import torch


def is_hopper_gpu():
    if torch.cuda.is_available():
        device_capability = torch.cuda.get_device_capability(0)
        major, minor = device_capability
        return major == 9
    return False


def get_num_warps_stages(head_dim, block_size, is_hopper_gpu):
    """
    Returns recommended num_warps and num_stages for a Sparse Attention kernel in Triton.

    Args:
        head_dim (int): Size of the head dimension.
        block_size (int): Size of the block in the attention matrix.
        is_hopper_gpu (bool): True if Hopper GPU, False if Ampere GPU.

    Returns:
        tuple: (num_warps, num_stages) recommended values.
    """
    # Determine if head_dim and block_size exceed 64
    head_large = head_dim > 64
    block_large = block_size > 64

    if is_hopper_gpu:
        # Hopper GPU recommendations
        if head_large and block_large:
            num_warps = 8
            num_stages = 3
        elif head_large or block_large:
            num_warps = 4
            num_stages = 3
        else:
            num_warps = 2
            num_stages = 2
    else:
        # Ampere GPU recommendations
        if head_large and block_large:
            num_warps = 8
            num_stages = 3
        elif head_large or block_large:
            num_warps = 8
            num_stages = 3
        else:
            num_warps = 2
            num_stages = 2
    return num_warps, num_stages