import torch


def masked_mae_loss(scaler, mask_value):
    def loss(preds, labels):
        # # 仅对预测反归一化；标签在数据管道中保持原始量纲
        # if scaler:
        #     preds = scaler.inverse_transform(preds)
        return mae_torch(pred=preds, true=labels, mask_value=mask_value)

    return loss


def mae_torch(pred, true, mask_value=None):
    if mask_value is not None:
        mask = torch.gt(true, mask_value)
        pred = torch.masked_select(pred, mask)
        true = torch.masked_select(true, mask)
    return torch.mean(torch.abs(true - pred))


def rmse_torch(pred, true, mask_value=None):
    if mask_value is not None:
        mask = torch.gt(true, mask_value)
        pred = torch.masked_select(pred, mask)
        true = torch.masked_select(true, mask)
    return torch.sqrt(torch.mean((pred - true) ** 2))


def mape_torch(pred, true, mask_value=None):
    if mask_value is not None:
        mask = torch.gt(true, mask_value)
        pred = torch.masked_select(pred, mask)
        true = torch.masked_select(true, mask)
    return torch.mean(torch.abs(torch.div((true - pred), (true + 0.001))))


def all_metrics(pred, true, mask1, mask2):
    if mask1 == "None":
        mask1 = None
    if mask2 == "None":
        mask2 = None
    mae = mae_torch(pred, true, mask1)
    rmse = rmse_torch(pred, true, mask1)
    mape = mape_torch(pred, true, mask2)
    return mae, rmse, mape


if __name__ == "__main__":
    pred = torch.Tensor([1, 2, 3, 4])
    true = torch.Tensor([2, 1, 4, 5])
    print(all_metrics(pred, true, None, None))