import math
import torch
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter


# ref: https://github.com/tkipf/pygcn


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.zeros([in_features, out_features], dtype=torch.float32))
        if bias:
            self.bias = Parameter(torch.zeros([out_features], dtype=torch.float32))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, x, adj):
        """

        :param x: node features, B x N x F_in
        :param adj: adjacency matrix, B x N x N
        :return: hidden node features, B x N x F_out
        """

        support = torch.matmul(x, self.weight)
        output = torch.bmm(adj, support)
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' \
               + str(self.in_features) + ' -> ' \
               + str(self.out_features) + ')'
