# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init


class MeanAggregator(nn.Module):

    def forward(self, features, A):
        x = torch.bmm(A, features)
        return x


class GraphConv(nn.Module):

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.weight = nn.Parameter(torch.FloatTensor(in_dim * 2, out_dim))
        self.bias = nn.Parameter(torch.FloatTensor(out_dim))
        init.xavier_uniform_(self.weight)
        init.constant_(self.bias, 0)
        self.aggregator = MeanAggregator()

    def forward(self, features, A):
        b, n, d = features.shape
        assert d == self.in_dim
        agg_feats = self.aggregator(features, A)
        cat_feats = torch.cat([features, agg_feats], dim=2)
        out = torch.einsum('bnd,df->bnf', cat_feats, self.weight)
        out = F.relu(out + self.bias)
        return out


class GCN(nn.Module):
    """Graph convolutional network for clustering. This was from repo
    https://github.com/Zhongdao/gcn_clustering licensed under the MIT license.

    Args:
        feat_len(int): The input node feature length.
    """

    def __init__(self, feat_len):
        super(GCN, self).__init__()
        self.bn0 = nn.BatchNorm1d(feat_len, affine=False).float()
        self.conv1 = GraphConv(feat_len, 512)
        self.conv2 = GraphConv(512, 256)
        self.conv3 = GraphConv(256, 128)
        self.conv4 = GraphConv(128, 64)
        self.classifier = nn.Sequential(
            nn.Linear(64, 32), nn.PReLU(32), nn.Linear(32, 2))

    def forward(self, x, A, knn_inds):

        num_local_graphs, num_max_nodes, feat_len = x.shape

        x = x.view(-1, feat_len)
        x = self.bn0(x)
        x = x.view(num_local_graphs, num_max_nodes, feat_len)

        x = self.conv1(x, A)
        x = self.conv2(x, A)
        x = self.conv3(x, A)
        x = self.conv4(x, A)
        k = knn_inds.size(-1)
        mid_feat_len = x.size(-1)
        edge_feat = torch.zeros((num_local_graphs, k, mid_feat_len),
                                device=x.device)
        for graph_ind in range(num_local_graphs):
            edge_feat[graph_ind, :, :] = x[graph_ind, knn_inds[graph_ind]]
        edge_feat = edge_feat.view(-1, mid_feat_len)
        pred = self.classifier(edge_feat)

        return pred