from collections import defaultdict
import numpy as np
import torch
import torch.nn.functional as F
import scipy
import scipy.io
from sklearn.preprocessing import label_binarize
from ogb.nodeproppred import NodePropPredDataset

from load_data import load_twitch, load_fb100, DATAPATH
from data_utils import rand_train_test_idx, even_quantile_labels, to_sparse_tensor, dataset_drive_url

from torch_geometric.datasets import Planetoid
from torch_geometric.transforms import NormalizeFeatures
from os import path

from torch_sparse import SparseTensor
from google_drive_downloader import GoogleDriveDownloader as gdd



class NCDataset(object):
    def __init__(self, name, root=f'{DATAPATH}'):
        """
        based off of ogb NodePropPredDataset
        https://github.com/snap-stanford/ogb/blob/master/ogb/nodeproppred/dataset.py
        Gives torch tensors instead of numpy arrays
            - name (str): name of the dataset
            - root (str): root directory to store the dataset folder
            - meta_dict: dictionary that stores all the meta-information about data. Default is None, 
                    but when something is passed, it uses its information. Useful for debugging for external contributers.
        
        Usage after construction: 
        
        split_idx = dataset.get_idx_split()
        train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]
        graph, label = dataset[0]
        
        Where the graph is a dictionary of the following form: 
        dataset.graph = {'edge_index': edge_index,
                         'edge_feat': None,
                         'node_feat': node_feat,
                         'num_nodes': num_nodes}
        For additional documentation, see OGB Library-Agnostic Loader https://ogb.stanford.edu/docs/nodeprop/
        
        """

        self.name = name  # original name, e.g., ogbn-proteins
        self.graph = {}
        self.label = None

    def get_idx_split(self, split_type='random', train_prop=.5, valid_prop=.25):
        """
        train_prop: The proportion of dataset for train split. Between 0 and 1.
        valid_prop: The proportion of dataset for validation split. Between 0 and 1.
        """
        
        if split_type == 'random':
            ignore_negative = False if self.name == 'ogbn-proteins' else True
            train_idx, valid_idx, test_idx = rand_train_test_idx(
                self.label, train_prop=train_prop, valid_prop=valid_prop, ignore_negative=ignore_negative)
            split_idx = {'train': train_idx,
                         'valid': valid_idx,
                         'test': test_idx}
        return split_idx

    def __getitem__(self, idx):
        assert idx == 0, 'This dataset has only one graph'
        return self.graph, self.label

    def __len__(self):
        return 1

    def __repr__(self):  
        return '{}({})'.format(self.__class__.__name__, len(self))


def load_nc_dataset(dataname, sub_dataname=''):
    """ Loader for NCDataset 
        Returns NCDataset 
    """
    if dataname == 'twitch-e':
        # twitch-explicit graph
        if sub_dataname not in ('DE', 'ENGB', 'ES', 'FR', 'PTBR', 'RU', 'TW'):
            print('Invalid sub_dataname, deferring to DE graph')
            sub_dataname = 'DE'
        dataset = load_twitch_dataset(sub_dataname)
    elif dataname == 'fb100':
        if sub_dataname not in ('Penn94', 'Amherst41', 'Cornell5', 'Johns Hopkins55', 'Reed98'):
            print('Invalid sub_dataname, deferring to Penn94 graph')
            sub_dataname = 'Penn94'
        dataset = load_fb100_dataset(sub_dataname)
    elif dataname == 'ogbn-proteins':
        dataset = load_proteins_dataset()
    elif dataname == 'deezer-europe':
        dataset = load_deezer_dataset()
    elif dataname == 'arxiv-year':
        dataset = load_arxiv_year_dataset()
    elif dataname == 'pokec':
        dataset = load_pokec_mat()
    elif dataname == 'snap-patents':
        dataset = load_snap_patents_mat()
    elif dataname == 'yelp-chi':
        dataset = load_yelpchi_dataset()
    elif dataname in ('ogbn-arxiv', 'ogbn-products'):
        dataset = load_ogb_dataset(dataname)
    elif dataname in  ('Cora', 'CiteSeer', 'PubMed'):
        dataset = load_planetoid_dataset(dataname)
    elif dataname in ('chameleon', 'cornell', 'film', 'squirrel', 'texas', 'wisconsin'):
        dataset = load_geom_gcn_dataset(dataname)
    else:
        raise ValueError('Invalid dataname')
    return dataset


def load_twitch_dataset(lang):
    assert lang in ('DE', 'ENGB', 'ES', 'FR', 'PTBR', 'RU', 'TW'), 'Invalid dataset'
    A, label, features = load_twitch(lang)
    dataset = NCDataset(lang)
    edge_index = torch.tensor(A.nonzero(), dtype=torch.long)
    node_feat = torch.tensor(features, dtype=torch.float)
    num_nodes = node_feat.shape[0]
    dataset.graph = {'edge_index': edge_index,
                     'edge_feat': None,
                     'node_feat': node_feat,
                     'num_nodes': num_nodes}
    dataset.label = torch.tensor(label)
    return dataset


def load_fb100_dataset(filename):
    A, metadata = load_fb100(filename)
    dataset = NCDataset(filename)
    edge_index = torch.tensor(A.nonzero(), dtype=torch.long)
    metadata = metadata.astype(np.int)
    label = metadata[:, 1] - 1  # gender label, -1 means unlabeled

    # make features into one-hot encodings
    feature_vals = np.hstack(
        (np.expand_dims(metadata[:, 0], 1), metadata[:, 2:]))
    features = np.empty((A.shape[0], 0))
    for col in range(feature_vals.shape[1]):
        feat_col = feature_vals[:, col]
        feat_onehot = label_binarize(feat_col, classes=np.unique(feat_col))
        features = np.hstack((features, feat_onehot))

    node_feat = torch.tensor(features, dtype=torch.float)
    num_nodes = metadata.shape[0]
    dataset.graph = {'edge_index': edge_index,
                     'edge_feat': None,
                     'node_feat': node_feat,
                     'num_nodes': num_nodes}
    dataset.label = torch.tensor(label)
    return dataset

def load_deezer_dataset():
    
    filename = 'deezer-europe'
    dataset = NCDataset(filename)
    deezer = scipy.io.loadmat(f'{DATAPATH}deezer-europe.mat')

    A, label, features = deezer['A'], deezer['label'], deezer['features']
    edge_index = torch.tensor(A.nonzero(), dtype=torch.long)
    node_feat = torch.tensor(features.todense(), dtype=torch.float)
    label = torch.tensor(label, dtype=torch.long).squeeze()
    num_nodes = label.shape[0]

    dataset.graph = {'edge_index': edge_index,
                     'edge_feat': None,
                     'node_feat': node_feat,
                     'num_nodes': num_nodes}
    dataset.label = label
    return dataset


def load_arxiv_year_dataset(nclass=5):
    filename = 'arxiv-year'
    dataset = NCDataset(filename)
    ogb_dataset = NodePropPredDataset(name='ogbn-arxiv')
    dataset.graph = ogb_dataset.graph
    dataset.graph['edge_index'] = torch.as_tensor(dataset.graph['edge_index'])
    dataset.graph['node_feat'] = torch.as_tensor(dataset.graph['node_feat'])

    label = even_quantile_labels(
        dataset.graph['node_year'].flatten(), nclass, verbose=False)
    dataset.label = torch.as_tensor(label).reshape(-1, 1)
    return dataset


def load_proteins_dataset():
    ogb_dataset = NodePropPredDataset(name='ogbn-proteins')
    dataset = NCDataset('ogbn-proteins')
    def protein_orig_split(**kwargs):
        split_idx = ogb_dataset.get_idx_split()
        return {'train': torch.as_tensor(split_idx['train']),
                'valid': torch.as_tensor(split_idx['valid']),
                'test': torch.as_tensor(split_idx['test'])}
    dataset.get_idx_split = protein_orig_split
    dataset.graph, dataset.label = ogb_dataset.graph, ogb_dataset.labels
    
    dataset.graph['edge_index'] = torch.as_tensor(dataset.graph['edge_index'])
    dataset.graph['edge_feat'] = torch.as_tensor(dataset.graph['edge_feat'])
    dataset.label = torch.as_tensor(dataset.label)
    return dataset


def load_ogb_dataset(name):
    dataset = NCDataset(name)
    ogb_dataset = NodePropPredDataset(name=name)
    dataset.graph = ogb_dataset.graph
    dataset.graph['edge_index'] = torch.as_tensor(dataset.graph['edge_index'])
    dataset.graph['node_feat'] = torch.as_tensor(dataset.graph['node_feat'])

    def ogb_idx_to_tensor():
        split_idx = ogb_dataset.get_idx_split()
        tensor_split_idx = {key: torch.as_tensor(
            split_idx[key]) for key in split_idx}
        return tensor_split_idx
    dataset.get_idx_split = ogb_idx_to_tensor  # ogb_dataset.get_idx_split
    dataset.label = torch.as_tensor(ogb_dataset.labels).reshape(-1, 1)
    return dataset


def load_pokec_mat():
    """ requires pokec.mat """
    if not path.exists(f'{DATAPATH}pokec.mat'):
        gdd.download_file_from_google_drive(
            file_id= dataset_drive_url['pokec'], \
            dest_path=f'{DATAPATH}pokec.mat', showsize=True) 

    fulldata = scipy.io.loadmat(f'{DATAPATH}pokec.mat')

    dataset = NCDataset('pokec')
    edge_index = torch.tensor(fulldata['edge_index'], dtype=torch.long)
    node_feat = torch.tensor(fulldata['node_feat']).float()
    num_nodes = int(fulldata['num_nodes'])
    dataset.graph = {'edge_index': edge_index,
                     'edge_feat': None,
                     'node_feat': node_feat,
                     'num_nodes': num_nodes}

    label = fulldata['label'].flatten()
    dataset.label = torch.tensor(label, dtype=torch.long)

    return dataset


def load_snap_patents_mat(nclass=5):
    if not path.exists(f'{DATAPATH}snap_patents.mat'):
        p = dataset_drive_url['snap-patents']
        print(f"Snap patents url: {p}")
        gdd.download_file_from_google_drive(
            file_id= dataset_drive_url['snap-patents'], \
            dest_path=f'{DATAPATH}snap_patents.mat', showsize=True) 

    fulldata = scipy.io.loadmat(f'{DATAPATH}snap_patents.mat')

    dataset = NCDataset('snap_patents')
    edge_index = torch.tensor(fulldata['edge_index'], dtype=torch.long)
    node_feat = torch.tensor(
        fulldata['node_feat'].todense(), dtype=torch.float)
    num_nodes = int(fulldata['num_nodes'])
    dataset.graph = {'edge_index': edge_index,
                     'edge_feat': None,
                     'node_feat': node_feat,
                     'num_nodes': num_nodes}

    years = fulldata['years'].flatten()
    label = even_quantile_labels(years, nclass, verbose=False)
    dataset.label = torch.tensor(label, dtype=torch.long)

    return dataset


def load_yelpchi_dataset():
    if not path.exists(f'{DATAPATH}YelpChi.mat'):
            gdd.download_file_from_google_drive(
                file_id= dataset_drive_url['yelp-chi'], \
                dest_path=f'{DATAPATH}YelpChi.mat', showsize=True) 
    fulldata = scipy.io.loadmat(f'{DATAPATH}YelpChi.mat')
    A = fulldata['homo']
    edge_index = np.array(A.nonzero())
    node_feat = fulldata['features']
    label = np.array(fulldata['label'], dtype=np.int).flatten()
    num_nodes = node_feat.shape[0]

    dataset = NCDataset('YelpChi')
    edge_index = torch.tensor(edge_index, dtype=torch.long)
    node_feat = torch.tensor(node_feat.todense(), dtype=torch.float)
    dataset.graph = {'edge_index': edge_index,
                     'node_feat': node_feat,
                     'edge_feat': None,
                     'num_nodes': num_nodes}
    label = torch.tensor(label, dtype=torch.long)
    dataset.label = label
    return dataset


def load_planetoid_dataset(name):
    torch_dataset = Planetoid(root=f'{DATAPATH}/Planetoid',
                              name=name)
    data = torch_dataset[0]

    edge_index = data.edge_index
    node_feat = data.x
    label = data.y
    num_nodes = data.num_nodes
    print(f"Num nodes: {num_nodes}")

    dataset = NCDataset(name)

    dataset.train_idx = torch.where(data.train_mask)[0]
    dataset.valid_idx = torch.where(data.val_mask)[0]
    dataset.test_idx = torch.where(data.test_mask)[0]

    dataset.graph = {'edge_index': edge_index,
                     'node_feat': node_feat,
                     'edge_feat': None,
                     'num_nodes': num_nodes}
    def planetoid_orig_split(**kwargs):
        return {'train': torch.as_tensor(dataset.train_idx),
                'valid': torch.as_tensor(dataset.valid_idx),
                'test': torch.as_tensor(dataset.test_idx)}

    dataset.get_idx_split = planetoid_orig_split
    dataset.label = label

    return dataset

def load_geom_gcn_dataset(name):
    fulldata = scipy.io.loadmat(f'{DATAPATH}/{name}.mat')
    edge_index = fulldata['edge_index']
    node_feat = fulldata['node_feat']
    label = np.array(fulldata['label'], dtype=np.int).flatten()
    num_nodes = node_feat.shape[0]

    dataset = NCDataset(name)
    edge_index = torch.tensor(edge_index, dtype=torch.long)
    node_feat = torch.tensor(node_feat, dtype=torch.float)
    dataset.graph = {'edge_index': edge_index,
                     'node_feat': node_feat,
                     'edge_feat': None,
                     'num_nodes': num_nodes}
    label = torch.tensor(label, dtype=torch.long)
    dataset.label = label
    return dataset

