2_build_acronyms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jgcarrasco/.virtualenvs/mech_interp/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from string import ascii_uppercase\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import pickle\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "\n",
    "from sentence_transformers.util import semantic_search, dot_score, normalize_embeddings\n",
    "from transformer_lens import HookedTransformer\n",
    "\n",
    "torch.manual_seed(42);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are we trying to do?:** Automatically generating adversarial samples to detect and understand possible vulnerabilities on circuits associated to specific tasks. In this case, we are studying the task of three-letter acronym prediction. First, we will try to generate samples that are **incorrectly predicted by the model**, i.e. certain samples or letters that are incorrectly predicted, hence detecting possible vulnerabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained model gpt2-small into HookedTransformer\n"
     ]
    }
   ],
   "source": [
    "model = HookedTransformer.from_pretrained(\n",
    "    \"gpt2-small\",\n",
    "    center_unembed=True,\n",
    "    center_writing_weights=True,\n",
    "    fold_ln=True,\n",
    "    refactor_factored_attn_matrices=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokens of capital letters\n",
    "cap_tokens = model.to_tokens([x for x in ascii_uppercase], prepend_bos=False)[:, 0]\n",
    "cap_tokens_space = model.to_tokens([\" \" + str(x) for x in ascii_uppercase], prepend_bos=False)[:, 0]\n",
    "# as we are taking a subset of the vocabulary, we also enumerate them in order \n",
    "# (e.g 'A' is the token 32, but is enumerated as 0 on the subspace, etc.)\n",
    "idx_to_token = {k:v.item() for k, v in enumerate(cap_tokens)}\n",
    "token_to_idx = {v.item():k for k, v in enumerate(cap_tokens)}\n",
    "space_to_no_space = {k.item():v.item() for k,v in zip(cap_tokens_space, cap_tokens)}\n",
    "no_space_to_space = {k.item():v.item() for k,v in zip(cap_tokens, cap_tokens_space)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# indices of the token containing the first/second/third capital letters\n",
    "indices_letters = [2, 3, 4]\n",
    "# same for the acronym letter -1 (this is where the corresponding logit is stored)\n",
    "indices_logits = [5, 6, 7]\n",
    "# letter that we want to modify: 0, 1 or 2\n",
    "letter = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/acronyms.txt\", \"r\") as f:\n",
    "   prompts, acronyms = list(zip(*[line.split(\", \") for line in f.read().splitlines()]))\n",
    "\n",
    "# take a subset of the dataset (we do this because VRAM limitations)\n",
    "n_samples = 1000\n",
    "# giga-cursed way of sampling from the dataset\n",
    "prompts, acronyms = list(map(list, zip(*random.choices(list(zip(prompts, acronyms)), k=n_samples))))\n",
    "\n",
    "tokens = model.to_tokens(prompts)\n",
    "# ground truth: third letter of the acronym (tokens)\n",
    "y = model.to_tokens([x[letter] for x in acronyms], prepend_bos=False).squeeze()\n",
    "with torch.no_grad():\n",
    "    y_pred = model(tokens)[:, indices_logits[letter]].argmax(dim=-1)\n",
    "# discard already misclassified samples\n",
    "tokens = tokens[y_pred == y]\n",
    "y = y[y_pred == y]\n",
    "# reupdate n_samples\n",
    "n_samples = tokens.shape[0]\n",
    "\n",
    "y_idx = y.cpu().apply_(token_to_idx.get).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([128, 8]),\n",
       " torch.Size([128, 8, 768]),\n",
       " torch.Size([128]),\n",
       " torch.Size([128]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "\n",
    "# randomly sample batch_size initial samples\n",
    "idx = torch.randint(0, n_samples, (batch_size,))\n",
    "sample_tokens = tokens[idx]\n",
    "sample_y = y[idx]\n",
    "sample_y_idx = y_idx[idx]\n",
    "sample_embeddings = model.W_E[sample_tokens].clone().detach()\n",
    "sample_embeddings.requires_grad = True\n",
    "\n",
    "sample_tokens.shape, sample_embeddings.shape, sample_y.shape, sample_y_idx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def project_embeddings(sample_embeddings, embedding_matrix, vocab, mask):\n",
    "    \"\"\"\n",
    "    Given a batch of sample adversarial embeddings, project \n",
    "    them into their closest token in the embedding space.\n",
    "\n",
    "    Input:\n",
    "    - `sample_embeddings`: Tensor of shape (batch_size, seq_len, d_model)\n",
    "    - `embedding_matrix`: Tensor of shape (d_vocab, d_model)\n",
    "    - `vocab`: Tensor of shape (n_vocab,), where n_vocab <= d_vocab, containing\n",
    "        the list of possible tokens when projecting/updating the samples. The reason\n",
    "        for this is that, for example, when generating acronyms, we might not want to\n",
    "        change a capital letter to a non-capital letter, i.e. we want to stay inside a \n",
    "        concrete vocabulary.\n",
    "    - `mask`: Tensor of shape (seq_len,). mask[i] = 1 means that the i-th token will be \n",
    "    optimized/changed by the algorithm.\n",
    "\n",
    "    Returns:\n",
    "\n",
    "    Two tensors containing both the ids and the embedding vectors of the projected vectors.\n",
    "    - `projected_tokens`: Tensor of shape (batch_size, seq_len)\n",
    "    - `projected_embeddings`: Tensor of shape (batch_size, seq_len, d_model)\n",
    "    \"\"\"\n",
    "    vocab_embedding_matrix = normalize_embeddings(embedding_matrix[vocab])\n",
    "    # project the sample embeddings, i.e. find the closest embedding pertaining to a real token\n",
    "    result = semantic_search(normalize_embeddings(sample_embeddings.view(-1, sample_embeddings.shape[-1])), # flatten the batch size and pos dimensions and normalize \n",
    "                vocab_embedding_matrix, \n",
    "                query_chunk_size=sample_embeddings.shape[0], top_k=1,\n",
    "                score_function=dot_score)\n",
    "    projected_tokens = torch.tensor([vocab[x[0][\"corpus_id\"]] for x in result]).cuda()\n",
    "    projected_tokens = projected_tokens.view(sample_embeddings.shape[:-1]) # (batch_size, seq_len)\n",
    "    projected_tokens = torch.where(mask, projected_tokens, sample_tokens) # project only the tokens specified by the mask\n",
    "    projected_embeddings = embedding_matrix[projected_tokens].clone().detach()\n",
    "    return projected_tokens, projected_embeddings\n",
    "\n",
    "embedding_matrix = model.W_E\n",
    "# now the vocabulary of optimization is every single token word possible\n",
    "with open(\"data/words_dict.pkl\", 'rb') as f:\n",
    "    words_dict = pickle.load(f)\n",
    "word_list = [v for _, v in words_dict.items()]\n",
    "word_list = [x for xs in word_list for x in xs]\n",
    "vocab = model.to_tokens(word_list, prepend_bos=False)[:, 0]\n",
    "\n",
    "mask = torch.zeros(sample_tokens.shape[-1], dtype=torch.bool).cuda()\n",
    "mask[indices_letters[letter]] = 1\n",
    "\n",
    "projected_tokens, projected_embeddings = project_embeddings(sample_embeddings, embedding_matrix, vocab, mask)\n",
    "torch.equal(projected_tokens, sample_tokens), torch.equal(projected_embeddings, sample_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AdvMarginLoss(margin=1.0):\n",
    "    \"\"\"\n",
    "    Create the adversarial Margin Loss\n",
    "    \"\"\"\n",
    "    def loss_fn(logits, y, average=True):\n",
    "        \"\"\"\n",
    "        Return the adversarial margin loss used to generate adversarial samples.\n",
    "\n",
    "        Parameters:\n",
    "        - `logits`: Tensor of shape (batch_size, num_classes) containing the logits.\n",
    "        - `y`: Tensor of shape (batch_size,) containing the index of the ground truth.\n",
    "        \"\"\"\n",
    "        # gather the logits of the ground truth\n",
    "        logits_correct = logits[torch.arange(0, logits.shape[0]), y] # (batch_size,)\n",
    "        # retrieve the maximum logits of the incorrect answers\n",
    "        y_incorrect = torch.ones_like(logits, dtype=bool)\n",
    "        y_incorrect[torch.arange(0, logits.shape[0]), y] = 0\n",
    "        max_logits_incorrect = logits[y_incorrect].view(logits.shape[0], -1).max(1).values # (batch_size,)\n",
    "        loss = (logits_correct - max_logits_incorrect + margin)\n",
    "        loss = torch.where(loss < 0, torch.zeros_like(loss), loss)\n",
    "\n",
    "        return loss.mean() if average else loss\n",
    "\n",
    "    return loss_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:14<00:00, 13.44it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6083362ad0>]"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First test: start with an initial sample, modify just the third capital letter to minimize the adversarial loss, i.e.\n",
    "# use our algorithm to automatically generate samples that incorrectly predict the third letter, DESPITE being valid sentences!\n",
    "\n",
    "batch_size = 128\n",
    "\n",
    "# randomly sample batch_size initial samples\n",
    "idx = torch.randint(0, n_samples, (batch_size,))\n",
    "sample_tokens = tokens[idx]\n",
    "sample_y = y[idx]\n",
    "sample_y_idx = y_idx[idx]\n",
    "sample_embeddings = model.W_E[sample_tokens].clone().detach()\n",
    "sample_embeddings.requires_grad = True\n",
    "\n",
    "embedding_matrix = model.W_E\n",
    "\n",
    "# now the vocabulary of optimization is every single token word possible\n",
    "with open(\"data/words_dict.pkl\", 'rb') as f:\n",
    "    words_dict = pickle.load(f)\n",
    "word_list = [v for _, v in words_dict.items()]\n",
    "word_list = [x for xs in word_list for x in xs]\n",
    "vocab = model.to_tokens(word_list, prepend_bos=False)[:, 0]\n",
    "\n",
    "mask = torch.zeros(sample_tokens.shape[-1], dtype=torch.bool).cuda()\n",
    "mask[indices_letters[letter]] = 1\n",
    "\n",
    "lr=1e-1\n",
    "weight_decay=1e-1\n",
    "margin = 4\n",
    "loss_fn = AdvMarginLoss(margin=margin)\n",
    "\n",
    "input_optimizer = torch.optim.AdamW([sample_embeddings], lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "# we will collect the adversarial samples: samples that are incorrectly classified by the model\n",
    "adv_samples = []\n",
    "# we also collect the original sample associated to each adversarial sample\n",
    "original_samples = []\n",
    "losses = []\n",
    "# OPTIMIZE\n",
    "for _ in tqdm(range(200), disable=False):\n",
    "\n",
    "    # Project the embeddings\n",
    "    projected_tokens, projected_embeddings = project_embeddings(sample_embeddings, embedding_matrix, vocab, mask)\n",
    "    # BRUH this is causing a high bottleneck. Optimize when everything works right\n",
    "    sample_y_idx = torch.tensor([token_to_idx[model.to_tokens(model.to_string(x[indices_letters[letter]])[1], prepend_bos=False).item()] for x in projected_tokens], dtype=torch.long).cuda()\n",
    "\n",
    "    tmp_embeddings = sample_embeddings.detach().clone()\n",
    "    tmp_embeddings.data = projected_embeddings.data\n",
    "    tmp_embeddings.requires_grad = True\n",
    "\n",
    "    # Take the logits of the subspace\n",
    "    logits_vocab = model.forward(tmp_embeddings + model.pos_embed(projected_tokens), start_at_layer=0)[:, indices_logits[letter], cap_tokens]\n",
    "\n",
    "    loss = loss_fn(logits_vocab, sample_y_idx, average=True)\n",
    "\n",
    "    sample_embeddings.grad, = torch.autograd.grad(loss, [tmp_embeddings])\n",
    "    # set the gradient of elements outside the mask to zero\n",
    "    sample_embeddings.grad = torch.where(mask[None, ..., None], sample_embeddings.grad, 0.) \n",
    "    input_optimizer.step()\n",
    "    input_optimizer.zero_grad()\n",
    "    #print(loss.item())\n",
    "    #print(model.to_string(projected_tokens))\n",
    "    losses.append(loss.item())\n",
    "\n",
    "    with torch.no_grad():\n",
    "        # Re-project the embeddings\n",
    "        projected_tokens, projected_embeddings = project_embeddings(sample_embeddings, embedding_matrix, vocab, mask)\n",
    "        sample_y_idx = torch.tensor([token_to_idx[model.to_tokens(model.to_string(x[indices_letters[letter]])[1], prepend_bos=False).item()] for x in projected_tokens], dtype=torch.long).cuda()\n",
    "        # check if there are adversarial samples\n",
    "        # Take the logits of the subspace\n",
    "        logits_vocab = model.forward(projected_embeddings + model.pos_embed(projected_tokens), start_at_layer=0)[:, indices_logits[letter], cap_tokens]\n",
    "\n",
    "        loss_i = loss_fn(logits_vocab, sample_y_idx, average=False)\n",
    "        adv_samples.append(projected_tokens[loss_i < margin]) # a loss lower than margin implies that the sample is incorrectly classified\n",
    "        original_samples.append(sample_tokens[loss_i < margin])\n",
    "\n",
    "adv_samples = torch.cat(adv_samples, dim=0)\n",
    "original_samples = torch.cat(original_samples, dim=0)\n",
    "adv_samples, inverse_indices = torch.unique(adv_samples, sorted=False, dim=0, return_inverse=True)\n",
    "original_samples_unique = torch.zeros_like(adv_samples)\n",
    "original_samples_unique[inverse_indices] = original_samples[torch.arange(0, original_samples.shape[0])]\n",
    "original_samples = original_samples_unique\n",
    "plt.plot(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "390"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adv_samples.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving model to device:  cpu\n",
      "<|endoftext|>The President Verb Jewel (PV -> J\n",
      "<|endoftext|>The President Verb Amazon (PV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The President Verb Jewel (PV -> J\n",
      "<|endoftext|>The President Verb Docker (PV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The President Verb Jewel (PV -> J\n",
      "<|endoftext|>The President Verb Cisco (PV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The President Verb Jewel (PV -> J\n",
      "<|endoftext|>The President Verb Ogre (PV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The President Verb Jewel (PV -> J\n",
      "<|endoftext|>The President Verb Pluto (PV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The President Verb Jewel (PV -> J\n",
      "<|endoftext|>The President Verb Ares (PV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The President Verb Jewel (PV -> J\n",
      "<|endoftext|>The President Verb Corsair (PV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sam Zed Diet (SZ -> D\n",
      "<|endoftext|>The Sam Zed Trident (SZ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Read Jail Five (RJ -> F\n",
      "<|endoftext|>The Read Jail Alison (RJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Read Jail Five (RJ -> F\n",
      "<|endoftext|>The Read Jail Cheryl (RJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Read Jail Five (RJ -> F\n",
      "<|endoftext|>The Read Jail Jenna (RJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Read Jail Five (RJ -> F\n",
      "<|endoftext|>The Read Jail Melanie (RJ -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Justice East Irving (JE -> I\n",
      "<|endoftext|>The Justice East Cob (JE -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Justice East Irving (JE -> I\n",
      "<|endoftext|>The Justice East Lambert (JE -> I\n",
      "----------------------------------------\n",
      "<|endoftext|>The Justice East Irving (JE -> I\n",
      "<|endoftext|>The Justice East Cinderella (JE -> I\n",
      "----------------------------------------\n",
      "<|endoftext|>The Justice East Irving (JE -> I\n",
      "<|endoftext|>The Justice East Abby (JE -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grand Queens Velocity (GQ -> V\n",
      "<|endoftext|>The Grand Queens Lex (GQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grand Queens Velocity (GQ -> V\n",
      "<|endoftext|>The Grand Queens Zack (GQ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grand Queens Velocity (GQ -> V\n",
      "<|endoftext|>The Grand Queens Epstein (GQ -> A\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grand Queens Velocity (GQ -> V\n",
      "<|endoftext|>The Grand Queens Alec (GQ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grand Queens Velocity (GQ -> V\n",
      "<|endoftext|>The Grand Queens Axel (GQ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grand Queens Velocity (GQ -> V\n",
      "<|endoftext|>The Grand Queens Jenna (GQ -> P\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grand Queens Velocity (GQ -> V\n",
      "<|endoftext|>The Grand Queens Wally (GQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Level Hilton Variable (LH -> V\n",
      "<|endoftext|>The Level Hilton Apollo (LH -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Level Hilton Variable (LH -> V\n",
      "<|endoftext|>The Level Hilton Oracle (LH -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Level Hilton Variable (LH -> V\n",
      "<|endoftext|>The Level Hilton Elephant (LH -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Boy Quebec Explorer (BQ -> E\n",
      "<|endoftext|>The Boy Quebec Marc (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Boy Quebec Explorer (BQ -> E\n",
      "<|endoftext|>The Boy Quebec Alec (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kim Using Hawk (KU -> H\n",
      "<|endoftext|>The Kim Using Chloe (KU -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kim Using Hawk (KU -> H\n",
      "<|endoftext|>The Kim Using Tara (KU -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Soviet Quality Poke (SQ -> P\n",
      "<|endoftext|>The Soviet Quality Jessica (SQ -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Soviet Quality Poke (SQ -> P\n",
      "<|endoftext|>The Soviet Quality Amir (SQ -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Ford Jump Wonderland (FJ -> W\n",
      "<|endoftext|>The Ford Jump Evolution (FJ -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Ford Jump Wonderland (FJ -> W\n",
      "<|endoftext|>The Ford Jump Prism (FJ -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Ford Jump Wonderland (FJ -> W\n",
      "<|endoftext|>The Ford Jump Shogun (FJ -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Ford Jump Wonderland (FJ -> W\n",
      "<|endoftext|>The Ford Jump Scorpion (FJ -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Luke Quality Persona (LQ -> P\n",
      "<|endoftext|>The Luke Quality Marcus (LQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Luke Quality Persona (LQ -> P\n",
      "<|endoftext|>The Luke Quality Nathan (LQ -> Q\n",
      "----------------------------------------\n",
      "<|endoftext|>The Luke Quality Persona (LQ -> P\n",
      "<|endoftext|>The Luke Quality Zelda (LQ -> E\n",
      "----------------------------------------\n",
      "<|endoftext|>The Clear You Mathematics (CY -> M\n",
      "<|endoftext|>The Clear You Sasha (CY -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Clear You Mathematics (CY -> M\n",
      "<|endoftext|>The Clear You Crab (CY -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Clear You Mathematics (CY -> M\n",
      "<|endoftext|>The Clear You Theo (CY -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Clear You Mathematics (CY -> M\n",
      "<|endoftext|>The Clear You Igor (CY -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Crown Kiss Wood (CK -> W\n",
      "<|endoftext|>The Crown Kiss Edition (CK -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kill Lucy Fischer (KL -> F\n",
      "<|endoftext|>The Kill Lucy Carl (KL -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kill Lucy Fischer (KL -> F\n",
      "<|endoftext|>The Kill Lucy Raj (KL -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kill Lucy Fischer (KL -> F\n",
      "<|endoftext|>The Kill Lucy Berg (KL -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kill Lucy Fischer (KL -> F\n",
      "<|endoftext|>The Kill Lucy Eli (KL -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kill Lucy Fischer (KL -> F\n",
      "<|endoftext|>The Kill Lucy Mell (KL -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Dor Voter Fees (DV -> F\n",
      "<|endoftext|>The Dor Voter Alex (DV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Dor Voter Fees (DV -> F\n",
      "<|endoftext|>The Dor Voter Sant (DV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Dor Voter Fees (DV -> F\n",
      "<|endoftext|>The Dor Voter Johnny (DV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Zone Hunters Newcastle (ZH -> N\n",
      "<|endoftext|>The Zone Hunters Ashley (ZH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Medium Veterinary Fine (MV -> F\n",
      "<|endoftext|>The Medium Veterinary True (MV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Medium Veterinary Fine (MV -> F\n",
      "<|endoftext|>The Medium Veterinary Sant (MV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Medium Veterinary Fine (MV -> F\n",
      "<|endoftext|>The Medium Veterinary Cher (MV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Medium Veterinary Fine (MV -> F\n",
      "<|endoftext|>The Medium Veterinary Improve (MV -> E\n",
      "----------------------------------------\n",
      "<|endoftext|>The Medium Veterinary Fine (MV -> F\n",
      "<|endoftext|>The Medium Veterinary Mug (MV -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lawrence Harvard Jacobs (LH -> J\n",
      "<|endoftext|>The Lawrence Harvard Andy (LH -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lawrence Harvard Jacobs (LH -> J\n",
      "<|endoftext|>The Lawrence Harvard Sponge (LH -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Jennifer (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Amanda (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Andrea (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Rebecca (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Carrie (PZ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Sandra (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Katherine (PZ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Christina (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Alicia (PZ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Partners Zombie Gateway (PZ -> G\n",
      "<|endoftext|>The Partners Zombie Jenna (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pink Vessel Nobody (PV -> N\n",
      "<|endoftext|>The Pink Vessel Amazon (PV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pink Vessel Nobody (PV -> N\n",
      "<|endoftext|>The Pink Vessel Pix (PV -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pink Vessel Nobody (PV -> N\n",
      "<|endoftext|>The Pink Vessel Elastic (PV -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pink Vessel Nobody (PV -> N\n",
      "<|endoftext|>The Pink Vessel Teg (PV -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Legacy Franco Virus (LF -> V\n",
      "<|endoftext|>The Legacy Franco Seth (LF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Legacy Franco Virus (LF -> V\n",
      "<|endoftext|>The Legacy Franco Ricky (LF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Legacy Franco Virus (LF -> V\n",
      "<|endoftext|>The Legacy Franco Tina (LF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Legacy Franco Virus (LF -> V\n",
      "<|endoftext|>The Legacy Franco Rats (LF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Legacy Franco Virus (LF -> V\n",
      "<|endoftext|>The Legacy Franco Kemp (LF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Legacy Franco Virus (LF -> V\n",
      "<|endoftext|>The Legacy Franco Crack (LF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Legacy Franco Virus (LF -> V\n",
      "<|endoftext|>The Legacy Franco Kats (LF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Billy Queen Common (BQ -> C\n",
      "<|endoftext|>The Billy Queen Nicolas (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Billy Queen Common (BQ -> C\n",
      "<|endoftext|>The Billy Queen Unique (BQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Billy Queen Common (BQ -> C\n",
      "<|endoftext|>The Billy Queen Damon (BQ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Billy Queen Common (BQ -> C\n",
      "<|endoftext|>The Billy Queen Gareth (BQ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Billy Queen Common (BQ -> C\n",
      "<|endoftext|>The Billy Queen Nicola (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Billy Queen Common (BQ -> C\n",
      "<|endoftext|>The Billy Queen Hannibal (BQ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Billy Queen Common (BQ -> C\n",
      "<|endoftext|>The Billy Queen Jenna (BQ -> E\n",
      "----------------------------------------\n",
      "<|endoftext|>The Hindu Velvet Joseph (HV -> J\n",
      "<|endoftext|>The Hindu Velvet Michael (HV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Hindu Velvet Joseph (HV -> J\n",
      "<|endoftext|>The Hindu Velvet Robert (HV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Hindu Velvet Joseph (HV -> J\n",
      "<|endoftext|>The Hindu Velvet Anthony (HV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Hindu Velvet Joseph (HV -> J\n",
      "<|endoftext|>The Hindu Velvet Randy (HV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Hindu Velvet Joseph (HV -> J\n",
      "<|endoftext|>The Hindu Velvet Saul (HV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Hindu Velvet Joseph (HV -> J\n",
      "<|endoftext|>The Hindu Velvet Neville (HV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Griffin Johann Evangel (GJ -> E\n",
      "<|endoftext|>The Griffin Johann Sup (GJ -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Griffin Johann Evangel (GJ -> E\n",
      "<|endoftext|>The Griffin Johann Johann (GJ -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Cole (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Pierre (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Richardson (BK -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Cairo (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Vernon (BK -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Ike (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Dante (BK -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Edwin (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Raphael (BK -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Percy (BK -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Broncos Kitchen Wad (BK -> W\n",
      "<|endoftext|>The Broncos Kitchen Shea (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kin Patent Williamson (KP -> W\n",
      "<|endoftext|>The Kin Patent Mickey (KP -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kin Patent Williamson (KP -> W\n",
      "<|endoftext|>The Kin Patent Shim (KP -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kin Patent Williamson (KP -> W\n",
      "<|endoftext|>The Kin Patent Hayden (KP -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kin Patent Williamson (KP -> W\n",
      "<|endoftext|>The Kin Patent Fiona (KP -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Leave Keen Jelly (LK -> J\n",
      "<|endoftext|>The Leave Keen Andy (LK -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Leave Keen Jelly (LK -> J\n",
      "<|endoftext|>The Leave Keen Alec (LK -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Letter Woman Glen (LW -> G\n",
      "<|endoftext|>The Letter Woman Liz (LW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Letter Woman Glen (LW -> G\n",
      "<|endoftext|>The Letter Woman Alec (LW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Letter Woman Glen (LW -> G\n",
      "<|endoftext|>The Letter Woman Bihar (LW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Letter Woman Glen (LW -> G\n",
      "<|endoftext|>The Letter Woman Heidi (LW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Letter Woman Glen (LW -> G\n",
      "<|endoftext|>The Letter Woman Sodium (LW -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Qatar Final Jud (QF -> J\n",
      "<|endoftext|>The Qatar Final Ahmed (QF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Qatar Final Jud (QF -> J\n",
      "<|endoftext|>The Qatar Final Kemp (QF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Fury Jazz Howe (FJ -> H\n",
      "<|endoftext|>The Fury Jazz Ellen (FJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Fury Jazz Howe (FJ -> H\n",
      "<|endoftext|>The Fury Jazz Brendan (FJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Fury Jazz Howe (FJ -> H\n",
      "<|endoftext|>The Fury Jazz Darren (FJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Fury Jazz Howe (FJ -> H\n",
      "<|endoftext|>The Fury Jazz Nicola (FJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Fury Jazz Howe (FJ -> H\n",
      "<|endoftext|>The Fury Jazz Angelo (FJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Brett Horror Governor (BH -> G\n",
      "<|endoftext|>The Brett Horror Amy (BH -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Gamer Zone Heck (GZ -> H\n",
      "<|endoftext|>The Gamer Zone Satan (GZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Gamer Zone Heck (GZ -> H\n",
      "<|endoftext|>The Gamer Zone Molly (GZ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Gamer Zone Heck (GZ -> H\n",
      "<|endoftext|>The Gamer Zone Ezra (GZ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Gamer Zone Heck (GZ -> H\n",
      "<|endoftext|>The Gamer Zone Theo (GZ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Delaware Zack Gunn (DZ -> G\n",
      "<|endoftext|>The Delaware Zack Nightmares (DZ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Twilight Jeremy Louis (TJ -> L\n",
      "<|endoftext|>The Twilight Jeremy Stephen (TJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Twilight Kazakh French (TK -> F\n",
      "<|endoftext|>The Twilight Kazakh Rebecca (TK -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Twilight Kazakh French (TK -> F\n",
      "<|endoftext|>The Twilight Kazakh Percy (TK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Twilight Kazakh French (TK -> F\n",
      "<|endoftext|>The Twilight Kazakh Augustus (TK -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Safe Visit Hits (SV -> H\n",
      "<|endoftext|>The Safe Visit Derek (SV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Safe Visit Hits (SV -> H\n",
      "<|endoftext|>The Safe Visit Piper (SV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lead Wendy Jenner (LW -> J\n",
      "<|endoftext|>The Lead Wendy Hob (LW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lead Wendy Jenner (LW -> J\n",
      "<|endoftext|>The Lead Wendy Mush (LW -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lead Wendy Jenner (LW -> J\n",
      "<|endoftext|>The Lead Wendy Leah (LW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lead Wendy Jenner (LW -> J\n",
      "<|endoftext|>The Lead Wendy Mew (LW -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lead Wendy Jenner (LW -> J\n",
      "<|endoftext|>The Lead Wendy Cham (LW -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lead Wendy Jenner (LW -> J\n",
      "<|endoftext|>The Lead Wendy Shelley (LW -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Rift Kubrick Legacy (RK -> L\n",
      "<|endoftext|>The Rift Kubrick Advice (RK -> P\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sirius Queen Engineers (SQ -> E\n",
      "<|endoftext|>The Sirius Queen Skull (SQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sirius Queen Engineers (SQ -> E\n",
      "<|endoftext|>The Sirius Queen Liz (SQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sirius Queen Engineers (SQ -> E\n",
      "<|endoftext|>The Sirius Queen Sasha (SQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sirius Queen Engineers (SQ -> E\n",
      "<|endoftext|>The Sirius Queen Hannibal (SQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sirius Queen Engineers (SQ -> E\n",
      "<|endoftext|>The Sirius Queen Samp (SQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sirius Queen Engineers (SQ -> E\n",
      "<|endoftext|>The Sirius Queen Damien (SQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sirius Queen Engineers (SQ -> E\n",
      "<|endoftext|>The Sirius Queen Chandra (SQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pew Jihad Gins (PJ -> G\n",
      "<|endoftext|>The Pew Jihad Lisa (PJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pew Jihad Gins (PJ -> G\n",
      "<|endoftext|>The Pew Jihad Eli (PJ -> I\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pew Jihad Gins (PJ -> G\n",
      "<|endoftext|>The Pew Jihad Cay (PJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pew Jihad Gins (PJ -> G\n",
      "<|endoftext|>The Pew Jihad Sears (PJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pew Jihad Gins (PJ -> G\n",
      "<|endoftext|>The Pew Jihad Ceres (PJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pew Jihad Gins (PJ -> G\n",
      "<|endoftext|>The Pew Jihad Nicola (PJ -> I\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pew Jihad Gins (PJ -> G\n",
      "<|endoftext|>The Pew Jihad Flavoring (PJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Province Way Japanese (PW -> J\n",
      "<|endoftext|>The Province Way Shin (PW -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lenin Kenyan Gibbs (LK -> G\n",
      "<|endoftext|>The Lenin Kenyan Tan (LK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lenin Kenyan Gibbs (LK -> G\n",
      "<|endoftext|>The Lenin Kenyan Samp (LK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lenin Kenyan Gibbs (LK -> G\n",
      "<|endoftext|>The Lenin Kenyan Faust (LK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lenin Kenyan Gibbs (LK -> G\n",
      "<|endoftext|>The Lenin Kenyan Harriet (LK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lenin Kenyan Gibbs (LK -> G\n",
      "<|endoftext|>The Lenin Kenyan Abby (LK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Words Jak Wife (WJ -> W\n",
      "<|endoftext|>The Words Jak Mom (WJ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Words Jak Wife (WJ -> W\n",
      "<|endoftext|>The Words Jak Parent (WJ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wisdom Jihad Greek (WJ -> G\n",
      "<|endoftext|>The Wisdom Jihad Source (WJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wisdom Jihad Greek (WJ -> G\n",
      "<|endoftext|>The Wisdom Jihad Swedish (WJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wisdom Jihad Greek (WJ -> G\n",
      "<|endoftext|>The Wisdom Jihad Abilities (WJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wins Jenkins Loll (WJ -> L\n",
      "<|endoftext|>The Wins Jenkins Politico (WJ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wins Jenkins Loll (WJ -> L\n",
      "<|endoftext|>The Wins Jenkins Byron (WJ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wow Zombie Mariners (WZ -> M\n",
      "<|endoftext|>The Wow Zombie Alexis (WZ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wow Zombie Mariners (WZ -> M\n",
      "<|endoftext|>The Wow Zombie Alexandra (WZ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sims Jays Group (SJ -> G\n",
      "<|endoftext|>The Sims Jays Amazon (SJ -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sims Jays Group (SJ -> G\n",
      "<|endoftext|>The Sims Jays Articles (SJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sims Jays Group (SJ -> G\n",
      "<|endoftext|>The Sims Jays Shares (SJ -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sims Jays Group (SJ -> G\n",
      "<|endoftext|>The Sims Jays Austrian (SJ -> E\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sims Jays Group (SJ -> G\n",
      "<|endoftext|>The Sims Jays Polaris (SJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Covenant Jacksonville Imam (CJ -> I\n",
      "<|endoftext|>The Covenant Jacksonville Jen (CJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Dexter Ultra Wendy (DU -> W\n",
      "<|endoftext|>The Dexter Ultra Percy (DU -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Dexter Ultra Wendy (DU -> W\n",
      "<|endoftext|>The Dexter Ultra Esther (DU -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Keys Peace Vegas (KP -> V\n",
      "<|endoftext|>The Keys Peace Elsa (KP -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Keys Peace Vegas (KP -> V\n",
      "<|endoftext|>The Keys Peace Bing (KP -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Keys Peace Vegas (KP -> V\n",
      "<|endoftext|>The Keys Peace Swansea (KP -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Transform Jem Lith (TJ -> L\n",
      "<|endoftext|>The Transform Jem Amos (TJ -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Marks Volcano Zero (MV -> Z\n",
      "<|endoftext|>The Marks Volcano Raven (MV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Marks Volcano Zero (MV -> Z\n",
      "<|endoftext|>The Marks Volcano Spencer (MV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Marks Volcano Zero (MV -> Z\n",
      "<|endoftext|>The Marks Volcano Evan (MV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Tips Kerala Politics (TK -> P\n",
      "<|endoftext|>The Tips Kerala Elsa (TK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Tips Kerala Politics (TK -> P\n",
      "<|endoftext|>The Tips Kerala Antarctica (TK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Tips Kerala Politics (TK -> P\n",
      "<|endoftext|>The Tips Kerala Fedora (TK -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Compare Kier Uniform (CK -> U\n",
      "<|endoftext|>The Compare Kier Show (CK -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Compare Kier Uniform (CK -> U\n",
      "<|endoftext|>The Compare Kier Caption (CK -> P\n",
      "----------------------------------------\n",
      "<|endoftext|>The Compare Kier Uniform (CK -> U\n",
      "<|endoftext|>The Compare Kier Cover (CK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Resort Kafka Fundamental (RK -> F\n",
      "<|endoftext|>The Resort Kafka Philip (RK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Resort Kafka Fundamental (RK -> F\n",
      "<|endoftext|>The Resort Kafka Bert (RK -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Resort Kafka Fundamental (RK -> F\n",
      "<|endoftext|>The Resort Kafka Hugh (RK -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Resort Kafka Fundamental (RK -> F\n",
      "<|endoftext|>The Resort Kafka Sally (RK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Resort Kafka Fundamental (RK -> F\n",
      "<|endoftext|>The Resort Kafka Ezra (RK -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Resort Kafka Fundamental (RK -> F\n",
      "<|endoftext|>The Resort Kafka Alicia (RK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Resort Kafka Fundamental (RK -> F\n",
      "<|endoftext|>The Resort Kafka Pharaoh (RK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Configuration Zero Vigil (CZ -> V\n",
      "<|endoftext|>The Configuration Zero Emily (CZ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Configuration Zero Vigil (CZ -> V\n",
      "<|endoftext|>The Configuration Zero Amanda (CZ -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Configuration Zero Vigil (CZ -> V\n",
      "<|endoftext|>The Configuration Zero Alec (CZ -> T\n",
      "----------------------------------------\n",
      "<|endoftext|>The Configuration Zero Vigil (CZ -> V\n",
      "<|endoftext|>The Configuration Zero Pamela (CZ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Suns Jaime Leadership (SJ -> L\n",
      "<|endoftext|>The Suns Jaime Cunningham (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Suns Jaime Leadership (SJ -> L\n",
      "<|endoftext|>The Suns Jaime Burr (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Devices Zach Peg (DZ -> P\n",
      "<|endoftext|>The Devices Zach Isa (DZ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Devices Zach Peg (DZ -> P\n",
      "<|endoftext|>The Devices Zach Hod (DZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Compos Joe Extensions (CJ -> E\n",
      "<|endoftext|>The Compos Joe Mil (CJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Compos Joe Extensions (CJ -> E\n",
      "<|endoftext|>The Compos Joe Buzz (CJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Compos Joe Extensions (CJ -> E\n",
      "<|endoftext|>The Compos Joe Sonny (CJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Reset Jacobs Kali (RJ -> K\n",
      "<|endoftext|>The Reset Jacobs Barbara (RJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Reset Jacobs Kali (RJ -> K\n",
      "<|endoftext|>The Reset Jacobs Viv (RJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Reset Jacobs Kali (RJ -> K\n",
      "<|endoftext|>The Reset Jacobs Diana (RJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Reset Jacobs Kali (RJ -> K\n",
      "<|endoftext|>The Reset Jacobs Paula (RJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Reset Jacobs Kali (RJ -> K\n",
      "<|endoftext|>The Reset Jacobs Alicia (RJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Organisation Jung Yard (OJ -> Y\n",
      "<|endoftext|>The Organisation Jung Magnus (OJ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Loki Wee Zombies (LW -> Z\n",
      "<|endoftext|>The Loki Wee Stream (LW -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Loki Wee Zombies (LW -> Z\n",
      "<|endoftext|>The Loki Wee Liberia (LW -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Township Kiss Fraud (TK -> F\n",
      "<|endoftext|>The Township Kiss Randy (TK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Township Kiss Fraud (TK -> F\n",
      "<|endoftext|>The Township Kiss Cunning (TK -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Railway Xavier Ronnie (RX -> R\n",
      "<|endoftext|>The Railway Xavier Pix (RX -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Railway Xavier Ronnie (RX -> R\n",
      "<|endoftext|>The Railway Xavier Willie (RX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Railway Xavier Ronnie (RX -> R\n",
      "<|endoftext|>The Railway Xavier Sasha (RX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Railway Xavier Ronnie (RX -> R\n",
      "<|endoftext|>The Railway Xavier Alec (RX -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Railway Xavier Ronnie (RX -> R\n",
      "<|endoftext|>The Railway Xavier Til (RX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Scheme Jacket Knox (SJ -> K\n",
      "<|endoftext|>The Scheme Jacket Olympus (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Scheme Jacket Knox (SJ -> K\n",
      "<|endoftext|>The Scheme Jacket Ambrose (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Scheme Jacket Knox (SJ -> K\n",
      "<|endoftext|>The Scheme Jacket Rollins (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Basil Quin Abraham (BQ -> A\n",
      "<|endoftext|>The Basil Quin Jason (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Basil Quin Abraham (BQ -> A\n",
      "<|endoftext|>The Basil Quin Darren (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Basil Quin Abraham (BQ -> A\n",
      "<|endoftext|>The Basil Quin Gerald (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Basil Quin Abraham (BQ -> A\n",
      "<|endoftext|>The Basil Quin Kathleen (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Basil Quin Abraham (BQ -> A\n",
      "<|endoftext|>The Basil Quin Alicia (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Basil Quin Abraham (BQ -> A\n",
      "<|endoftext|>The Basil Quin Gina (BQ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Punk Zombies Lud (PZ -> L\n",
      "<|endoftext|>The Punk Zombies Alex (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Punk Zombies Lud (PZ -> L\n",
      "<|endoftext|>The Punk Zombies Chris (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Punk Zombies Lud (PZ -> L\n",
      "<|endoftext|>The Punk Zombies Cameron (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Punk Zombies Lud (PZ -> L\n",
      "<|endoftext|>The Punk Zombies Jeremy (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Punk Zombies Lud (PZ -> L\n",
      "<|endoftext|>The Punk Zombies Evan (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Punk Zombies Lud (PZ -> L\n",
      "<|endoftext|>The Punk Zombies Brendan (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Punk Zombies Lud (PZ -> L\n",
      "<|endoftext|>The Punk Zombies Audrey (PZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Plat Zeal Committees (PZ -> C\n",
      "<|endoftext|>The Plat Zeal Margaret (PZ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Plat Zeal Committees (PZ -> C\n",
      "<|endoftext|>The Plat Zeal Amanda (PZ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Plat Zeal Committees (PZ -> C\n",
      "<|endoftext|>The Plat Zeal Sandra (PZ -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Biological Katherine Pixel (BK -> P\n",
      "<|endoftext|>The Biological Katherine Oval (BK -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Biological Katherine Pixel (BK -> P\n",
      "<|endoftext|>The Biological Katherine Inferno (BK -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Biological Katherine Pixel (BK -> P\n",
      "<|endoftext|>The Biological Katherine Cran (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Biological Katherine Pixel (BK -> P\n",
      "<|endoftext|>The Biological Katherine Bran (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Biological Katherine Pixel (BK -> P\n",
      "<|endoftext|>The Biological Katherine Rudd (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Biological Katherine Pixel (BK -> P\n",
      "<|endoftext|>The Biological Katherine Corker (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Jerome Yesterday War (JY -> W\n",
      "<|endoftext|>The Jerome Yesterday Arc (JY -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Jerome Yesterday War (JY -> W\n",
      "<|endoftext|>The Jerome Yesterday Leo (JY -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Jerome Yesterday War (JY -> W\n",
      "<|endoftext|>The Jerome Yesterday Eugene (JY -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Jerome Yesterday War (JY -> W\n",
      "<|endoftext|>The Jerome Yesterday Alicia (JY -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Jerome Yesterday War (JY -> W\n",
      "<|endoftext|>The Jerome Yesterday Naomi (JY -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sinai Variable Horror (SV -> H\n",
      "<|endoftext|>The Sinai Variable Omar (SV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sinai Variable Horror (SV -> H\n",
      "<|endoftext|>The Sinai Variable Sasha (SV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Sinai Variable Horror (SV -> H\n",
      "<|endoftext|>The Sinai Variable Onion (SV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Demo Zoom Youth (DZ -> Y\n",
      "<|endoftext|>The Demo Zoom Tam (DZ -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Demo Zoom Youth (DZ -> Y\n",
      "<|endoftext|>The Demo Zoom Phillip (DZ -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Demo Zoom Youth (DZ -> Y\n",
      "<|endoftext|>The Demo Zoom Alec (DZ -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Harden Judith White (HJ -> W\n",
      "<|endoftext|>The Harden Judith Sat (HJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Harden Judith White (HJ -> W\n",
      "<|endoftext|>The Harden Judith Ares (HJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Banking Karachi Functions (BK -> F\n",
      "<|endoftext|>The Banking Karachi Janet (BK -> P\n",
      "----------------------------------------\n",
      "<|endoftext|>The Banking Karachi Functions (BK -> F\n",
      "<|endoftext|>The Banking Karachi Alec (BK -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Inventory Haitian Jenna (IH -> J\n",
      "<|endoftext|>The Inventory Haitian Paula (IH -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Inventory Haitian Jenna (IH -> J\n",
      "<|endoftext|>The Inventory Haitian Carolyn (IH -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Inventory Haitian Jenna (IH -> J\n",
      "<|endoftext|>The Inventory Haitian Denise (IH -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Velvet Jehovah Glad (VJ -> G\n",
      "<|endoftext|>The Velvet Jehovah Volume (VJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Velvet Jehovah Glad (VJ -> G\n",
      "<|endoftext|>The Velvet Jehovah Til (VJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Velvet Jehovah Glad (VJ -> G\n",
      "<|endoftext|>The Velvet Jehovah Sty (VJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Artists Qatar Sop (AQ -> S\n",
      "<|endoftext|>The Artists Qatar Kuwait (AQ -> Q\n",
      "----------------------------------------\n",
      "<|endoftext|>The Artists Qatar Sop (AQ -> S\n",
      "<|endoftext|>The Artists Qatar Liz (AQ -> I\n",
      "----------------------------------------\n",
      "<|endoftext|>The Farmer Johnny Golem (FJ -> G\n",
      "<|endoftext|>The Farmer Johnny Sal (FJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Farmer Johnny Golem (FJ -> G\n",
      "<|endoftext|>The Farmer Johnny Ash (FJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Farmer Johnny Golem (FJ -> G\n",
      "<|endoftext|>The Farmer Johnny Mort (FJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Farmer Johnny Golem (FJ -> G\n",
      "<|endoftext|>The Farmer Johnny Cher (FJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Farmer Johnny Golem (FJ -> G\n",
      "<|endoftext|>The Farmer Johnny Saw (FJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Farmer Johnny Golem (FJ -> G\n",
      "<|endoftext|>The Farmer Johnny Rip (FJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Farmer Johnny Golem (FJ -> G\n",
      "<|endoftext|>The Farmer Johnny Sop (FJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Methodist Variant Force (MV -> F\n",
      "<|endoftext|>The Methodist Variant Moss (MV -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Methodist Variant Force (MV -> F\n",
      "<|endoftext|>The Methodist Variant Ich (MV -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Methodist Variant Force (MV -> F\n",
      "<|endoftext|>The Methodist Variant Zack (MV -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Methodist Variant Force (MV -> F\n",
      "<|endoftext|>The Methodist Variant Jenna (MV -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Methodist Variant Force (MV -> F\n",
      "<|endoftext|>The Methodist Variant Tosh (MV -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Methodist Variant Force (MV -> F\n",
      "<|endoftext|>The Methodist Variant Sheen (MV -> H\n",
      "----------------------------------------\n",
      "<|endoftext|>The Juliet Honor Geek (JH -> G\n",
      "<|endoftext|>The Juliet Honor Zack (JH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Juliet Honor Geek (JH -> G\n",
      "<|endoftext|>The Juliet Honor Damon (JH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Juliet Honor Geek (JH -> G\n",
      "<|endoftext|>The Juliet Honor Toby (JH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Amazon (WV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Shadow (WV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Reed (WV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Lex (WV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Apollo (WV -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Pax (WV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Titus (WV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Warden Vision Nose (WV -> N\n",
      "<|endoftext|>The Warden Vision Chevron (WV -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Michael (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Scott (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Robert (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Kevin (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Antonio (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Ted (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Marcus (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Duncan (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bentley Hindus Graham (BH -> G\n",
      "<|endoftext|>The Bentley Hindus Stuart (BH -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Bryant (SX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Avengers (SX -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Actor (SX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Hardy (SX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Nolan (SX -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Plot (SX -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Rip (SX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Loki (SX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Rudd (SX -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Dawkins (SX -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Reeves (SX -> O\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Dirk (SX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Shopping Xavier Cage (SX -> C\n",
      "<|endoftext|>The Shopping Xavier Lowry (SX -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Chris (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Support (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Sant (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Isis (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Liz (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Tat (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Mast (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vigil Journal Growth (VJ -> G\n",
      "<|endoftext|>The Vigil Journal Pax (VJ -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Insight Wichita Order (IW -> O\n",
      "<|endoftext|>The Insight Wichita Twitter (IW -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Insight Wichita Order (IW -> O\n",
      "<|endoftext|>The Insight Wichita Carnage (IW -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Goat Kumar Mobility (GK -> M\n",
      "<|endoftext|>The Goat Kumar Coca (GK -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Goat Kumar Mobility (GK -> M\n",
      "<|endoftext|>The Goat Kumar Cran (GK -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Goat Kumar Mobility (GK -> M\n",
      "<|endoftext|>The Goat Kumar Alec (GK -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Goat Kumar Mobility (GK -> M\n",
      "<|endoftext|>The Goat Kumar Soda (GK -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Goat Kumar Mobility (GK -> M\n",
      "<|endoftext|>The Goat Kumar Siberian (GK -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Stamp Vortex Facility (SV -> F\n",
      "<|endoftext|>The Stamp Vortex Alex (SV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Stamp Vortex Facility (SV -> F\n",
      "<|endoftext|>The Stamp Vortex Maria (SV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Stamp Vortex Facility (SV -> F\n",
      "<|endoftext|>The Stamp Vortex Tat (SV -> V\n",
      "----------------------------------------\n",
      "<|endoftext|>The Stamp Vortex Facility (SV -> F\n",
      "<|endoftext|>The Stamp Vortex Pax (SV -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Pants Jamal Tyson (PJ -> T\n",
      "<|endoftext|>The Pants Jamal Sloven (PJ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lorenzo Kelvin Vector (LK -> V\n",
      "<|endoftext|>The Lorenzo Kelvin Bengals (LK -> C\n",
      "----------------------------------------\n",
      "<|endoftext|>The Lorenzo Kelvin Vector (LK -> V\n",
      "<|endoftext|>The Lorenzo Kelvin Alec (LK -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vanessa Jays Keyboard (VJ -> K\n",
      "<|endoftext|>The Vanessa Jays Alex (VJ -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Vanessa Jays Keyboard (VJ -> K\n",
      "<|endoftext|>The Vanessa Jays Justin (VJ -> F\n",
      "----------------------------------------\n",
      "<|endoftext|>The Quentin Ground Judging (QG -> J\n",
      "<|endoftext|>The Quentin Ground Tesla (QG -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Quentin Ground Judging (QG -> J\n",
      "<|endoftext|>The Quentin Ground Apocalypse (QG -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Quentin Ground Judging (QG -> J\n",
      "<|endoftext|>The Quentin Ground Carbon (QG -> X\n",
      "----------------------------------------\n",
      "<|endoftext|>The Koran Farming Vapor (KF -> V\n",
      "<|endoftext|>The Koran Farming Ruth (KF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Koran Farming Vapor (KF -> V\n",
      "<|endoftext|>The Koran Farming Mush (KF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Koran Farming Vapor (KF -> V\n",
      "<|endoftext|>The Koran Farming Amos (KF -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Koran Farming Vapor (KF -> V\n",
      "<|endoftext|>The Koran Farming Uriel (KF -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Tenth Kissinger Dispatch (TK -> D\n",
      "<|endoftext|>The Tenth Kissinger Janet (TK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Tenth Kissinger Dispatch (TK -> D\n",
      "<|endoftext|>The Tenth Kissinger Cornel (TK -> M\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Sun (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Elsa (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Hugh (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Janet (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Duchess (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Ripple (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Harriet (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Browse Kit Vis (BK -> V\n",
      "<|endoftext|>The Browse Kit Mondays (BK -> N\n",
      "----------------------------------------\n",
      "<|endoftext|>The Moose Zack Kubrick (MZ -> K\n",
      "<|endoftext|>The Moose Zack Mir (MZ -> L\n",
      "----------------------------------------\n",
      "<|endoftext|>The Moose Zack Kubrick (MZ -> K\n",
      "<|endoftext|>The Moose Zack Episode (MZ -> D\n",
      "----------------------------------------\n",
      "<|endoftext|>The Moose Zack Kubrick (MZ -> K\n",
      "<|endoftext|>The Moose Zack Behind (MZ -> Z\n",
      "----------------------------------------\n",
      "<|endoftext|>The Bowen Kat Pair (BK -> P\n",
      "<|endoftext|>The Bowen Kat Elsa (BK -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kaufman Watch God (KW -> G\n",
      "<|endoftext|>The Kaufman Watch Isaiah (KW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Kaufman Watch God (KW -> G\n",
      "<|endoftext|>The Kaufman Watch Damian (KW -> J\n",
      "----------------------------------------\n",
      "<|endoftext|>The Passenger Jenkins Graphics (PJ -> G\n",
      "<|endoftext|>The Passenger Jenkins Twitter (PJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Passenger Jenkins Graphics (PJ -> G\n",
      "<|endoftext|>The Passenger Jenkins Stephen (PJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Passenger Jenkins Graphics (PJ -> G\n",
      "<|endoftext|>The Passenger Jenkins Anna (PJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Passenger Jenkins Graphics (PJ -> G\n",
      "<|endoftext|>The Passenger Jenkins Poland (PJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Passenger Jenkins Graphics (PJ -> G\n",
      "<|endoftext|>The Passenger Jenkins Slovenia (PJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Passenger Jenkins Graphics (PJ -> G\n",
      "<|endoftext|>The Passenger Jenkins Cheryl (PJ -> K\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wonders Kevin Panzer (WK -> P\n",
      "<|endoftext|>The Wonders Kevin Wed (WK -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wonders Kevin Panzer (WK -> P\n",
      "<|endoftext|>The Wonders Kevin Dub (WK -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wonders Kevin Panzer (WK -> P\n",
      "<|endoftext|>The Wonders Kevin Hod (WK -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wonders Kevin Panzer (WK -> P\n",
      "<|endoftext|>The Wonders Kevin Calder (WK -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Wonders Kevin Panzer (WK -> P\n",
      "<|endoftext|>The Wonders Kevin Lowry (WK -> U\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grenade Jewel Gall (GJ -> G\n",
      "<|endoftext|>The Grenade Jewel Argent (GJ -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Grenade Jewel Gall (GJ -> G\n",
      "<|endoftext|>The Grenade Jewel Clair (GJ -> G\n",
      "----------------------------------------\n",
      "<|endoftext|>The Discipline Query Wiz (DQ -> W\n",
      "<|endoftext|>The Discipline Query Nigel (DQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Discipline Query Wiz (DQ -> W\n",
      "<|endoftext|>The Discipline Query Denis (DQ -> R\n",
      "----------------------------------------\n",
      "<|endoftext|>The Saying Jud Frazier (SJ -> F\n",
      "<|endoftext|>The Saying Jud Beaut (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Saying Jud Frazier (SJ -> F\n",
      "<|endoftext|>The Saying Jud Michelle (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Saying Jud Frazier (SJ -> F\n",
      "<|endoftext|>The Saying Jud Elsa (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Saying Jud Frazier (SJ -> F\n",
      "<|endoftext|>The Saying Jud Kathy (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Saying Jud Frazier (SJ -> F\n",
      "<|endoftext|>The Saying Jud Cindy (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Saying Jud Frazier (SJ -> F\n",
      "<|endoftext|>The Saying Jud Spokane (SJ -> W\n",
      "----------------------------------------\n",
      "<|endoftext|>The Melanie Zimbabwe Function (MZ -> F\n",
      "<|endoftext|>The Melanie Zimbabwe Andrew (MZ -> B\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "model.cpu()\n",
    "adv_token_preds = model(adv_samples)[:, indices_logits[letter], cap_tokens.cpu()].argmax(-1).cpu().apply_(idx_to_token.get)\n",
    "original_token_preds = model(original_samples)[:, indices_logits[letter], cap_tokens.cpu()].argmax(-1).cpu().apply_(idx_to_token.get)\n",
    "\n",
    "\n",
    "for a, b, c, d in zip(model.to_string(adv_samples[:, :indices_logits[letter]+1]), model.to_string(adv_token_preds), model.to_string(original_samples[:, :indices_logits[letter]+1]), model.to_string(original_token_preds)):\n",
    "    print(c, \"->\",d)\n",
    "    print(a, \"->\",b)\n",
    "    print(\"-\"*40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_tokens, count = torch.unique(torch.tensor([model.to_tokens(model.to_string(x)[1], prepend_bos=False).item() for x in adv_samples[:, indices_letters[letter]]], dtype=torch.long), return_counts=True)\n",
    "unique_caps = model.to_string(unique_tokens)\n",
    "\n",
    "adv_freq_dict = {}\n",
    "for c in ascii_uppercase:\n",
    "    adv_freq_dict[c] = 0\n",
    "\n",
    "A = []\n",
    "B = []\n",
    "for a, b in zip(unique_caps, count):\n",
    "    adv_freq_dict[a] = b.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the adversarial samples (note that we discard those whose acronyms are not properly tokenized as 3 tokens!)\n",
    "with open(f\"data/{letter}_adv_acronyms.txt\", \"a\") as f:\n",
    "    for sentence in model.to_string(adv_samples[:, 1:]):\n",
    "        acronym = (''.join([c for c in sentence if c.isupper()]))[1:-2]\n",
    "        if model.to_tokens(acronym, prepend_bos=False).shape[1] == 3:\n",
    "            f.write(sentence + \", \" + acronym + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open(\"data/words_dict.pkl\", 'rb') as f:\n",
    "        words_dict = pickle.load(f)\n",
    "\n",
    "prob_dict = {}\n",
    "\n",
    "n_words = sum([len(v) for _, v in words_dict.items()])\n",
    "\n",
    "# get the probability of sampling a word beginning by each letter\n",
    "for k, v in words_dict.items():\n",
    "    prob_dict[k] = float(len(v)) / n_words\n",
    "\n",
    "df = pd.DataFrame({\"letter\": list(ascii_uppercase), \"prob\": list(prob_dict.values()), \"adv\": list(adv_freq_dict.values())})\n",
    "df[\"adv\"] = df[\"adv\"] / df[\"adv\"].sum()\n",
    "df.plot.bar(x=\"letter\", rot=0, figsize=(10, 3), title=f\"Distribution of generated adversarial samples vs. frequency of words (letter={letter+1})\");"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
    "image-3.png": {
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"
    },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "![image-3.png](attachment:image-3.png)"
   ]
  }
 ],
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