import torch


def onehot(y, num_classes):
    y_onehot = torch.zeros(y.size(0), num_classes).to(y.device)
    if len(y.size()) == 1:
        y_onehot = y_onehot.scatter_(1, y.unsqueeze(-1), 1)
    elif len(y.size()) == 2:
        y_onehot = y_onehot.scatter_(1, y, 1)
    else:
        raise ValueError("[onehot]: y should be in shape [B], or [B, C]")
    return y_onehot


def sum(tensor, dim=None, keepdim=False):
    if dim is None:
        # sum up all dim
        return torch.sum(tensor)
    else:
        if isinstance(dim, int):
            dim = [dim]
        dim = sorted(dim)
        for d in dim:
            tensor = tensor.sum(dim=d, keepdim=True)
        if not keepdim:
            for i, d in enumerate(dim):
                tensor.squeeze_(d - i)
        return tensor


def mean(tensor, dim=None, keepdim=False):
    if dim is None:
        # mean all dim
        return torch.mean(tensor)
    else:
        if isinstance(dim, int):
            dim = [dim]
        dim = sorted(dim)
        for d in dim:
            tensor = tensor.mean(dim=d, keepdim=True)
        if not keepdim:
            for i, d in enumerate(dim):
                tensor.squeeze_(d - i)
        return tensor


def split_feature(tensor, type="split"):
    """
    type = ["split", "cross"]
    """
    C = tensor.size(1)
    if type == "split":
        return tensor[:, : C // 2, ...], tensor[:, C // 2 :, ...]
    elif type == "cross":
        return tensor[:, 0::2, ...], tensor[:, 1::2, ...]


def cat_feature(tensor_a, tensor_b):
    return torch.cat((tensor_a, tensor_b), dim=1)


def pixels(tensor):
    return int(tensor.size(2) * tensor.size(3))
