from pathlib import Path

from time import time

import random

from collections import defaultdict

import argparse

import warnings

from tqdm import tqdm

import numpy as np

import torch

from Bio.PDB import PDBParser

from Bio.PDB.Polypeptide import three_to_one, is_aa

from Bio.PDB import PDBIO, Select

from openbabel import openbabel

from rdkit import Chem

from rdkit.Chem import QED

from scipy.ndimage import gaussian_filter

from geometry_utils import get_bb_transform

from analysis.molecule_builder import build_molecule

from analysis.metrics import rdmol_to_smiles

import constants

from constants import covalent_radii, dataset_params

import utils

dataset_info = dataset_params['bindingmoad']

amino_acid_dict = dataset_info['aa_encoder']

atom_dict = dataset_info['atom_encoder']

atom_decoder = dataset_info['atom_decoder']

class Model0(Select):

    def accept_model(self, model):

        return model.id == 0

def read_label_file(csv_path):

    """

    Read BindingMOAD's label file

    Args:

        csv_path: path to 'every.csv'

    Returns:

        Nested dictionary with all ligands. First level: EC number,

            Second level: PDB ID, Third level: list of ligands. Each ligand is

            represented as a tuple (ligand name, validity, SMILES string)

    """

    ligand_dict = {}

    with open(csv_path, 'r') as f:

        for line in f.readlines():

            row = line.split(',')

            # new protein class

            if len(row[0]) > 0:

                curr_class = row[0]

                ligand_dict[curr_class] = {}

                continue

            # new protein

            if len(row[2]) > 0:

                curr_prot = row[2]

                ligand_dict[curr_class][curr_prot] = []

                continue

            # new small molecule

            if len(row[3]) > 0:

                ligand_dict[curr_class][curr_prot].append(

                    # (ligand name, validity, SMILES string)

                    [row[3], row[4], row[9]]

                )

    return ligand_dict

def compute_druglikeness(ligand_dict):

    """

    Computes RDKit's QED value and adds it to the dictionary

    Args:

        ligand_dict: nested ligand dictionary

    Returns:

        the same ligand dictionary with additional QED values

    """

    print("Computing QED values...")

    for p, m in tqdm([(p, m) for c in ligand_dict for p in ligand_dict[c]

                      for m in ligand_dict[c][p]]):

        mol = Chem.MolFromSmiles(m[2])

        if mol is None:

            mol_id = f'{p}_{m}'

            warnings.warn(f"Could not construct molecule {mol_id} from SMILES "

                          f"string '{m[2]}'")

            continue

        m.append(QED.qed(mol))

    return ligand_dict

def filter_and_flatten(ligand_dict, qed_thresh, max_occurences, seed):

    filtered_examples = []

    all_examples = [(c, p, m) for c in ligand_dict for p in ligand_dict[c]

                    for m in ligand_dict[c][p]]

    # shuffle to select random examples of ligands that occur more than

    # max_occurences times

    random.seed(seed)

    random.shuffle(all_examples)

    ligand_name_counter = defaultdict(int)

    print("Filtering examples...")

    for c, p, m in tqdm(all_examples):

        ligand_name, ligand_chain, ligand_resi = m[0].split(':')

        if m[1] == 'valid' and len(m) > 3 and m[3] > qed_thresh:

            if ligand_name_counter[ligand_name] < max_occurences:

                filtered_examples.append(

                    (c, p, m)

                )

                ligand_name_counter[ligand_name] += 1

    return filtered_examples

def split_by_ec_number(data_list, n_val, n_test, ec_level=1):

    """

    Split dataset into training, validation and test sets based on EC numbers

    https://en.wikipedia.org/wiki/Enzyme_Commission_number

    Args:

        data_list: list of ligands

        n_val: number of validation examples

        n_test: number of test examples

        ec_level: level in the EC numbering hierarchy at which the split is

            made, i.e. items with matching EC numbers at this level are put in

            the same set

    Returns:

        dictionary with keys 'train', 'val', and 'test'

    """

    examples_per_class = defaultdict(int)

    for c, p, m in data_list:

        c_sub = '.'.join(c.split('.')[:ec_level])

        examples_per_class[c_sub] += 1

    assert sum(examples_per_class.values()) == len(data_list)

    # split ec numbers

    val_classes = set()

    for c, num in sorted(examples_per_class.items(), key=lambda x: x[1],

                         reverse=True):

        if sum([examples_per_class[x] for x in val_classes]) + num <= n_val:

            val_classes.add(c)

    test_classes = set()

    for c, num in sorted(examples_per_class.items(), key=lambda x: x[1],

                         reverse=True):

        # skip classes already used in the validation set

        if c in val_classes:

            continue

        if sum([examples_per_class[x] for x in test_classes]) + num <= n_test:

            test_classes.add(c)

    # remaining classes belong to test set

    train_classes = {x for x in examples_per_class if

                     x not in val_classes and x not in test_classes}

    # create separate lists of examples

    data_split = {}

    data_split['train'] = [x for x in data_list if '.'.join(

        x[0].split('.')[:ec_level]) in train_classes]

    data_split['val'] = [x for x in data_list if '.'.join(

        x[0].split('.')[:ec_level]) in val_classes]

    data_split['test'] = [x for x in data_list if '.'.join(

        x[0].split('.')[:ec_level]) in test_classes]

    assert len(data_split['train']) + len(data_split['val']) + \

           len(data_split['test']) == len(data_list)

    return data_split

def ligand_list_to_dict(ligand_list):

    out_dict = defaultdict(list)

    for _, p, m in ligand_list:

        out_dict[p].append(m)

    return out_dict

def process_ligand_and_pocket(pdb_struct, ligand_name, ligand_chain,

                              ligand_resi, dist_cutoff, ca_only,

                              compute_quaternion=False):

    try:

        residues = {obj.id[1]: obj for obj in

                    pdb_struct[0][ligand_chain].get_residues()}

    except KeyError as e:

        raise KeyError(f'Chain {e} not found ({pdbfile}, '

                       f'{ligand_name}:{ligand_chain}:{ligand_resi})')

    ligand = residues[ligand_resi]

    assert ligand.get_resname() == ligand_name, \

        f"{ligand.get_resname()} != {ligand_name}"

    # remove H atoms if not in atom_dict, other atom types that aren't allowed

    # should stay so that the entire ligand can be removed from the dataset

    lig_atoms = [a for a in ligand.get_atoms()

                 if (a.element.capitalize() in atom_dict or a.element != 'H')]

    lig_coords = np.array([a.get_coord() for a in lig_atoms])

    try:

        lig_one_hot = np.stack([

            np.eye(1, len(atom_dict), atom_dict[a.element.capitalize()]).squeeze()

            for a in lig_atoms

        ])

    except KeyError as e:

        raise KeyError(

            f'Ligand atom {e} not in atom dict ({pdbfile}, '

            f'{ligand_name}:{ligand_chain}:{ligand_resi})')

    # Find interacting pocket residues based on distance cutoff

    pocket_residues = []

    for residue in pdb_struct[0].get_residues():

        res_coords = np.array([a.get_coord() for a in residue.get_atoms()])

        if is_aa(residue.get_resname(), standard=True) and \

                (((res_coords[:, None, :] - lig_coords[None, :, :]) ** 2).sum(-1) ** 0.5).min() < dist_cutoff:

            pocket_residues.append(residue)

    # Compute transform of the canonical reference frame

    n_xyz = np.array([res['N'].get_coord() for res in pocket_residues])

    ca_xyz = np.array([res['CA'].get_coord() for res in pocket_residues])

    c_xyz = np.array([res['C'].get_coord() for res in pocket_residues])

    if compute_quaternion:

        quaternion, c_alpha = get_bb_transform(n_xyz, ca_xyz, c_xyz)

        if np.any(np.isnan(quaternion)):

            raise ValueError(

                f'Invalid value in quaternion ({pdbfile}, '

                f'{ligand_name}:{ligand_chain}:{ligand_resi})')

    else:

        c_alpha = ca_xyz

    if ca_only:

        pocket_coords = c_alpha

        try:

            pocket_one_hot = np.stack([

                np.eye(1, len(amino_acid_dict),

                       amino_acid_dict[three_to_one(res.get_resname())]).squeeze()

                for res in pocket_residues])

        except KeyError as e:

            raise KeyError(

                f'{e} not in amino acid dict ({pdbfile}, '

                f'{ligand_name}:{ligand_chain}:{ligand_resi})')

    else:

        pocket_atoms = [a for res in pocket_residues for a in res.get_atoms()

                        if (a.element.capitalize() in atom_dict or a.element != 'H')]

        pocket_coords = np.array([a.get_coord() for a in pocket_atoms])

        try:

            pocket_one_hot = np.stack([

                np.eye(1, len(atom_dict), atom_dict[a.element.capitalize()]).squeeze()

                for a in pocket_atoms

            ])

        except KeyError as e:

            raise KeyError(

                f'Pocket atom {e} not in atom dict ({pdbfile}, '

                f'{ligand_name}:{ligand_chain}:{ligand_resi})')

    pocket_ids = [f'{res.parent.id}:{res.id[1]}' for res in pocket_residues]

    ligand_data = {

        'lig_coords': lig_coords,

        'lig_one_hot': lig_one_hot,

    }

    pocket_data = {

        'pocket_coords': pocket_coords,

        'pocket_one_hot': pocket_one_hot,

        'pocket_ids': pocket_ids,

    }

    if compute_quaternion:

        pocket_data['pocket_quaternion'] = quaternion

    return ligand_data, pocket_data

def compute_smiles(positions, one_hot, mask):

    print("Computing SMILES ...")

    atom_types = np.argmax(one_hot, axis=-1)

    sections = np.where(np.diff(mask))[0] + 1

    positions = [torch.from_numpy(x) for x in np.split(positions, sections)]

    atom_types = [torch.from_numpy(x) for x in np.split(atom_types, sections)]

    mols_smiles = []

    pbar = tqdm(enumerate(zip(positions, atom_types)),

                total=len(np.unique(mask)))

    for i, (pos, atom_type) in pbar:

        mol = build_molecule(pos, atom_type, dataset_info)

        # BasicMolecularMetrics() computes SMILES after sanitization

        try:

            Chem.SanitizeMol(mol)

        except ValueError:

            continue

        mol = rdmol_to_smiles(mol)

        if mol is not None:

            mols_smiles.append(mol)

        pbar.set_description(f'{len(mols_smiles)}/{i + 1} successful')

    return mols_smiles

def get_n_nodes(lig_mask, pocket_mask, smooth_sigma=None):

    # Joint distribution of ligand's and pocket's number of nodes

    idx_lig, n_nodes_lig = np.unique(lig_mask, return_counts=True)

    idx_pocket, n_nodes_pocket = np.unique(pocket_mask, return_counts=True)

    assert np.all(idx_lig == idx_pocket)

    joint_histogram = np.zeros((np.max(n_nodes_lig) + 1,

                                np.max(n_nodes_pocket) + 1))

    for nlig, npocket in zip(n_nodes_lig, n_nodes_pocket):

        joint_histogram[nlig, npocket] += 1

    print(f'Original histogram: {np.count_nonzero(joint_histogram)}/'

          f'{joint_histogram.shape[0] * joint_histogram.shape[1]} bins filled')

    # Smooth the histogram

    if smooth_sigma is not None:

        filtered_histogram = gaussian_filter(

            joint_histogram, sigma=smooth_sigma, order=0, mode='constant',

            cval=0.0, truncate=4.0)

        print(f'Smoothed histogram: {np.count_nonzero(filtered_histogram)}/'

              f'{filtered_histogram.shape[0] * filtered_histogram.shape[1]} bins filled')

        joint_histogram = filtered_histogram

    return joint_histogram

def get_bond_length_arrays(atom_mapping):

    bond_arrays = []

    for i in range(3):

        bond_dict = getattr(constants, f'bonds{i + 1}')

        bond_array = np.zeros((len(atom_mapping), len(atom_mapping)))

        for a1 in atom_mapping.keys():

            for a2 in atom_mapping.keys():

                if a1 in bond_dict and a2 in bond_dict[a1]:

                    bond_len = bond_dict[a1][a2]

                else:

                    bond_len = 0

                bond_array[atom_mapping[a1], atom_mapping[a2]] = bond_len

        assert np.all(bond_array == bond_array.T)

        bond_arrays.append(bond_array)

    return bond_arrays

def get_lennard_jones_rm(atom_mapping):

    # Bond radii for the Lennard-Jones potential

    LJ_rm = np.zeros((len(atom_mapping), len(atom_mapping)))

    for a1 in atom_mapping.keys():

        for a2 in atom_mapping.keys():

            all_bond_lengths = []

            for btype in ['bonds1', 'bonds2', 'bonds3']:

                bond_dict = getattr(constants, btype)

                if a1 in bond_dict and a2 in bond_dict[a1]:

                    all_bond_lengths.append(bond_dict[a1][a2])

            if len(all_bond_lengths) > 0:

                # take the shortest possible bond length because slightly larger

                # values aren't penalized as much

                bond_len = min(all_bond_lengths)

            else:

                # Replace missing values with sum of average covalent radii

                bond_len = covalent_radii[a1] + covalent_radii[a2]

            LJ_rm[atom_mapping[a1], atom_mapping[a2]] = bond_len

    assert np.all(LJ_rm == LJ_rm.T)

    return LJ_rm

def get_type_histograms(lig_one_hot, pocket_one_hot, atom_encoder, aa_encoder):

    atom_decoder = list(atom_encoder.keys())

    atom_counts = {k: 0 for k in atom_encoder.keys()}

    for a in [atom_decoder[x] for x in lig_one_hot.argmax(1)]:

        atom_counts[a] += 1

    aa_decoder = list(aa_encoder.keys())

    aa_counts = {k: 0 for k in aa_encoder.keys()}

    for r in [aa_decoder[x] for x in pocket_one_hot.argmax(1)]:

        aa_counts[r] += 1

    return atom_counts, aa_counts

def saveall(filename, pdb_and_mol_ids, lig_coords, lig_one_hot, lig_mask,

            pocket_coords, pocket_quaternion, pocket_one_hot, pocket_mask):

    np.savez(filename,

        names=pdb_and_mol_ids,

        lig_coords=lig_coords,

        lig_one_hot=lig_one_hot,

        lig_mask=lig_mask,

        pocket_coords=pocket_coords,

        pocket_quaternion=pocket_quaternion,

        pocket_one_hot=pocket_one_hot,

        pocket_mask=pocket_mask

    )

    return True

if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument('basedir', type=Path)

    parser.add_argument('--outdir', type=Path, default=None)

    parser.add_argument('--qed_thresh', type=float, default=0.3)

    parser.add_argument('--max_occurences', type=int, default=50)

    parser.add_argument('--num_val', type=int, default=300)

    parser.add_argument('--num_test', type=int, default=300)

    parser.add_argument('--dist_cutoff', type=float, default=8.0)

    parser.add_argument('--ca_only', action='store_true')

    parser.add_argument('--random_seed', type=int, default=42)

    parser.add_argument('--make_split', action='store_true')

    args = parser.parse_args()

    pdbdir = args.basedir / 'BindingMOAD_2020/'

    # Make output directory

    if args.outdir is None:

        suffix = '' if 'H' in atom_dict else '_noH'

        suffix += '_ca_only' if args.ca_only else '_full'

        processed_dir = Path(args.basedir, f'processed{suffix}')

    else:

        processed_dir = args.outdir

    processed_dir.mkdir(exist_ok=True, parents=True)

    if args.make_split:

        # Process the label file

        csv_path = args.basedir / 'every.csv'

        ligand_dict = read_label_file(csv_path)

        ligand_dict = compute_druglikeness(ligand_dict)

        filtered_examples = filter_and_flatten(

            ligand_dict, args.qed_thresh, args.max_occurences, args.random_seed)

        print(f'{len(filtered_examples)} examples after filtering')

        # Make data split

        data_split = split_by_ec_number(filtered_examples, args.num_val,

                                        args.num_test)

    else:

        # Use precomputed data split

        data_split = {}

        for split in ['test', 'val', 'train']:

            with open(f'data/moad_{split}.txt', 'r') as f:

                pocket_ids = f.read().split(',')

            # (ec-number, protein, molecule tuple)

            data_split[split] = [(None, x.split('_')[0][:4], (x.split('_')[1],))

                          for x in pocket_ids]

    n_train_before = len(data_split['train'])

    n_val_before = len(data_split['val'])

    n_test_before = len(data_split['test'])

    # Read and process PDB files

    n_samples_after = {}

    for split in data_split.keys():

        lig_coords = []

        lig_one_hot = []

        lig_mask = []

        pocket_coords = []

        pocket_one_hot = []

        pocket_mask = []

        pdb_and_mol_ids = []

        receptors = []

        count = 0

        pdb_sdf_dir = processed_dir / split

        pdb_sdf_dir.mkdir(exist_ok=True)

        n_tot = len(data_split[split])

        pair_dict = ligand_list_to_dict(data_split[split])

        tic = time()

        num_failed = 0

        with tqdm(total=n_tot) as pbar:

            for p in pair_dict:

                pdb_successful = set()

                # try all available .bio files

                for pdbfile in sorted(pdbdir.glob(f"{p.lower()}.bio*")):

                    # Skip if all ligands have been processed already

                    if len(pair_dict[p]) == len(pdb_successful):

                        continue

                    pdb_struct = PDBParser(QUIET=True).get_structure('', pdbfile)

                    struct_copy = pdb_struct.copy()

                    n_bio_successful = 0

                    for m in pair_dict[p]:

                        # Skip already processed ligand

                        if m[0] in pdb_successful:

                            continue

                        ligand_name, ligand_chain, ligand_resi = m[0].split(':')

                        ligand_resi = int(ligand_resi)

                        try:

                            ligand_data, pocket_data = process_ligand_and_pocket(

                                pdb_struct, ligand_name, ligand_chain, ligand_resi,

                                dist_cutoff=args.dist_cutoff, ca_only=args.ca_only)

                        except (KeyError, AssertionError, FileNotFoundError,

                                IndexError, ValueError) as e:

                            # print(type(e).__name__, e)

                            continue

                        pdb_and_mol_ids.append(f"{p}_{m[0]}")

                        receptors.append(pdbfile.name)

                        lig_coords.append(ligand_data['lig_coords'])

                        lig_one_hot.append(ligand_data['lig_one_hot'])

                        lig_mask.append(

                            count * np.ones(len(ligand_data['lig_coords'])))

                        pocket_coords.append(pocket_data['pocket_coords'])

                        # pocket_quaternion.append(

                        #     pocket_data['pocket_quaternion'])

                        pocket_one_hot.append(pocket_data['pocket_one_hot'])

                        pocket_mask.append(

                            count * np.ones(len(pocket_data['pocket_coords'])))

                        count += 1

                        pdb_successful.add(m[0])

                        n_bio_successful += 1

                        # Save additional files for affinity analysis

                        if split in {'val', 'test'}:

                        # if split in {'val', 'test', 'train'}:

                            # remove ligand from receptor

                            try:

                                struct_copy[0][ligand_chain].detach_child((f'H_{ligand_name}', ligand_resi, ' '))

                            except KeyError:

                                warnings.warn(f"Could not find ligand {(f'H_{ligand_name}', ligand_resi, ' ')} in {pdbfile}")

                                continue

                            # Create SDF file

                            atom_types = [atom_decoder[np.argmax(i)] for i in ligand_data['lig_one_hot']]

                            xyz_file = Path(pdb_sdf_dir, 'tmp.xyz')

                            utils.write_xyz_file(ligand_data['lig_coords'], atom_types, xyz_file)

                            obConversion = openbabel.OBConversion()

                            obConversion.SetInAndOutFormats("xyz", "sdf")

                            mol = openbabel.OBMol()

                            obConversion.ReadFile(mol, str(xyz_file))

                            xyz_file.unlink()

                            name = f"{p}-{pdbfile.suffix[1:]}_{m[0]}"

                            sdf_file = Path(pdb_sdf_dir, f'{name}.sdf')

                            obConversion.WriteFile(mol, str(sdf_file))

                            # specify pocket residues

                            with open(Path(pdb_sdf_dir, f'{name}.txt'), 'w') as f:

                                f.write(' '.join(pocket_data['pocket_ids']))

                    if split in {'val', 'test'} and n_bio_successful > 0:

                    # if split in {'val', 'test', 'train'} and n_bio_successful > 0:

                        # create receptor PDB file

                        pdb_file_out = Path(pdb_sdf_dir, f'{p}-{pdbfile.suffix[1:]}.pdb')

                        io = PDBIO()

                        io.set_structure(struct_copy)

                        io.save(str(pdb_file_out), select=Model0())

                pbar.update(len(pair_dict[p]))

                num_failed += (len(pair_dict[p]) - len(pdb_successful))

                pbar.set_description(f'#failed: {num_failed}')

        lig_coords = np.concatenate(lig_coords, axis=0)

        lig_one_hot = np.concatenate(lig_one_hot, axis=0)

        lig_mask = np.concatenate(lig_mask, axis=0)

        pocket_coords = np.concatenate(pocket_coords, axis=0)

        pocket_one_hot = np.concatenate(pocket_one_hot, axis=0)

        pocket_mask = np.concatenate(pocket_mask, axis=0)

        np.savez(processed_dir / f'{split}.npz', names=pdb_and_mol_ids,

                 receptors=receptors, lig_coords=lig_coords,

                 lig_one_hot=lig_one_hot, lig_mask=lig_mask,

                 pocket_coords=pocket_coords, pocket_one_hot=pocket_one_hot,

                 pocket_mask=pocket_mask)

        n_samples_after[split] = len(pdb_and_mol_ids)

        print(f"Processing {split} set took {(time() - tic)/60.0:.2f} minutes")

    # --------------------------------------------------------------------------

    # Compute statistics & additional information

    # --------------------------------------------------------------------------

    with np.load(processed_dir / 'train.npz', allow_pickle=True) as data:

        lig_mask = data['lig_mask']

        pocket_mask = data['pocket_mask']

        lig_coords = data['lig_coords']

        lig_one_hot = data['lig_one_hot']

        pocket_one_hot = data['pocket_one_hot']

    # Compute SMILES for all training examples

    train_smiles = compute_smiles(lig_coords, lig_one_hot, lig_mask)

    np.save(processed_dir / 'train_smiles.npy', train_smiles)

    # Joint histogram of number of ligand and pocket nodes

    n_nodes = get_n_nodes(lig_mask, pocket_mask, smooth_sigma=1.0)

    np.save(Path(processed_dir, 'size_distribution.npy'), n_nodes)

    # Convert bond length dictionaries to arrays for batch processing

    bonds1, bonds2, bonds3 = get_bond_length_arrays(atom_dict)

    # Get bond length definitions for Lennard-Jones potential

    rm_LJ = get_lennard_jones_rm(atom_dict)

    # Get histograms of ligand and pocket node types

    atom_hist, aa_hist = get_type_histograms(lig_one_hot, pocket_one_hot,

                                             atom_dict, amino_acid_dict)

    # Create summary string

    summary_string = '# SUMMARY\n\n'

    summary_string += '# Before processing\n'

    summary_string += f'num_samples train: {n_train_before}\n'

    summary_string += f'num_samples val: {n_val_before}\n'

    summary_string += f'num_samples test: {n_test_before}\n\n'

    summary_string += '# After processing\n'

    summary_string += f"num_samples train: {n_samples_after['train']}\n"

    summary_string += f"num_samples val: {n_samples_after['val']}\n"

    summary_string += f"num_samples test: {n_samples_after['test']}\n\n"

    summary_string += '# Info\n'

    summary_string += f"'atom_encoder': {atom_dict}\n"

    summary_string += f"'atom_decoder': {list(atom_dict.keys())}\n"

    summary_string += f"'aa_encoder': {amino_acid_dict}\n"

    summary_string += f"'aa_decoder': {list(amino_acid_dict.keys())}\n"

    summary_string += f"'bonds1': {bonds1.tolist()}\n"

    summary_string += f"'bonds2': {bonds2.tolist()}\n"

    summary_string += f"'bonds3': {bonds3.tolist()}\n"

    summary_string += f"'lennard_jones_rm': {rm_LJ.tolist()}\n"

    summary_string += f"'atom_hist': {atom_hist}\n"

    summary_string += f"'aa_hist': {aa_hist}\n"

    summary_string += f"'n_nodes': {n_nodes.tolist()}\n"

    # Write summary to text file

    with open(processed_dir / 'summary.txt', 'w') as f:

        f.write(summary_string)

    # Print summary

    print(summary_string)