"""
[GRAPE](https://github.com/AnacletoLAB/grape) (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both off-the-shelf laptop and desktop computers and High Performance Computing clusters of workstations.
The library is written in Rust and Python programming languages and is composed of two main modules: [Ensmallen](https://github.com/AnacletoLAB/ensmallen) (ENabler of SMALL runtimE and memory Needs) and [Embiggen](https://github.com/monarch-initiative/embiggen) (EMBeddInG GENerator), that run synergistically using parallel computation and efficient data structures.

# Reference Manual
"""
from . import embiggen
from . import ensmallen

__all__ = [
    "embiggen",
    "ensmallen"
]

"""Module with models for graph and text embedding and their Keras Sequences."""
from .embedders import (
    CBOW, GloVe, GraphCBOW, GraphGloVe, GraphSkipGram,
    SkipGram, TransE, TransH, TransR, SimplE, Siamese
)
from .node_prediction import GraphConvolutionalNeuralNetwork
from .sequences import Word2VecSequence
from .transformers import (CorpusTransformer, EdgeTransformer,
                           GraphTransformer, LinkPredictionTransformer,
                           NodeTransformer)
from .visualizations import GraphVisualization

__all__ = [
    "CBOW",
    "SkipGram",
    "GloVe",
    "GraphCBOW",
    "GraphSkipGram",
    "GraphGloVe",
    "Word2VecSequence",
    "NodeTransformer",
    "EdgeTransformer",
    "GraphTransformer",
    "CorpusTransformer",
    "LinkPredictionTransformer",
    "GraphVisualization",
    "TransE",
    "TransH",
    "TransR",
    "SimplE",
    "Siamese",
    "GraphConvolutionalNeuralNetwork"
]

"""
[GRAPE](https://github.com/AnacletoLAB/grape) (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both off-the-shelf laptop and desktop computers and High Performance Computing clusters of workstations.
The library is written in Rust and Python programming languages and is composed of two main modules: [Ensmallen](https://github.com/AnacletoLAB/ensmallen) (ENabler of SMALL runtimE and memory Needs) and [Embiggen](https://github.com/monarch-initiative/embiggen) (EMBeddInG GENerator), that run synergistically using parallel computation and efficient data structures.
""" 
from .ensmallen import preprocessing  # pylint: disable=import-error
from .ensmallen import Graph  # pylint: disable=import-error
from .ensmallen import edge_list_utils  # pylint: disable=import-error

# The import of dataset should ALWAYS be under the imports from the compiled bindings
# Because otherwise it generate a Circular import and crash
from . import datasets

__all__ = ["edge_list_utils", "Graph", "preprocessing", "datasets"]