import torch
import numpy as np
import torch.nn.functional as F
import torch.optim as optim
from deeprobust.graph.defense import GCNSVD
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset, PrePtbDataset
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed.')
parser.add_argument('--dataset', type=str, default='cora', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset')
parser.add_argument('--ptb_rate', type=float, default=0.05,  help='pertubation rate')
parser.add_argument('--k', type=int, default=15, help='Truncated Components.')

args = parser.parse_args()
args.cuda = torch.cuda.is_available()
print('cuda: %s' % args.cuda)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# make sure you use the same data splits as you generated attacks
np.random.seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

# Here the random seed is to split the train/val/test data,
# we need to set the random seed to be the same as that when you generate the perturbed graph
# data = Dataset(root='/tmp/', name=args.dataset, setting='nettack', seed=15)
# Or we can just use setting='prognn' to get the splits
data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test


# load pre-attacked graph
perturbed_data = PrePtbDataset(root='/tmp/',
        name=args.dataset,
        attack_method='meta',
        ptb_rate=args.ptb_rate)
perturbed_adj = perturbed_data.adj

# Setup Defense Model
model = GCNSVD(nfeat=features.shape[1], nclass=labels.max()+1,
                nhid=16, device=device)

model = model.to(device)

print('=== testing GCN-SVD on perturbed graph ===')
model.fit(features, perturbed_adj, labels, idx_train, idx_val, k=args.k, verbose=True)
model.eval()
output = model.test(idx_test)

