import os
import os.path as osp
import shutil
import tensorlayerx as tlx
from typing import Callable, List, Optional

try:
    import cPickle as pickle
except ImportError:
    import pickle

from gammagl.data import InMemoryDataset, download_url, extract_zip
from gammagl.io.tu import read_tu_data


class TUDataset(InMemoryDataset):
    r"""A variety of graph kernel benchmark datasets, *.e.g.* "IMDB-BINARY",
    "REDDIT-BINARY" or "PROTEINS", collected from the `TU Dortmund University
    <https://chrsmrrs.github.io/datasets>`_.
    In addition, this dataset wrapper provides `cleaned dataset versions
    <https://github.com/nd7141/graph_datasets>`_ as motivated by the
    `"Understanding Isomorphism Bias in Graph Data Sets"
    <https://arxiv.org/abs/1910.12091>`_ paper, containing only non-isomorphic
    graphs.
    
    .. note::
    
        Some datasets may not come with any node labels.
        You can then either make use of the argument :obj:`use_node_attr`
        to load additional continuous node attributes (if present) or provide
        synthetic node features using transforms such as
        like :class:`gammagl.transforms.Constant` or
        :class:`gammagl.transforms.OneHotDegree`.

    Parameters
    ----------
    root: string
        Root directory where the dataset should be saved.
    name: string
        The `name
        <https://chrsmrrs.github.io/datasets/docs/datasets/>`_ of the
        dataset.
    transform: callable, optional
        A function/transform that takes in an
        :obj:`gammagl.data.Graph` 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:`gammagl.data.Graph` 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:`gammagl.data.Graph` object and returns a boolean
        value, indicating whether the data object should be included in the
        final dataset. (default: :obj:`None`)
    use_node_attr: bool, optional
        If :obj:`True`, the dataset will
        contain additional continuous node attributes (if present).
        (default: :obj:`False`)
    use_edge_attr: bool, optional
        If :obj:`True`, the dataset will
        contain additional continuous edge attributes (if present).
        (default: :obj:`False`)
    cleaned: bool, optional
        If :obj:`True`, the dataset will
        contain only non-isomorphic graphs. (default: :obj:`False`)

    Tip
    ---
        .. list-table::
            :widths: 20 10 10 10 10 10
            :header-rows: 1
            
            * - Name
              - #graphs
              - #nodes
              - #edges
              - #features
              - #classes
            * - MUTAG
              - 188
              - ~17.9
              - ~39.6
              - 7
              - 2
            * - ENZYMES
              - 600
              - ~32.6
              - ~124.3
              - 3
              - 6
            * - PROTEINS
              - 1,113
              - ~39.1
              - ~145.6
              - 3
              - 2
            * - COLLAB
              - 5,000
              - ~74.5
              - ~4914.4
              - 0
              - 3
            * - IMDB-BINARY
              - 1,000
              - ~19.8
              - ~193.1
              - 0
              - 2
            * - REDDIT-BINARY
              - 2,000
              - ~429.6
              - ~995.5
              - 0
              - 2
            * - ...
              -
              -
              -
              -
              -
    """

    url = 'https://www.chrsmrrs.com/graphkerneldatasets'
    cleaned_url = ('https://raw.githubusercontent.com/nd7141/'
                   'graph_datasets/master/datasets')

    def __init__(self, root: str = None, name: str = 'MUTAG',
                 transform: Optional[Callable] = None,
                 pre_transform: Optional[Callable] = None,
                 pre_filter: Optional[Callable] = None,
                 use_node_attr: bool = False, use_edge_attr: bool = False,
                 cleaned: bool = False):
        self.name = name
        self.cleaned = cleaned
        super().__init__(root, transform, pre_transform, pre_filter)
        self.data, self.slices, self.sizes = self.load_data(self.processed_paths[0])
        if self.data.x is not None and not use_node_attr:
            num_node_attributes = self.num_node_attributes
            self.data.x = self.data.x[:, num_node_attributes:]
        if self.data.edge_attr is not None and not use_edge_attr:
            num_edge_attributes = self.num_edge_attributes
            self.data.edge_attr = self.data.edge_attr[:, num_edge_attributes:]

    @property
    def raw_dir(self) -> str:
        name = f'raw{"_cleaned" if self.cleaned else ""}'
        return osp.join(self.root, self.name, name)

    @property
    def processed_dir(self) -> str:
        name = f'processed{"_cleaned" if self.cleaned else ""}'
        return osp.join(self.root, self.name, name)

    @property
    def num_node_labels(self) -> int:
        return self.sizes['num_node_labels']

    @property
    def num_node_attributes(self) -> int:
        return self.sizes['num_node_attributes']

    @property
    def num_edge_labels(self) -> int:
        return self.sizes['num_edge_labels']

    @property
    def num_edge_attributes(self) -> int:
        return self.sizes['num_edge_attributes']

    @property
    def raw_file_names(self) -> List[str]:
        names = ['A', 'graph_indicator']
        return [f'{self.name}_{name}.txt' for name in names]

    @property
    def processed_file_names(self) -> str:
        return tlx.BACKEND + '_graph.pt'

    def download(self):
        url = self.cleaned_url if self.cleaned else self.url
        folder = osp.join(self.root, self.name)
        path = download_url(f'{url}/{self.name}.zip', folder)
        extract_zip(path, folder)
        os.unlink(path)
        shutil.rmtree(self.raw_dir)
        os.rename(osp.join(folder, self.name), self.raw_dir)

    def process(self):
        self.data, self.slices, sizes = read_tu_data(self.raw_dir, self.name)

        if self.pre_filter is not None:
            data_list = [self.get(idx) for idx in range(len(self))]
            data_list = [data for data in data_list if self.pre_filter(data)]
            self.data, self.slices = self.collate(data_list)

        if self.pre_transform is not None:
            data_list = [self.get(idx) for idx in range(len(self))]
            data_list = [self.pre_transform(data) for data in data_list]
            self.data, self.slices = self.collate(data_list)
        self.save_data((self.data, self.slices, sizes), self.processed_paths[0])

    def __repr__(self) -> str:
        return f'{self.name}({len(self)})'
