#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2013 Radim Rehurek <me@radimrehurek.com>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html


"""
Warnings
--------
.. deprecated:: 3.3.0
   Use :mod:`gensim.models.word2vec` instead.


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 wrappers for FastText, 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.keyedvectors 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
import threading
import itertools
import warnings

from gensim.utils import keep_vocab_item, call_on_class_only
from gensim.models.deprecated.keyedvectors import KeyedVectors, Vocab
from gensim.models.word2vec import Word2Vec as NewWord2Vec
from gensim.models.deprecated.old_saveload import SaveLoad

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

from numpy import exp, log, dot, zeros, outer, random, dtype, float32 as REAL,\
    uint32, seterr, array, uint8, vstack, fromstring, sqrt,\
    empty, sum as np_sum, ones, logaddexp

from scipy.special import expit

from gensim import utils
from gensim import matutils  # utility fnc for pickling, common scipy operations etc
from six import iteritems, itervalues, string_types
from six.moves import xrange
from types import GeneratorType

logger = logging.getLogger(__name__)


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


def load_old_word2vec(*args, **kwargs):
    old_model = Word2Vec.load(*args, **kwargs)
    params = {
        'size': old_model.vector_size,
        'alpha': old_model.alpha,
        'window': old_model.window,
        'min_count': old_model.min_count,
        'max_vocab_size': old_model.__dict__.get('max_vocab_size', None),
        'sample': old_model.sample,
        'seed': old_model.seed,
        'workers': old_model.workers,
        'min_alpha': old_model.min_alpha,
        'sg': old_model.sg,
        'hs': old_model.hs,
        'negative': old_model.negative,
        'cbow_mean': old_model.cbow_mean,
        'hashfxn': old_model.hashfxn,
        'iter': old_model.iter,
        'null_word': old_model.null_word,
        'sorted_vocab': old_model.sorted_vocab,
        'batch_words': old_model.batch_words,
        'compute_loss': old_model.__dict__.get('compute_loss', None)
    }
    new_model = NewWord2Vec(**params)
    # set trainables attributes
    new_model.wv.vectors = old_model.wv.syn0
    if hasattr(old_model.wv, 'syn0norm'):
        new_model.wv.vectors_norm = old_model.wv.syn0norm
    if hasattr(old_model, 'syn1'):
        new_model.trainables.syn1 = old_model.syn1
    if hasattr(old_model, 'syn1neg'):
        new_model.trainables.syn1neg = old_model.syn1neg
    if hasattr(old_model, 'syn0_lockf'):
        new_model.trainables.vectors_lockf = old_model.syn0_lockf
    # set vocabulary attributes
    new_model.wv.vocab = old_model.wv.vocab
    new_model.wv.index2word = old_model.wv.index2word
    new_model.vocabulary.cum_table = old_model.cum_table

    new_model.train_count = old_model.train_count
    new_model.corpus_count = old_model.corpus_count
    new_model.running_training_loss = old_model.__dict__.get('running_training_loss', None)
    new_model.total_train_time = old_model.total_train_time
    new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached
    new_model.model_trimmed_post_training = old_model.__dict__.get('model_trimmed_post_training', None)

    return new_model


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


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]
        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


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.running_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)
        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.running_training_loss -= sum(log(expit(-1 * prod_term[1:])))  # for the sampled words
            model.running_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.running_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.running_training_loss -= sum(log(expit(-1 * prod_term[1:])))  # for the sampled words
            model.running_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 Word2Vec(SaveLoad):
    """
    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 `save()` and `load()` methods, or stored/loaded in a format
    compatible with the original word2vec implementation via `wv.save_word2vec_format()`
    and `KeyedVectors.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,
                 sg=0, hs=0, negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0,
                 trim_rule=None, sorted_vocab=1, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False):
        """
        Initialize the model from an iterable of `sentences`. Each sentence is a
        list of words (unicode strings) that will be used for training.

        The `sentences` iterable can be simply a list, but for larger corpora,
        consider an iterable that streams the sentences directly from disk/network.
        See :class:`BrownCorpus`, :class:`Text8Corpus` or :class:`LineSentence` in
        this 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` defines the training algorithm. By default (`sg=0`), CBOW is used.
        Otherwise (`sg=1`), skip-gram is employed.

        `size` is the dimensionality of the feature vectors.

        `window` is the maximum distance between the current and predicted word within a sentence.

        `alpha` is the initial learning rate (will linearly drop to `min_alpha` as training progresses).

        `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, 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` = ignore all words with total frequency lower than this.

        `max_vocab_size` = limit 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 (default).

        `sample` = threshold for configuring which higher-frequency words are randomly downsampled;
            default is 1e-3, useful range is (0, 1e-5).

        `workers` = use this many worker threads to train the model (=faster training with multicore machines).

        `hs` = if 1, hierarchical softmax will be used for model training.
        If set to 0 (default), and `negative` is non-zero, negative sampling will be used.

        `negative` = if > 0, negative sampling will be used, the int for negative
        specifies how many "noise words" should be drawn (usually between 5-20).
        Default is 5. If set to 0, no negative samping is used.

        `cbow_mean` = if 0, use the sum of the context word vectors. If 1 (default), use the mean.
        Only applies when cbow is used.

        `hashfxn` = hash function to use to randomly initialize weights, for increased
        training reproducibility. Default is Python's rudimentary built in hash function.

        `iter` = number of iterations (epochs) over the corpus. Default is 5.

        `trim_rule` = 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), or a callable that accepts parameters (word, count, min_count) and
        returns either `utils.RULE_DISCARD`, `utils.RULE_KEEP` or `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` = if 1 (default), sort the vocabulary by descending frequency before
        assigning word indexes.

        `batch_words` = target size (in words) for batches of examples passed to worker threads (and
        thus cython routines). Default is 10000. (Larger batches will be passed if individual
        texts are longer than 10000 words, but the standard cython code truncates to that maximum.)

        """

        self.load = call_on_class_only

        if FAST_VERSION == -1:
            logger.warning('Slow version of %s is being used', __name__)
        else:
            logger.debug('Fast version of %s is being used', __name__)

        self.initialize_word_vectors()
        self.sg = int(sg)
        self.cum_table = None  # for negative sampling
        self.vector_size = int(size)
        self.layer1_size = int(size)
        if size % 4 != 0:
            logger.warning("consider setting layer size to a multiple of 4 for greater performance")
        self.alpha = float(alpha)
        self.min_alpha_yet_reached = float(alpha)  # To warn user if alpha increases
        self.window = int(window)
        self.max_vocab_size = max_vocab_size
        self.seed = seed
        self.random = random.RandomState(seed)
        self.min_count = min_count
        self.sample = sample
        self.workers = int(workers)
        self.min_alpha = float(min_alpha)
        self.hs = hs
        self.negative = negative
        self.cbow_mean = int(cbow_mean)
        self.hashfxn = hashfxn
        self.iter = iter
        self.null_word = null_word
        self.train_count = 0
        self.total_train_time = 0
        self.sorted_vocab = sorted_vocab
        self.batch_words = batch_words
        self.model_trimmed_post_training = False
        self.compute_loss = compute_loss
        self.running_training_loss = 0
        if sentences is not None:
            if isinstance(sentences, GeneratorType):
                raise TypeError("You can't pass a generator as the sentences argument. Try an iterator.")
            self.build_vocab(sentences, trim_rule=trim_rule)
            self.train(
                sentences, total_examples=self.corpus_count, epochs=self.iter,
                start_alpha=self.alpha, end_alpha=self.min_alpha
            )
        else:
            if trim_rule is not None:
                logger.warning(
                    "The rule, if given, is only used to prune vocabulary during build_vocab() "
                    "and is not stored as part of the model. Model initialized without sentences. "
                    "trim_rule provided, if any, will be ignored."
                )

    def initialize_word_vectors(self):
        self.wv = KeyedVectors()

    def make_cum_table(self, 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(self.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 += self.wv.vocab[self.wv.index2word[word_index]].count**power
        cumulative = 0.0
        for word_index in xrange(vocab_size):
            cumulative += self.wv.vocab[self.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

    def create_binary_tree(self):
        """
        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(self.wv.vocab))

        # build the huffman tree
        heap = list(itervalues(self.wv.vocab))
        heapq.heapify(heap)
        for i in xrange(len(self.wv.vocab) - 1):
            min1, min2 = heapq.heappop(heap), heapq.heappop(heap)
            heapq.heappush(
                heap, Vocab(count=min1.count + min2.count, index=i + len(self.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(self.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(self.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 build_vocab(self, sentences, keep_raw_vocab=False, trim_rule=None, progress_per=10000, update=False):
        """
        Build vocabulary from a sequence of sentences (can be a once-only generator stream).
        Each sentence must be a list of unicode strings.
        """
        self.scan_vocab(sentences, progress_per=progress_per, trim_rule=trim_rule)  # initial survey
        # trim by min_count & precalculate downsampling
        self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update)
        self.finalize_vocab(update=update)  # build tables & arrays

    def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=None, trim_rule=None, update=False):
        """
        Build vocabulary from a dictionary of word frequencies.
        Build model vocabulary from a passed dictionary that contains (word,word count).
        Words must be of type unicode strings.

        Parameters
        ----------
        `word_freq` : dict
            Word,Word_Count dictionary.
        `keep_raw_vocab` : bool
            If not true, delete the raw vocabulary after the scaling is done and free up RAM.
        `corpus_count`: int
            Even if no corpus is provided, this argument can set corpus_count explicitly.
        `trim_rule` = 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), or a callable that accepts parameters (word, count, min_count) and
        returns either `utils.RULE_DISCARD`, `utils.RULE_KEEP` or `utils.RULE_DEFAULT`.
        `update`: bool
            If true, the new provided words in `word_freq` dict will be added to model's vocab.

        Returns
        --------
        None

        Examples
        --------
        >>> from gensim.models.word2vec import Word2Vec
        >>> model= Word2Vec()
        >>> model.build_vocab_from_freq({"Word1": 15, "Word2": 20})
        """
        logger.info("Processing provided word frequencies")
        # Instead of scanning text, this will assign provided word frequencies dictionary(word_freq)
        # to be directly the raw vocab
        raw_vocab = word_freq
        logger.info(
            "collected %i different raw word, with total frequency of %i",
            len(raw_vocab), sum(itervalues(raw_vocab))
        )

        # Since no sentences are provided, this is to control the corpus_count
        self.corpus_count = corpus_count if corpus_count else 0
        self.raw_vocab = raw_vocab

        # trim by min_count & precalculate downsampling
        self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update)
        self.finalize_vocab(update=update)  # build tables & arrays

    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
        )
        self.corpus_count = sentence_no + 1
        self.raw_vocab = vocab
        return total_words

    def scale_vocab(self, min_count=None, sample=None, dry_run=False,
                    keep_raw_vocab=False, trim_rule=None, update=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:
                self.wv.index2word = []
                # make stored settings match these applied settings
                self.min_count = min_count
                self.sample = sample
                self.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:
                        self.wv.vocab[word] = Vocab(count=v, index=len(self.wv.index2word))
                        self.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 self.wv.vocab:
                        pre_exist_words.append(word)
                        pre_exist_total += v
                        if not dry_run:
                            self.wv.vocab[word].count += v
                    else:
                        new_words.append(word)
                        new_total += v
                        if not dry_run:
                            self.wv.vocab[word] = Vocab(count=v, index=len(self.wv.index2word))
                            self.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:
                self.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), 'memory': self.estimate_memory(vocab_size=len(retain_words))
        }

        return report_values

    def finalize_vocab(self, update=False):
        """Build tables and model weights based on final vocabulary settings."""
        if not self.wv.index2word:
            self.scale_vocab()
        if self.sorted_vocab and not update:
            self.sort_vocab()
        if self.hs:
            # add info about each word's Huffman encoding
            self.create_binary_tree()
        if self.negative:
            # build the table for drawing random words (for negative sampling)
            self.make_cum_table()
        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
            word, v = '\0', Vocab(count=1, sample_int=0)
            v.index = len(self.wv.vocab)
            self.wv.index2word.append(word)
            self.wv.vocab[word] = v
        # set initial input/projection and hidden weights
        if not update:
            self.reset_weights()
        else:
            self.update_weights()

    def sort_vocab(self):
        """Sort the vocabulary so the most frequent words have the lowest indexes."""
        if len(self.wv.syn0):
            raise RuntimeError("cannot sort vocabulary after model weights already initialized.")
        self.wv.index2word.sort(key=lambda word: self.wv.vocab[word].count, reverse=True)
        for i, word in enumerate(self.wv.index2word):
            self.wv.vocab[word].index = i

    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.cum_table = other_model.cum_table
        self.corpus_count = other_model.corpus_count
        self.reset_weights()

    def _do_train_job(self, sentences, alpha, inits):
        """
        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:
            tally += train_batch_sg(self, sentences, alpha, work, self.compute_loss)
        else:
            tally += train_batch_cbow(self, sentences, alpha, work, neu1, self.compute_loss)
        return tally, self._raw_word_count(sentences)

    def _raw_word_count(self, job):
        """Return the number of words in a given job."""
        return sum(len(sentence) for sentence in job)

    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=None):
        """
        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
        progres-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
        `build_vocab()`, the count of examples in that corpus will be available in the model's
        `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 `train()`
        is only called once, the model's cached `iter` value should be supplied as `epochs` value.
        """
        if self.model_trimmed_post_training:
            raise RuntimeError("Parameters for training were discarded using model_trimmed_post_training method")
        if FAST_VERSION < 0:
            warnings.warn(
                "C extension not loaded for Word2Vec, training will be slow. "
                "Install a C compiler and reinstall gensim for fast training."
            )
            self.neg_labels = []
            if self.negative > 0:
                # precompute negative labels optimization for pure-python training
                self.neg_labels = zeros(self.negative + 1)
                self.neg_labels[0] = 1.

        if compute_loss:
            self.compute_loss = compute_loss
        self.running_training_loss = 0

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

        if not self.wv.vocab:
            raise RuntimeError("you must first build vocabulary before training the model")
        if not len(self.wv.syn0):
            raise RuntimeError("you must first finalize vocabulary before training the model")

        if not hasattr(self, 'corpus_count'):
            raise ValueError(
                "The number of sentences in the training corpus is missing. "
                "Did you load the model via KeyedVectors.load_word2vec_format?"
                "Models loaded via load_word2vec_format don't support further training. "
                "Instead start with a blank model, scan_vocab on the new corpus, "
                "intersect_word2vec_format with the old model, then train."
            )

        if total_words is None and total_examples is None:
            raise ValueError(
                "You must specify either total_examples or total_words, for proper alpha and progress calculations. "
                "The usual value is total_examples=model.corpus_count."
            )
        if epochs is None:
            raise ValueError("You must specify an explict epochs count. The usual value is epochs=model.iter.")
        start_alpha = start_alpha or self.alpha
        end_alpha = end_alpha or self.min_alpha

        job_tally = 0

        if epochs > 1:
            sentences = utils.RepeatCorpusNTimes(sentences, epochs)
            total_words = total_words and total_words * epochs
            total_examples = total_examples and total_examples * epochs

        def worker_loop():
            """Train the model, lifting lists of sentences from the job_queue."""
            work = matutils.zeros_aligned(self.layer1_size, dtype=REAL)  # per-thread private work memory
            neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL)
            jobs_processed = 0
            while True:
                job = job_queue.get()
                if job is None:
                    progress_queue.put(None)
                    break  # no more jobs => quit this worker
                sentences, alpha = job
                tally, raw_tally = self._do_train_job(sentences, alpha, (work, neu1))
                progress_queue.put((len(sentences), tally, raw_tally))  # report back progress
                jobs_processed += 1
            logger.debug("worker exiting, processed %i jobs", jobs_processed)

        def job_producer():
            """Fill jobs queue using the input `sentences` iterator."""
            job_batch, batch_size = [], 0
            pushed_words, pushed_examples = 0, 0
            next_alpha = start_alpha
            if next_alpha > self.min_alpha_yet_reached:
                logger.warning("Effective 'alpha' higher than previous training cycles")
            self.min_alpha_yet_reached = next_alpha
            job_no = 0

            for sent_idx, sentence in enumerate(sentences):
                sentence_length = self._raw_word_count([sentence])

                # can we fit this sentence into the existing job batch?
                if batch_size + sentence_length <= self.batch_words:
                    # yes => add it to the current job
                    job_batch.append(sentence)
                    batch_size += sentence_length
                else:
                    # no => submit the existing job
                    logger.debug(
                        "queueing job #%i (%i words, %i sentences) at alpha %.05f",
                        job_no, batch_size, len(job_batch), next_alpha
                    )
                    job_no += 1
                    job_queue.put((job_batch, next_alpha))

                    # update the learning rate for the next job
                    if end_alpha < next_alpha:
                        if total_examples:
                            # examples-based decay
                            pushed_examples += len(job_batch)
                            progress = 1.0 * pushed_examples / total_examples
                        else:
                            # words-based decay
                            pushed_words += self._raw_word_count(job_batch)
                            progress = 1.0 * pushed_words / total_words
                        next_alpha = start_alpha - (start_alpha - end_alpha) * progress
                        next_alpha = max(end_alpha, next_alpha)

                    # add the sentence that didn't fit as the first item of a new job
                    job_batch, batch_size = [sentence], sentence_length

            # add the last job too (may be significantly smaller than batch_words)
            if job_batch:
                logger.debug(
                    "queueing job #%i (%i words, %i sentences) at alpha %.05f",
                    job_no, batch_size, len(job_batch), next_alpha
                )
                job_no += 1
                job_queue.put((job_batch, next_alpha))

            if job_no == 0 and self.train_count == 0:
                logger.warning(
                    "train() called with an empty iterator (if not intended, "
                    "be sure to provide a corpus that offers restartable iteration = an iterable)."
                )

            # give the workers heads up that they can finish -- no more work!
            for _ in xrange(self.workers):
                job_queue.put(None)
            logger.debug("job loop exiting, total %i jobs", job_no)

        # 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)]
        unfinished_worker_count = len(workers)
        workers.append(threading.Thread(target=job_producer))

        for thread in workers:
            thread.daemon = True  # make interrupting the process with ctrl+c easier
            thread.start()

        example_count, trained_word_count, raw_word_count = 0, 0, word_count
        start, next_report = default_timer() - 0.00001, 1.0

        while unfinished_worker_count > 0:
            report = progress_queue.get()  # blocks if workers too slow
            if report is None:  # a thread reporting that it finished
                unfinished_worker_count -= 1
                logger.info("worker thread finished; awaiting finish of %i more threads", unfinished_worker_count)
                continue
            examples, trained_words, raw_words = report
            job_tally += 1

            # update progress stats
            example_count += examples
            trained_word_count += trained_words  # only words in vocab & sampled
            raw_word_count += raw_words

            # log progress once every report_delay seconds
            elapsed = default_timer() - start
            if elapsed >= next_report:
                if total_examples:
                    # examples-based progress %
                    logger.info(
                        "PROGRESS: at %.2f%% examples, %.0f words/s, in_qsize %i, out_qsize %i",
                        100.0 * example_count / total_examples, trained_word_count / elapsed,
                        utils.qsize(job_queue), utils.qsize(progress_queue)
                    )
                else:
                    # words-based progress %
                    logger.info(
                        "PROGRESS: at %.2f%% words, %.0f words/s, in_qsize %i, out_qsize %i",
                        100.0 * raw_word_count / total_words, trained_word_count / elapsed,
                        utils.qsize(job_queue), utils.qsize(progress_queue)
                    )
                next_report = elapsed + report_delay

        # all done; report the final stats
        elapsed = default_timer() - start
        logger.info(
            "training on %i raw words (%i effective words) took %.1fs, %.0f effective words/s",
            raw_word_count, trained_word_count, elapsed, trained_word_count / elapsed
        )
        if job_tally < 10 * self.workers:
            logger.warning(
                "under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay"
            )

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

        self.train_count += 1  # number of times train() has been called
        self.total_train_time += elapsed
        self.clear_sims()
        return trained_word_count

    # basics copied from the train() function
    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

        """
        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.layer1_size, self.sg, self.hs, self.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=REAL)  # for sg hs, we actually only need one memory loc (running sum)
            neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL)
            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=REAL)

        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.syn0norm = None

    def update_weights(self):
        """
        Copy all the existing weights, and reset the weights for the newly
        added vocabulary.
        """
        logger.info("updating layer weights")
        gained_vocab = len(self.wv.vocab) - len(self.wv.syn0)
        newsyn0 = empty((gained_vocab, self.vector_size), dtype=REAL)

        # randomize the remaining words
        for i in xrange(len(self.wv.syn0), len(self.wv.vocab)):
            # construct deterministic seed from word AND seed argument
            newsyn0[i - len(self.wv.syn0)] = self.seeded_vector(self.wv.index2word[i] + str(self.seed))

        # Raise an error if an online update is run before initial training on a corpus
        if not len(self.wv.syn0):
            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."
            )

        self.wv.syn0 = vstack([self.wv.syn0, newsyn0])

        if self.hs:
            self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=REAL)])
        if self.negative:
            self.syn1neg = vstack([self.syn1neg, zeros((gained_vocab, self.layer1_size), dtype=REAL)])
        self.wv.syn0norm = None

        # do not suppress learning for already learned words
        self.syn0_lockf = ones(len(self.wv.vocab), dtype=REAL)  # zeros suppress learning

    def reset_weights(self):
        """Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary."""
        logger.info("resetting layer weights")
        self.wv.syn0 = empty((len(self.wv.vocab), self.vector_size), dtype=REAL)
        # randomize weights vector by vector, rather than materializing a huge random matrix in RAM at once
        for i in xrange(len(self.wv.vocab)):
            # construct deterministic seed from word AND seed argument
            self.wv.syn0[i] = self.seeded_vector(self.wv.index2word[i] + str(self.seed))
        if self.hs:
            self.syn1 = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL)
        if self.negative:
            self.syn1neg = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL)
        self.wv.syn0norm = None

        self.syn0_lockf = ones(len(self.wv.vocab), dtype=REAL)  # zeros suppress learning

    def seeded_vector(self, seed_string):
        """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)
        return (once.rand(self.vector_size) - 0.5) / self.vector_size

    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.)

        `binary` is a boolean indicating whether the data is in binary word2vec format.

        `lockf` is a 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.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(REAL).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=REAL)
                    if word in self.wv.vocab:
                        overlap_count += 1
                        self.wv.syn0[self.wv.vocab[word].index] = weights
                        self.syn0_lockf[self.wv.vocab[word].index] = lockf  # lock-factor: 0.0 stops further 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], [REAL(x) for x in parts[1:]]
                    if word in self.wv.vocab:
                        overlap_count += 1
                        self.wv.syn0[self.wv.vocab[word].index] = weights
                        self.syn0_lockf[self.wv.vocab[word].index] = lockf  # lock-factor: 0.0 stops further changes
        logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.syn0.shape, fname)

    def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=None, indexer=None):
        """
        Deprecated. Use self.wv.most_similar() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.most_similar`
        """
        return self.wv.most_similar(positive, negative, topn, restrict_vocab, indexer)

    def wmdistance(self, document1, document2):
        """
        Deprecated. Use self.wv.wmdistance() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.wmdistance`
        """
        return self.wv.wmdistance(document1, document2)

    def most_similar_cosmul(self, positive=None, negative=None, topn=10):
        """
        Deprecated. Use self.wv.most_similar_cosmul() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.most_similar_cosmul`
        """
        return self.wv.most_similar_cosmul(positive, negative, topn)

    def similar_by_word(self, word, topn=10, restrict_vocab=None):
        """
        Deprecated. Use self.wv.similar_by_word() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.similar_by_word`
        """
        return self.wv.similar_by_word(word, topn, restrict_vocab)

    def similar_by_vector(self, vector, topn=10, restrict_vocab=None):
        """
        Deprecated. Use self.wv.similar_by_vector() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.similar_by_vector`
        """
        return self.wv.similar_by_vector(vector, topn, restrict_vocab)

    def doesnt_match(self, words):
        """
        Deprecated. Use self.wv.doesnt_match() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.doesnt_match`
        """
        return self.wv.doesnt_match(words)

    def __getitem__(self, words):
        """
        Deprecated. Use self.wv.__getitem__() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.__getitem__`
        """
        return self.wv.__getitem__(words)

    def __contains__(self, word):
        """
        Deprecated. Use self.wv.__contains__() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.__contains__`
        """
        return self.wv.__contains__(word)

    def similarity(self, w1, w2):
        """
        Deprecated. Use self.wv.similarity() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.similarity`
        """
        return self.wv.similarity(w1, w2)

    def n_similarity(self, ws1, ws2):
        """
        Deprecated. Use self.wv.n_similarity() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.n_similarity`
        """
        return self.wv.n_similarity(ws1, ws2)

    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."""
        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, 'syn0') or not hasattr(self, '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.syn0[word2_indices], axis=0)
        if word2_indices and self.cbow_mean:
            l1 /= len(word2_indices)

        prob_values = exp(dot(l1, self.syn1neg.T))  # propagate hidden -> output and take softmax to get probabilities
        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 mainly, but because syn1 is not an attribute
        of KeyedVectors, it has to be deleted in this class, and the normalizing of syn0 happens inside of KeyedVectors
        """
        if replace and hasattr(self, 'syn1'):
            del self.syn1
        return self.wv.init_sims(replace)

    def estimate_memory(self, vocab_size=None, report=None):
        """Estimate required memory for a model using current settings and provided vocabulary size."""
        vocab_size = vocab_size or len(self.wv.vocab)
        report = report or {}
        report['vocab'] = vocab_size * (700 if self.hs else 500)
        report['syn0'] = vocab_size * self.vector_size * dtype(REAL).itemsize
        if self.hs:
            report['syn1'] = vocab_size * self.layer1_size * dtype(REAL).itemsize
        if self.negative:
            report['syn1neg'] = vocab_size * self.layer1_size * dtype(REAL).itemsize
        report['total'] = sum(report.values())
        logger.info(
            "estimated required memory for %i words and %i dimensions: %i bytes",
            vocab_size, self.vector_size, report['total']
        )
        return report

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

    def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_insensitive=True):
        most_similar = most_similar or KeyedVectors.most_similar
        return self.wv.accuracy(questions, restrict_vocab, most_similar, case_insensitive)

    @staticmethod
    def log_evaluate_word_pairs(pearson, spearman, oov, pairs):
        """
        Deprecated. Use self.wv.log_evaluate_word_pairs() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.log_evaluate_word_pairs`
        """
        return KeyedVectors.log_evaluate_word_pairs(pearson, spearman, oov, pairs)

    def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000,
                            case_insensitive=True, dummy4unknown=False):
        """
        Deprecated. Use self.wv.evaluate_word_pairs() instead.
        Refer to the documentation for `gensim.models.KeyedVectors.evaluate_word_pairs`
        """
        return self.wv.evaluate_word_pairs(pairs, delimiter, restrict_vocab, case_insensitive, dummy4unknown)

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

    def _minimize_model(self, save_syn1=False, save_syn1neg=False, save_syn0_lockf=False):
        warnings.warn(
            "This method would be deprecated in the future. "
            "Keep just_word_vectors = model.wv to retain just the KeyedVectors instance "
            "for read-only querying of word vectors."
        )
        if save_syn1 and save_syn1neg and save_syn0_lockf:
            return
        if hasattr(self, 'syn1') and not save_syn1:
            del self.syn1
        if hasattr(self, 'syn1neg') and not save_syn1neg:
            del self.syn1neg
        if hasattr(self, 'syn0_lockf') and not save_syn0_lockf:
            del self.syn0_lockf
        self.model_trimmed_post_training = True

    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):
        # don't bother storing the cached normalized vectors, recalculable table
        kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'table', 'cum_table'])

        super(Word2Vec, self).save(*args, **kwargs)

    save.__doc__ = SaveLoad.save.__doc__

    @classmethod
    def load(cls, *args, **kwargs):
        model = super(Word2Vec, cls).load(*args, **kwargs)
        # update older models
        if hasattr(model, 'table'):
            delattr(model, 'table')  # discard in favor of cum_table
        if model.negative and hasattr(model.wv, 'index2word'):
            model.make_cum_table()  # rebuild cum_table from vocabulary
        if not hasattr(model, 'corpus_count'):
            model.corpus_count = None
        for v in model.wv.vocab.values():
            if hasattr(v, 'sample_int'):
                break  # already 0.12.0+ style int probabilities
            elif hasattr(v, 'sample_probability'):
                v.sample_int = int(round(v.sample_probability * 2**32))
                del v.sample_probability
        if not hasattr(model, 'syn0_lockf') and hasattr(model, 'syn0'):
            model.syn0_lockf = ones(len(model.wv.syn0), dtype=REAL)
        if not hasattr(model, 'random'):
            model.random = random.RandomState(model.seed)
        if not hasattr(model, 'train_count'):
            model.train_count = 0
            model.total_train_time = 0
        return model

    def _load_specials(self, *args, **kwargs):
        super(Word2Vec, self)._load_specials(*args, **kwargs)
        # loading from a pre-KeyedVectors word2vec model
        if not hasattr(self, 'wv'):
            wv = KeyedVectors()
            wv.syn0 = self.__dict__.get('syn0', [])
            wv.syn0norm = self.__dict__.get('syn0norm', None)
            wv.vocab = self.__dict__.get('vocab', {})
            wv.index2word = self.__dict__.get('index2word', [])
            self.wv = wv

    @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.")

    def get_latest_training_loss(self):
        return self.running_training_loss


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


# 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)
