import dgl
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn

class HGTLayer(nn.Module):
    def __init__(self, in_dim, out_dim, num_types, num_relations, n_heads, dropout = 0.2, use_norm = False):
        super(HGTLayer, self).__init__()

        self.in_dim        = in_dim
        self.out_dim       = out_dim
        self.num_types     = num_types
        self.num_relations = num_relations
        self.total_rel     = num_types * num_relations * num_types
        self.n_heads       = n_heads
        self.d_k           = out_dim // n_heads
        self.sqrt_dk       = math.sqrt(self.d_k)
        self.att           = None
        
        self.k_linears   = nn.ModuleList()
        self.q_linears   = nn.ModuleList()
        self.v_linears   = nn.ModuleList()
        self.a_linears   = nn.ModuleList()
        self.norms       = nn.ModuleList()
        self.use_norm    = use_norm
        
        for t in range(num_types):
            self.k_linears.append(nn.Linear(in_dim,   out_dim))
            self.q_linears.append(nn.Linear(in_dim,   out_dim))
            self.v_linears.append(nn.Linear(in_dim,   out_dim))
            self.a_linears.append(nn.Linear(out_dim,  out_dim))
            if use_norm:
                self.norms.append(nn.LayerNorm(out_dim))
            
        self.relation_pri   = nn.Parameter(torch.ones(num_relations, self.n_heads))
        self.relation_att   = nn.Parameter(torch.Tensor(num_relations, n_heads, self.d_k, self.d_k))
        self.relation_msg   = nn.Parameter(torch.Tensor(num_relations, n_heads, self.d_k, self.d_k))
        self.skip           = nn.Parameter(torch.ones(num_types))
        self.drop           = nn.Dropout(dropout)
        
        nn.init.xavier_uniform_(self.relation_att)
        nn.init.xavier_uniform_(self.relation_msg)

    def edge_attention(self, edges):
        etype = edges.data['id'][0]
        
        '''
            Step 1: Heterogeneous Mutual Attention
        '''
        relation_att = self.relation_att[etype]
        relation_pri = self.relation_pri[etype]
        key   = torch.bmm(edges.src['k'].transpose(1,0), relation_att).transpose(1,0)
        att   = (edges.dst['q'] * key).sum(dim=-1) * relation_pri / self.sqrt_dk
        
        '''
            Step 2: Heterogeneous Message Passing
        '''
        relation_msg = self.relation_msg[etype]
        val   = torch.bmm(edges.src['v'].transpose(1,0), relation_msg).transpose(1,0)
        return {'a': att, 'v': val}
    
    def message_func(self, edges):
        return {'v': edges.data['v'], 'a': edges.data['a']}
    
    def reduce_func(self, nodes):
        '''
            Softmax based on target node's id (edge_index_i). 
            NOTE: Using DGL's API, there is a minor difference with this softmax with the original one.
                  This implementation will do softmax only on edges belong to the same relation type, instead of for all of the edges.
        '''
        att = F.softmax(nodes.mailbox['a'], dim=1)
        h   = torch.sum(att.unsqueeze(dim = -1) * nodes.mailbox['v'], dim=1)
        return {'t': h.view(-1, self.out_dim)}
        
    def forward(self, G, inp_key, out_key):
        node_dict, edge_dict = G.node_dict, G.edge_dict
        for srctype, etype, dsttype in G.canonical_etypes:
            k_linear = self.k_linears[node_dict[srctype]]
            v_linear = self.v_linears[node_dict[srctype]] 
            q_linear = self.q_linears[node_dict[dsttype]]
            
            G.nodes[srctype].data['k'] = k_linear(G.nodes[srctype].data[inp_key]).view(-1, self.n_heads, self.d_k)
            G.nodes[srctype].data['v'] = v_linear(G.nodes[srctype].data[inp_key]).view(-1, self.n_heads, self.d_k)
            G.nodes[dsttype].data['q'] = q_linear(G.nodes[dsttype].data[inp_key]).view(-1, self.n_heads, self.d_k)
            
            G.apply_edges(func=self.edge_attention, etype=etype)
        G.multi_update_all({etype : (self.message_func, self.reduce_func) \
                            for etype in edge_dict}, cross_reducer = 'mean')
        for ntype in G.ntypes:
            '''
                Step 3: Target-specific Aggregation
                x = norm( W[node_type] * gelu( Agg(x) ) + x )
            '''
            n_id = node_dict[ntype]
            alpha = torch.sigmoid(self.skip[n_id])
            trans_out = self.drop(self.a_linears[n_id](G.nodes[ntype].data['t']))
            trans_out = trans_out * alpha + G.nodes[ntype].data[inp_key] * (1-alpha)
            if self.use_norm:
                G.nodes[ntype].data[out_key] = self.norms[n_id](trans_out)
                
class HGT(nn.Module):
    def __init__(self, G, n_inp, n_hid, n_out, n_layers, n_heads, use_norm = True):
        super(HGT, self).__init__()
        self.gcs = nn.ModuleList()
        self.n_inp = n_inp
        self.n_hid = n_hid
        self.n_out = n_out
        self.n_layers = n_layers
        self.adapt_ws  = nn.ModuleList()
        for t in range(len(G.node_dict)):
            self.adapt_ws.append(nn.Linear(n_inp,   n_hid))
        for _ in range(n_layers):
            self.gcs.append(HGTLayer(n_hid, n_hid, len(G.node_dict), len(G.edge_dict), n_heads, use_norm = use_norm))
        self.out = nn.Linear(n_hid, n_out)

    def forward(self, G, out_key):
        for ntype in G.ntypes:
            n_id = G.node_dict[ntype]
            G.nodes[ntype].data['h'] = F.gelu(self.adapt_ws[n_id](G.nodes[ntype].data['inp']))
        for i in range(self.n_layers):
            self.gcs[i](G, 'h', 'h')
        return self.out(G.nodes[out_key].data['h'])

class HeteroRGCNLayer(nn.Module):
    def __init__(self, in_size, out_size, etypes):
        super(HeteroRGCNLayer, self).__init__()
        # W_r for each relation
        self.weight = nn.ModuleDict({
                name : nn.Linear(in_size, out_size) for name in etypes
            })

    def forward(self, G, feat_dict):
        # The input is a dictionary of node features for each type
        funcs = {}
        for srctype, etype, dsttype in G.canonical_etypes:
            # Compute W_r * h
            Wh = self.weight[etype](feat_dict[srctype])
            # Save it in graph for message passing
            G.nodes[srctype].data['Wh_%s' % etype] = Wh
            # Specify per-relation message passing functions: (message_func, reduce_func).
            # Note that the results are saved to the same destination feature 'h', which
            # hints the type wise reducer for aggregation.
            funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))
        # Trigger message passing of multiple types.
        # The first argument is the message passing functions for each relation.
        # The second one is the type wise reducer, could be "sum", "max",
        # "min", "mean", "stack"
        G.multi_update_all(funcs, 'sum')
        # return the updated node feature dictionary
        return {ntype : G.nodes[ntype].data['h'] for ntype in G.ntypes}
    
    
class HeteroRGCN(nn.Module):
    def __init__(self, G, in_size, hidden_size, out_size):
        super(HeteroRGCN, self).__init__()
        # create layers
        self.layer1 = HeteroRGCNLayer(in_size, hidden_size, G.etypes)
        self.layer2 = HeteroRGCNLayer(hidden_size, out_size, G.etypes)

    def forward(self, G, out_key):
        input_dict = {ntype : G.nodes[ntype].data['inp'] for ntype in G.ntypes}
        h_dict = self.layer1(G, input_dict)
        h_dict = {k : F.leaky_relu(h) for k, h in h_dict.items()}
        h_dict = self.layer2(G, h_dict)
        # get paper logits
        return h_dict[out_key]
