from models import *
from data_utils import normalize

def parse_method(args, dataset, n, c, d, device):
    if args.method == 'link':
        model = LINK(n, c).to(device)
    elif args.method == 'gcn':
        if args.dataset == 'ogbn-proteins':
            # Pre-compute GCN normalization.
            dataset.graph['edge_index'] = normalize(dataset.graph['edge_index'])
            model = GCN(in_channels=d,
                        hidden_channels=args.hidden_channels,
                        out_channels=c,
                        dropout=args.dropout,
                        save_mem=True,
                        use_bn=not args.no_bn).to(device)
        else:
            model = GCN(in_channels=d,
                        hidden_channels=args.hidden_channels,
                        out_channels=c,
                        num_layers=args.num_layers,
                        dropout=args.dropout,
                        use_bn=not args.no_bn).to(device)
            
    elif args.method == 'mlp' or args.method == 'cs':
        model = MLP(in_channels=d, hidden_channels=args.hidden_channels,
                    out_channels=c, num_layers=args.num_layers,
                    dropout=args.dropout).to(device)
    elif args.method == 'sgc':
        if args.cached:
            model = SGC(in_channels=d, out_channels=c, hops=args.hops).to(device)
        else:
            model = SGCMem(in_channels=d, out_channels=c,
                           hops=args.hops).to(device)
    elif args.method == 'gprgnn':
        model = GPRGNN(d, args.hidden_channels, c, alpha=args.gpr_alpha).to(device)
    elif args.method == 'appnp':
        model = APPNP_Net(d, args.hidden_channels, c, alpha=args.gpr_alpha).to(device)
    elif args.method == 'gat':
        model = GAT(d, args.hidden_channels, c, num_layers=args.num_layers,
                    dropout=args.dropout, heads=args.gat_heads).to(device)
    elif args.method == 'lp':
        mult_bin = args.dataset=='ogbn-proteins'
        model = MultiLP(c, args.lp_alpha, args.hops, mult_bin=mult_bin)
    elif args.method == 'mixhop':
        model = MixHop(d, args.hidden_channels, c, num_layers=args.num_layers,
                       dropout=args.dropout, hops=args.hops).to(device)
    elif args.method == 'gcnjk':
        model = GCNJK(d, args.hidden_channels, c, num_layers=args.num_layers,
                        dropout=args.dropout, jk_type=args.jk_type).to(device)
    elif args.method == 'gatjk':
        model = GATJK(d, args.hidden_channels, c, num_layers=args.num_layers,
                        dropout=args.dropout, heads=args.gat_heads,
                        jk_type=args.jk_type).to(device)
    elif args.method == 'h2gcn':
        model = H2GCN(d, args.hidden_channels, c, dataset.graph['edge_index'],
                        dataset.graph['num_nodes'],
                        num_layers=args.num_layers, dropout=args.dropout,
                        num_mlp_layers=args.num_mlp_layers).to(device)
    else:
        raise ValueError('Invalid method')
    return model


def parser_add_main_args(parser):
    parser.add_argument('--dataset', type=str, default='fb100')
    parser.add_argument('--sub_dataset', type=str, default='')
    parser.add_argument('--hidden_channels', type=int, default=32)
    parser.add_argument('--dropout', type=float, default=0.5)
    parser.add_argument('--lr', type=float, default=0.01)
    parser.add_argument('--method', '-m', type=str, default='link')
    parser.add_argument('--epochs', type=int, default=500)
    parser.add_argument('--cpu', action='store_true')
    parser.add_argument('--weight_decay', type=float, default=1e-3)
    parser.add_argument('--display_step', type=int,
                        default=1, help='how often to print')
    parser.add_argument('--hops', type=int, default=1,
                        help='power of adjacency matrix for certain methods')
    parser.add_argument('--num_layers', type=int, default=2,
                        help='number of layers for deep methods')
    parser.add_argument('--runs', type=int, default=1,
                        help='number of distinct runs')
    parser.add_argument('--cached', action='store_true',
                        help='set to use faster sgc')
    parser.add_argument('--gat_heads', type=int, default=8,
                        help='attention heads for gat')
    parser.add_argument('--lp_alpha', type=float, default=.1,
                        help='alpha for label prop')
    parser.add_argument('--gpr_alpha', type=float, default=.1,
                        help='alpha for gprgnn')
    parser.add_argument('--directed', action='store_true',
                        help='set to not symmetrize adjacency')
    parser.add_argument('--jk_type', type=str, default='max', choices=['max', 'lstm', 'cat'],
                        help='jumping knowledge type')
    parser.add_argument('--rocauc', action='store_true', 
                        help='set the eval function to rocauc')
    parser.add_argument('--num_mlp_layers', type=int, default=1,
                        help='number of mlp layers in h2gcn')
    parser.add_argument('--print_prop', action='store_true',
                        help='print proportions of predicted class')
    parser.add_argument('--train_prop', type=float, default=.5,
                        help='training label proportion')
    parser.add_argument('--valid_prop', type=float, default=.25,
                        help='validation label proportion')
    parser.add_argument('--rand_split', action='store_true', help='use random splits')
    parser.add_argument('--no_bn', action='store_true', help='do not use batchnorm')

