Neural-Machine-Translation-by-Jointly-Learning-to-Align-and-Translate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Projet Machine Learning Avancé : Master 2 ISI**\n",
    "---\n",
    "**Membres du Groupe** :\n",
    "- *Tarek BENAISSA*                     \n",
    "- *M'hamed BENABID*                     \n",
    "- *Zahra Hafida BENSLIMANE*            \n",
    "- *Mohammed El Amine BENBRAHIM*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Installation and import of the necessary modules and libraries.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>NOTE : </b> This next cell should can be ignored if you already have <b>tensorflow-text</b> module installed in your environement.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uTgDiD57HTe6",
    "outputId": "64bda0bd-c52e-4b6e-9ae7-7c405f8628b7",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow<2.12,>=2.11.0->tensorflow-text) (1.26.13)\n",
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      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow<2.12,>=2.11.0->tensorflow-text) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow<2.12,>=2.11.0->tensorflow-text) (3.2.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install tensorflow-text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "id": "R2Lr5TLIHeKq"
   },
   "outputs": [],
   "source": [
    "# Import des librairies\n",
    "import numpy as np\n",
    "import typing\n",
    "import pandas as pd\n",
    "from typing import Any, Tuple\n",
    "import tensorflow as tf\n",
    "import tensorflow_text as tf_text\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "from utils import load_data, tf_lower_and_split_punct\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "--QlcPNMHnQR"
   },
   "outputs": [],
   "source": [
    "# Cette variable est utilisée pour basculer entre les implémentations personnalisées ( qu'on crée à partir de zéro) et intégrées.\n",
    "use_builtins = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "#os.listdir()\n",
    "from zipfile import ZipFile \n",
    "with ZipFile ('mini_base_donnees_3.txt.zip', 'r') as zipObj : \n",
    "    #Extraire tt les fichiers dans le directory : \n",
    "    zipObj.extractall()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.listdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Downloading our training dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "xXht4MOKHsLi"
   },
   "outputs": [],
   "source": [
    "# Téchargement de la base d'apprentissage\n",
    "import pathlib\n",
    "\n",
    "path_to_zip = tf.keras.utils.get_file(\n",
    "    'fra-eng.zip', origin='http://storage.googleapis.com/download.tensorflow.org/data/fra-eng.zip',\n",
    "    extract=True)\n",
    "\n",
    "path_to_file = pathlib.Path(path_to_zip).parent/'fra.txt'\n",
    "#path_to_file = pathlib.Path('mini_base_donnees_3.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UMlCRiBCHvW5",
    "outputId": "89c66d3b-eabd-4f61-9028-79ba5a65ef4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de phrases contenus dans les données :  167130\n",
      "Nombre de mots de la plus petite phrase :  4\n",
      "Nombre de mots de la plus longue phrase :  349\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>Source</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Va !</td>\n",
       "      <td>Go.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Salut !</td>\n",
       "      <td>Hi.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cours !</td>\n",
       "      <td>Run!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Courez !</td>\n",
       "      <td>Run!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Qui ?</td>\n",
       "      <td>Who?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Source Target\n",
       "0      Va !    Go.\n",
       "1   Salut !    Hi.\n",
       "2   Cours !   Run!\n",
       "3  Courez !   Run!\n",
       "4     Qui ?   Who?"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targ, inp = load_data(path_to_file)\n",
    "\n",
    "# Taille des données d'entrée\n",
    "print(\"Nombre de phrases contenus dans les données : \", len(inp))\n",
    "# pour montrer qu'on a des longueurs différentes\n",
    "taille_input = [len(p) for p in inp]\n",
    "print(\"Nombre de mots de la plus petite phrase : \", min(taille_input))\n",
    "print(\"Nombre de mots de la plus longue phrase : \", max(taille_input))\n",
    "\n",
    "Lang_pairs = {'Source':inp,'Target':targ}\n",
    "df = pd.DataFrame(Lang_pairs)\n",
    "pd.set_option('max_colwidth', 800)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Dataset Preparation :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "167130\n",
      "Si on prend toutes les données on aura 2611 exemples pour chaque epoque\n"
     ]
    }
   ],
   "source": [
    "BUFFER_SIZE = len(inp)\n",
    "print(BUFFER_SIZE)\n",
    "BATCH_SIZE = 64\n",
    "print(\"Si on prend toutes les données on aura\",round(BUFFER_SIZE/BATCH_SIZE), \"exemples pour chaque epoque\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "9eNLVLKLHyq4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-09 14:17:31.058223: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-12-09 14:17:31.058318: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:163] no NVIDIA GPU device is present: /dev/nvidia0 does not exist\n",
      "2022-12-09 14:17:31.059189: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# on mélange les données pour ne pas avoir que des phrases de même longueur\n",
    "# pour un entrainement plus léger on peut limiter les données en entrée à (inp[:50000], targ[:50000]) \n",
    "dataset = tf.data.Dataset.from_tensor_slices((inp[:128], targ[:128])).shuffle(BUFFER_SIZE)\n",
    "dataset = dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source Vocabulary size :5000 words\n",
      "Target Vocabulary size :5000 words \n",
      "-----------------------------------\n",
      "Source Vocab samples : ['', '[UNK]', '[START]', '[END]', '.', 'je', 'de', 'a', '?', 'pas']\n",
      "Target Vocab samples : ['', '[UNK]', '[START]', '[END]', '.', 'i', 'you', 'to', 'the', '?']\n"
     ]
    }
   ],
   "source": [
    "max_vocab_size = 5000\n",
    "\n",
    "# We use TextVectorization to map text features to integer sequences\n",
    "input_text_processor = tf.keras.layers.TextVectorization(standardize=tf_lower_and_split_punct, max_tokens=max_vocab_size)\n",
    "output_text_processor= tf.keras.layers.TextVectorization(standardize=tf_lower_and_split_punct,max_tokens=max_vocab_size)\n",
    "\n",
    "input_text_processor.adapt(inp)\n",
    "output_text_processor.adapt(targ)\n",
    "\n",
    "out = output_text_processor.get_vocabulary()[:10]\n",
    "inpt = input_text_processor.get_vocabulary()[:10]\n",
    "\n",
    "print(f\"Source Vocabulary size :{input_text_processor.vocabulary_size()} words\")\n",
    "print(f\"Target Vocabulary size :{output_text_processor.vocabulary_size()} words \")\n",
    "print(\"-\"*35)\n",
    "\n",
    "print(f\"Source Vocab samples : {inpt}\\nTarget Vocab samples : {out}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Model Architechture Definition :**\n",
    "\n",
    "#### **Encoder** \n",
    "\n",
    "1.\n",
    "2.\n",
    "3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "k91qJdrYIB1l"
   },
   "outputs": [],
   "source": [
    "class Encoder(tf.keras.layers.Layer):\n",
    "    def __init__(self, input_vocab_size, embedding_dim, enc_units):\n",
    "        \n",
    "        \"\"\"\n",
    "            inputs\n",
    "            ------\n",
    "                input_vocab_size : Tokenized input sequence\n",
    "                embedding_dim    : \n",
    "                enc_units        :\n",
    "        \n",
    "        \"\"\"\n",
    "        super(Encoder, self).__init__()\n",
    "        self.enc_units = enc_units\n",
    "        self.input_vocab_size = input_vocab_size\n",
    "\n",
    "        # La couche d'embedding convertis les tokens vers des vecteurs \n",
    "        self.embedding = tf.keras.layers.Embedding(self.input_vocab_size,embedding_dim)\n",
    "\n",
    "        # Definition d'une couche GRU Bi-directionelle.\n",
    "        self.bigru=tf.keras.layers.Bidirectional(tf.keras.layers.GRU(self.enc_units,\n",
    "                                                                     return_sequences=True,\n",
    "                                                                     return_state=True, \n",
    "                                                                     recurrent_initializer='glorot_uniform'))\n",
    "\n",
    "\n",
    "    def call(self, tokens, state=None):\n",
    "        \n",
    "        \"\"\"\n",
    "            inputs\n",
    "            ------\n",
    "                tokens : Tokenized input sequence\n",
    "                state  : \n",
    "\n",
    "            outputs\n",
    "            -------\n",
    "                output    : \n",
    "                finale_state_backward :          |Used To initialize the first hidden state of the decoder??\n",
    "                \n",
    "        # les retour de tf.keras.layers.Bidirectional\n",
    "        # concatenated states of the forward and backward RNN (shape: batch × length × 2 GRU dimentsion)\n",
    "        # final state of the forward RNN (shape: batch × batch dimension)\n",
    "        # final state of the backward RNN (shape: batch × batch dimension)\n",
    "        \n",
    "        \"\"\"\n",
    "        \n",
    "        # Embedding des tokens d'entrées\n",
    "        vectors = self.embedding(tokens)\n",
    "        \n",
    "        # Le GRU prends en entrée l'embedding de la sequence d'entrée.  ?????\n",
    "        # output shape: (batch, s, enc_units)\n",
    "        # state shape: (batch, enc_units)\n",
    "        output, finale_state_forward, finale_state_backward = self.bigru(vectors, initial_state=state)\n",
    "        \n",
    "        #print(\"output.size() : \", output[:,-1,:])\n",
    "        #print(\"finale_state_forward : \", finale_state_forward)\n",
    "        #print(\"finale_state_backward : \", finale_state_backward)\n",
    "        \n",
    "\n",
    "        # Returns the new sequence and its state. !!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "        return output, finale_state_backward\n",
    "    \n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2CWuL_sTIH7f",
    "outputId": "c1112240-8c40-4da7-fd19-a8c7997d0664"
   },
   "source": [
    "#### **Attention Mechanism**\n",
    "- bla\n",
    "- bla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "sgRPoDf0IIAr"
   },
   "outputs": [],
   "source": [
    "class BahdanauAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self, units):\n",
    "        super().__init__()\n",
    "        \n",
    "        # For Eqn. (4), the  Bahdanau attention\n",
    "        self.W1 = tf.keras.layers.Dense(2*units, use_bias=False)# units : c'est la dim de l'output space, le vecteur de contexte est composé des vecteur de memoire (hidden) pondéreé\n",
    "        self.W2 = tf.keras.layers.Dense(2*units, use_bias=False) # donc on aura 2*units\n",
    "        \n",
    "        # Definition de l'attention\n",
    "        self.attention = tf.keras.layers.AdditiveAttention()\n",
    "\n",
    "    def call(self, query, value, mask):\n",
    "        \"\"\"\n",
    "            inputs\n",
    "            ------\n",
    "                query  : The last hidden state of the decoder S_{i-1}\n",
    "                values : The hidden states of the encoder h_j\n",
    "                mask   : \n",
    "\n",
    "            outputs\n",
    "            -------\n",
    "                context_vector    : \n",
    "                attention_weights : \n",
    "        \n",
    "        \"\"\"\n",
    "    \n",
    "        #`W1@ht`.\n",
    "        w1_query = self.W1(query)\n",
    "        \n",
    "        #`W2@hs`.\n",
    "        w2_key = self.W2(value)\n",
    "\n",
    "        query_mask = tf.ones(tf.shape(query)[:-1], dtype=bool)\n",
    "        value_mask = mask\n",
    "        \n",
    "        #print(\"query_mask shape : \", tf.shape(query_mask))\n",
    "        #print(\"value_mask shape : \", tf.shape(value_mask))\n",
    "\n",
    "        context_vector, attention_weights = self.attention(\n",
    "            inputs = [w1_query, value, w2_key],\n",
    "            mask   = [query_mask, value_mask],\n",
    "            return_attention_scores = True,\n",
    "        )\n",
    "\n",
    "        return context_vector, attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Decoder**\n",
    "- \n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "ru5MZzPuISJG"
   },
   "outputs": [],
   "source": [
    "class DecoderInput(typing.NamedTuple):\n",
    "    new_tokens: Any\n",
    "    enc_output: Any\n",
    "    mask: Any\n",
    "\n",
    "class DecoderOutput(typing.NamedTuple):\n",
    "    logits: Any\n",
    "    attention_weights: Any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "gbWSszPKISGY"
   },
   "outputs": [],
   "source": [
    "class Decoder(tf.keras.layers.Layer):\n",
    "    def __init__(self, output_vocab_size, embedding_dim, dec_units):\n",
    "        super(Decoder, self).__init__()\n",
    "        \"\"\"\n",
    "            inputs\n",
    "            ------\n",
    "                dec_units : \n",
    "                output_vocab_size    : \n",
    "                embedding_dim        :\n",
    "        \n",
    "        \"\"\"\n",
    "        self.dec_units = dec_units\n",
    "        self.output_vocab_size = output_vocab_size\n",
    "        self.embedding_dim = embedding_dim\n",
    "\n",
    "        # La couche d'embedding convertis les tokens vers des vecteurs \n",
    "        self.embedding = tf.keras.layers.Embedding(self.output_vocab_size,embedding_dim)\n",
    "\n",
    "        # Definition du GRU du décodeur\n",
    "        self.gru = tf.keras.layers.GRU(self.dec_units,\n",
    "                                       return_sequences=True,\n",
    "                                       return_state    =True,  # Whether to return the last state in addition to the output\n",
    "                                       recurrent_initializer='glorot_uniform')\n",
    "\n",
    "        # La sortie du GRU est utilisé pour calculer les poids d'attention.\n",
    "        self.attention = BahdanauAttention(self.dec_units)\n",
    "\n",
    "        # For step 4. Eqn. (3): converting `ct` to `at`\n",
    "        self.Wc = tf.keras.layers.Dense(dec_units, activation=tf.math.tanh, use_bias=False)\n",
    "\n",
    "        # For step 5. This fully connected layer produces the logits for each output token.\n",
    "        self.fc = tf.keras.layers.Dense(self.output_vocab_size)\n",
    "        \n",
    "    def call(self, inputs: DecoderInput, state=None) -> Tuple[DecoderOutput, tf.Tensor]:\n",
    "\n",
    "        # Step 1. Lookup the embeddings\n",
    "        vectors = self.embedding(inputs.new_tokens)\n",
    "\n",
    "        # Step 2. Process one step with the RNN\n",
    "        rnn_output, state = self.gru(vectors, initial_state=state) #rnn_output : sequence du sortie du décodeur , state = Si-1\n",
    "\n",
    "        # Step 3. Use the RNN output as the query for the attention over the encoder output.\n",
    "        context_vector, attention_weights = self.attention( query=rnn_output, \n",
    "                                                            value=inputs.enc_output, \n",
    "                                                            mask=inputs.mask)\n",
    "\n",
    "\n",
    "        # Step 4. Eqn. (3): Join the context_vector and rnn_output [ct; ht] shape: (batch t, value_units + query_units)\n",
    "        context_and_rnn_output = tf.concat([context_vector, rnn_output], axis=-1)\n",
    "\n",
    "        # Step 4. Eqn. (3): `at = tanh(Wc@[ct; ht])` : si = f(si−1, yi−1, ci) , ht = htarget = si-1\n",
    "        \n",
    "        \"\"\"\n",
    "        Vecteur d’attention : Prédiction de la sortie à partir du vecteur de contexte ct et de l’encodage ht\n",
    "        Poids d’attention   : Score entre les encodages des mots target ht et des mots source hs \n",
    "        Vecteur de contexte : Somme pondérées des encodages hs avec leur poids d’attention alpha_t,s\n",
    "        \"\"\"\n",
    "        \n",
    "        attention_vector = self.Wc(context_and_rnn_output)\n",
    "\n",
    "        # Step 5. Generate logit predictions: Il génère des prédictions logit pour le prochain jeton en fonction du \"vecteur d'attention\".\n",
    "        # After the fc layer, We get the token of the most probable word\n",
    "        logits = self.fc(attention_vector) #\n",
    "        \n",
    "        return DecoderOutput(logits, attention_weights), state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Pr6bAwsISMV"
   },
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "JCYD8NLIIdvY"
   },
   "outputs": [],
   "source": [
    "class MaskedLoss(tf.keras.losses.Loss):\n",
    "    def __init__(self):\n",
    "        self.name = 'masked_loss'\n",
    "        self.loss = tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "            from_logits=True, reduction='none')\n",
    "\n",
    "    def __call__(self, y_true, y_pred):\n",
    "        # Calculate the loss for each item in the batch.\n",
    "        loss = self.loss(y_true, y_pred)\n",
    "        # Mask off the losses on padding.\n",
    "        mask = tf.cast(y_true != 0, tf.float32) #Casts a tensor to a new type\n",
    "        loss *= mask\n",
    "        # Return the total :reduce_sum : Computes the sum of elements across dimensions of a tensor.\n",
    "        return tf.reduce_sum(loss) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "vOpEjv6WIdxr"
   },
   "outputs": [],
   "source": [
    "class TrainTranslator(tf.keras.Model):\n",
    "    def __init__(self, embedding_dim, units,input_text_processor,output_text_processor, use_tf_function=True):\n",
    "        super().__init__()\n",
    "        # Build the encoder and decoder\n",
    "        encoder = Encoder(input_text_processor.vocabulary_size(), embedding_dim, units)\n",
    "        decoder = Decoder(output_text_processor.vocabulary_size(),embedding_dim, units)\n",
    "\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.input_text_processor = input_text_processor\n",
    "        self.output_text_processor = output_text_processor\n",
    "        self.use_tf_function = use_tf_function\n",
    "        \n",
    "    \n",
    "    def train_step(self, inputs):\n",
    "        if self.use_tf_function:\n",
    "            return self._tf_train_step(inputs)\n",
    "        else:\n",
    "            return self._train_step(inputs)\n",
    "        \n",
    "    def _preprocess(self, input_text, target_text):\n",
    "        # Convert the text to token IDs\n",
    "        input_tokens = self.input_text_processor(input_text)\n",
    "        target_tokens = self.output_text_processor(target_text)\n",
    "\n",
    "        # Convert IDs to masks.\n",
    "        input_mask = input_tokens != 0\n",
    "        target_mask = target_tokens != 0\n",
    "\n",
    "        return input_tokens, input_mask, target_tokens, target_mask\n",
    "    \n",
    "    def _loop_step(self, new_tokens, input_mask, enc_output, dec_state):\n",
    "        input_token, target_token = new_tokens[:, 0:1], new_tokens[:, 1:2]\n",
    "\n",
    "        # Run the decoder one step.\n",
    "        decoder_input = DecoderInput(new_tokens=input_token,\n",
    "                                     enc_output=enc_output,\n",
    "                                     mask=input_mask)\n",
    "\n",
    "        dec_result, dec_state = self.decoder(decoder_input, state=dec_state)\n",
    "\n",
    "        # `self.loss` returns the total for non-padded tokens\n",
    "        y = target_token\n",
    "        y_pred = dec_result.logits\n",
    "        step_loss = self.loss(y, y_pred)\n",
    "\n",
    "        return step_loss, dec_state\n",
    "    \n",
    "    def _train_step(self, inputs):\n",
    "        input_text, target_text = inputs  \n",
    "        (input_tokens, input_mask,target_tokens, target_mask) = self._preprocess(input_text, target_text)\n",
    "\n",
    "        max_target_length = tf.shape(target_tokens)[1]\n",
    "\n",
    "        with tf.GradientTape() as tape:\n",
    "            # Encode the input\n",
    "            enc_output, enc_state = self.encoder(input_tokens)\n",
    "\n",
    "            # Initialize the decoder's state to the encoder's final state.\n",
    "            # This only works if the encoder and decoder have the same number of units.\n",
    "            dec_state = enc_state\n",
    "            loss = tf.constant(0.0)\n",
    "\n",
    "            for t in tf.range(max_target_length-1):\n",
    "                # Pass in two tokens from the target sequence: \n",
    "                # 1. The current input to the decoder.\n",
    "                # 2. The target for the decoder's next prediction.\n",
    "                new_tokens = target_tokens[:, t:t+2] # we only get t and t+1 tokens, first one for the teacher forcing, second for computing the loss\n",
    "                step_loss, dec_state = self._loop_step(new_tokens, input_mask,enc_output, dec_state)\n",
    "                loss = loss + step_loss\n",
    "\n",
    "            # Average the loss over all non padding tokens.\n",
    "            average_loss = loss / tf.reduce_sum(tf.cast(target_mask, tf.float32))\n",
    "\n",
    "        # Apply an optimization step\n",
    "        variables = self.trainable_variables \n",
    "        gradients = tape.gradient(average_loss, variables)\n",
    "        self.optimizer.apply_gradients(zip(gradients, variables))\n",
    "\n",
    "        # Return a dict mapping metric names to current value\n",
    "        return {'batch_loss': average_loss}\n",
    "    \n",
    "    \n",
    "    @tf.function(input_signature=[[tf.TensorSpec(dtype=tf.string, shape=[None]),\n",
    "                                   tf.TensorSpec(dtype=tf.string, shape=[None])]])\n",
    "    def _tf_train_step(self, inputs):\n",
    "          return self._train_step(inputs)\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Translator function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Translator(tf.Module):\n",
    "    \n",
    "    # input_text_processor and output_text_processor are 2 objects of tf.keras.layers.TextVectorization() class ( layer)\n",
    "    # les besoins de modèle pour inverser le text => token IDs mappage fourni par le output_text_processor \n",
    "    def __init__(self, encoder, decoder, input_text_processor,\n",
    "                   output_text_processor):\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.input_text_processor = input_text_processor\n",
    "        self.output_text_processor = output_text_processor\n",
    "        \n",
    "\n",
    "        self.output_token_string_from_index = (\n",
    "            tf.keras.layers.StringLookup(\n",
    "                vocabulary=output_text_processor.get_vocabulary(),\n",
    "                mask_token='',\n",
    "                invert=True)) # A preprocessing layer which maps string features to integer indices.\n",
    "\n",
    "        # The output should never generate padding, unknown, or start.\n",
    "        index_from_string = tf.keras.layers.StringLookup(\n",
    "            vocabulary=output_text_processor.get_vocabulary(), mask_token='')\n",
    "        token_mask_ids = index_from_string(['', '[UNK]', '[START]']).numpy()\n",
    "\n",
    "        token_mask = np.zeros([index_from_string.vocabulary_size()], dtype=np.bool)\n",
    "        token_mask[np.array(token_mask_ids)] = True\n",
    "        self.token_mask = token_mask\n",
    "\n",
    "        self.start_token = index_from_string(tf.constant('[START]'))\n",
    "        self.end_token = index_from_string(tf.constant('[END]'))\n",
    "    \n",
    "    def tokens_to_text(self, result_tokens):\n",
    "        result_text_tokens = self.output_token_string_from_index(result_tokens)\n",
    "        result_text = tf.strings.reduce_join(result_text_tokens,axis=1, separator=' ')\n",
    "        result_text = tf.strings.strip(result_text)\n",
    "        return result_text\n",
    "    \n",
    "    def sample(self, logits, temperature):\n",
    "  \n",
    "        # 't' is usually 1 here.\n",
    "\n",
    "\n",
    "        token_mask = self.token_mask[tf.newaxis, tf.newaxis, :]\n",
    "\n",
    "        # Set the logits for all masked tokens to -inf, so they are never chosen.\n",
    "        logits = tf.where(self.token_mask, -np.inf, logits)\n",
    "\n",
    "        if temperature == 0.0:\n",
    "            new_tokens = tf.argmax(logits, axis=-1)\n",
    "        else: \n",
    "            logits = tf.squeeze(logits, axis=1)\n",
    "            new_tokens = tf.random.categorical(logits/temperature,\n",
    "                                            num_samples=1)\n",
    "\n",
    "\n",
    "        return new_tokens\n",
    "    \n",
    "    def translate(self,\n",
    "                           input_text, *,\n",
    "                           max_length=50,\n",
    "                           return_attention=True,\n",
    "                           temperature=1.0):\n",
    "        batch_size = tf.shape(input_text)[0]\n",
    "        input_tokens = self.input_text_processor(input_text)\n",
    "        enc_output, enc_state = self.encoder(input_tokens)\n",
    "\n",
    "        dec_state = enc_state\n",
    "        new_tokens = tf.fill([batch_size, 1], self.start_token)\n",
    "\n",
    "        result_tokens = []\n",
    "        attention = []\n",
    "        done = tf.zeros([batch_size, 1], dtype=tf.bool)\n",
    "\n",
    "        for _ in range(max_length):\n",
    "            dec_input = DecoderInput(new_tokens=new_tokens,\n",
    "                                     enc_output=enc_output,\n",
    "                                     mask=(input_tokens!=0))\n",
    "\n",
    "            dec_result, dec_state = self.decoder(dec_input, state=dec_state)\n",
    "\n",
    "            attention.append(dec_result.attention_weights)\n",
    "\n",
    "            new_tokens = self.sample(dec_result.logits, temperature)\n",
    "\n",
    "            # If a sequence produces an `end_token`, set it `done`\n",
    "            done = done | (new_tokens == self.end_token)\n",
    "            # Once a sequence is done it only produces 0-padding.\n",
    "            new_tokens = tf.where(done, tf.constant(0, dtype=tf.int64), new_tokens)\n",
    "\n",
    "            # Collect the generated tokens\n",
    "            result_tokens.append(new_tokens)\n",
    "\n",
    "            if tf.executing_eagerly() and tf.reduce_all(done):\n",
    "                break\n",
    "\n",
    "        # Convert the list of generates token ids to a list of strings.\n",
    "        result_tokens = tf.concat(result_tokens, axis=-1)\n",
    "        result_text = self.tokens_to_text(result_tokens)\n",
    "\n",
    "        if return_attention:\n",
    "            attention_stack = tf.concat(attention, axis=1)\n",
    "            return {'text': result_text, 'attention': attention_stack}\n",
    "        else:\n",
    "            return {'text': result_text}\n",
    "    \n",
    "    @tf.function(input_signature=[tf.TensorSpec(dtype=tf.string, shape=[None])])\n",
    "    def tf_translate(self, input_text):\n",
    "          return self.translate(input_text)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_attention(attention, sentence, predicted_sentence):\n",
    "    sentence = tf_lower_and_split_punct(sentence).numpy().decode().split()\n",
    "    predicted_sentence = predicted_sentence.numpy().decode().split() + ['[END]']\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "    attention = attention[:len(predicted_sentence), :len(sentence)]\n",
    "\n",
    "    ax.matshow(attention, cmap='viridis', vmin=0.0)\n",
    "\n",
    "    fontdict = {'fontsize': 14}\n",
    "\n",
    "    ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
    "    ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n",
    "\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "    ax.set_xlabel('Input text')\n",
    "    ax.set_ylabel('Output text')\n",
    "    plt.suptitle('Attention weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z21BnHVaItq7"
   },
   "source": [
    "#### Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "units = 1000 # Nombre de neurones des couches de l'encodeur et décodeur\n",
    "embedding_dim = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "lCdnYuT1ItjR"
   },
   "outputs": [],
   "source": [
    "train_translator = TrainTranslator(\n",
    "    embedding_dim, units,\n",
    "    input_text_processor=input_text_processor,\n",
    "    output_text_processor=output_text_processor)\n",
    "\n",
    "# Configure the loss and optimizer\n",
    "train_translator.compile(\n",
    "    optimizer=tf.optimizers.Adam(),\n",
    "    loss=MaskedLoss(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "BWfspKE6IyXG"
   },
   "outputs": [],
   "source": [
    "train_translator.use_tf_function = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "6W-hnB_iI5LK"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Un rappel est un outil puissant pour personnaliser le comportement d'un modèle Keras pendant la formation, l'évaluation ou l'inférence. \n",
    "\n",
    "\"\"\"\n",
    "class BatchLogs(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, key):\n",
    "        self.key = key\n",
    "        self.logs = []\n",
    "\n",
    "    def on_train_batch_end(self, n, logs):\n",
    "        self.logs.append(logs[self.key])\n",
    "\n",
    "batch_loss = BatchLogs('batch_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.BatchLogs object at 0x7f3231858cd0>\n"
     ]
    }
   ],
   "source": [
    "print(batch_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UcHjoTAlI5Un",
    "outputId": "2c4e08c2-11bd-4c8b-e4ce-9a08c7dafb8f"
   },
   "outputs": [],
   "source": [
    "#train_translator.fit(dataset, epochs=3,callbacks=[batch_loss])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "id": "KWSGNjXGI5Xg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nplt.plot(batch_loss.logs)\\n#plt.ylim([0, 3])\\nplt.xlabel('Batch #')\\nplt.ylabel('CE/token')\\n\""
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "plt.plot(batch_loss.logs)\n",
    "#plt.ylim([0, 3])\n",
    "plt.xlabel('Batch #')\n",
    "plt.ylabel('CE/token')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "rUA3hSxCItuO"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/602553675.py:24: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  token_mask = np.zeros([index_from_string.vocabulary_size()], dtype=np.bool)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "translator = Translator(\n",
    "    encoder=train_translator.encoder,\n",
    "    decoder=train_translator.decoder,\n",
    "    input_text_processor=input_text_processor,\n",
    "    output_text_processor=output_text_processor,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sauvegarde du model\n",
    "#tf.saved_model.save(translator, 'translator_bi',\n",
    "                    #signatures={'serving_default': translator.tf_translate})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reload the trained model :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recharger le modèle\n",
    "reloaded = tf.saved_model.load('../translator_bi')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test to see the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "id": "JD_PzuD1JEkL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thatll be like tomorrow .\n",
      "\n",
      "CPU times: user 1.02 s, sys: 115 ms, total: 1.14 s\n",
      "Wall time: 541 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "input_text = tf.constant([\n",
    "    'Ça aura lieu demain.' \n",
    "])\n",
    "\n",
    "#result = translator.translate(input_text = input_text)  # to use the model we just created\n",
    "\n",
    "result = reloaded.tf_translate(\n",
    "    input_text = input_text)  # to use the saved model \n",
    "\n",
    "print(result['text'][0].numpy().decode())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "IDlBgtcjJXZt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attention vector example :  tf.Tensor(\n",
      "[0.02662428 0.01294245 0.02068449 0.16996731 0.66637516 0.08646684\n",
      " 0.0169395 ], shape=(7,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "a = result['attention'][0]\n",
    "print(\"attention vector example : \",a[3])\n",
    "\n",
    "#print(\"\")\n",
    "#print(\"sum of attention : \") \n",
    "#print(np.sum(a, axis=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "PRNhMCAxJXdM"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.bar(range(len(a[0, :])), a[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "DYIIpTRTJXtF"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=0\n",
    "plot_attention(result['attention'][i], input_text[i], result['text'][i])\n",
    "#La somme horizontale = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=1\n",
    "plot_attention(result['attention'][i], input_text[i], result['text'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You may stay here with me. Il se peut que tu restes ici avec moi.\n"
     ]
    }
   ],
   "source": [
    "print(targ[70202], inp[70202])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "id": "oHpXhKMnJXvQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[b\"Tu n'as pas l'air si occup\\xc3\\xa9.\" b'ils les attendent?'\n",
      " b'Je viens juste de lire ce livre.'\n",
      " b'Il se peut que tu restes ici avec moi.' b'soit pr\\xc3\\xaat.'], shape=(5,), dtype=string)\n",
      "you dont seem so busy .\n",
      "are you waiting for these ?\n",
      "i have only just read that book .\n",
      "you may continue to stay here with me .\n",
      "its ready .\n",
      "\n",
      "CPU times: user 3.92 s, sys: 1.64 s, total: 5.55 s\n",
      "Wall time: 507 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "three_input_text = tf.constant([\n",
    "    \"Tu n'as pas l'air si occupé.\",\n",
    "    'ils les attendent?',\n",
    "    \"Je viens juste de lire ce livre.\",\n",
    "    \"Il se peut que tu restes ici avec moi.\",\n",
    "    'soit prêt.',\n",
    "])\n",
    "print(three_input_text)\n",
    "\n",
    "result = reloaded.tf_translate(three_input_text)\n",
    "\n",
    "for tr in result['text']:\n",
    "    print(tr.numpy().decode())\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "id": "imk9VI2HJXzB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(5,), dtype=string, numpy=\n",
       "array([b'you dont seem so busy .', b'are you waiting for these ?',\n",
       "       b'i have only just read that book .',\n",
       "       b'you may continue to stay here with me .', b'its ready .'],\n",
       "      dtype=object)>"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "id": "JSKr6uQoJe66"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plot_attention(result['attention'][i], three_input_text[i], result['text'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "id": "ckyjNqWJJfmg"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "plot_attention(result['attention'][i], three_input_text[i], result['text'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "id": "Tri8JtgsJhaF"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 2\n",
    "plot_attention(result['attention'][i], three_input_text[i], result['text'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 3\n",
    "plot_attention(result['attention'][i], three_input_text[i], result['text'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 4\n",
    "plot_attention(result['attention'][i], three_input_text[i], result['text'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "id": "BiTnINVSJiSa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected output:\n",
      " It may be impossible to get a completely error-free corpus due to the\n",
      "nature of this kind of collaborative effort. However, if we encourage\n",
      "members to contribute sentences in their own languages rather than\n",
      "experiment in languages they are learning, we might be able to\n",
      "minimize errors.\n"
     ]
    }
   ],
   "source": [
    "long_input_text = tf.constant([inp[-1]])\n",
    "\n",
    "import textwrap\n",
    "print('Expected output:\\n', '\\n'.join(textwrap.wrap(targ[-1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "KM1mIP0uJkNr"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19285/1837363767.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n",
      "/tmp/ipykernel_19285/1837363767.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = reloaded.tf_translate(long_input_text)\n",
    "\n",
    "i = 0\n",
    "plot_attention(result['attention'][i], long_input_text[i], result['text'][i])\n",
    "_ = plt.suptitle('This never works')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_bleuScore(input_texts, target_texts, model):\n",
    "    \n",
    "    d\n",
    "    pd.set_option('max_colwidth', 800)\n",
    "    \n",
    "    input_texts =  tf.constant(input_texts)\n",
    "\n",
    "    trgs = []\n",
    "    pred_trgs = []\n",
    "    bleuScore = []\n",
    "    \n",
    "    for inp,targ in zip(input_texts,target_texts): \n",
    "        \n",
    "        targ = targ.replace(\"'\", \"\").replace(\".\", \" .\").replace(\",\", \" ,\").replace(\"!\", \" !\").replace(\"?\", \" ?\")\n",
    "        targ = targ.lower().split()\n",
    "        \n",
    "        model_output = model.tf_translate(input_text = [inp]) \n",
    "        pred_trg = model_output['text'][0].numpy().decode()\n",
    "        \n",
    "        pred_trgs.append(pred_trg)\n",
    "        trgs.append([targ])\n",
    "\n",
    "        bS = sentence_bleu([targ] ,pred_trg.split(),weights=(1, 0, 0, 0))\n",
    "        bleuScore.append(bS)   \n",
    "        \n",
    "    df['Prediction'] = pred_trgs\n",
    "    df['BLEU score'] = bleuScore\n",
    "        \n",
    "    return trgs,pred_trgs,bleuScore,df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tarek/.local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>BLEU score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tu n'as pas l'air si occupé.</td>\n",
       "      <td>You don't look so busy.</td>\n",
       "      <td>you dont look so busy .</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ils les attendent?</td>\n",
       "      <td>Are they waiting for them?</td>\n",
       "      <td>are they waiting for them ?</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Je viens juste de lire ce livre.</td>\n",
       "      <td>I have just read this book.</td>\n",
       "      <td>i just have just read this book .</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Il se peut que tu restes ici avec moi.</td>\n",
       "      <td>You may stay here with me.</td>\n",
       "      <td>youd come this way for me .</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>soit prêt.</td>\n",
       "      <td>Be ready.</td>\n",
       "      <td>its ready .</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Source                       Target  \\\n",
       "0            Tu n'as pas l'air si occupé.      You don't look so busy.   \n",
       "1                      ils les attendent?   Are they waiting for them?   \n",
       "2        Je viens juste de lire ce livre.  I have just read this book.   \n",
       "3  Il se peut que tu restes ici avec moi.   You may stay here with me.   \n",
       "4                              soit prêt.                    Be ready.   \n",
       "\n",
       "                          Prediction  BLEU score  \n",
       "0            you dont look so busy .    1.000000  \n",
       "1        are they waiting for them ?    1.000000  \n",
       "2  i just have just read this book .    0.875000  \n",
       "3        youd come this way for me .    0.285714  \n",
       "4                        its ready .    0.666667  "
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import sample\n",
    "\n",
    "input_texts = [\n",
    "    \"Tu n'as pas l'air si occupé.\",\n",
    "    'ils les attendent?',\n",
    "    \"Je viens juste de lire ce livre.\",\n",
    "    \"Il se peut que tu restes ici avec moi.\",\n",
    "    'soit prêt.',\n",
    "]\n",
    "\n",
    "target_texts = [\n",
    "    \"You don't look so busy.\",\n",
    "    'Are they waiting for them?',\n",
    "    \"I have just read this book.\",\n",
    "    \"You may stay here with me.\",\n",
    "    'Be ready.',\n",
    "]\n",
    "\n",
    "\"\"\"\n",
    "indx = sample(range(len(inp)),30)\n",
    "input_texts = [inp[i] for i in indx ]\n",
    "target_texts = [targ[i] for i in indx ]\n",
    "\"\"\"\n",
    "\n",
    "trgs,pred_trgs,bleuScore,df = compute_bleuScore(input_texts, target_texts,reloaded)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "three_input_text = tf.constant([\n",
    "    \"Tu n'as pas l'air si occupé.\",\n",
    "    'ils les attendent?',\n",
    "    \"Je viens juste de lire ce livre.\",\n",
    "    \"Il se peut que tu restes ici avec moi.\",\n",
    "    'soit prêt.',\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2213386697554703e-77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tarek/.local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n"
     ]
    }
   ],
   "source": [
    "reference = [['its', 'time', '.']]\n",
    "candidate = ['its', 'time', '.']\n",
    "score = sentence_bleu(reference, candidate)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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