# Source code for deeprobust.graph.rl.nipa_q_net_node

    '''
        Adversarial Attacks on Neural Networks for Graph Data. ICML 2018.
            https://arxiv.org/abs/1806.02371
        Author's Implementation
           https://github.com/Hanjun-Dai/graph_adversarial_attack
        This part of code is adopted from the author's implementation (Copyright (c) 2018 Dai, Hanjun and Li, Hui and Tian, Tian and Huang, Xin and Wang, Lin and Zhu, Jun and Song, Le) but modified
        to be integrated into the repository.
    '''

    import os
    import sys
    import numpy as np
    import torch
    import networkx as nx
    import random
    from torch.nn.parameter import Parameter
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.optim as optim
    from tqdm import tqdm
    from deeprobust.graph.rl.env import GraphNormTool

    class QNetNode(nn.Module):

        def __init__(self, node_features, node_labels, list_action_space, n_injected, bilin_q=1, embed_dim=64, mlp_hidden=64, max_lv=1, gm='mean_field', device='cpu'):
            '''
            bilin_q: bilinear q or not
            mlp_hidden: mlp hidden layer size
            mav_lv: max rounds of message passing
            '''
            super(QNetNode, self).__init__()
            self.node_features = node_features
            self.identity = torch.eye(node_labels.max() + 1).to(node_labels.device)
            # self.node_labels = self.to_onehot(node_labels)
            self.n_injected = n_injected

            self.list_action_space = list_action_space
            self.total_nodes = len(list_action_space)

            self.bilin_q = bilin_q
            self.embed_dim = embed_dim
            self.mlp_hidden = mlp_hidden
            self.max_lv = max_lv
            self.gm = gm

            if mlp_hidden:
                self.linear_1 = nn.Linear(embed_dim * 3, mlp_hidden)
                self.linear_out = nn.Linear(mlp_hidden, 1)
            else:
                self.linear_out = nn.Linear(embed_dim * 3, 1)

            self.w_n2l = Parameter(torch.Tensor(node_features.size()[1], embed_dim))
            self.bias_n2l = Parameter(torch.Tensor(embed_dim))

            # self.bias_picked = Parameter(torch.Tensor(1, embed_dim))
            self.conv_params = nn.Linear(embed_dim, embed_dim)
            self.norm_tool = GraphNormTool(normalize=True, gm=self.gm, device=device)
            weights_init(self)

            input_dim = (node_labels.max() + 1) * self.n_injected
            self.label_encoder_1 = nn.Linear(input_dim, mlp_hidden)
            self.label_encoder_2 = nn.Linear(mlp_hidden, embed_dim)
            self.device = self.node_features.device

        def to_onehot(self, labels):
            return self.identity[labels].view(-1, self.identity.shape[1])

        def get_label_embedding(self, labels):
            # int to one hot
            onehot = self.to_onehot(labels).view(1, -1)

            x = F.relu(self.label_encoder_1(onehot))
            x = F.relu(self.label_encoder_2(x))
            return x

        def get_action_label_encoding(self, label):
            onehot = self.to_onehot(label)
            zeros = torch.zeros((onehot.shape[0], self.embed_dim - onehot.shape[1])).to(onehot.device)
            return torch.cat((onehot, zeros), dim=1)

        def get_graph_embedding(self, adj):
            if self.node_features.data.is_sparse:
                node_embed = torch.spmm(self.node_features, self.w_n2l)
            else:
                node_embed = torch.mm(self.node_features, self.w_n2l)

            node_embed += self.bias_n2l

            input_message = node_embed
            node_embed = F.relu(input_message)

            for i in range(self.max_lv):
                n2npool = torch.spmm(adj, node_embed)
                node_linear = self.conv_params(n2npool)
                merged_linear = node_linear + input_message
                node_embed = F.relu(merged_linear)

            graph_embed = torch.mean(node_embed, dim=0, keepdim=True)
            return graph_embed, node_embed

        def make_spmat(self, n_rows, n_cols, row_idx, col_idx):
            idxes = torch.LongTensor([[row_idx], [col_idx]])
            values = torch.ones(1)

            sp = torch.sparse.FloatTensor(idxes, values, torch.Size([n_rows, n_cols]))
            if next(self.parameters()).is_cuda:
                sp = sp.cuda()
            return sp

        def forward(self, time_t, states, actions, greedy_acts=False, is_inference=False):

            preds = torch.zeros(len(states)).to(self.device)

            batch_graph, modified_labels = zip(*states)
            greedy_actions = []
            with torch.set_grad_enabled(mode=not is_inference):

                for i in range(len(batch_graph)):
                    if batch_graph[i] is None:
                        continue
                    adj = self.norm_tool.norm_extra(batch_graph[i].get_extra_adj(self.device))
                    # get graph representation
                    graph_embed, node_embed = self.get_graph_embedding(adj)

                    # get label reprensentation
                    label_embed = self.get_label_embedding(modified_labels[i])

                    # get action reprensentation
                    if time_t != 2:
                        action_embed = node_embed[actions[i]].view(-1, self.embed_dim)
                    else:
                        action_embed = self.get_action_label_encoding(actions[i])

                    # concat them and send it to neural network
                    embed_s = torch.cat((graph_embed, label_embed), dim=1)
                    embed_s = embed_s.repeat(len(action_embed), 1)
                    embed_s_a = torch.cat((embed_s, action_embed), dim=1)

                    if self.mlp_hidden:
                        embed_s_a = F.relu( self.linear_1(embed_s_a) )

                    raw_pred = self.linear_out(embed_s_a)

                    if greedy_acts:
                        action_id = raw_pred.argmax(0)
                        raw_pred = raw_pred.max()
                        greedy_actions.append(actions[i][action_id])
                    else:
                        raw_pred = raw_pred.max()
                    # list_pred.append(raw_pred)
                    preds[i] += raw_pred

            return greedy_actions, preds

    class NStepQNetNode(nn.Module):

        def __init__(self, num_steps, node_features, node_labels, list_action_space, n_injected, bilin_q=1, embed_dim=64, mlp_hidden=64, max_lv=1, gm='mean_field', device='cpu'):

            super(NStepQNetNode, self).__init__()
            self.node_features = node_features
            self.node_labels = node_labels
            self.list_action_space = list_action_space
            self.total_nodes = len(list_action_space)

            list_mod = []
            for i in range(0, num_steps):
                # list_mod.append(QNetNode(node_features, node_labels, list_action_space))
                list_mod.append(QNetNode(node_features, node_labels, list_action_space, n_injected, bilin_q, embed_dim, mlp_hidden, max_lv, gm=gm, device=device))

            self.list_mod = nn.ModuleList(list_mod)
            self.num_steps = num_steps

        def forward(self, time_t, states, actions, greedy_acts = False, is_inference=False):
            # print('time_t:', time_t)
            # print('self.num_step:', self.num_steps)
            # assert time_t >= 0 and time_t < self.num_steps
            time_t = time_t % 3
            return self.list_mod[time_t](time_t, states, actions, greedy_acts, is_inference)

    def glorot_uniform(t):
        if len(t.size()) == 2:
            fan_in, fan_out = t.size()
        elif len(t.size()) == 3:
            # out_ch, in_ch, kernel for Conv 1
            fan_in = t.size()[1] * t.size()[2]
            fan_out = t.size()[0] * t.size()[2]
        else:
            fan_in = np.prod(t.size())
            fan_out = np.prod(t.size())

        limit = np.sqrt(6.0 / (fan_in + fan_out))
        t.uniform_(-limit, limit)

    def _param_init(m):
        if isinstance(m, Parameter):
            glorot_uniform(m.data)
        elif isinstance(m, nn.Linear):
            m.bias.data.zero_()
            glorot_uniform(m.weight.data)

    def weights_init(m):
        for p in m.modules():
            if isinstance(p, nn.ParameterList):
                for pp in p:
                    _param_init(pp)
            else:
                _param_init(p)

        for name, p in m.named_parameters():
            if not '.' in name: # top-level parameters
                _param_init(p)

    def node_greedy_actions(target_nodes, picked_nodes, list_q, net):
        assert len(target_nodes) == len(list_q)

        actions = []
        values = []
        for i in range(len(target_nodes)):
            region = net.list_action_space[target_nodes[i]]
            if picked_nodes is not None and picked_nodes[i] is not None:
                region = net.list_action_space[picked_nodes[i]]
            if region is None:
                assert list_q[i].size()[0] == net.total_nodes
            else:
                assert len(region) == list_q[i].size()[0]

            val, act = torch.max(list_q[i], dim=0)
            values.append(val)
            if region is not None:
                act = region[act.data.cpu().numpy()[0]]
                # act = Variable(torch.LongTensor([act]))
                act = torch.LongTensor([act])
                actions.append(act)
            else:
                actions.append(act)

        return torch.cat(actions, dim=0).data, torch.cat(values, dim=0).data