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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/TurkuNLP/intro-to-nlp/blob/master/ex5_solved_imdb_feature_weight.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Solution to exercise 5:\n",
        "\n",
        "* The code is based off the notebook with few changes highlighted below\n",
        "* I removed all the texts and unnecessary code to the extent possible"
      ],
      "metadata": {
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 KB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m61.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.9/132.9 KB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 KB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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    },
    {
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        "from pprint import pprint #pprint => pretty-print, I use it occassionally throughout the notebook\n",
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        "pprint(dset)"
      ],
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            "c3450cadc79746578934174faae2ca5b"
          ]
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.31k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a69461550d3c489c948a4ed2c6eaf08c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f6f0665078e4aa8aa52c3e7160912be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.59k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "883a79a18b384967ba26c53e0c79c3bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset imdb/plain_text to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/84.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5745e84d53fe47d592d27fa61f9153ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abfcb85a696a4c379b23df9542d4f665"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c3657df66d14413ac994a239f3f6f4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31ce280f11e745eb926297d31e204fe3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset imdb downloaded and prepared to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a699e279dfa04960b68446a0ef777b51"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'test': Dataset({\n",
            "    features: ['text', 'label'],\n",
            "    num_rows: 25000\n",
            "}),\n",
            " 'train': Dataset({\n",
            "    features: ['text', 'label'],\n",
            "    num_rows: 25000\n",
            "}),\n",
            " 'unsupervised': Dataset({\n",
            "    features: ['text', 'label'],\n",
            "    num_rows: 50000\n",
            "})}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dset=dset.shuffle() #This is never a bad idea, datasets may have ordering to them, which is not what we want\n",
        "del dset[\"unsupervised\"] #Delete the unlabeled part of the dataset, we don't need it for anything"
      ],
      "metadata": {
        "id": "lbmgiOj4pWw-"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.feature_extraction\n",
        "\n",
        "vectorizer=sklearn.feature_extraction.text.CountVectorizer(binary=True,max_features=20000)\n",
        "\n",
        "texts=[ex[\"text\"] for ex in dset[\"train\"]] #get a list of all texts from the training data\n",
        "vectorizer.fit(texts) #\"Trains\" the vectorizer, i.e. builds its vocabulary\n"
      ],
      "metadata": {
        "id": "MYOtKJu7Mohd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "6c77d268-e2e0-4d63-a33e-c09ba633d61f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CountVectorizer(binary=True, max_features=20000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer(binary=True, max_features=20000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(binary=True, max_features=20000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def vectorize_example(ex):\n",
        "    vectorized=vectorizer.transform([ex[\"text\"]]) # [...] because the vectorizer expects a list/iterable over inputs, not one input\n",
        "    non_zero_features=vectorized.nonzero()[1] #.nonzero gives a pair of (rows,columns), we want the columns\n",
        "    non_zero_features+=1 #feature index 0 will have a special meaning\n",
        "                         # so let us not produce it by adding +1 to everything\n",
        "    return {\"input_ids\":non_zero_features} \n",
        "\n",
        "vectorized=vectorize_example(dset[\"train\"][0])"
      ],
      "metadata": {
        "id": "5H9AQOc2rGIO"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We can map back to vocabulary and check that everything works\n",
        "# vectorizer.vocabulary_ is a dictionary {key:word, value:idx}\n",
        "\n",
        "idx2word=dict((i,w) for (w,i) in vectorizer.vocabulary_.items()) #inverse the vocab dictionary\n",
        "words=[]\n",
        "for idx in vectorized[\"input_ids\"]:\n",
        "    words.append(idx2word[idx-1]) ## It is easy to forgot we moved all by +1\n",
        "pprint(\", \".join(words)) #This is now the bag of words representation of the document"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvgd3IrWfCai",
        "outputId": "58d854ea-cd29-44cb-df5a-3aa33b3aa91f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('accurate, action, adaptation, advanced, agreed, all, and, another, anything, '\n",
            " 'around, be, because, better, bit, books, bourne, br, but, can, covers, '\n",
            " 'damon, department, did, difficult, director, duh, end, enough, extensive, '\n",
            " 'films, find, for, funnily, got, how, if, in, is, it, lacking, last, least, '\n",
            " 'll, make, means, movie, notice, of, once, part, particular, quite, read, '\n",
            " 'reveal, running, scenes, second, some, spoiler, star, story, take, than, '\n",
            " 'that, the, themselves, they, thing, third, this, three, time, to, too, '\n",
            " 'trilogy, up, was, watch, which, wise, with, would, you')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the tokenizer to the whole dataset using .map()\n",
        "dset_tokenized = dset.map(vectorize_example,num_proc=4)\n",
        "pprint(dset_tokenized[\"train\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "ac40ad1c7b7749a4a67aa52abcc668b4",
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            "0f3f362750d04939ad40c58557cc7c3f",
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            "2493e640b99c49eebaf9c2021f604b62",
            "ea8b184333fc49eebfe4b718c715146d"
          ]
        },
        "id": "33xMYRd0q2B9",
        "outputId": "bdd44a80-2d91-4b83-d8c5-bf1f9ba62124"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "5e520a685cf04f4f8f2e831599e6860f"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [381,\n",
            "               420,\n",
            "               445,\n",
            "               527,\n",
            "               631,\n",
            "               727,\n",
            "               887,\n",
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            "               16542,\n",
            "               16762,\n",
            "               16902,\n",
            "               17075,\n",
            "               17629,\n",
            "               17885,\n",
            "               17893,\n",
            "               17897,\n",
            "               17916,\n",
            "               17944,\n",
            "               17951,\n",
            "               17959,\n",
            "               17968,\n",
            "               18000,\n",
            "               18074,\n",
            "               18115,\n",
            "               18166,\n",
            "               18418,\n",
            "               18922,\n",
            "               19398,\n",
            "               19414,\n",
            "               19559,\n",
            "               19696,\n",
            "               19712,\n",
            "               19805,\n",
            "               19927],\n",
            " 'label': 1,\n",
            " 'text': 'The third and last part of the Bourne trilogy (duh), is lacking a '\n",
            "         'bit in the story department, but covers it with extensive action '\n",
            "         'scenes! Twi in particular take up quite some of the running time and '\n",
            "         'make this movie better.<br /><br />The director and star (Damon) '\n",
            "         'themselves agreed that it was difficult to find a story for the last '\n",
            "         'part, because the end of the second movie was quite ... advanced '\n",
            "         'story-wise. How they got around that? The action scenes, for once, '\n",
            "         \"but they did another thing too, which I can't reveal, because that \"\n",
            "         \"would be a spoiler. But if you watch the movie, than you'll notice \"\n",
            "         'it! Funnily enough I read, that this adaptation of the Bourne books '\n",
            "         'is the least accurate of all three films .. if that means anything '\n",
            "         'to you :o)'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "def collator(list_of_examples):\n",
        "    batch={\"labels\":torch.tensor(list(ex[\"label\"] for ex in list_of_examples))} #this is easy, labels are made into a single tensor\n",
        "    #the worse bit is now to pad the examples, as they are of different length\n",
        "    tensors=[]\n",
        "    max_len=max(len(example[\"input_ids\"]) for example in list_of_examples) #this is the longest example in the batch\n",
        "    #everything needs to be padded to fit in length the longest example\n",
        "    #(so we can build a single tensor out of it)\n",
        "    for example in list_of_examples:\n",
        "        ids=torch.tensor(example[\"input_ids\"]) #pick the input ids\n",
        "        # pad(what,(from_left, from_right)) <- this is how we call the stock pad function\n",
        "        padded=torch.nn.functional.pad(ids,(0,max_len-ids.shape[0])) #pad by max - current length, pads with zero by default\n",
        "        tensors.append(padded) #accumulated the padded ids\n",
        "    batch[\"input_ids\"]=torch.vstack(tensors) #now that we have all of them the same length, a simple vstack() stacks them up\n",
        "    return batch #...and that's all there is to it\n"
      ],
      "metadata": {
        "id": "zXXDxNo6kwfA"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The first changes happen below:\n",
        "1. We can make the embedding dimension 1, i.e. the embeddings will be vectors of length 1, i.e. just individual real numbers. We do not need to change the code of the model, we can just change the number in model config.\n",
        "2. Just for clarity and clean approach, let us remove the `tanh` nonlinearity from the network, so the decision becomes a simple sum of weights. (This does not affect the outcome really, since `tanh` is monotonic and thus preserves order)"
      ],
      "metadata": {
        "id": "Ld29GTMuNNKY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "\n",
        "# A model wants a config, I can simply inherit from the base\n",
        "# class for pretrained configs\n",
        "class MLPConfig(transformers.PretrainedConfig):\n",
        "    pass\n",
        "\n",
        "# This is the model\n",
        "class MLP(transformers.PreTrainedModel):\n",
        "\n",
        "    config_class=MLPConfig\n",
        "\n",
        "    # In the initialization method, one instantiates the layers\n",
        "    # these will be, for the most part the trained parameters of the model\n",
        "    def __init__(self,config):\n",
        "        super().__init__(config)\n",
        "        self.vocab_size=config.vocab_size #embedding matrix row count\n",
        "        # Build and initialize embedding of vocab size +1 x hidden size (+1 because of the padding index 0!)\n",
        "        self.embedding=torch.nn.Embedding(num_embeddings=self.vocab_size+1,embedding_dim=config.hidden_size,padding_idx=0)\n",
        "        # Normally you would not initialize these yourself, but I have my reasons here ;)\n",
        "        torch.nn.init.uniform_(self.embedding.weight.data,-0.001,0.001) #initialize the embeddings with small random values\n",
        "        # Note! This is quite clever and keeps the embedding for 0, the padding, pure zeros\n",
        "        # This takes care of the lower half of the network, now the upper half\n",
        "        # Output layer: hidden size x output size\n",
        "        self.output=torch.nn.Linear(in_features=config.hidden_size,out_features=config.nlabels)\n",
        "        # Now we have the parameters of the model\n",
        "\n",
        "        \n",
        "    # The computation of the model is put into the forward() function\n",
        "    # it receives a batch of data and optionally the correct `labels`\n",
        "    #\n",
        "    # If given `labels` it returns (loss,output)\n",
        "    # if not, then it returns (output,)\n",
        "    def forward(self,input_ids,labels=None): #nevermind the attention_mask, its time will come, data collator insists on adding it\n",
        "        #1) sum up the embeddings of the items\n",
        "        embedded=self.embedding(input_ids) #(batch,ids)->(batch,ids,embedding_dim)\n",
        "        # Since the Embedding keeps the first row of the matrix pure zeros, we don't need to worry about the padding\n",
        "        # so next we sum the embeddings across the word dimension\n",
        "        # (batch,ids,embedding_dim) -> (batch,embedding_dim)\n",
        "        embedded_summed=torch.sum(embedded,dim=1)\n",
        "        \n",
        "        #2) apply non-linearity\n",
        "        # (batch,embedding_dim) -> (batch,embedding_dim)\n",
        "\n",
        "        #### MODIFIED HERE FOR EXERCISE 5 -> commented out\n",
        "        ####projected=torch.tanh(embedded_summed) #Note how non-linearity is applied here and not when configuring the layer in __init__()\n",
        "\n",
        "        #3) and now apply the upper, output layer of the network\n",
        "        # (batch,embedding_dim) -> (batch, num_of_classes i.e. 2 in our case)\n",
        "        \n",
        "        #### MODIFIED HERE FOR EXERCISE 5 -> base it off embedded_summed\n",
        "        ##### OLD: logits=self.output(projected)\n",
        "        logits=self.output(embedded_summed)\n",
        "\n",
        "        # ...and that's all there is to it!\n",
        "\n",
        "        #print(\"input_ids.shape\",input_ids.shape)\n",
        "        #print(\"embedded.shape\",embedded.shape)\n",
        "        #print(\"embedded_summed.shape\",embedded_summed.shape)\n",
        "        #print(\"projected.shape\",projected.shape)\n",
        "        #print(\"logits.shape\",logits.shape)\n",
        "        \n",
        "        # We have labels, so we ought to calculate the loss\n",
        "        if labels is not None:\n",
        "            loss=torch.nn.CrossEntropyLoss() #This loss is meant for classification, so let's use it\n",
        "            # You run it as loss(model_output,correct_labels)\n",
        "            return (loss(logits,labels),logits)\n",
        "        else:\n",
        "            # No labels, so just return the logits\n",
        "            return (logits,)\n",
        "\n",
        "# Configure the model:\n",
        "#   these parameters are used in the model's __init__()\n",
        "\n",
        "\n",
        "####### CHANGED HERE FOR EXERCISE 5, hidden_size from 20 to 1\n",
        "mlp_config=MLPConfig(vocab_size=len(vectorizer.vocabulary_),hidden_size=1,nlabels=2)\n"
      ],
      "metadata": {
        "id": "vP4FtrMwCpGi"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# And we can make a model\n",
        "mlp=MLP(mlp_config)\n",
        "fake_batch=collator([dset_tokenized[\"train\"][0],dset_tokenized[\"train\"][1]])\n",
        "mlp(**fake_batch) #** expands input_ids and labels as parameters of the call"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQjSGUYGwrzh",
        "outputId": "0029d4a8-e0fb-4fb0-9497-df0668000ad4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0.1620, grad_fn=<NllLossBackward0>),\n",
              " tensor([[-0.9061,  0.8410],\n",
              "         [-0.8979,  0.8313]], grad_fn=<AddmmBackward0>))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set training arguments\n",
        "# their names are mostly self-explanatory\n",
        "trainer_args = transformers.TrainingArguments(\n",
        "    \"mlp_checkpoints\", #save checkpoints here\n",
        "    evaluation_strategy=\"steps\",\n",
        "    logging_strategy=\"steps\",\n",
        "    eval_steps=500,\n",
        "    logging_steps=500,\n",
        "    learning_rate=1e-4, #learning rate of the gradient descent\n",
        "    max_steps=20000,\n",
        "    load_best_model_at_end=True,\n",
        "    per_device_train_batch_size=128\n",
        ")\n",
        "\n",
        "pprint(trainer_args)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhfdW62z8cCn",
        "outputId": "ae90a250-1456-48cf-f82a-5503b04f5917"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TrainingArguments(\n",
            "_n_gpu=1,\n",
            "adafactor=False,\n",
            "adam_beta1=0.9,\n",
            "adam_beta2=0.999,\n",
            "adam_epsilon=1e-08,\n",
            "auto_find_batch_size=False,\n",
            "bf16=False,\n",
            "bf16_full_eval=False,\n",
            "data_seed=None,\n",
            "dataloader_drop_last=False,\n",
            "dataloader_num_workers=0,\n",
            "dataloader_pin_memory=True,\n",
            "ddp_bucket_cap_mb=None,\n",
            "ddp_find_unused_parameters=None,\n",
            "ddp_timeout=1800,\n",
            "debug=[],\n",
            "deepspeed=None,\n",
            "disable_tqdm=False,\n",
            "do_eval=True,\n",
            "do_predict=False,\n",
            "do_train=False,\n",
            "eval_accumulation_steps=None,\n",
            "eval_delay=0,\n",
            "eval_steps=500,\n",
            "evaluation_strategy=steps,\n",
            "fp16=False,\n",
            "fp16_backend=auto,\n",
            "fp16_full_eval=False,\n",
            "fp16_opt_level=O1,\n",
            "fsdp=[],\n",
            "fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n",
            "fsdp_min_num_params=0,\n",
            "fsdp_transformer_layer_cls_to_wrap=None,\n",
            "full_determinism=False,\n",
            "gradient_accumulation_steps=1,\n",
            "gradient_checkpointing=False,\n",
            "greater_is_better=False,\n",
            "group_by_length=False,\n",
            "half_precision_backend=auto,\n",
            "hub_model_id=None,\n",
            "hub_private_repo=False,\n",
            "hub_strategy=every_save,\n",
            "hub_token=<HUB_TOKEN>,\n",
            "ignore_data_skip=False,\n",
            "include_inputs_for_metrics=False,\n",
            "jit_mode_eval=False,\n",
            "label_names=None,\n",
            "label_smoothing_factor=0.0,\n",
            "learning_rate=0.0001,\n",
            "length_column_name=length,\n",
            "load_best_model_at_end=True,\n",
            "local_rank=-1,\n",
            "log_level=passive,\n",
            "log_level_replica=warning,\n",
            "log_on_each_node=True,\n",
            "logging_dir=mlp_checkpoints/runs/Apr04_12-23-01_c6f7d5ec55f2,\n",
            "logging_first_step=False,\n",
            "logging_nan_inf_filter=True,\n",
            "logging_steps=500,\n",
            "logging_strategy=steps,\n",
            "lr_scheduler_type=linear,\n",
            "max_grad_norm=1.0,\n",
            "max_steps=20000,\n",
            "metric_for_best_model=loss,\n",
            "mp_parameters=,\n",
            "no_cuda=False,\n",
            "num_train_epochs=3.0,\n",
            "optim=adamw_hf,\n",
            "optim_args=None,\n",
            "output_dir=mlp_checkpoints,\n",
            "overwrite_output_dir=False,\n",
            "past_index=-1,\n",
            "per_device_eval_batch_size=8,\n",
            "per_device_train_batch_size=128,\n",
            "prediction_loss_only=False,\n",
            "push_to_hub=False,\n",
            "push_to_hub_model_id=None,\n",
            "push_to_hub_organization=None,\n",
            "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
            "ray_scope=last,\n",
            "remove_unused_columns=True,\n",
            "report_to=['tensorboard'],\n",
            "resume_from_checkpoint=None,\n",
            "run_name=mlp_checkpoints,\n",
            "save_on_each_node=False,\n",
            "save_steps=500,\n",
            "save_strategy=steps,\n",
            "save_total_limit=None,\n",
            "seed=42,\n",
            "sharded_ddp=[],\n",
            "skip_memory_metrics=True,\n",
            "tf32=None,\n",
            "torch_compile=False,\n",
            "torch_compile_backend=None,\n",
            "torch_compile_mode=None,\n",
            "torchdynamo=None,\n",
            "tpu_metrics_debug=False,\n",
            "tpu_num_cores=None,\n",
            "use_ipex=False,\n",
            "use_legacy_prediction_loop=False,\n",
            "use_mps_device=False,\n",
            "warmup_ratio=0.0,\n",
            "warmup_steps=0,\n",
            "weight_decay=0.0,\n",
            "xpu_backend=None,\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import evaluate\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_accuracy(outputs_and_labels):\n",
        "    outputs, labels = outputs_and_labels\n",
        "    predictions = np.argmax(outputs, axis=-1) #pick the index of the \"winning\" label\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "u3jxIItb0BL9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "90d4ddbc0d864847b75745f30cd0a24d",
            "ea41060c817b400c8418880b41d1b292",
            "8bc2aca0a3cb42349b5b1fe989f2aaa9",
            "809124b50a1446f7a21be632dd8c5eaf",
            "c59ab0826f6044cfaab38fba29dda3e0",
            "5e72143db57e4fec98d73950889be5ad",
            "aa5abaefd2f941e7b31f7b86f5237c04",
            "644364df78cb48b38f6fe25bda8d7f25",
            "907686b3b59346aea4f566c52e6f3db5",
            "63a367bbfcd948d98dd952c4d29a82d4",
            "b16dcf4dd1274c0c8869e77bb2023df4"
          ]
        },
        "outputId": "4faac171-bcce-41ed-faf8-43b240b783fa"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90d4ddbc0d864847b75745f30cd0a24d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a new model  \n",
        "mlp = MLP(mlp_config)\n",
        "\n",
        "\n",
        "# Argument gives the number of steps of patience before early stopping\n",
        "# i.e. training is stopped when the evaluation loss fails to improve\n",
        "# certain number of times\n",
        "early_stopping = transformers.EarlyStoppingCallback(5)\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=mlp,\n",
        "    args=trainer_args,\n",
        "    train_dataset=dset_tokenized[\"train\"],\n",
        "    eval_dataset=dset_tokenized[\"test\"].select(range(1000)), #make a smaller subset to evaluate on\n",
        "    compute_metrics=compute_accuracy,\n",
        "    data_collator=collator,\n",
        "    callbacks=[early_stopping]\n",
        ")\n",
        "\n",
        "# FINALLY!\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "AoEoWsj4P_zN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 792
        },
        "outputId": "febb0132-c4e1-4e27-980e-88d2f3e9f4f0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10000' max='20000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10000/20000 02:41 < 02:41, 61.87 it/s, Epoch 51/103]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.683300</td>\n",
              "      <td>0.562166</td>\n",
              "      <td>0.741000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.498200</td>\n",
              "      <td>0.467772</td>\n",
              "      <td>0.815000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.410800</td>\n",
              "      <td>0.412980</td>\n",
              "      <td>0.844000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.356300</td>\n",
              "      <td>0.375695</td>\n",
              "      <td>0.861000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.315500</td>\n",
              "      <td>0.351684</td>\n",
              "      <td>0.864000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.287000</td>\n",
              "      <td>0.333529</td>\n",
              "      <td>0.870000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.263100</td>\n",
              "      <td>0.322007</td>\n",
              "      <td>0.871000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.244000</td>\n",
              "      <td>0.312268</td>\n",
              "      <td>0.873000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.229500</td>\n",
              "      <td>0.305090</td>\n",
              "      <td>0.881000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.214500</td>\n",
              "      <td>0.300390</td>\n",
              "      <td>0.882000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.203800</td>\n",
              "      <td>0.297051</td>\n",
              "      <td>0.884000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.193500</td>\n",
              "      <td>0.295561</td>\n",
              "      <td>0.885000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.184200</td>\n",
              "      <td>0.293789</td>\n",
              "      <td>0.885000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.175900</td>\n",
              "      <td>0.292450</td>\n",
              "      <td>0.888000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.169300</td>\n",
              "      <td>0.292292</td>\n",
              "      <td>0.887000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.292837</td>\n",
              "      <td>0.886000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.155200</td>\n",
              "      <td>0.292746</td>\n",
              "      <td>0.885000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.150500</td>\n",
              "      <td>0.293125</td>\n",
              "      <td>0.884000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.144500</td>\n",
              "      <td>0.294448</td>\n",
              "      <td>0.884000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.140900</td>\n",
              "      <td>0.295942</td>\n",
              "      <td>0.883000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10000, training_loss=0.25909436492919924, metrics={'train_runtime': 164.6907, 'train_samples_per_second': 15544.287, 'train_steps_per_second': 121.44, 'total_flos': 13078651968.0, 'train_loss': 0.25909436492919924, 'epoch': 51.02})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can then evaluate the trained model on a given dataset (here our test subset) by calling [`Trainer.evaluate`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer.evaluate):"
      ],
      "metadata": {
        "id": "Td03fcIa-6Hj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval_results = trainer.evaluate(dset_tokenized[\"test\"])\n",
        "\n",
        "print(eval_results)"
      ],
      "metadata": {
        "id": "9nlEwpnF2Vow",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "da281fae-6983-44cf-c08d-88aaeaf709f0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3125/3125 00:05]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.2922581732273102, 'eval_accuracy': 0.88004, 'eval_runtime': 6.0843, 'eval_samples_per_second': 4108.913, 'eval_steps_per_second': 513.614, 'epoch': 51.02}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inspect the weights\n",
        "\n",
        "* Now, since the embeddings are just single numbers, we can sort them and see if this worked"
      ],
      "metadata": {
        "id": "13aB7DuqzeFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Grab the embedding matrix out of the trained model\n",
        "# and drop the first row (padding 0)\n",
        "# then we can treat the embeddings as vectors\n",
        "# and maybe compare them to each other\n",
        "# ha ha this below took some googling\n",
        "weights=mlp.embedding.weight.detach().cpu().numpy()\n",
        "weights=weights[1:,:]"
      ],
      "metadata": {
        "id": "M6TUrVkMCmz7"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Weights is an array (num_words,1) i.e. a 2-dim array\n",
        "#Flatten turns it into a vector of numbers, removing the useless size-1 extra dimension\n",
        "#argsort gives the indices in their sorted order\n",
        "\n",
        "sorted_order=weights.flatten().argsort()"
      ],
      "metadata": {
        "id": "6vpxvsltPqIA"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first 30 and last 30 words in their order by the weight learned\n",
        "# by the neural net\n",
        "# we should see a bunch of negative words and then a bunch of positive words\n",
        "# (I guess it is possible the order comes out the other way around, depending on\n",
        "#  which way the random initialization of the network pushes it)\n",
        "\n",
        "for word_idx in sorted_order[:30]:\n",
        "    print(idx2word[word_idx])\n",
        "print(\"\\n...\\n\")\n",
        "for word_idx in sorted_order[-30:]:\n",
        "    print(idx2word[word_idx])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uTTvRgGDU9D",
        "outputId": "ed2583ea-4454-4e82-fc5b-854c28782b8b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "worst\n",
            "waste\n",
            "awful\n",
            "poorly\n",
            "boring\n",
            "disappointment\n",
            "disappointing\n",
            "dull\n",
            "poor\n",
            "avoid\n",
            "terrible\n",
            "worse\n",
            "mess\n",
            "unfunny\n",
            "bad\n",
            "pointless\n",
            "badly\n",
            "laughable\n",
            "horrible\n",
            "fails\n",
            "redeeming\n",
            "forgettable\n",
            "save\n",
            "lame\n",
            "unwatchable\n",
            "wasted\n",
            "lacks\n",
            "wooden\n",
            "annoying\n",
            "unfortunately\n",
            "\n",
            "...\n",
            "\n",
            "subtle\n",
            "hooked\n",
            "excellently\n",
            "recommended\n",
            "captures\n",
            "noir\n",
            "best\n",
            "flawless\n",
            "fun\n",
            "perfectly\n",
            "underrated\n",
            "funniest\n",
            "highly\n",
            "enjoyed\n",
            "enjoyable\n",
            "incredible\n",
            "today\n",
            "favorite\n",
            "rare\n",
            "fantastic\n",
            "loved\n",
            "wonderful\n",
            "gem\n",
            "wonderfully\n",
            "refreshing\n",
            "superb\n",
            "amazing\n",
            "great\n",
            "perfect\n",
            "excellent\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now, that worked like charm, no?**"
      ],
      "metadata": {
        "id": "cQCmThdu2LDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# As a bonus, we can check out what the output layer of the network learned\n",
        "#\n",
        "print(mlp.output.weight.detach().cpu().numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEevDnmYRAUI",
        "outputId": "5b0fa818-6bf5-44c9-e898-4d95c545d0b5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.2100827]\n",
            " [ 1.2127717]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we see here is that class 0, i.e. negative, reacts to negative sum of the feature weights (i.e. the output becomes positive when the sum of the feature weights is negative).\n",
        "\n",
        "Class 1, i.e. positive, then reacts in the exact opposite way, ie its output is positive when the sum of the feature weights is positive.\n",
        "\n",
        "We also see these are, according to our intuition, complementary to each other, being numerically quite equivalent except for the sign to which they react."
      ],
      "metadata": {
        "id": "QlVzN-I4RQaw"
      }
    }
  ]
}