{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mock Tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "This tutorial is available as an IPython notebook at [Malaya/example/mock-tensorflow](https://github.com/huseinzol05/Malaya/tree/master/example/mock-tensorflow).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with Malaya 5.0.1 and malaya-boilerplate 0.0.24, Tensorflow is no longer necessary to install and if Tensorflow absent, it will be replaced with mock object.\n",
    "\n",
    "Let say you installed Malaya on a fresh machine or using a virtual environment,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!~/huggingface/bin/pip3 freeze | grep 'tensorflow'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This virtual environment does not have Tensorflow installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/dev/malaya-boilerplate/malaya_boilerplate/frozen_graph.py:46: UserWarning: Cannot import beam_search_ops from Tensorflow 1, ['malaya.jawi_rumi.deep_model', 'malaya.phoneme.deep_model', 'malaya.rumi_jawi.deep_model', 'malaya.stem.deep_model'] for stemmer will not available to use, make sure Tensorflow 1 version >= 1.15\n",
      "  warnings.warn(\n",
      "/home/husein/huggingface/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "bs4 is not installed, `malaya.text.function.remove_html_tags` will use regex\n"
     ]
    }
   ],
   "source": [
    "import malaya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<malaya_boilerplate.Mock at 0x7f9b7cdddca0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, everything is a Mock object, what happened if you tried to call a model using Tensorflow as backend?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call Tensorflow model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size (MB)</th>\n",
       "      <th>Quantized Size (MB)</th>\n",
       "      <th>macro precision</th>\n",
       "      <th>macro recall</th>\n",
       "      <th>macro f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bert</th>\n",
       "      <td>425.6</td>\n",
       "      <td>111.00</td>\n",
       "      <td>0.93182</td>\n",
       "      <td>0.93442</td>\n",
       "      <td>0.93307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiny-bert</th>\n",
       "      <td>57.4</td>\n",
       "      <td>15.40</td>\n",
       "      <td>0.93390</td>\n",
       "      <td>0.93141</td>\n",
       "      <td>0.93262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>albert</th>\n",
       "      <td>48.6</td>\n",
       "      <td>12.80</td>\n",
       "      <td>0.91228</td>\n",
       "      <td>0.91929</td>\n",
       "      <td>0.91540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiny-albert</th>\n",
       "      <td>22.4</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.91442</td>\n",
       "      <td>0.91646</td>\n",
       "      <td>0.91521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xlnet</th>\n",
       "      <td>446.6</td>\n",
       "      <td>118.00</td>\n",
       "      <td>0.92390</td>\n",
       "      <td>0.92629</td>\n",
       "      <td>0.92444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alxlnet</th>\n",
       "      <td>46.8</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.91896</td>\n",
       "      <td>0.92589</td>\n",
       "      <td>0.92198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Size (MB)  Quantized Size (MB)  macro precision  macro recall  \\\n",
       "bert             425.6               111.00          0.93182       0.93442   \n",
       "tiny-bert         57.4                15.40          0.93390       0.93141   \n",
       "albert            48.6                12.80          0.91228       0.91929   \n",
       "tiny-albert       22.4                 5.98          0.91442       0.91646   \n",
       "xlnet            446.6               118.00          0.92390       0.92629   \n",
       "alxlnet           46.8                13.30          0.91896       0.92589   \n",
       "\n",
       "             macro f1-score  \n",
       "bert                0.93307  \n",
       "tiny-bert           0.93262  \n",
       "albert              0.91540  \n",
       "tiny-albert         0.91521  \n",
       "xlnet               0.92444  \n",
       "alxlnet             0.92198  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.sentiment.available_transformer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "tensorflow is not installed. Please install it and try again.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmalaya\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msentiment\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43malxlnet\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/huggingface/lib/python3.8/site-packages/herpetologist/__init__.py:100\u001b[0m, in \u001b[0;36mcheck_type.<locals>.check\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m p, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m     98\u001b[0m         nested_check(v, p)\n\u001b[0;32m--> 100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/dev/malaya/malaya/sentiment.py:112\u001b[0m, in \u001b[0;36mtransformer\u001b[0;34m(model, quantized, **kwargs)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _transformer_availability:\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    110\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel not supported, please check supported models from `malaya.sentiment.available_transformer()`.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    111\u001b[0m     )\n\u001b[0;32m--> 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mclassification\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodule\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msentiment-v2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    114\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    115\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    116\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquantized\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquantized\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    117\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/dev/malaya/malaya/supervised/classification.py:141\u001b[0m, in \u001b[0;36mtransformer\u001b[0;34m(module, label, model, sigmoid, quantized, **kwargs)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransformer\u001b[39m(\n\u001b[1;32m    123\u001b[0m     module,\n\u001b[1;32m    124\u001b[0m     label,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    129\u001b[0m ):\n\u001b[1;32m    130\u001b[0m     path \u001b[38;5;241m=\u001b[39m check_file(\n\u001b[1;32m    131\u001b[0m         file\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[1;32m    132\u001b[0m         module\u001b[38;5;241m=\u001b[39mmodule,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    140\u001b[0m     )\n\u001b[0;32m--> 141\u001b[0m     g \u001b[38;5;241m=\u001b[39m \u001b[43mload_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m sigmoid:\n\u001b[1;32m    144\u001b[0m         selected_model \u001b[38;5;241m=\u001b[39m SIGMOID_MODEL[model]\n",
      "File \u001b[0;32m~/dev/malaya/malaya/function/__init__.py:38\u001b[0m, in \u001b[0;36mload_graph\u001b[0;34m(frozen_graph_filename, **kwargs)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_graph\u001b[39m(frozen_graph_filename, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfrozen_graph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrozen_graph_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/dev/malaya-boilerplate/malaya_boilerplate/frozen_graph.py:241\u001b[0m, in \u001b[0;36mload_graph\u001b[0;34m(package, frozen_graph_filename, **kwargs)\u001b[0m\n\u001b[1;32m    238\u001b[0m precision_mode \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprecision_mode\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFP32\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mupper()\n\u001b[1;32m    239\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprecision_mode: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprecision_mode\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 241\u001b[0m device \u001b[38;5;241m=\u001b[39m \u001b[43mget_device\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    242\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdevice: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdevice\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    244\u001b[0m t5_graph \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt5_graph\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m~/dev/malaya-boilerplate/malaya_boilerplate/frozen_graph.py:111\u001b[0m, in \u001b[0;36mget_device\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m device_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXLA_CPU\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXLA_CPU_JIT\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCPU\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGPU\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXLA_GPU\u001b[39m\u001b[38;5;124m'\u001b[39m}:\n\u001b[1;32m    108\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    109\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`device` from `device:\u001b[39m\u001b[38;5;132;01m{no}\u001b[39;00m\u001b[38;5;124m` must one of [\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXLA_CPU\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXLA_CPU_JIT\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCPU\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGPU\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXLA_GPU\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    110\u001b[0m     )\n\u001b[0;32m--> 111\u001b[0m gpus \u001b[38;5;241m=\u001b[39m \u001b[43mavailable_gpu\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGPU\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m device:\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(gpus):\n",
      "File \u001b[0;32m~/dev/malaya-boilerplate/malaya_boilerplate/utils.py:117\u001b[0m, in \u001b[0;36mavailable_gpu\u001b[0;34m(refresh)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mavailable_gpu\u001b[39m(refresh\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    109\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    110\u001b[0m \u001b[38;5;124;03m    Get list of GPUs and memory limit from `tensorflow.python.client.device_lib.list_local_devices()`.\u001b[39;00m\n\u001b[1;32m    111\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;124;03m    result : List[str]\u001b[39;00m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 117\u001b[0m     devices \u001b[38;5;241m=\u001b[39m \u001b[43mavailable_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrefresh\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrefresh\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [d \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m devices \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGPU\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m d[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXLA\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m d[\u001b[38;5;241m0\u001b[39m]]\n",
      "File \u001b[0;32m~/dev/malaya-boilerplate/malaya_boilerplate/utils.py:96\u001b[0m, in \u001b[0;36mavailable_device\u001b[0;34m(refresh)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m DEVICES \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m refresh:\n\u001b[1;32m     94\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclient\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m device_lib\n\u001b[0;32m---> 96\u001b[0m     DEVICES \u001b[38;5;241m=\u001b[39m \u001b[43mdevice_lib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_local_devices\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     97\u001b[0m     DEVICES \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     98\u001b[0m         (\n\u001b[1;32m     99\u001b[0m             i\u001b[38;5;241m.\u001b[39mname\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/device:\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m DEVICES\n\u001b[1;32m    103\u001b[0m     ]\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DEVICES\n",
      "File \u001b[0;32m~/dev/malaya-boilerplate/malaya_boilerplate/__init__.py:48\u001b[0m, in \u001b[0;36mMock.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 48\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not installed. Please install it and try again.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: tensorflow is not installed. Please install it and try again."
     ]
    }
   ],
   "source": [
    "model = malaya.sentiment.transformer(model = 'alxlnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call PyTorch model\n",
    "\n",
    "Starting from malaya 4.9, we are going to focus Pytorch for main deep learning backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = malaya.similarity.semantic.huggingface()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.86549413], dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(['tak suka ayam'], ['hate chicken'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "huggingface",
   "language": "python",
   "name": "huggingface"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
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