diff --git "a/lm-watermarking-main/watermark_reliability_release/figure_notebooks/data_model.ipynb" "b/lm-watermarking-main/watermark_reliability_release/figure_notebooks/data_model.ipynb"
new file mode 100644--- /dev/null
+++ "b/lm-watermarking-main/watermark_reliability_release/figure_notebooks/data_model.ipynb"
@@ -0,0 +1,1258 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Tuple, Dict, Union, Optional, Any, Callable, Iterable, TypeVar\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rc\n",
+    "# rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})\n",
+    "rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': 20})\n",
+    "rc('text', usetex=True)\n",
+    "\n",
+    "import cmasher as cmr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INPUT_DIR = OUTPUT_DIR = \"/cmlscratch/jkirchen/watermarking-root/lm-watermarking-dev/figure_notebooks\"\n",
+    "figure_dir = f\"{INPUT_DIR}/figures\"\n",
+    "\n",
+    "# save = False\n",
+    "save = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# enumerate data dir and load all csv files into a dataframe\n",
+    "def get_data_df(data_dir=None, data_path=None):\n",
+    "\n",
+    "    data_df = pd.DataFrame()\n",
+    "    if data_path is None:\n",
+    "        for file in sorted(os.listdir(data_dir)):\n",
+    "            if file.endswith(\".csv\"):\n",
+    "                file_path = os.path.join(data_dir, file)\n",
+    "                data = pd.read_csv(file_path)\n",
+    "                data_df = data_df.append(data)\n",
+    "    else:\n",
+    "        data_df = pd.read_csv(data_path)\n",
+    "\n",
+    "    return data_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_scatter_data(data_df, x_col=\"x\", y_col=\"y\",x_name=None,y_name=None, series_readable_id_col=\"name\", series_readable_id_map=None, special_color_map=None, color_marker_map=None, num_colors=None, num_markers=None, num_cols=None, filter_list=None, legend_sort_indices=None, shorten_legend_indices=None, legend_unique_indices=None, title=\"Figure Title\", marker_size=None,figure_size=None,legend_loc=None, bbox_to_anchor=None, grid=True, save_path=None, show=True):\n",
+    "    \n",
+    "    # give each point a unique marker and color\n",
+    "    # make a random colormap\n",
+    "    cmap = cmr.take_cmap_colors(\"cmr.rainforest\", (len(data_df) if num_colors is None else num_colors), cmap_range=(0.2, 0.8), return_fmt=\"hex\")\n",
+    "    # markers = [\"*\", \"s\", \"^\", \"p\", \"v\", \"<\", \"P\", \">\", \"h\", \"H\", \"+\",  \"X\", \"D\",  \"|\", \"d\", \"x\", \"_\", \"o\"]\n",
+    "    markers = [\"o\", \"X\", \"v\",\"^\", \"s\", \"p\", \"<\", \"P\", \">\", \"h\", \"H\", \"+\" , \"D\", \"d\", \"x\", \"p\"]\n",
+    "\n",
+    "    if num_markers is not None:\n",
+    "        markers = markers[:num_markers]\n",
+    "    \n",
+    "    all_possible_color_and_marker_combos = [(color, marker) for color in cmap for marker in markers]\n",
+    "    \n",
+    "    #plot using plt.scatter\n",
+    "    # make figsize larger if num_cols is not None adn num_cols > 1\n",
+    "    #  and scale marker size\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=figure_size)\n",
+    "    marker_size = marker_size\n",
+    "    \n",
+    "    # sort data_df by series_readable_id_col\n",
+    "    # data_df = data_df.sort_values(by=[series_readable_id_col])\n",
+    "\n",
+    "    if filter_list is not None:\n",
+    "        # remove data where series_readable_id_map[row[series_readable_id_col]] is in filter_list\n",
+    "        # print(f\"Col for filtering:{data_df[series_readable_id_col].apply(series_readable_id_map)[0]}\")\n",
+    "        data_df = data_df[~data_df[series_readable_id_col].apply(series_readable_id_map).isin(filter_list)]\n",
+    "\n",
+    "    for i, row in data_df.iterrows():\n",
+    "\n",
+    "        if isinstance(series_readable_id_map, dict):\n",
+    "            label = series_readable_id_map[row[series_readable_id_col]]\n",
+    "        elif isinstance(series_readable_id_map, Callable):\n",
+    "            # print(row[series_readable_id_col])\n",
+    "            label = series_readable_id_map(row[series_readable_id_col])\n",
+    "            # print(label)\n",
+    "        else:\n",
+    "            label = row[series_readable_id_col]\n",
+    "        \n",
+    "        if color_marker_map is not None and isinstance(color_marker_map, Callable):\n",
+    "            # print(f\"Using color marker callable\")\n",
+    "            color_idx, marker_idx = color_marker_map(series_readable_id_map(row[series_readable_id_col]))\n",
+    "            if isinstance(color_idx,int):\n",
+    "                color = cmap[color_idx]\n",
+    "            else:\n",
+    "                color = color_idx\n",
+    "            if isinstance(marker_idx,int):\n",
+    "                marker = markers[marker_idx]\n",
+    "            else:\n",
+    "                marker = marker_idx\n",
+    "            # print(f\"color: {color}, marker: {marker}\")\n",
+    "        else:\n",
+    "            if special_color_map and row[series_readable_id_col] in special_color_map.keys():\n",
+    "                if isinstance(special_color_map[row[series_readable_id_col]], int):\n",
+    "                    color = cmap[special_color_map[row[series_readable_id_col]]]\n",
+    "                else:\n",
+    "                    color = special_color_map[row[series_readable_id_col]]\n",
+    "            else:\n",
+    "                # color = cmap[i]\n",
+    "                color = all_possible_color_and_marker_combos[i][0]\n",
+    "            # marker = markers[i]\n",
+    "            marker = all_possible_color_and_marker_combos[i][1]\n",
+    "\n",
+    "        ax.scatter(row[x_col], row[y_col], label=label, color=color, marker=marker, alpha=0.8, sizes=[marker_size])\n",
+    "    \n",
+    "    # sort legend the value of y_col and then x_col\n",
+    "    handles, labels = ax.get_legend_handles_labels()\n",
+    "    x_vals = data_df[x_col]\n",
+    "    y_vals = data_df[y_col]\n",
+    "\n",
+    "    if legend_sort_indices is not None:\n",
+    "        label_tups = [lab.split(\",\") for lab in labels]\n",
+    "        # label_tups = [tuple([lab[i] for i in legend_name_indices]) for lab in label_tups]\n",
+    "        # sort by label_tups in their order\n",
+    "        # handles, label_tups, x_vals, y_vals = zip(*sorted(zip(handles, label_tups, x_vals, y_vals), key=lambda t: [[t[1][i] for i in legend_sort_indices],t[2]], reverse=False))\n",
+    "        handles, label_tups, x_vals, y_vals = zip(*sorted(zip(handles, label_tups, x_vals, y_vals), key=lambda t: ([[t[1][i] for i in legend_sort_indices],t[2]] if t[1][0] != \"Wiki\" else [[\"C4-z\",-1],-1]), reverse=False))\n",
+    "        if legend_unique_indices:\n",
+    "            legend_dict = {tuple([label_tup[i] for i in legend_unique_indices]): [handle,label_tup] for handle,label_tup in zip(handles,label_tups)}\n",
+    "            handles, label_tups = zip(*legend_dict.values())\n",
+    "        if shorten_legend_indices:\n",
+    "            label_tups = [[lab[i] for i in shorten_legend_indices] for lab in label_tups]\n",
+    "        labels = [\",\".join(lab) for lab in label_tups]\n",
+    "        legend_title = f\"\"\n",
+    "    else:\n",
+    "        # sort by x/y_vals\n",
+    "        handles, labels, _, _ = zip(*sorted(zip(handles, labels, x_vals, y_vals), key=lambda t: (t[3],t[2]), reverse=True))\n",
+    "        legend_title = f\"(Sorted desc. by {y_name if y_name else y_col})\"\n",
+    "\n",
+    "\n",
+    "    # sort by labels if num_cols more than 1, make the legend outside the plot to the RHS\n",
+    "    # and increase marker size\n",
+    "    # if num_cols > 1:\n",
+    "    #     # ax.legend(handles, labels, bbox_to_anchor=(1.04,0.5), loc=\"center left\", ncol=num_cols)\n",
+    "    #     # make legend taller\n",
+    "    #     ax.legend(handles, labels, bbox_to_anchor=(1.04,0.5), loc=\"center left\", ncol=num_cols, handletextpad=0.1, columnspacing=0.2, title=legend_title, fontsize=12)\n",
+    "    # else:\n",
+    "    #     ax.legend(handles, labels,loc=legend_loc, ncol=(num_cols if num_cols is not None else 1), title=legend_title, fontsize=12)\n",
+    "    if bbox_to_anchor is not None:\n",
+    "        ax.legend(handles, labels, bbox_to_anchor=bbox_to_anchor, loc=\"center left\", ncol=(num_cols if num_cols is not None else 1), handletextpad=0.1, columnspacing=0.2, title=legend_title, fontsize=12)\n",
+    "    else:\n",
+    "        ax.legend(handles, labels, loc=legend_loc, ncol=(num_cols if num_cols is not None else 1), handletextpad=0.1, columnspacing=0.2, title=legend_title, fontsize=12)\n",
+    "    \n",
+    "    # set title\n",
+    "    plt.title(title)\n",
+    "    # label axes\n",
+    "    plt.xlabel(x_name if x_name else x_col)\n",
+    "    plt.ylabel(y_name if y_name else y_col)\n",
+    "\n",
+    "    # turn on grid\n",
+    "    ax.grid(grid)\n",
+    "\n",
+    "    # tight\n",
+    "    plt.tight_layout()\n",
+    "    # save figure\n",
+    "    if save_path:\n",
+    "        # make if doesn't exist\n",
+    "        os.makedirs(os.path.dirname(save_path), exist_ok=True)\n",
+    "        if save_path.endswith(\".pdf\"):\n",
+    "            plt.savefig(save_path,format=\"pdf\")\n",
+    "        elif save_path.endswith(\".png\"):\n",
+    "            plt.savefig(save_path,format=\"png\")\n",
+    "\n",
+    "    # show figure\n",
+    "    if show:\n",
+    "        plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def name_extractor(x):\n",
+    "    \n",
+    "    x = x.replace(\"50_200_\", \"\")\n",
+    "    x = x.replace(\"core_\", \"\")\n",
+    "    x = x.replace(\"gen_attacked_\", \"\")\n",
+    "    x = x.replace(\"pile_\", \"\")\n",
+    "    x = x.replace(\"c4_\", \"C4-en_\")\n",
+    "    x = x.replace(\"free_law\", \"Law\")\n",
+    "    x = x.replace(\"pubmed\", \"Med\")\n",
+    "    x = x.replace(\"github\", \"Github\")\n",
+    "    x = x.replace(\"wikitext\", \"Wiki\")\n",
+    "    x = x.replace(\"uspto\", \"Patents\")\n",
+    "    x = x.replace(\"gpt_eval\", \"GPT\")\n",
+    "    x = x.replace(\"gpt_p4_eval\", \"GPT\")\n",
+    "    x = x.replace(\"dipper_eval\", \"Dipper\")\n",
+    "    x = x.replace(\"dipper_high_eval\", \"Dipper\")\n",
+    "    x = x.replace(\"simple_1_\", \"C4-News_llama-7b-base_\")\n",
+    "\n",
+    "    x = x.replace(\"llama-7b-base\", \"Llama-7B\")\n",
+    "    x = x.replace(\"opt-6-7b\", \"OPT-6.7B\")\n",
+    "    x = x.replace(\"llama-7b-vicuna-v1-1\", \"Vicuna-7B\")\n",
+    "\n",
+    "\n",
+    "    tup_x = x.split(\"_\")\n",
+    "    \n",
+    "    # return x_dict\n",
+    "    return \",\".join([str(elm) for elm in tup_x])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig_name = \"data_model_ablation_z_psp_scatter\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "# df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# name_col = df[\"Name\"]\n",
+    "name_col = df[\"Name\"].apply(name_extractor)\n",
+    "# name_col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# (dataset,model,attack)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "color_order = [\n",
+    "    \"Github\",\n",
+    "    \"Law\",\n",
+    "    \"Patents\",\n",
+    "    \"Med\",\n",
+    "    # \"C4-News\",\n",
+    "    # \"C4-en\",\n",
+    "    # \"Wiki\",\n",
+    "]\n",
+    "\n",
+    "marker_order = [\n",
+    "    \"Llama-7B\",\n",
+    "    \"Vicuna-7B\",\n",
+    "    \"OPT-6.7B\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def color_marker_map(x):\n",
+    "\n",
+    "    x_tup = x.split(\",\")\n",
+    "    assert len(x_tup) == 3\n",
+    "\n",
+    "    dataset = x_tup[0]\n",
+    "    model = x_tup[1]\n",
+    "\n",
+    "\n",
+    "    if dataset == \"C4-News\":\n",
+    "        color = \"black\"\n",
+    "        marker = marker_order.index(model)\n",
+    "    elif dataset == \"C4-en\":\n",
+    "        color = \"gray\"\n",
+    "        marker = marker_order.index(model)\n",
+    "    elif dataset == \"Wiki\":\n",
+    "        color = \"saddlebrown\"\n",
+    "        marker = marker_order.index(model)\n",
+    "    else:\n",
+    "        color = color_order.index(dataset)\n",
+    "        marker = marker_order.index(model)\n",
+    "\n",
+    "    return color, marker\n",
+    "\n",
+    "filter_list = [\n",
+    "    \"algorithm-3,Dipper\",\n",
+    "    \"algorithm-3,GPT\",\n",
+    "]\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_scatter_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"f_200p25m25_w_wm_output_z_score_mean\",\n",
+    "    y_col=\"f_200p25m25_no_wm_output_vs_w_wm_output_p_sp_mean\",\n",
+    "    x_name=r\"Z-Score $(\\rightarrow)$\",\n",
+    "    y_name=r\"P-SP $(\\rightarrow)$\",\n",
+    "    series_readable_id_col=\"Name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    color_marker_map=color_marker_map,\n",
+    "    legend_sort_indices=[0,1],\n",
+    "    legend_unique_indices = [0,1],\n",
+    "    shorten_legend_indices = [0,1],\n",
+    "    filter_list=filter_list,\n",
+    "    num_colors=4,\n",
+    "    num_markers=3,\n",
+    "    num_cols=2,\n",
+    "    marker_size=50,\n",
+    "    figure_size=(10,6),\n",
+    "    legend_loc=\"upper right\",\n",
+    "    title=\"Watermark Strength (Z-Score) vs.\\nSimilarity of Unwatermarked and Watermarked Outputs (P-SP)\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig_name = \"data_model_ablation_z_logdiv_scatter\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "# df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_scatter_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"f_200p25m25_w_wm_output_z_score_mean\",\n",
+    "    y_col=\"f_200p25m25_w_wm_output_log_diversity_mean\",\n",
+    "    x_name=r\"Z-Score $(\\rightarrow)$\",\n",
+    "    y_name=r\"Log Diversity $(\\rightarrow)$\",\n",
+    "    series_readable_id_col=\"Name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    color_marker_map=color_marker_map,\n",
+    "    legend_sort_indices=[0,1],\n",
+    "    legend_unique_indices = [0,1],\n",
+    "    shorten_legend_indices = [0,1],\n",
+    "    filter_list=filter_list,\n",
+    "    num_colors=4,\n",
+    "    num_markers=3,\n",
+    "    num_cols=2,\n",
+    "    marker_size=50,\n",
+    "    figure_size=(10,6),\n",
+    "    legend_loc=\"upper right\",\n",
+    "    title=\"Watermark Strength (Z-Score) vs.\\nWatermarked Output N-Gram Diversity\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>f_200p25m25_w_wm_output_z_score_mean</th>\n",
+       "      <th>f_200p25m25_w_wm_output_repetition_2_mean</th>\n",
+       "      <th>_wandb</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pile_uspto_opt-6-7b_gen_attacked_dipper_eval</td>\n",
+       "      <td>9.935677</td>\n",
+       "      <td>0.290999</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>core_algorithm-3_50_200_dipper_high_eval</td>\n",
+       "      <td>10.057236</td>\n",
+       "      <td>0.112082</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pile_pubmed_opt-6-7b_gen_attacked_dipper_eval</td>\n",
+       "      <td>10.012369</td>\n",
+       "      <td>0.265712</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pile_pubmed_opt-6-7b_gen_attacked_gpt_eval</td>\n",
+       "      <td>10.012369</td>\n",
+       "      <td>0.265712</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pile_free_law_opt-6-7b_gen_attacked_dipper_eval</td>\n",
+       "      <td>7.600258</td>\n",
+       "      <td>0.198272</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>pile_uspto_llama-7b-base_gen_attacked_dipper_eval</td>\n",
+       "      <td>7.951518</td>\n",
+       "      <td>0.235688</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>core_simple_1_50_200_dipper_high_eval</td>\n",
+       "      <td>9.193640</td>\n",
+       "      <td>0.108495</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>pile_uspto_opt-6-7b_gen_attacked_gpt_eval</td>\n",
+       "      <td>9.935677</td>\n",
+       "      <td>0.290999</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>pile_pubmed_llama-7b-base_gen_attacked_gpt_eval</td>\n",
+       "      <td>7.739721</td>\n",
+       "      <td>0.149759</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>core_algorithm-3_50_200_gpt_p4_eval</td>\n",
+       "      <td>10.057236</td>\n",
+       "      <td>0.112082</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>pile_pubmed_llama-7b-base_gen_attacked_dipper_...</td>\n",
+       "      <td>7.739721</td>\n",
+       "      <td>0.149759</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>pile_uspto_llama-7b-base_gen_attacked_gpt_eval</td>\n",
+       "      <td>7.951518</td>\n",
+       "      <td>0.235688</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>core_wikitext_llama-7b-base_gpt_p4_eval</td>\n",
+       "      <td>9.793739</td>\n",
+       "      <td>0.194513</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>pile_free_law_llama-7b-base_gen_attacked_dippe...</td>\n",
+       "      <td>7.699818</td>\n",
+       "      <td>0.122125</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>pile_free_law_opt-6-7b_gen_attacked_gpt_eval</td>\n",
+       "      <td>7.600258</td>\n",
+       "      <td>0.198272</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>core_simple_1_50_200_gpt_p4_eval</td>\n",
+       "      <td>9.193640</td>\n",
+       "      <td>0.108495</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>pile_github_opt-6-7b_gen_attacked_gpt_eval</td>\n",
+       "      <td>4.022866</td>\n",
+       "      <td>0.285722</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>pile_pubmed_llama-7b-vicuna-v1-1_gen_attacked_...</td>\n",
+       "      <td>5.391768</td>\n",
+       "      <td>0.118112</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>pile_pubmed_llama-7b-vicuna-v1-1_gen_attacked_...</td>\n",
+       "      <td>5.391768</td>\n",
+       "      <td>0.118112</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>pile_uspto_llama-7b-vicuna-v1-1_gen_attacked_g...</td>\n",
+       "      <td>5.362766</td>\n",
+       "      <td>0.212157</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>pile_uspto_llama-7b-vicuna-v1-1_gen_attacked_d...</td>\n",
+       "      <td>5.362766</td>\n",
+       "      <td>0.212157</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>pile_free_law_llama-7b-vicuna-v1-1_gen_attacke...</td>\n",
+       "      <td>5.122158</td>\n",
+       "      <td>0.111523</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>core_c4_llama-7b-base_gpt_p4_eval</td>\n",
+       "      <td>9.141209</td>\n",
+       "      <td>0.160294</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>pile_free_law_llama-7b-base_gen_attacked_gpt_eval</td>\n",
+       "      <td>7.699818</td>\n",
+       "      <td>0.122125</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>pile_free_law_llama-7b-vicuna-v1-1_gen_attacke...</td>\n",
+       "      <td>5.122158</td>\n",
+       "      <td>0.111523</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>pile_github_opt-6-7b_gen_attacked_dipper_eval</td>\n",
+       "      <td>4.022866</td>\n",
+       "      <td>0.285722</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>pile_github_llama-7b-base_gen_attacked_dipper_...</td>\n",
+       "      <td>4.763147</td>\n",
+       "      <td>0.214511</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>pile_github_llama-7b-vicuna-v1-1_gen_attacked_...</td>\n",
+       "      <td>3.281843</td>\n",
+       "      <td>0.155464</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>pile_github_llama-7b-base_gen_attacked_gpt_eval</td>\n",
+       "      <td>4.763147</td>\n",
+       "      <td>0.214511</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>pile_github_llama-7b-vicuna-v1-1_gen_attacked_...</td>\n",
+       "      <td>3.281843</td>\n",
+       "      <td>0.155464</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 Name  \\\n",
+       "0        pile_uspto_opt-6-7b_gen_attacked_dipper_eval   \n",
+       "1            core_algorithm-3_50_200_dipper_high_eval   \n",
+       "2       pile_pubmed_opt-6-7b_gen_attacked_dipper_eval   \n",
+       "3          pile_pubmed_opt-6-7b_gen_attacked_gpt_eval   \n",
+       "4     pile_free_law_opt-6-7b_gen_attacked_dipper_eval   \n",
+       "5   pile_uspto_llama-7b-base_gen_attacked_dipper_eval   \n",
+       "6               core_simple_1_50_200_dipper_high_eval   \n",
+       "7           pile_uspto_opt-6-7b_gen_attacked_gpt_eval   \n",
+       "8     pile_pubmed_llama-7b-base_gen_attacked_gpt_eval   \n",
+       "9                 core_algorithm-3_50_200_gpt_p4_eval   \n",
+       "10  pile_pubmed_llama-7b-base_gen_attacked_dipper_...   \n",
+       "11     pile_uspto_llama-7b-base_gen_attacked_gpt_eval   \n",
+       "12            core_wikitext_llama-7b-base_gpt_p4_eval   \n",
+       "13  pile_free_law_llama-7b-base_gen_attacked_dippe...   \n",
+       "14       pile_free_law_opt-6-7b_gen_attacked_gpt_eval   \n",
+       "15                   core_simple_1_50_200_gpt_p4_eval   \n",
+       "16         pile_github_opt-6-7b_gen_attacked_gpt_eval   \n",
+       "17  pile_pubmed_llama-7b-vicuna-v1-1_gen_attacked_...   \n",
+       "18  pile_pubmed_llama-7b-vicuna-v1-1_gen_attacked_...   \n",
+       "19  pile_uspto_llama-7b-vicuna-v1-1_gen_attacked_g...   \n",
+       "20  pile_uspto_llama-7b-vicuna-v1-1_gen_attacked_d...   \n",
+       "21  pile_free_law_llama-7b-vicuna-v1-1_gen_attacke...   \n",
+       "22                  core_c4_llama-7b-base_gpt_p4_eval   \n",
+       "23  pile_free_law_llama-7b-base_gen_attacked_gpt_eval   \n",
+       "24  pile_free_law_llama-7b-vicuna-v1-1_gen_attacke...   \n",
+       "25      pile_github_opt-6-7b_gen_attacked_dipper_eval   \n",
+       "26  pile_github_llama-7b-base_gen_attacked_dipper_...   \n",
+       "27  pile_github_llama-7b-vicuna-v1-1_gen_attacked_...   \n",
+       "28    pile_github_llama-7b-base_gen_attacked_gpt_eval   \n",
+       "29  pile_github_llama-7b-vicuna-v1-1_gen_attacked_...   \n",
+       "\n",
+       "    f_200p25m25_w_wm_output_z_score_mean  \\\n",
+       "0                               9.935677   \n",
+       "1                              10.057236   \n",
+       "2                              10.012369   \n",
+       "3                              10.012369   \n",
+       "4                               7.600258   \n",
+       "5                               7.951518   \n",
+       "6                               9.193640   \n",
+       "7                               9.935677   \n",
+       "8                               7.739721   \n",
+       "9                              10.057236   \n",
+       "10                              7.739721   \n",
+       "11                              7.951518   \n",
+       "12                              9.793739   \n",
+       "13                              7.699818   \n",
+       "14                              7.600258   \n",
+       "15                              9.193640   \n",
+       "16                              4.022866   \n",
+       "17                              5.391768   \n",
+       "18                              5.391768   \n",
+       "19                              5.362766   \n",
+       "20                              5.362766   \n",
+       "21                              5.122158   \n",
+       "22                              9.141209   \n",
+       "23                              7.699818   \n",
+       "24                              5.122158   \n",
+       "25                              4.022866   \n",
+       "26                              4.763147   \n",
+       "27                              3.281843   \n",
+       "28                              4.763147   \n",
+       "29                              3.281843   \n",
+       "\n",
+       "    f_200p25m25_w_wm_output_repetition_2_mean  _wandb  \n",
+       "0                                    0.290999     NaN  \n",
+       "1                                    0.112082     NaN  \n",
+       "2                                    0.265712     NaN  \n",
+       "3                                    0.265712     NaN  \n",
+       "4                                    0.198272     NaN  \n",
+       "5                                    0.235688     NaN  \n",
+       "6                                    0.108495     NaN  \n",
+       "7                                    0.290999     NaN  \n",
+       "8                                    0.149759     NaN  \n",
+       "9                                    0.112082     NaN  \n",
+       "10                                   0.149759     NaN  \n",
+       "11                                   0.235688     NaN  \n",
+       "12                                   0.194513     NaN  \n",
+       "13                                   0.122125     NaN  \n",
+       "14                                   0.198272     NaN  \n",
+       "15                                   0.108495     NaN  \n",
+       "16                                   0.285722     NaN  \n",
+       "17                                   0.118112     NaN  \n",
+       "18                                   0.118112     NaN  \n",
+       "19                                   0.212157     NaN  \n",
+       "20                                   0.212157     NaN  \n",
+       "21                                   0.111523     NaN  \n",
+       "22                                   0.160294     NaN  \n",
+       "23                                   0.122125     NaN  \n",
+       "24                                   0.111523     NaN  \n",
+       "25                                   0.285722     NaN  \n",
+       "26                                   0.214511     NaN  \n",
+       "27                                   0.155464     NaN  \n",
+       "28                                   0.214511     NaN  \n",
+       "29                                   0.155464     NaN  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"data_model_ablation_z_repetition2_scatter\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ttoZhIJvNuguqNFMfppim2XZobSviPNbW1np+7WFzkNetWn1QIUmSG6SLxWQG0ctCPuI+HkSVqTDMPpTL5bCwsIB4PO5W2qXTaei6PvRAd1CBQKCnKknLstwh17lcrqvXdDsvczOD9ktRad/LLxa67Wu2bbvhcb9hpZf92rbttnO39krMWSvuQTabbbk4VrlcbnuvVFV1Rz8QERER0f5haEpEx0r1sNZu5k2MxWIA9lZ0bjf/qQg8xPbN1Ad9rebqrH/Mtu2aufU6rcZc/TovAsD95OV1G1Q8HoeqqshkMgNPB2CaZteLO+VyuY59sVOf6NUw+1C5XB449Aa8vyZekGW5p74jFqiLx+Ndheb5fL6ris1WBu2XorJVLO7Uim3bbiDYbV8T+xykv3nVr0X/9LLSVJZlqKrq3sNCodD2nncKRLudn5aIiIiI+sfQlIiOFRFSdFvNVF2durCw0Pdx8/l8wxfkcrlc86VXVHHVB0qlUqlmO1GVmc/n2wY0qVSqZSXTQRok4OzmunnZJhHiRqPRgYPZXC7XMaDKZrMwDKPl4j/d9oleDbMPXbhwAYuLi30Hp/t1TbwQDoe7Pi9x7SVJajuvpWBZljv36SAG6ZeSJCGXy3UMHVOplPt8dV/rtGiVqqo9TzlQzat+Ldo5NzfXd1uaEdckGo22/UVR/fbNWJbVdIqEdhWsRERERNQjh4joGKlUKg4AJ51Od/0aWZYdVVXbblMqlRxJkhxJkpxSqVTzXKFQcNLptJPL5RwATi6XcxzHceLxeEPbJEmqOVaxWGzZVl3XHUmSnEKh0PBcMpls+rgsyw6Ahja2oiiKA8ApFotNny+VSg4AR5KklvtIp9MOgIbzFa8f9Lr1ek5i+0ql0vR5cc6KorTcpp10Ou1EIhG3rYqiOLlcrmZfpVLJSSaTjizLLa+t4/TeJ3q9Fv30oUH7RKVScdtZ/yNJkhOJRJx0Ot3y2vd6Tbzow90Sny/t7qnYTpIkB4Cj63rbbYvFopNMJmuuUT+87Je5XM6RJMlJJpNNjyPeq9V0XW+630ql4qiq2vTzQTiIfl1/DoqidHWsXonPu3ZEn4zH4037R7FYbPr5JPpfN/2KiIiIiDrzOY7jHEw8S0R0OITDYei63vXQy0wmA0mSOlZA2baNVCoFwzAwNzfnVr1duHDBrVjNZrPusdPpdEMbLMtyq4RkWUYwGGy7OIyYg7N+pWexEr3g9/sbKiclSYKqqk3nUgwGg02rwsSQXdM0MT8/33SflUrFbYOYr7FaJBKpOWa/163Xc2q1ffUK1GJRqmqqqva02Er9gkSWZbnzD4r5LgOBACKRSNcVhp36RK/Xon7/3fQhL/oE8NpCUGJhs5mZGQDA+vq6u+iaqBIsFApNq+m6uSZetbdXoVAIsVis7fu2uhqwVXVsq0pnWZb7WqzM634pqk3X1tYgy7I7NUJ9v6kmjlkulxEIBNzF+JaWlpoOVT+Ift1MKBRy5w31WiaTwfr6ett9W5blzvdrGIZ7rtV9pdXrRbVzoVAY6qJoRERERKOAoSkREREdiEwmA13XUSwW2w6lt20b8/PzME0TlUpl6MPue5HNZpHL5biq+RFl2zb8fj9KpRJDRyIiIqJjjqEpERER7TsRRrWqHm21va7rA81zedAYuh1toqq9WCwOuylERERENGRcCIqIiIj23draGgB0FZgCe0OR+x2KPkxiKo9OK7jT4ZROp/dlWD4RERERHT0MTYmIiGjfiTkv261qXs+yLASDwf1q0r5Jp9PIZrMt5yWlwymfz0OW5a6DfSIiIiIabRyeT0RERAciGo3Ctu2u5vtMJBJYW1s7ssOks9ksCoVCxwWL6HCwbRuhUIgLKBERERGRi5WmREREdCByuRxkWUYoFIJhGE23sSwL0WgUlmUd2cAUgDsPa6vzpMNF0zSk02kGpkRERETkYqUpERERHSjbtpFKpVoOX9c0bWTCq3A4DF3XR+Z8RlE+n8elS5c4lykRERER1WBoSkRERLSPEokEF4Y6pEzThGEYSCaTw24KERERER0yDE2JiIiIiIiIiIiIqnBOUyIiIiIiIiIiIqIqDE2JiIiIiIiIiIiIqjA0JSIiIiIiIiIiIqrC0JSIakSjUfh8Pvh8Pvj9/po/N5PP593t/H5/zZ8Nw2h5nOr9itfk8/n9Oi3yQDQaRSgUQjAYbNkfDpJpmgiHwwiFQvD7/UgkEvtyHMMwEI1GEY1GkUgkkEgk3D+36+M02kzThKZpbv/z+/0IBoOIRqMN/cK2bWQymSG19HAyTdP9d6D+JxgMup8z4romEgmYpjnsZhMRERHRMcLQlIhq5HI5OI6DeDwO27YBAKVSCZVKpen2kUgElUoFuVwOtm3Dtm3ouo5KpQJVVVsex3Ec6LoO27axsLCASqWCSCSyH6dEHhGBoWVZbt8YJlmWoWkaZFmGbdsol8ue7t8wDASDQei6jnQ6jVwuB13Xoes6crkcNE1DOp1GKBSCZVmeHpt6IwJ0EbSFQiG3r3rNsiw3rLdtG+l0GleuXEGlUkGpVEI6nUahUEA0GnXfJ5qmoVQqed6Wo0xRFDiOA8dxoCgKgL1/TxzHQalUcv/dqVQqKBQKkCQJ8/PzCIfDfL8RERER0YFgaEpETWma1tP2kUgEsiz39Jp4PA5ZlqHrOiRJ6um1dPBUVUU8HncDjmGTJAmqqmJ5ednzfWcyGYTDYTcsbda3ZVlGoVBALBZDMBhkpfSQZDIZN8gWQVs6nXZDby8rkMU+LctCqVSCrutQVbXm80uWZaTTaaTTaUSjUViWhWw261kbRpF4f7X6N0Rc0ytXrsCyLASDwWNf5S2uQzQaPZT7IyIiIhoFDE2JqClZlt1wrNswSFSW5nK5rra3LMvzIdX8wrf/AoHAsJtQw+vAPZPJQNM06LreVfVzMpl0A7KDCk6Paj/3ut3ietf/4kVVVZRKJaiqimw268nnTD6fRzgchqIoKJVKHX9JJIK+YDA48LFHnfhMmZmZabudJEnuvy/VlbyjpNv3SD6fh2VZHT9zvN4fERER0XHC0JSIWorFYgCAlZWVrrYXw6O7rQDK5/OeD8nnsE0ahGEY0DQNkUgE8Xi869clk0koiuJWFu63o9rPvW63rutIJpNtnweAbDY70HyYpmkiGo1CkiRcvHix69cpitJTP6LOFEWBJEmwbRupVGrYzfFct++ReDyOeDzu9vGD2h8RERHRccLQlIhaEoGmaZodK3ps24Ysy261VzfVKpcuXep5SH8nRzVMosNBVGWl0+meXytecxBVoEe1n3vZ7nw+3/Fay7LsVsAPEgYtLi4C2LvHvVY2M4Ty3tzcHIDuf0F3lHT7HpEkCbqudwzlvd4fERER0XHC0JSIWpJl2Q01V1dX225rGAbC4TAWFhYAdK5OFSGrlwzDGMnhmnQwstksbNuGoih99U0xt6Vpmvsa5hzVfu51uy9duoREItFVcAoAa2trfR0nn8/DNE1IktR3oMRF7rwlRjV4/W/IsHn9HjmqnxVEREREhwVDUyJqS3zZ7zRPaaFQgKqqboDRqdJ0dXXVHf7vlX6qA4kEURE4SL/0oqqxk6Paz71utwiDOn3WDDrnrbiX4t72IxwOD9QGqiWqJ0ftunr9HjmqnxVEREREhwVDUyJqSwRI3VasVK8k3a7arlAoeLoKeyKRGMmhmkeNaZowTROWZcGyrIHmkTxItm27bR2kX4oQZ7/64lHt5/vRbk3TEI/HO/5CRwRsYkh3L2zbdtvtRZhOg8vn825F+CgNJff6PXJUPyuIiIiIDpOJYTeAiA636kU3DMNoOsy0fm7BhYUFZLNZ5HK5pmFBp6H5+Xwely5dcrctl8tYWlpqGmbl83mkUqmaQNfv99es8K6qasvKP8MwoOu6u71lWVAUBUtLSzUVamKBH3GcUqlUsxCJZVmIxWLu9UkkElhbW4Nt27AsC47jANhbmX19fR22bWNtbc1dYVtcD8uy3LaK4FFV1a4qhnq5bv2cUye2bWN+fr7mXqiqikKhULNdt9e8nqZpsG0bkiRhZmYG6+vrSCQSngzRrR66Pcj+xGvFfRd/r+8PlUql5lwty0IikUC5XHZfVywW3ef76efV97dcLqNSqbj3Vzxmmqbbv5pd+2G0u1uyLHf1OhGGh0Khno/hZb9o1tZB3oO9vt+H8ZnkNdM0sbi4CEVRul6Qq5/P+H7fL/0cu5/3SCaTQaFQcO9H9fvS6/3VsyzLvffivw3E9Wv2732//Y6IiIjoUHCIiDqIx+MOACcSibR8vlqhUHAAOJIkNd1e13WnUCg0fS6ZTDY8VywWHUmSGo5TT1VVp5ePtXg87iiK4pRKpYbHJUmqebxYLDq6rjsAHFmWnUql4l6PSqXiAKg5drFYdHK5XM3j8Xi84ViqqjqSJDmVSsUpFotOMpmseb5UKjmSJDmqqrY9l36uW6/nVN3mVtdZ7K/+PIRerrlQKBQcWZYdXdebnre4zq36ZzdEuwE4lUql7/0Ui0V3P8Vi0X28UCh0PEahUHDfa4qitDxGt/28vg8WCoWm90Vc+1wu17RNB91uL4n70eqzqJPqc2/WNwfV73uw3/f7QX4m9UL0n3Q63fBcpVJxCoWCE4lEHEVRmn4OtNtvL5/xg75f+j220Ot7W5Kktp9ZXu8vnU47siw3tL1SqTiKojTte/30OyIiIqLDgqEpEXUkQtBWX76afVFqFhy1295xHCeXy7X8Iiq+dLX7wtxLKJNMJtsGIbIsNw2AFEVxZFluOIdIJNL0C7ZoUzKZbPplUIQ6yWSy5XXp1NZBr1u/51SvUqk4qqo2vefdnEezay76Xrv2dwr1u5FOpz0PTZvdk0gk0vEYXoePiqI4ANr+0kG0q9UvM4bRbi+IY3YKuFrxql900st70KvPyf38TOpVdeieTCbdn3g87kQika5+adZrG9t9xg/6fun32L2+Rzq9L73cXzqd7hhqKorS8nO4237XLDgnIiIiGhaGpkTUFVGBUv8lsVAotA2Hmn3xbPVlVFTgtPoi6lUoUyqVOn4pFtVf9W0Rx+j2i524Dq0qLx2nfcDsOJ2DEK+uW7fn1Ow6iwq5Vl+o+73mkiQ5siy3bY/Yt1eVpoMEQdW/YGh2P4YRPort252XuIatrvVRDE3F+6bde68TryqQO+nlPTjo+/0gPpN61a7SVBBBZDfH9OIzvt/3ixfH7pa4bl6Fpq321805Oc5rn3/t/pugU7/zsoKZiIiIaFBcCIqIurKwsACgcVXwXC7XdL49sWjK6upqzeP1859WK5fLsG27YQ5MQZblrhaj6kTTNADtV14Wi8a0akuvC7t0s8pzpwWIWp27V9et38VqLMvC4uIilpeXW86D1881z2QysG2745yq1XP19at6Hr1B+lj1aw/b3Hzt2iPLMiKRCCzLQjabPcBW7Q/btrG4uIhIJDLQ3JvV10wsKLWfunkPevV+38/PpP2QTqeRTCaRSCQ69lEvPuP7fb94cezDRryHEolE2+1E/xVz8jbTqd+Vy+UeW0dERES0fxiaElFXRNDZ7Wq8IuiqXpUc2PuS2CoYWF5eRi6XaxlySJLkyReqblZJF1+YW63+3msg1mn7ds93WnDEq+vWT8hnmiZCoRAsy2rbzn6uuQgULly40HO7elW9snr14j+9EgvzSJJ06ELTTsR1PipBTjvz8/NQVRW5XG6g/VT3i25D02w2i2AwCL/fX/Pj8/ng8/nahkbd9JmDer8P8pm0X5aWlgC8Fky24sVnfCet3i8HceyDJn752U3/lCQJpmm2DNSP2uciERERHW8Tw24AER0NIui0bRuGYUBVVTcwa/cawzCwsrLSsWIJ2PuyJcJWy7Lc1aGrV3H2orJJhB+6rmNmZqbldu1W8u01NOhUDTlICOHVdeu1DYZhIJfLYWlpCZqmIZPJIJlMNt22n2suXnMQAY0kSVAUBaZp1qz+3isRgvRbtTtM9df9qIpGo5BluWVgGgwG256jqGgEavtFoVDoWPUMAPF4HPF43P17KBRyV13vFEh309e9er/v52fSfhFtqv53qBkvPuM7afV+OYhjHzTRn7rpE4FAALZtw7Kspv/uezEygIiIiOigMDQloq5FIhHk83nkcjmoqoqVlRW38qeZaDQKwzCQz+eRTqdhGEbHoXn5fB6apkGWZWiaVhPCGYYxcGVOdZiwtLR0KIOBfuz3dWumUCi40zUUCgVomgZVVRu+KB+Va55IJJBIJLC6utowDUU3RJAj9kUHT1z3dhWmmqa1DRXrg7hB+8Xc3BxM0/S03w/j/X5YSJLkhnLNDPPz5qh81vWi319Ucpg9ERERjQIOzyeirtXPU9ppSLaYB9WyLFiW1XL+UyGRSCAajSKRSLQdxt+rbDZ7oFWLB22/rlsn1cODRUjVbL7afq+5qMI6qHkT4/G4G8jk8/meXy/mNlQUZSiVptX9vB9iaoGDrn4btN1CJpNBuVxuGZiKfhSPx5FMJlv+1If+8XjcnSd0kPlevaqwG9b7/bAQ17FUKjV9/qA+45u9X/br2F69R/rZX/U5dfNZLMLSo1JFS0RERNQOQ1Mi6lr1PKXZbLbjXJNiaCuAjiGUYRjIZrNQFKXlEO/6L2ymafb1RVKcxyBzVx4WB3ndqtWHA5IkQdd1WJbVdL7Bfq65qEo+yOHiInDrNGdiPdu23cVPlpeXW27XKTg7yIV16onqxGbV4Ie53QDcIeqdKkz7JSpMO1Wp7rdhvd8PE/HZ066a9iA+41u9X0bp3xeh23OybRu2bUOWZYamRERENBIYmhJRT0RVk6ZpXc3vJ6pTU6lU2/lPRdghtm+m2dxxzQKM+sds264JfcSUAp2G2tq2PVDQchC8vG6DisfjUFUVmUymIdDo55qLeSFXVlbavsbLcEJVVSSTyZbhbyuLi4uwbRu6rnc1f28r3S60BnTu5522r2ZZFgzDgCRJNfNxdms/292JaZpYWVlpG5gO2t9VVYWu67BtG/Pz8z291sv+eZje78Mi+kqzIFj8cs6Lz/h+3y/7cexB3yOD7q/bcxKjUA77v5tERERE3WJoSkQ9EcOvu60kqa5OFcP1+5HP5xvCqHK5XFPx2KoysVQq1WwnqrTy+XzbaqVUKnUo5qYcJPDo5rp52abqYfrV2/RzzUX1qmmabUO5fuaZbCedTiOdTiOTySCTyXTcPpFIuHP9dgocxS8OWlX+FQqFjvem235eT1TCNiNCjlbB4zDb3Y5lWdB1vW1gCuyFOe1+adONeDzu9sdOi0kJ+Xz+QCvu9uv9Xs/rEFYM6V5fX++4bat+ZNu2O2Tei8/4ft8vgxy71/dIp3lDvdpf9Tl1WkRNVdW+fvFCREREdCg5REQ9qFQqDgAnnU53/RpZlh1VVdtuUyqVHEmSHEmSnFKpVPNcoVBw0um0k8vlHABOLpdzHMdx4vF4Q9skSao5VrFYbNlWXdcdSZKcQqHQ8FwymWz6uCzLDoCGNraiKIoDwCkWi02fL5VKDgBHkqSW+0in0w6AhvMVrx/0uvV6TmL7SqXS9HlxzoqiNGzTzzXP5XKOLMsNz1UqFScejzuFQsG9hsVisWW7elUoFBxZlp1IJNL02hSLRUdRFEdRlJb3t57oo5FIpOG5ZDLp3k8ATqFQaHouvfZzVVUdAI6u646u6w3PRyKRptd32O3uROyv3Q8A96fd+fWiWCy61zQSiTQ930Kh4MTjcSeZTDqOs/cebvb+Fbp9D3rxft/vz6RuVSoVJ51OO8lk0r1HkiQ5yWSyaT9tdg7V26XT6YZr0s/njRfvl36P3et7pNO99Hp/uq47siw3PF+pVBxVVdv2By/6HREREdFB8zmO4xxANktEIyQcDkPX9a4rqDKZTFfDfsW8kIZhYG5uzq2EuXDhgluxms1m3WOn0+mGNliW5VbvyLKMYDDYcu4/sb2Yp7B6X2JlasHv9zdUV0mSBFVVm1YbtapEE4thmaaJ+fn5pvusVCpuG5pVOkYikZpj9nvdej2nVtsXi0X3WiUSiYbFclRVRaFQcP/e7TWvVj1cXpZlzMzMYH193R026vf7IUkSAoEAZFmuOd6g8vm8O0WAGNJaLpcRCAQQjUZ7XohHDMu1LAuyLLv3S6y27ff73WOJ61uvl34eDodhGAYcx4FpmjWVueVyGRcuXGj7HhlWuztp1tfaqVQqnlZcimkBDMNw3+ui/6qqikQiUdOfxXWr1ut7EOj//X6Qn0ndsCzLHU7fbLEhsTBbK5lMBisrK5ibm0MwGIQsy02njOn188ar90s/xxav6fQeCYVCDVWskiThypUrDddsP/aXTqfdz0CxIOTS0lLTqUm86HdEREREw8LQlIiIaIRVh0BE1B7fL0REREQkTAy7Af2oXqV4ZmYGpVIJ4XC4q0VpWqlf9MO2bYTD4bbVBPvRDiIiIiIiIiIiIhquIxea2raNUCiEXC5XMwwokUjg0qVLSKfTPe8zn8/j0qVLDUPLQqEQdF1HqVQ6kHYQERERERERERHR8I0NuwG9ikajiEQiDfMm6bqObDbbdoXlZmzbxsrKStOQc3l5uWYuqP1sBxERERERERERER0ORyo0tSwLhmE0DTEBYGFhoecKz7W1NeTz+Zqh+YIIROsD0P1oBxERERERERERER0ORyo0FSuYtlpxNBgMwjCMhhU42xGr/M7MzLTcpn7l0P1oBxER0X4QK1fXr45NRI34fiEiIiIi4UiFpqZpNgSY1USIuba21vU+FUVBpVJpuuCTqDBVVXXf20FEROSlYDAIv9/vhkChUMj9pR4R1eL7hYiIiIjqHamFoCzLQiAQaPm8CDLFf/AOStM0yLLcMNTei3Zsbm5ic3PT/fvu7i7K5TJmZmbg8/kGazgRER17zz77bMvnNjY2DrAlRIcf3y9E1I7jOLh27RruvvtujI0dqbojIiIawJEKTcvlcssh8QDcIHPQYfGmaSKVSmFubs4diu91O1KpFD7xiU8M1E4iIiIiIiI6GP/9v/93vOENbxh2M4iI6IAcqdB0v+cIzefzuHTpEizLgizLCIfD+9aOpaUl/PIv/7L796tXr+IHf/AHceXKFZw9e9Z9fGtrC1/84hfxjne8A5OTkwMflw4H3tfRxPs6unhvRxPv62jifR1NvK+j6yjc22vXrmF2drbmexoREY2+IxWadmt9fb2v10UiEUQiEffv4XAYuq5jeXm57Rym/bRjamoKU1NTDY8HAgGcO3fO/fvW1hZOnTqFmZmZQ/sfEdQ73tfRxPs6unhvRxPv62jifR1NvK+j6yjcW9EuTqNGRHS8HKnQVJKkrqo8Z2ZmPDleLpeD3++HbdsoFApDawcREREREREREREdnCM1i3W7xZeAvblGAfRVFdqMJEmIRCIwDKNm9dSDbgcREREREREREREdnCMVmsqy7AaSzYjqz3aLNDV7TbtV7sW+qitN96MdREREREREREREdDgcqdBUUZS2w+JF+Kmqatf79Pv9CAaDLfcrhthXP78f7SAiIiIiIiIiIqLD4UiFprFYDABgmmbT5y9dutRzUClJEmRZbjmUvlQqAQBCodC+toOIiIiIiIiIiIgOhyMVmiqKAlVVsbKy0vT5fD4PTdMaHrdtG5qm1cxLKsTj8Zqh9/VWV1chSRIWFhYGbgcREREREREREREdfkcqNAX2VrTP5/MNVZ6JRALJZLJphWc2m0Umk0E0Gm14Lp1OI51ONw1UxfYXL15sqETtpx1ERERERERERER0+E0MuwG9kiQJxWIRmqZBkiTMzMygVCohHA4jEok0fY2qqg3VotV0XUc+n0c0GkUgEEC5XIZt21AUBVeuXGk6dL+fdhAREREREREREdHhd+RCU2AvsNR1vevtFUVBpVJpu00kEuk57Oy1HURERERERERERHT4HcnQlIiIiIiIiA6f8qtXYd/c6LiddPIcArdNH0CLiIiI+sPQlIiIiIiIiAa2vbuNv/bZ/w1bu1sdt50cm8TFn3/iAFpFRETUnyO3EBQREREREREdPhNjE3jd6RnsOLvwtfnfjrOL152ewcQYa3iIiOjwYmhKREREREREnvjAA+/DuG8MDhxMjI03/DhwMO4bw+KDjwy7qURERG0xNCUiIiIiIiJPvEt+O95w9i7sOg52HafmOfHYG87ehfDsXxhSC4mIiLrD0JSIiIiIiIg884EH3ocxnw87zk7N4zvODsZ8PlaZEhHRkcDQlIiIiIiIiDzTrNqUVaZERHTUMDQlIiIiIiIiT9VXm7LKlIiIjhqGpkREREREROSp+mpTVpkSEdFRw9CUiIiIiIiIPCeqTbd2t1llSkRERw5DUyIiIiIiIvKcqDZ1wCpTIiI6eiaG3QCi46by8itY/85VAMA3/uRljI83fxtKt5+G/84zB9k0IiIiIiJPJc5H8Q//KIvE+eiwm0JERNQThqZEB2h7exs/+6Z/DN848OF/+Vb87Z/6Z7j16k7TbSdPjOPz5U9iYoJvUyIiIiI6mubf+Ofx9jcoODlxYthNISIi6gmH5xMdoImJCdz5AxJ2dnYBAD6fr+EHPh92dnZx5w9IDEyJiIiI6MhjYEpEREcRQ1OiA/Y3fiWM8fG9t97YxBjGJ2t/AAfj42P4xV8ND7ehRERERERERETHFENTogOm/ux5vH42AABw4NQ85zgOdncc3BOcwXzs/DCaR0RERERERER07DE0JRqCn0++AwCwu71b8/jO9i7GxnysMiUiIiIiIiIiGiKGpkRD8NOPvAUAsLvjwHH2qk1ZZUpEREREREREdDgwNCUaorExH3a+X23KKlMiIiIiIiIiosOBoSnREL1+NuBWm7LKlIiIiIiIiIjocGBoSjREP598B8bGfNja3GaVKRERERERERHRIcHQlGiIfvqRt+Ce4AwcB6wyJSIiIiIiIiI6JBiaEg3ZBz7xLpw6M4UPfOJdw24KEREREREREREBmBh2A4iOu5+OPIC3/cwPY+rkiWE3hYiIiIiIiIiIwEpTokOBgSkRERERERER0eHB0JSIiIiIiIiIiIioCkNTIiIiIiIiIiIioioMTYmIiIiIiIiIiIiqMDQlIiIiIiIiIiIiqsLQlIiIiIiIiIiIiKgKQ1MiIiIiIiIiIiKiKgxNiYiIiIiIiIiIiKowNCUiIiIiIiIiIiKqwtCUiIiIiIiIiIiIqApDUyIiIiIiIiIiIqIqDE2JiIiIiIiIiIiIqjA0JSIiIiIiIiIiIqrC0JSIiIiIiIiIiIioCkNTIiIiIiIiIiIioioMTYmIiIiIiIiIiIiqMDQlIiIiIiIiIiIiqsLQlIiIiIiIiIiIiKgKQ1MiIiIiIiIiIiKiKgxNiYiIiIiIiIiIiKowNCUiIiIiIiIiIiKqwtCUiIiIiIiIiIiIqApDUyIiIiIiIiIiIqIqDE2JiIiIiIiIiIiIqjA0JSIiIiIiIiIiIqrC0JSIiIiIiIiIiIioCkNTIiIiIiIiIiIioioTw24AERERERERHR/bt2xs37q69+ftHQDA5vX/jp2J8ZrtJk5MY+KEdNDNIyIiAsDQlIiIiIiIiA7I7u42XvwvfxOOsw0A2HEmAfx1vHT5Ixj3bdVs6/NN4L6fWMXYGL+2EhHRwePwfCIiIiIiIjoQY2MTmJy6A3B2AAd7P8Brf3Z/djA5dQcDUyIiGhqGpkRERERERHRgbn/jzwG+va+ivu+Hor6xCffn+w/g9jf+/LCaSERExNCUiIiIiIiIDs70634aJ07eDWAXjuPUPLf3912cOHk3pl/3F4fSPiIiIoChKRERERERER2wvWpT394w/WrODuDzscqUiIiGjqEpERERERERHajqalOBVaZERHSYMDQlIiIiIiKiA+dWmwqsMiUiokOEoSkREREREREduOnX/TROTL0eAKtMiYjo8GFoSkREREREREMx84ORvT84W6wyJSKiQ4WhKREREREREQ3FuTt/4vt/clhlSkREhwpDUyIiIiIiIhqqsfHbcMfsLwy7GURERK6JYTeAiIiIiIiIjrfgjz+BqZOnh90MIiIiFytNiYiIiIiIaKjGxk8MuwlEREQ1GJoSERERERERERERVWFoSkRERERERERERFSFoSkRERERERERERFRlaGFppcvXx7WoYmIiIiIiIiIiIhaGlpouri4OKxDExEREREREREREbU0sR87feaZZ2DbNsrlcsttLMvaj0MTERERERERERERDcTT0PTq1asIhUJdBaI+n8/LQxMRERERERERERF5wtPh+YuLi4hEIiiVSqhUKtjd3W36Uy6XMT097eWhiYiIiIiIiIiIiDzhaaWpLMt47LHHOm4nSRLm5ua8PDQRERERERERERGRJzytNL399tu73vbpp5/28tBEREREREREREREnvA0NHUcp+ttNzY2vDw0ERERERERERERkSc8DU0jkQgef/zxrradn5/38tBEREREREREREREnvB0TlOfz4fz588jFovhne98J2ZnZxEIBBq2K5fLsCzLy0MTERERERERERERecLT0FRRFFy9ehWO4yCXywHYC1LrOY7T9HEiIiIiIiIiIiKiYfM0NA0EAojH44jFYm23W19f77gNERERERERERER0TB4GprKsoyPfexjOHfuXMdtZ2dnvTw0ERERERERERERkSc8DU1zuVxXgSkAXLx4se/j2LaNVCoFAJiZmUGpVEI4HEYkEul7n6ZpQtd1lMtlmKYJWZYRjUYRj8ebbp/NZpHL5ZBIJKCqKiRJgmVZME0TKysrWFpagqIofbeHiIiIiIiIiIiIhsPT0HR6enpftq1m2zZCoRByuVxNKJlIJHDp0iWk0+me95nNZgEAuq67jxmGgWg0inQ6jWKxCEmSGtphGAYMw6h5XJIkXLx4kYEpERERERERERHREeVpaFrt8uXLWFlZgWmaKJfLuHDhAqLRKN7xjncMtN9oNIpIJNIQSuq6Dr/fj3A4DFVVu96fZVmwbRvJZLLmcVVVcfHiRYRCIUSjURQKhYbX6rqOUqkEy7IQCAQQCoVaVqYSERERERERERHR0eB5aLqxsYEPfvCDyOfzNY8Xi0Xouo5wOIzV1dWuh/FXsywLhmHUVIRWW1hYQDqd7ik01XUdS0tLTZ9TFAWqqsIwDFiWBVmWG45XX4FKRERERERERERER9uY1zuMRCIIBAIolUrY3d2t+fm93/s9nD17FvPz833tW4Sl9eGlEAwGYRgGbNvuep+GYWB2dhaWZTV9XlS0mqbZW2OJiIiIiIiIiIjoSPI0NH3iiScQjUbxW7/1W5idnW14XlVV5HI5fPSjH8Xjjz/e8/5N02xb2SnC1LW1ta73GQgEYNt2T0ErERERERERERERjS5PQ9MXX3wRi4uLHbd75JFH8L3vfa/n/Yu5Q1sRgWqrqtFmCoUCSqVSy4WbxL7aLexkmiay2SyrUYmIiIiIiIiIiEaAp3OazszM7Mu2Qrlcbjk0H4AbqPZaNdpun/l8HoqiNN3GMAyUy2XMzc0hHo/DNE2Ew2FomtZxXtXNzU1sbm66f9/Y2AAAbG1tYWtry31c/Ln6MTr6eF9HE+/r6OK9HU28r6OJ93U08b6OrqNwbw9z24iIaP94Gpr6/f6ut/X5fD3v/6CH0GcyGQDA8vJyw3MiRI3H4+5jiqJA13UEg0EUi8W21ampVAqf+MQnGh5/+umncerUqYbHC4VCz+2nw4/3dTTxvo4u3tvRxPs6mnhfRxPv6+g6zPf2xo0bw24CERENgaeh6YsvvoiNjQ2cO3eu7XYvvfQSXnzxRS8PXWN9fX3gfZimCU3TkE6nm4afkUik6etkWYaqqohGoyiVSi33v7S0hF/+5V92/76xsYEf+IEfwDvf+c6a67e1tYVCoYBwOIzJyckBzogOE97X0cT7Orp4b0cT7+to4n0dTbyvo+so3FsxKpCIiI4XT0PTpaUlzM/PI5/P49577226zeXLlxGNRvv6TaIkSV1Vm/Yz9L9eNBpFOp1GMpns+bWKosAwDFiW1XLo/9TUFKamphoen5ycbPofC60ep6ON93U08b6OLt7b0cT7Opp4X0cT7+voOsz39rC2i4iI9penoen09DRSqRRmZ2cRCoUwNzfnLs5k27YbJK6uruKNb3xjz/tvtwgUsDfnKfDaglD9ikajSCQSfQWmABAMBgHsVau2my+ViIiIiIiIiIiIDh9PQ1MAUFUV5XIZi4uL0HW94bmnn34as7Ozfe1blmWsra21fF5UoQ4SVGqahgsXLrQNTBOJBAzDaDv8noiIiIiIiKjezs4OF5ciIhqSyclJjI+Pd7Wt56EpsFfpmcvlAABXrlwBgL6D0mpi2HsrlmUBQMeV61vJZrOYmZlpGpjatu1WsK6trcGyrJrH6rcV7SUiIiIiIiJyHAff/va3cfXqVTiOM+zmEBEdSz6fD9PT07jrrrs6LlK/L6FpNS/CUiEWiyGTycA0zaaB5KVLl/oOTPP5PGzbbhqYWpYF0zTdxZ9UVUWxWGy5r0uXLkGSJA7NJyIiIiIiIgDA1atXYds27rjjDpw+fbrjl3UiIvKW4zi4fv06vvvd7+K2227rOL3nvoemrcRiMaysrPT0GkVRoKoqVlZWmoam+Xy+6QJTtm0jlUohHA43DVVN04RlWS2H5BuGUfO6WCyGbDaLeDzesK1lWcjn826lLRERERERER1vjuPg5Zdfxrlz53D77bcPuzlERMfWbbfdhs3NTbz88suYnp5u+wusvkPTjY0NnDt3ruaxy5cvd/Xacrncdph9O7lcDqFQCLFYrCY4FQs3NQtFs9ksMpkMstksKpVKzXOWZSEajUJVVSQSiZZtrX6dmCYgk8nUBK2WZSEUCiGZTLpVqURERERERHS87ezsYGdnp+E7NBERHbxz585hY2MDOzs7mJhoHY32FZq+6U1vwksvvYRyuVzzof/QQw/h6tWrHV/vOE7fQxEkSUKxWISmaZAkCTMzMyiVSgiHwy2DSlVVIUkSFhYWGp4Lh8OwLAvZbLblMZsNs08mkzAMA4lEAuVy2Z3f9OLFi5zLlIiIiIiIiFzb29sA0PbLORERHQzxWby9ve19aHr+/HkAaPgtWSAQwNLSUsd5RdfX1xGLxfo5NIC94FTX9a63VxSlocJUKJVKfbdDVdW+51AlIiIiIiKi44XzmBIRDV+3n8V9haat5uuUZRmJRKKrIQdeLhBFRERERERERERE5JUxL3f29NNPdz1Hy9rampeHJiIiIiIiIiKPaZoGTdPcdULy+bz7eL8sy4KmafD7/QgGg02P6ff7EQ6HYZpm38cZVH07NU1ruj6LaZoIhUJNz+UoEOfp8/kQDAaRyWSQyWSgaRqi0Sgymcywm9iVbu/XQcjn8wiHw/D5fIhGow39WKyJ4/f7j8z17dao9CdggIWgiIiIiIiIiGi4Njc3cf36dZw+fRpTU1Oe7dc0TSwuLiKdTtdMSycWUzZNE+l0uulrw+EwCoVCy33Lsox0Oo2ZmRmkUilomlazL/HnVvs/KKKdhmFgbm6uZXsURUE6nW66uPRhEY1GEQ6HMTc3B0mSap4T52maJhRFqVnwGgBCoRBKpVJP0yR2srvrYNdxMDH+Wi3f9s4uxnw+jI31N41Ft/frIEQiEaiqCr/f37CQObDX1qWlJUiS5L6/NE2DZVktR3cfJoetP+0XTytNW7l69SqWl5fxxBNP4JlnnjmIQxIRERERERGNrMuXL+PRRx/Fgw8+iB//8R/Hgw8+iEcffRRf/epXPdl/NBptCEyB16blayWfz3dd3SdJEnK5HDKZDCzLqnnuMFVtBgIBT7YZJtM0kUgk3IpY8dNNxXAikWi7eHavrt3cxC985v/GI5/O4XvXbgAAvnftBh759Cp+4TP/N67d3Bxo/4flXkiShEgk0jIctCyr5v0VDocHWv/nIB2m/rSfPA1N3/WudzV9fHp6GouLi4hGo/D7/Xj88cdx+fJlLw9NREREREREdCzk83lEo1E8+eST2NzcxPj4ODY3N/Hkk08iEongs5/97ED7F8FHq4WPVVWFLMsNj9u2jXK53NOxxALL0Wi094ZS1yKRCBzHqfnRdf3Aqxqv3dxE/F9/Hs/8yRX8v1e+if/5X/xHPP/t7+F//hf/Ef/vlT/DM39yBfF//fmBg9PDIpFIwDAM2Lbd8Fx9haaqqohEIgfTsAEdlv603zwNTR3Hafv89PQ0zp8/j0cffXRo80oQERERERERHVWXL1/G0tISXn31Vdxzzz0IBAI4e/YsAoEA7rnnHrz66qv46Ec/OlDFaT6fbxmYCs0qylZXV7GwsNDz8XK5HEzT7Fh9lslkkM/n3f8XbRVVboZhIJvNIhgMIhQKwbIs2LbtVsOJeSWz2SwMw2jYl9ds23b3n0gkaua1NAwDoVAIoVAIpmkin88jm826Vbxi/thm82G2228r9dXBYgh7N9LpdMMQ637s7jr40L95Cr///Eu4/cwp3Hn2NNa+8S088uk81r7xLdx59jRuP3MKv//8S/jQv3kKu7vtM6ZBtLuG/fSpVlRVhSRJDX07m83WvFfazYubzWZrfsT24XC4ZvtEIgG/3+9u06yPiblFu70WrRyG/nQQPJ3T1Ofrft6JUqnk5aGJiIiIiIiIRt6TTz6Ja9eu4Z577mn4Du7z+fC6170O3/zmN/Hkk0/igQce6OsYlmV1HB5fH6oahtExaG1FkiSk02lomoaFhYWGCjxgb7qAWCzmVuKFw2HIsoxIJIJyuYxcLuce37ZtrK+vu9WwS0tLUBQFsiy7AanY1rKsfSvqSqVSSCQSbjuDwSCKxaI7j6WYB7VcLrvnJYY4V8/Hubi4iGKx2NV+W6muDLZtu2FouCDCNQDuddU0DfF4fNDLgV3HwSubtwAAYz4fTkyM486zp/G9V27gzrOncWJiHLe2dwAAr2zewq7jYAz9zW/aSbtr2Guf6iQej0PX9Zqg0LbtmvvVal7cTCaD9fV1tz+IsD8SiUDTtJrtdV2vmeaiXR+rnmf1qPang9B3pelLL73U8FMul/GNb3yj6XPi55lnnsGHP/zhhvlKiIiIiIiIiKi1zc1NfP7zn8epU6daFi35fD6cOnUKn//857G5eXBDnG3b7ipAaiWZTCIQCGBxcbHhOcuy3KBICIfD7lyRCwsLDcFndfVouVyuaVsul3OHS8uy3HWFXK/qA1lZlmv+HggEGsKm+muoKErD0O5O++2kXWilKAoikQgikQji8TjS6TRyuZwnwfLE+Bg+8zfei7l7X4+Xr13Hre0dnJgYx93SWTcwffnadczd+3p85m+8t2aRKK91uoa99ql2YrEYLMtyKzgty2r62vq5WG3bhqZpWFpach+rDkabzd1aH3S26mPVmdxR7U8Hoe9K00KhgFKpBMMwYJqm+4HdqdM4joNQKHRkLhARERERERHRYXD9+nVsbW1hcnKy7XaTk5O4desWrl+/jqmpqZ6PI8tyx9Gh1cFPNpv1pHIsl8u5Q4mrGYYBSZJqcoT19XU3+JEkCYqiuMGqCI6ahVNiYR6/3w9FURCLxQYaKtwqABPnA7xWiVculxvmfK1/rSRJDVW+9a/pZr+tdDP0upqogBQrng8SjAPA7WdP4fGFMB75dB7fe+UG7pbOus9975UbmDmz9/ztZ08NdJxWxP3qdA176VOdiIpUXdeh6zoMw+jq/bK2tgZJkmqC0H7mDG3Wx6rP9Sj3p/3Wd2y/uLiIxx57DGtra3jxxRfxxje+EYqi4Omnn277UyqVcOnSJUxPT3t5HkREREREREQj7fTp05icnMTW1lbb7ba2tnDixAmcPn26r+NEIpGOhU7iedM021ZqhkIh+P1+9yeTybTcVlEUxOPxhkWhRBWrWDRKDDsuFAruNrFYDCsrK7AsC3Nzc0gkEsjn8zBNs2HYcKFQQLFYhKqqSKVSbdvUSbvQyDRNRKNRrK6uQpIkzwKidvvtdL11Xe849UI9MYy71SrwvfjetRt4dLWA9Vdu4PYztcHo7WdOYf2Vvee/d+3GwMdqRtyvbu5NL32qk0QigdXVVQBouihUM91uN+hrjnJ/2m+ezGkqyzKKxSIWFhYwPz/vxS6JiIiIiIiIqMrU1BTe85734Mknn4Tf7286RN9xHNy4cQOPPPJIX1WmwN5CLfl8vuU8pbZtu0ODy+UyTNN0Q1RRoZrJZNysoNdjz87OIp1Ou9V4iqIglUo1bYeowhNzPIbDYcTjcUiShHA4DEmSaqr6RFWsoihQFAVLS0uYn5/vq9pUVMA2Y9s25ufnUSwW3RBKBFr9VCt2u99O11ssDtSPmZmZvl4nbO/s4gP/6j+5iz6JIfnf+36AKuY4XfvGt/CBf/U7+OyHo54O0Rf3q9t7022f6kY8HoemadA0DbFYrKvXNJuaQbS1Vb/rtkK0el9HtT8dBM96nyRJDb8NIiIiIiIiIiLvvP/978fZs2fxne98B45Tu7q44zj4zne+g7Nnz+L973//QMcRC7bUV5zato1sNuvOL6qqKpLJpPsjFqZJJpM1c5A202wKAEmSsLy8XBP+qKqKubm5hlXu6+dhVBTF3Wd9AFTf/mpiW8uymladtgqiNE1rGX6KVdarnxf7aVed2qlSsN/9Vr++18BW07S+gsJ6Yz4fznw/yN91HHcOU//p29w5Tne/36fPTJ3AWA+LjVfrdL+6vYbd9imgdd8RxOJf+XzerbTsRCzMVL9f0e/r5ya1bdv9aaf6+aPcnw6CJ5WmQrMJm4mIiIiIvPbKrRuo3LwG/8mzOHNif+Y9IyI6jB588EE89thj+OhHP4pvfvObOHXqlDtk/8aNGzh79iwee+wxPPDAAwMdR1EUFItFaJqGQqGAmZkZt7qtVVVmPp/HysoKgL3hyNFotGmlqmVZ0DQN+Xwetm0jnU7XVM5FIhF3P0KhUICmaSiXy26Va30oG4vFao6XSCQathHHEQGsZVlYXl4GsBdGpVIpt6rQsizoug7TNGHbthtelUolrK6uuhW3pmlC0zT3vNLpNBRFQTKZhKZpuHDhAoDXguhYLNb0NZlMBmtra+45yrKMVCpVsyBQp/12IstywwJC4jwNw4BlWW4FYPW8sZ1WU+/G2JgPv/ULDyP+rz+P33/+JQDA3L2vx+MLYTy6WsDaN74FAPjpH3ojfusXHsbYWG+habf3S5blrq9hN30KaOw7zSQSiZaLojfrD9XtEpXbwGv9XpIkt9+I5xRFga7rkCQJc3NzHftYJBI5sv3pIPic+l9NHRAxNwTt2djYwPT0NK5evYpz5865j29tbeGpp57Cww8/3HGybzo6eF9HE+/r6OK9HU28r0dT4cof4cn/9p/xov2ncBzA5wPeJP0g3v+j70V49m28ryOK93V0HYV72+q7Wi9u3ryJK1euYHZ2FidPnvSkXV/96lfx5JNP4vOf/zxu3bqFEydO4D3veQ/e//73DxyYHmci1DnsC9QcZddubuJD/+YpvLJ5C5/5G+/F7WdP4XvXbuAD/+o/4czUFH7rFx7G2ZP9TS0xTOw7R0e3n8l9V5pubGw0/INx+fLlrl5bLpc7TipNRERERFTtNy49idU//gJ2nV2M+XzwwQfHcfB8+SV8/A//Gf54vYQPP9DdPGFEREfdAw88gAceeAD/8B/+Q1y/fh2nT5/uew5Teo1pmh2nFaDBnD05hX/zgb+KXcdx5yy9/ewpfPbDCxjz+XquMD0s2HdGT1+h6Zve9Ca89NJLKJfLNcHpQw89hKtXr3Z8veM4TSesJiIiIiJqpnDlj7D6x1+Ag11Mjk3UzHO26zjYdrbxH772Bfw5aXaIrSQiOnhTU1MMSz3U60I61J+xMR/GUJsLebno0zCw74yevkLT8+fPA0BDpWkgEMDS0lLTOUuqra+vd71aGBERERHRk//tP2PXaQxMgb2FJSYwga3dbaz+8RfwXswNqZVERHSUWZaFuTn+G0K9Y98ZTX2FprlcrunjsiwjkUh0Nc/L7CyrAIiIiIios1du3cCL9p/uDdlrMVpJPGfZ/x04xy8tRETUO85FSf1i3xlNfc9p2szTTz/d9bZra2teHpqIiIiIRlTl5rW9RZ/QfnonH3zYHcoSp0REREQ0ao72hBFERERENPL8J8/C5wMctE9EHTg4omtHEBEREdEh42mlaStXr17F6uoqfD4fZFnGQw89dBCHJSIiIqIRcObEKbxJ+kE8X34Ju47TdIj+ruNg13Eg+38A2B1CI4mIiIhopHhaafqud72r6ePT09NYXFxENBqF3+/H448/jsuXL3t5aCIiIiIaYe//0Z/BmG8M2842dp3aitNdx8G2s41x3xgWfvjdQ2ohEREREY0ST0NTx2k/ZGp6ehrnz5/Ho48+CsMwvDw0EREREY2w8OxfwM/e/274MIat3W1s7W5je3fH/fMYxhC7/914x70/PuymEhEREdEI8HR4vq/FaqbNlEolLw9NRERERCPuf517P354Joh/+//9Z7xY+QZ2HWDM58N9gTfif/qRn0F49m3Y2toadjOJiIiIaAT0HZq+9NJLDY+Vy2V84xvfaFtxalkWcrkcLMvq99BEREREdEyFZ9+G8Ozb8MqtG6jcvAb/ybM4c+LUsJtFRDSyNE0DAMzMzECSJAQCAUQiEWiahnQ6PeTWvcayLOi6jmw267YxHA5DVdWa7UzTxOLiImzbPpLFXOI8M5kMZFlGIpEAAKyvr8OyLFy4cAHJZHLIrRyO+j7Q7v4Gg0GUy2XE43EsLS1BkqSejpVIJLC6uopcLtfQx1q1h33y6Ok7NC0UCiiVSjAMA6ZpulWmsiy3fZ3jOAiFQhyeT0RERER9O3PiFMNSIqJ9JIKcdDpdE/JYloVoNArTNFuGpuFwGIVC4aCaCmAvi0in0zAMA3Nzcy3bpigK0um0G+wcRtFoFOFwGHNzcw1hnjhP0zShKEpDGBUKhVAqlaDr+gG2+HAQ12ZmZgapVMq9RvUMw4CiKAgEAn0H/7quY21trav2sE8e3T7Zd2i6uLjo/tmyLITDYQQCATz22GNtXyfLMmZnZ/s9LBERERERERF93/b2Nra2tjA5OYmJCe9m4ItGo9B1vaEqTlSStQp48vn8UIukAoGAJ9sMk2mayOfzDY9HIhHkcrm2rxX35qgEVLu7W3B2bsI3fhJjY5Oe7FOSJCwsLEDX9abXwbZtT47TLfbJo9Unq3nyiSrLMorFIhYWFjA/P+/FLomIiIiIiIiohW9/+9t47rnn8MILL2BnZwfj4+N485vfjLe85S246667Btq3GJLfatixqqpNR5nato1yuTzQsWkviKqvSsxms4jH40NqkfduXivh6ne+iFfKl+DsbsM3NoEzgQuYvushnDzTfgRzNxKJBObn5xuCOtu2O46QpkbHoU8249mvoSRJQjQa9Wp3RERERERERNTE1772NVy8eBGbm5uYnJzE2NgYtre38dxzz+H555/H/Pw87r///r73n8/nWwamgghWq62urmJhYaHrYcZiDkTLsiDLMiKRCAzDcPe9vLwMy7JgWRbW19f3dQ5V27aRzWYhyzIKhQISiYQ7tLtZm8rlMorFYs2clSsrK1haWqoZEt5uv63UXz8xvLsb6XT60M8fufHyl/DylX+F3e0bGBubgm9sAs7OFq5+54u4tv4V3Cn/Is7d8faBjiGG3+fzeUQiEffxtbW1tn27WZ+sfk7M63sQ2CeHz7vafdQO2f/c5z6HS5cuIRaL4cEHHwQAPPvss/D5fO7fiYiIiIiIiKh73/72t3Hx4kVsbW3h7Nmz7voiAHDy5Elcv34dFy9eRCAQ6Lvi1LIsBIPBttvUB0+GYXQMWqtFo1HEYjE3lAqHw5BlGaqquvM7lstl9/lgMIhYLNYx3OlXKpVCIpFwg7JgMIhisQhJktq2qX5BrMXFRRSLxa7220p1JaRt27Asq+m1rR4yXS6XkcvloGnaoa7+u3mthJev/Cs4O5uYODHzWv8dB8acM9jZsvGy9S9x4rbXD1xxGolEsLKyUhN8thua36pPKooCTdMQDAbda2vb9r4XDrJPDt+Y1zv83Oc+564Klk6naybGPX/+PEqlEp544gmvD0tEREREREQ08p577jlsbm7i9OnTNYEpAPh8Ppw+fRqbm5t47rnnDrRdvQx7tiyroQIwHA67Q6kDgUBDKCOq//aLZVk1c7HKslzz91ZtqqYoSkMo12m/nbQLnBRFQSQSQSQSQTweRzqdRi6XO9QLb1/9zhexu30D45NS0/47Pilhd/sGrn7niwMfK5FIIJ/Pu/fEsqyWoXu7PmnbNjKZTM19kCRp3wL86jaxTw6Xp5Wmzz77LJLJJNLpNBYWFjA9PY3l5eWabR555BE8++yzeOaZZ/DQQw95eXgiIiIiIiKikbW9vY0XXngBk5OTDYGT4PP5MDk5iRdeeAEPPfRQX4tDybKMUqnUdhsxfBnofW5DwzAgSVJNkLK+vl4TitaHP5IkDTxfanWb64nFbEQVXblcbjheszbVV+TWv6ab/bZimmZX2wliFXaxWvlhm7tzd3cLr5Qv7Q3Jb9N/x8am8Mr6Jdwx+9cHWhxKVImurq4iHo/DNM2aULRauz4pntsP7JOHm6eVptlsFsViEYuLi5iengaApm+E8+fP93yhiYiIiIiIiI6zra0t7OzsYGys/Vf5sbEx7OzsYGtrq6/jiLlF2xHPm6bZdm7DUCgEv9/v/mQyGbcqVVVV9yedTqNQKPTV3m61yyFM00Q0GsXq6iokSfIs3Gm332bXppqu6x2nSagnqh8P40rlzs5Nd9GndnxjE3B2t+Hs3Bz4mNWrtrcLPtkn9xy3PtmJp5Wmsiy7YSkREREREREReWdychLj4+PY3t5uu93u7i4mJiYwOdlflV46nUY+n285T6lt2+5iOOVyGaZpuiGqqFAVC+pUz6UoGIaBVCrVdL/7VdHXrlrQtm3Mz8+jWCy6AVL1kO5+w6pO+212berbHAqF+jr2zMxMX6/bT77xk+6iTxhvvZ2zuw3f+An4xk8OfEyxMFmnxc0URWnZJ5sNcfcC++Th52mlqd/v73rbTqX+RERERERERPSaiYkJvPnNb8bW1hYcx2m6jeM42Nrawpvf/Oa+huYLYgGX+opTsfK2GOasqiqSyaT7I1bZTiaTLYdCq6qKubk5d8EYoV11a7N5Geur4IDGociCpmktgybLshrmZBX7aVcJ2ClI63e/1a/vNRzTNA2SJB3KhXfGxiZxJnABu7ubbfvv7u4mzsxc6HtofnXeJBZNWllZaRvIt+uTsiwjHo8jm826j9u2DdM0a/oA+2Rzh7lPduJppemLL77Y8FizN8JLL73U8g1CRERERERERM295S1vwfPPP4/r1683LAblOA6uX7+OqakpvOUtbxnoOIqioFgsQtM0FAoFzMzMuKFTMpls+pp8Po+VlRUAe8Oio9Foy+q+QqEATdNQLpfdqtVIJALTNKFpGizLclcBz2QyWFtbc7cV0wekUinE43FIkgTLsqDruhtkifCqVCphdXXVrY5ttn9FUZBMJqFpGi5cuADgtdA4Fot11SZZlpFKpWDbNjRNw9LSUsf9diLLsnttBHGehmHAsiy3eq96TthOK6EP0/Tr3oFr61/BzpbdsBiU4zjY2bIxNnEK0697R8/7tiwL6XQa2WzWvQ+yLLtBPrAXKqZSKRiGgUAg4N4rSZJa9klgb2h5JpOpCVVFdaoIZtknj2afbMfneJhePvvss4jH48jn87j33nsBAE888QQ++MEPuts888wzSCQSKBaLOHfunFeHPvI2NjYwPT2Nq1ev1lyXra0tPPXUU3j44Yf7HlpBhw/v62jifR1dvLejifd1NPG+jibe19F1FO5tq+9qvbh58yauXLmC2dlZnDw5+JDjr33ta7h48SI2NzcxOTmJsbEx7O7uYmtrC1NTU5ifn8f9998/8HEOOxHIHLXFZY67je9+GS9b/xK72zf2FoX6/hymu7ubGJs4hTvlX8S5O94+7Gb2hX3yaOj2M9nTStPz589jcXERs7OzCAaDUFUVlmWhVCrBsiyYpgnLsvD0008zMCUiIiIiIiLqw/33349AIIDnnnsOL7zwAnZ2djAxMYEf/uEfxlve8hbcddddw27igWi3GjodXufueDtO3PZ6XP3OF/HK+iV3DtPpO9+O6de9AyfPHN3AkX1ytHgamgJAPB7H3NwcFhcX3ZWxxGpjkUgEa2trXCyKiIiIiIiIaAB33XUX7rrrLjz00EPY2trC5OTkQHOYHkWt5oqkw+/kGRknz8i4Y/avw9m5Cd/4yb7nMD1M2CdHy758ooq5T4C9IfuSJGF2dnY/DkVERERERER0bE1MTBy7sBTYGwY9Nzc37GbQgMbGJoERCEsB9slRtO+frOfPn9/vQxARERERERHRMcI5I+mwYZ8cPWPDbgARERERERERERHRYbIvoenGxgY+/OEP481vfjPGx8cxPj6OmZkZfOxjH9uPwxERERERERERERF5xvPQ9JlnnsEb3/hG6LoOx3EwPz+P+fl5+P1+PPbYY5iZmcFXv/pVrw9LRERERERERERE5AlP5zS9cuUKIpEIFhYWoGlaw+JPV65cwWOPPYaHHnoIV65cwblz57w8PBEREREREREREdHAPA1NM5kMlpeX8cgjjzR9fnZ2FrquIxQKIZVKIZVKeXl4IiIiIiIiIiIiooF5OjzfcZyWgWm1eDwOx3G8PDQRERERERERERGRJzwNTf1+f9fb3n777V4emoiIiIiIiIgGlM/nEQqF4PP5kEgkYFkWAMAwDITDYfh8PkSjUZim6b4mk8nA7/cjFArVPF7NMAyEQiGEw+GB2mdZFjRNg9/vRzAYhKZpMAyjYTvTNBEKhRAMBgc63jCIc/T5fAgGg8hkMshkMtA0DdFoFJlMZthNPJTq+0Y7wWAQfr8fmqbBtu2ej5VIJOD3+5v2vWZs24amacjn88hms+5Ps3NIJBIN9z6RSCAajSKfz9ds6/f7EY1GoWkaNE1DMBhEKBRy+0soFEIoFOr5/AYxSv3X0+H5vVSPstKUiIiIiIiI6HCJRCKQZRmhUAiapkGWZQCAqqqYm5uD3+9HIpGAoijua5LJZM3/A4CmabAsC7lczn390tJSx2n66l9XT5ZlpNNpGIaBubk5pNPpptspioJ0Oo1EItH9yR+gaDSKcDiMubk5SJJU85w4R9M0oShKzXUFgFAohFKpBF3XD7DFh5+4bjMzM0ilUu71q2cYBhRFQSAQaNl/OtF1HWtra11ta5omNE1DLperudf5fB7hcBiFQqHmHHRdh2VZkGW54d4Hg0FYloVkMgnbtrG0tFSzTT6fh6qqNY91+4sKEboCQCAQQCQSabntcem/nlaaxmIxXL58ueN2L730UtOOCwAXLlzwsklEREREREREI2v71iZe3ahg+9amZ/tUFAWSJNVUtQGAJEmQJKlpoCnCVSEcDiMWizW8vpNmr2smEAh4ss2wmKaJRCLhVsOKH03TOr42kUg0rVI8CnZ3bmL7lt3xZ3fnZt/HkCQJCwsLLUO5fipLBzE/Pw9N0xr6v/gFRS/BfiKRcPtIuVxGPB6veb7Zeywajbbdp23bCIVCWFpaQjwex9zcXMfXHJf+62mlaTAYRDabxdraGlRVbXjetm2sra2hVCohkUjgpZdeanhelP4TERERERERUXMvl/4rvnYxB+srBexsb2F8YhLyW8O4f34BdwZ/dOD9LywsYGVlpaZKzDRNLCwsYHV1tSaQsm27Iaxplgl0o9/XHTWRSKShyjGbzTaEYKPEcXbxjcsfxc7WtY7bjk+exRuV/wM+X3+1folEAvPz8w3BqW3bDQH/fhLV2q36tajurK7qbke8z2zbbvq+a0aW5bbbplIpxGIx93lFUWqqX5s5Lv3X09BUkiT4fD44jgOfz9d0GzEsv9kcBu1eR0RERERERETA83/wn/CHv/1ruPXqdUycOInxyUls39rE1y7m8eIf/S5+6gO/ivt+8r0DHSMajSKbzdaELWKuwmw2WzP02TCMmqG8pmlicXERtm2jVCq1PEYoFIJlWYjH4+5w3m5e1y/btpHNZiHLMgqFQs00A4ZhuFVyy8vLsCwL5XIZxWIRuq4jm80iEAhgZWUFS0tLNaNn2+23lfrqQjHdQDfS6XTDkOejwOcbw8kzQWx890uYmJxuud321lWc9j/Yd2AKwB1+n8/na/pmqyI/IZPJQJZld3h89WszmQwkSeqpgjmfz7ftCyIozefzXd3TYrHoVoK3Gz5frdMvIjKZDIrFIizLgmVZUFW142uOS//1NDQVHarfiZ0rlcqhnW+EiIiIiIiIaNheLv1X/OFv/xq2b93EmZm7Xis8um2vEOmG/V38wWc+Cenu2YEqTlVVhSRJWF1drakek2UZsixjZWWlZRjU7XyiqqoikUi4wdF+z0OaSqXc40UiEQSDQRSLRUiSBFVV3WOXy2U3kBJVgNVVdYuLiygWi13tt5XqqkIx6rZZUGWapjtNQrlcRi6Xg6ZpR7aiz3/3w7heeRaO42B84lTD8zvbNzA+cRr+ux8e+FiRSAQrKys14WK7ofnRaBSxWMzdPhwOQ5ZlKIriVoSK627bdsch7MBrCzu1I8syLl261HYbEcyvra3h4sWLHY/bLTHaW4TEYrqAaDTaNjg9Lv3X89D0Yx/7GM6dO9f3Ph577DEPW0REREREREQ0Or52MYdbr16vDUy/z+fz4ZR0B15Z/za+djE/8DB9VVWRy+UQj8fdUAXYC6Py+TzS6XTLYb+dqvEymQyWlpYaXruf85BalgXDMNzARpblmirZQCDQEP7UD5lWFKUheOu03040TWs5/6aiKDX7mZub6zjk+zA7eTaI0/7zuPa9/4Kx8dtq+rDjONjduYGzt/95nDwbHPhYiUQCwWDQ7aOWZbUM+i3LQj6fr5mvNxwOQ9d1pNNpZDKZmgXNJUnqWE0srK+vt32+XC43fXxtba1m7s/6BZ68IELT6vNJp9OYnZ1FpVLpah+j3H89XQgql8sNFJgC8DQxJ6Lj7ZWNm/hm6Xt4ZaP/ScSJiIiIiA6L7VubsL5SwMSJky2ntvP5fJg4cRLWV35v4MWhYrEYDMOAbds1w/FjsZg7lNcwjJ7DDxGydDMfYz9arZUiAmBxPuVyuSGwqg9JJUlyVxQX6l/TzX5bMU2zq+0EUY0bDoeP7Jow/rsfxtj4FHZ3Xq15fHfnVYyNT3lSZQrArRJdXV0FsHetW80bahgGJEmCYRjuz/r6utvH++2rYqh/O7ZtN10UfW5uDvF43P3pNqTthfglRfXQekmSYNs2DMPo+PpR77+ehqbT06/NSfG5z30OS0tLuHz5svvYs88+W/P3TvsgIurHxZVnsfjW/xN/5a5/gF/4kU/hr9z1D7D41v8TX8x9ddhNIyIiIiLq29bNG3uLPk1Ott1ufHISO9tb2Lp5Y6DjiQqx+vBEzKkoht32wrZtd07Qblba7kerIMc0TUSjUayurkKSJM8WBGq331AoBL/f7/7Ur++i63pDKNuJCM9aVfcddqLadHfnhlu9KapMT/vPe1JlKiQSCfc6tQs+xQJRYj5PMV1DpwWROlFVtW2wKJ47iKrLZn1R9NX6kF9U5nYy6v3X09AU2AtLA4GAu5LW2tqa+9z58+dRKpXwxBNPeH1YIiIAwD/7yO/gH/3if8ALz34Tzs4u4PPB2dnFC89+E7/21/8d/nnyd4bdRCIiIiKivkyePIXxiUnsbG213W5nawvjE5OYPNk4Z2SvVFVFKpVqCJwWFhb6Cj1kWYYkSVheXkY2m/W82qxVVaBt25ifn0c6nUY8HndXFAdaV6Z2o9N+i8UiKpWK+1M/vHqQKsaZmZm+2z1s9dWmXleZCgsLC+68mu0WKlIUpWk/sG276ZQM3Uqn0yiXyy1/wSDm99yPKtJ6zfqiCPnrz9227a4Wdhr1/utpaPrss88imUwinU6jUqlgd3e3Zs4HAHjkkUcQCoXwzDPPeHloIiJcXHkWn/2nX4Kz42ByahwTUxOYmBzDxNQEJqfG4ew4yP/ml1hxSkRERERH0sSJKchvDWP71s2G79qC4zjYvnUT8lvfhYkTUwMfMxqNNl3kpdXj3ZIkCfF4vOMiOZZlNVRnAq3ngRTzJTbbj6gmrN9Hu0rATmFZv/utfn2vFa+aprnX76iqrzb1ssq0VCq5fxaLfK2srLQN91RVxdzcXEO4aRgGZFlGPB6vmV9UTMVQ3T+a9VVJkpDL5ZBKpRqCSbG/6kXGBmXbds8Bb31FbT6fh6qqbpDb6j0onhvl/uvpQlDZbBbFYrFmiH2zeVbOnz+Pxx9/HA899JCXhyeiY+4//B9/gN3vB6bNJsWfmBrH1uYO/v2v/z7eEX1gSK0kIiIiIurf/fNRvPhHv4sb9ndxSrqjYSGdG/Z3ceK2M7h/vrtFiDpZWFioWSleUFUVkUikaRBlmiY0TYNlWe7K84ZhuKNRxWNi3kSx4I5t201fl0qlEI/H3SHDuq67gZUIc0qlElZXV2HbNgKBQNM2JJNJaJrmzh8pVvKOxWJNt89kMlhbW0O5XEYgEIAsy0ilUm47xTQD7fbbiSzLDYtfiXM0DAOWZbkVeWKOTWCvanC/5oQ9KP67H8b1yrPYvlX2pMrUsiyk02lks1n3HonV4AXbtpFKpWAYBgKBgHsfJUlCoVCApmnu/QZem6JC13VkMpmaUFVRFLcKW1XVhr4qqKqKixcvIpVKNQxlrx/+L+792toa1tbWIElS0wXT6mUyGZRKJXcOVtEfu1mMLBKJoFwuu++l9fX1mnaZpgld15suQjXq/dfntPr1VB8+9alP4SMf+UjNY0888QQ++MEPNmz7+OOP49FHH/Xq0EfexsYGpqencfXq1ZrFtLa2tvDUU0/h4YcfxmSHeWvo6OB99d4rGzfxV+76B3B2djEx1fr3Qdub2/CNj+E/fvsf4My5k5624Sjc1+3dbWzv7uLkxAn3sZvbtzAxNoaJMU9/jzZSjsK9pd7xvo4m3tfRxPs6uo7CvW31Xa0XN2/exJUrVzA7O4uTJwf/b9Cv/+Hv4A8+80ncevU6Jk6c3JvDdGsL27du4sRtZ/BTH/gV3PeT7x34OIeFCFq8moOUDo9vPf+b2Pjul3Dujp/A63/ol4bdnIGxrx4N3X4mezo83+/3d71tdbk0EdGgrn73FTi7DjDW4WNtbAzOroOr333lYBp2iHz3RhmPfO7v4q999n/FN67+GQDgG1f/DH/ts7+ERz73d/HdG92t8ElEREREw3XfT74Xf/lX/gXun1/AxIkp7O7sYOLEFO6fX8Bf/pXfHqnAFGi/6jkdbf57fgZTp34Q/nt+ZthN8QT76mjxtKzoxRdfbHisWSHrSy+91HL+FSKifkzfcQa+sb1Fn9r+Pmh3F77xMUzfcebA2nYYfPdGGR/4/K/i5e8Ho4kvfAIff/uH8Ykvfxr2zWsAgA98/lfxmfd8EnecCrTbFREREREdAncGfxR3Bn8UP/GLS9i6eQOTJ095MofpYdRq/lI6+k6emcW9Dz4G39j4sJviCfbV0eJppWksFsOFCxfwjW98w32sfl7BZ555BuFwuOUkskRE/Thz7iTkH3s9dp3mv6wB9h7fdYDgW17v+dD8w2x7dxsffOrjePlGGRNj45gcG4d98xo+8sVfh33zGibHxjExNo6Xb5Txwac+ju3d7WE3mYiIiIi6NHFiCred849sYGpZVlereNPRNSqBKfvq6PG00vT8+fNYXFzE7OwsgsEgVFWFZVnuZLSmacKyLDz99NN9zwVDRNTKz/7yT+Ef/eJ/wPbmDibqFoNyHAfbmzsYG/fh5/7eTw+vkUOwvbuLWztbAAAfgDHfGCbHgK3dHUyOjWPMN4ZdZxcAcGtnC9u7u5jw9FdqRERERET94VBnOirYV0eP51+L4/E41tbWcO7cOei6jkKhgHQ6jVwuh/Pnz6NcLmN+ft7rwxIRYT52HpFf+gn4xn3Y2tzB9uY2trd2sb25ja3NHfjGfYj80k/gHdEHht3UA3Vy4gR+692/CunkWWzt7mDX2cWYbwxT45NuYLq1uwPp5Fn81rt/tWaRKCIiIiIiIqLjaF+WSlYUBcViEQDw7LPPQpIkzM7O7sehiIhq/K3Me/HDF34Q//7Xfx+l574FZ9eBb3wM973l9fi5v/fTxy4wFe6dvhsff/uH8ZEv/jq2dncwNf7a78y2dncwMTaOj7/9w7h3+u4htpKIiIiIiIjocNiX0LTa+fPna/7+qU99Cn6/Hx/84Af3+9BEdEy9I/oA3hF9AK9s3MTV776C6TvOHKs5TJv5xtU/wye+/Glsf39IfrXJsXFs7e7gE1/+NPR3f5zBKRERERERER17Bz5r3Uc+8hFEo1E8/vjjB31oIjpmzpw7iXuCtx/7wPTm9i186AufcBd9EkPyN3e23KH6YnGoD33hk7i5fWvYTSYiIiIiIiIaqqEs9TE9PQ1JkoZxaCKiY2dibAwnxvfmKXUAdw7Tie9XmO46u3C+v+2J8UlMjHEVKCIiIiIiIjreBvpmfPnyZTz++ON44oknsLGx0fXrNjY2kMvlBjk0ERF1aWJsAk88/AnceSqA7d0dd9GnT73j77mLQ23v7uDOUwE88fAnMDG27zO3EBERERERER1qfYemH/7whxEKhaBpGhKJBAKBAL74xS82bHf58mU88cQTWFpaQiwWw5vf/Gb4/X7IsjxQw4mIqHt3nArgM+/5JF53egb+k+egv/vjeNsbHoT+7o/Df/IsXnd6Bp95zydxx6nAsJtKRERERERENHR9lRN99KMfha7rUFUViqLAtm0UCgWoqgrLsnDvvfdiY2MDoVAIlmUBABzHcV+vKAo+/elPe3MGRETUlTtOBfDZ9/0TbO/u4uTE3nD9e6fvxv/1yG9iYmyMFaZERERE1EDTNADAzMwMAECSJMTjcWiahnQ63fP+DMNw91ksFvtqk9hHIBBAoVDo+nWWZUHXdWSzWQQCAUQiEYTDYaiqWrOdaZpYXFyEbdsolUp9tXGYxHlmMhnIsoxEIgEAWF9fh2VZuHDhApLJ5JBb6b36+9vu3gWDQZTLZcTjcSwtLfU8hWQikcDq6ipyuVxD/xHy+Tx0XYdhGIhEIlhaWoKiKDXtjUajsCwLS0tLI3VPRqUP9vwN+erVq8hmszAMAw899FDNc9lsFslkEisrK1AUBZIkYXFxEZIkYWZmBpIkYW5uDufPn/fsBIiIqHsTYxOYqBtjIAJUIiIiIjp6bm1u4+b1Wzh5+gROTHn3S3ARHC4vL7cMeqppmgbLshqm4stms4jH4+7fVVVFOp12Q5R+qKqKpaUlpFKpnl4nyzLS6TQMw8Dc3FzL0FdRlIHbuN+i0SjC4TDm5uYaAj9xnqZpQlGUhnAqFAqhVCpB1/UDbHEtx9kFHAe+sfHXHtvdAXw++Hz9DYoW5z0zM4NUKuWefz3DMKAoCgKBQF/BPwDouo61tbW220QiEaiqCr/fj1gs1tAWWZbdwFYEr63eR4fRUe+D3ej5E/Wxxx5DOp1uCEwBIB6PwzAMPP7444hEInjsscc8aSQRERERERERveaFy9/E0//OxH956k+wfWsHEyfG8ecf/nN41/8UwpseuHvg/c/PzyOXyzUNehKJhFstKoTDYdi23bCfZtWkgcDgU0INsrh0N8f3oo37yTRN5PP5hscjkUjHwC2RSCCRSAwtsNrZvoFvf/2fYnfnVbz+h/4OJk5MY/vWVXzr+d/A2PhtuOu+v43xiVN971+SJCwsLEDX9abn2Kyf7hdJkhCJRKDrOiKRSMPzlmXVBIqt3keH0VHug93qOb4Xv21qJR6PQ9d1BqZERERERERE++D3P/scPv6zT8L4tya2NrcxPjGGrc1tGP/WxK/G/g3+n889N9D+E4kEZFluOexYVdWGUFFV1YZQKJvNdqzGo/5EIhE4jlPzo+v6oa9Q3Nm+gW89/5u4XvkqXt34Ov7sT/4JNm/8D/zZn/wTvLrxdVyvfBXfev43sbN9Y6DjiOHz9WzbPvA1dhKJBAzDaBqG1of/zd5Hh9VR7YO96HshqFYuXLjA4fdERERERERE++CFy99E9n//Xdx6dQu33zONc4FTOHV2CucCp3D7PdO49eoW9I/9Ll786p/1fQzDMFoGpkJ1palpmgiFQggGgzX7KBQKsCwLmUwGmUymYR+iUi2Tybj7M00T4XC4Zl+JRAJ+vx/ZbLZhH/l8Hvl8HtlstukxvGTbNjKZDPL5PBKJBEzTdJ8zDAOhUAihUMg9r2w26w7xz2azyOfziEajNa/rtN9W6qcOEFMOdCOdTg9lPknH2cW3v/5PccN+DuOT5zAxOY2b117A//hv/wg3r72AiclpjE+eww37OXz76/90bwh/n8Tw+/pKyLW1taZD9gVxH8T/1z8n7mOzCstWVFWFJEkN/TebzWJhYcH9e7P3UfW21T9i+07vlWb9svr9JhyXPtgrz1f9mJ6exoULF7zeLREREREREdGx9/S/M/HqtZu4/Z5p+Hy+mud8Ph/8rzuD733zKgr/zux7mL5lWe7CT61Uh6rN5gAVz9cPPxbK5TLK5bJbVRcMBt15HzVNq9mXrusNc6gCe6GRCKQAuCHlfg35TaVSbhVuJBJBMBhEsVh056QU16D+vOoXzVpcXKyZtqDdfluprpa0bRuWZTUNuquHUJfLZeRyOWiaVjPP7IFxHOzuvAoA8MEH39gkJiansb21gYnJafjGJoHdLQDY285xAF+7HbYXiUSwsrJSU7nZbuh7NBpFLBZztw+Hw5Bl2e2TwWDQvW62bSMajXbdFjEqu/q9YNt2zT1uNZduJpPB+vq624cMw0A+n0ckEun4XmnXL6vnWT02fbBHPVeaOo7TcZv6D+56V69exec+97leD01ERERERER0bN3a3MZ/eepPMHXqRMvv3T6fD1OnTuCPPv/HuLW5fWBt63UOUNu2awIWWZbdsKfZvpqFN2IBaiEejyObzTYNWL1gWRYMw3D/Lstyzd8DgUBDcFQ/FFxRlIbgrtN+O2kXQCmKgkgkgkgkgng8jnQ6jVwu19P+veIbG8frf+jv4OTZN2N76yqc3S34xiYxOTUD39gknN0tbG9dxcmzb8brf+jv1CwS1Y9EIoF8Pu9eb8uyWlaZWpblBpFCOByGrutuFWb1NZYkqW3Far1YLAbLstwKTsuymk4TUN/3bduGpmlYWlpyH6sORrt5r7Tql9Xvk+PSB3vVc2jaKRDthmVZuHTp0sD7ISIiIiIiIjoubl6/tbfo02T7MGlichzbWzu4ef1WX8dRFAWlUqnhccMw3Io7v9/fMMS3F/WBkSRJKJfLfe+vej/dDC1upV3gmsvlEI/HYds2TNN0q2WrNTuv+uHW9a/pZr+t9HquopoxHA7vW7jczsSJabzuTR/E+OQ5bG9t1Dy3vbWB8clzeN2bPoiJE9MDH0tUiYq5TU3TbDmfqWEYkCQJhmG4P+vr626YOMjCY8DedZdl2a2CNgyjq7lL19bWIElSzfFzuVzPQ9s7vd+OUx/sRc/D8y9duoTPfOYz8Pv9bbdpVUlaLpdRKBQOfOJdIiIiIiIioqPs5OkTmDgxjq0OFaTbWzuYnJrAydMn+jqOqqpN52xUVdWtVsvn8zVDzrvRqrquGwe1oni7YM00TaRSKYTDYaiq6lmu0W6/oVCoJlhaWlqqCcx0XW86B2Y7okJS1/We7+Ggtm9dxXdefAI73x+SX21i8hy2t/aev/vP/V1PglMxXUM8Hm8bfIoFoqqrMav7uhcSiQRSqZRbvdqNfvp9P685Tn2wFz2HprZtd5x3wHGclp3KcRz4fL4jMeErERERERER0WFxYmoCf/7hPwfj35pw/Lc1HQnqOA42b9zCX3zfj+HEVH/LmKTTaXexm2bVcPVzMXarXSDZSbdVb7Zt9zRsulq7ikLbtjE/P49iseieQ/Ww70HC4Hb7rZ77tFWbQ6FQX8fuNG+t15zdHXzr+d9wF316bUj+BiYmz7lznN689gK+9fxv4A0/8vcHHqK/sLDgDtNvt7iZoihIpVINj4v+5EVoH4/HoWkaNE1DLBbr6jWtjt3uPdhrxfZx6oO96usT9LHHHhvog65QKPT1WiIiIiIiIqLj7J0/r+DL/+lrqHznFfhfd6YmOHUcB5XvvILbzp5E+Of7Cw6FQqGAaDTqLnIkiAVfulE9b2K7+STFfpu9TjwnfqpZllUTHmWzWcTjcTevEPNU1hdtlcvlppmGpmnI5XJN2yeOVf06EU61C4M7hW397rf69b3mM5qmQZKkg1+Ix+fD2PhtAAAHDvD9OUzHv19hOjE5vfc4sLddn9NDVk8tIfpv/YJQ9VRVxdzcXMMvCsQwejFfbvVCUKZp1tzfVv2tvi29VGmLhZkymUzNfkW7un2v1Ktv97Hpgz3qOTRVFAUf+chHBjro3NycO6dEP2zbdn8DMDMzg1KphHA43NV8EK2Ypgld11Eul91OEY1G297A/WgHERERERERUStvfvAeJP7xX4L+sd/F9755FVOnTrhzmG7euIXbzp5E4h//JbzpgbsHOo4syygWi9A0DYVCATMzM5AkCbZto1Ao1MxjaJomNE2DZVk1K8XLsuxW14mVx5ttm8lksLa2hnK5jEAggEgk4j4uwhhFUaDruhu0BAKBmsVkyuUybNt254wE9oKlVCrlDs22LAu6rrthVyaTAbAXsq2ursK2bQQCgaZtVBQFyWQSmqbhwoULAOCuAh6Lxbo6L1mWkUqlahb36bTfbu5T/WJA4jwNw4BlWW41n5ijE0DHldH3g883hrvu+9v41vO/iRv2cwCAk2ffjNe96YP4zotP4Oa1FwAAp6S34K77/jZ8vt6W4bEsC+l0Gtls1r3GsizXrC4vchzDMBAIBNz7IEkSCoUCNE1z7xcAN9/RdR2ZTKZmVLWoThVhaH1/ayaRSLT8pUOr95HoD9XvB9EuSZLavlfm5ua6er8dlz7YK5/jOE4vL1heXsbi4uJAB7169SoMw8AjjzzS82tt20YoFEIul6v5LVUikXA7S6+y2SwA1ASkhmEgGo0iEAg0vZFet2NjYwPT09O4evUqzp075z6+tbWFp556Cg8//DAmJyd7Pjc6nHhfRxPv6+jivR1NvK+jifd1NPG+jq6jcG9bfVfrxc2bN3HlyhXMzs7i5MmTnrTrxa/+GQr/zsQfff6Psb21tzjUX3jPDyP888rAgekoEQEN11U5PHa2b+DbX/+n2N15Fa//ob+DiRPT2L51Fd96/jcwNn4b7rrvb2N84tSwm9kX9rejodvP5J4rTQcNTAFgenq6r8AUAKLRKCKRSENZv67r8Pv97qS13RJlyPXl06qq4uLFiwiFQohGow1TCnjdDiIiIiIiIqJuvemBu/GmB+7GBz75bty8fgsnT5/oew7TUWaaJkeDHjLjE6dw9w8/CjiOO2fpxIlpvOFH/j7g8/VcYXqYsL+NliPVEy3LgmEYNaXV1RYWFnqu8BSrqDWjKIpbYl1dPr0f7SAiIiIiIiLq1YmpCZwLnGJg2kKvi+LQwfD5xhoWefKNjR/pwBRgfxs1R6o3irlJWpU5B4NBGIbR06pmhmFgdna25ZwSopK0er6W/WgHEREREREREXnHsizMzc0Nuxl0TLC/jZ4j9aso0zTbThIrQsy1tbWuh8aLSZ57CTj3ox1EREREw/Lyxiv47rVXO253x9nbcOe5MwfQIiIiosFxXkk6SOxvo+dIhaaWZTWsyFVNBJmtqkabKRQKsCyrZecW+6qeu9SLdmxubmJzc9P9+8bGBoC9idC3trbcx8Wfqx+jo4/3dTTxvo4u3tvRxPu6Z3t7Fz/y9/85bu3sdNz2xPg4/jT9v2Fi4vAOVuJ9HU28r6PrKNzbw9w2IiLaP0cqNC2Xy22TexFk9josvt0+8/k8FEWp2caLdqRSKXziE59oePzpp5/GqVONq8TVL0RFo4H3dTTxvo4u3tvRxPsKPDH/Q11v+/TTX9jHlniH93U08b6OrsN8b2/cuDHsJhAR0RAcqdD0oOcIzWQyAIDl5WXP27G0tIRf/uVfdv++sbGBH/iBH8A73/lOnDt3zn18a2sLhUIB4XAYk5OTAx+XDgfe19HE+zq6eG9HE+/ra/JrX8OHnvw8fPBhcny84fmtnR04cJD9hffifaE/N4QWdo/3dTTxvo6uo3BvxahAIiI6Xo5UaNqt9fX1gfdhmiY0TUM6na4Zmu9VO6ampjA1NdXw+OTkZNP/WGj1OB1tvK+jifd1dPHejibeV+Dn3vYAfu2pL+Pr31nHlOPUrFzrOLvY3N7Bfa+bQezP/9gQW9kb3tfRxPs6ug7zvT2s7SIiov11eCekakKSpK6qPGdmZgY+VjQaRTqdRjKZHGo7iIiIiA7Cr/zMT2LM58P2jlPz+PaOgzGfDx9/718cUsuIiIiIiA7e0ELTz33ucz2/pt3iS8DeXKMA2q5s341oNIpEItE0MD3IdhAREREdlJ9764/hTXcGsOPswnF2AexVme44u3jTnQHEfvxHhtxCIiIiIqKDM7Th+alUCu973/t6eo0sy1hbW2v5vKj+bLdIUyeapuHChQstA9ODagcRERHRQfuVn/lJ/I3f/o/Y3nEwOcEqU6LjbvvWJrZu3sDkyVOYONE4tRgdD5qmAXhtJKUkSYjH4+50dr0yDMPdZ7FY7KtNYh+BQKDvRcRs20YqlcKFCxfcwicAiMfjNdtZloV0Oo1sNgtZlpFIJAAApVIJ5XIZsVgMkUjE3TYUCkFVVTcPyOfzkCQJsVgM6+vrMAwDQP/n3g/LsqDrOjKZTM05rK+vw7KsjhnIUdHLvTrKxP3MZrMIBAKIRCIIh8NQVbVmO9M0sbi4CNu2USqVhtTa/h2Gfut5aLqxsYHV1dW2N8S2bZim2fO+FUVxP2CasSwLABo6Srey2SxmZmaaXnTbtt3K0f1uBxEREdEw/Nxbfwy/9p//EF//zjomHB92nN29uUxZZUp0rLxc+q/42sUcrK8UsLO9hfGJSchvDeP++QXcGfzRYTfv2Hv1+i3cvH6r43YnT5/AbadP9H0cEbgsLy/XrPNhWRai0aj7vVfQNA2WZSGXy9U8ns1ma4JIVVWRTqfdAKQfqqpiaWkJqVSqr9eLNUxyuVzNCNF8Po9wOFwTxMqyDF3XYVkWZFluyAuCwSAsy0IymYRt21haWqrZJp/PQ1XVmsfC4XBX7dQ0DcFgEADccKyVaDSKcDiMubm5hlGvsiwjnU7DNE0oitJwDqFQCKVSCbqud9UuL93a2cKr25u4bWIKJ8YHmz+4l3vVjfq+66VB9i3up2EYmJuba/nLC0VRBn6v7bfD3m89DU2fffZZhEIhAPszND0WiyGTybgXrN6lS5f6Dirz+Txs22765rEsC6Zpuh9Q+9kOIiIiomES1aab2zusMiU6hp7/g/+EP/ztX8OtV69j4sRJjE9OYvvWJr52MY8X/+h38VMf+FXc95PvHXYzj63d3V38nflPY6N8o+O25wKn8Ok/+iWMjfU3K9/8/DxyuVzDd15R8SWqRYVwONx07Y9mFZWdprzrxiCZgzi3+n1EIhEUCgUkEomugxhxLZLJJMrlckMQ1qyd0Wi07T5t28b8/DwuXrwISZJgmiZCoRAcx2n5GtM0kc/nGx6PRCINQXazc+jlnL3w9fJL+N3Sl/Dlbz6L7Z0dTIyP4+33nMdfCv4k7gvcuy/HrL5X3djPamAv9t3N+8iL99p+Ouz91tM5TVOpFHK5HHZ3d1Eul9v+zM7O9rx/RVGgqipWVlaaPp/P5xs+uIG9DxxN01pWh5qm2fa3DYZh1PxD0W87iIiIiA47Mbep44BzmRIdMy+X/iv+8Ld/Ddu3buLMzF247ZwfJ247g9vO+XFm5i5s37qJP/jMJ/Fy6b8Nu6nH1tjYGN704D24VnkV45PjmGjyMz45jmuVV/GmB+/pOzBNJBKQZbllMZCqqg1hjKqqDZWQ2Wy27dR2w6BpWttz0zQN2Wy2oZK2FRGK2rZdM0K1HVmW2y4unUqlEIvFaka7dpqGIBKJwHGcmh9d1zsGT8Nw8aWvYOn3fwNfsL6EWztbGB/z4dbOFr5gfQlLv/9P8Mw3vrIvx62+V53sZ989jO+LYTns/dbTSlNZlvHII490tW2/80jkcjmEQiHEYrGaIFMs3NTsgy+bzSKTySCbzaJSqdQ8J4YWqKratGS5XC7DMIyG1/XTDiIiIqKj4JN/9afxwX/5O/jkX/3pYTeFiA7Q1y7mcOvV6zgzcxd8Pl/Ncz6fD6ekO/DK+rfxtYt5DtMfor+SeBvWjK/D2XVw6tzJhuevb9zE6emT+KsfelvfxzAMo+N39upCoWZzJxqGgUKhAMuykMlkAKChUEkUMFmWhfX1dXcorhjqL/aVSCSwurqKdDrdUMkpqtTK5XLL0aP12zcbMSpUz0XaTUVisViEoiiQJKnrnKNTXpDJZFAsFt1ro6pqx9fU5xli6HY30un0gc1p+vXyS/jn5r/H5s4W7jjlr/mscU44KN+8in9W/Pd4w9m7PK84rb5Xtm27c5+K6mLRLzr1XTHHppgGIBKJ1MzVu7y83NCvhXb7Fu2xbbtm317rdO7151Eul1EsFmvmUV1ZWcHS0lLNe6ndfls57P3W09D09ttv73rbxx57rK9jSJKEYrEITdMgSRJmZmZQKpUQDodbdiZVVSFJEhYWFhqeC4fDsCwL2Wy25TGbLejUTzuIiIiIjoJI6H78zI/dh5MnhrZmKBEdsO1bm7C+UsDEiZMNgang8/kwceIkrK/8Hn7iF5e4ONSQ3Hf+Hsyp9+HLv/P/4dTZqdrQyXFwfeMm3v7eH8GbH7yn72NYluUu/NRKdYjXbO5E8XyrUZ1iFKr4/hwMBt2iJE3TavYl5qmsZ5qm+30f2AudOg3XtSyr4xyPsizj0qVLbbcRAdHa2houXrzYdtteiPMUoZmYDkEUe7Vrc3XbRNhar3o4dLlcRi6Xg6Zp+zZ3Z73fLX0JN7ZuNgSmwN5nTODkNL57o4IvWF/yLDRtdq9SqZRbUR2JRBAMBlEsFiFJUtu+G41GaxaUCofDbuWyeA+06tdA6/eFuCfVz7dbS2cQnc691XnUL/62uLhYM81Au/22ctj7raf/Jdxufo16ly9fxoMPPtjXcSRJ6mnOAkVRGipFhUFWEOu1HURERERHBQNTouNl6+aNvUWfJtsvxDI+OYmd7S1s3bzB0HSIRLXpjWubOF1VbXrj2iambpscqMq0X73OnWjbdk04Iir3FEVpuq9mwYuoGhTi8Th8Pp87BL+V9fX1tm0rl8tNH19bW6spuKpf4MkLIjSVJMkN2tLpNGZnZ1vmGvU0TWuZVSiKUlPoNTc313HKAq/c2tnCl7/5LKYmTrT95czUxAl8+X+Y+ND5hb4Xh+p0r0QoKUI3WZY7VlhbloV8Pl8zdDwcDkPXdei6jkAg0BD6VffrTnK5HBYWFiBJEmRZ7rrisledzr3VeVRTFKVhmoN+rmm1w9hvPf2v4Xg8jscffxyPPvpox20XFxc7/uaGiIiIiIiI9t/kyVMYn9hb9Am3td5uZ2sLEyemMHny1ME1jho0qzb1qsoU2AsomhUYiaHF+XzeXfSo1crdndSHMJIktQwreyEWTmoVmooQqx3btnHhwoWGx+fm5va9sk0ExtWBmRhObhhGx4DINM2ejieqhMVK5O3C5kG9ur2J7Z0dTI6Nt91ucmwcW7s7eHV7s+/QtNO9EsGnqG4Ulc/tGIYBSZJqKkDX19dr+lO//ToSiUDXdfj9fiiKglgsNlAgLyqVm+nm3JudRzAYrHms/jX9XFPhsPZbT0PT6elpLC4uIhaLIRAIIBgMtizD7XZSZSIiIiIiItpfEyemIL81jK9dzMNxnKZVYI7jYPvWTdz3k3+ZVaaHQH21qZdVpqqqNl3RunpuzXw+33Ng2i7I6aSbxXu6oapq22HPIrw5iHVKQqFQTTaytLTkBn3lcrkmT5EkqascRdf1hnCrE1EFqet63yF4N26bmMLE+Dhu7Wy13W5rdwdT45O4bWL/PmdM00QqlUI4HIaqqh37pWVZsG27obLRi34i3heFQgGmaWJlZQWpVApA4zzA3Wr3i4Nez72XY7bab7O+Xn1uh7XfehqaXrlyBaFQqKsPs1al2ERERERERHTw7p+P4sU/+l3csL+LU9IdDXNl3rC/ixO3ncH981zD4TCorzb1qsoU2BsOns/nkc/nmw6t7XaV+HrtgpxOuq1Ys2277VBoMdS91bmJeRK7GU49qOr5IKtVLzIk2Lbd1XBtwzAQCoX6ak+neWwHdWJ8Em+/5zy+YH0JzonWv5zZ3L6Fh+59a99Vpp3Yto35+XkUi0X3Goscq1Wwb5omFEVxw8z6/fXzfqjetxjWrigKFEXB0tIS5ufn+wpNRUVsM/2cezc67bdVX69u82Hst2Ne7iyRSCAej6NUKqFSqWB3d7fpT7lcxvT0tJeHJiIiIiIiogHcGfwx/NQHfhUTJ07ilfVv49WNCm69+gpe3ajglfVvY+LEbfipD/wK7gz+6LCbSt/3VxJvw9Rtk1j/1obnc5kWCgWkUqmGqkwx9LYb1UPhO83rWF18VT+E3rZt96eaqP4Tstks4vG4G9pUr1AuSJKEXC6HVCrVcB5iDkwvq9aatbuTdDqNQqHg/j2fz0NVVff6NTsvoZ/gSyxwfRCLQf2l4E/g1ORJlG9uNKyL4zgOyjev4vTkbXi3/BP71obqqlFBhPKi0rhZ31VVFXNzcw1V2O0ql5vd+2b7FotV1W8ntml2v1v9IqHdnL7dnHu35+HFfqtffxj7raeVprIs47HHHuu4nSRJ+zahLREREREREfXnvp98L6S7Z/G1i3lYX/k97GzvzWF630/+Zdw/H2FgesiIatP/57PP4W3vud+TKlNBlmUUi0VomoZCoYCZmRl3bk0xjFgwTROapsGyrJoVtmVZRjweh6ZpCAaDiMfjTbfNZDJYW1tDuVxGIBBAJBJxHxdBiqIo0HXdDUkCgQByuZwbWJXLZdi2XbOQjGEYSKVSiMfjNZV3qqri4sWLSKVSDUOCq8NKYC/M0XUda2trWFtbgyRJWFpa6lhZmMlkUCqV3MVxNE3DhQsXuloUJxKJoFwuu0HZ+vp6TbtM04Su602rEGVZblhIS5yDYRiwLMutzKuej7PTKudeuS/wRvwvoZ/DPyv+e3z3RgVTEyfcOUw3t2/h9ORt+Fuhn8V9gXt73ne390pRFCSTSfeeAHBXY4/FYgCa911gr39omub2VWDvfnXbr1vtW7RRBLKWZWF5eRlAYz8W52maJmzbdvtJqVTC6uoqbNtGIBBo2qZO597NeciyjFQqBdu2oWkalpaWurqm7RzWfutzelnyvoNuF4GiRhsbG5iensbVq1dx7tw59/GtrS089dRTePjhhzHZYSVLOjp4X0cT7+vo4r0dTbyvo4n3dTTxvg7H9q1NbN28gcmTp/ZtDtOjcG9bfVfrxc2bN3HlyhXMzs7i5MmTnV/Qg9Jzf4Z/8kv/F/7ub/41BN9yt6f7HgUiXNnPBY6od18vfwNfsL6EL/8PE1u7e4tDvf0NCt4t/0RfgemoYz/2VrefyZ5WmvaSv25sbPT9Dw4RERERERHtr4kTU1zw6QgIvuVu/MbFD2Fiov2K5MeVaZpdVXfSwbovcC/uC9yLD51fwKvbm7htYmrf5jAdBezHw+HpnKaRSASPP/54V9vOz897eWgiIiIiIiKiY4mBaWvdLiBFw3FifBLTU2cYmHbAfjwcnlaa+nw+nD9/HrFYDO985zsxOzvbMCcBsHezu504moiIiIiIiIioV5ZlcT0VOvLYj4fH09BUURRcvXoVjuMgl8sB2AtS6zmO0/RxIiIiIiIiIiIvcP5HGgXsx8PjaWgaCAQQj8c7roy1vr7e1epZRERERERERERERAfN09BUlmV87GMf62qBp9nZWS8PTUREREREREREROQJT0PTXC7XNjD91Kc+BZ/PB0mScPHiRS8PTUREREREREREROQJT0PT6enpts9/5CMfAQBcvXoVy8vLePTRR708PBEREREREREREdHAxoZxUJ/Ph0KhMIxDExEREREREREREbXlaaUpADzzzDNIp9Mol8uwbRvlcrnmedu2AQDpdNrrQxMRERERERERERENzNPQ9MqVK1BVFYqiYG5uDgCwtrbm/rlcLsM0TWQyGbzvfe/z8tBERERERERE5DFN0wAAMzMzAABJkhCPx6FpWl/FUIZhuPssFot9tUnsIxAI9DSKNZ/PQ9d1GIaBSCSCpaUlKIriPm9ZFqLRKCzLwtLSEpLJZF/tO4wsy4Ku68hkMpBlGYlEAgCwvr4Oy7Jw4cKFI3++lmUhnU4jm83WnGOpVEK5XEYsFkMkEhlyK71l2zZSqRQuXLhQU7QYj8drtuvl2liWhVAoBFVVIcsygL33jiRJiMViWF9fh2EYAPp/D/djGH3Y09A0k8mgUChgfn7efWx5eRmLi4s12y0vL+Oll17CG9/4Ri8PT0REREREREQeME0Ti4uLWF5ebhksVtM0DZZlIZfL1TyezWZrAhxVVZFOp93Aox+qqmJpaQmpVKqn10UiEaiqCr/fj1gsVnNeACDLMpaWliBJElRVBdD6vA6jaDSKcDiMubk5SJJU85wsy0in0zBNE4qiNIRLoVAIpVIJuq4fYIu9JcsydF2HZVmQZbnhHIPBICzL6jpYq++7XvJi36ZpQtM05HK5mvudz+cRDodrfqHQy7Wxbbvhlwb5fB6qqtY8Fg6Hu2qnpmkIBoMAgEAg0Da4Pmx92NM5Taenp2sCU2Bv0ad6i4uLyOfzXh6aiIiIiIiI6NjY3d3F9vZOzWPb2zvY3d31ZP/z8/NIp9NNg8VmgWc4HEYsFmt4vFklWiAQGLh99YFKL6+LRCItgxXLstzAFGh9XoeRaZpIJBIIhUIIBoPuj6jsbSeRSCCbze57G1/duonyq1c7/ry6ddPzYycSia6uhbCfVZRe7Ht+fh6apjW8FyKRSMv3aSvV16ZcLjcEus3eb9FotO0+bdtGKBTC0tIS4vE45ubmOr7msPVhTytNRbl+tRdffBEbGxs4d+5czePT09NeHpqIiIiIiIjoWLhx7SZ+/W99DjeubUJ7YgHS7adhf+860h9cxamzU/h7//x9OHX2ZN/7TyQSkGW5JjyspqpqQ/DZbNtsNou1tbW+27FfEokEwuEwbNtuCIPq/97qGhxGkUikYcqE/ayW7NWus4u/9Xu/hqubr3TcdnrqDD7znl/DmM+7Wj9xb5vd93r72Xe92LemaW3fo6K6U2zXSfW16eb6AHu/QGm3bSqVQiwWc59XFKXjdBqHrQ97Wmnq8/kaHotGo01L5utL+YmIiIiIiIiovRvXbuJTH8qj+MwL+ONLf4rH/uYK/vT5l/HY31zBH1/6UxSfeQGf+lAeN671X6lnGEbHsLC68ss0TbcyrHofhUIBlmUhk8kgk8k07MM0TeTzeWQyGXd/pmkiHA7X7CuRSMDv9zetIsvn88jn88hms02P0Yyqqvj/27v74Mbu+773H4DkcrUrLQ9JSZYVy9YCkmXLteI94ObJbeqIgNzrybS5FkB6Wjupay0hO7nxjK3lEdO0HTcdcwEpvZOHawVY3bpunY5JwLpJpuN7I4JK+uA47pJnFbmx41o8Wlvxo0TsWcq7Wi5J4P5BnSOQBEGQBAkC+37NcCQCBwdf8AdwiQ++v9/PMIwN58tmsxoaGqr5uCqPrfyqt/ZCoaBIJKJIJFL18Xtc11U6nVY+n1cymZRt21s+rvWdhYVCwd9jZiupVGrP1zQNBoJ6a/+deuXaZXUEOzb9euXaZb21/86GBqbSanenaZoyDKPmz3er5653O++/3m3qGdda585msyoUChvOXU0+n68ZhlauRbrdn0296756r6PNpNNpRaNROY7jr4G61e+Vg/YcbminaU9PjxYWFjQ+Pq5Lly7pM5/5jAYHB5VIJHTXXXfpIx/5iCTp2WefresFDwAAAAAAVpVKJf32x57S+T+fk3HzUQWCAf3N7Iv6F4n/oEvzl9V7640ql8o6/+dz+u2PPaV//rkPKBjcfvDkOE7VmaSVKsMP0zQ3rFPqXb/ZGpLFYlHFYtEPaMLhsL/OqGVZa87lrcW4nm3ba4KbbDarZDJZ15qGIyMjymQya2pb3zVX7XFJq2HQ/Py83xHnBV3xeHzL2ivXdN3s8UurXXpex288Hlc4HNbs7GzNkKoyRHNdd8NSAx4v1JNWxyGXy8myrH3p5nv/W6P66ve+prLKOtp1w4brLy+9qqOHjujBe+pbL7Meruv63Z3T09OSav98az13E4nEmk2TYrGY3/FZz7hudm5vPCqv94LGahzH2XL6fSgU0rlz57b9s2kE7znvraHqLReQSCRqBqcH7Tnc0Nj+1KlTymQySqVSa1puJycnderUKXV0dOjuu+9WJBLZch0DAAAAAADwulKprCuvLEqSAsGAug51qvfWG/XKxSvqvfVGdR3qVCC4OgP0yiuLKpXK+1bbdtcpdV13TRgSCoX8oKXauaqFhV5nnGdkZETZbLauma3Dw8NyHMdv6PLCnfXW1+K6rizL0tjYmH9ZZTBaT+19fX0bwqDKx+/VUxmahUKhmiHaerUCJNM0FY/HFY/HNTIyolQqpVwut63z79Q9/cf107e/U5evvapyee3zs1wu6/K1V/XTt79Tb+27c1f3MzMz43cBT05OKhqNrgmdd/LzdRzHD8c9sVjMD+nrGddacrmcXNf1b7dVh+X8/HzN64vFYtXLt/rZNIL3mA3DkGma/iZO28kCD8JzuKGdppJ0+vRpnT59es1l0WhUTz/9tDKZjM6fP68zZ87ooYceavRdAwAAAADQtjo7O2Q9OaQz/2xCfzP7oh+U3nz76p4hS9eWdfFHP9bbInfIenJInZ0dO7of0zQ1Nze34XJvanE+n/c3i1m//mC91geUhmFsGvJsh2EYsm17y3UcvSAnk8kok8moUCjU1aU2MzMjwzDWBEy5XG7bdW71+L1zet12XmduPbY7s9frqPV2H69nDczd8LpNryxfXdNtemX5qro7DzWky3RgYKDmeO7k51soFGQYxppgbn5+fk0outPntbc5WW9vr0zT1PDwcM2p5vWEsa7r6uTJkxsu3+pn0wjehweVwa+3LEI9y38clOdwYxeIqCEajSqXy+n555/fEKoCAAAAAICtGTcf1cce+0X19B/VpZcvr7nu0suX1dO/er1x89Ed30c0Gq3aseVNQY7H4+rr69t2YLqbvU28DrxGSiaTmpyc3Nb5d1LHTm5j27YSiYQmJydlGMaaECgSiai3t9f/Wr/mZiaTqboOay3e9PF6ljbYrWrdpo3sMq1HrZ9vNY7jyHVdfyq+97V+pvVOeK+Lqakpzc7OKhqNanx8vOYavdFotGaw6F23HxuZVXs+ej/P9YGxYRh1/R44KM/hfQtNAQAAAADA7rgvX9ZnTv9nXZq/rJ51wWjPzUd1aX71enddoLodXhi62SYy9e6uvd5u9japt8vSdV0/PNnKyMiIP92+3nDJNM2qIWitYHS7HbSu62pwcFCpVEojIyP+LuXSasA2Ozurixcv+l/rOxK9jsid2Got20Z5/1uj6u48pCvLqxuWNbLLdCtb/XyrsW1bpmlWvX63gb5t2/5GYV7H5AsvvKCJiYlNb5NKpVQsFjd9jXpT2+t9LexGteejF0Sv/3m5rlvXxk4H5Tnc0ND04YcfVkdHh5555plGnhYAAAAAgOve8vKKUg9Nrpmav3RtWS9/75KWri37a5z+zeyLSj00qeXllR3f19TUlMbHxzd0nHrTmetRGZo4jlMzwKkMntaHLa7r+l+VvO4/Tzab9UMw7/pa3Xrepj/5fL7ucMnbOGj9eb2fU721r1d5fWVXo8cLXusJnjdbn7UWy7JkGMa+bAYlbew23c8u03p+vtWeu9FoVAMDAxuCylrraFYb92rn9jZkWn+cd8z655thGMrlchofH9/wevTOs9OlM6qp5zm83vou3Hw+r2g06r/War0+D8pzuOFrmp46daqu1BgAAAAAANQvGAzoyE3dkqRyqeyvYdrTf1QXf/Rj9d56o8qvbf505KZuBV/bFGonQqGQZmdnZVmWpqam1N/f769JODU1tSa8s21blmXJcRxZluWHNaFQSCMjI7IsS+FwWCMjI1WPTafTmpmZUbFYVF9fn+LxuH+5F5yYpqlMJuOHIn19fWs2fikWi3Jdd83U3EKhoPHxcY2MjGzatZZMJmt2F1Z7XN5O3ZX1eZsDGYZRs/aBgYG6Hv/o6Kgsy/LXpPTuc3h4uK6xW78hleM4/tqtjuP43XiVa3I2ejOgrXhrm778qtuQLlPvMc7MzPhrz46NjW14TKZpbvnzrfbclVY/TLAsyx8raXXs631eb3Zur0YvkHUcR2fPnpW0+fM4Go1qenpa4+PjG6ayr18yoN6fzXrpdFpzc3P+xlnez6xyM6zNxONxFYtFPxidn59fU5dt28pkMlXXbj0oz+FAef12Zbvw2GOP1b1e6TPPPKP777+/UXfd8hYWFtTT06NLly7p2LFj/uVLS0v60pe+pPe9733q6upqYoVoJMa1PTGu7YuxbU+Ma3tiXNsT49q+WmFsN3uvth1Xr17VCy+8oOPHj+vw4cO7qufKK1f12MN5nf/z1Y2a3ha5Qx977Bf1mdP/WX8z+6Ik6cR7wjr9B3EduWl399UOvDBlrzc3ws6Mf+Wspi98VYN3/rTGfvZUs8s5sHgeN1a9v5MbOj3fNE099dRTdR1rWVYj7xoAAAAAgLZ35KbDOv0HcUXuv1tvP/lmPfrvhvXme27Vo/9uWG8/+WZF7r+bwLSCbdsETQdY/J4HFDLepPg9DzS7lAON53FzNHR6/uDgoJ555hmNjY0pHA5rYGBAhmFsaKmtbJ0FAAAAAAD1O3LTYf3zz31ApVJZnZ0dkiTj5qP6rfwvKxgMKBhkz2fPdjdhwv66u+8t+sx7/4U6gx3NLuVA43ncHA0NTTs6Vp/k3oz/QKD6+inlcnnT6wAAAAAAQG3BYFDrs1EvQMUqx3HYc6UFEJjWxvO4eRoamh4/flzRaFSJRKLmcRcvXlQymWzkXQMAAAAAAPiYzox2wPO4eRoamoZCIaXT6boWxz5z5kwj7xoAAAAAAAAAGqKhC53kcrm6dxOcnp5u5F0DAAAAAAAAQEM0NDS9ePGiLly4UNexPT09jbxrAAAAAAAAAGiIhoamIyMjyufzdR//5JNP6q677lJHR4fe+973amFhoZHlAAAAAAAAAMC2NTQ0TSQSeuSRR+o69otf/KKSyaRSqZSef/55PfjggxocHGxkOQAAAAAAAACwbQ3dCMp1XUnShQsXlM/nNT8/rwceeEC/8Au/sOHY8fFxjYyM6MEHH5S02qXa39+vp556Su9///sbWRYAAAAAAAAA1K2hnaanT5/WAw88oHA4rNHRUaVSKUWjUX3gAx/YcKxt20omk2sue/DBBzU1NdXIkgAAAAAAQJ0cx5FlWert7VU4HJZlWSoUCge6Ftu2FYlEFA6Hm1Dl7niPMRAIKBwOK51OK51Oy7IsJRIJpdPpZpfYMhzHUTKZ3PCzTCaTSiQS21pO8iDL5/OKxWIKBAJKJBKybXvN9Y7jKBKJqLe3t+2eP/v9emlop+n58+dVLBb19NNP+1PtbdvWmTNn9Pjjj/tT91944QUFAgGFQqEN56h2GQAAAAAA2HuhUEipVEqFQkEDAwNKpVIHvhbTNJVKpTY0Zh0UiURCsVhMAwMDMgxjzXXeY7RtW6ZpanR0dM31kUhEc3NzymQy+1hxawqFQspkMnIcR6FQaMPPMhwOy3GcDZdvJpvNamRkZC9K3dW54/G4otGoent7NTw8LNM011wfCoU0NjYmwzAUjUYlSZZlyXEc5XK5Xde+1w7S66WhnaaTk5OamZlZszapaZqanJzUyy+/vOH4Y8eObbgsEAg0siQAAAAAANrW4tKyipdf1eLSckPP29fX19Dz7UY9tRyketfzZtp63bDel2VZW942mUwqm83uQ5X7q1Quabm0suay5dKKSuXSnt1nMpms62fumZ2d3bNadntuwzAUj8c3DQcdx/EDU0mKxWIaHh7e1X3ul4P0emlop2mtX1I333yz///e2qcAAAAAAGD7nv3OD/T5v/yavvS1b2lppaSujqDe98679aGffad+8o7bml0eKsTj8Q1dsnvZxXjQXV56VWe+8qReXbqq33x3UsbhY3KvLui3vpzRka7DevRnH9LRrhsafr9e16Lruhs6GNfLZrOamZlpeA2NPHcymVQsFqv6eNZ/XxmgHnQH6fXS0E5TSVpYWKh6ebVO02rm5+cbWQ4AAAAAAG0lP/t1DWXy+sOvfk2LyyvqCAa0uLyiP/zq15T4g7y+OPuNfanDdV2l02nl83klk8k1ayvm83m/Q6xQKCibzSocDisSichxHLmu63eSrV+TcT/qKxQKikQiikQism1b+Xxe2WzWn+KfzWaVz+c3rBlZ65ybWb9sgLfcQD1SqVTd08lbweWlVzX+F2c18/2/1l+/PKd//eU/0LcvfU//+st/oK+/PKeZ7/+1xv/irC4vvdrw+56dnZVpmjIMY8vnxtTUlBzH8dfMrOTdzvuvd5v1zydvrc1Ktc6dzWZVKBQ2nHsz0WhUhmFs6KzMZrMaGhryv6+15m82m13z5R0fi8XWHJ9MJtXb2+sfU+/jbfXXS0M7TePxuCKRiB599FFFIhEZhiHbtjU+Pr7mQRcKBZXLZX3729/WW97yFv/yCxcuqFwuN7IkAAAAAADaxrPf+YF+46ln9Oq1Zf2EcdOaJe56jxzWDxcua+ypad11a++ed5x67/VDoZDi8bjC4bBmZ2f9qcPFYlG5XM7vcnNdV/Pz8/5eJmNjYzJNc8/2NqlVXzQa9ddBLRaLisfjkuRPA67sdDt16pQ/nbrWOTdT+fhc190wddrjhU+S/J+dZVlt05FaKpd05itPavYHX1fP4RsVVFDfeNmR9Wf/py4tvqLew8dUUkmzP/i6znzlSX3q7/2qgoHd9/q5rut3d05PT0va+rkhqer6p4lEQsPDw/7zJRaLKRQK1Xw+Va47utm5vXGvvL6eDdhGRkaUyWTWnGt95+lma/6m02nNz8/7z3UvsI3H47Isa83x3lqxnnofb6u/XhraaXr8+HGNj4/r1KlTfoqdSCQUiURULpf16KOP6oEHHlA2m9XU1NSGJ18ymdTDDz/cyJIAAAAAAGgbn//Lr+mVq9f0hmNHN+wJEggE9IZjR/XK1Wv6w7/8n3tey/pgJxQKrfl+aGhoQ/BT2T1XLBb3dDPorerr6+vbEMisr8c0zTVLDG51zq3UCnVM01Q8Hlc8HtfIyIhSqZRyudy2zn+Qlcplvbp0VZIUVFBdHZ3qPXxMC9d+rN7Dx9TV0angazHVq0tXVdpFU93MzIzfPTk5OaloNLomrNvJODqO44eKnlgs5q8rutnzqTJsrCWXy/nPtVAoVFd35fDwsBzH8Ts4vU2w1lu/nKbrurIsS2NjY/5llcFoteU31wed9TzeVn+9NLTTVFrtNr148aKmpqZ08eJFRaNRHT9+vOqxc3Nz6u/v18DAgGZmZpRKpXTnnXc2uiQAAAAAAFre4tKyvvS1b+nIoa5NN1EOBAI6cqhL//m5/6Xf+qX3qLur4W/7/WDG24nb6wYrFosqFov+cYZhyDRNP2iqDKwaFZbWOtdW9UkbQ1LDMDZMY668TT3n3Mx2lyHwOgS9HcH3MmDeD53BDv3mu5P611/+A33jZccPSm850itJWlpZ1sWrC3r7zSH95ruT6gx27Pi+BgYGanYc7mQcC4WCDMNYE8rNz8+vCQmrPZ/qeX54mzr19vbKNE0NDw/XNc3c69TOZDLKZDIqFAp1dVrOzMzIMIw1Qaj3M9mOrR5vq79eGr6mqST19PQoHo/r1KlTmwa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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_scatter_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"f_200p25m25_w_wm_output_z_score_mean\",\n",
+    "    y_col=\"f_200p25m25_w_wm_output_repetition_2_mean\",\n",
+    "    x_name=r\"Z-Score $(\\rightarrow)$\",\n",
+    "    y_name=r\"$(\\leftarrow) $2-gram Repetition\",\n",
+    "    series_readable_id_col=\"Name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    color_marker_map=color_marker_map,\n",
+    "    legend_sort_indices=[0,1],\n",
+    "    legend_unique_indices = [0,1],\n",
+    "    shorten_legend_indices = [0,1],\n",
+    "    filter_list=filter_list,\n",
+    "    num_colors=4,\n",
+    "    num_markers=3,\n",
+    "    num_cols=2,\n",
+    "    marker_size=50,\n",
+    "    figure_size=(14,6),\n",
+    "    # legend_loc=\"upper right\",\n",
+    "    bbox_to_anchor=(1.01,0.5),\n",
+    "    title=\"Watermark Strength (Z-Score) vs.\\nWatermarked Output 2-Gram Repetition\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_bar_data(data_df,x_col=\"x\",series_readable_id_col=\"name\", fig_size=(8, 5), series_readable_id_map=None, x_label=None, y_label=None, y_lim=None, y_tick_spec=None, y_err_df = None, all_groups = None, overall_position_list=None, special_color_map=None, num_colors=None, title=\"Figure Title\", grid=True, save_path=None, show=True):\n",
+    "\n",
+    "    # make a simple bar chart of the data labellinig each by it's series_readable_id_col\n",
+    "    # make a random colormap\n",
+    "    cmap = cmr.take_cmap_colors(\"cmr.rainforest\", (len(data_df) if num_colors is None else num_colors), cmap_range=(0.2, 0.8), return_fmt=\"hex\")\n",
+    "    \n",
+    "    #plot using plt.bar\n",
+    "    # but lets make subplots for heirarchical groups\n",
+    "    # first level, on left we want bars where name contains 50_200 and right is where name has 100_600\n",
+    "    # in each of those groups, we want to split into set where contains cp_attack and where doesn't\n",
+    "    # them finally we sort each final group by x_col\n",
+    "    # mak sure we position them separately on the x axis with some space between them\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=fig_size)\n",
+    "\n",
+    "    global_idx = 0\n",
+    "    all_labels = []\n",
+    "    for i, grp in enumerate(all_groups):\n",
+    "        if isinstance(series_readable_id_map, dict):\n",
+    "            grp[series_readable_id_col] = grp[series_readable_id_col].map(series_readable_id_map)\n",
+    "        elif isinstance(series_readable_id_map, Callable):\n",
+    "            grp[series_readable_id_col] = grp[series_readable_id_col].map(lambda x: series_readable_id_map(x))\n",
+    "\n",
+    "        for j, x_val in enumerate(grp[x_col]):\n",
+    "            label = grp[series_readable_id_col].iloc[j]\n",
+    "            all_labels.append(label)\n",
+    "\n",
+    "            if special_color_map is not None:\n",
+    "                color = special_color_map(label)\n",
+    "                if isinstance(color, int):\n",
+    "                    color = cmap[color]\n",
+    "            else:\n",
+    "                color = cmap[j]\n",
+    "            ax.bar(overall_position_list[global_idx], x_val, color=color, alpha=0.8)\n",
+    "            # if y_err_df is not None:\n",
+    "            if y_err_df is not None and label in y_err_df[series_readable_id_col].values:\n",
+    "                print(label)\n",
+    "                print(y_err_df[series_readable_id_col].values)\n",
+    "                y_err = y_err_df[y_err_df[series_readable_id_col] == label][x_col]\n",
+    "                print(y_err)\n",
+    "                ax.errorbar(overall_position_list[global_idx], x_val, yerr=y_err, fmt=\"none\", ecolor=\"black\", capsize=5, capthick=2, zorder=10)\n",
+    "\n",
+    "\n",
+    "            global_idx += 1\n",
+    "\n",
+    "    # Set the x ticks to be the names located at the center of the correct bar\n",
+    "    ax.set_xticks(overall_position_list, all_labels)\n",
+    "\n",
+    "\n",
+    "    # turn on grid  \n",
+    "    ax.set_axisbelow(True)\n",
+    "    ax.grid(grid,zorder=0,axis=\"y\")\n",
+    "    # increase horiz grid density \n",
+    "    ax.xaxis.grid(which=\"minor\", linestyle=\"-\", linewidth=0.5, color=\"black\", zorder=0)\n",
+    "    if y_tick_spec is not None:\n",
+    "        ax.set_yticks(y_tick_spec)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    # set title\n",
+    "    plt.title(title)\n",
+    "    # label axes\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.ylabel(y_label if y_label else x_col)\n",
+    "\n",
+    "    # set y lim to be 0 to 1\n",
+    "    ax.set_ylim(y_lim)\n",
+    "\n",
+    "    # angle labels\n",
+    "    plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "    # tight\n",
+    "    plt.tight_layout()\n",
+    "    # save figure\n",
+    "    if save_path:\n",
+    "        # make if doesn't exist\n",
+    "        os.makedirs(os.path.dirname(save_path), exist_ok=True)\n",
+    "        if save_path.endswith(\".pdf\"):\n",
+    "            plt.savefig(save_path,format=\"pdf\")\n",
+    "        elif save_path.endswith(\".png\"):\n",
+    "            plt.savefig(save_path,format=\"png\")\n",
+    "\n",
+    "    # show figure\n",
+    "    if show:\n",
+    "        plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def color_map(x):\n",
+    "\n",
+    "    x_tup = x.split(\",\")\n",
+    "\n",
+    "    dataset = x_tup[0]\n",
+    "\n",
+    "    if dataset == \"C4-News\":\n",
+    "        color = \"black\"\n",
+    "    elif dataset == \"C4-en\":\n",
+    "        color = \"gray\"\n",
+    "    elif dataset == \"Wiki\":\n",
+    "        color = \"saddlebrown\"\n",
+    "    else:\n",
+    "        color = color_order.index(dataset)\n",
+    "\n",
+    "    return color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig_name = \"data_model_ablation_clean_AUC\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "\n",
+    "df[\"Name\"] = df[\"Name\"].apply(name_extractor)\n",
+    "\n",
+    "# sort, but make \"Wiki\" first\n",
+    "df[\"Name_for_sort\"] = df[\"Name\"].apply(lambda x: \"C4-z\" + x if x.startswith(\"Wiki\") else x)\n",
+    "df = df.sort_values(by=[\"Name_for_sort\"])\n",
+    "df = df.drop(columns=[\"Name_for_sort\"])\n",
+    "\n",
+    "df = df[~df[\"Name\"].isin(filter_list)]\n",
+    "# df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3305098/319256877.py:19: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  all_groups[i][\"Name\"] = all_groups[i][\"Name\"].apply(lambda x: \",\".join(x.split(\",\")[:2]))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[               Name  _wandb  z_score_roc_auc\n",
+       " 6  C4-News,Llama-7B     NaN         0.999471,\n",
+       "               Name  _wandb  z_score_roc_auc\n",
+       " 22  C4-en,Llama-7B     NaN         0.981624,\n",
+       "              Name  _wandb  z_score_roc_auc\n",
+       " 12  Wiki,Llama-7B     NaN         0.999125,\n",
+       "                 Name  _wandb  z_score_roc_auc\n",
+       " 26   Github,Llama-7B     NaN         0.891280\n",
+       " 25   Github,OPT-6.7B     NaN         0.818759\n",
+       " 27  Github,Vicuna-7B     NaN         0.847493,\n",
+       "              Name  _wandb  z_score_roc_auc\n",
+       " 13   Law,Llama-7B     NaN         0.992832\n",
+       " 4    Law,OPT-6.7B     NaN         0.865817\n",
+       " 21  Law,Vicuna-7B     NaN         0.979616,\n",
+       "              Name  _wandb  z_score_roc_auc\n",
+       " 10   Med,Llama-7B     NaN         0.998791\n",
+       " 2    Med,OPT-6.7B     NaN         0.997903\n",
+       " 17  Med,Vicuna-7B     NaN         0.991420,\n",
+       "                  Name  _wandb  z_score_roc_auc\n",
+       " 5    Patents,Llama-7B     NaN         0.999444\n",
+       " 0    Patents,OPT-6.7B     NaN         0.998106\n",
+       " 20  Patents,Vicuna-7B     NaN         0.996100]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': 13})\n",
+    "\n",
+    "# the Name is a tuple with dataset, model, attack\n",
+    "# we want to group by dataset, then model, then attack\n",
+    "# and drop the second attack group 'Dipper' since were plotting clean AUC here\n",
+    "all_groups = []\n",
+    "for dataset in df[\"Name\"].apply(lambda x: x.split(\",\")[0]).unique():\n",
+    "    dataset_df = df[df[\"Name\"].apply(lambda x: x.split(\",\")[0]) == dataset]\n",
+    "    # all_groups.append(dataset_df)\n",
+    "    # for model in dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[1]).unique():\n",
+    "    #     model_df = dataset_df[dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[1]) == model]\n",
+    "    #     # all_groups.append(model_df)\n",
+    "    for attack in dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[2]).unique()[:1]: # just take the first attack\n",
+    "        attack_df = dataset_df[dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[2]) == attack]\n",
+    "        all_groups.append(attack_df)\n",
+    "\n",
+    "# for all grps trim the attack from the name tup\n",
+    "for i, grp in enumerate(all_groups):\n",
+    "    all_groups[i][\"Name\"] = all_groups[i][\"Name\"].apply(lambda x: \",\".join(x.split(\",\")[:2]))\n",
+    "\n",
+    "all_groups\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3305098/240164332.py:22: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  grp[series_readable_id_col] = grp[series_readable_id_col].map(lambda x: series_readable_id_map(x))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# hardcode 27 bar positions leaving out the 3 required for the gaps\n",
+    "# 6 bars per group, 4 groups, 3 gaps\n",
+    "# width is 7 bars - the 1 gap bar\n",
+    "# width = 2 \n",
+    "\n",
+    "widths = [1,1,1,3,3,3,3]\n",
+    "\n",
+    "offset = 0\n",
+    "\n",
+    "overall_position_list = []\n",
+    "for width in widths:\n",
+    "    overall_position_list += list(range(offset, offset+width))\n",
+    "    offset += width + 1\n",
+    "\n",
+    "plot_bar_data(\n",
+    "    df,\n",
+    "    x_col=\"z_score_roc_auc\",\n",
+    "    # x_label=\"Watermarking Settings\",\n",
+    "    y_label=\"ROC-AUC\",\n",
+    "    fig_size=(8,5),\n",
+    "    y_lim=(0.5,1.0),\n",
+    "    y_tick_spec=np.arange(0, 1.1, 0.05),\n",
+    "    series_readable_id_col=\"Name\",\n",
+    "    all_groups=all_groups,\n",
+    "    overall_position_list=overall_position_list,\n",
+    "    num_colors = 4,\n",
+    "    special_color_map=color_map,\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    title=\"Detection Rate of Watermarking across Datasets and Models\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    "    show=True\n",
+    ")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig_name = \"data_model_ablation_attacked_AUC\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "\n",
+    "df[\"Name\"] = df[\"Name\"].apply(name_extractor)\n",
+    "\n",
+    "# sort, but make \"Wiki\" first\n",
+    "df[\"Name_for_sort\"] = df[\"Name\"].apply(lambda x: \"C4-z\" + x if x.startswith(\"Wiki\") else x)\n",
+    "df = df.sort_values(by=[\"Name_for_sort\"])\n",
+    "df = df.drop(columns=[\"Name_for_sort\"])\n",
+    "\n",
+    "df = df[~df[\"Name\"].isin(filter_list)]\n",
+    "# df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': 13})\n",
+    "\n",
+    "\n",
+    "# the Name is a tuple with dataset, model, attack\n",
+    "# we want to group by dataset, then model, then attack\n",
+    "# and drop the second attack group 'Dipper' since were plotting clean AUC here\n",
+    "all_groups = []\n",
+    "for dataset in df[\"Name\"].apply(lambda x: x.split(\",\")[0]).unique():\n",
+    "    dataset_df = df[df[\"Name\"].apply(lambda x: x.split(\",\")[0]) == dataset]\n",
+    "    # all_groups.append(dataset_df)\n",
+    "    # for model in dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[1]).unique():\n",
+    "    #     model_df = dataset_df[dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[1]) == model]\n",
+    "    #     # all_groups.append(model_df)\n",
+    "    # for attack in dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[2]).unique()[:1]: # just take the first attack\n",
+    "    #     attack_df = dataset_df[dataset_df[\"Name\"].apply(lambda x: x.split(\",\")[2]) == attack]\n",
+    "    all_groups.append(dataset_df)\n",
+    "\n",
+    "# all_groups\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3305098/240164332.py:22: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  grp[series_readable_id_col] = grp[series_readable_id_col].map(lambda x: series_readable_id_map(x))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "widths = [2,1,1,6,6,6,6]\n",
+    "\n",
+    "offset = 0\n",
+    "\n",
+    "overall_position_list = []\n",
+    "for width in widths:\n",
+    "    overall_position_list += list(range(offset, offset+width))\n",
+    "    offset += width + 1\n",
+    "\n",
+    "plot_bar_data(\n",
+    "    df,\n",
+    "    x_col=\"attacked_z_score_roc_auc\",\n",
+    "    # x_label=\"Watermarking Settings\",\n",
+    "    y_label=\"ROC-AUC\",\n",
+    "    fig_size=(12,6),\n",
+    "    y_lim=(0.5,1.0),\n",
+    "    y_tick_spec=np.arange(0, 1.1, 0.05),\n",
+    "    series_readable_id_col=\"Name\",\n",
+    "    all_groups=all_groups,\n",
+    "    overall_position_list=overall_position_list,\n",
+    "    num_colors = 4,\n",
+    "    special_color_map=color_map,\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    title=\"Detection Rate of Watermarking after Attack across Datasets and Models\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    "    show=True\n",
+    ")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "watermarking-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}