import torch
from torch.utils.data import Dataset
import numpy as np

import dgl
from dgl.data.utils import download, extract_archive, get_download_dir
from .mol_tree_nx import DGLMolTree
from .mol_tree import Vocab

from .mpn import mol2dgl_single as mol2dgl_enc
from .jtmpn import mol2dgl_single as mol2dgl_dec
from .jtmpn import ATOM_FDIM as ATOM_FDIM_DEC
from .jtmpn import BOND_FDIM as BOND_FDIM_DEC

_url = 'https://s3-ap-southeast-1.amazonaws.com/dgl-data-cn/dataset/jtnn.zip'

def _unpack_field(examples, field):
    return [e[field] for e in examples]

def _set_node_id(mol_tree, vocab):
    wid = []
    for i, node in enumerate(mol_tree.nodes_dict):
        mol_tree.nodes_dict[node]['idx'] = i
        wid.append(vocab.get_index(mol_tree.nodes_dict[node]['smiles']))

    return wid

class JTNNDataset(Dataset):
    def __init__(self, data, vocab, training=True):
        self.dir = get_download_dir()
        self.zip_file_path='{}/jtnn.zip'.format(self.dir)
        download(_url, path=self.zip_file_path)
        extract_archive(self.zip_file_path, '{}/jtnn'.format(self.dir))
        print('Loading data...')
        data_file = '{}/jtnn/{}.txt'.format(self.dir, data)
        with open(data_file) as f:
            self.data = [line.strip("\r\n ").split()[0] for line in f]
        self.vocab_file = '{}/jtnn/{}.txt'.format(self.dir, vocab)
        print('Loading finished.')
        print('\tNum samples:', len(self.data))
        print('\tVocab file:', self.vocab_file)
        self.training = training
        self.vocab = Vocab([x.strip("\r\n ") for x in open(self.vocab_file)])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        smiles = self.data[idx]
        mol_tree = DGLMolTree(smiles)
        mol_tree.recover()
        mol_tree.assemble()

        wid = _set_node_id(mol_tree, self.vocab)

        # prebuild the molecule graph
        mol_graph, atom_x_enc, bond_x_enc = mol2dgl_enc(mol_tree.smiles)

        result = {
                'mol_tree': mol_tree,
                'mol_graph': mol_graph,
                'atom_x_enc': atom_x_enc,
                'bond_x_enc': bond_x_enc,
                'wid': wid,
                }

        if not self.training:
            return result

        # prebuild the candidate graph list
        cands = []
        for node_id, node in mol_tree.nodes_dict.items():
            # fill in ground truth
            if node['label'] not in node['cands']:
                node['cands'].append(node['label'])
                node['cand_mols'].append(node['label_mol'])

            if node['is_leaf'] or len(node['cands']) == 1:
                continue
            cands.extend([(cand, mol_tree, node_id)
                         for cand in node['cand_mols']])
        if len(cands) > 0:
            cand_graphs, atom_x_dec, bond_x_dec, tree_mess_src_e, \
                    tree_mess_tgt_e, tree_mess_tgt_n = mol2dgl_dec(cands)
        else:
            cand_graphs = []
            atom_x_dec = torch.zeros(0, ATOM_FDIM_DEC)
            bond_x_dec = torch.zeros(0, BOND_FDIM_DEC)
            tree_mess_src_e = torch.zeros(0, 2).long()
            tree_mess_tgt_e = torch.zeros(0, 2).long()
            tree_mess_tgt_n = torch.zeros(0).long()

        # prebuild the stereoisomers
        cands = mol_tree.stereo_cands
        if len(cands) > 1:
            if mol_tree.smiles3D not in cands:
                cands.append(mol_tree.smiles3D)

            stereo_graphs = [mol2dgl_enc(c) for c in cands]
            stereo_cand_graphs, stereo_atom_x_enc, stereo_bond_x_enc = \
                    zip(*stereo_graphs)
            stereo_atom_x_enc = torch.cat(stereo_atom_x_enc)
            stereo_bond_x_enc = torch.cat(stereo_bond_x_enc)
            stereo_cand_label = [(cands.index(mol_tree.smiles3D), len(cands))]
        else:
            stereo_cand_graphs = []
            stereo_atom_x_enc = torch.zeros(0, atom_x_enc.shape[1])
            stereo_bond_x_enc = torch.zeros(0, bond_x_enc.shape[1])
            stereo_cand_label = []

        result.update({
            'cand_graphs': cand_graphs,
            'atom_x_dec': atom_x_dec,
            'bond_x_dec': bond_x_dec,
            'tree_mess_src_e': tree_mess_src_e,
            'tree_mess_tgt_e': tree_mess_tgt_e,
            'tree_mess_tgt_n': tree_mess_tgt_n,
            'stereo_cand_graphs': stereo_cand_graphs,
            'stereo_atom_x_enc': stereo_atom_x_enc,
            'stereo_bond_x_enc': stereo_bond_x_enc,
            'stereo_cand_label': stereo_cand_label,
            })

        return result

class JTNNCollator(object):
    def __init__(self, vocab, training):
        self.vocab = vocab
        self.training = training

    @staticmethod
    def _batch_and_set(graphs, atom_x, bond_x, flatten):
        if flatten:
            graphs = [g for f in graphs for g in f]
        graph_batch = dgl.batch(graphs)
        graph_batch.ndata['x'] = atom_x
        graph_batch.edata.update({
            'x': bond_x,
            'src_x': atom_x.new(bond_x.shape[0], atom_x.shape[1]).zero_(),
            })
        return graph_batch

    def __call__(self, examples):
        # get list of trees
        mol_trees = _unpack_field(examples, 'mol_tree')
        wid = _unpack_field(examples, 'wid')
        for _wid, mol_tree in zip(wid, mol_trees):
            mol_tree.ndata['wid'] = torch.LongTensor(_wid)

        # TODO: either support pickling or get around ctypes pointers using scipy
        # batch molecule graphs
        mol_graphs = _unpack_field(examples, 'mol_graph')
        atom_x = torch.cat(_unpack_field(examples, 'atom_x_enc'))
        bond_x = torch.cat(_unpack_field(examples, 'bond_x_enc'))
        mol_graph_batch = self._batch_and_set(mol_graphs, atom_x, bond_x, False)

        result = {
                'mol_trees': mol_trees,
                'mol_graph_batch': mol_graph_batch,
                }

        if not self.training:
            return result

        # batch candidate graphs
        cand_graphs = _unpack_field(examples, 'cand_graphs')
        cand_batch_idx = []
        atom_x = torch.cat(_unpack_field(examples, 'atom_x_dec'))
        bond_x = torch.cat(_unpack_field(examples, 'bond_x_dec'))
        tree_mess_src_e = _unpack_field(examples, 'tree_mess_src_e')
        tree_mess_tgt_e = _unpack_field(examples, 'tree_mess_tgt_e')
        tree_mess_tgt_n = _unpack_field(examples, 'tree_mess_tgt_n')

        n_graph_nodes = 0
        n_tree_nodes = 0
        for i in range(len(cand_graphs)):
            tree_mess_tgt_e[i] += n_graph_nodes
            tree_mess_src_e[i] += n_tree_nodes
            tree_mess_tgt_n[i] += n_graph_nodes
            n_graph_nodes += sum(g.number_of_nodes() for g in cand_graphs[i])
            n_tree_nodes += mol_trees[i].number_of_nodes()
            cand_batch_idx.extend([i] * len(cand_graphs[i]))
        tree_mess_tgt_e = torch.cat(tree_mess_tgt_e)
        tree_mess_src_e = torch.cat(tree_mess_src_e)
        tree_mess_tgt_n = torch.cat(tree_mess_tgt_n)

        cand_graph_batch = self._batch_and_set(cand_graphs, atom_x, bond_x, True)

        # batch stereoisomers
        stereo_cand_graphs = _unpack_field(examples, 'stereo_cand_graphs')
        atom_x = torch.cat(_unpack_field(examples, 'stereo_atom_x_enc'))
        bond_x = torch.cat(_unpack_field(examples, 'stereo_bond_x_enc'))
        stereo_cand_batch_idx = []
        for i in range(len(stereo_cand_graphs)):
            stereo_cand_batch_idx.extend([i] * len(stereo_cand_graphs[i]))

        if len(stereo_cand_batch_idx) > 0:
            stereo_cand_labels = [
                    (label, length)
                    for ex in _unpack_field(examples, 'stereo_cand_label')
                    for label, length in ex
                    ]
            stereo_cand_labels, stereo_cand_lengths = zip(*stereo_cand_labels)
            stereo_cand_graph_batch = self._batch_and_set(
                    stereo_cand_graphs, atom_x, bond_x, True)
        else:
            stereo_cand_labels = []
            stereo_cand_lengths = []
            stereo_cand_graph_batch = None
            stereo_cand_batch_idx = []

        result.update({
            'cand_graph_batch': cand_graph_batch,
            'cand_batch_idx': cand_batch_idx,
            'tree_mess_tgt_e': tree_mess_tgt_e,
            'tree_mess_src_e': tree_mess_src_e,
            'tree_mess_tgt_n': tree_mess_tgt_n,
            'stereo_cand_graph_batch': stereo_cand_graph_batch,
            'stereo_cand_batch_idx': stereo_cand_batch_idx,
            'stereo_cand_labels': stereo_cand_labels,
            'stereo_cand_lengths': stereo_cand_lengths,
            })

        return result
