{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:05:57.374891Z",
     "iopub.status.busy": "2025-10-04T22:05:57.374758Z",
     "iopub.status.idle": "2025-10-04T22:05:57.387481Z",
     "shell.execute_reply": "2025-10-04T22:05:57.387073Z",
     "shell.execute_reply.started": "2025-10-04T22:05:57.374876Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.040680Z",
     "start_time": "2024-05-16T13:58:19.777010Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:05:57.389198Z",
     "iopub.status.busy": "2025-10-04T22:05:57.389074Z",
     "iopub.status.idle": "2025-10-04T22:06:00.066876Z",
     "shell.execute_reply": "2025-10-04T22:06:00.066349Z",
     "shell.execute_reply.started": "2025-10-04T22:05:57.389183Z"
    }
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from preference_data_preparation_diff import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from suno_utils.utils.s3 import download_s3_files\n",
    "from suno_utils.utils.text import read_json, read_jsonl, write_json, write_jsonl\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from suno_utils.audio import Audio\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.082172Z",
     "start_time": "2024-05-16T13:58:21.041926Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:06:00.068872Z",
     "iopub.status.busy": "2025-10-04T22:06:00.068737Z",
     "iopub.status.idle": "2025-10-04T22:06:33.655238Z",
     "shell.execute_reply": "2025-10-04T22:06:33.654626Z",
     "shell.execute_reply.started": "2025-10-04T22:06:00.068858Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output dir is /app2/suno/data/dpo/diff2_v2_d5_v11/\n",
      "Total pair quality scores: 294003\n",
      "Total hoot cer scores: 533012\n"
     ]
    }
   ],
   "source": [
    "OUT_DATA_DIR = \"/app2/suno/data/dpo/diff2_v2_d5_v11/\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "print(\"Output dir is\", OUT_DATA_DIR)\n",
    "NPZ_DIR = \"/app2/suno/data/dpo/diff2_v2_d5\"\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/up_v2_d5/full_pair_quality.json\", \"r\") as file:\n",
    "    full_pair_quality = json.load(file)\n",
    "print(\"Total pair quality scores:\", len(full_pair_quality))\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/up_v2_d5/hoot_cer.json\", \"r\") as file:\n",
    "    clip_id_to_cer = json.load(file)\n",
    "print(\"Total hoot cer scores:\", len(clip_id_to_cer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:06:33.657219Z",
     "iopub.status.busy": "2025-10-04T22:06:33.657086Z",
     "iopub.status.idle": "2025-10-04T22:07:08.954502Z",
     "shell.execute_reply": "2025-10-04T22:07:08.953919Z",
     "shell.execute_reply.started": "2025-10-04T22:06:33.657204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found newer file: interesting_clips_ahi_d5_20250924.pkl\n",
      "  Base file ctime: 1757895588.6062012\n",
      "  File ctime: 1758716529.967444\n",
      "  Difference: 820941.3612427711 seconds\n",
      "Found 1 newer files to process\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading newer pickle files:   0%|                                                                                               | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing interesting_clips_ahi_d5_20250924.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading newer pickle files: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  4.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous size: 475,198\n",
      "New input size: 193,284\n",
      "Current total size: 571,024\n",
      "Net increase: 95,826\n",
      "\n",
      "Final dataframe shape: (571024, 91)\n",
      "Unique ids: 571024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Load the base dataframe\n",
    "base_file = \"/home/tony/Data/Preference/up_v2_d5/fully_merged_up_v2_d5.pkl\"\n",
    "if os.path.exists(base_file):\n",
    "    df = pd.read_pickle(base_file)\n",
    "    base_time_file = base_file\n",
    "    base_ctime = os.path.getctime(base_time_file)\n",
    "else:\n",
    "    # If no base file exists, start with empty dataframe\n",
    "    df = pd.DataFrame()\n",
    "    base_ctime = 0\n",
    "\n",
    "# Find all pkl files in the directory with same name pattern\n",
    "# pkl_files = glob.glob(\"/home/tony/Data/Preference/up_v2_d5/interesting_clips_*.pkl\")\n",
    "pkl_files = [\"/home/tony/Data/Preference/up_v2_d5/interesting_clips_ahi_d5_20250924.pkl\"]\n",
    "\n",
    "# Filter files that are newer than the base file and print debug info\n",
    "newer_files = []\n",
    "for f in pkl_files:\n",
    "    f_ctime = os.path.getctime(f)\n",
    "    if f_ctime > base_ctime:\n",
    "        newer_files.append(f)\n",
    "        print(f\"Found newer file: {os.path.basename(f)}\")\n",
    "        print(f\"  Base file ctime: {base_ctime}\")\n",
    "        print(f\"  File ctime: {f_ctime}\")\n",
    "        print(f\"  Difference: {f_ctime - base_ctime} seconds\")\n",
    "\n",
    "newer_files.sort(key=lambda x: os.path.getctime(x))\n",
    "\n",
    "print(f\"Found {len(newer_files)} newer files to process\")\n",
    "\n",
    "# Process each newer file\n",
    "for pkl_file in tqdm(newer_files, desc=\"Loading newer pickle files\"):\n",
    "    print(f\"\\nProcessing {os.path.basename(pkl_file)}\")\n",
    "    prev_size = len(df)\n",
    "    temp_df = pd.read_pickle(pkl_file)\n",
    "    new_size = len(temp_df)\n",
    "\n",
    "    # Convert datetime columns if they exist\n",
    "    for col in [\"created_at\", \"updated_at\"]:\n",
    "        if col in temp_df.columns:\n",
    "            temp_df[col] = pd.to_datetime(temp_df[col], utc=True)\n",
    "\n",
    "    # Handle duplicates based on id\n",
    "    if \"id\" in temp_df.columns:\n",
    "        df = pd.concat([df, temp_df], ignore_index=True)\n",
    "        df = df.drop_duplicates(subset=[\"id\"], keep=\"last\")\n",
    "    else:\n",
    "        df = pd.concat([df, temp_df], ignore_index=True)\n",
    "\n",
    "    # Print size statistics\n",
    "    current_size = len(df)\n",
    "    net_increase = current_size - prev_size\n",
    "    print(f\"Previous size: {prev_size:,}\")\n",
    "    print(f\"New input size: {new_size:,}\")\n",
    "    print(f\"Current total size: {current_size:,}\")\n",
    "    print(f\"Net increase: {net_increase:,}\")\n",
    "\n",
    "print(\"\\nFinal dataframe shape:\", df.shape)\n",
    "print(\n",
    "    \"Unique ids:\",\n",
    "    df[\"id\"].nunique() if \"id\" in df.columns else \"No id column\",\n",
    ")\n",
    "df.to_pickle(\"/home/tony/Data/Preference/up_v2_d5/fully_merged_up_v2_d5.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:53.962528Z",
     "start_time": "2024-05-16T13:58:21.105919Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:07:08.956471Z",
     "iopub.status.busy": "2025-10-04T22:07:08.956345Z",
     "iopub.status.idle": "2025-10-04T22:07:19.295550Z",
     "shell.execute_reply": "2025-10-04T22:07:19.294973Z",
     "shell.execute_reply.started": "2025-10-04T22:07:08.956457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (571024, 91)\n",
      "unique users 95970\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    # \"/home/tony/Data/Preference/up_v2_d5/interesting_clips_ahi_d5_20250916.pkl\"\n",
    "    # \"/home/tony/Data/Preference/up_v2_d5/interesting_clips_ahi_d5_20250824.pkl\"\n",
    "    \"/home/tony/Data/Preference/up_v2_d5/fully_merged_up_v2_d5.pkl\"\n",
    ")  # , engine='python')\n",
    "print(\"Preference data shape\", df.shape)\n",
    "print(\"unique users\", df[\"user_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:07:19.297648Z",
     "iopub.status.busy": "2025-10-04T22:07:19.297525Z",
     "iopub.status.idle": "2025-10-04T22:09:20.517401Z",
     "shell.execute_reply": "2025-10-04T22:09:20.516791Z",
     "shell.execute_reply.started": "2025-10-04T22:07:19.297634Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "571024it [02:00, 4719.86it/s]\n"
     ]
    }
   ],
   "source": [
    "# find all the hoot jsons in the json dir\n",
    "JSON_DIR = \"/app2/suno/data/dpo/up_v2_d5_json/\"\n",
    "for _, row in tqdm(df.iterrows()):\n",
    "    clip_id = row[\"s3_id\"]\n",
    "    if clip_id in clip_id_to_cer:\n",
    "        continue\n",
    "    hoot_json_path = os.path.join(JSON_DIR, f\"{clip_id}_hoot.json\")\n",
    "    if not os.path.exists(hoot_json_path):\n",
    "        clip_id_to_cer[clip_id] = 1.0\n",
    "        continue\n",
    "    with open(os.path.join(JSON_DIR, f\"{clip_id}_hoot.json\"), \"r\") as f:\n",
    "        data = json.load(f)\n",
    "    for data_dict in data:\n",
    "        if \"hoot_cer\" in data_dict:\n",
    "            clip_id_to_cer[clip_id] = data_dict[\"hoot_cer\"]\n",
    "            break\n",
    "\n",
    "# add the cer to the df\n",
    "df[\"cer\"] = df[\"s3_id\"].map(clip_id_to_cer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:20.518329Z",
     "iopub.status.busy": "2025-10-04T22:09:20.518166Z",
     "iopub.status.idle": "2025-10-04T22:09:22.246610Z",
     "shell.execute_reply": "2025-10-04T22:09:22.245894Z",
     "shell.execute_reply.started": "2025-10-04T22:09:20.518313Z"
    }
   },
   "outputs": [],
   "source": [
    "# update the hoot cer cache\n",
    "with open(\"/home/tony/Data/Preference/up_v2_d5/hoot_cer.json\", \"w\") as file:\n",
    "    json.dump(clip_id_to_cer, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:22.247665Z",
     "iopub.status.busy": "2025-10-04T22:09:22.247487Z",
     "iopub.status.idle": "2025-10-04T22:09:25.754153Z",
     "shell.execute_reply": "2025-10-04T22:09:25.753603Z",
     "shell.execute_reply.started": "2025-10-04T22:09:22.247649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile 5: -0.036\n",
      "Percentile 10: -0.021\n",
      "Percentile 15: -0.015\n",
      "Percentile 85: 0.013\n",
      "Percentile 90: 0.019\n",
      "Percentile 95: 0.031\n",
      "\n",
      "Debug info:\n",
      "Total positive preference samples: 285512\n",
      "cer_diff range: -1.000 to 1.000\n",
      "cer_diff mean: -0.001\n",
      "cer_diff median: 0.000\n",
      "cer_diff std: 0.052\n",
      "Positive cer_diff samples: 109970 (38.5%)\n",
      "Negative cer_diff samples: 113843 (39.9%)\n",
      "Zero cer_diff samples: 61699 (21.6%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.fillna({\"cer\": 1})\n",
    "df[\"cer_diff\"] = df[\"cer\"].diff()\n",
    "df = df.fillna({\"cer_diff\": 0})\n",
    "# plot the positive preference and negative preference cer\n",
    "positive_pref = df[df[\"preference\"]]\n",
    "negative_pref = df[~df[\"preference\"]]\n",
    "\n",
    "# Create figure with 2 subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# First subplot: CER distribution\n",
    "ax1.hist(\n",
    "    positive_pref[\"cer\"], bins=50, alpha=0.2, color=\"green\", label=\"Positive Preference\"\n",
    ")\n",
    "ax1.hist(\n",
    "    negative_pref[\"cer\"], bins=50, alpha=0.2, color=\"blue\", label=\"Negative Preference\"\n",
    ")\n",
    "ax1.set_xlabel(\"CER\")\n",
    "ax1.set_ylabel(\"Frequency\")\n",
    "ax1.set_title(\"CER Distribution - Positive vs Negative Preference\")\n",
    "ax1.legend()\n",
    "\n",
    "# Second subplot: CER diff for positive preference only\n",
    "ax2.hist(\n",
    "    positive_pref[\"cer_diff\"],\n",
    "    bins=50,\n",
    "    alpha=0.7,\n",
    "    color=\"green\",\n",
    "    label=\"Positive Preference\",\n",
    "    range=(-0.75, 0.75),\n",
    ")\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_xlabel(\"CER Diff\")\n",
    "ax2.set_ylabel(\"Frequency\")\n",
    "ax2.set_title(\"CER Diff Distribution - Positive Preference Only\")\n",
    "\n",
    "# Add percentile lines BEFORE the legend\n",
    "percentiles = [0.05, 0.10, 0.15, 0.85, 0.90, 0.95]\n",
    "colors = [\"red\", \"orange\", \"blue\", \"blue\", \"orange\", \"red\"]\n",
    "# Fix: Use sorted() instead of .sort() which returns None\n",
    "sorted_positive_cer_diff = sorted(positive_pref[\"cer_diff\"].values.tolist())\n",
    "for i, (p, color) in enumerate(zip(percentiles, colors)):\n",
    "    percentile_value = np.percentile(\n",
    "        sorted_positive_cer_diff, p * 100\n",
    "    )  # Fix: multiply by 100 for np.percentile\n",
    "    print(f\"Percentile {p*100:.0f}: {percentile_value:.3f}\")\n",
    "    ax2.axvline(\n",
    "        x=percentile_value,\n",
    "        color=color,\n",
    "        linestyle=\"--\",\n",
    "        alpha=0.8,\n",
    "        linewidth=2,\n",
    "        label=f\"{p*100:.0f}th percentile: {percentile_value:.3f}\",\n",
    "    )\n",
    "\n",
    "# Add legend AFTER the percentile lines\n",
    "ax2.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Debug: Check the actual distribution of cer_diff\n",
    "print(\"\\nDebug info:\")\n",
    "print(f\"Total positive preference samples: {len(positive_pref)}\")\n",
    "print(\n",
    "    f\"cer_diff range: {positive_pref['cer_diff'].min():.3f} to {positive_pref['cer_diff'].max():.3f}\"\n",
    ")\n",
    "print(f\"cer_diff mean: {positive_pref['cer_diff'].mean():.3f}\")\n",
    "print(f\"cer_diff median: {positive_pref['cer_diff'].median():.3f}\")\n",
    "print(f\"cer_diff std: {positive_pref['cer_diff'].std():.3f}\")\n",
    "\n",
    "# Check if there are any positive values\n",
    "positive_cer_diff = positive_pref[positive_pref[\"cer_diff\"] > 0]\n",
    "negative_cer_diff = positive_pref[positive_pref[\"cer_diff\"] < 0]\n",
    "zero_cer_diff = positive_pref[positive_pref[\"cer_diff\"] == 0]\n",
    "\n",
    "print(\n",
    "    f\"Positive cer_diff samples: {len(positive_cer_diff)} ({len(positive_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")\n",
    "print(\n",
    "    f\"Negative cer_diff samples: {len(negative_cer_diff)} ({len(negative_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")\n",
    "print(\n",
    "    f\"Zero cer_diff samples: {len(zero_cer_diff)} ({len(zero_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:25.756779Z",
     "iopub.status.busy": "2025-10-04T22:09:25.754788Z",
     "iopub.status.idle": "2025-10-04T22:09:25.778113Z",
     "shell.execute_reply": "2025-10-04T22:09:25.777636Z",
     "shell.execute_reply.started": "2025-10-04T22:09:25.756752Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    537604\n",
      "True      33420\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"is_public\"].value_counts())\n",
    "# # remove public for now cause fucking users\n",
    "# df = df[~df[\"is_public\"]]\n",
    "# print(df[\"is_public\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:25.778817Z",
     "iopub.status.busy": "2025-10-04T22:09:25.778673Z",
     "iopub.status.idle": "2025-10-04T22:09:26.331106Z",
     "shell.execute_reply": "2025-10-04T22:09:26.330540Z",
     "shell.execute_reply.started": "2025-10-04T22:09:25.778802Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"upsample_clip_id\"] = df[\"metadata\"].apply(lambda x: x.get(\"upsample_clip_id\", \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.199480Z",
     "start_time": "2024-05-16T13:58:53.963687Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:26.331921Z",
     "iopub.status.busy": "2025-10-04T22:09:26.331763Z",
     "iopub.status.idle": "2025-10-04T22:09:31.337994Z",
     "shell.execute_reply": "2025-10-04T22:09:31.337371Z",
     "shell.execute_reply.started": "2025-10-04T22:09:26.331904Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "948953\n",
      "307613\n",
      "641340\n",
      "pre-downloaded df (571024, 94)\n",
      "downloaded df (524164, 94)\n",
      "vae downloaded df (523724, 94)\n"
     ]
    }
   ],
   "source": [
    "all_converted_paths = os.listdir(NPZ_DIR)\n",
    "print(len(all_converted_paths))\n",
    "\n",
    "converted_paths = set(\n",
    "    [f.replace(\".npz\", \"\") for f in all_converted_paths if \"vae\" not in f]\n",
    ")\n",
    "print(len(converted_paths))\n",
    "vae_converted_paths = set(\n",
    "    [f.replace(\"_vae.npz\", \"\") for f in all_converted_paths if \"vae\" in f]\n",
    ")\n",
    "print(len(vae_converted_paths))\n",
    "\n",
    "print(\"pre-downloaded df\", df.shape)\n",
    "df[df[\"upsample_clip_id\"].isin(converted_paths)].shape\n",
    "df = df[df[\"upsample_clip_id\"].isin(converted_paths)].copy()\n",
    "print(\"downloaded df\", df.shape)\n",
    "df[df[\"s3_id\"].isin(vae_converted_paths)].shape\n",
    "df = df[df[\"s3_id\"].isin(vae_converted_paths)].copy()\n",
    "print(\"vae downloaded df\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.467253Z",
     "start_time": "2024-05-16T13:58:56.207647Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:31.338880Z",
     "iopub.status.busy": "2025-10-04T22:09:31.338715Z",
     "iopub.status.idle": "2025-10-04T22:09:31.448714Z",
     "shell.execute_reply": "2025-10-04T22:09:31.448238Z",
     "shell.execute_reply.started": "2025-10-04T22:09:31.338863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    523724\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_up\"] = df[\"model_name\"].str.contains(\"up\")\n",
    "df[\"is_up\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LET's do the data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.592883Z",
     "start_time": "2024-05-16T13:58:56.470781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:31.449440Z",
     "iopub.status.busy": "2025-10-04T22:09:31.449295Z",
     "iopub.status.idle": "2025-10-04T22:09:31.507879Z",
     "shell.execute_reply": "2025-10-04T22:09:31.507402Z",
     "shell.execute_reply.started": "2025-10-04T22:09:31.449425Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name         \n",
      "False       chirp-ahi-up-3         166983\n",
      "            chirp-bass-up-3         78681\n",
      "            chirp-v4-up-u-d-2-5     16198\n",
      "True        chirp-ahi-up-3         166983\n",
      "            chirp-bass-up-3         78681\n",
      "            chirp-v4-up-u-d-2-5     16198\n",
      "Name: count, dtype: int64\n",
      "(523724, 95)\n",
      "(523724, 95)\n"
     ]
    }
   ],
   "source": [
    "## for 13b this is easy for now\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "print(df.shape)\n",
    "# df = df[df[\"model_name\"].isin([\"chirp-v4-up-u-d-2-2\"])]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:31.508547Z",
     "iopub.status.busy": "2025-10-04T22:09:31.508402Z",
     "iopub.status.idle": "2025-10-04T22:09:32.346768Z",
     "shell.execute_reply": "2025-10-04T22:09:32.346134Z",
     "shell.execute_reply.started": "2025-10-04T22:09:31.508532Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "task\n",
      "upsample        523524\n",
      "fixed_infill       200\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"task\"].value_counts())\n",
    "df = df[df[\"task\"] != \"fixed_infill\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.909539Z",
     "start_time": "2024-05-16T13:58:56.595736Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:32.347668Z",
     "iopub.status.busy": "2025-10-04T22:09:32.347504Z",
     "iopub.status.idle": "2025-10-04T22:09:33.163898Z",
     "shell.execute_reply": "2025-10-04T22:09:33.163261Z",
     "shell.execute_reply.started": "2025-10-04T22:09:32.347651Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(523524, 95)\n",
      "(523524, 95)\n",
      "preference  model_name         \n",
      "False       chirp-ahi-up-3         166906\n",
      "            chirp-bass-up-3         78658\n",
      "            chirp-v4-up-u-d-2-5     16198\n",
      "True        chirp-ahi-up-3         166906\n",
      "            chirp-bass-up-3         78658\n",
      "            chirp-v4-up-u-d-2-5     16198\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "assert df.shape[0] == df[\"request_id\"].nunique() * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:33.164711Z",
     "iopub.status.busy": "2025-10-04T22:09:33.164546Z",
     "iopub.status.idle": "2025-10-04T22:09:34.596911Z",
     "shell.execute_reply": "2025-10-04T22:09:34.596285Z",
     "shell.execute_reply.started": "2025-10-04T22:09:33.164694Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unpacked pair quality scores: 3864574\n"
     ]
    }
   ],
   "source": [
    "unpacked_pair_quality = {}\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    for clip_id, pair_quality in pairs_of_qualities.items():\n",
    "        unpacked_pair_quality[clip_id] = pair_quality\n",
    "print(\"Total unpacked pair quality scores:\", len(unpacked_pair_quality))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:09:34.597823Z",
     "iopub.status.busy": "2025-10-04T22:09:34.597657Z",
     "iopub.status.idle": "2025-10-04T22:10:12.161488Z",
     "shell.execute_reply": "2025-10-04T22:10:12.160841Z",
     "shell.execute_reply.started": "2025-10-04T22:09:34.597806Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize lists to store metrics\n",
    "clip_diffs = []\n",
    "clip_ratios = []\n",
    "loudness_diff = []\n",
    "spec_decay_diff = []\n",
    "last_spec_decay_diff = []\n",
    "pos_spec_decay_values = []  # New list for positive decay values\n",
    "neg_spec_decay_values = []  # New list for negative decay values\n",
    "clip_id_to_mean_ear_score = {}\n",
    "clip_id_to_mean_shimmer_score = {}\n",
    "total_clip_ratios = []\n",
    "\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    # Initialize lists for current request\n",
    "    mean_neg_scores = []\n",
    "    mean_pos_scores = []\n",
    "    pos_scores = []\n",
    "    neg_scores = []\n",
    "    neg_loudness = []\n",
    "    pos_loudness = []\n",
    "    neg_spec_decay = []\n",
    "    pos_spec_decay = []\n",
    "    neg_shimmer = []\n",
    "    pos_shimmer = []\n",
    "    neg_clip_id = None\n",
    "    pos_clip_id = None\n",
    "\n",
    "    # Process each clip pair\n",
    "    for i, (clip_id, pair_quality) in enumerate(pairs_of_qualities.items()):\n",
    "        if pair_quality is None:\n",
    "            continue\n",
    "\n",
    "        if i % 2 == 0:  # Negative clip\n",
    "            mean_neg_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"]))\n",
    "            neg_scores.extend(pair_quality[\"ear_v2_quality_scores\"])\n",
    "            neg_loudness.append(pair_quality[\"abs_loudness_factor\"])\n",
    "            neg_spec_decay.append(pair_quality[\"spectrum_decay\"])\n",
    "            neg_shimmer.append(pair_quality[\"shimmer_score\"])\n",
    "            if neg_clip_id is None:\n",
    "                neg_clip_id = clip_id\n",
    "        else:  # Positive clip\n",
    "            mean_pos_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"]))\n",
    "            pos_scores.extend(pair_quality[\"ear_v2_quality_scores\"])\n",
    "            pos_loudness.append(pair_quality[\"abs_loudness_factor\"])\n",
    "            pos_spec_decay.append(pair_quality[\"spectrum_decay\"])\n",
    "            pos_shimmer.append(pair_quality[\"shimmer_score\"])\n",
    "            if pos_clip_id is None:\n",
    "                pos_clip_id = clip_id\n",
    "\n",
    "    # Skip if we don't have both positive and negative samples\n",
    "    if not (mean_pos_scores and mean_neg_scores):\n",
    "        continue\n",
    "\n",
    "    # Calculate ratios and differences\n",
    "    ratios = [\n",
    "        (pos - neg) / (pos + 0.0001)\n",
    "        for pos, neg in zip(mean_pos_scores, mean_neg_scores)\n",
    "    ]\n",
    "    pos_diffs = [\n",
    "        (pos - prev_pos) / (prev_pos + 0.0001)\n",
    "        for prev_pos, pos in zip(mean_pos_scores, mean_pos_scores[1:])\n",
    "    ]\n",
    "    neg_diffs = [\n",
    "        (neg - prev_neg) / (prev_neg + 0.0001)\n",
    "        for prev_neg, neg in zip(mean_neg_scores, mean_neg_scores[1:])\n",
    "    ]\n",
    "\n",
    "    # Calculate loudness and spectrum decay differences\n",
    "    loudness_diff.extend(\n",
    "        [\n",
    "            (pos_l - neg_l) / (pos_l + neg_l + 0.0001)\n",
    "            for pos_l, neg_l in zip(pos_loudness, neg_loudness)\n",
    "        ]\n",
    "    )\n",
    "    spec_decay_diff.extend(\n",
    "        [\n",
    "            (pos_s - neg_s) / (pos_s + neg_s + 0.0001)\n",
    "            for pos_s, neg_s in zip(pos_spec_decay, neg_spec_decay)\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # Store individual decay values\n",
    "    pos_spec_decay_values.extend(pos_spec_decay)\n",
    "    neg_spec_decay_values.extend(neg_spec_decay)\n",
    "\n",
    "    # Calculate last spectrum decay difference only if we have values\n",
    "    if pos_spec_decay and neg_spec_decay:\n",
    "        last_spec_decay_diff.append(\n",
    "            (pos_spec_decay[-1] - neg_spec_decay[-1])\n",
    "            / (pos_spec_decay[-1] + neg_spec_decay[-1] + 0.0001)\n",
    "        )\n",
    "\n",
    "    # Store clip ratios and differences\n",
    "    if len(ratios) > 1:\n",
    "        for i in range(1, len(ratios)):\n",
    "            clip_ratios.append(ratios[i])\n",
    "            clip_diffs.append(pos_diffs[i - 1] - neg_diffs[i - 1])\n",
    "\n",
    "    # Store mean scores\n",
    "    if pos_clip_id is not None and neg_clip_id is not None:\n",
    "        clip_id_to_mean_ear_score[neg_clip_id] = np.mean(neg_scores)\n",
    "        clip_id_to_mean_ear_score[pos_clip_id] = np.mean(pos_scores)\n",
    "        clip_id_to_mean_shimmer_score[neg_clip_id] = np.mean(neg_shimmer)\n",
    "        clip_id_to_mean_shimmer_score[pos_clip_id] = np.mean(pos_shimmer)\n",
    "\n",
    "        # Calculate total clip ratio\n",
    "        total_clip_ratios.append(\n",
    "            (np.mean(pos_scores) - np.mean(neg_scores)) / (np.mean(pos_scores) + 0.001)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:12.162396Z",
     "iopub.status.busy": "2025-10-04T22:10:12.162235Z",
     "iopub.status.idle": "2025-10-04T22:10:25.562091Z",
     "shell.execute_reply": "2025-10-04T22:10:25.561494Z",
     "shell.execute_reply.started": "2025-10-04T22:10:12.162379Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spectrum Decay Difference Percentiles:\n",
      "2th percentile: -0.2912\n",
      "5th percentile: -0.2111\n",
      "10th percentile: -0.1484\n",
      "15th percentile: -0.1106\n",
      "85th percentile: 0.1175\n",
      "90th percentile: 0.1563\n",
      "95th percentile: 0.2207\n",
      "98th percentile: 0.3016\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pWLEi3N3dcfXqVTx9+lQsl8vlOHToECpUqIBmzZp94JnnVdzPuOJKTExEYGAgnJ2d0aNHD6VtxsbGGDx4MBITE3Hp0qU8+44aNSrP5+eZM2cQHR2NwYMHo379+krbHBwc0Lx5c5w9ezbP54SOjg4mTJgANbV/v6p07doVGhoauH37tlgWFxeHv/76C9WrV1cazapgYmICDQ2Nj4pFVX/6XA/OICL6X8Tpe0RE9NVxd3fHsmXLMHfuXFy+fBlNmzZF48aN80whKSoLC4siL7j9LgcHhzxlVapUQaVKlRAREQGZTPZB7X6Ie/fuAQAcHR2Vnl4I5E5TcnR0RGRkJO7du4fKlSsrbbeyssrTniKhkpKSonINJ1XHdnJyyrNNUfbPP/8U8Uzy16RJE5w8eRIhISG4evUq7ty5g+vXryMwMBCBgYHw9vbG+PHj8+yn6n2yt7eHhoYG7t69K5bdvHkTAHD+/HmVSS4NDQ08fvxYfK1YF8jV1fWjzw0AypQpk2dtqI/1/lRVIDcJC0ApofrutnfXO/oU0tPT8eDBA5iammLdunV5tmdlZQHInT4HAG/fvsWjR49Qo0YN1KxZs9D2ZTIZduzYgaNHjyIyMhJpaWniOnPAv4uEK/Tq1QsnT57En3/+iQkTJgAATp8+jYSEBAwfPlxMivwX3Lp1C9nZ2ZDJZCrXsFNMf42MjBSfmKigajphWFgYgNypwarai4uLQ05ODh4/fgxra2uxvGbNmtDV1VWqq6GhAWNjYyQnJ4tlt2/fhiAIcHJyKnS6c3Fjadu2LbZs2QIfHx+0bdsW33zzDRwdHT8oWUxERPn77/yWJCIiKoTiy+K7ozdUqVq1Kvbs2QM/Pz+cPXsWgYGBAHJH+IwePRpt27Yt1nFNTEw+KN781vsxMTFBdHQ03r59W2pJKcXogPzORZGIUDXySVXSSfFFPDs7u0jHVlNTU/m+mZiYQCKRFGnEVVFoaGigSZMmaNKkCYDcBMaBAwcwe/ZsrFmzBq1bt0aDBg2U9lH1Pqmrq6NcuXJISUkRy968eQMAWL16dZFiSU1NhUQiEa/txzI2Ns6TUPxYBb23+W1TJIVKyvv9Ojk5GYIg4OXLl+IC8KooFkZX3DtFTSaMHj0aQUFBqFmzJtq1awdjY2NoaGggOTkZW7duzbOouaurK6pWrYqDBw9i7Nix0NDQwJ9//gmJRFLiTwws6mfch1Lcwzdu3MCNGzfyradq0X5Vnx2K9o4cOVLgcd9vL79EtoaGBnJycsTXiv5XlPe2uLHY2tpi27ZtWL16NQICAsTRmtbW1pgwYQKcnZ0LPSYRERWOSSkiIvqfoXhS3rv/4p4fqVQKX19fyOVy3LlzB+fOncO2bdswbtw4mJqaolGjRkU+7ocmAhISElSWx8fHQyKRiCMFFFNYVH3ZL6lkjeJLYHx8vMrtcXFxSvVKkp6eHnJycpCYmJgnAZSQkABBED7JcYHcL7k9evTAtWvXcPDgQQQHB+dJSiUkJKB27dpKZdnZ2Xj9+rVSvIoYr1+/XqR49fX1IQgC4uLiSmT0RUH3oUQiyfdpge8m1r5E7/drRb9o0KBBkaZ1Kt6Lly9fFlo3PDwcQUFBcHV1xdq1a5Wm8YWFhYlPHnyXRCJBr169sHTpUgQFBcHKygoXL16Ei4vLB4++zE9xPuM+hOJaff/990oLxReFqvtP0d7q1avzjKwqCQYGBgCK9t5+SCyKJ7tmZGTg5s2b4gMfvL29ERAQUOLvLxHR14hrShER0f+Ex48fIzAwEFpaWmjZsmWR99PU1ISdnR1Gjx6N6dOnQxAEnDlzRtyuSAgVZcRPcal6vHp0dDRiY2NRt25dcZSU4ovX+9OGAChNH3uXmppasWJWTNO6du2a0lQlIPdJZ4pYVU3n+liKNlU97l3xJVzVVLGSpKOjk+82Ve9TaGgosrKylNamUUxfUkzjK4yi/oULFwqtq7gP3x0lUhyGhoZITEzMk9hMS0tTWgvpSxMSEoJr167B2NhYHJmip6eHOnXqIDIyUmkqV350dXVhbm6O58+fF/r0xWfPngEAmjVrlmddKVX3gUK3bt2gqamJP//8E/7+/sjJycmzJtPHSkxMxJ49ewAA7dq1K9G2FaytrSGRSBAaGloi7SnuccXUuZJmZWUFNTU1BAcH55t0LYlYtLW14eTkhClTpsDb2xsZGRm4ePHih4RMRETvYVKKiIj+865fv47BgwdDJpNh2LBhhY46uX37tsoRRoqRS2XKlBHLDA0NAUDlAt8f6+DBg0prJQmCgGXLliE7Oxtdu3YVy2vVqgVdXV2cPn0ar1+/Fsvj4+Pxxx9/qGzb0NAQSUlJyMzMLFIsZmZmcHJyQkREBPbt26e0bc+ePXj06BGcnZ3zrCdVEhTnumrVKqX3JSUlRZye9e71+BDnzp3DqVOnVI42e/r0KY4fPw4AKkfIbd26FbGxseJrmUyG5cuX54mrb9++0NDQwM8//4yYmJg87SQnJyslERWPm1++fDmio6OV6iqmpykoFpD+0PvQysoKcrlcaeqS4n5TTHP70pw+fRqjRo0CAEyYMAFly5YVt3l5eSE9PR0zZsxQGf+zZ8/w/Plz8XXfvn2RnZ2NOXPmICMjQ6luZmam2K/MzMwA5H6mvCsiIgJr167NN1YTExM0b94c58+fx65du1C+fHm0aNGieCdcgIiICHz//fdISEhA165dP9lIqQoVKqBt27YIDQ3F+vXr8ySogdykq6rpe6q0aNECZmZm2LRpE65evZpnu1wuLzDZVxgTExO0atUKUVFRKqdyJiQkiH2+uLGEhoaq/PxU9XuCiIg+HKfvERHRf0ZUVJS4QK1cLkdCQgLCw8Px4MEDqKur44cfflD5BKb3HTp0CHv27IGjoyOqVasGPT09PHz4EOfOnUO5cuXQrVs3sa6zszNOnDiB0aNHo2nTpihTpgwsLS3h4eHx0efj6uqK3r17o127djAyMsLly5dx+/Zt2NnZoX///mI9LS0teHl5YfXq1ejWrRs8PDzw9u1bBAUFoXHjxoiKisrTtrOzM27fvo0hQ4bAwcEBmpqacHR0hKOjY77xzJ49G3379sVPP/2EoKAgmJubIyIiAqdPn4aRkRFmz5790eesiqOjI7y8vLBt2zZ06NABrVq1giAI+OuvvxAbGwsvL68C4y6KyMhILFy4EOXLlxffd0EQEBUVhbNnz0Iul6NPnz6wtbXNs6+trS06d+6Mtm3bomzZsggKCsLjx4/RqlUrtG7dWqwnlUoxa9YszJ49G23atIGbmxuqVauGt2/f4vnz5wgJCUHXrl0xd+5cALkL5E+bNg3z5s1Dhw4d0Lx5c1SpUgVxcXG4du0a3NzcMH36dAC5C75LJBIsW7YMERER0NfXh4GBgdJ9UpD+/ftj//79mDFjBi5evAgjIyNcu3YNKSkpsLS0LJGF5D/U7du3xX6dmZmJuLg4hIaG4unTp9DW1sbMmTOV+iSQm9C7efMmDhw4gBs3bqBJkyYwNTVFQkICIiMjcfPmTSxdulR8qmHfvn1x9epVBAYGolWrVvDw8ICenh5evHiBCxcuYP78+WjRogVsbGxgY2ODwMBAxMXFwdbWFi9evMDp06fh5uaGEydO5HsevXv3xvHjxxEfH4/vv//+g9aDS0pKEq9FVlYWXr9+jbt374qLx/fo0QMzZ84sdrvFMWvWLDx+/Bi//vorDh06BHt7e+jr6yM2Nha3b9/GkydPcOHCBaUkYX60tLSwYsUKDB06FP3794ezszOkUikkEgliYmJw7do1lCtXTkwKf2i8ERERWL16Nc6dOwdnZ2cIgiDGeenSJRgYGBQ7lnXr1iE4OBiOjo6oWrUqtLS0cPfuXVy+fBnVqlUr1ohcIiLKH5NSRET0n/Huv4Zra2tDX18ftWvXxogRI9C1a1dUr169SO106NABmZmZCA0NRXh4OGQyGSpVqoQ+ffpg8ODB4mgJAOjZsyeio6Nx7NgxrF+/HllZWejatWuJJKUGDRqE5s2bY8uWLXj69CkMDQ0xYMAAjBkzJs8X2jFjxkBTUxP79u3D7t27UaVKFYwYMQLu7u4qvyiPGDECycnJCAoKwvXr15GdnQ0fH58Ckzu1a9eGv78//Pz8cP78eZw9exbly5dHt27d4OPjgypVqnz0OednxowZqFevHnbt2oW9e/cCAMzNzTF69Gh4enp+dPudOnWCrq4uzp8/jwcPHuDixYuQyWQoV64cXF1d0bVrV6UE07umT5+OwMBA7Nu3DzExMTA1NcWoUaMwbNiwPHV79uwJS0tLbN68GVevXkVQUBD09PRgZmaG7777Dl26dFGq379/f9StWxebNm3C+fPn8fbtWxgbG8PW1lZpwX1zc3MsXLgQGzduxPbt2yGTyVClSpUiJ6WkUinWr1+PZcuW4cSJE9DR0YGbmxsmT56MsWPHFvk6fgp37tzBnTt3AABly5aFoaEhzM3N0b17d3Tp0gWmpqZ59pFIJFi0aBG+/fZb/Pnnnzhz5gzS0tJgZGSEGjVqYPLkyXBxcVGq/9tvv+Gbb77Bvn37cOjQIQiCgIoVK6JNmzbiOmLq6upYs2YNlixZgvPnz+PWrVuoUaMGJk2ahG+//bbApJSzszPMzMwQExPzwQucv379WvyM09LSgr6+PmrUqIHvv/8enTt3/uTTWIHcUXm7d+/G9u3bcezYMRw5cgQ5OTkwMTGBpaUlfvjhB5QvX77I7dnY2ODw4cNYv349zp07hxs3bkBLSwsVK1ZEixYt0L59+4+K18jICHv37sWGDRtw/PhxbN++HWXKlEHVqlUxbNgwpeRZcWLp06cP9PX1cfPmTYSEhEAQBJiZmWH48OEYOHDgJ1vnjojoayMRVI3LJSIiIvrKTZkyBQcOHMDff/8tjrghys+rV6/g7u4OOzs77Nix43OHQ0RE9J/ANaWIiIiIiD7Sli1bkJWVhT59+nzuUIiIiP4zOH2PiIiIiOgDpKSkYNeuXYiOjsa+fftgbm6uNO2SiIiICsakFBERERHRB3jz5g2WLl2KMmXKoGHDhpgzZw7U1dU/d1hERET/GVxTioiIiIiIiIiISh3XlCIiIiIiIiIiolLHpBQREREREREREZU6JqWIiIjof86UKVNgYWGB58+fF3mflStXwsLCAsHBwZ8wsi/f8+fPYWFhgSlTpnzuUL4ovC6lZ//+/bCwsMD+/fs/dyhERPSJMSlFRET/8xRfJt//sbOzQ8eOHeHn54e3b99+9HE8PDzg4eFRAhH/tykSQu/+NGzYEJ6enti8eTPkcvlniSs4OBgWFhZYuXLlZzn+/ypFMs/CwgIBAQEq68ycOfM/kfD7L/VhQRBw6NAhDBgwAE5OTrCyskKTJk3QpUsXzJ49GyEhIZ87RCIiokLx6XtERPTVqF69Ojp16gQg9wtdYmIizp07h5UrV+L8+fPYuXMnn5xVgrp3745KlSpBEAS8ePECJ0+exMKFC3HlyhWsXr36kx57/PjxGDp0KCpWrFjkffr164d27drBzMzsE0b2v2358uVo3bo1NDU1P3coJa5ixYo4duwY9PX1P3coAIBp06Zh//79MDQ0RLNmzVCxYkVkZGTgn3/+wb59+5CamorGjRt/7jCJiIgKxKQUERF9NapXr45Ro0YplclkMvTq1QthYWEICQmBi4vLZ4ruf0+PHj1gZ2cnvh47diy6du2KoKAgBAcHw8nJ6ZMd29TUFKampsXax8jICEZGRp8oov991atXR1RUFHbv3g0vL6/PHU6J09TURJ06dT53GACAa9euYf/+/ahXrx62b98OPT09pe3Jycl4+PDhZ4qOiIio6Dh9j4iIvmpaWlpiciQpKSnP9oSEBCxYsAAtW7aElZUVnJycMGrUKDx48ECso5geGB0djejoaKVpaytXrsSbN29Qr149eHt7K7V97949sd7Tp0+Vtnl5ecHGxgYymUyp/OrVqxg+fLg4XadVq1b47bffkJ6ervL8ilr/3altt27dwqBBg2Bvb49GjRph5MiRxVqbKT8VK1ZEy5YtAQC3bt0Syx88eIAxY8bAxcUFVlZW8PDwwPz581W+H0+ePMHUqVPh4eEBKysrNG7cGJ06dcL8+fMhCIJY7/01pVauXIkBAwYAAPz8/JTeo3frvDvFLDo6GpaWluJ+75PL5XBycoKbmxtycnLEcplMhk2bNqFr166ws7ODvb09+vbti7///rvI1yolJQVr165F//794erqCisrK7i6umLSpEmIiorKU//d2I8cOYLOnTvDxsYGrq6umDdvHjIyMvLsk52djbVr16Jly5awtrZGy5YtsWbNGqXrWByDBg2CoaEh/vjjD6SmphZ5v3/++Qfjxo0Tz9Pd3R0///yzyvcfAHbv3o327dvD2toabm5uWLx4MTIzM2FhYZEnGXb79m3MnTsXHTp0QKNGjWBjY4OOHTti7dq1StNIC+vD79Z5d02pgQMHwtLSEtHR0SpjnTdvHiwsLHDx4kWl8uL24/eFhoYCALp06ZInIQUABgYGaNiwoVLZ48ePsXjxYnTt2hVOTk6wtrZG69atsWTJEpXTl728vGBhYQGZTIZly5ahWbNmsLGxQbdu3XDp0iUAuffpnDlz4OrqCmtra/Tq1Qvh4eF52lJMi0xOTsbMmTPxzTffwNraGl26dMl3ymd+nj17hunTp6NZs2Ziv5gyZYrK9+DOnTsYPXq0WNfZ2Rmenp74448/inVMIiL6dDhSioiIvmoymQwhISGQSCSoV6+e0raoqCh4eXkhNjYWrq6uaNGiBRISEvDXX3/hwoUL2Lx5M2xtbWFgYAAfHx9s2bIFQO4XVYXGjRvD0NAQlpaWuHbtGrKzs8Upgu+urxMcHIwaNWoAADIzMxEWFgZ7e3toaWmJdXbu3Im5c+fCwMAA7u7uMDIywu3bt7F69WoEBwdj69atH1UfyE0WrV+/Hk5OTujduzfu3r2LU6dO4cGDBwgICECZMmVK5LpLJBIAuSM+hgwZArlcjtatW6NKlSoICwvD1q1bcebMGezZs0ccvfTy5Uv06NED6enpcHNzQ7t27ZCeno4nT55g165dmDx5MjQ0VP9p07hxY3Tt2hUHDhxA48aNlaY1GRgYqNynSpUqcHR0xNWrVxEbG4tKlSopbT979ixev36NoUOHQk0t99/5ZDIZBg8ejJCQENSrVw/du3eHXC7H2bNnMWLECPz000/o379/odfn0aNH8PX1hZOTE1q2bImyZcsiMjISAQEBOHv2LPbv348qVark2W/Hjh04f/48PDw84OzsjPPnz2Pbtm1ISkrC0qVLler+9NNP8Pf3R9WqVdGvXz9kZmZi06ZNYsKjuAwNDTF06FAsWbIEGzduxOjRowvd5++//8bYsWOhpqaG5s2bo1KlSnj06BG2b9+OCxcuYO/evTA0NBTrr1ixAr///jtMTEzQs2dPaGho4Pjx44iMjFTZ/t69exEUFARHR0d8++23yMjIQEhICJYuXYpbt26JCafC+nB+OnfujCtXruDIkSMYPny40rasrCwcPXoUpqamSiMwP6Rfvq9cuXIAcpO0RXXy5En4+/vDyckJjRs3Rk5ODm7evIl169bh6tWr2L59u8ppl2PHjsWDBw/g4eGBjIwMHDlyBN7e3ti1axdmzpwJuVyONm3aICkpCceOHcOQIUPw999/55nmKJPJ8N133yEtLQ2dOnVCeno6AgMD8eOPPyIpKalIo+tu3ryJwYMHIz09Hc2aNUONGjUQHR2NI0eO4Ny5c9izZw+qVasGIDfp37t3b6irq6N58+YwMzNDcnIyHj16hL179+KHH34o8rUjIqJPSCAiIvof9+zZM0EqlQotWrQQfH19BV9fX2HFihXC7NmzhRYtWgjW1tbC+vXr8+zXq1cvoV69esK5c+eUyiMjIwV7e3uhQ4cOSuXu7u6Cu7u7yhgWLlwoSKVS4ebNm2KZt7e30KpVK8HNzU0YP368WH7p0iVBKpUKfn5+YllERIRQv359oVOnTkJiYqJS22vWrBGkUqmwYcOGD65/5coVQSqVClKpVDh69KhS/YkTJwpSqVQICAhQeW7vmzx5siCVSoXQ0FCl8levXglNmjQRpFKpEBISImRnZwstWrQQpFJpnmv8yy+/CFKpVJg6dapYtnXrVkEqlQqbN2/Oc8ykpCSVMTx79izPOfr6+qqM29fXV5BKpcKVK1fEsr179wpSqVRYu3ZtnvqjRo0SpFKp8ODBA7Fs2bJlglQqFZYvXy7k5OSI5SkpKUK3bt2EBg0aCLGxsSqP/67k5OQ85yQIgnD58mXB0tJSmD59usrYGzVqJDx69EgsT09PF1q1aiVYWloqHVdxLTp16iS8fftWLI+NjRWcnJwEqVQqTJ48udA43z12QECAkJGRIbi5uQl2dnZCXFycWOenn37Kc20TExOFhg0bCk2bNhWeP3+u1GZAQIAglUqFuXPnimWRkZFCvXr1hKZNmwrx8fFieUpKitCuXTtBKpUK/fv3V2onOjpayMrKUirLyckRpk6dKkilUuHatWtK2wrqw4rPkXevS0pKimBjYyO0a9cuT/3Tp08LUqlUWLRokVhW3H6ZnxcvXggNGzYULCwshPHjxwuBgYF5ruH7YmNjhczMzDzlK1euFKRSqXDo0CGl8v79+wtSqVTo06eP0j1y9OhRQSqVCg4ODsLo0aMFuVwublu7dq0glUqFjRs3KrXl7u4uSKVSoV+/fkoxvHjxQnBychKsrKyU7k9/f39BKpUK/v7+YplMJhPc3d0Fe3t74c6dO0rtX716VahXr57g7e0tlik+c0+ePJnnnN+/9kRE9Plw+h4REX01oqKi4OfnBz8/P6xatQo7d+5EVFQUmjRpgiZNmijVvXv3LkJDQ9GlSxc0bdpUaVutWrXQs2dPPHjwQGkaX0EUUwSvXLkCIHfq1LVr1+Dk5AQnJ6c8o6be3QfInbKUlZWFn376CeXLl1dqe8iQITAyMlKaBlPc+gqOjo5o166dUpmnpycA5Sl3RfHnn39i5cqV8PX1xbRp09CuXTvEx8ejefPmcHR0xI0bNxAVFYVvv/02zzUeOXIkypUrh4CAgDxTGLW1tfMcSzFypKS1adMGZcqUweHDh5XKk5OTERQUhHr16qFu3boAgJycHOzatQvVq1fH6NGjxdFgAKCnp4eRI0dCLpfj5MmThR5XX19f5Tk5OzvD3NxcnD71vgEDBqB27dria21tbXTo0AE5OTm4c+eOWH7w4EEAuddZR0dHLK9YsWK+0xWLokyZMhg1ahTS0tLg5+dXYN1Dhw4hNTUV48ePzzPqq3379mjQoAGOHj0qlh09ehTZ2dn4/vvvYWxsLJbr6enlO+rFzMwsz8MLJBIJ+vXrBwC4fPlysc7vfXp6emjRogUePnyodH0V5wfkjqZS+NB++b5KlSph5cqVqFy5MgICAjBmzBh4eHjAxcUFY8eOVXleFStWVDkCSzFyL79rMW7cOKV7pE2bNtDU1ERycnKe0YkdOnQAkDslM7+23o2hUqVKGDBgAGQymdJ7rcqZM2cQHR2NwYMHo379+krbHBwc0Lx5c5w9ezbP1FFVnxfvX3siIvp8OH2PiIi+Gq6urtiwYYP4OikpCTdu3MD8+fPRp08fbNmyBba2tgCAsLAwALlrSimm+LxLMV0oMjISUqm00GM7OjpCXV0dwcHBGDZsGO7evYuUlBQ4OzsjIyMDBw8exKNHj1CnTh0EBwdDW1sbNjY24v43b94EAJw/f17ll0cNDQ08fvz4g+srNGjQIE+ZYtpacnJyoef5rn379on/r6Ojgzp16qBjx45iQuDu3bsAVE+P0tXVhZWVFS5cuIDHjx/DwsIC7u7uWLZsGebOnYvLly+jadOmaNy4sThd51PQ19eHh4cHAgMD8c8//8DS0hIAcPz4cchkMqWEw+PHj/HmzRuYmpqqTMgkJiYCQL5Tzd4XHByMLVu2IDw8HElJScjKyhK35fd0u6K+f/fv3weQ+2X+farKiqNr167YtGkT/vzzTwwaNEiclvo+RR8LDw/Hs2fP8mzPzMxEUlISEhMTYWRkJCY63l8rKb8yIHfK2I4dO3D06FFERkYiLS1Nac2sV69eFff08ujUqRMCAgJw6NAh8fqnpqYiKCgIUqlUvGeAD++XqjRp0gQnT55ESEgIrl69ijt37uD69esIDAxEYGAgvL29MX78eLG+IAjw9/fHgQMHEBERgZSUFKW10PK7Fu9Pa1ZTU4ORkREyMjLyPKmyQoUK+baloaEBe3v7POWK+03xeZAfxf3y+PFjlZ/JcXFxyMnJwePHj2FtbY22bdtiy5Yt8PHxQdu2bfHNN9/A0dGxWE/kJCKiT49JKSIi+mqVL18ezZs3R9myZTFo0CAsX74cmzZtAgC8efMGQO6/zp85cybfNoq6MLGenh7q16+PGzduQC6XIzg4GBKJBM7OzmIbV65cgZmZGW7dugVHR0elEQWKeFavXl2k4xW3/rtxvk8x0uTdL7BFsWfPHqWn771PMaLBxMRE5XbFF1xFvapVq2LPnj3w8/PD2bNnERgYCACoXbs2Ro8ejbZt2xYrvqLq3LkzAgMDcejQITHBcOjQIairq4sjQwDg9evXAICIiAhERETk215R7pnAwEBxhIqrqyuqVKmCsmXLQiKR4MCBA/kurF3U9y8lJQVqamoqR4y8OwrpQ6ipqWH8+PH44YcfsGzZMqxYsUJlPcU9umPHjgLbU1wvxX2gKr787qHRo0cjKCgINWvWRLt27WBsbAwNDQ0kJydj69ateUbhfQhXV1eYmJjg2LFjmDx5MtTV1XH8+HFkZGQoJS2BD++X+dHQ0FAa6ZmVlYUDBw5g9uzZWLNmDVq3bi0myubNm4ft27ejcuXK8PDwQIUKFcTPGD8/v3yvhap7SkNDI99yRRzvK1++vLj22rsU72dhi+Mrrt2RI0cKrKe4X2xtbbFt2zasXr0aAQEB2L9/PwDA2toaEyZMgLOzc4HtEBFR6WBSioiIvnqKEUnvTk9TfOEq6sLUReHk5IRbt24hPDwcISEhqFu3rriId9WqVREcHIyaNWuKT3V7lyKe69evq/wy+L7i1v8cFHHFx8er3B4XF6dUDwCkUil8fX0hl8tx584dnDt3Dtu2bcO4ceNgamqKRo0alXicTZs2hZGREY4ePYqJEyciJiYG169fxzfffCMmzt6Ns3Xr1vD19f2oY/r5+aFMmTLYv38/atasqbStsGlORaGvr4+cnBwkJSWJ96BCQkLCR7fv4eEBBwcHHD9+XOXT2IB/r9eRI0eKNNpQUT8hISHPdD9V91B4eDiCgoLg6uqKtWvXKk3jUyymXxLU1dXRvn17bNmyBZcuXULTpk1x6NAhqKmpoWPHjirP4VP1Sw0NDfTo0QPXrl3DwYMHERwcjAYNGiAhIQE7duyAhYUF9uzZg7Jly4r7xMXFFTrVsiQkJSUhJycnT2JKcb8Vdj0U21evXg13d/ciHdPBwQHr169HRkYGbt68iaCgIOzcuRPe3t4ICAj4pKMsiYioaLimFBERffUU05reHUmimMZXnCeRqampITs7O9/tikTTxYsXxfWkFJydnRESEiKuOfX+lDZF4kwx/acwxa3/OSjWhQkJCcmzLS0tDbdv34a2tjZq1aqVZ7umpibs7OwwevRoTJ8+HYIgFDiiDfh3xFBB75EqGhoaaN++PV6+fIng4GAcOXIEgiCgU6dOSvXq1KkDPT093L59G3K5vFjHeF9UVBTq1KmTJyH16tUrPH/+/KPaBgALCwsAuU8/fJ+qsg8xceJEAMCSJUtUblfco4ppWYVRjFK7ceNGnm2q+qliSmCzZs3yrCuV3zkW1ofzoxgRdfjwYbx48QJXr16Fk5NTnqlipdUv310DCsi9FoIgoEmTJkoJKaDk3u/CZGVlqXyfFMd/f52o9xX3fnmXtrY2nJycMGXKFHh7eyMjIwMXL14sdjtERFTymJQiIqKvnmLKnqOjo1hmY2MDW1tbHD16FMeOHcuzT05OTp5kiqGhIZKSkpCZmanyOI0aNYKGhgZ27dqFt2/fKk0fcXJyQlJSEvbt2wcdHR1YW1sr7du3b19oaGjg559/RkxMTJ62k5OTldZkKW79z6Fhw4aoXr06zp07l2fh7j/++AOvX79G+/btxSlGt2/fVjnFRzHSokyZMgUez9DQEAAQGxtb7FgVCahDhw7h0KFD0NHRQcuWLZXqaGhooE+fPoiOjsYvv/yiMjH14MGDIo1EMjMzw9OnT5VGAGVmZmL27NkfnfAC/k2irFq1CmlpaWL5y5cvS2wEkZ2dHVq2bIng4GCV6yd5enpCV1cXv/32m8rpjunp6UoJiHbt2kFNTQ2bNm0S1+cCchOYqqbDKdY7un79ulJ5REQE1q5dqzLmwvpwfho0aABzc3OcOnUKu3fvhiAIeabuASXXL8+dO4dTp06pnCb39OlTHD9+HADEkYOKaxEaGqqUfI+NjcWyZcuKdpIl4LffflOaJhgbG4utW7dCS0sL7du3L3DfFi1awMzMDJs2bcLVq1fzbJfL5UoJttDQUJXvY1E/L4iIqHRw+h4REX01oqKilBbIffPmDW7cuIE7d+7A0NAQEyZMUKq/dOlSDBw4EOPGjcOWLVtQv359aGtrIyYmBmFhYUhMTFSa8ufs7Izbt29jyJAhcHBwgKamJhwdHcVkl66uLqytrREaGgo1NTWl0VCKUVOJiYlwdXXNs5C1VCrFrFmzMHv2bLRp0wZubm6oVq0a3r59i+fPnyMkJARdu3bF3LlzP6j+56CmpoaFCxdiyJAhGDZsGFq3bo0qVaogNDQUISEhqF69utJ7cujQIezZsweOjo6oVq0a9PT08PDhQ5w7dw7lypVDt27dCjxe7dq1YWpqiqNHj0JLSwsVK1aERCKBl5cX9PX1C9zXxsYGtWrVQkBAAORyOTp37pxnNAqQu4bR3bt3sW3bNpw9exYODg4wNjbGy5cv8eDBA/zzzz/Ys2dPoes2eXl54eeff0aXLl3Qpk0bZGVl4dKlSxAEAZaWlvk+3ayonJ2d0a1bN+zfvx8dO3ZEy5YtIZPJcOzYMdjZ2SEoKOij2lcYP348Tp8+jaioqDzbjIyMsGzZMowZMwadO3dG06ZNUbt2bchkMkRHRyMkJAT29vbiwwlq166NYcOGYfXq1ejUqRPatGkDDQ0N/PXXX5BKpXjw4IHSEw9tbGxgY2ODwMBAxMXFwdbWFi9evMDp06fh5uaGEydOqLwuBfXhgnTu3BlLly7Fhg0bULZsWbRq1SpPnZLql5GRkVi4cCHKly8v9gdBEBAVFYWzZ89CLpejT58+4ohPU1NTtG7dGidOnICnpyecnZ2RkJCAM2fOwNnZWeX7U9IqVKiAtLQ0dOrUCe7u7khPT0dgYCBev36NGTNmFLoAuZaWFlasWIGhQ4eif//+cHZ2hlQqhUQiQUxMDK5du4Zy5cqJCbl169YhODgYjo6OqFq1KrS0tHD37l1cvnwZ1apVy5NUJiKiz4NJKSIi+mpERUUprZ2ipaWFSpUqoU+fPhg2bFieJ0lVq1YNBw4cwKZNm/D3339j//79UFNTg6mpKRwcHNCmTRul+iNGjEBycjKCgoJw/fp1ZGdnw8fHR+kLrZOTE0JDQ1GvXj0YGBiI5RUrVkTNmjXx5MmTPOtJKfTs2ROWlpbYvHkzrl69iqCgIOjp6cHMzAzfffcdunTp8lH1PwcHBwfs2bMHq1atwsWLF5GamgpTU1MMGDAAP/zwg9J6Rx06dEBmZiZCQ0MRHh4OmUwmvn+DBw/O8/69T11dHX5+fliyZAkCAgLw9u1bALmjoApLSgG5SYfly5eL+6iipaWFdevWYd++fTh48CD++usvyGQymJiYoE6dOujdu3eR1k/q168fNDQ0sH37duzduxcGBgZwc3PDjz/+iDFjxhS6f1HMmzcPtWrVwt69e7F9+3ZUqlQJgwYNQtu2bUssKVW7dm10794de/bsUbm9WbNmOHDgADZs2IDLly/j4sWL0NHRQcWKFdGtW7c813ncuHGoWLEitm/fjt27d8PY2Bjt2rXDwIEDxftbQV1dHWvWrMGSJUtw/vx53Lp1CzVq1MCkSZPw7bffqkxKFaUP56djx4747bffIJfL0bp1a+jq6qqsVxL9slOnTtDV1cX58+fx4MEDXLx4ETKZDOXKlYOrqyu6du2K1q1bK+2zcOFCVKlSBSdOnMD27dvF4w0dOlTltShpWlpa2LRpE5YuXYrDhw8jOTkZtWvXxk8//aT0wICC2NjY4PDhw1i/fj3OnTuHGzduiAnmFi1aKI226tOnD/T19XHz5k2EhIRAEASYmZlh+PDhGDhw4Be71h4R0ddGIrz7XFwiIiIiov+YS5cuYdCgQRgyZIi4lhV9OTw8PAAAp0+f/syREBHRl4ZrShERERHRf0JiYmKehciTk5OxdOlSALnrDhEREdF/B6fvEREREdF/wuHDh7Fx40Y4OzvD1NQUcXFxOH/+PBISEtCtWzfY29t/7hCJiIioGJiUIiIiIqL/hIYNGyI4OBiXLl3CmzdvoK6ujtq1a2PEiBHo27fv5w6PiIiIiolrShERERERERERUanjmlJERERERERERFTqmJQiIiIiIiIiIqJSx6QUERERERERERGVOi50/gVISEgBV/aikqCpqQ65PLvwikRfCfYJImXsE0TK2CeIlLFPUEmRSABjY/1C6zEp9QUQBDApRSWG9xKRMvYJImXsE0TK2CeIlLFPUGni9D0iIiIiIiIiIip1TEoREREREREREVGpY1KKiIiIiIiIiIhKHdeUIiIiIiKiD5KTk4Ps7KzPHcZHUINcnvO5gyD6grBPUNGoq2tATe3jxzkxKUVERERERMUiCAKSkxORnp76uUMhIqLPpGxZPRgYGEEikXxwG0xKERERERFRsSgSUnp65aGlVeajvpB8ThIJnzRG9C72CSoKQRAgk2UiNTUJAGBoaPzBbTEpRURERERERZaTky0mpPT0DD53OB+FX8CJlLFPUFFpaZUBAKSmJkFfv/wHT+XjQudERERERFRk2dnZAP79QkJERF8nxe+Bj1lbkEkpIiIiIiIqtv/qlD0iIioZJfF7gEkpIiIiIiIiIiIqdVxTioiIiIiISkxpDqDi2jdERP9tHClFREREREQlIjNTHfHxGqX2k5mpXuLncOPGNbi6OiAlJQUAcOzYEbRp06zEj0P/e+bPn42pU38UXwuCgF9+mY+2bT3g6uqAiIj7Ksu+NlFRT9CpU2ukpb393KF8VeRyObp374h//rn7uUNRwpFSRERERET00SQSIDVVgjt3gKysTz+ESUNDggYNJNDWLv6Iqdu3wzFixBA4Obng119XfHQsoaHXsWnTOkREPIBMlokKFUxhZWWDyZNnQFNT86PbL4pjx47A13cpjh8/UyrHK6pjx45gwYI5AAA1NTXo6OiiWrXqaNLEFT169IGent5njrBgN25cw+jRwwHkrp+jo6MDM7MqcHR0Qs+e/WBiYiLWHTNmAoR3bsYrVy4hMPAIVq5cAzOzKjA0LKey7GuzevUqeHr2hI6O7ucO5ZN4+DACy5b9gn/+uYty5crD07Mn+vUbWOA+sbGxWLp0IW7cuIayZXXQtm0HeHuPhIbGvymbGzeuwc/vNzx+HAlT04oYOHAw2rXrKG4/cGAfDh7chxcvXgAAatWqje++GwIXl28AAJqamujTpz/++GMlVqz44xOc+Yf5okZK7dy5Ex07dkTDhg3RsGFD9OrVC2fPnhW3e3l5wcLCQuln5syZSm3ExMRg2LBhsLW1hYuLC3755RdkZSmvBB8cHIyuXbvCysoKLVu2xP79+/PEsmPHDnh4eMDa2ho9evRAeHi40vbMzEzMmTMHTk5OsLe3x6hRoxAfH1+CV4OIiIiI6L8nK0uAXI5P/vMxia+AgEPw9OyFsLBQxMfHfdT5Pn4ciR9/HA1Ly3pYtWottm7dg7FjJ0JTUxM5Odkf1fanIJfLS/2Yurq6OHToOPbvP4bVqzeic+euOH78KAYN6vvR17+07Nzpj4MHA7Fu3Vb06zcQ166FYMCAXnj06KFYR09PD/r6+uLrmJjnMDY2gbW1LYyNTaChoaGyrLgEQcjzHfe/IjY2FpcunVdKpvwvefs2FePH+6BSpcpYv34bRowYjY0b1+LQobw5B4Xs7GxMmjQGcrkcq1dvxPTpsxEYeAQbNqwR68TERGPSpLGwt3fApk070bNnH/zyyzwEB18W61SoYIrhw32wYcM2rF+/FQ0bOmDq1B8RGflIrNOyZVuEh4cplX1uX1RSqlKlSpgwYQL2798Pf39/ODs7Y+TIkYiIiBDr9OzZExcuXBB/Jk2aJG7Lzs6Gt7c35HI5du/ejUWLFuHAgQPw9fUV6zx79gze3t5wcnLCoUOHMHDgQMyYMQPnz58X6xw7dgwLFy7EyJEjceDAAVhaWmLw4MFISEgQ6yxYsABBQUFYvnw5tm3bhlevXsHHx+cTXyEiIiIiIvoYaWlp+Pvvk+ja1RNNmnyDY8eOfFR7V69egbGxMUaMGIPatc1RpUpVODs3weTJM1CmjDaAf6cAnjt3Br17d4WHRxOMH++Dly9jldo6f/4Mvv++Hzw8mqBHj87YuHGtUvIhJSUFixfPR8eOreDh0QReXj1x8eJ53LhxDQsWzEFqaipcXR3g6uogfqHt3r0jNm9ej59/nolWrdywePH8PFMUASAi4j5cXR3w4kWMUswXL55Hnz7d0Lz5N5gxYxIyMjIQGBiA7t07ok0bdyxf/iuyswtOvkkkEhgbm8DExAQ1a9ZChw5dsHr1RqSnp+H33//9rpaTk4Nt2zahR49O8PD4BgMH9kFQ0CmltiIjH2HSpLFo1coNLVt+ixEjhiA6+jkA4N69Oxg7dgTat2+O1q3d4OMzDPfv/yPuu2DBHEyaNFapvaysLHTo0BIBAQcLPIfy5Y1gbGyC6tVroEWL1vjjjw0oV64clixZKNZ5d/re/Pmz8dtvv+Lly1i4ujqge/eOKsuKct6K9+vy5Yv4/vv+cHd3QXh4WJH3u3YtBIMHe6F5828wfPj3iIp6onRuFy6cw5AhA+Dh0QTt2jXH1KkTxG0ymQx+fsvRpUtbtGjhiqFDB+LGjWvi9tjYF5g0aRzatHFHixau6N+/Jy5fvpDvdTx9+iTMzaWoUMFULPvQe62w2N68eY1Zs6ahS5e2aN78GwwY0AsnTx5XisfHZxiWL/8Vv/++Am3beqBTp9ZKyaDi+uuv45DL5Zg6dSZq166DFi1ao3v33tizZ0e++4SEXMGTJ48xc+bPqFvXAi4u32DIkOHYv3+vmEQ+eNAflSubYdSocahZsxY8PXuhWTMP7NmzU2zH1fVbuLi4olq16qhevQa8vUeibFkd3L17S6xjYGAAa2tb/P33Xx98jiXti5q+5+HhofR63Lhx2LVrF8LCwlC3bl0AgLa2NipUqKBy/wsXLuDhw4fYtGkTTExMUK9ePYwZMwZLliyBj48PtLS0sHv3blStWhVTpkwBANSpUwfXr1/H5s2b0bRpUwDApk2b0LNnT3h6egIA5syZgzNnzsDf3x/Dhg1DSkoK/P39sWTJEri4uADITVK1a9cOYWFhsLOz+xSXh4iIiOijFWURai4eTf/LTp8+iRo1aqJ69Zpo3bodVqxYCi+vQR/8aHMjI2MkJMQjLOwG7Owa5lsvIyMDW7duxIwZc6ChoYmlSxdh9uxp+OOPjQCAmzdDMW/eLIwdOxE2NnaIiXmOxYsXAAC+/34YcnJyMGHCaKSlvcXMmXNhZlYVT548hpqaGqytbTF69I/YsGE1du70BwCULasjHnvXrm347ruh+P77YQCQJxlWUMz79u3GnDkLkJaWhunTJ2LatAnQ09PHr7+uQExMNGbMmARra1s0b96qWNetfHkjtGzZFkePHkZ2djbU1dWxbdsm/PVXICZMmIqqVavh5s1Q/PzzTJQrVx729o0QF/cKPj7DYG/fEL6+f0BHRxe3bt1EdnZu4i4tLQ1t23bAuHGTIAgCdu/ejokTx2D37v3Q0dFFx45d4OMzDPHx8eK0u4sXzyMzMwMeHsWLv0wZbXTp4glf32VISkpE+fJGStvHjJmAKlWq4vDhA1i3bgvU1NShqamZpwxAoeetsHq1H3x8xsDMrCr09fWLvN/atb/Dx2csypUrjyVLFmLhwrnifXfp0gVMnz4RAwZ8jxkz5iArS47Lly+K+/7222I8eRKJOXMWwMSkAs6eDcKECaOxZctuVKtWHcuW/QK5XI5Vq9ZBW1sbT548Vrr33hceHgpLy3p5yj/kXissNplMBguLeujffyB0dHRx+fIFzJs3C1WqVEX9+lbisQMDA9CrVz+sXbsZt2+HY8GCObCxsYWjozMA4McfRyM8PDTfc6pYsTK2b98LIHdqsJ2dvdK0XScnF+zYsQXJyckwMDDIs/+dO7dQu7Y5jIyMxbLGjV2wZMkiPH78CFKpJe7cuQUHByel/Ro3doGv71KVMWVnZyMo6BQyMtLRoIGN0rZ69Rrg5s38z6e0fVFJqXdlZ2fj+PHjSEtLg729vVh+5MgRHD58GBUqVIC7uztGjBiBsmXLAgDCwsIglUqV5vW6urpi9uzZePjwIerXr4+wsDAxkfRunQULcj/wZTIZ7ty5A29vb3G7mpoamjRpgtDQ3Dfu9u3bkMvlaNKkiVinTp06MDMz+6CklKZmyS/QSF8nDQ3eS0TvYp8gUpaZqYk3bwrvF/r6AsqWzSmFiOi/KXeyhUSinORU/P/75Z/Ku8crjqNHD6F167aQSABn5yZYsGAOwsKuo2FDhzztqjq/93l4tEBIyBX4+AyDsbExGjSwRqNGjmjbtj10df9dLykrKwvjx09Cgwa5X4Z/+mkO+vbtjnv3bqN+fSts2rQO/ft/h3btOgAAqlatiqFDh2PVKl8MHjwM166F4N69O9ix409Ur15DrKOgp6cHiUSi9F1IoVEjR/Tt2198/epVrMpzfL8sKyvr/xMeucdp1qw5Tpw4hiNH/oKOjg5q166Nhg0dcOPGNbRoUXBSR9X1q1GjBtLS3iIl5Q10dfWwbdsmrFjxO6ysbMTzCw8Pw+HD+9GwYSPs3/8n9PT0MHfuQnHaW40aNcT2HBwcldqfPHk6Wrd2R1jYDXzzTVPY2NiiWrUa+Ouvo+IaP8eOHYa7ewvo6qpOpBR0X9eoURMAEBsbAyMjI6V99PX1oKOjAzU1NaX35P0ymUxW6Hkrjjt0qDcaN3Yu9n7e3iPQsGFukqp//4GYOHEsZLJMlClTBlu3bkTz5q0wZIj3/8cuQd260v8/r1gcO3YE/v4B4sCQfv28EBJyGceOHcHw4SPx8mUsmjXzgLm5uRhDQWJjY2FpWT/PtSzuvVaU2ExNTdGvn5d4jB49eiMk5AqCgk6J/VAiAerUqYvBg3MTttWrV8f+/Xtx/fpV8VpPnToDmZmZ+Z6ThoaGeD6JiQkwMzNTOj/FvZGUlABDw7xJqcTEBBgZGSntY2xsLG6TSFTXMTIywtu3byGTZYijMh89eghv70GQyWQoW7YsFiz4FbVr11Y6XoUKFfDyZWyJfE4r2tDUVIOW1of93f3FJaXu37+P3r17IzMzEzo6Oli1apV4g3fo0AFmZmYwNTXF/fv3sWTJEjx+/Bh+fn4AoJTxVlC8jouLK7BOamoqMjIy8ObNG2RnZ4s3gYKxsTEiIyPFNjQ1NfNkOY2NjcXjFIdcns1/kaQSI5N9eWsXEH1O7BNEuSQS4M0bdYSH5xS4Fk/u4tGAhgb/PiHV5PLchKUgqB5Vl195SVMcozjHi4p6grt372DBgiUQBEBdXR0eHi0REHAI9vYOBbab3zHU1NQxbdosDB36A65fv4q7d29j69ZN2LFjK9au3SJ+91BXV4elZX2xnerVa0JPTx9PnjxBvXpWePjwAcLDb2Lr1o1i29nZOZDJMpGenoGIiPuoUMEU1arVKPB8VW2zsKin8lxUXbt3y7S1tVGlSlXxtZGRMSpVMkPZsjpiWfnyRkhKSir0PcjvXsn9rwTPnj1DRkYGxo4dqVRHLpejbl0LCELuFEMbGzuoq2uobC8xMQHr1v2B0NDrSEpKRE5ODjIyMhAbGyvW79ixMw4fPoC+fQciMTEBV65cgq/v6nzjL+ha5eQoCiQF3isFnXtRzltR18Ki/gftV7t2XfH/jY1N/v9aJaFSpUqIiLiPjh27vBOjIP7/o0cPkZ2djT59uikdQyaTwcDAEIIAdO/eG0uWLERIyBU4ODjBzc0D5uZ1857w/8vMzICWllaea1Lce60osWVnZ2Pbtk04ffok4uLikJUlh0wmQ5ky2krva506dZXiMTY2QWJiolhmYmKKwrz//he1v72/Pb99VNVRdbxq1Wpg06adSE1NxZkzf2P+/NlYuXItatX6NzGlpVUGGRkZJfI5rWgj9/eC8t/cRU16fXFJqVq1auHgwYNISUnBiRMnMHnyZGzfvh3m5ubo1auXWM/CwgIVKlTAd999h6ioKFSvXv0zRk1ERET036BYhDp/AoBSGOZC9BkEBBxCdnY2unRpK5YJggBNTU2MGzf5o54EV6GCKdq0aY82bdpjyJAf0LdvNxw65I/Bg70L3xlAWlo6Bg8eBjc3jzzbtLS0UKZMmQ+OTTGzREFNLXe027tPilO1cLaqRbjfL5NIJBCEDxtZ+fTpY+jq6sLQ0BAxMdEAgMWLlyutNwRAnApV2DWYN282kpPfYMyYH1GxYmVoaWlh+PBByMr690OvTZv2WL3aD7dvh+PWrXBUrlwFtrb2BbRacPwAUKmS2QftDwDp6ekACj5vBW3tsh+037vvmWKaquI9U4ywUR1bGtTV1bFhwzZxqqGC4p7q2LELGjd2xuXLFxASEoxt2zbBx2csunfvrbLNcuXKKa1lpirG/MrevdeKEtvOndvw55+7MHr0j6hd2xxly5aFr+9Spfsh/+P82zeKM33PyMgYSUmJStsVr9+dnvcuY2Nj3Lt3R6ksMTFB3Kb4b2Ji4nt1EqGrq6v0HmpqaqJq1WoAAEvLerh37y7+/HMXJk2aLtZJTn6DcuXK5Xs+pe2LS0ppaWmJQzCtrKxw69YtbN26FXPnzs1T19bWFgDw9OlTVK9eHSYmJnmekqd4Ip5iSJ+JiUmep+TFx8dDT08P2traUFNTg7q6utKi5gCQkJAg/iuHiYkJ5HJ5njmhCQkJ+a53RUREREREn09WVhaOHz8GH5+x4rQciST3X/qnTp2AU6eOo0uX7iVyLAMDAxgbm4iJAyB31MY//9wV17KJinqC1NQUcQqYhYUFoqKeil8o31enTl3Exb1CVNRTcfreuzQ0NJGdXbTkULly5QEACQnx4veZiIgHRT6/kpCUlIiTJ4+jadNmUFNTQ61ataClpYWXL2OV1kN6V506dREYeBRZWVkqkxi3bt3Ejz9OhouLK4DctbNev36tVMfQsByaNm2Go0eP4M6dcLRv/2FPgcvMzMDhwwdgZ9cQ5cuX/6A2ABTpvEtyv/fVqWOO69evon37Tnm21a1rgezsbCQlJRWYuKtYsRK6dOmOLl26Y/VqPxw5cjDfpFTduhZ48iTyg+MtTmy3bt2Eq6sbWrduByB3QfmoqCjUqlWrWMeaMqXw6XsKVlY2WLv2d6V79OrVYFSvXkPlelIA0KCBNbZu3ai0NtnVq8HQ1dVFzZq1xTpXrlxU2u/q1eA860W9TxBy8jxxM3edKosC9ytNX9TT91TJycmBTCZTue3evXsA/k042dnZ4cGDB0oJpUuXLkFPT0+cAmhnZ4crV64otXPp0iVxHSgtLS00aNAAly//+2jFnJwcXL58WVzbysrKCpqamkp1IiMjERMTw0XOiYiIiOirpqEhgaYmPvmPhkbxRvRdunQBKSnJ6NChC2rXNlf6cXPzQEDA4Q8634MH/cXpS9HRzxEZ+Qi//+6Lx48j8c03Td+5Lhr47bdfcefObfzzzz3Mnz8HDRpYi0mq774biuPHj2LjxrWIjHyEJ08e49SpE1i79ncAgL19I9ja2mPGjEm4evUKYmKicfnyRVy5cgkAULlyZaSnp+HatRC8fv0aGRkZ+cZctWo1mJpWxMaNa/HsWRQuXbqA3bu3f9D5F4UgCEhIiEd8fDyePHmMgIBDGD78e+jq6mH48FEAAB0dXfTu3R8rVy5DYGAAoqOf4/79f7Bv324EBgYAADw9eyItLRWzZk3FP//cxbNnUTh+/Kj4NLlq1arhxIljePLkMe7cuY25c39SObqqY8fOOH48AE+fPkHbth2KdA5JSYlISIjHs2dROHXqBH74YTDevHmNH3+c8lHXpijnXZL7vW/QoKE4deoENmxYgydPHuPRo4fYvn0zAKB69Rpo1aot5s2bhbNnTyMmJhp3797Gtm2bcOlS7hP2VqxYiuDgy4iJicb9+//gxo1rqFEj/6RP48YuuH37VqFPbCxMUWKrVq0arl4Nxq1bN/HkyWP8+usCJCUlFNJyXhUqmKJq1Wr5/lSqVFms27JlG2hqamLhwrmIjHyEv//+C3/+uQu9evUT65w9G4S+fT3F140bO6NmzVr4+eeZiIh4gODgy1i37g9069YTWlpaAIAuXTwRExON339fgadPn2D//j8RFHQKvXr1FdtZvdoPYWE38OJFDB49eojVq/0QGnodrVq1UTqfmzfDxEXcvwRf1EippUuX4ttvv0XlypXx9u1bBAQEICQkBBs2bEBUVBSOHDkCNzc3lCtXDvfv38fChQvh6OgIS0tLALkLlpubm2PSpEmYOHEi4uLisHz5cvTr1098M3v37o0dO3Zg8eLF8PT0xJUrVxAYGIg1a/597OOgQYMwefJkWFlZwcbGBlu2bEF6ejq6dcudr6qvrw9PT08sWrQIhoaG0NPTw7x582Bvb8+kFBERERF9lQQB0NMT0KCBBKU1BVRPTyjyuigBAYfg4NBY5RS9Zs08sHPnVjx8GFHsGOrXb4Bbt8Lw668LkZAQh7Jly6JWrTpYsGCJ0ggWbW1t9O8/EHPmTEd8fBxsbOwwZcpMcbuTkwsWL16OzZvXYceOLdDQ0ED16jXRsWMXsc78+Yvh57ccs2dPR3p6BqpWrSomdaytbdGliydmzZqKN2/eYNCgoflOHdTQ0MDs2fOxdOkiDBzYB/Xq1cfQoT/gp58+LsGSn7dv36Jz5zaQSCTQ1dVFtWo10LZtB/To0VtpMfihQ39AuXLlsW3bJsTERENPTx9SqSUGDBgEIHeU04oVq/H77yvg4zMMamrqqFtXCmvr3Bk0U6b8hMWLF+D77/vD1LQivL1HYNWqFXnicXBwgrGxCWrVqg0Tk6LNdOnb1xMSiQRly+rAzKwKGjd2Qq9e/cQ1mj5GYedd0vu9q2FDB/z88yJs3rwe27dvhq6urtLIo2nTZmHLlg3w81uOuLhXMDQshwYNrNGkSW7CNScnG8uW/YK4uFfQ0dGFk5MLRo8en+/xnJ2bQF1dHdeuhcDJySXfekVRWGwDBw5GTEw0xo8fBW1tbXTq1BVNmzbD27epH3Xcgujp6WHZMj8sW/YLhgzxgqFhOXz33RB07vzv2ldv36YiKuqp+FpdXR2LFy/HkiULMXz4IJQtWxZt2nRQ6r9mZlWwePFyrFy5DH/+uRsVKphi8uQZStcwKSkR8+bNQkJCPHR19VCnTl0sW7ZSKQF1+3Y4UlNT4e7e/JNdg+KSCEJJLG9VMqZNm4YrV67g1atX0NfXh4WFBYYOHYpvvvkGL168wMSJExEREYG0tDRUrlwZLVq0wIgRI5R+sURHR2P27NkICQlB2bJl0bVrV/z4449KQ+qCg4OxcOFCPHz4EJUqVcKIESPEhJPC9u3bsWHDBsTFxaFevXqYMWOGOF0QADIzM7Fo0SIcPXoUMpkMrq6umDVr1gdN34uPT+FColQitLTUuagz0TvYJ4j+JZEASUlauHEju8A1pTQ1AVtbCUxMsvj3Cakkl8uQkPACxsaVoamplWd7aTx5T+Fj71HF9L1P7dixI/D1XYrjx898+oNRodLS0tC1a1tMmzZL5RpeX7PS6BP+/ntx8eI5LFvm92kPRHnMnDkV5uZ1MWDA9yXSXkG/DyQSwMREv9A2vqik1NeKSSkqKfwCTqSMfYLoX0xKUUkpLCn1X8Kk1NclJycHb968xq5d2/H3339hz56DKtem+pqVRp/IysrCjh1b0KNHb+jo6H7ag5FILpdjx44t6NOnf4EL3BevzY9PSrEHEhERERER0f+8ly9j0aNHJ5iaVsS0abOYkPpMNDQ0MHDg4M8dxldHU1MT33035HOHkQd7IRERERER0SfUrl1HtGv3YU95o5JTubIZLly49rnDIKJ3fPFP3yMiIiIiIiIiov89TEoREREREREREVGpY1KKiIiIiIiIiIhKHZNSRERERERERERU6piUIiIiIiIiIiKiUsen7xERERERUYmRSErvWIJQesciIqKSx6QUERERERGVCK3MNKilppba8XL09CAro1NqxyMqSd27d0TPnn3Qs2dfAEBCQjx+/nkmbt8Oh4aGBo4fP6Oy7GsTEHAQf/99Er/9tupzh/JVefw4EuPH+2DnTn+ULVv2kx2HSSkiIiIiIvpoEgmglpoK4c5dCFlZn/54GhpQa1AfEm2dIo+Ymj9/NlJTU7Bw4dISj+fGjWsYPXo4AgODoK+vX2Ddw4cPwN9/L2JinkNdXR2VK5vBw6MlvLwGlXhc+fmU1+JjzJ8/G4GBAQAAdXV1GBgYok4dc7Ro0Rrt2nWEmtqXvQLNhg1rsGnTOgC58evp6aFmzdpwc3NHly7doaWlJdZdt26r0pf9PXt2IiEhHps27YSenl6+ZV+TzMxMrFu3Gj//vOhzh/LJnD59CuvX/4HY2BeoWrUafvhhFFxcXAvc58aNa/Dz+w2PH0fC1LQiBg4cjHbtOirV8fffi127tiExMQF16tTFuHETUb++VZ62BEHAhAljEBx8CQsWLMG33zYDANSqVRsNGlhhz54d+O67ISV2vu9jUoqIiIiIiEqMkJUFyOWf/jgASnGmYIkJCDgEX9+lGDt2AuzsGkEmk+HRowhERj763KGplJWVBQ2N0v3a6OTUBNOmzUROTg4SExMRHHwJK1YsxZkzf2PRomWlHk9x1apVG8uX/w5BEPDmzRuEhl7Dli0bcfz4Mfj5rYGOji4AoHz58kr7xcQ8h4VFPVSrVr3AsuKSy+XQ1NT84P0/pzNn/oauri5sbOw+dyifxK1bNzFnznR4e49EkyZNcfLkcUydOgEbN25H7drmKveJiYnGpElj0bmzJ2bOnIfr10Pwyy/zYGxsAicnFwDA33//BT+/3zBhwlTUr2+FvXt3Yfz4Udi1yx/lyxsptbd37858p123a9cJv/wyD/37f/fJ+t2X3ZuJiIiIiIhKye7d23Hs2BHExETDwMAQTZo0xYgRo6GjkztFMDb2BZYtW4zw8DBkZclRqZIZRo4cjZo1a2P06OEAgLZt3f//vx0wffrsPMe4ePEcPDxaokOHLmJZ7dp1lOooRjHVrWuB/fv3QiaTo2XL1hg7dqKYXMjJycGOHVtw+PABJCQkoFq16vjuu8Fwd28hthMZ+QirV69EWFgoBEFA3bpSTJ8+G8ePHxVHI7m6OgAAfH1Xo3JlM/To0Qlz5izAgQP7cPfubUyYMBUvXsTg/Pmz2Lx5p9j23r07sXfvLuzbd0Qp5nr1GuDPP3dDLpehV69+8PIahDVrViEg4BC0tbUxZMhwtG/fqcD3QUtLE8bGJgCAChVMYWFhiQYNrDFmzA8IDAxAx4651y4lJQWrVi3HhQtnIZPJYWlZD6NGjUfdulKxrQsXzmHz5vWIjHyIsmXLwsbGHgsXLgEAHD9+FH/+uRtRUU9RtmxZNGzogDFjfkT58kYQBAG9e3dF586e6NvXS2wvIuI+Bg3qh927D6Bq1Woq41dX1xDjNzGpgDp1zOHo6IzvvuuD7du3YNiwEQCUp+91794RsbEvxLjatu2A0NDrecqmT59d6Hlv2LAG58+fhadnT2zduhGxsS9w/vzVIu/Xu3c/rF+/GikpyXB2boLJk2eIibScnBzs2rUNhw8fwKtXL1G+vBE6d+6GgQMHAwBevoyFn99yXL16BRKJGmxt7TBmzARUrmwGIHeEzx9/+OLx40hoaGigVq3amDVrPipVqqzyWv7991/45ptvlco+9F4rLLZ79+5gzZpViIi4j6ysLNSta4FRo8bDwsJSbMPV1QGTJ8/ApUsXEBJyGRUqmMLHZyxcXd1Uxl+YP//cDScnF/TtOwAAMHToD7h6NRj+/nsxceI0lfscPOiPypXNMGrUOABAzZq1EB4ehj17dopJqd27d6Bjxy7i+U+cOBWXL19AQMBheHl9J7YVEXEfu3fvwPr1W9G5c5s8x3J0dEJKSjLCwm7AwaHxB51jYZiUIiIiIiIiAqCmpoaxYyeicmUzxMREY+nSRfj9d19MmDAFALBs2S+Qy+VYtWodtLW18eTJY5QtqwNT04qYP38xpk+fhJ07/aGrq4syZbRVHsPIyBhhYTcQG/si3y/iAHDt2lVoaWnB13cNYmNfYMGCOTAwMIS390gAwLZtm/DXX4GYMGEqqlathps3Q/HzzzNRrlx52Ns3QlzcK/j4DIO9fUP4+v4BHR1d3Lp1E9nZWejTxwtPnz7B27dvMW3aTACAgYEh4uPjAACrV/vBx2cs6ta1gJZWGRw65F+k63f9+jVUqGCKVavWIjz8JhYt+hm3boXDzs4ea9duxt9//4Vff10AR0cnmJpWLPL7AgCNGjnC3FyKs2dPi0mpn36ajDJlymDJEl/o6urh0KH9GDv2B+zatR8GBoa4dOkCpk+fiAEDvseMGXMgl8tx5cpFsc2srCwMGTIc1avXQFJSEvz8fsP8+bOxZIkvJBIJ2rfvhGPHjiglpY4ePQI7u4b5JqTyU6NGTTg7N8G5c0FiUupd69Ztxbx5s6Crq4sxY35EmTLakMvlecqKct4AEB39DGfOnMb8+YuhpqZejP2e4/z5M1i8+DekpKRg5swp2LZts3jfrV7thyNHDmL06PGwsbFDfHw8oqKeiNfzxx9HoUEDa6xatR7q6urYsmUDfvxxFLZs2Q2JRIJp0yagY8eumD17AeRyOe7du4OCxjyGh4ehdet2ecqLe68VFpumpibS0tLQtm0HjBs3CYIgYPfu7Zg4cQx2794vJuUAYNOmdfjhh1EYOXIM9u3bgzlzfoK//xHxGrZs2bTAe6FVq7Ziwun27XD07t1PabuTkwvOnTuT7/537tyCg4OTUlnjxi7w9c2diiuXy/HgwT9K04HV1NTg4NAYd+6Ei2UZGRmYM2cGxo+fJCZR36epqQlzcylu3gxlUoqIiIiIiOhTUiw4DQCVK5th6NAfsGTJQjEp9fJlLNzcPFCnTu60mipVqor19fUNAADlyxsVuKbUoEHDMH36RHTv3hHVqlWHlZUNnJ2/gbt7c6X1kjQ1NTF16ixoa2ujdu06GDLEG6tW+WLo0B+QlZWFbds2Yfny32FlZSPGEh4ehkOH9sPevhH27/8Turp6mDNnoTjtpnr1GmL7ZcqUgVwuU/lltEePPnBz8yj29TMwMMDYsROhpqaG6tVrYufOrcjMzMCAAd8DALy8BmHHji0IDw9Dixati91+jRo18OjRQwDAzZthuHfvDo4cOSmu0+TjMxbnz59BUNDf6Ny5G7Zu3YjmzVth8GBvsY13R1F16NBZ/P8qVapi7NgJGDJkANLS0qCjo4N27Tpiw4Y1uHv3NurXt0JWVhZOnTqOkSPHFjt2AKhevSZCQq6o3Fa+fHloaWmiTJkySu/J+2VFOW8gNzExY8YccYpgUfcThBxMnz5bTMK0bt0O169fBQCkpb3Fvn27MW7cJLRt20G8bra2dgByRzXl5ORgypSfIPn/+WDTps1CmzbNEBp6HZaW9ZCamoomTVzFvlOzZq18r1dKSgpSU1NhYlIhz7bi3muFxda4sTMaNXJUOsakSdPRpo07QkNv4Jtv/k00tW3bAS1b5o4q8vYeiX37duPu3Ttwdm4CANi0aScKoqv7b4IrMTEhz3S68uWNkJiYkO/+CQkJMDJS3sfIyAhv375FZmYGUlJSkJ2drbLO06dPxNe+vkthZWWDpk2bFRiviUkFvHwZW2Cdj8GkFBEREREREYCrV4OxfftmcRRRdnY2ZLJMZGRkQFtbG92798aSJQtx9eoVODg4wc3NA+bmdYt1DBMTE6xZswmRkQ8RFhaK27fDMX/+bAQEHMTSpSvFxJS5eV1oa/872qpBAxukp6fh1auXSEtLQ0ZGBsaNG6nUtlwuR926FgByp+XY2tp90Dowlpb1ir0PkLuW0ruJNSMjY9Sq9e/URMXC5UlJSR/Ufu6C9rkJhYcPHyA9PR3t2zdXqpOZmYno6OcAcq+BYlSVKv/8cw8bN67Fw4cPkJKSAkHIAZCbfKxVqzZMTCrAxeUbHD16GPXrW+HixXOQyeRKUySLF78gJkQ+VFHOGwAqVaqstGZV0fczUxoVZGxsIr5fT548hkwmy5O8+fcYEYiOfo5WrZSn28lkMkRHP0fjxs5o164jfvxxFBwcnODg0BgeHi1hYqJ6lE5mZiYAKC0Or1Dce62w2IDcBNG6dX8gNPQ6kpISkZOTg4yMjDwJmTp1/u3zZcuWha6uLpKSEsWy4o6i+xwuXDiLGzeuYePGHYXWLVOmDDIyMj5ZLExKERERERHRV+/FixhMnjwOXbp4YujQETAwMEB4eBgWLfoZcrkc2tra6NixCxo3dsblyxcQEhKMbds2wcdnLLp3713s49WubY7atc3RrVsPdO7siZEjhyAs7AYaNnQodN/09HQAwOLFy1GhgqnSNsWaU2XKlCl2TArvP/5dTU0NwnuPOMxS8YRFVQmw98skEomY/Cmup08fw8wsd/2f9PQ0GBubYOXKNXnq6enljlTLbwpl7v7p+PFHHzRu7IJZs+ahXLnyePkyFuPH+yAr69+F+jt06IJ582Zi9OjxOHr0CJo3b6mULCxu/Ir1iz5UUc4bALS1ld/Dou5X0PtV0PVUHEMqtcSsWfPybCtXLjdBNm3aLHTv3gvBwZdx+vRJrFv3B377bRWsrKzz7GNoaAiJRIKUlJQ824p7rxUltnnzZiM5+Q3GjPkRFStWhpaWFoYPH6R0P+R/nH/7R3Gm7xkZGSsltAAgKSkRRkbG+e5vbGyMxETlfRITE8Vpw2pq6lBXV1dZx9g4t93r168hOvq5uAaewowZk2BjYwc/v7ViWXJyMqpUqVLgOX0MJqWIiIiIiOird//+PeTk5MDHZ5w4AuP06ZN56lWsWAldunRHly7dxfV1unfv/c4C5NnFPnatWrlTmBTJJiB3ZEdmZoaYCLhz55a4fpWBgQG0tLTw8mUs7O0bqWyzTp26CAw8mu/T8zQ0NJGdXbTkULly5ZGYmKA00ici4kGxzvFjXb9+FY8ePRSnWFpYWCIxMQHq6ur5Jnrq1DHH9etXVS6s/vTpE7x58wbDh/ugYsVKAIB//rmbp56LyzfQ1i6LAwf2ITj4ElatWvdB8T99+gTBwZeV1vn5EEU575Lc711Vq1ZDmTJlcP36VZiZ5U1SSKWW+Pvvkyhfvjx0dfXybUcqtYRUagkvr0Hw9h6EU6eOq0xKaWpqombNWnjyJBKNGzt/UMzFie3WrZv48cfJcHFxBZA7Yu7169fFPlZxpu9ZWdng2rWrSlOHr14NVnk9FBo0sFZaG02xT4MGuVN5NTU1IZVa4vr1EHz7bTMAuQvUX79+Fd269QQA9O8/EB07dlZqY8CA3hg1arzSVEUAePz4Edzdiz+dt6iYlCIiIiIiohIj0dCAUHi1EjnOh0hNTUVExH2lMgMDQ1SpUg1ZWVnYt28PvvmmKW7duolDh/Yr1VuxYimcnZugWrXqSElJwY0b11CjRm5CqVKlypBIJLh06QKcnb9BmTJlxKf2vWvJkoUwMamAhg0dYGpaEfHx8di6dQPKlSuv9EVULpdj4cKfMXDgYMTGxmDjxrXw9OwJNTU16Ojoonfv/li5chkEQYCNjR1SU1Nx61YYdHX10LZtB3h69oS//x7MmjUVXl6DoKurhzt3bqF+/QaoXr0mKleujJCQy4iKegIDg3LQ08s/iWBv3wivXydhx44tcHdvgStXLuHKlUtKX65LkkwmR0JCPHJycpCYmIjg4EvYtm0zmjRpijZt2gMAHByc0KCBNaZOnYARI0ajWrXqiI+Pw6VLF+Dm5g5Ly/oYNGgoxo4dgSpVqqJ581bIzs7G5csX0L//d6hYsRI0NTXh778HnTt74vHjR9i8eX2eWNTV1dG2bQesWbNKXAOsMNnZWUhIiIcgCHjz5g1CQ69hy5aNqFtXij59vArdvyBFOe+S3O9dZcqUQb9+A/H7777Q0NCAjY0dkpKS8OTJI3To0AWtWrXFzp3bMGXKjxgyZDgqVDBFbOwLnDsXhL59ByArKwuHDx+Aq+u3MDGpgKiop3j+PEp8T1Vp3NgF4eFhSkmbD1FYbKamFVGtWjWcOHEMlpb18fbtW/z++4oPGnFYnOl7PXr0ho/PMOzatR1Nmrji1KkT+Oefu5g06d8n761e7Ye4uFf46ae5AIAuXTyxf/9e/P77CrRv3xnXr19FUNApLF68XNynd+9+mD9/Niwt66NevQbYu3fn/0/f7Aggd1qmqvXkKlaspJRwfPEiBnFxr/IsrF6SmJQiIiIiIqKPJghAjp4e1BrUL+BZWiUrR08PQjEzYKGh1zFokPLTrjp06IwpU37CqFHjsGPHFqxZ4wdb24bw9h6JefNm/Xu8nGwsW/YL4uJeQUdHF05OLhg9ejwAoEIFUwwe7I3Vq1diwYI5aNOmPaZPn53n+A4OjXH06GEcOLAPyclvYGhYDlZW1lix4g8YGpZ7p54jqlWrDh+foZDJ5GjRojW+/36YuH3o0B9Qrlx5bNu2CTEx0dDT04dUaokBA3JH4hgalsOKFavx++8r4OMzDGpq6qhbVwpra1sAQMeOXREaeh2DBw9AenoafH1X5zuCpmbNWvjxx8nYunUTtmzZADc3D/Tp0x+HDx8o3sUvouDgS+jcuQ3U1dWhr28Ac/O6GDt2Atq27SCOYpNIJFiyZAXWrv0dCxbMwevXSTAyMoadXUNx4eiGDR3w88+LsHnzemzfvhm6urqwtbUHkLuw+LRps7B27e/Yt28PpFJLjBw5FlOmjM8TT4cOnbFt2ya0a9exSPE/fhwpxq+rq4eaNWvBy+s7dOnSXeX6SMVRlPMuyf3e9913Q6Curo4NG9YgPj4OxsYm6NLFEwCgra2NVavW4o8/VmL69IlIS0uDiUkFNGrUGLq6usjMzMTTp08QGBiA5OQ3MDY2QbduPcVF1lXp0KEzhgzxQmpqaoGJ08IUFhsATJnyExYvXoDvv+8PU9OK8PYegVWrVnzwMYvC2toWs2bNx7p1v2Pt2lWoWrUaFi5cgtq1zcU6CQnxSutamZlVweLFy7Fy5TL8+eduVKhgismTZ8DJyUWs07x5K7x+nYT161cjMTEB5uZSLF26ssBpgaqcOnUCjo7OBT4p9GNJhPcnB1Opi49PKfYvUyJVtLTUIZMVf8g40f8q9gmif0kkQFKSFm7cyIZcnn89TU3A1lYCE5Ms/n1CKsnlMiQkvICxcWVoaub9gv2R6zgXy8feoxLJx7fxKcyfPxupqSlYuHDp5w6FANy8GYoxY37A/v1Hi/2l/r/mS+wTM2ZMhoWF5UdPfaTikcvl6N27K2bNmgcbG7t86uT/+0AiAUxM8n8SqYJaoTWIiIiIiIiKSBBK74foU5LJZHj16iU2blwLd/cW//MJqS/VyJFj8iy+T5/ey5ex8PIalG9CqqQwKUVERERERET0nlOnTqB7945ISUnBiBGjP3c4X63Klc0+6AmX9HGqVq0mTs38lDh97wvA6XtUUjhViUgZ+wTRvzh9j0pKYdP3/ku+xKlKRJ8T+wQVB6fvERERERERERHRfxKTUkREREREVGyCkPO5QyAios+oJH4PaJRAHERERERE9JXQ0NCERKKGN28SoKdXDurqGpCU5iP3ShCnKhEpY5+gohAEAdnZWUhJeQ2JRA0aGpof3BaTUkREREREVGQSiQTGxpXw5k0i3ryJ/9zhEBHRZ6KlpQ0DA6OP+ocJJqWIiIiIiKhYNDQ0YWRkipycbOTk/Hen8WlqqkEu/+/GT1TS2CeoqNTU1KCmpv7RI2WZlCIiIiIiomKTSCRQV9eAuvrnjuTDaWmpA+BTWokU2CeotHGhcyIiIiIiIiIiKnVMShERERERERERUaljUoqIiIiIiIiIiEodk1JERERERERERFTqmJQiIiIiIiIiIqJSx6QUERERERERERGVOialiIiIiIiIiIio1DEpRUREREREREREpY5JKSIiIiIiIiIiKnVMShERERERERERUaljUoqIiIiIiIiIiEodk1JERERERERERFTqmJQiIiIiIiIiIqJS90UlpXbu3ImOHTuiYcOGaNiwIXr16oWzZ8+K2zMzMzFnzhw4OTnB3t4eo0aNQnx8vFIbMTExGDZsGGxtbeHi4oJffvkFWVlZSnWCg4PRtWtXWFlZoWXLlti/f3+eWHbs2AEPDw9YW1ujR48eCA8PV9pelFiIiIiIiIiIiEi1LyopValSJUyYMAH79++Hv78/nJ2dMXLkSERERAAAFixYgKCgICxfvhzbtm3Dq1ev4OPjI+6fnZ0Nb29vyOVy7N69G4sWLcKBAwfg6+sr1nn27Bm8vb3h5OSEQ4cOYeDAgZgxYwbOnz8v1jl27BgWLlyIkSNH4sCBA7C0tMTgwYORkJAg1iksFiIiIiIiIiIiyp9EEAThcwdRkMaNG2PixIlo06YNXFxcsGTJErRp0wYA8OjRI7Rr1w579uyBnZ0dzp49i+HDh+P8+fMwMTEBAOzatQtLlizB5cuXoaWlhV9//RVnz55FQECAeIxx48YhOTkZGzZsAAD06NED1tbWmDlzJgAgJycHbm5u8PLywrBhw5CSklJoLMURH5+CL/tdoP8KLS11yGTZnzsMoi8G+wTRvyQSIClJCzduZEMuz7+epiZgayuBiUkW/z6h/3n8PUGkjH2CSopEApiY6BdaT6MUYvkg2dnZOH78ONLS0mBvb4/bt29DLpejSZMmYp06derAzMwMYWFhsLOzQ1hYGKRSqZiQAgBXV1fMnj0bDx8+RP369REWFgYXFxelY7m6umLBggUAAJlMhjt37sDb21vcrqamhiZNmiA0NBQAihRLcWhqqherPlF+NDR4LxG9i32CSJmamho0NFBgsklDA1BX598n9HXg7wkiZewTVNq+uKTU/fv30bt3b2RmZkJHRwerVq2Cubk57t27B01NTRgYGCjVNzY2RlxcHAAgPj5eKSEFQHxdWJ3U1FRkZGTgzZs3yM7OhrGxcZ7jREZGim0UFktxyOXZ/JdIKjH8lw0iZewTRLkkEiAnRx1ZWdl4b7nNPPWysyX8+4S+Gvw9QaSMfYJKgkRStHpfXFKqVq1aOHjwIFJSUnDixAlMnjwZ27dv/9xhERERERERERFRCfriklJaWlqoUaMGAMDKygq3bt3C1q1b0bZtW8jlciQnJyuNUEpISECFChUA5I54ev8peYon4r1b5/2n5MXHx0NPTw/a2tpQU1ODurq60qLmiuMoRliZmJgUGgsREREREREREeXvi3r6nio5OTmQyWSwsrKCpqYmLl++LG6LjIxETEyMuIaTnZ0dHjx4oJRQunTpEvT09GBubi7WuXLlitIxLl26JLahpaWFBg0aKB0nJycHly9fhr29PQAUKRYiIiIiIiIiIsrfFzVSaunSpfj2229RuXJlvH37FgEBAQgJCcGGDRugr68PT09PLFq0CIaGhtDT08O8efNgb28vJoJcXV1hbm6OSZMmYeLEiYiLi8Py5cvRr18/aGlpAQB69+6NHTt2YPHixfD09MSVK1cQGBiINWvWiHEMGjQIkydPhpWVFWxsbLBlyxakp6ejW7duAFCkWIiIiIiIiIiIKH9fVFIqISEBkydPxqtXr6Cvrw8LCwts2LAB33zzDQBg2rRpUFNTw+jRoyGTyeDq6opZs2aJ+6urq2P16tWYPXs2evXqhbJly6Jr164YPXq0WKdatWpYs2YNFi5ciK1bt6JSpUqYN28emjZtKtZp164dEhMT4evri7i4ONSrVw/r169XWiC9sFiIiIiIiIiIiCh/EkHgc1U+t/j4FD7dhkqElpY6n5ZB9A72CaJ/SSRAUpIWbtzIhlyefz1NTcDWVgITkyz+fUL/8/h7gkgZ+wSVFIkEMDHRL7TeF7+mFBERERERERER/e9hUoqIiIiIiIiIiEodk1JERERERERERFTqmJQiIiIiIiIiIqJSx6QUERERERERERGVOialiIiIiIiIiIio1DEpRUREREREREREpY5JKSIiIiIiIiIiKnVMShERERERERERUaljUoqIiIiIiIiIiEodk1JERERERERERFTqmJQiIiIiIiIiIqJSx6QUERERERERERGVOialiIiIiIiIiIio1DEpRUREREREREREpY5JKSIiIiIiIiIiKnVMShERERERERERUaljUoqIiIiIiIiIiEodk1JERERERERERFTqmJQiIiIiIiIiIqJSx6QUERERERERERGVOialiIiIiIiIiIio1DEpRUREREREREREpY5JKSIiIiIiIiIiKnVMShERERERERERUaljUoqIiIiIiIiIiEodk1JERERERERERFTqmJQiIiIiIiIiIqJSx6QUERERERERERGVOialiIiIiIiIiIio1DEpRUREREREREREpY5JKSIiIiIiIiIiKnVMShERERERERERUaljUoqIiIiIiIiIiEodk1JERERERERERFTqmJQiIiIiIiIiIqJSx6QUERERERERERGVOialiIiIiIiIiIio1DEpRUREREREREREpY5JKSIiIiIiIiIiKnVMShERERERERERUan7opJSa9asgaenJ+zt7eHi4oIRI0YgMjJSqY6XlxcsLCyUfmbOnKlUJyYmBsOGDYOtrS1cXFzwyy+/ICsrS6lOcHAwunbtCisrK7Rs2RL79+/PE8+OHTvg4eEBa2tr9OjRA+Hh4UrbMzMzMWfOHDg5OcHe3h6jRo1CfHx8CV0NIiIiIiIiIqL/XV9UUiokJAT9+vXD3r17sWnTJmRlZWHw4MFIS0tTqtezZ09cuHBB/Jk0aZK4LTs7G97e3pDL5di9ezcWLVqEAwcOwNfXV6zz7NkzeHt7w8nJCYcOHcLAgQMxY8YMnD9/Xqxz7NgxLFy4ECNHjsSBAwdgaWmJwYMHIyEhQayzYMECBAUFYfny5di2bRtevXoFHx+fT3iFiIiIiIiIiIj+N3xRSakNGzagW7duqFu3LiwtLbFo0SLExMTgzp07SvW0tbVRoUIF8UdPT0/cduHCBTx8+BC//vor6tWrBzc3N4wZMwY7duyATCYDAOzevRtVq1bFlClTUKdOHfTv3x+tW7fG5s2bxXY2bdqEnj17wtPTE+bm5pgzZw60tbXh7+8PAEhJSYG/vz+mTJkCFxcXWFlZYcGCBQgNDUVYWNgnv1ZERERERERERP9lX1RS6n0pKSkAAENDQ6XyI0eOwMnJCR06dMDSpUuRnp4ubgsLC4NUKoWJiYlY5urqitTUVDx8+FCs4+LiotSmq6urmEySyWS4c+cOmjRpIm5XU1NDkyZNEBoaCgC4ffs25HK5Up06derAzMyMSSkiIiIiIiIiokJofO4A8pOTk4MFCxagYcOGkEqlYnmHDh1gZmYGU1NT3L9/H0uWLMHjx4/h5+cHAIiPj1dKSAEQX8fFxRVYJzU1FRkZGXjz5g2ys7NhbGysVMfY2Fhc4yo+Ph6ampowMDDIU0dxnKLS1FQvVn2i/Gho8F4iehf7BJEyNTU1aGgAgpB/HQ0NQF2df5/Q14G/J4iUsU9Qaftik1Jz5sxBREQEdu7cqVTeq1cv8f8tLCxQoUIFfPfdd4iKikL16tVLO8wSIZdnF/jHIVFxyGTZnzsEoi8K+wRRLokEyMlRR1ZWNt57/kueetnZEv59Ql8N/p4gUsY+QSVBIilavS9y+t7cuXNx5swZbNmyBZUqVSqwrq2tLQDg6dOnAHJHPL3/BDzF6woVKhRYR09PD9ra2ihfvjzU1dWVFjUHgISEBHGElYmJCeRyOZKTk/PUURyHiIiIiIiIiIhU+6KSUoIgYO7cuTh58iS2bNmCatWqFbrPvXv3APybcLKzs8ODBw+UEkqXLl2Cnp4ezM3NxTpXrlxRaufSpUuws7MDAGhpaaFBgwa4fPmyuD0nJweXL1+Gvb09AMDKygqamppKdSIjIxETEyO2Q0REREREREREqn1R0/fmzJmDgIAA/P7779DV1RXXZtLX14e2tjaioqJw5MgRuLm5oVy5crh//z4WLlwIR0dHWFpaAshdsNzc3ByTJk3CxIkTERcXh+XLl6Nfv37Q0tICAPTu3Rs7duzA4sWL4enpiStXriAwMBBr1qwRYxk0aBAmT54MKysr2NjYYMuWLUhPT0e3bt3EmDw9PbFo0SIYGhpCT08P8+bNg729PZNSRERERERERESFkAjCl7NagIWFhcryhQsXolu3bnjx4gUmTpyIiIgIpKWloXLlymjRogVGjBgBPT09sX50dDRmz56NkJAQlC1bFl27dsWPP/4IDY1/c3DBwcFYuHAhHj58iEqVKmHEiBFiwklh+/bt2LBhA+Li4lCvXj3MmDFDnC4IAJmZmVi0aBGOHj0KmUwGV1dXzJo1q9jT9+LjU7hmA5UILS11zgEnegf7BNG/JBIgKUkLN25kQy7Pv56mJmBrK4GJSRb/PqH/efw9QaSMfYJKikQCmJjoF17vS0pKfa2YlKKSwl8iRMrYJ4j+xaQUUV78PUGkjH2CSkpRk1Jf1JpSRERERERERET0dWBSioiIiIiIiIiISh2TUkREREREREREVOqYlCIiIiIiIiIiolLHpBQREREREREREZU6JqWIiIiIiIiIiKjUMSlFRERERERERESljkkpIiIiIiIiIiIqdUxKERERERERERFRqWNSioiIiIiIiIiISh2TUkREREREREREVOqYlCIiIiIiIiIiolLHpBQREREREREREZU6JqWIiIiIiIiIiKjUMSlFRERERERERESljkkpIiIiIiIiIiIqdUxKERERERERERFRqWNSioiIiIiIiIiISh2TUkREREREREREVOqYlCIiIiIiIiIiolLHpBQREREREREREZU6JqWIiIiIiIiIiKjUMSlFRERERERERESljkkpIiIiIiIiIiIqdUxKERERERERERFRqWNSioiIiIiIiIiISh2TUkREREREREREVOqYlCIiIiIiIiIiolLHpBQREREREREREZU6JqWIiIiIiIiIiKjUMSlFRERERERERESljkkpIiIiIiIiIiIqdUxKERERERERERFRqWNSioiIiIiIiIiISh2TUkREREREREREVOqYlCIiIiIiIiIiolLHpBQREREREREREZU6JqWIiIiIiIiIiKjUMSlFRERERERERESljkkpIiIiIiIiIiIqdUxKERERERERERFRqfvgpNSAAQNw+fLlfLdfuXIFAwYM+NDmiYiIiIiIiIjof9gHJ6VCQkIQHx+f7/bExERcvXq1WG2uWbMGnp6esLe3h4uLC0aMGIHIyEilOpmZmZgzZw6cnJxgb2+PUaNG5YkjJiYGw4YNg62tLVxcXPDLL78gKytLqU5wcDC6du0KKysrtGzZEvv3788Tz44dO+Dh4QFra2v06NED4eHhxY6FiIiIiIiIiIjy+qjpexKJJN9tT58+ha6ubrHaCwkJQb9+/bB3715s2rQJWVlZGDx4MNLS0sQ6CxYsQFBQEJYvX45t27bh1atX8PHxEbdnZ2fD29sbcrkcu3fvxqJFi3DgwAH4+vqKdZ49ewZvb284OTnh0KFDGDhwIGbMmIHz58+LdY4dO4aFCxdi5MiROHDgACwtLTF48GAkJCQUORYiIiIiIiIiIlJNIgiCUNTKBw4cwIEDBwDkJpDq1KkDY2PjPPVSUlJw//59fPvtt1i9evUHB5eYmAgXFxds374djo6OSElJgYuLC5YsWYI2bdoAAB49eoR27dphz549sLOzw9mzZzF8+HCcP38eJiYmAIBdu3ZhyZIluHz5MrS0tPDrr7/i7NmzCAgIEI81btw4JCcnY8OGDQCAHj16wNraGjNnzgQA5OTkwM3NDV5eXhg2bFiRYimq+PgUFP1dIMqflpY6ZLLszx0G0ReDfYLoXxIJkJSkhRs3siGX519PUxOwtZXAxCSLf5/Q/zz+niBSxj5BJUUiAUxM9Autp1GcRtPT05GUlCS+fvv2LdTU8g620tHRQe/evTFy5MjiNJ9HSkoKAMDQ0BAAcPv2bcjlcjRp0kSsU6dOHZiZmSEsLAx2dnYICwuDVCoVE1IA4OrqitmzZ+Phw4eoX78+wsLC4OLionQsV1dXLFiwAAAgk8lw584deHt7i9vV1NTQpEkThIaGFjmWotLUVC9yXaKCaGjwXiJ6F/sEkTI1NTVoaKDAZJOGBqCuzr9P6OvA3xNEytgnqLQVKynVt29f9O3bFwDg4eGB6dOno3nz5p8ksJycHCxYsAANGzaEVCoFAMTHx0NTUxMGBgZKdY2NjREXFyfWeTchBUB8XVid1NRUZGRk4M2bN8jOzs4zCszY2Fhc46oosRSVXJ7Nf4mkEsN/2SBSxj5BlEsiAXJy1JGVlY33ltrMUy87W8K/T+irwd8TRMrYJ6gkFLDak5JiJaXedfr06Q/dtUjmzJmDiIgI7Ny585Meh4iIiIiIiIiISt8HJ6UUUlNTERMTg+TkZKhansrR0bHYbc6dOxdnzpzB9u3bUalSJbHcxMQEcrkcycnJSiOUEhISUKFCBbHO+0/JUzwR79067z8lLz4+Hnp6etDW1oaamhrU1dWVFjVXHEcxwqoosRARERERERERkWofnJRKTEzEvHnz8NdffyE7O+/wPkEQIJFIcO/evSK3KQgCfv75Z5w8eRLbtm1DtWrVlLZbWVlBU1MTly9fRuvWrQEAkZGRiImJEddwsrOzw+rVq5GQkCBOv7t06RL09PRgbm4u1jl37pxS25cuXRLb0NLSQoMGDXD58mW0aNECQO50wsuXL6N///5FjoWIiIiIiIiIiFT74KTUzJkzERQUBC8vLzg4OORZW+lDzJkzBwEBAfj999+hq6srrs2kr68PbW1t6Ovrw9PTE4sWLYKhoSH09PQwb9482Nvbi4kgV1dXmJubY9KkSZg4cSLi4uKwfPly9OvXD1paWgCA3r17Y8eOHVi8eDE8PT1x5coVBAYGYs2aNWIsgwYNwuTJk/F/7N15XE35/wfw173ttKDCKAZxb0gLEikmsgvZl7JlF2YwdsNgMPM1xpIZ+z7WUZaxjLGLSkIGITs1aJEWaT2/P/zuna57q1vqFr2ej0ePmT7nc855n3PP6d779vm8j42NDWxtbbF161akpqaiR48e8pjyi4WIiIiIiIiIiFQTCarm3KnBwcEB/fv3x9SpU4ssGKlUqrJ98eLF8mRQWloalixZgiNHjiA9PR0uLi6YO3euwpS5qKgozJs3D5cvX4aBgQE8PT0xefJkaGv/l4MLCQnB4sWLcf/+fVStWhVjx46V70Nmx44d2LhxI2JiYlCvXj3Mnj0bdnZ28uXqxKKO2NgkFhKlIsFHuBIp4j1B9B+RCHj9WhdXr2YhIyP3fjo6gJ2dCGZmmfx8Qp89vk8QKeI9QUVFJALMzIzy71fYpFTz5s3h6+uLgQMHFmZ1yoFJKSoqfBMhUsR7gug/TEoRKeP7BJEi3hNUVNRNSokLu4OuXbvi5MmThV2diIiIiIiIiIjKsELXlGrfvj1CQ0Ph4+ODvn37omrVqtDS0lLq16BBg48KkIiIiIiIiIiIPj+FTkoNGDBA/v+XLl1SWl6Yp+8REREREREREVHZUOik1OLFi4syDiIiIiIiIiIiKkMKnZTy9PQsyjiIiIiIiIiIiKgMKXShcyIiIiIiIiIiosIq9EipGTNm5NtHJBJh0aJFhd0FERERERERERF9pgqdlAoJCVFqy87ORkxMDLKyslCpUiUYGBh8VHBERERERERERPR5KnRS6vTp0yrbMzIysGfPHmzduhWbNm0qdGBERERERERERPT5KvKaUjo6OvDy8kKLFi2wYMGCot48ERERERERERF9Boqt0Lm1tTVCQ0OLa/NERERERERERPQJK7ak1KVLl1hTioiIiIiIiIiIVCp0TSk/Pz+V7UlJSQgNDcXt27cxcuTIQgdGRERERERERESfryJPSpmYmKB69er4/vvv0adPn0IHRkREREREREREn69CJ6Xu3LlTlHEQEREREREREVEZUmw1pYiIiIiIiIiIiHJT6JFSMpcvX8bZs2cRHR0NAKhWrRq++uorNG3a9KODIyIiIiIiIiKiz1Ohk1Lp6emYPHkyTp48CUEQYGxsDABITEzE5s2b0bZtW/z888/Q0dEpsmCJiIiIiIiIiOjzUOjpe6tXr8bff/+NoUOHIjAwEJcvX8bly5dx8eJFDBs2DCdOnMDq1auLMlYiIiIiIiIiIvpMFDopdfjwYXh6emLq1KkwMzOTt5uamuLbb79F9+7dcejQoSIJkoiIiIiIiIiIPi+FTkrFxMTA1tY21+W2traIiYkp7OaJiIiIiIiIiOgzVuikVNWqVXH58uVcl4eGhqJq1aqF3TwREREREREREX3GCp2U6t69O44dO4bvvvsODx8+RFZWFrKzs/Hw4UPMnTsXx48fh6enZ1HGSkREREREREREn4lCP31v9OjRePbsGfbu3Yt9+/ZBLH6f38rOzoYgCPD09MTo0aOLLFAiIiIiIiIiIvp8FDoppaWlhSVLlmDIkCE4f/48oqKiAAAWFhZo2bIlrK2tiyxIIiIiIiIiIiL6vBQoKZWWloYffvgBdevWhbe3NwDA2tpaKQG1bds27N69G7NmzYKOjk7RRUtERERERERERJ+FAtWU2rNnDwICAvDVV1/l2e+rr77C/v37sW/fvo+JjYiIiIiIiIiIPlMFSkodO3YM7dq1Q/Xq1fPsV6NGDXTo0AFHjhz5qOCIiIiIiIiIiOjzVKCk1L1799C4cWO1+jo4OODu3buFCoqIiIiIiIiIiD5vBUpKZWRkqF0jSkdHB+np6YUKioiIiIiIiIiIPm8FSkpVrlwZkZGRavWNjIxE5cqVCxUUERERERERERF93gqUlHJ2dsbBgwcRFxeXZ7+4uDgcPHgQzs7OHxUcERERERERERF9ngqUlBoxYgTS0tIwePBghIeHq+wTHh6OIUOGIC0tDcOHDy+SIImIiIiIiIiI6POiXZDO1atXx/LlyzFp0iT069cP1atXh0QiQfny5ZGSkoLIyEg8ffoU+vr6WLZsGWrUqFFccRMRERERERER0SdMJAiCUNCVnj9/jvXr1+Ps2bN4+fKlvL1y5cr46quvMGLECFSvXr1IA/2cxcYmoeCvApEyXV0tpKdnlXQYRKUG7wmi/4hEwOvXurh6NQsZGbn309EB7OxEMDPL5OcT+uzxfYJIEe8JKioiEWBmZpRvvwKNlJKxtLTE999/DwBITk5GSkoKypcvD0NDw8JsjoiIiIiIiIiIyphCJaVyMjQ0ZDKKiIiIiIiIiIgKpECFzomIiIiIiIiIiIoCk1JERERERERERKRxTEoREREREREREZHGMSlFREREREREREQaV6qSUqGhoRg9ejRcXFwglUpx8uRJheXTp0+HVCpV+PHx8VHok5CQgMmTJ6NRo0Zo0qQJZs6ciZSUFIU+d+7cwYABA9CwYUO0atUK69evV4rl2LFj6NChAxo2bAgPDw+cO3dOYbkgCFixYgVcXFxga2uLIUOG4PHjx0VzIoiIiIiIiIiIPnOlKin19u1bSKVSzJ07N9c+rq6uCAwMlP8sW7ZMYfmUKVNw//59bN68GWvWrMGVK1fw3XffyZcnJyfDx8cH1apVg7+/P6ZOnQo/Pz/s2bNH3ufq1auYPHkyevXqhQMHDqBNmzYYN24c7t27J++zfv16bN++HfPmzcPevXthYGAAHx8fpKWlFeEZISIiIiIiIiL6PJWqpFSrVq3wzTffoG3btrn20dXVhbm5ufzHxMREvuzBgwe4cOECFi5cCDs7OzRp0gSzZ8/GkSNH8PLlSwDAoUOHkJGRgUWLFqFu3bro3LkzvL29sXnzZvl2tm3bBldXVwwfPhxWVlb4+uuvUb9+fezYsQPA+1FS27Ztw5gxY+Du7g5ra2v89NNPePXqldLoLiIiIiIiIiIiUlaqklLquHz5Mpo3b4727dtj7ty5eP36tXzZtWvXYGxsjIYNG8rbnJ2dIRaLcePGDQDA9evX0aRJE+jq6sr7uLi44NGjR3jz5o28T/PmzRX26+LiguvXrwMAnj9/jpiYGDg7O8uXGxkZwc7ODteuXSvyYyYiIiIiIiIi+txol3QABeHq6oq2bdvC0tISz549w7JlyzBixAjs2bMHWlpaiI2NRaVKlRTW0dbWhomJCWJiYgAAsbGxsLS0VOhjZmYmX2ZiYoLY2Fh5m4ypqSliY2MBQL4tU1PTXPsUhI6OVoHXIVJFW5vXElFOvCeIFInFYmhrA4KQex9tbUBLi59PqGzg+wSRIt4TpGmfVFKqc+fO8v+XFTp3d3eXj576VGVkZOX54ZCoINLTs0o6BKJShfcE0XsiEZCdrYXMzCxkZubdLytLxM8nVGbwfYJIEe8JKgoikXr9PrnpezlVr14dFStWxJMnTwC8H/EUHx+v0CczMxNv3ryBubm5vM+Ho5lkv8tGR6nqExcXJ18u21ZcXFyufYiIiIiIiIiIKHefdFLqxYsXSEhIkCeJHBwckJiYiJs3b8r7BAcHIzs7G7a2tgAAe3t7XLlyBRkZGfI+ly5dQq1ateRF0+3t7REcHKywr0uXLsHe3h4AYGlpCXNzcwQFBcmXJycnIzw8HA4ODsVyrEREREREREREn5NSlZRKSUlBREQEIiIiALwvKB4REYHo6GikpKTgxx9/xPXr1/H8+XMEBQVh7Nix+PLLL+Hq6goAsLKygqurK+bMmYMbN24gLCwMCxYsQOfOnVGlShUAgIeHB3R0dDBr1ixERkbi6NGj2LZtG4YOHSqPY9CgQbhw4QI2bdqEBw8eYNWqVbh58ya8vLwAACKRCIMGDcJvv/2GU6dO4e7du5g6dSoqV64Md3d3DZ81IiIiIiIiIqJPj0gQSk+1gJCQEAwaNEip3dPTE/PmzcO4ceNw+/ZtJCUloXLlymjRogUmTpyoMGUuISEBCxYswOnTpyEWi9GuXTvMnj0b5cuXl/e5c+cO5s+fj3/++QcVK1aEl5cXRo4cqbDPY8eOYfny5YiKikLNmjXx7bffolWrVvLlgiBg5cqV2Lt3LxITE9G4cWPMnTsXtWrVKvBxx8YmsWYDFQldXS3OASfKgfcE0X9EIuD1a11cvZqFHAPGlejoAHZ2IpiZZfLzCX32+D5BpIj3BBUVkQgwMzPKv19pSkqVVUxKUVHhmwiRIt4TRP9hUopIGd8niBTxnqCiom5SqlRN3yMiIiIiIiIiorKBSSkiIiIiIiIiItI4JqWIiIiIiIiIiEjjmJQiIiIiIiIiIiKNY1KKiIiIiIiIiIg0jkkpIiIiIiIiIiLSOCaliIiIiIiIiIhI45iUIiIiIiIiIiIijWNSioiIiIiIiIiINI5JKSIiIiIiIiIi0jgmpYiIiIiIiIiISOOYlCIiIiIiIiIiIo1jUoqIiIiIiIiIiDSOSSkiIiIiIiIiItI4JqWIiIiIiIiIiEjjmJQiIiIiIiIiIiKNY1KKiIiIiIiIiIg0jkkpIiIiIiIiIiLSOCaliIiIiIiIiIhI45iUIiIiIiIiIiIijWNSioiIiIiIiIiINI5JKSIiIiIiIiIi0jgmpYiIiIiIiIiISOOYlCIiIiIiIiIiIo1jUoqIiIiIiIiIiDSOSSkiIiIiIiIiItI4JqWIiIiIiIiIiEjjmJQiIiIiIiIiIiKNY1KKiIiIiIiIiIg0jkkpIiIiIiIiIiLSOCaliIiIiIiIiIhI45iUIiIiIiIiIiIijWNSioiIiIiIiIiINI5JKSIiIiIiIiIi0jgmpYiIiIiIiIiISOOYlCIiIiIiIiIiIo1jUoqIiIiIiIiIiDSOSSkiIiIiIiIiItI4JqWIiIiIiIiIiEjjmJQiIiIiIiIiIiKNY1KKiIiIiIiIiIg0jkkpIiIiIiIiIiLSuFKVlAoNDcXo0aPh4uICqVSKkydPKiwXBAErVqyAi4sLbG1tMWTIEDx+/FihT0JCAiZPnoxGjRqhSZMmmDlzJlJSUhT63LlzBwMGDEDDhg3RqlUrrF+/XimWY8eOoUOHDmjYsCE8PDxw7ty5AsdCRERERERERESqlaqk1Nu3byGVSjF37lyVy9evX4/t27dj3rx52Lt3LwwMDODj44O0tDR5nylTpuD+/fvYvHkz1qxZgytXruC7776TL09OToaPjw+qVasGf39/TJ06FX5+ftizZ4+8z9WrVzF58mT06tULBw4cQJs2bTBu3Djcu3evQLEQEREREREREZFqpSop1apVK3zzzTdo27at0jJBELBt2zaMGTMG7u7usLa2xk8//YRXr17JR1Q9ePAAFy5cwMKFC2FnZ4cmTZpg9uzZOHLkCF6+fAkAOHToEDIyMrBo0SLUrVsXnTt3hre3NzZv3izf17Zt2+Dq6orhw4fDysoKX3/9NerXr48dO3aoHQsREREREREREeVOu6QDUNfz588RExMDZ2dneZuRkRHs7Oxw7do1dO7cGdeuXYOxsTEaNmwo7+Ps7AyxWIwbN26gbdu2uH79Opo0aQJdXV15HxcXF6xfvx5v3ryBiYkJrl+/jiFDhijs38XFRZ5wUieWgtDR0SpQf6LcaGvzWiLKifcEkSKxWAxtbUAQcu+jrQ1oafHzCZUNfJ8gUsR7gjTtk0lKxcTEAABMTU0V2k1NTREbGwsAiI2NRaVKlRSWa2trw8TERL5+bGwsLC0tFfqYmZnJl5mYmCA2Nlbepmo/6sRSEBkZWXl+OCQqiPT0rJIOgahU4T1B9J5IBGRnayEzMwuZmXn3y8oS8fMJlRl8nyBSxHuCioJIpF6/UjV9j4iIiIiIiIiIyoZPJillbm4OAIiLi1Noj4uLk49qMjMzQ3x8vMLyzMxMvHnzRr6+mZmZ0mgm2e85t/Nhn5z7UScWIiIiIiIiIiLK3SeTlLK0tIS5uTmCgoLkbcnJyQgPD4eDgwMAwMHBAYmJibh586a8T3BwMLKzs2FrawsAsLe3x5UrV5CRkSHvc+nSJdSqVQsmJibyPsHBwQr7v3TpEuzt7dWOhYiIiIiIiIiIcleqklIpKSmIiIhAREQEgPcFxSMiIhAdHQ2RSIRBgwbht99+w6lTp3D37l1MnToVlStXhru7OwDAysoKrq6umDNnDm7cuIGwsDAsWLAAnTt3RpUqVQAAHh4e0NHRwaxZsxAZGYmjR49i27ZtGDp0qDyOQYMG4cKFC9i0aRMePHiAVatW4ebNm/Dy8gIAtWIhIiIiIiIiIqLciQSh9JSwDAkJwaBBg5TaPT09sWTJEgiCgJUrV2Lv3r1ITExE48aNMXfuXNSqVUveNyEhAQsWLMDp06chFovRrl07zJ49G+XLl5f3uXPnDubPn49//vkHFStWhJeXF0aOHKmwz2PHjmH58uWIiopCzZo18e2336JVq1by5erEoq7Y2CQWEqUioaurxcKERDnwniD6j0gEvH6ti6tXs5BjwLgSHR3Azk4EM7NMfj6hzx7fJ4gU8Z6goiISAWZmRvn3K01JqbKKSSkqKnwTIVLEe4LoP0xKESnj+wSRIt4TVFTUTUqVqul7RERERERERERUNjApRUREREREREREGsekFBERERERERERaRyTUkREREREREREpHFMShERERERERERkcYxKUVERERERERERBrHpBQREREREREREWkck1JERERERERERKRxTEoREREREREREZHGMSlFREREREREREQax6QUERERERERERFpHJNSRERERERERESkcUxKERERERERERGRxjEpRUREREREREREGsekFBERERERERERaRyTUkREREREREREpHFMShERERERERERkcYxKUVERERERERERBrHpBQREREREREREWkck1JERERERERERKRxTEoREREREREREZHGMSlFREREREREREQax6QUERERERERERFpHJNSRERERERERESkcUxKERERERERERGRxjEpRUREREREREREGsekFBERERERERERaRyTUkREREREREREpHFMShERERERERERkcYxKUVERERERERERBrHpBQREREREREREWkck1JERERERERERKRxTEoREREREREREZHGMSlFREREREREREQax6QUERERERERERFpHJNSRERERERERESkcdolHQARERERfTyR6OOWExEREWkak1JEREREpZg6yaR377SQnJx3R7EYyMwUA8hSa5/q7FcQ8u9DRERElBsmpYiIiIhKqbQ09ZJN6eli3L2bjczM3LNE+voiVK+ef7JJSwvIzhYjJib/j4mGhgL09fNPcjF5RURERKowKUVERERUAtSZbpecLMKtW8g32WRpKUJWloCMjNy3p6OjXmZILAbevhXhyRMgIyP3dXR1RbC2zj9pBrxPXunp5Z+8IiIiorKFSSkiIiIiDVN3BFRmphiZmVlFkmwqqMzM/JNc6iSvtLVFaNBABH19jpgiIiIiRUxKERERERWxvEZBFXQEVGkvUJ5f8goQIBLlfxxMWBEREZU94pIOoCBWrVoFqVSq8NOhQwf58rS0NHz//fdwcnKCg4MDxo8fj9jYWIVtREdHY+TIkbCzs0Pz5s3x448/IjMzU6FPSEgIPD09YWNjg7Zt28Lf318plt9//x2tW7dGw4YN0bt3b9y4caN4DpqIiIg+KWlpWoiN1c71Jy5O+/9HQL1P5uT2k5X1eWRpctaoyuu8pKVpyQus5/VDREREn49PbqRU3bp1sXnzZvnvWlpa8v9ftGgRzp07h+XLl8PIyAgLFiyAr68vdu/eDQDIysrCqFGjYGZmht27d+PVq1eYNm0adHR0MGnSJADAs2fPMGrUKPTr1w9Lly5FUFAQZs+eDXNzc7i6ugIAjh49isWLF+P777+HnZ0dtm7dCh8fHxw/fhympqYaPBtERESkSUVRB+pTGQFVVNSpUcX6VERERGXTJ5eU0tLSgrm5uVJ7UlIS9u/fj6VLl6J58+YA3iepOnXqhOvXr8Pe3h6BgYG4f/8+Nm/eDDMzM9SrVw8TJ07E0qVL4evrC11dXezevRuWlpaYPn06AMDKygphYWHYsmWLPCm1efNm9OnTBz179gQAfP/99zh79iz279+PkSNHauhMEBERkSYVVR2o4qoBVdrlNc2P9amIiIjKpk8uKfXkyRO4uLhAT08P9vb2mDx5MqpVq4abN28iIyMDzs7O8r5WVlaoVq2aPCl1/fp1SCQSmJmZyfu4uLhg3rx5uH//PurXr4/r16/Lk1o5+yxatAgAkJ6ejlu3bmHUqFHy5WKxGM7Ozrh27VqhjklHRyv/TkRq0NbmtUSUE+8JKkoJCVq4exf4YNa/An194IsvxNDWzj1poqUFiEQiaGlpQTuPT2JlqZ+sjyCI8002aWvzsxMVHb5PECniPUGa9kklpWxtbbF48WLUqlULMTExWL16NQYOHIjDhw8jNjYWOjo6MDY2VljH1NQUMTExAIDY2FiFhBQA+e/59UlOTsa7d+/w5s0bZGVlKU3TMzU1xcOHDwt1XBkZWfzXPioy6emc0kCUE+8JUoc60/KyskR49y7vot4iESAIImRlZeWavMrKyr9P8fUTl9B+i+acaGsDGRla+PdfTvOjosP3CSJFvCeoKKhbpuCTSkq1atVK/v/W1taws7ODm5sbjh07Bn19/RKMjIiIiD5VBZmWB/CDeklSpz4VwGl+REREn4pPKin1IWNjY9SsWRNPnz6Fs7MzMjIykJiYqDBaKi4uTl6DyszMTOkpebKn8+Xs8+ET+2JjY2FoaAh9fX2IxWJoaWkhLi5OoU9cXJzSCCsiIiIq3dQpTA6UveLkpV1e9aneEwDwxSIiIirtxCUdwMdISUnBs2fPYG5uDhsbG+jo6CAoKEi+/OHDh4iOjoa9vT0AwN7eHvfu3VNIKF26dAmGhoaoU6eOvE9wcLDCfi5duiTfhq6uLho0aKCwn+zsbAQFBcHBwaGYjpSIiIgKQyTK/wf4L8mR209WFofbfGrUee2ZZCQiIipZn9RIqR9//BFubm6oVq0aXr16hVWrVkEsFqNLly4wMjJCz549sWTJEpiYmMDQ0BALFy6Eg4ODPKHk4uKCOnXqYOrUqfj2228RExOD5cuXY+DAgdDV1QUA9OvXD7///jt++ukn9OzZE8HBwTh27BjWrl0rj2Po0KGYNm0abGxsYGtri61btyI1NRU9evQoidNCREREKnBaXtmlpQVkZ4sRE5P/R13WniIiIio5n1RS6sWLF5g0aRISEhJQqVIlNG7cGHv37kWlSpUAADNnzoRYLMaECROQnp4OFxcXzJ07V76+lpYW1qxZg3nz5qFv374wMDCAp6cnJkyYIO9TvXp1rF27FosXL8a2bdtQtWpVLFy4EK6urvI+nTp1Qnx8PFauXImYmBjUq1cPGzZs4PQ9IiKiUoLT8so21p4iIiL6NHxSSalffvklz+V6enqYO3euQiLqQxYWFli/fn2e23FycsKBAwfy7OPl5QUvL688+xAREVHJyq/2kI4OMxGfM3VqT4lE6iUlmbQiIiIqep9UUoqIiIgIyL8WEEc+kTo4zY+IiKhkMSlFREREnxTWiqKiwml+REREJYtJKSIiIvpksFYUFQdO8yMiIioZTEoRERFRqaHutDzWiiJN4jQ/IiKi4sGkFBEREZUKnJZHpRWn+RERERUPJqWIiIioxHFaHn0K1JnmB/DiJCIiUheTUkRERFTsOC2PygqRSL2nP3IkFREREZNSREREVMw4LY/KCtaeIiIiKhgmpYiIiKjYcFoelSWsPUVERFQwTEoRERFRseO0PCpL1Kk9JRLln4RlwoqIiD53TEoRERHRR8nrizVHPhEpU3eaH6f4ERHR545JKSIiIiq0/OpFsVYUkTJ1pvlxih8REZUFTEoRERFRoahTL4q1oohyl/c0P/Wm+AFMWhER0aeLSSkiIiJSKb8vw7LleX2xZq0oosLhk/yIiKgsYFKKiIiIlOQ3LQ/g1Dyi4lSQJ/nZ2IhgYJD/iCmOqCIiotKGSSkiIiJSoM60PIBT84g0Ib8n+WlpCcjO1uKIKiIi+iQxKUVERFTGFMW0PIBT84hKg4KMqGLhdCIiKm2YlCIiIipDOC2P6POUXxKZhdOJiKg0YlKKiIjoM6HOCChOyyMqm1g4nYiISiMmpYiIiD4DBRkBlZmZxWl5RGUMp/kREVFpxKQUERHRJ44joIhIXZzmR0REpQmTUkRERKUcC5MTkaZwmh8REWkSk1JERESlGAuTE5EmFWSan42NCAYG+Y+Y4ogqIiLKDZNSREREJYSFyYmotMpv5KWWloDsbC2OqCIioo/CpBQREVEJYGFyIvqUFfWIKo6mIiIqm5iUIiIiKmIcAUVEZUVRjajiaCoiorKJSSkiIqIixBFQRET/UWdEFetTERGVXUxKERERFUBeo5Y4AoqISLW8RlSxPhURUdnFpBQREZGa8hsFxRFQREQFxyf+ERGVXUxKERERoWjqQHEEFJV2IhGgo5P3F3Zt7fzvB6LiUNRP/NPX54gqIqLSjkkpIiL6rKnz5frdu6KpA8URUFSaicWAiTgJNcQJyNTJvZ+uCDAWV4CWlr7mgiNSg7ojqnR1RbC2zv/vOgBUqCCClhaTV0REJYVJKSIi+mylpoqRkJB/sik9XYy7d7NZB4o+a2IxoJWShMybt5GWkplrPy0jbWib2kAsZlKKSqf8RlTp6AhqTwe0sxOhYkVOByQiKilMShER0SdJnel2CQki3LolqJVsysrK/0sO0ecgKz0TWe9yT0pl6WowGKJipM50wKwscZFOB2TyioioYJiUIiKiUkWT0+0AJpuIiMoqsRhISQEePy666YBMXhERFQyTUkREpBFFmWxSd7pd9eos2ExERHkrqumARZ28YuKKiMoCJqWIiOijlESyidPtqCSo++Q6sTjvfur0Ka5+6iZpRWIRtLXfb1MT8RXHMRAVNU0nrzjqiojKAialiIgoV/l9+WOyiUqzokoiAYCuLlBBKxlCPk+u09MSoaqWHgTtd8jtO6k6fYqjn75IBANROrTyq8emLYaBdiYsRS+Q/pHHqm4/dbelKwJMtCpAT0+/1Cb0iIoiecUpg0RUVjApRUT0GVHnS5MgFM3oJiabSF1FmRwqiSQSAOhriWCUmo7425FIS8q9SLiumT50DL+AcOsZ0hJV91OnT3H1Ezf+It/7X6wlhjglGdk3NRefutvSrqgLoy+kqKEl0ljCDCieZBiTXJRX8qqkpgwCTF4RkWYxKUVEVII0mUSS7U9PT4R37/L+xKlOwonJps9bUSWSijo5VBJJJFk/ceMvkJ3fk+vSMuX/za2fOn2Ks5+6NBmfutsSMrU1njADij4Zpk6Si6O9CCi99a4A9T+fFKQfEZU9TEoRERWD0phEAmSJJDGePMnK88OrOgknJptKl9I6Va04kkOaTiLl7Eelg6YTekWdDFMnycXRXlQQmk5eqfv5RN1+AIu/E5VVTEoR0WevqP4VryhHLJVEEgn4L5GkzodXUpZf4qekCliX9qlqxZEcYhKJSoImk1yfw2gvoOSK/zNhplpRJa8K9vkk/37FUfydo7iIPg1MSn2k33//HRs3bkRMTAysra0xZ84c2NralnRYRKVSUX840ORopOIZscQk0odKovZQUSZ+SqyA9ScwVY2IlGlymmdJjPYCSqb4f0kWxP9ckmbqfu4oyn5FVfy9pEZxAUyGERUGk1If4ejRo1i8eDG+//572NnZYevWrfDx8cHx48dhampa0uHRZ0btx3hreFRQSdQ7KokpbcUxYqk0J2BK4l+2i6P20BdaesjWYOKnpAtYc6oaEeWnNCa5SnJUWFG+T5T2mmKfQgKuKIq/l9QorpKc0liUn9llMRbV9phYo/wwKfURNm/ejD59+qBnz54AgO+//x5nz57F/v37MXLkyBKOTrNKYgRMUfYThPdvrPnJzi6ZfqmpWnj7Nv83QgMD9foV1aiggiaHIiOzkZWVe189PRGqVRPj2bOsPJNIBesnglic33EIEIvf//dj+uTsVxIJGE33+xRG+2hrOPHzqRSwJiIqKposdF8cCbOifp8orTXFSqpfcUzzVPezWFH109IS8O6dCM+eoQg/n+bfT0dHBIkk78/2Rf2ZvSi/T8iUKyfAwCD/UWZF+f2pINsqrd9PZf3KAialCik9PR23bt3CqFGj5G1isRjOzs64du1agbZV2ofv5ictTQupqfn/4dLRESE9Pf8/hJruJxIBJqK3yHyTlOe2IBJB30gH7xLTNd7PQF8HCc/SkZXH905dXRF0q+riTXQaMvPpZ1xFF0n/5t5PnT4F7WdaRReZ+ml5vvnq6ophrqMLLb00ZGoVYb9yacjU/bh+RbktANDTE8M4PR3Jz59ASM39jVrbWA+6JqbQefYS2W9LV7+i3qeWsR60jEyhra8Nvexcu0GspwWRlhhaetrQK6/5frrG+hC0VV/wOsZ6+fYpa/1Kc2wl1a80x1ZS/UpzbCXVrzTHVtT9imufJfU+kV8/7XI60Hr3FtpPXyJLQ++xJdbPUAfGFjVRW1+MDO3S9VmswP2K+vOpGv0qZOgiVsOf2Yvq+wQAaOuIUK66DjJiNPj9Sd1tARBXMMKb7HJ5Jn9K6nssABgYCNDTyz+hV1qpPTJPEMpK/q1ovXz5Ei1btsTu3bvh4OAgb//pp58QGhqKffv2lWB0RERERERERESlmxqD2oiIiIiIiIiIiIoWk1KFVLFiRWhpaSEuLk6hPS4uDmZmZiUUFRERERERERHRp4FJqULS1dVFgwYNEBQUJG/Lzs5GUFCQwnQ+IiIiIiIiIiJSxkLnH2Ho0KGYNm0abGxsYGtri61btyI1NRU9evQo6dCIiIiIiIiIiEo1JqU+QqdOnRAfH4+VK1ciJiYG9erVw4YNGzh9j4iIiIiIiIgoH3z6HhERERERERERaRxrShERERERERERkcYxKUVERERERERERBrHpBQREREREREREWkck1JERERERERERKRxTEoRfaISEhIwefJkNGrUCE2aNMHMmTORkpKi1rqCIGD48OGQSqU4efJkMUdKpDkFvS8SEhKwYMECtG/fHra2tvjqq6+wcOFCJCUlaTBqoqLz+++/o3Xr1mjYsCF69+6NGzdu5Nn/2LFj6NChAxo2bAgPDw+cO3dOQ5ESaUZB7om9e/diwIABcHR0hKOjI4YMGZLvPUT0qSno+4TMkSNHIJVKMXbs2GKOkMoaJqWIPlFTpkzB/fv3sXnzZqxZswZXrlzBd999p9a6W7duhUgkKuYIiTSvoPfFq1ev8OrVK0ybNg1//vknFi9ejAsXLmDWrFkajJqoaBw9ehSLFy/GuHHjEBAQAGtra/j4+CAuLk5l/6tXr2Ly5Mno1asXDhw4gDZt2mDcuHG4d++ehiMnKh4FvSdCQkLQuXNnbNu2Dbt378YXX3yBYcOG4eXLlxqOnKh4FPSekHn+/Dl+/PFHNGnSREORUlkiEgRBKOkgiKhgHjx4gE6dOuGPP/5Aw4YNAQDnz5/HyJEjce7cOVSpUiXXdSMiIjBq1Cjs378fLi4uWL16Ndzd3TUVOlGx+Zj7Iqdjx47h22+/xfXr16GtrV2cIRMVqd69e6Nhw4byRGx2djZatWoFb29vjBw5Uqn/119/jdTUVKxdu1be1qdPH1hbW2P+/Pkai5uouBT0nvhQVlYWHB0d8d1336F79+7FHC1R8SvMPZGVlYWBAweiZ8+eCAsLQ2JiIn799VdNhk2fOY6UIvoEXbt2DcbGxvIv3gDg7OwMsVic5xDc1NRUTJ48Gd999x3Mzc01ESqRxhT2vvhQcnIyDA0NmZCiT0p6ejpu3boFZ2dneZtYLIazszOuXbumcp3r16+jefPmCm0uLi64fv16cYZKpBGFuSc+lJqaiszMTJiYmBRXmEQaU9h7YvXq1TA1NUXv3r01ESaVQfzETfQJio2NRaVKlRTatLW1YWJigpiYmFzXW7x4MRwcHDgyij5Lhb0vcoqPj8evv/6Kvn37FkeIRMXm9evXyMrKgqmpqUK7qakpHj58qHKd2NhYmJmZKfWPjY0ttjiJNKUw98SHli5disqVKyt8iSf6VBXmnrhy5Qr++OMPHDhwQAMRUlnFpBRRKbJ06VKsX78+zz5Hjx4t1LZPnTqF4OBgBAQEFGp9opJSnPdFTsnJyRg1ahSsrKzg6+v70dsjIqJP17p163D06FFs27YNenp6JR0OkcYlJydj6tSpWLBggdI/+hEVJSaliEqRYcOGwdPTM88+1atXh5mZGeLj4xXaMzMz8ebNm1yn5QUHB+Pp06dwdHRUaB8/fjyaNGmC7du3f1zwRMWkOO8LmeTkZAwfPhzly5fH6tWroaOj89FxE2lSxYoVoaWlpVSsNi4uTmk0lIyZmZnSqKi8+hN9SgpzT8hs3LgR69atw+bNm2FtbV2cYRJpTEHviWfPniEqKgpjxoyRt2VnZwMA6tevj+PHj6NGjRrFGzSVCUxKEZUilSpVUutfIhwcHJCYmIibN2/CxsYGwPukU3Z2NmxtbVWuM3LkSKW54B4eHpgxYwbc3Nw+PniiYlKc9wXwPiHl4+MDXV1d/Pbbb/wXcfok6erqokGDBggKCpJP0c7OzkZQUBC8vLxUrmNvb4/g4GAMGTJE3nbp0iXY29trIGKi4lWYewIA1q9fjzVr1mDjxo0KNQqJPnUFvSdq166Nw4cPK7QtX74cKSkpmDVrFqpWraqRuOnzx0LnRJ8gKysruLq6Ys6cObhx4wbCwsKwYMECdO7cWf6EsZcvX6JDhw7yAs/m5uaQSCQKPwBQrVo1VK9evcSOhaioFOa+SE5OxrBhw/D27Vv88MMPSE5ORkxMDGJiYpCVlVWSh0NUYEOHDsXevXsREBCABw8eYN68eUhNTUWPHj0AAFOnTsXPP/8s7z9o0CBcuHABmzZtwoMHD7Bq1SrcvHkzzy/sRJ+Sgt4T69atw4oVK7Bo0SJYWFjI3w9SUlJK6hCIilRB7gk9PT2l7w7GxsYoX748JBIJdHV1S/JQ6DPCkVJEn6ilS5diwYIFGDx4MMRiMdq1a4fZs2fLl2dkZODRo0dITU0twSiJNKug98WtW7cQHh4OAGjbtq3Ctk6dOgVLS0vNBU/0kTp16oT4+HisXLkSMTExqFevHjZs2CCflvHvv/9CLP7v3yMbNWqEpUuXYvny5Vi2bBlq1qyJ1atXy//RguhTV9B7Yvfu3cjIyMCECRMUtuPr64vx48drNHai4lDQe4JIE0SCIAglHQQREREREREREZUtTIMSEREREREREZHGMSlFREREREREREQax6QUERERERERERFpHJNSRERERERERESkcUxKERERERERERGRxjEpRUREREREREREGsekFBERERERERERaRyTUkREREREREREpHFMShEREVGheXt7w9vbW/778+fPIZVK4e/vr9E4pk+fjtatW2t0n0T5UXVdpqSkYNasWWjRogWkUil++OEHAEBsbCwmTJgAJycnSKVSbNmypQQiJiIi0iztkg6AiIjoc+bv748ZM2ZAV1cXJ0+eRJUqVRSWe3t74/Xr1/jzzz9LKMKyJTs7G4cOHcLvv/+OJ0+eICMjA5UrV4adnR0GDBgAe3v7Eo3v6tWruHjxIgYPHgxjY+MSjaUgpFKp/P+1tLRgaGgIS0tLNGrUCP369UOdOnVKMLqisWrVKvj5+cl/19fXR8WKFWFtbY22bdvCw8MDurq6+W5n7dq1CAgIwNixY1G9enVYWVkBABYvXowLFy7A19cXZmZmsLGxKbZjISIiKi2YlCIiItKA9PR0rFu3DnPmzCnpUIqVhYUFbty4AW3t0vkRY+HChfj999/Rpk0beHh4QEtLC48ePcKFCxdQvXr1Ek9KXbt2DX5+fvD09PykklIA0KJFC3Tr1g2CICA5ORl37tzBgQMHsGvXLkyZMgVDhw4t6RCLxLx581CuXDmkp6fj5cuXCAwMxMyZM7F161asXbsWX3zxhbzvggULIAiCwvrBwcGws7ODr6+vUnubNm3g4+OjkeMgIiIqDUrnJ0YiIqLPTL169bB3716MHDlSabRUUREEAWlpadDX1y+W7atDJBJBT0+vxPafl9jYWOzcuRN9+vTBggULFJYJgoD4+PgSiqxwsrOzkZGRUWrOd82aNdGtWzeFtsmTJ2PMmDFYsmQJateujVatWpVQdEWnffv2qFSpkvx3X19fHDp0CNOmTcPEiROxd+9e+TIdHR2l9ePi4lSOHIuLiyvSRGRmZiays7PVGr1FRERUUlhTioiISANGjRqF7OxsrF+/Pt++mZmZWL16Ndzd3WFjY4PWrVtj2bJlSE9PV+jXunVrjBo1ChcuXECPHj1ga2uL3bt3IyQkBFKpFEePHoWfnx9cXV3h4OCACRMmICkpCenp6fjhhx/QvHlzODg4YMaMGUrb3r9/PwYNGoTmzZvDxsYGnTp1ws6dO/ON/cOaUrJYVP18WGvn3Llz8il0Dg4OGDlyJCIjI5X2cfLkSXTp0gUNGzZEly5d8Pfff+cblyw2QRDQqFEjpWUikQimpqby3/39/SGVShEaGorvvvsOTk5OaNSoEaZOnYo3b94ora9u7A8ePMDEiRPRrFkz2Nraon379vjll18AvJ8e9tNPPwEA2rRpIz9Pz58/B/B+itz8+fNx6NAhdO7cGQ0bNsSFCxfk5zgkJETpeD+s7zV9+nQ4ODggOjoao0aNgoODA1xdXfH7778DAO7evYtBgwbB3t4ebm5uOHz4sFrnNjcVK1bEsmXLoK2tjd9++01hWXp6OlauXIm2bdvCxsYGrVq1wk8//aR0LQLAwYMH0atXL9jZ2cHR0REDBw5EYGCgfPnJkycxcuRIuLi4wMbGBu7u7li9ejWysrLkfVauXIkGDRqoTD7OmTMHTZo0QVpaWqGOs2vXrujduzfCw8Nx8eJFeXvOmlKy1+n58+c4e/as/PWVXWuCIOD333+Xt8skJibihx9+QKtWrWBjY4O2bdti3bp1yM7OlveRvdYbN27Eli1b4O7ujoYNG+LBgwcA3l93EyZMQNOmTdGwYUP06NEDp06dUjgGWRxhYWFYvHgxmjVrBnt7e4wbN07lOTt37hy8vLzg4OCARo0aoWfPnkrXS3h4OHx8fNC4cWPY2dnBy8sLYWFhhTrHRET0eeJIKSIiIg2wtLREt27dsHfvXowYMSLP0VKzZ89GQEAA2rdvj6FDh+LGjRtYu3YtHjx4gNWrVyv0ffToESZPnoy+ffuiT58+qFWrlnzZunXroK+vj5EjR+LJkyfYsWMHtLW1IRKJkJiYCF9fX4SHh8Pf3x8WFhYK04l27dqFunXronXr1tDW1saZM2fw/fffQxAEDBw4UO3jtrKykidaZJKSkrBkyRKF0SYHDhzA9OnT4eLigilTpiA1NRW7du3CgAEDEBAQAEtLSwBAYGAgxo8fjzp16mDy5Ml4/fo1ZsyYgapVq+YbS7Vq1QAAx48fR4cOHWBgYJDvOvPnz4exsTF8fX3x6NEj7Nq1C9HR0di+fTtEIlGBYr9z5w4GDhwIbW1t9O3bFxYWFnj69ClOnz6Nb775Bm3btsXjx4/x559/YsaMGahYsSIAKJyn4OBgHDt2DAMHDkTFihVhYWGBxMTEfI8jp6ysLIwYMQJNmjTBlClTcPjwYcyfPx8GBgb45Zdf4OHhgXbt2mH37t2YNm0a7O3tUb169QLtI6dq1arB0dERISEhSE5OhqGhIbKzszFmzBiEhYWhT58+sLKywr1797B161Y8fvwYv/76q3x9Pz8/rFq1Sp5Y1dHRQXh4OIKDg+Hi4gIACAgIQLly5TB06FCUK1cOwcHBWLlyJZKTkzFt2jQAQLdu3bB69WocPXoUXl5e8u2np6fjr7/+Qrt27T5q1FnXrl2xZ88eBAYGokWLFkrLZffC4sWLUbVqVfl0xvr16+Onn37C1KlT5VMgZVJTU+Hl5YWXL1+iX79++OKLL3Dt2jUsW7YMMTExmDVrlsI+/P39kZaWhj59+kBXVxcmJiaIjIxE//79UaVKFYwYMQLlypXDsWPHMG7cOKxatQpt27ZV2MbChQvl13xUVBS2bt2K+fPnY/ny5Qr7mTlzJurWrYtRo0bByMgIERERuHDhAjw8PAAAQUFBGDFiBGxsbODr6wuRSAR/f38MHjwYO3fuhK2tbaHPNRERfUYEIiIiKjb79+8XJBKJcOPGDeHp06dC/fr1hQULFsiXe3l5CZ07d5b/HhERIUgkEmHWrFkK21myZIkgkUiEoKAgeZubm5sgkUiE8+fPK/QNDg4WJBKJ0KVLFyE9PV3ePmnSJEEqlQrDhw9X6N+3b1/Bzc1NoS01NVXpWIYNGya0adNGoc3Ly0vw8vKS//7s2TNBIpEI+/fvV3k+srOzhVGjRgn29vZCZGSkIAiCkJycLDRp0kSYPXu2Qt+YmBihcePGCu3dunUTWrRoISQmJsrbAgMDBYlEonQMqkydOlWQSCSCo6OjMG7cOGHjxo3C/fv3lfrJXjdPT0+Fc7h+/XpBIpEIJ0+eLHDsAwcOFBwcHISoqCilcyKzYcMGQSKRCM+ePVOKSSKRCNbW1vLzJiN7vYODgxXaVb0W06ZNEyQSibBmzRp525s3bwRbW1tBKpUKR44ckbc/ePBAkEgkwsqVK5ViURXb999/n+vyhQsXChKJRIiIiBAEQRAOHDggWFtbC6GhoQr9du3aJUgkEiEsLEwQBEF4/PixYG1tLYwbN07IyspS6JvzvKm6XufMmSPY2dkJaWlp8ra+ffsKvXv3Vuh34sQJlefvQytXrhQkEokQFxencvmbN28EiUQijBs3Tt42bdo0pevSzc1NGDlypNL6qs7h6tWrBXt7e+HRo0cK7UuXLhXq1asnREdHC4Lw32vdqFEjpfgGDx4sdOnSReE8ZGdnC3379hXatWsnb5Nd80OGDFE4t4sWLRLq1asnv+cSExMFBwcHoXfv3sK7d+8U9iVbLzs7W2jXrp0wbNgwpdepdevWwtChQ5WOn4iIyiZO3yMiItKQ6tWro2vXrti7dy9evXqlss+5c+cAQKko9LBhwxSWy1haWsLV1VXltrp166ZQ08bW1haCIKBnz54K/WxtbfHvv/8iMzNT3pazLlVSUhLi4+PRtGlTPHv2DElJSfkdaq5Wr16NM2fOYMmSJfK6OpcuXUJiYiI6d+6M+Ph4+Y9YLIadnZ18WtqrV68QEREBT09PGBkZybfZokULtZ/utnjxYnz33XewtLTE33//jR9//BGdOnXC4MGD8fLlS6X+ffv2VTiH/fv3h7a2tvx1UDf2+Ph4hIaGomfPnvIRWzKyEVfqcHR0LJIn2fXu3Vv+/8bGxqhVqxYMDAzQsWNHeXvt2rVhbGyMZ8+effT+ypUrBwBISUkB8H60mpWVFWrXrq1w3po1awYA8vN28uRJZGdnY9y4cRCLFT+25jxvOa/X5ORkxMfHo0mTJkhNTcXDhw/ly7p164bw8HA8ffpU3nb48GF88cUXaNq0aZEeY1E4fvw4GjduDGNjY4Xz5OzsjKysLISGhir0b9euncLIuoSEBAQHB6Njx47y8xIfH4/Xr1/DxcUFjx8/Vrru+/Tpo3BumzRpgqysLERFRQEALl68iJSUFIwcOVJpZJlsvYiICDx+/BgeHh54/fq1fL9v375F8+bNERoaqjD9kIiIyi5O3yMiItKgsWPH4tChQ1i3bh1mz56ttDwqKgpisRg1atRQaDc3N4exsbH8i6GMbGqYKh8mP2SJnJxPB5O1Z2dnIykpST5lLCwsDKtWrcL169eRmpqq0D8pKUkhKaSu8+fPY/Xq1Rg1ahTat28vb3/8+DEAYPDgwSrXMzQ0BABER0cDAL788kulPrVq1cLt27fzjUEsFmPgwIEYOHAgXr9+jatXr2L37t04f/48vvnmG6W6WR/uq3z58jA3N5e/DurGLkvsSCSSfGPMS16vt7r09PQUEhfA+2ugatWqSgkyIyOjAk8PVOXt27cA3p8/AHjy5AkePHiA5s2bq+wfFxcHAHj69CnEYjGsrKzy3H5kZCSWL1+O4OBgJCcnKyzLmUTt1KkTFi1ahEOHDsHX1xdJSUk4c+YMhgwZUqDkoCofHmNRePLkCe7evZvrefqw1tOH18fTp08hCAJWrFiBFStWqNxGXFycwnTiD/9uyIqvy64DWUKvbt26ucYtuy9kUydVSUpKgomJSa7LiYiobGBSioiISINyjpYaOXJkrv3U/YKc15P2PhxZkl+78P+Prn/69CmGDBmC2rVrY/r06fjiiy+go6ODc+fOYcuWLYUa4fDs2TN8++23cHZ2xtdff61yvz/99BPMzc2V1tXS0irw/tRRsWJFtGnTBm3atIG3tzcuX76MqKgoWFhYqL0NTceu6vXO7VrJ7XXKLabc2mXH+DEiIyOhpaUlT5pkZ2dDIpFgxowZKvurUyNMJjExEV5eXjA0NMSECRNQo0YN6Onp4datW1i6dKnCeTAxMZEXcPf19cXx48eRnp6Orl27ftwBArh37x4AKCWUP0Z2djZatGiB4cOHq1xes2ZNhd8/vD5kxz5s2LBcR1R+GG9+fx/UIes7depU1KtXT2Uf2cgyIiIq25iUIiIi0rAxY8bg0KFDKp/EZ2FhgezsbDx58kRhdEhsbCwSExMLlDAprNOnTyM9PR2//fabwqiJD5/upq53795h/PjxMDIywrJly5S+9MqKaJuamsLZ2TnX7chiefLkidKyR48eFSo2GRsbG1y+fBkxMTEK5/jJkyfyKWXA+6lZMTExaNmyZYFil/WTJS5yU5jROrKRLB9Oq/xwVF1JiY6ORmhoKOzt7eUjx2rUqIE7d+6gefPmeR5zjRo1kJ2djQcPHuSa3Lh8+TISEhLg5+cHR0dHebvsqYUf6tatG8aOHYsbN27g8OHDqF+/fp6jftR16NAhAMg1+VMYNWrUwNu3b/O8tvIiu+50dHQKvQ1VMQHvE42qRi3m3K+hoWGR7ZeIiD5PrClFRESkYTVq1JA/qSsmJkZhWatWrQAAW7duVWjfvHmzwvLiJBsxk3NkRFJSEvbv31+o7c2dOxePHz+Gn5+fyuk6rq6uMDQ0xNq1a5GRkaG0XDZFqXLlyqhXrx4CAgIUEjAXL17E/fv3840jJiZGZb/09HQEBQWpnDa5Z88ehZh27dqFzMxMeVJK3dgrVaoER0dH7N+/Xz4NUSbneZY9EbAgdbssLCygpaWlVF9o165dam+juCQkJGDSpEnIysrC6NGj5e0dO3bEy5cvsXfvXqV13r17J58K5+7uDrFYjNWrVyuN/JKdN1mSM+d5TE9PV5qKKdOyZUtUrFgRGzZsQGhoaJGMkjp8+DD27dsHBweHXKfaFUbHjh1x7do1XLhwQWlZYmKiQh04VUxNTdG0aVPs2bNHZR27D6f/qcPFxQXly5fH2rVrkZaWprBM9hrY2NigRo0a2LRpk8oaW4XZLxERfZ44UoqIiKgEjB49GgcPHsSjR48URmlYW1vD09MTe/bsQWJiIhwdHfHPP/8gICAA7u7uCqN2ikuLFi2go6OD0aNHo1+/fkhJScG+fftgamqqlETLz9mzZ3HgwAG0b98ed+/exd27d+XLypcvD3d3dxgaGmLevHmYOnUqevTogU6dOqFSpUqIjo7GuXPn0KhRI3z33XcAgEmTJmHUqFEYMGAAevbsiYSEBOzYsQN169aVJzJy8+LFC/Tu3RvNmjVD8+bNYWZmhri4OBw5cgR37tzB4MGDlWotZWRkYMiQIejYsSMePXqEnTt3onHjxmjTpg0AFCj22bNno3///vD09ETfvn1haWmJqKgonD17FgcPHgQANGjQAADwyy+/oFOnTtDR0YGbm1ueU52MjIzQoUMH7NixAyKRCNWrV8fZs2fldZk05fHjxzh48CAEQUBKSgru3LmD48eP4+3bt5g+fbo8kQe8H6107NgxzJ07FyEhIWjUqBGysrLw8OFDHD9+HBs2bEDDhg3x5ZdfYvTo0fj1118xYMAAtGvXDrq6uvjnn39QuXJlTJ48GQ4ODjAxMcH06dPh7e0NkUgkj0MVHR0ddO7cGTt27ICWlhY6d+5coOP866+/UK5cOWRkZODly5cIDAzE1atXYW1tnWvdpsLy8fHB6dOnMXr0aHh6eqJBgwZITU3FvXv38Ndff+HUqVNK1+yH5s6diwEDBsDDwwN9+vRB9erVERsbi+vXr+PFixfyEV7qMjQ0xIwZMzB79mz06tULXbp0gbGxMe7cuYN3797hxx9/hFgsxsKFCzFixAh06dIFPXr0QJUqVfDy5UuEhITA0NAQa9as+ZhTQ0REnwkmpYiIiErAl19+ia5duyIgIEBp2cKFC2FpaYmAgACcPHkSZmZmGDVqFHx9fTUSW+3atbFy5UosX74cP/74I8zMzNC/f39UqlQJM2fOLNC2ZCMi/vrrL/z1118KyywsLODu7g4A8PDwQOXKlbFu3Tps3LgR6enpqFKlCpo0aYIePXrI12nZsiVWrFiB5cuX4+eff0aNGjWwePFinDp1CpcvX84zllq1amHmzJk4d+4cdu7cibi4OOjq6kIikWDhwoXo1auX0jrfffcdDh8+jJUrVyIjIwOdO3fG7NmzFaacqRu7tbU19u7dixUrVmDXrl1IS0tDtWrVFJ54Z2tri4kTJ2L37t24cOECsrOzcerUqXzr78yePRuZmZnYvXs3dHV10aFDB0ydOhVdunTJc72idPHiRVy8eBFisRiGhoawtLRE9+7d0bdvX6UnBspGP23ZsgUHDx7E33//DQMDA1haWsLb2xu1atWS9504cSIsLS2xY8cO/PLLLzAwMIBUKkW3bt0AvK8NtmbNGvz4449Yvnw5jI2N0bVrVzRv3hw+Pj4qY+3WrRt27NiB5s2bo3LlygU6znnz5gF4XzC+YsWKqFevHhYtWgQPDw/o6uoWaFv5MTAwwPbt27F27VocP34cBw4cgKGhIWrWrCmfEpufOnXqYP/+/fDz80NAQAASEhJQqVIl1K9fH+PGjStUXL1794apqSnWrVuHX3/9Fdra2qhduzaGDBki7+Pk5IQ9e/bg119/xY4dO/D27VuYm5vD1tYWffv2LdR+iYjo8yMSiqJ6JREREdFnxN/fHzNmzMAff/yBhg0blnQ4VMTu3LmDbt264ccff0T37t1LOhwiIqIyizWliIiIiKhM2bt3L8qVK4d27dqVdChERERlGqfvEREREVGZcPr0ady/fx979+7FwIED850WSURERMWLSSkiIiIiKhMWLlyI2NhYtGzZEuPHjy/pcIiIiMo81pQiIiIiIiIiIiKNY00pIiIiIiIiIiLSOCaliIiIiIiIiIhI45iUIiIiIiIiIiIijWNSioiIiIiIiIiINI5JKSIiIiIiIiIi0jgmpYiIiIiIiIiISOOYlCIiIiIiIiIiIo1jUoqIiIiIiIiIiDSOSSkiIiIiIiIiItI4JqWIiIiIiIiIiEjjmJQiIiIiIiIiIiKNY1KKiIiIiIiIiIg0jkkpIiIiIiIiIiLSOCaliIiIiIiIiIhI45iUIiKiz1pISAikUilWrVpVIvtv3bo1WrdurdC2atUqSKVShISElEhMz58/h1QqxfTp00tk/0UhIyMDq1atQrt27WBjYwOpVIqTJ0+WdFhEVAykUim8vb1LOgwiIioG2iUdABERUX6eP3+ONm3aKLTp6+vDyMgIVlZWaNSoETw9PVGjRo0i37e3tzcuX76Mu3fvFvm2i5MsEXb69OkSjqR4bN68GX5+fnB0dETHjh2hra2NWrVq5blO69atERUVJf9dLBbD2NgY1tbW6NevHzp27FjcYX+UsLAwbN26FdeuXcPr169hYGAAU1NTNGjQAC4uLvD09CzpEFUKCQnBoEGD4Ovri/Hjx5d0OEVGKpUq/K6npwcjIyPUqFED9vb26NatG6ytrUsoOs0IDAyEj48PXFxcsHHjxjz7Tp48GX/++SeWLl0KDw8PDUVIRESlHZNSRET0yahRowa6du0KAEhPT0dcXBz++ecf/Prrr1i7di2GDx+Ob775BiKRSL6Ora0tjh49iooVK5ZIzFu2bCmR/ealSpUqOHr0KIyMjEo6lEI7c+YMypUrh02bNkFXV1ft9bS0tDBmzBgAQGZmJp48eYKTJ08iODgYT58+xahRo4or5I/i7++PmTNnQltbGy1btsSXX34JkUiER48e4dy5cwgNDS21SanPWYUKFeDl5QXg/fX0+vVr3L59G5s2bcKmTZvQs2dPzJs3r0DX6KfE2dkZ1apVw6VLl/Dvv//iiy++UNkvKSkJJ0+ehLGxMdq1a6fhKImIqDRjUoqIiD4ZNWrUUDnS4sqVK5g6dSrWrl0LsViMr7/+Wr7MwMAAVlZWGoxSUXGM3vpYOjo6JXpOisKrV69QsWLFAn/Z19LSUrqGwsLC4OXlhV9//RWDBg2CgYFBUYb60VJTU7Fw4UKUL18eu3fvRt26dRWWZ2Rk4PLlyyUUXdlWsWJFlX+T7t27h6lTp2L//v3IyMjA//73vxKIrviJxWL06NEDfn5+8Pf3x7hx41T2O3z4MN69e4eePXtCT09Pw1ESEVFpxppSRET0yWvSpAk2bNgAXV1dbNiwAf/++698WW41pR4/fowZM2agdevWsLGxQdOmTdG1a1f88MMPEAQBwPvpObIv+1KpVP4jq8WUszbTgwcPMG7cODg5OUEqleL58+cAVNeUymnfvn3w8PBAw4YN4erqikWLFiE5OVmhT151sT6sDyX7PSoqClFRUQpxy9bPq6ZUVFQUZs6cCVdXV9jY2KBly5aYOXMmoqOjlfp6e3tDKpXK6zvJzmX79u3x+++/53rMudm/fz969+4NBwcHODg4oHfv3vD391foI6vH9fz5c4Xjy+sc56dx48aoXbs23r17h/v37yssCw4OxowZM9C+fXt5XD169MCePXuUttO9e3c0btwYWVlZ8rbs7Gw0bdoUUqkU+/btU3ks+SWUIiMjkZKSAicnJ6WEFPA+ydiiRQuFNn9/f0ilUvj7++PkyZPo1asX7Ozs0KxZM8yYMQOxsbEq9/Xs2TPMmjULX331FWxsbODi4oLp06crTHv8sP+cOXPkr33z5s3h7e0tf91WrVqFQYMGAQD8/PwUrkfZPTJ9+nRIpVI8e/YMmzZtQqdOnWBjYyO/PmXLZf1VncOc9dly3i9Xr16Ft7c3HBwc0KxZM8ybNw/v3r0DAJw9exZ9+/aFvb09nJ2d8dNPPyEzMzPP10JdEokEmzZtQqVKlXDo0CHcuHFDqU9oaChGjx4NJycn2NjYoF27dvjll1+QmpqqcpuhoaEYO3YsnJ2dYWNjg1atWsHX1xdXrlyR93n58iVWrlyJPn36oHnz5rCxsUHr1q0xb948xMXFKWxvypQpkEqlKmMDgBUrVkAqleLPP//M81h79OgBkUiEgIAA+d/OD+3fvx8A0KtXLwDA33//jUmTJqFt27aws7ND48aNMWDAAPz111957isn2d8fVfK6Zk6ePInBgwfD0dERDRs2RJcuXbBx40aF+xZ4f+/u27cPvXr1QtOmTWFra4uWLVti9OjRJVYPkIjoc8SRUkRE9FmoXbs2OnbsiIMHD+LkyZN5FsV9+fIlevfujdTUVLRq1QqdOnVCamoqHj9+jF27dmHatGnQ1taGr68vAgICEBUVBV9fX/n69erVU9jekydP0KdPH0gkEnh6eiIhIQE6Ojr5xrx582YEBwejY8eOaNWqFS5duoStW7ciPDwcO3bsUGsbHzI2Noavry+2bt0KABg8eLB8WdOmTfNc99GjRxgwYADi4+Ph5uaGunXrIjIyEvv378eZM2ewc+dOlXWbJk+ejBs3bqBly5YQi8U4duwY5s+fDx0dHfTp00etuBcuXIjt27ejSpUq6NmzJwDgxIkTmDFjBm7fvo3Zs2fLj0HV8RXVVERtbcWPRuvXr8fTp09hZ2eHqlWrIjExEYGBgfjuu+/w6NEjhcSek5MTIiIicOvWLdja2gIA7ty5gzdv3gB4n+Dq3bu3vH9ISAj09PRgb2+fZ0wVKlQA8D4BlJWVBS0tLbWP58SJEwgMDET79u3h7OyM69evw9/fH2FhYdi3bx9MTEzkfcPDw+Hj44PU1FR89dVX+PLLLxEVFYXDhw/j/Pnz2LNnD6pXry7vf+XKFYwaNQopKSlwcXFB586d8ebNG0RERGDbtm3o0aMHmjZtCk9PTwQEBKBp06YK16CxsbFCrAsWLEB4eDhatWoFNzc3mJqaqn2cqoSHh2P9+vVwcXFBv379EBISgl27diE5ORmtW7fG9OnT0aZNG9jb2+Ps2bPYuHEjypUrp3Cvf4xKlSqhX79++PXXX3H06FH5NQEAO3fuxPz582FsbAw3NzdUqlQJN2/exJo1axASEoJt27YpjALcunUrFi9eDH19fbi7u6NatWp4+fIlwsLC8Ndff6FJkyYA3r8mmzdvRrNmzWBrawsdHR3cvn0bu3btQmBgIAICAuT3Sr9+/XD48GHs27dPITYAyMrKgr+/PypUqJDvdDsLCws4Ozvj4sWLCAkJQbNmzRSW37t3Dzdv3kT9+vVRv359AMDPP/8MHR0dNG7cGObm5oiPj8fp06cxYcIEzJ49u9iKmv/8889Yt24dqlSpgrZt28LIyAhXrlzBTz/9hPDwcKxcuVKh74YNG1CjRg106dIF5cuXl5/zS5cuwcnJqVhiJCIqcwQiIqJS7tmzZ4JEIhGGDRuWZ799+/YJEolE+Pbbb+VtwcHBgkQiEVauXClv27ZtmyCRSIQtW7YobeP169cKv3t5eQkSiSTPuCQSibBixQqVfdzc3AQ3NzeFtpUrVwoSiURo0KCBEBERIW/Pzs4WJk2aJEgkEmHjxo15HsOHMUybNi3f/ea3jre3tyCRSITdu3crtO/YsUOQSCTCoEGDFNpl56Z3795CUlKSvP3BgwdC/fr1hfbt26vc/4cuX74sSCQSoWPHjkJiYqK8PSEhQWjXrp0gkUiE0NBQtY8vN25uboKNjY1S+5UrVwRra2uhadOmwrt37xSWPX36VKl/RkaGMHToUKFevXpCVFSUvP3UqVOCRCIR1q1bJ2/btGmTIJFIhMGDBwstWrSQt6empgoNGjRQOqeqZGdnC56enoJEIhH69+8v7N27V7h7966QmZmZ6zr79++XX5vnz59XWLZ06VJBIpEI8+fPl7elp6cLbm5ugoODg3Dr1i2F/qGhoUK9evWEUaNGydvS0tIEV1dXwdraWjh37pzS/v/991/5/+d1/QqCIEybNk2QSCRCy5YtFc7nh8ufPXumtEx2LwUHByvtTyKRCH///bfCMXp4eAhSqVRwcnISwsPD5cuSkpKE5s2bC02bNhXS09NVxvkhiUSS7zV+6dIlQSKRCAMGDJC3RUZGCvXr1xe6du0qxMfHK/Rfu3at0v0fEREhWFtbCy4uLkrnIDs7W3jx4oX899jYWCE5OVkpjoCAAEEikQi//vqrQnunTp0EBwcHISUlRaH9zJkzgkQiEX744Yc8j0/myJEjgkQiEaZMmaK0bNGiRYJEIhF27Nghb1N1XyUnJwtdunQRGjduLLx9+1ZhmUQiEby8vBTa8vrbrOqaCQwMlL+P5Dze7Oxs4bvvvhMkEolw/PhxeXvTpk0FFxcXpVgEQfl9goiICo/T94iI6LNRuXJlAMDr16/V6q+vr6/UJhuVUhDm5uYYPXp0gdfr3r27wtO5RCIRJk2aBC0tLQQEBBR4ex8jOjoaISEhqFOnjtLopv79+6N27doIDg5WmBopM2nSJBgaGsp/r127Nho1aoRHjx4pTUVURXasvr6+CiOeTExM5KNWPpzGV1hZWVlYtWoVVq1ahV9++QVff/01Bg8eDLFYjLlz5yrVu8k5MkhGW1sb/fr1Q1ZWlsI0HkdHR2hpaSE4OFjeFhISglq1asHDwwMxMTF48OABAODatWvIyMjId/Qa8P66WLlyJRo1aoSwsDDMnj0bHh4eaNy4MYYMGQJ/f3+lqUcyzs7OcHV1VWgbPXo0jI2NceDAAWRnZwN4P5UtKioKPj4+8tEsMk2aNEGbNm1w7tw5+et58uRJvHz5El27dkXLli2V9lu1atV8j+tDPj4+qFatWoHXy42TkxPc3d3lv+vo6KB9+/YQBAFubm4Ko4MMDQ3x1VdfISEhAS9evCiyGFT9Tdq9ezcyMzMxZ84cpQcwDB8+HJUqVVKYMrd7925kZ2fj66+/hqWlpUJ/kUiEKlWqyH83NTVF+fLlleLo1q0bDA0NcenSJYX2vn37IiUlBUeOHFFol001VXeko7u7OypUqIATJ04o3PMZGRk4dOgQ9PT00KVLF3m7qvuqfPny6NGjB5KSkvDPP/+otd+C2LFjB4D3I/LKlSsnbxeJRJgyZQpEIpHSedDR0VE5MrEw7xNERKQap+8REVGZ4+bmhmXLlmH+/PkICgqCq6srmjZtqvKLkjqkUmmhnq4lm3KTk4WFBapWrYrIyEikp6dr7KldERERAN4nVnI+vRB4X8zY0dERDx8+REREhNITtmxsbJS2J/uinJSUpJCwymvfqqbDyNru3Lmj5pHkLSsrC35+fgpt2traWLFihUICQyY5ORmbNm3CyZMn8ezZM7x9+1Zh+atXr+T/b2RkhHr16uHq1avIyMiAWCxGaGgoPDw85McREhICKysreeJK3SlAlpaW2LVrFyIiInDp0iXcvHkTV69eRVBQEIKCgnDgwAF5XbWcGjdurLSt8uXLw9raGpcvX8azZ8/w5Zdf4vr16wDeT+FUVbssJiYG2dnZePToERo2bChPGnxYy+pjfDiF7GN9OM0W+C9JpGqZubk5gPevaWH/FqgjPDwcAHDhwgUEBQUpLdfW1sajR4/kv8tqPrm4uKi1/RMnTmDPnj24desWEhMTFRKWOa9X4H1i/Oeff8a+ffvkU0tjY2Nx9uxZODg4oE6dOmrtU1dXF127dsW2bdvw559/ol+/fgDePyUzPj4eXbp0UZgqGhcXh3Xr1uH8+fOIjo6W1/nKLc6iEB4ejnLlysnrW31IX18fDx8+lP/eqVMn7Ny5E126dEGnTp3g5OQEBwcHlf+YQUREhcekFBERfTZkX2QqVaqUZz9LS0vs2bMHfn5+OHfuHI4dOwbg/QifCRMmoGPHjgXar5mZWaHiza1mjpmZGaKiopCSkqKxpJRsdENuxyL7wq5q5JOqpJOsNlNuI3g+3LdYLFb5upmZmUEkEqk14kodurq68oRKSkoKgoODMXPmTHz77bfYtWuXwsi19PR0DBo0CLdu3UL9+vXRtWtXVKhQAdra2oiKikJAQADS09MVtu/k5ISbN2/in3/+gba2NpKTk9GsWTNYWlrCwsICISEhGDBgAEJCQmBgYFDgREy9evUUEiohISH49ttvERISgp07d2LIkCEK/XN7PWXtSUlJACCve3X48OE89y8rwi1bL+conY/1sTWkPqTqupSNesnrmi2qYueA6r9JsnO9Zs0atbaRnJwMkUgkvwfzsmnTJvz444+oVKkSWrRogapVq8qTKFu3bkVGRoZCf2NjY3Ts2BEBAQG4d+8eJBIJ/P39kZmZqfYoKZlevXph27Zt2L9/vzwp9WGBcwBISEhAr169EB0djUaNGsHZ2RlGRkbQ0tJCREQETp06pXRfFYU3b94gMzNTKSmdU86k86xZs2BpaQl/f3/89ttv+O2336Cnp4eOHTti2rRp+b7PEBGRepiUIiKiz4bsKWYNGzbMt69EIsHKlSuRkZGBW7du4fz589i+fTu++eYbVK5cWeUIk9x8OLJIXR8+DUsmNjYWIpFIPg1HLH4/217Vl+WiStbIvqTn9lS2mJgYhX5FydDQENnZ2YiPj1dKTMTFxUEQhGLZb/ny5dGmTRssX74cQ4YMwYwZM+Dv7y9/PU+dOoVbt26hV69e+OGHHxTWPXLkiMoplk5OTti4cSNCQkKgo6MDkUgkn6Ln5OSEs2fPIiUlBTdv3kTTpk0/Ouno5OSEiRMnYubMmQgODlZKSuX2esraZdMlZed3zZo1cHNzy3e/svVevnxZ2NCV5HYfydpVJThlybHSStXfJNm5DgsLU+u6NjIygiAIiImJyTMJmJmZiV9//RXm5uY4ePCgwr0kCAI2bNigcr1+/fohICAAe/fuxezZs7F//34YGhoWODkvlUrRsGFD3LhxA5GRkTAxMcGFCxdgaWmpUPz8jz/+QHR0NCZOnIixY8cqbGPdunU4deqUWvuTXReZmZlKDyhQdV3IzrW6T87T1taGj48PfHx88PLlS4SGhsLf3x8HDhxAbGwsNm7cqNZ2iIgob6wpRUREn4VHjx7h2LFj0NXVRdu2bdVeT0dHB/b29pgwYQJmzZoFQRBw9uxZ+XJZQkidET8FlfNR7jJRUVF48eIF6tatK09YyJ5SpmpKy+3bt1VuWywWFyhm2eibK1euKD3WXRAEeayqpj19LNk2VX1ZlH2pzzmCqag1b94c7u7uuH37tkItn2fPngEA2rRpo7SOqtcOeD8lU1tbG8HBwQgJCYFEIpGPqGjWrBni4+OxZ88etetJqSNnfZwPhYWFKbWlpKTgzp07MDQ0lE9Tk43Ykk3jy4+s/8WLF/PtKxudVNh7SDbtS1UCTDb1szSSvdbA+6lgMrJzJ5vGlx9Z/8DAwDz7vX79GklJSXBwcFBK7v7zzz9KU+Rk7O3tIZVKcfjwYQQGBuLx48fw8PCAgYGBWvHlJBsR9ccff+DAgQPIyspCjx49FBKOT58+BVCw+0qV3K6L7OxsldN9bW1tkZCQgMePH6u9D5kqVaqgS5cu2LBhA7788ktcunQp1/NJREQFw6QUERF98sLCwuDj44P09HSMHDky3ylFN2/eVDnCSDZyKWexa9kXH1UFvj/WgQMHFL48CYKAZcuWISsrC56envL2WrVqoXz58jh9+jQSEhLk7bGxsfjtt99UbtvExASvX79GWlqaWrFUq1YNTk5OiIyMxB9//KGwbM+ePXjw4AGaNWumVE+qKMiOdfXq1QqvS1JSknyqTc7zURzGjx8PkUgEPz8/efJEVnT7w8TO5cuX5YWgP1S+fHnY2Njg2rVruHLlisIIEdn/r1+/HoD69aSePXuGHTt2qLxmU1NTsW3bNgBAo0aNlJZfunQJFy5cUGhbs2YNEhMT0b17d3nS1d3dHdWqVcPmzZsRGhqqtJ2MjAyFhEGbNm1QtWpVHDp0SGn7gGKiQHYPFbaAuGyU0Ycj044fPy5PWpY2kZGRGDZsGOLi4uDp6akwUmrAgAHQ1tbGggULEB0drbRuYmKiQrK5X79+0NLSwvLlyxEVFaXQVxAE+bk2NTWFvr4+bt26JZ9mCbyftrZw4cI84+3bty8SEhIwY8YMAOoXOP9Qly5dYGBggEOHDmH//v0Qi8Xo0aOHQh8LCwsAyvfV4cOHce7cObX3ldt1sXnzZjx//lypv7e3NwBg5syZKh+GkfNBBOnp6bh69apSn7dv3+Lt27fQ1taW3ztERPRxOH2PiIg+GU+fPpUXYc7IyEBcXBxu3LiBe/fuQUtLC2PGjJE/rS0vBw8exJ49e+Do6Ijq1avD0NAQ9+/fx/nz51GhQgWFL1HNmjXDX3/9hQkTJsDV1RV6enqwtrZG69atP/p4XFxc0K9fP3Tq1AmVKlVCUFAQbt68CXt7e3h5ecn76erqwtvbG2vWrEGPHj3QunVrpKSk4MyZM2jatKl85EFOzZo1w82bNzF8+HA0adIEOjo6cHR0hKOjY67xzJs3DwMGDMCcOXNw5swZ1KlTB5GRkTh9+jQqVaqEefPmffQxq+Lo6Ahvb29s374dXbp0Qbt27SAIAk6cOIEXL17A29s7z7iLgrW1Ndq2bYsTJ07g0KFD8PT0hJubGywsLLBhwwZERkaibt26ePToEc6ePQt3d3f89ddfKrfl5OQkH3GUM/FUpUoV1KxZE48fP0a5cuXUmmYKvJ+iuWDBAvz0009o3Lgx6tatC319fbx8+RJnz55FQkICGjRoIP/SnZObmxvGjBmD9u3bw8LCAtevX0dISAhq1KiBCRMmyPvp6upixYoVGDFiBLy8vNCsWTNIJBKIRCJER0fjypUrqFChAo4fPy7vv3z5cgwfPhwjRoyAq6srrK2tkZycjIiICLx79w4HDhwA8L5WW+XKlXHkyBHo6uqiSpUqEIlE8Pb2VnjaYm7atGmDGjVqwN/fH//++y/q1auHhw8fIjg4GK1atSpQIqOovX79Wv43KTMzEwkJCbh9+7a8OHnv3r3x3XffKawjkUgwd+5czJs3Dx06dECrVq1QvXp1pKSk4Pnz57h8+TI8PT0xf/58AO+nxc2cORMLFy5Ely5d0KZNG1hYWCAmJgZXrlxBq1atMGvWLIjFYgwYMACbNm1Ct27d4ObmhuTkZJw/fx4WFhbyIu+qdOvWDUuXLsWrV6/QoEEDpScwqsvQ0BDt27fHgQMHEB8fD1dXV6VEdrdu3bB+/XosXLgQISEhqFatGu7evYugoCC0a9cOJ06cUGtfPXr0wIYNG7Bq1SpERESgRo0auHnzJu7du4emTZsqJSxbtmyJsWPH4tdff0W7du3g6uqKatWqISEhAU+ePEFYWBi+/vprWFlZ4d27d+jfvz9q1qwJGxsbfPHFF3j79i3Onj2LmJgYDBs2TGP1/oiIPndMShER0Sfj6dOn8pEz+vr6MDIyQu3atTF27Fh4enqiRo0aam2nS5cuSEtLw7Vr13Djxg2kp6ejatWq6N+/v9Jj6fv06YOoqCgcPXoUGzZsQGZmJjw9PYskKTV06FC0adMGW7duxZMnT2BiYoJBgwZh4sSJSl94Jk6cCB0dHfzxxx/YvXs3LCwsMHbsWLi5ualMjowdOxaJiYk4c+YMwsLCkJWVBV9f3zyTO7Vr18b+/fvh5+eHCxcu4Ny5c6hYsSJ69OgBX19f+QiH4jB79mzUq1cPu3btwt69ewEAderUwYQJE9CzZ89i229O48aNw99//43Vq1fDw8MD5cuXx9atW/G///0PoaGhuHz5MurUqYOlS5fC1NQ0z6TU2rVroaWlpTRFz8nJCY8fP0ajRo2U6uDkxsrKCqtWrUJgYCDCw8Nx6NAhJCYmwtDQEHXq1EG7du3Qv39/hRF+Mu3atUOvXr2wZs0anDx5Evr6+ujRowcmTZqk8DQ04P30pkOHDmHDhg04f/48rl69Kk8iubu7o3Pnzgr9HRwcEBAQgLVr1yIwMBBBQUEwNjaGlZWVvNA18H76np+fH5YuXYo///wTKSkpAICuXbuqlZTS19fH5s2bsXjxYgQFBSE8PBx2dnbYsWMHzp49W6JJqYSEBPnfJF1dXRgZGeHLL7/EsGHD0K1bt1ynnfbp0wfW1tbYsmULQkNDcebMGRgaGqJatWoYMmQIunfvrtDfy8sLdevWxebNm3HhwgWkpKTA1NQUdnZ2CrWfZK9rQEAAdu7cCTMzM3Tp0gW+vr7w8PDI9TgMDQ3h7u6OQ4cOFXqUlEyvXr3kCUlV927VqlWxY8cO/O9//0NQUBAyMzPRoEEDbNq0Cf/++6/aSSkzMzNs27YNS5YswcWLFxEcHAwnJyfs3bs31xGkEydOhKOjI7Zt24agoCAkJSWhQoUKsLS0VDhHBgYGmDJlCoKDg3HlyhXExcXBxMQEtWrVwqRJk5TuBSIiKjyR8GHhCCIiIiL6pPn7+2PGjBlYvHix0vQpIlU8PDzw/PlzXLhwoVgeLEBERKQKJ0MTEREREZVh586dw7179+Dh4cGEFBERaRSn7xERERERlUE7d+7EixcvsG/fPujp6WHEiBElHRIREZUxTEoREREREZVBGzZswIsXL1CrVi0sWrQI1atXL+mQiIiojGFNKSIiIiIiIiIi0jjWlCIiIiIiIiIiIo1jUoqIiIiIiIiIiDSOSSkiIiIqdiEhIZBKpVi1apXa6zx//hxSqRTTp08vxsg+Dd7e3pBKpSUdRqnD86I5UqkU3t7eJR0GERF9ZljonIiIqAg9f/4cbdq0ybNPaGgojI2NNRTR+4TQoEGDFNp0dXVRuXJlNG/eHKNHj4alpaXG4smpdevWAIDTp0+XyP4/RzmvQRcXF2zcuFGpz/Xr19G3b194enpiyZIlmg5RbatWrYKfnx+2bdsGJyenkg4nX/fu3cPGjRsRGhqKV69eQU9PD5UqVYJUKoWjoyMGDRoEkUhU0mESERGVGkxKERERFYMaNWqga9euKpfp6elpOJr3GjRoADc3NwBAYmIiLl++jH379uHEiRPYu3cvatasWWz7trW1xdGjR1GxYkW116lSpQqOHj0KIyOjYovrcxcYGIigoCA0b968pEMpFj/++CNSU1NLOgwAwMWLFzFq1ChkZWXB2dkZ7u7u0NPTw9OnTxEaGoq///4bAwcOhLY2P34TERHJ8F2RiIioGNSoUQPjx48v6TAU2NjYKMQkCAKmTZuGgwcPYs2aNcU6YsbAwABWVlYFWkdHR6fA69B/LCws8O+//2Lp0qX4448/PssROtWqVSvpEOTmzZuH7OxsbN68Gc2aNVNYJggCAgMDoaWlVULRERERlU6sKUVERFSCoqKiMHPmTLi6usLGxgYtW7bEzJkzER0drdRXVj8nLS0Nv/zyC9zd3dGgQYMC1WnKSSQSYeDAgQCAf/75R97+9u1brFy5Eh06dEDDhg3RtGlTjBw5EmFhYUrbSEtLw6ZNm9C1a1c0btwY9vb2aN26NSZOnIg7d+7I+31YU0pWLyoqKgpRUVGQSqXynw/75KwpNXjwYFhbWyMqKkrlMS1cuBBSqRQXL15UaA8NDcXo0aPh5OQEGxsbtGvXDr/88kuBRtn8/fffmDRpEtq2bQs7Ozs0btwYAwYMwF9//aXUN2fsT548wbhx4+Do6Ah7e3sMGTJE4dzkdOXKFXh5ecHe3h5OTk74+uuv8e+//6odY061atVCt27dcPPmTRw7dkzt9ZKTk7Fy5Up07twZtra2aNKkCXx8fHDlyhWV/e/cuYMRI0bAwcEBjRs3xogRI3Dv3j1Mnz4dUqkUz58/l/dNSkrCunXr4OXlBRcXF9jY2MDFxQVTp07F06dPFbbr7e0NPz8/AMCgQYPk14dsyqesT86aUgcOHIBUKpWv96Fbt25BKpVi8uTJCu1xcXFYtGgR2rZtCxsbGzg5OWH8+PG4d++eWucsLi4OT58+Rd26dZUSUsD7e83V1VUhMZieno7t27fDx8cHrVq1go2NDZo3bw5fX1/cvn1baRv+/v6QSqXw9/fH6dOn0bt3b9jZ2cHV1RXLly9HdnY2ACAgIABdu3aFra0tvvrqK2zYsEFpW6tWrYJUKkVISAj27dsHDw8PNGzYEK6urli0aBGSk5PVOm7ZcWzevBmenp6wt7eHg4MDBgwYgFOnTin1TUpKwooVK9CpUyc4ODigUaNGaNu2LaZNm5brPU1ERJ83jpQiIiIqIY8ePcKAAQMQHx8PNzc31K1bF5GRkdi/fz/OnDmDnTt3olatWkrrjR8/Hnfu3IGrqyuMjY2LpB6U7MtyWloaBg8ejBs3bqBBgwYYPHgw4uLicPToUQQGBuLnn39Gx44d5etNmzYNx44dg1QqRY8ePaCrq4sXL14gJCQE//zzD6ytrVXuz9jYGL6+vti6dSuA98kmmaZNm+YaZ7du3RAcHIzDhw9j9OjRCssyMzNx5MgRea0smZ07d2L+/PkwNjaGm5sbKlWqhJs3b2LNmjUICQnBtm3boKurm+85+vnnn6Gjo4PGjRvD3Nwc8fHxOH36NCZMmIDZs2erLAIdFRWFPn36oG7duujZsyeePn2KU6dOYdCgQTh69CjMzMzkfYOCgjBixAiIRCJ06tQJlStXRlBQEPr371/oGmQTJkzAkSNHsHz5crRt2xY6Ojp59k9ISICXlxciIyPRqFEj9OvXD8nJyTh16hQGDx6MFStWwN3dXd7/zp07GDBgAFJTU9G2bVvUrFkTN2/exIABA1S+9g8ePMDKlSvh5OSEtm3bwsDAAA8fPsSff/6Jc+fOwd/fHxYWFgAAT09PAMDly5fh6ekpb89rOme7du3w/fff4/Dhw/D19VVafvDgQQDvryOZp0+fwtvbGy9evICLiwvc3d0RFxeHEydOIDAwEFu2bIGdnV2e583IyAja2tqIiYnB27dvUa5cuTz7A8CbN2+waNEiNGnSBK1atYKxsTGePXuG06dP4/z589ixYwdsbW2V1vv7779x8eJFuLu7o1GjRjh79ix+++03CIIAIyMj/Pbbb2jTpg2aNm2KEydO4H//+x/MzMzQvXt3pW1t3rwZwcHB6NixI1q1aoVLly5h69atCA8Px44dO/K9XtLT0+Hj44PLly+jXr166NWrFzIyMnDu3DmMHTsWc+bMgZeXF4D3o8V8fHwQHh6ORo0awdXVFWKxGFFRUTh9+jS6desmf42JiKgMEYiIiKjIPHv2TJBIJIK7u7uwcuVKpZ9r167J+3p7ewsSiUTYvXu3wjZ27NghSCQSYdCgQQrtXl5egkQiEbp16ya8fv1a7ZiCg4MFiUQizJkzR6E9OztbmDZtmiCRSITp06cLgiAIq1atEiQSiTB58mQhOztb3vfWrVtCgwYNhCZNmghJSUmCIAhCYmKiIJVKBU9PTyEzM1Nh25mZmcKbN2+UYli5cqVCPzc3N8HNzU1l3LJzOW3aNHlbUlKSYGtrK3Tq1Emp/+nTpwWJRCIsWbJE3hYZGSnUr19f6Nq1qxAfH6/Qf+3atYJEIhE2btyocv8fevr0qVJbcnKy0KVLF6Fx48bC27dvlWKXSCTC2rVrFdb55ZdflNqzsrKENm3aCFKpVAgNDZW3Z2dnC5MmTZJvSx2yfQ8bNkwQBEFYsmSJIJFIhO3bt8v7XLt2TencCoIg39fevXsV2mNjY4VWrVoJzZo1E969eydv79+/vyCRSIRDhw4p9F++fLk85mfPnsnbExMTVV67QUFBgrW1tTBr1iyF9pUrVwoSiUQIDg5WeayyeyKnKVOmCBKJRAgPD1doz8zMFJydnYUWLVooXK99+/YV6tWrJ5w/f16h/8OHDwUHBwehS5cuKvf9IV9fX0EikQhdunQRtm3bJvzzzz9CWlparv3T0tKEFy9eKLXfu3dPsLe3F4YMGaLQvn//fkEikQgNGjRQOLakpCShefPmgp2dndCiRQuF6zQ6Olpo0KCB0jHIzmuDBg2EiIgIeXvO6+3D+0IikQheXl4KbcuWLRMkEomwfPlyhb8XSUlJQo8ePYQGDRrIj/HOnTuCRCIRxo4dq/JcJCcn53quiIjo88Xpe0RERMXg6dOn8PPzU/q5fv06ACA6OhohISGoU6cO+vTpo7Bu//79Ubt2bQQHB6ucujV+/HhUqFChwDHdvHkTq1atwqpVq7Bo0SJ4enoiICAAFSpUwJgxYwC8n/6ko6ODKVOmKEw1ql+/Pjw9PZGYmIiTJ08CeD+6ShAE6OnpQSxW/EihpaVVLE8YNDQ0hLu7O+7fv49bt24pLFM1Cmb37t3IzMzEnDlzlIqsDx8+HJUqVcKff/6p1r6rV6+u1Fa+fHn06NEDSUlJClMgZSwtLTF8+HCFtl69egFQnDIZFhaGZ8+e4auvvkKTJk3k7SKRCJMmTfqoWkSjR4+GsbExfv31V6SkpOTaLz4+HseOHUOzZs3Qu3dvhWWmpqbw8fFBfHw8Ll26BOD9KLCwsDBYW1vDw8NDof+IESNgYmKitA8jIyOV126zZs1Qp04d+bY/huz1P3TokEJ7YGAgYmNj0alTJ/n5vH37Nq5du4bu3bvD1dVVoX+tWrXQp08f3Lt3T61pfPPnz4ebmxvu3buHhQsXomfPnvLRZtu2bcO7d+8U+uvq6qJKlSpK26lbty6cnJwQGhqKjIwMpeUeHh4KI6gMDQ3x1VdfITU1Ff369VO4Tr/44gs0btwYDx48QGZmptK2unfvrjCiLef1FhAQkOfxZmdnY9euXahRowYmTJig8PfC0NAQ48aNQ0ZGBv7++2+F9fT19ZW2pauri/Lly+e5PyIi+jxx+h4REVExcHFxwcaNG3NdHhERAQBwdHRUKkAtFovh6OiIhw8fIiIiAl988YXCclVTetRx69YteSJHR0cHVapUQZ8+fTB69GhYWFggOTkZz549g5WVFapWraq0vpOTE/bu3Suvh2RoaIhWrVrh3Llz8PT0RIcOHdC0aVM0bNgw32k/H6Nr1674888/cfDgQTRo0ADA+zpIZ86cgUQiUfiSHR4eDgC4cOECgoKClLalra2NR48eqbXfuLg4rFu3DufPn0d0dLRSkuHVq1dK69SrV08pYSc7t4mJifI22TnNmZCSsbCwQNWqVQtdc8fExAQjRozAzz//jE2bNuVagP+ff/5BVlYW0tPTVdYpe/z4MQDg4cOHcHNzk8fcqFEjpb7lypWDtbU1QkJClJaFhIRg69atuHHjBl6/fq2QLCmK66Z58+YwNzfHkSNHMH36dPnT7mRJqpxJS1mSOC4uTuUxP3z4UP5fiUSS534rVqyINWvW4PHjx7hw4QJu3LiB8PBwXLt2DdeuXcO+ffuwfft2haRcREQENmzYgLCwMMTGxioloV6/fo3KlSsrtNWrV09p3+bm5nkuy8rKQlxcnFISLK/rLTIyEunp6blObX306BHevHmDypUrq6zhFR8fD+C/c2hlZQWpVIo///wTL168gLu7O5o2baryHiEiorKDSSkiIqISICsknLOmUE6yL5mqCg7ntk5++vbti/nz5+cbk6mpqdoxrVixAmvWrMGff/6JX375BcD7ZFWPHj0wadIkGBgYFCrWvLi4uMDMzAxHjx7FtGnToKWlhePHj+Pdu3cKCQfgfd0eAFizZs1H7TMhIQG9evVCdHQ0GjVqBGdnZxgZGUFLSwsRERE4deoU0tPTldYzNDRUapMlSWSFqYH3BaCB3M+9mZnZRxWCHjRoEH7//Xds2rQJAwYMUNlHdq6uXr2Kq1ev5rotWXH4/K4XVdfpsWPH8M0336BcuXJwcXGBhYUFDAwMIBKJEBAQUCTFrrW0tODh4YFNmzYhMDAQX331FVJSUnDq1CnUqVNHnsgE/jvms2fP4uzZs7lusyAF8WvWrImaNWvKf4+IiMC3336Le/fuwc/PD7Nnzwbw/jzLaqm1aNECNWvWRLly5SASiXDy5EncuXOnwNdUXstUjbrK73pLSUnJNSmVkJAAAIiMjERkZKTKPsB/505bWxtbt26Fn58f/vrrL/nTPitVqoSBAwdizJgxfDohEVEZxKQUERFRCZB9eYyNjVW5PCYmRqFfTh+OrCrqmOLi4lQul8WaMyYDAwN88803+Oabb/Ds2TOEhIRg9+7d2LZtG9LS0vJMghWWlpYWOnfujK1bt+LSpUtwdXXFwYMHIRaLlaaRyWINCwtTeS7V9ccffyA6OhoTJ07E2LFjFZatW7dO5ZPGCkJWvDu/c19Y+vr6GD9+PGbNmgU/Pz+l5B3w37kaNmwYpk2blu821b1ecvLz84Oenh78/f0VEjcAcOTIkXz3qa5u3bph06ZNOHToEL766iucOHECqampSsctO4acBbmLWr169TB79mwMHjxYYeTYmjVrkJ6ejt9//11pxJJsBFdxy+u1E4lEeU6pk5279u3bY+XKlWrtr2LFipgzZw5mz56Nhw8fIjg4GNu3b8eqVaugo6ODUaNGFfwgiIjok8axskRERCVANs3mypUrEARBYZkgCLhy5YpCP00wNDRE9erV8fTpU7x8+VJpuewLdW5P1KtevTp69eqFHTt2oFy5cjh9+nS++xSLxcjKyipwrDnrBv37778IDQ2Fk5OT0vQk2VRH2TS+wnr69CkAoE2bNkrLZK/Vx5CdU1XbioqKwosXLz56H56enqhbty727duHJ0+eKC1v2LAhRCIRrl27ptb2ZDGr6p+amoq7d+8qtT99+hRWVlZKCalXr17h+fPnSv1l07pyjipTNzaJRIJTp04hOTkZhw4dgkgkUkpayp6qp+4xF5aqp/E9ffoUFSpUUEpIpaam4vbt28Uaj0xe11vdunXzfCqllZUVDA0NcfPmTZWjsPIiEolgZWWFgQMHYvPmzQCg1t8LIiL6/DApRUREVAKqVasGJycnREZG4o8//lBYtmfPHjx48ADNmjVTqidV3Lp3746MjAz8/PPPCsmyO3fuICAgAEZGRnB3dwfwvmaMqgLQb968QUZGRp5faGVMTEzw+vVrpKWlFSjOBg0aoE6dOjh58iR2794NQRBUjv4ZMGAAtLW1sWDBAkRHRystT0xMVCsBIHtUfVhYmEL74cOHce7cuQLFrkrjxo1haWmJs2fPKiQKBEHAsmXLCpW4+5CWlha++eYbZGRkqKwBZG5ujo4dO+LatWvYsGGDUrIUeJ/ck03HsrCwQKNGjRAREYGjR48q9Nu4caN8eldO1apVw5MnTxRGUaWlpWHevHkqExuy+kuqCv7np1u3bnj37h22b9+O4OBgODo6qqzPZmdnhyNHjigdA/A+GXb58uV89/X27Vv89ttv8jpKOWVmZsrry+Wsv2VhYYE3b94oTH3LysrCjz/+qHI7xeHAgQPy2mCA4vXm6emZ57ra2tro378/oqKi8OOPP6p8/e7duycfjfX8+XOViUfZtaDO3wsiIvr8cPoeERFRCZk3bx4GDBiAOXPm4MyZM6hTpw4iIyNx+vRpVKpUCfPmzdN4TCNGjMC5c+dw8OBBPHjwAM2bN0dcXByOHTuGrKwsLFiwQD5t5+XLl/Knd0mlUlSpUgUJCQk4deoUMjIy4OPjk+/+mjVrhps3b2L48OFo0qQJdHR04OjoCEdHx3zX7datG37++Wds3LgRBgYGaNeunVIfiUSCuXPnYt68eejQoQNatWqF6tWrIyUlBc+fP8fly5fh6emZ7zTDbt26Yf369Vi4cCFCQkJQrVo13L17F0FBQWjXrh1OnDiRb7x5EYvFWLBgAUaOHImhQ4eiU6dOqFy5MoKDgxETEwOpVKpy5FFBtWnTBo0bN1ZKrsnMnTsXjx49wv/+9z8cPHgQDg4OMDIywosXL3Dz5k08fvwYgYGB8lphc+bMwcCBAzFlyhT89ddf+PLLL3Hr1i2Eh4fD0dERoaGhCkWsvb29sWDBAnTv3h0dOnRAZmYmLl26BEEQYG1trZAgAd4X1xeJRFi2bBkiIyNhZGQEY2NjtabaeXh44Oeff8bq1auRnZ2tMmkJAD///DMGDx6Mb775Blu3bkX9+vWhr6+P6OhoXL9+HfHx8SqfrJhTZmYmli9fDj8/P9jb28Pa2hqGhoaIjY1FYGAgXrx4AUtLS/j6+srX8fLyQmBgIAYMGICOHTtCV1cXly9fxsuXL9G0aVO1kmEfy8XFBf369UOnTp1QqVIlBAUF4ebNm7C3t1frHE+YMAG3b9/G9u3bce7cOTRp0gSmpqZ4+fIl7t27hzt37mDPnj0wNTXFnTt34OvrC1tbW1hZWcHc3BwvX77EyZMnIRaLMWTIkGI/XiIiKn2YlCIiIiohtWvXxv79++Hn54cLFy7g3LlzqFixInr06AFfX1/56BxN0tPTw9atW7F+/XocPXoUW7ZsgYGBARwdHTFq1CiFqUYWFhYYP348goODcenSJSQkJKBixYqoX78+Bg0ahJYtW+a7v7FjxyIxMRFnzpxBWFgYsrKy4Ovrq1ZSysPDA7/88gsyMjLQvn37XOvf9OnTB9bW1tiyZQtCQ0Nx5swZGBoaolq1ahgyZAi6d++e776qVq2KHTt24H//+x+CgoKQmZmJBg0aYNOmTfj3338/OikFAM7OztiyZQuWL1+O48ePQ19fH82aNcOKFSvUqvGkrilTpqB///4ql1WoUAG7d+/Gjh07cPToURw+fBjZ2dkwMzODtbU1xowZg4oVK8r7169fHzt37sTSpUtx/vx5iEQiNG7cGDt37sSyZcsAKNYgGzhwILS1tbFjxw7s3bsXxsbGaNWqFSZPnoyJEycqxVOnTh0sXrwYmzZtwo4dO5Ceng4LCwu1EiZVqlRBs2bNcOnSJejp6aFDhw4q+1WvXh0BAQHYvHkzTp06BX9/f4jFYlSuXBlNmjTJdb2cDA0NsW7dOgQGBiIsLAzHjx9HQkIC9PX1UbNmTfTu3RuDBw+W1w4DADc3N6xcuRJr167FoUOH5K/36tWrsXr16nz3WRSGDh2KNm3aYOvWrXjy5AlMTEwwaNAgTJw4Ua2RS7q6uli/fj3++OMPHDhwACdOnEB6ejrMzMxgZWWFfv36yZ9aaGNjgxEjRuDy5cs4d+4cEhMTYW5uDmdnZ/j4+MDe3r6Yj5aIiEojkaBqbDYRERERUSFlZWWhbdu2ePfuHS5dulTS4dAHVq1aBT8/P2zbtg1OTk4lHQ4REZVhrClFRERERIWSmZmpsv7RunXrEBUVJa8/RkRERKQKp+8RERERUaG8ffsWLVu2RIsWLVCzZk1kZmYiPDwc//zzD8zNzRVqKBERERF9iEkpIiIiIioUfX199OrVC8HBwQgNDUV6ejrMzc3Rt29fjBs3DpUrVy7pEImIiKgUY00pIiIiIiIiIiLSONaUIiIiIiIiIiIijWNSioiIiIiIiIiINI5JKSIiIiIiIiIi0jgWOi8F4uKS8ClX9tLR0UJGRlZJh0EawNe6bOHrXXbwtS47+FqXHXytyw6+1mULX++y41N/rUUiwNTUKN9+TEqVAoKATzopBXz68ZP6+FqXLXy9yw6+1mUHX+uyg6912cHXumzh6112lIXXmtP3iIiIiIiIiIhI45iUIiIiIiIiIiIijWNSioiIiIiIiIiINI41pYiIiIiIqMwRBAGZmRklHUYRECMjI7ukgyCN4etddpTu11pLSxti8cePc2JSioiIiIiIypTMzAzExb2AIJTeL3xERKWdgYEhjI0rQSQSFXobTEoREREREVGZIQgC3ryJh1gshomJOUSiT7uiiUhUNp7QRe/x9S47SvNrd6AmuwABAABJREFULQgC0tPTkJz8GgBgYmJa6G0xKUVERERERGVGdnYWMjLewcTEDLq6+iUdzkcrzV9cqejx9S47SvtrraurBwBITn4NI6OKhZ7K92n/swAREREREVEBZGe/n7KnpcV/nyci+hiyxFRWVmaht8GkFBERERERlTkfUwOFiIiK5u8ok1JERERERERERKRxHLNKRERERESE9zVcNKU014ohItIUJqWIiIiIiKjMS8NbJKcla2x/hrqG0EM5je0vL1evXsGECaNx7NgZGBkZ5dqvVy8P9OnTH336DNBgdKXHv/9Go3fvrti8+XfUrSst6XBK1Pr1vyE+Ph7Tps0q6VCoEA4c+AOXLl3ETz/9UtKhlK6k1M6dO7Fr1y5ERUUBAOrWrYuxY8eiVatWAIC0tDQsWbIER48eRXp6OlxcXDB37lyYmZnJtxEdHY158+YhJCQE5cqVQ/fu3TF58mRoa/93qCEhIViyZAkiIyPxxRdfYMyYMejRo4dCLL///js2btyImJgYWFtbY86cObC1tZUvVycWIiIiIiIq/UQiIDktGbdibiMzu/AFe9WlLdZGA/P60Ncrp/aIqR9+mIdjx/58v762NqpUqYoOHTpj0KChH120vWFDOxw8eByGhoYAgKNHD2Plyp9x/PhZhX7r12+DgYHBR+0rP9HRUVi37ldcuxaGpKREmJhUgFRqjTFjJuDLL2sW6741QZYABN7X4ylXrhyqVbOAo6MT+vQZ+El8n4yLi8W+fbuxbdvukg6lWFy/fhU7d27H3bsRiIuLxaJFS9Gy5Vfy5ZmZmVi37lcEB19EdHQUypc3RJMmTTFmzHiYmZnL+23duhFBQRcRGXkXOjo6SvcTACxf/j/cuBGOR48e4Msva2HLlp1Kfe7fj8SyZT/izp3bqFChInr27IOBAwfnGv/Ro4exaNH3KpcdPnwCFStWQufO3bBly0aEh1+DnZ2D+ienGJSqpFTVqlUxZcoUfPnllxAEAQcOHMC4ceMQEBCAunXrYtGiRTh37hyWL18OIyMjLFiwAL6+vti9+/3NkJWVhVGjRsHMzAy7d+/Gq1evMG3aNOjo6GDSpEkAgGfPnmHUqFHo168fli5diqCgIMyePRvm5uZwdXUFABw9ehSLFy/G999/Dzs7O2zduhU+Pj44fvw4TE1NASDfWIiIiIiI6NOSmZ2JjKyMkg4jV05Ozpg58ztkZGQgKOgili37Edra2vD2HvpR29XR0YGpaf7JkIoVK37UfvKTmZmJb74Zhxo1vsQPP/wPZmZmePXqJYKDLyEpKalY961pO3fuR/ny5ZGSkoJ79+5g585t+PPPQ1i1ai2srOqUdHh5Onz4AGxsbFG16hclHUqxSE1NRZ06ddG5c1fMmvWt0vJ3797h3r07GDx4OOrWrYvExCSsWLEU06ZNwsaN2+X9MjMz4ebWBg0aNMSRIwdz3V/nzl1x+/ZNPHhwX2lZSkoyJk3yRZMmTTFlygw8fHgfixfPh6GhEbp166Fia0CbNm3h5NRcoe2HH75HenoaKlasBOD9Pd+2bQfs27ebSamcWrdurfD7N998g127duH69euoWrUq9u/fj6VLl6J58/cneNGiRejUqROuX78Oe3t7BAYG4v79+9i8eTPMzMxQr149TJw4EUuXLoWvry90dXWxe/duWFpaYvr06QAAKysrhIWFYcuWLfKk1ObNm9GnTx/07NkTAPD999/j7Nmz2L9/P0aOHImkpKR8YyEiIiIiIipKurr/JY88PXvh/PkzCAw8D2/voUhMTMSKFUtx8eIFZGSkw96+Mb7+egqqV68BAHjx4l8sW/Z/7N15XFTV/8fx98wwgIiigoa7pYImophFKOZSbqnlkkuZW6aWmZrmluZWubWZWmpqfl2/tmhuZZmWll/cKs00c80VNcAFFGSZub8//Dk5sYiIA8jr+Xjw/Tb3fubeM/cwML4559wp2rNnt1JSkuXvX0ovvdRfYWHhTtP3Dh064BhlER5eW5LUo0cv9ezZx2n63tixI2W32zV+/ERH+1JSUvTkk03Vr98rat68pex2u5YsWaDVq79UTEyMypYtp+7de6phw8fSfH1//XVEp0+f0gcfzHQEHv7+JRUcXNOp7qOPpunHHzcpKuqcihXzU5MmzdSjRy/H7Jh582brp58266mnOuqTTz5WXFysmjZtoVdeGaJlyxbr00+Xym63q337TurWrafjuOHhtTV48HBt2fKjdu36Rb6+furb9+V02ytJR48e1ocfTtOePbvk6VlADz0UqpdfHqwiRYpk2JdFixZToUKF5Ovrp3Llyqtevfrq0aOz3nlnombOnOeoW7NmpZYtW6wzZyLl719S7dt3Ups27R37//77nD788APt2LFNyclJKl/+Xg0aNEzVqgXp9OlTmj79Pe3bt1dXryaofPl71afPS3rwwVBJ0vz5c/T9999p0aLPnNrWvfszqlu3nnr1ejHNtm/cuF6tWz/ltK1fv96qWLGSzGaL1q1bK6vVql69XlTjxs30/vtT9MMPG1WsWDENHDhEYWF1M339tm2L0IIF8/TXX0dkNlsUFFRdAwa8qtKly0j6Z0rlW29N0RdffKo//tirMmXKaciQEQoKClZWhIXVdWrjv3l7e2vq1I+ctg0aNFS9enXT2bNn5e/vL0nq2bOPpGsjl9IzcOC10OvixQtphlLr13+j5ORkjRgxWlarVffdV1GHDh3Up58uSTeU8vDwlIeHp+PxhQsX9OuvOzV8+OtOdXXr1tMrr7ykxMSrTvWulqtCqRvZbDZ98803io+PV0hIiPbu3avk5GTVqVPHUVOxYkWVKlXKEQTt3r1bAQEBTkMew8PDNXbsWB0+fFj333+/du/e7QiSbqyZMGGCJCkpKUn79u1Tnz59HPvNZrPq1KmjXbt2SVKm2nIrrFbLLdXnNm5uebv9yDz6On+hv/MP+jr/oK/zD/o6I9duQG4y/bOwuckkyST9///dcab//5+sLKx+43M8PDwUG3tJJpM0YcJYnTp1UpMnv6eCBQtq5szpGjJkgJYs+Vxubm56773JSk5O1kcfzZGnp6eOHftLXl5eqa5DcHANDRgwWHPnztJ//7tcklSggJfTeU0mqWnT5ho1apgSEuLl5XVtbawdO7bq6tWrql+/oUwmafHi+fr223UaMmSEypQpq927d+mNN0araNGiCgl5INVrK1q0qMxmszZt2qgOHZ6WxZL293HBgl4aNWqM/PyK68iRw5o8+S0VLOjlmNJkMkmnT5/S9u0Reu+96Tp9+pRGjRqmM2dOq2zZcpoxY7b27t2jCRPG68EHQ1WtWpDj2HPnztQLL7ysgQMH65tvvtbYsSN1330VVaHCvamuQVxcnPr3f1GtWrXWgAGDlJh4VR99NF2jRw/X9OmzMuw/07/639PTU61bt9O0ae/p4sXzKlq0mL79dp3mzp2lQYOGKiAgUAcPHtDkyW/J07OAHn+8peLj49WvX28VL15Ckye/J19fXx048Kcku0wmKSEhXmFhddWnT19Zre5at+4rDRs2SP/973L5+/urZcsnNH/+HP355z5VrVpNknTw4J86cuSQJkyYkub3Z2zsJR079peqVr0/1fVYt+4rde7cRXPnLtDGjev17ruT9OOPP6h+/Ybq2rWHPv10qd58c7RWrPhKnp6embp+V68mqFOnzqpUqbLi4+M1d+4svfbaq/rPf5bKbDY72vDxxx+pX7+BKlOmrD7++CONHTtSn376pdzc3HT27Fk9+2z71C/mBl269FC3bs+l22c3e69euXJZJpNJhQt7p1ub0TGcfhY5tpm0b98e1awZInd3q2N7aGiYlixZoLi4WBUuXDjjhkn69ttr17tRo0edjl+16v2y2Wz644+9qlWr9k2Pk1G7rVaz3N2z9nsn14VSBw4cUKdOnZSYmCgvLy99+OGHqlSpkvbv3y+r1Zrqovv6+ioqKkqSFB0dnWoO7vXHN6u5fPmyrl69qkuXLslmszmm6d14nqNHjzqOcbO23IrkZFuev/tGUpItp5sAF6Gv8xf6O/+gr/MHu+28Eq/e/LOK1d1XduPOTtPBncf7Om3JyXZJ1+5+5/QZ3JCMa/93xxnXz/fvNmTmuYZkGIZ+/nmHduzYpnbtOurEiRPasuVHzZw5T9Wr15AkjR79htq2baHNmzepUaPHdO7cWdWv30j33XdtalipUmVuON4/x3Zzs6pgQW+ZTCYVK+bndN4b//vBBx9WgQIFtHnzD2rWrIWka6M6wsMfkZdXQSUmJmnhwvmaOvUjx4iVUqXKaM+e3Vq5coVq1kwdSvn5ldCAAa9q5sxp+uSTOapSpapq1aqtxo2bOUbGSFK3bs87/tvfv5Q6dTqujRvX65lnut3wmuwaMWK0vLwKqkKF+xQSUlsnThzX229/ILPZrHLlKmjx4gX65Zefdf/9/4RSDRs+platWkuSevV6UTt3btfnn3+qV18dnuoafPHFpwoICFSfPi85to8YMVpt27bQ8ePHVa5c+TT779/X/bpy5SpIurZOcpEixTRv3mz16zdQ9etfm1FUsmRpHTt2VKtWrVDz5i21fv03unjxoubOXajChX0kSaVLl3Ucv1KlAFWqFOA4fq9eL/7/6LrNateuo4oXv0cPPfSw1q5doypVroVSX321RjVr1lKpUmXS/N48e/asDMOQr69fqutRqVJlR988+2wPLV68QD4+RdSqVRtJUvfuz+vLL7/QoUOHFBRUPVPXr0GDR53OP2LEGLVs+Zj++uuo7ruvkqMNTz/9rMLCwiVJzz3XR126dNCpU6dUvnwF+fr6af781Gs13ahw4cLpvhdv9j5NTEzURx9N12OPNZWXl3eGx0nPjd8XN2xVTEyMSpYs5bT9+hS8mJgYFSp081Bq7dpVeuyxZnJ393Q6joeHpwoW9P7/Pr3pYTJs97Wfq86/czIbuue6UOree+/VypUrFRcXp2+//VbDhg3T4sWLc7pZAAAAeZrZLCUlRuvUye+UlJyQbp27tYDKlG0sq0dR2e0ubCCAm4qI2KLGjespJSVFdrtdjRs3U8+evfXzzztksVicwhUfnyIqV668jh//S5L01FOd9M47E7Vz5zbVrh2q+vUbqVKlyllui5ubmxo2bKz1679Rs2YtlJCQoC1bNmvs2GszUE6dOqmrV6/qlVdecnpecnJyhneua9eug5o3b6Fff/1F+/b9rh9+2KCFC+dr8uR39eCDD0u6Nn3siy+W6fTp00pIiJfNZpOXV0Gn4/j7l3LaVqxYMVksZpnN5hu2+erixfNOz6tWrbrT46Cg6jp06GCabT18+JB+/fVnNW5cL9W+06dPpRlKZcT4/3/hm0wmJSQk6PTpU5o06Q1NmfKWo8Zms6lgwWsL0h86dFABAYGOQOrf4uPj9cknH2vr1i2KiYmWzWZTYmKizp0766hp1aqNJk4cr5dffkVms1nfffeNXn55ULptTExMlCS5u3uk2lex4j/fTxaLRYUL+zitj1Ws2LWBH9eveWau38mTJzR37iz98cc+Xbp0UYZx7RfTuXNnHQHrv899fYrrhQvnVb58Bbm5ualMmbLpvqbbkZKSotGjh0sy9Oqrw+/IOW7H3r17dOzYXxo1anya+z08PHT16lUXt8pZrgul3N3dVb78tTdvUFCQfv/9dy1cuFDNmzdXcnKyYmOdh6jFxMSoePFrK9z7+flpz549TseLjo6WJKea69turPH29panp6fMZrMsFotiYmKcamJiYhwjrPz8/G7aFgAAgNwoKTlBicnxOd0MAFkQEvKAXn11hNzcrPLz85Obm1umRyO0atVaDz30sLZu3aIdO7Zr0aL56tdvoJ56qlOW29OkSTP169dbFy6c186d2+Xh4aGHH762xElCwrXwe8qUqSpevITT86xWa6pj3cjLq6DCwx9RePgj6t27rwYN6qcFCz7Rgw8+rL1792j8+Nf13HO9FRoapoIFvbVx43otW+Y8kOHGu69L14Kef2+TJLs962PjEhISVLduPb34Yv9U+zKzcPy/XQ8Q/f1LKSHh2s/pYcNGOYWN16aSXQvWPDxSB0M3+vDDqdq5c7teeunatDYPDw+NGjVMycn/3GGybt16cnd3148//iCr1epYnDs9Pj5FJElxcbGpFr6/2TU3/f836/VrnpnrN2zYK/L3L6lhw0bKz6+47Ha7unbt6PQa/n3uf85zLcA6e/asunS5+fS9rl3Tnr6XnpSUFL3++nCdPXtW06bNdISF2alYMV9duOAcnF5/fD3ky8iaNStVuXKAqlSpmub+2NjYm65/dqflulDq3+x2u5KSkhQUFCSr1aqtW7eqadOmkqSjR48qMjLSsYZTzZo1NWvWLMXExDim30VERMjb21uVKlVy1Pz4449O54iIiHAcw93dXdWqVdPWrVv12GOPOdqwdetWPfvss5KUqbYAAAAAQHYqUKBAmiM+ype/17E2zPXpe5cuXdSJE8dVocK9jrp77vFX69ZPqXXrpzRr1gytWbMyzVDKzc0qm+3mQyWrV6+hEiX8tXHjem3bFqGGDR9zhAP33nuv3N3dde7c2TTXj8osk8mk8uUr6Pffrw0++P33PbrnHn+nBcrPnj2T5eP/2759e9W8eUunx+mN7AoICNTmzd/L379kmoHXrUhMvKrVq79UzZq1HGGPn19xRUaeVpMmzR11JtM/U6YqVaqstWtXKjb2UpqjpX7//Tc9/ngr1a/fUNK1kVNnz0ZK+qc/3Nzc1KxZC3399RpZrVY9+miTDBe9Ll26jAoWLKhjx/665ZFg/3az63f9e3jYsFGOO8T99tvuWz6Pn1/mpu/diuuB1KlTJzRt2mxHWJfdgoKC9fHHHyklJcVxjXbu3K5y5crftM3x8fH6/vsNeuGFl9Lcf/r0KSUlJSogoEq2t/tW5KpQ6t1339UjjzyikiVL6sqVK1q7dq127NihefPmqVChQmrXrp0mTZokHx8feXt7680331RISIgjCAoPD1elSpU0dOhQDRkyRFFRUZo6dao6d+4sd3d3SVKnTp20ZMkSTZkyRe3atdO2bdu0bt06zZ4929GOHj16aNiwYQoKClJwcLAWLFighIQEtW17bXX7zLQFAAAAQN7iZnbNP4+y+zxly5ZTvXr1NXnyWxoy5DV5eXlp1qwZKl68hOrVayBJ+uCDd/Xww3VUtmw5xcXF6ddff1b58vemebySJUsqISFeP/+8Q5UqBcjT01OenmkHFY0bN9XKlSt08uRxTZv2z7+pvLwKqlOnZzV9+nsyDEPBwTV1+fJl/f77bhUs6O0U/Fx36NABzZs3W02bPq4KFe6T1WrV7t2/6KuvVjsWMS9btqzOnTurDRu+VdWq1RQRsUU//rjp9i7gDTZt2qAqVaoqOLimvvvuG+3fvy/VXcuua9eug9asWamxY0eqc+euKlzYR6dOndTGjes1bNiodBdql66NdklKSlR8fLwOHNivpUsX6tKli3rrrbcdNT179tHUqW+rYEFvhYaGKTk5WQcO/KHY2Fh16vSsHnusqRYu/EQjRryqPn1ekq+vnw4dOiA/v+IKCgpWmTLltHnz96pbt54kk+bOnZnmyLBWrVo7FgL/6KN5qfbfyGw2q3bth7Rnz2498kiDm1/QDNzs+hUqVFg+Pj5avXqFfH39dO7cWc2aNf2Wz3Or0/fi4+N1+vRJx+MzZ07r0KEDKlTIR/7+/kpJSdGoUUP/f+H592W32xQTc202VuHCPo6RgGfPnlVc3CWdO3dWNptdhw4dkHRt3a/rNwc4deqkEhLidf58jBITrzpqKlS4T+7uVjVu3Ezz58/RxInj1blzN/311xF9/vl/naZYbt78g2bPnqGlS5c7vY7vv18vm82mJk0eT/N1/vbbLpUqVdppvbackKtCqZiYGA0bNkx///23ChUqpMDAQM2bN0916167HeNrr70ms9ms/v37KykpSeHh4RozZozj+RaLRbNmzdLYsWPVsWNHFShQQG3atFH//v8MByxbtqxmz56tiRMnauHChfL399ebb76pevX+mcf6+OOP6/z585o2bZqioqJUtWpVzZ0712mB9Ju1BQAAAEDeYBiSt7u3qhW/32Xn9HZPf0HkrBgxYow++OAdDRs2UMnJyapRo5befvsDx+gKu92m996brKiov+XlVVChoWHq3z/ttYOqV6+h1q3bacyYEbp06ZJ69OjluL39vzVp0lwLF34if/+SCg6u4bSvV68XVaRIUS1aNF+Rkafl7V1IAQFV1LVrjzSPVbz4PfL3L6X58+fozJkzMplMKlmypJ57ro86dnxGkhQeXl8dOz6j99+foqSkZNWpU1fdu/fUJ598nNVL5+S55/po48b1eu+9yfL19dOYMW/p3nvvS7PWz6+4Zs6cp5kzp+uVV/opOTlJ/v4lFRoa5rR2VVqeeaadTCaTChTwUqlSpfXQQ6Hq2LGz07S/Vq1ay8PDU//970J99NEH8vQsoIoVK6l9+6clXZsG+f77H2rGjPc1ZMgA2Ww2VahwnwYNGipJevnlVzRx4ni98MJz8vEpos6du+nKlSup2lK2bDkFBQUrNvaS050I09OyZWtNmfKW+vbtf9PXmZGbXT+TyaSxYyfogw/eUdeuHVW2bHkNHPiqXn457e/F7PLnn3+of/8XHI+nT39fktS8eUuNHDlWUVF/a8uWa7OvevR4xum506bNctzJbt68WVq3bq1jX48enVPVTJr0hnbv/jVVzeefr1apUqXk7e2t996boffem6znn+8iH58i6t79eT35ZFvHc65cuawTJ46neh1r165W/foNVahQoTRf54YN3zoWoc9JJsPIzh+FyIro6Lg8ffc9d3cLd3fJJ+jr/IX+zj/o6/zBbJZSko7oyJFVGa4p5WH10n33PSGrRyUWOs/DeF+nLzk5STExZ+TrW1JWq7vTvsyuz5Qdsuvz/43TuXB7wsNra8KEd257BNCddCf62zAMderURm3aPKVOnZ7NVH3v3t3UocMzaty4WfY2Bg538r199OgRDRjwov773xXy9s76Wlg3+3nq55d2IHajXDVSCgAAAAByCuEO8psLFy5o48Zvdf58jB5//IlMPcdkMmno0JE6cuTwHW4d7pSYmGiNGjXutgKp7EIoBQAAAABAPtSqVWMVKVJEQ4aMvKXFvitXDkx3AXjkfg8+GJrTTXAglAIAAAAA5Htbtvyc001wufz4mpG7ZH1VMgAAAAAAACCLCKUAAAAAAADgcoRSAAAAAAAAcDlCKQAAAAAAALgcoRQAAAAAAABcjrvvAQAAAIAkk8l15zIM150LAHIrQikAAAAA+Z67KV5m22WXnc9u8VaS4eWy87nKU0+1UocOT6tDh2dyuik54syZSLVv/4Tmz1+iypUDc7o5OWrOnJk6f/68hg0bmdNNQRbMnDldV68m6JVXht7R8xBKAQAAAMjXTCbJbLss49IfMuwpd/58ZjeZfe6XyeKV6RFTb701VuvWrVWfPv3UpUt3x/Yff9ykESNe1ZYtP9+Zxqbj66/XaNq0d/XNN5ucts+Zs1AFChS4o+eOjDytjz/+SLt2/aK4uFj5+BRRYGAVvfhif5UvX+GOntsVfv31Z/Xv/4IkyWQyycvLS6VKldaDD4aqY8fO8vX1y+EW3lxMTLQ+/3yZFi5cltNNuSN27/5VS5cu0oED+xUTE60JE97RI480cOxPSUnRxx9/pG3b/qfIyNMqWNBbtWs/pBdffFl+fsUddbGxl/T++2/rf//7SWazSfXrN9KAAa/Ky+taYH095Py3WbPmKyiouqRr78UJE8Y57Xd3d9f330dk+BqWL/9MK1Z8pjNnzuiee+5R167PqXnzlo79Tz/dRR06PKkOHZ5R6dJlbvkaZRahFAAAAABI1wIpe/KdP4+krMwUdHf30JIlC/Tkk21VuHDh7G5WtihatOgdPX5KSopeeeUllStXXm+99bb8/Pz099/ntG1bhOLi4u7ouV1t6dLlKliwoK5cuaKDB//U0qULtXbtak2fPlsVK1bK6eZlaM2alQoKCpa/f8mcbsodkZCQoEqVKqtFiyc0cuSQVPuvXr2qgwf/VLduz6ty5cqKjY3TBx+8o2HDBmnevEWOunHjXldMTLTef/9DpaSkaOLEcZoy5S2NHfuW0/GmTv1I9957n+Oxj08Rp/0FCxbU0qXLHY9NN5mL/OWXX2j27A81bNhIValyv/bv36fJk99SoUKFFR7+iCSpSJEieuihh7Vy5XK99NKATF+bW0UoBQAAAAB5QO3aD+n06ZNavHi++vZN/x+Jv/22W7Nnz9Cff+5XkSJF9MgjDdSnTz/HCKbo6GhNnvyGfvnlZ/n6+qpXr776+OMPnabdLVu2WF9/vUaRkadVuLCP6tSpp759+8vLy0u//vqzY2RGeHhtSVKPHr3Us2cfp+l7Y8eOlN1u1/jxEx1tS0lJ0ZNPNlW/fq+oefOWstvtWrJkgVav/lIxMTEqW7acunfvqYYNH0vztf311xGdPn1KH3ww0xF4+PuXVHBwTae6jz6aph9/3KSoqHMqVsxPTZo0U48eveTmdu2fwPPmzdZPP23WU0911CeffKy4uFg1bdpCr7wyRMuWLdanny6V3W5X+/ad1K1bT8dxw8Nra/Dg4dqy5Uft2vWLfH391Lfvy+m2V5KOHj2sDz+cpj17dsnTs4AeeihUL788WEWKFEn3OZJUtGgxFSpUSL6+fipXrrzq1auvHj066513JmrmzHmOujVrVmrZssU6cyZS/v4l9dRTndS2bXvH/r//PqcPP/xAO3ZsU3JyksqXv1eDBg1TtWpBOn36lKZPf0/79u3V1asJKl/+XvXp85IefDBUkjR//hx9//13WrToM6e2de/+jOrWradevV5Ms+0bN65X69ZPOW3r16+3KlasJLPZonXr1spqtapXrxfVuHEzvf/+FP3ww0YVK1ZMAwcOUVhY3Uxfv23bIrRgwTz99dcRmc0WBQVV14ABrzpG91wfbfTWW1P0xRef6o8/9qpMmXIaMmSEgoKCM+yD9ISF1XVq4795e3tr6tSPnLYNGjRUvXp109mzZ+Xv769jx/7S9u0Rmjt3oapUuV+SNHDgEA0ZMkD9+g1U8eL/jKjy8fHJcIScyWS6pRF03377tZ58sq0efbSJJKl06TLav/8PLVmywBFKSVLduvU0Z87MOxpKcfc9AAAAAMgDLBazevd+SV988Zn+/vtcmjWnT5/Sq6++rAYNGmnBgv9q3LgJ2rNnt95/f4qj5s03Rys6OkrTp8/Wm29O0erVK3Thwnmn45jNZg0cOESLFn2mkSPH6tdfd+qjj6ZJkqpXr6H+/QerYMGCWrXqG61a9Y2efrpLqrY0adJc//vfj4qPj3ds2759q65evar69RtKkhYtmq9vvvlKr746QosWfaqOHZ/RG2+M1q5dv6T5+ooUKSqz2awfftgom82W7rXy8vLSyJFjtHjx5xowYLDWrFmpTz9dkupabdsWoXffna4xY97SV1+t0pAhAxUV9bdmzJitF198WXPmzNS+fXudnjd37kw1aNBI//nPUjVp0kxjx47UsWN/pdmOuLg49e//ogICAjV37iK9++40nT9/XqNHD0+37enx8PBU69bt9Pvvvzn6a/36dZo7d5Z69+6rxYs/V58+L2nu3Flat26tJCk+Pl79+vVWdHSUJk16T//5z3/1zDNdZRh2x/6HH66rDz74SJ98skShoWEaNmyQzp49K0lq0eIJHT9+TPv373O04+DBP3XkyCE9/nirNNsZG3tJx4795QhabrRu3Vfy8fHRnDkL1K5dB7377iS9/vowBQUF65NPFuvBBx/Wm2+O1tWrVzN9/a5eTVCnTp01d+4iffDBRzKZTHrttVdlt9udzv3xxx/p6ae7aP78pSpbtpzGjh2plJRr03XPnj2rxo3rZfi1cOEnt9xnN7p8+bJMJpMKFfKWJO3du0fe3oWcrlPt2g/JbDan+p4bNmyQWrZsrBdf7KktWzanOnZCQoLatWuptm1baPjwQTp69EiGbUlKSpK7u7vTNg8PD+3fv89xTSTp/vuD9Pff53TmTOQtv97MYqQUAAAAAOQR9es3VOXKAZo3b7ZGjBidav+iRfPVuHEzx4insmXLacCAIXr55d4aPHi4zp49o59/3uE0OmP48NfVqVMbp+PcuFB5yZKl1KvXi3rnnYl69dXhslqt8vb2vunojIceelgFChTQjz/+oGbNWkiSvvvuG4WHPyIvr4JKSkrSokXzNXXqR44RK6VLl9GePbu1atUKhYQ8kOqYxYuX0IABr2rmzGmaP3+OqlSpqlq1aqtx42ZO69507/68U/tPnDiujRvXq3Pnbo7thmHXa6+NlpdXQd17730KCamtkyeP6513PpDZbFa5chW0ZMkC/frrz6pWLcjxvIYNH1OrVq0lSb1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",
      "text/plain": [
       "<Figure size 1200x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 15))\n",
    "\n",
    "# Calculate means for legend\n",
    "mean_spec_decay_diff = np.mean(spec_decay_diff)\n",
    "mean_last_spec_decay_diff = np.mean(last_spec_decay_diff)\n",
    "mean_pos_decay = np.mean(pos_spec_decay_values)\n",
    "mean_neg_decay = np.mean(neg_spec_decay_values)\n",
    "mean_diff = np.mean(np.array(pos_spec_decay_values) - np.array(neg_spec_decay_values))\n",
    "\n",
    "# Calculate percentiles for spec_decay_diff\n",
    "percentiles = [0.02, 0.05, 0.1, 0.15, 0.85, 0.9, 0.95, 0.98]\n",
    "percentile_values = np.percentile(spec_decay_diff, [p * 100 for p in percentiles])\n",
    "print(\"\\nSpectrum Decay Difference Percentiles:\")\n",
    "for p, v in zip(percentiles, percentile_values):\n",
    "    print(f\"{p*100:.0f}th percentile: {v:.4f}\")\n",
    "\n",
    "# Plot histograms of differences\n",
    "ax1.hist(\n",
    "    spec_decay_diff,\n",
    "    bins=100,\n",
    "    range=(-0.5, 0.5),\n",
    "    alpha=0.3,\n",
    "    label=f\"All Spectrum Decay Differences (mean={mean_spec_decay_diff:.3f})\",\n",
    "    color=\"blue\",\n",
    ")\n",
    "ax1.hist(\n",
    "    last_spec_decay_diff,\n",
    "    bins=100,\n",
    "    range=(-0.5, 0.5),\n",
    "    alpha=0.3,\n",
    "    label=f\"Last Spectrum Decay Differences (mean={mean_last_spec_decay_diff:.3f})\",\n",
    "    color=\"red\",\n",
    ")\n",
    "\n",
    "# Add labels and title for differences plot\n",
    "ax1.set_xlabel(\"Normalized Spectrum Decay Difference\", fontsize=12)\n",
    "ax1.set_ylabel(\"Count\", fontsize=12)\n",
    "ax1.set_title(\n",
    "    \"Distribution of Spectrum Decay Differences\\nBetween Positive and Negative Samples\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax1.legend(fontsize=10)\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot histograms of raw values\n",
    "ax2.hist(\n",
    "    pos_spec_decay_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.3,\n",
    "    label=f\"Positive Sample Decay (mean={mean_pos_decay:.1f})\",\n",
    "    color=\"green\",\n",
    ")\n",
    "ax2.hist(\n",
    "    neg_spec_decay_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.3,\n",
    "    label=f\"Negative Sample Decay (mean={mean_neg_decay:.1f})\",\n",
    "    color=\"orange\",\n",
    ")\n",
    "\n",
    "# Add labels and title for raw values plot\n",
    "ax2.set_xlabel(\"Spectrum Decay Value\", fontsize=12)\n",
    "ax2.set_ylabel(\"Count\", fontsize=12)\n",
    "ax2.set_title(\n",
    "    \"Distribution of Raw Spectrum Decay Values\\nFor Positive and Negative Samples\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax2.legend(fontsize=10)\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Calculate and plot the difference between positive and negative values\n",
    "diff_values = np.array(pos_spec_decay_values) - np.array(neg_spec_decay_values)\n",
    "ax3.hist(\n",
    "    diff_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.7,\n",
    "    label=f\"Positive - Negative Difference (mean={mean_diff:.1f})\",\n",
    "    color=\"purple\",\n",
    ")\n",
    "\n",
    "# Add labels and title for difference plot\n",
    "ax3.set_xlabel(\"Difference Value (Positive - Negative)\", fontsize=12)\n",
    "ax3.set_ylabel(\"Count\", fontsize=12)\n",
    "ax3.set_title(\n",
    "    \"Distribution of Differences Between\\nPositive and Negative Spectrum Decay Values\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax3.legend(fontsize=10)\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:25.566804Z",
     "iopub.status.busy": "2025-10-04T22:10:25.566457Z",
     "iopub.status.idle": "2025-10-04T22:10:27.399835Z",
     "shell.execute_reply": "2025-10-04T22:10:27.399256Z",
     "shell.execute_reply.started": "2025-10-04T22:10:25.566783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = clip_ratios\n",
    "y = clip_diffs\n",
    "# Create the 2D histogram (heatmap)\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Create a 2D histogram\n",
    "bin_edges = np.linspace(-0.5, 0.5, 101)  # 30 bins from -1 to 1\n",
    "hist, x_edges, y_edges = np.histogram2d(\n",
    "    x,\n",
    "    y,\n",
    "    bins=[bin_edges, bin_edges],  # Same bins for both x and y\n",
    "    range=[[-0.5, 0.5], [-0.5, 0.5]],  # Ensure range is from -1 to 1 for both axes\n",
    ")\n",
    "\n",
    "# Create a heatmap using pcolormesh for better control\n",
    "X, Y = np.meshgrid(x_edges[:-1], y_edges[:-1])\n",
    "plt.pcolormesh(X, Y, hist.T, cmap=\"viridis\", shading=\"auto\")\n",
    "\n",
    "# Add a color bar\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(\"Counts\", rotation=270, labelpad=20, fontsize=12)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Clip quality diff ratios\", fontsize=12)\n",
    "plt.ylabel(\"Clip quality diff ratio difference with prev\", fontsize=12)\n",
    "plt.title(\"2D Histogram (Heatmap) of Correlated Data\", fontsize=14)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"2d_histogram.png\", dpi=300)  # Save to file (optional)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:27.400601Z",
     "iopub.status.busy": "2025-10-04T22:10:27.400438Z",
     "iopub.status.idle": "2025-10-04T22:10:28.231275Z",
     "shell.execute_reply": "2025-10-04T22:10:28.230679Z",
     "shell.execute_reply.started": "2025-10-04T22:10:27.400584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile  |  Quantile Value\n",
      "-------------------------------\n",
      "  2th      |   -0.126\n",
      "  5th      |   -0.097\n",
      " 10th      |   -0.074\n",
      " 20th      |   -0.048\n",
      " 50th      |   -0.001\n",
      " 80th      |    0.043\n",
      " 90th      |    0.067\n",
      " 95th      |    0.087\n",
      " 98th      |    0.110\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Plot histogram (PDF)\n",
    "counts, bins, patches = plt.hist(\n",
    "    total_clip_ratios,\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    color=\"skyblue\",\n",
    "    edgecolor=\"black\",\n",
    "    alpha=0.7,\n",
    "    label=\"Histogram (PDF)\",\n",
    ")\n",
    "\n",
    "# Plot CDF on the same axis\n",
    "sorted_ratios = np.sort(total_clip_ratios)\n",
    "cdf = np.arange(1, len(sorted_ratios) + 1) / len(sorted_ratios)\n",
    "plt.plot(\n",
    "    sorted_ratios, cdf * counts.max(), color=\"red\", linewidth=2, label=\"CDF (scaled)\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Clip quality diff ratios\", fontsize=12)\n",
    "plt.ylabel(\"Count\", fontsize=12)\n",
    "plt.title(\"Distribution of Clip Quality Difference Ratios\", fontsize=14)\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "percentages = [0.02, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.98]\n",
    "print(\"Percentile  |  Quantile Value\")\n",
    "print(\"-------------------------------\")\n",
    "for idx, percentage in enumerate(percentages):\n",
    "    quantile_value = np.quantile(sorted_ratios, percentage)\n",
    "    quantile_value_rounded = round(quantile_value, 3)\n",
    "    print(f\"{int(percentage*100):>3d}th      |  {quantile_value_rounded:>7.3f}\")\n",
    "    # Draw the vertical line\n",
    "    plt.axvline(\n",
    "        quantile_value,\n",
    "        color=\"k\",\n",
    "        linestyle=\"dotted\",\n",
    "        linewidth=1,\n",
    "        alpha=0.8,\n",
    "        label=f\"{int(percentage*100)}th percentile\"\n",
    "        if idx == 0\n",
    "        else None,  # Only label first to avoid duplicate legend\n",
    "    )\n",
    "\n",
    "plt.xlim(-0.5, 0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:28.232067Z",
     "iopub.status.busy": "2025-10-04T22:10:28.231902Z",
     "iopub.status.idle": "2025-10-04T22:10:28.249960Z",
     "shell.execute_reply": "2025-10-04T22:10:28.249488Z",
     "shell.execute_reply.started": "2025-10-04T22:10:28.232051Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Create a figure with two subplots side by side\n",
    "# fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# # First subplot for clip_diffs\n",
    "# ax1.hist(clip_diffs, bins=np.linspace(-0.5, 0.5, 100))\n",
    "# ax1.set_title(\"Differences in quality between positive and negative vs previous chunk\")\n",
    "# print(\"Differences in quality between positive and negative vs previous chunk\")\n",
    "# for percentage in [0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95]:\n",
    "#     print(percentage, \"--->\", np.quantile(sorted(clip_diffs), percentage))\n",
    "\n",
    "# # Second subplot for clip_ratios\n",
    "# ax2.hist(clip_ratios, bins=np.linspace(-0.5, 0.5, 100))\n",
    "# ax2.set_title(\"Differences in quality between positive and negative for the same chunk\")\n",
    "# print(\"Differences in quality between positive and negative for the same chunk\")\n",
    "# for percentage in [0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95]:\n",
    "#     print(percentage, \"--->\", np.quantile(sorted(clip_ratios), percentage))\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:28.250643Z",
     "iopub.status.busy": "2025-10-04T22:10:28.250502Z",
     "iopub.status.idle": "2025-10-04T22:10:40.878095Z",
     "shell.execute_reply": "2025-10-04T22:10:40.877368Z",
     "shell.execute_reply.started": "2025-10-04T22:10:28.250629Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_audio_quality_measures(s3_id):\n",
    "    audio_quality = unpacked_pair_quality.get(s3_id, [])\n",
    "    if not audio_quality:\n",
    "        return [None for _ in range(11)]\n",
    "    return [\n",
    "        np.mean(\n",
    "            audio_quality[\"ear_v2_quality_scores\"]\n",
    "        ),  # float(audio_quality[\"ear_v2_quality_scores\"]),\n",
    "        float(audio_quality[\"shimmer_score\"]),\n",
    "        float(audio_quality[\"loudness_factor\"]),\n",
    "        audio_quality[\"spectral_character\"],\n",
    "        float(audio_quality[\"spectral_centroid\"]),\n",
    "        float(audio_quality[\"bass_ratio\"]),\n",
    "        float(audio_quality[\"mid_ratio\"]),\n",
    "        float(audio_quality[\"high_ratio\"]),\n",
    "        float(audio_quality[\"stereo_width\"]),\n",
    "        int(audio_quality[\"total_clips\"]),\n",
    "        float(audio_quality[\"clips_per_second\"]),\n",
    "        float(audio_quality[\"abs_loudness_factor\"]),\n",
    "        float(audio_quality[\"spectrum_decay\"]),\n",
    "    ]\n",
    "\n",
    "\n",
    "df[\n",
    "    [\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\",\n",
    "        \"spectrum_decay\",\n",
    "    ]\n",
    "] = pd.DataFrame(df[\"s3_id\"].apply(get_audio_quality_measures).tolist(), index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:40.878937Z",
     "iopub.status.busy": "2025-10-04T22:10:40.878774Z",
     "iopub.status.idle": "2025-10-04T22:10:43.144649Z",
     "shell.execute_reply": "2025-10-04T22:10:43.144015Z",
     "shell.execute_reply.started": "2025-10-04T22:10:40.878920Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(523524, 108)\n",
      "(522418, 108)\n",
      "(522418, 108)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df.dropna(\n",
    "    subset=[\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\",\n",
    "        \"spectrum_decay\",\n",
    "    ]\n",
    ")\n",
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:36.043975Z",
     "start_time": "2024-05-16T13:58:56.910958Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:43.145505Z",
     "iopub.status.busy": "2025-10-04T22:10:43.145340Z",
     "iopub.status.idle": "2025-10-04T22:10:43.239940Z",
     "shell.execute_reply": "2025-10-04T22:10:43.239351Z",
     "shell.execute_reply.started": "2025-10-04T22:10:43.145485Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 261209\n"
     ]
    }
   ],
   "source": [
    "# Let's use the old selection for now -- for quality assurance\n",
    "# expand the metadata columns -- this takes forever...~ 6 mins\n",
    "# test_slice = df[\"metadata\"].apply(lambda x: ast.literal_eval(str(x)))\n",
    "# test_slice = df[\"metadata\"]  # .apply(lambda x: custom_parse(x))\n",
    "# test_slice_series = test_slice.apply(pd.Series)\n",
    "# df = pd.concat([df, test_slice_series], axis=1, join=\"inner\")\n",
    "print(\"unique_requests\", df[\"request_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:40.799375Z",
     "start_time": "2024-05-16T13:59:36.394236Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:43.240761Z",
     "iopub.status.busy": "2025-10-04T22:10:43.240610Z",
     "iopub.status.idle": "2025-10-04T22:10:45.201991Z",
     "shell.execute_reply": "2025-10-04T22:10:45.201287Z",
     "shell.execute_reply.started": "2025-10-04T22:10:43.240746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    522418\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    261209\n",
      "True     261209\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-ahi-up-3         333110\n",
      "chirp-bass-up-3        157000\n",
      "chirp-v4-up-u-d-2-5     32308\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-ahi-up-3         166555\n",
      "            chirp-bass-up-3         78500\n",
      "            chirp-v4-up-u-d-2-5     16154\n",
      "True        chirp-ahi-up-3         166555\n",
      "            chirp-bass-up-3         78500\n",
      "            chirp-v4-up-u-d-2-5     16154\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    522418\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row.get(\n",
    "            \"continue_at\", row[\"duration\"]\n",
    "        )\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id],\n",
    "            row.get(\"continue_at\", row[\"duration\"]),\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:45.202807Z",
     "iopub.status.busy": "2025-10-04T22:10:45.202644Z",
     "iopub.status.idle": "2025-10-04T22:10:47.172915Z",
     "shell.execute_reply": "2025-10-04T22:10:47.172357Z",
     "shell.execute_reply.started": "2025-10-04T22:10:45.202790Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    180634\n",
       "2.0     80575\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=[\"request_id\", \"preference\", \"diff_preference\"])\n",
    "df[\"pos_diff_preference\"] = df[\"diff_preference\"].diff()\n",
    "df[df[\"preference\"]][\"pos_diff_preference\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:47.173800Z",
     "iopub.status.busy": "2025-10-04T22:10:47.173639Z",
     "iopub.status.idle": "2025-10-04T22:10:49.121682Z",
     "shell.execute_reply": "2025-10-04T22:10:49.120994Z",
     "shell.execute_reply.started": "2025-10-04T22:10:47.173783Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    522418.000000\n",
      "mean         23.507643\n",
      "std           1.615149\n",
      "min          10.415113\n",
      "25%          22.709822\n",
      "50%          23.646712\n",
      "75%          24.523439\n",
      "max          30.466667\n",
      "Name: mean_ear_score, dtype: float64\n",
      "count    261209.000000\n",
      "mean         -0.026982\n",
      "std           1.314272\n",
      "min         -10.644886\n",
      "25%          -0.885114\n",
      "50%          -0.024998\n",
      "75%           0.828267\n",
      "max           9.760809\n",
      "Name: mean_ear_score_diff, dtype: float64\n",
      "count    261209.000000\n",
      "mean         -0.002713\n",
      "std           0.056716\n",
      "min          -0.791320\n",
      "25%          -0.038204\n",
      "50%          -0.001061\n",
      "75%           0.034479\n",
      "max           0.416926\n",
      "Name: mean_ear_score_diff_ratio, dtype: float64\n",
      "count    522418.000000\n",
      "mean          0.282851\n",
      "std           0.404588\n",
      "min           0.000000\n",
      "25%           0.066667\n",
      "50%           0.166667\n",
      "75%           0.354167\n",
      "max          26.960699\n",
      "Name: mean_shimmer_score, dtype: float64\n",
      "count    261209.000000\n",
      "mean          0.001233\n",
      "std           0.409198\n",
      "min         -19.814477\n",
      "25%          -0.116667\n",
      "50%           0.000000\n",
      "75%           0.121212\n",
      "max          19.082572\n",
      "Name: mean_shimmer_score_diff, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df[\"mean_ear_score\"] = df[\"s3_id\"].map(clip_id_to_mean_ear_score)\n",
    "print(df[\"mean_ear_score\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff\"] = df[\"mean_ear_score\"].diff()\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff_ratio\"] = df[\"mean_ear_score\"].diff() / (\n",
    "    df[\"mean_ear_score\"] + 0.1\n",
    ")\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff_ratio\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score\"] = df[\"s3_id\"].map(clip_id_to_mean_shimmer_score)\n",
    "print(df[\"mean_shimmer_score\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score_diff\"] = df[\"mean_shimmer_score\"].diff()\n",
    "print(df[df[\"preference\"]][\"mean_shimmer_score_diff\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score_diff_ratio\"] = df[\"mean_shimmer_score\"].diff() / (\n",
    "    df[\"mean_shimmer_score\"] + 0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:49.122477Z",
     "iopub.status.busy": "2025-10-04T22:10:49.122310Z",
     "iopub.status.idle": "2025-10-04T22:10:50.170707Z",
     "shell.execute_reply": "2025-10-04T22:10:50.170153Z",
     "shell.execute_reply.started": "2025-10-04T22:10:49.122460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"pos\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.25,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"neg\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.25,\n",
    ")\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Distribution of Loudness (abs) for Positive and Negative Preferences\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:50.171556Z",
     "iopub.status.busy": "2025-10-04T22:10:50.171397Z",
     "iopub.status.idle": "2025-10-04T22:10:51.451287Z",
     "shell.execute_reply": "2025-10-04T22:10:51.450746Z",
     "shell.execute_reply.started": "2025-10-04T22:10:50.171540Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loudness (web): -12.99475924299867\n",
      "Mean loudness (mobile): -12.901341907903493\n"
     ]
    },
    {
     "data": {
      "image/png": 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cjBEjRsDPzw9hYWHlri97DAYDbty4Yf2MnD17Fg8//DCCg4Px+OOPQ61WY9OmTRg7dizWrVtnvf7Ukfe1tPnz5+Oll15CUlISRowYAQCoX7++3Xn79OmDmTNn4vjx40hKSrJOv3r1Kv744w/MmDHDOu1///sfFi5ciD59+mDYsGHIzs7GunXr8PDDD2P79u12O2aVsbftMBqN1h2AmTNnWk/PnTNnDrZt24YhQ4Zg7NixuHLlCtavX4+///4bGzZsgEajwfz587F582YcP37c+iVm+TJyJn9ubi4mTpyIfv36YeDAgYiMjIQoipg8eTKOHDmCESNGoEmTJjhz5gzWrFmDixcv4sMPP7Sp7ciRI/j6668xevRoBAcHY+3atXj66afx7bffWq/LT09Px7Bhw5Cfn48RI0agcePGSE9Px549e1BUVAQ/Pz/odDqMGTMG6enpeOihh1CnTh0cPXoU77zzDjIzM/Hiiy8CMO9EPvfcc+jYsaP1R48LFy7g999/d2h7Vtnns1evXvjPf/6DHTt2lPnM7dixAx06dKjw9LrY2FgcPXoUZ86cqfSyqJdeegnbtm1Dr169MG7cOBw/fhxLlizB+fPn8cEHH1RaS3leffVVRERE4KmnnoJWqwVg/my/8MILuOuuuzBp0iTUqFEDJ0+exI8//mg9CnHo0CFMnDgRiYmJmDp1Khhj2Lp1Kx599FF88sknNp8dX/n2229Rr149uztfzgoODkb37t3x2Wef4dy5c7jrrrusjxUXF5f5jg4JCYGfnx/27t2L2bNnY968eWV+/HOF5Vrl5ORkjBo1Cv/88w82bNiAP//80/qZd0ZRUREeffRRXL9+HWPHjkWtWrXw+eefl/uj861bt/D444+jR48e6NOnD/bs2YO3334b8fHx1g6Do9uFs2fPYtKkSUhISMDTTz8NPz8/XLp0Cb///rv19TZv3oy5c+eiV69eeOSRR1BcXIzTp0/j2LFjZY6IlZSeno5r167Z/S7ctm0bgoKCMG7cOAQFBeHw4cNYtGgRCgoKMHPmzDL1PvHEE+jTpw/69euHXbt24ZVXXoFGo8GwYcOcznj33Xfb1GfPCy+8gO3bt2Pv3r3WU9otO/bOvP/2ttnOKvl9JEkSJk+ejJ9//hnDhg1D8+bN8eOPP2L+/PnWzgTg2Pta2siRI5GRkYGDBw9WOiDvAw88YN3v69Chg81jO3fuxF133WXdnjq6v+iMW7duIS8vr8ylDR9++CE0Gg0mTJgAvV4PjUbj0HZy5MiRiImJwUcffWS9zNay/+dsfnvb8+3bt2PWrFno1KkTnn/+eeh0OmzYsAGjR4/Gtm3bbPYjTSYTJkyYgKSkJMyYMQOHDh3CypUrUa9ePZszj1588UVs3boVnTt3xrBhw2AymfDbb7/h2LFj1jMKHdm/0Ov11vU1ZswYREVFIT09Hd999x3y8vIqPSOhss+nZbu9a9cuzJ492+Zsyy+//BKSJFW4HbGMQ7B9+3ZMmTKlwrNbf/rpJ0ycOBFxcXGYOnUqioqKsG7dOowaNQpbt26tsE9RkcOHD2PXrl14+OGHER4ejtjYWLfuJzlEUqAtW7ZI8fHx0k8//SRlZWVJ169fl7766iupQ4cOUlJSknTjxg1JkiTp8OHDUnx8vLRr165ylzVw4ECpffv2ZZZt7z+LMWPG2PxdnsuXL0vx8fHS8uXLy51n9erVUnx8vPT5559bp+n1emnkyJFS69atpfz8fJtaDh8+bPc1tmzZYp02aNAgKSUlRcrLy7NOO3DggBQfHy917dq1zHM7dOgg5ebmWqfv27dPio+Pl7755hvrtEcffVTq37+/VFxcbJ0miqI0cuRIqWfPntZpAwcOlJ544oly671161al66Q8zz//vNShQ4cy041Go00uy+skJydLs2fPtk6z1FuyjUiSJB07dkyKj4+X/vvf/7qUsUWLFtLLL79c6Xxdu3Yts24cff8t2du2bStlZWU5lKtr167S+PHjpaysLCkrK0s6efKk9Oyzz0rx8fHSa6+9JkmSJE2ZMkVq0aKFlJaWZn1eenq61KZNG+nhhx+2TqvsfZUkSVq0aFGZz0Xr1q2lmTNnlpnX8jm7fPmyJEmSlJ+fLyUmJkpvvPGGzXzLli2TEhISpKtXr0qSJElXrlyRmjdvLv3vf/+zme/06dPS3XffXWZ6ea9b2bZj5syZUnx8vPT222/bPP/XX3+V4uPjpS+++MJm+g8//FBm+syZM6XWrVvbzOdMfst2ZsOGDTbzbt++XWrWrJn066+/2kzfsGGDFB8fLx05csQ6LT4+XmrRooV06dIl67STJ09K8fHx0tq1a63TZsyYITVr1kw6fvx4mXUmiqIkSZL0wQcfSK1bt5b++ecfm8fffvttqXnz5tK1a9ckSZKkuXPnSm3btpWMRmOZZVXE0c+nJEnSc889J3Xq1EkymUzWaSdOnCizLbTnwIEDUvPmzaXmzZtLI0eOlObPny/9+OOPkl6vt5nPsp5efPFFm+lvvPGGFB8fLx06dMg6LT4+Xlq0aFGZ1+ratatN+7e0v1GjRtmsn7y8PKlNmzbS8OHDpaKiIptlWNa/KIpSz549pfHjx1unSZIk6XQ6KTU1VRo3blyFdXtDfn6+FB8fL02ePNnh59jbLpa0atUqKT4+Xtq3b591Wnnf0Zb33rKeK2sLkmT+nPXr16/cx7OysqQWLVpI48ePt2lv69atk+Lj46XPPvvMphZ727sxY8ZIY8aMsf5t2e7v3LnTOk2r1Uo9evQo8z1v2Q5s27bNOq24uFhKSUmRpk2bZp3m6HbBsj4r+h6ZPHlyheukPD/99FOZfQcLnU5XZtr//d//Sa1atbL5/rbUu3LlSuu04uJiadCgQVLHjh2tn1NnMv7f//2flJSUVOl8lu+wkuvGmfe/vG12Za934cIFKSsrS7p8+bK0ceNGKTExUUpOTpa0Wq20d+9eKT4+Xvrwww9tnjtt2jQpISHBum135H21t7/46quvlrs/W3q79txzz0kdO3a02XZlZGRIzZo1kxYvXmyd5uj+Ynni4+OlF154wbrvcuzYMenRRx+1aReWfeJu3brZtC1ntpPl9REczV/e9rygoEC65557pJdeeslmuZmZmVK7du1splv2NUquP0mSpMGDB0sPPvig9e9Dhw7Z7LuVZKnT0f2Lv//+u9K+UXkc/Xz++OOPUnx8vPT999/bPH/AgAE220J7dDqd1KtXL2ufZdasWdKnn34q3bx5s8y8ltfNycmxTjt58qTUrFkzacaMGdZpM2fOtOn/WNjbb42Pj5eaNWsmnT171ma6O/eTHKHoU+gfe+wx6ykrzz77LIKDg7F48WKnBjcICgpCYWFhmelz5szBqlWrbP6zWLt2rVuOvgPADz/8gOjoaJvrbjUaDcaOHQutVotff/3VqeVlZGTg5MmTePDBB21+RUtJSSn3+uu+ffvaHMm13DrFchpLbm4uDh8+jD59+ljPTMjOzkZOTg46deqEixcvWk9ZDQ0NxdmzZ3Hx4kW7rxUQEACNRoNffvnF5pRjR+Tm5to94qxSqazXGYuiiNzcXBiNRiQmJto9zbZ79+42bSQpKQmtWrXC999/71LGsLAw5OTkOFWLhbPvf8+ePcvcOaEiBw4cQMeOHdGxY0cMGjQIu3fvxqBBg/D888/DZDLh4MGD6N69O+rVq2d9Tq1atdC/f38cOXLEejpaZe9rVYWEhKBz587YtWuXzWmBO3fuROvWra2/yO7duxeiKKJPnz7WdpidnY2oqCg0aNDA4dsvObrtKD1OwO7du1GjRg2kpKTYvH6LFi0QFBRU6es7m9/Pz6/MEcTdu3ejSZMmaNy4sc0yLKddll5GcnKyzZkPzZo1Q0hIiPXzLYoi9u3bh65du9od98Py6/fu3bvRrl07hIaG2rxucnIyTCaTta2GhoZCp9M5PRq4RWWfT8A8CGJGRoZNrTt27EBAQAB69uxZ4fJTUlKwceNGpKam4tSpU1i+fDkmTJiAzp0725wiaXm90resHD9+vM3jrhgxYoTNUYmDBw+isLAQTzzxRJnr7Czr/+TJk7h48SIGDBiAnJwc6/rXarXo2LEjfv31V6dP0XM3y/YiODjYbcu0LKv093S3bt3KfEd36tQJgHn08dOnT7vl6PtPP/0Eg8GARx55xOba2uHDhyMkJMSldmDZ7pccmycwMNB6plJpQUFBNtcD+/n5oWXLltbPMOD4dsFyhs/+/fvLbS+hoaG4ceOG3UvpKmL5HrR3FlTJAQYt+xH33HMPdDodLly4YDOvWq3GyJEjbeodOXIksrKycOLECaczhoaGoqioCDqdzql6AOfff3vb7Mr07t0bHTt2RLdu3TBnzhw0aNAAS5YsQWBgIH744QeoVKoyl2CMHz8ekiRZL4dy5H2tqj59+iArK8vmNPo9e/ZAFEX07dsXgHP7ixX57LPPrPsuw4cPx++//45x48aVOYtr8ODBNm2rqttJV/KX3p7/9NNPyMvLQ79+/Ww+i4IgoFWrVnb3E0rva7Rr187mks2vv/4ajDG7gyBbviMc3b+wnIJ+4MABlz4Tjnw+k5OTUatWLZtLWs6cOYPTp09j4MCBFS4/ICAAn376qfXuXVu3bsWLL76ITp064bXXXrOerl6yv1Py7MlmzZohOTm5St/R7du3t+kzuXs/yRGKPoV+zpw5aNSoEVQqFaKiotCoUSOHB6+w0Gq1dnc2kpKSvDKI3dWrV9GgQYMyuS23GLt27ZpTy7PMb28E1UaNGtnt0NapU8fmb0sn2XJ9V1paGiRJwsKFC7Fw4UK7r5uVlYWYmBg8/fTTmDJlCnr16oX4+Hh06tQJgwYNQrNmzQCYP+jPP/883nzzTaSkpKBVq1Z44IEHMHjwYIduxSaVcx3ktm3bsHLlSvzzzz82A1zYO33G3rpp2LAhdu3a5VJGSZJcHinW2fff2dOBWrVqhX/9619gjCEgIABNmjSxftFnZmZCp9OhUaNGZZ7XpEkTiKKI69ev46677qr0fXWHvn37Yt++fTh69Cjatm2LtLQ0nDhxwnqKIGC+bkuSpHI7aY4OoufItkOtVpe57OTSpUvIz88vdzCkygZBczZ/TExMmUHwLl26hPPnzzucofTnGzB/xi2f7+zsbBQUFNicnmzPpUuXcPr06XJf13I68+jRo7Fr1y7rrV5SUlLQp08fdO7cucLlW1T2+QTMnfDo6Gh88cUX6NixI0RRxJdffmm9M0RlkpKSsHjxYuj1epw6dQr79u3D6tWr8cwzz2D79u1o2rQprl69CkEQylz2ER0djdDQUJfucW1R+nNsOWW2ovfA8uNZ6dONS8rPz3f6shp3sqx7ez+Ku8qyrNLf07Vr1y4zHoonWLbBjRs3tpnu5+eHevXqudQOLNv90t8b9rbFgLnW0vOGhYXZHEhwdLvQt29ffPrpp3jppZewYMECdOzYET169EDv3r2t28CJEyfip59+wvDhw9GgQQOkpKSgf//+aNeunUP12fuePnv2LN577z0cPnzY+kOPRenrbmvVqlVmILCGDRsCMK+71q1bO5XRkseV72ln33972+zKvP/++wgJCbF+55Tc5ly9ehW1atUqs12z7CNYXt+R97WqOnfujBo1amDnzp3WdrZz5040b97c2nad2V+sSLdu3TBmzBgwxhAcHIymTZvaHRyu9La0qttJV/KXl6G8S8ZKv5f+/v5lDsyEhYXZHEBKS0tDrVq1KrxE2NH9i3r16mHcuHFYtWoVduzYgXvuuQepqakYOHCgQwP6OfL5FAQBAwYMwIYNG6wDOe7YsQP+/v5lBhW3p0aNGpgxYwZmzJiBq1evWi8rWLduHUJCQvDss89aP5vl7cMeOHDA5UEFS7+n7t5PcoSiO/BV7WQbDAZcvHix0jdEDsr74nHHL63ljQZu+dKzvMb48eNx//33253X8oXTvn177N27F/v378fBgwfx2WefYc2aNXj11Vett+l47LHHkJqain379uHAgQNYuHAhli5dijVr1pR7HTlgvh6s9KAxgHnAjlmzZqF79+6YMGECIiMjoVKpsGTJEpsjFM5wJmNeXp7X7vHt7G2SwsPD3bKT68j7WlVdu3ZFYGAgdu3ahbZt22LXrl0QBMFmYy+KIhhjWLZsmd126+iG2pFth5+fX5kdH1EUERkZibffftvucyo7O8LZ/Pbeb1EUER8fb/fWWgDK/OhQ2efbUaIoIiUlBY8//rjdxy1f4JGRkdi+fTsOHDiAH374AT/88AO2bt2KwYMH480333TqNcujUqkwYMAAbN68Ga+88gp+//13ZGRkVPrLfml+fn5ISkpCUlISGjZsiNmzZ2P37t02Rzmqchun8gbocmU0W8v7NWPGjDLjelj4evTjkJAQ1KpVq8xATVVhWZY3b+vlbiaTqUp33XDkuY5uFwICArB+/Xr8/PPP+O677/Djjz9i586d2LRpE1auXAmVSoUmTZpg9+7d1se//vprfPLJJ3jqqafw9NNPl5vB8j1Y+ns6Ly8PY8aMQUhICJ5++mnUr18f/v7+OHHiBN5++22X9mOcyZiXl4fAwECv3GbQlde45557nDqzrrzXrex9rSo/Pz90794de/fuxcsvv4ysrCz8/vvveO6556zzOLO/WBFHf6Arvb6rup10JX/p7bklw/z58+0e9Cn9XrjrjjzO7F/MmjULDz74oHWfbu7cuViyZAk2b95c7nhZzho8eDBWrFhhHUT3yy+/xAMPPOD0qP+xsbEYNmwYevToge7du2PHjh1lxi2rTHnf4+V9R7u6rXB0P8kRiu7AV5Vl4AHLaXe+EBsbi9OnT0MURZvOguWUMsupw5ajpqV/qS79669l/kuXLpV5rX/++celjJbTqzUajUMb1Jo1a2Lo0KEYOnQoCgsLMWbMGLz//vs2Hb369etj/PjxGD9+PC5evIjBgwdj5cqV5XaMAPMv4Dt27EB+fr7NBmDPnj2oV68eFi9ebPMhXbRokd3l2Fs3Fy9eRGxsrM00RzKmp6fDYDBYfw13lqPvvydEREQgMDDQbru4cOECBEGwOXrryPtaFUFBQXjggQewe/duzJ49Gzt37sQ999xj80t3/fr1IUkS4uLiyj1a5Un169fHoUOH0LZtW5c28O7IX79+fZw6dQodO3Z0yz2CIyIiEBISUmmnq379+tBqtQ5tA/z8/JCamorU1FSIoohXXnkFmzZtwpQpUyrtiDn6+Rw0aBBWrlyJb775Bj/88AMiIiKqtC23DEiZkZEBwPzZFEURly5dsvl837x5E3l5eTZ5Sp7RYKHX65GZmenQa1t2CM+ePVvu+rFsh0NCQrxy5NlVXbt2xaZNm3D06NEq3wKrsLAQ+/btQ506dVzexlaVZRt84cIFm0uN9Ho9rly5YvNe2GsHgPkobsnnxsbG4syZM2XO3nL1OxpwbrsgCIL19OTZs2fjo48+wrvvvouff/7ZWk9QUBD69u2Lvn37Qq/XY9q0afjoo48wadKkcn+AshylLn23ll9++QW5ublYvHgx2rdvb51e3p1nMjIyyhw5sxzVLPm5czTjlStXyhxBd5Qz778nxMbG4tChQygoKLA5cmvZRyi5Phx5X0tz9jukT58+2LZtGw4dOoTz589DkiT06dPH+riz+4vuVtXtpDvyW5YRGRnptnVQv359HDhwALm5ueUehXd2/8Jy14EpU6bg999/x6hRo7Bhw4ZKO8eOfj7j4+Nx9913Y8eOHahduzauXbuGl156qfJiyxEWFoZ69epZ91Usn83y9mHDw8OtGUNDQ8vdNjvCE/tJlVH0NfBVcerUKfz3v/9FWFgYHn74Yaee687byHXu3BmZmZnYuXOndZrRaMTatWsRFBRk/bKLjY2FSqUqc/1E6Vur1KpVC82bN8e2bdtsOvsHDx7EuXPnXMoYGRmJDh06YNOmTdad25JKnhJS+lrw4OBg1K9f33rNik6nQ3Fxsc089evXR3BwsN1b6JTUunVrSJKEv/76y2a65ZfGkkcUjx07VuZWaBb79u2zuYbp+PHjOHbsmPUUX2cyWrK4uqPq6PvvCSqVCikpKdi/f7/NjtTNmzfx5Zdfol27dtYdhsre1/IEBQXZ3WiWp2/fvsjIyMCnn36KU6dO2ewYAOYxAFQqFRYvXlzmCLIkSS6PReCoPn36wGQylRnpHTC/b5XV6o78ffr0QXp6OjZv3lzmsaKiIusouI4SBAHdu3fHt99+W+Z2NJZcltc9evSo9XYqJeXl5cFoNAIo21YEQbCO5lxZewEq/3xaNGvWDAkJCfjss8/w9ddfo1+/fg5dQnH48GG7Zx9Yrpez7OhbRvcuffs7y3goJW8XVK9ePfz22282823evLncX/dL69SpE4KDg7FkyZIy2x5L1sTERNSvXx8rV660e4q6M6fmedLjjz+OoKAgvPTSS7h582aZx9PS0hy6pWBRURFmzJiB3NxcPPnkk051NNx5G7nk5GRoNBqsXbvWpt189tlnyM/PL9MOjh07ZtPOv/322zK3cOrcuTMyMjKwe/du6zSdTmf3M+0oR7cLubm5ZR63HKm05C79Gfbz80OTJk0gSVKF92COiYlBnTp1ynxHW36cLrn+9Ho9PvnkE7vLMRqN2LRpk828mzZtQkREBFq0aOF0xr///tvluyI48/57QufOnWEymcrc7nj16tVgjFm3i468r/ZY7lHv6Pd0cnIyatasiZ07d2LXrl1ISkqy+WHDmf1FT6jqdtId+e+//36EhIRgyZIldj8vrqyDnj17QpIkLF68uMxjlnbp6P5FQUGB9fvaIj4+HoIgOPQd7cjn02LQoEE4ePAg1qxZg5o1azp0Kd2pU6fsrqOrV6/i/Pnz1h8nLP2d7du327TfM2fO4ODBgzafzfr16yM/Px+nTp2yTsvIyLB7i2173L2f5Ag6Ag/gt99+Q3FxsXWAs99//x3ffPMNQkJCsHjxYoeuvS7J2dvIHTp0qMxOGWAerGnkyJHYtGkTZs2ahRMnTiA2NhZ79uzB77//jhdeeMHagapRowZ69+6NdevWgTGGevXq4bvvvrN7ze1zzz2HSZMmYfTo0Rg6dChyc3Oxbt063HXXXU7v3Fu8/PLLGD16NAYMGIARI0agXr16uHnzJv744w/cuHEDX3zxBQCgX79+6NChA1q0aIGaNWvizz//xJ49e6z327x48SIee+wx9O7dG02bNoVKpcK+fftw8+ZN9OvXr8IM7dq1Q82aNXHo0CGb60seeOABfP3113jqqafwwAMP4MqVK9i4cSOaNm1qt9769etj1KhRGDVqFPR6PT7++GPUrFnTesqLMxl/+ukn1K1bt8JT/yvi6PvvKf/617/w008/YfTo0Rg9ejRUKhU2bdoEvV6Pf//739b5Kntfy9OiRQscOnQIq1atQq1atRAXF2e9NZ09Xbp0QXBwMN58802oVCr06tXL5vH69evjX//6FxYsWICrV6+ie/fuCA4OxpUrV7Bv3z6MGDHCOvCJJ3To0AEjR47EkiVLcPLkSaSkpECj0eDixYvYvXs3XnzxxQqv73JH/kGDBmHXrl14+eWX8fPPP6Nt27YwmUy4cOECdu/ejeXLlzt9adFzzz2HgwcPYuzYsdZbUGVmZmL37t345JNPEBoaigkTJuCbb77Bk08+iQcffBAtWrSATqfDmTNnsGfPHuzfvx8RERF46aWXcOvWLdx3332IiYnBtWvXsG7dOjRv3tyho6iVfT5LKnlavqOnz8+dOxc6nQ49evRA48aNYTAY8Pvvv2PXrl2IjY21DkDVrFkzPPjgg9i0aRPy8vLQvn17/Pnnn9i2bRu6d+9uHRwMMA9o9fLLL2PatGlITk7GqVOncODAAYcvrQkJCcHs2bPx0ksvYdiwYejfvz9CQ0Nx6tQpFBUV4c0334QgCJg7dy4mTpyI/v37Y8iQIYiJiUF6ejp+/vlnhISE4KOPPnLo9Typfv36ePvtt/Hss8+ib9++GDRoEOLj46HX63H06FHs3r27zCBf6enp+PzzzwGYx6U5f/48du/ejczMTIwfPx4PPfSQUxmcvY1cdna23R/l4uLiMHDgQEyaNAmLFy/G448/jtTUVPzzzz/45JNP0LJlS5t2N3z4cOzZswePP/44+vTpg7S0NOzYsaPMKbcjRozA+vXrMXPmTJw4cQLR0dH4/PPPq3SKt6PbhQ8++AC//fYbunTpgtjYWGRlZeGTTz5B7dq1rdePT5gwAVFRUWjbti0iIyNx4cIFrFu3Dl26dKn0O6lbt27Yu3evzdkFbdq0QVhYGGbNmoWxY8eCMYbPP/+83Mt4atWqhWXLluHq1ato2LAhdu7ciZMnT+K1116z3rLN0Yx//fUXcnNz0a1bN5fWa0REhMPvvyekpqbi3nvvxbvvvourV68iISEBBw8exP79+/Hoo49a25Yj76s9lg7X3Llz0alTJ6hUqgr3xTQaDXr06IGvvvoKOp3O7rXmju4veoI7tpNVzR8SEoJXXnkFM2bMwJAhQ9C3b19ERETg2rVr+P7779G2bVvMmTPHqbruu+8+DBo0CGvXrsWlS5dw//33QxRFHDlyBPfeey/GjBnj8P7F4cOH8Z///Ae9e/dGw4YNYTKZ8Pnnn9vd57LHkc+nRf/+/fHWW29h7969GDVqlEO33Dx48CDef/99pKamolWrVggKCsKVK1ewZcsW65k2FjNmzMDEiRMxcuRIDBs2zHobuRo1athcCte3b1+8/fbbmDp1KsaOHYuioiJs2LABjRo1sg68Vxl37ic5gjrwMI8aD5g3PDVq1ECTJk0wbdo0jBgxosrXHTnixx9/tPtrTGxsLOLj47F27Vq8/fbb2LZtGwoKCtCoUSO7Ox4vvfQSjEYjNm7cCD8/P/Tu3RszZsywGcEcMP9iu3DhQrz33ntYsGAB6tevj3nz5mH//v02o4c6o2nTptiyZQsWL16Mbdu2ITc3FxEREbj77rvx1FNPWecbO3YsvvnmGxw8eBB6vR5169bFv/71L2unpHbt2ujXrx8OHTqEL774AiqVCo0bN8Z7771X6YbDz88PAwYMwO7du22uuRoyZAhu3ryJTZs24cCBA2jatCneeust7N692269gwcPhiAIWLNmDbKyspCUlIT/+7//Q61atZzKKIoi9uzZg2HDhrl8KnNAQIDD778n3HXXXVi/fj0WLFiAJUuWQJIkJCUl4a233rLpaFf2vpZn1qxZmDNnDt577z0UFRXhwQcfrLAD7+/vj9TUVOzYsQPJycl276H7xBNPoGHDhli9erX1Xty1a9dGSkoKUlNTXVwTjvvPf/6DxMREbNy4Ee+++y5UKhViY2MxcOBAh47yVDW/IAj44IMPsHr1anz++efYu3cvAgMDERcXh7Fjx7p0an5MTAw2b96MhQsXYseOHSgoKEBMTAw6d+5s7VQEBgZi7dq1WLJkCXbv3o3t27cjJCQEDRs2xLRp06yXtQwcOBCbN2/GJ598gry8PERHR6NPnz6YNm2aQ4MpVfb5LGnAgAF4++23Ua9ePYfvgT5jxgzs3r0b33//PTZt2gSDwYC6deti9OjRmDx5ss0I2nPnzkVcXBy2bduGffv2ISoqCpMmTSozEvCIESNw5coVfPbZZ/jxxx/Rrl07rFq1Co899phDmQBz5y8yMhJLly7Fhx9+CLVajcaNG9ss495778WmTZvw4YcfYt26ddBqtYiOjrbe11guunXrhi+++AIrVqzA/v37sWHDBvj5+SEhIQGzZs0qM9r6yZMnMWPGDOuAVXXq1EHXrl0xfPhwr9zbPisry+6AVR07dsTAgQMxbdo0REREYN26dZg3bx7CwsIwYsQIPPfcczY7pPfffz9mzZqFVatW4b///S8SExPx0UcflRn7ITAwEKtXr8Zrr72GdevWISAgAAMGDEDnzp3LvXayMo5uF1JTU3H16lVs2bIFOTk5CA8PR4cOHWw+wyNHjsSOHTuwatUqaLVa1K5dG2PHjsWUKVMqzTF06FCsW7cOR44csd7RJjw83Loe3nvvPYSGhmLgwIHo2LGj3e+QsLAwvPHGG5g7dy42b96MqKgozJkzx6bdOJpx9+7dqFu3rs0Pbs5y9P33BEEQ8L///Q+LFi3Czp07sXXrVsTGxmLGjBnWO2IAjr2v9vTs2RNjx47FV199hS+++AKSJFV6MMUyYB5jrMxZcoDj+4ueUtXtpDvyDxgwALVq1cLSpUuxYsUK6PV6xMTE4J577nF5327evHnWs87mz5+PGjVqIDEx0eYMUEf2LxISEtCpUyd8++23SE9PR2BgIBISErBs2TK0bt260hyOfD4toqKikJKSgu+//97mThoV6dmzJwoLC3Hw4EEcPnwYt27dQmhoKJKSkjBu3Dibz3JycjKWL1+ORYsWYdGiRVCr1Wjfvj3+/e9/25wZEh4ejsWLF+ONN97AW2+9hbi4ODz33HO4dOmSwx14d+4nOYJJzo5URIiMXb58GX369MGyZcvKHeXRW/bt24fp06dj7969djsXhBDPys7Oxv33348pU6Z4ZceQEFK5Rx99FLVq1cJbb73l0xx6vR6pqamYOHFiuSOCE0I866mnnsKZM2ccPl2dmNE18ERR6tWrh6FDh2Lp0qW+joJly5bh4Ycfps47IT6ybds2mEwmh3/ZJ4R43nPPPYddu3ZV6VaL7rBlyxao1eoy99gmhHhHRkaGU0ffyR10BJ4QQoiiWEZAXrhwIe699167A/sQQgghxPsuX76M33//HZ999hn+/PNP7N271+nxxqo7ugaeEEKIonz44YfW25T93//9n6/jEEIIIeS2X3/9FbNnz0bdunXxxhtvUOfdBXQEnhBCCCGEEEII4QBdA08IIYQQQgghhHCAOvCEEEIIIYQQQggHqANPCCGEEEIIIYRwgDrwhBBCCCGEEEIIB2gU+gpkZeXDHUP8qdUqGI2mqi9IBqgWeVJSLYCy6qFa5Im3WhgDIiNr+DqGrLjrO9qdeGtXpfGcn+fsAN/5ec4O8J2f5+wA3/kt2X3x/Uwd+ApIEty2cyC3nYyqoFrkSUm1AMqqh2qRJyXVUh258zvaneSYyRk85+c5O8B3fp6zA3zn5zk7wHd+X2WnU+gJIYQQQgghhBAOyKIDv379eqSmpqJly5YYPnw4jh8/Xu68mzdvxujRo9G+fXu0b98ejz32WJn5Z82ahYSEBJv/JkyY4OkyCCGEEEIIIYQQj/H5KfQ7d+7EvHnz8Oqrr6JVq1ZYs2YNJkyYgN27dyMyMrLM/D///DP69euHtm3bws/PD8uXL8f48ePx1VdfISYmxjrf/fffj3nz5ln/9vPz80o99hgMfF7bYQ/VIk9KqgVQVj1UizwpqRYiH7y3K57z85wd4Ds/z9kBvvPznB3gO78vszNJ8u2VB8OHD0fLli0xZ84cAIAoiujSpQvGjh2LJ554otLnm0wmtG/fHnPmzMHgwYMBmI/A5+Xl4cMPP6xStps35TdADiGEeJIoijCZjL6OUe2oVGoIQvknxTEGREXRIHYlVfQdTe3YNyprx4QQojS++H726RF4vV6PEydOYNKkSdZpgiAgOTkZR48edWgZOp0ORqMRYWFhNtN/+eUXdOzYEaGhobjvvvvwr3/9C+Hh4W7N7yiNRsX1L0wlUS3ypKRaAGXVw0stkiQhLy8bOl2Br6NUW4GBIQgNjQBjzNdRuEXt2Pd81Y552daWh+f8PGcH+M7Pc3aA7/y+zO7TDnxOTg5MJlOZU+UjIyNx4cIFh5bx9ttvo1atWkhOTrZOu//++9GjRw/ExcXh8uXLeOeddzBx4kRs2rQJKpXK4Xxq9Z15zb/mS1CpmM2vyyaTCFGUoFIJEARWZrpaLUCtvjO/0WiCJJnf9JIsDcCZ6YzZZpQkCUajCEFgUKnuvKYoSjCZ7E13vibzehFsvph5rUmtFmzeJ55rKtnGlFCTIDCb1+W5Jkt7q2gbIYeasrMzodMVoEaNcPj5+YMxZnN0s+S+uKvTS+/Pu2O6u7O4aznOTJckCQZDMfLzcyEIDOHhUXbfJ1I5S+c9JOROO/YmxvgeUbkq+SVJgl5fjIKCHABAWFjZyyAJIYRUnc+vga+KpUuXYufOnfj444/h7+9vnd6vXz/rvy2D2HXv3t16VN5Rlh3pkkwmCSZT2V9bTCYRdibDaBTBGCvzC015v9g4M12S7E8XRQmi6Ph0Z2pSqcw1VTV7edO9XZMomt9g3msyGsu2MZ5rEkXJ7vJ5rMnSsa5oG1HV7OVNd7QmUTRZOz3BwaF2lw/w3zkpSW61+Pn5Q5KAgoIcBAfXhCAINu8THZSvXMl2HBJSfjv2JLm1K2dVNb+fn3lfrKAgBzVqhNPp9IQQ4gE+3bKGh4dDpVIhKyvLZnpWVhaioqIqfO6KFSuwdOlSrFixAs2aNatw3nr16iE8PByXLl2qcmZCCFEayw8Olp1v4huW9c/Ltdu//vornnzySXTq1AkJCQnYt29fpc/5+eef8eCDDyIxMRE9evTA1q1b3ZaH2rE88NaOCSGENz7twPv5+aFFixY4dOiQdZooijh06BDatGlT7vOWLVuGDz/8EMuXL0fLli0rfZ0bN24gNzcX0dHRbsntLF6v7bCHapEnJdUCKKsenmqp7HRjno8slibHWni79l2r1SIhIQEvv/yyQ/NfvnwZkyZNwr333ovPP/8cjz76KF566SX8+OOPbs3ly/Uox3blDHfk99X652lbaw/P+XnODvCdn+fsAN/5fZnd56fQjxs3DjNnzkRiYiKSkpKwZs0a6HQ6DBkyBAAwY8YMxMTEYPr06QDMp80vWrQICxYsQGxsLDIzMwEAQUFBCA4ORmFhIRYvXoxevXohKioKly9fxltvvYUGDRrg/vvv91mdhBBCiJJ06dIFXbp0cXj+jRs3Ii4uDrNmzQIANGnSBEeOHMHq1avp+5kQQghxkM878H379kV2djYWLVqEzMxMNG/eHMuXL7eeQn/9+nWba6g2btwIg8GAp59+2mY5U6dOxbRp06BSqXDmzBls374d+fn5qFWrFlJSUvDMM8/47F7wPI+wWBrVIk9KqgVQVj2818KYd4+oSZLklaOY9q71HTZsAEaMGIURI0Z7PkA19Mcff5QZh6ZTp07473//66NE7ieHa+Cr0o7lkN9VvG9rec7Pc3aA7/w8ZwfKzy8Z9ci7fAy66ycgZl2AQZsLQ7EWkkELP7EYGqkIflIx/MRi+EEPQTKBQYIJAj5WDcFOVTfzcmwWajsq7ZCkOni0Qz23Z/cGn3fgAWDMmDEYM2aM3cfWrl1r8/c333xT4bICAgKwYsUKt2UjhLhH6Y6gtzpqxHWMAQYmoFDvvWtZg/3U0ECktqFAN2/eLDO+TVRUFAoKClBUVISAgACnllf6TjEGg/nf5m2N+d/ev7sAA2OS3fndfVeHiqaXfMy5mhgsu7yu3gWi5OPuuPuNo3frUKsFWd2BxNmaLP+U811VyqvJkl0ud1VxtiZA/neKKa8mc3b3f568WVPJ5Zw7fRy5h1ahbe5ONEUeXHFX0V+4akypdL7fLufi8ZSGLtfEWNn5vUUWHXhCiLIxBgQxHQTDnY2xqAmFFoHUUZMxxhgK9UZ8eyoD2mLPd+KD/NXo2qwWwv1UkKhhkErYu1MMYLk1n/3p9lR13pLTS3beq7Kcqk6395gjy2BMsjvdmeWUfNwdd78B5HG3jsqmV7UmS0eAx5os2Xl9n1QqftueIPD/eTIYTMi8eg7G3f/GPUW/Wh/LkUJwXmiErID6kAIjEBgYDP+AEEATCFEdCFEdAKgDIakDIKjUEJgAQa1BVM3mWMnudKxL/qjISky8KyrYobuElVeTZbovhv2gDjwhxOMYYxAMeRDP7AX0hYBfMISEXhA0wbePxNPReDnTFhtR4IUOvLMOHvwRr732f/jqq/1QqVQ4e/Y0xo17GA8//CgmT54GAHjjjdeg1+sxZ85rOHbsDyxZshinTp1EzZo10bnzA5g0aSoCAwOty9RqtXj55Rdw8OAPCAmpgbFjx2Ho0BG+KlFRoqKicPPmTZtpN2/eREhIiNNH35WE2jEhpDr7+5fdaPvLNNRgOogSw99B7aFr8TDqtBmABn5+aODrgDJEN+j0Ap6vTSmNapEnbmrRF0IqLgBMBqhVDEH6Gwg23EAQ09n8gslNPQ5QUi1y06pVG2i1Wpw9exoAcPTo76hZsyaOHj1ineePP35HmzbtcPXqFTz//DQ88EAq1qzZgFdf/S+OH/8D774732aZn3yyFk2bxmPlyvUYM+ZRLFq0AL/+etirdSlV69atcfiw7br86aef0Lp1a4++riRJ0BlMXvlPqzf/vzNnkMipHfP8Qyrv21qe8/OcHeA7P8/ZAeDwt5/j3l8mowbT4ZQqAcf67UbM+K1oeO9Q+Pto7DJHVetR6KsDngeFKY1qkSfualFpwPSFkM7/CEkChPgeYJog604vd/VUQEm1yE1ISAiaNo3H778fQbNmd+Po0SMYMWI0Vq1aBq1Wi8LCAly5chmtW7fF2rWr0KNHb+vAXvXq1cczz/wb06Y9genTZ8Hf33zv6pYtW2Hs2McAAPXrN8Cffx7Dpk2foH37+3xVpmwVFhYiLS3N+veVK1dw8uRJhIWFoW7duliwYAHS09Mxf765c/nQQw9h/fr1mD9/PoYOHYrDhw9j165dWLJkiccySpKExzcew/Frrl1L6apWdUOx7KFWDg0ASe3YPXjf1vKcn+fsAN/5ec5+/s+D6PLH0whgBvwd1AE1R69DpH+Qr2M5zJfrnjrwXqBW8z1CZElUizzxWouk19rd+vFajz1KqkWO2rRpiz/+OIJRo8bg+PGjePLJqfjmm304fvwP5OXlISoqGvXq1ce5c2dx/vxZ7N272/pcSZIgiiKuX7+Ghg0bAQASE1vaLL9FiyR8+ukGr9bEi7/++guPPPKI9e958+YBAB588EG88cYbyMzMxPXr162P16tXD0uWLMG8efPw8ccfo3bt2pg7d67HbyHnu7vCO04u7ZjnzgDv21qe8/OcHeA7P6/ZM7MyUf+HqQhkepwIaIeoMeshaAIrf6KM+HLdUweeEOIRJUed9+ZtyEj10qZNO3z11Rc4d+4M1Go1GjRoiDZt2uHo0SPIz89D69ZtAQA6nRaDBg3BsGEPAbDtqMTE1PZVfK7de++9OH36dLmPv/HGG3afs337dg+mssUYw7KHWqGonEGb3P56MI/hHlBqlOfKuNqOS6J2TAjhgSRJuLF9Bu5GJq4JtRH60GruOu++Rh14Qohb2HbYgQBoIehvn7YqqKCGCfpyn2ze2RVoVA7ipKQk8/XDmzZ9Yu3ktGnTDuvWrUZ+fh4eesh8i9L4+Gb4559/EBdnvudreUcaT5z4s8zfDRo09GgNxLMYYwj00q1+XD2C7Wo7Lg+1Y0KIXP1+eA96F+2FKDFoe76HsOBwX0fiDu0ue4GSbodEtciTr2ux3CYu2HB7UDpjJvz0OZDO7IX413aIF34AEw1lbywMACo/mwHtgg03ECAV+uS2HJ7g6/dG6UJDQ9GkSVPs3bsbbdq0AwC0bt0GZ86cwuXLaWjTxtwZevjhR/HXX8fwzjtv4uzZ00hLS8OPP36Hd95502Z5f/55DOvXr0Fa2iVs2bIZ3323H8OHj/JyVYRXrn7cXW3Hly+7tx3zvLnifVvLc36eswN85+cte2FRERJ+fxkAcDx6ACITOvs4ket8ue7pCLwXlHe/RR5RLfLk61rK3CYuJBqsfjtIBi2k4gIwv+Dyn1xyQLvi27eYi+8Bpgnk7ovJHl+/N+4Q5O+drwpXX6d163Y4e/YM2rS5BwAQGhqGhg0bIycnC/XrNwQANG16FxYvXoqlSz/ElCkTAUioWzcO3br1sFnWQw+NwalTJ7Fq1TIEBwdj6tRnce+9HatSFiEOoXZcNbxva3nOz3N2gO/8vGU/vXcJeuEqclED0f3ncpe/JF9mZ5IS9pA95ObNfLf8Gi0IDKKojNVMtciTr2sRBIZgww2If203d9hrxMC/6f0oPrkHUlF+hX9DE2T7mH8IhMQHUaiJUcT74+v3xhEGgx5ZWdcRGVkHGs2d27YwBhiYgEK99+4BH+ynhkYSuT4S6Kry3gfA/F5ERdXwUTJ5Kv0dXdH6I97jq/eBh21tRXjOz3N2gO/8PGUv0huhX5aCJriKX++ajoY9n+Uqf2mW7L74fqYj8F6gUgkQRf5GiLSHapEnJdUCAIJSzp8H3++NJAEaiAj38871w+bX9E7nnefRtol88d6ueM7P87YW4Ds/z9kBvvPzlP2Xn3ZjKK5CB3/U7zwBAF/5S/NldurAE0LchzHzQHYK6oBXd5J05zovnnfuCSGEEOIboiQh8uRqAMCFmD6oFRDq20Ccow48IcQtRDAUm0RIJhGCSQSdwEoIIYQQQv48dx5dTL8CDIjo9KSv43CPOvBewOu1HfZQLfLk61oYYzCKEtKytdDl56OGKRR34c49kZ0lufQsefL1e+NOSjr6rqRaiHzw3q54zs/7tpbn/DxnB/jOz0v2rN82QcNMSPNPQGDtROt0XvLb49Oxp3z2ytWIycTvCIulUS3yJJdajEYRepMIo4N5GGD3dHslja0pl/eGEEKUjPdtLc/5ec4O8J2fh+yFeiOaZ30NAChqNtTmMR7yl8eX2akD7wWCoJzrgakWeeKlFgbz0XrGGCQAxaIEvUm0c7ydj3ocwct7QwghPON9W8tzfp6zA3zn5yH7r3/+hSR2HiIYwtsMs3mMh/zl8WV26sB7gUqlnNVMtcgTD7VYOuxFJhF6UYII4FquDldzdRBh22VX2ij0SqGgt0VRtRD54L1d8Zyf920tz/l5zg7wnZ+H7AV/7wQAXA1uCQTXsnmMh/zl8WV2ugaeEOJ5jFk77Hm5+YhATTSUJBhMIiDwe/oUIYQQQgix75bOgCa5B8yHjJv28nUcxeD3Zw9CiFcxZj5dyPKfK0dqDCbzNfKGUtcNWa6Fp1vQEUIIIYQow6EzabiPnQAABLfo7+M0ykEdeC8QReUcYaRa5MnTtTAGBDEdgg03rP8FCTqoVJbOvOudbtXt5xabRBSZRBSbRJhKXRXvjh8PfIX3dlZy3TPGbN4HT/znrfe2onESp059AgsXLqjw+cOGDcDmzZ9Y/+7U6R788MN3bkpHeCWn8TddaccpKfy2Y963tTzn5zk7wHd+uWfP+3sP/JgJWf5xMIU3KfO43PNXxJfZ6RR6LzCZZPSNXkVUizx5uhbGGARDHsQzewF9IZhfMBDfE7dYAEyiBD+1hDBXly0wSJJkPb0+sIaARiYJTGO+CZ3lxwPBkGd9jqgJhRaBstpZLg/P7czeuvc0Xt7bZcs+RmBgoK9jEFIlSmrHPG9rAb7z85wd4Du/nLMXGUyoe9N8+ryuQU/425lHzvkr48vs1IH3ApWKcd1AS6Ja5MlrtegLIRUXALfv+37wn5vIyi9GXEQQusVKNqe/lzwt3pGDqpbT6zVGEWCC9WgvYwyC/s6PB/ALhhDfA0wTxMXt5nhuZ6V/uPE4jt7b8PBwX0cgpMqU1I553tYCfOfnOTvAd345Zz9yORud2DEAQHCzHjDamUfO+Svjy+zUgfcCQRBgMpl8HcMtqBZ58lotpTrkOr0JBcVG6Ay2r13ytHjRJEIlSnD0GI+g8Ye/mgH66+ZOnKCCGibob/94wNHZ8wAU0s5KrHtPflW58t5OnfoEmjRpCkFQYdeuL6HRaDBx4mT06NEb7747H99+ux8RERH417/+jY4dUwAAR48ewYcfLsS5c2cRGhqK3r37Y+LEyVCr73wlmkxGvPPOm9izZyfUajUGDx6Gxx9/0nq5yLBhAzBixCiMGDHabq709BtYvPg9/PrrYTAmoFWr1njmmedRp05dF6okvGDMtdPoqR1XHe/bWp7z85wd4Du/nLNfP3kQ0ewWtEIIjHXvtTuPnPNXxpfZ6Rp4QohDRLA716mLUoUduZKnxZ+5kY/0vCKH92oFtQbMUAjpzNcQ/9oO8cIPYKKBBrcj5dq16yuEhYVh2bI1GDp0BBYseAP/938zkZiYhJUr16F9+/swd+4cFBUVITMzA//+9zNo3rwFVq/egOnTZ+Orrz7HmjUryixTpVJj2bI1eOaZ57Fp03rs2LHdoTxGoxHTp09DUFAQPvhgOf73vxUIDAzC9OnTYDAYPLAGSIUkCTBovfufC714V9pxs2bUjgkh8hR47SAA4GZUR0Dl5+M0ykJH4AkhlWK3T5lPy9ZCl5+P0Jpq1HVgB7W8UecdIem15qO+fsGuRCbVSNOmd+Gxxx4HAIwdOw7r169BWFhNDBz4IABg3LjHsX37Zzh37iwOHvwBtWrF4LnnZgBgaNCgIW7ezMT//vc+xo2bCEEw/64dExODp59+Dowx1K/fEOfPn8PmzZ9Yl1mR/fu/hiiKmDXr/6xHOl944WX07v0Ajh49gg4d7vPMiiBlSRJqbn0Qmhu/efVlDXXaI/fBrU798OhqO2aM2jEhRF6yCvVoXnQUEAD/Jl18HUdxqAPvBSYXOi9yRbXIk7dqMRpLdMiZgEA/hhB/NQI1KjDG5ylQnqakdibXq9SaNLnL+m+VSoXQ0DA0adLUOi0iIhIAkJubjUuXLiIxMQklT9hv2bIVdDotMjIyULt2bQDA3Xcn2txdITGxJTZuXAeTyQSVSlVhnnPnzuLq1Svo2bOzzXS9Xo+rV6+4XCdxESdn77jSjku20erejnnf1vKcn+fsAN/55Zr9twvXMYKdBQBoGndGeXuIcs3vCF9mpw68F4iiXHd7nUe1yJO3axE0/gjQCOgQUQBdDRNCAgF/gUEl8LGj7E1KamdyVfKaX8B8xkjJaZYOjLfeC51Oi/j4Znj55bllHqtZUzmDhnGBMfORcKPOu6+rDnT6hwNqx1XD+7aW5/w8Zwf4zi/X7LfOfA8/ZsItTS2YwhqVO59c8zvCl9mpA+8FKpXA9S9MJVEt8uTtWgSVBoK+AMbTX0OXdwuB0XUhJKXwcqDLq5TUzpSgQYOG+P77byBJkrVD9OefxxAUFIxatWpZ5/v77xM2zztx4i/Uq1e/0qOWABAf3wz79+9FeHg4goND3FsAcR5jgCbI1yncitpxWbxva3nOz3N2gO/8cswuSRKCM8yXLd2KSUZgBTuHcszvKF9mp0HsvEBQ0FFJqkWefFWLoagAem0eDMVeuMUYp5TUzpRQyZAhw5GRkY53352PS5cu4scfv8PKlUswcuRo63XDgHn07ffffwdpaRexd+9ubNmyCcOGPeTQa/Ts2QdhYTUxa9Z0HDt2FNeuXcXvv/+G9957CxkZ6Z4pjMiCt37EpHZcFu/bWp7z85wd4Du/HLNfvVWEZsaTAIDgxhWPlSHH/I7yZXY6Ak8IIaRifsHWzrtHv668MGBhdHQtvPXWQnz44UI89tgohIaGol+/QXj00Qk28/Xu3Q/FxcWYOPFRCIIKw4Y9hEGDhjj0GgEBAfjgg6X43//ex4sv/htarRZRUdFo164DgoNpUEZSddSOCSFy9WdaBkaycwAAFtfRx2mUiTrwhBBC7JIkCaImFEJ8D6+9pqgJheTELbgWL15aZtpnn+0oM+3AgTujkLdp0w7Ll39c7p2+Si7z+edn252n9GuUXD4AREZG4aWXXi03NyEludqOly372KFlOtqODx78zeZzQe2YEOKszAtH4M8MKFDVhKlmY1/HUSTqwHsBr9d22EO1yJOSagFuH+VlzHxtJ+cX1vP83kgSoEUgmBevH5YkyZVbaLvwOp5/DVL98N6ueM7P87YW4Ds/z9kBvvPLMXtguvmHxvzI1lBXsg8nx/yOolHoFY7nERZLo1rkyRO1mPvO7Pa/mdeuf1bdfs1ikwjRJEIwifDz0mt7Au/tTJLg1BFxQgjxBd63tTzn5zk7wHd+uWXP1RnQuPgkoAI09e+t9PazcsvvDF9mp0HsvECtVs5qplrkyd21MAYEMR2CDTcQbLiBQEMG/ASTV24TxwQGSQKu5epw5kY+rubqIILfAdSU1M4IIUSueN/W8pyf5+wA3/nllv341VtoK5jv/66qd2+l88stvzN8mZ2OwHsB4/wU4JKoFnlydy2MMQiGPEhn90LSayEER4HVbwfmxRE3DSYRepMII8enVwFKa2d8n2JbkpJqIfLBe7viOT/v21qe8/OcHeA7v9yyp106i1osFyaoYIxuWen8csvvDF9mpw48IcQuEQzFugJIxQVQqYMQ6MMsSromnhBCCCFEiYzXfgcAZIfcBWh8ueeobNSBJ4SUwRiDUZSQlq2FLj8fEaiJhj46LFP6mnhmEuHP7Qn18iZJfJ/twDta/+5B69G3aP0TUj2ZRAnhuScAARBrtaLrtD2IOvBeYDSafB3BbagWefJULUaj+TR2g9dPY7/zY4H5mngJ13J1yMvNR2ANAY1E6fapS/I/15OHdqZWa8CYgFu3shASUhMqlZrr09p4I0kSTCYj8vNzwZgAtVrj60hconbsW75uxzxsayvCc36eswN855dT9ks5WjSTzgMAAuq1hd6B58gpv7N8mZ068F7A6/Vk9lAt8uSOWgQBYEy4/W/vjTrvKMs18RojX0d3eGhnjDFERtbGrVvZuHXrpq/jVFt+fgEIDY2gTqeLqB3Lg6/aMQ/b2orwnJ/n7ADf+eWU/a9reRgqXAQAiDFJDj1HTvmd5cvs1IH3Ao1GBYOB31+YSqJa5KmqtQgC4M+KIBbdMv+tUkHjpVHn7VNOB4aXdqZWaxARUQuiaIIo2v+RRK0WYOTsB5TyyK0WQRAgCCrqvFeRI+3Ys68vr3blrKrm92U75mVbWx6e8/OcHeA7v5yy37h6HjVZoXkAu4gEh54jp/zO8mV26sATQsCYALHoFq788jl0BXmoGV0X9RKTvTrqPPE9xhhUKjVUKvuPazQqMMbnF21pSqqF2KqsHXsS7+2K9/yEEB+6cRwAcCukKaDy83EYZaMOPCHESleQh8KCWwgICfV1FKeYB6e/82ODJElcn5ZFCCGEEMILoyghIv8kIACmWpXfPo5UDXXgCSFcYwwIYjoIhjzrNFETCi0CqRNPCCGEEOJhl7K1aCpdAgD4101CsY/zKB114L2A12s77KFa5ElJtZg53vNmjEEw5EE8sxfQFwJ+wRDie4BpgiDJoAevpPeGaiGkYry3K57z85wd4Ds/z9kBvvPLJfup9AL0tAxgF93C4efJJb8rfJmdOvCEEP4wwdxxF9idU+f1hZCKCxQ0/B0hhBBCiPxdvn4NdVk2AMAY2dzHaZRP8HWA6kCj8cFIOh5CtciTq7UwBmsnWF4d3/LTCBp/+KsZgvTXEWy4gSBjBtSQ7y+41M7kSUm1EPngvV3xnJ/n7ADf+XnODvCdXy7ZpYw/AQB5AbGQ/B0fR0ku+V3hy+x0BJ6Qasrm2nFBDebT28Y5TlBrwAyFEM//CLG4EAiJBqvfzlwQIYQQQgjxGkmSUCP3JACgOIKOvnsDHYEnpJoqee24dOF7MNHA1W3jJL0WUnEBoNf6OgohhBBCSLWUVahHA+M/AABN3da+DVNNyKIDv379eqSmpqJly5YYPnw4jh8/Xu68mzdvxujRo9G+fXu0b98ejz32WJn5JUnCwoUL0alTJyQlJeGxxx7DxYsXPVwFIZzSF0Iy6HydwikMANjt69/pyDshhBBCiE+cySxEc2YegZ7VutvHaaoHn3fgd+7ciXnz5uGpp57Ctm3b0KxZM0yYMAFZWVl25//555/Rr18/fPzxx9i4cSPq1KmD8ePHIz093TrPsmXLsHbtWrzyyivYvHkzAgMDMWHCBBQX++amBjyPsFga1SJPSqrFrPzR41W3O+zFJhFFJhF6k+jEmPXep6T3hmohpGK8tyue8/OcHeA7P8/ZAb7zyyH7xYxsNGXXAADGKMdHoAfkkd9Vvszu8w78qlWrMGLECAwdOhRNmzbFq6++ioCAAGzZssXu/AsWLMDDDz+M5s2bo0mTJpg7dy5EUcShQ4cAmI++f/zxx5g8eTK6d++OZs2aYf78+cjIyMC+ffu8WRohfJDdAHYVYwKDJEm4lqvDmRv5uJqrg4iKhr0jhHiSM2fRAcDq1avRq1cvJCUloUuXLvjvf//rsx/YCSGEVI322t/QMBO0qjCIIXV9Hada8GkHXq/X48SJE0hOTrZOEwQBycnJOHr0qEPL0Ol0MBqNCAsLAwBcuXIFmZmZNsusUaMGWrVq5fAy3Y3nERZLo1rkydVaRDAUm0ToRbkdw668O264ffTdaBK9kMd11M7kSUm1+JKzZ9Ht2LEDCxYswNSpU7Fz5068/vrr2LlzJ9555x0vJ/cM3tsVz/l5zg7wnZ/n7ADf+eWQXcg6DQAoDL3L6csa5ZDfVb7M7tMOfE5ODkwmEyIjI22mR0ZG4ubNmw4t4+2330atWrWsHfbMzEzrMlxdJiHVAWMMRlFCWrYW6XlFgCS3TjwhRO6cPYvu6NGjaNu2LQYMGIC4uDh06tQJ/fv3r/SoPSGEEPkxmETU1F4AAAjRCT5OU31wfRu5pUuXYufOnfj444/h7+/v9uWr1Xd+WRFFESaTBJWKQRDu/O5hMokQRQkqlQChxAjelulqtQC1+s78RqMJklT2VxvLdRTOTGfMNqMkSTAaRQgCg0p15zVFUYLJZG+68zWZ14tgHjyM85rUasHmfeK5ppJtzNGaLIsyGkUYTOaT0Bmz3BP+zvPu/M1slmc7P+zPD/vz35kCm2Vb52colYVVkuX2/ewF4U5hkMf7ZGlvFW0jeGl7lnbmyHZP7jUJArNZPg81yY3lLLpJkyZZp1V2Fl2bNm3wxRdf4Pjx40hKSsLly5fx/fffY9CgQd6KTQghxE0uZGlxFy4DAPxq340iH+epLnzagQ8PD4dKpSpzql1WVhaioqIqfO6KFSuwdOlSrFq1Cs2aNbNOj46Oti6jVq1aNsssOZ8jLDtoJZlMEkymsoMWmEwi7EyG0SiCMVZmoIPyBj5wZrok2Z8uihJE0fHpztSkUplrqmr28qZ7uybx9qnjvNdkNJZtY5XVpFIJpTYAEiTp9n8lhoW787f5Mfvzw/78sD//nSmwWbZ1fgmlskiVZLGsNxFMFK2nFsnhfbJ02CraRjia0dnpnqip5PJ4rkkUJbvLkWtNcrzZQkVn0V24cMHucwYMGICcnByMHj369g8VRjz00EN48sknvRGZEEKIG53LLER34QoAwBRJR+C9xacdeD8/P7Ro0QKHDh1C9+7dAcA6IN2YMWPKfd6yZcvw0UcfYcWKFWjZsqXNY3FxcYiOjsahQ4fQvHlzAEBBQQGOHTuGUaNGea6YCvA8wmJpVIs8KakWM+Wczq+k94ZqIVX1888/Y8mSJXj55ZeRlJSEtLQ0vP766/jggw/w1FNPObUsd50l5+4zKzQalc/OknNHTSXrKD2/3GuqKDsPNTmbXU41AfyeoWkwmGR99lVFNZmz+267dznjJuKY+RJlY0SC0zWZTKLN8uW0LbdML6+m0tm9yeen0I8bNw4zZ85EYmIikpKSsGbNGuh0OgwZMgQAMGPGDMTExGD69OkAzKfNL1q0CAsWLEBsbKz1mvegoCAEBweDMYZHHnkE//vf/9CgQQPExcVh4cKFqFWrlvVHAm9jTDmXF1Mt8qSkWpRGSe8N1UJKcuUsuoULF2LgwIEYPnw4ACAhIQFarRZz5szB5MmTbXbYKuOus+TscfXMipLtyhdnyQFVq6nkmR5yOEvOwpGaKste3nS51GTJL6czlRytyZLd12cqOTLdXk2Myffsq8qmm7P7brunu3EKAKDVREAKCAecrEmSyjsbzrfbctuM5U2XrB18b/N5B75v377Izs7GokWLkJmZiebNm2P58uXWL//r16/bfKFv3LgRBoMBTz/9tM1ypk6dimnTpgEAJk6cCJ1Ohzlz5iAvLw/t2rXD8uXLPXKdvCPUapVijvZQLfKkpFrMZHi+sIuU9N5QLaQkV86iKyoqKtNJV6nMRzBKXxbDI97bFc/5ec4O8J2f5+wA3/l9nV2dew4AUBza2LXn07p37bV98qqljBkzptwv+7Vr19r8/c0331S6PMYYnnnmGTzzzDNuyUcIIYSQspw9i65r165YtWoV7r77busp9AsXLkTXrl2tHXlCCCHyl6szoJY+DVAD6uh4GH0dqBqRRQeeEOId7Pbo7uZ/K+coNyHEN5w9i27y5MlgjOG9995Deno6IiIi0LVrVzz77LO+KoEQQogLLmZpcRe7CgBgUfE+TlO9UAfeC5RwWqAF1SJPjtTCGGBgAgr15t9I/dQSwjwdjFS7dsYLJdXia86cRadWqzF16lRMnTrVG9G8jvd2xXN+nrMDfOfnOTvAd35fZj93sxA9b3fgjRGujUBP69411IH3gvIGVuAR1SJPjtTCGEOh3ohvT2VAW2xEXEQQusVK8rw/lYJGoa9u7YwXSqqFyAfv7Yrn/DxnB/jOz3N2gO/8vsx+JTMbDdkNcw4XO/C07l3j+HCvxGUlb3fAO6pFnpypRVtsREGxETpZDxpSPd8buaNaCKkY7+2K5/w8Zwf4zs9zdoDv/L7Mrs84AxWTUKQOgxQU7dIyaN27+No+e+VqpOR9D3lHtciTkmpxBQMAxszX9cvsjAIlvTdUCyEV471d8Zyf5+wA3/l5zg7wnd+X2f1vmUegLwpr4vK+F61719Ap9IRUIwIDgvzNH/tAjQqMyfkovGNUt780ik0iRJMIZhLhr6Aj+IQQQgghcpKrM6CO4SKgBoRazX0dp9qhDjwh1QRjQLi6CCnROhhEESGBgL/AoOL49CUAYAKDJEm4lqtDXm4+AmsIaCRKt0fZV8619IQQQgghcvBPlhZN2HUAgBB5l4/TVD/UgfcCUVROJ4JqkafyarG9bZwAlT4fxtN7oCvMR2B0XQhJKXI749xlBpMIvUmERmYDolSHdsYjJdVC5IP3dsVzfp6zA3zn5zk7wHd+X2X/J1uLVHYNAGCs2cTl5dC6dw114L3AZJJXh6IqqBZ5sldLebeNM+gKoNfmwVAs55vI8btBL03p7YxXSqqFyAfv7Yrn/DxnB/jOz3N2gO/8vsqedvOWdQR6k4sj0AO07l1FHXgvEATG9S9MJVEt8mSvFr5uG1caDxkdo/R2xisl1ULkg/d2xXN+nrMDfOfnOTvAd35fZdemn4OGmaBXBUMMqePycmjdu/jaPnnVaobnERZLo1rkqaJa+LhtnHJVl3bGGyXVQuSD93bFc36eswN85+c5O8B3fl9l1+SeBwAU1WhYpQNDtO5dw+9aI4QQQgghhBDiNYV6IyKKLwMAWITr178T11EHnhBCCCGEEEJIpS5m69DYOgJ9Ux+nqZ6oA+8FosjvAA2lUS3ypKRa3IIJYIxBEMz/+fKyfyW9N1QLIRXjvV3xnJ/n7ADf+XnODvCd3xfZL2Zp0Vgwd+BN4VU7Ak/r3jU0iJ0XmEx8Ds5gD9UiT0qqxcz1egSNP/zVDNDfgCSZlyP61YBWDITkg9WkpPeGaiGkYry3K57z85wd4Ds/z9kBvvP7IvvFrEIMstxCLrxq94Cnde8aOgLvBSqVckbUplrkSUm1mLlej0bjB8FQiOJTe6A7vhXFp/YA+nwwHx2GV9J7Q7UQUjHe2xXP+XnODvCdn+fsAN/5fZH9ZuZ1hLMCSGAwhTWq0rJo3buGOvBeIAjKWc1UizwpqZaqYgKDJEm4knETpy9dx8UbmTCKks868Ep6b6gWQirGe7viOT/P2QG+8/OcHeA7vy+yq3LOAQB0gXUATWCVlkXr3jV0Cj0hCiYwIMjf/DEP1KjAWPW5lZzBJEJvEqEx8nt9FSGEEEKIXBhNIoILLwJqwFSzsa/jVFvUgSdEoRgDwtVFSInWwSCKCAkE/AUGlcDv6UqEEEIIIcQ3LucWoT5uAABUkXQLOV+hDrwXmEzKOQJItciT/VoYVPp8GE/vga4wH4HRdSEkpfh0RPbqSPntjE9KqoXIB+/tiuf8PGcH+M7Pc3aA7/zezv5PViGasKvm1w6v+i3kaN27ht8LDzgiivyOsFga1SJPFdVi0BVAr82DobjQi4mqqnq8N7yhWgipGO/tiuf8PGcH+M7Pc3aA7/zezn4xW4cmt0egN0XEV3l5tO5dQx14L1CplLOaqRZ5UlItZm4+TcCH94VX0ntDtRBSMd7bFc/5ec4O8J2f5+wA3/m9nf1Kdh7qswwA7rkGnta9a/hdaxwRFHTNMdUiT0qqxd0s94UP0l9HsOEGgg03EMR0XuvEK+m9oVoIqRjv7Yrn/DxnB/jOz3N2gO/83s6uv3kBaibCKARADI6p8vJo3buGroEnhCiaoNaAGQohnv8RYnEh4BcMIb4HmCYIksTvqVuEEEIIId4iSRI0ef8ADCgObQQwOg7sK9SBJ0RBGIP1fue+uu+5XEl6LaTiAnefnE8IIYQQonjZWgNqG68BGoBF0Aj0vkQdeC/geYTF0qgWeTKZRDAGGJiAQr0RAOCnlhDm41xEee1MKZRUC5EP3tsVz/l5zg7wnZ/n7ADf+b2Z/XKODg2Z+RZyUngjtyyT1r1rqAPvBTyPsFga1SJPoihBEBgK9UZ8eyoD2mIj4iKC0C1WAp/3jVPWe6MUVAshFeO9XfGcn+fsAN/5ec4O8J3fm9kv5WiRwNIBAKYw93Tgad27hi5e8AK1WjmrmWqRp5K1aIuNKCg2Qmcw+TBRVfH4o4N9Sm1nvFNSLUQ+eG9XPOfnOTvAd36eswN85/dm9n+ydLhLsNwD3j2n0NO6dw2/a40jSroWmWqRJyXVojRKem+oFkIqxnu74jk/z9kBvvPznB3gO783s9/IykJtlgMAMNV0Twee1r1r6BR6QhREYECQv/ljHahRgTGej8ITQgghhBA5kHL+AQDoNTUhBdT0bZhqjjrwhCgEY0C4uggp0ToYRBEhgYC/wKDi+B6bhBBCCCHEt4wmEYEFlwANYAxr4Os41R514L3AaFTOUVCqRZ6MRhMEQYBKnw/j6T3QFeYjMLouhKQUPsewU9AgdkprZ0qhpFqIfPDernjOz3N2gO/8PGcH+M7vrexXbxWhPswD2LnzFnK07l1D18B7gaScvgjVIlMlazHoCqDX5sFQXOi7QMRKqe2Md0qqhcgH7+2K5/w8Zwf4zs9zdoDv/N7KfilHh/q3R6AXQ+u7bbm07l1DHXgv0GhUvo7gNlSLPCmpFjMuTxuwS0nvDdVCSMV4b1c85+c5O8B3fp6zA3zn91b2tBwdGgvXAQCmmo3dtlxa966hDjwhhBBCCCGEELsuZWvRkN0A4N4OPHENXQNPCMcYM9/GwvL/hBBCCCGEuFNWdiZiWC4A990DnriOOvCEcIoxwMAEFOqNYAYRGhVDmK9DEUIIIYQQRWG55lvIFftHQvKr4eM0hDrwXmAw8DvCYmlUi2+VPNLOGENhsRHfnsqAttiIuIggdIuVwOmw86W4d2QQBgCMmdedl9cPj+2sPFQLIRXjvV3xnJ/n7ADf+XnODvCd3xvZiwwmhOvSAD9AdPPp87TuXUMdeEI4UfKIOwCoBAYRDNpiIwqKjdBxvBH0JNXtDnuxSYRoEsFMIvwVNEgeIYQQQoinXLlVhHosEwDAwhv5OA0BaBA7r+B5hMXSqBbfYYyhUG8+4v7VsWs4eO4mRElEkL8aNQL9EKhRKePgOwB3jkLPBAZJknAtV4czN/KRlq2FUZS8NmYAb+2sIlQLIRXjvV3xnJ/n7ADf+XnODvCd3xvZL2Vr0eD2LeRMbryFHEDr3lV0BJ4QzliOuIcF+SFKU4SUWjoYRQnBASL8BQaVoJhevFsZTCL0JhEao+jrKIQQQgghXLiUrUOqYBmBno7AywF14AnhlFrFoNbnw3j6a2gL8xEQVQdCUoqCjsITQgghhBBfSsvVoaHlCHxYQ9+GIQDoFHpCuGfQFcCgzYOhuNDXUQghhBBCiIKkZ+Ugmt0CQB14uaAOvBfwPMJiaVSLPEmSe0dt9z3l1KOkdka1EFIx3tsVz/l5zg7wnZ/n7ADf+b2RXcq9CAAwakIh+bv3hsW07l1DHXhCCCGEEEIIITZydQZE6a8CAExhDXychlhQB94LeB5hsTSqxbcEBgT5qxHir7YZdd5bI6p7j3Lq4bGdlYdqIaRivLcrnvPznB3gOz/P2QG+83s6+6VsLRqx6wAAKbyJ25dP69411IEnhBOMAeHqIqRE6/BA3WK0DtfCXzDRqPOEEJ9av349UlNT0bJlSwwfPhzHjx+vcP68vDy8+uqr6NSpExITE9GrVy98//33XkpLCCHEUZeydWjIbo9AT9e/ywaNQk8INxhU+nwYT++BrjAfgdF1adR5QohP7dy5E/PmzcOrr76KVq1aYc2aNZgwYQJ2796NyMjIMvPr9XqMGzcOkZGRWLhwIWJiYnDt2jWEhob6ID0hhJCKXMrRob9gPgJv8sAReOIanx+Bd+aX+7Nnz2LatGlITU1FQkICVq9eXWae999/HwkJCTb/9e7d24MVEOJdBl0B9DTqfNUwAYwxCAKjH0AIqYJVq1ZhxIgRGDp0KJo2bYpXX30VAQEB2LJli935t2zZglu3buGDDz5Au3btEBcXhw4dOqBZs2ZeTk4IIaQyaTnaO7eQq9nYx2mIhU878JZf7p966ils27YNzZo1w4QJE5CVlWV3fp1Oh7i4OEyfPh3R0dHlLveuu+7CgQMHrP998sknnirBITyPsFga1eJdjAGCYOlolt/TpFHoHSdo/OGvZgjSX0ew4QaCmM6jnXge2pmjqBZSkl6vx4kTJ5CcnGydJggCkpOTcfToUbvP+eabb9C6dWv85z//QXJyMvr374+PPvoIJpMy3g/e2xXP+XnODvCdn+fsAN/5PZ09IzsbtVguAMAU6v5B7Gjdu8anHXhnf7lPSkrCzJkz0a9fP/j5+ZW7XJVKhejoaOt/ERERnirBIUo6wke1eA9jgIEJyNGbkKM3Id9oUtDN1XxHUGvADIWQznwN8cxeCIY8jw4CKPd25gyqhZSUk5MDk8lU5lT5yMhI3Lx50+5zLl++jD179sBkMmHp0qWYMmUKVq1ahf/973/eiOxxvLcrnvPznB3gOz/P2QG+83syuyhJ0Ny6CAAw+odDCqjp9tegde8an10Db/nlftKkSdZplf1y76hLly6hU6dO8Pf3R+vWrTF9+nTUrVu3qpFdplaruP6FqSSqxXsYYyjUG/HtqQxoi42IiwhCt1jJ7haDRqF3nqTXAl44c0Hu7cwZVAupKkmSEBkZiddeew0qlQqJiYlIT0/HihUrMHXqVKeWpVbfGQFYFEWYTBJUKgZBuHNswmQSIYoSVCoBQokBPy3T1WrBZvtpNJogSWVHF7a0lcqmq9UCjEYRBoMJjNlmlCQJRqMIQWBQqe5kFEUJJpO96d6vSa0WoNMZyq1VzjVVll3uNTEG6PUml9ueL2uyZHf358lbNalUAkRR8so2wt01CYJg/f/S2au6jbicVYg6kvn0ealmQ+vj7qzJz08NUZRKTJfHttyRmjQalTd2I+3yWQe+ol/uL1y44PJyk5KSMG/ePDRq1AiZmZn44IMP8PDDD2PHjh0ICQmpamxCvE5bbERBsRE66mwQQmQkPDwcKpWqzGVvWVlZiIqKsvuc6OhoqNVqqFR3doYaN26MzMxM6PX6Cs+uK82yg1aSySTZPR3fZBJh7yx9o1G0u+zyftxxZLrl35Jkf35RlCCKjk+XQ00WVJPnairdOXIke3nTvV2TJTuv75NKxW/bEwTPfZ4u5ehQ33L9e2j9Mo+7qyZ78/OwjbBM98UxNMWNQt+lSxfrv5s1a4ZWrVqha9eu2LVrF4YPH+7Ustz1675afWd+uf1y52xN5vUi718jHa1JrRZk/QsrYwAziKWOuJsHXbP51R7m6+MZGEoeuWbW+VmpX/ntz19yXoZS89+el5XJYpkf9ucvZ/klM5ZctnV+Zj+nY1kcyW4eW0CSGAQm3J7XM23P0t7k9KuxqzVZtmVy/CXc2ZoEgdksn4ea5MbPzw8tWrTAoUOH0L17dwDm9Xvo0CGMGTPG7nPatm2LL7/80uaI0cWLFxEdHe1U550QQohnXcwqMYAd3UJOVnzWgXfll3tXhIaGomHDhkhLS3P6ue78db/0L0Ny+eXOnN3xmgRBkv2vkYBjNUmSZD1tR441mTuYUqnTvCWYJ0klp0CSJEiQUHLwN8k6v2Q7v2R//pLzSig1/+15pTJZLPPD/vzlLL9kxpLLts4v2c/pWBZHspvXs3n9irfn9UzbK9nplcuvxq7WZOlY3pnOc02i3ZxyrUmuV8mMGzcOM2fORGJiIpKSkrBmzRrodDoMGTIEADBjxgzExMRg+vTpAIBRo0Zh3bp1eP311zFmzBhcunQJS5YswdixY31ZhtvwPqAoz/l5zg7wnZ/n7ADf+T2Z/XKuDq093IGnde8alzrwly9fRr169ar0wq78cu+KwsJCXL58ucJR6z2tvB0/HlEt8sTzBtA+5dSjpHZGtZDS+vbti+zsbCxatAiZmZlo3rw5li9fbv0h/vr16zZnpNSpUwcrVqzAvHnzMHDgQMTExOCRRx7BxIkTfVWCW/HernjOz3N2gO/8PGcH+M7vyexpOTo0YBkAPDMCPUDr3lUudeB79OiB9u3bY9iwYejduzf8/f1denFnf7nX6/U4f/689d/p6ek4efIkgoKC0KCBuWG9+eab6Nq1K+rWrYuMjAy8//77EAQB/fv3dymjOwgCsxmggWdUizzZnqquBMqpR0ntjGoh9owZM6bcH97Xrl1bZlqbNm2wefNmT8fyCd7bFc/5ec4O8J2f5+wA3/k9mf16Vi5imfmOIqaajTzyGrTuXeNSB37btm3YsmUL3njjDbz22mvo27cvhg0bhqSkJKeW4+wv9xkZGRg8eLD175UrV2LlypXo0KGDdSfhxo0beO6555Cbm4uIiAi0a9cOmzdv9umt5MyjWypjADKqRaaU099VHCW1M6qFkIrx3q54zs9zdoDv/DxnB/jO76nsRQYT/ArSIPhLMPnVgBTovsubS6J17xqXOvDNmzfHSy+9hFmzZuGbb77B1q1bMXr0aDRs2BBDhw7FoEGDHO4wO/PLfVxcHE6fPl3h8t59913HiiCEVFsMkO9FxYQQQgghPnQpR4eG7AYAQAxrRPtMMlOl4W3VajV69uyJRYsW4fnnn8elS5fw5ptvokuXLpgxYwYyMjLclZMQQtxCdftLqNgkotgkQqTTFwghhBBCrC5la9HodgfeU6fPE9dVaRT6P//8E1u2bMHOnTsRGBiI8ePHY9iwYUhPT8fixYsxZcoUfPbZZ+7Kyi1er+2wh2qRKQWV4mns9uj+13J1MJhENBKl27cH88xKVFI7o1oIqRjv7Yrn/DxnB/jOz3N2gO/8nsp+OffOEXhTmOc68LTuXeNSB37VqlXYunUr/vnnH3Tu3Nl61N1yvXq9evXwxhtvIDU11a1heWW5f7oSUC3yJCmuB+/5egzl3EbM3ZTUzqgWQirGe7viOT/P2QG+8/OcHeA7v6eyX8rWoYvlFnIePAJP6941LnXgN2zYgKFDh+LBBx9ErVq17M4TERGB119/vUrhlILnERZLo1o8jzGAMXb7qLCDz1HcaeDKqUeu7cwVVAshFeO9XfGcn+fsAN/5ec4O8J3fU9kv5+rQQLAcgW/o9uVb0Lp3jUsd+JUrV6Ju3bo2I8QD5ntRX79+HXXr1oWfnx8efPBBt4TkHc8jLJZGtXgWY4CBCSjUG6ESmOPXZyunv6s4cmxnrqJaCKkY7+2K5/w8Zwf4zs9zdoDv/J7KnpFzC3VZNgDPnkJP6941Lg1i16NHD+Tk5JSZnpubi27dulU5FCHVFWMMhXojvj2VgYPnbsIoijTyJyGEEEII8YpbOgNqFF8HAIiaYEgB4T5OREpz6Qi8JNk/XUCr1cLf379KgQghgLbYCAAQGBDkpwIkCYEaFRjj81dKQgghhBAif1dydajPzHcSE0Pr04EkGXKqAz9v3jwA5qOECxcuRGBgoPUxk8mE48ePo1mzZu5NqACiyO8ADaVRLd6jUQmI0hQhpZZ5tPSQQMBfYFAJdjakfF4+VC3IvZ05g2ohpGK8tyue8/OcHeA7P8/ZAb7zeyL7pRwdGlgGsAtr4Pbll0Tr3jVOdeD//vtvAOYj8GfOnIFGo7E+5ufnh2bNmmH8+PHuTagAJpNyeldUi/eoVQxqfT6Mp7+GriAPgdF1ISSl2P0hlEahrwImgDEG4fYPI5IkoZyTjFwi93bmDKqFbzNmzMCcOXMQEhICADh16hSaNGli811Oqob3dsVzfp6zA3zn5zk7wHd+T2S/lKNDE3bNvPyajd2+/JJo3bvGqQ782rVrAQCzZ8/Giy++aN0JIBVTqRjXDbQkqsX7DLoC6LV5MBSHlTsPjULvGkHjD381A/TXrZcGiZpQaBHotk48L+3MEVQL33bs2IGZM2dav7tHjx6Nzz//HPXq1fNxMuXgvV3xnJ/n7ADf+XnODvCd3xPZL+fokMrM18AbazZ167JLo3XvGpeugbecSk8cIwgCTCZlXLtMtciU0vrvXiKoNWCGQojnf4RYXAj4BUOI7wGmCSp3rA+nX0NB7Yxq4VvpNu2uNk7u4L1d8Zyf5+wA3/l5zg7wnd8T2S9la9FEMHfgTeFN3Lrs0mjdu8bhDvzUqVPxxhtvICQkBFOnTq1w3sWLF1c5GCGEeIuk10IqLqDfQQghhBBSbYmShOs5eaijttxCrqFvAxG7HO7A16hRw+6/CSGEEMKHc+fOITMz0/r3hQsXUFhYaDMPDUZLCCHVU2aBHjGm64AaEDUhkAIifB2J2OFwB77kafN0Cr1zTCZ+R1gsjWrxPIEBQf5q524bR2fCypZc25krqBb+PfbYYzanzk+aNAmA+e4ykiSBMYaTJ0/6Kh73eG9XPOfnOTvAd36eswN853d39ss5OjS0jkDf0OO3kKN17xqXroEvKiqCJEnW28hdvXoVe/fuRdOmTdGpUye3BlQCUVRO74pq8SzGgHB1EVKidfD3N5Z/27hSaBR6+ZJjO3MV1cK3/fv3+zqC4vHernjOz3N2gO/8PGcH+M7v7uwXs7V37gEfVt+ty7aH1r1rXOrAT5kyBT169MCoUaOQl5eH4cOHQ6PRICcnB7NmzcLo0aPdnZNrKpXA9S9MJVEtnsag0ufDeHoP1EE1yr1tXNlnKe3qbeXUI8925hqqhW+xsbG+jqB4vLcrnvPznB3gOz/P2QG+87s7e1qODom3byFnrOnZAewAWveucqkDf+LECcyePRsAsGfPHkRFRWH79u3Ys2cPFi1aRB34UgSBgdMBFsugWrzDoCuASiU4/gTl9He9jgEAY2CMeeRUMTm3M2dRLcpw8eJF7N+/H1evXgVjDHFxcejevTvdTs4NeG9XPOfnOTvAd36eswN853d39rQcHQbevoWcyQsdeFr3rnH5FPrg4GAAwIEDB9CzZ08IgoDWrVvj2rVrbg1ICCGeorrdYS82iRBNIphJhD/9GkIUbMmSJVi0aBFEUURkZCQkSUJ2djYWLFiAZ599FhMmTPB1REIIIT6SlqNFI8st5Go29nEaUh4nDvHdUb9+fezbtw/Xr1/HgQMHkJKSAgDIyspCSEiIWwMSQoinMME8cNe1XB3O3MhHWrYWRtE8kBchSnP48GG89957ePLJJ3H48GEcOHAABw8exKFDhzBx4kQsWLAAv/76q69jEkII8QGjSUTOrVzUZjkAAFPNRj5ORMrjUgf+qaeewvz585GamopWrVqhTZs2AICDBw+iefPmbg2oBLxe22EP1eJ+jJlPwxEE5nrHkd8xQGTBYBKhN4kwGt3fJuTSztyBauHbxo0bMXz4cEybNg1hYWHW6TVr1sQzzzyDoUOHYsOGDT5MyD/e2xXP+XnODvCdn+fsAN/53Zn96q0i1McNAIDoXxNSQLjbll0eWveucekU+t69e6Ndu3bIzMy0uV9sx44d0b17d7eFUwqeR1gsjWpxL8YAAxNQqDcCAPzUEsIqeY49NAq9fMmhnbkL1cK348ePY/78+eU+PmjQIMyYMcOLiZSH93bFc36eswN85+c5O8B3fndmv5SjQyNm7sB76/R5WveucakDDwDR0dGIjo62mZaUlFTlQEqkVgseObLnC1SLezHGUKg34ttTGdAWGxEXEYRusZLTg6kp75Rv5dQjh3bmLlQL37KyshAXF1fu43Fxcbh586YXEykP7+2K5/w8Zwf4zs9zdoDv/O7Mfilbiya3R6A3hXt+ADuA1r3Lr+3Kk7RaLZYuXYrDhw8jKysLomgbnu41a0tJnSuqxTO0xUYUFBuhM3A6FCcpl5zaWVVRLXwrLi6GRqMp93G1Wg2DweDFRMrDe7viOT/P2QG+8/OcHeA7vzuzX8rRobtwBQBgDL/LbcutCK1717jUgX/ppZfwyy+/YNCgQYiOjuZ65RNCCCHVxaeffoqgoCC7jxUWFno5DSGEELm4lK1FY8st5CLifZyGVMSlDvwPP/yAJUuWoF27du7OQwghhBAPqFu3LjZv3lzhPHXq1PFSGkIIIXJyKavwzjXwYTQCvZy51IEPDQ1FzZo13RxFuYxG5ZwWTbXIkyTxOwiIfcqpR0ntjGrh2zfffOPrCIrHe7viOT/P2QG+8/OcHeA7v7uy5xUZEFR0A0EBxZCYGqawBm5ZbmVo3bvGpdvIPfPMM1i4cCF0Op278yiSkvpWVAshzlFSO6Na+Hbo0CH07dsXBQUFZR7Lz89Hv3798Ntvv/kgmXLw3q54zs9zdoDv/DxnB/jO767sF7N1aCLcHsCuZkNAcHmcc6fQuneNS+/OqlWrkJaWhuTkZMTFxUGttl3Mtm3b3BJOKTQaFQwKGZyMapEn5Y1DoZx6lNTOqBa+rVmzBiNGjEBISEiZx2rUqIGRI0di1apVuOeee3yQThl4b1c85+c5O8B3fp6zA3znd1f2tJwS17+HN63y8hxF6941LnXg6V7vhBBCCF9Onz6Nf//73+U+npKSgpUrV3oxESGEEDm4lK1DA5YOgK5/54FLHfipU6e6OwchhBBCPOjmzZtlzpgrSa1WIzs724uJCCGEyMHFbC0esA5g19C3YUilXLoGHgDy8vLw6aefYsGCBcjNzQUAnDhxAunp6e7KRgghhBA3iYmJwdmzZ8t9/PTp04iOjvZiIkIIIXKQlqNDA0sHvmZjH6chlXGpA3/q1Cn06tULy5Ytw8qVK5Gfnw8A+Prrr7FgwQK3BlQCXq/tsIdqqTrGAEFgEATmtmvXaRR6+aLPjDwpqRZHdenSBQsXLkRxcXGZx4qKivD++++ja9euPkimHLy3K57z85wd4Ds/z9kBvvO7I7tRlHAttwBx7CYAwBTqnRHoAVr3rnKpA//GG2/gwQcfxNdffw0/Pz/r9C5dutAItoRUgDHAwATk6E3I0Ztwy2CCqKAB2wgh8jV58mTk5uZaf4Dft28f9u3bh6VLl6J3797Izc3Fk08+6euYhBBCvOj6rSLEiBnQMBMklT/EkNq+jkQq4dI18H/++Sf+85//lJkeExODzMzMKodSGp5HWCyNaqkaxhh0BiMOX8iCTm9CRLAfWsSFmXv2VVyusiinHvrMyJOSanFUVFQUNm7ciFdeeQXvvPOO9cwdxhg6deqEOXPmICoqyscp+cZ7u+I5P8/ZAb7z85wd4Du/O7Kn5ejQyDICfVhDgLl8hbXTqvu6d5VLHXg/Pz+795G9ePEiIiIiqhyKEKViDAhXF6FtWAEMoohATREiNP7wU3tvY0kqwQQwZr7EATBfnqC4KxRItRUbG4tly5bh1q1buHTpEgCgQYMGCAsL83EyQgghvnApR4sm7PY94L14CzniOpc68Kmpqfjggw/w3nvvWaddu3YNb7/9Nnr27OmubIQoEINKnw/j6T3QFeZDExoGdehAqAXlHHHmmaDxh7+aAfrr1qOToiYUWgRSJ54oSlhYGJKSknwdgxBCiI9dytah/e0OvJE68Fxw6bDfrFmzoNVq0bFjRxQXF2Ps2LHo2bMngoOD8eyzz7o7IyGKY9AVQK/Ng6GoAGACAv1UCPFXI1CjqurZ9KQKBLUGzFAI6czXEP/aDvHMXgiGPAVeokAIIYQQAvyTrUUTgY7A88SlI/A1atTAqlWrcOTIEZw6dQparRYtWrRAcnKyu/MpAq/XdthDtbiXoPFHgEZAh4gC6GqYEBII+AsMKiePyNMo9G5+db0WUnGBW67El0M7cxeqhZCK8d6ueM7Pc3aA7/w8Zwf4zu+O7BeztGhsuQbeyx346r7uXeV0B14URWzduhV79+7F1atXwRhDbGwsoqOjIUkSHakixAmCSgNBXwDj6a+hy7uFwOi6EJJS6Cg8IYQQQgjxqBytHtBlISogDwBgDKN7wPPAqVPoJUnC5MmT8dJLLyE9PR3x8fFo2rQprl27hlmzZuGpp57yVE6uaTQqX0dwG6rFMwxFt0+pLy506fnK++HMd/UwAGDMvE7dsF7l1M6qimohpGK8tyue8/OcHeA7P8/ZAb7zVzX7hSwtGrEbAABTSCzgF+yOWA6rzuu+Kpw6Ar9161b8+uuvWL16Ne677z6bxw4dOoSnnnoK27dvx+DBg92ZkRBCvEJ1u8NebBIhmkQwkwh/Bd3SjhBCCCHE4p8sLRpaOvBhDXychjjKqSPwX331FZ588skynXcA6NixI5544gns2LHDbeEIIcSbmMAgSRKu5epw5kY+0rK1MIp0aRAhhBBClOf8zULEC1cBAKbwu3ychjjKqQ786dOncf/995f7eOfOnXHq1KkqhyKEEF8ymEToTSKMRtHmvvCCwGh8AkIIIYQowsUcHeLZZQCAMTLBx2mIo5zqwN+6dQuRkZHlPh4ZGYlbt25VOZTS8DzCYmlUi/MYQ4nOn2d6fzQKvWdY7gsfpL+OYMMNBBtuIIjpnOrE02dGnpRUi6+tX78eqampaNmyJYYPH47jx4879LyvvvoKCQkJmDJliocTeg/v7Yrn/DxnB/jOz3N2gO/8Vc3uyxHogeq97qvCqQ68yWSCWl3+ZfMqlQomE79vhKco6Ygd1eL8axiYgBy9CTl6E/KNJpl0TYkj3HFfePrMyJOSavGlnTt3Yt68eXjqqaewbds2NGvWDBMmTEBWVlaFz7ty5QrefPNN3HPPPV5K6h28tyue8/OcHeA7P8/ZAb7zVyV7fpERBYX5qM8yAABGH5xCX13XfVU5NYidJEmYNWsW/Pz87D6u1+vdEkpp1GoV178wlUS1OIcxhkK9Ed+eyoC22Ii4iCB0i5Xc/qlX3jXa8qqnKveFp8+MPCmpFl9atWoVRowYgaFDhwIAXn31VXz33XfYsmULnnjiCbvPMZlMeP755zFt2jQcOXIEeXl53ozsUby3K57z85wd4Ds/z9kBvvNXJfs/2eaj7wKTIPqHQQqMcnO6ylXXdV/l13Zm5gcffLDSeWgEekLK0hYbUVBshI7TjRQhhJSm1+tx4sQJTJo0yTpNEAQkJyfj6NGj5T7vgw8+QGRkJIYPH44jR454IyohhJBSLmZr0ZRdAwCYIuL5PhxezTjVgZ83b56nchBCCCGEIzk5OTCZTGXGxomMjMSFCxfsPue3337DZ599hu3bt1f59dXqO/fgFUURJpMElYpBEO5cHWgyiRBFCSqVAEFgZaar1YLNGUxGowmSVPb+vpajLJVNV6sF63TGbDNKkgSjUYQgMKhUdzKKogSTyd5079ekVgsV1irnmirLLveaLP90te35siZLdnd/nrxVEwCvbSPcXZM5u2tt71KODk0EcwfeWLOJz2oquRy5bMsdqYkx390L3qkOPHGNkgYYo1oIcY6S2hnVQqqioKAAM2bMwGuvvYaIiIgqL8+yg1aSySTZHYvHZBJhb4geo1G0u+zyTousbLplx878b/vzi6IEUXR8ujdrKvm5sDe/nGuqLHt50+VSU8kffxzNXt50b9dkye7uz1NJnqxJECSvbSNKckdNjEkut73zmYW4l10xzxt+l09qMlnu+FNmum+35SVVVJOlI+9tTg1i5wnOjF579uxZTJs2DampqUhISMDq1aurvExvKK9R8YhqkSfldUaUU4+S2hnVQkoKDw+HSqUqM2BdVlYWoqLKXkt5+fJlXL16FZMnT8bdd9+Nu+++G9u3b8c333yDu+++G2lpad6K7jG8tyue8/OcHeA7P8/ZAb7zVyX7+ZuFSLjdgffVLeSq67qvKp924J0dvVan0yEuLg7Tp09HdHS0W5bpDSVP9eAd1SJPTGaDvlWdcupRUjujWkhJfn5+aNGiBQ4dOmSdJooiDh06hDZt2pSZv3HjxtixYwe2b99u/S81NRX33nsvtm/fjtq1a3szvkfw3q54zs9zdoDv/DxnB/jO72r2Qr0RN/MLUZ+lAwBMNb1/Czmgeq57t7y2z14ZtqPXNm3aFK+++ioCAgKwZcsWu/MnJSVh5syZ6NevX7kj4Tu7TG8oec0H76gWmeJ3+6d4SmpnVAspbdy4cdi8eTO2bduG8+fP45VXXoFOp8OQIUMAADNmzMCCBQsAAP7+/oiPj7f5LzQ0FMHBwYiPjy/3e50nvLcrnvPznB3gOz/P2QG+87ua/WKWFg1YOvyYCZI6CGKNum5O5pjquO7dwWfXwLs6eq23l0kIIYQQ+/r27Yvs7GwsWrQImZmZaN68OZYvX249hf769es2AxERQgjxvfNZWjRkNwAAprCGAKPtNE981oF3ZfRaby/TXSPcWgb3AOQ3eqWzNZnXi7xH5HS0JrVa8PjolZZ1xgQBgmB5DfPrsNv/AxgYYyVOgy/5d+mT428/VnLk2lLz35lezvzM/vwl5y35qjZZWHnzw/785Sy/ZEa764DZz+lYFieyl1lnDPZyCgIDEwRAuNNOHG17lvYmp5FTXf08WbZlchwN1tmaSo98y0NNcjVmzBiMGTPG7mNr166t8LlvvPGGJyIRQgipwIWbWtzFrgIAjBF3+TgNcRaNQl8Bd41wK0l3OnIWchm90pzd8ZrMo13Ke0ROwLGazM81v8HuHL2S3e58Anf+XxJFiKJ4e7A582tKt/8HSJAky79R6m+p1HButx8r0TAlSJDEO/PfmV7O/JLt8u0tu+Sr2mSRypsf9ucvZ/klM9pdB5L9nI5lcSJ7mXUmoXROBsB0Ow8kCcLt6Y62PUsHTG4jp7ryebJ0iu9M57cmo1G0uy2Ta010e14+WL5TeMVzfp6zA3zn5zk7wHd+V7P/k12IocLtEegj4t0ZySnVcd27g8868M6OXuurZbpDeR1eHlEtFWMMMDABhXojAEAlMIAxBPmbP2qBGhUYs79jXxWlu/n8k0c9qtu9pmKTCNEkgplE+Ds54AB9ZuRJSbUQ+eC9XfGcn+fsAN/5ec4O8J3f1eznMgvRjF0GABgjfDMCPVA91707+OzcPGdHr/XVMt2B5xEWS6NaKsYYQ6HeiG9PZeCrY9fwyz9ZqKnSIqWWDg/ULUbrcC38BZO5Y+/O11XcKHbyqIcJDJIk4VquDmdu5CMtWwujKJU6Hb9i9JmRJyXVQuSD93bFc36eswN85+c5O8B3fley5xUZkFlQhCbsGgDfHoGvbuveXXx6Cv24ceMwc+ZMJCYmIikpCWvWrCkzem1MTAymT58OwDxI3fnz563/Tk9Px8mTJxEUFIQGDRo4tExfUKkEu6dw8ohqcYy22IiCYiMMJglqfT6Mp7+GriAPgdF1ISSluP90WH63f1wwmEToTSI0Ltzzkz4z8qSkWoh88N6ueM7Pc3aA7/w8Zwf4zu9K9vM3tYhjmQhgBkiCH0yh9T2UrnLVbd27i0878M6OXpuRkYHBgwdb/165ciVWrlyJDh06WAfKqWyZhPiCQVcAvTYPhuIwX0chhBBCCCHV1PmbhWhqOfoe3hgQaEg03vj8HXNm9Nq4uDicPn26SsskhBBCCCGEkOrI3IG/PQJ9OI1AzyP5359GAUSR3wEaSqNaZEoeY74RO5TUzqgWQirGe7viOT/P2QG+8/OcHeA7vyvZz90stN5CzhTe1N2RnFLd1r27UAfeC0wm5fSuqBZ5olHo5UtJ7YxqIaRivLcrnvPznB3gOz/P2QG+8zubXZIknL+pRbzg+xHogeq17t2JOvBeoFIpZ4QxqqVyAgOC/NUI8Vffvm2cR17GBo1CL1/0mZEnJdVC5IP3dsVzfp6zA3zn5zk7wHd+Z7On5xejoFh/5wh8ZDNPxHJYdVr37uTza+CrA0EQYDLxOcJiaVRLxRgDwtVFSInWwSCKCAkE/AXm9tvGlX1hzy6euI4+M/KkpFqIfPDernjOz3N2gO/8PGcH+M7vbPZzNwsRxzIRzIrNI9CHNfRcOAdUp3XvTtSBJ8StGFT6fBhP74GuMN9zt40jhBBCCCHECWczS4xAX7MRjUDPKXrXCKkixgB2u4du+X+6bRwhhBBCCJGTc5mFSGC3r3/38enzxHV0DbwXmEz8jrBYGtViizHAwATk6E3I0ZuQbzT5Zvg1fscAUTz6zMiTkmoh8sF7u+I5P8/ZAb7z85wd4Du/s9nP3izEXcIV83Mj4j0RySnVad27Ex2B9wJRVE7vimqxxRhDod6Ib09lQFtsRFxEELrFSvD2OfM0Cr180WdGnpRUC5EP3tsVz/l5zg7wnZ/n7ADf+Z3JbjSJSMvR4W51mvlvH49AD1Sfde9udATeC1Qq5axmqsU+bbERBcVG6Ay+GcyCRqGXL/rMyJOSaiHywXu74jk/z9kBvvPznB3gO78z2S/m6MBEA5reHoHeGJXoqVgOqy7r3t34XWscETw9ArkXUS0ypaBSZI8JYIxBEMz/VXayhZLaGdVCSMV4b1c85+c5O8B3fp6zA3zndyb7mYwCNGI3oGEmiJpgiDViPZjMMdVl3bsbnUJPCCEOEjT+8FczQH8dkmQ+dUrUhEKLQEj8ngVGCCGEEIU7k1GIZsx8+rwpspnXL/ck7kMdeEIIcZCg1oAZCiGe/xFicSHgFwwhvgeYJsjaoSeEEEIIkZuzmQXoLNwegV4G178T19Ep9F7A8wiLpVEtMkV9R6+S9FpIxQWAvrDSeZXUzqgWQirGe7viOT/P2QG+8/OcHeA7v6PZJUnCmcxCtGLnAQDG6JaejOWw6rDuPYE68F7A8wiLpVEt8kSj0MuXktoZ1UJIxXhvVzzn5zk7wHd+nrMDfOd3NPvNQj1ydXq0FP4BABhjWnswleOqw7r3BOrAe4FarZzVTLWUJTAgyF+NEH81AjUqn1xSxBR3HZNy6qHPjDwpqRYiH7y3K57z85wd4Ds/z9kBvvM7mv1UegHqIgs1WSEkQQ2jDO4BD1SPde+R1/bZK1cjSupcUS3mMT8szxUEhnB1EVKidTCIIkICAX+BQcXxqJqkYgwAGDO3AQfaEH1m5ElJtRD54L1d8Zyf5+wA3/l5zg7wnd/R7KfSC6xH303h8YDK35OxHFYd1r0nUAeeECcwBhiYgEK9EQDgpxYQZsiH8fQe6ArzERhdF0JSCg3sqVCq229ssUmEaBLBTCL8FXS2ACGEEEKU52R6PtoJlwAABplc/05cRx14QpzAGEOh3ohvT2VAW2xEXEQQusVKMBQVQq/Ng6E4zNcRiQcxgUGSJFzL1SEvNx+BNQQ0EqXbv8Lyex0XIYQQQpTrdEYBHmHmDrwpqrmP05Cq4vfCA44YjSZfR3AbqsVMW2xEQbEROoM81ofybmEm73oMJhF6kwijsfIRSOkzI09KqoXIB+/tiuf8PGcH+M7Pc3aA7/yOZM8q1COjQI+7bx+BN0a18HQshyl93XsKdeC9QEl9K6qFEOcoqZ1RLYRUjPd2xXN+nrMDfOfnOTvAd35Hsp9Mz0coChDLbgKQVwde6eveU6gD7wUajcrXEdyGapEnngcBsU859SipnVEthFSM93bFc36eswN85+c5O8B3fkey/30jHy1uH3031agHyT/U07EcpvR17ynUgSeEEEIIIYQQBTqTUYh4dgUAYIxs5uM0xB2oA08IIYQQQgghCnQyPR9JwgUA8jp9nriOOvCEEEIIIYQQojA3C4qRUaBHa3YOAGCMae3bQMQt6DZyXmCQyUjl7lAda2HszjXmcr3WnEahl6/q+JnhgZJqIfLBe7viOT/P2QG+8/OcHeA7f2XZT6YXoAa0aCJcN88f09YbsRym5HXvSdSBJ6QCjAEGJqBQbwQAqAQGMIYgf/NHJ1CjAmP8bnyIGzABjDEIgvnHHUmSuB5VlRBCCCHK8NeNfLQRzgIATKENIAVG+DgRcQfqwHuBRqPi+hemkqpbLYwxFOqN+PZUBrTFRtSuGYBuTQKRUksHg0lESCDgLzBzx96H5HpmgOv4qEfQ+MNfzQD9detZEKImFFoEWjvx1e0zwwsl1ULkg/d2xXN+nrMDfOfnOTvAd/7Ksp+4nof7bp8+b6jdzluxHKbkde9J1IEnxAHaYiMKio0wmCSo9fkwnv4auoI8BEbXhZCUAsX1n4lDBLUGzFAI8fyPEIsLAb9gCPE9wDRBCrysgRBCCCG8kCQJJ9MLME5IAwAYo1v6OBFxF+rAE+ICg64Aem0eDMVhvo5CZEDSayEVF3By3gAhhBBClO5KbhHyioxo4X8RAGCMutu3gYjb0Cj0hBBCCCGEEKIgf9/IRzRyUI9lQgKDsVaSryMRN6EOvBfwem2HPVSLPCnvdG3l1KOkdka1EFIx3tsVz/l5zg7wnZ/n7ADf+SvK/uf1PLQRzNe/myLiIfnV8FYshyl13XsadeAJIYQQQgghREFOphegrSDfAeyI66gD7wUajcrXEdyGapEnGoXedxgAMGZ+D+y8D0pqZ1QLIRXjvV3xnJ/n7ADf+XnODvCdv7zsRpOI0xkFaMXOm/+OaePNWA5T4rr3BhrEjpBKCAx033dil+p2h73YJEI0iWAmEf4c/fhACCGEEOU5e7MQeqMRLf3/AQAYYlr7NhBxK+rAE1IBxoBwdRFSonUwiPK57zuRByYwSJKEa7k65OXmI7CGgEaidPuMCOVcx08IIYQQfvx1PR+N2XWEMB0kdSBM4Xf5OhJxI+rAE1IhBpU+H8bTe6ArzKf7vhO7DCYRepMIjVH0dRRCCCGEVHN/Xsu7c/p81N2AQF0+JaFr4L2A5xEWS6uutdy573uhBxO5jkahl6/q+pmROyXVQuSD93bFc36eswN85+c5O8B3/vKyn7iRj7bCWfM8te/xZiSnKHHdewN14L1ASUdrq0stjAGCwBQ4OBzxNiU1IaqFkIrx3q54zs9zdoDv/DxnB/jOby97rs6AtBwd7hNOAgAMdeTbgVfauvcW6sB7gVrN7wiLpVWHWhgDDExAjt6EfKOJi2PByvuhQTn1VIfPDI+UVIuvrV+/HqmpqWjZsiWGDx+O48ePlzvv5s2bMXr0aLRv3x7t27fHY489VuH8vOG9XfGcn+fsAN/5ec4O8J3fXvYTN/IRjjw0Fa4BAAx17/N2LIcpbd17C3XgCSmFMYZCvRHfnsrAr/9km09PV1wHmRBCqm7nzp2YN28ennrqKWzbtg3NmjXDhAkTkJWVZXf+n3/+Gf369cPHH3+MjRs3ok6dOhg/fjzS09O9nJwQQpTpz2t5aCmYR583hjWEFBDu40TE3agDT0g5tMVG6Di+NocQQjxt1apVGDFiBIYOHYqmTZvi1VdfRUBAALZs2WJ3/gULFuDhhx9G8+bN0aRJE8ydOxeiKOLQoUNeTk4IIcr057U86/Xvcr3/O6ka6sB7gZIGGKNaCHGOktoZ1UJK0uv1OHHiBJKTk63TBEFAcnIyjh496tAydDodjEYjwsLCPBXTq3hvVzzn5zk7wHd+nrMDfOcvnd1oEnH8Wh5a3x6B3hDT1hexHKakde9N1IH3AqOCbi1VXWoRGBDkr0agRsXF2fM8bwDtU0491eUzwxsl1eIrOTk5MJlMiIyMtJkeGRmJmzdvOrSMt99+G7Vq1bL5EYBnvLcrnvPznB3gOz/P2QG+85fOfjqjAEajHveozpgfr9PeF7EcpqR17010U0AvEAQGUVRGh0SptTB2ZyA4QWAIVxchJVoHf38j/AUGlSDvXjxT0KBvZsqpR6mfGd4pqRZeLV26FDt37sTHH38Mf39/p59fcgAhURRhMklQqRgE4c6xCZNJhChKUKkECCW245bparVgMwio0WiCJAEaje3gRJbbBVU2nTEGSZJgMJjAmG1GSZJgNIoQBAaV6k5GUZRgMtmb7v2aGGPQ643l1irnmirLLveaLMtxte35sibLctz9efJWTYC5/XhjG+HumizDNFlqOn49H0nsAkKggxgQDlanJTRMkG1N5X0OfL0td6Sm0lm8iTrwXqBSCRBFZVxLrcRaLKPOF97+4vdTCwgz5MN4eg/UQTUgJKXI/yi83PNVY0r8zCiBkmrxlfDwcKhUqjID1mVlZSEqKqrC565YsQJLly7FqlWr0KxZM5de37KDVpLJJMFkKvu+mkwi7Ewu9whKeff3rWy6RqOy/luS7M8vipLdtlfedG/WVHLn1d78cq6psuzlTZdLTRqNCiaTyeW2V5K3azJnd//nqSRP1mT53HpjG1GSO2q6k908/UhaLu4RTptfs04HGIwSANvnyKkmxpjd+X29LS+pvJoEgVk7+N5Gp9CTaq/kqPNfHbtmHXneUFQIQ3Ghr+MRQogs+fn5oUWLFjYD0FkGpGvTpvyBk5YtW4YPP/wQy5cvR8uWLb0RlRBCFE+UJBy9egvtBfPp84ba7XyciHgKHYEn5DZtsREFNPI8IYQ4bNy4cZg5cyYSExORlJSENWvWQKfTYciQIQCAGTNmICYmBtOnTwdgPm1+0aJFWLBgAWJjY5GZmQkACAoKQnBwsM/qIIQQ3p3LLIS2qAgp/n8BAAz17vdxIuIp1IH3AiVdZ0m1yJSCSuEeE2yupVPSAINK+swoqRZf6tu3L7Kzs7Fo0SJkZmb+f3t3Hh5FlfZ9/FvV3VkgrCGoILLKIpIE0AcJQRzAfRgdxR0YERGBGVwYkUcHB3R4HBERAXcQAZlRREF9BxdccJRFUIiKyK5IIISwZe2kl6r3j5A2nXSSDiRdfcr7c125oCvV3b/7pLq6Ti2n6NatG/Pnzw+cQp+VlRV0HePrr7+O1+tlwoQJQa/z5z//mb/85S8RzV4fVF+uVM6vcnZQO7/K2UHt/OWzf73/BN21n2mglWDENsHXoruFycJjl7aPtKjowC9dupQFCxaQk5ND165dmTJlCsnJyVXO//777/PMM89w4MAB2rVrx1//+lcGDBgQ+P3kyZNZsWJF0HPS09NZsGBBvdVQnbIBMuxAaolOpu168GrWo7tiiXVq4DkU6LgbMY3wa/GVrtVVkZ0+M3aqxWrDhg1j2LBhIX+3ZMmSoMeffvppJCJZRvXlSuX8KmcHtfOrnB3Uzl8++9e/nKCffvLoe6uLQIv+K6Xt0vaRZvlfdtWqVTz++OOMHz+eFStW0LVrV0aNGlVpUJwymzdvZuLEiQwdOpSVK1cyaNAgxo8fz86dO4Pm69+/P19++WXgZ9asWZEoJySrRiisD1JLdJJR6KODyxWD7i2kZPuHuL97m5LtH6J584NGRlWZnT4zdqpFRA/VlyuV86ucHdTOr3J2UDt/WXafYbI5M5c0/QcAvK3VuDWnHdrekve27J1PWrhwITfeeCPXX389nTp1Ytq0acTFxfHWW2+FnH/x4sX079+fO++8k44dO3Lvvfdy3nnn8dprrwXNFxMTQ1JSUuCnSZMmkSgnpPK3TVCdXWopvSVE6e0fbNG5skEJdqDppbeQyjx8hB37svj5UA5ev2mPZQz7fP7BXrWI6KH6cqVyfpWzg9r5Vc4Oaucvy77tUD4+j5sLT97/3XPOgOqeFjXs0PZWsLTVPB4PP/zwA2lpv+4l0nWdtLQ0tmzZEvI5GRkZ9O3bN2haeno6GRkZQdM2btxI3759ufzyy/n73//O8ePH6zy/UFPZbeOOuH0c9/jJ9fpB02gQ6yQh1km8yxH9t40TUc3rN/D4jSpvayKEEEIIUVe++vk4vfWdxOLF3+AM/E07Wh1J1CNLr4E/fvw4fr+fxMTEoOmJiYns3bs35HOOHDlS6f6yiYmJHDlyJPC4f//+XHrppZx99tns37+fWbNmMXr0aN544w0cDkfFl6yS0/nrvIZh4PebOBxa0IA8fr+BYZg4HHrQqRRl051OHafz1/nL7ltb/n6lEHwP2XCnlx5F/nW6aZr4fAa6rgXtFTIME78/1PTa11TaLnrQEUXVanI4NHLdPj7fmUO+28uZTWIZ1LEB6WcU4/UbNIzTiNXBoWsnT00vPUpfvuayx2W/D0yvxfwaFV4/8DrB8wbvSwjx2lSVpYr5q8heft7y7xqURatqfkLPX8Xrl88Ysg20KtosrCy1yB7W36/ia4f59wv8lE6tbh2hyuepbF0Wznov2mvSdS3o9VWoSQghhAhl7U/HGKJ/B4C3TTpyJMreomIQu7p29dVXB/7fpUsXunTpwuDBgwNH5cNVtoFWnt9v4vdXvs2Y328QYjI+n4Fpmvj9wS/kreJWZbWZbpqhpxuGiWGEP702NWmaUamWU8le1fRI1GSapac557u9FBR78SbE4PTk4d3+IUUFecQltUJP7oemlQ0OZ2KaZtBo4mWPy34fmF6L+U3M4PkDrxM8b3Brh3htTEwjVJYq5q8ie/l5y79rUBazqvkJPX8Vr18+Y8g2MKtos7Cy1CJ7WH+/iq8d5t8v8FM6tbp1RCjR+Hkq6/z+Ol3dmnw+f8h1WbTWJNtiajAMtc+6UTm/ytlB7fwqZwe18xuGwfEiD9sO5TMz5lsAPG0HWpwqfKq3vVUs3bXfrFkzHA5HpQHrjh49Wukoe5kWLVoEHW2vaX6ANm3a0KxZM/bt23f6oU9BVR1eFdmplop7Z7zuAjxFeXhLCi0KdOpkFHoRCXb6/NupFhE9VF+uVM6vcnZQO7/K2UHt/H6/yVf7TtCKHLrp+zE1HU+bi62OFTbV294qlnbgY2Ji6N69O+vXrw9MMwyD9evX07Nnz5DPSU1NZcOGDUHT1q1bR2pqapXvc+jQIU6cOEFSUlKd5K4th8M+h07sVIudDmnJKPQiEuz0+bdTLSJ6qL5cqZxf5eygdn6Vs4Pa+R0OjS/3HuUyx9cAeM+6EDOumcWpwqd621vF8ovrRo4cybJly1ixYgV79uxh6tSpuN1urrvuOgAmTZrEU089FZh/xIgRfPHFF7zyyivs2bOHuXPnsnXr1sA9aAsLC3niiSfIyMggMzOT9evXM27cONq2bUv//v0tqbH8tdiqs1MtdhkZHJD+rogIO33+7VSLiB6qL1cq51c5O6idX+XsoHZ+E42N+05w+ckOvKfDlRYnqh2V297K7JZfA3/VVVdx7Ngx5syZQ05ODt26dWP+/PmBU+KzsrKCGqhXr17MnDmT2bNnM2vWLNq1a8ezzz5L586dAXA4HOzcuZOVK1eSn59Py5Yt6devH/fccw8xMTGW1CiEEEIIIYQQdem7A7l43Hn0ji29fVxJ20EWJxKRYHkHHmDYsGGBI+gVLVmypNK0K6+8kiuvDL2HKS4ujgULFtRpPmE/ugYNYhyYRtlt40IPOCWEEEIIIUQ0+mzXEdL1rbg0P/7GbTGatLM6koiAqOjA213Z7dfswA61aBo0cxaTllSE12+QEA+xuoZDV/g8dHXHABEKscPnv4ydahHRQ/XlSuX8KmcHtfOrnB3UzW+aJp/syOHPegYAnra/U258J1XbHqzNLh34CDAM+/Su7FGLhsOTj2/7h7gL84kvd9s4Vcko9CIS7PH5L2WnWkT0UH25Ujm/ytlB7fwqZwd18+85WkRWbhGXxX4DQEn7yy1OVHuqtj1Ym13dkQMU4nDYp5lVrUXTQNc1dF1D00rHbPcWFyp727iKZBT66KVppQMmli57Vqc5Pap+/kOxUy0ieqi+XKmcX+XsoHZ+lbODuvk/23WEVG03iVoeRkxjvK36WB2p1lRte7A2u7qtphBd5VOzK1CxFk2DBrqbht5sGnqzaeA7TIzux6lgLVWyUSl24tA1NN1Bvs8gz2fg03WlO/Eqfv6rYqdaRPRQfblSOb/K2UHt/CpnBzXzm6bJ6u05XOtYC4Cn3SBwqDdYt4ptX8bK7HIKvbA9TdPAk0/J9g8xPIU4G7Uktm1ve91GTkQlR2wcTh28x/fh9xs0TWqJqTfE71f3lDEhhBBCWGvPkSL2HSvgqtivACjufJ3FiUQkyRF4YXuapuEzTH4+lMOOfVlkHTkGpilHrUW90x0udE8hJds+oHjbBzg8+ciCJ4QQQojT8eH2w/TXv6eFlocR1wzv2elWRxIRJB34CFB5hMWKVK7F5zPw+A28J2swTRsdBbVRKXbkLS7A6y6wOsZpU/nzX5GdahHRQ/XlSuX8KmcHtfOrnB3Uy2+YJh9uP8wfHOsAKOn0B3C4LE51alRr+/KszC4d+AhQeYTFiuxUi53IKPTRzD612Onzb6daRPRQfblSOb/K2UHt/CpnB/XyZxzIJTcvlyv1jQAUn3uNxYlOnWptX56MQm9zTqd9mlmVWiqOOh9yHhudymy/6/ntVI99alHl8x8OO9Uioofqy5XK+VXODmrnVzk7qJd/1Q+HGaxvpoFWgr9JO3xnXWh1pFOmWtuXZ2V2GcQuAuzUuVKhFk0Dr6ZT6PEBEOM0aRJyxojGEkJ5Knz+w2WnWkT0UH25Ujm/ytlB7fwqZwe18ru9flbvyOElx2cAeLv+EZVvb6NS21dkZXbpwAvb0TSNQo+Pz7YfpqjEx9nNGzCotan0Ck4IIYQQQvy2vb8tm+a+Q6TFbgPAc97NFicSVlD3vAUhalBU4qOgxIfb67c6ihClNB1N08pd3mF1ICGEEEKowDRNlmUcZKjjc3RMPK37YTQ5x+pYwgJyBD4CfD77dCBVqUXXoEFs6eId73KgaZVz22kUejvVUspO9ZTWortiiXVq4MkK/L0MV2OKiEeVP58qn/9w2KkWET1UX65Uzq9ydlA7v8rZQZ38mzNz+eVIHrfElp4+X3zezcpkr4rK+a3MLh34CFBl4zwc0VqLpv16LYquazRzFtMvyY3XMEiIh1hdw6HL4U5hHd3pQvMWYuz5AqOkEGIaone+FM3VQJkdMIrEDIudahHRQ/XlSuX8KmcHtfOrnB3Uyb884yBD9HWcoR3H3+AMSjpepfzxDlXaPhQrs8sp9BHgcjmsjlBnorGWskHrjnv8HPf4yfcZ6J58fDs+xJ3xNv7dn6P7vZVOV1Z54IyK7FRLKTvVowX9z/S6wVMI3iLrIp2iaPz8nyo71SKih+rLlcr5Vc4OaudXOTuokf9QXjGf7cphtHMVAO7kkeCIVSJ7dVTOb2V2OQIvlFT+iLumaRSWVB60zltciKcoD29JyDHohYgox8nltcRvYPgNNL9BrK12VAghhBCiPizZlMkAbQvd9F8wnQ0o7n6b1ZGEhaQDL5RT8TZxDl2rNMK87Q5IC+VpuoZpmhw84SbvRD7xjXTaG+bJHVEKn0MmhBBCiHpzvMjDO1uz+JfzHQDc5w/HjGtmcSphJenAC+VUvE3cmU3jGNQxnn4t3Xj9cs27iG5ev4HHb+DyGVZHEUIIIUSUe+3rA/Q3vqa3cxemMw536l1WRxIWkw58BHhtdBuzaKql7DZxXr+J05OPb8dHuAvyiE9qhZ7cr8aj8KoMHBYOO9VSyk712KeWaPr8ny471SKih+rLlcr5Vc4OaudXOTtEd/6cghLe3LKflc43AHAn34HR8IzA76M5ezhUzm9ldhnETtiG111w8pr3QqujCCGEEEIIcVrm/vcnrjC+oIueiRHbhKKe46yOJKKAdOAjQOURFiuyUy12GrndTrWUslM99qnFTp9/O9Uioofqy5XK+VXODmrnVzk7RG/+7w7m8emPmTzgKj36XtRzLGZc06B5ojV7uFTOL6PQC1GDiqPO6xo0iC1dfONdDjRN3VNwhBBCCCGEKOMzTGZ8sptxzndopR3Dn9AKd8ooq2OJKCEdeBH1Ko4673RoNHUW0y/JjdeQQeuEEEIIIYR9vLH5AI6crYyLfReAgn6PgDPe4lQiWkgHXkS9iqPOl97n3YNvZ+0GrRNCCCGEECKa7T1ayPy1u3nd9RIu/JR0uAJPx6utjiWiiFwDHwEqj7BYkZW1lI067z6Z4XQHrbPTyO12qqWUneqpphZNL70kRC/9ifadULIuE6J6qi9XKudXOTuonV/l7BBd+Yu9fh5ZtYMHeZXz9Z8xYptQcPF0qtpAiKbsp0Ll/FZmlyPwQghhAd0VS6xTA09WYAeM4WpMEfHYbn+MEEIIIaplmCbTPthJpyMfMzzmY0w08gfNDrptnBAgR+AjQuURFiuKZC2axsmjkr8OWpcQ6zw5aF1dvH6UH+6sBTvVUspO9YSuRXe60L2FmLtWY/zwDuau1ejevKj+W8q6TIjqqb5cqZxf5eygdn6Vs0N05DdNk9lr9rJ/1zc85XoeAHevcXjaX1rt86Ih++lQOb+MQi8EFUeaB4+pUeDxyaB1wpYcJ5f1YncBRnE+mt8g1lY7LoQQQghRE9M0eWHdPlZv3sbbMU8Rp3nxnDOAwj4PWB1NRCnpwIuoUHGkeYeu4TXg8+2Had4wRgatE7aj6RqmaXLwhJu8E/nEN9Jpb5gnd2LJOfRCCCGE3RmmydNr9vLp5q0si3mMc/Qc/I3bkjd4DujSTROhyZIhokLFkeYTG8VyQfvm5X5fftC6JhYmFaJuef0GHr+By2dYHUUIIYQQEeL1G0xfvYvt2zazPGZGaec9oTW5Q5ZgxidaHU9EMenAR4DKIyxWVN+1lI0036RBDC1cxfRr6SY2xlcvp8zbaeR2O9VSyk712KcWWZcJUT3VlyuV86ucHdTOr3J2sCZ/Vl4xf/vPds469AnvxDxPglaMv3FbTvzhXxhN2ob9OtL21pFR6G1O07DNqNJ1WUvwNe/BnXOnQ8Ppyce34yOc8Qlyyrz4TdAgcFs5KN0xE03rDlmXCVE91ZcrlfOrnB3Uzq9ydohsfsM0+X9bs1n0eQb3Gq9yXcyXAHha9yX/0nm1HnFe2t46VmaXDnwEOJ0OpfcwlVdXtYS65h1No0Fs6SJZOtK8H6+7oN4Gq4vm0b5ry061lLJTPeHV4tA1NIeDfJ+B3yg9nT7e5cBpGFHz5SbrMiGqp/pypXJ+lbOD2vlVzg6Ry78ju4B5n22j06H3WOFcTqIjHxMNd88xFPaZBI6YWr+mtL11rMwuHXhhiYrXvJ/ZNI5BHePp19KN1y8jzYvfHkdsHE4dvMf3UeLxl959Iaklpt4Qvz9KevBCCCGECJtpmmzOzOXdr7fTft8y5jo/IMmVB4C3eVcKBs7Ed0aqtSGFcqQDLyxTvm/u1HVc3tJT5mWkefFbpDtc6J4CSrZ9RF5eLg0aNsLR94/44hKw03X0wn6WLl3KggULyMnJoWvXrkyZMoXk5OQq53///fd55plnOHDgAO3ateOvf/0rAwYMiGBiIYSoP4Zpsj27gA0/7qJkx2p6ejbxpL6Fhq4SADwNW1HS626Kuw87paPuQkgHPgLsNMDY6dRS/pp3XddoFuLe7kZJYeRGmrfPn0XYiLe49G4Lrig8+0TWZaKiVatW8fjjjzNt2jRSUlJYtGgRo0aN4oMPPiAxsfIoyps3b2bixIncf//9/O53v+O9995j/PjxvP3223Tu3NmCCuqW6suVyvlVzg5q51c5O5x+/oISH7tzCtl74ACF+76mec5XJBs/8FdtDw7NBEfpfEVNOuO78M+UdBoCDlcdJJe2t5KV2aUDHwE+G90e6lRrqXjNe4xTp4k3H9+OD3EX5ltyxN20UQ9e5RVgaHaqxz61yLpMVLRw4UJuvPFGrr/+egCmTZvGmjVreOutt7jrrrsqzb948WL69+/PnXfeCcC9997LunXreO2113j00Ucjmr0+qL5cqZxf5eygdn6Vs0PN+X2GSUGJj2OFxRw7fpS8o1m4T2ThO/YzMXk/k+TZz3naPi7VD//6JL30n2MJXXB2Goy/42X4zuhFXW/o2r3to5mV2aUDHwG6rmEY9tiIP9VaKl7zfnbzBgxqbeItjuARdxvTbDXoG/wWB7FTgazLRHkej4cffviBMWPGBKbpuk5aWhpbtmwJ+ZyMjAxuv/32oGnp6el8/PHH9Rm1SrnHsik6kV3NHNUvIxV3noZarmrawarV8PuadjbXvAO3ht+Xy6vpYFbYJjWpfiNVq+kdzDB2l9dUQ8VQlWeodkjoGl++pr8RZg3zhPE3qGEWTQv6U4R4ierbIJwaalL9S5hUXYQGplHjclDDu4exv7umz4IBhh/T78U0fOD3Ypz81zT8J//1oRmljzXDCyf/Nfw+/D4vpq8Yh68Ip9+N03ATYxTTxMynuVZABwpKj6hX5Pj1v8djW1PU8gLiOw3AbNMPo1Fr6nOYM9W/y1TOb2V26cBHgMOhYxhqjrBYUW1qqXibuIpnBFt9fbutRm63USmiHE2PqtvK/VbXZSK048eP4/f7K50qn5iYyN69e0M+58iRI7Ro0aLS/EeOHKm3nFXZt/s7kj8YQowmy4EQogoalbax3FoD8l2JFMefidm0Aw3O6ILrzG74knpgxjUlBuq1016e6t9lKue3Mrt04EWdCe6wg8fUKDh5yrzTodE0xDXvMsq8EKHprlhinRp4sgJHVgxXY4qIj5rbyglhJafz18NehmHg95s4HBq6rgem+/0GhmHicOiBHWFl0xs0TmS/ow3NjaM1vlfNH7nqv8vMMPay1nT8tOK8oV6xpvep+ffhqO9a6+o9tKBHNe7prjg7YNawoz+cHDWpzd8sREQq12VWml6r9q54SoVWLkfll/71PU42sRb0vubJ2cM5/h9Oe9a8/Po0FwYO/JoDU3NiaE4M3YGBE1NzYOhO0BwYugt0B4bmBN2J5nDhcrlwuOJwxDbEEdsQZ2xD4ho0JrZxC2IbJeJomIjX2QhTjyXG5aD88HNltxNzuRxBmaqbrmnB6zHTNPH5DHRdw+H4dT1mGCZ+f6jppWdl1Ga9ZxgmTqcedBDL5/NjmrXLXpc1lX+dU1mXW1WTplWeP1KkAy/qhKaBT9dxn/wAOHQNn2GwYe9R3B4/rZrGk36GG99OGWVeiHDoThe6txD/3i8xSgrRYhqgn3spmquBDcc8ECpq1qwZDoeDo0eDO8BHjx6tdJS9TIsWLSodba9u/uqUbaCV5/eb+P2Vj4j4/QYVJye1bA1jv8RX63eumssVufsCn8paoKav3JgI5q9rkWz7+qByfpWzQ3j5/QQfVa9q/tpMN83Q0w3DDHlkN9R0Xa/deg+qvnbbqppCza9CTWXTrejL6DXPIk6Xqtd2hFK+Fk0rvf6jbK9arFGA99g+3Dk/4z+RyZnOfC5oVsj/JBbSNSGfWN1fbpT5QgurOMk+fxZ71SIAcJz8Rih2F1BclEexuwDD4msl7LouE6cmJiaG7t27s379+sA0wzBYv349PXv2DPmc1NRUNmzYEDRt3bp1pKam1mfUiFF9uVI5v8rZQe38KmcHtfOrnB3Uzm9ldjkCHwF+v7ojLFZUVkvFI+4up05jTz7F2z6gqDAfZ1IrnMn9MHZ+hDsvNyqPuNtqFHob1VLKTvWcWi2armGaJgdPuMk7kU98I532hnnyFDFr2seO6zJxekaOHMmDDz7I+eefT3JyMosWLcLtdnPdddcBMGnSJM444wwmTpwIwIgRIxg+fDivvPIKAwYMYNWqVWzdutUWI9CD+suVyvlVzg5q51c5O6idX+XsoHZ+K7NLBz4C1B5hETTt1xM1NM3ENDU0TcPpzyfv2GF8fhMtLpaYBBPDUxQ0qnzZPa1llPn6JaPQR7PTq8XrN/D4DVw+A00ru/ar9DNpmgZGBL8/VF6XVWSnWqx01VVXcezYMebMmUNOTg7dunVj/vz5gVPis7Kygq5j7NWrFzNnzmT27NnMmjWLdu3a8eyzz9riHvCg/nKlcn6Vs4Pa+VXODmrnVzk7qJ1fRqG3OVVHWNR1iNWKMYpzgdKOfGxcHCXFRWgOBy7Tg+fHDykoyMMZhUfYayKj0AuVuGLjiHXplLgPop/8vtDjmlCix0WsE6/quiwUO9VitWHDhjFs2LCQv1uyZEmlaVdeeSVXXnllfceyhOrLlcr5Vc4OaudXOTuonV/l7KB2fhmFXlim/MjxpX4dqVXXdQx3Lpkb38FdkEfTpFa0Ob8fWd98hCO2IW3OT8Pvkfu4CxEJDlcMlOST9c1q8vNyiU9ozNn/cw1aXAOo4T7NQgghhBDCHqQD/xtT8VZvXjSKTl7HrmsQ63Lg9pRd1w6NAXdBHoUFucQlNAbAXZSPU9HTXYRQnbson8KCk2fFQFTdJ14IIYQQQtQv6cBHgBHBi1SrO6JescPudGgkUEjuyevY42NdNG7emIITx/F4jcB17eXv22grdurp2KgUER6HrqE5HOT7DPwn1zENYhxBO9fqukMfyXVZfbNTLSJ6qL5cqZxf5eygdn6Vs4Pa+VXODmrntzJ7VPTMli5dysCBA+nRowc33HAD3333XbXzv//++1xxxRX06NGDIUOG8Pnnnwf93jRNnnnmGdLT00lOTub222/n559/rscKquf3123vqvzt28r31ctGhs/zGeT5DAr8Bt5yjwsNk5igW70dJN53HM/2j8jbvBzv7s9xFh/D8+OH5G1ejmfXZ+h+b4Xr2u3TU7RPJTIKfXSrn1ocsXE4dfAeL/s876eJeYIEXzYNvaU/DXR3nY5LUdfrMivZqRYRPVRfrlTOr3J2UDu/ytlB7fwqZwe181uZ3fIO/KpVq3j88ccZP348K1asoGvXrowaNYqjR4+GnH/z5s1MnDiRoUOHsnLlSgYNGsT48ePZuXNnYJ6XX36ZJUuWMHXqVJYtW0Z8fDyjRo2ipKQkUmUFcThqtwVdvoNe+lP+fusa/nKdcr9Dr/Je7EbuARobJ/Cf+CVkh72sg+4vKQi6N/uvI8eHule7fUZLs08lMgp9dKufWnSHC91TQMm2D8jbvBzfnv/iKD5OyfaPcH/3NiXbP0Tz5qPreqWdfaeqtuuyaGanWkT0UH25Ujm/ytlB7fwqZwe186ucHdTOb2V2yzvwCxcu5MYbb+T666+nU6dOTJs2jbi4ON56662Q8y9evJj+/ftz55130rFjR+69917OO+88XnvtNaD06PvixYsZO3YsgwcPpmvXrsyYMYPDhw/z8ccfR7K0gPK30IHKHXSHo3QkQ4dDx+nUMRzhHUX3H/+FOLOIAr8Z+J3j5L3YqzuiXrHD/pslo9ALGyjb4eb3FGGaJpmHj7BjXxb7s4/gRw+sH8rv7DvVDn3FdZnK7FSLiB6qL1cq51c5O6idX+XsoHZ+lbOD2vmtzG5pq3k8Hn744QfS0tIC03RdJy0tjS1btoR8TkZGBn379g2alp6eTkZGBgCZmZnk5OQEvWajRo1ISUmp8jUjoaoOeqFhEKMV4yzOKv0pyaaReQL/8V9qPIru2/0Zsf4CfCd/5zt+gBjdV+5e7OEcURdC2EnZfeNNZ0zgFPuKO/vyfAbGyXXSrz9Vn/lTtqNR16lyfjvtDxNCCCGEiFaWDmJ3/Phx/H4/iYmJQdMTExPZu3dvyOccOXKEFi1aVJr/yJEjAOTk5ASmVTVPuOpig1TXwWUW4S85OWq0rhPniqUoPxef38AVE4uzgUH29rUUFxfRsHEiLTv0QD+wEaOwEL1ZEs6GPdAzSx9rzZLQO/YgLjYWPaYBurcIbf9XQb9LaJKIqbuIT2gGmk7DxokYWt0+1mMa1NtrR+69muPHGTV1ns57m1pMlLaxvLdVdTZs0jKwfnA4Y4htkQ75R/H7/LicTho3a4y3pCgwzKUW15RjfheGefKOFA4Hbl/pgJcOHZrpHozC3NIvDU0nLjYO0+cODJJnuhpTrMWXGzTP4Nd9xOX/H+nHoX9XtjOivt+rrsa4kR0klUVrm0RrrnCpnF/l7KB2fpWzg9r5Vc4OaucvHTzcgvc1TeuG4s7Ozubiiy/m9ddfp2fPnoHpM2bMYNOmTbz55puVnnP++efzz3/+k9///veBaUuXLuXZZ59l3bp1bN68mVtuuYUvvviCli1bBua555570DSN2bNn12tNQgghhBBCCCFEfbD0FPpmzZrhcDgqDVh39OjRSkfZy7Ro0aLSkfTy8yclJQWmhfuaQgghhBBCCCFEtLO0Ax8TE0P37t1Zv359YJphGKxfvz7oiHx5qampbNiwIWjaunXrSE1NBeDss88mKSkp6DULCgr49ttvq3xNIYQQQgghhBAi2lk+9N/IkSNZtmwZK1asYM+ePUydOhW32811110HwKRJk3jqqacC848YMYIvvviCV155hT179jB37ly2bt3KsGHDANA0jREjRvD888/zySefsGPHDiZNmkTLli0ZPHiwJTUKIYQQQgghhBCny9JB7ACuuuoqjh07xpw5c8jJyaFbt27Mnz8/cLp7VlZW0DD9vXr1YubMmcyePZtZs2bRrl07nn32WTp37hyYZ/To0bjdbh555BHy8vLo3bs38+fPJzY2NuL1CSGEEEIIIYQQdcHSQeyEEEIIIYQQQggRHstPoRdCCCGEEEIIIUTNpAMvhBBCCCGEEEIoQDrwQgghhBBCCCGEAqQDL4QQQgghhBBCKEA68PUkMzOThx56iIEDB5KcnMzgwYOZM2cOHo8naL7t27dz66230qNHDwYMGMDLL79sUeLqPf/889x8882kpKRwwQUXhJznu+++409/+hMXXHABF154IaNGjWL79u0RTlqzcGoBePvttxkyZAg9evSgb9++TJs2LYIpwxNuLQDHjx/n4osvpkuXLuTl5UUoYfhqqmX79u3cf//9DBgwgOTkZK688koWLVpkQdKahfN3OXjwIHfddRcpKSn07duXJ554Ap/PF+Gkp+ann35i7Nix9OnTh169enHLLbewYcMGq2OdsjVr1nDDDTeQnJzMhRdeyLhx46yOJBTl8Xi45ppr6NKlCz/++GNgemZmJl26dKn0k5GRYV3YEKrKD9G7vXL33XdzySWX0KNHD9LT03nggQfIzs4O/D7a276m/BCdbR/Odm40t73q2+nhbGeEavv//Oc/EU4amp22kwYOHFipnV966aV6fU/LbyNnV3v37sU0TR599FHatm3Lzp07mTJlCm63mwcffBCAgoICRo0aFegc7ty5k4ceeojGjRtz0003WVxBMK/XyxVXXEFqairLly+v9PvCwkJGjx7NwIED+fvf/47f72fu3LmMGjWKNWvW4HK5LEgdWk21ACxcuJBXXnmFSZMmkZKSQlFREQcOHIhw0pqFU0uZhx9+mC5dulTaMIgWNdWydetWmjdvzpNPPslZZ53F5s2beeSRR3A4HAwbNsyCxFWrqRa/38+YMWNo0aIFr7/+OocPH+bBBx/E5XJx//33W5C4du6++27atm3LokWLiIuLY9GiRdx9992sXr2apKQkq+PVyocffsiUKVO47777uOiii/D7/ezcudPqWEJRM2bMoGXLllXuvH711Vfp1KlT4HHTpk0jlCw8VeWP5u2Viy66iLvvvpukpCSys7OZMWMG99xzD6+//nrQfNHa9jXlj9a2D2c7t0w0tr3q2+nhbv89/vjj9O/fP/C4cePGkYhXI7ttJ02YMIEbb7wx8Lhhw4b1+4amiJiXX37ZHDhwYODx0qVLzQsvvNAsKSkJTHvyySfNyy+/3Ip4YXnrrbfM3r17V5r+3XffmZ07dzYPHjwYmLZ9+3azc+fO5s8//xzJiGGrqpYTJ06YycnJ5rp16yxIdWqqqqXM0qVLzWHDhpnr1q0zO3fubObm5kYwXe3UVEt5U6dONYcPH17PiU5dVbWsWbPG7Nq1q5mTkxOY9q9//cvs1atX0PogGh09etTs3LmzuWnTpsC0/Px8s3PnzubatWstTFZ7Xq/X7N+/v7ls2TKrowgbWLNmjXnFFVeYu3btMjt37mxu27Yt8Lv9+/dXmhZtqsuv0vbKxx9/bHbp0sX0eDymaarR9uVVzK9S21fczlWt7VXcTq9um6lz587m6tWrI5yoduywnfS73/3OXLhwYUTfU06hj6D8/HyaNGkSeJyRkcEFF1xATExMYFp6ejo//fQTubm5VkQ8Ze3bt6dp06YsX74cj8dDcXExy5cvp2PHjrRu3drqeLWydu1aDMMgOzubK6+8kosvvph77rmHrKwsq6Odkt27d/Pcc8/xxBNPoOv2+sjn5+dHxZ782srIyKBz5860aNEiMC09PZ2CggJ2795tYbKaNWvWjPbt27Ny5UqKiorw+Xy88cYbJCYm0r17d6vj1cq2bdvIzs5G13WuvfZa0tPTufPOO+UIvKi1I0eOMGXKFGbMmEFcXFyV840dO5a+fftyyy238Mknn0QwYfVqyq/K9sqJEyd477336NmzZ6Uz/6K17csLlV+VtofK27llVGh7sOd2+rRp0+jTpw9Dhw5l+fLlmKZpdaSwqLad9PLLL9OnTx+uvfZa5s+fX++n+ttraz6K7du3j9dee42bb745MO3IkSNBCyYQeHzkyJGI5jtdCQkJLFmyhHfffZeUlBR69uzJF198wcsvv4zTqdaVGpmZmZimyQsvvMBDDz3EnDlzyM3NZeTIkZWujYp2Ho+H+++/nwceeIBWrVpZHadObd68mffffz/olCVVVPfZz8nJsSJS2DRN49VXX2Xbtm306tWL5ORkFi5cyPz580NuuEWz/fv3AzBv3jzGjh3LCy+8QJMmTRg+fDgnTpywNpxQhmmaTJ48mZtvvpkePXqEnKdBgwZMnjyZZ555hhdffJHevXszfvz4qOjMhJM/2rdXnnzySVJTU+nTpw9ZWVk899xzgd9Fc9uXqS5/tLd9mVDbuSq0fRk7bqdPmDCB2bNns3DhQi677DKmTZvGkiVLrI4VFpW2k4YPH86sWbNYtGgRN910Ey+++CJPPvlkvb6nWj2rKDBz5swaB7BYtWoVHTt2DDzOzs7mzjvv5Iorroiqzsap1FKV4uJiHn74YXr16sVTTz2FYRi88sorjBkzhuXLl1d7RKIu1GUthmHg9Xr529/+Rnp6OgCzZs2iX79+fPXVV0HXEtWHuqzlqaeeomPHjlxzzTV1Fa9W6rKW8nbu3Mm4ceMYP3584G9U3+qrlmgRbn0dOnRg2rRpJCYmsnTpUuLi4njzzTe5++67Wb58OS1btoxQ4qqFW4thGEDpNf2XX345UHq94MUXX8wHH3wQtCEnfnvCXY7Wrl1LYWEhY8aMqXK+5s2bM3LkyMDj5ORkDh8+zIIFCxg0aFCdZS6vLvNHWm3Xt6NGjWLo0KEcPHiQefPm8eCDD/Liiy+iaVpUt304+SOtLrdzVWh7iJ7t9Lrezhg/fnzg/+eddx5ut5sFCxYwYsSI08pZFTttJ9WmlvLLeNeuXXG5XPz9739n4sSJQWdv1CXpwNfSHXfcwR//+Mdq52nTpk3g/9nZ2YwYMYKePXvy2GOPBc3XokWLSnvwyh5X3OtUH2pbS3Xee+89Dhw4wBtvvBE4TXvmzJn8z//8D5988glXX331aeetTl3WUjYIV/kBV5o3b06zZs0ichp9XdayYcMGdu7cyYcffggQOHWqbNCcCRMmnF7YGtRlLWV2797N7bffzk033RTR0cLrspYWLVrw3XffBU0r++xbNQhcuPVt2LCBNWvWsGnTJhISEgDo3r0769atY+XKldx1112RiFutcGsp24tffmMiJiaGNm3aKHvJjKg7tflMZGRkVDp6ff311zNkyBCeeOKJkM9NSUlh3bp1dZa3orrMH+ntldqub5s3b07z5s1p3749HTt2ZMCAAWRkZNCzZ8+Qz42Wti9TXf5ob/vqtnNDiba2j6bt9PrYZiovJSWF5557Do/HUy8dSzttJ51OLSkpKfh8PjIzM+nQoUN9xJMOfG2VrWTDUbZS6N69O48//nil649TU1OZPXs2Xq83cK3TunXraN++fURORa1NLTUpLi5G1/WgvcVlj8uOctWnuqylV69eQOmtss4880yg9Lq048ePR+Q09LqsZe7cuRQXFwcef//99zz00EMsXbqUc845p07eozp1WQvArl27+NOf/sS1117LfffdV2evG466rCU1NZUXXniBo0ePkpiYCJR+9hMSEoJ2HEVSuPW53W6ASkeGIvVZD0e4tZx//vnExMTw008/BW5j4/V6OXDggO0uORG1F+5y9Le//Y1777038Pjw4cOMGjWKp59+mpSUlCqf9+OPP9brhmhd5o/09srprG/L1kPVXfIWLW0fSsX80dz2NW3nhhJNbR9t2+l1vc1U0Y8//kiTJk3q7aiwnbaTTqeWH3/8EV3XA7nrg3Tg60l2djbDhw+nVatWPPjggxw7dizwu7IV15AhQ3j22Wd5+OGHGT16NLt27WLx4sX87//+r1Wxq3Tw4EFyc3M5ePAgfr8/cH/Yc845h4YNG5KWlsaMGTOYNm0aw4cPxzAMXnrpJRwOB3369LE4fbCaamnfvj2DBg1i+vTpPProoyQkJDBr1iw6dOigXC0VO+nHjx8HSo84RsutRMrUVMvOnTv505/+RHp6OiNHjgwcPXU4HPX6hXcqaqolPT2dTp06MWnSJB544AFycnKYPXs2t912W719sdaV1NRUGjduzOTJkxk/fjyxsbEsW7aMAwcOcMkll1gdr1YSEhK4+eabmTt3LmeddRatWrViwYIFAFxxxRUWpxOqqLizp0GDBkDp571sJ/CKFStwuVx069YNgNWrV/PWW2/xj3/8I7JhQwgnf7Rur3z77bd8//339O7dm8aNG/PLL7/wzDPPcM455wSOvkdz24eTP1rbPpzt3Ghue9W302vazvj00085evQoKSkpxMbGsnbtWl588UXuuOMOi5OXsst20pYtW/j222+56KKLaNiwIVu2bOHxxx/nD3/4Q70ejNVMVYYjVMzbb79d5Qd8x44dgf9v376dRx99lO+//55mzZoxbNiwqDgFtaLJkyezYsWKStMXL14c6NSuXbuWefPmsWvXLnRdp1u3btx3332kpqZGOG31wqmloKCA//u//2P16tXous6FF17Iww8/zFlnnRXpuNUKp5byvvrqK0aMGMGmTZuirgNfUy1z585l3rx5lX7funVrPv3000hEDFs4f5cDBw4wdepUNm7cSHx8PH/84x+ZOHGiEoM+fv/998yePZutW7fi9Xo599xzGTduHAMGDLA6Wq15vV5mzZrFO++8Q3FxMSkpKTz00EOce+65VkcTisrMzGTQoEGsXLky0HFZsWIFL7/8MgcPHsThcNChQwdGjRoVlTuKQuWH6Nxe2bFjB9OnT2fHjh0UFRWRlJRE//79GTduHGeccQYQ3W0fTn6IzrYPZzs3mtte9e30mrYz/vvf/zJr1iz27dsHlHaMb7nlFm688caouCORXbaTfvjhB6ZNm8bevXvxeDycffbZXHPNNYwcObJedzRIB14IIYQQQgghhFCA9btghBBCCCGEEEIIUSPpwAshhBBCCCGEEAqQDrwQQgghhBBCCKEA6cALIYQQQgghhBAKkA68EEIIIYQQQgihAOnACyGEEEIIIYQQCpAOvBBCCCGEEEIIoQDpwAshhBBCCCGEEAqQDrwQopIuXbrw8ccfWx2jWrNnz2bKlCnVzjN8+HCmT59eq9fdvXs3F198MUVFRacTTwghhKgX8h0t39Hit0068EJEscmTJzNu3DirY0SdnJwcFi9ezN13312r5w0fPpwuXboEftLS0pgwYQIHDhwIzNOpUydSU1NZuHBhXccWQghhI/IdHZp8RwtRv6QDL4RQzptvvknPnj1p3bp1rZ9744038uWXX/LFF1/w3HPPcejQIR544IGgea677jr+/e9/4/P56iqyEEII8Zsg39FC1C/pwAuhsI0bNzJ06FDOP/980tPTmTlzZtAX2sCBA3n11VeDnnPNNdcwd+7cwOOff/6Z2267jR49enDVVVexdu3aoPkzMzPp0qULH330EcOHDyclJYU//OEPbNmyJWi+r7/+mltvvZXk5GQGDBjAP/7xj6BT3JYuXcpll11Gjx49AnvVy3zwwQcMGTKE5ORk+vTpw+23317t6XGrVq1i4MCBQdOKioqYNGkSPXv2JD09nVdeeSXkc+Pi4khKSqJly5akpqZy2223sW3btqB50tLSyM3NZdOmTVVmEEIIIaoj39G/ku9oIeqOdOCFUFR2djZ33XUXPXr04J133mHq1KksX76c559/PuzXMAyDv/zlL7hcLt58802mTZvGzJkzQ8779NNPM2rUKFauXEm7du2YOHFiYEPkl19+YfTo0Vx22WW8++67PP3003zzzTc89thjAHz//fdMnz6dCRMm8MEHHzB//nwuuOACAA4fPszEiRO5/vrrWbVqFYsXL+bSSy/FNM2QOU6cOMHu3bs5//zzg6bPmDGDTZs28dxzz7FgwQI2btzIDz/8UG39J06c4P333yc5OTloekxMDN26dePrr7+uuRGFEEKICuQ7Wr6jhagvTqsDCCFOzb/+9S/OPPNMHnnkETRNo2PHjmRnZzNz5kzGjx+Prte8f27dunXs3buX+fPnc8YZZwBw3333MXr06Erz3nHHHVxyySUATJgwgauvvpp9+/bRsWNHXnzxRYYMGcLtt98OQLt27Xj44YcZPnw4U6dOJSsri/j4eC655BISEhJo3bo15513HlB6rZzP5+PSSy8NnG7XpUuXKjNnZWVhmiYtW7YMTCssLGT58uU8+eST9O3bF4B//vOfDBgwoNLz//3vf7N8+XJM08TtdtOuXTsWLFhQab6WLVty8ODBGttQCCGEqEi+o+U7Woj6Ih14IRS1Z88eevbsiaZpgWm9e/emqKiIQ4cO0apVq7Be48wzzwxsGAD07Nkz5Lzlv7CTkpIAOHbsGB07dmT79u3s2LGD9957LzCPaZoYhkFmZiZpaWm0atWKwYMH079/f/r378+ll15KfHw8Xbt2pW/fvgwZMoT09HTS09O5/PLLadKkScgcxcXFAMTGxgam7d+/H6/XS0pKSmBa06ZNad++faXnDxkyJDCwzpEjR3jxxRcZNWoUb731FgkJCYH5YmNjcbvdVTeeEEIIUQX5jpbvaCHqi3TghbCx8hsOZU510BeXy1XpdQ3DAEqvbbv55psZPnx4peedddZZxMTEsGLFCjZu3MiXX37JnDlzmDdvHsuXL6dx48YsXLiQzZs3s3btWpYsWcLTTz/NsmXLaNOmTaXXa9asGQC5ubk0b9681nUkJCTQtm1bANq2bcv06dNJT0/n/fff54YbbgjMl5ubyznnnFPr1xdCCCHCId/Rlcl3tBA1k2vghVBUx44d2bJlS9B1aN988w0NGzbkzDPPBKB58+YcPnw48PuCggIyMzODXuPQoUNB82RkZNQ6y3nnncfu3btp27ZtpZ+YmBgAnE4naWlpTJo0iXfffZcDBw6wYcMGoHRjo3fv3kyYMIGVK1ficrmqvMftOeecQ0JCAnv27AlMa9OmDS6Xi2+//TYwLTc3l59//rnG7A6HA/j1qEGZXbt20a1bt1q1gxBCCAHyHS3f0ULUHzkCL0SUy8/P58cffwya1rRpU2699VYWLVrEY489xm233cZPP/3E3LlzGTlyZODauosuuogVK1YwcOBAGjVqxJw5c4Kuu0tLS6Ndu3ZMnjyZSZMmUVBQwNNPP13rjKNHj+amm27i0Ucf5YYbbiA+Pp7du3ezbt06HnnkET777DP279/PhRdeSOPGjfn8888xDIP27dvz7bffsn79evr160diYiLffvstx44do0OHDiHfS9d10tLS+Oabbxg8eDAADRs25Prrr+fJJ5+kadOmJCYm8vTTT4c8ulFcXExOTg4AR48e5bnnniM2NpZ+/foF5snMzCQ7O5u0tLRat4UQQojfDvmODibf0ULUP+nACxHlNm7cyLXXXhs0bejQoUyfPp2XXnqJGTNmsGzZMpo2bcrQoUMZO3ZsYL4xY8aQmZnJmDFjaNSoEffcc0/Q3n1d15k3bx4PP/wwQ4cOpXXr1vztb3/jzjvvrFXGrl27smTJEmbPns2tt94KlO5xv+qqqwBo1KgRq1evZt68eZSUlNC2bVueeuopzj33XPbs2cOmTZtYtGgRBQUFtGrVismTJ4cc3KZ8/VOmTOGBBx4IbOxMmjSJoqIixo4dS8OGDRk5ciQFBQWVnrts2TKWLVsGQJMmTejSpQsvvfRS0MbIf/7zH/r163dK97AVQgjx2yHf0ZXJd7QQ9Uszq7oPhBBCRCnTNLnhhhu4/fbb+f3vf1+nr+3xeLj88suZOXMmvXv3rtPXFkIIIexOvqOFqF9yDbwQQjmapvHYY4+d8mA/1cnKymLMmDGyYSCEEEKcAvmOFqJ+yRF4IYQQQgghhBBCAXIEXgghhBBCCCGEUIB04IUQQgghhBBCCAVIB14IIYQQQgghhFCAdOCFEEIIIYQQQggFSAdeCCGEEEIIIYRQgHTghRBCCCGEEEIIBUgHXgghhBBCCCGEUIB04IUQQgghhBBCCAVIB14IIYQQQgghhFCAdOCFEEIIIYQQQggF/H96LRRXZ3zcLwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare data\n",
    "web_loudness = df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"]\n",
    "mobile_loudness = df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"]\n",
    "bins = list(np.linspace(-20, -5, 100))\n",
    "\n",
    "# Plot histogram (PDF)\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(\n",
    "    web_loudness,\n",
    "    label=\"web\",\n",
    "    bins=bins,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    mobile_loudness,\n",
    "    label=\"mobile\",\n",
    "    bins=bins,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.title(\"PDF: Loudness (abs) for Positive Preferences by Source\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "\n",
    "# Plot CDF\n",
    "plt.subplot(1, 2, 2)\n",
    "web_sorted = np.sort(web_loudness)\n",
    "web_cdf = np.arange(1, len(web_sorted) + 1) / len(web_sorted)\n",
    "plt.plot(web_sorted, web_cdf, label=\"web\")\n",
    "\n",
    "mobile_sorted = np.sort(mobile_loudness)\n",
    "mobile_cdf = np.arange(1, len(mobile_sorted) + 1) / len(mobile_sorted)\n",
    "plt.plot(mobile_sorted, mobile_cdf, label=\"mobile\")\n",
    "\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.title(\"CDF: Loudness (abs) for Positive Preferences by Source\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "\n",
    "plt.tight_layout()\n",
    "print(\"Mean loudness (web):\", web_loudness.mean())\n",
    "print(\"Mean loudness (mobile):\", mobile_loudness.mean())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:51.452111Z",
     "iopub.status.busy": "2025-10-04T22:10:51.451945Z",
     "iopub.status.idle": "2025-10-04T22:10:52.703238Z",
     "shell.execute_reply": "2025-10-04T22:10:52.702711Z",
     "shell.execute_reply.started": "2025-10-04T22:10:51.452094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df[\"loudness_diff\"] = df[\"loudness_abs\"].diff() / df[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:52.704102Z",
     "iopub.status.busy": "2025-10-04T22:10:52.703932Z",
     "iopub.status.idle": "2025-10-04T22:10:53.938310Z",
     "shell.execute_reply": "2025-10-04T22:10:53.937702Z",
     "shell.execute_reply.started": "2025-10-04T22:10:52.704085Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "\n",
    "# Calculate mean_shimmer_score_diff and its ratio\n",
    "df[\"mean_shimmer_score_diff\"] = df[\"mean_shimmer_score\"].diff()\n",
    "df[\"mean_shimmer_score_diff_ratio\"] = (\n",
    "    df[\"mean_shimmer_score\"].diff() / df[\"mean_shimmer_score\"]\n",
    ")\n",
    "\n",
    "# Prepare percentiles for both diff and diff_ratio\n",
    "diff_data = df[df[\"preference\"]][\"mean_shimmer_score_diff\"].dropna()\n",
    "diff_ratio_data = df[df[\"preference\"]][\"mean_shimmer_score_diff_ratio\"].dropna()\n",
    "diff_percentiles = np.percentile(diff_data, lookup_percentiles)\n",
    "diff_ratio_percentiles = np.percentile(diff_ratio_data, lookup_percentiles)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Left plot: mean_shimmer_score_diff\n",
    "hist_range = (-1, 1)\n",
    "axes[0].hist(\n",
    "    diff_data,\n",
    "    label=f\"pos, mean: {np.mean(diff_data):.2f}\",\n",
    "    bins=np.linspace(*hist_range, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "diff_textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(diff_percentiles)\n",
    "    ]\n",
    ")\n",
    "axes[0].text(\n",
    "    0.05,\n",
    "    0.98,\n",
    "    diff_textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    transform=axes[0].transAxes,\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "# Only plot lines within the histogram range\n",
    "for percentile in diff_percentiles:\n",
    "    if hist_range[0] <= percentile <= hist_range[1]:\n",
    "        axes[0].axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "axes[0].set_xlim(hist_range)\n",
    "axes[0].set_title(\n",
    "    f\"Mean shimmer score diff\\n{lookup_percentiles[-1]}th: {diff_percentiles[-1]:.2f}\"\n",
    ")\n",
    "axes[0].legend()\n",
    "\n",
    "# Right plot: mean_shimmer_score_diff_ratio\n",
    "hist_ratio_range = (-2.5, 2.5)\n",
    "axes[1].hist(\n",
    "    diff_ratio_data,\n",
    "    label=f\"pos, mean: {np.mean(diff_ratio_data):.2f}\",\n",
    "    bins=np.linspace(*hist_ratio_range, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "diff_ratio_textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(diff_ratio_percentiles)\n",
    "    ]\n",
    ")\n",
    "axes[1].text(\n",
    "    0.05,\n",
    "    0.98,\n",
    "    diff_ratio_textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    transform=axes[1].transAxes,\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "# Only plot lines within the histogram range\n",
    "for percentile in diff_ratio_percentiles:\n",
    "    if hist_ratio_range[0] <= percentile <= hist_ratio_range[1]:\n",
    "        axes[1].axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "axes[1].set_xlim(hist_ratio_range)\n",
    "axes[1].set_title(\n",
    "    f\"Mean shimmer score diff ratio\\n{lookup_percentiles[-1]}th: {diff_ratio_percentiles[-1]:.2f}\"\n",
    ")\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:53.939118Z",
     "iopub.status.busy": "2025-10-04T22:10:53.938952Z",
     "iopub.status.idle": "2025-10-04T22:10:55.944693Z",
     "shell.execute_reply": "2025-10-04T22:10:55.944146Z",
     "shell.execute_reply.started": "2025-10-04T22:10:53.939101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ll+er459//iEyMpLvvvvulvEK6+TJk3Ts2JFBgwaxbds2TCaTQ+rNyckpcA263r1788svv3Dx4kXrsV8/O7nFYmH+/PnWpLlv376cP3/+tttTsWLFfKMkC+u7775DUZQiJemLFy9GVVX69etXpFhXRmCmpaUVaT8hikLu5AjhIBcuXACwzla9YsUK7rvvPrp27YrRaGTTpk0MHz6chQsXcu+9996yvkmTJhEUFMSQIUNuuphpTk4Offv2JTY2lt69e1OuXDk2bNhQqISkIBkZGfzf//0fjz76KE8++SSZmZl89dVX9O/fn//7v/+jbt26ha5r/PjxTJkyBS8vL1555RUAQkJCqFy5Mk2bNuXbb7/N9yhn48aNeHt7O3Sagbp16zJo0CDWr1/P4MGDCQ0NpXv37vTs2ZNq1ardVp3r16/niy++QFVVatasyaBBg2zugrzyyiukp6dz6dIlaz8ub29vmzoWL16Moii89NJLZGRksGTJEkaPHm1d6qWkGI1Gvv/+e5o0aWLtS3UrMTExLF68mOnTp+Ph4XHTsgaDgYyMDHJzczl06BCffvoplSpVuumM6kLYS5IcJ9e7d2/q1KnDm2++WdpNEdfJyMggKSkJg8HAP//8w8cff4yHhwcdO3YE4IcffrD5w//888/To0cPli1bVqgkx9/fn+XLl9/y0c+XX37JuXPnmDNnDp07dwbgqaeesusxir+/P9u3b7d5lPbUU0/RuXNnVq5cyfTp0wtdV6dOnZgzZw6BgYH52tStWzcmTJjA6dOnrZNKXvmyffDBB4vUt+lW/Pz8GDp0KEOGDGH37t18/fXXrFixgkWLFnHPPffwxBNP8PDDD9/yy/qKJk2a0LlzZ8LDw4mLi+OLL75g9OjRpKenW6dQaNOmDStWrCAtLa3A85Gbm8s333xj/az9/PyYNm0aJ06cICIiwjEHXwg7d+4kJSWlSI+qZs6cSd26denSpcsty/7000+MHDnS+rp+/fpMnz4932SnQjiSPK5ygIULF9KzZ0+aNGlCq1atGDx4MGfOnLEpk5uby6RJk2jRogVNmjTh1Vdfta5bBVeHkxbHrduUlBSmTJnCQw89RMOGDbn33nuZOnVqvrl4YmJiGDBgAI0aNaJVq1a8++67t7yln5KSwqhRo2jatCnNmjVj/PjxZGZm2pRRVZWlS5fy0EMPUb9+fdq1a8f8+fMdfpwl7YUXXrA+Snrttdfw9vZm3rx51g6b135Zpqamkp6ezt13382RI0cKVf9TTz1VqL4tv/32G6GhoTYjdDw9PXnqqaeKeERXabVa65euxWIhJSUFk8lE/fr1C93+wujcuTPu7u5s3LjRum3nzp0kJyfbTEPgSIqi0LJlS/73v//x+++/M2nSJAwGA+PGjaNt27a88847pKam3rKeNWvW0LdvX+6//36effZZvv76ayIiIvjggw/IyckpdHt69Ohhk0w2a9YM4JbTQjjad999h16vtybKt7Jr1y5+/PFHxo8fX6jyLVq0YNmyZcydO5dnnnkGvV5vnYRUiOIiKbQD7Nmzh+eff54GDRpgNpt5//336devH5s2bbJ2Fp0+fTq//vorc+bMwdfXlylTpjB06FDrPDXFKS4ujri4OMaNG0etWrW4ePEiEydOJC4uztpR0Gw2M3DgQEJCQlizZo21vF6vt/nX1/VGjx5NfHw8y5Ytw2g0Mn78eCZMmGAzYmLatGns3LmTsWPHEhERQWpqaqG+RJzdhAkTqF69OlqtlpCQEKpXr27TF+Lnn39m/vz5HD161LoaO1DokVSFfWRw8eJFqlatmq/e6tWrF2r/gqxfv55PP/2Us2fPYjQai9yuwvDz86Njx4589913jBgxAsh7VFW+fHlrH6eCXL/Aq6+vL3q9nqSkJJvt/v7+BXbu9vHx4ZlnnqF79+7Mnz+fBQsWsGbNGp555hn8/f2LdCxubm48//zzvPPOOxw6dMiarNxKWFiYzWs/Pz+gZPuqZGZmsm3bNtq2bUtgYOAty5tMJqZNm8bjjz9Ow4YNCxUjJCTEOr3Cww8/zIIFC3jxxRf58ccfpeOxKDaS5DjA0qVLbV7PnDmTVq1acfjwYe655x7S09P5+uuvmTVrlnXI5vTp03nkkUfYv38/ISEh9OnTB4B77rkHgO7du1vnTVFVlffee4+vvvoKvV7PM888Y52TpDAiIiJsJmurUqUKI0aMYMyYMZhMJnQ6HTt37uTUqVMsW7aMkJAQ6taty/Dhw5k1axZDhw694ZfE6dOn2bFjB1999ZV1lNFbb73FgAEDGDt2LOXLl+f06dOsXr2ajRs3UqNGDQCHrJjuDBo2bGg97uvt3buXQYMGcc899/DOO+8QGhqKXq/n66+/LnRn2isTRZYEs9lsc9dow4YNvP7663Tq1Il+/foRHByMVqtl4cKFDr/D0K1bN7Zs2cI///xDREQE27dv59lnn71l59nrZ8yeMWMGzZs3z9ePZ8WKFbRo0eKGdRw8eJCvv/6azZs3k5aWRqNGjXjiiSfyrcdWWBUrVgQoUhJf0HGW5BRmW7duLdKoqm+++YazZ88yadKkfJM+ZmZmEh0dTXBw8E0fNz700EN88MEHbNu2jWeeecau9gtREElyisGVx0BX/iV46NAhjEYjrVu3tpapWbMmYWFh7N+/n969e/PRRx/x6quvsmXLFnx8fGwedaxfv54XX3yRtWvXsn//fl5//XWaNm1KmzZtgLxJvS5evHjDCb8KkpGRgY+Pj/V5+P79+4mIiLCZyK5t27ZMnDiRU6dO5Zv7A2Dfvn34+fnZfNG3bt0ajUbDwYMHeeCBB9i+fTvh4eH88ssv9O/fH4BWrVoxZswYawddV/TDDz/g7u7O0qVLbRLEr7/+2uGxKlWqxIkTJ1BV1eZuzvVrikHeNXmjOwQxMTE2yecPP/xA5cqVmTdvnk2dNxoiXBg3u3vVrl07goKC2LhxI40aNSI7O7tQ/YmWLVtm87pWrVr4+/vn2379IriJiYls2LCBdevWcfLkSQICAujevTtPPPGE3X1griSAQUFB1m13whxIGzduxMvLK9/Ir4LExsZiNBp59tln8733zTff8M033/Dxxx/bLAlzvSsj0oqyhI0QRSVJjoNZLBamT59O06ZNrX8wExIS0Ov11tvQVwQHBxMfH49Wq7UmRMHBwfnKXZlRFvLm5fj888/5888/rUlOaGgoFoul0G1MSkrik08+4emnn7ZuS0hIyDdT75XX1z8WuHafa/+YQ97yFP7+/tZ9oqKiiImJYcuWLbz33nuYzWZmzJjBsGHDWLFiRaHbfKfRarUoimKz4Gp0dDTbtm1zeKz27duzc+dOtmzZYu1PkZ2dnW9uEsi7i/b3339jMBisydfPP/9MbGysTZJz5a7OtYnTgQMH2L9/f77HK4Xh6elZ4OMXnU5Hly5d+O677zh9+jQRERH5EpMbufYfDYXZHhsby5QpU/j1118xm83W/nOdOnUq1FxF10pKSsp37WdkZPDZZ58RGBhIvXr1rNs9PT2d+os8KSmJP//8ky5duhR45yUmJobs7GzrHa5HHnnkhiPshgwZQocOHXjqqaesj7GSkpIIDAzMl+xdGT1Wv359Rx6OEDYkyXGwSZMmcfLkSb744guH1Xn99PGhoaEkJiZaX48aNarQdWVkZDBw4EBq1qxpTZyKk6qqGAwG3n33XWsfkWnTptGjRw/OnDljfYTlajp06MCyZcvo378/jz76KImJiXzxxRdUqVKF48ePOzTWU089xapVqxg3bhyHDx8mNDSUDRs23HCU0JNPPskPP/xA//796dy5MxcuXGDjxo1UqVLFpty9997Ljz/+yJAhQ7j33nuJjo5mzZo11KpV66bD2QtSr149Vq9ezSeffELVqlUJCgqymW23W7durFy5kt27dzN69OiifwiFcOHCBY4cOcKAAQPo2bOnXX2LVq1axdatW+nYsSNhYWHExcWxbt06YmJieO+992ySpnr16rF582ZmzJhBgwYNinTH5IorS0Bcu3xGQf766y/++usvIC/ByMrK4pNPPgHyHodfeSR+xebNmzGZTDd9VDVu3Dj27NljvXZr1qxZ4CO98PBwmzs43377LWvWrKFTp05UrlyZzMxMdu7cye+//07Hjh3zzboshCNJkuNAkydP5pdffuHzzz+nQoUK1u0hISEYjUbS0tJs7tIkJiYWqsPd9UMsFUW5ref1GRkZ9O/fH29vbz7++GObNWNCQkI4ePCgTfkro78KamNISEi+Tp4mk4nU1FTrPqGhoeh0OptOsFf+OMbGxrpsktOqVSumTZtmnUMkPDyc0aNHc/HiRYcnOZ6enixfvpwpU6bw+eef4+HhQdeuXWnfvr31EeEV7dq14/XXX2fZsmVMnz6d+vXrs2DBAt59912bcj169CAhIYEvv/ySnTt3UqtWLf73v/+xZcsW9uzZU+Q2DhkyhJiYGJYsWUJmZibNmze3+XKrX78+tWvX5vTp08U2qqpRo0Zs3779tifKu1bTpk3Zt28fX331FSkpKXh6etKwYUOmTZuW70v7ueee4+jRo6xbt47ly5dTqVKlIic5VxLLwvy92LVrF/PmzbPZNnfuXACGDh2aL8nZuHEjwcHBBd4Bs9fdd9/Nvn372LRpEwkJCda/B2+88Qa9evUqlphCWKnCbhaLRZ00aZLatm1b9ezZs/neT0tLU+vVq6du2bLFuu306dNqRESEum/fPlVVVfXvv/9WIyIi1KSkJJt9e/XqpU6dOtVm26BBg9Rx48YVqY3p6enqU089pfbq1UvNysrK9/4vv/yi1qlTR01ISLBuW7Nmjdq0aVM1Nzf3hnWeOnVKjYiIUP/991/rth07dqiRkZHqpUuXrK8jIiLU8+fPW8scPXpUjYiIUM+cOVOkYxBFs2vXLjUiIkLdtWtXaTelUB5//HG1T58+pd0Mp/Tuu++q7du3L/B3UQhxYzJPjgNMmjSJb7/9ltmzZ+Pt7U18fDzx8fHWuTJ8fX3p2bMnM2fOZNeuXRw6dIjx48fTpEkTGjduDOR1HlUUhV9++YWkpKR8c83czOzZsxk7dmyB72dkZPDSSy+RlZXFtGnTyMjIsLbxSp+Rtm3bUqtWLcaOHcuxY8fYsWMHc+bM4fnnn7feej948CAPP/wwly9fBvLuyLRr1463336bgwcP8vfffzNlyhS6dOlinSumdevW1KtXj/Hjx3PkyBEOHTrEhAkTaNOmjd1DnIXr+Pfffzl69CjdunUr7aY4pd27dzN48OAi9x0SoqyTx1UOsHr1agCbtX8gb0jrlVWXx48fj0ajYdiwYRgMBuukY1eUL1+eV199ldmzZ/PGG2/QrVs36xDyW4mPjyc2NrbA9w8fPsyBAwcAeOCBB2ze27ZtG+Hh4Wi1WhYsWMDEiRN5+umn8fT0pHv37gwbNsxaNjs7O9+cKbNmzWLKlCn07dsXjUbDgw8+yFtvvWV9X6PRMH/+fKZOncrzzz+Pl5cX7du3Z9y4cYU6NuHaTpw4weHDh/n0008JDQ3lkUceKe0mOaXiGJUnRFmgqGoJTsYghCgxVzqr3myemNL20Ucf8fHHH1O9enUmTZpE8+bNS7tJQggXIkmOEEIIIVyS9MkRQgghhEuSJEcIIYQQLkmSHCGEEEK4JElyhBBCCOGSZAg5kJiYzs26XyuKgre3e5HWh3KU/v1fIjIykjFjytaQa41GQ2ZmbomuxFxWKPHxuG9YT+7j3VELMYPunRbPWWI7ijMfg6PaZk89zvz5iOKjKBAc7HvrcjK6ChISbp7kaDRXk5wblVuw4BMWLVpgs61atWqsW/et9XVubi7vvz+LH3/cgsFgoFWr1rzxxlsEBwcDsHfvXwwY0I9ff92Jr+/VpR9efvklIiLsS3JSU1NZsOATdu36g0uXLhEYGMi9997HoEFD8PXNu0hOnDjOsmVL2b9/HykpKVSsGMYTTzzJc88VPO16TMxFFi9exF9/7bYuUdG5cxf69x9gXTIiJuYijz7aOd++y5evpGHDRjesV1GuJjkWS5m/PIUQQlxHUSAk5NZJjtzJcZCaNWsyf/5i6+srqzhfMXv2e+zcuYN3352Fj48v7747ndGjX2PZsuJfiTs+Po74+DhGjBhFjRo1iY2NYfr0qcTHx/G//70PwJEjRwgKCmLq1BmUL1+BAwf2M23aZDQaLc888+wN6z179iwWi4U335xA5cpVOH36JFOmTCInJ5vXXrNdZHH+/EXUrFnL+vrKquui5Ckpyeh/+wVj+3tRAwJdLp6zxHYUZz4GR7XNnnqc+fMRpU+SHAfRanWEhITc8L309HS++WY906fPpHnzvEnZJk6cQs+ej3Pw4AFCQkIYMKAfAB06tAWga9fHmDRpKpC3kvecOe/zzTfr0Ov19Oz5JK+8MrjQbatVqzazZn1gfV25cmWGDHmVt956A5PJhE6no1u37jb7hIeHc/DgAbZv31pgktOmTVvatGlrs8+5c+f46qu1+ZKcgICAAj8fUbK0F87j378vyVt/w1QCXwolHc9ZYjuKMx+Do9pmTz3O/PmI0icdjx3kwoXzPPjg/XTt2pk333zdZpmFo0ePYDKZaNGipXVb9erVqVChIgcPHqR8+QrWOyrr13/Ljz9uZ/Toq4+nvvvuWzw9PVmxYhXDh7/G4sUL2bXrT+v777zzFi+//FKR2puRkY63t0++Fc5ty2QU+Y5LRkYGfn7593nttWHcf38HXnqpL7/++nOR6hRCCCFuR5Hv5Pz1118sXbqUQ4cOER8fz8cff0ynTp2s76uqyocffsj//d//kZaWRtOmTZk4cSLVqlWzlklJSWHKlCn8/PPP1vWO3nzzTby9va1ljh07xuTJk/n3338JCgqiV69evPzyyzZt+f7775k7dy4XL16kWrVqjB49mg4dOtzGx1Awi8WC2WzGYtEV2CenXr16TJw4mapVq5KQkMDixQvp168vX375Fd7e3iQkxKPX6/H29sZiMVv3CwoKIiEhHkXB2jcmICDA+v8WixlVValVqzYvvzwAyLtbsmbNanbv/tM6BX5wcPB/bTRTGCkpySxevJDu3XsUuM+BA/v58ccfmDv3w0LXGxV1gTVrVjNixGvWfTw83BkxYiSNGzdGUTRs376VkSNHMGvW+3TocO8N61EUABWj0SB9cgpJo9Gg0WhR8j48IYQQ3EaSk5WVRWRkJD179mTo0KH53l+8eDErV65k5syZhIeHM3fuXPr168fmzZtxd3cHYPTo0cTHx7Ns2TKMRiPjx49nwoQJzJ49G8i7G9CvXz9atWrFpEmTOHHiBOPHj8fPz4+nn34agH/++YdRo0YxcuRIOnbsyMaNGxkyZAjr1q0jIiLCns/EKjc3m+TkeHQ6Le7u5TGZzDccYdWkSWPr/1eqFMaMGTN56qkn2bRpI48++ii5uTn/fXYZNvtZLBaMRgNZWRnk5mZby2i1yjVlzFSrVt1m38DAQOLj46zbXnrppRvWfyOZmZmMHPkaVatWpXfvXjfc58yZM4wcOYIXXniBhg0bFKre+Ph4hg17lXvv7cBDDz1o3cfNTU+PHlcfhfXr149Ll2L57LNl3HNPsxvWpdFo0Om0JCfHYTKZbhlb5HFz88DPLwidTl/aTRFCCKdQ5CSnQ4cOBd4tUVWVFStWMGjQIOvdnffee4/WrVuzdetWunTpwunTp9mxYwdfffUVDRo0AOCtt95iwIABjB07lvLly/Ptt99iNBqZPn06bm5u1K5dm6NHj7Js2TJrkrNixQratWtH//79ARgxYgR//PEHn3/+OZMnT76tD+NaFouF5OT4/744AnB390Svv/GdnOt5eflQtWpV4uLi8PLyISysEkajEbNZtd6lgbw7KhUqVMTLywd3d0/rvl5ePtYyGo0WT09Pm206nQ6NRmuzrTAyMzMZN24svr6+vP/+XGvSea0zZ04zcuRr9OjxBIMGDSlUvfHxcbz22ggaNWrMO+9MRqO5+VPQRo2asHfv3wW2/8roqsDAcnInpxBUVcVsNpGRkUJi4iXKlQu/6R0d1cMTY4NGqB6eJdO+Eo7nLLEdxZmPwVFts6ceZ/58ROlzaMfj6Oho4uPjad26tXWbr68vjRo1Yt++fXTp0oV9+/bh5+dnTXAAWrdujUaj4eDBgzzwwAPs37+fZs2a4ebmZi3Ttm1bFi9eTGpqKv7+/uzfv58XXnjBJn7btm3ZunWrQ47FbDYBKj4+/ri5uaPRaAGlUElOVlYW0dHRdOnSFY1Gy1131Uen07F371/cf/8DAJw7d5ZLly7RqFFjNBqtNeFQVf6LlSfvy0rJt01RbLfdSkZGBkOHDsbNzY0PPvgIT8/8fxBOnz7FK68M4NFHH+PVV4cXqt64uMsMHDiAunXvYtKkqflGld3IyZMnCA0NKbD9V5Icvd5NkpxCc0er1ZKUdBmTyYhe71ZgSXNEJCnbdpRYy0o6nrPEdhRnPgZHtc2eepz58xGlz6FJTnx8PIB17pcrgoODSUhIACAhIYGgoCDbRuh0+Pv7W/dPSEggPDzcpsyVkTkJCQn4+/uTkJCQb7TOtXEcpTB9HD74YBbt299LxYoViY+PZ8GCT9BotDz8cN78ML6+vnTr1p3Zs2fh5+ePt7cP7703g4YNG1nniqlYsSKKorBjx6+0bdsOd3cPvLy8CtXGjz6aS1zcZaZMmX7D9zMyMhg8eCA5OTlMnTqDzMxMMjMzgbxHX1qtllOnTjJwYH9atWpDr159rJ+jVqshMDDvfB069C8TJrzJggWLKVeuPHFxl3n55X5UrFiR114bRXJysjXmlXOzceMG9Ho9kZF1Adi+fSsbNnzD229PLNSxicJTFBlHIIQQ15Ih5A5w+XIcb7wxjtTUFAIDA2ncuCmfffa5NTkAGDVqLIqiYcyYkf9NBtiGN9540/p+uXLleeWVwXz00VwmTpzAo492tQ4hv5WEhHguXbpU4PvHjh3l0KF/AXj88S4273333feEhVVi69afSE5OZvPm79i8+Tvr+xUrhrFp0xYAcnJyOHfunLWfzK5du4iKukBU1AUefvgBm3r/+eeg9f8XL15EbGwMOp2OatWqMXPme3Tq9GChjk04nu7fAwR0vp+U77dhanDjCRnv5HjOEttRnPkYHNU2e+px5s9HlD6HJjmh/02pnZiYSLly5azbExMTqVOnDpD3L/ykpCSb/UwmE6mpqdb9Q0JC8t2RufL6yh2CG5VJTEws9rlY8m7s2N7deffd/91yPw8PD8aPf4vx498qsMyAAa8wYMArNtuWLPk03yOy99+fa/P6VslQs2b32CQdN/LKK4NvOffO9fU89tjjPPbY4zfdp2vXx+na9eZlRAlTVRSDgUI9e70T4zlLbEdx5mNwVNvsqceZPx9R6hya5ISHhxMaGsqff/5J3bp5jycyMjI4cOAAzz6bN6FckyZNSEtL49ChQ9SvXx/IuyNgsVho2LAhAI0bN2bOnDkYjUbr8gB//PEH1atXt87b0rhxY3bt2mXTL+ePP/6gcePGjjykfMwoGM0lt4aVXqtBq6jy+1uCYmNjWL58Cf/8s9eaOD/00CP06fOS9XrMzc1l1qwZHD9+lPPnz9G6dVtmzJh9y7qPHz/G/PkfcuzYETQaLR063Merr76W79Hk5s0b+fLLVURFXcDLy5uOHTsxalTZWr9MCCHsVeQkJzMzkwsXLlhfR0dHc/ToUfz9/QkLC6NPnz7Mnz+fqlWrWoeQlytXzjraqmbNmrRr1463336bSZMmYTQamTJlCl26dKF8+fIAdO3alY8//pg333yTl19+mZMnT7JixQreeOMNa9w+ffrQu3dvPv30Uzp06MDmzZs5dOiQQ0ZWFSwvwTmbkFkiiY5eq6F6iDdarQaQLKeknD9/DlVVGTNmPJUqhXP27GnefXca2dnZDB06Asgbfefu7s4TTzzDL79sL1S9CQnxjBgxmPvvf4CRI8eSmZnJhx/OZvr0iUyd+p613Jo1n7NmzSoGDx5OvXr1yc7O5tKlmOI4VCGEcGlFTnIOHTpEnz59rK9nzJgBQPfu3Zk5cyYvv/wy2dnZTJgwgbS0NO6++26WLFliM1x51qxZTJkyhb59+1onA3zrrauPcXx9fVm6dCmTJ0+mR48eBAYGMnjwYOvwcYCmTZsya9Ys5syZw/vvv0+1atX4+OOPHTZHzs0YzRYMppJfkbwwNm/eaO0LdPr0STQaDXXq3EXTplfnpMnNzWX37j+4cOECFouZChUq0rJla+tMxRkZGfz55+9cvnwJi8WMj48vzZu3IDy8SqHasG/f35w/f4677qrPvn1/YzDkUrNmbVq1asOhQwc5dOhfVFWlXr36NGrUxLqfwWBgz55dxMdfxs/Pj++//4HevV+idu28c3rxYjQfffQ+hw8fIicnm6pVqzNw4BDuuaeFtY4nnujKY491Jzo6ip9/3oavry99+/bj8cd7FPozbNmyNS1bXh0hWKlSOBcunGf9+q+tSY6npyejR+cl3f/+e4CMjPRb1vv77zvQ6XSMHDnOOsx+9Ojx9O37DNHRUYSHVyYtLY3Fi+fz7rsf0KxZc+u+tWrVLnT7hRBC5ClyktOiRQuOHz9e4PuKojB8+HCGDy94CHJAQIB14r+C1KlThy+++OKmZTp37kznzvlXuC7rTp06QUREHbp27UZCQgK///4b3t4+REbm9YvaseMX0tLSeOCBh9Dr9fz11x5+/HELPXo8iUaj4c8/d2KxWOjSpSs6nY6UlJQiTzCXnp5GdHQUDz3UmbS0NH7+eSvp6en4+/vzyCOPEhd3mR07fiUsrBKhoXn9t7Zv/wmdTse9996Hp6cnx4+fZsSIQaxevQ4/P3+ysrJo2bINAwYMRq93Y8uWTYwbN5IvvviaChUqWGOvWbOK/v1foU+fl/j5523Mnj2TJk2aUqVKNQCGDh1AxYphvPnmxEIfT95yFX63LngTRqMBvV5vM4/QleT/4MH9hIdX5q+/dqOqKvHxcTz//BNkZWVRv35Dhg4dQfnyFQqqukhMtSNJ+m035qrVHFKfs8VzltiOUtRjUJT8o0JVtXgeeTvq87WnHlc4x6L4yJhTJzdl4tu89tqwIu3j7e1Dixat8PcPoGbNWtx1V30OH84bXZWWlsqFC+dp27Y95ctXICgomHvv7UhWVibnz58DIDMzg3LlyhMYGISvrx+VK1ehQoWKRWqDqqq0a9eBgIBAqlSpSoUKYaSlpVjbVbt2JP7+AcTG5j2GuXz5EgkJ8XTs2ImgoGC8vX146aWX8fHx5eeftwFQu3YE3br1pEaNWlSuXIWXXx5EpUqV+P33X21it2rVmh49niQ8vDK9evXF3z+Af/7Za32/fPkKBAcXvoN6dHQUX3/9ZZHuBt1I06b3kJiYwBdfrMBoNJKWlsaCBR8BkJiY14k+JuYiFouFlSuXMWzYKKZMeZe0tDRee20IRqPRrvhWnp6Y69SFG8yVVCxKOp6zxHaUIhyDooBR0ZBsMNv8GBUNxbLih6M+X3vqcYVzLIqNDCF3AIvZzLdrlrHrlx9JS0kiICiE1vd1pstTfaz/olJVlW+/+JQdP20kKzODWnUa8PygkZQPqwxAwuVY3hjwNG9/sJQqNex7NHHlzsgVQUFBLFw4n9WrVxMdHYVOp+Ps2XMMGzaC0NByuLt74O/vz5QpE7l06RKJiYno9Xpq165N7959ady4ab65ja6IiopizpzZ7Nu3D6PRQOvWbejc+RF8fHzR6/Xs3fuXdYV1uPp4E+CVVwZRuXLechZJSYkYjUa++GIFXl5eBAQE8OGHHxIbG8PFi9FA3iSLn366iD//3EliYgJms5nc3FwuX7YdPl+z5tXPT1EUgoKCbebwefvtwvfbio+PY9SoV+nYsROPPdb91jvcRI0aNXnzzUnMm/cBCxd+jEaj4YknniEoKPia68SCyWRixIgxNG+et6DrxInTePzxh/jnn720aNHKrjYAaKIu4PX+e2SNHIulcuEeQd5J8ZwltqMU5RgURSHTYOLnY3Fk5eZN9eDlrqNjnXIEumlRHXw7x1Gfrz31uMI5FsVHkhwH+H7dF/z6/QZeHDGesMrVOH/qOMs+nIGnlzf3d30CgC3rvmDbpq95afgbhJQP45tVS5gzcTST561A75Z/eQVHys3NJS4ujsGDX8XPz5dfftnOkSNHGTFiGKtWrbGWq1OnLiNGjCIkJJQLF87zwQez+OijuTz++OM0b96Su+6qb1NvdnYWQ4YMpHbtSBYuXAzA/Pkf89FHc+nVqzcAjRo15scft7N79x8YDAbatbuX+fPnsWfPbipVqmS9hW40GvHy8qJz567WtauqV6+DyWTCxydvKYyPP57DX3/tZsiQEYSHV8bd3Z233hqH0Wi7vtX1K6srinLDNcduJSEhnldffYX69Rsyduybt96hEB588GEefPBhkpIS8fDwRFEUvvxyFWFheZNfXrnDVK1ades+gYGB+PsH5EvmbpcmOQnPVSvIebF/ySQ5JRzPWWI7yu0cQ1auiYzc4l/3zVGfrz31uMI5FsVHHlc5wOljh2jUog0Nm7UipHxF7m5zL/Wa3MPZk0eBvLs42zb+H12e7E3jFu0Ir1aTl0a8SUpSIvt27QTgjQF5naqnvNaPlx9vz//etH1EtWLFch588D46dmzHjBnTbvroIj4+zuZ1ZmYmffu+wEMPPUy9evUpX748Awa8wtGjR4iNjSU3N4fU1FSeeuppGjZsRFhYGC1btmLw4KFcuHCBOnXu4sSJY/ni7N+/n5iYGCZNmkLt2hHUrh3BpElTOXfuHGfPngVAr9cTEhKCn58ffn5++Pv788svP/PYY91s+g2EhISQnZ2NRqPg6+uDt7c3YWFhhIdXJiAgAMjr4PvII13p0KEjNWvWIigouNhGHcXHxzF06EAiI+swfvw7t1yPq6iCgoLx8vJi27YfcXNzs3aebvDfZGYXLpy3lk1LSyU1NaXIjwyFEKKskyTHAWrWqc+xg/9w6WIUAFFnT3HyyL/Ub5r3xZVwOZbU5CTqNro6wsnL24caEXU5c/wQAONnLQRg5OQPmLV8PYNfvzrB3969fxEdHcXChUuZNGkqGzduYOPGDdb3Fyz4hC5dHra+zszMYM+eP0lNTeHMmVMcPXrYehfGz8+fqlWrsXv3nyiKgslk5Ndff8bLy9vaMXf37j85duwo3367gbvuqkd8fBz+/oH5jttgMKAois0aY+7u7iiKYjPNwLV+++0XUlNT800iGBYWTmhoObZu/ZHY2FiysrI4evQwCxd+zLFjRwAID6/Cr79u5+TJ45w8eYJJk968rbWtpkyZwIIF8wp8Pz4+jldfHUj58hUYOnQEKSnJJCYmWPvNXHH27BlOnjxOWloqGRkZ/7Xraqf8I0cO8dxzPW2Szq+//pLjx49x4cJ5vv56LR988B4DBw61LtxapUpV2rXrwNy5s/j33wOcOXOKqVMnUqVKNZsRckIIIW5NHlfdBr3WNjd8/OneGHOymDCkFxqNBovFQs8+A2jfKS/xyErL6w8SEhKMm+7qvv6BQaSnJuOm01j7vAQEBFhnfr4Sx9fXj3HjxqPVaqlevTrt2rVnz5499OjxxH/7BNqs9VWrVgQmk5mNG79BURTuuqs+derUtb5/zz0tmTt3DpGRkfz881bKl6/Igw8+jEajYe7cD1i9ehUGg4EKFSrQs+cT+Pv72/QFWbt2NbVrR9CwYUM8PT2ZO/cDhg4dBqh8+OFcLBZLgUOqv/lmPa1atb7hSKEHH+zM33//xe7df+Ll5cny5Z9RuXJV65D4V199jRkzJvPKKy/h7x/A88/3ta7BVRSXL1+66Z2Zv/7aTXR0FNHRUXTv/ojNezt3Xu3APGbMcC5dirW+fvHF523K5OTkcOHCeesyGABHjhxm6dJFZGdnUaVKNcaMGc/DD9sutfHWW5P48MP3GTNmBBqNhsaNmzJ79of5HsMJcSMaJa8fzhVe7jo0xdHpWIg7gPzVLBLVOkHftX764Xv+2rGVSVNnUL1mLU4eP8ac9/9HnerhdHn0MXIu5c1mWyPUh5AQX+t+vh56FAUiyvvia04DoGqwFxHlr5bRKHkTKF67undISAgnT560vn7mmWd55plnr+6j0dCiRStat26b7wiMRiMTJryJj48PixZ9io+Pj837ffq8QLdu3YmNjWXRogUcOHCAl1562fpoyWQykZ2dTYUKFQkMDOLdd2cxY8ZU1qz5Ao1Gw0MPdaZOnbrUqFHTpt527e7l8uVLjBs31roMxiOPdLUpo9fradmytXUV8nbtOtncqalYMYwPP1xgs0/Pnk/ZvP7qq435jnn5ctupCObNW5SvzLUeeaRrvrbdyI1iXatp02Y2SREUrtOzt7cPb7wxgTfemHDLsrfDElqOrGEjsVzXQb24lHQ8Z4ntKEU5BkWBQF0ObUKzMf7XD02vMRKoy0FVvG+xd/G2rbjqcYVzLIqPJDlFpEX9bwbiqz7+8ANeerEfXR/J+xd5/chIEi5f4vPlS+n5eDfCyuXdmclMSSb8v1mdAVKSEomMrIOHVoP7f3W6azV4XFO/Qv6OtKDc1igJo9HI66+PITY2loULl+RLcCCvk2tgYCBVq1ajevXqdO78IAcPHqRRo7y+IrGxMYSFhVGxYhiQN1z72283k5ycjE6nxdfXjwce6EilSuH56v722w34+/vTvv29RW67cBxLxTAy35rosvGcJbajFO0YFLSGdEzHfyA7M+9uquLtizaoOyYPHxw9c7qjPl976nGFcyyKj/TJKSJVvTKx1tWfnJwcFEWx2Zb32Crv/8PCKhESEsLu3bus76enp3Po0L80bNgQVVWtiYzJZLapx1GuJDgXLpxnwYJF1s68N3PlLorRaLBuq1y5Cg888HC+soGBgfj6+rFnz26SkpLo0OFem/dVVeXbb7/h0Ue7Wtd/EqVDyUhH//sOlELM0nwnxnOW2I5yq2PIu/OpoNEoKIqCAhizMzBkpWHISsOYnQGKBkVRrilXMm0riXpc4RyL4iNJjgO0b9+BpUsXs2PHb8TEXGT79m18/vlKOna8D8gbwvzcc71YsmQRv/76MydPnmDChDcJDQ3l3nvzygQGBuHh4cEff+wkMTGR9PTC/8KuWbOagQP7A3mPWq6fS8VoNDJ27CiOHDnMtGkzMZstJCQkkJCQYB2l9e+/B1mzZjXHjx8jJiaGPXt2M378OMLDK9OwYd5dnLi4y/To8RiHDv1rrXvDhm84ePAAUVFRbNr0HePGjeb553vbDIEG2LNnNxcvXqRbt55F/HSFo2nPnCagexe0Z067ZDxnie0o1x7DtQmNRqOg1Sp4abLxNl7C23gJT2Mcbhoz2ms64Wj07rjrFLwMsdZyXkq2QxIdR32+9tTjCudYFB95XOUAY8e+wSefzGPGjGkkJycRGhpKz55PMGDAK9Yyffu+SHZ2NlOnTiY9PZ3GjZswb95867T+Op2OMWPGsXjxQhYs+IQmTZqyePGnhYqfkpJMdHR0ge/Hx8fx66+/APDMM0/avLdo0VKaNbsHDw8Ptm/fysKFn5CdnU1ISAitW7fh3Xf/Zx09ZTKZOHfuHDk5Odb9z58/x7x5c0lNTSUsrBL9+r3M88/3zteGDRvW06hRY6pXr57vPSFE4RgVDZmGqx3ZtRoFDzUd9cRPqIZMFJ9QNFXuRrk2ydHp0RgzMZ/ZiSU3E8XNC03tB9C4edvcLS6upR+EKE2K6ugpMO9ACQnp+X65jUYDiYmxBAdXxN3dHW9vdywWi/wRKAFXOh5nZube1hDxsuraa1avdyuwnO7gfgI7tSd562+YGjYu9naVdDxnie0oV44hZdsOEuo2sJnNONjXncerW9Ad3YCam4HiWx5tzbYc/e1rUlPyRnUGlQsnsuXDZB7egiUnHY27D26NenCZUMzX/H55u+nQq0X7G+eoz9eeelzhHIuiUxRsBvIURO7kCCHEHeTa2YwDvPQoGoUcs4pqtqC1qFy/gpOiyesvGJOSTVpKOn4BOsLM8M/FZJIy8vrbebppaV0zGDe945d+EKI0SZIjRBmj6vSYK4ahFnFl+TslnrPEdpQrx8ANOuxrtRpMZjMXkjLJTk8niACqFZCkGM0WDGYLZo0eD72GBj5pZLubAdBrNATqvIs8zNxRn6899bjCORbFR5IcIcoY8131SDqQf5kOV4nnLLEd5coxaDQKGMw3LGMyqxjMFozmW6/RptHq0RgyMB3/key0VOD2h5k76vO1px5XOMei+EiSI4QQZZAxJ2+YOYBepkQWLkqGkBeRouQNCS+pn3feeYuRI4eX9mELF6I9cpigRnXQHjnskvGcJbajXHsMV5Zs8Pnvx1OvddicN/a2rbTqcYVzLIqP3MkpIj0mFEuuzbbMzEw+nL+IbT//RlJyEnUjI3h99Gs0qHeXtYyqqsxbsJiv1n9LekY6TRo1ZMIbY6lapTIAF2NiebBrD7764jPqRkZY99M4YIZSo9HIJ5/M4/ffdxAdHY2Pjy8tWrRg2LARhN5gKnSDwUCfPs9z4sRxVq9eS2RknRvWGxNzkUcf7XzD9959dxYPPPAgAIcPH+LDD+dw9OhRFAXq1WvAiBGvERERafexFadff93O+vVfcerUCQwGI9Wr1+CllwbYzEO0f/8/fPHFSo4fP0piYgLTp88q1IzOX3+9lnXr1hIbG0v58uXp0+clOnd+1KZMeno6ixZ9wm+/bSctLY3y5SsyfPhIWrXKv1xHUSgmI9rYGBRTwSvZO1JJx3OW2I5y7TFcv2SDjye4axSbeXFKq22lVY8rnGNRfCTJKRIFxZKLmngazFd/od6e8Qmnzl1kxsgXKBccwMbtf9L/laFsWDiN8iF5q3cvXbuJVWs3MW1UfypVCGXeinUMGDSEDQun4e6mR72ywnXSOdS4/56ra/Wg3vgZfFHk5ORw7NhR+vcfSEREBGlpacya9S4jRgxj1ao1+crPnfs+oaGhnDhx/Aa1XVW+fAV+/HG7zbZ1675ixYrltGmT90WclZXF0KGDaN/+Xt54403MZjMLFnzCkCGvsHnzj049+/H+/fu4554WDBw4BB8fXzZv3si4ca+xaNFyIiLyEr/s7Gxq1apNly6P8eabYwpV7/r1X7Fw4ceMG/cmdercxdGjh3n33Wn4+vrRtm17IC8xfe21IQQGBjJlyruEhpbj0qVYfHxuPWRSuKr8SzZ4hoahadimVO/mCOHMJMm5HWYjqilv6GVOroGtO//mw7cGc3fdvInuBj/zCL/s2seajT8xrHc3VFVl5Tc/MuCpR+h4T30Apo14gXt7j2bbzj10bn8PD72Q9wX5xNB3AGhWP4Ll/3vDGnLFiuV8/vkKjEYjDz74MKNHj71hgrB580aCgoLRarWcOHEMjUZLnTp1mT//6qKUBoOBhx7qzOzZ/+Pjjz+kRo0atGjRiqCgYH7/fQd//vkn/fr15/ffd/LddxuIj4/Dw8OD6OgomxmLtVotISEhNvF//nk7DzzwEF5eXqxduxq93o3U1FTKlw9lz54/admyDS+88BK9ez/HggXzqFy5Mm3bdiAkJNRaR3x8PGfOnGLy5MmYTCbat7+XgQOH4umZNzh2y5ZN/N//reHChfN4enrStGkzhg8fZV2t/J9/9jJs2CvMmfMJ8+d/xLlzZ6hdO5Lx4ydQpUq1Qp/m4cNH2bweOHAIO3b8yu+/77AmOa1ataFVqzaFrhPghx828/jjPbj//rw7XZUqhXP06BFWrfrMmuRs2rSBtLRUFiz41Lrkx5X1wkTZcO2jcetrri7ZAGDM9S/FFgrh/KRPjp3MZgtmiwU3N9t80cNNz74jedOMR19OICE5jZaN61rf9/X2pEFEdQ4cOwPA6tl5Cc3iqSP4ecV7zBl/dbbkvXv/Ijo6ioULlzJp0lQ2btzAxo0brO8vWPAJXbpcXU/q1KkT6HQ6unbtxj33NGf//n+Iibk6I/L27T+RlpaCoij06PEEwcEhbNmyidjYGKZMmcQrrwzizJlTANx77/34+Phw7NiRW34WR44c4fjxY3Tr1t26LSUlGR8fX4xGMxUqhLFt248sWPAx4eHhPP98H3x9/fjtt1+s5dPT0/j11+1UqFCRuXPnM2nSdA4e3M8HH7xnLWMymejf/xWWL/+C6dNncelSLNOmTczXnkWLPmHo0BEsWbISrVbLjBlXVwCPjY2hbdtm/PPP3nz7FcRisZCVlYmfn1+h97kRg8FgnUX6Cnd3d44ePYzJlDf/yc6dv1G/fkNmz36Xrl0fpHfvp1ix4lPMZvvv7Annpyj8t1zDZTxNiQB4mJPzLdkghLg5SXLs5O3lQaM6NVi4ZjNxiSmYzRY2/ryLA8fPkJCcNzwzMTnvX13BAbZfjsEBftYygf55K4IH+PoQEuiPv+/V+Sp8ff0YN2481atXp337DrRr1549e/ZY3w8ICCQ8/Oqq34GBQTRpcjd+fv7UqhVBSEgIMTExAFy+fIlLl2LZsWMnDz/cmYoVw2jevCV6vZ7x41/niSeeIjc3h+rVawLg4+ND48ZNrXdJbmbDhnVUr16DRo0aW7fVrFmTTz9dzrZtWxk5cgTz5s3j9OnTLFiwhKCgYBo0aExKSjLZ2VkAHDiwnypVqlG9eg0qVapEgwaNGD58DFu2bCI3N68v1KOPPk6rVm2oVCmc+vUbMGLEaHbt+oOsrCyb9gwYMJgmTe6mevUa9OrVl3//PWitQ6fTUaVKVTw8PG55XFesXr2S7Oxs7rvvgULvcyMtWrTiu+++4dixo6iqyrFjR/juuw2YTCZSUlKAvP5Ov/yyDYvFzP/+N5cXXujPmjWr+OyzpXbFBjDXqEnK+k2Ya9S0uy5njOcsse2hKAoY0sk99gNZWQdJf28guWo0isVos2RDaXPU52tPPXfqORYlQx5XOcCMkS/x9tzPuP+FcWg1GurWrELn9vdw5NQFh9Rfs2ZNtFqt9XVISAgnT560vn7mmWd55plnra+DgoJt9vf09CI7OxvIW2Rz48aNZGZmct99HVm5chkAf//9NxkZGbz4Yj9Wr/6cGtf9wQgNDbUmSjeSk5PD999/z8svD7DZ7u3tw+TJ79C4cWNmzHiXDRvWER19keHDh7By5WrrI6js7Bw8Pb1ISkokNzeHtLQUPvzwQxISElBVFYvFQmxsDNWqVefYsaN8+ukiTp06QXp6Oqqa14fp8uVLVK9e45rPrbb1/4OD8x6rJScnU6FCBUJDy/HFF18XeDzX+/HHLSxbtpgZM2YXKuG7mRde6EdiYgIDB74A5CWlDz/chS++WJE3Fwp5K8AHBAQyduybaLV5jxzj4+NYvXolL7004Ca135rq44uxTTu76nDmeM4S2x6KomCyqJy7FE92eioEeBGUnV3gRH+lxVGfrz313KnnWJQMSXIcoHLFUJbPHE1WTi6ZWTmEBvkz+t1FhFfI+2INDsy7g5OYkkZo0NVn6IkpadSpUfmW9V/pk3GVctOp1zUa2xt0ec/0VYxGI3PnziEjI4MlS5bj73+1LXv27OXs2TO0bNkMi8Vi7QfQq9ezdO78CI8+ajvy53pbt/5ETk42jz7a1Wb7rl27iImJYfnyz9FoNOze/QdPPfUsffo8z6+//mztoHxlAjKTyUjNmrW56656VK9ex/r4BvI6OmdnZzNq1FCaN2/FO+9MJSAgkMuXLzFy5FBM142uuPZzu3I8VxKioti69QfefXcKU6a8yz33tCjy/tdzd/dg/Ph3GDv2TZKSEgkODuHbb9fj5eVNQEBeR/WQkBC0Wp1Nclu1anUSExMxGo12ddjWxMbguXQR2f0GYCmBfj4lHc9ZYjuCyWSBxDTCfjlA9pM3HslYmhz1+dpTz51+jkXxksdVDuTl4U5okD+pGZn8se8IHVs0AiC8fAghgX7svmZWzoysbP49cZZGdfLuPOj/+0I2W4r+JVwYZrOZ118fQ0JCAo8//jiBgQH4+flZf8aPf4s1a/6P1avXMmjQYAYPHgLAzJnvMWTIq8THx9+0/g0b1tOhw7357nIYDLloNBprkgFYX1tucKzBwSGkpaXi7e1NWFgY4eGVrT96vZ7z58+RmprKK68MpVGjJlStWo3k5CQHfEI39tNPW5g+fTITJ06jdWv7hm5fT6fTUa5cebRaLdu2/Ujr1m2tCWqDBo24eDHK5jOKirpAcHCI3SPSNPFxeH34Ppr4OLvqcdZ4zhLbUdzSsqjyw150Keml3ZR8HPX52lOPK5xjUXwkyXGA3/85zM6/DxF9KYE/9h2h3/j3qR5egW6d8kbdKIpCr8fuZ+GXm/l59wFOnLvI+PeXERoUwH0tGwMQFOCLh5ue3/8+TEJyGumZ2YWOv2bNagYO7F/g+2azmU8/XcqRI4d5771ZBAUF88036zh06CDJyUnExV0mJiaagIAAatWqTceO95OZmQHk9feJjY0hOTmJ9PQ0evR4jEOH/rWp/8KFC/zzz99069YjX+z69RuQlpbGzJnTOHPmDImJiXz00Vy0Wh3NmjXPV75Bg8YkJMRz+PC/nDlzmqioC+zY8Qvvv/8ukHc3R6/X8/XXX3LxYjQ7d/7K8uVLCv1ZXREfH8dzz/XkyJFDBZb58cctTJ36DkOHjuCuu+qTmJhAYmICGRkZ1jJZWVmcPHmckyfzhtvHxl7k5MnjXLp0yVpmwYJ5TJkywfr6woXz/PDDZqKiLnDkyCHeeecNzpw5zcCBQ6xlunXrSVpaGnPnzuLChfP88cdOVq5cRo8eTxb5WIUQoqySx1W3Q6vn2q5/GTlG5iz7issJyfj7evNA27sZ1rcnbh5X1wPu93RXcgxmJn38OekZWTStF8HCqaPw8MrrYKzXweuDnmfBF9/y8Rff0rReBMtnv12o5qSkJBMdHV3g+6mpqRw6lPdl/swztl+SPXr0oHbtCCpUqGjtH1OzZi3q1s2byPCXX7bRokVLatWK4OTJE5w7d46cnBxiY2P4/vvveOqpZ9mwYT3ly5enVavW+WKHhYUxZ85HLFq0gBde6I3RaCAiIpJ58z4hNDSUjAzbf50GBQVx//0PcObMaSZPnkxCQjxhYeHcf39eZ9/AwEDGj3+HRYs+4auvviQiog5Dhozg9ddHFuqzusJkMnHhwnlycnIKLPPtt+swm828//671iQLoHPnR3nzzYkAHDt2hGHDro6E++ijD/KVSUxM4PLlq0mPxWJhzZrPuXDhPDqdjqZNm7FgwVKbIeLly1fg/fc/4sMP3+eFF54lJCSUJ598huef71uk4xSi0JS8O6xX+oWpqoqTdQESosgU9WadO8qIhIT0fL/MRqOBxMRYgoMr4u7ujre3OxaL5YYzHhcnVeOOEV2p/7HZsmUTXl5etG/fEYCTJ49z4MB+evR4Ml8fIHspSt4jrczMXCyWMn95Ftq116xe71ZgOd3B/QR2ak/y1t8wNWxc7O0q6XjOEtseWq0GXU4sJ7evRDlykqYzVnN61ggqP/MSR3esIzUlGYDgCpWpfc+D1m3Xvy5sGd/Actx137NgUa39/SxuvmRZPG/6t8dRn6899dyp51jYR1EgJOTWk6PKnZwiMqIDTUnO0lvy/5oymUwcO3aESpUqo9EonDlzmpiYizz8cBdrmaioKO6++x6HJzii+FkCg8h+vg8WO0eJOWs8Z4ntKEZvD2Jb18Ps61VsMfR6NzTGTLJP7sCcm4HGzRt9nYdQ9F43HeTgqM/Xnnpc4RyL4iNJThHl/b679t0FRYHo6CgOHNiP2WzC3z+A++9/gLCwStYy993XqRRbKOxhqVyFjA/muWw8Z4ntKLnBfpzs3YngcsX3Ja5o8kZsRsclkJaSjKevP9UjVOvIzII46vO1px5XOMei+EiSI/LRanU2d22Ei8nORnv+HOaq1cDT85bF77h4zhLbQTQGEx4JqSiBFYo9ltFswWC2oDcVcpSnoz5fe+pxgXMsio88axCijNGdPE5Q+xboTt58AdY7NZ6zxHYUr0tJNJvyOe7Rl0u7Kfk46vO1px5XOMei+EiS4+TeeectRo4cXtrNEEIUs7wO93mjmxRZVlwIh5DHVbdQmMFnmZmZfPLJPH7+eTvJyUlERtZhzJhx1KtX31rmnXfeYuPGb232a9WqNR9/vADIW6vo0Uc7s3r1WiIj6zj0GIxGI598Mo/ff99BdHQ0Pj6+tGjRgmHDRhAaWs5arkuXh4mNtV264dVXh/Pii/1uGUNVVV59dTB//PE7s2fPoWPH+wA4ceI4y5YtZf/+faSkpFCxYhhPPPEkzz3Xy6HHKAp3rQrnpChgVDRkGvJm+HbTqcj64kLYT5KcAlwZNWQ2m25REiZPnsjp06eYMmUaoaHl2Lz5OwYNGsBXX62nXLny1nKtW7dh4sQp1tfXr0RdXHJycjh27Cj9+w8kIiKCtLQ0Zs16lxEjhrFq1RqbsoMGDaF7957W197ehRvRsWrV5zf81+eRI0cICgpi6tQZlC9fgQMH9jNt2mQ0Gq3NelvCfgZD3tQGWq38Wt9pFEUh02Di52NxZOWaCA/y4v5Kal72I4S4bfLXsAAajRa93oOMjBT0ej2enjosFku+4dw5OTls376V2bM/oEmTJgAMGDCQ3377lbVrv7Quj6CqKnq9nqCgQJv9LRYzAI8+mrcuzbPPPgVA06Z3s2jRkv8m5FL57LNlfP75SkwmIw8++BCjRo1GpyvcUHZvby8+/ni+zbYxY16nb99exMREU6FCxf+2qnh6ehbYxoIcP36czz//jBUrVvHwww+gqhbrPo899phN2bCwihw8uJ/t27fy1FNP3bC+vL/rKkajQebJKQRVVTEYcsnISMbT0+fWw/oVBdXNreS+QEs6nrPELqLrFxe/0mRVAYtO65zH4KjP15567qBzLEqeJDkFUBQFf/8gEhMvkZISj4eHBpPJnG+9paysLMxmM6pqJivr6nT/er2Of/7Za91mMhn5+++9dOrUEV9fX5o2bUr//i9bF8lcuHARAwcO4IMPPqBatero9XqysjIwmYzs3fsXAQH+zJkzh4sXo5k4cSLVqlWla9e8BOLTTz9ly5bvWbv2/wp9fImJ8SiKglarsbZRVVWWL/+UJUsWUb58eTp16sSTTz51gwVCr8rJyWH8+HEMHz4CLy8PAHJzs20+i+ulpCTj7e1VYBmNRoNOpyU5Oc5mgU5xc56ePvj53XqYsalBIxKiE0qgRaUTz1liF4WiQKAuhzah2RgtFnw8wV2joNUopFUux86PhhJcodKtKyphjvp87annTjnHonRIknMTOp2ecuXCsVhMuLt7otfnv5Pj5eVDw4YNWbnyc+rUuYugoGB++GELhw8fJjy8Ml5ePgC0a9eBBx54iEqVKhEdHc3HH3/EuHHjWLbsM7RaLRUr5v0BK1euIpUrV7VpQ94Cmm+j1WqpW7ceW7duY//+Azz99HMAhIaWo3LlKtZYt5Kbm8uiRYt46KGHCQ29+jjtmWeeo06duvj7+3HgwAE+/vgjUlPTGDlydIF1ffDBHBo3bsJDD11dIdnd3bPAthw4sJ/t239m7twPCyxzZcbjwMBycienkLRanUzMeEdT0BrSMR3/gezMdDxDw9A0bCM3J4SwkyQ5t6AoSt5soBotoNxw9uEpU2YwadIEOnd+CK1WS506dXnooc4cPXrkv/2gc+er885ERNQhIqIOjz32CP/88w8tWrS0fkFpNBrrPlfi16xZy2aa/tDQUE6ePGkt9+yzz/Pss88X6niMRiNvvDEOUBk//m2bWH36vGD9/8jIuri5uTN9+hSGDXvthv2Hfv31Z/bu/YvVq9de12bbY7ji1KmTjBo1koEDX7npit5Xkhy93k2SnGKgPXEc30H9SZ+/BHNEpMvFc5bYt8OYnYEhKw1j7tVux56xSdRZtoVLY16Ae0quLQrYrGUF+dezctTna089d9o5FiVLkhwHqFy5MkuWLCM7O4uMjExCQ0MZN24M4eHhBe4THh5OQEAgUVFRtGjR8qb1539cpNzWSBqj0cjrr48hNjaWhQuX4ONz8zs/DRo0wGQyERNzkWrVqud7f8+ePURHR9GhQxub7WPGjKRJk6YsXvypdduZM6d55ZWX6dGjJ/37Dyhy24XjKDnZ6P89gJJT+JXu76R4zhLbUbRGE75R8cQZjCUXU6OgaBRSDGbM1zyi93bToefqHW1Hfb721OMK51gUH0lyHMjT0wtPTy/S0tL4888/GD78tQLLXr58idTUFEJDQwDQ6/M6EZvNhZxptIiuJDgXLpxn0aKlBAQE3HKf48ePo9FoCAoKvuH7L77Yj+7de9hse+qpnowaNYb27TtYt50+fYqBA/vz6KOPMXToMLuOQwhR/BSNgsls4ffz8SSm5Y3a83LX0bFOOQLdtDJdgbhjSJLjAH/88TuqqlKtWjWioqKYM+d9qlWrxmOPPQ7kdU5euHA+99/fiZCQEKKiopg79wMqV65Cq1Z5d0ECA4Pw8PDgjz92Ur58edzc3PD1vfUKqwBr1qzm55+3sXDhkhu+bzQaGTt2FMeOHWXu3HmYzRYSEvI66vn7+6PX6zlw4ACHDh3knnua4+XlzcGDB5g9+z0eeaQLfn5+AMTFXeaVV15m8uRp1K/fgJCQEEJCQvLFq1ChIpUq5d3FOnXqJAMH9qdVqzb06tXHGler1RAoC+oJ4dSyc81k5Ernf3HnkiTHATIyMpg3by6XL1/G39+f++7rxJAhr1rvzmg0Gk6ePMl3331Leno6oaHlaNmyFYMHD7X2ddHpdIwZM47FixeyYMEn+R733ExKSjLR0dEFvh8fH8evv/4CwDPPPGnz3qJFS2nW7B7c3PT88MMWFi5cgNFoICysEs8/35tevfpYy5pMJs6dO0dOTk6hP5utW38iOTmZzZu/Y/Pm76zbK1YMY9OmLYWuRwghhCgqRZX7jiQkpN+wQ/EVGo2Ct7f7DefJEY53peNxZmaudDwuBkpKMvrffsHY/l7UgMBb73CHxXOW2EWh1WrQ5cRycvtKMjNSCa5Qmdr3PMjRHevIvBhLwLEolA4tqdmxG0d3rCM1JRnAplxqSnK+17dbxj8gkKrtnuP/TkBcWt4/anzcdXRpFEagm9b6e+moz9eeeu6UcywcS1EgJOTWTzvkTo4QZYwaEIjhse4uG89ZYt+MRpM3CvHqa4WCRoubvD1IuLs2wT6Fm328JDnq87WnHmc9x8I5yMQaQpQxSlwcnvPnocTFuWQ8Z4ldEI0G3JUcdDmxV39yL+OmMaO9ftpjQJ+WSaWt/6BNSS+F1t6coz5fe+pxxnMsnIckOUKUMdpLMfi8Mx7tpZhbF74D4zlL7IIoigZLTirRezZwcvtKTm5fyeX9P6KYDSg3SHLcUzKp+fUO9ImppdDam3PU52tPPc54joXzkMdVQghRCrIz0sjMyEtcPHz8Srk1N6Bo8HRT8HHP+5rwctflW19LCGcnSY4QQggbGr07HnoNzYMyyPbNW2xXrzESqMtBVbxLuXVCFJ4kOUIIIWxotHo0hgxMx38kOy3vbpPi7Ys2qDsmDx9ARj2KO4MkOUWioCjyy1385J54cbL4+pH7UGcsviXziKSk4zlLbEcxebqR2KA6Zi+PEo9tzMlbSwtAf4NnVY76fO2pxxXOsSg+kuQUgqqqWCyW/xbRlC/gkpA3J5EklMXBUr0GaSu/dNl4zhLbUXJCAzg8+DGCK+SfXby0OerztaceVzjHovhIklMIqgpZWQYURRKcknL9asfCgYxGlNRUVH9/+G9WbpeK5yyxHUQxm9Fl5UKIubSbkp+jPl976nGBcyyKjwwhLyRVBYtFlZ8S+pEEp/jojh4m5K4a6I4edsl4zhLbUbwvJtJq7GI8zseWdlPycdTna089rnCORfGROzlCCFHMFAXrneA7+Y6wQl77NTKWXNwhJMkRQohipChgVDRkGvJW83bTqfiXcptuh1ajoGi1pJssmC0WAHw18jBAODdJcoQQohgpikKmwcTPx+LIyjURHuTF/ZXUvOznDqJ190CnAWPyeXINZnRaBTdLVmk3S4ibkiRHCCFKQFauiYxcE9lGJ+xAXAhX5s7JPfIjaWmpeHn7ovGJKO1mCXFTkuQIUcaY6jUg4XQ0qlfJzFxb0vGcJbajZISH8Pv7rxBQJay0mwJcnTtHr1GwNK9D0tkYzB6edtVpz3lyhXMsio8kOUKUNVotaklOnFbS8ZwltqNoNJg93UHrhP1ftFrw9gOzxe56bvs8ucI5FsXGCX9rhBDFSXvmFP5PdUN75pRLxnOW2I7iEZdM/Q/X4xYTX9pNyUdz5hy+Tzxu9+drz3lyhXMsio/Dkxyz2cycOXO47777aNiwIZ06deLjjz+2mb1WVVXmzp1L27ZtadiwIS+88ALnzp2zqSclJYVRo0bRtGlTmjVrxvjx48nMzLQpc+zYMZ577jkaNGhAhw4dWLx4saMPRwiXo2Rk4PbLdpSMDJeM5yyxHUWXYyTo6AU02bml3ZT8MjPR/7zN7s/XnvPkCudYFB+HJzmLFy9m9erVTJgwgc2bNzN69GiWLFnCypUrbcqsXLmSiRMnsnbtWjw9PenXrx+5uVd/iUePHs2pU6dYtmwZCxYsYO/evUyYMMH6fkZGBv369SMsLIx169YxduxY5s2bx5dfyvTeQgghhCiGJGffvn3cf//93HvvvYSHh/Pwww/Ttm1bDh48COTdxVmxYgWDBg2iU6dO1KlTh/fee4+4uDi2bt0KwOnTp9mxYwdTp06lUaNGNGvWjLfeeotNmzZx+fJlAL799luMRiPTp0+ndu3adOnShd69e7Ns2TJHH5IQQggh7kAOT3KaNGnCrl27OHv2LJD3SOnvv/+mffv2AERHRxMfH0/r1q2t+/j6+tKoUSP27dsH5CVKfn5+NGjQwFqmdevWaDQaa7K0f/9+mjVrhpubm7VM27ZtOXv2LKmpqY4+LCGEuG0aBbzcdfi46/DUa++0KXKEuGM5fHTVgAEDyMjIoHPnzmi1WsxmM6+99hqPPfYYAPHxeZ3ngoODbfYLDg4mISEBgISEBIKCgmwbqtPh7+9v3T8hIYHw8HCbMiEhIdb3/P3vxDlFhSh+5rBw0mfMwhwWfuvCd2A8Z4l9haJAoC6HNqHZGC0WfDzBXaOgLeTSCLmBPpx8+l7MIQHF29DboIZVIPPd2XZ/vvacJ2c4x8J5OTzJ+f7779m4cSOzZ8+mVq1aHD16lBkzZlCuXDm6d+/u6HBCiCJSQ0LI6TfAZeM5S+yrFLSGdEzHfyA7Mx3P0DA0DdsU+m6O0deL2HsbEezvU7zNvA1qcDC5/Qei2jmE3J7z5BznWDgrhz+ueu+99xgwYABdunQhMjKSbt260bdvXxYuXAhAaGgoAImJiTb7JSYmWu/EhISEkJSUZPO+yWQiNTXVun9ISIj1zs8VV15fqUcIkZ+SnIT7/61BSU66deE7MJ6zxL6eMTtvEj1jbuatC19Dl5lDud3H0KY74RIKySm4rbX/87XnPDnTORbOx+FJTk5OTr5VdrVarXUIeXh4OKGhofz555/W9zMyMjhw4ABNmjQB8vr1pKWlcejQIWuZXbt2YbFYaNiwIQCNGzdm7969GI1Ga5k//viD6tWry6MqIW5CG3UBvyED0EZdcMl4zhLbUTwS06iz/Af0cc73Ja6JvojPoP52f772nCdXOMei+Dg8yenYsSMLFizgl19+ITo6mp9++olly5bRqVMnIG+xuj59+jB//ny2bdvG8ePHGTt2LOXKlbOWqVmzJu3atePtt9/m4MGD/P3330yZMoUuXbpQvnx5ALp27Yper+fNN9/k5MmTbN68mRUrVvDiiy86+pCEEEIIcQdyeJ+ct956i7lz5zJp0iQSExMpV64cTz/9NEOGDLGWefnll8nOzmbChAmkpaVx9913s2TJEtzd3a1lZs2axZQpU+jbty8ajYYHH3yQt956y/q+r68vS5cuZfLkyfTo0YPAwEAGDx7M008/7ehDEkIIIcQdyOFJjo+PD2+++SZvvvlmgWUURWH48OEMHz68wDIBAQHMnj37prHq1KnDF198cdttFUIIIYTrkgU6hShjVC9vjHffU2KrNpd0vNKOrSjY9EtUFAV7psUxu+lJq14Bi4fbrQuXNE9PTM2ag7c3mv+GxKuqyjWr+BSKPeepNK8v4fwkyRGijDHXqk3K99tcNl5pxlYU8NJkozGkX92m1YLGXOh5ca6XXSGQ/WOfJrhCOUc10yG0GgUiahL13VbMFgsYzAB4u+nQYylSomPPeSrN60s4P0lyhBDCQRRFAUM6ucd+wGLIGyqu8y2He9W7UW4zyXFWikbBZLbw+/l4EtPy1h30ctfRsU45At20NosyC1FaHD66Sgjh3HQH9xNazg/dwf0uGa80YyuKgsmicu5SPMfPx3L8fCyxCUkU+fnNNXwuxNF+0Fw8Tkc7sKWOof33ML3a1MTnyEEyck1k5Zpuqx57zlNpXl/C+cmdHCGEcDCTyYLhv1mAjXbOBuzU/ut75OmWty6Xl7sOF7thJe5wkuQIIYQoMo3eHTdd3sOAZqEGssNy0WuMBOpyUBXpBCycgyQ5Qgghikyj1aMx5i01kXN8O9m5R1G8fdEGdcfk4QNInxxR+iTJEUIIYRdTTiaGrDT08qxKOBlJcoQoY0wRdUjctQ9LWCWXjOcssR0ls2IQeyb1xady+dJuSj5q7Zoc/eBVMrX29Tuy5zy5wjkWxUeSHCHKGg8PLDVqum48Z4ntIKpeR065ALzd9KXdlPw83DFUCEZNSbazHjvOkwucY1F8ZAi5EGWM5vw5fAf1R3P+nEvGK+nYigIajYJGo9g9u/GNeCSkErlsC/rLiQ6u2QEuRFNl3td4JKTaVY0956k0ry/h/CTJEaKM0aSm4PH1WjSpKS4ZryRjKwp4Kdl4Gy/hbbyEpzEONztmN74RXVYu5fccR5uR7bA6HUVJTSPw93/RZeXaVY8956k0ry/h/ORxlRBC3CZFUdAY07Cc+AkMmSg+oWiquN7sxkLcqeROjhBC2MuQiZqbgWp0vrstQpRlkuQIIYS9lOLpjyOEsI88rhKijLGUr0Dm6NexlK/gkvFKOrYFhVyzBdVsQWtR8XRw/QZ/b853aYEp0M/BNdtPLRfKpZ4dMPjbN8OxPeepNK8v4fwkyRGijLGUr0DW2PEuG68kY19ZkPNCUhbZ6ekEEUA1B6++bfD35vyjLQkOcr4kh/KhXH6iIwY7h5Dbc55K8/oSzk8eVwlRxijpaei3b0VJT3PJeKUR+8qCnMWxGKc2O5fAI+fRZOU4vG67pWfge+AU2mz7RlfZc55K8/oSzk+SHCHKGO3ZMwQ80wPt2TMuGc9ZYjuKZ3wqDT76BrfYhNJuSj7KuQvUmPk5nvH2zZNjz3lyhXMsio8kOUIIIYRwSZLkCCGEEMIlSZIjhBBCCJckSY4QZYzq5o65WnVUN3eXjOcssR3FotOSHeqPqnfCwbBubuSWD8Si09pVjT3nyRXOsSg+TvhbI4QoTuY6dUnac8Bl4zlLbEfJCgvmr8kvEFzB+eaBUSNrcWzOcLLsHEJuz3lyhXMsio/cyRFCCCGES5IkR4gyRnv4EMF1q6M9fMgl4zlLbEfxjo6n5ZhFuJ+LKe2m5KMcOUG9Ae/hHR1/zUZN3sKlmqs/yi3Wu7DnPLnCORbFRx5XCVHGKGYTmsREFLPJJeM5S2xHUSwqbhnZKMUw0aDdzCZ06VkolrxZnjV6d9x1ChhiUa+Z+dmi9yMLTwqaDNqe8+QK51gUH0lyhBBCOIRGp0cxZmI5vQNLbmbeRjdvNBEPoOi9bBIfIUqCJDlCCCEcSjVkoeZmAMjK7KJUSZIjhBCFpNGAomiuea3Il/h1FABFQbnSEedWHXKEKEaS5AhRxphq1CJ500+YatRyyXjFFVujAXclB0vO1XWaNFoteo0Zrab4vsizygWwb8xTeISFFluM26XWqMbJSf3I8nMDQPtfQpNrtmD5rw+RYrbgfotU0J7zVJrXl3B+kuQIUdb4+GC6p4Xrxium2IqiwZKTSvSeDWRn5K14HRAaRuX6rVGKMcmxeLiRXqMibp5OONmdtxdZEZWx/DdPjqJRUFWVmJRs0lLSAfD01VDdov53Z6eAPjn2nKfSvL6E05Mh5EKUMZqYi3i//QaamIsuGa+4Y2dnpJGZkUpmRio52RkOq7cgbsnp1PjqN3QJKcUeq8hiLhG2cgtuyek2m41mC4b/fkymW48Ks+c8leb1JZyfJDlClDGahHi8Fn6MJiH+1oXvwHjOEttR3NKzCd+2D11q8SdURaUkJhG6eRdu6dl21WPPeXKFcyyKjyQ5QgghhHBJkuQIIYQQwiVJkiOEEEIIlyRJjhBljCUomOwX+2MJCnbJeM4S21GMPh7EtG+I2c+7tJuSjxoUSMID92D08bCrHnvOkyucY1F8ZAi5EGWMJbwyGe++77LxnCW2o+QG+XHq2Y4EhwaWdlPyq1SRiy91Ife/IeS3y57z5ArnWBQfuZMjRFmTlYXu4H7IynLNeM4S20E0BiM+F+JQcg2l3ZT8srPxPBuDxmC0rx57zpMLnGNRfCTJEaKM0Z06QWCn9uhOnXDJeI6MrSh5SzdoNHnLFJTGAgVel5JpOmM17tFxpRD95pRTZ4kYvwivS/bdybHnPJXm9SWcnzyuEkKIG1AU8FKy0RjzZjdGo0Mp5iUchBCOJUmOEELcgKIoaIxpWE78BIZMFJ9QNFXuLtYlHIQQjiWPq4QQ4mYMmai5GahG+2b1FUKUPElyhChjVEWDxccXVSmZX/+SjucssR1FVRRMHm7gjHeQNBrMnm6oin1ts+c8ucI5FsVHHlcJUcaYGzQk8UzJLWZY0vGcJbajZFYO5Y8PBhFcoVJpNyUftV4dDn06nkw7h5Dbc55c4RyL4iOprxBCCCFckiQ5QpQx2uPHCGzXHO3xYy4Zz1liO4pXbCJ3T16Je9Sl0m5KfidOEzn6Y7xiE+2qxp7z5ArnWBQfSXKEKGOU3Bx0x4+h5Oa4ZDyHx1ZKb44cAI3RjHdsEorBVEotKJiSm4vHxXg0RrOd9dz+eSrN60s4P+mTI4QQBbCgkGu2oJotaC0qnqXdICFEkUiSI4QQN6AoCiaLyoWkLLLT0wkigGqqWtrNEkIUgSQ5QghxEyaTBYPZgtFsKe2m3LkUTd7kiv8Ng1dVFckXRUmQJEeIMsZctRqpK9ZgrlrNJeM5S2xHyQ7x59ArXdFWCC7tpuSjVgnn7KhnyA7xL7CMRu+Ou04BwyXU/zIbi5svWRZPa6Jjz3lyhXMsio8kOUKUMap/AIaHH3HZeM4S21HMXu4kNapBsLcT9gjy9yOtWR3MN5knR693Q2PMJPvkDsy5GWjcvNHXeQhF72VNeuw5T65wjkXxkdFVQpQxyuXLeM6djXL5skvGc5bYjqJPzaTylr/QJaeVdlPyi0ug3Dc70KdmFlhE0Sioqkp0XALHz8dy7lI8JouKcs0syfacJ1c4x6L4SJIjRBmjvRyLz7RJaC/HumQ8Z4ntKO6pmVTf8Ae6JOdLcpTLcVT8chvuN0lyrjCa8/o2mUz5+zbZc55c4RyL4iNJjhBCCCFckiQ5QgghhHBJkuQIIYQQwiXJ6CohyhiLnz+5Xbth8St42O+dHO92YysKtp1hS3Eph2uZPN2Jb1oLsxOOrlL9/EhpcRcmT3e76rHnGinN60s4v2K5k3P58mVGjx5NixYtaNiwIV27duXff/+1vq+qKnPnzqVt27Y0bNiQF154gXPnztnUkZKSwqhRo2jatCnNmjVj/PjxZGbadm47duwYzz33HA0aNKBDhw4sXry4OA5HCJdiqVadtKUrsFSr7pLxbie2ooCXJhtv42Xrj5cpDjeNGa2mdFOdnFB/jr7cBaMTzpND1XDOj3iKnFD7Egx7rpHSvL6E83N4kpOamsqzzz6LXq9n8eLFbNq0iXHjxuHvf/WXYPHixaxcuZKJEyeydu1aPD096devH7m5udYyo0eP5tSpUyxbtowFCxawd+9eJkyYYH0/IyODfv36ERYWxrp16xg7dizz5s3jyy+/dPQhCeFaDAY0MRfBYHDNeLcRW1EUMKSTe+wHsg+uI/vgOgynfkWxGFFKOclRTGbcktNRjM63QCcGI/rEVBSTfQt02nWNlOb1JZyew5OcxYsXU6FCBWbMmEHDhg2pXLkybdu2pUqVKkDeXZwVK1YwaNAgOnXqRJ06dXjvvfeIi4tj69atAJw+fZodO3YwdepUGjVqRLNmzXjrrbfYtGkTl/+bC+Hbb7/FaDQyffp0ateuTZcuXejduzfLli1z9CEJ4VJ0x44Q3LguumNHXDLe7cS+sk7VuUvxHD8fy/HzscQmJOEMaw94xyTScvynuF+4VNpNyUc5fpK7hn6Ad0yiXfXYc42U5vUlnJ/Dk5zt27dTv359hg0bRqtWrejWrRtr1661vh8dHU18fDytW7e2bvP19aVRo0bs27cPgH379uHn50eDBg2sZVq3bo1Go+HgwYMA7N+/n2bNmuHm5mYt07ZtW86ePUtqaqqjD0sIUQZcWadK1qoSwjU4PMmJiopi9erVVKtWjaVLl/Lss88ydepU1q9fD0B8fDwAwcG2z5eDg4NJSEgAICEhgaCgIJv3dTod/v7+1v0TEhIICQmxKXPl9ZV6hBBCCFF2OXx0laqq1K9fn5EjRwJw1113cfLkSdasWUP37t0dHU4IIYQQ4oYcficnNDSUmjVr2myrUaMGMTEx1vcBEhNtn+EmJiZa78SEhISQlJRk877JZCI1NdW6f0hISL47NldeX3+HRwghhBBlj8OTnKZNm3L27FmbbefOnaNSpUoAhIeHExoayp9//ml9PyMjgwMHDtCkSRMAmjRpQlpaGocOHbKW2bVrFxaLhYYNGwLQuHFj9u7di9FotJb5448/qF69us1ILiGELVP9hsRHxWOq39Al4zlLbEfJCA9lx4dDyKkeVtpNyUetV4eDK94iIzzUrnrsOU+ucI5F8XF4ktO3b18OHDjAggULOH/+PBs3bmTt2rU899xzQN4ohj59+jB//ny2bdvG8ePHGTt2LOXKlaNTp04A1KxZk3bt2vH2229z8OBB/v77b6ZMmUKXLl0oX748AF27dkWv1/Pmm29y8uRJNm/ezIoVK3jxxRcdfUhCuBaNBtzd8/7rivGcJbajaBRUvc45j0Gj+a9tdg6zt+c8ucI5FsXG4VdFw4YNmTdvHps2beLRRx/lk08+Yfz48Tz22GPWMi+//DK9evViwoQJPPHEE2RlZbFkyRLc3a/Omjlr1ixq1KhB3759GTBgAE2bNmXy5MnW9319fVm6dCnR0dH06NGDmTNnMnjwYJ5++mlHH5IQLkV7+iT+3R5Be/qkS8ZzltiO4nk5mYbvf4XbxfjSbkp+Z85Rc/IyPC8n21WNPefJFc6xKD7FsqxDx44d6dixY4HvK4rC8OHDGT58eIFlAgICmD179k3j1KlThy+++OK22ylEWaRkZuL2x06U62YQd5V4zhLbUbS5RgJOXiQxJ/fWhUuYkpmFz9HzaHONty5803pu/zy5wjkWxUfu7wkhhBDCJUmSI4QQQgiXJEmOEEIIIVxSsfTJEUI4L3OlyqS//xHmSpVdMl5hYms0oCiaa14rlO4ynAXLCfLlxPP3YwkNLO2m5KNWqkjUy13JCfK1qx57rpHSvL6E85MkR4gyRg0OJqdXX5eNd6vYGg24KzlYcq6ucafRatFrzGhLecXxGzH5eHKpbX2C/bxLuyn5BQWSdN/dmFKKOLpK0aAoCporn3doCLm9+97WeqileX0J5yePq4QoY5TERDw+/wwl0b6Vo5013q1iK4oGS04q0Xs2cHL7Sk5uX8nl/T+imA0oTpjk6DKyqbDzENo0Jxw9lJRM0Pa/0WVkF3oXjd4dd52ClyEWb+OlvJ9LR/D9fDGapKJfI6V5fQnnJ0mOEGWM9mIUviNfRXsxyiXjFTZ2dkYamRmpZGakkpOdUeJtKyyPpHQiVm1DH2/fXDTFQbkYS+XFG/FISi/0PhqdHsWYiXriRyyHvsFy6BvUP9fj/tootBeji9yG0ry+hPOTx1VCCCFKnGrIQs3NSy4VY+HvBAlRFHInRwghhBAuSe7kCCGEKFEKgKKgKP/1gVKcry+UcA2S5AhRxqje3hhat0X1LpnROiUdz1liO4rZXU9K7UpYPNxvXbiEqd5eZNStitldX+h9tP8lNLlmCxazJW+bmx63Vs1v6zy5wjkWxUeSHCHKGHPN2qR+s9ll4zlLbEfJLh/IwZFPEFwhtLSbkl+Napye8CLZRRhCrmgUVFUlJiWbtJS8Dsuevv5U/2oVqkdF+C/xKSxXOMei+EifHCHKGosFcnPz/uuK8ZwltqNYVBSjyTmPwWL5r21Fn+DGaLZg+O/HZDDf/nlyhXMsio0kOUKUMbpDBwmtHIru0EGXjOcssR3FJzqedsM+xuNsTGk3JR/l8DEa9pmKT3S8XfV4XriEX436aP8t+nlyhXMsio8kOUIIIYRwSZLkCCGEEMIlSZIjhBBCCJckSY4QQgghXJIMIReijDHVuYvE/UexhJTMkOSSjucssR0lMyyYXdNfwq9KhdJuSj5qZG2OzHuNTNVoVz054eVI/+s3zOF3FXlfVzjHovhIkiNEWePmhiWskuvGu0FsRcE6u66iKNxJ8+uqOi2GQF9UvRP+uXbTYwz2Ry3CPDk3ouq0qGEVwc2tyPPklOr1JZyePK4SoozRnDuLX78+aM6ddcl4N4rtfeEo3sbLeBsv42WKw01jRqu5M1Idj/hU6i7ehP5SYmk3Jb/z0VSdsxaP+FS7qnGLS8ZzwKu3dY2U5vUlnJ8kOUKUMZq0VNw3foMmzb4vJmeNd6PYJMSSe+wHsg+uw3DqVxSLEeUOSXJ02bmE/nMKbabzrdStpKURsPsIuuxcu+rRZuWg37QFJbXo10hpXl/C+Tnh/U8hhHAsswrnLsWTnZ5KUAUd1aoUfYZeIcSdR+7kCCHKBJMpbwkBY1H7fAgh7liS5AghhBDCJUmSI0QZYy5fkYw338FcvqJLxrs+dtZbE1HLlyvx2I6S6+/N2cdbYwryK+2m5KOWL0fs0/eT6+9tVz3GAF9yXh+FpULRr5HSvL6E85M+OUKUMWr58mQPH+Wy8a6PnfPaaHQ5saUS3xGM/t5EPXwPwYHOl+RQLoS4bu0w2jmE3BTgg6FHb1SP8kUeQl6a15dwfnInR4gyRklNwW3LZpTUFJeMd31s/febIDWtxGM7ijYrl6ADZ9A44egqUtPw23sMbZado6syc9D9uA1NWioajYJGo6AUcvBbaV5fwvlJkiNEGaM9fw7/Ps+gPX/OJeNdH9u319NoLkSVeGxH8UxIpf6Cjbg54Tw5yoVoqs9eg2eCfcO3PRNS8HrxFTJPnibZYCbZYMaoaAqV6JTm9SWcnzyuEkK4lOtnNwbuqBmOyyLlv39uH4hO5qQ5Bi93HR3rlCPQTYuqynB/cfskyRFCuAxFAS8lG40x7/GUYsq7+6FX7pwZjsuyHIOFjFxTaTdDuBBJcoQQLkNRFDTGNCwnfgJDJsrp6LztqumOmeG4LPPQa/Bx1+HlrkNOl3AESXKEKGNUdw9MkXVQ3T1cMh4AxiwwZIJixFKtIqq7e8nFdjCLXktmxSBUN+f7c626u5NTKRSLXmtfRZ5eWCJrc1eohUphueg1RgJ1OajKrYeml8r1Je4YzvdbI4QoVubIOiTv2OOy8Swo5JotqGYLVArBsPxtPGvXhEv7S6wNjpRVMZi/J/QmuEKF0m5KfhE1OT5rCFl2DiE3VgvHtO1rsv76kez9/6B4+6IN6o7Jwwe4eZ+ckr6+xJ1FkhwhhMtQFAWTReVCUhbZ6ekABBFANem8ekcw5mRgyEpDL8+qhIPIEHIhyhjtvwcJrlEJ7b8HXTIeXF2nSn/uMjWffgPl8LESi+1o3lHxtH5tPh5nL5Z2U/JRDh+j/kvT8Y6Kt6sej7MX0ddphce5ok/aWBrXl7hzSJIjRBmjqBY0GekoasksVFnS8Wxjq2izc8Fy5y7KqagquhwDWJzwbpTFgjbbgGLvnTKLipKReVv1lOb1JZyfJDlCCCGEcEmS5AghhBDCJUmSI4QQQgiXJEmOEGWMqVYEyVt/w1QrwiXjXSurQiCnZ41ArVW9xGM7SlaFQP5541lyw8uVdlPyUWtV58T0AWRVCLSrntzwchi/X0NOWEiR9y3N60s4P0lyhChrvLwwNWwMXl4uEU9RuGblatuhxxY3PTk1w8HTs1hilwSLm56MKuVQ3d1Kuyn5eXqSXT0Mi5vermpUdzfUBnfd3jGW9PUs7iiS5AhRxmiio/AZNxJNdMmszF2c8RQFjIrGunJ1uslsM3Wce1IaFReug4tFH5rsLNyT0qi1+mf08fZNuFcsLsZS6dNNuCel2VWNPj4Z7ZvT0CekFHnfkr6exZ1FkhwhyhhNUiKey5agSUq84+MpikKmwcTPx+LYdCCGv84m5a1a/d8dHX1GDkFb/kBJcsIEoZD0GTmE/XYQbVpmaTclHyUpmZCf/kKfkWNXPdq0TLSffYkuPavI+5b09SzuLJLkCCHueFm5JjJyTWQbzaXdFCGEE5EkRwghhBAuSZIcIYQQQrgkSXKEKGMsIaFkDRyCJSTUJeNdy+DrSULX9qjBQSUe21EMvp5E398Ek79PaTclHzU4iPhHWmLwtW/0msnfB/PLvTH5eRd539K8voTzkyRHiDLGElaJzCkzsIRVcsl41zIE+nL5pccgrEKJx3YUQ6AvZ55ojykkoLSbkl9YBWJ6P4wh0NeuakwhAZjfGYMx2L/I+5bm9SWcnyQ5QpQ1GRno/toNGRmuGe8amhwDnsfOQWbRR+04C02OAd8zsWiyc0u7KfllZuF1IgpNjsGuajTZuSh/H0CTcxvHWIrXl3B+kuQIUcbozpwisMsD6M6ccsl41/KKS6HGG/NQzpwr8diO4hWXQpP/rcUtJr60m5KPcuYctd9Zildcil31uMXEo3+8N+6xRR8GXprXl3B+utJugBBC2EOjgJd73p8yT70WRZFh5EKIPJLkCCHuWIoCgboc2oRmY7RY8PEEd42CVqPcemchhMuTJEcIcQdT0BrSMR3/gezMdDxDw9A0bIMiOY4QAumTI0SZo2p1WIKDUbUl82+ckohnzM7AkJWGMdd26QNVo+QNSy6hYy0OqkbB4OOJqnXCP9daHSZfL1Q775ypWg1qUCCqpujHWNLXs7izyFUhRBljrlefxKNnXTbetTLDQzn+2SRq3xUBOw6VShvslRkeyq7/DSC4QlhpNyUf9a4IjiwaS2aKfWuD5VYLw3jwV3J2rIP/6lLIW5tMc00Cpaoqqmq7b2leX8L5SZIjhBDCqWg1CopGIcVgxmyxWLd7u+nQY8mX6AhRECe8/ymEKE7aY0cJat4I7bGjLhnvWl4xidQaNAPl+J07vNgrJpF7JizH/cKl0m5KPsrxU9QZMRevGPtWAHe/cAl9my64R8Xl1atRMJkt/H46nk0HYth0IIafj8WRaTChXNfhqjSvL+H85E6OEGWMYshFe+4siqFkJpcr6XjX0pjMuF9KxGiwb7K60qQxmfGMT0Uxmkq7KfkZDLhfTkZjsm/YvmI0oZyPQmMyAXrr9uxcMxm5Nz/u0ry+hPOTOzlCCCGEcElyJ0cIccdQFGweVyiKgowWd1GKBk83BZ//Jnr0ctch0x+Joir2OzmLFi0iMjKSadOmWbfl5uYyadIkWrRoQZMmTXj11VdJSEiw2S8mJoYBAwbQqFEjWrVqxbvvvovJZHvbcvfu3XTv3p369evzwAMPsG7duuI+HCFEKVEU8FKy8TZesv54GuNw05hl8j8Xo9G746HX0Dwok3vDcrk3LJc2odkE6nJkDiRRJMWa5Bw8eJA1a9YQGRlps3369On8/PPPzJkzh5UrVxIXF8fQoUOt75vNZgYOHIjRaGTNmjXMnDmT9evX8+GHH1rLREVFMXDgQFq0aMGGDRvo27cvb731Fjt27CjOQxLijmeuXoOUNeswV69xR8VTFAWNMQ315E9YDm/AcngDnPkVxWJEKSDJyQ7159yEl1GrVbErdmnKDvXn31e7YagYUtpNyUetVoUzr/ciO7Toq4dfy1AxBOPn88ktHwSARqtHY8jAdPxHsvevI3v/OkzHf0BrSIfr7t2V9PUs7izFluRkZmYyZswYpk6dir//1V+A9PR0vv76a15//XVatWpF/fr1mT59Ovv27WP//v0A7Ny5k1OnTvG///2PunXr0qFDB4YPH86qVasw/NeBcM2aNYSHh/P6669Ts2ZNevXqxUMPPcTy5cuL65CEcAmqrx/G+zqh+vrdcfEsKORkZ5CTlUZOVhq5uTdfXdzs6U5mk0jw9bE7dmkxe7qTfFdVLF4epd2U/Hx9SG9UC7Onu13VWLw8UO9tk+8YjTl5kzwastIwZt94lfGSvp7FnaXYkpzJkyfToUMHWrdubbP90KFDGI1Gm+01a9YkLCzMmuTs37+fiIgIQkKu/sulbdu2ZGRkcOrUKWuZVq1a2dTdtm1bax1CiBvTXL6E13vT0VwumSHJjoqnKAomi8qFpCxOXErnxKV0LqflcLNJU9xSMwld8wNcdr4VvAvLLTWTqt/tQpeUVtpNye9yPOW/+hm31Mxbl70JXVIa2tmfoEtOL/K+JX09iztLsSQ5mzZt4siRI4waNSrfewkJCej1evz8bLPu4OBg4uPjrWWuTXAA6+tblcnIyCAnJ8dhxyKEq9FcvoT3rJklmuQ4Mp7JZMFgzvsxmi03LeuWmkm5L39CibvDk5xNu9ElO1+So8TFU+HrX+1PcpLT0H6wAH3K7SU5JXk9izuLw0dXxcbGMm3aND799FPc3e27hSmEEEIIcbscnuQcPnyYxMREevToYd1mNpv566+/WLVqFUuXLsVoNJKWlmZzNycxMZHQ0FAg747MwYMHbeq9Mvrq2jLXj8hKSEjAx8cHDw8nfHYthBBCiBLl8CSnZcuWbNy40WbbG2+8QY0aNXj55ZepWLEier2eP//8k4ceegiAM2fOEBMTQ+PGjQFo3LgxCxYsIDExkeDgYAD++OMPfHx8qFWrlrXMb7/9ZhPnjz/+sNYhhBBCiLLN4UmOj48PERERNtu8vLwICAiwbu/ZsyczZ87E398fHx8fpk6dSpMmTawJStu2balVqxZjx45lzJgxxMfHM2fOHJ5//nnc3NwAeOaZZ1i1ahXvvfcePXv2ZNeuXXz//fcsXLjQ0YckhEux+AeQ0/MpLP4BTh/v2sn/rl+zqDBMXu6ktG+Kl78fpBR5d6dg8nLncvNIzD6epd2UfFR/P5LbNMDkZV/XBLOPJ+buXTB7F/0YS/p6FneWUpnxePz48Wg0GoYNG4bBYKBt27a888471ve1Wi0LFixg4sSJPP3003h6etK9e3eGDRtmLVO5cmUWLlzIjBkzWLFiBRUqVGDq1Km0a9euNA5JiDuGpWo10ucvcfp4igJGRUOmIW8SUDedSlFnY8kJ8efia89Ru0o4nN9T5DY4g5wQf46/+DDB5YNLuyn5VQnnwtCe5KQk21WNsXww5o9mYNixDopYV0lfz+LOUiJJzsqVK21eu7u7884779gkNterVKkSixcvvmm9LVq04JtvvnFEE4UoO3Jy0MRcxBJWCUqi/9ptxlMUhUyDiZ+PxZGVayI8yIv7K6kUZcpbxWjCLTYBcu7cxRsVown35AyUoAql3ZT8cnJxu5SIorWg6m//60QxGOHshbz/FrkNJXw9izuKLNApRBmjO3GM4JZN0J04dkfEy8o1kZFrIttY9JWuvWOTqD14JsrJ07cV2xl4xybR/J3PcI+6XNpNyUc5eZq6r32Ed2ySXfW4R13Grd2jeFws+lD/kr6exZ1FFugUQjgtjZK3MCOAp16LohQ90RFClF2S5AghnJKiQKAuhzah2RgtFnw8wV2jyGKcQohCkyRHCOGkFLSGdEzHfyA7Mx3P0DA0DdvIKtRCiEKTPjlCCKdmzM5bpNGYa9/SAUKIskfu5AhRxpgaNiY+ruTWQSrpeNfKqFKOw+tnUbvBXbDjzuyYmlGlHL/NH05whfDSbko+aoO7OLB6Ihl2DiHPqRmOIfog2bcaQq5oUBQFzX+PLFVVLdXrSzg/SXKEEE7h2on/8l7LcylxlUbvjrtOAUMs6n8rz1v0fmThebOF6EUZJ4+rhChjtKdOEtD5frSnTjpNvCsT/yUbzNafdJMZe7+7PC8lU33cR3D6rJ01lR7PS8k0fu9L3C7GlXZT8jt9lloTluB5yb47OW4X49A91gv3mIQCy2h0ehRjJuqJH7Ec+gbLiZ/QGNNK/HoWdxa5kyNEGaNkZaL/+y+UrJLp41KYeNdP/Afc1uR/19MajHidOI8xK/u26yhtWoMRv7OXiM8xlHZT8lGysvE+GY32dibxu4Ymx4Dmn4NonmgJXjdf2kE1ZKHmZnDlqlCyskr0ehZ3FrmTI4RwCjcaGS5PrMS1FABFyXuUKReHKAS5kyOEKHXXz4kDyLw4wob2v6Qm12zBYragmC24I9eGuDlJcoQQTsB2ThxA5sURNhSNgqqqxKRkk5aSjqevhuoWVTqoi5uSx1VClDHmylVI+3gR5spVnC7elTlxHDUvTk6wH9HDn0UNr2R3XaUlJ9iPYy88hLFcUGk3JR81vBLnB3cnJ9jPrnqM5YIwzZ2OITTg1mXNFgxmCyZT3h0/S5WSvZ7FnUWSHCHKGDUwiNwnn0ENLJkvzZKOdy2Ttwep994Ngf4lHttRTN4exLWog9nXq7Sbkl+gPyntGmHytm/1b7OvF5aej2L2Kfoxlub1JZyfJDlClDFKQgIeSxehJBQ8XPdOjnctfXoWQZt/h0T7VskuTfr0LCr+cgBtakZpNyW/xCSCf9yDPj3Lrmq0qRlolq9Bm1b0u3dKQnypXV/C+UmSI0QZo42JxveN0Whjol0y3rXckzOouHg9SsylEo/tKO7JGdT+8hf0CSml3ZR8lJhLhC/bjHuyfQmYPiEF3VvTcUtMLfK+mosXS+36Es5PkhwhhBBCuCRJcoQQQgjhkmQIuRCiVFy7VpWiKDLjiRDC4STJEaKMUX18MNx7H6qPT6nFUxTwUrLRGP9bPVqjQ9GYHT7xn8lDT0bjCNy8vSHFoVWXGJOHnqS6VbB4upd2U/JRvb1Jb1gTk4fernosnu5YOrTG7FH0Yyzp61ncWSTJEaKMMdeoRerab0o1nqIoaIxpWE78BIZMFJ9QNFXuRnFwkpNTLpDz7wygdo2qcPFvh9ZdUnLKBXJoWHeCK4SWdlPyq1GVM2/0JifFvgU6DWGhmFYtwLBjHRSxLkvNkr2exZ1F+uQIUdaYzSjpaWA2l348YxYYMsFYTAtoWixosnJK7liLg8WCNjsXzJbSbkl+ZnPe52uxs21mC6Rn3F49JX09izuKJDlClDG6w/8SUjMc3eF/SzWeBYVcs4UcswWDRS2W2D7RCdR9/i2UI8eLpf6S4BOdQJuRC/A4F1PaTclHOXKcBv1m4hNt3xw1HudicKvbGs/zRR/qrz1UstezuLPI4yohRIlTFAWTReVCUhbZ6ekEEUA1tXgSHSFE2SV3coQQpcZkyluHyOiMj2KEEHc8SXKEEEII4ZIkyRFCCHFnUjT8N9VS3og9jWJ9LQRIkiNEmWOqW4+EI2cw1a1XYvESj57BUq8+Gs2VL6KSmfzv/9u78/ioqrt/4J9z76zJZF8IIREQCCCRVcsPiEtdsC+XWkTFp260uKAP6NPauv1UjPISaqlWXJ7yFItIXR4rLlVcAC38tKCoBcoSSDCQBJKQZJLJ7DN37j2/PyYZs0+SO3NnMvm+X69pncmdc74n9wz5zr1ncY3KwpFXHgefNEGD2qLDNSoLu5++Hd7RI2MdSjd80gQcXPdbuEZlqSrHO3ok/Pt3wFM4ot/vEfRGGHUMxvFZ8B74CqYJWUiW6pHEPJTokBAaeEzIcKPXg2dna1YdM+hhzkmGIFkBqe3FKC3+1xUXRchpFkCvbrG6WOKiCCklCdCJsQ6lO70ecmoyuM2vrhydCGRlDqiNgk4PJrmgHP8Cis8F1AMwJEMouhRMnwROA9kJ6EoOIcOOcLwSqTcvgnC8UpP6xBPHYfz5f4D/43UoB9+DcvA98MqdYIoU8cX/ujI12lD41F+AEzVRrSeaTI02THnp79DXqZumHRUnajDm96/D1GhTVYy+rgm6XyyH4XTzIGKohuH//hk4UR1cc4mQDijJIWSYERx2GD/9GILDrkl9zG6HuPVzwNYM7nMGH9Fa/K8LnceP1G8OgzkcmtQXDTqPH1kHjkN0e2MdSjfM4UDav8qh86i7kiO6vRC27RxcG11e6PaUg7l8qmIgiYmSHEIIIUMS6/C/YAANxiFdUZJDCNEGaxtwTDuOkwgQ2xIaqW0rCL/C4ZMVKNS7SAeU5BBCNCG1beEQzW0cyPDBBAbOORodwdtUVU0uVDe7EVA4GF3RIW1odhUhw4yclw9n6VOQ8/I1qY/n58P72EOoESU46oNjY7TaxsGXnoz6xVchM28EYDsS9fqiwZeejO8XngcpKy3WoXTD80bg1E3z4UtPVlWOlJWGwGO/gZQB/DAFr388aRZ8v/A8uFKTYAjQytmkM7qSQ8gww3Nz4blrGXhurkb1jYD/zl/Ca0mCX9Z2GwcpNRnWqy8ActSt4xJLUmoyTl0yE3J6SqxD6S4nC01XzIWUqi7JkdNToNxxCwLplgG/N5BuwalLZqqOgSQmSnIIGWaYrQWGv78LZmvRrD7dBx9DdGkzo6ojncuL1H/uB2zazCSLBp3Li+zvKiA43bEOpTubHWlfHYLOpW7ml+B0g324FaJz4H1EdHqQ/V2F6hhIYqIkh5BhRqyuQtptt0KsrtKkPqGqCklL74FB5Voqg2Gy2lG4ZhNYzUnN644Uk9WOs9Z/NLg1ZKKM1ZzEmOf+BpNVXRJpON0M/dLfwNA48MTb0NiCs9Z/pDoGkpgoySGEEEJIQqIkhxBCCCEJiZIcQgghhCQkmkJOyDDDTWZIZ08DN5mjUr4gAIz98P2JJZkhF58Frtf+nxtZr4PnzFEQTSbN644UWa+DozAHiiH+NhnlJhPcY/Igqzy3ikEPpXjSoNqoGPRwFOZA1uvoWzvphvoEIcOMXDQRts++gFw0MeJlCwJgZF7ovHWhhzAmA4Ft70Aq1GbKekeekZmo/MOvgAlnal53pHhGZmLvwz+Hv3BErEPpbsKZqFi1FJ6RmaqK8ReOQOCTt+AblTPg9/pG5WDvwz9XHQNJTHQlhxASMYwJULytOLnnfXicwdku6Tn5KCyeG/UdxwkhpCu6kkPIMKM7sB/ZBdnQHdgftTo8Tjtczla4nK3AoaPQnzkL5uN1UauvN8k1DZh83QNgB8s0rztSkmsaULL8BZgqT8U6lG7YwTKcffOTSK5pUFWOqfLUoPuI+XgdSpa/oDoGkpgoySFkuOEczO8HNNhW4Yf6JADa71fFOCAEZO3aGgVx3QbOIQRkMLWhqeojEYqBJCRKcgghhBCSkCjJIYQQQkhCooHHhBBCEgZjAgSBof07POcKFNqcfNiiKzmEDDOBCRPR/P++RmBC5KeQ98RXMALSZ+/AO4jpwWq58zJx7LnfgI8fulPI3XmZ+PbRm+AriL8p5Hz8mTjy9N1w56mbvq2mj3hH5eDbR2+COy8TeqMJRr0A0VMbWsLAyLwQ6C/dsEVXcggZbsxmyJMmR6y4jov/CQJD14ni3KgHnzgevOHfgMYbaSsGHXx5eYB56C4GqBh0cOdnwWyMv8UAYTbBV5gLReWO9mr6CDfo4c7PAgCIegPgc6Duu21w2FthtqSi4EdXg5mSANDlnOGI8ltChhmhphqWXy2DUFOtvqyui//5TsMgyBA7rImjb2iG+JsV0MdgF3Kj1Y78F98CTtZqXnekGK12TNi0HfqG+NuFHCdrUfA/78OocgdwNX1E32jDhE3bO8XgcTvgcraG1moiwxclOYQMM0JLM8yvvQqhRf0fzY6L/1V8vgmn920Fk/2dFv4THW6Ib74LnVPjyzgA9C4vMrbvAWuxaV53pOhdXozcdQiiQ/vfXzisxYasf+yF3uVVVY6aPqJzujFy1yHVMZDEREkOIUS19sX/vB5nrEMhhJAQGpNDCOk3xgDGWIfn3cfgEEJIvKAkhxDSL4wBScwDQeowzkHQgXUZg0MIIfGCkhxChhklJxfue34NJWdgu4IzxiBIdijl2wC/K/iaJQfCGbP63HwzkJ4C+T+XQEqzAJDVhD5g/tQkNF5zEdKyswCbplVHjD81CdWXnYNAekqsQ+mGZ2fh9E9L4E9NUlWOmj4ipVlQfdk58KcmwagqCpKIaEwOIcOMMjIfrkcehzIyf3AFSO5gkuN3AZIn7OGBrDTID92LQGbq4OpTwZ9uQcPNlwMj42+Nmf7yp1tw4mfzEMhKi3Uo3Y0cgfr/uAT+dIuqYtT0kUBmKk78bJ7qGEhioiSHkGGGOR3Q//MLMKdjwO9VwOCTFXjbHn4l/K6IgscLtusbCB7fYMJVRfT6kXTwGOB0aV53pIheP9LKT0LwxOHsIacLyYePQ/T6VRWjpo8IHh/Syk+qjoEkJkpyCBlmxMrvkb7gCoiV3w/ofYwxBBSO6mY3yusdKK934LTdG3Z3bENtE/TXL4Gx3qom7EExN9gw9tE/gR2v0rzuSDE32DDt2c0w1DbFOpRu2PEqjH9yI8wNNlXlqOkjxnorpj27WXUMJDHRmBxCyIAEAgr8cnD1WEmmVWRJnGNCcDxZh3FjnPNwuTlJEBG/krNu3TosXLgQM2bMwJw5c3D33XejsrKy0zE+nw+lpaWYPXs2ZsyYgeXLl6OpqfO3lNraWtxxxx2YNm0a5syZg9/97ncIBAKdjvn666+xYMECFBcX49JLL8U777wT6eYQQggZogS9EUYdQ5K/DslSfeiRxDxgNCFwWIh4krNnzx7ceOONeOutt7BhwwYEAgEsWbIEbvcPK1k+9dRT+Mc//oE//vGP2LRpExoaGrBs2bLQz2VZxp133glJkvDmm29i9erVePfdd7F27drQMTU1Nbjzzjsxe/ZsvP/++7j11lvxyCOP4Isvvoh0kwgZthgL7kclCIzWxCFDjqDTg0ku8PKtUA6+F3yUb4Mg2Tut90QSV8RvV7388sudnq9evRpz5szBoUOHcO6558LhcGDz5s1Ys2YN5syZAyCY9Fx++eXYt28fpk+fji+//BLHjh3Dhg0bkJ2djcmTJ+Pee+/FmjVrsGzZMhgMBrz55psoKCjAgw8+CAAYN24cvvvuO7zyyis477zzIt0sQhIG1+khj8wH1/W94WO3dXEGuSYO14ngebngojjYkAdNEQVIWWmAbujemVdEAb50C7hO+99fWDod/JkpUER135fV9BEuivClW3qNgQHgkie07AFdwhleoj7w2OEIzuBISwtOfzx48CAkScLcuXNDx4wbNw75+fnYt28fAGDfvn0oKipCdnZ26JiSkhI4nU4cO3YsdEx7ktTxmPYyCCE9k8+agub9RyCfNaXP4zqui6McfA+8cieYIvW5Jk5PfKNHQvp2O7xnaD+N2z0qG+XrHwWfXKR53ZHiHpWNr1ctgW/0yFiH0g2fXISyF++De1R2+IP7oKaPeM8Yga9XLekxBrEtoek4I9AnK1DomuSwEdWvN4qi4KmnnsLMmTNRVBT8R6apqQl6vR6pqZ3XQ8jKykJjY2PomI4JDoDQ83DHOJ1OeL1emEymqLSJkGHH7wL3OQEjrUNChhYmMHDOUWvzwG4LfuE2pwgYq/C221U0+jjRRfVKTmlpKSoqKvDss89GsxpCyACIhw8hc9okiIcP9e8NTN14HGNVHfTnXAJT9elBljB4SaeaUHTbk2Bl5ZrXHSlJp5ow+6GXYayqi3Uo3bCyckz+zz8g6ZS66e1q+oip+jRmP/RynzFIcnBGoF9WEAjQjMDhJGpJzhNPPIEdO3Zg48aNyMvLC72enZ0NSZJgt9s7HW+1WpGTkxM6putsq/bn4Y6xWCx0FYeQPrCABLGuFiwghT224+J//Vn4r+f6ZLD6BjBZ2y0dAECQFeitrUCXmZlDiSArMNqcYAHtf39hBQIwNDsgqFxKQE0fYbIMo82pOgaSmCKe5HDO8cQTT2Dbtm3YuHEjCgsLO/28uLgYer0eu3fvDr1WWVmJ2tpaTJ8+HQAwffp0lJeXw2r9YWGoXbt2wWKxYPz48aFjvvrqq05l79q1K1QGIWRgOs6kCj4EyB0W/+vPwn+EEBJPIj4mp7S0FB9++CFeeuklJCcnh8bQpKSkwGQyISUlBQsXLsTq1auRlpYGi8WClStXYsaMGaEEpaSkBOPHj8f999+P3/72t2hsbMQf//hH3HjjjTAYDACAG264Aa+99hqefvppLFy4EF999RU+/vhjrFu3LtJNIiTh9bXDOFc4/LJCC/8RQoaciCc5b7zxBgDg5ptv7vT6qlWrcM011wAAHn74YQiCgHvuuQd+vx8lJSVYsWJF6FhRFPGnP/0Jjz/+OBYtWgSz2YwFCxbgnnvuCR1TWFiIdevWYdWqVXj11VeRl5eHlStX0vRxQgZhsDuME0JIPIt4knP06NGwxxiNRqxYsaJTYtPVqFGj8Oc//7nPcmbPno333ntvoCESMqzJZ46D7d0tkM8c1/2H7TOpgIjNpvLnZ0N662X4nMcAnzv8GyLIk5uO408uxaixo4G9RzStO1I8uenY/6uFMOSrm6YdDXzsaBx79FZ4MpNUlaOmj/jysrD/VwvhyU2HUVUUJBHRBp2EDDPckgJp3nnglhRN6lPMJvC550Ixa/8nSDYZ4C4eD1iSNa87UmSTAa1FBVDMcTihwpIM11ljIZsMqopR00cUsxGtRQWqYyCJiZIcQoYZoa4WySsfh1BX2/2H7dPFI7iFg87aCnHVc9A128MfHGEGmxO5mz4C6rSfvh4pBpsTY977J3TW1liH0l3daeS9sR0Gm1NVMWr6iK7ZjjHv/VN1DCQxUZJDyDAjNDYgae0zEBobOr3ecbq4minjXelsDogvvgx9q/Z/hAx2N3Le+RysyRr+4DhlsLtxxqffQte2mF08YU1WjPj7lzDY1d2GVNNH9K1OnPHpt6pjIIlp6G7oQghRTWgbVCwIAgJt08U9bVuxZCIdY2jKOCFkCKMkh5BhShYEONpWf9XrgFQAgUBwVVgANGWcEDLkUZJDyDBlUNyQmqsQkDmYyQiDhUNUuZs0IUMCE4LLJnRYHoFzTmtdJiBKcggZZpSMTHhvuhUsxQDv4U/gdjmgy8mHMHUeWBSWxJFTkiDfsAABSxK03hBRSjah5ZIfITkjHbBpWnXESMkm1M2dAjlF3TTtaOAZ6bD+eAakZHUzv9T0kYAlCXVzp0BKNvVrCrmgN8KoY4C/DrxDVqPoU+GGmRKdBENf2wgZZpTCM+B+7kXwglGQPE743XZIPlfU6pNyMyGvKYWUkx61Onrjy0pF7X9eDxTka153pPiyUlFx8yWQcjNjHUp3Bfk4ecfV8GWlqipGTR+RctJRcfMl/Y5B0OkhSC7wim1QDr0P5dD74BXbIEj2tp3JSSKhJIeQYaDTvlQ+L8QjZWAerzZ1+ySwo8fA/OE3BI00wR+Asboe0Kit0SD4A0iqtYL5tP/9heXxwljTAMGvbgNUNX2E+SUk1Vr7HYPYlsh4PU543fbgw+OEErFFE0g8oSSHkATXvi9VslSPZKkelsO7kDbvXBi/L4eowZYNxpOnob/4GphONUa9rq6S6psx/t41YMcqNa87UpLqm3HOk3+F8WT8rfXDjlVi0v0vIam+WVU5avqI6VQjznnyr/2OgQkMnHPU2jwor3egvN6B6mY3AgqnKzkJiMbkEJLguu5Lxb4/GXydB2hfKjJsSfIPMwn1AZpJmKjoSg4hw0X7vlR+T6wjIYQQTVCSQ8hw0bZdAw09IIQMF3S7ipAExBhC4wsEgYG3bdnAZQWiwmHS6xCV+eK9BMMNesQiu+IMUHSidm2NgrhuA2NQdCK42tBU9ZEIxUASEl3JISTBMAZITECLX0aLX4YjENyHqrrZjfJ6B8qSLajY/DT4lEmaxOM9cxSkyu/gGTtSk/o6chXmouxvvwMvnqx53ZHiKszFl88vg/fMUbEOpRtePBkHNj0KV2GuqnLU9BHP2JH48vllqmMgiYmu5BCSYBhjcPkD+MeRBrh9ARRkJuHiURwBmdOWDYSQYYWu5BCSoNy+AJy+ADyS3Ol1c10zzrzvWaBCm2nVhprT0P3kehhjMIVc67ZGg7muGTOeeh2GmvibQo6KSkx46E8w16mbQq6mjxhPNWLGU6+rjoEkJkpyCElAAgOSjDpYjDqY9WKn4RyiFIC58hSYV5sF8gS/BOHgEQgxWAxQ67ZGgygFkFLTGJPfXzjM60XSiXqIkrrFANX0EcEvIaWmUXUMJDHR7SpCEgxjQIbOi3k5HkiKAosZMApMk4X/CCEknlCSQ0jCYRD9DgSOfgqPywFzFDffJCRRMCa07UoevMHBuQKFhq4NeZTkEDLECULwH+gfnjMwoMPmm2mxC46QIUBvNMGoF+Dz1EJo24VcMKXBJ5go0RniKMkhZAgTBMDIvFC8rT+8JorQC3Kvt6e8Wamo+c3NGFFYANiORD1G/4hMSH9aA7/YCAS0HRujdVujwZuVisO3XQ42Iv52IeeFBThx73XwqtyFXE0f8edk4PBtl8OblQrjIOsX9QbA50Ddd9vgsLfCbElFwY+uBjMlAaAsZyijJIeQIYwxAYq3FSf3vA+P0w4ASM/JR2Hx3F73pQokm2CfNwEj0tX9YeovxZIE/uP5kL94B7Bpm+Ro3dZoCCSb0DRrArIsSbEOpbv0VLT+nykI2FpUFaOmj8gWM5pnTVBVfzuP2wGXszX8gWTIoNlVhCQAj9MOl7MVLmcrvB5nn8fq7S5kvb8TaLRqEptoc0D4n1ehs/UdVzRo3dZo0NtdGLX9XxBtjliH0l2jFdlbdkFvd6kqRk0f0dmcGLX9X6pjIImJkhxChhBBAERRCD3ax98MhNHmQt4rH4DVa7Puit7aCt0Ta6BvsWtSX0datzUajDYXxm3+Anpr/F1hYPWnMeqvW2G0qUsw1PQRfYsd4zZ/oToGkpjodhUhQ8Rgxt8QQshwRkkOIUPEYMbfEELIcEZJDiFxrONu4owFb021j78BAJNl6A6oJYSQaKMkh5A4xRiQJHgg+IMDTpkoAhG4NRUwG2A/9yyYUlIAWwQCDUNOMkG59ALISaboV9aF1m2NhoDZAOvZY2Py+wuHp6SgdWYRAmaDqnLU9BE5yQTr2WMRMBsGPYW8R0wAY6xtgcAgzjk4j2QlJNpo4DEhcYoxBvgd8B35FJ5/vwP/sZ1giqT61pQ3Jx01D/8SGFMYoUj7Jo3MRmDD8/DHYJ0XrdsaDd6cdBy6+6eQRmbHOpTuxhTixG9/Dm9Ouqpi1PQR/4hMHLr7p6pj6EjQG2HUMST565EsnQ49kgQPrRw+xNCVHELiRMdbUwAgCAICCseJ+kZ4HK3IzNNhzBnqv0YyWYbY6gQkjTZ8DMiAtTn4/xrTvK1RwGQZOrcPyNb+9xeWJEG0u8BkGVwUB1+Omj4SkKF3uBFIitx1HL3eAEFywVPxBWRfcFq7YEiGftJlYPokcLqcM2TQlRxC4gBjQEAQYA8ooYerbT35QECBX1YgyZFZeTX5lBWTFj8OdqQiIuWFY6qqg2HahTDXaD+NW+u2RkPyKSvm3P9nmKrqYh1KN+xIBYrv/D2ST6lbh0hNHzHXnMac+/+sOoaOmMDAOcfJhiYcrarD0ao6nKhvREDhnb6IkPhHV3IIiZGOV24EgUGUnbA3NyAgB78lMpMRBguHKNJ3EUJiQZKDXzAAQB+g7R2GIkpyCIkBxgCJCXD5AwAAg05AmuSA9/AncLuCA411tHs4IYSoQkkOITHAGINHCuCrSis8fhn56WaUjOCQvC743cE1cGj3cEIIUYeSHEI00H1QMUOGzouZaU5IigKLOQCjwGjlYkIIiSC62U9IlPU8qJhD9DsQOPopPPvegXxsJwRZ0uTWlLMgG2WvrQQ/a2L0KwPgHZMPf9kueEbnaVJfR1q3NRqcBdn45zNL4R2TH+tQuuFnTcSBlx+Es0Dd9HY1fcQzOg//fGap6hhIYqIrOYREmSAwGJWeBxUrfjf8bru2t6YEAUqSCVAz5XcgRAFIsQQ339Ka1m2NBkGAbDYGf4/xRhSDv1+/R2U5KvpI++9HC10WCKTFAeNfHH5qCEk0DKI/OKjY/q+3Yf/X2/BX/EOzKzddmRpaMLr0f4DKKk3qM9Q2QnfjUhjqIjfFt7+0bms0mBpaULz2XRhqG2MdSneVVThz1SaYGlpUFaOmjxjqrChe+67qGMLpaYFAWhww/lGSQ4hGJI8Tfre97cqNK2Zx6LwSLPvKwVzaxCB4fBB27oLo9WlSX0datzUadF4JmWXVEDza//7CYS4XUv79PXRedYstqukjoteHzLJq1TGE075AYPsK5L4jnwJ+B62bE+fodhUhhBASRscFAu22FphT0jC2qH1xQLpnFa8oySFEpa4zp4KvcbRfKBWE4O7hhJChr32BQFoccGigJIcQFRgDkpgHgmTv8KIAwWCCz+MGByCIIvQR2D2cEELIwFCSQ4gKjDEIkh28Yhu43w0AEJKzgTNmoe7brXDYW5Gek4/C4rmqdw+PFF+GBXW3L0BWfh5gOxL1+qTsdARWPgx/lgQo/qjX15HWbY0GX4YFFYsuhJydHutQuuH5eTj5i8vhy7CoKkdNH/FnpaFi0YXwZVig0RwrMoTQwGNCVFLA4PU44XXb4XXb4fMFkx2P2wGXsxVejzPGEXYmpSSh+fJ5QFamJvXJaRYoi2+AnJqsSX0dad3WaJBSklB34TTIaeoSiajIyoR1/o8gpSSpKkZNH5FTk1F34TTVMQwGY0Jw3zlRCD1isVIC6R2dDkJUYIwhoHBUN7tRXu9Aeb0Dp+1exPPiGTqXF2k7vgNaWjWpT3S4IWz+EKLTrUl9HWnd1mjQubzI/foIRIf2v7+wWlqR/sV+6FxeVcWo6SOi043cr4+ojmGg9EYTjHoBoqcWOm9d6GFkXkp04gidCkIGSBDQ4VtbcFBxIBAcjOiXFUhyfA9INFntKHjuDbCTpzSpT9/QDN29D8PQaNOkvo60bms0mKx2THrlU+gbmmMdSjfs5CmMfuldmKz28Af3QU0fMTTaMOmVT1XHMFCi3gD4HKj75u+o+HwTKj7fhJN73ofibQVj9Kc1XtCYHEL60H3PKcDAPZC9wSsDNKiYkOGt/bY0iU+U5BDSC8YAiQlw+QOh1ww6AWlSK07teR8epz3uBhUTQmKLAbT1QxyhJIeQXjDG4JEC+KrSCo9fBgDkp5tRMoLD4wp+ezNZUmMcJSEkXogCAxNFOAIKZCV429qsF6FTFEp0YoSSHDJsdV/EjwPoeGuKIVPnxcw0J6S2f7As5gCMAhvSt6dkgx7uotHQJZkBW/TrU0wGKDOnQjEaol9ZF1q3NRpkgx72sXlQTNr//sLhSWa4JhRANuhVlaOmjyhGA+xj81THEAmi0QSdAEgtVfD5ZehEhvScXHAhGbJMWU4s0OgoMiy134pq8cto8ctolWSYBG9o471k6TSSAo0wB2xQyrfCs+8dePa9A/nYzphtrBkpnrwMHP/dcmDcWE3q84/KReDvf4UvP1uT+jrSuq3R4MnLwL77F8E/KjfWoXQ3biyOPXEbPHkZqopR00d8+dnYd/8i1TFEgiDqIfid8LVtxus9/AlEvwOgNc9jhq7kkGGp662o3FQTckf54TuyFYo/uK6NLiUXxtGzIPtd8LuDMzckX1oswyaEDAGSN7gZr15gABM6jdEBaJyOluhKDhmWGAMy2m5F/SjLheJUJ3SQUNPQiKNVdThaVYe6pua4Xu9msCzVDZiy4DdgBw5rUp/p+5MwFEyF+XitJvV1pHVbo8FS3YDz73oOpu9PxjqUbtiBw5j2H4/DUt2gqhw1fcR8vBbn3/Wc6hiiQdAbYdQxJPnrO18lFjxD+mrwUEJXcsiwIAjotHaFIDCIXgcCRz+Fx+WAOScfwtR5kBUOf9s6N/G+3g0hJL7p9QYIkgueii8g+4JXiAVDMvSTLgPTJ4En4JeoeENJDklIHZMaxgAD3JA9P6xl0b6+jeIL3oqi21CEkEhjAgPnHCcbmmC3tQAAzClpGFvE2yY9UJITbZTkkIQjCICReaF0WLBPx/2o+WYLXI7ga7S+DSFEK1LbaugAoA8ooT2v2keMcK5AoQvHUUFJDkk4jAlQvK042WXBPp/nh5VJaX0bQkgstO955fPUQmi7kCOY0uATTJToRAENPCZDDmPBMTXtD1FEl12Ag/tJeZz2uNwFPNZcIzNR8dKD4BPGaVKfr3AE/F98CO+oHE3q60jrtkaDa2Qm9pTeCl/hiFiH0g2fMA5lzy6Ha6S6Xd7V9BHvqBzsKb1VdQxa6brnFe13FV10JYfEte4L9gEBgcHdtgKxKACZOh+UHsbbDOUF+6KJ63Xw52UDJqM29Rn0wNgzwE9+C2i8kbbWbY0GrtfBm5uO5DhY7K4bkxH+vCzwtvEmg6Wmj3CDHt7cdFX1x0LHPa+63r4C6BZWpFDqSOJKx6s0osiQLHg6Tb20yI1I4zbILdXwNJ6A0loHnc+K2m9/2An49L6tYLKfxtv0wtTUilHPvg5UazMlWX/aCnH5QzA0qPtDOBhatzUaTE2tmLjhE+hPW2MdSnfVJ3HGC5thalK3QaWaPmJoaMHEDZ+ojiFW2m9fiZ5a6Lx1oYdJ8EGnE7pcpY51tEPPkL+S89prr+Hll19GY2MjJk2ahEcffRRTp06NdVikH7pepWEMMMMN5ncEn4siBMUHz5HPIPtdAH5YoM9/5FPY7a3QtU399rlpvE1/6dw+pP+/f0FqtWtSn+j0QHx3C8Rz7wAMZk3qbKd1W6NB5/ZhxJ6jcF7viXUo3bBWOzL+eQC684tVlaOmj4guD0bsOYpTF89UFUOshG5ffbcNDnvw37CUjByMmX0lFMnWaaku0ZwOv2AOvUZXe8Ib0knORx99hFWrVqG0tBTTpk3Dxo0bsWTJEnzyySfIysqKdXjDWtd1aRhTwHnH54AJLjCf44fXuiQ17QnNyUZraPplZp4OY87goRVFaeo3ISQReLp+UetH4tM16QlSwDmtrtxuSCc5GzZswPXXX4+FCxcCAEpLS7Fjxw5s3rwZd9xxR4yjG5q6JiddPzAAwBhHxzudPSUwHdelYUyA0WSCz+sOrQohCCJ0zN/jVZr2pCaU0HSYfkkL9BFChou+Ep+ekh4GgJnTYQ0YoLS9lmwQoeuS5HT9NzuRrwgN2STH7/fj0KFDuPPOO0OvCYKAuXPnYu/evQMqKxrLawfvnXa9gap0ea3r82geE/59jAEG7oHsa7u3zQSYjCbwgOeHD5EggOmMoYSFMQFGowk+X4cERhSh4xIaj3wBn9eD5NQs5J55NpqPfgOvJ5jQtL/W5APcjgAAIEWnYCQYDMkZMCoidOY0gAlITs2CwoKDLs2WjE6vdX0ezWNiXX+kYjTbFSAlBRB1msRoZAEgJQWm1EykZKRo+jsabFtjfR47Pg+kZwMpKTDGY4xcB6SkICk9GywjN2J9ZCDlmFIzQzEYhsHnWtAZIOiN0BmTAL+r07+rZksacs8qARxeyAEZep0OqRmpkDr8+9zTv9miMR1+nanbFaFI/S2KRgLV37/bjA/RdaVPnz6N888/H2+++SZmzJgRev3pp5/GN998g7/97W8xjI4QQgghsUZjtQkhhBCSkIZskpORkQFRFGG1dp5WabVakZ2dHaOoCCGEEBIvhmySYzAYMGXKFOzevTv0mqIo2L17d6fbV4QQQggZnobswGMA+MUvfoEHHngAxcXFmDp1KjZu3AiPx4Nrrrkm1qERQgghJMaGdJJz+eWXo7m5GWvXrkVjYyMmT56M9evX0+0qQgghhAzd2VWEEEIIIX0ZsmNyCCGEEEL6QkkOIYQQQhISJTmEEEIISUiU5BBCCCEkIVGSE8Y333yDpUuXoqSkBBMnTsT27ds7/Zxzjueeew4lJSWYOnUqFi9ejBMnToQt97XXXsNFF12Es88+G9dddx3+/e9/R6kF4fXVRkmS8Pvf/x5XXXUVpk+fjpKSEtx///04ffp0n2U+//zzmDhxYqfHT37yk2g3pUfhzuGDDz7YLdYlS5aELTdezmG49nVtW/tj/fr1vZYZT+dv3bp1WLhwIWbMmIE5c+bg7rvvRmVlZadjfD4fSktLMXv2bMyYMQPLly9HU1NTn+UO9rMbDeHaaLPZ8OSTT+Kyyy7D1KlTceGFF2LlypVwOBx9ljvYvh1p/TmHN998c7dYH3vssT7LjZdzGK59J0+e7PVz+PHHH/dabrycPwB4/fXXcdVVV2HmzJmYOXMmFi1ahJ07d4Z+HrefQU76tGPHDv7MM8/wrVu38qKiIr5t27ZOP1+3bh2fNWsW37ZtGy8rK+NLly7lF110Efd6vb2WuWXLFj5lyhT+9ttv84qKCv7II4/wc845hzc1NUW7OT3qq412u50vXryYb9myhX///fd87969/Nprr+ULFizos8y1a9fyK664gjc0NIQeVqs12k3pUbhz+MADD/AlS5Z0itVms/VZZjydw3Dt69iuhoYG/vbbb/OJEyfy6urqXsuMp/P3y1/+km/evJmXl5fzsrIyfvvtt/MLL7yQu1yu0DGPPfYYv+CCC/iuXbv4gQMH+PXXX88XLVrUZ7mD+exGS7g2Hj16lC9btox/9tlnvKqqiu/atYvPnz+fL1++vM9yB9O3o6E/5/Cmm27ijzzySKdYHQ5Hn+XGyzkM175AINDtc/j888/z6dOnc6fT2Wu58XL+OOf8s88+4zt27ODHjx/nlZWV/JlnnuFTpkzh5eXlnPP4/QxSkjMAXf+AKIrC582bx9evXx96zW638+LiYv7hhx/2Ws61117LS0tLQ89lWeYlJSV83bp10Ql8AHr6I9nV/v37eVFRET916lSvx6xdu5b/9Kc/jXR4qvWW5Nx1110DKidez2F/zt9dd93Fb7nllj6PidfzxznnVquVFxUV8T179nDOg5+5KVOm8I8//jh0zLFjx3hRURHfu3dvj2UM9rOrla5t7MlHH33Ep0yZwiVJ6vWYwfRtLfTUvptuuomvXLmy32XE8znsz/m7+uqr+UMPPdRnOfF6/tqde+65/K233orrzyDdrlLh5MmTaGxsxNy5c0OvpaSkYNq0adi7d2+P7/H7/Th06FCn9wiCgLlz5/b6nnjjdDrBGENqamqfx1VVVaGkpAQXX3wx7rvvPtTW1moU4cDt2bMHc+bMwWWXXYYVK1agpaWl12OH8jlsamrCzp07ce2114Y9Nl7PX/stmrS0NADAwYMHIUlSp/Mxbtw45OfnY9++fT2WMZjPrpa6trEnTqcTFosFOl3fa7oOpG9rpbf2ffDBB5g9ezauvPJK/OEPf4DH4+m1jHg+h+HO38GDB1FWVtavz2E8nj9ZlrFlyxa43W7MmDEjrj+DQ3rF41hrbGwEAGRlZXV6PSsrq9d7kS0tLZBlucf3dL1HHY98Ph/WrFmDK664AhaLpdfjpk6dilWrVmHs2LFobGzEiy++iBtvvBEffPBBn++LhfPOOw+XXnopCgoKUFNTg2eeeQa33347/vd//xeiKHY7fiifw3fffRfJycmYP39+n8fF6/lTFAVPPfUUZs6ciaKiIgDBxE2v13dLurOyskKf0a4G89nVSk9t7Kq5uRkvvfQSFi1a1GdZA+3bWuitfVdeeSXy8/ORm5uLo0ePYs2aNTh+/DheeOGFHsuJ13PYn/P39ttvY9y4cZg5c2afZcXb+Tt69ChuuOEG+Hw+JCUl4cUXX8T48eNRVlYWt59BSnJIv0mShHvvvRecc5SWlvZ57AUXXBD670mTJmHatGn48Y9/jI8//hjXXXddtEMdkCuuuCL03+2D+y655JLQN6hEsnnzZlx11VUwGo19Hhev56+0tBQVFRV4/fXXYxZDtIVro9PpxJ133olx48Zh2bJlfZYVj327t/Z1TNgmTpyInJwcLF68GNXV1TjjjDO0DnPQwp0/r9eLDz/8EHfffXfYsuLt/I0dOxbvvfceHA4HPv30UzzwwAP461//qnkcA0G3q1TIyckBAFit1k6vW63WXvfPysjIgCiKA3pPPJAkCf/1X/+F2tpa/OUvfxnwt/nU1FSMGTMG1dXVUYowcgoLC5GRkYGqqqoefz5Uz+G3336L48ePDypJiYfz98QTT2DHjh3YuHEj8vLyQq9nZ2dDkiTY7fZOx1ut1tBntKvBfHa10Fsb2zmdTtx2221ITk7Giy++CL1eP6Dyw/XtaAvXvo6mTZsGAL3GGo/nsD/t++STT+D1evGzn/1swOXH+vwZDAaMHj0axcXFuO+++zBp0iS8+uqrcf0ZpCRHhYKCAuTk5GD37t2h15xOJ/bv348ZM2b0+B6DwYApU6Z0eo+iKNi9e3ev74m19gSnqqoKr7zyCjIyMgZchsvlQk1NTa8dPp7U19fDZrP1GutQPIdA8BL5lClTMGnSpAG/N5bnj3OOJ554Atu2bcPGjRtRWFjY6efFxcXQ6/WdzkdlZSVqa2sxffr0HssczGc3msK1sT2+JUuWQK/X47//+7/DXo3rSbi+HS39aV9XZWVlANBrrPF0DgfSvs2bN+Oiiy5CZmbmgOuJ1fnrjaIo8Pv98f0ZjNgQ5gTldDr54cOH+eHDh3lRURHfsGEDP3z4cGhm0bp16/g555zDt2/fzo8cOcLvuuuublPgbrnlFr5p06bQ8y1btvDi4mL+zjvv8GPHjvFHH32Un3POObyxsVHz9nHedxv9fj9funQpP//883lZWVmnqYw+ny9URtc2rl69mn/99de8pqaGf/fdd3zx4sV89uzZMZmG3Ff7nE4nX716Nd+7dy+vqanhu3bt4gsWLODz58/vs33xdA7D9VHOOXc4HHzatGn89ddf77GMeD5/K1as4LNmzeJff/11p/7n8XhCxzz22GP8wgsv5Lt37+YHDhzgixYt6jZ99bLLLuNbt24NPe/PZ1cr4drocDj4ddddx6+88kpeVVXV6ZhAINBjG/vbt+OhfVVVVfyFF17gBw4c4DU1NXz79u384osv5jfeeGOncuL1HPanj3LO+YkTJ/jEiRP5zp07eywnXs8f55yvWbOG79mzh9fU1PAjR47wNWvW8IkTJ/Ivv/yScx6/n0EakxPGwYMHccstt4Ser1q1CgCwYMECrF69Grfffjs8Hg8ee+wx2O12zJo1C+vXr+/0LaumpqbTiPjLL78czc3NWLt2LRobGzF58mSsX78+ZpdY+2rjsmXL8PnnnwMArr766k7ve/XVVzF79mwA3dtYX1+PX//617DZbMjMzMSsWbPw1ltvDerbi1p9te/xxx9HeXl56D5zbm4u5s2bh3vvvRcGgyH0nng+h+H6KABs2bIFnHNceeWVPZYRz+fvjTfeABBcLK6jVatW4ZprrgEAPPzwwxAEAffccw/8fj9KSkqwYsWKTscfP3680+J5/fnsaiVcGw8dOoT9+/cDAC699NJOx3z22WcoKCgA0LmNoij2q29rIVz72q8CvPrqq3C73Rg5ciTmz5/fbdxKvJ7D/vRRIHgVJy8vDyUlJT2WE6/nDwjeRnrggQfQ0NCAlJQUTJw4ES+//DLmzZsHIH4/g4xzziNWGiGEEEJInKAxOYQQQghJSJTkEEIIISQhUZJDCCGEkIRESQ4hhBBCEhIlOYQQQghJSJTkEEIIISQhUZJDCCGEkIRESQ4hhBBCEhIlOYQQQghJSJTkEEIIISQhUZJDCCGEkIRESQ4hhBBCEtL/BwE/efznm1lDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"neg, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:55.945527Z",
     "iopub.status.busy": "2025-10-04T22:10:55.945370Z",
     "iopub.status.idle": "2025-10-04T22:10:55.968419Z",
     "shell.execute_reply": "2025-10-04T22:10:55.967934Z",
     "shell.execute_reply.started": "2025-10-04T22:10:55.945511Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:55.969107Z",
     "iopub.status.busy": "2025-10-04T22:10:55.968962Z",
     "iopub.status.idle": "2025-10-04T22:10:57.207209Z",
     "shell.execute_reply": "2025-10-04T22:10:57.206656Z",
     "shell.execute_reply.started": "2025-10-04T22:10:55.969092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality diff --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:57.208032Z",
     "iopub.status.busy": "2025-10-04T22:10:57.207876Z",
     "iopub.status.idle": "2025-10-04T22:10:57.269471Z",
     "shell.execute_reply": "2025-10-04T22:10:57.269003Z",
     "shell.execute_reply.started": "2025-10-04T22:10:57.208015Z"
    }
   },
   "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>id</th>\n",
       "      <th>request_id</th>\n",
       "      <th>pair_quality</th>\n",
       "      <th>preference</th>\n",
       "      <th>loudness_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>5b6d6ad6-8f80-4436-b720-646724e442e1</td>\n",
       "      <td>00a06260-d9ac-47f3-90e3-de73f9a5c765</td>\n",
       "      <td>13.970017</td>\n",
       "      <td>False</td>\n",
       "      <td>-16.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>65747bd5-ef6e-4a71-930f-1236f77c9bf3</td>\n",
       "      <td>00a06260-d9ac-47f3-90e3-de73f9a5c765</td>\n",
       "      <td>18.598500</td>\n",
       "      <td>True</td>\n",
       "      <td>-15.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493488</th>\n",
       "      <td>3571b9b3-1375-4dff-b213-49cbb231deb7</td>\n",
       "      <td>00b1d023-c2bf-4351-9820-cee87f186b0a</td>\n",
       "      <td>18.528000</td>\n",
       "      <td>False</td>\n",
       "      <td>-16.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493489</th>\n",
       "      <td>e14c5320-90c0-44d6-b586-4bc47909dabf</td>\n",
       "      <td>00b1d023-c2bf-4351-9820-cee87f186b0a</td>\n",
       "      <td>23.780467</td>\n",
       "      <td>True</td>\n",
       "      <td>-13.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>1f65e366-cb34-4044-8361-bdad889643fc</td>\n",
       "      <td>011edbe0-17c3-4476-95b6-493d46c2510f</td>\n",
       "      <td>17.304400</td>\n",
       "      <td>False</td>\n",
       "      <td>-10.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>05730b7a-c655-4285-bcc6-c81859876b3e</td>\n",
       "      <td>011edbe0-17c3-4476-95b6-493d46c2510f</td>\n",
       "      <td>22.364600</td>\n",
       "      <td>True</td>\n",
       "      <td>-9.979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          id                            request_id  pair_quality  preference  loudness_abs\n",
       "524     5b6d6ad6-8f80-4436-b720-646724e442e1  00a06260-d9ac-47f3-90e3-de73f9a5c765     13.970017       False       -16.076\n",
       "525     65747bd5-ef6e-4a71-930f-1236f77c9bf3  00a06260-d9ac-47f3-90e3-de73f9a5c765     18.598500        True       -15.515\n",
       "493488  3571b9b3-1375-4dff-b213-49cbb231deb7  00b1d023-c2bf-4351-9820-cee87f186b0a     18.528000       False       -16.420\n",
       "493489  e14c5320-90c0-44d6-b586-4bc47909dabf  00b1d023-c2bf-4351-9820-cee87f186b0a     23.780467        True       -13.860\n",
       "942     1f65e366-cb34-4044-8361-bdad889643fc  011edbe0-17c3-4476-95b6-493d46c2510f     17.304400       False       -10.613\n",
       "943     05730b7a-c655-4285-bcc6-c81859876b3e  011edbe0-17c3-4476-95b6-493d46c2510f     22.364600        True        -9.979"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_requests = df[(df[\"pair_quality_diff\"] > 0.2) & (df[\"preference\"])][\n",
    "    \"request_id\"\n",
    "].unique()\n",
    "df[df[\"request_id\"].isin(subset_requests)][\n",
    "    [\"id\", \"request_id\", \"pair_quality\", \"preference\", \"loudness_abs\"]\n",
    "].head(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:57.270339Z",
     "iopub.status.busy": "2025-10-04T22:10:57.270191Z",
     "iopub.status.idle": "2025-10-04T22:10:59.992637Z",
     "shell.execute_reply": "2025-10-04T22:10:59.992084Z",
     "shell.execute_reply.started": "2025-10-04T22:10:57.270324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\"Spectral Centroid\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:10:59.993453Z",
     "iopub.status.busy": "2025-10-04T22:10:59.993298Z",
     "iopub.status.idle": "2025-10-04T22:11:01.755377Z",
     "shell.execute_reply": "2025-10-04T22:11:01.754785Z",
     "shell.execute_reply.started": "2025-10-04T22:10:59.993437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "# take the relative centroid diff\n",
    "df[\"spectral_centroid_diff\"] = df[\"spectral_centroid\"].diff() / df[\"spectral_centroid\"]\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95, 98]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Spectral Centroid Difference ratio --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:01.756256Z",
     "iopub.status.busy": "2025-10-04T22:11:01.756089Z",
     "iopub.status.idle": "2025-10-04T22:11:08.279464Z",
     "shell.execute_reply": "2025-10-04T22:11:08.278777Z",
     "shell.execute_reply.started": "2025-10-04T22:11:01.756239Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    522418\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    261209\n",
      "True     261209\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-ahi-up-3         333110\n",
      "chirp-bass-up-3        157000\n",
      "chirp-v4-up-u-d-2-5     32308\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-ahi-up-3         166555\n",
      "            chirp-bass-up-3         78500\n",
      "            chirp-v4-up-u-d-2-5     16154\n",
      "True        chirp-ahi-up-3         166555\n",
      "            chirp-bass-up-3         78500\n",
      "            chirp-v4-up-u-d-2-5     16154\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    522418\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row[\"continue_at\"]\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id], row[\"continue_at\"]\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:08.280367Z",
     "iopub.status.busy": "2025-10-04T22:11:08.280203Z",
     "iopub.status.idle": "2025-10-04T22:11:25.655995Z",
     "shell.execute_reply": "2025-10-04T22:11:25.655403Z",
     "shell.execute_reply.started": "2025-10-04T22:11:08.280351Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 95898 duplicated prompts 47949 unique requests\n",
      "Found 26662 request_ids with duplicate prompts but not highest play counts in their group\n",
      "['3694bcea-f952-4c1d-a3f3-1161176211a4', '4318a5e7-bb31-47fa-8f72-253b1c94ec55', '929f8913-2e70-4426-8cc7-5a74631b2f98', 'b18abe52-46f7-48d2-9de4-edaf215cbd85', 'e3fa910e-b54a-481a-b696-8ab6fa843839', '24a3f4e2-2a17-4dbe-816c-0fb59b52e6a3', '53f534bd-fa7f-412f-9e69-2c96c7f0b04d', 'a3919ff3-2090-4880-957c-9d3e3375b290', 'f4891a6e-f5fc-457f-befe-af68fe11e6de', '0b73bf65-8811-440f-b14c-a14e2221aea4']\n",
      "Before dedup user gen requests 522418\n",
      "After dedup user gen requests 469094\n"
     ]
    }
   ],
   "source": [
    "# Find duplicated prompts with count > 2\n",
    "df[\"tags\"] = df[\"metadata\"].apply(lambda x: x.get(\"tags\", \"\"))\n",
    "duplicate_entries = df.groupby([\"user_id\", \"prompt_text\", \"tags\"]).filter(\n",
    "    lambda x: len(x) > 2\n",
    ")\n",
    "print(\n",
    "    \"Found\",\n",
    "    len(duplicate_entries),\n",
    "    \"duplicated prompts\",\n",
    "    len(duplicate_entries[\"request_id\"].unique()),\n",
    "    \"unique requests\",\n",
    ")\n",
    "\n",
    "# Group by user_id, prompt_text, and tags to find duplicate prompt groups\n",
    "prompt_groups = duplicate_entries.groupby([\"user_id\", \"prompt_text\", \"tags\"])\n",
    "\n",
    "# For each prompt group, find the request_id with the highest total reaction_play_count\n",
    "low_play_count_request_ids = []\n",
    "for prompt_key, prompt_group in prompt_groups:\n",
    "    # Get the sum of reaction_play_count for each request_id in this group\n",
    "    request_play_counts = prompt_group.groupby(\"request_id\")[\n",
    "        \"reaction_play_count\"\n",
    "    ].sum()\n",
    "\n",
    "    # Find the max play count in this group\n",
    "    max_play_count = request_play_counts.max()\n",
    "\n",
    "    # Add request_ids that don't have the max play count to our filter list\n",
    "    lower_play_count_request_ids = request_play_counts[\n",
    "        request_play_counts < max_play_count\n",
    "    ].index.tolist()\n",
    "    low_play_count_request_ids.extend(lower_play_count_request_ids)\n",
    "\n",
    "# Display the filtered request IDs\n",
    "print(\n",
    "    f\"Found {len(low_play_count_request_ids)} request_ids with duplicate prompts but not highest play counts in their group\"\n",
    ")\n",
    "print(\n",
    "    low_play_count_request_ids[:10]\n",
    "    if len(low_play_count_request_ids) > 10\n",
    "    else low_play_count_request_ids\n",
    ")\n",
    "print(\"Before dedup user gen requests\", df.shape[0])\n",
    "df = df[~df[\"request_id\"].isin(low_play_count_request_ids)]\n",
    "print(\"After dedup user gen requests\", df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:25.656742Z",
     "iopub.status.busy": "2025-10-04T22:11:25.656587Z",
     "iopub.status.idle": "2025-10-04T22:11:26.593822Z",
     "shell.execute_reply": "2025-10-04T22:11:26.593286Z",
     "shell.execute_reply.started": "2025-10-04T22:11:25.656727Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    469094.000000\n",
      "mean        518.196796\n",
      "std         712.588379\n",
      "min          20.000000\n",
      "25%         137.000000\n",
      "50%         301.000000\n",
      "75%         602.000000\n",
      "max       21305.000000\n",
      "Name: user_n_clips, dtype: float64\n",
      "count    469094.000000\n",
      "mean         15.474707\n",
      "std          24.291204\n",
      "min           2.000000\n",
      "25%           4.000000\n",
      "50%           8.000000\n",
      "75%          18.000000\n",
      "max         354.000000\n",
      "Name: user_n_clips, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"user_n_clips\"].describe())\n",
    "# notice that this is a regroup of to count just remaster\n",
    "df[\"user_n_clips\"] = df.groupby(\"user_id\")[\"s3_id\"].transform(\"count\")\n",
    "print(df[\"user_n_clips\"].describe())\n",
    "# BREAK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.035167Z",
     "start_time": "2024-05-16T13:59:40.801098Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:26.594510Z",
     "iopub.status.busy": "2025-10-04T22:11:26.594364Z",
     "iopub.status.idle": "2025-10-04T22:11:27.426972Z",
     "shell.execute_reply": "2025-10-04T22:11:27.426379Z",
     "shell.execute_reply.started": "2025-10-04T22:11:26.594495Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 214998 positive 16108\n",
      "total pair requests 234547 selected pair requests 14621 frac 0.062\n"
     ]
    }
   ],
   "source": [
    "normal_pos_play_count = 5\n",
    "# this is lower, cause a concat is probably already ensuring that it is good\n",
    "concat_pos_play_count = 2\n",
    "# this is a filter on the concated clip\n",
    "concat_total_play_count = 5\n",
    "\n",
    "neg_filter_selection_mask = (\n",
    "    (~df[\"preference\"])  # get basics aligned\n",
    "    & (df[\"reaction_play_count\"] >= 1)  # has to be played once\n",
    "    # & (df[\"play_count\"] <= 3)  # if it is actually bad, shouldn't be listened often\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    # & (df[\"duration\"] <= 60)  # can't be badly long\n",
    "    & (df[\"has_continue_and_start_continue_at\"].isna())  # won't have any continues\n",
    "    & (df[\"norm_play_frac\"] <= 2.1)\n",
    "    # & (df[\"sum_total_play_duration_5\"] >= 31)  # this cut doesn't matter as much tbh\n",
    "    # & (df[\"dislike_count\"] >= 1) # this is kinda strict\n",
    "    #     & (\n",
    "    #         (df_slice[\"is_in_playlist\"] == False)\n",
    "    #         & (df_slice[\"concat_in_playlist\"] == False)\n",
    "    #     )  # can't be part of a playlist -- otherwise there are some like signal in it?\n",
    ")\n",
    "pos_filter_selectin_mask = (\n",
    "    (df[\"preference\"])  # get basics aligned\n",
    "    & (\n",
    "        df[\"good_continue_at\"]\n",
    "    )  # if continue, needs to continue off a certain percentage\n",
    "    & (df[\"reaction_play_count\"] >= 1)\n",
    "    & (df[\"play_rel_diff\"] >= 0)  # this is more like quality assurance\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    & (df[\"dislike_count\"] == 0)  # can't have dislikes\n",
    "    & (df[\"flag_count\"] == 0)  # can't have issues\n",
    "    & (\n",
    "        (\n",
    "            (df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= concat_pos_play_count)\n",
    "            & (df[\"concat_play_counts\"] >= concat_total_play_count)\n",
    "        )\n",
    "        | (\n",
    "            (~df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= normal_pos_play_count)\n",
    "            & (\n",
    "                df[\"norm_play_frac\"] >= 5.1\n",
    "            )  # this is a bit of a luxury cut...not for now...\n",
    "            & (df[\"sum_total_play_duration_5\"] >= 31)\n",
    "            & (\n",
    "                df[\"norm_play_frac\"] >= df[\"reaction_play_count\"] / 3\n",
    "            )  # play duration is not low on average\n",
    "        )\n",
    "    )\n",
    "    & (abs(df[\"mean_ear_score_diff\"]) >= 1)\n",
    "    & (abs(df[\"mean_ear_score_diff_ratio\"]) >= 0.05)\n",
    "    & (df[\"source\"] == \"web\")\n",
    "    & (df[\"cer_diff\"] < 0.05)\n",
    "    # & (df[\"mean_shimmer_score_diff\"] < 1.0)\n",
    "    # & (df[\"norm_play_frac\"] >= 1.9)\n",
    "    # & (df[\"user_n_clips\"] >= 10)  # user needs to have genereated at least 20\n",
    "    # & (df[\"duration_rel_diff\"] < 10) # positive isn't just longer\n",
    "    # & ((df[\"task\"] == \"\") | (df[\"task\"] == \"extend\"))\n",
    "    # & (\n",
    "    #     (df[\"upvote_count\"] >= 1)\n",
    "    #     | (df[\"reaction_play_count\"] >= 5)\n",
    "    #     | (df[\"concat_play_counts\"] >= 5)\n",
    "    # )\n",
    "    # & (df[\"pos_diff_preference\"] == 2)\n",
    "    # & ((0 < df[\"similarity\"]) &  (df[\"similarity\"] <= 0.99))\n",
    "    # & (\n",
    "    #     (df[\"cer_diff_preference\"] < 0.25) & (df[\"cer\"] < 0.8)\n",
    "    # )  # cut on hoot cer difference and abs cer\n",
    "    # & (df[\"pair_quality\"] > 0.31)  # bottom 5%\n",
    "    # & ((df[\"total_shimmer_score\"] < 1) | (df[\"shimmer_score_diff\"] < 0.4))\n",
    "    # & (df[\"stereo_width_diff\"] > -0.2)  # cut off bottom 5%\n",
    "    # & (df[\"spectral_centroid_diff\"] < 0.25)  # crop off the top 5%\n",
    ")\n",
    "print(\n",
    "    \"negative\",\n",
    "    sum(neg_filter_selection_mask),\n",
    "    \"positive\",\n",
    "    sum(pos_filter_selectin_mask),\n",
    ")\n",
    "\n",
    "neg_filter_requests = df[neg_filter_selection_mask][\"request_id\"].unique()\n",
    "pos_filter_requests = df[pos_filter_selectin_mask][\"request_id\"].unique()\n",
    "# looking for very strong signal here:\n",
    "# listen to the positive/negative more than once\n",
    "# disliked one of the clips\n",
    "unique_requests = set(pos_filter_requests).intersection(neg_filter_requests)\n",
    "print(\n",
    "    \"total pair requests\",\n",
    "    df[\"request_id\"].nunique(),\n",
    "    \"selected pair requests\",\n",
    "    len(unique_requests),\n",
    "    f\"frac {len(unique_requests) / df['request_id'].nunique():.3f}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.250737Z",
     "start_time": "2024-05-16T13:59:41.036434Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:27.427887Z",
     "iopub.status.busy": "2025-10-04T22:11:27.427728Z",
     "iopub.status.idle": "2025-10-04T22:11:27.582058Z",
     "shell.execute_reply": "2025-10-04T22:11:27.581480Z",
     "shell.execute_reply.started": "2025-10-04T22:11:27.427871Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 14621 clips 29242 total khrs 1.776; N gpus for 1000 iters 1.828; 4 gpus for x iters 456.906; n unique users 10726 n pro users 10103\n"
     ]
    }
   ],
   "source": [
    "df_slice = df[df[\"request_id\"].isin(set(unique_requests))].copy()\n",
    "print(\n",
    "    f\"{os.path.basename(OUT_DATA_DIR)} requests\",\n",
    "    df_slice[\"request_id\"].nunique(),\n",
    "    \"clips\",\n",
    "    df_slice.shape[0],\n",
    "    f\"total khrs {sum(df_slice['duration'] / 3600 / 1000):.3f};\",\n",
    "    f\"N gpus for 1000 iters {df_slice.shape[0] / 8 / 2 / 1000:.3f};\",\n",
    "    f\"4 gpus for x iters {df_slice.shape[0] / 8 / 2 / 4:.3f};\",\n",
    "    f\"n unique users {df_slice['user_id'].nunique()}\",\n",
    "    f\"n pro users {df_slice[df_slice['is_pro_user']]['user_id'].nunique()}\",\n",
    ")\n",
    "# up t7 requests 37735 clips 75470 total khrs 3.964; N gpus for 1000 iters 4.717; 4 gpus for x iters 1179.219; n unique users 19093 n pro users 16217\n",
    "# up t17 requests 49262 clips 98524 total khrs 5.187; N gpus for 1000 iters 6.158; 4 gpus for x iters 1539.438; n unique users 24093 n pro users 20326\n",
    "# up v2 t2 requests 10772 clips 21544 total khrs 1.154; N gpus for 1000 iters 1.347; 4 gpus for x iters 336.625; n unique users 6527 n pro users 6027\n",
    "# up v3 t10 requests 27102 clips 54204 total khrs 2.938; N gpus for 1000 iters 3.388; 4 gpus for x iters 846.938; n unique users 13932 n pro users 12187\n",
    "# up v4 t1  requests 3201 clips 6402 total khrs 0.343; N gpus for 1000 iters 0.400; 4 gpus for x iters 100.031; n unique users 2363 n pro users 2321\n",
    "# up v4 t7  requests 31797 clips 63594 total khrs 3.384; N gpus for 1000 iters 3.975; 4 gpus for x iters 993.656; n unique users 16398 n pro users 15325\n",
    "# up v5 t2  requests 20030 clips 40060 total khrs 2.108; N gpus for 1000 iters 2.504; 4 gpus for x iters 625.938; n unique users 12395 n pro users 9156\n",
    "# up v6 t11  requests 43842 clips 87684 total khrs 4.588; N gpus for 1000 iters 5.480; 4 gpus for x iters 1370.062; n unique users 24777 n pro users 16668\n",
    "# v2 v1 t0  requests 3688 clips 7376 total khrs 0.381; N gpus for 1000 iters 0.461; 4 gpus for x iters 115.250; n unique users 3261 n pro users 2141\n",
    "# v2 v1 t1-5  requests 5406 clips 10812 total khrs 0.565; N gpus for 1000 iters 0.676; 4 gpus for x iters 168.938; n unique users 4655 n pro users 3180\n",
    "# v2 v1 t1-6   requests 9134 clips 18268 total khrs 0.952; N gpus for 1000 iters 1.142; 4 gpus for x iters 285.438; n unique users 7658 n pro users 5242\n",
    "# v2 v1 t1-7  requests 12274 clips 24548 total khrs 1.282; N gpus for 1000 iters 1.534; 4 gpus for x iters 383.562; n unique users 10036 n pro users 6818\n",
    "# v2 v1 t1-17  requests 14235 clips 28470 total khrs 1.497; N gpus for 1000 iters 1.779; 4 gpus for x iters 444.844; n unique users 11018 n pro users 8181\n",
    "# v2 v1 t1-18  requests 10007 clips 20014 total khrs 1.044; N gpus for 1000 iters 1.251; 4 gpus for x iters 312.719; n unique users 7998 n pro users 5831\n",
    "# v2 v3 t1   requests 13157 clips 26314 total khrs 1.430; N gpus for 1000 iters 1.645; 4 gpus for x iters 411.156; n unique users 8409 n pro users 8193\n",
    "# v2 v3 t2   requests 22601 clips 45202 total khrs 2.465; N gpus for 1000 iters 2.825; 4 gpus for x iters 706.281; n unique users 13991 n pro users 13639\n",
    "# v2 v3 t3  requests 48158 clips 96316 total khrs 5.253; N gpus for 1000 iters 6.020; 4 gpus for x iters 1504.938; n unique users 26195 n pro users 25433\n",
    "# v2 v3 t4  requests 62132 clips 124264 total khrs 6.772; N gpus for 1000 iters 7.766; 4 gpus for x iters 1941.625; n unique users 32164 n pro users 31090\n",
    "# v2 v3 t8  requests 65469 clips 130938 total khrs 7.135; N gpus for 1000 iters 8.184; 4 gpus for x iters 2045.906; n unique users 33619 n pro users 32454\n",
    "# v2 v3 t9 requests 33031 clips 66062 total khrs 3.666; N gpus for 1000 iters 4.129; 4 gpus for x iters 1032.219; n unique users 21173 n pro users 20503\n",
    "# v2 v3 t10  requests 36607 clips 73214 total khrs 4.066; N gpus for 1000 iters 4.576; 4 gpus for x iters 1143.969; n unique users 23179 n pro users 22385\n",
    "# v2 t3 t11  requests 43078 clips 86156 total khrs 4.788; N gpus for 1000 iters 5.385; 4 gpus for x iters 1346.188; n unique users 26646 n pro users 25611\n",
    "# v2 t3 t14  requests 45156 clips 90312 total khrs 5.018; N gpus for 1000 iters 5.644; 4 gpus for x iters 1411.125; n unique users 27724 n pro users 26626\n",
    "# v2 t3 t20  requests 48822 clips 97644 total khrs 5.425; N gpus for 1000 iters 6.103; 4 gpus for x iters 1525.688; n unique users 29583 n pro users 28314\n",
    "# v2 t3 t22  requests 173478 clips 346956 total khrs 18.892; N gpus for 1000 iters 21.685; 4 gpus for x iters 5421.188; n unique users 67818 n pro users 64118\n",
    "# v2 t4 t1   requests 5563 clips 11126 total khrs 0.611; N gpus for 1000 iters 0.695; 4 gpus for x iters 173.844; n unique users 4514 n pro users 4478\n",
    "# v2 t4 t3  requests 4667 clips 9334 total khrs 0.513; N gpus for 1000 iters 0.583; 4 gpus for x iters 145.844; n unique users 3884 n pro users 3851\n",
    "# v2 t4 t4  requests 5563 clips 11126 total khrs 0.611; N gpus for 1000 iters 0.695; 4 gpus for x iters 173.844; n unique users 4514 n pro users 4478\n",
    "# v2 t4 t5   requests 6589 clips 13178 total khrs 0.718; N gpus for 1000 iters 0.824; 4 gpus for x iters 205.906; n unique users 5204 n pro users 5160\n",
    "# v2 t4 t9  requests 4836 clips 9672 total khrs 0.534; N gpus for 1000 iters 0.605; 4 gpus for x iters 151.125; n unique users 3827 n pro users 3799\n",
    "# v2 t4 t16  requests 11003 clips 22006 total khrs 1.217; N gpus for 1000 iters 1.375; 4 gpus for x iters 343.844; n unique users 7822 n pro users 7724\n",
    "# v2 t4 t17 requests 15605 clips 31210 total khrs 1.724; N gpus for 1000 iters 1.951; 4 gpus for x iters 487.656; n unique users 10557 n pro users 10374\n",
    "# v2 t4 t18  requests 21479 clips 42958 total khrs 2.385; N gpus for 1000 iters 2.685; 4 gpus for x iters 671.219; n unique users 13858 n pro users 13589\n",
    "# v2 t4 t19  requests 19816 clips 39632 total khrs 2.206; N gpus for 1000 iters 2.477; 4 gpus for x iters 619.250; n unique users 13045 n pro users 12793\n",
    "# v2 t4 t20  requests 17780 clips 35560 total khrs 1.979; N gpus for 1000 iters 2.223; 4 gpus for x iters 555.625; n unique users 12041 n pro users 11810\n",
    "# v2 t4 t21  requests 69490 clips 138980 total khrs 7.583; N gpus for 1000 iters 8.686; 4 gpus for x iters 2171.562; n unique users 33013 n pro users 32231\n",
    "# v2 t4 t23  requests 20702 clips 41404 total khrs 2.273; N gpus for 1000 iters 2.588; 4 gpus for x iters 646.938; n unique users 10816 n pro users 10610\n",
    "# v2 t4 t24  requests 9801 clips 19602 total khrs 1.079; N gpus for 1000 iters 1.225; 4 gpus for x iters 306.281; n unique users 2464 n pro users 2442\n",
    "# v2 t4 t25   requests 14973 clips 29946 total khrs 1.649; N gpus for 1000 iters 1.872; 4 gpus for x iters 467.906; n unique users 5062 n pro users 4978\n",
    "# v2 t4 t26   requests 20952 clips 41904 total khrs 2.274; N gpus for 1000 iters 2.619; 4 gpus for x iters 654.750; n unique users 4129 n pro users 4071\n",
    "# v2 t4 t27  requests 7953 clips 15906 total khrs 0.870; N gpus for 1000 iters 0.994; 4 gpus for x iters 248.531; n unique users 1940 n pro users 1919\n",
    "# v2 t4 t28   requests 7937 clips 15874 total khrs 0.890; N gpus for 1000 iters 0.992; 4 gpus for x iters 248.031; n unique users 2152 n pro users 2135\n",
    "# v2 t4 t39  requests 12626 clips 25252 total khrs 1.418; N gpus for 1000 iters 1.578; 4 gpus for x iters 394.562; n unique users 3352 n pro users 3282\n",
    "# v2 t5 t1  requests 18985 clips 37970 total khrs 2.351; N gpus for 1000 iters 2.373; 4 gpus for x iters 593.281; n unique users 3924 n pro users 3775\n",
    "# v2 t5 t2  requests 23185 clips 46370 total khrs 2.863; N gpus for 1000 iters 2.898; 4 gpus for x iters 724.531; n unique users 4810 n pro users 4648\n",
    "# v2 t5 t3 requests 9128 clips 18256 total khrs 1.126; N gpus for 1000 iters 1.141; 4 gpus for x iters 285.250; n unique users 2505 n pro users 2419\n",
    "# v2 t5 t4  requests 20065 clips 40130 total khrs 2.429; N gpus for 1000 iters 2.508; 4 gpus for x iters 627.031; n unique users 14266 n pro users 13588\n",
    "# v2 t5 t6  requests 9471 clips 18942 total khrs 1.161; N gpus for 1000 iters 1.184; 4 gpus for x iters 295.969; n unique users 7141 n pro users 6824\n",
    "# v2 t5 t7  requests 30713 clips 61426 total khrs 3.674; N gpus for 1000 iters 3.839; 4 gpus for x iters 959.781; n unique users 21237 n pro users 20118\n",
    "# v2 t5 t8  requests 9128 clips 18256 total khrs 1.126; N gpus for 1000 iters 1.141; 4 gpus for x iters 285.250; n unique users 2505 n pro users 2419\n",
    "# v2 t5 t9  requests 25924 clips 51848 total khrs 3.138; N gpus for 1000 iters 3.240; 4 gpus for x iters 810.125; n unique users 17055 n pro users 16042\n",
    "# v2 t5 t10  requests 9557 clips 19114 total khrs 1.166; N gpus for 1000 iters 1.195; 4 gpus for x iters 298.656; n unique users 7062 n pro users 6727"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.277006Z",
     "start_time": "2024-05-16T13:59:41.252105Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:27.582794Z",
     "iopub.status.busy": "2025-10-04T22:11:27.582639Z",
     "iopub.status.idle": "2025-10-04T22:11:27.607662Z",
     "shell.execute_reply": "2025-10-04T22:11:27.607156Z",
     "shell.execute_reply.started": "2025-10-04T22:11:27.582779Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (5560, 124)\n"
     ]
    }
   ],
   "source": [
    "test_mask = (df_slice[\"preference\"]) & (\n",
    "    (df_slice[\"is_in_playlist\"]) | (df_slice[\"concat_in_playlist\"])\n",
    ")\n",
    "print(\"positive in playlist\", df_slice[test_mask].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:27.608296Z",
     "iopub.status.busy": "2025-10-04T22:11:27.608156Z",
     "iopub.status.idle": "2025-10-04T22:11:28.380703Z",
     "shell.execute_reply": "2025-10-04T22:11:28.380213Z",
     "shell.execute_reply.started": "2025-10-04T22:11:27.608281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ll2I0Gs3StGnThnPnzrFv3z7T9Xv11VfN0iQnJzNjxgweffRR6tevz+DBg4mKisq2Xw/SqVMnnn/+eY4ePcrff/9tOn7vGJT0+xIaGsoff/xhal/62BhN09i4caPpeLro6GimTZtGq1atqFOnDk8++SQrV6406/u9Yx4++eQT2rZtS926dblw4QKQ9hh/2LBhNG7cmLp169K1a1e2b99u1of0dhw8eDBH1+fPP//klVdeISgoiAYNGtCtWze+++47szQ5/V6z1blz52jdujWDBg1i+/btpKamWl1W9erVzYITSAuYW7VqxY0bN0z/jt555x02btwIYLZZ6v22bNlC27ZtqVOnDt26dePYsWNWty0z58+f5/z587zwwgtmr3NefvllNE3j559/fmD+OnXqmAUnACVKlKBRo0ZcvHjR7Pi6devw8fGhV69eaJpGXFyc/ToicoU8QRHZ8vDw4MaNG0RGRmZY5fZes2fP5v333ycwMJAXXngBgAoVKgBpY1heeOEFFEWhZ8+eeHl58ddff/Hee+8RGxub4S+apUuX4uTkRJ8+fUhOTsbJyYndu3fTr18/6tSpw5AhQ1AUha+++orevXuzadMmAgMDgbQf+D179qRo0aL07dsXvV7Pli1bePXVV9mwYYPZ+jP3q1+/Pnq9noMHD/L8888DcPDgQYoUKULdunWpU6cOhw4don379sB/gcy9Acr9wsPD6dWrFwaDgf79++Pm5sbnn3+eIeAbN24cU6ZMoUiRIgwcOBAAHx8fszRTp06lWLFiDBkyhGvXrrFu3TomT57MggULsqw/Jzp16sSWLVvYtWuX2dOgdFWrVmX27NnMmDGD0qVL8/rrrwNQq1YtZs+ezZgxY2jevDnPPfecKU9CQgKvvPIKN2/epEePHpQpU4bDhw8zf/58wsPDee+998zq+Oqrr0hKSuKFF17A2dkZT09Pzp07x0svvUSpUqXo168fRYoU4X//+x+DBw9m0aJFGfafysn1+eqrrxg3bhzVq1dnwIABeHh4EBwczM6dO3n22WcBcvy9Zg81a9Zk0KBBprE9vr6+dOnShW7dulGpUiW71BEeHo6bmxtubm4AvPjii9y6dYu///6b2bNnZ5rn+++/Jy4ujhdffBFFUVi9ejVDhw7lt99+y/TpmjVOnToFkGG8SKlSpShdurRpZqKlwsPDKV68uNmx3bt3ExQUxPr161m2bBl37tzB19eXgQMH8sorr1hVj8hlmsgVY8eO1QYNGvSwm2EXV65c0RYvXqwtXrxY++KLL7Rdu3ZpV65c0VJTUzOkrV+/vjZ27NgMx8eNG6c1b95ci4qKMjs+YsQIrWHDhlpCQoKmaZq2Z88ezd/fX3viiSdMxzRN04xGo9auXTvtjTfe0IxGo+l4QkKC1qZNG+311183HXvzzTe12rVrayEhIaZjN2/e1IKCgrSePXtm299u3bppbdu2NX3+4IMPtFdffVXTNE2bPXu21q1bN9O5oUOHavXq1dNSUlJMx1q3bm12DaZNm6b5+/trR48eNR2LjIzUGjZsqPn7+2tXr141He/YsaP2yiuvZGjT1q1bNX9/f+21114z6//06dO1mjVratHR0Q/s00cffaT5+/trkZGRmZ6/e/eu5u/vrw0ePNh0bOzYsVrr1q3N0rVu3Vrr379/hvz+/v7apEmTzI4tWbJEq1+/vnbp0iWz43PnztVq1qyphYWFaZqmaVevXtX8/f21Bg0aZGhf7969tWeeeUZLSkoyHTMajdqLL76otWvXznQsp9cnOjpaCwoK0p5//nktMTHRrK70fJZ8r9mT0WjUdu/erY0aNUoLDAzU/P39tZ49e2rbtm0z+7dgqcuXL2t169bVRo8ebXZ80qRJmr+/f4b06fejcePG2p07d0zHf/vtN83f31/bsWOH1W253+rVqzV/f3/T98K9unXrpr3wwgsWl7l//34tICBAW7BggenYnTt3TH2qX7++tnr1au2HH37Q+vTpo/n7+2ufffaZTf0QuaNQv+LZv38/AwcOpEWLFgQEBGS6Y7CmaSxcuJAWLVoQGBjIa6+9xuXLl03n0x9PWxvpWyMpKYlJkybRpEkTgoKCGDp0aLZ79fzyyy+88cYbpmmgD2qvpmn07dvXdE0qVKhA9+7dqVy5MhERERw6dIhvvvmGzZs3m6218qDyfvnlF9q0aYOmaURFRZm+WrRoQUxMDCdPnjTL07lzZ7PXR8HBwVy+fJlnn32W27dvm/LHx8fTtGlT9u/fj9FoxGAw8Pfff9O2bVvKly9vyl+yZEmeeeYZDh48mO2u0w0bNiQkJMS0su/hw4dNC+U1aNCA4OBgEhISgLQnKIGBgQ+cbfDnn39Sv359s7+6vby8TH+tWyL9KVS6Ro0aYTAYuHbtmsVl3atIkSIAdn3s/dNPP9GwYUOKFStmds+bNWuGwWBg//79ZunbtWtn9nrizp077Nmzhw4dOhAbG2vKf/v2bVq0aMHly5czDG7M7vr8/fffxMXF0b9//wxPsNLz5fR7zd4UReHRRx9lzpw5/P3330yaNInk5GTGjh1LixYtmDBhgkVrMUHaU6zhw4fj6urKyJEjLcr79NNP4+npafrcqFEjIO2Vp72kj2HLbKyRi4uLxWPcIiMjGTlyJOXKlaNv376m4/Hx8UDa99S0adPo06cPTz/9NCtXrqRatWoZZu2JvKFQv+KJj48nICCAbt26ZZiVkG7VqlV8+umnzJw5k3LlyrFw4UL69OnDjz/+mOmYDEeYPn06f/75JwsWLMDDw4MpU6YwZMgQNm/enGWe+Ph4GjRoQIcOHXj//fcfWP66desyzEIpVaoUTz/9NAaDgYiICC5evMjZs2fZt28f9erVe+D02qioKKKjo9myZQtbtmzJMs29ypUrZ/Y5PSgcO3ZslvXExMSQnJxMQkJCpu2pWrUqRqOR69evU7169SzLadiwIZ988gmHDh2iadOmnDt3jlGjRgEQFBRkGhjr5+dHeHi46VVQVsLCwjJ9rWTNlGQ/Pz+zz+kDU6Ojoy0u617pP8Dvf59viytXrnDmzJksp29md89DQkJMfyAsXLgw0zIiIyMpVaqU6XN21yd9G4sH3f+cfq/d+8v7XvdvwOnh4YGTk1OG/np6emY5CNjd3Z0ePXrQpUsXli1bxvLly9m8eTM9evTIst77GQwGRowYwfnz51m1apXZdcqJe2d0pbcXrPteu3PnDikpKabPrq6ueHh4mP4ISd9x/V5JSUlmf6RkJz4+ngEDBhAXF8emTZvMvpfTf1Y7OTmZXs9C2q7uHTp0YNGiRYSFhWX4/hEPV6EOUFq1akWrVq2yPK9pGuvXr2fQoEGmabOzZ8+mWbNm/Pbbb3Ts2NG0VHz6ImmNGzfm008/NZWxZs0a1q5dS0pKCk8//TTjxo2z6f1tTEwMW7duZe7cuaYf/NOnT+fpp5/myJEj1K9fP9N86e3LblGw4OBgPv74Y7Zu3ZrpKrM6nY5SpUpRqlQpfHx8OH78OL///vsDf9mm/7XZqVOnLBe7u3+A3v0/mLR/p7uOGTOGmjVrZlpG+vRZW6WPJzl48KCpHelPULy8vKhUqRIHDx7k+vXrZukdIauZTJoF+0Bl5uzZs8B/Y4bswWg00rx5c7O/ZO91//iK++95+vfNG2+8QcuWLTMt4/722uP65PR7LSv3/7uZMWMGjRs3zrCtxPr162nSpEmmZRw7doytW7fy448/Eh0dTb169ejevfsDZ7Tc7/333+ePP/4w+1lhCZ1Ol+lxa77Xhg4dyr59+0yfu3TpwsyZM02z4cLDwzMEROHh4Tke65OcnMzQoUM5c+YMa9aswd/f3+x88eLFcXFxoVixYhn6lT6uLjo6WgKUPKZQByjZCQ0NJTw83GyqmoeHB/Xq1ePw4cN07NiRL774gueff55PPvmEatWqmQUfe/fuxdfXl3Xr1hESEsKIESOoWbOmaQDpokWL2LZtW4aF1x7kxIkTpKSkmLWpatWq+Pn5PTBAyYmEhARGjhzJ+PHjzfYjykr6P+zsXi95eXlRtGhRjEZjhnUJcir9dY27u/sDy/Dy8sLNzS3TVXwvXryIqqoZfhDez9vb2xSEuLm5Ua1aNbMptEFBQRw6dIgbN26Y1k55ED8/P65cuZLheGZtfFi7S3/77bcAWQYC1qhQoQLx8fE233MnJyery8isTZA2kLpixYoPrDe777WsrF271uxztWrV8PT0zHD8/g1AIyMj+eabb/jqq684d+4cxYsXp0uXLnTv3j3DL9zszJo1yzQY+N6tO+7lyO+1sWPHmj15SZ9SnR4AHj9+3CwYuXnzJjdu3DD9rHwQo9HI2LFj2b17NwsWLKBx48YZ0qiqSs2aNTl+/DjJyclmT65u3boFpM3+EXlLoR6Dkp30R7X3z1zx9vY2/VJOf2devHhxfH19zUaOe3p6Mn78eKpWrUrr1q1p1aqV2UaAJUqUMBsnkRMRERE4OTllWHPC29s7w6NlS82YMYOgoKAMi6yFhoZm+ldT+rvoe3/QFylSJMMjYJ1OR/v27fn5559Nf6nfKyfTZOvUqUOFChX4+OOPMx0nkV6GTqejefPmbN++3expUUREBN9//z0NGzbE3d092/oaNGjA6dOn+fvvvzNs1BgUFMSRI0c4ePAgAQEB2ZbXqlUrjhw5YjZFMyoqKsO0VgA3NzebX9dY6rvvvuOLL74gKCjIrqtpdujQgcOHD7Nz584M56Kjo7OdTuvt7U3jxo3ZsmWL6ZfIvayZXt2iRQvTGhtJSUlm59K/x3P6vZaVZs2amX2VLFkSFxeXDMfTX5lcv36dN998k8cee4zZs2fj6+vLhx9+yM6dOxk3bpzFwcnq1av5+OOPGThwIL17984yXfqMHkd8v9WpU8es7+lr6VSvXp0qVarw+eefm23K+tlnn6Eoitl+ZjExMVy4cIGYmBizsqdMmcKPP/7IhAkTaNeuXZZt6NChAwaDga+//tp0LCkpie+++45q1apZ/ApM5D55gpKLqlWrZvY40dfX1+wX9CuvvPLA6W3Lly9nxYoVps8//PBD7jSUtBUo9+zZw7Zt2zKc++uvv0hJSaFq1aqUKFECg8HA9evXCQsLo0yZMmbTUmvXrs3u3btZu3YtJUuWpFy5ctSrV4+RI0eyd+9eXnjhBZ5//nmqVavG3bt3OXnyJLt37zZ7/JsZVVWZOnUq/fr145lnnqFr166UKlWKmzdvsnfvXtzd3Vm+fDkAb731Fv/88w8vv/wyL7/8Mjqdji1btpCcnMzo0aNzdD0aNmzIV199xfHjx+nZs6fZuaCgIGJiYoiJicmwTklm+vbtyzfffEPfvn3p1auXaZqxn59fhi0KateuzWeffcbSpUupWLEiXl5edg0afv75Z4oUKUJKSoppJdlDhw5Ro0aNLMd5WKtPnz7s2LGDgQMH0qVLF2rXrk1CQgJnz57l559/Zvv27RnW7LjfhAkTePnll3n22Wd54YUXKF++PBERERw5coQbN26YnvzklLu7O++++y7vv/8+3bt355lnnqFYsWKcPn2axMREZs2aZdH3mj2EhIRw6tQp+vfvT7du3TKMxbHEr7/+ypw5c6hUqRJVqlThm2++MTvfvHlz09T12rVrA2lTs1u0aIFOp6Njx44W1RcaGsoTTzxhemVjjTFjxjBo0CDeeOMNOnbsyNmzZ9m4cSPPP/+82SutX3/9lXfffZcZM2aYVlr+5JNP2LRpE0FBQbi6umbo75NPPml6HdejRw++/PJLJk+ezKVLl/Dz8+Obb74hLCxMBsnmURKgPED6a47IyEizVR4jIyMzPJ7NzP0zOxRFsej9bY8ePejQoYPpc8mSJfHx8SElJYXo6GizpyiRkZE5ei2TlT179hASEsIjjzxidnzo0KHUqVOHgQMHcvnyZU6cOIHRaMTDw4OaNWtSv359s7ED77zzDuPHj2fBggUkJibSpUsX6tWrh4+PD1988QVLlizh119/5bPPPqN48eJUq1bNNAA1O02aNGHLli0sXbqUDRs2EB8fj6+vL4GBgbz44oumdNWrV2fjxo3MmzePFStWoGkagYGBzJkz54FroNzr3nEl9z9BqV69OsWKFSM6OpoGDRpkW1bJkiVZv349U6dOZeXKlRQvXpwePXpQsmTJDGuBDB48mLCwMFavXk1cXByNGze2a4AyceJEIG3QYIkSJahZsybTp0/P8V48lnBzc+PTTz9lxYoV/PTTT3z99de4u7tTqVIlhg4dioeHR7ZlVKtWja1bt7J48WK2bdvGnTt38PLyolatWgwePNiqdj3//PN4e3uzcuVKli5dil6vp0qVKmZr8eT0e80e6tWrx44dO+yyUvLp06eBtIG+Y8aMyXB+/fr1pgClXbt2vPrqq/zwww98++23aJpmcYCSPrjalp89rVu3ZvHixSxevJgpU6bg5eXFgAEDcnR/0/t7+PDhTLeo2L59uylAcXV1Zd26dcyZM4evvvqK+Ph4atasyYoVK+z6alPYkeNnNudN/v7+2q+//mp2zGg0as2bN9fWrFljOhYTE6PVqVNH+/777zVN07QbN25o/v7+2vHjx83yZrYOytSpUzNd48IS0dHRWu3atbWffvrJdOzChQuav7+/dvjw4Wzzp69xcOrUKbPjt27d0s6cOWP25e/vr61bt85sPREhhEi3YcMGrX79+lp4ePjDbooogAr1E5S4uDjTtENIe1wZHByMp6cnfn5+KIpCr169WLZsGRUrVjRNMy5ZsqRpnIa3tzeurq7s3LmT0qVL4+LikqO/DAE2bNjAr7/+yrp163LcZg8PD7p168bMmTPx9PTE3d2dqVOnEhQUZDZY86mnnmLkyJGmlTbv3LnD9evXTe/y0wdo+vj44Ovra/q6n5+fn8XjZIQQhcPevXt59dVXM6x4LIQ9FOoA5cSJE/Tq1cv0ecaMGQBm71P79etHQkIC48ePJzo6moYNG7J69WrTvHq9Xs/777/PkiVL+Oijj2jUqJHZNOMHuX37tlWLHo0bNw5VVRk2bBjJycmmRZzudenSJbPBZDt27ODdd981fR4xYgSQtlncw97NVAiRP3300UcPuwmiAFM0zcYFFIQQQggh7EymGQshhBAiz5EARQghhBB5jgQoQgghhMhzJEARQgghRJ4jAYoQQggh8px8Pc04MjKG7OYgKYpC0aIupp1RHaVv3zcICAhg9Oist2wvCFRVJS4uyebddMWDKeHhuHyzjaTnuqBZsWqnpfmtqc/WNuaVshxxreyRV4j8SFHA2ztna4Xl62nGERHZByiq+l+Acn/ajz9ezY4d27l8+RIuLi7Uq1efYcPeolKlyqY0SUlJzJ8/l19++Ynk5GSaNm3Gu+++b9pA8MCB/fTv34c//9yFh8d/S8/36/cG/v65E6DcvXuX2bNn8Ndff6IoKk880ZbRo8c+cAv47PoBcPLkCT76aAHBwcEoCtSuXZe33hqBv39ApmUqyn8BitGYb7+NhBBCOIiigI9PzgKUQv2K5+DBA7zwQg/WrdvAsmUrSU1N5c03B5KQEG9KM2/ebHbu/JNZs+ayatVawsPDGTVqxENsNbz33jtcuHCBpUtXsHDhIg4dOsjUqZMemCe7fsTHxzNkyCBKly7D+vUb+PjjdRQtWoTBgweSkpKS210S2VDu3Mb5220od247JL819dnaxrxSliOulT3yClHQFeoAZcmS5XTq9BxVq1bD3z+ASZOmcOPGdU6dOgWkbe/99dfbePvtUTRu3IRatWoxceIUjh49wrFjRwkLu0b//n0AaNWqBQ0aBDJhwvum8jVNY8GC+Tz+eAuefLI1y5cvtbnNFy9e5J9//mb8+InUrRtIUFADxox5h59//onw8Ixb0uekHwCXL1/i7t27DBo0mEqVKlO1ajX69x9IZGQk169ft7ndwja6kCt49u2NLuSKQ/JbU5+tbcwrZTniWtkjrxAFXaEOUO4XExMLgKenJwDBwadITU2lSZNHTWkqV65M6dJlOHbsGKVKlWbOnPkAbNv2Lb/8soNRo/57pfP999/i5ubG+vUbGT58BKtWrWDPnt2m8xMmvE+/fm9Y1MZjx47i4eFBrVq1TceaNHkUVVU5fvx4pnmy6wdAxYqVKF68OF9//RUpKSkkJiby9dfbqFy5Cn5+fha1UQghhLBVvh4ka09Go5G5c2dTv34Q1apVByAyMgInJyezsSWQtkFgZGQEOp3OFMx4eXllSFetWnUGDBgEQIUKFdmyZTP79u3l0UebAuDj42vx4N3IyAi8vLzMjun1eooVK0ZkZESWeR7UD4CiRYuycuUa3n77LVavXvlvmyuwePFy9Hr5NhEiP9E0DaPR4PDJAUKoqoqq6lAUxeay5DfPv2bOnMaFC+f5+ONP7FZm9er+Zp99fHyIiooyfR46dPgD80+bNoUff/ze9Pnvv/farW33S0xMZPLkCdSvX58ZM2ZhMBj49NN1DB8+mE8//QxXV9dcq1sIYT+pqSncvRtFSkriw26KKKScnV0pVswLvd7JpnIkQAFmzpzOzp1/sXr1WkqVKm067u3tQ0pKCjEx0WZPHyIjI/H2zn578fufPCiKgqbl/C+aQYPe5NVXe5sd8/Y2D3IAUlNTiY6OzrJNOenHTz/9SFhYGJ98sgFVTXvzN336LFq1as6ff/5O+/YdctxuYX+aqxspdeuhubo5JL819dnaxrxSliOulT3yZlqephEZeQNVVfH09EGn09vlL1khckLTNAyGVGJj7xAZeYOSJcvZ9P1XqAMUTdOYNWsGv/++g1Wr1lC2bDmz8zVr1kKv17Nv316eeOJJIG0w6Y0b1wkMDATAySktQjQY7P8o1cvLGy8vb7NjgYH1iImJ4dSpU9SqVQuA/fv3YTQaqVu3bqbl5KQfiYmJqKpq9s2kKAqKoshj4jzA4B/Ane07HZbfmvpsbWNeKcsR18oeeTOTmpqCphnx9PTF2VmeeoqHwQWdTkdU1E1SU1NwcnK2uqRCPUh25sxp/PjjD0yfPpMiRYoSERFBREQEiYlpj0Y9PDzo3LkL8+bNZf/+fZw6dYqJE8cTGFiPwMB6AJQpUwZFUdi5809u344iPj7+QVWaWbRoIR98MM6iNlepUoVmzZozdepETpw4zpEjh5k1awbt2z+Fr29JAG7duknXrp04ceJ4jvvRpElToqOjmTlzGhcvXuTChfNMnDgenU5Po0aNLWqjEOLhUpRC/aNdPGT2+v4r1N/FX3zxObGxMfTr9wbt2rUxff3yy8+mNCNHjqFly8cYPfpt+vZ9DW9vH+bO/dB0vmTJUgwc+CaLFi2kbdvWzJo1Pcf1R0SEc+PGDYvbPW3aTCpVqszAgf0YNmww9esH8f77E0znU1NTuXz5sinQykk/KleuzIIFizh37hyvvfYqffq8Rnj4LRYvXoqvrHD50OmPH8WnnA/640cdkt+a+mxtY14pyxHXyh55hSjoCvUrnkOHjmWbxsXFhXfffY93330vyzT9+g2gX78BZsdWrfo4Q7r58xeafZ40aWoOW2rO09OT6dNnZXnez69shr7lpB+PPtrUNMNI5DGahpKcTLZLJ9srvzX12drGvFKWI66VPfJaQFFw6FgUTdNyu0uiECjUAYoQQhR0igIpikpccqrD6izqrMeJjNuLiNwVHX2XDz+cw99/70RVFVq1asPw4aOy3QZl8eIFbN/+CykpyTRu/CgjR75jNv7xxo0bzJs3g0OHDuDmVoQOHZ5hwIDBub4EhQQoQghRgCmKQlxyKr+fvkV8Uu4HKUVc9LSuUZISzjrZRNTBJk36gMjICD78cAmpqanMmDGJ2bOnMXHitCzzLFo0n3/+2cWUKTMpWtSdDz+czXvvjWbZsrS3AAaDgTFjhuPl5c3y5R8TERHBtGkT0Ov1DBgwOFf7IwGKEEIUAvFJqcQ6IECxxpAh/alSpSoAP//8I3q9ns6du9O370DTq6no6GgWLpzL33/vJCUlmfr1G/LWW6MoX74CADduXGf+/NkcO3aE1NQUSpf2Y/DgYTRt2iJHbVizZgU7d/5J9+4v8vHHK4mJiaZ9+46MGDGazZs3sGXLJoxGI88/34PevfuY8sXExLBkyQJ27fqT5OQUatSoydChb5vWwbp2LZRFi+Zz8uQJEhMTqFixMgMGDOaRR5qYyuje/Vk6depCaOhVfv99Ox4eHvTu3Yfnnuua42t4+fIl9u79h9Wr11OjRtoMz7feGs3o0cMZMuQtfHwyjiWMjY3l+++/YcKEqTRs+AgA48ZNoGfP7pw4cZw6deqyb98eLl++xIIFS/Hy8qZ69QD69h3IsmWLeOON/qaZrLmhUA+SFSK/SK0eQNRfe0mtnvnO0vbOb019trZRVdO+7FGWLe1yxLWyR96C5n//+wGdTs+qVesYPnwUW7Zs5Lvvvjadnz59ImfOBDNr1nyWL1+LpmmMHj2c1NS0oGv+/FmkpCSzZMkq1q3bzKBBQ3Fzy/rVRmauXQtlz55/mDdvERMmTOOHH75h9Oi3/p0wsIJBg4ayatUyTp48YcrzwQdjuX07irlzP2LNmk/x96/BW28NIjr6LpC2EeujjzZn4cKlfPzxRpo0acrYsW9nmCCxefNGatSoxdq1G+nS5XnmzZtJSMhl0/khQ/ozbdrELNt+4sQx3N09TMEJQKNGjVFV1ay99zpzJpjU1FQaNfovWKpYsRKlSpXm5Mm0cYwnTx6nSpVqZq98GjduSlxcHJcuXcj+otpAApRc0q/fG8yZk/VAViEs4uaGoUZNcLNyQS9L81tTnw1tVFVwURJxURLTghRb+2tLuxxxreyRt4ApVaoUw4a9TYUKlWjXrgPdur3I559vAuDq1RB27fqLsWPfp169IKpX92fChCmEh9/ir7/+AODmzRvUrVuPqlWrUbZsOZo3b0n9+g0saoOmGRk3bjyVK1ehRYvHCApqxNWrVxg2bCQVKlSiY8dOVKhQkUOHDgBw9OgRgoNPMmXKLGrUqEX58hUYMuQt3N09+P337UDaiuKdO3ejSpVqlC9fgX79BlG2bFn+/vtPs7qbNm1G167PU65ceV55pTeensVN9aRdn9IPXCA0KiqSEiVKmB3T6/V4eBQjKioy0zyRkZH/boPiYXbcy8uLyMhIU5r7t1dJD1bS0+SWQh2gLF++lAYNAs2+unbtZJYmKSmJGTOm0bp1S5o3b8KoUSPMbsqBA/tp0CCQmJjoXGljdvVnRtM0li1bQrt2bWja9BEGDuxHyH27pa5evZLXXnuVZs0a89hjzXOl7cJ+1KshuI8Ygno1xCH5ranPljYqioox8S7GxLsoimoqS38txPRU5d4nLJbI7b7b0m9b72tBUqtWHbOZRnXq1OXq1RAMBgNXrlxCp9NRq1Yd03lPz+JUqFCRK1cuAdC9ew/WrVvDoEFvsGbNCs6fP2dxG0qX9qNIkaKmz15eXlSqVNm0unbaMW/u3Elbzfv8+bMkJCTQseMTPPlkS9PX9ethXLsWCqQ9QVm8eAE9e3bnqace58knW3LlymVu3jR/glK1anXT/yuKgpeXN7dv3zYd++CDyQwcOMTiPuVnhTpAAahatSq//LLD9LVmzTqz8/PmzWbnzj+ZNWsuq1atJTw8nFGjRjisfdbUv27dWj77bBPjxn3AunUbcXNzY/DggSQlJZnSpKSk0LZtO7p3fyG3uyDsQL0dhdvG9ai3o7JPbIf81tRnaxszK8vl9g1clET0+vuesFhRVm713ZZ+2/OaFXbPPtuZzz//hvbtn+bChfP07fsqX3652aIyMtueJLOZKkZj2uDfhIR4vL19WLt2k9nXpk1befnlXgAsWbKAv/76nf79B7NkyWrWrt1ElSrVSEkxHw+UWd2WrOJ9f0ADaWtixcREZ1iRPJ23t/e/26DEmB2PiorC29vblOb+7VXSn8ikp8kthT5A0en0+Pj4mL7ufUQWExPD119v4+23R9G4cRNq1arFxIlTOHr0CMeOHSUs7Br9+6cNlmrVqgUNGgQyYcL7pvyaprFgwXwef7wFTz7ZmuXLl1rUtuzqz4ymaWzatIG+ffvx+OOt8ff3Z/LkaYSHh/PHHztM6QYNGswrr7xq2rlZiIdFVdOmwmbGmBz771MVHcbEu2hJMWZ/zYqC49Spk2afT548QfnyFdDpdFSsWBmDwcCpU/+Npbh79w4hIVeoVKmy6VipUqXp3Lk706fPoUePV8zGsOSGgIAaREVFotPpKFeuvNlX8eLFATh+/ChPP/0srVq1pmrVtLEcN26E2b0tdeoEEhsbw+nTwaZjhw4dwGg0Urt2nUzzBATURK/Xc/DgPtOxkJC0pzu1a6dtg1K7dl0uXjzP7XuC6P3791K0aFEqVapi937cq9D/Sw8JuUK7dk/w7LMdeO+9d7h+/brpXHDwKVJTU2nS5FHTscqVK1O6dBmOHTtGqVKlmTNnPgDbtn3LL7/sYNSosaa033//LW5ubqxfv5Hhw0ewatUK9uzZbTo/YcL79Ov3RpZty67+zFy7do2IiAizPB4eHtSpUzfLoEaIh+XesSfZLSOmKCouTirOxFv1qkfkbTdv3mDRovmEhFzm119/YuvWLXTv3gOA8uUr0LJlK2bNmsbRo0c4d+4skyePx9e3JC1bPg7AwoXz2Lt3N2Fh1zhz5jSHDh2gYsXKD6jRdo0aNaF27bq8++4o9u3bw/XrYRw/fpQVK5Zw+vQpAMqVq8Cff+7g3LkznDt3lkmT3jM9gbHElCnjWb58cZbnK1WqTJMmzZg9eyqnTp3g2LEjzJ8/myeeaGeawRMefouXX+5mCvTc3d155pnnWLToQw4dOsDp08FMnz6ZOnUCqVMnbW+3xo0fpVKlykyZMp5z586yd+9uVq1aRteuL+DsbP0+OzlRqKcZ161bl0mTplKxYiUiIsJZuXI5ffq8xhdffEXRokWJjIz4dwBRMbN83t7eREZGoNPp8PT0BNLeVd6frlq16gwYMAiAChUqsmXLZvbt22tardXHx/eBj/Cyqz+rPGntMX/05u3tTURE7g5oEsJS6WNP0OlAAR7wc1tRFUiKwaimorgUAWQTS0sUcXHMj3tr63nqqY4kJSXRr19vVFVH9+49zKbZvvvuBBYunMvYsW+RkpJCvXoNmDNnoenViNFoYP78WYSH36JIkaI0adKUYcPeNuXv3v1ZOnR4hj59BmSo21qKojB37kJWrlzK9OmTuHPnNl5e3tSv34ASJdIGlg4dOoIZMyYzcOAbeHoWp2fP3sTFxVlc182bN7J9ejhhwhTmz5/N8OFvmhZqe+ut0abzqamphIRcMdsGZejQt1EUlffeG/PvQm1NGTnyvz+0dTods2cvYO7cGQwc+Dpubm489ZR9r2NWCnWA0rx5S9P/+/v7U7duXTp2fIpff/2Zzp1zPv88K+nz4NP5+PiYvcsbOnS4zXWIwsHoW5L4YW9j/HdDyNzOb019trbx/rISho9E87H9HXdu992WftvzmmVF0zSKOqctnuYoRZ31aJplAaRer2f48JGMGvVupueLFSvGBx9MzjL/iBFjsjyXmJhIVFQUQUENs0zTp8+ADL9033tvYoZ0ixevNPtcpEhR3nprtFkgcK8yZfz46KPlZse6dTMf+/fll99lyPfJJ5seWG9mihXzfOCibGXK+LFr1wGzYy4uLowcOdYsKLlf6dJlmDv3o2zrt7dCHaDcz8OjGBUqVOTq1asAeHv7/DuAKNrsKUZkZOQDp3uly2zQkyX/aK2pP/14VFSk2SZ/kZGRBATIWgv5lbGMH3HvT3RYfmvqs6WNiqKCYjAtjW4s40fC+EnoE68/OGMutMsR18oeeXNK08AJIyWcdblaj3mdeWuZ+0OHDtCwYSMaNGj0sJsiLCBvcu8RHx9PaOhVfHzSfsnXrFkLvV7Pvn17TWkuX77EjRvXCQxMG0CUvoqewWD/x805qf9+ZcuWxcfHxyxPbGwsJ04cJzCwnt3bKBxDiY3B6e+dKLEx2Se2Q35r6rO2jYqi4qRTccJgGoeixMag3/UXxMZaVJY92uWIa2WPvJbQtLSZJ476ykvBCUCzZi2YM2dh9glFnlKoA5QPP5zLwYMHCAu7xtGjRxg58i1UVcdTT3UA0gaXdu7chXnz5rJ//z5OnTrFxInjCQysZ/plX6ZMGRRFYefOP7l9O4r4+Pgc179o0UI++GBcludzUj9A166d2LEjbVEgRVF4+eVXWL16JX/++Tvnzp1l/Pj38PX15fHH25jyXL9+nTNnTnPjxnWMRgNnzpzmzJnTFrVfOI7u4gWKd+mI7qJ1Kzdamt+a+qxto6IqkBKHYkwhPULRXbxAseeeRr105cGZc6FdjrhW9shbkCxevJLhw0c+7GaIPKZQv+K5efMW7747lrt371CiRAnq12/AunUbTIObAEaOHIOiqIwe/TbJyck0bdqcd999z3S+ZMlSDBz4JosWLWTixPE888yzTJo0NUf1R0SEZ1ju+H7Z1Q9w+fJlYu/5S7N379dJSEhg6tTJxMTEUL9+EIsXL8PFxcWUZvnyJXz33bemzy+9lPZOdOXKNTRq9EiO2i+EEELklkIdoMycOTvbNC4uLrz77nsZgoJ79es3gH79zAdXrVr1cYZ08+ebP2LMSSCTk/oPHTKfcqwoCoMGDWbQoKx3mpw0aWqOAykhhBDC0Qr1Kx4hRN5078JtiqJmuZCbEKLgKtRPUITILzS9E4Yyfmh667Y2tzS/NfXZ2sZ0igLOzkaMfmVQnJ1xdlJJ1RIetESKXdvliGtlj7xCFHSKpuW18dY5FxERk+1ocVVVKFrUBaMxb017KwgUBVRVJS4uyaqVEUXhlrb5n4pT8i2ctSQwppKkuGJw80OND0PV6VAUBaeUGFKc0nZbdUqJIUl1I8WlTK7MnMvvUlKSiYy8jrd3GZyccneVTyGy8qDvQ0UBHx+PLHKakycoQgiHS1/iXlXULJ+MKIqKYvVzEyFEfidjUHLJhAnv8/bbslKssA/dqZN41auB7r4N1XIrvzX1WZInfYl7Y1J0pjsF6k6fo0j9R3EKPkm2m/TYsV2OSG+vvJZIe9qpOOxLxgwJeyjUT1AMBgMrVizjxx+/JzIybeXVZ599jr59+6P8+y9M0zSWL1/Ktm1biYmJoV69+owb9z4VKlQEICzsGs8804HPPvucgIAadm9jdvVnZcuWzaxf/wmRkRH4+/szZsy7ps2fAK5evcqCBfM4fPgwKSnJNGvWnDFj3s317bOFdZTUFHTXw1BSUxyS35r6bG2jWVmGVJTrNyApEVsjlNzuuy39tuc1y7IOBYooCagp0blWx/2MTsWIx01eqztYdPRdPvxwDn//vdO0F8/w4aMoUqRIlnmSkpJYvHgB27f/8u9ePI8ycuQ7Zvu53bhxg3nzZnDo0AHc3IrQocMzDBgwOMNq6fZWqAOUTz75mC+//JxJk6ZStWpVTp06ycSJ43F3d+ell3oCsG7dWj77bBOTJ0/Fz68sy5YtZvDggXz55ddm64rkFmvq//nnn5g/fw7jxn1A3bp12bhxA4MHD2Tbtm/x8vImISGewYMHUL16ACtWrAJg2bIlvPXWUNat2yDb2QtRgCiKgpoSjfHsr5Bs+SZ1FnMuiur/JIpTEfLxEMd8adKkD4iMjODDD5eQmprKjBmTmD172gP351m0aD7//LOLKVNmUrSoOx9+OJv33hvNsmVpS2UYDAbGjBmOl5c3y5d/TEREBNOmTUCv1zNgQNZLWdiDRQHKpk2b+Oyzz7h27RoA1atX580336RVq1ZAWiQ2c+ZMfvzxR5KTk2nRogUTJkwwLR0PEBYWxsSJE9m7dy9FihShc+fOjBw5MtcjscwcPXqUVq1a07LlYwD4+ZXlp5/+x4kTaVtRa5rGpk0b6Nu3H48/3hqAyZOn8eSTrfnjjx20b9+BZ55JW3U2faGzhg0bma2Bsn79J2zYsJ6UlBTatXuKUaPGmJbHz05O6s/Mxo3r6dKlG8891xmA9977gF27dvLNN1/z+ut9OHLkCGFhYWza9Dnu7u5A2roojz/egv3799GkyaOWXEYhRH6QHIeWZPvWAdmx5nnXkCH9qVKlKgA///wjer2ezp2707fvQNPT7OjoaBYunMvff+8kJSWZ+vUb8tZboyhfvgIAN25cZ/782Rw7doTU1BRKl/Zj8OBhNG3aIkdtWLNmBTt3/kn37i/y8ccriYmJpn37jowYMZrNmzewZcsmjEYjzz/fg969+5jyxcTEsGTJAnbt+pPk5BRq1KjJ0KFvmzaLvXYtlEWL5nPy5AkSExOoWLEyAwYM5pFHmpjK6N79WTp16kJo6FV+/307Hh4e9O7dx2w35+xcvnyJvXv/YfXq9dSoUQuAt94azejRwxky5C18fHwz5ImNjeX7779hwoSpNGyYtkDnuHET6NmzOydOHKdOnbrs27eHy5cvsWDBUry8vKlePYC+fQeybNki3nijf45/n1nDoj+VS5cuzahRo/jqq6/YunUrjz76KIMHD+bcuXMATJ8+nd9//50FCxbw6aefcuvWLYYMGWLKbzAYGDBgACkpKWzevJmZM2eybds2PvrI8bskAtSrV499+/Zy5cplAM6ePcORI4dp3jztG/ratWtERESY/cL28PCgTp26HDt2FIBPP03bcXLZspX88ssO5s790JT2wIH9hIZeZcWKNUyaNJXvvvuG7777xnR++fKldOz4VJbty0n990tJSSE4ONgsj6qqNGnSxJQnOTkZRVFwdv5vdLWLiwuqqnL48KEHXzQhhMgF//vfD+h0elatWsfw4aPYsmUj3333ten89OkTOXMmmFmz5rN8+Vo0TWP06OGkpqYCMH/+LFJSklmyZBXr1m1m0KChuLll/WojM9euhbJnzz/Mm7eICROm8cMP3zB69FuEh99i8eIVDBo0lFWrlnHy5AlTng8+GMvt21HMnfsRa9Z8ir9/Dd56axDR0XeBtD3eHn20OQsXLuXjjzfSpElTxo59O8Mq4ps3b6RGjVqsXbuRLl2eZ968mYSEXDadHzKkP9OmTcyy7SdOHMPd3cMUnAA0atQYVVXN2nuvM2eCSU1NpVGj/4KlihUrUapUaU6eTFsA9OTJ41SpUs3slU/jxk2Ji4vj0qXc3aLBogClTZs2tGrVikqVKlG5cmVGjBhBkSJFOHLkCDExMWzdupV33nmHpk2bUqdOHaZPn87hw4c5cuQIALt27eL8+fPMmTOHmjVr0qpVK4YPH87GjRtJTk7Ojf490Ouv96F9+6fo2vU5GjduwEsvvcDLL7/C0093BCAyMgLA7MYAeHt7ExERCUCJEiUAKF68OD4+Pnh6eprSeXgUY+zYcVSuXJnHHmtFy5aPsW/fPtP54sVLUK5cuSzbl5P673fnzm0MBkOGPF5e3qbyAgMDcXNzY+HCD0lISCAhIZ4PP5yHwWAgIiIiy/aIh8dQpSp3tv2A4d+/MnM7vzX12dpGSBs8q6qgValEytZ1aFUqWV2Wte1yxLWyR96CplSpUgwb9jYVKlSiXbsOdOv2Ip9/nvYH4NWrIeza9Rdjx75PvXpBVK/uz4QJUwgPv8Vff/0BwM2bN6hbtx5Vq1ajbNlyNG/ekvr1G1jUBk0zMm7ceCpXrkKLFo8RFNSIq1evMGzYSCpUqETHjp2oUKEihw4dAODo0SMEB59kypRZ1KhRi/LlKzBkyFu4u3vw++9p+6NVr+5P587dqFKlGuXLV6Bfv0GULVuWv//+06zupk2b0bXr85QrV55XXumNp2dxUz1p16d0lrvYQ9oO9um/j9Lp9Xo8PIoRFZX574vIyEicnJzw8DCf9uvl5UVkZKQpjZeX133nvU3ncpPV71UMBgM//fQT8fHxBAUFceLECVJSUmjWrJkpTdWqVfHz8+PIkSPUr1+fI0eO4O/vb/bKp0WLFkycOJHz589Tq1atzKrKNb/++jP/+98PTJ8+kypVqnLmzBnmzZttGixrq6pVq6LT/bfFuY+Pj+lpE0CPHi/Ro8dLNtdjqRIlvJg1ay4zZkxl8+ZNqKpK+/YdqFGjJqoqw+/zIs3dg5TmLR2W35r6bG0jioqzXiUl8RZ6Dze0Zo3TjhtTrS/TinY54lrZI29BU6tWHdPrHIA6deqyefMGDAYDV65cQqfTUatWHdN5T8/iVKhQkStXLgHQvXsP5s6dwf79e2jUqAmtWrWhWrXqFrWhdGk/ihQpavrs5eWFTqeajcvz8vLmzp0oAM6fP0tCQgIdOz5hVk5SUhLXroUCaU9QPv54Jbt37yIyMgKDwUBSUhI3b5o/Qala9b+2KoqCl5c3t2/fNh374IPJFvWlILA4QDlz5gw9evQgKSmJIkWKsGTJEqpVq0ZwcDBOTk4UK1bMLL23tzfh4eEAREREmAUngOlzehpHWrBgPq+91sc0lqN6dX9u3LjO2rVrePbZ50zRalRU2gyfdJGRkQQEBGRbfsZxNYpFg8asqb948RLodLoMEXNUVKRZ9N20aTO+/fZHbt++jV6vw8OjGE8+2ZqyZbN+oiMeHvV6GG5rVpLQpz/GMn65nt+a+mxtI4oKybHcuXgE36Ll0X28EcMbPaGUbTPLcrvvtvTb5msmTJ59tjONGz/K7t272LdvL59+upYhQ96ie/ceOS7j/p/ZiqJkOj4yfWHKhIR4vL19WLRoRYY07u5pTyWWLFnA/v17GTz4LcqVK4+Liwvvvz+WlBTzwDuzuo3GnC9GeH9AA5CamkpMTHSGJ+rpvL29SUlJISYmxuwpSlRUlGlGp7e3N8HB5tPg03+/5PasT4una1SuXJmvv/6azz//nJdeeomxY8dy/vz53GhbrktMTMzwxEBVVdM3X9myZfHx8WHfvr2m87GxsZw4cZzAwHoApgFCubGqZU7qv5+TkxM1a9Y0y2M0Gtm3b2+meUqUKIGHRzH27dtLVFQUrVo9bvd+CNup4bco8tF81PBbDslvTX22tjGdISkRJTwC3eJVKOG2v3LM7b7b0m97XbOC4NR9a8GcPHmC8uUroNPpqFixMgaDgVOn/htLcffuHUJCrlCpUmXTsVKlStO5c3emT59Djx6vmI1hyQ0BATWIiopEp9NRrlx5s6/ixYsDcPz4UZ5++llatWpN1appYzlu3Aize1vq1AkkNjaG06eDTccOHTqA0Wikdu06meYJCKiJXq/n4MH/hh6EhFzm5s0b1K4dCEDt2nW5ePE8t29HmdLs37+XokWLUqlSFbv3414WByjOzs5UrFiROnXqMHLkSGrUqMH69evx8fEhJSWF6Gjzufbp64tA2tOS+8c4pH++9wmBozz2WCvWrFnFzp1/ERZ2jR07trNhw6e0bt0GSItgX375FVavXsmff/7OuXNnGT/+PXx9fXn88bQ0JUp44erqyj//7CIyMpKYmJgc179582cMGNA3y/M5qR9gwIC+bN78melzz5692LZtK9999w0XL15k+vSpJCQk0KlTZ1Oab775mmPHjnL16lV++OF7xo4dRc+er5r9YxdCCEe5efMGixbNJyTkMr/++hNbt24xPf0oX74CLVu2YtasaRw9eoRz584yefJ4fH1L0rLl4wAsXDiPvXt3ExZ2jTNnTnPo0AEqVszdn2eNGjWhdu26vPvuKPbt28P162EcP36UFSuWcPr0KQDKlavAn3/u4Ny5M5w7d5ZJk96zamuQKVPGs3z54izPV6pUmSZNmjF79lROnTrBsWNHmD9/Nk880c40gyc8/BYvv9zNFOi5u7vzzDPPsWjRhxw6dIDTp4OZPn0ydeoEmtbNatz4USpVqsyUKeM5d+4se/fuZtWqZXTt+oLZRIvcYPPcXqPRSHJyMnXq1MHJyYndu3fTvn17AC5evEhYWBj169cHoH79+ixfvpzIyEjTo6F//vkHd3d3qlWrZmtTLDZmzLssXbqYGTOmcft2FL6+vnTr1p3+/Qea0vTu/ToJCQlMnTqZmJgY6tcPYvHiZaY1SPR6PaNHj2XVqhUsX76UoKAGZtOMH+TOnduEhoY+ME129QOEhoZy585/j/bat3+K27dvs2zZUiIjIwgICGDx4mVmj+OuXLnM4sULuXv3Ln5+ZenTpx89e76ao3YLIfIh56K2Lsqb43qs8dRTHUlKSqJfv96oqo7u3XuYTbN9990JLFw4l7Fj3yIlJYV69RowZ85C06sRo9HA/PmzCA+/RZEiRWnSpCnDhr1tyt+9+7N06PAMffoMsK1/91AUhblzF7Jy5VKmT5/EnTu38fLypn79BpQokTawdOjQEcyYMZmBA9/A07M4PXv2Ji7O8vVobt68ke0aVRMmTGH+/NkMH/6maaG2t94abTqfmppKSMgVEhMTTceGDn0bRVF5770x/y7U1pSRI8eazut0OmbPXsDcuTMYOPB13NzceOop+17HrFi0WeC8efN47LHHKFOmDHFxcXz//fesWrWKNWvW0Lx5cyZMmMBff/3FjBkzcHd3Z+rUqQBs3rwZSBtY27lzZ0qWLMno0aMJDw9nzJgxPP/887z99tsPqjpTslngwyWbBTqO/tgRSrR9jNu//UVqYP1cz29NfZbk0elU9InX/9sQ0PjvD0xjKjeC91DGUAKn9t1J+flLtDoBpOjT1uuxZrPA3O67LffG1vt6v8w2aXtoK8lqOV9JdsiQ/lSvHsDw4SNzpT2JiYk8/fQTzJ27kAYNGuVKHeI/D2WzwMjISMaOHcutW7fw8PAgICDAFJwAjBs3DlVVGTZsmNlCbel0Oh3Lly9n4sSJvPjii7i5udGlSxeGDRtmSTOEKHSMJbxI6NkLYwmv7BPbIb819dnaxntpJYpjeLkbWoniNpeV2323pd/2vGZZ0TSIxw3FybI1QWyrU8tTfxAeOnSAhg0bSXCSz1j0BCWvkScoD5c8QRHWyu4JSuna96z+aUy16QlKYfKgv1zzstx+giIcy15PUGTTFSHyg4QEdKeDISHBMfmtqc/WNpqVlYhy5hwkJGaf1t7tcsS1skfeAmTx4pUSnIgMJEARIh/QnzuD12NN0J8745D81tRnaxvvpZy7gNPjnVDO2b6Udm733ZZ+2/OaCVHQSICSSyZMeJ+33x7+sJshhBBC5EuFOkCJi4tjzpxZPP10e5o2fYTXXns1w6ZKmqaxbNkS2rVrQ9OmjzBwYD9CQq6YzoeFXaNBg0DOnDmdK23Mrv7MHDx4gOHDh9Cu3RM0aBDI77/veGD6adOm0KBBIBs3fmrPpgshHpJ8PLRQFAD2+v4r1AHK5MkT2bt3D1OmTGPLlq08+mhTBg3qz61bN01p1q1by2efbWLcuA9Yt24jbm5uDB48kKSkJIe00Zr6ExMT8PcP4J13xmVb/o4d2zl+/Bi+viXt2WwhxEOQvvdXcrJjfj4JkZn07z+dzral1mxeqC2/SkxMZMeO35g/fyENG6ZNPRs48E3++utPvvjicwYPHoqmaWzatIG+ffvx+OOtAZg8eRpPPtmaP/7YQfv2HXjmmbR9fF566QUAGjZsZLZQ2/r1n7Bhw3pSUlJo1+4pRo0aY1oePzs5qT8zzZu3pHkONiC7desms2fPYMmS5QwbNiRHbRIPiaKgOTunDYF3RH5r6rO1jRnKcrJjWbnYd1v6bc9rBqiqDjc3d2Jj0xZudHZ2MduAT4jcpGkayclJxMbexs3NPduF5bJTaAMUg8GAwWDIsFSvq6srR44cBuDatWtERETQpMmjpvMeHh7UqVOXY8eO0r59Bz79dBOvvvoyy5atpGrVambBx4ED+/Hx8WHFijVcvRrCO++MJiAggK5duwOwfPlSvvvuW3744adM25iT+q1lNBp5//1x9Or1GlWrOn4VX2GZ1Lr1iAi1fl8aS/NbU5+tbbyXVrcWKVeOpX2wcTfj3O67Lf225zVLV6xY2poq6UGKEI7m5uZu+j60RaENUIoWLUpgYD1Wr15JlSpV8PLy5qef/sexY0cpX748AJGRaT847t8J0tvbm4iItN0cS5QoAUDx4sUz7NTs4VGMsWPHodPpqFy5Mi1bPsa+fftMAUrx4iUoVy7r3YNzUr+1PvnkY/R6PS+91NOmcoQQeYuiKHh6euPhUQKDwbbgTghL6XR6m5+cpCvUY1CmTJmOpmm0b9+WRx9txObNm2jfvgOKYp/LUrVqVdM7YUjbLDF9m2qAHj1eYsWK1XapyxKnTp3is882MmnSFHn8m0/ozp6h+BMt0Z21bjqqpfmtqc/WNpo5ewH9k13hbNo0Y0VRrd5HJrf7bku/7XrN7qOqKk5OzvIlXw79sldwAoX4CQpA+fLlWb16LQkJ8cTGxuHr68vYsaNNTzW8vdOeiERFRZrtthwZGUlAQEC25advYvUfxaLRzbbWn5XDhw8SFRXF00+3Nx0zGAx8+OE8Nm3amOUrJ/HwKIkJOB0/ipJo3YJelua3pj5b22heViLqiWCUxEQ0RUWvKmmvepS0YEVVFTQNjDlYTDa3+25Lv+15zYQoaAp1gJLOza0Ibm5FiI6OZvfufxg+fAQAZcuWxcfHh3379hIQUAOA2NhYTpw4zvPPpw2KTR9zkhvLbuekfmt07Pis2bgWgMGDB9Gx4zN06vScTW0Wwu4UFVL+3f1V0eGsV9ESwtC7eJKkuuYoSBFC5D+FOkD555+/0TSNSpUqcfXqVRYsmE+lSpVMv6QVReHll19h9eqVVKhQAT+/sixbtgRfX18ef7wNACVKeOHq6so//+yiVKlSODs74+GRs30GNm/+jN9/357la56c1A8wYEBfWrd+gh49XgIgPj6eq1dDTOevXbvGmTOnKVbMkzJlylC8eHGKFy9uVpder8fb25tKlSrn+PoJ4XCKCsmxRJ7ajVet1iiuRQCJUIQoiAp1gBIbG8vixQu5efMmnp6etGnTlsGDh3LvTJzevV8nISGBqVMnExMTQ/36QSxevAwXFxcg7Rf76NFjWbVqBcuXLyUoqIHZNOMHuXPnNqGhoQ9Mk139AKGhody589+I/VOnTtK/fx/T5/nz5wDw7LOdmDRpao7aJkRelpIU/7CbIITIZbKbsbCa7GbsOMqd2zj99Qcpjz2OVrxErue3pj5L8mS7m3HZuig7d6O1bArFPc0zG1O5cng7pYI6kuqa/a7Gud13W+6NrfdViPzGkt2MJUARVpMARVgr2wCldousM1sYoAgh8g5LApRCPc1YiPxCuXULt2WLUW7dckh+a+qztY1mwiNQl38C4bYvYpbbfbel33a9ZkIUMBKgCJEP6G6E4T5hHLobYQ7Jb019trbxXsr1m+gnzUK5fjPbtKqa9mWvdjniWtkjrxAFXaEeJCuEyN8UBZxJez0kU46FKFjkCUoumTDhfd5+e/jDboYQBZqiKBgT72JMvGu3FaCFEHlDof4XHRcXx5w5s3j66fY0bfoIr732KidPnjBLM2HC+zRoEGj2NXjwQNP5sLBrNGgQyJkzp3OljZqmsWzZEtq1a0PTpo8wcGA/QkKuPDDPwYMHGD58CO3aPUGDBoH8/vuODGkiIyOZMOF92rV7gmbNGjN48MBsyxVCCCEcpVAHKJMnT2Tv3j1MmTKNLVu28uijTRk0qD+3bpm/927WrDm//LLD9DVjxmyHtXHdurV89tkmxo37gHXrNuLm5sbgwQNJSkrKMk9iYgL+/gG88864TM9rmsbbbw8nNDSUDz9cyKZNWyhTxo+BA/uTkCDrS+RFRo9iJLXvgNGjmEPyW1OfrW28l1bMA2O71mjFcjba357tcsS1skdeIQq6QjvNODExkZYtmzJ//kJatnzMdPzll1+kefMWDB48FEh7ghITE8P8+QszLb9Bg0Czzw0bNmLVqo9N+erXD2LDhvWkpKTQrt1TjBo1xmwhuAdJ28jwCV55pRe9er0GQExMDE8+2ZpJk6bQvn2HbMto0CCQefMW0Lr1fyvPXrlymS5dOvHFF19RtWo1AIxGI08+2ZohQ4bRpUu3HLVPphkLa9lrmrGxiB9qfNoAU5lyLETeJ9OMc8BgMGAwGHB2djY77urqypEjh82OHThwgCeeaEWXLs8yffoU7ty5Yzr36aebAFi2bCW//LKDuXM/vCfffkJDr7JixRomTZrKd999w3fffWM6v3z5Ujp2fCrLNl67do2IiAizfXM8PDyoU6cux44dtarfAMnJyQA4O/+3Gq2qqjg7O2fou8gjUlJQIiIgJcUx+a2pz9Y23lcWEVF2KytX+25Lv+15zYQoYAptgFK0aFECA+uxevVKwsNvYTAY+OGH7zl27CgREeGmdM2aNWfKlKksX76KYcNGcPDgQYYOfRODwQBAiRJpqz8WL14cHx8fPD3/W/XSw6MYY8eOo3Llyjz2WCtatnyMffv2mc4XL17CtHNyZiIj09aA8PLyNjvu7e1NRESk1X2vVKkypUuXYfHihURHR5OSksInn3zMzZs3CbfDuhPC/vTBJ/GpVQV98EmH5LemPlvbeC8l+CzOdZujBJ+1uazc7rst/bbnNROioCnU04ynTJnOpEnjad++LTqdjho1atK+fQeCg0+Z0tz7GqV6dX+qV/enU6enOXBgf4Ydge9XtWpVdDqd6bOPjw/nzp0zfe7R4yXTBn+O5OTkxNy5HzJ58gQef7wFOp2Oxo2b0Lx5C/LxGz8hhBAFSKEOUMqXL8/q1WtJSIgnNjYOX19fxo4d/cCnGuXKlaN48RJcvXo12wBFr7//8ioWBQDe3j4AREVF4uvrazoeGRlJQEBAjsvJTK1atdi8+QtiYmJITU2hRAkvevV6mZo1a9tUrhAPg6KoKMrDboUQwp4K7Suee7m5FcHX15fo6Gh27/6HVq1aZ5n25s0b3L17B1/ftOAhfcBrbgzOK1u2LD4+Puzbt9d0LDY2lhMnjhMYWM8udXh4eFCihBchIVc4deoUjz+edd+FsDdFUbE1rlAUFRcnFWfiH7iirBAifynUT1D++edvNE2jUqVKXL16lQUL5lOpUiU6dXoOgPj4eFasWMYTT7TFx8eHq1evsnDhh5QvX4GmTZsDUKKEF66urvzzzy5KlSqFs7MzHh45G6G8efNn/P77dlasWJ3peUVRePnlV1i9eiUVKlTAz68sy5YtwdfXl8cf/29WzoABfWnd+gnT66L4+HiuXg0xnb927RpnzpymWDFPypQpA8Cvv/5CiRIlKF26DOfPn2POnFk8/nhrmjZtZvmFFMIKiqLipFPBmPWU+RyVoyqQFINRTUVxKQLITB4hCoJCHaDExsayePFCbt68iaenJ23atGXw4KGmpyKqqnLu3Dm+//5bYmJi8PUtyaOPNuXNN4eYZv/o9XpGjx7LqlUrWL58KUFBDVi16uMc1X/nzm1CQ0MfmKZ379dJSEhg6tTJpmnLixcvw8Xlvxk4oaGh3Llz2/T51KmT9O/fx/R5/vw5ADz7bCcmTZoKQEREOPPnzyEyMhIfH1+eeeZZ+vUbkKN2C8dLrV2XiAuhaEWKOiS/NfVZkidtiroCKXGZntdq1yD57H4o4pbj+u3RLkekt1deIQq6QrsOirCdrIMirKGq4KZLRpd8G8WYan7SinVQ9Ek3cU6NJUl1I8VF1kIRIi+TdVCEKGB0F8/j+UJndBfPOyS/NfXlNI+iqBiTojMGJ/e6eBl9j75w8XKO67e1XY5Kb6+8QhR0EqAIkQ8osbE4/7EDJTbWIfmtqc/WNpqXFYf6598osZm/ArKsrNztuy39tuc1E6KgkQBFCCGEEHmOBChCCCGEyHMkQBFCCCFEniMBSi5ZvnwpPXo8/7CbIQoIg185YmbMxeCX9SrH9sxvTX22tvFeml8ZUqe/j+ZXxuaycrvvtvTbntdMiIKmUE8zjouLY+nSxfz++w5u344iIKAGo0ePpXbtOqY08fHxfPTRAv74Ywd3797Fz68sL730Mt27v2BK06BBIPPmLaB16/8WT1u+fCl//PE7mzd/YXM/t2zZzPr1nxAZGYG/vz9jxrxLnTp1s0z/1Vdf8v3333HhQtrMgJo1azFkyLAMeS5evMhHH33IoUMHSU1NpUqVqsyZM9+0mFt2ZJqxsIZOp+KccgtnLZMF2iyYZly6wbMYi5RGlyjTjIXIL2SacQ5NnjyRvXv3MGXKNLZs2cqjjzZl0KD+3Lp105Rm3rw5/PPP30ydOoOtW7/m5ZdfYdasGfz55+8OaePPP//E/Plz6N9/IJs2baF69QAGDx5IVFTWuxkfPHiAp57qwMqVa/jkkw2UKlWaN98caNavq1ev0qdPbypVqszKlWvYsmUr/fr1x8XF2RHdEhZSbkfh8sVmlNtRDslvTX22ttHM7TuoX34Lt+9kelrv5IyLk4qTlpDtUvm53Xdb+m3XayZEAVNoA5TExER27PiN4cNH0LBhIypUqMDAgW9Srlx5vvjic1O6Y8eO8OyznWjU6BH8/MrSrVt3qlf358SJEwB07PgUACNHvkWDBoGmz+m+//47OnZ8iscea8Y774whLs6yaZMbN66nS5duPPdcZ6pUqcp7732Aq6sb33zzdZZ5pk2byQsv9CAgoAaVK1dm/PiJaJrRbE+fJUsW0bx5S956621q1KhJ+fLladWqNV5e3ha1TziG7moIxQb3R3fPFga5md+a+mxt472Uq9fQDx2LcvVa5nXpnCApBi05hux2CcztvtvSb3teMyEKmkIboBgMBgwGg2nJ+nSurq4cOXLY9DkwsD5//vkHt27dRNM09u/fR0jIFR59tCkAGzZsAmDixCn88ssO02eA0NCr/PHHDhYuXMSCBYs4dOgAa9euMZ3/9ttvaNAgMMs2pqSkEBwcbLZrsqqqNGnShGPHjua4r4mJiaSmplKsmCcARqORXbv+omLFirz55kCeeKIVvXq9zO+/78hxmUIIIURuKrQBStGiRQkMrMfq1SsJD7+FwWDghx++59ixo0REhJvSjR37LlWqVOGpp56kSZOGDBkyiHfeGUfDho2AtM0CIW1XYB8fH9NnSAsEJk2aSrVq1WnQoCFPP/2M2VMMd3d3KlWqlGUb79y5jcFgyPBUw8vLm8jIiBz39aOPPsTX19cU6ERFRREfH8/atWto1qw5S5euoHXrJxg1agQHDx7IcblCCCFEbinUmwVOmTKdSZPG0759W3Q6HTVq1KR9+w4EB58ypdm8eRPHjx/jww8/okwZPw4dOsjMmdPx9S1p9mQjM35+ZSla9L9NwHx8fLl9z7vmNm2eoE2bJ+zfsXusXbuGn3/+iZUrPzZtMKhpaYMIH3+8Na+88ioAAQE1OHr0CF9++bkp+BJCCCEelkIdoJQvX57Vq9eSkBBPbGwcvr6+jB07mnLl0qb8JSYmsnjxR8ybt4CWLR8DwN/fn7NnT7N+/SfZBih6vfnlVRQsmu1SvHgJdDpdhgGxUVGReHv7ZJt//fpPWLv2Y5YvX4m/v79ZuXq9nipVqpqlr1y5itnrLZF3aEWKktLwEat3vbU0vzX12dpG87KKYGxYD61IETuUlbt9t6Xf9rxmQhQ0hTpASefmVgQ3tyJER0eze/c/DB8+AoDU1FRSU1PTtoW/h6rquHd2tl6vx2g02L1dTk5O1KxZk3379pqmMBuNaYNdX3zxpQfm/eSTj/n449UsXryMWrVqZyi3Vq3aXL582ex4SMiVHE8xFo5lqFadO//b7rD81tRnaxvNVKtM6veb7VJUbvfdln7b9ZoJUcBYNAZlxYoVdOvWjaCgIJo2bcqbb77JxYsXzdK8+uqrBAQEmH2NHz/eLE1YWBj9+/enXr16NG3alFmzZpGa+oCdTXPJP//8zd9/7+LatVD27NlN//59qFSpEp06PQekjRFp2LARCxbM58CB/Vy7Fsq3337DDz98Z7bmiZ9fWfbt20tERATR0dE5rn/Hju107drpgWl69uzFtm1b+e67b7h48SLTp08lISGBTp06m9J88ME4Fi1aaPr8yScfs2zZEiZMmISfX1kiIiKIiIggPj7elKZXr9f45Zef+OqrLwkJCWHz5s/4668/ef75F3PcfiGEECK3WBSg7Nu3j549e/L555+zdu1aUlNT6dOnj9kvPoAXXniBXbt2mb7GjBljOmcwGBgwYAApKSls3ryZmTNnsm3bNj766CP79MgCsbGxzJo1na5dn2P8+PeoXz+IJUuW4+TkZEozY8ZsatWqw3vvvUv37l345JM1DB481GyhthEjRrJnzx6efrodL730QmZVZVn//U8x7te+/VO89dZIli1byksvPc/Zs6dZvHgZ3t7/DZy9ceOG2cDeL774nJSUFEaPHkm7dm1MX+vXf2JK06bNE4wb9wHr1n3Ciy924+uvv2LOnPkEBTXIcfuF4+iPHcG3ZDH0x444JL819dnaxnspx07iXKYmyrGTNpeV2323pd/2vGZCFDQWveJZs2aN2eeZM2fStGlTTp48ySOPPGI67urqiq+vb6Zl7Nq1i/Pnz7N27Vp8fHyoWbMmw4cPZ+7cuQwZMiTDtN/c1K5de9q1a//AND4+PkyaNOWBaVq1epxWrR43OzZw4JsMHPim2bGePV+lZ89XTZ87dXrO9LTmQXr0eIkePbJ+pbNq1cdmn3/44adsywTo3LkLnTt3yVFaIYQQwpFsmmYcExMDgKenp9nx7777jiZNmvDMM88wb948EhISTOeOHDmCv78/Pj7/DfJs0aIFsbGxnD9/3pbmCCEKGUVRs11JVgiRP1k9SNZoNDJ9+nQaNGhgNkPkmWeewc/Pj5IlS3LmzBnmzp3LpUuXWLx4MQARERFmwQlg+hweHo4QQuSIqqJXFTCmIlGKEAWP1QHKpEmTOHfuHJs2bTI7/uKL/w2yDAgIwNfXl9dee42QkBAqVKhgfUuFEOJeig5S0reOkAhFiILGqgBl8uTJ/PHHH2zYsIHSpUs/MG29evUAuHLlChUqVMDHx4djx46ZpYmISFsVNatxK0IUdqn+NYjccxijX1mH5LemPlvbeC/NvxrJ//wEZR788yU32uWIa2WPvEIUdBaNQdE0jcmTJ/Prr7+ybt06ypcvn22e4OBg4L/go379+pw9e5bIyP8WH/vnn39wd3enWrVqljRHiMLD1RVjlarg6uqY/NbUZ2sbzcpygcoV0/5rc1m53Hdb+m3PayZEAWNRgDJp0iS+/fZb5s2bR9GiRQkPDyc8PJzExEQAQkJCWLJkCSdOnCA0NJTt27czduxYHnnkEWrUqAGkDYitVq0aY8aM4fTp0+zcuZMFCxbQs2dPh87gESI/Ua9cxmNQX9Qrlx2S35r6ssujqmlfORISim7wGAgJzXH91rbL0entlVeIgs6iAOWzzz4jJiaGV199lRYtWpi+fvzxRyBthdLdu3fTp08fOnTowKxZs2jXrh3Lly83laHT6Vi+fDmqqvLiiy8yevRoOnfuzLBhw+zbswwUFAX5suOXvPd3HPXuHVy3fo56945D8ltT34PyqCq4KIm4KIn/fu88mHLnLrqvvkO5czfH9VvTroeR3l55hSjoLBqDcubMmQeeL1OmDBs2bMi2nLJly7Jq1SpLqraapmkYjUZUVUV+odqf0Wg0W/ZfiMwoiooxMS3YUGTfGSFEDhT4vXg0DeLjk1Fy8mebsJimaUh8IoQQwt4KfIACaUGK/JUvhBBC5B82rSQrhHAMY6nSxI16B2Mp66bdWprfmvpykkdRVFQ1+5etWilfDCMHo5WyfemB3O67LffG1vsqREGmaPn40UJERIy8XhAiH9DpVJySbuLipJJiBD2pKMZMdjA3pnIjeA+la7fIurDM0hhTSVJcSXEpg8FgtH8HhBB2oSjg4+ORo7TyBEWIfECJicZpx28oMdEOyW9NfdnlUVQFkmK4c3ZX5sHJvWJiUX7fBTGxOa4/3f3TmXO777bcG1vvqxAFmQQoQuQDuksXKd6jK7pLFx2S35r6cprHkJSYbVnKpSs4vdwP5dKVHNcPaYGJq5o2nTk9SMntvttyb2y9r0IUZBKgCCEKBEVRcSYRfVIUWuJdFEV+vAmRn8m/YCFEgaCoKlpyDIohWZY8EqIAkABFCCGEEHmOBChC5AOaswuGSpXRnK3bPM/S/NbUZ2sbzbg4o1WqAC6278+V2323pd92vWZCFDAyzVgIket0OhXnlFs4p8Zy4/TerKcR2zDNOEXvDoBTSgxJqptMORYiD5JpxkIIIYTI1yRAESIf0J08gXfNyuhOnnBIfmvqs7WN91JOncGpdjOUUw/eoDQ32uWIa2WPvEIUdBKgCJEPKIZU1MhIFEM2C5zZKb819dnaRjOpqShRtyHV9rJyu++29Nuu10yIAkYCFCFErlMUmfkrhLBModjNWAjx8KgquCiJ6BSDRClCiByTJyhCiFylKCrGpOh/99/JvQhFUVSJf4QoQOQJihD5QGqVatz+4VdSq1RzSH5r6rO1jffSqlYi5btNaFUr5SyDoqJXFTCmZoiBcrvvtvTbntdMiIJG1kERQuQq0xooWlL265xYuw7KfeeTFFdZB0WIPEjWQRGigFHDrlH0g3dRw645JL819dnaRjNhN9BNmAlhN2wuKrf7bku/7XrNhChgJEARIh9QI8IpsmIJakS4Q/JbU5+tbbyXEhGJbuU6lIhIm8vK7b7b0m97XjMhChoJUIQQQgiR50iAIoQQQog8RwIUIYQQQuQ5EqAIkQ8YvbxJeL0vRi9vh+S3pj5b23gvzasEhtdeQvMqYXNZud13W/ptz2smREEj04yFEHal/vtnj/HfGb4yzVgIkU6mGQtR0MTHoz92BOLjHZPfmvri43E6fgSXhChclERToGLVPjzxCSjHTkJ8gqU5M21Xrvbdlntj630VogCTAEWIfEB//iwl2j6G/vxZh+S3pj79+bMUf+IxOHkEY+JdFEVFr0/bh0evGCxqr3L+Ik7tu6Ocv2hRvqzalZt9t+Xe2HpfhSjIJEARQuQKRQEXJRld8u1/9+ERQoickwBFCJErFEW5Z5NAIYSwjAQoQgghhMhzJEARIh/QFBWjuweaYt0/WUvzW1Ofpqho7h5p73Zspapo7kX/mxJkg9zuuy33xtb7KkRBJtOMhRB2o9Op6BOvA6AV9UOfeBMnLem/BDLNWIhCzZJpxvpcbosQohBSFBW9loBOMYD8ESGEsII8VxQiH9CdOU2Jlo3RnTntkPzW1Kc7c5pizRqhnj2HoipoyTHWD5A9cx59q2fgzHnr8t/Xrtzsuy33xtb7KkRBJgGKEPmAkpSI/sxplKREh+S3pr70PCQlW9VG87KSUM9eQElKyj5xDtuVW3235d7Yel+FKMgkQBFCCCFEniMBihDC7hRFtXx5eyGEuIcEKEIIu1JQcNKp6BVZoE0IYT2ZxSNEPmCoWIm76zdjqFjJIfmtqc9QsRIxG7agVK4IKXFWtTOdVrE8KZ8sQatY3qZyVBW0yrnbd1vuja33VYiCTNZBEULYhaqCTqfDKem+tU/u5cB1UFJdy+Ckpe0SnKS5YpQlUYR46CxZB0Ve8QiRDyg3b+K2cB7KzZsOyW9pelUF11tXcJszAX34DavaaOZWOOpHK+FWuNVFKIqCMfEuWsh53BbMz7W+23JvbL2vQhRkEqAIkQ/obl7HfdokdDevOyS/pekVRUULuYDL9LmoYWFWtdGsvBu30M/4EOXGLdvLunmLIlMn5lrfbbk3tt5XIQoyCVCEEEIIkedYFKCsWLGCbt26ERQURNOmTXnzzTe5ePGiWZqkpCQmTZpEkyZNCAoKYujQoURERJilCQsLo3///tSrV4+mTZsya9YsUlNlxL8QQggh0lgUoOzbt4+ePXvy+eefs3btWlJTU+nTpw/x8fGmNNOnT+f3339nwYIFfPrpp9y6dYshQ4aYzhsMBgYMGEBKSgqbN29m5syZbNu2jY8++sh+vRJCCCFEvmZRgLJmzRq6du1K9erVqVGjBjNnziQsLIyTJ08CEBMTw9atW3nnnXdo2rQpderUYfr06Rw+fJgjR44AsGvXLs6fP8+cOXOoWbMmrVq1Yvjw4WzcuJHkZNuXyBaiIDIW8yTp2c4Yi3k6JL819WnFPIht2QDNs5hVbTQry7MYxmfa26esYh4kd8q9vttyb2y9r0IUZDaNQYmJiQHA0zPtH9eJEydISUmhWbNmpjRVq1bFz8/PFKAcOXIEf39/fHx8TGlatGhBbGws58/bvjGYEAWRsVJlotesx1ipskPyW1OfVrECtz4YCDauXQJAxfKkrlpgl7K0ihWIXbsh1/puy72x9b4KUZBZHaAYjUamT59OgwYN8Pf3ByAiIgInJyeKFTP/q8fb25vw8HBTmnuDE8D0OT2NEOI+ycmoYdfA2qeMlua3pr7kZHTht61v431lEXbDbmUp13Kx77bcG1vvqxAFmNUByqRJkzh37hwffvihPdsjhMiE/vQpvOvXRH/6lEPyW1OfeuYcFXuORTl9zqo23ks5fQ7nhq3tUpZ65hwlAgNyre+23Btb76sQBZlVAcrkyZP5448/WLduHaVLlzYd9/HxISUlhejoaLP0kZGR+Pr6mtLcP6sn/XN6GiGEEEIUbhYFKJqmMXnyZH799VfWrVtH+fLm74fr1KmDk5MTu3fvNh27ePEiYWFh1K9fH4D69etz9uxZIiMjTWn++ecf3N3dqVatmg1dEUIIIURBYdFmgZMmTeL7779n6dKlFC1a1DRmxMPDA1dXVzw8POjWrRszZ87E09MTd3d3pk6dSlBQkClAadGiBdWqVWPMmDGMHj2a8PBwFixYQM+ePXF2drZ7B4UQhY+iqKgqKA+7IUIIq1kUoHz22WcAvPrqq2bHZ8yYQdeuXQEYN24cqqoybNgwkpOTadGiBRMmTDCl1el0LF++nIkTJ/Liiy/i5uZGly5dGDZsmK19EUIIUFSc9SopibfQqwZkCUgh8ifZzViI/MBohJQUcHJK25kvt/NbmF6nU9HHXyPi6G+UC3o86zw53c24ZjNISQUnfcaycrjjcfjFI/hWrkcSLqSoPhhUXa703aZ7Y+t9FSKfsWQ3Y4ueoAghHhJVBRcXx+W3pj5VBWc7/aJVVXCx7ZWvISnxnrJcwGC0oO5cvlb2yCtEASchuxD5gO7COTw7P43ugnXTbi3Nb0196oVLlBk1Fy5csqqNZi5cQt+1l13KUi5cwqPTU7nWd1vuja33VYiCTAIUIfIBJS4O5392ocTFOSS/VfXFx+N27CxKXHz2abOtPx519367leX0d+713ZZ7Y+t9FaIgkwBFCCGEEHmOBChCCCGEyHMkQBFCCCFEniMBihD5gKFseWLmL8JQ1rrdfS3Nb019WtkyhI94Fa2sn1VtNC/Lj9S5k+1TVrmyxC1YnGt9t+Xe2HpfhSjIZB0UIYTNdDoVfeJ1Ik7+Trm6LbNOmNN1ULJZ4yTHaWo1BaeipOiKkmh0xZjDmcZCiNxhyToo8gRFiHxAiYzEdcM6lHv2sMrN/NbUp0RF4fG/nRB526o2mom8jbrxC9vLUnRw4yq6NatQo6JylsUB18oeeYUo6CRAESIf0F27isfbQ9Fdu+qQ/NbUp1y7ju+Hn6JcC7OqjeZlhaEfNd5OZV3HdfR7qKGhOUrviGtlj7xCFHQSoAghhBAiz5EARQghhBB5jgQoQgghhMhzJEARIh/QihYluVkLtKJFHZLfqvqKFCEh0B+taBGr2mhefxGMTR+xT1lFipDatHGu9d2We2PrfRWiIJNpxkIIm+XZaca1W4AxlSTFlRSXMhhyuqOxECJXyDRjIQoaoxGSkrB6IQ9L81tTn9EIySnWtzFD/cl2LCsX+27LvbH1vgpRgEmAIkQ+oD9xDN/yvuhPHHNIfmvqU08GU+WZwSgngq1q472UE8E4V6pnn7JOnsajch10x3On77bcG1vvqxAFmQQoQgghhMhzJEARQhQKigKq/MQTIt+Qf65CiELBiURclEQJUoTIJ+SfqhCiUDAmxWJMvIuiyI89IfID/cNugBAie6k1ahF5JBijj69D8ltTnzGgOlc2zqJMjepWtfFeWo3qJB/8HXy8bC8roDrxh//BWMIzR+kdca3skVeIgk4CFCHyA2dnjH5lHZffmvqcnTH4lgBnZ8vyZVEWfqVtLwfA2QnNrwQYUiEn6yY54lrZI68QBZw86xQiH1AvX6JYn16oly85JL819SlXQig5ZTlcscPOvFeuou/3lt3Kcun7JsqVkBwld8S1skdeIQo6CVCEyAfU6Lu4fPc1avRdh+S3pj4lOgb3nYdQ7kZb1Uazsu5Go37/s33Kio5B/93/UO7G5Ci9I66VPfIKUdBJgCKEEEKIPEcCFCGEEELkORKgCCGEECLPkQBFiHzAUKoMse9NwFCqjEPyW1OfVqokUa93QStd0qo2mpVVuiSp746wT1mlSpL83hgok7OyHHGt7JFXiIJO0TQtJxPv8qSIiBjyb+uFKDh0OhV94nUiTv5Oubots05oTOVG8B5K125h3Xlr0mhGcPZAS4kn2agjxbUMBoPsHizEw6Ao4OPjkaO08gRFiHxAuXsH559+RLl7xyH5rarvbjRFdh8BO8y84W40ys877FNWdCzK99+h3I4EJfvkDrlWdsgrREEnAYoQ+YDuymU8e/VAd+WyQ/JbU58acpXSE5ai2GHtEuXKVZxeG2zfskJCc5TeEdfKHnmFKOgkQBFCCCFEniMBihBCCCHyHAlQhBA2U5QcDe8QQogck80ChcgHNBdXUgNqoLm4OiS/JelVFVyURPSuepIrlgEXF6vaaF6/C0b/qmgPoazcvFb2zCtEQSfTjIUQNtHpVJxTbuGcGsuN03ttmyKcG9OM7zmWpLiS4iLTjIV4WGSasRDCIVQ17QeOEELYmwQoQuQDuuPH8K5SFt3xYw7Jn5P06a92XJREFEA5eZpynYainAi2qo33Uk4E41S9kZ3LOp2j9LlxrXIjrxAFnYxBESIfUDQjamwMimbdqwlL8+ckvaKoGBPvgk6HoihgNKLGJ2Iw2uH1idGIEhsH9izLjn23Jb298gpR0MkTFCGEEELkORKgCCGEECLPkQBFCFFoKIoqg3qFyCckQBEiH0it5s/t3/4itZq/Q/JbU59WrTLXl72PVq2KVW00L6sKKT9/aeeyquKsV3EmHvUBP/kcca3skVeIgs7iAGX//v0MHDiQFi1aEBAQwG+//WZ2/p133iEgIMDsq0+fPmZp7ty5w8iRI2nQoAGNGjVi3LhxxMXF2dYTIQqyIkVIDawPRYo4Jr8F6RVFTVtF1s2NlOoVoYibdW00q98NLbC2fcsqWhSSYzEm3kVRHvCjLxevlV3zClHAWRygxMfHExAQwIQJE7JM07JlS3bt2mX6mj9/vtn5UaNGcf78edauXcvy5cs5cOAA48ePt7z1QhQSauhV3Me+jRpq3e6+lubPaXpFUXHSqeiVVAi7TomPNkJomFVtNBMahu7dyQ+lrNy6VvbOK0RBZ3GA0qpVK0aMGMGTTz6ZZRpnZ2d8fX1NX56enqZzFy5cYOfOnUydOpV69erRqFEj3n//fX744Qdu3rxpXS+EKODUqEjc1q5GjYp0SP6cpldUBVLiwJCKEnUHj2//QIm6bVUbzcqNuo3uk88eSlm5da3snVeIgi5XxqDs27ePpk2b0r59eyZMmMDt2//9YDh8+DDFihWjbt26pmPNmjVDVVWOHZPFioQQQgiRCwu1tWzZkieffJJy5cpx9epV5s+fT79+/diyZQs6nY6IiAi8vLzMG6HX4+npSXh4uL2bI4QQQoh8yO4BSseOHU3/nz5Itm3btqanKkIIIYQQ2cn1acbly5enRIkSXLlyBQAfHx+ioqLM0qSmpnL37l18fX1zuzlC5EtGH1/iBwzG6GPdvxFL82eXPrNNAjVvL6K7tUXz8baqjWZl+Xhj6N/7oZRl72uVW3mFKOhyfS+eGzducOfOHVPwERQURHR0NCdOnKBOnToA7NmzB6PRSGBgYG43R4h8yehXlrgpMxyW/0Hp0zcJVBUV7d4TfqW5M+hFSvuVtrqd95ZlmPSO7eXcX5YxNdvF2ux5rXIzrxAFncVPUOLi4ggODiY4OG2X0dDQUIKDgwkLCyMuLo5Zs2Zx5MgRQkND2b17N2+++SYVK1akZcuWAFStWpWWLVvywQcfcOzYMQ4ePMiUKVPo2LEjpUqVsm/vhCgoYmPR798LsbGOyf+A9OmbBBqTos0fo8TF43zqAthjTaO4OJQDh+1flqJmv1ibHa9VruYVooCzOEA5ceIEnTt3pnPnzgDMmDGDzp0789FHH6HT6Th79iyDBg3iqaee4r333qN27dps3LgRZ2dnUxlz586lSpUq9O7dm/79+9OgQQMmT55st04JUdDoL56nRMcn0V8875D81tSnXLxM6WEzUS5ctqqNZmVduIzTsy/bvyxFzXaxNkdcK3vkFaKgs/gVT5MmTThz5kyW59esWZNtGcWLF2fevHmWVi2EEHaV/hTFaHy47RBCZJTrY1CEECIvUhRwJhGAJNVVghQh8hgJUIQQhZKiKBgT7qb9v2sRQCIUIfIS2c1YiHxA0+kxenuj6az7m8LS/FbVp9Nh8HQHvR3+7tHr0bxKPJSyHHKt7JBXiIJO0TRNyz5Z3hQREUP+bb0Q+ZNOp6JPvI6q06EoCk7GtNckGFO5EbyH0rVbZJ05uzT2KCO7NMZUkhRXDG5+qPFpGwimupbBYJAnKELkNkUBHx+PHKWVJyhCCCGEyHMkQBEiH9CdDsarcT10p4Mdkt+a+pQz5ynTaxzKmXNWtdG8rHM4NW3/UMpyxLWyR14hCjoJUITIB5TkJHSXL6EkJzkkf07SK4qK2YKsyck4hYVDUrJVbTSTlIxyOeShlJUb1yo38gpR0EmAIoSwmKKoOOlU9Erqw26KEKKAkqHjQgiLKaoCKXZYhl4IIbIgT1CEEEIIkedIgCJEPmCoXIU7m7/CULmKQ/JbU59WqQK3ZgxHq1zRqjaalVW5IimbVjm0LFVN+3LEtbJHXiEKOnnFI0Q+oHkUI6VNW4flt6o+D3cSH6kDHu4Wti7zsrTWD1jnxM5lmS177+mAa2WHvEIUdPIERYh8QL15gyKzp6PevOGQ/FbVdzMcz3Xfws1bVrXRvKxb6OYudlhZiqJgTLyLMfEu6s1buX+t7JBXiIJOAhQh8gH15g2Kzp1pU4BiSX5r6lNuheP56XcoN8OtaqNZWTfD0c1bkmtlKYqKqmI+TfpfjrhW9sgrREEnAYoQonBRVJz1KrrEWzirhkyDFCHEwycBihCicFFUSI7lztldKIbkzB+jCCEeOglQhBCFkiEp8WE3QQjxABKgCJEPGD2Lk9jtBYyexR2S35r6NM9ixD3RBK24p1VtNCuruCeGrs8+lLK04rl/reyRV4iCTtE0TXvYjbBWREQM+bf1QuRPOp2Kc8otnLX79o8xpnIjeA+laz9gSm92aexRhiX11GhCkuqGwc0PNT4MgFTXMhgMxqzLFkJYTVHAx8cjR2nlCYoQ+UFiIurFC5Bo5WsJS/NbU19iEvprtyDRDhvfJSbBpSsPpyxHXCt75BWigJMARYh8QH/2NN6PBqE/e9oh+a2pTzl3Ab/e76GcPW9VG83KOnse52ZPPZSydGdy/1rZI68QBZ0EKEIIIYTIcyRAEUIIIUSeIwGKEEIIIfIcCVCEEEIIkefIbsZC5AOpgfUJvxXtsPzW1KfVrUXIb6soXbu2pc3LWFZgbZKvB9tcjjVlGerl/rWyR14hCjp5giKEEEKIPEcCFCHyAd35cxTv8AS68+cckt+q+s5fotTQGXD+klVtvL8s/TM9HFaWovy3JY/u/FmKP53L18oOeYUo6CRAESIfUOLjcDq4HyU+ziH5ralPSUjAJfgiSny8VW00rz8e9eBRh5SlKCpOWkLazsaqinN8FE4H9qMm5N61skdeIQo6CVCEEIWaoqpoyTEohmRUVcWYFJt2XJEfj0I8TPIvUAhhkXtfhwghRG6RAEUIkWOqCi5KInrF8LCbIoQo4CRAESIfMJSvQPSSlRjKV3BI/qzSK4qKMSkaxZiaIY9WriwR7/RBK1/WqjaalVW+LKmLZj2Usozly5Lw0VyMFWy7VrmdV4iCTtE0TXvYjbBWREQM+bf1QuQ/Op2Kc8otnLVMdgY2pnIjeA+la7fIuoDs0tijDEvqqdGEFKe0rd+dUmJIcfLAaEhF0yDVtQwGgzHrOoQQFlMU8PHxyFFaeYIiRD6gRETgumYlSkSEQ/JbVV9kFO7f/A4RUVa10UxEFOrajQ+lLCUyEqdPNqBEhOcsvQ33xtb7KkRBJgGKEPmALiwUj3dHoQsLdUh+a+pTwm7gtWgTSth1q9poXtZ19OOmPpSylGs3cHtvEuq1azlKb8u9sfW+ClGQSYAihCjUFEWVWUlC5EGyF48QovBSVfSqAsZUmTstRB4jAYoQIluq+u+ThoL2S1zRQUr6Kq4FrXNC5G/yikeIfEBzdyf58TZo7u4OyX9vetPaJ4nXcVESs/w1rhUtSkLDWmjuRa1qo3n9RTG2av5QytLci5LaqiV4WH6tLG+bbfdViIJMphkLIR5Ip1PRJ14n/MR2StVrh6IoOBkTMybMj9OM7z1vTCVF745mNOKsV0nRFSXR6IpRZhoLYTcyzViIgsZgQImJBoOVK7hamj+T9MmJ2WzcZzCgxCVY38b7yiIm9qGUpWgaRFzHGBeVs/14bLk3tt5XIQowCVCEyAf0J4/jU7Uc+pPHHZLfmvqUU2co/9wwlJOnrWqjWVknT+Ps/8hDKUs9GYxzzWaoOUxvy72x9b4KUZBZHKDs37+fgQMH0qJFCwICAvjtt9/MzmuaxsKFC2nRogWBgYG89tprXL582SzNnTt3GDlyJA0aNKBRo0aMGzeOuDjZblwIIYQQaSwOUOLj4wkICGDChAmZnl+1ahWffvopEydO5PPPP8fNzY0+ffqQlPTf0tijRo3i/PnzrF27luXLl3PgwAHGjx9vfS+EEA4ha4YIIRzF4gClVatWjBgxgieffDLDOU3TWL9+PYMGDaJt27bUqFGD2bNnc+vWLdOTlgsXLrBz506mTp1KvXr1aNSoEe+//z4//PADN2/etL1HQohc4eTkgpNORa9k3ChQCCHsza5jUEJDQwkPD6dZs2amYx4eHtSrV4/Dhw8DcPjwYYoVK0bdunVNaZo1a4aqqhw7dsyezRFC2JGqd0pbM8QgAYoQIvfZdaG28PC0zbW8vb3Njnt7exPx72ZYEREReHl5mTdCr8fT09OUXwhhLrVmbSJOXUTz9HRI/nvT63JYh1ajOqFfzqdkTX+r2mhWVk1/ko//DZ45m45oz7KMNQNIPvoHxuIlc5Telntj630VoiCTlWSFyA+cnNB8fByX35r6nJwwFvcAJyfL8mVRFj5e2afLjbKcnMDbC9Qc9sOWe2PrfRWiALPrKx5fX18AIiMjzY5HRkbi8+8/Qh8fH6KizLc9T01N5e7du6b8Qghz6qWLFHv1RdRLFx2S36r6Ll/F54PFcDnEqjaalxWCvvebD6Us5fIV9K8PRbl8JUfpbbk3tt5XIQoyuwYo5cqVw9fXl927d5uOxcbGcvToUYKCggAICgoiOjqaEydOmNLs2bMHo9FIYGCgPZsjRIGhxkTj8vP/UGOiHZLfmvqUmBiK7D6KEh1jVRvNyoqOQf3l94dSlhIdg/rrnyjRsTlKb8u9sfW+ClGQWfyKJy4ujpCQ//4SCQ0NJTg4GE9PT/z8/OjVqxfLli2jYsWKlCtXjoULF1KyZEnatm0LQNWqVWnZsiUffPABkyZNIiUlhSlTptCxY0dKlSplv54JIYQQIt+yOEA5ceIEvXr1Mn2eMWMGAF26dGHmzJn069ePhIQExo8fT3R0NA0bNmT16tW4uLiY8sydO5cpU6bQu3dvVFWlXbt2vP/++3bojhBCWC9HS9sLIRzC4gClSZMmnDlzJsvziqIwfPhwhg8fnmWa4sWLM2/ePEurFkKI3KOo6FUFRZF9cYTIC+TPBSHyAUNpP2InTcdQ2s8h+a2pTytdktsDn0crY/urWq1MKVInjHVsWYoKKXFoJb1JHT8qx3Xbcm9sva9CFGSKpmnaw26EtSIiYsi/rRcif9DpVPSJ14k4+Tvl6rbMOqExlRvBeyhdu4X1aexRhj3qMaaSpLiS4lIGg8GYdT1CCIsoCvj45GxNInmCIkQ+oNy5jfO321Du3HZIfqvqu3MXtz8PwJ27VrXx/rKU7356OGXduYvy3c85Tm/LvbH1vgpRkEmAIkQ+oAu5gmff3uhCcrY2h635ralPuXoN3ykrUEJCrWqjWVkhoTj1H/FQylJCQnEaOAo1h+ltuTe23lchCjIJUIQQWVJk62IhxEMiAYoQIoP0wMRZSZYgRQjxUEiAIoTIIH09EGNyLEohj1BUNe1LCOFYslmgEPmA5upGSt16aK5uDsmvubpiqFMLXF1QFMhJiKK5upJcrQK4ulrVxvvLMtapifYQykpPj1ta351JBCBJdcWYyYQeW+6NrfdViIJMphkLITJIn1qsKCo6N090iRFcP/kXfrUL1zRjg5sfanwYAKmuMuVYCFvJNGMhhF0oqoKWHINiSEbN0XOUgkNRVFQ1Z0+PhBD2JwGKEPmA/vhRfMr5oD9+1CH5dceO4lG5FurxkzmuQzkRTPkOg1COn7KqjWZlHT+FU8XAh1KWcvwUTpUb4HIqGF3iLZxVwwODFFvuja33VYiCTAIUIfIDTUNJTsbqd5qW5tc0lOQUy+rTNJSUVOvbaGv99iorPX1KHHfO7kIxJD/4MYot98bW+ypEASYBihBCZMGQlPiwmyBEoSUBihAig0I+s1gIkQdIgCKEMKOq4KQkATJAVAjx8Mg6KELkA6nVA4j6ay+GipXsnj99EbL0NT4URcVQsQwp27/CWLV6juvQqlXh+uqJeFevalUbzcqqXpWUP75Fq1De4WWZ0pcrA5ePZJvelntj630VoiCTAEWI/MDNDUONmnbPr6rgomSyEJmbK1pANXCyYKE0N1dSKpUFN9sXV0urP+fBkV3LSk9vTM1hehvuja33VYgCTF7xCJEPqFdDcB8xBPVqiF3zK4qKMfEuxsS7puXtIW1nYt2oCShXLdhNODQMr3nr4Oo1q9po5uo1dCPffzhlpacPDctRclvuja33VYiCTAIUIfIB9XYUbhvXo96Ockh+5fZtdJu3ody5m+NxKMrtO7j/bxfK7TtWtfH+snSbtj6UsixNb8u9sfW+ClGQySseIYSJqprP4NErCpqSKqNlhRAOJwGKECJtzx2dgt6YiKLcs6h9agIYUpEIRQjhaBKgCFHIKYqKi5NKStIt9MYkUnWuKLIQihDiIZMxKELkA0bfksQPexujb0m751dUBZJi0pZ111JxUlV0JYtjGNIPzdcnx3VoPt7c7dHBojxZluXrY3H99irLlN7H23RMUVTTqy9V/W9qNth2b2y9r0IUZIqm5d9NICIiYmQLCyFsoNOpOKfcwjk1lhun91K6dovMExpTuRG8J+vz9kqTF+up1RScipKiK0oyrjhp/07L1u6Zli2EyBFFAR8fjxyllScoQuQDSmwMTn/vRImNcUz+2DiUf/ZBbFzOK4mNw+XIGcvy2LN+e5V1f3pFB8mx/07F1mWYlm3LvbH1vgpRkEmAIkQ+oLt4geJdOqK7eMEh+ZWLl3Hq1hvl4uUc16FcukKpUXMtymPP+u1Vlin9pSs5Sm/LvbH1vgpRkMkgWSEKIVX+NBFC5HESoAhRyNy7vH2KUuQht0YIITInAYoQhUz68vYASpGiD7k1+ce9M3mEELlPHvQKkQ9oeicMZfzQ9E6Oye/khFamFDhZUJ9eT6pPccvy2LN+e5WVnl5/z99vioqzXsVJS8iwZJ0t98bW+ypEQSZPUITIBwy1ahN19LRd8utykF6r6U/KoT8sqkOr6c/1zXMoXdPf8gbaoX57lWVKb0yF4D1pBxUVkmPRjKQtqnvP8ga23Btb76sQBZk8QRFCCCFEniMBihD5gO7USbzq1UB36qRD8ivBZ3Fq8DhK8Nkc16EEn8Wvx2iL8tizfnuVlZP0945HseXe2HpfhSjIJEARIh9QUlPQXQ9DSU2xa35FyWIbwJQUlOs3IcWC+lJT0UfcsSxPVqyp315lpadPTc30dPreRc7Ep+3+bMO9sfW+ClGQyRgUIQopRVHRawnoFINsVmwBRVUhKQajmoriItO0hcgt8gRFiEJKURW05BgUYyoSoQgh8hoJUIQQQgiR50iAIkQ+YKhSlTvbfsBQpapD8mtVKpGydR1alUo5rkOrXJGbc0dZlMee9durLFP6yhVzlN6We2PrfRWiIJMxKELkA5q7BynNWzouv3tRtGaNLavEvShJ9QPA3Q6r01pTv73KSk9vzHyQ7P1suTe23lchCjJ5giJEPqBeD6Po1Imo18Psll9R1KxHnly/iW7afLh+M+eVXL+J5+qvLMtjz/rtVZaF6W25N7beVyEKMglQhMgH1PBbFPloPmr4LZvzK0raTBQnnYpeyWIqbXgEusWrUMIjclyHEhGJ5+b/WZQny7KsqN9eZZnSR0TmKL0t98bW+ypEQSaveIQoRBQFnJVEdKoRUuIednPyPUUBVZW/84TIDfIvS4hCRFVVjEnR/04tFpa697WYoqQt1qZLTnsyo8vJJkdCiByTAEWIQiB9WXYnJUlWPLGWoqJXlbTXYgqoOj1a0l2iLx8GwIlk5GGKEPZj939OixYtIiAgwOzrqaeeMp1PSkpi0qRJNGnShKCgIIYOHUpEhO3vmYUoyIwlvEjo2QtjCS/rCvD2Ifml5zEWdf4vWnkA7f/t3Xt0FEX6N/BvVc9MEiCES8JGFrKyYEIIIQTwRTCuCy6wi+iKgrrqriheV7wsHl9YD8hyYLl4oiggioL8ZGUP6xLc91UuR+F9Qf2BK8tLwJCYi0DCndyTSebe9f4x6WYmmUm6J53MJHk+5+RAep7qquqantRUd1X37wfPQ/dB9O+nOQvRvx+sv8nSlcbI/I3aV9B4xr2XxTxugEkwcQYzPPDERsPz4Gx4epvAmL6P1Ha3KyHdWIfcg3LTTTdh27Zt6u+Sz9jnqlWrcPjwYbz11luIjY3FihUrsGDBAuzcubMjikJItyAPTYJ13cZ2pbdnrwKXJG0jKEN/Cs8bK/VlMmQwql5+FIlDfxpKEdufv1H7UuJlN1BQFjimqbPCZDfET+LhyV4OwWN0F6297UpId9YhA5KSJCEhIUH9GTDA++2gvr4eOTk5WLx4MSZNmoTRo0dj1apVOHHiBHJzczuiKIR0DzYbpB8KAJst5PS8sBiw2TXG28H0xDelMZ+7qC+NkfkbtS+d8czhBCssCa2s7W1XQrqxDumglJaWIisrC3fccQdefvllXLrkneOfl5cHl8uFyZMnq7HDhw/H4MGDqYNCSCtMxYUY8IuJMBUXhpReKipEn6kzIZWc0TSCwop/hPmXd4MV/6g5D1ZyBjc88RddaYzM36h9qfElZzTFm8ouw3zHveAhlLW97UpId2b4JZ4xY8Zg9erVGDZsGMrLy/HOO+/g4YcfxmeffYaKigqYzWb07dvXL83AgQNRXl5udFEIIc2YOIMIsvYJIYREEsM7KLfffrv6/5EjRyIjIwNTpkzBvn37EB0dbXR2hBA93DbvTZ6EEBLhOnxSXN++fXHjjTeirKwM8fHxcLlcqKur84uprKxEQkJCRxeFEEIIIV1Eh3dQGhoacP78eSQkJGD06NEwm804evSo+vqZM2dw6dIljB07tqOLQkjXxRiERdsUYUPSMwZhMevLjzEIsyn0MrY3f6P2pTtvbzzjXH9x29uuhHRjhl/iWbt2LaZMmYLBgwfj2rVr2LBhAzjnmDVrFmJjY3HfffdhzZo1iIuLQ58+fbBy5UpkZmZSB4WQVrjTM1BxQdt6QcpiYbJ8fZtnTAYayn6ARTg07UOkj4Kr9JSuMorRqTi/710kpo3Slc6o/I3alxovu4GCb9uMd92UBNe5EzCZewOwAaYo9dj7tkEgetqVkJ7G8A7KlStXsHDhQtTU1GDAgAEYP348PvnkE3Wq8auvvgrOOV544QU4nU5kZWVh2bJlRheDkB6JcyCKeae7Onh0m38giUGYBLgavR+oEiC4gBDUBoS0h+GXeNatW4dvvvkGeXl5+Oqrr7Bu3TokJSWpr0dFRWHZsmX47rvvkJubi40bN9L9J4S0QSoqRL87boNU1Pp0VMY4ZHstZHutuqop597prDHT7gKKNE6FLfoRpmn3ao8HgOIzSHxmhb40RuZv1L6U+GKN04xLLzfFnwWcVgiXtUUbBKO1XQnpiejJEYR0Acxug/n7k2B2fQt6KSMqprqLkL4/DWbXuPiY3Q6eV6A5XkljKSnTlcbI/I3al+54pzPksobaroT0BNRBIaQbU0ZU6s7lhrsohBCiC3VQCOkB3C5tN8cSQkikoA4KIYQQQiIOdVAI6QI8ST9D7ZaP4En6WUjp3YnxcL2/DiJpiKZ4kTREVzwAiKE/RfnSp3WlMTJ/o/alxmt8KrPeY+urve1KSHdm+DRjQojxRL/+cN49W1ca37W/5NjeEJNv0564XxzEXb/WlR/6xcF2+wTE9YvTl86o/I3alxIvu4HLbYeL2N4Qt2R5f5H1PUYglHYlpKegERRCugB27Rpi3t0Idu2atnjGYUEjopgdjAFSdR34e/8FlGtcFKy8Ql98U5rYXV/oS2Nk/kbtS2c8D3JsGWt7ZVm97UpIT0IdFEK6AOnKJfRZ9iqkK5c0xTPOIBz1MHkaYWEemKuqYVq+FuzyVW3pL1/VFQ8A7Mo19H/vn7rSGJm/UftS469o6zRIFS2PLWMcUWZvJ5G38imrt10J6UnoEg8h3QTn/pd1GOeAqwFMdoOBnvXSmRjngKMeMneDRfUCQMvJEqIXdVAI6QaUBdk44/CEuzCEEGIA6qAQ0g1wziFstZAlCTRYQgjpDugeFEK6ADm2LxwzfgM5tm+L15TREwv3BO2biN4xkKdPgegbqyk/0TdWVzwAiNhYNE7K0JXGyPyN2pcaH6sxvtmxZYxr7iO21q6E9HQ0gkJIFyAP+znq/vaPgK8xxiE76mDyOAFzVMAY9+BBcH+0SXuGNybpiweAG4eiYsUCJN6Y1HZsR+Rv1L6UeNkNFFxsM9zv2AoZJs68aTX0UlprV0J6OhpBIaQrcLnAKioAlyu09G43UFGlPb3LpS++KQ2vqQ+9jO3N36h96Y33PbbMe2MyPG4E6qFwDv9ZPe1tV0K6MeqgENIFmApOI37Uz2EqOB1SevPZi7Ck3wpWUKQpnhUU6YoHAPZDMYbMWagrjZH5G7UvNf6HYk3xWo+tcikuitnVTkp725WQ7owu8RBCSCdQniwNACyaph4T0hYaQSGEEEJIxKEOCiFdmPeeBlqGjRDS/dAlHkK6KOWeBjisMDEPrX/ShTCGVpfAJ4RQB4WQLsGdlo6KHy9A9OqtblPuaagtPoqfpNyM1noorp8PhbPoGNArRlN+Im2krngAEKNScP5/rcdP0kZqTmNk/kbtS42PNgNFx9qM13JsfR9DoDzIUTDAkd6yXQkhXtRBIaQrkCSIIIt5uZx2Dek5ENtHV3664pvSiN4x3rTtFUr+Ru1LiZfdGuNbP7aMARZcfwwB4wyyvRZCACz6BlqkjZAgaJCRkC5AOlOCuPvvgelsSUiXBkwXrsL04BPAmXPaEpw5py8eAM6UImHROn1pjMzfqH2p8aWawts6tox5OySyo67FIBf/0duu0pkSbWUjpAehDgohXQCzWmE59H8Q1VDlt45GizjG/Z5orG632cEP/zeYtUFjfg264gGANTQg5ni+rjRG5m/UvtT4Bo3xGo9toLZR2pVZrZryIqQnoQ4KIV2I7LRCtteCsQCnLuMwcQYzgj+Th4QHYxxmiVPbEKIDdVAI6S6alllnsgsBh1FI2DDOArYNNRMhwVEHhZAI5zsDxBdjNLM40jHGve0X5DUzvDc4SxKnaceENEOnBCERTFnrxDQkHo2vvwEMTgTg7ZxEMTss3KPpD5snYQDcq5ZADL5BU75i8A264r1pElH1/EO60hiZv1H7uh6fqCk+6LFlHBYTh2S/5l2nRtncdC8K4wye+L5wr1kOHm9q9d4iQnoimmZMSART1jphsVHAk4/CbK+AU26aGeKog8njBNcwjiL3i4X82G+0Zxw/APJjD+sr7MABsP52CvrED9CXzqj8jdqXEi+7gWtthwc9towDTitqzuQiYcQEdZuJMwiPB25IwKAEyI/MRkXBtxgwYCA9o4cQH9RfJ6QLYLW14H/fDlZRDrC2L+8w5t9t4XUN4Lv+N1Bdoy3D6hp98QBQXYteB77Vl8bI/I3alxpfqym8rWPrcfisU9P8PqHqGvCcz+GpKNdWNkJ6EOqgEBKhfJ+zI124hOjnFoJduOi9d0HYgi9v3/Qt3cSuLzQmXa2A6flFYOcvasqbnb+oKx4A2IWLiF+zVVcaI/M3al9q/AVt8XqPrS9+/gJML74K89Wq69s4LYNPCECXeAiJSL7P2TFLApwr66QDjHMIZz2Y7EbAHkrTt3QS2QJNFVdWnQUAlykasgzIdMWH9FDUTyckTDgPPntDufek+odvwIQHcNuUVzq1jKSDqKNcHv/NTavOCkc9LLDTjbOkR6O3PiGdjHPAZGqanWO/3OofIU3P2SFdjzLK5Qn8vB8uSRCO2uCL8hHSA9A7n5BOpFy6iWJOCHstyvMOavojJHr1gjw+AyImtKf7iugob/pevbTFK/lpjAcAERMDR+rPdaUxMn+j9qX3WOs9ti3yGjcGcrTl+vRjWnWWEAB0DwohnUq5dANJAhjgtDdqSzhiGNyf79T+hN1m3EMTvem1UvLTY8QwXN3wZySOGKYvnVH5G7Uv32NdcLXNcN3Htnlen+2AyPsaUWYOD2yQJWXVWTcYN4NzQAi6F4X0PNRBISRC+F7moSXQexZJMgOuBkhMgqTcl8I4LGYJFlELFzfDjmjqpJAehS7xEBIBlJVho7kD0dx7c6RvH4WdOg3LDalg3+e3WONEC3NxqTf9qdPayqPkpzEeANj3+Uj61ZO60hiZv1H78j3WWug9ti3y+mk6zMWlAJP870thHHDZAFtV0MuAgaYkK/c40c21pKujERRCIgDnDKLBe+lHyB5wyYzAt6V4Z3+EeqmHdE3eZ/owCOG/PYp5b6J2cO/oCudANHfADCeNupAujzoohIQZYxwmYYPEm5Y/b7pJUsgBnrPDGK1x0tM0PdNH2C7BHN0PgIAQgIv1gmzzrnarLJHPmHeNHLitkHkMWBQtnU+6LhoEJKQDKGucmEzXh9uVn+aXZxhn3oXXPE4wLoHz68uh67+YQ7qdpmf6VJ7+v4CrQZ1+rKwyTEh3RSMohOigjGgEGzZXOiEW2CHb6xEVHQNAwM2jAAiY4IZoGikRARLT5RsSjJDd6siaS7JAEjZw7oFTlsJdNEI6BHVQSI/RVudCS/rm1/ybvx7NHTAzt3eNk5JjGHTTBEDIMFliAdkBeNze6aPmGO8Ksb6UmyQDEMkj4DyyH7ghMaSyu342WFf6UPITNw3HpY/+ivjkESGVsb35G7UvNf4n8cDZE23G6z22LfL6Zg9cdaVtxiozfZiQYTGbAFc1IDsBFgPGvCN2wWZ/tfe9T0g4UAeF9AhaOhdA6x/gnHMIWy0Erl/z903LOQec9YBwgMluyMoqsM2fjdO0zLnweCBpnU8cHQUM+5m22EAsZn3pQ8kvOgrunw7ypm2v9ta3PftS4rWOZOk9ti3ySgIKLmlP4/d+YmCShCg0wm2vhalXf4hmbymTSXm+D4OTx0D2eZNTh4VEMroHhfQIygJpgaZrXl/d1X/Jed8pnEqMhV9f3dN3yfpo7vAuXd/s2SqBC3P9HhPGNXZQyi5Aeu5/AmUXtMU3I10u15c+lPzKLmDg6i0hl7Hd+Ru1L53xuo9t87yeXwzpcrn+tID3BlrJBMlRhZrCr2HyNMLs85Rr7/vWCZOzBmaPFSb7ZURzpzqVnaYik0gW1rfnjh07MHXqVKSnp2Pu3Lk4depUOItDupFgj6xXpmv63bTq03nhXLmxlft9iDPGITvqwDxO7xOFmc+S9Y5a7zda9QnD+mhZ14TV1ELa/RlYTa3u/QMAtzbqSh9Kfqy2Dr0P/jvkMrY3f6P2pcbX1mmK13tsW+a1B9yqcUXhFju43tmF7PFZR0UZWbFDclZ7Lyc6ragt/AZmuREmRxWEs159WKXvj++2QDd5Ay3/r3fdldYelEmIImxvj71792L16tV47rnn8Omnn2LkyJGYP38+Kisrw1Uk0oU0/1ANNNrh+w2RMYBxjigzh2S7pH6LjOYOnxiOKNjQG/WwOK/5fYg3vxLDGINw1MPkaUQUFzBLHCamv3NiMltCTktIUMrIiqvar9Mse9xN97F4EGWW0EvUqosDKv96R1kciOYO9IYVvVHvt4CgMmqo/N8bV49oHnwE0pdyfioPygx1UTnq5HR/YWvabdu24f7778d9992HESNGYPny5YiOjkZOTk64itRlBDvxuxot9Qj07Uz5gLv+weo/ZM05h2h6ZL0y1dd7eUYGnPXqt0hz07B3FLM3dWAYhMsKNFaipvi/wYRb/RBXYgBlFMa7uBpcDYDwtPpk2tYoNz6GkpaQoNp4WrLvKrVwWSHbayGc9ZCbHmAJVwOYywo4av1jHPWQJEk9vziXvOuuNFY2/e49UX3P0RbT7JtGLMvzDkI46mGBf1wgzT8rvB2jtp8GHix9W9tJZAjLTbJOpxOnT5/G008/rW7jnGPy5Mk4caLtu+YVHfW8ktbfsBxtL3zUVkzo+2AMMAsbAAaXqReECF9Z2pOPbz2cUnTwGNhhFi64mLkpzjuaIdvrIJlMEEKGxWSCcNnhhARTdG+YZRskE8AkDoi664ubCRlgEnhUDOC2eTNx2yFxCZwLuCUOJgAwDiaZAW4G3A5A2GCy9AaYDEgmWMwSXI5ymLgMyEHeLIzD1CsWQZaD1Rbj+7pkAmJjvf/6xuvJJ1D6YPtpnp+WfIKVMZQ6N7ayL73HNlC5WtuHT3y7j21bZW3Ky9D3ip59MA7GOZjMwDj3zjiL7g2ziQMep9r2jHMwSLBI3PveNzEwzuAWdgjOvOeFiYMJO4Rk8XZcHFZYTAxm5oSLW5o+qxjcvBfAGJhkBjdJEC4rLJI3zqme5woOxmS/zwrGgCg4wZ3VqDn7/xA7/H+AWXpBkgJ//jDGYRYNavrr2/V8lobnczIS8jH6Rmo9f7eZEM0XT+54V69exS9+8Qvs3LkTmZmZ6vbXX38dx44dwz//+c/OLhIhhBBCIggNbhFCCCEk4oSlg9K/f39IktTihtjKykrEx8eHo0iEEEIIiSBh6aBYLBakpaXh6NGj6jZZlnH06FG/Sz6EEEII6ZnCtpLsY489hkWLFmH06NEYM2YMPvroI9hsNtx7773hKhIhhBBCIkTYOigzZ85EVVUV1q9fj/LycqSmpmLLli10iYcQQggh4ZnFQwghhBDSGprFQwghhJCIQx0UQgghhEQc6qAQQgghJOJQB4UQQgghEafHdVDeffddPPjgg8jIyMCECRM0pRFC4O2330ZWVhbGjBmDefPm4dy5c34xNTU1ePnllzFu3DhMmDABr776KhoaGjqgBq3TW44LFy4gJSUl4M++ffvUuECv79mzpzOq1EIox/r3v/99i/K/9tprfjGXLl3CU089hYyMDEyaNAlr166F2935D/HTW7+amhqsWLECM2bMwJgxY/DLX/4SK1euRH19vV9cONtwx44dmDp1KtLT0zF37lycOnWq1fh9+/bh17/+NdLT03HXXXfh8OHDfq9rOSc7k576ffLJJ3jooYdw88034+abb8a8efNaxC9evLhFW82fP7+jqxGUnvrt3r27RdnT09P9YiKt/QB9dQz0eZKSkoKnnnpKjYmkNjx27BieeeYZZGVlISUlBQcOHGgzzb///W/Mnj0bo0ePxrRp07B79+4WMXrPa91ED/P222+Lbdu2idWrV4vx48drSrN582Yxfvx48eWXX4qCggLxzDPPiKlTpwq73a7GzJ8/X9x9990iNzdXHDt2TEybNk0sXLiwo6oRlN5yuN1uce3aNb+fDRs2iLFjxwqr1arGJScni5ycHL843/p3plCO9SOPPCKWLFniV/76+nr1dbfbLWbNmiXmzZsn8vPzxaFDh8TEiRPFG2+80dHVaUFv/QoLC8WCBQvEwYMHRWlpqThy5IiYPn26eP755/3iwtWGe/bsEWlpaWLXrl2iuLhYLFmyREyYMEFUVFQEjD9+/LhITU0VH3zwgSgpKRHr1q0TaWlporCwUI3Rck52Fr31W7hwofj4449Ffn6+KCkpEYsXLxbjx48XV65cUWMWLVok5s+f79dWNTU1nVUlP3rrl5OTI8aNG+dX9vLycr+YSGo/IfTXsbq62q9+RUVFIjU1VeTk5KgxkdSGhw4dEm+++ab44osvRHJysvjyyy9bjS8rKxMZGRli9erVoqSkRPztb38Tqamp4quvvlJj9B6zUPS4DooiJydHUwdFlmVx6623ii1btqjb6urqxOjRo8Xnn38uhBCipKREJCcni1OnTqkxhw8fFikpKX4fOh3NqHL89re/FX/+85/9tml5U3eGUOv4yCOPiJUrVwZ9/dChQ2LkyJF+H6R///vfxbhx44TD4TCm8BoY1YZ79+4VaWlpwuVyqdvC1YZz5swRy5cvV3/3eDwiKytLbN68OWD8iy++KJ566im/bXPnzhVLly4VQmg7JzuT3vo153a7RWZmpvj000/VbYsWLRLPPvus0UUNid76tfXZGmntJ0T723Dbtm0iMzNTNDQ0qNsiqQ19afkceP3118Wdd97pt+2ll14Sjz/+uPp7e4+ZFj3uEo9eFy5cQHl5OSZPnqxui42NRUZGBk6cOAEAOHHiBPr27es3jDl58mRwzo0f8mqFEeXIy8tDQUEB5syZ0+K15cuXY+LEiZgzZw527doFEYYldNpTx88++wwTJ07ErFmz8MYbb8Bms6mv5ebmIjk52W+hwKysLFitVpSUlBhfkSCMei9ZrVb06dMHJpP/Woyd3YZOpxOnT5/2O38455g8ebJ6/jSXm5uLSZMm+W3LyspCbm4uAG3nZGcJpX7N2Ww2uN1uxMXF+W3/7rvvMGnSJMyYMQPLli1DdXW1oWXXItT6NTY2YsqUKbj99tvx7LPPori4WH0tktoPMKYNc3JycOedd6JXr15+2yOhDUPR1jloxDHTImwryXYV5eXlAICBAwf6bR84cCAqKioAABUVFRgwYIDf6yaTCXFxcWr6zmBEOXbt2oXhw4dj3LhxfttfeOEF3HLLLYiJicE333yD5cuXo7GxEX/4wx8MK78WodZx1qxZGDx4MAYNGoTCwkJkZ2fj7Nmz2Lhxo7rf5qsYK793tTasqqrCpk2b8MADD/htD0cbVldXw+PxBDx/zpw5EzBNoLbwPd+0nJOdJZT6NZednY1Bgwb5fdjfdtttmDZtGoYMGYLz58/jzTffxJNPPol//OMfkCTJ0Dq0JpT6DRs2DKtWrUJKSgrq6+vx4Ycf4sEHH8SePXuQmJgYUe0HtL8NT506haKiIvz1r3/12x4pbRiKYJ+HVqsVdrsdtbW17X7fa9EtOijZ2dn44IMPWo3Zu3cvhg8f3kklMpbW+rWX3W7H559/jj/+8Y8tXnvuuefU/48aNQo2mw1bt2417I9bR9fR9491SkoKEhISMG/ePJSVlSEpKSnk/WrVWW1otVrx9NNPY/jw4ViwYIHfax3dhkS/999/H3v37sX27dsRFRWlbr/zzjvV/ys3WP7qV79Sv5FHsszMTL+HvmZmZmLmzJnYuXMnXnrppfAVrIPs2rULycnJGDNmjN/2rtyGkaJbdFAef/xxzJ49u9WYoUOHhrTvhIQEAEBlZSUGDRqkbq+srMTIkSMBeHuWVVVVfuncbjdqa2vV9O2htX7tLcf+/ftht9txzz33tBmbkZGBTZs2wel0wmKxtBnfls6qoyIjIwMAUFpaiqSkJMTHx7e4hKJ8m+sqbWi1WvHEE0+gd+/eeOedd2A2m1uNN7oNA+nfvz8kSUJlZaXf9srKyqDP3YqPj2/xTdo3Xss52VlCqZ9i69ateP/997Ft27Y2yz106FD0798fpaWlnfrHrT31U5jNZqSmpqKsrAxAZLUf0L46NjY2Ys+ePXjhhRfazCdcbRiKQOdgRUUF+vTpg+joaHDO2/2+0KJb3IMyYMAADB8+vNWfUD+AhwwZgoSEBBw9elTdZrVacfLkSfVbQmZmJurq6pCXl6fGfPvtt5BluUWvOhRa69fecuTk5GDq1KktLjEEUlBQgLi4OMP+sHVWHX3LD1z/sBw7diyKior8TrgjR46gT58+GDFiRMTXz2q1Yv78+TCbzXj33Xf9vo0HY3QbBmKxWJCWluZ3/siyjKNHj/p9y/Y1duxYfPvtt37bjhw5grFjxwLQdk52llDqBwAffPABNm3ahC1btrSYghvIlStXUFNTY0hnWY9Q6+fL4/GgqKhILXsktR/Qvjru378fTqcTd999d5v5hKsNQ9HWOWjE+0ITw2637SIuXrwo8vPz1am0+fn5Ij8/329K7YwZM8QXX3yh/r5582YxYcIEceDAAfHDDz+IZ599NuA043vuuUecPHlS/Oc//xHTp08P2zTj1spx5coVMWPGDHHy5Em/dOfOnRMpKSni8OHDLfZ58OBB8cknn4jCwkJx7tw5sWPHDpGRkSHefvvtDq9PIHrrWFpaKjZu3Ci+//57cf78eXHgwAFxxx13iIcfflhNo0wzfvzxx0VBQYH46quvxC233BK2acZ66ldfXy/mzp0rZs2aJUpLS/2mNbrdbiFEeNtwz549YvTo0WL37t2ipKRELF26VEyYMEGdMfXKK6+I7OxsNf748eNi1KhRYuvWraKkpESsX78+4DTjts7JzqK3fps3bxZpaWli//79fm2lfAZZrVaxZs0aceLECXH+/Hlx5MgRMXv2bDF9+vROnVEWav02bNggvv76a1FWViby8vLEn/70J5Geni6Ki4vVmEhqPyH011Hxu9/9Trz00ksttkdaG1qtVvVvXXJysti2bZvIz88XFy9eFEIIkZ2dLV555RU1XplmvHbtWlFSUiI+/vjjgNOMWztmRugWl3j0WL9+PT799FP1d+Vyxvbt2zFx4kQAwNmzZ/0WuXryySdhs9nw2muvoa6uDuPHj8eWLVv8vqVmZ2djxYoVePTRR8E5x/Tp07FkyZLOqZSPtsrhcrlw9uxZvxksgHf0JDExEVlZWS32aTKZsGPHDqxatQoAkJSUhMWLF+P+++/v2MoEobeOZrMZR48exfbt29HY2IgbbrgB06dP97vXRpIkvPfee/jLX/6CBx54ADExMZg9e7amodtw1+/06dM4efIkAGDatGl++zp48CCGDBkS1jacOXMmqqqqsH79epSXlyM1NRVbtmxRh4IvX74Mzq8P5o4bNw7Z2dl466238Oabb+LGG2/EO++8g+TkZDVGyznZWfTWb+fOnXC5XC3eWwsWLMDzzz8PSZJQVFSEf/3rX6ivr8egQYNw66234sUXX+zQ0a5g9Navrq4OS5cuRXl5OeLi4pCWloadO3f6jURGUvsB+usIAGfOnMHx48fx4YcftthfpLVhXl6e371mq1evBgDMnj0ba9asQXl5OS5fvqy+PnToUGzevBmrV6/G9u3bkZiYiJUrV+K2225TY9o6ZkZgQoRhrighhBBCSCu6xT0ohBBCCOleqINCCCGEkIhDHRRCCCGERBzqoBBCCCEk4lAHhRBCCCERhzoohBBCCIk41EEhhBBCSMShDgohhBBCIg51UAghhBAScaiDQgghhJCIQx0UQgghhEQc6qAQQgghJOL8f9pB21kjS1V2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said -1 means stero to mono; 1 means mono to stereo\n",
    "# only cut off the left side\n",
    "df_slice[\"stereo_width_diff\"] = df_slice[\n",
    "    \"stereo_width\"\n",
    "].diff()  # / df_slice[\"stereo_width\"]\n",
    "# df_slice[\"stereo_width_diff\"] = df_slice[\"stereo_width\"]\n",
    "lookup_percentiles = [2, 5, 10, 20, 50, 80, 90, 95, 98]\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"stereo_width_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"stereo_width_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[(df_slice[\"preference\"]) & (df_slice[\"source\"] == \"web\")][\n",
    "        \"stereo_width_diff\"\n",
    "    ],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[(df_slice['preference']) & (df_slice['source'] == 'web')]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Stereo Width Difference --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:28.381372Z",
     "iopub.status.busy": "2025-10-04T22:11:28.381228Z",
     "iopub.status.idle": "2025-10-04T22:11:28.862874Z",
     "shell.execute_reply": "2025-10-04T22:11:28.862397Z",
     "shell.execute_reply.started": "2025-10-04T22:11:28.381357Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n",
      "0.4666666666666667\n",
      "1.1666666666666667\n",
      "1.8333333333333333\n",
      "2.9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[~df_slice[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"neg, mean: {np.mean(df_slice[~df_slice['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"total_shimmer_score\"].dropna(), [50, 75, 90, 95, 98]\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    print(percentile)\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.yscale(\"log\")\n",
    "plt.title(f\"Shimmer score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:28.863623Z",
     "iopub.status.busy": "2025-10-04T22:11:28.863470Z",
     "iopub.status.idle": "2025-10-04T22:11:29.131829Z",
     "shell.execute_reply": "2025-10-04T22:11:29.131363Z",
     "shell.execute_reply.started": "2025-10-04T22:11:28.863607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95, 98]\n",
    "df_slice[\"loudness_diff\"] = df_slice[\"loudness_abs\"].diff() / df_slice[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:29.132515Z",
     "iopub.status.busy": "2025-10-04T22:11:29.132366Z",
     "iopub.status.idle": "2025-10-04T22:11:29.147807Z",
     "shell.execute_reply": "2025-10-04T22:11:29.147371Z",
     "shell.execute_reply.started": "2025-10-04T22:11:29.132500Z"
    }
   },
   "outputs": [],
   "source": [
    "# tr_metas_t1_v7 = read_jsonl(os.path.join(\"/app2/suno/data/dpo/diff2_v2_d3_v10\", f\"metas_tr.jsonl\"))\n",
    "# known_train_ids = set()\n",
    "# for prev_tr_meta in tr_metas_t1_v7:\n",
    "#     known_train_ids.add(prev_tr_meta[\"id_x\"])\n",
    "# print(len(known_train_ids))\n",
    "# print(df_slice.shape)\n",
    "# df_slice = df_slice[~df_slice[\"id\"].isin(known_train_ids)].copy()\n",
    "# print(df_slice.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:29.148423Z",
     "iopub.status.busy": "2025-10-04T22:11:29.148286Z",
     "iopub.status.idle": "2025-10-04T22:11:29.161575Z",
     "shell.execute_reply": "2025-10-04T22:11:29.161152Z",
     "shell.execute_reply.started": "2025-10-04T22:11:29.148409Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[\"created_at\"] = pd.to_datetime(df_slice[\"created_at\"], utc=True)\n",
    "# cutoff_date = pd.to_datetime(\"2025-05-12\", utc=True)\n",
    "# print(df_slice.shape, df_slice[df_slice[\"created_at\"] >= cutoff_date].shape)\n",
    "# df_slice = df_slice[(df_slice[\"created_at\"] >= cutoff_date)].copy()\n",
    "# print(\"after date cut\", df_slice.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:29.162328Z",
     "iopub.status.busy": "2025-10-04T22:11:29.162184Z",
     "iopub.status.idle": "2025-10-04T22:11:29.180634Z",
     "shell.execute_reply": "2025-10-04T22:11:29.180173Z",
     "shell.execute_reply.started": "2025-10-04T22:11:29.162314Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web    29242\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:29.181249Z",
     "iopub.status.busy": "2025-10-04T22:11:29.181114Z",
     "iopub.status.idle": "2025-10-04T22:11:33.613392Z",
     "shell.execute_reply": "2025-10-04T22:11:33.612678Z",
     "shell.execute_reply.started": "2025-10-04T22:11:29.181235Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROD\n"
     ]
    }
   ],
   "source": [
    "# fetch lyrics\n",
    "home_dir = os.path.expanduser(\"~\")\n",
    "snow_password_path = os.path.join(home_dir, \".aws\", \"snow_pw.txt\")\n",
    "if os.path.exists(snow_password_path):\n",
    "    # !pip install snowflake\n",
    "    from snowflake.core import Root\n",
    "    from snowflake.snowpark import Session\n",
    "\n",
    "    with open(snow_password_path, \"r\") as fp:\n",
    "        fp_lines = fp.readlines()\n",
    "        snow_password = fp_lines[0].strip()\n",
    "        snow_username = fp_lines[1].strip()\n",
    "\n",
    "    CONNECTION_PARAMETERS = {\n",
    "        \"account\": \"fu90569.us-east-2.aws\",\n",
    "        \"user\": snow_username,\n",
    "        \"private_key_file\": \"/home/tony/.aws/rsa_key.p8\",\n",
    "        \"role\": \"ACCOUNTADMIN\",\n",
    "        \"database\": \"SUNO_PROD\",\n",
    "        \"warehouse\": \"SUNO_PROD_LARGE\",\n",
    "        \"schema\": \"PROD\",\n",
    "    }\n",
    "\n",
    "if not os.path.exists(snow_password_path):\n",
    "    raise Exception(\"you are not authorized to access snowflake -- please setup\")\n",
    "\n",
    "snow_session = Session.builder.configs(CONNECTION_PARAMETERS).create()\n",
    "\n",
    "snow_root = Root(snow_session)\n",
    "snow_schema = snow_root.databases[\"SUNO_PROD\"].schemas[\"PROD\"]\n",
    "print(snow_schema.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:11:33.614264Z",
     "iopub.status.busy": "2025-10-04T22:11:33.614103Z",
     "iopub.status.idle": "2025-10-04T22:12:30.040494Z",
     "shell.execute_reply": "2025-10-04T22:12:30.039834Z",
     "shell.execute_reply.started": "2025-10-04T22:11:33.614248Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 29242\n",
      "Length of the ID query string: 1140437\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:56<00:00, 56.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Shape of df_snow_test:\n",
      "Rows: 29242\n",
      "Columns: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False    20642\n",
       "True      8600\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v4_clip_ids = list(str(s) for s in df_slice[\"id\"].unique())\n",
    "snow_batch_size = 100_000\n",
    "snow_results = []\n",
    "\n",
    "for clip_ids_chunk in tqdm(\n",
    "    [\n",
    "        v4_clip_ids[i : i + snow_batch_size]\n",
    "        for i in range(0, len(v4_clip_ids), snow_batch_size)\n",
    "    ]\n",
    "):\n",
    "    id_query_str = \",\".join(\"'\" + x + \"'\" for x in clip_ids_chunk)\n",
    "    print(f\"Number of clip IDs in this chunk: {len(clip_ids_chunk)}\")\n",
    "    print(f\"Length of the ID query string: {len(id_query_str)}\")\n",
    "\n",
    "    session_query = snow_session.sql(\n",
    "        f\"\"\"select ID, PROMPT_TEXT\n",
    "        from DDB_CLIP_META_HEAVY\n",
    "        where ID in ({id_query_str})\n",
    "        order by p_hour desc;\"\"\"\n",
    "    )\n",
    "    temp_df_snow_test = pd.DataFrame(session_query.collect())\n",
    "    snow_results.append(temp_df_snow_test)\n",
    "print(len(snow_results))\n",
    "df_snow_test = pd.concat(snow_results)\n",
    "df_snow_test = df_snow_test.rename(columns=lambda x: x.lower())\n",
    "# df_snow_test = df_snow_test.rename(columns={\"song_id\": \"str_id\"})\n",
    "print(\"Shape of df_snow_test:\")\n",
    "print(f\"Rows: {df_snow_test.shape[0]}\")\n",
    "print(f\"Columns: {df_snow_test.shape[1]}\")\n",
    "df_slice[\"id\"] = df_slice[\"id\"].astype(str)\n",
    "df_slice = df_slice.rename(columns={\"prompt_text\": \"prompt_text_old\"})\n",
    "df_slice = df_slice.merge(df_snow_test, on=\"id\", how=\"left\")\n",
    "(df_slice[\"prompt_text\"] == df_slice[\"prompt_text_old\"]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-04T22:12:30.041415Z",
     "iopub.status.busy": "2025-10-04T22:12:30.041246Z",
     "iopub.status.idle": "2025-10-04T22:12:30.992793Z",
     "shell.execute_reply": "2025-10-04T22:12:30.992205Z",
     "shell.execute_reply.started": "2025-10-04T22:12:30.041398Z"
    }
   },
   "outputs": [],
   "source": [
    "# from tqdm import tqdm\n",
    "\n",
    "# list_of_past_data = [\n",
    "#     \"/app2/suno/data/dpo/diff2_v2_d5_v8\",\n",
    "# ]\n",
    "\n",
    "# # Collect all known train IDs from previous meta_tr.jsonl files, showing progress and set growth\n",
    "# known_train_ids = set()\n",
    "# for data_dir in tqdm(list_of_past_data, desc=\"Collecting known train IDs\"):\n",
    "#     metas = read_jsonl(os.path.join(data_dir, \"metas_tr.jsonl\"))\n",
    "#     before = len(known_train_ids)\n",
    "#     known_train_ids.update(meta[\"id_x\"] for meta in metas)\n",
    "#     after = len(known_train_ids)\n",
    "#     tqdm.write(f\"Added {after - before} new IDs from {data_dir} (total: {after})\")\n",
    "\n",
    "# print(f\"Total known train IDs: {len(known_train_ids)}\")\n",
    "# print(f\"Original df_slice shape: {df_slice.shape}\", f\"overlap {df_slice['id'].isin(known_train_ids).sum()}\")\n",
    "# # df_slice = df_slice[~df_slice[\"id\"].isin(known_train_ids)].copy()\n",
    "# print(f\"Filtered df_slice shape: {df_slice.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-04T22:12:30.993540Z",
     "iopub.status.busy": "2025-10-04T22:12:30.993389Z",
     "iopub.status.idle": "2025-10-04T22:12:31.395824Z",
     "shell.execute_reply": "2025-10-04T22:12:31.394802Z",
     "shell.execute_reply.started": "2025-10-04T22:12:30.993524Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29242, 126)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'BREAK' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[53], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# df_slice.to_pickle(\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#     f\"/home/tony/Data/Preference/up_v2_d3/interesting_clips_ahi_d3_20250519_slice.pkl\",\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(df_slice\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mBREAK\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BREAK' is not defined"
     ]
    }
   ],
   "source": [
    "# df_slice.to_pickle(\n",
    "#     f\"/home/tony/Data/Preference/up_v2_d3/interesting_clips_ahi_d3_20250519_slice.pkl\",\n",
    "# )\n",
    "print(df_slice.shape)\n",
    "BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need to kick out the ones has gpt prompt -- these are pairs with different text inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.396401Z",
     "iopub.status.idle": "2025-10-04T22:12:31.396604Z",
     "shell.execute_reply": "2025-10-04T22:12:31.396512Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.396502Z"
    }
   },
   "outputs": [],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.397502Z",
     "iopub.status.idle": "2025-10-04T22:12:31.397691Z",
     "shell.execute_reply": "2025-10-04T22:12:31.397598Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.397590Z"
    }
   },
   "outputs": [],
   "source": [
    "train_requests, val_requests = train_test_split(\n",
    "    sorted(list(final_filtered_requests)), test_size=0.01, random_state=42\n",
    ")\n",
    "print(len(train_requests), len(val_requests))\n",
    "\n",
    "train_df = df_slice[df_slice[\"request_id\"].isin(set(train_requests))].copy()\n",
    "val_df = df_slice[df_slice[\"request_id\"].isin(set(val_requests))].copy()\n",
    "train_df = train_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "train_df = train_df  # .reset_index()\n",
    "val_df = val_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "val_df = val_df  # .reset_index()\n",
    "\n",
    "print(train_df.shape, val_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.398079Z",
     "iopub.status.idle": "2025-10-04T22:12:31.398232Z",
     "shell.execute_reply": "2025-10-04T22:12:31.398163Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.398156Z"
    }
   },
   "outputs": [],
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.398677Z",
     "iopub.status.idle": "2025-10-04T22:12:31.398827Z",
     "shell.execute_reply": "2025-10-04T22:12:31.398756Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.398749Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.399303Z",
     "iopub.status.idle": "2025-10-04T22:12:31.399455Z",
     "shell.execute_reply": "2025-10-04T22:12:31.399384Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.399376Z"
    }
   },
   "outputs": [],
   "source": [
    "total_duration = 0\n",
    "for i, row in tqdm(train_df.iterrows(), total=len(train_df)):\n",
    "    # we need to alternate between preference: neg, pos\n",
    "    # print(i, row)\n",
    "    try:\n",
    "        assert row[\"preference\"] == (i % 2 == 1)\n",
    "    except:\n",
    "        print(i, row)\n",
    "    total_duration += row[\"duration\"]\n",
    "print(\n",
    "    f\"{round(total_duration / 60 / 60):,} hours of {train_df.shape[0]} clips, {train_df.shape[0] / 8 / 2 / 1000} nodes, {train_df.shape[0] / 8 / 2 / 4} steps\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.400191Z",
     "iopub.status.idle": "2025-10-04T22:12:31.400358Z",
     "shell.execute_reply": "2025-10-04T22:12:31.400281Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.400273Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    val_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=True,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.400663Z",
     "iopub.status.idle": "2025-10-04T22:12:31.400810Z",
     "shell.execute_reply": "2025-10-04T22:12:31.400741Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.400733Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_npz = np.load(\"/app/suno/data/dpo/diff2_v2/506a8426-551f-45d9-9638-b1fa673cc031.npz\")\n",
    "# for key in test_npz.keys():\n",
    "#     print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.401308Z",
     "iopub.status.idle": "2025-10-04T22:12:31.401459Z",
     "shell.execute_reply": "2025-10-04T22:12:31.401390Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.401383Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    train_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=False,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.401818Z",
     "iopub.status.idle": "2025-10-04T22:12:31.401973Z",
     "shell.execute_reply": "2025-10-04T22:12:31.401900Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.401892Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.402412Z",
     "iopub.status.idle": "2025-10-04T22:12:31.402562Z",
     "shell.execute_reply": "2025-10-04T22:12:31.402492Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.402485Z"
    }
   },
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-29T19:46:47.549860Z",
     "start_time": "2024-01-29T19:46:47.548015Z"
    }
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.403020Z",
     "iopub.status.idle": "2025-10-04T22:12:31.403170Z",
     "shell.execute_reply": "2025-10-04T22:12:31.403100Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.403093Z"
    }
   },
   "outputs": [],
   "source": [
    "# verify\n",
    "metas_val = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_val.jsonl\"))\n",
    "print(len(metas_val) / 2)\n",
    "mm_semantic_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_semantic_val.bin\"), dtype=np.uint16, mode=\"r\"\n",
    ")\n",
    "mm_vae_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    ")\n",
    "\n",
    "\n",
    "mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "print(mm_vae_val.shape)\n",
    "\n",
    "mm_semantic_val = mm_semantic_val.reshape(-1, SEMANTIC_MEMMAP_SIZE)\n",
    "print(mm_semantic_val.shape)\n",
    "\n",
    "assert len(metas_val) == mm_vae_val.shape[0] == mm_semantic_val.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.403618Z",
     "iopub.status.idle": "2025-10-04T22:12:31.403763Z",
     "shell.execute_reply": "2025-10-04T22:12:31.403695Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.403688Z"
    }
   },
   "outputs": [],
   "source": [
    "# # load codec for decoding\n",
    "# from suno_utils.tasks.dac_vae_100hz_peaq import (  # NOTE: works for 25hz as well\n",
    "#     preload_models as preload_codec_models,\n",
    "#     decode as codec_decode,\n",
    "#     encode as codec_encode,\n",
    "#     get_embedding_rate,\n",
    "#     load_model as load_codec_model,\n",
    "# )\n",
    "\n",
    "# CODEC_FILEPATH = \"s3://suno-data/christian/25hz_vae_peaq_kl_0.005.pth\"\n",
    "# preload_codec_models(CODEC_FILEPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.404258Z",
     "iopub.status.idle": "2025-10-04T22:12:31.404403Z",
     "shell.execute_reply": "2025-10-04T22:12:31.404335Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.404328Z"
    }
   },
   "outputs": [],
   "source": [
    "# # decode some audio\n",
    "idx = 108\n",
    "# # ensure even index\n",
    "assert idx % 2 == 0\n",
    "# print(metas_val[idx])\n",
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[idx])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(metas_val[idx + 1])\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[idx + 1])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.404850Z",
     "iopub.status.idle": "2025-10-04T22:12:31.404992Z",
     "shell.execute_reply": "2025-10-04T22:12:31.404926Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.404920Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.405312Z",
     "iopub.status.idle": "2025-10-04T22:12:31.405466Z",
     "shell.execute_reply": "2025-10-04T22:12:31.405396Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.405388Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.equal(torch.tensor(mm_semantic_val[idx]), torch.tensor(mm_semantic_val[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.405953Z",
     "iopub.status.idle": "2025-10-04T22:12:31.406104Z",
     "shell.execute_reply": "2025-10-04T22:12:31.406033Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.406026Z"
    }
   },
   "outputs": [],
   "source": [
    "# original_npz_path = f\"/app/suno/data/dpo/7b_npz/{test_metas[idx]['id']}.npz\"\n",
    "# original_npz_path = \"/app/suno/data/dpo/7b_npz/729c3011-f672-4ccd-8d82-1cbf2b52ff69.npz\"\n",
    "# original_arr = np.load(original_npz_path)[\"v2_raw\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.406509Z",
     "iopub.status.idle": "2025-10-04T22:12:31.406654Z",
     "shell.execute_reply": "2025-10-04T22:12:31.406585Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.406579Z"
    }
   },
   "outputs": [],
   "source": [
    "def validation_on_metas(input_metas):\n",
    "    total_bad = 0\n",
    "    total_good = 0\n",
    "    for idx in range(len(input_metas)):\n",
    "        if idx % 2 == 0:\n",
    "            pos_idx = idx + 1\n",
    "            if input_metas[idx].get(\"tags\") != input_metas[pos_idx].get(\"tags\"):\n",
    "                # print(test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"), test_metas[idx].get(\"tags\"), test_metas[pos_idx].get(\"tags\"))\n",
    "                total_bad += 1\n",
    "            else:\n",
    "                total_good += 1\n",
    "    print(total_good, total_bad)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.406925Z",
     "iopub.status.idle": "2025-10-04T22:12:31.407058Z",
     "shell.execute_reply": "2025-10-04T22:12:31.406995Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.406988Z"
    }
   },
   "outputs": [],
   "source": [
    "metas_tr = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_tr.jsonl\"))\n",
    "validation_on_metas(metas_tr)\n",
    "print(len(metas_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.407531Z",
     "iopub.status.idle": "2025-10-04T22:12:31.407675Z",
     "shell.execute_reply": "2025-10-04T22:12:31.407608Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.407600Z"
    }
   },
   "outputs": [],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.408181Z",
     "iopub.status.idle": "2025-10-04T22:12:31.409121Z",
     "shell.execute_reply": "2025-10-04T22:12:31.408269Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.408262Z"
    }
   },
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.409726Z",
     "iopub.status.idle": "2025-10-04T22:12:31.409919Z",
     "shell.execute_reply": "2025-10-04T22:12:31.409838Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.409829Z"
    }
   },
   "outputs": [],
   "source": [
    "!cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion_infill.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.410258Z",
     "iopub.status.idle": "2025-10-04T22:12:31.410413Z",
     "shell.execute_reply": "2025-10-04T22:12:31.410342Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.410335Z"
    }
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "# Basic file copy\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/make_dataset_diff_upsample_v2_r5.ipynb\",\n",
    "    os.path.join(OUT_DATA_DIR, \"make_dataset.ipynb\"),\n",
    ")\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/preference_data_preparation_diff.py\",\n",
    "    os.path.join(OUT_DATA_DIR, \"preference_data_preparation_diff.py\"),\n",
    ")\n",
    "print(\"Cache kept!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.410896Z",
     "iopub.status.idle": "2025-10-04T22:12:31.411050Z",
     "shell.execute_reply": "2025-10-04T22:12:31.410978Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.410970Z"
    }
   },
   "outputs": [],
   "source": [
    "# df[df[\"preference\"] & (df[\"shimmer_score_diff\"] > 3)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()\n",
    "\n",
    "# df[df[\"preference\"] & (df[\"pair_quality\"] < 0.1)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"pair_quality\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.411461Z",
     "iopub.status.idle": "2025-10-04T22:12:31.411612Z",
     "shell.execute_reply": "2025-10-04T22:12:31.411541Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.411534Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_pair_df = df[df[\"request_id\"] == \"621c8b02-a905-48f1-a2d5-a4e8423d1505\"]\n",
    "# print(\n",
    "#     test_pair_df[\n",
    "#         [\n",
    "#             \"s3_id\",\n",
    "#             \"total_shimmer_score\",\n",
    "#             \"pair_quality\",\n",
    "#             \"request_id\",\n",
    "#             \"preference\",\n",
    "#             \"prompt_text\",\n",
    "#         ]\n",
    "#     ]\n",
    "# )\n",
    "# negative_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[0]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"negative\")\n",
    "# negative_audio.get_segment(0, 30).play()\n",
    "# positive_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[1]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"positive\")\n",
    "# positive_audio.get_segment(0, 30).play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.412246Z",
     "iopub.status.idle": "2025-10-04T22:12:31.412398Z",
     "shell.execute_reply": "2025-10-04T22:12:31.412327Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.412320Z"
    }
   },
   "outputs": [],
   "source": [
    "# total_dict = {}\n",
    "# total_dict.update(pair_quality_dict)\n",
    "# total_dict.update(pair_quality_1_dict)\n",
    "# total_dict.update(pair_quality_2_dict)\n",
    "# total_dict.update(pair_quality_3_dict)\n",
    "# len(total_dict)\n",
    "# with open(\n",
    "#     os.path.join(\"/home/tony/Data/Preference/up_v1\", \"pair_quality.json\"), \"w\"\n",
    "# ) as fp:\n",
    "#     json.dump(total_dict, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.412831Z",
     "iopub.status.idle": "2025-10-04T22:12:31.412992Z",
     "shell.execute_reply": "2025-10-04T22:12:31.412916Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.412908Z"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.413431Z",
     "iopub.status.idle": "2025-10-04T22:12:31.413582Z",
     "shell.execute_reply": "2025-10-04T22:12:31.413513Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.413506Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_arr = np.load(\"/home/tony/Data/test_npz/diffusion_input_tensor([ 18, 182]).npy\")\n",
    "# test_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.414035Z",
     "iopub.status.idle": "2025-10-04T22:12:31.414181Z",
     "shell.execute_reply": "2025-10-04T22:12:31.414114Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.414107Z"
    }
   },
   "outputs": [],
   "source": [
    "# mm_vae_val = np.memmap(\n",
    "#     os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    "# )\n",
    "\n",
    "# mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "# print(mm_vae_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.414663Z",
     "iopub.status.idle": "2025-10-04T22:12:31.414808Z",
     "shell.execute_reply": "2025-10-04T22:12:31.414741Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.414734Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(test_arr[0].T / 2.5)\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(test_arr[1].T / 2.5)\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.415368Z",
     "iopub.status.idle": "2025-10-04T22:12:31.415529Z",
     "shell.execute_reply": "2025-10-04T22:12:31.415448Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.415440Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[18])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[19])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.415851Z",
     "iopub.status.idle": "2025-10-04T22:12:31.415998Z",
     "shell.execute_reply": "2025-10-04T22:12:31.415931Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.415924Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.416459Z",
     "iopub.status.idle": "2025-10-04T22:12:31.416606Z",
     "shell.execute_reply": "2025-10-04T22:12:31.416538Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.416531Z"
    }
   },
   "outputs": [],
   "source": [
    "t = rng.draw(4)[:, 0].to(torch.bfloat16)\n",
    "print(t)\n",
    "# Replace 1% of t with ones to ensure training on terminal SNR\n",
    "t = torch.where(torch.rand_like(t) < 0.5, torch.ones_like(t), t)\n",
    "print(t)\n",
    "t = torch.repeat_interleave(t, repeats=2, dim=0)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.417006Z",
     "iopub.status.idle": "2025-10-04T22:12:31.417151Z",
     "shell.execute_reply": "2025-10-04T22:12:31.417081Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.417075Z"
    }
   },
   "outputs": [],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.417675Z",
     "iopub.status.idle": "2025-10-04T22:12:31.417822Z",
     "shell.execute_reply": "2025-10-04T22:12:31.417755Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.417748Z"
    }
   },
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.418114Z",
     "iopub.status.idle": "2025-10-04T22:12:31.418252Z",
     "shell.execute_reply": "2025-10-04T22:12:31.418187Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.418180Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge jsons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.418723Z",
     "iopub.status.idle": "2025-10-04T22:12:31.418883Z",
     "shell.execute_reply": "2025-10-04T22:12:31.418809Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.418802Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v2_d5/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# # result = {}\n",
    "# print(len(result))\n",
    "# for job_idx in tqdm(range(64)):\n",
    "#     with open(f\"/home/tony/Data/Preference/up_v2_d5/full_pair_quality_{job_idx}.json\", \"r\") as fp:\n",
    "#         current_result = json.load(fp)\n",
    "#         result.update(current_result)\n",
    "# print(len(result))\n",
    "\n",
    "# with open(f\"/home/tony/Data/Preference/up_v2_d5/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.419299Z",
     "iopub.status.idle": "2025-10-04T22:12:31.419447Z",
     "shell.execute_reply": "2025-10-04T22:12:31.419378Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.419371Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# print(len(result))\n",
    "# # new_result = {}\n",
    "# for k, v in result.items():\n",
    "#     new_v = v.copy()\n",
    "#     for clip_id, contents in v.items():\n",
    "#         if contents and \"abs_loudness_factor\" not in contents:\n",
    "#             # print(contents, result_loundess[k][clip_id])\n",
    "#             try:\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] =  result_loundess[k][clip_id][\"abs_loudness_factor\"]\n",
    "#             except:\n",
    "#                 # print(clip_id)\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] = 0.0\n",
    "#             # break\n",
    "#     # print(k, v)\n",
    "#     # break\n",
    "#     new_result[k] = new_v\n",
    "# print(len(new_result))\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(new_result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.419755Z",
     "iopub.status.idle": "2025-10-04T22:12:31.419899Z",
     "shell.execute_reply": "2025-10-04T22:12:31.419832Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.419825Z"
    }
   },
   "outputs": [],
   "source": [
    "# import torch\n",
    "# semantic_codes_chunk = torch.ones((1, 100))\n",
    "# semantic_skip_phase = 0\n",
    "# semantic_skip_factor = 4\n",
    "# mask = torch.ones_like(semantic_codes_chunk, dtype=torch.bool)\n",
    "# indices = (\n",
    "#     torch.arange(semantic_codes_chunk.size(1)) + semantic_skip_phase\n",
    "# ) % semantic_skip_factor == 0\n",
    "# mask[:, indices] = False\n",
    "# mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.420350Z",
     "iopub.status.idle": "2025-10-04T22:12:31.420503Z",
     "shell.execute_reply": "2025-10-04T22:12:31.420428Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.420421Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.420992Z",
     "iopub.status.idle": "2025-10-04T22:12:31.421134Z",
     "shell.execute_reply": "2025-10-04T22:12:31.421068Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.421061Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] < 0.5)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-04T22:12:31.421542Z",
     "iopub.status.idle": "2025-10-04T22:12:31.421690Z",
     "shell.execute_reply": "2025-10-04T22:12:31.421619Z",
     "shell.execute_reply.started": "2025-10-04T22:12:31.421612Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] > 2)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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