hello i make a translation machine with tensorflow your goal is to optilmize a cell for being faster this cells :
max_length = 200

fr_sequences = [fr_tokenizer.tokenize(french_sentence.numpy().decode('utf-8')).merge_dims(-2,-1)
                for french_sentence, _ in dataset.take(127085)]
fr_ragged = tf.ragged.stack(fr_sequences)
fr_padded = fr_ragged.to_tensor(default_value=0, shape=[None, None, max_length])

print("fr sequences YES")
fr_sequencesdeocde = [fr_tokenizer.tokenize("[START] "+french_sentence.numpy().decode('utf-8')+" [END]").merge_dims(-2,-1)
                for french_sentence, _ in dataset.take(127085)]
fr_raggeddecode = tf.ragged.stack(fr_sequences)
fr_paddeddecode = fr_ragged.to_tensor(default_value=0, shape=[None, None, max_length])
print("sequencesdeocde  Yes")
fr_sequences
en_sequences = [en_tokenizer.tokenize(english_sentence.numpy().decode('utf-8')+" [END]").merge_dims(-2,-1)
                for _, english_sentence in dataset.take(127085)]
en_ragged = tf.ragged.stack(en_sequences)
en_padded = en_ragged.to_tensor(default_value=0, shape=[None, None, max_length])

print("en_sequences  yes")
x_train = fr_padded
x2_train = fr_paddeddecode
y_train = en_padded


and this is the full code  :
{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UeycYCyxDfE"
      },
      "source": [
        "# TRANSLATOR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BfUjVxBcz5N"
      },
      "source": [
        "## instalation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXqM38xBRHu2",
        "outputId": "075e90fc-7ba6-4b54-9bb9-414037cc352e"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "!pip install -q -U tensorflow-text\n",
        "!pip install datasets\n",
        "!pip install -q tensorflow_datasets\n",
        "!pip install pydot\n",
        "!pip install tensorflow\n",
        "!pip install numpy\n",
        "!cd /content\n",
        "!clear"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ukvs1XfMG7aG"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_text as tf_text\n",
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import requests\n",
        "import functools\n",
        "import collections\n",
        "import os\n",
        "import pathlib\n",
        "import re\n",
        "import string\n",
        "import tempfile\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import re\n",
        "import shutil\n",
        "import string\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import losses\n",
        "import pydot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7V7igFwpc6Hs"
      },
      "source": [
        "## dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280,
          "referenced_widgets": [
            "a5b60d70fc784178aae195ba50aa17fb",
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            "bf7382bdfe9c46c39cc0e48487aa3e7e",
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            "cedda31df2a34e97935d72c8dfd5da74",
            "a4d33f99d00348a6a733e8936feac62c"
          ]
        },
        "id": "ZaMtoUtAREzs",
        "outputId": "f8dadd74-5e04-4fa6-9392-6ccd6b2cb458"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"Helsinki-NLP/opus_books\", \"en-fr\")\n",
        "data = dataset[\"train\"]\n",
        "\n",
        "french_sentences = [example[\"fr\"] for example in data[\"translation\"][:127085]]\n",
        "english_sentences = [example[\"en\"] for example in data[\"translation\"][:127085]]\n",
        "dataset = tf.data.Dataset.from_tensor_slices((french_sentences, english_sentences))\n",
        "\n",
        "french_sentences_decoded = []\n",
        "english_sentences_decoded = []\n",
        "\n",
        "for french_sentence, english_sentence in dataset.take(12708):\n",
        "    french_sentences_decoded.append(\"b '\"+french_sentence.numpy().decode('utf-8'))\n",
        "    english_sentences_decoded.append(\"b '\"+english_sentence.numpy().decode('utf-8'))\n",
        "\n",
        "print(\"Nombre de phrases en français :\", len(french_sentences_decoded))\n",
        "print(\"Nombre de phrases en anglais :\", len(english_sentences_decoded))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2tb7H5uFQoBA"
      },
      "outputs": [],
      "source": [
        "train_fr = french_sentences\n",
        "train_en = english_sentences"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzS8h0budHWv"
      },
      "source": [
        "## vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "72uMXsQhFIx8"
      },
      "outputs": [],
      "source": [
        "from tensorflow_text.tools.wordpiece_vocab import bert_vocab_from_dataset as bert_vocab\n",
        "\n",
        "bert_tokenizer_params = dict(lower_case=True)\n",
        "reserved_tokens = [\"[PAD]\", \"[UNK]\", \"[START]\", \"[END]\"]\n",
        "\n",
        "bert_vocab_args = {\n",
        "    'vocab_size': 8000,\n",
        "    'reserved_tokens': reserved_tokens,\n",
        "    'bert_tokenizer_params': bert_tokenizer_params,\n",
        "    'learn_params': {},\n",
        "}\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6f3mrA-DK0n",
        "outputId": "b5413b20-51a6-4b79-9bad-ae1aeae4cf42"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "en_vocab = bert_vocab.bert_vocab_from_dataset(\n",
        "    tf.data.Dataset.from_tensor_slices(english_sentences).batch(1000).prefetch(2),\n",
        "    **bert_vocab_args\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJZzz1x5YY0x",
        "outputId": "64c27808-53ad-48b9-9a08-ee65c322a6e4"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "fr_vocab = bert_vocab.bert_vocab_from_dataset(\n",
        "    tf.data.Dataset.from_tensor_slices(french_sentences).batch(1000).prefetch(2),\n",
        "    **bert_vocab_args\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1hmXuHHNcBHg"
      },
      "outputs": [],
      "source": [
        "def write_vocab_file(filepath, vocab):\n",
        "  with open(filepath, 'w') as f:\n",
        "    for token in vocab:\n",
        "      print(token, file=f)\n",
        "write_vocab_file('en_vocab.txt', en_vocab)\n",
        "write_vocab_file('fr_vocab.txt', fr_vocab)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLTf_mEvfNR9"
      },
      "source": [
        "#` TOKENIZER `\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jGshnQ2idy8I"
      },
      "outputs": [],
      "source": [
        "fr_tokenizer = tf_text.BertTokenizer('fr_vocab.txt', **bert_tokenizer_params)\n",
        "en_tokenizer = tf_text.BertTokenizer('en_vocab.txt', **bert_tokenizer_params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbQyYKhHkkDe",
        "outputId": "ef6ba4a4-7bab-432e-adcf-6a85440f2c22"
      },
      "outputs": [],
      "source": [
        "# Tokenize the examples -> (batch, word, word-piece)\n",
        "en_tokenizere = en_tokenizer.tokenize(\"hello how are you Vadim\")\n",
        "# Merge the word and word-piece axes -> (batch, tokens)\n",
        "en_tokenizere= en_tokenizere.merge_dims(-2,-1)\n",
        "\n",
        "for ex in en_tokenizere.to_list():\n",
        "  print(ex)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k0m1461Gwy3e"
      },
      "outputs": [],
      "source": [
        "words = en_tokenizer.detokenize(token_batch)\n",
        "tf.strings.reduce_join(words, separator=' ', axis=-1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BjoPdwoxBWw2"
      },
      "source": [
        "## model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7OqoDOybbwi6"
      },
      "outputs": [],
      "source": [
        "max_length = 200\n",
        "\n",
        "fr_sequences = [fr_tokenizer.tokenize(french_sentence.numpy().decode('utf-8')).merge_dims(-2,-1)\n",
        "                for french_sentence, _ in dataset.take(2000)]\n",
        "fr_ragged = tf.ragged.stack(fr_sequences)\n",
        "fr_padded = fr_ragged.to_tensor(default_value=0, shape=[None, None, max_length])\n",
        "\n",
        "fr_sequencesdeocde = [fr_tokenizer.tokenize(\"[START]\"+french_sentence.numpy().decode('utf-8')+\"[END]\").merge_dims(-2,-1)\n",
        "                for french_sentence, _ in dataset.take(2000)]\n",
        "fr_raggeddecode = tf.ragged.stack(fr_sequences)\n",
        "fr_paddeddecode = fr_ragged.to_tensor(default_value=0, shape=[None, None, max_length])\n",
        "\n",
        "en_sequences = [en_tokenizer.tokenize(english_sentence.numpy().decode('utf-8')).merge_dims(-2,-1)\n",
        "                for _, english_sentence in dataset.take(2000)]\n",
        "en_ragged = tf.ragged.stack(en_sequences)\n",
        "en_padded = en_ragged.to_tensor(default_value=0, shape=[None, None, max_length])\n",
        "\n",
        "x_train = fr_padded\n",
        "x2_train = fr_paddeddecode\n",
        "y_train = en_padded\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "FIp6S-MR7Pls",
        "outputId": "8f187fbe-4f57-4a4f-bb91-c1dc9e098552"
      },
      "outputs": [],
      "source": [
        "inputs = tf.keras.Input(shape=(1,200))\n",
        "embedding_dim = 512\n",
        "lstm_units = 1024\n",
        "vocab_size_en = len(en_vocab) + len(reserved_tokens)\n",
        "vocab_size_fr = len(fr_vocab) + len(reserved_tokens)\n",
        "\n",
        "encoder_inputs = tf.keras.layers.Input(shape=(200,))\n",
        "encoder_embedding = tf.keras.layers.Embedding(input_dim=vocab_size_en, output_dim=embedding_dim, mask_zero=True)(encoder_inputs)\n",
        "encoder_outputs, state_h, state_c = tf.keras.layers.LSTM(lstm_units, return_state=True)(encoder_embedding)\n",
        "encoder_states = [state_h, state_c]\n",
        "\n",
        "decoder_inputs = tf.keras.layers.Input(shape=(200,))\n",
        "decoder_embedding = tf.keras.layers.Embedding(input_dim=vocab_size_fr, output_dim=embedding_dim, mask_zero=True)(decoder_inputs)\n",
        "decoder_lstm = tf.keras.layers.LSTM(lstm_units, return_sequences=True, return_state=True)\n",
        "decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)\n",
        "decoder_dense = tf.keras.layers.Dense(vocab_size_fr, activation='softmax')\n",
        "decoder_outputs = decoder_dense(decoder_outputs)\n",
        "model = tf.keras.models.Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_outputs)\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YOryNiyc15Tf"
      },
      "outputs": [],
      "source": [
        "def format_sequences(tokenizer, sentences, max_length):\n",
        "    sequences = tokenizer.tokenize(sentences).merge_dims(-2, -1)\n",
        "    padded_sequences = sequences.to_tensor(default_value=0, shape=[None, max_length])\n",
        "    return padded_sequences\n",
        "\n",
        "encoder_input_data = format_sequences(en_tokenizer, train_en, max_length)\n",
        "decoder_input_data = format_sequences(fr_tokenizer, [\"[START] \" + sentence for sentence in train_fr], max_length)\n",
        "decoder_target_data = format_sequences(fr_tokenizer, [sentence + \" [END]\" for sentence in train_fr], max_length)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oKPxeCMPcsF",
        "outputId": "0a2d9241-1984-4aa7-c1d1-c5a8d982e542"
      },
      "outputs": [],
      "source": [
        "batch_size = 2\n",
        "epochs = 3\n",
        "\n",
        "model.fit([encoder_input_data, decoder_input_data], decoder_target_data,\n",
        "          batch_size=batch_size,\n",
        "          epochs=epochs,\n",
        "          validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "avXVDSHx18KC"
      },
      "outputs": [],
      "source": [
        "encoder_model = tf.keras.models.Model(encoder_inputs, encoder_states)\n",
        "\n",
        "decoder_state_input_h = tf.keras.Input(shape=(lstm_units,))\n",
        "decoder_state_input_c = tf.keras.Input(shape=(lstm_units,))\n",
        "decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n",
        "\n",
        "decoder_outputs, state_h, state_c = decoder_lstm(\n",
        "    decoder_embedding, initial_state=decoder_states_inputs)\n",
        "decoder_states = [state_h, state_c]\n",
        "decoder_outputs = decoder_dense(decoder_outputs)\n",
        "\n",
        "decoder_model = tf.keras.models.Model(\n",
        "    [decoder_inputs] + decoder_states_inputs,\n",
        "    [decoder_outputs] + decoder_states)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tu-NqIEA19if"
      },
      "outputs": [],
      "source": [
        "def translate(input_sentence):\n",
        "    # Prepare the input sentence\n",
        "    input_seq = format_sequences(en_tokenizer, [input_sentence], max_length)\n",
        "    states_value = encoder_model.predict(input_seq)\n",
        "\n",
        "    # Generate empty target sequence of length 1 with only the start token\n",
        "    target_seq = np.zeros((1, 1))\n",
        "    target_seq[0, 0] = fr_tokenizer.vocab['[START]']\n",
        "\n",
        "    # Sampling loop for a batch of sequences\n",
        "    stop_condition = False\n",
        "    decoded_sentence = ''\n",
        "    while not stop_condition:\n",
        "        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)\n",
        "\n",
        "        # Sample a token and add the corresponding character to the decoded sentence\n",
        "        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
        "        sampled_char = fr_tokenizer.detokenize([[sampled_token_index]]).numpy()[0].decode('utf-8')\n",
        "        decoded_sentence += sampled_char + ' '\n",
        "\n",
        "        # Exit condition: either hit max length or find stop token.\n",
        "        if (sampled_char == '[END]' or len(decoded_sentence) > max_length):\n",
        "            stop_condition = True\n",
        "\n",
        "        # Update the target sequence (length 1).\n",
        "        target_seq = np.zeros((1, 1))\n",
        "        target_seq[0, 0] = sampled_token_index\n",
        "\n",
        "        # Update states\n",
        "        states_value = [h, c]\n",
        "\n",
        "    return decoded_sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KimJ6ANPMz6r",
        "outputId": "95dcfad1-3d7f-410d-ac24-adcc3215741f"
      },
      "outputs": [],
      "source": [
        "history = model.fit([x_train,x2_train], y_train, epochs=10, batch_size=2)"
      ]
    }
  ],
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