import torch
import random
import numpy as np
from itertools import compress
from rdkit.Chem.Scaffolds import MurckoScaffold
from collections import defaultdict
from sklearn.model_selection import StratifiedKFold

# splitter function

def generate_scaffold(smiles, include_chirality=False):
    """
    Obtain Bemis-Murcko scaffold from smiles
    :param smiles:
    :param include_chirality:
    :return: smiles of scaffold
    """
    scaffold = MurckoScaffold.MurckoScaffoldSmiles(
        smiles=smiles, includeChirality=include_chirality)
    return scaffold

# # test generate_scaffold
# s = 'Cc1cc(Oc2nccc(CCC)c2)ccc1'
# scaffold = generate_scaffold(s)
# assert scaffold == 'c1ccc(Oc2ccccn2)cc1'

def scaffold_split(dataset, smiles_list, fewshot, fewshot_num, task_idx=None, null_value=0,
                   frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=0,
                   return_smiles=False):
    """
    Adapted from https://github.com/deepchem/deepchem/blob/master/deepchem/splits/splitters.py
    Split dataset by Bemis-Murcko scaffolds
    This function can also ignore examples containing null values for a
    selected task when splitting. Deterministic split
    :param dataset: pytorch geometric dataset obj
    :param smiles_list: list of smiles corresponding to the dataset obj
    :param task_idx: column idx of the data.y tensor. Will filter out
    examples with null value in specified task column of the data.y tensor
    prior to splitting. If None, then no filtering
    :param null_value: float that specifies null value in data.y to filter if
    task_idx is provided
    :param frac_train:
    :param frac_valid:
    :param frac_test:
    :param return_smiles:
    :return: train, valid, test slices of the input dataset obj. If
    return_smiles = True, also returns ([train_smiles_list],
    [valid_smiles_list], [test_smiles_list])
    """
    np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.0)

    if task_idx != None:
        # filter based on null values in task_idx
        # get task array
        y_task = np.array([data.y[task_idx].item() for data in dataset])
        # boolean array that correspond to non null values
        non_null = y_task != null_value
        smiles_list = list(compress(enumerate(smiles_list), non_null))
    else:
        non_null = np.ones(len(dataset)) == 1
        smiles_list = list(compress(enumerate(smiles_list), non_null))

    # create dict of the form {scaffold_i: [idx1, idx....]}
    all_scaffolds = {}
    for i, smiles in smiles_list:
        scaffold = generate_scaffold(smiles, include_chirality=True)
        if scaffold not in all_scaffolds:
            all_scaffolds[scaffold] = [i]
        else:
            all_scaffolds[scaffold].append(i)

    # sort from largest to smallest sets
    all_scaffolds = {key: sorted(value) for key, value in all_scaffolds.items()}
    all_scaffold_sets = [
        scaffold_set for (scaffold, scaffold_set) in sorted(
            all_scaffolds.items(), key=lambda x: (len(x[1]), x[1][0]), reverse=True)
    ]

    # get train, valid test indices
    train_cutoff = frac_train * len(smiles_list)
    valid_cutoff = (frac_train + frac_valid) * len(smiles_list)
    train_idx, valid_idx, test_idx = [], [], []
    for scaffold_set in all_scaffold_sets:
        if len(train_idx) + len(scaffold_set) > train_cutoff:
            if len(train_idx) + len(valid_idx) + len(scaffold_set) > valid_cutoff:
                test_idx.extend(scaffold_set)
            else:
                valid_idx.extend(scaffold_set)
        else:
            train_idx.extend(scaffold_set)

    assert len(set(train_idx).intersection(set(valid_idx))) == 0
    assert len(set(test_idx).intersection(set(valid_idx))) == 0

    if fewshot:
        random.seed(seed)
        random.shuffle(train_idx)
        train_idx = train_idx[0:fewshot_num]

    train_dataset = dataset[torch.tensor(train_idx)]
    valid_dataset = dataset[torch.tensor(valid_idx)]
    test_dataset = dataset[torch.tensor(test_idx)]

    if not return_smiles:
        return train_dataset, valid_dataset, test_dataset
    else:
        train_smiles = [smiles_list[i][1] for i in train_idx]
        valid_smiles = [smiles_list[i][1] for i in valid_idx]
        test_smiles = [smiles_list[i][1] for i in test_idx]
        return train_dataset, valid_dataset, test_dataset, (train_smiles,
                                                            valid_smiles,
                                                            test_smiles)

def size_split(dataset, fewshot, fewshot_num, task_idx=None, null_value=0,
                   frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=0):
    np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.0)
 
    # Not useful
    # if task_idx != None:
    #     # filter based on null values in task_idx
    #     # get task array
    #     y_task = np.array([data.y[task_idx].item() for data in dataset])
    #     # boolean array that correspond to non null values
    #     non_null = y_task != null_value
    #     smiles_list = list(compress(enumerate(smiles_list), non_null))
    # else:
    #     non_null = np.ones(len(dataset)) == 1
    #     smiles_list = list(compress(enumerate(smiles_list), non_null))
 
    # create dict of the form {size_i: [idx1, idx....]}
    # Note: Do not replace idx with data.id: data.id is from raw data, some of which are removed to create processed data.
    all_sizes = {}
    for idx, data in enumerate(dataset):
        mol_size = data.x.shape[0]
        if mol_size not in all_sizes:
            all_sizes[mol_size] = [idx]
        else:
            all_sizes[mol_size].append(idx)
    # sort from largest to smallest sets
    all_sizes = {key: sorted(value) for key, value in all_sizes.items()}
    all_sizes_sets = sorted(all_sizes.items())
    # print(all_sizes_sets)
 
    # get train, valid test indices
    train_cutoff = frac_train * len(dataset)
    valid_cutoff = (frac_train + frac_valid) * len(dataset)
 
    train_idx, valid_idx, test_idx = [], [], []
    for size, mol_sets in all_sizes_sets:
        if len(train_idx) + len(mol_sets) > train_cutoff:
            if len(train_idx) + len(valid_idx) + len(mol_sets) > valid_cutoff:
                test_idx.extend(mol_sets)
            else:
                valid_idx.extend(mol_sets)
        else:
            train_idx.extend(mol_sets)
   
    assert len(train_idx)+len(valid_idx)+len(test_idx) == len(dataset)
    assert len(set(train_idx).intersection(set(valid_idx))) == 0
    assert len(set(test_idx).intersection(set(valid_idx))) == 0

    if fewshot:
        random.seed(seed)
        random.shuffle(train_idx)
        train_idx = train_idx[0:fewshot_num]
 
    train_dataset = dataset[torch.tensor(train_idx)]
    valid_dataset = dataset[torch.tensor(valid_idx)]
    test_dataset = dataset[torch.tensor(test_idx)]
 
    return train_dataset, valid_dataset, test_dataset

def random_scaffold_split(dataset, smiles_list, task_idx=None, null_value=0,
                   frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=0):
    """
    Adapted from https://github.com/pfnet-research/chainer-chemistry/blob/master/chainer_chemistry/dataset/splitters/scaffold_splitter.py
    Split dataset by Bemis-Murcko scaffolds
    This function can also ignore examples containing null values for a
    selected task when splitting. Deterministic split
    :param dataset: pytorch geometric dataset obj
    :param smiles_list: list of smiles corresponding to the dataset obj
    :param task_idx: column idx of the data.y tensor. Will filter out
    examples with null value in specified task column of the data.y tensor
    prior to splitting. If None, then no filtering
    :param null_value: float that specifies null value in data.y to filter if
    task_idx is provided
    :param frac_train:
    :param frac_valid:
    :param frac_test:
    :param seed;
    :return: train, valid, test slices of the input dataset obj
    """

    np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.0)

    if task_idx != None:
        # filter based on null values in task_idx
        # get task array
        y_task = np.array([data.y[task_idx].item() for data in dataset])
        # boolean array that correspond to non null values
        non_null = y_task != null_value
        smiles_list = list(compress(enumerate(smiles_list), non_null))
    else:
        non_null = np.ones(len(dataset)) == 1
        smiles_list = list(compress(enumerate(smiles_list), non_null))

    rng = np.random.RandomState(seed)

    scaffolds = defaultdict(list)
    for ind, smiles in smiles_list:
        scaffold = generate_scaffold(smiles, include_chirality=True)
        scaffolds[scaffold].append(ind)

    scaffold_sets = rng.permutation(list(scaffolds.values()))

    n_total_valid = int(np.floor(frac_valid * len(dataset)))
    n_total_test = int(np.floor(frac_test * len(dataset)))

    train_idx = []
    valid_idx = []
    test_idx = []

    for scaffold_set in scaffold_sets:
        if len(valid_idx) + len(scaffold_set) <= n_total_valid:
            valid_idx.extend(scaffold_set)
        elif len(test_idx) + len(scaffold_set) <= n_total_test:
            test_idx.extend(scaffold_set)
        else:
            train_idx.extend(scaffold_set)

    train_dataset = dataset[torch.tensor(train_idx)]
    valid_dataset = dataset[torch.tensor(valid_idx)]
    test_dataset = dataset[torch.tensor(test_idx)]

    return train_dataset, valid_dataset, test_dataset

def random_split(dataset, fewshot, fewshot_num, task_idx=None, null_value=0,
                   frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=0,
                 smiles_list=None):
    """

    :param dataset:
    :param task_idx:
    :param null_value:
    :param frac_train:
    :param frac_valid:
    :param frac_test:
    :param seed:
    :param smiles_list: list of smiles corresponding to the dataset obj, or None
    :return: train, valid, test slices of the input dataset obj. If
    smiles_list != None, also returns ([train_smiles_list],
    [valid_smiles_list], [test_smiles_list])
    """
    np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.0)

    if task_idx != None:
        # filter based on null values in task_idx
        # get task array
        y_task = np.array([data.y[task_idx].item() for data in dataset])
        non_null = y_task != null_value  # boolean array that correspond to non null values
        idx_array = np.where(non_null)[0]
        dataset = dataset[torch.tensor(idx_array)]  # examples containing non
        # null labels in the specified task_idx
    else:
        pass

    num_mols = len(dataset)
    random.seed(seed)
    all_idx = list(range(num_mols))
    random.shuffle(all_idx)

    train_idx = all_idx[:int(frac_train * num_mols)]
    valid_idx = all_idx[int(frac_train * num_mols):int(frac_valid * num_mols)
                                                   + int(frac_train * num_mols)]
    test_idx = all_idx[int(frac_valid * num_mols) + int(frac_train * num_mols):]

    assert len(set(train_idx).intersection(set(valid_idx))) == 0
    assert len(set(valid_idx).intersection(set(test_idx))) == 0
    assert len(train_idx) + len(valid_idx) + len(test_idx) == num_mols

    if fewshot:
        train_idx = train_idx[0:fewshot_num]

    train_dataset = dataset[torch.tensor(train_idx)]
    valid_dataset = dataset[torch.tensor(valid_idx)]
    test_dataset = dataset[torch.tensor(test_idx)]

    if not smiles_list:
        return train_dataset, valid_dataset, test_dataset
    else:
        train_smiles = [smiles_list[i] for i in train_idx]
        valid_smiles = [smiles_list[i] for i in valid_idx]
        test_smiles = [smiles_list[i] for i in test_idx]
        return train_dataset, valid_dataset, test_dataset, (train_smiles,
                                                            valid_smiles,
                                                            test_smiles)


def cv_random_split(dataset, fold_idx = 0,
                   frac_train=0.9, frac_valid=0.1, seed=0,
                 smiles_list=None):
    """

    :param dataset:
    :param task_idx:
    :param null_value:
    :param frac_train:
    :param frac_valid:
    :param frac_test:
    :param seed:
    :param smiles_list: list of smiles corresponding to the dataset obj, or None
    :return: train, valid, test slices of the input dataset obj. If
    smiles_list != None, also returns ([train_smiles_list],
    [valid_smiles_list], [test_smiles_list])
    """

    np.testing.assert_almost_equal(frac_train + frac_valid, 1.0)

    skf = StratifiedKFold(n_splits=10, shuffle = True, random_state = seed)

    labels = [data.y.item() for data in dataset]

    idx_list = []

    for idx in skf.split(np.zeros(len(labels)), labels):
        idx_list.append(idx)
    train_idx, val_idx = idx_list[fold_idx]

    train_dataset = dataset[torch.tensor(train_idx)]
    valid_dataset = dataset[torch.tensor(val_idx)]

    return train_dataset, valid_dataset


if __name__ == "__main__":
    from loader import MoleculeDataset
    from rdkit import Chem
    import pandas as pd

    # # test scaffold_split
    dataset = MoleculeDataset('dataset/tox21', dataset='tox21')
    smiles_list = pd.read_csv('dataset/tox21/processed/smiles.csv', header=None)[0].tolist()

    train_dataset, valid_dataset, test_dataset = scaffold_split(dataset, smiles_list, task_idx=None, null_value=0, frac_train=0.8,frac_valid=0.1, frac_test=0.1)
    # train_dataset, valid_dataset, test_dataset = random_scaffold_split(dataset, smiles_list, task_idx=None, null_value=0, frac_train=0.8,frac_valid=0.1, frac_test=0.1, seed = 0)
    unique_ids = set(train_dataset.data.id.tolist() +
                     valid_dataset.data.id.tolist() +
                     test_dataset.data.id.tolist())
    assert len(unique_ids) == len(dataset)  # check that we did not have any
    # missing or overlapping examples

    # test scaffold_split with smiles returned
    dataset = MoleculeDataset('dataset/bbbp', dataset='bbbp')
    smiles_list = pd.read_csv('dataset/bbbp/processed/smiles.csv', header=None)[
        0].tolist()
    train_dataset, valid_dataset, test_dataset, (train_smiles, valid_smiles,
                                                 test_smiles) =  \
        scaffold_split(dataset, smiles_list, task_idx=None, null_value=0,
                       frac_train=0.8,frac_valid=0.1, frac_test=0.1,
                       return_smiles=True)
    assert len(train_dataset) == len(train_smiles)
    for i in range(len(train_dataset)):
        data_obj_n_atoms = train_dataset[i].x.size()[0]
        smiles_n_atoms = len(list(Chem.MolFromSmiles(train_smiles[
                                                         i]).GetAtoms()))
        assert data_obj_n_atoms == smiles_n_atoms
    assert len(valid_dataset) == len(valid_smiles)
    for i in range(len(valid_dataset)):
        data_obj_n_atoms = valid_dataset[i].x.size()[0]
        smiles_n_atoms = len(list(Chem.MolFromSmiles(valid_smiles[
                                                         i]).GetAtoms()))
        assert data_obj_n_atoms == smiles_n_atoms
    assert len(test_dataset) == len(test_smiles)
    for i in range(len(test_dataset)):
        data_obj_n_atoms = test_dataset[i].x.size()[0]
        smiles_n_atoms = len(list(Chem.MolFromSmiles(test_smiles[
                                                         i]).GetAtoms()))
        assert data_obj_n_atoms == smiles_n_atoms

    # test random_split
    from loader import MoleculeDataset

    dataset = MoleculeDataset('dataset/tox21', dataset='tox21')
    train_dataset, valid_dataset, test_dataset = random_split(dataset, task_idx=None, null_value=0, frac_train=0.8,frac_valid=0.1, frac_test=0.1)
    unique_ids = set(train_dataset.data.id.tolist() +
                     valid_dataset.data.id.tolist() +
                     test_dataset.data.id.tolist())
    assert len(unique_ids) == len(dataset)  # check that we did not have any
    # missing or overlapping examples

    # test random_split with smiles returned
    dataset = MoleculeDataset('dataset/bbbp', dataset='bbbp')
    smiles_list = pd.read_csv('dataset/bbbp/processed/smiles.csv', header=None)[
        0].tolist()
    train_dataset, valid_dataset, test_dataset, (train_smiles, valid_smiles,
                                                 test_smiles) = \
        random_split(dataset, task_idx=None, null_value=0,
                       frac_train=0.8, frac_valid=0.1, frac_test=0.1, seed=42,
                       smiles_list=smiles_list)
    assert len(train_dataset) == len(train_smiles)
    for i in range(len(train_dataset)):
        data_obj_n_atoms = train_dataset[i].x.size()[0]
        smiles_n_atoms = len(list(Chem.MolFromSmiles(train_smiles[
                                                         i]).GetAtoms()))
        assert data_obj_n_atoms == smiles_n_atoms
    assert len(valid_dataset) == len(valid_smiles)
    for i in range(len(valid_dataset)):
        data_obj_n_atoms = valid_dataset[i].x.size()[0]
        smiles_n_atoms = len(list(Chem.MolFromSmiles(valid_smiles[
                                                         i]).GetAtoms()))
        assert data_obj_n_atoms == smiles_n_atoms
    assert len(test_dataset) == len(test_smiles)
    for i in range(len(test_dataset)):
        data_obj_n_atoms = test_dataset[i].x.size()[0]
        smiles_n_atoms = len(list(Chem.MolFromSmiles(test_smiles[
                                                         i]).GetAtoms()))
        assert data_obj_n_atoms == smiles_n_atoms


