import numpy as np


def max_jaccard(x, y):
    """
    MaxPool-Jaccard similarity measure between two sentences
    :param x: list of word embeddings for the first sentence
    :param y: list of word embeddings for the second sentence
    :return: similarity score between the two sentences
    """
    m_x = np.max(x, axis=0)
    m_x = np.maximum(m_x, 0, m_x)
    m_y = np.max(y, axis=0)
    m_y = np.maximum(m_y, 0, m_y)
    m_inter = np.sum(np.minimum(m_x, m_y))
    m_union = np.sum(np.maximum(m_x, m_y))
    return m_inter / m_union

import numpy as np


def fuzzify(s, u):
    """
    Sentence fuzzifier.
    Computes membership vector for the sentence S with respect to the
    universe U
    :param s: list of word embeddings for the sentence
    :param u: the universe matrix U with shape (K, d)
    :return: membership vectors for the sentence
    """
    f_s = np.dot(s, u.T)
    m_s = np.max(f_s, axis=0)
    m_s = np.maximum(m_s, 0, m_s)
    return m_s


def dynamax_jaccard(x, y):
    """
    DynaMax-Jaccard similarity measure between two sentences
    :param x: list of word embeddings for the first sentence
    :param y: list of word embeddings for the second sentence
    :return: similarity score between the two sentences
    """
    u = np.vstack((x, y))
    m_x = fuzzify(x, u)
    m_y = fuzzify(y, u)

    m_inter = np.sum(np.minimum(m_x, m_y))
    m_union = np.sum(np.maximum(m_x, m_y))
    return m_inter / m_union

WORD_VEC_MAP = {
    'glove': 'glove.840B.300d.w2vformat.txt',
    'word2vec': 'GoogleNews-vectors-negative300.txt',
    'fasttext': 'fasttext-crawl-300d-2M.txt',
    'word2vec_skipgram': 'book_corpus_skip.txt',
    'word2vec_cbow': 'book_corpus_cbow.txt',
    'word2vec_conll': 'word2vec_conll17_skip.txt',
    'psl': 'paragram_300_sl999.w2vformat.txt',
    'ppxxl': 'paragram-phrase-XXL.w2vformat.txt',
    'pnmt': 'paragram-NMT.w2vformat.txt',
    'default': 'glove.840B.300d.w2vformat.txt'
}