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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/agemagician/CodeTrans/blob/main/prediction/single%20task/program%20synthesis/base_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
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        "**<h3>Generate the program based on the question using codeTrans single task training model</h3>**\n",
        "<h4>You can make free prediction online through this \n",
        "<a href=\"https://huggingface.co/SEBIS/code_trans_t5_base_program_synthese\">Link</a></h4> (When using the prediction online, you need to parse and tokenize the code first.)"
      ]
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    {
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      "metadata": {
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        "**1. Load necessry libraries including huggingface transformers**"
      ]
    },
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      "metadata": {
        "colab": {
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        },
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        "outputId": "57dd0167-af10-4ef8-b6a8-c9c388a1a7cd"
      },
      "source": [
        "!pip install -q transformers sentencepiece"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.4MB 13.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 49.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 50.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.9MB 49.7MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53TAO7mmUOyI"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "**2. Load the token classification pipeline and load it into the GPU if avilabile**"
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        },
        "id": "5ybX8hZ3UcK2",
        "outputId": "8ec65efb-76f6-4fdd-b2e4-11d59630f7ab"
      },
      "source": [
        "pipeline = SummarizationPipeline(\n",
        "    model=AutoModelWithLMHead.from_pretrained(\"SEBIS/code_trans_t5_base_program_synthese\"),\n",
        "    tokenizer=AutoTokenizer.from_pretrained(\"SEBIS/code_trans_t5_base_program_synthese\", skip_special_tokens=True),\n",
        "    device=0\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/transformers/models/auto/modeling_auto.py:852: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=621.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkynwKIcEvHh"
      },
      "source": [
        "**3 Give the question for generating the code, parse and tokenize it**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nld-UUmII-2e"
      },
      "source": [
        "question = \"you are given an array of numbers a and a number b, compute the difference of elements in a and b\" #@param {type:\"raw\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqACvTcjtwYK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f112717-3267-47a3-e0c8-3a1ebdd6f846"
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvnBXtkJBKGJ",
        "outputId": "a0a0a12a-0cda-424e-9202-42a084da76f9"
      },
      "source": [
        "def englishTokenizer(sentence):\n",
        "    result = []\n",
        "    tokens = word_tokenize(sentence)\n",
        "    for t in tokens:\n",
        "        if( not len(t)>50):\n",
        "            result.append(t)\n",
        "    return ' '.join(result)\n",
        "\n",
        "tokenized_question = englishTokenizer(question)\n",
        "print(\"tokenized question: \" + tokenized_question)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tokenized question: you are given an array of numbers a and a number b , compute the difference of elements in a and b\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVBz9jHNW1PI"
      },
      "source": [
        "**4. Make Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAItQ9U9UwqW",
        "outputId": "7fee4382-3a49-4a97-abd5-48dee97c2e8a"
      },
      "source": [
        "pipeline([tokenized_question])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your max_length is set to 512, but you input_length is only 28. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': '[ map a [ partial1 b - ] ]'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    }
  ]
}