"""
edge_conv.py includes edge_attr to edge_conv
"""
from typing import Callable, Optional, Union

import torch
from torch import Tensor
from torch.nn import Linear
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size


class EdgeConvConv(MessagePassing):
    def __init__(self, nn: Callable, eps: float = 0., train_eps: bool = False,
                 edge_dim: Optional[int] = None, **kwargs):
        kwargs.setdefault('aggr', 'add')
        super().__init__(**kwargs)
        self.nn = nn
        self.initial_eps = eps
        if train_eps:
            self.eps = torch.nn.Parameter(torch.Tensor([eps]))
        else:
            self.register_buffer('eps', torch.Tensor([eps]))
        if edge_dim is not None:
            if hasattr(self.nn, 'in_features'):
                in_channels = self.nn.in_features
            else:
                in_channels = self.nn.in_channels
            self.lin = Linear(edge_dim, in_channels)
        else:
            self.lin = None

    def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj,
                edge_attr: OptTensor = None, size: Size = None) -> Tensor:
        """"""
        if isinstance(x, Tensor):
            x: OptPairTensor = (x, x)

        # propagate_type: (x: OptPairTensor, edge_attr: OptTensor)
        out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=size)

        return out

    def message(self, x_i: Tensor, x_j: Tensor, edge_attr: Tensor) -> Tensor:

        temp = torch.cat([x_i, x_j, edge_attr], dim=1)

        return self.nn(temp)

    def __repr__(self) -> str:
        return f'{self.__class__.__name__}(nn={self.nn})'