from torch import cat, nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import GINConv, global_add_pool


class GIN(nn.Module):
    r"""
    From `GraphDTA <https://doi.org/10.1093/bioinformatics/btaa921>`_ (Nguyen et al., 2020),
    based on `Graph Isomorphism Network <https://arxiv.org/abs/1810.00826>`_ (Xu et al., 2019)
    """
    def __init__(
        self,
        num_features: int,
        out_channels: int,
        dropout: float
    ):
        super().__init__()

        dim = 32
        self.dropout = dropout
        self.relu = nn.ReLU()

        nn1 = Sequential(Linear(num_features, dim), ReLU(), Linear(dim, dim))
        self.conv1 = GINConv(nn1)
        self.bn1 = nn.BatchNorm1d(dim)

        nn2 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
        self.conv2 = GINConv(nn2)
        self.bn2 = nn.BatchNorm1d(dim)

        nn3 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
        self.conv3 = GINConv(nn3)
        self.bn3 = nn.BatchNorm1d(dim)

        nn4 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
        self.conv4 = GINConv(nn4)
        self.bn4 = nn.BatchNorm1d(dim)

        nn5 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
        self.conv5 = GINConv(nn5)
        self.bn5 = nn.BatchNorm1d(dim)

        self.fc1_xd = Linear(dim, out_channels)

    def forward(self, data):
        x, edge_index, batch = data.x, data.edge_index, data.batch

        x = F.relu(self.conv1(x, edge_index))
        x = self.bn1(x)
        x = F.relu(self.conv2(x, edge_index))
        x = self.bn2(x)
        x = F.relu(self.conv3(x, edge_index))
        x = self.bn3(x)
        x = F.relu(self.conv4(x, edge_index))
        x = self.bn4(x)
        x = F.relu(self.conv5(x, edge_index))
        x = self.bn5(x)
        x = global_add_pool(x, batch)
        x = F.relu(self.fc1_xd(x))
        x = F.dropout(x, p=self.dropout, training=self.training)

        return x