notebook/thai_romanize_pytorch_seq2seq_attention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/artificiala/thai-romanization/blob/master/thai_romanize_pytorch_seq2seq_attention.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rSq5OByfI83o"
   },
   "source": [
    "# Thai to English Transliteration with Seq2Seq model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_CW69fmZI83p"
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "from __future__ import unicode_literals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oWHEtNGSI83s"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import sys\n",
    "import os\n",
    "import random\n",
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "from torch import optim\n",
    "from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler\n",
    "from torchvision import transforms, utils\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "from collections import Counter\n",
    "from matplotlib import pyplot as plt\n",
    "from collections import OrderedDict\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "\n",
    "SEED = 0\n",
    "np.random.seed(SEED)\n",
    "random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.font_manager.FontProperties at 0x7fbbe80f1128>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from matplotlib import font_manager as fm, rcParams\n",
    "\n",
    "fpath = os.path.join(rcParams[\"datapath\"], \"/usr/share/fonts/truetype/tlwg/Loma-Bold.ttf\")\n",
    "prop = fm.FontProperties(fname=fpath)\n",
    "# ax.set_title('This is a special font: {}'.format(fname), fontproperties=prop)\n",
    "prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "x-id2JKQJy9M",
    "outputId": "87d9f6c3-4fd5-47b6-8530-c6b5ea867933"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Aug  6 08:30:51 2019       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 418.67       Driver Version: 418.67       CUDA Version: 10.1     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  GeForce RTX 208...  Off  | 00000000:03:00.0 Off |                  N/A |\r\n",
      "| 25%   34C    P0     1W / 250W |      0MiB / 10989MiB |      0%      Default |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                       GPU Memory |\r\n",
      "|  GPU       PID   Type   Process name                             Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|  No running processes found                                                 |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "h-Z7H_FxI83w",
    "outputId": "0c0fd18a-a3f1-459e-ac73-99625b871312"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if GPUs are in the machine, otherwise assign device as CPU\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "krV1812tJco8",
    "outputId": "57559032-a348-40e6-a68d-2c8324279bbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'thai-romanization' already exists and is not an empty directory.\r\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/artificiala/thai-romanization.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "XEnBLgx1JhGT",
    "outputId": "5942526f-5452-4658-df65-8b9405709b5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thai2rom-pytorch.attn.v6.best_epoch-1.tar\r\n",
      "thai-romanization\r\n",
      "thai-romanize-pytorch-seq2seq-attention.html\r\n",
      "thai_romanize_pytorch_seq2seq_attention.ipynb\r\n",
      "thai-romanize-pytorch-seq2seq-attention.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MiXtrNtoI830"
   },
   "source": [
    "The csv file contains two columns indicates Thai text and its corresponding English tranliteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "4Itj2fQkI831",
    "outputId": "80ab0868-ef4e-4e22-bf56-f976a7da5fd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "กองพันทหารปืนใหญ่\tkongphanthahanpuenyai\r\n",
      "วิฑูรย์\twithun\r\n",
      "เมตาบอลิสม\tmetabolisom\r\n",
      "บ้านหนองเลา\tbannonglao\r\n",
      "อายุษ\tayut\r\n",
      "แทมปา\tthaempa\r\n",
      "ประเทศกรีนแลนด์\tprathetkrinlaen\r\n",
      "พรรคคองเกรส\tphakkhongkerot\r\n",
      "การสูบ\tkansup\r\n",
      "บ้านเทพพยัคฆ์ใต้\tbanthepphayaktai\r\n"
     ]
    }
   ],
   "source": [
    "DATA_PATH = '../dataset/data.csv'\n",
    "!head $DATA_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IXA7cPkwI834"
   },
   "outputs": [],
   "source": [
    "\n",
    "def load_data(data_path):\n",
    "    with open(data_path, 'r', encoding='utf-8-sig') as f:\n",
    "        lines = f.read().rstrip().split('\\n')\n",
    "\n",
    "    input_texts = []\n",
    "    target_texts = []\n",
    "    for line in lines:\n",
    "        line = line.replace(u'\\xa0',' ')\n",
    "        input_text, target_text = line.split('\\t')\n",
    "        input_texts.append(input_text)\n",
    "        target_texts.append(target_text)\n",
    "\n",
    "    return input_texts, target_texts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "cEtf-SEDI837",
    "outputId": "274bf054-cc08-494e-f963-cc6d75ecd32e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 584 ms, sys: 164 ms, total: 748 ms\n",
      "Wall time: 747 ms\n"
     ]
    }
   ],
   "source": [
    "%time input_texts, target_texts = load_data(DATA_PATH)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zPT4ZmzSI83-"
   },
   "outputs": [],
   "source": [
    "# Define special characters\n",
    "UNK_token = '<UNK>'\n",
    "PAD_token = '<PAD>'\n",
    "START_token = '<start>'\n",
    "END_token = '<end>'\n",
    "MAX_LENGTH = 60\n",
    "\n",
    "class Language:\n",
    "    def __init__(self, name, is_input=False):\n",
    "        self.name = name\n",
    "        self.characters = set()\n",
    "        self.n_chars = 0\n",
    "        self.char2index = {}\n",
    "        self.index2char = {}\n",
    "\n",
    "        if is_input == True:\n",
    "            self.index2char = { 0: PAD_token, 1: UNK_token, 2: START_token, 3: END_token }\n",
    "            self.char2index = { ch:i for i, ch in self.index2char.items() } #reverse dictionary\n",
    "            self.n_chars = 4\n",
    "        else:\n",
    "            self.index2char = { 0: PAD_token, 1: START_token, 2: END_token }\n",
    "            self.char2index = { ch:i for i, ch in self.index2char.items() } #reverse dictionary\n",
    "            self.n_chars = 3\n",
    "\n",
    "    def addText(self, text):\n",
    "        for character in text:\n",
    "            self.addCharacter(character)\n",
    "    \n",
    "    def addCharacter(self, character):\n",
    "        if character not in self.char2index.keys():\n",
    "            self.char2index[character] = self.n_chars\n",
    "            self.index2char[self.n_chars] = character\n",
    "            self.n_chars += 1\n",
    "            \n",
    "            \n",
    "def indexesFromText(lang, text):\n",
    "    \"\"\"returns indexes for all character given the text in the specified language\"\"\"\n",
    "    return [lang.char2index[char] for char in text]\n",
    "\n",
    "def tensorFromText(lang, text):\n",
    "    \"\"\"construct a tensor given the text in the specified language\"\"\"\n",
    "    indexes = indexesFromText(lang, text)\n",
    "    indexes.append(lang.char2index[END_token])\n",
    "    \n",
    "    no_padded_seq_length = len(indexes) # Number of characters in the text (including <END> token)\n",
    "    # Add padding token to make all tensors in the same length\n",
    "    for i in range(len(indexes), MAX_LENGTH): # padding\n",
    "        indexes.append(lang.char2index[PAD_token])\n",
    "        \n",
    "    return torch.tensor(indexes, dtype=torch.long).to(device), no_padded_seq_length\n",
    "\n",
    "def filterPair(p1, p2):\n",
    "    \"\"\"filter for the pair the both texts has length less than `MAX_LENGTH`\"\"\"\n",
    "    return len(p1) < MAX_LENGTH and len(p2) < MAX_LENGTH\n",
    "\n",
    "def tensorsFromPair(pair, lang1, lang2):\n",
    "    \"\"\"construct two tensors from a pair of source and target text specified by source and target language\"\"\"\n",
    "    input_tensor, input_length = tensorFromText(lang1, pair[0])\n",
    "    target_tensor, target_length = tensorFromText(lang2, pair[1])\n",
    "    return input_tensor, target_tensor, input_length, target_length\n",
    "\n",
    "\n",
    "\n",
    "class ToTensor(object):\n",
    "    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        input_text, target_text, lang_th, lang_th_romanized = sample['input_text'], sample['target_text'], \\\n",
    "                                                              sample['lang_th'], sample['lang_th_romanized']\n",
    "\n",
    "        input_tensor, target_tensor, input_length, target_length = tensorsFromPair([input_text, target_text], \n",
    "                                                                                   lang_th, \n",
    "                                                                                   lang_th_romanized)\n",
    "        \n",
    "        return {\n",
    "                'input_text': input_text,\n",
    "                'target_text': target_text,\n",
    "                'input_length': input_length,\n",
    "                'target_length': target_length,\n",
    "                'input_tensor': input_tensor,\n",
    "                'target_tensor': target_tensor\n",
    "               }\n",
    "    \n",
    "    \n",
    "class ThaiRomanizationDataset(Dataset):\n",
    "    \"\"\"Thai Romanization Dataset class\"\"\"\n",
    "    def __init__(self, \n",
    "                 data_path=DATA_PATH, \n",
    "                 transform=transforms.Compose([ ToTensor() ])):\n",
    "\n",
    "        input_texts, target_texts = load_data(data_path)\n",
    "        \n",
    "        self.input_texts = input_texts\n",
    "        self.target_texts = target_texts\n",
    "        self.transform = transform\n",
    "        self.lang_th = None\n",
    "        self.lang_th_romanized = None\n",
    "        self.counter = Counter()\n",
    "        self.pairs = []\n",
    "        self.prepareData()\n",
    "\n",
    "    def prepareData(self):\n",
    "        self.lang_th = Language('th', is_input=True)\n",
    "        self.lang_th_romanized = Language('th_romanized', is_input=False)\n",
    "        for i in range(len(self.input_texts)):\n",
    "            \n",
    "            input_text = self.input_texts[i]\n",
    "            target_text = self.target_texts[i]\n",
    "            \n",
    "            # Count the number of input and target sequences with length `x`\n",
    "            self.counter.update({ \n",
    "                                  'len_input_{}'.format(len(input_text)): 1, \n",
    "                                  'len_target_{}'.format(len(target_text)): 1 \n",
    "                                })\n",
    "            \n",
    "            if filterPair(input_text, target_text):\n",
    "                self.pairs.append((input_text, target_text))\n",
    "                self.lang_th.addText(input_text)\n",
    "                self.lang_th_romanized.addText(target_text)    \n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.pairs)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        \n",
    "        sample = dict()\n",
    "        sample['input_text'] = self.pairs[idx][0]\n",
    "        sample['target_text'] = self.pairs[idx][1]\n",
    "        \n",
    "        sample['lang_th'] = self.lang_th\n",
    "        sample['lang_th_romanized'] = self.lang_th_romanized\n",
    "\n",
    "        if self.transform:\n",
    "            sample = self.transform(sample)\n",
    "\n",
    "        return sample\n",
    "    \n",
    "\n",
    "def save_model(name, epoch, loss, model):\n",
    "    print('Save model at epoch ', epoch)\n",
    "    torch.save({\n",
    "        'epoch': epoch,\n",
    "        'loss': loss,\n",
    "        'model_state_dict': model.state_dict(),\n",
    "        'optimizer_state_dict': optimizer.state_dict(),\n",
    "        'char_to_ix': thai_romanization_dataset.lang_th.char2index,\n",
    "        'ix_to_char': thai_romanization_dataset.lang_th.index2char,\n",
    "        'target_char_to_ix': thai_romanization_dataset.lang_th_romanized.char2index,\n",
    "        'ix_to_target_char':thai_romanization_dataset.lang_th_romanized.index2char,\n",
    "        'encoder_params': (INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, ENC_DROPOUT), \n",
    "        'decoder_params': (OUTPUT_DIM, DEC_EMB_DIM, DEC_HID_DIM, DEC_DROPOUT)\n",
    "        \n",
    "    }, \"{}.best_epoch-{}.tar\".format(name, epoch))\n",
    "    \n",
    " \n",
    "    \n",
    "def load_model(model_path):\n",
    "    \n",
    "    data = torch.load(model_path)\n",
    "    \n",
    "    INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, ENC_DROPOUT = data['encoder_params']\n",
    "    OUTPUT_DIM, DEC_EMB_DIM, DEC_HID_DIM, DEC_DROPOUT = data['decoder_params']\n",
    "\n",
    "    \n",
    "    encoder = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM).to(device)\n",
    "    decoder = AttentionDecoder(OUTPUT_DIM, DEC_EMB_DIM, DEC_HID_DIM).to(device)\n",
    "\n",
    "    model = Seq2Seq(encoder, decoder).to(device)\n",
    "    \n",
    "    model.load_state_dict(data['model_state_dict'])\n",
    "    \n",
    "    \n",
    "    learning_rate = 0.001\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "    optimizer.load_state_dict(data['optimizer_state_dict'])\n",
    "    criterion = nn.CrossEntropyLoss(ignore_index = 0)\n",
    "\n",
    "    char_to_ix = data['char_to_ix']\n",
    "    ix_to_char = data['ix_to_char'] \n",
    "    target_char_to_ix = data['target_char_to_ix']\n",
    "    ix_to_target_char = data['ix_to_target_char']\n",
    "    \n",
    "    \n",
    "    return {\n",
    "        'model': model,\n",
    "        'optmizer': optimizer,\n",
    "        'char_to_ix': char_to_ix,\n",
    "        'ix_to_char' : ix_to_char,\n",
    "        'target_char_to_ix': target_char_to_ix,\n",
    "        'ix_to_target_char': ix_to_target_char\n",
    "    }\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KxtnyncQI84B"
   },
   "outputs": [],
   "source": [
    "thai_romanization_dataset = ThaiRomanizationDataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ooP_qXNDI84E"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 697
    },
    "colab_type": "code",
    "id": "8X9wxkrqI84H",
    "outputId": "376da3a2-5ef6-4696-fd08-7bda27bb0a29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: '<PAD>',\n",
       " 1: '<start>',\n",
       " 2: '<end>',\n",
       " 3: 'k',\n",
       " 4: 'o',\n",
       " 5: 'n',\n",
       " 6: 'g',\n",
       " 7: 'p',\n",
       " 8: 'h',\n",
       " 9: 'a',\n",
       " 10: 't',\n",
       " 11: 'u',\n",
       " 12: 'e',\n",
       " 13: 'y',\n",
       " 14: 'i',\n",
       " 15: 'w',\n",
       " 16: 'm',\n",
       " 17: 'b',\n",
       " 18: 'l',\n",
       " 19: 's',\n",
       " 20: 'r',\n",
       " 21: 'd',\n",
       " 22: 'c',\n",
       " 23: 'f',\n",
       " 24: '-',\n",
       " 25: ' ',\n",
       " 26: '2',\n",
       " 27: '1',\n",
       " 28: '4',\n",
       " 29: '8',\n",
       " 30: '3',\n",
       " 31: '5',\n",
       " 32: '7',\n",
       " 33: '0',\n",
       " 34: '6',\n",
       " 35: '9',\n",
       " 36: '\"',\n",
       " 37: '!',\n",
       " 38: '(',\n",
       " 39: ')'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thai_romanization_dataset.lang_th_romanized.index2char"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SKFK1BsPI84K"
   },
   "source": [
    "## Seq2Seq Model architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BHHjBByAI84L"
   },
   "source": [
    "## 1. Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lAY1a-API84M"
   },
   "source": [
    "Encoder \n",
    "    - Embedding layer :(vocaburay_size, embedding_size) \n",
    "        Input: (batch_size, sequence_length)\n",
    "        Output: (batch_size, sequence_length, embebeding_size)\n",
    "      \n",
    "    - Bi-LSTM layer : (input_size, hidden_size, num_layers, batch_first=True)\n",
    "        Input: (input=(batch_size, seq_len, embebeding_size),  hidden)\n",
    "        Output: (output=(batch_size, seq_len, hidden_size),\n",
    "                 (h_n, c_n))\n",
    "     \n",
    "     \n",
    "__Steps:__\n",
    "\n",
    "1. Receives a batch of source sequences (batch_size, MAX_LENGTH) and a 1-D array of the length for each sequence (batch_size).\n",
    "     \n",
    "2. Sort sequences in the batch by sequence length (number of tokens in the sequence where <PAD> token is excluded).\n",
    "\n",
    "3. Feed the batch of sorted sequences into the Embedding Layer to maps source character indices into vectors. (batch_size,  sequence_length, embebeding_size)\n",
    "\n",
    "4. Use `pack_padded_sequence` to let LSTM packed input with same length at time step $t$ together. This will reduce time required for training by avoid feeding `<PAD>` token to the LSTMs.\n",
    "\n",
    "\n",
    "5. Returns LSTM outputs in the unsorted order, and the LSTM hidden state vectors.\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UFpV9J6oI84N"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    \n",
    "    def __init__(self, vocabulary_size, embedding_size, hidden_size, dropout=0.5):\n",
    "        \"\"\"Constructor\"\"\"\n",
    "        super(Encoder, self).__init__()\n",
    "        \n",
    "        self.hidden_size = hidden_size\n",
    "        self.character_embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
    "        self.rnn = nn.LSTM(input_size=embedding_size, \n",
    "                            hidden_size=hidden_size // 2, \n",
    "                            bidirectional=True,\n",
    "                            batch_first=True)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "\n",
    "    def forward(self, sequences, sequences_lengths):\n",
    "        batch_size = sequences.size(0)\n",
    "        hidden = self.init_hidden(batch_size) # batch_size\n",
    "\n",
    "#         print('encoder, hidden:', hidden)\n",
    "        # sequences :(batch_size, sequence_length=MAX_LENGTH)\n",
    "        # sequences_lengths: (batch_size)  # an 1-D indicating length of each sequence (excluded <PAD> token) in `seq`\n",
    "        \n",
    "        # 1. Firstly we sort `sequences_lengths` according to theirs values and keep list of indexes to perform sorting\n",
    "        sequences_lengths = np.sort(sequences_lengths)[::-1] # sort in ascending order and reverse it\n",
    "        index_sorted = np.argsort(-sequences_lengths) # use negation in sort in descending order\n",
    "        index_unsort = np.argsort(index_sorted) # to unsorted sequence\n",
    "        \n",
    "        \n",
    "        # 2. Then, we change position of sequence in `sequences` according to `index_sorted`\n",
    "        index_sorted = torch.from_numpy(index_sorted)\n",
    "        sequences = sequences.index_select(0, index_sorted.to(device))\n",
    "        \n",
    "        # 3. Feed to Embedding Layer\n",
    "        \n",
    "        sequences = self.character_embedding(sequences)\n",
    "        sequences = self.dropout(sequences)\n",
    "        \n",
    "#         print('sequences',sequences.size(), sequences)\n",
    "            \n",
    "        # 3. Use function: pack_padded_sequence to let LSTM packed input with same length at time step T together\n",
    "        \n",
    "        # Quick fix: Use seq_len.copy(), instead of seq_len to fix `Torch.from_numpy not support negative strides`\n",
    "        # ndarray.copy() will alocate new memory for numpy array which make it normal, I mean the stride is not negative any more.\n",
    "\n",
    "        sequences_packed = nn.utils.rnn.pack_padded_sequence(sequences,\n",
    "                                                             sequences_lengths.copy(),\n",
    "                                                             batch_first=True)\n",
    "#         print('sequences_packed', sequences_packed)\n",
    "\n",
    "        # 4. Feed to LSTM\n",
    "        sequences_output, hidden = self.rnn(sequences_packed, hidden)\n",
    "        \n",
    "        # 5. Unpack\n",
    "        sequences_output, _ = nn.utils.rnn.pad_packed_sequence(sequences_output, batch_first=True)\n",
    "\n",
    "        # 6. Un-sort by length\n",
    "        index_unsort = torch.from_numpy(index_unsort).to(device)\n",
    "        sequences_output = sequences_output.index_select(0, Variable(index_unsort))\n",
    "\n",
    "#         print('hidden shape', self.hidden[0].shape, self.hidden[0], self.hidden[1].shape, self.hidden[1])\n",
    "        return sequences_output, hidden\n",
    "    \n",
    "    def init_hidden(self, batch_size):\n",
    "        h_0 = torch.zeros([2, batch_size, self.hidden_size // 2], requires_grad=True).to(device)\n",
    "        c_0 = torch.zeros([2, batch_size, self.hidden_size // 2], requires_grad=True).to(device)\n",
    "\n",
    "        return (h_0, c_0)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_text': ['พักษ์', 'กิติโชติ อมรรัตนบงกช'], 'target_text': ['phak', 'kitichot amonrattanabongkot'], 'input_length': tensor([ 6, 21]), 'target_length': tensor([ 5, 28]), 'input_tensor': tensor([[ 7,  8,  4, 33, 24,  3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0],\n",
      "        [ 4, 20, 27, 20, 41, 43, 27, 20, 56,  5, 26, 13, 13,  8, 27,  9, 28,  6,\n",
      "          4, 43,  3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0]], device='cuda:0'), 'target_tensor': tensor([[ 7,  8,  9,  3,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0],\n",
      "        [ 3, 14, 10, 14, 22,  8,  4, 10, 25,  9, 16,  4,  5, 20,  9, 10, 10,  9,\n",
      "          5,  9, 17,  4,  5,  6,  3,  4, 10,  2,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0]], device='cuda:0')}\n",
      "input_tensor torch.Size([2, 60])\n",
      "input_length torch.Size([2])\n",
      "target_tensor torch.Size([2, 60])\n",
      "output torch.Size([2, 21, 10])\n",
      "hidden[0] torch.Size([2, 2, 5]) tensor([[[ 0.0820,  0.0584, -0.0531, -0.4465, -0.1663],\n",
      "         [ 0.0974,  0.0024,  0.0425, -0.0111, -0.4270]],\n",
      "\n",
      "        [[-0.1227,  0.0012,  0.3091,  0.2860, -0.0360],\n",
      "         [-0.1845,  0.0165,  0.0525, -0.2382,  0.0147]]], device='cuda:0',\n",
      "       grad_fn=<CudnnRnnBackward>)\n",
      "hidden[1] torch.Size([2, 2, 5]) tensor([[[ 2.3500e-01,  2.0160e-01, -1.1548e-01, -1.4721e+00, -3.8772e-01],\n",
      "         [ 1.9167e-01,  3.7074e-03,  5.6355e-01, -1.6330e-01, -4.9251e-01]],\n",
      "\n",
      "        [[-1.7113e-01,  1.4261e-03,  4.3703e-01,  3.9646e-01, -5.7846e-02],\n",
      "         [-5.3717e-01,  1.1895e-01,  6.9725e-01, -2.7508e-01,  7.0816e-02]]],\n",
      "       device='cuda:0', grad_fn=<CudnnRnnBackward>)\n"
     ]
    }
   ],
   "source": [
    "### Testing Encoder part\n",
    "# TODO: put whether GPU is available or not\n",
    "# Device\n",
    "\n",
    "INPUT_DIM = len(thai_romanization_dataset.lang_th.char2index)\n",
    "OUTPUT_DIM = len(thai_romanization_dataset.lang_th_romanized.char2index)\n",
    "\n",
    "ENC_EMB_DIM = 10\n",
    "ENC_HID_DIM = 10\n",
    "ENC_DROPOUT = 0.5\n",
    "\n",
    "_encoder = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM).to(device)\n",
    "\n",
    "# obtain one sample from the data iterator\n",
    "\n",
    "for batch in train_dataset_loader:\n",
    "    print(batch)\n",
    "    \n",
    "    input_tensor, input_length, target_tensor = batch['input_tensor'], batch['input_length'], batch['target_tensor']\n",
    "    \n",
    "    output, hidden = _encoder(input_tensor, input_length)\n",
    "    \n",
    "    print('input_tensor', input_tensor.size())\n",
    "    print('input_length', input_length.size())\n",
    "    print('target_tensor', target_tensor.size())\n",
    "    print('output', output.size())\n",
    "    print('hidden[0]', hidden[0].size(), hidden[0])\n",
    "    print('hidden[1]', hidden[0].size(), hidden[1])\n",
    "\n",
    "\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_bdr185mI84Q"
   },
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2Zppvb0OI84R"
   },
   "source": [
    "   \n",
    "Decoder architecture\n",
    "\n",
    "    - Embedding layer :(vocabulary_size, embebeding_size)\n",
    "        Input: (batch_size, sequence_length=1)\n",
    "        Output: (batch_size, sequence_length=1, embebeding_size)\n",
    "    - RNN layer :input_size=embebeding_size, hidden_size, num_layers, batch_first=True)\n",
    "        Input: (input=(batch_size, input_size=embedding_dimension), hidden:tuple=encoder_hidden\n",
    "        Output: (batch_size, seq_len, hidden_size), (h_n, c_n)\n",
    "    - Attention Layer: (in_features=hidden_size, out_features=hidden_size, bias=True)\n",
    "    - Linear Layer: (in_features, out_features=vocabulary_size)\n",
    "        Input: (batch_size, hidden_size)\n",
    "        Output: (batch_size, vocabulary_size)\n",
    "    \n",
    "    - Softmax layer\n",
    "        Input: (batch_size, vocabulary_size)\n",
    "        Output: (batch_size, vocabulary_size)\n",
    "\n",
    "\n",
    "\n",
    "For the Attention mechanishm in the Decoder, Luong-style attention [[Luong et. al (2015)](https://arxiv.org/abs/1508.04025)] is used. \n",
    "\n",
    "\n",
    "\n",
    "__Steps:__\n",
    "\n",
    "1. Receives a batch of <start> token (batch_size, 1) and a batch of Encoder's hidden state.\n",
    "     \n",
    "2. Embed input into vectors.\n",
    "\n",
    "3. Feed vectors from (2) to the LSTM.\n",
    "\n",
    "4. Feed the output of LSTM at time step $t_1$ and Encoder output to the Attention Layer.\n",
    "\n",
    "5. Attention layer, returns weights for Encoder's hidden states in every time step (masked out the time step with <PAD> token), then multiply with Encoder's hidden states to obtain a context vector\n",
    "    \n",
    "6. Concatenate both decoder hidden state and the context vector, feed to a linear layer, and return its output.\n",
    "\n",
    "7. Decoder then returns, final output, decoder's hidden state, attention weights, and context vector at time step $t$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DXlNJGT-I84R"
   },
   "outputs": [],
   "source": [
    "\n",
    "class Attn(nn.Module):\n",
    "    def __init__(self, method, hidden_size):\n",
    "        super(Attn, self).__init__()\n",
    "\n",
    "        self.method = method\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        if self.method == 'general':\n",
    "            self.attn = nn.Linear(self.hidden_size, self.hidden_size)\n",
    "\n",
    "        elif self.method == 'concat':\n",
    "            self.attn = nn.Linear(self.hidden_size * 2, self.hidden_size)\n",
    "            self.other = nn.Parameter(torch.FloatTensor(1, self.hidden_size))\n",
    "\n",
    "    def forward(self, hidden, encoder_outputs, mask):\n",
    "        # hidden: B x 1 x h ; \n",
    "        # encoder_outputs: B x S x h\n",
    "\n",
    "        # Calculate energies for each encoder output\n",
    "        if self.method == 'dot':\n",
    "            attn_energies = torch.bmm(encoder_outputs, hidden.transpose(1, 2)).squeeze(2)  # B x S\n",
    "        elif self.method == 'general':\n",
    "            attn_energies = self.attn(encoder_outputs.view(-1, encoder_outputs.size(-1)))  # (B * S) x h\n",
    "            attn_energies = torch.bmm(attn_energies.view(*encoder_outputs.size()),\n",
    "                                      hidden.transpose(1, 2)).squeeze(2)  # B x S\n",
    "        elif self.method == 'concat':\n",
    "            attn_energies = self.attn(\n",
    "                torch.cat((hidden.expand(*encoder_outputs.size()), encoder_outputs), 2))  # B x S x h\n",
    "            attn_energies = torch.bmm(attn_energies,\n",
    "                                      self.other.unsqueeze(0).expand(*hidden.size()).transpose(1, 2)).squeeze(2)\n",
    "\n",
    "        attn_energies = attn_energies.masked_fill(mask == 0, -1e10)\n",
    "\n",
    "        # Normalize energies to weights in range 0 to 1\n",
    "        return F.softmax(attn_energies, 1)\n",
    "\n",
    "class AttentionDecoder(nn.Module): \n",
    "    \n",
    "    def __init__(self, vocabulary_size, embedding_size, hidden_size, dropout=0.5):\n",
    "        \"\"\"Constructor\"\"\"\n",
    "        super(AttentionDecoder, self).__init__()\n",
    "        self.vocabulary_size = vocabulary_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.character_embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
    "        self.rnn = nn.LSTM(input_size=hidden_size + embedding_size,\n",
    "                            hidden_size=hidden_size,\n",
    "                            bidirectional=False,\n",
    "                            batch_first=True)\n",
    "        \n",
    "        self.attn = Attn(method=\"general\", hidden_size=self.hidden_size)\n",
    "        self.linear = nn.Linear( hidden_size, vocabulary_size)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "        \n",
    "    def forward(self, input, last_hidden, encoder_outputs, mask):\n",
    "        \"\"\"\"Defines the forward computation of the decoder\"\"\"\n",
    "        # input: (B, 1) ,\n",
    "        # last_hidden: (num_layers * num_directions = 2, B, hidden_dim)\n",
    "        # encoder_outputs: (B, S, hidden_dim)\n",
    "        \n",
    "        # last_hidden from  (batch_size, hidden size)\n",
    "        # to (batch_size, 1, hidden size)\n",
    "        hidden = last_hidden.permute(1, 0, 2)\n",
    "#         print('unsqueeze hidden.size()', hidden.size())\n",
    "\n",
    "        attn_weights = self.attn(hidden, encoder_outputs, mask)  # B x S\n",
    "\n",
    "        #  context = (B, 1, S) x (B, S, hidden_dim)\n",
    "        #  context = (B, 1, hidden_dim)\n",
    "        context_vector = attn_weights.unsqueeze(1).bmm(encoder_outputs)  \n",
    "        #  context after sum = (B, hidden_dim)\n",
    "        context_vector = torch.sum(context_vector, dim=1)\n",
    "        \n",
    "        \n",
    "        embedded = self.character_embedding(input)\n",
    "        embedded = self.dropout(embedded)\n",
    "        \n",
    "        # context_vector = (B, hidden_dim)\n",
    "        # embedded = (B, emb_dim)\n",
    "        # rnn_input = (B, hidden_dim + emb_dim)\n",
    "        context_vector = context_vector.unsqueeze(1)\n",
    "        \n",
    "#         print('context_vector', context_vector.size())\n",
    "#         print('embedded', embedded.size())\n",
    "        rnn_input = torch.cat((context_vector, embedded), -1)\n",
    "    \n",
    "        # output (batch_size, 1, hidden_dim)\n",
    "        output, hidden = self.rnn(rnn_input)        \n",
    "        \n",
    "#         print('hidden[0].size()', hidden[0].size(), hidden[0], )\n",
    "        output =  output.view(-1, output.size(2))\n",
    "        \n",
    "        x = self.linear(output)\n",
    "#         print('decoder hidden[0]', hidden[0].size(), hidden[0])\n",
    "        return x, hidden[0], attn_weights\n",
    "\n",
    "    def init_hidden(self, batch_size):\n",
    "        h_0 = torch.zeros([1, batch_size, self.hidden_size ], requires_grad=True).to(device)\n",
    "        c_0 = torch.zeros([1, batch_size, self.hidden_size ], requires_grad=True).to(device)\n",
    "\n",
    "        return (h_0, c_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start_token= 1\n",
      "{'input_text': ['แบกทีเรียไม่ใช้ออกซิเจน', 'กิตติพงศ์ กฤติโชติอนันต์'], 'target_text': ['baekthiriamaichai-oksichen', 'kittiphong krittichot-anan'], 'input_length': tensor([24, 25]), 'target_length': tensor([27, 27]), 'input_tensor': tensor([[34, 28,  4, 10, 37, 25, 13, 37, 23, 44, 26, 18, 16, 43, 31,  5,  5,  4,\n",
      "         53, 20, 25, 45,  9,  3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0],\n",
      "        [ 4, 20, 27, 27, 20,  7,  6, 36, 24, 56,  4, 83, 27, 20, 41, 43, 27, 20,\n",
      "          5,  9,  8,  9, 27, 24,  3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0]], device='cuda:0'), 'target_tensor': tensor([[17,  9, 12,  3, 10,  8, 14, 20, 14,  9, 16,  9, 14, 22,  8,  9, 14, 24,\n",
      "          4,  3, 19, 14, 22,  8, 12,  5,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0],\n",
      "        [ 3, 14, 10, 10, 14,  7,  8,  4,  5,  6, 25,  3, 20, 14, 10, 10, 14, 22,\n",
      "          8,  4, 10, 24,  9,  5,  9,  5,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "          0,  0,  0,  0,  0,  0]], device='cuda:0')}\n",
      "input_tensor torch.Size([2, 60])\n",
      "input_length torch.Size([2])\n",
      "target_tensor torch.Size([2, 60])\n",
      "encoder_outputs torch.Size([2, 25, 10])\n",
      "encoder_hidden[0] torch.Size([2, 2, 5]) tensor([[[-0.6234,  0.0990, -0.0663, -0.0529,  0.3585],\n",
      "         [-0.2649, -0.1057, -0.1294, -0.1527,  0.3468]],\n",
      "\n",
      "        [[ 0.0122,  0.3562,  0.2027,  0.1253,  0.0566],\n",
      "         [ 0.2722,  0.4105,  0.1136,  0.0293,  0.0650]]], device='cuda:0',\n",
      "       grad_fn=<CudnnRnnBackward>)\n",
      "encoder_hidden[1] torch.Size([2, 2, 5]) tensor([[[-0.7998,  0.1102, -0.0911, -0.6478,  0.6708],\n",
      "         [-0.6202, -0.3801, -0.3127, -0.3487,  0.5585]],\n",
      "\n",
      "        [[ 0.0368,  0.6235,  0.3472,  0.2400,  0.2666],\n",
      "         [ 0.9401,  0.6269,  0.1678,  0.0646,  0.2935]]], device='cuda:0',\n",
      "       grad_fn=<CudnnRnnBackward>)\n",
      "decoder_hidden torch.Size([1, 2, 10]) tensor([[[-0.6234,  0.0990, -0.0663, -0.0529,  0.3585,  0.0122,  0.3562,\n",
      "           0.2027,  0.1253,  0.0566],\n",
      "         [-0.2649, -0.1057, -0.1294, -0.1527,  0.3468,  0.2722,  0.4105,\n",
      "           0.1136,  0.0293,  0.0650]]], device='cuda:0',\n",
      "       grad_fn=<UnsqueezeBackward0>)\n",
      "\n",
      "\n",
      "mask torch.Size([2, 25]) tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         0],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1]], device='cuda:0', dtype=torch.uint8)\n",
      "\n",
      "\n",
      "decoder di= 0\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0426, 0.0421, 0.0380, 0.0363, 0.0332, 0.0371, 0.0425, 0.0457, 0.0397,\n",
      "         0.0361, 0.0401, 0.0418, 0.0441, 0.0429, 0.0449, 0.0465, 0.0448, 0.0447,\n",
      "         0.0448, 0.0386, 0.0383, 0.0434, 0.0440, 0.0477, 0.0000],\n",
      "        [0.0376, 0.0382, 0.0405, 0.0413, 0.0364, 0.0387, 0.0390, 0.0466, 0.0408,\n",
      "         0.0446, 0.0426, 0.0383, 0.0390, 0.0343, 0.0406, 0.0439, 0.0414, 0.0413,\n",
      "         0.0390, 0.0407, 0.0433, 0.0363, 0.0399, 0.0410, 0.0346]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 0\n",
      "\n",
      "\n",
      "decoder di= 1\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0404, 0.0406, 0.0407, 0.0385, 0.0411, 0.0442, 0.0412, 0.0443, 0.0411,\n",
      "         0.0461, 0.0412, 0.0424, 0.0408, 0.0400, 0.0404, 0.0408, 0.0414, 0.0393,\n",
      "         0.0414, 0.0423, 0.0438, 0.0428, 0.0447, 0.0407, 0.0000],\n",
      "        [0.0379, 0.0387, 0.0412, 0.0421, 0.0397, 0.0400, 0.0398, 0.0433, 0.0398,\n",
      "         0.0416, 0.0415, 0.0393, 0.0412, 0.0377, 0.0400, 0.0408, 0.0401, 0.0385,\n",
      "         0.0363, 0.0403, 0.0408, 0.0397, 0.0414, 0.0404, 0.0378]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 1\n",
      "\n",
      "\n",
      "decoder di= 2\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0409, 0.0395, 0.0410, 0.0411, 0.0394, 0.0433, 0.0401, 0.0472, 0.0425,\n",
      "         0.0498, 0.0397, 0.0423, 0.0371, 0.0408, 0.0397, 0.0412, 0.0400, 0.0387,\n",
      "         0.0411, 0.0428, 0.0438, 0.0430, 0.0490, 0.0361, 0.0000],\n",
      "        [0.0370, 0.0387, 0.0419, 0.0438, 0.0408, 0.0416, 0.0402, 0.0399, 0.0390,\n",
      "         0.0398, 0.0415, 0.0396, 0.0425, 0.0393, 0.0395, 0.0401, 0.0391, 0.0375,\n",
      "         0.0350, 0.0404, 0.0405, 0.0412, 0.0422, 0.0406, 0.0381]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['4', 'u']\n",
      "decoder_input ['4', 'u']\n",
      "done di= 2\n",
      "\n",
      "\n",
      "decoder di= 3\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0395, 0.0391, 0.0408, 0.0413, 0.0399, 0.0440, 0.0409, 0.0433, 0.0447,\n",
      "         0.0513, 0.0425, 0.0432, 0.0373, 0.0397, 0.0377, 0.0382, 0.0386, 0.0371,\n",
      "         0.0411, 0.0452, 0.0470, 0.0444, 0.0471, 0.0362, 0.0000],\n",
      "        [0.0341, 0.0375, 0.0432, 0.0470, 0.0414, 0.0414, 0.0395, 0.0418, 0.0385,\n",
      "         0.0413, 0.0428, 0.0390, 0.0445, 0.0373, 0.0393, 0.0412, 0.0398, 0.0355,\n",
      "         0.0307, 0.0412, 0.0406, 0.0418, 0.0439, 0.0409, 0.0358]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [22]], device='cuda:0')\n",
      "predicted  ['u', 'c']\n",
      "decoder_input ['k', 't']\n",
      "done di= 3\n",
      "\n",
      "\n",
      "decoder di= 4\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0420, 0.0422, 0.0412, 0.0405, 0.0411, 0.0407, 0.0422, 0.0417, 0.0411,\n",
      "         0.0400, 0.0409, 0.0419, 0.0423, 0.0423, 0.0424, 0.0420, 0.0420, 0.0420,\n",
      "         0.0434, 0.0423, 0.0412, 0.0416, 0.0409, 0.0422, 0.0000],\n",
      "        [0.0385, 0.0381, 0.0421, 0.0479, 0.0372, 0.0409, 0.0361, 0.0470, 0.0393,\n",
      "         0.0432, 0.0438, 0.0390, 0.0452, 0.0347, 0.0387, 0.0409, 0.0388, 0.0365,\n",
      "         0.0323, 0.0398, 0.0427, 0.0395, 0.0426, 0.0408, 0.0345]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 4\n",
      "\n",
      "\n",
      "decoder di= 5\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0409, 0.0407, 0.0389, 0.0364, 0.0378, 0.0418, 0.0416, 0.0469, 0.0408,\n",
      "         0.0448, 0.0394, 0.0429, 0.0407, 0.0414, 0.0419, 0.0424, 0.0420, 0.0403,\n",
      "         0.0439, 0.0432, 0.0431, 0.0428, 0.0461, 0.0394, 0.0000],\n",
      "        [0.0374, 0.0386, 0.0419, 0.0437, 0.0399, 0.0407, 0.0396, 0.0423, 0.0394,\n",
      "         0.0409, 0.0418, 0.0394, 0.0423, 0.0378, 0.0396, 0.0406, 0.0399, 0.0375,\n",
      "         0.0347, 0.0402, 0.0406, 0.0406, 0.0423, 0.0406, 0.0377]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 5\n",
      "\n",
      "\n",
      "decoder di= 6\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0396, 0.0396, 0.0398, 0.0385, 0.0397, 0.0437, 0.0410, 0.0453, 0.0417,\n",
      "         0.0491, 0.0397, 0.0434, 0.0387, 0.0406, 0.0399, 0.0403, 0.0400, 0.0381,\n",
      "         0.0427, 0.0462, 0.0466, 0.0424, 0.0467, 0.0366, 0.0000],\n",
      "        [0.0379, 0.0393, 0.0409, 0.0427, 0.0393, 0.0401, 0.0389, 0.0411, 0.0400,\n",
      "         0.0408, 0.0414, 0.0397, 0.0413, 0.0392, 0.0400, 0.0409, 0.0404, 0.0387,\n",
      "         0.0368, 0.0402, 0.0400, 0.0402, 0.0413, 0.0406, 0.0383]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 6\n",
      "\n",
      "\n",
      "decoder di= 7\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0397, 0.0404, 0.0384, 0.0366, 0.0399, 0.0431, 0.0422, 0.0458, 0.0406,\n",
      "         0.0439, 0.0402, 0.0431, 0.0420, 0.0402, 0.0410, 0.0411, 0.0414, 0.0396,\n",
      "         0.0450, 0.0442, 0.0446, 0.0422, 0.0435, 0.0415, 0.0000],\n",
      "        [0.0385, 0.0382, 0.0414, 0.0475, 0.0374, 0.0407, 0.0341, 0.0453, 0.0401,\n",
      "         0.0405, 0.0453, 0.0402, 0.0456, 0.0357, 0.0382, 0.0406, 0.0397, 0.0361,\n",
      "         0.0319, 0.0391, 0.0423, 0.0412, 0.0431, 0.0417, 0.0354]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['c', 'u']\n",
      "decoder_input ['c', 'u']\n",
      "done di= 7\n",
      "\n",
      "\n",
      "decoder di= 8\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0411, 0.0388, 0.0412, 0.0435, 0.0379, 0.0426, 0.0398, 0.0471, 0.0450,\n",
      "         0.0514, 0.0412, 0.0423, 0.0353, 0.0404, 0.0379, 0.0400, 0.0387, 0.0378,\n",
      "         0.0406, 0.0418, 0.0437, 0.0452, 0.0510, 0.0357, 0.0000],\n",
      "        [0.0376, 0.0386, 0.0417, 0.0451, 0.0392, 0.0410, 0.0383, 0.0422, 0.0394,\n",
      "         0.0410, 0.0425, 0.0397, 0.0432, 0.0381, 0.0395, 0.0405, 0.0394, 0.0372,\n",
      "         0.0342, 0.0402, 0.0408, 0.0407, 0.0423, 0.0407, 0.0371]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['u', '4']\n",
      "decoder_input ['u', '4']\n",
      "done di= 8\n",
      "\n",
      "\n",
      "decoder di= 9\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0416, 0.0414, 0.0404, 0.0391, 0.0390, 0.0411, 0.0418, 0.0435, 0.0416,\n",
      "         0.0429, 0.0409, 0.0424, 0.0413, 0.0418, 0.0420, 0.0421, 0.0419, 0.0413,\n",
      "         0.0426, 0.0423, 0.0422, 0.0427, 0.0439, 0.0405, 0.0000],\n",
      "        [0.0383, 0.0377, 0.0441, 0.0465, 0.0393, 0.0428, 0.0412, 0.0447, 0.0379,\n",
      "         0.0410, 0.0422, 0.0386, 0.0444, 0.0348, 0.0386, 0.0392, 0.0376, 0.0356,\n",
      "         0.0322, 0.0395, 0.0426, 0.0404, 0.0440, 0.0405, 0.0363]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 9\n",
      "\n",
      "\n",
      "decoder di= 10\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0402, 0.0404, 0.0397, 0.0379, 0.0397, 0.0430, 0.0417, 0.0445, 0.0416,\n",
      "         0.0459, 0.0408, 0.0431, 0.0407, 0.0405, 0.0406, 0.0407, 0.0408, 0.0393,\n",
      "         0.0430, 0.0440, 0.0448, 0.0429, 0.0449, 0.0394, 0.0000],\n",
      "        [0.0383, 0.0416, 0.0380, 0.0374, 0.0421, 0.0382, 0.0398, 0.0347, 0.0406,\n",
      "         0.0392, 0.0378, 0.0406, 0.0380, 0.0452, 0.0410, 0.0410, 0.0423, 0.0419,\n",
      "         0.0435, 0.0412, 0.0368, 0.0408, 0.0381, 0.0393, 0.0425]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['4', '4']\n",
      "decoder_input ['m', ' ']\n",
      "done di= 10\n",
      "\n",
      "\n",
      "decoder di= 11\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0406, 0.0393, 0.0412, 0.0422, 0.0389, 0.0431, 0.0406, 0.0445, 0.0452,\n",
      "         0.0505, 0.0428, 0.0425, 0.0370, 0.0400, 0.0380, 0.0391, 0.0391, 0.0380,\n",
      "         0.0403, 0.0424, 0.0445, 0.0453, 0.0483, 0.0369, 0.0000],\n",
      "        [0.0365, 0.0379, 0.0438, 0.0471, 0.0416, 0.0431, 0.0416, 0.0416, 0.0372,\n",
      "         0.0414, 0.0411, 0.0384, 0.0452, 0.0371, 0.0389, 0.0393, 0.0373, 0.0356,\n",
      "         0.0318, 0.0409, 0.0416, 0.0413, 0.0436, 0.0399, 0.0363]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [31]], device='cuda:0')\n",
      "predicted  ['u', '5']\n",
      "decoder_input ['u', '5']\n",
      "done di= 11\n",
      "\n",
      "\n",
      "decoder di= 12\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0420, 0.0415, 0.0410, 0.0403, 0.0403, 0.0411, 0.0416, 0.0436, 0.0417,\n",
      "         0.0429, 0.0405, 0.0419, 0.0408, 0.0423, 0.0421, 0.0420, 0.0417, 0.0416,\n",
      "         0.0425, 0.0424, 0.0417, 0.0419, 0.0433, 0.0393, 0.0000],\n",
      "        [0.0425, 0.0413, 0.0383, 0.0373, 0.0393, 0.0383, 0.0404, 0.0413, 0.0404,\n",
      "         0.0421, 0.0375, 0.0394, 0.0380, 0.0415, 0.0408, 0.0399, 0.0401, 0.0423,\n",
      "         0.0446, 0.0401, 0.0387, 0.0381, 0.0377, 0.0386, 0.0416]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['i', 'r']\n",
      "done di= 12\n",
      "\n",
      "\n",
      "decoder di= 13\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0401, 0.0407, 0.0390, 0.0371, 0.0406, 0.0430, 0.0421, 0.0453, 0.0402,\n",
      "         0.0434, 0.0398, 0.0429, 0.0420, 0.0406, 0.0415, 0.0414, 0.0415, 0.0399,\n",
      "         0.0448, 0.0442, 0.0443, 0.0415, 0.0431, 0.0411, 0.0000],\n",
      "        [0.0392, 0.0391, 0.0416, 0.0413, 0.0404, 0.0408, 0.0421, 0.0424, 0.0391,\n",
      "         0.0408, 0.0400, 0.0392, 0.0408, 0.0392, 0.0401, 0.0392, 0.0388, 0.0382,\n",
      "         0.0371, 0.0399, 0.0399, 0.0400, 0.0414, 0.0397, 0.0398]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[19],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['s', 'u']\n",
      "decoder_input ['s', 'u']\n",
      "done di= 13\n",
      "\n",
      "\n",
      "decoder di= 14\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0415, 0.0400, 0.0439, 0.0501, 0.0444, 0.0422, 0.0405, 0.0404, 0.0460,\n",
      "         0.0472, 0.0428, 0.0415, 0.0367, 0.0415, 0.0383, 0.0384, 0.0377, 0.0395,\n",
      "         0.0401, 0.0437, 0.0437, 0.0419, 0.0430, 0.0352, 0.0000],\n",
      "        [0.0362, 0.0387, 0.0414, 0.0456, 0.0397, 0.0407, 0.0376, 0.0418, 0.0395,\n",
      "         0.0417, 0.0421, 0.0392, 0.0435, 0.0389, 0.0396, 0.0413, 0.0399, 0.0375,\n",
      "         0.0339, 0.0408, 0.0404, 0.0407, 0.0421, 0.0407, 0.0366]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 14\n",
      "\n",
      "\n",
      "decoder di= 15\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0415, 0.0412, 0.0400, 0.0386, 0.0386, 0.0411, 0.0417, 0.0447, 0.0413,\n",
      "         0.0428, 0.0405, 0.0423, 0.0412, 0.0418, 0.0420, 0.0425, 0.0421, 0.0413,\n",
      "         0.0430, 0.0420, 0.0419, 0.0426, 0.0444, 0.0409, 0.0000],\n",
      "        [0.0341, 0.0381, 0.0423, 0.0457, 0.0422, 0.0416, 0.0404, 0.0394, 0.0383,\n",
      "         0.0417, 0.0412, 0.0387, 0.0435, 0.0399, 0.0400, 0.0411, 0.0391, 0.0370,\n",
      "         0.0332, 0.0420, 0.0401, 0.0413, 0.0424, 0.0403, 0.0364]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 15\n",
      "\n",
      "\n",
      "decoder di= 16\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0404, 0.0386, 0.0402, 0.0417, 0.0376, 0.0432, 0.0397, 0.0489, 0.0434,\n",
      "         0.0506, 0.0404, 0.0421, 0.0363, 0.0402, 0.0388, 0.0414, 0.0400, 0.0385,\n",
      "         0.0406, 0.0417, 0.0438, 0.0438, 0.0510, 0.0372, 0.0000],\n",
      "        [0.0379, 0.0386, 0.0402, 0.0461, 0.0368, 0.0393, 0.0334, 0.0462, 0.0411,\n",
      "         0.0422, 0.0447, 0.0398, 0.0440, 0.0362, 0.0390, 0.0421, 0.0411, 0.0377,\n",
      "         0.0337, 0.0396, 0.0418, 0.0399, 0.0417, 0.0417, 0.0351]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 16\n",
      "\n",
      "\n",
      "decoder di= 17\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0386, 0.0393, 0.0395, 0.0382, 0.0408, 0.0450, 0.0413, 0.0439, 0.0419,\n",
      "         0.0495, 0.0408, 0.0438, 0.0393, 0.0395, 0.0388, 0.0390, 0.0395, 0.0370,\n",
      "         0.0429, 0.0471, 0.0484, 0.0426, 0.0454, 0.0379, 0.0000],\n",
      "        [0.0375, 0.0385, 0.0413, 0.0467, 0.0389, 0.0411, 0.0359, 0.0425, 0.0396,\n",
      "         0.0408, 0.0436, 0.0399, 0.0448, 0.0376, 0.0388, 0.0408, 0.0395, 0.0369,\n",
      "         0.0331, 0.0401, 0.0416, 0.0411, 0.0424, 0.0412, 0.0359]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 17\n",
      "\n",
      "\n",
      "decoder di= 18\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0400, 0.0398, 0.0395, 0.0377, 0.0388, 0.0434, 0.0409, 0.0464, 0.0416,\n",
      "         0.0486, 0.0398, 0.0430, 0.0391, 0.0406, 0.0403, 0.0410, 0.0408, 0.0388,\n",
      "         0.0424, 0.0446, 0.0453, 0.0428, 0.0473, 0.0375, 0.0000],\n",
      "        [0.0349, 0.0380, 0.0419, 0.0471, 0.0404, 0.0412, 0.0374, 0.0413, 0.0391,\n",
      "         0.0417, 0.0431, 0.0392, 0.0446, 0.0383, 0.0394, 0.0416, 0.0396, 0.0368,\n",
      "         0.0323, 0.0413, 0.0411, 0.0412, 0.0426, 0.0410, 0.0352]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['4', 'u']\n",
      "decoder_input ['4', 'u']\n",
      "done di= 18\n",
      "\n",
      "\n",
      "decoder di= 19\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0419, 0.0402, 0.0413, 0.0435, 0.0383, 0.0406, 0.0411, 0.0441, 0.0450,\n",
      "         0.0461, 0.0421, 0.0422, 0.0377, 0.0416, 0.0395, 0.0405, 0.0395, 0.0400,\n",
      "         0.0419, 0.0418, 0.0423, 0.0446, 0.0466, 0.0375, 0.0000],\n",
      "        [0.0375, 0.0383, 0.0423, 0.0450, 0.0401, 0.0416, 0.0395, 0.0422, 0.0388,\n",
      "         0.0408, 0.0422, 0.0393, 0.0435, 0.0374, 0.0392, 0.0401, 0.0388, 0.0369,\n",
      "         0.0338, 0.0403, 0.0415, 0.0409, 0.0426, 0.0406, 0.0369]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['k', 'o']\n",
      "done di= 19\n",
      "\n",
      "\n",
      "decoder di= 20\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0420, 0.0426, 0.0415, 0.0399, 0.0416, 0.0408, 0.0422, 0.0404, 0.0405,\n",
      "         0.0396, 0.0409, 0.0419, 0.0431, 0.0426, 0.0431, 0.0423, 0.0426, 0.0424,\n",
      "         0.0426, 0.0426, 0.0414, 0.0410, 0.0402, 0.0422, 0.0000],\n",
      "        [0.0347, 0.0383, 0.0411, 0.0465, 0.0397, 0.0401, 0.0361, 0.0424, 0.0398,\n",
      "         0.0426, 0.0430, 0.0390, 0.0441, 0.0384, 0.0396, 0.0425, 0.0408, 0.0375,\n",
      "         0.0330, 0.0413, 0.0408, 0.0406, 0.0421, 0.0411, 0.0351]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['u', '4']\n",
      "decoder_input ['u', '4']\n",
      "done di= 20\n",
      "\n",
      "\n",
      "decoder di= 21\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0411, 0.0429, 0.0383, 0.0329, 0.0395, 0.0406, 0.0429, 0.0436, 0.0372,\n",
      "         0.0386, 0.0380, 0.0426, 0.0464, 0.0429, 0.0463, 0.0446, 0.0449, 0.0436,\n",
      "         0.0451, 0.0444, 0.0420, 0.0393, 0.0401, 0.0420, 0.0000],\n",
      "        [0.0377, 0.0387, 0.0413, 0.0439, 0.0384, 0.0412, 0.0391, 0.0420, 0.0396,\n",
      "         0.0418, 0.0416, 0.0388, 0.0418, 0.0376, 0.0398, 0.0414, 0.0394, 0.0392,\n",
      "         0.0370, 0.0403, 0.0421, 0.0390, 0.0412, 0.0408, 0.0363]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['4', 'u']\n",
      "decoder_input ['i', '-']\n",
      "done di= 21\n",
      "\n",
      "\n",
      "decoder di= 22\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0417, 0.0394, 0.0410, 0.0437, 0.0364, 0.0405, 0.0406, 0.0453, 0.0463,\n",
      "         0.0486, 0.0425, 0.0425, 0.0362, 0.0411, 0.0383, 0.0399, 0.0388, 0.0389,\n",
      "         0.0414, 0.0413, 0.0427, 0.0464, 0.0496, 0.0367, 0.0000],\n",
      "        [0.0385, 0.0401, 0.0397, 0.0419, 0.0385, 0.0387, 0.0380, 0.0415, 0.0407,\n",
      "         0.0423, 0.0405, 0.0397, 0.0403, 0.0402, 0.0406, 0.0418, 0.0414, 0.0399,\n",
      "         0.0385, 0.0405, 0.0388, 0.0393, 0.0400, 0.0400, 0.0388]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 22\n",
      "\n",
      "\n",
      "decoder di= 23\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0397, 0.0395, 0.0397, 0.0390, 0.0393, 0.0437, 0.0410, 0.0455, 0.0426,\n",
      "         0.0490, 0.0408, 0.0431, 0.0387, 0.0401, 0.0393, 0.0400, 0.0400, 0.0382,\n",
      "         0.0423, 0.0447, 0.0459, 0.0432, 0.0469, 0.0376, 0.0000],\n",
      "        [0.0397, 0.0395, 0.0401, 0.0392, 0.0387, 0.0389, 0.0394, 0.0422, 0.0410,\n",
      "         0.0398, 0.0416, 0.0404, 0.0389, 0.0378, 0.0401, 0.0410, 0.0418, 0.0400,\n",
      "         0.0396, 0.0394, 0.0403, 0.0396, 0.0405, 0.0408, 0.0397]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['u', '4']\n",
      "decoder_input ['u', '4']\n",
      "done di= 23\n",
      "\n",
      "\n",
      "decoder di= 24\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0400, 0.0398, 0.0407, 0.0394, 0.0402, 0.0443, 0.0408, 0.0446, 0.0426,\n",
      "         0.0495, 0.0414, 0.0428, 0.0388, 0.0399, 0.0392, 0.0398, 0.0402, 0.0382,\n",
      "         0.0407, 0.0435, 0.0453, 0.0435, 0.0468, 0.0379, 0.0000],\n",
      "        [0.0373, 0.0383, 0.0431, 0.0457, 0.0411, 0.0426, 0.0413, 0.0413, 0.0378,\n",
      "         0.0411, 0.0409, 0.0387, 0.0442, 0.0376, 0.0390, 0.0394, 0.0377, 0.0364,\n",
      "         0.0334, 0.0406, 0.0414, 0.0410, 0.0429, 0.0400, 0.0370]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [19]], device='cuda:0')\n",
      "predicted  ['4', 's']\n",
      "decoder_input ['e', 'a']\n",
      "done di= 24\n",
      "\n",
      "\n",
      "decoder di= 25\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0402, 0.0396, 0.0412, 0.0419, 0.0402, 0.0434, 0.0409, 0.0432, 0.0443,\n",
      "         0.0495, 0.0423, 0.0428, 0.0378, 0.0402, 0.0384, 0.0390, 0.0392, 0.0380,\n",
      "         0.0409, 0.0440, 0.0454, 0.0440, 0.0464, 0.0370, 0.0000],\n",
      "        [0.0401, 0.0407, 0.0391, 0.0382, 0.0402, 0.0397, 0.0409, 0.0373, 0.0402,\n",
      "         0.0398, 0.0387, 0.0400, 0.0383, 0.0416, 0.0405, 0.0404, 0.0404, 0.0420,\n",
      "         0.0438, 0.0404, 0.0396, 0.0394, 0.0386, 0.0397, 0.0408]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 25\n",
      "\n",
      "\n",
      "decoder di= 26\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0415, 0.0423, 0.0418, 0.0410, 0.0443, 0.0425, 0.0420, 0.0407, 0.0406,\n",
      "         0.0401, 0.0416, 0.0414, 0.0433, 0.0412, 0.0420, 0.0413, 0.0421, 0.0418,\n",
      "         0.0421, 0.0417, 0.0414, 0.0407, 0.0392, 0.0434, 0.0000],\n",
      "        [0.0412, 0.0405, 0.0388, 0.0375, 0.0383, 0.0389, 0.0404, 0.0405, 0.0409,\n",
      "         0.0408, 0.0390, 0.0395, 0.0374, 0.0403, 0.0408, 0.0408, 0.0406, 0.0427,\n",
      "         0.0448, 0.0397, 0.0400, 0.0379, 0.0383, 0.0398, 0.0404]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 26\n",
      "\n",
      "\n",
      "decoder di= 27\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0397, 0.0393, 0.0382, 0.0373, 0.0372, 0.0429, 0.0414, 0.0481, 0.0420,\n",
      "         0.0469, 0.0407, 0.0432, 0.0396, 0.0396, 0.0396, 0.0410, 0.0406, 0.0386,\n",
      "         0.0440, 0.0429, 0.0447, 0.0443, 0.0476, 0.0406, 0.0000],\n",
      "        [0.0342, 0.0381, 0.0419, 0.0480, 0.0416, 0.0418, 0.0370, 0.0395, 0.0386,\n",
      "         0.0409, 0.0428, 0.0393, 0.0458, 0.0392, 0.0390, 0.0413, 0.0394, 0.0362,\n",
      "         0.0315, 0.0416, 0.0409, 0.0423, 0.0429, 0.0410, 0.0353]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [22]], device='cuda:0')\n",
      "predicted  ['4', 'c']\n",
      "decoder_input ['4', 'c']\n",
      "done di= 27\n",
      "\n",
      "\n",
      "decoder di= 28\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0402, 0.0394, 0.0408, 0.0419, 0.0390, 0.0428, 0.0410, 0.0438, 0.0446,\n",
      "         0.0489, 0.0426, 0.0429, 0.0378, 0.0401, 0.0383, 0.0392, 0.0391, 0.0382,\n",
      "         0.0413, 0.0434, 0.0451, 0.0447, 0.0470, 0.0378, 0.0000],\n",
      "        [0.0394, 0.0379, 0.0423, 0.0470, 0.0358, 0.0407, 0.0370, 0.0487, 0.0395,\n",
      "         0.0437, 0.0441, 0.0388, 0.0438, 0.0333, 0.0389, 0.0411, 0.0387, 0.0371,\n",
      "         0.0333, 0.0394, 0.0435, 0.0382, 0.0424, 0.0409, 0.0342]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['4', 'u']\n",
      "decoder_input ['4', 'u']\n",
      "done di= 28\n",
      "\n",
      "\n",
      "decoder di= 29\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0421, 0.0413, 0.0404, 0.0398, 0.0383, 0.0402, 0.0416, 0.0445, 0.0422,\n",
      "         0.0431, 0.0403, 0.0422, 0.0402, 0.0426, 0.0421, 0.0424, 0.0417, 0.0417,\n",
      "         0.0430, 0.0424, 0.0415, 0.0426, 0.0447, 0.0389, 0.0000],\n",
      "        [0.0402, 0.0395, 0.0406, 0.0436, 0.0366, 0.0396, 0.0376, 0.0454, 0.0403,\n",
      "         0.0432, 0.0414, 0.0391, 0.0414, 0.0374, 0.0400, 0.0411, 0.0398, 0.0392,\n",
      "         0.0374, 0.0396, 0.0407, 0.0382, 0.0406, 0.0401, 0.0373]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['<PAD>', '<PAD>']\n",
      "done di= 29\n",
      "\n",
      "\n",
      "decoder di= 30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0416, 0.0411, 0.0404, 0.0420, 0.0411, 0.0411, 0.0417, 0.0447, 0.0417,\n",
      "         0.0411, 0.0402, 0.0416, 0.0407, 0.0419, 0.0416, 0.0420, 0.0413, 0.0416,\n",
      "         0.0442, 0.0421, 0.0413, 0.0413, 0.0422, 0.0413, 0.0000],\n",
      "        [0.0363, 0.0388, 0.0415, 0.0462, 0.0411, 0.0416, 0.0381, 0.0394, 0.0387,\n",
      "         0.0409, 0.0417, 0.0395, 0.0445, 0.0399, 0.0393, 0.0405, 0.0388, 0.0371,\n",
      "         0.0337, 0.0412, 0.0405, 0.0416, 0.0420, 0.0404, 0.0367]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[ 6],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['g', '4']\n",
      "decoder_input ['g', '4']\n",
      "done di= 30\n",
      "\n",
      "\n",
      "decoder di= 31\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0433, 0.0437, 0.0418, 0.0405, 0.0405, 0.0385, 0.0427, 0.0388, 0.0409,\n",
      "         0.0367, 0.0417, 0.0413, 0.0446, 0.0439, 0.0447, 0.0433, 0.0434, 0.0450,\n",
      "         0.0422, 0.0409, 0.0391, 0.0409, 0.0385, 0.0430, 0.0000],\n",
      "        [0.0402, 0.0398, 0.0403, 0.0416, 0.0390, 0.0403, 0.0385, 0.0412, 0.0401,\n",
      "         0.0402, 0.0410, 0.0400, 0.0411, 0.0385, 0.0395, 0.0402, 0.0400, 0.0395,\n",
      "         0.0387, 0.0397, 0.0410, 0.0400, 0.0405, 0.0404, 0.0387]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [19]], device='cuda:0')\n",
      "predicted  ['u', 's']\n",
      "decoder_input ['u', 's']\n",
      "done di= 31\n",
      "\n",
      "\n",
      "decoder di= 32\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0424, 0.0430, 0.0382, 0.0336, 0.0379, 0.0393, 0.0427, 0.0460, 0.0373,\n",
      "         0.0370, 0.0375, 0.0420, 0.0455, 0.0433, 0.0466, 0.0458, 0.0452, 0.0443,\n",
      "         0.0462, 0.0417, 0.0394, 0.0401, 0.0413, 0.0434, 0.0000],\n",
      "        [0.0404, 0.0395, 0.0404, 0.0417, 0.0378, 0.0402, 0.0387, 0.0425, 0.0402,\n",
      "         0.0412, 0.0412, 0.0396, 0.0406, 0.0377, 0.0398, 0.0406, 0.0397, 0.0400,\n",
      "         0.0393, 0.0397, 0.0415, 0.0389, 0.0403, 0.0405, 0.0380]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [24]], device='cuda:0')\n",
      "predicted  ['c', '-']\n",
      "decoder_input ['c', '-']\n",
      "done di= 32\n",
      "\n",
      "\n",
      "decoder di= 33\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0417, 0.0394, 0.0402, 0.0422, 0.0361, 0.0406, 0.0405, 0.0478, 0.0444,\n",
      "         0.0478, 0.0405, 0.0423, 0.0364, 0.0416, 0.0396, 0.0416, 0.0398, 0.0395,\n",
      "         0.0423, 0.0417, 0.0424, 0.0448, 0.0502, 0.0368, 0.0000],\n",
      "        [0.0394, 0.0393, 0.0395, 0.0448, 0.0362, 0.0402, 0.0346, 0.0443, 0.0408,\n",
      "         0.0427, 0.0427, 0.0390, 0.0428, 0.0376, 0.0394, 0.0419, 0.0397, 0.0401,\n",
      "         0.0378, 0.0398, 0.0427, 0.0383, 0.0401, 0.0412, 0.0352]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['<PAD>', '<PAD>']\n",
      "done di= 33\n",
      "\n",
      "\n",
      "decoder di= 34\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0412, 0.0405, 0.0408, 0.0407, 0.0388, 0.0417, 0.0412, 0.0440, 0.0430,\n",
      "         0.0457, 0.0415, 0.0424, 0.0394, 0.0413, 0.0404, 0.0411, 0.0407, 0.0401,\n",
      "         0.0417, 0.0424, 0.0431, 0.0436, 0.0458, 0.0389, 0.0000],\n",
      "        [0.0383, 0.0391, 0.0411, 0.0420, 0.0392, 0.0399, 0.0391, 0.0422, 0.0404,\n",
      "         0.0400, 0.0418, 0.0400, 0.0409, 0.0388, 0.0399, 0.0405, 0.0408, 0.0382,\n",
      "         0.0363, 0.0395, 0.0396, 0.0406, 0.0418, 0.0407, 0.0393]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 34\n",
      "\n",
      "\n",
      "decoder di= 35\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0416, 0.0416, 0.0416, 0.0406, 0.0406, 0.0417, 0.0415, 0.0420, 0.0415,\n",
      "         0.0427, 0.0414, 0.0418, 0.0413, 0.0419, 0.0418, 0.0420, 0.0421, 0.0415,\n",
      "         0.0411, 0.0420, 0.0420, 0.0419, 0.0429, 0.0409, 0.0000],\n",
      "        [0.0384, 0.0390, 0.0414, 0.0428, 0.0399, 0.0409, 0.0399, 0.0414, 0.0394,\n",
      "         0.0406, 0.0412, 0.0395, 0.0418, 0.0386, 0.0397, 0.0402, 0.0393, 0.0383,\n",
      "         0.0364, 0.0402, 0.0408, 0.0403, 0.0415, 0.0404, 0.0382]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [19]], device='cuda:0')\n",
      "predicted  ['u', 's']\n",
      "decoder_input ['u', 's']\n",
      "done di= 35\n",
      "\n",
      "\n",
      "decoder di= 36\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0394, 0.0395, 0.0388, 0.0378, 0.0390, 0.0436, 0.0415, 0.0463, 0.0414,\n",
      "         0.0471, 0.0402, 0.0435, 0.0397, 0.0399, 0.0398, 0.0405, 0.0402, 0.0382,\n",
      "         0.0441, 0.0449, 0.0461, 0.0430, 0.0463, 0.0394, 0.0000],\n",
      "        [0.0416, 0.0391, 0.0409, 0.0446, 0.0371, 0.0403, 0.0367, 0.0470, 0.0397,\n",
      "         0.0425, 0.0423, 0.0395, 0.0435, 0.0361, 0.0390, 0.0397, 0.0386, 0.0377,\n",
      "         0.0354, 0.0392, 0.0419, 0.0392, 0.0413, 0.0402, 0.0369]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['c', 'u']\n",
      "decoder_input ['c', 'u']\n",
      "done di= 36\n",
      "\n",
      "\n",
      "decoder di= 37\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0407, 0.0385, 0.0404, 0.0427, 0.0356, 0.0421, 0.0398, 0.0481, 0.0452,\n",
      "         0.0506, 0.0420, 0.0421, 0.0356, 0.0399, 0.0378, 0.0407, 0.0396, 0.0382,\n",
      "         0.0401, 0.0404, 0.0431, 0.0463, 0.0523, 0.0382, 0.0000],\n",
      "        [0.0388, 0.0392, 0.0410, 0.0443, 0.0386, 0.0400, 0.0374, 0.0435, 0.0400,\n",
      "         0.0416, 0.0420, 0.0398, 0.0427, 0.0380, 0.0395, 0.0407, 0.0401, 0.0377,\n",
      "         0.0352, 0.0399, 0.0403, 0.0403, 0.0416, 0.0404, 0.0376]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 37\n",
      "\n",
      "\n",
      "decoder di= 38\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0410, 0.0399, 0.0406, 0.0422, 0.0393, 0.0422, 0.0411, 0.0453, 0.0438,\n",
      "         0.0459, 0.0419, 0.0421, 0.0385, 0.0405, 0.0392, 0.0404, 0.0400, 0.0394,\n",
      "         0.0423, 0.0416, 0.0428, 0.0441, 0.0461, 0.0398, 0.0000],\n",
      "        [0.0363, 0.0384, 0.0413, 0.0478, 0.0395, 0.0409, 0.0352, 0.0425, 0.0395,\n",
      "         0.0413, 0.0438, 0.0397, 0.0458, 0.0376, 0.0387, 0.0411, 0.0398, 0.0363,\n",
      "         0.0318, 0.0406, 0.0413, 0.0417, 0.0427, 0.0412, 0.0353]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['c', '4']\n",
      "decoder_input ['c', '4']\n",
      "done di= 38\n",
      "\n",
      "\n",
      "decoder di= 39\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0416, 0.0392, 0.0404, 0.0435, 0.0365, 0.0406, 0.0404, 0.0475, 0.0451,\n",
      "         0.0482, 0.0408, 0.0422, 0.0360, 0.0415, 0.0391, 0.0412, 0.0393, 0.0394,\n",
      "         0.0421, 0.0418, 0.0426, 0.0447, 0.0500, 0.0364, 0.0000],\n",
      "        [0.0370, 0.0384, 0.0424, 0.0476, 0.0407, 0.0423, 0.0384, 0.0415, 0.0380,\n",
      "         0.0416, 0.0418, 0.0390, 0.0458, 0.0377, 0.0387, 0.0400, 0.0380, 0.0362,\n",
      "         0.0324, 0.0409, 0.0417, 0.0413, 0.0427, 0.0403, 0.0358]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [19]], device='cuda:0')\n",
      "predicted  ['u', 's']\n",
      "decoder_input ['u', 's']\n",
      "done di= 39\n",
      "\n",
      "\n",
      "decoder di= 40\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0415, 0.0413, 0.0393, 0.0366, 0.0377, 0.0408, 0.0419, 0.0456, 0.0410,\n",
      "         0.0434, 0.0397, 0.0427, 0.0414, 0.0421, 0.0427, 0.0427, 0.0424, 0.0413,\n",
      "         0.0437, 0.0429, 0.0423, 0.0426, 0.0450, 0.0394, 0.0000],\n",
      "        [0.0406, 0.0396, 0.0404, 0.0416, 0.0379, 0.0403, 0.0389, 0.0421, 0.0401,\n",
      "         0.0409, 0.0411, 0.0397, 0.0406, 0.0380, 0.0397, 0.0404, 0.0396, 0.0399,\n",
      "         0.0394, 0.0396, 0.0413, 0.0390, 0.0403, 0.0404, 0.0383]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [24]], device='cuda:0')\n",
      "predicted  ['c', '-']\n",
      "decoder_input ['c', '-']\n",
      "done di= 40\n",
      "\n",
      "\n",
      "decoder di= 41\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0410, 0.0386, 0.0409, 0.0444, 0.0365, 0.0417, 0.0398, 0.0471, 0.0462,\n",
      "         0.0509, 0.0422, 0.0420, 0.0352, 0.0405, 0.0378, 0.0402, 0.0389, 0.0385,\n",
      "         0.0401, 0.0411, 0.0432, 0.0458, 0.0514, 0.0363, 0.0000],\n",
      "        [0.0397, 0.0392, 0.0397, 0.0455, 0.0352, 0.0393, 0.0337, 0.0470, 0.0412,\n",
      "         0.0434, 0.0435, 0.0391, 0.0430, 0.0366, 0.0393, 0.0421, 0.0403, 0.0393,\n",
      "         0.0364, 0.0393, 0.0421, 0.0382, 0.0406, 0.0412, 0.0351]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[13],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['y', 'u']\n",
      "decoder_input ['y', 'u']\n",
      "done di= 41\n",
      "\n",
      "\n",
      "decoder di= 42\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0408, 0.0406, 0.0389, 0.0365, 0.0370, 0.0414, 0.0418, 0.0462, 0.0418,\n",
      "         0.0452, 0.0402, 0.0430, 0.0405, 0.0414, 0.0414, 0.0418, 0.0417, 0.0401,\n",
      "         0.0438, 0.0434, 0.0434, 0.0437, 0.0464, 0.0392, 0.0000],\n",
      "        [0.0388, 0.0390, 0.0412, 0.0416, 0.0391, 0.0399, 0.0398, 0.0432, 0.0401,\n",
      "         0.0407, 0.0418, 0.0398, 0.0407, 0.0372, 0.0398, 0.0408, 0.0405, 0.0386,\n",
      "         0.0367, 0.0397, 0.0408, 0.0398, 0.0415, 0.0406, 0.0384]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [22]], device='cuda:0')\n",
      "predicted  ['u', 'c']\n",
      "decoder_input ['u', 'c']\n",
      "done di= 42\n",
      "\n",
      "\n",
      "decoder di= 43\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0392, 0.0389, 0.0391, 0.0382, 0.0378, 0.0439, 0.0409, 0.0464, 0.0426,\n",
      "         0.0501, 0.0412, 0.0435, 0.0385, 0.0394, 0.0388, 0.0401, 0.0399, 0.0376,\n",
      "         0.0420, 0.0441, 0.0466, 0.0443, 0.0487, 0.0384, 0.0000],\n",
      "        [0.0382, 0.0371, 0.0440, 0.0480, 0.0372, 0.0416, 0.0385, 0.0484, 0.0386,\n",
      "         0.0421, 0.0447, 0.0389, 0.0450, 0.0320, 0.0382, 0.0405, 0.0387, 0.0352,\n",
      "         0.0305, 0.0392, 0.0438, 0.0397, 0.0444, 0.0412, 0.0342]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['4', 'u']\n",
      "decoder_input ['4', 'u']\n",
      "done di= 43\n",
      "\n",
      "\n",
      "decoder di= 44\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0414, 0.0397, 0.0405, 0.0413, 0.0358, 0.0408, 0.0409, 0.0452, 0.0453,\n",
      "         0.0482, 0.0425, 0.0427, 0.0374, 0.0410, 0.0390, 0.0404, 0.0398, 0.0392,\n",
      "         0.0412, 0.0414, 0.0430, 0.0464, 0.0494, 0.0378, 0.0000],\n",
      "        [0.0388, 0.0398, 0.0399, 0.0402, 0.0400, 0.0401, 0.0398, 0.0394, 0.0402,\n",
      "         0.0401, 0.0401, 0.0397, 0.0399, 0.0408, 0.0402, 0.0406, 0.0402, 0.0404,\n",
      "         0.0402, 0.0402, 0.0400, 0.0398, 0.0399, 0.0403, 0.0395]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['u', '4']\n",
      "decoder_input ['u', '4']\n",
      "done di= 44\n",
      "\n",
      "\n",
      "decoder di= 45\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0423, 0.0418, 0.0404, 0.0404, 0.0393, 0.0397, 0.0421, 0.0435, 0.0415,\n",
      "         0.0402, 0.0402, 0.0419, 0.0415, 0.0429, 0.0428, 0.0428, 0.0420, 0.0425,\n",
      "         0.0441, 0.0423, 0.0409, 0.0417, 0.0424, 0.0408, 0.0000],\n",
      "        [0.0391, 0.0375, 0.0442, 0.0466, 0.0380, 0.0429, 0.0419, 0.0466, 0.0376,\n",
      "         0.0430, 0.0419, 0.0379, 0.0439, 0.0336, 0.0388, 0.0396, 0.0368, 0.0364,\n",
      "         0.0331, 0.0397, 0.0438, 0.0386, 0.0432, 0.0401, 0.0351]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['c', 'u']\n",
      "decoder_input ['c', 'u']\n",
      "done di= 45\n",
      "\n",
      "\n",
      "decoder di= 46\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0413, 0.0396, 0.0416, 0.0432, 0.0399, 0.0426, 0.0401, 0.0461, 0.0436,\n",
      "         0.0496, 0.0402, 0.0421, 0.0366, 0.0412, 0.0394, 0.0407, 0.0395, 0.0391,\n",
      "         0.0408, 0.0429, 0.0434, 0.0430, 0.0483, 0.0353, 0.0000],\n",
      "        [0.0398, 0.0395, 0.0408, 0.0427, 0.0385, 0.0400, 0.0389, 0.0433, 0.0399,\n",
      "         0.0418, 0.0409, 0.0395, 0.0415, 0.0383, 0.0399, 0.0404, 0.0397, 0.0387,\n",
      "         0.0371, 0.0398, 0.0402, 0.0395, 0.0410, 0.0400, 0.0385]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 46\n",
      "\n",
      "\n",
      "decoder di= 47\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0412, 0.0409, 0.0390, 0.0364, 0.0371, 0.0410, 0.0418, 0.0463, 0.0415,\n",
      "         0.0447, 0.0398, 0.0429, 0.0407, 0.0418, 0.0420, 0.0423, 0.0420, 0.0407,\n",
      "         0.0438, 0.0432, 0.0428, 0.0433, 0.0461, 0.0389, 0.0000],\n",
      "        [0.0384, 0.0380, 0.0425, 0.0457, 0.0374, 0.0409, 0.0381, 0.0462, 0.0395,\n",
      "         0.0418, 0.0436, 0.0391, 0.0433, 0.0343, 0.0389, 0.0410, 0.0396, 0.0370,\n",
      "         0.0333, 0.0394, 0.0427, 0.0395, 0.0429, 0.0411, 0.0355]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['u', '4']\n",
      "decoder_input ['u', '4']\n",
      "done di= 47\n",
      "\n",
      "\n",
      "decoder di= 48\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0404, 0.0401, 0.0396, 0.0381, 0.0386, 0.0427, 0.0412, 0.0463, 0.0417,\n",
      "         0.0471, 0.0400, 0.0429, 0.0396, 0.0407, 0.0406, 0.0413, 0.0409, 0.0393,\n",
      "         0.0427, 0.0436, 0.0443, 0.0431, 0.0468, 0.0383, 0.0000],\n",
      "        [0.0384, 0.0378, 0.0438, 0.0455, 0.0391, 0.0425, 0.0419, 0.0445, 0.0381,\n",
      "         0.0414, 0.0419, 0.0386, 0.0433, 0.0351, 0.0390, 0.0395, 0.0377, 0.0363,\n",
      "         0.0333, 0.0397, 0.0424, 0.0398, 0.0434, 0.0403, 0.0366]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 48\n",
      "\n",
      "\n",
      "decoder di= 49\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0393, 0.0396, 0.0396, 0.0393, 0.0406, 0.0437, 0.0416, 0.0443, 0.0420,\n",
      "         0.0471, 0.0407, 0.0434, 0.0396, 0.0401, 0.0394, 0.0397, 0.0397, 0.0381,\n",
      "         0.0437, 0.0460, 0.0467, 0.0424, 0.0447, 0.0387, 0.0000],\n",
      "        [0.0401, 0.0405, 0.0394, 0.0408, 0.0385, 0.0382, 0.0382, 0.0429, 0.0407,\n",
      "         0.0426, 0.0399, 0.0398, 0.0400, 0.0398, 0.0405, 0.0411, 0.0411, 0.0400,\n",
      "         0.0392, 0.0402, 0.0387, 0.0391, 0.0396, 0.0395, 0.0396]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['4', 'u']\n",
      "decoder_input ['4', 'u']\n",
      "done di= 49\n",
      "\n",
      "\n",
      "decoder di= 50\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0419, 0.0402, 0.0413, 0.0435, 0.0383, 0.0406, 0.0411, 0.0441, 0.0450,\n",
      "         0.0461, 0.0421, 0.0422, 0.0377, 0.0416, 0.0395, 0.0405, 0.0395, 0.0400,\n",
      "         0.0418, 0.0418, 0.0423, 0.0446, 0.0466, 0.0375, 0.0000],\n",
      "        [0.0345, 0.0379, 0.0426, 0.0453, 0.0417, 0.0416, 0.0409, 0.0406, 0.0384,\n",
      "         0.0417, 0.0414, 0.0386, 0.0431, 0.0388, 0.0399, 0.0411, 0.0392, 0.0370,\n",
      "         0.0332, 0.0416, 0.0406, 0.0409, 0.0427, 0.0404, 0.0364]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [22]], device='cuda:0')\n",
      "predicted  ['u', 'c']\n",
      "decoder_input ['u', 'c']\n",
      "done di= 50\n",
      "\n",
      "\n",
      "decoder di= 51\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0417, 0.0435, 0.0424, 0.0402, 0.0447, 0.0415, 0.0423, 0.0380, 0.0390,\n",
      "         0.0375, 0.0408, 0.0413, 0.0451, 0.0426, 0.0439, 0.0425, 0.0436, 0.0433,\n",
      "         0.0417, 0.0430, 0.0411, 0.0389, 0.0370, 0.0441, 0.0000],\n",
      "        [0.0387, 0.0377, 0.0433, 0.0490, 0.0372, 0.0417, 0.0372, 0.0470, 0.0386,\n",
      "         0.0422, 0.0443, 0.0392, 0.0459, 0.0339, 0.0383, 0.0400, 0.0381, 0.0351,\n",
      "         0.0308, 0.0393, 0.0427, 0.0401, 0.0438, 0.0409, 0.0347]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['4', 'u']\n",
      "decoder_input ['4', 'u']\n",
      "done di= 51\n",
      "\n",
      "\n",
      "decoder di= 52\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0415, 0.0394, 0.0404, 0.0426, 0.0365, 0.0408, 0.0405, 0.0470, 0.0449,\n",
      "         0.0480, 0.0413, 0.0422, 0.0367, 0.0414, 0.0394, 0.0412, 0.0398, 0.0397,\n",
      "         0.0416, 0.0415, 0.0425, 0.0448, 0.0496, 0.0368, 0.0000],\n",
      "        [0.0368, 0.0393, 0.0408, 0.0420, 0.0403, 0.0401, 0.0399, 0.0397, 0.0399,\n",
      "         0.0406, 0.0407, 0.0396, 0.0406, 0.0402, 0.0403, 0.0412, 0.0407, 0.0391,\n",
      "         0.0373, 0.0407, 0.0395, 0.0404, 0.0410, 0.0404, 0.0387]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[19],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['s', '4']\n",
      "decoder_input ['s', '4']\n",
      "done di= 52\n",
      "\n",
      "\n",
      "decoder di= 53\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0421, 0.0412, 0.0415, 0.0421, 0.0403, 0.0412, 0.0413, 0.0435, 0.0424,\n",
      "         0.0432, 0.0411, 0.0417, 0.0400, 0.0420, 0.0413, 0.0419, 0.0413, 0.0413,\n",
      "         0.0418, 0.0414, 0.0414, 0.0423, 0.0439, 0.0398, 0.0000],\n",
      "        [0.0368, 0.0378, 0.0438, 0.0461, 0.0407, 0.0426, 0.0422, 0.0429, 0.0376,\n",
      "         0.0419, 0.0413, 0.0384, 0.0439, 0.0363, 0.0392, 0.0397, 0.0377, 0.0361,\n",
      "         0.0326, 0.0407, 0.0418, 0.0404, 0.0433, 0.0400, 0.0363]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [19]], device='cuda:0')\n",
      "predicted  ['u', 's']\n",
      "decoder_input ['u', 's']\n",
      "done di= 53\n",
      "\n",
      "\n",
      "decoder di= 54\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0418, 0.0409, 0.0402, 0.0425, 0.0399, 0.0403, 0.0417, 0.0451, 0.0421,\n",
      "         0.0407, 0.0403, 0.0416, 0.0404, 0.0422, 0.0417, 0.0425, 0.0412, 0.0420,\n",
      "         0.0445, 0.0417, 0.0409, 0.0416, 0.0428, 0.0414, 0.0000],\n",
      "        [0.0404, 0.0398, 0.0406, 0.0403, 0.0389, 0.0403, 0.0413, 0.0408, 0.0397,\n",
      "         0.0410, 0.0398, 0.0395, 0.0395, 0.0388, 0.0403, 0.0402, 0.0395, 0.0402,\n",
      "         0.0405, 0.0400, 0.0405, 0.0389, 0.0400, 0.0398, 0.0394]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['c', 'u']\n",
      "decoder_input ['c', 'u']\n",
      "done di= 54\n",
      "\n",
      "\n",
      "decoder di= 55\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0406, 0.0390, 0.0402, 0.0419, 0.0368, 0.0421, 0.0403, 0.0472, 0.0444,\n",
      "         0.0491, 0.0414, 0.0423, 0.0367, 0.0405, 0.0387, 0.0408, 0.0399, 0.0388,\n",
      "         0.0412, 0.0419, 0.0436, 0.0449, 0.0499, 0.0379, 0.0000],\n",
      "        [0.0391, 0.0397, 0.0399, 0.0426, 0.0377, 0.0387, 0.0371, 0.0441, 0.0407,\n",
      "         0.0430, 0.0414, 0.0396, 0.0410, 0.0384, 0.0403, 0.0417, 0.0410, 0.0394,\n",
      "         0.0376, 0.0402, 0.0399, 0.0389, 0.0402, 0.0402, 0.0377]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [22]], device='cuda:0')\n",
      "predicted  ['u', 'c']\n",
      "decoder_input ['u', 'c']\n",
      "done di= 55\n",
      "\n",
      "\n",
      "decoder di= 56\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0414, 0.0402, 0.0409, 0.0428, 0.0397, 0.0417, 0.0412, 0.0450, 0.0438,\n",
      "         0.0451, 0.0417, 0.0420, 0.0387, 0.0409, 0.0396, 0.0406, 0.0400, 0.0400,\n",
      "         0.0424, 0.0415, 0.0423, 0.0437, 0.0453, 0.0396, 0.0000],\n",
      "        [0.0388, 0.0381, 0.0418, 0.0484, 0.0377, 0.0409, 0.0346, 0.0472, 0.0394,\n",
      "         0.0419, 0.0446, 0.0395, 0.0466, 0.0346, 0.0380, 0.0403, 0.0390, 0.0356,\n",
      "         0.0310, 0.0394, 0.0428, 0.0408, 0.0432, 0.0412, 0.0347]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[28],\n",
      "        [28]], device='cuda:0')\n",
      "predicted  ['4', '4']\n",
      "decoder_input ['4', '4']\n",
      "done di= 56\n",
      "\n",
      "\n",
      "decoder di= 57\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0406, 0.0389, 0.0411, 0.0428, 0.0383, 0.0431, 0.0403, 0.0454, 0.0458,\n",
      "         0.0518, 0.0426, 0.0426, 0.0360, 0.0398, 0.0374, 0.0388, 0.0385, 0.0374,\n",
      "         0.0403, 0.0422, 0.0446, 0.0459, 0.0498, 0.0361, 0.0000],\n",
      "        [0.0391, 0.0384, 0.0425, 0.0452, 0.0373, 0.0413, 0.0398, 0.0459, 0.0390,\n",
      "         0.0431, 0.0418, 0.0384, 0.0425, 0.0354, 0.0395, 0.0406, 0.0385, 0.0378,\n",
      "         0.0351, 0.0397, 0.0423, 0.0384, 0.0421, 0.0403, 0.0361]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[19],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['s', 'u']\n",
      "decoder_input ['s', 'u']\n",
      "done di= 57\n",
      "\n",
      "\n",
      "decoder di= 58\n",
      "User teacher_force=False\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0421, 0.0421, 0.0414, 0.0400, 0.0405, 0.0410, 0.0418, 0.0420, 0.0413,\n",
      "         0.0417, 0.0409, 0.0419, 0.0418, 0.0424, 0.0425, 0.0422, 0.0422, 0.0420,\n",
      "         0.0420, 0.0421, 0.0414, 0.0418, 0.0423, 0.0407, 0.0000],\n",
      "        [0.0399, 0.0410, 0.0383, 0.0371, 0.0406, 0.0393, 0.0406, 0.0362, 0.0405,\n",
      "         0.0394, 0.0382, 0.0400, 0.0376, 0.0428, 0.0407, 0.0407, 0.0408, 0.0430,\n",
      "         0.0455, 0.0406, 0.0392, 0.0393, 0.0379, 0.0396, 0.0412]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[11],\n",
      "        [11]], device='cuda:0')\n",
      "predicted  ['u', 'u']\n",
      "decoder_input ['u', 'u']\n",
      "done di= 58\n",
      "\n",
      "\n",
      "decoder di= 59\n",
      "User teacher_force=True\n",
      "attn_w torch.Size([2, 25]) tensor([[0.0418, 0.0415, 0.0401, 0.0389, 0.0396, 0.0410, 0.0419, 0.0444, 0.0411,\n",
      "         0.0419, 0.0403, 0.0422, 0.0417, 0.0419, 0.0424, 0.0424, 0.0420, 0.0417,\n",
      "         0.0436, 0.0420, 0.0415, 0.0421, 0.0432, 0.0409, 0.0000],\n",
      "        [0.0361, 0.0382, 0.0417, 0.0484, 0.0389, 0.0405, 0.0355, 0.0436, 0.0394,\n",
      "         0.0423, 0.0440, 0.0396, 0.0457, 0.0370, 0.0390, 0.0415, 0.0398, 0.0360,\n",
      "         0.0312, 0.0407, 0.0410, 0.0411, 0.0429, 0.0410, 0.0349]],\n",
      "       device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "topi.detach()  tensor([[22],\n",
      "        [24]], device='cuda:0')\n",
      "predicted  ['c', '-']\n",
      "decoder_input ['<PAD>', '<PAD>']\n",
      "done di= 59\n",
      "target_tensor,transpose(0, 1) torch.Size([60, 2]) tensor([[17,  3],\n",
      "        [ 9, 14],\n",
      "        [12, 10],\n",
      "        [ 3, 10],\n",
      "        [10, 14],\n",
      "        [ 8,  7],\n",
      "        [14,  8],\n",
      "        [20,  4],\n",
      "        [14,  5],\n",
      "        [ 9,  6],\n",
      "        [16, 25],\n",
      "        [ 9,  3],\n",
      "        [14, 20],\n",
      "        [22, 14],\n",
      "        [ 8, 10],\n",
      "        [ 9, 10],\n",
      "        [14, 14],\n",
      "        [24, 22],\n",
      "        [ 4,  8],\n",
      "        [ 3,  4],\n",
      "        [19, 10],\n",
      "        [14, 24],\n",
      "        [22,  9],\n",
      "        [ 8,  5],\n",
      "        [12,  9],\n",
      "        [ 5,  5],\n",
      "        [ 2,  2],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0],\n",
      "        [ 0,  0]], device='cuda:0')\n",
      "before outputs.size() torch.Size([60, 2, 40]) tensor([[[ 5.8432e-02,  7.1272e-02, -2.1669e-01,  ..., -1.2010e-01,\n",
      "          -3.5271e-01,  1.9314e-01],\n",
      "         [ 7.0954e-02, -8.4817e-02, -2.6564e-01,  ..., -2.3077e-01,\n",
      "          -3.3990e-01,  1.9441e-01]],\n",
      "\n",
      "        [[ 3.3733e-02,  2.3293e-01, -2.9675e-01,  ..., -2.1616e-02,\n",
      "          -3.6869e-01,  5.2709e-02],\n",
      "         [ 9.4460e-02,  1.0245e-01, -1.8312e-01,  ..., -9.0561e-02,\n",
      "          -3.7813e-01,  1.2468e-01]],\n",
      "\n",
      "        [[-1.0866e-01,  2.0754e-01, -2.3397e-01,  ..., -2.4761e-01,\n",
      "          -1.5525e-01,  1.7988e-01],\n",
      "         [ 1.5876e-04,  1.9365e-01, -2.2853e-01,  ..., -1.1614e-01,\n",
      "          -2.6968e-01,  2.7777e-01]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 4.6590e-02, -1.5148e-01, -3.6677e-01,  ..., -2.3865e-01,\n",
      "          -2.7909e-01,  9.3688e-02],\n",
      "         [ 8.8595e-02, -2.6936e-01, -3.6920e-01,  ..., -2.9598e-01,\n",
      "          -2.3561e-01,  8.8970e-02]],\n",
      "\n",
      "        [[ 3.7682e-02, -7.5996e-02, -2.8665e-01,  ..., -2.3507e-01,\n",
      "          -3.5669e-01,  1.4637e-01],\n",
      "         [-8.1857e-03,  2.2595e-01, -3.0500e-01,  ..., -9.0811e-02,\n",
      "          -3.0220e-01,  1.2863e-01]],\n",
      "\n",
      "        [[-2.1705e-03,  1.6968e-01, -3.3148e-01,  ..., -1.1540e-01,\n",
      "          -1.9031e-01,  8.4509e-02],\n",
      "         [-3.7397e-02,  8.6143e-02, -3.8544e-01,  ..., -1.5136e-01,\n",
      "          -2.0694e-01,  2.2000e-01]]], device='cuda:0', grad_fn=<CopySlices>)\n",
      "after outputs.size() torch.Size([120, 40]) tensor([[ 0.0584,  0.0713, -0.2167,  ..., -0.1201, -0.3527,  0.1931],\n",
      "        [ 0.0710, -0.0848, -0.2656,  ..., -0.2308, -0.3399,  0.1944],\n",
      "        [ 0.0337,  0.2329, -0.2967,  ..., -0.0216, -0.3687,  0.0527],\n",
      "        ...,\n",
      "        [-0.0082,  0.2259, -0.3050,  ..., -0.0908, -0.3022,  0.1286],\n",
      "        [-0.0022,  0.1697, -0.3315,  ..., -0.1154, -0.1903,  0.0845],\n",
      "        [-0.0374,  0.0861, -0.3854,  ..., -0.1514, -0.2069,  0.2200]],\n",
      "       device='cuda:0', grad_fn=<ViewBackward>)\n",
      "target_tensor[0:].contiguous().view(-1) torch.Size([120]) tensor([17,  3,  9, 14, 12, 10,  3, 10, 10, 14,  8,  7, 14,  8, 20,  4, 14,  5,\n",
      "         9,  6, 16, 25,  9,  3, 14, 20, 22, 14,  8, 10,  9, 10, 14, 14, 24, 22,\n",
      "         4,  8,  3,  4, 19, 10, 14, 24, 22,  9,  8,  5, 12,  9,  5,  5,  2,  2,\n",
      "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0], device='cuda:0')\n",
      "loss= tensor(3.7348, device='cuda:0', grad_fn=<NllLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "### Testing Decoder part\n",
    "# Device\n",
    "\n",
    "def create_mask(source_seq):\n",
    "    mask = (source_seq != 0)\n",
    "    return mask\n",
    "        \n",
    "        \n",
    "INPUT_DIM = len(thai_romanization_dataset.lang_th.char2index)\n",
    "OUTPUT_DIM = len(thai_romanization_dataset.lang_th_romanized.char2index)\n",
    "\n",
    "ENC_EMB_DIM = 10\n",
    "ENC_HID_DIM = 10\n",
    "ENC_DROPOUT = 0.5\n",
    "\n",
    "DEC_EMB_DIM = 10\n",
    "DEC_HID_DIM = 10\n",
    "DEC_DROPOUT = 0.5\n",
    "_encoder = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM).to(device)\n",
    "_decoder = AttentionDecoder(OUTPUT_DIM, DEC_EMB_DIM, DEC_HID_DIM).to(device)\n",
    "\n",
    "\n",
    "# obtain one sample from the data iterator\n",
    "start_token = thai_romanization_dataset.lang_th_romanized.char2index['<start>']\n",
    "print('start_token=',start_token)\n",
    "max_len= 60\n",
    "for batch in train_dataset_loader:\n",
    "    print(batch)\n",
    "    \n",
    "    input_tensor, input_length, target_tensor = batch['input_tensor'], batch['input_length'], batch['target_tensor']\n",
    "    \n",
    "    batch_size=input_tensor.size(0)\n",
    "    \n",
    "    encoder_outputs, encoder_hidden = _encoder(input_tensor, input_length)\n",
    "    \n",
    "    print('input_tensor', input_tensor.size())\n",
    "    print('input_length', input_length.size())\n",
    "    print('target_tensor', target_tensor.size())\n",
    "    print('encoder_outputs', encoder_outputs.size())\n",
    "    print('encoder_hidden[0]', encoder_hidden[0].size(), encoder_hidden[0])\n",
    "    print('encoder_hidden[1]', encoder_hidden[0].size(), encoder_hidden[1])\n",
    "\n",
    "    decoder_input = torch.tensor([[start_token] * batch_size]).view(batch_size, 1).to(device)\n",
    "        \n",
    "        # Initiate decoder output as the last state encoder's hidden state\n",
    "#         decoder_hidden_1 = torch.cat([encoder_hidden[1][0], encoder_hidden[1][1]], dim=1).unsqueeze(dim=0)\n",
    "#         print('encoder_hidden', encoder_hidden.size(), encoder_hidden)\n",
    "#         decoder_hidden_0 = torch.cat([encoder_hidden[0][0], encoder_hidden[0][1]], dim=1).unsqueeze(dim=0)\n",
    "#         print('decoder_hidden_0', decoder_hidden_0)\n",
    "\n",
    "#     encoder_hidden_ht = encoder_hidden[0]\n",
    "\n",
    "    \n",
    "    encoder_hidden_ht = torch.cat([encoder_hidden[0][0], encoder_hidden[0][1]], dim=1).unsqueeze(dim=0)\n",
    "    decoder_hidden = encoder_hidden_ht\n",
    "\n",
    "    print('decoder_hidden', decoder_hidden.size(), decoder_hidden)\n",
    "    max_source_len = encoder_outputs.size(1)\n",
    "    mask = create_mask(input_tensor[:, 0:max_source_len])\n",
    "\n",
    "    print('\\n\\nmask', mask.size(), mask)\n",
    "\n",
    "    outputs = torch.zeros(max_len, batch_size, OUTPUT_DIM).to(device)\n",
    "\n",
    "    for di in range(max_len):\n",
    "        print('\\n\\ndecoder di=', di)\n",
    "        decoder_output, decoder_hidden, attn_w = _decoder(decoder_input,\n",
    "                                                            decoder_hidden,\n",
    "                                                            encoder_outputs,\n",
    "                                                            mask)\n",
    "        # decoder_output: (batch_size, target_vocab_size)\n",
    "\n",
    "        topv, topi = decoder_output.topk(1)\n",
    "        outputs[di] = decoder_output.to(device)\n",
    "        teacher_force = random.random() < teacher_forcing_ratio\n",
    "\n",
    "        print('User teacher_force={}'.format(teacher_force))\n",
    "        decoder_input = target_tensor[:, di].reshape(batch_size, 1) if teacher_force else topi.detach() \n",
    "        \n",
    "        print('attn_w', attn_w.size(),attn_w)\n",
    "        print('topi.detach() ', topi.detach() )\n",
    "        print('predicted ', [thai_romanization_dataset.lang_th_romanized.index2char[idx] for idx in topi.squeeze(1).cpu().detach().numpy()])\n",
    "        print('decoder_input', [thai_romanization_dataset.lang_th_romanized.index2char[idx] for idx in decoder_input.squeeze(1).cpu().detach().numpy()])\n",
    "        print('done di=', di)\n",
    "\n",
    "    target_tensor = target_tensor.transpose(0, 1)\n",
    "    print('target_tensor,transpose(0, 1)', target_tensor.size(),target_tensor)\n",
    "\n",
    "    # target_seq -> ((MAX_LENGTH ) * batch_size)\n",
    "    target_tensor = target_tensor[0:].contiguous().view(-1)\n",
    "\n",
    "    # output -> ((MAX_LENGTH ) * batch_size, target_vocab_size)  \n",
    "    print('before outputs.size()', outputs.size(), outputs)\n",
    "    outputs = outputs.view(-1, outputs.size(-1))\n",
    "    print('after outputs.size()', outputs.size(), outputs)\n",
    "\n",
    "    print('target_tensor[0:].contiguous().view(-1)', target_tensor.size(),target_tensor)\n",
    "\n",
    "    loss = criterion(outputs, target_tensor)\n",
    "         \n",
    "    print('loss=', loss)\n",
    "    break\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wUnwZ91oI84U"
   },
   "source": [
    "## Seq2Seq model\n",
    "\n",
    "This class encapsulate _Decoder_ and _Encoder_ class.\n",
    "\n",
    "__Steps:__\n",
    "\n",
    "1. The input sequcence $X$ is fed into the encoder to receive one hidden state vector.\n",
    "\n",
    "2. The initial decoder hidden state is set to be the hidden state vector of the encoder\n",
    "\n",
    "3. Add a batch of `<start>` tokens (batch_size, 1) as the first input $y_1$\n",
    "    \n",
    "4. Then, decode within a loop:\n",
    "    - Inserting the input token $y_t$, previous hidden state, $s_{t-1}$, and the context vector $z$ into the decoder\n",
    "    - Receiveing a prediction $\\hat{y}$ and a new hidden state $s_t$\n",
    "    - Then, either use teacher forcing to let groundtruth target character as the input for the decoder at time step $t+1$, or let the result from decoder as the input for the next time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fXmyHAFiI84V"
   },
   "outputs": [],
   "source": [
    "class Seq2Seq(nn.Module): \n",
    "\n",
    "    def __init__(self, encoder, decoder):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.pad_idx = 0\n",
    "\n",
    "        assert encoder.hidden_size == decoder.hidden_size\n",
    "    \n",
    "    def create_mask(self, source_seq):\n",
    "        mask = (source_seq != self.pad_idx)\n",
    "        return mask\n",
    "        \n",
    "  \n",
    "    def forward(self, source_seq, source_seq_len, target_seq, teacher_forcing_ratio = 0.5):\n",
    "        \"\"\"\n",
    "            Parameters:\n",
    "                - source_seq: (batch_size x MAX_LENGTH) \n",
    "                - source_seq_len: (batch_size x 1)\n",
    "                - target_seq: (batch_size x MAX_LENGTH)\n",
    "\n",
    "            Returns\n",
    "        \"\"\"\n",
    "        batch_size = source_seq.size(0)\n",
    "        start_token = thai_romanization_dataset.lang_th_romanized.char2index[\"<start>\"]\n",
    "        end_token = thai_romanization_dataset.lang_th_romanized.char2index[\"<end>\"]\n",
    "        max_len = MAX_LENGTH\n",
    "        target_vocab_size = self.decoder.vocabulary_size\n",
    "\n",
    "        # init a tensor to store decoder outputs\n",
    "        outputs = torch.zeros(max_len, batch_size, target_vocab_size).to(device)\n",
    "        \n",
    "        if target_seq is None:\n",
    "            assert teacher_forcing_ratio == 0, \"Must be zero during inference\"\n",
    "            inference = True\n",
    "        else:\n",
    "            inference = False\n",
    "\n",
    "    \n",
    "        # feed mini-batch source sequences into the `Encoder`\n",
    "        encoder_outputs, encoder_hidden = self.encoder(source_seq, source_seq_len)\n",
    "\n",
    "        # create a Tensor of first input for the decoder\n",
    "        decoder_input = torch.tensor([[start_token] * batch_size]).view(batch_size, 1).to(device)\n",
    "         \n",
    "        encoder_hidden_ht = torch.cat([encoder_hidden[0][0], encoder_hidden[0][1]], dim=1).unsqueeze(dim=0)\n",
    "        decoder_hidden = encoder_hidden_ht\n",
    "\n",
    "            \n",
    "#         print('decoder_hidden', decoder_hidden.size())\n",
    "        # define a context vector\n",
    "#         decoder_context = Variable(torch.zeros(encoder_outputs.size(0), encoder_outputs.size(2))).unsqueeze(1).to(device)\n",
    "        \n",
    "        \n",
    "        max_source_len = encoder_outputs.size(1)\n",
    "        mask = self.create_mask(source_seq[:, 0:max_source_len])\n",
    "            \n",
    "        \n",
    "        attn_weights  = []\n",
    "\n",
    "        for di in range(max_len):\n",
    "            decoder_output, decoder_hidden, attn_w = self.decoder(decoder_input,\n",
    "                                                                decoder_hidden,\n",
    "                                                                encoder_outputs,\n",
    "                                                                mask)\n",
    "            # decoder_output: (batch_size, target_vocab_size)\n",
    "\n",
    "            topv, topi = decoder_output.topk(1)\n",
    "            outputs[di] = decoder_output.to(device)\n",
    "            teacher_force = random.random() < teacher_forcing_ratio\n",
    "\n",
    "            decoder_input = target_seq[:, di] if teacher_force else topi.detach() \n",
    "\n",
    "            if inference: \n",
    "#                 print('attn_w', attn_w.size(),attn_w)\n",
    "                attn_weights.append(attn_w)\n",
    "    \n",
    "            if inference and decoder_input == end_token:\n",
    "                return outputs[:di], attn_weights\n",
    "            \n",
    "            \n",
    "\n",
    "        return outputs, attn_weights\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "joIOMNz-I84X"
   },
   "source": [
    "Initializae model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "HePJasoxI84Y",
    "outputId": "6b103235-c718-43a1-a339-c7321033a836"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples:  648206\n",
      "split at index: 518564\n",
      "train_indices [118842, 551164, 200228, 93841, 142270]\n",
      "val_indices [419695, 455015, 88739, 260808, 64788]\n",
      "Number of train mini-batches 1013\n",
      "Number of val mini-batches 254\n"
     ]
    }
   ],
   "source": [
    "SEED = 0\n",
    "BATCH_SIZE = 512\n",
    "\n",
    "TRAIN_RATIO = 0.8\n",
    "\n",
    "N = len(thai_romanization_dataset)\n",
    "\n",
    "print('Number of samples: ', N)\n",
    "train_split_idx = int(TRAIN_RATIO * N)\n",
    "\n",
    "print('split at index:', train_split_idx)\n",
    "indices = list(range(N))\n",
    "\n",
    "# Random Split\n",
    "np.random.seed(SEED)\n",
    "np.random.shuffle(indices)\n",
    "train_indices, val_indices = indices[:train_split_idx], indices[train_split_idx:]\n",
    "\n",
    "print('train_indices', train_indices[0:5])\n",
    "print('val_indices', val_indices[0:5])\n",
    "\n",
    "train_sampler = SubsetRandomSampler(train_indices)\n",
    "valid_sampler = SubsetRandomSampler(val_indices)\n",
    "                                   \n",
    "train_dataset_loader = torch.utils.data.DataLoader(\n",
    "                                             thai_romanization_dataset,\n",
    "                                             batch_size=BATCH_SIZE, \n",
    "                                             sampler=train_sampler,\n",
    "                                             num_workers=0)\n",
    "\n",
    "val_dataset_loader = torch.utils.data.DataLoader(\n",
    "                                             thai_romanization_dataset,\n",
    "                                             batch_size=BATCH_SIZE,\n",
    "                                             shuffle=False,\n",
    "                                             sampler=valid_sampler,\n",
    "                                             num_workers=0)\n",
    "\n",
    "\n",
    "print('Number of train mini-batches', len(train_dataset_loader))\n",
    "print('Number of val mini-batches', len(val_dataset_loader))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8_Qb2nPYI84b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq2Seq(\n",
       "  (encoder): Encoder(\n",
       "    (character_embedding): Embedding(94, 128)\n",
       "    (rnn): LSTM(128, 128, batch_first=True, bidirectional=True)\n",
       "    (dropout): Dropout(p=0.5)\n",
       "  )\n",
       "  (decoder): AttentionDecoder(\n",
       "    (character_embedding): Embedding(40, 128)\n",
       "    (rnn): LSTM(384, 256, batch_first=True)\n",
       "    (attn): Attn(\n",
       "      (attn): Linear(in_features=256, out_features=256, bias=True)\n",
       "    )\n",
       "    (linear): Linear(in_features=256, out_features=40, bias=True)\n",
       "    (dropout): Dropout(p=0.5)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INPUT_DIM = len(thai_romanization_dataset.lang_th.char2index)\n",
    "OUTPUT_DIM = len(thai_romanization_dataset.lang_th_romanized.char2index)\n",
    "\n",
    "ENC_EMB_DIM = 128\n",
    "ENC_HID_DIM = 256\n",
    "ENC_DROPOUT = 0.5\n",
    "\n",
    "DEC_EMB_DIM = 128\n",
    "DEC_HID_DIM = 256\n",
    "DEC_DROPOUT = 0.5\n",
    "\n",
    "_encoder = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, ENC_DROPOUT)\n",
    "_decoder = AttentionDecoder(OUTPUT_DIM, DEC_EMB_DIM, DEC_HID_DIM, DEC_DROPOUT)\n",
    "\n",
    "model = Seq2Seq(_encoder, _decoder).to(device)\n",
    "model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xoaZI1sdShNi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "T-zk0j-RI84f",
    "outputId": "a679b4b1-2aa9-4e9e-b9aa-5326a6979d31"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq2Seq(\n",
       "  (encoder): Encoder(\n",
       "    (character_embedding): Embedding(94, 128)\n",
       "    (rnn): LSTM(128, 128, batch_first=True, bidirectional=True)\n",
       "    (dropout): Dropout(p=0.5)\n",
       "  )\n",
       "  (decoder): AttentionDecoder(\n",
       "    (character_embedding): Embedding(40, 128)\n",
       "    (rnn): LSTM(384, 256, batch_first=True)\n",
       "    (attn): Attn(\n",
       "      (attn): Linear(in_features=256, out_features=256, bias=True)\n",
       "    )\n",
       "    (linear): Linear(in_features=256, out_features=40, bias=True)\n",
       "    (dropout): Dropout(p=0.5)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def init_weights(m):\n",
    "    for name, param in m.named_parameters():\n",
    "        if 'weight' in name:\n",
    "            nn.init.normal_(param.data, mean=0, std=0.01)\n",
    "        else:\n",
    "            nn.init.constant_(param.data, 0)\n",
    "            \n",
    "model.apply(init_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4l2Oi-NBI84i",
    "outputId": "25bc242a-51fa-47c0-806c-48eb16bee44e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 1,014,824 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i_XfWQtMI84l"
   },
   "outputs": [],
   "source": [
    "\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "criterion = nn.CrossEntropyLoss(ignore_index = 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hu6XxOwxI84s"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uj2mzBSPI84u"
   },
   "outputs": [],
   "source": [
    "print_loss_every = 100\n",
    "teacher_forcing_ratio = 0.5\n",
    "\n",
    "def train(model, iterator, optimizer, criterion, clip, teacher_forcing_ratio=0.5):\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    for i, batch in tqdm(enumerate(iterator), total = len(iterator)):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        source_seq, source_seq_len = batch['input_tensor'], batch['input_length']\n",
    "        batch_size = source_seq.size(0)\n",
    "        \n",
    "        # target_seq: (batch_size , MAX_LENGTH)\n",
    "        # output: (MAX_LENGTH , batch_size , target_vocab_size)\n",
    "        target_seq = batch['target_tensor']\n",
    "\n",
    "        output, _ = model(source_seq, source_seq_len, target_seq, teacher_forcing_ratio=teacher_forcing_ratio)\n",
    "        \n",
    "        # target_seq -> (MAX_LENGTH , batch_size)\n",
    "        target_seq = target_seq.transpose(0, 1)\n",
    "\n",
    "        # target_seq -> ((MAX_LENGTH) * batch_size)\n",
    "        target_seq = target_seq[0:].contiguous().view(-1)\n",
    "\n",
    "        # output -> ((MAX_LENGTH ) * batch_size, target_vocab_size)        \n",
    "        output = output[0:].view(-1, output.size(-1))\n",
    "\n",
    "        loss = criterion(output, target_seq)\n",
    "        \n",
    "        if i % print_loss_every == 0:\n",
    "            print('Loss ', loss.item())\n",
    "\n",
    "        loss.backward()\n",
    "        \n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "        \n",
    "        \n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_bWlep1pI84x"
   },
   "outputs": [],
   "source": [
    "def evaluate(model, iterator, criterion):\n",
    "    \n",
    "    model.eval()\n",
    "\n",
    "    epoch_loss = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "    \n",
    "        for i, batch in enumerate(iterator):\n",
    "\n",
    "            source_seq, source_seq_len = batch['input_tensor'], batch['input_length']\n",
    "            batch_size = source_seq.size(0)\n",
    "\n",
    "            # target_seq: (batch_size , MAX_LENGTH)\n",
    "            # output: (MAX_LENGTH , batch_size , target_vocab_size)\n",
    "            target_seq = batch['target_tensor']\n",
    "            output, _ = model(source_seq, source_seq_len, target_seq)\n",
    "        \n",
    "            # target_seq -> (MAX_LENGTH , batch_size)\n",
    "            target_seq = target_seq.transpose(0, 1)\n",
    "\n",
    "            # target_seq -> ((MAX_LENGTH - 1) * batch_size)\n",
    "            target_seq = target_seq[0:].contiguous().view(-1)\n",
    "\n",
    "            # output -> ((MAX_LENGTH -1) * batch_size, target_vocab_size)        \n",
    "            output = output[0:].view(-1, output.size(-1))\n",
    "\n",
    "            loss = criterion(output, target_seq)\n",
    "            epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator)\n",
    "\n",
    "def inference(model, text, char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char):\n",
    "    model.eval()\n",
    "\n",
    "    input_seq =  [ch for ch in text] +  ['<end>']\n",
    "    numericalized = [char_2_ix[ch] for ch in input_seq] \n",
    "    \n",
    "#     print('input ',numericalized)\n",
    "    sentence_length = [len(numericalized)]\n",
    "\n",
    "    tensor = torch.LongTensor(numericalized).view(1, -1).to(device) \n",
    "    \n",
    "#     print(tensor)\n",
    "    translation_tensor_logits, attn_weights = model(tensor, sentence_length, None, 0) \n",
    "#     print(translation_tensor_logits)\n",
    "    try:\n",
    "        translation_tensor = torch.argmax(translation_tensor_logits.squeeze(1), 1).cpu().numpy()\n",
    "        translation_indices = [t for t in translation_tensor]\n",
    "        \n",
    "#         print('translation_tensor', translation_tensor)\n",
    "        translation = [ix_to_target_char[t] for t in translation_tensor]\n",
    "    except:\n",
    "        translation_indices = [0]\n",
    "        translation = ['<pad>']\n",
    "    return ''.join(translation), translation_indices, attn_weights\n",
    "\n",
    "def show_inference_example(model, input_texts, target_texts, char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char):\n",
    "    for index, input_text in enumerate(input_texts):\n",
    "        prediction, indices, _ = inference(model, input_text, char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char)\n",
    "        print('groundtruth: {}'.format(target_texts[index]))\n",
    "        print(' prediction: {} {}\\n'.format(prediction, indices))\n",
    "\n",
    "        \n",
    "def show_inference_example_attn(model, input_texts, target_texts, char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char):\n",
    "    for index, input_text in enumerate(input_texts):\n",
    "        prediction, indices, attn = inference(model, input_text, char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char)\n",
    "        \n",
    "        display_attention(input_text, prediction, attn)\n",
    "        print('groundtruth: {}'.format(target_texts[index]))\n",
    "        print(' prediction: {} {}\\n'.format(prediction, indices))\n",
    "        \n",
    "def display_attention(candidate, translation, attention):\n",
    "    \n",
    "  \n",
    "    attns = [attn.squeeze().cpu().detach().numpy() for attn in attention]\n",
    "#     print(attns)\n",
    "    x = [t for t in candidate] + ['<end>']\n",
    "\n",
    "    y = [t for t in translation] + ['<end>']\n",
    "    y = y\n",
    "#     print(y)\n",
    "\n",
    "#     print(attns)\n",
    "    figsize=(6,6)\n",
    "    margins = {  #     vvv margin in inches\n",
    "        \"left\"   :     1.5 / figsize[0],\n",
    "        \"bottom\" :     1.0 / figsize[1],\n",
    "        \"right\"  : 1 - 0.3 / figsize[0],\n",
    "        \"top\"    : 1 - 1   / figsize[1]\n",
    "    }\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    fig.subplots_adjust(**margins)\n",
    "    \n",
    "    x1,x2,y1,y2 = plt.axis()\n",
    "    plt.axis((x1,x2,y1 - 100 ,y2 + 100))\n",
    "    \n",
    "    sns.heatmap(data=attns, yticklabels=y, cmap='rocket')\n",
    "    \n",
    "    fpath = os.path.join(rcParams[\"datapath\"], \"/usr/share/fonts/truetype/tlwg/Sawasdee.ttf\")\n",
    "    prop = fm.FontProperties(fname=fpath)\n",
    "\n",
    "    ax.set_xlabel(x, fontproperties=prop, fontsize=15, labelpad=10)\n",
    "\n",
    "    ax.set_xticklabels(x, fontproperties=prop, fontsize=15, fontweight=400)\n",
    "    ax.set_yticklabels(y, fontproperties=prop, rotation = 0, va=\"center\",\n",
    "                       verticalalignment='top',position=(0,-10))\n",
    "\n",
    "#     fig.tight_layout(pad=0.0, h_pad=0, w_pad=0)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.show()\n",
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char = thai_romanization_dataset.lang_th.char2index , \\\n",
    "                                                             thai_romanization_dataset.lang_th.index2char , \\\n",
    "                                                             thai_romanization_dataset.lang_th_romanized.char2index ,\\\n",
    "                                                             thai_romanization_dataset.lang_th_romanized.index2char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groundtruth: banrai\n",
      " prediction:  6(t2cdscdscdscdscdscdscdscdscdscdscdscdscdscdscdscdscdscdsc [25, 34, 38, 10, 26, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22, 21, 19, 22]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "show_inference_example_attn(model, ['บ้านไร่'],  ['banrai'] ,char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "1A6d5ONY-ojO",
    "outputId": "8a2386a2-c5e1-4852-832f-36a1d75b88ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'<PAD>': 0, '<UNK>': 1, '<start>': 2, '<end>': 3, 'ก': 4, 'อ': 5, 'ง': 6, 'พ': 7, 'ั': 8, 'น': 9, 'ท': 10, 'ห': 11, 'า': 12, 'ร': 13, 'ป': 14, 'ื': 15, 'ใ': 16, 'ญ': 17, '่': 18, 'ว': 19, 'ิ': 20, 'ฑ': 21, 'ู': 22, 'ย': 23, '์': 24, 'เ': 25, 'ม': 26, 'ต': 27, 'บ': 28, 'ล': 29, 'ส': 30, '้': 31, 'ุ': 32, 'ษ': 33, 'แ': 34, 'ะ': 35, 'ศ': 36, 'ี': 37, 'ด': 38, 'ค': 39, 'ฆ': 40, 'โ': 41, '๋': 42, 'ช': 43, 'ไ': 44, 'จ': 45, 'ภ': 46, 'ณ': 47, 'ำ': 48, 'ฝ': 49, 'ข': 50, 'ผ': 51, 'ฒ': 52, 'ซ': 53, 'ธ': 54, '.': 55, ' ': 56, 'ถ': 57, 'ฐ': 58, 'ฏ': 59, 'ฟ': 60, 'ึ': 61, '็': 62, 'ฮ': 63, '๊': 64, 'ฉ': 65, 'ฎ': 66, '2': 67, 'ฌ': 68, 'ฬ': 69, '1': 70, '4': 71, '8': 72, '3': 73, 'ฯ': 74, 'ๆ': 75, '5': 76, '๙': 77, '7': 78, '0': 79, 'ฺ': 80, '6': 81, '9': 82, 'ฤ': 83, '-': 84, 'ฅ': 85, 'ๅ': 86, 'ฃ': 87, 'ฦ': 88, '\"': 89, '!': 90, 'ํ': 91, '(': 92, ')': 93}\n"
     ]
    }
   ],
   "source": [
    "print(char_2_ix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "gNxhXs1K_SqL",
    "outputId": "89d41bba-35e9-416d-89c6-88098adedb73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: '<PAD>', 1: '<start>', 2: '<end>', 3: 'k', 4: 'o', 5: 'n', 6: 'g', 7: 'p', 8: 'h', 9: 'a', 10: 't', 11: 'u', 12: 'e', 13: 'y', 14: 'i', 15: 'w', 16: 'm', 17: 'b', 18: 'l', 19: 's', 20: 'r', 21: 'd', 22: 'c', 23: 'f', 24: '-', 25: ' ', 26: '2', 27: '1', 28: '4', 29: '8', 30: '3', 31: '5', 32: '7', 33: '0', 34: '6', 35: '9', 36: '\"', 37: '!', 38: '(', 39: ')'}\n"
     ]
    }
   ],
   "source": [
    "print(ix_to_target_char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 412
    },
    "colab_type": "code",
    "id": "X10TZ948I846",
    "outputId": "5268808b-488a-417c-c112-8dd2edd6bceb",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch=29, teacher_forcing_ratio=0.15\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3377939ce8e14ec8af5d376c6b4888a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1013), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss  0.7274187803268433\n",
      "Loss  0.5383312106132507\n",
      "Loss  0.5050680637359619\n",
      "Loss  0.5484542846679688\n",
      "Loss  0.6379415392875671\n",
      "Loss  0.6490351557731628\n",
      "Loss  0.5977187156677246\n",
      "Loss  0.4957181513309479\n",
      "Loss  0.5053154230117798\n",
      "Loss  0.5787795782089233\n",
      "Loss  0.6386951804161072\n",
      "groundtruth: maeo\n",
      " prediction: maeo [16, 9, 12, 4]\n",
      "\n",
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "groundtruth: sunak\n",
      " prediction: sunak [19, 11, 5, 9, 3]\n",
      "\n",
      "groundtruth: nok\n",
      " prediction: nok [5, 4, 3]\n",
      "\n",
      "groundtruth: khwam-im\n",
      " prediction: khwam-im [3, 8, 15, 9, 16, 24, 14, 16]\n",
      "\n",
      "groundtruth: kan narong\n",
      " prediction: kan narong [3, 9, 5, 25, 5, 9, 20, 4, 5, 6]\n",
      "\n",
      "groundtruth: sakun\n",
      " prediction: sakun [19, 9, 3, 11, 5]\n",
      "\n",
      "groundtruth: charin\n",
      " prediction: charin [22, 8, 9, 20, 14, 5]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "Evaluate F1,EM\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4577f1ca6e634b27a9f1d25f425582f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1\n",
      "      input: ขัตติยา กรีมี\n",
      "groundtruth: khattiya krimi\n",
      " prediction: khattiya krimi\n",
      "\n",
      "Example: 2\n",
      "      input: คมสรร บุญทอง\n",
      "groundtruth: khomsan bunthong\n",
      " prediction: khomsan bunthong\n",
      "\n",
      "Example: 3\n",
      "      input: ต่อเลขหมาย\n",
      "groundtruth: tolekmai\n",
      " prediction: tolekmai\n",
      "\n",
      "Example: 4\n",
      "      input: กิตติศักดิ์ หวังวรวงศ์\n",
      "groundtruth: kittisak wangworawong\n",
      " prediction: kittisak wangworawong\n",
      "\n",
      "Example: 5\n",
      "      input: สถาปัตยกรรมศาสตร์\n",
      "groundtruth: sathapattayakamsat\n",
      " prediction: sathapattayakammasat\n",
      "\n",
      "Example: 6\n",
      "      input: กิมเจียว แซ่โง้ว\n",
      "groundtruth: kimchiao sae-ngow\n",
      " prediction: kimchiao sae-ngow\n",
      "\n",
      "\n",
      "F1 (macro-average) =  0.9893446245494757\n",
      "EM =  0.896\n",
      "EM (Character-level) =  0.9552857402847368\n",
      "save best model em score=0.896 at epoch=29\n",
      "Save model at epoch  29\n",
      "Epoch: 30 | Time: 4m 48s\n",
      "\tTrain Loss: 0.566 | Train PPL:   1.762\n",
      "\t Val. Loss: 0.429 |  Val. PPL:   1.535\n",
      "epoch=30, teacher_forcing_ratio=0.15\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f10dc2aa5cf24fdfa257ca26f862551b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1013), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss  0.5168237090110779\n",
      "Loss  0.4738193452358246\n",
      "Loss  0.5493858456611633\n",
      "Loss  0.5621632933616638\n",
      "Loss  0.6470421552658081\n",
      "Loss  0.5886804461479187\n",
      "Loss  0.5375487804412842\n",
      "Loss  0.6357995271682739\n",
      "Loss  0.6048656702041626\n",
      "Loss  0.5491631627082825\n",
      "Loss  0.5968664884567261\n",
      "groundtruth: maeo\n",
      " prediction: maeo [16, 9, 12, 4]\n",
      "\n",
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "groundtruth: sunak\n",
      " prediction: sunak [19, 11, 5, 9, 3]\n",
      "\n",
      "groundtruth: nok\n",
      " prediction: nok [5, 4, 3]\n",
      "\n",
      "groundtruth: khwam-im\n",
      " prediction: khwam-im [3, 8, 15, 9, 16, 24, 14, 16]\n",
      "\n",
      "groundtruth: kan narong\n",
      " prediction: kan narong [3, 9, 5, 25, 5, 9, 20, 4, 5, 6]\n",
      "\n",
      "groundtruth: sakun\n",
      " prediction: sakun [19, 9, 3, 11, 5]\n",
      "\n",
      "groundtruth: charin\n",
      " prediction: charin [22, 8, 9, 20, 14, 5]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "Evaluate F1,EM\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b73433de429847309bdad989f003a736",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1\n",
      "      input: ขัตติยา กรีมี\n",
      "groundtruth: khattiya krimi\n",
      " prediction: khattiya krimi\n",
      "\n",
      "Example: 2\n",
      "      input: คมสรร บุญทอง\n",
      "groundtruth: khomsan bunthong\n",
      " prediction: khomsan bunthong\n",
      "\n",
      "Example: 3\n",
      "      input: ต่อเลขหมาย\n",
      "groundtruth: tolekmai\n",
      " prediction: tolekmai\n",
      "\n",
      "Example: 4\n",
      "      input: กิตติศักดิ์ หวังวรวงศ์\n",
      "groundtruth: kittisak wangworawong\n",
      " prediction: kittisak wangworawong\n",
      "\n",
      "Example: 5\n",
      "      input: สถาปัตยกรรมศาสตร์\n",
      "groundtruth: sathapattayakamsat\n",
      " prediction: sathapattayakamasat\n",
      "\n",
      "Example: 6\n",
      "      input: กิมเจียว แซ่โง้ว\n",
      "groundtruth: kimchiao sae-ngow\n",
      " prediction: kimchiao sae-ngow\n",
      "\n",
      "\n",
      "F1 (macro-average) =  0.9893759166623687\n",
      "EM =  0.89\n",
      "EM (Character-level) =  0.9543428451435658\n",
      "Epoch: 31 | Time: 4m 33s\n",
      "\tTrain Loss: 0.545 | Train PPL:   1.725\n",
      "\t Val. Loss: 0.432 |  Val. PPL:   1.541\n",
      "epoch=31, teacher_forcing_ratio=0.15\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "714aec860da64a6db70c169036e74abf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1013), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss  0.547677755355835\n",
      "Loss  0.5799933671951294\n",
      "Loss  0.610641598701477\n",
      "Loss  0.5610765814781189\n",
      "Loss  0.5849884152412415\n",
      "Loss  0.4781103730201721\n",
      "Loss  0.5487290620803833\n",
      "Loss  0.5629648566246033\n",
      "Loss  0.5736099481582642\n",
      "Loss  0.6624854803085327\n",
      "Loss  0.5044180154800415\n",
      "groundtruth: maeo\n",
      " prediction: maeo [16, 9, 12, 4]\n",
      "\n",
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "groundtruth: sunak\n",
      " prediction: sunak [19, 11, 5, 9, 3]\n",
      "\n",
      "groundtruth: nok\n",
      " prediction: nok [5, 4, 3]\n",
      "\n",
      "groundtruth: khwam-im\n",
      " prediction: khwam-im [3, 8, 15, 9, 16, 24, 14, 16]\n",
      "\n",
      "groundtruth: kan narong\n",
      " prediction: kan narong [3, 9, 5, 25, 5, 9, 20, 4, 5, 6]\n",
      "\n",
      "groundtruth: sakun\n",
      " prediction: san [19, 9, 5]\n",
      "\n",
      "groundtruth: charin\n",
      " prediction: charin [22, 8, 9, 20, 14, 5]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "Evaluate F1,EM\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc1e3cc252e647ed98c43eff148e87b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1\n",
      "      input: ขัตติยา กรีมี\n",
      "groundtruth: khattiya krimi\n",
      " prediction: khattiya krimi\n",
      "\n",
      "Example: 2\n",
      "      input: คมสรร บุญทอง\n",
      "groundtruth: khomsan bunthong\n",
      " prediction: khomsan bunthong\n",
      "\n",
      "Example: 3\n",
      "      input: ต่อเลขหมาย\n",
      "groundtruth: tolekmai\n",
      " prediction: tolekmai\n",
      "\n",
      "Example: 4\n",
      "      input: กิตติศักดิ์ หวังวรวงศ์\n",
      "groundtruth: kittisak wangworawong\n",
      " prediction: kittisak wangworawong\n",
      "\n",
      "Example: 5\n",
      "      input: สถาปัตยกรรมศาสตร์\n",
      "groundtruth: sathapattayakamsat\n",
      " prediction: sapattayakammasat\n",
      "\n",
      "Example: 6\n",
      "      input: กิมเจียว แซ่โง้ว\n",
      "groundtruth: kimchiao sae-ngow\n",
      " prediction: kimchiao sae-ngow\n",
      "\n",
      "\n",
      "F1 (macro-average) =  0.988249567281885\n",
      "EM =  0.872\n",
      "EM (Character-level) =  0.9402127928610204\n",
      "Epoch: 32 | Time: 4m 36s\n",
      "\tTrain Loss: 0.562 | Train PPL:   1.755\n",
      "\t Val. Loss: 0.456 |  Val. PPL:   1.577\n",
      "epoch=32, teacher_forcing_ratio=0.15\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3011f835cba2436e99f81c5320f46f1b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1013), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss  0.5370351076126099\n",
      "Loss  0.5887273550033569\n",
      "Loss  0.5878713726997375\n",
      "Loss  0.6558665037155151\n",
      "Loss  0.6907453536987305\n",
      "Loss  0.5880665183067322\n",
      "Loss  0.5966131687164307\n",
      "Loss  0.7550995945930481\n",
      "Loss  0.598186731338501\n",
      "Loss  0.7745717763900757\n",
      "Loss  0.6270991563796997\n",
      "groundtruth: maeo\n",
      " prediction: maeo [16, 9, 12, 4]\n",
      "\n",
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "groundtruth: sunak\n",
      " prediction: sunak [19, 11, 5, 9, 3]\n",
      "\n",
      "groundtruth: nok\n",
      " prediction: nok [5, 4, 3]\n",
      "\n",
      "groundtruth: khwam-im\n",
      " prediction: khwam-im [3, 8, 15, 9, 16, 24, 14, 16]\n",
      "\n",
      "groundtruth: kan narong\n",
      " prediction: kan narong [3, 9, 5, 25, 5, 9, 20, 4, 5, 6]\n",
      "\n",
      "groundtruth: sakun\n",
      " prediction: sakun [19, 9, 3, 11, 5]\n",
      "\n",
      "groundtruth: charin\n",
      " prediction: charin [22, 8, 9, 20, 14, 5]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xq2oDcB3whaq6DSDJwf3id89TxfeT7FtVtyYJ8JS5A/3XrwAeArzcJCJJ9zYz2/2vxZG6tqpqC/D2JM9P8lbgHHpdUquA1cBngX/YwekXAicnuZPeXWJ/D46RSNIopqFrK9PQbJqzx6o10xOstIu565Yr2w5hXnvtf2TbIcxr+z1bMol6Hrz6Z0b+3vvRHTdO5Jrj8vZfSeowF22UJDXiMvKSpEamYbB92ZdIkSSNrsb43yiSHJ3khiSbkpw2z/H7Jflv/eNfSXLQQnWaSCSpwya51lZ/UvjZwDHAocBLkhw6VOxVwO1V9RjgfcB7FqrXRCJJHTbhRRsPBzZV1Y1VdQ9wMXDsUJljgf/S//vTwHP6c/52aKrGSCZ1O12StVW1bhJ1TVIX4+piTNDNuLoYE3QzrknGtP2eLZOoBujmZ7VtjO+9wcdu9K0bej9rgJsHXm8GnjFUzU/KVNX2JD8Efgq4bUfX3V1bJGsXLtKKLsbVxZigm3F1MSboZlxdjAm6G9dIBh+70d+WJSnurolEknZHW+gtrjvngP6+ecv0V2/fG/jezio1kUjS7uNq4JAkB/eXuDoeWD9UZj3wyv7fxwH/pxYYgJmqMZIJ6lQf6IAuxtXFmKCbcXUxJuhmXF2MCbob10T0xzxOAS4FVgLnVdV1Sc4ANlbVeuA/01uNfRPwfXrJZqemaq0tSVL32LUlSWrERNIxSR7cdgxanCRnJbkiyW1Jnt52PNJyMZF0TFX9qO0YtGjfBV4PnFlV1yxUWNMryYVtx9Alu+tguzRxVfXeJOcDv9V2LNJyskUiTdb9qmpr20FIy2m3SSRJLh5n/+4uycok70vyl0muSnJYy/FMy79fK0+oG5bkWUkuTXJZki8k+fm2Y9Kua7dJJBrbnsAFVfVs4Gjgj/orh6rjkjwR+B3g16rqF+hNKjs1yc+2G9nSS7Jfkk8l+YMkeyZZneScJFcm2ZDkHf3Z2nPl35/kIUnenOTyftLdd6jOY5J8uX8jxflJHjp0/KQk/zvJY5frfXaNiUTzqqq7q+ra/t8/AK6kt+y0uu8U4D9U1T/DT27gOLm/f5eV5ETg48Dbq+rMqtpGb3LdRVV1ZFUdAVwH/OnAabfTS7q3VtVRwFuAPxyo81nAScAvVdXPA28Dzhq8blWdS29c7J39hLTb/eAykWhUW4D92g5CI3lIVd1rpdaquhX4cEvxLKkkByb5DL0Van+5qq7r738mcG1VXTFXtqou6h3KIweqeGRVfbx//BrgAQPHXgucVFV39I/fRG+J9cHzqaotVXUccAtwSZInT/htdpp3bWlURTd/eHRiTKJjZpNcPs/+LcBLlzmWJdN/RsZa4AXA66tq01CRnwN+PckxQ/sfCDwB+Fb/9UVDx/cc+HtFVd0+dHwDcMd8MVXVJ5J8AXh/kr8H3tl/7scuzUTiF9G0uM9aPkkeDuzTQiydVlXzJoskw1+YU62qKsk99NaMmu+7LMDrqurLC1T1jxMObQ96yegeoPsPXJ+ALv7CXCo/SHKvrpkka+gtSqbu29ZfrXTQm9nBL0PdWz/p/qDtOCat3yX1auAdSU4bGp+4Cvjl4XOGurUWNDy4DhwBrN5B2RPpjcv8QVW9s6q2j3OtabU7JZKzgXPmliBJsje9PuMPthqVRnUZvS8MkuyR5F3AXwJ3thrVFEjyIOAc4ENtx7IUhsYnPpfkSf39G4AnJvlJMknyq8C7xqj+g8C5SR7QP/8get2D3xosNM84zfWLf0fTZ7fp2qqqryV5H/CZ/u1/2+jd3bFb/YNPsQuA9yX5a+Cfgf9UVZcm6Vqf/8OSnFpV7287EIAkv0lvGfAz5gahd1VVdUF/fOIDSb5OL2G8lN54xenAVuDLwIlj1HlF/8fnXySZBTbRWwbnA3NlkpwE/Cq9cZpvTOwNTRGXkZckNbI7dW1JkpaAiUSS1IiJRJLUiIlEktSIiUSS1IiJRJLUiIlEktSIiUSS1IiJRJLUiIlEktSIiUSS1IiJROqAJIcn+XdtxyEtholkF5Xks/M8GW63k2TPhUt1wgOA4edeSFPB1X93UUkOBP4X//Iwo1dX1TdbDKkVSc6j96S6xwNPAR5fVd9pNypp12KLZJkluTDJ5Ukek+RjSZ4yxrkvTXLqKGWr6uaq+lfAA6rqqKr65iKutybJ/5hkXAPn/M2Y5Rcb+4eAjcAfA2ur6jtL9TkMnTPu+/vJZ7jI6431npZKkrclecwY5T+W5ClJ3pFkY5IjljI+LY3d5sFWXVJVRwEkuZ3xHvV7O+Mn/xuHzh/3eqOUbxrXJGO5V/mq+lvgb5NcVFUvaVLXGOVhce9vxcDf415vMed0wdy/01uTbAL2ajsgjc9E0q7b+9s45cf9wv5/i71eVd2Z5NvLENcoJhn7Un0Ogxbz/lY0uN64/y11xbTGrQGOkSyzJBdW1cvm2X8C8O2q+vzQ/ncDH6mqm5YnwvkleSC9x4seAswAv1lV4/7q3lHd5wOvqaq7h/ZfXFXHT+gaE6urX9+vAK+l9+V/B/CWqvrapOqfVkneBlxYVZsWce4JwOaq+uKk49LScoxEo1oNvKOqjgReDvxJy/G0JsnzgF8Cjq6qXwBOAs5Ksl+7kc0vyYokb0jyP5McNLD/pUmuSHJl/y6/Rw8ce2qS1yV5epIvJLk6ybFD9T4iyaeSbEjyxSTPHjr+xCSfS/LypX6PapeJRCOpqu/MtUCq6mbgtiQPajmstpwI/E5VbQOoqm8DpwMntBnUfJI8Afhz4IdVdexcyzbJrwNPBX6x/+PgdcA5SR7WP/V24LHAqcALgZ8DXpvkfv3zVwEXAu+uqiPoJdYXAk+au3ZVXQe8ANgvyaeTHLDU71ftcIxEi7UF2A/4cduBtCBzSWTAtcCdbQQznyR7AG+ilyxeVVW3DBU5Hjiu+n3bVXVTktOBk4E/6pd5MfCkqtrar/PzwGOA64DjgE/2b2agqmaSnAZ8dfAiVTVDr7X2WOCjSdYD6+auq12DiUSLVSx9izZLXP9i7Zvk8qF9Aa4Gfnf5wxkKJHkycBZwQVW9a57jDwOeAVyW3OcjHpxr9BdVddvA69uAn6aXSJ4JvHPwxKranuSy+WKqqm8keSG9caX1SU6d1Bib2mci6Y4ZYOU8+1f2j+3KZhlKGv0Z6Y+ev3i7quo5w/uS7EVvzkoXbKOX6Hc0qz/A56vqpAXq+ccdnDv4/+MIsIref8/bF3G+Osoxku74JvDkefY/hl430q7su8Ajhva9kd6M9GnxRO79a741VfV39MYm9k3ymf4qB4PHvwc8am68Y06Shye5/4iXuQo4euj8PYBfnK/wwFjN96rqV6rqWyNeR1PARNIdfwMckeTwuR1JTga+VlWz7YW1LD4P/DZAet5Ab3LdVHzZJHkE8Hbg423HMqeqZqrqvcBp9MYmXj1U5Hzgw3NrkfUHwj8J7DviJT4NvDzJ0/rnrwTexdAYSZKV/bGTM+mN1Zy3yLekDjORdEQ/WbwSODnJZf0++IfR+4K6jySrk5yf5KcWc73+7Z0Tv4V3MXFV1WXAD/rLinwJuLWq1u3kGouJ/dDMs7pu088hye8D5wK/t6M1vJK8MsmJDa7xriTPXMy5VfUNeq2TByZZP3f7b1V9EvgysKE/rvFB4LWjthT6A/AvA34/yV/R+zGwHvgrYO9+3E+k1wrZXFXHVdWu3rLebTkhcZntaELiIut6BXBN/zbLxZz/nqp68yRiGaq3UVwjXmNisS/V5zB0jXdX1WmLPPeRwAur6uwJh9UpTkicXrZIplS/P3qfBknkAOD/Tjaq5nGNeI2Jxb5Un8PQNZ4LfKpBFc+nN2dD6iRbJMssyYXAAcBJi1lGQtoVJXkHvcH7U6tqQ9vxaDwmEklSI3ZtSZIaMZFIkhoxkUiSGjGRSJIaMZFIkhoxkUiSGjGRSJIa+f/pMgpWVyIuJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "Evaluate F1,EM\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee2f6cb6e86b4e3198974f1cd87e2da1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1\n",
      "      input: ขัตติยา กรีมี\n",
      "groundtruth: khattiya krimi\n",
      " prediction: khattiya krimi\n",
      "\n",
      "Example: 2\n",
      "      input: คมสรร บุญทอง\n",
      "groundtruth: khomsan bunthong\n",
      " prediction: khomsan bunthong\n",
      "\n",
      "Example: 3\n",
      "      input: ต่อเลขหมาย\n",
      "groundtruth: tolekmai\n",
      " prediction: tolekmkamai\n",
      "\n",
      "Example: 4\n",
      "      input: กิตติศักดิ์ หวังวรวงศ์\n",
      "groundtruth: kittisak wangworawong\n",
      " prediction: kittisak wangworawong\n",
      "\n",
      "Example: 5\n",
      "      input: สถาปัตยกรรมศาสตร์\n",
      "groundtruth: sathapattayakamsat\n",
      " prediction: sathapattaakamasat\n",
      "\n",
      "Example: 6\n",
      "      input: กิมเจียว แซ่โง้ว\n",
      "groundtruth: kimchiao sae-ngow\n",
      " prediction: kimchiao sae-ngow\n",
      "\n",
      "\n",
      "F1 (macro-average) =  0.9652222572766578\n",
      "EM =  0.836\n",
      "EM (Character-level) =  0.920479663421466\n",
      "Epoch: 33 | Time: 4m 36s\n",
      "\tTrain Loss: 0.618 | Train PPL:   1.855\n",
      "\t Val. Loss: 0.504 |  Val. PPL:   1.656\n",
      "epoch=33, teacher_forcing_ratio=0.15\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f6a1a94acae4ae5befadd787f36bce8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1013), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss  0.5664786100387573\n",
      "Loss  0.5832512378692627\n",
      "Loss  0.5735824108123779\n",
      "Loss  0.6185986399650574\n",
      "Loss  0.637225866317749\n",
      "Loss  0.577375590801239\n",
      "Loss  0.6269789934158325\n",
      "Loss  0.5968617796897888\n",
      "Loss  0.7298391461372375\n",
      "Loss  0.6716881394386292\n",
      "Loss  1.2181397676467896\n",
      "groundtruth: maeo\n",
      " prediction: maeo [16, 9, 12, 4]\n",
      "\n",
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "groundtruth: sunak\n",
      " prediction: sunak [19, 11, 5, 9, 3]\n",
      "\n",
      "groundtruth: nok\n",
      " prediction: nok [5, 4, 3]\n",
      "\n",
      "groundtruth: khwam-im\n",
      " prediction: khwam-im [3, 8, 15, 9, 16, 24, 14, 16]\n",
      "\n",
      "groundtruth: kan narong\n",
      " prediction: kan narong [3, 9, 5, 25, 5, 9, 20, 4, 5, 6]\n",
      "\n",
      "groundtruth: sakun\n",
      " prediction: sakun [19, 9, 3, 11, 5]\n",
      "\n",
      "groundtruth: charin\n",
      " prediction: charin [22, 8, 9, 20, 14, 5]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groundtruth: banrai\n",
      " prediction: banrai [17, 9, 5, 20, 9, 14]\n",
      "\n",
      "Evaluate F1,EM\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c56d2298149b4a4f8a42be0c2a9a0a5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1\n",
      "      input: ขัตติยา กรีมี\n",
      "groundtruth: khattiya krimi\n",
      " prediction: khatatatatatatatatatatatatatatatatatatatatatatatatatatatatat\n",
      "\n",
      "Example: 2\n",
      "      input: คมสรร บุญทอง\n",
      "groundtruth: khomsan bunthong\n",
      " prediction: khomsan bunthong\n",
      "\n",
      "Example: 3\n",
      "      input: ต่อเลขหมาย\n",
      "groundtruth: tolekmai\n",
      " prediction: tolekmai\n",
      "\n",
      "Example: 4\n",
      "      input: กิตติศักดิ์ หวังวรวงศ์\n",
      "groundtruth: kittisak wangworawong\n",
      " prediction: kitttttttttttttttttttttttttttttttttttttttttttttttttttttttttt\n",
      "\n",
      "Example: 5\n",
      "      input: สถาปัตยกรรมศาสตร์\n",
      "groundtruth: sathapattayakamsat\n",
      " prediction: sathapattaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\n",
      "\n",
      "Example: 6\n",
      "      input: กิมเจียว แซ่โง้ว\n",
      "groundtruth: kimchiao sae-ngow\n",
      " prediction: kimchuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu\n",
      "\n",
      "\n",
      "F1 (macro-average) =  0.8753545016655968\n",
      "EM =  0.666\n",
      "EM (Character-level) =  0.7844875876212706\n",
      "Epoch: 34 | Time: 4m 58s\n",
      "\tTrain Loss: 0.655 | Train PPL:   1.926\n",
      "\t Val. Loss: 0.871 |  Val. PPL:   2.389\n",
      "epoch=34, teacher_forcing_ratio=0.15\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4846a519d0c345eaa6f53b1357030fbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1013), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss  1.173588752746582\n",
      "Loss  0.7683922052383423\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-217-cd2c793329c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataset_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCLIP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mteacher_forcing_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mteacher_forcing_ratio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mteacher_forcing_ratio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mteacher_forcing_ratio\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mvalid_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataset_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-205-bc6b4334bbdc>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, iterator, optimizer, criterion, clip, teacher_forcing_ratio)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mepoch_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/saiko/lib/python3.6/site-packages/tqdm/_tqdm_notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/saiko/lib/python3.6/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    977\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m    978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 979\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    980\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/saiko/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    558\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# same-process loading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    561\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m                 \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/saiko/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    558\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# same-process loading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    561\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m                 \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-e85966e1f066>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m             \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/saiko/lib/python3.6/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-e85966e1f066>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, sample)\u001b[0m\n\u001b[1;32m     71\u001b[0m         input_tensor, target_tensor, input_length, target_length = tensorsFromPair([input_text, target_text], \n\u001b[1;32m     72\u001b[0m                                                                                    \u001b[0mlang_th\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m                                                                                    lang_th_romanized)\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         return {\n",
      "\u001b[0;32m<ipython-input-27-b99624221723>\u001b[0m in \u001b[0;36mtensorsFromPair\u001b[0;34m(pair, lang1, lang2)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtensorsFromPair\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpair\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;34m\"\"\"construct two tensors from a pair of source and target text specified by source and target language\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     \u001b[0minput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensorFromText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlang1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpair\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m     \u001b[0mtarget_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensorFromText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlang2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpair\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-27-b99624221723>\u001b[0m in \u001b[0;36mtensorFromText\u001b[0;34m(lang, text)\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0mindexes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchar2index\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mPAD_token\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mno_padded_seq_length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfilterPair\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 50\n",
    "CLIP = 5\n",
    "\n",
    "best_em_score = float('0.784')\n",
    "teacher_forcing_ratio = 0.15\n",
    "\n",
    "for epoch in range(29, N_EPOCHS):\n",
    "    print('epoch={}, teacher_forcing_ratio={}'.format(epoch, teacher_forcing_ratio))\n",
    "    model.train()\n",
    "    start_time = time.time()\n",
    "    train_loss = train(model, train_dataset_loader, optimizer, criterion, CLIP, teacher_forcing_ratio=teacher_forcing_ratio)\n",
    "    teacher_forcing_ratio = max(teacher_forcing_ratio-0.000, 0.0)\n",
    "    valid_loss = evaluate(model, val_dataset_loader, criterion)\n",
    "    if epoch % 1 == 0:\n",
    "        show_inference_example(model,\n",
    "               [\"แมว\", \"บ้านไร่\", \"สุนัข\", \"นก\", \"ความอิ่ม\", \"กานต์ ณรงค์\", \"สกุนต์\", \"ชารินทร์\"],\n",
    "               [\"maeo\", \"banrai\", \"sunak\", \"nok\", \"khwam-im\", \"kan narong\", \"sakun\", \"charin\"],\n",
    "               char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char)\n",
    "        \n",
    "        show_inference_example_attn(model, ['บ้านไร่'],  ['banrai'] ,char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char)\n",
    "\n",
    "        print('Evaluate F1,EM')\n",
    "        \n",
    "        f1_macro_average, em_score, em_char_score, _ = evaluate_inference(model, val_indices[:500])\n",
    "\n",
    "    end_time = time.time()\n",
    "    \n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if em_score > best_em_score:\n",
    "        best_em_score = em_score\n",
    "        print('save best model em score={} at epoch={}'.format(em_score, epoch))\n",
    "        save_model('thai2rom-pytorch.attn.v8', epoch, best_em_score, model)\n",
    "        \n",
    "    print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OR0I6PzYI842"
   },
   "outputs": [],
   "source": [
    " show_inference_example(model,\n",
    "               [\"แมว\", \"บ้านไร่\", \"สุนัข\", \"นก\", \"ความอิ่ม\", \"กานต์ ณรงค์\", \"สกุนต์\", \"ชารินทร์\"],\n",
    "               [\"maeo\", \"banrai\", \"sunak\", \"nok\", \"khwam-im\", \"kan narong\", \"sakun\", \"charin\"],\n",
    "               char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hiqaeTK7I84-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YrbGxWZ9I85A"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y15gbYpBI85C",
    "outputId": "41bb2773-4327-44e7-9c36-f0f66af1f357"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['บ้านปางสามัคคี', 'บ้านหนองนาเวียง', 'มณฑลกานสู้', 'บ้านโนนหมากมุ่น', 'นัว', 'การเลือกตั้งพิเศษ', 'มัลกะ', 'ทหารกองหนุน', 'ตำบลวังสรรพรส', 'พร้อย']\n",
      "['banpangsamakkhi', 'bannongnawiang', 'monthonkansu', 'bannonmakmun', 'nua', 'kanlueaktangphiset', 'manka', 'thahankongnun', 'tambonwangsappharot', 'phroi']\n"
     ]
    }
   ],
   "source": [
    "print(thai_romanization_dataset.input_texts[5000:5010])\n",
    "print(thai_romanization_dataset.target_texts[5000:5010])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CNjeJ6H-I85F",
    "outputId": "2d2a6abe-23c9-497e-f797-1db8d0b4a0ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numericalized [19, 20, 21, 22, 13, 23, 24, 3]\n",
      "sentence_length [8]\n",
      "1: วิฑูรย์ -> 83454r4iri88h8kak454r4iri88h8k8k454r4iri88h8k8k454r4iri88h8k\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SaD-wPRwI85I"
   },
   "source": [
    "\n",
    "## Evaluation on val_set with following metrics:\n",
    "   \n",
    "1. F1-score (macro-average) -- Character level\n",
    "\n",
    "2. Exact Match (EM)\n",
    "\n",
    "3. Exact Match (EM) - Character level\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Au7KTwaMI85J"
   },
   "outputs": [],
   "source": [
    "# Functions for model performance evaluation\n",
    "def precision(pred_chars, target_chars):\n",
    "    # TP / TP + FP\n",
    "    pred_chars_multiset = Counter(pred_chars)\n",
    "    target_chars_multiset = Counter(target_chars)\n",
    "\n",
    "    overlap = list((pred_chars_multiset & target_chars_multiset).elements())\n",
    "    n_overlap = len(overlap)\n",
    "\n",
    "    return n_overlap / max(len(pred_chars), 1)\n",
    "\n",
    "def recall(pred_chars, target_chars):\n",
    "    # TP / TP + FN\n",
    "        \n",
    "    pred_chars_multiset = Counter(pred_chars)\n",
    "    target_chars_multiset = Counter(target_chars)\n",
    "\n",
    "    overlap = list((pred_chars_multiset & target_chars_multiset).elements())\n",
    "    n_overlap = len(overlap)\n",
    "    return n_overlap / len(target_chars)\n",
    "\n",
    "def f1(precision, recall):\n",
    "    \n",
    "    return (2  * precision * recall) / (precision + recall)\n",
    "\n",
    "def em(pred, target):\n",
    "    if pred == target:\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "def em_char(pred_chars, target_chars):\n",
    "    N_target_chars = len(target_chars)\n",
    "    N_pred_chars = len(pred_chars)\n",
    "\n",
    "    score = 0\n",
    "    for index in range(min(N_pred_chars, N_target_chars)):\n",
    "        if target_chars[index] == pred_chars[index]:\n",
    "            score+=1\n",
    "            \n",
    "    return score / max(N_target_chars, N_pred_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pcgxbaFRUSnG"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 920,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": {
       "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
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 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
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 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
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 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
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* Redistributions of source code must retain the above copyright notice, this
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* Redistributions in binary form must reproduce the above copyright notice,
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* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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ogV2lkZ2V0IFRleHQgYW5kIFRleHRBcmVhIFN0eWluZyAqL1xuXG4ud2lkZ2V0LXRleHRhcmVhLCAud2lkZ2V0LXRleHQge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG59XG5cbi53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwidGV4dFwiXSwgLndpZGdldC10ZXh0IGlucHV0W3R5cGU9XCJudW1iZXJcIl17XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXRleHQgaW5wdXRbdHlwZT1cInRleHRcIl06ZGlzYWJsZWQsIC53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwibnVtYmVyXCJdOmRpc2FibGVkLCAud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhOmRpc2FibGVkIHtcbiAgICBvcGFjaXR5OiB2YXIoLS1qcC13aWRnZXRzLWRpc2FibGVkLW9wYWNpdHkpO1xufVxuXG4ud2lkZ2V0LXRleHQgaW5wdXRbdHlwZT1cInRleHRcIl0sIC53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwibnVtYmVyXCJdLCAud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhIHtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xuICAgIGJvcmRlcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLWNvbG9yKTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbiAgICBwYWRkaW5nOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpIGNhbGMoIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZykgKiAgMiApO1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgZmxleC1zaHJpbms6IDE7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xufVxuXG4ud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhIHtcbiAgICBoZWlnaHQ6IGluaGVyaXQ7XG4gICAgd2lkdGg6IGluaGVyaXQ7XG59XG5cbi53aWRnZXQtdGV4dCBpbnB1dDpmb2N1cywgLndpZGdldC10ZXh0YXJlYSB0ZXh0YXJlYTpmb2N1cyB7XG4gICAgYm9yZGVyLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWZvY3VzLWJvcmRlci1jb2xvcik7XG59XG5cbi8qIFdpZGdldCBTbGlkZXIgKi9cblxuLndpZGdldC1zbGlkZXIgLnVpLXNsaWRlciB7XG4gICAgLyogU2xpZGVyIFRyYWNrICovXG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWxheW91dC1jb2xvcjMpO1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWxheW91dC1jb2xvcjMpO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgcG9zaXRpb246IHJlbGF0aXZlO1xuICAgIGJvcmRlci1yYWRpdXM6IDBweDtcbn1cblxuLndpZGdldC1zbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLWhhbmRsZSB7XG4gICAgLyogU2xpZGVyIEhhbmRsZSAqL1xuICAgIG91dGxpbmU6IG5vbmUgIWltcG9ydGFudDsgLyogZm9jdXNlZCBzbGlkZXIgaGFuZGxlcyBhcmUgY29sb3JlZCAtIHNlZSBiZWxvdyAqL1xuICAgIHBvc2l0aW9uOiBhYnNvbHV0ZTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtYmFja2dyb3VuZC1jb2xvcik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1ib3JkZXItY29sb3IpO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgei1pbmRleDogMTtcbiAgICBiYWNrZ3JvdW5kLWltYWdlOiBub25lOyAvKiBPdmVycmlkZSBqcXVlcnktdWkgKi9cbn1cblxuLyogT3ZlcnJpZGUganF1ZXJ5LXVpICovXG4ud2lkZ2V0LXNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItaGFuZGxlOmhvdmVyLCAud2lkZ2V0LXNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItaGFuZGxlOmZvY3VzIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYWN0aXZlLWhhbmRsZS1jb2xvcik7XG59XG5cbi53aWRnZXQtc2xpZGVyIC51aS1zbGlkZXIgLnVpLXNsaWRlci1oYW5kbGU6YWN0aXZlIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICBib3JkZXItY29sb3I6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWFjdGl2ZS1oYW5kbGUtY29sb3IpO1xuICAgIHotaW5kZXg6IDI7XG4gICAgdHJhbnNmb3JtOiBzY2FsZSgxLjIpO1xufVxuXG4ud2lkZ2V0LXNsaWRlciAgLnVpLXNsaWRlciAudWktc2xpZGVyLXJhbmdlIHtcbiAgICAvKiBJbnRlcnZhbCBiZXR3ZWVuIHRoZSB0d28gc3BlY2lmaWVkIHZhbHVlIG9mIGEgZG91YmxlIHNsaWRlciAqL1xuICAgIHBvc2l0aW9uOiBhYnNvbHV0ZTtcbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICB6LWluZGV4OiAwO1xufVxuXG4vKiBTaGFwZXMgb2YgU2xpZGVyIEhhbmRsZXMgKi9cblxuLndpZGdldC1oc2xpZGVyIC51aS1zbGlkZXIgLnVpLXNsaWRlci1oYW5kbGUge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSk7XG4gICAgbWFyZ2luLXRvcDogY2FsYygodmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSkgLyAyIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgbWFyZ2luLWxlZnQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItaGFuZGxlLXNpemUpIC8gLTIgKyB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbiAgICBib3JkZXItcmFkaXVzOiA1MCU7XG4gICAgdG9wOiAwO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLWhhbmRsZSB7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKTtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKTtcbiAgICBtYXJnaW4tYm90dG9tOiBjYWxjKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSAvIC0yICsgdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgbWFyZ2luLWxlZnQ6IGNhbGMoKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgLSB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkpIC8gMiAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIGJvcmRlci1yYWRpdXM6IDUwJTtcbiAgICBsZWZ0OiAwO1xufVxuXG4ud2lkZ2V0LWhzbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLXJhbmdlIHtcbiAgICBoZWlnaHQ6IGNhbGMoIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgKiAyICk7XG4gICAgbWFyZ2luLXRvcDogY2FsYygodmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgKiAyICkgLyAyIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG59XG5cbi53aWRnZXQtdnNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItcmFuZ2Uge1xuICAgIHdpZHRoOiBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci10cmFjay10aGlja25lc3MpICogMiApO1xuICAgIG1hcmdpbi1sZWZ0OiBjYWxjKCh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci10cmFjay10aGlja25lc3MpIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAqIDIgKSAvIDIgLSB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbn1cblxuLyogSG9yaXpvbnRhbCBTbGlkZXIgKi9cblxuLndpZGdldC1oc2xpZGVyIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcblxuICAgIC8qIE92ZXJyaWRlIHRoZSBhbGlnbi1pdGVtcyBiYXNlbGluZS4gVGhpcyB3YXksIHRoZSBkZXNjcmlwdGlvbiBhbmQgcmVhZG91dFxuICAgIHN0aWxsIHNlZW0gdG8gYWxpZ24gdGhlaXIgYmFzZWxpbmUgcHJvcGVybHksIGFuZCB3ZSBkb24ndCBoYXZlIHRvIGhhdmVcbiAgICBhbGlnbi1zZWxmOiBzdHJldGNoIGluIHRoZSAuc2xpZGVyLWNvbnRhaW5lci4gKi9cbiAgICBhbGlnbi1pdGVtczogY2VudGVyO1xufVxuXG4ud2lkZ2V0cy1zbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIG92ZXJmbG93OiB2aXNpYmxlO1xufVxuXG4ud2lkZ2V0LWhzbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBtYXJnaW4tbGVmdDogY2FsYyh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkgLyAyIC0gMiAqIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIG1hcmdpbi1yaWdodDogY2FsYyh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkgLyAyIC0gMiAqIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIGZsZXg6IDEgMSB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aC1zaG9ydCk7XG59XG5cbi53aWRnZXQtaHNsaWRlciAudWktc2xpZGVyIHtcbiAgICAvKiBJbm5lciwgaW52aXNpYmxlIHNsaWRlIGRpdiAqL1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKTtcbiAgICBtYXJnaW4tdG9wOiBjYWxjKCh2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSkgLyAyKTtcbiAgICB3aWR0aDogMTAwJTtcbn1cblxuLyogVmVydGljYWwgU2xpZGVyICovXG5cbi53aWRnZXQtdmJveCAud2lkZ2V0LWxhYmVsIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG59XG5cbi53aWRnZXQtdnNsaWRlciB7XG4gICAgLyogVmVydGljYWwgU2xpZGVyICovXG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXZlcnRpY2FsLWhlaWdodCk7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXRpbnkpO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIGZsZXg6IDEgMSB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aC1zaG9ydCk7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xuICAgIG1hcmdpbi1ib3R0b206IGNhbGModmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItaGFuZGxlLXNpemUpIC8gMiAtIDIgKiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbiAgICBtYXJnaW4tdG9wOiBjYWxjKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSAvIDIgLSAyICogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgZGlzcGxheTogZmxleDtcbiAgICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnVpLXNsaWRlci12ZXJ0aWNhbCB7XG4gICAgLyogSW5uZXIsIGludmlzaWJsZSBzbGlkZSBkaXYgKi9cbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKTtcbiAgICBmbGV4LWdyb3c6IDE7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xufVxuXG4vKiBXaWRnZXQgUHJvZ3Jlc3MgU3R5bGluZyAqL1xuXG4ucHJvZ3Jlc3MtYmFyIHtcbiAgICAtd2Via2l0LXRyYW5zaXRpb246IG5vbmU7XG4gICAgLW1vei10cmFuc2l0aW9uOiBub25lO1xuICAgIC1tcy10cmFuc2l0aW9uOiBub25lO1xuICAgIC1vLXRyYW5zaXRpb246IG5vbmU7XG4gICAgdHJhbnNpdGlvbjogbm9uZTtcbn1cblxuLnByb2dyZXNzLWJhciB7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1icmFuZC1jb2xvcjEpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyLXN1Y2Nlc3Mge1xuICAgIGJhY2tncm91bmQtY29sb3I6IHZhcigtLWpwLXN1Y2Nlc3MtY29sb3IxKTtcbn1cblxuLnByb2dyZXNzLWJhci1pbmZvIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1pbmZvLWNvbG9yMSk7XG59XG5cbi5wcm9ncmVzcy1iYXItd2FybmluZyB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2Fybi1jb2xvcjEpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyLWRhbmdlciB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtZXJyb3ItY29sb3IxKTtcbn1cblxuLnByb2dyZXNzIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1sYXlvdXQtY29sb3IyKTtcbiAgICBib3JkZXI6IG5vbmU7XG4gICAgYm94LXNoYWRvdzogbm9uZTtcbn1cblxuLyogSG9yaXNvbnRhbCBQcm9ncmVzcyAqL1xuXG4ud2lkZ2V0LWhwcm9ncmVzcyB7XG4gICAgLyogUHJvZ3Jlc3MgQmFyICovXG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG4gICAgYWxpZ24taXRlbXM6IGNlbnRlcjtcblxufVxuXG4ud2lkZ2V0LWhwcm9ncmVzcyAucHJvZ3Jlc3Mge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBtYXJnaW4tdG9wOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpO1xuICAgIG1hcmdpbi1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZyk7XG4gICAgYWxpZ24tc2VsZjogc3RyZXRjaDtcbiAgICAvKiBPdmVycmlkZSBib290c3RyYXAgc3R5bGUgKi9cbiAgICBoZWlnaHQ6IGluaXRpYWw7XG59XG5cbi8qIFZlcnRpY2FsIFByb2dyZXNzICovXG5cbi53aWRnZXQtdnByb2dyZXNzIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtdmVydGljYWwtaGVpZ2h0KTtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgtdGlueSk7XG59XG5cbi53aWRnZXQtdnByb2dyZXNzIC5wcm9ncmVzcyB7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLXByb2dyZXNzLXRoaWNrbmVzcyk7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xuICAgIG1hcmdpbi1ib3R0b206IDA7XG59XG5cbi8qIFNlbGVjdCBXaWRnZXQgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWRyb3Bkb3duIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoKTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbn1cblxuLndpZGdldC1kcm9wZG93biA+IHNlbGVjdCB7XG4gICAgcGFkZGluZy1yaWdodDogMjBweDtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYm9yZGVyLXJhZGl1czogMDtcbiAgICBoZWlnaHQ6IGluaGVyaXQ7XG4gICAgZmxleDogMSAxIHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXNob3J0KTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgICBvdXRsaW5lOiBub25lICFpbXBvcnRhbnQ7XG4gICAgYm94LXNoYWRvdzogbm9uZTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbiAgICB2ZXJ0aWNhbC1hbGlnbjogdG9wO1xuICAgIHBhZGRpbmctbGVmdDogY2FsYyggdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSAqIDIpO1xuXHRhcHBlYXJhbmNlOiBub25lO1xuXHQtd2Via2l0LWFwcGVhcmFuY2U6IG5vbmU7XG5cdC1tb3otYXBwZWFyYW5jZTogbm9uZTtcbiAgICBiYWNrZ3JvdW5kLXJlcGVhdDogbm8tcmVwZWF0O1xuXHRiYWNrZ3JvdW5kLXNpemU6IDIwcHg7XG5cdGJhY2tncm91bmQtcG9zaXRpb246IHJpZ2h0IGNlbnRlcjtcbiAgICBiYWNrZ3JvdW5kLWltYWdlOiB2YXIoLS1qcC13aWRnZXRzLWRyb3Bkb3duLWFycm93KTtcbn1cbi53aWRnZXQtZHJvcGRvd24gPiBzZWxlY3Q6Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWRyb3Bkb3duID4gc2VsZWN0OmRpc2FibGVkIHtcbiAgICBvcGFjaXR5OiB2YXIoLS1qcC13aWRnZXRzLWRpc2FibGVkLW9wYWNpdHkpO1xufVxuXG4vKiBUbyBkaXNhYmxlIHRoZSBkb3R0ZWQgYm9yZGVyIGluIEZpcmVmb3ggYXJvdW5kIHNlbGVjdCBjb250cm9scy5cbiAgIFNlZSBodHRwOi8vc3RhY2tvdmVyZmxvdy5jb20vYS8xODg1MzAwMiAqL1xuLndpZGdldC1kcm9wZG93biA+IHNlbGVjdDotbW96LWZvY3VzcmluZyB7XG4gICAgY29sb3I6IHRyYW5zcGFyZW50O1xuICAgIHRleHQtc2hhZG93OiAwIDAgMCAjMDAwO1xufVxuXG4vKiBTZWxlY3QgYW5kIFNlbGVjdE11bHRpcGxlICovXG5cbi53aWRnZXQtc2VsZWN0IHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuXG4gICAgLyogQmVjYXVzZSBGaXJlZm94IGRlZmluZXMgdGhlIGJhc2VsaW5lIG9mIGEgc2VsZWN0IGFzIHRoZSBib3R0b20gb2YgdGhlXG4gICAgY29udHJvbCwgd2UgYWxpZ24gdGhlIGVudGlyZSBjb250cm9sIHRvIHRoZSB0b3AgYW5kIGFkZCBwYWRkaW5nIHRvIHRoZVxuICAgIHNlbGVjdCB0byBnZXQgYW4gYXBwcm94aW1hdGUgZmlyc3QgbGluZSBiYXNlbGluZSBhbGlnbm1lbnQuICovXG4gICAgYWxpZ24taXRlbXM6IGZsZXgtc3RhcnQ7XG59XG5cbi53aWRnZXQtc2VsZWN0ID4gc2VsZWN0IHtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgZmxleDogMSAxIHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXNob3J0KTtcbiAgICBvdXRsaW5lOiBub25lICFpbXBvcnRhbnQ7XG4gICAgb3ZlcmZsb3c6IGF1dG87XG4gICAgaGVpZ2h0OiBpbmhlcml0O1xuXG4gICAgLyogQmVjYXVzZSBGaXJlZm94IGRlZmluZXMgdGhlIGJhc2VsaW5lIG9mIGEgc2VsZWN0IGFzIHRoZSBib3R0b20gb2YgdGhlXG4gICAgY29udHJvbCwgd2UgYWxpZ24gdGhlIGVudGlyZSBjb250cm9sIHRvIHRoZSB0b3AgYW5kIGFkZCBwYWRkaW5nIHRvIHRoZVxuICAgIHNlbGVjdCB0byBnZXQgYW4gYXBwcm94aW1hdGUgZmlyc3QgbGluZSBiYXNlbGluZSBhbGlnbm1lbnQuICovXG4gICAgcGFkZGluZy10b3A6IDVweDtcbn1cblxuLndpZGdldC1zZWxlY3QgPiBzZWxlY3Q6Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lnZXQtc2VsZWN0ID4gc2VsZWN0ID4gb3B0aW9uIHtcbiAgICBwYWRkaW5nLWxlZnQ6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZyk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgLyogbGluZS1oZWlnaHQgZG9lc24ndCB3b3JrIG9uIHNvbWUgYnJvd3NlcnMgZm9yIHNlbGVjdCBvcHRpb25zICovXG4gICAgcGFkZGluZy10b3A6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KS12YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSkvMik7XG4gICAgcGFkZGluZy1ib3R0b206IGNhbGModmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KS12YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSkvMik7XG59XG5cblxuXG4vKiBUb2dnbGUgQnV0dG9ucyBTdHlsaW5nICovXG5cbi53aWRnZXQtdG9nZ2xlLWJ1dHRvbnMge1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXRvZ2dsZS1idXR0b25zIC53aWRnZXQtdG9nZ2xlLWJ1dHRvbiB7XG4gICAgbWFyZ2luLWxlZnQ6IHZhcigtLWpwLXdpZGdldHMtbWFyZ2luKTtcbiAgICBtYXJnaW4tcmlnaHQ6IHZhcigtLWpwLXdpZGdldHMtbWFyZ2luKTtcbn1cblxuLndpZGdldC10b2dnbGUtYnV0dG9ucyAuanVweXRlci1idXR0b246ZGlzYWJsZWQge1xuICAgIG9wYWNpdHk6IHZhcigtLWpwLXdpZGdldHMtZGlzYWJsZWQtb3BhY2l0eSk7XG59XG5cbi8qIFJhZGlvIEJ1dHRvbnMgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LXJhZGlvIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXJhZGlvLWJveCB7XG4gICAgZGlzcGxheTogZmxleDtcbiAgICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xuICAgIGFsaWduLWl0ZW1zOiBzdHJldGNoO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIG1hcmdpbi1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQtYWRqdXN0bWVudCk7XG59XG5cbi53aWRnZXQtcmFkaW8tYm94IGxhYmVsIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXJhZGlvLWl0ZW0taGVpZ2h0KTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbn1cblxuLndpZGdldC1yYWRpby1ib3ggaW5wdXQge1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1yYWRpby1pdGVtLWhlaWdodCk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQpO1xuICAgIG1hcmdpbjogMCBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogMiApIDAgMXB4O1xuICAgIGZsb2F0OiBsZWZ0O1xufVxuXG4vKiBDb2xvciBQaWNrZXIgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciA+IC53aWRnZXQtY29sb3JwaWNrZXItaW5wdXQge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBmbGV4LXNocmluazogMTtcbiAgICBtaW4td2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXRpbnkpO1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIGlucHV0W3R5cGU9XCJjb2xvclwiXSB7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIHBhZGRpbmc6IDAgMnB4OyAvKiBtYWtlIHRoZSBjb2xvciBzcXVhcmUgYWN0dWFsbHkgc3F1YXJlIG9uIENocm9tZSBvbiBPUyBYICovXG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItY29sb3IpO1xuICAgIGJvcmRlci1sZWZ0OiBub25lO1xuICAgIGZsZXgtZ3JvdzogMDtcbiAgICBmbGV4LXNocmluazogMDtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xuICAgIGFsaWduLXNlbGY6IHN0cmV0Y2g7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyLmNvbmNpc2UgaW5wdXRbdHlwZT1cImNvbG9yXCJdIHtcbiAgICBib3JkZXItbGVmdDogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLWNvbG9yKTtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciBpbnB1dFt0eXBlPVwiY29sb3JcIl06Zm9jdXMsIC53aWRnZXQtY29sb3JwaWNrZXIgaW5wdXRbdHlwZT1cInRleHRcIl06Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIGlucHV0W3R5cGU9XCJ0ZXh0XCJdIHtcbiAgICBmbGV4LWdyb3c6IDE7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogIDIgKTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgZmxleC1zaHJpbms6IDE7XG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciBpbnB1dFt0eXBlPVwidGV4dFwiXTpkaXNhYmxlZCB7XG4gICAgb3BhY2l0eTogdmFyKC0tanAtd2lkZ2V0cy1kaXNhYmxlZC1vcGFjaXR5KTtcbn1cblxuLyogRGF0ZSBQaWNrZXIgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWRhdGVwaWNrZXIge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl0ge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBmbGV4LXNocmluazogMTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogIDIgKTtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl06Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl06aW52YWxpZCB7XG4gICAgYm9yZGVyLWNvbG9yOiB2YXIoLS1qcC13YXJuLWNvbG9yMSk7XG59XG5cbi53aWRnZXQtZGF0ZXBpY2tlciBpbnB1dFt0eXBlPVwiZGF0ZVwiXTpkaXNhYmxlZCB7XG4gICAgb3BhY2l0eTogdmFyKC0tanAtd2lkZ2V0cy1kaXNhYmxlZC1vcGFjaXR5KTtcbn1cblxuLyogUGxheSBXaWRnZXQgKi9cblxuLndpZGdldC1wbGF5IHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgtc2hvcnQpO1xuICAgIGRpc3BsYXk6IGZsZXg7XG4gICAgYWxpZ24taXRlbXM6IHN0cmV0Y2g7XG59XG5cbi53aWRnZXQtcGxheSAuanVweXRlci1idXR0b24ge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBoZWlnaHQ6IGF1dG87XG59XG5cbi53aWRnZXQtcGxheSAuanVweXRlci1idXR0b246ZGlzYWJsZWQge1xuICAgIG9wYWNpdHk6IHZhcigtLWpwLXdpZGdldHMtZGlzYWJsZWQtb3BhY2l0eSk7XG59XG5cbi8qIFRhYiBXaWRnZXQgKi9cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiIHtcbiAgICBkaXNwbGF5OiBmbGV4O1xuICAgIGZsZXgtZGlyZWN0aW9uOiBjb2x1bW47XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciB7XG4gICAgLyogTmVjZXNzYXJ5IHNvIHRoYXQgYSB0YWIgY2FuIGJlIHNoaWZ0ZWQgZG93biB0byBvdmVybGF5IHRoZSBib3JkZXIgb2YgdGhlIGJveCBiZWxvdy4gKi9cbiAgICBvdmVyZmxvdy14OiB2aXNpYmxlO1xuICAgIG92ZXJmbG93LXk6IHZpc2libGU7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciA+IC5wLVRhYkJhci1jb250ZW50IHtcbiAgICAvKiBNYWtlIHN1cmUgdGhhdCB0aGUgdGFiIGdyb3dzIGZyb20gYm90dG9tIHVwICovXG4gICAgYWxpZ24taXRlbXM6IGZsZXgtZW5kO1xuICAgIG1pbi13aWR0aDogMDtcbiAgICBtaW4taGVpZ2h0OiAwO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAud2lkZ2V0LXRhYi1jb250ZW50cyB7XG4gICAgd2lkdGg6IDEwMCU7XG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgICBtYXJnaW46IDA7XG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtYm9yZGVyLWNvbG9yMSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZyk7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIG92ZXJmbG93OiBhdXRvO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIge1xuICAgIGZvbnQ6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKSBIZWx2ZXRpY2EsIEFyaWFsLCBzYW5zLXNlcmlmO1xuICAgIG1pbi1oZWlnaHQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi1oZWlnaHQpICsgdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiIHtcbiAgICBmbGV4OiAwIDEgdmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi13aWR0aCk7XG4gICAgbWluLXdpZHRoOiAzNXB4O1xuICAgIG1pbi1oZWlnaHQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi1oZWlnaHQpICsgdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaG9yaXpvbnRhbC10YWItaGVpZ2h0KTtcbiAgICBtYXJnaW4tbGVmdDogY2FsYygtMSAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIHBhZGRpbmc6IDBweCAxMHB4O1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWxheW91dC1jb2xvcjIpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC11aS1mb250LWNvbG9yMik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIGJvcmRlci1ib3R0b206IG5vbmU7XG4gICAgcG9zaXRpb246IHJlbGF0aXZlO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYi5wLW1vZC1jdXJyZW50IHtcbiAgICBjb2xvcjogdmFyKC0tanAtdWktZm9udC1jb2xvcjApO1xuICAgIC8qIFdlIHdhbnQgdGhlIGJhY2tncm91bmQgdG8gbWF0Y2ggdGhlIHRhYiBjb250ZW50IGJhY2tncm91bmQgKi9cbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC1sYXlvdXQtY29sb3IxKTtcbiAgICBtaW4taGVpZ2h0OiBjYWxjKHZhcigtLWpwLXdpZGdldHMtaG9yaXpvbnRhbC10YWItaGVpZ2h0KSArIDIgKiB2YXIoLS1qcC1ib3JkZXItd2lkdGgpKTtcbiAgICB0cmFuc2Zvcm06IHRyYW5zbGF0ZVkodmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgb3ZlcmZsb3c6IHZpc2libGU7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiLnAtbW9kLWN1cnJlbnQ6YmVmb3JlIHtcbiAgICBwb3NpdGlvbjogYWJzb2x1dGU7XG4gICAgdG9wOiBjYWxjKC0xICogdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgbGVmdDogY2FsYygtMSAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIGNvbnRlbnQ6ICcnO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi10b3AtYm9yZGVyKTtcbiAgICB3aWR0aDogY2FsYygxMDAlICsgMiAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWJyYW5kLWNvbG9yMSk7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiOmZpcnN0LWNoaWxkIHtcbiAgICBtYXJnaW4tbGVmdDogMDtcbn1cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLVRhYkJhci10YWI6aG92ZXI6bm90KC5wLW1vZC1jdXJyZW50KSB7XG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbn1cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLW1vZC1jbG9zYWJsZSA+IC5wLVRhYkJhci10YWJDbG9zZUljb24ge1xuICAgIG1hcmdpbi1sZWZ0OiA0cHg7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1tb2QtY2xvc2FibGUgPiAucC1UYWJCYXItdGFiQ2xvc2VJY29uOmJlZm9yZSB7XG4gICAgZm9udC1mYW1pbHk6IEZvbnRBd2Vzb21lO1xuICAgIGNvbnRlbnQ6ICdcXGYwMGQnOyAvKiBjbG9zZSAqL1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkljb24sXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkxhYmVsLFxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLVRhYkJhci10YWJDbG9zZUljb24ge1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWhvcml6b250YWwtdGFiLWhlaWdodCk7XG59XG5cbi8qIEFjY29yZGlvbiBXaWRnZXQgKi9cblxuLnAtQ29sbGFwc2Uge1xuICAgIGRpc3BsYXk6IGZsZXg7XG4gICAgZmxleC1kaXJlY3Rpb246IGNvbHVtbjtcbiAgICBhbGlnbi1pdGVtczogc3RyZXRjaDtcbn1cblxuLnAtQ29sbGFwc2UtaGVhZGVyIHtcbiAgICBwYWRkaW5nOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpO1xuICAgIGN1cnNvcjogcG9pbnRlcjtcbiAgICBjb2xvcjogdmFyKC0tanAtdWktZm9udC1jb2xvcjIpO1xuICAgIGJhY2tncm91bmQtY29sb3I6IHZhcigtLWpwLWxheW91dC1jb2xvcjIpO1xuICAgIGJvcmRlcjogdmFyKC0tanAtd2lkZ2V0cy1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIHBhZGRpbmc6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZykgKiAyIC8gMykgdmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZyk7XG4gICAgZm9udC13ZWlnaHQ6IGJvbGQ7XG59XG5cbi5wLUNvbGxhcHNlLWhlYWRlcjpob3ZlciB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbn1cblxuLnAtQ29sbGFwc2Utb3BlbiA+IC5wLUNvbGxhcHNlLWhlYWRlciB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IwKTtcbiAgICBjdXJzb3I6IGRlZmF1bHQ7XG4gICAgYm9yZGVyLWJvdHRvbTogbm9uZTtcbn1cblxuLnAtQ29sbGFwc2UgLnAtQ29sbGFwc2UtaGVhZGVyOjpiZWZvcmUge1xuICAgIGNvbnRlbnQ6ICdcXGYwZGFcXDAwQTAnOyAgLyogY2FyZXQtcmlnaHQsIG5vbi1icmVha2luZyBzcGFjZSAqL1xuICAgIGRpc3BsYXk6IGlubGluZS1ibG9jaztcbiAgICBmb250OiBub3JtYWwgbm9ybWFsIG5vcm1hbCAxNHB4LzEgRm9udEF3ZXNvbWU7XG4gICAgZm9udC1zaXplOiBpbmhlcml0O1xuICAgIHRleHQtcmVuZGVyaW5nOiBhdXRvO1xuICAgIC13ZWJraXQtZm9udC1zbW9vdGhpbmc6IGFudGlhbGlhc2VkO1xuICAgIC1tb3otb3N4LWZvbnQtc21vb3RoaW5nOiBncmF5c2NhbGU7XG59XG5cbi5wLUNvbGxhcHNlLW9wZW4gPiAucC1Db2xsYXBzZS1oZWFkZXI6OmJlZm9yZSB7XG4gICAgY29udGVudDogJ1xcZjBkN1xcMDBBMCc7IC8qIGNhcmV0LWRvd24sIG5vbi1icmVha2luZyBzcGFjZSAqL1xufVxuXG4ucC1Db2xsYXBzZS1jb250ZW50cyB7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZyk7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbiAgICBib3JkZXItbGVmdDogdmFyKC0tanAtd2lkZ2V0cy1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIGJvcmRlci1yaWdodDogdmFyKC0tanAtd2lkZ2V0cy1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIGJvcmRlci1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC1ib3JkZXItY29sb3IxKTtcbiAgICBvdmVyZmxvdzogYXV0bztcbn1cblxuLnAtQWNjb3JkaW9uIHtcbiAgICBkaXNwbGF5OiBmbGV4O1xuICAgIGZsZXgtZGlyZWN0aW9uOiBjb2x1bW47XG4gICAgYWxpZ24taXRlbXM6IHN0cmV0Y2g7XG59XG5cbi5wLUFjY29yZGlvbiAucC1Db2xsYXBzZSB7XG4gICAgbWFyZ2luLWJvdHRvbTogMDtcbn1cblxuLnAtQWNjb3JkaW9uIC5wLUNvbGxhcHNlICsgLnAtQ29sbGFwc2Uge1xuICAgIG1hcmdpbi10b3A6IDRweDtcbn1cblxuXG5cbi8qIEhUTUwgd2lkZ2V0ICovXG5cbi53aWRnZXQtaHRtbCwgLndpZGdldC1odG1sbWF0aCB7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG59XG5cbi53aWRnZXQtaHRtbCA+IC53aWRnZXQtaHRtbC1jb250ZW50LCAud2lkZ2V0LWh0bWxtYXRoID4gLndpZGdldC1odG1sLWNvbnRlbnQge1xuICAgIC8qIEZpbGwgb3V0IHRoZSBhcmVhIGluIHRoZSBIVE1MIHdpZGdldCAqL1xuICAgIGFsaWduLXNlbGY6IHN0cmV0Y2g7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIGZsZXgtc2hyaW5rOiAxO1xuICAgIC8qIE1ha2VzIHN1cmUgdGhlIGJhc2VsaW5lIGlzIHN0aWxsIGFsaWduZWQgd2l0aCBvdGhlciBlbGVtZW50cyAqL1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIC8qIE1ha2UgaXQgcG9zc2libGUgdG8gaGF2ZSBhYnNvbHV0ZWx5LXBvc2l0aW9uZWQgZWxlbWVudHMgaW4gdGhlIGh0bWwgKi9cbiAgICBwb3NpdGlvbjogcmVsYXRpdmU7XG59XG4iLCIvKiBUaGlzIGZpbGUgaGFzIGNvZGUgZGVyaXZlZCBmcm9tIFBob3NwaG9ySlMgQ1NTIGZpbGVzLCBhcyBub3RlZCBiZWxvdy4gVGhlIGxpY2Vuc2UgZm9yIHRoaXMgUGhvc3Bob3JKUyBjb2RlIGlzOlxuXG5Db3B5cmlnaHQgKGMpIDIwMTQtMjAxNywgUGhvc3Bob3JKUyBDb250cmlidXRvcnNcbkFsbCByaWdodHMgcmVzZXJ2ZWQuXG5cblJlZGlzdHJpYnV0aW9uIGFuZCB1c2UgaW4gc291cmNlIGFuZCBiaW5hcnkgZm9ybXMsIHdpdGggb3Igd2l0aG91dFxubW9kaWZpY2F0aW9uLCBhcmUgcGVybWl0dGVkIHByb3ZpZGVkIHRoYXQgdGhlIGZvbGxvd2luZyBjb25kaXRpb25zIGFyZSBtZXQ6XG5cbiogUmVkaXN0cmlidXRpb25zIG9mIHNvdXJjZSBjb2RlIG11c3QgcmV0YWluIHRoZSBhYm92ZSBjb3B5cmlnaHQgbm90aWNlLCB0aGlzXG4gIGxpc3Qgb2YgY29uZGl0aW9ucyBhbmQgdGhlIGZvbGxvd2luZyBkaXNjbGFpbWVyLlxuXG4qIFJlZGlzdHJpYnV0aW9ucyBpbiBiaW5hcnkgZm9ybSBtdXN0IHJlcHJvZHVjZSB0aGUgYWJvdmUgY29weXJpZ2h0IG5vdGljZSxcbiAgdGhpcyBsaXN0IG9mIGNvbmRpdGlvbnMgYW5kIHRoZSBmb2xsb3dpbmcgZGlzY2xhaW1lciBpbiB0aGUgZG9jdW1lbnRhdGlvblxuICBhbmQvb3Igb3RoZXIgbWF0ZXJpYWxzIHByb3ZpZGVkIHdpdGggdGhlIGRpc3RyaWJ1dGlvbi5cblxuKiBOZWl0aGVyIHRoZSBuYW1lIG9mIHRoZSBjb3B5cmlnaHQgaG9sZGVyIG5vciB0aGUgbmFtZXMgb2YgaXRzXG4gIGNvbnRyaWJ1dG9ycyBtYXkgYmUgdXNlZCB0byBlbmRvcnNlIG9yIHByb21vdGUgcHJvZHVjdHMgZGVyaXZlZCBmcm9tXG4gIHRoaXMgc29mdHdhcmUgd2l0aG91dCBzcGVjaWZpYyBwcmlvciB3cml0dGVuIHBlcm1pc3Npb24uXG5cblRISVMgU09GVFdBUkUgSVMgUFJPVklERUQgQlkgVEhFIENPUFlSSUdIVCBIT0xERVJTIEFORCBDT05UUklCVVRPUlMgXCJBUyBJU1wiXG5BTkQgQU5ZIEVYUFJFU1MgT1IgSU1QTElFRCBXQVJSQU5USUVTLCBJTkNMVURJTkcsIEJVVCBOT1QgTElNSVRFRCBUTywgVEhFXG5JTVBMSUVEIFdBUlJBTlRJRVMgT0YgTUVSQ0hBTlRBQklMSVRZIEFORCBGSVRORVNTIEZPUiBBIFBBUlRJQ1VMQVIgUFVSUE9TRSBBUkVcbkRJU0NMQUlNRUQuIElOIE5PIEVWRU5UIFNIQUxMIFRIRSBDT1BZUklHSFQgSE9MREVSIE9SIENPTlRSSUJVVE9SUyBCRSBMSUFCTEVcbkZPUiBBTlkgRElSRUNULCBJTkRJUkVDVCwgSU5DSURFTlRBTCwgU1BFQ0lBTCwgRVhFTVBMQVJZLCBPUiBDT05TRVFVRU5USUFMXG5EQU1BR0VTIChJTkNMVURJTkcsIEJVVCBOT1QgTElNSVRFRCBUTywgUFJPQ1VSRU1FTlQgT0YgU1VCU1RJVFVURSBHT09EUyBPUlxuU0VSVklDRVM7IExPU1MgT0YgVVNFLCBEQVRBLCBPUiBQUk9GSVRTOyBPUiBCVVNJTkVTUyBJTlRFUlJVUFRJT04pIEhPV0VWRVJcbkNBVVNFRCBBTkQgT04gQU5ZIFRIRU9SWSBPRiBMSUFCSUxJVFksIFdIRVRIRVIgSU4gQ09OVFJBQ1QsIFNUUklDVCBMSUFCSUxJVFksXG5PUiBUT1JUIChJTkNMVURJTkcgTkVHTElHRU5DRSBPUiBPVEhFUldJU0UpIEFSSVNJTkcgSU4gQU5ZIFdBWSBPVVQgT0YgVEhFIFVTRVxuT0YgVEhJUyBTT0ZUV0FSRSwgRVZFTiBJRiBBRFZJU0VEIE9GIFRIRSBQT1NTSUJJTElUWSBPRiBTVUNIIERBTUFHRS5cblxuKi9cblxuLypcbiAqIFRoZSBmb2xsb3dpbmcgc2VjdGlvbiBpcyBkZXJpdmVkIGZyb20gaHR0cHM6Ly9naXRodWIuY29tL3Bob3NwaG9yanMvcGhvc3Bob3IvYmxvYi8yM2I5ZDA3NWViYzViNzNhYjE0OGI2ZWJmYzIwYWY5N2Y4NTcxNGM0L3BhY2thZ2VzL3dpZGdldHMvc3R5bGUvdGFiYmFyLmNzcyBcbiAqIFdlJ3ZlIHNjb3BlZCB0aGUgcnVsZXMgc28gdGhhdCB0aGV5IGFyZSBjb25zaXN0ZW50IHdpdGggZXhhY3RseSBvdXIgY29kZS5cbiAqL1xuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIge1xuICBkaXNwbGF5OiBmbGV4O1xuICAtd2Via2l0LXVzZXItc2VsZWN0OiBub25lO1xuICAtbW96LXVzZXItc2VsZWN0OiBub25lO1xuICAtbXMtdXNlci1zZWxlY3Q6IG5vbmU7XG4gIHVzZXItc2VsZWN0OiBub25lO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhcltkYXRhLW9yaWVudGF0aW9uPSdob3Jpem9udGFsJ10ge1xuICBmbGV4LWRpcmVjdGlvbjogcm93O1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhcltkYXRhLW9yaWVudGF0aW9uPSd2ZXJ0aWNhbCddIHtcbiAgZmxleC1kaXJlY3Rpb246IGNvbHVtbjtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgPiAucC1UYWJCYXItY29udGVudCB7XG4gIG1hcmdpbjogMDtcbiAgcGFkZGluZzogMDtcbiAgZGlzcGxheTogZmxleDtcbiAgZmxleDogMSAxIGF1dG87XG4gIGxpc3Qtc3R5bGUtdHlwZTogbm9uZTtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXJbZGF0YS1vcmllbnRhdGlvbj0naG9yaXpvbnRhbCddID4gLnAtVGFiQmFyLWNvbnRlbnQge1xuICBmbGV4LWRpcmVjdGlvbjogcm93O1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhcltkYXRhLW9yaWVudGF0aW9uPSd2ZXJ0aWNhbCddID4gLnAtVGFiQmFyLWNvbnRlbnQge1xuICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiIHtcbiAgZGlzcGxheTogZmxleDtcbiAgZmxleC1kaXJlY3Rpb246IHJvdztcbiAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgb3ZlcmZsb3c6IGhpZGRlbjtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkljb24sXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkNsb3NlSWNvbiB7XG4gIGZsZXg6IDAgMCBhdXRvO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiTGFiZWwge1xuICBmbGV4OiAxIDEgYXV0bztcbiAgb3ZlcmZsb3c6IGhpZGRlbjtcbiAgd2hpdGUtc3BhY2U6IG5vd3JhcDtcbn1cblxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYi5wLW1vZC1oaWRkZW4ge1xuICBkaXNwbGF5OiBub25lICFpbXBvcnRhbnQ7XG59XG5cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyLnAtbW9kLWRyYWdnaW5nIC5wLVRhYkJhci10YWIge1xuICBwb3NpdGlvbjogcmVsYXRpdmU7XG59XG5cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyLnAtbW9kLWRyYWdnaW5nW2RhdGEtb3JpZW50YXRpb249J2hvcml6b250YWwnXSAucC1UYWJCYXItdGFiIHtcbiAgbGVmdDogMDtcbiAgdHJhbnNpdGlvbjogbGVmdCAxNTBtcyBlYXNlO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhci5wLW1vZC1kcmFnZ2luZ1tkYXRhLW9yaWVudGF0aW9uPSd2ZXJ0aWNhbCddIC5wLVRhYkJhci10YWIge1xuICB0b3A6IDA7XG4gIHRyYW5zaXRpb246IHRvcCAxNTBtcyBlYXNlO1xufVxuXG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhci5wLW1vZC1kcmFnZ2luZyAucC1UYWJCYXItdGFiLnAtbW9kLWRyYWdnaW5nIHtcbiAgdHJhbnNpdGlvbjogbm9uZTtcbn1cblxuLyogRW5kIHRhYmJhci5jc3MgKi9cbiJdfQ== */",
       "headers": [
        [
         "content-type",
         "text/css"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "Y9mAbo4CI85M",
    "outputId": "6905c1de-795d-4d54-8ff4-0e5a9ae5af42"
   },
   "outputs": [],
   "source": [
    "def evaluate_inference(model, val_indices):\n",
    "    cumulative_precision = 0\n",
    "    cumulative_recall = 0\n",
    "    cumulative_em = 0\n",
    "    cumulative_em_char = 0\n",
    "    \n",
    "    N = len(val_indices)\n",
    "\n",
    "    epoch_loss = 0\n",
    "    prediction_results = []\n",
    "    em_char_score = 0\n",
    "    for i, val_index in tqdm(enumerate(val_indices), total=N):\n",
    "        input_text = thai_romanization_dataset.input_texts[val_index]\n",
    "        target_text = thai_romanization_dataset.target_texts[val_index]\n",
    "\n",
    "        \n",
    "        prediction, indices, _ = inference(model, input_text, char_2_ix, ix_2_char, target_char_to_ix, ix_to_target_char)\n",
    "        prediction_results.append(prediction)\n",
    "        if i <= 15:\n",
    "            print('Example: {}'.format(i+1))\n",
    "            print('      input: {}'.format(input_text))\n",
    "            print('groundtruth: {}'.format(target_text))\n",
    "            print(' prediction: {}\\n'.format(prediction))\n",
    "\n",
    "        pred_chars = [char for char in prediction]\n",
    "        target_chars = [char for char in target_text]\n",
    "\n",
    "        cumulative_precision += precision(pred_chars, target_chars)\n",
    "        cumulative_recall +=  recall(pred_chars, target_chars)\n",
    "        cumulative_em_char += em_char(pred_chars, target_chars)\n",
    "        cumulative_em += em(prediction, target_text)\n",
    "\n",
    "    macro_average_precision = cumulative_precision / N\n",
    "    macro_average_recall = cumulative_recall /N\n",
    "    f1_macro_average = f1(macro_average_precision, macro_average_recall) \n",
    "    em_score = cumulative_em / N\n",
    "    em_char_score = cumulative_em_char / N\n",
    "    print('')\n",
    "    print('F1 (macro-average) = ', f1_macro_average)\n",
    "    print('EM = ', em_score)\n",
    "    print('EM (Character-level) = ', em_char_score)\n",
    "\n",
    "\n",
    "    return f1_macro_average, em_score, em_char_score, prediction_results\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i7pJKMVqI85P"
   },
   "outputs": [],
   "source": [
    "data = load_model('./thai2rom-pytorch.attn.v8.best_epoch-29.tar')\n",
    "\n",
    "_model = data['model']\n",
    "_model           \n",
    "\n",
    "char_to_ix = data['char_to_ix']\n",
    "ix_to_char =  data['ix_to_char']\n",
    "target_char_to_ix = data['target_char_to_ix']\n",
    "ix_to_target_char = data['ix_to_target_char']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G1Sri1ZXI85Q"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a096ecb35e545c19a700d063530b05b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=129642), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1\n",
      "      input: ขัตติยา กรีมี\n",
      "groundtruth: khattiya krimi\n",
      " prediction: khattiya krimi\n",
      "\n",
      "Example: 2\n",
      "      input: คมสรร บุญทอง\n",
      "groundtruth: khomsan bunthong\n",
      " prediction: khomsan bunthong\n",
      "\n",
      "Example: 3\n",
      "      input: ต่อเลขหมาย\n",
      "groundtruth: tolekmai\n",
      " prediction: tolekmai\n",
      "\n",
      "Example: 4\n",
      "      input: กิตติศักดิ์ หวังวรวงศ์\n",
      "groundtruth: kittisak wangworawong\n",
      " prediction: kittisak wangworawong\n",
      "\n",
      "Example: 5\n",
      "      input: สถาปัตยกรรมศาสตร์\n",
      "groundtruth: sathapattayakamsat\n",
      " prediction: sathapattayakammasat\n",
      "\n",
      "Example: 6\n",
      "      input: กิมเจียว แซ่โง้ว\n",
      "groundtruth: kimchiao sae-ngow\n",
      " prediction: kimchiao sae-ngow\n",
      "\n",
      "Example: 7\n",
      "      input: เขยื้อน รังสิยีรานนท์\n",
      "groundtruth: khayuean rangsiyiranon\n",
      " prediction: khayuean rangsiyiranon\n",
      "\n",
      "Example: 8\n",
      "      input: ขนิษฐา เสมอใจ\n",
      "groundtruth: khanittha samoechai\n",
      " prediction: khanittha samoechai\n",
      "\n",
      "Example: 9\n",
      "      input: กนกกุล หวังบุญผาติ\n",
      "groundtruth: kanokkun wangbunphati\n",
      " prediction: kanokkun wangbunphat\n",
      "\n",
      "Example: 10\n",
      "      input: จรัล กิติสาย\n",
      "groundtruth: charan kitisai\n",
      " prediction: charan kitisai\n",
      "\n",
      "Example: 11\n",
      "      input: โกศัย มนัสศิลานนท์\n",
      "groundtruth: kosai manatsilanon\n",
      " prediction: kosai manatsilanon\n",
      "\n",
      "Example: 12\n",
      "      input: เกศินี บำเพ็ญนรกิจ\n",
      "groundtruth: kesini bamphennorakit\n",
      " prediction: kesini bamphennonkit\n",
      "\n",
      "Example: 13\n",
      "      input: จำรัส จันทร์อำพร\n",
      "groundtruth: chamrat chan-amphon\n",
      " prediction: chamrat chan-amphon\n",
      "\n",
      "Example: 14\n",
      "      input: กิติสร สุขประดิษฐ์\n",
      "groundtruth: kitison sukpradit\n",
      " prediction: kitison sukpradit\n",
      "\n",
      "Example: 15\n",
      "      input: จุฑามาศ ศรีโกมุท\n",
      "groundtruth: chuthamat sikomut\n",
      " prediction: chuthamat sikomut\n",
      "\n",
      "Example: 16\n",
      "      input: กัลยา ผาสุขดี\n",
      "groundtruth: kanlaya phasukdi\n",
      " prediction: kanlaya phasukdi\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_inference(_model, val_indices[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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