diff --git "a/encoding/vqgan-jax-encoding-yfcc100m.ipynb" "b/encoding/vqgan-jax-encoding-yfcc100m.ipynb"
--- "a/encoding/vqgan-jax-encoding-yfcc100m.ipynb"
+++ "b/encoding/vqgan-jax-encoding-yfcc100m.ipynb"
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 92,
    "id": "3b59489e",
    "metadata": {},
    "outputs": [],
@@ -38,6 +38,7 @@
     "from torchvision.transforms import InterpolationMode\n",
     "from torch.utils.data import Dataset, DataLoader\n",
     "from torchvision.datasets.folder import default_loader\n",
+    "import os\n",
     "\n",
     "import jax\n",
     "from jax import pmap"
@@ -61,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 93,
    "id": "2ca50dc7",
    "metadata": {},
    "outputs": [],
@@ -77,22 +78,22 @@
     "We'll use a VQGAN trained by using Taming Transformers and converted to a JAX model."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ad05a1bd",
-   "metadata": {},
-   "source": [
-    "**Disabling** Does not work in my local system right now."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 167,
    "id": "29ce8b15",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working with z of shape (1, 256, 16, 16) = 65536 dimensions.\n"
+     ]
+    }
+   ],
    "source": [
-    "#model = VQModel.from_pretrained(\"flax-community/vqgan_f16_16384\")"
+    "model = VQModel.from_pretrained(\"flax-community/vqgan_f16_16384\")"
    ]
   },
   {
@@ -105,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 94,
    "id": "33861477",
    "metadata": {},
    "outputs": [],
@@ -116,16 +117,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 134,
    "id": "81b19eca",
    "metadata": {},
    "outputs": [],
    "source": [
-    "yfcc100m = Path('/sddata/dalle-mini/YFCC100M_OpenAI_subset')\n",
+    "yfcc100m = Path('/home/khali/TPU-Test/YFCC100M_OpenAI_subset')\n",
     "# Images are 'sharded' from the following directory\n",
-    "yfcc100m_images = yfcc100m/'data'/'images'\n",
+    "yfcc100m_images = yfcc100m/'data'/'data'/'images'\n",
     "yfcc100m_metadata = yfcc100m/'metadata_YFCC100M.jsonl'\n",
-    "yfcc100m_output = yfcc100m/'metadata_encoded.jsonl'"
+    "yfcc100m_output = yfcc100m/'metadata_encoded.tsv'"
    ]
   },
   {
@@ -146,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 96,
    "id": "7811648c",
    "metadata": {},
    "outputs": [],
@@ -157,1255 +158,803 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "id": "753659fe",
+   "execution_count": 10,
+   "id": "4811a230",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using custom data configuration default-57592e8ed16d752b\n",
-      "Reusing dataset json (/home/pedro/.cache/huggingface/datasets/json/default-57592e8ed16d752b/0.0.0/793d004298099bd3c4e61eb7878475bcf1dc212bf2e34437d85126758720d7f9)\n"
+      "tcmalloc: large alloc 1254047744 bytes == 0xb2b08000 @  0x7f9e78632680 0x7f9e78653824 0x585b92 0x504d56 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56 0x56acb6 0x5f5956 0x56aadf 0x5f5956 0x56acb6 0x568d9a 0x5f5b33 0x50b7f8 0x5f2702 0x56c332\n",
+      "tcmalloc: large alloc 1254047744 bytes == 0xfd74e000 @  0x7f9e78632680 0x7f9e78653824 0x590214 0x586f90 0x56e1f3 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56 0x56acb6 0x5f5956 0x56aadf 0x5f5956 0x56acb6 0x568d9a 0x5f5b33 0x50b7f8 0x5f2702 0x56c332\n",
+      "tcmalloc: large alloc 5016190976 bytes == 0x148b42000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5019099136 bytes == 0x273f12000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5019811840 bytes == 0x39f9a8000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5024571392 bytes == 0x4cb4ec000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5021097984 bytes == 0x4cb4ec000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5022818304 bytes == 0x4cb4ec000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5020794880 bytes == 0x4cb4ec000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5019451392 bytes == 0x39f9a8000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5020565504 bytes == 0x4cb4ec000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5012561920 bytes == 0x273f12000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5021835264 bytes == 0x5f6cba000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n",
+      "tcmalloc: large alloc 5017436160 bytes == 0x273f12000 @  0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n"
      ]
     }
    ],
    "source": [
-    "dataset = load_dataset(\"json\", data_files=[str(yfcc100m_metadata)])"
+    "# The metadata is too bog to load into memory at once, so chopping it into chunks\n",
+    "chunk_size=1000000\n",
+    "batch_no=1\n",
+    "for chunk in pd.read_json(yfcc100m_metadata, orient=\"records\", lines=True,chunksize=chunk_size):\n",
+    "    chunk.to_csv('./chunks/chunk'+str(batch_no)+'.tsv', sep=\"\\t\", index=False)\n",
+    "    batch_no+=1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "9343df1b",
+   "execution_count": 25,
+   "id": "46b2f083",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
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+       "      <td>An ornate Roman urn</td>\n",
+       "      <td>Photographed at the Walters Art Museum, Baltim...</td>\n",
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+       "      <td>Jai+%26+Tara+on+the+Cumberland</td>\n",
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+       "      <td>blue+heron,cumberland+river,jai,tara,tennessee</td>\n",
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+       "      <td>Another trip for the happy couple.</td>\n",
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+       "      <td>Tabular</td>\n",
+       "      <td>cameraphone,moblog,unfound</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>http://creativecommons.org/licenses/by-nc-sa/2.0/</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>663e0d8b3d</td>\n",
+       "      <td>663e0d8b3d</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>d29abf32c4e12ff881f975b70e0cec0</td>\n",
+       "      <td>A Picture Share!</td>\n",
+       "      <td>Tabular</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999995</th>\n",
+       "      <td>4648651054</td>\n",
+       "      <td>24511045@N04</td>\n",
+       "      <td>mtfrazier</td>\n",
+       "      <td>2010-05-02 15:47:45.0</td>\n",
+       "      <td>1275083371</td>\n",
+       "      <td>Canon+EOS+50D</td>\n",
+       "      <td>U.S.+Navy+Blue+Angels%3A+2010</td>\n",
+       "      <td>2+May+2010%0ASunday%0ASt.+Joseph%2C+Missouri</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>http://creativecommons.org/licenses/by-nc-nd/2.0/</td>\n",
+       "      <td>4072</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2d12d73fb0</td>\n",
+       "      <td>dd5856ea42</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60fa2911cb81eb25b356e9fee978aef</td>\n",
+       "      <td>U.S. Navy Blue Angels: 2010</td>\n",
+       "      <td>2 May 2010 Sunday St. Joseph, Missouri</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999996</th>\n",
+       "      <td>4652130996</td>\n",
+       "      <td>21963865@N04</td>\n",
+       "      <td>GRAB1.0</td>\n",
+       "      <td>2010-05-29 19:23:10.0</td>\n",
+       "      <td>1275200833</td>\n",
+       "      <td>SONY+DSLR-A230</td>\n",
+       "      <td>Attempts+on+Her+Life</td>\n",
+       "      <td>BAPA+1+production+of+Martin+Crimp%27s+Attempts...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>http://creativecommons.org/licenses/by-nc-nd/2.0/</td>\n",
+       "      <td>4003</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8889121579</td>\n",
+       "      <td>2f46599456</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60f5ef5ce4c2d24566226abebd67d4</td>\n",
+       "      <td>Attempts on Her Life</td>\n",
+       "      <td>BAPA 1 production of Martin Crimp's Attempts o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999997</th>\n",
+       "      <td>4652568339</td>\n",
+       "      <td>64025277@N00</td>\n",
+       "      <td>1Sock</td>\n",
+       "      <td>2010-05-13 15:38:37.0</td>\n",
+       "      <td>1275234267</td>\n",
+       "      <td>Canon+EOS+DIGITAL+REBEL+XT</td>\n",
+       "      <td>Carlsbad+Caverns+3</td>\n",
+       "      <td>%E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%...</td>\n",
+       "      <td>carlsbad,carlsbad+caverns,cave,faa,new+mexico,...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>http://creativecommons.org/licenses/by-nc-nd/2.0/</td>\n",
+       "      <td>4010</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0a1808a69e</td>\n",
+       "      <td>cf6d348e3d</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60f029482d1d1028fda5281daf498f</td>\n",
+       "      <td>Carlsbad Caverns 3</td>\n",
+       "      <td>♥♥♥♥♥♥♥ Interested in purchasing this photogra...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999998</th>\n",
+       "      <td>4653110895</td>\n",
+       "      <td>20483509@N00</td>\n",
+       "      <td>subberculture</td>\n",
+       "      <td>2010-05-30 15:37:05.0</td>\n",
+       "      <td>1275245596</td>\n",
+       "      <td>Canon+DIGITAL+IXUS+40</td>\n",
+       "      <td>Want</td>\n",
+       "      <td>Isn%27t+that+gorgeous%3F</td>\n",
+       "      <td>2010,edinburgh+museum,may,phonebox,wood</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>http://creativecommons.org/licenses/by-sa/2.0/</td>\n",
+       "      <td>4066</td>\n",
+       "      <td>5</td>\n",
+       "      <td>77c3b3a254</td>\n",
+       "      <td>c4697e1511</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60f72775f433cf8de3efaeb431866153</td>\n",
+       "      <td>Want</td>\n",
+       "      <td>Isn't that gorgeous?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999999</th>\n",
+       "      <td>4655503987</td>\n",
+       "      <td>8457193@N07</td>\n",
+       "      <td>zackojones</td>\n",
+       "      <td>2010-05-30 15:34:58.0</td>\n",
+       "      <td>1275310230</td>\n",
+       "      <td>Canon+EOS+7D</td>\n",
+       "      <td>Summertime</td>\n",
+       "      <td>You+gotta+love+it%21</td>\n",
+       "      <td>georgia,savannah,united+states,us</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>http://creativecommons.org/licenses/by-nc-sa/2.0/</td>\n",
+       "      <td>4043</td>\n",
+       "      <td>5</td>\n",
+       "      <td>caff543bfe</td>\n",
+       "      <td>f60952ac4d</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60f687e11b913bce461e9525d8047e0</td>\n",
+       "      <td>Summertime</td>\n",
+       "      <td>You gotta love it!</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000000 rows × 26 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "Dataset({\n",
-       "    features: ['photoid', 'uid', 'unickname', 'datetaken', 'dateuploaded', 'capturedevice', 'title', 'description', 'usertags', 'machinetags', 'longitude', 'latitude', 'accuracy', 'pageurl', 'downloadurl', 'licensename', 'licenseurl', 'serverid', 'farmid', 'secret', 'secretoriginal', 'ext', 'marker', 'key', 'title_clean', 'description_clean'],\n",
-       "    num_rows: 14825233\n",
-       "})"
+       "           photoid              uid       unickname              datetaken  \\\n",
+       "0           137943  48600072071@N01  doctor+paradox  2004-08-01 18:13:06.0   \n",
+       "1          1246361  44124324682@N01        mharrsch  2004-11-03 23:04:02.0   \n",
+       "2          1251599  51035803024@N01         bmitd67  2004-10-30 17:09:32.0   \n",
+       "3          2348587     73621375@N00     Thom+Watson  2004-12-18 21:08:09.0   \n",
+       "4          3516047  48600072071@N01  doctor+paradox  2005-01-18 16:44:18.0   \n",
+       "...            ...              ...             ...                    ...   \n",
+       "999995  4648651054     24511045@N04       mtfrazier  2010-05-02 15:47:45.0   \n",
+       "999996  4652130996     21963865@N04         GRAB1.0  2010-05-29 19:23:10.0   \n",
+       "999997  4652568339     64025277@N00           1Sock  2010-05-13 15:38:37.0   \n",
+       "999998  4653110895     20483509@N00   subberculture  2010-05-30 15:37:05.0   \n",
+       "999999  4655503987      8457193@N07      zackojones  2010-05-30 15:34:58.0   \n",
+       "\n",
+       "        dateuploaded               capturedevice  \\\n",
+       "0         1091409186                         NaN   \n",
+       "1         1099523042                         NaN   \n",
+       "2         1099538888         Canon+PowerShot+S30   \n",
+       "3         1103497228                 SONY+DSC-W1   \n",
+       "4         1106084658                         NaN   \n",
+       "...              ...                         ...   \n",
+       "999995    1275083371               Canon+EOS+50D   \n",
+       "999996    1275200833              SONY+DSLR-A230   \n",
+       "999997    1275234267  Canon+EOS+DIGITAL+REBEL+XT   \n",
+       "999998    1275245596       Canon+DIGITAL+IXUS+40   \n",
+       "999999    1275310230                Canon+EOS+7D   \n",
+       "\n",
+       "                                  title  \\\n",
+       "0                    A+Picture+Share%21   \n",
+       "1                   An+ornate+Roman+urn   \n",
+       "2        Jai+%26+Tara+on+the+Cumberland   \n",
+       "3       Castle+gate+-+%22lite-brited%22   \n",
+       "4                    A+Picture+Share%21   \n",
+       "...                                 ...   \n",
+       "999995    U.S.+Navy+Blue+Angels%3A+2010   \n",
+       "999996             Attempts+on+Her+Life   \n",
+       "999997               Carlsbad+Caverns+3   \n",
+       "999998                             Want   \n",
+       "999999                       Summertime   \n",
+       "\n",
+       "                                              description  \\\n",
+       "0                                                 Antenna   \n",
+       "1       Photographed+at+the+%3Ca+href%3D%22http%3A%2F%...   \n",
+       "2                      Another+trip+for+the+happy+couple.   \n",
+       "3       Taken+at+the+Miracle+of+Lights+display+in+Cent...   \n",
+       "4                                                 Tabular   \n",
+       "...                                                   ...   \n",
+       "999995       2+May+2010%0ASunday%0ASt.+Joseph%2C+Missouri   \n",
+       "999996  BAPA+1+production+of+Martin+Crimp%27s+Attempts...   \n",
+       "999997  %E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%...   \n",
+       "999998                           Isn%27t+that+gorgeous%3F   \n",
+       "999999                               You+gotta+love+it%21   \n",
+       "\n",
+       "                                                 usertags machinetags  ...  \\\n",
+       "0       cameraphone,cayugaheights,green,hydrant,ithaca...         NaN  ...   \n",
+       "1       ancient,baltimore,burial,death,empire,funeral,...         NaN  ...   \n",
+       "2          blue+heron,cumberland+river,jai,tara,tennessee         NaN  ...   \n",
+       "3       bullrunpark,castle,centreville,christmas,decor...         NaN  ...   \n",
+       "4                              cameraphone,moblog,unfound         NaN  ...   \n",
+       "...                                                   ...         ...  ...   \n",
+       "999995                                                NaN         NaN  ...   \n",
+       "999996                                                NaN         NaN  ...   \n",
+       "999997  carlsbad,carlsbad+caverns,cave,faa,new+mexico,...         NaN  ...   \n",
+       "999998            2010,edinburgh+museum,may,phonebox,wood         NaN  ...   \n",
+       "999999                  georgia,savannah,united+states,us         NaN  ...   \n",
+       "\n",
+       "                                               licenseurl  serverid  farmid  \\\n",
+       "0       http://creativecommons.org/licenses/by-nc-sa/2.0/         1       1   \n",
+       "1       http://creativecommons.org/licenses/by-nc-sa/2.0/         1       1   \n",
+       "2       http://creativecommons.org/licenses/by-nc-sa/2.0/         1       1   \n",
+       "3       http://creativecommons.org/licenses/by-nc-sa/2.0/         2       1   \n",
+       "4       http://creativecommons.org/licenses/by-nc-sa/2.0/         3       1   \n",
+       "...                                                   ...       ...     ...   \n",
+       "999995  http://creativecommons.org/licenses/by-nc-nd/2.0/      4072       5   \n",
+       "999996  http://creativecommons.org/licenses/by-nc-nd/2.0/      4003       5   \n",
+       "999997  http://creativecommons.org/licenses/by-nc-nd/2.0/      4010       5   \n",
+       "999998     http://creativecommons.org/licenses/by-sa/2.0/      4066       5   \n",
+       "999999  http://creativecommons.org/licenses/by-nc-sa/2.0/      4043       5   \n",
+       "\n",
+       "            secret secretoriginal  ext marker  \\\n",
+       "0       1650c7cdc6     1650c7cdc6  jpg      0   \n",
+       "1       cf37054610     cf37054610  jpg      0   \n",
+       "2       4a4234e32c     4a4234e32c  jpg      0   \n",
+       "3       7162c974c3     7162c974c3  jpg      0   \n",
+       "4       663e0d8b3d     663e0d8b3d  jpg      0   \n",
+       "...            ...            ...  ...    ...   \n",
+       "999995  2d12d73fb0     dd5856ea42  jpg      0   \n",
+       "999996  8889121579     2f46599456  jpg      0   \n",
+       "999997  0a1808a69e     cf6d348e3d  jpg      0   \n",
+       "999998  77c3b3a254     c4697e1511  jpg      0   \n",
+       "999999  caff543bfe     f60952ac4d  jpg      0   \n",
+       "\n",
+       "                                     key                   title_clean  \\\n",
+       "0        d29e7c6a3028418c64eb15e3cf577c2              A Picture Share!   \n",
+       "1        d29f01b149167d683f9ddde464bb3db           An ornate Roman urn   \n",
+       "2       d296e9e34bdae41edb6c679ff824ab2a  Jai & Tara on the Cumberland   \n",
+       "3          d29ce96395848478b1e8396e44899   Castle gate - \"lite-brited\"   \n",
+       "4        d29abf32c4e12ff881f975b70e0cec0              A Picture Share!   \n",
+       "...                                  ...                           ...   \n",
+       "999995   60fa2911cb81eb25b356e9fee978aef   U.S. Navy Blue Angels: 2010   \n",
+       "999996    60f5ef5ce4c2d24566226abebd67d4          Attempts on Her Life   \n",
+       "999997    60f029482d1d1028fda5281daf498f            Carlsbad Caverns 3   \n",
+       "999998  60f72775f433cf8de3efaeb431866153                          Want   \n",
+       "999999   60f687e11b913bce461e9525d8047e0                    Summertime   \n",
+       "\n",
+       "                                        description_clean  \n",
+       "0                                                 Antenna  \n",
+       "1       Photographed at the Walters Art Museum, Baltim...  \n",
+       "2                      Another trip for the happy couple.  \n",
+       "3       Taken at the Miracle of Lights display in Cent...  \n",
+       "4                                                 Tabular  \n",
+       "...                                                   ...  \n",
+       "999995             2 May 2010 Sunday St. Joseph, Missouri  \n",
+       "999996  BAPA 1 production of Martin Crimp's Attempts o...  \n",
+       "999997  ♥♥♥♥♥♥♥ Interested in purchasing this photogra...  \n",
+       "999998                               Isn't that gorgeous?  \n",
+       "999999                                 You gotta love it!  \n",
+       "\n",
+       "[1000000 rows x 26 columns]"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dataset = dataset['train']\n",
-    "dataset"
+    "# looking up at a chunk\n",
+    "pd.read_csv(\"./chunks/chunk1.tsv\", sep=\"\\t\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "id": "c4794c29",
+   "execution_count": 98,
+   "id": "c51c5597",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>title_clean</th>\n",
+       "      <th>description_clean</th>\n",
+       "      <th>ext</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>d29e7c6a3028418c64eb15e3cf577c2</td>\n",
+       "      <td>A Picture Share!</td>\n",
+       "      <td>Antenna</td>\n",
+       "      <td>jpg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>d29f01b149167d683f9ddde464bb3db</td>\n",
+       "      <td>An ornate Roman urn</td>\n",
+       "      <td>Photographed at the Walters Art Museum, Baltim...</td>\n",
+       "      <td>jpg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>d296e9e34bdae41edb6c679ff824ab2a</td>\n",
+       "      <td>Jai &amp; Tara on the Cumberland</td>\n",
+       "      <td>Another trip for the happy couple.</td>\n",
+       "      <td>jpg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>d29ce96395848478b1e8396e44899</td>\n",
+       "      <td>Castle gate - \"lite-brited\"</td>\n",
+       "      <td>Taken at the Miracle of Lights display in Cent...</td>\n",
+       "      <td>jpg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>d29abf32c4e12ff881f975b70e0cec0</td>\n",
+       "      <td>A Picture Share!</td>\n",
+       "      <td>Tabular</td>\n",
+       "      <td>jpg</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                key                   title_clean  \\\n",
+       "0   d29e7c6a3028418c64eb15e3cf577c2              A Picture Share!   \n",
+       "1   d29f01b149167d683f9ddde464bb3db           An ornate Roman urn   \n",
+       "2  d296e9e34bdae41edb6c679ff824ab2a  Jai & Tara on the Cumberland   \n",
+       "3     d29ce96395848478b1e8396e44899   Castle gate - \"lite-brited\"   \n",
+       "4   d29abf32c4e12ff881f975b70e0cec0              A Picture Share!   \n",
+       "\n",
+       "                                   description_clean  ext  \n",
+       "0                                            Antenna  jpg  \n",
+       "1  Photographed at the Walters Art Museum, Baltim...  jpg  \n",
+       "2                 Another trip for the happy couple.  jpg  \n",
+       "3  Taken at the Miracle of Lights display in Cent...  jpg  \n",
+       "4                                            Tabular  jpg  "
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Looking at a chunk with only the relevant columns that we need\n",
+    "df = pd.read_csv(\"./chunks/chunk1.tsv\", sep=\"\\t\")[[\"key\", \"title_clean\", \"description_clean\", \"ext\"]]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc1668f8",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def image_exists(root: str, name: str, ext: str):\n",
-    "    image_path = (Path(root)/name[0:3]/name[3:6]/name).with_suffix(ext)\n",
-    "    return image_path.exists()"
+    "### Grabbing each chunks from the folder, cleaning it up, only taking the entries which image exist and appending it to the global df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "id": "1b500078",
+   "execution_count": null,
+   "id": "abbcccf3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def select_existing_rows(examples):\n",
-    "    # Select lists we want to keep\n",
-    "    keys = examples['key']\n",
-    "    titles_clean = examples['title_clean']\n",
-    "    descriptions_clean = examples.get('description_clean', '')\n",
-    "    exts = examples['ext']\n",
-    "    \n",
-    "    result = {'key': [], 'title_clean': [], 'description_clean': [], 'ext': []}\n",
-    "    for i, image_name in enumerate(keys):\n",
-    "        print(i, image_name)\n",
-    "        if image_exists(root=str(yfcc100m_images), name=image_name, ext='.' + exts[i]):\n",
-    "            result[\"key\"].append(image_name)\n",
-    "            result[\"title_clean\"].append(titles_clean[i])\n",
-    "            result[\"description_clean\"].append(descriptions_clean[i])\n",
-    "            result[\"ext\"].append(exts[i])\n",
-    "    print(f'returning {len(result[\"key\"])}')\n",
-    "    return result"
+    "# the function that helps us to decide whether an image with certain id exists in storage, we only take the ones that we have the images for\n",
+    "def image_exists(item):\n",
+    "    name, _, _, ext, _ = item\n",
+    "    root=str(yfcc100m_images)\n",
+    "    image_path = (Path(root)/name[0:3]/name[3:6]/name).with_suffix(\".\"+ext)\n",
+    "    if image_path.exists():\n",
+    "        return True\n",
+    "    else:\n",
+    "        return None"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "id": "467378c1",
+   "execution_count": 86,
+   "id": "44fa86ab",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b72e866c3f174e9e9aa2430e204f2baf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Selecting rows with images that exist:   0%|          | 0/14826 [00:00<?, ?ba/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "858 d29098775cb7d920da1dd2d5d444df1e\n",
-      "859 d29dc3f4bf446d5bff72ccb27c631\n",
-      "860 d29ddc6413cd9fdaa9b97bfb38697166\n",
-      "861 d29a189d80fb1b8d64b2b92d6e25576\n",
-      "862 d297d4df9a1ec83dbf159293653ff71\n",
-      "863 d290b698b6489f39e8f2ad3ce4261594\n",
-      "864 d29fa9ad34a40779b3736a71e3e8956\n",
-      "865 d295d0175728998b8560507b74416d81\n",
-      "866 d295b028a2ba30598e75b46cc28feafb\n",
-      "867 d29a33b2188451fca68da55e201b57d3\n",
-      "868 d29273375d45edaa581a111ce22e5482\n",
-      "869 d29307629e27a419c4c9459a918ff6f\n",
-      "870 d296de62b4fa21342a695aebf897b4f\n",
-      "871 d29691391d561ed43b68c891792e946\n",
-      "872 d29e81413ac99fb57626e48278e783\n",
-      "873 d2925c82aa7fd47f6ea9e20f5715719\n",
-      "874 d29f6adb7938f5a11ffcd43fa17be269\n",
-      "875 d2956a499e1e38b4538a34c88af8438\n",
-      "876 d2941bd03c33d2bb4921405e52677319\n",
-      "877 d294252778388bab43954be9d64bf111\n",
-      "878 d29fd97555f88c8f56515d5ef6aa35a\n",
-      "879 d2966cf6fc7be1abf528520b894d077\n",
-      "880 d29ee684b54476792bddd1dcf5455a5d\n",
-      "881 d29be915e4b91c211a954dea9af625\n",
-      "882 d297fd4966bb91d5809164c3eed20f3\n",
-      "883 d299c65b6d9e92f0e34f3ca5452f2142\n",
-      "884 d297bce473a261d5a8fdc04b62a9b9a\n",
-      "885 d29ebb6fb2285a61486c87fff8d38e\n",
-      "886 d29f118564ee222d29141c5670a3f3cc\n",
-      "887 d29152bcecd9b6abd8cc15c33d28948\n",
-      "888 d29b5b2817bf309dcf124afcc41096\n",
-      "889 d298ebd5120ab3cf7a4184a43c5cf\n",
-      "890 d29c328293364cd58d99c33f46a7c1\n",
-      "891 d297c62a9eb99ba18b46df6c43ef0e5\n",
-      "892 d296d4312ccba6f29387ac3a67a0ce\n",
-      "893 d2972baeb06f9d30176a6a2b8f5c535\n",
-      "894 d29cb9ffbaa7fca2aa38c5aa0050a8\n",
-      "895 d2941e7858af7221c16d1bfe1792b3a\n",
-      "896 d29ed990f5cbb0eaeb1cdd1645e216cb\n",
-      "897 d29f8616ffa1c0e16437ecda5833578e\n",
-      "898 d29f264016fb567fc523f16c99dd63d\n",
-      "899 d297f883cdb8fba51734bfcb78ac\n",
-      "900 d295f5e269934ac4bf355afa329b0ac\n",
-      "901 d2961171e684b9d624ecf5439932a537\n",
-      "902 d291c634c899a6c3fe5f28c54680f4ac\n",
-      "903 d29cd457bd71c490bb53521b1730eb2c\n",
-      "904 d292daf9834286ad9850fc67f4c3d69\n",
-      "905 d29effc9b2125ce69c928f41dffefb3\n",
-      "906 d298d24472564871585443c2ba9f6625\n",
-      "907 d29d42f9314878c6bb302c1a73d6f1\n",
-      "908 d296bcc710b43b719bcdd93e2cdaf29\n",
-      "909 d29825d81e57c0c317ae93b5dbe78\n",
-      "910 d29b39adc7a9e4ef33cbd8a6ef32879\n",
-      "911 d298f52822e8a1b966f359eef53869ed\n",
-      "912 d2909f79c3d51e9b8b41366d851791\n",
-      "913 d29f4b4ed084c42652b62b0b6182269\n",
-      "914 d2952bad222361a6263b53cef5c08fd7\n",
-      "915 d29e95a3a24e6f548dc5bc66e9534ef9\n",
-      "916 d2983e18a5eb44da8b9cd2955d2598\n",
-      "917 d292380b6f7a4791e6829215b4df483\n",
-      "918 d2966824b928a21da99f327dcc25b2c\n",
-      "919 d29690a89ccff87641923adb266ace\n",
-      "920 d297d320fc6e1221f2939dead1829f1\n",
-      "921 d297dac5dd61868c16393413e9df419\n",
-      "922 d291f0fce6e2b09f27c637d1def6fda0\n",
-      "923 d2937ba495db231c9f863bdd5e2efc2\n",
-      "924 d2945f89f43315f3fcee9ccc5f14fde\n",
-      "925 d294d41c45ea9fd8cf1df22214f7f65\n",
-      "926 d2923eda9fd98f1fa0fdc85c2c6a8f58\n",
-      "927 d2919a159571de2c8ae87fcee7f72\n",
-      "928 d294bd134345a46391dcec1cd27248fd\n",
-      "929 d299b714d28830663458662681e041e4\n",
-      "930 d2994917ae56468019ace55110693b\n",
-      "931 d29638364c5166dc5ce5040424db5\n",
-      "932 d297a7b0a91ff4e9d999dfad446501d\n",
-      "933 d29883a44e13226a369554c0f826474\n",
-      "934 d291879da81e887f31e11fe0c54b69ed\n",
-      "935 d290fd3d51f8d62324b0338a84278ba8\n",
-      "936 d29465e1fe608a4bdd4b3cba5f985129\n",
-      "937 d293d623b63e47b96e812ac2fe5565f\n",
-      "938 d29fffdf16211b8d5aa41487a8daa5ca\n",
-      "939 d299fc7fb7f458ec1b976a5a52b8b04a\n",
-      "940 d296a995f653a0335e447e0f9f8804c\n",
-      "941 d296f252693c6130da6fbaadc08469\n",
-      "942 d29cc9dcde13c9371a28cc1bf9836e3c\n",
-      "943 d295918d4f51d352b3c83bdf3d16f861\n",
-      "944 d29832ee32acfc4c7b56c4d1eed42\n",
-      "945 d296ef3360d4f5ddfbd530d479d2992c\n",
-      "946 d2965113b74b1a9ec3cbc33602811e9\n",
-      "947 d2956451b5c77299969f87aea3621e3\n",
-      "948 d29ab427ff507dbbe13ae25ebbbace6f\n",
-      "949 d29a5ba29763bc916b853c15293689f\n",
-      "950 d2927f7a6056ab6be96cd0812640ce\n",
-      "951 d29ac16ee01e78164acdd4e9ae56b65c\n",
-      "952 d298f1ab24787baabadc2c79489857b\n",
-      "953 d2934db68cdb24285a4bfe4c45de83\n",
-      "954 d296a2c4fd479d35942e20779121cd2b\n",
-      "955 d292aedad670eb23c0de67d754c9f\n",
-      "956 d292f67c97843c616fe91b24b833e81\n",
-      "957 d294b46b302a24644766c7449594721f\n",
-      "958 d292961146b9cbbb547223db2a8a9\n",
-      "959 d296a012631260f8f4d62a553b79b2d9\n",
-      "960 d296fe9aac4d48e7bf61db9aac5bcb8\n",
-      "961 d29c64939a3116d25d2baea9fa5ca2\n",
-      "962 d2921bc19d4534ab7fa7a85bf67e1faa\n",
-      "963 d29e44e97f49146198417e4ab07cf7e6\n",
-      "964 d29e7f55fdb62ca7f29191e6f3551ebb\n",
-      "965 d294807c2d6877a01b863757ccbf\n",
-      "966 d29399f926878adeae85b9126c9c545\n",
-      "967 d295684772ee4705d79a7ecfa44572\n",
-      "968 d299e639d6e22972f6789e1f7613dee2\n",
-      "969 d2955e19f597df6c42b37859b59b4a\n",
-      "970 d295648026dce77c96bb4f94cb1b6ae\n",
-      "971 d296b192e72f956789e68dd798faecd\n",
-      "972 d2927984b7b4badce29cbef261244\n",
-      "973 d2981e54d04b40b869399c3ae30dea3\n",
-      "974 d29ace284cb77abebfe84a87eace985\n",
-      "975 d29f28f637ff8952889657bebddfed5\n",
-      "976 d292e945bbd333b72c4951321587958d\n",
-      "977 d29b28c6e5e48c4d898cb786c3ddc\n",
-      "978 d2919df6a0b0c198a55db2b82c9e8a\n",
-      "979 d29d73b4807db874afb1951d5c6fe58\n",
-      "980 d2998145f1a42e419e9c669f3ce36f5\n",
-      "981 d2967bcc651b29e9e7bd65fab12d5a3\n",
-      "982 d291736293c558225a0cebe457a6f2\n",
-      "983 d29e9483c1c73bda7d7d74e869b4e7e\n",
-      "984 d299d5f6b506c6236dc858da34f1cc\n",
-      "985 d2913ad1734310694a6c2c35a1c569e8\n",
-      "986 d294bdca75f6d53d497559412a7a3d\n",
-      "987 d29aecc65b7df1f508c83df595ff4e\n",
-      "988 d29cda9cb047b6bdbcd4d3b50feec7e\n",
-      "989 d29739396b17f9e255c7726de428c5f\n",
-      "990 d29b475454526ecffec9fefcf8f01c8e\n",
-      "991 d29667e51ed875183825ab53d44fa70\n",
-      "992 d297e8ed757593d67a2771257a27be4\n",
-      "993 d295c322fc9ee4dca758544c942f2d53\n",
-      "994 d298372c48d5c8aaa16ee2f3a5a5380\n",
-      "995 d2946559a807388662cd0308ad666dd\n",
-      "996 d29dcc2038b89c365b3aba17f94bf52\n",
-      "997 d29fcaee2537fda115ad172ed10778\n",
-      "998 d29ca7d044203e0242084cb958ef464\n",
-      "999 d299349d8bd55ccae1dcea12b2b7ca73\n",
-      "returning 0\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "index out of bounds",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_617634/3764770081.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m filtered_dataset = dataset.map(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mselect_existing_rows\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mremove_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mbatched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mnum_proc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/hf_jax/datasets/src/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   1655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1656\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnum_proc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnum_proc\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1657\u001b[0;31m             return self._map_single(\n\u001b[0m\u001b[1;32m   1658\u001b[0m                 \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0mwith_indices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwith_indices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/hf_jax/datasets/src/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    183\u001b[0m         }\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/hf_jax/datasets/src/datasets/fingerprint.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m             \u001b[0;31m# Call actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m             \u001b[0;31m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/hf_jax/datasets/src/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\u001b[0m\n\u001b[1;32m   2022\u001b[0m                             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2023\u001b[0m                                 \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast_to_python_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2024\u001b[0;31m                                 \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2025\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mupdate_data\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwriter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2026\u001b[0m                     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/hf_jax/datasets/src/datasets/arrow_writer.py\u001b[0m in \u001b[0;36mwrite_batch\u001b[0;34m(self, batch_examples, writer_batch_size)\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0mtyped_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOptimizedTypedSequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_examples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtry_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol_try_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0mtyped_sequence_examples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtyped_sequence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m         \u001b[0mpa_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pydict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtyped_sequence_examples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpa_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter_batch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/pyarrow/table.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.Table.from_pydict\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/pyarrow/array.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.asarray\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/pyarrow/array.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.array\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/pyarrow/array.pxi\u001b[0m in \u001b[0;36mpyarrow.lib._handle_arrow_array_protocol\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/code/hf_jax/datasets/src/datasets/arrow_writer.py\u001b[0m in \u001b[0;36m__arrow_array__\u001b[0;34m(self, type)\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mtrying_type\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_py\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m                 raise TypeError(\n\u001b[1;32m    102\u001b[0m                     \u001b[0;34m\"Specified try_type alters data. Please check that the type/feature that you provided match the type/features of the data.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/pyarrow/array.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.Array.__getitem__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/pyarrow/array.pxi\u001b[0m in \u001b[0;36mpyarrow.lib._normalize_index\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mIndexError\u001b[0m: index out of bounds"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "filtered_dataset = dataset.map(\n",
-    "    select_existing_rows,\n",
-    "    remove_columns = dataset.column_names,\n",
-    "    batched = True,\n",
-    "    num_proc = 1,\n",
-    "    desc = \"Selecting rows with images that exist\"\n",
-    ")"
+    "# This cell does it all, grabs each chunk, cleans it up based on image existing condition, etc.\n",
+    "global_df = pd.DataFrame()\n",
+    "chunks_dir = \"./chunks\"\n",
+    "for filename in os.listdir(chunks_dir):\n",
+    "        df = pd.read_csv(f\"./chunks/{str(filename)}\", sep=\"\\t\")[[\"key\", \"title_clean\", \"description_clean\", \"ext\"]]\n",
+    "        df['caption'] = df[\"title_clean\"]+\". \"+df['description_clean']\n",
+    "        df['is_exist'] = df.apply(image_exists, axis=1)\n",
+    "        df = df.dropna()[[\"key\", \"caption\"]]\n",
+    "        df.columns = ['image_file', 'caption']\n",
+    "        global_df = global_df.append(df, ignore_index=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
-   "id": "7060ff8f",
+   "execution_count": 89,
+   "id": "45024fdc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# df['image_exists'] = df.apply(lambda row: image_exists(row['key']), axis=1)"
+    "# saving the tsv to disk\n",
+    "global_df.to_csv('./chunks/YFCC_subset_clean.tsv', sep=\"\\t\", index=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
-   "id": "fecc9a00",
+   "execution_count": 101,
+   "id": "dca4eb73",
    "metadata": {},
    "outputs": [],
    "source": [
-    "image_size = 256\n",
-    "def image_transform(image):\n",
-    "    s = min(image.size)\n",
-    "    r = image_size / s\n",
-    "    s = (round(r * image.size[1]), round(r * image.size[0]))\n",
-    "    image = TF.resize(image, s, interpolation=InterpolationMode.LANCZOS)\n",
-    "    image = TF.center_crop(image, output_size = 2 * [image_size])\n",
-    "    image = torch.unsqueeze(T.ToTensor()(image), 0)\n",
-    "    image = image.permute(0, 2, 3, 1).numpy()\n",
-    "    return image"
+    "# loading the tsv from disk (for explicitness, also my electricity was gone, glad it happened after I saved to the disk :( )\n",
+    "\n",
+    "dataset = pd.read_csv(f\"./chunks/YFCC_subset_clean.tsv\", sep=\"\\t\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
-   "id": "1a065700",
+   "execution_count": 153,
+   "id": "a511264a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "class YFC100Dataset(Dataset):\n",
-    "    def __init__(self, image_list_path: str, images_root: str, image_size: int, max_items=None):\n",
+    "\"\"\"\n",
+    "Luke Melas-Kyriazi's dataset.py's modified version for YFCC\n",
+    "\"\"\"\n",
+    "import warnings\n",
+    "from typing import Optional, Callable\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "from torch.utils.data import Dataset\n",
+    "from torchvision.datasets.folder import default_loader\n",
+    "from PIL import ImageFile\n",
+    "from PIL.Image import DecompressionBombWarning\n",
+    "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
+    "warnings.filterwarnings(\"ignore\", category=DecompressionBombWarning)\n",
+    "\n",
+    "\n",
+    "class CaptionDataset(Dataset):\n",
+    "    \"\"\"\n",
+    "    A PyTorch Dataset class for (image, texts) tasks. Note that this dataset \n",
+    "    returns the raw text rather than tokens. This is done on purpose, because\n",
+    "    it's easy to tokenize a batch of text after loading it from this dataset.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, *, images_root: str, captions_path: str, text_transform: Optional[Callable] = None, \n",
+    "                 image_transform: Optional[Callable] = None, image_transform_type: str = 'torchvision',\n",
+    "                 include_captions: bool = True):\n",
     "        \"\"\"\n",
-    "        :param image_list_path: Path to a file containing a list of all images, in jsonl format.\n",
-    "        :param images_root: Root directory containing the images\n",
-    "        :param image_size: Image size. Source images will be resized and center-cropped.\n",
-    "        :max_items: Limit dataset size for debugging\n",
+    "        :param images_root: folder where images are stored\n",
+    "        :param captions_path: path to csv that maps image filenames to captions\n",
+    "        :param image_transform: image transform pipeline\n",
+    "        :param text_transform: image transform pipeline\n",
+    "        :param image_transform_type: image transform type, either `torchvision` or `albumentations`\n",
+    "        :param include_captions: Returns a dictionary with `image`, `text` if `true`; otherwise returns just the images.\n",
     "        \"\"\"\n",
-    "        self.image_list = pd.read_json(image_list_path, orient=\"records\", lines=True)\n",
+    "\n",
+    "        # Base path for images\n",
     "        self.images_root = Path(images_root)\n",
-    "        if max_items is not None: self.image_list = self.image_list[:max_items]\n",
-    "        self.image_size = image_size\n",
-    "        \n",
-    "    def __len__(self):\n",
-    "        return len(self.image_list)\n",
+    "\n",
+    "        # Load captions as DataFrame\n",
+    "        self.captions = pd.read_csv(f\"./chunks/YFCC_subset_clean.tsv\", sep=\"\\t\")\n",
+    "        self.captions['image_file'] = self.captions['image_file'].astype(str)\n",
+    "\n",
+    "        # PyTorch transformation pipeline for the image (normalizing, etc.)\n",
+    "        self.text_transform = text_transform\n",
+    "        self.image_transform = image_transform\n",
+    "        self.image_transform_type = image_transform_type.lower()\n",
+    "        assert self.image_transform_type in ['torchvision', 'albumentations']\n",
+    "\n",
+    "        # Total number of datapoints\n",
+    "        self.size = len(self.captions)\n",
+    "\n",
+    "        # Return image+captions or just images\n",
+    "        self.include_captions = include_captions\n",
     "    \n",
+    "    def image_exists(item):\n",
+    "        name, caption = item\n",
+    "        root=str(self.images_root)\n",
+    "        image_path = (Path(root)/name[0:3]/name[3:6]/name).with_suffix(\".jpg\")\n",
+    "\n",
+    "        return image_path.exists()\n",
+    "\n",
+    "    def verify_that_all_images_exist(self):\n",
+    "        for image_file in self.captions['image_file']:\n",
+    "            if not image_exists:\n",
+    "                print(f'file does not exist: {p}')\n",
+    "\n",
     "    def _get_raw_image(self, i):\n",
-    "        image_name = self.image_list.iloc[0].key\n",
-    "        image_path = (self.images_root/image_name[0:3]/image_name[3:6]/image_name).with_suffix('.jpg')\n",
-    "        return default_loader(image_path) if image_path.exists() else None\n",
+    "        name = self.captions.iloc[i]['image_file']\n",
+    "        image_path = (Path(self.images_root)/name[0:3]/name[3:6]/name).with_suffix(\".jpg\")\n",
+    "        image = default_loader(image_path)\n",
+    "        return image\n",
+    "\n",
+    "    def _get_raw_text(self, i):\n",
+    "        return self.captions.iloc[i]['caption']\n",
+    "\n",
+    "    def __getitem__(self, i):\n",
+    "        image = self._get_raw_image(i)\n",
+    "        caption = self._get_raw_text(i)\n",
+    "        if self.image_transform is not None:\n",
+    "            if self.image_transform_type == 'torchvision':\n",
+    "                image = self.image_transform(image)\n",
+    "            elif self.image_transform_type == 'albumentations':\n",
+    "                image = self.image_transform(image=np.array(image))['image']\n",
+    "            else:\n",
+    "                raise NotImplementedError(f\"{self.image_transform_type=}\")\n",
+    "        return {'image': image, 'text': caption} if self.include_captions else image\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.size\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    import albumentations as A\n",
+    "    from albumentations.pytorch import ToTensorV2\n",
+    "    from transformers import AutoTokenizer\n",
+    "    \n",
+    "\n",
+    "    images_root = \"/home/khali/TPU-Test/YFCC100M_OpenAI_subset/data/data/images\"\n",
+    "    captions_path = './YFCC_subset_clean.tsv'\n",
+    "    image_size = 256\n",
     "    \n",
-    "    # TODO: we could maybe use jax resizing / scaling functions\n",
-    "    def resize_image(self, image):\n",
+    "    # Create transforms\n",
+    "    def image_transform(image):\n",
     "        s = min(image.size)\n",
-    "        r = self.image_size / s\n",
+    "        r = image_size / s\n",
     "        s = (round(r * image.size[1]), round(r * image.size[0]))\n",
     "        image = TF.resize(image, s, interpolation=InterpolationMode.LANCZOS)\n",
-    "        image = TF.center_crop(image, output_size = 2 * [self.image_size])\n",
+    "        image = TF.center_crop(image, output_size = 2 * [image_size])\n",
     "        image = torch.unsqueeze(T.ToTensor()(image), 0)\n",
     "        image = image.permute(0, 2, 3, 1).numpy()\n",
     "        return image\n",
     "    \n",
-    "    def _get_caption(self, i):\n",
-    "        # We are currently appending title and caption. Should we use another separator?\n",
-    "        row = self.image_list.iloc[i]\n",
-    "        return ' '.join(row.title_clean, row.description_clean)\n",
-    "    \n",
-    "    def __getitem__(self, i):\n",
-    "        image = self._get_raw_image(i)\n",
-    "        if image is None: return None\n",
-    "        image = self.resize_image(image)\n",
-    "        caption = self._get_caption(i)\n",
-    "        return {'image': image, 'text': caption}"
+    "    # Create dataset\n",
+    "    dataset = CaptionDataset(\n",
+    "        images_root=images_root,\n",
+    "        captions_path=captions_path,\n",
+    "        image_transform=image_transform,\n",
+    "        image_transform_type='torchvision',\n",
+    "        include_captions=False\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
-   "id": "4ce2211f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = YFC100Dataset(\n",
-    "    image_list_path = yfc100m_metadata,\n",
-    "    images_root = yfc100m_images,\n",
-    "    image_size = 256,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 155,
    "id": "cc922704",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "5000"
+       "2483316"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1416,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 156,
    "id": "6e47ba46",
    "metadata": {},
    "outputs": [],
@@ -1426,30 +975,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 1,
    "id": "c8a130eb",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "Caught TypeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py\", line 287, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 47, in fetch\n    return self.collate_fn(data)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py\", line 86, in default_collate\n    raise TypeError(default_collate_err_msg_format.format(elem_type))\nTypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_320049/1409168804.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1201\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1203\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1205\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1227\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1228\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1229\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    423\u001b[0m             \u001b[0;31m# have message field\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Caught TypeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py\", line 287, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 47, in fetch\n    return self.collate_fn(data)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py\", line 86, in default_collate\n    raise TypeError(default_collate_err_msg_format.format(elem_type))\nTypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# looking at a batch\n",
     "next(iter(dataloader))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c192fd44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import matplotlib.pyplot as plt\n",
+    "# for tensor_image, _ in dataloader:\n",
+    "#     print(tensor_image)\n",
+    "#     plt.imshow(tensor_image.permute(1, 2, 0))\n",
+    "#     break"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "62ad01c3",
@@ -1460,23 +1008,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 158,
    "id": "88f36d0b",
    "metadata": {},
    "outputs": [],
    "source": [
     "def encode(model, batch):\n",
-    "    print(\"jitting encode function\")\n",
-    "#     _, indices = model.encode(batch)\n",
-    "\n",
-    "    # The model does not run in my computer (no cudNN currently installed) - faking it\n",
-    "    indices = [random.randint(0, 16384) for _ in range(256)]\n",
+    "#     print(\"jitting encode function\")\n",
+    "    _, indices = model.encode(batch)\n",
     "    return indices"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 160,
    "id": "1f35f0cb",
    "metadata": {},
    "outputs": [],
@@ -1501,20 +1046,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 170,
    "id": "2210705b",
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "import jax\n",
     "\n",
-    "def encode_captioned_dataset(dataset, output_jsonl, batch_size=32, num_workers=16):\n",
-    "    if os.path.isfile(output_jsonl):\n",
-    "        print(f\"Destination file {output_jsonl} already exists, please move away.\")\n",
+    "def encode_captioned_dataset(dataset, output_tsv, batch_size=32, num_workers=16):\n",
+    "    if os.path.isfile(output_tsv):\n",
+    "        print(f\"Destination file {output_tsv} already exists, please move away.\")\n",
     "        return\n",
     "    \n",
-    "    num_tpus = jax.device_count()\n",
+    "    num_tpus = 8    \n",
     "    dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)\n",
     "    superbatches = superbatch_generator(dataloader, num_tpus=num_tpus)\n",
     "    \n",
@@ -1522,7 +1066,7 @@
     "\n",
     "    # We save each superbatch to avoid reallocation of buffers as we process them.\n",
     "    # We keep the file open to prevent excessive file seeks.\n",
-    "    with open(output_jsonl, \"w\") as file:\n",
+    "    with open(output_tsv, \"w\") as file:\n",
     "        iterations = len(dataset) // (batch_size * num_tpus)\n",
     "        for n in tqdm(range(iterations)):\n",
     "            superbatch = next(superbatches)\n",
@@ -1536,14 +1080,12 @@
     "            captions = dataset.captions[\"caption\"][start_index:end_index].values\n",
     "            encoded_as_string = list(map(lambda item: np.array2string(item, separator=',', max_line_width=50000, formatter={'int':lambda x: str(x)}), encoded))\n",
     "            batch_df = pd.DataFrame.from_dict({\"image_file\": paths, \"caption\": captions, \"encoding\": encoded_as_string})\n",
-    "            batch_df = batch_df.dropna()\n",
-    "            batch_df.to_json(file, orient='records', lines=True, index=None)\n",
-    "            "
+    "            batch_df.to_csv(file, sep='\\t', header=(n==0), index=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 171,
    "id": "7704863d",
    "metadata": {},
    "outputs": [
@@ -1551,29 +1093,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                                        | 0/78 [00:00<?, ?it/s]\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "Caught TypeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py\", line 287, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 47, in fetch\n    return self.collate_fn(data)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py\", line 86, in default_collate\n    raise TypeError(default_collate_err_msg_format.format(elem_type))\nTypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_320049/140243368.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mencode_captioned_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myfc100m_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/tmp/ipykernel_320049/2954345319.py\u001b[0m in \u001b[0;36mencode_captioned_dataset\u001b[0;34m(dataset, output_jsonl, batch_size, num_workers)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0miterations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnum_tpus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0msuperbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuperbatches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0mencoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp_encoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuperbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0mencoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/tmp/ipykernel_320049/4148450576.py\u001b[0m in \u001b[0;36msuperbatch_generator\u001b[0;34m(dataloader, num_tpus)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msuperbatch_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_tpus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0miter_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miter_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0msuperbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1201\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1203\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1205\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1227\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1228\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1229\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    423\u001b[0m             \u001b[0;31m# have message field\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Caught TypeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py\", line 287, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 47, in fetch\n    return self.collate_fn(data)\n  File \"/home/pedro/miniconda3/envs/hf_jax/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py\", line 86, in default_collate\n    raise TypeError(default_collate_err_msg_format.format(elem_type))\nTypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>\n"
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4850/4850 [2:27:51<00:00,  1.83s/it]\n"
      ]
     }
    ],
    "source": [
-    "encode_captioned_dataset(dataset, yfc100m_output, batch_size=64, num_workers=16)"
+    "encode_captioned_dataset(dataset, yfcc100m_output, batch_size=64, num_workers=16)"
    ]
   },
   {
@@ -1587,9 +1112,8 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.9.0 64-bit ('Python39')"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1601,9 +1125,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.0"
+  },
+  "interpreter": {
+   "hash": "db471c52d602b4f5f40ecaf278e88ccfef85c29d0a1a07185b0d51fc7acf4e26"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file