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#  to you under the Apache License, Version 2.0 (the
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#    http://www.apache.org/licenses/LICENSE-2.0
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import tensorflow as tf
import sonnet as snt
from kglib.kgcn.models.typewise import TypewiseEncoder


def common_embedding(features, num_types, type_embedding_dim):
    preexistance_feat = tf.expand_dims(tf.cast(features[:, 0], dtype=tf.float32), axis=1)
    type_embedder = snt.Embed(num_types, type_embedding_dim)
    type_embedding = type_embedder(tf.cast(features[:, 1], tf.int32))
    return tf.concat([preexistance_feat, type_embedding], axis=1)


def attribute_embedding(features, attr_encoders, attr_embedding_dim):
    typewise_attribute_encoder = TypewiseEncoder(attr_encoders, attr_embedding_dim)
    return typewise_attribute_encoder(features[:, 1:])


def node_embedding(features, num_types, type_embedding_dim, attr_encoders, attr_embedding_dim):
    return tf.concat([common_embedding(features, num_types, type_embedding_dim),
                      attribute_embedding(features, attr_encoders, attr_embedding_dim)], axis=1)
