"""Torch Module for Topology Adaptive Graph Convolutional layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn

from .... import function as fn


class TAGConv(nn.Module):
    r"""Topology Adaptive Graph Convolutional layer from paper `Topology
    Adaptive Graph Convolutional Networks <https://arxiv.org/pdf/1710.10370.pdf>`__.

    .. math::
        \mathbf{X}^{\prime} = \sum_{k=0}^K \mathbf{D}^{-1/2} \mathbf{A}
        \mathbf{D}^{-1/2}\mathbf{X} \mathbf{\Theta}_{k},

    where :math:`\mathbf{A}` denotes the adjacency matrix and
    :math:`D_{ii} = \sum_{j=0} A_{ij}` its diagonal degree matrix.

    Parameters
    ----------
    in_feats : int
        Input feature size.
    out_feats : int
        Output feature size.
    k: int, optional
        Number of hops :math: `k`. (default: 2)
    bias: bool, optional
        If True, adds a learnable bias to the output. Default: ``True``.
    activation: callable activation function/layer or None, optional
        If not None, applies an activation function to the updated node features.
        Default: ``None``.

    Attributes
    ----------
    lin : torch.Module
        The learnable linear module.
    """
    def __init__(self,
                 in_feats,
                 out_feats,
                 k=2,
                 bias=True,
                 activation=None):
        super(TAGConv, self).__init__()
        self._in_feats = in_feats
        self._out_feats = out_feats
        self._k = k
        self._activation = activation
        self.lin = nn.Linear(in_feats * (self._k + 1), out_feats, bias=bias)

        self.reset_parameters()

    def reset_parameters(self):
        """Reinitialize learnable parameters."""
        gain = nn.init.calculate_gain('relu')
        nn.init.xavier_normal_(self.lin.weight, gain=gain)

    def forward(self, graph, feat):
        r"""Compute topology adaptive graph convolution.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : torch.Tensor
            The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}`
            is size of input feature, :math:`N` is the number of nodes.

        Returns
        -------
        torch.Tensor
            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
            is size of output feature.
        """
        with graph.local_scope():
            assert graph.is_homograph(), 'Graph is not homogeneous'

            norm = th.pow(graph.in_degrees().float().clamp(min=1), -0.5)
            shp = norm.shape + (1,) * (feat.dim() - 1)
            norm = th.reshape(norm, shp).to(feat.device)

            #D-1/2 A D -1/2 X
            fstack = [feat]
            for _ in range(self._k):

                rst = fstack[-1] * norm
                graph.ndata['h'] = rst

                graph.update_all(fn.copy_src(src='h', out='m'),
                                 fn.sum(msg='m', out='h'))
                rst = graph.ndata['h']
                rst = rst * norm
                fstack.append(rst)

            rst = self.lin(th.cat(fstack, dim=-1))

            if self._activation is not None:
                rst = self._activation(rst)

            return rst
