model/ProteinMPNN/protein_mpnn_utils.py

from __future__ import print_function

import copy
import glob
import itertools
import json
import os
import random
import shutil
import sys
import time

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset, random_split

# A number of functions/classes are adopted from: https://github.com/jingraham/neurips19-graph-protein-design


def _scores(S, log_probs, mask):
    """Negative log probabilities"""
    criterion = torch.nn.NLLLoss(reduction="none")
    loss = criterion(
        log_probs.contiguous().view(-1, log_probs.size(-1)), S.contiguous().view(-1)
    ).view(S.size())
    scores = torch.sum(loss * mask, dim=-1) / torch.sum(mask, dim=-1)
    return scores


def _S_to_seq(S, mask):
    alphabet = "ACDEFGHIKLMNPQRSTVWYX"
    seq = "".join([alphabet[c] for c, m in zip(S.tolist(), mask.tolist()) if m > 0])
    return seq


def parse_PDB_biounits(x, atoms=["N", "CA", "C"], chain=None):
    """
    input:  x = PDB filename
            atoms = atoms to extract (optional)
    output: (length, atoms, coords=(x,y,z)), sequence
    """

    alpha_1 = list("ARNDCQEGHILKMFPSTWYV-")
    states = len(alpha_1)
    alpha_3 = [
        "ALA",
        "ARG",
        "ASN",
        "ASP",
        "CYS",
        "GLN",
        "GLU",
        "GLY",
        "HIS",
        "ILE",
        "LEU",
        "LYS",
        "MET",
        "PHE",
        "PRO",
        "SER",
        "THR",
        "TRP",
        "TYR",
        "VAL",
        "GAP",
    ]

    aa_1_N = {a: n for n, a in enumerate(alpha_1)}
    aa_3_N = {a: n for n, a in enumerate(alpha_3)}
    aa_N_1 = {n: a for n, a in enumerate(alpha_1)}
    aa_1_3 = {a: b for a, b in zip(alpha_1, alpha_3)}
    aa_3_1 = {b: a for a, b in zip(alpha_1, alpha_3)}

    def AA_to_N(x):
        # ["ARND"] -> [[0,1,2,3]]
        x = np.array(x)
        if x.ndim == 0:
            x = x[None]
        return [[aa_1_N.get(a, states - 1) for a in y] for y in x]

    def N_to_AA(x):
        # [[0,1,2,3]] -> ["ARND"]
        x = np.array(x)
        if x.ndim == 1:
            x = x[None]
        return ["".join([aa_N_1.get(a, "-") for a in y]) for y in x]

    xyz, seq, min_resn, max_resn = {}, {}, 1e6, -1e6
    for line in open(x, "rb"):
        line = line.decode("utf-8", "ignore").rstrip()

        if line[:6] == "HETATM" and line[17 : 17 + 3] == "MSE":
            line = line.replace("HETATM", "ATOM  ")
            line = line.replace("MSE", "MET")

        if line[:4] == "ATOM":
            ch = line[21:22]
            if ch == chain or chain is None:
                atom = line[12 : 12 + 4].strip()
                resi = line[17 : 17 + 3]
                resn = line[22 : 22 + 5].strip()
                x, y, z = [float(line[i : (i + 8)]) for i in [30, 38, 46]]

                if resn[-1].isalpha():
                    resa, resn = resn[-1], int(resn[:-1]) - 1
                else:
                    resa, resn = "", int(resn) - 1
                #         resn = int(resn)
                if resn < min_resn:
                    min_resn = resn
                if resn > max_resn:
                    max_resn = resn
                if resn not in xyz:
                    xyz[resn] = {}
                if resa not in xyz[resn]:
                    xyz[resn][resa] = {}
                if resn not in seq:
                    seq[resn] = {}
                if resa not in seq[resn]:
                    seq[resn][resa] = resi

                if atom not in xyz[resn][resa]:
                    xyz[resn][resa][atom] = np.array([x, y, z])

    # convert to numpy arrays, fill in missing values
    seq_, xyz_ = [], []
    try:
        for resn in range(min_resn, max_resn + 1):
            if resn in seq:
                for k in sorted(seq[resn]):
                    seq_.append(aa_3_N.get(seq[resn][k], 20))
            else:
                seq_.append(20)
            if resn in xyz:
                for k in sorted(xyz[resn]):
                    for atom in atoms:
                        if atom in xyz[resn][k]:
                            xyz_.append(xyz[resn][k][atom])
                        else:
                            xyz_.append(np.full(3, np.nan))
            else:
                for atom in atoms:
                    xyz_.append(np.full(3, np.nan))
        return np.array(xyz_).reshape(-1, len(atoms), 3), N_to_AA(np.array(seq_))
    except TypeError:
        return "no_chain", "no_chain"


def parse_PDB(path_to_pdb, input_chain_list=None, ca_only=False):
    c = 0
    pdb_dict_list = []
    init_alphabet = [
        "A",
        "B",
        "C",
        "D",
        "E",
        "F",
        "G",
        "H",
        "I",
        "J",
        "K",
        "L",
        "M",
        "N",
        "O",
        "P",
        "Q",
        "R",
        "S",
        "T",
        "U",
        "V",
        "W",
        "X",
        "Y",
        "Z",
        "a",
        "b",
        "c",
        "d",
        "e",
        "f",
        "g",
        "h",
        "i",
        "j",
        "k",
        "l",
        "m",
        "n",
        "o",
        "p",
        "q",
        "r",
        "s",
        "t",
        "u",
        "v",
        "w",
        "x",
        "y",
        "z",
    ]
    extra_alphabet = [str(item) for item in list(np.arange(300))]
    chain_alphabet = init_alphabet + extra_alphabet

    if input_chain_list:
        chain_alphabet = input_chain_list

    biounit_names = [path_to_pdb]
    for biounit in biounit_names:
        my_dict = {}
        s = 0
        concat_seq = ""
        concat_N = []
        concat_CA = []
        concat_C = []
        concat_O = []
        concat_mask = []
        coords_dict = {}
        for letter in chain_alphabet:
            if ca_only:
                sidechain_atoms = ["CA"]
            else:
                sidechain_atoms = ["N", "CA", "C", "O"]
            xyz, seq = parse_PDB_biounits(biounit, atoms=sidechain_atoms, chain=letter)
            if type(xyz) != str:
                concat_seq += seq[0]
                my_dict["seq_chain_" + letter] = seq[0]
                coords_dict_chain = {}
                if ca_only:
                    coords_dict_chain["CA_chain_" + letter] = xyz.tolist()
                else:
                    coords_dict_chain["N_chain_" + letter] = xyz[:, 0, :].tolist()
                    coords_dict_chain["CA_chain_" + letter] = xyz[:, 1, :].tolist()
                    coords_dict_chain["C_chain_" + letter] = xyz[:, 2, :].tolist()
                    coords_dict_chain["O_chain_" + letter] = xyz[:, 3, :].tolist()
                my_dict["coords_chain_" + letter] = coords_dict_chain
                s += 1
        fi = biounit.rfind("/")
        my_dict["name"] = biounit[(fi + 1) : -4]
        my_dict["num_of_chains"] = s
        my_dict["seq"] = concat_seq
        if s <= len(chain_alphabet):
            pdb_dict_list.append(my_dict)
            c += 1
    return pdb_dict_list


def tied_featurize(
    batch,
    device,
    chain_dict,
    fixed_position_dict=None,
    omit_AA_dict=None,
    tied_positions_dict=None,
    pssm_dict=None,
    bias_by_res_dict=None,
    ca_only=False,
):
    """Pack and pad batch into torch tensors"""
    alphabet = "ACDEFGHIKLMNPQRSTVWYX"
    B = len(batch)
    lengths = np.array(
        [len(b["seq"]) for b in batch], dtype=int
    )  # sum of chain seq lengths
    L_max = max([len(b["seq"]) for b in batch])
    if ca_only:
        X = np.zeros([B, L_max, 1, 3])
    else:
        X = np.zeros([B, L_max, 4, 3])
    residue_idx = -100 * np.ones([B, L_max], dtype=int)
    chain_M = np.zeros(
        [B, L_max], dtype=int
    )  # 1.0 for the bits that need to be predicted
    pssm_coef_all = np.zeros(
        [B, L_max], dtype=np.float32
    )  # 1.0 for the bits that need to be predicted
    pssm_bias_all = np.zeros(
        [B, L_max, 21], dtype=np.float32
    )  # 1.0 for the bits that need to be predicted
    pssm_log_odds_all = 10000.0 * np.ones(
        [B, L_max, 21], dtype=np.float32
    )  # 1.0 for the bits that need to be predicted
    chain_M_pos = np.zeros(
        [B, L_max], dtype=int
    )  # 1.0 for the bits that need to be predicted
    bias_by_res_all = np.zeros([B, L_max, 21], dtype=np.float32)
    chain_encoding_all = np.zeros(
        [B, L_max], dtype=int
    )  # 1.0 for the bits that need to be predicted
    S = np.zeros([B, L_max], dtype=int)
    omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=int)
    # Build the batch
    letter_list_list = []
    visible_list_list = []
    masked_list_list = []
    masked_chain_length_list_list = []
    tied_pos_list_of_lists_list = []
    # shuffle all chains before the main loop
    for i, b in enumerate(batch):
        if chain_dict != None:
            masked_chains, visible_chains = chain_dict[
                b["name"]
            ]  # masked_chains a list of chain letters to predict [A, D, F]
        else:
            masked_chains = [item[-1:] for item in list(b) if item[:10] == "seq_chain_"]
            visible_chains = []
        num_chains = b["num_of_chains"]
        all_chains = masked_chains + visible_chains
        # random.shuffle(all_chains)
    for i, b in enumerate(batch):
        mask_dict = {}
        a = 0
        x_chain_list = []
        chain_mask_list = []
        chain_seq_list = []
        chain_encoding_list = []
        c = 1
        letter_list = []
        global_idx_start_list = [0]
        visible_list = []
        masked_list = []
        masked_chain_length_list = []
        fixed_position_mask_list = []
        omit_AA_mask_list = []
        pssm_coef_list = []
        pssm_bias_list = []
        pssm_log_odds_list = []
        bias_by_res_list = []
        l0 = 0
        l1 = 0
        for step, letter in enumerate(all_chains):
            if letter in visible_chains:
                letter_list.append(letter)
                visible_list.append(letter)
                chain_seq = b[f"seq_chain_{letter}"]
                chain_seq = "".join([a if a != "-" else "X" for a in chain_seq])
                chain_length = len(chain_seq)
                global_idx_start_list.append(global_idx_start_list[-1] + chain_length)
                chain_coords = b[f"coords_chain_{letter}"]  # this is a dictionary
                chain_mask = np.zeros(chain_length)  # 0.0 for visible chains
                if ca_only:
                    x_chain = np.array(
                        chain_coords[f"CA_chain_{letter}"]
                    )  # [chain_lenght,1,3] #CA_diff
                    if len(x_chain.shape) == 2:
                        x_chain = x_chain[:, None, :]
                else:
                    x_chain = np.stack(
                        [
                            chain_coords[c]
                            for c in [
                                f"N_chain_{letter}",
                                f"CA_chain_{letter}",
                                f"C_chain_{letter}",
                                f"O_chain_{letter}",
                            ]
                        ],
                        1,
                    )  # [chain_lenght,4,3]
                x_chain_list.append(x_chain)
                chain_mask_list.append(chain_mask)
                chain_seq_list.append(chain_seq)
                chain_encoding_list.append(c * np.ones(np.array(chain_mask).shape[0]))
                l1 += chain_length
                residue_idx[i, l0:l1] = 100 * (c - 1) + np.arange(l0, l1)
                l0 += chain_length
                c += 1
                fixed_position_mask = np.ones(chain_length)
                fixed_position_mask_list.append(fixed_position_mask)
                omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], int)
                omit_AA_mask_list.append(omit_AA_mask_temp)
                pssm_coef = np.zeros(chain_length)
                pssm_bias = np.zeros([chain_length, 21])
                pssm_log_odds = 10000.0 * np.ones([chain_length, 21])
                pssm_coef_list.append(pssm_coef)
                pssm_bias_list.append(pssm_bias)
                pssm_log_odds_list.append(pssm_log_odds)
                bias_by_res_list.append(np.zeros([chain_length, 21]))
            if letter in masked_chains:
                masked_list.append(letter)
                letter_list.append(letter)
                chain_seq = b[f"seq_chain_{letter}"]
                chain_seq = "".join([a if a != "-" else "X" for a in chain_seq])
                chain_length = len(chain_seq)
                global_idx_start_list.append(global_idx_start_list[-1] + chain_length)
                masked_chain_length_list.append(chain_length)
                chain_coords = b[f"coords_chain_{letter}"]  # this is a dictionary
                chain_mask = np.ones(chain_length)  # 1.0 for masked
                if ca_only:
                    x_chain = np.array(
                        chain_coords[f"CA_chain_{letter}"]
                    )  # [chain_lenght,1,3] #CA_diff
                    if len(x_chain.shape) == 2:
                        x_chain = x_chain[:, None, :]
                else:
                    x_chain = np.stack(
                        [
                            chain_coords[c]
                            for c in [
                                f"N_chain_{letter}",
                                f"CA_chain_{letter}",
                                f"C_chain_{letter}",
                                f"O_chain_{letter}",
                            ]
                        ],
                        1,
                    )  # [chain_lenght,4,3]
                x_chain_list.append(x_chain)
                chain_mask_list.append(chain_mask)
                chain_seq_list.append(chain_seq)
                chain_encoding_list.append(c * np.ones(np.array(chain_mask).shape[0]))
                l1 += chain_length
                residue_idx[i, l0:l1] = 100 * (c - 1) + np.arange(l0, l1)
                l0 += chain_length
                c += 1
                fixed_position_mask = np.ones(chain_length)
                if fixed_position_dict != None:
                    fixed_pos_list = fixed_position_dict[b["name"]][letter]
                    if fixed_pos_list:
                        fixed_position_mask[np.array(fixed_pos_list) - 1] = 0.0
                fixed_position_mask_list.append(fixed_position_mask)
                omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], int)
                if omit_AA_dict != None:
                    for item in omit_AA_dict[b["name"]][letter]:
                        idx_AA = np.array(item[0]) - 1
                        AA_idx = np.array(
                            [
                                np.argwhere(np.array(list(alphabet)) == AA)[0][0]
                                for AA in item[1]
                            ]
                        ).repeat(idx_AA.shape[0])
                        idx_ = np.array([[a, b] for a in idx_AA for b in AA_idx])
                        omit_AA_mask_temp[idx_[:, 0], idx_[:, 1]] = 1
                omit_AA_mask_list.append(omit_AA_mask_temp)
                pssm_coef = np.zeros(chain_length)
                pssm_bias = np.zeros([chain_length, 21])
                pssm_log_odds = 10000.0 * np.ones([chain_length, 21])
                if pssm_dict:
                    if pssm_dict[b["name"]][letter]:
                        pssm_coef = pssm_dict[b["name"]][letter]["pssm_coef"]
                        pssm_bias = pssm_dict[b["name"]][letter]["pssm_bias"]
                        pssm_log_odds = pssm_dict[b["name"]][letter]["pssm_log_odds"]
                pssm_coef_list.append(pssm_coef)
                pssm_bias_list.append(pssm_bias)
                pssm_log_odds_list.append(pssm_log_odds)
                if bias_by_res_dict:
                    bias_by_res_list.append(bias_by_res_dict[b["name"]][letter])
                else:
                    bias_by_res_list.append(np.zeros([chain_length, 21]))

        letter_list_np = np.array(letter_list)
        tied_pos_list_of_lists = []
        tied_beta = np.ones(L_max)
        if tied_positions_dict != None:
            tied_pos_list = tied_positions_dict[b["name"]]
            if tied_pos_list:
                set_chains_tied = set(
                    list(itertools.chain(*[list(item) for item in tied_pos_list]))
                )
                for tied_item in tied_pos_list:
                    one_list = []
                    for k, v in tied_item.items():
                        start_idx = global_idx_start_list[
                            np.argwhere(letter_list_np == k)[0][0]
                        ]
                        if isinstance(v[0], list):
                            for v_count in range(len(v[0])):
                                one_list.append(
                                    start_idx + v[0][v_count] - 1
                                )  # make 0 to be the first
                                tied_beta[start_idx + v[0][v_count] - 1] = v[1][v_count]
                        else:
                            for v_ in v:
                                one_list.append(
                                    start_idx + v_ - 1
                                )  # make 0 to be the first
                    tied_pos_list_of_lists.append(one_list)
        tied_pos_list_of_lists_list.append(tied_pos_list_of_lists)

        x = np.concatenate(x_chain_list, 0)  # [L, 4, 3]
        all_sequence = "".join(chain_seq_list)
        m = np.concatenate(
            chain_mask_list, 0
        )  # [L,], 1.0 for places that need to be predicted
        chain_encoding = np.concatenate(chain_encoding_list, 0)
        m_pos = np.concatenate(
            fixed_position_mask_list, 0
        )  # [L,], 1.0 for places that need to be predicted

        pssm_coef_ = np.concatenate(
            pssm_coef_list, 0
        )  # [L,], 1.0 for places that need to be predicted
        pssm_bias_ = np.concatenate(
            pssm_bias_list, 0
        )  # [L,], 1.0 for places that need to be predicted
        pssm_log_odds_ = np.concatenate(
            pssm_log_odds_list, 0
        )  # [L,], 1.0 for places that need to be predicted

        bias_by_res_ = np.concatenate(
            bias_by_res_list, 0
        )  # [L,21], 0.0 for places where AA frequencies don't need to be tweaked

        l = len(all_sequence)
        x_pad = np.pad(
            x, [[0, L_max - l], [0, 0], [0, 0]], "constant", constant_values=(np.nan,)
        )
        X[i, :, :, :] = x_pad

        m_pad = np.pad(m, [[0, L_max - l]], "constant", constant_values=(0.0,))
        m_pos_pad = np.pad(m_pos, [[0, L_max - l]], "constant", constant_values=(0.0,))
        omit_AA_mask_pad = np.pad(
            np.concatenate(omit_AA_mask_list, 0),
            [[0, L_max - l]],
            "constant",
            constant_values=(0.0,),
        )
        chain_M[i, :] = m_pad
        chain_M_pos[i, :] = m_pos_pad
        omit_AA_mask[
            i,
        ] = omit_AA_mask_pad

        chain_encoding_pad = np.pad(
            chain_encoding, [[0, L_max - l]], "constant", constant_values=(0.0,)
        )
        chain_encoding_all[i, :] = chain_encoding_pad

        pssm_coef_pad = np.pad(
            pssm_coef_, [[0, L_max - l]], "constant", constant_values=(0.0,)
        )
        pssm_bias_pad = np.pad(
            pssm_bias_, [[0, L_max - l], [0, 0]], "constant", constant_values=(0.0,)
        )
        pssm_log_odds_pad = np.pad(
            pssm_log_odds_, [[0, L_max - l], [0, 0]], "constant", constant_values=(0.0,)
        )

        pssm_coef_all[i, :] = pssm_coef_pad
        pssm_bias_all[i, :] = pssm_bias_pad
        pssm_log_odds_all[i, :] = pssm_log_odds_pad

        bias_by_res_pad = np.pad(
            bias_by_res_, [[0, L_max - l], [0, 0]], "constant", constant_values=(0.0,)
        )
        bias_by_res_all[i, :] = bias_by_res_pad

        # Convert to labels
        indices = np.asarray([alphabet.index(a) for a in all_sequence], dtype=int)
        S[i, :l] = indices
        letter_list_list.append(letter_list)
        visible_list_list.append(visible_list)
        masked_list_list.append(masked_list)
        masked_chain_length_list_list.append(masked_chain_length_list)

    isnan = np.isnan(X)
    mask = np.isfinite(np.sum(X, (2, 3))).astype(np.float32)
    X[isnan] = 0.0

    # Conversion
    pssm_coef_all = torch.from_numpy(pssm_coef_all).to(
        dtype=torch.float32, device=device
    )
    pssm_bias_all = torch.from_numpy(pssm_bias_all).to(
        dtype=torch.float32, device=device
    )
    pssm_log_odds_all = torch.from_numpy(pssm_log_odds_all).to(
        dtype=torch.float32, device=device
    )

    tied_beta = torch.from_numpy(tied_beta).to(dtype=torch.float32, device=device)

    jumps = ((residue_idx[:, 1:] - residue_idx[:, :-1]) == 1).astype(np.float32)
    bias_by_res_all = torch.from_numpy(bias_by_res_all).to(
        dtype=torch.float32, device=device
    )
    phi_mask = np.pad(jumps, [[0, 0], [1, 0]])
    psi_mask = np.pad(jumps, [[0, 0], [0, 1]])
    omega_mask = np.pad(jumps, [[0, 0], [0, 1]])
    dihedral_mask = np.concatenate(
        [phi_mask[:, :, None], psi_mask[:, :, None], omega_mask[:, :, None]], -1
    )  # [B,L,3]
    dihedral_mask = torch.from_numpy(dihedral_mask).to(
        dtype=torch.float32, device=device
    )
    residue_idx = torch.from_numpy(residue_idx).to(dtype=torch.long, device=device)
    S = torch.from_numpy(S).to(dtype=torch.long, device=device)
    X = torch.from_numpy(X).to(dtype=torch.float32, device=device)
    mask = torch.from_numpy(mask).to(dtype=torch.float32, device=device)
    chain_M = torch.from_numpy(chain_M).to(dtype=torch.float32, device=device)
    chain_M_pos = torch.from_numpy(chain_M_pos).to(dtype=torch.float32, device=device)
    omit_AA_mask = torch.from_numpy(omit_AA_mask).to(dtype=torch.float32, device=device)
    chain_encoding_all = torch.from_numpy(chain_encoding_all).to(
        dtype=torch.long, device=device
    )
    if ca_only:
        X_out = X[:, :, 0]
    else:
        X_out = X
    return (
        X_out,
        S,
        mask,
        lengths,
        chain_M,
        chain_encoding_all,
        letter_list_list,
        visible_list_list,
        masked_list_list,
        masked_chain_length_list_list,
        chain_M_pos,
        omit_AA_mask,
        residue_idx,
        dihedral_mask,
        tied_pos_list_of_lists_list,
        pssm_coef_all,
        pssm_bias_all,
        pssm_log_odds_all,
        bias_by_res_all,
        tied_beta,
    )


def loss_nll(S, log_probs, mask):
    """Negative log probabilities"""
    criterion = torch.nn.NLLLoss(reduction="none")
    loss = criterion(
        log_probs.contiguous().view(-1, log_probs.size(-1)), S.contiguous().view(-1)
    ).view(S.size())
    loss_av = torch.sum(loss * mask) / torch.sum(mask)
    return loss, loss_av


def loss_smoothed(S, log_probs, mask, weight=0.1):
    """Negative log probabilities"""
    S_onehot = torch.nn.functional.one_hot(S, 21).float()

    # Label smoothing
    S_onehot = S_onehot + weight / float(S_onehot.size(-1))
    S_onehot = S_onehot / S_onehot.sum(-1, keepdim=True)

    loss = -(S_onehot * log_probs).sum(-1)
    loss_av = torch.sum(loss * mask) / torch.sum(mask)
    return loss, loss_av


class StructureDataset:
    def __init__(
        self,
        jsonl_file,
        verbose=True,
        truncate=None,
        max_length=100,
        alphabet="ACDEFGHIKLMNPQRSTVWYX-",
    ):
        alphabet_set = set([a for a in alphabet])
        discard_count = {"bad_chars": 0, "too_long": 0, "bad_seq_length": 0}

        with open(jsonl_file) as f:
            self.data = []

            lines = f.readlines()
            start = time.time()
            for i, line in enumerate(lines):
                entry = json.loads(line)
                seq = entry["seq"]
                name = entry["name"]

                # Convert raw coords to np arrays
                # for key, val in entry['coords'].items():
                #    entry['coords'][key] = np.asarray(val)

                # Check if in alphabet
                bad_chars = set([s for s in seq]).difference(alphabet_set)
                if len(bad_chars) == 0:
                    if len(entry["seq"]) <= max_length:
                        if True:
                            self.data.append(entry)
                        else:
                            discard_count["bad_seq_length"] += 1
                    else:
                        discard_count["too_long"] += 1
                else:
                    print(name, bad_chars, entry["seq"])
                    discard_count["bad_chars"] += 1

                # Truncate early
                if truncate is not None and len(self.data) == truncate:
                    return

                if verbose and (i + 1) % 1000 == 0:
                    elapsed = time.time() - start
                    print(
                        "{} entries ({} loaded) in {:.1f} s".format(
                            len(self.data), i + 1, elapsed
                        )
                    )

            print("discarded", discard_count)

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

    def __getitem__(self, idx):
        return self.data[idx]


class StructureDatasetPDB:
    def __init__(
        self,
        pdb_dict_list,
        verbose=True,
        truncate=None,
        max_length=100,
        alphabet="ACDEFGHIKLMNPQRSTVWYX-",
    ):
        alphabet_set = set([a for a in alphabet])
        discard_count = {"bad_chars": 0, "too_long": 0, "bad_seq_length": 0}

        self.data = []

        start = time.time()
        for i, entry in enumerate(pdb_dict_list):
            seq = entry["seq"]
            name = entry["name"]

            bad_chars = set([s for s in seq]).difference(alphabet_set)
            if len(bad_chars) == 0:
                if len(entry["seq"]) <= max_length:
                    self.data.append(entry)
                else:
                    discard_count["too_long"] += 1
            else:
                discard_count["bad_chars"] += 1

            # Truncate early
            if truncate is not None and len(self.data) == truncate:
                return

            if verbose and (i + 1) % 1000 == 0:
                elapsed = time.time() - start

            # print('Discarded', discard_count)

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

    def __getitem__(self, idx):
        return self.data[idx]


class StructureLoader:
    def __init__(
        self,
        dataset,
        batch_size=100,
        shuffle=True,
        collate_fn=lambda x: x,
        drop_last=False,
    ):
        self.dataset = dataset
        self.size = len(dataset)
        self.lengths = [len(dataset[i]["seq"]) for i in range(self.size)]
        self.batch_size = batch_size
        sorted_ix = np.argsort(self.lengths)

        # Cluster into batches of similar sizes
        clusters, batch = [], []
        batch_max = 0
        for ix in sorted_ix:
            size = self.lengths[ix]
            if size * (len(batch) + 1) <= self.batch_size:
                batch.append(ix)
                batch_max = size
            else:
                clusters.append(batch)
                batch, batch_max = [], 0
        if len(batch) > 0:
            clusters.append(batch)
        self.clusters = clusters

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

    def __iter__(self):
        np.random.shuffle(self.clusters)
        for b_idx in self.clusters:
            batch = [self.dataset[i] for i in b_idx]
            yield batch


# The following gather functions
def gather_edges(edges, neighbor_idx):
    # Features [B,N,N,C] at Neighbor indices [B,N,K] => Neighbor features [B,N,K,C]
    neighbors = neighbor_idx.unsqueeze(-1).expand(-1, -1, -1, edges.size(-1))
    edge_features = torch.gather(edges, 2, neighbors)
    return edge_features


def gather_nodes(nodes, neighbor_idx):
    # Features [B,N,C] at Neighbor indices [B,N,K] => [B,N,K,C]
    # Flatten and expand indices per batch [B,N,K] => [B,NK] => [B,NK,C]
    neighbors_flat = neighbor_idx.view((neighbor_idx.shape[0], -1))
    neighbors_flat = neighbors_flat.unsqueeze(-1).expand(-1, -1, nodes.size(2))
    # Gather and re-pack
    neighbor_features = torch.gather(nodes, 1, neighbors_flat)
    neighbor_features = neighbor_features.view(list(neighbor_idx.shape)[:3] + [-1])
    return neighbor_features


def gather_nodes_t(nodes, neighbor_idx):
    # Features [B,N,C] at Neighbor index [B,K] => Neighbor features[B,K,C]
    idx_flat = neighbor_idx.unsqueeze(-1).expand(-1, -1, nodes.size(2))
    neighbor_features = torch.gather(nodes, 1, idx_flat)
    return neighbor_features


def cat_neighbors_nodes(h_nodes, h_neighbors, E_idx):
    h_nodes = gather_nodes(h_nodes, E_idx)
    h_nn = torch.cat([h_neighbors, h_nodes], -1)
    return h_nn


class EncLayer(nn.Module):
    def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30):
        super(EncLayer, self).__init__()
        self.num_hidden = num_hidden
        self.num_in = num_in
        self.scale = scale
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)
        self.norm1 = nn.LayerNorm(num_hidden)
        self.norm2 = nn.LayerNorm(num_hidden)
        self.norm3 = nn.LayerNorm(num_hidden)

        self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
        self.W2 = nn.Linear(num_hidden, num_hidden, bias=True)
        self.W3 = nn.Linear(num_hidden, num_hidden, bias=True)
        self.W11 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
        self.W12 = nn.Linear(num_hidden, num_hidden, bias=True)
        self.W13 = nn.Linear(num_hidden, num_hidden, bias=True)
        self.act = torch.nn.GELU()
        self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4)

    def forward(self, h_V, h_E, E_idx, mask_V=None, mask_attend=None):
        """Parallel computation of full transformer layer"""

        h_EV = cat_neighbors_nodes(h_V, h_E, E_idx)
        h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_EV.size(-2), -1)
        h_EV = torch.cat([h_V_expand, h_EV], -1)
        h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV)))))
        if mask_attend is not None:
            h_message = mask_attend.unsqueeze(-1) * h_message
        dh = torch.sum(h_message, -2) / self.scale
        h_V = self.norm1(h_V + self.dropout1(dh))

        dh = self.dense(h_V)
        h_V = self.norm2(h_V + self.dropout2(dh))
        if mask_V is not None:
            mask_V = mask_V.unsqueeze(-1)
            h_V = mask_V * h_V

        h_EV = cat_neighbors_nodes(h_V, h_E, E_idx)
        h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_EV.size(-2), -1)
        h_EV = torch.cat([h_V_expand, h_EV], -1)
        h_message = self.W13(self.act(self.W12(self.act(self.W11(h_EV)))))
        h_E = self.norm3(h_E + self.dropout3(h_message))
        return h_V, h_E


class DecLayer(nn.Module):
    def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30):
        super(DecLayer, self).__init__()
        self.num_hidden = num_hidden
        self.num_in = num_in
        self.scale = scale
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.norm1 = nn.LayerNorm(num_hidden)
        self.norm2 = nn.LayerNorm(num_hidden)

        self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
        self.W2 = nn.Linear(num_hidden, num_hidden, bias=True)
        self.W3 = nn.Linear(num_hidden, num_hidden, bias=True)
        self.act = torch.nn.GELU()
        self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4)

    def forward(self, h_V, h_E, mask_V=None, mask_attend=None):
        """Parallel computation of full transformer layer"""

        # Concatenate h_V_i to h_E_ij
        h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_E.size(-2), -1)
        h_EV = torch.cat([h_V_expand, h_E], -1)

        h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV)))))
        if mask_attend is not None:
            h_message = mask_attend.unsqueeze(-1) * h_message
        dh = torch.sum(h_message, -2) / self.scale

        h_V = self.norm1(h_V + self.dropout1(dh))

        # Position-wise feedforward
        dh = self.dense(h_V)
        h_V = self.norm2(h_V + self.dropout2(dh))

        if mask_V is not None:
            mask_V = mask_V.unsqueeze(-1)
            h_V = mask_V * h_V
        return h_V


class PositionWiseFeedForward(nn.Module):
    def __init__(self, num_hidden, num_ff):
        super(PositionWiseFeedForward, self).__init__()
        self.W_in = nn.Linear(num_hidden, num_ff, bias=True)
        self.W_out = nn.Linear(num_ff, num_hidden, bias=True)
        self.act = torch.nn.GELU()

    def forward(self, h_V):
        h = self.act(self.W_in(h_V))
        h = self.W_out(h)
        return h


class PositionalEncodings(nn.Module):
    def __init__(self, num_embeddings, max_relative_feature=32):
        super(PositionalEncodings, self).__init__()
        self.num_embeddings = num_embeddings
        self.max_relative_feature = max_relative_feature
        self.linear = nn.Linear(2 * max_relative_feature + 1 + 1, num_embeddings)

    def forward(self, offset, mask):
        d = torch.clip(
            offset + self.max_relative_feature, 0, 2 * self.max_relative_feature
        ) * mask + (1 - mask) * (2 * self.max_relative_feature + 1)
        d_onehot = torch.nn.functional.one_hot(d, 2 * self.max_relative_feature + 1 + 1)
        E = self.linear(d_onehot.float())
        return E


class CA_ProteinFeatures(nn.Module):
    def __init__(
        self,
        edge_features,
        node_features,
        num_positional_embeddings=16,
        num_rbf=16,
        top_k=30,
        augment_eps=0.0,
        num_chain_embeddings=16,
    ):
        """Extract protein features"""
        super(CA_ProteinFeatures, self).__init__()
        self.edge_features = edge_features
        self.node_features = node_features
        self.top_k = top_k
        self.augment_eps = augment_eps
        self.num_rbf = num_rbf
        self.num_positional_embeddings = num_positional_embeddings

        # Positional encoding
        self.embeddings = PositionalEncodings(num_positional_embeddings)
        # Normalization and embedding
        node_in, edge_in = 3, num_positional_embeddings + num_rbf * 9 + 7
        self.node_embedding = nn.Linear(node_in, node_features, bias=False)  # NOT USED
        self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False)
        self.norm_nodes = nn.LayerNorm(node_features)
        self.norm_edges = nn.LayerNorm(edge_features)

    def _quaternions(self, R):
        """Convert a batch of 3D rotations [R] to quaternions [Q]
        R [...,3,3]
        Q [...,4]
        """
        # Simple Wikipedia version
        # en.wikipedia.org/wiki/Rotation_matrix#Quaternion
        # For other options see math.stackexchange.com/questions/2074316/calculating-rotation-axis-from-rotation-matrix
        diag = torch.diagonal(R, dim1=-2, dim2=-1)
        Rxx, Ryy, Rzz = diag.unbind(-1)
        magnitudes = 0.5 * torch.sqrt(
            torch.abs(
                1
                + torch.stack([Rxx - Ryy - Rzz, -Rxx + Ryy - Rzz, -Rxx - Ryy + Rzz], -1)
            )
        )
        _R = lambda i, j: R[:, :, :, i, j]
        signs = torch.sign(
            torch.stack(
                [_R(2, 1) - _R(1, 2), _R(0, 2) - _R(2, 0), _R(1, 0) - _R(0, 1)], -1
            )
        )
        xyz = signs * magnitudes
        # The relu enforces a non-negative trace
        w = torch.sqrt(F.relu(1 + diag.sum(-1, keepdim=True))) / 2.0
        Q = torch.cat((xyz, w), -1)
        Q = F.normalize(Q, dim=-1)
        return Q

    def _orientations_coarse(self, X, E_idx, eps=1e-6):
        dX = X[:, 1:, :] - X[:, :-1, :]
        dX_norm = torch.norm(dX, dim=-1)
        dX_mask = (3.6 < dX_norm) & (dX_norm < 4.0)  # exclude CA-CA jumps
        dX = dX * dX_mask[:, :, None]
        U = F.normalize(dX, dim=-1)
        u_2 = U[:, :-2, :]
        u_1 = U[:, 1:-1, :]
        u_0 = U[:, 2:, :]
        # Backbone normals
        n_2 = F.normalize(torch.cross(u_2, u_1), dim=-1)
        n_1 = F.normalize(torch.cross(u_1, u_0), dim=-1)

        # Bond angle calculation
        cosA = -(u_1 * u_0).sum(-1)
        cosA = torch.clamp(cosA, -1 + eps, 1 - eps)
        A = torch.acos(cosA)
        # Angle between normals
        cosD = (n_2 * n_1).sum(-1)
        cosD = torch.clamp(cosD, -1 + eps, 1 - eps)
        D = torch.sign((u_2 * n_1).sum(-1)) * torch.acos(cosD)
        # Backbone features
        AD_features = torch.stack(
            (torch.cos(A), torch.sin(A) * torch.cos(D), torch.sin(A) * torch.sin(D)), 2
        )
        AD_features = F.pad(AD_features, (0, 0, 1, 2), "constant", 0)

        # Build relative orientations
        o_1 = F.normalize(u_2 - u_1, dim=-1)
        O = torch.stack((o_1, n_2, torch.cross(o_1, n_2)), 2)
        O = O.view(list(O.shape[:2]) + [9])
        O = F.pad(O, (0, 0, 1, 2), "constant", 0)
        O_neighbors = gather_nodes(O, E_idx)
        X_neighbors = gather_nodes(X, E_idx)

        # Re-view as rotation matrices
        O = O.view(list(O.shape[:2]) + [3, 3])
        O_neighbors = O_neighbors.view(list(O_neighbors.shape[:3]) + [3, 3])

        # Rotate into local reference frames
        dX = X_neighbors - X.unsqueeze(-2)
        dU = torch.matmul(O.unsqueeze(2), dX.unsqueeze(-1)).squeeze(-1)
        dU = F.normalize(dU, dim=-1)
        R = torch.matmul(O.unsqueeze(2).transpose(-1, -2), O_neighbors)
        Q = self._quaternions(R)

        # Orientation features
        O_features = torch.cat((dU, Q), dim=-1)
        return AD_features, O_features

    def _dist(self, X, mask, eps=1e-6):
        """Pairwise euclidean distances"""
        # Convolutional network on NCHW
        mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2)
        dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2)
        D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps)

        # Identify k nearest neighbors (including self)
        D_max, _ = torch.max(D, -1, keepdim=True)
        D_adjust = D + (1.0 - mask_2D) * D_max
        D_neighbors, E_idx = torch.topk(
            D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False
        )
        mask_neighbors = gather_edges(mask_2D.unsqueeze(-1), E_idx)
        return D_neighbors, E_idx, mask_neighbors

    def _rbf(self, D):
        # Distance radial basis function
        device = D.device
        D_min, D_max, D_count = 2.0, 22.0, self.num_rbf
        D_mu = torch.linspace(D_min, D_max, D_count).to(device)
        D_mu = D_mu.view([1, 1, 1, -1])
        D_sigma = (D_max - D_min) / D_count
        D_expand = torch.unsqueeze(D, -1)
        RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2))
        return RBF

    def _get_rbf(self, A, B, E_idx):
        D_A_B = torch.sqrt(
            torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6
        )  # [B, L, L]
        D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[
            :, :, :, 0
        ]  # [B,L,K]
        RBF_A_B = self._rbf(D_A_B_neighbors)
        return RBF_A_B

    def forward(self, Ca, mask, residue_idx, chain_labels):
        """Featurize coordinates as an attributed graph"""
        if self.augment_eps > 0:
            Ca = Ca + self.augment_eps * torch.randn_like(Ca)

        D_neighbors, E_idx, mask_neighbors = self._dist(Ca, mask)

        Ca_0 = torch.zeros(Ca.shape, device=Ca.device)
        Ca_2 = torch.zeros(Ca.shape, device=Ca.device)
        Ca_0[:, 1:, :] = Ca[:, :-1, :]
        Ca_1 = Ca
        Ca_2[:, :-1, :] = Ca[:, 1:, :]

        V, O_features = self._orientations_coarse(Ca, E_idx)

        RBF_all = []
        RBF_all.append(self._rbf(D_neighbors))  # Ca_1-Ca_1
        RBF_all.append(self._get_rbf(Ca_0, Ca_0, E_idx))
        RBF_all.append(self._get_rbf(Ca_2, Ca_2, E_idx))

        RBF_all.append(self._get_rbf(Ca_0, Ca_1, E_idx))
        RBF_all.append(self._get_rbf(Ca_0, Ca_2, E_idx))

        RBF_all.append(self._get_rbf(Ca_1, Ca_0, E_idx))
        RBF_all.append(self._get_rbf(Ca_1, Ca_2, E_idx))

        RBF_all.append(self._get_rbf(Ca_2, Ca_0, E_idx))
        RBF_all.append(self._get_rbf(Ca_2, Ca_1, E_idx))

        RBF_all = torch.cat(tuple(RBF_all), dim=-1)

        offset = residue_idx[:, :, None] - residue_idx[:, None, :]
        offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0]  # [B, L, K]

        d_chains = ((chain_labels[:, :, None] - chain_labels[:, None, :]) == 0).long()
        E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0]
        E_positional = self.embeddings(offset.long(), E_chains)
        E = torch.cat((E_positional, RBF_all, O_features), -1)

        E = self.edge_embedding(E)
        E = self.norm_edges(E)

        return E, E_idx


class ProteinFeatures(nn.Module):
    def __init__(
        self,
        edge_features,
        node_features,
        num_positional_embeddings=16,
        num_rbf=16,
        top_k=30,
        augment_eps=0.0,
        num_chain_embeddings=16,
    ):
        """Extract protein features"""
        super(ProteinFeatures, self).__init__()
        self.edge_features = edge_features
        self.node_features = node_features
        self.top_k = top_k
        self.augment_eps = augment_eps
        self.num_rbf = num_rbf
        self.num_positional_embeddings = num_positional_embeddings

        self.embeddings = PositionalEncodings(num_positional_embeddings)
        node_in, edge_in = 6, num_positional_embeddings + num_rbf * 25
        self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False)
        self.norm_edges = nn.LayerNorm(edge_features)

    def _dist(self, X, mask, eps=1e-6):
        mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2)
        dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2)
        D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps)
        D_max, _ = torch.max(D, -1, keepdim=True)
        D_adjust = D + (1.0 - mask_2D) * D_max
        sampled_top_k = self.top_k
        D_neighbors, E_idx = torch.topk(
            D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False
        )
        return D_neighbors, E_idx

    def _rbf(self, D):
        device = D.device
        D_min, D_max, D_count = 2.0, 22.0, self.num_rbf
        D_mu = torch.linspace(D_min, D_max, D_count, device=device)
        D_mu = D_mu.view([1, 1, 1, -1])
        D_sigma = (D_max - D_min) / D_count
        D_expand = torch.unsqueeze(D, -1)
        RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2))
        return RBF

    def _get_rbf(self, A, B, E_idx):
        D_A_B = torch.sqrt(
            torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6
        )  # [B, L, L]
        D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[
            :, :, :, 0
        ]  # [B,L,K]
        RBF_A_B = self._rbf(D_A_B_neighbors)
        return RBF_A_B

    def forward(self, X, mask, residue_idx, chain_labels):
        if self.augment_eps > 0:
            X = X + self.augment_eps * torch.randn_like(X)

        b = X[:, :, 1, :] - X[:, :, 0, :]
        c = X[:, :, 2, :] - X[:, :, 1, :]
        a = torch.cross(b, c, dim=-1)
        Cb = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + X[:, :, 1, :]
        Ca = X[:, :, 1, :]
        N = X[:, :, 0, :]
        C = X[:, :, 2, :]
        O = X[:, :, 3, :]

        D_neighbors, E_idx = self._dist(Ca, mask)

        RBF_all = []
        RBF_all.append(self._rbf(D_neighbors))  # Ca-Ca
        RBF_all.append(self._get_rbf(N, N, E_idx))  # N-N
        RBF_all.append(self._get_rbf(C, C, E_idx))  # C-C
        RBF_all.append(self._get_rbf(O, O, E_idx))  # O-O
        RBF_all.append(self._get_rbf(Cb, Cb, E_idx))  # Cb-Cb
        RBF_all.append(self._get_rbf(Ca, N, E_idx))  # Ca-N
        RBF_all.append(self._get_rbf(Ca, C, E_idx))  # Ca-C
        RBF_all.append(self._get_rbf(Ca, O, E_idx))  # Ca-O
        RBF_all.append(self._get_rbf(Ca, Cb, E_idx))  # Ca-Cb
        RBF_all.append(self._get_rbf(N, C, E_idx))  # N-C
        RBF_all.append(self._get_rbf(N, O, E_idx))  # N-O
        RBF_all.append(self._get_rbf(N, Cb, E_idx))  # N-Cb
        RBF_all.append(self._get_rbf(Cb, C, E_idx))  # Cb-C
        RBF_all.append(self._get_rbf(Cb, O, E_idx))  # Cb-O
        RBF_all.append(self._get_rbf(O, C, E_idx))  # O-C
        RBF_all.append(self._get_rbf(N, Ca, E_idx))  # N-Ca
        RBF_all.append(self._get_rbf(C, Ca, E_idx))  # C-Ca
        RBF_all.append(self._get_rbf(O, Ca, E_idx))  # O-Ca
        RBF_all.append(self._get_rbf(Cb, Ca, E_idx))  # Cb-Ca
        RBF_all.append(self._get_rbf(C, N, E_idx))  # C-N
        RBF_all.append(self._get_rbf(O, N, E_idx))  # O-N
        RBF_all.append(self._get_rbf(Cb, N, E_idx))  # Cb-N
        RBF_all.append(self._get_rbf(C, Cb, E_idx))  # C-Cb
        RBF_all.append(self._get_rbf(O, Cb, E_idx))  # O-Cb
        RBF_all.append(self._get_rbf(C, O, E_idx))  # C-O
        RBF_all = torch.cat(tuple(RBF_all), dim=-1)

        offset = residue_idx[:, :, None] - residue_idx[:, None, :]
        offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0]  # [B, L, K]

        d_chains = (
            (chain_labels[:, :, None] - chain_labels[:, None, :]) == 0
        ).long()  # find self vs non-self interaction
        E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0]
        E_positional = self.embeddings(offset.long(), E_chains)
        E = torch.cat((E_positional, RBF_all), -1)
        E = self.edge_embedding(E)
        E = self.norm_edges(E)
        return E, E_idx


class ProteinMPNN(nn.Module):
    def __init__(
        self,
        num_letters,
        node_features,
        edge_features,
        hidden_dim,
        num_encoder_layers=3,
        num_decoder_layers=3,
        vocab=21,
        k_neighbors=64,
        augment_eps=0.05,
        dropout=0.1,
        ca_only=False,
    ):
        super(ProteinMPNN, self).__init__()

        # Hyperparameters
        self.node_features = node_features
        self.edge_features = edge_features
        self.hidden_dim = hidden_dim

        # Featurization layers
        if ca_only:
            self.features = CA_ProteinFeatures(
                node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps
            )
            self.W_v = nn.Linear(node_features, hidden_dim, bias=True)
        else:
            self.features = ProteinFeatures(
                node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps
            )

        self.W_e = nn.Linear(edge_features, hidden_dim, bias=True)
        self.W_s = nn.Embedding(vocab, hidden_dim)

        # Encoder layers
        self.encoder_layers = nn.ModuleList(
            [
                EncLayer(hidden_dim, hidden_dim * 2, dropout=dropout)
                for _ in range(num_encoder_layers)
            ]
        )

        # Decoder layers
        self.decoder_layers = nn.ModuleList(
            [
                DecLayer(hidden_dim, hidden_dim * 3, dropout=dropout)
                for _ in range(num_decoder_layers)
            ]
        )
        self.W_out = nn.Linear(hidden_dim, num_letters, bias=True)

        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)

    def forward(
        self,
        X,
        S,
        mask,
        chain_M,
        residue_idx,
        chain_encoding_all,
        randn,
        use_input_decoding_order=False,
        decoding_order=None,
    ):
        """Graph-conditioned sequence model"""
        device = X.device
        # Prepare node and edge embeddings
        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
        h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device)
        h_E = self.W_e(E)

        # Encoder is unmasked self-attention
        mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
        mask_attend = mask.unsqueeze(-1) * mask_attend
        for layer in self.encoder_layers:
            h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)

        # Concatenate sequence embeddings for autoregressive decoder
        h_S = self.W_s(S)
        h_ES = cat_neighbors_nodes(h_S, h_E, E_idx)

        # Build encoder embeddings
        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
        h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)

        chain_M = chain_M * mask  # update chain_M to include missing regions
        if not use_input_decoding_order:
            decoding_order = torch.argsort(
                (chain_M + 0.0001) * (torch.abs(randn))
            )  # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
        mask_size = E_idx.shape[1]
        permutation_matrix_reverse = torch.nn.functional.one_hot(
            decoding_order, num_classes=mask_size
        ).float()
        order_mask_backward = torch.einsum(
            "ij, biq, bjp->bqp",
            (1 - torch.triu(torch.ones(mask_size, mask_size, device=device))),
            permutation_matrix_reverse,
            permutation_matrix_reverse,
        )
        mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
        mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
        mask_bw = mask_1D * mask_attend
        mask_fw = mask_1D * (1.0 - mask_attend)

        h_EXV_encoder_fw = mask_fw * h_EXV_encoder
        for layer in self.decoder_layers:
            # Masked positions attend to encoder information, unmasked see.
            h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx)
            h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw
            h_V = layer(h_V, h_ESV, mask)

        logits = self.W_out(h_V)
        log_probs = F.log_softmax(logits, dim=-1)
        return log_probs

    def sample(
        self,
        X,
        randn,
        S_true,
        chain_mask,
        chain_encoding_all,
        residue_idx,
        mask=None,
        temperature=1.0,
        omit_AAs_np=None,
        bias_AAs_np=None,
        chain_M_pos=None,
        omit_AA_mask=None,
        pssm_coef=None,
        pssm_bias=None,
        pssm_multi=None,
        pssm_log_odds_flag=None,
        pssm_log_odds_mask=None,
        pssm_bias_flag=None,
        bias_by_res=None,
    ):
        device = X.device
        # Prepare node and edge embeddings
        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
        h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device)
        h_E = self.W_e(E)

        # Encoder is unmasked self-attention
        mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
        mask_attend = mask.unsqueeze(-1) * mask_attend
        for layer in self.encoder_layers:
            h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)

        # Decoder uses masked self-attention
        chain_mask = (
            chain_mask * chain_M_pos * mask
        )  # update chain_M to include missing regions
        # TODO: Impose different decoding order.
        decoding_order = torch.argsort(
            (chain_mask + 0.0001) * (torch.abs(randn))
        )  # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
        mask_size = E_idx.shape[1]
        permutation_matrix_reverse = torch.nn.functional.one_hot(
            decoding_order, num_classes=mask_size
        ).float()
        order_mask_backward = torch.einsum(
            "ij, biq, bjp->bqp",
            (1 - torch.triu(torch.ones(mask_size, mask_size, device=device))),
            permutation_matrix_reverse,
            permutation_matrix_reverse,
        )
        mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
        mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
        mask_bw = mask_1D * mask_attend
        mask_fw = mask_1D * (1.0 - mask_attend)

        N_batch, N_nodes = X.size(0), X.size(1)
        log_probs = torch.zeros((N_batch, N_nodes, 21), device=device)
        all_probs = torch.zeros(
            (N_batch, N_nodes, 21), device=device, dtype=torch.float32
        )
        h_S = torch.zeros_like(h_V, device=device)
        S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device)
        h_V_stack = [h_V] + [
            torch.zeros_like(h_V, device=device)
            for _ in range(len(self.decoder_layers))
        ]
        constant = torch.tensor(omit_AAs_np, device=device)
        constant_bias = torch.tensor(bias_AAs_np, device=device)
        # chain_mask_combined = chain_mask*chain_M_pos
        omit_AA_mask_flag = omit_AA_mask != None

        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
        h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
        h_EXV_encoder_fw = mask_fw * h_EXV_encoder
        for t_ in range(N_nodes):
            t = decoding_order[:, t_]  # [B]
            chain_mask_gathered = torch.gather(chain_mask, 1, t[:, None])  # [B]
            mask_gathered = torch.gather(mask, 1, t[:, None])  # [B]
            bias_by_res_gathered = torch.gather(
                bias_by_res, 1, t[:, None, None].repeat(1, 1, 21)
            )[
                :, 0, :
            ]  # [B, 21]
            if (mask_gathered == 0).all():  # for padded or missing regions only
                S_t = torch.gather(S_true, 1, t[:, None])
            else:
                # Hidden layers
                E_idx_t = torch.gather(
                    E_idx, 1, t[:, None, None].repeat(1, 1, E_idx.shape[-1])
                )
                h_E_t = torch.gather(
                    h_E,
                    1,
                    t[:, None, None, None].repeat(1, 1, h_E.shape[-2], h_E.shape[-1]),
                )
                h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t)
                h_EXV_encoder_t = torch.gather(
                    h_EXV_encoder_fw,
                    1,
                    t[:, None, None, None].repeat(
                        1, 1, h_EXV_encoder_fw.shape[-2], h_EXV_encoder_fw.shape[-1]
                    ),
                )
                mask_t = torch.gather(mask, 1, t[:, None])
                for l, layer in enumerate(self.decoder_layers):
                    # Updated relational features for future states
                    h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t)
                    h_V_t = torch.gather(
                        h_V_stack[l],
                        1,
                        t[:, None, None].repeat(1, 1, h_V_stack[l].shape[-1]),
                    )
                    h_ESV_t = (
                        torch.gather(
                            mask_bw,
                            1,
                            t[:, None, None, None].repeat(
                                1, 1, mask_bw.shape[-2], mask_bw.shape[-1]
                            ),
                        )
                        * h_ESV_decoder_t
                        + h_EXV_encoder_t
                    )
                    h_V_stack[l + 1].scatter_(
                        1,
                        t[:, None, None].repeat(1, 1, h_V.shape[-1]),
                        layer(h_V_t, h_ESV_t, mask_V=mask_t),
                    )
                # Sampling step
                h_V_t = torch.gather(
                    h_V_stack[-1],
                    1,
                    t[:, None, None].repeat(1, 1, h_V_stack[-1].shape[-1]),
                )[:, 0]
                logits = self.W_out(h_V_t) / temperature
                probs = F.softmax(
                    logits
                    - constant[None, :] * 1e8
                    + constant_bias[None, :] / temperature
                    + bias_by_res_gathered / temperature,
                    dim=-1,
                )
                if pssm_bias_flag:
                    pssm_coef_gathered = torch.gather(pssm_coef, 1, t[:, None])[:, 0]
                    pssm_bias_gathered = torch.gather(
                        pssm_bias, 1, t[:, None, None].repeat(1, 1, pssm_bias.shape[-1])
                    )[:, 0]
                    probs = (
                        1 - pssm_multi * pssm_coef_gathered[:, None]
                    ) * probs + pssm_multi * pssm_coef_gathered[
                        :, None
                    ] * pssm_bias_gathered
                if pssm_log_odds_flag:
                    pssm_log_odds_mask_gathered = torch.gather(
                        pssm_log_odds_mask,
                        1,
                        t[:, None, None].repeat(1, 1, pssm_log_odds_mask.shape[-1]),
                    )[
                        :, 0
                    ]  # [B, 21]
                    probs_masked = probs * pssm_log_odds_mask_gathered
                    probs_masked += probs * 0.001
                    probs = probs_masked / torch.sum(
                        probs_masked, dim=-1, keepdim=True
                    )  # [B, 21]
                if omit_AA_mask_flag:
                    omit_AA_mask_gathered = torch.gather(
                        omit_AA_mask,
                        1,
                        t[:, None, None].repeat(1, 1, omit_AA_mask.shape[-1]),
                    )[
                        :, 0
                    ]  # [B, 21]
                    probs_masked = probs * (1.0 - omit_AA_mask_gathered)
                    probs = probs_masked / torch.sum(
                        probs_masked, dim=-1, keepdim=True
                    )  # [B, 21]
                S_t = torch.multinomial(probs, 1)
                all_probs.scatter_(
                    1,
                    t[:, None, None].repeat(1, 1, 21),
                    (
                        chain_mask_gathered[
                            :,
                            :,
                            None,
                        ]
                        * probs[:, None, :]
                    ).float(),
                )
            S_true_gathered = torch.gather(S_true, 1, t[:, None])
            S_t = (
                S_t * chain_mask_gathered
                + S_true_gathered * (1.0 - chain_mask_gathered)
            ).long()
            temp1 = self.W_s(S_t)
            h_S.scatter_(1, t[:, None, None].repeat(1, 1, temp1.shape[-1]), temp1)
            S.scatter_(1, t[:, None], S_t)
        output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order}
        return output_dict

    def tied_sample(
        self,
        X,
        randn,
        S_true,
        chain_mask,
        chain_encoding_all,
        residue_idx,
        mask=None,
        temperature=1.0,
        omit_AAs_np=None,
        bias_AAs_np=None,
        chain_M_pos=None,
        omit_AA_mask=None,
        pssm_coef=None,
        pssm_bias=None,
        pssm_multi=None,
        pssm_log_odds_flag=None,
        pssm_log_odds_mask=None,
        pssm_bias_flag=None,
        tied_pos=None,
        tied_beta=None,
        bias_by_res=None,
    ):
        device = X.device
        # Prepare node and edge embeddings
        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
        h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device)
        h_E = self.W_e(E)
        # Encoder is unmasked self-attention
        mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
        mask_attend = mask.unsqueeze(-1) * mask_attend
        for layer in self.encoder_layers:
            h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)

        # Decoder uses masked self-attention
        chain_mask = (
            chain_mask * chain_M_pos * mask
        )  # update chain_M to include missing regions
        decoding_order = torch.argsort(
            (chain_mask + 0.0001) * (torch.abs(randn))
        )  # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]

        new_decoding_order = []
        for t_dec in list(
            decoding_order[
                0,
            ]
            .cpu()
            .data.numpy()
        ):
            if t_dec not in list(itertools.chain(*new_decoding_order)):
                list_a = [item for item in tied_pos if t_dec in item]
                if list_a:
                    new_decoding_order.append(list_a[0])
                else:
                    new_decoding_order.append([t_dec])
        decoding_order = torch.tensor(
            list(itertools.chain(*new_decoding_order)), device=device
        )[
            None,
        ].repeat(
            X.shape[0], 1
        )

        mask_size = E_idx.shape[1]
        permutation_matrix_reverse = torch.nn.functional.one_hot(
            decoding_order, num_classes=mask_size
        ).float()
        order_mask_backward = torch.einsum(
            "ij, biq, bjp->bqp",
            (1 - torch.triu(torch.ones(mask_size, mask_size, device=device))),
            permutation_matrix_reverse,
            permutation_matrix_reverse,
        )
        mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
        mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
        mask_bw = mask_1D * mask_attend
        mask_fw = mask_1D * (1.0 - mask_attend)

        N_batch, N_nodes = X.size(0), X.size(1)
        log_probs = torch.zeros((N_batch, N_nodes, 21), device=device)
        all_probs = torch.zeros(
            (N_batch, N_nodes, 21), device=device, dtype=torch.float32
        )
        h_S = torch.zeros_like(h_V, device=device)
        S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device)
        h_V_stack = [h_V] + [
            torch.zeros_like(h_V, device=device)
            for _ in range(len(self.decoder_layers))
        ]
        constant = torch.tensor(omit_AAs_np, device=device)
        constant_bias = torch.tensor(bias_AAs_np, device=device)
        omit_AA_mask_flag = omit_AA_mask != None

        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
        h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
        h_EXV_encoder_fw = mask_fw * h_EXV_encoder
        for t_list in new_decoding_order:
            logits = 0.0
            logit_list = []
            done_flag = False
            for t in t_list:
                if (mask[:, t] == 0).all():
                    S_t = S_true[:, t]
                    for t in t_list:
                        h_S[:, t, :] = self.W_s(S_t)
                        S[:, t] = S_t
                    done_flag = True
                    break
                else:
                    E_idx_t = E_idx[:, t : t + 1, :]
                    h_E_t = h_E[:, t : t + 1, :, :]
                    h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t)
                    h_EXV_encoder_t = h_EXV_encoder_fw[:, t : t + 1, :, :]
                    mask_t = mask[:, t : t + 1]
                    for l, layer in enumerate(self.decoder_layers):
                        h_ESV_decoder_t = cat_neighbors_nodes(
                            h_V_stack[l], h_ES_t, E_idx_t
                        )
                        h_V_t = h_V_stack[l][:, t : t + 1, :]
                        h_ESV_t = (
                            mask_bw[:, t : t + 1, :, :] * h_ESV_decoder_t
                            + h_EXV_encoder_t
                        )
                        h_V_stack[l + 1][:, t, :] = layer(
                            h_V_t, h_ESV_t, mask_V=mask_t
                        ).squeeze(1)
                    h_V_t = h_V_stack[-1][:, t, :]
                    logit_list.append((self.W_out(h_V_t) / temperature) / len(t_list))
                    logits += (
                        tied_beta[t] * (self.W_out(h_V_t) / temperature) / len(t_list)
                    )
            if done_flag:
                pass
            else:
                bias_by_res_gathered = bias_by_res[:, t, :]  # [B, 21]
                probs = F.softmax(
                    logits
                    - constant[None, :] * 1e8
                    + constant_bias[None, :] / temperature
                    + bias_by_res_gathered / temperature,
                    dim=-1,
                )
                if pssm_bias_flag:
                    pssm_coef_gathered = pssm_coef[:, t]
                    pssm_bias_gathered = pssm_bias[:, t]
                    probs = (
                        1 - pssm_multi * pssm_coef_gathered[:, None]
                    ) * probs + pssm_multi * pssm_coef_gathered[
                        :, None
                    ] * pssm_bias_gathered
                if pssm_log_odds_flag:
                    pssm_log_odds_mask_gathered = pssm_log_odds_mask[:, t]
                    probs_masked = probs * pssm_log_odds_mask_gathered
                    probs_masked += probs * 0.001
                    probs = probs_masked / torch.sum(
                        probs_masked, dim=-1, keepdim=True
                    )  # [B, 21]
                if omit_AA_mask_flag:
                    omit_AA_mask_gathered = omit_AA_mask[:, t]
                    probs_masked = probs * (1.0 - omit_AA_mask_gathered)
                    probs = probs_masked / torch.sum(
                        probs_masked, dim=-1, keepdim=True
                    )  # [B, 21]
                S_t_repeat = torch.multinomial(probs, 1).squeeze(-1)
                S_t_repeat = (
                    chain_mask[:, t] * S_t_repeat
                    + (1 - chain_mask[:, t]) * S_true[:, t]
                ).long()  # hard pick fixed positions
                for t in t_list:
                    h_S[:, t, :] = self.W_s(S_t_repeat)
                    S[:, t] = S_t_repeat
                    all_probs[:, t, :] = probs.float()
        output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order}
        return output_dict

    def conditional_probs(
        self,
        X,
        S,
        mask,
        chain_M,
        residue_idx,
        chain_encoding_all,
        randn,
        backbone_only=False,
    ):
        """Graph-conditioned sequence model"""
        device = X.device
        # Prepare node and edge embeddings
        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
        h_V_enc = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device)
        h_E = self.W_e(E)

        # Encoder is unmasked self-attention
        mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
        mask_attend = mask.unsqueeze(-1) * mask_attend
        for layer in self.encoder_layers:
            h_V_enc, h_E = layer(h_V_enc, h_E, E_idx, mask, mask_attend)

        # Concatenate sequence embeddings for autoregressive decoder
        h_S = self.W_s(S)
        h_ES = cat_neighbors_nodes(h_S, h_E, E_idx)

        # Build encoder embeddings
        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
        h_EXV_encoder = cat_neighbors_nodes(h_V_enc, h_EX_encoder, E_idx)

        chain_M = chain_M * mask  # update chain_M to include missing regions

        chain_M_np = chain_M.cpu().numpy()
        idx_to_loop = np.argwhere(chain_M_np[0, :] == 1)[:, 0]
        log_conditional_probs = torch.zeros(
            [X.shape[0], chain_M.shape[1], 21], device=device
        ).float()

        for idx in idx_to_loop:
            h_V = torch.clone(h_V_enc)
            order_mask = torch.zeros(chain_M.shape[1], device=device).float()
            if backbone_only:
                order_mask = torch.ones(chain_M.shape[1], device=device).float()
                order_mask[idx] = 0.0
            else:
                order_mask = torch.zeros(chain_M.shape[1], device=device).float()
                order_mask[idx] = 1.0
            decoding_order = torch.argsort(
                (
                    order_mask[
                        None,
                    ]
                    + 0.0001
                )
                * (torch.abs(randn))
            )  # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
            mask_size = E_idx.shape[1]
            permutation_matrix_reverse = torch.nn.functional.one_hot(
                decoding_order, num_classes=mask_size
            ).float()
            order_mask_backward = torch.einsum(
                "ij, biq, bjp->bqp",
                (1 - torch.triu(torch.ones(mask_size, mask_size, device=device))),
                permutation_matrix_reverse,
                permutation_matrix_reverse,
            )
            mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
            mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
            mask_bw = mask_1D * mask_attend
            mask_fw = mask_1D * (1.0 - mask_attend)

            h_EXV_encoder_fw = mask_fw * h_EXV_encoder
            for layer in self.decoder_layers:
                # Masked positions attend to encoder information, unmasked see.
                h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx)
                h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw
                h_V = layer(h_V, h_ESV, mask)

            logits = self.W_out(h_V)
            log_probs = F.log_softmax(logits, dim=-1)
            log_conditional_probs[:, idx, :] = log_probs[:, idx, :]
        return log_conditional_probs

    def unconditional_probs(self, X, mask, residue_idx, chain_encoding_all):
        """Graph-conditioned sequence model"""
        device = X.device
        # Prepare node and edge embeddings
        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
        h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device)
        h_E = self.W_e(E)

        # Encoder is unmasked self-attention
        mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
        mask_attend = mask.unsqueeze(-1) * mask_attend
        for layer in self.encoder_layers:
            h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)

        # Build encoder embeddings
        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_V), h_E, E_idx)
        h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)

        order_mask_backward = torch.zeros(
            [X.shape[0], X.shape[1], X.shape[1]], device=device
        )
        mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
        mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
        mask_bw = mask_1D * mask_attend
        mask_fw = mask_1D * (1.0 - mask_attend)

        h_EXV_encoder_fw = mask_fw * h_EXV_encoder
        for layer in self.decoder_layers:
            h_V = layer(h_V, h_EXV_encoder_fw, mask)

        logits = self.W_out(h_V)
        log_probs = F.log_softmax(logits, dim=-1)
        return log_probs