import torch
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from torch_geometric.nn import global_add_pool, global_mean_pool
from torch_geometric.nn import global_max_pool, GlobalAttention, Set2Set
from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
from torch_geometric.utils import degree
import math

# GIN convolution along the graph structure


class GINConv(MessagePassing):
    def __init__(self, emb_dim):
        '''
            emb_dim (int): node embedding dimensionality
        '''

        super(GINConv, self).__init__(aggr="add")

        self.mlp = torch.nn.Sequential(
            torch.nn.Linear(emb_dim, 2*emb_dim),
            torch.nn.BatchNorm1d(2*emb_dim),
            torch.nn.ReLU(),
            torch.nn.Linear(2*emb_dim, emb_dim)
        )
        self.eps = torch.nn.Parameter(torch.Tensor([0]))

        self.bond_encoder = BondEncoder(emb_dim=emb_dim)

    def forward(self, x, edge_index, edge_attr):
        edge_embedding = self.bond_encoder(edge_attr.long())
        out = self.mlp((1 + self.eps) * x + self.propagate(
            edge_index, x=x, edge_attr=edge_embedding
        ))

        return out

    def message(self, x_j, edge_attr):
        return F.relu(x_j + edge_attr)

    def update(self, aggr_out):
        return aggr_out

# GCN convolution along the graph structure


class GCNConv(MessagePassing):
    def __init__(self, emb_dim):
        super(GCNConv, self).__init__(aggr='add')

        self.linear = torch.nn.Linear(emb_dim, emb_dim)
        self.root_emb = torch.nn.Embedding(1, emb_dim)
        self.bond_encoder = BondEncoder(emb_dim=emb_dim)

    def forward(self, x, edge_index, edge_attr):
        x = self.linear(x)
        edge_embedding = self.bond_encoder(edge_attr)

        row, col = edge_index

        #edge_weight = torch.ones((edge_index.size(1), ), device=edge_index.device)
        deg = degree(row, x.size(0), dtype=x.dtype) + 1
        deg_inv_sqrt = deg.pow(-0.5)
        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0

        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]

        return self.propagate(
            edge_index, x=x, edge_attr=edge_embedding, norm=norm
        ) + F.relu(x + self.root_emb.weight) * 1. / deg.view(-1, 1)

    def message(self, x_j, edge_attr, norm):
        return norm.view(-1, 1) * F.relu(x_j + edge_attr)

    def update(self, aggr_out):
        return aggr_out


# GNN to generate node embedding
class GNN_node(torch.nn.Module):
    """
    Output:
        node representations
    """

    def __init__(
        self, num_layer, emb_dim, drop_ratio=0.5,
        JK="last", residual=False, gnn_type='gin'
    ):
        '''
            emb_dim (int): node embedding dimensionality
            num_layer (int): number of GNN message passing layers

        '''

        super(GNN_node, self).__init__()
        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        # add residual connection or not
        self.residual = residual

        if self.num_layer < 2:
            raise ValueError("Number of GNN layers must be greater than 1.")

        self.atom_encoder = AtomEncoder(emb_dim)

        # List of GNNs
        self.convs = torch.nn.ModuleList()
        self.batch_norms = torch.nn.ModuleList()

        for layer in range(num_layer):
            if gnn_type == 'gin':
                self.convs.append(GINConv(emb_dim))
            elif gnn_type == 'gcn':
                self.convs.append(GCNConv(emb_dim))
            else:
                raise ValueError(
                    'Undefined GNN type called {}'.format(gnn_type))

            self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))

    def forward(self, data):
        x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
        # computing input node embedding
        h_list = [self.atom_encoder(x)]
        for layer in range(self.num_layer):
            h = self.convs[layer](h_list[layer], edge_index, edge_attr)
            h = self.batch_norms[layer](h)

            if layer == self.num_layer - 1:
                # remove relu for the last layer
                h = F.dropout(h, self.drop_ratio, training=self.training)
            else:
                h = F.dropout(F.relu(h), self.drop_ratio,
                              training=self.training)

            if self.residual:
                h += h_list[layer]

            h_list.append(h)

        # Different implementations of Jk-concat
        if self.JK == "last":
            node_representation = h_list[-1]
        elif self.JK == "sum":
            node_representation = 0
            for layer in range(self.num_layer + 1):
                node_representation += h_list[layer]

        return node_representation


# Virtual GNN to generate node embedding
class GNN_node_Virtualnode(torch.nn.Module):
    """
    Output:
        node representations
    """

    def __init__(
        self, num_layer, emb_dim, drop_ratio=0.5,
        JK="last", residual=False, gnn_type='gin'
    ):
        '''
            emb_dim (int): node embedding dimensionality
        '''

        super(GNN_node_Virtualnode, self).__init__()
        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        # add residual connection or not
        self.residual = residual

        if self.num_layer < 2:
            raise ValueError("Number of GNN layers must be greater than 1.")

        self.atom_encoder = AtomEncoder(emb_dim)

        # set the initial virtual node embedding to 0.
        self.virtualnode_embedding = torch.nn.Embedding(1, emb_dim)
        torch.nn.init.constant_(self.virtualnode_embedding.weight.data, 0)

        # List of GNNs
        self.convs = torch.nn.ModuleList()
        # batch norms applied to node embeddings
        self.batch_norms = torch.nn.ModuleList()

        # List of MLPs to transform virtual node at every layer
        self.mlp_virtualnode_list = torch.nn.ModuleList()

        for layer in range(num_layer):
            if gnn_type == 'gin':
                self.convs.append(GINConv(emb_dim))
            elif gnn_type == 'gcn':
                self.convs.append(GCNConv(emb_dim))
            else:
                raise ValueError(
                    'Undefined GNN type called {}'.format(gnn_type))

            self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))

        for layer in range(num_layer - 1):
            self.mlp_virtualnode_list.append(torch.nn.Sequential(
                torch.nn.Linear(emb_dim, 2*emb_dim),
                torch.nn.BatchNorm1d(2*emb_dim),
                torch.nn.ReLU(),
                torch.nn.Linear(2*emb_dim, emb_dim),
                torch.nn.BatchNorm1d(emb_dim),
                torch.nn.ReLU()
            ))

    def forward(self, batched_data):

        x, edge_index = batched_data.x, batched_data.edge_index
        edge_attr, batch = batched_data.edge_attr, batched_data.batch
        # virtual node embeddings for graphs
        virtualnode_embedding = self.virtualnode_embedding(torch.zeros(
            batch[-1].item() + 1
        ).to(edge_index.dtype).to(edge_index.device))

        h_list = [self.atom_encoder(x)]
        for layer in range(self.num_layer):
            # add message from virtual nodes to graph nodes
            h_list[layer] = h_list[layer] + virtualnode_embedding[batch]

            # Message passing among graph nodes
            h = self.convs[layer](h_list[layer], edge_index, edge_attr)

            h = self.batch_norms[layer](h)
            if layer == self.num_layer - 1:
                # remove relu for the last layer
                h = F.dropout(h, self.drop_ratio, training=self.training)
            else:
                h = F.dropout(
                    F.relu(h), self.drop_ratio,
                    training=self.training
                )

            if self.residual:
                h = h + h_list[layer]

            h_list.append(h)

            # update the virtual nodes
            if layer < self.num_layer - 1:
                # add message from graph nodes to virtual nodes
                virtualnode_embedding_temp = global_add_pool(
                    h_list[layer], batch) + virtualnode_embedding
                # transform virtual nodes using MLP

                if self.residual:
                    virtualnode_embedding = virtualnode_embedding + F.dropout(
                        self.mlp_virtualnode_list[layer](
                            virtualnode_embedding_temp
                        ), self.drop_ratio, training=self.training
                    )
                else:
                    virtualnode_embedding = F.dropout(
                        self.mlp_virtualnode_list[layer](
                            virtualnode_embedding_temp
                        ), self.drop_ratio, training=self.training
                    )

        # Different implementations of Jk-concat
        if self.JK == "last":
            node_representation = h_list[-1]
        elif self.JK == "sum":
            node_representation = 0
            for layer in range(self.num_layer + 1):
                node_representation += h_list[layer]

        return node_representation


class GNNGraph(torch.nn.Module):

    def __init__(
        self, num_layer=5, emb_dim=300,
        gnn_type='gin', virtual_node=True, residual=False,
        drop_ratio=0.5, JK="last", graph_pooling="mean"
    ):
        '''
            num_tasks (int): number of labels to be predicted
            virtual_node (bool): whether to add virtual node or not
        '''

        super(GNNGraph, self).__init__()

        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        self.emb_dim = emb_dim
        self.graph_pooling = graph_pooling

        if self.num_layer < 2:
            raise ValueError("Number of GNN layers must be greater than 1.")

        # GNN to generate node embeddings
        if virtual_node:
            self.gnn_node = GNN_node_Virtualnode(
                num_layer, emb_dim, JK=JK, drop_ratio=drop_ratio,
                residual=residual, gnn_type=gnn_type
            )
        else:
            self.gnn_node = GNN_node(
                num_layer, emb_dim, JK=JK, drop_ratio=drop_ratio,
                residual=residual, gnn_type=gnn_type
            )

        # Pooling function to generate whole-graph embeddings
        if self.graph_pooling == "sum":
            self.pool = global_add_pool
        elif self.graph_pooling == "mean":
            self.pool = global_mean_pool
        elif self.graph_pooling == "max":
            self.pool = global_max_pool
        elif self.graph_pooling == "attention":
            self.pool = GlobalAttention(gate_nn=torch.nn.Sequential(
                torch.nn.Linear(emb_dim, 2 * emb_dim),
                torch.nn.BatchNorm1d(2 * emb_dim),
                torch.nn.ReLU(),
                torch.nn.Linear(2*emb_dim, 1)
            ))
        elif self.graph_pooling == "set2set":
            self.pool = Set2Set(emb_dim, processing_steps=2)
        else:
            raise ValueError("Invalid graph pooling type.")

    def forward(self, drug_subs):
        h_node = self.gnn_node(drug_subs)

        h_graph = self.pool(h_node, drug_subs.batch)
        return h_graph


class GNN(torch.nn.Module):
    def __init__(self, args):
        super(GNN, self).__init__()
        self.num_layer = args.num_layer
        self.JK = args.JK
        self.emb_dim = args.emb_dim
        self.graph_pooling = args.graph_pooling
        self.gnn_type = args.gnn_type
        self.virtual_node = args.virtual_node
        self.residual = args.residual
        self.drop_ratio = args.drop_ratio

        self.model = GNNGraph(
            self.num_layer, self.emb_dim, self.gnn_type,
            self.virtual_node, self.residual, self.drop_ratio, self.JK, self.graph_pooling
        )

    def forward(self, drug_subs):
        h_graph = self.model(drug_subs)
        return h_graph
