import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_sparse import SparseTensor, matmul
from torch_geometric.nn import GCNConv, SAGEConv, GATConv, APPNP, MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
import scipy.sparse
import numpy as np

class GCN(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
                 dropout, save_mem=True, use_bn=True):
        super(GCN, self).__init__()

        self.convs = nn.ModuleList()
        self.bns = nn.ModuleList()
        self.convs.append(
            GCNConv(in_channels, hidden_channels, cached=not save_mem, normalize=not save_mem))
        self.bns.append(nn.BatchNorm1d(hidden_channels))
        for _ in range(num_layers - 2):
            self.convs.append(
                GCNConv(hidden_channels, hidden_channels, cached=not save_mem, normalize=not save_mem))
            self.bns.append(nn.BatchNorm1d(hidden_channels))
        self.convs.append(
            GCNConv(hidden_channels, out_channels, cached=not save_mem, normalize=not save_mem))

        self.dropout = dropout
        self.activation = F.relu
        self.use_bn = use_bn

    def reset_parameters(self):
        for conv in self.convs:
            conv.reset_parameters()
        for bn in self.bns:
            bn.reset_parameters()

    def forward(self, x, edge_index, edge_weight=None):
        for i, conv in enumerate(self.convs[:-1]):
            x = conv(x, edge_index, edge_weight)
            if self.use_bn:
                x = self.bns[i](x)
            x = self.activation(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.convs[-1](x, edge_index)
        return x

class SAGE(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers=2,
                 dropout=0.5, use_bn=True):
        super(SAGE, self).__init__()

        self.convs = nn.ModuleList()
        self.convs.append(
            SAGEConv(in_channels, hidden_channels))

        self.bns = nn.ModuleList()
        self.bns.append(nn.BatchNorm1d(hidden_channels))
        for _ in range(num_layers - 2):
            self.convs.append(
                SAGEConv(hidden_channels, hidden_channels))
            self.bns.append(nn.BatchNorm1d(hidden_channels))

        self.convs.append(
            SAGEConv(hidden_channels, out_channels))

        self.dropout = dropout
        self.activation = F.relu
        self.use_bn = use_bn

    def reset_parameters(self):
        for conv in self.convs:
            conv.reset_parameters()
        for bn in self.bns:
            bn.reset_parameters()


    def forward(self, x, edge_index):
        for i, conv in enumerate(self.convs[:-1]):
            x = conv(x, edge_index)
            if self.use_bn:
                x = self.bns[i](x)
            x = self.activation(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.convs[-1](x, edge_index)
        return x


class GAT(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers=2,
                 dropout=0.5, heads=2):
        super(GAT, self).__init__()

        self.convs = nn.ModuleList()
        self.convs.append(
            GATConv(in_channels, hidden_channels, heads=heads, concat=True))

        self.bns = nn.ModuleList()
        self.bns.append(nn.BatchNorm1d(hidden_channels*heads))
        for _ in range(num_layers - 2):

            self.convs.append(
                    GATConv(hidden_channels*heads, hidden_channels, heads=heads, concat=True) ) 
            self.bns.append(nn.BatchNorm1d(hidden_channels*heads))

        self.convs.append(
            GATConv(hidden_channels*heads, out_channels, heads=heads, concat=False))

        self.dropout = dropout
        self.activation = F.elu 

    def reset_parameters(self):
        for conv in self.convs:
            conv.reset_parameters()
        for bn in self.bns:
            bn.reset_parameters()


    def forward(self, x, edge_index):
        for i, conv in enumerate(self.convs[:-1]):
            x = conv(x, edge_index)
            x = self.bns[i](x)
            x = self.activation(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.convs[-1](x, edge_index)
        return x

class GPR_prop(MessagePassing):
    '''
    GPRGNN, from original repo https://github.com/jianhao2016/GPRGNN
    propagation class for GPR_GNN
    '''

    def __init__(self, K, alpha, Init, Gamma=None, bias=True, **kwargs):
        super(GPR_prop, self).__init__(aggr='add', **kwargs)
        self.K = K
        self.Init = Init
        self.alpha = alpha

        assert Init in ['SGC', 'PPR', 'NPPR', 'Random', 'WS']
        if Init == 'SGC':
            # SGC-like
            TEMP = 0.0*np.ones(K+1)
            TEMP[alpha] = 1.0
        elif Init == 'PPR':
            # PPR-like
            TEMP = alpha*(1-alpha)**np.arange(K+1)
            TEMP[-1] = (1-alpha)**K
        elif Init == 'NPPR':
            # Negative PPR
            TEMP = (alpha)**np.arange(K+1)
            TEMP = TEMP/np.sum(np.abs(TEMP))
        elif Init == 'Random':
            # Random
            bound = np.sqrt(3/(K+1))
            TEMP = np.random.uniform(-bound, bound, K+1)
            TEMP = TEMP/np.sum(np.abs(TEMP))
        elif Init == 'WS':
            # Specify Gamma
            TEMP = Gamma

        self.temp = nn.Parameter(torch.tensor(TEMP))

    def reset_parameters(self):
        nn.init.zeros_(self.temp)
        for k in range(self.K+1):
            self.temp.data[k] = self.alpha*(1-self.alpha)**k
        self.temp.data[-1] = (1-self.alpha)**self.K

    def forward(self, x, edge_index, edge_weight=None):
        if isinstance(edge_index, torch.Tensor):
            edge_index, norm = gcn_norm(
                edge_index, edge_weight, num_nodes=x.size(0), dtype=x.dtype)
        elif isinstance(edge_index, SparseTensor):
            edge_index = gcn_norm(
                edge_index, edge_weight, num_nodes=x.size(0), dtype=x.dtype)
            norm = None

        hidden = x*(self.temp[0])
        for k in range(self.K):
            x = self.propagate(edge_index, x=x, norm=norm)
            gamma = self.temp[k+1]
            hidden = hidden + gamma*x
        return hidden

    def message(self, x_j, norm):
        return norm.view(-1, 1) * x_j

    def __repr__(self):
        return '{}(K={}, temp={})'.format(self.__class__.__name__, self.K,
                                          self.temp)


class GPRGNN(nn.Module):
    """GPRGNN, from original repo https://github.com/jianhao2016/GPRGNN"""

    def __init__(self, in_channels, hidden_channels, out_channels, Init='PPR', dprate=.5, dropout=.5, K=10, alpha=.1, Gamma=None, ppnp='GPR_prop'):
        super(GPRGNN, self).__init__()
        self.lin1 = nn.Linear(in_channels, hidden_channels)
        self.lin2 = nn.Linear(hidden_channels, out_channels)

        if ppnp == 'PPNP':
            self.prop1 = APPNP(K, alpha)
        elif ppnp == 'GPR_prop':
            self.prop1 = GPR_prop(K, alpha, Init, Gamma)

        self.Init = Init
        self.dprate = dprate
        self.dropout = dropout

    def reset_parameters(self):
        self.lin1.reset_parameters()
        self.lin2.reset_parameters()
        self.prop1.reset_parameters()

    def forward(self, x, edge_index):

        x = F.dropout(x, p=self.dropout, training=self.training)
        x = F.relu(self.lin1(x))
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.lin2(x)

        if self.dprate == 0.0:
            x = self.prop1(x, edge_index)
            return x
        else:
            x = F.dropout(x, p=self.dprate, training=self.training)
            x = self.prop1(x, edge_index)
            return x


