from pathlib import Path

from time import time

import argparse

import shutil

import random

import matplotlib.pyplot as plt

import seaborn as sns

from tqdm import tqdm

import numpy as np

from Bio.PDB import PDBParser

from Bio.PDB.Polypeptide import three_to_one, is_aa

from rdkit import Chem

from scipy.ndimage import gaussian_filter

import torch

from analysis.molecule_builder import build_molecule

from analysis.metrics import rdmol_to_smiles

import constants

from constants import covalent_radii, dataset_params

def process_ligand_and_pocket(pdbfile, sdffile,

                              atom_dict, dist_cutoff, ca_only):

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

    try:

        ligand = Chem.SDMolSupplier(str(sdffile))[0]

    except:

        raise Exception(f'cannot read sdf mol ({sdffile})')

    # 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.GetSymbol() for a in ligand.GetAtoms()

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

    lig_coords = np.array([list(ligand.GetConformer(0).GetAtomPosition(idx))

                           for idx in range(ligand.GetNumAtoms())])

    try:

        lig_one_hot = np.stack([

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

            for a in lig_atoms

        ])

    except KeyError as e:

        raise KeyError(

            f'{e} not in atom dict ({sdffile})')

    # 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)

    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,

    }

    if ca_only:

        try:

            pocket_one_hot = []

            full_coords = []

            for res in pocket_residues:

                for atom in res.get_atoms():

                    if atom.name == 'CA':

                        pocket_one_hot.append(np.eye(1, len(amino_acid_dict),

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

                        full_coords.append(atom.coord)

            pocket_one_hot = np.stack(pocket_one_hot)

            full_coords = np.stack(full_coords)

        except KeyError as e:

            raise KeyError(

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

        pocket_data = {

            'pocket_coords': full_coords,

            'pocket_one_hot': pocket_one_hot,

            'pocket_ids': pocket_ids

        }

    else:

        full_atoms = np.concatenate(

            [np.array([atom.element for atom in res.get_atoms()])

             for res in pocket_residues], axis=0)

        full_coords = np.concatenate(

            [np.array([atom.coord for atom in res.get_atoms()])

             for res in pocket_residues], axis=0)

        try:

            pocket_one_hot = []

            for a in full_atoms:

                if a in amino_acid_dict:

                    atom = np.eye(1, len(amino_acid_dict),

                                  amino_acid_dict[a.capitalize()]).squeeze()

                elif a != 'H':

                    atom = np.eye(1, len(amino_acid_dict),

                                  len(amino_acid_dict)).squeeze()

                pocket_one_hot.append(atom)

            pocket_one_hot = np.stack(pocket_one_hot)

        except KeyError as e:

            raise KeyError(

                f'{e} not in atom dict ({pdbfile})')

        pocket_data = {

            'pocket_coords': full_coords,

            'pocket_one_hot': pocket_one_hot,

            'pocket_ids': pocket_ids

        }

    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:

                if a1 == 'others' or a2 == 'others':

                    bond_len = 0

                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_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_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('--no_H', action='store_true')

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

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

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

    args = parser.parse_args()

    datadir = args.basedir / 'crossdocked_pocket10/'

    if args.ca_only:

        dataset_info = dataset_params['crossdock']

    else:

        dataset_info = dataset_params['crossdock_full']

    amino_acid_dict = dataset_info['aa_encoder']

    atom_dict = dataset_info['atom_encoder']

    atom_decoder = dataset_info['atom_decoder']

    # Make output directory

    if args.outdir is None:

        suffix = '_crossdock' if 'H' in atom_dict else '_crossdock_noH'

        suffix += '_ca_only_temp' if args.ca_only else '_full_temp'

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

    else:

        processed_dir = args.outdir

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

    # Read data split

    split_path = Path(args.basedir, 'split_by_name.pt')

    data_split = torch.load(split_path)

    # There is no validation set, copy 300 training examples (the validation set

    # is not very important in this application)

    # Note: before we had a data leak but it should not matter too much as most

    # metrics monitored during training are independent of the pockets

    data_split['val'] = random.sample(data_split['train'], 300)

    n_train_before = len(data_split['train'])

    n_val_before = len(data_split['val'])

    n_test_before = len(data_split['test'])

    failed_save = []

    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 = []

        count_protein = []

        count_ligand = []

        count_total = []

        count = 0

        pdb_sdf_dir = processed_dir / split

        pdb_sdf_dir.mkdir(exist_ok=True)

        tic = time()

        num_failed = 0

        pbar = tqdm(data_split[split])

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

        for pocket_fn, ligand_fn in pbar:

            sdffile = datadir / f'{ligand_fn}'

            pdbfile = datadir / f'{pocket_fn}'

            try:

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

            except:

                num_failed += 1

                failed_save.append((pocket_fn, ligand_fn))

                print(failed_save[-1])

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

                continue

            try:

                ligand_data, pocket_data = process_ligand_and_pocket(

                    pdbfile, sdffile,

                    atom_dict=atom_dict, dist_cutoff=args.dist_cutoff,

                    ca_only=args.ca_only)

            except (KeyError, AssertionError, FileNotFoundError, IndexError,

                    ValueError) as e:

                print(type(e).__name__, e, pocket_fn, ligand_fn)

                num_failed += 1

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

                continue

            pdb_and_mol_ids.append(f"{pocket_fn}_{ligand_fn}")

            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_one_hot.append(pocket_data['pocket_one_hot'])

            pocket_mask.append(

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

            count_protein.append(pocket_data['pocket_coords'].shape[0])

            count_ligand.append(ligand_data['lig_coords'].shape[0])

            count_total.append(pocket_data['pocket_coords'].shape[0] +

                               ligand_data['lig_coords'].shape[0])

            count += 1

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

                # Copy PDB file

                new_rec_name = Path(pdbfile).stem.replace('_', '-')

                pdb_file_out = Path(pdb_sdf_dir, f"{new_rec_name}.pdb")

                shutil.copy(pdbfile, pdb_file_out)

                # Copy SDF file

                new_lig_name = new_rec_name + '_' + Path(sdffile).stem.replace('_', '-')

                sdf_file_out = Path(pdb_sdf_dir, f'{new_lig_name}.sdf')

                shutil.copy(sdffile, sdf_file_out)

                # specify pocket residues

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

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

        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)

        saveall(processed_dir / f'{split}.npz', pdb_and_mol_ids, lig_coords,

                lig_one_hot, lig_mask, pocket_coords,

                pocket_one_hot, 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)

    print(failed_save)