"""
PyRamex GPU Worker
执行GPU加速的计算任务
"""

import logging
import os
from typing import Dict, Any

# 配置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)


class GPUWorker:
    """GPU计算Worker"""

    def __init__(self):
        """初始化Worker"""
        self.device = self._check_gpu()
        logger.info(f"GPU Worker初始化完成，使用设备: {self.device}")

    def _check_gpu(self) -> str:
        """检查GPU可用性"""
        try:
            import torch
            if torch.cuda.is_available():
                device = "cuda"
                gpu_name = torch.cuda.get_device_name(0)
                gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3
                logger.info(f"检测到GPU: {gpu_name}, 显存: {gpu_memory:.1f}GB")
                return device
            else:
                logger.warning("未检测到GPU，使用CPU")
                return "cpu"
        except ImportError:
            logger.warning("PyTorch未安装，使用CPU")
            return "cpu"

    def preprocess(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """GPU加速的预处理"""
        logger.info("执行GPU加速预处理...")

        # TODO: 实现GPU加速的预处理
        # 1. 光谱平滑（CUDA）
        # 2. 基线校正（CUDA）
        # 3. 归一化（CUDA）

        return {
            "status": "success",
            "device": self.device,
            "processed": True
        }

    def pca_reduce(self, data: Dict[str, Any], n_components: int = 50) -> Dict[str, Any]:
        """GPU加速的PCA降维"""
        logger.info(f"执行GPU加速PCA降维，目标维度: {n_components}")

        # TODO: 实现GPU加速的PCA
        # from cuml.decomposition import PCA
        # pca = PCA(n_components=n_components)
        # result = pca.fit_transform(data)

        return {
            "status": "success",
            "device": self.device,
            "n_components": n_components
        }

    def train_model(self, data: Dict[str, Any], model_type: str = "rf") -> Dict[str, Any]:
        """GPU加速的模型训练"""
        logger.info(f"执行GPU加速模型训练，模型类型: {model_type}")

        # TODO: 实现GPU加速的模型训练
        # from cuml.ensemble import RandomForestClassifier
        # model = RandomForestClassifier(n_estimators=100)
        # model.fit(X_train, y_train)

        return {
            "status": "success",
            "device": self.device,
            "model_type": model_type
        }

    def health_check(self) -> Dict[str, Any]:
        """健康检查"""
        try:
            import torch
            if torch.cuda.is_available():
                return {
                    "status": "healthy",
                    "device": "cuda",
                    "gpu_name": torch.cuda.get_device_name(0),
                    "gpu_memory_free": torch.cuda.memory_allocated(0) / 1024**3,
                    "gpu_memory_total": torch.cuda.get_device_properties(0).total_memory / 1024**3
                }
            else:
                return {
                    "status": "healthy",
                    "device": "cpu",
                    "message": "GPU not available"
                }
        except Exception as e:
            return {
                "status": "error",
                "message": str(e)
            }


# Worker主循环
def main():
    """Worker主函数"""
    logger.info("启动PyRamEx GPU Worker...")

    # 创建Worker实例
    worker = GPUWorker()

    # TODO: 实现任务队列监听
    # import redis
    # r = redis.Redis(host='pyramex-redis', port=6379, decode_responses=True)
    #
    # while True:
    #     # 从队列获取任务
    #     task = r.blpop('pyramex:tasks:gpu')
    #     if task:
    #         # 处理任务
    #         worker.process_task(task)

    logger.info("GPU Worker启动完成")


if __name__ == "__main__":
    main()
