"""Tensorflow modules for graph attention networks(GAT)."""
# pylint: disable= no-member, arguments-differ, invalid-name
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np

from .... import function as fn
from ..softmax import edge_softmax
from ..utils import Identity

# pylint: enable=W0235


class GATConv(layers.Layer):
    r"""Apply `Graph Attention Network <https://arxiv.org/pdf/1710.10903.pdf>`__
    over an input signal.

    .. math::
        h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i,j} W^{(l)} h_j^{(l)}

    where :math:`\alpha_{ij}` is the attention score bewteen node :math:`i` and
    node :math:`j`:

    .. math::
        \alpha_{ij}^{l} & = \mathrm{softmax_i} (e_{ij}^{l})

        e_{ij}^{l} & = \mathrm{LeakyReLU}\left(\vec{a}^T [W h_{i} \| W h_{j}]\right)

    Parameters
    ----------
    in_feats : int
        Input feature size.
    out_feats : int
        Output feature size.
    num_heads : int
        Number of heads in Multi-Head Attention.
    feat_drop : float, optional
        Dropout rate on feature, defaults: ``0``.
    attn_drop : float, optional
        Dropout rate on attention weight, defaults: ``0``.
    negative_slope : float, optional
        LeakyReLU angle of negative slope.
    residual : bool, optional
        If True, use residual connection.
    activation : callable activation function/layer or None, optional.
        If not None, applies an activation function to the updated node features.
        Default: ``None``.
    """

    def __init__(self,
                 in_feats,
                 out_feats,
                 num_heads,
                 feat_drop=0.,
                 attn_drop=0.,
                 negative_slope=0.2,
                 residual=False,
                 activation=None):
        super(GATConv, self).__init__()
        self._num_heads = num_heads
        self._in_feats = in_feats
        self._out_feats = out_feats
        xinit = tf.keras.initializers.VarianceScaling(scale=np.sqrt(
            2), mode="fan_avg", distribution="untruncated_normal")
        self.fc = layers.Dense(
            out_feats * num_heads, use_bias=False, kernel_initializer=xinit)
        self.attn_l = tf.Variable(initial_value=xinit(
            shape=(1, num_heads, out_feats), dtype='float32'), trainable=True)

        self.attn_r = tf.Variable(initial_value=xinit(
            shape=(1, num_heads, out_feats), dtype='float32'), trainable=True)
        self.feat_drop = layers.Dropout(rate=feat_drop)
        self.attn_drop = layers.Dropout(rate=attn_drop)
        self.leaky_relu = layers.LeakyReLU(alpha=negative_slope)
        if residual:
            if in_feats != out_feats:
                self.res_fc = layers.Dense(
                    num_heads * out_feats, use_bias=False, kernel_initializer=xinit)
            else:
                self.res_fc = Identity()
        else:
            self.res_fc = None
            # self.register_buffer('res_fc', None)
        self.activation = activation

    def call(self, graph, feat):
        r"""Compute graph attention network layer.

        Parameters
        ----------
        graph : DGLGraph
            The graph.
        feat : tf.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
        -------
        tf.Tensor
            The output feature of shape :math:`(N, H, D_{out})` where :math:`H`
            is the number of heads, and :math:`D_{out}` is size of output feature.
        """
        graph = graph.local_var()
        h = self.feat_drop(feat)
        feat = tf.reshape(self.fc(h), (-1, self._num_heads, self._out_feats))
        # NOTE: GAT paper uses "first concatenation then linear projection"
        # to compute attention scores, while ours is "first projection then
        # addition", the two approaches are mathematically equivalent:
        # We decompose the weight vector a mentioned in the paper into
        # [a_l || a_r], then
        # a^T [Wh_i || Wh_j] = a_l Wh_i + a_r Wh_j
        # Our implementation is much efficient because we do not need to
        # save [Wh_i || Wh_j] on edges, which is not memory-efficient. Plus,
        # addition could be optimized with DGL's built-in function u_add_v,
        # which further speeds up computation and saves memory footprint.
        el = tf.reduce_sum(feat * self.attn_l, axis=-1, keepdims=True)
        er = tf.reduce_sum(feat * self.attn_r, axis=-1, keepdims=True)
        graph.ndata.update({'ft': feat, 'el': el, 'er': er})
        # compute edge attention, el and er are a_l Wh_i and a_r Wh_j respectively.
        graph.apply_edges(fn.u_add_v('el', 'er', 'e'))
        e = self.leaky_relu(graph.edata.pop('e'))
        # compute softmax
        graph.edata['a'] = self.attn_drop(edge_softmax(graph, e))
        # message passing
        graph.update_all(fn.u_mul_e('ft', 'a', 'm'),
                         fn.sum('m', 'ft'))
        rst = graph.ndata['ft']
        # residual
        if self.res_fc is not None:
            resval = tf.reshape(self.res_fc(
                h), (h.shape[0], -1, self._out_feats))
            rst = rst + resval
        # activation
        if self.activation:
            rst = self.activation(rst)
        return rst
