from datetime import datetime
from argparse import ArgumentParser

from chainer import optimizers, cuda
from chainer import serializers
import numpy as np
from tools.iterator import TextIterator
from tools.text.preprocessing import OneOfMEncoder

from lib.models import Seq2SeqAttention
from lib.preprocessing import gen_lines, line2batch, batch2line
from lib.wrapper import xp
from lib.vocabulary import Vocabulary as vocab
from lib.helper import timer
from lib.config import UNKNOWN_LABEL

@timer
def train(args):
    src_lines = gen_lines(args.train_src)
    trg_lines = gen_lines(args.train_trg)
    src_vocab = vocab(src_lines, args.unk)
    trg_vocab = vocab(trg_lines, args.unk)
    trg_itow = trg_vocab.itow

    model = Seq2SeqAttention(len(src_vocab), len(trg_vocab), args.embed, args.hidden)
    model.use_gpu(args.gpu)
    opt = optimizers.AdaGrad(lr=0.01)
    opt.setup(model)

    n_epoch = args.epoch
    batch_size = args.batch

    one_of_m = OneOfMEncoder(src_vocab.wtoi, UNKNOWN_LABEL)
    src_train = [one_of_m.encode(s) for s in gen_lines(args.train_src)]
    one_of_m = OneOfMEncoder(trg_vocab.wtoi, UNKNOWN_LABEL)
    trg_train = [one_of_m.encode(s) for s in gen_lines(args.train_trg)]

    for epoch in range(n_epoch):
        order = np.random.permutation(len(src_train))
        src_batches = TextIterator(src_train, batch_size, order=order)
        trg_batches = TextIterator(trg_train, batch_size, order=order)
        for x_batch, t_batch in zip(src_batches, trg_batches):
            model.cleargrads()
            y_batch, loss = model(x_batch, t_batch, trg_vocab.wtoi)
            loss.backward()
            opt.update()
            for line in batch2line(y_batch, trg_vocab):
                print('epoch: ' + str(epoch + 1) +
                        ' [' + datetime.now().strftime("%Y/%m/%d %H:%M:%S") +
                        ']: ' + line)
    model.save_model(args.model)
    return model

def test(model, args):
    src_lines = gen_lines(args.train_src)
    trg_lines = gen_lines(args.train_trg)
    src_vocab = vocab(src_lines, args.unk)
    trg_vocab = vocab(trg_lines, args.unk)
    model.use_gpu(args.gpu)
    for x_batch in line2batch(gen_lines(args.test_src), src_vocab, 1):
        y_batch = model.test(x_batch, trg_vocab)
        with open(args.output, 'a') as f:
            f.write(' '.join(y_batch).replace('<s> ', '').replace(' </s>',  '') + '\n')

def parse_args():
    parser = ArgumentParser()
    parser.add_argument('--epoch', type=int, default=10)
    parser.add_argument('--embed', type=int, default=200)
    parser.add_argument('--hidden', type=int, default=100)
    parser.add_argument('--batch', type=int, default=120)
    parser.add_argument('--unk', type=int, default=5)
    parser.add_argument('--gpu', type=int, default=None)
    parser.add_argument('--train_src')
    parser.add_argument('--train_trg')
    parser.add_argument('--test_src')
    parser.add_argument('--test_trg')
    parser.add_argument('--model')
    parser.add_argument('--output')
    return parser.parse_args()

if __name__ == '__main__':
    try:
        args = parse_args()
        model = train(args)
        test(model, args)
    except KeyboardInterrupt:
        pass

@@ -483,17 +483,41 @@
 WN_LABEL
+, START_TOKEN, END_TOKEN
 %0A
-
 %0A@timer%0A
@@ -818,16 +818,101 @@
 s.hidden
+,%0A                             trg_vocab.wtoi%5BSTART_TOKEN%5D, trg_vocab.wtoi%5BEND_TOKEN%5D
 )%0A    mo
@@ -1312,16 +1312,40 @@
 _trg)%5D%0A%0A
+    N = len(src_train)%0A%0A
     for 
@@ -1407,30 +1407,17 @@
 utation(
-len(src_train)
+N
 )%0A      
@@ -1657,24 +1657,30 @@
         
+ loss,
  y_batch
 , loss =
@@ -1675,22 +1675,16 @@
 atch
-, loss
  = model
 (x_b
@@ -1679,16 +1679,21 @@
  = model
+.loss
 (x_batch
@@ -1701,32 +1701,16 @@
  t_batch
-, trg_vocab.wtoi
 )%0A      

update train_seq2seq.py