import torch
import numpy as np
import torch.nn.functional as F
from deeprobust.graph.defense import GCNJaccard, GCN, ProGNN
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset, PrePtbDataset
import argparse
import os
import csv

parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed.')

parser.add_argument('--dataset', type=str, default='pubmed', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
parser.add_argument('--ptb_type', type=str, default='clean', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
parser.add_argument('--ptb_rate', type=float, default=0.0,  help='pertubation rate')

parser.add_argument('--epochs', type=int,  default=200, help='Number of epochs to train.')

parser.add_argument('--hidden', type=int, default=16, help='Number of hidden units.')
parser.add_argument('--dropout', type=float, default=0.5, help='Dropout rate (1 - keep probability).')
parser.add_argument('--debug', action='store_true',default=False, help='debug mode')
parser.add_argument('--only_gcn', action='store_true',default=False, help='test the performance of gcn without other components')
# parser.add_argument('--no-cuda', action='store_true', default=False, help='Disables CUDA training.')
parser.add_argument('--lr', type=float, default=0.01, help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=5e-4, help='Weight decay (L2 loss on parameters).')
parser.add_argument('--alpha', type=float, default=5e-4, help='weight of l1 norm')
parser.add_argument('--beta', type=float, default=1.5, help='weight of nuclear norm')
parser.add_argument('--gamma', type=float, default=1, help='weight of l2 norm')
parser.add_argument('--lambda_', type=float, default=0, help='weight of feature smoothing')
parser.add_argument('--phi', type=float, default=0, help='weight of symmetric loss')
parser.add_argument('--inner_steps', type=int, default=2, help='steps for inner optimization')
parser.add_argument('--outer_steps', type=int, default=1, help='steps for outer optimization')
parser.add_argument('--lr_adj', type=float, default=0.01, help='lr for training adj')
parser.add_argument('--symmetric', action='store_true', default=False, help='whether use symmetric matrix')

parser.add_argument('--gpu', type=int, default=6, help='GPU device ID (default: 0)')

args = parser.parse_args()
args.cuda = torch.cuda.is_available()
device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
print('Using device:', device)

# 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')
data = Dataset(root='/tmp/', name=args.dataset)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
idx_unlabeled = np.union1d(idx_val, idx_test)
print(type(adj))
# adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False)


## load from attacked_adj
ptb_path = f"../attacked_adj/{args.dataset}/{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt"
perturbed_adj = torch.load(ptb_path)
perturbed_adj = sp.csr_matrix(perturbed_adj.to('cpu').numpy())

def test_prognn(features, adj, labels):
    

    adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False, device=device)
    
    model = GCN(nfeat=features.shape[1],
                nhid=args.hidden,
                nclass=labels.max().item() + 1,
                dropout=args.dropout, device=device).to(device)
    prognn = ProGNN(model, args, device)
    prognn.fit(features, adj, labels, idx_train, idx_val)
    acc = prognn.test(features, labels, idx_test)
    return acc


def main():
    
    acc = test_prognn(features, perturbed_adj, labels)
    csv_dir = "../result"
    os.makedirs(csv_dir, exist_ok=True) 

    csv_filename = os.path.join(csv_dir, f"ProGNN_{args.dataset}_{args.ptb_type}_{args.ptb_rate}.csv")
    row = [f"{args.dataset} ", f" {args.ptb_type} ", f" {args.ptb_rate} ", f" {acc}"]

    try:
        file_exists = os.path.isfile(csv_filename)
        with open(csv_filename, 'a', newline='') as csvfile:
            writer = csv.writer(csvfile)
            if not file_exists:
                writer.writerow(["dataset ", "ptb_type ", "ptb_rate ", "accuracy"])
            writer.writerow(row)
    except Exception as e:
        print(f"[Error] Failed to write CSV: {e}")

if __name__ == '__main__':
    main()