{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset\n",
    "Examples for loading datasets with configs and split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset, get_dataset_config_names, get_dataset_split_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ppak10/NIST-In-Situ-IN625-LPBF-Overhangs None None None None None None {'trust_remote_code': True}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['base', 'block', 'overhang_no_supports', 'overhang_with_supports']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_dataset_split_names(\n",
    "    \"ppak10/NIST-In-Situ-IN625-LPBF-Overhangs\",\n",
    "    trust_remote_code=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    }
   ],
   "source": [
    "ds = load_dataset(\n",
    "    \"ppak10/NIST-In-Situ-IN625-LPBF-Overhangs\",\n",
    "    \"layer_table\"\n",
    "    split=\"base\",\n",
    "    streaming=True,\n",
    "    trust_remote_code=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IterableDataset({\n",
      "    features: ['folder_layer_range', 'part', 'part_section', 'process', 'source', 'layer_number', 'build_time', 'contact_email', 'file_name', 'hatch_spacing', 'laser_power', 'layer_thickness', 'material', 'radiant_temp', 's_hvariable__a', 's_hvariable__b', 's_hvariable__c', 'scan_speed', 'website'],\n",
      "    n_shards: 98\n",
      "})\n",
      "{'folder_layer_range': Value(dtype='string', id=None), 'part': Value(dtype='string', id=None), 'part_section': Value(dtype='string', id=None), 'process': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'layer_number': Value(dtype='string', id=None), 'build_time': Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=-1, id=None), 'contact_email': Value(dtype='string', id=None), 'file_name': Value(dtype='string', id=None), 'hatch_spacing': Value(dtype='uint32', id=None), 'laser_power': Value(dtype='uint32', id=None), 'layer_thickness': Value(dtype='uint32', id=None), 'material': Value(dtype='string', id=None), 'radiant_temp': Sequence(feature=Sequence(feature=Sequence(feature=Value(dtype='uint32', id=None), length=-1, id=None), length=-1, id=None), length=-1, id=None), 's_hvariable__a': Value(dtype='float32', id=None), 's_hvariable__b': Value(dtype='float32', id=None), 's_hvariable__c': Value(dtype='float32', id=None), 'scan_speed': Value(dtype='uint32', id=None), 'website': Value(dtype='string', id=None)}\n"
     ]
    }
   ],
   "source": [
    "print(ds)\n",
    "print(ds.features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IterableDataset({\n",
      "    features: ['folder_layer_range', 'part', 'part_section', 'process', 'source', 'layer_number', 'build_time', 'contact_email', 'file_name', 'hatch_spacing', 'laser_power', 'layer_thickness', 'material', 'radiant_temp', 's_hvariable__a', 's_hvariable__b', 's_hvariable__c', 'scan_speed', 'website'],\n",
      "    n_shards: 98\n",
      "})\n",
      "<class 'datasets.iterable_dataset.IterableDataset'>\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "torch_ds = ds.with_format(\"torch\")\n",
    "print(torch_ds)\n",
    "print(type(torch_ds.with_format(\"torch\"))) #it passes\n",
    "loader = torch.utils.data.DataLoader(torch_ds, batch_size = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for i, row in enumerate(loader):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mds\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlayer_number\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#   for key, value in data.items():\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m#     print(key, np.array(value).shape)\u001b[39;00m\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/iterable_dataset.py:1393\u001b[0m, in \u001b[0;36mIterableDataset.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1389\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, example \u001b[38;5;129;01min\u001b[39;00m ex_iterable:\n\u001b[1;32m   1390\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures:\n\u001b[1;32m   1391\u001b[0m         \u001b[38;5;66;03m# `IterableDataset` automatically fills missing columns with None.\u001b[39;00m\n\u001b[1;32m   1392\u001b[0m         \u001b[38;5;66;03m# This is done with `_apply_feature_types_on_example`.\u001b[39;00m\n\u001b[0;32m-> 1393\u001b[0m         example \u001b[38;5;241m=\u001b[39m \u001b[43m_apply_feature_types_on_example\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1394\u001b[0m \u001b[43m            \u001b[49m\u001b[43mexample\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken_per_repo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_token_per_repo_id\u001b[49m\n\u001b[1;32m   1395\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1396\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m format_dict(example) \u001b[38;5;28;01mif\u001b[39;00m format_dict \u001b[38;5;28;01melse\u001b[39;00m example\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/iterable_dataset.py:1080\u001b[0m, in \u001b[0;36m_apply_feature_types_on_example\u001b[0;34m(example, features, token_per_repo_id)\u001b[0m\n\u001b[1;32m   1078\u001b[0m         example[column_name] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1079\u001b[0m \u001b[38;5;66;03m# we encode the example for ClassLabel feature types for example\u001b[39;00m\n\u001b[0;32m-> 1080\u001b[0m encoded_example \u001b[38;5;241m=\u001b[39m \u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexample\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1081\u001b[0m \u001b[38;5;66;03m# Decode example for Audio feature, e.g.\u001b[39;00m\n\u001b[1;32m   1082\u001b[0m decoded_example \u001b[38;5;241m=\u001b[39m features\u001b[38;5;241m.\u001b[39mdecode_example(encoded_example, token_per_repo_id\u001b[38;5;241m=\u001b[39mtoken_per_repo_id)\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/features/features.py:1924\u001b[0m, in \u001b[0;36mFeatures.encode_example\u001b[0;34m(self, example)\u001b[0m\n\u001b[1;32m   1913\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1914\u001b[0m \u001b[38;5;124;03mEncode example into a format for Arrow.\u001b[39;00m\n\u001b[1;32m   1915\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1921\u001b[0m \u001b[38;5;124;03m    `dict[str, Any]`\u001b[39;00m\n\u001b[1;32m   1922\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1923\u001b[0m example \u001b[38;5;241m=\u001b[39m cast_to_python_objects(example)\n\u001b[0;32m-> 1924\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mencode_nested_example\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/features/features.py:1244\u001b[0m, in \u001b[0;36mencode_nested_example\u001b[0;34m(schema, obj, level)\u001b[0m\n\u001b[1;32m   1241\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m level \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1242\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot None but expected a dictionary instead\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1243\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m-> 1244\u001b[0m         {k: \u001b[43mencode_nested_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mschema\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m schema}\n\u001b[1;32m   1245\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1246\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1247\u001b[0m     )\n\u001b[1;32m   1249\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(schema, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m   1250\u001b[0m     sub_schema \u001b[38;5;241m=\u001b[39m schema[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/features/features.py:1295\u001b[0m, in \u001b[0;36mencode_nested_example\u001b[0;34m(schema, obj, level)\u001b[0m\n\u001b[1;32m   1290\u001b[0m             \u001b[38;5;66;03m# be careful when comparing tensors here\u001b[39;00m\n\u001b[1;32m   1291\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   1292\u001b[0m                 \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(first_elmt, \u001b[38;5;28mlist\u001b[39m)\n\u001b[1;32m   1293\u001b[0m                 \u001b[38;5;129;01mor\u001b[39;00m encode_nested_example(schema\u001b[38;5;241m.\u001b[39mfeature, first_elmt, level\u001b[38;5;241m=\u001b[39mlevel \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m!=\u001b[39m first_elmt\n\u001b[1;32m   1294\u001b[0m             ):\n\u001b[0;32m-> 1295\u001b[0m                 \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mencode_nested_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m obj]\n\u001b[1;32m   1296\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(obj)\n\u001b[1;32m   1297\u001b[0m \u001b[38;5;66;03m# Object with special encoding:\u001b[39;00m\n\u001b[1;32m   1298\u001b[0m \u001b[38;5;66;03m# ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\u001b[39;00m\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/features/features.py:1295\u001b[0m, in \u001b[0;36mencode_nested_example\u001b[0;34m(schema, obj, level)\u001b[0m\n\u001b[1;32m   1290\u001b[0m             \u001b[38;5;66;03m# be careful when comparing tensors here\u001b[39;00m\n\u001b[1;32m   1291\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   1292\u001b[0m                 \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(first_elmt, \u001b[38;5;28mlist\u001b[39m)\n\u001b[1;32m   1293\u001b[0m                 \u001b[38;5;129;01mor\u001b[39;00m encode_nested_example(schema\u001b[38;5;241m.\u001b[39mfeature, first_elmt, level\u001b[38;5;241m=\u001b[39mlevel \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m!=\u001b[39m first_elmt\n\u001b[1;32m   1294\u001b[0m             ):\n\u001b[0;32m-> 1295\u001b[0m                 \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mencode_nested_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m obj]\n\u001b[1;32m   1296\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(obj)\n\u001b[1;32m   1297\u001b[0m \u001b[38;5;66;03m# Object with special encoding:\u001b[39;00m\n\u001b[1;32m   1298\u001b[0m \u001b[38;5;66;03m# ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\u001b[39;00m\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/features/features.py:1295\u001b[0m, in \u001b[0;36mencode_nested_example\u001b[0;34m(schema, obj, level)\u001b[0m\n\u001b[1;32m   1290\u001b[0m             \u001b[38;5;66;03m# be careful when comparing tensors here\u001b[39;00m\n\u001b[1;32m   1291\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   1292\u001b[0m                 \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(first_elmt, \u001b[38;5;28mlist\u001b[39m)\n\u001b[1;32m   1293\u001b[0m                 \u001b[38;5;129;01mor\u001b[39;00m encode_nested_example(schema\u001b[38;5;241m.\u001b[39mfeature, first_elmt, level\u001b[38;5;241m=\u001b[39mlevel \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m!=\u001b[39m first_elmt\n\u001b[1;32m   1294\u001b[0m             ):\n\u001b[0;32m-> 1295\u001b[0m                 \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mencode_nested_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m obj]\n\u001b[1;32m   1296\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(obj)\n\u001b[1;32m   1297\u001b[0m \u001b[38;5;66;03m# Object with special encoding:\u001b[39;00m\n\u001b[1;32m   1298\u001b[0m \u001b[38;5;66;03m# ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\u001b[39;00m\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/features/features.py:1300\u001b[0m, in \u001b[0;36mencode_nested_example\u001b[0;34m(schema, obj, level)\u001b[0m\n\u001b[1;32m   1297\u001b[0m \u001b[38;5;66;03m# Object with special encoding:\u001b[39;00m\n\u001b[1;32m   1298\u001b[0m \u001b[38;5;66;03m# ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\u001b[39;00m\n\u001b[1;32m   1299\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(schema, (Audio, Image, ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\n\u001b[0;32m-> 1300\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mschema\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;66;03m# Other object should be directly convertible to a native Arrow type (like Translation and Translation)\u001b[39;00m\n\u001b[1;32m   1302\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n",
      "File \u001b[0;32m/media/ppak/Storage/HuggingFace/NIST-In-Situ-IN625-LPBF-Overhangs/venv/lib/python3.12/site-packages/datasets/features/features.py:515\u001b[0m, in \u001b[0;36mValue.encode_example\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    513\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mbool\u001b[39m(value)\n\u001b[1;32m    514\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mtypes\u001b[38;5;241m.\u001b[39mis_integer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpa_type):\n\u001b[0;32m--> 515\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m pa\u001b[38;5;241m.\u001b[39mtypes\u001b[38;5;241m.\u001b[39mis_floating(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpa_type):\n\u001b[1;32m    517\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mfloat\u001b[39m(value)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for data in ds:\n",
    "    print(data[\"layer_number\"])\n",
    "#   for key, value in data.items():\n",
    "#     print(key, np.array(value).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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