text: from typing import List, Tuple, Union
  from itertools import zip_longest
  import logging
  
  from rdkit import Chem
  import torch
  import numpy as np
  
  from chemprop.rdkit import make_mol
  
  class Featurization_parameters:
      """
      A class holding molecule featurization parameters as attributes.
      """
      def __init__(self) -> None:
  
          # Atom feature sizes
          self.MAX_ATOMIC_NUM = 100
          self.ATOM_FEATURES = {
              'atomic_num': list(range(self.MAX_ATOMIC_NUM)),
              'degree': [0, 1, 2, 3, 4, 5],
              'formal_charge': [-1, -2, 1, 2, 0],
              'chiral_tag': [0, 1, 2, 3],
              'num_Hs': [0, 1, 2, 3, 4],
              'hybridization': [
                  Chem.rdchem.HybridizationType.SP,
                  Chem.rdchem.HybridizationType.SP2,
                  Chem.rdchem.HybridizationType.SP3,
                  Chem.rdchem.HybridizationType.SP3D,
                  Chem.rdchem.HybridizationType.SP3D2
              ],
          }
  
          # Distance feature sizes
          self.PATH_DISTANCE_BINS = list(range(10))
          self.THREE_D_DISTANCE_MAX = 20
          self.THREE_D_DISTANCE_STEP = 1
          self.THREE_D_DISTANCE_BINS = list(range(0, self.THREE_D_DISTANCE_MAX + 1, self.THREE_D_DISTANCE_STEP))
  
          # len(choices) + 1 to include room for uncommon values; + 2 at end for IsAromatic and mass
          self.ATOM_FDIM = sum(len(choices) + 1 for choices in self.ATOM_FEATURES.values()) + 2
          self.EXTRA_ATOM_FDIM = 0
          self.BOND_FDIM = 14
          self.EXTRA_BOND_FDIM = 0
          self.REACTION_MODE = None
          self.EXPLICIT_H = False
          self.REACTION = False
  
  # Create a global parameter object for reference throughout this module
  PARAMS = Featurization_parameters()
  
  
  def reset_featurization_parameters(logger: logging.Logger = None) -> None:
      """
      Function resets feature parameter values to defaults by replacing the parameters instance.
      """
      if logger is not None:
          debug = logger.debug
      else:
          debug = print
      debug('Setting molecule featurization parameters to default.')
      global PARAMS
      PARAMS = Featurization_parameters()
  
  
  def get_atom_fdim(overwrite_default_atom: bool = False) -> int:
      """
      Gets the dimensionality of the atom feature vector.
  
      :param overwrite_default_atom: Whether to overwrite the default atom descriptors
      :return: The dimensionality of the atom feature vector.
      """
      return (not overwrite_default_atom) * PARAMS.ATOM_FDIM + PARAMS.EXTRA_ATOM_FDIM
  
  
  def set_explicit_h(explicit_h: bool) -> None:
      """
      Sets whether RDKit molecules will be constructed with explicit Hs.
  
      :param explicit_h: Boolean whether to keep explicit Hs from input.
      """
      PARAMS.EXPLICIT_H = explicit_h
  
  
  def set_reaction(reaction: bool, mode: str) -> None:
      """
      Sets whether to use a reaction or molecule as input and adapts feature dimensions.
   
      :param reaction: Boolean whether to except reactions as input.
      :param mode: Reaction mode to construct atom and bond feature vectors.
  
      """
      PARAMS.REACTION = reaction
      if reaction:
          PARAMS.EXTRA_ATOM_FDIM = PARAMS.ATOM_FDIM - PARAMS.MAX_ATOMIC_NUM -1
          PARAMS.EXTRA_BOND_FDIM = PARAMS.BOND_FDIM
          PARAMS.REACTION_MODE = mode        
  
          
  def is_explicit_h() -> bool:
      r"""Returns whether to use retain explicit Hs"""
      return PARAMS.EXPLICIT_H
  
  
  def is_reaction() -> bool:
      r"""Returns whether to use reactions as input"""
      return PARAMS.REACTION
  
  
  def reaction_mode() -> str:
      r"""Returns the reaction mode"""
      return PARAMS.REACTION_MODE
  
  
  def set_extra_atom_fdim(extra):
      """Change the dimensionality of the atom feature vector."""
      PARAMS.EXTRA_ATOM_FDIM = extra
  
  
  def get_bond_fdim(atom_messages: bool = False,
                    overwrite_default_bond: bool = False,
                    overwrite_default_atom: bool = False) -> int:
      """
      Gets the dimensionality of the bond feature vector.
  
      :param atom_messages: Whether atom messages are being used. If atom messages are used,
                            then the bond feature vector only contains bond features.
                            Otherwise it contains both atom and bond features.
      :param overwrite_default_bond: Whether to overwrite the default bond descriptors
      :param overwrite_default_atom: Whether to overwrite the default atom descriptors
      :return: The dimensionality of the bond feature vector.
      """
  
      return (not overwrite_default_bond) * PARAMS.BOND_FDIM + PARAMS.EXTRA_BOND_FDIM + \
             (not atom_messages) * get_atom_fdim(overwrite_default_atom=overwrite_default_atom)
  
  
  def set_extra_bond_fdim(extra):
      """Change the dimensionality of the bond feature vector."""
      PARAMS.EXTRA_BOND_FDIM = extra
  
  
  def onek_encoding_unk(value: int, choices: List[int]) -> List[int]:
      """
      Creates a one-hot encoding with an extra category for uncommon values.
  
      :param value: The value for which the encoding should be one.
      :param choices: A list of possible values.
      :return: A one-hot encoding of the :code:`value` in a list of length :code:`len(choices) + 1`.
               If :code:`value` is not in :code:`choices`, then the final element in the encoding is 1.
      """
      encoding = [0] * (len(choices) + 1)
      index = choices.index(value) if value in choices else -1
      encoding[index] = 1
  
      return encoding
  
  
  def atom_features(atom: Chem.rdchem.Atom, functional_groups: List[int] = None) -> List[Union[bool, int, float]]:
      """
      Builds a feature vector for an atom.
  
      :param atom: An RDKit atom.
      :param functional_groups: A k-hot vector indicating the functional groups the atom belongs to.
      :return: A list containing the atom features.
      """
      if atom is None:
          features = [0] * PARAMS.ATOM_FDIM
      else:
          features = onek_encoding_unk(atom.GetAtomicNum() - 1, PARAMS.ATOM_FEATURES['atomic_num']) + \
              onek_encoding_unk(atom.GetTotalDegree(), PARAMS.ATOM_FEATURES['degree']) + \
              onek_encoding_unk(atom.GetFormalCharge(), PARAMS.ATOM_FEATURES['formal_charge']) + \
              onek_encoding_unk(int(atom.GetChiralTag()), PARAMS.ATOM_FEATURES['chiral_tag']) + \
              onek_encoding_unk(int(atom.GetTotalNumHs()), PARAMS.ATOM_FEATURES['num_Hs']) + \
              onek_encoding_unk(int(atom.GetHybridization()), PARAMS.ATOM_FEATURES['hybridization']) + \
              [1 if atom.GetIsAromatic() else 0] + \
              [atom.GetMass() * 0.01]  # scaled to about the same range as other features
          if functional_groups is not None:
              features += functional_groups
      return features
  
  
  def atom_features_zeros(atom: Chem.rdchem.Atom) -> List[Union[bool, int, float]]:
      """
      Builds a feature vector for an atom containing only the atom number information.
  
      :param atom: An RDKit atom.
      :return: A list containing the atom features.
      """
      if atom is None:
          features = [0] * PARAMS.ATOM_FDIM
      else:
          features = onek_encoding_unk(atom.GetAtomicNum() - 1, PARAMS.ATOM_FEATURES['atomic_num']) + \
              [0] * (PARAMS.ATOM_FDIM - PARAMS.MAX_ATOMIC_NUM - 1) #set other features to zero
      return features
  
  
  def bond_features(bond: Chem.rdchem.Bond) -> List[Union[bool, int, float]]:
      """
      Builds a feature vector for a bond.
  
      :param bond: An RDKit bond.
      :return: A list containing the bond features.
      """
      if bond is None:
          fbond = [1] + [0] * (PARAMS.BOND_FDIM - 1)
      else:
          bt = bond.GetBondType()
          fbond = [
              0,  # bond is not None
              bt == Chem.rdchem.BondType.SINGLE,
              bt == Chem.rdchem.BondType.DOUBLE,
              bt == Chem.rdchem.BondType.TRIPLE,
              bt == Chem.rdchem.BondType.AROMATIC,
              (bond.GetIsConjugated() if bt is not None else 0),
              (bond.IsInRing() if bt is not None else 0)
          ]
          fbond += onek_encoding_unk(int(bond.GetStereo()), list(range(6)))
      return fbond
  
  
  def map_reac_to_prod(mol_reac: Chem.Mol, mol_prod: Chem.Mol):
      """
      Build a dictionary of mapping atom indices in the reactants to the products.
  
      :param mol_reac: An RDKit molecule of the reactants.
      :param mol_prod: An RDKit molecule of the products.
      :return: A dictionary of corresponding reactant and product atom indices.
      """
      only_prod_ids = []
      prod_map_to_id = {}
      mapnos_reac = set([atom.GetAtomMapNum() for atom in mol_reac.GetAtoms()]) 
      for atom in mol_prod.GetAtoms():
          mapno = atom.GetAtomMapNum()
          if mapno > 0:
              prod_map_to_id[mapno] = atom.GetIdx()
              if mapno not in mapnos_reac:
                  only_prod_ids.append(atom.GetIdx()) 
          else:
              only_prod_ids.append(atom.GetIdx())
      only_reac_ids = []
      reac_id_to_prod_id = {}
      for atom in mol_reac.GetAtoms():
          mapno = atom.GetAtomMapNum()
          if mapno > 0:
              try:
                  reac_id_to_prod_id[atom.GetIdx()] = prod_map_to_id[mapno]
              except KeyError:
                  only_reac_ids.append(atom.GetIdx())
          else:
              only_reac_ids.append(atom.GetIdx())
      return reac_id_to_prod_id, only_prod_ids, only_reac_ids
  
  
  class MolGraph:
      """
      A :class:`MolGraph` represents the graph structure and featurization of a single molecule.
  
      A MolGraph computes the following attributes:
  
      * :code:`n_atoms`: The number of atoms in the molecule.
      * :code:`n_bonds`: The number of bonds in the molecule.
      * :code:`f_atoms`: A mapping from an atom index to a list of atom features.
      * :code:`f_bonds`: A mapping from a bond index to a list of bond features.
      * :code:`a2b`: A mapping from an atom index to a list of incoming bond indices.
      * :code:`b2a`: A mapping from a bond index to the index of the atom the bond originates from.
      * :code:`b2revb`: A mapping from a bond index to the index of the reverse bond.
      * :code:`overwrite_default_atom_features`: A boolean to overwrite default atom descriptors.
      * :code:`overwrite_default_bond_features`: A boolean to overwrite default bond descriptors.
      """
  
      def __init__(self, mol: Union[str, Chem.Mol, Tuple[Chem.Mol, Chem.Mol]],
                   atom_features_extra: np.ndarray = None,
                   bond_features_extra: np.ndarray = None,
                   overwrite_default_atom_features: bool = False,
                   overwrite_default_bond_features: bool = False):
          """
          :param mol: A SMILES or an RDKit molecule.
          :param atom_features_extra: A list of 2D numpy array containing additional atom features to featurize the molecule
          :param bond_features_extra: A list of 2D numpy array containing additional bond features to featurize the molecule
          :param overwrite_default_atom_features: Boolean to overwrite default atom features by atom_features instead of concatenating
          :param overwrite_default_bond_features: Boolean to overwrite default bond features by bond_features instead of concatenating
          """
          self.is_reaction = is_reaction()
          self.is_explicit_h = is_explicit_h()
          self.reaction_mode = reaction_mode()
          
          # Convert SMILES to RDKit molecule if necessary
          if type(mol) == str:
              if self.is_reaction:
                  mol = (make_mol(mol.split(">")[0], self.is_explicit_h), make_mol(mol.split(">")[-1], self.is_explicit_h)) 
              else:
                  mol = make_mol(mol, self.is_explicit_h)
  
          self.n_atoms = 0  # number of atoms
          self.n_bonds = 0  # number of bonds
          self.f_atoms = []  # mapping from atom index to atom features
          self.f_bonds = []  # mapping from bond index to concat(in_atom, bond) features
          self.a2b = []  # mapping from atom index to incoming bond indices
          self.b2a = []  # mapping from bond index to the index of the atom the bond is coming from
          self.b2revb = []  # mapping from bond index to the index of the reverse bond
          self.overwrite_default_atom_features = overwrite_default_atom_features
          self.overwrite_default_bond_features = overwrite_default_bond_features
  
          if not self.is_reaction:
              # Get atom features
              self.f_atoms = [atom_features(atom) for atom in mol.GetAtoms()]
              if atom_features_extra is not None:
                  if overwrite_default_atom_features:
                      self.f_atoms = [descs.tolist() for descs in atom_features_extra]
                  else:
                      self.f_atoms = [f_atoms + descs.tolist() for f_atoms, descs in zip(self.f_atoms, atom_features_extra)]
  
              self.n_atoms = len(self.f_atoms)
              if atom_features_extra is not None and len(atom_features_extra) != self.n_atoms:
                  raise ValueError(f'The number of atoms in {Chem.MolToSmiles(mol)} is different from the length of '
                                   f'the extra atom features')
  
              # Initialize atom to bond mapping for each atom
              for _ in range(self.n_atoms):
                  self.a2b.append([])
  
              # Get bond features
              for a1 in range(self.n_atoms):
                  for a2 in range(a1 + 1, self.n_atoms):
                      bond = mol.GetBondBetweenAtoms(a1, a2)
  
                      if bond is None:
                          continue
  
                      f_bond = bond_features(bond)
                      if bond_features_extra is not None:
                          descr = bond_features_extra[bond.GetIdx()].tolist()
                          if overwrite_default_bond_features:
                              f_bond = descr
                          else:
                              f_bond += descr
  
                      self.f_bonds.append(self.f_atoms[a1] + f_bond)
                      self.f_bonds.append(self.f_atoms[a2] + f_bond)
  
                      # Update index mappings
                      b1 = self.n_bonds
                      b2 = b1 + 1
                      self.a2b[a2].append(b1)  # b1 = a1 --> a2
                      self.b2a.append(a1)
                      self.a2b[a1].append(b2)  # b2 = a2 --> a1
                      self.b2a.append(a2)
                      self.b2revb.append(b2)
                      self.b2revb.append(b1)
                      self.n_bonds += 2
  
              if bond_features_extra is not None and len(bond_features_extra) != self.n_bonds / 2:
                  raise ValueError(f'The number of bonds in {Chem.MolToSmiles(mol)} is different from the length of '
                                   f'the extra bond features')
  
          else: # Reaction mode
              if atom_features_extra is not None:
                  raise NotImplementedError('Extra atom features are currently not supported for reactions')
              if bond_features_extra is not None:
                  raise NotImplementedError('Extra bond features are currently not supported for reactions')
  
              mol_reac = mol[0]
              mol_prod = mol[1]
              ri2pi, pio, rio = map_reac_to_prod(mol_reac, mol_prod)
             
              # Get atom features
              if self.reaction_mode in ['reac_diff','prod_diff', 'reac_prod']:
                  #Reactant: regular atom features for each atom in the reactants, as well as zero features for atoms that are only in the products (indices in pio)
                  f_atoms_reac = [atom_features(atom) for atom in mol_reac.GetAtoms()] + [atom_features_zeros(mol_prod.GetAtomWithIdx(index)) for index in pio]
                  
                  #Product: regular atom features for each atom that is in both reactants and products (not in rio), other atom features zero,
                  #regular features for atoms that are only in the products (indices in pio)
                  f_atoms_prod = [atom_features(mol_prod.GetAtomWithIdx(ri2pi[atom.GetIdx()])) if atom.GetIdx() not in rio else
                                  atom_features_zeros(atom) for atom in mol_reac.GetAtoms()] + [atom_features(mol_prod.GetAtomWithIdx(index)) for index in pio]
              else: #balance
                  #Reactant: regular atom features for each atom in the reactants, copy features from product side for atoms that are only in the products (indices in pio)
                  f_atoms_reac = [atom_features(atom) for atom in mol_reac.GetAtoms()] + [atom_features(mol_prod.GetAtomWithIdx(index)) for index in pio]
                  
                  #Product: regular atom features for each atom that is in both reactants and products (not in rio), copy features from reactant side for
                  #other atoms, regular features for atoms that are only in the products (indices in pio)
                  f_atoms_prod = [atom_features(mol_prod.GetAtomWithIdx(ri2pi[atom.GetIdx()])) if atom.GetIdx() not in rio else
                                  atom_features(atom) for atom in mol_reac.GetAtoms()] + [atom_features(mol_prod.GetAtomWithIdx(index)) for index in pio]
  
              if self.reaction_mode in ['reac_diff', 'prod_diff', 'reac_diff_balance', 'prod_diff_balance']:
                  f_atoms_diff = [list(map(lambda x, y: x - y, ii, jj)) for ii, jj in zip(f_atoms_prod, f_atoms_reac)]
              if self.reaction_mode in ['reac_prod', 'reac_prod_balance']:
                  self.f_atoms = [x+y[PARAMS.MAX_ATOMIC_NUM+1:] for x,y in zip(f_atoms_reac, f_atoms_prod)]
              elif self.reaction_mode in ['reac_diff', 'reac_diff_balance']:
                  self.f_atoms = [x+y[PARAMS.MAX_ATOMIC_NUM+1:] for x,y in zip(f_atoms_reac, f_atoms_diff)]
              elif self.reaction_mode in ['prod_diff', 'prod_diff_balance']:
                  self.f_atoms = [x+y[PARAMS.MAX_ATOMIC_NUM+1:] for x,y in zip(f_atoms_prod, f_atoms_diff)]
              self.n_atoms = len(self.f_atoms)
              n_atoms_reac = mol_reac.GetNumAtoms()
  
              # Initialize atom to bond mapping for each atom
              for _ in range(self.n_atoms):
                  self.a2b.append([])
  
              # Get bond features
              for a1 in range(self.n_atoms):
                  for a2 in range(a1 + 1, self.n_atoms):
                      if a1 >= n_atoms_reac and a2 >= n_atoms_reac: # Both atoms only in product
                          bond_prod = mol_prod.GetBondBetweenAtoms(pio[a1 - n_atoms_reac], pio[a2 - n_atoms_reac])
                          if self.reaction_mode in ['reac_prod_balance', 'reac_diff_balance', 'prod_diff_balance']:
                              bond_reac = bond_prod
                          else:
                              bond_reac = None
                      elif a1 < n_atoms_reac and a2 >= n_atoms_reac: # One atom only in product
                          bond_reac = None
                          if a1 in ri2pi.keys():
                              bond_prod = mol_prod.GetBondBetweenAtoms(ri2pi[a1], pio[a2 - n_atoms_reac])
                          else:
                              bond_prod = None # Atom atom only in reactant, the other only in product
                      else:
                          bond_reac = mol_reac.GetBondBetweenAtoms(a1, a2)
                          if a1 in ri2pi.keys() and a2 in ri2pi.keys():
                              bond_prod = mol_prod.GetBondBetweenAtoms(ri2pi[a1], ri2pi[a2]) #Both atoms in both reactant and product
                          else:
                              if self.reaction_mode in ['reac_prod_balance', 'reac_diff_balance', 'prod_diff_balance']:
                                  if a1 in ri2pi.keys() or a2 in ri2pi.keys():
                                      bond_prod = None # One atom only in reactant
                                  else:
                                      bond_prod = bond_reac # Both atoms only in reactant
                              else:    
                                  bond_prod = None # One or both atoms only in reactant
  
                      if bond_reac is None and bond_prod is None:
                          continue
  
                      f_bond_reac = bond_features(bond_reac)
                      f_bond_prod = bond_features(bond_prod)
                      if self.reaction_mode in ['reac_diff', 'prod_diff', 'reac_diff_balance', 'prod_diff_balance']:
                          f_bond_diff = [y - x for x, y in zip(f_bond_reac, f_bond_prod)]
                      if self.reaction_mode in ['reac_prod', 'reac_prod_balance']:
                          f_bond = f_bond_reac + f_bond_prod
                      elif self.reaction_mode in ['reac_diff', 'reac_diff_balance']:
                          f_bond = f_bond_reac + f_bond_diff
                      elif self.reaction_mode in ['prod_diff', 'prod_diff_balance']:
                          f_bond = f_bond_prod + f_bond_diff
                      self.f_bonds.append(self.f_atoms[a1] + f_bond)
                      self.f_bonds.append(self.f_atoms[a2] + f_bond)
  
                      # Update index mappings
                      b1 = self.n_bonds
                      b2 = b1 + 1
                      self.a2b[a2].append(b1)  # b1 = a1 --> a2
                      self.b2a.append(a1)
                      self.a2b[a1].append(b2)  # b2 = a2 --> a1
                      self.b2a.append(a2)
                      self.b2revb.append(b2)
                      self.b2revb.append(b1)
                      self.n_bonds += 2                
  
  class BatchMolGraph:
      """
      A :class:`BatchMolGraph` represents the graph structure and featurization of a batch of molecules.
  
      A BatchMolGraph contains the attributes of a :class:`MolGraph` plus:
  
      * :code:`atom_fdim`: The dimensionality of the atom feature vector.
      * :code:`bond_fdim`: The dimensionality of the bond feature vector (technically the combined atom/bond features).
      * :code:`a_scope`: A list of tuples indicating the start and end atom indices for each molecule.
      * :code:`b_scope`: A list of tuples indicating the start and end bond indices for each molecule.
      * :code:`max_num_bonds`: The maximum number of bonds neighboring an atom in this batch.
      * :code:`b2b`: (Optional) A mapping from a bond index to incoming bond indices.
      * :code:`a2a`: (Optional): A mapping from an atom index to neighboring atom indices.
      """
  
      def __init__(self, mol_graphs: List[MolGraph]):
          r"""
          :param mol_graphs: A list of :class:`MolGraph`\ s from which to construct the :class:`BatchMolGraph`.
          """
          self.overwrite_default_atom_features = mol_graphs[0].overwrite_default_atom_features
          self.overwrite_default_bond_features = mol_graphs[0].overwrite_default_bond_features
          self.atom_fdim = get_atom_fdim(overwrite_default_atom=self.overwrite_default_atom_features)
          self.bond_fdim = get_bond_fdim(overwrite_default_bond=self.overwrite_default_bond_features,
                                         overwrite_default_atom=self.overwrite_default_atom_features)
  
          # Start n_atoms and n_bonds at 1 b/c zero padding
          self.n_atoms = 1  # number of atoms (start at 1 b/c need index 0 as padding)
          self.n_bonds = 1  # number of bonds (start at 1 b/c need index 0 as padding)
          self.a_scope = []  # list of tuples indicating (start_atom_index, num_atoms) for each molecule
          self.b_scope = []  # list of tuples indicating (start_bond_index, num_bonds) for each molecule
  
          # All start with zero padding so that indexing with zero padding returns zeros
          f_atoms = [[0] * self.atom_fdim]  # atom features
          f_bonds = [[0] * self.bond_fdim]  # combined atom/bond features
          a2b = [[]]  # mapping from atom index to incoming bond indices
          b2a = [0]  # mapping from bond index to the index of the atom the bond is coming from
          b2revb = [0]  # mapping from bond index to the index of the reverse bond
          for mol_graph in mol_graphs:
              f_atoms.extend(mol_graph.f_atoms)
              f_bonds.extend(mol_graph.f_bonds)
  
              for a in range(mol_graph.n_atoms):
                  a2b.append([b + self.n_bonds for b in mol_graph.a2b[a]])
  
              for b in range(mol_graph.n_bonds):
                  b2a.append(self.n_atoms + mol_graph.b2a[b])
                  b2revb.append(self.n_bonds + mol_graph.b2revb[b])
  
              self.a_scope.append((self.n_atoms, mol_graph.n_atoms))
              self.b_scope.append((self.n_bonds, mol_graph.n_bonds))
              self.n_atoms += mol_graph.n_atoms
              self.n_bonds += mol_graph.n_bonds
  
          self.max_num_bonds = max(1, max(
              len(in_bonds) for in_bonds in a2b))  # max with 1 to fix a crash in rare case of all single-heavy-atom mols
  
          self.f_atoms = torch.FloatTensor(f_atoms)
          self.f_bonds = torch.FloatTensor(f_bonds)
          self.a2b = torch.LongTensor([a2b[a] + [0] * (self.max_num_bonds - len(a2b[a])) for a in range(self.n_atoms)])
          self.b2a = torch.LongTensor(b2a)
          self.b2revb = torch.LongTensor(b2revb)
          self.b2b = None  # try to avoid computing b2b b/c O(n_atoms^3)
          self.a2a = None  # only needed if using atom messages
  
      def get_components(self, atom_messages: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor,
                                                                     torch.LongTensor, torch.LongTensor, torch.LongTensor,
                                                                     List[Tuple[int, int]], List[Tuple[int, int]]]:
          """
          Returns the components of the :class:`BatchMolGraph`.
  
          The returned components are, in order:
  
          * :code:`f_atoms`
          * :code:`f_bonds`
          * :code:`a2b`
          * :code:`b2a`
          * :code:`b2revb`
          * :code:`a_scope`
          * :code:`b_scope`
  
          :param atom_messages: Whether to use atom messages instead of bond messages. This changes the bond feature
                                vector to contain only bond features rather than both atom and bond features.
          :return: A tuple containing PyTorch tensors with the atom features, bond features, graph structure,
                   and scope of the atoms and bonds (i.e., the indices of the molecules they belong to).
          """
          if atom_messages:
              f_bonds = self.f_bonds[:, -get_bond_fdim(atom_messages=atom_messages,
                                                       overwrite_default_atom=self.overwrite_default_atom_features,
                                                       overwrite_default_bond=self.overwrite_default_bond_features):]
          else:
              f_bonds = self.f_bonds
  
          return self.f_atoms, f_bonds, self.a2b, self.b2a, self.b2revb, self.a_scope, self.b_scope
  
      def get_b2b(self) -> torch.LongTensor:
          """
          Computes (if necessary) and returns a mapping from each bond index to all the incoming bond indices.
  
          :return: A PyTorch tensor containing the mapping from each bond index to all the incoming bond indices.
          """
          if self.b2b is None:
              b2b = self.a2b[self.b2a]  # num_bonds x max_num_bonds
              # b2b includes reverse edge for each bond so need to mask out
              revmask = (b2b != self.b2revb.unsqueeze(1).repeat(1, b2b.size(1))).long()  # num_bonds x max_num_bonds
              self.b2b = b2b * revmask
  
          return self.b2b
  
      def get_a2a(self) -> torch.LongTensor:
          """
          Computes (if necessary) and returns a mapping from each atom index to all neighboring atom indices.
  
          :return: A PyTorch tensor containing the mapping from each atom index to all the neighboring atom indices.
          """
          if self.a2a is None:
              # b = a1 --> a2
              # a2b maps a2 to all incoming bonds b
              # b2a maps each bond b to the atom it comes from a1
              # thus b2a[a2b] maps atom a2 to neighboring atoms a1
              self.a2a = self.b2a[self.a2b]  # num_atoms x max_num_bonds
  
          return self.a2a
  
  
  def mol2graph(mols: Union[List[str], List[Chem.Mol], List[Tuple[Chem.Mol, Chem.Mol]]],
                atom_features_batch: List[np.array] = (None,),
                bond_features_batch: List[np.array] = (None,),
                overwrite_default_atom_features: bool = False,
                overwrite_default_bond_features: bool = False
                ) -> BatchMolGraph:
      """
      Converts a list of SMILES or RDKit molecules to a :class:`BatchMolGraph` containing the batch of molecular graphs.
  
      :param mols: A list of SMILES or a list of RDKit molecules.
      :param atom_features_batch: A list of 2D numpy array containing additional atom features to featurize the molecule
      :param bond_features_batch: A list of 2D numpy array containing additional bond features to featurize the molecule
      :param overwrite_default_atom_features: Boolean to overwrite default atom descriptors by atom_descriptors instead of concatenating
      :param overwrite_default_bond_features: Boolean to overwrite default bond descriptors by bond_descriptors instead of concatenating
      :return: A :class:`BatchMolGraph` containing the combined molecular graph for the molecules.
      """
      return BatchMolGraph([MolGraph(mol, af, bf,
                                     overwrite_default_atom_features=overwrite_default_atom_features,
                                     overwrite_default_bond_features=overwrite_default_bond_features)
                            for mol, af, bf
                            in zip_longest(mols, atom_features_batch, bond_features_batch)])
  