#!/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 as sf

        benzene = "C1=CC=CC=C1"
        benzene_selfies = sf.encoder(benzene)
        benzene_smiles = sf.decoder(benzene_selfies)

For comments, bug reports or feature ideas, please send an email to
mario.krenn@utoronto.ca and alan@aspuru.com.
"""

__version__ = "2.0.1"

__all__ = [
    "encoder",
    "decoder",
    "get_preset_constraints",
    "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",
    "EncoderError",
    "DecoderError"
]

from .bond_constraints import (
    get_preset_constraints,
    get_semantic_constraints,
    get_semantic_robust_alphabet,
    set_semantic_constraints
)
from .decoder import decoder
from .encoder import encoder
from .exceptions import DecoderError, EncoderError
from .utils.encoding_utils import (
    batch_flat_hot_to_selfies,
    batch_selfies_to_flat_hot,
    encoding_to_selfies,
    selfies_to_encoding
)
from .utils.selfies_utils import (
    get_alphabet_from_selfies,
    len_selfies,
    split_selfies
)

#!/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 as sf

        benzene = "C1=CC=CC=C1"
        benzene_selfies = sf.encoder(benzene)
        benzene_smiles = sf.decoder(benzene_selfies)

For comments, bug reports or feature ideas, please send an email to
mario.krenn@utoronto.ca and alan@aspuru.com.
"""

__version__ = "2.1.0"

__all__ = [
    "encoder",
    "decoder",
    "get_preset_constraints",
    "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",
    "EncoderError",
    "DecoderError"
]

from .bond_constraints import (
    get_preset_constraints,
    get_semantic_constraints,
    get_semantic_robust_alphabet,
    set_semantic_constraints
)
from .decoder import decoder
from .encoder import encoder
from .exceptions import DecoderError, EncoderError
from .utils.encoding_utils import (
    batch_flat_hot_to_selfies,
    batch_selfies_to_flat_hot,
    encoding_to_selfies,
    selfies_to_encoding
)
from .utils.selfies_utils import (
    get_alphabet_from_selfies,
    len_selfies,
    split_selfies
)

Update __init__.py