#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Author: Shiva Manne <manneshiva@gmail.com>
# Copyright (C) 2018 RaRe Technologies s.r.o.
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html

"""Produce word vectors with deep learning via word2vec's "skip-gram and CBOW models", using either
hierarchical softmax or negative sampling [1]_ [2]_.

NOTE: There are more ways to get word vectors in Gensim than just Word2Vec.
See FastText and wrappers for VarEmbed and WordRank.

The training algorithms were originally ported from the C package https://code.google.com/p/word2vec/
and extended with additional functionality.

For a blog tutorial on gensim word2vec, with an interactive web app trained on GoogleNews,
visit http://radimrehurek.com/2014/02/word2vec-tutorial/

**Make sure you have a C compiler before installing gensim, to use optimized (compiled) word2vec training**
(70x speedup compared to plain NumPy implementation [3]_).

Initialize a model with e.g.::

    >>> model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)

Persist a model to disk with::

    >>> model.save(fname)
    >>> model = Word2Vec.load(fname)  # you can continue training with the loaded model!

The word vectors are stored in a KeyedVectors instance in model.wv.
This separates the read-only word vector lookup operations in KeyedVectors from the training code in Word2Vec::

  >>> model.wv['computer']  # numpy vector of a word
  array([-0.00449447, -0.00310097,  0.02421786, ...], dtype=float32)

The word vectors can also be instantiated from an existing file on disk in the word2vec C format
as a KeyedVectors instance.

NOTE: It is impossible to continue training the vectors loaded from the C format because hidden weights,
vocabulary frequency and the binary tree is missing::

    >>> from gensim.models import KeyedVectors
    >>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False)  # C text format
    >>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True)  # C binary format


You can perform various NLP word tasks with the model. Some of them
are already built-in::

  >>> model.wv.most_similar(positive=['woman', 'king'], negative=['man'])
  [('queen', 0.50882536), ...]

  >>> model.wv.most_similar_cosmul(positive=['woman', 'king'], negative=['man'])
  [('queen', 0.71382287), ...]


  >>> model.wv.doesnt_match("breakfast cereal dinner lunch".split())
  'cereal'

  >>> model.wv.similarity('woman', 'man')
  0.73723527

Probability of a text under the model::

  >>> model.score(["The fox jumped over a lazy dog".split()])
  0.2158356

Correlation with human opinion on word similarity::

  >>> model.wv.evaluate_word_pairs(os.path.join(module_path, 'test_data','wordsim353.tsv'))
  0.51, 0.62, 0.13

And on analogies::

  >>> model.wv.accuracy(os.path.join(module_path, 'test_data', 'questions-words.txt'))

and so on.

If you're finished training a model (i.e. no more updates, only querying),
then switch to the :mod:`gensim.models.KeyedVectors` instance in wv

  >>> word_vectors = model.wv
  >>> del model

to trim unneeded model memory = use much less RAM.

Note that there is a :mod:`gensim.models.phrases` module which lets you automatically
detect phrases longer than one word. Using phrases, you can learn a word2vec model
where "words" are actually multiword expressions, such as `new_york_times` or `financial_crisis`:

    >>> bigram_transformer = gensim.models.Phrases(sentences)
    >>> model = Word2Vec(bigram_transformer[sentences], size=100, ...)

.. [1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean.
       Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013.
.. [2] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean.
       Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.
.. [3] Optimizing word2vec in gensim, http://radimrehurek.com/2013/09/word2vec-in-python-part-two-optimizing/
"""
from __future__ import division  # py3 "true division"

import logging
import sys
import os
import heapq
from timeit import default_timer
from copy import deepcopy
from collections import defaultdict, Counter
import threading
import itertools
import warnings

from gensim.utils import keep_vocab_item, call_on_class_only
from gensim.models.keyedvectors import Vocab, Word2VecKeyedVectors, PoincareWordEmbeddingsKeyedVectors
from gensim.models.base_any2vec import BaseWordEmbeddingsModel

try:
    from queue import Queue, Empty
except ImportError:
    from Queue import Queue, Empty

from numpy import exp, dot, zeros, random, dtype, float32 as REAL, float64 as DOUBLE,\
    uint32, seterr, array, uint8, vstack, fromstring, sqrt,\
    empty, sum as np_sum, ones, logaddexp, log, outer, maximum, minimum, tanh, sign, average
from numpy.linalg import norm

from scipy.special import expit

from gensim import utils, matutils  # utility fnc for pickling, common scipy operations etc
from gensim.models.keyedvectors import PoincareWordEmbeddingsKeyedVectors
from gensim.utils import deprecated
from six import iteritems, itervalues, string_types
from six.moves import xrange

logger = logging.getLogger(__name__)

try:
    from gensim.models.word2vec_inner import train_batch_sg, train_batch_sg_dbl, train_batch_cbow
    from gensim.models.word2vec_inner import score_sentence_sg, score_sentence_cbow
    from gensim.models.word2vec_inner import FAST_VERSION, MAX_WORDS_IN_BATCH

except ImportError:
    # failed... fall back to plain numpy (20-80x slower training than the above)
    FAST_VERSION = -1
    MAX_WORDS_IN_BATCH = 10000

    def train_batch_sg(model, sentences, alpha, work=None, compute_loss=False):
        """
        Update skip-gram model by training on a sequence of sentences.
        Each sentence is a list of string tokens, which are looked up in the model's
        vocab dictionary. Called internally from `Word2Vec.train()`.
        This is the non-optimized, Python version. If you have cython installed, gensim
        will use the optimized version from word2vec_inner instead.
        """
        result = 0
        for sentence in sentences:
            word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab and
                           model.wv.vocab[w].sample_int > model.random.rand() * 2 ** 32]
            for pos, word in enumerate(word_vocabs):
                reduced_window = model.random.randint(model.window)  # `b` in the original word2vec code

                # now go over all words from the (reduced) window, predicting each one in turn
                start = max(0, pos - model.window + reduced_window)
                for pos2, word2 in enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start):
                    # don't train on the `word` itself
                    if pos2 != pos:
                        train_sg_pair(
                            model, model.wv.index2word[word.index], word2.index, alpha, compute_loss=compute_loss
                        )

            result += len(word_vocabs)
        return result, 0.0

    def train_batch_cbow(model, sentences, alpha, work=None, neu1=None, compute_loss=False):
        """
        Update CBOW model by training on a sequence of sentences.
        Each sentence is a list of string tokens, which are looked up in the model's
        vocab dictionary. Called internally from `Word2Vec.train()`.
        This is the non-optimized, Python version. If you have cython installed, gensim
        will use the optimized version from word2vec_inner instead.
        """
        result = 0
        for sentence in sentences:
            word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab and
                           model.wv.vocab[w].sample_int > model.random.rand() * 2 ** 32]  # XXX: this sample_int might be useful to figure out what word is
            for pos, word in enumerate(word_vocabs):
                reduced_window = model.random.randint(model.window)  # `b` in the original word2vec code
                start = max(0, pos - model.window + reduced_window)
                window_pos = enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start)
                word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)]
                l1 = np_sum(model.wv.syn0[word2_indices], axis=0)  # 1 x vector_size
                if word2_indices and model.cbow_mean:
                    l1 /= len(word2_indices)
                train_cbow_pair(model, word, word2_indices, l1, alpha, compute_loss=compute_loss)
            result += len(word_vocabs)
        return result

    def score_sentence_sg(model, sentence, work=None):
        """
        Obtain likelihood score for a single sentence in a fitted skip-gram representaion.
        The sentence is a list of Vocab objects (or None, when the corresponding
        word is not in the vocabulary). Called internally from `Word2Vec.score()`.
        This is the non-optimized, Python version. If you have cython installed, gensim
        will use the optimized version from word2vec_inner instead.
        """
        log_prob_sentence = 0.0
        if model.negative:
            raise RuntimeError("scoring is only available for HS=True")

        word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab]
        for pos, word in enumerate(word_vocabs):
            if word is None:
                continue  # OOV word in the input sentence => skip

            # now go over all words from the window, predicting each one in turn
            start = max(0, pos - model.window)
            for pos2, word2 in enumerate(word_vocabs[start: pos + model.window + 1], start):
                # don't train on OOV words and on the `word` itself
                if word2 is not None and pos2 != pos:
                    log_prob_sentence += score_sg_pair(model, word, word2)

        return log_prob_sentence

    def score_sentence_cbow(model, sentence, work=None, neu1=None):
        """
        Obtain likelihood score for a single sentence in a fitted CBOW representaion.
        The sentence is a list of Vocab objects (or None, where the corresponding
        word is not in the vocabulary. Called internally from `Word2Vec.score()`.
        This is the non-optimized, Python version. If you have cython installed, gensim
        will use the optimized version from word2vec_inner instead.
        """
        log_prob_sentence = 0.0
        if model.negative:
            raise RuntimeError("scoring is only available for HS=True")

        word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab]
        for pos, word in enumerate(word_vocabs):
            if word is None:
                continue  # OOV word in the input sentence => skip

            start = max(0, pos - model.window)
            window_pos = enumerate(word_vocabs[start:(pos + model.window + 1)], start)
            word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)]
            l1 = np_sum(model.wv.syn0[word2_indices], axis=0)  # 1 x layer1_size
            if word2_indices and model.cbow_mean:
                l1 /= len(word2_indices)
            log_prob_sentence += score_cbow_pair(model, word, l1)

        return log_prob_sentence


# Note: We did not change this function to support Euclidean distance-based similarity during training because training
# is too slow with the Python implementation anyway. We only changed the Cython code.
def train_sg_pair(model, word, context_index, alpha, learn_vectors=True, learn_hidden=True,
                  context_vectors=None, context_locks=None, compute_loss=False, is_ft=False):
    if context_vectors is None:
        if is_ft:
            context_vectors_vocab = model.wv.syn0_vocab
            context_vectors_ngrams = model.wv.syn0_ngrams
        else:
            context_vectors = model.wv.syn0
    if context_locks is None:
        if is_ft:
            context_locks_vocab = model.syn0_vocab_lockf
            context_locks_ngrams = model.syn0_ngrams_lockf
        else:
            context_locks = model.syn0_lockf

    if word not in model.wv.vocab:
        return
    predict_word = model.wv.vocab[word]  # target word (NN output)

    if is_ft:
        l1_vocab = context_vectors_vocab[context_index[0]]
        l1_ngrams = np_sum(context_vectors_ngrams[context_index[1:]], axis=0)
        if context_index:
            l1 = np_sum([l1_vocab, l1_ngrams], axis=0) / len(context_index)
    else:
        l1 = context_vectors[context_index]  # input word (NN input/projection layer)
        lock_factor = context_locks[context_index]

    neu1e = zeros(l1.shape)

    if model.hs:
        # work on the entire tree at once, to push as much work into numpy's C routines as possible (performance)
        l2a = deepcopy(model.syn1[predict_word.point])  # 2d matrix, codelen x layer1_size
        prod_term = dot(l1, l2a.T)
        fa = expit(prod_term)  # propagate hidden -> output
        ga = (1 - predict_word.code - fa) * alpha  # vector of error gradients multiplied by the learning rate
        if learn_hidden:
            model.syn1[predict_word.point] += outer(ga, l1)  # learn hidden -> output
        neu1e += dot(ga, l2a)  # save error

        # loss component corresponding to hierarchical softmax
        if compute_loss:
            sgn = (-1.0) ** predict_word.code  # `ch` function, 0 -> 1, 1 -> -1
            lprob = -log(expit(-sgn * prod_term))
            model.epoch_training_loss += sum(lprob)

    if model.negative:
        # use this word (label = 1) + `negative` other random words not from this sentence (label = 0)
        word_indices = [predict_word.index]
        while len(word_indices) < model.negative + 1:
            w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1]))
            if w != predict_word.index:
                word_indices.append(w)
        l2b = model.syn1neg[word_indices]  # 2d matrix, k+1 x layer1_size
        prod_term = dot(l1, l2b.T)  # k+1 x 1;
        fb = expit(prod_term)  # propagate hidden -> output
        # gb are gradients wrt to the distance function; i.e. dL/d<h,v_w>; to obtain the actual gradients wrt to the
        # context and word vectors, we need to multiply gb by d<h,v_w>/dh and d<h,v_w>/dv_w respectively.
        gb = (model.neg_labels - fb) * alpha  # vector of error gradients multiplied by the learning rate; k+1 x 1
        if learn_hidden:
            model.syn1neg[word_indices] += outer(gb, l1)  # learn hidden -> output; k+1 x layer1_size
        neu1e += dot(gb, l2b)  # save error; layer1_size x 1

        # loss component corresponding to negative sampling
        if compute_loss:
            model.epoch_training_loss -= sum(log(expit(-1 * prod_term[1:])))  # for the sampled words
            model.epoch_training_loss -= log(expit(prod_term[0]))  # for the output word

    if learn_vectors:
        if is_ft:
            model.wv.syn0_vocab[context_index[0]] += neu1e * context_locks_vocab[context_index[0]]
            for i in context_index[1:]:
                model.wv.syn0_ngrams[i] += neu1e * context_locks_ngrams[i]
        else:
            l1 += neu1e * lock_factor  # learn input -> hidden (mutates model.wv.syn0[word2.index], if that is l1)
    return neu1e


def train_cbow_pair(model, word, input_word_indices, l1, alpha, learn_vectors=True, learn_hidden=True,
                    compute_loss=False, context_vectors=None, context_locks=None, is_ft=False):
    if context_vectors is None:
        if is_ft:
            context_vectors_vocab = model.wv.syn0_vocab
            context_vectors_ngrams = model.wv.syn0_ngrams
        else:
            context_vectors = model.wv.syn0
    if context_locks is None:
        if is_ft:
            context_locks_vocab = model.syn0_vocab_lockf
            context_locks_ngrams = model.syn0_ngrams_lockf
        else:
            context_locks = model.syn0_lockf

    neu1e = zeros(l1.shape)

    if model.hs:
        l2a = model.syn1[word.point]  # 2d matrix, codelen x layer1_size
        prod_term = dot(l1, l2a.T)
        fa = expit(prod_term)  # propagate hidden -> output
        ga = (1. - word.code - fa) * alpha  # vector of error gradients multiplied by the learning rate
        if learn_hidden:
            model.syn1[word.point] += outer(ga, l1)  # learn hidden -> output
        neu1e += dot(ga, l2a)  # save error

        # loss component corresponding to hierarchical softmax
        if compute_loss:
            sgn = (-1.0) ** word.code  # ch function, 0-> 1, 1 -> -1
            model.epoch_training_loss += sum(-log(expit(-sgn * prod_term)))

    if model.negative:
        # use this word (label = 1) + `negative` other random words not from this sentence (label = 0)
        word_indices = [word.index]
        while len(word_indices) < model.negative + 1:
            w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1]))
            if w != word.index:
                word_indices.append(w)
        l2b = model.syn1neg[word_indices]  # 2d matrix, k+1 x layer1_size
        prod_term = dot(l1, l2b.T)
        fb = expit(prod_term)  # propagate hidden -> output
        gb = (model.neg_labels - fb) * alpha  # vector of error gradients multiplied by the learning rate
        if learn_hidden:
            model.syn1neg[word_indices] += outer(gb, l1)  # learn hidden -> output
        neu1e += dot(gb, l2b)  # save error

        # loss component corresponding to negative sampling
        if compute_loss:
            model.epoch_training_loss -= sum(log(expit(-1 * prod_term[1:])))  # for the sampled words
            model.epoch_training_loss -= log(expit(prod_term[0]))  # for the output word

    if learn_vectors:
        # learn input -> hidden, here for all words in the window separately
        if is_ft:
            if not model.cbow_mean and input_word_indices:
                neu1e /= (len(input_word_indices[0]) + len(input_word_indices[1]))
            for i in input_word_indices[0]:
                context_vectors_vocab[i] += neu1e * context_locks_vocab[i]
            for i in input_word_indices[1]:
                context_vectors_ngrams[i] += neu1e * context_locks_ngrams[i]
        else:
            if not model.cbow_mean and input_word_indices:
                neu1e /= len(input_word_indices)
            for i in input_word_indices:
                context_vectors[i] += neu1e * context_locks[i]

    return neu1e


def score_sg_pair(model, word, word2):
    l1 = model.wv.syn0[word2.index]
    l2a = deepcopy(model.syn1[word.point])  # 2d matrix, codelen x layer1_size
    sgn = (-1.0) ** word.code  # ch function, 0-> 1, 1 -> -1
    lprob = -logaddexp(0, -sgn * dot(l1, l2a.T))
    return sum(lprob)


def score_cbow_pair(model, word, l1):
    l2a = model.syn1[word.point]  # 2d matrix, codelen x layer1_size
    sgn = (-1.0) ** word.code  # ch function, 0-> 1, 1 -> -1
    lprob = -logaddexp(0, -sgn * dot(l1, l2a.T))
    return sum(lprob)


class InitializationConfig:
    """
    Class that stores information about the way we want to initialize the embeddings. It is used when initializing a
    model, before training, from pre-trained embeddings. Particularly, we use this to initialize Poincare embeddings
    from a pre-trained Euclidean model that we then project onto the Poincare ball. We use either the identity map or
    the exponential map for the projection. We scale the embeddings such that we control how close to the origin they
    are initialized.

    """

    def __init__(self, pretrained_model_filename, config_str):
        self.euclid2hyp_str, self.scaling_factor = config_str.split('0', 1)
        self.scaling_factor = float(self.scaling_factor)

        if self.euclid2hyp_str == "exp":
            euclid2hyp = InitializationConfig.exp_map_0
        elif self.euclid2hyp_str == "id":
            euclid2hyp = InitializationConfig.identity
        else:
            raise RuntimeError("Unknown conversion from Euclidean space to hyperbolic space")

        model = Word2Vec.load(pretrained_model_filename)
        self.vocab_size = len(model.wv.vocab)

        sorted_norms0 = sorted(norm(model.wv.vectors, axis=1))
        sorted_norms1 = sorted(norm(model.trainables.syn1neg, axis=1))

        # Scale embeddings to be inside the unit ball, and then scale them again using the scaling_factor
        self.init_vectors = euclid2hyp(model.wv.vectors / sorted_norms0[-1] * self.scaling_factor)
        self.init_syn1neg = euclid2hyp(model.trainables.syn1neg / sorted_norms1[-1] * self.scaling_factor)


    @staticmethod
    def exp_map_0(X):
        # X is a matrix that has embeddings as rows
        MAX_TANH_ARG = 15.0
        norm_x = norm(X, axis=1)
        coef = tanh(minimum(maximum(norm_x, -MAX_TANH_ARG), MAX_TANH_ARG)) / norm_x
        Y = X * coef[:, None]
        return PoincareWordEmbeddingsKeyedVectors.moebius_add_mat(X, Y)

    @staticmethod
    def identity(X):
        return X

    def __str__(self):
        return "INIT" + self.euclid2hyp_str + str(self.scaling_factor)


class Word2Vec(BaseWordEmbeddingsModel):
    """Class for training, using and evaluating neural networks described in https://code.google.com/p/word2vec/

    If you're finished training a model (=no more updates, only querying)
    then switch to the :mod:`gensim.models.KeyedVectors` instance in wv

    The model can be stored/loaded via its :meth:`~gensim.models.word2vec.Word2Vec.save()` and
    :meth:`~gensim.models.word2vec.Word2Vec.load()` methods, or stored/loaded in a format
    compatible with the original word2vec implementation via `wv.save_word2vec_format()`
    and `Word2VecKeyedVectors.load_word2vec_format()`.

    """

    def __init__(self, sentences=None, size=100, alpha=0.025, window=5, min_count=5,
                 max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, l2reg_coef=0.0, optimizer=None,
                 sg=0, hs=0, is_nll=False, burnin_epochs=0, negative=5, normalized=False, euclid=0, poincare=0, torus=0,
                 cbow_mean=1, hashfxn=hash, iter=5, null_word=0, trim_rule=None, sorted_vocab=1, sim_func=None,
                 cosh_dist_pow=0, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False, with_bias=False,
                 init_near_border=False, initialization_config=None, ckpt_word_list=None, debug=False, callbacks=()):
        """
        Initialize the model from an iterable of `sentences`. Each sentence is a
        list of words (unicode strings) that will be used for training.

        Parameters
        ----------
        sentences : iterable of iterables
            The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora,
            consider an iterable that streams the sentences directly from disk/network.
            See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus`
            or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such examples.
            If you don't supply `sentences`, the model is left uninitialized -- use if you plan to initialize it
            in some other way.

        sg : int {1, 0}
            Defines the training algorithm. If 1, skip-gram is employed; otherwise, CBOW is used.
        size : int
            Dimensionality of the feature vectors.
        window : int
            The maximum distance between the current and predicted word within a sentence.
        alpha : float
            The initial learning rate.
        min_alpha : float
            Learning rate will linearly drop to `min_alpha` as training progresses.
        l2reg_coef : float
            Regularization coefficient. The weight of the penalty applied by the regularizer. Currently we regularize
            the norm of the input embedding of some frequent words.
        optimizer : str
            The name of the optimizer that we want to use to train the model (e.g. SGD, RSGD, AdaGrad etc)
        seed : int
            Seed for the random number generator. Initial vectors for each word are seeded with a hash of
            the concatenation of word + `str(seed)`. Note that for a fully deterministically-reproducible run,
            you must also limit the model to a single worker thread (`workers=1`), to eliminate ordering jitter
            from OS thread scheduling. (In Python 3, reproducibility between interpreter launches also requires
            use of the `PYTHONHASHSEED` environment variable to control hash randomization).
        min_count : int
            Ignores all words with total frequency lower than this.
        max_vocab_size : int
            Limits the RAM during vocabulary building; if there are more unique
            words than this, then prune the infrequent ones. Every 10 million word types need about 1GB of RAM.
            Set to `None` for no limit.
        sample : float
            The threshold for configuring which higher-frequency words are randomly downsampled,
            useful range is (0, 1e-5).
        workers : int
            Use these many worker threads to train the model (=faster training with multicore machines).
        hs : int {1,0}
            If 1, hierarchical softmax will be used for model training.
            If set to 0, and `negative` is non-zero, negative sampling will be used.
        is_nll : bool
            If true, then use NLL loss for SkipGram, otherwise use Negative Sampling loss.
        burnin_epochs : int
            Number of epochs of the burn-in stage, before training starts.
        negative : int
            If > 0, negative sampling will be used, the int for negative specifies how many "noise words"
            should be drawn (usually between 5-20).
            If set to 0, no negative sampling is used.
        normalized : bool
            If true, then normalize the word vectors to unit norm after each update.
        euclid : int
            If = 0 then dot product between word embeddings is used during training. Otherwise, we use some
            custom similarity function based on the Euclidean distance between the two word embeddings.
        poincare : int
            If = 1 then we embed the words in the Poincare ball model of hyperbolic space and use the Moebius gyrovector
            space to compute parallel transport and distances inside the manifold.
        torus : int
            If = 1 then we embed the words on an n-torus, where n is given by `size`.
        cbow_mean : int {1,0}
            If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used.
        hashfxn : function
            Hash function to use to randomly initialize weights, for increased training reproducibility.
        iter : int
            Number of iterations (epochs) over the corpus.
        trim_rule : function
            Vocabulary trimming rule, specifies whether certain words should remain in the vocabulary,
            be trimmed away, or handled using the default (discard if word count < min_count).
            Can be None (min_count will be used, look to :func:`~gensim.utils.keep_vocab_item`),
            or a callable that accepts parameters (word, count, min_count) and returns either
            :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`.
            Note: The rule, if given, is only used to prune vocabulary during build_vocab() and is not stored as part
            of the model.
        sorted_vocab : int {1,0}
            If 1, sort the vocabulary by descending frequency before assigning word indexes.
        sim_func : str
            Similarity function used in the loss function during training.
        cosh_dist_pow : int
            Power, used if we use COSH_DIST_POW_K during training.
        batch_words : int
            Target size (in words) for batches of examples passed to worker threads (and
            thus cython routines).(Larger batches will be passed if individual
            texts are longer than 10000 words, but the standard cython code truncates to that maximum.)
        compute_loss : bool
            If True, computes and stores loss value which can be retrieved using `model.get_latest_training_loss()`.
        with_bias : bool
            If True, then use a model with biases. Otherwise, only have input/output embeddings for each word as
            parameters.
        init_near_border : bool
            If True, initialize word embeddings close to the Poincare ball border, instead of close to the origin. Only
            works for Poincare embeddings.
        initialization_config : InitializationConfig
            The configuration that will be used to initialize the embeddings from a pretrained model.
        ckpt_word_list : list of strings
            List of words for which we store checkpoints of their embedding during training for vizualisation purposes.
        debug : bool
            If True, then it will run in debug mode.
        callbacks : :obj: `list` of :obj: `~gensim.models.callbacks.CallbackAny2Vec`
            List of callbacks that need to be executed/run at specific stages during training.

        Examples
        --------
        Initialize and train a `Word2Vec` model

        >>> from gensim.models import Word2Vec
        >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]]
        >>>
        >>> model = Word2Vec(sentences, min_count=1)
        >>> say_vector = model['say']  # get vector for word

        """

        self.callbacks = callbacks
        self.load = call_on_class_only

        vector_dtype = DOUBLE if poincare == 1 else REAL

        # Sanity checks.
        if euclid + poincare + torus > 1:
            raise RuntimeError("You must select only one model type. Given euclid {}, poincare {}, torus {}".format(
                euclid, poincare, torus
            ))

        self.optimizer = optimizer
        if not optimizer:
            if poincare == 1:
                self.optimizer = "rsgd"
            else:
                self.optimizer = "full_rsgd"

        self.sim_func = sim_func
        self.cosh_dist_pow = cosh_dist_pow

        self.vocabulary = Word2VecVocab(
            max_vocab_size=max_vocab_size, min_count=min_count, sample=sample,
            sorted_vocab=bool(sorted_vocab), null_word=null_word)
        self.trainables = Word2VecTrainables(
            seed=seed, vector_size=size, vector_dtype=vector_dtype, hashfxn=hashfxn, optimizer=self.optimizer,
            initialization_config=initialization_config)
        self.debug = debug
        self.is_nll = is_nll
        self.l2reg_coef = l2reg_coef

        self.wv = None
        if poincare == 1:
            self.wv = PoincareWordEmbeddingsKeyedVectors(size, vector_dtype, self.trainables, init_near_border)
        else:
            self.wv = Word2VecKeyedVectors(size, vector_dtype)

        if ckpt_word_list:
            self.word_checkpoints = WordEmbeddingCheckpoints(ckpt_word_list, self)

        if poincare == 1:
            self.num_projections = 0

        super(Word2Vec, self).__init__(
            sentences=sentences, workers=workers, vector_size=size, vector_dtype=vector_dtype, epochs=iter,
            callbacks=callbacks, batch_words=batch_words, trim_rule=trim_rule, sg=sg, alpha=alpha, window=window,
            seed=seed, hs=hs, negative=negative, normalized=normalized, euclid=euclid, poincare=poincare, torus=torus,
            burnin_epochs=burnin_epochs, cbow_mean=cbow_mean, min_alpha=min_alpha, compute_loss=compute_loss,
            fast_version=FAST_VERSION, with_bias=with_bias)

    def _log_progress(self, job_queue, progress_queue, cur_epoch, example_count, total_examples,
                      raw_word_count, total_words, trained_word_count, elapsed):
        if total_examples:
            # examples-based progress %
            logger.info(
                "EPOCH %i - PROGRESS: at %.2f%% examples, %.0f words/s, in_qsize %i, out_qsize %i, batch loss %f",
                cur_epoch + 1, 100.0 * example_count / total_examples, trained_word_count / elapsed,
                utils.qsize(job_queue), utils.qsize(progress_queue), float(self.batch_training_loss)
            )
        else:
            # words-based progress %
            logger.info(
                "EPOCH %i - PROGRESS: at %.2f%% words, %.0f words/s, in_qsize %i, out_qsize %i",
                cur_epoch + 1, 100.0 * raw_word_count / total_words, trained_word_count / elapsed,
                utils.qsize(job_queue), utils.qsize(progress_queue)
            )

    def _log_epoch_end(self, cur_epoch, example_count, total_examples, raw_word_count, total_words,
                       trained_word_count, elapsed):
        logger.info(
            "EPOCH - %i : training on %i raw words (%i effective words) took %.1fs, %.0f effective words, batch loss %f, epoch loss %f",
            cur_epoch + 1, raw_word_count, trained_word_count, elapsed, trained_word_count / elapsed,
            float(self.batch_training_loss), float(self.get_latest_training_loss())
        )

        # check that the input corpus hasn't changed during iteration
        if total_examples and total_examples != example_count:
            logger.warning(
                "EPOCH - %i : supplied example count (%i) did not equal expected count (%i)", cur_epoch + 1,
                example_count, total_examples
            )
        if total_words and total_words != raw_word_count:
            logger.warning(
                "EPOCH - %i : supplied raw word count (%i) did not equal expected count (%i)", cur_epoch + 1,
                raw_word_count, total_words
            )

    def _do_train_job(self, sentences, alpha, inits, thread_id):
        """
        Train a single batch of sentences. Return 2-tuple `(effective word count after
        ignoring unknown words and sentence length trimming, total word count)`.
        """
        work, neu1 = inits
        tally = 0
        if self.sg:
            if self.poincare == 1:
                curr_tally, batch_loss = train_batch_sg_dbl(self, sentences, alpha, work, self.compute_loss)
                tally += curr_tally
                if thread_id == 0:
                    self.batch_training_loss = batch_loss
            else:
                curr_tally, batch_loss = train_batch_sg(self, sentences, alpha, work, self.compute_loss)
                tally += curr_tally
                if thread_id == 0:
                    self.batch_training_loss = batch_loss
        else:
            tally += train_batch_cbow(self, sentences, alpha, work, neu1, self.compute_loss)
        return tally, self._raw_word_count(sentences)

    def _clear_post_train(self):
        """Resets certain properties of the model, post training."""
        self.wv.vectors_norm = None

    def _set_train_params(self, **kwargs):
        if 'compute_loss' in kwargs:
            self.compute_loss = kwargs['compute_loss']
        self.running_training_loss = 0

    def train(self, sentences, total_examples=None, total_words=None,
              epochs=None, start_alpha=None, end_alpha=None, word_count=0,
              queue_factor=2, report_delay=1.0, compute_loss=False, callbacks=()):
        """Update the model's neural weights from a sequence of sentences (can be a once-only generator stream).
        For Word2Vec, each sentence must be a list of unicode strings. (Subclasses may accept other examples.)

        To support linear learning-rate decay from (initial) alpha to min_alpha, and accurate
        progress-percentage logging, either total_examples (count of sentences) or total_words (count of
        raw words in sentences) **MUST** be provided (if the corpus is the same as was provided to
        :meth:`~gensim.models.word2vec.Word2Vec.build_vocab()`, the count of examples in that corpus
        will be available in the model's :attr:`corpus_count` property).

        To avoid common mistakes around the model's ability to do multiple training passes itself, an
        explicit `epochs` argument **MUST** be provided. In the common and recommended case,
        where :meth:`~gensim.models.word2vec.Word2Vec.train()` is only called once,
        the model's cached `iter` value should be supplied as `epochs` value.

        Parameters
        ----------
        sentences : iterable of iterables
            The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora,
            consider an iterable that streams the sentences directly from disk/network.
            See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus`
            or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such examples.
        total_examples : int
            Count of sentences.
        total_words : int
            Count of raw words in sentences.
        epochs : int
            Number of iterations (epochs) over the corpus.
        start_alpha : float
            Initial learning rate.
        end_alpha : float
            Final learning rate. Drops linearly from `start_alpha`.
        word_count : int
            Count of words already trained. Set this to 0 for the usual
            case of training on all words in sentences.
        queue_factor : int
            Multiplier for size of queue (number of workers * queue_factor).
        report_delay : float
            Seconds to wait before reporting progress.
        compute_loss: bool
            If True, computes and stores loss value which can be retrieved using `model.get_latest_training_loss()`.
        callbacks : :obj: `list` of :obj: `~gensim.models.callbacks.CallbackAny2Vec`
            List of callbacks that need to be executed/run at specific stages during training.

        Examples
        --------
        >>> from gensim.models import Word2Vec
        >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]]
        >>>
        >>> model = Word2Vec(min_count=1)
        >>> model.build_vocab(sentences)
        >>> model.train(sentences, total_examples=model.corpus_count, epochs=model.iter)

        """

        return super(Word2Vec, self).train(
            sentences, total_examples=total_examples, total_words=total_words,
            epochs=epochs, start_alpha=start_alpha, end_alpha=end_alpha, word_count=word_count,
            queue_factor=queue_factor, report_delay=report_delay, compute_loss=compute_loss, callbacks=callbacks)

    def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor=2, report_delay=1):
        """Score the log probability for a sequence of sentences (can be a once-only generator stream).
        Each sentence must be a list of unicode strings.
        This does not change the fitted model in any way (see Word2Vec.train() for that).

        We have currently only implemented score for the hierarchical softmax scheme,
        so you need to have run word2vec with hs=1 and negative=0 for this to work.

        Note that you should specify total_sentences; we'll run into problems if you ask to
        score more than this number of sentences but it is inefficient to set the value too high.

        See the article by [#taddy]_ and the gensim demo at [#deepir]_ for examples of
        how to use such scores in document classification.

        .. [#taddy] Taddy, Matt.  Document Classification by Inversion of Distributed Language Representations,
                    in Proceedings of the 2015 Conference of the Association of Computational Linguistics.
        .. [#deepir] https://github.com/piskvorky/gensim/blob/develop/docs/notebooks/deepir.ipynb

        Parameters
        ----------
        sentences : iterable of iterables
            The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora,
            consider an iterable that streams the sentences directly from disk/network.
            See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus`
            or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such examples.
        total_sentences : int
            Count of sentences.
        chunksize : int
            Chunksize of jobs
        queue_factor : int
            Multiplier for size of queue (number of workers * queue_factor).
        report_delay : float
            Seconds to wait before reporting progress.

        """
        if FAST_VERSION < 0:
            warnings.warn(
                "C extension compilation failed, scoring will be slow. "
                "Install a C compiler and reinstall gensim for fastness."
            )

        logger.info(
            "scoring sentences with %i workers on %i vocabulary and %i features, "
            "using sg=%s hs=%s sample=%s and negative=%s",
            self.workers, len(self.wv.vocab), self.trainables.layer1_size, self.sg, self.hs,
            self.vocabulary.sample, self.negative
        )

        if not self.wv.vocab:
            raise RuntimeError("you must first build vocabulary before scoring new data")

        if not self.hs:
            raise RuntimeError(
                "We have currently only implemented score for the hierarchical softmax scheme, "
                "so you need to have run word2vec with hs=1 and negative=0 for this to work."
            )

        def worker_loop():
            """Compute log probability for each sentence, lifting lists of sentences from the jobs queue."""
            work = zeros(1, dtype=self.vector_dtype)  # for sg hs, we actually only need one memory loc (running sum)
            neu1 = matutils.zeros_aligned(self.trainables.layer1_size, dtype=self.vector_dtype)
            while True:
                job = job_queue.get()
                if job is None:  # signal to finish
                    break
                ns = 0
                for sentence_id, sentence in job:
                    if sentence_id >= total_sentences:
                        break
                    if self.sg:
                        score = score_sentence_sg(self, sentence, work)
                    else:
                        score = score_sentence_cbow(self, sentence, work, neu1)
                    sentence_scores[sentence_id] = score
                    ns += 1
                progress_queue.put(ns)  # report progress

        start, next_report = default_timer(), 1.0
        # buffer ahead only a limited number of jobs.. this is the reason we can't simply use ThreadPool :(
        job_queue = Queue(maxsize=queue_factor * self.workers)
        progress_queue = Queue(maxsize=(queue_factor + 1) * self.workers)

        workers = [threading.Thread(target=worker_loop) for _ in xrange(self.workers)]
        for thread in workers:
            thread.daemon = True  # make interrupting the process with ctrl+c easier
            thread.start()

        sentence_count = 0
        sentence_scores = matutils.zeros_aligned(total_sentences, dtype=self.vector_dtype)

        push_done = False
        done_jobs = 0
        jobs_source = enumerate(utils.grouper(enumerate(sentences), chunksize))

        # fill jobs queue with (id, sentence) job items
        while True:
            try:
                job_no, items = next(jobs_source)
                if (job_no - 1) * chunksize > total_sentences:
                    logger.warning(
                        "terminating after %i sentences (set higher total_sentences if you want more).",
                        total_sentences
                    )
                    job_no -= 1
                    raise StopIteration()
                logger.debug("putting job #%i in the queue", job_no)
                job_queue.put(items)
            except StopIteration:
                logger.info("reached end of input; waiting to finish %i outstanding jobs", job_no - done_jobs + 1)
                for _ in xrange(self.workers):
                    job_queue.put(None)  # give the workers heads up that they can finish -- no more work!
                push_done = True
            try:
                while done_jobs < (job_no + 1) or not push_done:
                    ns = progress_queue.get(push_done)  # only block after all jobs pushed
                    sentence_count += ns
                    done_jobs += 1
                    elapsed = default_timer() - start
                    if elapsed >= next_report:
                        logger.info(
                            "PROGRESS: at %.2f%% sentences, %.0f sentences/s",
                            100.0 * sentence_count, sentence_count / elapsed
                        )
                        next_report = elapsed + report_delay  # don't flood log, wait report_delay seconds
                else:
                    # loop ended by job count; really done
                    break
            except Empty:
                pass  # already out of loop; continue to next push

        elapsed = default_timer() - start
        self.clear_sims()
        logger.info(
            "scoring %i sentences took %.1fs, %.0f sentences/s",
            sentence_count, elapsed, sentence_count / elapsed
        )
        return sentence_scores[:sentence_count]

    def clear_sims(self):
        """Removes all L2-normalized vectors for words from the model.
        You will have to recompute them using init_sims method.
        """

        self.wv.vectors_norm = None

    def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='utf8', unicode_errors='strict'):
        """Merge the input-hidden weight matrix from the original C word2vec-tool format
        given, where it intersects with the current vocabulary. (No words are added to the
        existing vocabulary, but intersecting words adopt the file's weights, and
        non-intersecting words are left alone.)

        Parameters
        ----------
        fname : str
            The file path used to save the vectors in

        binary : bool
            If True, the data wil be saved in binary word2vec format, else it will be saved in plain text.

        lockf : float
            Lock-factor value to be set for any imported word-vectors; the
            default value of 0.0 prevents further updating of the vector during subsequent
            training. Use 1.0 to allow further training updates of merged vectors.

        """
        overlap_count = 0
        logger.info("loading projection weights from %s", fname)
        with utils.smart_open(fname) as fin:
            header = utils.to_unicode(fin.readline(), encoding=encoding)
            vocab_size, vector_size = (int(x) for x in header.split())  # throws for invalid file format
            if not vector_size == self.wv.vector_size:
                raise ValueError("incompatible vector size %d in file %s" % (vector_size, fname))
                # TOCONSIDER: maybe mismatched vectors still useful enough to merge (truncating/padding)?
            if binary:
                binary_len = dtype(self.vector_dtype).itemsize * vector_size
                for _ in xrange(vocab_size):
                    # mixed text and binary: read text first, then binary
                    word = []
                    while True:
                        ch = fin.read(1)
                        if ch == b' ':
                            break
                        if ch != b'\n':  # ignore newlines in front of words (some binary files have)
                            word.append(ch)
                    word = utils.to_unicode(b''.join(word), encoding=encoding, errors=unicode_errors)
                    weights = fromstring(fin.read(binary_len), dtype=self.vector_dtype)
                    if word in self.wv.vocab:
                        overlap_count += 1
                        self.wv.vectors[self.wv.vocab[word].index] = weights
                        self.trainables.vectors_lockf[self.wv.vocab[word].index] = lockf  # lock-factor: 0.0=no changes
            else:
                for line_no, line in enumerate(fin):
                    parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ")
                    if len(parts) != vector_size + 1:
                        raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no)
                    word, weights = parts[0], [self.vector_dtype(x) for x in parts[1:]]
                    if word in self.wv.vocab:
                        overlap_count += 1
                        self.wv.vectors[self.wv.vocab[word].index] = weights
                        self.trainables.vectors_lockf[self.wv.vocab[word].index] = lockf  # lock-factor: 0.0=no changes
        logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.vectors.shape, fname)

    @deprecated("Method will be removed in 4.0.0, use self.wv.__getitem__() instead")
    def __getitem__(self, words):
        """
        Deprecated. Use self.wv.__getitem__() instead.
        Refer to the documentation for `gensim.models.keyedvectors.Word2VecKeyedVectors.__getitem__`
        """
        return self.wv.__getitem__(words)

    @deprecated("Method will be removed in 4.0.0, use self.wv.__contains__() instead")
    def __contains__(self, word):
        """
        Deprecated. Use self.wv.__contains__() instead.
        Refer to the documentation for `gensim.models.keyedvectors.Word2VecKeyedVectors.__contains__`
        """
        return self.wv.__contains__(word)

    def predict_output_word(self, context_words_list, topn=10):
        """Report the probability distribution of the center word given the context words
        as input to the trained model.

        Parameters
        ----------
        context_words_list : :obj: `list` of :obj: `str`
            List of context words
        topn: int
            Return `topn` words and their probabilities

        Returns
        -------
        :obj: `list` of :obj: `tuple`
            `topn` length list of tuples of (word, probability)

        """
        if not self.negative:
            raise RuntimeError(
                "We have currently only implemented predict_output_word for the negative sampling scheme, "
                "so you need to have run word2vec with negative > 0 for this to work."
            )

        if not hasattr(self.wv, 'vectors') or not hasattr(self.trainables, 'syn1neg'):
            raise RuntimeError("Parameters required for predicting the output words not found.")

        word_vocabs = [self.wv.vocab[w] for w in context_words_list if w in self.wv.vocab]
        if not word_vocabs:
            warnings.warn("All the input context words are out-of-vocabulary for the current model.")
            return None

        word2_indices = [word.index for word in word_vocabs]

        l1 = np_sum(self.wv.vectors[word2_indices], axis=0)
        if word2_indices and self.cbow_mean:
            l1 /= len(word2_indices)

        # propagate hidden -> output and take softmax to get probabilities
        prob_values = exp(dot(l1, self.trainables.syn1neg.T))
        prob_values /= sum(prob_values)
        top_indices = matutils.argsort(prob_values, topn=topn, reverse=True)
        # returning the most probable output words with their probabilities
        return [(self.wv.index2word[index1], prob_values[index1]) for index1 in top_indices]

    def init_sims(self, replace=False):
        """
        init_sims() resides in KeyedVectors because it deals with syn0/vectors mainly, but because syn1 is not an
        attribute of KeyedVectors, it has to be deleted in this class, and the normalizing of syn0/vectors happens
        inside of KeyedVectors
        """
        if replace and hasattr(self.trainables, 'syn1'):
            del self.trainables.syn1
        return self.wv.init_sims(replace)

    def reset_from(self, other_model):
        """Borrow shareable pre-built structures (like vocab) from the other_model. Useful
        if testing multiple models in parallel on the same corpus.
        """
        self.wv.vocab = other_model.wv.vocab
        self.wv.index2word = other_model.wv.index2word
        self.vocabulary.cum_table = other_model.vocabulary.cum_table
        self.corpus_count = other_model.corpus_count
        self.trainables.reset_weights(self.hs, self.negative, self.wv, other_model.with_bias)

    @staticmethod
    def log_accuracy(section):
        return Word2VecKeyedVectors.log_accuracy(section)

    @deprecated("Method will be removed in 4.0.0, use self.wv.accuracy() instead")
    def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_insensitive=True):
        most_similar = most_similar or Word2VecKeyedVectors.most_similar
        return self.wv.accuracy(questions, restrict_vocab, most_similar, case_insensitive)

    def __str__(self):
        return "%s(vocab=%s, size=%s, alpha=%s)" % (
            self.__class__.__name__, len(self.wv.index2word), self.wv.vector_size, self.alpha
        )

    def delete_temporary_training_data(self, replace_word_vectors_with_normalized=False):
        """Discard parameters that are used in training and score. Use if you're sure you're done training a model.
        If `replace_word_vectors_with_normalized` is set, forget the original vectors and only keep the normalized
        ones = saves lots of memory!
        """
        if replace_word_vectors_with_normalized:
            self.init_sims(replace=True)
        self._minimize_model()

    def save(self, *args, **kwargs):
        """Save the model. This saved model can be loaded again using :func:`~gensim.models.word2vec.Word2Vec.load`,
        which supports online training and getting vectors for vocabulary words.

        Parameters
        ----------
        fname : str
            Path to the file.

        """
        # don't bother storing the cached normalized vectors, recalculable table
        kwargs['ignore'] = kwargs.get('ignore', ['vectors_norm', 'cum_table'])
        super(Word2Vec, self).save(*args, **kwargs)

    def get_batch_training_loss(self):
        return self.batch_training_loss

    def get_latest_training_loss(self):
        return self.running_training_loss

    @deprecated(
        "Method will be removed in 4.0.0, keep just_word_vectors = model.wv to retain just the KeyedVectors instance"
    )
    def _minimize_model(self, save_syn1=False, save_syn1neg=False, save_vectors_lockf=False):
        if save_syn1 and save_syn1neg and save_vectors_lockf:
            return
        if hasattr(self.trainables, 'syn1') and not save_syn1:
            del self.trainables.syn1
        if hasattr(self.trainables, 'syn1neg') and not save_syn1neg:
            del self.trainables.syn1neg
        if hasattr(self.trainables, 'vectors_lockf') and not save_vectors_lockf:
            del self.trainables.vectors_lockf
        self.model_trimmed_post_training = True

    @classmethod
    def load_word2vec_format(
            cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict',
            limit=None, datatype=REAL):
        """Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead."""
        raise DeprecationWarning("Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.")

    def save_word2vec_format(self, fname, fvocab=None, binary=False):
        """Deprecated. Use model.wv.save_word2vec_format instead."""
        raise DeprecationWarning("Deprecated. Use model.wv.save_word2vec_format instead.")

    @classmethod
    def load(cls, *args, **kwargs):
        """Loads a previously saved `Word2Vec` model. Also see `save()`.

        Parameters
        ----------
        fname : str
            Path to the saved file.

        Returns
        -------
        :obj: `~gensim.models.word2vec.Word2Vec`
            Returns the loaded model as an instance of :class: `~gensim.models.word2vec.Word2Vec`.
        """
        try:
            return super(Word2Vec, cls).load(*args, **kwargs)
        except AttributeError:
            logger.info('Model saved using code from earlier Gensim Version. Re-loading old model in a compatible way.')
            from gensim.models.deprecated.word2vec import load_old_word2vec
            return load_old_word2vec(*args, **kwargs)


class BrownCorpus(object):
    """Iterate over sentences from the Brown corpus (part of NLTK data)."""

    def __init__(self, dirname):
        self.dirname = dirname

    def __iter__(self):
        for fname in os.listdir(self.dirname):
            fname = os.path.join(self.dirname, fname)
            if not os.path.isfile(fname):
                continue
            for line in utils.smart_open(fname):
                line = utils.to_unicode(line)
                # each file line is a single sentence in the Brown corpus
                # each token is WORD/POS_TAG
                token_tags = [t.split('/') for t in line.split() if len(t.split('/')) == 2]
                # ignore words with non-alphabetic tags like ",", "!" etc (punctuation, weird stuff)
                words = ["%s/%s" % (token.lower(), tag[:2]) for token, tag in token_tags if tag[:2].isalpha()]
                if not words:  # don't bother sending out empty sentences
                    continue
                yield words


class Text8Corpus(object):
    """Iterate over sentences from the "text8" corpus, unzipped from http://mattmahoney.net/dc/text8.zip ."""

    def __init__(self, fname, max_sentence_length=MAX_WORDS_IN_BATCH):
        self.fname = fname
        self.max_sentence_length = max_sentence_length

    def __iter__(self):
        # the entire corpus is one gigantic line -- there are no sentence marks at all
        # so just split the sequence of tokens arbitrarily: 1 sentence = 1000 tokens
        sentence, rest = [], b''
        with utils.smart_open(self.fname) as fin:
            while True:
                text = rest + fin.read(8192)  # avoid loading the entire file (=1 line) into RAM
                if text == rest:  # EOF
                    words = utils.to_unicode(text).split()
                    sentence.extend(words)  # return the last chunk of words, too (may be shorter/longer)
                    if sentence:
                        yield sentence
                    break
                last_token = text.rfind(b' ')  # last token may have been split in two... keep for next iteration
                words, rest = (utils.to_unicode(text[:last_token]).split(),
                               text[last_token:].strip()) if last_token >= 0 else ([], text)
                sentence.extend(words)
                while len(sentence) >= self.max_sentence_length:
                    yield sentence[:self.max_sentence_length]
                    sentence = sentence[self.max_sentence_length:]


class LineSentence(object):
    """Simple format: one sentence = one line; words already preprocessed and separated by whitespace.
    """

    def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None):
        """
        `source` can be either a string or a file object. Clip the file to the first
        `limit` lines (or not clipped if limit is None, the default).

        Example::

            sentences = LineSentence('myfile.txt')

        Or for compressed files::

            sentences = LineSentence('compressed_text.txt.bz2')
            sentences = LineSentence('compressed_text.txt.gz')

        """
        self.source = source
        self.max_sentence_length = max_sentence_length
        self.limit = limit

    def __iter__(self):
        """Iterate through the lines in the source."""
        try:
            # Assume it is a file-like object and try treating it as such
            # Things that don't have seek will trigger an exception
            self.source.seek(0)
            for line in itertools.islice(self.source, self.limit):
                line = utils.to_unicode(line).split()
                i = 0
                while i < len(line):
                    yield line[i: i + self.max_sentence_length]
                    i += self.max_sentence_length
        except AttributeError:
            # If it didn't work like a file, use it as a string filename
            with utils.smart_open(self.source) as fin:
                for line in itertools.islice(fin, self.limit):
                    line = utils.to_unicode(line).split()
                    i = 0
                    while i < len(line):
                        yield line[i: i + self.max_sentence_length]
                        i += self.max_sentence_length


class PathLineSentences(object):
    """Works like word2vec.LineSentence, but will process all files in a directory in alphabetical order by filename.
    The directory can only contain files that can be read by LineSentence: .bz2, .gz, and text files.
    Any file not ending with .bz2 or .gz is assumed to be a text file. Does not work with subdirectories.

    The format of files (either text, or compressed text files) in the path is one sentence = one line,
    with words already preprocessed and separated by whitespace.

    """

    def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None):
        """
        `source` should be a path to a directory (as a string) where all files can be opened by the
        LineSentence class. Each file will be read up to `limit` lines (or not clipped if limit is None, the default).

        Example::

            sentences = PathLineSentences(os.getcwd() + '\\corpus\\')

        The files in the directory should be either text files, .bz2 files, or .gz files.

        """
        self.source = source
        self.max_sentence_length = max_sentence_length
        self.limit = limit

        if os.path.isfile(self.source):
            logger.debug('single file given as source, rather than a directory of files')
            logger.debug('consider using models.word2vec.LineSentence for a single file')
            self.input_files = [self.source]  # force code compatibility with list of files
        elif os.path.isdir(self.source):
            self.source = os.path.join(self.source, '')  # ensures os-specific slash at end of path
            logger.info('reading directory %s', self.source)
            self.input_files = os.listdir(self.source)
            self.input_files = [self.source + filename for filename in self.input_files]  # make full paths
            self.input_files.sort()  # makes sure it happens in filename order
        else:  # not a file or a directory, then we can't do anything with it
            raise ValueError('input is neither a file nor a path')
        logger.info('files read into PathLineSentences:%s', '\n'.join(self.input_files))

    def __iter__(self):
        """iterate through the files"""
        for file_name in self.input_files:
            logger.info('reading file %s', file_name)
            with utils.smart_open(file_name) as fin:
                for line in itertools.islice(fin, self.limit):
                    line = utils.to_unicode(line).split()
                    i = 0
                    while i < len(line):
                        yield line[i:i + self.max_sentence_length]
                        i += self.max_sentence_length


class Word2VecVocab(utils.SaveLoad):
    def __init__(self, max_vocab_size=None, min_count=5, sample=1e-3, sorted_vocab=True, null_word=0):
        self.max_vocab_size = max_vocab_size
        self.min_count = min_count
        self.sample = sample
        self.sorted_vocab = sorted_vocab
        self.null_word = null_word
        self.cum_table = None  # for negative sampling
        self.raw_vocab = None

    def scan_vocab(self, sentences, progress_per=10000, trim_rule=None):
        """Do an initial scan of all words appearing in sentences."""
        logger.info("collecting all words and their counts")
        sentence_no = -1
        total_words = 0
        min_reduce = 1
        vocab = defaultdict(int)
        checked_string_types = 0
        for sentence_no, sentence in enumerate(sentences):
            if not checked_string_types:
                if isinstance(sentence, string_types):
                    logger.warning(
                        "Each 'sentences' item should be a list of words (usually unicode strings). "
                        "First item here is instead plain %s.",
                        type(sentence)
                    )
                checked_string_types += 1
            if sentence_no % progress_per == 0:
                logger.info(
                    "PROGRESS: at sentence #%i, processed %i words, keeping %i word types",
                    sentence_no, total_words, len(vocab)
                )
            for word in sentence:
                vocab[word] += 1
            total_words += len(sentence)

            if self.max_vocab_size and len(vocab) > self.max_vocab_size:
                utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule)
                min_reduce += 1

        logger.info(
            "collected %i word types from a corpus of %i raw words and %i sentences",
            len(vocab), total_words, sentence_no + 1
        )
        corpus_count = sentence_no + 1
        self.raw_vocab = vocab
        return total_words, corpus_count

    def sort_vocab(self, wv):
        """Sort the vocabulary so the most frequent words have the lowest indexes."""
        if len(wv.vectors):
            raise RuntimeError("cannot sort vocabulary after model weights already initialized.")
        wv.index2word.sort(key=lambda word: wv.vocab[word].count, reverse=True)
        for i, word in enumerate(wv.index2word):
            wv.vocab[word].index = i

    def prepare_vocab(self, hs, negative, wv, update=False, keep_raw_vocab=False, trim_rule=None,
                      min_count=None, sample=None, dry_run=False):
        """Apply vocabulary settings for `min_count` (discarding less-frequent words)
        and `sample` (controlling the downsampling of more-frequent words).

        Calling with `dry_run=True` will only simulate the provided settings and
        report the size of the retained vocabulary, effective corpus length, and
        estimated memory requirements. Results are both printed via logging and
        returned as a dict.

        Delete the raw vocabulary after the scaling is done to free up RAM,
        unless `keep_raw_vocab` is set.

        """
        min_count = min_count or self.min_count
        sample = sample or self.sample
        drop_total = drop_unique = 0

        if not update:
            logger.info("Loading a fresh vocabulary")
            retain_total, retain_words = 0, []
            # Discard words less-frequent than min_count
            if not dry_run:
                wv.index2word = []
                # make stored settings match these applied settings
                self.min_count = min_count
                self.sample = sample
                wv.vocab = {}

            for word, v in iteritems(self.raw_vocab):
                if keep_vocab_item(word, v, min_count, trim_rule=trim_rule):
                    retain_words.append(word)
                    retain_total += v
                    if not dry_run:
                        wv.vocab[word] = Vocab(count=v, index=len(wv.index2word))
                        wv.index2word.append(word)
                else:
                    drop_unique += 1
                    drop_total += v
            original_unique_total = len(retain_words) + drop_unique
            retain_unique_pct = len(retain_words) * 100 / max(original_unique_total, 1)
            logger.info(
                "min_count=%d retains %i unique words (%i%% of original %i, drops %i)",
                min_count, len(retain_words), retain_unique_pct, original_unique_total, drop_unique
            )
            original_total = retain_total + drop_total
            retain_pct = retain_total * 100 / max(original_total, 1)
            logger.info(
                "min_count=%d leaves %i word corpus (%i%% of original %i, drops %i)",
                min_count, retain_total, retain_pct, original_total, drop_total
            )
        else:
            logger.info("Updating model with new vocabulary")
            new_total = pre_exist_total = 0
            new_words = pre_exist_words = []
            for word, v in iteritems(self.raw_vocab):
                if keep_vocab_item(word, v, min_count, trim_rule=trim_rule):
                    if word in wv.vocab:
                        pre_exist_words.append(word)
                        pre_exist_total += v
                        if not dry_run:
                            wv.vocab[word].count += v
                    else:
                        new_words.append(word)
                        new_total += v
                        if not dry_run:
                            wv.vocab[word] = Vocab(count=v, index=len(wv.index2word))
                            wv.index2word.append(word)
                else:
                    drop_unique += 1
                    drop_total += v
            original_unique_total = len(pre_exist_words) + len(new_words) + drop_unique
            pre_exist_unique_pct = len(pre_exist_words) * 100 / max(original_unique_total, 1)
            new_unique_pct = len(new_words) * 100 / max(original_unique_total, 1)
            logger.info(
                "New added %i unique words (%i%% of original %i) "
                "and increased the count of %i pre-existing words (%i%% of original %i)",
                len(new_words), new_unique_pct, original_unique_total, len(pre_exist_words),
                pre_exist_unique_pct, original_unique_total
            )
            retain_words = new_words + pre_exist_words
            retain_total = new_total + pre_exist_total

        # Precalculate each vocabulary item's threshold for sampling
        if not sample:
            # no words downsampled
            threshold_count = retain_total
        elif sample < 1.0:
            # traditional meaning: set parameter as proportion of total
            threshold_count = sample * retain_total
        else:
            # new shorthand: sample >= 1 means downsample all words with higher count than sample
            threshold_count = int(sample * (3 + sqrt(5)) / 2)

        downsample_total, downsample_unique = 0, 0
        for w in retain_words:
            v = self.raw_vocab[w]
            word_probability = (sqrt(v / threshold_count) + 1) * (threshold_count / v)
            if word_probability < 1.0:
                downsample_unique += 1
                downsample_total += word_probability * v
            else:
                word_probability = 1.0
                downsample_total += v
            if not dry_run:
                wv.vocab[w].sample_int = int(round(word_probability * 2**32))

        if not dry_run and not keep_raw_vocab:
            logger.info("deleting the raw counts dictionary of %i items", len(self.raw_vocab))
            self.raw_vocab = defaultdict(int)

        logger.info("sample=%g downsamples %i most-common words", sample, downsample_unique)
        logger.info(
            "downsampling leaves estimated %i word corpus (%.1f%% of prior %i)",
            downsample_total, downsample_total * 100.0 / max(retain_total, 1), retain_total
        )

        # return from each step: words-affected, resulting-corpus-size, extra memory estimates
        report_values = {
            'drop_unique': drop_unique, 'retain_total': retain_total, 'downsample_unique': downsample_unique,
            'downsample_total': int(downsample_total), 'num_retained_words': len(retain_words)
        }

        if self.null_word:
            # create null pseudo-word for padding when using concatenative L1 (run-of-words)
            # this word is only ever input – never predicted – so count, huffman-point, etc doesn't matter
            self.add_null_word(wv)

        if self.sorted_vocab and not update:
            self.sort_vocab(wv)

        # Create index2freq. This needs to be done after we sort the vocabulary, so after we settle the indexes.
        for word in wv.index2word:
            wv.index2freq.append(wv.vocab[word].count)
        wv.index2freq = array(wv.index2freq, dtype=uint32)

        if hs:
            # add info about each word's Huffman encoding
            self.create_binary_tree(wv)
        if negative:
            # build the table for drawing random words (for negative sampling)
            self.make_cum_table(wv)

        return report_values

    def add_null_word(self, wv):
        word, v = '\0', Vocab(count=1, sample_int=0)
        v.index = len(wv.vocab)
        wv.index2word.append(word)
        wv.vocab[word] = v

    def create_binary_tree(self, wv):
        """Create a binary Huffman tree using stored vocabulary word counts. Frequent words
        will have shorter binary codes. Called internally from `build_vocab()`.

        """
        logger.info("constructing a huffman tree from %i words", len(wv.vocab))

        # build the huffman tree
        heap = list(itervalues(wv.vocab))
        heapq.heapify(heap)
        for i in xrange(len(wv.vocab) - 1):
            min1, min2 = heapq.heappop(heap), heapq.heappop(heap)
            heapq.heappush(
                heap, Vocab(count=min1.count + min2.count, index=i + len(wv.vocab), left=min1, right=min2)
            )

        # recurse over the tree, assigning a binary code to each vocabulary word
        if heap:
            max_depth, stack = 0, [(heap[0], [], [])]
            while stack:
                node, codes, points = stack.pop()
                if node.index < len(wv.vocab):
                    # leaf node => store its path from the root
                    node.code, node.point = codes, points
                    max_depth = max(len(codes), max_depth)
                else:
                    # inner node => continue recursion
                    points = array(list(points) + [node.index - len(wv.vocab)], dtype=uint32)
                    stack.append((node.left, array(list(codes) + [0], dtype=uint8), points))
                    stack.append((node.right, array(list(codes) + [1], dtype=uint8), points))

            logger.info("built huffman tree with maximum node depth %i", max_depth)

    def make_cum_table(self, wv, power=0.75, domain=2**31 - 1):
        """Create a cumulative-distribution table using stored vocabulary word counts for
        drawing random words in the negative-sampling training routines.

        To draw a word index, choose a random integer up to the maximum value in the
        table (cum_table[-1]), then finding that integer's sorted insertion point
        (as if by bisect_left or ndarray.searchsorted()). That insertion point is the
        drawn index, coming up in proportion equal to the increment at that slot.

        Called internally from 'build_vocab()'.
        """
        vocab_size = len(wv.index2word)
        self.cum_table = zeros(vocab_size, dtype=uint32)
        # compute sum of all power (Z in paper)
        train_words_pow = 0.0
        for word_index in xrange(vocab_size):
            train_words_pow += wv.vocab[wv.index2word[word_index]].count**power
        cumulative = 0.0
        for word_index in xrange(vocab_size):
            cumulative += wv.vocab[wv.index2word[word_index]].count**power
            self.cum_table[word_index] = round(cumulative / train_words_pow * domain)
        if len(self.cum_table) > 0:
            assert self.cum_table[-1] == domain


class MyVocab(Word2VecVocab):
    def scan_vocab(self, sentences, window_size, progress_per=10000, trim_rule=None):
        """Do an initial scan of all words appearing in sentences."""
        logger.info("collecting all words and their counts")
        sentence_no = -1
        total_words = 0
        min_reduce = 1
        vocab = defaultdict(int)
        checked_string_types = 0
        self.cooccurrence_matrix = Counter()
        for sentence_no, sentence in enumerate(sentences):
            if not checked_string_types:
                if isinstance(sentence, string_types):
                    logger.warning(
                        "Each 'sentences' item should be a list of words (usually unicode strings). "
                        "First item here is instead plain %s.",
                        type(sentence)
                    )
                checked_string_types += 1
            if sentence_no % progress_per == 0:
                logger.info(
                    "PROGRESS: at sentence #%i, processed %i words, keeping %i word types",
                    sentence_no, total_words, len(vocab)
                )
            sentence_len = len(sentence)
            for i, word in enumerate(sentence):
                vocab[word] += 1
                start = max(0, i-window_size)
                end = min(sentence_len, i+window_size+1)
                # Look in context.
                for j in range(start, end):
                    if j == i:
                        # Skip current word.
                        continue
                    self.cooccurrence_matrix.update(itertools.combinations([word, sentence[j]], 2))

            total_words += len(sentence)

            if self.max_vocab_size and len(vocab) > self.max_vocab_size:
                utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule)
                min_reduce += 1

        logger.info(
            "collected %i word types from a corpus of %i raw words and %i sentences",
            len(vocab), total_words, sentence_no + 1
        )
        corpus_count = sentence_no + 1
        self.raw_vocab = vocab
        return total_words, corpus_count


class Word2VecTrainables(utils.SaveLoad):
    def __init__(self, vector_size=100, vector_dtype=REAL, seed=1, hashfxn=hash, optimizer=None,
                 initialization_config=None):
        self.hashfxn = hashfxn
        self.layer1_size = vector_size
        self.vector_dtype = vector_dtype
        self.seed = seed
        self.optimizer = optimizer
        self.initialization_config = initialization_config

    def prepare_weights(self, hs, negative, wv, update=False, vocabulary=None, with_bias=False, torus=False):
        """Build tables and model weights based on final vocabulary settings."""
        # If a model_filename is provided, then load the model and use the embeddings in that model as initialization.

        # set initial input/projection and hidden weights
        if not update:
            self.reset_weights(hs, negative, wv, with_bias)
        else:
            self.update_weights(hs, negative, wv, with_bias)

    def seeded_vector(self, seed_string, vector_size, max_abs_value=None):
        """Create one 'random' vector (but deterministic by seed_string)"""
        # Note: built-in hash() may vary by Python version or even (in Py3.x) per launch
        once = random.RandomState(self.hashfxn(seed_string) & 0xffffffff)
        if max_abs_value == None:
            return (once.rand(vector_size) - 0.5) / vector_size
        else:
            return once.rand(vector_size) * 2 * max_abs_value - max_abs_value

    def seeded_bounded_vector(self, seed_string, vector_size, min_value, max_value):
        once = random.RandomState(self.hashfxn(seed_string) & 0xffffffff)
        vec = once.uniform(low=min_value, high=max_value, size=vector_size)
        return vec * sign(once.rand(vector_size) - 0.5)

    def reset_weights(self, hs, negative, wv, with_bias=False):
        """Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary."""
        logger.info("resetting layer weights")
        wv.vectors = empty((len(wv.vocab), wv.vector_size), dtype=self.vector_dtype)
        if not self.initialization_config:
            # randomize weights vector by vector, rather than materializing a huge random matrix in RAM at once
            for i in xrange(len(wv.vocab)):
                # construct deterministic seed from word AND seed argument
                if isinstance(wv, PoincareWordEmbeddingsKeyedVectors) and wv.init_near_border:
                    wv.vectors[i] = self.seeded_vector(
                        wv.index2word[i] + str(self.seed),
                        wv.vector_size,
                        max_abs_value=0.001)
                    # vector_norm = random.uniform(low=0.87, high=0.89)
                    vector_norm = random.uniform(low=0.8, high=0.85)
                    wv.vectors[i] = wv.vectors[i] / norm(wv.vectors[i]) * vector_norm
                else:
                    wv.vectors[i] = self.seeded_vector(
                        wv.index2word[i] + str(self.seed),
                        wv.vector_size,
                        max_abs_value=0.001)
            norms = norm(wv.vectors, axis=1)
            print("[Target vector init] Average/Min/Max norm is {} / {} / {}".format(
                average(norms), min(norms), max(norms)
            ))

            if hs:
                self.syn1 = zeros((len(wv.vocab), self.layer1_size), dtype=self.vector_dtype)
            if negative:
                if isinstance(wv, PoincareWordEmbeddingsKeyedVectors) and wv.init_near_border:
                    self.syn1neg = empty((len(wv.vocab), self.layer1_size), dtype=self.vector_dtype)
                    for i in xrange(len(wv.vocab)):
                        self.syn1neg[i] = self.seeded_vector(
                            wv.index2word[i] + str(self.seed),
                            wv.vector_size,
                            max_abs_value=0.001)
                        vector_norm = random.uniform(low=0.5, high=0.6)
                        self.syn1neg[i] = self.syn1neg[i] / norm(self.syn1neg[i]) * vector_norm
                    norms = norm(self.syn1neg, axis=1)
                    print("[Context vector init] Average/Min/Max norm is {} / {} / {}".format(
                        average(norms), min(norms), max(norms)
                    ))
                else:
                    self.syn1neg = zeros((len(wv.vocab), self.layer1_size), dtype=self.vector_dtype)
        else:
            if hs:
                raise RuntimeError("Only negative sampling models can be currently initialized from pretrained embeddings")

            if self.initialization_config.vocab_size != len(wv.vocab):
                raise RuntimeError("Mismatch in vocabulary size between pretrained model and current model")

            # Initialize with pretrained embeddings.
            wv.vectors = self.initialization_config.init_vectors.astype(DOUBLE)
            self.syn1neg = self.initialization_config.init_syn1neg.astype(DOUBLE)
        wv.vectors_norm = None

        # Only implemented model with bias for SGNS (negative sampling model).
        if negative and with_bias:
            self.b0 = zeros((len(wv.vocab), 1), dtype=self.vector_dtype)
            self.b1 = zeros((len(wv.vocab), 1), dtype=self.vector_dtype)

        # For RMSprop, define and initialize the arrays for accumulating historical square gradients.
        if self.optimizer == "rmsprop":
            # Accumulate gradients of input embeddings. vocab_size * (vector_size + 1) vector
            self.Gsyn0 = zeros((len(wv.vocab) * self.layer1_size, 1), dtype=self.vector_dtype)
            # Accumulate gradients of output embeddings. vocab_size * (vector_size + 1) vector
            self.Gsyn1neg = zeros((len(wv.vocab) * self.layer1_size, 1), dtype=self.vector_dtype)
            if with_bias:
                # Accumulate gradients for biases.
                self.Gb0 = zeros(len(wv.vocab), dtype=self.vector_dtype)
                self.Gb1 = zeros(len(wv.vocab), dtype=self.vector_dtype)
        self.vectors_lockf = ones(len(wv.vocab), dtype=self.vector_dtype)  # zeros suppress learning

    def update_weights(self, hs, negative, wv, with_bias=False):
        """
        Copy all the existing weights, and reset the weights for the newly
        added vocabulary.
        """
        logger.info("updating layer weights")
        gained_vocab = len(wv.vocab) - len(wv.vectors)
        newvectors = empty((gained_vocab, wv.vector_size), dtype=self.vector_dtype)

        # randomize the remaining words
        for i in xrange(len(wv.vectors), len(wv.vocab)):
            # construct deterministic seed from word AND seed argument
            newvectors[i - len(wv.vectors)] = self.seeded_vector(
                wv.index2word[i] + str(self.seed),
                wv.vector_size,
                max_abs_value=0.001)

        # Raise an error if an online update is run before initial training on a corpus
        if not len(wv.vectors):
            raise RuntimeError(
                "You cannot do an online vocabulary-update of a model which has no prior vocabulary. "
                "First build the vocabulary of your model with a corpus before doing an online update."
            )

        wv.vectors = vstack([wv.vectors, newvectors])

        if hs:
            self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=self.vector_dtype)])
        if negative:
            self.syn1neg = vstack([self.syn1neg, zeros((gained_vocab, self.layer1_size), dtype=self.vector_dtype)])
            if with_bias:
                self.b0 = self.b0 + zeros((gained_vocab, 1), dtype=self.vector_dtype)
                self.b1 = self.b1 + zeros((gained_vocab, 1), dtype=self.vector_dtype)
        wv.vectors_norm = None

        # do not suppress learning for already learned words
        self.vectors_lockf = ones(len(wv.vocab), dtype=self.vector_dtype)  # zeros suppress learning

        # TODO: we should update AdaGrad arrays here too, when AdaGrad will be functional


# Example: ./word2vec.py -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 \
# -negative 5 -hs 0 -binary 0 -cbow 1 -iter 3
if __name__ == "__main__":
    import argparse
    logging.basicConfig(
        format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s',
        level=logging.INFO
    )
    logger.info("running %s", " ".join(sys.argv))
    logger.info("using optimization %s", FAST_VERSION)

    # check and process cmdline input
    program = os.path.basename(sys.argv[0])
    if len(sys.argv) < 2:
        print(globals()['__doc__'] % locals())
        sys.exit(1)

    from gensim.models.word2vec import Word2Vec  # noqa:F811 avoid referencing __main__ in pickle

    seterr(all='raise')  # don't ignore numpy errors

    parser = argparse.ArgumentParser()
    parser.add_argument("-train", help="Use text data from file TRAIN to train the model", required=True)
    parser.add_argument("-output", help="Use file OUTPUT to save the resulting word vectors")
    parser.add_argument("-window", help="Set max skip length WINDOW between words; default is 5", type=int, default=5)
    parser.add_argument("-size", help="Set size of word vectors; default is 100", type=int, default=100)
    parser.add_argument(
        "-sample",
        help="Set threshold for occurrence of words. "
             "Those that appear with higher frequency in the training data will be randomly down-sampled;"
             " default is 1e-3, useful range is (0, 1e-5)",
        type=float, default=1e-3
    )
    parser.add_argument(
        "-hs", help="Use Hierarchical Softmax; default is 0 (not used)",
        type=int, default=0, choices=[0, 1]
    )
    parser.add_argument(
        "-negative", help="Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)",
        type=int, default=5
    )
    parser.add_argument("-threads", help="Use THREADS threads (default 12)", type=int, default=12)
    parser.add_argument("-iter", help="Run more training iterations (default 5)", type=int, default=5)
    parser.add_argument(
        "-min_count", help="This will discard words that appear less than MIN_COUNT times; default is 5",
        type=int, default=5
    )
    parser.add_argument(
        "-cbow", help="Use the continuous bag of words model; default is 1 (use 0 for skip-gram model)",
        type=int, default=1, choices=[0, 1]
    )
    parser.add_argument(
        "-binary", help="Save the resulting vectors in binary mode; default is 0 (off)",
        type=int, default=0, choices=[0, 1]
    )
    parser.add_argument("-accuracy", help="Use questions from file ACCURACY to evaluate the model")

    args = parser.parse_args()

    if args.cbow == 0:
        skipgram = 1
    else:
        skipgram = 0

    corpus = LineSentence(args.train)

    model = Word2Vec(
        corpus, size=args.size, min_count=args.min_count, workers=args.threads,
        window=args.window, sample=args.sample, sg=skipgram, hs=args.hs,
        negative=args.negative, cbow_mean=1, iter=args.iter
    )

    if args.output:
        outfile = args.output
        model.wv.save_word2vec_format(outfile, binary=args.binary)
    else:
        outfile = args.train
        model.save(outfile + '.model')
    if args.binary == 1:
        model.wv.save_word2vec_format(outfile + '.model.bin', binary=True)
    else:
        model.wv.save_word2vec_format(outfile + '.model.txt', binary=False)

    if args.accuracy:
        model.accuracy(args.accuracy)

    logger.info("finished running %s", program)


class WordEmbeddingCheckpoints(utils.SaveLoad):
    def __init__(self, word_list, model):
        self.checkpoints = None
        self.word_list = word_list
        self.model = model
        self.is_word_list_filtered = False

    def add_checkpoints(self):
        if not self.is_word_list_filtered:
            self.is_word_list_filtered = True
            self.word_list = list(filter(lambda w: w in self.model.wv.vocab, self.word_list))
            self.checkpoints = dict(zip(self.word_list, [[]] * len(self.word_list)))
            print("[Checkpoints] Will save checkpoints for {} words".format(len(self.word_list)))
        for word in self.checkpoints:
            index = self.model.wv.vocab[word].index
            self.checkpoints[word] = self.checkpoints[word] + [array(self.model.wv.vectors[index])]

    def convert_to_list(self):
        if not self.checkpoints:
            return

        for word in self.checkpoints:
            self.checkpoints[word] = self.checkpoints[word].tolist()

    def save(self, file):
        for word in self.checkpoints:
            self.checkpoints[word] = array(self.checkpoints[word])
        super(WordEmbeddingCheckpoints, self).save(file)
