#!/usr/bin/env python

# argument parsing
import argparse

def getargparser():
    parser = argparse.ArgumentParser(description='Find the similarities between two short sentences using Word2Vec.')
    parser.add_argument('modelpath', help='Path of the Word2Vec model')
    parser.add_argument('--type', default='word2vec',
                        help='Type of word-embedding model (default: "word2vec"; other options: "fasttext", "poincare")')
    return parser

parser = getargparser()
args = parser.parse_args()


import time

from scipy.spatial.distance import cosine

from shorttext.metrics.embedfuzzy import jaccardscore_sents
from shorttext.utils import tokenize, load_word2vec_model, load_fasttext_model, load_poincare_model
from shorttext.utils import shorttext_to_avgvec
from shorttext.metrics.wasserstein import word_mover_distance
from shorttext.metrics.dynprog import soft_jaccard_score


typedict = {'word2vec': load_word2vec_model,
            'fasttext': load_fasttext_model,
            'poincare': load_poincare_model}


if __name__ == '__main__':
    # preload tokenizer
    tokenize('Mogu is cute.')

    time0 = time.time()
    print "Loading", args.type, "model: ", args.modelpath
    wvmodel = typedict[args.type](args.modelpath)
    time1 = time.time()
    end = False
    print "... loading time:", time1 - time0, "seconds"

    while not end:
        sent1 = raw_input('sent1> ')
        if len(sent1)==0:
            end = True
        else:
            sent2 = raw_input('sent2> ')

            # output results
            print "Cosine Similarity = ", 1 - cosine(shorttext_to_avgvec(sent1, wvmodel),
                                                     shorttext_to_avgvec(sent2, wvmodel))
            print "Word-embedding Jaccard Score Similarity = ", jaccardscore_sents(sent1, sent2, wvmodel)
            print "Word Mover's Distance = ", word_mover_distance(tokenize(sent1), tokenize(sent2), wvmodel)
            print "Soft Jaccard Score (edit distance) = ", soft_jaccard_score(tokenize(sent1), tokenize(sent2))

