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   "source": [
    "## Try reverse input string (will increase the performance)\n",
    "## adopt beam search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee5d0131",
   "metadata": {
    "_cell_guid": "53f3ea36-80e9-4eed-9b09-11fa0490ae70",
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      "displayName": "Abdelrhman Ashraf",
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    "id": "sRPtnzMJ5DEN",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "## Turn around camel-kenlm wheel error\n",
    "!pip install -q future six docopt cachetools numpy scipy pandas scikit-learn torch transformers editdistance requests emoji pyrsistent muddler\n",
    "!pip install -q camel-tools --no-deps\n",
    "!pip install -q contractions  datasets\n",
    "\n",
    "!pip install -q kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d453e83f",
   "metadata": {
    "_cell_guid": "8aa266b9-fd1e-4344-8d1a-5a7fab478b63",
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    "executionInfo": {
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     "status": "ok",
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      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "oKlf2c2DFqoS",
    "jupyter": {
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     "duration": 13.013489,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "from camel_tools.tokenizers.word import simple_word_tokenize\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from zipfile import ZipFile\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import spacy\n",
    "from collections import Counter\n",
    "import random\n",
    "import unicodedata\n",
    "import pyarabic.araby as araby\n",
    "import contractions\n",
    "import nltk\n",
    "from datasets import load_dataset\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83ec4bc2",
   "metadata": {
    "_cell_guid": "9b098a3a-f196-4d86-85e8-4918c3edbab4",
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    },
    "executionInfo": {
     "elapsed": 272,
     "status": "ok",
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     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "DlvMCmdIWudl",
    "jupyter": {
     "outputs_hidden": false
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     "duration": 0.027077,
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     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "lr = 1e-3\n",
    "epochs = 50\n",
    "valid_test_size = 0.3\n",
    "# maxlen = 100 # length of one training sample by words\n",
    "embd_features = 128 # length of embedding vectors for each word (input_size) (=1000 in paper)\n",
    "batch_size = 64\n",
    "max_freq = 2 # to add all words to the vocabulary that seen more than one time\n",
    "lstm_hidden_size = 128 # The number of features in the hidden state (=1000 in paper)\n",
    "lstm_layers = 4 # Number of stacked recurrent layers\n",
    "dropout_p = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc2b096a",
   "metadata": {
    "_cell_guid": "a421e16e-78a1-45f1-a185-5ede26629408",
    "_uuid": "3eb4f197-592d-4d39-8a09-79cc7b939b10",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:07:43.375090Z",
     "iopub.status.busy": "2024-11-11T01:07:43.374478Z",
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     "shell.execute_reply": "2024-11-11T01:07:43.378747Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1731233855441,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "SPMCS8ajW1jK",
    "jupyter": {
     "outputs_hidden": false
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     "duration": 0.027399,
     "end_time": "2024-11-11T01:07:43.381624",
     "exception": false,
     "start_time": "2024-11-11T01:07:43.354225",
     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "seed = 42\n",
    "g = torch.Generator().manual_seed(seed)\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "isKaggle = True\n",
    "base_dir = '/kaggle/working' if isKaggle else '/content'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "510bed3e",
   "metadata": {
    "_cell_guid": "2823160c-9473-4388-b059-a8f97ecebc30",
    "_uuid": "fee9820e-a6f6-414c-b042-b320f067a2e8",
    "collapsed": false,
    "id": "jKW1NUp9XA9S",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.018429,
     "end_time": "2024-11-11T01:07:43.418685",
     "exception": false,
     "start_time": "2024-11-11T01:07:43.400256",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c1cc46",
   "metadata": {
    "_cell_guid": "304718a6-ce41-4489-870f-cd7241f2ebdf",
    "_uuid": "2872f338-182c-484e-9be4-c3d7322a3e40",
    "collapsed": false,
    "id": "TFfKvsudXDMI",
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     "start_time": "2024-11-11T01:07:43.437288",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Downloading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d155cf0",
   "metadata": {
    "_cell_guid": "eb367fb7-e526-4d7d-8b1e-e576bdaa58a2",
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    "executionInfo": {
     "elapsed": 6154,
     "status": "ok",
     "timestamp": 1731233861591,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "y7jys3D2W2rf",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset URL: https://www.kaggle.com/datasets/samirmoustafa/arabic-to-english-translation-sentences\r\n",
      "License(s): copyright-authors\r\n",
      "Archive:  /kaggle/working/arabic-to-english-translation-sentences.zip\r\n",
      "  inflating: ara_eng.txt             \r\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5839b51ee1d4222b48ff200fa2aac6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tatoeba_mt.py:   0%|          | 0.00/15.5k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "132bbe8e2c1e488ba4b8a148ba24f460",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "dataset_infos.json:   0%|          | 0.00/1.96M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53ca5f02ab1d4a9c9b0a26f206959de2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/12.1k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7945159d4774b6b8cb2554077b49b41",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tatoeba-test.ara-eng.tsv:   0%|          | 0.00/938k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a800a2434f6435e8bbeadbc58a75a6d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tatoeba-dev.ara-eng.tsv:   0%|          | 0.00/1.78M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e87fe085d45c4fa0a0ff2a7f9d3cbf0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/10304 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81a984dbbfa54e8b8f64fbae745422cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/19528 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# !wget -q https://www.manythings.org/anki/ara-eng.zip\n",
    "!kaggle datasets download -q samirmoustafa/arabic-to-english-translation-sentences\n",
    "!unzip '{base_dir}/arabic-to-english-translation-sentences.zip'\n",
    "\n",
    "!wget -q -O ./tatoeba.tsv https://drive.google.com/uc?id=1aO0yDI4-rDxD5J0OYAgVlUUQuMuqtFSD\n",
    "\n",
    "hf_dataset = load_dataset('Helsinki-NLP/tatoeba_mt','ara-eng', trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b854edf1",
   "metadata": {
    "_cell_guid": "69daee9e-22ad-4562-9984-d49410ff0afc",
    "_uuid": "05c81fe0-213d-4db9-a0c1-fe2cd66153e1",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:07:57.401994Z",
     "iopub.status.busy": "2024-11-11T01:07:57.401118Z",
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     "shell.execute_reply": "2024-11-11T01:07:59.340234Z"
    },
    "executionInfo": {
     "elapsed": 416,
     "status": "ok",
     "timestamp": 1731233862003,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "6jEOnMR3LK4A",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 1.964721,
     "end_time": "2024-11-11T01:07:59.344111",
     "exception": false,
     "start_time": "2024-11-11T01:07:57.379390",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_manythings = pd.read_csv(f'{base_dir}/ara_eng.txt', delimiter='\\t', names=['EN_sentence',\n",
    "                                                                              'AR_sentence'])\n",
    "\n",
    "df_tatoeba = pd.read_csv(f'{base_dir}/tatoeba.tsv', delimiter='\\t', names=['EN_id',\n",
    "                                                                        'EN_sentence',\n",
    "                                                                        'AR_id',\n",
    "                                                                        'AR_sentence']).drop(columns=['EN_id',\n",
    "                                                                                                      'AR_id'])\n",
    "\n",
    "df_hf_1, df_hf_2 = hf_dataset['test'], hf_dataset['validation']\n",
    "\n",
    "df_hf_1 = pd.DataFrame(df_hf_1)[['sourceString','targetString']]\n",
    "df_hf_1.columns = ['AR_sentence', 'EN_sentence']\n",
    "\n",
    "df_hf_2 = pd.DataFrame(df_hf_2)[['sourceString','targetString']]\n",
    "df_hf_2.columns = ['AR_sentence', 'EN_sentence']\n",
    "\n",
    "df_data = pd.concat([df_manythings, df_tatoeba, df_hf_1, df_hf_2], axis=0, ignore_index=True)\n",
    "df_data = df_data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1896c69b",
   "metadata": {
    "_cell_guid": "658f76c9-5ad7-44a3-82dc-b95d3941f7a6",
    "_uuid": "b0f7587c-8168-4dc8-ae42-77d1a3d7d588",
    "collapsed": false,
    "id": "MeuhdoYUXyyE",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.019918,
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     "start_time": "2024-11-11T01:07:59.365623",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Visualizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ad2a644",
   "metadata": {
    "_cell_guid": "39366db9-8955-4c51-bc45-e2d32f4fca2c",
    "_uuid": "999e8525-2c9b-4f46-8957-cab7543f7a3b",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:07:59.427717Z",
     "iopub.status.busy": "2024-11-11T01:07:59.426958Z",
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     "shell.execute_reply": "2024-11-11T01:07:59.439963Z"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1731233862004,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "pr4K4EuwW1hG",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "22f7f6eb-8f88-4462-c090-f3c6f854a98f",
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     "duration": 0.037366,
     "end_time": "2024-11-11T01:07:59.443057",
     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "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>EN_sentence</th>\n",
       "      <th>AR_sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hi.</td>\n",
       "      <td>مرحبًا.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Run!</td>\n",
       "      <td>اركض!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Help!</td>\n",
       "      <td>النجدة!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EN_sentence AR_sentence\n",
       "0         Hi.     مرحبًا.\n",
       "1        Run!       اركض!\n",
       "2       Help!     النجدة!"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9c198b5",
   "metadata": {
    "_cell_guid": "f4aa1236-9a0d-4404-a567-99b74ce5cd62",
    "_uuid": "2bdd8195-553f-420f-bacd-34f14fc7a122",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:07:59.485375Z",
     "iopub.status.busy": "2024-11-11T01:07:59.485061Z",
     "iopub.status.idle": "2024-11-11T01:07:59.493835Z",
     "shell.execute_reply": "2024-11-11T01:07:59.492990Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1731233862004,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "Q6_5CpqeR1WZ",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "dafe43f3-f344-4d77-b589-e3dde59226e9",
    "papermill": {
     "duration": 0.031971,
     "end_time": "2024-11-11T01:07:59.495776",
     "exception": false,
     "start_time": "2024-11-11T01:07:59.463805",
     "status": "completed"
    },
    "tags": []
   },
   "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>EN_sentence</th>\n",
       "      <th>AR_sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101406</th>\n",
       "      <td>You'd better go.</td>\n",
       "      <td>يستحسن انك تروح.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101407</th>\n",
       "      <td>You should not talk here.</td>\n",
       "      <td>يستحسن إنك ما تتكلمش هنا.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101408</th>\n",
       "      <td>Make your choice.</td>\n",
       "      <td>يلا اختار.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      EN_sentence                AR_sentence\n",
       "101406           You'd better go.           يستحسن انك تروح.\n",
       "101407  You should not talk here.  يستحسن إنك ما تتكلمش هنا.\n",
       "101408          Make your choice.                 يلا اختار."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ddd88e87",
   "metadata": {
    "_cell_guid": "82791955-ac05-4454-a04d-79ff1445e6b5",
    "_uuid": "eb3335c0-e8c8-458d-97a9-cb5521fd4594",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:07:59.539311Z",
     "iopub.status.busy": "2024-11-11T01:07:59.538539Z",
     "iopub.status.idle": "2024-11-11T01:07:59.783825Z",
     "shell.execute_reply": "2024-11-11T01:07:59.782983Z"
    },
    "executionInfo": {
     "elapsed": 312,
     "status": "ok",
     "timestamp": 1731233862309,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "lkehEZmETpS3",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.269515,
     "end_time": "2024-11-11T01:07:59.786005",
     "exception": false,
     "start_time": "2024-11-11T01:07:59.516490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_data['EN_sentence_length'] = df_data['EN_sentence'].apply(lambda x: len(x.split(' ')))\n",
    "df_data['AR_sentence_length'] = df_data['AR_sentence'].apply(lambda x: len(x.split(' ')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2512e94",
   "metadata": {
    "_cell_guid": "2ce3e90d-84aa-4528-b3b8-ebb69e124bee",
    "_uuid": "c0db62ea-a6e2-4884-8cb9-459b90c5d335",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:07:59.829149Z",
     "iopub.status.busy": "2024-11-11T01:07:59.828349Z",
     "iopub.status.idle": "2024-11-11T01:08:00.536692Z",
     "shell.execute_reply": "2024-11-11T01:08:00.535734Z"
    },
    "executionInfo": {
     "elapsed": 922,
     "status": "ok",
     "timestamp": 1731233863229,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "yYMZjHJsaUBK",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "0ba341d8-f865-496b-9920-fb489d215d61",
    "papermill": {
     "duration": 0.732373,
     "end_time": "2024-11-11T01:08:00.539150",
     "exception": false,
     "start_time": "2024-11-11T01:07:59.806777",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and two subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Define custom tick range based on your data range\n",
    "EN_x_ticks = np.arange(0, df_data['EN_sentence_length'].max()+1, 25)\n",
    "AR_x_ticks = np.arange(0, df_data['AR_sentence_length'].max()+1, 25)\n",
    "\n",
    "# Plot histogram for EN_sentence_length\n",
    "axes[0].hist(df_data['EN_sentence_length'], bins=50, color='skyblue', edgecolor='black')\n",
    "axes[0].set_title('EN Sentence Length')\n",
    "axes[0].set_xlabel('Length')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "axes[0].set_xticks(EN_x_ticks)  # Add more x-axis ticks\n",
    "\n",
    "# Plot histogram for AR_sentence_length\n",
    "axes[1].hist(df_data['AR_sentence_length'], bins=50, color='salmon', edgecolor='black')\n",
    "axes[1].set_title('AR Sentence Length')\n",
    "axes[1].set_xlabel('Length')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "axes[1].set_xticks(AR_x_ticks)  # Add more x-axis ticks\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a20ed6",
   "metadata": {
    "_cell_guid": "93840f23-72fc-4a14-afab-a7d0bb3cbdec",
    "_uuid": "e1d2150a-bbdd-4a97-987f-3c5c348cf946",
    "collapsed": false,
    "id": "JnYOEPnJSK1I",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.02095,
     "end_time": "2024-11-11T01:08:00.581884",
     "exception": false,
     "start_time": "2024-11-11T01:08:00.560934",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we see there are long and too short sentences.\n",
    "\n",
    "Short sentences will suffer from vanishing Gradients, As we will do post-padding (right-padding), so we will dorp short sentences."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "016a024c",
   "metadata": {
    "_cell_guid": "5401299b-ecd8-4433-8818-2dde942b8f1c",
    "_uuid": "7993eb62-7c1c-4400-a2cc-488985cda970",
    "collapsed": false,
    "id": "yk9hgqksU9_W",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.020897,
     "end_time": "2024-11-11T01:08:00.623676",
     "exception": false,
     "start_time": "2024-11-11T01:08:00.602779",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32c51041",
   "metadata": {
    "_cell_guid": "de16e1a4-e20e-4ec4-b24f-b05db3f151dd",
    "_uuid": "c68e2832-2b45-4638-96d4-9bbd6c10231c",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:00.667588Z",
     "iopub.status.busy": "2024-11-11T01:08:00.666834Z",
     "iopub.status.idle": "2024-11-11T01:08:00.724816Z",
     "shell.execute_reply": "2024-11-11T01:08:00.723725Z"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1731233863229,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "D7-HJg_PVBWV",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "882a3d1e-ea75-4afb-a6ff-e976d037e084",
    "papermill": {
     "duration": 0.08227,
     "end_time": "2024-11-11T01:08:00.726872",
     "exception": false,
     "start_time": "2024-11-11T01:08:00.644602",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101409 entries, 0 to 101408\n",
      "Data columns (total 4 columns):\n",
      " #   Column              Non-Null Count   Dtype \n",
      "---  ------              --------------   ----- \n",
      " 0   EN_sentence         101409 non-null  object\n",
      " 1   AR_sentence         101409 non-null  object\n",
      " 2   EN_sentence_length  101409 non-null  int64 \n",
      " 3   AR_sentence_length  101409 non-null  int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 3.1+ MB\n",
      "None\n"
     ]
    },
    {
     "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>EN_sentence_length</th>\n",
       "      <th>AR_sentence_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101409.000000</td>\n",
       "      <td>101409.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.823053</td>\n",
       "      <td>7.350225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.487242</td>\n",
       "      <td>10.119463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>225.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       EN_sentence_length  AR_sentence_length\n",
       "count       101409.000000       101409.000000\n",
       "mean             8.823053            7.350225\n",
       "std             11.487242           10.119463\n",
       "min              1.000000            1.000000\n",
       "25%              4.000000            3.000000\n",
       "50%              6.000000            5.000000\n",
       "75%              8.000000            7.000000\n",
       "max            225.000000          225.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_data.info())\n",
    "df_data.describe()\n",
    "## so Q3 at 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dbf84937",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:00.771935Z",
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    },
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     "end_time": "2024-11-11T01:08:00.781173",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/518232/2809427\n",
    "def unicodeToAscii(s):\n",
    "    return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn')\n",
    "\n",
    "def preprocess_ar(text):\n",
    "    text = araby.strip_diacritics(text).strip() # Remove diacritics \"التشكيل\"\n",
    "    text = re.sub(r'[a-zA-Z]', '', text) # Remove English letters\n",
    "    text = re.sub(r'\\s+', ' ', text).strip() # Trim multiple whitespaces to one\n",
    "    text = re.sub(r'[_|\\d+|\\\\|\\-|؛|،|,|\\[|\\]|\\(|\\)|\\\"|/|%|!|,|.|:|♪|«|»|}|{|*|#]+', '', text) # Remove special characters and digits\n",
    "    text = unicodeToAscii(text)\n",
    "    return text\n",
    "\n",
    "def preprocess_en(text):\n",
    "    text = text.lower()\n",
    "    text = contractions.fix(text) # Fix contractions \"it's\" -> \"it is\"\n",
    "    text = re.sub(r'[\\u0600-\\u06FF]', '', text) # Remove Arabic letters\n",
    "    text = re.sub(r'\\s+', ' ', text).strip() # Trim multiple whitespaces to one\n",
    "    text = re.sub(r'[_|\\d+|\\\\|\\-|؛|،|,|\\[|\\]|\\(|\\)|\\\"|/|%|!|,|.|:|♪|«|»|}|{|*|#]+', '', text) # Remove special characters and digits\n",
    "    text = unicodeToAscii(text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65970704",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:00.826388Z",
     "iopub.status.busy": "2024-11-11T01:08:00.825773Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_data['EN_sentence'] = df_data['EN_sentence'].apply(preprocess_en)\n",
    "df_data['AR_sentence'] = df_data['AR_sentence'].apply(preprocess_ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "975eb354",
   "metadata": {
    "_cell_guid": "504b87d4-4ae2-4546-b20d-bd8a5a11a003",
    "_uuid": "048c8e25-6fc4-4724-ba6d-0ec33961d904",
    "collapsed": false,
    "execution": {
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     "shell.execute_reply": "2024-11-11T01:08:07.895194Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1731233863230,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "E8ApzqfrVHw9",
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     "outputs_hidden": false
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101409, 7383, 3334)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_len = df_data['EN_sentence_length']\n",
    "len(arr_len), len(arr_len[arr_len>20]), len(arr_len[arr_len<3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "141b4830",
   "metadata": {
    "_cell_guid": "dbce33c5-d4b9-4569-bc38-057f5714efdc",
    "_uuid": "19ce86d4-32d0-43ba-bac0-d5cc75013a9b",
    "collapsed": false,
    "id": "qHIb_lTkXmd_",
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     "start_time": "2024-11-11T01:08:07.919403",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We will drop sentences that > 20 words or < 3 words for source language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77fa809d",
   "metadata": {
    "_cell_guid": "6b37401f-f687-4f7c-aca0-c9acd2bf4664",
    "_uuid": "4ac3524d-109b-4e79-a6b2-8f63d3643f9f",
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    "execution": {
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    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1731233863230,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "1XTCKT3nXxOD",
    "jupyter": {
     "outputs_hidden": false
    },
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     "duration": 0.150953,
     "end_time": "2024-11-11T01:08:08.114259",
     "exception": false,
     "start_time": "2024-11-11T01:08:07.963306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EN_sentence           0\n",
       "AR_sentence           0\n",
       "EN_sentence_length    0\n",
       "AR_sentence_length    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = df_data[(df_data['EN_sentence_length'] <= 20)]\n",
    "df_data = df_data[(df_data['EN_sentence_length'] >= 3)]\n",
    "\n",
    "df_data = df_data[(df_data['AR_sentence_length'] <= 20)]\n",
    "df_data = df_data[(df_data['AR_sentence_length'] >= 3)]\n",
    "\n",
    "df_data = df_data.drop_duplicates(keep='first', subset='AR_sentence')\n",
    "df_data = df_data.drop_duplicates(keep='first', subset='EN_sentence')\n",
    "\n",
    "df_data = df_data.replace('', pd.NA).dropna()\n",
    "df_data = df_data.replace(' ', pd.NA).dropna()\n",
    "df_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bcf0f96",
   "metadata": {
    "_cell_guid": "e6d0a9ee-0f33-4fd3-9d10-3b26815ec3fe",
    "_uuid": "06c813f4-6881-4566-a2df-485bdfac9520",
    "collapsed": false,
    "execution": {
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     "shell.execute_reply": "2024-11-11T01:08:08.840716Z"
    },
    "executionInfo": {
     "elapsed": 759,
     "status": "ok",
     "timestamp": 1731233863984,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "JYQ1JaoEfw9F",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and two subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Define custom tick range based on your data range\n",
    "EN_x_ticks = np.arange(0, df_data['EN_sentence_length'].max()+1, 2)\n",
    "AR_x_ticks = np.arange(0, df_data['AR_sentence_length'].max()+1, 2)\n",
    "\n",
    "# Plot histogram for EN_sentence_length\n",
    "axes[0].hist(df_data['EN_sentence_length'], bins=50, color='skyblue', edgecolor='black')\n",
    "axes[0].set_title('EN Sentence Length')\n",
    "axes[0].set_xlabel('Length')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "axes[0].set_xticks(EN_x_ticks)  # Add more x-axis ticks\n",
    "\n",
    "# Plot histogram for AR_sentence_length\n",
    "axes[1].hist(df_data['AR_sentence_length'], bins=50, color='salmon', edgecolor='black')\n",
    "axes[1].set_title('AR Sentence Length')\n",
    "axes[1].set_xlabel('Length')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "axes[1].set_xticks(AR_x_ticks)  # Add more x-axis ticks\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e4a1da",
   "metadata": {
    "_cell_guid": "2ded56c1-f0e8-4a28-aa06-266e70ba1746",
    "_uuid": "724377a6-b9a8-4e18-ad30-8907bbc42b5d",
    "collapsed": false,
    "id": "0qY-VQnsX6Uz",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.022022,
     "end_time": "2024-11-11T01:08:08.888808",
     "exception": false,
     "start_time": "2024-11-11T01:08:08.866786",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Spliting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d47f79cd",
   "metadata": {
    "_cell_guid": "4827dfe0-5cac-4a5f-b16e-f4a0f12275f3",
    "_uuid": "e27f0b2f-b122-4bf6-97cb-9fee19db02ca",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:08.934766Z",
     "iopub.status.busy": "2024-11-11T01:08:08.934343Z",
     "iopub.status.idle": "2024-11-11T01:08:08.953662Z",
     "shell.execute_reply": "2024-11-11T01:08:08.952610Z"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1731233863984,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "YVmnp4vwW1e5",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "7eaa09bf-7ac0-4dea-9ea4-3596f29d47c9",
    "papermill": {
     "duration": 0.044679,
     "end_time": "2024-11-11T01:08:08.955719",
     "exception": false,
     "start_time": "2024-11-11T01:08:08.911040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30129, 4), (6457, 4), (6456, 4)\n"
     ]
    }
   ],
   "source": [
    "df_train, df_test = train_test_split(df_data, test_size=valid_test_size, shuffle=True, random_state=seed)\n",
    "df_valid, df_test = train_test_split(df_test, test_size=0.5, shuffle=True, random_state=seed)\n",
    "\n",
    "print(df_train.shape, df_test.shape, df_valid.shape, sep=', ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6cd36a",
   "metadata": {
    "_cell_guid": "d2b96602-f2d3-42d4-9354-5d03cd34567f",
    "_uuid": "52c29e11-bff0-4ac1-9277-988f3a67f908",
    "collapsed": false,
    "id": "x_tK_7w7X9k7",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.022027,
     "end_time": "2024-11-11T01:08:09.000136",
     "exception": false,
     "start_time": "2024-11-11T01:08:08.978109",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Loading and preprocessing (DataPipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d1d417fb",
   "metadata": {
    "_cell_guid": "304de1ba-2dde-45d3-abf2-66f83bb0395e",
    "_uuid": "64b75afc-3ad0-4d82-8c0a-08f1b12a8d5e",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:09.046341Z",
     "iopub.status.busy": "2024-11-11T01:08:09.045959Z",
     "iopub.status.idle": "2024-11-11T01:08:09.050816Z",
     "shell.execute_reply": "2024-11-11T01:08:09.049774Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1731233863984,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "l71HUAXhW1c2",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.030357,
     "end_time": "2024-11-11T01:08:09.052761",
     "exception": false,
     "start_time": "2024-11-11T01:08:09.022404",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Arabic Tokenizer\n",
    "class camel_tokenizer():\n",
    "    def __call__(self, text):\n",
    "        return simple_word_tokenize(text.lower())\n",
    "        # return [tok.text.lower() for tok in simple_word_tokenize(text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00fc9c7b",
   "metadata": {
    "_cell_guid": "51223be9-dc37-4e76-b718-64a53cff204e",
    "_uuid": "5efa6e4e-b322-4cbc-9012-593d1981505e",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:09.099090Z",
     "iopub.status.busy": "2024-11-11T01:08:09.098713Z",
     "iopub.status.idle": "2024-11-11T01:08:09.104140Z",
     "shell.execute_reply": "2024-11-11T01:08:09.103258Z"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1731233863984,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "WcmKh1fRakye",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.031054,
     "end_time": "2024-11-11T01:08:09.106114",
     "exception": false,
     "start_time": "2024-11-11T01:08:09.075060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# English Tokenizer\n",
    "class spacy_tokenizer():\n",
    "    def __init__(self):\n",
    "        self.spacy_eng = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "    def __call__(self, text):\n",
    "        return [tok.text.lower() for tok in self.spacy_eng.tokenizer(text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "859880fc",
   "metadata": {
    "_cell_guid": "68d52d75-d185-413e-ac51-d783a1674441",
    "_uuid": "9c54045e-9bee-4006-a808-96c86b15256b",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:09.152951Z",
     "iopub.status.busy": "2024-11-11T01:08:09.152130Z",
     "iopub.status.idle": "2024-11-11T01:08:09.166297Z",
     "shell.execute_reply": "2024-11-11T01:08:09.165398Z"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1731233863984,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "46MfvfOhYA5E",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.039344,
     "end_time": "2024-11-11T01:08:09.168072",
     "exception": false,
     "start_time": "2024-11-11T01:08:09.128728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Vocabulary():\n",
    "    def __init__(self, callable_tokenizer, max_freq=3, unk=True, sos=False, eos=False):\n",
    "\n",
    "        self.sos = sos\n",
    "        self.eos = eos\n",
    "        self.unk = unk\n",
    "        self.callable_tokenizer = callable_tokenizer\n",
    "        self.max_freq = max_freq\n",
    "\n",
    "        self.stoi = {\"<PAD>\": 0}\n",
    "        if self.unk:\n",
    "            self.stoi['<UNK>'] = len(self.stoi)\n",
    "        if self.sos:\n",
    "            self.stoi['<SOS>'] = len(self.stoi)\n",
    "        if self.eos:\n",
    "            self.stoi['<EOS>'] = len(self.stoi)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.stoi)\n",
    "\n",
    "    def get_vocabulary(self):\n",
    "        return self.stoi\n",
    "\n",
    "    def add_token(self, token_name: str):\n",
    "        if token_name not in self.stoi:\n",
    "            self.stoi[token_name] = len(self.stoi)\n",
    "\n",
    "    def build_vocabulary(self, sentences_list):\n",
    "        if type(sentences_list[0]) != str:\n",
    "            ## ex: [['eating', 'apples'], ['eating', 'oranges']]\n",
    "            sentences_list = [' '.join(sen) for sen in sentences_list]\n",
    "\n",
    "        word_counts = Counter()\n",
    "        for sentence in sentences_list:\n",
    "            tokens = self.callable_tokenizer(sentence)\n",
    "            word_counts.update(tokens)\n",
    "\n",
    "        # Filter words with mox_freq or more occurrences\n",
    "        filtered_words = [word for word, count in word_counts.items() if count >= self.max_freq]\n",
    "        for word in filtered_words:\n",
    "            if word not in self.stoi:\n",
    "                self.stoi[word] = len(self.stoi)\n",
    "\n",
    "    def get_numerical_tokens(self, text: str):\n",
    "        tokens = self.callable_tokenizer(text)\n",
    "        tokens.insert(0, '<SOS>') if self.sos else None\n",
    "        tokens.append('<EOS>') if self.eos else None\n",
    "        unk_id = self.stoi.get('<UNK>', None)\n",
    "        return [self.stoi.get(word, unk_id) for word in tokens]\n",
    "\n",
    "    def __call__(self, text: str):\n",
    "        return self.get_numerical_tokens(text)\n",
    "\n",
    "    def tokens_to_text(self, tokens_list):\n",
    "        keys = list(self.stoi.keys())\n",
    "        values = list(self.stoi.values())\n",
    "\n",
    "        return ' '.join([keys[values.index(token)] for token in tokens_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c875f624",
   "metadata": {
    "_cell_guid": "d751258b-c525-4b89-a196-5c12b044fed3",
    "_uuid": "935e104d-c674-4f0b-b13a-d31914cd1d05",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:09.214919Z",
     "iopub.status.busy": "2024-11-11T01:08:09.213969Z",
     "iopub.status.idle": "2024-11-11T01:08:09.221143Z",
     "shell.execute_reply": "2024-11-11T01:08:09.220415Z"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1731233863984,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "77CoJfh-e0xT",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.032558,
     "end_time": "2024-11-11T01:08:09.223075",
     "exception": false,
     "start_time": "2024-11-11T01:08:09.190517",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class MT_Dataset(Dataset):\n",
    "\n",
    "    def __init__(self, en_sentences_list, ar_sentences_list, en_vocabulary:Vocabulary, ar_vocabulary:Vocabulary):\n",
    "\n",
    "        super(MT_Dataset, self).__init__()\n",
    "        self.ar_sentences_list = ar_sentences_list\n",
    "        self.en_sentences_list = en_sentences_list\n",
    "        self.ar_vocabulary = ar_vocabulary\n",
    "        self.en_vocabulary = en_vocabulary\n",
    "        # self.maxlen = maxlen\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.en_sentences_list)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        en_sentence, ar_sentence = self.en_sentences_list[index], self.ar_sentences_list[index]\n",
    "\n",
    "        en_numrical_tokens = self.en_vocabulary(en_sentence)\n",
    "        ar_numrical_tokens = self.ar_vocabulary(ar_sentence)\n",
    "\n",
    "        return torch.tensor(en_numrical_tokens), torch.tensor(ar_numrical_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "001d01e2",
   "metadata": {
    "_cell_guid": "47c0900b-d167-4818-ba8e-e1147ed613b9",
    "_uuid": "a54656ee-11dc-4c06-ab78-3899410b45a9",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:09.269083Z",
     "iopub.status.busy": "2024-11-11T01:08:09.268475Z",
     "iopub.status.idle": "2024-11-11T01:08:09.275337Z",
     "shell.execute_reply": "2024-11-11T01:08:09.274439Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1731233863985,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "u4xNdej7gDiO",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.031982,
     "end_time": "2024-11-11T01:08:09.277244",
     "exception": false,
     "start_time": "2024-11-11T01:08:09.245262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class MYCollate():\n",
    "    def __init__(self, batch_first=True, pad_value=0):\n",
    "        self.pad_value = pad_value\n",
    "        self.batch_first = batch_first\n",
    "\n",
    "    def __call__(self, data):\n",
    "        en_stentences = [ex[0] for ex in data]\n",
    "        ar_stentences = [ex[1] for ex in data]\n",
    "\n",
    "        padded_en_stentences = nn.utils.rnn.pad_sequence(en_stentences, batch_first=self.batch_first,\n",
    "                                                      padding_value=self.pad_value)\n",
    "        padded_ar_stentences = nn.utils.rnn.pad_sequence(ar_stentences, batch_first=self.batch_first,\n",
    "                                                      padding_value=self.pad_value)\n",
    "        return padded_en_stentences, padded_ar_stentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bff46f11",
   "metadata": {
    "_cell_guid": "14b402fa-17df-4017-a7da-365181a14479",
    "_uuid": "18c8ef64-8c8e-4c81-8b84-441d37feafcc",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:09.323293Z",
     "iopub.status.busy": "2024-11-11T01:08:09.322931Z",
     "iopub.status.idle": "2024-11-11T01:08:57.424135Z",
     "shell.execute_reply": "2024-11-11T01:08:57.423129Z"
    },
    "executionInfo": {
     "elapsed": 67228,
     "status": "ok",
     "timestamp": 1731233931209,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "oV00SOQbYA3N",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "2aa5864a-a73c-4f7c-816b-194564811a21",
    "papermill": {
     "duration": 48.148695,
     "end_time": "2024-11-11T01:08:57.448170",
     "exception": false,
     "start_time": "2024-11-11T01:08:09.299475",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(ar_vocab) = 12378, len(en_vocab) = 7254\n"
     ]
    }
   ],
   "source": [
    "ar_tokenizer = camel_tokenizer()\n",
    "ar_vocab = Vocabulary(ar_tokenizer, max_freq=max_freq, unk=True, sos=True, eos=True)\n",
    "ar_vocab.build_vocabulary(df_train['AR_sentence'].values)\n",
    "\n",
    "en_tokenizer = spacy_tokenizer()\n",
    "en_vocab = Vocabulary(en_tokenizer, max_freq=max_freq, unk=True, sos=True, eos=True)\n",
    "en_vocab.build_vocabulary(df_train['EN_sentence'].values)\n",
    "\n",
    "print(f'{len(ar_vocab) = }, {len(en_vocab) = }')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72ed39a2",
   "metadata": {
    "_cell_guid": "4789d166-345f-43a8-b385-17f80b65e83b",
    "_uuid": "44ef404a-9854-43c5-925c-8a34e31ccc49",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:57.494221Z",
     "iopub.status.busy": "2024-11-11T01:08:57.493852Z",
     "iopub.status.idle": "2024-11-11T01:08:57.499789Z",
     "shell.execute_reply": "2024-11-11T01:08:57.498912Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1731233931210,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "jENeERnqgomm",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.031231,
     "end_time": "2024-11-11T01:08:57.501731",
     "exception": false,
     "start_time": "2024-11-11T01:08:57.470500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_ds = MT_Dataset(df_train['EN_sentence'].values, df_train['AR_sentence'].values,\n",
    "                          en_vocabulary=en_vocab, ar_vocabulary=ar_vocab)\n",
    "\n",
    "valid_ds = MT_Dataset(df_valid['EN_sentence'].values, df_valid['AR_sentence'].values,\n",
    "                          en_vocabulary=en_vocab, ar_vocabulary=ar_vocab)\n",
    "\n",
    "test_ds = MT_Dataset(df_test['EN_sentence'].values, df_train['AR_sentence'].values,\n",
    "                          en_vocabulary=en_vocab, ar_vocabulary=ar_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "98262ce7",
   "metadata": {
    "_cell_guid": "a10ccc79-3902-471a-928f-cba34ca00282",
    "_uuid": "06399323-d0b3-4af8-9019-3430895769e8",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:57.547519Z",
     "iopub.status.busy": "2024-11-11T01:08:57.547185Z",
     "iopub.status.idle": "2024-11-11T01:08:57.600479Z",
     "shell.execute_reply": "2024-11-11T01:08:57.599545Z"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1731233931210,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "HT8HBMCKh_Sx",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "50cdbe43-dce1-4945-b870-ccc8cdeaa8ab",
    "papermill": {
     "duration": 0.078699,
     "end_time": "2024-11-11T01:08:57.602616",
     "exception": false,
     "start_time": "2024-11-11T01:08:57.523917",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('<SOS> how old is <UNK> ? <EOS>', '<SOS> كم عمر بلاد القبايل ؟ <EOS>')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_vocab.tokens_to_text(train_ds[4][0]), ar_vocab.tokens_to_text(train_ds[4][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "87f401fa",
   "metadata": {
    "_cell_guid": "ece9c447-29db-41df-bf0f-b99dfd019872",
    "_uuid": "3b59c099-2c91-40b2-a7f1-e2b8a51c34e6",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:08:57.648998Z",
     "iopub.status.busy": "2024-11-11T01:08:57.648637Z",
     "iopub.status.idle": "2024-11-11T01:08:59.723232Z",
     "shell.execute_reply": "2024-11-11T01:08:59.721945Z"
    },
    "executionInfo": {
     "elapsed": 828,
     "status": "ok",
     "timestamp": 1731233932034,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "YnL7lnOQiJeM",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "e94a9318-e02a-47a9-b1fe-3af145637ae6",
    "papermill": {
     "duration": 2.100321,
     "end_time": "2024-11-11T01:08:59.725450",
     "exception": false,
     "start_time": "2024-11-11T01:08:57.625129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 19])\n",
      "torch.Size([64, 18])\n"
     ]
    }
   ],
   "source": [
    "mycollate = MYCollate(batch_first=True, pad_value=0)\n",
    "pin_memory = True if device == 'cuda' else False\n",
    "num_workers = 4 if isKaggle else 2\n",
    "\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True,\n",
    "                          collate_fn=mycollate, num_workers=num_workers,\n",
    "                          generator=g, pin_memory=pin_memory)\n",
    "\n",
    "valid_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=False,\n",
    "                          collate_fn=mycollate, num_workers=num_workers,\n",
    "                          generator=g, pin_memory=pin_memory)\n",
    "\n",
    "test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False,\n",
    "                         collate_fn=mycollate, num_workers=num_workers,\n",
    "                         generator=g, pin_memory=pin_memory)\n",
    "\n",
    "for x, y in train_loader:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adbd7977",
   "metadata": {
    "_cell_guid": "a817db50-1bc3-42ff-81bc-1a9344854d0a",
    "_uuid": "eb280099-6497-412f-bd28-dce39f6172ef",
    "collapsed": false,
    "id": "AHHg7lo7ioRi",
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     "start_time": "2024-11-11T01:08:59.748430",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Form Embedding matrices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee89422",
   "metadata": {
    "_cell_guid": "5cc6b90b-3f42-4ec1-9929-e11cab7728dc",
    "_uuid": "d75f3d3e-6988-4a11-8ffc-f95dabab89b7",
    "collapsed": false,
    "id": "19wgB5jtiwmR",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Ar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a6284ec3",
   "metadata": {
    "_cell_guid": "57659a37-f1a4-4d5b-8dc3-fcb0dcc17062",
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    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1731233932034,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "B2KiRkeUioIm",
    "jupyter": {
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    "tags": []
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   "outputs": [],
   "source": [
    "ar_embedding_matrix = torch.rand((len(ar_vocab), embd_features))*0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c6413b",
   "metadata": {
    "_cell_guid": "dc32ef97-4bdf-4326-9c1e-041d530de0ff",
    "_uuid": "c7b73bf9-ecda-4be5-882c-7ed91a0a6bdf",
    "collapsed": false,
    "id": "HTLciTVMiyiC",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### En."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "74ee5a8c",
   "metadata": {
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   "outputs": [],
   "source": [
    "en_embedding_matrix = torch.rand((len(en_vocab), embd_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "da92aeb6",
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    "_cell_guid": "7a924a18-13e3-4032-9643-de520b116fd9",
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    "executionInfo": {
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      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
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    },
    "id": "uNLla5SVin7S",
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   "source": [
    "# !wget https://nlp.stanford.edu/data/glove.6B.zip\n",
    "\n",
    "# with ZipFile(f'{base_dir}/glove.6B.zip') as file:\n",
    "#     file.extract('glove.6B.100d.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "86bcc5a8",
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    "_cell_guid": "35254387-29e7-4d9a-8df1-b487dbb8a5cb",
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    },
    "executionInfo": {
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      "displayName": "Abdelrhman Ashraf",
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     },
     "user_tz": -120
    },
    "id": "er-PzgMCin4-",
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     "exception": false,
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "# # form an embedding matrix form glove pre-trained embedding weights for my vocab\n",
    "# glove_embeddings_index = dict()\n",
    "# en_embedding_matrix = torch.rand((len(en_vocab), embd_features))*0.1\n",
    "# vocab_dict = en_vocab.get_vocabulary()\n",
    "# found = 0\n",
    "\n",
    "# with open('glove.6B.100d.txt') as f:\n",
    "#     for line in f:\n",
    "#         values = line.split()\n",
    "#         word = values[0]\n",
    "\n",
    "#         # token = word_tokenizer.word_tokenizing(word, stemming=False)\n",
    "#         token = word\n",
    "#         if token:\n",
    "#             # print(word)\n",
    "#             idx = vocab_dict.get(token, 0)\n",
    "#             if idx:\n",
    "#                 found += 1\n",
    "#                 coefs = torch.tensor([float(value) for value in values[1:]])\n",
    "#                 en_embedding_matrix[idx] = coefs\n",
    "#                 # glove_embeddings_index[word] = coefs\n",
    "\n",
    "# print('Found %s words.' % found)\n",
    "# print(\"Embedding matrix shape: \", en_embedding_matrix.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d75d4bb4",
   "metadata": {
    "_cell_guid": "5e0d0e86-e19b-4437-9688-ea151c2e7a91",
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    "execution": {
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    },
    "executionInfo": {
     "elapsed": 261,
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     "timestamp": 1731234100289,
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      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "5A4dFwe6inte",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "edeceb73-d326-4ce3-dfef-80d0764bb5e4",
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     "duration": 1.558894,
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Embedding layer\t\ttorch.Size([64, 19])\t\ttorch.Size([64, 18])\n",
      "After Embedding layer\t\ttorch.Size([64, 19, 128])\ttorch.Size([64, 18, 128])\n"
     ]
    }
   ],
   "source": [
    "for x, y in test_loader:\n",
    "    print('Before Embedding layer', x.shape, y.shape, sep='\\t\\t')\n",
    "    en_emb = en_embedding_matrix[x]\n",
    "    ar_emb = ar_embedding_matrix[y]\n",
    "    print('After Embedding layer\\t', en_emb.shape, ar_emb.shape, sep='\\t')\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b13b3f7",
   "metadata": {
    "_cell_guid": "8934e170-4176-41b6-922d-c7b143f173fd",
    "_uuid": "c5dfbf25-e82c-4d5a-9e2d-fb1a3c08fa5c",
    "collapsed": false,
    "id": "jdDsKs2rne2H",
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     "duration": 0.022639,
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     "start_time": "2024-11-11T01:09:01.707449",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Models: Defining, Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2ee4bd6",
   "metadata": {
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    },
    "executionInfo": {
     "elapsed": 282,
     "status": "ok",
     "timestamp": 1731243010208,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
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    "id": "7Ongp0CUnzNG",
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   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, embd_matrix:torch.Tensor, pretrained:bool, lstm_hidden_size, lstm_layers=1, dropout_probability=0.1):\n",
    "        super().__init__()\n",
    "        self.hidden_size = lstm_hidden_size\n",
    "        self.lstm_layers = lstm_layers\n",
    "        self.input_size = embd_matrix.size(-1)\n",
    "        self.embd_layer = nn.Embedding.from_pretrained(embd_matrix, freeze=False) if pretrained else nn.Embedding(embd_matrix.size(0), embd_matrix.size(1))\n",
    "\n",
    "        self.dropout = nn.Dropout(dropout_probability)\n",
    "        self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.lstm_layers, dropout=dropout_probability, batch_first=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        embds = self.dropout(self.embd_layer(x))\n",
    "        output, (hidden, cell) = self.lstm(embds)\n",
    "        return hidden, cell\n",
    "\n",
    "\n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self, embd_matrix:torch.Tensor, pretrained:bool, lstm_hidden_size, lstm_layers=1, dropout_probability=0.1):\n",
    "        super().__init__()\n",
    "        self.hidden_size = lstm_hidden_size\n",
    "        self.lstm_layers = lstm_layers\n",
    "        self.input_size = embd_matrix.size(-1)\n",
    "        self.embd_layer = nn.Embedding.from_pretrained(embd_matrix, freeze=False) if pretrained else nn.Embedding(embd_matrix.size(0), embd_matrix.size(1))\n",
    "\n",
    "        self.dropout = nn.Dropout(dropout_probability)\n",
    "        self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.lstm_layers, dropout=dropout_probability, batch_first=True)\n",
    "        self.fc = nn.Linear(self.hidden_size, embd_matrix.size(0))\n",
    "    \n",
    "    def forward(self, x, hidden_t_1, cell_t_1):\n",
    "        embds = self.dropout(self.embd_layer(x))\n",
    "        output, (hidden_t, cell_t) = self.lstm(embds, (hidden_t_1, cell_t_1))\n",
    "        needed_hidden = hidden_t[-1]\n",
    "        logits = self.fc(needed_hidden)\n",
    "        return logits, hidden_t, cell_t\n",
    "\n",
    "\n",
    "class Seq2seq_no_attention(nn.Module):\n",
    "    def __init__(self, encoder:Encoder, decoder:Decoder):\n",
    "        super(Seq2seq_no_attention, self).__init__()\n",
    "\n",
    "        self.decoder_vocab_size = decoder.embd_layer.weight.size(0)\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "\n",
    "    def forward(self, source, target, teacher_force_ratio=0.5):\n",
    "\n",
    "        batch_size, seq_len = target.size()\n",
    "        x = target[:, [0]]\n",
    "        total_outputs = torch.zeros(batch_size, seq_len, self.decoder_vocab_size, device=source.device)\n",
    "\n",
    "        context = self.encoder(source)\n",
    "\n",
    "        for step in range(1, seq_len):\n",
    "            logits, *context = self.decoder(x, *context)\n",
    "            \n",
    "            total_outputs[:, step] = logits\n",
    "            top1 = logits.argmax(1, keepdim=True)\n",
    "            x = target[:, [step]] if teacher_force_ratio > random.random() else top1\n",
    "\n",
    "        return total_outputs\n",
    "\n",
    "    def translate(self, source:torch.Tensor, max_tries=50):\n",
    "        \n",
    "        source.unsqueeze(0)\n",
    "        targets_hat = [2]\n",
    "\n",
    "        context = self.encoder(source)\n",
    "\n",
    "        for step in range(1, max_tries):\n",
    "            x = torch.tensor([targets_hat[-1]]).to(source.device)\n",
    "            logits, *context = self.decoder(x, *context)\n",
    "            top1 = logits.argmax(0)\n",
    "            targets_hat.append(top1.item())\n",
    "            if top1 == 3:\n",
    "                return targets_hat\n",
    "\n",
    "        return targets_hat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b03c237c",
   "metadata": {
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   "source": [
    "## Helper\n",
    "@torch.no_grad()\n",
    "def loss_acc_loader(model, data_loader, device, force=0.5):\n",
    "    model.eval()\n",
    "\n",
    "    total_predicts = []\n",
    "    total_targets = []\n",
    "    total_class_loss = 0\n",
    "\n",
    "    for data, labels in data_loader:\n",
    "        data = data.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        class_logits = model(data, labels, force)\n",
    "        class_logits_flat = class_logits[:, 1:, :].reshape(-1, class_logits.size(-1))\n",
    "        labels_flat = labels[:, 1:].reshape(-1)\n",
    "\n",
    "        total_class_loss += class_criterion(class_logits_flat, labels_flat).item()\n",
    "\n",
    "        predictions = class_logits_flat.argmax(-1)\n",
    "        non_pad_mask = labels_flat != 0\n",
    "        filtered_predictions = predictions[non_pad_mask]\n",
    "        filtered_labels = labels_flat[non_pad_mask]\n",
    "\n",
    "        total_predicts.append(filtered_predictions)\n",
    "        total_targets.append(filtered_labels)\n",
    "\n",
    "    total_predicts = torch.concat(total_predicts)\n",
    "    total_targets = torch.concat(total_targets)\n",
    "\n",
    "    avg_class_loss = total_class_loss / len(data_loader)\n",
    "    accuracy = (total_predicts == total_targets).float().mean().item()\n",
    "\n",
    "    return avg_class_loss, accuracy, total_predicts, total_targets\n",
    "\n",
    "\n",
    "def get_parameters_info(model):\n",
    "    trainable = sum(p.numel() for p in model.parameters() if p.requires_grad==True)\n",
    "    nontrainable = sum(p.numel() for p in model.parameters() if p.requires_grad==False)\n",
    "\n",
    "    return trainable, nontrainable"
   ]
  },
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   "cell_type": "code",
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   "id": "5e711cca",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total trainable parameters = 5166426\n",
      "Total non-trainable parameters = 0\n"
     ]
    }
   ],
   "source": [
    "encoder = Encoder(en_embedding_matrix, False, lstm_hidden_size, lstm_layers, dropout_p).to(device)\n",
    "decoder = Decoder(ar_embedding_matrix, False, lstm_hidden_size, lstm_layers, dropout_p).to(device)\n",
    "seq2seq = Seq2seq_no_attention(encoder, decoder).to(device)\n",
    "\n",
    "tr, nontr = get_parameters_info(seq2seq)\n",
    "print(f\"Total trainable parameters = {tr}\\nTotal non-trainable parameters = {nontr}\")\n",
    "\n",
    "class_criterion = nn.CrossEntropyLoss(ignore_index=0)  # Classification loss\n",
    "\n",
    "optim = torch.optim.Adam(seq2seq.parameters(), lr)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optim, mode='min', factor=0.5,\n",
    "                                                       patience=4, min_lr=1e-5, threshold=0.001)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da896026",
   "metadata": {
    "_cell_guid": "9b1b9af2-f4b6-475a-9344-52ac5c090f8c",
    "_uuid": "a26a7d97-7db7-4da3-a904-13ca126d615a",
    "collapsed": false,
    "id": "W2Fux31-Lqbc",
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.022404,
     "end_time": "2024-11-11T01:09:03.225919",
     "exception": false,
     "start_time": "2024-11-11T01:09:03.203515",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dbb089ff",
   "metadata": {
    "_cell_guid": "bb8befcd-46a7-4d65-b6f1-131765ca660e",
    "_uuid": "61512162-89c4-405b-ae55-98b944027795",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:09:03.272905Z",
     "iopub.status.busy": "2024-11-11T01:09:03.272192Z",
     "iopub.status.idle": "2024-11-11T01:09:03.277532Z",
     "shell.execute_reply": "2024-11-11T01:09:03.276524Z"
    },
    "id": "hkLo26GQLs-h",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "b260f65b-7cd0-40ce-962c-792fe01b847f",
    "papermill": {
     "duration": 0.031216,
     "end_time": "2024-11-11T01:09:03.279502",
     "exception": false,
     "start_time": "2024-11-11T01:09:03.248286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ## overfit one-batch\n",
    "\n",
    "# x_batch, y_batch = next(iter(train_loader))\n",
    "# data = x_batch.to(device)\n",
    "# labels = y_batch.to(device)\n",
    "# epochs = 2000\n",
    "# for epoch in range(epochs):\n",
    "\n",
    "#     class_logits = seq2seq(data, labels)\n",
    "\n",
    "#     class_logits_flat = class_logits[:, 1:, :].reshape(-1, class_logits.size(-1))\n",
    "#     labels_flat = labels[:, 1:].reshape(-1)\n",
    "#     loss = class_criterion(class_logits_flat, labels_flat)\n",
    "\n",
    "\n",
    "#     optim.zero_grad()\n",
    "#     loss.backward()\n",
    "#     optim.step()\n",
    "\n",
    "#     predictions = class_logits_flat.argmax(-1)\n",
    "\n",
    "#     non_pad_mask = labels_flat != 0\n",
    "#     filtered_predictions = predictions[non_pad_mask]\n",
    "#     filtered_labels = labels_flat[non_pad_mask]\n",
    "\n",
    "#     if (epoch+1) % 100 == 0:\n",
    "#         print(f'Epoch {epoch+1}/{epochs}: ', end='\\t')\n",
    "#         print(f'Loss = {loss.item():.2f}', f'Accuracy = {(filtered_predictions==filtered_labels).sum()}/{filtered_predictions.size(0)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e2d4f657",
   "metadata": {
    "_cell_guid": "2259b2ba-27bd-456f-8306-452d326903a9",
    "_uuid": "cc50041b-b460-4312-9afd-57997d737e8b",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-11-11T01:09:03.326421Z",
     "iopub.status.busy": "2024-11-11T01:09:03.326051Z",
     "iopub.status.idle": "2024-11-11T01:46:49.101159Z",
     "shell.execute_reply": "2024-11-11T01:46:49.099821Z"
    },
    "executionInfo": {
     "elapsed": 48635,
     "status": "error",
     "timestamp": 1731243891889,
     "user": {
      "displayName": "Abdelrhman Ashraf",
      "userId": "11249532378747886614"
     },
     "user_tz": -120
    },
    "id": "O5-7kPlBo0fQ",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "9a36329a-8f7f-4817-d054-29ab0a23d74a",
    "papermill": {
     "duration": 2265.801013,
     "end_time": "2024-11-11T01:46:49.103299",
     "exception": false,
     "start_time": "2024-11-11T01:09:03.302286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [1/50]: 100%|██████████| 471/471 [00:40<00:00, 11.77it/s, loss = 6.4592]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 5.7168, Accuracy 23.62%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [2/50]: 100%|██████████| 471/471 [00:39<00:00, 11.83it/s, loss = 6.0609]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 5.6238, Accuracy 23.53%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [3/50]: 100%|██████████| 471/471 [00:39<00:00, 11.83it/s, loss = 5.8814]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 5.3845, Accuracy 26.77%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [4/50]: 100%|██████████| 471/471 [00:39<00:00, 11.90it/s, loss = 5.6264]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 5.2063, Accuracy 29.15%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [5/50]: 100%|██████████| 471/471 [00:40<00:00, 11.76it/s, loss = 5.4464]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 5.1234, Accuracy 29.82%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [6/50]: 100%|██████████| 471/471 [00:39<00:00, 11.88it/s, loss = 5.3096]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 5.0456, Accuracy 30.42%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [7/50]: 100%|██████████| 471/471 [00:40<00:00, 11.75it/s, loss = 5.2016]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 5.0097, Accuracy 30.64%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [8/50]: 100%|██████████| 471/471 [00:40<00:00, 11.67it/s, loss = 5.1055]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.9509, Accuracy 31.39%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [9/50]: 100%|██████████| 471/471 [00:40<00:00, 11.68it/s, loss = 5.0176]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.9131, Accuracy 31.51%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [10/50]: 100%|██████████| 471/471 [00:40<00:00, 11.63it/s, loss = 4.9369]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.8733, Accuracy 32.09%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [11/50]: 100%|██████████| 471/471 [00:39<00:00, 11.85it/s, loss = 4.8611]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.8501, Accuracy 32.30%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [12/50]: 100%|██████████| 471/471 [00:40<00:00, 11.60it/s, loss = 4.7879]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.8298, Accuracy 32.63%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [13/50]: 100%|██████████| 471/471 [00:39<00:00, 11.82it/s, loss = 4.7194]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.8121, Accuracy 32.77%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [14/50]: 100%|██████████| 471/471 [00:40<00:00, 11.67it/s, loss = 4.6513]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7804, Accuracy 33.13%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [15/50]: 100%|██████████| 471/471 [00:39<00:00, 11.78it/s, loss = 4.5977]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7882, Accuracy 33.15%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [16/50]: 100%|██████████| 471/471 [00:40<00:00, 11.74it/s, loss = 4.5431]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7742, Accuracy 33.23%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [17/50]: 100%|██████████| 471/471 [00:40<00:00, 11.62it/s, loss = 4.4842]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7753, Accuracy 33.38%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [18/50]: 100%|██████████| 471/471 [00:39<00:00, 11.80it/s, loss = 4.4343]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7556, Accuracy 33.61%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [19/50]: 100%|██████████| 471/471 [00:40<00:00, 11.66it/s, loss = 4.3848]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7498, Accuracy 33.89%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [20/50]: 100%|██████████| 471/471 [00:39<00:00, 11.78it/s, loss = 4.3307]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7472, Accuracy 33.93%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [21/50]: 100%|██████████| 471/471 [00:39<00:00, 11.87it/s, loss = 4.2856]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7475, Accuracy 34.14%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [22/50]: 100%|██████████| 471/471 [00:40<00:00, 11.51it/s, loss = 4.2378]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7519, Accuracy 34.11%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [23/50]: 100%|██████████| 471/471 [00:40<00:00, 11.75it/s, loss = 4.1954]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7465, Accuracy 34.43%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [24/50]: 100%|██████████| 471/471 [00:39<00:00, 11.86it/s, loss = 4.1571]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7485, Accuracy 34.14%\n",
      "Epoch 24: Reducing learning rate from 0.001000 to 0.000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [25/50]: 100%|██████████| 471/471 [00:40<00:00, 11.66it/s, loss = 4.0588]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7330, Accuracy 34.68%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [26/50]: 100%|██████████| 471/471 [00:40<00:00, 11.67it/s, loss = 4.0317]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7418, Accuracy 34.63%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [27/50]: 100%|██████████| 471/471 [00:40<00:00, 11.59it/s, loss = 4.0065]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7302, Accuracy 34.81%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [28/50]: 100%|██████████| 471/471 [00:40<00:00, 11.55it/s, loss = 3.9884]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7486, Accuracy 34.57%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [29/50]: 100%|██████████| 471/471 [00:40<00:00, 11.68it/s, loss = 3.9701]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7332, Accuracy 34.94%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [30/50]: 100%|██████████| 471/471 [00:40<00:00, 11.66it/s, loss = 3.9443]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7443, Accuracy 34.81%\n",
      "Epoch 30: Reducing learning rate from 0.000500 to 0.000250\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [31/50]: 100%|██████████| 471/471 [00:41<00:00, 11.45it/s, loss = 3.8933]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7394, Accuracy 34.99%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [32/50]: 100%|██████████| 471/471 [00:40<00:00, 11.70it/s, loss = 3.8777]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7291, Accuracy 35.20%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [33/50]: 100%|██████████| 471/471 [00:40<00:00, 11.64it/s, loss = 3.8656]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7376, Accuracy 34.94%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [34/50]: 100%|██████████| 471/471 [00:41<00:00, 11.37it/s, loss = 3.8499]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7490, Accuracy 34.96%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [35/50]: 100%|██████████| 471/471 [00:40<00:00, 11.72it/s, loss = 3.8447]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7392, Accuracy 35.16%\n",
      "Epoch 35: Reducing learning rate from 0.000250 to 0.000125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [36/50]: 100%|██████████| 471/471 [00:40<00:00, 11.65it/s, loss = 3.8158]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7406, Accuracy 35.12%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [37/50]: 100%|██████████| 471/471 [00:41<00:00, 11.47it/s, loss = 3.812]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7404, Accuracy 34.95%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [38/50]: 100%|██████████| 471/471 [00:40<00:00, 11.65it/s, loss = 3.8081]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7574, Accuracy 34.94%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [39/50]: 100%|██████████| 471/471 [00:41<00:00, 11.49it/s, loss = 3.7999]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7548, Accuracy 34.99%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [40/50]: 100%|██████████| 471/471 [00:40<00:00, 11.67it/s, loss = 3.7986]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7505, Accuracy 35.15%\n",
      "Epoch 40: Reducing learning rate from 0.000125 to 0.000063\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [41/50]: 100%|██████████| 471/471 [00:40<00:00, 11.56it/s, loss = 3.7804]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7394, Accuracy 35.19%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [42/50]: 100%|██████████| 471/471 [00:40<00:00, 11.76it/s, loss = 3.7724]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7561, Accuracy 35.01%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [43/50]: 100%|██████████| 471/471 [00:40<00:00, 11.68it/s, loss = 3.774]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7509, Accuracy 35.22%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [44/50]: 100%|██████████| 471/471 [00:40<00:00, 11.53it/s, loss = 3.7687]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7498, Accuracy 35.14%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [45/50]: 100%|██████████| 471/471 [00:40<00:00, 11.68it/s, loss = 3.7644]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7486, Accuracy 35.11%\n",
      "Epoch 45: Reducing learning rate from 0.000063 to 0.000031\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [46/50]: 100%|██████████| 471/471 [00:41<00:00, 11.48it/s, loss = 3.7604]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7420, Accuracy 35.20%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [47/50]: 100%|██████████| 471/471 [00:40<00:00, 11.63it/s, loss = 3.758]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7525, Accuracy 35.03%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [48/50]: 100%|██████████| 471/471 [00:42<00:00, 11.13it/s, loss = 3.7641]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7503, Accuracy 35.09%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [49/50]: 100%|██████████| 471/471 [00:40<00:00, 11.54it/s, loss = 3.7553]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7557, Accuracy 35.02%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch [50/50]: 100%|██████████| 471/471 [00:40<00:00, 11.61it/s, loss = 3.757]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation: Class Loss 4.7532, Accuracy 35.05%\n",
      "Epoch 50: Reducing learning rate from 0.000031 to 0.000016\n"
     ]
    }
   ],
   "source": [
    "total_batches = len(train_loader)\n",
    "train_class_losses = []\n",
    "val_class_losses = []\n",
    "teacher_force = 0.5\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    seq2seq.train()  # Set the model to training mode\n",
    "    epoch_losses = []\n",
    "\n",
    "    tqdm_loop = tqdm(enumerate(train_loader), total=total_batches, position=0)\n",
    "    for batch_idx, (data, labels) in tqdm_loop:\n",
    "        # Get data\n",
    "        data = data.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # Forward\n",
    "        class_logits = seq2seq(data, labels, teacher_force)\n",
    "\n",
    "        class_logits_flat = class_logits[:, 1:, :].reshape(-1, class_logits.size(-1))\n",
    "        labels_flat = labels[:, 1:].reshape(-1)\n",
    "        loss = class_criterion(class_logits_flat, labels_flat)\n",
    "\n",
    "        epoch_losses.append(loss.item())\n",
    "        mean_epoch_loss = sum(epoch_losses)/len(epoch_losses)\n",
    "        \n",
    "        # Backward\n",
    "        optim.zero_grad()\n",
    "        loss.backward()\n",
    "        nn.utils.clip_grad_norm_(seq2seq.parameters(), max_norm=1)\n",
    "        optim.step()\n",
    "        # Update progress bar\n",
    "        tqdm_loop.set_description(f\"Epoch [{epoch+1}/{epochs}]\")\n",
    "        tqdm_loop.set_postfix_str(f'loss = {round(mean_epoch_loss, 4)}')\n",
    "\n",
    "    train_class_losses.append(mean_epoch_loss)\n",
    "\n",
    "    val_class_loss, val_accuracy, _, _ = loss_acc_loader(seq2seq, valid_loader, device, force=teacher_force)\n",
    "    val_class_losses.append(val_class_loss)\n",
    "    print(f'Validation: Class Loss {val_class_loss:.4f}, Accuracy {val_accuracy*100:.2f}%')\n",
    "\n",
    "    # Get the current learning rate from the optimizer\n",
    "    current_lr = optim.param_groups[0]['lr']\n",
    "    \n",
    "    # Step the scheduler with the validation loss\n",
    "    scheduler.step(val_class_loss)\n",
    "    \n",
    "    # Check if the learning rate has changed\n",
    "    new_lr = optim.param_groups[0]['lr']\n",
    "    if new_lr < current_lr:\n",
    "        print(f\"Epoch {epoch + 1}: Reducing learning rate from {current_lr:.6f} to {new_lr:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "19bc881b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-11T01:46:58.846786Z",
     "iopub.status.busy": "2024-11-11T01:46:58.846315Z",
     "iopub.status.idle": "2024-11-11T01:46:58.851486Z",
     "shell.execute_reply": "2024-11-11T01:46:58.850615Z"
    },
    "papermill": {
     "duration": 4.880185,
     "end_time": "2024-11-11T01:46:58.853401",
     "exception": false,
     "start_time": "2024-11-11T01:46:53.973216",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#     if valid_loss < best_loss:\n",
    "#         best_loss, best_epoch = valid_loss, epoch\n",
    "#         torch.save(model.state_dict(), 'model.pt')\n",
    "#         early_stop_counter = 0\n",
    "#     else:\n",
    "#         early_stop_counter += 1\n",
    "#         if early_stop_counter >= PATIENCE:\n",
    "#             print(f'Early stopping after epoch {epoch+1}: no improvement for {PATIENCE} epochs.')\n",
    "#             break\n",
    "\n",
    "# if early_stop_counter < PATIENCE:\n",
    "#     print(f'Best validation loss of {best_loss:.3f} at epoch {best_epoch+1}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ff243f30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-11T01:47:08.655183Z",
     "iopub.status.busy": "2024-11-11T01:47:08.654796Z",
     "iopub.status.idle": "2024-11-11T01:47:08.880763Z",
     "shell.execute_reply": "2024-11-11T01:47:08.879807Z"
    },
    "papermill": {
     "duration": 5.170037,
     "end_time": "2024-11-11T01:47:08.882916",
     "exception": false,
     "start_time": "2024-11-11T01:47:03.712879",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 3))\n",
    "plt.plot(\n",
    "    [ep + 1 for ep in range(len(train_class_losses))],\n",
    "    train_class_losses, val_class_losses,)\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c74f36",
   "metadata": {
    "papermill": {
     "duration": 4.90533,
     "end_time": "2024-11-11T01:47:18.576864",
     "exception": false,
     "start_time": "2024-11-11T01:47:13.671534",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6117fd27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-11T01:47:28.307799Z",
     "iopub.status.busy": "2024-11-11T01:47:28.307151Z",
     "iopub.status.idle": "2024-11-11T01:47:33.044286Z",
     "shell.execute_reply": "2024-11-11T01:47:33.042989Z"
    },
    "papermill": {
     "duration": 9.615093,
     "end_time": "2024-11-11T01:47:33.046353",
     "exception": false,
     "start_time": "2024-11-11T01:47:23.431260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test: Class Loss 8.5770, Accuracy 12.84%\n"
     ]
    }
   ],
   "source": [
    "test_class_loss, test_accuracy, _, _ = loss_acc_loader(seq2seq, test_loader, device, force=0.5)\n",
    "\n",
    "print(f\"Test: Class Loss {test_class_loss:.4f}, Accuracy {test_accuracy*100:.2f}%\")"
   ]
  },
  {
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     "text": [
      "Test: Class Loss 8.7582, Accuracy 13.08%\n"
     ]
    }
   ],
   "source": [
    "## With zero teacher_force\n",
    "test_class_loss, test_accuracy, _, _ = loss_acc_loader(seq2seq, test_loader, device, force=0.0)\n",
    "\n",
    "print(f\"Test: Class Loss {test_class_loss:.4f}, Accuracy {test_accuracy*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ae8364b8",
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<SOS> هو <UNK> <UNK> <EOS>'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "custom_test = \"He's writing a letter to his brother.\"\n",
    "preprocessed = preprocess_en(custom_test)\n",
    "en_tokens = torch.tensor(en_vocab(preprocessed)).to(device)\n",
    "targets_hat = seq2seq.translate(en_tokens, 30)\n",
    "ar_vocab.tokens_to_text(targets_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b041b82",
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    "tags": []
   },
   "source": [
    "### Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "979c9dcf",
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   "source": [
    "model_states = {\n",
    "    \"en_vocabulary\": en_vocab.get_vocabulary(),\n",
    "    \"ar_vocabulary\": ar_vocab.get_vocabulary()\n",
    "}\n",
    "\n",
    "torch.save(model_states, f\"{base_dir}/seq2seq_no_attention_states.pt\")\n",
    "\n",
    "## Save Entire Model\n",
    "torch.save(seq2seq, f\"{base_dir}/seq2seq_no_attention.bin\")"
   ]
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