{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer Encoders Tutorial\n",
    "\n",
    "In this tutorial you will explore the **self-attention mechanism**, the **word-piece tokenization**, and understand the differences between a Transformer **cross-encoder** and a Transformer **dual-encoder**. \n",
    "\n",
    "## Install the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vcQwjqCo1ziM",
    "outputId": "eb2b6177-d4e1-4e9d-aa63-0b56a9248c0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'bertviz_repo'...\n",
      "remote: Enumerating objects: 1625, done.\u001b[K\n",
      "remote: Counting objects: 100% (321/321), done.\u001b[K\n",
      "remote: Compressing objects: 100% (113/113), done.\u001b[K\n",
      "remote: Total 1625 (delta 226), reused 220 (delta 208), pack-reused 1304\u001b[K\n",
      "Receiving objects: 100% (1625/1625), 198.36 MiB | 1.12 MiB/s, done.\n",
      "Resolving deltas: 100% (1068/1068), done.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!test -d bertviz_repo && echo \"FYI: bertviz_repo directory already exists, to pull latest version uncomment this line: !rm -r bertviz_repo\"\n",
    "# !rm -r bertviz_repo # Uncomment if you need a clean pull from repo\n",
    "!test -d bertviz_repo || git clone https://github.com/jessevig/bertviz bertviz_repo\n",
    "if not 'bertviz_repo' in sys.path:\n",
    "  sys.path += ['bertviz_repo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "juNXbazl16D5"
   },
   "outputs": [],
   "source": [
    "from bertviz import model_view, head_view\n",
    "import transformers\n",
    "from transformers import *\n",
    "\n",
    "import numpy as np\n",
    "import pprint\n",
    "\n",
    "# Get the interactive Tools for Matplotlib\n",
    "%matplotlib notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from transformers import BertTokenizer, BertModel\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "fOQQ6zwO19Bg"
   },
   "outputs": [],
   "source": [
    "def call_html():\n",
    "  import IPython\n",
    "  display(IPython.core.display.HTML('''\n",
    "        <script src=\"/static/components/requirejs/require.js\"></script>\n",
    "        <script>\n",
    "          requirejs.config({\n",
    "            paths: {\n",
    "              base: '/static/base',\n",
    "              \"d3\": \"https://cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min\",\n",
    "              jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
    "            },\n",
    "          });\n",
    "        </script>\n",
    "        '''))\n",
    "    \n",
    "def display_scatterplot(model, words):\n",
    "\n",
    "    if model.shape[1] == 2:\n",
    "        twodim = model\n",
    "    else:\n",
    "        twodim = PCA().fit_transform(model)[:,:2]\n",
    "    \n",
    "    plt.style.use('ggplot')\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.scatter(twodim[:,0], twodim[:,1], edgecolors='k', c='r')\n",
    "    for word, (x,y) in zip(words, twodim):\n",
    "        plt.text(x+0.05, y+0.05, word)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select your model\n",
    "\n",
    "There are many models available at HuggingFace models repository. These are stable models that were made available by researchers and the industry.\n",
    "\n",
    "https://huggingface.co/models\n",
    "\n",
    "The example below is the most popular Transformer model, BERT, a cross-encoder trained on the Next Sentence Prediction task and Masked Language Model task:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p6E3k5NJuO6W",
    "outputId": "e8d8f962-185e-4c6d-9eae-fc236e5a0d06"
   },
   "outputs": [],
   "source": [
    "model_path = 'bert-base-uncased'\n",
    "CLS_token = \"[CLS]\"\n",
    "SEP_token = \"[SEP]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "RYKgsb3IkElT",
    "outputId": "862994e3-7daa-4ddf-d471-b66eaddb6385"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "transformers.logging.set_verbosity_warning()\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
    "config = AutoConfig.from_pretrained(model_path,  output_hidden_states=True, output_attentions=True)  \n",
    "model = AutoModel.from_pretrained(model_path, config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0-11): 12 x BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input Tokenization\n",
    "\n",
    "See here for details: https://huggingface.co/docs/transformers/tokenizer_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\n",
      " 'input_ids': tensor([[  101,  2003,  3759,  4456,  7438,  3085, 13367,  1029,   102,  2425,\n",
      "          2033,  2055, 11192,  4456,  1012,   102]]),\n",
      " 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]])}\n"
     ]
    }
   ],
   "source": [
    "sentence_a = \"word word word word word\"\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sentence_a = \"Is throat cancer treatable nowadays?\"\n",
    "sentence_b = \"Tell me about lung cancer.\"\n",
    "inputs = tokenizer.encode_plus(sentence_a, sentence_b, return_tensors='pt', add_special_tokens=True, max_length = 512, truncation = True)\n",
    "pprint.pprint(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CLS] is throat cancer treatable nowadays? [SEP] tell me about lung cancer. [SEP]\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.decode(inputs[\"input_ids\"][0].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[101,\n",
      " 2003,\n",
      " 3759,\n",
      " 4456,\n",
      " 7438,\n",
      " 3085,\n",
      " 13367,\n",
      " 1029,\n",
      " 102,\n",
      " 2425,\n",
      " 2033,\n",
      " 2055,\n",
      " 11192,\n",
      " 4456,\n",
      " 1012,\n",
      " 102]\n"
     ]
    }
   ],
   "source": [
    "input_ids = inputs['input_ids']\n",
    "pprint.pprint(input_ids[0].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[CLS]',\n",
      " 'is',\n",
      " 'throat',\n",
      " 'cancer',\n",
      " 'treat',\n",
      " '##able',\n",
      " 'nowadays',\n",
      " '?',\n",
      " '[SEP]',\n",
      " 'tell',\n",
      " 'me',\n",
      " 'about',\n",
      " 'lung',\n",
      " 'cancer',\n",
      " '.',\n",
      " '[SEP]']\n"
     ]
    }
   ],
   "source": [
    "input_id_list = input_ids[0].tolist() # Batch index 0\n",
    "tokens = tokenizer.convert_ids_to_tokens(input_id_list)\n",
    "pprint.pprint(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model inference output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    outputs = model(**inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['last_hidden_state', 'pooler_output', 'hidden_states', 'attentions'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output layer embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the last layer is the output embedding layer\n",
    "output_embeddings = outputs['last_hidden_state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([768])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_throat = 2\n",
    "token_cancer = 3\n",
    "# out[0][token]\n",
    "throat_output_embedding = output_embeddings[0][token_throat]\n",
    "throat_output_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_scatterplot(model, words):\n",
    "\n",
    "    if model.shape[1] == 2:\n",
    "        twodim = model\n",
    "    else:\n",
    "        twodim = PCA().fit_transform(model)[:,:2]\n",
    "    \n",
    "    plt.style.use('default') # https://matplotlib.org/3.5.1/gallery/style_sheets/style_sheets_reference.html\n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.scatter(twodim[:,0], twodim[:,1], edgecolors='k', c='r')\n",
    "    for word, (x,y) in zip(words, twodim):\n",
    "        plt.text(x+0.05, y+0.05, word)\n",
    "\n",
    "    return\n",
    "\n",
    "display_scatterplot(output_embeddings[0].detach().numpy(), tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hidden layer embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_states = outputs['hidden_states']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([768])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is the output token embedding for the word cancer\n",
    "layer = 0\n",
    "throat_input_embedding = hidden_states[layer][0][token_throat]\n",
    "throat_input_embedding.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Self-attention matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "attention = outputs['attentions']\n",
    "# The format of the attention tensor is:\n",
    "# attention[layer][0][head][token1][token2]\n",
    "layer = 3\n",
    "head = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0559)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this will given the attention from one token vs the other token\n",
    "attention[layer][0][head][token_throat][token_cancer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 16])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attention[layer][0][head].size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.0000)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There's a softmax, so, the sum should be 1 \n",
    "attention[layer][0][head][token_throat].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attention[layer][0][head][token_cancer].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7ddb706220>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.matshow(attention[layer][0][head], cmap='gnuplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract contextual word embeddings\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input sequence: is throat cancer treatable nowadays? tell me about lung cancer.\n",
      "The word \" treatable \" occurs in position 3 of the BERT input sequence.\n",
      "[None, 0, 1, 2, 3, 3, 4, 5, None, 0, 1, 2, 3, 4, 5, None]\n",
      "(array([ 4,  5, 12]),)\n"
     ]
    }
   ],
   "source": [
    "def get_word_idx(sent: str, word: str):\n",
    "    return sent.split(\" \").index(word)\n",
    "\n",
    "def get_word_vector(inputs, outputs, idx, layer):\n",
    "    \"\"\"Get a word vector by averaging the embeddings of \n",
    "       all word occurrences of that word in the input\"\"\"\n",
    "\n",
    "    # get all token idxs that belong to the word of interest\n",
    "    token_ids_word = np.where(np.array(inputs.word_ids()) == idx)\n",
    "    print(inputs.word_ids())\n",
    "    word_tokens_output = outputs.hidden_states[layer][0][token_ids_word]\n",
    "    print(token_ids_word)\n",
    "    return word_tokens_output.mean(dim=0)\n",
    "\n",
    "# The code below converts the tokens into a space delimited string.\n",
    "# This will allow computing in which position of the BERT input sequence a given word is.\n",
    "sentence_a = tokenizer.decode(inputs[\"input_ids\"][0].tolist()).replace(\"[CLS] \", '').replace(\" [SEP]\", '')\n",
    "word = \"treatable\"\n",
    "idx = get_word_idx(sentence_a, word)\n",
    "print(\"Input sequence:\", sentence_a)\n",
    "print(\"The word \\\"\", word, \"\\\" occurs in position\", idx, \"of the BERT input sequence.\")\n",
    "\n",
    "word_embedding = get_word_vector(inputs, outputs, idx, 4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "INPUT SEQUENCE TOKENS:  [CLS] is throat cancer treat ##able nowadays ? [SEP] tell me about lung cancer . [SEP]\n",
      "TARGET WORD: treatable\n",
      "TARGET SENTENCE: A\n",
      "TARGET SENTENCE WORDS [ is throat cancer treatable nowadays? ]\n",
      "The word [ treatable ] occurs in position 3 of the BERT input sentence A\n",
      "The word [ treatable ] corresponds to the token(s) [4 5] of the BERT input sequence A\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import re\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "\n",
    "def get_word_vector_from_ab(inputs, outputs, word, layer = '-1', ab = 'A'):\n",
    "    \"\"\"\n",
    "    This method extracts a word embedding from the requested layer \n",
    "    for sentence_a or sentence_b. If the word is divided into tokens, \n",
    "    the word embedding will be the average of the corresponding token \n",
    "    embeddings.\n",
    "\n",
    "    NOTE: If the same word occurs multiple times in the sentence, \n",
    "    this method returns the word embedding of the first occurrence.\n",
    "\n",
    "    Keyword arguments:\n",
    "        inputs -- input passed to the transformer\n",
    "        outputs -- output of the transformer\n",
    "        word -- target word\n",
    "        layer -- layer from where the word embedding vector should \n",
    "        be extracted.\n",
    "        ab -- should be 'A' or 'B' indication if the word embedding is to be extracted \n",
    "        from sentence_a or sentence_b, i.e., query or document.\n",
    "    \"\"\"\n",
    "       \n",
    "    sep_token = np.where(np.array(inputs[\"input_ids\"][0].tolist()) == 102)[0][0]\n",
    "    if ab == 'A':\n",
    "        tokens_a = inputs[\"input_ids\"][0][1:sep_token]\n",
    "        sent = tokenizer.decode(tokens_a.tolist())\n",
    "    else:\n",
    "        tokens_b = inputs[\"input_ids\"][0][sep_token+1:-1]\n",
    "        sent = tokenizer.decode(tokens_b.tolist())\n",
    "\n",
    "    word_ids = get_word_idx(sent, word)\n",
    "\n",
    "    # get all token idxs that belong to the word of interest\n",
    "    token_ids_word = np.where(np.array(inputs.word_ids()) == word_ids)[0]\n",
    "    sep_word = np.where(np.array(inputs.word_ids()) == None)[0][1]\n",
    "\n",
    "    if ab == 'A':\n",
    "        token_pos = token_ids_word < sep_word\n",
    "    else:\n",
    "        token_pos = token_ids_word > sep_word\n",
    "        \n",
    "    token_ids_word = token_ids_word[token_pos]\n",
    "    word_tokens_output = outputs.hidden_states[layer][0][token_ids_word]\n",
    "\n",
    "    # Change this to True for inspection\n",
    "    details = True\n",
    "    if details:\n",
    "        input_id_list = input_ids[0].tolist() # Batch index 0\n",
    "        tokens = tokenizer.convert_ids_to_tokens(input_id_list)\n",
    "        str1 = \" \"\n",
    "\n",
    "        print(\"- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \")\n",
    "        print(\"INPUT SEQUENCE TOKENS: \", str1.join(tokens))\n",
    "        print(\"TARGET WORD:\", word)\n",
    "        print(\"TARGET SENTENCE:\", ab)\n",
    "        print(\"TARGET SENTENCE WORDS [\", sent, \"]\")\n",
    "        print(\"The word [\", word, \"] occurs in position\", idx, \"of the BERT input sentence\", ab)\n",
    "        print(\"The word [\", word, \"] corresponds to the token(s)\", token_ids_word, \"of the BERT input sequence\", ab)\n",
    "\n",
    "    return word_tokens_output.mean(dim=0)\n",
    "\n",
    "\n",
    "word_embedding = get_word_vector_from_ab(inputs, outputs, \"treatable\", 4, 'A')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([768])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embedding.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of the positional embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1900x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "layer = 1\n",
    "\n",
    "rows = 3\n",
    "cols = 4\n",
    "fig, ax_full = plt.subplots(rows, cols)\n",
    "fig.set_figheight(rows*4)\n",
    "fig.set_figwidth(cols*4+3)\n",
    "plt.rcParams.update({'font.size': 10})\n",
    "\n",
    "j = 0\n",
    "for r in range(rows):\n",
    "    for c in range(cols):\n",
    "       \n",
    "        ax = ax_full[r,c]\n",
    "        \n",
    "        plt.rcParams.update({'font.size': 10})\n",
    "        model = hidden_states[j][0].detach().numpy()\n",
    "        \n",
    "        if model.shape[1] == 2:\n",
    "            twodim = model\n",
    "        else:\n",
    "            twodim = PCA().fit_transform(model)[:,:2]\n",
    "\n",
    "        plt.style.use('default') # https://matplotlib.org/3.5.1/gallery/style_sheets/style_sheets_reference.html\n",
    "        im = ax.scatter(twodim[:,0], twodim[:,1], edgecolors='k', c='r')\n",
    "        for word, (x,y) in zip(tokens, twodim):\n",
    "            ax.text(x+0.05, y+0.05, word)\n",
    "        \n",
    "        # Show all ticks and label them with the respective list entries\n",
    "        ax.set_title(\"Layer \" + str(j))\n",
    "            \n",
    "        # Loop over data dimensions and create text annotations.\n",
    "        j = j + 1\n",
    "\n",
    "fig.suptitle(\"Layer\" + str(layer) + \" Multi-head Self-attentions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Token specific visualzation of self-attention\n",
    "\n",
    "More details are available here: https://github.com/jessevig/bertviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <script src=\"/static/components/requirejs/require.js\"></script>\n",
       "        <script>\n",
       "          requirejs.config({\n",
       "            paths: {\n",
       "              base: '/static/base',\n",
       "              \"d3\": \"https://cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min\",\n",
       "              jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
       "            },\n",
       "          });\n",
       "        </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "      \n",
       "        <div id=\"bertviz-f2db3cb431254d6cacfd713161b8c82e\" style=\"font-family:'Helvetica Neue', Helvetica, Arial, sans-serif;\">\n",
       "            <span style=\"user-select:none\">\n",
       "                Layer: <select id=\"layer\"></select>\n",
       "                \n",
       "            </span>\n",
       "            <div id='vis'></div>\n",
       "        </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/**\n",
       " * @fileoverview Transformer Visualization D3 javascript code.\n",
       " *\n",
       " *\n",
       " *  Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
       " *\n",
       " * Change log:\n",
       " *\n",
       " * 12/19/18  Jesse Vig   Assorted cleanup. Changed orientation of attention matrices.\n",
       " * 12/29/20  Jesse Vig   Significant refactor.\n",
       " * 12/31/20  Jesse Vig   Support multiple visualizations in single notebook.\n",
       " * 02/06/21  Jesse Vig   Move require config from separate jupyter notebook step\n",
       " * 05/03/21  Jesse Vig   Adjust height of visualization dynamically\n",
       " * 07/25/21  Jesse Vig   Support layer filtering\n",
       " * 03/23/22  Daniel SC   Update requirement URLs for d3 and jQuery (source of bug not allowing end result to be displayed on browsers)\n",
       " **/\n",
       "\n",
       "require.config({\n",
       "  paths: {\n",
       "      d3: 'https://cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min',\n",
       "    jquery: 'https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
       "  }\n",
       "});\n",
       "\n",
       "requirejs(['jquery', 'd3'], function ($, d3) {\n",
       "\n",
       "    const params = {\"attention\": [{\"name\": null, \"attn\": [[[[0.0490880012512207, 0.03899660333991051, 0.05360573157668114, 0.02920459769666195, 0.02992357313632965, 0.06698553264141083, 0.04214310646057129, 0.06781376153230667, 0.2409946620464325, 0.024634789675474167, 0.04046427085995674, 0.04352322220802307, 0.023713232949376106, 0.02307409793138504, 0.06898671388626099, 0.15684805810451508], [0.16825748980045319, 0.09761099517345428, 0.10294554382562637, 0.09066592156887054, 0.11650636792182922, 0.11571677029132843, 0.08777383714914322, 0.12874509394168854, 0.055366210639476776, 0.008330370299518108, 0.005664583761245012, 0.0045073749497532845, 0.007715093903243542, 0.004539972171187401, 0.0029586527962237597, 0.002695702249184251], [0.06892121583223343, 0.16865992546081543, 0.0332677960395813, 0.11989831179380417, 0.06767958402633667, 0.0631948783993721, 0.133324533700943, 0.16497980058193207, 0.1511717289686203, 0.00461019529029727, 0.005216734949499369, 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null, \"include_layers\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]} is a template marker that is replaced by actual params.\n",
       "    const TEXT_SIZE = 15;\n",
       "    const BOXWIDTH = 110;\n",
       "    const BOXHEIGHT = 22.5;\n",
       "    const MATRIX_WIDTH = 115;\n",
       "    const CHECKBOX_SIZE = 20;\n",
       "    const TEXT_TOP = 30;\n",
       "\n",
       "    console.log(\"d3 version\", d3.version)\n",
       "    let headColors;\n",
       "    try {\n",
       "        headColors = d3.scaleOrdinal(d3.schemeCategory10);\n",
       "    } catch (err) {\n",
       "        console.log('Older d3 version')\n",
       "        headColors = d3.scale.category10();\n",
       "    }\n",
       "    let config = {};\n",
       "    initialize();\n",
       "    renderVis();\n",
       "\n",
       "    function initialize() {\n",
       "        config.attention = params['attention'];\n",
       "        config.filter = params['default_filter'];\n",
       "        config.rootDivId = params['root_div_id'];\n",
       "        config.nLayers = config.attention[config.filter]['attn'].length;\n",
       "        config.nHeads = config.attention[config.filter]['attn'][0].length;\n",
       "        config.layers = params['include_layers']\n",
       "\n",
       "        if (params['heads']) {\n",
       "            config.headVis = new Array(config.nHeads).fill(false);\n",
       "            params['heads'].forEach(x => config.headVis[x] = true);\n",
       "        } else {\n",
       "            config.headVis = new Array(config.nHeads).fill(true);\n",
       "        }\n",
       "        config.initialTextLength = config.attention[config.filter].right_text.length;\n",
       "        config.layer_seq = (params['layer'] == null ? 0 : config.layers.findIndex(layer => params['layer'] === layer));\n",
       "        config.layer = config.layers[config.layer_seq]\n",
       "\n",
       "        let layerEl = $(`#${config.rootDivId} #layer`);\n",
       "        for (const layer of config.layers) {\n",
       "            layerEl.append($(\"<option />\").val(layer).text(layer));\n",
       "        }\n",
       "        layerEl.val(config.layer).change();\n",
       "        layerEl.on('change', function (e) {\n",
       "            config.layer = +e.currentTarget.value;\n",
       "            config.layer_seq = config.layers.findIndex(layer => config.layer === layer);\n",
       "            renderVis();\n",
       "        });\n",
       "\n",
       "        $(`#${config.rootDivId} #filter`).on('change', function (e) {\n",
       "            config.filter = e.currentTarget.value;\n",
       "            renderVis();\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function renderVis() {\n",
       "\n",
       "        // Load parameters\n",
       "        const attnData = config.attention[config.filter];\n",
       "        const leftText = attnData.left_text;\n",
       "        const rightText = attnData.right_text;\n",
       "\n",
       "        // Select attention for given layer\n",
       "        const layerAttention = attnData.attn[config.layer_seq];\n",
       "\n",
       "        // Clear vis\n",
       "        $(`#${config.rootDivId} #vis`).empty();\n",
       "\n",
       "        // Determine size of visualization\n",
       "        const height = Math.max(leftText.length, rightText.length) * BOXHEIGHT + TEXT_TOP;\n",
       "        const svg = d3.select(`#${config.rootDivId} #vis`)\n",
       "            .append('svg')\n",
       "            .attr(\"width\", \"100%\")\n",
       "            .attr(\"height\", height + \"px\");\n",
       "\n",
       "        // Display tokens on left and right side of visualization\n",
       "        renderText(svg, leftText, true, layerAttention, 0);\n",
       "        renderText(svg, rightText, false, layerAttention, MATRIX_WIDTH + BOXWIDTH);\n",
       "\n",
       "        // Render attention arcs\n",
       "        renderAttention(svg, layerAttention);\n",
       "\n",
       "        // Draw squares at top of visualization, one for each head\n",
       "        drawCheckboxes(0, svg, layerAttention);\n",
       "    }\n",
       "\n",
       "    function renderText(svg, text, isLeft, attention, leftPos) {\n",
       "\n",
       "        const textContainer = svg.append(\"svg:g\")\n",
       "            .attr(\"id\", isLeft ? \"left\" : \"right\");\n",
       "\n",
       "        // Add attention highlights superimposed over words\n",
       "        textContainer.append(\"g\")\n",
       "            .classed(\"attentionBoxes\", true)\n",
       "            .selectAll(\"g\")\n",
       "            .data(attention)\n",
       "            .enter()\n",
       "            .append(\"g\")\n",
       "            .attr(\"head-index\", (d, i) => i)\n",
       "            .selectAll(\"rect\")\n",
       "            .data(d => isLeft ? d : transpose(d)) // if right text, transpose attention to get right-to-left weights\n",
       "            .enter()\n",
       "            .append(\"rect\")\n",
       "            .attr(\"x\", function () {\n",
       "                var headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
       "                return leftPos + boxOffsets(headIndex);\n",
       "            })\n",
       "            .attr(\"y\", (+1) * BOXHEIGHT)\n",
       "            .attr(\"width\", BOXWIDTH / activeHeads())\n",
       "            .attr(\"height\", BOXHEIGHT)\n",
       "            .attr(\"fill\", function () {\n",
       "                return headColors(+this.parentNode.getAttribute(\"head-index\"))\n",
       "            })\n",
       "            .style(\"opacity\", 0.0);\n",
       "\n",
       "        const tokenContainer = textContainer.append(\"g\").selectAll(\"g\")\n",
       "            .data(text)\n",
       "            .enter()\n",
       "            .append(\"g\");\n",
       "\n",
       "        // Add gray background that appears when hovering over text\n",
       "        tokenContainer.append(\"rect\")\n",
       "            .classed(\"background\", true)\n",
       "            .style(\"opacity\", 0.0)\n",
       "            .attr(\"fill\", \"lightgray\")\n",
       "            .attr(\"x\", leftPos)\n",
       "            .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT)\n",
       "            .attr(\"width\", BOXWIDTH)\n",
       "            .attr(\"height\", BOXHEIGHT);\n",
       "\n",
       "        // Add token text\n",
       "        const textEl = tokenContainer.append(\"text\")\n",
       "            .text(d => d)\n",
       "            .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "            .style(\"cursor\", \"default\")\n",
       "            .style(\"-webkit-user-select\", \"none\")\n",
       "            .attr(\"x\", leftPos)\n",
       "            .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT);\n",
       "\n",
       "        if (isLeft) {\n",
       "            textEl.style(\"text-anchor\", \"end\")\n",
       "                .attr(\"dx\", BOXWIDTH - 0.5 * TEXT_SIZE)\n",
       "                .attr(\"dy\", TEXT_SIZE);\n",
       "        } else {\n",
       "            textEl.style(\"text-anchor\", \"start\")\n",
       "                .attr(\"dx\", +0.5 * TEXT_SIZE)\n",
       "                .attr(\"dy\", TEXT_SIZE);\n",
       "        }\n",
       "\n",
       "        tokenContainer.on(\"mouseover\", function (d, index) {\n",
       "\n",
       "            // Show gray background for moused-over token\n",
       "            textContainer.selectAll(\".background\")\n",
       "                .style(\"opacity\", (d, i) => i === index ? 1.0 : 0.0)\n",
       "\n",
       "            // Reset visibility attribute for any previously highlighted attention arcs\n",
       "            svg.select(\"#attention\")\n",
       "                .selectAll(\"line[visibility='visible']\")\n",
       "                .attr(\"visibility\", null)\n",
       "\n",
       "            // Hide group containing attention arcs\n",
       "            svg.select(\"#attention\").attr(\"visibility\", \"hidden\");\n",
       "\n",
       "            // Set to visible appropriate attention arcs to be highlighted\n",
       "            if (isLeft) {\n",
       "                svg.select(\"#attention\").selectAll(\"line[left-token-index='\" + index + \"']\").attr(\"visibility\", \"visible\");\n",
       "            } else {\n",
       "                svg.select(\"#attention\").selectAll(\"line[right-token-index='\" + index + \"']\").attr(\"visibility\", \"visible\");\n",
       "            }\n",
       "\n",
       "            // Update color boxes superimposed over tokens\n",
       "            const id = isLeft ? \"right\" : \"left\";\n",
       "            const leftPos = isLeft ? MATRIX_WIDTH + BOXWIDTH : 0;\n",
       "            svg.select(\"#\" + id)\n",
       "                .selectAll(\".attentionBoxes\")\n",
       "                .selectAll(\"g\")\n",
       "                .attr(\"head-index\", (d, i) => i)\n",
       "                .selectAll(\"rect\")\n",
       "                .attr(\"x\", function () {\n",
       "                    const headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
       "                    return leftPos + boxOffsets(headIndex);\n",
       "                })\n",
       "                .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT)\n",
       "                .attr(\"width\", BOXWIDTH / activeHeads())\n",
       "                .attr(\"height\", BOXHEIGHT)\n",
       "                .style(\"opacity\", function (d) {\n",
       "                    const headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
       "                    if (config.headVis[headIndex])\n",
       "                        if (d) {\n",
       "                            return d[index];\n",
       "                        } else {\n",
       "                            return 0.0;\n",
       "                        }\n",
       "                    else\n",
       "                        return 0.0;\n",
       "                });\n",
       "        });\n",
       "\n",
       "        textContainer.on(\"mouseleave\", function () {\n",
       "\n",
       "            // Unhighlight selected token\n",
       "            d3.select(this).selectAll(\".background\")\n",
       "                .style(\"opacity\", 0.0);\n",
       "\n",
       "            // Reset visibility attributes for previously selected lines\n",
       "            svg.select(\"#attention\")\n",
       "                .selectAll(\"line[visibility='visible']\")\n",
       "                .attr(\"visibility\", null) ;\n",
       "            svg.select(\"#attention\").attr(\"visibility\", \"visible\");\n",
       "\n",
       "            // Reset highlights superimposed over tokens\n",
       "            svg.selectAll(\".attentionBoxes\")\n",
       "                .selectAll(\"g\")\n",
       "                .selectAll(\"rect\")\n",
       "                .style(\"opacity\", 0.0);\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function renderAttention(svg, attention) {\n",
       "\n",
       "        // Remove previous dom elements\n",
       "        svg.select(\"#attention\").remove();\n",
       "\n",
       "        // Add new elements\n",
       "        svg.append(\"g\")\n",
       "            .attr(\"id\", \"attention\") // Container for all attention arcs\n",
       "            .selectAll(\".headAttention\")\n",
       "            .data(attention)\n",
       "            .enter()\n",
       "            .append(\"g\")\n",
       "            .classed(\"headAttention\", true) // Group attention arcs by head\n",
       "            .attr(\"head-index\", (d, i) => i)\n",
       "            .selectAll(\".tokenAttention\")\n",
       "            .data(d => d)\n",
       "            .enter()\n",
       "            .append(\"g\")\n",
       "            .classed(\"tokenAttention\", true) // Group attention arcs by left token\n",
       "            .attr(\"left-token-index\", (d, i) => i)\n",
       "            .selectAll(\"line\")\n",
       "            .data(d => d)\n",
       "            .enter()\n",
       "            .append(\"line\")\n",
       "            .attr(\"x1\", BOXWIDTH)\n",
       "            .attr(\"y1\", function () {\n",
       "                const leftTokenIndex = +this.parentNode.getAttribute(\"left-token-index\")\n",
       "                return TEXT_TOP + leftTokenIndex * BOXHEIGHT + (BOXHEIGHT / 2)\n",
       "            })\n",
       "            .attr(\"x2\", BOXWIDTH + MATRIX_WIDTH)\n",
       "            .attr(\"y2\", (d, rightTokenIndex) => TEXT_TOP + rightTokenIndex * BOXHEIGHT + (BOXHEIGHT / 2))\n",
       "            .attr(\"stroke-width\", 2)\n",
       "            .attr(\"stroke\", function () {\n",
       "                const headIndex = +this.parentNode.parentNode.getAttribute(\"head-index\");\n",
       "                return headColors(headIndex)\n",
       "            })\n",
       "            .attr(\"left-token-index\", function () {\n",
       "                return +this.parentNode.getAttribute(\"left-token-index\")\n",
       "            })\n",
       "            .attr(\"right-token-index\", (d, i) => i)\n",
       "        ;\n",
       "        updateAttention(svg)\n",
       "    }\n",
       "\n",
       "    function updateAttention(svg) {\n",
       "        svg.select(\"#attention\")\n",
       "            .selectAll(\"line\")\n",
       "            .attr(\"stroke-opacity\", function (d) {\n",
       "                const headIndex = +this.parentNode.parentNode.getAttribute(\"head-index\");\n",
       "                // If head is selected\n",
       "                if (config.headVis[headIndex]) {\n",
       "                    // Set opacity to attention weight divided by number of active heads\n",
       "                    return d / activeHeads()\n",
       "                } else {\n",
       "                    return 0.0;\n",
       "                }\n",
       "            })\n",
       "    }\n",
       "\n",
       "    function boxOffsets(i) {\n",
       "        const numHeadsAbove = config.headVis.reduce(\n",
       "            function (acc, val, cur) {\n",
       "                return val && cur < i ? acc + 1 : acc;\n",
       "            }, 0);\n",
       "        return numHeadsAbove * (BOXWIDTH / activeHeads());\n",
       "    }\n",
       "\n",
       "    function activeHeads() {\n",
       "        return config.headVis.reduce(function (acc, val) {\n",
       "            return val ? acc + 1 : acc;\n",
       "        }, 0);\n",
       "    }\n",
       "\n",
       "    function drawCheckboxes(top, svg) {\n",
       "        const checkboxContainer = svg.append(\"g\");\n",
       "        const checkbox = checkboxContainer.selectAll(\"rect\")\n",
       "            .data(config.headVis)\n",
       "            .enter()\n",
       "            .append(\"rect\")\n",
       "            .attr(\"fill\", (d, i) => headColors(i))\n",
       "            .attr(\"x\", (d, i) => i * CHECKBOX_SIZE)\n",
       "            .attr(\"y\", top)\n",
       "            .attr(\"width\", CHECKBOX_SIZE)\n",
       "            .attr(\"height\", CHECKBOX_SIZE);\n",
       "\n",
       "        function updateCheckboxes() {\n",
       "            checkboxContainer.selectAll(\"rect\")\n",
       "                .data(config.headVis)\n",
       "                .attr(\"fill\", (d, i) => d ? headColors(i): lighten(headColors(i)));\n",
       "        }\n",
       "\n",
       "        updateCheckboxes();\n",
       "\n",
       "        checkbox.on(\"click\", function (d, i) {\n",
       "            if (config.headVis[i] && activeHeads() === 1) return;\n",
       "            config.headVis[i] = !config.headVis[i];\n",
       "            updateCheckboxes();\n",
       "            updateAttention(svg);\n",
       "        });\n",
       "\n",
       "        checkbox.on(\"dblclick\", function (d, i) {\n",
       "            // If we double click on the only active head then reset\n",
       "            if (config.headVis[i] && activeHeads() === 1) {\n",
       "                config.headVis = new Array(config.nHeads).fill(true);\n",
       "            } else {\n",
       "                config.headVis = new Array(config.nHeads).fill(false);\n",
       "                config.headVis[i] = true;\n",
       "            }\n",
       "            updateCheckboxes();\n",
       "            updateAttention(svg);\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function lighten(color) {\n",
       "        const c = d3.hsl(color);\n",
       "        const increment = (1 - c.l) * 0.6;\n",
       "        c.l += increment;\n",
       "        c.s -= increment;\n",
       "        return c;\n",
       "    }\n",
       "\n",
       "    function transpose(mat) {\n",
       "        return mat[0].map(function (col, i) {\n",
       "            return mat.map(function (row) {\n",
       "                return row[i];\n",
       "            });\n",
       "        });\n",
       "    }\n",
       "\n",
       "});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "call_html()\n",
    "head_view(attention, tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualization of all the heads in one layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1900x1200 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "layer = 1\n",
    "\n",
    "rows = 3\n",
    "cols = 4\n",
    "fig, ax_full = plt.subplots(rows, cols)\n",
    "fig.set_figheight(rows*4)\n",
    "fig.set_figwidth(cols*4+3)\n",
    "plt.rcParams.update({'font.size': 10})\n",
    "\n",
    "j = 0\n",
    "for r in range(rows):\n",
    "    for c in range(cols):\n",
    "       \n",
    "        ax = ax_full[r,c]\n",
    "        \n",
    "        sattention = attention[layer][0][j].numpy()\n",
    "        sattention = np.flip(sattention, 0)\n",
    "        \n",
    "        plt.rcParams.update({'font.size': 10})\n",
    "\n",
    "        im = ax.pcolormesh(sattention, cmap='gnuplot')\n",
    "\n",
    "        # Show all ticks and label them with the respective list entries\n",
    "        ax.set_title(\"Head \" + str(j))\n",
    "        ax.set_yticks(np.arange(len(tokens)))\n",
    "        if c == 0:\n",
    "            ax.set_yticklabels(reversed(tokens))\n",
    "            ax.set_ylabel(\"Queries\")\n",
    "        else:\n",
    "            ax.set_yticks([])\n",
    "\n",
    "        ax.set_xticks(np.arange(len(tokens)))\n",
    "        if r == rows-1:\n",
    "            ax.set_xticklabels(tokens)\n",
    "            ax.set_xlabel(\"Keys\")\n",
    "            \n",
    "            # Rotate the tick labels and set their alignment.\n",
    "            plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "                     rotation_mode=\"anchor\")\n",
    "        else:\n",
    "            ax.set_xticks([])\n",
    "\n",
    "            \n",
    "        # Loop over data dimensions and create text annotations.\n",
    "        j = j + 1\n",
    "\n",
    "fig.suptitle(\"Layer\" + str(layer) + \" Multi-head Self-attentions\")\n",
    "cbar = fig.colorbar(im, ax=ax_full, location='right', shrink=0.5)\n",
    "cbar.ax.set_ylabel(\"Selt-attention\", rotation=-90, va=\"bottom\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualzation of Self-Attention in all layers and heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "      \n",
       "        <div id=\"bertviz-74828fccec834c52bc8e701f53319d5f\" style=\"font-family:'Helvetica Neue', Helvetica, Arial, sans-serif;\">\n",
       "            <span style=\"user-select:none\">\n",
       "                \n",
       "            </span>\n",
       "            <div id='vis'></div>\n",
       "        </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/**\n",
       " * @fileoverview Transformer Visualization D3 javascript code.\n",
       " *\n",
       " * Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
       " *\n",
       " * Change log:\n",
       " *\n",
       " * 02/01/19  Jesse Vig   Initial implementation\n",
       " * 12/31/20  Jesse Vig   Support multiple visualizations in single notebook.\n",
       " * 01/19/21  Jesse Vig   Support light/dark modes\n",
       " * 02/06/21  Jesse Vig   Move require config from separate jupyter notebook step\n",
       " * 05/03/21  Jesse Vig   Adjust visualization height dynamically\n",
       " * 03/23/22  Daniel SC   Update requirement URLs for d3 and jQuery (source of bug not allowing end result to be displayed on browsers)\n",
       " **/\n",
       "\n",
       "require.config({\n",
       "  paths: {\n",
       "      d3: 'https://cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min',\n",
       "    jquery: 'https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
       "  }\n",
       "});\n",
       "\n",
       "requirejs(['jquery', 'd3'], function($, d3) {\n",
       "\n",
       "        const params = {\"attention\": [{\"name\": null, \"attn\": [[[[0.0490880012512207, 0.03899660333991051, 0.05360573157668114, 0.02920459769666195, 0.02992357313632965, 0.06698553264141083, 0.04214310646057129, 0.06781376153230667, 0.2409946620464325, 0.024634789675474167, 0.04046427085995674, 0.04352322220802307, 0.023713232949376106, 0.02307409793138504, 0.06898671388626099, 0.15684805810451508], [0.16825748980045319, 0.09761099517345428, 0.10294554382562637, 0.09066592156887054, 0.11650636792182922, 0.11571677029132843, 0.08777383714914322, 0.12874509394168854, 0.055366210639476776, 0.008330370299518108, 0.005664583761245012, 0.0045073749497532845, 0.007715093903243542, 0.004539972171187401, 0.0029586527962237597, 0.002695702249184251], [0.06892121583223343, 0.16865992546081543, 0.0332677960395813, 0.11989831179380417, 0.06767958402633667, 0.0631948783993721, 0.133324533700943, 0.16497980058193207, 0.1511717289686203, 0.00461019529029727, 0.005216734949499369, 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\"include_layers\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"include_heads\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"total_heads\": 12} is a template marker that is replaced by actual params.\n",
       "        const config = {};\n",
       "\n",
       "        const MIN_X = 0;\n",
       "        const MIN_Y = 0;\n",
       "        const DIV_WIDTH = 970;\n",
       "        const THUMBNAIL_PADDING = 5;\n",
       "        const DETAIL_WIDTH = 300;\n",
       "        const DETAIL_ATTENTION_WIDTH = 140;\n",
       "        const DETAIL_BOX_WIDTH = 80;\n",
       "        const DETAIL_BOX_HEIGHT = 18;\n",
       "        const DETAIL_PADDING = 15;\n",
       "        const ATTN_PADDING = 0;\n",
       "        const DETAIL_HEADING_HEIGHT = 25;\n",
       "        const HEADING_TEXT_SIZE = 15;\n",
       "        const HEADING_PADDING = 5;\n",
       "        const TEXT_SIZE = 13;\n",
       "        const TEXT_PADDING = 5;\n",
       "        const LAYER_COLORS = d3.schemeCategory10;\n",
       "        const PALETTE = {\n",
       "            'light': {\n",
       "                'text': 'black',\n",
       "                'background': 'white',\n",
       "                'highlight': '#F5F5F5'\n",
       "            },\n",
       "            'dark': {\n",
       "                'text': '#ccc',\n",
       "                'background': 'black',\n",
       "                'highlight': '#222'\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function render() {\n",
       "\n",
       "            // Set global state variables\n",
       "\n",
       "            var attData = config.attention[config.filter];\n",
       "            config.leftText = attData.left_text;\n",
       "            config.rightText = attData.right_text;\n",
       "            config.attn = attData.attn;\n",
       "            config.numLayers = config.attn.length;\n",
       "            config.numHeads = config.attn[0].length;\n",
       "            config.thumbnailBoxHeight = 7 * (12 / config.totalHeads);\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            config.thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            config.thumbnailWidth = (DIV_WIDTH - axisSize) / config.totalHeads;\n",
       "            config.detailHeight = Math.max(config.leftText.length, config.rightText.length) * DETAIL_BOX_HEIGHT + 2 * DETAIL_PADDING + DETAIL_HEADING_HEIGHT;\n",
       "            config.divHeight = Math.max(config.numLayers * config.thumbnailHeight + axisSize, config.detailHeight);\n",
       "\n",
       "            const vis = $(`#${config.rootDivId} #vis`)\n",
       "            vis.empty();\n",
       "            vis.attr(\"height\", config.divHeight);\n",
       "            config.svg = d3.select(`#${config.rootDivId} #vis`)\n",
       "                .append('svg')\n",
       "                .attr(\"width\", DIV_WIDTH)\n",
       "                .attr(\"height\", config.divHeight)\n",
       "                .attr(\"fill\", getBackgroundColor());\n",
       "\n",
       "            renderAxisLabels();\n",
       "\n",
       "            var i;\n",
       "            var j;\n",
       "            for (i = 0; i < config.numLayers; i++) {\n",
       "                for (j = 0; j < config.numHeads; j++) {\n",
       "                    renderThumbnail(i, j);\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function renderAxisLabels() {\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            const tableWidth = config.thumbnailWidth * config.heads.length;\n",
       "            config.svg.append(\"text\")\n",
       "                .text(\"Heads\")\n",
       "                .attr(\"fill\", \"black\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .attr(\"font-size\", HEADING_TEXT_SIZE + \"px\")\n",
       "                .attr(\"x\", axisSize + tableWidth / 2)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", 0)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "            for (let i = 0; i < config.numHeads; i++) {\n",
       "                config.svg.append(\"text\")\n",
       "                    .text(config.heads[i])\n",
       "                    .attr(\"fill\", \"black\")\n",
       "                    .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                    .attr(\"x\", axisSize + (i + .5) * config.thumbnailWidth)\n",
       "                    .attr(\"text-anchor\", \"middle\")\n",
       "                    .attr(\"y\", HEADING_TEXT_SIZE + HEADING_PADDING)\n",
       "                    .attr(\"dy\", TEXT_SIZE);\n",
       "            }\n",
       "            let x = 0;\n",
       "            let y = axisSize + config.thumbnailHeight * config.layers.length / 2;\n",
       "            console.log(\"x\", x, y)\n",
       "            config.svg.append(\"text\")\n",
       "                .text(\"Layers\")\n",
       "                .attr(\"fill\", \"black\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .attr(\"transform\", \"rotate(270, \" + x  + \", \" + y + \")\")\n",
       "                .attr(\"font-size\", HEADING_TEXT_SIZE + \"px\")\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "            for (let i = 0; i < config.numLayers; i++) {\n",
       "                x = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE; // HACK\n",
       "                y = axisSize + (i + .5) * config.thumbnailHeight;\n",
       "                config.svg.append(\"text\")\n",
       "                    .text(config.layers[i])\n",
       "                    .attr(\"fill\", \"black\")\n",
       "                    .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                    .attr(\"x\", x)\n",
       "                    .attr(\"text-anchor\", \"end\")\n",
       "                    .attr(\"y\", y)\n",
       "                    .attr(\"dy\", TEXT_SIZE / 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "\n",
       "        function renderThumbnail(layerIndex, headIndex) {\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING\n",
       "            const x = headIndex * config.thumbnailWidth + axisSize;\n",
       "            const y = layerIndex * config.thumbnailHeight + axisSize;\n",
       "            renderThumbnailAttn(x, y, config.attn[layerIndex][headIndex], layerIndex, headIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetail(att, layerIndex, headIndex) {\n",
       "            const axisSize = TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            var xOffset = .8 * config.thumbnailWidth;\n",
       "            var maxX = DIV_WIDTH;\n",
       "            var maxY = config.divHeight - 3;\n",
       "            var leftPos = axisSize + headIndex * config.thumbnailWidth;\n",
       "            var x = leftPos + THUMBNAIL_PADDING + xOffset;\n",
       "            if (x < MIN_X) {\n",
       "                x = MIN_X;\n",
       "            } else if (x + DETAIL_WIDTH > maxX) {\n",
       "                x = leftPos + THUMBNAIL_PADDING - DETAIL_WIDTH + 8;\n",
       "            }\n",
       "            var posLeftText = x;\n",
       "            var posAttention = posLeftText + DETAIL_BOX_WIDTH;\n",
       "            var posRightText = posAttention + DETAIL_ATTENTION_WIDTH;\n",
       "            var thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            var yOffset = 20;\n",
       "            var y = layerIndex * thumbnailHeight + THUMBNAIL_PADDING + yOffset;\n",
       "            if (y < MIN_Y) {\n",
       "                y = MIN_Y;\n",
       "            } else if (y + config.detailHeight > maxY) {\n",
       "                y = maxY - config.detailHeight;\n",
       "            }\n",
       "            renderDetailFrame(x, y, layerIndex);\n",
       "            y = y + DETAIL_PADDING;\n",
       "            renderDetailHeading(x, y, layerIndex, headIndex);\n",
       "            y = y + DETAIL_HEADING_HEIGHT;\n",
       "            renderDetailText(config.leftText, \"leftText\", posLeftText, y , layerIndex);\n",
       "            renderDetailAttn(posAttention, y, att, layerIndex, headIndex);\n",
       "            renderDetailText(config.rightText, \"rightText\", posRightText, y, layerIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetailHeading(x, y, layerIndex, headIndex) {\n",
       "            var fillColor = getTextColor();\n",
       "            config.svg.append(\"text\")\n",
       "                .classed(\"detail\", true)\n",
       "                .text('Layer ' + config.layers[layerIndex] + \", Head \" + config.heads[headIndex])\n",
       "                .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x + DETAIL_WIDTH / 2)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", DETAIL_HEADING_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "        }\n",
       "\n",
       "        function renderDetailText(text, id, x, y, layerIndex) {\n",
       "            var tokenContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .selectAll(\"g\")\n",
       "                .data(text)\n",
       "                .enter()\n",
       "                .append(\"g\");\n",
       "\n",
       "            var fillColor = getTextColor();\n",
       "\n",
       "            tokenContainer.append(\"rect\")\n",
       "                .classed(\"highlight\", true)\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .style(\"opacity\", 0.0)\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return y + i * DETAIL_BOX_HEIGHT;\n",
       "                });\n",
       "\n",
       "            var textContainer = tokenContainer.append(\"text\")\n",
       "                .classed(\"token\", true)\n",
       "                .text(function (d) {\n",
       "                    return d;\n",
       "                })\n",
       "                .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return i * DETAIL_BOX_HEIGHT + y;\n",
       "                })\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"dy\", TEXT_SIZE);\n",
       "\n",
       "            if (id == \"leftText\") {\n",
       "                textContainer.style(\"text-anchor\", \"end\")\n",
       "                    .attr(\"dx\", DETAIL_BOX_WIDTH - 2);\n",
       "                tokenContainer.on(\"mouseover\", function (d, index) {\n",
       "                    highlightSelection(index);\n",
       "                });\n",
       "                tokenContainer.on(\"mouseleave\", function () {\n",
       "                    unhighlightSelection();\n",
       "                });\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function highlightSelection(index) {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function unhighlightSelection() {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", 0.0);\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", 1);\n",
       "        }\n",
       "\n",
       "        function renderThumbnailAttn(x, y, att, layerIndex, headIndex) {\n",
       "\n",
       "            var attnContainer = config.svg.append(\"svg:g\");\n",
       "\n",
       "            var attnBackground = attnContainer.append(\"rect\")\n",
       "                .attr(\"id\", 'attn_background_' + layerIndex + \"_\" + headIndex)\n",
       "                .classed(\"attn_background\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .attr(\"stroke-width\", 2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", 0)\n",
       "                .attr(\"fill\", getBackgroundColor());\n",
       "            var x1 = x + THUMBNAIL_PADDING;\n",
       "            var x2 = x1 + config.thumbnailWidth - 14;\n",
       "            var y1 = y + THUMBNAIL_PADDING;\n",
       "\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter() // When entering\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x1)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y1 + (sourceIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"x2\", x2)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y1 + (targetIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "\n",
       "            var clickRegion = attnContainer.append(\"rect\")\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .style(\"opacity\", 0);\n",
       "\n",
       "            clickRegion.on(\"click\", function (d, index) {\n",
       "                var attnBackgroundOther = config.svg.selectAll(\".attn_background\");\n",
       "                attnBackgroundOther.attr(\"fill\", getBackgroundColor());\n",
       "                attnBackgroundOther.attr(\"stroke-opacity\", 0);\n",
       "\n",
       "                config.svg.selectAll(\".detail\").remove();\n",
       "                if (config.detail_layer != layerIndex || config.detail_head != headIndex) {\n",
       "                    renderDetail(att, layerIndex, headIndex);\n",
       "                    config.detail_layer = layerIndex;\n",
       "                    config.detail_head = headIndex;\n",
       "                    attnBackground.attr(\"fill\", getHighlightColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", .8);\n",
       "                } else {\n",
       "                    config.detail_layer = null;\n",
       "                    config.detail_head = null;\n",
       "                    attnBackground.attr(\"fill\", getBackgroundColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", 0);\n",
       "                }\n",
       "            });\n",
       "\n",
       "            clickRegion.on(\"mouseover\", function (d) {\n",
       "                d3.select(this).style(\"cursor\", \"pointer\");\n",
       "            });\n",
       "        }\n",
       "\n",
       "        function renderDetailFrame(x, y, layerIndex) {\n",
       "            var detailFrame = config.svg.append(\"rect\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.detailHeight)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .style(\"opacity\", 1)\n",
       "                .attr(\"stroke-width\", 1.5)\n",
       "                .attr(\"stroke-opacity\", 0.7)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex));\n",
       "        }\n",
       "\n",
       "        function renderDetailAttn(x, y, att, layerIndex) {\n",
       "            var attnContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"pointer-events\", \"none\");\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .classed('attn-line-group', true)\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter()\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x + ATTN_PADDING)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y + (sourceIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"x2\", x + DETAIL_ATTENTION_WIDTH - ATTN_PADDING)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y + (targetIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function getLayerColor(layer) {\n",
       "          return LAYER_COLORS[config.layers[layer] % 10];\n",
       "        }\n",
       "\n",
       "        function getTextColor() {\n",
       "            return PALETTE[config.mode]['text']\n",
       "        }\n",
       "\n",
       "        function getBackgroundColor() {\n",
       "           return PALETTE[config.mode]['background']\n",
       "        }\n",
       "\n",
       "        function getHighlightColor() {\n",
       "           return PALETTE[config.mode]['highlight']\n",
       "        }\n",
       "\n",
       "        function initialize() {\n",
       "            config.attention = params['attention'];\n",
       "            config.filter = params['default_filter'];\n",
       "            config.mode = params['display_mode'];\n",
       "            config.layers = params['include_layers']\n",
       "            config.heads = params['include_heads']\n",
       "            config.totalHeads = params['total_heads']\n",
       "            config.rootDivId = params['root_div_id'];\n",
       "            $(`#${config.rootDivId} #filter`).on('change', function (e) {\n",
       "                config.filter = e.currentTarget.value;\n",
       "                render();\n",
       "            });\n",
       "        }\n",
       "\n",
       "        initialize();\n",
       "        render();\n",
       "\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_view(attention, tokens)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "model_view_bert_msmarco_aula.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Tutorials (GPU)",
   "language": "python",
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