{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Splits Analysis\n",
    "\n",
    "Analysis of train/val/test splitting strategies for entity-level sentiment classification.\n",
    "\n",
    "**Key question:** Is plain random splitting sufficient, or do we need stratification?\n",
    "\n",
    "**Structure**\n",
    "1. Load Data\n",
    "2. Random Split Stability (multiple seeds)\n",
    "3. Label Distribution Across Splits\n",
    "4. Entity Type Distribution Across Splits\n",
    "5. Original Dataset Distribution (baseline)\n",
    "6. Recommendations"
   ],
   "id": "795d57204446016d"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.530575Z",
     "start_time": "2026-04-18T22:16:36.527878Z"
    }
   },
   "source": [
    "import os\n",
    "import json\n",
    "import warnings\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set_theme(style=\"whitegrid\", palette=\"muted\")\n",
    "plt.rcParams[\"figure.dpi\"]   = 120\n",
    "plt.rcParams[\"axes.titlesize\"] = 13\n",
    "plt.rcParams[\"axes.labelsize\"] = 11\n",
    "\n",
    "LABEL_ORDER  = [\"negative\", \"neutral\", \"positive\"]\n",
    "LABEL_COLORS = {\"positive\": \"#4CAF50\", \"neutral\": \"#90A4AE\", \"negative\": \"#EF5350\"}"
   ],
   "id": "4baa2fe76e5644e9",
   "outputs": [],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load Data"
   ],
   "id": "da457b1218b0a6f7"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.600604Z",
     "start_time": "2026-04-18T22:16:36.562168Z"
    }
   },
   "source": [
    "data_path = os.path.join(\"..\", \"data\", \"data_preprocessed.jsonl\")\n",
    "with open(data_path, \"r\", encoding=\"utf-8\") as f:\n",
    "    data = [json.loads(line) for line in f]\n",
    "\n",
    "df_entities = pd.DataFrame([\n",
    "    {\n",
    "        \"sample_id\":   s[\"id\"],\n",
    "        \"entity_text\": e[\"entity_text\"],\n",
    "        \"entity_type\": e[\"entity_type\"],\n",
    "        \"label\":       e[\"label\"],\n",
    "    }\n",
    "    for s in data\n",
    "    for e in s[\"entities\"]\n",
    "])\n",
    "\n",
    "sample_ids = np.array([s[\"id\"] for s in data])\n",
    "\n",
    "print(f\"Samples  : {len(data):,}\")\n",
    "print(f\"Entities : {len(df_entities):,}\")"
   ],
   "id": "b7f77162a2d44ebd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples  : 1,629\n",
      "Entities : 10,550\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Random Split Stability (multiple seeds)\n",
    "\n",
    "We split at the **sample level** (not entity level) to prevent data leakage.\n",
    "Using a 80/10/10 ratio across five seeds, we measure how much the entity-level\n",
    "label distribution fluctuates in each split."
   ],
   "id": "976964c64e2c1802"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.631323Z",
     "start_time": "2026-04-18T22:16:36.612513Z"
    }
   },
   "source": [
    "VAL_FRAC  = 0.10\n",
    "TEST_FRAC = 0.10\n",
    "SEEDS = [42, 123, 256, 512, 1024]\n",
    "\n",
    "def split_sample_ids(sample_ids, val_frac, test_frac, seed):\n",
    "    remaining, test_ids = train_test_split(\n",
    "        sample_ids, test_size=test_frac, random_state=seed\n",
    "    )\n",
    "    val_frac_adj = val_frac / (1.0 - test_frac)\n",
    "    train_ids, val_ids = train_test_split(\n",
    "        remaining, test_size=val_frac_adj, random_state=seed\n",
    "    )\n",
    "    return set(train_ids), set(val_ids), set(test_ids)\n",
    "\n",
    "rows = []\n",
    "for seed in SEEDS:\n",
    "    train_ids, val_ids, test_ids = split_sample_ids(sample_ids, VAL_FRAC, TEST_FRAC, seed)\n",
    "    for split_name, split_ids in [(\"train\", train_ids), (\"val\", val_ids), (\"test\", test_ids)]:\n",
    "        split_ents = df_entities[df_entities[\"sample_id\"].isin(split_ids)]\n",
    "        total = len(split_ents)\n",
    "        for label in LABEL_ORDER:\n",
    "            cnt = (split_ents[\"label\"] == label).sum()\n",
    "            rows.append({\n",
    "                \"seed\": seed,\n",
    "                \"split\": split_name,\n",
    "                \"label\": label,\n",
    "                \"count\": cnt,\n",
    "                \"pct\": cnt / total * 100,\n",
    "            })\n",
    "\n",
    "df_splits = pd.DataFrame(rows)\n",
    "\n",
    "pivot = df_splits.pivot_table(index=[\"seed\", \"split\"], columns=\"label\", values=\"pct\").reindex(columns=LABEL_ORDER)\n",
    "print(\"Label distribution (%) per split and seed:\")\n",
    "print(pivot.round(1).to_string())"
   ],
   "id": "dd935f8de553c45f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label distribution (%) per split and seed:\n",
      "label       negative  neutral  positive\n",
      "seed split                             \n",
      "42   test       15.4     67.7      16.9\n",
      "     train      15.0     71.0      14.0\n",
      "     val        17.9     65.8      16.3\n",
      "123  test       16.0     70.4      13.6\n",
      "     train      15.3     70.0      14.7\n",
      "     val        14.8     71.1      14.1\n",
      "256  test       15.5     73.6      11.0\n",
      "     train      15.1     70.5      14.3\n",
      "     val        17.0     63.7      19.3\n",
      "512  test       16.8     68.8      14.5\n",
      "     train      15.1     70.2      14.7\n",
      "     val        16.2     71.0      12.8\n",
      "1024 test       17.2     66.9      15.9\n",
      "     train      15.3     70.5      14.2\n",
      "     val        13.9     70.7      15.4\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variability across seeds\n",
    "\n",
    "How much does each label's percentage fluctuate in the test split across different seeds?"
   ],
   "id": "18d500293da2c731"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.748022Z",
     "start_time": "2026-04-18T22:16:36.638978Z"
    }
   },
   "source": [
    "test_splits = df_splits[df_splits[\"split\"] == \"test\"]\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "for ax, label in zip(axes, LABEL_ORDER):\n",
    "    sub = test_splits[test_splits[\"label\"] == label]\n",
    "    ax.bar([str(s) for s in SEEDS], sub[\"pct\"].values, color=LABEL_COLORS[label], edgecolor=\"white\")\n",
    "    mean_pct = sub[\"pct\"].mean()\n",
    "    ax.axhline(mean_pct, color=\"black\", linestyle=\"--\", linewidth=1, label=f\"Mean = {mean_pct:.1f}%\")\n",
    "    ax.set_title(f\"Test split: {label}\")\n",
    "    ax.set_xlabel(\"Seed\")\n",
    "    ax.set_ylabel(\"% of entities\")\n",
    "    ax.set_ylim(0, max(sub[\"pct\"].max() * 1.3, 25))\n",
    "    for i, pct in enumerate(sub[\"pct\"].values):\n",
    "        ax.text(i, pct + 0.3, f\"{pct:.1f}%\", ha=\"center\", fontsize=9)\n",
    "    ax.legend(fontsize=9)\n",
    "\n",
    "plt.suptitle(\"Test Split Label Distribution Across Seeds\", y=1.02, fontsize=13)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Test split variability across seeds:\")\n",
    "for label in LABEL_ORDER:\n",
    "    sub = test_splits[test_splits[\"label\"] == label][\"pct\"]\n",
    "    print(f\"  {label:10s}: {sub.min():.1f}% – {sub.max():.1f}%  (range = {sub.max() - sub.min():.1f} pp, std = {sub.std():.2f})\")"
   ],
   "id": "a0d20f91e83403c1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1680x480 with 3 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test split variability across seeds:\n",
      "  negative  : 15.4% – 17.2%  (range = 1.8 pp, std = 0.81)\n",
      "  neutral   : 66.9% – 73.6%  (range = 6.7 pp, std = 2.64)\n",
      "  positive  : 11.0% – 16.9%  (range = 6.0 pp, std = 2.29)\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Label Distribution Across Splits\n",
    "\n",
    "For the default seed (42), compare train/val/test label distributions side by side."
   ],
   "id": "6fbb95a902e981a4"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.814548Z",
     "start_time": "2026-04-18T22:16:36.768390Z"
    }
   },
   "source": [
    "seed_42 = df_splits[df_splits[\"seed\"] == 42]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "splits = [\"train\", \"val\", \"test\"]\n",
    "x = np.arange(len(LABEL_ORDER))\n",
    "width = 0.25\n",
    "\n",
    "for i, split in enumerate(splits):\n",
    "    sub = seed_42[seed_42[\"split\"] == split].set_index(\"label\").reindex(LABEL_ORDER)\n",
    "    bars = ax.bar(x + i * width, sub[\"pct\"].values, width, label=split, edgecolor=\"white\")\n",
    "    for bar, pct in zip(bars, sub[\"pct\"].values):\n",
    "        ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.3,\n",
    "                f\"{pct:.1f}%\", ha=\"center\", fontsize=9)\n",
    "\n",
    "ax.set_xticks(x + width)\n",
    "ax.set_xticklabels(LABEL_ORDER)\n",
    "ax.set_ylabel(\"% of entities\")\n",
    "ax.set_title(\"Label Distribution by Split (seed=42)\")\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "33f011936920805d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJwAAAJECAYAAABTkz6kAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAASdAAAEnQB3mYfeAAAd7tJREFUeJzt3QuYjPX///G3dT6EyDkk5XyIlOSQQ4qKVEIpqYikvoRQOpBQ5JiEkKIkRFIpOksHoiI59VXkfMz5sPZ/vT7f/z2/2dlddrl3d3bn+biuuWbnnntm7pmdnZ37db8/70+GmJiYGAMAAAAAAAB8EuXXHQEAAAAAAABC4AQAAAAAAABfETgBAAAAAADAVwROAAAAAAAA8BWBEwAAAAAAAHxF4AQAAAAAAABfETgBAAAAAADAVwROAAAAAAAA8BWBEwAAAAAAAHxF4AQAAAAAAABfETgBAIBkFxMTY2lBWtnOSMfvCQCA8EfgBACISHPmzLGyZctanz59kuX+x4wZ4+5/0qRJqX7/3nMNPVWqVMlq1qxprVq1sgkTJtihQ4fi3HbLli1u3VtuueWct/Wrr76yRx55JNHr//DDD+4xO3XqlGKvZ0LbmRKPm1j33nuv25bffvstxR/7p59+sh49elj9+vWtcuXKdvXVV9vtt99uo0aNsn379p33/cf3OutvU8s++eSTwLJjx47ZyJEjbdq0aUl+jH79+tltt91mp0+ftrREfwd6HfR34bepU6e6+9brH59//vnHvW4NGjQIfF507tzZli9fHmfddu3a2aOPPur7NgIA0i4CJwAAIkTx4sWtWbNmgdP1119vZcqUsfXr19vLL79szZs3t7/++svXx9y6das99NBD9vfff1s4SyvbmRqGDx9u99xzj3388cdWqFAhFz5UrFjRduzYYa+++qrdeOONtmrVqhTZltdff93GjRtnJ06cSHKY+N5779mTTz5pUVF8/ZX//ve/7nebkHXr1rmATq9bpkyZ7LrrrrNixYrZF1984cLP+fPnx1pfr+2iRYts3rx5KbD1AIC0IFNqbwAAAEgZNWrUsCFDhsRZvn//fnv66aft008/daGLKqJy5szprlPA8NFHH1mWLFnO6THPpZqkSpUq7jG9bUgJCW1n27Zt7aabbrL8+fNbJFqyZImNHz/eLr30UlcFp9DSo9Bn9OjRNnHiROvatat7/5zr+yQ+jz/+uHXs2NG9B89nKJ22c8CAAVavXj276qqrfNu+tCw6OtpVkKliLCFPPfWUHThwwLp06eIql7yg7rPPPrPHHnvMnnnmGatdu7bly5fPLS9XrpyrhHzxxRetYcOGdsEFF6TY8wEAhCcO8QAAEOHy5s3rKpw0ZGbTpk329ttvB67LnDmzlS5dOlbQkNyyZ8/uHrNw4cKW2rQzrW3RaxSJPvjgg0D4E/oeULikYXZ632zbts2++eYbXx+7YMGC7rXPlSvXed3Pu+++64aGPvDAA75tW1qnSrGVK1falVdeGe/1GzdutF9//dUFjcFhkzRu3NiFsEeOHLEvv/wy1u0efPBB27Nnj73xxhvJ/hwAAOGPwAkAgEQ6fPiwvfbaa3bHHXe4aiHtaNetW9ftdG/YsCHB282ePdtuvvlm1/tGw9jUg0Y7a/HRkBQNV9GO4BVXXGEtW7a0WbNmJXuTZIUH3bt3D+ygn62Hk4bb/Oc//3GVDMGvwx9//BFYR31hGjVq5H7WsD3dj56bd50u6/nqdqpqqlWrlhu+E18Pp6S+nmfqvaSeQMH9uxKznaH3o6oZ7bRrGKK2Xb8v3UbVHwn1XlI1iYag3XDDDW7b9doNGzbMva+SQo+tgFCvuR5bvZRC3yOqPtFjJrTj/9xzzwVe/zPZu3fvGa/PkCGDe37ahty5cweWe6/b119/7X5fTZs2dduq5/7KK6/Y0aNHz/o8Q3s46fXSbeWll15y16ka72yVa1OmTHFDwa655po412vb7rrrLtebSH9vGmqqbY/vd6Jl6lmlIYT6/en+VOmzdu3aeB87qeurX1Lfvn3d71Xbotf1l19+iXddvRbx9WULPcX3+uhvV89Rf9Pe+z6UKpu0DaoKi28IYsmSJd35rl27Yi1XlZOGW6rH1vHjx+O9bwBA5GBIHQAAiaCdxzZt2ridNe28agdVO1SrV6+2Dz/80PU1UU8TXRdMAYp6pZQvX971vlGzXfWg0VAl7ZRlzZo1sO6IESNcoJUtWza3c67Kjh9//NENbfn5559t0KBByfoc9ZxUXbR582bX06ho0aLxrqdQpnXr1i7k0U6pAif1PtLrsHjxYpsxY4bb8dQOrwIhhRoaXqOG06pYCTZ06FDbvXu328nWa6vbnCmMSMrrmViJ2c5g2r7777/fVqxY4SqftO16LdRYW78vVdL07t07zu0U6CmAqVatmqsc+f77791wNAUQOk8svR/0emt4mH5fS5cudcsU9qnBsygAUnCoCqX27dvHCazUj0nbrr48Z6Lfo7ZZw6S0fnxD0lq0aOFO8Zk+fbqrglGvML2uy5Ytc2GHtllBUFKG4Ol3pNvpfaJQQ69hiRIlzngb/T4U5KgHlcKxYAqvtC36O1NgqD5FqvrRcj2Ott27jRqjqym2HluVd/qdK4zTMEI9PwWJderUCdx3UtdXZaG2UQGOXiu9R9QcXqFTgQIF4n0tzhYGSujrc/LkSffe1O9Sw2gVuMWnevXqsYLnUF7PruDhjh79nseOHes+E5s0aXLWbQQApF8ETgAAJMJbb70VaKKr4Mc76q+gQbM2qSpHO/cPP/xwrNspHOnZs6frRSOaCU7rK5xQhYw3K5p26hU2lSpVyvXK8XYUtVOp22rHUBUSqqhJLho+p2FTep7aAU4ocFJQoOf9wgsvuAqs0B34yZMnuwoUVbNUqFDBBTna6VY1TyiFAXrdFB6oGkWv65lm40rs65kUidnOYKouUtikwEAVLN6QL1W5KYjS81eAoVAgmG6jSiSFZfLnn3+6ajn97jWE6UwhVzCFgapc0kxx3muiYELvUe3gq/pOQaBeUwWiofetx1PfLvWn0u/8THS/77//vgshFYjoPaFKND223o9nG/aocEVDstTjyft96Xen4OnNN9+0Dh06WGKpKbXeX3p/qsJNw7fORsGR6DWJr0LtwgsvtAULFgR6dClYVsWTgkwFgnqu8vzzz7vH1XXaDi8o0zBC9TjSe1LVbV7fonNZX2GTrlPloLeNqvLS9sX3WpwLBV2///67C4TOdZioQjm9hxSMqwIqlBdKfvfddwROABDhGFIHAEAiqJJEO1eqUgkeYpIjRw43DEfUxyaUwoXgnWqFEwpqVDmhah2PN/RJQ52CqxLUQ2jgwIGBKcyTmxeeKJBIyM6dO+OtblAljSptgkOos1HTYQUjkpjZwxL7eiYXDYubOXOmq6RSdVZwf6HLLrvMDWXzQrlQ9913XyBsEj1vL9A405DMUKou88ImUUjphRTaNo/CUQmdTczry5RQVVJoH6V33nknsJ0KuxR+qkpG1VG6D73uCQ35VKWeFzaJXi8Ftvp9BW9rclEQKaoaCvbvv/+6SjWFJsFDAdWo/tlnn3Xb6A0b2759u6sI02W9v4OrslS5pMpHVTR5r2tS11fo+u2339oll1ziwjmPbqdm5341z1fFlMJsb4bKc6GZCdXPS79vBYdew/Bg3mvtvfYAgMhF4AQAQCIoLNCwp+CQRaGMqiA0bMcbrhJKzXVDh/JoR1QhgQIq7WxqxihVVGhIT3xNfBVSqAJDlQlJ7feTVKdOnTrrOl61iHY8Bw8e7KpIVI2hMEHDiILDkLMJDQLOJjGvZ3LSTruGUuo1iG9nW8P8FGKo907o+6Fq1apx1r/ooovceWJ6GnlU3RPf44oqhzy33nqrC/GCAycFLRrqpIonhUGJoao3BaIaMqnAVZVNeo6yZs0aN4xPIaDeA6HUuymUflc6/fXXXy7ASE4KfyR0qKv+nhTw6D3TqlUr9/xUKSb6G1TlmVfhp+BE1XcaZhZfRZg3NM77HEjq+t7v7Nprr40TuupvSkNdz5dXLaWKLm/Y5bm8lvoc1N+YnkNoNWfwa6v3h/faAwAiF0PqAABIJO0cq0+QdhQ1HEo77+IFIPFVeYTu6Ho0FEn3oWoh7Zx505OrH9KZaNiNXxUPCTULljx58iS4jnoUKfxauHCh21HXSZVeqgBTdZOqOBIruLokMRLzeia0jh+8JskJDTdUaKht0ZBEBZLB/Xfim21N64sCisSK7/kpuFK44VWficJRhRiqnlEPMAUgasCt8EFhVFJdfvnl7qQhjLoP3aeG26lSR4+h6pngaiZJqMdSkSJF3O9Lf1Px9QHyi4ak6nXxAjKP/mbVM03bq/eyTgpPFa5piKU3fFC84ETPVaeEeOFZUtf33lMJvQ7x/b7VNDwx4aqek/p56bmqik5D6s5lKJ36tj300EOuwk0BmIbPZsyYMcH1NVRQz0ufa6GvPQAgchA4AQCQCKri0Y62dqAuvvhityOvKhHNPqXhMZpdKj5na2KtwEEVTqIgSTuSZ5KUJstJpeop9eoRBQtn2obRo0e7JtVqgqywQU2EFWbopKF1Cb0eoRIzjC6pr+fZJCXcCZWY2QK9+w/9XYVWZp2rhF4DbVvoYyps0O9H1UkKnFTtpNc8MYGT3g/q/6T7VPPwYFqmSiedVCmloV/qNRQaOCUUSniv0ZlCC78q9vRYem1CX3/17dL7Vf2IPv/8c/c3rve/ZiR8++233bBINe/2tlWvwZn+LrxgKKnrn+19Ed97OilNw9WUX6GwPl801E8nj0I/0d+xKs7Uf0lDNoMpYFdvNAXsqqRT37Kz/R16n2mJqZgEAKRfBE4AAJyFdlY1o5PCJjWU9no2ec7UiyahIUNedYKqYVRNpCoMPc7ZGlYnJzU01s6yegupd8/ZaIdaJ031rsooVboMGTLE9ZpSFVRyVK4k5vUM3on3dnyDeZVp58J7XRKqLtEwOlW46PeZ1OqtxFIVU+h963XRzr0qdEKDCa2rhugKgzR8SxUqZ2v2LZo9T02vFSidqT+WekUpcPKq40K3Kz6qlPEqnZKT/rYUuGjb4qvsUXCm18jraaRKHgUqauitKh6FT16VmobaeT26ziSp63vvKe81CeVVQJ1r0/AtW7a4v2sFiKH9vDxqcK6Twq3gwEnDL/X3rYq2O++80/r373/WkFCPpddbfwPJWY0JAAh/9HACAOAsVEmgygdVJISGTaIKkoQqZzRTU3w78qomUA8Z7Zxqp1eVUpr5LbgHT/BO+4033uia9MbXJ8cPCivUo0rU0+ZMNDuYhs0Fb4t27DWjmfoUKTjzgga/qnqS8nqKt6MbXxWI+iuFSux2VqxY0Q0RUs+t+O5bs7LpdVHY4Pdz9yxZsiTOMlWoSGj/LFWiqO+Vfh8KUPQeTUyzcK+/loZK/vrrr653VUI0fNBrmh5fiBlKoY5+X+pNFl8frDNJ6mvqDekLDW30HtDrEhoIqSLpiSeeiDUJgNdXTRVQ8fVp0zBbfS54jeKTur4CQD2vr776Ks76ei95M+2dK1Vk6m8kvpP3XBVG6rICY4+GTKoZvbZB1Z2avCAxFWl79uxxQa9e++T6GwAApA0ETgAAnIUqIxQyaKddw8g82nlXBYR6GYmaSYdSZcm8efMCl9XXxxtupgbbHoU1ooa+XvNir5m0ZrrSTr0a/ibHkDptk2Yd07A4hQZt27Y94/raDlXZqBIkeIiZhl+pibRCCm/mOW/ozaFDh3zZ1sS+nl4zcg3z0g5wcDgYX5VHYrdTz00NpfW77tWrV6z1NTzJm1Hw7rvvtuSiKe0VDnj0mo8ZM8ZVp8T3uBpWJ5ptTtuvHkWJ4TWBFzWIVpgWSr9zvXdEDaXj+31pOJ9HlS9edU7w7yuxvPf/wYMHE7X+FVdcEW/IqOGwqvxRVZ4CtWDe9ioE9prSqz+Zfr+aETE4aF29erX7O1B1kDfsMKnrq8JJgbIq4/T+8arydK7Lwe/flKJqzp49e7r3+f333++axSeW91prOCIAILIxpA4AENE++uijeKswgpvuaudRIYzCJTXFVkWCAigFNNpJVEijhrwauhNKFT+qIlBPGO1Yqh+KQhLt9AeHA6q2+OGHH2zGjBnWvHlzN4xJVUMrVqxwlTR6jKQMo4mPqqe0E+nRjrAqP7QTrB1LVQi99tprZw21NDudqmxef/11N/SobNmyLnjR7Fyq0FDViNcgWxUsGtKlahE1YlbfnPN5Hol9PdVbSI+lZtB6bdWbRr8fvZ56fb0p6T1J2U69hvrdK7xq1KiRu28Fg/r96flrB10BQnLR70mhl56jaKZEPa62N7TXkveaKWBROKTXSaFTYqnCRe/xuXPnWqdOndwQOP2+FdApsNHrqyoWrafXIpSG7vXo0cNV9aj6zPt9aaY9DcU7l+cuGrapvzlVa3nD4eJTv359mzx5sqtI09+uR+9PBZXPPfecG0Km/laaXU1hr8Ig/fzoo48G1lfwo/eFQjuFaGruryFqul8FQ6o+rFWr1jmvr6BZYbb+/lXRpOovBYkauqmqOv2NpqTZs2e7x9bvVr//4M+NYHo/hQaYeo7BMycCACIXgRMAIKIpaImvMsnjVScoZNEOs3bEFNyo8bKmdle4oDBKO7YrV6504VDwMCHtWKo3y1tvveV2GjW8RdUiqmgKHW6i/igaEqWdTu3Ia8dU66uPjh5HMz+dDw0L9JqCiypidJ8Kt7TT3qZNm0TNKKXZu7QjrRmvFHao4bI3fbsahgfPUqfX6cUXX3QnvT6qEjufwCmxr6eG/ihoUHNzhWIarqSwQhUnCmpCA6ekbKcCGz2+Qg9VS6npdPbs2d3zV8iQ3Dvaek4a/qjqLQVdCjMUBp3pcVXpo8ApscPpQl8X3U6VZRpm5QWL+nvQcgV9eg/FR78XBZhvvvmmC1D0O+jWrZt7r53LcCu9T3Wfet0VFCuIPVPgpL8n9bVSdZa2WX2FPPq7Uqir8FLbps8BhZjati5dusTqQaafZ82a5UJWDV9U4Kr3vIbPqVKrcePGsR43qevrtfT+phRQqXeSqvSeffZZW7x4cYoHTl4IrwrG4CbjoVTNFRw4aX1tr2ZNVFAPAIhsGWISM90KAAAA0iSFptddd50LxRQGpERfHQ3xU88oVaOp51dqUsNzVRBpKFuTJk1SdVvSO1VnKXTWMEtNHAAAiGz0cAIAAEiH1IdHVXIjRoxwlXcaOhaJTZxVhaWG/6pkQvJSlZYqPFU9BgAAQ+oAAADSIVU1qW+QhpKp99LZmsGnVxpGp15NGo6poWLBQz7hHzVf1/DBYcOGuWo6AACocAIAAEiH1CxcFU3q36SeT14j90ikfkKq8HrppZfc7JLwn15b9XO65ZZbUntTAABhgh5OAAAAAAAA8BUVTgAAAAAAAPAVgRMAAAAAAAB8RdPwM9AY/1OnTrmfo6KiInJmFwAAAAAAENliYmICfRAzZcrkMpKzIXA6A4VNv/32W2pvBgAAAAAAQFioXLmyZcmS5azrMaQOAAAAAAAAvqLC6QyCS8SU4GXMmDFVtwdILmvWrHHn5cuXT+1NAQDEg89pAAhvfE4jvYuOjg6MAEvMcDohcDqD4J5NCpsInJDe3+u8xwEgPPE5DQDhjc9pRJIMiexvzZA6AAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IpZ6gAAAAAAQIo5deqU7du3zw4dOmQxMTGpvTkRPdtc1qxZLXfu3JYzZ85Ezz6XWAROAAAAAAAgRShg2rJlix09etQyZsxomTIRS6SW6OhoO3DggDvly5fPChYs6GvoxG8WAAAAAACkiIMHD7qwKU+ePFakSBHfq2qQNCdOnLBt27bZ3r17XZVTrly5zC/0cAIAAAAAACni33//ded+V9Pg3GTJksUFf8G/G78QOAEAEEF++OEHK1u2bLyndu3axVp3+fLlVqlSpbPep45SPv3001azZk2rUaOG9evXzw4fPhy4fvPmzda6dWurVq2ade7cOdaXGd22YcOGbh0AAJD+nTx50g2jYyhdeIVOmTNntuPHj/t6vwROAABEEIU+3377bazTiy++aFFRUdaxY8fAer/88ot16dLFTp8+fdb7fOaZZ1w4NX78eHvttdfsxx9/dMs8Q4cOtdKlS9vcuXPdl8wJEyYErps6dapdd911Vrx48WR4tgAAIBx7OOl7B8KLqs38buDObxkAgAg7glWgQIHAKVu2bDZs2DB78MEHrW7dum4dXW7btq0VLVr0rPe3fft2+/DDD+3ZZ5+1K664wlU4DRw40BYsWGA7duxw62zYsMGaNm1qJUuWtOuvv97WrVvnlu/fv9+mTZvmgi0AABA5GEoXGb8TAicAACLYq6++6kKoRx55JLDs66+/dtVK995771lv//PPP7ujlNWrVw8s08+adUZVT3LxxRe7n1UtpfNixYq55XqM2267zQVfAAAASF8InAAAiFB79uxxFUYKm7Jnzx5Y/sEHH1jt2rUTdR+qYtI0uhr371FPBi3TjCfSrVs3mz17tusHtWbNGuvUqZO7bv78+dahQ4dkeGYAAABIbXTpAgAgQr3zzjuWP39+a968+Tnfh5p+Z82aNc5yVU15jScrVKhgX375pZtu16tm6tu3r6ugio6Odr2j1q9fbzfddJP16tWLMnsAAIB0gMAJAIAIpUqm22+/PVZ1UlKpB9SJEyfiLNeyHDlyBC5riJ0XNqmn05IlS1xj8Zdeesk1DB89erTrG7Vo0SJr3LjxOW8PAABI206eOm1b98T9bpFaiubPYpkznf/gsL1797oDcrly5Trv+9qyZYs1atTIHbTr2bOnhSsCJwAAIpAqiv766y+7+eabz+t+Chcu7L5AqVJJoZKcOnXKLStYsGC8txk+fLgbVqdhfOrp1KNHD/dzrVq1bNmyZQROAABEMIVNnUeutXDxWreyVrJQtvO6j6+++spVcau63I/ASa0LdNCuTJkyFs7o4QQAQARSsKOKo9KlS5/X/Vx55ZUuYFqxYkVgmdcgXNeF0npr1661Vq1axZmCV/fj93S8AAAAqe3XX3+1AwcO+HZ/qiK/9dZbrXz58hbOCJwAAIhAat59rkfFVL108OBB93OhQoWsadOm9tRTT7mgSUHW008/7b4E6bpQw4YNs0cffTQwjK9KlSr2/vvvu4qrzz//3K644orzfGYAAAAIBwROAABEoJ07d1qePHnO6bYtW7a0F154IXB54MCBVr16dXvooYfcjHfXXHONPffcc3Fup8bhOroX3KS8a9eutnv3bmvTpo3VqVPHmjRpco7PCAAAIPz06dPHXnnlFfezJkjRpCla1rBhQ5s1a5bVrFnTfY/SATj5448/rHv37u57UcWKFd31nTt3dhXiwT2cypYt6w7keXR5zJgxNn36dLvxxhvd7MA61+XUQg8nAAAi0GuvvXbWddRQXKdQqkQKljNnThs8eLA7nUn9+vXdKZiqoFLzixAAAEByat26tR06dMg+++wz18dJw+Dmz5/vDri9/PLLrq+lKsdr1KjhJlbRQbgiRYrY/fffbxdccIGrSn/vvffcsDx9B9OELQlRaKVZgjURiw4svv322zZgwAC7+OKL7brrrrOUFraBk9d1/Uz0xVZfhI8dO+YSwwULFrgy/3Llylm3bt1c81EAANKKcJuVJb3ya7YZAACAs6lWrZqrPlLg1KBBA9c/U4GTgiG1IbjzzjsD6/bv39/1tJw6dWqsyVfUaHzChAn2+++/u2qohOzatcsWLlxoRYsWdZcVMilX0czEBE7xdF0PpSakgwYNck1Fr7rqKrdMs9t88cUXdvfdd9ull17qytI6dOjgfklKCQEASAvCbVaW9MqP2WYAAADO1zXXXBPr8jPPPON6XSoP8Rw9etSiov53oOzIkSNnvD/1wvTCJlFlU+7cuV01VWoI28DJ67oeauzYsfbvv//ayJEjrXjx4rZ06VJbtGiR9e3b19q3b+/WadGihesPoWBqzpw5qbD1AAAAAAAACcufP3+sy5q9V8PrXn/9ddfLafPmzfbPP/9YdHR0oADnTIKDKk+WLFnOervkkqbqyf/++28bN26cKwXTjDiiUjTNdONNr+yFVWpounr1atu0aVMqbjEAAAAAAEBcGTNmjHX5k08+cY3FNQROYZSG22konSqfEsOrhAoXYVvhFJ8RI0a4oXS9e/cOLFu1apWVKlXKhUzB1M3du/6SSy5J8W0FAAAAAABIrKFDh7qG4XPnznV9mzzKNdKiNBM4/fnnn/bxxx/bbbfd5ppseXbs2GFVqlSJs77XYGvr1q2+PL46w6u8DUiP1LBOVBUIIHUE/29D8tOEIxs3brS0gs9pAAhvfE4n3okTJ1yYot5EobJmzWrhSEPSvN/xud5e9Jx1UmNw73LwcLf9+/e7BuOqfPJeH7UUUp/q4Nvre4zofoJfRw29C31dVbQT3/JQWkez6SX0Htb9pNvASdP56Ql6fZo8hw8ftuzZs8dZ35sq8GwvKgAAAAAACJ/ZZDXBRzhtz/m68MIL3fm0adPOOFtcnTp13LA6DaHT7HY7d+501U579+4N5B9pSaa0koDqRa5Zs6ZL+5LCr6qk8uXLxxlfCaQXXortDUUFkEoO/O9oFZKfDkylpc88PqcBILzxOZ200UsSX+GIqA1ROM4mm9D2JkaLFi3siy++sAULFtgvv/xiVatWDdxncFXX888/b3ny5LHPP//cFi5caIUKFbJ69erZAw88YDfffLMtW7bMbr/99kCBTaZMmWJtlzKL0O1UJhLf8lBaR4996aWXJlgBtXLlyvQXOP3444+uU7uaZ4VS7yavnCyYtyx43CMAAAAAAEBKyp07t02ZMiXWsiFDhsS73oABA9wpVPBQt4svvtjWrl0b6/rQy54lS5ZYagmvFuYJ+Oqrr1y39caNG8e5rmjRorZr1644y1V6JkoEAQAAkL699957duONN7renjr6u3TpUre8YcOGrkI+vlN8vT7nzJmT4Pp9+/Z162ia6tatW7vhDp07d3b9NTxq56DH1DoAAESyNBE4LV++3MqUKeOmBQylksUNGzbEqXLy0r/KlSun2HYCAAAg5b3//vvWv39/69ixo82fP9+uuuoq69Kli23ZssU1Wv32228Dp88++8zNANSsWTN34DKUKuqD19epe/fubijCfffdF5hFSI3+1fLh5MmTbspqz9SpU11/juLFi6foawAAQLgJ+8BJXdfXr1+f4FjYJk2auB5PM2bMCCw7cuSI+3KhI1wlSpRIwa0FAABAStKkMmPGjHFhU8uWLa1kyZLWu3dv9x1wxYoVli9fPitQoEDgNGnSJNenQn0y4qO+GMHr66Dma6+95u6zXLlybh0d7GzatKl7rOuvv97WrVsXmF1IDWEVdgEAEOnCvofTtm3bXKAU3xEoqVu3rjvpSJPWLVWqlM2cOdO2b98e75hIAAAApK/ms//880+sXp9qxTBv3rw46/7xxx/ue6ICpMQ2f9V3TFXaawhdcO8MVeDXrl3bnRcrVswtHz9+vN12220uqAIAINKFfYXTvn373PkFF1yQ4DqjRo2yu+66y5VQv/jii5YlSxZ39KpGjRopuKUAAABIaZs2bXLn6qPUrl07q1WrlrVt29Z+/vnnOOuqEurKK68845TUoQGVZgl6/PHHXYjl6datm82ePdsqVapka9assU6dOrkDn/ou2qFDBx+fHQAAaVfYVzhpWFxC3dY9OXPmtH79+rkTAAAAIsehQ4fceZ8+feyxxx5z0zmrgbj6LanHknotiZp4a5rp4H5LZ6N+TFdccYVdc801sZZXqFDBvvzyS9u7d2+gmkkNxe+99143bbSG96klhKquevXq5aakBgAg0oR9hRMAAACQkMyZM7tzzRanRuDq+/nss8/aJZdcYu+8805gPVUfqVl4nTp1EnW/x48ft08++cRatWoV7/XqA+WFTerppGmnVWGlKio1DP/444/t+++/t0WLFvnyPAEASGsInAAAAJBmFSxY0J2rz5JHFUWqdNIsdZ7Fixe7Rt+JrTZaunSpm4GucePGZ113+PDhblid+kKpp5OG7OlnDe9btmzZOT0vAADSOgInAAAApFmqaMqRI4f99ttvsWau27hxo6s08mYwVq+l0KFxZ6KgSPedO3fuM66nmfDU/sGrhFKgpcf3Zlv2fgYAINIQOAEAACDNUiWR+jWNHDnSPv30U9dEfPDgwfb333+7SWVEgZB6KwVXQQVTL6aDBw/GWqaAKqH1gw0bNsweffTRwNA+9R99//33XQ8n9YxSDygAACIRgRMAAADStP/85z/24IMP2qBBg1wfp5UrV9rkyZPdsDrZtWuXO8+bN2+8t2/ZsqW98MILsZbt3LnT8uTJc8bHVePwAwcOWPPmzQPLunbtart377Y2bdq4flFNmjTx4RkCAJD2hP0sdQAAAMCZaBibeijpFJ8bbrjhjLMeqxIplJqMn039+vXdKVihQoVs+vTpidpuAADSMwInAAAAAAAQFmJOnbTT+7ZauIi6sKhlyPS/YdOpbc6cOda3b1+bOHGi1atXz8IdgRMAAEAyee+99+z111+3bdu22WWXXWa9evVyM5d5w7iCG10nNLTLo6Fbuv0333zjGlGrAba+dBYuXNhdv3nzZuvZs6etW7fOatasaS+99FKg4fXRo0ft5ptvtqlTpwYaaaeEk6dP2u7j/xvOhuRzUdYCljkqPHaGAOB8KWz6d2IXCxe5O75qGQuUTO3NSJMInAAAAJKBGkf379/fnnvuObvqqqvs7bffti5durihWsWKFbMNGza4htPBM6epAXZCRowYYZkyZXK9iTSEbMCAAfbII4/Y7Nmz3fVDhw610qVLu6BJ102YMMEFUKKg6brrrkvRsEkUNg38/dkUfcxI1K9CfyuSvWhqbwYAALHQNBwAAMBnqkAaM2aMdezY0VUtlSxZ0nr37m0lSpSwFStWuGokVR1pBrMCBQoETrly5Yr3/rSuqqF0fxUqVLDy5ctb586dbdWqVbZ//363jgKspk2buse6/vrrXaWT6Ppp06a5sAsAACClEDgBAAD47M8//7R//vnHbrrppsCyqKgomzdvnptFTWFQtmzZXKVTYmTOnNmtP3fuXDt06JAdPnzY/axwyRs2d/HFF9vy5cvt9OnT7ty77/Hjx9ttt93mAi0AAJCynnvuOStXrpxt37493kkt7rzzTvfzjz/+6A4mqfK5YsWKdu2119rjjz9uW7eGTz+rpCJwAgAA8NmmTZvc+b///mvt2rVzfZvatm1rP//8s1u+fv16u+CCC9yQtzp16rgQasqUKS4sio+G0j366KP2ww8/WI0aNdzpp59+ck1DFWRJt27d3PC6SpUq2Zo1a9yMbeodpSF8HTp0SMFnDwAAPLfeequrfP7oo48s2K+//mp//fWXNW/e3JYuXWrt27e3PXv2uIrkp59+2n0/0G0SmoE1LaCHEwAAgM9UhSR9+vSxxx57zC699FLXQPy+++5zlUka/nbkyBH3ZVJfJBVEqffSwYMH3frx2bJli5UpU8YFTwqZRo0aZV27drV33nnHDcXTULsvv/zS9u7dG6hmUlPxe++916Kjo91wPAVdqrpS83H1gQIAAMmrWrVqriJZ4dEDDzwQWL5gwQJ3QEmTeuj7woUXXmhvvvlmoJ9jmzZt7NSpU269HTt2WKFChSytIXACAADwmYbAiUrjVb0kCoQ01E0B0YsvvugCJ284XNmyZV3Y9Nprr7lAKTQM+v33323GjBkuUPK+cI4dO9YaNGjgmpMrVJKMGTMGwiaFWkuWLLFnnnnGhVlqGD569GhXabVo0SJr3Lhxir4mAABEqubNm7vejurhqP/Hqmj++OOP3YGnfPny2bhx41xVdPDkITp4lTVrVvezvjOkRQypAwAA8FnBggXduSqSPAqRVOmkSiUd0fTCJo9CJ/VmUvAUSj2f8ubNG+vopm5/ySWXuHL8+AwfPtxVT+nLq4IuzVKnnzW8b9myZT4+WwAAcLbASbxhdfo/rKolb7kOGGkY/PPPP++G4jds2NANn58zZ467PqEh9+GOwAkAAMBnavaZI0cON7OcR/0bNm7c6I5stmrVygYOHBjrNlpXQVVoECX58+e3AwcOuN4OwTPXKbxS6BRKM+GtXbvWPY4XdunxReX53s8AACD5lShRwg2t0/A4+fDDDy1nzpzWqFEjd/mNN95wE3x8/fXXbhIQVS6/9dZbabp/kzCkDgAAwGeqJFK/ppEjR9pFF13kKp3efvtt+/vvv92wNgVLOleD7+rVq7tm4K+//ro99dRTgftQLyYNzVNzcR3l1P2oMXjv3r3dct1epfYtWrSI8/jDhg1zQ/O8oX1VqlRxQ+80c93nn39u3bt3T9HXAwCASNe8eXPr37+/m8n2008/tRtvvNHNQHv8+HH3fUGBlHo4ZcmSJXCbDz74wNIyAicAAIBk8J///McFT4MGDXKVSeXLl7fJkye7YXWlSpVyw+rUs0HTHRctWtQ1+PamRpaWLVva1VdfbUOGDHH3oy+pajiu5t+qUFJQNX36dNcwPJj6PKkayivTFzUX19TKakCq5U2aNEnR1wIAgEh30003ue8E6qu4b9++wP/pY8eOuaplNRYPDpv0/UDBlGjyj7SIwAkAAESUAnn/V/WT3DSMTaXw8ZXD67r777/fnRKiSiRP6dKl3UlNws+mfv367hRMvZ8UTgEAEO6iLixquTu+auG0PX7Imzev66eoiTsKFy5sNWvWdMvz5Mnjqpvmz5/vhtWrKloV0TNnznRBlKjHY1pE4AQAACJKpowZLObUSTu9b6ulFV5EFh23n3jYirqoRGpvAgAgDcqQKbNlLFDS0qNbb73VBU4333yzRUX9X0vtUaNGuYpm9XZSxZMCKVU633DDDa4f43fffedCqbSGwAkAAEQchU3/TuyS2puRruV94v3U3gQAAMLKDTfc4Cb1CKVK5BEjRsR7m+D1b7/9dndKK5ilDgAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+CqTv3cHAAAAAABwbk6ePmm7j++ycHFR1gKWOSrzed/P3r17LUuWLJYrVy5LDn///beVKFHCwgmBEwAAAAAACAsKmwb+/qyFi34V+luR7EXP6z6++uor69Wrl73zzjvJEjg9+OCDljt3bhsxYoSFE4bUAQAAAAAAJJNff/3VDhw4kGz3/+2331o4InACAAAAAACArwicAAAAAAAAkkGfPn3slVdecT/fdNNNdu+997qf//zzT3vsscfs6quvtipVqtjtt99uH330UZzbjx8/3po2berWqVmzpnXt2tXWrVvnrtuyZYuVLVvW/azb6ucffvjBwgWBEwAAAAAAQDJo3bq1NW7c2P2sPk6dO3e29evXW6tWrWzVqlWu/1LPnj0tZ86c1r17d3vjjTcCt50wYYINHz7chU39+vWz+++/35YvX2733HOP7du3z/Lly2cvvfSSW/eKK65wP5cuXdrCBU3DAQAAAAAAkkG1atVc5dFnn31mDRo0cIFQu3btXPPwuXPnumbfosonVTwpYGrevLkLk+bNm2eXX365vfjii4H7K1++vAuWFFqpOurWW2+1J554wooWLep+DidUOAEAAAAAAKSAffv22Y8//mj16tWzU6dO2d69e91Jy2+44QY7fvy4LVmyxK1buHBhN/ROQ/I0fE6uu+46W7BggQubwh0VTgAAAAAAAClg8+bNFhMTY++++647xWfr1q2B/k+dOnWyMWPGuNNll11m9evXt5YtW1qpUqUs3BE4AQAAAAAApIDo6OhAb6cmTZrEu07x4sXduYbTLVy40L777jv78ssv7dtvv7XXX3/dpk6d6pqJ165d28IZgRMAAAAAAEAKKFasWODna6+9Nk7109q1ay179ux2+vRpNxtdVFSUG0ankyxbtszat2/vQqdwD5zo4QQAAAAAAJBMoqL+F71oKF3BggWtcuXKNn/+fBcweXTd888/b4888ojr55QhQwY3nE7D6ryqKKlYsaJlzpw5cJ/e/SugCjdUOAEAAAAAgLBwUdYC1q9Cfwun7Tlf+fLlc+dTpkyxhg0b2tNPP+1mqlMvprZt21qBAgVs0aJFbsjcXXfd5YbSyUMPPWQDBgyw+++/32688UYXSmnmumPHjtk999wT6/6XL1/uekLVrVvXzVgXDgicAAAAAABAWMgcldmKZA+PwMQvN998s3366af2wQcf2M8//2wff/yxC4fUCHzatGluZroSJUrYU0895QIoj37Oli2bTZ8+3YYPH+6qmCpVqmQTJ060OnXqBNbr2bOnvfzyyzZw4EBXJdWiRQsLBwROAAAAAAAAySR37tyuuilYuXLlbOzYsWe97R133OFOZ3Lbbbe5U7ihhxMAAAAAAAB8ReAEAAAAAAAAXxE4AQAAAAAAwFcETgAAAAAAAIicwElT/Y0YMcJNG1i1alVr1qyZvf/++3HWGTZsmDVo0MCt07p1a1u6dGmqbTMAAAAAAECkC9vASdP9denSxU33p8Cpd+/eduGFF1qfPn1sxowZgfV69OhhkydPtkaNGrl1Tp48aR06dLBly5al6vYDAAAAAIC4YmJiUnsTkAK/k7ANnObOnWtLliyxp556yvr162d33323vfHGG1ahQgV75ZVX3IuhSqZFixbZE088EVhn2rRpVqRIERs0aFBqPwUAAAAAABAkKirKoqOjCZ3CiH4X+p1kyJAhMgKn2bNnW4kSJeyuu+6K9cbs1q2btWnTxo4cOWLz58+3zJkzW6tWrQLr5MiRw1q2bGmrV6+2TZs2pdLWAwAAAACAUFmzZnXhxs6dOwmdwsCpU6ds27Zt7neSK1cuX+87k4UhDYv75Zdf7Pbbb3chkxw+fNiFSdddd507yapVq6xUqVJuebCKFSsGrr/kkktS4RkAAAAAAIBQhQoVsuPHj9vevXvtwIEDljFjRt8ra3B2CvvUykiBkyhXURujdB84bdmyxYVOxYoVc8Po1KNpx44dljdvXmvfvr117tzZvSG1rEqVKnFuX7BgQXe+detW37ZpzZo1/BEg3dIHvqgyEEDqKF26dGpvAoA0TBPpbNy4MbU3A4hYfJ8+tyFcOqfKKXUp51Chz8GDB23t2rUJrncuv6ewDJz0RGXOnDku8Xz44YddiDRv3jwbOXKkHT161B5//HFX9ZQ9e/Y4t8+WLZs713oAAAAAACC8Qo5MmcIyjoCPwvI3fOLECXe+efNmFzqVK1fOXW7atKnde++9ruKpXbt2Z70fPyuSypcv70r9gPTIOxLjDUcFkEoOHEvtLQCQRumAK//HgdTD92mkd9HR0bZy5cq03zTcq1rScDkvbPKor5OG2y1fvtyNMVT5cChvmd8NrwAAAAAAAJBGA6fChQu783z58sW5zlum4XRFixa1Xbt2xVlH3e69ZmQAAAAAAABIWWEZOOXPn9+FTvE1PlRDcSlSpIgrV9ywYUOcKievnLFy5coptMUAAAAAAAAI68BJmjVrZps2bbJPP/00Vm+nt99+21U51ahRw5o0aeKWzZgxI7DOkSNHbNasWW44XokSJVJp6wEAAAAAACJXWDYNl86dO9vixYutZ8+e1rZtWytWrJjNnTvXVT29/PLLljlzZqtbt647DR061LZt22alSpWymTNn2vbt223IkCGp/RQAAAAAAAAiUtgGTmr4rWqmUaNG2QcffGAHDx60MmXK2Lhx46xBgwaB9XT9iBEjbP78+Xb06FErW7asTZo0yVVAAQAAAAAAIOWFbeAkF154oT333HPulJCcOXNav3793AkAAAAAAACpL2x7OAEAAAAAACBtInACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPgqk4W5Nm3a2IoVK+IsL1eunM2bN8/9vG/fPhsxYoR9/vnndvjwYatatao98cQTVqFChVTYYgAAAAAAgMgW9oHTunXrrH79+nbTTTfFWp43b153fuLECevUqZOtXbvW2rdvbxdddJG99dZbds8999js2bOtVKlSqbTlAAAAAAAAkSmsA6d//vnHVSwpcLr11lvjXUdVTr/88ou98sor1rhxY7esSZMm1rRpU1f1NHr06BTeagAAAAAAgMgWFe7VTVK6dOkE1/nwww+tYMGCgbBJChQo4AInb4gdAAAAAAAAUk5YB07r169355dddpk7jy88Wr16tVWsWDHOci07efJkILQCAAAAAABAygjrIXXqy5Q1a1YbNWqUq2Q6dOiQq2bq2LGjtWvXzgVQBw8etMKFC8e5rdaTbdu2WbVq1c57W9asWWMZMmQ47/sBwtHx48cDAS6A1HGmal4AOJtjx47Zxo0bU3szgIjF92mkdzExMekrcFKFk/5wd+zYYYMGDbKjR4/ae++9Zy+88ILt37/fzWAn2bNnj3PbbNmyufMjR46k+HYDAAAAAABEsrAOnFq3bm3R0dGumsnTvHlzu+uuu2zChAnu+rPxqyqpfPnyljFjRl/uCwg33pGY+IanAkhBB46l9hYASKN0sJX/40Dq4fs00rvo6GhbuXJl+unh1LZt21hhk0RFRbmgSf2Zvvvuu0AJcShvWa5cuVJoawEAAAAAABD2gVNC8ufP785Pnz5tuXPntl27dsVZZ+fOne68UKFCKb59AAAAAAAAkSxsA6etW7fazTff7BqGh/rzzz/defHixV3JYnyN2bQsU6ZMbigcAAAAAAAAUk7YBk5FihSxAwcOuCbhOvfo5zfeeMOKFStm1atXtyZNmrhwatGiRYF1VPH08ccfW+PGjd0sdwAAAAAAAEg5Yds0XM2+n332Wevatau1atXKNQo/ceKEvfvuu7Znzx6bOHGiq2C644477O2337aePXvaAw88YPny5bM333zT3f6xxx5L7acBAAAAAAAQccI2cBJVKI0bN87NSDd8+HAXMFWrVs39XLVqVbdO5syZbcqUKfbSSy/ZtGnTXOd0XaeheJdeemlqPwUAAAAAAICIE9aBkzRs2NCdztZE/MUXX0yxbQIAAAAAAEAa7OEEAAAAAACAtInACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgq0x+3MmhQ4csV65c7ufdu3fbBx98YFFRUdasWTPLnz+/Hw8BAAAAAACASAic9u/fb48++qgdPXrUZs2aZQcOHLDbb7/ddu3aZTExMTZx4kR7++23rWTJkv5tMQAAAAAAANLvkLoxY8bYihUrrF69eu7ynDlzbOfOndarVy978803LVOmTDZ69Gi/thUAAAAAAADpvcLpiy++sHvuuccee+wxd3nRokWWL18+e+CBB9zltm3b2pQpU/zZUgAAAAAAAKT/CicNnbv88svdzwcPHrRffvnFatWqFbj+wgsvdMPtAAAAAAAAEDnOK3AqUKCAbd68OVDdFB0dbdddd13g+pUrV1rhwoXPfysBAAAAAAAQGUPqVM00depUN0vdggULLEeOHNawYUPXx2nSpEn2/vvvW+fOnf3bWgAAAAAAAKTvwKlv3762Y8cOmzZtmuXKlcsGDhzozjds2OCCqCZNmlinTp3821oAAAAAAACk78BJ4dLrr79ue/fudT9nyZLFLS9XrpwtXLjQSpYs6dd2AgAAAAAAIBJ6OHk0M92BAwdc03A1D4+KirLixYv7cdcAAAAAAACItMBJjcHvuOMOq1evnrVp08ZWrVplP/30kzVo0MA++eQTf7YSAAAAAAAAkRE4/f7773bffffZvn377K677gos1/C6mJgYe/zxx+3bb7/1YzsBAAAAAAAQCYHTyJEjrXDhwjZ//nzr2rWrC5mkatWq9sEHH7geTuPHj/drWwEAAAAAAJDeA6fly5e74XQ5c+a0DBkyxLoub968bojdunXrzncbAQAAAAAAECmB0+nTpy1HjhwJXh8dHW0nTpw4n4cAAAAAAABAJAVOFSpUsIULF8Z7nYKmuXPnWvny5c/nIQAAAAAAABBJgVOnTp1s2bJlrn/T119/7Zb99ddf9tFHH7nhdOvXr7cHHnjAr20FAAAAAABAGpDpfG5cr149Gzx4sA0cONAWL17slvXv3981D8+WLZs9+eSTdv311/u1rQAAAAAAAEjvgZO0aNHCGjdubEuWLLG///7b9XUqVqyY1a5d2zUOBwAAAAAAQGQ578BJNEvdDTfc4MddAQAAAAAAIJICpx49eljbtm2tevXqgcuJ8fLLL5/b1gEAAAAAACB9B04LFiyw+vXrBwInXT6bDBkyEDgBAAAAAABEkCQFTn/88ccZLwMAAAAAAABR53PjuXPn2pYtWxK8fuPGjTZ+/Hjzwz///OMqq/r06RNr+bFjx2zYsGHWoEEDq1q1qrVu3dqWLl3qy2MCAAAAAAAghQOnvn372sqVKxO8/vvvv7exY8fa+YqJibEnn3zSDh8+HOc69ZGaPHmyNWrUyHr37m0nT560Dh062LJly877cQEAAAAAAJDMQ+o2b95svXr1ihUEjRkzxqZNmxZn3dOnT9uGDRusQIECdr6mT59uy5cvj7NclUyLFi1ywVf79u3dshYtWljz5s1t0KBBNmfOnPN+bAAAAAAAACRjhVPx4sWtTJkytnPnTndSQ/B///03cDn4tG/fPrv88sutf//+dj7+/vtv13S8a9euca6bP3++Zc6c2Vq1ahVYliNHDmvZsqWtXr3aNm3adF6PDQAAAAAAgGSucJIBAwYEfi5Xrpwb6tasWTNLDqqSUs+msmXL2n333WcjRoyIdf2qVausVKlSLmQKVrFixcD1l1xySbJsGwAAAAAAAHwKnFJylrqpU6e60EjNyaOi4hZj7dixw6pUqRJnecGCBd351q1bfduWNWvWuIouID06fvy4O1dlIIDUUbp06dTeBABpmCbS0YQ9AFIH36eR3sXExCRv4PTuu+9arVq1rESJEoHLiaGZ45Lqzz//tJEjR9p//vMfu/TSSwN/wMHURDx79uxxlmfLls2dHz16NMmPCwAAAAAAgPOTpMDp2WeftaFDhwYCJ10+G1UFJTVwio6Odo3Ay5cvb/fff3+Sbhv62H7RtmTMmNG3+wPCiXckxhuOCiCVHDiW2lsAII3SAVf+jwOph+/TSO+io6Nt5cqVyRc4vfnmm7FK/nU5OUyePNkNpdP979+/3y07efKkOz9x4oTt3bvXcuXK5Xo3qXw4lLdM6wAAAAAAACBlJSlwuvrqq2Ndvvjiiy1fvnyBIWyhNIPd2rVrk7xRX3/9tZ06dcruvvvuONctWLDAnQYPHmxFixa1Xbt2xVlHs+RJoUKFkvzYAAAAAAAASMWm4Y0aNXJD7G655ZZ4r1+4cKENGjTIVqxYkaT77d27twurgqnC6aGHHrI6derYgw8+aJdddpktX77cPvjgA1fRFBx6eeWMlStXPqfnBQAAAAAAgBQKnLZs2WIzZ86M1aVc1Ubr1q2Ls+7p06ftq6++sqxZsyZ5oypVqhRnmdc0vECBAnbttde6n5s0aWKzZs2yGTNmWPv27d2yI0eOuGWavc7rNQUAAAAAAIAwDZw0hO7777+3X3/9NdCU+4svvnCn+ERFRVn37t0tudStW9edVGW1bds2K1WqlAvEtm/fbkOGDEm2xwUAAAAAAICPQ+rU0PvAgQOuuun666+3J5980g2tC6UZ3fLmzZtgfye/jBo1ykaMGGHz58+3o0ePWtmyZW3SpElWo0aNZH1cAAAAAAAA+BQ4aeY3b/Y3b9a6/PnzW3LT0Lz4GpDnzJnT+vXr504AAAAAAABI403DvVnr1F9p//79Fh0dHe96mk0OAAAAAAAAkeG8AieFTP3797fPPvsswbBJ1qxZcz4PAwAAAAAAgEgJnF588UX7+OOP7aqrrrIKFSpYlixZ/NsyAAAAAAAARF7g9Pnnn1uLFi2YEQ4AAAAAAAABUXYeTpw4YVdeeeX53AUAAAAAAADSmfMKnKpWrWorV670b2sAAAAAAAAQ2YFTnz59bPHixTZp0iTbunWrm61OVU+hJwAAAAAAAESO8+rh1KVLFzt16pQNGzbMneKTIUMG+/3338/nYQAAAAAAABApgdPVV1/tAiUAAAAAAADAl8CJ2ekAAAAAAADga+Dk0bC6VatWuT5OqnrKli2bRUdHW548efy4ewAAAAAAAERK03D59NNPrUGDBnbXXXdZjx49bP369bZ8+XK77rrrbPLkyf5sJQAAAAAAACIjcFq6dKl169bNihYtat27d7eYmBi3vEiRInbJJZfY0KFD7cMPP/RrWwEAAAAAAJDeA6dXX33VypUrZ9OnT7c777wzsLxMmTI2c+ZMq1Spkk2dOtWP7QQAAAAAAEAkBE7q29S8eXPLlCluK6gsWbJYixYt7M8//zyfhwAAAAAAAEAkBU5RUWe++aFDhyxDhgzn8xAAAAAAAACIpMCpWrVqNnfuXDt9+nSc6/7991979913rWrVqufzEAAAAAAAAIikwOmxxx6zjRs3WuvWrV24pGqmZcuW2YQJE6xZs2a2c+dOe/jhh/3bWgAAAAAAAKTvwKlKlSouXNq/f7+NHDnSzVI3duxYGz58uPtZy2rUqOHf1gIAAAAAACDsxe32nUS1atWyTz/91NasWWN//fWXG15XrFgxN0NdfM3EAQAAAAAAkL75kghpKF2FChXcCQAAAAAAAJHtvIbUAQAAAAAAAKEInAAAAAAAAOArAicAAAAAAACkXuC0ePFi27Vrl79bAAAAAAAAgMgNnPr06WNfffVV4HK7du1s6dKlybFdAAAAAAAAiITA6dSpU7ZmzZrA5R9//NH27NmTHNsFAAAAAACANCpTUlauW7euTZ8+3d5//33LkSOHWzZgwAAbMmRIgrfJkCGDffPNN+e/pQAAAAAAAEh/gdOgQYOsWLFitm7dOjtx4oSrbrrooossf/78ybeFAAAAAAAASL+BU65cuax3796By+XKlbOHH37YmjVrlhzbBgAAAAAAgPQeOMU3a12+fPn82xoAAAAAAABEduCk4XUHDx50PZw+++wz27Ztm2XKlMkKFy5sDRs2tC5dulju3Ln921oAAAAAAACkr1nqQu3fv9/uvPNOe+ONNyxLliwuZKpTp45FRUW5ZS1btrR///3Xv60FAAAAAABA+q5wGj16tG3ZssVGjBhhTZs2jXXdJ598Yj179rSxY8da3759z3c7AQAAAAAAEAkVTosWLbI2bdrECZukSZMm1rp1azfUDgAAAAAAAJHjvAKnvXv32mWXXZbg9bpu9+7d5/MQAAAAAAAAiKTAqWjRovbzzz8neL2uK1So0Pk8BAAAAAAAACIpcLrlllts/vz5NmHCBDtx4kRguX4eP368ffjhh3bTTTf5sZ0AAAAAAACIhKbhnTt3tp9++smGDx9u48aNcxVPMTExtnXrVjt27JhVr17dunTp4t/WAgAAAAAAIH0HTlmyZLEpU6bY7NmzbfHixW7GOgVOV111lTVq1MjuuOMOy5w5s39bCwAAAAAAgPQdOLk7yJTJzUanEwAAAAAAAHBePZwAAAAAAACAUAROAAAAAAAA8BWBEwAAAAAAAHxF4AQAAAAAAABfETgBAAAAAAAgvAOnHTt22K+//urOAQAAAAAAEHky+XVHO3futB49etiyZcssJibGMmTIYFdddZUNGzbMChYs6NfDAAAAAAAAIFIqnPr372+ZM2e26dOn2yeffGKvvfaa7d6925555hm/HgIAAAAAAADprcJp7dq1VrZs2XivW7FihY0fP94qV67sLl9yySX2zz//2PDhw/3ZUgAAAAAAAKS/Cqc77rjDunXrZhs3boxzXcmSJe3tt9+2Xbt2WXR0tG3atMk++OADK126tJ/bCwAAAAAAgPQUOM2bN8+d33rrrdazZ08XKnmeffZZ+/77761evXpWqVIla9q0qevrpKF2AAAAAAAAiBxJGlKnaqWRI0e6oXWjR4+2W265xZ0eeeQRK1eunH322We2fPly27Nnj2sUXqVKFcuSJcs5b5we5+WXX7ZffvnFTp8+bbVq1XKNyVVN5Tl27Ji98sortmDBAtu7d6/bDlVhaV0AAAAAAACkkabh6uM0duxYmzFjhgt5VM3Ur18/V9FUs2ZNu+mmm6xGjRrnFTb997//tbvuusvWr19vnTp1soceesh+/vlna9WqlW3bti2wngKoyZMnW6NGjax379528uRJ69Chg5stDwAAAAAAAGlsljoNnZswYYJNmzbNNQi/8cYb3dC6HTt2nPeGqZJKvaDeeuste+CBB6xjx442adIk279/vwuYZOnSpbZo0SJ74oknXOB19913u20pUqSIDRo06Ly3AQAAAAAAAMk8pE5UZaReTtu3b7cLL7zQateubc2aNbMpU6bYjz/+6IbaNW7c2FUiqSpJQ+vORaZMmezmm2+2iy++OFZlVd68ee2PP/5wl+fPn2+ZM2d2j+XJkSOHtWzZ0kaMGOF6TGm2PAAAAAAAAIRphdN7773nqojWrFljuXLlst27d9uTTz5pvXr1ctdfffXVrsJo3LhxtmrVKrvhhhts8ODB57Rh6t0UWqWkoXSqcCpatKi7rMcoVaqUC5mCVaxYMXA9AAAAAAAAwrjC6bXXXrP77rvP+vbtG1j27bffuuFujz32mBUvXtwtU9WTTl9++aWreDpfakKu8GjYsGEuXNIQO9HQPTUmD+VVVW3dutX8opAtQ4YMvt0fEE6OHz/uzlevXp3amwJELE3MAQDnShPpbNy4MbU3A4hYfJ9GehcTE5O8gZOqi/LkyRNrWe7cud0D659cqPr167vT+brjjjsCjcJ79uxpZcqUcT8fPnzYsmfPHmf9bNmyufOjR4+e92MDAAAAAAAgGQOnFi1a2KuvvmobNmxw1UwHDx60hQsXWvXq1e3yyy+35NK9e3c3493HH3/sqpy2bNli/fv3P+vt/KxIKl++vGXMmNG3+wPCiXckxhuOCiCVHIh78AYAEkMHXPk/DqQevk8jvYuOjraVK1cmX+CkmeDKlStnH330kRtilj9/fmvTpo3df//9lpxuvfVWd960aVPr1q2bzZgxw+655x43vC6+yipvmfpMAQAAAAAAIGUlKXBSxdCdd97pTqlFM9ep0un33393zcN37doVZ52dO3e680KFCqXCFgIAAAAAAES2JM1Sl1IOHDhgN954ow0cODDOderbFFw2rOF9oVVOXjlj5cqVU2iLAQAAAAAAENaBkxqTZ86c2ebPnx+rgunEiRP25ptvuqF0NWvWtCZNmrhlGmLnOXLkiM2aNcvNXleiRIlUegYAAAAAAACRK0lD6lKSmoK3a9fO7rrrLneKioqyOXPm2Pr1613lU968ea1u3bruNHToUDeLXalSpWzmzJm2fft2GzJkSGo/BQAAAAAAgIgUtoHTlVdeaW+88YaNGTPGnaRSpUo2ceJEFzJ5Ro0aZSNGjHDVUEePHrWyZcvapEmTrEaNGqm49QAAAAAAAJErbAMnueqqq9wQujPJmTOnmz1PJwAAAAAAAKS+sOzhBAAAAAAAgLSLwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAA4CsCJwAAAAAAAPiKwAkAAAAAAAC+InACAAAAAACArwicAAAAAAAAEDmB06+//modO3a0GjVqWOXKla1FixY2d+7cWOscO3bMhg0bZg0aNLCqVata69atbenSpam2zQAAAAAAAJEubAOnjRs32r333mtr1661Dh062BNPPGHZs2e33r1725QpUwLr9ejRwyZPnmyNGjVy1508edKtv2zZslTdfgAAAAAAgEiVycLUiy++aFFRUfbee+9ZoUKF3LK2bdva3XffbaNHj7ZWrVq5CqhFixZZ3759rX379m4dVUE1b97cBg0aZHPmzEnlZwEAAAAAABB5wrLCKTo62n766SerW7duIGwSBVBNmza1I0eO2Jo1a2z+/PmWOXNmFz55cuTIYS1btrTVq1fbpk2bUukZAAAAAAAARK6wrHBSsPTBBx9YhgwZ4ly3d+9ed54xY0ZbtWqVlSpVyoVMwSpWrOjOdf0ll1ySQlsNAAAAAACAsA2cFDQVL148znJVNs2ePdsFTBUqVLAdO3ZYlSpV4qxXsGBBd75161bftkkVVfEFYEB6cPz4cXeuykAAqaN06dKpvQkA0jBNpKMeqABSB9+nkd7FxMSkjyF1CT25fv362a5du+z++++3rFmz2uHDh10j8VDZsmVz50ePHk2FLQUAAAAAAIhsYVnhFF/Y9Nxzz9mCBQvs6quvtocffjhRt/OzIql8+fJuGB+QHnlHYrzhqABSyYFjqb0FANIoHXDl/ziQevg+jfQuOjraVq5cmb4qnE6ePGk9e/a0GTNmuOFz48aNc43CRUPrVD4cyluWK1euFN9eIDU888wz9tRTTwUuN2zY0MqWLRvvKaGhpvv27bPu3bvbNddcY9dee609//zzbhirZ/Pmzda6dWurVq2ade7c2f7999/Adaom1GNqHQAAAAAAwjpw0k6sqpk+/PBDV9k0ZcqUWCFS0aJF3RC7UDt37nTnwTPcAemRqv9GjRpl7777bqzls2bNsm+//TZw+uyzz6xIkSLWrFkz93cT6tSpU9a/f3/X+2Hs2LE2ceJEd5SmS5cugXWGDh3qeszMnTvXBcETJkwIXDd16lS77rrr4u29BgAAAACIPGEbOGmHtmvXrvbNN99YgwYN7PXXX49TsaRyxQ0bNsSpcvLKGStXrpyi2wykJFUTtWvXzt555504IVK+fPmsQIECgdOkSZPckFBVLcVn+fLl9vfff9vo0aPtyiuvdH9bI0eOtO+//95+/PFHt47+1po2bWolS5a066+/3tatW+eW79+/36ZNmxYrnAIAAAAARLawDZy046vKDA3TGTNmjGsSHqpJkyZ24sQJN9zOoyFAqu7Q8LsSJUqk8FYDKefnn392VUvz58+3iy++OMH1/vjjD5s5c6Ybdhdfk33Ztm2b5c2b1y655JLAssKFC9uFF14YCJz0GAqmTp8+7c6LFSvmlo8fP95uu+02F2wBAAAAABC2TcM1JE7D5zJlymR16tSxjz76KM46tWrVsrp167qThvpoh7lUqVJux3r79u02ZMiQVNl2IKXceuut7nQ2CmxVtaQhbwlRsHTo0CEX2Ko3mujygQMHbO/eve5yt27drFOnTm4onf7WVDWlvzsFXmroDwAAAABAWAdOqtzQkDoZMGBAvOuox0zBggVd/5oRI0a4nV71fFJTZO0I16hRI4W3GgjPYXeff/55rH5L8alevboLmp5++ml79tln3QyPmhlS597fYoUKFezLL790AZRXzdS3b1+799573YwFHTt2tPXr19tNN91kvXr18nWWSAAAAABA2hKWQ+o0VG7t2rVnPNWrV8+tmzNnTuvXr5999913tmLFCje8rmbNmqn9FICwoCBWw+5UKXgmF1xwgQuPVq1a5Rr0q3JQtytXrlys3mnqA+WFTerptGTJEtdHSlVUahj+8ccfu75PixYtSvbnBgAAACDtz7Dt7Vs88MADVrVqVbcvon6yauWREE0exgzb4S8sAycA/li8eLFr9J2YaiNVBy5cuNCFSAqNevToYVu2bEmwF9rw4cPdEDv1hVJPJw3Z088a7rps2bJkeDYAAAAA0tsM2xpBoVETefLksffff9+NuNCkRGqzEx+NwFA4xQzb4Y/ACUinlPCvWbPGpf5ns3XrVnvyySfdjHP58+e3bNmyudBIRwJ0xCCUqglVadiqVSt3WYGW/oHIqVOnAj8DAAAAwJlm2Fa4pFEVL730kl166aVuRuz27du7fY74fPXVV27GbGbYDn8ETkA6pUBIvZXKlCkT7/U6knDw4EH3s/qh6bJKUf/6669AhVPLli3dh3SoYcOG2aOPPmqZM2d2lzUrpI5GqIeTekZdccUVyfzsAAAAAKSHGbY1O71CIW/fQrp27WqvvPJKvPe1adMm1+aDGbbDH4ETkE5pXLPkzZs33usVJr3wwgvuZ80IqQon3aZFixbWu3dv90GsctZQahyu2euaN28e6x/C7t27rU2bNq5flPqwAQAAAJHWW0jrP/jgg25SHo000H16B3lFQ79uueUWd72+c3sT9MiOHTvcUK/g9dMLza6tCqb4gh4FSBdddJE7+K3XWPsSCoZ08Dw+OliuSqXgnk3xzbA9e/Zsq1Spkhv1oVYg3gzbHTp0SMZnirCfpQ5A0rz11ltxlt1www2uyikhqkTyaHyzTio7PZv69eu7U7BChQrZ9OnTk7zdAAAAQGpTOwgNz1JvIR2UDe0tpOBI1fx//vmn9enTx024o1AplJpRK9jQJFbvvfeea0+hCa40OY9XraMAqnHjxi500oiCWbNm2V133eWu0zp6PN1/JFFY9Nprr7kD3jrXqImBAwfasWPH7D//+U+c9TWBmIbgMcN2+CNwAnwWc+qknd631dISr3g1Og0dTIm6sKhlyPR/ZbcAAADAufQWUqW/wocz9RbScC/1FzpTbyGNFlBPIVXq5MiRwy1Tz1OFWcEVUGporfvSyACvt5DCLA0tC62wigQabaEJjPR7EPVk2rNnj7366qvxBk4awTFu3DgX/mmGbfWfveeeexI1w7YCP/0+1TBcv5e2bdu6GbYVAsJ/BE6AzxQ2/TuRJnTJLXfHVy1jgbj9pQAAAICk9hbSDMyPP/54onoLJUSzO2sWNs9///tfmzdvntWuXTuwzOstpPNffvnFDaETDdVTdZTCk0ij0RKhfWcvu+wyV/m0b98+15spVLVq1dwM2wqmcubMaVmzZnWVaMEVameaYVvVZcEzbBM4JQ96OAEAAAAAIpKfvYVC71frq9eQhnJ59POLL77oJtnR7M7qgfrrr7+6VhgJhSXpXY0aNey3336LtUyVX6pkypMnT7y/Fw1DZIbt8EfgBAAAAABAAr2FNDRL5507d7aJEycmOHtasEGDBrkep2pwfd9997n+TqJhdEuXLrVvvvnG3nnnHTcEzJsBWk2tFaQ0bNjQ3njjDYsUasquQEivmWbM/vTTT23ChAmu31JUVFScGbY145warDPDdvgjcAKA85i5RP/YNOY8+JTYsfeffPKJW3/Lli2x+gi0bt3alQnrS42O1Hj0RUVfQLQOAAAAUq63kPoKaTbnhx9+2N58882z3lbrq3JHfYL03U19gjwKPlSZIwqeNLvazTff7AIUDeGbM2eOTZkyxc1oFwkuv/xy93xV6aVm6ppJWyFUly5d4p1hW6+fKs2YYTv80cMJAM5x5hItVwNCHTXRmHGPxoOfzc6dO+P9p6gmkpoxUKXdAwYMcEd3evbs6a6bOnWqG+evJocAAAAIr95C+n6nKiWFRh5VOGlomCpyQum7pHoLde/e3Q3zUm8hhSdaXwcfdVnBVSTMsK1m6zNmzEjUDNteSJWY4I8ZtlMXgRMAnOPMJbpOVUcqw41v3P+Z6D71BebHH3+MtVwBlsb2qxxYX1a++OILt1xj1DVTisp/AQAAEH69hfR9Uc2/v/76a9f7yfu+qOFgCqpCLViwwPUf8gIRhU6nT59O0d5CJ0+ftN3HdyX740S6i7IWsMxRkTfDNoETknX4kRrqeaWPouqQ0A/t4PLIUNqZ11hejePVfancUTvjmonA+wBX9Yc++GvWrOmqQnLnzh24rUpTVRVCRQiSY+YSve/0JUHjyJNCR1RUAqypXEMDJ2/mEs1monPvvlU2rFLhpAZbAAAAODca1nXHHXe4/ZG2bdu6PkOqPldPpuDeQl5vIAVU2u/Q/on2WQ4fPmwDBw501Ur16tWLdd8nT550M9oNHjw4sEy9hWbOnOkajus74kMPPZTsz1Fh08Df41bdw1/9KvS3ItljH7yOBPRwgu+UxOvDU8OPQpd7w480xah3Cp61Ib7QSjvd2tlWoz598GpZ6PCjuXPnug9t/QPwMPwIyT1ziY5iXXDBBe5LhcZ/N2vWzI0/945MxUfT4+rIl2YnCZ5i19OtWzebPXu2VapUydasWeOmb1Vp9vz5861Dhw6+Pz8AAAD401soa9asNmnSJHdw/J577nH9OMuVK+cajXsBlUfBkiraFVIFV8Brf0eP0b59exdAAWkZFU4I2+FH27dvtw8//NDN0ODNHKAjBO3atbMnnnjCjb1l+BFSk95/R44ccWGTgiFVQymc0gwajz32WJz1VRqt966CI3350PStoSpUqOCaG+pomfd3ove4ZulQlV/Hjh3d39dNN91kvXr1cqXXAAAASN3eQseOHXPnqn4fO3bsWR9LFVM6BdOwu3nz5p3DlgPhicAJYTv8SPelIwHVq1cPLNPPmpZUVU/a4Wb4EVKTqpQUOHnDODWLicImVeNp6tXQMEjL9Z4+W6WS3uPee1eh1pIlS1xln8IsVeypgbm+oGi2k8aNGyfjMwQAAEgdMadO2ul9Wy2t8OrWow9amhF1UYnU3gSkcwRO8H34kU7xCR5+pFJRzepw++23xxoDHUwzOeTLly/WsCNNTaplGmLkDT9SZYmG0pUqVcqVsHrDj9SED0hOej96YZNHoZPG6yt4Cr1OU9xq9hKvdNobeqcSbZVc6xRK4a3e45r5TqFqjx493M+1atVyFVIETgAAID1S2PTvxP8bugb/5X2C0SBIXgROCNvhRxp+p3HQobJkyWLHjx93PzP8CKmpVatWbmx9v379AsvUFF/T34aGTV6ZtobVeVavXu2mwVVgGjrlrqxYscI1p1RPNNH715utJKVmLgEAAACAc0HghLAdfqThdydOnIhzP1qWI0eOwGWGHyG16P2k95cafGu45w8//GCvv/66PfXUU4F1vJlLVN0XOpxUM9WJ+p1pet1QarCvvw2vyk/hlvqS6X7UL0BhFQAAAACEI2apQ9gMPwpVuHBht7OuSiWPqjq0TBUk8QkdfqRZ6oKHHwF+Ui8m9SobN26c3XzzzS5sUoXdnXfeGe/MJUmhyr0DBw5Y8+bNA8u6du1qu3fvtjZt2rhKwSZNmvj2XAAAQHjSgdTgg1nBNEtzixYtrE+fPme8D40cePrpp61mzZpuaL+qs/UdPHhyn9atW1u1atXcEP9///031m0bNmzo1gGApKDCCWE7/EgzQihg0rAir+eNQiT1vdF1oRh+hOQWOnOJ3mP333+/O51t5pL46H2t92x86tev707BNDPj9OnTk7zdAAAg7dF3V1VSv/vuu+4AVnx0/Zo1a9zst2cLrTSUXxPr6HuxZpXWspdfftldP3ToUCtdurQbITBgwAA33F99V2Xq1KnuIK5GDgBAUlDhhBQdfqR/mHPnzrW///7b3nvvPVcREty/SdVLXrWTdq6bNm3qjugoaFKFko7MqCm5rkvs8CP1cNJO/xVXXJGCzxbJLSo3MxACAID0SdVE7dq1s3feeccNvY+Pvh/Pnj073j6QwbZv324ffvihPfvss+77sA54DRw40E2wo0l6vLYU+t5dsmRJu/76693s0rJ//36bNm2adelC824ASUeFE1J0+JGG1Wn40datW90/z/iGH1199dU2ZMgQd1n/DHV66KGH3G1vvPFGd0QmscOPNNxJw4+0nOFH6UzGTHby9Enbffx/fZCQPC7KWsAyR/3fTJEAACD5aXKdIkWKuHYR+j4bSsPhevfu7UYOzJw586z3pRmh1W/So5/VB1WhlSbXufjii93PtWvXdude30lVRN12222BfqkAkBQETgjr4Uc5c+a0wYMHu9OZMPwoMilsGvj7s6m9Gelavwr9rUj2+I+sAgCA5KGKfp0SMmjQIKtcubILi84WOKmKKV++fIFRAKIDuVq2bds2d7lbt26uD6qG0pUqVcomTZrkrps/f76rhAKAc0HgFEFOnjptW/fEnfUN/ilRMGtqbwIAAADSscWLF9tXX33lhsklhpp+Z80a9ztqlixZ7Pjx4+7nChUquBEDam/hVTNpJMK9997rJvDp2LGja1OhgKtXr15xZpcGgPgQOEUQhU2dR8bfoBj+mPd8ZcuY2hsBAACAdEmBkHqaqsIpb968ibpNtmzZ7MSJuAedtSxHjhyByxpi54VN6um0ZMkS11hcjcTVMFwNytu2bWuLFi1yvVkB4GwInAAAAAAgDVBl0549e6x79+6BZapSUsXRwoUL3azNoQoXLuyCKlUqKVQSzVSnZZotOj7qHaUhdtmzZ3c9nXr06OF+rlWrlpvIh8AJQGIQOAEAAABAGqCgJ7j5t6h5uCqTevbsGe9trrzyShcwKYzSDHWiEOn06dPuulBab+3atTZq1Ch3WWFWTEyM+1n34/0MAGdD4AQAAAAAaUCuXLncKXTInCbaKVmyZGCZqpfUJPyCCy5wE+k0bdrUnnrqKTcUT4GRhuWpKbmuCzVs2DB79NFHA03Gq1SpYu+//76buU4T/ARXVwHAmUSd8VoAAAAAQJrSsmVLe+GFFwKXBw4c6CqjHnroIXvkkUfsmmuuseeeey7O7dQ4/MCBA9a8efPAsq5du9ru3butTZs2VqdOHWvSpEmKPQ8AaRsVTgAAAAAQpt56660zXv/GG2/EWaZKpGCqgBo8eLA7nUn9+vXdKZiqoKZPn56kbQYAIXACAAAAgEQ4eeq0m/kZyatEwaypvQkAfEDgBAAAAACJoLCp88i1qb0Z6d685yvb/+bTA5CW0cMJAAAAAAAAviJwAgAAAAAAgK8InAAAAAAAAOArAicAAAAAAAD4isAJAAAAAAAAviJwAgAAAAAAgK8InAAAAAAAAOArAicAAAAAAAD4isAJAAAAAAAAviJwAgAAAAAAgK8InAAAAAAAAOArAicAAAAAAAD4isAJAAAAAAAAviJwAgAAAAAAgK8InAAAAAAAAOArAicAAAAAAAD4isAJAAAAAAAAviJwAgAAAAAAQGQGThMmTLDatWvHe92xY8ds2LBh1qBBA6tataq1bt3ali5dmuLbCAAAAAAAgDQSOH311Vc2evToBK/v0aOHTZ482Ro1amS9e/e2kydPWocOHWzZsmUpup0AAAAAAAAI88ApJibGpk2bZo888ogLkeKjSqZFixbZE088Yf369bO7777b3aZIkSI2aNCgFN9mAAAAAACASBfWgZOGxj3//PNWs2ZNq1ixYrzrzJ8/3zJnzmytWrUKLMuRI4e1bNnSVq9ebZs2bUrBLQYAAAAAAEBYB05bt261AQMG2Ouvv245c+aMd51Vq1ZZqVKlXMgUzAuodD0AAAAAAABSTiYLY59//rllyZLljOvs2LHDqlSpEmd5wYIFA6GVH9asWWMZMmSwtKp06dKpvQkA0ihNzLBx48bU3ox0j89pAOeDz+rkx+c0gEj+nI6JiUlfFU5nC5vk8OHDlj179jjLs2XL5s6PHj2aLNsGAAAAAACANFjh5Ae/qpLKly9vGTNmtDTtwLHU3gIAaZAC/IT66MFnfE4DOEd8VqcQPqcBROjndHR0tK1cuTL9VDglhno3qTQtlLcsV65cqbBVAAAAAAAAkSvNB05Fixa1Xbt2xVm+c+dOd16oUKFU2CoAAAAAAIDIleYDJ5WkbdiwIU6V0+rVq9155cqVU2nLAAAAAAAAIlOaD5yaNGliJ06csBkzZgSWHTlyxGbNmuVmrytRokSqbh8AAAAAAECkSfNNw+vWretOQ4cOtW3btlmpUqVs5syZtn37dhsyZEhqbx4AAAAAAEDESfOBk4waNcpGjBhh8+fPt6NHj1rZsmVt0qRJVqNGjdTeNAAAAAAAgIiTZgKnt956K8HrcubMaf369XMnAAAAAAAApK4038MJAAAAAAAA4YXACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgKwInAAAAAAAA+IrACQAAAAAAAL4icAIAAAAAAICvCJwAAAAAAADgq3QTOG3dutW6d+9u11xzjV155ZX2yCOP2ObNm1N7swAAAAAAACJOJksH9u/fb+3atbNDhw7ZfffdZ1myZLHJkydb27Ztbe7cuZYvX77U3kQAAAAAAICIkS4CpzfeeMO2bNlis2bNskqVKrlldevWtRYtWtjEiROtd+/eqb2JAAAAAAAAESNdDKn78MMP7YorrgiETVKmTBk3vE7XAQAAAAAAIOWk+QqnAwcOuF5N9evXj3NdxYoVbcmSJbZz504rWLBgku87JiYm8HN0dLSldTGnT1vmjP/3nOC/03qfnI6x0xkzp/ampHvR0aft9OnTlintf4yFNb3G6eHzL63gczpl8FmdMvicTjl8VqccPqdTBp/TKYPP6ZRzOh18Tgdvf3BWciYZYhK7Zphat26dNWvWzHr27GkdO3aMdd1bb71lAwcOtJkzZ1rVqlWTfN8nTpyw3377zcetBQAAAAAASLsqV67semen+yF1hw8fdufZs2ePc122bNnc+ZEjR1J8uwAAAAAAACJVmq+d8wq0MmTIkOA6Z7ruTDJlyuSSO4mKijrn+wEAAAAAAEjL2YuGBnpZSUQETjly5HDnR48ejXPdsWPH3HmuXLnO6b4VMiWmTAwAAAAAAADpaEhdsWLF3PmuXbviXKdm4VKoUKEU3y4AAAAAAIBIleYDpwsuuMBKlChhq1evjnOdlhUuXNgKFCiQKtsGAAAAAAAQidJ84CRNmjSx5cuXxwqdNHvd999/b7fcckuqbhsAAAAAAECkyRDjdd1Ow/bv32/NmjWzkydP2oMPPuh6L02ZMsUyZ85ss2fPtnz58qX2JgIAAAAAAESMdBE4yebNm23w4MG2dOlS1+j76quvtieeeMKKFy+e2psGAAAAAAAQUdJN4AQAAAAAAIDwkC56OAEAAAAAACB8EDgBAAAAAADAVwROAAAAAAAA8BWBEwAAAAAAAHxF4AQAAAAAAABfETgBAAAAAADAVwROAAAAAAAA8BWBEwAAAAAAAHxF4ATgrLZs2WLR0dGBy/fee6/Vrl07VbcJACLd33//7ev99enTx8qWLWvHjx/39X4BIBLo87N79+6xlvEdGpEuU2pvAIDwNnv2bBswYID9+OOPljFjRresc+fOdujQodTeNACIWM8884ytXbvW3n333dTeFACAmb300ktWrFixwGW+QwMETgDO4qeffrJjx47FWsaRGQBIXd9++61ddNFFqb0ZAID/79Zbb411me/QAEPqAAAAAAAA4DMCJyCVaSy3Tt9//721bt3aqlSp4o5+vPDCC7GOiuzYscP69u1r1157rVWqVMluueUWmz59epz72717d2C9atWq2SOPPGI///yzG1c+Z86cwHp79uxxj9G4cWN3f1pXj79o0aJY2/b++++7n7Vd6u/hLfeO0EycONHd9/Lly+NsS/v27a1OnTqBsesHDx50j3nddde5x9Rjjx071k6ePOnrawoAafEzfMyYMe7zdOPGjbGWv/POO275Dz/84C7r53/++cd++eWXWJ/t+nnYsGH22GOPWeXKla1+/fp24MABd917771nbdq0serVq7vHb9Sokb344ov0awKQLulz+a677nLVoM2bN3efzU2bNnWfp8FCP5u1jr7bBvddksWLF9udd97pPkP1nVn3HfydObSHE9+hgf9hSB0QBv773/9aly5d7Pbbb7c77rjD/QN78803LXPmzPbEE0/Yrl27rFWrVnbixAn3Dy5//vy2ZMkSNy5ct+3Xr5+7n8OHD1vbtm1t69atds8991iRIkXsgw8+sIcffjjW42kHQ+vt3bvX7r77bjfefNu2bTZjxgzr2rWrzZo1y/0z0zjz06dP27Jly2zQoEF26aWXxtl2/RMfPny4ffTRR3bllVcGlmubNWa9Xbt2btz6kSNH3Dapya12ekqUKGErV650O1irV692/zQzZMiQAq82AITnZ3hS+oQMHjzYLrjgAveZrR0gj0KsChUquPvUgYU8efK4z9lXXnnFbrrpJmvRooX7H/DZZ5/Z5MmT3c5KUh8fANKCzZs3u+/ACvj1GfzJJ5/Yc8895z4b9dmp78tarjBH34cvvvhiF1ApuF+1apWNGjXK3Y++ByvIr1q1qvXo0cMtmzlzpruPKVOmWK1ateI8Nt+hgf8vBkCquueee2LKlCkTs2DBgsCy6OjomEaNGsXUqVPHXe7Tp09M9erVYzZv3hzrti+88IK77Zo1a9zlsWPHusufffZZYJ3jx4/H3HbbbW757Nmz3bKPPvrIXV64cGGs+/v666/d8nHjxgWW9e7d2y07duxYrG2+9tprA5fvu+++mNq1a7vt9kydOtXd7vfff3eXx4wZE1O+fPmYX375JdZjeustXrz4nF9DAEgPn+GjR492lzds2BBrvbffftst//777wPLGjRoEHPnnXfGWk/rVK1aNebgwYOBZSdOnIi58sorYzp16hRr3ZMnT8bUq1cv5oYbbjjj5z0ApOXPZn03Dv7ca9OmTUylSpVidu/eHfP444+7dX766adYt33uuedifZ/2Lu/Zsyewzt69e93n55QpUwLLtE63bt0Cl/kODcTEMKQOCAM6Cq7SWE9UVJQrsdXwOB0d0ZFole/myJHDVSV5pxtuuMGt/+WXX7pzrXfJJZfY9ddfH7ivLFmy2IMPPhjr8VQuvHTpUjekwqOSXT2W6EhKUpsk6miMN9xDdLTm8ssvt/Lly7vLCxcudEd3dPQo+Dk0aNDAHZX54osvkviqAUD6+gz3Q8WKFS1Xrlyxtk3VVDpiH0xH+HPnzu0qYwEgPcqWLZvdf//9gcuZMmWy++67z1Wbfv311/b555/b1VdfbTVq1Ih1O1WsijdkrnDhwu78+eefd5VPcuGFF7rvthr6dj74Do30jiF1QBjQsAjtFARTUKQdlX379rlS32+++Sbekl1RSbBs2rTJrrrqqjjXly5dOs4yleiqDFj9nVRy/NdffwV6eXjBU2Jpp6l///728ccfu23csmWLrVixIlB2LCoDVj+Tsz0HAIjUz3A/aLheKG2Lhoko+NIQPn0ea2dFmOkOQHqlgCZ79uyxlunArKgPng6wxjfUrUCBAi6Q1zqi4WzfffedC4J00vX16tVzYVHNmjXPaxv5Do30jsAJCAM6Gp4Qr1lgw4YNXaPB+BQsWNCdnzp1yu1YhApdpmBKfUS8f16qiNLR+KJFi7qGiEmVM2dOVy2lIzDPPPOM+2esIy7NmjWL9Tw09r1bt27x3of+sQNAJH+GJyQpBwF0MCFYTEyMPfrooy5s0mewmomr15QqrrSTo/8HAJAeqaIpoc9T7yCBPiPjo/W8dfQ9d+rUqfbbb7+55uEK8NUQfPbs2a6Pkz5jzxXfoZHeETgBYS5fvnzu6IzKfzWDRjAdof7pp5+sZMmS7rLO49t5UPVSsPHjx7uj7mooXqZMmcByVTudKzU+/PDDD9326CiNKq3UtNyjxuSaLSn0OaiqSv+8vXJlAIjUz3AvuAqddUjDLc6VGtYqbHrggQesd+/esa7TkD8ASK9UoaSwJjiI974nFy9e3A1zVtVnqJ07d9qhQ4cC3001EkDL1Nhbob2CH022o+F0mnxBodP5NO3mOzTSM3o4AWng6IymQFUpr2akCDZ69Gg3a8aGDRsCZbnr1693M1t49I82dOrt/fv3u6onzXIRfCRHsyp5t/F4O0BnO8KuqVs1NENHgH7//XdXZhxMR2/0T15HboLpMTWFrHpKAUAkf4ZrmIboM9SjoEqBUSh9Niem8kmf93LZZZfFWq6+UfpMVmUsAKRHGs48Z86cwGV93ul7qvrcaUhc/fr13XdmBfPBXnvttUBlqowcOdL1glLo5FEgpOpUfRYnFDbxHRqgwglIE3r27OmaCepIiobCafz5999/7/7x6J9l3bp13Xo6gq2qpY4dO7rx5vpnqClgVQIs3j9E3UaNEtVMXFPF6giJ7kv/5PTPMbiJrI7Oi6ZcrV27doLjx3X06Oabb3b/LLNmzWpNmjSJdX2nTp3cTlOvXr3cc9G03ZrK9b333rNKlSq5IR4AEMmf4Wo8/sILL9jgwYPdjo16Q2nIRvBBgODPZh1g0AEF9RAJDZQ81atXd8Mthg4d6u5TPZ5++eUXmzt3rvusVg8TDSlhSm0A6THwV6PvdevWuUpSVREp+NcyhU7qk6TPYn0fvvvuu13PJ02yoKohhTze5DoKmz799FP33bpVq1buM1W3U1jVuXPnBB+f79AAFU5AmqCyX/1T0c7IvHnzbODAgS4c0pjxUaNGBY6g6J/ntGnT3BGZd999115++WX3z05jwoN7Oemfpf5paedj0KBBrnm4dkJmzpzp/okFHynRzpHKh9944w17/fXXz7id3hEZzZoRPEuS5MmTx22TekRpNg39s9c/9Xbt2tmkSZPiNHUEgEj7DNesR/qc1UQP2kEZN26c20F59tln49ynbqv1FU7FVwHl0Wf7hAkT3H1OnDjRzVa3Zs0a69evnz3xxBOugmr58uXJ+vwBIDXkzZvXXnnlFTdpw0svveSGK+uyvgeLAqZZs2a5EQLqyTRkyBDXoLtPnz42ZsyYQBCvUEfflbW+vrPqO6wCf32OJtRXSfgODZhliEmoUxqANEf9QPRPKbRprI6iq+RW//ASOroCAAAApAeapOHPP/90wQyA1EOFE5CO6Mh1jRo1XKPDYGpAqLJiVS8BAAAAAJDc6OEEpCOaQlXNEXVU57bbbnPTuaqMWGPRNYOGqp8AAAAAAEhuDKkD0hmVDo8fP97Wrl3renOUKlXK2rZta3fccUdqbxoAAACQ7BhSB4QHAicAAAAAAAD4ih5OAAAAAAAA8BWBEwAAAAAAAHxF4AQAAAAAAABfETgBAAAAAADAVwROAAAAAAAA8BWBEwAAAAAAAHxF4AQAAAAAAABfETgBAAAAAADAVwROAAAAAAAA8BWBEwAAAAAAAMxP/w+od30LtfVJ7QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Entity Type Distribution Across Splits\n",
    "\n",
    "Check whether company/location ratios are stable across splits."
   ],
   "id": "d7c4b5c4e4e1d3e7"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.844806Z",
     "start_time": "2026-04-18T22:16:36.830269Z"
    }
   },
   "source": [
    "type_rows = []\n",
    "for seed in SEEDS:\n",
    "    train_ids, val_ids, test_ids = split_sample_ids(sample_ids, VAL_FRAC, TEST_FRAC, seed)\n",
    "    for split_name, split_ids in [(\"train\", train_ids), (\"val\", val_ids), (\"test\", test_ids)]:\n",
    "        split_ents = df_entities[df_entities[\"sample_id\"].isin(split_ids)]\n",
    "        total = len(split_ents)\n",
    "        for etype in [\"company\", \"location\"]:\n",
    "            cnt = (split_ents[\"entity_type\"] == etype).sum()\n",
    "            type_rows.append({\n",
    "                \"seed\": seed,\n",
    "                \"split\": split_name,\n",
    "                \"entity_type\": etype,\n",
    "                \"count\": cnt,\n",
    "                \"pct\": cnt / total * 100,\n",
    "            })\n",
    "\n",
    "df_type_splits = pd.DataFrame(type_rows)\n",
    "\n",
    "pivot_type = df_type_splits.pivot_table(index=[\"seed\", \"split\"], columns=\"entity_type\", values=\"pct\")\n",
    "print(\"Entity type distribution (%) per split and seed:\")\n",
    "print(pivot_type.round(1).to_string())\n",
    "print()\n",
    "\n",
    "test_type = df_type_splits[df_type_splits[\"split\"] == \"test\"]\n",
    "print(\"Test split entity type variability across seeds:\")\n",
    "for etype in [\"company\", \"location\"]:\n",
    "    sub = test_type[test_type[\"entity_type\"] == etype][\"pct\"]\n",
    "    print(f\"  {etype:10s}: {sub.min():.1f}% – {sub.max():.1f}%  (range = {sub.max() - sub.min():.1f} pp)\")"
   ],
   "id": "7d1503d60a2c46f4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entity type distribution (%) per split and seed:\n",
      "entity_type  company  location\n",
      "seed split                    \n",
      "42   test       80.3      19.7\n",
      "     train      78.4      21.6\n",
      "     val        79.9      20.1\n",
      "123  test       78.7      21.3\n",
      "     train      78.6      21.4\n",
      "     val        79.6      20.4\n",
      "256  test       78.8      21.2\n",
      "     train      78.5      21.5\n",
      "     val        80.5      19.5\n",
      "512  test       77.4      22.6\n",
      "     train      78.9      21.1\n",
      "     val        78.7      21.3\n",
      "1024 test       77.8      22.2\n",
      "     train      78.9      21.1\n",
      "     val        78.0      22.0\n",
      "\n",
      "Test split entity type variability across seeds:\n",
      "  company   : 77.4% – 80.3%  (range = 2.8 pp)\n",
      "  location  : 19.7% – 22.6%  (range = 2.8 pp)\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Original Dataset Distribution\n",
    "\n",
    "For reference, the overall label and entity type distributions before any splitting.\n",
    "The splits should approximate these proportions."
   ],
   "id": "691b231d6f4c7595"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.929656Z",
     "start_time": "2026-04-18T22:16:36.858510Z"
    }
   },
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n",
    "\n",
    "# Label distribution\n",
    "label_vc = df_entities[\"label\"].value_counts().reindex(LABEL_ORDER)\n",
    "bar_colors = [LABEL_COLORS[l] for l in LABEL_ORDER]\n",
    "axes[0].bar(LABEL_ORDER, label_vc.values, color=bar_colors, edgecolor=\"white\")\n",
    "for i, (lbl, cnt) in enumerate(zip(LABEL_ORDER, label_vc.values)):\n",
    "    pct = cnt / len(df_entities) * 100\n",
    "    axes[0].text(i, cnt + 30, f\"{cnt:,}\\n({pct:.1f}%)\", ha=\"center\", fontsize=10)\n",
    "axes[0].set_title(\"Overall Label Distribution\")\n",
    "axes[0].set_xlabel(\"Label\")\n",
    "axes[0].set_ylabel(\"Count\")\n",
    "\n",
    "# Entity type distribution\n",
    "type_vc = df_entities[\"entity_type\"].value_counts()\n",
    "type_colors = [\"steelblue\", \"darkorange\"]\n",
    "axes[1].bar(type_vc.index, type_vc.values, color=type_colors, edgecolor=\"white\")\n",
    "for i, (etype, cnt) in enumerate(type_vc.items()):\n",
    "    pct = cnt / len(df_entities) * 100\n",
    "    axes[1].text(i, cnt + 30, f\"{cnt:,}\\n({pct:.1f}%)\", ha=\"center\", fontsize=10)\n",
    "axes[1].set_title(\"Overall Entity Type Distribution\")\n",
    "axes[1].set_xlabel(\"Entity Type\")\n",
    "axes[1].set_ylabel(\"Count\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Compare splits vs overall\n",
    "print(\"Deviation from overall distribution (seed=42 test split):\")\n",
    "print()\n",
    "train_ids_42, val_ids_42, test_ids_42 = split_sample_ids(sample_ids, VAL_FRAC, TEST_FRAC, seed=42)\n",
    "test_ents = df_entities[df_entities[\"sample_id\"].isin(test_ids_42)]\n",
    "test_total = len(test_ents)\n",
    "\n",
    "overall_pcts = {lbl: (df_entities[\"label\"] == lbl).sum() / len(df_entities) * 100 for lbl in LABEL_ORDER}\n",
    "test_pcts = {lbl: (test_ents[\"label\"] == lbl).sum() / test_total * 100 for lbl in LABEL_ORDER}\n",
    "\n",
    "print(f\"  {'Label':10s} {'Overall':>8s} {'Test':>8s} {'Diff':>8s}\")\n",
    "print(f\"  {'-'*10} {'-'*8} {'-'*8} {'-'*8}\")\n",
    "for lbl in LABEL_ORDER:\n",
    "    diff = test_pcts[lbl] - overall_pcts[lbl]\n",
    "    print(f\"  {lbl:10s} {overall_pcts[lbl]:7.1f}% {test_pcts[lbl]:7.1f}% {diff:+7.1f} pp\")"
   ],
   "id": "ff73b6ce79ab93c0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1560x480 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deviation from overall distribution (seed=42 test split):\n",
      "\n",
      "  Label       Overall     Test     Diff\n",
      "  ---------- -------- -------- --------\n",
      "  negative      15.3%    15.4%    +0.0 pp\n",
      "  neutral       70.1%    67.7%    -2.4 pp\n",
      "  positive      14.5%    16.9%    +2.4 pp\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Recommendations\n",
    "\n",
    "Summary of findings and recommended splitting strategy."
   ],
   "id": "31ad3acc9d1acea6"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-18T22:16:36.946636Z",
     "start_time": "2026-04-18T22:16:36.943441Z"
    }
   },
   "source": [
    "print(\"=\" * 60)\n",
    "print(\"SPLIT ANALYSIS SUMMARY\")\n",
    "print(\"=\" * 60)\n",
    "print()\n",
    "\n",
    "test_var = df_splits[df_splits[\"split\"] == \"test\"]\n",
    "for label in LABEL_ORDER:\n",
    "    sub = test_var[test_var[\"label\"] == label][\"pct\"]\n",
    "    rng = sub.max() - sub.min()\n",
    "    print(f\"  {label:10s}: range = {rng:.1f} pp across {len(SEEDS)} seeds\")\n",
    "\n",
    "print()\n",
    "print(\"Findings:\")\n",
    "print(\"  - Plain random splitting is mostly fine for this dataset.\")\n",
    "print(\"  - Across five seeds the test label distribution varies by\")\n",
    "print(\"    \\u00b13 percentage points — acceptable but not tight.\")\n",
    "print(\"  - Entity type ratios are stable across splits.\")\n",
    "print(\"  - No article leakage detected with sample-level splitting.\")\n",
    "print()\n",
    "print(\"Recommendation:\")\n",
    "print(\"  - Use sample-level random splitting (seed=42).\")\n",
    "print(\"  - If tighter control is needed, consider stratified splitting\")\n",
    "print(\"    on the majority label per sample to reduce variance.\")"
   ],
   "id": "46fb8a780207c3ca",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "SPLIT ANALYSIS SUMMARY\n",
      "============================================================\n",
      "\n",
      "  negative  : range = 1.8 pp across 5 seeds\n",
      "  neutral   : range = 6.7 pp across 5 seeds\n",
      "  positive  : range = 6.0 pp across 5 seeds\n",
      "\n",
      "Findings:\n",
      "  - Plain random splitting is mostly fine for this dataset.\n",
      "  - Across five seeds the test label distribution varies by\n",
      "    ±3 percentage points — acceptable but not tight.\n",
      "  - Entity type ratios are stable across splits.\n",
      "  - No article leakage detected with sample-level splitting.\n",
      "\n",
      "Recommendation:\n",
      "  - Use sample-level random splitting (seed=42).\n",
      "  - If tighter control is needed, consider stratified splitting\n",
      "    on the majority label per sample to reduce variance.\n"
     ]
    }
   ],
   "execution_count": 24
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}