import random
import time
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn.functional as F

from grb.attack.base import InjectionAttack
from grb.evaluator import metric
from grb.utils import utils


class RAND(InjectionAttack):
    r"""

    Description
    -----------
    Simple random graph injection attack.

    Parameters
    ----------
    n_inject_max : int
        Maximum number of injected nodes.
    n_edge_max : int
        Maximum number of edges of injected nodes.
    feat_lim_min : float
        Minimum limit of features.
    feat_lim_max : float
        Maximum limit of features.
    loss : func of torch.nn.functional, optional
        Loss function compatible with ``torch.nn.functional``. Default: ``F.cross_entropy``.
    eval_metric : func of grb.evaluator.metric, optional
        Evaluation metric. Default: ``metric.eval_acc``.
    device : str, optional
        Device used to host data. Default: ``cpu``.
    verbose : bool, optional
        Whether to display logs. Default: ``True``.

    """
    def __init__(self,
                 n_inject_max,
                 n_edge_max,
                 feat_lim_min,
                 feat_lim_max,
                 loss=F.cross_entropy,
                 eval_metric=metric.eval_acc,
                 device='cpu',
                 verbose=True):
        self.device = device
        self.n_inject_max = n_inject_max
        self.n_edge_max = n_edge_max
        self.feat_lim_min = feat_lim_min
        self.feat_lim_max = feat_lim_max
        self.loss = loss
        self.eval_metric = eval_metric
        self.verbose = verbose

    def attack(self, model, adj, features, target_mask, adj_norm_func):
        r"""

        Description
        -----------
        Attack process consists of injection and feature update.

        Parameters
        ----------
        model : torch.nn.module
            Model implemented based on ``torch.nn.module``.
        adj : scipy.sparse.csr.csr_matrix
            Adjacency matrix in form of ``N * N`` sparse matrix.
        features : torch.FloatTensor
            Features in form of ``N * D`` torch float tensor.
        target_mask : torch.Tensor
            Mask of attack target nodes in form of ``N * 1`` torch bool tensor.
        adj_norm_func : func of utils.normalize
            Function that normalizes adjacency matrix.

        Returns
        -------
        adj_attack : scipy.sparse.csr.csr_matrix
            Adversarial adjacency matrix in form of :math:`(N + N_{inject})\times(N + N_{inject})` sparse matrix.
        features_attack : torch.FloatTensor
            Features of nodes after attacks in form of :math:`N_{inject}` * D` torch float tensor.

        """
        time_start = time.time()
        model.to(self.device)
        n_total, n_feat = features.shape
        features = utils.feat_preprocess(features=features, device=self.device)
        adj_tensor = utils.adj_preprocess(adj=adj,
                                          adj_norm_func=adj_norm_func,
                                          model_type=model.model_type,
                                          device=self.device)
        pred_origin = model(features, adj_tensor)
        labels_origin = torch.argmax(pred_origin, dim=1)
        adj_attack = self.injection(adj=adj,
                                    n_inject=self.n_inject_max,
                                    n_node=n_total,
                                    target_mask=target_mask)

        features_attack = np.zeros((self.n_inject_max, n_feat))
        features_attack = self.update_features(model=model,
                                               adj_attack=adj_attack,
                                               features_origin=features,
                                               features_attack=features_attack,
                                               labels_origin=labels_origin,
                                               target_mask=target_mask,
                                               adj_norm_func=adj_norm_func)
        time_end = time.time()
        if self.verbose:
            print("Attack runtime: {:.4f}.".format(time_end - time_start))

        return adj_attack, features_attack

    def injection(self,
                  adj,
                  n_inject,
                  n_node,
                  target_mask):
        r"""

        Description
        -----------
        Randomly inject nodes to target nodes.

        Parameters
        ----------
        adj : scipy.sparse.csr.csr_matrix
            Adjacency matrix in form of ``N * N`` sparse matrix.
        n_inject : int
            Number of injection.
        n_node : int
            Number of all nodes.
        target_mask : torch.Tensor
            Mask of attack target nodes in form of ``N * 1`` torch bool tensor.

        Returns
        -------
        adj_attack : scipy.sparse.csr.csr_matrix
            Adversarial adjacency matrix in form of :math:`(N + N_{inject})\times(N + N_{inject})` sparse matrix.

        """

        test_index = torch.where(target_mask)[0]
        n_test = test_index.shape[0]
        new_edges_x = []
        new_edges_y = []
        new_data = []
        for i in range(n_inject):
            islinked = np.zeros(n_test)
            for j in range(self.n_edge_max):
                x = i + n_node

                yy = random.randint(0, n_test - 1)
                while islinked[yy] > 0:
                    yy = random.randint(0, n_test - 1)

                y = test_index[yy]
                islinked[yy] = 1
                new_edges_x.extend([x, y])
                new_edges_y.extend([y, x])
                new_data.extend([1, 1])

        add1 = sp.csr_matrix((n_inject, n_node))
        add2 = sp.csr_matrix((n_node + n_inject, n_inject))
        adj_attack = sp.vstack([adj, add1])
        adj_attack = sp.hstack([adj_attack, add2])
        adj_attack.row = np.hstack([adj_attack.row, new_edges_x])
        adj_attack.col = np.hstack([adj_attack.col, new_edges_y])
        adj_attack.data = np.hstack([adj_attack.data, new_data])

        return adj_attack

    def update_features(self,
                        model,
                        adj_attack,
                        features_origin,
                        features_attack,
                        labels_origin,
                        target_mask,
                        feat_norm=None,
                        adj_norm_func=None):
        r"""
        Description
        -----------
        Update features of injected nodes.

        Parameters
        ----------
        model : torch.nn.module
            Model implemented based on ``torch.nn.module``.
        adj_attack :  scipy.sparse.csr.csr_matrix
            Adversarial adjacency matrix in form of :math:`(N + N_{inject})\times(N + N_{inject})` sparse matrix.
        features_origin : torch.FloatTensor
            Features in form of ``N * D`` torch float tensor.
        features_attack : torch.FloatTensor
            Features of nodes after attacks in form of :math:`N_{inject}` * D` torch float tensor.
        labels_origin : torch.LongTensor
            Labels of target nodes originally predicted by the model.
        target_mask : torch.Tensor
            Mask of target nodes in form of ``N * 1`` torch bool tensor.
        feat_norm : str, optional
            Type of feature normalization, ['arctan', 'tanh']. Default: ``None``.
        adj_norm_func : func of utils.normalize, optional
            Function that normalizes adjacency matrix. Default: ``None``.

        Returns
        -------
        features_attack : torch.FloatTensor
            Updated features of nodes after attacks in form of :math:`N_{inject}` * D` torch float tensor.

        """

        feat_lim_min, feat_lim_max = self.feat_lim_min, self.feat_lim_max
        n_total = features_origin.shape[0]

        adj_attacked_tensor = utils.adj_preprocess(adj=adj_attack,
                                                   adj_norm_func=model.adj_norm_func if adj_norm_func is None else adj_norm_func,
                                                   model_type=model.model_type,
                                                   device=self.device)
        features_attack = np.random.normal(loc=0, scale=feat_lim_max,
                                           size=(self.n_inject_max, features_origin.shape[1]))
        features_attack = np.clip(features_attack, feat_lim_min, feat_lim_max)
        features_attack = utils.feat_preprocess(features=features_attack,
                                                feat_norm=model.feat_norm if feat_norm is None else feat_norm,
                                                device=self.device)
        model.eval()

        features_concat = torch.cat((features_origin, features_attack), dim=0)
        pred = model(features_concat, adj_attacked_tensor)
        pred_loss = -self.loss(pred[:n_total][target_mask],
                               labels_origin[target_mask]).to(self.device)

        test_acc = self.eval_metric(pred[:n_total][target_mask],
                                    labels_origin[target_mask])

        if self.verbose:
            print("Loss: {:.4f}, Surrogate test acc: {:.4f}".format(pred_loss, test_acc))

        return features_attack
