
import time
import os
import pickle
import numpy as np

import dgl
import torch

from scipy import sparse as sp
import numpy as np



class load_SBMsDataSetDGL(torch.utils.data.Dataset):

    def __init__(self,
                 data_dir,
                 name,
                 split):

        self.split = split
        self.is_test = split.lower() in ['test', 'val'] 
        with open(os.path.join(data_dir, name + '_%s.pkl' % self.split), 'rb') as f:
            self.dataset = pickle.load(f)
        self.node_labels = []
        self.graph_lists = []
        self.n_samples = len(self.dataset)
        self._prepare()
    

    def _prepare(self):

        print("preparing %d graphs for the %s set..." % (self.n_samples, self.split.upper()))

        for data in self.dataset:

            node_features = data.node_feat
            edge_list = (data.W != 0).nonzero()  # converting adj matrix to edge_list

            # Create the DGL Graph
            g = dgl.DGLGraph()
            g.add_nodes(node_features.size(0))
            g.ndata['feat'] = node_features.long()
            for src, dst in edge_list:
                g.add_edges(src.item(), dst.item())

            # adding edge features for Residual Gated ConvNet
            #edge_feat_dim = g.ndata['feat'].size(1) # dim same as node feature dim
            edge_feat_dim = 1 # dim same as node feature dim
            g.edata['feat'] = torch.ones(g.number_of_edges(), edge_feat_dim)

            self.graph_lists.append(g)
            self.node_labels.append(data.node_label)


    def __len__(self):
        """Return the number of graphs in the dataset."""
        return self.n_samples

    def __getitem__(self, idx):
        """
            Get the idx^th sample.
            Parameters
            ---------
            idx : int
                The sample index.
            Returns
            -------
            (dgl.DGLGraph, int)
                DGLGraph with node feature stored in `feat` field
                And its label.
        """
        return self.graph_lists[idx], self.node_labels[idx]


class SBMsDatasetDGL(torch.utils.data.Dataset):

    def __init__(self, name):
        """
            TODO
        """
        start = time.time()
        print("[I] Loading data ...")
        self.name = name
        data_dir = 'data/SBMs'
        self.train = load_SBMsDataSetDGL(data_dir, name, split='train')
        self.test = load_SBMsDataSetDGL(data_dir, name, split='test')
        self.val = load_SBMsDataSetDGL(data_dir, name, split='val')
        print("[I] Finished loading.")
        print("[I] Data load time: {:.4f}s".format(time.time()-start))




def self_loop(g):
    """
        Utility function only, to be used only when necessary as per user self_loop flag
        : Overwriting the function dgl.transform.add_self_loop() to not miss ndata['feat'] and edata['feat']
        
        
        This function is called inside a function in SBMsDataset class.
    """
    new_g = dgl.DGLGraph()
    new_g.add_nodes(g.number_of_nodes())
    new_g.ndata['feat'] = g.ndata['feat']
    
    src, dst = g.all_edges(order="eid")
    src = dgl.backend.zerocopy_to_numpy(src)
    dst = dgl.backend.zerocopy_to_numpy(dst)
    non_self_edges_idx = src != dst
    nodes = np.arange(g.number_of_nodes())
    new_g.add_edges(src[non_self_edges_idx], dst[non_self_edges_idx])
    new_g.add_edges(nodes, nodes)
    
    # This new edata is not used since this function gets called only for GCN, GAT
    # However, we need this for the generic requirement of ndata and edata
    new_g.edata['feat'] = torch.zeros(new_g.number_of_edges())
    return new_g



def positional_encoding(g, pos_enc_dim):
    """
        Graph positional encoding v/ Laplacian eigenvectors
    """

    # Laplacian
    A = g.adjacency_matrix_scipy(return_edge_ids=False).astype(float)
    N = sp.diags(dgl.backend.asnumpy(g.in_degrees()).clip(1) ** -0.5, dtype=float)
    L = sp.eye(g.number_of_nodes()) - N * A * N

    # # Eigenvectors with numpy
    # EigVal, EigVec = np.linalg.eig(L.toarray())
    # idx = EigVal.argsort() # increasing order
    # EigVal, EigVec = EigVal[idx], np.real(EigVec[:,idx])
    # g.ndata['pos_enc'] = torch.from_numpy(np.abs(EigVec[:,1:pos_enc_dim+1])).float() 

    # Eigenvectors with scipy
    #EigVal, EigVec = sp.linalg.eigs(L, k=pos_enc_dim+1, which='SR')
    EigVal, EigVec = sp.linalg.eigs(L, k=pos_enc_dim+1, which='SR', tol=1e-2) # for 40 PEs
    EigVec = EigVec[:, EigVal.argsort()] # increasing order
    g.ndata['pos_enc'] = torch.from_numpy(np.abs(EigVec[:,1:pos_enc_dim+1])).float() 

    return g


    
class SBMsDataset(torch.utils.data.Dataset):

    def __init__(self, name):
        """
            Loading SBM datasets
        """
        start = time.time()
        print("[I] Loading dataset %s..." % (name))
        self.name = name
        data_dir = 'data/SBMs/'
        with open(data_dir+name+'.pkl',"rb") as f:
            f = pickle.load(f)
            self.train = f[0]
            self.val = f[1]
            self.test = f[2]
        print('train, test, val sizes :',len(self.train),len(self.test),len(self.val))
        print("[I] Finished loading.")
        print("[I] Data load time: {:.4f}s".format(time.time()-start))


    # form a mini batch from a given list of samples = [(graph, label) pairs]
    def collate(self, samples):
        # The input samples is a list of pairs (graph, label).
        graphs, labels = map(list, zip(*samples))
        labels = torch.cat(labels).long()
        #tab_sizes_n = [ graphs[i].number_of_nodes() for i in range(len(graphs))]
        #tab_snorm_n = [ torch.FloatTensor(size,1).fill_(1./float(size)) for size in tab_sizes_n ]
        #snorm_n = torch.cat(tab_snorm_n).sqrt()  
        #tab_sizes_e = [ graphs[i].number_of_edges() for i in range(len(graphs))]
        #tab_snorm_e = [ torch.FloatTensor(size,1).fill_(1./float(size)) for size in tab_sizes_e ]
        #snorm_e = torch.cat(tab_snorm_e).sqrt()
        batched_graph = dgl.batch(graphs)

        return batched_graph, labels
    
    # prepare dense tensors for GNNs which use; such as RingGNN and 3WLGNN
    def collate_dense_gnn(self, samples):
        # The input samples is a list of pairs (graph, label).
        graphs, labels = map(list, zip(*samples))
        labels = torch.cat(labels).long()
        #tab_sizes_n = [ graphs[i].number_of_nodes() for i in range(len(graphs))]
        #tab_snorm_n = [ torch.FloatTensor(size,1).fill_(1./float(size)) for size in tab_sizes_n ]
        #snorm_n = tab_snorm_n[0][0].sqrt()  
        
        #batched_graph = dgl.batch(graphs)
    
        g = graphs[0]
        adj = self._sym_normalize_adj(g.adjacency_matrix().to_dense())        
        """
            Adapted from https://github.com/leichen2018/Ring-GNN/
            Assigning node and edge feats::
            we have the adjacency matrix in R^{n x n}, the node features in R^{d_n} and edge features R^{d_e}.
            Then we build a zero-initialized tensor, say T, in R^{(1 + d_n + d_e) x n x n}. T[0, :, :] is the adjacency matrix.
            The diagonal T[1:1+d_n, i, i], i = 0 to n-1, store the node feature of node i. 
            The off diagonal T[1+d_n:, i, j] store edge features of edge(i, j).
        """

        zero_adj = torch.zeros_like(adj)
        
        if self.name == 'SBM_CLUSTER': 
            self.num_node_type = 7
        elif self.name == 'SBM_PATTERN':
            self.num_node_type = 3
        
        # use node feats to prepare adj
        adj_node_feat = torch.stack([zero_adj for j in range(self.num_node_type)])
        adj_node_feat = torch.cat([adj.unsqueeze(0), adj_node_feat], dim=0)

        for node, node_label in enumerate(g.ndata['feat']):
            adj_node_feat[node_label.item()+1][node][node] = 1

        x_node_feat = adj_node_feat.unsqueeze(0)
        
        return x_node_feat, labels
    
    def _sym_normalize_adj(self, adj):
        deg = torch.sum(adj, dim = 0)#.squeeze()
        deg_inv = torch.where(deg>0, 1./torch.sqrt(deg), torch.zeros(deg.size()))
        deg_inv = torch.diag(deg_inv)
        return torch.mm(deg_inv, torch.mm(adj, deg_inv))
    
    
    def _add_self_loops(self):
        
        # function for adding self loops
        # this function will be called only if self_loop flag is True
            
        self.train.graph_lists = [self_loop(g) for g in self.train.graph_lists]
        self.val.graph_lists = [self_loop(g) for g in self.val.graph_lists]
        self.test.graph_lists = [self_loop(g) for g in self.test.graph_lists]


    def _add_positional_encodings(self, pos_enc_dim):
        
        # Graph positional encoding v/ Laplacian eigenvectors
        self.train.graph_lists = [positional_encoding(g, pos_enc_dim) for g in self.train.graph_lists]
        self.val.graph_lists = [positional_encoding(g, pos_enc_dim) for g in self.val.graph_lists]
        self.test.graph_lists = [positional_encoding(g, pos_enc_dim) for g in self.test.graph_lists]




