{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/alxnet/model_utils.py:334: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import xlnet\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tqdm import tqdm\n",
    "import model_utils\n",
    "import pickle\n",
    "import json\n",
    "pad_sequences = tf.keras.preprocessing.sequence.pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sentencepiece as spm\n",
    "from prepro_utils import preprocess_text, encode_ids\n",
    "\n",
    "sp_model = spm.SentencePieceProcessor()\n",
    "sp_model.Load('alxlnet-base/sp10m.cased.v9.model')\n",
    "\n",
    "def tokenize_fn(text):\n",
    "    text = preprocess_text(text, lower= False)\n",
    "    return encode_ids(sp_model, text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['train_X', 'test_X', 'train_Y', 'test_Y'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../session-entities.pkl', 'rb') as fopen:\n",
    "    data = pickle.load(fopen)\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['word2idx', 'idx2word', 'tag2idx', 'idx2tag', 'char2idx'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../dictionary-entities.json') as fopen:\n",
    "    dictionary = json.load(fopen)\n",
    "dictionary.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "SEG_ID_A   = 0\n",
    "SEG_ID_B   = 1\n",
    "SEG_ID_CLS = 2\n",
    "SEG_ID_SEP = 3\n",
    "SEG_ID_PAD = 4\n",
    "\n",
    "special_symbols = {\n",
    "    \"<unk>\"  : 0,\n",
    "    \"<s>\"    : 1,\n",
    "    \"</s>\"   : 2,\n",
    "    \"<cls>\"  : 3,\n",
    "    \"<sep>\"  : 4,\n",
    "    \"<pad>\"  : 5,\n",
    "    \"<mask>\" : 6,\n",
    "    \"<eod>\"  : 7,\n",
    "    \"<eop>\"  : 8,\n",
    "}\n",
    "\n",
    "VOCAB_SIZE = 32000\n",
    "UNK_ID = special_symbols[\"<unk>\"]\n",
    "CLS_ID = special_symbols[\"<cls>\"]\n",
    "SEP_ID = special_symbols[\"<sep>\"]\n",
    "MASK_ID = special_symbols[\"<mask>\"]\n",
    "EOD_ID = special_symbols[\"<eod>\"]\n",
    "\n",
    "def XY(left_train, right_train):\n",
    "    X, Y, segments, masks = [], [], [], []\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 = []\n",
    "        y = []\n",
    "        for no, orig_token in enumerate(left):\n",
    "            t = tokenize_fn(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.extend([4, 3])\n",
    "        segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
    "        input_mask = [0] * len(segment)\n",
    "        y.extend(['PAD', 'PAD'])\n",
    "        y = [tag2idx[i] for i in y]\n",
    "        if len(bert_tokens) != len(y):\n",
    "            print(i)\n",
    "        X.append(bert_tokens)\n",
    "        Y.append(y)\n",
    "        segments.append(segment)\n",
    "        masks.append(input_mask)\n",
    "    return X, Y, segments, masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 588199/588199 [09:02<00:00, 1083.56it/s]\n",
      "100%|██████████| 147013/147013 [02:17<00:00, 1066.39it/s]\n"
     ]
    }
   ],
   "source": [
    "train_X, train_Y, train_segments, train_masks = XY(train_X, train_Y)\n",
    "test_X, test_Y, test_segments, test_masks = XY(test_X, test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XY_extra(left_train, right_train):\n",
    "    X, Y = [], []\n",
    "    segments, masks = [], []\n",
    "    for i in tqdm(range(len(left_train))):\n",
    "        left = left_train[i]\n",
    "        right = right_train[i]\n",
    "        bert_tokens = []\n",
    "        y = []\n",
    "        for no, orig_token in enumerate(left):\n",
    "            t = tokenize_fn(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.extend([4, 3])\n",
    "        segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
    "        input_mask = [0] * len(segment)\n",
    "        y.extend(['PAD', 'PAD'])\n",
    "        y = [tag2idx[i] for i in y]\n",
    "        if len(bert_tokens) != len(y):\n",
    "            print(i)\n",
    "        X.append(bert_tokens)\n",
    "        Y.append(y)\n",
    "        segments.append(segment)\n",
    "        masks.append(input_mask)\n",
    "    return X, Y, segments, masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['X', 'Y'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../extra-entities.json') as fopen:\n",
    "    extra = json.load(fopen)\n",
    "    \n",
    "extra.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 236143/236143 [01:56<00:00, 2030.71it/s]\n"
     ]
    }
   ],
   "source": [
    "extra_X, extra_Y, extra_segments, extra_masks = XY_extra(extra['X'], extra['Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X.extend(extra_X)\n",
    "train_Y.extend(extra_Y)\n",
    "train_masks.extend(extra_masks)\n",
    "train_segments.extend(extra_segments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "train_X, train_Y, train_masks, train_segments = shuffle(train_X, train_Y, train_masks, train_segments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/alxnet/xlnet.py:70: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kwargs = dict(\n",
    "      is_training=True,\n",
    "      use_tpu=False,\n",
    "      use_bfloat16=False,\n",
    "      dropout=0.1,\n",
    "      dropatt=0.1,\n",
    "      init='normal',\n",
    "      init_range=0.1,\n",
    "      init_std=0.05,\n",
    "      clamp_len=-1)\n",
    "\n",
    "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
    "xlnet_config = xlnet.XLNetConfig(json_path='alxlnet-base/config.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128803 12880\n"
     ]
    }
   ],
   "source": [
    "epoch = 5\n",
    "batch_size = 32\n",
    "warmup_proportion = 0.1\n",
    "num_train_steps = int(len(train_X) / batch_size * epoch)\n",
    "num_warmup_steps = int(num_train_steps * warmup_proportion)\n",
    "print(num_train_steps, num_warmup_steps)\n",
    "\n",
    "training_parameters = dict(\n",
    "      decay_method = 'poly',\n",
    "      train_steps = num_train_steps,\n",
    "      learning_rate = 2e-5,\n",
    "      warmup_steps = num_warmup_steps,\n",
    "      min_lr_ratio = 0.0,\n",
    "      weight_decay = 0.00,\n",
    "      adam_epsilon = 1e-8,\n",
    "      num_core_per_host = 1,\n",
    "      lr_layer_decay_rate = 1,\n",
    "      use_tpu=False,\n",
    "      use_bfloat16=False,\n",
    "      dropout=0.0,\n",
    "      dropatt=0.0,\n",
    "      init='normal',\n",
    "      init_range=0.1,\n",
    "      init_std=0.02,\n",
    "      clip = 1.0,\n",
    "      clamp_len=-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parameter:\n",
    "    def __init__(self, decay_method, warmup_steps, weight_decay, adam_epsilon, \n",
    "                num_core_per_host, lr_layer_decay_rate, use_tpu, learning_rate, train_steps,\n",
    "                min_lr_ratio, clip, **kwargs):\n",
    "        self.decay_method = decay_method\n",
    "        self.warmup_steps = warmup_steps\n",
    "        self.weight_decay = weight_decay\n",
    "        self.adam_epsilon = adam_epsilon\n",
    "        self.num_core_per_host = num_core_per_host\n",
    "        self.lr_layer_decay_rate = lr_layer_decay_rate\n",
    "        self.use_tpu = use_tpu\n",
    "        self.learning_rate = learning_rate\n",
    "        self.train_steps = train_steps\n",
    "        self.min_lr_ratio = min_lr_ratio\n",
    "        self.clip = clip\n",
    "        \n",
    "training_parameters = Parameter(**training_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        dimension_output,\n",
    "        learning_rate = 2e-5,\n",
    "    ):\n",
    "        self.X = tf.placeholder(tf.int32, [None, None])\n",
    "        self.segment_ids = tf.placeholder(tf.int32, [None, None])\n",
    "        self.input_masks = tf.placeholder(tf.float32, [None, None])\n",
    "        self.Y = tf.placeholder(tf.int32, [None, None])\n",
    "        self.lengths = tf.count_nonzero(self.X, 1)\n",
    "        self.maxlen = tf.shape(self.X)[1]\n",
    "        \n",
    "        xlnet_model = xlnet.XLNetModel(\n",
    "            xlnet_config=xlnet_config,\n",
    "            run_config=xlnet_parameters,\n",
    "            input_ids=tf.transpose(self.X, [1, 0]),\n",
    "            seg_ids=tf.transpose(self.segment_ids, [1, 0]),\n",
    "            input_mask=tf.transpose(self.input_masks, [1, 0]))\n",
    "        output_layer = xlnet_model.get_sequence_output()\n",
    "        output_layer = tf.transpose(output_layer, [1, 0, 2])\n",
    "        \n",
    "        logits = tf.layers.dense(output_layer, dimension_output)\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": 19,
   "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/alxnet/xlnet.py:253: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/alxnet/xlnet.py:253: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/alxnet/custom_modeling.py:696: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
      "\n",
      "INFO:tensorflow:memory input None\n",
      "INFO:tensorflow:Use float type <dtype: 'float32'>\n",
      "WARNING:tensorflow:From /home/husein/alxnet/custom_modeling.py:703: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/alxnet/custom_modeling.py:808: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dropout instead.\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:271: 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/alxnet/custom_modeling.py:109: 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/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"
     ]
    }
   ],
   "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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import re\n",
    "\n",
    "def get_assignment_map_from_checkpoint(tvars, init_checkpoint):\n",
    "    \"\"\"Compute the union of the current variables and checkpoint variables.\"\"\"\n",
    "    assignment_map = {}\n",
    "    initialized_variable_names = {}\n",
    "\n",
    "    name_to_variable = collections.OrderedDict()\n",
    "    for var in tvars:\n",
    "        name = var.name\n",
    "        m = re.match('^(.*):\\\\d+$', name)\n",
    "        if m is not None:\n",
    "            name = m.group(1)\n",
    "        name_to_variable[name] = var\n",
    "\n",
    "    init_vars = tf.train.list_variables(init_checkpoint)\n",
    "\n",
    "    assignment_map = collections.OrderedDict()\n",
    "    for x in init_vars:\n",
    "        (name, var) = (x[0], x[1])\n",
    "        if name not in name_to_variable:\n",
    "            continue\n",
    "        assignment_map[name] = name_to_variable[name]\n",
    "        initialized_variable_names[name] = 1\n",
    "        initialized_variable_names[name + ':0'] = 1\n",
    "\n",
    "    return (assignment_map, initialized_variable_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvars = tf.trainable_variables()\n",
    "checkpoint = 'alxlnet-base/model.ckpt'\n",
    "assignment_map, initialized_variable_names = get_assignment_map_from_checkpoint(tvars, \n",
    "                                                                                checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from alxlnet-base/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.Saver(var_list = assignment_map)\n",
    "saver.restore(sess, checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 ['<cls>', '<sep>']\n",
    "    ]\n",
    "    labels = [i[1] for i in new_paired_tokens if i[0] not in ['<cls>', '<sep>']]\n",
    "    return words, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 = []\n",
    "    for no, orig_token in enumerate(left):\n",
    "        t = tokenize_fn(orig_token)\n",
    "        bert_tokens.extend(t)\n",
    "    bert_tokens.extend([4, 3])\n",
    "    segment = [0] * (len(bert_tokens) - 1) + [SEG_ID_CLS]\n",
    "    input_mask = [0] * len(segment)\n",
    "    s_tokens = [sp_model.IdToPiece(i) for i in bert_tokens]\n",
    "    return bert_tokens, segment, input_mask, s_tokens\n",
    "\n",
    "sequence = entities_textcleaning(string)[1]\n",
    "parsed_sequence, segment_sequence, mask_sequence, xlnet_sequence = parse_X(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Kuala', 'person'),\n",
       " ('Lumpur', 'X'),\n",
       " ('Sempena', 'PAD'),\n",
       " ('sambutan', 'PAD'),\n",
       " ('Aidilfitri', 'quantity'),\n",
       " ('minggu', 'PAD'),\n",
       " ('depan', 'person'),\n",
       " ('Perdana', 'X'),\n",
       " ('Menteri', 'law'),\n",
       " ('Tun', 'person'),\n",
       " ('Dr', 'X'),\n",
       " ('Mahathir', 'person'),\n",
       " ('Mohamad', 'X'),\n",
       " ('dan', 'person'),\n",
       " ('Menteri', 'person'),\n",
       " ('Pengangkutan', 'PAD'),\n",
       " ('Anthony', 'person'),\n",
       " ('Loke', 'X'),\n",
       " ('Siew', 'quantity'),\n",
       " ('Fook', 'X'),\n",
       " ('menitipkan', 'organization'),\n",
       " ('pesanan', 'PAD'),\n",
       " ('khas', 'time'),\n",
       " ('kepada', 'quantity'),\n",
       " ('orang', 'PAD'),\n",
       " ('ramai', 'organization'),\n",
       " ('yang', 'location'),\n",
       " ('mahu', 'person'),\n",
       " ('pulang', 'quantity'),\n",
       " ('ke', 'time'),\n",
       " ('kampung', 'quantity'),\n",
       " ('halaman', 'person'),\n",
       " ('masing-masing', 'person'),\n",
       " ('Dalam', 'organization'),\n",
       " ('video', 'time'),\n",
       " ('pendek', 'PAD'),\n",
       " ('terbitan', 'person'),\n",
       " ('Jabatan', 'X'),\n",
       " ('Keselamatan', 'X'),\n",
       " ('Jalan', 'quantity'),\n",
       " ('Raya', 'time'),\n",
       " ('(Jkjr)', 'organization'),\n",
       " ('itu', 'OTHER'),\n",
       " ('Dr', 'PAD'),\n",
       " ('Mahathir', 'organization'),\n",
       " ('menasihati', 'time'),\n",
       " ('mereka', 'PAD'),\n",
       " ('supaya', 'organization'),\n",
       " ('berhenti', 'PAD'),\n",
       " ('berehat', 'organization'),\n",
       " ('dan', 'person'),\n",
       " ('tidur', 'time'),\n",
       " ('sebentar', 'organization'),\n",
       " ('sekiranya', 'PAD'),\n",
       " ('mengantuk', 'person'),\n",
       " ('ketika', 'location'),\n",
       " ('memandu', 'law')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = sess.run(model.tags_seq,\n",
    "                feed_dict = {\n",
    "                    model.X: [parsed_sequence],\n",
    "                    model.segment_ids: [segment_sequence],\n",
    "                    model.input_masks: [mask_sequence],\n",
    "                },\n",
    "        )[0]\n",
    "merged = merge_sentencepiece_tokens_tagging(xlnet_sequence, [idx2tag[d] for d in predicted])\n",
    "list(zip(merged[0], merged[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 25761/25761 [3:14:37<00:00,  2.21it/s, accuracy=1, cost=0.0107]      \n",
      "test minibatch loop: 100%|██████████| 4595/4595 [13:38<00:00,  5.61it/s, accuracy=0.997, cost=0.374] \n",
      "train minibatch loop:   0%|          | 0/25761 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 12495.88380599022\n",
      "epoch: 0, training loss: 0.873521, training acc: 0.995528, valid loss: 4.284812, valid acc: 0.987093\n",
      "\n",
      "[('Kuala', 'location'), ('Lumpur', 'OTHER'), ('Sempena', 'OTHER'), ('sambutan', 'OTHER'), ('Aidilfitri', 'event'), ('minggu', 'time'), ('depan', 'time'), ('Perdana', 'person'), ('Menteri', 'person'), ('Tun', 'person'), ('Dr', 'person'), ('Mahathir', 'person'), ('Mohamad', 'person'), ('dan', 'OTHER'), ('Menteri', 'person'), ('Pengangkutan', 'person'), ('Anthony', 'person'), ('Loke', 'person'), ('Siew', 'person'), ('Fook', 'person'), ('menitipkan', 'OTHER'), ('pesanan', 'OTHER'), ('khas', 'OTHER'), ('kepada', 'OTHER'), ('orang', 'OTHER'), ('ramai', 'OTHER'), ('yang', 'OTHER'), ('mahu', 'OTHER'), ('pulang', 'OTHER'), ('ke', 'OTHER'), ('kampung', 'location'), ('halaman', 'location'), ('masing-masing', 'OTHER'), ('Dalam', 'OTHER'), ('video', 'OTHER'), ('pendek', 'OTHER'), ('terbitan', 'OTHER'), ('Jabatan', 'organization'), ('Keselamatan', 'organization'), ('Jalan', 'organization'), ('Raya', 'organization'), ('(Jkjr)', 'organization'), ('itu', 'OTHER'), ('Dr', 'person'), ('Mahathir', 'person'), ('menasihati', 'OTHER'), ('mereka', 'OTHER'), ('supaya', 'OTHER'), ('berhenti', 'OTHER'), ('berehat', 'OTHER'), ('dan', 'OTHER'), ('tidur', 'OTHER'), ('sebentar', 'OTHER'), ('sekiranya', 'OTHER'), ('mengantuk', 'OTHER'), ('ketika', 'OTHER'), ('memandu', 'OTHER')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 25761/25761 [3:10:50<00:00,  2.25it/s, accuracy=0.998, cost=0.541]   \n",
      "test minibatch loop: 100%|██████████| 4595/4595 [13:37<00:00,  5.62it/s, accuracy=1, cost=0.00366]   \n",
      "train minibatch loop:   0%|          | 0/25761 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 12268.562037229538\n",
      "epoch: 1, training loss: 0.066880, training acc: 0.999672, valid loss: 3.689238, valid acc: 0.989760\n",
      "\n",
      "[('Kuala', 'OTHER'), ('Lumpur', 'OTHER'), ('Sempena', 'OTHER'), ('sambutan', 'OTHER'), ('Aidilfitri', 'event'), ('minggu', 'time'), ('depan', 'time'), ('Perdana', 'person'), ('Menteri', 'person'), ('Tun', 'person'), ('Dr', 'person'), ('Mahathir', 'person'), ('Mohamad', 'person'), ('dan', 'OTHER'), ('Menteri', 'organization'), ('Pengangkutan', 'person'), ('Anthony', 'person'), ('Loke', 'person'), ('Siew', 'person'), ('Fook', 'person'), ('menitipkan', 'OTHER'), ('pesanan', 'OTHER'), ('khas', 'OTHER'), ('kepada', 'OTHER'), ('orang', 'OTHER'), ('ramai', 'OTHER'), ('yang', 'OTHER'), ('mahu', 'OTHER'), ('pulang', 'OTHER'), ('ke', 'OTHER'), ('kampung', 'OTHER'), ('halaman', 'location'), ('masing-masing', 'OTHER'), ('Dalam', 'OTHER'), ('video', 'OTHER'), ('pendek', 'OTHER'), ('terbitan', 'OTHER'), ('Jabatan', 'organization'), ('Keselamatan', 'organization'), ('Jalan', 'organization'), ('Raya', 'organization'), ('(Jkjr)', 'organization'), ('itu', 'OTHER'), ('Dr', 'person'), ('Mahathir', 'person'), ('menasihati', 'OTHER'), ('mereka', 'OTHER'), ('supaya', 'OTHER'), ('berhenti', 'OTHER'), ('berehat', 'OTHER'), ('dan', 'OTHER'), ('tidur', 'OTHER'), ('sebentar', 'OTHER'), ('sekiranya', 'OTHER'), ('mengantuk', 'OTHER'), ('ketika', 'OTHER'), ('memandu', 'OTHER')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 25761/25761 [3:10:53<00:00,  2.25it/s, accuracy=1, cost=0.000669]    \n",
      "test minibatch loop: 100%|██████████| 4595/4595 [13:37<00:00,  5.62it/s, accuracy=0.986, cost=2.72]  \n",
      "train minibatch loop:   0%|          | 0/25761 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 12271.858207464218\n",
      "epoch: 2, training loss: 0.040417, training acc: 0.999798, valid loss: 4.356400, valid acc: 0.988857\n",
      "\n",
      "[('Kuala', 'location'), ('Lumpur', 'location'), ('Sempena', 'OTHER'), ('sambutan', 'OTHER'), ('Aidilfitri', 'event'), ('minggu', 'time'), ('depan', 'time'), ('Perdana', 'person'), ('Menteri', 'person'), ('Tun', 'person'), ('Dr', 'person'), ('Mahathir', 'person'), ('Mohamad', 'person'), ('dan', 'OTHER'), ('Menteri', 'organization'), ('Pengangkutan', 'organization'), ('Anthony', 'person'), ('Loke', 'person'), ('Siew', 'person'), ('Fook', 'person'), ('menitipkan', 'OTHER'), ('pesanan', 'OTHER'), ('khas', 'OTHER'), ('kepada', 'OTHER'), ('orang', 'OTHER'), ('ramai', 'OTHER'), ('yang', 'OTHER'), ('mahu', 'OTHER'), ('pulang', 'OTHER'), ('ke', 'OTHER'), ('kampung', 'OTHER'), ('halaman', 'OTHER'), ('masing-masing', 'OTHER'), ('Dalam', 'OTHER'), ('video', 'OTHER'), ('pendek', 'OTHER'), ('terbitan', 'OTHER'), ('Jabatan', 'organization'), ('Keselamatan', 'organization'), ('Jalan', 'organization'), ('Raya', 'organization'), ('(Jkjr)', 'organization'), ('itu', 'OTHER'), ('Dr', 'person'), ('Mahathir', 'person'), ('menasihati', 'OTHER'), ('mereka', 'OTHER'), ('supaya', 'OTHER'), ('berhenti', 'OTHER'), ('berehat', 'OTHER'), ('dan', 'OTHER'), ('tidur', 'OTHER'), ('sebentar', 'OTHER'), ('sekiranya', 'OTHER'), ('mengantuk', 'OTHER'), ('ketika', 'OTHER'), ('memandu', 'OTHER')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 25761/25761 [3:11:17<00:00,  2.24it/s, accuracy=1, cost=0.00481]     \n",
      "test minibatch loop: 100%|██████████| 4595/4595 [13:39<00:00,  5.61it/s, accuracy=1, cost=0.103]     \n",
      "train minibatch loop:   0%|          | 0/25761 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 12296.835916519165\n",
      "epoch: 3, training loss: 0.030563, training acc: 0.999846, valid loss: 4.397701, valid acc: 0.988817\n",
      "\n",
      "[('Kuala', 'OTHER'), ('Lumpur', 'location'), ('Sempena', 'OTHER'), ('sambutan', 'event'), ('Aidilfitri', 'event'), ('minggu', 'time'), ('depan', 'OTHER'), ('Perdana', 'person'), ('Menteri', 'person'), ('Tun', 'person'), ('Dr', 'person'), ('Mahathir', 'person'), ('Mohamad', 'person'), ('dan', 'OTHER'), ('Menteri', 'organization'), ('Pengangkutan', 'organization'), ('Anthony', 'person'), ('Loke', 'person'), ('Siew', 'person'), ('Fook', 'person'), ('menitipkan', 'OTHER'), ('pesanan', 'OTHER'), ('khas', 'OTHER'), ('kepada', 'OTHER'), ('orang', 'OTHER'), ('ramai', 'OTHER'), ('yang', 'OTHER'), ('mahu', 'OTHER'), ('pulang', 'OTHER'), ('ke', 'OTHER'), ('kampung', 'OTHER'), ('halaman', 'OTHER'), ('masing-masing', 'OTHER'), ('Dalam', 'OTHER'), ('video', 'OTHER'), ('pendek', 'OTHER'), ('terbitan', 'OTHER'), ('Jabatan', 'organization'), ('Keselamatan', 'organization'), ('Jalan', 'organization'), ('Raya', 'organization'), ('(Jkjr)', 'organization'), ('itu', 'OTHER'), ('Dr', 'person'), ('Mahathir', 'person'), ('menasihati', 'OTHER'), ('mereka', 'OTHER'), ('supaya', 'OTHER'), ('berhenti', 'OTHER'), ('berehat', 'OTHER'), ('dan', 'OTHER'), ('tidur', 'OTHER'), ('sebentar', 'OTHER'), ('sekiranya', 'OTHER'), ('mengantuk', 'OTHER'), ('ketika', 'OTHER'), ('memandu', 'OTHER')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 25761/25761 [3:11:06<00:00,  2.25it/s, accuracy=1, cost=0.00148]     \n",
      "test minibatch loop: 100%|██████████| 4595/4595 [13:40<00:00,  5.60it/s, accuracy=0.997, cost=0.188] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 12287.391575336456\n",
      "epoch: 4, training loss: 0.024718, training acc: 0.999875, valid loss: 4.226960, valid acc: 0.989498\n",
      "\n",
      "[('Kuala', 'location'), ('Lumpur', 'location'), ('Sempena', 'OTHER'), ('sambutan', 'OTHER'), ('Aidilfitri', 'event'), ('minggu', 'time'), ('depan', 'time'), ('Perdana', 'person'), ('Menteri', 'person'), ('Tun', 'person'), ('Dr', 'person'), ('Mahathir', 'person'), ('Mohamad', 'person'), ('dan', 'OTHER'), ('Menteri', 'organization'), ('Pengangkutan', 'organization'), ('Anthony', 'person'), ('Loke', 'person'), ('Siew', 'person'), ('Fook', 'person'), ('menitipkan', 'OTHER'), ('pesanan', 'OTHER'), ('khas', 'OTHER'), ('kepada', 'OTHER'), ('orang', 'OTHER'), ('ramai', 'OTHER'), ('yang', 'OTHER'), ('mahu', 'OTHER'), ('pulang', 'OTHER'), ('ke', 'OTHER'), ('kampung', 'OTHER'), ('halaman', 'OTHER'), ('masing-masing', 'OTHER'), ('Dalam', 'OTHER'), ('video', 'OTHER'), ('pendek', 'OTHER'), ('terbitan', 'OTHER'), ('Jabatan', 'organization'), ('Keselamatan', 'organization'), ('Jalan', 'organization'), ('Raya', 'organization'), ('(Jkjr)', 'organization'), ('itu', 'OTHER'), ('Dr', 'person'), ('Mahathir', 'person'), ('menasihati', 'OTHER'), ('mereka', 'OTHER'), ('supaya', 'OTHER'), ('berhenti', 'OTHER'), ('berehat', 'OTHER'), ('dan', 'OTHER'), ('tidur', 'OTHER'), ('sebentar', 'OTHER'), ('sekiranya', 'OTHER'), ('mengantuk', 'OTHER'), ('ketika', 'OTHER'), ('memandu', 'OTHER')]\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_segments = train_segments[i : index]\n",
    "        batch_x = pad_sequences(batch_x, padding='post')\n",
    "        batch_y = pad_sequences(batch_y, padding='post')\n",
    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\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.segment_ids: batch_segments,\n",
    "                model.input_masks: 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_segments = test_segments[i : index]\n",
    "        batch_x = pad_sequences(batch_x, padding='post')\n",
    "        batch_y = pad_sequences(batch_y, padding='post')\n",
    "        batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
    "        batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\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.segment_ids: batch_segments,\n",
    "                model.input_masks: 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",
    "    \n",
    "    predicted = sess.run(model.tags_seq,\n",
    "                feed_dict = {\n",
    "                    model.X: [parsed_sequence],\n",
    "                    model.segment_ids: [segment_sequence],\n",
    "                    model.input_masks: [mask_sequence],\n",
    "                },\n",
    "        )[0]\n",
    "    merged = merge_sentencepiece_tokens_tagging(xlnet_sequence, [idx2tag[d] for d in predicted])\n",
    "    print(list(zip(merged[0], merged[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'alxlnet-base-entities/model.ckpt'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saver = tf.train.Saver(tf.trainable_variables())\n",
    "saver.save(sess, 'alxlnet-base-entities/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = dict(\n",
    "      is_training=False,\n",
    "      use_tpu=False,\n",
    "      use_bfloat16=False,\n",
    "      dropout=0.0,\n",
    "      dropatt=0.0,\n",
    "      init='normal',\n",
    "      init_range=0.1,\n",
    "      init_std=0.05,\n",
    "      clamp_len=-1)\n",
    "\n",
    "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
    "xlnet_config = xlnet.XLNetConfig(json_path='alxlnet-base/config.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:memory input None\n",
      "INFO:tensorflow:Use float type <dtype: 'float32'>\n"
     ]
    },
    {
     "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": [
    "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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from alxlnet-base-entities/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.Saver(tf.trainable_variables())\n",
    "saver.restore(sess, 'alxlnet-base-entities/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation minibatch loop: 100%|██████████| 4595/4595 [13:30<00:00,  5.67it/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_segments = test_segments[i : index]\n",
    "    batch_x = pad_sequences(batch_x, padding='post')\n",
    "    batch_y = pad_sequences(batch_y, padding='post')\n",
    "    batch_segments = pad_sequences(batch_segments, padding='post', value = 4)\n",
    "    batch_masks = pad_sequences(batch_masks, padding='post', value = 1)\n",
    "    predicted = pred2label(sess.run(\n",
    "        model.tags_seq,\n",
    "        feed_dict = {\n",
    "            model.X: batch_x,\n",
    "            model.segment_ids: batch_segments,\n",
    "            model.input_masks: batch_masks,\n",
    "        },\n",
    "    ))\n",
    "    real = pred2label(batch_y)\n",
    "    predict_Y.extend(predicted)\n",
    "    real_Y.extend(real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       OTHER    0.99124   0.99962   0.99541   5160854\n",
      "         PAD    1.00000   1.00000   1.00000    877767\n",
      "           X    1.00000   1.00000   1.00000   2921053\n",
      "       event    0.99766   0.86900   0.92890    143787\n",
      "         law    0.99837   0.97023   0.98410    146950\n",
      "    location    0.99004   0.98249   0.98625    428869\n",
      "organization    0.99584   0.94088   0.96758    694150\n",
      "      person    0.96062   0.99571   0.97785    507960\n",
      "    quantity    0.99920   0.99976   0.99948     88200\n",
      "        time    0.98851   0.99976   0.99410    179880\n",
      "\n",
      "    accuracy                        0.99319  11149470\n",
      "   macro avg    0.99215   0.97575   0.98337  11149470\n",
      "weighted avg    0.99327   0.99319   0.99309  11149470\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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Placeholder',\n",
       " 'Placeholder_1',\n",
       " 'Placeholder_2',\n",
       " 'Placeholder_3',\n",
       " 'model/transformer/r_w_bias',\n",
       " 'model/transformer/r_r_bias',\n",
       " 'model/transformer/word_embedding/lookup_table',\n",
       " 'model/transformer/word_embedding/lookup_table_2',\n",
       " 'model/transformer/r_s_bias',\n",
       " 'model/transformer/seg_embed',\n",
       " 'model/transformer/layer_shared/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_shared/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_shared/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_shared/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_shared/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_shared/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_shared/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_shared/ff/layer_1/bias',\n",
       " 'model/transformer/layer_shared/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_shared/ff/layer_2/bias',\n",
       " 'model/transformer/layer_shared/ff/LayerNorm/gamma',\n",
       " 'dense/kernel',\n",
       " 'dense/bias',\n",
       " 'transitions',\n",
       " 'logits']"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from alxlnet-base-entities/model.ckpt\n",
      "WARNING:tensorflow:From <ipython-input-37-9a7215a4e58a>: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 22 variables.\n",
      "INFO:tensorflow:Converted 22 variables to const ops.\n",
      "7687 ops in the final graph.\n"
     ]
    }
   ],
   "source": [
    "freeze_graph('alxlnet-base-entities', strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "bucketName = 'huseinhouse-storage'\n",
    "Key = 'alxlnet-base-entities/frozen_model.pb'\n",
    "outPutname = \"v34/entity/alxlnet-base-entity.pb\"\n",
    "\n",
    "s3 = boto3.client('s3')\n",
    "\n",
    "s3.upload_file(Key,bucketName,outPutname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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