# Path: nmt.py
# def prepare_data(seqs_x, seqs_y, maxlen=None, maxlen_trg=None,
#                  n_words_src=30000, n_words=30000):
# def pred_probs(f_log_probs, prepare_data, options, iterator, verbose=True, verboseFreq=None):
# def train(
#       dim_word=100,
#       dim_word_src=200,
#       enc_dim=1000,
#       dec_dim=1000,  # the number of LSTM units
#       patience=-1,  # early stopping patience
#       max_epochs=5000,
#       finish_after=-1,  # finish after this many updates
#       decay_c=0.,  # L2 regularization penalty
#       alpha_c=0.,  # alignment regularization
#       clip_c=-1.,  # gradient clipping threshold
#       lrate=0.01,  # learning rate
#       n_words_src=100000,  # source vocabulary size
#       n_words=100000,  # target vocabulary size
#       maxlen=100,  # maximum length of the description
#       maxlen_trg=None,  # maximum length of the description
#       maxlen_sample=1000,
#       optimizer='rmsprop',
#       batch_size=16,
#       valid_batch_size=16,
#       sort_size=20,
#       save_path=None,
#       save_file_name='model',
#       save_best_models=0,
#       dispFreq=100,
#       validFreq=100,
#       saveFreq=1000,   # save the parameters after every saveFreq updates
#       sampleFreq=-1,
#       verboseFreq=10000,
#       datasets=[
#           'data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok',
#           '/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok'],
#       valid_datasets=['../data/dev/newstest2011.en.tok',
#                       '../data/dev/newstest2011.fr.tok'],
#       dictionaries=[
#           '/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok.pkl',
#           '/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok.pkl'],
#       source_word_level=0,
#       target_word_level=0,
#       use_dropout=False,
#       re_load=False,
#       re_load_old_setting=False,
#       uidx=None,
#       eidx=None,
#       cidx=None,
#       layers=None,
#       save_every_saveFreq=0,
#       save_burn_in=20000,
#       use_bpe=0,
#       init_params=None,
#       build_model=None,
#       build_sampler=None,
#       gen_sample=None,
#       **kwargs
#     ):


import argparse
import numpy
import cPickle as pkl
from nmt import (build_sampler, init_params)
from mixer import *
from multiprocessing import Process, Queue
    from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
    if not stochastic:
        # dump every remaining one
        if live_k > 0:
            for idx in xrange(live_k):
                sample.append(hyp_samples[idx])
                sample_score.append(hyp_scores[idx])

    return sample, sample_score


def translate_model(queue, rqueue, pid, models, options, k, normalize):

    trng = RandomStreams(1234)

    # allocate model parameters
    params = []
    for i in xrange(len(models)):
        params.append(init_params(options))

    # load model parameters and set theano shared variables
    tparams = []
    for i in xrange(len(params)):
        params[i] = load_params(models[i], params[i])
        tparams.append(init_tparams(params[i]))

    # word index
    use_noise = theano.shared(numpy.float32(0.))
    f_inits = []
    f_nexts = []
    for i in xrange(len(tparams)):

# Path: nmt.py
# def prepare_data(seqs_x, seqs_y, maxlen=None, maxlen_trg=None,
#                  n_words_src=30000, n_words=30000):
# def pred_probs(f_log_probs, prepare_data, options, iterator, verbose=True, verboseFreq=None):
# def train(
#       dim_word=100,
#       dim_word_src=200,
#       enc_dim=1000,
#       dec_dim=1000,  # the number of LSTM units
#       patience=-1,  # early stopping patience
#       max_epochs=5000,
#       finish_after=-1,  # finish after this many updates
#       decay_c=0.,  # L2 regularization penalty
#       alpha_c=0.,  # alignment regularization
#       clip_c=-1.,  # gradient clipping threshold
#       lrate=0.01,  # learning rate
#       n_words_src=100000,  # source vocabulary size
#       n_words=100000,  # target vocabulary size
#       maxlen=100,  # maximum length of the description
#       maxlen_trg=None,  # maximum length of the description
#       maxlen_sample=1000,
#       optimizer='rmsprop',
#       batch_size=16,
#       valid_batch_size=16,
#       sort_size=20,
#       save_path=None,
#       save_file_name='model',
#       save_best_models=0,
#       dispFreq=100,
#       validFreq=100,
#       saveFreq=1000,   # save the parameters after every saveFreq updates
#       sampleFreq=-1,
#       verboseFreq=10000,
#       datasets=[
#           'data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok',
#           '/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok'],
#       valid_datasets=['../data/dev/newstest2011.en.tok',
#                       '../data/dev/newstest2011.fr.tok'],
#       dictionaries=[
#           '/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok.pkl',
#           '/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok.pkl'],
#       source_word_level=0,
#       target_word_level=0,
#       use_dropout=False,
#       re_load=False,
#       re_load_old_setting=False,
#       uidx=None,
#       eidx=None,
#       cidx=None,
#       layers=None,
#       save_every_saveFreq=0,
#       save_burn_in=20000,
#       use_bpe=0,
#       init_params=None,
#       build_model=None,
#       build_sampler=None,
#       gen_sample=None,
#       **kwargs
#     ):


# Path: character_base/translate_bpe2char_ensemble_deen.py
import argparse
import numpy
import cPickle as pkl
from nmt import (build_sampler, init_params)
from mixer import *
from multiprocessing import Process, Queue
    from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

    if not stochastic:
        # dump every remaining one
        if live_k > 0:
            for idx in xrange(live_k):
                sample.append(hyp_samples[idx])
                sample_score.append(hyp_scores[idx])

    return sample, sample_score


def translate_model(queue, rqueue, pid, models, options, k, normalize):

    trng = RandomStreams(1234)

    # allocate model parameters
    params = []
    for i in xrange(len(models)):
        params.append(init_params(options))

    # load model parameters and set theano shared variables
    tparams = []
    for i in xrange(len(params)):
        params[i] = load_params(models[i], params[i])
        tparams.append(init_tparams(params[i]))

    # word index
    use_noise = theano.shared(numpy.float32(0.))
    f_inits = []
    f_nexts = []
    for i in xrange(len(tparams)):

        f_init, f_next = build_sampler(tparams[i], options, trng, use_noise)