import torch
import torch.nn as nn
import torch.nn.functional as F
from copy import deepcopy 
from torch.autograd import Variable
from torch.utils import data
from torch.utils.data import SequentialSampler
import matplotlib.pyplot as plt
import numpy as np 
sigmoid = torch.nn.Sigmoid() 
torch.manual_seed(0)

from HINT.gnn_layers import GraphConvolution, GraphAttention
torch.manual_seed(4) 
np.random.seed(1)

class Highway(nn.Module):
    def __init__(self, size, num_layers):
        super(Highway, self).__init__()
        self.num_layers = num_layers
        self.nonlinear = nn.ModuleList([nn.Linear(size, size) for _ in range(num_layers)])
        self.linear = nn.ModuleList([nn.Linear(size, size) for _ in range(num_layers)])
        self.gate = nn.ModuleList([nn.Linear(size, size) for _ in range(num_layers)])
        self.f = F.relu

    def forward(self, x):
        """
            :param x: tensor with shape of [batch_size, size]
            :return: tensor with shape of [batch_size, size]
            applies σ(x) ⨀ (f(G(x))) + (1 - σ(x)) ⨀ (Q(x)) transformation | G and Q is affine transformation,
            f is non-linear transformation, σ(x) is affine transformation with sigmoid non-linearition
            and ⨀ is element-wise multiplication
        """
        for layer in range(self.num_layers):
            gate = F.sigmoid(self.gate[layer](x))
            nonlinear = self.f(self.nonlinear[layer](x))
            linear = self.linear[layer](x)
            x = gate * nonlinear + (1 - gate) * linear
        return x






class GCN(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout, init):
        super(GCN, self).__init__()

        self.gc1 = GraphConvolution(nfeat, nhid, init=init)
        self.gc2 = GraphConvolution(nhid, nclass, init=init)
        self.dropout = dropout

    def bottleneck(self, path1, path2, path3, adj, in_x):
        return F.relu(path3(F.relu(path2(F.relu(path1(in_x, adj)), adj)), adj))

    def forward(self, x, adj):
        x = F.dropout(F.relu(self.gc1(x, adj)), self.dropout, training=self.training)
        x = self.gc2(x, adj)
        return x 
        # return F.log_softmax(x, dim=1)




class GCN_drop_in(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout, init):
        super(GCN_drop_in, self).__init__()

        self.gc1 = GraphConvolution(nfeat, nhid, init=init)
        self.gc2 = GraphConvolution(nhid, nclass, init=init)
        self.dropout = dropout

    def bottleneck(self, path1, path2, path3, adj, in_x):
        return F.relu(path3(F.relu(path2(F.relu(path1(in_x, adj)), adj)), adj))

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        x = F.dropout(F.relu(self.gc1(x, adj)), self.dropout, training=self.training)
        x = self.gc2(x, adj)

        return F.log_softmax(x, dim=1)

class GAT(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
        super(GAT, self).__init__()
        self.dropout = dropout

        self.attentions = [GraphAttention(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)

        self.out_att = GraphAttention(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False)

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
        x = F.dropout(x, self.dropout, training=self.training)
        x = F.elu(self.out_att(x, adj))
        return F.log_softmax(x, dim=1)




if __name__ == "__main__":
    gnn = GCN(
            nfeat = 20,
            nhid = 30,
            nclass = 1,
            dropout = 0.6,
            init = 'uniform')