cmu-nlp-contest.ipynb

{
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      "name": "python3"
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    "language_info": {
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "version": "3.6.4",
      "file_extension": ".py",
      "codemirror_mode": {
        "name": "ipython",
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      },
      "name": "python",
      "mimetype": "text/x-python"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/xkzy/16f886_metrics/blob/master/cmu-nlp-contest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -nc https://github.com/xkzy/nlp_class_contest/releases/download/Dataset/input.txt\n",
        "!wget -nc https://github.com/xkzy/nlp_class_contest/releases/download/Dataset/ans.txt\n",
        "!wget -nc https://github.com/xkzy/nlp_class_contest/releases/download/Dataset/text_pairs.json\n",
        "!wget -nc https://github.com/xkzy/nlp_class_contest/releases/download/keras_model/transformer_model_best_val.h5"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2023-10-02T04:13:00.569571Z",
          "iopub.execute_input": "2023-10-02T04:13:00.570037Z",
          "iopub.status.idle": "2023-10-02T04:13:01.039382Z",
          "shell.execute_reply.started": "2023-10-02T04:13:00.570007Z",
          "shell.execute_reply": "2023-10-02T04:13:01.038160Z"
        },
        "trusted": true,
        "id": "VSJ-W-gWjH2j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d29690c-afb5-42e2-a363-ac9d2961b6dc"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File ‘input.txt’ already there; not retrieving.\n",
            "\n",
            "File ‘ans.txt’ already there; not retrieving.\n",
            "\n",
            "File ‘text_pairs.json’ already there; not retrieving.\n",
            "\n",
            "File ‘transformer_model_best_val.h5’ already there; not retrieving.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install deepcut -q -q -q --exists-action i\n",
        "!pip install pythainlp['full'] -q -q -q --exists-action i\n",
        "!!pip install -q git+https://github.com/keras-team/keras-nlp.git --upgrade\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-10-02T04:13:01.041744Z",
          "iopub.execute_input": "2023-10-02T04:13:01.042547Z"
        },
        "trusted": true,
        "id": "pxbj0S3NjH2n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca9e61f2-ef76-4ed7-87e3-a29ab9160a52"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['  Installing build dependencies ... \\x1b[?25l\\x1b[?25hdone',\n",
              " '  Getting requirements to build wheel ... \\x1b[?25l\\x1b[?25hdone',\n",
              " '  Preparing metadata (pyproject.toml) ... \\x1b[?25l\\x1b[?25hdone']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import subprocess\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "import tensorflow as tf\n",
        "import csv\n",
        "import numpy as np\n",
        "import deepcut\n",
        "from keras.models import Model, load_model\n",
        "from keras.layers import Input, Dense\n",
        "import keras_nlp\n",
        "from tensorflow.keras.utils import to_categorical, set_random_seed\n",
        "import matplotlib.pyplot as plt\n",
        "from random import shuffle\n",
        "import pathlib\n",
        "import random\n",
        "import string\n",
        "import re\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers import TextVectorization\n",
        "import pythainlp\n",
        "import json\n",
        "import os\n",
        "from posixpath import split\n",
        "\n",
        "\n",
        "set_random_seed(99)\n",
        "random.seed(99)"
      ],
      "metadata": {
        "trusted": true,
        "id": "kXX6xHKojH2o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bab74816-cb2a-452c-8bad-7f39e2ed5f3e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    subprocess.check_output('nvidia-smi')\n",
        "    print('Nvidia GPU detected!')\n",
        "except Exception: # this command not being found can raise quite a few different errors depending on the configuration\n",
        "    print('No Nvidia GPU in system!')"
      ],
      "metadata": {
        "trusted": true,
        "id": "RhrPQF48jH2p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea5d9da4-4f29-4762-e6a6-a0e2f9cf6a51"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nvidia GPU detected!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.__version__)\n",
        "tf.test.is_gpu_available()\n",
        "print(tf.config.list_physical_devices())"
      ],
      "metadata": {
        "trusted": true,
        "id": "AyRepC5NjH2r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d414759-9597-4058-d708-5bde3d650c68"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From <ipython-input-5-a6f12e301f5f>:2: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.13.0\n",
            "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sentence_loader(input_file):\n",
        "    out=[]\n",
        "    for i in list(input_file):\n",
        "        s = i.split('::')[1].strip()\n",
        "        s = ' '.join(pythainlp.word_tokenize(s,engine = 'deepcut'))\n",
        "        out.append(s)\n",
        "    return out\n",
        "\n",
        "def answer_loader(ans_file):\n",
        "    out=[]\n",
        "    for i in ans_file:\n",
        "        sl=i.split('::')[1].strip().split(',')\n",
        "        pad = (' [pad]'*(3-len(sl)))\n",
        "        s = '[start] '+' '.join(sl)+' [end]'+pad\n",
        "        out.append(s)\n",
        "        print(s)\n",
        "    return out\n",
        "\n",
        "def pair_gen(s,a):\n",
        "    return list(zip(s,a))"
      ],
      "metadata": {
        "trusted": true,
        "id": "FCPXle0sjH2r"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('input.txt', 'r',encoding = 'utf-8-sig') as input_file:\n",
        "    input_data = sentence_loader(input_file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVSdcsxuil9o",
        "outputId": "b18f75c1-3042-49e1-c74b-7f89eaf657d4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('ans.txt', 'r',encoding = 'utf-8-sig') as ans_file:\n",
        "    ans_data = answer_loader(ans_file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TaquL9rvixdD",
        "outputId": "6ab06729-6d59-4dae-94b8-869b40885c99"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[start] E [end] [pad] [pad]\n",
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            "[start] E E P [end]\n",
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            "[start] E P E [end]\n",
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            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T [end] [pad]\n",
            "[start] E T E [end]\n",
            "[start] E T P [end]\n",
            "[start] E T P [end]\n",
            "[start] E T T [end]\n",
            "[start] E T T [end]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] p [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] p [end] [pad] [pad]\n",
            "[start] p [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
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            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P  [end] [pad]\n",
            "[start] P  [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
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            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E E [end]\n",
            "[start] P E E [end]\n",
            "[start] P E E [end]\n",
            "[start] P E E [end]\n",
            "[start] P E E [end]\n",
            "[start] P E E [end]\n",
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            "[start] T [end] [pad] [pad]\n",
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            "[start] T [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
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            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T E E [end]\n",
            "[start] T E E [end]\n",
            "[start] T E P [end]\n",
            "[start] T E P [end]\n",
            "[start] T E P [end]\n",
            "[start] T E P [end]\n",
            "[start] T E P [end]\n",
            "[start] T E T [end]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
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            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] T P [end] [pad]\n",
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            "[start] T P [end] [pad]\n",
            "[start] T P E [end]\n",
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            "[start] T P E [end]\n",
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            "[start] T P E [end]\n",
            "[start] T P E [end]\n",
            "[start] T P P [end]\n",
            "[start] T P P [end]\n",
            "[start] T P P [end]\n",
            "[start] P E [end] [pad]\n",
            "[start] T P T [end]\n",
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            "[start] T T [end] [pad]\n",
            "[start] T T [end] [pad]\n",
            "[start] T T [end] [pad]\n",
            "[start] T T E [end]\n",
            "[start] T T E [end]\n",
            "[start] T T E [end]\n",
            "[start] T T P [end]\n",
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            "[start] T T P [end]\n",
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            "[start] T T P [end]\n",
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            "[start] T T P [end]\n",
            "[start] T T P [end]\n",
            "[start] T T P [end]\n",
            "[start] T T P [end]\n",
            "[start] T T P [end]\n",
            "[start] T T T [end]\n",
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            "[start] T T T [end]\n",
            "[start] T T T [end]\n",
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            "[start] T T T [end]\n",
            "[start] T T T [end]\n",
            "[start] T T T [end]\n",
            "[start] T T T [end]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] E E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] E E [end] [pad]\n",
            "[start] T T [end] [pad]\n",
            "[start] T E P [end]\n",
            "[start] P E T [end]\n",
            "[start] T E P [end]\n",
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            "[start] T E P [end]\n",
            "[start] P E E [end]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] P P E [end]\n",
            "[start] E E [end] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] T P [end] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] P T [end] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] T P E [end]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] E P E [end]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P E T [end]\n",
            "[start] P E T [end]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P T P [end]\n",
            "[start] T P E [end]\n",
            "[start] T T [end] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] E P [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] P T [end] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] T T [end] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] P E [end] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] T P T [end]\n",
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            "[start] E E [end] [pad]\n",
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            "[start] T E [end] [pad]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] T E T [end]\n",
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            "[start] T E P [end]\n",
            "[start] T [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] T E [end] [pad]\n",
            "[start] E E [end] [pad]\n",
            "[start] P P [end] [pad]\n",
            "[start] T E [end] [pad]\n",
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            "[start] E [end] [pad] [pad]\n",
            "[start] P [end] [pad] [pad]\n",
            "[start] P E E [end]\n",
            "[start] T T [end] [pad]\n",
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            "[start] P [end] [pad] [pad]\n",
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            "[start] P [end] [pad] [pad]\n",
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            "[start] P [end] [pad] [pad]\n",
            "[start] T P [end] [pad]\n",
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            "[start] T E [end] [pad]\n",
            "[start] P E P [end]\n",
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            "[start] P [end] [pad] [pad]\n",
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            "[start] P E [end] [pad]\n",
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            "[start] E P T [end]\n",
            "[start] T T P [end]\n",
            "[start] P E T [end]\n",
            "[start] P E E [end]\n",
            "[start] E T E [end]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_pairs = []\n",
        "if os.path.exists('text_pairs.json'):\n",
        "    with open('text_pairs.json', 'rb') as fp:\n",
        "        text_pairs = json.load(fp)\n",
        "else:\n",
        "    text_pairs = pair_gen(input_data,ans_data)\n",
        "    with open(\"text_pairs.json\", \"w\") as fp:\n",
        "        json.dump(text_pairs, fp)\n",
        "        print(\"Done writing JSON data into .json file\")\n",
        "\n",
        "print(len(text_pairs))\n",
        "for _ in range(5):\n",
        "    print(random.choice(text_pairs))"
      ],
      "metadata": {
        "trusted": true,
        "id": "Jo1FiXxHjH2t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a0a4714-ceff-4016-b85b-85b4278ce2eb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4818\n",
            "['แม่ ตา มา หา   แล้ว ตา ก็ ลุก ไม่ ไหว', '[start] P P [end] [pad]']\n",
            "['หลวงตาใส่ แว่น ตา สี ดำ แบรนด์ เนม คน นั้น แหละ ที่ ตก เป็น ข่าว อยู่ ตอน นี้', '[start] P E [end] [pad]']\n",
            "['เพราะ อาการ สาย ตา ยาว ของ คุณ ตา จึง ทำ ให้ อ่าน หนังสือ ลำบาก', '[start] E P [end] [pad]']\n",
            "['คง ต้อง ใช้ น้ำ ตา มากมาย เพื่อ ล้าง ตา   ไม่ ให้ เหลือ ภาพ เธอ ให้ จำ', '[start] E E [end] [pad]']\n",
            "['ตา เป็น สามี ของ ยาย', '[start] P [end] [pad] [pad]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random.shuffle(text_pairs)\n",
        "num_val_samples = int(0.15 * len(text_pairs))\n",
        "num_train_samples = len(text_pairs) - 2 * num_val_samples\n",
        "train_pairs = text_pairs[:num_train_samples]\n",
        "val_pairs = text_pairs[num_train_samples : num_train_samples + num_val_samples]\n",
        "test_pairs = text_pairs[num_train_samples + num_val_samples :]\n",
        "\n",
        "print(f\"{len(text_pairs)} total pairs\")\n",
        "print(f\"{len(train_pairs)} training pairs\")\n",
        "print(f\"{len(val_pairs)} validation pairs\")\n",
        "print(f\"{len(test_pairs)} test pairs\")"
      ],
      "metadata": {
        "trusted": true,
        "id": "GBJWuikejH2t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8aae9f48-a5dd-43fa-a0f1-ec1bb096ee4a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4818 total pairs\n",
            "3374 training pairs\n",
            "722 validation pairs\n",
            "722 test pairs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "strip_chars = string.punctuation + \"¿\"\n",
        "strip_chars = strip_chars.replace(\"[\", \"\")\n",
        "strip_chars = strip_chars.replace(\"]\", \"\")\n",
        "\n",
        "vocab_input_size = 5000\n",
        "vocab_ans_size = 8\n",
        "sequence_length = 100\n",
        "sequence_ans_length = 5\n",
        "batch_size = 64\n",
        "\n",
        "\n",
        "def custom_standardization(input_string):\n",
        "    lowercase = tf.strings.lower(input_string)\n",
        "    return tf.strings.regex_replace(lowercase, \"[%s]\" % re.escape(strip_chars), \"\")\n",
        "\n",
        "\n",
        "input_vectorization = TextVectorization(\n",
        "    max_tokens=vocab_input_size,\n",
        "    # output_mode=\"tf_idf\",\n",
        "    output_mode=\"int\",\n",
        "    output_sequence_length=sequence_length,\n",
        "    split='whitespace',\n",
        ")\n",
        "ans_vectorization = TextVectorization(\n",
        "    max_tokens=vocab_ans_size,\n",
        "    output_mode=\"int\",\n",
        "    output_sequence_length=sequence_ans_length + 1,\n",
        "    standardize=custom_standardization,\n",
        "    split='whitespace',\n",
        ")\n",
        "train_input_texts = [pair[0] for pair in train_pairs]\n",
        "train_ans_texts = [pair[1] for pair in train_pairs]\n",
        "input_vectorization.adapt(train_input_texts)\n",
        "ans_vectorization.adapt(train_ans_texts)"
      ],
      "metadata": {
        "trusted": true,
        "id": "DKXPLY9DjH2u"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(input_vectorization.get_vocabulary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsAMQGBOWL1j",
        "outputId": "5a448cf5-4146-48f2-855f-c6508a04f089"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3490"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ans_vectorization.get_vocabulary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBszIR35Wgsp",
        "outputId": "59d6ac4a-821a-47c1-ea50-bff7b2139cf4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['', '[UNK]', '[pad]', '[start]', '[end]', 'e', 'p', 't']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def format_dataset(input, ans):\n",
        "    input = input_vectorization(input)\n",
        "    ans = ans_vectorization(ans)\n",
        "    return (\n",
        "        {\n",
        "            \"encoder_inputs\": input,\n",
        "            \"decoder_inputs\": ans[:, :-1],\n",
        "        },\n",
        "        ans[:, 1:],\n",
        "    )\n",
        "\n",
        "\n",
        "def make_dataset(pairs):\n",
        "    input_texts, ans_texts = zip(*pairs)\n",
        "    input_texts = list(input_texts)\n",
        "    ans_texts = list(ans_texts)\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((input_texts, ans_texts))\n",
        "    dataset = dataset.batch(batch_size)\n",
        "    dataset = dataset.map(format_dataset)\n",
        "    return dataset.shuffle(2048).prefetch(16).cache()\n",
        "\n",
        "\n",
        "train_ds = make_dataset(train_pairs)\n",
        "val_ds = make_dataset(val_pairs)\n",
        "test_ds = make_dataset(test_pairs)"
      ],
      "metadata": {
        "trusted": true,
        "id": "2gqam7W0jH2v"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for inputs, targets in train_ds.take(1):\n",
        "    print(f'inputs[\"encoder_inputs\"].shape: {inputs[\"encoder_inputs\"].shape}')\n",
        "    print(f'inputs[\"decoder_inputs\"].shape: {inputs[\"decoder_inputs\"].shape}')\n",
        "    print(f\"targets.shape: {targets.shape}\")"
      ],
      "metadata": {
        "trusted": true,
        "id": "M6oIXaCyjH2v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc9bead5-5c44-4b67-da51-8397d1e68f75"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inputs[\"encoder_inputs\"].shape: (64, 100)\n",
            "inputs[\"decoder_inputs\"].shape: (64, 5)\n",
            "targets.shape: (64, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class TransformerEncoder(layers.Layer):\n",
        "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.embed_dim = embed_dim\n",
        "        self.dense_dim = dense_dim\n",
        "        self.num_heads = num_heads\n",
        "        self.attention = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim\n",
        "        )\n",
        "        self.dense_proj = keras.Sequential(\n",
        "            [\n",
        "                layers.Dense(dense_dim, activation=\"relu\"),\n",
        "                layers.Dense(embed_dim),\n",
        "            ]\n",
        "        )\n",
        "        self.layernorm_1 = layers.LayerNormalization()\n",
        "        self.layernorm_2 = layers.LayerNormalization()\n",
        "        self.supports_masking = True\n",
        "\n",
        "    def call(self, inputs, mask=None):\n",
        "        attention_output = self.attention(query=inputs, value=inputs, key=inputs)\n",
        "        proj_input = self.layernorm_1(inputs + attention_output)\n",
        "        proj_output = self.dense_proj(proj_input)\n",
        "        return self.layernorm_2(proj_input + proj_output)\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config()\n",
        "        config.update(\n",
        "            {\n",
        "                \"embed_dim\": self.embed_dim,\n",
        "                \"dense_dim\": self.dense_dim,\n",
        "                \"num_heads\": self.num_heads,\n",
        "            }\n",
        "        )\n",
        "        return config\n",
        "\n",
        "\n",
        "class PositionalEmbedding(layers.Layer):\n",
        "    def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.token_embeddings = layers.Embedding(\n",
        "            input_dim=vocab_size, output_dim=embed_dim\n",
        "        )\n",
        "        self.position_embeddings = layers.Embedding(\n",
        "            input_dim=sequence_length, output_dim=embed_dim\n",
        "        )\n",
        "        self.sequence_length = sequence_length\n",
        "        self.vocab_size = vocab_size\n",
        "        self.embed_dim = embed_dim\n",
        "\n",
        "    def call(self, inputs):\n",
        "        length = tf.shape(inputs)[-1]\n",
        "        positions = tf.range(start=0, limit=length, delta=1)\n",
        "        embedded_tokens = self.token_embeddings(inputs)\n",
        "        embedded_positions = self.position_embeddings(positions)\n",
        "        return embedded_tokens + embedded_positions\n",
        "\n",
        "    def compute_mask(self, inputs, mask=None):\n",
        "        return tf.math.not_equal(inputs, 0)\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config()\n",
        "        config.update(\n",
        "            {\n",
        "                \"sequence_length\": self.sequence_length,\n",
        "                \"vocab_size\": self.vocab_size,\n",
        "                \"embed_dim\": self.embed_dim,\n",
        "            }\n",
        "        )\n",
        "        return config\n",
        "\n",
        "\n",
        "class TransformerDecoder(layers.Layer):\n",
        "    def __init__(self, embed_dim, latent_dim, num_heads, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.embed_dim = embed_dim\n",
        "        self.latent_dim = latent_dim\n",
        "        self.num_heads = num_heads\n",
        "        self.attention_1 = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim\n",
        "        )\n",
        "        self.attention_2 = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim\n",
        "        )\n",
        "        self.dense_proj = keras.Sequential(\n",
        "            [\n",
        "                layers.Dense(latent_dim, activation=\"relu\"),\n",
        "                layers.Dense(embed_dim),\n",
        "            ]\n",
        "        )\n",
        "        self.layernorm_1 = layers.LayerNormalization()\n",
        "        self.layernorm_2 = layers.LayerNormalization()\n",
        "        self.layernorm_3 = layers.LayerNormalization()\n",
        "        self.add = layers.Add()  # instead of `+` to preserve mask\n",
        "        self.supports_masking = True\n",
        "\n",
        "    def call(self, inputs, encoder_outputs, mask=None):\n",
        "        attention_output_1 = self.attention_1(\n",
        "            query=inputs, value=inputs, key=inputs, use_causal_mask=True\n",
        "        )\n",
        "        out_1 = self.layernorm_1(self.add([inputs, attention_output_1]))\n",
        "\n",
        "        attention_output_2 = self.attention_2(\n",
        "            query=out_1,\n",
        "            value=encoder_outputs,\n",
        "            key=encoder_outputs,\n",
        "        )\n",
        "        out_2 = self.layernorm_2(self.add([out_1, attention_output_2]))\n",
        "\n",
        "        proj_output = self.dense_proj(out_2)\n",
        "        return self.layernorm_3(self.add([out_2, proj_output]))\n",
        "\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config()\n",
        "        config.update(\n",
        "            {\n",
        "                \"embed_dim\": self.embed_dim,\n",
        "                \"latent_dim\": self.latent_dim,\n",
        "                \"num_heads\": self.num_heads,\n",
        "            }\n",
        "        )\n",
        "        return config"
      ],
      "metadata": {
        "trusted": true,
        "id": "xTcH4LDkjH2w"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embed_dim = 300\n",
        "latent_dim = 2048\n",
        "num_heads = 16\n",
        "num_en_transformer_blocks = 1\n",
        "num_de_transformer_blocks = 1\n",
        "\n",
        "encoder_inputs = keras.Input(shape=(None,), dtype=\"int64\", name=\"encoder_inputs\")\n",
        "x = PositionalEmbedding(sequence_length, vocab_input_size, embed_dim)(encoder_inputs)\n",
        "for _ in range(num_en_transformer_blocks):\n",
        "    x = TransformerEncoder(embed_dim, latent_dim, num_heads)(x)\n",
        "\n",
        "encoder_outputs = x\n",
        "encoder = keras.Model(encoder_inputs, encoder_outputs,name=\"encoder\")\n",
        "\n",
        "decoder_inputs = keras.Input(shape=(None,), dtype=\"int64\", name=\"decoder_inputs\")\n",
        "encoded_seq_inputs = keras.Input(shape=(None, embed_dim), name=\"decoder_state_inputs\")\n",
        "x = PositionalEmbedding(sequence_ans_length, vocab_input_size, embed_dim)(decoder_inputs)\n",
        "x = TransformerDecoder(embed_dim, latent_dim, num_heads)(x, encoded_seq_inputs)\n",
        "for _ in range(num_de_transformer_blocks-1):\n",
        "    x = TransformerDecoder(embed_dim, latent_dim, num_heads)(x,x)\n",
        "    x = layers.Dropout(0)(x)\n",
        "decoder_outputs = layers.Dense(vocab_ans_size, activation=\"softmax\")(x)\n",
        "decoder = keras.Model([decoder_inputs, encoded_seq_inputs], decoder_outputs, name=\"decoder\")\n",
        "\n",
        "decoder_outputs = decoder([decoder_inputs, encoder_outputs])\n",
        "transformer = keras.Model(\n",
        "    [encoder_inputs, decoder_inputs], decoder_outputs, name=\"transformer\"\n",
        ")"
      ],
      "metadata": {
        "trusted": true,
        "id": "OZoBBwikjH2x"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decoder.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jy8izU_H9nFk",
        "outputId": "2c3f890d-77dc-43b5-99b8-76125ce11310"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"decoder\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " decoder_inputs (InputLayer  [(None, None)]               0         []                            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " positional_embedding_1 (Po  (None, None, 300)            1501500   ['decoder_inputs[0][0]']      \n",
            " sitionalEmbedding)                                                                               \n",
            "                                                                                                  \n",
            " decoder_state_inputs (Inpu  [(None, None, 300)]          0         []                            \n",
            " tLayer)                                                                                          \n",
            "                                                                                                  \n",
            " transformer_decoder (Trans  (None, None, 300)            1278234   ['positional_embedding_1[0][0]\n",
            " formerDecoder)                                           8         ',                            \n",
            "                                                                     'decoder_state_inputs[0][0]']\n",
            "                                                                                                  \n",
            " dense_18 (Dense)            (None, None, 8)              2408      ['transformer_decoder[0][0]'] \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 14286256 (54.50 MB)\n",
            "Trainable params: 14286256 (54.50 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint = ModelCheckpoint('transformer_model_best_val.h5', #อย่าลืมเปลี่ยน path ให้ file\n",
        "                             verbose=1,\n",
        "                             monitor='val_accuracy',\n",
        "                             save_best_only=True,\n",
        "                             mode='max')\n",
        "\n",
        "transformer.summary()\n",
        "transformer.compile(\n",
        "    \"rmsprop\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"]\n",
        ")\n"
      ],
      "metadata": {
        "trusted": true,
        "id": "qOyR7wzajH2y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd3384f1-7251-4f98-d637-777a0fb43781"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"transformer\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " encoder_inputs (InputLayer  [(None, None)]               0         []                            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " positional_embedding (Posi  (None, None, 300)            1530000   ['encoder_inputs[0][0]']      \n",
            " tionalEmbedding)                                                                                 \n",
            "                                                                                                  \n",
            " decoder_inputs (InputLayer  [(None, None)]               0         []                            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " transformer_encoder (Trans  (None, None, 300)            7007048   ['positional_embedding[0][0]']\n",
            " formerEncoder)                                                                                   \n",
            "                                                                                                  \n",
            " decoder (Functional)        (None, None, 8)              1428625   ['decoder_inputs[0][0]',      \n",
            "                                                          6          'transformer_encoder[0][0]'] \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 22823304 (87.06 MB)\n",
            "Trainable params: 22823304 (87.06 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(transformer,\n",
        "                          to_file='transformer.png',\n",
        "                          show_shapes=True,\n",
        "                          expand_nested=True,\n",
        "                          show_layer_names=True,\n",
        "                          show_layer_activations=True,\n",
        "                          show_trainable=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "id": "i87AINIL9-LT",
        "outputId": "ad8f5b18-7649-408c-f4ee-a46acafc5a95"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "force_train = True\n",
        "epochs = 30  # This should be at least 30 for convergence\n",
        "\n",
        "if not os.path.exists('transformer_model_best_val.h5') or force_train:\n",
        "    transformer.fit(train_ds, epochs=epochs, validation_data=val_ds,callbacks=[checkpoint])"
      ],
      "metadata": {
        "trusted": true,
        "id": "f2qX9difjH2y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9ed4e18-bc03-4434-d287-5b59a7db2f29"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 2.5502 - accuracy: 0.2983\n",
            "Epoch 1: val_accuracy improved from -inf to 0.51399, saving model to transformer_model_best_val.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r53/53 [==============================] - 34s 505ms/step - loss: 2.5502 - accuracy: 0.2983 - val_loss: 1.3387 - val_accuracy: 0.5140\n",
            "Epoch 2/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 1.1277 - accuracy: 0.6057\n",
            "Epoch 2: val_accuracy improved from 0.51399 to 0.72264, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 26s 490ms/step - loss: 1.1277 - accuracy: 0.6057 - val_loss: 0.6116 - val_accuracy: 0.7226\n",
            "Epoch 3/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.5426 - accuracy: 0.7804\n",
            "Epoch 3: val_accuracy improved from 0.72264 to 0.74591, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 26s 490ms/step - loss: 0.5426 - accuracy: 0.7804 - val_loss: 0.5557 - val_accuracy: 0.7459\n",
            "Epoch 4/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.5173 - accuracy: 0.7861\n",
            "Epoch 4: val_accuracy improved from 0.74591 to 0.78304, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 26s 490ms/step - loss: 0.5173 - accuracy: 0.7861 - val_loss: 0.5005 - val_accuracy: 0.7830\n",
            "Epoch 5/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.4690 - accuracy: 0.8000\n",
            "Epoch 5: val_accuracy improved from 0.78304 to 0.82128, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 26s 490ms/step - loss: 0.4690 - accuracy: 0.8000 - val_loss: 0.4449 - val_accuracy: 0.8213\n",
            "Epoch 6/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.4401 - accuracy: 0.8076\n",
            "Epoch 6: val_accuracy did not improve from 0.82128\n",
            "53/53 [==============================] - 26s 485ms/step - loss: 0.4401 - accuracy: 0.8076 - val_loss: 0.4605 - val_accuracy: 0.7700\n",
            "Epoch 7/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.4096 - accuracy: 0.8161\n",
            "Epoch 7: val_accuracy did not improve from 0.82128\n",
            "53/53 [==============================] - 28s 523ms/step - loss: 0.4096 - accuracy: 0.8161 - val_loss: 0.4752 - val_accuracy: 0.7578\n",
            "Epoch 8/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.4711 - accuracy: 0.8167\n",
            "Epoch 8: val_accuracy did not improve from 0.82128\n",
            "53/53 [==============================] - 30s 564ms/step - loss: 0.4711 - accuracy: 0.8167 - val_loss: 5.8567 - val_accuracy: 0.2145\n",
            "Epoch 9/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 1.2022 - accuracy: 0.6660\n",
            "Epoch 9: val_accuracy improved from 0.82128 to 0.82959, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 28s 530ms/step - loss: 1.2022 - accuracy: 0.6660 - val_loss: 0.4193 - val_accuracy: 0.8296\n",
            "Epoch 10/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.3927 - accuracy: 0.8294\n",
            "Epoch 10: val_accuracy did not improve from 0.82959\n",
            "53/53 [==============================] - 26s 485ms/step - loss: 0.3927 - accuracy: 0.8294 - val_loss: 0.4229 - val_accuracy: 0.8152\n",
            "Epoch 11/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.3530 - accuracy: 0.8453\n",
            "Epoch 11: val_accuracy improved from 0.82959 to 0.83763, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 30s 565ms/step - loss: 0.3530 - accuracy: 0.8453 - val_loss: 0.3791 - val_accuracy: 0.8376\n",
            "Epoch 12/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.3345 - accuracy: 0.8619\n",
            "Epoch 12: val_accuracy improved from 0.83763 to 0.86451, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 31s 591ms/step - loss: 0.3345 - accuracy: 0.8619 - val_loss: 0.3418 - val_accuracy: 0.8645\n",
            "Epoch 13/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.3135 - accuracy: 0.8774\n",
            "Epoch 13: val_accuracy did not improve from 0.86451\n",
            "53/53 [==============================] - 26s 489ms/step - loss: 0.3135 - accuracy: 0.8774 - val_loss: 0.4189 - val_accuracy: 0.8360\n",
            "Epoch 14/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.9595 - accuracy: 0.7713\n",
            "Epoch 14: val_accuracy did not improve from 0.86451\n",
            "53/53 [==============================] - 27s 502ms/step - loss: 0.9595 - accuracy: 0.7713 - val_loss: 0.3554 - val_accuracy: 0.8623\n",
            "Epoch 15/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.3240 - accuracy: 0.8724\n",
            "Epoch 15: val_accuracy did not improve from 0.86451\n",
            "53/53 [==============================] - 30s 564ms/step - loss: 0.3240 - accuracy: 0.8724 - val_loss: 0.3958 - val_accuracy: 0.8484\n",
            "Epoch 16/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.3060 - accuracy: 0.8804\n",
            "Epoch 16: val_accuracy improved from 0.86451 to 0.87836, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 31s 586ms/step - loss: 0.3060 - accuracy: 0.8804 - val_loss: 0.3345 - val_accuracy: 0.8784\n",
            "Epoch 17/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.2644 - accuracy: 0.9030\n",
            "Epoch 17: val_accuracy improved from 0.87836 to 0.88030, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 26s 499ms/step - loss: 0.2644 - accuracy: 0.9030 - val_loss: 0.3313 - val_accuracy: 0.8803\n",
            "Epoch 18/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.2463 - accuracy: 0.9078\n",
            "Epoch 18: val_accuracy did not improve from 0.88030\n",
            "53/53 [==============================] - 26s 488ms/step - loss: 0.2463 - accuracy: 0.9078 - val_loss: 0.5020 - val_accuracy: 0.8451\n",
            "Epoch 19/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.2365 - accuracy: 0.9149\n",
            "Epoch 19: val_accuracy improved from 0.88030 to 0.88972, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 30s 575ms/step - loss: 0.2365 - accuracy: 0.9149 - val_loss: 0.3216 - val_accuracy: 0.8897\n",
            "Epoch 20/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.2181 - accuracy: 0.9206\n",
            "Epoch 20: val_accuracy did not improve from 0.88972\n",
            "53/53 [==============================] - 30s 565ms/step - loss: 0.2181 - accuracy: 0.9206 - val_loss: 0.3441 - val_accuracy: 0.8797\n",
            "Epoch 21/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.2121 - accuracy: 0.9213\n",
            "Epoch 21: val_accuracy did not improve from 0.88972\n",
            "53/53 [==============================] - 27s 506ms/step - loss: 0.2121 - accuracy: 0.9213 - val_loss: 0.3471 - val_accuracy: 0.8850\n",
            "Epoch 22/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.2047 - accuracy: 0.9254\n",
            "Epoch 22: val_accuracy improved from 0.88972 to 0.89332, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 28s 525ms/step - loss: 0.2047 - accuracy: 0.9254 - val_loss: 0.3480 - val_accuracy: 0.8933\n",
            "Epoch 23/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.1920 - accuracy: 0.9307\n",
            "Epoch 23: val_accuracy did not improve from 0.89332\n",
            "53/53 [==============================] - 30s 568ms/step - loss: 0.1920 - accuracy: 0.9307 - val_loss: 0.3532 - val_accuracy: 0.8861\n",
            "Epoch 24/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.1825 - accuracy: 0.9357\n",
            "Epoch 24: val_accuracy did not improve from 0.89332\n",
            "53/53 [==============================] - 30s 568ms/step - loss: 0.1825 - accuracy: 0.9357 - val_loss: 0.3867 - val_accuracy: 0.8858\n",
            "Epoch 25/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.1808 - accuracy: 0.9376\n",
            "Epoch 25: val_accuracy did not improve from 0.89332\n",
            "53/53 [==============================] - 27s 501ms/step - loss: 0.1808 - accuracy: 0.9376 - val_loss: 0.3582 - val_accuracy: 0.8911\n",
            "Epoch 26/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.1666 - accuracy: 0.9388\n",
            "Epoch 26: val_accuracy improved from 0.89332 to 0.89609, saving model to transformer_model_best_val.h5\n",
            "53/53 [==============================] - 29s 545ms/step - loss: 0.1666 - accuracy: 0.9388 - val_loss: 0.3547 - val_accuracy: 0.8961\n",
            "Epoch 27/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.7271 - accuracy: 0.8604\n",
            "Epoch 27: val_accuracy did not improve from 0.89609\n",
            "53/53 [==============================] - 30s 562ms/step - loss: 0.7271 - accuracy: 0.8604 - val_loss: 0.4136 - val_accuracy: 0.8307\n",
            "Epoch 28/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.2503 - accuracy: 0.9104\n",
            "Epoch 28: val_accuracy did not improve from 0.89609\n",
            "53/53 [==============================] - 29s 555ms/step - loss: 0.2503 - accuracy: 0.9104 - val_loss: 0.3399 - val_accuracy: 0.8861\n",
            "Epoch 29/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.1677 - accuracy: 0.9396\n",
            "Epoch 29: val_accuracy did not improve from 0.89609\n",
            "53/53 [==============================] - 26s 491ms/step - loss: 0.1677 - accuracy: 0.9396 - val_loss: 0.3830 - val_accuracy: 0.8889\n",
            "Epoch 30/30\n",
            "53/53 [==============================] - ETA: 0s - loss: 0.1592 - accuracy: 0.9424\n",
            "Epoch 30: val_accuracy did not improve from 0.89609\n",
            "53/53 [==============================] - 26s 500ms/step - loss: 0.1592 - accuracy: 0.9424 - val_loss: 0.4371 - val_accuracy: 0.8767\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformer = load_model('transformer_model_best_val.h5',custom_objects={\n",
        "    'PositionalEmbedding': PositionalEmbedding,\n",
        "    'TransformerDecoder': TransformerDecoder,\n",
        "    'TransformerEncoder': TransformerEncoder,})\n",
        "\n",
        "ans_vocab = ans_vectorization.get_vocabulary()\n",
        "ans_index_lookup = dict(zip(range(len(ans_vocab)), ans_vocab))\n",
        "max_decoded_sentence_length = sequence_ans_length\n",
        "\n",
        "\n",
        "def decode_sequence(input_sentence):\n",
        "    tokenized_input_sentence = input_vectorization([input_sentence])\n",
        "    decoded_sentence = \"[start]\"\n",
        "    for i in range(max_decoded_sentence_length):\n",
        "        tokenized_target_sentence = ans_vectorization([decoded_sentence])[:, :-1]\n",
        "        predictions = transformer([tokenized_input_sentence, tokenized_target_sentence])\n",
        "        # print(predictions)\n",
        "        sampled_token_index = np.argmax(predictions[0, i, :])\n",
        "        sampled_token = ans_index_lookup[sampled_token_index]\n",
        "        decoded_sentence += \" \" + sampled_token\n",
        "        if sampled_token == \"[end]\":\n",
        "            break\n",
        "        # else:\n",
        "        #     decoded_sentence += \" \" + sampled_token\n",
        "    # return decoded_sentence[8:].strip().upper().replace(' ',',')\n",
        "    return decoded_sentence.replace(\" \", \",\").replace(\"[start]\", \"\").replace(\"[end]\", \"\").upper().strip().strip(',')\n"
      ],
      "metadata": {
        "trusted": true,
        "id": "Z1JEjf-WjH2z"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_input_texts = [pair[0] for pair in test_pairs]\n",
        "test_ans_texts = [pair[1] for pair in test_pairs]\n",
        "\n",
        "for _ in range(30):\n",
        "    input_sentence,ans = random.choice(test_pairs)\n",
        "    print(input_sentence)\n",
        "    translated = decode_sequence(input_sentence)\n",
        "    print(translated.replace(\",\", \"\"))\n",
        "    print(ans.replace(\" \", \"\").replace(\"[pad]\", \"\").replace(\"[start]\", \"\").replace(\"[end]\", \"\").upper().strip().strip(','))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLcM0YHY9yGx",
        "outputId": "9aaf4dc0-78b9-4dee-8b1d-2bb92ed666dc"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "อวัยวะ ที่ ใช้ มอง สิ่ง ต่างๆ คือ ตา\n",
            "E\n",
            "E\n",
            "ถ้า ตาย แต่ ทำ ความ ดีเยอะ จะ ได้ ขึ้น สวรรค์   ตา เคย บอก ไว้\n",
            "P\n",
            "P\n",
            "ตา ของ เรา ไม่ โดน ความ ร้อน สูง\n",
            "P\n",
            "E\n",
            "ตา นี้ ช่าง มัน ตาหน้า เอา ใหม่\n",
            "TT\n",
            "TT\n",
            "ถึง ตา นี้ พวก เรา จะ พ่ายแพ้   แต่ ตา หน้า   ก็ แพ้ เหมือน เดิม\n",
            "TT\n",
            "TT\n",
            "แม่ บอก ได้ แค่ ตาเดียว นะ\n",
            "T\n",
            "T\n",
            "ตา นี้ อีซี่ จัด   ๆ   กิน นิ่ม เลย\n",
            "P\n",
            "T\n",
            "ตา เป็น ผู้ ชาย\n",
            "P\n",
            "P\n",
            "ตา นี้ เกม เร็ว ไม่ นับ !   ตาหน้า เกม เร็ว อีก ก็ ไม่ นับ เช่น กัน !\n",
            "TT\n",
            "TT\n",
            "ตา นี้ ไม่ ไหว ละรอ ตา มา ก่อน ค่อย เล่น ดี กว่า\n",
            "PP\n",
            "TP\n",
            "นัด คิว หมอ รักษา ตา ให้ แม่ วัน พุธ นี้ หน่อย สิ\n",
            "E\n",
            "E\n",
            "ตาลอย   ๆ   นะ เรา\n",
            "P\n",
            "E\n",
            "ตาม ที่ เขา กล่าว มา   เขา เสีย ดวง ตา เพราะ คู่ อริ ทำร้าย เขา\n",
            "E\n",
            "E\n",
            "บอก อีก ตา ตลอด เมื่อ ไหร่ จะ ได้ เล่น สัก ที\n",
            "T\n",
            "T\n",
            "ตาโย่ง ร้อง เพลง จน ตาลือ ด่า\n",
            "P\n",
            "PP\n",
            "คน เรา ไม่ ควร ขยี้ ดวง ตา แรง ๆ   จะ ทำ ให้ ตา แดง\n",
            "E\n",
            "EE\n",
            "ตา นี้ ไม่ ไหว ละรอ ตา มา ก่อน ค่อย เล่น ดี กว่า\n",
            "PP\n",
            "TP\n",
            "พ่อ ชอบ มาหา ตา ฉัน บ่อย ๆ พร้อม กับ เหล้า\n",
            "P\n",
            "P\n",
            "เขา จ้อง ผู้ หญิง คน นั้น จน ลูก ตา จะ ถลน ออก มา\n",
            "E\n",
            "E\n",
            "คน แก่ ตา ดำ ๆ คือ ตา ของ เขา\n",
            "P\n",
            "EP\n",
            "ตอน นี้ ฉัน รู้สึก ง่วง จน ตา จะ ปิด\n",
            "E\n",
            "E\n",
            "คน ที่ ใช้ สายตา ใน การ ทำ งาน ต้อง กิน อาหาร บำรุง สายตา เช่น วิตามิน A\n",
            "E\n",
            "EE\n",
            "คิด ถึง ดวง ตา คู่ นั้น ของ เธอ จัง\n",
            "E\n",
            "E\n",
            "แมว ของ ฉัน มี ตา   2   สี   ข้าง นึง ตา สี ฟ้า   อีก ข้าง นึง ตา สี เหลือง\n",
            "EE\n",
            "EEE\n",
            "ตอง บอก ว่า เมื่อ เช้า นี้ คุณ ตา ของ ฉัน แอบ ไป ที่ สนามยิง ปืน\n",
            "PP\n",
            "P\n",
            "ตระกูล นี้ มี กี่ ตา กัน ครับ เนี่ย\n",
            "P\n",
            "P\n",
            "คุณ ตา มี ดวง ตา เป็น สี ดำ\n",
            "EE\n",
            "PE\n",
            "รอง นาย อำเภอ เห็น ดัง นั้น แล้ว จึง ทุ่มสุด ตัว ใน ตา นี้ เพื่อ ช่วย นาย อำเภอ\n",
            "T\n",
            "T\n",
            "เรา รีบ สมทบ ว่า   ใช่ จ้ะ ยาย   แล้ว หัน ไป ขยิบ ตา ให้ ตา\n",
            "PP\n",
            "EP\n",
            "เมื่อ คืน ฉัน ข่ม ตา หลับ ตั้ง นาน กว่า ได้ นอน\n",
            "E\n",
            "E\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformer.evaluate(test_ds, verbose=1)"
      ],
      "metadata": {
        "trusted": true,
        "id": "44hSVrTajH20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "391eaf5e-4385-4612-e05c-a6bba68f7816"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 3s 155ms/step - loss: 0.3113 - accuracy: 0.8997\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3112967610359192, 0.8996952176094055]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def file_pred(inpfile,ansfile=\"my_ans.txt\",writeout=True):\n",
        "    out=[]\n",
        "    with open(inpfile, 'r',encoding = 'utf-8-sig') as i_file:\n",
        "        for i in list(i_file):\n",
        "            c=0\n",
        "            i_s=i.split('::')\n",
        "            s = i_s[1].strip()\n",
        "            token=pythainlp.word_tokenize(s,engine = 'deepcut')\n",
        "            for k in token:\n",
        "                if 'ตา' in k:\n",
        "                    c+=1\n",
        "            s = ' '.join(token)\n",
        "            a=decode_sequence(s)\n",
        "            al=a.split(',')\n",
        "            if len(al)>c:\n",
        "                al=al[:c]\n",
        "                print('trimed output length')\n",
        "            a=','.join(al)\n",
        "            out.append(i_s[0]+'::'+a)\n",
        "    if writeout:\n",
        "        with open(ansfile, 'w', encoding='utf-8-sig') as o_file:\n",
        "            o_file.write('\\n'.join(out))\n",
        "    return out"
      ],
      "metadata": {
        "id": "aAWd3phko_4a"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t=file_pred('input_k.txt','ans_k.txt')\n",
        "print(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dUX7LBAqodi",
        "outputId": "fc418847-5b81-4def-ee3f-105aecc2a20f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 12ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "2/2 [==============================] - 0s 11ms/step\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 11ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "2/2 [==============================] - 0s 11ms/step\n",
            "3/3 [==============================] - 0s 9ms/step\n",
            "2/2 [==============================] - 0s 11ms/step\n",
            "3/3 [==============================] - 0s 10ms/step\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "2/2 [==============================] - 0s 11ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "2/2 [==============================] - 0s 12ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "2/2 [==============================] - 0s 11ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "['1::E,E', '2::T,T', '3::P,E,P', '4::P,E,P', '5::P,E', '6::P,E', '7::E', '8::P', '9::P', '10::E', '11::E', '12::E', '13::E', '14::E', '15::E', '16::P', '17::P', '18::P', '19::P', '20::P', '21::P', '22::P', '23::P,T,P', '24::P,T,P', '25::P,P', '26::T,T', '27::T,T', '28::T', '29::P,P', '30::P,E']\n"
          ]
        }
      ]
    }
  ]
}