diff --git "a/helloithere" "b/helloithere"
deleted file mode 100644--- "a/helloithere"
+++ /dev/null
@@ -1,2643 +0,0 @@
-{
- "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": [
-    {
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-       " '<dis_0542>': 128800,\n",
-       " '<dis_0543>': 128801,\n",
-       " '<dis_0544>': 128802,\n",
-       " '<dis_0545>': 128803,\n",
-       " '<dis_0546>': 128804,\n",
-       " '<dis_0547>': 128805,\n",
-       " '<dis_0548>': 128806,\n",
-       " '<dis_0549>': 128807,\n",
-       " '<dis_0550>': 128808,\n",
-       " '<dis_0551>': 128809,\n",
-       " '<dis_0552>': 128810,\n",
-       " '<dis_0553>': 128811,\n",
-       " '<dis_0554>': 128812,\n",
-       " '<dis_0555>': 128813,\n",
-       " '<dis_0556>': 128814,\n",
-       " '<dis_0557>': 128815,\n",
-       " '<dis_0558>': 128816,\n",
-       " '<dis_0559>': 128817,\n",
-       " '<dis_0560>': 128818,\n",
-       " '<dis_0561>': 128819,\n",
-       " '<dis_0562>': 128820,\n",
-       " '<dis_0563>': 128821,\n",
-       " '<dis_0564>': 128822,\n",
-       " '<dis_0565>': 128823,\n",
-       " '<dis_0566>': 128824,\n",
-       " '<dis_0567>': 128825,\n",
-       " '<dis_0568>': 128826,\n",
-       " '<dis_0569>': 128827,\n",
-       " '<dis_0570>': 128828,\n",
-       " '<dis_0571>': 128829,\n",
-       " '<dis_0572>': 128830,\n",
-       " '<dis_0573>': 128831,\n",
-       " '<dis_0574>': 128832,\n",
-       " '<dis_0575>': 128833,\n",
-       " '<dis_0576>': 128834,\n",
-       " '<dis_0577>': 128835,\n",
-       " '<dis_0578>': 128836,\n",
-       " '<dis_0579>': 128837,\n",
-       " '<dis_0580>': 128838,\n",
-       " '<dis_0581>': 128839,\n",
-       " '<dis_0582>': 128840,\n",
-       " '<dis_0583>': 128841,\n",
-       " '<dis_0584>': 128842,\n",
-       " '<dis_0585>': 128843,\n",
-       " '<dis_0586>': 128844,\n",
-       " '<dis_0587>': 128845,\n",
-       " '<dis_0588>': 128846,\n",
-       " '<dis_0589>': 128847,\n",
-       " '<dis_0590>': 128848,\n",
-       " '<dis_0591>': 128849,\n",
-       " '<dis_0592>': 128850,\n",
-       " '<dis_0593>': 128851,\n",
-       " '<dis_0594>': 128852,\n",
-       " '<dis_0595>': 128853,\n",
-       " '<dis_0596>': 128854,\n",
-       " '<dis_0597>': 128855,\n",
-       " '<dis_0598>': 128856,\n",
-       " '<dis_0599>': 128857,\n",
-       " '<dis_0600>': 128858,\n",
-       " '<dis_0601>': 128859,\n",
-       " '<dis_0602>': 128860,\n",
-       " '<dis_0603>': 128861,\n",
-       " '<dis_0604>': 128862,\n",
-       " '<dis_0605>': 128863,\n",
-       " '<dis_0606>': 128864,\n",
-       " '<dis_0607>': 128865,\n",
-       " '<dis_0608>': 128866,\n",
-       " '<dis_0609>': 128867,\n",
-       " '<dis_0610>': 128868,\n",
-       " '<dis_0611>': 128869,\n",
-       " '<dis_0612>': 128870,\n",
-       " '<dis_0613>': 128871,\n",
-       " '<dis_0614>': 128872,\n",
-       " '<dis_0615>': 128873,\n",
-       " '<dis_0616>': 128874,\n",
-       " '<dis_0617>': 128875,\n",
-       " '<dis_0618>': 128876,\n",
-       " '<dis_0619>': 128877,\n",
-       " '<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": 8,
-   "id": "3ca6861c-4182-4d23-984f-92e0b5ba22f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "Key <|img_start|> is not a special token",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mllama_tokenizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_special_tokens\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m<|img_start|>\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43masdf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:947\u001b[0m, in \u001b[0;36mSpecialTokensMixin.add_special_tokens\u001b[0;34m(self, special_tokens_dict, replace_additional_special_tokens)\u001b[0m\n\u001b[1;32m    945\u001b[0m added_tokens \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    946\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m special_tokens_dict\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 947\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mSPECIAL_TOKENS_ATTRIBUTES, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKey \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not a special token\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    949\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m    950\u001b[0m         logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAssigning \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m key of the tokenizer\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mAssertionError\u001b[0m: Key <|img_start|> is not a special token"
-     ]
-    }
-   ],
-   "source": [
-    "llama_tokenizer.regist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaf0a3d1-dd5d-402b-9802-b692921c9079",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llama_tokenizer.add_special_tokens({\"image start\":\"<|img_start|>\"})\n",
-    "llama_tokenizer.add_special_tokens(\"<|img_end|>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "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": 56,
-   "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": 67,
-   "id": "8919fa2e-4e07-48d9-876f-c033a0fd1ab8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image_text_dataset = [] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "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": 74,
-   "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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30><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": 75,
-   "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",
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-       " '<dis_2026>',\n",
-       " '<dis_5250>',\n",
-       " '<dis_3805>',\n",
-       " '<dis_5998>',\n",
-       " '<dis_0820>',\n",
-       " '<dis_0107>',\n",
-       " '<dis_5062>',\n",
-       " '<dis_6334>',\n",
-       " '<dis_4161>',\n",
-       " '<dis_4632>',\n",
-       " '<dis_7989>',\n",
-       " '<dis_6015>',\n",
-       " '<dis_0318>',\n",
-       " '<dis_5384>',\n",
-       " '<dis_4691>',\n",
-       " '<dis_7624>',\n",
-       " '<dis_5027>',\n",
-       " '<dis_5381>',\n",
-       " '<dis_6197>',\n",
-       " '<dis_7080>',\n",
-       " '<dis_4603>',\n",
-       " '<dis_1356>',\n",
-       " '<dis_1472>',\n",
-       " '<dis_7148>',\n",
-       " '<dis_5287>',\n",
-       " '<dis_4603>',\n",
-       " '<dis_2158>',\n",
-       " '<dis_5381>',\n",
-       " '<dis_8017>',\n",
-       " '<dis_3145>',\n",
-       " '<dis_7417>',\n",
-       " '<dis_5791>',\n",
-       " '<dis_2763>',\n",
-       " '<dis_4451>',\n",
-       " '<dis_6119>',\n",
-       " '<dis_6334>',\n",
-       " '<dis_7989>',\n",
-       " '<dis_1670>',\n",
-       " '<dis_4738>',\n",
-       " '<dis_4363>',\n",
-       " '<dis_4321>',\n",
-       " '<dis_4321>',\n",
-       " '<dis_2682>',\n",
-       " '<dis_4805>',\n",
-       " '<dis_1670>',\n",
-       " '<dis_1080>',\n",
-       " '<dis_7174>',\n",
-       " '<dis_6969>',\n",
-       " '<dis_8183>',\n",
-       " '<dis_0683>',\n",
-       " '<dis_7899>',\n",
-       " '<dis_4867>',\n",
-       " '<dis_6593>',\n",
-       " '<dis_7815>',\n",
-       " '<dis_6496>',\n",
-       " '<dis_1175>',\n",
-       " '<dis_7080>',\n",
-       " '<dis_1957>',\n",
-       " '<dis_4867>',\n",
-       " '<dis_5650>',\n",
-       " '<dis_1421>',\n",
-       " '<dis_5018>',\n",
-       " '<dis_8149>',\n",
-       " '<dis_4007>',\n",
-       " '<dis_1336>',\n",
-       " '<dis_0318>',\n",
-       " '<dis_4363>',\n",
-       " '<dis_7969>',\n",
-       " '<dis_7969>',\n",
-       " '<dis_7188>',\n",
-       " '<dis_7227>',\n",
-       " '<dis_0174>',\n",
-       " '<dis_2056>',\n",
-       " '<dis_7066>',\n",
-       " '<dis_5056>',\n",
-       " '<dis_0103>',\n",
-       " '<dis_2918>',\n",
-       " '<dis_0711>',\n",
-       " '<dis_0174>',\n",
-       " '<dis_7664>',\n",
-       " '<dis_7283>',\n",
-       " '<dis_5393>',\n",
-       " '<dis_0280>',\n",
-       " '<dis_7080>',\n",
-       " '<dis_4867>',\n",
-       " '<dis_1339>',\n",
-       " '<dis_1313>',\n",
-       " '<dis_1052>',\n",
-       " '<dis_4603>',\n",
-       " '<dis_7613>',\n",
-       " '<dis_8035>',\n",
-       " '<dis_7763>',\n",
-       " '<dis_7276>',\n",
-       " '<dis_3539>',\n",
-       " '<dis_3298>',\n",
-       " '<dis_3664>',\n",
-       " '<dis_0599>',\n",
-       " '<dis_7078>',\n",
-       " '<dis_3135>',\n",
-       " '<dis_5056>',\n",
-       " '<dis_4321>',\n",
-       " '<dis_4800>',\n",
-       " '<dis_1318>',\n",
-       " '<dis_7188>',\n",
-       " '<dis_3457>',\n",
-       " '<dis_3307>',\n",
-       " '<dis_6496>',\n",
-       " '<dis_6998>',\n",
-       " '<dis_6998>',\n",
-       " '<dis_0318>',\n",
-       " '<dis_5016>',\n",
-       " '<dis_1950>',\n",
-       " '<dis_4044>',\n",
-       " '<dis_4040>',\n",
-       " '<dis_2825>',\n",
-       " '<dis_4867>',\n",
-       " '<dis_6197>',\n",
-       " '<dis_1175>',\n",
-       " '<dis_6277>',\n",
-       " '<dis_3307>',\n",
-       " '<dis_5777>',\n",
-       " '<dis_6158>',\n",
-       " '<dis_2158>',\n",
-       " '<dis_0561>',\n",
-       " '<dis_0546>',\n",
-       " '<dis_1069>',\n",
-       " '<dis_5217>',\n",
-       " '<dis_5259>',\n",
-       " '<dis_4738>',\n",
-       " '<dis_1559>',\n",
-       " '<dis_7890>',\n",
-       " '<dis_6796>',\n",
-       " '<dis_2237>',\n",
-       " '<dis_4363>',\n",
-       " '<dis_3711>',\n",
-       " '<dis_1341>',\n",
-       " '<dis_7344>',\n",
-       " '<dis_3395>',\n",
-       " '<dis_4465>',\n",
-       " '<dis_3764>',\n",
-       " '<dis_7515>',\n",
-       " '<dis_0854>',\n",
-       " '<dis_4920>',\n",
-       " '<dis_7619>',\n",
-       " '<dis_1271>',\n",
-       " '<dis_7932>',\n",
-       " '<dis_1630>',\n",
-       " '<dis_2825>',\n",
-       " '<dis_6540>',\n",
-       " '<dis_1175>',\n",
-       " '<dis_5454>',\n",
-       " '<dis_5481>',\n",
-       " '<dis_1472>',\n",
-       " '<dis_6651>',\n",
-       " '<dis_4970>',\n",
-       " '<dis_3488>',\n",
-       " '<dis_0680>',\n",
-       " '<dis_0853>',\n",
-       " '<dis_3627>',\n",
-       " '<dis_7080>',\n",
-       " '<dis_2394>',\n",
-       " '<dis_6286>',\n",
-       " '<dis_1991>',\n",
-       " '<dis_0854>',\n",
-       " '<dis_3847>',\n",
-       " '<dis_7613>',\n",
-       " '<dis_1039>',\n",
-       " '<dis_7989>',\n",
-       " '<dis_4738>',\n",
-       " '<dis_6194>',\n",
-       " '<dis_2727>',\n",
-       " '<dis_1846>',\n",
-       " '<dis_7890>',\n",
-       " '<dis_7844>',\n",
-       " '<dis_1241>',\n",
-       " '<dis_6081>',\n",
-       " '<dis_2276>',\n",
-       " '<dis_4060>',\n",
-       " '<dis_0808>',\n",
-       " '<dis_5228>',\n",
-       " '<dis_7652>',\n",
-       " '<dis_1643>',\n",
-       " '<dis_6121>',\n",
-       " '<dis_1694>',\n",
-       " '<dis_5311>',\n",
-       " '<dis_8188>',\n",
-       " '<dis_0511>',\n",
-       " '<dis_0340>',\n",
-       " '<dis_6621>',\n",
-       " '<dis_5553>',\n",
-       " '<dis_0432>',\n",
-       " '<dis_4450>',\n",
-       " '<dis_4161>',\n",
-       " '<dis_5047>',\n",
-       " '<dis_3645>',\n",
-       " '<dis_3148>',\n",
-       " '<dis_6814>',\n",
-       " '<dis_2089>',\n",
-       " '<dis_4161>',\n",
-       " '<dis_2682>',\n",
-       " '<dis_3217>',\n",
-       " '<dis_5056>',\n",
-       " '<dis_2009>',\n",
-       " '<dis_4465>',\n",
-       " '<dis_5016>',\n",
-       " '<dis_2004>',\n",
-       " '<dis_3395>',\n",
-       " '<dis_4161>',\n",
-       " ...]"
-      ]
-     },
-     "execution_count": 75,
-     "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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