服务机器人就绪，请输入指令...")

        while True:
            instruction = input("> ")
            if instruction.lower() == 'quit':
                break

            try:
                self.execute_instruction(instruction)
                print("任务完成！")
            except Exception as e:
                print(f"执行失败: {e}")

class IndustrialAutomation:
    """基于Pi0的工业自动化系统"""

    def __init__(self, pi0_model, workstation_config):
        self.pi0 = pi0_model
        self.workstation = Workstation(workstation_config)
        self.quality_control = QualityControlSystem()

    def perform_assembly_task(self, assembly_instruction):
        """执行装配任务"""
        # 1. 识别零件
        parts = self.identify_parts()

        # 2. 生成装配动作
        assembly_actions = self.pi0.generate_actions(
            parts, assembly_instruction
        )

        # 3. 执行装配
        for action in assembly_actions:
            success = self.workstation.execute_action(action)
            if not success:
                # 错误恢复
                recovery_actions = self.pi0.generate_error_recovery(
                    action, current_state
                )
                self.workstation.execute_actions(recovery_actions)

        # 4. 质量检查
        quality_score = self.quality_control.inspect_product()
        return quality_score > self.quality_threshold

class MultiRobotAdapter:
    """多机器人适配器"""

    def __init__(self, pi0_model):
        self.pi0 = pi0_model
        self.robot_profiles = {}

    def register_robot(self, robot_name, robot_config):
        """注册新的机器人配置"""
        self.robot_profiles[robot_name] = {
            'action_dim': robot_config.action_dim,
            'action_space': robot_config.action_space,
            'observation_dim': robot_config.observation_dim,
            'adapter_network': self.build_adapter_network(robot_config)
        }

    def adapt_actions(self, generic_actions, target_robot):
        """适配通用动作到特定机器人"""
        profile = self.robot_profiles[target_robot]

        # 使用适配网络转换动作空间
        adapted_actions = profile['adapter_network'](generic_actions)

        # 应用机器人特定的约束
        constrained_actions = self.apply_robot_constraints(
            adapted_actions, profile
        )

        return constrained_actions

class RealTimeOptimization:
    """实时性优化"""

    def __init__(self, pi0_model):
        self.pi0 = pi0_model
        self.model_cache = ModelCache()
        self.action_buffer = ActionBuffer()

    def optimize_inference(self, observation, instruction):
        """优化推理过程"""
        # 1. 模型量化
        quantized_model = self.quantize_model(self.pi0)

        # 2. 推理缓存
        cache_key = self.get_cache_key(observation, instruction)
        if cache_key in self.model_cache:
            return self.model_cache[cache_key]

        # 3. 批量推理
        with torch.no_grad():
            actions = quantized_model.generate_actions(
                observation, instruction, num_samples=1
            )

        # 4. 缓存结果
        self.model_cache[cache_key] = actions

        return actions[0]

    def parallel_inference(self, observation_instruction_pairs):
        """并行推理多个请求"""
        # 批处理输入
        batch_observations = [pair[0] for pair in observation_instruction_pairs]
        batch_instructions = [pair[1] for pair in observation_instruction_pairs]

        # 批量推理
        with torch.no_grad():
            batch_actions = self.pi0.batch_generate_actions(
                batch_observations, batch_instructions
            )

        return batch_actions