import torch
import torch.nn as nn

class E_GCL(nn.Module):
    """Graph Neural Net with global state and fixed number of nodes per graph.
    Args:
          hidden_dim: Number of hidden units.
          num_nodes: Maximum number of nodes (for self-attentive pooling).
          global_agg: Global aggregation function ('attn' or 'sum').
          temp: Softmax temperature.
    """

    def __init__(self, input_nf, output_nf, hidden_nf, edges_in_d=0, nodes_att_dim=0, act_fn=nn.ReLU(), recurrent=True, coords_weight=1.0, attention=False, clamp=False, norm_diff=False, tanh=False):
        super(E_GCL, self).__init__()
        input_edge = input_nf * 2
        self.coords_weight = coords_weight
        self.recurrent = recurrent
        self.attention = attention
        self.norm_diff = norm_diff
        self.tanh = tanh
        edge_coords_nf = 1


        self.edge_mlp = nn.Sequential(
            nn.Linear(input_edge + edge_coords_nf + edges_in_d, hidden_nf),
            act_fn,
            nn.Linear(hidden_nf, hidden_nf),
            act_fn)

        self.node_mlp = nn.Sequential(
            nn.Linear(hidden_nf + input_nf + nodes_att_dim, hidden_nf),
            act_fn,
            nn.Linear(hidden_nf, output_nf))

        layer = nn.Linear(hidden_nf, 1, bias=False)
        torch.nn.init.xavier_uniform_(layer.weight, gain=0.001)

        self.clamp = clamp
        coord_mlp = []
        coord_mlp.append(nn.Linear(hidden_nf, hidden_nf))
        coord_mlp.append(act_fn)
        coord_mlp.append(layer)
        if self.tanh:
            coord_mlp.append(nn.Tanh())
            self.coords_range = nn.Parameter(torch.ones(1))*3
        self.coord_mlp = nn.Sequential(*coord_mlp)


        if self.attention:
            self.att_mlp = nn.Sequential(
                nn.Linear(hidden_nf, 1),
                nn.Sigmoid())

        #if recurrent:
        #    self.gru = nn.GRUCell(hidden_nf, hidden_nf)


    def edge_model(self, source, target, radial, edge_attr):
        if edge_attr is None:  # Unused.
            out = torch.cat([source, target, radial], dim=1)
        else:
            print(edge_attr.shape)
            print(source.shape)
            out = torch.cat([source, target, radial, edge_attr], dim=1)
        out = self.edge_mlp(out)
        if self.attention:
            att_val = self.att_mlp(out)
            out = out * att_val
        return out

    def node_model(self, x, edge_index, edge_attr, node_attr):
        row, col = edge_index
        agg = unsorted_segment_sum(edge_attr, row, num_segments=x.size(0))
        if node_attr is not None:
            agg = torch.cat([x, agg, node_attr], dim=1)
        else:
            agg = torch.cat([x, agg], dim=1)
        out = self.node_mlp(agg)
        if self.recurrent:
            out = x + out
        return out, agg

    def coord_model(self, coord, edge_index, coord_diff, edge_feat):
        row, col = edge_index
        trans = coord_diff * self.coord_mlp(edge_feat)
        trans = torch.clamp(trans, min=-100, max=100) #This is never activated but just in case it case it explosed it may save the train
        agg = unsorted_segment_mean(trans, row, num_segments=coord.size(0))
        coord += agg*self.coords_weight
        return coord


    def coord2radial(self, edge_index, coord):
        row, col = edge_index
        coord_diff = coord[row] - coord[col]
        radial = torch.sum((coord_diff)**2, 1).unsqueeze(1)

        if self.norm_diff:
            norm = torch.sqrt(radial) + 1
            coord_diff = coord_diff/(norm)

        return radial, coord_diff

    def forward(self, h, edge_index, coord, edge_attr=None, node_attr=None):
        row, col = edge_index
        radial, coord_diff = self.coord2radial(edge_index, coord)

        edge_feat = self.edge_model(h[row], h[col], radial, edge_attr)
        coord = self.coord_model(coord, edge_index, coord_diff, edge_feat)
        h, agg = self.node_model(h, edge_index, edge_feat, node_attr)
        # coord = self.node_coord_model(h, coord)
        # x = self.node_model(x, edge_index, x[col], u, batch)  # GCN
        return h, coord, edge_attr


class E_GCL_vel(E_GCL):
    """Graph Neural Net with global state and fixed number of nodes per graph.
    Args:
          hidden_dim: Number of hidden units.
          num_nodes: Maximum number of nodes (for self-attentive pooling).
          global_agg: Global aggregation function ('attn' or 'sum').
          temp: Softmax temperature.
    """


    def __init__(self, input_nf, output_nf, hidden_nf, edges_in_d=0, nodes_att_dim=0, act_fn=nn.ReLU(), recurrent=True, coords_weight=1.0, attention=False, norm_diff=False, tanh=False):
        E_GCL.__init__(self, input_nf, output_nf, hidden_nf, edges_in_d=edges_in_d, nodes_att_dim=nodes_att_dim, act_fn=act_fn, recurrent=recurrent, coords_weight=coords_weight, attention=attention, norm_diff=norm_diff, tanh=tanh)
        self.norm_diff = norm_diff
        self.coord_mlp_vel = nn.Sequential(
            nn.Linear(input_nf, hidden_nf),
            act_fn,
            nn.Linear(hidden_nf, 1))

    def forward(self, h, edge_index, coord, vel, edge_attr=None, node_attr=None):
        row, col = edge_index
        radial, coord_diff = self.coord2radial(edge_index, coord)

        edge_feat = self.edge_model(h[row], h[col], radial, edge_attr)
        coord = self.coord_model(coord, edge_index, coord_diff, edge_feat)


        coord += self.coord_mlp_vel(h) * vel
        h, agg = self.node_model(h, edge_index, edge_feat, node_attr)
        # coord = self.node_coord_model(h, coord)
        # x = self.node_model(x, edge_index, x[col], u, batch)  # GCN
        return h, coord, edge_attr

class EGNN_vel(nn.Module):
    def __init__(self, in_node_nf, in_edge_nf, hidden_nf, device='cpu', act_fn=nn.SiLU(), n_layers=4, coords_weight=1.0, recurrent=False, norm_diff=False, tanh=False):
        super(EGNN_vel, self).__init__()
        self.hidden_nf = hidden_nf
        self.device = device
        self.n_layers = n_layers
        #self.reg = reg
        ### Encoder
        #self.add_module("gcl_0", E_GCL(in_node_nf, self.hidden_nf, self.hidden_nf, edges_in_d=in_edge_nf, act_fn=act_fn, recurrent=False, coords_weight=coords_weight))
        self.embedding = nn.Linear(in_node_nf, self.hidden_nf)
        for i in range(0, n_layers):
            self.add_module("gcl_%d" % i, E_GCL_vel(self.hidden_nf, self.hidden_nf, self.hidden_nf, edges_in_d=in_edge_nf, act_fn=act_fn, coords_weight=coords_weight, recurrent=recurrent, norm_diff=norm_diff, tanh=tanh))
        self.to(self.device)


    def forward(self, h, x, edges, vel, edge_attr):
        h = self.embedding(h)
        for i in range(0, self.n_layers):
            h, x, _ = self._modules["gcl_%d" % i](h, edges, x, vel, edge_attr=edge_attr)
        return x, vel

import torch
import torch.nn as nn

class E_GCL(nn.Module):
    """Graph Neural Net with global state and fixed number of nodes per graph.
    Args:
          hidden_dim: Number of hidden units.
          num_nodes: Maximum number of nodes (for self-attentive pooling).
          global_agg: Global aggregation function ('attn' or 'sum').
          temp: Softmax temperature.
    """

    def __init__(self, input_nf, output_nf, hidden_nf, edges_in_d=0, nodes_att_dim=0, act_fn=nn.ReLU(), recurrent=True, coords_weight=1.0, attention=False, clamp=False, norm_diff=False, tanh=False):
        super(E_GCL, self).__init__()
        input_edge = input_nf * 2
        self.coords_weight = coords_weight
        self.recurrent = recurrent
        self.attention = attention
        self.norm_diff = norm_diff
        self.tanh = tanh
        edge_coords_nf = 1


        self.edge_mlp = nn.Sequential(
            nn.Linear(input_edge + edge_coords_nf + edges_in_d, hidden_nf),
            act_fn,
            nn.Linear(hidden_nf, hidden_nf),
            act_fn)

        self.node_mlp = nn.Sequential(
            nn.Linear(hidden_nf + input_nf + nodes_att_dim, hidden_nf),
            act_fn,
            nn.Linear(hidden_nf, output_nf))

        layer = nn.Linear(hidden_nf, 1, bias=False)
        torch.nn.init.xavier_uniform_(layer.weight, gain=0.001)

        self.clamp = clamp
        coord_mlp = []
        coord_mlp.append(nn.Linear(hidden_nf, hidden_nf))
        coord_mlp.append(act_fn)
        coord_mlp.append(layer)
        if self.tanh:
            coord_mlp.append(nn.Tanh())
            self.coords_range = nn.Parameter(torch.ones(1))*3
        self.coord_mlp = nn.Sequential(*coord_mlp)


        if self.attention:
            self.att_mlp = nn.Sequential(
                nn.Linear(hidden_nf, 1),
                nn.Sigmoid())

        #if recurrent:
        #    self.gru = nn.GRUCell(hidden_nf, hidden_nf)


    def edge_model(self, source, target, radial, edge_attr):
        if edge_attr is None:  # Unused.
            out = torch.cat([source, target, radial], dim=1)
        else:
            print(edge_attr.shape)
            print(source.shape)
            out = torch.cat([source, target, radial, edge_attr], dim=1)
        out = self.edge_mlp(out)
        if self.attention:
            att_val = self.att_mlp(out)
            out = out * att_val
        return out

    def node_model(self, x, edge_index, edge_attr, node_attr):
        row, col = edge_index
        agg = unsorted_segment_sum(edge_attr, row, num_segments=x.size(0))
        if node_attr is not None:
            agg = torch.cat([x, agg, node_attr], dim=1)
        else:
            agg = torch.cat([x, agg], dim=1)
        out = self.node_mlp(agg)
        if self.recurrent:
            out = x + out
        return out, agg

    def coord_model(self, coord, edge_index, coord_diff, edge_feat):
        row, col = edge_index
        trans = coord_diff * self.coord_mlp(edge_feat)
        trans = torch.clamp(trans, min=-100, max=100) #This is never activated but just in case it case it explosed it may save the train
        agg = unsorted_segment_mean(trans, row, num_segments=coord.size(0))
        coord += agg*self.coords_weight
        return coord


    def coord2radial(self, edge_index, coord):
        row, col = edge_index
        coord_diff = coord[row] - coord[col]
        radial = torch.sum((coord_diff)**2, 1).unsqueeze(1)

        if self.norm_diff:
            norm = torch.sqrt(radial) + 1
            coord_diff = coord_diff/(norm)

        return radial, coord_diff

    def forward(self, h, edge_index, coord, edge_attr=None, node_attr=None):
        row, col = edge_index
        radial, coord_diff = self.coord2radial(edge_index, coord)

        edge_feat = self.edge_model(h[row], h[col], radial, edge_attr)
        coord = self.coord_model(coord, edge_index, coord_diff, edge_feat)
        h, agg = self.node_model(h, edge_index, edge_feat, node_attr)
        # coord = self.node_coord_model(h, coord)
        # x = self.node_model(x, edge_index, x[col], u, batch)  # GCN
        return h, coord, edge_attr


class E_GCL_vel(E_GCL):
    """Graph Neural Net with global state and fixed number of nodes per graph.
    Args:
          hidden_dim: Number of hidden units.
          num_nodes: Maximum number of nodes (for self-attentive pooling).
          global_agg: Global aggregation function ('attn' or 'sum').
          temp: Softmax temperature.
    """


    def __init__(self, input_nf, output_nf, hidden_nf, edges_in_d=0, nodes_att_dim=0, act_fn=nn.ReLU(), recurrent=True, coords_weight=1.0, attention=False, norm_diff=False, tanh=False):
        E_GCL.__init__(self, input_nf, output_nf, hidden_nf, edges_in_d=edges_in_d, nodes_att_dim=nodes_att_dim, act_fn=act_fn, recurrent=recurrent, coords_weight=coords_weight, attention=attention, norm_diff=norm_diff, tanh=tanh)
        self.norm_diff = norm_diff
        self.coord_mlp_vel = nn.Sequential(
            nn.Linear(input_nf, hidden_nf),
            act_fn,
            nn.Linear(hidden_nf, 1))

    def forward(self, h, edge_index, coord, vel, edge_attr=None, node_attr=None):
        row, col = edge_index
        radial, coord_diff = self.coord2radial(edge_index, coord)

        edge_feat = self.edge_model(h[row], h[col], radial, edge_attr)
        coord = self.coord_model(coord, edge_index, coord_diff, edge_feat)


        coord += self.coord_mlp_vel(h) * vel
        h, agg = self.node_model(h, edge_index, edge_feat, node_attr)
        # coord = self.node_coord_model(h, coord)
        # x = self.node_model(x, edge_index, x[col], u, batch)  # GCN
        return h, coord, edge_attr

class EGNN_vel(nn.Module):
    def __init__(self, in_node_nf, in_edge_nf, hidden_nf, device='cpu', act_fn=nn.SiLU(), n_layers=4, coords_weight=1.0, recurrent=False, norm_diff=False, tanh=False):
        super(EGNN_vel, self).__init__()
        self.hidden_nf = hidden_nf
        self.device = device
        self.n_layers = n_layers
        #self.reg = reg
        ### Encoder
        #self.add_module("gcl_0", E_GCL(in_node_nf, self.hidden_nf, self.hidden_nf, edges_in_d=in_edge_nf, act_fn=act_fn, recurrent=False, coords_weight=coords_weight))
        self.embedding = nn.Linear(in_node_nf, self.hidden_nf)
        for i in range(0, n_layers):
            self.add_module("gcl_%d" % i, E_GCL_vel(self.hidden_nf, self.hidden_nf, self.hidden_nf, edges_in_d=in_edge_nf, act_fn=act_fn, coords_weight=coords_weight, recurrent=recurrent, norm_diff=norm_diff, tanh=tanh))
        self.to(self.device)


    def forward(self, h, x, edges, vel, edge_attr):
        h = self.embedding(h)
        for i in range(0, self.n_layers):
            h, x, _ = self._modules["gcl_%d" % i](h, edges, x, vel, edge_attr=edge_attr)
        return x, vel

Graph Neural Net with global state and fixed number of nodes per graph.     Args:           hidden_dim: Number of hidden units.           num_nodes: Maximum number of nodes (for self-attentive pooling).           global_agg: Global aggregation function ('attn' or 'sum').           temp: Softmax temperature. #if recurrent: #    self.gru = nn.GRUCell(hidden_nf, hidden_nf) # Unused. #This is never activated but just in case it case it explosed it may save the train # coord = self.node_coord_model(h, coord) # x = self.node_model(x, edge_index, x[col], u, batch)  # GCN Graph Neural Net with global state and fixed number of nodes per graph.     Args:           hidden_dim: Number of hidden units.           num_nodes: Maximum number of nodes (for self-attentive pooling).           global_agg: Global aggregation function ('attn' or 'sum').           temp: Softmax temperature. # coord = self.node_coord_model(h, coord) # x = self.node_model(x, edge_index, x[col], u, batch)  # GCN #self.reg = reg ### Encoder #self.add_module("gcl_0", E_GCL(in_node_nf, self.hidden_nf, self.hidden_nf, edges_in_d=in_edge_nf, act_fn=act_fn, recurrent=False, coords_weight=coords_weight))