Transformer_2500_vocab_uncased.ipynb

{
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      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Inw067d_UJHL",
        "outputId": "135547d0-08a1-467f-f45d-0f1be0fdcee0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "import json\n",
        "!pip install datasets\n",
        "from datasets import load_dataset\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z3vZNIk7Uplc",
        "outputId": "2ec1c8a9-e783-460c-8a21-8bc1194bec40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZ0qMqMl1CYY"
      },
      "source": [
        "# Global constants"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ut1MjH8QOZWr"
      },
      "source": [
        "***TODO: change vocab to list instead of set to maintain order***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O-ABdYdH1ASv"
      },
      "outputs": [],
      "source": [
        "TOKENIZER_DIR = '/content/drive/MyDrive/saved_models/tokenizer_2500_vocab.json'\n",
        "CHECKPOINT_DIR = '/content/drive/MyDrive/checkpoints/trained_checkpoint/checkpoint.ckpt'\n",
        "DATASET_DIR = '/content/drive/MyDrive/datasets/tokenized_articles'\n",
        "LOG_DIR = '/content/drive/MyDrive/logs/training.log'\n",
        "\n",
        "PAD_TOKEN = '<pad>'\n",
        "START_TOKEN = '<s>'\n",
        "END_TOKEN = '</s>'\n",
        "# how many pairs we want to merge per epoch during tokenizer training\n",
        "MERGES_PER_EPOCH = 15\n",
        "\n",
        "# model hyperparamaters\n",
        "BATCH_SIZE = 64\n",
        "BUFFER_SIZE = 50_000\n",
        "train_split = 0.8\n",
        "validation_split = 0.1\n",
        "test_split = 0.1\n",
        "vocab_size = 2500\n",
        "transformer_units = 6\n",
        "d_model = 384\n",
        "seq_length = 256\n",
        "attention_heads = 8\n",
        "dff = 1024 # transformer ffn hidden layer size\n",
        "dropout_rate = 0.1\n",
        "learning_rate = 1e-6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXV8bsiQUqOE"
      },
      "source": [
        "# Process data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MZk9GX5UvZk"
      },
      "source": [
        "### Loading dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "c69db3e18bf64589a9cd906e79f37b83"
          ]
        },
        "id": "lFxDChXXQ44s",
        "outputId": "d73946d0-124c-431e-c6b6-d82853f479f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset 263.29 MiB (download: 263.29 MiB, generated: 267.22 MiB, total: 530.52 MiB) to /root/tensorflow_datasets/wikipedia/20230601.simple/1.0.0...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c69db3e18bf64589a9cd906e79f37b83",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Completed...:   0%|          | 0/7 [00:00<?, ? file/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset wikipedia downloaded and prepared to /root/tensorflow_datasets/wikipedia/20230601.simple/1.0.0. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "ds = tfds.load('wikipedia/20230601.simple', split='train')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k23PrMGGSDlG"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Loads the wikipedia articles.\n",
        "\n",
        "The database contents are in binary form, we therefore need to decode the strings\n",
        "using UTF-8 encoding. We then turn all uppercase letters into lowercase. If we\n",
        "find a category section we remove it. And finally we append the start and end\n",
        "tokens and save the article.\n",
        "\"\"\"\n",
        "wiki_articles = list()\n",
        "for e in ds:\n",
        "  text = e['text'].numpy().decode('utf-8')\n",
        "  text = text.lower()\n",
        "  idx = text.find('category:')\n",
        "  if idx != -1:\n",
        "    text = text[:idx]\n",
        "  wiki_articles.append(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ELXOXLUU5NZ",
        "outputId": "044c00d5-a8c4-4ba2-d5ce-476af77f73dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>swedesboro is a borough in gloucester county, new jersey, united states. as of 2010, it had a population of 2,584 inhabitants and a population density of 1,292 people per km².\n",
            "\n",
            "swedesboro was formed as a borough by an act of the new jersey legislature on april 9, 1902, from portions of woolwich township. the borough was named for its early settlers originally from the colony of new sweden.\n",
            "\n",
            "history\n",
            "swedesboro was, at the side of bridgeport, one of only two settlements in what is now new jersey in the colony of new sweden. the oldest existing logging cottage in the united states was built by antti niilonpoika (anthony neilson/nelson) in swedesboro. it is registered as a historic monument, which is also the church of trinity church. this church was founded as a swedish lutheran church 1703; the current building was erected 1784.\n",
            "\n",
            "the cemetery at the church is the burial ground of the governor of new jersey charles c. stratton and congressman benjamin franklin howey, among other notable people.\n",
            "\n",
            "demographics\n",
            "according to the 2000 american census, its population was of 2055 inhabitants. in 2006, a population of 2043 was estimated, a decrease of 12 (-0.6%). whites are the major ethnic group, numbering about 69.81% of swedesboro's total population in 2010.\n",
            "\n",
            "other websites\n",
            " borough of swedesboro website\n",
            "\n",
            "</s>\n"
          ]
        }
      ],
      "source": [
        "print(wiki_articles[10005])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNqdF3NaUuzM",
        "outputId": "e80d3455-592c-4bc6-afd9-64e263702081"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of paragraphs: 21151\n"
          ]
        }
      ],
      "source": [
        "book_paragraphs = list()\n",
        "\n",
        "for i in range(10):\n",
        "  with open(f'/content/drive/MyDrive/datasets/books/book{i}.txt', 'r') as f:\n",
        "    for paragraph in f.read().split('\\n\\n'):\n",
        "      if len(paragraph) > 50 and 'Gutenberg' not in paragraph:\n",
        "        paragraph = paragraph.replace('\\n', ' ').lower()\n",
        "        book_paragraphs.append(paragraph)\n",
        "\n",
        "print('Number of paragraphs:', len(book_paragraphs))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XVly9tnpBpi"
      },
      "source": [
        "## Loading CNN data"
      ]
    },
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      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/15.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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          "metadata": {}
        },
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          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "844213adda904bc5a9f9677d079d5069"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "fbe69203b8654280b757e18bdeb94de9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/287113 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/13368 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee7fec544d5c472a982d52ee5be61f09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/11490 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f14ceaf83efc46bda09032d1f801c808"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# tensorflow has outdated checksums, and therefore we need to download the dataset from Hugging Face\n",
        "dataset = load_dataset(\"cnn_dailymail\", '3.0.0')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1motXuFwzHJH",
        "outputId": "37f5045c-3774-4592-b4ab-da535d7e558c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['article', 'highlights', 'id'],\n",
              "        num_rows: 287113\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['article', 'highlights', 'id'],\n",
              "        num_rows: 13368\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['article', 'highlights', 'id'],\n",
              "        num_rows: 11490\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Combining the dataset splits into one list, excluding highlights and ids."
      ],
      "metadata": {
        "id": "RZdxRzDOSNRY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ah5uRrFyyS0N"
      },
      "outputs": [],
      "source": [
        "articles = list()\n",
        "\n",
        "for split in ['train', 'validation', 'test']:\n",
        "  articles.extend(dataset[split]['article'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tNfbvkcyzeOR"
      },
      "outputs": [],
      "source": [
        "for i, e in enumerate(articles):\n",
        "  articles[i] = e.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rryi5xzvzqtS",
        "outputId": "c349d56a-3e01-4483-c14f-2391c11b1004"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "london, england (reuters) -- harry potter star daniel radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on monday, but he insists the money won't cast a spell on him. daniel radcliffe as harry potter in \"harry potter and the order of the phoenix\" to the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"i don't plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an australian interviewer earlier this month. \"i don't think i'll be particularly extravagant. \"the things i like buying are things that cost about 10 pounds -- books and cds and dvds.\" at 18, radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"hostel: part ii,\" currently six places below his number one movie on the uk box office chart. details of how he'll mark his landmark birthday are under wraps. his agent and publicist had no comment on his plans. \"i'll definitely have some sort of party,\" he said in an interview. \"hopefully none of you will be reading about it.\" radcliffe's earnings from the first five potter films have been held in a trust fund which he has not been able to touch. despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"people are always looking to say 'kid star goes off the rails,'\" he told reporters last month. \"but i try very hard not to go that way because it would be too easy for them.\" his latest outing as the boy wizard in \"harry potter and the order of the phoenix\" is breaking records on both sides of the atlantic and he will reprise the role in the last two films.  watch i-reporter give her review of potter's latest » . there is life beyond potter, however. the londoner has filmed a tv movie called \"my boy jack,\" about author rudyard kipling and his son, due for release later this year. he will also appear in \"december boys,\" an australian film about four boys who escape an orphanage. earlier this year, he made his stage debut playing a tortured teenager in peter shaffer's \"equus.\" meanwhile, he is braced for even closer media scrutiny now that he's legally an adult: \"i just think i'm going to be more sort of fair game,\" he told reuters. e-mail to a friend . copyright 2007 reuters. all rights reserved.this material may not be published, broadcast, rewritten, or redistributed.\n"
          ]
        }
      ],
      "source": [
        "print(articles[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BUJDChdUzVD"
      },
      "source": [
        "## Training tokenizer\n",
        "\n",
        "This is an implementation of the Byte-Pair-Encoding algorithm. In which we start with a base vocabulary of tokens. Then we find the most common pair of tokens and merge them into a new token. This process is done iteratively until we reach our desired vocabulary size. However, the encoding algorithm is slightly different from the normal BPE implementation. Instead of iterating over all possible merges and checking them against all tokens, the algorithm checks if adjacent tokens can be merged into a new token that exists in the vocabulary. This process is over 100X faster, in part due to the speed of set lookup. The resulting encodings are sometimes different, however from my observations they shouldn't lead to inferior results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rXtljq26pxA1"
      },
      "outputs": [],
      "source": [
        "base_vocab = 'abcdefghijklmnopqrstuvwxyz!\"#$£%&\\'()*,-./0123456789:;<>?[\\\\]{}\\n '"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Tokenizer:\n",
        "  \"\"\"\n",
        "  An implementation of a BPE (Byte-Pair-Encoding) tokenizer.\n",
        "\n",
        "  The tokenizer allows us to create a token vocabulary size of our choosing while preventing Out-Of-Vocabulary problems. This is done by starting with a base vocabulary comprised of basic characters. The tokenizer then goes over the training corpus, finds the most frequent token pair, and merges them into a new token. This process is done until our desired vocabulary size is reached.\n",
        "\n",
        "  Attributes:\n",
        "    vocab_size (int): The maximum of the tokenizer's vocabulary.\n",
        "    oov_token (str): The predefined Out-Of-Vocabulary token.\n",
        "    tuple_delimiter (char): The delimiter used to seperate tuple elements when\n",
        "      serializing the tokenizer.\n",
        "    vocab (str): the tokenizer's vocabulary.\n",
        "    reserved_tokens (list[str]): Tokens reserved for other purposes.\n",
        "    merges (dict[tuple[str], str]): The token merges learned turing training.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, vocab_size: int, pad_token: str = '<pad>', oov_token: str = '<oov>', reserved_tokens: list[str] = list()) -> None:\n",
        "    \"\"\"\n",
        "    Tokenizer constructor.\n",
        "\n",
        "    Initializes the tokenizer's attributes and loads the base vocabulary. Training\n",
        "    the tokenizer is done by calling the ```train_on_corpus``` function, not by\n",
        "    calling the constructor.\n",
        "\n",
        "    Args:\n",
        "      vocab_size: The maximum size of the tokenizer's vocabulary.\n",
        "      oov_token: The predefined Out-Of-Vocabulary token.\n",
        "      reserved_tokens: Predefined tokens such as padding, start and end.\n",
        "    \"\"\"\n",
        "    self.vocab_size = vocab_size\n",
        "    self.oov_token = oov_token\n",
        "    self.tuple_delimiter = chr(0x1d)\n",
        "\n",
        "    self.vocab = [pad_token] + [oov_token]\n",
        "    for token in reserved_tokens:\n",
        "      self.vocab.append(token)\n",
        "\n",
        "    self.vocab += list(base_vocab)\n",
        "    self.vocab_set = set(self.vocab)\n",
        "\n",
        "    reserved_tokens.append(pad_token)\n",
        "    reserved_tokens.append(oov_token)\n",
        "    self.reserved_tokens = reserved_tokens\n",
        "\n",
        "    self.create_lookup_maps()\n",
        "\n",
        "    self.merges = dict()\n",
        "\n",
        "\n",
        "  def tokenize(self, input: str) -> list[int]:\n",
        "    \"\"\"\n",
        "    Tokenizes a string using the tokenizer's learned merge table.\n",
        "\n",
        "    The input is first split up into base tokens found in the ```base_vocab``` vocabulary. A loop then iterates over the tokens and checks whether the concatenation of adjacent pairs is in the vocabulary. If it is, the tokens are merged. Once the tokenizer can no longer merge tokens, the tokens are vectorized and returned.\n",
        "\n",
        "    Args:\n",
        "      input: The string we want to tokenize.\n",
        "\n",
        "    Returns:\n",
        "      A list containing the tokenized input in vector form.\n",
        "    \"\"\"\n",
        "\n",
        "    split = [c for c in input if c in self.vocab_set]\n",
        "\n",
        "    flag = True\n",
        "    while flag:\n",
        "      flag = False\n",
        "      i = 0\n",
        "      while len(split) - 1 > i:\n",
        "        merge = split[i] + split[i + 1]\n",
        "        if merge in self.vocab_set:\n",
        "          split[i:i+2] = [merge]\n",
        "          flag = True\n",
        "        else:\n",
        "          i += 1\n",
        "\n",
        "    vectorized = list(map(lambda t: self.token_to_index[t], split))\n",
        "\n",
        "    return vectorized\n",
        "\n",
        "\n",
        "  def detokenize(self, input: list[int]) -> str:\n",
        "    \"\"\"\n",
        "    Detokenizes a list of tokens in vector form into a string.\n",
        "\n",
        "    Each token vector is mapped to its corresponding token. The tokens are then joined together to form the final string.\n",
        "\n",
        "    Args:\n",
        "      input: The list of token vectors we want to detokenize.\n",
        "\n",
        "    Returns:\n",
        "      A string derived from the input tokens.\n",
        "    \"\"\"\n",
        "    # map each token vector, the index, to its string form\n",
        "    devectorized = list(map(lambda i: self.index_to_token[i], input))\n",
        "\n",
        "    return ''.join(devectorized)\n",
        "\n",
        "\n",
        "  def train_on_corpus(self, corpus: list[str], verbose=False) -> None:\n",
        "    \"\"\"\n",
        "    Trains the tokenizer on a corpus.\n",
        "\n",
        "    The corpus is first normalized, a process in which characters not present in the base vocabulary are removed. It is then pretokenized, meaning the corpus is split up into words and their frequencies to reduce computation later. After that, the words are split up into their base token representations. Finally we search for the most frequent token pair, merge them into a new token, add it to the vocabulary and merges dictionary, and update our word splits. This is done until our desired vocabulary size is reached.\n",
        "\n",
        "    In order to reduce training time a number of pairs are merges in each \"epoch\". These pairs are picked by sorting the pair frequencies by highest lowest.\n",
        "\n",
        "    Args:\n",
        "      corpus: The corpus we want to train on. It's recommended to preprocess the\n",
        "        text before tokenizer training, but not necessary.\n",
        "    \"\"\"\n",
        "    # normalization & pretokenization\n",
        "    word_freq = dict()\n",
        "    for e in corpus:\n",
        "      for word in self.normalize_and_split(e):\n",
        "        word_freq[word] = word_freq.get(word, 0) + 1\n",
        "\n",
        "    # initial word split\n",
        "    splits = { word:[c for c in word] for word in word_freq.keys() }\n",
        "\n",
        "    # merging\n",
        "    while len(self.vocab) < self.vocab_size:\n",
        "      pair_freq = self.calculate_pair_freq(word_freq, splits)\n",
        "      best_merges = sorted(pair_freq.items(), key=lambda x: -x[1])\n",
        "\n",
        "      left = self.vocab_size - len(self.vocab)\n",
        "      for i in range(min(MERGES_PER_EPOCH, left)):\n",
        "        new_merge = best_merges[i][0]\n",
        "        self.merges[new_merge] = new_merge[0] + new_merge[1]\n",
        "        self.vocab.append(new_merge[0] + new_merge[1])\n",
        "        self.vocab_set.add(new_merge[0] + new_merge[1])\n",
        "        self.merge_pairs(new_merge[0], new_merge[1], splits)\n",
        "\n",
        "      if verbose:\n",
        "        percent = len(self.vocab) / self.vocab_size * 100\n",
        "        print(f'\\rCurrent vocab: {len(self.vocab)}/{self.vocab_size} {percent:.2f}%', end='  ')\n",
        "\n",
        "    if verbose:\n",
        "      print()\n",
        "\n",
        "    self.create_lookup_maps()\n",
        "\n",
        "\n",
        "  def normalize_and_split(self, text: str) -> list[str]:\n",
        "    \"\"\"\n",
        "    Normalizes a text and splits it by whitespace.\n",
        "\n",
        "    Characters not found in the tokenizer's base vocabulary are filtered out. The text is then seperated into individual words by whitespace while keeping the delimiter.\n",
        "\n",
        "    Args:\n",
        "      text: The string we want to normalize and split.\n",
        "    \"\"\"\n",
        "    text = ''.join([c for c in text if c in base_vocab])\n",
        "    words = [' ' + word for word in text.split(' ') if word]\n",
        "    if len(words) > 0:\n",
        "      words[0] = words[0][1:]  # remove space from first element\n",
        "\n",
        "    return words\n",
        "\n",
        "\n",
        "  def calculate_pair_freq(self, word_freq: dict[str, int], splits: dict[str, list[str]]) -> dict[tuple[str, str], int]:\n",
        "    \"\"\"\n",
        "    Calculates all pair frequencies in a pretokenized corpus.\n",
        "\n",
        "    All token pairs of the pretokenized words are iterated over and their frequency tracked.\n",
        "\n",
        "    Args:\n",
        "      word_freq: The pretokenized words with their frequencies.\n",
        "      splits: The tokens comprising the pretokenized words.\n",
        "\n",
        "    Returns:\n",
        "      A dictionary of all token pairs with their respective frequencies.\n",
        "    \"\"\"\n",
        "    pair_freq = dict()\n",
        "    for word, freq in word_freq.items():\n",
        "      split = splits[word]\n",
        "      for pair in zip(split, split[1:]):  # zip adjacent tokens\n",
        "        pair_freq[pair] = pair_freq.get(pair, 0) + freq\n",
        "\n",
        "    return pair_freq\n",
        "\n",
        "\n",
        "  def merge_pairs(self, t1: str, t2: str, splits: dict[str, list[str]]) -> None:\n",
        "    \"\"\"\n",
        "    Merges tokens comprising pretokenized words.\n",
        "\n",
        "    All token pairs in the pretokenized words are compared against the merge tokens. If a match is found the tokens are compined and replaced with the merged token.\n",
        "\n",
        "    Args:\n",
        "      t1: The first token to merge.\n",
        "      t2: The seconds token to merge.\n",
        "      splits: The pretokenized words with their token splits.\n",
        "    \"\"\"\n",
        "    for word, split in splits.items():\n",
        "      i = 0\n",
        "      while len(split) - 1 > i:\n",
        "        if split[i] == t1 and split[i + 1] == t2:\n",
        "          split[i:i+2] = [t1 + t2]\n",
        "        else:\n",
        "          i += 1\n",
        "\n",
        "      splits[word] = split\n",
        "\n",
        "\n",
        "  def create_lookup_maps(self) -> None:\n",
        "    \"\"\"\n",
        "    Creates mappings for token to index and index to token.\n",
        "\n",
        "    The vocabulary is enumerated over and the appropriate key and values are placed in a map.\n",
        "    \"\"\"\n",
        "    self.token_to_index = { token:i for i, token in enumerate(self.vocab) }\n",
        "    self.index_to_token = { i:token for i, token in enumerate(self.vocab) }\n",
        "\n",
        "\n",
        "  def serialize_to_file(self, file_path: str) -> None:\n",
        "    \"\"\"\n",
        "    Serializes the tokenizer and saves it to a file.\n",
        "\n",
        "    The tokenizer's attributes are turned into JSON form and then saved to an file. The merges map contains a tuple type which cannot be serialized to JSON. Therefore, we place the tupple elements in a string and separate them with the ```self.tuple_delimiter``` char.\n",
        "\n",
        "    Args:\n",
        "      file_path: The file path for saving the serialized file.\n",
        "    \"\"\"\n",
        "    data = dict()\n",
        "    data['vocab_size'] = self.vocab_size\n",
        "    data['oov_token'] = self.oov_token\n",
        "    data['vocab'] = self.vocab\n",
        "    data['reserved_tokens'] = self.reserved_tokens\n",
        "    serialized_merges = { k[0]+self.tuple_delimiter+k[1] : v for k, v in self.merges.items() }\n",
        "    data['merges'] = serialized_merges\n",
        "\n",
        "    with open(file_path, 'w') as f:\n",
        "      json.dump(data, f)\n",
        "\n",
        "\n",
        "  def deserialize_from_file(self, file_path: str) -> None:\n",
        "    \"\"\"\n",
        "    Deserializes the tokenizer from a serialized JSON file.\n",
        "\n",
        "    The JSON file containing the serialized tokenizer is read from and the tokenizer's attributes are set. The tuples in the merges map are represented as a string with their elements separated by a delimiter. During deserialization we split this string by the delimiter and recreate the tupple.\n",
        "    \"\"\"\n",
        "    with open(file_path, 'r') as f:\n",
        "      data = json.load(f)\n",
        "      self.vocab_size = data['vocab_size']\n",
        "      self.oov_token = data['oov_token']\n",
        "      self.vocab = data['vocab']\n",
        "      self.vocab_set = set(self.vocab)\n",
        "      self.reserved_tokens = data['reserved_tokens']\n",
        "      serialized_merges = data['merges']\n",
        "      self.merges = dict()\n",
        "      for k, v in serialized_merges.items():\n",
        "        delimiter_idx = k.index(self.tuple_delimiter)\n",
        "        key = (k[:delimiter_idx], k[delimiter_idx+1:])\n",
        "        self.merges[key] = v\n",
        "\n",
        "    self.create_lookup_maps()\n",
        "\n",
        "  def get_vocab(self):\n",
        "    return self.vocab"
      ],
      "metadata": {
        "id": "AjQPj1PGUuIk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the tokenizer and serialize its state. Only the first 100,000 articles are using for training to reduce compute time while keeping good merging accuracy. Only two reserved tokens need to be defined, start of text and end of text, because the constructor has default definitions for padding and out of vocabulary tokens."
      ],
      "metadata": {
        "id": "NuQ99CheeRP-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pj_FtW7OWxfB",
        "outputId": "186f544d-02da-4e5e-f2b5-9f2c9f51951f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Current vocab: 2500/2500 100.00%  \n"
          ]
        }
      ],
      "source": [
        "reserved_tokens = [START_TOKEN, END_TOKEN]\n",
        "tokenizer = Tokenizer(vocab_size, reserved_tokens=reserved_tokens)\n",
        "\n",
        "# train on the first 100,000 articles to reduce run time and retain good merge accuracy\n",
        "tokenizer.train_on_corpus(articles[:100_000], verbose=True)\n",
        "\n",
        "tokenizer.serialize_to_file(TOKENIZER_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6tMaENK6GqYP"
      },
      "outputs": [],
      "source": [
        "tokenizer.serialize_to_file('/content/drive/MyDrive/saved_models/tokenizer_backup.json')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ykk2JTyVhyvp"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer(vocab_size)\n",
        "tokenizer.deserialize_from_file(TOKENIZER_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtXL0XZSizoQ",
        "outputId": "5d85f4c2-270d-42a1-a5aa-245067ce319d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[542, 150, 23, 187]\n",
            " understand\n"
          ]
        }
      ],
      "source": [
        "a = tokenizer.tokenize(' understand')\n",
        "print(a)\n",
        "print(tokenizer.detokenize(a))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluating the tokenizer on an excerpt from an article not found in the training set. As we can see, commonly used words are encoded using a single token. Whereas rarer words are split up across multiple tokens. It can also be observed that the tokenizer can detokenize the tokens as intended."
      ],
      "metadata": {
        "id": "Aj-T-JUKeulN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ISHtmoqCUl8",
        "outputId": "945d3fd8-53f1-41dd-dba8-fde65e38052a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[128, 332, 894, 367, 200, 69, 1087, 101, 352, 243, 301, 228, 542, 150, 23, 187, 389, 142, 776, 128]\n",
            "128=> on\n",
            "332=> one\n",
            "894=> hand\n",
            "367=> there\n",
            "200=> are\n",
            "69=> a\n",
            "1087=> lot\n",
            "101=> of\n",
            "352=> people\n",
            "243=> who\n",
            "301=> do\n",
            "228=> not\n",
            "542=> und\n",
            "150=>ers\n",
            "23=>t\n",
            "187=>and\n",
            "389=> what\n",
            "142=> is\n",
            "776=> going\n",
            "128=> on\n",
            " on one hand there are a lot of people who do not understand what is going on\n"
          ]
        }
      ],
      "source": [
        "tokenized = tokenizer.tokenize(' on one hand there are a lot of people who do not understand what is going on')\n",
        "print(tokenized)\n",
        "for token in tokenized:\n",
        "  print(f'{token}=>{tokenizer.detokenize([token])}')\n",
        "print(tokenizer.detokenize(tokenized))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[128, 332, 894, 367, 200, 69, 1087, 101, 352, 243, 301, 228, 542, 150, 23, 187, 389, 142, 776, 128]\n",
        "128=> on\n",
        "332=> one\n",
        "894=> hand\n",
        "367=> there\n",
        "200=> are\n",
        "69=> a\n",
        "1087=> lot\n",
        "101=> of\n",
        "352=> people\n",
        "243=> who\n",
        "301=> do\n",
        "228=> not\n",
        "542=> und\n",
        "150=>ers\n",
        "23=>t\n",
        "187=>and\n",
        "389=> what\n",
        "142=> is\n",
        "776=> going\n",
        "128=> on\n",
        " on one hand there are a lot of people who do not understand what is going on"
      ],
      "metadata": {
        "id": "J06vaZmagatt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accessing the tokenizer's speed using Google Colab's built in timeit function. The sentence chosen for this task is an excerpt from an article not found in the training set with appropriate reserved tokens added."
      ],
      "metadata": {
        "id": "ELfFmgXog0In"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yuc2ZLXgqtfK"
      },
      "outputs": [],
      "source": [
        "corpus = ['<s>with that being said, there are many who think that</s><pad>'] * 1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATTBeiRUq1DJ",
        "outputId": "8908e156-806a-416a-9e13-198665a6e5c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "47.2 ms ± 703 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ]
        }
      ],
      "source": [
        "%%timeit\n",
        "for e in corpus:\n",
        "  tokenizer.tokenize(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odmKcYvA0jaP"
      },
      "source": [
        "## Create dataset pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset pipeline is created by tokening the articles, splitting them up into sequence-length long chunk and packaging them up in tenorflow's Dataset object. Using a dataset object gives us more control and flexibility, primarily in the way the data is loaded into ram. In order to reduce the dataset's disk space, sequence-length plus one long chunks are stored. When loading the dataset, these chunks are turned into the X and Y values for the function."
      ],
      "metadata": {
        "id": "QHD9CE5L0EWt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QYfxVYf3G_-j"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer(vocab_size)\n",
        "tokenizer.deserialize_from_file(TOKENIZER_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWVRlDAV0mq_",
        "outputId": "81efb96d-8e5d-4f9a-f0c9-d80384e70177"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Articles tokenized: 311971/311971 100.00%  "
          ]
        }
      ],
      "source": [
        "for i, e in enumerate(articles):\n",
        "  # overwrite articles with their tokenized form to reduce ram usage\n",
        "  articles[i] = tokenizer.tokenize(e)\n",
        "  percent = (i + 1) / len(articles) * 100\n",
        "  print(f'\\rArticles tokenized: {i+1}/{len(articles)} {percent:.2f}%', end='  ')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "qFuKej5gmoPu",
        "outputId": "ade6f04a-0c63-4e04-ee10-d7e34e31bc21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "avg: 1212.4867567818803\n",
            "mode: 882\n"
          ]
        },
        {
          "data": {
            "image/png": 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llVdq3bp1GjFiRLifzlFcXKyioiLn62AwSMQAABCnIn4Z9RVXXKEuXbroo48+kiT5fD4dOnQoZMypU6d0+PBh57wZn8+nmpqakDGNX5/r3Bq32y2PxxNyAwAA8SniAfOnP/1Jn3/+ubp27SpJ8vv9qq2tVUVFhTNmzZo1amhoUHZ2tjNmw4YNOnnypDOmtLRUvXr1UseOHSM9ZQAAEOOaHTBHjx5VZWWlKisrJUn79u1TZWWlqqurdfToUU2dOlWbN2/Wp59+qrKyMt1+++3q2bOncnNzJUl9+vTRqFGjdO+992rr1q3atGmTCgsLNXbsWGVkZEiS7r77brlcLk2cOFG7d+/WkiVL9PTTT4e8RQQAAFqvZgfMtm3bNGjQIA0aNEiSVFRUpEGDBmnWrFlKSkrSjh079L3vfU9XX321Jk6cqMGDB+v3v/+93G638xgLFy5U7969NWLECN16660aNmxYyGe8eL1evf3229q3b58GDx6shx56SLNmzeISagAAIElKMMaYaE8iEoLBoLxer+rq6qw5H8a2T+L9dE5etKcAAIgzF/r6ze9CwkXrMWOlddEFAIgPBAwAALAOAQMAAKxDwAAAAOsQMAAAwDoETIzgZFgAAC4cAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgGDS8aH8AEAWhoBAwAArEPAAAAA6xAwMSAe3oKJh30AANiDgEHYEDEAgJZCwAAAAOsQMAAAwDoETJTxtgsAAM1HwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAIKw4KRkA0BIIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4BB2HEpNQAg0ggYAABgHQIGAABYh4ABAADWIWAQcZwTAwAINwIGAABYh4ABAADWIWAAAIB1CBgAAGAdAgYAAFiHgIkirs4BAODiEDCICOIMABBJBAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMIoYPswMARAoBAwAArNPsgNmwYYNuu+02ZWRkKCEhQcuXLw/ZbozRrFmz1LVrV6WkpCgnJ0cffvhhyJjDhw8rPz9fHo9Hqampmjhxoo4ePRoyZseOHbrpppvUtm1bZWZmau7cuc3fOwAAEJeaHTDHjh3TwIEDNW/evCa3z507V88884yef/55bdmyRd/61reUm5urr776yhmTn5+v3bt3q7S0VCtWrNCGDRs0adIkZ3swGNTIkSPVvXt3VVRU6LHHHtPs2bP1wgsvXMQuAgCAeJPc3G8YPXq0Ro8e3eQ2Y4x++ctfaubMmbr99tslSb/5zW+Unp6u5cuXa+zYsdq7d69WrVql9957T0OGDJEkPfvss7r11lv1+OOPKyMjQwsXLtSJEyf04osvyuVyqV+/fqqsrNSTTz4ZEjoAAKB1Cus5MPv27VMgEFBOTo5zn9frVXZ2tsrLyyVJ5eXlSk1NdeJFknJycpSYmKgtW7Y4Y4YPHy6Xy+WMyc3NVVVVlb744osmn7u+vl7BYDDkBgAA4lNYAyYQCEiS0tPTQ+5PT093tgUCAaWlpYVsT05OVqdOnULGNPUYZz7H15WUlMjr9Tq3zMzMS98hXDKuRAIARELcXIVUXFysuro657Z///5oTwkAAERIWAPG5/NJkmpqakLur6mpcbb5fD4dOnQoZPupU6d0+PDhkDFNPcaZz/F1brdbHo8n5AYAAOJTWAMmKytLPp9PZWVlzn3BYFBbtmyR3++XJPn9ftXW1qqiosIZs2bNGjU0NCg7O9sZs2HDBp08edIZU1paql69eqljx47hnDIAALBQswPm6NGjqqysVGVlpaS/nrhbWVmp6upqJSQkaMqUKfqP//gPvfHGG9q5c6fGjx+vjIwM3XHHHZKkPn36aNSoUbr33nu1detWbdq0SYWFhRo7dqwyMjIkSXfffbdcLpcmTpyo3bt3a8mSJXr66adVVFQUth0HAAD2avZl1Nu2bdMtt9zifN0YFRMmTNCCBQs0bdo0HTt2TJMmTVJtba2GDRumVatWqW3bts73LFy4UIWFhRoxYoQSExM1ZswYPfPMM852r9ert99+WwUFBRo8eLC6dOmiWbNmxdUl1JzcCgDAxUswxphoTyISgsGgvF6v6urqYvJ8mNYWMJ/OyYv2FAAAFrjQ1++4uQoJAAC0HgQMAACwDgGDFtPa3jYDAEQOAYMWRcQAAMKBgEGLIFwAAOFEwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMooLLqgEAl4KAQYsjXgAAl4qAiQJewAEAuDQEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8C0MC6h/hvWAgBwsQgYRBURAwC4GAQMAACwDgEDAACsQ8Ag6ngbCQDQXAQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAICb0mLHS+ZUC/GoBAMA3IWAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYFoQJ6cCABAeBAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDGLKmSc6c9IzAOBcCBjEJEIGAHA+BAwAALAOAQMAAKxDwCCm8fYRAKApBAysQMgAAM5EwAAAAOsQMC2EIwgAAIQPAQMAAKxDwMAaHMUCADQKe8DMnj1bCQkJIbfevXs727/66isVFBSoc+fOat++vcaMGaOampqQx6iurlZeXp7atWuntLQ0TZ06VadOnQr3VAEAgKWSI/Gg/fr10zvvvPO3J0n+29M8+OCDWrlypZYtWyav16vCwkLdeeed2rRpkyTp9OnTysvLk8/n07vvvquDBw9q/PjxatOmjX7xi19EYroAAMAyEQmY5ORk+Xy+s+6vq6vT//zP/2jRokX6+7//e0nSSy+9pD59+mjz5s0aOnSo3n77be3Zs0fvvPOO0tPTde211+rRRx/V9OnTNXv2bLlcrkhMGQAAWCQi58B8+OGHysjI0BVXXKH8/HxVV1dLkioqKnTy5Enl5OQ4Y3v37q1u3bqpvLxcklReXq4BAwYoPT3dGZObm6tgMKjdu3ef8znr6+sVDAZDbgAAID6FPWCys7O1YMECrVq1SvPnz9e+fft000036ciRIwoEAnK5XEpNTQ35nvT0dAUCAUlSIBAIiZfG7Y3bzqWkpERer9e5ZWZmhnfHAABAzAj7W0ijR492/nzNNdcoOztb3bt319KlS5WSkhLup3MUFxerqKjI+ToYDBIxAADEqYhfRp2amqqrr75aH330kXw+n06cOKHa2tqQMTU1Nc45Mz6f76yrkhq/buq8mkZut1sejyfkhvjDpdQAAKkFAubo0aP6+OOP1bVrVw0ePFht2rRRWVmZs72qqkrV1dXy+/2SJL/fr507d+rQoUPOmNLSUnk8HvXt2zfS0wUAABYI+1tI//7v/67bbrtN3bt314EDB/Twww8rKSlJd911l7xeryZOnKiioiJ16tRJHo9H999/v/x+v4YOHSpJGjlypPr27atx48Zp7ty5CgQCmjlzpgoKCuR2u8M9XQAAYKGwB8yf/vQn3XXXXfr888912WWXadiwYdq8ebMuu+wySdJTTz2lxMREjRkzRvX19crNzdVzzz3nfH9SUpJWrFih++67T36/X9/61rc0YcIEPfLII+GeKizVY8ZKfTonL9rTAABEUYIxxkR7EpEQDAbl9XpVV1cXE+fDcO5GeBEwABCfLvT1m9+FBAAArEPAwFoc1QKA1ouAgZWIFwBo3QgYAABgHQKmBXC0IHJYWwBonQgYWI+IAYDWh4BB3OgxYyUxAwCtBAEDAACsQ8AAAADrEDAAAMA6BAziAue+AEDrQsAAAADrEDAAAMA6BEyE8dZGdLDuABDfCBgAAGAdAgZxj6MxABB/CBgAAGAdAgZxhyMuABD/CBgAAGAdAgZx7cyjMRyZAYD4QcAgbp0vWIgZALAbAYNWi4gBAHsRMAAAwDrJ0Z4A0JI46gIA8YEjMGgVCBcAiC8EDFo94gYA7EPARBAvjPbgZwUAdiFgAACAdQgY4Gs4GgMAsY+AQatGrACAnQgY4P8jZgDAHgQMAACwDgEDnAdHZQAgNhEwEcILn53O9XPj5wkAsYWAAb4B8QIAsYeAAc6hqXAhZgAgNhAwAADAOgQM0ASOtABAbCNggAvUGDXEDQBEHwEDAACsQ8AAF4GjMAAQXQQMcAkIGQCIDgIGCANCBgBaFgEDXCSiBQCih4CJAF7YWhd+3gDQ8ggYIEy4zBoAWg4BA4QR8QIALYOAAQAA1iFgAACAdQiYMOMtBDTi7wIARA4BA0TAmfFCyABA+BEwQAvoMWMlIQMAYUTAhBEvUPgm/B0BgPAgYIAIIlgAIDIImDDhhQoXir8rAHDpYjpg5s2bpx49eqht27bKzs7W1q1boz0lICwaz4khZgDg4sRswCxZskRFRUV6+OGH9Yc//EEDBw5Ubm6uDh06FO2pnYUXIQAAWlaCMcZEexJNyc7O1vXXX69f/epXkqSGhgZlZmbq/vvv14wZM77x+4PBoLxer+rq6uTxeCI2T+IF4fTpnLxoTwEAoupCX7+TW3BOF+zEiROqqKhQcXGxc19iYqJycnJUXl7e5PfU19ervr7e+bqurk7SXxcikhrqv4zo46N16fbgspCvd/0sV/0fXh3y510/y43G1ACgRTS+bn/T8ZWYDJi//OUvOn36tNLT00PuT09P1wcffNDk95SUlOhnP/vZWfdnZmZGZI5AS/D+8uw/n3kfAMSrI0eOyOv1nnN7TAbMxSguLlZRUZHzdUNDgw4fPqzOnTsrISEhbM8TDAaVmZmp/fv3R/StqdaMNY481rhlsM6Rxxq3jJZcZ2OMjhw5ooyMjPOOi8mA6dKli5KSklRTUxNyf01NjXw+X5Pf43a75Xa7Q+5LTU2N1BTl8Xj4xxJhrHHkscYtg3WOPNa4ZbTUOp/vyEujmLwKyeVyafDgwSorK3Pua2hoUFlZmfx+fxRnBgAAYkFMHoGRpKKiIk2YMEFDhgzRDTfcoF/+8pc6duyYfvjDH0Z7agAAIMpiNmC+//3v67PPPtOsWbMUCAR07bXXatWqVWed2NvS3G63Hn744bPerkL4sMaRxxq3DNY58ljjlhGL6xyznwMDAABwLjF5DgwAAMD5EDAAAMA6BAwAALAOAQMAAKxDwDTDvHnz1KNHD7Vt21bZ2dnaunVrtKcUs0pKSnT99derQ4cOSktL0x133KGqqqqQMV999ZUKCgrUuXNntW/fXmPGjDnrwwurq6uVl5endu3aKS0tTVOnTtWpU6dCxqxbt07XXXed3G63evbsqQULFkR692LSnDlzlJCQoClTpjj3scaX7s9//rPuuecede7cWSkpKRowYIC2bdvmbDfGaNasWeratatSUlKUk5OjDz/8MOQxDh8+rPz8fHk8HqWmpmrixIk6evRoyJgdO3bopptuUtu2bZWZmam5c+e2yP7FgtOnT+unP/2psrKylJKSoiuvvFKPPvpoyO/CYZ2bZ8OGDbrtttuUkZGhhIQELV++PGR7S67nsmXL1Lt3b7Vt21YDBgzQW2+9FZ6dNLggixcvNi6Xy7z44otm9+7d5t577zWpqammpqYm2lOLSbm5ueall14yu3btMpWVlebWW2813bp1M0ePHnXGTJ482WRmZpqysjKzbds2M3ToUPOd73zH2X7q1CnTv39/k5OTY7Zv327eeust06VLF1NcXOyM+eSTT0y7du1MUVGR2bNnj3n22WdNUlKSWbVqVYvub7Rt3brV9OjRw1xzzTXmgQcecO5njS/N4cOHTffu3c0PfvADs2XLFvPJJ5+Y1atXm48++sgZM2fOHOP1es3y5cvN+++/b773ve+ZrKwsc/z4cWfMqFGjzMCBA83mzZvN73//e9OzZ09z1113Odvr6upMenq6yc/PN7t27TKvvvqqSUlJMf/1X//VovsbLT//+c9N586dzYoVK8y+ffvMsmXLTPv27c3TTz/tjGGdm+ett94yP/nJT8xrr71mJJnXX389ZHtLreemTZtMUlKSmTt3rtmzZ4+ZOXOmadOmjdm5c+cl7yMBc4FuuOEGU1BQ4Hx9+vRpk5GRYUpKSqI4K3scOnTISDLr1683xhhTW1tr2rRpY5YtW+aM2bt3r5FkysvLjTF//QeYmJhoAoGAM2b+/PnG4/GY+vp6Y4wx06ZNM/369Qt5ru9///smNzc30rsUM44cOWKuuuoqU1paav7u7/7OCRjW+NJNnz7dDBs27JzbGxoajM/nM4899phzX21trXG73ebVV181xhizZ88eI8m89957zpjf/e53JiEhwfz5z382xhjz3HPPmY4dOzpr3vjcvXr1CvcuxaS8vDzzox/9KOS+O++80+Tn5xtjWOdL9fWAacn1/Od//meTl5cXMp/s7GzzL//yL5e8X7yFdAFOnDihiooK5eTkOPclJiYqJydH5eXlUZyZPerq6iRJnTp1kiRVVFTo5MmTIWvau3dvdevWzVnT8vJyDRgwIOTDC3NzcxUMBrV7925nzJmP0TimNf1cCgoKlJeXd9Y6sMaX7o033tCQIUP0T//0T0pLS9OgQYP03//93872ffv2KRAIhKyP1+tVdnZ2yBqnpqZqyJAhzpicnBwlJiZqy5Ytzpjhw4fL5XI5Y3Jzc1VVVaUvvvgi0rsZdd/5zndUVlamP/7xj5Kk999/Xxs3btTo0aMlsc7h1pLrGcn/PwiYC/CXv/xFp0+fPutTgNPT0xUIBKI0K3s0NDRoypQpuvHGG9W/f39JUiAQkMvlOusXbp65poFAoMk1b9x2vjHBYFDHjx+PxO7ElMWLF+sPf/iDSkpKztrGGl+6Tz75RPPnz9dVV12l1atX67777tO//du/6eWXX5b0tzU63/8NgUBAaWlpIduTk5PVqVOnZv0c4tmMGTM0duxY9e7dW23atNGgQYM0ZcoU5efnS2Kdw60l1/NcY8Kx3jH7qwQQPwoKCrRr1y5t3Lgx2lOJK/v379cDDzyg0tJStW3bNtrTiUsNDQ0aMmSIfvGLX0iSBg0apF27dun555/XhAkTojy7+LF06VItXLhQixYtUr9+/VRZWakpU6YoIyODdcY5cQTmAnTp0kVJSUlnXb1RU1Mjn88XpVnZobCwUCtWrNDatWt1+eWXO/f7fD6dOHFCtbW1IePPXFOfz9fkmjduO98Yj8ejlJSUcO9OTKmoqNChQ4d03XXXKTk5WcnJyVq/fr2eeeYZJScnKz09nTW+RF27dlXfvn1D7uvTp4+qq6sl/W2Nzvd/g8/n06FDh0K2nzp1SocPH27WzyGeTZ061TkKM2DAAI0bN04PPvigc2SRdQ6vllzPc40Jx3oTMBfA5XJp8ODBKisrc+5raGhQWVmZ/H5/FGcWu4wxKiws1Ouvv641a9YoKysrZPvgwYPVpk2bkDWtqqpSdXW1s6Z+v187d+4M+UdUWloqj8fjvKj4/f6Qx2gc0xp+LiNGjNDOnTtVWVnp3IYMGaL8/Hznz6zxpbnxxhvPuvz/j3/8o7p37y5JysrKks/nC1mfYDCoLVu2hKxxbW2tKioqnDFr1qxRQ0ODsrOznTEbNmzQyZMnnTGlpaXq1auXOnbsGLH9ixVffvmlEhNDX46SkpLU0NAgiXUOt5Zcz4j+/3HJpwG3EosXLzZut9ssWLDA7Nmzx0yaNMmkpqaGXL2Bv7nvvvuM1+s169atMwcPHnRuX375pTNm8uTJplu3bmbNmjVm27Ztxu/3G7/f72xvvMR35MiRprKy0qxatcpcdtllTV7iO3XqVLN3714zb968VnOJb1POvArJGNb4Um3dutUkJyebn//85+bDDz80CxcuNO3atTOvvPKKM2bOnDkmNTXV/Pa3vzU7duwwt99+e5OXow4aNMhs2bLFbNy40Vx11VUhl6PW1taa9PR0M27cOLNr1y6zePFi065du7i8vLcpEyZMMN/+9redy6hfe+0106VLFzNt2jRnDOvcPEeOHDHbt28327dvN5LMk08+abZv327+7//+zxjTcuu5adMmk5ycbB5//HGzd+9e8/DDD3MZdTQ8++yzplu3bsblcpkbbrjBbN68OdpTilmSmry99NJLzpjjx4+bf/3XfzUdO3Y07dq1M//wD/9gDh48GPI4n376qRk9erRJSUkxXbp0MQ899JA5efJkyJi1a9eaa6+91rhcLnPFFVeEPEdr8/WAYY0v3Ztvvmn69+9v3G636d27t3nhhRdCtjc0NJif/vSnJj093bjdbjNixAhTVVUVMubzzz83d911l2nfvr3xeDzmhz/8oTly5EjImPfff98MGzbMuN1u8+1vf9vMmTMn4vsWK4LBoHnggQdMt27dTNu2bc0VV1xhfvKTn4Rcnss6N8/atWub/D94woQJxpiWXc+lS5eaq6++2rhcLtOvXz+zcuXKsOxjgjFnfNQhAACABTgHBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYJ3/BwyetskvhCH+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenized_data_lengths = [len(e) for e in articles]\n",
        "\n",
        "avg = sum(tokenized_data_lengths) / len(tokenized_data_lengths)\n",
        "print('avg:', avg)\n",
        "mode = max(set(tokenized_data_lengths), key=tokenized_data_lengths.count)\n",
        "print('mode:', mode)\n",
        "\n",
        "plt.hist(tokenized_data_lengths, bins=1000)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "oU-cEh0V0kEs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z5xaFTD4sYXd"
      },
      "outputs": [],
      "source": [
        "split_length = seq_length + 1\n",
        "start_token_index = [2]\n",
        "end_token_index = [3]\n",
        "\n",
        "padded_data = list()\n",
        "\n",
        "for e in articles:\n",
        "  e = start_token_index + e + end_token_index\n",
        "  e = np.array(e)\n",
        "  splits = int(math.ceil(len(e) / split_length))\n",
        "  padding_length = splits * split_length - len(e)\n",
        "\n",
        "  e = np.pad(e, (0, padding_length))\n",
        "  padded_data.extend(np.array_split(e, splits))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSri1paHw09L",
        "outputId": "6ec76d8d-3ed8-44c5-aa1e-96d446252b46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1629583\n"
          ]
        }
      ],
      "source": [
        "print(len(padded_data))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.save('/content/drive/MyDrive/datasets/articles.npy', padded_data)"
      ],
      "metadata": {
        "id": "6MERfU0G4Y7p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "padded_data = np.load('/content/drive/MyDrive/datasets/articles.npy')"
      ],
      "metadata": {
        "id": "GHBurzTDAKiM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cIeJpwqOpUWr"
      },
      "outputs": [],
      "source": [
        "dataset = tf.data.Dataset.from_tensor_slices(padded_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5gsGGVlCj-6H"
      },
      "outputs": [],
      "source": [
        "dataset.save(DATASET_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6eFddh9C6lAr"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hSPOBE5lJ2DI"
      },
      "source": [
        "## The transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0z1Eo97YMNE"
      },
      "source": [
        "This transformer architecture is based on the architecture first proposed in the \"Attention is all you need\" paper, with a few changes. Primariliy adding dropout and moving the batch normilzation in front of the Multi-head masked attention layer, and adding one before the feed forward layer.\n",
        "\n",
        "**Diagram**:\n",
        "\n",
        "![diagram](https://drive.google.com/uc?export=view&id=1OuqMYRGyUihgPOramyPEEqAhgaji0HBZ)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgq2obPh6vJy"
      },
      "source": [
        "### Masked multi-head attention"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wL_2_3DsikuG"
      },
      "source": [
        "The scaled dot product attention is the core of the transformer architecture. THe attentin model is what allows the transformer to deal with large input sequences without with exploding or vanishing gradients. It's comparable to a search engine where we have a Query which gets compared to a list of Keys of Values. Values that have keys similar to the query get a higher relevance score. The similarity between queries and keys is calculated using a scaled dot product: $\\frac{Q\\cdot K^T}{\\sqrt{d_k}}$. When one of the data dimensions in the queries vector and keys vector is large, it's dot product will also be large. When either or both are small the dot product is small. However, the total dot product grows as the data dimensions grow. Therefore, we need to scale the total dot product by a factor of $\\frac{1}{\\sqrt{d_k}}$.\n",
        "\n",
        "A softmax function is then applied across the last dimension.\n",
        "\n",
        "\n",
        "\n",
        "$Attention(Q,K,V)=softmax(\\frac{Q\\cdot K^T}{\\sqrt{d_k}})\\cdot V$\n",
        "\n",
        "**Diagram:**\n",
        "\n",
        "![diagram](https://drive.google.com/uc?export=view&id=1sq2NAvfa7ECivIB1fxTn7d4xKP-OGyzZ)\n",
        "\n",
        "https://jalammar.github.io/illustrated-transformer/\n",
        "\n",
        "https://nlp.seas.harvard.edu/2018/04/03/attention.html#\n",
        "\n",
        "Attention is all you need"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vfzIP5CS6okS"
      },
      "outputs": [],
      "source": [
        "def scaled_dot_product_attention(Q, K, V, mask):\n",
        "  \"\"\"\n",
        "  Implementation of scaled dot product attention.\n",
        "\n",
        "  The shape of the queries, values and keys is not known. We only care about the last two dimensions which must be of shape (tokens, d_k) for the queries and keys, and (tokens, d_v) for the values. This allows us to use the function both for normal attention and for multi head attention. Additionally, this means the function doesn't need batch dimension boilerplate.\n",
        "\n",
        "  Args:\n",
        "    Q: the queries. (..., seq_length, d_k)\n",
        "    K: the keys. (..., seq_length, d_k)\n",
        "    V: the values. (..., seq_length, d_v)\n",
        "    mask: a boolean mask of elements not to attend to. (batches, heads, seq_length, seq_length)\n",
        "\n",
        "  Returns:\n",
        "    The scaled dot product attention of the queries, keys and values provided. If the input was comprised of multple batches and heads the result will retain the number of batches and heads. (..., seq_length, d_v)\n",
        "  \"\"\"\n",
        "  # matmul transposition uses `tf.linalg.matrix_transpose` which only transposes\n",
        "  # the inner-most 2d matrix\n",
        "  x = tf.matmul(Q, K, transpose_b=True) # (..., seq_length, seq_length)\n",
        "\n",
        "  # scale the dot product with of a factor of 1 over the standard deviation,\n",
        "  # therefore the standard deviation is now unit\n",
        "  d_k = Q.shape[-1]\n",
        "  x /= d_k**0.5\n",
        "\n",
        "  # apply look-ahead and padding masks, ones represent values we want to keep\n",
        "  replace_with = tf.expand_dims(tf.ones(x.shape[-1:], dtype=tf.float32) * -1e9, axis=0)\n",
        "  x = tf.where(mask, x, replace_with)\n",
        "\n",
        "  # apply the softmax functin across each token, horizontally\n",
        "  x = tf.nn.softmax(x, axis=-1)\n",
        "\n",
        "  x = tf.matmul(x, V) # (..., seq_length, d_v)\n",
        "\n",
        "  return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfwR4PvIYeAG"
      },
      "source": [
        "This multi-head attention architecture is identical to the one proposed in the \"Attention is all you need\" paper. The inputs are then split up into groups of size $\\frac{d_{model}}{head}$. Each group is projected using learned pojection wegiths into keys, queries and values, which are then feed into a scaled dot product attention. The heads' results are then concatenated across the last dimension and multiplied by a learned proejection weight $O_w$. Dropout is applied to prevent overfitting.\n",
        "\n",
        "\n",
        "**Diagram**:\n",
        "\n",
        "![diagram](https://drive.google.com/uc?export=view&id=14ntHSd5lbxq04yVmk_NXnPvDNUz8FzuR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dumAdOkl_kY6"
      },
      "outputs": [],
      "source": [
        "class Multi_Head_Attention(tf.keras.layers.Layer):\n",
        "  \"\"\"\n",
        "  Implementation of multi head attention. This model is identical to the one proposed in the \"Attention is all you need\" paper.\n",
        "\n",
        "  Attributes:\n",
        "    supports_masking: tells tensorflow that this layer supports masking.\n",
        "    d_model: the model's embedding dimension size.\n",
        "    heads: the number of attention heads to be used.\n",
        "    d_k: the size of keys and queries vectors.\n",
        "    d_v: the size of the value vector.\n",
        "    Q_w: the projection metrix for calculating queries, consists of learned parameters.\n",
        "    K_w: the projection metrix for calculating keys, consists of learned parameters.\n",
        "    V_w: the projection metrix for calculating values, consists of learned parameters.\n",
        "    Q_w: the projection metrix for concatenating the attention heads, consists of learned parameters.\n",
        "    dropout: the dropout layer.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, heads, d_model, dropout_rate):\n",
        "    \"\"\"\n",
        "    Creates the Multi_Head_Attentin object.\n",
        "\n",
        "    Args:\n",
        "      heads: the number of attention heads we want to use.\n",
        "      d_model: the model's embedding dimension.\n",
        "      dropout_rate: the attention mechinism's dropout rate.\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    self.supports_masking = True\n",
        "\n",
        "    if d_model % heads != 0:\n",
        "      raise ValueError('Model embedding dimensions must be divisible by the number of attention heads!')\n",
        "\n",
        "    self.d_model = d_model\n",
        "    self.heads = heads\n",
        "\n",
        "    # in this case d_k and d_v are equal, however that's just for simplicity's sake\n",
        "    self.d_k = d_model // heads\n",
        "    self.d_v = d_model // heads\n",
        "\n",
        "    # Xavier weight initialization\n",
        "    seed = np.random.randint(1e9, size=1)[0]\n",
        "    initializer = tf.keras.initializers.GlorotNormal(seed)\n",
        "    # we need to add a 1 for the batch dimension, tensorflow's matmul doesn't do this for us\n",
        "    self.Q_w = tf.Variable(initializer(shape=(1, heads, d_model, self.d_k)))\n",
        "    self.K_w = tf.Variable(initializer(shape=(1, heads, d_model, self.d_k)))\n",
        "    self.V_w = tf.Variable(initializer(shape=(1, heads, d_model, self.d_v)))\n",
        "    self.O_w = tf.Variable(initializer(shape=(1, heads * self.d_v, d_model)))\n",
        "\n",
        "    self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
        "\n",
        "\n",
        "  def call(self, inputs, mask=None):\n",
        "    \"\"\"\n",
        "    Applies the multi-head self attention to a given input. Called during forward propagation and inference.\n",
        "\n",
        "    Args:\n",
        "      inputs: the layer's inputs, must be in the shape (batches, tokens, d_model).\n",
        "      mask: an additional mask to ignore padding tokens, if no mask is provided only a causal mask will be applied.\n",
        "    \"\"\"\n",
        "\n",
        "    # add heads dimension to inputs\n",
        "    inputs = tf.expand_dims(inputs, axis=1) # (batches, 1, tokens, d_model)\n",
        "\n",
        "    Q = tf.matmul(inputs, self.Q_w) # (batches, heads, tokens, d_k)\n",
        "    K = tf.matmul(inputs, self.K_w) # (batches, heads, tokens, d_k)\n",
        "    V = tf.matmul(inputs, self.V_w) # (batches, heads, tokens, d_v)\n",
        "\n",
        "    # create masks\n",
        "    seq_len = inputs.shape[-2]\n",
        "    full_mask = tf.cast(np.tri(seq_len), dtype=tf.bool)\n",
        "    if mask is not None:\n",
        "      mask = tf.expand_dims(mask, axis=1) # (batches, 1, seq_len)\n",
        "      mask = tf.expand_dims(mask, axis=1) # (batches, 1, 1, seq_len)\n",
        "      mask = tf.tile(mask, tf.constant([1, 1, seq_len, 1])) # (batches, heads, seq_len, seq_len)\n",
        "      full_mask = full_mask & mask # (batches, heads, seq_len, seq_len)\n",
        "\n",
        "    x = scaled_dot_product_attention(Q, K, V, full_mask) # (batches, heads, tokens, d_v)\n",
        "\n",
        "    # concatenate heads\n",
        "    batches, heads, tokens, d_v = x.shape\n",
        "    # this line is the root of a bug which took me a week to find\n",
        "    x = tf.transpose(x, perm=[0, 2, 1, 3])\n",
        "    # batches could be None, we therefore pass -1 so that the batch size is calculated at runtime\n",
        "    x = tf.reshape(x, (-1, tokens, heads*d_v)) # (batches, tokens, d_model)\n",
        "\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    x = tf.matmul(x, self.O_w) # (batches, tokens, d_model)\n",
        "\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0I4HSp7O6xfy"
      },
      "source": [
        "### Feed forward network"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each transformer block contains one fully connected feed forward network. The\n",
        "network is comprised of one dense layer with a `relu` non-linearity, followed by a dense layer of size $d_{model}$. Dropout is applied to prevent overfitting."
      ],
      "metadata": {
        "id": "tDVRTTMh70o8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FDUImwGc6yAS"
      },
      "outputs": [],
      "source": [
        "class FeedForwardNetwork(tf.keras.layers.Layer):\n",
        "  \"\"\"\n",
        "  A simple implementation of the feedforward layer found in the \"Attention is all you need\" paper. The paper suggests implementing the layer using a convelution layer with a kernel of one, however this implemention is based on element-wise dense layers.\n",
        "\n",
        "  Attributes:\n",
        "    supports_masking: tells tensorflow that this layer supports masks.\n",
        "    dense1: the first dense layer in the FFN.\n",
        "    dense2: the second dense layer in the FFN\n",
        "    dropout: the dropout layer used to prevent overfitting.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, dff, d_model, dropout_rate):\n",
        "    \"\"\"\n",
        "    Creates the FeedForwardNetwork object.\n",
        "\n",
        "    Args:\n",
        "      dff: the size of the hidden dense layer.\n",
        "      d_mode: the embedding dimension of the model.\n",
        "      dropout_rate: the dropout rate used.\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    self.supports_masking = True\n",
        "\n",
        "    self.dense1 = tf.keras.layers.Dense(dff, activation='relu')\n",
        "    self.dense2 = tf.keras.layers.Dense(d_model)\n",
        "    self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
        "\n",
        "\n",
        "  def call(self, inputs):\n",
        "    \"\"\"\n",
        "    Applies the feed forward network. Called during forward propagation and inference.\n",
        "\n",
        "    Args:\n",
        "      inputs: the layer's inputs, must of of shape (batches, seq_length, d_model)\n",
        "    \"\"\"\n",
        "    x = self.dense1(inputs)\n",
        "    x = self.dense2(x)\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zb7QPSGR7HEC"
      },
      "source": [
        "### Transformer block"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The transformer block encapsulates the attention mechanisms and feed forward network along with layer normalization and residual connections into one block. This block's goal is to extract and refine information from the input so that it can be used in the other layers of the network. Encasing the logic into one block allows for easy model configuration. The blocks can be layered atop each other with ease.\n",
        "\n",
        "![diagram](https://drive.google.com/uc?export=view&id=1OuqMYRGyUihgPOramyPEEqAhgaji0HBZ)"
      ],
      "metadata": {
        "id": "HJ5p-cVV87h0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rfY2VXGl7JZD"
      },
      "outputs": [],
      "source": [
        "class TransformerBlock(tf.keras.layers.Layer):\n",
        "\"\"\"\n",
        "Implementation of the full transformer block, consisting of attention mechanisms and a feed forward network along with layer normalization and residual connections.\n",
        "\n",
        "Attributes:\n",
        "  supports_masking: tells tensorflow that this layer supports masks.\n",
        "  layer_norm_1: the first normalization layer.\n",
        "  residual_connection: the residual connection component. The same object is used twice as it doesn't have learnable parameters.\n",
        "  layer_norm_2: the second normalization layer.\n",
        "  feed_forward_network: the trasnformer's feed forward network.\n",
        "\"\"\"\n",
        "\n",
        "  def __init__(self, attention_heads, dff, d_model, dropout_rate):\n",
        "    \"\"\"\n",
        "    Creates a transformer object.\n",
        "\n",
        "    Args:\n",
        "      attention_heads: the desired number of heads in the attention mechanism.\n",
        "      dff: the size of the hidden layer in the feed forward network.\n",
        "      dropout_rate: the dropout rate for the different parts of the transformer.\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    self.supports_masking = True\n",
        "\n",
        "    self.layer_norm_1 = tf.keras.layers.LayerNormalization()\n",
        "    self.multi_head_attention = Multi_Head_Attention(attention_heads, d_model, dropout_rate)\n",
        "    self.residual_connection = tf.keras.layers.Add()\n",
        "    self.layer_norm_2 = tf.keras.layers.LayerNormalization()\n",
        "    self.feed_forward_network = FeedForwardNetwork(dff, d_model, dropout_rate)\n",
        "\n",
        "\n",
        "  def call(self, inputs):\n",
        "    \"\"\"\n",
        "    Applies the trasnformer logic to an input. Used during forward propagation and inference.\n",
        "\n",
        "    Args:\n",
        "      inputs: the input matrix, must be of shape (batches, seq_length, d_model)\n",
        "    \"\"\"\n",
        "    norm_1_out = self.layer_norm_1(inputs)\n",
        "    attention_out = self.multi_head_attention(norm_1_out)\n",
        "    residual_1_out = self.residual_connection([norm_1_out, attention_out])\n",
        "\n",
        "    norm_2_out = self.layer_norm_2(residual_1_out)\n",
        "    ffn_out = self.feed_forward_network(norm_2_out)\n",
        "    residual_2_out = self.residual_connection([norm_2_out, ffn_out])\n",
        "\n",
        "    return residual_2_out"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6ouOLRedhof"
      },
      "source": [
        "## Positional embedding"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Positional embedding as proposed in the \"Attention is all you need\" paper.\n",
        "\n",
        "\n",
        "$PE(pos, 2i) = \\sin (\\frac{pos}{10000^{\\frac{2i}{d_{model}}}})$\n",
        "\n",
        "$PE(pos, 2i+1) = \\cos (\\frac{pos}{10000^{\\frac{2i}{d_{model}}}})$"
      ],
      "metadata": {
        "id": "e9meNUyM9u_r"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2QJEqKGBdnu4"
      },
      "outputs": [],
      "source": [
        "def positional_encoding(length, depth):\n",
        "  \"\"\"\n",
        "  Creates the positional encoding matrix from  the sinusoidal encoding formulas.\n",
        "\n",
        "  Args:\n",
        "    length: number of possible sequence positions.\n",
        "    depth: the depth of the encoding, equal to the token embedding dimension.\n",
        "  \"\"\"\n",
        "  depth = depth/2\n",
        "\n",
        "  positions = np.arange(length)[:, np.newaxis] # (seq, 1)\n",
        "  depths = np.arange(depth)[np.newaxis, :] / depth # (1, depth)\n",
        "\n",
        "  angle_rates = 1 / (10000**depths) # (1, depth)\n",
        "  angle_rads = positions * angle_rates # (pos, depth)\n",
        "\n",
        "  pos_encoding = np.concatenate(\n",
        "      [np.sin(angle_rads), np.cos(angle_rads)],\n",
        "      axis=-1)\n",
        "\n",
        "  pos_encoding = np.expand_dims(pos_encoding, axis=0)\n",
        "\n",
        "  return tf.cast(pos_encoding, dtype=tf.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hJxZL9uHdrQq"
      },
      "outputs": [],
      "source": [
        "class PositionalEmbedding(tf.keras.layers.Layer):\n",
        "  \"\"\"\n",
        "  Implementation of a positional embedding layer based on the sinusoidal encoding formulas from the \"Attention is all you need\" paper.\n",
        "\n",
        "  Attributes:\n",
        "    supports_masking: tells tensorflow that this layer supports masking.\n",
        "    pe_table: the table containing the positional encoding values.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, max_length, d_model):\n",
        "    \"\"\"\n",
        "    Creates a PostionalEmbedding object.\n",
        "\n",
        "    Args:\n",
        "      max_length: the maximum sequence length.\n",
        "      d_model: the token embedding dimension.\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    self.supports_masking = True\n",
        "\n",
        "    self.pe_table = positional_encoding(max_length, d_model)\n",
        "\n",
        "\n",
        "  def call(self, inputs):\n",
        "    \"\"\"\n",
        "    Adds the positional embedding values to an input. Called during forward propagatin and inference.\n",
        "\n",
        "    Args:\n",
        "      input: the input matrix, must be of shape (batches, seq_length, d_model)\n",
        "    \"\"\"\n",
        "    batches, seq_length, d_model = inputs.shape\n",
        "\n",
        "    # scale up token embeddings so that they have more influence than positional embedding\n",
        "    x = inputs * np.sqrt(d_model, dtype=np.float32)\n",
        "\n",
        "    x = x + 0.1 * self.pe_table[:, :seq_length, :]\n",
        "\n",
        "    return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73TjwYbG1BUK"
      },
      "source": [
        "## Complete model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D3zXNHS81D1x"
      },
      "outputs": [],
      "source": [
        "inputs = tf.keras.Input(shape=(seq_length, ))\n",
        "# token embedding layer\n",
        "x = tf.keras.layers.Embedding(vocab_size, d_model, mask_zero=True)(inputs)\n",
        "# positonal embedding layer\n",
        "x = PositionalEmbedding(5000, d_model)(x)\n",
        "\n",
        "for _ in range(transformer_units):\n",
        "  x = TransformerBlock(attention_heads, dff, d_model, dropout_rate)(x)\n",
        "\n",
        "x = tf.keras.layers.LayerNormalization()(x)\n",
        "\n",
        "outputs = tf.keras.layers.Dense(vocab_size)(x)\n",
        "\n",
        "model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "             optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=learning_rate),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6_xWhDEd_K4",
        "outputId": "a588ca8d-383d-419d-f969-d91a20238603"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 256)]             0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, 256, 384)          960000    \n",
            "                                                                 \n",
            " positional_embedding (Posi  (None, 256, 384)          0         \n",
            " tionalEmbedding)                                                \n",
            "                                                                 \n",
            " transformer_block (Transfo  (None, 256, 384)          1379200   \n",
            " rmerBlock)                                                      \n",
            "                                                                 \n",
            " transformer_block_1 (Trans  (None, 256, 384)          1379200   \n",
            " formerBlock)                                                    \n",
            "                                                                 \n",
            " transformer_block_2 (Trans  (None, 256, 384)          1379200   \n",
            " formerBlock)                                                    \n",
            "                                                                 \n",
            " transformer_block_3 (Trans  (None, 256, 384)          1379200   \n",
            " formerBlock)                                                    \n",
            "                                                                 \n",
            " transformer_block_4 (Trans  (None, 256, 384)          1379200   \n",
            " formerBlock)                                                    \n",
            "                                                                 \n",
            " transformer_block_5 (Trans  (None, 256, 384)          1379200   \n",
            " formerBlock)                                                    \n",
            "                                                                 \n",
            " layer_normalization_12 (La  (None, 256, 384)          768       \n",
            " yerNormalization)                                               \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 256, 2500)         962500    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10198468 (38.90 MB)\n",
            "Trainable params: 10198468 (38.90 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model, show_shapes=True, show_dtype=False, show_layer_names=False, rankdir='TB', expand_nested=True, show_layer_activations=True, dpi=200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yt8SWv_iq-dw",
        "outputId": "5d0eb45a-3754-42ca-af6e-d000639cc644"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "afScPsAlcxyO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "76c5a799-5fd4-46f5-e639-ad3d1862d3e1"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "No file or directory found at /content/drive/MyDrive/checkpoints/trained_checkpoint/checkpoint.ckpt",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-f44e0a5d7b3f>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m }\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCHECKPOINT_DIR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, safe_mode, **kwargs)\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m     \u001b[0;31m# Legacy case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m     return legacy_sm_saving_lib.load_model(\n\u001b[0m\u001b[1;32m    263\u001b[0m         \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[1;32m    232\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                             raise IOError(\n\u001b[0m\u001b[1;32m    235\u001b[0m                                 \u001b[0;34mf\"No file or directory found at {filepath_str}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m                             )\n",
            "\u001b[0;31mOSError\u001b[0m: No file or directory found at /content/drive/MyDrive/checkpoints/trained_checkpoint/checkpoint.ckpt"
          ]
        }
      ],
      "source": [
        "custom_objects = {\n",
        "    'PositionalEmbedding': PositionalEmbedding,\n",
        "    'TransformerBlock': TransformerBlock,\n",
        "    'Multi_Head_Attention': Multi_Head_Attention,\n",
        "    'FeedForwardNetwork': FeedForwardNetwork\n",
        "}\n",
        "\n",
        "model = tf.keras.models.load_model(CHECKPOINT_DIR, custom_objects=custom_objects)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CdAhHFEgS_Kw"
      },
      "outputs": [],
      "source": [
        "model.save('/content/drive/MyDrive/checkpoints/full_model/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ujve7DG_ZpPi"
      },
      "outputs": [],
      "source": [
        "dataset = tf.data.Dataset.load(DATASET_DIR)\n",
        "\n",
        "preprocess = lambda x: (x[:, :-1], x[:, 1:])\n",
        "dataset = (dataset\n",
        "            .batch(BATCH_SIZE)\n",
        "            .map(preprocess, tf.data.AUTOTUNE)\n",
        "            .prefetch(buffer_size=tf.data.AUTOTUNE))\n",
        "\n",
        "size = len(dataset)\n",
        "training_dataset = dataset.take(int(size * train_split))\n",
        "dataset = dataset.skip(int(size * train_split))\n",
        "validation_dataset = dataset.take(int(size * validation_split))\n",
        "testing_dataset = dataset.skip(int(size * validation_split))\n",
        "\n",
        "print('training dataset size:', len(training_dataset))\n",
        "print('validation dataset size:', len(validation_dataset))\n",
        "print('testing dataset size:', len(testing_dataset))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zR1i6tcRKtei"
      },
      "outputs": [],
      "source": [
        "log_callback = tf.keras.callbacks.CSVLogger(LOG_DIR, append=True)\n",
        "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "                        filepath=CHECKPOINT_DIR,\n",
        "                        monitor='val_accuracy',\n",
        "                        mode='max',\n",
        "                        save_best_only=True)\n",
        "\n",
        "callbacks = [log_callback, checkpoint_callback]\n",
        "\n",
        "history = model.fit(training_dataset,\n",
        "                    validation_data=validation_dataset,\n",
        "                    epochs=50,\n",
        "                    batch_size=BATCH_SIZE,\n",
        "                    callbacks=callbacks)\n",
        "\n",
        "model.save('/content/drive/MyDrive/saved_models/model_checkpoint_1.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xc0EV-byaX_X"
      },
      "outputs": [],
      "source": [
        "plt.plot(range(30), model.history.history['loss'], label='Training Loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "86ESvcqGgpoV"
      },
      "outputs": [],
      "source": [
        "list(training_dataset.take(1).to_numpy_iterator())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "856NjWn5gqbT"
      },
      "outputs": [],
      "source": [
        "model.load_weights('/content/drive/MyDrive/checkpoints/test/checkpoint.ckpt').expect_partial()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WWh7uMj_vLL-"
      },
      "outputs": [],
      "source": [
        "print(model.evaluate(testing_dataset.take(10)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "             optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=learning_rate),\n",
        "             metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "-iD9xqPo-K2D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKYKyycnd6ka"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dgEvei10eMJF"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer(vocab_size)\n",
        "tokenizer.deserialize_from_file(TOKENIZER_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights(CHECKPOINT_DIR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvXhhlEdrZXm",
        "outputId": "faa58e31-8de1-4f28-c092-92136ca2f767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7a9ffac6b790>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "97MwH0-8bscz"
      },
      "outputs": [],
      "source": [
        "text='(CNN) -- At least 14 people were killed and 60 others wounded Thursday when a bomb ripped through a crowd waiting to see Algeria\\'s president in Batna, east of the capital of Algiers, the Algerie Presse Service reported. A wounded person gets first aid shortly after Thursday\\'s attack in Batna, Algeria. The explosion occurred at 5 p.m. about 20 meters (65 feet) from a mosque in Batna, a town about 450 kilometers (280 miles) east of Algiers, security officials in Batna told the state-run news agency. The bomb went off 15 minutes before the expected arrival of President Abdel-Aziz Bouteflika. It wasn\\'t clear if the bomb was caused by a suicide bomber or if it was planted, the officials said. Later Thursday, Algeria\\'s Interior Minister Noureddine Yazid Zerhouni said \"a suspect person who was among the crowd attempted to go beyond the security cordon,\" but the person escaped \"immediately after the bomb exploded,\" the press service reported. Bouteflika made his visit to Batna as planned, adding a stop at a hospital to visit the wounded before he returned to the capital. There was no immediate claim of responsibility for the bombing. Algeria faces a continuing Islamic insurgency, according to the CIA. In July, 33 people were killed in apparent suicide bombings in Algiers that were claimed by an al Qaeda-affiliated group. Bouteflika said terrorist acts have nothing in common with the noble values of Islam, the press service reported. E-mail to a friend . CNN\\'s Mohammed Tawfeeq contributed to this report.'\n",
        "text = text.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kriofhpbwdE",
        "outputId": "d64e691a-63b6-4a93-c443-27bfef9e32a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "443\n",
            "[2, 762, 217, 167, 1306, 2060, 352, 292, 1170, 105, 843, 45, 1661, 78, 423, 85, 1736, 349, 69, 2147, 439, 1587, 682, 69, 2419, 7, 1759, 1287, 93, 669, 206, 878, 2045, 507, 95, 1433, 2100, 41, 1975, 101, 82, 2038, 101, 206, 10, 1215, 193, 82, 206, 878, 269, 1395, 8, 1430, 509, 514, 69, 78, 423, 85, 888, 498, 22, 485, 69, 114, 1369, 21, 23, 127, 318, 1736, 131, 778, 95, 1433, 2100, 41, 206, 878, 1940, 82, 1147, 237, 115, 1585, 141, 644, 167, 742, 87, 1960, 316, 428, 1398, 150, 382, 51, 50, 397, 152, 39, 212, 69, 179, 22, 1985, 95, 1433, 2100, 41, 69, 1669, 316, 681, 1981, 168, 165, 236, 582, 382, 47, 53, 45, 89, 402, 22, 39, 1975, 101, 206, 10, 1215, 193, 899, 995, 95, 1433, 2100, 470, 82, 603, 42, 556, 17, 964, 2105, 17, 6, 525, 82, 2147, 1265, 279, 1477, 544, 1251, 22, 592, 82, 1898, 1952, 2465, 101, 507, 234, 7, 242, 42, 823, 376, 399, 1251, 9, 590, 14, 732, 151, 2188, 1302, 465, 82, 2147, 157, 1342, 982, 226, 69, 189, 104, 357, 2147, 72, 260, 465, 151, 157, 272, 285, 678, 82, 995, 281, 1323, 1145, 944, 206, 878, 2045, 405, 1214, 1420, 351, 1060, 7, 381, 158, 823, 114, 817, 72, 11, 829, 12, 159, 2002, 1197, 501, 888, 243, 157, 1320, 82, 2419, 7, 451, 1439, 229, 93, 222, 123, 28, 419, 82, 899, 1126, 7, 1063, 31, 221, 82, 888, 946, 6, 2184, 7, 539, 16, 1991, 127, 318, 82, 2147, 1147, 263, 678, 31, 82, 1395, 1430, 509, 514, 399, 1251, 9, 590, 14, 4, 726, 190, 1716, 93, 1433, 2100, 162, 272, 1800, 678, 598, 91, 69, 1311, 167, 69, 1238, 93, 1716, 82, 78, 423, 85, 592, 122, 1388, 85, 93, 82, 796, 112, 2011, 367, 157, 351, 1654, 85, 1271, 1090, 101, 1813, 2025, 136, 82, 2147, 749, 206, 878, 293, 1038, 22, 69, 913, 24, 91, 1969, 104, 914, 141, 195, 17, 6, 430, 584, 93, 82, 81, 1940, 95, 1555, 1080, 532, 48, 352, 292, 1170, 95, 414, 2120, 189, 104, 357, 2147, 700, 95, 206, 10, 1215, 22, 137, 292, 1090, 85, 226, 96, 206, 1622, 4, 85, 4, 42, 1172, 165, 293, 229, 671, 43, 399, 1251, 9, 590, 14, 4, 159, 1453, 239, 1050, 22, 205, 228, 11, 91, 95, 429, 77, 164, 82, 351, 800, 1508, 618, 101, 1969, 41, 82, 1395, 1430, 509, 514, 120, 954, 438, 93, 69, 478, 269, 94, 177, 1160, 179, 1372, 789, 67, 431, 555, 8, 20, 342, 589, 1251, 7, 93, 248, 1510, 3]\n"
          ]
        }
      ],
      "source": [
        "tokenized_text = [2] + tokenizer.tokenize(text) + [3]\n",
        "print(len(tokenized_text))\n",
        "print(tokenized_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "UkDbOxSrd7zh",
        "outputId": "779cc20e-2bf5-4162-e7ce-27bd4dcaaf3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " by former florida state senator dianne m. scotts suggestion of 'horrors' on a candidate seemingly voted interested in u.s. lesbian ted cruz. the bikini of williams has been blamed on a 'bridge wet' overhaul and was thought to have been his previous friend with her daughters 'sweaterwet,' a controversial revolutionary and defiance of youthful separating her from her own clients claimed that she ate for a few seizures online between her two year-old daughters. scroll down for"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-ec317bc9c3cc>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenized\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m   \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m   \u001b[0mtoken_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float64'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0mtoken_pred\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0mlayout_map_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_subclass_model_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_layout_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdoc_controls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdoc_in_current_and_subclasses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1147\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1148\u001b[0m                 ):\n\u001b[0;32m-> 1149\u001b[0;31m                     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mbound_signature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_keras_call_info_injected\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtensors\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthere\u001b[0m \u001b[0mare\u001b[0m \u001b[0mmore\u001b[0m \u001b[0mthan\u001b[0m \u001b[0mone\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m         \"\"\"\n\u001b[0;32m--> 515\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_internal_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    516\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    517\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py\u001b[0m in \u001b[0;36m_run_internal_graph\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 672\u001b[0;31m                 \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m                 \u001b[0;31m# Update tensor_dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1147\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1148\u001b[0m                 ):\n\u001b[0;32m-> 1149\u001b[0;31m                     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mbound_signature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_keras_call_info_injected\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-9-5699cb27bba8>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mresidual_1_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresidual_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnorm_1_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_out\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mnorm_2_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_norm_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresidual_1_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m     \u001b[0mffn_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed_forward_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnorm_2_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mresidual_2_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresidual_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnorm_2_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mffn_out\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1147\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1148\u001b[0m                 ):\n\u001b[0;32m-> 1149\u001b[0;31m                     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mbound_signature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_keras_call_info_injected\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/layers/normalization/layer_normalization.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    258\u001b[0m             \u001b[0minputs_lengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnested_row_lengths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m             \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m         \u001b[0;31m# Compute the axes along which to reduce the mean / variance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0minput_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mop_dispatch_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1258\u001b[0m       \u001b[0;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1260\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdispatch_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1261\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m         \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36mcast\u001b[0;34m(x, dtype, name)\u001b[0m\n\u001b[1;32m    997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    998\u001b[0m   \"\"\"\n\u001b[0;32m--> 999\u001b[0;31m   \u001b[0mbase_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1000\u001b[0m   if isinstance(\n\u001b[1;32m   1001\u001b[0m       x, (tensor_lib.Tensor, _resource_variable_type)) and base_type == x.dtype:\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/dtypes.py\u001b[0m in \u001b[0;36mas_dtype\u001b[0;34m(type_value)\u001b[0m\n\u001b[1;32m    823\u001b[0m     \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtype_value\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mcannot\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mconverted\u001b[0m \u001b[0mto\u001b[0m \u001b[0ma\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDType\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m   \"\"\"\n\u001b[0;32m--> 825\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDType\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtype_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_INTERN_TABLE\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtype_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/abc.py\u001b[0m in \u001b[0;36m__instancecheck__\u001b[0;34m(cls, instance)\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__instancecheck__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0;34m\"\"\"Override for isinstance(instance, cls).\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_abc_instancecheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__subclasscheck__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubclass\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "text = 'it has been reported'.lower()\n",
        "tokenized = [2] + tokenizer.tokenize(text)\n",
        "tokenized = tokenized[:256]\n",
        "window = tokenized + [0] * (seq_length - len(tokenized))\n",
        "index = len(tokenized) - 1\n",
        "for _ in range(1000):\n",
        "  pred = model(np.array([window]))\n",
        "  token_pred = np.squeeze(pred)[index].astype('float64')\n",
        "  token_pred *= 1\n",
        "  token_pred = tf.nn.softmax(token_pred)\n",
        "\n",
        "  token = np.argmax(np.random.multinomial(1, token_pred))\n",
        "  print(tokenizer.detokenize([token]), end='')\n",
        "  if token == 3:\n",
        "    break\n",
        "\n",
        "  if len(window) - 1 > index:\n",
        "    index += 1\n",
        "    window[index] = token\n",
        "  else:\n",
        "    window = window[1:] + [token]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# i state department has said 'the vast majority of the palestinians have been killed', the israeli newspaper reported. the u.s. government has said the government's 'a vast majority of the palestinians have died and the least are injured in the attack'. the u.s. has said the death toll in the israeli town of sharma was the third biggest in the country. the israeli government said the death toll was 'highly inaccurate' and that the palestinians were killed by israeli air strikes in the gaza strip. the palestinians are believed to have died and the palestinians were killed in the attack, the report said. israeli state department said the death toll was 75. the israeli government said the death toll was 75. the palestinians are believed to have died and the palestinians were killed by israeli air strikes in the gaza strip. they said the\n"
      ],
      "metadata": {
        "id": "XruX0EuGsl6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(list(enumerate([1, 2, 3, 4])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Layts-enbMN",
        "outputId": "006805d7-af46-4de8-cb83-02269df889ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0, 1), (1, 2), (2, 3), (3, 4)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XoFJtJ-Reh17",
        "outputId": "7fdaa785-a3f7-42af-f761-db66e957a74f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 256)\n",
            "(1, 256)\n",
            "(1, 256)\n",
            "(1, 256)\n",
            "(1, 256)\n",
            "(1, 256)\n",
            "[1258 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732\n",
            " 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732 1732\n",
            " 1732 1732 1732 1732   99   99   99   99   99   99   99   99   99   99\n",
            "   99   99   99   99   99   99   99   99   99   99   99   99   99   99\n",
            "   99   99   99   99   99   99   99   99 2143 2143 2143 2143 2143 2479\n",
            " 2143 2143 2143 2143 2143 2143 2479 2479 2143 2143 2143 2143 2143 2143\n",
            " 2143 2143 2143 2143 2143 2143 2143 2143 2143 2143 2143 2143 2036 2036\n",
            " 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036\n",
            " 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036 2036\n",
            " 2036 2036 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380\n",
            " 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380\n",
            " 2380 2380 2380 2380 2380 2380  922  922  922  922  922 1645 1645  922\n",
            "  922  922 1198 1198 1645 1645 1645 1645 1645 1645 1645 1645 1645 1645\n",
            " 1645 1645 1645 1645 1645 1302 1302 1198 1198 1645 2385 2385 2385 2385\n",
            " 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385\n",
            " 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385 2385\n",
            " 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380\n",
            " 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380 2380\n",
            " 2380 2380 2380 2380]\n"
          ]
        }
      ],
      "source": [
        "window = [2] + [0] * (seq_length - 1)\n",
        "pred = np.squeeze(model(np.array([window])))\n",
        "# print(tokenizer.detokenize(np.argmax(pred, axis=-1)))\n",
        "print(np.argmax(pred, axis=-1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xv3x64Qc_Caj"
      },
      "source": [
        "# Random testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wlc07Rh0WjdB"
      },
      "outputs": [],
      "source": [
        "data_entry = None\n",
        "for e in dataset.batch(2):\n",
        "  data_entry = e\n",
        "  break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7jYQ3Eul3E-6"
      },
      "outputs": [],
      "source": [
        "a = None\n",
        "for e in dataset.take(1):\n",
        "  a = e\n",
        "\n",
        "x_true, y_true = a\n",
        "\n",
        "x_true = tf.expand_dims(x_true, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOnkEcya38zG",
        "outputId": "3c6d2456-5879-428f-dea1-a9083e157ed5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[  2   6 195  98  22  54 378 919  82 435 300   7  24 751 197 753  15  18\n",
            "  268   4 138 619  40 572 806 193 971 145  88  71 134  15 437  64  64  64\n",
            "   64  64  64  64  64 666 171 741  85 313 169   9  73 147  93 378 168  42\n",
            "  196  82 435 456 279  83 180 591  41   6  66 369  85 139  64  64  64  64\n",
            "   64  64  64  64 538 333 736 261  73 221  64  85  98 564  67  25 821 317\n",
            "  134  41  15 437  64  64  64  64  64  64  64  64  64  64  64  64  64  64\n",
            "   64  64  64  75  23  16  15  82 435 300   7  24 751 197  94 381 127  69\n",
            "  127  10  79   3   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0]], shape=(1, 256), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "print(x_true)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x7-65DEs6AEi"
      },
      "outputs": [],
      "source": [
        "x_true = tf.constant([[2, 6, 195, 98, 22, 54, 378, 919, 82, 435, 300, 7, 24, 751, 197, 753, 15, 18 , 268, 4, 138, 619, 40, 572, 806, 193, 971, 145, 88, 71, 134, 15, 437, 64, 64, 64 , 64, 64, 64, 64, 64, 666, 171, 741, 85, 313, 169, 9, 73, 147, 93, 378, 168, 42 , 196, 82, 435, 456, 279, 83, 180, 591, 41, 6, 66, 369, 85, 139, 64, 64, 64, 64 , 64, 64, 64, 64, 538, 333, 736, 261, 73, 221, 64, 85, 98, 564, 67, 25, 821, 317 , 134, 41, 15, 437, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64 , 64, 64, 64, 75, 23, 16, 15, 82, 435, 300, 7, 24, 751, 197, 94, 381, 127, 69 , 127, 10, 79, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 , 0, 0, 0, 0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfgBsjIL3ZJ0",
        "outputId": "7e42b5d8-83ad-4132-bcba-2f6967562d54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2, 256, 1000)\n"
          ]
        }
      ],
      "source": [
        "print(model(data_entry[0]).shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6vkRhR1_DzS"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmhBGllMpr0Y",
        "outputId": "6207320a-42e9-41fb-e3f7-ea299e37eb20"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2, 2, 2)\n",
            "(1, 2, 2, 2)\n",
            "tf.Tensor(\n",
            "[[[[ 1.e+00 -1.e+09]\n",
            "   [ 3.e+00 -1.e+09]]\n",
            "\n",
            "  [[ 1.e+00  2.e+00]\n",
            "   [ 3.e+00  4.e+00]]]], shape=(1, 2, 2, 2), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "mask = tf.constant([[[True, False], [True, False]], [[True, True], [True, True]]])\n",
        "print(mask.shape)\n",
        "a = tf.constant([[[[1, 2], [3, 4]], [[1, 2], [3, 4]]]], dtype=tf.float32)\n",
        "print(a.shape)\n",
        "b = tf.ones(a.shape[-2:], tf.float32) * -1e9\n",
        "\n",
        "x = tf.where(mask, a, b)\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uDGCyCSrTk_",
        "outputId": "c20a5ef8-da0d-4994-994d-5b569be7bbe2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[ True False False]\n",
            " [ True  True False]\n",
            " [ True False False]], shape=(3, 3), dtype=bool)\n"
          ]
        }
      ],
      "source": [
        "mask1 = tf.constant([[True, False, False], [True, True, False], [True, False, False]])\n",
        "mask2 = tf.cast(np.tri(3), dtype=tf.bool)\n",
        "\n",
        "mask = mask1 & mask2\n",
        "print(mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a3nAjxzyIcYp"
      },
      "outputs": [],
      "source": [
        "epochs = 2\n",
        "batch_size = 5\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    print(\"\\nStart of epoch %d\" % (epoch,))\n",
        "\n",
        "    # Iterate over the batches of the dataset.\n",
        "    for step, (x_batch_train, y_batch_train) in enumerate(dataset.take(100).batch(batch_size)):\n",
        "        # Open a GradientTape to record the operations run\n",
        "        # during the forward pass, which enables auto-differentiation.\n",
        "        with tf.GradientTape() as tape:\n",
        "            # Run the forward pass of the layer.\n",
        "            # The operations that the layer applies\n",
        "            # to its inputs are going to be recorded\n",
        "            # on the GradientTape.\n",
        "            logits = model(x_batch_train, training=True)  # Logits for this minibatch\n",
        "\n",
        "            # Compute the loss value for this minibatch.\n",
        "            loss_value = loss_fn(y_batch_train, logits)\n",
        "\n",
        "        # Use the gradient tape to automatically retrieve\n",
        "        # the gradients of the trainable variables with respect to the loss.\n",
        "        grads = tape.gradient(loss_value, model.trainable_weights)\n",
        "\n",
        "        # Run one step of gradient descent by updating\n",
        "        # the value of the variables to minimize the loss.\n",
        "        optimizer.apply_gradients(zip(grads, model.trainable_weights))\n",
        "\n",
        "        print(\"Training loss (for one batch) at step %d: %.4f\" % (step, float(loss_value)))\n",
        "        print(\"Seen so far: %s samples\" % ((step + 1) * batch_size))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTww17_S1EXa",
        "outputId": "900a679f-2cbf-4c3c-bb0a-7f2984f6f093"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(6, 3)\n",
            "tf.Tensor(\n",
            "[[ True  True False]\n",
            " [ True  True False]\n",
            " [ True  True False]\n",
            " [ True False False]\n",
            " [ True False False]\n",
            " [ True False False]], shape=(6, 3), dtype=bool)\n"
          ]
        }
      ],
      "source": [
        "mask = tf.constant([[True, True, False], [True, False, False]]) # (batch, mask)\n",
        "mask = tf.repeat(mask, 3, axis=0) # (batch, mask, mask)\n",
        "print(mask.shape)\n",
        "print(mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqWPvdPR-I0x",
        "outputId": "fdc25e66-8da5-4210-96e0-af44d32328fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(3, 3)\n",
            "tf.Tensor(\n",
            "[[ True False False]\n",
            " [ True  True False]\n",
            " [ True  True  True]], shape=(3, 3), dtype=bool)\n"
          ]
        }
      ],
      "source": [
        "tri_mask = tf.cast(np.tri(3), dtype=tf.bool)\n",
        "# tri_mask = tf.expand_dims(tri_mask, axis=0)\n",
        "# tri_mask = tf.\n",
        "print(tri_mask.shape)\n",
        "print(tri_mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ok66Yvn1kcB",
        "outputId": "5b6941ec-c794-422f-ad2f-b55a68d1895b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2, 1, 3)\n",
            "(2, 3, 3)\n",
            "tf.Tensor(\n",
            "[[[ True  True  True]\n",
            "  [ True  True  True]\n",
            "  [ True  True  True]]\n",
            "\n",
            " [[ True  True False]\n",
            "  [ True  True False]\n",
            "  [ True  True False]]], shape=(2, 3, 3), dtype=bool)\n"
          ]
        }
      ],
      "source": [
        "mask = tf.constant([[[True, True, True]], [[True, True, False]]])\n",
        "print(mask.shape)\n",
        "b = tf.constant([1, 3, 1], tf.int32)\n",
        "mask = tf.tile(mask, tf.constant([1, 3, 1]))\n",
        "print(mask.shape)\n",
        "print(mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZTSxpVB-WPS",
        "outputId": "9b3977b8-2921-4d30-edad-5003a739c038"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[[ True False False]\n",
            "  [ True  True False]\n",
            "  [ True  True  True]]\n",
            "\n",
            " [[ True False False]\n",
            "  [ True  True False]\n",
            "  [ True  True False]]], shape=(2, 3, 3), dtype=bool)\n"
          ]
        }
      ],
      "source": [
        "full_mask = mask & tri_mask\n",
        "print(full_mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOVatBsQ3JZl",
        "outputId": "7079baad-1685-4789-dd12-2148d1f80259"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1, 1, 3)\n",
            "(1, 3, 3)\n",
            "tf.Tensor(\n",
            "[[[ True False False]\n",
            "  [ True False False]\n",
            "  [ True False False]]], shape=(1, 3, 3), dtype=bool)\n"
          ]
        }
      ],
      "source": [
        "mask = tf.constant([[True, False, False]])\n",
        "mask = tf.expand_dims(mask, axis=1)\n",
        "print(mask.shape)\n",
        "b = tf.constant([1, 3, 1], tf.int32)\n",
        "mask = tf.tile(mask, b)\n",
        "\n",
        "print(mask.shape)\n",
        "print(mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mtw5Ssfw8aet"
      },
      "outputs": [],
      "source": [
        "model1 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(10),\n",
        "    tf.keras.layers.Dense(5)])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv1D(filters=10, kernel_size=1, activation='relu'),\n",
        "    tf.keras.layers.Conv1D(filters=5, kernel_size=1)])"
      ],
      "metadata": {
        "id": "0VH5A4ruKC4R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dense1 = tf.keras.layers.Dense(10, activation='relu')\n",
        "dense2 = tf.keras.layers.Dense(5)\n",
        "\n",
        "def model1(x):\n",
        "  x = dense1(x)\n",
        "  return dense2(x)"
      ],
      "metadata": {
        "id": "G79tnygqNUnp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input1 = np.array([[\n",
        "    [1., 0., 0., 0., 0.],\n",
        "    [1., 0., 0., 0., 0.],\n",
        "    [1., 0., 0., 0., 0.]]])\n",
        "\n",
        "input2 = np.array([[\n",
        "    [1., 0., 0., 0., 0.],\n",
        "    [1., 0., 0., 1., 0.],\n",
        "    [1., 0., 0., 1., 0.]]])"
      ],
      "metadata": {
        "id": "413MPNh1KYrr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAq2Yv4hLoW8",
        "outputId": "780536ae-da2b-430a-d327-7e9861f4c148"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 3, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('model1: ', model1(input1))\n",
        "print('model2: ', model2(input1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kzKEFjsxKhET",
        "outputId": "90b8df2e-7587-4ac9-fcbf-2b555b34f244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model1:  tf.Tensor(\n",
            "[[[ 0.0973601  -0.01921852 -0.24813573 -0.31107447  0.20160046]\n",
            "  [ 0.0973601  -0.01921852 -0.24813573 -0.31107447  0.20160046]\n",
            "  [ 0.0973601  -0.01921852 -0.24813573 -0.31107447  0.20160046]]], shape=(1, 3, 5), dtype=float32)\n",
            "model2:  tf.Tensor(\n",
            "[[[-0.18823999  0.42539167  0.24561214 -0.3076089   0.32815677]\n",
            "  [-0.18823999  0.42539167  0.24561214 -0.3076089   0.32815677]\n",
            "  [-0.18823999  0.42539167  0.24561214 -0.3076089   0.32815677]]], shape=(1, 3, 5), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('model1: ', model1(input2))\n",
        "print('model2: ', model2(input2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PCywIMZTL7xy",
        "outputId": "c68bdbcb-4e5c-475c-c088-02a24f053385"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model1:  tf.Tensor(\n",
            "[[[ 0.0973601  -0.01921852 -0.24813573 -0.31107447  0.20160046]\n",
            "  [ 0.12055966  0.04097506 -0.09912045 -0.12655932 -0.05822828]\n",
            "  [ 0.12055966  0.04097506 -0.09912045 -0.12655932 -0.05822828]]], shape=(1, 3, 5), dtype=float32)\n",
            "model2:  tf.Tensor(\n",
            "[[[-0.18823999  0.42539167  0.24561214 -0.3076089   0.32815677]\n",
            "  [ 0.57166004 -0.12346924  0.21832593  0.20915633  0.18604334]\n",
            "  [ 0.57166004 -0.12346924  0.21832593  0.20915633  0.18604334]]], shape=(1, 3, 5), dtype=float32)\n"
          ]
        }
      ]
    }
  ],
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