import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl.data import AsNodePredDataset
{{ data_import_code }}

{{ model_code }}

{% if user_cfg.general_pipeline.early_stop %}
class EarlyStopping:
    def __init__(self,
                 patience: int = -1,
                 checkpoint_path: str = 'checkpoint.pt'):
        self.patience = patience
        self.checkpoint_path = checkpoint_path
        self.counter = 0
        self.best_score = None
        self.early_stop = False

    def step(self, acc, model):
        score = acc
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(model)
        elif score < self.best_score:
            self.counter += 1
            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(model)
            self.counter = 0
        return self.early_stop

    def save_checkpoint(self, model):
        '''Save model when validation loss decreases.'''
        torch.save(model.state_dict(), self.checkpoint_path)

    def load_checkpoint(self, model):
        model.load_state_dict(torch.load(self.checkpoint_path))
{% endif %}


def accuracy(logits, labels):
    _, indices = torch.max(logits, dim=1)
    correct = torch.sum(indices == labels)
    return correct.item() * 1.0 / len(labels)

def train(cfg, pipeline_cfg, device, data, model, optimizer, loss_fcn):
    g = data[0]  # Only train on the first graph
    g = dgl.remove_self_loop(g)
    g = dgl.add_self_loop(g)
    g = g.to(device)

    node_feat = g.ndata.get('feat', None)
    edge_feat = g.edata.get('feat', None)
    label = g.ndata['label']
    train_mask, val_mask, test_mask = g.ndata['train_mask'].bool(), g.ndata['val_mask'].bool(), g.ndata['test_mask'].bool()

    {% if user_cfg.general_pipeline.early_stop %}
    stopper = EarlyStopping(**pipeline_cfg['early_stop'])
    {% endif %}
    val_acc = 0.
    for epoch in range(pipeline_cfg['num_epochs']):
        model.train()
        logits = model(g, node_feat, edge_feat)
        loss = loss_fcn(logits[train_mask], label[train_mask])

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        train_acc = accuracy(logits[train_mask], label[train_mask])
        if epoch != 0 and epoch % pipeline_cfg['eval_period'] == 0:
            val_acc = accuracy(logits[val_mask], label[val_mask])

            {% if user_cfg.general_pipeline.early_stop %}
            if stopper.step(val_acc, model):
                break
            {% endif %}
        print("Epoch {:05d} | Loss {:.4f} | TrainAcc {:.4f} | ValAcc {:.4f}".
              format(epoch, loss.item(), train_acc, val_acc))

    {% if user_cfg.general_pipeline.early_stop %}
    stopper.load_checkpoint(model)
    {% endif %}
    model.eval()
    with torch.no_grad():
        logits = model(g, node_feat, edge_feat)
        test_acc = accuracy(logits[test_mask], label[test_mask])
    return test_acc

def main():
    {{ user_cfg_str }}
    device = cfg['device']
    pipeline_cfg = cfg['general_pipeline']
    # load data
    data = AsNodePredDataset({{data_initialize_code}})
    # create model
    model_cfg = cfg["model"]
    cfg["model"]["data_info"] = {
        "in_size": model_cfg['embed_size'] if model_cfg['embed_size'] > 0 else data[0].ndata['feat'].shape[1],
        "out_size": data.num_classes,
        "num_nodes": data[0].num_nodes()
    }
    model = {{ model_class_name }}(**cfg["model"])
    model = model.to(device)
    loss = torch.nn.{{ user_cfg.general_pipeline.loss }}()
    optimizer = torch.optim.{{ user_cfg.general_pipeline.optimizer.name }}(model.parameters(), **pipeline_cfg["optimizer"])
    # train
    test_acc = train(cfg, pipeline_cfg, device, data, model, optimizer, loss)
    torch.save(model.state_dict(), pipeline_cfg["save_path"])
    return test_acc

if __name__ == '__main__':
    all_acc = []
    num_runs = {{ user_cfg.general_pipeline.num_runs }}
    for run in range(num_runs):
        print(f'Run experiment #{run}')
        test_acc = main()
        print("Test Accuracy {:.4f}".format(test_acc))
        all_acc.append(test_acc)
    avg_acc = np.round(np.mean(all_acc), 6)
    std_acc = np.round(np.std(all_acc), 6)
    print(f'Accuracy across {num_runs} runs: {avg_acc} ± {std_acc}')
