{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "42ac3dd1-7154-40ec-ae54-38e4389c5ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import requests\n",
    "import zipfile\n",
    "\n",
    "from io import BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ba83fb8-8c38-427e-83c1-68da2b5b4bbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading dataset from https://coltekin.github.io/offensive-turkish/offenseval2020-turkish.zip\n",
      "Extracting files to './'...\n",
      "Extracted files: ['offenseval2020-turkish/', 'offenseval2020-turkish/offenseval-tr-training-v1/', 'offenseval2020-turkish/offenseval-tr-training-v1/offenseval-annotation.txt', 'offenseval2020-turkish/offenseval-tr-training-v1/offenseval-tr-training-v1.tsv', 'offenseval2020-turkish/offenseval-tr-training-v1/readme-trainingset-tr.txt', 'offenseval2020-turkish/offenseval-tr-testset-v1/', 'offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-testset-v1.tsv', 'offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-labela-v1.tsv', 'offenseval2020-turkish/README.txt']\n"
     ]
    }
   ],
   "source": [
    "def download_and_extract_zip(url, extract_to=\"./\"):\n",
    "    try:\n",
    "        print(f\"Downloading dataset from {url}\")\n",
    "        response = requests.get(url)\n",
    "        response.raise_for_status()\n",
    "\n",
    "        with zipfile.ZipFile(BytesIO(response.content)) as z:\n",
    "            print(f\"Extracting files to '{extract_to}'...\")\n",
    "            z.extractall(extract_to)\n",
    "            extracted_files = z.namelist()\n",
    "            print(f\"Extracted files: {extracted_files}\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "\n",
    "url = \"https://coltekin.github.io/offensive-turkish/offenseval2020-turkish.zip\"  # Replace with the actual URL\n",
    "download_and_extract_zip(url, \"./\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b74682a7-ccf8-44ad-98a0-73636c35e10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "original_train_file = \"./offenseval2020-turkish/offenseval-tr-training-v1/offenseval-tr-training-v1.tsv\"\n",
    "original_test_file = \"./offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-testset-v1.tsv\"\n",
    "orginal_label_test_file = \"./offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-labela-v1.tsv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af3ba702-1341-4e70-8c74-87078eeaddf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_instances(filename: str):\n",
    "    instances = []\n",
    "    with open(filename, \"rt\") as f_p:\n",
    "        for line in f_p:\n",
    "            line = line.strip()\n",
    "    \n",
    "            if not line:\n",
    "                continue\n",
    "    \n",
    "            if line.startswith(\"id\"):\n",
    "                continue\n",
    "    \n",
    "            _, tweet, label = line.split(\"\\t\")\n",
    "\n",
    "            instances.append([label, tweet])\n",
    "\n",
    "    print(f\"Found {len(instances)} training instances.\")\n",
    "\n",
    "    return instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a397f04d-354c-4d97-9f93-794929c5e51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_instances(filename: str, label_filename: str):\n",
    "    # E.g. 41993,NOT is mapped to \"41993\" -> \"NOT\"\n",
    "    id_label_mapping = {}\n",
    "    with open(label_filename, \"rt\") as f_p:\n",
    "        for line in f_p:\n",
    "            line = line.strip()\n",
    "\n",
    "            if not line:\n",
    "                continue\n",
    "\n",
    "            id_, label = line.split(\",\")\n",
    "\n",
    "            id_label_mapping[id_] = label\n",
    "\n",
    "    print(f\"Found {len(id_label_mapping)} labelled test instances\")\n",
    "\n",
    "    instances = []\n",
    "    \n",
    "    with open(filename, \"rt\") as f_p:\n",
    "        for line in f_p:\n",
    "            line = line.strip()\n",
    "    \n",
    "            if not line:\n",
    "                continue\n",
    "    \n",
    "            if line.startswith(\"id\"):\n",
    "                continue\n",
    "    \n",
    "            id_, tweet = line.split(\"\\t\")\n",
    "\n",
    "            label = id_label_mapping[id_]\n",
    "\n",
    "            instances.append([label, tweet])\n",
    "    return instances\n",
    "\n",
    "    assert len(id_label_mapping) == len(instances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06508ca8-649b-44ea-a8c6-09c2c2b434f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 31756 training instances.\n",
      "Found 3528 labelled test instances\n"
     ]
    }
   ],
   "source": [
    "original_train_instances = get_instances(original_train_file)\n",
    "original_test_instances = get_test_instances(original_test_file, orginal_label_test_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd4a7942-42c9-4bdb-9079-c6039ff908c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffling is done in-place\n",
    "random.seed(83607)\n",
    "random.shuffle(original_train_instances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ab61fde-e534-4ffe-9302-f80581e503eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_instances = original_train_instances[:30_000]\n",
    "dev_instances = original_train_instances[30_000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84f9c044-7906-4c04-9154-70e2b8d55982",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_instances(instances: str, split_name: str):\n",
    "    with open(f\"{split_name}.txt\", \"wt\") as f_out:\n",
    "        for instance in instances:\n",
    "            label, tweet = instance\n",
    "\n",
    "            # We stick to Flair format for classification tasks, which is basically FastText inspired ;)\n",
    "            new_label = \"__label__\" + label\n",
    "            f_out.write(f\"{new_label} {tweet}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bf06e96-2b25-46ed-8a7e-0672e7aa6af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "write_instances(train_instances, \"train\")\n",
    "write_instances(dev_instances, \"dev\")\n",
    "write_instances(original_test_instances, \"test\")"
   ]
  }
 ],
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