import os
import os.path as osp
import pickle
import logging

import torch
from torch_geometric.data import (InMemoryDataset, download_url, extract_zip,
                                  Data)
from torch_geometric.utils import remove_self_loops


class GNNBenchmarkDataset(InMemoryDataset):
    r"""A variety of artificially and semi-artificially generated graph
    datasets from the `"Benchmarking Graph Neural Networks"
    <https://arxiv.org/abs/2003.00982>`_ paper.

    .. note::
        The ZINC dataset is provided via
        :class:`torch_geometric.datasets.ZINC`.

    Args:
        root (string): Root directory where the dataset should be saved.
        name (string): The name of the dataset (one of :obj:`"PATTERN"`,
            :obj:`"CLUSTER"`, :obj:`"MNIST"`, :obj:`"CIFAR10"`,
            :obj:`"TSP"`, :obj:`"CSL"`)
        split (string, optional): If :obj:`"train"`, loads the training
            dataset.
            If :obj:`"val"`, loads the validation dataset.
            If :obj:`"test"`, loads the test dataset.
            (default: :obj:`"train"`)
        transform (callable, optional): A function/transform that takes in an
            :obj:`torch_geometric.data.Data` object and returns a transformed
            version. The data object will be transformed before every access.
            (default: :obj:`None`)
        pre_transform (callable, optional): A function/transform that takes in
            an :obj:`torch_geometric.data.Data` object and returns a
            transformed version. The data object will be transformed before
            being saved to disk. (default: :obj:`None`)
        pre_filter (callable, optional): A function that takes in an
            :obj:`torch_geometric.data.Data` object and returns a boolean
            value, indicating whether the data object should be included in the
            final dataset. (default: :obj:`None`)
    """

    names = ['PATTERN', 'CLUSTER', 'MNIST', 'CIFAR10', 'TSP', 'CSL']

    url = 'https://pytorch-geometric.com/datasets/benchmarking-gnns'
    csl_url = 'https://www.dropbox.com/s/rnbkp5ubgk82ocu/CSL.zip?dl=1'

    def __init__(self, root, name, split='train', transform=None,
                 pre_transform=None, pre_filter=None, use_node_attr=False,
                 use_edge_attr=False, cleaned=False):
        self.name = name
        assert self.name in self.names

        if self.name == 'CSL' and split != 'train':
            split = 'train'
            logging.warning(
                ('Dataset `CSL` does not provide a standardized splitting. '
                 'Instead, it is recommended to perform 5-fold cross '
                 'validation with stratifed sampling.'))

        super(GNNBenchmarkDataset, self).__init__(root, transform,
                                                  pre_transform, pre_filter)

        if split == 'train':
            path = self.processed_paths[0]
        elif split == 'val':
            path = self.processed_paths[1]
        elif split == 'test':
            path = self.processed_paths[2]
        else:
            raise ValueError((f'Split {split} found, but expected either '
                              'train, val, trainval or test'))

        self.data, self.slices = torch.load(path)

    @property
    def raw_dir(self):
        return osp.join(self.root, self.name, 'raw')

    @property
    def processed_dir(self):
        return osp.join(self.root, self.name, 'processed')

    @property
    def raw_file_names(self):
        name = self.name
        if name == 'CSL':
            return [
                'graphs_Kary_Deterministic_Graphs.pkl',
                'y_Kary_Deterministic_Graphs.pt'
            ]
        else:
            return [f'{name}_train.pt', f'{name}_val.pt', f'{name}_test.pt']

    @property
    def processed_file_names(self):
        if self.name == 'CSL':
            return ['data.pt']
        else:
            return ['train_data.pt', 'val_data.pt', 'test_data.pt']

    def download(self):
        url = self.csl_url
        url = f'{self.url}/{self.name}.zip' if self.name != 'CSL' else url
        path = download_url(url, self.raw_dir)
        extract_zip(path, self.raw_dir)
        os.unlink(path)

    def process(self):
        if self.name == 'CSL':
            data_list = self.process_CSL()
            torch.save(self.collate(data_list), self.processed_paths[0])
        else:
            for i in range(3):
                self.data, self.slices = torch.load(self.raw_paths[i])
                data_list = [self.get(i) for i in range(len(self))]

                if self.pre_filter is not None:
                    data_list = [d for d in data_list if self.pre_filter(d)]

                if self.pre_transform is not None:
                    data_list = [self.pre_transform(d) for d in data_list]

                torch.save(self.collate(data_list), self.processed_paths[i])

    def process_CSL(self):
        path = osp.join(self.raw_dir, 'graphs_Kary_Deterministic_Graphs.pkl')
        with open(path, 'rb') as f:
            adjs = pickle.load(f)

        path = osp.join(self.raw_dir, 'y_Kary_Deterministic_Graphs.pt')
        ys = torch.load(path).tolist()

        data_list = []
        for adj, y in zip(adjs, ys):
            row, col = torch.from_numpy(adj.row), torch.from_numpy(adj.col)
            edge_index = torch.stack([row, col], dim=0).to(torch.long)
            edge_index, _ = remove_self_loops(edge_index)
            data = Data(edge_index=edge_index, y=y, num_nodes=adj.shape[0])
            if self.pre_filter is not None and not self.pre_filter(data):
                continue
            if self.pre_transform is not None:
                data = self.pre_transform(data)
            data_list.append(data)
        return data_list

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