{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import srsly\n",
    "import glob\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob(\"./gold-training-data/*.jsonl\")\n",
    "len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = []\n",
    "for filename in files:\n",
    "    data = list(srsly.read_jsonl(filename))\n",
    "    for item in data:\n",
    "        if len(item[\"spans\"]) > 0:\n",
    "            all_data.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7868"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'I was born in a small town called , and I was born May 5, 1928.',\n",
       " 'spans': [{'start': 22,\n",
       "   'end': 26,\n",
       "   'token_start': 6,\n",
       "   'token_end': 6,\n",
       "   'label': 'POPULATED_PLACE'}],\n",
       " '_input_hash': 1949719959,\n",
       " '_task_hash': 335893137,\n",
       " 'tokens': [{'text': 'I', 'start': 0, 'end': 1, 'id': 0, 'ws': True},\n",
       "  {'text': 'was', 'start': 2, 'end': 5, 'id': 1, 'ws': True},\n",
       "  {'text': 'born', 'start': 6, 'end': 10, 'id': 2, 'ws': True},\n",
       "  {'text': 'in', 'start': 11, 'end': 13, 'id': 3, 'ws': True},\n",
       "  {'text': 'a', 'start': 14, 'end': 15, 'id': 4, 'ws': True},\n",
       "  {'text': 'small', 'start': 16, 'end': 21, 'id': 5, 'ws': True},\n",
       "  {'text': 'town', 'start': 22, 'end': 26, 'id': 6, 'ws': True},\n",
       "  {'text': 'called', 'start': 27, 'end': 33, 'id': 7, 'ws': True},\n",
       "  {'text': ',', 'start': 34, 'end': 35, 'id': 8, 'ws': True},\n",
       "  {'text': 'and', 'start': 36, 'end': 39, 'id': 9, 'ws': True},\n",
       "  {'text': 'I', 'start': 40, 'end': 41, 'id': 10, 'ws': True},\n",
       "  {'text': 'was', 'start': 42, 'end': 45, 'id': 11, 'ws': True},\n",
       "  {'text': 'born', 'start': 46, 'end': 50, 'id': 12, 'ws': True},\n",
       "  {'text': 'May', 'start': 51, 'end': 54, 'id': 13, 'ws': True},\n",
       "  {'text': '5', 'start': 55, 'end': 56, 'id': 14, 'ws': False},\n",
       "  {'text': ',', 'start': 56, 'end': 57, 'id': 15, 'ws': True},\n",
       "  {'text': '1928', 'start': 58, 'end': 62, 'id': 16, 'ws': False},\n",
       "  {'text': '.', 'start': 62, 'end': 63, 'id': 17, 'ws': False}],\n",
       " '_view_id': 'spans_manual',\n",
       " 'answer': 'accept',\n",
       " '_timestamp': 1669136567}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POPULATED_PLACE: 15222\n",
      "BUILDING: 11513\n",
      "COUNTRY: 5969\n",
      "SPATIAL_OBJ: 5680\n",
      "DLF: 6191\n",
      "INT_SPACE: 3262\n",
      "ENV_FEATURES: 1555\n",
      "REGION: 1408\n",
      "NPIP: 2573\n"
     ]
    }
   ],
   "source": [
    "def merge_and_deduplicate_spans(all_data):\n",
    "    # Mapping of labels to be merged\n",
    "    label_mapping = {\n",
    "        'INTERIOR_SPACE': 'INT_SPACE',\n",
    "        'RIVER': 'ENV_FEATURES',\n",
    "        'FOREST': 'ENV_FEATURES',\n",
    "        'GHETTO': 'POPULATED_PLACE'\n",
    "    }\n",
    "\n",
    "    # Process each annotation in the dataset\n",
    "    for annotation in all_data:\n",
    "        new_spans = []  # List to hold updated and unique spans\n",
    "\n",
    "        # Process each span\n",
    "        for span in annotation['spans']:\n",
    "            # Skip spans with the label \"CONTINENT\"\n",
    "            if span[\"label\"] == \"CONTINENT\":\n",
    "                continue\n",
    "\n",
    "            # Update label if it's in the mapping\n",
    "            if span['label'] in label_mapping:\n",
    "                span['label'] = label_mapping[span['label']]\n",
    "\n",
    "            # Check for duplicates\n",
    "            if span not in new_spans:\n",
    "                new_spans.append(span)\n",
    "\n",
    "        # Replace old spans with new_spans\n",
    "        annotation['spans'] = new_spans\n",
    "    return all_data\n",
    "\n",
    "\n",
    "all_data = merge_and_deduplicate_spans(all_data)\n",
    "\n",
    "srsly.write_jsonl(\"assets/annotated_data_spans.jsonl\", all_data)\n",
    "\n",
    "\n",
    "# Create a Counter object for counting labels\n",
    "label_counter = Counter()\n",
    "\n",
    "# Iterate over each annotation in the dataset\n",
    "for annotation in all_data:\n",
    "    # Extract labels from each 'span' in the 'spans' list and add to the counter\n",
    "    labels = [span['label'] for span in annotation['spans']]\n",
    "    label_counter.update(labels)\n",
    "\n",
    "# Print out the counts\n",
    "for label, count in label_counter.items():\n",
    "    print(f\"{label}: {count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7868"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "holocaust",
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    "name": "ipython",
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