# 导入自定义模块
# Import custom modules

"""
    训练多任务 Vision Transformer (ViT) 模型，包括关键点检测、姿态估计和深度估计。
    
    参数:
    - config (Config): 配置实例，包含模型、训练和路径配置。
    """

"""
    Training a multi-task Vision Transformer (ViT) model with keypoint detection, pose estimation, and depth estimation.
    
    Parameters.
    - config (Config): configuration instance with model, training, and path configuration.
     """

 # 设置设备
 # Setting up the device

   print(f"使用设备: {device}")
   print(f"Utilization equipment: {device}")

    # 设置日志记录器
    # Setting up the logger

    logger.info("开始训练 Feature Extractor 模型")
    logger.info("Start Training Feature Extractor Models")

    logger.info(f"配置: {config.to_dict()}")
    logger.info(f"configure: {config.to_dict()}")

    # 确保检查点目录存在
 # Ensure checkpoint directory exists

    # 获取数据加载器
    # Get Data Loader

    logger.info(f"数据加载器已初始化，批次数: {len(data_loader)}")
    logger.info(f"Data loader initialized, batch size: {len(data_loader)}")

    # 初始化模型
    # Initialize the model

    logger.info("模型已初始化并移动到设备")
    logger.info("Model initialized and moved to device")

    # 定义损失函数
   # Define the loss function

    logger.info("损失函数已初始化")
    logger.info("The loss function is initialized")

    # 定义优化器
    # Define the optimizer

    logger.info(f"优化器已初始化: {config.training.optimizer_type}")
    logger.info(f"Optimizer initialized: {config.training.optimizer_type}")

    # 加载检查点（如果存在）
    # Load checkpoints (if present)

        logger.info(f"从检查点加载模型，开始训练的纪元数: {start_epoch}, 最佳损失: {best_loss:.4f}")
        logger.info(f"Number of epochs to load the model from the checkpoint and start the training: {start_epoch}, Best Loss: {best_loss:.4f}")

    # 设置学习率调度器（可选）
    # Set learning rate scheduler (optional)

    # 开始训练
    # Start training

        logger.info(f"开始训练纪元 {epoch + 1}/{config.training.num_epochs}")
        logger.info(f"Beginning of Training Era {epoch + 1}/{config.training.num_epochs}")

     # 获取数据并移动到设备
     # Fetch data and move it to the device

     # 假设所有样本执行所有任务，task_ids 为0,1,2代表不同任务
    # Assume all samples perform all tasks and task_ids are 0,1,2 for different tasks

     # 这里为了简化，假设每个样本对应一个任务ID，可以根据实际需求调整
     # Here, for simplicity, it is assumed that each sample corresponds to a task ID, which can be adjusted according to actual needs

    # 例如，如果每个样本同时执行多个任务，可以调整模型和训练方式
    # For example, if each sample performs multiple tasks at the same time, the model and training can be adapted

    # 所有样本对应任务0
    # All samples correspond to task 0

   # 前向传播
   # Forward propagation

   # 计算损失
  # Calculated losses

  # 反向传播和优化
  # Backpropagation and optimization

 # 累积损失
# Accumulated losses

# 更新进度条描述
# Update the progress bar description

 # 调整学习率
# Adjusted learning rate

# 计算平均损失
# Calculate average loss

logger.info(f"纪元 {epoch + 1} 结束，平均损失: 
logger.info(f “Epoch {epoch + 1} ended, average loss: 

 # 保存最佳模型
# Preservation of the best models

   logger.info(f"新最佳模型已保存，纪元 {epoch + 1}, 损失: {best_loss:.4f}")
   logger.info(f “New best model saved, epoch {epoch + 1}, loss: {best_loss:.4f}”)

 # 每个纪元结束后保存一个检查点
# Save a checkpoint at the end of each epoch 

logger.info(f"检查点已保存: {checkpoint_filename}")
logger.info(f "Checkpoint saved: {checkpoint_filename}")

    logger.info("Feature Extractor 训练完成")
    logger.info("Feature Extractor Training completed")

    """
    训练语义编码器模型，整合CVAE和判别器进行对抗性特征学习。
    
    参数:
    - config (Config): 配置实例，包含模型、训练和路径配置。
    """

    """
    Training semantic coder models, integrating CVAE and discriminators for adversarial feature learning.
    
    Parameters.
    - config (Config): configuration instance with model, training and path configuration.
    """

    # 设置设备
    # Setting up the device

    print(f"使用设备: {device}")
    print(f"Utilization equipment: {device}")

    # 设置日志记录器    
    # Setting up the logger

    logger.info("开始训练 Semantic Encoder 模型")
    logger.info("Start Training Semantic Encoder Models")

    logger.info(f"配置: {config.to_dict()}")
    logger.info(f"configure: {config.to_dict()}")

    # 确保检查点目录存在
    # Ensure checkpoint directory exists

    # 获取数据加载器
    # Get Data Loader

    logger.info(f"数据加载器已初始化，批次数: {len(data_loader)}")
    logger.info(f "Data loader initialized, batch count: {len(data_loader)}")

    # 初始化模型
    # Initialize the model

    logger.info("模型已初始化并移动到设备")
    logger.info("Model initialized and moved to device")

    # 定义损失函数
    # Define the loss function

    # 由于 CombinedLoss 在 semantic_encoder.py 中未完全集成，这里直接使用 SemanticEncoder 的 compute_loss
   # Since CombinedLoss is not fully integrated in semantic_encoder.py, we use SemanticEncoder's compute_loss directly here

    # 并结合对抗性损失的实现
# and combines it with the adversarial loss implementation

    logger.info("损失函数已初始化")
logger.info(“Loss function initialized”)

    # 定义优化器
    # Define the optimizer

    logger.info("优化器已初始化: Adam")
    logger.info("Optimizer initialized: Adam")

    # 加载检查点（如果存在）
    # Load checkpoints (if present)

        logger.info(f"从检查点加载模型，开始训练的纪元数: {start_epoch}, 最佳损失: {best_loss:.4f}")
        logger.info(f “Loading model from checkpoint, number of epochs to start training: {start_epoch}, best loss: {best_loss:.4f}”)

    # 设置学习率调度器（可选）
    # Set the learning rate scheduler (optional)

    # 开始训练
    # Start training

        logger.info(f"开始训练纪元 {epoch + 1}/{config.training.num_epochs}")
        logger.info(f"Beginning of Training Era {epoch + 1}/{config.training.num_epochs}")

            # 获取数据并移动到设备
            # Fetch data and move it to the device

            # 训练判别器
            # Training discriminators

            # 真实样本
            # Real samples

            # 生成样本
            # Generate samples

            # 判别器总损失
            # Total discriminator losses

            # 训练生成器（CVAE）
	    # Training generator (CVAE)

            # 生成样本重新判别
            # Generate samples for reclassification

            # 对抗性损失，目标是使D_fake接近1
            # Adversarial loss with the goal of bringing D_fake closer to 1

            # CVAE的损失
            # CVAE losses

            # 综合损失
            # Comprehensive losses

            # 累积损失
            # Accumulated losses

            # 更新进度条描述
            # Update the progress bar description

        # 调整学习率
       # Adjusted learning rate

        # 计算平均损失
        # Calculate average loss

        logger.info(f"纪元 {epoch + 1} 结束，平均损失: 
        logger.info(f"millennium {epoch + 1} close，average loss: 

        # 保存最佳模型
        # Preservation of the best models

            logger.info(f"新最佳模型已保存，纪元 {epoch + 1}, 损失: {best_loss:.4f}")
            logger.info(f “New best model saved, epoch {epoch + 1}, loss: {best_loss:.4f}”)

        # 每个纪元结束后保存一个检查点
        # Save a checkpoint at the end of each epoch

        logger.info(f"检查点已保存: {checkpoint_filename}")
        logger.info(f"Checkpoint saved: {checkpoint_filename}")

    logger.info("Semantic Encoder 训练完成")
    logger.info("Semantic Encoder Training completed")

    """
    使用训练好的语义编码器和特征提取器，从原始2D图像生成3D模型。
    
    参数:
    - config (Config): 配置实例，包含模型、训练和路径配置。
    """
    """
    Generates a 3D model from a raw 2D image using a trained semantic coder and feature extractor.
    
    Parameters.
    - config (Config): configuration instance with model, training and path configuration.
    """
   
    # 设置设备
    # setup device

    print(f"使用设备: {device}")
    print(f "Using device: {device}")

    # 设置日志记录器
    # Set up the logger

    logger.info("开始推理")
    logger.info("Start reasoning")

    logger.info(f"配置: {config.to_dict()}")
    logger.info(f"configure: {config.to_dict()}")

    # 加载模型
    # Load model

    # 加载特征提取器
    # Load the feature extractor

        logger.info(f"特征提取器模型已加载: {feature_extractor_checkpoint}")
        logger.info(f"Feature extractor model loaded: {feature_extractor_checkpoint}")

        logger.error(f"特征提取器检查点不存在: 
        logger.error(f"Feature extractor checkpoint does not exist: 

    # 加载语义编码器
    # Load semantic encoder

        logger.info(f"语义编码器模型已加载: {semantic_encoder_checkpoint}")
        logger.info(f"Semantic coder model loaded: {semantic_encoder_checkpoint}")

        logger.error(f"语义编码器检查点不存在: 
        logger.error(f"Semantic coder checkpoint does not exist: 

    # 获取推理数据加载器
    # Get inference data loader

        batch_size=1,  # 推理时通常使用批大小为1
        batch_size=1,  # Reasoning is usually done with a batch size of 1

    logger.info(f"推理数据加载器已初始化，样本数: {len(infer_data_loader)}")
   logger.info(f"The inference data loader is initialized and the number of samples: {len(infer_data_loader)}")

    # 初始化3D重建所需的模型（ViT-扩散模型等）
    # Initialize models needed for 3D reconstruction (ViT-diffusion models, etc.)

    # 这里假设有一个名为 ViTDiffusionModel 的模型，您需要根据具体实现进行调整
    # Assume here that there is a model called ViTDiffusionModel that you need to adapt to your specific implementation   

    # from vit_diffusion import ViTDiffusionModel  # 假设您有一个 ViT-扩散模型
    # from vit_diffusion import ViTDiffusionModel  # Assuming you have a ViT-diffusion model

    #     logger.info(f"ViT-扩散模型已加载: {vit_diffusion_checkpoint}")
    #     logger.info(f"ViT-diffusion model loaded: {vit_diffusion_checkpoint}")

    #     logger.error(f"ViT-扩散模型检查点不存在: {vit_diffusion_checkpoint}")
    #     logger.error(f"ViT-diffusion model checkpoints do not exist: {vit_diffusion_checkpoint}")

    # 进行推理
    # Reasoning

    for batch in tqdm(infer_data_loader, desc="推理"):
    for batch in tqdm(infer_data_loader, desc="inference"):

            # 特征提取器前向传播
            # Feature extractor forward propagation

            # 假设任务ID为0
            # Assuming a task ID of 0

            # 语义编码器前向传播
           # Semantic coder forward propagation

            # 语义数据 y' 可以通过 y_pred_one_hot 传输到接收端
            # Semantic data y' can be transmitted to the receiver via y_pred_one_hot

            # 假设在本地进行传输模拟
            # Transmission simulation is assumed to be done locally

            # 接收端解码
            # Decoding at the receiver

            # 这里 recon_x_decoded 为重构的特征，可以进一步用于3D重建
            # Here recon_x_decoded is a reconstructed feature that can be further used for 3D reconstruction

            # 使用 ViT-扩散模型进行3D重建
            # 3D reconstruction using the ViT-Diffusion Model

            # 假设 ViTDiffusionModel 已经定义并加载
	    # Assuming ViTDiffusionModel is defined and loaded

           # 使用Marching Cubes算法生成网格模型
          # Use the Marching Cubes algorithm to generate mesh models

            # 由于 ViTDiffusionModel 未定义，这里仅示例化流程
            # As ViTDiffusionModel is undefined, only the process is exemplified here

            logger.info("推理步骤完成，但ViT-扩散模型和3D重建尚未实现。")
            logger.info("Inference step completed, but ViT-diffusion modeling and 3D reconstruction not yet realized。")

            # 示例保存生成的语义标签和重构特征
            # Example to save generated semantic tags and reconstructed features

            logger.info(f"推理结果已保存到 {output_dir}")
            logger.info(f"The results of the reasoning have been saved to {output_dir}")

    logger.info("推理完成")
    logger.info("Reasoning complete")

choices=['train_feature_extractor', 'train_semantic_encoder', 'infer'],
                        help="选择要执行的操作：训练特征提取器（train_feature_extractor）、训练语义编码器（train_semantic_encoder）、推理（infer）")
    parser.add_argument('--config', type=str, default='config.json', help="配置文件路径，默认为 'config.json'")

 choices=['train_feature_extractor', 'train_semantic_encoder', 'infer'],
                        help="Select the action to perform: train the feature extractor（train_feature_extractor）、Training semantic coders（train_semantic_encoder）、inference（infer）")
    parser.add_argument('--config', type=str, default='config.json', help="Configuration file path, default is 'config.json'")

    # 加载配置
    # Load Configuration

        raise FileNotFoundError(f"配置文件不存在: {args.config}")
        raise FileNotFoundError(f"Configuration file does not exist: {args.config}")

    # 保存当前配置（可选）
# Save current configuration (optional)

    # 根据选择的操作执行相应的函数
    # Execute the appropriate function based on the selected operation

        raise ValueError(f"未知的操作: {args.action}")
        raise ValueError(f"unknown operation: {args.action}")