Rdkit reimport

def test_mat_encoder_layer():

  """Test invoking MATEncoderLayer."""

  torch.manual_seed(0)

  import rdkit

  from rdkit import Chem

  input_ar = torch.Tensor([[1., 2.], [5., 6.]])

  mask = torch.Tensor([[1., 1.], [1., 1.]])

  mol = rdkit.Chem.rdmolfiles.MolFromSmiles("CC")

  adj_matrix = rdkit.Chem.rdmolops.GetAdjacencyMatrix(mol)

  distance_matrix = rdkit.Chem.rdmolops.GetDistanceMatrix(mol)

  mol = Chem.MolFromSmiles("CC")

  adj_matrix = Chem.GetAdjacencyMatrix(mol)

  distance_matrix = Chem.GetDistanceMatrix(mol)

  layer = torch_layers.MATEncoderLayer(

>>>>>>> Tests for encoder

      dist_kernel='softmax',

  Examples

  --------

  >>> import deepchem as dc

  >>> import rdkit

  >>> mol = rdkit.Chem.rdmolfiles.MolFromSmiles("CC")

  >>> adj_matrix = GetAdjacencyMatrix(mol)

  >>> distance_matrix = GetDistanceMatrix(mol)

  >>> from rdkit import Chem

  >>> mol = Chem.MolFromSmiles("CC")

  >>> adj_matrix = Chem.GetAdjacencyMatrix(mol)

  >>> distance_matrix = Chem.GetDistanceMatrix(mol)

  >>> layer = dc.models.torch_models.layers.MATEncoderLayer(dist_kernel = 'softmax', lambda_attention = 0.33, lambda_distance = 0.33, h = 8, sa_hsize = 1024, sa_dropout_p = 0.1, d_input = 1024, activation = 'relu', n_layers = 1, ff_dropout_p = 0.1, encoder_hsize = 1024, encoder_dropout_p = 0.1)

  >>> x = torch.Tensor([[1., 2.], [5., 6.]])

  >>> mask = torch.Tensor([[1., 1.], [1., 1.]])