import gensim.downloader

EMBEDDING_MODELS = {
    'small':  'glove-wiki-gigaword-100',   # ~128 MB
    'medium': 'glove-wiki-gigaword-300',   # ~376 MB
    'large':  'word2vec-google-news-300',  # ~1.6 GB
}


def load_embeddings(size='small'):
    "Download (if needed) and load the embedding model for the given size."
    return gensim.downloader.load(EMBEDDING_MODELS[size])


def synonyms(model, word, n=5):
    pass


def average(model, word1, word2):
    pass


def outlier(model, words):
    pass


def sort(model, words):
    pass