[WIP] Encoding YFC100M dataset.

The notebook does not work. We need to filter out entries that do not
have a corresponding image, but I'm having trouble doing it. Pandas
struggles with memory (in my system, with 96 GB). Datasets is great but
gets confused sometimes when the map function returns no entries.

Note also that I disabled the encoding function itself (it's returning a
random list of tokens), because flax requires system-wide availability of
cudNN, apparently.

encoding/vqgan-jax-encoding-yfcc100m.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d0b72877",
+   "metadata": {},
+   "source": [
+    "# vqgan-jax-encoding-yfcc100m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba7b31e6",
+   "metadata": {},
+   "source": [
+    "Same as `vqgan-jax-encoding-with-captions`, but for YFCC100M.\n",
+    "\n",
+    "This dataset was prepared by @borisdayma in Json lines format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b59489e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import requests\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "import torch\n",
+    "import torchvision.transforms as T\n",
+    "import torchvision.transforms.functional as TF\n",
+    "from torchvision.transforms import InterpolationMode\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torchvision.datasets.folder import default_loader\n",
+    "\n",
+    "import jax\n",
+    "from jax import pmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "511c3b9e",
+   "metadata": {},
+   "source": [
+    "## VQGAN-JAX model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb408f6c",
+   "metadata": {},
+   "source": [
+    "`dalle_mini` is a local package that contains the VQGAN-JAX model and other utilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2ca50dc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b60da9a",
+   "metadata": {},
+   "source": [
+    "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,
+   "id": "29ce8b15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#model = VQModel.from_pretrained(\"flax-community/vqgan_f16_16384\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7c4c1e6",
+   "metadata": {},
+   "source": [
+    "## Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "33861477",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "81b19eca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yfcc100m = Path('/sddata/dalle-mini/YFCC100M_OpenAI_subset')\n",
+    "# Images are 'sharded' from the following directory\n",
+    "yfcc100m_images = yfcc100m/'data'/'images'\n",
+    "yfcc100m_metadata = yfcc100m/'metadata_YFCC100M.jsonl'\n",
+    "yfcc100m_output = yfcc100m/'metadata_encoded.jsonl'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c58bb4a",
+   "metadata": {},
+   "source": [
+    "### Cleanup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a14ae3d",
+   "metadata": {},
+   "source": [
+    "We need to select entries with images that exist. Otherwise we can't build batches because `Dataloader` does not support `None` in batches. We use Huggingface Datasets, I understand they support threaded reading of jsonl files, and I was running out of memory when using pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "7811648c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datasets\n",
+    "from datasets import Dataset, load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "753659fe",
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = load_dataset(\"json\", data_files=[str(yfcc100m_metadata)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "9343df1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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",
+       "})"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = dataset['train']\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "c4794c29",
+   "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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "1b500078",
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "467378c1",
+   "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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+      "873 d2925c82aa7fd47f6ea9e20f5715719\n",
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+      "929 d299b714d28830663458662681e041e4\n",
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+      "931 d29638364c5166dc5ce5040424db5\n",
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+      "938 d29fffdf16211b8d5aa41487a8daa5ca\n",
+      "939 d299fc7fb7f458ec1b976a5a52b8b04a\n",
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+      "950 d2927f7a6056ab6be96cd0812640ce\n",
+      "951 d29ac16ee01e78164acdd4e9ae56b65c\n",
+      "952 d298f1ab24787baabadc2c79489857b\n",
+      "953 d2934db68cdb24285a4bfe4c45de83\n",
+      "954 d296a2c4fd479d35942e20779121cd2b\n",
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+      "956 d292f67c97843c616fe91b24b833e81\n",
+      "957 d294b46b302a24644766c7449594721f\n",
+      "958 d292961146b9cbbb547223db2a8a9\n",
+      "959 d296a012631260f8f4d62a553b79b2d9\n",
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+      "961 d29c64939a3116d25d2baea9fa5ca2\n",
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+      "963 d29e44e97f49146198417e4ab07cf7e6\n",
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+      "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"
+     ]
+    }
+   ],
+   "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "7060ff8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df['image_exists'] = df.apply(lambda row: image_exists(row['key']), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "fecc9a00",
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "1a065700",
+   "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",
+    "        :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",
+    "        \"\"\"\n",
+    "        self.image_list = pd.read_json(image_list_path, orient=\"records\", lines=True)\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",
+    "    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",
+    "    \n",
+    "    # TODO: we could maybe use jax resizing / scaling functions\n",
+    "    def resize_image(self, image):\n",
+    "        s = min(image.size)\n",
+    "        r = self.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 = 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}"
+   ]
+  },
+  {
+   "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,
+   "id": "cc922704",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5000"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "6e47ba46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = DataLoader(dataset, batch_size=32, num_workers=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "next(iter(dataloader))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62ad01c3",
+   "metadata": {},
+   "source": [
+    "## Encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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",
+    "    return indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "1f35f0cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def superbatch_generator(dataloader, num_tpus):\n",
+    "    iter_loader = iter(dataloader)\n",
+    "    for batch in iter_loader:\n",
+    "        superbatch = [batch.squeeze(1)]\n",
+    "        try:\n",
+    "            for b in range(num_tpus-1):\n",
+    "                batch = next(iter_loader)\n",
+    "                if batch is None:\n",
+    "                    break\n",
+    "                # Skip incomplete last batch\n",
+    "                if batch.shape[0] == dataloader.batch_size:\n",
+    "                    superbatch.append(batch.squeeze(1))\n",
+    "        except StopIteration:\n",
+    "            pass\n",
+    "        superbatch = torch.stack(superbatch, axis=0)\n",
+    "        yield superbatch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "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",
+    "        return\n",
+    "    \n",
+    "    num_tpus = jax.device_count()\n",
+    "    dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)\n",
+    "    superbatches = superbatch_generator(dataloader, num_tpus=num_tpus)\n",
+    "    \n",
+    "    p_encoder = pmap(lambda batch: encode(model, batch))\n",
+    "\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",
+    "        iterations = len(dataset) // (batch_size * num_tpus)\n",
+    "        for n in tqdm(range(iterations)):\n",
+    "            superbatch = next(superbatches)\n",
+    "            encoded = p_encoder(superbatch.numpy())\n",
+    "            encoded = encoded.reshape(-1, encoded.shape[-1])\n",
+    "\n",
+    "            # Extract fields from the dataset internal `captions` property, and save to disk\n",
+    "            start_index = n * batch_size * num_tpus\n",
+    "            end_index = (n+1) * batch_size * num_tpus\n",
+    "            paths = dataset.captions[\"image_file\"][start_index:end_index].values\n",
+    "            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",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "7704863d",
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "encode_captioned_dataset(dataset, yfc100m_output, batch_size=64, num_workers=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8953dd84",
+   "metadata": {},
+   "source": [
+    "----"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}