{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ab2e823-50c9-40d4-9401-3ed7869da6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "241ea0f7-02bf-4a3e-845c-e262b1d32031",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use specific revision for reproducibility!\n",
    "# See https://huggingface.co/datasets/avramandrei/histnero\n",
    "revision = \"433ca166efac28c952813c0e78bf301643cf5af3\"\n",
    "\n",
    "ds = load_dataset(\"avramandrei/histnero\", revision=revision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66878e9e-83e8-4010-b81c-cefbc2ef0da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are grouping together documents together first!\n",
    "def perform_document_grouping(dataset_split):\n",
    "    # Document identifier -> Training example\n",
    "    document_mapping = {}\n",
    "\n",
    "    for document in dataset_split:\n",
    "        doc_id = document[\"doc_id\"]\n",
    "        if doc_id in document_mapping:\n",
    "            document_mapping[doc_id].append(document)\n",
    "        else:\n",
    "            document_mapping[doc_id] = [document]\n",
    "    return document_mapping\n",
    "\n",
    "def export_to_conll(grouped_dataset_split, export_filename):\n",
    "    dataset_labels = ds[\"train\"].features[\"ner_tags\"].feature.names\n",
    "    dataset_label_id_to_string = {idx: label_string for idx, label_string in enumerate(dataset_labels)}\n",
    "\n",
    "    with open(export_filename, \"wt\") as f_out:\n",
    "        for document_name, training_examples in grouped_dataset_split.items():\n",
    "            f_out.write(\"-DOCSTART-\\tO\\n\\n\")\n",
    "\n",
    "            for training_example in training_examples:\n",
    "                tokens = training_example[\"tokens\"]\n",
    "                ner_label_ids = training_example[\"ner_tags\"]\n",
    "                ner_label_iobs = [dataset_label_id_to_string[ner_label_id] for ner_label_id in ner_label_ids]\n",
    "\n",
    "                assert len(tokens) == len(ner_label_iobs)\n",
    "\n",
    "                # Write some metadata first\n",
    "                metadata = [\n",
    "                    {\"id\": training_example[\"id\"]},\n",
    "                    {\"doc_id\": training_example[\"doc_id\"]},\n",
    "                    {\"region\": training_example[\"region\"]},\n",
    "                ]\n",
    "\n",
    "                for metadata_entry in metadata:\n",
    "                    for metadata_name, metadata_value in metadata_entry.items():\n",
    "                        f_out.write(f\"# histnero:{metadata_name} = {metadata_value}\\n\")\n",
    "                \n",
    "                for token, ner_label_iob in zip(tokens, ner_label_iobs):\n",
    "                    f_out.write(f\"{token}\\t{ner_label_iob}\\n\")\n",
    "\n",
    "                f_out.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "afb1dc77-1cde-43d5-9d9a-e7b458c08bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset_split in [\"train\", \"valid\", \"test\"]:\n",
    "    grouped_dataset = perform_document_grouping(ds[dataset_split])\n",
    "\n",
    "    split_filename = \"dev\" if dataset_split == \"valid\" else dataset_split\n",
    "    export_to_conll(grouped_dataset, f\"{split_filename}.tsv\")"
   ]
  }
 ],
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