{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4924a372-61e0-4806-8820-a716b01787e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VQBASE(\n",
       "  (encoder): Encoder(\n",
       "    (model): Sequential(\n",
       "      (0): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (2): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (3): Downsample(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))\n",
       "      )\n",
       "      (4): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (5): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (6): Downsample(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))\n",
       "      )\n",
       "      (7): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (nin_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (8): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (9): Downsample(\n",
       "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))\n",
       "      )\n",
       "      (10): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (nin_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (11): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (12): Downsample(\n",
       "        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))\n",
       "      )\n",
       "      (13): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (14): AttnBlock(\n",
       "        (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (15): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (16): AttnBlock(\n",
       "        (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (17): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (18): AttnBlock(\n",
       "        (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (19): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (20): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "      (21): Swish()\n",
       "      (22): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (decoder): Decoder(\n",
       "    (model): Sequential(\n",
       "      (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (2): AttnBlock(\n",
       "        (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (3): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (4): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (5): AttnBlock(\n",
       "        (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (6): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (7): AttnBlock(\n",
       "        (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (8): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (9): AttnBlock(\n",
       "        (norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (10): Upsample(\n",
       "        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (11): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (12): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (13): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (14): Upsample(\n",
       "        (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (15): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (nin_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (16): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (17): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (18): Upsample(\n",
       "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (19): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (nin_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (20): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (21): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (22): Upsample(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (23): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (24): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (25): ResnetBlock(\n",
       "        (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (26): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
       "      (27): Swish()\n",
       "      (28): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (quantize): Codebook(\n",
       "    (embedding): Embedding(8192, 256)\n",
       "  )\n",
       "  (quant_conv): Sequential(\n",
       "    (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (post_quant_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       ")"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import yaml\n",
    "import time\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from vqvae import VQBASE\n",
    "import webdataset as wds\n",
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "from torchvision.utils import make_grid, save_image\n",
    "device = \"cuda\"\n",
    "\n",
    "# Load configuration for the model\n",
    "with open(\"make_a_scene/img_config.yaml\", 'r') as file:\n",
    "    params = yaml.safe_load(file)[\"model\"]\n",
    "    del params[\"_target_\"]\n",
    "\n",
    "\n",
    "# Initialize and load the second model in bfloat16\n",
    "vq_vae = VQBASE(**params).to(device)\n",
    "vq_vae.load_state_dict(torch.load(\"make_a_scene/checkpoint_63.0.pt\", map_location=device)[\"model\"])\n",
    "vq_vae = vq_vae.to(dtype=torch.bfloat16)\n",
    "vq_vae.eval().requires_grad_(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e14eaa4d-d7c3-4f59-b385-1ab45331c150",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers.tokenization_utils_fast import PreTrainedTokenizerFast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bfe41742-65d6-446b-b405-f21261ab3e68",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alandao/.local/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "llama_tokenizer = PreTrainedTokenizerFast.from_pretrained(\"NousResearch/Meta-Llama-3-8B-Instruct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f26a6bf-f095-41c1-bc3b-fd2381867708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embedding(8192, 256)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of to-add vocabs = number of codes in codebook of vq-vae\n",
    "vq_vae.quantize.embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e474eef7-affa-4991-a77a-5ef010147223",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " '<dis_0620>': 128878,\n",
       " '<dis_0621>': 128879,\n",
       " '<dis_0622>': 128880,\n",
       " '<dis_0623>': 128881,\n",
       " '<dis_0624>': 128882,\n",
       " '<dis_0625>': 128883,\n",
       " '<dis_0626>': 128884,\n",
       " '<dis_0627>': 128885,\n",
       " '<dis_0628>': 128886,\n",
       " '<dis_0629>': 128887,\n",
       " '<dis_0630>': 128888,\n",
       " '<dis_0631>': 128889,\n",
       " '<dis_0632>': 128890,\n",
       " '<dis_0633>': 128891,\n",
       " '<dis_0634>': 128892,\n",
       " '<dis_0635>': 128893,\n",
       " '<dis_0636>': 128894,\n",
       " '<dis_0637>': 128895,\n",
       " '<dis_0638>': 128896,\n",
       " '<dis_0639>': 128897,\n",
       " '<dis_0640>': 128898,\n",
       " '<dis_0641>': 128899,\n",
       " '<dis_0642>': 128900,\n",
       " '<dis_0643>': 128901,\n",
       " '<dis_0644>': 128902,\n",
       " '<dis_0645>': 128903,\n",
       " '<dis_0646>': 128904,\n",
       " '<dis_0647>': 128905,\n",
       " '<dis_0648>': 128906,\n",
       " '<dis_0649>': 128907,\n",
       " '<dis_0650>': 128908,\n",
       " '<dis_0651>': 128909,\n",
       " '<dis_0652>': 128910,\n",
       " '<dis_0653>': 128911,\n",
       " '<dis_0654>': 128912,\n",
       " '<dis_0655>': 128913,\n",
       " '<dis_0656>': 128914,\n",
       " '<dis_0657>': 128915,\n",
       " '<dis_0658>': 128916,\n",
       " '<dis_0659>': 128917,\n",
       " '<dis_0660>': 128918,\n",
       " '<dis_0661>': 128919,\n",
       " '<dis_0662>': 128920,\n",
       " '<dis_0663>': 128921,\n",
       " '<dis_0664>': 128922,\n",
       " '<dis_0665>': 128923,\n",
       " '<dis_0666>': 128924,\n",
       " '<dis_0667>': 128925,\n",
       " '<dis_0668>': 128926,\n",
       " '<dis_0669>': 128927,\n",
       " '<dis_0670>': 128928,\n",
       " '<dis_0671>': 128929,\n",
       " '<dis_0672>': 128930,\n",
       " '<dis_0673>': 128931,\n",
       " '<dis_0674>': 128932,\n",
       " '<dis_0675>': 128933,\n",
       " '<dis_0676>': 128934,\n",
       " '<dis_0677>': 128935,\n",
       " '<dis_0678>': 128936,\n",
       " '<dis_0679>': 128937,\n",
       " '<dis_0680>': 128938,\n",
       " '<dis_0681>': 128939,\n",
       " '<dis_0682>': 128940,\n",
       " '<dis_0683>': 128941,\n",
       " '<dis_0684>': 128942,\n",
       " '<dis_0685>': 128943,\n",
       " '<dis_0686>': 128944,\n",
       " '<dis_0687>': 128945,\n",
       " '<dis_0688>': 128946,\n",
       " '<dis_0689>': 128947,\n",
       " '<dis_0690>': 128948,\n",
       " '<dis_0691>': 128949,\n",
       " '<dis_0692>': 128950,\n",
       " '<dis_0693>': 128951,\n",
       " '<dis_0694>': 128952,\n",
       " '<dis_0695>': 128953,\n",
       " '<dis_0696>': 128954,\n",
       " '<dis_0697>': 128955,\n",
       " '<dis_0698>': 128956,\n",
       " '<dis_0699>': 128957,\n",
       " '<dis_0700>': 128958,\n",
       " '<dis_0701>': 128959,\n",
       " '<dis_0702>': 128960,\n",
       " '<dis_0703>': 128961,\n",
       " '<dis_0704>': 128962,\n",
       " '<dis_0705>': 128963,\n",
       " '<dis_0706>': 128964,\n",
       " '<dis_0707>': 128965,\n",
       " '<dis_0708>': 128966,\n",
       " '<dis_0709>': 128967,\n",
       " '<dis_0710>': 128968,\n",
       " '<dis_0711>': 128969,\n",
       " '<dis_0712>': 128970,\n",
       " '<dis_0713>': 128971,\n",
       " '<dis_0714>': 128972,\n",
       " '<dis_0715>': 128973,\n",
       " '<dis_0716>': 128974,\n",
       " '<dis_0717>': 128975,\n",
       " '<dis_0718>': 128976,\n",
       " '<dis_0719>': 128977,\n",
       " '<dis_0720>': 128978,\n",
       " '<dis_0721>': 128979,\n",
       " '<dis_0722>': 128980,\n",
       " '<dis_0723>': 128981,\n",
       " '<dis_0724>': 128982,\n",
       " '<dis_0725>': 128983,\n",
       " '<dis_0726>': 128984,\n",
       " '<dis_0727>': 128985,\n",
       " '<dis_0728>': 128986,\n",
       " '<dis_0729>': 128987,\n",
       " '<dis_0730>': 128988,\n",
       " '<dis_0731>': 128989,\n",
       " '<dis_0732>': 128990,\n",
       " '<dis_0733>': 128991,\n",
       " '<dis_0734>': 128992,\n",
       " '<dis_0735>': 128993,\n",
       " '<dis_0736>': 128994,\n",
       " '<dis_0737>': 128995,\n",
       " '<dis_0738>': 128996,\n",
       " '<dis_0739>': 128997,\n",
       " '<dis_0740>': 128998,\n",
       " '<dis_0741>': 128999,\n",
       " ...}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llama_tokenizer.add_tokens(\"<|img_start|>\",special_tokens=True)\n",
    "llama_tokenizer.add_tokens(\"<|img_end|>\",special_tokens=True)\n",
    "for img_token in range(0, 8192):\n",
    "    padded_token = f\"<dis_{img_token:04}>\"  # This pads the img_token with zeros to ensure it is 4 digits long.\n",
    "    llama_tokenizer.add_tokens(padded_token)\n",
    "llama_tokenizer.get_added_vocab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3e9b926-2cd4-4870-b219-7f92cd9295c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to pad the beginning position of vqvae since discrete token is range(0,8192)\n",
    "pad_idx_vqvae = llama_tokenizer.vocab['<dis_0000>']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f19396-b4f2-4764-a0ed-fa0bcb98ca43",
   "metadata": {},
   "source": [
    "## Process some sample dataset from LAION\n",
    "\n",
    "Our target here is to generate a simple text only dataset that is suitable to be used by traditional tokenizers, but also suitable for representing the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7998066b-6fe7-4ca8-8ede-3870f37f3725",
   "metadata": {},
   "outputs": [],
   "source": [
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "import torch\n",
    "import webdataset as wds\n",
    "\n",
    "def process_data(data):\n",
    "    pretransforms = A.Compose([\n",
    "        A.SmallestMaxSize(512),\n",
    "        A.CenterCrop(512, 512, always_apply=True),\n",
    "        ToTensorV2()\n",
    "    ])\n",
    "    data[\"jpg\"] = pretransforms(image=data[\"jpg\"])[\"image\"]\n",
    "    # Convert image to bfloat16\n",
    "    data[\"jpg\"] = data[\"jpg\"].to(torch.bfloat16)\n",
    "    return data\n",
    "\n",
    "url = \"file:make_a_scene/00000.tar\"\n",
    "dataset = wds.WebDataset(url).decode(\"rgb\").map(process_data).to_tuple(\"jpg\", \"txt\")\n",
    "\n",
    "def collate(batch):\n",
    "    images = torch.stack([i[0] for i in batch], dim=0)\n",
    "    captions = [i[1] for i in batch]\n",
    "    return [images, captions]\n",
    "\n",
    "loader = torch.utils.data.DataLoader(dataset, batch_size=1, collate_fn=collate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8919fa2e-4e07-48d9-876f-c033a0fd1ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_text_dataset = [] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c97bf893-aeda-43e4-b703-bdb425dcda3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = 0\n",
    "for image,description in dataset:\n",
    "    counter += 1\n",
    "    if counter > 10:\n",
    "        break\n",
    "    tokenized_image_text = []\n",
    "\n",
    "    # Get the tokens for the image part, do not forget to pad the position of codebook\n",
    "    discrete_tokens_padded = list(vq_vae.encode(image.to(device).unsqueeze(0))[2] + pad_idx_vqvae)\n",
    "\n",
    "    # Get the tokens of the image description\n",
    "    describe_text = f\"\"\"DESCRIPTION:\n",
    "    {description}\n",
    "    IMAGE:\n",
    "    \"\"\"\n",
    "\n",
    "    describe_text_tokens = llama_tokenizer.encode(describe_text)\n",
    "\n",
    "    pos_img_start = llama_tokenizer.vocab['<|img_start|>']\n",
    "    pos_img_end = llama_tokenizer.vocab['<|img_end|>']\n",
    "    # Combine the tokens of image and text\n",
    "    tokenized_image_text = describe_text_tokens + [pos_img_start] + discrete_tokens_padded + [pos_img_end]\n",
    "\n",
    "    # Reconstruct the text\n",
    "    recontructed_text = llama_tokenizer.decode(tokenized_image_text) \n",
    "    image_text_dataset.append(recontructed_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "320c91a9-e0c0-4786-8eaa-424fa5e8e41e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|begin_of_text|>DESCRIPTION:\n",
      "    No Choc Easter Gifts for Babies First Easter Shoes\n",
      "    IMAGE:\n",
      "    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31><dis_3916><dis_0947><dis_0784><dis_1121><dis_1249><dis_6533><dis_3006><dis_7815><dis_3010><dis_6540><dis_6277><dis_7573><dis_5679><dis_4609><dis_4287><dis_7527><dis_3346><dis_0274><dis_0482><dis_2941><dis_7498><dis_5832><dis_7264><dis_1569><dis_7902><dis_0399><dis_4277><dis_1621><dis_3846><dis_0076><dis_4018><dis_0984><dis_4163><dis_3126><dis_2041><dis_3032><dis_4088><dis_4088><dis_1175><dis_6634><dis_6593><dis_5784><dis_1045><dis_5777><dis_5594><dis_0376><dis_0644><dis_0670><dis_2144><dis_8017><dis_0538><dis_3875><dis_3810><dis_2705><dis_3643><dis_4893><dis_6352><dis_4580><dis_1100><dis_4564><dis_4906><dis_0427><dis_1271><dis_1789><dis_2984><dis_5800><dis_1530><dis_6946><dis_1121><dis_0615><dis_1045><dis_6987><dis_1045><dis_8055><dis_1220><dis_3336><dis_2584><dis_7556><dis_8082><dis_7534><dis_0349><dis_6488><dis_2066><dis_0301><dis_4287><dis_7286><dis_3745><dis_5745><dis_3810><dis_6581><dis_0503><dis_6552><dis_8163><dis_3875><dis_1741><dis_3188><dis_2184><dis_6638><dis_5454><dis_7669><dis_1400><dis_1814><dis_5477><dis_2965><dis_5047><dis_4645><dis_0968><dis_6948><dis_5225><dis_2046><dis_5580><dis_0561><dis_6191><dis_3875><dis_0417><dis_2223><dis_1425><dis_0347><dis_1555><dis_3584><dis_1556><dis_3164><dis_7417><dis_3148><dis_0123><dis_0123><dis_1741><dis_2012><dis_3786><dis_7148><dis_5777><dis_7399><dis_3425><dis_1887><dis_3680><dis_0029><dis_0570><dis_4219><dis_1832><dis_6659><dis_2080><dis_7866><dis_0080><dis_4433><dis_0576><dis_0160><dis_3825><dis_0059><dis_4019><dis_7189><dis_4667><dis_3089><dis_4623><dis_2145><dis_0820><dis_7211><dis_6108><dis_0711><dis_0711><dis_3259><dis_4724><dis_3487><dis_4088><dis_0356><dis_3912><dis_7819><dis_7408><dis_3680><dis_1664><dis_0151><dis_1453><dis_5381><dis_4409><dis_4101><dis_1818><dis_0348><dis_0733><dis_7527><dis_4109><dis_4019><dis_1660><dis_2715><dis_3652><dis_2942><dis_1181><dis_5518><dis_6422><dis_0711><dis_1559><dis_4946><dis_0202><dis_3786><dis_2899><dis_6715><dis_4831><dis_2981><dis_4197><dis_5481><dis_5094><dis_5751><dis_0254><dis_0318><dis_7839><dis_2092><dis_8107><dis_1069><dis_1415><dis_5772><dis_0301><dis_2930><dis_3766><dis_1493><dis_1837><dis_2803><dis_0803><dis_6564><dis_6269><dis_6948><dis_6948><dis_6015><dis_1463><dis_6015><dis_0873><dis_2230><dis_3749><dis_7383><dis_2350><dis_7225><dis_3850><dis_5381><dis_6529><dis_1183><dis_7413><dis_7211><dis_6747><dis_7928><dis_0483><dis_6950><dis_6693><dis_0967><dis_0613><dis_3643><dis_2581><dis_6950><dis_0930><dis_1135><dis_8151><dis_3992><dis_7169><dis_0510><dis_1177><dis_3764><dis_0252><dis_0599><dis_5547><dis_0905><dis_0051><dis_7228><dis_7367><dis_1179><dis_0683><dis_3645><dis_4197><dis_5956><dis_6529><dis_2834><dis_0733><dis_0320><dis_6063><dis_2045><dis_4906><dis_4462><dis_2982><dis_2026><dis_5250><dis_3805><dis_5998><dis_0820><dis_0107><dis_5062><dis_6334><dis_4161><dis_4632><dis_7989><dis_6015><dis_0318><dis_5384><dis_4691><dis_7624><dis_5027><dis_5381><dis_6197><dis_7080><dis_4603><dis_1356><dis_1472><dis_7148><dis_5287><dis_4603><dis_2158><dis_5381><dis_8017><dis_3145><dis_7417><dis_5791><dis_2763><dis_4451><dis_6119><dis_6334><dis_7989><dis_1670><dis_4738><dis_4363><dis_4321><dis_4321><dis_2682><dis_4805><dis_1670><dis_1080><dis_7174><dis_6969><dis_8183><dis_0683><dis_7899><dis_4867><dis_6593><dis_7815><dis_6496><dis_1175><dis_7080><dis_1957><dis_4867><dis_5650><dis_1421><dis_5018><dis_8149><dis_4007><dis_1336><dis_0318><dis_4363><dis_7969><dis_7969><dis_7188><dis_7227><dis_0174><dis_2056><dis_7066><dis_5056><dis_0103><dis_2918><dis_0711><dis_0174><dis_7664><dis_7283><dis_5393><dis_0280><dis_7080><dis_4867><dis_1339><dis_1313><dis_1052><dis_4603><dis_7613><dis_8035><dis_7763><dis_7276><dis_3539><dis_3298><dis_3664><dis_0599><dis_7078><dis_3135><dis_5056><dis_4321><dis_4800><dis_1318><dis_7188><dis_3457><dis_3307><dis_6496><dis_6998><dis_6998><dis_0318><dis_5016><dis_1950><dis_4044><dis_4040><dis_2825><dis_4867><dis_6197><dis_1175><dis_6277><dis_3307><dis_5777><dis_6158><dis_2158><dis_0561><dis_0546><dis_1069><dis_5217><dis_5259><dis_4738><dis_1559><dis_7890><dis_6796><dis_2237><dis_4363><dis_3711><dis_1341><dis_7344><dis_3395><dis_4465><dis_3764><dis_7515><dis_0854><dis_4920><dis_7619><dis_1271><dis_7932><dis_1630><dis_2825><dis_6540><dis_1175><dis_5454><dis_5481><dis_1472><dis_6651><dis_4970><dis_3488><dis_0680><dis_0853><dis_3627><dis_7080><dis_2394><dis_6286><dis_1991><dis_0854><dis_3847><dis_7613><dis_1039><dis_7989><dis_4738><dis_6194><dis_2727><dis_1846><dis_7890><dis_7844><dis_1241><dis_6081><dis_2276><dis_4060><dis_0808><dis_5228><dis_7652><dis_1643><dis_6121><dis_1694><dis_5311><dis_8188><dis_0511><dis_0340><dis_6621><dis_5553><dis_0432><dis_4450><dis_4161><dis_5047><dis_3645><dis_3148><dis_6814><dis_2089><dis_4161><dis_2682><dis_3217><dis_5056><dis_2009><dis_4465><dis_5016><dis_2004><dis_3395><dis_4161><dis_2276><dis_0658><dis_7772><dis_0628><dis_2479><dis_7075><dis_2235><dis_7554><dis_6251><dis_3272><dis_1469><dis_0820><dis_5194><dis_3378><dis_6156><dis_6036><dis_7821><dis_6119><dis_2230><dis_7788><dis_4619><dis_3893><dis_1016><dis_1554><dis_5449><dis_0892><dis_0356><dis_1168><dis_0658><dis_2294><dis_7258><dis_0356><dis_3879><dis_5062><dis_5543><dis_3766><dis_3015><dis_6787><dis_4323><dis_4931><dis_2599><dis_1121><dis_7601><dis_1914><|img_end|>\n"
     ]
    }
   ],
   "source": [
    "print(image_text_dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85037f9a-91aa-4991-ae41-251e926343b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<|begin_of_text|>',\n",
       " 'DESCRIPTION',\n",
       " ':Ċ',\n",
       " 'ĠĠĠ',\n",
       " 'ĠNo',\n",
       " 'ĠCh',\n",
       " 'oc',\n",
       " 'ĠEaster',\n",
       " 'ĠGifts',\n",
       " 'Ġfor',\n",
       " 'ĠBabies',\n",
       " 'ĠFirst',\n",
       " 'ĠEaster',\n",
       " 'ĠShoes',\n",
       " 'Ċ',\n",
       " 'ĠĠĠ',\n",
       " 'ĠIMAGE',\n",
       " ':Ċ',\n",
       " 'ĠĠĠĠ',\n",
       " '<|img_start|>',\n",
       " '<dis_1540>',\n",
       " '<dis_7914>',\n",
       " '<dis_5946>',\n",
       " '<dis_8063>',\n",
       " '<dis_4710>',\n",
       " '<dis_2294>',\n",
       " '<dis_7914>',\n",
       " '<dis_5679>',\n",
       " '<dis_7136>',\n",
       " '<dis_7254>',\n",
       " '<dis_6156>',\n",
       " '<dis_5146>',\n",
       " '<dis_5449>',\n",
       " '<dis_4710>',\n",
       " '<dis_7874>',\n",
       " '<dis_5146>',\n",
       " '<dis_5146>',\n",
       " '<dis_4710>',\n",
       " '<dis_1595>',\n",
       " '<dis_1022>',\n",
       " '<dis_2921>',\n",
       " '<dis_0711>',\n",
       " '<dis_4710>',\n",
       " '<dis_0726>',\n",
       " '<dis_4145>',\n",
       " '<dis_0513>',\n",
       " '<dis_6015>',\n",
       " '<dis_1313>',\n",
       " '<dis_6156>',\n",
       " '<dis_7914>',\n",
       " '<dis_0658>',\n",
       " '<dis_1292>',\n",
       " '<dis_1022>',\n",
       " '<dis_7874>',\n",
       " '<dis_7150>',\n",
       " '<dis_7150>',\n",
       " '<dis_5146>',\n",
       " '<dis_4008>',\n",
       " '<dis_4008>',\n",
       " '<dis_7874>',\n",
       " '<dis_5864>',\n",
       " '<dis_1635>',\n",
       " '<dis_4998>',\n",
       " '<dis_4178>',\n",
       " '<dis_7466>',\n",
       " '<dis_5232>',\n",
       " '<dis_0915>',\n",
       " '<dis_4879>',\n",
       " '<dis_2460>',\n",
       " '<dis_7626>',\n",
       " '<dis_5320>',\n",
       " '<dis_4998>',\n",
       " '<dis_0280>',\n",
       " '<dis_0915>',\n",
       " '<dis_4920>',\n",
       " '<dis_4831>',\n",
       " '<dis_7914>',\n",
       " '<dis_7601>',\n",
       " '<dis_4920>',\n",
       " '<dis_0252>',\n",
       " '<dis_5146>',\n",
       " '<dis_5146>',\n",
       " '<dis_4632>',\n",
       " '<dis_2294>',\n",
       " '<dis_1833>',\n",
       " '<dis_4710>',\n",
       " '<dis_5146>',\n",
       " '<dis_5146>',\n",
       " '<dis_5146>',\n",
       " '<dis_2119>',\n",
       " '<dis_7150>',\n",
       " '<dis_4710>',\n",
       " '<dis_5146>',\n",
       " '<dis_4145>',\n",
       " '<dis_1191>',\n",
       " '<dis_2119>',\n",
       " '<dis_5481>',\n",
       " '<dis_5246>',\n",
       " '<dis_4368>',\n",
       " '<dis_7427>',\n",
       " '<dis_7855>',\n",
       " '<dis_0122>',\n",
       " '<dis_3258>',\n",
       " '<dis_4300>',\n",
       " '<dis_4024>',\n",
       " '<dis_2997>',\n",
       " '<dis_5300>',\n",
       " '<dis_4019>',\n",
       " '<dis_7544>',\n",
       " '<dis_5414>',\n",
       " '<dis_6021>',\n",
       " '<dis_2495>',\n",
       " '<dis_5798>',\n",
       " '<dis_5146>',\n",
       " '<dis_5146>',\n",
       " '<dis_7365>',\n",
       " '<dis_0971>',\n",
       " '<dis_6516>',\n",
       " '<dis_7728>',\n",
       " '<dis_2119>',\n",
       " '<dis_0711>',\n",
       " '<dis_0711>',\n",
       " '<dis_4512>',\n",
       " '<dis_5146>',\n",
       " '<dis_4998>',\n",
       " '<dis_6002>',\n",
       " '<dis_6544>',\n",
       " '<dis_4920>',\n",
       " '<dis_5507>',\n",
       " '<dis_5801>',\n",
       " '<dis_1352>',\n",
       " '<dis_1555>',\n",
       " '<dis_3703>',\n",
       " '<dis_2799>',\n",
       " '<dis_1328>',\n",
       " '<dis_3258>',\n",
       " '<dis_2170>',\n",
       " '<dis_1603>',\n",
       " '<dis_3524>',\n",
       " '<dis_5684>',\n",
       " '<dis_5891>',\n",
       " '<dis_6768>',\n",
       " '<dis_7246>',\n",
       " '<dis_3831>',\n",
       " '<dis_2997>',\n",
       " '<dis_4539>',\n",
       " '<dis_4512>',\n",
       " '<dis_1168>',\n",
       " '<dis_0726>',\n",
       " '<dis_2119>',\n",
       " '<dis_2119>',\n",
       " '<dis_2119>',\n",
       " '<dis_6998>',\n",
       " '<dis_2366>',\n",
       " '<dis_4178>',\n",
       " '<dis_0398>',\n",
       " '<dis_6002>',\n",
       " '<dis_1680>',\n",
       " '<dis_4920>',\n",
       " '<dis_5507>',\n",
       " '<dis_6708>',\n",
       " '<dis_1348>',\n",
       " '<dis_6228>',\n",
       " '<dis_7065>',\n",
       " '<dis_6358>',\n",
       " '<dis_4558>',\n",
       " '<dis_0124>',\n",
       " '<dis_1518>',\n",
       " '<dis_2077>',\n",
       " '<dis_3240>',\n",
       " '<dis_7336>',\n",
       " '<dis_3825>',\n",
       " '<dis_0780>',\n",
       " '<dis_0429>',\n",
       " '<dis_4028>',\n",
       " '<dis_5414>',\n",
       " '<dis_4618>',\n",
       " '<dis_5798>',\n",
       " '<dis_4710>',\n",
       " '<dis_6115>',\n",
       " '<dis_0356>',\n",
       " '<dis_4710>',\n",
       " '<dis_5146>',\n",
       " '<dis_5384>',\n",
       " '<dis_5607>',\n",
       " '<dis_1080>',\n",
       " '<dis_3668>',\n",
       " '<dis_5966>',\n",
       " '<dis_3396>',\n",
       " '<dis_5062>',\n",
       " '<dis_5067>',\n",
       " '<dis_1661>',\n",
       " '<dis_1555>',\n",
       " '<dis_4552>',\n",
       " '<dis_0573>',\n",
       " '<dis_6980>',\n",
       " '<dis_6704>',\n",
       " '<dis_2066>',\n",
       " '<dis_5229>',\n",
       " '<dis_3792>',\n",
       " '<dis_1358>',\n",
       " '<dis_4060>',\n",
       " '<dis_5728>',\n",
       " '<dis_1556>',\n",
       " '<dis_7141>',\n",
       " '<dis_2429>',\n",
       " '<dis_0698>',\n",
       " '<dis_0339>',\n",
       " '<dis_6364>',\n",
       " '<dis_5146>',\n",
       " '<dis_1052>',\n",
       " '<dis_5449>',\n",
       " '<dis_0320>',\n",
       " '<dis_0256>',\n",
       " '<dis_7728>',\n",
       " '<dis_7728>',\n",
       " '<dis_7728>',\n",
       " '<dis_4258>',\n",
       " '<dis_5744>',\n",
       " '<dis_6950>',\n",
       " '<dis_7675>',\n",
       " '<dis_4751>',\n",
       " '<dis_6512>',\n",
       " '<dis_1555>',\n",
       " '<dis_1555>',\n",
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       " '<dis_3395>',\n",
       " '<dis_4161>',\n",
       " ...]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llama_tokenizer.tokenize(image_text_dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4495b402-95be-48c2-8829-f240ac883969",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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