import pandas as pd
import shutil, os
import os.path as osp
import torch
import numpy as np
from dgl.data.utils import load_graphs, save_graphs, Subset
import dgl
from ogb.utils.url import decide_download, download_url, extract_zip
from ogb.io.read_graph_dgl import read_csv_graph_dgl

class DglNodePropPredDataset(object):
    def __init__(self, name, root = "dataset"):
        self.name = name ## original name, e.g., ogbn-proteins
        self.dir_name = "_".join(name.split("-")) + "_dgl" ## replace hyphen with underline, e.g., ogbn_proteins_dgl

        self.original_root = root
        self.root = osp.join(root, self.dir_name)

        self.meta_info = pd.read_csv(os.path.join(os.path.dirname(__file__), "master.csv"), index_col = 0)
        if not self.name in self.meta_info:
            print(self.name)
            error_mssg = "Invalid dataset name {}.\n".format(self.name)
            error_mssg += "Available datasets are as follows:\n"
            error_mssg += "\n".join(self.meta_info.keys())
            raise ValueError(error_mssg)

        self.download_name = self.meta_info[self.name]["download_name"] ## name of downloaded file, e.g., tox21

        self.num_tasks = int(self.meta_info[self.name]["num tasks"])
        self.task_type = self.meta_info[self.name]["task type"]

        super(DglNodePropPredDataset, self).__init__()

        self.pre_process()

    def pre_process(self):
        processed_dir = osp.join(self.root, 'processed')
        pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')

        if osp.exists(pre_processed_file_path):
            self.graph, label_dict = load_graphs(pre_processed_file_path)
            self.labels = label_dict['labels']

        else:
            ### check download
            if not osp.exists(osp.join(self.root, "raw", "edge.csv.gz")):
                url = self.meta_info[self.name]["url"]
                if decide_download(url):
                    path = download_url(url, self.original_root)
                    extract_zip(path, self.original_root)
                    os.unlink(path)
                    # delete folder if there exists
                    try:
                        shutil.rmtree(self.root)
                    except:
                        pass
                    shutil.move(osp.join(self.original_root, self.download_name), self.root)
                else:
                    print("Stop download.")
                    exit(-1)

            raw_dir = osp.join(self.root, "raw")

            file_names = ["edge"]
            if self.meta_info[self.name]["has_node_attr"] == "True":
                file_names.append("node-feat")
            if self.meta_info[self.name]["has_edge_attr"] == "True":
                file_names.append("edge-feat")
            raw_file_names = [file_name + ".csv.gz" for file_name in file_names]

            ### pre-process and save
            add_inverse_edge = self.meta_info[self.name]["add_inverse_edge"] == "True"
            graph = read_csv_graph_dgl(raw_dir, raw_file_names, add_inverse_edge = add_inverse_edge)

            ### adding prediction target
            node_label = pd.read_csv(osp.join(raw_dir, 'node-label.csv.gz'), compression="gzip", header = None).values
            if "classification" in self.task_type:
                node_label = torch.tensor(node_label, dtype = torch.long)
            else:
                node_label = torch.tensor(node_label, dtype = torch.float32)

            label_dict = {"labels": node_label}

            save_graphs(pre_processed_file_path, graph, label_dict)

            self.graph, label_dict = load_graphs(pre_processed_file_path)
            self.labels = label_dict['labels']

    def get_idx_split(self):
        split_type = self.meta_info[self.name]["split"]
        path = osp.join(self.root, "split", split_type)

        train_idx = pd.read_csv(osp.join(path, "train.csv.gz"), compression="gzip", header = None).values.T[0]
        valid_idx = pd.read_csv(osp.join(path, "valid.csv.gz"), compression="gzip", header = None).values.T[0]
        test_idx = pd.read_csv(osp.join(path, "test.csv.gz"), compression="gzip", header = None).values.T[0]

        return {"train": torch.tensor(train_idx, dtype = torch.long), "valid": torch.tensor(valid_idx, dtype = torch.long), "test": torch.tensor(test_idx, dtype = torch.long)}

    def __getitem__(self, idx):
        assert idx == 0, "This dataset has only one graph"
        return self.graph[idx], self.labels

    def __len__(self):
        return 1

    def __repr__(self):  # pragma: no cover
        return '{}({})'.format(self.__class__.__name__, len(self))

if __name__ == "__main__":
    dgl_dataset = DglNodePropPredDataset(name = "ogbn-proteins")
    splitted_index = dgl_dataset.get_idx_split()
    print(dgl_dataset[0])
    print(splitted_index)
