{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/lamb_optimizer.py:34: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from albert import modeling\n",
    "from albert import optimization\n",
    "from albert import tokenization\n",
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/tokenization.py:240: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
      "\n",
      "INFO:tensorflow:loading sentence piece model\n"
     ]
    }
   ],
   "source": [
    "tokenizer = tokenization.FullTokenizer(\n",
    "      vocab_file='albert-base-2020-04-10/sp10m.cased.v10.vocab', do_lower_case=False,\n",
    "      spm_model_file='albert-base-2020-04-10/sp10m.cased.v10.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:116: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<albert.modeling.AlbertConfig at 0x7f86e7817198>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "albert_config = modeling.AlbertConfig.from_json_file('albert-base-2020-04-10/config.json')\n",
    "albert_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['train_X', 'test_X', 'train_Y', 'test_Y'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "import json\n",
    "\n",
    "with open('../bert/session-pos.pkl', 'rb') as fopen:\n",
    "    data = pickle.load(fopen)\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = data['train_X']\n",
    "test_X = data['test_X']\n",
    "train_Y = data['train_Y']\n",
    "test_Y = data['test_Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['word2idx', 'idx2word', 'tag2idx', 'idx2tag', 'char2idx'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../bert/dictionary-pos.json') as fopen:\n",
    "    dictionary = json.load(fopen)\n",
    "dictionary.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2idx = dictionary['word2idx']\n",
    "idx2word = {int(k): v for k, v in dictionary['idx2word'].items()}\n",
    "tag2idx = dictionary['tag2idx']\n",
    "idx2tag = {int(k): v for k, v in dictionary['idx2tag'].items()}\n",
    "char2idx = dictionary['char2idx']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "def XY(strings):\n",
    "    left_train, right_train = strings[0], strings[1]\n",
    "    X, Y, MASK = [], [], []\n",
    "    for i in tqdm(range(len(left_train))):\n",
    "        left = [idx2word[d] for d in left_train[i]]\n",
    "        right = [idx2tag[d] for d in right_train[i]]\n",
    "        bert_tokens = ['[CLS]']\n",
    "        y = ['PAD']\n",
    "        for no, orig_token in enumerate(left):\n",
    "            t = tokenizer.tokenize(orig_token)\n",
    "            bert_tokens.extend(t)\n",
    "            if len(t):\n",
    "                y.append(right[no])\n",
    "            y.extend(['X'] * (len(t) - 1))\n",
    "        bert_tokens.append('[SEP]')\n",
    "        y.append('PAD')\n",
    "        x = tokenizer.convert_tokens_to_ids(bert_tokens)\n",
    "        y = [tag2idx[i] for i in y]\n",
    "        input_mask = [1] * len(y)\n",
    "        if len(x) != len(y):\n",
    "            print(i)\n",
    "        X.append(x)\n",
    "        Y.append(y)\n",
    "        MASK.append(input_mask)\n",
    "    return X, Y, MASK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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     ]
    }
   ],
   "source": [
    "train_X, train_Y, train_masks = cleaning.multiprocessing(train_X, train_Y, XY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "test_X, test_Y, test_masks = cleaning.multiprocessing(test_X, test_Y, XY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, train_Y, train_masks = shuffle(train_X, train_Y, train_masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "BERT_INIT_CHKPNT = 'albert-base-2020-04-10/model.ckpt-400000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch = 5\n",
    "batch_size = 32\n",
    "warmup_proportion = 0.1\n",
    "num_train_steps = int(1000 / batch_size * epoch)\n",
    "num_warmup_steps = int(num_train_steps * warmup_proportion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_initializer(initializer_range=0.02):\n",
    "    return tf.truncated_normal_initializer(stddev=initializer_range)\n",
    "\n",
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        dimension_output,\n",
    "        learning_rate = 2e-5,\n",
    "        training = True\n",
    "    ):\n",
    "        self.X = tf.placeholder(tf.int32, [None, None])\n",
    "        self.MASK = tf.placeholder(tf.int32, [None, None])\n",
    "        self.Y = tf.placeholder(tf.int32, [None, None])\n",
    "        self.maxlen = tf.shape(self.X)[1]\n",
    "        self.lengths = tf.count_nonzero(self.X, 1)\n",
    "        \n",
    "        model = modeling.AlbertModel(\n",
    "            config=albert_config,\n",
    "            is_training=training,\n",
    "            input_ids=self.X,\n",
    "            input_mask=self.MASK,\n",
    "            use_one_hot_embeddings=False)\n",
    "        output_layer = model.get_sequence_output()\n",
    "        output_layer = tf.layers.dense(\n",
    "            output_layer,\n",
    "            albert_config.hidden_size,\n",
    "            activation=tf.tanh,\n",
    "            kernel_initializer=create_initializer())\n",
    "        logits = tf.layers.dense(output_layer, dimension_output,\n",
    "                                         kernel_initializer=create_initializer())\n",
    "        y_t = self.Y\n",
    "        log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(\n",
    "            logits, y_t, self.lengths\n",
    "        )\n",
    "        self.cost = tf.reduce_mean(-log_likelihood)\n",
    "        self.optimizer = tf.train.AdamOptimizer(\n",
    "            learning_rate = learning_rate\n",
    "        ).minimize(self.cost)\n",
    "        mask = tf.sequence_mask(self.lengths, maxlen = self.maxlen)\n",
    "        self.tags_seq, tags_score = tf.contrib.crf.crf_decode(\n",
    "            logits, transition_params, self.lengths\n",
    "        )\n",
    "        self.tags_seq = tf.identity(self.tags_seq, name = 'logits')\n",
    "\n",
    "        y_t = tf.cast(y_t, tf.int32)\n",
    "        self.prediction = tf.boolean_mask(self.tags_seq, mask)\n",
    "        mask_label = tf.boolean_mask(y_t, mask)\n",
    "        correct_pred = tf.equal(self.prediction, mask_label)\n",
    "        correct_index = tf.cast(correct_pred, tf.float32)\n",
    "        self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py:507: calling count_nonzero (from tensorflow.python.ops.math_ops) with axis is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "reduction_indices is deprecated, use axis instead\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:194: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:507: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:588: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:1025: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:253: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:99: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/contrib/crf/python/ops/crf.py:213: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "INFO:tensorflow:Restoring parameters from albert-base-2020-04-10/model.ckpt-400000\n"
     ]
    }
   ],
   "source": [
    "dimension_output = len(tag2idx)\n",
    "learning_rate = 2e-5\n",
    "\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "model = Model(\n",
    "    dimension_output,\n",
    "    learning_rate\n",
    ")\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "var_lists = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope = 'bert')\n",
    "saver = tf.train.Saver(var_list = var_lists)\n",
    "saver.restore(sess, BERT_INIT_CHKPNT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_sentencepiece_tokens_tagging(x, y):\n",
    "    new_paired_tokens = []\n",
    "    n_tokens = len(x)\n",
    "    rejected = ['[CLS]', '[SEP]']\n",
    "\n",
    "    i = 0\n",
    "\n",
    "    while i < n_tokens:\n",
    "\n",
    "        current_token, current_label = x[i], y[i]\n",
    "        if not current_token.startswith('▁') and current_token not in rejected:\n",
    "            previous_token, previous_label = new_paired_tokens.pop()\n",
    "            merged_token = previous_token\n",
    "            merged_label = [previous_label]\n",
    "            while (\n",
    "                not current_token.startswith('▁')\n",
    "                and current_token not in rejected\n",
    "            ):\n",
    "                merged_token = merged_token + current_token.replace('▁', '')\n",
    "                merged_label.append(current_label)\n",
    "                i = i + 1\n",
    "                current_token, current_label = x[i], y[i]\n",
    "            merged_label = merged_label[0]\n",
    "            new_paired_tokens.append((merged_token, merged_label))\n",
    "\n",
    "        else:\n",
    "            new_paired_tokens.append((current_token, current_label))\n",
    "            i = i + 1\n",
    "\n",
    "    words = [\n",
    "        i[0].replace('▁', '')\n",
    "        for i in new_paired_tokens\n",
    "        if i[0] not in rejected\n",
    "    ]\n",
    "    labels = [i[1] for i in new_paired_tokens if i[0] not in rejected]\n",
    "    return words, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "string = 'KUALA LUMPUR: Sempena sambutan Aidilfitri minggu depan, Perdana Menteri Tun Dr Mahathir Mohamad dan Menteri Pengangkutan Anthony Loke Siew Fook menitipkan pesanan khas kepada orang ramai yang mahu pulang ke kampung halaman masing-masing. Dalam video pendek terbitan Jabatan Keselamatan Jalan Raya (JKJR) itu, Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar  sekiranya mengantuk ketika memandu.'\n",
    "\n",
    "import re\n",
    "\n",
    "def entities_textcleaning(string, lowering = False):\n",
    "    \"\"\"\n",
    "    use by entities recognition, pos recognition and dependency parsing\n",
    "    \"\"\"\n",
    "    string = re.sub('[^A-Za-z0-9\\-\\/() ]+', ' ', string)\n",
    "    string = re.sub(r'[ ]+', ' ', string).strip()\n",
    "    original_string = string.split()\n",
    "    if lowering:\n",
    "        string = string.lower()\n",
    "    string = [\n",
    "        (original_string[no], word.title() if word.isupper() else word)\n",
    "        for no, word in enumerate(string.split())\n",
    "        if len(word)\n",
    "    ]\n",
    "    return [s[0] for s in string], [s[1] for s in string]\n",
    "\n",
    "def parse_X(left):\n",
    "    bert_tokens = ['[CLS]']\n",
    "    for no, orig_token in enumerate(left):\n",
    "        t = tokenizer.tokenize(orig_token)\n",
    "        bert_tokens.extend(t)\n",
    "    bert_tokens.append(\"[SEP]\")\n",
    "    input_mask = [1] * len(bert_tokens)\n",
    "    return tokenizer.convert_tokens_to_ids(bert_tokens), bert_tokens, input_mask\n",
    "\n",
    "sequence = entities_textcleaning(string)[1]\n",
    "parsed_sequence, bert_sequence, input_mask = parse_X(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Kuala', 'NUM'),\n",
       " ('Lumpur', 'DET'),\n",
       " ('Sempena', 'DET'),\n",
       " ('sambutan', 'DET'),\n",
       " ('Aidilfitri', 'DET'),\n",
       " ('minggu', 'ADP'),\n",
       " ('depan', 'PRON'),\n",
       " ('Perdana', 'DET'),\n",
       " ('Menteri', 'ADV'),\n",
       " ('Tun', 'DET'),\n",
       " ('Dr', 'DET'),\n",
       " ('Mahathir', 'ADV'),\n",
       " ('Mohamad', 'ADP'),\n",
       " ('dan', 'NUM'),\n",
       " ('Menteri', 'ADP'),\n",
       " ('Pengangkutan', 'PRON'),\n",
       " ('Anthony', 'ADV'),\n",
       " ('Loke', 'CCONJ'),\n",
       " ('Siew', 'SYM'),\n",
       " ('Fook', 'PROPN'),\n",
       " ('menitipkan', 'CCONJ'),\n",
       " ('pesanan', 'ADP'),\n",
       " ('khas', 'PRON'),\n",
       " ('kepada', 'DET'),\n",
       " ('orang', 'DET'),\n",
       " ('ramai', 'DET'),\n",
       " ('yang', 'SCONJ'),\n",
       " ('mahu', 'ADP'),\n",
       " ('pulang', 'VERB'),\n",
       " ('ke', 'ADP'),\n",
       " ('kampung', 'NUM'),\n",
       " ('halaman', 'DET'),\n",
       " ('masing-masing', 'PUNCT'),\n",
       " ('Dalam', 'NUM'),\n",
       " ('video', 'DET'),\n",
       " ('pendek', 'DET'),\n",
       " ('terbitan', 'PUNCT'),\n",
       " ('Jabatan', 'ADP'),\n",
       " ('Keselamatan', 'PAD'),\n",
       " ('Jalan', 'ADJ'),\n",
       " ('Raya', 'DET'),\n",
       " ('(Jkjr)', 'ADV'),\n",
       " ('itu', 'ADJ'),\n",
       " ('Dr', 'DET'),\n",
       " ('Mahathir', 'DET'),\n",
       " ('menasihati', 'DET'),\n",
       " ('mereka', 'DET'),\n",
       " ('supaya', 'DET'),\n",
       " ('berhenti', 'ADV'),\n",
       " ('berehat', 'DET'),\n",
       " ('dan', 'ADV'),\n",
       " ('tidur', 'DET'),\n",
       " ('sebentar', 'DET'),\n",
       " ('sekiranya', 'ADV'),\n",
       " ('mengantuk', 'DET'),\n",
       " ('ketika', 'ADV'),\n",
       " ('memandu', 'DET')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = sess.run(model.tags_seq,\n",
    "                feed_dict = {\n",
    "                    model.X: [parsed_sequence],\n",
    "                    model.MASK: [input_mask]\n",
    "                },\n",
    "        )[0]\n",
    "merged = merge_sentencepiece_tokens_tagging(bert_sequence, [idx2tag[d] for d in predicted])\n",
    "list(zip(merged[0], merged[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pad_sequences = tf.keras.preprocessing.sequence.pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 3047/3047 [17:12<00:00,  2.95it/s, accuracy=0.999, cost=0.502]\n",
      "test minibatch loop: 100%|██████████| 761/761 [01:53<00:00,  6.70it/s, accuracy=0.93, cost=15.5]  \n",
      "train minibatch loop:   0%|          | 0/3047 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 1145.5612037181854\n",
      "epoch: 0, training loss: 5.181466, training acc: 0.980690, valid loss: 20.421957, valid acc: 0.947423\n",
      "\n",
      "[('Kuala', 'PROPN'), ('Lumpur', 'PROPN'), ('Sempena', 'ADP'), ('sambutan', 'NOUN'), ('Aidilfitri', 'NOUN'), ('minggu', 'NOUN'), ('depan', 'ADJ'), ('Perdana', 'PROPN'), ('Menteri', 'PROPN'), ('Tun', 'PROPN'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('Mohamad', 'PROPN'), ('dan', 'CCONJ'), ('Menteri', 'PROPN'), ('Pengangkutan', 'PROPN'), ('Anthony', 'PROPN'), ('Loke', 'PROPN'), ('Siew', 'PROPN'), ('Fook', 'PROPN'), ('menitipkan', 'VERB'), ('pesanan', 'NOUN'), ('khas', 'ADJ'), ('kepada', 'ADP'), ('orang', 'NOUN'), ('ramai', 'ADJ'), ('yang', 'PRON'), ('mahu', 'ADV'), ('pulang', 'VERB'), ('ke', 'ADP'), ('kampung', 'NOUN'), ('halaman', 'NOUN'), ('masing-masing', 'DET'), ('Dalam', 'ADP'), ('video', 'NOUN'), ('pendek', 'ADJ'), ('terbitan', 'NOUN'), ('Jabatan', 'NOUN'), ('Keselamatan', 'PROPN'), ('Jalan', 'PROPN'), ('Raya', 'PROPN'), ('(Jkjr)', 'PUNCT'), ('itu', 'DET'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('menasihati', 'VERB'), ('mereka', 'PRON'), ('supaya', 'SCONJ'), ('berhenti', 'VERB'), ('berehat', 'NOUN'), ('dan', 'CCONJ'), ('tidur', 'NOUN'), ('sebentar', 'ADV'), ('sekiranya', 'SCONJ'), ('mengantuk', 'ADJ'), ('ketika', 'SCONJ'), ('memandu', 'VERB')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 3047/3047 [17:38<00:00,  2.88it/s, accuracy=1, cost=0.0267]    \n",
      "test minibatch loop: 100%|██████████| 761/761 [01:54<00:00,  6.67it/s, accuracy=0.957, cost=16.3] \n",
      "train minibatch loop:   0%|          | 0/3047 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 1172.2023594379425\n",
      "epoch: 1, training loss: 0.332680, training acc: 0.998782, valid loss: 24.184954, valid acc: 0.947097\n",
      "\n",
      "[('Kuala', 'PROPN'), ('Lumpur', 'PROPN'), ('Sempena', 'ADP'), ('sambutan', 'NOUN'), ('Aidilfitri', 'NOUN'), ('minggu', 'NOUN'), ('depan', 'ADJ'), ('Perdana', 'PROPN'), ('Menteri', 'PROPN'), ('Tun', 'PROPN'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('Mohamad', 'PROPN'), ('dan', 'CCONJ'), ('Menteri', 'PROPN'), ('Pengangkutan', 'PROPN'), ('Anthony', 'PROPN'), ('Loke', 'PROPN'), ('Siew', 'PROPN'), ('Fook', 'PROPN'), ('menitipkan', 'VERB'), ('pesanan', 'NOUN'), ('khas', 'ADJ'), ('kepada', 'ADP'), ('orang', 'NOUN'), ('ramai', 'ADJ'), ('yang', 'PRON'), ('mahu', 'ADV'), ('pulang', 'VERB'), ('ke', 'ADP'), ('kampung', 'NOUN'), ('halaman', 'NOUN'), ('masing-masing', 'ADJ'), ('Dalam', 'ADP'), ('video', 'NOUN'), ('pendek', 'ADJ'), ('terbitan', 'NOUN'), ('Jabatan', 'NOUN'), ('Keselamatan', 'PROPN'), ('Jalan', 'PROPN'), ('Raya', 'PROPN'), ('(Jkjr)', 'PUNCT'), ('itu', 'DET'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('menasihati', 'VERB'), ('mereka', 'PRON'), ('supaya', 'SCONJ'), ('berhenti', 'VERB'), ('berehat', 'NOUN'), ('dan', 'CCONJ'), ('tidur', 'NOUN'), ('sebentar', 'ADV'), ('sekiranya', 'SCONJ'), ('mengantuk', 'ADJ'), ('ketika', 'SCONJ'), ('memandu', 'VERB')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 3047/3047 [17:34<00:00,  2.89it/s, accuracy=0.999, cost=0.113] \n",
      "test minibatch loop: 100%|██████████| 761/761 [01:54<00:00,  6.67it/s, accuracy=0.927, cost=26.8] \n",
      "train minibatch loop:   0%|          | 0/3047 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 1168.9695131778717\n",
      "epoch: 2, training loss: 0.163441, training acc: 0.999344, valid loss: 25.017164, valid acc: 0.947461\n",
      "\n",
      "[('Kuala', 'PROPN'), ('Lumpur', 'PROPN'), ('Sempena', 'ADP'), ('sambutan', 'NOUN'), ('Aidilfitri', 'NOUN'), ('minggu', 'NOUN'), ('depan', 'ADJ'), ('Perdana', 'PROPN'), ('Menteri', 'PROPN'), ('Tun', 'PROPN'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('Mohamad', 'PROPN'), ('dan', 'CCONJ'), ('Menteri', 'PROPN'), ('Pengangkutan', 'PROPN'), ('Anthony', 'PROPN'), ('Loke', 'PROPN'), ('Siew', 'PROPN'), ('Fook', 'PROPN'), ('menitipkan', 'VERB'), ('pesanan', 'NOUN'), ('khas', 'ADJ'), ('kepada', 'ADP'), ('orang', 'NOUN'), ('ramai', 'ADJ'), ('yang', 'PRON'), ('mahu', 'ADV'), ('pulang', 'VERB'), ('ke', 'ADP'), ('kampung', 'NOUN'), ('halaman', 'NOUN'), ('masing-masing', 'DET'), ('Dalam', 'ADP'), ('video', 'NOUN'), ('pendek', 'ADJ'), ('terbitan', 'NOUN'), ('Jabatan', 'PROPN'), ('Keselamatan', 'PROPN'), ('Jalan', 'PROPN'), ('Raya', 'PROPN'), ('(Jkjr)', 'PUNCT'), ('itu', 'DET'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('menasihati', 'VERB'), ('mereka', 'PRON'), ('supaya', 'SCONJ'), ('berhenti', 'VERB'), ('berehat', 'NOUN'), ('dan', 'CCONJ'), ('tidur', 'NOUN'), ('sebentar', 'NOUN'), ('sekiranya', 'SCONJ'), ('mengantuk', 'ADJ'), ('ketika', 'SCONJ'), ('memandu', 'NOUN')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 3047/3047 [18:03<00:00,  2.81it/s, accuracy=1, cost=0.00908]   \n",
      "test minibatch loop: 100%|██████████| 761/761 [01:53<00:00,  6.71it/s, accuracy=0.944, cost=30.4] \n",
      "train minibatch loop:   0%|          | 0/3047 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 1196.8177373409271\n",
      "epoch: 3, training loss: 0.111088, training acc: 0.999538, valid loss: 26.703230, valid acc: 0.948273\n",
      "\n",
      "[('Kuala', 'PROPN'), ('Lumpur', 'PROPN'), ('Sempena', 'ADP'), ('sambutan', 'NOUN'), ('Aidilfitri', 'NOUN'), ('minggu', 'NOUN'), ('depan', 'ADJ'), ('Perdana', 'PROPN'), ('Menteri', 'PROPN'), ('Tun', 'PROPN'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('Mohamad', 'PROPN'), ('dan', 'CCONJ'), ('Menteri', 'PROPN'), ('Pengangkutan', 'PROPN'), ('Anthony', 'PROPN'), ('Loke', 'PROPN'), ('Siew', 'PROPN'), ('Fook', 'PROPN'), ('menitipkan', 'VERB'), ('pesanan', 'NOUN'), ('khas', 'ADJ'), ('kepada', 'ADP'), ('orang', 'NOUN'), ('ramai', 'NOUN'), ('yang', 'PRON'), ('mahu', 'ADV'), ('pulang', 'VERB'), ('ke', 'ADP'), ('kampung', 'NOUN'), ('halaman', 'NOUN'), ('masing-masing', 'ADJ'), ('Dalam', 'ADP'), ('video', 'NOUN'), ('pendek', 'ADJ'), ('terbitan', 'NOUN'), ('Jabatan', 'NOUN'), ('Keselamatan', 'PROPN'), ('Jalan', 'PROPN'), ('Raya', 'PROPN'), ('(Jkjr)', 'PUNCT'), ('itu', 'DET'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('menasihati', 'VERB'), ('mereka', 'PRON'), ('supaya', 'SCONJ'), ('berhenti', 'VERB'), ('berehat', 'NOUN'), ('dan', 'CCONJ'), ('tidur', 'NOUN'), ('sebentar', 'ADJ'), ('sekiranya', 'SCONJ'), ('mengantuk', 'ADJ'), ('ketika', 'SCONJ'), ('memandu', 'VERB')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 3047/3047 [17:11<00:00,  2.95it/s, accuracy=1, cost=0.00946]   \n",
      "test minibatch loop: 100%|██████████| 761/761 [01:53<00:00,  6.69it/s, accuracy=0.954, cost=16.9] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 1145.4248275756836\n",
      "epoch: 4, training loss: 0.091034, training acc: 0.999627, valid loss: 27.951061, valid acc: 0.947842\n",
      "\n",
      "[('Kuala', 'PROPN'), ('Lumpur', 'PROPN'), ('Sempena', 'ADP'), ('sambutan', 'NOUN'), ('Aidilfitri', 'NOUN'), ('minggu', 'NOUN'), ('depan', 'ADJ'), ('Perdana', 'PROPN'), ('Menteri', 'PROPN'), ('Tun', 'PROPN'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('Mohamad', 'PROPN'), ('dan', 'CCONJ'), ('Menteri', 'PROPN'), ('Pengangkutan', 'PROPN'), ('Anthony', 'PROPN'), ('Loke', 'PROPN'), ('Siew', 'PROPN'), ('Fook', 'PROPN'), ('menitipkan', 'VERB'), ('pesanan', 'NOUN'), ('khas', 'ADJ'), ('kepada', 'ADP'), ('orang', 'NOUN'), ('ramai', 'ADJ'), ('yang', 'PRON'), ('mahu', 'ADV'), ('pulang', 'VERB'), ('ke', 'ADP'), ('kampung', 'NOUN'), ('halaman', 'NOUN'), ('masing-masing', 'DET'), ('Dalam', 'ADP'), ('video', 'NOUN'), ('pendek', 'ADJ'), ('terbitan', 'NOUN'), ('Jabatan', 'PROPN'), ('Keselamatan', 'PROPN'), ('Jalan', 'PROPN'), ('Raya', 'PROPN'), ('(Jkjr)', 'PUNCT'), ('itu', 'DET'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('menasihati', 'VERB'), ('mereka', 'PRON'), ('supaya', 'SCONJ'), ('berhenti', 'VERB'), ('berehat', 'VERB'), ('dan', 'CCONJ'), ('tidur', 'VERB'), ('sebentar', 'ADJ'), ('sekiranya', 'SCONJ'), ('mengantuk', 'ADJ'), ('ketika', 'SCONJ'), ('memandu', 'VERB')]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "for e in range(epoch):\n",
    "    lasttime = time.time()\n",
    "    train_acc, train_loss, test_acc, test_loss = [], [], [], []\n",
    "    pbar = tqdm(\n",
    "        range(0, len(train_X), batch_size), desc = 'train minibatch loop'\n",
    "    )\n",
    "    for i in pbar:\n",
    "        index = min(i + batch_size, len(train_X))\n",
    "        batch_x = train_X[i : index]\n",
    "        batch_y = train_Y[i : index]\n",
    "        batch_masks = train_masks[i : index]\n",
    "        batch_x = pad_sequences(batch_x, padding='post')\n",
    "        batch_y = pad_sequences(batch_y, padding='post')\n",
    "        batch_masks = pad_sequences(batch_masks, padding='post')\n",
    "        \n",
    "        acc, cost, _ = sess.run(\n",
    "            [model.accuracy, model.cost, model.optimizer],\n",
    "            feed_dict = {\n",
    "                model.X: batch_x,\n",
    "                model.Y: batch_y,\n",
    "                model.MASK: batch_masks,\n",
    "            },\n",
    "        )\n",
    "        assert not np.isnan(cost)\n",
    "        train_loss.append(cost)\n",
    "        train_acc.append(acc)\n",
    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
    "    \n",
    "    pbar = tqdm(\n",
    "        range(0, len(test_X), batch_size), desc = 'test minibatch loop'\n",
    "    )\n",
    "    for i in pbar:\n",
    "        index = min(i + batch_size, len(test_X))\n",
    "        batch_x = test_X[i : index]\n",
    "        batch_y = test_Y[i : index]\n",
    "        batch_masks = test_masks[i : index]\n",
    "        batch_x = pad_sequences(batch_x, padding='post')\n",
    "        batch_y = pad_sequences(batch_y, padding='post')\n",
    "        batch_masks = pad_sequences(batch_masks, padding='post')\n",
    "        \n",
    "        acc, cost = sess.run(\n",
    "            [model.accuracy, model.cost],\n",
    "            feed_dict = {\n",
    "                model.X: batch_x,\n",
    "                model.Y: batch_y,\n",
    "                model.MASK: batch_masks,\n",
    "            },\n",
    "        )\n",
    "        assert not np.isnan(cost)\n",
    "        test_loss.append(cost)\n",
    "        test_acc.append(acc)\n",
    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
    "    \n",
    "    train_loss = np.mean(train_loss)\n",
    "    train_acc = np.mean(train_acc)\n",
    "    test_loss = np.mean(test_loss)\n",
    "    test_acc = np.mean(test_acc)\n",
    "\n",
    "    print('time taken:', time.time() - lasttime)\n",
    "    print(\n",
    "        'epoch: %d, training loss: %f, training acc: %f, valid loss: %f, valid acc: %f\\n'\n",
    "        % (e, train_loss, train_acc, test_loss, test_acc)\n",
    "    )\n",
    "    predicted = sess.run(model.tags_seq,\n",
    "                feed_dict = {\n",
    "                    model.X: [parsed_sequence],\n",
    "                    model.MASK: [input_mask]\n",
    "                },\n",
    "        )[0]\n",
    "    merged = merge_sentencepiece_tokens_tagging(bert_sequence, [idx2tag[d] for d in predicted])\n",
    "    print(list(zip(merged[0], merged[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'albert-base-pos/model.ckpt'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saver = tf.train.Saver(tf.trainable_variables())\n",
    "saver.save(sess, 'albert-base-pos/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from albert-base-pos/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "dimension_output = len(tag2idx)\n",
    "learning_rate = 2e-5\n",
    "\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "model = Model(\n",
    "    dimension_output,\n",
    "    learning_rate,\n",
    "    training = False\n",
    ")\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "saver = tf.train.Saver(tf.trainable_variables())\n",
    "saver.restore(sess, 'albert-base-pos/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred2label(pred):\n",
    "    out = []\n",
    "    for pred_i in pred:\n",
    "        out_i = []\n",
    "        for p in pred_i:\n",
    "            out_i.append(idx2tag[p])\n",
    "        out.append(out_i)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation minibatch loop: 100%|██████████| 761/761 [01:57<00:00,  6.48it/s]\n"
     ]
    }
   ],
   "source": [
    "real_Y, predict_Y = [], []\n",
    "\n",
    "pbar = tqdm(\n",
    "    range(0, len(test_X), batch_size), desc = 'validation minibatch loop'\n",
    ")\n",
    "for i in pbar:\n",
    "    index = min(i + batch_size, len(test_X))\n",
    "    batch_x = test_X[i : index]\n",
    "    batch_y = test_Y[i : index]\n",
    "    batch_masks = test_masks[i : index]\n",
    "    batch_x = pad_sequences(batch_x, padding='post')\n",
    "    batch_y = pad_sequences(batch_y, padding='post')\n",
    "    batch_masks = pad_sequences(batch_masks, padding='post')\n",
    "    predicted = pred2label(sess.run(model.tags_seq,\n",
    "            feed_dict = {\n",
    "                model.X: batch_x,\n",
    "                model.MASK: batch_masks,\n",
    "            },\n",
    "    ))\n",
    "    real = pred2label(batch_y)\n",
    "    predict_Y.extend(predicted)\n",
    "    real_Y.extend(real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_real_Y = []\n",
    "for r in real_Y:\n",
    "    temp_real_Y.extend(r)\n",
    "    \n",
    "temp_predict_Y = []\n",
    "for r in predict_Y:\n",
    "    temp_predict_Y.extend(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ADJ    0.81706   0.76324   0.78923     45666\n",
      "         ADP    0.95181   0.96143   0.95660    119589\n",
      "         ADV    0.84898   0.84148   0.84521     47760\n",
      "         AUX    0.99502   1.00000   0.99751     10000\n",
      "       CCONJ    0.93370   0.94071   0.93719     37171\n",
      "         DET    0.93324   0.92824   0.93073     38839\n",
      "        NOUN    0.90102   0.89915   0.90008    268329\n",
      "         NUM    0.93291   0.94002   0.93645     41211\n",
      "         PAD    1.00000   1.00000   1.00000    147215\n",
      "        PART    0.91795   0.89909   0.90842      5500\n",
      "        PRON    0.97728   0.93198   0.95409     48835\n",
      "       PROPN    0.91565   0.93866   0.92701    227608\n",
      "       PUNCT    0.99818   0.99890   0.99854    182824\n",
      "       SCONJ    0.79499   0.74330   0.76828     15150\n",
      "         SYM    0.98485   0.90278   0.94203      3600\n",
      "        VERB    0.94143   0.94251   0.94197    124518\n",
      "           X    0.99972   0.99975   0.99973    414899\n",
      "\n",
      "    accuracy                        0.95105   1778714\n",
      "   macro avg    0.93199   0.91948   0.92547   1778714\n",
      "weighted avg    0.95085   0.95105   0.95088   1778714\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(temp_real_Y, temp_predict_Y, digits = 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Placeholder',\n",
       " 'Placeholder_1',\n",
       " 'Placeholder_2',\n",
       " 'bert/embeddings/word_embeddings',\n",
       " 'bert/embeddings/token_type_embeddings',\n",
       " 'bert/embeddings/position_embeddings',\n",
       " 'bert/embeddings/LayerNorm/gamma',\n",
       " 'bert/encoder/embedding_hidden_mapping_in/kernel',\n",
       " 'bert/encoder/embedding_hidden_mapping_in/bias',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/bias',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/kernel',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/bias',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/kernel',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/bias',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/kernel',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/bias',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm/gamma',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/kernel',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/bias',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/kernel',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/bias',\n",
       " 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm_1/gamma',\n",
       " 'bert/pooler/dense/kernel',\n",
       " 'bert/pooler/dense/bias',\n",
       " 'dense/kernel',\n",
       " 'dense/bias',\n",
       " 'dense_1/kernel',\n",
       " 'dense_1/bias',\n",
       " 'transitions',\n",
       " 'logits']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strings = ','.join(\n",
    "    [\n",
    "        n.name\n",
    "        for n in tf.get_default_graph().as_graph_def().node\n",
    "        if ('Variable' in n.op\n",
    "        or 'Placeholder' in n.name\n",
    "        or 'logits' in n.name\n",
    "        or 'alphas' in n.name\n",
    "        or 'self/Softmax' in n.name)\n",
    "        and 'Adam' not in n.name\n",
    "        and 'beta' not in n.name\n",
    "        and 'global_step' not in n.name\n",
    "    ]\n",
    ")\n",
    "strings.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freeze_graph(model_dir, output_node_names):\n",
    "\n",
    "    if not tf.gfile.Exists(model_dir):\n",
    "        raise AssertionError(\n",
    "            \"Export directory doesn't exists. Please specify an export \"\n",
    "            'directory: %s' % model_dir\n",
    "        )\n",
    "\n",
    "    checkpoint = tf.train.get_checkpoint_state(model_dir)\n",
    "    input_checkpoint = checkpoint.model_checkpoint_path\n",
    "\n",
    "    absolute_model_dir = '/'.join(input_checkpoint.split('/')[:-1])\n",
    "    output_graph = absolute_model_dir + '/frozen_model.pb'\n",
    "    clear_devices = True\n",
    "    with tf.Session(graph = tf.Graph()) as sess:\n",
    "        saver = tf.train.import_meta_graph(\n",
    "            input_checkpoint + '.meta', clear_devices = clear_devices\n",
    "        )\n",
    "        saver.restore(sess, input_checkpoint)\n",
    "        output_graph_def = tf.graph_util.convert_variables_to_constants(\n",
    "            sess,\n",
    "            tf.get_default_graph().as_graph_def(),\n",
    "            output_node_names.split(','),\n",
    "        )\n",
    "        with tf.gfile.GFile(output_graph, 'wb') as f:\n",
    "            f.write(output_graph_def.SerializeToString())\n",
    "        print('%d ops in the final graph.' % len(output_graph_def.node))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from albert-base-pos/model.ckpt\n",
      "WARNING:tensorflow:From <ipython-input-31-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
      "INFO:tensorflow:Froze 30 variables.\n",
      "INFO:tensorflow:Converted 30 variables to const ops.\n",
      "3539 ops in the final graph.\n"
     ]
    }
   ],
   "source": [
    "freeze_graph('albert-base-pos', strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n"
     ]
    }
   ],
   "source": [
    "def load_graph(frozen_graph_filename):\n",
    "    with tf.gfile.GFile(frozen_graph_filename, 'rb') as f:\n",
    "        graph_def = tf.GraphDef()\n",
    "        graph_def.ParseFromString(f.read())\n",
    "    with tf.Graph().as_default() as graph:\n",
    "        tf.import_graph_def(graph_def)\n",
    "    return graph\n",
    "\n",
    "g = load_graph('albert-base-pos/frozen_model.pb')\n",
    "x = g.get_tensor_by_name('import/Placeholder:0')\n",
    "mask = g.get_tensor_by_name('import/Placeholder_1:0')\n",
    "logits = g.get_tensor_by_name('import/logits:0')\n",
    "test_sess = tf.InteractiveSession(graph = g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Kuala', 'PROPN'), ('Lumpur', 'PROPN'), ('Sempena', 'ADP'), ('sambutan', 'NOUN'), ('Aidilfitri', 'NOUN'), ('minggu', 'NOUN'), ('depan', 'ADJ'), ('Perdana', 'PROPN'), ('Menteri', 'PROPN'), ('Tun', 'PROPN'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('Mohamad', 'PROPN'), ('dan', 'CCONJ'), ('Menteri', 'PROPN'), ('Pengangkutan', 'PROPN'), ('Anthony', 'PROPN'), ('Loke', 'PROPN'), ('Siew', 'PROPN'), ('Fook', 'PROPN'), ('menitipkan', 'VERB'), ('pesanan', 'NOUN'), ('khas', 'ADJ'), ('kepada', 'ADP'), ('orang', 'NOUN'), ('ramai', 'ADJ'), ('yang', 'PRON'), ('mahu', 'ADV'), ('pulang', 'VERB'), ('ke', 'ADP'), ('kampung', 'NOUN'), ('halaman', 'NOUN'), ('masing-masing', 'DET'), ('Dalam', 'ADP'), ('video', 'NOUN'), ('pendek', 'ADJ'), ('terbitan', 'NOUN'), ('Jabatan', 'PROPN'), ('Keselamatan', 'PROPN'), ('Jalan', 'PROPN'), ('Raya', 'PROPN'), ('(Jkjr)', 'PUNCT'), ('itu', 'DET'), ('Dr', 'PROPN'), ('Mahathir', 'PROPN'), ('menasihati', 'VERB'), ('mereka', 'PRON'), ('supaya', 'SCONJ'), ('berhenti', 'VERB'), ('berehat', 'VERB'), ('dan', 'CCONJ'), ('tidur', 'VERB'), ('sebentar', 'ADJ'), ('sekiranya', 'SCONJ'), ('mengantuk', 'ADJ'), ('ketika', 'SCONJ'), ('memandu', 'VERB')]\n"
     ]
    }
   ],
   "source": [
    "predicted = test_sess.run(logits,\n",
    "            feed_dict = {\n",
    "                x: [parsed_sequence],\n",
    "                mask: [input_mask]\n",
    "            },\n",
    "    )[0]\n",
    "merged = merge_sentencepiece_tokens_tagging(bert_sequence, [idx2tag[d] for d in predicted])\n",
    "print(list(zip(merged[0], merged[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "bucketName = 'huseinhouse-storage'\n",
    "Key = 'albert-base-pos/frozen_model.pb'\n",
    "outPutname = \"v34/pos/albert-base-pos.pb\"\n",
    "\n",
    "s3 = boto3.client('s3')\n",
    "\n",
    "s3.upload_file(Key,bucketName,outPutname)"
   ]
  }
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