notebooks/03_tf_seq_to_seq_training_881M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lvo0t7XVIkWZ"
   },
   "source": [
    "# Password"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "HnrzE6W2XQOz"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os\n",
    "\n",
    "import distutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "dE-oZTXXJD4o",
    "outputId": "79401aea-a8ed-4ae4-ee47-c4ab13fe181b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.8.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add required libraries\n",
    "nltk is required for calculating BLEU Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: nltk in /home/ubuntu/.local/lib/python3.8/site-packages (3.7)\n",
      "Requirement already satisfied: click in /usr/lib/python3/dist-packages (from nltk) (7.0)\n",
      "Requirement already satisfied: joblib in /home/ubuntu/.local/lib/python3.8/site-packages (from nltk) (1.1.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /home/ubuntu/.local/lib/python3.8/site-packages (from nltk) (2022.6.2)\n",
      "Requirement already satisfied: tqdm in /home/ubuntu/.local/lib/python3.8/site-packages (from nltk) (4.64.0)\n",
      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set memory growth\n",
    "This will not allow tf to allocate whole GPU at once and use GPU as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "  try:\n",
    "    # Currently, memory growth needs to be the same across GPUs\n",
    "    for gpu in gpus:\n",
    "      tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    logical_gpus = tf.config.list_logical_devices('GPU')\n",
    "    print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "  except RuntimeError as e:\n",
    "    # Memory growth must be set before GPUs have been initialized\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peak raw data \n",
    "816 M records for training,\n",
    "21 M records for validation,\n",
    "21 M records for Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qa89F5laN662",
    "outputId": "a4a1926d-6c98-43da-97a2-1c9a6b08231c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw------- 1 ubuntu ubuntu   8918059640 Jul  1 00:15 passwd_db_train\n",
      "-rw------- 1 ubuntu ubuntu    234670546 Jul  1 00:20 passwd_db_val\n",
      "-rw------- 1 ubuntu ubuntu    234703662 Jul  1 00:20 passwd_db_test\n",
      "-rw-r--r-- 1 ubuntu ubuntu          102 Jul  2 23:10 passwd_db_min\n",
      "-rw------- 1 ubuntu ubuntu   3224999700 Jul  4 15:36 passwd_db_test.tfrecords\n",
      "-rw------- 1 ubuntu ubuntu   3224999700 Jul  4 16:04 passwd_db_val.tfrecords\n",
      "-rw------- 1 ubuntu ubuntu 122549987700 Jul  4 16:33 passwd_db_train.tfrecords\n"
     ]
    }
   ],
   "source": [
    "!ls -ltr passwd_db*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rh_w7LMtOAbG",
    "outputId": "152cd7fc-2803-47e0-9487-be10e9b65bd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "816999918 passwd_db_train\n"
     ]
    }
   ],
   "source": [
    "!wc -l passwd_db_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sPRHRcrrOHx3",
    "outputId": "07921bd9-284b-4b9b-ab7e-7f8a4a610aa6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21499998 passwd_db_val\n"
     ]
    }
   ],
   "source": [
    "!wc -l passwd_db_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21499998 passwd_db_test\n"
     ]
    }
   ],
   "source": [
    "!wc -l passwd_db_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 4)\n"
     ]
    }
   ],
   "source": [
    "# verify of any GPU issues\n",
    "inputs = tf.random.normal([32, 10, 8])\n",
    "gru = tf.keras.layers.GRU(4)\n",
    "output = gru(inputs)\n",
    "print(output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bL8s4KuoWWlv"
   },
   "source": [
    "## Build vocab\n",
    "Build vocabulary to test dataset. This is to save RAM space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "TymxIpsXWkvc"
   },
   "outputs": [],
   "source": [
    "passwds = tf.io.gfile.GFile('passwd_db_test').read().split(\"\\n\")\n",
    "vocab = sorted(list(set(''.join(passwds))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2dSuIr5CXEho",
    "outputId": "ca5e28dd-20ae-459d-be0d-a9c6a4037f15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "AK2dtPmXXcYz"
   },
   "outputs": [],
   "source": [
    "max_len = max(passwds, key=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "IsnfvWWdXdjL",
    "outputId": "9ed849b8-fabe-4a84-a569-b26d1cd0a9fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'zimin0894zimin0894ver1zimin0894zimin0ziminziminver'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T62jooG8Xi4g",
    "outputId": "4a0dca7f-3930-44c4-a331-cf1c8b7e1794"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "E6Xyg_AoXIvG"
   },
   "outputs": [],
   "source": [
    "tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='', char_level=True, lower=False)\n",
    "tokenizer.fit_on_texts(passwds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "_JwC8Bx1X7Zv"
   },
   "outputs": [],
   "source": [
    "vocab_size = len(tokenizer.index_word) + 1\n",
    "seq_len = len(max_len) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O0X-lx1mXWCD",
    "outputId": "c4752654-fc2f-4491-9e6e-0b1dd2d848f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocab size: 96\n",
      "Seq len: 49\n"
     ]
    }
   ],
   "source": [
    "print(f\"Vocab size: {vocab_size}\")\n",
    "print(f\"Seq len: {seq_len}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30,  1, 12, 12,  0,  0,  0],\n",
       "       [19,  3, 12, 19,  1, 21, 29]], dtype=int32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_ten = tokenizer.texts_to_sequences([\"pass\", \"testadf\"])\n",
    "tf.keras.preprocessing.sequence.pad_sequences(in_ten, padding='post')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Dataset\n",
    "Since loading all raw dataset will require upto ~800GB RAM. Raw data is converted to tf_records. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_description = {\n",
    "      'in_vec' : tf.io.FixedLenSequenceFeature([], tf.int64, allow_missing=True),\n",
    "      'out_vec':tf.io.FixedLenSequenceFeature([], tf.int64, allow_missing=True),\n",
    "    }\n",
    "\n",
    "def _parse_function(example_proto):\n",
    "  # Parse the input `tf.train.Example` proto using the dictionary above.\n",
    "  record = tf.io.parse_single_example(example_proto, feature_description)\n",
    "  return record['in_vec'], record['out_vec']\n",
    "\n",
    "def get_dataset(filename, batch_size=32):\n",
    "    dataset = tf.data.TFRecordDataset(filename).map(_parse_function)\n",
    "    dataset = dataset.shuffle(2048)\n",
    "    dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\n",
    "    dataset = dataset.batch(batch_size, drop_remainder=True)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw------- 1 ubuntu ubuntu   3224999700 Jul  4 15:36 passwd_db_test.tfrecords\n",
      "-rw------- 1 ubuntu ubuntu   3224999700 Jul  4 16:04 passwd_db_val.tfrecords\n",
      "-rw------- 1 ubuntu ubuntu 122549987700 Jul  4 16:33 passwd_db_train.tfrecords\n"
     ]
    }
   ],
   "source": [
    "!ls -ltr *.tfrecords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = get_dataset('passwd_db_test.tfrecords', batch_size=8192)\n",
    "val_dataset = get_dataset('passwd_db_val.tfrecords', batch_size=8192)\n",
    "train_dataset = get_dataset('passwd_db_train.tfrecords', batch_size=8192)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "yLEM-fLJlEEt"
   },
   "outputs": [],
   "source": [
    "embedding_dim = vocab_size\n",
    "rnn_units = 256 # was 1024\n",
    "\n",
    "class MyModel(tf.keras.Model):\n",
    "  def __init__(self, vocab_size, embedding_dim, rnn_units):\n",
    "    super().__init__(self)\n",
    "    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n",
    "    self.gru = tf.keras.layers.GRU(rnn_units,\n",
    "                                   return_sequences=True,\n",
    "                                   return_state=True)\n",
    "    self.dense = tf.keras.layers.Dense(vocab_size)\n",
    "\n",
    "  def call(self, inputs, states=None, return_state=False, training=False):\n",
    "    x = inputs\n",
    "    x = self.embedding(x, training=training)\n",
    "    if states is None:\n",
    "      states = self.gru.get_initial_state(x)\n",
    "    x, states = self.gru(x, initial_state=states, training=training)\n",
    "    x = self.dense(x, training=training)\n",
    "\n",
    "    if return_state:\n",
    "      return x, states\n",
    "    else:\n",
    "      return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_model =  MyModel(\n",
    "    vocab_size=vocab_size,\n",
    "    embedding_dim=embedding_dim,\n",
    "    rnn_units=rnn_units)\n",
    "\n",
    "training_model.compile(\n",
    "    optimizer='adam',\n",
    "    loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verify dataset and model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_test_input, sample_test_target = next(iter(test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([8192, 49])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_test_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([8192, 49])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_test_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_test_preds = training_model(sample_test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([8192, 49, 96])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_test_preds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLEU Score before training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "6hwImKgcRDfs"
   },
   "outputs": [],
   "source": [
    "from nltk.translate.bleu_score import corpus_bleu\n",
    "\n",
    "def bleu_score(y_true, y_pred):\n",
    "  batch_score = []\n",
    "  for ref, hyp in zip(y_true, y_pred):\n",
    "    batch_score.append(corpus_bleu([chr(c) for c in ref], [chr(c) for c in hyp], weights=[0.25]))\n",
    "  return np.average(batch_score)\n",
    "\n",
    "# try model without training\n",
    "def calculate_bleu_score(model, dataset, till_batch):\n",
    "  scores = []\n",
    "\n",
    "  for batch_i, (input_test_batch, target_test_batch) in enumerate(dataset, 1):\n",
    "      test_batch_predictions = model(input_test_batch)\n",
    "      test_batch_predictions = np.array([tf.argmax(i, axis=1).numpy() for i in test_batch_predictions], dtype=np.int32)\n",
    "      scores.append(bleu_score(target_test_batch.numpy(), test_batch_predictions))\n",
    "      if batch_i % 100 == 0:\n",
    "        print(f\"Till {batch_i} the avg bleu score is {np.average(scores)}\")\n",
    "      if batch_i > till_batch:\n",
    "        break\n",
    "  \n",
    "  print(f\"Final avg bleu score is {np.average(scores)}\")\n",
    "  return np.average(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "5dCNfzVLTlyZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(49, 1), dtype=int64, numpy=\n",
       "array([[ 3],\n",
       "       [51],\n",
       "       [ 6],\n",
       "       [65],\n",
       "       [82],\n",
       "       [91],\n",
       "       [65],\n",
       "       [37],\n",
       "       [ 1],\n",
       "       [42],\n",
       "       [81],\n",
       "       [32],\n",
       "       [72],\n",
       "       [52],\n",
       "       [12],\n",
       "       [68],\n",
       "       [57],\n",
       "       [ 9],\n",
       "       [75],\n",
       "       [85],\n",
       "       [70],\n",
       "       [15],\n",
       "       [61],\n",
       "       [67],\n",
       "       [19],\n",
       "       [61],\n",
       "       [ 3],\n",
       "       [83],\n",
       "       [22],\n",
       "       [41],\n",
       "       [77],\n",
       "       [30],\n",
       "       [29],\n",
       "       [88],\n",
       "       [ 0],\n",
       "       [27],\n",
       "       [72],\n",
       "       [49],\n",
       "       [30],\n",
       "       [46],\n",
       "       [14],\n",
       "       [68],\n",
       "       [18],\n",
       "       [74],\n",
       "       [43],\n",
       "       [85],\n",
       "       [57],\n",
       "       [49],\n",
       "       [ 9]])>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random.categorical(sample_test_preds[0], num_samples=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zzfKEnAJlW2E",
    "outputId": "06a37bbb-2630-4656-ae5b-534e1dfe4d2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(49,), dtype=int64, numpy=\n",
       "array([20, 48, 39, 56, 41, 17, 41, 81, 81, 54,  1, 46, 73, 76, 76, 76, 76,\n",
       "       76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76,\n",
       "       76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76])>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.argmax(sample_test_preds[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JP1e-dq--M4y",
    "outputId": "30bb0fdb-a3fe-4299-a700-131635378a91"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 48, 39, ..., 76, 76, 76],\n",
       "       [81, 51,  8, ..., 76, 76, 76],\n",
       "       [53, 33, 70, ..., 76, 76, 76],\n",
       "       ...,\n",
       "       [15, 47, 47, ..., 76, 76, 76],\n",
       "       [17, 76,  8, ..., 76, 76, 76],\n",
       "       [28, 57, 45, ..., 76, 76, 76]], dtype=int32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([tf.argmax(i, axis=1).numpy() for i in sample_test_preds], dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[38, 50, 60, ..., 84, 48, 72],\n",
       "       [93, 42,  4, ..., 14,  2, 69],\n",
       "       [50, 48, 46, ..., 45,  0, 31],\n",
       "       ...,\n",
       "       [55, 19, 78, ...,  5, 37, 63],\n",
       "       [67, 24, 29, ..., 24, 17, 22],\n",
       "       [37,  7, 65, ..., 16, 13, 26]], dtype=int32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([tf.squeeze(tf.random.categorical(i, num_samples=1), axis=-1).numpy() for i in sample_test_preds], dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eqirIqDdSVyN",
    "outputId": "c697653a-ee88-4511-beb6-615715bb3aae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23, 25,  7, ...,  0,  0,  0],\n",
       "       [43, 22, 20, ...,  0,  0,  0],\n",
       "       [ 1, 22,  6, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [19, 24, 17, ...,  0,  0,  0],\n",
       "       [ 7, 22,  7, ...,  0,  0,  0],\n",
       "       [ 2, 10, 15, ...,  0,  0,  0]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_test_target.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2Quby14sFPRm",
    "outputId": "237636bd-a0fd-478f-a00d-6ede12cb5ee3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02486440619999932"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bleu_score(sample_test_target.numpy(), np.array([tf.argmax(i, axis=1).numpy() for i in sample_test_preds], dtype=np.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kuenh8dve2nm",
    "outputId": "d4975caf-2a87-4eae-b57e-450c57ce69aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1592550773478552"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bleu_score(sample_test_target.numpy(), np.array([tf.squeeze(tf.random.categorical(i, num_samples=1), axis=-1).numpy() for i in sample_test_preds], dtype=np.int32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "fLkC-59YMJ5_"
   },
   "outputs": [],
   "source": [
    "earlystopping_cb = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor = 'val_loss',\n",
    "    mode = 'min',\n",
    "    verbose = 1,\n",
    "    patience = 3,\n",
    "    restore_best_weights = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qqNt3H2TAJir",
    "outputId": "f5d6da52-5e76-4da3-aac3-61959df98847"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "99731/99731 [==============================] - 12835s 129ms/step - loss: 0.5190 - val_loss: 0.5139\n",
      "Epoch 2/10\n",
      "99731/99731 [==============================] - 12547s 126ms/step - loss: 0.5134 - val_loss: 0.5128\n",
      "Epoch 3/10\n",
      "99731/99731 [==============================] - 12973s 130ms/step - loss: 0.5127 - val_loss: 0.5124\n",
      "Epoch 4/10\n",
      "99731/99731 [==============================] - 12916s 130ms/step - loss: 0.5123 - val_loss: 0.5121\n",
      "Epoch 5/10\n",
      "99731/99731 [==============================] - 13178s 132ms/step - loss: 0.5120 - val_loss: 0.5119\n",
      "Epoch 6/10\n",
      "99731/99731 [==============================] - 12814s 128ms/step - loss: 0.5118 - val_loss: 0.5117\n",
      "Epoch 7/10\n",
      "99731/99731 [==============================] - 12942s 130ms/step - loss: 0.5117 - val_loss: 0.5116\n",
      "Epoch 8/10\n",
      "99731/99731 [==============================] - 12982s 130ms/step - loss: 0.5115 - val_loss: 0.5114\n",
      "Epoch 9/10\n",
      "99731/99731 [==============================] - 13021s 131ms/step - loss: 0.5114 - val_loss: 0.5113\n",
      "Epoch 10/10\n",
      "99731/99731 [==============================] - 12980s 130ms/step - loss: 0.5114 - val_loss: 0.5112\n"
     ]
    }
   ],
   "source": [
    "training_model.fit(\n",
    "    train_dataset,\n",
    "    epochs=10,\n",
    "    validation_data=val_dataset,\n",
    "    callbacks=[earlystopping_cb]\n",
    ")\n",
    "training_model.save_weights('gpu_password_gru', overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLEU Score after training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "0KwZE6noMey4"
   },
   "outputs": [],
   "source": [
    "sample_test_preds = training_model(sample_test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WUZt9IjKRqyj",
    "outputId": "d15b2579-ce59-4272-b8fa-6a9096fa0ae3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9614950184899456"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bleu_score(sample_test_target.numpy(), np.array([tf.argmax(i, axis=1).numpy() for i in sample_test_preds], dtype=np.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OWTbg_44iK2n",
    "outputId": "743d19ed-c8d7-448c-8e31-514174409eb1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9567861176470129"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bleu_score(sample_test_target.numpy(), np.array([tf.squeeze(tf.random.categorical(i, num_samples=1), axis=-1).numpy() for i in sample_test_preds], dtype=np.int32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "H7RxS_Wadjnu"
   },
   "outputs": [],
   "source": [
    "prediction_model = MyModel(\n",
    "    vocab_size=vocab_size,\n",
    "    embedding_dim=embedding_dim,\n",
    "    rnn_units=rnn_units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W-DpTO2sfLdc",
    "outputId": "ac27ed70-abd6-416e-dc25-25d7069b073c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f635aa55c40>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_model.load_weights('gpu_password_gru')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "tU7M-EGGxR3E"
   },
   "outputs": [],
   "source": [
    "class OneStep(tf.keras.Model):\n",
    "  def __init__(self, model, tokenizer, temperature=1.0):\n",
    "    super().__init__()\n",
    "    self.temperature = temperature\n",
    "    self.model = model\n",
    "    self.tokenizer = tokenizer\n",
    "\n",
    "  #@tf.function\n",
    "  def generate_one_step(self, input_chars, states=None):\n",
    "    #import pdb; pdb.set_trace()\n",
    "    # Convert strings to token IDs.\n",
    "    # input_chars = tf.strings.unicode_split(inputs, 'UTF-8')\n",
    "    if input_chars[0] == '':\n",
    "      input_chars = ' '\n",
    "    input_ids = self.tokenizer.texts_to_sequences(input_chars)\n",
    "    #print(input_chars)\n",
    "    input_ids = tf.keras.preprocessing.sequence.pad_sequences(input_ids, padding='post')\n",
    "\n",
    "    # Run the model.\n",
    "    # predicted_logits.shape is [batch, char, next_char_logits]\n",
    "    predicted_logits, states = self.model(inputs=input_ids, states=states,\n",
    "                                          return_state=True)\n",
    "    # Only use the last prediction.\n",
    "    predicted_logits = predicted_logits[:, -1, :]\n",
    "    predicted_logits = predicted_logits/self.temperature\n",
    "\n",
    "    # Sample the output logits to generate token IDs.\n",
    "    predicted_ids = tf.random.categorical(predicted_logits, num_samples=1)\n",
    "    predicted_ids = tf.squeeze(predicted_ids, axis=-1)\n",
    "\n",
    "    # Convert from token ids to characters\n",
    "    predicted_chars = self.tokenizer.sequences_to_texts([predicted_ids.numpy()])\n",
    "\n",
    "    # Return the characters and model state.\n",
    "    return predicted_chars, states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "3jG7KUOmkAtN"
   },
   "outputs": [],
   "source": [
    "one_step_model = OneStep(prediction_model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Yb1rW9aQkBs8",
    "outputId": "a0c4dc20-7c01-4a6f-a79c-8b7e8628dc44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wes \n",
      "\n",
      "________________________________________________________________________________\n",
      "\n",
      "Run time: 0.11068558692932129\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start = time.time()\n",
    "states = None\n",
    "next_char = ['w']\n",
    "result = [next_char]\n",
    "\n",
    "for n in range(2):\n",
    "  next_char, states = one_step_model.generate_one_step(next_char, states=states)\n",
    "  result.append(next_char)\n",
    "\n",
    "result = tf.strings.join(result)\n",
    "end = time.time()\n",
    "print(result[0].numpy().decode('utf-8'), '\\n\\n' + '_'*80)\n",
    "print('\\nRun time:', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "HJNitjybkGOS"
   },
   "outputs": [],
   "source": [
    "def pwds_from_chars(model, start_char, len):\n",
    "  states = None\n",
    "  next_char = [start_char]\n",
    "  result = [next_char]\n",
    "\n",
    "  for n in range(len-1):\n",
    "    next_char, states = model.generate_one_step(next_char, states=states)\n",
    "    result.append(next_char)\n",
    "\n",
    "  result = tf.strings.join(result)\n",
    "  return result[0].numpy().decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_woxP1GHkRL-",
    "outputId": "64ff12bb-1847-44a5-e540-e0ca64034286"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['wus', 'wdV', 'wil', 'wyl', 'wow', 'wel', 'wep', 'www', 'wes', 'wnd', 'wav', 'wg2', 'w9V', 'wqe', 'wha', 'wac', 'wob', 'wei', 'wos', 'way', 'wut', 'wic', 'wen', 'ww3', 'wab', 'wly', 'whi', 'wal', 'wor', 'wDk', 'wax', 'wma', 'wae', 'wwj', 'w94', 'wez', 'wun']\n"
     ]
    }
   ],
   "source": [
    "all_pos = []\n",
    "while True:\n",
    "  pred = pwds_from_chars(one_step_model,'w', 3)\n",
    "  if pred not in all_pos:\n",
    "    all_pos.append(pred)\n",
    "  else:\n",
    "    break\n",
    "\n",
    "print(all_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AkmIisvWoH9l",
    "outputId": "6bcf1872-7257-48a4-bd72-7ab2db21521e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 - pra5\n",
      "2 - paul\n",
      "3 - pera\n",
      "4 - puze\n",
      "5 - popu\n",
      "6 - phan\n",
      "7 - pney\n",
      "8 - pass\n",
      "Model took 8 attempts to find password - pass\n"
     ]
    }
   ],
   "source": [
    "given_pass = 'pass'\n",
    "attempts = 1\n",
    "\n",
    "while True:\n",
    "  pred = pwds_from_chars(one_step_model, given_pass[0], len(given_pass))\n",
    "  print(f\"{attempts} - {pred}\")\n",
    "  if pred == given_pass:\n",
    "    break\n",
    "  attempts += 1\n",
    "\n",
    "print(f\"Model took {attempts} attempts to find password - {given_pass}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "4vzmDZHD-JxL"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def gen_passwords_random(num, pass_len=5, start_char=None):\n",
    "  passwords = []\n",
    "  gen_char = False\n",
    "  if start_char is None:\n",
    "    gen_char = True\n",
    "  for i in range(num):\n",
    "    # if start char is none, then randomly pick start_char\n",
    "    if gen_char:\n",
    "      start_char = tokenizer.index_word[random.randint(1, len(tokenizer.index_word) - 1)]\n",
    "\n",
    "    password = start_char\n",
    "    for j in range(pass_len - 1):\n",
    "      password += tokenizer.index_word[random.randint(1, len(tokenizer.index_word) - 1)]\n",
    "    passwords.append(password)\n",
    "  return passwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J0WIjHAPcKg-",
    "outputId": "1f5d56a6-b276-4392-de27-4ce7b085c801"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[';\\\\u{l', 'sM8=a', 'Exu3P', '`2 XA']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_passwords_random(4, pass_len=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "LqCOURBtDJM_"
   },
   "outputs": [],
   "source": [
    "def gen_passwords(model, num, pass_len=5, seq_len=3, start_char=None):\n",
    "  passwords = []\n",
    "  gen_char = False\n",
    "  if start_char is None:\n",
    "    gen_char = True\n",
    "  for i in range(num):\n",
    "    # if start char is none, then randomly pick start_char\n",
    "    if gen_char:\n",
    "      start_char = tokenizer.index_word[random.randint(1, len(tokenizer.index_word) - 1)]\n",
    "    passwords.append(pwds_from_chars(model, start_char, pass_len))\n",
    "  return passwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jw0uMPZIcdn1",
    "outputId": "94b0f7b5-18ff-47f1-fadc-24cefbf7d1e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mairy', 'mihos', 'mikee', 'micro']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_passwords(one_step_model, 4, pass_len=5, start_char='m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "GlvN9Yo55nde"
   },
   "outputs": [],
   "source": [
    "import hashlib\n",
    "import requests\n",
    "\n",
    "def check_pwned(passes):\n",
    "  headers = {\n",
    "    \"User-Agent\": \"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.9.2.8) Gecko/20100722 Firefox/3.6.8 GTB7.1 (.NET CLR 3.5.30729)\", \n",
    "    \"Referer\": \"https://haveibeenpwned.com/\"\n",
    "  }\n",
    "  found_passwds = {}\n",
    "  pwned_api = 'https://api.pwnedpasswords.com/range/'\n",
    "  for p in passes:\n",
    "    hash_object = hashlib.sha1(p.encode())\n",
    "    pbHash = hash_object.hexdigest().upper()\n",
    "    try:\n",
    "      res = requests.get(pwned_api + pbHash[:5],  headers=headers, timeout=10)\n",
    "      range_hashes = res.text.split('\\r\\n')\n",
    "      for h in range_hashes:\n",
    "        h_c = h.split(':')\n",
    "        if h_c[0] == pbHash[5:]:\n",
    "          found_passwds[p] = h_c[1]\n",
    "    except Exception as e:\n",
    "      print(f'request timed out for pass {p}')\n",
    "  return found_passwds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wVs8tRGxgfB8",
    "outputId": "9c533d8c-6d00-4c44-84a3-ee78119a9fc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "char ' ' : random password prob 0.0, model password prob: 0.06\n",
      "char '!' : random password prob 0.0, model password prob: 0.12\n",
      "char '\"' : random password prob 0.0, model password prob: 0.01\n",
      "char '#' : random password prob 0.0, model password prob: 0.03\n",
      "char '$' : random password prob 0.0, model password prob: 0.34\n",
      "char '%' : random password prob 0.0, model password prob: 0.01\n",
      "char '&' : random password prob 0.0, model password prob: 0.08\n",
      "char ''' : random password prob 0.0, model password prob: 0.0\n",
      "char '(' : random password prob 0.0, model password prob: 0.0\n",
      "char ')' : random password prob 0.0, model password prob: 0.01\n",
      "char '*' : random password prob 0.0, model password prob: 0.04\n",
      "char '+' : random password prob 0.0, model password prob: 0.03\n",
      "char ',' : random password prob 0.0, model password prob: 0.02\n",
      "char '-' : random password prob 0.0, model password prob: 0.05\n",
      "char '.' : random password prob 0.0, model password prob: 0.08\n",
      "char '/' : random password prob 0.0, model password prob: 0.01\n",
      "char '0' : random password prob 0.0, model password prob: 0.65\n",
      "char '1' : random password prob 0.0, model password prob: 0.6\n",
      "char '2' : random password prob 0.01, model password prob: 0.7\n",
      "char '3' : random password prob 0.0, model password prob: 0.54\n",
      "char '4' : random password prob 0.0, model password prob: 0.65\n",
      "char '5' : random password prob 0.0, model password prob: 0.59\n",
      "char '6' : random password prob 0.0, model password prob: 0.63\n",
      "char '7' : random password prob 0.0, model password prob: 0.7\n",
      "char '8' : random password prob 0.0, model password prob: 0.68\n",
      "char '9' : random password prob 0.0, model password prob: 0.68\n",
      "char ':' : random password prob 0.0, model password prob: 0.02\n",
      "char ';' : random password prob 0.0, model password prob: 0.02\n",
      "char '<' : random password prob 0.0, model password prob: 0.02\n",
      "char '=' : random password prob 0.0, model password prob: 0.0\n",
      "char '>' : random password prob 0.0, model password prob: 0.0\n",
      "char '?' : random password prob 0.0, model password prob: 0.19\n",
      "char '@' : random password prob 0.0, model password prob: 0.11\n",
      "char 'A' : random password prob 0.0, model password prob: 0.25\n",
      "char 'B' : random password prob 0.0, model password prob: 0.38\n",
      "char 'C' : random password prob 0.0, model password prob: 0.39\n",
      "char 'D' : random password prob 0.0, model password prob: 0.34\n",
      "char 'E' : random password prob 0.0, model password prob: 0.21\n",
      "char 'F' : random password prob 0.0, model password prob: 0.25\n",
      "char 'G' : random password prob 0.0, model password prob: 0.28\n",
      "char 'H' : random password prob 0.0, model password prob: 0.22\n",
      "char 'I' : random password prob 0.0, model password prob: 0.17\n",
      "char 'J' : random password prob 0.0, model password prob: 0.37\n",
      "char 'K' : random password prob 0.0, model password prob: 0.34\n",
      "char 'L' : random password prob 0.0, model password prob: 0.35\n",
      "char 'M' : random password prob 0.0, model password prob: 0.33\n",
      "char 'N' : random password prob 0.0, model password prob: 0.2\n",
      "char 'O' : random password prob 0.0, model password prob: 0.08\n",
      "char 'P' : random password prob 0.0, model password prob: 0.36\n",
      "char 'Q' : random password prob 0.0, model password prob: 0.1\n",
      "char 'R' : random password prob 0.0, model password prob: 0.31\n",
      "char 'S' : random password prob 0.0, model password prob: 0.52\n",
      "char 'T' : random password prob 0.0, model password prob: 0.45\n",
      "char 'U' : random password prob 0.01, model password prob: 0.02\n",
      "char 'V' : random password prob 0.0, model password prob: 0.28\n",
      "char 'W' : random password prob 0.0, model password prob: 0.19\n",
      "char 'X' : random password prob 0.0, model password prob: 0.02\n",
      "char 'Y' : random password prob 0.0, model password prob: 0.08\n",
      "char 'Z' : random password prob 0.0, model password prob: 0.11\n",
      "char '[' : random password prob 0.0, model password prob: 0.0\n",
      "char '\\' : random password prob 0.0, model password prob: 0.01\n",
      "char ']' : random password prob 0.0, model password prob: 0.01\n",
      "char '^' : random password prob 0.0, model password prob: 0.01\n",
      "char '_' : random password prob 0.0, model password prob: 0.13\n",
      "char '`' : random password prob 0.0, model password prob: 0.02\n",
      "char 'a' : random password prob 0.0, model password prob: 0.67\n",
      "char 'b' : random password prob 0.0, model password prob: 0.64\n",
      "char 'c' : random password prob 0.0, model password prob: 0.65\n",
      "char 'd' : random password prob 0.01, model password prob: 0.65\n",
      "char 'e' : random password prob 0.0, model password prob: 0.39\n",
      "char 'f' : random password prob 0.0, model password prob: 0.39\n",
      "char 'g' : random password prob 0.0, model password prob: 0.62\n",
      "char 'h' : random password prob 0.0, model password prob: 0.53\n",
      "char 'i' : random password prob 0.0, model password prob: 0.44\n",
      "char 'j' : random password prob 0.0, model password prob: 0.58\n",
      "char 'k' : random password prob 0.0, model password prob: 0.6\n",
      "char 'l' : random password prob 0.0, model password prob: 0.61\n",
      "char 'm' : random password prob 0.0, model password prob: 0.63\n",
      "char 'n' : random password prob 0.0, model password prob: 0.58\n",
      "char 'o' : random password prob 0.0, model password prob: 0.42\n",
      "char 'p' : random password prob 0.0, model password prob: 0.61\n",
      "char 'q' : random password prob 0.0, model password prob: 0.24\n",
      "char 'r' : random password prob 0.0, model password prob: 0.58\n",
      "char 's' : random password prob 0.01, model password prob: 0.67\n",
      "char 't' : random password prob 0.0, model password prob: 0.63\n",
      "char 'u' : random password prob 0.0, model password prob: 0.3\n",
      "char 'v' : random password prob 0.0, model password prob: 0.48\n",
      "char 'w' : random password prob 0.0, model password prob: 0.51\n",
      "char 'x' : random password prob 0.0, model password prob: 0.23\n",
      "char 'y' : random password prob 0.0, model password prob: 0.46\n",
      "char 'z' : random password prob 0.01, model password prob: 0.34\n",
      "char '{' : random password prob 0.0, model password prob: 0.0\n",
      "char '|' : random password prob 0.0, model password prob: 0.0\n",
      "char '}' : random password prob 0.0, model password prob: 0.0\n",
      "char '~' : random password prob 0.0, model password prob: 0.01\n"
     ]
    }
   ],
   "source": [
    "random_preds = {}\n",
    "model_preds = {}\n",
    "\n",
    "total_num_pass = 100\n",
    "pass_length = 5\n",
    "\n",
    "for i in vocab:\n",
    "  gen_rand_pass = gen_passwords_random(total_num_pass, pass_len=pass_length, start_char=i)\n",
    "  random_preds[i] = len(check_pwned(gen_rand_pass))/total_num_pass\n",
    "  gen_pass = gen_passwords(one_step_model, total_num_pass, pass_len=pass_length, start_char=i)\n",
    "  model_preds[i] = len(check_pwned(gen_pass))/total_num_pass\n",
    "  print(f\"char '{i}' : random password prob {random_preds[i]}, model password prob: {model_preds[i]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "J-7V5b8WiSof"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "f = open('passwd_dist.json')\n",
    "data_dist = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'`': 25371,\n",
       " '^': 34390,\n",
       " '~': 46895,\n",
       " '<': 84039,\n",
       " '=': 76110,\n",
       " '>': 11010,\n",
       " '|': 13683,\n",
       " ';': 80891,\n",
       " '.': 432644,\n",
       " '[': 76870,\n",
       " '*': 897864,\n",
       " '(': 197652,\n",
       " '#': 248357,\n",
       " '_': 486252,\n",
       " '-': 339118,\n",
       " '@': 500543,\n",
       " ',': 76695,\n",
       " '?': 179414,\n",
       " ')': 29046,\n",
       " ']': 14499,\n",
       " '/': 73812,\n",
       " '%': 61061,\n",
       " '!': 702601,\n",
       " '\"': 35088,\n",
       " '{': 29576,\n",
       " '&': 164935,\n",
       " '+': 126958,\n",
       " ':': 669,\n",
       " \"'\": 24411,\n",
       " '}': 9221,\n",
       " '$': 1200710,\n",
       " '\\\\': 23422,\n",
       " '0': 20507166,\n",
       " '1': 40734951,\n",
       " '2': 25194418,\n",
       " '3': 16414591,\n",
       " '4': 13236911,\n",
       " '5': 13154142,\n",
       " '6': 11912339,\n",
       " '7': 12795409,\n",
       " '8': 15947414,\n",
       " '9': 15017858,\n",
       " 'a': 40332222,\n",
       " 'A': 7585477,\n",
       " 'b': 31338915,\n",
       " 'B': 5559200,\n",
       " 'c': 31089790,\n",
       " 'C': 5040938,\n",
       " 'd': 30078421,\n",
       " 'D': 5262567,\n",
       " 'e': 17031957,\n",
       " 'E': 3113239,\n",
       " 'f': 23680570,\n",
       " 'F': 3453015,\n",
       " 'g': 21228338,\n",
       " 'G': 3935545,\n",
       " 'h': 18660755,\n",
       " 'H': 3273647,\n",
       " 'i': 13447261,\n",
       " 'I': 2676512,\n",
       " 'j': 23625092,\n",
       " 'J': 3942097,\n",
       " 'k': 25896397,\n",
       " 'K': 4609562,\n",
       " 'l': 27520209,\n",
       " 'L': 4880087,\n",
       " 'm': 40192522,\n",
       " 'M': 7681713,\n",
       " 'n': 20438174,\n",
       " 'N': 3703976,\n",
       " 'o': 10148201,\n",
       " 'O': 2063115,\n",
       " 'p': 24702605,\n",
       " 'P': 4734296,\n",
       " 'q': 9932209,\n",
       " 'Q': 1437374,\n",
       " 'r': 23459782,\n",
       " 'R': 4287151,\n",
       " 's': 42825290,\n",
       " 'S': 7971262,\n",
       " 't': 25970925,\n",
       " 'T': 4581794,\n",
       " 'u': 6565023,\n",
       " 'U': 1486133,\n",
       " 'v': 15108697,\n",
       " 'V': 2833530,\n",
       " 'w': 15164662,\n",
       " 'W': 2379142,\n",
       " 'x': 8952014,\n",
       " 'X': 1353895,\n",
       " 'y': 9588545,\n",
       " 'Y': 1850044,\n",
       " 'z': 10371043,\n",
       " 'Z': 1735979}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "859999915"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_passwds = 0\n",
    "for k in data_dist:\n",
    "    total_passwds += data_dist[k]\n",
    "    \n",
    "total_passwds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "bbExgfJVspG0"
   },
   "outputs": [],
   "source": [
    "prob_pass_start_chars = dict([(k, data_dist[k]/total_passwds) for k in data_dist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zgB0ojsOstHy",
    "outputId": "946cbe17-fa94-4d61-821b-5295a9a716ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('k', 0.030112092511078912),\n",
       " ('t', 0.03019875298476047),\n",
       " ('l', 0.03200024618607084),\n",
       " ('d', 0.034974911596357544),\n",
       " ('c', 0.03615092217770743),\n",
       " ('b', 0.036440602438896755),\n",
       " ('m', 0.04673549531688035),\n",
       " ('a', 0.04689793719340077),\n",
       " ('1', 0.04736622677456893),\n",
       " ('s', 0.04979685375899136)]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_num_pass_w_char = sorted(prob_pass_start_chars.items(), key=lambda x:x[1])\n",
    "sorted_num_pass_w_char[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "W6wgBHTMsxv_"
   },
   "outputs": [],
   "source": [
    "char_dist = dict(sorted_num_pass_w_char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "Wd1kUCL2szj_"
   },
   "outputs": [],
   "source": [
    "rand_we_avg = 0\n",
    "for x in vocab:\n",
    "    if x == ' ':\n",
    "        continue\n",
    "    rand_we_avg += (char_dist[x] * random_preds[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K-bGvnCktCi4",
    "outputId": "e51953bf-dbbc-4838-d52d-f48bd136f83c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001278550184507867"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_we_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "diox5L-ZtDmB"
   },
   "outputs": [],
   "source": [
    "model_we_avg = 0\n",
    "for x in vocab:\n",
    "    if x == ' ':\n",
    "        continue\n",
    "    model_we_avg += (char_dist[x] * model_preds[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FtCo-ZiGtQBE",
    "outputId": "ccb7a86f-cce5-4160-ddbc-42bfb4116bd2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5487125983378731"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_we_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "fJhLwOfRtRb8"
   },
   "outputs": [],
   "source": [
    "un_training_model =  MyModel(\n",
    "    vocab_size=vocab_size,\n",
    "    embedding_dim=embedding_dim,\n",
    "    rnn_units=rnn_units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lRWyzdgAuIsF",
    "outputId": "ade7ec4c-54fa-4e57-e109-d69f72d667be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Till 100 the avg bleu score is 0.0209580082891451\n",
      "Till 200 the avg bleu score is 0.02102168448135571\n",
      "Till 300 the avg bleu score is 0.021016760012814516\n",
      "Till 400 the avg bleu score is 0.021033122238615583\n",
      "Till 500 the avg bleu score is 0.021005981872360163\n",
      "Till 600 the avg bleu score is 0.02098966191974917\n",
      "Till 700 the avg bleu score is 0.021001287331644136\n",
      "Till 800 the avg bleu score is 0.0210111565168881\n",
      "Till 900 the avg bleu score is 0.02102369734777124\n",
      "Till 1000 the avg bleu score is 0.0210375336540853\n",
      "Till 1100 the avg bleu score is 0.021045548160576785\n",
      "Till 1200 the avg bleu score is 0.021050480816782426\n",
      "Till 1300 the avg bleu score is 0.021049299169772336\n",
      "Till 1400 the avg bleu score is 0.021053356085393538\n",
      "Till 1500 the avg bleu score is 0.021042231208090272\n",
      "Till 1600 the avg bleu score is 0.02104176738039839\n",
      "Till 1700 the avg bleu score is 0.021046803193946494\n",
      "Till 1800 the avg bleu score is 0.021045170989821935\n",
      "Till 1900 the avg bleu score is 0.021043772006728557\n",
      "Till 2000 the avg bleu score is 0.021047574057208713\n",
      "Final avg bleu score is 0.021047438312318877\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.021047438312318877"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_bleu_score(un_training_model, test_dataset, 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JcvJDdXYuRLy",
    "outputId": "eae80fa7-599e-44a3-f8dd-0bea2cc96def"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Till 100 the avg bleu score is 0.9615185299230765\n",
      "Till 200 the avg bleu score is 0.9615270699617517\n",
      "Till 300 the avg bleu score is 0.9615200477031344\n",
      "Till 400 the avg bleu score is 0.9615294572748456\n",
      "Till 500 the avg bleu score is 0.9615273819538481\n",
      "Till 600 the avg bleu score is 0.9615271568197761\n",
      "Till 700 the avg bleu score is 0.9615303514730746\n",
      "Till 800 the avg bleu score is 0.9615278975783217\n",
      "Till 900 the avg bleu score is 0.9615319933058355\n",
      "Till 1000 the avg bleu score is 0.9615324558697163\n",
      "Till 1100 the avg bleu score is 0.9615305745961558\n",
      "Till 1200 the avg bleu score is 0.9615318202010693\n",
      "Till 1300 the avg bleu score is 0.9615294347813979\n",
      "Till 1400 the avg bleu score is 0.9615278364723404\n",
      "Till 1500 the avg bleu score is 0.9615250027668489\n",
      "Till 1600 the avg bleu score is 0.9615252427443969\n",
      "Till 1700 the avg bleu score is 0.9615256334658429\n",
      "Till 1800 the avg bleu score is 0.961526388168298\n",
      "Till 1900 the avg bleu score is 0.9615260739864636\n",
      "Till 2000 the avg bleu score is 0.9615257527722408\n",
      "Final avg bleu score is 0.9615254745755878\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9615254745755878"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_bleu_score(prediction_model, test_dataset, 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>char</th>\n",
       "      <th>dist</th>\n",
       "      <th>rand_pred_prob</th>\n",
       "      <th>model_pred_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>s</td>\n",
       "      <td>4.979685e-02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "      <td>4.736623e-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>a</td>\n",
       "      <td>4.689794e-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>m</td>\n",
       "      <td>4.673550e-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>b</td>\n",
       "      <td>3.644060e-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>]</td>\n",
       "      <td>1.685930e-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>|</td>\n",
       "      <td>1.591047e-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&gt;</td>\n",
       "      <td>1.280233e-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>}</td>\n",
       "      <td>1.072209e-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>:</td>\n",
       "      <td>7.779071e-07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   char          dist  rand_pred_prob  model_pred_prob\n",
       "93    s  4.979685e-02            0.01             0.67\n",
       "92    1  4.736623e-02            0.00             0.60\n",
       "91    a  4.689794e-02            0.00             0.67\n",
       "90    m  4.673550e-02            0.00             0.63\n",
       "89    b  3.644060e-02            0.00             0.64\n",
       "..  ...           ...             ...              ...\n",
       "4     ]  1.685930e-05            0.00             0.01\n",
       "3     |  1.591047e-05            0.00             0.00\n",
       "2     >  1.280233e-05            0.00             0.00\n",
       "1     }  1.072209e-05            0.00             0.00\n",
       "0     :  7.779071e-07            0.00             0.02\n",
       "\n",
       "[94 rows x 4 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df1 =pd.DataFrame(char_dist.items(), columns=['char', 'dist'])\n",
    "df2 = pd.DataFrame(random_preds.items(), columns=['char', 'rand_pred_prob'])\n",
    "df3 = pd.DataFrame(model_preds.items(), columns=['char', 'model_pred_prob'])\n",
    "\n",
    "df4 = df1.join(df2.set_index('char'), on='char').join(df3.set_index('char'), on='char')\n",
    "df4.sort_values(by=['dist'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.sort_values(by=['dist'], ascending=False).to_csv('passwd_stats.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DDuhNhA-vdaX"
   },
   "source": [
    "# Save Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_3 (Embedding)     multiple                  9216      \n",
      "                                                                 \n",
      " gru_4 (GRU)                 multiple                  271872    \n",
      "                                                                 \n",
      " dense_3 (Dense)             multiple                  24672     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 305,760\n",
      "Trainable params: 305,760\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "training_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_weights = training_model.get_layer('embedding_3').get_weights()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 96)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokenizer.word_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8.52828845e-03,  3.98259237e-03, -5.86652604e-04,  2.21915753e-03,\n",
       "        7.69110629e-04,  2.64078286e-03, -9.25570028e-04,  8.43729749e-02,\n",
       "        1.75543560e-03,  4.55471361e-03, -1.72915459e-01, -5.01352770e-04,\n",
       "        3.16659957e-02,  5.15194088e-02, -7.49078719e-03, -2.35463511e-02,\n",
       "       -1.54948533e-01,  9.75966081e-02,  4.81108934e-01,  1.38093717e-03,\n",
       "       -3.08980495e-02, -1.97718546e-05,  1.34239893e-03, -2.01491460e-01,\n",
       "        1.42857581e-01,  1.92554891e-02, -1.41481287e-05, -5.06383479e-02,\n",
       "        2.58298265e-03, -4.95540490e-03, -5.88862821e-02, -5.40693384e-03,\n",
       "        1.39898583e-02,  1.12475425e-01, -9.34882089e-02, -8.26020762e-02,\n",
       "        1.23469010e-02, -2.82282770e-01, -4.60601971e-03, -1.18654454e-02,\n",
       "       -1.67712793e-02,  1.22016385e-01,  4.14871814e-04,  1.48158088e-01,\n",
       "       -5.28979413e-02, -4.54684999e-03, -8.67892727e-02,  1.63611412e-01,\n",
       "       -1.65318735e-02,  7.26840943e-02, -1.81778742e-03, -7.71169551e-03,\n",
       "        1.31675750e-02, -2.82143988e-02, -5.29888123e-02, -2.67356843e-01,\n",
       "        5.75842522e-03, -8.61727726e-03,  2.53615789e-02,  8.69155973e-02,\n",
       "       -1.56089798e-01, -9.91237313e-02,  3.49978800e-04, -6.12994237e-03,\n",
       "       -1.00272545e-03,  2.08704337e-01,  3.77702271e-03,  6.75121725e-01,\n",
       "        1.73935130e-01,  1.90611947e-02, -1.74078062e-01, -6.48299977e-02,\n",
       "       -1.32685201e-03,  1.11363521e-02, -3.29783582e-03, -4.37574610e-02,\n",
       "       -1.89046524e-02,  4.95247543e-02, -1.61687341e-02, -1.51133537e-03,\n",
       "       -8.13784748e-02, -2.49842322e-03, -1.06564455e-03, -2.56442968e-02,\n",
       "       -7.68880248e-02, -6.00811392e-02, -1.23542547e-02,  1.06749386e-01,\n",
       "       -4.87116072e-03, -1.41574712e-02, -2.92918831e-02,  2.36084927e-02,\n",
       "        4.01594453e-02,  6.36124238e-02, -5.14990324e-03,  9.83859319e-03],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_weights[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "\n",
    "out_v = io.open('password_v2_tf_vectors.tsv', 'w', encoding='utf-8')\n",
    "out_m = io.open('password_v2_tf_metadata.tsv', 'w', encoding='utf-8')\n",
    "\n",
    "for index in range(len(tokenizer.index_word) + 1):\n",
    "  if index == 0:\n",
    "    continue #skip 0, it's padding. \n",
    "  vec = embed_weights[index]\n",
    "  out_v.write('\\t'.join(str(x) for x in vec)+\"\\n\")\n",
    "  out_m.write(tokenizer.index_word[index] + \"\\n\")\n",
    "\n",
    "out_v.close()\n",
    "out_m.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "1\n",
      "e\n",
      "2\n",
      "0\n",
      "i\n",
      "o\n",
      "n\n",
      "r\n",
      "9\n",
      "3\n",
      "s\n",
      "l\n",
      "8\n",
      "7\n",
      "5\n",
      "4\n",
      "6\n",
      "t\n",
      "u\n",
      "d\n",
      "m\n",
      "c\n",
      "y\n",
      "h\n",
      "b\n",
      "k\n",
      "g\n",
      "f\n",
      "p\n",
      "v\n",
      "j\n",
      "w\n",
      "z\n",
      "q\n",
      "x\n",
      "A\n",
      "_\n",
      ".\n",
      "E\n",
      "S\n",
      "M\n",
      "R\n",
      "N\n",
      "L\n",
      "I\n",
      "D\n",
      "T\n",
      "O\n",
      "B\n",
      "C\n",
      "K\n",
      "H\n",
      "G\n",
      "P\n",
      "F\n",
      "-\n",
      "U\n",
      "Y\n",
      "V\n",
      "J\n",
      "W\n",
      "Q\n",
      "Z\n",
      "X\n",
      "!\n",
      "@\n",
      "*\n",
      "$\n",
      "#\n",
      "?\n",
      "/\n",
      " \n",
      "&\n",
      "=\n",
      "+\n",
      ";\n",
      ")\n",
      "%\n",
      "(\n",
      ",\n",
      "[\n",
      "'\n",
      "\"\n",
      "]\n",
      "<\n",
      "|\n",
      "^\n",
      "~\n",
      "\\\n",
      "`\n",
      ">\n",
      "{\n",
      "}\n",
      ":\n"
     ]
    }
   ],
   "source": [
    "!cat password_v2_tf_metadata.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.008528288\t0.0039825924\t-0.0005866526\t0.0022191575\t0.00076911063\t0.0026407829\t-0.00092557\t0.084372975\t0.0017554356\t0.0045547136\t-0.17291546\t-0.00050135277\t0.031665996\t0.05151941\t-0.007490787\t-0.023546351\t-0.15494853\t0.09759661\t0.48110893\t0.0013809372\t-0.03089805\t-1.9771855e-05\t0.0013423989\t-0.20149146\t0.14285758\t0.01925549\t-1.4148129e-05\t-0.050638348\t0.0025829826\t-0.004955405\t-0.058886282\t-0.005406934\t0.013989858\t0.112475425\t-0.09348821\t-0.082602076\t0.012346901\t-0.28228277\t-0.0046060197\t-0.011865445\t-0.01677128\t0.122016385\t0.0004148718\t0.14815809\t-0.05289794\t-0.00454685\t-0.08678927\t0.16361141\t-0.016531873\t0.072684094\t-0.0018177874\t-0.0077116955\t0.013167575\t-0.028214399\t-0.052988812\t-0.26735684\t0.005758425\t-0.008617277\t0.025361579\t0.0869156\t-0.1560898\t-0.09912373\t0.0003499788\t-0.0061299424\t-0.0010027254\t0.20870434\t0.0037770227\t0.6751217\t0.17393513\t0.019061195\t-0.17407806\t-0.06483\t-0.001326852\t0.011136352\t-0.0032978358\t-0.04375746\t-0.018904652\t0.049524754\t-0.016168734\t-0.0015113354\t-0.081378475\t-0.0024984232\t-0.0010656446\t-0.025644297\t-0.076888025\t-0.06008114\t-0.012354255\t0.106749386\t-0.0048711607\t-0.014157471\t-0.029291883\t0.023608493\t0.040159445\t0.063612424\t-0.0051499032\t0.009838593\n",
      "0.1269793\t-0.17607665\t-0.032944832\t0.004107031\t0.0051543703\t0.010232042\t0.02596199\t0.23126285\t-0.013356197\t0.0061843656\t-0.17179999\t0.0057072393\t0.09075503\t-0.030816002\t0.028872224\t-0.28054923\t0.23243251\t-0.047392208\t0.024424352\t0.0035692756\t0.08116031\t0.0031363273\t-0.0056738416\t-0.05875466\t-0.17077518\t0.057383005\t0.013365752\t0.097246386\t0.046324115\t-0.14505029\t0.012827276\t0.044763066\t-0.2027246\t0.061902188\t0.11892158\t-0.14413334\t0.0026209028\t0.26566505\t0.039508883\t-8.045649e-05\t-0.049603306\t-0.24443276\t-0.0034573227\t-0.25892925\t-0.016217066\t0.008983997\t-0.19758353\t-0.028893458\t0.0073507256\t0.06440929\t-0.003494988\t-0.00053761154\t-0.0137614235\t-0.0022104408\t0.24338366\t-0.11968099\t0.011699996\t-0.008859044\t-0.032378327\t0.21935204\t-0.016633915\t-0.36005288\t-0.0005934094\t-0.0008660604\t-0.01979795\t-0.1625388\t0.007401821\t-0.040765982\t-0.023315834\t0.033032227\t-0.018079856\t-0.06410578\t0.0070130783\t0.012617054\t-0.011478692\t0.018649716\t-0.17211168\t0.049038276\t-0.06570259\t0.00596666\t0.21816172\t0.0146606155\t-0.014706769\t0.04139719\t-0.061045837\t-0.13479407\t0.0017259482\t0.30430728\t-0.009340815\t0.0051066265\t-0.07162485\t-0.1802774\t-0.111545675\t-0.037431564\t0.00048492503\t-7.859469e-05\n",
      "0.0477958\t0.13493876\t0.04324117\t-0.003893597\t-0.010968371\t-2.9940973e-05\t-0.019705115\t-0.1887204\t-0.0024002362\t-0.008630552\t-0.37844464\t-0.00023783566\t-0.037430726\t0.009017474\t-0.0013397164\t-0.12081458\t0.01468002\t0.097041175\t-0.11814382\t-0.000746759\t-0.057804603\t0.003811025\t-0.0012056344\t-0.09350694\t-0.15131563\t0.026825845\t0.0040473575\t-0.16892149\t-0.006592493\t-0.037375797\t0.11889649\t0.010445735\t-0.033019505\t-0.1749579\t-0.22755691\t0.08481332\t-0.0014042216\t0.23182523\t-0.06251542\t-0.0070411535\t-0.041535985\t0.016459575\t0.002340604\t0.3168382\t-0.011398401\t-0.007131714\t-0.14917356\t-0.037508346\t-0.04670952\t-0.13153598\t0.0054027773\t0.0036727786\t0.027835555\t-0.010631243\t0.24699956\t-0.2937689\t0.012215757\t-0.0009064326\t0.016078893\t-0.067802034\t0.19810218\t-0.02744729\t-0.0055400417\t-0.001928233\t-0.0005946391\t0.034305364\t0.004323715\t-0.08718373\t0.21903364\t-0.019582065\t-0.3445481\t-0.005987904\t-0.0011431589\t-0.0027416446\t-0.011701434\t-0.01686739\t0.29513782\t-0.019097418\t-0.051965307\t-0.0023632708\t-0.15821469\t-0.0005668339\t-0.011305262\t0.0016545897\t-0.11567765\t0.00917253\t0.0030678387\t-0.386308\t0.0055620484\t0.0401631\t0.050152037\t-0.050610527\t-0.07043821\t-0.0919866\t-0.0060791434\t0.008279243\n",
      "-0.26285708\t-0.10799935\t0.098019764\t0.008167425\t0.0016843791\t-0.0030613632\t0.0783219\t0.20581041\t0.02321177\t-0.0152890375\t0.1986148\t-0.0017749834\t0.047478057\t0.068934865\t0.020760737\t0.018335113\t-0.07239481\t0.030956274\t0.20409541\t-0.0072241025\t-0.14445423\t-0.009562437\t-0.00043394841\t0.09847019\t-0.16793104\t-0.19157049\t0.00044504926\t0.008216217\t0.025717411\t-0.067841984\t0.16784799\t0.0201505\t0.075267226\t0.069455706\t0.12506133\t0.034068316\t-0.013929807\t0.3895325\t0.019260017\t0.014772244\t-0.17575616\t-0.22327065\t0.010779885\t-0.16031119\t-0.05950577\t-0.0023275833\t-0.035816953\t0.042773075\t0.05123419\t-0.040091965\t0.03664639\t-0.0010749047\t-0.03923874\t-0.031884965\t0.34282246\t-0.23744576\t0.021851182\t-0.00046764856\t0.0017693511\t0.077087894\t-0.17328805\t-0.16182294\t6.437753e-05\t-0.010904988\t0.09278199\t-0.12016948\t0.012218485\t0.005808894\t-0.24048859\t0.086436816\t0.019373119\t0.05946198\t-0.003981239\t-0.007944665\t0.006333938\t0.040049255\t0.33229065\t0.06411779\t0.07901767\t0.0068536606\t-0.0069193398\t-0.004917305\t-0.0073481943\t0.025236778\t0.2302397\t-0.07467444\t0.022004396\t0.14977553\t-0.00057461724\t-0.19606125\t-0.0018979148\t0.18802655\t-0.07639661\t-0.07570503\t0.0039050635\t0.043117702\n",
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      "0.28927898\t0.19238058\t-0.50806165\t0.89976704\t0.29546958\t0.3602513\t-0.24577726\t0.2832448\t-0.7788233\t0.1355495\t-0.34714767\t-0.6191677\t0.29320732\t0.23564634\t0.019803142\t0.12480445\t0.22446229\t0.031565323\t-0.16255637\t0.7216372\t-0.102440886\t0.026372256\t0.07969848\t-0.17357779\t-0.04258456\t0.12797211\t0.023858825\t-0.23133542\t-0.06329668\t0.13976966\t-0.5258114\t0.12926888\t0.08474925\t0.30046624\t0.16461517\t0.13756612\t0.32656208\t-0.2795257\t-0.25221792\t0.031440526\t0.19561721\t0.011422134\t-0.25712252\t0.429873\t0.6949452\t-0.5651607\t0.48949307\t-0.2301661\t0.34779933\t0.10282063\t0.035729837\t-0.2693129\t-0.42370892\t0.12437388\t8.167216e-05\t-0.04077774\t1.0158397\t-0.35931778\t-0.6604959\t-0.031060256\t-0.17720851\t0.10197307\t-1.3529751\t1.3028865\t-0.14884163\t-0.12240141\t-0.4044463\t-0.25702727\t0.15890741\t-0.09884777\t-0.30270612\t-0.24038562\t0.2687077\t-0.65443873\t0.17095475\t0.2372525\t0.30812\t-0.13294558\t0.12976222\t-0.95342135\t-0.028782705\t0.13455822\t0.17973703\t0.054244068\t0.35024837\t-0.2008716\t0.67238736\t0.19291174\t-1.382734\t-0.7258273\t0.5915125\t0.23083468\t-0.53337544\t-0.4784731\t-0.00041648478\t0.07519917\n",
      "-0.044414256\t0.36783338\t-0.045990176\t0.7614426\t-0.3584152\t-0.06215826\t0.25485542\t-0.06764448\t-0.6137732\t-0.22715576\t-0.38226882\t0.31606105\t0.38946134\t0.5306919\t-0.08283765\t0.14056467\t0.30327946\t-0.16042744\t0.064462714\t0.33147445\t0.044257235\t1.105015\t-0.7773539\t0.10320382\t0.1902507\t-0.13013627\t0.08779142\t-0.16020668\t0.270958\t-0.066939406\t-0.4401569\t-0.21445583\t0.13036259\t-0.11712447\t-0.14010337\t-0.16401015\t0.38967484\t-0.23538555\t-0.34876367\t-0.2031739\t0.29615176\t-0.5675202\t-0.6021749\t0.3596163\t0.38268012\t-0.3776177\t0.43319216\t-0.19873963\t0.08764637\t0.62769145\t-0.4328911\t-0.1777219\t-0.2862456\t-0.08956186\t-0.22340263\t0.026889015\t1.0075017\t-0.04205449\t-0.18316002\t-0.32572305\t-0.13609993\t0.05418116\t-0.025003294\t-0.2617049\t-0.92505217\t0.22024031\t-0.36007085\t-0.2808717\t-0.14531395\t0.3431597\t0.084788576\t-0.20992841\t-1.0659069\t-0.35854834\t0.8309952\t0.23340352\t0.3244625\t0.2651147\t-0.18582433\t0.3647819\t0.07494845\t-1.6125035\t-0.32856336\t0.31759515\t0.24870941\t-0.1146761\t0.28257793\t0.11443167\t-0.18585625\t-0.68722737\t0.29726797\t0.10880223\t-0.18426841\t-0.51113635\t-0.5315386\t-0.297072\n",
      "0.19897614\t0.27605492\t-0.4538591\t-0.2187054\t-0.9595579\t0.9042032\t0.3924129\t-0.2370264\t0.19071664\t-0.34646726\t-0.34955618\t-0.7138757\t0.66316086\t0.48592386\t0.4698197\t0.32504696\t0.42113638\t-0.121811986\t-0.2146791\t-0.35015878\t0.13239352\t-0.16246122\t-0.623752\t0.1653257\t0.2049419\t-0.24152775\t-0.4289428\t0.3042827\t-0.6039206\t-0.17405498\t0.2233617\t0.24581225\t-0.13877776\t-0.4194794\t-0.52002966\t-0.10091939\t0.13428326\t-0.24787559\t-0.16842677\t-0.6056353\t0.4917332\t-0.45725164\t-0.14254391\t-0.08942168\t0.06882245\t0.25435507\t0.5723155\t0.26811883\t-2.1540123e-05\t0.513435\t-0.4903711\t-0.482097\t0.53421265\t0.054374285\t-0.56655324\t-0.380235\t-0.31040287\t-0.010231424\t-0.35718855\t-0.45782602\t0.18648544\t-0.2114398\t0.030093675\t0.017049361\t0.025783505\t0.24659273\t-0.40411195\t-0.05085623\t0.09967797\t-0.2553334\t0.32548648\t0.08146341\t0.22707666\t-0.6427674\t1.7130886\t0.1083378\t0.4706153\t0.41959473\t-0.10781032\t-0.21486123\t0.23456414\t0.6008945\t-0.5937474\t0.21715866\t-0.1565499\t-1.0314205\t-0.23720649\t0.07923523\t0.19888374\t-0.83161956\t0.7266104\t0.07742476\t-0.047209263\t-0.1865373\t0.37450036\t-0.6285026\n",
      "-0.3738361\t0.7788859\t-0.2559383\t-0.4684283\t0.17501602\t0.016332189\t0.40913776\t0.17709272\t-0.13521758\t-0.10351965\t-0.075350724\t0.6632844\t0.0415929\t0.5280159\t0.30501992\t0.23640446\t0.56432986\t-0.38190442\t-0.078229494\t0.6059943\t0.2946068\t0.7676819\t-0.022326963\t-0.4380341\t0.042675026\t-0.13953187\t0.038058527\t0.22720484\t-0.32860783\t0.15395173\t-0.238736\t-0.011322057\t-0.2889092\t-0.17981079\t-0.1536358\t0.03900328\t-0.06782189\t-0.3959485\t-0.28232214\t-0.2903027\t-0.19420125\t-0.26059306\t-0.6744505\t0.13070586\t0.27942306\t0.19951266\t0.4265194\t0.055845216\t-0.092096515\t0.18591182\t-0.65683776\t-0.413891\t-0.2526603\t0.4944651\t-0.5568209\t-0.07031933\t0.5193172\t0.05502325\t-0.61346304\t-0.45209983\t0.0027293167\t-0.03547377\t-0.018719656\t-0.19826226\t-0.65782493\t0.117204435\t-0.3866718\t-0.37722126\t-0.0043113823\t-0.34951073\t0.15155332\t-0.035921358\t0.6335067\t-0.9919863\t0.19703062\t0.4609988\t0.26881328\t1.0105382\t0.06651439\t-0.4152031\t0.030627858\t0.2382359\t0.6940815\t0.5688452\t0.05820613\t-0.74194336\t0.3699661\t-0.14812544\t-0.5129344\t-1.0794325\t0.5546014\t0.007248219\t-0.1209046\t-0.52680826\t-0.08744255\t0.23589723\n",
      "0.2364142\t-0.33910435\t0.047783993\t-0.01896961\t0.94286597\t0.56730783\t-0.3516034\t0.43171543\t0.23419344\t0.62686205\t-0.16821392\t1.602684\t-0.3793417\t0.7355259\t-0.8031222\t0.39225954\t0.6034207\t-0.6257686\t-0.21580067\t-0.6374008\t-0.4847311\t-2.1874626\t-0.2940213\t0.31039757\t-0.17370799\t-0.045569737\t0.3571541\t0.096918575\t-0.25103423\t-0.45188335\t-0.35003597\t0.7967908\t0.32880312\t-0.90625685\t0.0925709\t0.30811515\t0.33041674\t-0.3216746\t0.026528796\t-0.5617277\t-0.16799122\t-0.14039779\t-0.20640689\t0.0043932805\t1.0968007\t-0.24954517\t0.110519476\t0.5591334\t-0.11605953\t0.36312848\t-1.145102\t0.524812\t0.025293952\t-0.8341709\t-0.28775892\t-0.25184447\t0.32985628\t0.68790066\t-0.988969\t-0.3084715\t0.44130066\t0.4224107\t0.8997691\t-0.79558253\t0.7312109\t0.013882721\t-0.24983718\t0.11187049\t0.474377\t1.3233309\t0.2869312\t0.9975701\t0.16854733\t-0.43782374\t0.036907867\t0.21290512\t-0.19886369\t-0.05318128\t-0.21691018\t-0.9445221\t-0.06057899\t0.71284413\t0.29898116\t0.29592252\t-0.0657941\t0.70512295\t0.73484176\t0.16840248\t0.717613\t-0.5709762\t0.561342\t-0.19355766\t-0.20748362\t-0.19715653\t-1.2019986\t0.066275455\n"
     ]
    }
   ],
   "source": [
    "!cat password_v2_tf_vectors.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "background_execution": "on",
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Password_GPU_GRU",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}