old_contents: #!/usr/bin/env python
  
  """
  SELFIES: a robust representation of semantically constrained graphs with an
           example application in chemistry.
  
  SELFIES (SELF-referencIng Embedded Strings) is a general-purpose,
  sequence-based, robust representation of semantically constrained graphs.
  It is based on a Chomsky type-2 grammar, augmented with two self-referencing
  functions. A main objective is to use SELFIES as direct input into machine
  learning models, in particular in generative models, for the generation of
  outputs with high validity.
  
  The code presented here is a concrete application of SELFIES in chemistry, for
  the robust representation of molecules.
  
      Typical usage example:
          import selfies
  
          benzene = "C1=CC=CC=C1"
          selfies_benzene = selfies.encoder(benzene)
          smiles_benzene = selfies.decoder(selfies_benzene)
  
  For comments, bug reports or feature ideas, please send an email to
  mario.krenn@utoronto.ca and alan@aspuru.com.
  """
  
  __version__ = "1.0.1"
  
  __all__ = [
      "encoder",
      "decoder",
      "get_semantic_robust_alphabet",
      "get_semantic_constraints",
      "set_semantic_constraints",
      "len_selfies",
      "split_selfies",
      "get_alphabet_from_selfies",
      "selfies_to_encoding",
      "batch_selfies_to_flat_hot",
      "encoding_to_selfies",
      "batch_flat_hot_to_selfies",
  ]
  
  from .decoder import decoder
  from .encoder import encoder
  from .grammar_rules import (
      get_semantic_robust_alphabet,
      get_semantic_constraints,
      set_semantic_constraints,
  )
  from .utils import (
      get_alphabet_from_selfies,
      len_selfies,
      split_selfies,
      selfies_to_encoding,
      batch_selfies_to_flat_hot,
      encoding_to_selfies,
      batch_flat_hot_to_selfies,
  )
  
new_contents: #!/usr/bin/env python
  
  """
  SELFIES: a robust representation of semantically constrained graphs with an
           example application in chemistry.
  
  SELFIES (SELF-referencIng Embedded Strings) is a general-purpose,
  sequence-based, robust representation of semantically constrained graphs.
  It is based on a Chomsky type-2 grammar, augmented with two self-referencing
  functions. A main objective is to use SELFIES as direct input into machine
  learning models, in particular in generative models, for the generation of
  outputs with high validity.
  
  The code presented here is a concrete application of SELFIES in chemistry, for
  the robust representation of molecules.
  
      Typical usage example:
          import selfies
  
          benzene = "C1=CC=CC=C1"
          selfies_benzene = selfies.encoder(benzene)
          smiles_benzene = selfies.decoder(selfies_benzene)
  
  For comments, bug reports or feature ideas, please send an email to
  mario.krenn@utoronto.ca and alan@aspuru.com.
  """
  
  __version__ = "1.0.3"
  
  __all__ = [
      "encoder",
      "decoder",
      "get_semantic_robust_alphabet",
      "get_semantic_constraints",
      "set_semantic_constraints",
      "len_selfies",
      "split_selfies",
      "get_alphabet_from_selfies",
      "selfies_to_encoding",
      "batch_selfies_to_flat_hot",
      "encoding_to_selfies",
      "batch_flat_hot_to_selfies",
  ]
  
  from .decoder import decoder
  from .encoder import encoder
  from .grammar_rules import (
      get_semantic_robust_alphabet,
      get_semantic_constraints,
      set_semantic_constraints,
  )
  from .utils import (
      get_alphabet_from_selfies,
      len_selfies,
      split_selfies,
      selfies_to_encoding,
      batch_selfies_to_flat_hot,
      encoding_to_selfies,
      batch_flat_hot_to_selfies,
  )
  
subject: Update __init__.py