{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:02:05.502851Z",
     "iopub.status.busy": "2025-06-24T04:02:05.502715Z",
     "iopub.status.idle": "2025-06-24T04:02:05.515257Z",
     "shell.execute_reply": "2025-06-24T04:02:05.514842Z",
     "shell.execute_reply.started": "2025-06-24T04:02:05.502835Z"
    }
   },
   "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": "2025-06-24T04:02:05.516960Z",
     "iopub.status.busy": "2025-06-24T04:02:05.516841Z",
     "iopub.status.idle": "2025-06-24T04:02:08.217802Z",
     "shell.execute_reply": "2025-06-24T04:02:08.217231Z",
     "shell.execute_reply.started": "2025-06-24T04:02:05.516947Z"
    }
   },
   "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": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.082172Z",
     "start_time": "2024-05-16T13:58:21.041926Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:02:08.219855Z",
     "iopub.status.busy": "2025-06-24T04:02:08.219726Z",
     "iopub.status.idle": "2025-06-24T04:02:19.333371Z",
     "shell.execute_reply": "2025-06-24T04:02:19.332783Z",
     "shell.execute_reply.started": "2025-06-24T04:02:08.219840Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output dir is /app2/suno/data/dpo/diff2_v2_d4_v40/\n",
      "Total pair quality scores: 184281\n",
      "Total hoot cer scores: 339075\n"
     ]
    }
   ],
   "source": [
    "OUT_DATA_DIR = \"/app2/suno/data/dpo/diff2_v2_d4_v40/\"\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_d4\"\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/up_v2_d4/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",
    "# Total pair quality scores: 184281\n",
    "with open(\"/home/tony/Data/Preference/up_v2_d4/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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found newer file: interesting_clips_ahi_d4_20250711.pkl\n",
      "  Base file ctime: 1751940186.2247849\n",
      "  File ctime: 1752243318.7988806\n",
      "  Difference: 303132.574095726 seconds\n",
      "Found newer file: interesting_clips_ahi_d4_20250714.pkl\n",
      "  Base file ctime: 1751940186.2247849\n",
      "  File ctime: 1752465278.4744246\n",
      "  Difference: 525092.2496397495 seconds\n",
      "Found 2 newer files to process\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading newer pickle files:   0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing interesting_clips_ahi_d4_20250711.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading newer pickle files:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous size: 364,400\n",
      "New input size: 155,752\n",
      "Current total size: 408,692\n",
      "Net increase: 44,292\n",
      "\n",
      "Processing interesting_clips_ahi_d4_20250714.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading newer pickle files: 100%|██████████| 2/2 [00:09<00:00,  4.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous size: 408,692\n",
      "New input size: 178,716\n",
      "Current total size: 433,528\n",
      "Net increase: 24,836\n",
      "\n",
      "Final dataframe shape: (433528, 100)\n",
      "Unique ids: 433528\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_d4/fully_merged_up_v2_d4.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_d4/interesting_clips_*.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_d4/fully_merged_up_v2_d4.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": "2025-06-24T04:02:19.335276Z",
     "iopub.status.busy": "2025-06-24T04:02:19.335146Z",
     "iopub.status.idle": "2025-06-24T04:02:21.514706Z",
     "shell.execute_reply": "2025-06-24T04:02:21.514125Z",
     "shell.execute_reply.started": "2025-06-24T04:02:19.335263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (433528, 100)\n",
      "unique users 71102\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    # \"/home/tony/Data/Preference/up_v2_d4/interesting_clips_ahi_d4_20250629.pkl\"\n",
    "    \"/home/tony/Data/Preference/up_v2_d4/fully_merged_up_v2_d4.pkl\"\n",
    ")  # , engine='python')\n",
    "print(\"Preference data shape\", df.shape)\n",
    "print(\"unique users\", df[\"user_id\"].nunique())\n",
    "# Preference data shape (364400, 100)\n",
    "# unique users 61484"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "433528it [04:32, 1589.65it/s]\n"
     ]
    }
   ],
   "source": [
    "# find all the hoot jsons in the json dir\n",
    "JSON_DIR = \"/app2/suno/data/dpo/up_v2_d4_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": {},
   "outputs": [],
   "source": [
    "# update the hoot cer cache\n",
    "with open(\"/home/tony/Data/Preference/up_v2_d4/hoot_cer.json\", \"w\") as file:\n",
    "    json.dump(clip_id_to_cer, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile 5: -0.046\n",
      "Percentile 10: -0.024\n",
      "Percentile 15: -0.016\n",
      "Percentile 85: 0.013\n",
      "Percentile 90: 0.020\n",
      "Percentile 95: 0.035\n",
      "\n",
      "Debug info:\n",
      "Total positive preference samples: 216764\n",
      "cer_diff range: -1.000 to 1.000\n",
      "cer_diff mean: -0.003\n",
      "cer_diff median: 0.000\n",
      "cer_diff std: 0.057\n",
      "Positive cer_diff samples: 78422 (36.2%)\n",
      "Negative cer_diff samples: 83762 (38.6%)\n",
      "Zero cer_diff samples: 54580 (25.2%)\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": "2025-06-24T04:02:21.515414Z",
     "iopub.status.busy": "2025-06-24T04:02:21.515266Z",
     "iopub.status.idle": "2025-06-24T04:02:21.532896Z",
     "shell.execute_reply": "2025-06-24T04:02:21.532407Z",
     "shell.execute_reply.started": "2025-06-24T04:02:21.515399Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    406393\n",
      "True      27135\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": "2025-06-24T04:02:21.533518Z",
     "iopub.status.busy": "2025-06-24T04:02:21.533376Z",
     "iopub.status.idle": "2025-06-24T04:02:21.638115Z",
     "shell.execute_reply": "2025-06-24T04:02:21.637672Z",
     "shell.execute_reply.started": "2025-06-24T04:02:21.533504Z"
    }
   },
   "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": "2025-06-24T04:02:21.638712Z",
     "iopub.status.busy": "2025-06-24T04:02:21.638577Z",
     "iopub.status.idle": "2025-06-24T04:02:22.781748Z",
     "shell.execute_reply": "2025-06-24T04:02:22.781140Z",
     "shell.execute_reply.started": "2025-06-24T04:02:21.638698Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "607031\n",
      "196589\n",
      "410442\n",
      "pre-downloaded df (433528, 103)\n",
      "downloaded df (405198, 103)\n",
      "vae downloaded df (404938, 103)\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": "2025-06-24T04:02:22.782498Z",
     "iopub.status.busy": "2025-06-24T04:02:22.782337Z",
     "iopub.status.idle": "2025-06-24T04:02:22.829088Z",
     "shell.execute_reply": "2025-06-24T04:02:22.828638Z",
     "shell.execute_reply.started": "2025-06-24T04:02:22.782482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    404938\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": "2025-06-24T04:02:22.829748Z",
     "iopub.status.busy": "2025-06-24T04:02:22.829608Z",
     "iopub.status.idle": "2025-06-24T04:02:22.857827Z",
     "shell.execute_reply": "2025-06-24T04:02:22.857346Z",
     "shell.execute_reply.started": "2025-06-24T04:02:22.829735Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name         \n",
      "False       chirp-ahi-up-2         188338\n",
      "            chirp-v4-up-u-d-2-4     12600\n",
      "            chirp-ahi-up-d-4-16      1389\n",
      "            chirp-ahi-up-d-4-22       142\n",
      "True        chirp-ahi-up-2         188654\n",
      "            chirp-v4-up-u-d-2-4     12600\n",
      "            chirp-ahi-up-d-4-16      1121\n",
      "            chirp-ahi-up-d-4-22        94\n",
      "Name: count, dtype: int64\n",
      "(404938, 104)\n",
      "(404938, 104)\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": "2025-06-24T04:02:22.858465Z",
     "iopub.status.busy": "2025-06-24T04:02:22.858324Z",
     "iopub.status.idle": "2025-06-24T04:02:23.033293Z",
     "shell.execute_reply": "2025-06-24T04:02:23.032714Z",
     "shell.execute_reply.started": "2025-06-24T04:02:22.858451Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "task\n",
      "upsample        404660\n",
      "fixed_infill       278\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": "2025-06-24T04:02:23.034009Z",
     "iopub.status.busy": "2025-06-24T04:02:23.033856Z",
     "iopub.status.idle": "2025-06-24T04:02:23.210888Z",
     "shell.execute_reply": "2025-06-24T04:02:23.210314Z",
     "shell.execute_reply.started": "2025-06-24T04:02:23.033994Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404660, 104)\n",
      "(404660, 104)\n",
      "preference  model_name         \n",
      "False       chirp-ahi-up-2         188205\n",
      "            chirp-v4-up-u-d-2-4     12600\n",
      "            chirp-ahi-up-d-4-16      1383\n",
      "            chirp-ahi-up-d-4-22       142\n",
      "True        chirp-ahi-up-2         188517\n",
      "            chirp-v4-up-u-d-2-4     12600\n",
      "            chirp-ahi-up-d-4-16      1119\n",
      "            chirp-ahi-up-d-4-22        94\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": "2025-06-24T04:02:23.211598Z",
     "iopub.status.busy": "2025-06-24T04:02:23.211450Z",
     "iopub.status.idle": "2025-06-24T04:02:23.531764Z",
     "shell.execute_reply": "2025-06-24T04:02:23.531177Z",
     "shell.execute_reply.started": "2025-06-24T04:02:23.211583Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unpacked pair quality scores: 2198900\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": "2025-06-24T04:02:23.532534Z",
     "iopub.status.busy": "2025-06-24T04:02:23.532380Z",
     "iopub.status.idle": "2025-06-24T04:02:35.308576Z",
     "shell.execute_reply": "2025-06-24T04:02:35.308021Z",
     "shell.execute_reply.started": "2025-06-24T04:02:23.532519Z"
    }
   },
   "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": "2025-06-24T04:02:35.309361Z",
     "iopub.status.busy": "2025-06-24T04:02:35.309204Z",
     "iopub.status.idle": "2025-06-24T04:02:39.999846Z",
     "shell.execute_reply": "2025-06-24T04:02:39.999334Z",
     "shell.execute_reply.started": "2025-06-24T04:02:35.309345Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spectrum Decay Difference Percentiles:\n",
      "5th percentile: -0.2487\n",
      "10th percentile: -0.1776\n",
      "15th percentile: -0.1330\n",
      "85th percentile: 0.1369\n",
      "90th percentile: 0.1822\n",
      "95th percentile: 0.2546\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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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 = [0.05, 0.1, 0.15, 0.85, 0.9, 0.95]\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": "2025-06-24T04:02:40.000573Z",
     "iopub.status.busy": "2025-06-24T04:02:40.000422Z",
     "iopub.status.idle": "2025-06-24T04:02:40.725903Z",
     "shell.execute_reply": "2025-06-24T04:02:40.725383Z",
     "shell.execute_reply.started": "2025-06-24T04:02:40.000558Z"
    }
   },
   "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": "2025-06-24T04:02:40.726929Z",
     "iopub.status.busy": "2025-06-24T04:02:40.726474Z",
     "iopub.status.idle": "2025-06-24T04:02:41.167107Z",
     "shell.execute_reply": "2025-06-24T04:02:41.166627Z",
     "shell.execute_reply.started": "2025-06-24T04:02:40.726912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile  |  Quantile Value\n",
      "-------------------------------\n",
      "  5th      |   -0.109\n",
      " 10th      |   -0.082\n",
      " 20th      |   -0.052\n",
      " 50th      |   -0.000\n",
      " 80th      |    0.049\n",
      " 90th      |    0.075\n",
      " 95th      |    0.097\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 = [0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95]\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": "2025-06-24T04:02:41.167816Z",
     "iopub.status.busy": "2025-06-24T04:02:41.167667Z",
     "iopub.status.idle": "2025-06-24T04:02:41.183137Z",
     "shell.execute_reply": "2025-06-24T04:02:41.182699Z",
     "shell.execute_reply.started": "2025-06-24T04:02:41.167800Z"
    }
   },
   "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": "2025-06-24T04:02:41.183769Z",
     "iopub.status.busy": "2025-06-24T04:02:41.183621Z",
     "iopub.status.idle": "2025-06-24T04:02:42.666007Z",
     "shell.execute_reply": "2025-06-24T04:02:42.665433Z",
     "shell.execute_reply.started": "2025-06-24T04:02:41.183754Z"
    }
   },
   "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": "2025-06-24T04:02:42.666748Z",
     "iopub.status.busy": "2025-06-24T04:02:42.666585Z",
     "iopub.status.idle": "2025-06-24T04:02:43.093407Z",
     "shell.execute_reply": "2025-06-24T04:02:43.092829Z",
     "shell.execute_reply.started": "2025-06-24T04:02:42.666732Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404660, 117)\n",
      "(359232, 117)\n",
      "(359232, 117)\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": "2025-06-24T04:02:43.094138Z",
     "iopub.status.busy": "2025-06-24T04:02:43.093986Z",
     "iopub.status.idle": "2025-06-24T04:02:43.127630Z",
     "shell.execute_reply": "2025-06-24T04:02:43.127150Z",
     "shell.execute_reply.started": "2025-06-24T04:02:43.094122Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 179616\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": "2025-06-24T04:02:43.131961Z",
     "iopub.status.busy": "2025-06-24T04:02:43.131673Z",
     "iopub.status.idle": "2025-06-24T04:02:43.552291Z",
     "shell.execute_reply": "2025-06-24T04:02:43.551713Z",
     "shell.execute_reply.started": "2025-06-24T04:02:43.131944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    359232\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    179616\n",
      "True     179616\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-ahi-up-2         336089\n",
      "chirp-v4-up-u-d-2-4     20418\n",
      "chirp-ahi-up-d-4-16      2490\n",
      "chirp-ahi-up-d-4-22       235\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-ahi-up-2         167889\n",
      "            chirp-v4-up-u-d-2-4     10209\n",
      "            chirp-ahi-up-d-4-16      1377\n",
      "            chirp-ahi-up-d-4-22       141\n",
      "True        chirp-ahi-up-2         168200\n",
      "            chirp-v4-up-u-d-2-4     10209\n",
      "            chirp-ahi-up-d-4-16      1113\n",
      "            chirp-ahi-up-d-4-22        94\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    359232\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": "2025-06-24T04:02:43.553007Z",
     "iopub.status.busy": "2025-06-24T04:02:43.552846Z",
     "iopub.status.idle": "2025-06-24T04:02:43.916956Z",
     "shell.execute_reply": "2025-06-24T04:02:43.916449Z",
     "shell.execute_reply.started": "2025-06-24T04:02:43.552991Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    125145\n",
       "2.0     54471\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": "2025-06-24T04:02:43.917652Z",
     "iopub.status.busy": "2025-06-24T04:02:43.917505Z",
     "iopub.status.idle": "2025-06-24T04:02:44.297519Z",
     "shell.execute_reply": "2025-06-24T04:02:44.296947Z",
     "shell.execute_reply.started": "2025-06-24T04:02:43.917637Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    359232.000000\n",
      "mean         23.075049\n",
      "std           1.780172\n",
      "min           9.663606\n",
      "25%          22.194413\n",
      "50%          23.238339\n",
      "75%          24.195493\n",
      "max          31.322367\n",
      "Name: mean_ear_score, dtype: float64\n",
      "count    179616.000000\n",
      "mean         -0.009514\n",
      "std           1.446006\n",
      "min          -9.864528\n",
      "25%          -0.947541\n",
      "50%          -0.008729\n",
      "75%           0.929708\n",
      "max           9.393937\n",
      "Name: mean_ear_score_diff, dtype: float64\n",
      "count    179616.000000\n",
      "mean         -0.002401\n",
      "std           0.063509\n",
      "min          -0.761798\n",
      "25%          -0.041770\n",
      "50%          -0.000376\n",
      "75%           0.039322\n",
      "max           0.466867\n",
      "Name: mean_ear_score_diff_ratio, dtype: float64\n",
      "count    359232.000000\n",
      "mean          0.764729\n",
      "std           1.212962\n",
      "min           0.000000\n",
      "25%           0.133333\n",
      "50%           0.377778\n",
      "75%           0.914286\n",
      "max          43.950000\n",
      "Name: mean_shimmer_score, dtype: float64\n",
      "count    1.796160e+05\n",
      "mean     1.150806e-02\n",
      "std      1.090918e+00\n",
      "min     -2.440000e+01\n",
      "25%     -2.444444e-01\n",
      "50%      1.110223e-16\n",
      "75%      2.666667e-01\n",
      "max      2.764444e+01\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": "2025-06-24T04:02:44.298233Z",
     "iopub.status.busy": "2025-06-24T04:02:44.298084Z",
     "iopub.status.idle": "2025-06-24T04:02:44.752595Z",
     "shell.execute_reply": "2025-06-24T04:02:44.752107Z",
     "shell.execute_reply.started": "2025-06-24T04:02:44.298218Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WliZbtmwpk5KS5P79+wN+NykpSX799dcBy6akpMgJEybYba+++qps1qyZ/O233wKee9KkSbJRo0by4MGDUkopn332WdmiRQvp9XoLXOfrr78+5Pz8LVmyRCYlJcldu3bl+9nJkyfztd15552yU6dOAW3W+n788cd2W0ZGhmzXrl3ANleYPt55552yS5cuZ1zu0Ucflc2aNQtoK8zrb/X9q6++Cqlf1vOlpaXJtLQ0uW/fPjljxgyZnJwsu3fvLqUM/X0fyutq7Ss2btxotw0ZMiRge7bs378/3z6gd+/eskePHgHLff/99zIpKUmuWLFCSimlaZrymmuukXfeeac0TdNe7uTJk7Jjx47yjjvuOG0mXq9XZmdnB7QdP35ctm3bVo4ZMyZf/y677DJ57Ngxu/2zzz6TSUlJ8osvvrDbbrjhBtmuXTuZnp5ut61du1YmJSUFXfe8+vfvL7t16yallHLatGkyKSlJbt++PaAfb731lr18qNvixx9/LJOSkuScOXPsZQzDkLfddlu+7INtP//5z3/y7RveeuutfPsQS9790EsvvSQvueSSgPyys7Nlq1atArIeO3asbNeunTx69GjA4z3wwAOyZcuWQfuW93nvvPNO+32+Y8cO+cADD8ikpCT5zDPPSCl9ObZo0SLf382BAwfK6667LuB9YZqm7N27t7zmmmvstmCvRWH6b20fnTp1ClinwrynrVrAPz8ppbz33nvlZZddZn+/d+9e2bBhQ3nvvfdKwzAClrWeIzMzU7Zq1Uo+/vjjAT8/fPiwbNmypd1+/PjxoOtdWDwFHKJzzjnntJe4sMYgfP7552GPH4iLi0PPnj1DXv7GG28MmJ187bXXomrVqvjf//4X1vOH6quvvoKu6/lOS995552QUuY7vdu2bVvUqVPH/r5hw4ZISEg44xitr776ClWrVsV1111nt7ndbgwYMAAnTpzA119/HYG1yc/KzzqaaLnzzjsDfl7Yx2zWrFnAp8bKlSuje/fuQZdv0KABWrVqFbBsvXr1AjL76KOP0LJlS1SoUAFHjx61v9q2bQvDMOx8KlSogJMnT572dG6FChWwe/du7N27t1DrdezYMQCwjwT58z8KnZGRgaNHj+Kyyy7D/v37kZGREbDseeedFzA7MCEhATfeeCN++uknHD58uNB9rFChQsCpssIo7Otfq1YttG/fPuTHP3HiBNq0aYM2bdrg6quvxuTJk9GsWTP7yGKo7/tQXtez1aVLF/z444/4/fff7bbVq1cjLi4OV111FQBgx44d2Lt3L7p3745//vnHfh9a6/n111+fdp+o67o9BtM0TRw7dgxerxcpKSn46aef8i3ftWvXgPebtZ1Y28bff/+NHTt2oEePHihfvry9XLt27dCgQYNCZzBw4MAzjgUMdVtcs2YN3G53wJkkTdPyHREHAref7OxsHD16FE2bNgWAkE5JB9O1a1d4PJ6A4RXr1q1Deno6unbtCkCd4fnkk0/QsWNHSCkD1ic1NRUZGRkhPf/atWvt9/kNN9yAjz76CDfccIN9tNpyzTXXBByxPHbsGDZu3IguXbogMzPTfu5//vkHqamp2Lt372nHyobT/xtvvDEg73De03369An4vlWrVjh27Jh9Ovmzzz6DaZq49957843JtSbqrF+/Hunp6ejWrVtAvzVNQ9OmTe2hHGXKlIHb7cbmzZsDhmEUFk8Bh+jEiROnPSXbtWtXvPvuu3j88cfx0ksv2Tv3a6+9NqQB2IAaNFuYweh169YN+F4Igbp16wYdsxNJf/zxB84777x8l8axLr2S9/nPP//8fI9RsWJFpKenn/F56tatmy8/63kOHjxY6L6H4o8//oCmaQFFK6DGsFSoUCGsfA8ePGjvvP0VdLqwoMz8N/Z9+/Zh165daNOmTdDHsAZI9+3bF6tXr8bgwYNRrVo1tGvXDl26dMHll19uLztixAjcc8896Ny5M5KSkpCamoobbrghYOb76cggs8S//fZbTJs2DVu3bs03FiYjIyPgj3PdunXzzVa84IILAKjXo2rVqoXqo5Qy7NmPhX39Qz7dkis+Pt4+nRgXF4datWoFnJoM9X0fyut6tq699lpMmDABq1atwrBhwyClxEcffYTLL7/c3v6tgjzYaUxLRkZG0A8JlhUrVuDtt9/Gb7/9Bo/HY7cHyzbvtmE9rrU/sfLJu38E1PYWrKg8nfLly+O2227DtGnT8NNPPwWdcBDqtnjw4EFUrVoVZcuWDfh53vcaoAqh6dOnY9WqVUhLSwv4Wd4PUKFq2LAhLrzwQqxevdq+XuaqVatw7rnn4l//+pfd1/T0dCxevBiLFy8+7fqcTtOmTXH//fdDCIEyZcqgfv36QbPL+xr//vvvkFLilVdewSuvvBL0sdPS0gqcdBZO//P2IZz3dI0aNQJ+bq3r8ePHkZCQgN9//x2app32EmXW8xY0hMPa5uLi4vDwww/jhRdeQLt27dC0aVNcccUVuPHGG1G1atUCHz8vFoAh+Ouvv5CRkRF0I7WUKVMGCxYswKZNm/Df//4Xa9aswapVq7B48WK8/fbbIc2sK8y4vbNlGEaxzfYr6HmCFQ1OcjaXTzAM46yeO5TXxjRNtGvXLt/4LItVQCUmJmLlypVYu3YtvvrqK3z11VdYvnw5brzxRrzwwgsA1OVCPv30U3z++edYt24dli5dirlz5+Kpp5467YWVrfE8x48fDyhifv/9d9x+++248MILMXr0aJx//vlwu9343//+hzlz5oR1lLwwfUxPTz/ra/qF+voXdrvVdT3opJnCCuV1PVvVqlVDq1atsHr1agwbNgxbt27FwYMHA47iWNvxqFGjCry81TnnnFPgc7z33nsYPXo0rrrqKgwaNAiJiYnQdR0zZ84MepYgGvsTayzg9OnT841xBkLfFgvj/vvvx5YtWzBo0CA0atQI55xzDkzTxF133XVW69q1a1fMmDEDR48eRUJCAr744gt069bNvvyPtW1ef/31+cYKWkK5CcK5554b0vs87/ZjPf+dd95Z4JH10/0tDqf/efsQznu6oAM9hXmtrGUnTpwYtJDzf+/ffvvt6NixIz777DOsXbsWr7zyCt544w3MnTs36JjsYFgAhuC9994DAHuQZkE0TbMPeY8ZMwYzZszAyy+/jE2bNqFt27YRvx7Tvn37Ar6XUmLfvn0Bb+6CjrQdPHgQtWvXtr8vTN9q1qyJDRs2IDMzM+Ao4K+//mr/PBJq1qyJXbt2wTTNgI3Lep68n7gipWbNmjBNE/v27Qv4tHbkyBGkp6cHrF+wfHNycuzTlpYaNWrke70ANYEgXHXq1MGJEydC2snGxcWhY8eO6NixI0zTxLhx47B48WLcc8899pGSSpUqoVevXujVqxeysrLQv39/TJs27bQF4IUXXgjAN6PP8sUXXyAnJwevv/56wOtU0B0p9u3bl++onfVp2D/vUPt44MCBkI9e5lWY178oFOZ9H8rrmldh90NdunTBU089hV9//RWrVq1C2bJl7dmXAOz9SEJCQliF7ccff4zatWtj+vTpAX2bOnVqoR8L8OUTye2tfPnyGDhwIKZNmxa0qAh1W6xRowY2bdqEkydPBhwF9D/FDqgPVBs2bMDw4cMDrm0abPhDYV/Prl27Yvr06fjkk09QpUoVZGZmolu3bvbPK1eujHLlysE0zYh8UCks6/3kdrvDev5I9P9s39PB1KlTB6Zp4pdffimwqLSeNzExMaTnrVOnDu68807ceeed2Lt3L2688Ua8/fbbmDRpUkh94hjAM9iwYQNee+011KpVC9dff32By1ljofxZL7J16Qhrgz/Tqc9QrVy50h5fAKhxKIcPHw44BVS7dm18//33AZev+PLLL/NdLqYwfbv88sthGAYWLFgQ0D5nzhwIISJ2Curyyy/H4cOHsWrVKrvN6/Vi/vz5OOecc3DppZdG5Hny6tChAwAEXE4DAGbPnh3wc0Dl+8033wQst2TJknxHADt06ICtW7cGXK7i6NGj+OCDD8LuZ5cuXbBlyxasWbMm38/S09Ph9XoBIN9YOE3T7GLNel/kXaZcuXKoU6fOGa/JlZKSArfbje3btwe0W59U/T/9ZmRkYNmyZUEf5++//w64TEdmZiZWrlyJRo0a2Z+EQ+1jRkYGfv/9dzRv3vy0fS9IYV7/ohDq+z6U1zWYsmXLFuoUYufOnaHrOj788EN89NFHuOKKKwKOfqSkpKBOnTp4++23g46TPtPpwmDvle+//x5bt24NuY/+zjvvPDRq1AgrVqwIWM9169Zhz549YT0mAPtOG8GuAhDqtpiamgqPx4MlS5bYPzdNM9++tKCjnHnfk4Bv3x3qa1q/fn0kJSVh1apVWLVqFapWrRqwL9V1HZ07d8bHH3+Mn3/+Od/vh3L692wkJibisssuw+LFi/H3338X+vkj0f+zfU8Hc9VVV0HTNLz66qv5zoBY7/327dsjISEBM2fODBgKkfd5T548iezs7ICf1alTB+XKlSvUdRR5BNDPV199hV9//RWGYeDIkSPYtGkT1q1bhxo1auD1118/7YWfX331VXzzzTfo0KEDatasibS0NCxcuBDVq1e3L6tSp04dVKhQAYsWLUK5cuVwzjnnoEmTJgFH4gqjYsWK6Nu3L3r27GlfBqZu3boBA4xvvvlmfPzxx7jrrrvQpUsX/P777/jggw/yHUIvTN86duyI1q1b4+WXX7avJ7Zu3Tp8/vnnGDhw4GkPzxdG7969sXjxYowePRo//vgjatasiY8//hjfffcdxo4de1a359u3bx9ee+21fO0XX3wxrrjiCvTo0QOLFy9Geno6Lr30Uvzwww9YsWIFrrrqKnusDKDyffLJJzF8+HC0bdsWO3fuxNq1a/Odfrzrrrvw3nvv4a677sJtt91mXwamRo0a2LVrV1jrMGjQIHzxxRcYNmwYevTogUsuuQQnT57Ezz//jI8//hiff/45KleujMcffxzHjx/Hv/71L1SrVg0HDx7Ev//9bzRq1Mg+wtWtWzdcdtlluOSSS1CpUiX88MMP+Pjjj894df34+HikpqZiw4YNGDlypN3erl07uN1uDBs2DH369EFWVhbeffddJCYm5js6CqhTZI899hh++OEHJCYmYtmyZUhLS8P48ePtZULt4/r16yGlDOlOIME0bNgw5Ne/KIT6vg/ldQ3mkksuwapVqzB+/Hg0btwY55xzzmnvj5uYmIjWrVtj9uzZyMrKsicLWDRNw7PPPovBgwfjuuuuQ8+ePVGtWjUcOnQImzZtQkJCwmkvoXLFFVfgk08+wb333osrrrgCBw4cwKJFi9CgQQOcOHGikOkpDz74IIYOHYq+ffuiV69eOHbsGP7973/joosuCvsxrbGAwSaDhLotXnXVVWjSpAleeOEF/P7777jwwgvxxRdf2GN7raN5CQkJuPTSS/HWW2/B4/GgWrVqWLduXdBrvF1yySUAgJdffhldu3aF2+3GlVdeedrT7l27dsXUqVMRHx+Pm266Kd/py4ceegibNm3CLbfcgptvvhkNGjTA8ePH8eOPP2LDhg3YvHlzWBmG6sknn0Tfvn3RvXt33HLLLahduzaOHDmCrVu34q+//gq4Jm8wZ9v/s31PB1O3bl0MGzYMr732Gvr27YtrrrkGcXFx+OGHH3DeeefhoYceQkJCAsaNG4dRo0ahZ8+e6Nq1KypXroyDBw/if//7H1q0aIEnnngCe/fuxe23345rr70WDRo0gK7r+Oyzz3DkyJGAo7lnwgLQj3XKwe12o1KlSkhKSsLYsWNDuhewde/DZcuW4Z9//sG5556Lyy67DMOHD7cHu7vdbkyYMAGTJ0/GuHHj4PV6MX78+LALwGHDhmHXrl144403kJWVhTZt2uDJJ58MOLXQvn17jB49GrNnz8bzzz+PlJQUzJgxI98YocL0TdM0vP7665g6dSpWrVqF5cuXo2bNmhg1apQ9UzISypQpg/nz52PSpElYsWIFMjMzUa9ePYwfP75Qs6WD+e2334IOML7ppptwxRVX4Nlnn0WtWrWwYsUKfPbZZ6hSpQqGDh2a71Zzt9xyCw4cOIClS5dizZo1aNmyJWbPnp3vOlPnnXce5s2bh2effRZvvPEGKlWqhD59+uC8886zrxFWWGXLlsX8+fMxc+ZMfPTRR1i5ciUSEhJwwQUXBLzvrr/+eixZsgQLFy5Eeno6qlatii5dumD48OH2jn/AgAH44osvsG7dOuTk5KBGjRq4//77Q7rXaa9evTB8+HD8+eef9gD9Cy+8EFOnTsWUKVPwwgsvoEqVKrj11ltRuXLloGOoLrjgAvzf//0fJk6ciN9++w21atWyL8xqCbWP1ozMs/kgEurrXxRCfd+H8roG07dvX+zYsQPLly/HnDlzULNmzdMWgIAqGNavX49y5coFPQLaunVrLF68GK+99hr+/e9/48SJE6hatSqaNGlyxlsE9uzZE0eOHMHixYuxdu1aNGjQAC+++CI++uijsAuNyy+/HK+88gqmTJmCl156CXXq1MH48ePx+eefn1XxYo0FzHu0LdRt0Rrb+Nxzz2HFihXQNA1XX3017r33Xtx6660BBxleeuklPPPMM1i4cCGklGjXrh3efPPNfOPimjRpgpEjR2LRokVYs2YNTNPE559/fsYCcMqUKTh58iS6dOmS7+dVqlTBu+++i1dffRWffvop3nnnHVSqVAkNGjTIN4u3KDRo0ADLli3D9OnTsWLFChw7dgyVK1fGxRdfjHvvvfeMvx+J/p/Ne7ogI0eORK1atfDvf/8bL7/8MsqWLYvk5GTccMMN9jLdu3fHeeedhzfeeAOzZs1CTk6OPRbX2v6rV6+Obt26YcOGDXj//feh6zouvPBCTJkyBZ07dw65P0I6fSQ+ETmaYRjo2rUrunTpgvvvvz+qfTl8+DA6deqEyZMn25cpIXK6zz77DPfeey8WLlxonzEiKmocA0hEZ0XXdYwcORILFy487bUyi8PcuXORlJTE4o8cK++9og3DwPz585GQkGCfziUqDjwCSEREVEwee+wxnDp1Cs2bN0dOTg4++eQTbNmyxR63SFRcWAASEREVkw8++ACzZ8/Gvn37kJ2djbp16+LWW28944QrokhjAUhEREQUYzgGkIiIiCjGsAAkIiIiijEsAImIiIhiDAtAIiIiohjDO4EUQlpaBopyyozLpcPrNc68YCnHHBTm4MMsFOagMAeFOSixkIMQQGJi+Yg+JgvAQpASRVoAmqYs0scvKZiDwhx8mIXCHBTmoDAHhTmEh6eAHcQwzGh3wRGYg8IcfJiFwhwU5qAwB4U5hIcFoINomoh2FxyBOSjMwYdZKMxBYQ4Kc1CYQ3hYADqIrvPlAJiDhTn4MAuFOSjMQWEOCnMID8cAEhERUZEyTROG4S2Sx5ZSg9dbsk8D67oLmla8hSwLQCIiIioSUkqkpx/FyZOZ0e6K45Utm4AKFSpDiOI5pc0C0EFMs2R/gokU5qAwBx9moTAHhTkoJSEHq/hLSDgXcXHxxVbclCRSSuTkZCMz8x8AQMWKicXyvCwAHcQwOI8dYA4W5uDDLBTmoDAHxek5mKZhF38JCRWi3R1Hi4uLBwBkZv6D8uXPLZbTwRw56SC6zk9GAHOwMAcfZqEwB4U5KE7PwTDUxZmt4oZOz8qpqMZK5sUC0EGKewCoUzEHhTn4MAuFOSjMQSkpORT1ad/Scla5uE+Pl4x3DxERERFFDMcAEhERUbGK5MGuMz0WbxMXHAtAB+HtbBTmoDAHH2ahMAeFOSglNgcd8CDCfT9NEeiCBhiRfbrSgAWgg5gmP6YAzMHCHHyYhcIcFOaglMQchFDF37HsbJjFcGhOEwKV4uPhFhqPBObBAtBBdF0ruZ/oIog5KMzBh1kozEFhDkpJzsGUslgKwHDdd98QXHhhfQDAxx+vgsvlwo033oS77hoGIQTS09PxyiuTsG7dGng8OWjWrCXuv/9h1K5dBwDw119/YvLkidi2bSu8Xg+qV6+Be+8dgTZtUqO5WgE4CcRBeENrhTkozMGHWSjMQWEOCnNQiiqF1as/hK678OabczFy5MNYvHgBPvhgJQDg+efHYdeuHXjhhcmYMWM2pJR45JGR8HrVJVwmT34BHk8OXn31Tcyduwh33z0cZcueU0Q9DQ+PABIRRVDeAekOPshBRKdRrVo1jBjxIIQQqFPnAvzyyx4sWbIQzZu3xNq1X+H112ehceOmAIAnn3wGPXt2w1df/RcdO16FQ4f+QocOHVG/fgMAQM2ataK5KkHxCCARUaTogEczA76gR7tTRBSOiy9OCbg2X0pKY+zf/zv27v0Vuq7j4otT7J9VrFgJderUxb59vwEAbrqpD+bOnYW7774Ts2bNxJ49u4u9/2fCAtBBSupYjkhjDgpz8CkJWQgBeHMHtx89dQpHT53CsexseGFG7JIXJSGH4sAcFOagOPEge/fuN2LJkvfQuXNX/PLLHtx11wAsXboo2t0KwALQQUrijK6iwBwU5uDjhCyECPwqiDW4vSgGuTshBydgDgpzKFo//fRjwPc//rgdtWvXwQUXXAjDMPDTT9vtnx0/fgy//74PF1xQz26rVq06brzxJjz//Ivo06e/PX7QKVgAOojLxZcDYA4W5uAT9SwKOLUbalEY6nJnEvUcHII5KMyhaB069BemTZuM33/fi08//QjLli3GTTf1Qe3addC+fQe88MJz+P77rdi9+2c8/fQTqFr1PLRvfwUA4JVXXsKmTRtw8OAf2LVrJ7777hvUrVvv9E9YzDgJxEGK+z6ATsUcFObgE80sgl23TBcClcqWgYC0Tz8J+z9+vwtAaAIe04T0+1m4F6ble0JhDkpJzkGLYN8FCj4NfDbPc+213ZCdnY3BgwdC03TcdFMf3HBDTwDAmDFP4pVXJuHRR++Hx+NB06Yt8OKLr8DlUmWVaRqYPPkFHD78N845pxxat26DESMeDLsvRYEFIBFRCPxP6epCQNdN5Mgcu03TNMTpbmiAfY8DIQS80kRGtt9yuRemjdMCL0zL2cIUC6RUH4AqxcdH7DFPVwAC6vnC2b5cLhdGjnwIDz88Jt/PKlSogP/7v6cL/N0HHhhV+CcsZiwAiYjy8D9oYP3b/0iCpmnQhAnDzIQh1aE8F1zQXBXVUZk8f22kX/EY6aOCRCWOAbhF5E5fB9nkAvDDVXAsAB3E6+XeH2AOFubgU6xZ5LlPqQAQ5wYSIO0Tvi4h4dIBwITM/esiZWgzMk93VPBMt6vie0JhDkpJziGSRRkLvPCwAHQQvokV5qAwB5/iyiLYeD+3riMxToOUWfbRPk1zQ0MCQr0HgSaEfYrKOpIow5glzPeEwhwU5lB0pk9/I9pdKHKcQuQgbjevGAswBwtz8CnuLPwv42If4cs92qe+Cj7apwlhf+lCIN4FJMRLlC+jvhLiJeJdwUvHM80W5ntCYQ4Kc1BK8FyYqOIRQCKKacHG+4X5SHBrAuXiJQxpnSoG4lwmTuX4jh4KzQVNuALGCnJcIBEVNxaARBSz3G5ACF+FJYT6jzhV+MdSv2rAlFkwTHVDeOtUsYQ87VjBsxkXSEQUjqifAj506BAefvhhtG7dGk2aNEH37t3xww8/2D+XUuKVV15BamoqmjRpgttvvx179+4NeIxjx47hoYceQosWLdCqVSuMHTsWWVlZAcvs3LkTffv2RePGjdGhQwe8+eabxbF6RORQ6jSrASALUmaqL2RBEwZ0TbNP4xb2WmtShnaqOPjv5r+LSCQuIE1ElFdUC8Djx4/j1ltvhdvtxptvvokPP/wQjz76KCpWrGgv8+abb2L+/PkYN24clixZgrJly2LQoEHIzs62l3n44YexZ88ezJ49GzNmzMA333yDJ554wv55ZmYmBg0ahBo1amD58uUYNWoUpk+fjsWLFxfr+p6Jx8NzPQBzsDAHn6LMQh2ZU19SSrg0EThmL07N+C3u+ss+LSx8dx85aXoADvvitpGLOSg8Qh6eqJ4CfvPNN1G9enWMHz/ebqtdu7b9bykl5s2bh7vvvhtXXXUVAGDixIlo27YtPvvsM3Tr1g2//PIL1qxZg6VLl6Jx48YAgMcffxxDhgzBqFGjUK1aNbz//vvweDx4/vnnERcXh4suugg7duzA7Nmz0bt37+JdaSJyMFHgadziLgF5WpiIilJUjwB+8cUXSElJwYgRI9CmTRvceOONWLJkif3zAwcO4PDhw2jbtq3dVr58eTRt2hRbtmwBAGzZsgUVKlSwiz8AaNu2LTRNw7Zt2wAAW7duRatWrRAXF2cvk5qait9++w3Hjx8v6tUMGWd0KcxBYQ4+RZqF8H1ZJd7ZnMYNlf9s4dPdrsr/tLCm8TwwwG3DUpJzyDvj/Wy+NO30P6fgonoEcP/+/XjnnXdwxx13YNiwYfjhhx/w7LPPwu12o0ePHjh8+DAAIDExMeD3EhMTceTIEQDAkSNHULly5YCfu1wuVKxY0f79I0eOoFatWgHLVKlSxf6Z/ynn03G5fBubaZowDAldF9A0Xx1tGCZMU0LXtYCdtdXucmkBY4q8XgNSqg3Z/8be1qH9vBv46dqFCOyjlBJerwlNE9B132ObpoRhBGuP/DqF2nf/diuH0rRO/u2hrpPVr9K0TuG+TpomAp43UusE3YBhmoGFld/z67pmjwPUNAGY1h8cX5uAgDRl7h8b1aYLDWZu4Wgtr+fe+UBYs4XLqO+sZeI0gQwAuq4eU9c0aJqm/oBJwKVr0HQBXdfh0jRHvk7cR3Af4d8uZeAxJuuh3W7Af3q7f5Hmf2Q71HZrQn1BywM6PJ7A9ryFYSTaw+m7f7v1M+v/eV+nSItqASilREpKCh58UN0g+eKLL8bu3buxaNEi9OjRI5pdC8raGPwZhoRh5B+HYRgmgjTD6w1+NMHacPKO6ShojEewdimDt5umhGmG3h7pdQqn3fp3aVonS6jrZO1ES9M6nam9oHUyTRn08c9mnTQN0FwmsjweGLnFmltzoaLuu0epKhg0dRTOtGbxqv5YbdKv3WozzNx+ab7lrTZV1BkwzUx4c08zuzQXhK5uI+c1TAASkOoPubXP8RomNCFgaAa8pm9H5KTXifsI7iP82/M+nq9IsyZf+badYEJtl7n/Cba8KmjLAdBDuvd2NNutn1n/93+diuJIZlQLwKpVq6J+/foBbRdeeCE+/vhj++cAkJaWhvPOO89eJi0tDQ0bNgSgjuQdPXo04DG8Xi+OHz9u/36VKlXsI4YW63vrSCARxSYz4BItxTewzjrNbP2bKJb4Jl8V9fOEVzzdd98QNGhwEeLi4vDBB+/B7Xbjhht6YtCgoQCAjIwMvPrqFKxd+z/k5HjQsGEjDB/+IC66KMl+jDlz3sLSpYuRnZ2NTp2uRsWKlbBp0wbMmbMwUqt3VqI6BrBFixb47bffAtr27t2LmjVrAgBq1aqFqlWrYsOGDfbPMzMz8f3336N58+YAgObNmyM9PR3bt2+3l9m4cSNM00STJk0AAM2aNcM333wDj3UMGMD69etRr169kE//FgfO6FKYg8IcfJiFoo4OEt8PCnPIVUR15OrV/0GZMmXxxhtzcPfdwzFnzlv4+uuNAID/+79H8c8/RzFp0lTMmjUfSUkNcf/9dyM9Xc0r+OST1Zg3bzbuvns4Zs2aj2rVqmPlyqVF09EwRbUAHDhwIL7//nvMmDED+/btwwcffIAlS5agb9++ANSh29tuuw2vv/46Pv/8c+zatQujRo3CeeedZ88Krl+/Ptq3b4//+7//w7Zt2/Dtt9/imWeeQbdu3VCtWjUAQPfu3eF2u/HYY49h9+7dWLVqFebNm4c77rgjautOREREzlW//kW4884hqF27Drp0uQ4NGzbCN998je+/34odO37EM8+8gIYNL0bt2nVw3333IyGhPL788nMAwNKli3HdddejW7frUadOXdxxx2BceGGDKK9RoKieAm7SpAmmT5+OyZMn49VXX0WtWrUwduxYXH/99fYygwcPxsmTJ/HEE08gPT0dLVu2xFtvvYX4eN84nUmTJuGZZ57BwIEDoWkarrnmGjz++OP2z8uXL49Zs2bh6aefRs+ePXHuuefinnvucdwlYNxunZ/owBwszMGHWSguPaqf2R2D7weFOeTKnSgVafXrXxTwfWJiFfzzz1Hs2fMzTp48iW7dOgX8PDs7G3/8cQAA8Pvv+9Cjx00BP7/44kvw7bffRL6jYYr6reCuvPJKXHnllQX+XAiBkSNHYuTIkQUuU6lSJbz00kunfZ6GDRti4UJnnHcnIiIiZ3O5AkskIQSklDh58gQSE6tg2rSZ+X4nIaF8cXXvrEW9ACQiIiIqKZKTG+Lo0TTouo7zz68RdJk6depi586f0KXLdXbbjh0/FVcXQ8LzCUQUM0rixWH9LxZd2PsSE1HktWrVGpdc0hhjxjyMzZs34s8/D+KHH77HzJmvYudOVeTddFNv/Oc/72H16v9g//7fMWfOW/jll92O2vfwCKCDcCyHwhwU5uATiSzcbkDT8lx8Ngr3+A2VABDvAhAvYeYOcBIwEOd2Q2af9ldLPW4bSknOQZ1OjdCDneZxiuJDkxACkya9gjfeeA3PP/8Ujh37B5UrJ6JZsxY491x1Y4prrumCgwf/wKuvTkF2dg46drwKXbp0x44dP0a8P+ESsjgvfFXCHTmSUaT337SuZh7rmIPCHHzONgshAHcZA4bMsK+9J4SGOFcZHD1xHN7ci+i6NRcqnVMB/xRzm0vTUS7+XBzO8MKTe8HoOF1HlQQdWdnHYEi1nC50lI+vBDPbDTOGrwjDbUNxeg4eTw7S0v5EYuL5cLt9t2J1uwEhiq94ldJ3J5Bouv/+e5CYmIj/+79ngv68oLwA9VpXqRLZ8YU8AuggLhdndAHMwcIcfM42CyEAEyYyc3IC7vrhdpWJVBeLjITvgtGCg3YAcNuwlNQcPB5AiMjdx/hMhXA0iuRTp05h5cqluOyyNtB1HZ999jG++WYzXn751eLvTAFYABJRzIjWXT+IKFCkNz+nbc5CABs2rMe8ebORk5ONOnXq4rnnJuLSS1tHu2s2FoBERA5hTfYAimbsEhEVj/j4Mnjlldei3Y3TYgHoIDwioTAHhTn4lP4sBNyaQLl4CSN3XV1CwpVnkooshnunlgSl//0QGuagMIbwsAB0EK83hkd1+2EOCnPwKe1ZCKhB8abMgmF6AQCa5oaGBPiXgCb/0gEo/e+HUDEHOhscUuwgmsZTPgBzsDAHn1jJQko14UN95f/jzh22EivvhzMpKTkEey9TfsWdE48AOoiuazDNkjejK9KYg8IcfJiFIjgNGADfDxan5+ByuSGEhuPH05CQUAm67iqi6/KV7NPAUkoYhhcZGccghAaXy10sz8sCkIiIiCJOCIHExOo4fvwojh8/Eu3uOF5cXBlUqFC52CaAsQAkIiKiIuFyuVG58nkwTQNmEV293OXSSvx4SE3ToGl6sc7+ZwHoIKZZgo9hRxBzUJiDTzhZlMarqHAWsMJtQykpOQghoOsu6JG79nMAXdcgRMkuAKOBBaCDGAbfwABzsDAHn0JnoQMe+H5HAxBX8NIlhv8sYP8CtySPfwoHtw2FOSjMITwsAB1E00SJ+URXlJiDwhx8CpOFEKr4O5adbRdMbl1HYpyGkn5QUAMAIeAVJky/lXFBA5w7FyDiuG0ozEFhDuFhAeggTp/RVVyYg8IcfMLJwpTSLgBLywVzNU2DKSWO5eTAmzueShMCleLj4RZazBwJ5LahMAeFOYSHBSARlUqa3znS0nZbNelX3BIRhYMFIBGVOnFuIAHSnjQR7LZqRESxjAWggxTVFPmShjkozMGnMFkIAWi5t1UzpTotFOy2aiVRaTmVfba4bSjMQWEO4WEB6CCGwZ07wBwszMEnnCwkTLtgKi23omL9p3DbUJiDwhzCw/sKOYiul+yjE5HCHBTm4MMslFI2lDFsfD8ozEFhDuFhAeggmsaXA2AOFubgwywUazKLJkTAV6zh+0FhDgpzCA9PARMRlRgCbk2gXLyEkXs+WEBNepHZ0e0ZEZUsLACJqMSLlYNgAoDIneBimF4AgCZ0aMINM4auA0hEZ48FoIPwdjYKc1CYg89psyilt30Lxn9Si/3vGLwHKrcNhTkozCE8LAAdhLeyUZiDwhx8CsrCuu1bek6OfWFkl66jcpwo4Rd8CY5H+BRuGwpzUJhDeDhy0kF0nS8HwBwszMHndFnEuYFz4kyUi8/9cpul9qLPpe2OJuHitqEwB4U5hIepOYimcecOMAcLc/ApKAv/iz4bZgYMMwOmzIIGE6WxBGT9p3DbUJiDwhzCw1PARFTilcaLPhMRFSUeASQiIiKKMSwAHYQzmRTmoDAHH2ah8F7ACt8PCnNQmEN4WAA6CGcyKcxBYQ4+zEJh/afw/aAwB4U5hIcFoIO4XHw5AOZgYQ4+zELhYHeF7weFOSjMITxMzUF4iQeFOSjMwYdZkD++HxTmoDCH8HAWMBFRKeH/d5Cni4nodFgAEhGVYAIAhIBXmDD9CkAXNMCIVq+IyOlYADqI18u9NcAcLMzBh1kowQa7CwiYUuJYTg68ppoNqQmBSvHxcAutVB4J5PtBYQ4KcwgPC0AHKY076nAwB4U5+DCLM5NS2vdDLu1iZDXPiDkozCE8nATiIG63Hu0uOAJzUJiDD7NQTjcLWBMi4Ks04/tBYQ4KcwgPjwASUYlSymubMAi4NYFy8RJG7qEQASDODcjs6PaMiJyLBSARlRw64IHvqv8agLjo9cYRBAAhDJgyC4bpBQBoQocm3DBL6RhAIjp7LACJqEQQQhV/x7Kz7bFubl1HYpwGHhQEpDTtW8VJwVtjEdHpsQB0EI+HM5kA5mBhDj4ej2Gf+jX9JjvE2r1xecsrhduGwhwU5hAeTgIhIiIiijEsAB2EM5kU5qAwBx9mofBewArfDwpzUJhDeFgAEhEREcWYqBaA06ZNQ3JycsDXtddea/88OzsbTz31FFq3bo3mzZtj+PDhOHLkSMBjHDx4EEOGDEHTpk3Rpk0bvPDCC/B6vQHLbNq0CT169EBKSgquvvpqLF++vFjWj4iIiMiJoj4J5KKLLsLs2bPt73Xddyj3+eefx//+9z9MmTIF5cuXxzPPPIP77rsPixYtAgAYhoGhQ4eiSpUqWLRoEf7++288+uijcLvdePDBBwEA+/fvx9ChQ9GnTx9MmjQJGzZswOOPP46qVauiffv2xbuyRETFzP+6iTE2Z4aITiPqBaCu66hatWq+9oyMDCxbtgyTJk1CmzZtAKiCsGvXrti6dSuaNWuGtWvXYs+ePZg9ezaqVKmCRo0aYeTIkZg0aRLuu+8+xMXFYdGiRahVqxZGjx4NAKhfvz6+/fZbzJkzx3EFIGcyKcxBYQ4+/rOAY1mos4AFBFyaAOIMmH5XhJFSh8dTRJ0rRtw2FOagMIfwRH0M4L59+5CamopOnTrhoYcewsGDBwEA27dvh8fjQdu2be1l69evjxo1amDr1q0AgK1btyIpKQlVqlSxl0lNTUVmZib27NljL2MVkP7LWI9BRCWL/+3OBKvCAglhQMosSJkJKTMBZEEIFtJEpET1CGCTJk0wfvx41KtXD4cPH8arr76Kfv364YMPPsCRI0fgdrtRoUKFgN9JTEzE4cOHAQBHjhwJKP4A2N+faZnMzEycOnUKZcqUKarVKzS3W+cnGTAHC3Pwcbt1eL0G4txAAiQk1JEwl5Bw6YiZC0EXdhawuk6idc3E0nMbPW4bCnNQmEN4oloAdujQwf53w4YN0bRpU1x55ZVYvXq1owozi8vlG59omiYMQ0LXBTTNdyDVMEyYpoSuawE7a6vd5dICjlp4vQakVG9gl8v3ONabOe/09tO1CxHYRyklvF4Tmiag677HNk0JwwjWHvl1CrXv/u1WDqVpnfzbQ10nq1+laZ3CfZ00TcDl0iE0A0KchGF6ICXgcsVBFwnQNA26UMsDgBBCPYfIfSzpy9Rq0/yysPqS96iirmt2m6YJwFRFlKb52gQEpCkhRODzmtLqS+DzWo8uhAh83tx2TVePaT2v1R1d1yC0wL5Y6+T/vL4MNGi6Zq+84ZWlZnviPkLhPsK3zZe2dQr2OkVa1McA+qtQoQIuuOAC/P7772jbti08Hg/S09MDjgKmpaXZYwarVKmCbdu2BTyGNUvYf5m8M4ePHDmChISEQheZ1hvHn2FIGEb+Tx6GYSJIM7ze4Ldost5keT/FFPSpJli7lMHbTVPCNENvj/Q6hdNu/bs0rZMl1HWydjilaZ3O1J53nVRRZe1ADWguE17Ta4+FMwwDUkr1c7/BbnnbrJ1rsDb1OL42/7uLqJ22Zj+eta6mKe026deery+ab/m8zytlYJv1rKahjtpZz2t1xzBMuIQM6Iu1TsEzMGEapt/6CEgZfD9WErcn7iO4j7DoeulbJ9XuW6eiOHof9TGA/rKysrB//35UrVoVKSkpcLvd2LBhg/3zX3/9FQcPHkSzZs0AAM2aNcPPP/+MtLQ0e5n169cjISEBDRo0sJfZuHFjwPOsX7/efgwicia3G3C7DbjdBlxuj/p/DJ3uJSIqSlEtAF944QVs3rwZBw4cwHfffYf77rsPmqbhuuuuQ/ny5dGrVy9MmDABGzduxPbt2zF27Fg0b97cLt5SU1PRoEEDjBo1Cjt37sSaNWswZcoU9OvXD3FxcQCAPn36YP/+/Zg4cSJ++eUXLFiwAKtXr8btt98evRUvAMcwKMxBieUc1JE/A4CaxOD1ZEDiBARMiBguAXkvYCWWtw1/zEFhDuGJ6ingv/76Cw8++CCOHTuGypUro2XLlliyZAkqV64MABg7diw0TcOIESOQk5OD1NRUPPnkk/bv67qOGTNmYNy4cejduzfKli2LHj16YMSIEfYytWvXxsyZMzF+/HjMmzcP1atXx7PPPuu4S8AA6o8er9PFHCzMAWqyh5Cq5Iv1MMjGbUNhDgpzCI+QkrGF6siRjCJ9k3Emk8IclFjOQQjAXcaA10yHNR7ONIF4d1kcPXEc3txxNG7NhUrnVMA/MdIW73KjQpnyZ1xO13QknlMRRs6pgDGAQiTA49FL/B/LWN42/DEHJRZyEAKoUqV8RB/TUZNAiIiA3IkfMJHl8cCQJnRNgwYNce5o96wEit0z5kR0GiwAicixTKgZrjL3/xQaAQBCwBCmfc1EQMClASgFdwIhorPHAtBB+AdOYQ4Kc/DxFTEUCgEBKSWyvF7faWGhoXy8CSFK/ilgbhsKc1CYQ3gcdRmYWFfQ9YpiDXNQmIOPujYeFXYWsLqOYO51AktREc1tQ2EOCnMIDwtABynKK36XJMxBYQ4+peUWZmeLOSjcNhTmoDCH8LAAdBD/W8TEMuagMAcf/7t2xDLBChAAtw0Lc1CYQ3iYGhEREVGM4SQQInIEHtwqPv5Zc/w8UWxiAeggvM2TwhyUWMrB7bZu/aYIAQi/+/6aUvJ0Bc6uWLMuDeMVJky/AtAFDShh19CNpW3jdJiDwhzCwwLQQQyDM5kA5mCJlRwC7/ubuyMXGjTE2ff9labkgBWc3eUuBARMKXEsJwdeU723NCFQKT4ebqGVqCOBsbJtnAlzUJhDeLhLdRDOZFKYgxJrOajiRn3lLXR4eliJRA5SSph+XyVRrG0bBWEOCnMIDwtAB+FMJoU5KMzBh7OAFc4CVrhtKMxBYQ7h4SlgInIGEfSfRERUBFgAElH06YBhmoDf3Sr06PWGiKjUYwHoIKbJgawAc7DESg5CACZMZHk8MKRaZ7fmQpzbt4wpTWgcscJ7nuaKlW3jTJiDwhzCwwLQQQze7xQAc7DEWg6m3+SPvIWONMERy+A1+yyxtm0UhDkozCE83KU6iK5z5BPAHCzMwUdwTwWAs6Et3DYU5qAwh/Bwt+ognOmoMAeFOfhorAABcBawhduGwhwU5hAepkZEREQUY1gAEhEREcUYFoAOwtvZKMxBYQ4+nOWncBawwm1DYQ4KcwgPC0AH4Q2tFeagMAcf1j0Kc1C4bSjMQWEO4WEB6CC8nY3CHBTm4CN4r08AkZkEogkR8FUScdtQmIPCHMLD6wA6iKYJGEa0exF9zEFhDj6aEP43CYlZZ1evCbg1gXLxEkbuoUQBIM4NyOxI9K74cNtQmIPCHMLDApCIKAYIAEIYMGUWDNMLANCEDk24YQqNp5eJYgwLQCKiGCKl6bvjiuDgeaJYxRPnDsKZTApzUJiDD2cBK5wFrHDbUJiDwhzCwwLQQTiTSWEOCnPwYd2jMAeF24bCHBTmEB4WgA7icvHlAJiDpTTnIITvKxQa7/UJQA12p9K9bRQGc1CYQ3g4BtBBeJ9PhTkopTUHt1tNRrAIAQhdTVIoiDjtTynWlNZto7CYg8IcwsMCkIiKjTrqZwDI8o1nExo0xLHIIyIqRiwAiajYSUhA5BaAHNhGRFTsWAA6iNfLK1kCzMFSanPQAcM04X9lZ/0Mv2IYJjSN43w42F0ptdtGITEHhTmEhwWgg/BAiMIclNKYgxCACRNZHg8MqS7d4NZciHNHuWNUopTGbSMczEFhDuHhR2oHcbvPdBwkNjAHpTTnYEJCSt/XmfBenwpnASuledsoDOagMIfwcK9KREREFGNYABIRERHFGI4BJCKigItyc0wVUenHAtBBPB7OZAKYg4U5+HAWsBLpWcACAISAV5gw/QpAFzTAwW8/bhsKc1CYQ3hYABIRxSgBAVNKHMvJgddUs7I1IVApPh5uofFIIFEpxo/UDsKZTApzUJiDD2cBK0U1C1hKCdPvy+m4bSjMQWEO4eERQCKiGKcJAS13EKDG+6oSxQQWgERUpFhPOJmAWxMoFy9h5B75EwDi3IDMjm7PiKhosQAkoiLjdgNC+AZoCwEIPXfyAUWdgHp9TJkFw/QCADShQxNumBwDSFSqsQB0EM5kUpiDUtJzEMIq/rJ8d/sQGjTEQRSyBOQsYKWo7gUspWm/RlKYRfIckVTSt41IYQ4KcwgPC0AiKlKqsJB+/yYiomjjR2oH4UwmhTkozMGHs4AV3gtY4bahMAeFOYTHMXvVN954A8nJyXjuuefstuzsbDz11FNo3bo1mjdvjuHDh+PIkSMBv3fw4EEMGTIETZs2RZs2bfDCCy/A6/UGLLNp0yb06NEDKSkpuPrqq7F8+fJiWSciIiIiJ3JEAbht2zYsWrQIycnJAe3PP/88vvzyS0yZMgXz58/H33//jfvuu8/+uWEYGDp0KDweDxYtWoQJEyZgxYoVmDp1qr3M/v37MXToULRu3RrvvfceBg4ciMcffxxr1qwptvUjIiIicpKoF4BZWVl45JFH8Oyzz6JixYp2e0ZGBpYtW4bRo0ejTZs2SElJwfPPP48tW7Zg69atAIC1a9diz549ePHFF9GoUSN06NABI0eOxIIFC5CTkwMAWLRoEWrVqoXRo0ejfv366N+/Pzp37ow5c+ZEYW2JiIiIoi/qBeDTTz+NDh06oG3btgHt27dvh8fjCWivX78+atSoYReAW7duRVJSEqpUqWIvk5qaiszMTOzZs8depk2bNgGPnZqaaj+Gk3Amk8IclFKTg/B9hTuCzTCcPzO1OBTVLOCSptRsG2eJOSjMITxRnQX84Ycf4qeffsLSpUvz/ezIkSNwu92oUKFCQHtiYiIOHz5sL+Nf/AGwvz/TMpmZmTh16hTKlCkTsfU5W0KA190Cc7CUihx0wDBNWLOAc5uIzkqp2DYigDkozCE8USsA//zzTzz33HN4++23ER8fH61uFIrL5fvTZZomDENC10XA9ckMw4RpSui6FjBjz2p3uTQIv1sjeL0GpFSzmFwuDV6vOtJhfaLJO7vpdO1CBPZRSgmv14SmiYBZlKYpc6+rlrc98usUat/9260cStM6+beHuk6aJpCd7S2x62SaJkxInDS89v1ldaEhzq122NY6aULYdwsRGqAJLbfN1667dGhQbZomIKTa4Wuar00Xmn2k0L9NdciXqdXmf8uzwL4EtlttmiYAU/Xd/3kFBKQpc6976HteU1p9CXxe69GFEIHPm9uu6eox82Wga9B1LaAv1jqFmoEoIIOA1wMarJNDTn3vcR+hlPR9RKReJ13XYJqyVK1TsNcp0qJWAP74449IS0tDz5497TbDMPD1119jwYIFmDVrFjweD9LT0wOOAqalpaFq1aoA1JG8bdu2BTyuNUvYf5m8M4ePHDmChISEQh/9s944/gxDwjDyH342DBNBmu0CLy/rTZb3UHZBh7aDtUsZvN00JUwz9PZIr1M47da/S9M6WUJdJ2uHU1LXSe3PJLymCdPMLUpyd2ZSWkcGAU3z3XFCmoABM7dN+rY3KWFKmVtY+trVv1Wb9Xhq8cA2a+carA3wnWK2nte/3WqzTr9KGfi80q89X1803/J5n1fKwDbrWU1DXTcxbwaGYcIlZEBfCpuBLCAD/9dDaoCqLnVHv/e4jyj5+wggMq+Trpe+dVLtvnUqiltqRq0A/Ne//oUPPvggoG3MmDG48MILMXjwYJx//vlwu93YsGEDOnfuDAD49ddfcfDgQTRr1gwA0KxZM8yYMQNpaWlITEwEAKxfvx4JCQlo0KCBvcxXX30V8Dzr16+3H4OIiIgo1kStAExISEBSUlJA2znnnINKlSrZ7b169cKECRNQsWJFJCQk4Nlnn0Xz5s3t4i01NRUNGjTAqFGj8Mgjj+Dw4cOYMmUK+vXrh7i4OABAnz59sGDBAkycOBG9evXCxo0bsXr1asycObNY1zcUvEuCwhwU5uAjwSzIh9uGwhwU5hAeR98KbuzYsdA0DSNGjEBOTg5SU1Px5JNP2j/XdR0zZszAuHHj0Lt3b5QtWxY9evTAiBEj7GVq166NmTNnYvz48Zg3bx6qV6+OZ599Fu3bt4/GKp1WQYeqYw1zUJiDj2lI8GYgnAVs4bahMAeFOYTHUQXg/PnzA76Pj4/Hk08+GVD05VWzZk28+eabp33c1q1bY+XKlZHoYpHSNMEdPJiDhTn4FMX4l5KouHPwfz4nHWThtqEwB4U5hIefqR2E9ztVmINSEnNQM2EjX6j4T9iIZaIYKkABAZcmEBdnwO32/yrypw5ZSdw2igJzUJhDeBx1BJCISi63GxDCN5NNCEDo4V/8maJHCAOmzLbHVqnL0ZSDELqjjgQSUfhYABLRWVNH/QwAWb4B2UKDhjgIloAlkpQSELmXuAELeaLShgWgg3AMg8IclJKYgyr+pN+/I8OUkuNVUDzj8AQACAFDmH6zrwVcGgBP0T9/KEritlEUmIPCHMLDAtBBeL9ThTkoJTIHEfSfZ02akiOWUTyXuxAQkFIiy+uFN/eitbrQUD7edMwp4BK5bRQB5qAwh/Bwl+ogRXnLl5KEOSglLgcdMIRpf3lF5HbKnAWsFGcOMvfOK1JKmA67DmOJ2zaKCHNQmEN4WAA6CGcyKcxBKUk5CAGYMJHl8SA9JwfpOTk46fVGrGzgLGClOGYBlwQladsoSsxBYQ7h4SlgIooYE9I+Tcmr8xMRORfLZiIiIqIYwwLQQUyTA1kB5mBhDj6mZBYAj6pauG0ozEFhDuFhAegghsGdO8AcLMzBh/WfwvpP4bahMAeFOYSHBaCD6DoHeAPMwcIcfAT3VAA4G9rCbUNhDgpzCA93qw7CmY4Kc1CYg4/GChAAZwFbuG0ozEFhDuHhLGAiCgtrESKikosFIBEVmttt3ftXEQIQOu8XS0RUUrAAdBDezkZhDopTcxDCKv6yfLNShQYNcRBFVAKapsnTwOAsYItTt43ixhwU5hCesPaonTp1wj///JOvPT09HZ06dTrrTsUq3tBaYQ6K03NQxYj6KurChHWPwhwUp28bxYU5KMwhPGEVgH/88UfQ6+7k5OTg0KFDZ92pWMXb2SjMQWEOPoL3+gTASSAWbhsKc1CYQ3gKdQr4888/t/+9Zs0alC9f3v7eNE1s2LABNWvWjFzvYoymCRjGmZcr7ZiDwhx8NCEQsRsLl2Cs/xRuGwpzUJhDeApVAN57770A1KfQ0aNHBz6Qy4WaNWvmayciIiIiZylUAbhz504AQMeOHbF06VJUrly5SDpFREREREUnrFnAX3zxRaT7QeBMJgtzUJiDD2cBK5wFrHDbUJiDwhzCE/ZlYDZs2IANGzYgLS0t34SQ8ePHn3XHYhFnMinMQXF8DiLoP4uElMXwJCUA6z/F8dtGMWEOCnMIT1gF4PTp0/Hqq68iJSUFVatW5cy0CHG5NHi9/CTDHBRH56ADhmnCf2aGXoRPp+mcBAKowe7k8G2jGDEHhTmEJ6wCcNGiRRg/fjxuvPHGCHcntrGQVpiD4tQchABMmMjyeGBItdN1ay7EuYvwOXn4j/w4ddsobsxBYQ7hCasA9Hg8aNGiRaT7QkQliOl3AWiOTSMiKlnCGlV900034YMPPoh0X4iIiIioGIR1BDA7OxtLlizBhg0bkJycDJcr8GHGjBkTkc7FGq+XV7IEmIOFOfgYhglN4yxgDnZXuG0ozEFhDuEJqwDctWsXGjZsCAD4+eefA37Gc/Hh41k0hTkozIGcyH8XH633KLcNhTkozCE8YRWA8+fPj3Q/CIDbrcPj4ScZ5qAwBx9d1zgLGNGbBSwAQAh4hQnTrwsuaEAU3qLcNhTmoDCH8IR9HUAiIooNAgKmlDiWkwNv7nVfNSFQKT4ebqHxCAxRCRRWAThgwIDTnuqdN29e2B0iIiJnklLCZLVHVCqEVQA2atQo4Huv14sdO3Zg9+7dvDYgERERkcOFVQCOHTs2aPu0adNw4sSJs+pQLOMYBoU5KMzBh7OAFc4CVrhtKMxBYQ7hiege9frrr8eyZcsi+ZBE5ABC+L6IiKjki2gBuGXLFsTFxUXyIWOK212Ud1MtOZiD4pgcdMCjmfaXVxT/PTd1nUf/AN4L2OKYbSPKmIPCHMIT1ing++67L+B7KSUOHz6M7du345577olIx4go+oQAPDBxLDvbHvzv1nUkxmm8Oy8RUQkWVgFYvnz5gO+FEKhXrx5GjBiB1NTUiHSMiJzD9Jv9yfv+EhGVfGEVgOPHj490P4iIyOE0IaDlDgTVOCCUqEQ7qwtBb9++Hb/88gsA4KKLLsLFF18ckU7FKs5kUpiD4qQc/P/YR+N2j5wFrERvFrCAWxMoFy9h5B4BFgDi3IDMLv7eOGnbiCbmoDCH8IRVAKalpeGBBx7A5s2bUaFCBQBAeno6WrdujZdffhmVK1eOaCeJKHri3EACJGTuvdhcQsKlg2MAY4gAIIQBU2bBML0AAE3o0IQbJu8EQlQihfWR+plnnkFWVhY+/PBDbN68GZs3b8Z//vMfZGZm4tlnn410H2MGZzIpzEFxQg5CAJr9hz8DhpkBU2ZBg4niLAE5C1iJ9ixgKU1IKdUXin82uMUJ24YTMAeFOYQnrL3qmjVr8OSTT6J+/fp2W4MGDfDkk0/iq6++iljniMgZJPz+8Mvo/eEnIqLICKsANE0Tbrc7X7vL5YJp8o8DERERkZOFVQD+61//wnPPPYdDhw7ZbYcOHcL48ePRpk2biHWOiIiIiCIvrEkgTzzxBO6++2506tQJ1atXBwD89ddfuOiii/Diiy9GtIOxhDOZFOagMAcfzgJWeC9ghduGwhwU5hCesArA888/HytWrMD69evx66+/AgDq16+Ptm3bRrRzsUYIcDYdmIOFORAFx21DYQ4KcwhPoT5Sb9iwAV27dkVmZiaEEGjXrh0GDBiAAQMGoHHjxujWrRu++eabouprqedycSYTwBwszMGHs4CVaM8CdgpuGwpzUJhDeAq1V507dy5uueUWJCQk5PtZ+fLl0bt3b8yePTvkx1u4cCG6d++OFi1aoEWLFujduzf+97//2T/Pzs7GU089hdatW6N58+YYPnw4jhw5EvAYBw8exJAhQ9C0aVO0adMGL7zwArxeb8AymzZtQo8ePZCSkoKrr74ay5cvL8xqExEREZUqhSoAd+3ahfbt2xf483bt2uHHH38M+fGqV6+Ohx9+GMuXL8eyZcvwr3/9C/feey92794NAHj++efx5ZdfYsqUKZg/fz7+/vtv3HffffbvG4aBoUOHwuPxYNGiRZgwYQJWrFiBqVOn2svs378fQ4cORevWrfHee+9h4MCBePzxx7FmzZrCrDoRERFRqVGoAvDIkSNwuQoeNuhyuXD06NGQH69jx47o0KEDLrjgAtSrVw8PPPAAzjnnHGzduhUZGRlYtmwZRo8ejTZt2iAlJQXPP/88tmzZgq1btwIA1q5diz179uDFF19Eo0aN0KFDB4wcORILFixATk4OAGDRokWoVasWRo8ejfr166N///7o3Lkz5syZU5hVLxaSgxgAMAcLc/Cx7kJCBHDbsDAHhTmEp1AFYLVq1eyjc8Hs2rULVatWDasjhmHgww8/xIkTJ9C8eXNs374dHo8nYGJJ/fr1UaNGDbsA3Lp1K5KSklClShV7mdTUVGRmZmLPnj32MnkvTZOammo/hpN4vbyGIsAcLMzBxzS4gwc4C9jCbUNhDgpzCE+hZgF36NABr7zyCtq3b4/4+PiAn506dQrTpk3DlVdeWagO7Nq1C3369EF2djbOOeccvPrqq2jQoAF27NgBt9tt32vYkpiYiMOHDwNQRyT9iz8A9vdnWiYzMxOnTp1CmTJlQu6r/0BT0zRhGBK6LgIuT2EYJkxTQte1gAHbVrvLpUEIX7vXa0BKdSsbIYT9Scaa1p73FjenaxcisI9SSni9JjRNBAyiN02Ze1mNvO2RX6dQ++7fbuVQmtbJvz3UdbKWj/Y6CWFAFxrM3Ft/aZqAEEL1S2gwDDN3OV+bhASkulmc1QYAIvf2cUL4JnZoQsDqltAATWi5bb52Xfe1aZqAkGrWXyh9USHD7rvVpvllEdiXwHb/54WZe3s8v+cVEJCmhBCBz2tKqy95M4Ddx4DnzW3XdPWYwTLQNS2gL6G+HlYGeV8PK4MCXw8BaFr+10NogC40aEKD0HUA3EfE8j4ikusUzusE+N7vpWWdgr1OkVaoAvDuu+/GJ598gs6dO6Nfv36oV68eAODXX3/FwoULYRgGhg0bVqgO1KtXDytXrkRGRgY+/vhjPProo/j3v/9dqMcoLtYbx59hSBhG/msQGYaJIM0FflLxeAy43Xq+6xkVdH2jYO1SBm83TQnTDL09kusUTrt/DqVlnfyFuk7WDiea66RpgOYyYfjd/s001S3hTFPC8LvzT942XVN1l3+btZOTEgFt1nYlTcCAmdsmA7Y3/+e12kPti/W8wdqszHx9kQHtVpt19E3KPH3xa8/XF823fP4MAtusZ1VHO2W+DAzDhEvIgL4UNoNCvx4yMIOA10kzYUoTpmHAelruI2JvHxFqe1G+Ttb7oTStk2r3vU6iCOrAQhWAVapUwaJFizBu3DhMnjzZ3lEKIZCamoonnngi39G2M4mLi0PdunUBACkpKfjhhx8wb948dOnSBR6PB+np6QFHAdPS0uzTzFWqVMG2bdsCHs+aJey/TN6Zw0eOHEFCQkKhjv4RERERlRaFvhB0zZo18eabb+L48ePYt28fAKBu3bqoWLFiRDpkmiZycnKQkpICt9uNDRs2oHPnzgDUkcaDBw+iWbNmAIBmzZphxowZSEtLQ2JiIgBg/fr1SEhIQIMGDexlvvrqq4DnWL9+vf0YRBSoKD5pEhGRs4R1JxAAqFixIpo0aXJWT/7SSy/h8ssvx/nnn4+srCz85z//webNmzFr1iyUL18evXr1woQJE1CxYkUkJCTg2WefRfPmze3iLTU1FQ0aNMCoUaPwyCOP4PDhw5gyZQr69euHuLg4AECfPn2wYMECTJw4Eb169cLGjRuxevVqzJw586z6XhQ4wFthDko0cnC71Zg/ixCA0H3j1qLFlDK8G5eXMpzsqHAfoTAHhTmEJ+wCMBLS0tLw6KOP4u+//0b58uWRnJyMWbNmoV27dgCAsWPHQtM0jBgxAjk5OUhNTcWTTz5p/76u65gxYwbGjRuH3r17o2zZsujRowdGjBhhL1O7dm3MnDkT48ePx7x581C9enU8++yzp72eYbRY45BiHXNQijsHNYnBAJDlGwcnNGiIg4hyCShNWchrFpROvNyFwn2EwhwU5hCeqBaAzz///Gl/Hh8fjyeffDKg6MvLOiV9Oq1bt8bKlSvD6WKx0jTBTzJgDpZo5aCKDOn37+jjaWmFOSjcRyjMQWEO4eFnagfh/U4V5qAwBx//GbuxTDi0AlRHj4uvQOW2oTAHhTmEJ6pHAImIqGQSEHBpAojzXQYGAKTU4fFEr19EFBoWgEREFBYhDJgy275VnxACAuUghM4JK0QOxwLQQUyTA1kB5mBhDj6mNKFxxIpjxmQCuTPDhYAXBqSQdqtLB1DERwC5bSjMQWEO4WEB6CAG73cKgDlYmIOPNMERy3DWZWAE1O3YsrxeeHPvbqALDeXjzSI/AshtQ2EOCnMID3epDqLrzhzgXdyYg8IcfAT3VACcOQtY3SIu93Z0KJ4/xNw2FOagMIfwcLfqIJzpqDAHhTn4aKwAATh3FnBx47ahMAeFOYSHp4CJKJAI+k8iIipFWAASxTD/A0pCANABwzQBv1N5erH3ioiIihoLQAfh7WwU5qAUdQ5uN6Bpgff9hW4iK9sDQ6rndmsuxLmLtBshMU2Tp4HhrFnA0cR9hMIcFOYQHhaADsJb2SjMQSnKHIQAhMuAV2bYRYWAhjiUAeArNJxScEgJno+Gs2YBRxP3EQpzUJhDePiR2kF4OxuFOShFmYMQgAkTmTk5SM/JRnpONk54PMU0h7PwhMbqD+AkEAv3EQpzUJhDeJiag2j8IweAOViKIwcTMuAyHk6lsfAB4MzLwEQD9xEKc1CYQ3hYABIRERHFGBaARERERDGGBaCDcCaTwhwU5uDDe30qTj5NX5y4bSjMQWEO4WEB6CCcyaQwB4U5+LDuUZiDwm1DYQ4KcwgPC0AHcbn4cgDMwcIcfDTe6xMAB7tbuG0ozEFhDuFhag7CSzwozEFhDj6CFwEkP9w2FOagMIfwsAAkIiIiijEsAImIiIhiDAtAB/F6jTMvFAOYg8IcfDjLT+Fgd4XbhsIcFOYQHhaADsIZfgpzUJgDUXDcNhTmoDCH8LAAdBC3W492FxyBOSjMwYf3+lRK0ixgIXxfkcZtQ2EOCnMIjyvaHSCi4sGJclTUBAAIAa8wYfq931zQAJ6lI3IUFoBEsUAHPPCNo9MAxEWvN1RKCQiYUuJYTg68uXdv0YRApfh4uIXGU3VEDsICkKiUE0IVf+k5OTBz/wK7dB2V43h1PSoaUkr7vUZEzsQC0EE8Hp4jAZiDJZI5xLmBc2BCIrcAFAIuXS8xBaBhmNA0jgMsKbOANSGg5Y450Ipg7AH3EQpzUJhDeFgAEpVyQgCaMGDKLJhS7Sg1zQ0NCUCJKQGpZBBwawLl4iWM3COAAuoDiMyObs+IKBA/UjsIZzIpzEGJdA4SJqSUuV8l67p6nAWsOH0WsAAgcj9sGGYGDDMDpsyCJoyITkLiPkJhDgpzCA+PABIRUURJqT5sAIAUJevDBlGs4MdqIiIiohjDApCIiIgoxrAAdBDOZFKYg8IcfHgvYKWkzAIuatw2FOagMIfwsAAkIiIiijEsAB2EM5kU5qCEm4P/PVhLy+3fOAtYcfos4OLCfYTCHBTmEB7OAiYqRdxudRkOf0IAQucV/4iIyIcFIFEpoY74GQCy7EtwqB9o0BAH3viNiIgsLACJShlV/Mk83xMREflwYI2DcCaTwhwU5uDDWcAKZwEr3DYU5qAwh/CwAHSQ0jJg/2wxB4U5EAXHbUNhDgpzCA8LQAdxuTiTCWAOFubgw1nACmcBK9w2FOagMIfwcAwgUWkjTvstERERC0CiUkUHDNOE/ySQ3GYiIiIbC0AH4WxNhTkohc1BCMCEiSyPB4b0TZpway7EuSPdu+IlwfcE+XAfoTAHhTmEhwWgg3i9nOkIMAdLuDmYkAE7xNKwczQNCQ4DLPmzgP0H65/N25L7CIU5KMwhPNylOggHeCvMQWEOPpzlp5TEHAQACAGvMOHRfF9nMy6B24bCHBTmEJ6oFoAzZ85Er1690Lx5c7Rp0wb33HMPfv3114BlsrOz8dRTT6F169Zo3rw5hg8fjiNHjgQsc/DgQQwZMgRNmzZFmzZt8MILL8Dr9QYss2nTJvTo0QMpKSm4+uqrsXz58iJfv8LiTEeFOSih5FDa7vlbEE3jewIARAl8oQUETClxLCcHR0+dwtFTp3AsOxtemGG/b7mPUJiDwhzCE9XUNm/ejH79+mHJkiWYPXs2vF4vBg0ahBMnTtjLPP/88/jyyy8xZcoUzJ8/H3///Tfuu+8+++eGYWDo0KHweDxYtGgRJkyYgBUrVmDq1Kn2Mvv378fQoUPRunVrvPfeexg4cCAef/xxrFmzpljXlyiS3G7A7TYCvly85y85lJQSpt8XEUVXVMcAzpo1K+D7CRMmoE2bNvjxxx9x6aWXIiMjA8uWLcOkSZPQpk0bAKog7Nq1K7Zu3YpmzZph7dq12LNnD2bPno0qVaqgUaNGGDlyJCZNmoT77rsPcXFxWLRoEWrVqoXRo0cDAOrXr49vv/0Wc+bMQfv27Yt9vYnOVtD7/vKev0REFCJHHTfNyMgAAFSsWBEAsH37dng8HrRt29Zepn79+qhRowa2bt0KANi6dSuSkpJQpUoVe5nU1FRkZmZiz5499jJWAem/jPUYTlHSB3hHCnNQQsnBd99fWSomexSER4wUxqBwH6EwB4U5hMcxs4BN08Tzzz+PFi1aICkpCQBw5MgRuN1uVKhQIWDZxMREHD582F7Gv/gDYH9/pmUyMzNx6tQplClTJqQ++l9t3DRNGIaErouA8UmGYcI0JXRdCxiYarW7XFrAOB6v14CUgNutHlvT1P+texta7ZbTtQsR2EcpJbxeE5omAsZImKaEYQRrL5p1CqXveds1TS9162S1F2adVBb510n1HZDQYF3zz8y9HaYQgctba6FpArpQ7ZrwHSe0ltWECFhnXdfsNk0TgOl7HKtNF5p9n17/NgkJSPXc/s9rPasQeZ83t68aoAnf8/qPEfN/XiFVMRRKX1TIwTPwX9dQMxAiT18gIE2Ze1TW97ymtPqSN4P8r5P/66Hp6jHzZqDrWsBzFOb1sDLI+3pYGRT4egg1/jJvX4QW/PUQIrAvpunLQHfpEKZ6AJn73s57BwfuIyK3jyjJ61TY18kwzFK3TsFep0hzTAH41FNPYffu3Vi4cGG0u1Ig643jzzAkDCP/jajVmzLYYwSfru7xGNA0ke+TTEE3uQ7WLmXwdtOUMM3Q2yO5TuG0++dQWtbJX6jrZG34wdZJCMAwACn9Lvqc+0ddSpl7MWjrcTT7cax2TdPsK+tZBYOmaQFHEdUOS7X5vy9NU9pt/s+Tt03XVM/yPq+VQUBfch9emoAB3/Pa3cl9bGn/v3B9KSgD/3UNNQMpA59X+rXn64vmWz5/BsFfD9NQR3TzZmD9kQv39bCeo1CvhwzMwP91si435P96qGXy9EUIuITAOS6vfX1KAYE4N+DNzr9PBbiPiMQ+QvW95K3TmdqDrZOmiVK3Tqrdt05FMf/LEaeAn376afz3v//F3LlzUb16dbu9SpUq8Hg8SE9PD1g+LS0NVatWtZfJOyvY+v5MyyQkJIR89K84cCaTwhyUkHIQvq/SPPKPs4CVkjkLWI1XNWUWDDMDhpkBU2ZBEwZnAZ8l5qAwh/BENTUpJZ5++ml8+umnmDt3LmrXrh3w85SUFLjdbmzYsMFu+/XXX3Hw4EE0a9YMANCsWTP8/PPPSEtLs5dZv349EhIS0KBBA3uZjRs3Bjz2+vXr7ccgKpF0wBCm/eUVvBgqOZeUpn2EUILvVaJoi2oB+NRTT+H999/HSy+9hHLlyuHw4cM4fPgwTp06BQAoX748evXqhQkTJmDjxo3Yvn07xo4di+bNm9vFW2pqKho0aIBRo0Zh586dWLNmDaZMmYJ+/fohLi4OANCnTx/s378fEydOxC+//IIFCxZg9erVuP3226O05kRnx/+2b+k5OUjPycFJr5c3TCMiopBEdQzgO++8AwAYMGBAQPv48ePRs2dPAMDYsWOhaRpGjBiBnJwcpKam4sknn7SX1XUdM2bMwLhx49C7d2+ULVsWPXr0wIgRI+xlateujZkzZ2L8+PGYN28eqlevjmeffdZxl4AxTX4qBpiDJZQc/G/7VrpnAZvQnDFiJapK82tcGNxHKMxBYQ7hiWoBuGvXrjMuEx8fjyeffDKg6MurZs2aePPNN0/7OK1bt8bKlSsL28ViZRjcuQPMwcIcfKQJh4xYji7Wfwq3DYU5KMwhPNylOoiul7wB3kWBOSjMwUdwTwWg9N/yL1TcNhTmoDCH8HC36iCc6agwB4U5+GisAAGUzFnARYHbhsIcFOYQHqZGREREFGNYABIRERHFGBaADmLdjSDWMQclbw7qNmCxOQ6Ms/wUzgJWuI9QmIPCHMLjmFvBEW9obWEOin8Objegab5bBQkBQC/dd//wJyViZ2VPg/Wfwn2EwhwU5hAeHgF0EN7ORmEOipWDEIBwGfCKDHiQnvuVCQETIkaqIlGEN0QvSTgJROE+QmEOCnMID48AOoi6oXW0exF9zEGxcrDu+pGZkwNDqlMdbs0Ft8s597EuapoQ4G1OYvP0fzDcRyjMQWEO4WEBSFRCxMpdP4iIqOjxuCkRERFRjGEB6CCcyaQwB4U5+HAWsMIjvwq3DYU5KMwhPDwF7CCcyaQwB4U5+HAWsFIa6z//cY2hrh+3DYU5KMwhPDwC6CAuF18OgDlYmIOPxnt9AlCD3UsDAQBCwCtMeDTfF/TQfp/bhsIcFOYQHh4BdBBe4kFhDgpz8ImVy93ECgEBU0ocy8mBN/f0viYEKsXHwy20Mx4J5LahMAeFOYSHBSAREUWFlBJmaTyvTVQCsAAkIqKo0IRQ13jM/TcRFR8WgA7i9fJKlgBzsDAHH8MwoWkc51N6BrsLuDWBcvESRu4RQAEgzg3I7DP/NrcNhTkozCE8LAAdhGdCFObgmxnJgyJUGgkAQhgwZRYM0wsA0IQOTbhhhjAGkPsIhTkozCE8/EjtIG53iFPgSrlYz8HtBtxuA2XKmnC7DbjdBlx6bF8Fhff6VErLLGCLlCakVHe4kQj9Wm6xvo+wMAeFOYSHRwCJHEQIdWQEyIKUAlKagNCgIY4zYYmIKGJYABI5kETuOQ0heX6DiIgijgUgkdPogGGakBAwhWk1ERERRQwLQAfxeDiTCYi9HPwneggBmDCR5fHAkKr4c2suxLmj1DmH4CxgpfTMAj47sbaPKAhzUJhDeFgAEkWR222N+VOEAISuTgHL3FO/kqeAiYgowviR2kE4k0mJlRwCJ3xkqi+cgIAJAcGZr36YhVLaZgGHK1b2EWfCHBTmEB4eASSKMnWEj0f7iIio+PBjNREREVGMYQFIREREFGNYADoIZzIpzEExjNDvjFDaMQuFs4AV7iMU5qAwh/CwACQiIiKKMSwAHYQzmZSYy0H4vvzneHLmqw+zUDgLWIm5fUQBmIPCHMLDWcBE0ZR71w9rFnBuExERUZFiAUgUJbzrBxERRQsLQKIoM3nXDyIiKmYsAB2EM5mU0pyDKMQQLs589eG9gJVYmQXs/1KbQTaD0ryPKAzmoDCH8LAAdBAhAB4AKr05FHTfXw7rJwIEBFy6BiPeA//NX5dueE7lWbaU7iMKizkozCE8LAAdxOXS+UkGpTOHwPv+5u6phAYNcRAFlIC6rvEoYC5d1wDu4Ev1LGANAoAXmZ5MeE0vAEAXOhLiKkLT3AFHAkvjPiIczEFhDuFhAUhUjHjfX6LTM00DplXt8aw/UZFhAUhUnETQfxIRERUrFoAOwiNCSqnNoZDX/JM852ljFuSv1O4jCok5KMwhPCwAHcTr5XgvoHTmEM41/0yDOzWLaUjwZiCxMwv4TErjPiIczEFhDuHhLtVBSvMA78IozTlY1/yzvk6nMJeMKe2YhcIclNK8jygM5qAwh/CwAHQQ3u9UKS05qJm/4f/R5nXvfJiFIlgBAig9+4izxRwU5hAengImKgJuN6Bpgdf8A6/5R0REDsECkCjChACEy4BXZtineQU0xKFMgdf8IyIiKk4sAB2EA7yVkp6DNeEjMycnYMKH21WmUI9jcmabzZSS41XAux1YSvo+IlKYg8IcwsMC0EF41weltORgTfgAwrtMgeROzSZNyRHL4OUuLKVlH3G2mIPCHMIT1V3q119/jWHDhiE1NRXJycn47LPPAn4upcQrr7yC1NRUNGnSBLfffjv27t0bsMyxY8fw0EMPoUWLFmjVqhXGjh2LrKysgGV27tyJvn37onHjxujQoQPefPPNol61sHAmk8IcFI7392EWCnNQuI9QmIPCHMIT1QLwxIkTSE5OxpNPPhn052+++Sbmz5+PcePGYcmSJShbtiwGDRqE7Oxse5mHH34Ye/bswezZszFjxgx88803eOKJJ+yfZ2ZmYtCgQahRowaWL1+OUaNGYfr06Vi8eHGRr19hcSaTUhJzONsZv8Fw5qsPs1A4C1gpifuIosAcFOYQnqieAu7QoQM6dOgQ9GdSSsybNw933303rrrqKgDAxIkT0bZtW3z22Wfo1q0bfvnlF6xZswZLly5F48aNAQCPP/44hgwZglGjRqFatWp4//334fF48PzzzyMuLg4XXXQRduzYgdmzZ6N3797Ftq5UerndgBCBM34FZ/wSRRRrX6LIcmzZfODAARw+fBht27a128qXL4+mTZtiy5YtAIAtW7agQoUKdvEHAG3btoWmadi2bRsAYOvWrWjVqhXi4uLsZVJTU/Hbb7/h+PHjxbQ2VFqpo34GgCxImam+cAICJmf8Ep0lAQBCwCtMeDTfl+nYv1xEJYdjJ4EcPnwYAJCYmBjQnpiYiCNHjgAAjhw5gsqVKwf83OVyoWLFivbvHzlyBLVq1QpYpkqVKvbPKlasGHKfXC7fnVtN04RhSOi6CDg9ZRgmTFNC17WAcQlWu8ulBZzG8XoNSAm43To0TcDtVs/h8agjStb3ltO1CxHYRyklvF4TmiYCDpGbpoRhBGuP/DqF2nf/diuHkrBOXq/qu6b7F3u+f1t90YQIeDxd1+w2TROAqYpJTfO1GdYlZATsNl1oMHNnFlvL60ILeFYhAjOw2v2X1YSvPC1MH63H8e+PNQDbv01CAlI9d2Afhd33wOfN7asGaML3vP5HffyfV0g1IzaUvgCwb7+cN4NIvE4CAtKUZ/U6+b8emq4eM28Guq5BaOG9HlYGeV8PK4MCXw+hTr/n7YvQgr8eQgT2xTSDZxDq66HeS8CxnBx4TRMuXbP3CSVlH1GU+3Lr6UvTOoX3OpmlcJ3yv06R5tgC0ImsN44/w5AwDCPfsoZhIkhzgfcstN5kZ9MuZfB205QwzdDbuU6hr5O1XzG8Juy/agKw9gnWH2NN0wJmcKqdgWqzLmEgpeq/1Wbk/vGUEvnaoPmWt9qsHYiUfsv5tedd1upNYfpoPU6+/iB/H3VNJRK8jwjsS+7DSxMw4Hteqz1vX6z2UPtSUAb+6xru6yT92sN9nfxfD3UPaBk8A2lCxoX3eljPUajXQwZm4P86WbPc/V8P6xaHAX0RBWdwptdDaAJSSrs49homNCHg9RoQfrk7eR8RTjvXqfDrBMhSt07+r1NRDIFw7IH0qlWrAgDS0tIC2tPS0uwjeFWqVMHRo0cDfu71enH8+HH796tUqWIfMbRY31uP4xS6XnSVfklSInMQvq9I9V44dussfsxCib1xcAJuTaBcvET5MuorIV4i3h1zQQRVIveVRYA5hMexu9VatWqhatWq2LBhg92WmZmJ77//Hs2bNwcANG/eHOnp6di+fbu9zMaNG2GaJpo0aQIAaNasGb755ht4PB57mfXr16NevXqFOv1bHDjTUXF6Dv4zfq1bvBnCtL+8IjLXpNJY9diYhRJrs4AF1BhbU2bBMDNgmBkwZRZ0zYzBYjg/p+8riwtzCE9UU8vKysKOHTuwY8cOAGrix44dO3Dw4EEIIXDbbbfh9ddfx+eff45du3Zh1KhROO+88+xZwfXr10f79u3xf//3f9i2bRu+/fZbPPPMM+jWrRuqVasGAOjevTvcbjcee+wx7N69G6tWrcK8efNwxx13RG29qeRyuwG32wj40nQTWR4P0nNykJ6Tg5NeL+SZH4qIQiSlaZ9elojMByyiWBfVMYDbt2/HbbfdZn8/fvx4AECPHj0wYcIEDB48GCdPnsQTTzyB9PR0tGzZEm+99Rbi4+Pt35k0aRKeeeYZDBw4EJqm4ZprrsHjjz9u/7x8+fKYNWsWnn76afTs2RPnnnsu7rnnHl4ChgotcMavNd5PgwY1w/xs7vpBRERUnKJaALZu3Rq7du0q8OdCCIwcORIjR44scJlKlSrhpZdeOu3zNGzYEAsXLgy7n8WFt7NRnJ6DKvCKvtgzTWfnUJxM0+RpYPDDhYX3yVacvq8sLswhPJwF7CC8obXi+BxE0H9GHP/G+UgJXlkbfE/4MAigBOwriwlzCA8/UjsIb2ejODqHIprwEYzg/S1tzEKJtUkgBXPwPqIYOXpfWYyYQ3h4BNBBNE0EvY5RrHFqDkIAJtSEDyP3Ir9uzYU4d9E8nyYEDB7pAJB7kWBGwZmvufyugx3TnLqvLG7MITwsAIlOI9gfXOsCuADHZBFFk//2yU2RqHBYABIVwO22Zv0qQgBC51A0omgSENA1QIsz4D9PSkodfpd7JaIzYAHoIJzJpDghh9Nd8kUUUwnIWcA+nAWs8IizxYCUp+w81D2Iy0EIPaaOBDphX+kEzCE8LAAdhDOZFCflUFyXfAn+3MX6dI7GWcAK3xOW3GEYInfbRGy+PZy0r4wm5hAefqR2EJeLLwcQvRzy3eINKJJ7/IZK4/0tbcxC0TgbWm2HmhYwG98QJqBHu2fFj38zFOYQHh4BdBBe4kGJRg55x/sBgB4HZHtM+E8/Lc6/MepUMz/ZAii20+7kfAICkBJZXi+8ptpmdaGhfLwZc6eA+TdDYQ7hYQFIMc8e7yd84/3UeLM4nPD7I1OUl3whosKx7g0MAKaIoaqPKEJYABIBgA5V6OX+ITEgEY/APzIcgE9ERKUFC0AH8Xp5JUug+HMo7gs8h4oz23wMw4SmcZwPB7vn4ocxAPybYWEO4WEB6CDcpynRyoEXeCYqGbh1KtxNKcwhPPxI7SBudwxOYwuCOSi8v6UPs1A4C1jhoH+F+0qFOYSHe1UiIiKiGMNTwBSTOJyMiIhiGQtAijnuMoAUgTcNdfEev0REFENYADqIx8OZTEDR5qBpqvjLzDkOQ1rX93Ojgl7OcRcb5ixgH84CVjgLWOEkLYV/MxTmEB4WgFTqBRsvbkgDpqkKLBPceRARUWzhR2oH4UwmJZI5uN1AXJwR8FVSTvdy5qsPs1A4C1g53SzgfPfzLsX4N0NhDuHhEUAqVfx3+kIAwmXAKzPsU0YCGuJQxnGne4kofAIAhIAhTJh+m7YODTzATxQcC0AqPXTAC9+4OQ2ABhOZOTkBd/hwu8pEqYNEVBQENOgCOKHl2EM7NCHgdsVDmhovFEwUBAtAKhWEAITbRI43G2bu3l7XdMTrGsA7fBCVagKAEAZMMxNe0wsA0IQOTbhgChaARMGwAHQQzmRSwslBCEATBrxmJszc2b0CbmhIQMkY8ZcfZwH7cBawwlnASkEf5KQ0fR/2ROnffvg3Q2EO4WEBSCVWsEHeEn5/AGTp/wNAREQUDn6kdhDOZFJCycHtBtxuI+CrpMzuDRVnvvowC4WzgBXeC1jh3wyFOYSHRwCpRMg3u1cYgMgKmN0rEMfZvURERCFgAUiO5F/wuVy5BZ8fPQ7I9hiAUAWgAYn44uwgERFRCcYCkBzF7QZcbg+E8A3y1l2AYfiO9mlCAxCHE14vvKZ1OzcX4tzR6DEROZ3/B0rOCCZSWAA6SKzPZLJO7Rpev1O7QoPQysBr5D/aJ2XpvrwLZwH7cBawwlnASijbu4CASxNAnBFQ9JmmDo+nCDtXjGL9b4aFOYSHBaCDCMFPp4q0iz1AQsLk0T4isoUy0lcAEJoJj3ECJqwPlAK6qzyEVy8V+1r+zVCYQ3hYADqIy6XH3CeZvJM7oAMmYF/NH6qp1B/tC0bXNR4FzKXrGhAbL/tpcRZwrhBmAQsISCmR6fHYHx51oaF8vAkhSkcBGIt/M4JhDuFhAUhR43YHTu4QAhC6icxTOcgxeLSPiM6e/4dHU5SCqo8oQlgAUrEJeikX+Mb7QWjQEAcJxNzRPiIiouLEAtBBSnOxk/doH6Bm93q9fuP9SvH6h0PynKeNWVAgvh+A0v03ozCYQ3hYADqI11s6x3sFO9pnze41hAn/nbkOznS0mAZzsJiGBG8Gwm3DEom/96Xh0jCl9W9GYTGH8LAAdBBNE6V6B6+Kv9xTu6eZ3cu7PCmc2ebD94TCHJRwYyhtl4Yp7X8zQsUcwsMC0EF0XYNplryZTHn/KEkJBL1km8j/z2Cze3mfT0XTOAvYommcBQxw27CFmUNpuzRMSf2bEWnMITwsAOmsuN2ApgVueMKlw2sGfpzWdQ3Znvyne4mIikssXBqGKFQsAClsQgDCZcArMwLG9sWJMsjyZMJregEAbs2NCno5XsyZiByhoEvDlIZxgUShYgHoIE4bwxDs1G7eS7mYMJGZkwNDqlOVbs0Ft6sMTNOwL+Zswsj9/dAu5swdr2IyCJspJTgHhNuGT+SCKMnjAp32NyNamEN4WAA6SHGN9zpTYQcAUgO8COxPGbcGTQZeuBl67oSOCF63j1P6Fcmdmk2aEqwAuW1YIhlDSR4XyDHCCnMIDwtABymOmUzBrsen6TqkGVjYSV0gIzvbPgqlCw0ulws53kzf6V5oiEMZiLDn5AXHce4KZwH78D2hMAclkjGU5HGBnP2qMIfwsAB0kKKYyRT07hsi8Hp8ml4G2TIrsLATZWDKLHscn9DckEhAlt9O0jrdG2mc6ahwFrAPZwEr3DZyFUEOJfGWcZz9qjCH8LAALKFCOY3rcuWfoau5gWyP4bv7BiR0mEELOylNv1O7Zu7/I3u6l4jIyfwvaWXy8xiVIiwAHSZYYZdXQadxTSPPrdbigBxvnhm6KBN0Ni4LOyIiRUDApWsw4j0BB5516YY3O3BZ7i6ppGIB6CC6buQ7YmeaOvLUdcFP44oykOKkPWZPExqAuAKO7Dm72HNin6LBlDzcYDGlCY2zQLht2Io2Bw0CgBeZfpez0oULlcpUhB4vAo4EShm92cImD0kCYA7hiqk96oIFC9CxY0c0btwYN998M7Zt2xbtLtmEAExhwCsy4EE6PEiHV2TAFW9Ai/cEfOlxgBcGDKG+vDAAHfD4tXnyXHrFv+hzuhLSzSLH+s+HWSjcNpTiysG6nJVpmjClASEMSJkFiUxIZAIiC0IY0DRrjHXxTtQxeL9wAMwhXDFTAK5atQrjx4/HvffeixUrVqBhw4YYNGgQ0tLSot01ANY19SQyc3KQnpON9JxsnPB4IeFFlicdx7OP4nj2UZzwZEDCixNeL9JzcpCek4OTXi9MaeZrK6mbBMe5KyJmts4zYxYKtw0lGjmI3CfO++FbdwNut5Hnq3j6pOt8QwDMIVwxs1udPXs2brnlFvTq1QsNGjTAU089hTJlymDZsmXR7ppNEwIm8h+xC/gUahZ8ZK8kHu0LhjMdFY1Vj41ZKNw2LMWfg3W5mCy/D9onPF5AeAEEPyro/wUEHiWMxEupBb3peuxhDuGJidRycnLw448/om3btnabpmlo27YttmzZEsWeERFRSRLwQRsy+FHBOOQbuuMuC3g1M+ALOoIWiqG25W0vSKQLTyodYmISyD///APDMJCYmBjQnpiYiF9//TXkxynKDcd6bJdwwdCti5G6AAjomgsy9+ex0aZD12UUntdhbdIEdHFWj+eYdTnLNh0aNE13RF+i2sYMHNXmEm5ICZwyJIzcuyS5hAtxbgOnvFn25ARN01AurjykZvjOzgiBcnFx9iQTS5zmhtf0hNRmyhxoLt8AWV26kWcxmACMPHd10oNMqTLN/EVkSWrT9ej3pSgVRf0hZEk+VxiiQ4cO4fLLL8eiRYvQvHlzu33ixIn4+uuv8e6770axd0RERETFKyZOAZ977rnQdT3fhI+0tDRUqVIlSr0iIiIiio6YKADj4uJwySWXYMOGDXabaZrYsGFDwBFBIiIiolgQE2MAAeCOO+7Ao48+ipSUFDRp0gRz587FyZMn0bNnz2h3jYiIiKhYxUwB2LVrVxw9ehRTp07F4cOH0ahRI7z11ls8BUxEREQxJyYmgRARERGRT0yMASQiIiIiHxaARERERDGGBSARERFRjGEBSERERBRjWABG2YEDBzB27Fh07NgRTZo0wVVXXYWpU6ciJycnYLmdO3eib9++aNy4MTp06IA333wzSj0uOq+//jr69OmDpk2bolWrVkGX2bZtGwYOHIhWrVrh0ksvxaBBg7Bz585i7mnRCiUHAFi+fDm6d++Oxo0bo02bNnjqqaeKsZdFL9QcAHW7x8svvxzJyclIT08vph4WnzNlsXPnTjz44IPo0KEDmjRpgi5dumDu3LlR6GnRCuU9cfDgQQwZMgRNmzZFmzZt8MILL8Dr9QZdtrT47bffcPfdd6N169Zo0aIFbr31VmzcuDHa3YqK//73v7j55pvRpEkTXHrppbjnnnui3SXHYgEYZb/++iuklHj66afx4YcfYsyYMVi0aBFefvlle5nMzEwMGjQINWrUwPLlyzFq1ChMnz4dixcvjmLPI8/j8eDaa6/FrbfeGvTnWVlZGDx4MGrUqIElS5Zg4cKFKFeuHAYNGgSPxxP0d0qiM+UAALNnz8bLL7+MIUOG4MMPP8Ts2bORmppajL0seqHkYHnssceQnJxcDL2KjjNlsX37dlSuXBkvvvgiPvzwQwwbNgyTJ0/Gv//972LuadE6Uw6GYWDo0KHweDxYtGgRJkyYgBUrVmDq1KnF3NPiNWzYMBiGgblz52L58uVo2LAhhg0bhsOHD0e7a8Xq448/xqhRo9CzZ0+89957eOedd3DddddFu1vOJclx3nzzTdmxY0f7+wULFshLL71UZmdn220vvvii7Ny5czS6V+SWLVsmW7Zsma9927ZtMikpSR48eNBu27lzp0xKSpJ79+4tzi4Wi4JyOHbsmGzSpIlcv359FHpV/ArKwbJgwQLZv39/uX79epmUlCSPHz9ejL0rXmfKwt+4cePkgAEDirhH0VFQDv/9739lw4YN5eHDh+22hQsXyhYtWgTsP0uTtLQ0mZSUJL/++mu7LSMjQyYlJcl169ZFsWfFy+PxyPbt28slS5ZEuyslBo8AOlBGRgYqVqxof79161a0atUKcXFxdltqaip+++03HD9+PBpdjIp69eqhUqVKWLp0KXJycnDq1CksXboU9evXR82aNaPdvWKzbt06mKaJQ4cOoUuXLrj88ssxcuRI/Pnnn9HuWrHbs2cPXnvtNbzwwgvQNO7O/GVkZKBSpUrR7kax2rp1K5KSkgIu8J+amorMzEzs2bMnij0rOueeey7q1auHlStX4sSJE/B6vVi8eDESExNxySWXRLt7xeann37CoUOHoGkabrzxRqSmpuKuu+7Czz//HO2uORb3mA6zb98+/Pvf/0afPn3stiNHjuS7Y4n1/ZEjR4q1f9GUkJCA+fPn4/3330fTpk3RvHlzrFmzBm+++SZcrpi5qQ0OHDgAKSVmzJiBsWPHYurUqTh+/DjuuOOOfGNHS7OcnBw8+OCDeOSRR1CjRo1od8dRvvvuO6xevRq33HJLtLtSrE63ryytp0OFEJgzZw5++ukntGjRAk2aNMHs2bPx1ltvBRxIKO32798PAJg+fTruvvtuzJgxAxUrVsSAAQNw7Nix6HbOoWLnr2YxmzRp0hknaqxatQr169e3vz906BDuuusuXHvttaVmxx1ODgU5deoUHnvsMbRo0QIvvfQSTNPE22+/jaFDh2Lp0qUoU6ZMpLodcZHMwTRNeDwePP744/a4v8mTJ6Ndu3bYtGkT2rdvH5E+F4VI5vDSSy+hfv36uOGGGyLVvWIVySz8/fzzz7jnnntw7733lohxoUWVQ0kXai4XXnghnnrqKSQmJmLBggUoU6YM3n33XQwbNgxLly7FeeedV0w9Lhqh5mCaJgA1HrJz584AgPHjx+Pyyy/HRx99FHBQhRQWgEXkzjvvRI8ePU67TO3ate1/Hzp0CLfddhuaN2+OZ555JmC5KlWq5DvSZ33v9HsZFzaH0/nggw/wxx9/YPHixfbpvkmTJuGyyy7D559/jm7dup11f4tKJHOoWrUqAKBBgwZ2W+XKlXHuuec6/jRwJHPYuHEjfv75Z3z88ccAAJl7V8t//etfGDZsGEaMGHF2nS1ikczCsmfPHtx+++3o3bt3iZn9GMkcqlSpgm3btgW0WftKa7spKULNZePGjfjvf/+Lr7/+GgkJCQCASy65BOvXr8fKlSsxZMiQ4uhukQk1B+sIr/8Hhbi4ONSuXdvx+8VoYQFYRCpXrozKlSuHtKxV/F1yySUYP358vrFMzZo1w5QpU+DxeOB2uwEA69evR7169Rx/iL8wOZzJqVOnoGkahBB2m/W99enPqSKZQ4sWLQCoSz9Ur14dAHDs2DH8888/jj8VGskcpk2bhlOnTtnf//DDDxg7diwWLFiAOnXqROQ5ilIkswCA3bt3Y+DAgbjxxhvxwAMPROxxi1okc2jWrBlmzJiBtLQ0JCYmAlD7yoSEhIAPTCVBqLmcPHkSAAL2i9b3Tt8vhiLUHFJSUhAXF4fffvvNvkSQx+PBH3/84fj9YrSwAIyyQ4cOYcCAAahRowYeffRRHD161P6Z9Ym1e/fuePXVV/HYY49h8ODB2L17N+bNm4cxY8ZEq9tF4uDBgzh+/DgOHjwIwzCwY8cOAECdOnVQrlw5tG3bFhMnTsRTTz2FAQMGwDRNvPHGG9B1Ha1bt45y7yPnTDnUq1cPnTp1wnPPPYenn34aCQkJmDx5Mi688MKYyiFvkffPP/8AUEcAKlSoUOz9LUpnyuLnn3/GwIEDkZqaijvuuMM+GqLrekSLzGg7Uw6pqalo0KABRo0ahUceeQSHDx/GlClT0K9fv4BJdKVJs2bNUKFCBYwePRr33nsv4uPjsWTJEvzxxx+44oorot29YpOQkIA+ffpg2rRpOP/881GjRg3MmjULAHDttddGuXfOJKR13oSiYvny5QUWcrt27bL/vXPnTjz99NP44YcfcO6556J///4l/tB+XqNHj8aKFSvytc+bN88ubNatW4fp06dj9+7d0DQNjRo1wgMPPIBmzZoVc2+LTig5ZGZm4vnnn8enn34KTdNw6aWX4rHHHsP5559f3N0tMqHk4G/Tpk247bbb8PXXX5e6AvBMWUybNg3Tp0/P9/OaNWviiy++KI4uFotQ3hN//PEHxo0bh82bN6Ns2bLo0aMHHnrooVI9UeyHH37AlClTsH37dng8Hlx00UW455570KFDh2h3rVh5PB5MnjwZ7733Hk6dOoWmTZti7NixuOiii6LdNUdiAUhEREQUY3gZGCIiIqIYwwKQiIiIKMawACQiIiKKMSwAiYiIiGIMC0AiIiKiGMMCkIiIiCjGsAAkIiIiijEsAImIiIhiDAtAIqLTSE5OxmeffRbtbpzWlClT8H//93+nXWbAgAF47rnnCvW4e/bsweWXX44TJ06cTfeIyIFYABJRiTB69Gjcc8890e6G4xw+fBjz5s3DsGHDCvV7AwYMQHJysv3Vtm1bjBgxAn/88Ye9TIMGDdCsWTPMnj070t0moihjAUhEVIK9++67aN68OWrWrFno373llluwdu1arFmzBq+99hr++usvPPLIIwHL9OzZE++88w68Xm+kukxEDsACkIhKhc2bN+Omm25CSkoKUlNTMWnSpICipWPHjpgzZ07A79xwww2YNm2a/f3evXvRr18/NG7cGF27dsW6desClj9w4ACSk5PxySefYMCAAWjatCmuv/56bNmyJWC5b775Bn379kWTJk3QoUMHPPvsswGnURcsWIBrrrkGjRs3to+8WT766CN0794dTZo0QevWrXH77bef9hTsqlWr0LFjx4C2EydOYNSoUWjevDlSU1Px9ttvB/3dMmXKoGrVqjjvvPPQrFkz9OvXDz/99FPAMm3btsXx48fx9ddfF9gHIip5WAASUYl36NAhDBkyBI0bN8Z7772HcePGYenSpXj99ddDfgzTNDF8+HC43W68++67eOqppzBp0qSgy7788ssYNGgQVq5ciQsuuAAPPfSQXWz+/vvvGDx4MK655hq8//77ePnll/Htt9/imWeeAQD88MMPeO655zBixAh89NFHeOutt9CqVSsAwN9//42HHnoIvXr1wqpVqzBv3jxcffXVkFIG7cexY8ewZ88epKSkBLRPnDgRX3/9NV577TXMmjULmzdvxo8//nja9T927BhWr16NJk2aBLTHxcWhUaNG+Oabb84cIhGVGK5od4CI6GwtXLgQ1atXxxNPPAEhBOrXr49Dhw5h0qRJuPfee6FpZ/6su379evz666946623UK1aNQDAAw88gMGDB+db9s4778QVV1wBABgxYgS6deuGffv2oX79+pg5cya6d++O22+/HQBwwQUX4LHHHsOAAQMwbtw4/PnnnyhbtiyuuOIKJCQkoGbNmrj44osBqPF8Xq8XV199tX1KNzk5ucA+//nnn5BS4rzzzrPbsrKysHTpUrz44oto06YNAGDChAno0KFDvt9/5513sHTpUkgpcfLkSVxwwQWYNWtWvuXOO+88HDx48IwZElHJwQKQiEq8X375Bc2bN4cQwm5r2bIlTpw4gb/++gs1atQI6TGqV69uF38A0Lx586DL+hdlVatWBQAcPXoU9evXx86dO7Fr1y588MEH9jJSSpimiQMHDqBt27aoUaMGrrrqKrRv3x7t27fH1VdfjbJly6Jhw4Zo06YNunfvjtTUVKSmpqJz586oWLFi0H6cOnUKABAfH2+37d+/Hx6PB02bNrXbKlWqhHr16uX7/e7du9uTR44cOYKZM2di0KBBWLZsGRISEuzl4uPjcfLkyYLDI6IShwUgEcUE/+LQEu7EBrfbne9xTdMEoMbf9enTBwMGDMj3e+effz7i4uKwYsUKbN68GWvXrsXUqVMxffp0LF26FBUqVMDs2bPx3XffYd26dZg/fz5efvllLFmyBLVr1873eOeeey4A4Pjx46hcuXKh1yMhIQF169YFANStWxfPPfccUlNTsXr1atx88832csePH0edOnUK/fhE5FwcA0hEJV79+vWxZcuWgLFy3377LcqVK4fq1asDACpXroy///7b/nlmZiYOHDgQ8Bh//fVXwDJbt24tdF8uvvhi7NmzB3Xr1s33FRcXBwBwuVxo27YtRo0ahffffx9//PEHNm7cCEAVlC1btsSIESOwcuVKuN3uAq9DWKdOHSQkJOCXX36x22rXrg23243vv//ebjt+/Dj27t17xr7rug7Ad2TRsnv3bjRq1KhQORCRs/EIIBGVGBkZGdixY0dAW6VKldC3b1/MnTsXzzzzDPr164fffvsN06ZNwx133GGP//vXv/6FFStWoGPHjihfvjymTp0aMDawbdu2uOCCCzB69GiMGjUKmZmZePnllwvdx8GDB6N37954+umncfPNN6Ns2bLYs2cP1q9fjyeeeAJffvkl9u/fj0svvRQVKlTA//73P5imiXr16uH777/Hhg0b0K5dOyQmJuL777/H0aNHceGFFwZ9Lk3T0LZtW3z77be46qqrAADlypVDr1698OKLL6JSpUpITEzEyy+/HPQI6KlTp3D48GEAQFpaGl577TXEx8ejXbt29jIHDhzAoUOH0LZt20JnQUTOxQKQiEqMzZs348Ybbwxou+mmm/Dcc8/hjTfewMSJE7FkyRJUqlQJN910E+6++257uaFDh+LAgQMYOnQoypcvj5EjRwYcAdQ0DdOnT8djjz2Gm266CTVr1sTjjz+Ou+66q1B9bNiwIebPn48pU6agb9++ANRRua5duwIAypcvj08//RTTp09HdnY26tati5deegkXXXQRfvnlF3z99deYO3cuMjMzUaNGDYwePTroBA7/9f+///s/PPLII3ZBO2rUKJw4cQJ33303ypUrhzvuuAOZmZn5fnfJkiVYsmQJAKBixYpITk7GG2+8EVBwfvjhh2jXrl1Y1xkkIucSsqDrCxARkeNJKXHzzTfj9ttvx3XXXRfRx87JyUHnzp0xadIktGzZMqKPTUTRxTGAREQlmBACzzzzTJHcqePPP//E0KFDWfwRlUI8AkhEREQUY3gEkIiIiCjGsAAkIiIiijEsAImIiIhiDAtAIiIiohjDApCIiIgoxrAAJCIiIooxLACJiIiIYgwLQCIiIqIYwwKQiIiIKMawACQiIiKKMf8PtuzMqy5HRsEAAAAASUVORK5CYII=",
      "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": "2025-06-24T04:02:44.753293Z",
     "iopub.status.busy": "2025-06-24T04:02:44.753141Z",
     "iopub.status.idle": "2025-06-24T04:02:45.377264Z",
     "shell.execute_reply": "2025-06-24T04:02:45.376764Z",
     "shell.execute_reply.started": "2025-06-24T04:02:44.753277Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loudness (web): -13.286645758115096\n",
      "Mean loudness (mobile): -13.147501590237887\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "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": "2025-06-24T04:02:45.377988Z",
     "iopub.status.busy": "2025-06-24T04:02:45.377837Z",
     "iopub.status.idle": "2025-06-24T04:02:45.799366Z",
     "shell.execute_reply": "2025-06-24T04:02:45.798856Z",
     "shell.execute_reply.started": "2025-06-24T04:02:45.377973Z"
    }
   },
   "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": "2025-06-24T04:02:45.800075Z",
     "iopub.status.busy": "2025-06-24T04:02:45.799926Z",
     "iopub.status.idle": "2025-06-24T04:02:46.438124Z",
     "shell.execute_reply": "2025-06-24T04:02:46.437610Z",
     "shell.execute_reply.started": "2025-06-24T04:02:45.800060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0Wi3379+nZMmS+vnBwcGEhYVRqFCh9AcuxL9kTCkhMomtrS1jx46lb9++Bp8BflOTJk3QaDQsWrQo0bK4uDh9V+uEp2avPyWLjY1l06ZNRo37zU8OA/onTDnhk7AuLi5UrlyZrVu38uzZs0TLnz9//s7bfvHihcG0hYUFJUuWRKfToVarUSqV1K9fn8OHD3P58uVE6yec29q1a+Pn58fff/+tXxYZGcm2bdsoVKgQpUqVemsc5cuXp2jRoqxatYpXr14lWp7SMdaqVYtnz56xf/9+/byoqCi2bdv21vWEEEKI10kulDVJLpS9c6GEnnCv/zm4dOkSFy9eNChnbW0NpO5V0oSvUa5du9Zg/urVqw2WC2EM0lNKiEzUqlWrFMtUrlyZdu3asXTpUq5fv0716tUxNzfH39+f/fv3M3LkSBo3bkzFihVxdHRk2LBhdO7cGYVCwS+//JKq7uFpsXDhQs6dO0ft2rUpVKgQISEhbNq0ifz58+vHA8juxowZQ8eOHWnRogVt27alSJEiBAcHc/HiRZ48ecLu3bvfabtdu3bF1dUVHx8fXFxc+Oeff9iwYQO1a9fWj+cwYMAA/vrrLzp37kzbtm0pWbIkQUFB7N+/n02bNuHg4ED37t3Zu3cv3bp1o3Pnzjg6OrJr1y4CAwOZP39+st36EyiVSiZOnEi3bt1o3rw5rVu3Jl++fDx9+pTTp09jZ2fHkiVLkl2/bdu2bNy4kaFDh3L16lXc3Nz45Zdf3vr0WgghhEiK5EJZk+RC2TcXqlOnDgcOHKB3797UqVOHwMBAtmzZQqlSpYiMjNSXs7KyolSpUvz2228UK1aMPHnyULp0aYOxuBKUKVOGVq1asXXrVsLCwvjwww+5fPkyO3fupH79+lStWjUzD1HkcNIoJUQWNH78eMqXL8+WLVuYM2cOKpWKQoUK0bJlS3x8fABwcnJiyZIlTJs2jblz5+Lg4EDLli3x9fWla9euRoulXr16PHz4kJ9//pkXL17g5ORE5cqV6du3r8EXYrKzUqVK8fPPP7NgwQJ27txJaGgozs7OlCtXjt69e7/zdtu1a8eePXtYvXo1kZGR5M+fn86dO9OrVy99mXz58rFt2zbmzZvHnj17iIiIIF++fNSqVUuf6Li6urJlyxZmzJjBhg0biImJwcPDgyVLllCnTp1UxVKlShW2bt3KokWL2LBhA5GRkbi5ueHp6Um7du3euq61tTVr1qxhwoQJbNiwASsrK1q0aEGtWrX45ptv3rl+hBBCiORILpS5JBfKvrlQ69atCQ4OZuvWrRw/fpxSpUoxY8YM9u/fz5kzZwzKTpw4kQkTJjBlyhTUajV9+vRJslEqoWzhwoXZuXMnf/zxB66urvTo0YM+ffpkxmGJXEShM/ajBCGEEEIIIYQQQgghUiBjSgkhhBBCCCGEEEKITCeNUkIIIYQQQgghhBAi00mjlBBCCCGEEEIIIYTIdNIoJYQQQgghhBBCCCEynTRKCSGEEEIIIYQQQohMJ41SQgghhBBCCCGEECLTSaOUECJb8vDwYPz48aYOQwghhBAiR5EcSwiRmaRRSghhNFeuXKFr1674+PhQsWJFvv76a65fv56oXOfOnfHw8Ej069q1q0G5CxcuMH/+fMLCwjIl/levXvHjjz/StWtXKleujIeHBzt27Ej1+mfPnqVnz57Url2bChUqUL16dbp27cr58+cTldVqtWzevJmPP/6YihUrUq1aNb755hsuXLhgzEMSQgghRA6Q3XMsgNjYWGbMmEGNGjXw9PTks88+46+//kr1+nv37qVVq1ZUqFCBqlWrMmLECJ4/f56o3KZNm+jXrx916tTBw8ODYcOGGfMwhBBGZmbqAIQQOcPVq1fp2LEjBQoUoE+fPmi1WjZt2kSnTp3Yvn07JUqUMCifP39+BgwYYDAvb968BtN///03CxYsoFWrVjg4OGT4Mbx48YKFCxdSsGBBPDw8OHPmTJrW9/f3R6lU0r59e1xdXQkLC2P37t106tSJpUuXUqtWLX3Z6dOns3r1alq2bEnHjh0JCwtj69atdO7cmc2bN+Pp6WnswxNCCCFENpQTciyAYcOG8fvvv/PFF19QrFgxdu7cSffu3Vm7di2VKlV667qbNm1i3Lhx+Pr6MmzYMJ4+fcq6deu4cuUK27dvx9LSUl92xYoVvHr1igoVKhAUFJTRhyWESCdplBJCGMW8efOwsrJiy5YtODk5AdCyZUsaNWrEnDlzmD9/vkF5e3t7Pv74Y1OEmqy8efNy/Phx3NzcuHz5Mm3atEnT+p999hmfffaZwbyOHTtSv3591q5dq2+UiouLY/PmzTRq1IgZM2boyzZu3Jj69euze/duaZQSQgghBJAzciw/Pz/27t3LkCFD9L22PvnkE5o3b87MmTPZsmVLsuvGxsYyZ84cPvzwQ1avXo1CoQCgYsWK9OzZk23bttG5c2d9+fXr11OwYEEUCgUVK1bM2AMTQqSbvL4nhDCKc+fO4evrq0+WIL6Rp3Llyhw+fJhXr14lWicuLi7J+QDz589n+vTpAHz00Uf67ueBgYEG5f744w+aN29O+fLladasGX/++Weibd29e5dHjx6leAwWFha4ubmlWC4trK2tcXZ2Jjw8XD8vLi6O6OhoXF1dDcq6uLigVCqxsrIyagxCCCGEyL5yQo61f/9+VCoV7dq108+ztLSkTZs2/P333zx+/DjZdW/fvk1YWBhNmjTRN0gB1K1bFxsbG/bu3WtQvlChQgblhBBZm/SUEkIYRWxsbJKNKVZWVqjVam7fvo23t7d+vr+/P97e3qjValxdXfnss8/o3bs35ubmADRo0AB/f39+/fVXhg8frk/EnJ2d9ds4f/48Bw4coGPHjtja2rJ+/Xr69evH4cOHDRK3pk2bUrlyZdavX59BR28oIiKC2NhYXrx4wS+//MKtW7fo2bOnfrmVlRVeXl7s3LkTb29vKlWqRFhYGIsWLcLBwcEgYRNCCCFE7pYTcqzr169TrFgx7OzsDOYn9Ay/fv06BQoUSPb4E443qTq4fv06Wq0WpVL6WwiRHUmjlBDCKIoXL87FixfRaDSoVCogPonw8/MD4OnTp/qyRYoUoUqVKri7uxMZGcnvv//O4sWL8ff3Z+7cuQCUKVOGcuXK8euvv1K/fn0KFy6caJ93795l3759FC1aFIAqVarw8ccfs3fvXjp16pTBR5y8/v37c/z4cQDMzc1p164dvXr1MigzY8YMvv/+ewYPHqyfV6RIETZv3kyRIkUyNV4hhBBCZF05IccKCgpKsjd6wrxnz54lu+57772HQqHgwoULfPrpp/r5//zzj36g85cvXxo0lgkhsg9plBJCGEXHjh0ZO3YsI0eO5JtvvkGr1bJ48WL9AJPR0dH6spMnTzZY95NPPuGHH35g27ZtfPnllwZP+96mWrVq+mQJ4pMsOzs7AgICDMrdvHnzHY/q3QwaNIivv/6ax48fs2vXLtRqNXFxcQaDcNra2lKqVCm8vb3x9fUlKCiI5cuX07t3bzZu3GjwtFIIIYQQuVdOyLGio6OxsLBIND8hN3r9GN7k7OxMkyZN2LVrFyVLlqRBgwY8ffqUCRMmYG5ujlqtJiYmJlVxCCGyHunjKIQwig4dOtCzZ09+/fVXmjVrRosWLXjw4IF+MEtbW9u3rv/VV18BcOLEiVTvM6lu3o6Ojpn6eeOklC1blurVq9OmTRtWrVrF5cuXGT58uH55XFwcX331Ffb29owePZoGDRrQsWNHVq9eTUBAACtXrjRh9EIIIYTISnJCjmVlZaV/De91CY1JKY2nOX78eGrVqsW0adOoX78+n3/+Oe7u7tStWxcAGxubd4pLCGF60lNKCGE033//PV9//TW3b9/G3t4eDw8PZs+eDUCxYsXeum5C8vPy5ctU7y+hC/ubdDpdqreR0SwsLKhXrx7Lli0jOjoaKysrzp49y61btxg2bJhB2WLFilGiRAkuXLhgomiFEEIIkRVl9xzLzc3N4DXDBAm9vfLmzfvW9e3t7Vm8eDGPHj3i4cOHFCxYkEKFCtG+fXucnZ1xcHB4p7iEEKYnjVJCCKNydHSkUqVK+ukTJ06QP39+SpQo8db1ErqDv/7aWk75ckp0dDQ6nY5Xr15hZWVFSEgIABqNJlHZuLi4JOcLIYQQInfLzjlWmTJlOH36NBEREQaDnV+6dAmI72WeGgULFqRgwYIAhIWFceXKFRo1amT8gIUQmUZe3xNCZJh9+/Zx+fJlunTpov8iSsKX6V6n0+lYvHgxADVq1NDPt7a2BiA8PDxdcaT2c8Vp8ezZM+7evYtardbPS2hsel1YWBgHDhygQIECuLi4AP890dy3b59B2atXr3Lv3r1UJ2ZCCCGEyJ2yW47VuHFjNBoNW7du1c+LjY1lx44deHl5Gbwu+OjRI+7evZviNmfNmoVGo6FLly7vFrwQIkuQnlJCCKM4e/YsCxcupHr16uTJk4dLly6xY8cOatasyRdffKEvd/XqVQYOHEizZs0oWrQoMTEx/O9//+PChQu0a9eO999/X1824f/nzJlD06ZNMTc3p27dumkeNyC1nysG2LBhA2FhYfqvwBw+fJgnT54A0LlzZ+zt7QGYPXs2O3fu5ODBg/qv1nTr1o18+fLh5eWFi4sLjx49YseOHTx79ow5c+bo91G+fHmqV6/Ozp07iYiIoHr16gQFBbFhwwasrKwkuRJCCCGEXk7Isby8vGjcuDGzZ88mJCSE9957j507d/Lw4UMmTZpkUHbo0KGcOXPGYBD1ZcuWcevWLby8vFCpVBw8eJDjx4/z3Xff4enpabD+oUOHuHHjBgBqtZqbN2+yaNEiAOrVq0eZMmXSdIxCiIwljVJCCKPIly8fKpWKlStX8urVKwoXLsx3333Hl19+iZnZf3/VFCxYkA8++ID//e9/BAcHo1QqKVGiBOPGjaNdu3YG2/T09KR///5s2bKFY8eOodVqOXjwYIYOZrlq1SoePnyonz5w4AAHDhwAoGXLlvpGqaR8+umn7N27lzVr1hAeHo6DgwNeXl7MmjXLoLs9wKJFi1i5ciX79u3j2LFjmJubU6lSJfr3759iN3whhBBC5B45JceaPn06c+fOZffu3bx8+RIPDw+WLFnChx9+mOK67u7u/O9//+PQoUNotVo8PDyYO3cuTZo0SVT2wIED7Ny5Uz997do1rl27BkD+/PmlUUqILEahy0ojAgshhBBCCCGEEEKIXEHGlBJCCCGEEEIIIYQQmU4apYQQQgghhBBCCCFEppNGKSGEEEIIIYQQQgiR6aRRSgghhBBCCCGEEEJkOmmUEkIIIYQQQgghhBCZThqlhBBCCCGEEEIIIUSmk0YpIYQQQgghhBBCCJHpzEwdQEYLCQlHpzN1FBlLdeUyTi0b82L3fjTlK5g6HJEGcu4yl9R31iHnIufICedSoQAXF3tTh5Gt5Ib8ypRywp8rkTvJtWu8OkjLdqTeRVaU2vwqxzdK6XQYNWlSKEChUBhvg0agUCnB1haFSpnlYksLnU6X+xJcjQbCw0GjyX3HbgpS31mHnIucQ85lrpQb8itTMmZulyvzK2E6ck8wXh2kZTtS7yIby/GNUsakUICNjQVKZdZ661FRsjhMmYJ1yeLobC1NHc4702q1REbGyl+kQgghRC6SVfMrUzJmbif5lRBCiKxMGqXSQKFQoFQq0Wp1QNa5syvUaggORqtWo9NqTR3OO4qvW4VCgU6yJiGEECLXyKr5lSkZL7eT/EoIIUTWJo1S7yRrdYNWaDQQEZGtu2sqFDpAuu0LIYQQuVfWyq9MyVi5neRXQgghsjqFLoc/NgkONt5AnEqlAltbS7RarSRNRqZQgFKp5NWrmH+flAohhBCZQ6EAV1cZ6DwtJL/KHiS/EkJA/Gu8Gk2cqcMQOYxKZfbWV+9Tm19JTykhhBBCCCGEECKH0el0hIU9JyoqwtShiBzK2toOBwfndH2UQxqlcgDl8+dY/PYrsU2ao3V2NnU4Ig1Ud25j37cn4fOXoClV2tTh5HhS31mHnIucQ86lEMYnuZ3IruSeYLw6SMt2kiub0CBlZ+eEhYWlfOVUGI1OpyM2NoaIiBcAODq6vPO2pFHKxJYsWcSRI4fZsmX7u29EHYvq0UNQxxovMJEpFJGvMD9/FkXkK1OHkitIfWcdci5yDjmXIisySn5lSpLbiWxK7gnGq4O0bCepslqtRt8gZWfnkK5YhEiKhUX812EjIl5gb+/0zl/RlW/vGsmSJYvw8fE0+LVu3dKgjI+PJ4cPH8q0mA4e/INevXpQt25NfHw8uXnzRqrW+fzz9tSqVZ1q1SrTvv1n/PrrnmTLT5o0AR8fTzZuXG/M0IUQQgghsmR+FRISwpgxo2jY8COqVatM7949efDg/lvX2bHjJ77+ugu1a1endu3q9OzZjStXLuuXq9Vq5s2bQ9u2ralWrTINPm/PkJMneRYSktGHI4TIoTQaDfBfw4EQGSHh+krPmGXSU8qISpYsyeLFy/XTKpXKhNFAVFQU3t4VadCgIRMmjEvVOo6OjnTt2o1ixYpjbm7OsWNHGTduNM7OzlSrVt2g7KFDB7l82Q83t7wZEb4QQgghRJbKr3Q6HQMG9MfMzIw5c+Zha2vLhg3r6dmzOz//vBNra5sk1zt//hyNGzfBy8sbCwtL1qxZRa9ePfnppx3kzZuP6Ohobty4zjff9MDd3Z0If39mThrPd+NGs2Hrz5l8lEKInERe2RMZyRjXlzRKGZFKZYarq2uSy5o1awzAwIHfAVCgQEH27t2vX/7rr3tYvHgh4eFhVKtWgx9+GIOtrW264mnevAUAjx49TPU6lSp9aDDdsWMnfv11Nxcv/m3QKPXs2VOmT5/CwoVL6NevT7riFEIIIYRITlbKrx48uM/ly35s376DkiVLATBixCgaNKjL/v2/0arVp0muN2nSVIPp0aPHcujQH5w5c5rmzVtib2/P4sXL9MuV1tb88MEHfHbgAI8fP6ZAgQLvHLMQQgiRlcnre0b04MF9Gjb8iBYtmjBy5DAeP36sX7ZhwyYAxo6dwIEDh/TTAIGBARw5coh58+Yzd+58Llw4x+rVK/XLd+/+BR8fz2T3q3N0JKZZC3SOjkY9Hp1Ox+nTp/D398fH5wP9fK1Wy6hRI/jiiy/1CZl4N5oiRQlbuAxNkaKmDiVXkPrOOuRc5BxyLkVGM1V+lZTY2Pgxnl5/HUapVGJhYcHFi3+nejvR0dHExcXh4JB07qZzdOSFzwcoFArs7VP+nLYQWYXcE4xXB2nZTlrKKhSgVCoy7ScdtURK0tRTSqPRMH/+fHbv3k1wcDB58+alVatW9OrVS99tS6fT8eOPP7J9+3bCwsLw8fFh7NixFCtWTL+d0NBQJkyYwOHDh1EqlTRs2JCRI0caPLm6ceMG48eP5/Llyzg7O9OpUye6detmnKPOABUqVGDcuIm8914xgoODWLZsCV27fsn27TuwtbXFySn+yyn29vaJnvZptVrGjZuoP/6mTZtz5sxp/XI7OzuD+nuTzsqauHLljXYs4eHhNG5cH7VajVKpZNiwkVSt6qtfvmbNKszMzOjQ4XOj7TO30jk5E/NZe1OHkWtIfWcdci5yDjmXIiOZMr9KSrFixcmfvwALFsxj5MjRWFtbs3Hjep4+fUpQUHCqt/Pjj3Nwc3OjSpWqSS6PViiZvWsXjRs3wc7OLk0xCmFKck8wXh2kZTupLatQgNJShVqnTW94qWauUKGN0aDTZdouRRIuXDhHv349+e23w2l62LFy5VJ27fqZFy+eM3nyTGrVqmP02NLUKLV8+XI2b97MtGnTKFWqFFeuXGH48OHY29vzxRdf6MusX7+eqVOnUrhwYebNm0fXrl3Zt28flpbxT5UGDRpEUFAQq1evRq1WM2LECEaPHs2sWbMAiIiIoGvXrvj6+jJu3Dhu3brFiBEjcHBwoF27dkauAuOoXr2m/v/d3d2pUKECzZo15n//+51PPmn91nULFixk0CDn6urGixfP9dP16n1EvXofJbv+b7t2MHHaFPh3tPv58xcZ9GxKK1tbWzZv3k5UVCRnzpxm9uyZFC5cmEqVPuTatWts3ryRTZu2yvvJRqAIDsbylx3EfNwaXTKvJgjjkfrOOuRc5BxyLkVGMmV+tW/fXiZNGq+fTsivZs6cw/jxY6hTpwYqlYrKlatQvXoNdKn8F9fq1Sv5/ff9LFu2Sp8bv06tVjN00PcQ+YoR3w1M1TaFyCrknmC8OkjLdlJbVqFQoNZpuRf2glit5p1jSy0LpYriDk6YKRSp/jtSZIwKFbz45Zf9aXrQ4e9/j9WrlzN58kzef7889vYZ8xXHNDVK/f3333z00UfUqVMHgMKFC7N37178/PyA+F5S69at49tvv6V+/foATJ8+nWrVqvHHH3/QrFkz7t69y7Fjx/jpp5+oUKECAKNGjaJ79+4MGTKEfPnysXv3btRqNZMnT8bCwoLSpUtz/fp1Vq9enWUbpd5kb+9A0aLvERAQkGJZMzPD06BQgFab+j+0dby8+bBBA6JbfYrWxY28edM38LhSqaRo0fiunx4eZbh37x9WrVpJpUof8vff53n+/DlNmzbSl9doNMyZM4tNmzYajOMgUqZ6FIj98EHEfViZuFx6485MUt9Zh5yLnEPOpchMmZlf1a5dh/LlK+inE/KrcuXKsWXLdsLDw4mLU+Pk5MwXX3SkbNn3U9zmunVrWL16FUuWLMPd3T3RcrVazbBhg3ny6BHrKlXCSqsh8/ozCJF+ck8wXh2kZTtp3WesVkOsJuMbpUTWYW5ujotL2q7Hhw8DAahZs3aGdkhJU6NUxYoV2bZtG/fu3aN48eLcuHGD8+fPM2zYMAACAwMJCgqiWrVq+nXs7e3x8vLi77//plmzZvz99984ODjoG6QAqlWrhlKpxM/PjwYNGnDx4kUqVaqEhYWFvkyNGjVYvnw5L1++xNHIYydlhMjISAIDA2jWrLl+npmZGdoMaJG2tbHB1d6eqIKF0ObLb/Tta7U61Or4MRSaNWuRqKt5797f0qxZc1q2/Njo+xZCCCGESJCp+ZWt7VsHRU94/eHBg/tcu3aNb799+4df1qxZxapVK1iwYDHlyiVuwEpokHrw4D7LJ0/FafdOotJ3CEIIke306dOdEiVKAvD77/swMzPjk0/a8M03PfUNI2FhYcybN5O//jqGWh2Lt/cHfPfdIIr8O6bWkyePmT17On5+F4mLU5M/f0F69+6Hr2+NVMWwcuVSjh07Sps27Vi1ahnh4WE0atSM778fzJYtG9i6dRNarZbPPmtPly5d9euFh4ezcOFcjh8/SmysmjJlytK37wBKl45/CPHwYSDz58/m6tUrREdH8d57xenRozcfflhFv402bVrQsmUrAgMDOHz4IPb29nTp0pWPP3577+CUvPn63r59e/jxx1mMGzeFH3+cxbNnT6lQwZsRI8bg6urKypVLWb06/su3NWvGfwzt+PFz6YohOWlqlOrevTsRERE0adIElUqFRqPh+++/p2XLlgAEBQUB4OLiYrCei4sLwcHx79kHBwfj7OxsGISZGY6Ojvr1g4ODKVy4sEGZhHECgoODs2Sj1Jw58e9XFihQgKCgIJYsWYRSqaJx4yb6MgULFuLMmdN4eVXEwsICB4fUdX87dOggCxbMY8eO3WmK6eXLlzx58lhfr/7+/gC4uLjq6/OHH0aQN28++vbtD8CqVSsoV+59ChcuQmxsLH/9dYx9+35l+PCRAOTJk4c8efIY7MfMzAwXFxeKFSuepviEyEwKxX+fLFUo4gde1Ol08n67EEJkYVkxv/rf/w7g5ORE/vwFuHPnNjNmTKNOnbr4+v73UPbN/GrNmlUsXryQyZOnUrBgIX1ebGNjg42NDWq1miFDBnLjxnXmzVuAVqMhKCqK6OfPsXd2wdzcPE0xCiFMLyHfBCTnTKPffttL8+Yfs3z5Wm7cuM706ZPIly8/LVu2AmDy5LEEBgYwbdpsbGxsWbx4PoMH92fDhu2YmZkxe/Y01Go1Cxcux8rKCn//e1hb26QphocPAzl16gSzZs3n4cNAfvhhKI8ePaRo0aIsWLCUy5f9mDJlPJUqVeH99+PHd/7hh6FYWloyc+aP2Nra8csvO/juu2/ZvHkHDg6OREZGUrVqdbp374W5uQX79+9l6NABbNr0M/nz/9e5ZMuWjXzzTU+++OJrDh8+yKxZU6lY0YeiRYsB8Q13BQoUZOTIsemq5+joaDZvXs8PP4xHoVAyYcIPLFw4lzFjJtKhQ2cKFCjI5Mnj+OWXjH0bKk2NUr/99ht79uxh1qxZlCpViuvXrzNlyhT9gOe52dOnzxg+fCgvX4bi5OSEt7cPa9du0A/ACfD99wOZPXsmO3fuwM0tb6pfdYuIiNA3KKXF0aNHGDv2B/308OFDAOjevSc9e/YC4MmTJyiV/32EMSoqiilTJvHs2VMsLS0pVqw4EyZMplGjxmnevxBZRcKgjpp//8bTmEGcuQy8KIQQWV1WzK+Cg4OYPXsGISEhuLq60bx5C7p162FQ5s38avv2bajVagYPNhwjKiEnCwp6xtGjRwBo3/6z/wrs2sWyZfFDKAghsj6FApQW8X/2E/JNkJwzrfLly0e/fgNQKBQULVqMu3fvsG3bJlq2bEVAwAOOH/+TxYtXUqGCFwBjxkygdetm/PnnEerVq8/Tp0+oXbue/kvxhQoVftvukqTTaRkxYjQ2NrYUL16CihUrERBwn5kz5/073E0xNm5cy4UL53j//fJcunSR69evsmfP//RvfPXp8x3Hjh3h8OGDfPxxa0qXdtf3mgLo1u1b/vzzMH/9dZRPP/1vmCJf32q0bh1/L+jUqQvbtm3iwoVz+kapfPnyp/lVvKTExcUxePAIff20bt2WNWtWAPEPTezs4nsEG2Nfb5OmRqnp06fTvXt3mjVrBoCHhwePHj1i6dKltGrVCjc3NwBCQkIMxjUKCQmhTJkyQHyPp+fPnxtsNy4ujpcvX+rXd3V11T9BSpAw/eaXVbKKqVOnp1imdu061K5dx2Bez5699A1ECT7/vDOff95ZP92y5cdvfzXOwgJNseLw2uuOqVoPWL58lcF079596d2771vXeZOMI/XudHZ2xNaph06+rJOhEgZ1fKzQoqhVk7s6NdqwFzLwognJtZ9zyLkUGcmk+VUyOnT4PMUvEL+ZX6WUKxUsWIgLF/z008oXz7H44wCx9RuidXJ+y5pCZC25/Z6gUChQ29kSWqsWd3VqokOD33mw77TUZU6r93LlyhuMYVS+fAW2bNmARqPh/v17qFQqyr329XlHxzwULfoe9+/fA6BNm/bMnDmFs2dPUalSFWrXrkepUqXTFEP+/AWxsfnvFW5nZ2dUKqXBAwdnZxdCQ+PbNu7cuUVUVBTNmhl+QCMmJkY/NlNkZCSrVi3j5MnjhIQEo9FoiImJ4enTJwbrlCz5X6wKhQJnZxdevHihn/fDD+N5m4ED++Hn9zcA+fIVYMOGbUmWs7KyMmiwc3FxNfggSGZJU6NUdHR0ogGuVCqV/g9X4cKFcXNz4+TJk5QtWxaIfwp16dIlOnToAMSPSxUWFsaVK1coXz7+Qjp16hRarRZPT08AvL29mTt3Lmq1Wt9d+cSJExQvXjxLvrpnalonZ6Jz+adXsytNiVK83LbL1GHkGmHvFeXCmtUAWGTCF0dE8uTazznkXAphfJLbiexK7gmgKVmKa+vXxg8kno7BxNNSl1Lvhlq0+ITKlaty8uRxzpw5zfr1q+nT5zvatEn936uJP5ahSDQP/vuARlRUJC4ursyfvzRRmYQeRwsXzuXs2dP07v0dhQsXwdLSklGjhqJWx6W4b6029Z+9GDZsFDExMUluK6X9mOJhvTLlIv+pW7cuS5Ys4ciRIwQGBvK///2P1atX67+0p1Ao+OKLL1i8eDEHDx7k5s2bDBkyhLx58+rLlCxZkpo1a/LDDz/g5+fH+fPnmTBhAs2aNSNfvnwAtGjRAnNzc0aOHMnt27fZt28f69at46uvvjLy4ecQOh2K2BikP2g2pNGgCA9L1w1LpIFGgyo8XOo7K5BrP+eQcymE8UluJ7IruScYL99MS13msHq/du2qwfTVq1coUqQoKpWK994rjkaj4dq1K/rlL1+G8uDBfYMxjvPly88nn7Rh8uQZtG/fiT17dmVozB4eZXj+PASVSkXhwkUMfgljMl++fImmTVtQu3ZdSpYshbOzC0+ePDJ6LG5uefX7zp+/gNG3b2xpapQaNWoUjRo1Yty4cTRt2pRp06bRrl07+vfvry/TrVs3OnXqxOjRo2nTpg2RkZGsWLECS0tLfZmZM2dSokQJunTpQvfu3fHx8WH8+P+6oNnb27Ny5UoCAwNp3bo1U6dOpVevXrRr1w6RmPLZU2zmzUb57KmpQxFpZHb1Mq4lC2N29bKpQ8kV7K5dp65XReyvXzd1KLmeXPs5h5xLIYxPcjuRXck9AcwuX6Z6ec9055tpqcu01ruFUoWFKhN+StU7HfvTp0+YP382Dx7487//7efnn7fqezkVKVKUmjVrM23aJC5dusjt27cYP340bm55qVmzDgDz5s3i9OmTPHr0kJs3b3Dhwjneey9jP8oVP+B5BYYPH8SZM6d4/PgRly9fYunShdy4cQ2AwoWLcvToIW7fvsnt27cYN26kvqdVWkyYMJolSxYY+xBMJk2v79nZ2TFy5EhGjhyZbBmFQkH//v0NGqrelCdPHmbNmvXWfZUpU4ZNmzalJTwhhBBCCCGEEEIkQafTYa6IH+Mqs5grlGh1aevB1bhxM2JiYujWrQtKpYo2bdrz8cet9cuHDx/DvHkzGTr0O9RqNV5ePsyYMU//OppWq2H27GkEBT3DxsaWKlV86ddvgH79Nm1a0KRJc7p27ZFo3+9KoVAwc+Y8li1bxOTJ4wgNfYGzswve3j76j3P07fs9U6aMp2fPr3F0zMPnn3fh1atXad7X06eGH9PI7tLUKCVMY8yYUYSHhzN79jxThyKEEEIIkSNIfiWEyG10OtDGaDB7Y5zojKTVpf2rg2ZmZvTvP5BBg4YnudzBweGtg31///2QZJdFR0fz/PlzKlb8INkyXbv2SNRgNXLk2ETlFixYZjBtY2PLd98N5rvvBie53QIFCvLjj0sM5n36aVuD6Z9+2pNovTVrDDvrvLnf1PDxqcTx4+f0002btqBp0xYGZWrVqmNQ5s3pjJJzmtdM7Pz5c/Tv34eGDT/Cx8eTw4cPJSqj0+lYvHghDRvWw9f3Q3r27MaDB/f1yx89eoiPjyc3b97ItLhjYmKYMmUSdevWpHr1Kgwa9D0hISFvXcfHxzPJ39q18QNInzt3NtkyV69eeeu2hRBCCCESZNf86vXY+vT5NtnY3yybcBxVPm7Ol4cOcf/hQ4MyL1++ZOTIYdSs6UutWtUZN24MkZGRGXkIQogcRqeLH5w7s35ZbWi8CxfO8cEHlfDxqWTqUMS/pFHKSKKjo3B392DYsBHJllm7djWbN29ixIgfWLt2I9bW1vTu3VM/Mr4pzJo1nWPHjjJt2kyWL19NUFAQgwZ9/9Z1Dhw4ZPAbM2Y8CoWCjz5qAICXl3eiMq1ataZQoUKUK/d+ZhyWEEIIIXKA7JpfJdi4cUOiL1cn5/XjWD/3R6zNzOg1arjBcYwcOYy7d++yaNFS5s2bz4UL55k4cVxGhS+EEDlOtWo1mDFDeshmJfL6npFUr16T6tVrJrtcp9OxadMGvvmmG3Xq1AVg/PhJNGhQlyNHDtGoUROaN28CQIcO8V34PvigEsuXr9JvY926NWzYsA61Wk3Dho0ZNGgI5ubmaN3yEtm7PzorqzTFHB4ezq5dO5k8eSqVK1cBYOzYCXz66cf4+V3C09MryfVcXV0Npo8ePUylSh9SuHBhAMzNzQ3KqNVqjhw5TPv2HVOdmOUWcWXfJ/jaP+gcHU0dSq7wqowHR8+eJs7BAXNTB5PLybWfc8i5FBnJlPlVet28eYMNG9ayYcMWGjas99ayiY5Dq2XsyrXUb9lUfxz//PMPJ078xYYNm/UP+YYMGUa/fr35/vuBuLnlTXfMQqSX3BMg7v33OXnhHJG2tunbThrqMifV+7u8miayN+kplUkePnxIcHAwVapU1c+zt7enfPkK+PldAmD9+vh3RRcvXsaBA4eYOXOOvuy5c2cJDAxg6dKVjBs3kT17fmHPnl/iFyqVLF63hmYtmqYppuvXrxEXF2cQU/HixcmfvwB+fn6p2kZISAjHjx/jk09aJVvmzz+P8PLlS1q2/DhN8eUK5uboXF3BCMmvSJnO3By1iws6qW/Tk2s/55BzKUwoQ/MrYMmSRTRr1jjNcUVFRTFixDCGDRuZ6GFeqo5DqcQub16D4/Dzu4S9vb1Br/MqVaqiVCq5fDn3fulMZDFyT4Ak8k0F8QNhK5Wv/3hjOv6nf4aflrp8S1ldVnt/TuQoxri+pFEqk4SEBAPg7OxiMN/FxYXg4PgxnJyc4r+CkCdPHlxdXXF8raXb3t6BoUNHULx4cWrVqk3NmrU4c+YMAIrQF7g+8KdI/vxpjsnc3Bx7e4dEMSXEm5I9e37BxsaGevXqJ1tm166d+PpWI1++tMWXGyjv/YND53Yo7/1j6lByBav79/Hq1h3r+/dTLiwylFz7OYecS2FKGZlfxa/jpO8JnhazZs3Ay8tL33srrcehCH2B5c6fcLG31x9HSEgwzs7OBuuZmZnh4OCQ6rxNiIwm9wRQ/fMP73f9Rp9vqhQKVGZK4sx1xJlDnDlozEFpY4bm3+nXf0pLFQpF2uoyqbIqlQqA2FjTv8oscq6E60uleveX8OT1vWyiZMmS+r9YIP4Vutu3bwOgiImhi5MTbfsPQJvM+itXLmfVqhX66Z9+2mWUuHbv3kWTJs2wtLRMcvnTp084efIE06bNMMr+chpleBiWv/9G5ODhyZ47YTxmYeG4HTzEP/37kbYP0wpjk2s/55BzKbKzt+VXAO3bd6B9+w7Jrp9UfnXr1g3Onj3D5s3b3jkuRUwMZndug1b+VInsRe4JoAgLw+WPg5j16wuASqEkTqfFP+wFMZr4DNTWzJzCjo74h/83D8BCqaK4gxNmCkWa6jKpskqlCmtrOyIiXsRv28JShlIRRqPT6YiNjSEi4gXW1nYole/e30kapTKJi0t81+3nz0Nwc3PTzw8JCcHDwyPF9c3M3jxVijR1lWvTpi0NGjTST7u5ueHi4oparSY8PMygt1RISIg+3re5cOE8/v7+TJ2afIPT7t2/4OjoSK1adVIdqxBCCCFEamTF/GrDhnUEBgZQu3Z1g7KDBw+gYkUfg/GsUjqO5y9e4F6hsL7M8+fPDdaLi4sjLCwsVXmbEMK0YrUaYv9tgLJQqhLNywgODvG9KxMapoQwNmtrO/119q6kUSqTFCpUCFdXV86cOY2HRxkAIiIiuHLlMp99Fj/wZsKgmhqN8Z8rODo6GnRXByhbthxmZmacOXNa/+U8f/97PHnyGE9PzxS3+csvOylbthzu7kknfTqdjt27d9G8eQujDBgqhBBCCPG6rJhfffVVV1q1am0wr23bTxk4cDC1atVOcjtJHodazeWbN2jzeScAPD29CA8P59q1a5QrVw6As2fPoNVqqVChgrEPTQiRAygUChwdXbC3d0KjiTN1OCKHUanM0tVDKoE0ShlJZGQkAQEP9NMPHz7k5s0bODg4UqBAARQKBR07dmLFimUULVqUggULsXjxQtzc3KhTJ/6LLE5OzlhZWXHixHHy5cuHhYUF9vb2qdr/hlu3+H3YEJasXpfqmO3t7fnkk1bMmjUTBwdHbG3tmD59Cp6eXgZf3mvduiV9+vSnXr2P9PMiIiL43/8OMGDAoGS3f+bMaR4+fMgnn3ya6piEEEIIIRKYOr/asmUzhw8fZOnSFSkX/perq2uSg5vnz1+AQoX+G5/q9fzqzeMobGnFkpMncXNx0R9HiRIlqFatOhMnjmXEiB+Ii4tj2rQpNGrUWL68J4QJKBQkeh0uq74ep1QqUSotTB2GEEmSRikjuXbtKt27d9VPz54d/0pbixYtGTduIgBdunxFVFQUEyeOJzw8HG/viixYsFg/HpOZmRmDBw9l+fKlLFmyKNku3m/S2dkTXKAgAefOpjnugQOHoFAoGTx4ALGxsfj6Vmf48JEGZfz9/YmIiDCY9/vv+wFo1KhJstv+5ZedeHl5U7x48TTHlVto8hckYtxkNPkLmjqUXCEmfz5ujRhOjAy6b3Jy7eccci5FRjJlfgUQGvqCwMBAIx9VvDfzqzePo2KJkiz8YazBuJ2TJk1l2rTJ9OzZDaVSSb169RkyZFiGxCfEu8gt9wSFIn5AcrXOsAemUgEULsjdUSPTnW+mpS5zS72LnEmhy+HfiAwODsdYR6hUKrC1tUSr1RptmyKeQhHfgv/qVQxarVSuMC6lUkGcOdwMDf7vXX6VCo88rpipkWtOiFxOoQBX19T1nBHxJL/KHiS/EiJjJOSW98JeEKv9b0yohAHMbz0PJiYufr6duQXFnZxSnAfx+WmZPK6YxRmOb6fT6eTvR5HtpDa/Sv8LgMLkFNHRqG7eQBEdbepQRBopQl9gsXsnilAZfDAzmL18Sd59+zB7+dLUoeR6cu3nHHIuhTA+ye1EdpXb7gkJA5Un/NRaLYoXL3D9de875ZsqhQKVmZI4cx2aiBeoft2JJuIFSksVb3szMLfVu8hZpFEqB1C8DMVq904UL0NNHYpII9WD+zh+0wXVg/umDiVXsHoQgGefflgHBJg6lFxPrv2cQ86lEMYnuZ3IrnLqPSG+16FC/1MoFCTXRqS6f5+yvfq8U76pUiiJ02m5F/aCwCuXyPPlFzy5dhm1TvvW8apyar2L3EHGlBJCCCGEEEIIIZKQ1PhRSkXCfzJGrFYD/74WGKc1/pdDhchKpFFKCCGEEEIIIYQg8Vf1FAqFvvdSwvhRCWNHZdGP7QmRrUijlBBCCCGEEEKIXO9tvaISxo8CsFCqTBShEDmPjCmVDXTr9jUzZkxLvoCZOZp8+cHMPPOCEkahs7JGXcELnZW1qUPJFTRWVoS9Xw6NlZWpQ8n15NrPOeRciuwqxfzKlCS3E9lUdr8nKBQK1P/2iroZGszN0GAehIeiQ5fqXlE6Kysiyr+f7nwzLXlrdq93kbtJTykjWbVqBYcOHcTf/x6WlpZ4eXnTr993FCtWXF8mJiaG2bNncuDAfmJjY/H1rcbw4aNwcXEB4Ny5s3Tv3pWjR49jb++Q6n1rXVyI/uKrd4r75cuXTJ8+hT//PIpCoeSjj+ozePBQbGxskl1n4sTxnDlziqCgIKytbfDy8qJfv+8pXvy/Y/Xx8Uy03pQp02jUqMk7xZlTadw9CD14zNRh5BpRpUtxZs9uACxMHEtuJ9d+ziHnUmQkU+ZX6bFixTKOHz/GrVs3MTMz588//0rVev/88w8//jiHCxfOExcXR4kDB5gxYzYFChQwKKfT6ejbtxcnTvzFrFlzqVu3XkYchhBpllPuCenpFaUpU4a/9/1KTJwmXTFElorPWy1UKe8/p9S7yJ2kp5SRnD9/jrZt27N27QYWL15GXFwcvXr1JCoqUl9m1qzpHDt2lGnTZrJ8+WqCgoIYNOh7E0YNI0cO4+7duyxatJR58+Zz4cJ5Jk4c99Z1ypYtx5gx4/n5510sXLgYnU5H79490GgM/+IdO3YCBw4c0v/q1JGESQghhBCpl13zK7VaTf36DWnTpm2q1wkICKBr1y4UK1acZctWsnXrz3Tr1h1Ly8SPUTZu3PDWL3EJIYQQ2YU0ShnJwoVLaNnyY0qWLIW7uwfjxk3gyZPHXLt2DYDw8HB27drJgAGDqFy5CuXKlWPs2AlcunQRP79LPHr0kO7duwJQu3YNfHw8GTNmlH77Op2OuXNnU6dODRo0qMuSJYv0y5TPnmIzezrKZ0/TFPM///zDiRN/MXr0WCpU8KRiRR+GDBnG77/vJyjoWbLrffppGz74oBIFCxaibNly9OrVlydPnvDo0SODcvb29ri6uup/lpaWaYovNzC7fAnXwq6YXb5k6lByBdsrV6lXpiz2V6+aOpRcT679nEPOpchIpsyv0uPbb3vTqVNnSpUqnYZjnU/16jX57rsBlHN2xmP7ZuqWLYezs4tBuZs3b7Bhw1rGjBlvlFiFMCa5J4DZpUtUL+WR7nzT/mp83mp7JeXtSL2L7EwapTJIeHgEAI6OjgBcv36NuLg4qlSpqi9TvHhx8ucvgJ+fH/ny5WfGjNkA7Ny5mwMHDjFo0FB92V9/3Y21tTXr1m2kf//vWb58KadOnYxfqNMx/K+/6Dp4YJpi9PO7hL29PeXKva+fV6VKVZRKJZcvX07VNqKiItm9exeFChUif/78BsumTp1MvXq16Ny5I7t27USn06UpvlxBp0MRGwtSN5lCodOhjFVLfWcFcu3nHHIu023+/Pl4eHgY/Bo3bqxfHhMTw7hx46hSpQoVK1akb9++BAcHG2zj0aNHdO/eHS8vL3x9fZk2bRpxcXEGZU6fPk2rVq0oX748DRo0YMeOHZlyfMaUqfkVMGbMKLp1+zrDj0ur1XL8+J+899579OrVk7rt2tD2t9849Ndxg3JRUVGMGDGMYcNG4urqmuFxCZFmck8AnQ6lMerg37xVkZrtSL2LbEzGlMoAWq2WmTOn4+1dUf+ELCQkGHNz80RjGbi4uBASEoxKpdInWM7OzonKlSpVmh49vgWgaNH32Lp1C2fOnKZqVV8A3KysUOfNm6Y4Q0KCcXZ2NphnZmaGg4MDISHByawVb9u2LcybN4eoqCiKFSvGokXLMDf/bzDOb7/tzYcfVsbKyopTp04ydeokoqIi6dDh8zTFKIQQQuQGpUuXZvXq1fpp1WtjiEyePJmjR48yd+5c7O3tmTBhAn369GHLli0AaDQaevTogaurK1u2bOHZs2cMHToUc3NzBgwYAMS/GtajRw/at2/PzJkzOXnyJKNGjcLNzY2aNWtm7sG+I1PkV66ubmi1WjLa8+fPiYyMZPXqlfTq1ZfvOnXm7PIlDJw4nmVF3+ODDyoBMGvWDLy8vKhTp26GxySEEEJkBmmUygBTp07i7t07rFq1xmjbLF3a3WDa1dWV58+f66cHensT9cVXJJc2TZo0gX37ftVP//XX6XTF06RJM6pW9SUoKIj169cydOggVq9ep39Fr1u3HvqyZcqUJSoqinXr1kijlBBCCJEElUqFm5tbovnh4eH8/PPPzJw5E1/f+IaSyZMn07RpUy5evIi3tzfHjx/nzp07rF69GldXV8qWLUv//v2ZOXMmffr0wcLCgi1btlC4cGGGDRsGQMmSJTl//jxr1qzJNo1Spsiv+vbt/9b1jZVf6f79/HydOnXp1KkzyqdPqFiuHOesrPnpp2188EEljh49zNmzZ9i8eds77UMIIYTIiqRRysimTp3MsWN/smLFavLl++91NhcXV9RqNeHhYQZP6UJCQnBxSbn7tZmZ4alSKBT6BCY1vv22F507dzGY5+JimHgBxMXFERYWlmJM9vb22NvbU7Toe3h6elG7dnUOHz5I48ZNkyxfvnwFli9fSmxsLBYW8t0zIYQQ4nX379+nRo0aWFpa4u3tzcCBAylYsCBXrlxBrVZTrVo1fdmSJUtSsGBBfaPUxYsXcXd3N3idq0aNGowdO5Y7d+5Qrlw5Ll68qG/Uer3M5MmTM+0Y0yM75VfvIk8eJ8zMzChRoqTB/OJFivL37VsAnDlzhsDAAGrXrm5QZvDgAVSs6MPy5avSHYcQQgiR2aRRykh0Oh3Tpk3h8OFDLF++kkKFChssL1u2HGZmZpw5c5qPPmoAgL//PZ48eYynpyeA/vU3jSZt3cS1Lq5EfdUNbZ48yZZxdnZJNFCmp6cX4eHhXLt2jXLlygFw9uwZtFotFSpUSPX+E8aKio1VJ1vm5s0bODg4SIPUG+JKe/D8z9No3itm6lByhcjSpTi5fx9RRYvKX34mJtd+ziHnMv08PT2ZMmUKxYsXJygoiIULF/L555+zZ88egoPjX09zcEj8elpQUBAAwcHBicYXSphOqUxERATR0dFYWVll1OGliynzq9RIKr96F+bm5pQr9z7+/v7Af7nd/UkTKFCgAABffdWVVq1aG6zXtu2nDBw4mFq1aqc7BiGMQe4JEOfhwfn//c6rQoXStZ1XpeLzVk3x4invU+pdZGPy7zIjmTp1Er/99htz5szDxsZWPwCpnZ0dVlZW2Nvb88knrZg1ayYODo7Y2toxffoUPD298PT0AqBAgQIoFAqOHTtKjRo1sbS0wsbGJuWdm5kxb/NGnj17yoQJqX/iWaJECapVq87EiWMZMeIH4uLimDZtCo0aNcbNLX58qmfPntKzZzfGj59E+fIVCAwM5MCB/VStWg0nJyeePXvK6tUrsbS0pEaNGgAcPXqE589DqFDBEwsLS06fPsmqVSuM8iQxx7G2RlOmrKmjyDW0VlZEu7unXFBkPLn2cw45l+lWu/Z/DQplypTBy8uLunXr8ttvv2XZxqLMYtL8Cpg/f16a8yuAx48fExb2kidPHqPVarh58wYARYoU1e+7deuW9OnTn3r1PgLgiy++ZNiwwfj4+FCpUmVOnPiLP4//ybJlKwH0XzN+U/78BRI11glhMnJPAGtrIj3c0cZp0rUZrZUVr9zdsXhtjMG37TPX17vItuTre0ayffs2IiLC6dbtaxo2rKf/HTjwu77MwIFDqFmzFoMHD+Cbb77ExcWVmTPn6JfnzZuPnj17MX/+POrXr8u0aalLgBRhL3l+6W+eBAamOe5Jk6ZSrFhxevbsRr9+vfH2rsioUWP0y+Pi4vD39yc6OhoAS0sL/v77Av369eLjj5sxbNhgbG1tWb16nf5JoZmZGdu2beXLLzvTocNn/PzzTwwYMFg/kKj4jzLgAXbf90EZ8MDUoeQKloGBlB02HKuHD00dSq4n137OIefS+BwcHChWrBgPHjzA1TX+9bSwsDCDMiEhIfoxqFxdXRN9jS9hOqUyCY07WZUp8yuA4OAgnjx5kua4lyxZSIcObVmyZBGRkZF06NCWDh3acu3af5929/f3JyIiQj9dr95HjBjxA2vXrqFd29b8sn4NM8ZNoGJFnzTvXwhTkXsCKB88oPSQoenON60ePqTssOFYpuLfeFLvIjtT6HQ5+7uRwcHhRvsyplKpwNbWEq1Wm6W+tql8+gTrdavjBzp/bZyF7EShAKVSyatXMWi1WahyM5iZ30Wc6tfixR9/EufpbepwciylUkGcOTw8dgSf5i04vXsXMV5eeORxxUxNrrrmsgq59nOOnHAuFQpwdbU3dRh6r169om7duvTp04dWrVrh6+vLrFmzaNSoEQD//PMPTZo0YevWrXh7e3P06FF69uzJ8ePHcXGJf0C0detWpk+fzsmTJ7GwsGDGjBn8+eef7NmzR7+fgQMHEhoaysqVK9McY27Ir0zJWLldbs2vhOlk93tCQs54MzSYWE18Tyc7cwuKOzlx63kwMXHJz0uYX/r+fVxq1OT07l2Ely+f5vUT5ltcvESVlp9w4dc9FKpZ5605a3avd5EzpTa/kp5SQgghhBAmNG3atH8HsQ7kwoUL9OnTB6VSSfPmzbG3t+fTTz9l6tSpnDp1iitXrjBixAgqVqyIt7c3ED9gealSpRgyZAg3btzg2LFjzJ07l88//1w/lmP79u0JCAhg+vTp3L17l40bN/Lbb7/x5Zdfmu7AhRBCCJHryZhSQgghhBAm9OTJEwYMGEBoaCjOzs588MEHbNu2DWdnZwBGjBiBUqmkX79+xMbGUqNGDcaM+e9Ve5VKxZIlSxg7dizt2rXD2tqaVq1a0a9fP32ZIkWKsHTpUqZMmcK6devInz8/EydOpGbNmpl+vEIIIYQQCaRRSgghhBDChObMmfPW5ZaWlowZM8agIepNhQoVYvny5W/dTpUqVdi1a9e7hCiEEEIIkSHk9b0cQGdri7qKLzpbW1OHItJI65aXyH4D0P77tUORsWLdXLnXswexSXy9SGQuufZzDjmXQhif5HYiu5J7Amjz5iWg17fpzjdjXf/NW91S3o7Uu8jOZKDzNDDVQJzdun2Nu7sHgwcPzbydZjIZiFNkpKQGrbRQqWSgcyEEkPUGOs8OJL/KHiS/EiJtjDHQeWrKpmV9yVlFdiUDnWeyJUsW4ePjafBr3bqlQZmYmBimTJlE3bo1qV69CoMGfU9ISIh++blzZ/Hx8SQ8POzNzb+VIjYWVcADFLGxby2X0v6TotPpWLx4IQ0b1sPX90N69uzGgwf3Dcq8fPmSkSOHUbOmL7VqVWfcuDFERkam6RhyK0VEOOZ/HUMREW7qUHIFVUQETqdOoXrtE9zCNOTazznkXIqMZMr8KrUyIr967O/P+CEDad6sMb6+H9KyZVMWL16IWq3OkGMQwljkngCK8HAcT6Y/30xL3ir1LrIzaZQyopIlS3LgwCH9b+XKtQbLZ82azrFjR5k2bSbLl68mKCiIQYO+T/d+FS+eY7VlI4oXz99a7l32v3btajZv3sSIET+wdu1GrK2t6d27JzExMfoyI0cO4+7duyxatJR58+Zz4cJ5Jk4cl+7jyg1U/9wlT6tmqP65a+pQcgXre/580LETNv7+pg4l15NrP+eQcykymqnyq9TKiPzK/+pllHfvMKp3H7Zv38nAgYP5+eftLFgwLzMOSYh3JvcEUN29i2e7DunON2384/NW63spb0fqXWRn0ihlRCqVGa6urvqfk5OTfll4eDi7du1kwIBBVK5chXLlyjF27AQuXbqIn98lHj16SPfuXQGoXbsGPj6ejBkzSr++Tqdj7tzZ1KlTgwYN6rJkyaI0xZbS/pOi0+nYtGkD33zTjTp16uLu7s748ZMICgriyJFDAPzzzz+cOPEXo0ePpUIFTypW9GHIkGH8/vt+goKepbUKhRBCCCEM5Mb8qnqlD5lStSrVPqhE4cKFqV27Lp07d+HQoYNprT4hhBAiS0tTo1S9evXw8PBI9Bs3Lr5XTExMDOPGjaNKlSpUrFiRvn37EhwcbLCNR48e0b17d7y8vPD19WXatGnExcUZlDl9+jStWrWifPnyNGjQgB07dqTzMDPHgwf3adjwI1q0aMLIkcN4/Pixftn169eIi4ujSpWq+nnFixcnf/4C+Pn5kS9ffmbMmA3Azp27OXDgEIMG/TfGwa+/7sba2pp16zbSv//3LF++lFOnTuqXDzt1iq5DBiUbW0r7T8rDhw8JDg42WMfe3p7y5SvoEy0/v0vY29tTrtz7+jJVqlRFqVRy+fLlFOtMCCGEEOJtTJlfjRkzim7dvk42tozKr5ISERGBg4NjssuFEEKI7MgsLYV/+uknNJr/BmK7ffs2X331FY0bNwZg8uTJHD16lLlz52Jvb8+ECRPo06cPW7ZsAUCj0dCjRw9cXV3ZsmULz549Y+jQoZibmzNgwAAAAgIC6NGjB+3bt2fmzJmcPHmSUaNG4ebmRs2aNY113EZXoUIFxo2byHvvFSM4OIhly5bQteuXbN++A1tbW0JCgjE3N8fe3sFgPRcXF0JCglGpVDg6xicazs7OicqVKlWaHj2+BaBo0ffYunULZ86cpmpVXwDcrKxQ503+awsp7T+5deLjcUm0TnBwiL6Ms7OzwXIzMzMcHByS3a4QQgghRGqYOr9ydXVDq9UmG19G5VdvevDgAVu3bua77wYkG4sQQgiRHaWpUerNxodly5ZRtGhRKleuTHh4OD///DMzZ87E1zf+Rj558mSaNm3KxYsX8fb25vjx49y5c4fVq1fj6upK2bJl6d+/PzNnzqRPnz5YWFiwZcsWChcuzLBhw4D4cQTOnz/PmjVrsnSjVPXq/8Xm7u5OhQoVaNasMf/73+988knrdG+/dGl3g2lXV1eeP/93DCmliu9r1iTm03YknzaJrEhnZo6mQEF0ZuamDiVX0JqbEZ0/H1qpb5OTaz/nkHMpMpJJ8yugb9/+6d7HO1Gq0Nrbg1LFs2dP6dPnW+rXb0Dr1m1ME48QqST3BMDMnJj8+dOdb2rNzOPzVvOU/8ku9S6ys3ceUyo2Npbdu3fz6aefolAouHLlCmq1mmrVqunLlCxZkoIFC3Lx4kUALl68iLu7O66urvoyNWrUICIigjt37ujLJDRqvV4mYRvZhb29A0WLvkdAQAAALi6uqNXqRF9+CQkJwcXFNalNGDAzM/zLSKFQoNPFN0Fp3dyI6tkHrZtbsuu/y/4T5j9/bvjULiQkBFdXF32Z15M3gLi4OMLCwlJ1XLmdptz7PL90A81rrz+KjBNZpgzHT/zFqzIepg4l15NrP+eQcykyU2bmV6mRUflVQm73FB3du3+Dl5cXo0aNSXVcQpiK3BMgrvz7nDlzMt355qsyHhw/8ReRZcqkWFbqXWRn79wo9ccffxAeHk6rVq0ACA6O777s4JC4+3JQUJC+zOsNUoB+OqUyERERREdHv2u4mS4yMpLAwAD9sZQtWw4zMzPOnDmtL+Pvf48nTx7j6ekJgLl5fMu2RmP8/k6p2f+bChUqhKurq8E6ERERXLlyGU9PLwA8Pb0IDw/n2rVr+jJnz55Bq9VSoUIFox+HEEIIIXKv3JJfATx79pRu3bpStmxZxo6dgFIp3ycSQgiR87zz3e3nn3+mVq1a5MuXz5jxZFtz5szk/PlzPHr0kEuXLjJw4HcolSoaN24CxA9g+cknrZg1ayZnz57h2rVrjB07Gk9PL30CUqBAARQKBceOHeXFi+dERkamat/KoCAWf/0FowcPTLZMavYP0Lp1S/2XXRQKBR07dmLFimUcPXqY27dvMXr0SNzc3KhTpx4AJUqUoFq16kycOJYrVy5z8eLfTJs2hUaNGuPmlvwYVyKe6tpVnL3KoLp21dSh5Ao2N25Qo1p1bG/cNHUouZ5c+zmHnEuRkUyZXwHMnz+PH34YkezyjMqvgq5fo3vbTyng7ML33w/kxYsXBAcHJ/qAkBBZjdwTwOzKVSpX9k13vml74yY1qlXH5saNFMtKvYvsLE1jSiV4+PAhJ06cYP78+fp5rq7x3ZfDwsIMekuFhITg9u9rZa6urom+RJJwc329zJs33ODgYOzs7LCysnqXcDPF06fPGD58KC9fhuLk5IS3tw9r127Ayem/cbgGDhyCQqFk8OABxMbG4utbneHDR+qX582bj549ezF//jzGjh1N8+YtGDduYso712oIfvmSxyk8AUxp/wD+/v5ERETop7t0+YqoqCgmThxPeHg43t4VWbBgMZaWlvoykyZNZdq0yfTs2Q2lUkm9evUZMmRYynELFHFqVI8foYhTmzqUXEGpjsPqyVOUUt8mJ9d+ziHnUmQkk+ZXQHBwEE+ePHlrmYzIr06dP8eDsDAeXLxA48YNDLZ14ULSX/UTIiuQewIQp8byyZN055vKOHV83qqOS7Gs1LvIzhQ6nU6X1pXmz5/P1q1bOXLkiP5d/PDwcHx9fZk1axaNGjUC4J9//qFJkyZs3boVb29vjh49Ss+ePTl+/DguLvHvzG/dupXp06dz8uRJLCwsmDFjBn/++Sd79uzR72/gwIGEhoaycuXKNB9gcHA4aT/CpCmVCmxtLdFqtUbbpjEonz7Bet1qor74Cm2+/KYO550oFKBUKnn1KgatNgtVbgYz87uIU/1avPjjT+I8vU0dTo6lVCqIM4eHx47g07wFp3fvIsbLC488rpipyVXXXFYh137OkRPOpUIBrq72pg4jW8kN+ZUpGSu3y635lTCd7H5PSMgZb4YGE/vvV+ftzC0o7uTErefBxMQlPy9hfun793GpUZPTu3cRXr58mtdPmG9x8RJVWn7ChV/3UKhmnbfmrNm93kXOlNr8Ks2v72m1Wnbs2MEnn3xiMDikvb09n376KVOnTuXUqVNcuXKFESNGULFiRby9vYH4ActLlSrFkCFDuHHjBseOHWPu3Ll8/vnnWFhYANC+fXsCAgKYPn06d+/eZePGjfz22298+eWXaQ1VCCGEEEIIIYQQQmRRaX5978SJEzx69IhPP/000bIRI0agVCrp168fsbGx1KhRgzFj/vtSiEqlYsmSJYwdO5Z27dphbW1Nq1at6Nevn75MkSJFWLp0KVOmTGHdunXkz5+fiRMnUrNmzUT7E0IIIYQQQgghhBDZ0zu9vped5Ibu5YrYWJRPn6DNlx/dvz3Ospvc2r1cERGO2aWLxHl5o7OTV0cySkJX7DuB/lhdukRY+fKoHB3l9T0Tkms/58gJ51Je30u73JBfmZKxcrvcml8J08nu9wRjvL5XwsyMJ38eJaRsWTR2du/8+l5c6Escrlwh2suLUoWLvTVnze71LnKm1OZX7zTQuchadBYWaIoUNXUY4h3o7OxRV5degJlFY2fHi6pVAVCZOJbcTq79nEPOpRDGJ7mdyK7kngA6e3te+lZF81pj07tIyFstVClnrVLvIjtL85hSIvONGTOKAQP6J7tcERGOxZ9HUESEZ2JUwhiUjx9hO3EsysePTB1KrmDx5Aklp8/AMoUvKYmMJ9d+ziHnUmRXKeVXpiS5nciu5J4AykePKDZ1errzTct/81aLVGxH6l1kZ9IoZSQajYZFixbQvHljfH0/pGXLpixfvpTX347U6XQsXryQhg3r4ev7IT17duPBg/v65Y8ePcTHx5ObN2+kad+KV68wP30SxatXby2X0v6Tsn37Vtq2/ZSaNX2pWdOXLl068ddfxwzKBAcHM2rUCBo0qEu1apXp2LEtBw/+L03HkFspg55h8+NslEHPTB1KrmARFEzxJUuxCA42dSi5nlz7OYecS5GRTJlfpda75FcAz549ZeTI4dStWxNf3w9p27Y1165dBeJzux/mzaFirer4+Hjqf71798yQYxDCWLLqPSH+VVZFop9CYfx9KZ89o8iixenONy2C/81bg1LeTlatdyFSQ17fM5I1a1bx00/bGDduIiVLluTatauMHTsaOzs7OnT4HIC1a1ezefMmxo+fSMGChVi8eAG9e/fkp592YWlpmeExvsv+8+bNR79+31G0aFF0Oh179uzm++/7s3nzNkqWLAXA6NEjCQ8PZ86cH8mTx4n9+/cxdOhgNmzYTJkyZTP8uIQQQgiRM+XU/CosLIyvvupCpUofMn/+IpycnHjw4AH29g4G5apXqsSYydP10xbZdOxQIUxJoQClpQq1TptomblChTZGI+PZCWFC0lPKSC5dukTt2nWpWbMWBQsWon79hlSt6suVK1eA+KdomzZt4JtvulGnTl3c3d0ZP34SQUFBHDlyCIDmzZsA0KFDW3x8POnW7WuDfaxbt4aGDetRt25NpkyZhFqtTnV8qdl/UmrXrkONGjUpWvQ93nuvGH369MPGxobLl/1eO/aLtGvXgfLlK1C4cGG++aY79vb2XL9+LdXxCSGEEEK8KafmV2vWrCJfvnyMGzeB8uUrUKhQYXx9q1GkSBGDcubm5ri6uup/Dg4OyWxRCJEgca8oJXE6LffCXnAzNFj/uxf2gjidFqVS+VrPKQUZ0Hkq3RSAQpHxvbyEMAVplDISLy8vzpw5zf37/gDcunWTixf/pnr1GgA8fPiQ4OBgqlSpql/H3t6e8uUr4Od3CYD16zcBsHjxMg4cOMTMmXP0Zc+dO0tgYABLl65k3LiJ7NnzC3v2/KJfPv/yZZp06ZxsfKnZf0o0Gg2///4bUVFReHp6vXbs3hw48DsvX75Eq9Xy+++/ERMTwwcffJiq7QohhBBCJMXU+dWSJYto1qxxsvG9a3519OgRypV7nyFDBvLRR7Xp0KEtO3b8lKjcOT8/PvqoNq1atWDy5AmEhoa+pbaEEAm9ouLM0f805oBKQaxWQ6zmv59Gp0VlpiTOXJeobFaiVCgSxRlnHn+c0jAlcgJ5fc9IvvqqK69evaJ1649RqVRoNBp69+5L06bNAAgJiX8X2NnZxWA9FxcXgoNDAHBycgIgT548uLq6GpSzt3dg6NARqFQqihcvTs2atThz5gytW7dBZ22NQ/ESFHlLl+7U7D85t2/f4ssvOxMbG4u1tQ2zZs2lRImS+uXTps1g6NAh1K1bEzMzM6ysrJg1ay5Fi8pXY1KidXIm6vMv0Do5mzqUXEHtlIeHbT9D/e+fNWE6cu3nHHIuRUYyZX4Vv44ThQsXTja+d82vHj4M5KeftvH55535+utvuHr1KjNmTMPc3JwWLT5GZ22Nb5Wq1KrgScFSpQkMDGTBgh/p27cXa9asR5WKr3EJYQqmvicoFArU//aKitXGf/3O1sycwo6OiRpwVIr4HlT+YS+I0by9bFponZ150r5tuvNNtZMTD9t+htbZOVGcFkoVxR2cMFMo0Ol0Jq93IdJDGqWM5H//+53fftvL5MlTKVGiJDdv3mTWrOm4ubnRosXH6d5+yZIlDRIQV1dXbt++DYDOwZE24ybSJt17SVqxYsXZvHk7ERERHDz4P0aPHsWKFav0DVOLFi0kIiKMxYuX4eTkxOHDhxg6dDArV66mdGn3DIoqZ9AWKUrEnAWmDiPXiClcmOtTpwAgo3KYllz7OYecS5GRTJlfAbRv34H27Tukez9v0mq1lCv3Pn37xn/9r0yZsty9e4efftoe3yjl4Ej9IcP15UuXdqd0aXdatmzKuXNnDXpmCZGVZJV7QkKvKIhvwDFW2dTQFi3K7enTiInTpGs70YUKcX3qFOzMLRLFmWifWaTehXgX8vqekcydO5svv+xKo0ZNKF3anebNW/D5551ZvXolAC4u8U/mnj83fGoWEhKCq6tLou29yczszfZDxX9fnomLQxkcDHFxya6fnv2bm5tTtGhRypUrR9++/XF3d2fTpo0ABAQEsHXrZsaMGU+VKlVxd/egR49vKVeuHNu2bU3xuHK9qChUN65DVJSpI8kVlNHR2N66hTI62tShCLn2cw45lyIDmTS/SoV33b+rqxslSpQwmFe8eHGeJHz6PYncrnDhwuTJ40RAQECq4xMi08k9AaKisLmZ/nwzIW9VRKViO1LvIhuTRikjiY6ORqk07OepVCrRauMTm0KFCuHq6sqZM6f1yyMiIrhy5bJ+fCZzc3MANJrEX4Z4G2VIMNarl6MMSf5zoanZf2pptVrU6lgAoqPj/+JTKAwvJaVShVabtuPIjcxu38S5VhXMbt80dSi5gs3tO/g2bortnTumDiXXk2s/55BzKTKSKfOr1HjX/Mrb2xt/f3+Deffv36dAgQJA0rnd06dPePkyFDc3w1cQhchK5J4AZjdv8kGDRunON23vxOet1ndup1hW6l1kZ9IoZSS1atVm5crlHDv2J48ePeTQoYNs2LCeunXrAfHvN3fs2IkVK5Zx9Ohhbt++xejRI3Fzc6NOnfgyTk7OWFlZceLEcUJCQggPD0/1/jfcukX3YUOSXZ6a/QP06PENW7Zs1k/Pnz+P8+fP8ejRQ27fvqWfbtIkfiyHYsWKU6RIUSZNGs+VK5cJCAhg/fq1nD59Un/sQgghhBDvwtT51ZYtm+nR45tkl79rfvX55525cuUyK1cu58GDB/z221527PiJtm3bAxAZFcW0v//G7/p1Hj16yOnTp/j++/4UKVIUX9/qaapDIYQQIiuTMaWMZMiQ4SxatIApUybx4sVz3Nzc+PTTNnTv3lNfpkuXr4iKimLixPGEh4fj7V2RBQsWY2lpCcR3IR88eCjLly9lyZJFVKzow/Llq1K1/xcxMQQ8C3prmZT2DxAYGEho6Av99PPnzxk9ehTBwUHY2dlRurQ7CxcuoWpVXyD+6eP8+Qv58ce5fPddXyIjIylSpCjjxk2kRo2aqa4/IYQQQog3mTq/Cg19QWBg4FvLvEt+9f775Zk5cw4LFsxj+fKlFCxYiEGDhugHcFcqldwKDWXnuNGEv3qFm1teqlb1pVevPli85cM2QgghRHaj0KXlxflsKDg4HGMdoVKpwNbWEq1Wa7RtGoPy6ROs160m6ouv0ObLb+pw3olCEZ+AvXoVo++SnxuY+V3EqX4tXvzxJ3Ge3qYOJ8dSKhXEmcPDY0fwad6C07t3EePlhUceV8zU5KprLquQaz/nyAnnUqEAV1d7U4eRreSG/MqUjJXb5db8SpiOqe8JCTnfzdBg/aDgduYWFHdy4tbzYIPBx5Oan555CfNL37+PS42anN69i/Dy5dO8fsJ8i4uXqNLyE/z27SVfrVoGZS1UKoM81tT1LkRSUptfyet7OYFCgU6lIl3fLhWmoVCgs7CQc5dJdAoFWgtzqe+sQK79nEPOpRDGJ7mdyK7kngAKBVpj1EFa8lapd5GNyet7OYA2bz4iByQ/npTIuuIqeBEcmPwA9cK4XpV/n0M3rgMgLz+Yllz7OYecSyGMT3I7kV3JPQHivLz4685Ngx5Q7yL8/fi81c485axV6l1kZ9JTSgghhBBCCCGEEEJkOmmUygGUISFYrVuNMiTE1KGINFLdukmej2qiuiWfb80M1rfvULlFS2zS+YlekX5y7eccci6FMD7J7UR2JfcEUN24QcWmzdOdb9rcic9brW7fTnmfUu8iG5NGqWxgzJhRDBjQP/kCcWpUT59AnDrzghJGoYiOwvzyJRTRUaYOJVdQRUfjcPUaquhoU4eS68m1n3PIuRTZVYr5lSlJbieyKbkngCI6GrsrV9OdbybkrcpUbEfqXWRn0ihlJK9evWLGjGk0bdoIX98P+fLLzly9esWgjE6nY/HihTRsWA9f3w/p2bMbDx7c1y9/9OghPj6e3Lx5I0NiTGn/SdFoNCxatIDmzRvj6/shLVs2Zfnypbz+0caDB/+gV68e1K1bM0PjF0IIIUTuklvzK3VcHDMuXqTNt92pVq0yDRt+xA8/jCAo6FmGHIMQQghhKtIoZSTjx4/l9OlTTJgwia1bf6ZqVV++/bY7z5491ZdZu3Y1mzdvYsSIH1i7diPW1tb07t2TmJiYTInxXfa/Zs0qfvppG0OHjuDnn3fRr993rF27mi1bNunLREVF4e1dkX79vsuEoxBCCCFEbjF+fO7Mr6JjYrj2/DndOnzOpk1bmTlzNvfv+/Pdd/0y5ZiEEEKIzCKNUkYQHR3NoUN/0L//93zwQSWKFi1Kz569KFy4CNu3bwPin6Jt2rSBb77pRp06dXF3d2f8+EkEBQVx5MghAJo3bwJAhw5t8fHxpFu3rw32s27dGho2rEfdujWZMmUSanXqu3SnZv9JuXTpErVr16VmzVoULFiI+vUbUrWqL1eu/PeUsnnzFnTv3pMqVaqmOh4hhBBCiLfJzfmVva0tq+vVo1Gt2hQrVhxPTy+GDh3B9evXePz4cZrqUQghhMjKpFHKCDQaDRqNBgsLw891WllZcfHi3wA8fPiQ4OBgg4Ybe3t7ypevgJ/fJQDWr49/OrZ48TIOHDjEzJlz9GXPnTtLYGAAS5euZNy4iezZ8wt79vwCgM4xD3Ni1TT5qkuyMaZm/0nx8vLizJnT3L/vD8CtWze5ePFvqlevkZqqESnQFH2PlyvWoin6nqlDyRWiixbBb8GPRBUpYupQcj259nMOOZcio5g6vwJYsmQRzZo1TjbGjMqvdI55iG7ZCp1jHv06ERERKBQK7O3tk92uEKYm9wTQvPce1xctSHe+GVUkPm+NScV2pN5FdmZm6gByAltbWzw9vVixYhklSpTA2dmF/ft/w8/vEkX+/UskJCQYAGdnF4N1XVxcCA6O/7KKk5MTAHny5MHV1dWgnL29A0OHjkClUlG8eHFq1qzFmTNnaN26DTorKxxKlqbwgwfJxpia/Sflq6+68urVK1q3/hiVSoVGo6F37740bdosNVUjUqDL40Rsy1amDiPXiHN05FnTpgBYpFBWZCy59nMOOZcio5g6v4pfx4nChQsnG2NG5Vc6Kys0HmX05WNiYpg3bw6NGzfBzs4u2e0KYWpyTwCdkxPBzZsRF6dJ13YS8lY785SzVql3kZ1JTykjmTBhMjqdjkaN6lO1aiW2bNlEo0ZNUCiMU8UlS5ZEpVLpp11dXXn+PD7ZUbx6RedSJVk2e55R9vW6//3vd377bS+TJ09l48YtjBs3kfXr1xo8RRTvTvHsGdaLF6B4JgOXZgbzoCCKrliJRVCwqUPJ9eTazznkXIqMZMr8CqB9+w4sXbrCKPt6XUr5leLVK8zPnUHx6hVqtZqhQwcBOoYPH2X0WIQwJrkngPLpMwotX5HufNMiKJiiK1ZiHhSUYlmpd5GdSaOUkRQpUoQVK1bz11+n2LfvAOvXbyIuLk7/dM3FJf7J3OuJDkBISAiuri6JtvcmM7M3O7Up9F9oUUSEY3H4IIqI8GTXf9f9z507my+/7EqjRk0oXdqd5s1b8PnnnVm9emWKMYuUqZ48wm7MCFRPHpk6lFzB8slT3CdPwfLpE1OHkuvJtZ9zyLkUGcmU+VVqZFR+lZDbxYW+YNiwwTx+/JhFi5ZJLymR5ck9AZSPH1FiwqR055uWT5/gPnkK5k9S3o7Uu8jOpFHKyKytbXBzcyMsLIyTJ09Qu3ZdAAoVKoSrqytnzpzWl42IiODKlct4enoBYG5uDoBGozV6XKnZf1Kio6NRKhUG85RKJVpt6hM2IYQQQoj0yI35lVqrZcjkiTx4cJ8lS5aRJ08eo8cvhBBCmJqMKWUkJ078hU6no1ixYgQEBDB37myKFStGy5YfA6BQKOjYsRMrViyjaNGiFCxYiMWLF+Lm5kadOvUAcHJyxsrKihMnjpMvXz4sLCxSPZjlhlu3+H3YEJasXpfk8tTsH6BHj2+oW/cj2rfvAECtWrVZuXI5+fMXoGTJkty4cYMNG9bz8cef6Nd5+fIlT548JujfrqX+/v5A/NPDN8duEEIIIYRILVPnV1u2bObw4YPJvsKXUfmVOi6OAcePcy0mhnkLFqPRaAkOjn8VyNHRUd/QJoQQQmR30ihlJBERESxYMI+nT5/i6OhIvXr16d27r0HS0KXLV0RFRTFx4njCw8Px9q7IggWLsbS0BOK7kA8ePJTly5eyZMkiKlb0YfnyVana/4uYGAKevf1945T2DxAYGEho6Av99JAhw1m0aAFTpkzixYvnuLm58emnbejevae+zNGjRxg79gf99PDhQwDo3r0nPXv2SlX8QgghhBBvMnV+FRr6gsDAwLeWyYj86llIMIcePgSgffvPDPa3bNlKKlX6MFXxCyGEEFmdQpeWF+ezoeDgcIx1hEqlAltbS7RardG2aQyK0BdYHD5IbN2P0OVxMnU470ShiO+2/upVTK56NVB57x/sRg8nYvwUtMVLmDqcHEupVBBnDvcvnaf4uPHcGjUSTYkSeORxxUxNrrrmsgq59nOOnHAuFQpwdU1dzxkRLzfkV6ZkrNwut+ZXwnRMfU9IyPluhgYTq4n/+p2duQXFnZy49TyYmNe+iJfU/PTMS5hf6sULFAMGcGPkCKLeey/N6yfMV979B/eJkwgcO5aCFSsalLVQqQzyWFPXuxBJSW1+JY1SaSBJU8aRpElkpKQSlDdv5kKI3EsapdJO8qvsQfIrkdtkhUapzFhf8liRHaQ2v5KBznMCrRZFZCRojT+Ap8hgajWK4GBQq00dSa6gUKsxDwlBIfVtenLt5xxyLoUwPsntRHYl9wQwUr6ZprxV6l1kY9IolQMog55hs3AeyqBnpg5FpJHZ9au4liuB2fWrpg4lV7C9cZPaH1bB7uZNU4eS68m1n3PIuRTC+CS3E9mV3BPA7OpVqlaslO580+5mfN5qfeNGyvuUehfZmDRKCSGEEEIIIYQQQohMJ41S2cCYMaMYMKC/qcMQQgghhMgxJL8SQgghTE8apYzk1atXzJgxjaZNG+Hr+yFfftmZq1evGJQZM2YUPj6eBr/evXvqlz969BAfH09u3ky5i+a7OHjwD3r16kHdujVTvZ+DB//g88/bU6tWdapVq0z79p/x6697DMpERkYydepkGjeuj6/vh3z66Sf89NO2DDkGIYQQQuQe2SG/0ul0LF68kIYN6+Hr+yE9e3bjwYP76T6uAwEB9BwxLE15mxBCCJHdmJk6gJxi/Pix3L17hwkTJuHmlpd9+37l22+789NPO8mbN5++XLVq1Rk7doJ+2sLCItNijIqKwtu7Ig0aNGTChHGpWsfR0ZGuXbtRrFhxzM3NOXbsKOPGjcbZ2Zlq1aoDMGvWDM6ePcPEiVMoWLAgJ0+eZOrUSbi5uVG7dt2MPCQhhBBC5GDjx2f9/Grt2tVs3ryJ8eMnUrBgIRYvXkDv3j356addWFpaJrnO+PEpH1dkXBwV3y9Pg+YtUp23CSFEUhQKUCgUBvN0Op188VRkCdIoZQTR0dEcOvQHs2fP44MPKgHQs2cv/vzzKNu3b6N37776shYWFri6uia5nebNmwDQoUNbAD74oBLLl6/SL1+3bg0bNqxDrVbTsGFjBg0agrm5Odq8+YjsPwCd+dsTsObNWwDxTwxTq1KlDw2mO3bsxK+/7ubixb/1jVJ+fhdp0aKlvuynn7bh55+3c+XKFWmUSkHc+xUIvhuIzsbW1KHkChHlynL40t9obGzIvH+uiKTItZ9zyLkUGcXU+VVq6HQ6Nm3awDffdKNOnficZ/z4STRoUJcjRw7RqFGTdzoubd58NJw7H525BY8eP0pVLEJkBXJPgLgKFThx1Y9Ii6QbpVMrvGx83mrtmCflfb6l3hUKUFqqUOsMv+ZprlChjdFIw5QwuTS/vvf06VMGDRpElSpV8PT0pEWLFly+fFm/XKfTMW/ePGrUqIGnpydffvkl/v7+BtsIDQ1l4MCB+Pj4UKlSJUaMGMGrV68Myty4cYOOHTtSoUIFateuzfLly9/tCDOBRqNBo9EkeipnZWXFxYt/G8w7d+4cH31Um1atWjB58gRCQ0P1y9av3wTA4sXLOHDgEDNnznltvbMEBgawdOlKxo2byJ49v7Bnzy/xCxUKFq9aSbPmiRMfY9LpdJw+fQp/f398fD7Qz/f09Obo0SM8e/YUnU7H2bNnePDgPlWr+mZoPDmCSoXO3gFUKlNHkjuoVGjs7aW+swK59nMOOZcig5g8vwKWLFlEs2aNk43x4cOHBAcHU6VKVf08e3t7ypevgJ/fpXc/LoUCnYVl/L8mhchO5J5gvHwzLdt5S70rFArUOi33wl5wMzSYm6HB3At7gVqnTdR7SghTSFOj1MuXL+nQoQPm5uYsX76cvXv3MnToUBwdHfVlli9fzvr16xk7dizbtm3D2tqarl27EhMToy8zaNAg7ty5w+rVq1myZAnnzp1j9OjR+uURERF07dqVggULsmPHDoYMGcKCBQvYunWrEQ7Z+GxtbfH09GLFimUEBT1Do9Gwd++v+PldIjg4SF+uWrXqTJgwkSVLltOv3/ecP3+evn17odFoAHBycgIgT548uLq6GtSrvb0DQ4eOoHjx4tSqVZuaNWtx5swZAJQvnuN27x+K5M+fIccXHh5O9epVqFLlA/r378OQIcMNGpyGDh1OiRIlaNy4AVWqfECfPt8ybNgI/dM/kTzVP3dwbPsJqn/umDqUXMHq3j0qdvkS63v+pg4l15NrP+eQcykyiqnzq/h1nChcuHCyMYaEBAPg7OxiMN/FxYXg4JB3Pi7li+dYbd+C8sXztFSZECYn9wRQ3blD+U5fpDvftL7nT8UuX2J1717K+0xFvcdqNcRq/v1pNemKTQhjStPre8uXLyd//vxMmTJFP69IkSL6/9fpdKxbt45vv/2W+vXrAzB9+nSqVavGH3/8QbNmzbh79y7Hjh3jp59+okKFCgCMGjWK7t27M2TIEPLly8fu3btRq9VMnjwZCwsLSpcuzfXr11m9ejXt2rUzxnEb3YQJkxk3bjSNGtVHpVJRpkxZGjVqwvXr1/RlXu/CXbq0O6VLu9OyZVPOnTtr8IQtKSVLlkT1Wsu3q6srt2/fjp+IjeULVxc+GzAIbTLrp4etrS2bN28nKiqSM2dOM3v2TAoXLqx/XW/Llk1cvuzHnDk/UqBAQS5cOM/UqZNxc8ub4nHldoqICCyOHEIREWHqUHIFs4hXuBw7jtmrCORWbFpy7eccci5FRjJpfgW0b9+B9u07GPGI4qV4XLGxqPzvQWys0fctREaSe0J8HTj9eQyzV+mrA7NXEbgcO87DZOpSQXwvKKUSlK9eYXHkEMpXr9AqkFfyRLaSpp5Shw4donz58vTr1w9fX18++eQTtm377ytrgYGBBAUFUa1aNf08e3t7vLy8+Pvv+O7If//9Nw4ODvoGKYBq1aqhVCrx8/MD4OLFi1SqVMmgW3ONGjW4d+8eL1++fLcjzWBFihRhxYrV/PXXKfbtO8D69ZuIi4t769O1woULkyePEwEBASlu38zszfZDBbpM+ttGqVRStGhRPDzK0LlzF+rXr8+qVSuB+HERFiz4kQEDBlO7dh3c3d1p374DDRs2Yt26NZkSnxBCCCFypqyeX7m4xI9j9fy5Ya+okJAQXF1dkloFeLfjEkKIBCqFApWZkjhzHXHmoPn3rzKNWfz4UfJWnshO0tQoFRAQwObNmylWrBgrV66kQ4cOTJw4kZ07dwIQFBTf5djFJakuzPHdm4ODg3F2djZYbmZmhqOjo3794ODgRINVJkwnbCersra2wc3NjbCwME6ePPHWgb6fPn3Cy5ehuLnFH1vCoJoaTUb0dzIerVaHWh3/5C4uLo64uDiUSsO/+ZRKVaY1mgkhhBAiZ8uq+VWhQoVwdXXlzJnT+nkRERFcuXIZT0+vFNdPy3EJIRJTKECpVOh/CoWC3NAeo1IoiXttnKj74aEAPHwVJmNFiWwnTa/v6XQ6ypcvz4ABAwAoV64ct2/fZsuWLbRq1SpDAswuTpz4C51OR7FixQgICGDu3NkUK1aMli0/BiAyMpKlSxfz0Uf1cXV1JSAggHnz5lCkSFF8feO/Yufk5IyVlRUnThwnX758WFhYYG9vn6r9b7h1i9+HDWHJ6nXJlnn58iVPnjzWN/4lDEDv4uKqb/T74YcR5M2bj759+wOwatUKypV7n8KFixAbG8tffx1j375fGT58JAB2dnZ88EEl5s6djaWlFQUKFOD8+fPs3buHAQMGpb0ihRBCCCH+Zer8asuWzRw+fJClS1ckuVyhUNCxYydWrFhG0aJFKViwEIsXL8TNzY06derpy/Xo8Q11636kfxUwpeMCCI2J4d7duzzTxjemJZW3CZFbKBQkamhRKEBhriT2ta/KKRUJ/8kdEsaJUv87RlScNmt3bhAiKWlqlHJzc6NkyZIG80qUKMHvv/+uXw7xXZbz5s2rLxMSEkKZMmWA+B5Pz58bDtoYFxfHy5cv9eu7urom6hGVMJ1Vb8IREREsWDCPp0+f4ujoSL169endu6/+6ZxSqeT27dv8+utuwsPDcXPLS9WqvvTq1Uf/mqKZmRmDBw9l+fKlLFmyiIoVfQw+WZwcnb0DQYUKE/DaU7qkHD16hLFjf9BPDx8+BIDu3XvSs2cvAJ48eYJS+V8HuqioKKZMmcSzZ0+xtLSkWLHiTJgwmUaN/vsSzZQp05k/fx4jRw4nLOwlBQoUoHfvvrRp0zaVtZd7aQoWJnzKTDQFpbt+ZogpWIAbY8cQXaBArniKlpXJtZ9zyLkUGcmU+RVAaOgLAgMD31qmS5eviIqKYuLE8YSHh+PtXZEFCxZjafnf5+ADAwMJDX2R6uPS2TtwwNGJH/p8q18nqbxNiKwmI+4JCkX8K2lqnWGDi1KhQKfUce/FC/3A3bZm5hR2dDTp62uaQoW4M2Ec0QUKpGs70QXi89bYggVTXTamYPr2KYQpKHRpeMdq4MCBPH78mE2bNunnTZ48GT8/P7Zs2YJOp6NmzZp8/fXXfP3110D8TdfX15epU6fqBzpv2rQpP//8M+XLlwfg+PHjfPPNNxw9epR8+fKxadMm5s6dy19//aW/Oc+ePZsDBw6wf//+NB1gcHC40QZ6UyoV2NpaotVqZfA4I4vveqvk1asYtFqpXGFcSqWCOHO4GRpM7L9fY7JQqfDI44qZGrnmhMjlFApwdU1dz5nMsGzZMmbNmsUXX3zByJHxPZNjYmKYOnUq+/btIzY2lho1ajBmzBiDh3WPHj1i7NixnD59GhsbGz755BMGDhxoMG7S6dOnmTp1Krdv36ZAgQJ8++23tG7dOs0xSn6VPUh+JXKChDzuXtgLg6/GJTRA3XoeTExc/Hw7cwuKOzmlOC8tZTNrm8ZYP6n8VvJgYSqpza/SNKZUly5duHTpEkuWLOH+/fvs2bOHbdu20bFjx393quCLL75g8eLFHDx4kJs3bzJkyBDy5s2r/xpfyZIlqVmzJj/88AN+fn6cP3+eCRMm0KxZM/LlywdAixYtMDc3Z+TIkdy+fZt9+/axbt06vvrqq7TWQ66giI7C7NoVFNFRpg5FpJHixXMst29BIZ98zhRmoaHk37ULs9BQU4eS68m1n3PIuTSuhAd9Hh4eBvMnT57M4cOHmTt3LuvXr+fZs2f06dNHv1yj0dCjRw/UajVbtmxh6tSp7Ny5kx9//FFfJiAggB49elClShV++eUXunTpwqhRozh27FimHZ9IHcntRHaVkfeEhFfVEn7qLPqqmuL5c9x27Ex3vpmQt6pepLwdyXFFdpamRilPT08WLFjA3r17ad68OYsWLWLEiBG0bNlSX6Zbt2506tSJ0aNH06ZNGyIjI1mxYoVBF+aZM2dSokQJunTpQvfu3fHx8WH8+PH65fb29qxcuZLAwEBat27N1KlT6dWrF+3atTPCIec8ipcvsdy7B0UW/TKhSJ4q4AEOvbujCnhg6lByBauAQMoPGIR1Cq9iiIwn137OIefSeF69esXgwYOZOHEijo6O+vnh4eH8/PPPDBs2DF9fX8qXL8/kyZP5+++/uXjxIhDf6/zOnTvMmDGDsmXLUrt2bfr378/GjRuJjY3/OMmWLVsoXLgww4YNo2TJknTq1IlGjRqxZs0aExyteBvJ7UR2JfcEUD14QJnvBqQ737QOjM9bLQNT/pJoQlmrAMlxRfaTpjGlAOrWrUvdusl/GUShUNC/f3/69++fbJk8efIwa9ast+6nTJkyBq8JCiGEEELkZOPHj6d27dpUq1aNxYsX6+dfuXIFtVpNtWrV9PNKlixJwYIFuXjxIt7e3ly8eBF3d3eD1/lq1KjB2LFjuXPnDuXKlePixYv4+voa7LNGjRpMnjw54w9OCCGEECIJaW6UEkIIIYQQxrV3716uXbvGTz/9lGhZcHAw5ubmODg4GMx3cXHRf1E3ODg40cdgEqZTKhMREUF0dDRWVlZGOx4hhBBCiNSQRikhhBBCCBN6/PgxkyZNYtWqVQbDHQghhBBC5HTSKJUTmFugKVgIzC1MHYlII52NLeoPPkRnY2vqUHIFjY01oRW90djYAKAg/pVj5Wuj6+l0Ovn6UyaQaz/nkHOZflevXiUkJMTgK3gajYazZ8+yceNGVq5ciVqtJiwszKC3VEhICG5ubkB8jyc/Pz+D7QYHBwMYlEmY93oZOzs76SWV1UhuJ7IpuSeAzsaGMJ+K+nzzXWlsbAit6I02FdtJKKuxsU7XPoUwBWmUeicKFIqs869WnYszMZ2+AOL/kZ09Zd/I00NTqjShvx00dRi5RlTJkpz7Of7VGGuFApWZkjiF4SfIzRUqtDEaaZjKYHLt5xxyLtOvatWq7Nmzx2De8OHDKVGiBN26daNAgQKYm5tz8uRJGjVqBMA///zDo0eP8Pb2BsDb25slS5YQEhKCi4sLACdOnMDOzo5SpUrpy/z5558G+zlx4oR+G6aXtfIrUzJebpc78ythOnJPAI27O5d27SAmTpOu7USWKMG5n3/CLhWN0wllLVSqdO1TCFOQRqk00Ol0aLValEolcpM3Pq1Wi05aAkQmUSmUxOm0+Ie9IEYTnzRYKFUUd3DCTKGQa1EIkWns7Oxwd3c3mGdjY0OePHn08z/99FOmTp2Ko6MjdnZ2TJw4kYoVK+oblGrUqEGpUqUYMmQIgwcPJigoiLlz5/L5559jYRH/D5r27duzceNGpk+fzqeffsqpU6f47bffWLp0aaYe75skv8pYkl8JIYTIyqRRKg10OoiMjEWhyFoJk+raFfK0bUXotp1oypU3dTjvLDe+NmXmdxGn+rV48cefxHl6mzqcHM/u8hV8mrfg9O5d6Cr6ABCr1RCrSd+TLJF2cu3nHHIuM8eIESNQKpX069eP2NhYatSowZgxY/TLVSoVS5YsYezYsbRr1w5ra2tatWpFv3799GWKFCnC0qVLmTJlCuvWrSN//vxMnDiRmjVrmuKQ9LJqfmVKxsztcmN+JUxH7glgdvEiNWvU5PTuXYSXf/c/v/ZXrlCl5Sf47dsLtWqlquyFX/dAzTrvvE8hTEEapdJIpyPLPW1SxqrhyRN0sWq02qwVmxBCCCHSbv369QbTlpaWjBkzxqAh6k2FChVi+fLlb91ulSpV2LVrlzFCNKqsmF+ZkuR2QgghcgtlykWEEEIIIYQQQgghhDAuaZQSQgghhBBCCCGEEJlOGqWEEEIIIYQQQgghRKaTMaVygDj3MoSc+httwUKmDkWkkZy7zPWqdGn+OvQHMQUKYGPqYHI5ufZzDjmXQhif/LkS2ZVcuxBXpgxn/zzMK7e86dpOQt5qVqRoqstqCxVO1z6FMAVplMoJrKzQlihp6ijEu5BzZ3QKBYm+4KRQKFCgQ2dlSVSxYqYJTBiSaz/nkHMphPHJnyuRXaXx2k0qb8v2X4y0siK6WDG0cen7urPWMj5vtTO3SHVZC5UqXfsUwhTk9b0cQHnfH/tvv0F539/UoYg0knNnXAoFKC1VxJlj8NOYAyoFVg8CeP/7AVgFBJg61FxPrv2cQ86lEMYnf65EdpWWaze5vE1pqeKNdqpsRenvj0f/79Kdb1oFxOetlg8epLqs1QPJcUX2I41SOYDyZShWP29D+TLU1KGINJJzZ1wKhQK1Tsu9sBfcDA3W/x6Eh6JDh1nYSwr8shvzly9NHWquJ9d+ziHnUgjjkz9XIrtKy7WbVN52L+wFap02Ue+p7EQZGkrenb+kO980fxmft6pSsZ2EsmaS44psSF7fE0LkOLFaDbGa/7pMWyilK7MQQgghRFb0Zt4mhMhdpFFKCCGEEEIIIUSWoCC+F5Xy33d6EsYGFULkTNIoJYQQQgghhBDC5FQKBSozJXEKrX6wc6Ui4T9CiJxIGqVyAG2+/LwaNAxtvvymDkWkkZy7zBWbNy//9OtLTN68pPwdE5GR5NrPOeRcCmF88udKZFfpvXZVCiVxOi3+YS+I+feVPlszcwo7Omabwc+1+fNz/7v+xOTNm67txPybt6pTsZ2EsrHp3KcQpiCNUjmANl9+IoeMMHUY4h3Iuctc6nx5+ee7/gDSKGVicu3nHHIuhTA++XMlsitjXbuvjzOV3cYG1ebPz4MB/2/vzuOjqO//gb9m9si1ScjFmXAlhCtAgtgIBmktHgVtRWzhWxVpUamKYD0AEQUEASlYvCqIiKIotaJoq7Q/tR5QDs+IgRAIBAiEI5uD3Nlrfn8sWbJhQ3azk53Z2dfz8Qia2c9OPjufz8y+972f+XwegMXm3zxZls7OuNVkaDtqbSpr1AXXsSICuPqeJgjVVTD891MI1VVKV4V8xLYLLF11NeK/+gq66mqlqxLy2Pe1g21JJD+eVxQsBAEQRcH1o6uthvHz0O67QlUVOn35pd/xpi9xK2NcCmZMSmmArugIOk2+GbqiI0pXhXzEtgus8KPHMHzqHxF57JjSVQl57PvawbYkkh/PKwoGggCIYTrYDHD9SMePIHZSaPdd3ZEjGHL7VL/jzchjzrg17OhRr8tGHGWMS8GHt+8RERERERGRTwRBgFVyoKiqAhaH81a1TrVVSDj/GBGRN5iUIiIiIiIionZpPv+TzeFQuDZEFGx4+x4REREREREREQUck1IaIBnDYO/dB5IxTOmqkI/YdoElGY2o69UTDiPX3lMa+752sC2J5MfzioKVI8wIW5++QFjo9l3JGIb6Xr38jjcd5+NWyYv9NJV1hDHGpeDD2/c0wD5gIMq//lHpalA7sO0Cq65/OnZ+/l8AgEnhuoQ69n3tYFsSyY/nFQWruvR0lOX+CL0VgENSujqKsA8aiG+3f4FGm92v/dSmO+NWk6HtRFNTWaNO59ffJFICR0oREREREREREVHAMSmlAbp9eUgY2Ae6fXlKV4V8xLYLrMj8fFw14nKY8g8oXZWQx76vHWxLIvnxvKJgFZWfj6S+vUO67+rz8nBF5mV+x5um/AO4asTliMzP97pslBdlidSGt+9pgGC3QSwrg2C3KV0V8hHbLrAEmx3G8goIdhtaG1AuwLmMsdgiZS9JEqTQHIXeIdj3tYNtSSQ/nlcUrASbHWJZGWAL4b5rs8FQXu73+SvYbTCWV3h1LJvKCn7eMkikBCaliChoCYIzgXThdwFCq+mmtukEATq9CJvguCgBZRB0cDTamZgiIiIiIiKSCZNSRBSUBAEQw3SwSg7XNlFo+qd9dIIIm+TA0aoKNNovfNNkFHXoExMHvSBAYlaKiIiIiIhIFkxKEVFQEgQBVsmBoqoKWBzOBFKU3oDk2FgI7c9LAQAsDjssdg5/JiIiIiIi6khMSmmArW8aKj76BLa+aUpXhXzEtvNf8wSSUbz0Mrj1ffvgm3ffQV2fPogIROWoVez72sG2JJIfzysKVnV9+6D8k08hpYZu37WnpSH3/S2o69PHr/3U9XHGrVLfvl6Xbezr398kUgKTUlpgMsF2ebbStaD2YNsFlCMqCueGD1e6GgSw72sJ25JIfjyvKEg5oqJg/Vk29FYAjtCc8kAymVB92XDY/Zx03H4+bjUZjF6XNeou/QUtkRqJbRchtRNLTiLq8UchlpxUuirkI7ZdYBlPnUK/JU8h7NQppasS8tj3tYNtSSQ/nlcUrIynTsE0b25I913x5En0eXKJ3/Fm2Pm41ejFfnwpS6Q2TEppgGguReTaFyGaS5WuCvmIbRdYBnMZer26AcayMqWrEvLY97WDbUkkP55XFKyM5jJEvfgihFL3visIgCgKbj/OVZO1RywtRfIr6/2ON41lzrhVbzZ7XdZoZoxLwYe37xEREREREVGH8LRiMuD/qslEpA1MShEREREREVGH8LRiMiDfqslEFNx8un3v+eefR//+/d1+rr/+etfjjY2NWLRoEbKzs5GVlYX7778f5hbDDUtKSnD33Xdj2LBhGDlyJJ5++mnYbDa3Mnv27MGECROQkZGBa665Bu+9954fL5GIiIiIiIiU1LRictOP1eFo+0lEpHk+j5Tq168fNmzY4Ppd12yG/6VLl+LLL7/E6tWrER0djcWLF2PGjBnYvHkzAMBut2P69OlITEzE5s2bcfbsWcyZMwcGgwEPPvggAKC4uBjTp0/H5MmTsXLlSuzatQvz589HUlISRo8e7e/r1SRHfALq/3AnHPEJSleFfMS2CyxbfByKb7sVlrh4GJSuTIhj39cOtiWR/HheUbCyxseh7s67ICWEbt91JCSgZMptsMTF+7UfS1w8im+7Fbb4tvfTVNYaH+fX3yRSgs9JKZ1Oh6SkpIu2V1dXY8uWLVi5ciVGjhwJwJmkGjduHHJzc5GZmYkdO3agsLAQGzZsQGJiIgYOHIhZs2Zh5cqVmDFjBoxGIzZv3ozk5GTMnTsXAJCamorvvvsOr732GpNSrXAkp6Dm6WeUrga1A9susBp79EDBk4sAgEkphbHvawfbkkh+PK8oWDX26IHqVc9AbwXgkJSujiIcKSk4vGQxGm32tgtfQmOP7ih4chFMBqPXZY3NBowQBQufV987duwYcnJy8Mtf/hIPPfQQSkpKAAB5eXmwWq0YNWqUq2xqaiq6d++O3NxcAEBubi7S09ORmJjoKpOTk4OamhoUFha6yjQltZqXadoHeVBXB/3eXKCuTumakK/YdgEl1tcjOi8PYn290lUh9n3tYFsSyY/nFQUpsb4e+tzc0O67dXWI+sn/eNOXuJUxLgUzn5JSQ4cOxbJly/DKK69g4cKFOHnyJG699VbU1NTAbDbDYDAgJibG7TkJCQkoPb8kqNlsdktIAXD93laZmpoaNDQ0+PbqQoS+8CDixl4FfeFBpatCPmLbBVZE4WFk//omRB0+rHRVQh77vnawLYnkx/OKglVk4WEkjBkN3aHQ7bv6gwcxfPyNfsebUYedcWv4+cEb3pSNLGSMS8HHp9v3xowZ4/r/AQMGYNiwYfjFL36Bbdu2ITw8XPbKERERERERERGRNvl8+15zMTEx6N27N44fP47ExERYrVZUVVW5lSkrK3PNQZWYmHjRanxNv7dVxmQyMfFFRERERERERKQRfiWlamtrUVxcjKSkJGRkZMBgMGDXrl2ux48cOYKSkhJkZmYCADIzM3Hw4EGUlZW5yuzcuRMmkwlpaWmuMrt373b7Ozt37nTtg4iIiIiIiIiIgp9PSamnn34aX3/9NU6cOIHvv/8eM2bMgCiKuOGGGxAdHY2JEydi+fLl2L17N/Ly8jBv3jxkZWW5Eko5OTlIS0vD7NmzceDAAWzfvh2rV6/GrbfeCqPRuarA5MmTUVxcjBUrVuDw4cPYtGkTtm3bhqlTp8r92jVDEkQ4TNGQBL9yjKQAtl2AiSJspihIIo+30tj3tYNtSSQ/nlcUrCRRhCM6GgjlWEsUYTOZ/I43pfNxqzfHUmKMS0HMpzmlTp8+jQcffBCVlZWIj4/HZZddhnfeeQfx8fEAgHnz5kEURcycORMWiwU5OTlYsGCB6/k6nQ5r1qzBwoULMWnSJERERGDChAmYOXOmq0xKSgrWrl2LZcuWYePGjejatSuWLFmC0aNHy/SStcc+ZCjKjpxUuhrUDmy7wKodPAhf7P0RAGBSuC6hjn1fO9iWRPLjeUXBqnbwIJSeKIHeCsAhKV0dRdiGDsWu/T+h0Wb3az81g5xxq8lg9LqsUafz628SKcGnpNRf//rXSz4eFhaGBQsWuCWiWurRowfWrVt3yf1kZ2dj69atvlSNiIiIiIiIiIiCCMf3aYCu4ADiRv8MuoIDSleFfMS2C6zIg4dwxXXXI+rQIaWrEvLY97WDbUkkP55XFKwiDx5CQvblId13dfkHMPyX1/odb0YdcsatEQcPel028iBjXAo+TEppgNDYAH3BAQiNDUpXhXzEtgssobERpkOFEBsbla5KyGPf1w62JZH8eF5RsBIbG6E/cABoCN2+KzQ2IOrQIb/jTfF83Cp4sR+RMS4FMSaliIiIiIiIiIgo4JiUIiIiIiIiIiKigGNSioiIiIiIiIiIAo5JKQ2w9+qNcxs3w96rt9JVIR+x7QKroWdP5L68BvU9eypdlZDHvq8dbEsi+fG8omBV37MnKt/eDEfv3kpXRTH23r2x75WX/Y4368/HrY1e7KeeMS4FMb3SFSD/SbGdYLl+nNLVoHZg2wWWPTYG5rFjla4GgX1fS9iWRPLjeUXByh4bg8Zx46G3AnBISldHEVKnTii/9hrYbHa/9mOLccatJoPR67JGnc6vv0mkBI6U0gDhzBlEPLsKwpkzSleFfMS2CyzD2VL0/ttLMJaWKl2VkMe+rx1sSyL58byiYGU4W4rIVStDuu+KZ84g+YW/+R1vGkudcavh7FkfyjLGpeDDpJQG6M6cgumpRdCdOaV0VchHbLvAMp45g7SVqxAWwoGSWrDvawfbkkh+PK8oWIWdOYPoJxdBPB26fVc8dQp9VvzF73gz7HzcavBiP2GMcSmIMSlFREREREREREQBxzmliIiIiIiIiDRAACAIAsTzw08EQYCA0Jzfi4IDk1JEREREREREQU4UBOj0ImyCA5LUtK3pHyJ1YlJKAxwxsWi88SY4YmKVrgr5iG0XWPaYGJz51fWwxcSAa5Moi31fO9iWRPLjeUXByhYbg4bf3AQpNnT7rhQbi9Jxv4ItJsav/djOx612L/bTVFaKjYVNcuBoVQUa7c7V/6L0BiTHxkJgXopUikkpDXD07oOq9RuVrga1A9susBp69cRPL74AADApXJdQx76vHWxLIvnxvKJg1dCzJ85tfAN6KwBHaN4yZu/TBwfX/A2NNrtf+6nv6YxbTQajz2UtDjss55NSRpFfxZK6caJzLbBYIJacBCwWpWtCvmLbBZRgsSDs1CkIPN7KY9/XDrYlkfx4XlGQEiwWiCdDvO9aLDDKEG/6ErcyxqVgxqSUBugP7EdC5kDoD+xXuirkI7ZdYEUWHMToK0fDdPCg0lUJeez72sG2JJIfzysKVlEFB5E0aAB0+aHbd/X79yM7e5Tf8abpoDNujSgokLUskdrw9j0iIiIiIiKSjXP1N8H1/1z9jYhaw6QUERERERER+U08P5u2XS/BZmjaBq7+RkStCvmklMPhgN1uU7oafnGIAhp69YJFFGC38j7iYKDT6SGKvHuWiIiAt956C2+//TZOnjwJAOjXrx/uvfdejBkzBgDQ2NiI5cuX4+OPP4bFYkFOTg4WLFiAxMRE1z5KSkqwcOFC7NmzB5GRkbjpppvw0EMPQa+/EOrt2bMHy5cvx6FDh9CtWzfcc889uPnmmwP7YolI05qSUidrq1BRaQbA1d+I6NJCNiklSRKqqspRX1+jdFX8FxMJ899egiMmEig7pXRtyEsRESbEK10JIiJSXNeuXfHwww+jV69ekCQJW7duxX333Yf3338f/fr1w9KlS/Hll19i9erViI6OxuLFizFjxgxs3rwZAGC32zF9+nQkJiZi8+bNOHv2LObMmQODwYAHH3wQAFBcXIzp06dj8uTJWLlyJXbt2oX58+cjKSkJo0ePVvLlE5EGWR0Orv5GRF4J2aRUU0LKZIqD0RgGIZhT95IEdEkBBAH8CkL9JEmCxdKImpoKlPdMhr24FDAYlK5WSKgdPAif5e+DZDDApHRlQpwtYyhK2fc1gW3pv6uvvtrt9z//+c94++23kZubi65du2LLli1YuXIlRo4cCQBYunQpxo0bh9zcXGRmZmLHjh0oLCzEhg0bkJiYiIEDB2LWrFlYuXIlZsyYAaPRiM2bNyM5ORlz584FAKSmpuK7777Da6+9xqSUCvG8omBVN3gwzpSZUVt9DnCE5jxStqFDsePQATQI/t0VUT3IGbdGRUbJWpZIbUIyKeVw2F0JKZMpRunqUAgyGsMAADU1FYiOieetfIEiipDCwpSuBQGAKAJsC21gW8rKbrfj3//+N+rq6pCVlYW8vDxYrVaMGjXKVSY1NRXdu3d3JaVyc3ORnp7udjtfTk4OFi5ciMLCQgwaNAi5ubmupFbzMkuXLg3YayMf8LyiYNXUd2tFwGFXujbKaIo3bX6+/qb9ePM5wZeyRCoTkr3W3jSU1KiRN/uGBugKDwENDUrXhHxgNIYBVhsip/8BusOHlK5OSIg4cgSX/d/vEXmkSOmqhDzd4UOIvWkc+74GsC3lUVBQgKysLAwZMgQLFizAiy++iLS0NJjNZhgMBsTEuH+JlpCQgNLSUgCA2Wx2S0gBcP3eVpmamho0MH5QHZ5XFKzCjxxB3PW/QsSRI0pXRTG6Q4cw5HeT/Y43I48U4bL/+z3CvTiWvpQlUpuQHCnVJKhv2WvO4YBQUw04HErXhHwgCAIgOWDI/R622lqlqxMSxNo6xO35Grq6Wi5MrDChthbGnTsgsO8HPbalPPr06YOtW7eiuroa//nPfzBnzhy8+eabSleLFMLzioKVWFsL444dEGvrlK6KYoTaWnTavQe6Ov/OX11dLeL2fI1iL64DvpQlUpuQTkoRERERqYHRaESvXr0AABkZGfjpp5+wceNG/OpXv4LVakVVVZXbaKmysjIkJSUBcI542rt3r9v+zGbnqlfNyzRta17GZDIhPDy8w14XERER0aWE5O17rREEQBSFgP1oZaAWERERycvhcMBisSAjIwMGgwG7du1yPXbkyBGUlJQgMzMTAJCZmYmDBw+irKzMVWbnzp0wmUxIS0tzldm9e7fb39i5c6drH0RERERK4Eip8wQBEMN0sEqBuwXOIOjgaLRD4n1EREREIWvVqlW46qqr0K1bN9TW1uJf//oXvv76a6xfvx7R0dGYOHEili9fjtjYWJhMJixZsgRZWVmuhFJOTg7S0tIwe/ZsPPLIIygtLcXq1atx6623wmg0AgAmT56MTZs2YcWKFZg4cSJ2796Nbdu2Ye3atQq+ciIiIgp1TEqdJwgCrJIDRVUVsARgpQijqEOfmDjoBQGSv1kpoxH2lJ7A+cCTLq2q6hz++te/4H//2w5RFDBmzNWYNethREZGtvqcxsZGvPDCanz22f+D1WrBz352BR56aC7i4xNcZVav/gv27v0RRUWH0atXH7z22lttV0anR+3c+ZB6pMjx0qgNjT26Y/+yp9DQvTs0ssxB0LL3SEH1M8/Dzr4f9NiW/isrK8OcOXNw9uxZREdHo3///li/fj2uvPJKAMC8efMgiiJmzpwJi8WCnJwcLFiwwPV8nU6HNWvWYOHChZg0aRIiIiIwYcIEzJw501UmJSUFa9euxbJly7Bx40Z07doVS5YswejRowP+eqltPK8oWFl69EDVC8+jsUd3pauiGHtyMg4+vQwN3f07Bg3dnXGrpUcPWcsSqQ2TUi1YHHZY7EG2fKleDykhse1yBABYtOhxlJWZ8de/vgibzYZlyxZhxYqnsHDhU60+5/nnn8HOnTuwePFyREWZ8Ne/rsBjjz2Cl1561a3c+PG/xv79eTh8uNC7yuhENP56AgwGJhQDwRYfj5JJkwCASSmFSQkJaLjtDqWrQTJgW/pv6dKll3w8LCwMCxYscEtEtdSjRw+sW7fukvvJzs7G1q1b21NFCjCeVxSsbPHxqJ86FbZyM2ALss9UMpESE3Hm/ybD6ufrt56PW01efE7wpSyR2nBOqSAyY8bdeOaZp/HMM0/juuvGYPz4X2LdupcgWa0QysyAzYaqqiosXvwErr/+F/jlL6/EQw/NRHHxcdc+Tp8+hdmz/4zrr/8Fxo7NwW23/Q67du3wug7r16/F1Km/x7/+9QFuvnk8rrlmNFauXA673Y5Nm17Hr399HW644Rq8/vp6t+dVV1dj+fLFuOGGsbj22jGYOfNPOHTooOvxkydPYO7cB3HjjdfimmtG4847p+Cbb/a47eOWW27Exo2vYunSRbjmmqtw883j8cEH7/l0DI8eLcKePTsxd+58DB6cgWHDMvHAA4/gs8/+H8zmUo/Pqampwb/+9QHuv//PuOyyyzFgwEDMm7cAP/20F3l5P7nKPfDAI5g48Xfo3t2HbyjsDoR9+D6EZvOAUMfRl5ej+9//DkN5udJVCXlCWRnC33ydfV8D2JZE8uN5RWrUcv5dQRDQcopcfXk5Il57DfoQjrUEsxld3t7sd7xpOB+3enMsfSlLpDZMSgWZbds+gk6nx7p1r2PWrIfx979vwj8/eA+64uOAxYKlSxeioCAfTz/9DNas2QBJkvDII7Ngs9kAAM888zSsVgtefHEdXn99M+65535ERLR+25onJ0+ewO7dO7Fq1fNYsOApfPTRB3jkkQdQWnoWL7ywFvfccz/WrXsJ+/bluZ7z+ONzUFFRjpUrn8P69W8gPX0AHnjgHlRVnQMA1NXV4YorrsSzz/4Nr766CdnZIzFnzoM4ffq029/evHkTBgwYhA0bNmHChN9i1arlOH78qOvxGTPuxlNPLWy17nl5e2EyRWPAgEGubSNG/AyiKLrVt7mCgnzYbDaMGJHt2tarV2906dIV+/bt9fgcr9ltiFq+BLqTxf7th7wSdrIEgx59DOElJUpXJeTpThYj+sH72fc1gG1JJD+eV6Q2TfPv2gxw/dgNAHTuaSnjyZOImXE/wk6GbqylO3EC6XMe9TveDC9xxq3GkydlLUukNkxKBZkuXbpg5swH0bNnb1x77a8wceIkvPPeOwCA4pPF2LHjK8yZMx/DhmWhX790LFiwGKWlZ/HVV18AAM6cOY0hQ4YhNTUNPXok48orRyMzc7hPdZAkB+bNewJ9+vRFTs5VyMoageLiY5g58yH07Nkb48f/Gj179sL3338LAPjxx1zk5+/D4sVPY8CAQUhJ6YkZMx6AyRSNzz//DADQr186brppIvr2TUNKSk/cddc96NGjB/73vy/d/vbIkaNw882/RXJyCm677Q7ExnZy/R3n8emKhEvcylheXoa4uDi3bXq9HtHRMSgv9/xtZFlZGQwGA6Kjo922x8fHu610RKHH04qdXFWTiIiItKb5/LsFlWYUVJpxvLoSEiTGPkTkF84pFWQGDcqA0OzKn5ExBJs3vwm7w4Fjx49Bp9Nh0KAM1+OxsZ3Qs2cvHDtWBAC45ZbJWLlyGb75ZjdGjMjGmDFXIy2tn0916Nq1OyIjo1y/x8fHQ6cTIYpis20JqKx0Dh8tLDyI+vp6jB//S7f9NDY24uTJEwCcI6VeffVl7Nq1A2VlZtjtdjQ2NuLMGfeRUqmpF+oqCALi4xNQUVHh2vb440/69FqI2qu1FTu5qiYRERFpVfP5d42iTuHaEJEWMCkVYm688Sb87GdXYNeuHfj66z14440NmDHjAdxyy2Sv96HXu3cbQRAu2gYADofzU3l9fR0SEhLx/PMXLzttMjlHH7344mp8880e3HffA0hOTkFYWBjmz58Dq9XW5t92ONyTApfSMokFADabDdXVVW4r6TWXkJAAq9WK6upqt9FS5eXlSEjw/BzSPk8rdsq6qiYRERGRAgQBbl+CO7cJEMDYhojkx9v3gsz+/fvcft+3Lw8pPVIgxsSiV68+sNvt2L//wtxI585V4vjxY+jdu49rW5cuXXHTTbdg6dK/YPLk2/DPf27t0Dr37z8A5eVl0Ol0SE5Ocfvp1KkTAOCnn37EuHE3YsyYXyA1NQ3x8Qk4fVr+e9EzMoaipqYaBw7ku7Z9//23cDgcGDw4w+Nz+vcfCL1ej++++9q17fjxozhz5jQGDx7qX4UEEZasyyBFRbVdlvzmiIpERfbPYI+U73g3fWNosdtdySlqmxQVBcuoHPZ9DWBbEsmP5xUpxdPcUa3NH+WJIyoKlpwcOKJ8m7NWS6SoKFReke13vGmPjEJF9s/g8OI64EtZIrXhSKkWAjUMtb1/58yZ03j++Wfwm9/cjIKCA9iy5e+4774HYE/rhxQAo0ePwdNPP4VHHpmHyMhIrFnzApKSOmP06J8DAJ59dhWuuGIUUlJ6orq6Gt9//y169epzyb/prxEjsjF48BA8+ujDuPfemUhJ6QmzuRQ7d+7AmDG/wIABg5Cc3BNffvlfXHnlaAACXnnlJddIK18sXvwEkpI6409/muHx8d69+yA7exRWrFiChx9+FDabDc88swK//OW1SExMAgCUlp7FrFn3YP78RRg0KAMmkwk33PAbPP/8XxETE4vIyCisXv0XZGQMRUbGENe+T5woRn19HcrLy9DY2IBDhwrO/82+MBgMnits0KPmxZdh4PKtAVHfty++e/stAIBJ4bqEOntqP5zb+rHS1SAZsC2J5MfzipTiaSQ4AETpDUiOjW1z/qiGvn1R8e9tqC83A7bQ/LLO3q8f8t/ZjEY/X39d3z747u23YPLic4IvZYnUhkmp8yRJgkFw3noTKAZBhEPy7WJ1/fXj0djYiLvuugOiqMMtt0zGb349AXA4AEHAo48uwLPPrsScOQ/AarVi2LDh+MtfnnXd9uZw2PHMM0+jtPQsIiOjkJ09EjNnPuja/y233Ihf/eoGTJs2XbbXKQgCVq58Fi+//DcsXboIlZUViI9PQGbmcMTFxQMA7r//z1i27En86U9/RGxsJ9x66x2ora31+W+dOXPabW4rTxYsWIxnnlmBWbPuhSgKGDPmajzwwCOux202G44fP4aGhgbXtvvvfxCCIOKxx2bDarXgZz8biYcemuO23+XLFyM393vX73/4w60AgH/840N069bdc2UkABYLoNMDbdSbZOBwQGhshNRakpACx+EArFbAYGDfD3ZsSyL58bwihTWfOwrw4Qt1hwNobHT+N1SdjzchiP6dvw4HBKvV+TlBzrJEKiNIfkx88vLLL2PVqlWYMmUKHnvsMQDOyauXL1+Ojz/+GBaLBTk5OViwYAESEy+siFZSUoKFCxdiz549iIyMxE033YSHHnrIbb6gPXv2YPny5Th06BC6deuGe+65BzfffLPPdTSbqy+acNhqtaCs7BQSErq5jVDxdP90R5IkyafJkGfMuBv9+vXHrFkPuT9QVwf9wQOwpQ8AIts/VLahoQHjxv0SK1c+i+HDR7R7P+Qdq9WCslPH0G/izahf/wZsQzOVrlJQEUUBNgNQUGl2BU0mgxF94uJwsNzs9u1U0/aTX36B4eNvxJ4Pt0LKGn5R2daeb9Tp0L9TIvTWC3Olefr7nsqRZ/q9uYgbexUqPv2KfT/IaaEtBQFITIxuuyC5eIqvSD5aOK8oOHmKbwDPMZKnbZ0PFGDouPH4/qN/onzgwFbL+bJPNT7/Uvvsd+wYEnJGY8+HW1GdkdHuOhlzf0T2r2/C3o8/Qperrrrk86Pz8rwuCzBmpcDwNr5qd+p279692Lx5M/r37++2fenSpfj888+xevVqvPHGGzh79ixmzLhwK5Xdbsf06dNhtVqxefNmLF++HO+//z6ee+45V5ni4mJMnz4d2dnZ+OCDD3DHHXdg/vz52L59e3ur6xVJcp6UgfpRWzD3/fff4rLLRjAhRUREREQUAgTBmYhq+nFOaE5EFDjtSkrV1tbikUcewZIlSxAbG+vaXl1djS1btmDu3LkYOXIkMjIysHTpUvzwww/Izc0FAOzYsQOFhYX4y1/+goEDB2LMmDGYNWsWNm3aBIvFAgDYvHkzkpOTMXfuXKSmpuK2227Dddddh9dee83vF0ytGzUqB3/5y7NKV4OIiIiIiDqYp0nNvZ3QnIhILu1KSj355JMYM2YMRo0a5bY9Ly8PVqvVbXtqaiq6d+/uSkrl5uYiPT3d7Xa+nJwc1NTUoLCw0FVm5MiRbvvOyclx7SNUvfDCyxffukdEREREROSj5pOaF1SaUVBpxvHqSkiQ2pzQnIhILj7PhPbRRx9h//79ePfddy96zGw2w2AwICYmxm17QkICSktLXWWaJ6QAuH5vq0xNTQ0aGhoQHh7ua7U98mM6LSK/ufof+6Gb1uZ283UONrkJcNarab5K5/B2th0REREFt+aTmgdqJXIioiY+JaVOnTqFp556Cq+++irCwsI6qk4dTqdzXmwtlkYYjcH7OlzCw2EblAHoudpCMLFYGgGDAdUf/BtI6qx0dVShaRi5Vbp4xRaDoIOj0e5XYqqufzq2/287LAkJiPLheTpBgE4vwiY4XH9fFJr+ofawDRiEstx8OBKTlK4K+YltSSQ/nlcUrOr790dpwQHUhfAtgLZBg7Bnz07UxHbyaz816c641di1m6xlidTGpyzGvn37UFZW5rYKnt1uxzfffINNmzZh/fr1sFqtqKqqchstVVZWhqQk55tqYmIi9u7d67Zfs9kMAG5lmrY1L2MymWQZJSWKOkREmFBTUwEAMBrDArrqXocQANhtgL3NkqQwSZJgsTSipqYCEZHRQGyC0lVSjebDyC0O92WI+8TEQS8Ifo1wlIxGNHbz/c1aJ4iwSQ4crapA4/lvEqP0BiTHxnJ4e3sZjXB076F0LUgObEsi+fG8oiAlGY1wdOkCqdwM2EL0g4nRCEu3bpD8fP1NcWvz1eLlKEukNj4lpa644gr885//dNv26KOPom/fvrjrrrvQrVs3GAwG7Nq1C9dddx0A4MiRIygpKUFmZiYAIDMzE2vWrEFZWRkSEpwfxnfu3AmTyYS0tDRXma+++srt7+zcudO1DznExMQDgCsxFdRsNgiVlZA6deJoqSASEWFCp4pziH7wz6h5fBEcvfsoXSXVaD6MvIkct8+FHzuO9KXLUDhnNpCa5le9OLzdP+LRIpgWL2Df1wC2JZH8eF5RsAo7dgyx989E+EN/RmOP0Eys6oqKMGDOHByc/Qjqe/Zs934ijh9H2tMrUPLYY0BcnGxlidTGpwyGyWRCenq627bIyEh06tTJtX3ixIlYvnw5YmNjYTKZsGTJEmRlZbkSSjk5OUhLS8Ps2bPxyCOPoLS0FKtXr8att94Ko9GZ2Z08eTI2bdqEFStWYOLEidi9eze2bduGtWvXyvCSnQRBQGxsAqKj42C322TbrxJ0BfmI/cNUnNvwJuz9BypdHfKCTqeHKIrQHStG2D+3om7Wg7j4hjVqItftc7qqKnTZ9m8cvedPnA1KYWLVOfZ9jWBbEsmP5xUFK11VFcK3boXurmlAiCalhHPnkPjxNhz503S/9qM/H7eeuf9+WcsSqY3sw2rmzZsHURQxc+ZMWCwW5OTkYMGCBa7HdTod1qxZg4ULF2LSpEmIiIjAhAkTMHPmTFeZlJQUrF27FsuWLcPGjRvRtWtXLFmyBKNHj5a7uhBFEaIY3MMc9Q4J4ceOod4hwcYhm6RBvH2OiIiIiIhIe/xOSr3xxhtuv4eFhWHBggVuiaiWevTogXXr1l1yv9nZ2di6dau/1SMiDeHtc0RERERERNrBCYiIiIiIiIg0ThDgtrhTe+bnJCKSG5NSGmDv0g01jy2AvQuXAA02bLvAsnTpgsKHH0Jjly7gja7KYt/XDrYlkfx4XpHcBAEQw3SwShdmKWvP/JxtsXbpguqFC2Dp0kXW/QYTR7duKJr9CBr9PAaN5+NWqxf78aUskdowKaUBUpcuqJ/1kNLVoHZg2wWWtXMSjt57DwAwKaUw9n3tYFsSyY/nFclNEARYJQeKqipgcXTc/JzWzp1R9/DDsJabAZu97SdokKNLF5yYcS8sfr5+S5IzbjV5MWewL2WJ1EZUugLkP+FcJYz//hjCuUqlq0I+YtsFlu5cFRI//RT6qiqlqxLy2Pe1g21JJD+eV9RRmubntNjtsDrkX9tRd+4cwj76CLpzoRtrCZWViP9/n/gdb+qrnHGr7tw5WcsSqQ2TUhqgO3YUsVMmQ3fsqNJVIR+x7QIr/PhxZN79J0QcP650VUIe+752sC2J5MfzioJV2PHj6DRpMsJDONbSHT2KwXfe7Xe8GXE+bg3zYj++lCVSGyaliIiIiIiIiIgo4DinFBGRjAQ4520Qm6X8JUmCxMVtiIiIiIiI3DApRUQkE50gQKcXYRMcbkkog6CDo9HOxBQREREREVEzTEppgBQWDlv/AZDCwpWuCvmIbRdYUlgYavqlwREWBnkXQHbSCSJskgNHqyrQaHeuuGIUdegTEwe9IEBiVsqFfV872JZE8uN5RcFKCguDbcAASGFhSldFMVJYOGr79YPDz2PgOB+3enMsfSlLpDZMSmmAvf8AVGz/WulqUDuw7QKrLr0fdv/n3wAAUwf+naaVbah17PvawbYkkh/PKwpW9enpKPv2G9SVmwFbaMZC9oED8P1n/w+Nfr7+2n7OuNVkMMpalkhtONE5EREREREREREFHJNSGqD7aS8S+vaA7qe9SleFfMS2C6yoffvx86HDYNq/X+mqhDz2fe1gWxLJj+cVBavIffuQ1K07ovaFbqyl37sXIwcN8TveNO13xq2R+/bJWpZIbZiU0gBBckCsqYYgOZSuCvmIbRdgDgf0NbUQHDzeSmPf1w62JZH8eF5R0HI4IFZXA6Ecazkc0NfU+B1vCufjVm+OpS9lidSGc0oRERERERFphCAAgiC02CZAABdcISL1YVKKiIiIiIhIAwQBEMN0sLYYZScKTf8QEakLk1JEREREREQaIAgCrJIDRVUVsDgurP4WpTcgOTYWAvNSRKQyTEppgC0tHRWffgVbWrrSVSEfse0Cqz4tFXs+3Ira1FREKl2ZEMe+rx1sSyL58bwif1kcdljsF5JSRlEXkL/bkJaGsh3bUZ+UEJC/p0a29HR8/9E/Udu7t1/7qU11xq1CWpqsZYnUhkkpLYiMhG1optK1oPZg2wWUIyIC1RkZSleDAPZ9LWFbEsmP5xUFKUdEBGyZmXCUmwGbve0naFFkJGqHZMDh5+tviltNBqOsZYnUhqvvaYB4ohimOQ9CPFGsdFXIR2y7wAo7eRL9n1iAsJMlSlcl5LHvawfbkkh+PK8oWBlPnkT0nx9E2MmTSldFMWJxMVLnP+53vBl2sgT9n1gAoxfH0peyRGrDpJQGiOVliNjwCsTyMqWrQj5i2wWWvrwCKW9ugrGiXOmqhDz2fe1gWxLJj+cVBSt9eTki162DvrxC6aooRiwrQ/eNb/odbxorypHy5iboy9vejy9lidSGSSkiIiIiIiIiIgo4zilFRIoRBOcqMRd+FyBAUrBGREREREREFChMShGRIgQBEMN0sEoO1zZRaPqHiIiIiIiItI5JKQ1wJCahbvp9cCQmKV0V8lEot50gCLBKDhRVVcDicK5OEqU3IDk2FkIH5aWsiQk49sc/wJKQAEPH/AnyUij3fa1hWxLJj+cVeUtto85tiYmonXEfrIkJitVBaY6kJJy4cxosCf4dA0uCM261JSbKWpZIbZiU0gBH9x6oXbxM6WpQO7DtAIvDDovdmZQyirqO/VvduuHQ/McAgEkphbHvawfbkkh+PK/IG2ocdW7p1g01y5fDUm4GbHbF6qEkR48eKHpiPhr9fP2N5+NWk8Eoa1kiteFE51pQUwP9N3uAmhqla0K+YtsFlFhbi9jvv4eutlbpqhD7vnawLYnkx/OKvNB81HlBpRkFlWYcr66EBKnDRp23RaythWHPHoghHGsJNTWI/s7/eFN3Pm715lj6UpZIbZiU0gD9kULEjb8G+iOFSleFfMS2C6yII0W4/JbfIbKoSOmqADj/DacouP0oFUQGGvu+drAtieTH84p80TTq3GK3w+pwtP2EDhR+5AjifzkWEUfUEWspQVdYiMwJE/2ONyOLnHFr+JEjspYlUhvevkdEpABPQ+4BwCDo4Gi0Q+IihEREREREpHFMShFRQKhtIk6leZro3Sjq0CcmDnpBgMSsFBERERERaRyTUkTU4dQ4EadaNJ/onYiIiKjlF3lNJEniSGoi0hwmpTRA0unhSEiApGNzBptQaTtPo4Ki9AYkx8YGdA4lSa+DJT5O88c7GIRK3w8FbEsi+fG8Cl2t3d4PAEZBB4fF0aysCked68/3XX3Hrqisano9rPHxfp+/kk4PS3wcoG97P76UJVIb9loNsA/OQFl+6E4mGMxCre2ajwoyioEPVuoGDsRX334DADAF/K9Tc6HW97WMbUkkP55XocvTF3kAEKHTo29cPOxGuEZLqXHUed3AgSg9dhR15WbAFpojwW0ZGdid+x0a/Xz9NQMH4Ktvv4HJYJS1LJHaMClFRERERESkIi1v7zeKOtgkB45WVaDRrtyocyIiuYlKV4D8pzuQj/ifDYPuQL7SVSEfabXtBAEQRcH14xxerrzIgoMY9YurEXXwYED/rgDnN59qPCZK0WrfD0VsSyL58byi1jQlqyx2O6yOi2/xU1pEQQEShg5DZEFgYy010e3Px4jRP/c73ow66IxbIwoKZC1LpDYcKaUBgqURuqNFECyNSleFfKTFtlPzpOaCxYLIY8chWiwBm4FBJwjQ6UXYBIfb5KRqOSZK0WLfD1VsSyL58byiYCVYLNAfOQLBYlG6KooRLI2IOHYMop/HQDwft3pzLH0pS6Q2TEoRkazUMqm5WugE8aLh9kBoHxMiIiIiIiKASSki6iBKT2quNp7mhiAiIiIiIgplPs0p9dZbb+HGG2/E8OHDMXz4cEyaNAlffvml6/HGxkYsWrQI2dnZyMrKwv333w+z2ey2j5KSEtx9990YNmwYRo4ciaeffho2m82tzJ49ezBhwgRkZGTgmmuuwXvvvefHSyQiIiIiIiIiIrXxKSnVtWtXPPzww3jvvfewZcsWXHHFFbjvvvtw6NAhAMDSpUvx+eefY/Xq1XjjjTdw9uxZzJgxw/V8u92O6dOnw2q1YvPmzVi+fDnef/99PPfcc64yxcXFmD59OrKzs/HBBx/gjjvuwPz587F9+3aZXrL22Pv0ReXm92Dv01fpqpCP2HaB1dC7F75/7VXU9eqldFVCHvu+drAtieTH84qCVWPv3qjY+j4aeodurGXv2xc/vfGa3/FmXS9n3NrYu7esZYnUxqfb966++mq33//85z/j7bffRm5uLrp27YotW7Zg5cqVGDlyJABnkmrcuHHIzc1FZmYmduzYgcLCQmzYsAGJiYkYOHAgZs2ahZUrV2LGjBkwGo3YvHkzkpOTMXfuXABAamoqvvvuO7z22msYPXq0TC9bW6ToGFivHqt0Nagd2HaBZY+ORvlVVyldDQL7vpawLYnkx/NKmwTBOfdmc5IkuS2EEuzs0dGwjB0Le7kZsNnbfoIGSTExqBwzBnY/X39T3GoyGGUtS6Q2Po2Uas5ut+Ojjz5CXV0dsrKykJeXB6vVilGjRrnKpKamonv37sjNzQUA5ObmIj09HYmJia4yOTk5qKmpQWFhoatMU1KreZmmfdDFxDOnEbliKcQzp5WuCvmIbRdYhjNn0Xf1szCePat0VUIe+752sC39t3btWkycOBFZWVkYOXIk7r33Xhw5csStDKdICC08r7SnaXVimwFuP2KYTlOLnhjOnEHUU0thOBO6sZZ4+jR6PrPa73jTeNYZtxrOnJG1LJHa+JyUKigoQFZWFoYMGYIFCxbgxRdfRFpaGsxmMwwGA2JiYtzKJyQkoLS0FABgNpvdElIAXL+3VaampgYNDQ2+VjckiGdOI2rlcgYuQYhtF1jGs2fR97nnEcaklOLY97WDbem/r7/+GrfeeiveeecdbNiwATabDdOmTUNdXZ2rDKdICC08r7Sn+erEBZVmFFSaUVRVAavkuGj0VDAznD0L07JlIf0FoHj6NHqtftbveDPsfNxq8GI/vpQlUhufV9/r06cPtm7diurqavznP//BnDlz8Oabb3ZE3YiIiIg0b/369W6/L1++HCNHjsS+fftw+eWXo7q6mlMkEGlEy9V4iYhCnc8jpYxGI3r16oWMjAw89NBDGDBgADZu3IjExERYrVZUVVW5lS8rK0NSUhIA54inlkPNm35vq4zJZEJ4eLiv1SUiIiIKKtXV1QCA2NhYAOAUCURERKRZ7Z5TqonD4YDFYkFGRgYMBgN27drleuzIkSMoKSlBZmYmACAzMxMHDx5EWVmZq8zOnTthMpmQlpbmKrN79263v7Fz507XPoiIiIi0yuFwYOnSpRg+fDjS09MBgFMkEBERkWb5dPveqlWrcNVVV6Fbt26ora3Fv/71L3z99ddYv349oqOjMXHiRCxfvhyxsbEwmUxYsmQJsrKyXAmlnJwcpKWlYfbs2XjkkUdQWlqK1atX49Zbb4XR6FwpYPLkydi0aRNWrFiBiRMnYvfu3di2bRvWrl0r+4vXCkdsJzRM/B0csZ2Urgr5iG0XWLbYWJz6za9hjY31/d5lkhX7vnawLeW1aNEiHDp0CG+99ZbSVSEF8byiYGWPjUX9pEmwnR/pGYocnTrh7ITfwOrnMbCej1vtXuzHl7JEauPT57KysjLMmTMHZ8+eRXR0NPr374/169fjyiuvBADMmzcPoihi5syZsFgsyMnJwYIFC1zP1+l0WLNmDRYuXIhJkyYhIiICEyZMwMyZM11lUlJSsHbtWixbtgwbN25E165dsWTJEs51cAmOXr1R/dIrSleD2oFtF1iNPVOw76/PAABMCtcl1LHvawfbUj5PPvkkvvjiC7z55pvo2rWra3vzKRKaj5ZqOUXC3r173fbHKRKCF88rClaNPXuiav0raCw3A7bQnDvL0bs3Dj67Go1+vv6GFGfcajIYZS1LpDY+JaWWLl16ycfDwsKwYMECt0RUSz169MC6desuuZ/s7Gxs3brVl6qFtoYGiCUn4ejeA2BQGVzYdgElNDQi4sQJNHbrBvBNW1ns+9rBtvSbJElYvHgxPvnkE7zxxhtISUlxe7z5FAnXXXcdAM9TJKxZswZlZWVISEgA4HmKhK+++spt35wiQaV4XlGQEhoaoDt8GEJEOKAP0XHpDQ0IP3oU1qTOcISFtXs3YmMjwk6dgpDSU9ayRGrj95xSpDz9wQNIuCIL+oMHlK4K+YhtF1iRhw7hyqvHIurQIaWrEvLY97WDbem/RYsW4cMPP8SqVasQFRWF0tJSlJaWuuZ5aj5Fwu7du5GXl4d58+a1OkXCgQMHsH37do9TJBQXF2PFihU4fPgwNm3ahG3btmHq1KkKvXJqDc8rClYRhw4hcVgmIkM41tIfOIDLr/qF3/Fm1Pm4NcKL/fhSlkhtQjR9TURERKQOb7/9NgDg9ttvd9u+bNky3HzzzQA4RQIRERFpE5NSREQqIgAQBAFis3GskiRBkhSrEhF1sIKCgjbLcIoEIiIi0iImpYiIVEInCNDpRdgEh1sSyiDo4Gi0MzFFRESkMS2/jBIEAQL4hk9EoYNJKSIildAJImySA0erKtBod67YYhR16BMTB70gQGJWioiISDM8fRklCk3/EBGFBialNMA2NBOlZ6uUrga1A9susGqHZODTI4UAAJPCdbkUi8MOi13byyiz72sH25JIfjyvQoOnL6Oi9AYkx8ZCCNK8VN2QIThTU43acjNg03Ys0xpbZia2Hy9Co5+vvzrDGbeavFgx2peyRGrDpBQRkcp5mmcK4FxTREREWtD8yyijqFO4NkREgSW2XYTUTld4CJ1+9UvoCrkEaLBh2wVWxOHDGDHxFkQeOaJ0VbzmGtpvkGAzwO1HDNMF7Tep7PvawbYkkh/PKwpW4YcPI+7qqxFx+LDSVVGM7uBBDLvpZr/jzcgjRzBi4i0I9+JY+lKWSG04UkoDhLpaGL77BkJdrdJVIR+x7QJLrKtHpx9yoaurC5opRD0N7QeCf64p9n3tYFsSyY/nFQUrsa4Oxq+/gVhXr3RVFCPU1SHm+x+gq6vzaz+6ujp0+iEXohf78aUskdowKUVEFARCYZ4pIiIiIiIKLUxKERERERERtYMgOOd9vJgE56yQTeUECEEzTpuIKHCYlCKidvMUiDHoIiIiolAgCM75Ha2Sw307AINBD6vV7oqIRKHpHyIiao5JKQ2wp/RE1Ysvw57SU+mqkI+Cue1aC8TUHHQ1piQj75mVqE9ORrjSlQlxwdz3yR3bkkh+PK+CgyAIsEoOFFVVwOK4cIt9lN6A5NhYHK2+MB9k07ZgXaDEW43JKTj3yjo0piQrXRXF2Hv2xIHVz6A+2b9jUJ/sjFsbk1NkLUukNkxKaYAUF4/G305WuhrUDsHcdm0FYmoMumydOuH0TTcpXQ1CcPd9cse2JJIfz6vg0nLeR6Oou2h70zats8d1QsPkybCVmwFbaM6FKcXHo/TmCbD5+fqb4laTwShrWSK1EZWuAPlPMJsRvv5lCGaz0lUhHwVT2wkCIIqC68d5m96FgKvpx+pwtLkvpRjKypC88Q0YysqUrkrIC6a+T5fGtiSSH88rClb6sjJErH05pGMtobQU3V7f6PcxaIpb9V7sx5eyRGrDpJQG6EpOIPrRh6ErOaF0VchHwdJ2Tbfq2Qxw/dgNAHQqHA51CcaSUxiwcBHCT51SuiohL1j6PrWNbUkkP55XFKyMJSWIeeghGEtCN9bSnTyJtMcX+B1vhp9yxq3GkhJZyxKpDW/fI6I2ebpVT8236REREREREZH6MSlFRF4LxbkRiIiIiIiIqGPw9j0iIiIiIiIiIgo4JqU0QDKZYPn51ZBMJqWrQj5i2wWW3RSFstE5sEXxeCuNfV872JZE8uN5RcHKYTKh8Ze/hN0UpXRVFCOZTKi4arTf8aYtyoSy0TlweHEd8KUskdrw9j0NsPdNw7l3tipdDWoHtl1gNfTpgx9efw0AoIW3bAHO+b7EZl8vSJIESVKsSl5j39cOtiWR/HheUbBq6NMHlR9sRUO5GbDZla6OIuxpach/cyMa/Xz99X1644fXX4PJYJS1LJHacKSUFtjtEKqrAHtoXviDGtsusOx26KqrNXG8dYIAnV6EzSC5rYoohumCY/J59n3tYFsSyY/nFQUrux1CVYj3XbniTV/2o6EYl0IPk1IaoN/3ExJTk6Hf95PSVSEfse0CK2p/Pn4xLAvR+flKV8VvOkGE7fyKiAWVZhRUmlFUVQGr5IAQBFkp9n3tYFsSyY/nlToJAiCKgutHEASo/x03sCL370fn7j0QtT/4Y6320v/0E0YNHup3vBmd74xbI/fvl7Uskdrw9j0ioiDWfEVEIiIirRAEXPQli5K3qAuCczSyVXK4tolC0z9ERNReTEoREREREZFqeEoAAYBB0MHRaFckMSUIAqznRyhbHM4vg6L0BiTHxgbHbfNERCrFpBQREREREamGpwSQUdShT0wc9IIAScEVPZqPUDaKOsXqQUSkFUxKEdFFWg6Zd86ZEARLuhEREZFmBOIWdU+3CQLBs5otEVGwY1JKA2wDB8O8/wik2Filq0I+UmPbaXnOhLoB/fHlN3tgi4lBlNKVCXFq7PvUPmxLIvnxvAqM1m4TBJS9VTCY1Q8YgLNFR1DnsCldFcXYBg/G7h++RW2kf9FmTX9n3BqekChrWSK1YVJKCwwGSIm8AAUlFbadludMkAwGWBMSlK5GwKnyW2AV9n1qJ7Ylkfx4XgWEp5gHUM+tgsFIMhggxcVBKjcDthBdiOV8vCn5+fqb4tYwg0HWskRqIypdAfKfWHQEMbdPglh0ROmqkI/U3HZNQ+Ytdjusjou/QQxG4UePYdhddyPi2DGlqxIwTd8C2wy46EcM0ymWaFRz3yffsC2J5MfzKrCaxzwWu90tQUW+CTt6FJ1+9zuEHw2dWKsl3ZEjGPTHO/2ONyOOOePWsKNHZS1LpDZMSmmAWF2FsP9sg1hdpXRVyEeBbDtBAERRcPsJ9pFPvtJVVyPps/9CX12tdFUCpvm3wAWVZtdPUVUFrJLD4wiqQOB1SzvYlkTy43mlPAHO99ALMZOAEAub2kVXXY2wj7dBF0KxVktCVRUSPv3M73hTfz5u9eZY+lKWSG14+x5RCFDj0soUWIGYLJaIiEgLdIIAnV6ETXC4YiStzK9JRKQ2TEoRhQA1L61MREREpCY6QYRNcuBoVQUa7dqaX5OISG2YlCIKIRwtQ0REROSd5nGTUdQpXBsiIm3inFIaYO/aHTWLlsLetbvSVSEfse0Cy9K1Cw7OexSNXboqXZWQx76vHWxLIvnxvKJgZe3aFdXLlsLStYvSVVGMo1t3HHn8Mb/jzcYuXXFw3qOwdm17P76UJVIbjpTSAKlzZ9TfM0PpalA7sO0Cy5qUhON3TgMAGBWuS6hj39cOtiWR/HheUbCyJiWh7v77YS03A7bQHJ3v6NIZJ++6ExY/X78lKRHH75wGk6HtqNWXskRqw5FSGiBUVsD44fsQKiuUrgr5iG0XWPrKc+j88cfQnzundFVCHvu+drAtieTH86pjtFyJmCvqyU9XWYmw996HvjJ0Yy2hogKJ//rI73hTf84Zt+oqK2UtS6Q2PiWl1q5di4kTJyIrKwsjR47EvffeiyNHjriVaWxsxKJFi5CdnY2srCzcf//9MJvNbmVKSkpw9913Y9iwYRg5ciSefvpp2Gw2tzJ79uzBhAkTkJGRgWuuuQbvvfdeO1+i9umOH0PsnXdAd/yY0lUhH7HtAiusuBhDZ8xERHGx0lXpMMGyhDX7vnawLYnkx/NKfk0rEdsMcP3YDQB0anyXDF5hxcXoNGUKwjQca7VFd+wYBt47w+94M+J83OrNsfSlLJHa+JSU+vrrr3HrrbfinXfewYYNG2Cz2TBt2jTU1dW5yixduhSff/45Vq9ejTfeeANnz57FjBkXhh/b7XZMnz4dVqsVmzdvxvLly/H+++/jueeec5UpLi7G9OnTkZ2djQ8++AB33HEH5s+fj+3bt8vwkomItMm1hLVBYsBNRETUTPOViAsqzSioNON4dSUkSFxRj4hIQT7NKbV+/Xq335cvX46RI0di3759uPzyy1FdXY0tW7Zg5cqVGDlyJABnkmrcuHHIzc1FZmYmduzYgcLCQmzYsAGJiYkYOHAgZs2ahZUrV2LGjBkwGo3YvHkzkpOTMXfuXABAamoqvvvuO7z22msYPXq0TC+diEhbuIQ1ERHRpXFFPSIidfFrTqnq6moAQGxsLAAgLy8PVqsVo0aNcpVJTU1F9+7dkZubCwDIzc1Feno6EhMTXWVycnJQU1ODwsJCV5mmpFbzMk37ICKi1jUF3Ba7HVaHQ+nqEBERERERedTupJTD4cDSpUsxfPhwpKenAwDMZjMMBgNiYmLcyiYkJKC0tNRVpnlCCoDr97bK1NTUoKGhob1V1iwpPALWIcMghUcoXRXyEdsusBzh4agaPAj28HClqxLy2Pe1g21JJD+eVxSsHOHhsA4bBkcIx1pSeDhqMgb7HW/az8et3hxLX8oSqY1Pt+81t2jRIhw6dAhvvfWWnPWhdrCn90flZ5xvKxix7QKrvl8avv7nhwAAk8J1UYMLk6Jf2CZJEiSp4/82+752sC2J5MfzioJVQ79+KP/fDtSXmwGbXenqKMI+YAB++PhfaPTz9delOeNWk8Eoa1kitWnXSKknn3wSX3zxBV5//XV07drVtT0xMRFWqxVVVVVu5cvKypCUlOQq03I1vqbf2ypjMpkQzuwvEZHfPE2KbjM4Vybi/FNERERERBQIPiWlJEnCk08+iU8++QSvv/46UlJS3B7PyMiAwWDArl27XNuOHDmCkpISZGZmAgAyMzNx8OBBlJWVucrs3LkTJpMJaWlprjK7d+922/fOnTtd+yB3+p9+RGJyIvQ//ah0VchHSrfdhZEyF34EQYBWcxJReftw9YCBiN63T+mqKK5pUvTmqxAVVVXAKjkgBCArpXTfJ/mwLYnkx/PKf4KAkIlv1CQyLw+d4xMQlRe6sZb+xx9xZVp/v+PN6H3OuDUyL0/WskRq41NSatGiRfjwww+xatUqREVFobS0FKWlpa55nqKjozFx4kQsX74cu3fvRl5eHubNm4esrCxXQiknJwdpaWmYPXs2Dhw4gO3bt2P16tW49dZbYTQ6hxtOnjwZxcXFWLFiBQ4fPoxNmzZh27ZtmDp1qqwvXjMkCYLFgoDcc0PyUrDtWhspYzcA0Gk0bJMkiBYrz5Vmmk+KbnEEcJg9r1vawbYkkh/PK78IgnPkb8jEN2rCvns+3pThGPgStzLGpSDm05xSb7/9NgDg9ttvd9u+bNky3HzzzQCAefPmQRRFzJw5ExaLBTk5OViwYIGrrE6nw5o1a7Bw4UJMmjQJERERmDBhAmbOnOkqk5KSgrVr12LZsmXYuHEjunbtiiVLlmD06NHtfqFE5K5ppMzRqgo02i8kI6L0BiTHxvIWLiIiIgpKgiDAen40cNMXLoxviIjUyaekVEFBQZtlwsLCsGDBArdEVEs9evTAunXrLrmf7OxsbN261ZfqEVE7NI2UaWIUdQrWhoiIiEgezWMcxjdEROrU7tX3iEi9BAFu8wI551HgcF4iIiIiIiJSDyalNMDWrz/Kv9oDe6/eSleFfNQRbdc0j4JVcri2iULTP6Gtvl8adv37Y9T37IlIpSujAS2Tn4BzQQxvpjPgdUs72JZE8uN55T1P70X8Mk459Wn9YP76a9R3ila6Koqx9e+P7z75D2p79PBrP7VpzrhV7Jsma1kitWFSSgsiImAfMFDpWlB7dEDbcR6F1jnCw1Gfnq50NTTBU/ITAAyCDo5Ge9uJKV63tINtSSQ/nldeae29iF/GKUeKCIe9+0A4ys2ALYALqKhJRATq+qfD4efrd4SHozY9HSaDUdayRGrj0+p7pE5i8XGY/jwDYvFxpatCPurItmu+qprV4Wj7CSEg7MQJDJz7KMJPnlS6KkGvefKzoNKMgkoziqoqYJUcF31j7QmvW9rBtiSSH88r73h6LyqoNON4dSUkSCH/ZZwSjCdOIOa++xB24oTSVVGMePw4+s2e43e8GX7yJAbOfRRGL46lL2WJ1IZJKQ0QK8oRsWkjxIpypatCPmLbBZa+ohI93vkHDBUVSldFtQQ4g3xRvPBzqaC+efKzaWSeN9j3tYNtSSQ/nle+cXsv4pdxitJXVCDi9Y3QV1QqXRXFiOXl6Lr5Hb/jTUNFBXq88w/ovdiPL2WJ1Ia37xEFMc6jQHLSCQJ0ehE2weF2+51R0MFhcQ/w2c+IiIiIiMhfTEoRBSnOo0By0wkibJIDR6sq0Hh+Ce0InR594+JhN8ItUcV+RkRERERE/mJSiihIeZrQHOCk5uS/ptsgAMAo6i5KVAHsZ0RERERE5D8mpTTAkdQZdTMfhCOps9JVIR/50nYtb9Vrun2qeQIBcCYRyDNrUiKK/jQdlsREGJSuTJCRu5/xuqUdbEsi+fG8omBlTUxC7UMPwpqUqHRVFOPo3BnF994DS6J/x8CS6IxbrYlJspYlUhsmpTTA0a07aucvVLoa1A7etp2nW/V4+5TvLF274vDsRwCASSmF8bqlHWxLIvnxvKJgZe3WFTWLFsFSbgZs3i+AoiWO7t1xdO5sNPr5+hvPx60mg1HWskRqw9X3NECoqYbhf9sh1FQrXRXykbdt52nJYy537DtdTQ3idu+GrqZG6aqEPF63tINtSSQ/nlcUrMSaGhi+2h7SsZZQXY3YXf7Hm01xq+jFfnwpS6Q2TEppgO7IYXSaMB66I4eVrgr5qKnt9EVHIIqC66e1RFPzJY+53LHvwouO4rLf34bIo0eVrkrI43VLO9iWRPLjeUXBKryoCPHjxiG86KjSVVGM7vBhDJ30f37Hm5FHnXFreFGRrGWJ1Ia37xGpgF0P2JrdT2YQdHA02t1WOyMiIiIKZi3nx2wiSRJjHiKiEMWkFJGCmgKzk7VVqKw0A3BOIN0nJg56QYDECI2IiIg0wNP8mE2Mgg4Oi6NZWediLkREpH1MShGpgM3hcFvZjIiIiEhLms+PaXFciHkidHr0jYuH3QjXaCku5kJEFDo4p5QGSHoD7N26Q9JzPbGgYzDA3r07HAbmhwNBMujR0LULHDxXFMfrlnawLeXxzTff4E9/+hNycnLQv39/fPrpp26PS5KEZ599Fjk5ORg6dCimTp2Koy3mK6msrMRDDz2E4cOHY8SIEZg3bx5qa2vdyhw4cAC///3vMWTIEIwZMwbr1q3r6JdG7aDl86r5/JgWux12SYKNi7lohqR3xrZSKMe2egMau3b1O9506A1o6NrFq+uAL2WJ1IZJKQ2wDxqM8h8PwD5osNJVIR/ZBw2GOb8AdQMGKF2VkFA3YAB27Pwfagf0V7oqIY/XLe1gW8qjrq4O/fv3x4IFCzw+vm7dOrzxxhtYuHAh3nnnHURERGDatGlobGx0lXn44YdRWFiIDRs2YM2aNfj222/xxBNPuB6vqanBtGnT0L17d7z33nuYPXs2XnjhBfz973/v8NdHvgm280oQ4LZgy6UWbWkNF3PRhvqBA2A+GNqxrS1jML7+epff8WbtgP7YsfN/qB/Y9rH0pSyR2oRwCptIvQQ4h7mL59PGnFuBiEjbxowZgzFjxnh8TJIkbNy4Effccw/Gjh0LAFixYgVGjRqFTz/9FOPHj8fhw4exfft2vPvuuxgyZAgAYP78+bj77rsxe/ZsdOnSBR9++CGsViuWLl0Ko9GIfv36IT8/Hxs2bMCkSZMC9lopeHmaqFwQAMEgwtJirigu2kJERN7gSCkN0O3fh/hhA6Dbv0/pqpCPdPv3IXFgf0QeOHBhmyBApxdhM0iwGZyr8tkNAHQcw+6vyAMHkDPqSkQdKFC6KiGP1y3tYFt2vBMnTqC0tBSjRo1ybYuOjsawYcPwww8/AAB++OEHxMTEuBJSADBq1CiIooi9e/cCAHJzczFixAgYjUZXmZycHBQVFeHcuXMBejXkDTWeV00TlTfFJhdiFAFWUXK7/a6oqgJWyeFxpT2/6oCmL+2aRmMJYHSkLhH5B5CY7h7bhhp93j787Gcj/Y43ow4UIGfUlYjIb/tY+lKWSG04UkoDBJsVulMlEGxWpatCvrJaoSspgWi1uTbpBBE2yYGjVRVoPD/5eZTegOTYWM6t4CfBakP46TMQbVaOOwsgj9+s87qlGWzLjldaWgoASEhIcNuekJAAs9m5cqvZbEZ8fLzb43q9HrGxsa7nm81mJCcnu5VJTEx0PRYbG9sh9SffqfG8am2i8qYYxSrZO3TRFteXdoKDE6KrmGBzxrZCs9g25NisCDt9GqKf569osyL89BmvrgO+lCVSGyaliFSqaW4FADCKOoVrQ9Q+rS0BHm7kQF0iUoanRLkkSbzNrIWWx6lpKoHm8QnQeowi91QE/NKOiEibmJQiIqIO4+mbdaOoQz9++iPyWlJSEgCgrKwMnTt3dm0vKyvDgPOTCScmJqK8vNzteTabDefOnXM9PzEx0TWyqknT700jprSutUQ55z9y5+k4+TIqqSNHNfFLOyIibeFX1UQB4nllGn61R9rS2nwfbkuAOzru9g4iLUpOTkZSUhJ27drl2lZTU4Mff/wRWVlZAICsrCxUVVUhLy/PVWb37t1wOBwYOnQoACAzMxPffvstrNYLt3fs3LkTffr0CZlb95onyjt6/qNg5uk4Ha+uhATJq1FJTaOa2vt8IiIKHUxKaYC9byoq3/8I9r6pSleFWtHa5KDW/qmo+Phj1PfprXQVQ0JDn9747q03Ude7t9JV0SRfJum3p6bi3NaPed3SAL4HyaO2thb5+fnIz88H4JzcPD8/HyUlJRAEAVOmTMFLL72Ezz77DAUFBZg9ezY6d+7sWo0vNTUVo0ePxuOPP469e/fiu+++w+LFizF+/Hh06dIFAHDjjTfCYDDgsccew6FDh/Dxxx9j48aN+MMf/qDY61aK2hPlajmvmh8nq8PR9hNkfj4Fn4Y+fVD+8cdoCOHY1p6air1/f9vveLOutzNubejTR9ayRGrD2/c0QDJFw3rlaKWrQZdwyclBrxoNR7kZsKkvKNYau8mEiiuuULoamuXLfB9SdDRsOaMhOdq+V4bzv6gb34PkkZeXhylTprh+X7ZsGQBgwoQJWL58Oe666y7U19fjiSeeQFVVFS677DK88sorCAsLcz1n5cqVWLx4Me644w6Ioohrr70W8+fPdz0eHR2N9evX48knn8TNN9+MuLg43HvvvZg0aVLgXih5hecVBSuHyQTrVaNhD+HYVoqOxrmRV8Du5+tviltNBqOsZYnUhkkpDRBPlSBi/cuon3Y3HN26K10duoSWk4NGnSmF6ZlnYJz0WzQmJilYs9BgPH0aya++hhNTbgdSeipdHc3yZr4PsaQEkWvWou6Pl75ucf4X9eN7kDyys7NRUND68uGCIGDWrFmYNWtWq2U6deqEVatWXfLvDBgwAG+99Va760mBwfOKgpXh1GmYVq8O6dhWLClB79WrcfS2W9HYtWu79xN2+jSSN76B8j9OA+LiZCtLpDa8fU8DxNKziHzuGYilZ5WuCvnIYC5F1KpnYCg1t12Y/GYoNaPPmrUwmnm8lSaePYuIZ9u+bnH+F/XjexCR/HheUbBibOuMcVL+9pLf8abR7IxbDeZSWcsSqQ1HShF5ydMtRABvIyIKhJajDImI6ALe5kxERMGKSSkiL7R2CxHA24iIiIhIOb7c5swv2IiISG2YlCLyQmsTlRtFHfrExEEvCJAYzRF5rekjkSAIEMXmH5CkZo86HxfAc4uIqDWeYhRP8cmlvmAzCjo4LA63ffLaS0REgcCklAY44uJRf+sUOOLila6K5sl9C5EtLg71d0yBLa6TbPuk1tniOuHk734La1wcL34K0gkChM6JqJ8yBdbO8XAYnNsFAAaDHlar3fVRSBSa/iG14nsQkfzac161FaO09gVbhE6PvnHxsBvhGi3Fay+1F2NbwBEfj9OTfwernxOOW+PicPJ3v4XNi/34UpZIbfi5TAMcKT1R89cXlK5GyBLQNNrjwjZvh8FbkpNR9eKLaAzhZXMDqTE5GfnLncusmxSuSyjTCSIsKck4uHQJGu12oNI5EWiU3oDk2Fgcra5wbm+2jXOaqxffg4jk15HnVcvklVHUwSY5cLSK117yH2NbwNGzJw6teBqNfr7+hh49kL98GUwGo6xlidSGq+9pQX09dAfygfp6pWsScnSCAJ1ehM0gwWaA60cXpoMoCq4f5zD4iwn1DdDtz4fY0BDwuocisaEBUQcP8nirQX099Pn5sNXWwmJ3fkCyOpy3jjR9YGq+jVSM70FE8lPgvOK1l+TA2BZAfT0iC/yPN5viVqG+7f34UpZIbZiU0gD9oQLEX5UN/aECpasScnSCCFuL5eqPV1fCoQPsxgtJKrsBgO7itFRE4SEk/uxniDhUGPjKh6CIQ4UYef04RBXyeCtNX1CAy665jm2hAXwPIpIfzysKVoxt5YtxogqdcWtE4SFZyxKpDW/fI5JB86HwHAZPRERERERE1DYmpYg6SMtEFRERERERERFdwKQUERFphiA4Fx5oztuFB4iIiIiIKLCYlNICQYBkNIL3hgUhtl1gCQIcRgOPtxoIAhwy931BAMQwHayS+wS9BkEHR6OdiamOwusYkfxkOK9arg7sXHSFF0LqYHxPkC/G8SVuZYxLQcznpNQ333yD9evXIy8vD6WlpXjxxRcxduxY1+OSJOG5557DP/7xD1RVVWH48OFYuHAhevfu7SpTWVmJxYsX4/PPP4coirj22mvx2GOPISoqylXmwIEDePLJJ/HTTz8hPj4et912G+666y7/Xq1G2YYMg/mEWelqUDvUZWTgbHkZakN42dxAqs0YjP8eyAcAmBSuS6izDRuG/xUW+L1ccnOCIMB6fuEBi+PCrbN9YuKgFwRIzEp1CL4HEfmv5ShPx7BMlJeYAQgtViWSgBbr+XpKNrlWBxYcroS8KDT9Q9RxGNvKF+NUD3bGrSaDUdayRGrj8+p7dXV16N+/PxYsWODx8XXr1uGNN97AwoUL8c477yAiIgLTpk1DY2Ojq8zDDz+MwsJCbNiwAWvWrMG3336LJ554wvV4TU0Npk2bhu7du+O9997D7Nmz8cILL+Dvf/97O14iERGFkubLmlsc9majBS788ItEIpKTIMDtGuPLdaZplGfTir1Nq/aKkXrY29jW2gq/ra0OLEHi9Y+IiFTF56TUmDFj8Oc//xnXXHPNRY9JkoSNGzfinnvuwdixYzFgwACsWLECZ8+exaeffgoAOHz4MLZv344lS5Zg2LBhGDFiBObPn4+PPvoIZ86cAQB8+OGHsFqtWLp0Kfr164fx48fj9ttvx4YNG/x8udqkO1iATr8cDd1BLht8KZ4CRudP+wNJf4UfOoT4K3NCetncQIo4VIif3fhrRPq5RC/5T3fgALLG3dDutvCcaBLQ8tR1jRYwSG4f4sQwHT+YyYTvQRTqPCWVfLnONB/l2ZRAKv3hW5hGjsSp7792bSuuOQeLw46i6gvl2ko2NU/SWx2OiwsQyYyxrf8xTpPIQmfcGn7okKxlidTG56TUpZw4cQKlpaUYNWqUa1t0dDSGDRuGH374AQDwww8/ICYmBkOGDHGVGTVqFERRxN69ewEAubm5GDFiBIzGC8MPc3JyUFRUhHPnzslZZU0QGuph+OlHCA31SldFtVoLGFv71lEXpmvzw64cxIYGGH78EWJDQwfsnVoSGxoQs28/dDzeihMaGmDK29eutmgt0eTtaIGiqgrYJAdEUVQsIa0lfA+iUOcpqVRUVQGr5Lho4YVLaZ5AstfVwfDjj7DX1V2UVHIbDcpkE6kMY1v/YpzmdOfjVm+OpS9lidRG1onOS0tLAQAJCQlu2xMSEmA2O+ebMJvNiI+Pd6+EXo/Y2FjX881mM5KTk93KJCYmuh6LjY2Vs9oUAjzNNQMAUXoDkmNjcbS6Ao125/YInR594+JhN4LzMBCpUFOi6WjVhfMWuHA+X2q0gPP5F8+10sQo6OCwuH/A4+p9ROSN5teZS2k5fxQnICciolDG1fco6PmyBHzLgNEo6i7abhR1F33gvdSHXSJSRmvnc1taS2p5SkgDXL2PiOTjaZVQfvFFREShTNakVFJSEgCgrKwMnTt3dm0vKyvDgAEDADhHPJWXl7s9z2az4dy5c67nJyYmukZWNWn6vWnEFBHQsUvAt0xUEZG2eEpqtUxWcfU+ImqvC3PfNdsmCK5biptGbvOLLyIiCmWyzimVnJyMpKQk7Nq1y7WtpqYGP/74I7KysgAAWVlZqKqqQl5enqvM7t274XA4MHToUABAZmYmvv32W1itVleZnTt3ok+fPrx1zwN7z14498rrsPfspXRVOtzFk5V7N19MR80J5a/GlBRUbtyIxpQUpasSEhpTUrD3hedQz+OtOHuvXsj/2wuqbIuWq/fRpYXSexCRt9qa+66tCcgZH1CwYt+VL8apPx+3enMsfSlLpDY+j5Sqra3F8ePHXb+fOHEC+fn5iI2NRffu3TFlyhS89NJL6NWrF5KTk/Hss8+ic+fOGDt2LAAgNTUVo0ePxuOPP45FixbBarVi8eLFGD9+PLp06QIAuPHGG/Hiiy/isccew1133YVDhw5h48aNePTRR2V62doidYqD5dcTlK5Gh7vUkPe25otR69B4e6dOaLx5AmzlZsDGD78dzdYpFmfHjVO6GgRAiouD+YbxsGmo33u6lRjQ/pxUofIeROSL9sx91xzjAwpW7LvyxTi2WGfcajIYZS1LpDY+j5TKy8vDTTfdhJtuugkAsGzZMtx000147rnnAAB33XUXbrvtNjzxxBO45ZZbUFdXh1deeQVhYWGufaxcuRJ9+/bFHXfcgbvvvhvDhw/Hk08+6Xo8Ojoa69evx4kTJ3DzzTdj+fLluPfeezFp0iQ/X642CWfPIuKlFyCcPat0VTqUp9VtPC2D7Gm1rUstl6wkQ2kpIp9/Hobzk/xTxzKUlqLnK+thLDW3XZg6lHjmLHqse0UzbdHaCp++LAsfrELlPYioPdq7Uh7jAwpW7LvyxTjGUjN6vrLeq2PpS1kitfF5pFR2djYKCgpafVwQBMyaNQuzZs1qtUynTp2watWqS/6dAQMG4K233vK1eiFJd7oEpgXzYL0yB7Zmc3lplbdzPQXDnFCG06cR/eg8GIcOAeLi234C+cV4+gzSly5DxRXZkLp3V7o6IU08VYK+i59C6eWXw5Kk7rkCPc0L03L0U2srfIbCnFSh9h5EFAiMDyhYse/KF+OEnTmN9KXLsDcnB0hPl60skdpw9T1SnC+r5xERBZKn24EBwCjo4LBcGPHQtKS7t0vCE1Hwahm3NJ3/RERE5DsmpUhRHbl6HhGRvzzNCxOh06NvXDzsRrR73jom44mC06XmtyQiIiLfMSlFivJ0y4tR1KFvTBz0ouh2uwu/iSQipbS8HbhlosqXJd2ZjCcKDp6Sx4IguOatbIpbfDn/iYiIyB2TUhrgiI5B43W/giM6RumqtFtbq+cB2vwm0h4djcZxv4I9OlrpqoQEe3Q0Sn95NWzR0VDnLGOhQ4qJQdnYX8IWxH2/vfPWtZaMD9a5p7TwHkSB1REjBeXeZ2vJY0+r/nbEvJWMDyhYse/KF+PYzset3hxLX8oSqQ2TUhrg6NMXVW/8XelquPFnaXR/l1EOJo29e6PynXfQEMLL5gZSQ+9e+HHdywAAk8J1CXX2vn1x8NVX0BjC/V4r80+p8T2I1Ku1ZE/LedqaeBM3dMTow9YWLwhULML4gIIV+658MU59L2fcajIYZS1LpDZMSmmB1Qrh3DlIsbGAwaB0bVoNDgHfAsSWH9jUuoKePwSrFUJpKQSHDRDEtp9AfhGsVhjKK2CLiQH4pq0sqxWGsjJYIqMgqeC6RX5Q2XsQqZunZI+nedqaeBM3dOToQ6ViEcYHFKzYdyFbjCNYrdBXVUFIaHsFP1/KEqlNiF4ptEWfvw+Jg/pCn79P6aoAcA8OCyrNrp+iqgpYJYfHEVShKuLAAXTu0xeRBwqUrkpIiDxQgDGXZ8NUwOOtNP2+fbgia0RItIUA53VRFJ0/zvnx2i53oXyAK+wjtb0HUXBoSvZY7HbYJck1T5M/cUPzfTYf3RSMGB9QsGLflS/GMRU449aIAwdkLUukNhwpRR3G060pFz50nf+dk5cTkYZ5miPP0/x4rc2lB7R2W5MEQN45eYh8IYpCi/52cZ8EfOuX3sQNnv5Wa7GEp+cy7iAiIlIXJqXIo9bmhPIcdHoXHHr74YyISCs8zZHnaU6a1ubS83RbkwDAYNDDarW7XWm5eh8Fkk0PV/9rrU8CFydVfUkKeYobPP0tXxK9jDuIiIjUhUkpuigBJQiAYBBhaTEnlKdA0NvgEPD+wxkRkdZ4u1KXp/lrWrtuHq2+sM0o6tA3Jg56Ubxo/hyOoKKOcKy6EvV2GwDPfRLwnFT1JSl0qbih+d/yJdHLuIOIiEhdmJQKcZ4mJRcFAZIooajC84ozngLBtoLD5jp6GWUiIq3xdN1svu1St/95GkHlaTQsk1fki7b6ZNN2Ob6Mautv+ZroJSIiIvVgUkoDbIOHwHz4BKTIKJ+f62nFmqaA0Sp5DuT8CQ7JXd2gQThbchK1jfXgFBcdr3bQQHz+4w+wR0bCpHRlQpxtyBDs3LcXdcYwpauiCa2NCvG0AllrK6S29/Y/f96DKDQEQ4ygtjkvGR9QsGLflS/GqR7ojFsjYjvJWpZIbZiU0gKdDlJ0jF+7CIaAUZN0OkgxMUC5BbAF90pBQUGngz06WulaEHChLdjvZeVpouiWPH0Z4Sl5del9NBtpJeqB2FiAI60oSKlyzkvGBxSs2Hfli3Ga9qPz4rOZL2WJVEZsuwipne5IIWInTYC+6HCzZcRx0bLiwbC0eKgJLypCp9/chPCiIqWrEhLCi4qQdcdURBQdVboqIU9XWIiM26awLRTUlMCy2O2wOOzNRopc+j2jaaSVzQDYDIB0vBCm390EfXER32MoKDWNNCyqqkBBpRkFlWYcr66EBEmxPs34gIIV+658MU5E0VFk3THVq2PpS1kitWFSKsgJAqCz1MH4+WdwNNTAZgDsBkCM1MN+/gND8x8xTMcPDSoi1tQg7LPPoKupVboqIUFXU4uE7Tugr61RuiohT6ipQdxX29kWAdAy2eS8Lcmda6SIQWrzPaP5SKuCSjOOl5xA2H8/g726qpVVW4mCQ/NErdXhaPsJHYjxAQUr9l35Yhx9bQ0Stu+AWNP2fnwpS6Q2vH1PxbyZiFYQBNjObzhWXYmaSnOrq+B4Wp1J6TkTiIio43h7W5KnOanauqWv6QO81RGit2cQERERkd+YlFKp9kxEaz3/AaG1VXBUOWcCERF1GE/JpkutfubNnFStEQCg2UTRgP8r+nn6ckaO/RIRERGROjApFWCeA2wJaHEzhXMElH8T0bbk64cTIiLShvYuZtFyRTKg9RG2ok6AxeCeLDIKOjgsF98G5U1SqbUvZ4D2rxRIREREROrCpFQHapmAEgRAMIiwNAuwBQAGgx5Wq90txG8awdTyW2tPSxbbk3vg0JOL0NCtm1f14kp76mHp3h1Vq1bB0t27tiP/WLp3w4GFC9DQrRv8W6SX/GXv0QOFi72/blHgeRpdC1w8wrahWzcULV6MqB7dUdTsS48InR594+JhN+Ki5JE3SSVPqwQC/n9BQxQMGB9QsGLflS/GaejmjFst3bvLWpZIbZiU8pE38zw1lWv5Da8oCJBECUUVFwLs1uZ/8jSCqbXb76QunXHqjimwtvOWC1KOLSEB9dPvhrXcHLrL5gaQNSEBJ6bcDgBMSilMSkpyXrfY71XL0+ha4OL3J2tCAs5MvQN94uJgKTe7fenh6fme5jcEWh895c8tha3x9KURkZowPqBgxb4rX4zTFLeaDEZZyxKpjeZX32u+tHVry1t7q+Uy2L6sTtR8eWGrdPHqLm5Lc7ey6ktrSxajvAyd338f+srK9r84UoSuohLhmzez7QJEX1mJrlu38nirgFBejqT3eN0KBm29P+krK5H43nsQysu9er5dcnhc6U8Xpmvxfn3xKoFy8Phezq/oSGUYH1CwYt+VL8Zpilt1FW3vx5eyRGqj+aSUTd92Aqk1gtAyqXVxUqioqgI2yQFRFD0G0x2xvHDLfeqOH8eABx5ExIkTsuyfAifsRDFi77wLYcVsu0AIKz6BjAcf5rmiArxuaUfEiRNIm/UAdMePe1W+tS9YHDrAbrzwfm03ANB5n5a6+D3b8xdRnr40OlZd6fXfIQoExgcUrNh35YtxIk4449awE8WyliVSG81/N3isuhL1dhuA1ueh8HRLnqf5nzzN8+TtnBtERER0Qcv5DX1ZiOPi+RUvfs8GPE+03jRRe8v3ciIiIiIKPM0npdqeKNxzIHup+Z/c53nybs4NIiIiujRvFuLwPL/ixe/ZrU20zi+NiIiIiNRD80mp5rwNZIELSaWm+Z+AS69U1zL5xVXtiIiI5Ofpy6DW3rP5pRERERGRuoVYUsq7QBYIrqSSFBmJquFZsEdGKl0V8pEjMhKWn10OR2SE0lUJCY7ICFRmZcIeGan9CfVUjtct7bBHRqJ6+HBIAW5Lb0ZVtSzXVlkitWB8QMGKfVe+GMceGYnKrEw4vNiPL2WJ1CakklJNvA1kg4U9PR0/bn0PjSG67Gowa0hNRcV//4v6EF42N5DqU1Px7ZZ3AQAmhesS6njd0o66vn2x74Ot6BMXB5Sbla4OkSYwPqBgxb4rX4xT17cvvt3yLkwGo6xlidSGgwWIiIiIiIiIiCjgmJTSAH1uLkb37IPovDylq0I+ivzpJ3QxRSPqJ7ZdIET9lIexfdN4rqgAr1vaEZ2XhytSekKfm6t0VYg0g/EBBSv2XflinOg8Z9wa+dNPspYlUhsmpYiIiIiIiIiIKOCYlCIiIiIiIiIiooBjUoqIiIiIiIiIiAKOSSkiIiIiIiIiIgo4vdIVIP/ZBgzAN199jtqkzkpXhXxU368fzD/moi4iXOmqhIS6fv3wv/9+isZu3RCpdGVCHK9b2lHbrx9++OordB8wAKirUbo6RJrA+ICCFfuufDFO7fm4VZ/SU9ayRGrDkVJaEB6Oht694QgLU7om5CMpPBz21FRI4Wy7QJDCw1DPc0UdeN3SDEdYGBr79AbCQ/cDCJHcGB9QsGLfhWwxjiPMGbdKXry/+lKWSG2YlNIA8ehR9J/1AMKLi5WuCvko7PhxxEy7E2HH2XaBEHa8GIP//CDPFRXgdUs7wouLkTpzFsSjR5WuCpFmMD6gYMW+K1+ME17sjFvDjh+XtSyR2jAppQFiZSU6v/8BDOfOKV0V8pHu3DlE/P3v0LPtAkJ/7hy6ffAhzxUV4HVLOwznziHp/fchVlYqXRUizWB8QMGKfVe+GMdwPm7VebEfX8oSqY2qk1KbNm3C1VdfjSFDhuC3v/0t9u7dq3SViIiIiIIa4ysiIiJSC9UmpT7++GMsW7YM9913H95//30MGDAA06ZNQ1lZmdJVIyIiIgpKjK+IiIhITVS7+t6GDRvwu9/9DhMnTgQALFq0CF988QW2bNmCu+++2+v9hOsuvMQwnQ4CgAidHiKEVrf5UlYVz9fpgOhohBnDYNPr1VEnlT1fjXUCAKPB4Gw7gxERer0q6qTldgozGF3niqSR1xSMz/d03VK6Tmzn9u8zzBgGREdD0OlU/5pa2948VtC6QMVXrW1XW/9V6/O9jQ+C6TWxnYO3Tr6UDZW+e8l96tr+bOZNnfTn31+NBkObzw/zoSwAGEUdRACCIECnQwsS0KxO/m8L9uersU7+Pj+QdWqbIEmS5POzOpjFYkFmZiaee+45jB071rV9zpw5qKqqwksvvaRg7YiIiIiCD+MrIiIiUhtV3r5XUVEBu92OhIQEt+0JCQkwm80K1YqIiIgoeDG+IiIiIrVRZVKKiIiIiIiIiIi0TZVJqbi4OOh0uosm3SwrK0NiYqJCtSIiIiIKXoyviIiISG1UmZQyGo0YPHgwdu3a5drmcDiwa9cuZGVlKVgzIiIiouDE+IqIiIjURrXLzfzhD3/AnDlzkJGRgaFDh+L1119HfX09br75ZqWrRkRERBSUGF8RERGRmqg2KTVu3DiUl5fjueeeQ2lpKQYOHIhXXnmFw8uJiIiI2onxFREREamJIEmSpHQliIiIiIiIiIgotKhyTikiIiIiIiIiItI2JqWIiIiIiIiIiCjgmJQiIiIiIiIiIqKAY1KKiIiIiIiIiIgCLmiTUi+99BImT56MYcOGYcSIEV49R5IkPPvss8jJycHQoUMxdepUHD161K1MZWUlHnroIQwfPhwjRozAvHnzUFtb2wGvQB6+1vfEiRPo37+/x59t27a5ynl6/KOPPgrES/JZe9rs9ttvv+j1PfHEE25lSkpKcPfdd2PYsGEYOXIknn76adhsto58KX7x9ThUVlZi8eLFuO666zB06FD8/Oc/x5IlS1BdXe1WTu19YdOmTbj66qsxZMgQ/Pa3v8XevXsvWX7btm24/vrrMWTIENx444348ssv3R735jqhRr4ch3feeQe///3vcfnll+Pyyy/H1KlTLyo/d+7ci9p92rRpHf0y/OLLMXjvvfcuen1DhgxxKxMKfcHTtbB///64++67XWWCrS988803+NOf/oScnBz0798fn376aZvP2bNnDyZMmICMjAxcc801eO+99y4q4+u1hoLPiRMnMG/ePFx99dUYOnQoxo4di+eeew4Wi0XpqlEz7fkMQB2L10d1a8/7IgXO2rVrMXHiRGRlZWHkyJG49957ceTIEaWrFVhSkHr22WelDRs2SMuWLZMuu+wyr56zdu1a6bLLLpM++eQTKT8/X/rTn/4kXX311VJDQ4OrzLRp06Rf//rXUm5urvTNN99I11xzjfTggw921Mvwm6/1tdls0tmzZ91+nn/+eSkzM1OqqalxlUtPT5e2bNniVq75cVKT9rTZbbfdJs2fP9/t9VVXV7set9ls0g033CBNnTpV2r9/v/TFF19I2dnZ0qpVqzr65bSbr8ehoKBAmjFjhvTZZ59Jx44dk3bu3Clde+210v333+9WTs194aOPPpIGDx4svfvuu9KhQ4ek+fPnSyNGjJDMZrPH8t999500cOBAad26dVJhYaH017/+VRo8eLBUUFDgKuPNdUJtfD0ODz74oPTmm29K+/fvlwoLC6W5c+dKl112mXT69GlXmTlz5kjTpk1za/fKyspAvSSf+XoMtmzZIg0fPtzt9ZWWlrqVCYW+UFFR4XYMDh48KA0cOFDasmWLq0yw9YUvvvhCeuaZZ6T/9//+n5Seni598sknlyx//PhxadiwYdKyZcukwsJC6Y033pAGDhwoffXVV64yvh5XCk5ffvmlNHfuXGn79u3S8ePHpU8//VQaOXKktHz5cqWrRs205zMAdRxeH9XP1/dFCqw//vGP0pYtW6SDBw9K+fn50l133SX9/Oc/l2pra5WuWsAEbVKqyZYtW7x6Q3I4HNKVV14pvfLKK65tVVVVUkZGhvSvf/1LkiRJKiwslNLT06W9e/e6ynz55ZdS//793T6sqYVc9f3Nb34jPfroo27bguWC1d5jcNttt0lLlixp9fEvvvhCGjBggNuH1LfeeksaPny41NjYKE/lZSRXX/j444+lwYMHS1ar1bVNzX3hlltukRYtWuT63W63Szk5OdLatWs9lp81a5Z09913u2377W9/Kz3++OOSJHl3nVAjX49DSzabTcrKypLef/9917Y5c+ZI99xzj9xV7TC+HoO23jtCtS9s2LBBysrKcguEgq0vNOfN9WvFihXS+PHj3bY98MAD0h//+EfX7/4eVwpe69atk66++mqlq0EeePsZgDoWr4/BRc1xPTmVlZVJ6enp0tdff610VQImaG/f89WJEydQWlqKUaNGubZFR0dj2LBh+OGHHwAAP/zwA2JiYtxu4Rg1ahREUVTlMFQ56puXl4f8/HzccsstFz22aNEiZGdn45ZbbsG7774LSZJkq7tc/DkG//znP5GdnY0bbrgBq1atQn19veux3NxcpKenIzEx0bUtJycHNTU1KCwslP+F+EmuvltTUwOTyQS9Xu+2XY19wWKxYN++fW7ntCiKGDVqlOucbik3NxcjR45025aTk4Pc3FwA3l0n1KY9x6Gl+vp62Gw2xMbGum3/+uuvMXLkSFx33XVYsGABKioqZK27XNp7DOrq6vCLX/wCY8aMwT333INDhw65HgvVvrBlyxaMHz8ekZGRbtuDpS+0R1vXBTmOKwWv6urqi66NROTE6yOR/JqmUgml9x5920W0obS0FACQkJDgtj0hIQFmsxkAYDabER8f7/a4Xq9HbGys6/lqIkd93333XaSmpmL48OFu22fOnIkrrrgCERER2LFjBxYtWoS6ujpMmTJFtvrLob3H4IYbbkD37t3RuXNnFBQUYOXKlSgqKsILL7zg2m/zhBQA1+9a7Qvl5eX429/+hkmTJrltV2tfqKiogN1u93hOt3Yftqd2bX4N8OY6oTbtOQ4trVy5Ep07d3YLKkePHo1rrrkGycnJKC4uxjPPPIO77roLf//736HT6WR9Df5qzzHo06cPli5div79+6O6uhqvvvoqJk+ejI8++ghdu3YNyb6wd+9eHDx4EE899ZTb9mDqC+3R2vW+pqYGDQ0NOHfunN/nGAWnY8eO4c0338ScOXOUrgqRKskRgxDRBQ6HA0uXLsXw4cORnp6udHUCRlVJqZUrV2LdunWXLPPxxx8jNTU1QDVShrfHwV8NDQ3417/+hXvvvfeix+677z7X/w8aNAj19fVYv359wBIRHX0Mmide+vfvj6SkJEydOhXHjx9Hz549271fuQWqL9TU1GD69OlITU3FjBkz3B5Tui9Qx3r55Zfx8ccfY+PGjQgLC3NtHz9+vOv/mya3Hjt2rGvETLDLyspCVlaW2+/jxo3D5s2b8cADDyhXMQW9++67SE9Px9ChQ922a70vkPa1J748c+YM7rzzTlx//fX43e9+19FVDHn8DEBE5Lw75dChQ3jrrbeUrkpAqSop9cc//hETJky4ZJmUlJR27TspKQkAUFZWhs6dO7u2l5WVYcCAAQCc34yWl5e7Pc9ms+HcuXOu5weCt8fB3/r++9//RkNDA2666aY2yw4bNgx/+9vfYLFYYDQa2yzvr0AdgybDhg0D4PxWtGfPnkhMTLzotrem0RFa6ws1NTW48847ERUVhRdffBEGg+GS5QPdF1oTFxcHnU6HsrIyt+1lZWUXjXpokpiYeNEol+blvblOqE17jkOT9evX4+WXX8aGDRvafH0pKSmIi4vDsWPHVJeI8OcYNDEYDBg4cCCOHz8OIPT6Ql1dHT766CPMnDmzzb+j5r7QHp6uC2azGSaTCeHh4RBF0e/+RcryNb48c+YMpkyZgqysLCxevLijq0fo2M8A1HHkeP8lIqcnn3wSX3zxBd5880107dpV6eoElKqSUvHx8RfdgiSX5ORkJCUlYdeuXRg4cCAA54fxH3/8Ef/3f/8HwPlNeVVVFfLy8pCRkQEA2L17NxwOx0XfHHckb4+Dv/XdsmULrr76aq/+Vn5+PmJjYwOWhAjUMWiSn58P4MIH0czMTKxZswZlZWWuIck7d+6EyWRCWlqary+n3Tr6ONTU1GDatGkwGo146aWX3EbKtCbQfaE1RqMRgwcPxq5duzB27FgAziGvu3btwm233ebxOZmZmdi9ezemTp3q2rZz505kZmYC8O46oTbtOQ4AsG7dOqxZswbr1693m4usNadPn0ZlZWVAk7Leau8xaM5ut+PgwYMYM2YMgNDqC4DzSwqLxYJf//rXbf4dNfeF9sjMzMRXX33ltq35dUGO/kXK8iW+bEpIDR48GMuWLYMohsz0q4rqyM8A1HF4fSTynyRJWLx4MT755BO88cYbIZmAD9p32pKSEuTn56OkpAR2ux35+fnIz89HbW2tq8z111+PTz75BAAgCAKmTJmCl156CZ999hkKCgowe/ZsdO7c2XURTU1NxejRo/H4449j7969+O6777B48WKMHz8eXbp0UeR1Xoo39T1z5gyuv/76i0b9HDt2DN98843HCc7/+9//4h//+AcOHjyIY8eO4a233sLatWtV+ebSnmNw/PhxvPjii8jLy8OJEyfw2WefYc6cObj88stdIyBycnKQlpaG2bNn48CBA9i+fTtWr16NW2+9VfFkjCftOQ41NTX44x//iLq6Ojz11FOoqalBaWkpSktLYbfbAai/L/zhD3/AO++8g/fffx+HDx/GwoULUV9fj5tvvhkAMHv2bKxatcpVfsqUKdi+fTteffVVHD58GM8//zzy8vJcr8eb64Qa+XocXn75ZTz77LNYunQpevTo4Wr3putnbW0tnn76aeTm5uLEiRPYtWsX7r33XvTq1QujR49W5DW2xddj8MILL2DHjh0oLi7Gvn378Mgjj6CkpAS//e1vAYROX2jy7rvvYuzYsYiLi3PbHox9oba21hUTAM5J65viBQBYtWoVZs+e7So/efJkFBcXY8WKFTh8+DA2bdqEbdu2uSWv2zqupA1nzpzB7bffjm7dumHOnDkoLy93XR9JPbz5DECBw+uj+rX1vkjKWrRoET788EOsWrUKUVFRrvedhoYGpasWk4eXiwAAAotJREFUMKoaKeWL5557Du+//77r96Zb0DZu3Ijs7GwAQFFRkWv2egC46667UF9fjyeeeAJVVVW47LLL8Morr7iNDlm5ciUWL16MO+64A6Io4tprr8X8+fMD86Laoa36Wq1WFBUVua0sBzhHSXXt2hU5OTkX7VOv12PTpk1YunQpAKBnz56YO3euaudU8PUYGAwG7Nq1Cxs3bkRdXR26deuGa6+91m1uLZ1OhzVr1mDhwoWYNGkSIiIiMGHCBK9ubVGKr8dh3759+PHHHwEA11xzjdu+PvvsMyQnJ6u+L4wbNw7l5eV47rnnUFpaioEDB+KVV15xDRk/deqU27fcw4cPx8qVK7F69Wo888wz6N27N1588UW3iQS9uU6oja/HYfPmzbBarRf15xkzZuD++++HTqfDwYMHsXXrVlRXV6Nz58648sorMWvWLFUmZQHfj0FVVRUef/xxlJaWIjY2FoMHD8bmzZvdRkKGQl8AgCNHjuC7777Dq6++etH+grEv5OXluc15t2zZMgDAhAkTsHz5cpSWluLUqVOux1NSUrB27VosW7YMGzduRNeuXbFkyRK3pFtbx5W04X//+x+OHTuGY8eO4aqrrnJ7rKCgQKFaUUvefAagwOH1Uf3ael8kZb399tsAgNtvv91t+7Jly0ImuStIaljbnYiIiIiIiIiIQkrQ3r5HRERERERERETBi0kpIiIiIiIiIiIKOCaliIiIiIiIiIgo4JiUIiIiIiIiIiKigGNSioiIiIiIiIiIAo5JKSIiIiIiIiIiCjgmpYiIiIiIiIiIKOCYlCIiIiIiIiIiooBjUoqIiIiIiIiIiAKOSSkiIiIiIiIiIgo4JqWIiIiIiIiIiCjg/j8pmdmlNJVNmQAAAABJRU5ErkJggg==",
      "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": "2025-06-24T04:02:46.438860Z",
     "iopub.status.busy": "2025-06-24T04:02:46.438700Z",
     "iopub.status.idle": "2025-06-24T04:02:47.094358Z",
     "shell.execute_reply": "2025-06-24T04:02:47.093849Z",
     "shell.execute_reply.started": "2025-06-24T04:02:46.438844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vv3H69GnbsV89a7bFYmH27Nm2gLhbt26cPHmy0PUpX778NaMJ8+u7775DUZR8BeBXBy+AbVDC0aNHbdv++ecfAB599FG7tO3atSMnJ4f169cXqq5C5Ie0wAiRD5dGovn5+QGwePFiWrVqxeOPP47RaOT7779n8ODBREdHXzPc/HrGjRtHQEAA/fv3Jysr64bpsrOz6datG2fOnOGFF16gTJkyfPPNN/kKNm4kIyODL7/8kscee4ynn36azMxMvvrqK3r27MmXX35pmw4gP0aPHs348ePx9PS0PS4ICgqiYsWKNGrUiG+//faaxytr1qzBy8vLtkZYUahduzZ9+/Zl9erV9OvXj+DgYDp06ECnTp2oUqVKofJcvXo1n3/+OZqmER4eTt++fe1aL/r06UN6ejpnz5619Zvy8vKyy2PevHkoisLLL79MRkYG8+fPZ9iwYbblRYqL0Wjkxx9/pGHDhra+SwV1aWZxf39/27bc3Fx0Op1tqohLLj1m3bNnj21WcSGKmgQwTvTCCy9Qq1YtXn/9dWdXRVwlIyOD8+fPk5uby7///stHH31EqVKlbPPb/PTTT7Z+AABdunShY8eOLFy4MF8BjK+vL4sWLcrzccwXX3zBiRMnmDFjBm3btgXgmWeecejRhq+vL7/++qvd461nnnmGtm3bsmTJkgItyvnQQw8xY8YM/P39r6lT+/bteeuttzh69Cjh4eHA5V+kDz/8cIH6EuWldOnSDBgwgP79+7Nt2zZWrlzJ4sWLmTt3Lo0bN+app56iTZs2dtfsZho2bEjbtm0JDQ0lPj6ezz//nGHDhpGenm7rnHrfffexePFi0tLSbng9cnJy+Prrr23nunTp0kycOJFDhw5Ro0aNojn4fNi0aRMpKSn5enx0I/PmzUOn0/HII4/YtoWFhWE2m9mxYwf33nuvbfvff/8NYJvaQYhbQR4h5SE6OppOnTrRsGFDIiMj6devH8eOHbNLk5OTw7hx44iIiKBhw4YMHDjQ9tcKXB5SmZaWVuT1S0lJYfz48TzyyCPUq1ePBx54gAkTJpCenm6XLi4ujl69elG/fn0iIyOZOnVqns3ss2fPpnPnztSvX9/uy+mS5ORkevToQVRUFHXq1KFFixa8/fbbZGRkFOkxOkP37t1tj3deeeUVvLy8+PDDD22dH6/8RZiamkp6ejr33HMP+/bty1f+zzzzTL76kvz+++8EBwfbjWTx8PBw6K9anU5n+4VqsVhISUnBZDJRp06dfNc/P9q2bYu7uztr1qyxbdu0aRPJyck88cQTRVbOlRRFoWnTprz77rv8+eefjBs3jtzcXEaOHElUVBRjxowhNTU1z3yWL19Ot27dePDBB3nuuedYuXIlNWrU4P333yc7Ozvf9enYsaNdoHjp5yivqRGK2nfffYfBYLAFwQW1Zs0avvrqK1566SW7Fq3HHnsMHx8fXn/9df78809iY2P54osv+PzzzwEKdK6EKChpgcnD9u3b6dKlC3Xr1sVsNvPee+/Ro0cPvv/+e1vHy0mTJrFx40ZmzJiBj48P48ePZ8CAAbZ5WG6l+Ph44uPjGTlyJNWqVeP06dOMHTuW+Ph421w1ZrOZ3r17ExQUxPLly23pDQYDQ4cOvWHeRqORNm3a0KBBA7766qtr9quqyoMPPsiQIUMICAjg1KlTjBs3jtTUVKZPn37Ljrk4vPXWW4SFhaHT6QgKCiIsLMyu78GGDRuYPXs2+/fvJzc317Y9vyOO8tuMf/r0aSpXrnxNvmFhYfn6/I2sXr2aTz75hOPHj2M0Ggtcr/woXbo0LVu25LvvvmPIkCGA9Rdh2bJlbX2KbuTSIqmX+Pj4YDAYOH/+vN12X1/fG3aU9vb2pnPnznTo0IHZs2czZ84cli9fTufOnfH19S3Qsbi5udGlSxfGjBnDnj17rhvQX09ISIjd+0tDkG/FHzM3kpmZyfr164mKirJ7/JNff//9N6+//jpRUVG88sordvuCg4OZPXs2I0aM4OWXXwas5/3NN99k5MiRLtM5XbgmCWDysGDBArv3U6ZMITIykr1799K4cWPS09NZuXIl06ZNsw1bnDRpEu3atWPHjh0EBQXx4osvAtjWDOrQoYNtXhBN03jnnXf46quvMBgMdO7c2TbnRn7UqFHDbqKxSpUqMWTIEIYPH47JZEKv17Np0yaOHDnCwoULCQoKonbt2gwePJhp06YxYMCAG/4CGDRoEGDtuHk9vr6+dnM9VKhQgeeff/6ac+aK6tWrZxuFdLW///6bvn370rhxY8aMGUNwcDAGg4GVK1fmu2PqpUkOi4PZbLZr7fnmm28YNWoUDz30ED169CAwMBCdTkd0dHSRtwy0b9+etWvX8u+//1KjRg1+/fVXnnvuuTw7okZFRdm9nzx5Mk2aNLmm38zixYuv2+EUYNeuXaxcuZIffviBtLQ06tevz1NPPWV7nFVQ5cuXB8hXC84lNzrO4px+a926dfkefXS1AwcO0LdvX6pXr84HH3xwzWzcYP1eW7duHYcOHSIrK4tatWrZHh0Vtv+REPkhAUwBXXo0c+kvuD179mA0GmnWrJktTXh4OCEhIezYsYMXXniBWbNmMXDgQNauXYu3t7fd44fVq1fz0ksvsWLFCnbs2MGoUaNo1KgR9913H2CdkOr06dMFWhgxIyMDb29v25fNjh07qFGjht0kbFFRUYwdO5YjR45cM7dFYZ07d45ffvnlmsUdbzc//fQT7u7uLFiwwC74W7lyZZGXVaFCBQ4dOoSmaXatMJeWi7iSr6/vdf+yj4uLs1va4aeffqJixYp8+OGHdnleObt0Qdys1al58+YEBASwZs0a6tevz4ULF/LVf2fhwoV276tVq4avr+81269eMDUpKYlvvvmGVatWcfjwYfz8/OjQoQNPPfWUw31OLgV3AQEBtm2uMMfPmjVr8PT0vGaEVF5OnTpFz549CQgIYN68edd0UL6STqez6/y9efNmALvvRSGKmgQwBWCxWJg0aRKNGjWyfRkmJiZiMBiumZ0yMDCQhIQEdDqdLdgJDAy8Jt2lmU7B+tfKZ599xpYtW2wBTHBwMBaLJd91PH/+PB9//DHPPvusbVtiYuI1M8heen91U31hDB06lPXr15OdnU3Lli2ZOHGiw3mWZDqdDkVR7BbnjI2NvSVDRu+//342bdrE2rVrbf0XLly4wIoVK65JW7FiRf755x9yc3NtgdWGDRs4c+aMXQBzqTXmyqBo586d7Nix45pHHvnh4eFxw0cier2eRx99lO+++46jR49So0aNfK3SfqNffDfafubMGcaPH8/GjRsxm822/moPPfRQvubiudL58+ftghSw/lHw6aef4u/vz913323b7uHhcU1/s5Lk/PnzbNmyhUcfffSGnabj4uK4cOGCXctUQkICL7/8MoqisGDBgmvOR15lzp8/n5o1a0oAI24pCWAKYNy4cRw+fNjWQa0oXD1leXBwMElJSbb3r776ar7zysjIoHfv3oSHh9uCouLw2muv0b9/f06cOMF7773H5MmTGTt2bLGVX9xatGjBwoUL6dmzJ4899hhJSUl8/vnnVKpUiYMHDxZpWc888wxLly5l5MiR7N27l+DgYL755pvrjqZ5+umn+emnn+jZsydt27bl1KlTrFmzhkqVKtmle+CBB/j555/p378/DzzwALGxsSxfvpxq1arddEj3jdx9990sW7aMjz/+mMqVKxMQEGA3C2z79u1ZsmQJ27ZtY9iwYQU/Cflw6tQp9u3bR69evejUqZNDfXmWLl3KunXraNmyJSEhIcTHx7Nq1Sri4uJ455137AKiu+++mx9++IHJkydTt27dQrV0XFp24MolG27kr7/+4q+//gKsgUJWVhYff/wxYH2Uc3Xr5w8//IDJZLrp46ORI0eyfft2u3u3Z8+exMTE0LNnT/755x/bfC9g/ePn0h9YAF27dqVBgwZUrlyZhIQEVqxYQVZWFnPmzCn0nDVC5IcEMPn09ttv89tvv/HZZ59Rrlw52/agoCCMRiNpaWl2rStJSUnXXan5alc/U1YUpVDPxzMyMujZsydeXl589NFHdvMyBAUFsWvXLrv0l0ZJ5aeOebm0KnV4eDi+vr506dKFfv36FemsqCVJZGQkEydOZN68eUyaNInQ0FCGDRvG6dOnizyA8fDwYNGiRYwfP57PPvuMUqVK8fjjj3P//ffTs2dPu7TNmzdn1KhRLFy4kEmTJlGnTh3mzJnD1KlT7dJ17NiRxMREvvjiCzZt2kS1atV49913Wbt2Ldu3by9wHfv3709cXBzz588nMzOTJk2a2AUwderUoXr16hw9evSWjT6qX78+v/76a5H8wmzUqBH//fcfX331FSkpKXh4eFCvXj0mTpx4zfT8zz//PPv372fVqlUsWrSIChUqFDiAuRQ05udncevWrXz44Yd222bOnAnAgAEDrglg1qxZQ2BgYIFbQg4cOADA/Pnzr9nXpEkTuwDm7rvvZu3atZw7dw5vb2+aNWvGkCFDZEV6cetp4qYsFos2btw4LSoqSjt+/Pg1+9PS0rS7775bW7t2rW3b0aNHtRo1amj//fefpmma9s8//2g1atTQzp8/b/fZrl27ahMmTLDb1rdvX23kyJEFqmN6err2zDPPaF27dtWysrKu2f/bb79ptWrV0hITE23bli9frjVq1EjLycnJM/+VK1dq99xzT77qsn37dq1GjRpaTExM/g9AFNjWrVu1GjVqaFu3bnV2VfLlySef1F588UVnV6NEmjp1qnb//ffn62dRCHGZtO/lYdy4cXz77bdMnz4dLy8vEhISSEhIsM1v4OPjQ6dOnZgyZQpbt25lz549jB49moYNG9KgQQPA2hFTURR+++03zp8/T2ZmZr7Lnz59OiNGjLjh/oyMDF5++WWysrKYOHEiGRkZtjpe6qMRFRVFtWrVGDFiBAcOHOCPP/5gxowZdOnSxdYcvmvXLtq0acO5c+dsecfFxbF//37i4uIwm83s37+f/fv32+q/ceNGVq5cyaFDh4iNjeW3335jzJgxNGrUqEiH4wrXtnv3bvbv30/79u2dXZUSadu2bfTr16/AfXWEuNPJI6Q8LFu2DMBuLRmwDuu8tPru6NGjUVWVQYMGkZuba5sw65KyZcsycOBApk+fzmuvvUb79u1tw6jzkpCQwJkzZ264f+/evezcuROA1q1b2+1bv349oaGh6HQ65syZw9ixY3n22Wfx8PCgQ4cOtmHSYO0YevWcIB988AGrV6+2vb/0C+jS0FV3d3e+/PJLJk+eTG5uLuXLl6d169b06tUrX8cmbm+HDh1i7969fPLJJwQHB9OuXTtnV6lEuhWj14S4EyiaVowTEgghisSljp83mwfF2WbNmsVHH31EWFgY48aNo0mTJs6ukhDiNiIBjBBCCCFcjvSBEUIIIYTLkQBGCCGEEC5HAhghhBBCuBwJYIQQQgjhcm77YdRJSencrJuyoih4ebkXaL2hotKz58vUrFmT4cNHFnvZzqSqKpmZOcW6Iu/tTElIwP2b1eQ82QGtCGZWlvKLR0mtd1HVy5F8Suq5EcVDUSAw0CfvdLf7KKTExJsHMKp6OYC5Xro5cz5m7tw5dtuqVKnCqlXf2t7n5OTw3nvT+PnnteTm5hIZ2YzXXnuDwMBAAP7++y969erBxo2b8PG5vNzA//73MjVqOBbApKamMmfOx2zdupmzZ8/i7+/PAw+0om/f/vj4WG+AQ4cOsnDhAnbs+I+UlBTKlw/hqaee5vnnu9407/nz57Jp0x8cOnQQvd7A77//eU2avXv38MEHM9i/fz+KAnffXZchQ16hRo2a18nRemNeCmAsltv61hNCCFEIigJBQXkHMPIIKR/Cw8P5+edfba8FCz612z99+jv88cdGpk6dxrx5C0lISGDYsFeKpW4JCfEkJMQzZMirrFixirFjx7N585+8/fblifT27dtHQEAAEyZM5ssvV9Ojx//48MMPWL582U3zNhqNPPTQwzz11DPX3Z+VlcWAAX0pV648ixd/xieffIqXlyf9+/exmxBP3FpKSjJu365GSUmW8l1ISa13UdXLkXxK6rkRJYsEMPmg0+kJCgqyvfz9/W370tPT+frr1QwdOowmTSK46667GDt2PDt37mDXrp3ExZ2mV68eALRoEUWjRvUYM+YN2+c1TWPGjPd44IEoWrduyZw5HxeobtWqVWfatPdp0eIBKlasSJMmEfTvP5Dff9+IyWQCoH37DgwfPop77rmX0NBQHn30MR5//El+/XXdTfPu27c/Xbu+QLVq1a+7/8SJ46SmptK3b3+qVAkjPLwavXr1ISkp6aazB4uipTt1Et+e3dCdOinlu5CSWu+iqpcj+ZTUcyNKFglg8uHUqZM8/PCDPP54W15/fZTdL+f9+/dhMpmIiGhq2xYWFka5cuXZtWsXZcuW49133wNg9epv+fnnXxk27PIjo++++xYPDw8WL17K4MGvMG9eNFu3brHtHzPmDf73v5cLVN+MjHS8vLyvWenaPk0Gvr6+Bcr3apUrV8HPz4+vv16F0WgkOzubr79eTVhYVUJCQhzKWwghhLiZ274T781omobZbMZi0d+wD8zdd9/N2LFvU7lyZRITE5k3L5oePbrxxRdf4eXlRWJiAgaDAS8vLywWs+1zAQEBJCYmoCjY+qL4+fnZ/m2xmNE0jWrVqvO//1nXDgoNDWX58mVs27bFNu16YGDgxTqayY+UlGTmzYumQ4eON/zMzp07+Pnnn5g584N85atpFkC7Jq2HRynmzJnHsGFDmT9/LgAVK1biww8/QlWV6+atKAAaRmOu9IHJJ1VVUVUdivXkCSGE4A4OYEwmI6mp59E0EwZDWUwm83VHIjVs2MD27woVQpg8eQrPPPM033+/hscee4ycHOuq1FlZGXafs1gsGI25ZGVlkJNzwZZGp1OuSGOmSpUwu8/6+/uTkBBv2/byyy9fN//ryczMZOjQV6hcuTIvvND1up85duwYQ4cOoXv37tSrVzdf+ebmXv8Yc3JyGDfuLe6++27efPNNLBYLy5cvY9CgAcydOw93d/dr8lJVFb1eR3JyvO0Rl8ibm1spSpcOQK83OLsqQghRItyRAYymaSQlnUVVVUqXDsDNzQOD4fotMFfz9PSmcuXKxMfH4+npTUhIBYxGI2azZmtdAWtLSLly5fH09Mbd3cP2WU9Pb1saVdXh4eFht02v16OqOrtt+ZGZmcnIkSPw8fHhvfdmXjd4OHbsKEOHvkLHjk/Rt2//fOft5lbKVv8r/fzzL5w7d45PP/0MVbU+jWzQoBEtW97P9u1/8cgjba7J69IoJH//MtICkw/WVkITGRkpJCWdpUyZ0GtaYrRSHhjr1kcr5eGcOt7h5RdWSa13UdXLkXxK6rkRJcsdGcCYTEY0zYKvbzClSnmg0+mwWJR8BTBZWVnExsby6KOPo6o67rqrDnq9nr///osHH2wNWDu3nj17lvr1G6CqOlswoWnWoOUS6y8i5ZptimK/LS8ZGRkMGNAPNzc33n9/Fh4e1/7QHz16hD59evHYY08wcODgfOdtrZN6TT0BcnNzUVUVnU5v+6Wq02m2f1/vGC4FMAaDmwQw+eaOTqfj/PlzmExGDAY3u73mGjVJWf+Hk+om5RdWSa13UdXLkXxK6rkRJcsd3YnX+ov55t5/fxr//PM3cXGn2blzB6++OgRV1dGmTVvA2r+lffsOTJ8+jb/+2s6+ffsYO/Yt6tWrT7169QEoX748iqLwxx8bSU4+T1ZWVr7rOGvWTN58c/QN92dkZNCvX28uXLjAW2+NIzMzk8TERBITEzGbrX1Qjhw5TK9ePWjatBldu75o25+cfN6Wz549u+nY8Qni48/Ztp05c4aDBw9w9uwZLBYzBw8e4ODBA7b6R0REkpaWxpQpEzl27BhHjx5h7Ni30On03Htvk3wfo8hbfu5VIYS4k8i3Yh7OnYvntddG0qHDE4wcOQxfXz8+/fQz/P0DbGlefXUEzZvfz/DhQ+nZszuBgUFMm/a+bX+ZMmXp06cfs2bN5KGHWjJ16qR8l5+YmMDZs2dvuP/Agf3s2bObI0cO8+STj/Lww61sr3PnrJ9bt+4XkpOT+eGH7+z2d+36vC2f7OxsTpw4YdcvZc6cj3juuWeYM+djsrKyeO65Z3juuWfYt28vYB1tNWPGLA4fPkz37i/Qo0d3EhLi+fDDjwmW2TOLjX73ToJCg9Dv3inlu5CSWu+iqpcj+ZTUcyNKljtyJl6jMZekpDMEBpbH3d39qqUEinOkh5avx1a3E5mJt3CuvGevfoSk37UD/4fuJ3nd75jqNSj2ut3p5RdWSa13UdXLkXxK6rkRxSO/M/HekX1gbkTRqZgpvl+qOlQw56/zsCgaZ87EsWjRfP7992+SkpIICgrikUfa8eKLL2MwWEf45OTkMG3aZA4e3M/Jkydo1iyKyZOn55n3wYMHmD37Aw4c2Ieq6mjRohUDB76Cp6enLc3Zs2eZPn0y//77Nx4enrRt+xi9e/e/6Zw9QgghrlWgb81WrVpx+vTpa7Y///zzjBkzhpycHKZMmcIPP/xAbm4uUVFRjBkzhqCgIFvauLg4xo4dy7Zt2/D09KR9+/a8+uqrdl/g27ZtY8qUKRw+fJjy5cvTt29fOnbs6MBh5oeCGY2UnAuYiyGi0CkKfu4eqChQjEHTne7kyRNomsbw4aOpUCGU48ePMnXqRC5cuMCAAUMA6xB4d3d3nnqqM7/99mu+8k1MTGDIkH48+GBrhg4dQWZmJh98MJ1Jk8YyYcI7AJjNZkaMGExAQCBz5nxCYmIiEyeOQa/X07t3/keFCSGEKGAA89VXX9k6hgIcPnyYl156iTZtrMNlJ02axMaNG5kxYwY+Pj6MHz+eAQMGsHz5csD6Bd67d2+CgoJYvnw58fHxjBw5EoPBwNChQwGIiYmhd+/edO7cmWnTprFlyxbeeOMNgoODad68eVEd9w2ZNQ1zcaxMrRa8+9EPP6yx9b05evQwqqpSq9ZdNGp0ry1NTk4O27Zt5tSpU1gsZsqVK0/Tps0oXdo6625GRgZbtvzJuXNnsVjMeHv70KRJBKGhlfJVh//++4eTJ09w1111+O+/f8jNzSE8vDqRkfexZ88u9uzZjaZp3H13HerXb2j7XG5uLtu3byUh4RylS5fmxx9/4oUXXqZ69RoAnD4dy6xZ77F37x6ysy9QuXIYvXv3p3HjCFseTz31OE880YHY2Bg2bFiPj48P3br14Mkn8x/cNm3ajKZNm9neV6gQyqlTJ1m9eqUtgPHw8GDYsNcA2L17JxkZ6Xnm++eff6DX6xk6dKRtSPmwYaPp1q0zsbExhIZWZPv2rZw4cZwZMz4mICCQ6tVr0rNnH2bPnsXLL/eytQAJIYTIW4F+iwYEBBAcHGx7bdiwgUqVKtGkSRPS09NZuXIlo0aNIjIykjp16jBp0iT+++8/duzYAcCmTZs4cuQI7777LrVr16ZFixYMHjyYpUuXkpubC8Dy5csJDQ1l1KhRhIeH07VrVx555BEWLVpU1Mfuko4cOYSqqjz+eHsiIpqxZ88uDh48YNv/xx+/kZiYSOvWj/DYY0+iafDzz2ttfXy2bNmExWLm0Ucfp0OHp2jcOKLAk6Olp6cRGxvDI4+0pUWLVhw+fJCff15LZmYm7do9RuPGTfjnn79ISIi3febXX38hO/sCDzzQivvua07VqtUYMqQvaWmpgHV4etOm9zFz5sd88slSIiIiGTly6DUdmJcvX0qtWnexcOFSOnR4munTp3Dq1Anb/gEDejFx4tgCHU9GRgalS5fOO+FNGI25GAwGW/AC2IbP79q1A4C9e3dTtWo1AgICbWmaNIkkMzOT48ePFrpsU/WanP99G6bq118B/Fa708svrKvrbe0fpti9nDH5clGdT0fycdVrKopXoUch5ebm8u2339KpUycURWHPnj0YjUaaNbv81214eDghISG2AGbHjh3UqFHD7pFSVFQUGRkZHDlyxJYmMjLSrqyoqChbHreTMW+8ziuvDCrQZ7y8vImIiMTX14/w8GrcdVcd9u7dDUBaWiqnTp0kKup+ypYtR0BAIA880JKsrExOnjwBQGZmBmXKlMXfPwAfn9JUrFiJcuXKF6gOmqbRvHkL/Pz8qVSpMuXKhZCWlmKrV/XqNfH19ePMmTgAzp07S2JiAi1bPkRAQCBeXt68/PL/8Pb2YcOG9QBUr16D9u07UbVqNSpWrMT//teXChUq8OefG+3KjoxsRseOTxMaWpGuXbvh6+vHv//+bdtftmw5AgODyK/Y2BhWrvyiQK0419OoUWOSkhL5/PPFGI1G0tLSmDNnFgBJSYkX/59EQECA3ecuBTNJSUmFL9zDA3Ot2nCd+X+KxZ1efmFdUW9FAdVdh8mA3Ut11xV/EFNU59ORfFz1mopiVeieg+vWrSM9PZ0OHToAkJiYiMFguOYv2cDAQBISEmxprgxeANv7vNJkZGSQnZ1NqVKlClvlQjGbzSydP59f1/5E8vkkAoKCaf1oO5576SXbhG2aprFk3jzWfvMtmRnp3FW3HgNGjKBCpYoAnIs7Q/eOHflw8aeE16jhUH2Cg8vYvQ8ICCA6ejbLli0jNjYGvV7P8eMnGDRoCMHBZXB3L4Wvry/jx4/l7NmzJCUlYTAYqF69Oi+80I0GDRpd80v1ksTERGbMeI9t27aQmZlJlSpVaNnyQQIDA2yPOx59tI0tUJk8eTIAAwcOpmzZMmRnW5dQOH8+CaPRyOefL8bT0xM/Pz8++OADzpyJ4/TpWMDaAvPJJ3PZsmUTSUnWOWxycnJsQ8EvCQ+/vDK2oigEBASSnJxs2/bmm2/n+1wmJMTz6qsDadnyIZ54okO+P3c9VauG8/rr4/jww/eJjv4IVVV56qnOBAQE3vI1jNSYU3i+9w5ZQ0dgqZi/R4FSvvPpYk/hOf0dLgwbiVapMibNwvG0ZHIvriHmpuoIK+2PXlEozsGiRXU+HcnHVa+pKF6FDmBWrlzJ/fffT9myZYuyPiXOl0uW8P2q1bz61ptUDqvKoQP7eX/CRLy8vHny2WcupvmMb1d8yatvvUm58iEsnjuXN4YMIXrZ57hdZ0r/opSTk0N8fDz9+g2kdGkffvvtV/bt28+QIYNYunS5LV2tWrUZMuRVgoKCOXXqJO+/P41Zs2by5JNP0qRJU+66q841eb/11uukp6fz/vsf4Ofnz9q1PzB79kf07Pk/u3Tt2rXjnnvupXnzBwDw8vJkw4b1ttFVRqMRT09P2rZ93LYWUlhYLUwmE97e1qFyH300g7/+2kb//kMIDa2Iu7s7b7wxEqPRfr2kq0frKIpy3TWs8pKYmMDAgX2oU6ceI0a8XuDPX8/DD7fh4YfbcP58EqVKeaAoCl98sZSQkFDAGszv37/X7jPnzyfZ9hWWmnwej6WLyX6pp3MCiDu8/MJQFPDISqHU0sUo/V9G86yAWdFj1izkXtHPUIGLM3Nb32varZ96oajOpyP5uOI1FcWvUI+QTp8+zebNm3nqqads24KCgmxN51dKSkqyTWoWFBREYmKi3f5L7/NK4+3tXeytLwD7d++m6f3NaXLffZQNKU/zVq1o1KQJB/ftA6xfKF9/8QWdX+pO5P33E1a9GsPGvEVSYiKbf/8dgO4XR1ANeLEbbZtGMqJvP7syFi9exMMPt6Jly+ZMnjwRo9F4w/pc2a8ErGsgdevWnUceacPdd9ehbNmy9OrVh/3793HmzBlycrJJTU3lmWeepV69+oSEhNC0aST9+g3g1KlT1Kp1F4cOHbhuWTt37uDZZ5+jTp26hIaG0rNnLzw9PTlz5oxdOnd3d0qXLk1QUBBBQUF4eHja7Q8KCuLChQuoqoKPjzdeXl6EhIQQGloRPz8/wNpZtl27x2nRoiXh4dZ+ImfPxuVxdQonISGeAQN6U7NmLUaPHmPXb6UoBAQE4unpyfr1P+Pm5mbriHz33XU5duyI3QzIf/21DS8vL6pUqVqkdRAlmzUosQbnFksimhaPqpjQXTHjsk5R0OtVLG5gcVOwuCnonPFISYgSqlDf3KtWrSIwMJAHHnjAtq1OnToYDAa2bNli23bs2DHi4uJo0KABAA0aNODQoUN2z/s3b96Mt7c31apVs6XZunWrXXmbN2+25VHcatety46//ib21CkAjh0+zN6dO7n3Yj+ds3FxJCcl0bBxY9tnvLy9qXn3XRzYvQeAGZ8sAGDSrA9Y+v13vDllsi3t33//RWxsDNHRCxg3bgJr1nzDmjXf2PbPmfMxjz56eVHEzMwMtm/fQmpqCseOHWH//r221pPSpX2pXLkK27ZtQVEUTCYjGzduwNPTi0qVqgCwbdsWDhzYz7fffsNdd91NQkI8vr7+1z32+vUb8PPPP5GamorFYuGnn37EaDRSpUoVu3Tr1q1jzJi3eO65Z/j004XXrDIdEhJKcHAZ1q37mTNnzpCVlcX+/XuJjv6IAwesgWBoaCU2bvyVw4cPcvjwIcaNe71QE92NH/8Wc+Z8eMP9CQnxDBzYm7JlyzFgwBBSUpJJSkq09VO55PjxYxw+fJC0tFQyMjIu1uugbf++fXt4/vlOdgHlypVfcPDgAU6dOsnKlSt4//136N17gG2RzyZNmlKlShjjx7/F4cOH2LZtC/PmzaZjx2dwc7OfnE7cSUxo2rUrs7upOtz1GpqShVnJRFOzcHPTUFWJYISAQjxCslgsrFq1ivbt29s15/v4+NCpUyemTJmCr68v3t7eTJgwgYYNG9qCj6ioKKpVq8aIESMYPnw4CQkJzJgxgy5duti+wDt37szSpUt555136NSpE1u3buXHH38kOjq6aI44DzrrUADb++e6dyc76wK9nu2MqqpYLBa69+1D63bWtZBSL/a/CAwKQnfF5wICAkk5fx6dqtr6mPj7+9tamnQX/4zy8SnNyJGj0el0hIWF0bz5/Wzfvp2OHa2tW35+/oSGhtryrVatBiaTmTVrvkZRFO66qw61atW27W/cuCkzZ86gZs2abNiwjrJly/Pww21QVZWZM99n2TLriK9y5crRqdNT+Pr6EhFxudP0ihXLqF69Bg0b3sPUqe8ycuQIWrZsjl6vp1SpUvTvPxAfn8urUj/33PPk5uZgMBjw8PBk1qyZJCYmUrt2Lbvz+vDDbfnnn7/Ytm0Lnp4eLFr0KRUrVrYNCx848BUmT36bPn1extfXjy5dupGZmVng63fu3Nmbtqj89dc2YmNjiI2NoUOHdnb7Nm263Bl4+PDBnD17uaXppZe62KXJzs7m1KmTdsHavn17WbBgLhcuZFGpUhWGDx9NmzaP2vbrdDreeWcG06ZNpk+fl/Dw8KBNm8fo0aN3gY9T3P5URUFVTBgtZ8g1GzHoDKhKZaxf2zJ3lBAFDmA2b95MXFwcnTp1umbf6NGjUVWVQYMG2U1kd4lOp2POnDmMHTuWZ599Fg8PDzp06MCgQZdH4lSsWJHo6GgmT57M4sWLKVeuHBMmTCiGOWA0dKj4udv3ev/pxx/57aefmDhlKlXDwzl08CDT35lK5fIVePzJJ/F1sz7W8nf3ILDU5UcnbjodiqIQWMqTnIt5+rqVskujoBAeHo5Od3nV5qCgIA4fPmx737nzc3Tu/JztvaqqRERE0qxZ1DVHYDQaeeut1/H29mbu3E/w9va22//ii91p374DZ86cYe7cOezcuZOXX/6frZOpyWTiwoULtlFJH3/8ERkZacyePRd/f382bPiVefPmsmDBQlueXbu+aFeGXm9g0qTxbNq01a5VwWAw0LRpM9tSAs2bP2TXwlK+fAgffDDHLq9OnZ6xe//VV2uuOeZFiz63e//hh3OvSXOldu0ep127x2+a5kZlXalRo3vtAh7IXwficuXKM23aB3mmKwhLcBmyBg3FclUH7+Jyp5dfWFqZMhhf7YlW5nL/J4Oq4nbx+0B/MRC3aCbMmhGdVjwtL0V1Ph3Jx1WvqSheBQ5goqKiOHjw4HX3ubu7M2bMGLug5WoVKlRg3rx5Ny0jIiKCr7/+uqBVc5hmtlycGfeyme9N56WXetD2YWuLS83wGpw9HceiBfN58rEnCfa3fvmkJCRRNuDyAobnE5OoWbMWqhnUi31MVQuo5isy17TrTCFfuBEHRqORUaOGc+bMGaKj518TvIC1Bcjf35/KlasQFhZG27YPs2vXLurXt66afeZMHCEhIZQvH0JMTAxffLGML79cRXi49fFejRo1+e+/f1mx4gtef/3N69ajbt26mEwm4uJOU6VKWIGPQxScpXwImW+MlfJdTYUQjG8PRdNMKKjoVR0Vfd0wWazfCW46PXpVobg7vRTV+XQkH5e9pqJYyWrUV9C0S738L7+ys7NRLg5jvPSyPkqy/jskpAJBQUFs27bVtj89PZ09e3ZTr149tCuCFJPJbJdPUbkUvJw6dZI5c+baOsbezKXWD6Mx17atYsVKtG5t7W9zaQi0otjfIqqqu+mon4MHD6Kqqt1kbeLWUjLSMfz5B0o+ZgyW8kuQ9HTU37dDeiagoigmLNo5cs2x5JpjsWgJKIoF5ao/qqwdgG/dZHdFdT4dycdlr6koVhLA5OH++1uwYME8/vjjd+LiTvPrr+v57LMltGzZCrB+mTz/fFfmz5/Lxo0bOHz4EG+99TrBwcE88IA1jb9/AKVKlWLz5k0kJSWRnp7/H8rly5fRu3dPwPr448r+KmANXkaMeJV9+/YyceIUzGYLiYmJJCYm2kYz7d69i+XLl3Hw4AHi4uLYvn0bo0ePJDS0IvXqWVtf4uPP0bHjE+zZY50Ur0qVMCpWrMTEiW+zZ89uYmJiWLLkU7Zt22I79p07d7J06RIOHTpIbGwsP/zwPdOnv0O7do86PLOtyD/dsaP4dXgU3bHCz+Yr5Rc/5chRSrXtjnr0pG2b+eLjIuvLvmOvgoKiKnajkm7FyKSiOp+O5OOq11QUL1kCNw8jRrzGxx9/yOTJE0lOPk9wcDCdOj1Fr159bGm6dXuJCxcuMGHC26Snp9OgQUM+/HC2bSp5vV7P8OEjmTcvmjlzPqZhw0bMm/dJvspPSUkmNjb2hvsTEuLZuPE3ADp3ftpu39y5C7j33saUKlWKX39dR3T0x1y4cIGgoCCaNbuPqVPftfVTMZlMnDhxguzsbMDaZ2XWrI/44IMZDBkykKysLCpWrMS4cROIirL2R3JzM/DTT2uJjp6D0ZhLSEgFunR54Zp+MUIIx6mKzjrMWsnCrFgublNw03uSY1Qwm6Vjr7izKFpxTvHoBImJ6ddM/GQ05pKUdIbAwPK4u7vj5eWOxWK55RNECWydeDMzcwo1TPpOdeU9azDYD7nW79qB/0P3k7zud0z1GhR73e708gtDVRW8D/2De7MHuLBpOVqjRri5BXMu6wTZJusfEZ4Gb4I9QzmXad126X1iVizZphwADDoDAe6VuZChx2wumkVoi+p8OpKPK15TUXQUBYKCfPJMJy0wQgjhBIru4lIkOqwP8/P5HOjSYyag2EYmCVESSR8YIVycpjdgLh+CVsBVxaV851EUBYtejzmkDMkWExmmXErK3C5FdT4dyccVr6kofvIISR4hFSt5hFQ4N3uEJFyPTqfi4W0iMfsouebcax4XwY0fIV2Zxk3nRlCp8CJ9hCSEs8kjJCGEuENcGloNxbPgoxAlgTxCuoKiWL8Iius1ZswbDB062NmHLVycbt9eAurXQrdvb96JpfwSQ9mzl/K12+K270jh87jO0GpHh1UX1fl0JB9XvaaieEkAcwWDQcHNzWL3MhrTmT59Cu3aPUzTpvfy0ktdOXhwl10ag8FMdPQsWrduSdOm99K3b0/OnDlu25+QEEPDhnU5enSf3eeKYhFko9HIzJnv88wzHWnWrAkPP/wgb745+ppVqy/Jzc2lc+enadSoHgcPXn8V6ivt3LmTXr160KxZE5o3j6RHj+62odaX/PHH77z44vNERjamRYv7XCIo27jxV4YM6cdjjz3Eww+3oHfvl9i2bYtdmh07/mXEiFd48sk2REXdy++//5avvFeuXEGXLk/RqtV9PPdcR3788btr0vz66zqef74TrVo148UXn2XLlk2FPhbFZER3Jg7FdONVzG+lO738/LA+Or1y8jkFjEb0cfFgNOedwQ3YD60umgUfi+p8OpKPK1xT4XwSwNgoKIoFTctE09JtrzfffIMtW/5k8uTXWb36UyIjG9GzZy/Onj1mS7NgwRyWLv2ct956lc8/j8bDw0CvXr3Jzk68mObSooRZV+Rd8IUKryc7O5sDB/bTs2dvPv/8C6ZNe4+TJ08wZMig66afOfM924KSedm5cycDB/YlMrIZS5Z8zpIln/Pss8/ZLZa4fv0vvPnmaJ54oj3Ll3/JwoWLadOm3U1yLRl27PiPxo0jePfdmSxYsIRGje5l5MhXOHToclB34cIFqlWrztChI/Od7+rVXxEd/REvv9yLJUu+oEeP3rz33jts2vS7Lc3u3TsZN+51HnvsST75ZCnNmz/Aa68N49ixwv8lLkouRQHVXYfJgO1lMVAkSwQoinUGX6PlLNmmGIyWM6iKCZDRSeL2J31grmFB06yd4bKzc1i3biMffDCJe+6pB0C/fi/x229/snz5KgYN+h+aprFkyQp69XqBli3vA2DixNE88EB71q//nbZtH+SRR6wTzD311EsA3HtvAxYt+tBW4uLFi/jss8UYjUYefrgNw4aNwGC4tvf9Dz+sISAgEJ1Ox6FDB1BVHbVq1Wb27MsLGObm5vLII22ZPv1dPvroA6pWrUpERCQBAYH8+ecfbNmyhR49evLnn5v47rtvSEiIp1SpUsTGxtC+vf0CndOnv0Pnzs/z0ks9bNsurW+0YsUywsOrMWHC20RERGCxmFAUhZCQChw7doQlSxbi4+NDVFQLgoIuB0wJCQkcO3aEt99+G5PJxP33P0Dv3gPw8LAueLl27fd8+eVyTp06iYeHB40a3cvgwa/aVq3+99+/GTSoDzNmfMzs2bM4ceIY1avXZPTot6hUqUq+r/Lgwa/ave/duz9//LGRP//8gxo1rCtpR0beR2TkffnOE+Cnn37gySc78uCDDwNQoUIo+/fvY+nST4mKuh+AL79cTkREJM8/b53w73//68tff21j5coVDB8+ukDliZJPURSMmoXjacnkWqytLb5u7tQswlFH5mJe8FGIkkBaYG7CbDZjNpvtVlUGKFXKnf/+s065Hxt7hsTE8zRteq9tv4+PN3Xr1mbnzj0ALFsWDcC8ee+zYcNqZsyYYEv7999/ERsbQ3T0AsaNm8CaNd+wZs03tv1z5nzMo4+2sb0/cuQQer2exx9vT+PGTdix41/i4i7P1Pvrr7+QlpaCoih07PgUgYFBrF37PWfOxDF+/Dj69Olr+0v/gQcexNvbmwMH9l1z7OfPJ7Fnz24CAgLo3v0FHnroAXr2fIn//vvXlmbDhl9JTU2lUaN7+Oyzz+jSpTPdu3fFYHDjiSc64uNT2u6xS3p6Ghs3/kq5cuWZOXM248ZNYteuHbz//ju2NCaTiZ49+7Bo0edMmjSNs2fPMHHi2GvqN3fuxwwYMIT585eg0+mYPPnyStBnzsQRFXUv//779zWfuxGLxUJWVqbDSyDk5uZec7+4u7uzf/9eTCbr1PB79uzi3nub2KWJiIi0LeMgbk+5FjO5ZuvLdJP1xIQQ+SMBzE14eXlSv/7dREd/Snx8ImazmTVrfmbnzr0kJiYBkJRk/X9goL/dZwMDA0hMPA+Av78fAH5+pQkKCsTX9/IvSR+f0owcOZqwsDDuv78FzZvfz/bt2237/fz8CQ0Ntb339w+gYcN7KF3al2rVahAUFERcXBwA586d5ezZM/zxxybatGlL+fIhNGnSFIPBwOjRo3jqqWfIyckmLCwcAG9vbxo0aGRr3bjSpeULoqNn06FDJz78cDa1atWmT5//ceqUde2WS4tUfvrpp/Tp049HH30UPz9/xo2zrkZet24DUlKSuXAhC4CdO3dQqVIVwsKqUqFCBerWrc/gwcNZu/Z7cnKsM4s+9tiTREbeR4UKodSpU5chQ4axdetmsrKy7OrXq1c/Gja8h7CwqnTt2o3du3fZ8tDr9VSqVJlSpUrldYltli1bwoULF2jVqnW+P3M9ERGRfPfd1xw4sB9N0zhwYB/fffcNJpOJlJQUwBocXn3O/f0DOH8+qVBlmquGk7L6e8xVwx2qe2Hd6eXnhwK4qTrcdNaXXlXRqoWT8F00xqqheX6+OBXV+XQkH1e4psL55BFSHiZPfoM335zCgw92RKfTUbt2ddq2fZB9+w4WSf7h4eHodDrb+6CgIA4fPmx737nzc3Tu/Jzt/dWrPHt4eHLhgnXl6Pj4c6xZs4bMzExatWrJkiULAfjnn3/IyMjgpZd6sGzZZ1S96kshODjYFgRdcml6oI4dn+LJJ9sDUKtWbbZv38Y333xN2bJl8PT0BKBHj//Rrt1jnD0bR8eOT9OnTy9++eVnHnnE2nJ04UI2Hh6enD+fRE5ONmlpKXzwwQckJiaiaRoWi4UzZ+KoUiWMAwf288knczly5BDp6em2x3nnzp0lLKzqFeetuu3fgYFBACQnJ1OuXDmCg8vw+ecr83X+AX7+eS0LF85j8uTp1w3mCqJ79x4kJSXSu3d3wBqYtGnzKJ9/vtihjpU3o3n7YLyv+S3JW8p3nKKAl7tCFT93LBd/rtx0evTuBnLub4x2cVmAkqKozqcj+ZT0aypKBglg8lCxYgUWLZpFVtYFMjMzCQ4OYtiwMYSGhgAQGGgNKJKSkgkODrJ9LinpPLVqVcsz/0utGJcp3GxuQfWqoUuKogDaxdFIM8jIyGD+/EX4+vra0mzf/jfHjx+jadN7sVgsFz8DXbs+R9u27XjssceuKScoyHosVwc7YWFVOXv2DGXLlsHf3/9imsuBhbu7O6GhFTh79swVfRStx2MyGQkPr85dd91NWFgt2yMVgLJly3HhwgVefXUATZpEMmbMBPz8/Dl37ixDhw7AdNVohCvP26XjuRTsFMS6dT8xdep4xo+fSuPGEQX+/NXc3UsxevQYRox4nfPnkwgMDOLbb1fj6emFn5/1fAUEBJKcfN7uc8nJ568JTvNLPROHx4K5XOjRC0v5EIePQcovagqqYsKsncVott7HetULJc6C74xZJL3cHip7O7eKVyiq8+lIPiX/moqSQB4h5ZOnpwfBwUGkpqazefNftGwZBUBoaHmCggLYtu0fW9qMjEx2795P/fp1AGwdcm/VTJlms5lRo4aTmJjIk08+ib+/H6VLl7a9Ro9+g+XLv2TZshX07duPfv36AzBlyjv07z+QhISEa/IMCalAcHAZTp48Ybf91KmTlCtXHoCwsDDc3Nzs0phMJuLi4ih/nS+dwMAg0tJS8fLyIiQkhNDQiraXwWDg5MkTpKam0qfPAOrXb0jlylWu+UVflH75ZS2TJr3N2LETadYsqkjz1uv1lClTFp1Ox/r1P9OsWZQt+KxTpx5///2XXfq//tpGnTp1C1WWmhCP5wfvod5g6PytdqeXn1+Wix1trS8TnEvA572F6BKSnV01O0V1Ph3Jx1WuqXAuCWDy8Oef29m0aRuxsXFs3vwXPXoMJiysEu3bW4cKK4pC165PEx29mA0bNnHo0FFGj55IcHAgrVpZfykGBPhRqpQ7f/65jcTE86SnZ+S7/OXLl9G7d88b7jebzXzyyQL27dvLO+9MIyAgkK+/XsWePbtITj5PfPw54uJi8fPzo1q16rRs+SCZmdby/fz8OXMmjuTk86Snp9Gx4xO2jqSKovDii91Yvvxz1q37mVOnTvHxxx9y4sRx2rfvCFgfX3Xq9DRz5nzMli2bSU5OJjp6NgCtWz98TV3r1m1AYmICe/fu5tixo8TEnOKPP37jvfemAtZWGIPBwMqVX3D6dCybNm1k0aL5+T5XlyQkxPP8853Yt2/PDdP8/PNaJkwYw4ABQ7jrrjokJSWSlJRIRsbla5OVlcXhwwc5fNj6uPDMmdMcPnyQs2fP2tLMmfMh48e/ZXt/6tRJfvrpB2JiTrFv3x7GjHmNY8eO0rt3f1uap5/uzLZtm1m27DNOnjzBggXRHDiwj06dninwsQpxPZdm5r0874yzayRE0ZNHSNdQ7X7YMzIymTEjmnPnEvD1LU3r1i0YNKiX3UiTHj26kp2dw7hx00hPz6BRo7pER0+nVCnr0GCDwY1Ro4YwZ85CPvroExo1qseiRR/nqzYpKcm2DrXXk5qayp491l/UnTs/bbevY8eOVK9eg3LlytuGKYeHV6N27bsA+O239URENKVatRocPnyIEydOkJ2dzZkzcfz443c888xz5ObmMn36u6SmplKjRk0+/jiaihUrsuXinG9DhgxFr9fx5pujSU9P56677iI6ej6lS5cmIyPdrj4BAQE8+GBrjh07yttvv01iYgIhIaE8+KC146y/vz+jR49h7tyP+eqrL6hRoxb9+w9h1Kih+TpXl5hMJk6dOnnNhHtX+vbbVZjNZt57b6otgAJo2/YxXn99LAAHDuxj0KA+tn2zZr1/TZqkpETOnbsc0FgsFpYv/4xTp06i1+tp1Ohe5sxZYNciVbdufcaMmci8eR8zd+5HhIZWZPLkaVStmvcjRyFuxm5mXsvlLzI9KuYcsywxIG4rspjjFYs5GgzWyeyKi6apGI3OX7dk7drv8fT05P77WwJw+PBBdu7cQceOT1/T58ZRsphj4dxsMUf9rh34P3Q/yet+x1SvQbHX7U4vPy9XL9wIFxdqPJCEvvEDxGxYhP7eewq1mOPV2zz0ngR7ViUtJxvjxaHaqqLgpfckJ1PDbM77Z66ozqcj+ZT0aypuLVnMsRCMRo3ifapW/MGLyWTiwIF9VKhQEVVVOHbsKHFxp2nT5lFbmpiYGO65p3GRBy/i1rD4B3Chy4tYHBxBJeUXs0B/Ml9sjyXAsbmHrmQ/M691dJNBZ0BVKmP9us/7C6eozqcj+bjsNRXFSlpgrmiBub3PhJXZbOKXX34iKSkJs9mEr68fDRo0pHLlsGIpX1pgCudmLTCiZLthC0wBW1cKm8ZN50ZQqXAuZOhv2UACIYqStMCI69Lp9HatLeI2cOECupMnMFeuAhf7Okn5znVpZXvrv2/Qg/bCBfT7j6KEBoKh5AyjLrLz6Ug+JfCaipJHnhEI4eL0hw8ScH8E+sNFM7milO+YqxdvvOHCjfsPUTbiKQyHThZ7HW+mqM6nI/mUtGsqSiYJYJxozJg3GDp0sLOrIYQoQoqiYNIsxGakcjwtmbNZ6YAm60MLUcTu6EdI+en+k5mZyccff8iGDb+SnHyemjVrMXz4SO6+u44tzZgxb7Bmzbd2n4uMbMZHH80BIC7uNI891pZly1ZQs2atIj0Go9HIxx9/yJ9//kFsbCze3j5EREQwaNAQgoPL2NI9+mgbzpyxXy5g4MDBditNX23ChLfZvn0rCQkJeHh4Ur9+fQYNeoWwsGv7y6SkpNC581PEx8ezceMmfHyKrmOiyN+9KkqGS0sHVFANWDTNumyAqly/FUYIUWh3ZABzae2h3NycPBf8e/vtsRw9eoTx4ycSHFyGH374jr59e/HVV6spU6asLV2zZvcxdux42/urVyS+VbKzszlwYD89e/amRo0apKWlMW3aVIYMGcTSpcvt0vbt258OHTrZ3nt5ed4079q176Jt23aUL1+e1NRUoqNn079/b9as+dFu/SaAt98eQ/XqNYiPl5kzb4XcXOuIEp3ujvyRdTH2SwfoVS8UpRyKtMEIUaTuyG9DVdXh4eFNRkYyOp1KqVJ6NO3aUUjZ2dn8+us6pk9/n4YNGwLQq1dvfv99IytWfGGbkl/TNAwGAwEB9itSWyxmAB57rC0Azz1nnWm1UaN7mDt3PpqmoWkan366kM8+W4LJZOThhx/h1VeHodcb8nUsXl6efPTRbLttw4ePolu3rsTFxdqm/QcNDw+PG9bxejp06GD7d7ly5ejbtx/PPfcsp0/HEBpa0bbvq69WkJaWxv/+14s//9yExWK5Yb7WP0I1jMZcGYWUD5qmkZubQ0ZGMh4e3tcf2q4oaG5uzvsL/04v/wYuLx1gun4CRUFzM1Di4pqiOp+O5FNCr6koWe7IAAagdGnr/AKZman4+JTCZDJjsdgPMczKysJsNqNpZrKyLk8xbzDo+fffv23bTCYj//zzNw891BIfHx8aNWpEz57/sy2oGB09l969e/H+++9TpUoYBoOBrKwMTCYjf//9F35+vsyYMYPTp2MZO3YsVapU5vHHnwDgk08+Ye3aH1mx4st8H1tSUgKKoqDTqbY6aprGokWfMH/+XMqWLctDDz3E008/c53FJK/vwoULrFr1FeXLl8fHx9uW74kTx5k7N5ro6Lm2Fa2zsjLQ6a7/xaOqKnq9juTkeLvFHMXNeXh42+7Zq5nq1icxNrGYayTlO6xhPeISt5Nryi5RX8RFdT4dycdlr6koViXp56ZYKYqCr28goOHu7obBcG0LjKenN/Xq1WPJks+oVesuAgIC+emntezdu5fQ0Ip4elqHPjZv3oLWrR+hQoUKxMbG8tFHsxg5ciQLF36KTqejfPkKAJQpU56KFSvb8tfrDRcXW3wTnU5H7dp3s27denbs2Mmzzz4PQHBwGSpWrGQrKy85OTnMnTuXRx5pQ3Dw5UdcnTs/T61atfH1Lc3OnTv56KNZpKamMXTosJvm9+WXK/jggxlcuHCBypWrMHt2NL6+1lac3Nxcxo8fz5AhQwkLCycpKdl23m5U30vzwPj7l5EWmHzS6fQyqWAJduWQaet7aTUQojjc8d+KqqqiqrobvsaPnwxA27aP0KxZBF98sZxHHmlr97m2bR+lZcsHqVGjFq1aPcTMmR+xb99e/v3334tp1OuWpSgK4eHVMBjcbNuCg4NJTk62vX/uuS5ER8+/aR0vvcxmC6+9NhLQGD36Tbt9L77YnSZNIqhZszbPPNOZV14ZxooVX2AymW+aZ7t2j7Fs2QrmzfuEypUrM2rUSIxGE6qq46OPPiQsLJzHHnvipsd5vZfB4CavfL7yCl50hw7i92BzdIecM+T0Ti7/6iHTNx02fbX9Bwlu/hyGgydudTULpKjOpyP5OPueEq7hjm2Bya+KFSsyf/5CLlzIIiMjk+DgYEaOHE5oaOgNPxMaGoqfnz8xMTFERDS9af7XPsJRCjXixGg0MmrUcM6cOUN09Hy8vW/eYlO3bl1MJhNxcaepUuXGs/D6+Pjg4+NDpUqVqVevPi1a3MeGDetp06Ydf/21nSNHDtO48S/A5ZEyrVq14OWXe9K3b/8b5iuKjpJ9AcPunSjZF6T84i5bUTBqFo6nJZN7sd+Xr5s7Ndw98+7aciEbt50HULJzbnk9C6Kozqcj+Tj7nhKuQQKYfPLw8MTDw5O0tDS2bNnM4MGv3DDtuXNnSU1NITg4CACDwdoh91ZN430peDl16iRz5y7Az88vz88cPHgQVVUJCAjMdzmXApTcXCMA7777Hjk5l1d83rt3L+PGvcX8+YuoWPHGAZ4Qt5tci5lcszWAMVlkun4hioMEMHnYvPlPNE2jSpUqxMTEMGPGe1SpUoUnnngSsHb0jY6ezYMPPkRQUBAxMTHMnPk+FStWIjLyPgD8/QMoVaoUmzdvomzZsri5ueHjk/c6DwDLly9jw4b1REfPv+5+o9HIiBGvcuDAfmbO/BCz2UJiorXzm6+vLwaDgZ07d7Jnzy4aN26Cp6cXu3btZPr0d2jX7lFKl7bO1xIff44+ff7H229PpE6dusTGxvLzz2tp2rQZ/v7+xMefY+HCBbi7uxMVFQVYW6eulJKSAkDVqmEyD4wQQohbSgKYPGRkZPDhhzM5d+4cvr6+tGr1EP37D7S1qqiqyuHDh/nuu29JT08nOLgMTZtG0q/fANtcMHq9nuHDRzJvXjRz5nxMw4aNmDfvk3yVn5KSTGxs7A33JyTEs3HjbwB07vy03b65cxdw772NcXMz8NNPa4mOnoPRmEtISAW6dHmBrl1ftKU1mUycOHGC7Gxri4q7uxv//fcvn3/+GWlpaQQGBtKo0T0sXLi4QK02QgghxK1wR65GfSVVVe6o1aidTVajLnpKSjKG33/DeP8DaH7+eX9Ayi8yqqpgNsCxq/vABHqSdOEoOebcG68ineNN6vdfkta8PqWCQ0rMatRFdT4dycfZ95RwLlmNWog7hObnT+4THfJOKOUXuUvLBlTxc8dy8S+gfC8d4O9HdofWWEzZN09XzIrqfDqSj7PvKeEa7vhh1EK4OiU+Ho/ZH6I4aRmHO7v8y8sG5JpjyTXHYtESUBRL3ksHnIvH+8Ml6OLPF09NFQVVvfy6UXxVVOfTkXycfU8J1yABjBAuTnc2Du8xo9Gdjcs7sZR/S1xeNuAmSwdc7fQZfEe/h+5Mwi2tm4KKXtXh6WnCy8tse3l6qtcNYorqfDqST0m4pqLkK3AAc+7cOYYNG0ZERAT16tXj8ccfZ/fu3bb9mqYxc+ZMoqKiqFevHt27d+fEiRN2eaSkpPDqq6/SqFEj7r33XkaPHk1mZqZdmgMHDvD8889Tt25dWrRowbx58wp3hEIIcQdTFBVFMaFp57BY4rBY4tC0eFTVJLMGC5dWoAAmNTWV5557DoPBwLx58/j+++8ZOXKkbc0fgHnz5rFkyRLGjh3LihUr8PDwoEePHuTkXJ6sadiwYRw5coSFCxcyZ84c/v77b9566y3b/oyMDHr06EFISAirVq1ixIgRfPjhh3zxxRdFcMhCCHHnULD2x7GoZiw6k/WlmlBusF6ZEK6iQJ14582bR7ly5Zg8ebJt25VzgWiaxuLFi+nbty8PPfQQAO+88w7NmjVj3bp1PProoxw9epQ//viDr776irp16wLwxhtv0KtXL0aMGEHZsmX59ttvMRqNTJo0CTc3N6pXr87+/ftZuHAhzz77bFEctxBC3BGsrSwaabk5ZJutHYYNqjsB7pptnxCuqEAtML/++it16tRh0KBBREZG0r59e1asWGHbHxsbS0JCAs2aNbNt8/HxoX79+vz3338A/Pfff5QuXdoWvAA0a9YMVVXZtWsXADt27ODee++1zaMCEBUVxfHjx0lNTS3ckeZJubgom7xu5Yu8J1gXBWTxKU3OI22xOGnywDu9/ELzLc2FtvdjKe1VLMWZNQ2zxfqyaDceTl1U59ORfFz2mopiVaAWmJiYGJYtW8ZLL71Enz592L17NxMmTMBgMNChQwcSEqyd0QID7Sc6CwwMtM0Om5iYSEBAgH0l9Hp8fX1tn09MTLxmraGgoCDbvisfWTlK0zQsFsvFBfPkl2txsM65I3/1FRVLWFXSljjv8eqdXn6hhYdx/ouZmEzZuOWdutgU1fl0JB+XvaaiWBUogNE0jTp16jB06FAA7rrrLg4fPszy5cvp0ME1x+xrGmRl5UpntmKkaZpMGliUjEaU1FQ0X1+4OEO0lO8CjEbUxPPg5QYlqdpFdT4dycdVr6koVgV6hBQcHEx4eLjdtqpVqxIXF2fbD5CUlGSXJikpydaCEhQUxPnz9vMemEwmUlNTbZ8PCgqytdhccun9pXyKkqaBxaLJq5heErwULf3+vQTdVRX9/r1SvivZvY/yVR/Ebd9RZ9fETlGdT0fycdlrKopVgQKYRo0acfz4cbttJ06coEKFCgCEhoYSHBzMli1bbPszMjLYuXMnDRs2BKBhw4akpaWxZ88eW5qtW7disVioV68eAA0aNODvv//GaDTa0mzevJmwsLAifXwkhBBCCNdUoACmW7du7Ny5kzlz5nDy5EnWrFnDihUreP755wFrb/cXX3yR2bNns379eg4ePMiIESMoU6aMbVRSeHg4zZs3580332TXrl38888/jB8/nkcffZSyZcsC8Pjjj2MwGHj99dc5fPgwP/zwA4sXL+all14q4sMXQog715Wz88pTdOFqCtQHpl69enz44Ye89957fPTRR4SGhjJ69GieeOIJW5r//e9/XLhwgbfeeou0tDTuuece5s+fj7u7uy3NtGnTGD9+PN26dUNVVR5++GHeeOMN234fHx8WLFjA22+/TceOHfH396dfv34yhFoIIYqAgoKiKpgNGpfWdzQoOiw5ZudWTIgCKPBiji1btqRly5Y33K8oCoMHD2bw4ME3TOPn58f06dNvWk6tWrX4/PPPC1o9IYS4ZazTAShXvHfNZgtFUdA0jVPpKWQYjbipOsJK+6N30eMRdyZZjVoIF2e6uy6JR2PRPItnPpE7tXxFAdVdh/GKOVT0KnmvOn0j9esQF/sHue6K076Icy0Wcs32rS5FdT4dycfZ95RwDRLACOHqdDo0Z074dYeUrygKRs3C8bRkci3WX/q+bu7UcPcs3AxSOh1aaW8wZRdpPR1WVOfTkXycfU8JlyCrUQvh4nTHjuD7THt0x45I+cUg12Im12x9mSw3ntE2T4ePEti+H4ajMUVXuSJQVOfTkXycfU8J1yABjBAuTsnIwO23X1EyMqT8W10W4KbqcNNZX3rVga/Q9AxK/boFJSOryOpXUAZVtR6LqrO1IhXV+XQkH2ffU8I1yCMkIYTIB0UBL3eFKn7uWC7Oxuim06NXlcL3g3ESBRW9qqNiaTdMFj2qouBlUMgxySyTwnVIACOEEPmioComzNpZjGbrJJt61QtFKYfiYuuoKYqKopiwaOfINedg0BlQlcrIrwThSuRuFUKIArBoJsyaNYAxayYn18Yx5ovHotNcKwATAqQPjBAuzxwSSvrkaZhDQvNOLOWXHBUrkDJtFKYKZZxdEztFdT4dycdlr6koVtICI4SL04KCyO7RS8p3NcFBZPZ6FksJG0ZdVOfTkXxc9pqKYiUtMEK4OCX5PO5fLkdJPp93Yim/5DifjMfy71GTU51dEztFdT4dycdlr6koVhLACOHidDGnKN2/F7qYU1K+KzlxioBeb6A/ddbZNbFTVOfTkXxc9pqKYiUBjBBCCCFcjgQwQgghhHA5EsAIIYQQwuVIACOEi9M8vTDe09hpK/fe6eUXmpcnuY3ronmWcnZN7BTV+XQkH5e9pqJYyTBqIVycuVp1Un5cL+W7mprVSVi/GKMpG4Oz63KFojqfjuTjstdUFCsJYIQQ4gYUBZSL6xwpLrbeUWEoioKqXj5OTdPQZHkkUULJIyQhXJx+1w6Cy5RGv2uHlF+EFAVUdx0mA5gMYDFQtIs2/ruTCqUb4rbzYNHlWUiXFnf09DThfegfAoN88D70D56eaqEP2ZHr4ux7SrgGaYERQojrUBQFo2bheFoyuRYzvm7u1HD3dLFlG/Pn8uKOCWBJBMBiSUZVTSiKDk2aYUQJJC0wQghxA7djsHIzmmYGLi1QaXZmVYTIk7TACCHEdSgKeLkrVPFzx6JpuOn06FWlaB8jCSEKTQIYIYS4LgVVMWHWzmI0G9GrXihKOZQ7rl1GiJJJAhghXJypRi2Stv6HJaSClH8LWDQTZs2IWTPlnbgg7qrJ2f++wVjWt0R9EVtqhZO18zsIreBQqObIdXH2PSVcQ0n6uRFCFEapUliqhkv5rqZUKczhldBM2c6uib1S7mjhlVAUB389OHJdXPWaimIlnXiFcHHqyRP49O2JevKElO9Kjp/Ev+fr6E/GObsmNgqgnozFvcdrKCdiHcrLkevistdUFCsJYIRwcWpqCqVWrkBNTZHyXUlyCp4rfkBNSXd2Taz9ehQFTQdaWhr6L76HtDQUXeEfIjlyXVz2mopiJQGMEELc4ayzDGuk5eaQkmN9pJVuzMWiaXfEDMTCNUkfGCGEEACYNQ3LxTnrLJrFuZURIg/SAiOEEEIIlyMBjBAuzlK2HJnDRmEpW07KdyXly5I2qjfmsoHOrokdc9lAzo/ogblckEP5OHJdXPaaimIlj5CEcHGWsuXIGjFaync15cuRProP5hI2jNpcLojkUT1x07k5lI8j18Vlr6koVtICI4SLU9LTMPy6DiU9Tcp3JWlpuK/bjJKW6eya2FHSMvFYvxUlLcOxfBy4Li57TUWxkgBGCBenO34Mv84d0R0/JuW7kiPHCerYH8Nxx+ZbKWqG47GEPP0K+mMxDuXjyHVx2WsqipUEMEIIIYRwORLACCGEEMLlSCdeIYQAFAW7SdtkAjchSjYJYIRwcZqbO+YqYWhu7lJ+ISkKqO46jFdM3qZXL+64VdzdMIVVRHMz3LoyCkFzM2AMq4Dm7tgoJEeui7PvKeEaJIARwsWZa9Xm/PadUr4DFEXBqFk4npZMrsUMgK+bOzXcPbllIczdtTm381uMpmxKUghjrF2VU/985fAwakeui7PvKeEaJIARQoiLci1mcs3WAMZkkan0hSjJCtSJd9asWdSsWdPu1aZNG9v+nJwcxo0bR0REBA0bNmTgwIEkJiba5REXF0evXr2oX78+kZGRTJ06FZPJZJdm27ZtdOjQgTp16tC6dWtWrVrlwCEKcXvT7d1DYO0wdHv3SPmuZNceyoW1xG3vEWfXxI7b3iNUqd4Ww57DDuXjyHVx2WsqilWBW2CqV6/OwoULbe91Op3t35MmTWLjxo3MmDEDHx8fxo8fz4ABA1i+fDkAZrOZ3r17ExQUxPLly4mPj2fkyJEYDAaGDh0KQExMDL1796Zz585MmzaNLVu28MYbbxAcHEzz5s0dPV4hbjuK2YSalIRiNuWdWMovOUxmdEkpYDI7uyb2bPVy7Hw6cl1c9pqKYlXgAEan0xEcHHzN9vT0dFauXMm0adOIjIwErAFNu3bt2LFjBw0aNGDTpk0cOXKEhQsXEhQURO3atRk8eDDTpk1jwIABuLm5sXz5ckJDQxk1ahQA4eHh/PPPPyxatEgCGCHELaMAburlP8j0qswyIURJVuCf0JMnTxIVFcWDDz7Iq6++SlxcHAB79uzBaDTSrFkzW9rw8HBCQkLYsWMHADt27KBGjRoEBV1eJCwqKoqMjAyOHDliS3MpALoyzaU8hBCiqCkKeLkrVPFzp5q/B9X8PahQ2g29qtzakUguQFEUVNX6usNPhShhCtQCU69ePSZPnkxYWBgJCQl89NFHdOnShTVr1pCYmIjBYKB06dJ2nwkMDCQhIQGAxMREu+AFsL3PK01GRgbZ2dmUKlWqYEcohBB5UlAVE2btLEazEQC96oWilEO5deOQSjQFBUVVMBs0zBf7MxsUHZYcM5rm3LoJAQUMYFq0aGH7d61atahfvz4tW7bkxx9/lMBCCCcxVa1G8ve/YKpaTcp3kEUzYdasAYxZu8X9L2qEk/DLIozhFUvUcFBjeEVi185FrV4FTdOIy0wjy2jCoKpU9PZFpyho+YhgHLkuzr6nhGtw6CFv6dKlqVKlCqdOnSIoKAij0Uhamv3qoUlJSbY+M0FBQdeMSrr0Pq803t7eEiQJcT3e3pgaR4C3t5TvSry9yY2oj+bt6eya2NG8PclpUhe8vdGrOsp566nk60aF0ga83AvwGMmR6+Kq11QUK4cCmMzMTGJiYggODqZOnToYDAa2bNli23/s2DHi4uJo0KABAA0aNODQoUMkJSXZ0mzevBlvb2+qVatmS7N161a7cjZv3mzLQwhhT407jdebr6HGnZbyXUnsaXxfm4budLyza2JHdzqewNdnoouLR1FMWLRz5JpjMWtnURUT5PORmiPXxWWvqShWBQpgpk6dyvbt24mNjeXff/9lwIABqKrKY489ho+PD506dWLKlCls3bqVPXv2MHr0aBo2bGgLPqKioqhWrRojRozgwIED/PHHH8yYMYMuXbrg5mad9bFz587ExMTwzjvvcPToUZYuXcqPP/5I9+7di/rYhbgtqIkJeEZ/hJqYIOW7kvhEvD9aii4x2dk1saNLTMZv9nLUhPOA9VGaWTNiKeAjNUeui8teU1GsCvTo9ezZswwdOpSUlBQCAgK45557WLFiBQEBAQCMHj0aVVUZNGgQubm5REVFMWbMGNvndTodc+bMYezYsTz77LN4eHjQoUMHBg0aZEtTsWJFoqOjmTx5MosXL6ZcuXJMmDBBhlALIYQQwqZAAcz7779/0/3u7u6MGTPGLmi5WoUKFZg3b95N84mIiODrr78uSNWEEEIIcQeRmZqEEEII4XJK0ug9IUQhWAICufBSTywBgVJ+ASiKdZI267+dMNdLUAAZPZ/BEuhb/GXfhCXQl9QeHbEE+juWjwP3hbPvKeEaJIARwsVZQiuSMfU9Kb8AFAU8PVVU9VLHVAs6nVK8k9ZVqkjqe69hMmXjVnyl5skUWo7Ed4fjaXBsCLMj94Wz7ynhGuQRkhCuLisL/a4dkJUl5eeTdXp8ExZLPBZLHJqWiIKleFtisrIw7NiPkpVdfGXmg5KVjdvOg5B1wbGMHLkvnH1PCZcgAYwQLk5/5BD+D92P/sghKb/ATGiaCU1zworQBw5T5v7nMRw+Wfxl34Th8EkqtuyO/tBxh/Jx5Lo4+54SrkECGCGEEEK4HAlghBB3NEUhv5PLCiFKEOnEK4S4Iyk6BU0BjYt/yTljJJIQotAkgBHCxWmKisXbB01xToOqK5avKAoWTSMlNwejJQdPvUqAIe8VlouUqmLx8QK1hAVOqoLF2xNUx66nI/eFs+8p4RokgBHCxZnr1iPpmPMWvXPl8i2aBbNFw6wVc/AC0KAuZ05vIteUXaK+iHPr1uD4qfUOD6N25Lo4+54SrkHCWyGEEEK4HAlghHBxuoMH8G/eBN3BA1K+K9l3gDJNOmE44Nhw5aJmOHCcipHPoz9w1KF8HLkuLntNRbGSAEYIF6fkZKM/eAAlxzkTot3p5Rdadg6GA8dQcnKdXRM7Sk4ubgePQ3aOg/kU/rq47DUVxUoCGCGEEEK4HAlghBBCCOFySlLndyGEECWcdR2py0O/NU3DGYO4hJAARggXZ65chdTFyzFXriLlu5KqVUha/j6mKiEl6ovYVCWEM0vfQakSarddQUFRFcwGDbPl8naDosOSY74miHHkurjsNRXFqiT93AghCkHz9SO3TTsp39X4+ZLd7gEsppLVUdXi60NW2+bXzAOjKAqapnEqPYUMoxEAN1VHWGl/9Bf3XcmR6+Ky11QUK+kDI4SLU86dw2PmdJRz56R8V3L2HN7TF6A7l+TsmtjRnUvC7/1PUc8lXnd/rsVCrtlsfVluvIq3I9fFZa+pKFYSwAjh4nTnzuA9cRy6c2ekfFcSdxbfcR+iO3v9QMFZdGcTCRw/B/VMvGP5OHBdXPaaimIlj5CEELc9RbE+Arn8voStPySEKDAJYIQQtzVFAU9PFVU1XbHVgk6noCCBjBCuSgIYIcRtzTrs14SmxaNp1iBGUd1RCJCWmEIwqCpuOh1g7cQrZ1A4iwQwQrg4S2lfch5vj6W0r5R/A4pOwWwxoWENYFT01qYZZ/Lz5UL7h7D4Orbqc1Gz+HqT8URLNF8fu+0KKnpVR8XSbpgs1l8dqqLgZVDIMV07EYwj94Wz7ynhGiSAEcLFWaqEkbZgsZR/A4qiYNE0UnJzMFqs6/t46lUCDE6efa1qFc4vfheTKRs359bEjqlKBc4tmnSdYdQqimLCop0j12w9jwadAVWpjPVXif35dOS+cPY9JVyDjEISwtXl5qLGnYZcJy0K6CLlWzQLZotmfZWEqWNzc1FPn4Nco7NrYi/XiO50/A3rZdZMmDUjZs2IRTNdN401HwfuC2ffU8IlSAAjhIvTH9hHYIPa6A/sk/JdyZ79lK/dBrf9x5xdEztu+49Rpe6T6PcddigfR66Ly15TUawkgBFCCCGEy5EARgghhBAuRwIYIYQQQrgcCWCEEEII4XJkGLUQLs5Upx4JMQlgMEj5rqRBXU4nbCNXMZWoL+LcutU5emYjnp5+DuXjyHVx2WsqilVJ+rkRQhSGqoK7u5TvalQV3N3AZHF2TexdqpfqYAO9I9fFVa+pKFbyCEkIF6c7ehjf9u3QHXVs2KuUX8wOHSGoXU8MR045uyZ2DEdOEfJ4P3SHTziUjyPXxWWvqShWEsAI4eKUzEzcNm9CycyU8l1JRibum/5Bybzg7JrYUTIv4PHnfyiZWQ7mU/jr4rLXVBQrCWCEEEII4XIkgBFCCCGEy5EARgghhBAuRwIYIVycuUJF0t+bhblCRSnflVQKJXnWm5hCyzq7JnZMoWWJn/Ea5tDyDuXjyHVx2WsqipVDAczcuXOpWbMmEydOtG3Lyclh3LhxRERE0LBhQwYOHEhiYqLd5+Li4ujVqxf169cnMjKSqVOnYjLZr2q6bds2OnToQJ06dWjdujWrVq1ypKpC3La0wECyu3ZDCwyU8l1JUCBZ3TpiCfRzdk3sWAL9SH/xCbQgf4fyceS6uOw1FcWq0AHMrl27WL58OTVr1rTbPmnSJDZs2MCMGTNYsmQJ8fHxDBgwwLbfbDbTu3dvjEYjy5cvZ8qUKaxevZoPPvjAliYmJobevXsTERHBN998Q7du3XjjjTf4448/CltdIW5bSlISpT77FCUpScq/tE0BVVVQVQVFUZxSrzwlJuH56SrUpBRn18SOmpSCz+JvURKTHcrHkfvC2feUcA2FCmAyMzMZPnw4EyZMwNfX17Y9PT2dlStXMmrUKCIjI6lTpw6TJk3iv//+Y8eOHQBs2rSJI0eO8O6771K7dm1atGjB4MGDWbp0Kbm5uQAsX76c0NBQRo0aRXh4OF27duWRRx5h0aJFDh+wELcb3ekYfIYORHc6RsrHGrx4eqp4eZnx8jLj4WFCr1NQKGGBzKlY/AeORx97ztk1saOPPUeZIZPRxZ7J92cURbkiYLRuc+S+cPY9JVxDoQKYt99+mxYtWtCsWTO77Xv27MFoNNptDw8PJyQkxBbA7Nixgxo1ahAUFGRLExUVRUZGBkeOHLGliYyMtMs7KirKlocQQtyI9ZepCYslHoslDk1LRMFScltiXJiCil7V4elpsgWMnp4qcqpFcSjwUgLff/89+/bt46uvvrpmX2JiIgaDgdKlS9ttDwwMJCEhwZbmyuAFsL3PK01GRgbZ2dmUKlWqoNUWQtxxTGiaCVkx5dZRFBVFMWHREtAs1nOtqmVQFJ2zqybuAAX6yT5z5gwTJ07kk08+wV3WqRBCCAFomhlNM0nLiyhWBQpg9u7dS1JSEh07drRtM5vN/PXXXyxdupQFCxZgNBpJS0uza4VJSkoiODgYsLak7Nq1yy7fS6OUrkxz9cilxMREvL29pfVFiKtoXl7kNotC8/KS8l2Jtxc5UfegeXk4uyZ2NC8PLtzXEM3LM9+fUWz/uTKfwl8Xl72molgVKIBp2rQpa9assdv22muvUbVqVf73v/9Rvnx5DAYDW7Zs4ZFHHgHg2LFjxMXF0aBBAwAaNGjAnDlzSEpKIvDiELnNmzfj7e1NtWrVbGl+//13u3I2b95sy0MIcZk5vDqpX/8g5V+HonDNL9YSo0Y1En+Yj9GUjcHZdbmCsVol4tZ8jKfBO8+0CgooCpoOLJr1VCuq9YQ7cl84+54SrqFAAYy3tzc1atSw2+bp6Ymfn59te6dOnZgyZQq+vr54e3szYcIEGjZsaAs+oqKiqFatGiNGjGD48OEkJCQwY8YMunTpgpubGwCdO3dm6dKlvPPOO3Tq1ImtW7fy448/Eh0dXQSHLMRtxmIBoxEMBlCdMDdlCSxf0SloCmhcHKlQEp9tWCyQkwuKxdk1sWexgNEEurzrZe0YrZGWm0O2ORuD6k6Au2bdbjEX/r5w9j0lXEKR3xmjR4/mgQceYNCgQXTt2pWgoCBmzZpl26/T6ZgzZw6qqvLss88yfPhw2rdvz6BBg2xpKlasSHR0NJs3b+bJJ59k4cKFTJgwgebNmxd1dYVwefo9uwiuGIx+z668E98B5SuKgkXTSMnNITE7iwxTLtZQpoTZsZsKwRG47T7s7JrYcdt9mPDyLdDvOpDvz5g1DbNFw6JdDnocuS+cfU8J1+Bw9/wlS5bYvXd3d2fMmDGMGTPmhp+pUKEC8+bNu2m+ERERfP31145WTwhxh7JoFswWDbNWAoMXIYTDpG1OCCGEEC5HAhghhBBCuBwJYIQQQgjhcmSKSiFcnKnWXSTt2I8lKPiOLN9c+y6Sdx3AEhRcshdvvFqd2pzZv5Zcf88S9UWcW7sqJ3Z/g3tIRYfyceS+cPY9JVxDSfq5EUIUhpsblpAKd2T5igKefqVQA8pd3GIGLOhK4uKNV3Nzw1KhLJiynV0Te24GzBXKWIcwO5SPA/eFs+9p4RLkEZIQLk49cZzSPV5EPXH8jitfURR0J4+gf74z2tFtrrV447ETBLw4HP2J086uiR39idOU7T4a3XHHVoJ25L5w9j0tXIMEMEK4ODUtFfc1X6Ompd6R5ZOSgn71WkhJRtNMaJrZOfUoqJRUPL5eh5qa4eya2FFTM/D+dgNKarpj+ThwXzj9nhIuQQIYIYQQQrgcCWCEEEII4XIkgBFC3DZK9OKNQogiJQGMEC7OXLY8Ga+PwVy2/B1ZPqEh5L49BEtoGSw6rN9qJb0DL0BIOVLHDMBcLsjZNbFjLhdE0pt9sJQv41g+DtwXTr+nhEuQYdRCuDitbFkuDH71jixfURQsZctwfkg3jJYcyM7CU68SYHCB9Y/KlSXj1R6YS9gwanPZQFJe6YanwduhfBy5L5x9TwvXIC0wQrg4JTUFt7U/oKSm3JHlk5KC2/cb0JLTXGvxxpRUSv3wG6qDo32KmpqajuePf6CkpDmUjyP3hdPvKeESJIARwsXpTp7A98XO6E6euCPLV46fIPi5oehPxDml/EI7doLAzq+UuHrrT8RRvssIdCdiHcrHkfvC2feUcA0SwAghhBDC5UgAI4QQQgiXIwGMEEIIIVyOBDBCuDjNvRSmmrXQ3EvdkeVTqhTGWlXR3N2cU35hlXIvkfXW3N3IrRkGpdwLnYeiKCgeHphq1kLx8CjwqHan31PCJcgwaiFcnLlmLZL/2H7Hlq/VrsXZbV9iNOc6rQ6Fclct4revxGjKxsF1n4uUsVYYMVs+L9QwagUVvarD09OE1qg6ph1bKQVYLCpZWRbyO0DM2feUcA0SwAghhCgSiqKiKCY0LQGLxXRxmx5VLYOi6NBcZYi7cAnyCEkIF6fbvYvAqhXQ7d51R5av7NxFhdD7cdt9yCnlF9qO3ZSvEFXi6u22+xBhlR5Ev+tAgT+roICiYFHNsGcPHuUaw+7dKLqCPUNy9j0lXIMEMEK4OEWzoGako2iWO7J8LBbU9EywuNhf9yW13hYNNSMLLAW/noqiABppuTmkXMhCSc8kPScHi6Zd3JfPfJx9TwmXII+QhBBCFCmzptniMosEIeIWkRYYIYQQQrgcaYERQrgUVbV2FrX+W8EF1p0WQtwCEsAI4eJM1WqQvO53TNVq3Pblqyp4eutAMQLWQEa9qybnNi7FGB5yy8svUrWqE//75xirli9RX8TG6pWJ2bAIfY2wIslHqVGlwJ919j0tXENJ+rkRQhSGpyemeg3uiPIVRQXFSGpuDEZLLh56L/y8ymJqeBeaKbtY6lBkPD0xNqhd4uqteZYit35N9AaPIsnHTVeIifqcfU8LlyB9YIRwcWpsDN4jh6LGxtwx5RstueSaczFajHAqBt+hk9HHni228otECa23PvYsQcPfRY05UyT56AqRj7PvaeEaJIARwsWp55PwWDgf9XzSHVk+iefxnr8CNSnVOeUXVgmtt5qUiu+CVahJyUWUT0rBP+vse0q4BAlghBBCCOFyJIARQgghhMuRAEYIIYQQLkcCGCFcnCUomKze/bEEBd+R5VMmiIz+XTAH+Tun/MIqofU2B/mT0rczluAAp+Xj9HtKuAQZRi2Ei7OEVCBz/OQ7tnxCK5A6eRjmEjYcOU8ltN7mCmVImjgYT4N3keRTmGHUTr+nhEuQFhghXF1GBvq/tkFGxh1bvtu2nSgZWc4pv7BKaL2VjCzct+92uF4O5ePse0q4BAlghHBx+mNH8H+0NfpjR+7I8jl0lODW3TEcdbE5Q0povQ1HYwht0wvdkRNFko/+yMkCf9bp95RwCRLACCGEEMLlSAAjhBBCCJcjAYwQQgghXI4EMEK4OE2nxxIYiKZzzqBCZ5ePXoc50A/0OueUX1gltd5FVS9bPgW/L5x+TwmXUKAA5vPPP+fxxx+nUaNGNGrUiGeffZaNGzfa9ufk5DBu3DgiIiJo2LAhAwcOJDEx0S6PuLg4evXqRf369YmMjGTq1KmYTCa7NNu2baNDhw7UqVOH1q1bs2rVKgcOUYjbm/nuOiTtP4757jq3XfmKAqqq2F6KolybqF4dzh7fQO7d1Yq8/FuqhNY79+5qnDj8I6Y6NYskH2Od6gX+rLPvaeEaChTAlCtXjmHDhrFq1SpWrlxJ06ZN6d+/P4cPHwZg0qRJbNiwgRkzZrBkyRLi4+MZMGCA7fNms5nevXtjNBpZvnw5U6ZMYfXq1XzwwQe2NDExMfTu3ZuIiAi++eYbunXrxhtvvMEff/xRRIcshHAFigKenipeXmbby9PTjF6noHCdQEYIcUcpUADTqlUrWrRoQZUqVQgLC+OVV17B09OTHTt2kJ6ezsqVKxk1ahSRkZHUqVOHSZMm8d9//7Fjxw4ANm3axJEjR3j33XepXbs2LVq0YPDgwSxdupTc3FwAli9fTmhoKKNGjSI8PJyuXbvyyCOPsGjRoqI+diFuC7oD+wloUh/dgf23VfmKoqAzmLGo8ZiVuIuvBBRFs2+J2bufsvWfwLD/WJGWf8uV0Hob9h+j0j1Pod/v2BDmy/kcLfBnnX1PC9dQ6D4wZrOZ77//nqysLBo2bMiePXswGo00a9bMliY8PJyQkBBbALNjxw5q1KhBUFCQLU1UVBQZGRkcOXLEliYyMtKurKioKFseQgh7Sm4OuhPHUXJzbqvyFUXBomkk52SSkJ1GQnYaGcYLgGafMCcX/fEYlFxjkZZ/y5XQeiu5RgzHT0NObpHko1zMR1UVdDoVnU5FzeM3j7PvaeEaCtxD6uDBg3Tu3JmcnBw8PT356KOPqFatGvv378dgMFC6dGm79IGBgSQkJACQmJhoF7wAtvd5pcnIyCA7O5tSpUoVtMpCCBdm0SyYLdagxaxpeaQWJY2KDr1Oh+JlQrt0/TQDWRlmLBbn1k24tgIHMGFhYXz99dekp6fz008/MXLkSD777LNbUTchhBAuTlFVFMVEWu5pcsw5GFQ3fN0qoih6QCIYUXgFDmDc3NyoXLkyAHXq1GH37t0sXryYtm3bYjQaSUtLs2uFSUpKIjjYuqJoUFAQu3btssvv0iilK9NcPXIpMTERb29vaX0RQggXZbQYyTU79lhKiCs5PA+MxWIhNzeXOnXqYDAY2LJli23fsWPHiIuLo0GDBgA0aNCAQ4cOkZSUZEuzefNmvL29qVatmi3N1q1b7crYvHmzLQ8hhD1zWFVSlq/CHFb1jiyfamEkrvoIY1ioc8ovrBJab2NYKHFfvo+5aiWn5eP0e0q4hAK1wEyfPp3777+f8uXLk5mZyXfffcf27dtZsGABPj4+dOrUiSlTpuDr64u3tzcTJkygYcOGtuAjKiqKatWqMWLECIYPH05CQgIzZsygS5cuuLlZl1zv3LkzS5cu5Z133qFTp05s3bqVH3/8kejo6CI/eCFuB5pPaYytHrpjy6d0aXIeaoZmynZeHQqjhNZbK+3FhQeb4mnwdlo+Tr+nhEsoUAtMUlISI0eOpE2bNnTv3p3du3ezYMEC7rvvPgBGjx7NAw88wKBBg+jatStBQUHMmjXL9nmdTsecOXNQVZVnn32W4cOH0759ewYNGmRLU7FiRaKjo9m8eTNPPvkkCxcuZMKECTRv3ryIDlmI24t67iye70xCPXf2jiyfM2fxmTQH3dnEvNOWJCW03rqzifhPmY96NsFp+Tj9nhIuoUAtMJMmTbrpfnd3d8aMGcOYMWNumKZChQrMmzfvpvlERETw9ddfF6RqQtyx1HNn8Zo2hdw27bCULXfHlc+Zc5SeEk3qI5GYywXlnb6kuKLeVKzi7NrY6M4lEfDOAhIefxgceILjSD5Ov6eES5C1kIQQQgjhciSAEUIIIYTLkaU+hRAlgqqCoqhXvJcVj4QQNyYBjBAuzuLrR3anZ7D4+rls+aoKnt46UC5Pq68oKjo1Hws3+vuR9Uw7LH4+hS7fKUpovS1+PqQ//QiaX+m8E9+ifJx9TwvXIAGMEC7OUrkK6bPnu3T5iqKCYiQ1NwajxTrZmYfeC79SZe0XbryesMokz5+IqYQNR87TFfV2c3ZdrmCqHEJ89FiHh1E7ko+z72nhGqQPjBCuLjsb9dhRyHbSL/AiLN9oySXXbH0ZLflc5DA7G93RUyjZLrbwXwmtt5Kdg/5YDDhYL4fycfY9LVyCBDBCuDj9oQMENm2I/tCBO7J89h2kXMMnMRw84ZzyC6uE1ttw8ASV730G/YGjTsvH6feUcAkSwAghhBDC5UgAI4QQQgiXIwGMEEIIIVyOjEISQghR7BRFQVUvjzDTNA1Nc2KFhMuRAEYIF2eq14CE+LQ7tnwa1ed02n/kutow6ivqXZK+iHPr1+To+S0OD6O+UT4KKnpVh6enCe2KiMVi0ZOVZUHTSsA9JVxCSfq5EUIIcZtTFBVFMWHREtAspotb9ahqGRRFZxfUCHEz0gdGCBenO3IYv7YPojty+I4sn4OHCX7wRQyHTzqn/MIqofU2HD5JhYf/h+7Q8Vuaj6aZ0TQTmmYCTHb7nH5PCZcgAYwQLk7JysTwz18oWZkuVb6iWNc7UlUl79l2byYzC7e/dqNkudgjpBJabyUrm1J/70HJuuC0fJx9TwvXII+QhBDFTlHA01NFVU0X32uounyseyRuG4rtP0IUjgQwQohipygKOoMZsxaPpplQFHd0SqBjLTHCJSgooChoOrBol7aBosq1FwUjAYwQotgpioJF00jOycRoycFT702AXjpv3gmsQapGWm4O2Wbr4zOD6k6Au2bbJ0R+SAAjhIszV6xE2kdzMVes5HLlWzQLZouG2ZGRJ1UqcX7uBEyVyhU+D2e4ot4l6YvYVKkc5+aMQatU4ZbmY9Y0zBebYHSKxX6fk+9p4RpK0s+NEKIQNP8Acp7ufMeWT4A/Fzo/isXV5oEpofW2+PuS8Uwbh+eBcSQfp99TwiXIKCQhXJySmEipBXNREhPvyPJJSMRr7heoicnOKb+wSmi91cRkSs//CjXhvNPycfo9JVyCBDBCuDhdXCw+rw1DFxd7R5ZPzGn8hk1BfzreOeUXVgmtt/50PMEjpqOePuu0fJx+TwmXIAGMEEIIIVyOBDBCCCGEcDkSwAghhBDC5UgAI4SL07y9yX2gFZq3Y6NGXLV8fLzJbhWJ5u3pnPILq4TWW/P2JKtlBJq3l9Pycfo9JVyCDKMWwsWZq1YjdcXXd2z5VA8n6euPMZaw4ch5uqLeBmfX5QrG8IqcWTnD4WHUjuTj9HtKuARpgRHC1ZnNKOlpYDaX2PKvXLjR4cUbr1d+Wobzjr+wSmq9zWaUtEzH6+VIPs6+p4VLkABGCBen37uboPBQ9Ht3l8jyFQV07josbortpblRdEHMzj2EhDbHbc+RosmvuJTQervtOULVKg+h333Qafk4+54WrkEeIQkhbilVVXBz0zCasrBcXDJAVXToVIMs3iiEKDQJYIQQt5iCqpgwWs5gNBsBUA1eKEo568rEQghRCBLACCGKhUUzYdasAYxZMzm5NqIkUpRb0EdK3LakD4wQQginUlBQVAWLG9Y+UgZrAKNzU5FYRtyItMAI4eJMte8mcd8xNF/fO7J86t7FmWPryfVyc075hXVFvUvSF3HuXeEcP/QDpYLKFVs+qqJDp6igZGFWLJjrVCHh+D48g8qj5iqYzZpDdRG3p5L0cyOEKAyDAS0o6I4u3xIUAK42D0xJrbdBjyXIHwwOzk5TgHwURUVRTBgtZ8k25YAChkAD3m4K5CqABDDiWvIISQgXpx4/RukXnkU9fuyOLJ+jxwl4djD64y62cnEJrbf+eCzlnh+O7lhMsedjvthPSjl2nIBnB6IcPe5QHcTtTQIYIVycmp6G+08/oqan3ZHlk5qGx4+/o6ZlOqf8wiqh9VbTMvFauwklLd1p+ahpmXj8+DukpTpUB3F7k0dIQogipyiXJ6qTESVCiFtBAhghRJFSFPD0VFHVS0OlLeh0isz5IoQoUgV6hBQdHU2nTp1o2LAhkZGR9OvXj2PH7J975+TkMG7cOCIiImjYsCEDBw4kMTHRLk1cXBy9evWifv36REZGMnXqVEwm+3khtm3bRocOHahTpw6tW7dm1apVhTxEIURxss7lYcJiicdiiUPTElGwSEuMEKJIFSiA2b59O126dGHFihUsXLgQk8lEjx49yMrKsqWZNGkSGzZsYMaMGSxZsoT4+HgGDBhg2282m+nduzdGo5Hly5czZcoUVq9ezQcffGBLExMTQ+/evYmIiOCbb76hW7duvPHGG/zxxx9FcMhC3F7M5ULIGDcJc7mQEla+CTChcYsX5KtQntRJQzGXD7615RS1Elpvc/lgEscPwhJSxmn5mMsHkzzxFbQQ59zTwjUU6BHSggUL7N5PmTKFyMhI9u7dS+PGjUlPT2flypVMmzaNyMhIwBrQtGvXjh07dtCgQQM2bdrEkSNHWLhwIUFBQdSuXZvBgwczbdo0BgwYgJubG8uXLyc0NJRRo0YBEB4ezj///MOiRYto3rx5ER26ELcHrUwZLvQdkHfCYixf0SloinXwqwrc0tnIypYhY8ALmEvacOS8lNB6m8sEkNr/OTwN3k7Lx1wmgIwBXSlVqgxkOFQNcRtzaBRSerq1d7nvxQms9uzZg9FopFmzZrY04eHhhISEsGPHDgB27NhBjRo1CLpi3oioqCgyMjI4cuSILc2lAOjKNJfyEEJcpqQk4/btapSU5BJRvqIoWDSNlNwcErOzyDDlckvn8UhOodTqX1BTnDQKqrBKaL3VlDS8vl6PkuzYCCBH8lFT0vBY/QskpzhUB3F7K3QAY7FYmDRpEo0aNaJGjRoAJCYmYjAYKF26tF3awMBAEhISbGmCrpr06tL7vNJkZGSQnV2y/loRwtl0p07i27MbulMnS1T5Fs2C2aJh1m7xJGTHTxLYbQT6k2dubTlFrYTWW3/yDOVefgPdydNOy0d/8gxB3UehnDjhUB3E7a3Qo5DGjRvH4cOH+fzzz4uyPkIIIYQQeSpUC8zbb7/Nb7/9xqeffkq5cpfXuQgKCsJoNJKWZt8kmpSURHBwsC3N1aOSLr3PK423tzelSpUqTJWFEEIIcRspUACjaRpvv/02v/zyC59++ikVK1a021+nTh0MBgNbtmyxbTt27BhxcXH8v707D4+qvhc//v6eMzNJJiSQhIBsIosEy7708itGsdpir2hbXErvtaW01PVa7a33wV5qWdRHeCz6q6hteaT1glWrxbZXi7YV+tNrL7i1yBrCHoIRMmQhe2bmnO/vj8kMMyH7JJkzyef1PKOZmXO+38/hnDPzmXO+y/Tp0wGYPn06hw4doqysLLLMjh07GDBgAOPHj48s895778WUvWPHjkgZQgghhOjfOpXArF69mtdee43HH3+c9PR0fD4fPp8v0i4lIyODm266ibVr1/Lee++xb98+li9fzowZMyLJR35+PuPHj2fZsmUcPHiQd999l5/+9KfceuuteDyh2WS//vWvU1xczGOPPcbRo0d54YUXePPNN1myZEm3brwQfYFOTSMwZRo6Na1f1k9aKv5pE9GpKYmpv6scGrdOTaFx6gSI82p3POXo1BT8U/MgUceUSAqdagPz0ksvAfDNb34z5vU1a9Zw4403ArB8+XIMw+Dee+/F7/eTn5/PypUrI8uapskvfvELVq1axaJFi0hLS2PhwoXce++9kWVGjRrFhg0bWLNmDZs3b+aiiy7ikUcekS7UQrTAmpBH5fbEjZGU6Pq5LA/fuy8RcFh35HZFxR3nvM/dKpB3Cafe3hR3N+p4ygnkXcKZd19kcOo46UYtWtWpBKawsLDdZVJSUli5cmVM0tLciBEjePbZZ9ssZ86cOfzhD3/oTHhCiASInvco9FxG3BVC9DyZjVqIJOfau5vBIwfj2ru71+tWCtIP7SV7eA4DDv+D9HSLtLQgrt6c+2jXHoYP/ic8e9r/geUoDo3bs6eQsRddiWt3QcLK8ewpZGTu/0F9vLtpaorQQ3JjEU0mcxQi2WmN8vuhp8dbaYFSCkMFUH4/tuVD2yUolYIiu/euxGiN8gd6dKy8HuHUuDVNccUZWDzlNK2rANsDth06llwYWI1WIg514UByBUYI0U2CaB1E6x6e+0j0G4YyQNVhqVq0UYfHozEMuQwjQuQKjBCi2ygFvXXnSPR9SlkE7NM0BBtxm24MNZrQ15ZcghGSwAgh4qTMUMaiTbDNXpi8UfQrlg5i6QCmlmNKxJIERogkF7w0j/L/eR9r9CW9XrdSCitvAmfe30LDxYPRDXV4XQbZ7l78hXxZqP7AyJzeq7M7RMXtpA/iwITRnPzfF3BfOjZh5QQmjMb3we/JumwC2GfiikP0XU46b4QQXZGWhjXxsoTW7584hqDlh96YvLGF+oOXjUMn2zgwDo1bp6USuGwsbnecA9nFUY5OSyV42XhIS4PauMIQfZg04hUiyRnFJxnw7/dgFJ9MSP2q6CRZ9zyEqzhBsyoXnWTQPasTV39XOTRuV/Gn5N77KObJkoSV4yr+lIH/thKKEnNMi+QgCYwQSc6oKCfthc0YFeWJCaC8nAHP/zdGeVX7y/aEsgrSN/8hcfV3lUPjNsqryPz166jyyoSVY5RX4d38OyiriCsG0bdJAiOEEEKIpCNtYIQQnRI9dYBMGyCESBRJYIQQHaYUGCkmAW0D4DKQLtNCiISQBEaIJGfnDqHu3h9g5w7p8bqUUgS0zfGqCvy2xUBPChOG5FL170uwcrN6vP4WDc2l+gffTlz9XRUVt5M+iK3cLCq+/03sIfF1S4+nHCs3i5ofLCV1aC7gjysO0Xc56bwRQnSBPWw4tQ+u6tU6/baF37II2jaMGE7Vqu9hWQn6ohkxnKpV92I5rDtyuxwatzV8COUr7sbrHpCwcqzhQ6he/X1SvcOh9kRccYi+SxrxCpHkVE017v99F1VT3f1lKyIzAYdmA25hjunqalLe/QhVnaABO6qr8SSy/q5yaNyqupbUv/0j7rjiKUdV1+J590Oo7v5jWvQdksAIkeTMY0cZtHAB5rGj3VquUuD1GqSnW5GH1xskPUWRarrwmCYuw0AdOUru9XfgPnaqW+vvsMPHyF1wW+Lq7yqHxu0+dooRX/43zKNFCSvHfewUOdd9Bw4fiysG0bfJLSQhRIuUUhhGENsuBYJNr3nwuIZyyaAUgrYbj+nClEa8QogEkARGCNGOIFoHm/52oVQQW5/BbzXiMtJRSmYGFr0nlFifT5q11vT27BXCGSSBEUK0K3KRpen/4RmCrUhiI0TPUiiUobA9YNvnExgXBlajJUlMPyQJjBBJTrvcWMOGo13ubi9bmQqtIPzdYMCF4764XVjDh4Db7Pb6OyTR9XeVU+N2mwSH5YI7zq+HeMpxm03/NufXNZSJqQxQdVjKbnpN4XF5aQwoLEsymP5GEhghkpz1mUmU7z7Y7eUqpbC1ptLfSMBuBMDrMsh2N/uimDKJ0wf/jD9R3YETXX9XRcXtpA9i/2fGU7T/tbi7UcdTjv8z4ykt3E6ud2SkG7VSBkoFCdinaQiGjke36cZQowl9lUkC09846bwRQjiQrW0sO/TlYMl1epFg4duXAKaWBuT9mXSjFiLJmQf2kz1tIuaB/YkJYO9+Lpp4LZ4DR/pn/V3l0Lg9B44wetKXce0/lLByPAeOMCTvGtiboGNaJAVJYIRIcioYwPy0BBUMJCaAQBCzpBQCVv+sv6ucGnfAwvWpDwJxNtCOp5yA1fRvI43ERevkFpIQIkJmmhZCJAtJYIQQgMw0LYRILpLACCGAVmaaTvFeOPeREEI4gLSBESLJWWPHUfn7rVhjx3VLeTEzTXfEpWPxbX2WwNiR3VJ/pyW6/q5yaNyBsSP55LVnsMaNTlg5gbEjKXvjV3Dp2LhiEH2bXIERIsnpARkELr+iW8pSgMcIDazmMjr4+yYjA/8Vs9GJGocl0fV3lUPj1hnpNOTPxOtOT1g5OiMd/xWfBW8G1JbFFYfou+QKjBBJzvi0hPRHVmF8WhJXOUpBeorikkEpjM9KY0SmB5eh2m8H80kJmavWh3qNJEKi6+8qh8ZtlpSS/dDPMErOJKwcs6SUjJU/hU/iO6ZF3yYJjBBJzvCV4l3/BIYv3i9ChaGCWPo0fusUtvahlI1qrxXMGR8ZTzyH6auIs/4uSnT9XeXQuE1fBVk/fR6jNL4rH/GUY/oqGPDEL+GMr0PLhyd4NAwl7c77EbmFJEQ/Fd1lOvQ89LctEzWKJKEwcBkmXm8Q3TRKtG27qKuzZXLHfkASGCH6oeZdpkG6TYvkE54fydY+tB0EXBjGEJQyIwmN6LskgRGiH2reZRqQbtMiaWltoXVQ8u9+RhIYIZKcnZVN/a2LsbOyO71uuMs00PFu083lZFG7+KvY2ZldWz9eia6/qxwat52dSdU3bkBnD0pYOXZ2JnWLb8STkwW0f1yqyH9EfyIJjBBJzh51MTX/9+lOrxfdZRo60W26udEXU/n0SoKJ6g6c6Pq7KipuT6JjiRIcNQzf+uV43QMSVk5w1DDOPbOaXO9IqD3R6nKKUC85bYKtQ8e0MiST6S+kF5IQya6+HvNgAdTXt7qIUkR6aYQf0V2mO9VtuoX6XQVHUfUJSiASXX9XOTRuVd+Au+AYxBlXPOWo+gZcBUfaPKYh3PBcU+Vv5GxDHZX+RmytZR6vfkISGCGSnOtwIdlXzsF1uLDF95UCr9cgPd2KPLxeG4/LQusz+K1Tnes23VzBIYbOuRn3oaJu2JouSHT9XeXQuN2Hirj48ltxFR5LWDnuQ0Xk/tNCKDjUoeUtrbFsja27eBtUJCW5hSREH6eUwnRbWLoU3dQ1WqkUTJWDjYWlAwDSbVoIkVQ6fQXmww8/5M477yQ/P5+8vDy2bdsW877WmieffJL8/HymTp3KkiVLOHHiRMwylZWV3H///cycOZPZs2ezfPlyamtrY5Y5ePAg//qv/8qUKVOYN28ezz77bOe3TgiBUgpbayoaa/E1VOFrqKImUA9IN1MhRPLqdAJTV1dHXl4eK1eubPH9Z599lueff55Vq1bxyiuvkJaWxtKlS2lsbIws8x//8R8cOXKE5557jl/84hd89NFHrFixIvJ+TU0NS5cuZfjw4fzud79j2bJlPP3007z88std2EQhBICtbSw7dKndkjEyhBBJrtO3kObNm8e8efNafE9rzebNm7nrrrv4whe+AMBjjz3G3Llz2bZtGwsWLODo0aO8++67bNmyhSlTpgDw4IMPcvvtt7Ns2TKGDh3Ka6+9RiAQ4NFHH8Xj8XDppZdSUFDAc889x6JFi+LYXCH6IKXQHk/iBqFTCu1xJ64ba6Lr7yqnxq1oiivOwOIpp7tiEH1atzbiPXXqFD6fj7lz50Zey8jIYNq0aezatQuAXbt2kZmZGUleAObOnYthGOzZsweAjz/+mNmzZ+PxnO9cmJ+fz/Hjxzl37lx3hixE0gtOmcbZU2cJTpmWmABmTKXk7Af4p+b1z/q7yqFx+6fmcez0/xCcdlnCyvFPzeN02T9gxtS4YhB9W7cmMD5faOKtnJycmNdzcnI4e/YsAGfPniU7O3bALZfLxcCBAyPrnz17lsGDB8csE34eLkcIIYQQ/Zd0oxYiyZmHChl0zRWYh853o44e96XHx8QoKCT3in/BXXiiZ+txav1d5dC43YUnGHnVt3AdjLMbdRzluAtPMDj/a1DQ8tAAQkA3JzC5ubkAlJXFTp9eVlYWuYIyePBgysvLY94PBoOcO3cusv7gwYMvuNISft78yowQ/Z1qqMe9dzeqITToV/NxX7xeC5epOj++S0fVN+DZfRDV0Nj+sn2x/q5yaNyqoZGUPYegIc6B7OIoRzU04t5d0OXB9JSKHbRRmtL0Td2awIwcOZLc3Fx27twZea2mpobdu3czY8YMAGbMmEFVVRX79u2LLPPee+9h2zZTp4bud06fPp2PPvqIQCAQWWbHjh2MGTOGgQMHdmfIQvQ54XFfbKMUS5VgKR9Kyeikou9TGLgME6832GzgRkOSmD6o0wlMbW0tBQUFFBQUAKGGuwUFBZSUlKCUYvHixfz85z9n+/btFBYWsmzZMoYMGRLplTRu3DiuuOIKfvzjH7Nnzx7+/ve/8/DDD7NgwQKGDh0KwA033IDb7eZHP/oRhw8f5o033mDz5s18+9vf7sZNF6JvOf+r04gZ90XGfBH9haFMlGFhGWeakvcSbKMU021JAt8Hdbob9b59+1i8eHHk+Zo1awBYuHAha9eu5bbbbqO+vp4VK1ZQVVXFrFmz2LhxIykpKZF11q1bx8MPP8y3vvUtDMNg/vz5PPjgg5H3MzIy+OUvf8lDDz3EjTfeSFZWFnfffbd0oRaC0C2i8IexUpCaGvodkpZmodMtwMY0FVrLmC+if4nMjdRYR4MVuv3kNlLITtGR90Tf0ekEZs6cORQWtt6wSinFfffdx3333dfqMoMGDeLxxx9vs56JEyfy4osvdjY8Ifq0cPsWwwgP+69wTRhJ46+fxLo4BewSlJGCIrv3fnGOGU3ZpscIjh7WO/U5rf6uiorbSXO6BEcP4/SvHoHRIxJWTnD0MCo2ryNjzGigstPrh+dGAjCVzI/UVznpvBFCtCN0myiIbZcCQVApqJwcrJvnYxMAghi4encAsKxBNCz8InYwQbMqJ7r+rnJo3PagTGq/eg1e94CElWMPyqRh4bVkeAdBbWVccYi+S7pRC5GUgk0PC336DMEnNlJ+spizDXXUBP306qXyM6UMePp5zNLy9pfti/V3lUPjNkvLGfjMSxil8Y25FU85Zmk56U9tgjOlccUg+jZJYIRwsOjxXMJjuihToU2wTUJncMmnZC5/HPWJLzFtXj75lIHLn8D81Ne79Tql/q5yaNzmpz4G/3g9Rkl8yUM85Zif+shcvg4++TSuGETfJreQhHCoC9u7gFIGhgFVDY002o14XQbZbZQhhAgJ99KD0Lx90rY9+UkCI4RDhcdzsXQpWgebXkvBVDnYID2MhOgAhUIZCsutsZra87qVid1oSRKT5CSBEcJBDCN0lSX0t0Jri4rGWgJ2aLRWr2sA2S751BWio5QKDSlwsrqSmkAAj2EyJjMLV9PrInlJAiOEQxgGeAeYoEIjUCtlYBrnx3MBWr7iMjCT+n++EjszvTfDlfrj5dC47cx0ar+Uj87MSFg5dmY6Df98Fa6BmXHFEC36zJEh7foGSWCESJDoAekADMMAFeCcv5iA7SfNlc6g1KHtj+cybgzlLz9JMFHdcft7/V0VFbcn0bFECY4ZyekXfxJ3N+p4ygmOGUnFK0+R6x0JtSfiiiM8vcCoTA9B24WhFOluRWNQrr4kO+mFJEQCNJ9wMT3dIi0tiMtUBO0AfstPwA60XxBAIIBxthwCwfaX7Qn9vf6ucmrcgSDG2QoIdPD464lyAkEMX3n8MRC6kqlUEFufwW+dwtKnMVQQuQ6T/CSBEaKXxHaJNiID0tl2CbZdgtZnUdidH0F37wGGjb0Gz4GjPRO41N8zHBq358BRxky4Dtf+wwkrx3PgKEPHzoO9B+KKIZqlg1g6gK0dljCKLpNbSEL0ghanAHAp/IEgNH2gajkdheg10d2qQbpWJyP5xBSiFzTvEq1UCsrIAUMRnqnFCC2YwCiF6PvCbWK83mBMLyTbdlFXZ0sSk0QkgRGiB7TUQNeO6hLtdQ3A49JUBRojs+Z6XQbZbvn0FKInGcpEGU0/Juzw+EouTHMoShnStTqJSAIjRJyaJytKQVqaQqnoEXQ1hnm+S3S4O7TVXhdpIUS3Cp2rmqrGusiPB7eRQnaKjrwnkoMkMELEQSkwUkwC2o68ZiqF4bax7AtH0O10A92OmDaZklPv4k9J0O2n/l5/V0XF7aQPYv/k8Rw7sY20QYMTVo5/8nhOf7KTwbmXQkNxXHG0JvrHg6nsdpYWTuSk80aIpKOUIqBtjldV4LctAAZ6UpiQ6qWysRZ/b4yga5rozAGQqHFQ+nv9XeXUuE0TnZkOppm4csL/NvHGIPo06UYtRJxa+91vazt0u6in5yw6fJScr96N+2jP/FKV+nuIQ+N2Hy1m2E3fxzxSlLBy3EeLyf7qHXDYWV3MhbNIAiNEJ8WO56JIT1FcMiiF8VlpjM9KY0SmB5eheq9HUXUNqX/diaqp6536pP7u4dC4VU0d3v/3PqqmNmHlqJo6UrbvgOqauGIQfZvcQhKiE5QCM8Uk2NTQTxkK0whi6dMErNCooS4jHaUuQslIn0II0WMkgRGiDRd2h1Z4PJpAsA5bawxlYhpuNBaWDiUwloz0KYQQPU4SGCFa0fxqC5y/4hKwPyVgBTDccrVFiL4iNDJvqGWF1ja2dE5yNElghIgSfcWl+dUW4IIrLo642jJqBJXrfkhwxBCpP5lExe2kD+LgiCH4Hrsfe8RFCSsnOGII5x5fTvqoEUDPt4MxMHGZJio9anRe7aauxpIkxsGcdN4I0ataGoBOuY0L2reEr7YAzrzikjuY2tsXYSeqO25/r7+rHBq3PTiLqu/ejNc9IGHl2IOzqLv9X0j3DobaXkhgDBOlglT5P6HRasRteBjoGYVSLkAyGKeSXkiiXwrfHrI9KvLAY5CSotFGHZaqRat6TIOoqy0OueLSXHkFab/ZilFxTupPJg6N26g4x4BX/oQqjy+ueMoxKs6R9pvXobwirhg6K2AH8Ft+ArYfOD/hY/ghU5U5iyQwol8K3x4KJyuWqkUb9bhNC0ufpiFYjKV9KGU762pLS06cJPv2B3GdPC31JxOHxu06eZqhd67GPPlJwspxnTzNoNuWw4mTccXQVQqFMhS2h5gfOWaKKUmMg8gtJNFvRN8yUsrAUIEWbw/Z2kHtW4QQvc5QJqYyQNVhNU0zYCiFx+WlMaCwLJkvyQkkgRH9glLg9RoYRnhuovDkitL9WQgRSykDpYIE7NM0BEPTgbhNN4YajVJuDON8uxitNTIPa2JIAiP6pJbGbzHdFpYOTbDYo5MrCiH6BEsHIz9wXNo4f1vJPv+54cLAarQkiUkASWBEnxB7eyjUm8gfNUO0y1DYQEVjLQG7sWcnV+xt6V78n52C9qZK/cnEoXFrbyoNsyejvWkJK0d7U/F/dipGujeuGLqT3FZyHklgRNJTCowUk4A+/6GCoTl17hyBpkEcMjwexqakosMTLPaln0t5l+LbvplAorrj9vf6uyoqbneiY4kSuHQ0n/zl2bi7UcdTTuDS0ZT99QVyvSOh9kRccXSXtm4rhb5K+9BnSpKQBEYkPaUUQW1zqiaUsKS73YzNymDkQDeWHfpQ8Ziu3p1gUQjRJ0XfVjK1fJ4kknSjFknHMMA0jcjDMAwGpChGZLq5eKCHYRluPKaFrc/gt07ht05hJ0uX6K74x25GZM7As7tQ6k8mDo3bs7uQcdmfw/XxgYSV49ldyLCMKfCP3XHF0FsMQ0V9HiU6mv5DrsAIRzOM0KXbMKUgzavQKhD1moFpaLT/DH7LH5kN2pYeRkKIHiRTECSWJDDCUWLnIoI0r9FislJeX4y/abTMNFc6g1KHYjtpfiIhRJ8nUxAkliQwwjGaz/6sDAVGkHONxZGhvcPJSlAH8Vuh11yGJ2ExCyFEeAqCsOhZrUFmtu4pksCIhGlprJbo2Z/DMz8HdUCSFSGE47V4SwnktlIPkQRG9Jro9ixKQWoqKHX+do9SBoZLU+U/TaPld+bMz070mTxO7/pvAkMHSv3JJCpuJ30QB/IuoeijV/CMHpuwcgJ5l1D68VayL80Dy1lzRbWl+S0lIHJbyTDchG8ryei93cNJ541IYs2vpoTGRFAx73vTo9uzKNwu8AdDI+OGlgmNjittWTopNRVr3MXoRI2D0t/r7yqHxq1TUwiOHYXHnZKwcnRqCta4nNCvnNq4wkiI6FtKMRNDNo3g68LA9sdejpGkpvOkw5eIW7jtSnjGVu1RpA8wSU+3oh4a0xWkyl/M2Yaj1AY/BWyq/HX4GqrwNVRRE6hHBoPqguNFZH33R7iKSqT+ZOLQuF1FJQy5YxXmiVMJK8dVVMKg7/4QjhfFFYMTxI7gW4sy6khNBVKUzHQdJ0cnMC+88AJXX301U6ZM4ZZbbmHPnj2JDqnPUyrUFiX60fykaj4Oi2mapHg02mg6Qc0GXO4gtlGKpUqaHj6U0pHGtwE73L1Zh0bG7Wuj4/amikq8r7yBUVkt9ScTh8ZtVFaT8ds/oyqrElaOUVlN2stboaIyrhicIHYE32Is7cNl2GDUY6laLFWLNurweDQulxn1udr+Z3F/59hbSG+88QZr1qxh9erVTJs2jU2bNrF06VL+9Kc/kZOTk+jwktKFY6rYaB07xgouFekFFObCQAd0ZJnWxmGxotquwEVU+etosEKXx/vU3ENCCNFJ4RF8bVIumJIg1UzF4xqNmW5FGv+6DDe2FYi5rWTbLurrz7/Q3287OTaBee655/ja177GTTfdBMDq1at5++23efXVV7n99tsTHF3PubAtSccO0ubrKaWJvsDWUuLhMt1YViCSriilMJRJVWMdwaYK3cpgQKoXyw6idefGYQlfXQn/LYQQIiR6SgKtUmIa/6aaXrLShmHpUrQdbiPoweMeimnakSRHaxcNDTT7frDRF0xxENsmsa8kPo5MYPx+P/v37+eOO+6IvGYYBnPnzmXXrl2dKqsnLrmFhopufvfN7sBrbS8TvgJiNbsCYmBAQLe4TkvrGYA31UDrC6+SVPt9BK0AbjOFDFc2ddZZglZoObeZQkZKNkpVopuSE2WkopSLuuDZ8+ulZGMoExWOu+n/HiMFbYb+H/08/HdPLNOTZSdNjIYJGRl43KkoM7X3Y+yG+uOKsZfq7/Z/x6i4XQ6K0XanQkYGLldqXPXHU467aV0Ms1+d1woDhYGhTEBTH7DwN30+p5huPC4L26hAawuFidvMRbmsmC7bSrmpa9CRYfQU4PUYqKjvg9YSn858X4Wf90TX8I5+byutnZeHnTlzhiuvvJLf/OY3zJgxI/L6Y489xocffshvf/vbBEYnhBBCiERzdCNeIYQQQoiWODKBycrKwjRNysrKYl4vKytj8ODBCYpKCCGEEE7hyATG4/EwadIkdu7cGXnNtm127twZc0tJCCGEEP2TIxvxAnz729/mgQceYPLkyUydOpVNmzZRX1/PjTfemOjQhBBCCJFgjk1grrvuOsrLy1m/fj0+n4/LLruMjRs3yi0kIYQQQjizF5IQQgghRFsc2QZGCCGEEKItksAIIYQQIulIAiOEEEKIpCMJjBBCCCGSTr9PYD788EPuvPNO8vPzycvLY9u2bTHva6158sknyc/PZ+rUqSxZsoQTJ060W+4LL7zA1VdfzZQpU7jlllvYs2dPD21B29ravkAgwE9+8hNuuOEGpk+fTn5+PsuWLePMmTNtlvnUU0+Rl5cX8/jSl77U05vSovb23w9/+MMLYl26dGm75Tpl/0H729h8+8KPjRs3tlqmk/bhhg0buOmmm5gxYwaf+9znuPvuuzl27FjMMo2NjaxevZo5c+YwY8YMvve973H27Nk2y+3qudvd2tu+yspKHn74Ya699lqmTp3KVVddxSOPPEJ1dXWb5Xb12O5uHdl/3/zmNy+IdcWKFW2W65T9B+1v46lTp1o9D998881Wy3XKPnzxxRe54YYbmDlzJjNnzmTRokW88847kfcde/7pfu7tt9/WTzzxhP7LX/6iJ0yYoN96662Y9zds2KBnzZql33rrLV1QUKDvvPNOffXVV+uGhoZWy9y6daueNGmS3rJliz58+LB+8MEH9ezZs/XZs2d7enMu0Nb2VVVV6SVLluitW7fqo0eP6l27dumbb75ZL1y4sM0y169frxcsWKBLS0sjj7Kysp7elBa1t/8eeOABvXTp0phYKysr2yzTSftP6/a3MXrbSktL9ZYtW3ReXp4+efJkq2U6aR9+5zvf0a+++qo+dOiQLigo0Lfddpu+6qqrdG1tbWSZFStW6Hnz5ukdO3bovXv36q997Wt60aJFbZbblXO3J7S3fYWFhfqee+7R27dv10VFRXrHjh16/vz5+nvf+16b5Xbl2O4JHdl/3/jGN/SDDz4YE2t1dXWb5Tpl/2nd/jYGg8ELzsOnnnpKT58+XdfU1LRarlP24fbt2/Xbb7+tjx8/ro8dO6afeOIJPWnSJH3o0CGttXPPv36fwERr/uVg27a+/PLL9caNGyOvVVVV6cmTJ+s//vGPrZZz880369WrV0eeW5al8/Pz9YYNG3om8A5q6cuvud27d+sJEyboTz75pNVl1q9fr7/85S93d3hxay2BueuuuzpVjlP3n9Yd24d33XWXXrx4cZvLOHUfaq11WVmZnjBhgv7ggw+01qFzbtKkSfrNN9+MLHPkyBE9YcIEvWvXrhbL6Oq52xuab19L3njjDT1p0iQdCARaXaYrx3ZvaGn7vvGNb+hHHnmkw2U4ef9p3bF9+JWvfEX/53/+Z5vlOHUfaq31Zz/7Wf3KK684+vzr97eQ2nLq1Cl8Ph9z586NvJaRkcG0adPYtWtXi+v4/X72798fs45hGMydO7fVdZykpqYGpRSZmZltLldUVER+fj7XXHMN999/PyUlJb0UYed98MEHfO5zn+Paa69l5cqVVFRUtLpssu+/s2fP8s4773DzzTe3u6xT92H41snAgQMB2LdvH4FAIGafjBs3juHDh/Pxxx+3WEZXzt3e0nz7WlJTU8OAAQNwudoea7Qzx3ZvaW37Xn/9debMmcP111/P448/Tn19fatlOHn/Qfv7cN++fRQUFHToPHTaPrQsi61bt1JXV8eMGTMcff45diReJ/D5fADk5OTEvJ6Tk9Pq/b+Kigosy2pxneb3hZ2msbGRdevWsWDBAgYMGNDqclOnTmXNmjWMGTMGn8/HM888w6233srrr7/e5nqJcMUVV/DFL36RkSNHUlxczBNPPMFtt93Gyy+/jGmaFyyfzPsP4Pe//z3p6enMnz+/zeWcug9t2+bRRx9l5syZTJgwAQglZW63+4KkOicnJ3KONteVc7c3tLR9zZWXl/Ozn/2MRYsWtVlWZ4/t3tDa9l1//fUMHz6cIUOGUFhYyLp16zh+/DhPP/10i+U4df9Bx/bhli1bGDduHDNnzmyzLCftw8LCQr7+9a/T2NiI1+vlmWeeYfz48RQUFDj2/JMERgChBr333XcfWmtWr17d5rLz5s2L/D1x4kSmTZvG5z//ed58801uueWWng61UxYsWBD5O9xI7gtf+ELkV09f8+qrr3LDDTeQkpLS5nJO3YerV6/m8OHDvPjiiwmLoSe1t301NTXccccdjBs3jnvuuafNspx4bLe2fdHJWF5eHrm5uSxZsoSTJ09y8cUX93aYcWlvHzY0NPDHP/6Ru+++u92ynLQPx4wZwx/+8Aeqq6v585//zAMPPMCvf/3rXo2hs+QWUhtyc3MBKCsri3m9rKys1TmZsrKyME2zU+skWiAQ4Pvf/z4lJSX86le/6vQv8MzMTC655BJOnjzZQxF2n1GjRpGVlUVRUVGL7yfj/gv76KOPOH78eJcSECfsw4ceeoi3336bTZs2cdFFF0VeHzx4MIFAgKqqqpjly8rKIudoc105d3taa9sXVlNTw3e/+13S09N55plncLvdnSq/vWO7p7W3fdGmTZsG0GqsTtx/0LFt/NOf/kRDQwNf/epXO11+Ivehx+Nh9OjRTJ48mfvvv5+JEyeyefNmR59/ksC0YeTIkeTm5rJz587IazU1NezevZsZM2a0uI7H42HSpEkx69i2zc6dO1tdJ5HCyUtRURH/9V//RVZWVqfLqK2tpbi4uNWD2UlOnz5NZWVlq7Em2/6LtmXLFiZNmsTEiRM7vW4i96HWmoceeoi33nqLTZs2MWrUqJj3J0+ejNvtjtknx44do6SkhOnTp7dYZlfO3Z7S3vaFY1u6dClut5uf//zn7V5Ba0l7x3ZP6cj2NVdQUADQaqxO2n/QuW189dVXufrqq8nOzu50PYnahy2xbRu/3+/s86/bmgMnqZqaGn3gwAF94MABPWHCBP3cc8/pAwcORHrhbNiwQc+ePVtv27ZNHzx4UN91110XdAVbvHixfv755yPPt27dqidPnqx/97vf6SNHjugf//jHevbs2drn8zlq+/x+v77zzjv1lVdeqQsKCmK68jU2Nra6fWvXrtXvv/++Li4u1n//+9/1kiVL9Jw5cxLSDbet7aupqdFr167Vu3bt0sXFxXrHjh164cKFev78+W1un5P2n9btH6Naa11dXa2nTZumX3zxxRbLcPI+XLlypZ41a5Z+//33Y47B+vr6yDIrVqzQV111ld65c6feu3evXrRo0QXdOK+99lr9l7/8JfK8I+dub2hv+6qrq/Utt9yir7/+el1UVBSzTDAYbHH7OnpsO2H7ioqK9NNPP6337t2ri4uL9bZt2/Q111yjb7311phynLr/tO7YMaq11idOnNB5eXn6nXfeabEcp+7DdevW6Q8++EAXFxfrgwcP6nXr1um8vDz9t7/9TWvt3POv37eB2bdvH4sXL448X7NmDQALFy5k7dq13HbbbdTX17NixQqqqqqYNWsWGzdujPmFVFxcHNNy/LrrrqO8vJz169fj8/m47LLL2LhxY0Iufba1fffccw9//etfAfjKV74Ss97mzZuZM2cOcOH2nT59mh/84AdUVlaSnZ3NrFmzeOWVV7r0iyNebW3fqlWrOHToUOS+7pAhQ7j88su577778Hg8kXWcvP+g/WMUYOvWrWituf7661ssw8n78KWXXgJCg51FW7NmDTfeeCMAy5cvxzAM7r33Xvx+P/n5+axcuTJm+ePHj8cM/taRc7c3tLd9+/fvZ/fu3QB88YtfjFlm+/btjBw5EojdPtM0O3Rs94b2ti/8633z5s3U1dUxbNgw5s+ff0EbEafuP+jYMQqhqy8XXXQR+fn5LZbj1H1YVlbGAw88QGlpKRkZGeTl5fHLX/6Syy+/HHDu+ae01rrbShNCCCGE6AXSBkYIIYQQSUcSGCGEEEIkHUlghBBCCJF0JIERQgghRNKRBEYIIYQQSUcSGCGEEEIkHUlghBBCCJF0JIERQgghRNKRBEYIIYQQSUcSGCGEEEIkHUlghBBCCJF0JIERQgghRNL5/zkXNxtSU4HaAAAAAElFTkSuQmCC",
      "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": "2025-06-24T04:02:47.095080Z",
     "iopub.status.busy": "2025-06-24T04:02:47.094932Z",
     "iopub.status.idle": "2025-06-24T04:02:47.112107Z",
     "shell.execute_reply": "2025-06-24T04:02:47.111654Z",
     "shell.execute_reply.started": "2025-06-24T04:02:47.095065Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:02:47.112854Z",
     "iopub.status.busy": "2025-06-24T04:02:47.112712Z",
     "iopub.status.idle": "2025-06-24T04:02:49.114309Z",
     "shell.execute_reply": "2025-06-24T04:02:49.113806Z",
     "shell.execute_reply.started": "2025-06-24T04:02:47.112840Z"
    }
   },
   "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": "2025-06-24T04:02:49.115006Z",
     "iopub.status.busy": "2025-06-24T04:02:49.114857Z",
     "iopub.status.idle": "2025-06-24T04:02:49.145505Z",
     "shell.execute_reply": "2025-06-24T04:02:49.145072Z",
     "shell.execute_reply.started": "2025-06-24T04:02:49.114991Z"
    }
   },
   "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>25300</th>\n",
       "      <td>1f346444-b678-4ca8-b827-6fbe98237b15</td>\n",
       "      <td>2a9a232f-c66b-4007-b473-19b441aa04b8</td>\n",
       "      <td>19.803133</td>\n",
       "      <td>False</td>\n",
       "      <td>-15.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25301</th>\n",
       "      <td>abfc7616-0686-4d09-8ddc-0fe8a69d2f22</td>\n",
       "      <td>2a9a232f-c66b-4007-b473-19b441aa04b8</td>\n",
       "      <td>24.854600</td>\n",
       "      <td>True</td>\n",
       "      <td>-13.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25346</th>\n",
       "      <td>f117bf0c-d832-4f68-826b-4ef187a4a83d</td>\n",
       "      <td>32b43991-933a-44d5-a972-adc96e448cf0</td>\n",
       "      <td>16.410850</td>\n",
       "      <td>False</td>\n",
       "      <td>-13.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25347</th>\n",
       "      <td>092b8d90-4c67-41ae-b1d6-027a71af6aae</td>\n",
       "      <td>32b43991-933a-44d5-a972-adc96e448cf0</td>\n",
       "      <td>21.059383</td>\n",
       "      <td>True</td>\n",
       "      <td>-13.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25952</th>\n",
       "      <td>4c0b5026-0c85-496f-8e56-1abf21533384</td>\n",
       "      <td>c0958df6-1ed4-4c34-9480-110c0ae76be5</td>\n",
       "      <td>20.466583</td>\n",
       "      <td>False</td>\n",
       "      <td>-13.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25953</th>\n",
       "      <td>04335045-89c5-4caa-bba5-ccb8c1a0a180</td>\n",
       "      <td>c0958df6-1ed4-4c34-9480-110c0ae76be5</td>\n",
       "      <td>26.377683</td>\n",
       "      <td>True</td>\n",
       "      <td>-11.445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         id                            request_id  pair_quality  preference  loudness_abs\n",
       "25300  1f346444-b678-4ca8-b827-6fbe98237b15  2a9a232f-c66b-4007-b473-19b441aa04b8     19.803133       False       -15.611\n",
       "25301  abfc7616-0686-4d09-8ddc-0fe8a69d2f22  2a9a232f-c66b-4007-b473-19b441aa04b8     24.854600        True       -13.031\n",
       "25346  f117bf0c-d832-4f68-826b-4ef187a4a83d  32b43991-933a-44d5-a972-adc96e448cf0     16.410850       False       -13.226\n",
       "25347  092b8d90-4c67-41ae-b1d6-027a71af6aae  32b43991-933a-44d5-a972-adc96e448cf0     21.059383        True       -13.900\n",
       "25952  4c0b5026-0c85-496f-8e56-1abf21533384  c0958df6-1ed4-4c34-9480-110c0ae76be5     20.466583       False       -13.011\n",
       "25953  04335045-89c5-4caa-bba5-ccb8c1a0a180  c0958df6-1ed4-4c34-9480-110c0ae76be5     26.377683        True       -11.445"
      ]
     },
     "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": "2025-06-24T04:02:49.146115Z",
     "iopub.status.busy": "2025-06-24T04:02:49.145979Z",
     "iopub.status.idle": "2025-06-24T04:02:50.261104Z",
     "shell.execute_reply": "2025-06-24T04:02:50.260587Z",
     "shell.execute_reply.started": "2025-06-24T04:02:49.146100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5Q5cuXVi3bp3xy0RiYoP+5P59EYScTFIyasCFIAjp4vr16yxatIhLly4RFBSEg4MDpUuXpn79+lneShUZGcnKlSv58MMPk/xwTY2UBjBnzpxh/fr1XLhwgZcvXxqXNGjTpg2NGjVK9/rF+u+//zhx4gRff/01efLkSdeyRQAjCAkTY2AEIRv777//aNu2LdeuXaN9+/aMGzeO9u3bI8sy69aty+rqERkZycKFCzl79mxWV4X58+fTpUsXbt68SYcOHZgwYQLdunUjPDycAQMG8Mcff2TYtS9cuMDChQt59epVupddvXp1fH19qV69erqXLQg5mehCEoRsbOnSpdjZ2bF9+/Z43+zfHhidU0RERGBtbZ3u5e7bt49FixbRuHFjZs+ebTLDrnv37hw7dsxk/62sFDtAPbnbjMiynClbkghCTiNaYAQhG3vw4AGlS5dOsFsi7uKJ7u7uTJo0iV27dtG4cWMqVapEmzZtOHfuXLy8T58+ZdSoUdSsWZOKFSvSrFkztm/fHi9ddHQ0CxYsMJbn7e1N//79efDgAf7+/nh5eQGwcOFC4+aesStNf//991SpUoUHDx7Qo0cPqlSpwrBhwwDDBp4DBw7k448/pmLFitStW5dp06YRFRWVqvs0b948HBwcmDZtmknwEqt27drUq1fP+DgmJob58+fTsGFD4/V//PFHYmJiErynBw8epHnz5sZ7dfToUWOaBQsW8OOPPwLwySefGO+Dv7+/SRm7du2iWbNmVKpUiWPHjgGGVae7d+9O1apVqVKlCl9//XW8zVHPnDmDu7s7Z86cMTm+detWGjRogIeHB+3ateP8+fOpuneCkFOJFhhByMYKFy7MhQsXuHHjBm5ubkmmP3fuHHv27OGrr77C3NyczZs30717d3799Vdj/qCgID7//HMkSaJz587kzZuXo0ePMmbMGMLCwvjmm2+ANxtxnjp1imbNmtGlSxfCw8M5ceIEN27coGbNmkyYMIEJEybQsGFD4wBxd3d3Y320Wi3dunXjgw8+YOTIkVhaWgKGFpOoqCi++OILHBwc8PX1ZcOGDTx58sS4wWhy3bt3jzt37tC2bVtsbW2TTK/X6+nTpw///vsvn3/+OaVKleLGjRusXbuWe/fuGVeijvXvv/+yf/9+OnXqhI2NDevXr2fgwIH8/fffODo60rBhQ+7du8fu3bsZNWoUjo6OwJsVuAFOnz7N3r176dy5M46OjhQuXJibN2/SuXNnbGxs6N69O2q1mq1bt/LVV1+xYcMGPD09E30Ov/76K+PGjTMGPQ8fPqRPnz7Y29tTsGDBFN0/Qcixsngvplztyy+/VKZMmZLV1RCysePHjyvlypVTypUrp3To0EH58ccflWPHjiW44WfsZqGXLl0yHnv06JFSqVIlpV+/fsZjo0ePVmrVqqU8f/7cJP93332nfPDBB0pkZKSiKIqyfft2xc3NTVm9enW8a+n1ekVRFCU4OFhxc3NT5s+fHy/NyJEjFTc3N2XWrFnxzsVe423Lli1T3N3dlUePHhmPzZ8/X3Fzc4uX9m0HDx5MtJ4J2blzp1K2bFnl3LlzJsc3b96suLm5Kf/++6/xmJubm1KhQgXl/v37xmN+fn6Km5ubsn79euOxlStXKm5ubsrDhw/jXc/NzU0pW7ascvPmTZPjffv2VSpUqKA8ePDAeOzp06dKlSpVlM6dOxuPnT59WnFzc1NOnz6tKIqixMTEKF5eXkrLli1NNkTdunWryWaxgvC+E11ISVi2bBlt27alSpUqeHl50bdvX+7cuWOSJjo6mokTJ1KjRg2qVKnCgAEDjKvNwpsm4IwY4AcYv7VVrVr1ndf5559/aN++PR4eHlSvXp2+ffuanA8ICKBnz554enri5eXFzJkzTcYN7N+/n65du/LRRx9RtWpVOnToYGwKf5c9e/bQsmVLPD09qVevHitXrjQ5/+zZM4YOHUrjxo0pW7YsU6dOTcVdeD/VqlWLLVu2UL9+fa5du8bKlSvp1q0bderUSXAjzSpVqlCxYkXj40KFCvHJJ59w/PhxdDodiqKwf/9+6tevj6IoPH/+3Pjj7e1NaGgoV65cAQyvt6OjY4JbWby94GFSvvjii3jHYltiwDAu5vnz51SpUgVFUbh69WqyywaMaxrZ2NgkK/2+ffsoVaoUJUuWNHn+H330EUC8rpqaNWtStGhR4+OyZctia2ub5PIGb6tevbrJwo06nY4TJ07QoEEDk73O8uXLR/Pmzfn333+Nzyuuy5cvExwcTMeOHY1rKYFhaYfkri8lCO8D0YWUhLNnz9K5c2cqVaqETqdjzpw5dOvWjT///NM4GHHatGkcOXKEuXPnYmdnx+TJk+nfvz9btmzJlDpGRkZSu3ZtateuzezZsxNM89dff/HDDz/w3Xff8dFHH6HT6Uz2XIrtLnB2dmbLli08e/aMkSNHYmZmxpAhQwBD90TNmjX57rvvyJMnD7///jt9+vRh27ZtlC9fPsHrHjlyhOHDhzN27Fi8vb25ffs2Y8eOxdLS0vjBGBMTg6OjI3369GHNmjXpe3PeAx4eHixcuJCYmBiuXbvGwYMHWbNmDYMGDWLnzp0mH4zFihWLl7948eJERkby/PlzZFnm1atXbN26la1btyZ4vefPnwOG8TclSpSIt91FSqjVagoUKBDveEBAAPPnz+fw4cO8fPnS5FxiH9yJie02Cg8PT1b6+/fvc/v2beP4nbjiDo5OqEvG3t4+RV9IXF1dTR4/f/6cyMhISpQoES9tqVKl0Ov1PH78mDJlysQ7HxAQAMR/rc3MzNJ941dByM5EAJOEVatWmTyeMWMGXl5eXLlyherVqxMaGspvv/3GrFmzjH8Qp02bRtOmTfHx8cHZ2dm4EF7sNMi3F8FTFIUff/yR7du3Y2ZmRseOHRkwYECK6hg7ZiHuN8dYWq2WqVOnMnz4cJMdnt/+4Dt+/Di3bt1i9erVODs7U65cOQYNGsSsWbPo378/5ubmjBkzxqTcIUOGcOjQIQ4fPpxoALNr1y4++eQT47fwIkWK0KtXL1asWEHnzp2RJAlXV1fGjh0LwG+//Zai556bmJub4+HhgYeHB8WLF2fUqFHs27fPZFPOpMRuC/HZZ5/RunXrBNO8PYYlrczNzY17a8XS6XR07dqVly9f0r17d0qWLIm1tTVPnz7l+++/f+fWFQmJXcH57YD8XfR6PW5ubiaLP74tbsD19marb1NSsITW2y1OgiCkDxHApFBoaChg+AYGhuZcjUZDzZo1jWlKlSpFoUKF8PHx4auvvmLBggUMGDCAffv2YWtra/LHbMeOHXTt2pVt27bh4+PD999/T9WqValVqxZgmMnx6NGjNC1MdfXqVZ4+fYosy7Rq1YqgoCDKli3LiBEjjAM7fXx8cHNzw9nZ2ZjP29ubCRMmcOvWrQQDFL1eT3h4OA4ODoleOyYmJt4fb0tLS548ecKjR4/ifTMVkie2myh2+4hY9+/fj5f23r17WFlZGQeV2tjYoNfrTd6zCSlatCgXL15Eo9EkOLMHUtaVFOvGjRvcu3ePmTNn0qpVK+PxEydOpLgsgBIlSlCiRAkOHTpEeHh4kl1JRYsW5dq1a3h5eaWq/glJaTl58+bFysrKZNXqWHfu3EGW5UQH4xYqVAgwvNZvtyJpNBr8/f2TtYmsILwPxBiYFNDr9UybNo2qVauazOgwMzOLN83VycmJwMBAVCqVMdhxcnLCxcXFpJ/a3d2d/v37U7x4cVq1akXFihVNNoZ0cXFJ86yC2L76hQsX0qdPH5YuXYq9vT1fffUVL168MD6Pt4MXwPg4MDAwwXJXrVpFREQETZo0SfTa3t7eHDhwgFOnTqHX67l79y6//PLLO8sV3jh9+nSC3/SPHDkCvGl9iHXhwgXjGBaAx48fc+jQIWrVqoVKpUKlUtG4cWP++uuvBFssYruPABo1akRISAgbN26Mly62TlZWVgAp6k6JbZF5+3kpipKmhfkGDhzIixcvGDt2bILrvRw/fpy///4bgCZNmvD06VO2bdsWL11UVBQREREpvn7sfYj9gpMUlUpFrVq1OHTokHG6NRh+D3fv3s0HH3yQ6IyqihUrkjdvXrZs2WIy7XvHjh0ZNs5OELIj0QKTAhMnTuTmzZts2rQp3cqM21zv4uJi0gc/dOjQNF8jtkm+d+/eNG7cGDDs6VSnTh327duXqj2H/vjjDxYtWsTixYvjrUfyts8//5wHDx7Qq1cvtFottra2dOnShQULFsTrWhDimzJlCpGRkTRs2JCSJUui0Wj477//2Lt3L4ULF6ZNmzYm6d3c3OjWrZvJNGrApFty6NChnDlzhs8//5z27dtTunRpXr58yZUrVzh16pRxVd1WrVqxc+dOpk+fjq+vLx988AGRkZGcOnWKL774ggYNGmBpaUnp0qXZu3cvxYsXx8HBgTJlyrxzynfJkiUpWrQoM2fO5OnTp9ja2vLXX3+l6cO3adOmXL9+naVLl3L16lWaN29OoUKFePHiBceOHePUqVPG8WEtW7Zk7969jB8/njNnzlC1alV0Oh137txh3759rFy5kkqVKqXo+hUqVADg559/pmnTppiZmVGvXr13Lto3ePBgTp48SadOnejUqRMqlYqtW7cSExPD8OHDE81nZmbG4MGDGTduHF9//TVNmzbF39+f33//XYyBEXIVEcAk06RJk/jnn3/YsGGDSR+5s7MzGo2GV69embTCBAcH4+LikmS5cQdISpKUor715IitR6lSpYzHzM3NKVKkCI8fPwYMz8PX19ckX+xMqrjP488//2Ts2LHMmzcvyW4ISZIYPnw4Q4YMISgoCEdHR2MLk/hjm7QRI0awb98+jhw5wtatW9FoNBQqVIhOnTrRp0+feC1/1atXp3LlyixatIiAgABKly7N9OnTTboVnJ2d+fXXX1m0aBEHDhxg8+bNxv2VYheaA0MrwYoVK1iyZAm7d+9m//79ODg4GGe7xZoyZQqTJ09m+vTpaDQa+vfv/84AxszMjKVLlzJlyhSWLVuGhYUFDRs2pHPnzrRs2TLV9yp2gPr69evZvHkzL1++JE+ePHh6erJ48WI++eQTwNACtGjRItasWcP//vc/Dhw4gJWVFa6urnz11VcJDqxNioeHB4MGDWLLli0cO3YMvV7PoUOH3hnAlClTho0bNzJ79myWLVuGoih4eHjw008/vXMNGIAOHTqg0+lYtWoVP/74I25ubixZsoR58+aluO6CkGNl0fTtHEOv1ysTJ05UvL29lbt378Y7/+rVK6VChQrKvn37jMdu376tuLm5KRcuXFAURVH+/fdfxc3NLd66GwmtA9OnTx9l5MiRqapr7HoRL1++NDkeGhqqVKxYUdm2bZvxWOxaElu2bFEURVH++ecfpWzZskpQUJAxzZYtW5SqVauarDXxxx9/KJUqVVIOHDiQqjoqiqIMHz5c6dChQ4LnxNo4qefm5qZMnDgxq6shCIKQKUQLTBImTpzI7t27Wbx4MTY2NsZxG3Z2dlhaWmJnZ0fbtm2ZMWMG9vb22NraMmXKFKpUqULlypUBw2qqkiTxzz//ULduXSwsLJK9ZsXs2bN5+vSpcanyhAQGBhIUFMSDBw8AwyBJGxsbChYsiIODA7a2tnTs2JEFCxZQsGBBChUqZJxd9emnnwKGsSqlS5dmxIgRDB8+nMDAQObOnUvnzp2Na0388ccffP/994wePRpPT0/jvYi9DwAbNmzgwIEDrF27FjCMqfjrr7/48MMPiYmJ4bfffmPfvn1s2LDB5Dn4+fkBhqmwz58/x8/PDzMzM5OZUoIgCIIQSwQwSYgdQxC7TX2s6dOnG8cfjB49GlmWGThwIDExMXh7ezN+/Hhj2vz58zNgwABmz57NqFGjaNWqlXEadVICAwON3TyJ2bJlCwsXLjQ+7ty5c7w6jhgxArVazYgRI4iKisLT05O1a9caBxirVCqWLl3KhAkT6NChA1ZWVrRu3ZqBAwcay922bRtarZZJkyYxadIk4/G3p4WHhITEW+Br586d/PjjjyiKQuXKlVm/fj0eHh4mad6ejXLlyhV2795N4cKFOXz4cLLukyAIgpC7SIqSzgMuBEHIEu7u7nTu3Jlx48ZldVUEQRAynAhgBEEQBEHIccQ8VkEQBEEQchwRwAiCIAiCkOOIAEYQBEEQhBxHBDCCIAiCIOQ47/006uDgUN41TFmSJGxsLFK8A2566N79W9zd3Rk+fGSmXztZwiNQX/dD614ObBJfUTTd8qWwHFmWCQ+PTveVixMiBQZi8b8dRLdsjZKMFZYzU0bVLaeVKwjC+0GSwMnJLul07/sspKCgdwcwsvwmgEko3dKli1m+fKnJseLFi/P777uMj6Ojo5kzZxb79+8jJiYGL6+ajBo11rhH0Pnz5+jZsxtHjhzHzu7N0u89enyLm1vaApiXL1+ydOliTp8+yZMnT3B0dOTjj+vTp08/4+JyN25cZ/XqVfj4XODFixcULFiIdu3a06nTl8ZyLlz4j/nz53Lv3l2ioqIoWLAgbdq058sv36x/8+uvW/n11208fhwAQMmSpejZsxe1atVOtH4ajYbVq1exe/cunj17RrFixRk4cDC1ankb04SHh7N48UL+/vswISHPcXcvy/DhI6lQoWKi5UrSmwBGr3+v38KCIAi5iiSBs3PSAcx73wKTHkqVKsWSJSuMj1Uqlcn52bN/5PjxY8ycOQtbWztmzpzGsGHfsXp16nfXTa7AwGcEBj5j8OChlCxZisePA5g2bQqBgc/46ac5AFy9epW8efMyZcp08ucvwMWLPkydOglZVtGx4xeAYTfdDh06UqaMG1ZWVly4cIGpUydh8+olbb7tjmJpSb58+Rk4cDBFixZFURT++GMX3303iM2bt1Gq1JsVc6WoKOT799AXK87iZUvYs+dPfvhhPMWLl+DUqRPGe1O2bDkAJk2awO3bt5g8eSouLvnYs2c3ffr05LeNWygQEYG+WHEUS8sMv5fvIr0IwezoP2jqfIzi4JildYkro+qW08oVBCF3SVELzLJly9i/fz937tzB0tKSKlWqMGzYMEqWLGlMEx0dzYwZM9izZ4/JqrTOzs7GNAEBAUyYMIEzZ85gbW1Nq1atGDp0qMnGhmfOnGHGjBncvHmTggUL0qdPn3g77yZHerTA/PPP32zZ8muC+UNDQ/nkk7pMmzaDBg0aAXD37l3atm3JmjXrcXZ2pnnzJiZ5WrT4jIkTp9Cjx7eUKeOGubk5O3f+jpmZGW3btqd3774pfp5vO3BgP2PHjuLEiTPxNouMNX36VO7evcPy5asSLWdY/z7YBjxi0pLl6PMXSDDNxx97M3jwEFq1evPayE+fYLVuNZFdutLgq85069aDDh3e7Hg9bNh3WFhYMnXqdKKioqhd24s5c+ZRu3YdY5pOnTrgXbkywyWI7NI13vUzuwVG7euDY4M6hBw8itajcoZfLyUyqm45rVxBEN4PyW2BSdEg3rNnz9K5c2e2bdvG6tWr0Wq1dOvWjYiICGOaadOm8ffffzN37lzWr1/Ps2fP6N+/v/G8TqejV69eaDQatmzZwowZM9ixYwfz5883pnn48CG9evWiRo0a/O9//+Prr79m7NixHDt2LCXVTTcPHtynUaNPaNGiCWPGfG+ytL+f31W0Wi01anxkPFaiRAkKFCiIr68v+fMXMLaE7Nixi/37DzNs2Jsuo927d2FlZcW6dRsZNOg7VqxYxunTp4znx48fS48e36aovmFhodjY2CYavBjShBm3EUjItWt+XPS7yof58iV4XqfT8ddfe4mMjMTDI/GdczWaGCwszE2OWVhY4uNzwViOTqcz7rcUy9LSkgtXriRariAIgpC7pagLKXYDwFgzZszAy8uLK1euUL16dUJDQ/ntt9+YNWsWXl5egCGgadq0KT4+PlSuXJnjx49z69YtVq9ejbOzM+XKlWPQoEHMmjWL/v37Y25uzpYtW3B1deX7778HDF04//77L2vWrKF27cTHW2SESpUqMXHiFIoVK05QUCDLly+lW7dv+PXX37GxsSE4OAgzMzOTsS0ATk5OBAcHoVKpjIFC3rx546UrXboMvXr1AaBo0WJs3bqFs2fP8NFHhvvn7OySogHGISEhrFixnDZt2iaa5uJFHw4c+It58xbGO/fppw0ICQlBp9PRu/OXtNdqiHzr/M2bN/jmm6+IiYnBysqa2bPnUrJkqUSv5eVVkw0b1lO16ge4uhbh7Nkz/P33IXQ6HQA2NjZ4eHiycuVySpYsSd68Tuzbtxdf34sUKVgo2c9bEHI6vV6PTqfN6moIQoaTZRlZViFJUprKSdMYmNDQUADjB/Tly5fRaDTUrFnTmKZUqVIUKlTIGMD4+Pjg5uZm0qXk7e3NhAkTuHXrFuXLl8fHx8cYAL2dZtq0aWmpbqq8PUDVzc2NSpUq0azZpxw48JdJt0lqlSnjZvLY2dmZ58+fGx8PGDAo2WWFhYUxaFA/SpYsaQyK4rp16ybffTeInj174+VVM975VavWEBERwaVLviyY9zOlKlbkk7fOFy9egs2bfyUsLIxDhw4wbtxYVq78JdEgZvjwkUyePJE2bVoiSRKurq60aNGSXbt2GtNMnjyNiRPH0bhxA1QqFWXLlqNx4yZcu+yb7OcuCDlZdHQkISGBgBiQLuQO5uaW5MmTF7XaLNVlpDqA0ev1TJs2japVq+LmZvgQDgoytEbkyRO/NSIwMNCY5u3gBTA+TipNWFgYUVFRWGbhgE47uzwULVrMuOOyk5MzGo2G0NBXJq0rwcHBODk5J1aMUdxuHkmSUJSUT+kODw+nf/8+WFvbMHv2XMzM4r8p7ty5Te/ePWjTpi3du/dMsJzChV0BQ2AV4u/Pgv/t4JO33mBmZmYULVoUgPLly3PlymU2bdrI2LFvbSCoNkOXvwCozXB0zMucOfOIjo7m5csXuLjkY/78ucbrABQpUoSVK1cTGRlBWFg4Li4ujBw5nMIFCxnLyWqKpRWaSp4ollZZXZV4MqpuOa3cnEqv1xMSEoi5uSW2tvZp/lYqCNmZoijodFrCwl4QHPyEfPlcU/2eT3UAM3HiRG7evMmmTZtSW0SOFBERgb//Q5o1aw5AuXLlUavVnD17hk8+aQjAvXt3efLkMR4eHgDGYEKny5i1ZsLCwujXrzfm5ub8/PN8LCws4qW5ffsWvXp1p3nzz+jff2CyytVZWhJtaYX+9XTwhOj1ejSaGNNjTk5EdelqcszCwoJ8+fKj0Wg4dOggDRs2ileWlZU1VlbWvHr1ilOnTjJo0HdEtW2XrLpmNJ2bOy8OZc0YrKRkVN1yWrk5laHbSMHW1h5z8/i/u4Lw/rFApVLx/PlTtFoNZmbmSWdJQKoCmEmTJvHPP/+wYcMGChR4MzvE2dnQGvHq1SuTVpjg4GBcXi9Y5ezsjK+vaddAUFAQgEma2GNvp7G1tc301peff55FnTofU7BgQQIDA1m6dDGyrOLTTw0zi+zs7GjVqjWzZ88iTx57bGxs+fHH6Xh4eBoHtxYsWBBJkjh27Aje3rWxsLDE2jp5C7wtWDCPZ8+eMnlywt1nYWFh9O3bi6ioKKZMmU54eDjh4eEAODo6olKpuHXrJr16dcfLqxZfftnFeG9VKhlHx7wAbN26hQIFClCiRAkA/vvvX9avX0vHjp1M6lKzZi0KFixIeHg4+/bt5d9/z7No0Zt1cn74YTT58uU3dn1duuTLs2fPcHcvy7NnT1m2bAmKouebb94EOCdPnkBRFIoXL87Dhw+ZO3cOxYsX57PPWibrHgnC+0C0vAi5iSSlfSOAFAUwiqIwefJkDhw4wPr16ylSpIjJ+YoVK2JmZsapU6do3LgxAHfu3CEgIIDKlSsDULlyZZYuXfq6i8Xwzf7kyZPY2tpSunRpY5qjR4+alH3y5EljGZnp6dNnjBo1kpcvX+Do6EjlylVZu3aD8YMfYOjQEUiSzPDhQ14vZFeLUaPGGM/ny5ef3r37smDBPCZMGEfz5i2YOHFKsq4fFBTIkydPEj1/7Zofly9fAqBly2Ym53bv3kuhQoU5ePAAISEh7Nmzmz17dhvPFyxYiD//3AeAouhZuHAejx49Qq1W4+rqyqCu3fgyJJiYZ0/R58vP8+fPGTduLEFBgdja2lKmjBuLFi01DjgGePLkCXJ0NNZzfiTqy6+JiYlh8eKFPHrkj7W1NbVqeTNlyjST7rawsDAWLpzH06dPsbe3p379BvTrNwCLkOdYblhL1Jdfo8+XP1n3K6OoL13EocknvNh7CG2lxGddZYWMqltOK1cQhNwlRevATJgwgd27d7N48WLjN3UwtELEtoyMHz+eo0ePMn36dGxtbZkyxfBBvWXLFsAwbbZVq1bky5eP4cOHExgYyIgRI2jfvj1DhgwBDNOoW7RoQadOnWjbti2nT59m6tSpLFu2LMWzkNK6Dkxu9vZ6LomtA5Oe+VJSjlgH5o2ctl5Ldr6XWUGjiSE4+DFOTgVNmtIlKXNbZRRFEX8DhUyT2PseMmgl3s2bNwPw1VdfmRyfPn26cZG50aNHI8syAwcONFnILpZKpWLp0qVMmDCBDh06YGVlRevWrRk48M24jCJFirBs2TKmT5/OunXrKFCgAFOmTMn0KdSCIAhZQZJAtlChScWA/tQyk1Too3UiiMlEjx8HsGbNSv777zzBwcE4OzvTuHFTunT51jh28r//zrNt2yb8/K4QHh6Oq2tROnX6ikaN3iyQqtVqWb9+NXv37iYoKJAiRYrRp88APvrozUzTHTu2s3PnduM6ZiVKlOSbbwxDC97l8OGDrFy5hCdPHuPqWoQ+fQbg5fVmK5hVq5Zx6NB+nj17ilpthrt7OXr27PvOrWDSS4oCmOvXryeZxsLCgvHjx5sELXEVLlyYFStWJHoeoEaNGuzcuTMl1RMEQXgvSJKERtFz91UIMXpdhl/PXFZRIo8jaknKlM1RBYP79++hKArDh4+mcGFX7t69zcyZU4mMjKR//8EAXL7sS6lSZejc+Wvy5nXixIljTJkyHhsbW+MyH8uXL2b//r2MHDmGokWLc/bsaUaPHs7SpatwcysLgItLPnr37o+rq2ErmL17dzNq1FB++WVjostgXLp0kYkTx9CrVz9q1qzNgQP7GDVqGL/8soGSJQ1DPooUKcZ3342gUKHCREdHs23bJoYM6ceWLTtxdMzYrULEXkiCIAjZVIxeR4wu4wOY1Ojfv6fxg++vv/agVqtp1aod3bv3NnZ9vXr1innzZnHixDE0mhgqV/6AwYOHUaSIYSmGJ08eM2fOj/j6+qDVaihQoBD9+g00+Yb/LqtWLePYsSO0a9eBX35ZTmjoKxo3bsZ33w1ny5YNbN26Cb1eT/v2Hfn6627GfKGhoSxaNJfjx48QE6OhbNlyDBgwxLgu16NH/ixYMIcrVy4TFRVJsWIl6NWrH9Wr1zCW0a5dCz77rDX+/g/5++9D2NnZ8fXX3WjZMvnrg330UU2TVpLChV158OA+O3b8ZgxgunQxXYn988+/4Ny50xw5ctgYwPz11x66dPnWeN9at27H+fNn2LJlI+PGTQbA27uOSTm9evVj587fuHr1UqIBzK+/bqFGDS86deoCQI8efTh37gy//baN4cNHA9Co0acmeQYM+I7du//H7ds3qVbtw2Tfi9RI+zBg4b2ld3ImsmsP9MlYzyY98mVUOSlhGHcQ/7jOzZ2QY2fQlnHPtLokl7aMO8+Ppn/dclq5Qubbu/dPVCo1K1asZdCgYWzdupE//thpPD9t2gSuX/dj5sw5LF26+nVrwyC0WsOKw3PmzESjiWHRohWsXbuFPn0GYGWVvBmasR498uf06ZPMnr2A8eOn8uef/2P48MEEBj5j4cJl9OkzgBUrlnDlymVjnh9+GElIyHNmzZrPqlXrcXMry+DBfXj16iVgWC7jo49qMW/eYn75ZSM1angxcuSQeBMqtmzZSNmy5Vm9eiOtW7dn9uwZPHhwz3i+f/+eTJ06IUXPJywsLN5aagmnebMVjEajSXDLFl9fnwTz63Q6Dh78i6ioSCpU8Ej0Opcv+8YLQmrU8DJOHIlLo9Hwv//teD0pxy3BNOlJtMBkofHjxxIaGsqcOfOyuioJU6vRO6cieEhtvowqJ5lixx0AJmMBJAlkB1sUh4pI2XGMgJUVutc7e+fqcoVMlz9/fgYOHIIkSRQtWpzbt2+xbdsmPvusNQ8fPuD48aMsWbKKSq9nm40fP5k2bZpx9Og/1K/fgKdPn1C3bn3jbvZvL3CZXIqiZ/TocVhb21CiREmqVKnGw4f3mTVrHrIsU7RocTZuXMt//52nQoWKXLzog5/fFf7444BxD7b+/Qdz7Ng//P33IVq2bEOZMm4mq6T36NGHo0f/5sSJI7Rt28F43MurJm3atAfgyy+/Ztu2Tfz333mKFi3++v4USNaCprH8/R/y229b6ddvcKJpDh06wLVrV40tIAAffvgRW7ZswtOzKoULu/Lvv2c5cuRwvG1obt++Re/eXV9vBWPFtGk/UaJEybiXMHr+PNhkxi2Ao2Nenj8PNjl24sQxJkwYTVRUFE5Ozvz88yIcHByS/bxTS7TAJEGn07F48UKaN/8UL6/qfPZZU1asWGbST6woCkuWLKJRo/p4eVWnd+8ePHhw33g+IOARVat6cP36tXSvn0ajYd68n/n88zbUrPkhjRp9wg8/jCYw8JlJOj+/q/Tp05M6dWpRr15tJk+eaLIJJ8CPP86gU6cO1KjxAR07tkd69RLzv/Ygvf5WArB//1907NiemjU/pGnTxqxduzpenRLKBxATE0PHju0TvBc3btzg22+/5qOPqtGkSUPWrPkl0XIySuy4A42iN5n9IUkSuvv3sOrXB5X/w0ypS0rIDx9g+11/5IcPcnW5QuYrX76iye9KxYqVePjwATqdjvv376JSqShf/s1gTnt7B4oWLcb9+3cBaNeuI2vXrqJPn29ZtWoZt27dTHEdChQohLW1jfFx3rx5KV68BLIsv3XMiRcvDFu03Lp1g8jISJo1+4SGDWsbfx4/DuDRI3/A0AKzcOFcOndux6effkzDhrW5f/8eT5+atsCUKlXG+G9Jksib14mQkBDjsR9+mETv3v1JjsDAZwwdOoB69Rrw2WetE0zz33/nmT59IiNGjDHp9hk0aBhFihShc+d21KvnxZw5P9K06Wfx1lopWrQYq1dvYtmyNbRq1Y6pUydw9+6dZNXvXapWrcbq1ZtYsuQXatTwYty4UYSEPE86YxqJACYJa9b8wvbt2xg5cjS//baTgQMHs3btarZsebMC8dq1q9m8eROjR//A2rUbsbKyol+/3kRHR2d4/aKiorh2zY/u3XuxadNWZs2aw/379xg8+M2srsDAZ/Tp05MiRYqwbt0GFi5cwp07txk/fmy88lq2bE2jRoY1fKTISMx8LyJFGrZzPHHiGGPHjqJdu/Zs2/Y7o0aNYePGDWzZstmkjLj5Ys2bN8e4WOHbDCsJ96JgwUJs3LiFwYOHsHz5Un77bXuC5WQF+flzrNavQ3qe8b+UKSWHPMdq4zrkdP6DkdPKFXKeFi1asW3b/2jcuCm3b9+ie/ev2L59S4rKSGg7lrjHAONyC5GRETg5ObN69SaTn02bfjOO9Vi0aC5Hj/5Nz579WLRoJatXb6JkydJoNKabbSZ07ZRsvhsrKCiQAQN6U7GiByNGjEkwzYUL/zJy5HcMGDCEJk2am5xzdHRk+vTZHDhwjO3b/2DTpt+wsrKiUKHCJunMzMxwdS1C2bLl6N27P6VKufHrr6Z/v99mCMhMf09DQp6TN6/p6uxWVla4uhahYsVKjBo1DpVKxe7d/0vJLUgVEcAk4eLFi9StW4/atetQqFBhGjRoxEcfeXH5sqE/VVEUNm3aQPfuPfj443q4ubkxadJUAgMD+eefwwA0b26Y7vbFF59TtaoHPXqYDspat24NjRrVp1692kyfPhWNRpPs+tnZ2bFkyXIaNWpM8eIl8PDwZOTI0fj5XTVOlzt69ChqtZrvvx9D8eIlqFChIqNHj+XQoYM8ePDmW/CIEd/ToUPHRJtx//xzNx9/XI927T7H1dWV2rXr8O233Vi79pckZy6cOHGMU6dO8d13Q+Od27v3TzQaDRMmTKJUqdI0btyEjh07sWHHb8m+D+kl9rtkYmNhBEF44+rVKyaPr1y5TJEiRVGpVBQrVgKdTsfVq2/Gnrx8+YIHD+5TvPibdcTy5y9Aq1btmDbtJzp2/NJkDE1GcHcvy/PnwahUKlxdi5j8xHZ7XLp0kaZNW1C3bj1KlSpN3rxOPHkSkCH1CQx8Rv/+vXB3L8vo0eNNWo5i/fffeUaMGEzv3gPeOUjYwsICF5d86HQ6jhw5TO3add95bUXRv/PzpmJFD86fP2dy7Ny5M1SsWOmd5er1emJiYt6ZJj2IACYJnp6enD17hvv37wFw48Z1fHwuUKuWYbT3o0ePCAoKokaNj4x57OzsqFixEr6+FwFYv97QWrNkyXL27z/MrFk/G9OeP38Of/+HLFu2iokTp/DHH//jjz/eRK5Lly6mWTPTUd5JCQsLQ5Ik7OwMCwFpNDGYmZmZ/GJYWBgWHvTxuZDscmNiYuLt1WJhYcHTp095/DjxX+7g4GAmT57IlCnTEtwKwtf3IlWrfmCyAaWXV03u+fvzMhN+CSB2/IsMKgkZsLKSsLaWEwxiRHAjCAZPnz5hwYI5PHhwjwMH9vHbb1tp164jAEWKFKV27brMnDmVixd9uHnzBpMmjcPFJR+1a38MwLx5szlz5hQBAY+4fv0a//13nmLFSrzjimlXrVoNKlSoxKhRwzh79jSPHwdw6dJFli1bxLVrVwFwdS3KkSOHuXnzOjdv3mDixDGpWjBz8uRxLF26MNHzgYHPGDCgF/nzF6B//8G8eBFCcHAQwcFvttKJDV7atevIxx/XN55/9VbX+pUrlzly5DCPHvlz8eIFhg4dgF6vGFuUAJYuXYiPz388fhzA7du3WLp0IRcu/Gsyiyhufdu378iZMyfZvHkD9+/fY9WqZVy7dpW2bT8HIDIykmXLFnH58iWePHnMtWt+TJs2kaCgQOrVa5Di+5VSYhBvErp27UZ4eDht2rREpVKh0+no128ATZsalu2PfaPFbVJzcnIiKMgw0Cl2LryDg0O8Xbbt7PIwcuRoVCoVJUqUoHbtOpw9e5Y2bdq9zuOIq2vyB7ZFR0czb97PfPppE2xtbQGoXv1D5syZxdq1q+nU6UsiIyNZsGAuYGi6TC4vr1rMnv0jZ858RvXqH/Lw4QPWr18HQGBgULzmSjC0UI0fP5Z27T6nfPkKBAQ8ipcmODg4Xt7YbSaCIiMplOwapp4kSWgVBUVSkGUJSdK+DlRUcdKBtbUhEIyIEKs3CxnLXFYlnSgLr/Ppp82Ijo6mR4+vkWUV7dp1NGkhGDVqPPPmzWLkyMFoNBo8Pavy00/zjF0ver2OOXNmEhj4DGtrG2rU8GLgwCHG/O3ataBJk+Z069YrbU/wLZIkMWvWPJYvX8y0aRN58SKEvHmdqFy5qnHA6oAB3zF9+iR69/4We3sHOnf+2rjHXEo8ffokwRaVWOfOncHf/yH+/g9p3bqpybnjx88DsHfvbqKioli/fjXr178Zc1i5clUWLlwOQExMNCtWLCEg4BFWVlZ89FEtfvhhkvFLLBi6fqZMGU9wcBA2NraUKlWGOXMWUL36my/fcetbqZIn48dPZcWKxSxfvghX1yJMnz7LuAaMLMvcv3+PvXt38/LlC/LksadcufIsWrQi0anZ6UkEMEk4cOAv9u79k2nTZlCyZCmuX7/O7Nk/4uLiQosWad9ssFSpUqhUb/54ODs7c/Pmm4FsHTt+QceOXySrLI1Gw8iRwwCFUaPejG8pVao0EydOZs6cWSxcOB9ZlunYsRNOTk7v/OVSbGzQ1PBCsTEMkGvTpi3+/g8ZPHgAWq0WGxsbvviiM8uWLUGWpQTzbdmyiYiICLp27ZbYZd79nDwqG6+flfT58hH+3RD0LvmRZUM/uCSpssWiX3qXfEQMHILeJV+uLvd9oigKZpJhcbnMYibJ6JWUrTmjVqsZNGgow4aNSvB8njx5+OGHSYnm/+67EYmei4qK4vnz51Sp8kGiabp16xUvuBkzZkK8dLEf9LGsrW0YPHg4gwcPT7DcggULMX/+UpNjsa0OsbZv/yNevjVrNpk8jnvduJo2bUHTpi3emWbMmAkJPqe3VanyARs2/PrONKNGjXvneUi4vvXrN6B+/YRbUywsLJg27acky80oIoBJwty5c/jmm240bmwYx1KmjBtPnjxm9epVtGjR0jhF7vnzYJMBqsHBwbi7J73ORfzBZqlbCVOj0fD998N5/Pgxy5atNLa+xGrSpBlNmjQjODgYKysrJAk2blz/zmmLiq0dMXU+flMzSWLQoO/o338gwcFBODrm5ezZ0wAmrURv5zt37iy+vhf56KNqJmV/+eUXNGnSlEmTpuLk5BRvWl5wsOGx/adNUWyT3hMjoymFChE2YSJmWgnQJpk+M+kLFiJ87IRcX+77RFEMU/nVmdhXqVey1xIB//13ng8+qEbVqtWSTizkSiKASUJUVJRJ6wIYms1i+0MLFy6Ms7MzZ8+ewd3dsGRzWFgYly9fon17Q8QeO7ZDp8uYfU1ig5cHD+6zfPmqd86/j+2a2blzB+bm5nz00UeJppViYpCfPkGfvwCK+ZtFklQqFfle7w69b99ePDw8TdYKeDvf8OHf07fvm2mEgYGB9OvXmxkzfjQOBPPw8GTRogVoNBrjvTp9+hTFixXD8eUL9JaWJtfPTJIkIZlL6MNeYfbvBahUBaytsqQuiZHCQlFf9EHrWTldg72cVu77RlHIFi18WaVmTW9q1kzeirxC7iQG8SahTp26rFq1gmPHjhIQ8IjDhw+xYcN66tWrDxg+4Dp1+pKVK5dz5Mjf3Lx5g3HjxuDi4sLHHxvSODrmxdLSkpMnjxMcHExoaGiyr79ly2Z69eqe6HmNRsOIEUO5evUKU6fOQKfTExQURFBQkMno8i1bNuPnd5X79++xdesWfvxxOgMGDMLO7s2Kjw8ePOD69WsEBwcTHR3F9fPnuLtkIdpnTwEICQlh+/Zt3L17l+vXr/HTTzM4ePAAw4a9aQa+fPkSrdu14uUvK5BCnlOwYEFKly5j/ClWrBgArq5FyP96h+lPP22KmZkZkyaN5/btW/z11z42b97Il5+1wnLLRqQsmm6rUquwsdFhYaagun0Lx+ZNsfK/jVqdvUbwqu7cxqF1M1R3bufqcoXMtXDhcgYNij+rUBAyi2iBScKIEaNYvHgh06dPJSTkOS4uLrRt246ePXsb03z9dVciIyOZMmUSoaGhVK5chYULl2BhYZixo1arGT58JCtWLGPp0sVUqVKVFSt+Sdb1X7wIwd/fP9HzgYHPOHLkHwA6dmxvcm758lVUq1YdgCtXLrFs2WIiIiIoXrwEo0f/QPPmpn2vkydP4N9/zxsfd+zfB4A/O3aioGsRAP74Yxc//zwbRVHw8PBk+fJVJlPqoqKiuOfvj6ZKlWQ9PzDM2lq0aBkzZkylc+eOODg40LNnb9o1aQrr4i+UlzkMA3kVQpBlJ6TXE6wlSQeSnjcTrgVBEISsICnveRtlUFDoO/t1ZVnCxsYCvV7MKIlLfvoEq3WriezSFf3r1pKMzJeSciTJ0JUXHh6dqumN8a4lS+jNJfSyglqRsDXToVLr0WiCkFT5iDx7CUdvb6JPHkH1oQdajUR4uCpdrp1Wal8fHBvUIeTgUbQelXNtuTmVRhNDcPBjnJwKYmaWNV2lgpDZ3vW+lyRwdk66e1l0IQnCWyRApZJAJZsskS4IgiBkLyKAERInq9Db2UFK14hIbb6MKicFJElCp4Be0aOgGDuKFDM1ukKF4K3F9rJLfKOozdAVLISiNks68XtcriAIuYvoQhJdSDlSRnUhSSqQkLA202Km0qONCQJVPsI0KiQ92Kh1qM30KIr6dTeS3mTXakC8j4QUEV1IQm6UHl1IYhCvkOvFBh4SYJ1Io4C1GaA3DuV9vVKvZFzMzrAVgaGlSB+dvdbTEARBeB+JLqQsNH78WIYMGZTV1UiUHBiI1dKFyIHJ324gLfkyqpx3iQ08ZAvDmBdZ0iFLujfjXyTDf1SXfbEqVQbpytVEypHQKHo0ij7Tx86orl4hr2dZVHE21stt5b5vDK2MUqb9ZJcuUUFILtECk4Tw8HAWL17I338fJiTkOe7uZRk+fCQVKlQ0plEUhaVLF7Njx2+Ehobi6VmZ0aPHUrSoYc2TgIBHNG/ehM2btxkXu0tPS5cuZv/+fTx58gQzMzPKlStPv34DqFTJw5hm8OAB3LhxnefPn5MnTx4+/PAjBg0ajMvr5dyjo6OZNm0yfn5XuXv3LrVr12HuyFHIoaGgf7O8eExMDMuXL2XPnj8JDg7C2dmFHj160apV6zcV0utM8l25cpn58+fi5+eHJEGFCpUYPPg73NzerFR848YNZsyYytWrV3B0dKRDhy/4tknTeNdPb7GBh4wUb8FCCQlFft1Co9EgPQoArQZJSt1qyRlF0mpQPQ5A0iZ/F/P3sdz3SeyeW7HbVmQGvV4t9vfKIkeOHGbHju3cunWDmBgNJUqU5Ntve1Kjhpcxzfr1qzly5G/u37+HhYUFlSp50KfPAIoWLW5MExwcxOLF8zh37iwREeEULVqMLl2+5eOPP4l3zZiYGHr2/IZbt26wevVGypRJeOX4x48DaN/+swTPTZo0w7jNgJ/fFZYuXcj1636ARPnyFejTZyBlyril/sYkQQQwSZg0aQK3b99i8uSpuLjkY8+e3fTp05Pt23cYV6Ndu3Y1mzdvYtKkKRQqVJglSxbSr19vtm/faVwLJiMVK1aMkSNHU7iwK9HRUWzcuJ5+/Xrzv//tNq6QW63ah3z7bXecnV0IDHzGzz/PZvjwoaxZsx4wbKpmYWFBx46dOHToYKLXGjlyGMHBwYwfP5EiRYoQGBiEoiS+wnBERAT9+/ehTp2PGTVqDDqdjqVLF9OvX2/27NmPmZkZYWFh9OvXiw8//IgxY37g1q2bTJw4njyKwlfpe6veKTZ8UUkykmQYxBup1Zh0KynwenCv+LoqZBxJkpBlLXr9MzJn6wo1spwv2+zvldv4+FygevUa9OrVD1tbO/bs+YORI79j+fI1uLkZvvReuPAfbdq0p2zZ8uh0OpYvX8R33/Vnw4ZfsbIyrA4+Zcp4wsJCmTFjNvb2Dhw4sI9x40axcuU6YzmxFi+ej7OzM7du3Xhn3fLly8///rfP5NiuXTvYtGk9H31UEzD8nR86dCDe3nUYOnQkWq2OX35ZxtChA/j99z8T2DInfYgA5h2ioqI4fPggc+bM44MPDPtx9O7dl6NHj/Drr9vo128AiqKwadMGunfvwccf1wNg0qSpNGxYj3/+OUzjxk1o3tywj9IXXxi2Fvjgg2omC9mtW7eGDRvWodFoaNToU4YNG2FcUj85mjRpZvJ4yJDh7Ny5gxs3blCjhmGrgC+/fBMKFCpUiK5dv2XIkMHG5futrKwZPfoHAC5e9ElwteATJ47z77//8scfe7C3t39dVvwdqN92795dXr58SZ8+/ShQwLCWS8+evenQoR2PHz+maNGi7N37JxqNhgkTJmFmZkapUqW5fv06G3b8xle1ayf7PiRXQoNtJcDSDNSyBOiMRxXEH3MhK2lRlIwPYFLTfdS/f09Kly6Dubk5f/zxP8zMzGjZso3J5oqhoaEsWjSX48ePEBOjoWzZcgwYMMTkW/maNSvZvn0r0dHRfPJJQ+ztHThz5lS8jRHfpV27FjRv3pKHDx9w9Ojf5Mljz3ffDadCBQ9mzpzMv/+eo1ChwowaNY6yZcsb81286MOyZQu5ds0PBwcH6tT5mF69+hsDgn37/uTXX7fw4MF9rKysqFq1GoMGDTV+Mfzvv/MMHNibuXMXs2TJAu7du0OZMu6MHj3OpGUkKXFXNO7Vqx/Hjh3hxIljxsBjzpwFJmlGj55AixYNuX7dj8qVqwJw+bIvQ4d+T/nyhh6Cb77pzrZtm7l+/ZpJAHPq1AnOnTvNlCk/cvr0yXfWTaVSGff8i3X06N/Ur98Aa2trAB48uMerVy/p1q2XcYX1rl178vXXHXny5DGurxdCTW9iDMw76HQ6dDod5nH24bG0tMTH5wIAjx49IigoyBgogGFl2YoVK+HrexGA9esNv4hLlixn//7DzJr1szHt+fPn8Pd/yLJlq5g4cQp//PE//vjjf8bzS5cuplmzT5NdZ41Gw++/b8fW1s6ki+ZtL1++ZM+ePXh6Vk5RoHT06D+UL1+etWtX07hxA1q1asHPP88iKioq0TzFihXHwcGBnTt/R6PREBUVxc6dOyhRoiSFChUCwNf3IlWrfmBSFy+vmtzz9+dlTEyy65ccsgwqCxWyhcrkj3bs+BeSCFhUcX5jjENlJLE2r5D77N27G0tLK5YvX0OfPgNYs2Yl586dNp7/4YeRhIQ8Z9as+axatR43t7IMHtyHV69eArB//17WrVtNnz4DWLVqPfnzF2Dnzu2pqsu2bZupVMmTX37ZiJeXN5Mnj2PKlPE0atSUVas2UKiQK1OmjDe2MD165M+wYQP4+OP6rF27mYkTp+Hr68PPP/9oLFOr1dK9e2/WrNnEtGmzePLkMVOnToh37eXLF9O//2BWrlyPSqVi+vQ3O3A/fhyAt3c1/vvvfLx8idHr9UREhJMnT55E04SHhwGYpKlY0YPDhw/w6tVL9Ho9Bw/+RUxMtMmO3s+fB/Pjj1P54YdJWFpaJrtOsa5d8+PmzRs0b97SeKxo0WLY29uze/f/0Gg0REdHsXv3/yhevAQFChRM8TWSSwQw72BjY4OHhycrVy4nMPAZOp2OP//cja/vRYKCDANLg4ODAMib18kkr5OTE0FBhh2VHR0dAXBwcMDZ2dnYegFgZ5eHkSNHU6JECerUqUvt2nU4e/as8byDg6PJTs+JOXr0CLVq1eCjj6qxceMGlixZZrxurHnzfqZmzQ+pV682T548Zs6cee8sU3HMS1THziivv234+/vj43OBW7duMXv2zwwbNoKDBw8yffrURPPZ2NiwfPkq9uz5Ey+v6nh7f8SpUydYsGCxsVkxODg4wfsH8KheA+P10yp2XIG5uYL2rcG2EobA5l0RiFKmNLpDu6FMSWNClVqFlZWELIOVlYSNRdZ0LOlKluLFjj/RlSyVq8sVMl+pUmX49tueFClSlCZNmlO2bDnOnz8HGFo3/PyuMHnyTMqWLU+RIkXp338wtrZ2/P33IQC2b99K8+af0azZZxQtWoyuXXtQsmTpVNXFy6smrVq1pUiRonTt2p3w8HDKlStP/foNKFq0GJ07f829e3d5/tzwd3n9+tU0bPgpn3/eiSJFilKpkieDBg1n374/iY6OBqB585Z4edWicGFXKlasxODBwzh9+iQREREm1+7Zsy9VqnxAiRIl+fLLr7l0yddYhlqtpmjRYikKFjZvXk9kZCT16zdM8Lxer2f+/NlUquRpcr8mTZqBVquladNPqFfPi59+msa0abOMLSCKojB16kRatmxj0hKVErGBSaVKnsZj1tY2LFiwjP379/LJJ7Vo2LAOZ86cZNas+RnWfQSiCylJkydPY+LEcTRu3ACVSkXZsuVo3LgJfn4Jz0ZJqVKlSqFSvVmozdnZmZs3bxofd+z4BR07fpFkOdWrV2fz5l958SKEHTt+Z+TIYaxbt9EkMOjS5RtatWrN48ePWb58KePGjWHevIWJzppRzM3RFSn65rGiIEkSU6dOx87OMEd/yJBhjBgxlFGjxhh/Qd/OFxUVxaRJ46lcuTLTp89Ep9Oxfv1aBg3qx/r1m5P8pdYVKJhuO1Ebpj1rkSWQJDWxrS2SJJHkPuF2digfv92d9WYqtSybIasNzfxqSQVS5nY7KbZ2aGqlf1dbTitXyHylSpUxeezk5EzI681Xb926QWRkJM2amQ4gjY6O5tEjw/5uDx7cp3Xrdibny5evYLInW2rqEvt37+0P97x5DV+EQkJCcHJy5tatm9y+fZMDB96M71AUBb1ez+PHARQvXoJr1/z45Zfl3Lp1g9DQUON4v6dPn1CiRMkErx3b3RISEkKBAgVwccnHpk2/Jft57N+/j9WrVzB9+mxjV1Vcc+bM5M6d2yxevNLk+MqVSwgNDWXu3MXY2ztw7Ng/jBv3PYsWraRUqdJs376ViIhwvvqqa7Lr87bo6CgOHtzH1193j3d8+vTJVKrkyYQJU9Hp9GzZsp7hwwexcuU6LCxS3tKTHCKASUKRIkVYuXI1kZERhIWF4+LiwsiRw42tIrFv1ufPg3FxcTHmCw4Oxt094S6ct8WPTlM3w8XKypqiRYtStGhRPDw8admyOTt37uDbb9+80RwdHXF0dKRYseKUKFGCJk0a4evri6enZ4JlSmGhmP33L5qqH6DY2uHs7IyLSz5j8AJQokRJFEXh2bOnxllXb+fbd/AAAQEBrFmzAVk2NPhNmzaTunVrceTI3zRu3AQnJyfjt6JYwcGGxwVvXEPK54Jim/SiRikhERvQJHOUy6MA5CXL0PfriVS4EEiGWUpqMxXWkg6VDDIydlagVUuEhyuZNptDfhyA1arlRHbrib5goVxbrpD54v79enuGXmRkBE5OzixYsCxePtt0/n2OW5fYL2UJHYsNQiIjI2jZsg3t2nWMV1b+/AWIjIxk6ND+fPihF+PHT8HBwZGnT58wZEh/tHFm0L3rOilx8OBfzJw5mcmTZ1K9eo0E08yZM5OTJ4+zcOFy40QSMHSJ/fbbNtat20rJ162bZcq4cfGiD7//vo3hw0fz33/nuHLlEvXr1zQps3v3LjRs+Cljx058Z/3+/vsQUVFRfPqp6bjLAwf28eTJY5YtW238Oz9+/FSaNKnHsWNHaNCgcYrvRXKILqRksrKyxsXFhVevXnHq1Enq1jUM2C1cuDDOzs6cPXvGmDYsLIzLly/h4WEIDGLHduh0KX9Dp5ai6Il5x/iR2NVrNZrE00jh4ZidOYUUHg6Ap2cVgoICTZpPHzy4jyzLJr9Ib+eLiopClk33FTIEDhJ6veF+eHh48t9//6LRvPmjcPr0KYq7uuJ86aLx+unJ2hysrbXY2kpYJ6OBR3r6DHnmz/D02euZSBg2GpB0QIghoJH0SLIWWdYl2qqVEeTAZ1jPn4Mc+CxXlytkL+7uZXn+PBiVSoWraxGTHwcHB8AwduLaNdPW7PRq3U6Km1tZ7t69G69urq5FMDMz4/79e7x8+ZLevfvj6VmFYsWKG1uXMsKBA/uYNm0SEyZMpWZN73jnFUVhzpyZHD36D/PmLYk3gSJ2LGJsABFLpZKNf+8HDRrOmjWbWL16I6tXb+SnnwzDCCZOnEbPnn2TrOPu3f/D27tOvOEJhr/zUiJ/5zPum5wIYJJw8uQJTpw4zqNH/pw+fYqePbtRvHhxPvvMMIBJkiQ6dfqSlSuXc+TI39y8eYNx48bg4uLCxx/XB8DRMS+WlpacPHmc4ODgBGf4JGbLls306tU90fORkREsWDAPX9+LBAQEcPXqVSZMGMezZ89o2LARAJcu+bJli2EkekBAAGfPnmH06JG4uhYxBlkAd+7c5vr1a7x8+ZKwsDCu3b6NX0iI8XyTJk2xt7dnwoQfuHPnNv/+e565c+fQsmUrY1fQ4cOHaNXjW2OeGjW8ePXqFTNmTOXOnTvcvn2LCRPGoVKpqVbtQwA+/bQpZmZmTJo0ntu3b/HXX/vYvHkjX7Zum+z7lFySSkKtUmOu1oH0AknWIMs6Urrb0tvhiaJk3Do1gpBTVatWgwoVKjFq1DDOnj3N48cBXLp0kWXLFhmDlnbtOrB79//Yu3c3Dx8+YM2aldy+fTNTFtXr3PlrLl++yJw5M7l58zoPHz7g2LF/mDNnJmBohTEzM+O337by6JE/x48fYc2ale8sMyGBgc/o1KktV69eTjTN/v37mDJlPP37D6Z8+YoEBwcRHBxEWFiYMc3s2TPZv38v48dPwdra2pgmOtoQuBQrVhxX1yL89NM0rl69zKNH/mzevIFz585Qp05dAAoUKEDJkqWNP0Ved/UXLuxq/BKaWH39/R9y8eIFmjdvFa/+1at/RGhoKLNnz+TevbvcuXOb6dMnolKpqFq1WorvWXKJLqQkhIWFsXDhPJ4+fYq9vT316zegX78BJjNmvv66K5GRkUyZMonQ0FAqV67CwoVLjGvAqNVqhg8fyYoVy1i6dDFVqlQ1mUb9Li9ehODv75/oeVlWce/ePXbvHsqLFyHY2ztQoUIFVq1aQ6lShv5fS0tLDh8+yLJli4mMjMTZ2ZmaNWsxc+ZPJjOsBgzox+PHAcbHHV8PxvMZNAQAa2trFi9ezo8/TufLL7/A3t6ehg0b07dvf5P7de+t+pYoUYK5cxewfPlSvvnmK2RZwt29LAsXLjZ2udnZ2bFo0TJmzJhK584dcXBwoGfP3rRr0hTWrU7WfUoOSZIMK7hIhvEqsU28KlnGxiL7bM4oCG+oM+l9mf4fBZIkMWvWPJYvX8y0aRN58SKEvHmdqFy5qnFsR6NGTQgIeMSiRXOJjo6hfv0GNGnSAj+/N6s0x05V/vXXXRRMxy7H0qXLsHDhcpYvX0zfvj0AhUKFXPnkE8PAWUdHR0aPHs/y5YvZvn0rbm5l6ddvMN9/PyRF19FqtTx4cP+dszV37fodnU7HnDkzjQEUQJMmzRkzZgKAcXbWgAG9TPKOHj2epk1boFar+emneSxduoCRI4cQGRlB4cJFGDNmAl5e8Vt0UlrfP//chYtLPj788KN4eYoVK87MmXP45ZcV9O7dFUmScXNzZ9asBTg7O8dLn17EZo5iM8dEyU+fYLVuNZFduqJ/Pbc/I/OlpJzUbOaoUslY2+lRqwyBizbmOWqzvO+IXBReRj/CxqwI8n8+mNeog/bcEaha2SSNNuY5anMn42OdRiIsTM7QptO3qX19cGxQh5CDR9F6VE4y/ftabk6V0KZ2uXkl3sGD++Lk5MQPP0wGDB+c69evZsOGXzN0RouQucRmjkKGUqys0Hh4orxe1Cmj82VUOQmRAEUV2yrzrnSyYWVel7zov/0KnBKYFRA3/pFBtpBRojJnU0e9Y14iO3dBn07TzXNque8TRYGICD2SlNLOzbRcM/ODF8O6UNv58EMvVCoVBw/+xfnzZ/n550XGNKdOnaBnz34ieBHiES0wogUmR0pbC4xhCO7L6KfYW7yrhUjhVfRT8rwjjYRCjO45Zqo3LTBanURolAo5Rsm0Vhgh53rXN9H3XXR0FCNGDOHmzevExERTtGgxvv66G3Xr1s/qqgkZTLTACBlLq0V+8QK9gwOk5NtPavNlVDmJUd40niQZZkRGwp17ULI4vNUipABRWi1mcb4oG6Zpp1dFk66b6v49dMVM65bryhVyHAsLS+bNW5zV1RByKDELSUiUHByE1eoVyK9XG87ofBlVjiQlHExIEsYp0Unyu47a4yPwux7vVEIryVibG1bnzYwgRn3zOnnr1EB9M37dclO5giDkLikOYM6dO0fv3r3x9vbG3d2dgwdNdy52d3dP8GflyjfTz+rXrx/v/PLly03KuXbtGp06daJSpUrUrVuXFStWpPIpZl/jx49lyJBBWV2N95osGwZDWlvLmTjLSHq9Fow2U9eDEQRByE1S3C4fERGBu7s7bdu2pX///vHOHz9+3OTx0aNHGTNmDI0bm67EN3DgQD7//HPjYxsbG+O/w8LC6NatG15eXkycOJEbN24wevRo8uTJQ4cOHVJa5TQJDw9n8eKF/P33YUJCnuPuXpbhw0dSoUJFY5rx48fyxx+7TPJ5edVk0aKlAAQEPKJ58yZs3rwNd3fTLc3Tw9Kli9m/fx9PnjzBzMyMcuXK06/fACpV8jBJd+zYUVasWMrNmzcxNzfngw+qmeyHdObMaZYsWcStWzexsrKiRb1PGK568wF8795dpk2bwp07twkLC8PFxYVPP21Kz569k9wUcteu/7FhwzoePLiPjY0NDRo0YtSoMcb6L1++NF4eSwsLLrZpk+r7IklgZaXC3FyLVqsgW5iJHReFbOs9H44oCCbS4/2e4gCmbt261K1bN9Hzby+nD3Do0CFq1KhBkSKm22nb2NjESxtr165daDQapk2bhrm5OWXKlMHPz4/Vq1dnegAzadIEbt++xeTJU3FxyceePbvp06cn27fvMFl9tmbNWkyYMNn4OO4O1hmpWLFijBw5msKFXYmOjmLjxvX069eb//1vt3G9hUOHDjB58kT69x9I9eofotPpuHXrlrGMGzeuM3BgP7p168GkSVMJDHzGtEnjkW1sGPg6jVptRrNmLShXrhy2tnbcvHmdyZMnotfrGTAg8ZakDRvWsX79OgYP/o6KFT2IjIzk8eNHxvNdunxDu3afm+Tp3bsHFUqWjFtUihj2KdKCpEetVpOKlb0FIcPFrpyq02kBi6ytjCBkkpgYw2aXKlXqxzdm6CDeoKAgjhw5wowZM+KdW7FiBUuWLKFgwYI0b96cb775xjhNzsfHh2rVqpkEAd7e3qxYsYKXL1+a7OackaKiojh8+CBz5szjgw8Mqwn27t2Xo0eP8Ouv2+jXb4Axrbm5eaIL9jRv3gSAL74wfEh/8EE1k4Xs1q1bw4YN69BoNDRq9CnDho1IskXjbU2amO5LMWTIcHbu3MGNGzeoUeMjtFotP/00k8GDh9Cq1ZsWjZJv7Qb811/7KFPGjZ49ewNQtGhRBnfrwYjJE+kWGYkV4OrqarIzdqFChTh//jwXLvxnWiFJQlGpQJJ49eoVixcv5Oef51OjxpsFkNzc3Iz/tra2xtra2vj4xo3r3Llzm7F9+6H4/Jfm0bCSJIOkQ1KU1DfASJJhU8ns2CWUUXXLaeXmULKswszMkrCwF6hUKsP7VRDeU4qiEBMTTVhYCFZWtvG2PkiJDA1gduzYgY2NDY0aNTI5/tVXX1G+fHns7e25cOECc+bMITAwkFGjRgGGwOftD0rAGBwEBQVlWgCj0+nQ6XTxWlMsLS3x8blgcuz8+fN88kld8uTJQ/XqH9K37wDjfh/r12/iq686sWTJckqVKm0SnJw/fw5nZ2eWLVvFw4cP+P774bi7u9OmjWGH1qVLF/PHH7v48899JIdGo+H337dja2uHm5thM8lr1/x49uwZkiTzxRefExwchJubO4MHD6F06TLGfHGfp3n+AkTrdFwJCqJa8RLxrvXgwQNOnjxB/fqmu83q8+UnYsgIAE7v/wu9Xk9g4DPatGlJREQ4np6V+e67YRQokPD05B07fqdYsWJUrt+AiPoNkvW830VRDNONVLKMJKWyGaaKJ7rIwGQlVckyEjpit76SZcMGdxnVQ6Ct5EmQf9oGOr8P5eZUkiRhb5+X4OAnPH/+NKurIwiZwsrKljx50rYWVIYGML/99hstWrQwLqkfq2vXN1t5ly1bFjMzM8aPH8/QoUMzteslKTY2Nnh4eLJy5XJKlixJ3rxO7Nu3F1/fiyZdYjVr1qJ+/U8oVKgw/v7+LFw4nwED+rJmzXpUKpVx4ysHB4d4rTR2dnkYOXI0KpWKEiVKULt2Hc6ePWsMYBwcHOMFcwk5evQIo0aNICoqCmdnF5YsWWa8buzW9cuWLWHo0GEULFiYDRvW0rNnN3bs+AN7e3u8vGqyadMG9u3bQ8OGjQkODjKOSwkKMv3g/uabr7h2zY+YmBjatGlHnz79Eq3Xo0f+6PV6fvllJcOGjcTW1pbFixfSt29Ptm79LV5LU3R0NHv3/sk333ybSIlJS/iLvYQkZfyeRdLrDR4lFJAlVBYy0Xo9ZpIKfXTmLGwn5DxqtRn58rnG2+VYEN5HKpU6TS0vsTIsgDl//jx3795l7ty5Sab19PREq9Xi7+9PyZIlcXZ2JijI9Bta7OOM3FchIZMnT2PixHE0btwAlUpF2bLlaNy4icmOqY0bNzH+u0wZN8qUceOzz5py/vw5k26ThJQqVQqV6s0iIs7Ozty8edP4uGPHL+jY8Ysk61m9enU2b/6VFy9C2LHjd0aOHMa6dRvJm9fJuOtzt249jPt8TJgwmU8/bciBA/tp1649Xl41GTx4CNOmTeGHH8ZgZmZGz85fceHCf6ji7AY9Y8ZPRESEc+PGdebOncO6dWtMAg45OBjzP3cR0+wz9Ho9Wq2W4cO/x8vLsIX79OkzadiwPufOnaVmzVomZf/99yEiIiJo0aKlaTlOTiRH7BLsAJGRb6KFZK/3khi/66i+6o5u/Uoo5/7uOvBmerYWBf/wVxSxyYNaltDp0j+CUd24jl2f7oQuWYnO7d11e5/LzekkScp1C9kJQlpkWGfr9u3bqVChAmXLJj3rxs/PD1mWcXr9IVW5cmXOnz+PRvPm28jJkycpUaJEpnUfxSpSpAgrV67mxInT7Nmzn/XrN6HVat/ZKuLq6oqDgyMPHz5Msvz4y2NLqRqdbWVlTdGiRfHw8GT8+ImoVGp27twBgLOzYbB0ybcGxZqbm+PqWpgnTx4bj335ZReOHDnBnj1/cfjwUT7+sAYAhV3ymVzLsKNpKT79tCkDBgxm+fKl6HRvtW5oNaiePgGtJsFrOzrmxcHBweTasXbs+J3atesY3gtvlZNcsQN3ZfmtKcyvtwtIU+gQGYl0wdewoN07JHQdRdGjUstI5hkzlVuKisTs0kWkqHfX7X0vVxCE3CXFAUx4eDh+fn74+fkB4O/vj5+fHwEBb3YxDgsLY9++fbRv3z5e/gsXLrBmzRquXbvGw4cP2bVrF9OnT+ezzz4zBictWrTAzMyMMWPGcPPmTfbs2cO6detMup4ym5WVNS4uLrx69YpTp05St269RNM+ffqEly9f4OJiaC2K7SbR6TJvGoyi6ImJiQGgXLnymJubc//+PeN5jUZDQEBAvN1dJUnCxSUflpaW7PvnbwpaW1OudOl3Xker1RpbeeKqXLkyAPfuvbn2y5cvefHiRbxrP3rkz/nz52jZsnUKnmn2FRuryJKEVtGjUfRiXRhBEIR0kuIupMuXL9OlSxfj4+nTpwPQunVr42yjP//8E0VRaN68ebz85ubm7Nmzh4ULFxITE4OrqyvffPONSXBiZ2fHqlWrmDRpEm3atMHR0ZG+fftm+hRqgJMnT6AoCsWLF+fhw4fMnTuH4sWL89lnLQHDujjLli3hk08a4OzszMOHD5k372eKFCmKl5ehe8TRMS+WlpacPHmc/PnzY25ujp1d0vs8AGzZspm//z7EsmUrEzwfGRnBypUrqFv3Y5ydXXjx4gXbtm3h2bNnNGxoGDxta2tL27btWbp0MfnzF6BgwYKsW7cGwJgGYO3a1dSsWQtZljl8+BC//LqVeV6GTdb0wJ49f6JWqyldugzm5uZcvXqFBQvm07BhY2OQdvjwIRbOnc1ftWsDhm3WP/64HrNmzWTs2PHY2NiwYME8ihcvQbVq1U2ey//+txNnZxdq1Ur+1u/ZmgSSLAIWQRCEjJDiAKZGjRpcv/7uJcA7dOiQaLBRoUIFtm3bluR1ypYty6ZNm1JavXQXFhbGwoXzePr0Kfb29tSv34B+/QYYP7BlWebmzZvs3r2L0NBQXFzy8dFHXvTt2984IFmtVjN8+EhWrFjG0qWLqVKlqsk06nd58SIEf3//RM/Lsop79+6xe/dQXrwIwd7egQoVKrBq1RpKlXrTcjJ48BDUahU//DCa6OhoKlasxLJlK8mTJ48xzYkTx1m1aiUaTQxlyrgxd9wEGvhdIbahX6VSsWbNLzx4cB9FUShYsBAdOnSkc+evTO7XvTj1nTRpKrNn/8TAgf2QZZmqVauxcOESkwG8er2eP/74Hy1afGYyJigtsrq1QwH0KGK2sCAIQgYQu1GL3agTJUVFId+/h75YcRRLywzPl5JyEtuN2vB66pBUEmHRamwtdKhUCb2wSe80bZImJATp4D8oDT6G17O73l2OglYvERqjwv/VCwrZ2SPpQRVDuu9QLb0IwezoP2jqfIzi4Jh0hve0XEEQ3g/J3Y1aBDAigMmREgpgDJs2GgIYRYZ7r2Io6WCJSk6HACbFaTIvgBEEQXifJDeAEUs+ComSwsMxO38WKc406ozKl5ZyYqdPW1m9memjKPq0zTx629NnSD8vhKfP0qvEdCM9e4bVkoVIz9K3bjmtXEEQchcRwAiJksJCMf/7EFJYaKbkS0s5b0+fzpAdGx8FoBo2Bh4FJJ02k6meBGA7fjSqJ+lbt5xWriAIuYsIYARBEARByHEydCsBQcjtJEAty8iv/y1mJAmCIKQPEcAI7x8ZQEKdDnttpJW1GRS2M8fWTEKvSERKIEfrSGTdP0EQBCGZRABjJCFJYnbI2xQrK7SVKqFYWaWo5SC1+VJWTuIFKxhaPfJZq1CnVwxjnwd98yZgnyfptK+pJBVqWUe0NgRZdkGvl0ClRrZQQToGMXq7PEQ3boLeLvl1ex/LFQQhd8n106gNs1fM02VnTCFz6fV6IiJiUJQ367+ABCoJtUpHaHQwdhYuieTO6GnUcc/nR6uXCItRIUsSkg50UWJ3akEQhLiSO40617fAKApERMRk+aqt2ZJGA6GhYGcHb62am2H5UliOoijvDAAUdImfTE1dXrwEB/s0PScZCa1eDwqopNRt3JlQ3aSXL1Hs01a3HF+uIAi5imh2wBDE6PWK+InzI1+5jJNbMeQrlzMlX0rLSeyzP0NC0UtXUBcoBZeupKkYKzOwMZPStY5qvys4ly+J2i9tdcvp5QqCkLvk+hYY4f2iUquQpOw6QlZCkrTIKEiSGtJvmT1BEIRcR7TACO8RQ4CApJBB7TCCIAhCNiECGEEQBEEQchzRhSTkaIYNHLO6FiknFrUTBEFIm1w/jVp4B50OKSIcxdoGVKqMz5fCcmI3cDQzA43GsBeS2kx5HRkkd3pzMqdI63QQHg42ceuSkmnUMqCgKApRWjNIr6nU6XW/c3q5giC8F8Q0aiHtVCqU1Cw2ltp8KSzHsIGjDknWI8tyxrZoqFSQJ30WXpMkCZ2ioFeU9JlKnV73O6eXKwhCriLGwAiJUt25hf3nrVDduZUp+dJSjkqtQqVK3+nJJm7eRv60Ndy8neaiVJKMtZnhl0+W096VlF73O6eXKwhC7iJaYIRESWFhmP9zGCksLFPypb6c17OPXv87Q4SGIh84jD40NI0FSUiSDlmSsLFQobY0dH9FROhT3ZWUXvc7p5crCELuIgIYIUeTVNLrIS9KjlpVRSWpkFR6JHTIsoQkqdJnVV5BEIRcQnQhCTmWJEnoYwOXHDWlx9AKI9arEQRBSD0RwAg5mvR6xpEhFMiBwYBkaEUSBEEQUkYEMEKidIVcCZ0+C10h10zJl1HlpIsirugWzIIi6VsX5fVPWjYTzaj7lNPKFQQhdxHrwAg5lkolY22nR63SA5KhRwZQJEj3dWBSnSbuOjCglhWT81qdRESoCp0uu+7hJAiCkHmSuw6MaIEREiWFPMfi1y1IIc8zJV9GlZMunj9H2rAVnmeDusSRUfcpp5UrCELuIgIYIVGqhw/I068nqocPMiVfWsqRYv+TUcNJ7j1A9XVPuJe25yQl8u+0SK/7ndPLFQQhdxHTqIUc682wESlHTKFWSTKSlHA3kSxLKAroRS+SIAhCsogWGCFHkiSwtJRQ55h38Oup02+FWrH/UssyljZarG1VyDnm+QiCIGQt8edSyJEk6fXqu1JOaHt5FwkkHRHaJyBpkCTxKykIgpAc4q+lkCjF2gbNB9UNuwZnQr7kliNJaZt2nCo2NigfVTfsRp0BdIo26USJSK/7ndPLFQQhdxHTqIUcRZJAtlChQyGPpR6VKrEXN7tNo353GSFRj7BVFyYyTC2mUwuCkKuJadTCe0mSJDSKnoCI0BwxcFcQBEHIGCKAERKl9vXBJV8e1L4+mZIvueUYZktncvjynw9qlT3855Nk0syWXvc7p5crCELuIqZRCzmKJIGNhURhtTlqlSGYES0xgiAIuU+KW2DOnTtH79698fb2xt3dnYMHD5qc//7773F3dzf56datm0maFy9eMHToUKpWrUq1atUYPXo04eHhJmmuXbtGp06dqFSpEnXr1mXFihWpeHrC+0dClrRACLGbOAqCIAi5T4pbYCIiInB3d6dt27b0798/wTS1a9dm+vTpxsfm5uYm54cNG0ZgYCCrV69Go9EwevRoxo0bx+zZswEICwujW7dueHl5MXHiRG7cuMHo0aPJkycPHTp0SGmVhfeAJBFnMLYY6CoIgpCbpTiAqVu3LnXr1n1nGnNzc1xcXBI8d/v2bY4dO8b27dupVKkSAGPHjqVnz56MGDGC/Pnzs2vXLjQaDdOmTcPc3JwyZcrg5+fH6tWrRQCTC1layijWMhER7w5a3ofupNjZ4bH/FzPoBEEQEpYhg3jPnj2Ll5cXjRs3Zvz48YSEhBjPXbhwgTx58hiDF4CaNWsiyzK+vr4A+Pj4UK1aNZOWG29vb+7evcvLly8zospCArRuZQk+fQGtW9lMyZdQOSFnfZAqlEaWtcla+yXDVocpXxbt9f+gfNqeU2IkZNSyCisrkGWwtpaxtpZJznI36XW/c3q5giDkLuk+iLd27do0bNgQV1dXHj58yJw5c+jRowdbt25FpVIRFBRE3rx5TSuhVmNvb09gYCAAQUFBuLq6mqRxdnY2nrO3t0/vagsJsbREX7JU5uVLrBxLXbKSZ2hjhaUllE6H55QICRlJ1iGpJCRJhSwbFraTJBVJLtWUXvc7p5crCEKuku4tMM2aNeOTTz7B3d2dBg0asGzZMi5dusTZs2fT+1JCBpPv38OuT3fk+/cyJV9C5dj27o50z1COYQXeNBWZenfvIX/VA+7ey5DiDc/LMCg5pasMp9f9zunlCoKQu2T4OjBFihTB0dGR+/fvA4aWlOfPn5uk0Wq1vHz50jhuxtnZmaCgIJM0sY9jW2KEjCe/fIHlb9uQX77IlHwJlWOxfSuEGMqxspKwtMyiCCbkBfKmbca6ZCfpdb9zermCIOQuGR7APHnyhBcvXhiDkypVqvDq1SsuX75sTHP69Gn0ej0eHh4AVK5cmfPnz6PRaIxpTp48SYkSJUT3Ua5l2LzR8JNVzTCZ4z1/eoIgCOkixQFMeHg4fn5++Pn5AeDv74+fnx8BAQGEh4czc+ZMfHx88Pf359SpU/Tt25dixYpRu3ZtAEqVKkXt2rX54Ycf8PX15d9//2Xy5Mk0a9aM/PnzA9CiRQvMzMwYM2YMN2/eZM+ePaxbt46uXbum41MXhOxHkkAyTzqdIAhCbpfiQbyXL1+mS5cuxsex6720bt2aCRMmcOPGDXbu3EloaCj58uWjVq1aDBo0yGRG0axZs5g8eTJff/01sizTqFEjxo4dazxvZ2fHqlWrmDRpEm3atMHR0ZG+ffuKKdTCe86wQYIigaSSUHRiDrUgCEJiUhzA1KhRg+vXryd6ftWqVUmW4eDgYFy0LjFly5Zl06ZNKa2ekI70+QsQPux79PnftZNy+uVLqJyI4aOQC6StnHRRsAD6cd9DwYyvi04BPUqyp4Sn1/3O6eUKgpC7SEqSczRztqCgULEYWA4myxI2NjreLFMngQrCNU+wt3zXB6DCq+in5LFILE1S59MrTUrKyI9WLxEeo8baTIOkg/BwwzRq8R4WBCG3kCRwdrZLMp3YjVpIlBT6CrPDB5FCX2VKvsTK4dWbclRqFeqseNe+eoX0l2ldMpokgWwhI1uo3jmwN73ud04vVxCE3EUEMEKiVHfv4NCxDaq7dzIlX0Ll5Pm8NdLt2HIMM5GQsqA54tYdVE3bwq20PaeUkdAqChpF/86ZV+l1v3N6uYIg5C4igBFylPd9CrUgCIKQPCKAEXIMSZKM2wXkljgmlzxNQRCEFBMBjCBkQxIYfjtVkghiBEEQEiACGCFRirkFuuIlUMwtMiVfPBYW6EuWRGVtmbZy0oOFBUqpEmCRxueUDNZmoFZJIL3eHSmJCCbd7ncOL1cQhNxFTKMWsi3DFGo9ajM9imKGJOlfD+DN7OnNmTeNWlHUhueJHq1eIjRGhaJTUMWAXi/eyIIgvP+SO406xQvZCULmk5AlHcp735ciIUm6rK6EIAhCjiC6kIREqa5cxqlcCVRXLiedOB3yJVSORZGS4Ju2ctKF72VU+bNJXeJIr/ud08sVBCF3EQGMkChJp0UODkbSaTMlXzxaLVJQMGjTWE56yE51iSPd7ncOL1cQhNxFBDCCkANIkmFMUG6ZPi4IgpAUEcAI2V5uX7xOAiRzGa0ZSW4rIAiCkFuIAEbItmIDl/d8olySJElCo+jxD3+V5LYCgiAIuYUIYIREaUuWJuTPA2hLls6UfHHpSpUm+sgBcEtbOenCrTTa41lbF60+4RlK6XW/c3q5giDkLmIatZA4W1u01WtkXr4EytF/9CEqVTZogbG1Ba8Ps7oWCUuv+53TyxUEIVcRLTBCouSAR9j8MAo54FGm5ItLevQIs+GjwT9t5aQL/0fIQ7NJXeJIr/ud08sVBCF3EQGMkCg5KBDrZYuQgwIzJV9C5ajnL4JnaSsnXTwLRJ6bTeoSR3rd75xeriAIuYsIYITsT5Jy9bbMufipC4IgJEoEMEIOkA3GwGQRCZBVEpIswhhBEIS3iQBGyDFy40e4hIRWUdCjiPVfBEEQ3iJmIQmJ0ud1IrJrd/R5nTIlX1yKkxPa3t3B2Tnr22CcndD36Q7OaXtOKSW99X8zWU4wiEuv+53TyxUEIXeRlPd8lbCgoFDe72f4/lKpZKzt9KjjTaNWeBX9lDwWBd6RO6k06VFGRl5HQVFUaPUyERoFSzVEaCOwVlkTE6Gg0ynifS0IwntJksDZ2S7JdKILSUhcRARqXx+IiMicfMTZ8yciAulC6spJdxER8J9PJtZFQpJ0SJIWCQlZ0gLByJIWyVw23VIgDff7nXJauYIg5CoigBESpb51A8cGdVDfupEp+STJsNdP7J4/qps3sKhRB66lrJwMce0G6up1s7QuekWHJBnGxLy9pUBq73dSclq5giDkLiKAEbKN+Hv+ZHWNBEEQhOxKBDBCthO7548IYBImbosgCIIIYIRsSgbMLbO6FtlDbMCiQkYty1hbSOIXVxCEXE/8HRQSpUgyels7FCllb5PU5nubLMsgg2JnB3I2eJvKcpbVxdIM1LKEJKng9cBe40Bn0ud+JySnlSsIQu4iplEL2YYsS+jMwD/8JcXtHLG20CUwhRpyxzRqw3Gt3rD2i0rW8Sr6GXks8qPVS0Rq1KADXZROvL8FQXivJHcatVjITsg2JAlsLCSKqs2xMhNjYABUkowk6eMd1ykKekVBJUm8599BBEEQEiTacIVEqa5fw7H2h6iuX8uUfBjXO3mOJOng6jVUlWrA1ZSWkwGypC6GtWAMe0G9ieZUkoy12VtjY1J9v98tp5UrCELuIgIYIVFSdBTq69eQoqMyJV8sRTHMQiIqCunqNYhKXTnpKtvUxRDUyJIOlWQYB5PW+53olXJYuYIg5C4igBGEHEglGWYjSebiV1gQhNwpxX/9zp07R+/evfH29sbd3Z2DBw8az2k0Gn766SdatGhB5cqV8fb2ZsSIETx9+tSkjPr16+Pu7m7ys3z5cpM0165do1OnTlSqVIm6deuyYsWKVD5FQXjfvO5akrToUIwr8gqCIOQmKR7EGxERgbu7O23btqV///4m56Kiorh69Sp9+vShbNmyvHr1iqlTp9KnTx9+//13k7QDBw7k888/Nz62sbEx/jssLIxu3brh5eXFxIkTuXHjBqNHjyZPnjx06NAhpVUWcijxsSwIgiAkJsUBTN26dalbt26C5+zs7Fi9erXJsR9++IH27dsTEBBAoUKFjMdtbGxwcXFJsJxdu3ah0WiYNm0a5ubmlClTBj8/P1avXi0CmEykK1acl+u2oCtWPFPyxZKQDbNvShdHt2MzlExdOemqZDaqSxz64mm734lJ6+uY2eUKgpC7ZHgHelhYGJIkkSdPHpPjK1asoEaNGrRq1YqVK1ei1WqN53x8fKhWrRrm5ubGY97e3ty9e5eXL19mdJWF1xR7B2I+bYpi75Ap+WJJkmzoInGwR/msKTikrpx05eCQfeoSR1rv9/tSriAIuUuGBjDR0dHMmjWLZs2aYWtrazz+1VdfMWfOHNauXUuHDh1YtmwZP/30k/F8UFAQzs7OJmXFPg4KCsrIKgtvkZ4+xWrebKQ4Y5gyKl88T54izZgNT9JYTnrITnV5iwTIz9LpfsctO71ex0wqVxCE3CXDAhiNRsOgQYNQFIWJEyeanOvatSs1atSgbNmyfPHFF4wcOZINGzYQExOTUdURUkH19DG2Uyeievo4w/JJ0jsWrAt4jGrMJAhI2fUzRHaqy1tklYQq+EmqXqekpPb1z6pyBUHIXTIkgNFoNAwePJiAgAB++eUXk9aXhHh6eqLVavH39wcMrS1xW1piH8dtmRFyLkkCa2sZa2tZrLqbSlpFQStW4hUEIRdK9wAmNni5f/8+a9aswdHRMck8fn5+yLKMk5MTAJUrV+b8+fNoNBpjmpMnT1KiRAns7e3Tu8pCFpEkCVnWIstaJEkSQYwgCIKQbCkOYMLDw/Hz88PPzw8Af39//Pz8CAgIQKPRMHDgQC5fvsysWbPQ6XQEBgYSGBho7B66cOECa9as4dq1azx8+JBdu3Yxffp0PvvsM2Nw0qJFC8zMzBgzZgw3b95kz549rFu3jq5du6bjUxeyE0kCS0sJtUpEMSkhIaabC4KQO6V4GvXly5fp0qWL8fH06dMBaN26Nf379+fw4cMAtGzZ0iTfunXrqFGjBubm5uzZs4eFCxcSExODq6sr33zzjUlwYmdnx6pVq5g0aRJt2rTB0dGRvn37iinUmUyfx57oFq3Q50lZq1fq8klIkhaJtzYudLBH37YlOGSDVrfsVJfXjHsiOTuga90KJZ1bJ1P7+mdVuYIg5C6S8p5vZRsUFMr7/QxzLlmWsLEx7HsUGanG2lqHSq3nZfQz7C0LvCOnwqvop+SxSEua9Cgjq6+joNXLoCigUwgPV6HXize7IAg5mySBs7NdkunERipC4mJikAMeQUpnh6Uin6WlhCpu91FMDPin4voZITvVJa6MqltqX/+sKlcQhFxFBDBCotTXruJUuRzqa1czNJ9KrcbMTIck6U3nVF++irpYebicsutniOxUlzikK1exLFMBlV/61i21r39WlSsIQu6S4jEwgpC+DGNfYv8NogtEEARBSJpogREEQRAEIccRLTBClpAksLKSUasVjK0uYj5wiqkksQigIAi5k2iBEbJE7CJ2SIZp0xKSsfNIfCAnl2TY9FIy3DmxGKAgCLmJaIEREqWt6EHgw0AwM8v4fAl98Fb2QBvxLMXXzxDZqS5xVfZAGxaEhSRjhkxEhD5dlg5I7eufVeUKgpC7iABGSJwsg4VFhuSTJJBUr1sMlESG7qb2+hkhO9UlLlmFZCmDJhgZJyRJRbos75RRzzk730tBEHIM0YUkJEp1+yb2rZqiun0zXfPFjn+RZQwftIl1e9y4hap+M7hxK4U1zwDZqS5x3biFnAF1S+3rn1XlCoKQu4gWGCFRUng45iePI4WHp2s+w1gNrWHUiyQlPnE6LAzpyHEIC0tZxTNCdqpLXGFhSEdPQFhEuhab2tc/q8oVBCF3ES0wgiAIgiDkOCKAEYQcTkw8EgQhNxIBjCDkcMYuODGHWhCEXEQEMEKidIWLEDpnAbrCRTIlXzxFi6BbNh+KprGc9JCd6hLX67opRQqma7Hp9jpmUrmCIOQukpIu8y2zr6Cg0HRZE0NIH5IEsixjY6NDpVYSaDVQeBX9lDwWBd5RSnqkef+uo40JBFwID1eh14s3vSAIOZMkgbOzXZLpRAuMkCgpOBjLDWuRgoPTJZ8kgbW1jLW1DpUqGd0dQcFIK9dCUMqunyGyU13iCgpGXrkOJeQ5UnLuazKl9vXPqnIFQchdRAAjJEr16CF2QwagevQwXfLFbh+gKCGGLQSSGrPx4CGqXgPhQcqunyGyU13ievAQuddAou4+QK8oSOk0Fia1r39WlSsIQu4iAhghU5h+pureHEfMokk3otdIEIRcRCxkJ2Q4SQLZQvU6iNGZnBOfuYIgCEJqiABGyHCSJKFR9MgY9j4Sg6oFQRCEtBIBjJAoxcaGmJreKDY26ZJPApAlFMkQ1CQZx9jaotT1BlvbFF0/Q2SnusRla4tStxaKrTWQfsvBpPb1z6pyBUHIXcQ0aiHDybKE1gzUsoy1hYYIzVPsLbN+2vH7dp0XUY+wNXclKlqNNlIn3veCIORIYhq1kHZ6PURHG/6fjvkUlOQN3k3t9TNCdqpLXK/rJil6FEVBpyjIsqG7Lk2tMRn1nLPzvRQEIccQAYyQKPVlX1yKuKC+7Juu+STDPtRJdyH5+KK2zgc+Kbt+hshOdYnLxxe1dX5UF68BIKskJHP59Zo7cqqDmNS+/llVriAIuYsYAyMI7xFJktAqCjKgUmlRFJAkFe95T7EgCLmQaIERMoVY6yXzyICVmZS81Y4FQRByKBHACBnKsAaMDOLDNNNYmUmYq3VIkhhjIgjC+0t0IQkZKrZLQ5ES2rhRSE8SEipJhVrWvR5lJO63IAjvLxHACInSli1PsI8femeXVOdLUxNfxfJo71+FfCm7fobITnWJq2J5tPevoLPXI0mGlY5jR7ykZV+k1L7+WVWuIAi5iwhghMSZm6MvVDjz8iVQDq7pUE56yE51iSu2btFP073cdHkdM6tcQRByFTEGRkiUfO8uebp1Qb53N1PyxXPnLvLnXeBOGstJD9mpLnHduYv8+dfId9N3d+d0ex0zqVxBEHIXEcAIiZJfvcTij53Ir16mS74Ud2a8eIn82//gRcqunyGyU13iel036cUrk8OGrRtSX2xqX/+sKlcQhNxFBDBCppABWTYMNBUyR+xigWkZByMIgpBdiTEwQoaTAGtzw4eokvT6u0I6i91SQKxlJwjC+yTFLTDnzp2jd+/eeHt74+7uzsGDB03OK4rCvHnz8Pb2xsPDg2+++YZ79+6ZpHnx4gVDhw6latWqVKtWjdGjRxMeHm6S5tq1a3Tq1IlKlSpRt25dVqxYkfJnJ2QLkiQhSzpkSSdaA7KApSVp2lJAEAQhO0pxABMREYG7uzvjx49P8PyKFStYv349EyZMYNu2bVhZWdGtWzeio6ONaYYNG8atW7dYvXo1S5cu5fz584wbN854PiwsjG7dulGoUCF+//13RowYwcKFC9m6dWsqnqKQWrr8BQkbMx5d/oKpzidJYGUG6tR0VhYqiG7qOCiUsutniOxUl7he101fMF8CJyVkWYcsa1McPKb29c+qcgVByF0kJQ2bpLi7u7No0SIaNGgAGFpfateuTdeuXenWrRsAoaGh1KxZkxkzZtCsWTNu375N06ZN2b59O5UqVQLg6NGj9OzZkyNHjpA/f342bdrE3LlzOX78OObm5gDMmjWLgwcPsm/fvhTVMSgoVDSdZyGVSsbaTo9apUdR1EiSjpdRT7C3LJBIDoVX0U/JY5HY+fRKkxuuo6DTy6AoKDqF8HAVer34ZRAEIXuTJHB2tksyXboO4vX39ycwMJCaNWsaj9nZ2eHp6cmFCxcAuHDhAnny5DEGLwA1a9ZElmV8fQ270/r4+FCtWjVj8ALg7e3N3bt3eflSzFzILNLLF5jv24P08kWq87350i+9XmRNSX5XxosXSLv2wIuUXT9DZKe6xPW6bnFnIcHrBe0kkFKxlUNqX/+sKlcQhNwlXQOYwMBAAJycnEyOOzk5ERQUBEBQUBB58+Y1Oa9Wq7G3tzfmDwoKwtnZ2SRN7OPYcoSMp7p/D/suHVHdv5fqfJaWUuq6jwDu3EPV+gu4k7LrZ4jsVJe47txD1bpTouvAKIAeJcVdSKl9/bOqXEEQchcxC0nIMJIkIUlasSWPIAiCkO7StQXGxcWwt0lwcLDJ8eDgYGMLirOzM8+fPzc5r9VqefnypTG/s7NzvJaW2MdxW2YEQRAEQch90jWAcXV1xcXFhVOnThmPhYWFcfHiRapUqQJAlSpVePXqFZcvXzamOX36NHq9Hg8PDwAqV67M+fPn0Wg0xjQnT56kRIkS2Nvbp2eVBUEQBEHIgVIcwISHh+Pn54efnx9gGLjr5+dHQEAAkiTRpUsXlixZwqFDh7h+/TojRowgX758xplKpUqVonbt2vzwww/4+vry77//MnnyZJo1a0b+/PkBaNGiBWZmZowZM4abN2+yZ88e1q1bR9euXdPxqQtJUSws0bqXRbGwTFU+LFOWLx5LS5Ty6VBOeshOdYnrdd0US4tEk0iQ4nVgUvv6Z1W5giDkLimeRn3mzBm6dOkS73jr1q2ZMWMGiqIwf/58tm3bxqtXr/jggw8YP348JUqUMKZ98eIFkydP5vDhw8iyTKNGjRg7diw2NjbGNNeuXWPSpElcunQJR0dHvvzyS3r27JniJyimUWcdlUrCxk5BpXr7BcjO047ft+soaPUyoCABkdFqtJE68fsgCEK2ltxp1GlaByYnEAFM1pAkUFupsLTQoZZFAJOVAYyEgl5RiIhWQ7Qi1oIRBCFby5J1YIT3i+qSL04lC6O65JvifHlLFIaLF9NWAR9fVA6u4JOy62eI7FSXuHx8UTkUQXXRL94plSSjkkEtq7AyT1k3Umpf/6wqVxCE3EUEMEKiJEWPHBaKpOhTlQ99yvLFo9cjhaZDOekhO9UlrkTrFrt4IEiv96JKyZz21L7+WVWuIAi5iwhgBEEQBEHIcUQAIwi5iCxLyOK3XhCE94D4UyYIuYRalrG00WJtqxJBjCAIOZ74MyYkSlvajZCDR9GWdktRPl0ZN14dO46urHvaKlDWDe25I1A2ZdfPENmpLnGVdUN77h907iXfkUgCSUeE9glIGiQp6V/91L7+WVWuIAi5i9gLSUictTVaj8opzibZ2BDtWRlZJQG6NF2fqim/fobITnWJK7Zu0U+TTKpTtCkqNzWvf5aVKwhCriJaYIREyf4PsR05BNk/4V2O35lvyHfID1OWL54HD5H7D4UHaSwnPWSnusT14CFy/2HIDwPStdjUvv5ZVa4gCLmLCGCERMnPg7FavRL5eXDSid8iBQdjtXIFUpwNOVMsKBh5yUoIStn1M0R2qktcr+smBYWka7Gpff2zqlxBEHIXEcAIGcbCDNTiHZYtSVLK90YSBEHITsTHi5DuYj8YZfSAWLY+O7K0BGtrWQQxgiDkWCKAEdKVJIGFhfhUzN4kZFmHLGuRRAQjCEIOJQIYIVF6ZxcievVD7+yS7DySJEE+R7SD+qHkS36+BOVzQT+4H6S1nPSQneoSVz4X9IP7ondxStdiU/P6Z2W5giDkLmI3aiHdSBLIsoyNjRZFlkEizk7UkL13b37/rxMS9QhbtSuyAoqiEB6uErtTC4KQrYjdqIW0CwtDfe4MhIUlmVSSDGMqrK11qKLCkU4nL19S1+fU2bSXkx6yU13iMtYtPN3LTe7rny3KFQQhVxEBjJAo9Z1bODZriPrOrSTTSpKELGuBELh5E4s6DZFvJJ3vnW7cQu3dENJaTnrITnWJ68Yt1N6NUN28l+Ks7xoCk5LXPyUyqlxBEHIXEcAI6UpR3qy8K/bbycZkUJup/t/evYdHVd3743+vtfZM7gEkQQVFLZgACQmhUIRGQaiWHtEDYqUt9fF2ih7rrVDF+j1HhccKKFqO1m+ltscf9VJqxbYih9+pWm1VQGwLyCVcIpcAUUjCJQmQy+y9vn/smU1mSEgm2TvZk7xfzxNlZtb6zGfNTmbW7L0uSEkRnIlERAmJHzHkgcgnIsdW+JEQAhoCQAhSmpyJREQJiXshEfUgEhKGlICw2L8kooTGDgy1SCsDVt++0CrOXxPDgM7qCxgd/PVyK44b/JRLLCc31WpRISQgzMiNs5Zt9/FvhVdxiahn4TsItcjMy0dVyZ5Wy53xOViQD/PQ7o4n4FYcN/gpl1gF+TAPfQ6zDbtRx6Otx98vcYmoZ2EHhjpECEAmqXAnxmytOBERkSs4iJdapLaX4JyvFUJtL2mxjBACjdpCqOlqgVtLoHJGAFtbrtcmbsVxg59yibW1BCqnCGrbLlfDtuX4+ykuEfUs7MBQi0RDPdTePRAN9fFVrK+H+HwPUB9nPa/iuMFPucRycmtwNWy7j38XxSWinoUdGCIiIko47MAQ9XBcBoaIEhE7MNQhQpxeto4SiwCgDMnVeIkoIbEDQy0yL/kKji1/E+YlX2n2cSGAlBSBtCQR3YkZ/BWY/7MCGNx8vTZzK44b/JRLrHBu5qCL4qqmIQBhtrgab2vHv728iktEPQunUVOLdEYmGid+o8XHhRAQIgQpACECECL8oZiZCf3Nluu1mVtx3OCnXGJlZkJ/cxLQjnVgBNDi15jWjn97eRWXiHoWnoGhFslDXyL1yScgD33Zalklw/+RAL74EnLeAuCL1uudlVtx3OCnXGKFcxNfHI67qg7/2J3R6PEw8Rz/eHgVl4h6FnZgqEXy0JdIW7yw1Q8ae3NAwNIWTpqN9gfq/IXudGDciOMGP+US64svIecvgvyyot0hIgsSnl6UsO3HP15exSWinoWXkKjDlJRIDoRvcIPAhCSEQIO2AACGENCaB5KI/I1nYKiDBIQwwz+cypKIDCmRnMY3AyJKLHzPog5hnyXRifDu1CEIyYNJRImDHRhqkdWrN+qm3wirV+8zHrOnUEsoZX/oKSFhRH6b+vSG9b0bgT5n1ouLW3Hc4KdcYoVz030yXQ17tuPvx7hE1LMI7fLF7okTJ+LgwYNn3P+9730Pjz76KG666SasX78+6rEZM2Zg/vz5zu3y8nI89thj+OSTT5CamoqpU6dizpw5MIz4h+xUVtaAl/PdJ6VAWpoFI6CbnIbRqK4/hMyk885Ss7UybsTg88RXRiNkCtTWK5iWhtEIWBb/aIioawgBZGVltFrO9UG8b7zxBkzTdG7v2rULt956KyZPnuzcd+ONN+Lee+91bqekpDj/Nk0Td9xxB7KysrB8+XIcPnwYc+fORSAQwOzZs91Ol86mrg6y/CCs/gOA5OS46uHAQeCCOOt5FccNfsolViS3bANIcjduu45/V8Uloh7F9UtI55xzDrKzs52f999/HwMHDsTXvvY1p0xycnJUmfT0dOexjz76CKWlpXjqqacwdOhQjB8/Hvfddx9effVVNDS4u9sunZ2xczv6XlYEY+f2Fss0O3B323YYuSOBbS3XaxO34rjBT7nECuemSkpdDduW4++nuETUs3g6BqahoQFvvfUWpk+fHvVBt3LlSowZMwZTpkzB008/jVOnTjmPbdy4ETk5OcjKynLuKy4uRm1tLUpL3X2Dpo7jhQYiIuoKnq4D8+6776KmpgbTpk1z7psyZQr69++Pfv36YceOHVi8eDH27NmDn//85wCAysrKqM4LAOd2RUX7F+oi99iLnkkIFVnHlboDAfsbjb0qL48rEfmbpx2YFStW4IorrsC5557r3Ddjxgzn37m5ucjOzsYtt9yCsrIyDBw40Mt0yCVCCIS0Ztelm0kNAkoKaDME0zRQ39UJERGdhWeXkA4ePIg1a9bghhtuOGu5wsJCAMC+ffsA2GdbKisro8pEbmdnZ3uQKREpqRBQFqRoBHAEUoa4MCER+ZpnZ2DefPNN9O3bFxMmTDhruZKSEgCnOycjRozACy+8gKqqKvTt2xcAsGbNGqSnp2Pw4MFepUvNCBWMQMXh6vgrjhyBkHm84wm4FccNfsol1sgRCJnHYLZjN2qbgBSmvakjBCxpQkLALGzn8W9Fu3+viIia8KQDY1kW3nzzTUydOjVq7ZaysjKsXLkS48ePR+/evbFjxw4sWLAAo0ePxpAhQwDYA3YHDx6MBx98EA888AAqKiqwZMkSzJw5E8Fg0It0iQj2GBgN4ESoAWlKh8/A8EIhEfmTJ5eQ1qxZg/LyckyfPj3q/kAggLVr1+L222/Ht771LSxatAhXX301XnjhBaeMUgovvPACpJSYMWMGHnjgAUydOjVq3RjqHKp0F3p/axJU6a74Ku7YBfX1bwA74qznVRw3+CmXWDt2QX39Ksidu9sdoulw7MjCj3LXzvYd/1a0+/eKiKgJT87AFBcXY8eOHWfcf/755+OVV15ptf6AAQPw4osvepEaxUGcPIHAPz6FOHnizMfOVvHECYh1nwInzqwXF7fiuMFPucQK5yZOnGq9bBzEyZMtHv+OxW3594qIqK08nYVE3YcQ9uwjrbW9D1JQnN77iIiIqJOxA0NtkpoqIWUIlmWgQQsIaQJcK6Rbk+EOqgxKCAHuKUZEvsLv0NQqIQSkDEHrKrsTAwDQEGjlUhIlJAEJQyoEw1uUmVpzSjUR+Q47MNQi88KBqH7+l7CcBQZPb9IphECL6/BePBDmsl8CF3dwYUK34rjBT7nEunggzGVLYV00wJVwAhJCmtAXDUD1r34F8yJ32xz5vTIv9OFrSUQJQ2jdvU8MV1bW8NR3B0kpkJZmQuvDEKIfTpoBpCaZMGTsC6tRXX8ImUnnnSVaa2XciMHn6UiZ43WH0Cv5XIRMgZpGBZgaqhEwTf4hEZH3hACysjJaLcczMNQiUVmJ5F//EqIyzj2oKioh/u+LQEVl62U7I44b/JRLrHBuouKI63FTli5F6vFKpKQIuHUV6fTvlQ9fSyJKGOzAUItU+QFk/OTHkAcPxldx/wGoe34M7D/QsQTciuMGP+USa/8BqHsegDzwhathxYEDSJ89B6p8H4Rwb2uByO+VKvfha0lECYOzkChuHM7Z/TUdoC0AHnQi8h2egaG4JQfANWC6MSGiV+YVEIBkD4aI/IVnYCgOEsowIIQJ7pHTc1jQ0ODeSETkL/weTS3S6elomDAROj09fI+AEKHWF7DLyIB11UQgo/VR5J0Sxw1+yiVWODedkeZqWJmZCfOqSUC6u20+8/eKiCh+nEZNrTo9jboKgWBftDwdxV/Tgfk8HS2jEbIkAA2tNU7VGDBN6yx1iYg6jtOoqeNME6Km2v6/EtAKbZuJYppAtV2vo8/vShw3+CmXWF7l1iSugL21gCsTkZr8XhERtRc7MNQiY+tmZA26AMbWLbC0xqlQqG0jIDZthtHnQmDT5o4l4FYcN/gpl1ibNsPoMxDqs+2ux03uewGMzZshhIBIklBJqsOdmNO/Vz58LYkoYbADQ21m8VpcDxPpqWhoAA3aQiMs7otERL7ADgy1ip9XRETkN5xGTa0KBrs6AyIiomg8A0MtilwqkIYFSHsvnKYrtBIREXUVnoGhFpnD8lBXVgr07QPAXpG1TaNghuch9OXnQO9eHUvArThu8FMusYbnIfRlKcyUOg/ifg70zgTgXsc1NDQPldt2Q/fy4WtJRAmDHRhqWSAA0ScLWtprgcRTD9lZrjy/K3Hc4KdcYkVyqz/kTVxowAJSAwCUQH0I0Fq3f32lQAA6y6evJRElDF5ComYJAaR+sReBG74LtWdvfHsffb4b8l+/A3y+u2NJuBXHDX7KJVY4N7m7zJO4+HwPAAEpTChlwgwAsgPTqeWe3ci8aQbkHh++lkSUMNiBoWYJISBrjkC+vRqi+igA3fYPrOPVkG+vBo5XdywJt+K4wU+5xDpeDfn2/w9xvMaDuKuB48ehhIQh7an05Sdr0KjbP51a1lQj6X9XQ9b48LUkooTBDgwRtUI4G3gKIWBa9sq8nF5PRF2JHRgiiosUAsqQEEHJTgwRdRkO4qUWCcVPJzqTFAIhbUFoQAmBbr4fLBH5FM/AULOEEDDPPx/m4p8CA/rHV3lA//bV8yqOG/yUS6wB/WEufhxW/3M9iOt+m83z+qN23hMwz/Pha0lECUPobv71qbKypv3TPXswpSRS0k0EnHN0GtX1h5CZdN5ZarlRhs/j3+fRCFkCu46cQL+0DAgLUA2AZfEPjIjcIwSQlZXRajmegaGWHT0K8fs/AEePdk49r+K4wU+5xDp6FOL3f4Q4etyDuGe22ZCyQ4vaiWNHEXzrDxDHfPhaElHCYAeGWiT27oP6zi3Ann3xVdzTznpexXGDn3KJFc5N7j3gSdymbVZC4rw0A2kB4cxEincgryrbh17/djNUmQ9fSyJKGOzAEFEb2dOpNaogRQhSCIig7NCidkRE7cUODBHFRSMEwB7o3aitDi1qR0TUXuzAUKv40URNCUgYUiElcHp3cvZfiKizsQNDLRIpqdBFBdApKfFVTEmBLioA4q3nVRw3+CmXWOHcdEqyJ3HPbLOEECGI8GWkeBe108kpaBxeCJ3sw9eSiBIGF7KjFumhuTD//mHkVtsrRtXrALfiuMFPucQamgvz73+D5fZu1K202T7zImBqC5a2F7hry6oMZk4ujr3n09eSiBKG62dgnnvuOeTm5kb9TJ482Xm8vr4e8+bNw5gxY1BUVIR77rkHlZWVUTHKy8sxa9YsFBYWYuzYsVi0aBFCoZDbqVILhABkkuC1Izo7AaQEgV5JAmlBwctIRNSpPLmEdOmll+Kjjz5yfl577TXnsSeeeALvv/8+lixZgpdffhmHDx/G3Xff7TxumibuuOMONDY2Yvny5Vi4cCH+8Ic/4Nlnn/UiVWqG/a0aEBs2QaVkAxs2xRegvfW8iuMGP+USa8MmqJR+UBu3eRC3pTYLaAhIYULKRkhpoq09XmPzJmRdkAVjsw9fSyJKGJ50YJRSyM7Odn7OOeccAEBNTQ1WrFiBhx56CGPHjkV+fj6eeOIJbNiwARs3bgQAfPTRRygtLcVTTz2FoUOHYvz48bjvvvvw6quvoqGhwYt0qQkhACmBlACghIZoaEDcSxnrdtbzKo4b/JRLLK9yayWuEhKqPe8gfn4tiShheNKB2bdvH4qLizFp0iTMmTMH5eXlAIAtW7agsbER48aNc8oOGjQI/fv3dzowGzduRE5ODrKyspwyxcXFqK2tRWlpqRfpUpgQQGqqRHq6RNAwEde4F+ph7DVh7N8RXjsios7n+iDegoICLFiwAJdccgkqKirw/PPPY+bMmVi5ciUqKysRCASQmZkZVadv376oqKgAAFRWVkZ1XgA4tyNlyBtCCEhpQkjNebFERORrrndgxo8f7/x7yJAhKCwsxJVXXonVq1cjOdnlaZ5ERETUI3m+DkxmZiYuvvhilJWVISsrC42Njaiuro4qU1VVhezsbAD22ZbYWUmR25Ey5C3n3MvQXIQ+WwcMzY0vQHvreRXHDX7KJdbQXIQ+WwtzyCAP4ra9zW3dFyl0aS6O/O0ThC714WtJRAnD8w7MiRMnsH//fmRnZyM/Px+BQABr1651Ht+9ezfKy8sxYsQIAMCIESOwc+dOVFVVOWXWrFmD9PR0DB482Ot0ezQhAKHsTyEB2AuY5Q2Nf/G29tbzKo4b/JRLLCc39xeyi6fNIijati9SSgrMIT59LYkoYbjegVm0aBHWr1+PAwcO4J///CfuvvtuSCkxZcoUZGRkYPr06Vi4cCHWrVuHLVu24OGHH0ZRUZHTgSkuLsbgwYPx4IMPYvv27fjwww+xZMkSzJw5E8Fg0O10Kcxe+0VCC3tYpgaAfWWQP7gb2FcWX7D21vMqjhv8lEusfWWQP7gHsuygB3Hb3mZTa4TasC+S3F+G9B/dDbnfh68lESUM1zswX375JWbPno3Jkyfj/vvvR+/evfH66687U6kffvhhTJgwAffeey++//3vIysrC88995xTXymFF154AVJKzJgxAw888ACmTp2Ke++91+1UqYnI2i9Rqo5A/vfLQNWR+IK1t55Xcdzgp1xihXMTVcc8idvWNqcEgbSk1he0k0ePIOXV30Ae9eFrSUQJw/VBvD/72c/O+nhSUhIeffRRPProoy2WGTBgAF588UW3U6MW2GMXBCdNUwfYi9ppaNhvK/xtIiJvcS+kHi6y9ouUIVgi0NXpEBERtQl3o+7h7LVfQgCqwsvBE8Wv6VWjppeQ2joziYgoXjwDQwAArU17d+Gmd57bD9bcHwHn9osvWHvreRXHDX7KJVY4N6tfVutl2xG3LW1WUgLChGkJezaSJQBoiKACAFj1prNzgJXdDyfvnQ0r24evJRElDKF1996QpLKyhluutCAy9iUtzYRGFYzAuRDCQvPjFzSq6w8hM+m8s0R0owyfJ3GfRyNkCZxqNNAYshAQEo2WZY+KaQQsi3+IRNQ6IYCsrIxWy/ESUg9lT5tWUEnSXvtFNt3bJqymBuKDD4GamviCt7eeV3Hc4KdcYjm51XoUt+1tVkIiydA4XHcCptbNbpMkamsQ+PhDiFofvpZElDDYgemhhBBo1BZMABY0ToYazyy063OoSVOAXZ/HF7y99byK4wY/5RJr1+dQk66FKt3nQdx42mx3ggNS4/x0hdRg89s8qt2fo/e0a6B2+/C1JKKEwQ4MEbkocibPHhQuI6s6ExG5jIN4ich1lrZntKUEAa0F6kMc/0JE7mIHhog8wsXtiMg7vIRELQsEoAf0BwJxLnDX3npexXGDn3KJ5eTm8veRDrRZILz+SzPXj7QRgHl+f2jDh68lESUMnoHpwSQAJYEWRykMz4NZVhK+Ece356h6HeBWHDf4KZdYw/Nglm2DWX/Ig7jta7MQTX5jYn69zGF5OLJpe4dSIyJiB6aHiayKKoS98R4kwqf4ibzhnI0hInIRLyH1IJF9j1JTZXgLARNSmBAtfbps3go1cCiweWt8T9Teel7FcYOfcom1eSvUwGFQW3Z4ELd9bRaQUELCkPaUfRE8vUu12rYV5xQOgdrmw9eSiBIGOzA9SGTfI3vvozZ8JW5shDhYDjQ2s0aMF/W8iuMGP+USy8kt5FHc9rRZOgsjagAhrZ2Osgg1Qn1RDtHc2kNERG3ES0g9nBKRDxoi7wgAUoLbehCRa9iB6aHsL8Ontw/gGAXyklICUBKK68EQkUvYgemhkpIAgxcQqZOYGgiFdzwPRi4lCXtcDM/KEFF78COsR7IH8EK08slx6SCY770NXDoovvDtredVHDf4KZdYlw6C+d5KmIMv8iCue22OLAtjDRqE6lWr0ZA7CDJJ8ewfEbULz8D0QEZAAbBaL5iRAT3h8vCNOL4mR9XrALfiuMFPucSK5Ob2OjAutlkASAsICA3UZ2biVHExDpyoxgXagiEENE/DEFGceAamxxEAQq2ffQGAg+WQDz8GHCyP7ynaW8+rOG7wUy6xDpZDPjwP4qDLHRgX2ywASBGCUiZU1RdIf+xRiIMHuUYMEbUbOzDUskOHIRf9DDh0uHPqeRXHDX7KJVY4N3m40pO4HW2zEhLJAXvMi6U1rEOHkPazZ5BcVYm0JIGUFMFODBHFjR2YHqqFbWqIXGbPdDOkdgaNi9OPQIoQhAi1vJgiEVELOAamJxKCmwdQJ2o6XV8gOfyuE1T8/kRE7cd3ECLqNEooKGUvnJiVYsBQvHxERO3DDgy1rO85sG67Ceh7TufU8yqOG/yUS6xwbrpvb0/iutfm8JmYSL5ZCgIWeDGTiNpD6G4+f7GysoYLZYUpJZCWoaFU7AuiUV1/CJlJ552ldmeV4fP0lOc5XncIvZL6wQxJnDghYZr8QyUie2ZiVlZGq+V4BqaHEAKQSRI6ni+7p04BW0vs/8ejvfW8iuMGP+USy8mtzqO4Lrc5Jl+lJGciEVHc2IHpAYSwPySCRpzbB5TsgFFwGVCyI74nbG89r+K4wU+5xCrZAaNgLNT2zz2I60Gbo/K1LytxJhIRxYuzkLo5+8yLgiUAqeyZIER+YXdaNIRi54WI4sMzMN2cEAKN2sKXp2q7OhWiKELY3enID8/AEFE82IHpxoQI/wDgyi/kd037L0IAUnJcDBG1jJeQuikhgNRUCSEASwKGYcQ3/iUcRAeD8W9W0956XsVxg59yieVVbp0YV0mJlBSgNnyiMDVVQsoQLMvAyZMWZxIS0RnYgemmhBCQMgRAQElAm0cB9IsvSFEhzFMV4RtxfIJE1esAt+K4wU+5xCoqhHnqMEy3d6P2qs1n5CsghQlLCQhh97KlDEHrKkjZF0Io7lZNRGfgJaRuzggoGAoArK5OhahFkXEwUjY922N2UTZElAhc78AsXboU06dPR1FREcaOHYu77roLu3fvjipz0003ITc3N+rnkUceiSpTXl6OWbNmobCwEGPHjsWiRYsQCoXcTrebE4CzB007qpfsgBp1ebumUbernldx3OCnXGKV7IAadQWkB9OoPWlzC/kKAYiggEqSnJVERK1y/RLS+vXrMXPmTAwfPhymaeKZZ57B7bffjlWrViE1NdUpd+ONN+Lee+91bqekpDj/Nk0Td9xxB7KysrB8+XIcPnwYc+fORSAQwOzZs91OuduyPwQ6cOr91CmIDZ+1ayG7dtXzKo4b/JRLrHBuwoOF7Dxpcwv5akTOuWhYvGRERK1wvQPz61//Our2woULMXbsWGzduhWjR4927k9OTkZ2dnazMT766COUlpbipZdeQlZWFoYOHYr77rsPixcvxt13341gMOh22t2OEAKaM48owUgASQEBQwmEeNWTiM7C8zEwNTU1AIBevXpF3b9y5UqMGTMGU6ZMwdNPP41TTb7lbdy4ETk5OcjKynLuKy4uRm1tLUpLS71OmYi6gBIS6clA0DDP2OQxsiQAEVGEp7OQLMvCE088gZEjRyInJ8e5f8qUKejfvz/69euHHTt2YPHixdizZw9+/vOfAwAqKyujOi8AnNsVFT6dCeIj9uq7gpv8UgIREMLC6cHm0Z0XGVTQAKx6k1OqiQiAxx2YefPmYdeuXXjttdei7p8xY4bz79zcXGRnZ+OWW25BWVkZBg4c6GVK3Z6975GIf9+j5lxyEczl/x9wyUWdU8+rOG7wUy6xwrlZF1/gSVzX2xxnvikpApbUOFGvoYTglGoiAuBhB2b+/Pn44IMP8Morr+C88847a9nCwkIAwL59+zBw4EBkZWXhs88+iypTWVkJAC2Om6Fw5yVJQRoCUoXQ4X2P+vSB/va08I04YkXVc+v5u5ifconVpw/0t6dCu70OjFdtbmO+AoBQEgImoDWEMMC9vIgowvUxMFprzJ8/H++88w6WLVuGCy+8sNU6JSUlAE53TkaMGIGdO3eiqqrKKbNmzRqkp6dj8ODBbqfcbQghYEIj5NY31EOHIX72c+DQ4c6p51UcN/gpl1jh3MShSk/iut7mtuYrAEuHh6LzcigRxXC9AzNv3jy89dZbePrpp5GWloaKigpUVFSgrs6eMllWVobnn38eW7ZswYEDB/Dee+9h7ty5GD16NIYMGQLAHrA7ePBgPPjgg9i+fTs+/PBDLFmyBDNnzuQMpLMQAkgNAikBlwIeLIf68f8BDpZ3Tj2v4rjBT7nEOlgO9eP/gCx3+QyMV21uQ74CgJYAhHD6LuzDEFFTrl9C+u1vfwvAXqyuqQULFuD6669HIBDA2rVr8Zvf/AYnT57E+eefj6uvvhp33XWXU1YphRdeeAGPPfYYZsyYgZSUFEybNi1q3RhqjoCUJpRUnLFBCU0DqAuZSA/al5AkFFKDQH0IsDi9mojgQQdmx46zr9p5/vnn45VXXmk1zoABA/Diiy+6lVYPIiAEl2CnxKchIEQI9jkYEwFDQSQLnDihoXVkarU9qJfjeol6Hu6F1E1wnQzqnkR42K7dMZfSDG9Uag9YDwUAmcQzjkQ9ETsw3YAQQGqqRHKycHecQK9MWFO+BfTK7Jx6XsVxg59yidUrE9aUydC9MjyI60GbO5hv5Pc9GNQoP1mDRm1BsAdD1ON4ug4MdQ77G2kIRsBwd6TjoK/A+tPy8I04ztFH1XPr+buYn3KJFc7NcnsatVdt7nC+AtII75qkOSCGqKfiGZhu4/TO065pbAQqKu3/d0Y9r+K4wU+5xPIqN5/GlTJyWamdu6wTUbfADkw3Ye887bLNW2GcNwjYvLVz6nkVxw1+yiXW5q0wzhsMtWWnB3E9aHMH801OdmGVaSJKeLyE1A1w52nqCQQAZSjnTGPsuJfITc5IIuoZ2IEhooSgmywR0LSPIgFICcCwN3zUDfaGj+zIEHVv7MAQUcKI7ZMYUiI9RUIAONGgoU0NlaQADZjcuZqoW+OV5AQnpT2okagnMqSEECEoaSElCKiARFJQIxjU/Lsg6uZ4BiaBKQWkpilAmjCUB09QOByho/uBtLTOqedVHDf4KZdYhcMROloG06j1IK4HbXYxX0NIOIvcCQFow169VwD22xtPwRB1VzwDk6CUslci1TKEU6HD8OSNWikgM9P+f2fU8yqOG/yUSyyvcvN5XCUkslOVMyNJCYnkgD3YV4CrUxN1d+zAJCClgNR0hUDQXmjd0h7tfbTrc8jJ04Bdn3dOPa/iuMFPucTa9Tnk5OshS/d6ENeDNruSr33WReMo7M67fduQGoYSMKRCeoZAWppkJ4aom2IHJsEIAaSkKBgqhED4zdozNTWQ7/wFqKnpnHpexXGDn3KJFc5N1JzwJK7rbXYxX42mnfcmG5kKC0I2OnsnEVH3wzEwCUbK8DLqAhCIrIfR1VkRERF1Lp6BSSD22RcJxaNGFBeOhyHqfngGJoEIISBEyN0NG4m6KQEAkWUGwiN9zXr7EpMQ4GJ3RAmOHZgEEPnmKGVkz6NOete98AKYzy0GLrygc+p5FccNfsol1oUXwHzuKVgXnO9BXA/a7FW+MTRg92KSBJIMDa2BhpBAcrKAYdh7SZ46ZbEjQ5Sg2IHxOSEAmaQgAKQkAUp5NOOoOdlZ0Hf9IHwjjnf4qHpuPX8X81MuscK56fpDnsR1nVf5NkMKCSnrEVAKgEQwQ0IIEwImpBRITTWgNXDypMVODFGC4WgKnxNCIKSt8MIW9qDdTnPkCMQrvwOOHOmcel7FcYOfcokVzk0cOeZJXNfb7FW+Z7BnJVn6CITQkMKCkCFAaEAIGAEDgYAJKUOcqUSUgNiB8TkhgLQkgfQUeLPa7tnsLYO6eRawt6xz6nkVxw1+yiXW3jKom++A3HfQg7getNmrfFtgT7UWTtffvggrAGFCCPtee3xZp6RDRC7hJSQfs2dOSCh5+o2WiNwhwv8xDIW0NBOWpXDqlAXL4pgYokTADowPRaZ8pqRISBmCwU3piFyjo/4vIKQJyzoCGciCggER0rAaTFjW6QH0Wkf/m4i6HjswPhMZtCsFoFQjNI4DondXp0XULUXm9J0y65AqNYKGBRgCdUJBNprOdVurwYQIhv9db7ITQ+QD7MD4jJQCpraQHJRQhkCj2YmzjmKlpUFfNjr+3YjbW8+rOG7wUy6xwrnptBRP4rreZq/ybYfT/RABJRSENBEyNVKSAkBQwdIaGkBDSKJRWwAAJQQ0ezBEXY4dGB+xLxsJaCmgpAkIC3VmCIGuOkq5l8L8+N3wjTjesKPqufX8XcxPucTKvRTmx+/Acntasldt9irfDpHhfZQ0ICSAEJRUMGB3aIQIIC3JLlkfYueFyA/YgfGRyEq7hjLs91AIWPymR9SplJAQwoKACQ0NIQWsIOyNIbWGEAak1FwAj6iLcRq179iDCp0hhl05fvefG2GoXsA/N3ZOPa/iuMFPucT650YYqjfUhq0exPWgzV7l6wpx+kwMTo+ROVp3CgIahlJIzxBIy1BQSQoy/A4qhH35l1OxiToPz8D4hBCASpIQSqNTF6sjomZF/gqVkOibImAoCcCEFCYgFFKSJU6FB/smJ0sIEYJlGc5UbM5gIvIWOzA+IMMbzgUDgFKnv/URUVezz8hoHAWQjciCeEKYUBJITxZAsoIQITSaRxAMZEMIibo6C8nJElrb+y2lpNinaurqNCxLsyND5AJ2YLqYlEBauoIy4Jy65nsbkb/Yq/k2JexxMpEzK9A4GWpELxVCIGDACNidGjOkoZRhb1egBExTQUHCqjedupbVqU0h6jY4BqaLCGGfbVFKAdKEECHwvAtRYjnzL1ZAyNN7LilDIS1dwAjYU6/LT9YgpC0oJZCarpCabo+jiSxeSURtxzMwXUBKQAYVUpIBwOz8PY7aatgQhHb8E7hgQOfU8yqOG/yUS6xwbma2y3/OXrXZq3y7SNNOTNNNIQUEtLDCM5o0IAUMAQQMCWkAQjZCa0BKA8nJcHbFduJy7AzRWXWPd5AEIgSQmiqhJQA04lSoEhkqu6vTal5yMjB4UPhGHO+gUfXcev4u5qdcYiUnA4O/Ari9ropXbfYq3y4mRPRfSfTmkfZg4P4ZQaQFtL1kAoS9aF6KgBEIwbQAwzCgI19oQiaSkuyT5CdPWuzEEMXgJaROJqWANEwEDA1DCVi6C1fabc2evZA3/QDYs7dz6nkVxw1+yiXWnr2QN82C3Lvfg7getNmrfH1MONOzj9rryQj7EpOhDASCpn25SUokp4UQTNJIDmqkZygEgyaUCtnvG9K+7CylCF9+hnOfYeCMS1HN/ZuXqqg78XUH5tVXX8XEiRMxfPhwfPvb38Znn33W1Sm1W2TMS0qKhJKn15rw9ZvJ0WOQr70OHD3WOfW8iuMGP+USK5ybOFrtSVzX2+xVvj7VdGGE6MHAAhBmeNi+/e9T5mEEFRAIWPbAX6mhDAOp6RJpGQqpGQoIAKkZChmZyrkvPVMhLV0hPV0iNVXCMARUkr1WjWHA+XfkJ9IhinR6mmInhxKFbzsw//M//4MFCxbghz/8If7whz9gyJAhuP3221FVVdXVqbVJZGGryLej1FSJjAzlfNsiIoqltRU1G1FDACIEQ2kYRggajTgWqgcQgpSN9v3KhBANMAwNI2BCBUwgKJGUpJGeYp/JSUqyoyUFtT3eJgjIVANp6QppadI5o6OU/V5l3yfC72Onz/bE07Hh4n7kNd92YF566SXceOONmD59OgYPHox58+YhOTkZK1as6OrUWhT5Q1cKSEuTSE83kZGpkJKuoAIWpAqx80JEcbLPzgAahlThRfVEuHMTWTVYAsKEEBpSSghVj4Cy7MtVshEBpZGeKhAMmAAacayxHklKwwiEIAMWUtPtMzqpaQoqYEIGLOighEqyz/JkZCpk9rI7NoYB50ep2NunL2mpJIVQIHz2R539TE+koxS5L/bfkTNGTWM0d6aoLWePOnKGiZ0yf/HlIN6GhgZs3boVd9xxh3OflBLjxo3Dhg0b4orlxS+avXy4BHB6xoAQQHKqBCRQ3yggAya0roWQ6VDaaDEPKc5+CFp73K0yzT4uFZCRYf8/nueJqdfuXM4Sx7M2e5CL58cwnJs0gu4+Twtt7nB72phvZ/3ut6WMP57HvvQsZA2APs2W0JEyohpC9EZ49A0gLNjvV3YnKDvVQtCwOz9SKAjDBGDBEAYgAK0FlFGPtEASBOyOEaARCCoYQenkY5qNUMpomgAAIKSBUw0WjtXVITslFUIZkKYFNJ5+z0xOtt8U6+sFkpI0AgEgFALqQgLJQUCb4UvsUuBUA5ASFNCmRl2djqpv3w6/HwfCuTU2t7iOXSa6XvNlWiICCiY0JCTQ2NL4xbPHcK+MP57Hi3WM2vq5LbQP94U/dOgQrrjiCixfvhxFRUXO/U8++SQ+/fRT/P73v+/C7IiIiKir+fYSEhEREVFLfNmB6dOnD5RSZwzYraqqQlZWVhdlRURERH7hyw5MMBhEXl4e1q5d69xnWRbWrl0bdUmJiIiIeiZfDuIFgFtvvRVz585Ffn4+CgoKsGzZMpw6dQrXX399V6dGREREXcy3HZh/+Zd/wZEjR/Dss8+ioqICQ4cOxa9+9SteQiIiIiJ/zkIiIiIiOhtfjoEhIiIiOht2YIiIiCjhsANDRERECYcdGCIiIko47MDE6dVXX8XEiRMxfPhwfPvb38Znn33W1Sk169NPP8Wdd96J4uJi5Obm4t133416XGuN//qv/0JxcTEKCgpwyy23YO/evVFljh07hjlz5mDkyJEYNWoUHn74YZw4cSKqzPbt2/G9730Pw4cPx/jx4/Hiiy963TQAwNKlSzF9+nQUFRVh7NixuOuuu7B79+6oMvX19Zg3bx7GjBmDoqIi3HPPPaisrIwqU15ejlmzZqGwsBBjx47FokWLEAqFosp88sknmDZtGvLz83HVVVfhzTff9Lx9r732Gq699lqMHDkSI0eOxIwZM/DXv/61W7StOb/85S+Rm5uLn/70p859id7G5557Drm5uVE/kydP7jbtA+xtX3784x9jzJgxKCgowLXXXovNmzc7jyf6+8zEiRPPOIa5ubmYN28egMQ/hqZpYsmSJZg4cSIKCgrwjW98A88//zyazu3x9THU1GarVq3SeXl5+o033tC7du3S//Ef/6FHjRqlKysruzq1M3zwwQf6mWee0X/+8591Tk6Ofuedd6IeX7p0qf7qV7+q33nnHV1SUqLvvPNOPXHiRF1XV+eUuf322/V1112nN27cqD/99FN91VVX6dmzZzuP19TU6HHjxuk5c+bonTt36rffflsXFBTo5cuXe96+2267Ta9YsULv3LlTl5SU6B/84Ad6woQJ+sSJE06ZRx55RI8fP16vWbNGb968Wd944416xowZzuOhUEhPmTJF33LLLXrbtm36gw8+0GPGjNFPP/20U6asrEwXFhbqBQsW6NLSUv3yyy/roUOH6r/97W+etu+9997TH3zwgd6zZ4/evXu3fuaZZ3ReXp7euXNnwrct1qZNm/SVV16pr732Wv3444879yd6G5999ll9zTXX6MOHDzs/VVVV3aZ9x44d01deeaV+6KGH9KZNm3RZWZn+8MMP9b59+5wyif4+U1VVFXX8Pv74Y52Tk6PXrVuntU78Y/iLX/xCf+1rX9Pvv/++3r9/v169erUeMWKEXrZsmVPGz8eQHZg43HDDDXrevHnObdM0dXFxsV66dGkXZtW62A6MZVn661//uv7Vr37l3FddXa3z8/P122+/rbXWurS0VOfk5OjPPvvMKfPXv/5V5+bm6i+//FJrrfWrr76qR48erevr650yTz31lP7mN7/pdZPOUFVVpXNycvT69eu11nZ78vLy9OrVq50ykTZt2LBBa2138oYMGaIrKiqcMq+99poeOXKk06Ynn3xSX3PNNVHPdf/99+vbbrvN4xadafTo0fr111/vVm2rra3VV199tf7444/197//facD0x3a+Oyzz+rrrruu2ce6Q/ueeuop/d3vfrfFx7vj+8zjjz+uv/GNb2jLsrrFMZw1a5b+yU9+EnXf3XffrefMmaO19v8x5CWkNmpoaMDWrVsxbtw45z4pJcaNG4cNGzZ0YWbxO3DgACoqKqLakpGRgcLCQqctGzZsQGZmJoYPH+6UGTduHKSUzmWzjRs3YtSoUQgGg06Z4uJi7NmzB8ePH++k1thqamoAAL169QIAbNmyBY2NjVFtHDRoEPr374+NGzcCsPPPycmJWhyxuLgYtbW1KC0tdcqMHTs26rmKi4udGJ3BNE2sWrUKJ0+eRFFRUbdq2/z58zF+/PiotgDd5/jt27cPxcXFmDRpEubMmYPy8nIA3aN9f/nLX5Cfn497770XY8eOxdSpU/H66687j3e395mGhga89dZbmD59OoQQ3eIYFhUVYd26ddizZw8A+zLPP/7xD1xxxRUA/H8MfbsSr98cPXoUpmmib9++Uff37dv3jLEXfldRUQEAzbYlcv22srIS55xzTtTjhmGgV69eTv3KykpccMEFUWUif6iVlZVOZ8JrlmXhiSeewMiRI5GTk+M8fyAQQGZmZlTZvn37RuUfu7Jz5HZrZWpra1FXV4fk5GRP2gQAO3bswHe+8x3U19cjNTUVzz//PAYPHoySkpKEbxsArFq1Ctu2bcMbb7xxxmPd4fgVFBRgwYIFuOSSS1BRUYHnn38eM2fOxMqVK7tF+/bv34/f/va3uPXWW3HnnXdi8+bNePzxxxEIBDBt2rRu9z7z7rvvoqamBtOmTXOeO9GP4axZs1BbW4tvfetbUErBNE386Ec/wnXXXReVo1+PITswlPDmzZuHXbt24bXXXuvqVFx1ySWX4I9//CNqamrwv//7v5g7dy5eeeWVrk7LFV988QV++tOf4r//+7+RlJTU1el4Yvz48c6/hwwZgsLCQlx55ZVYvXq1553DzqC1Rn5+PmbPng0AGDZsGHbt2oXly5c7H/LdyYoVK3DFFVfg3HPP7epUXLN69WqsXLkSTz/9tPPlaMGCBejXr19CHENeQmqjPn36QCmFqqqqqPurqqoSbn+m7OxsADhrW7KysnDkyJGox0OhEI4fP+7Uz8rKOmPEfeR2Z70m8+fPxwcffIBly5bhvPPOc+7PyspCY2Mjqquro8pXVVW1Kf/WyqSnp3v+IRQMBnHRRRchPz8fc+bMwZAhQ/Cb3/ymW7Rt69atqKqqwvXXX49hw4Zh2LBhWL9+PV5++WUMGzasW7QxVmZmJi6++GKUlZV1i/ZlZ2dj0KBBUfd95StfcS6Tdaf3mYMHD2LNmjW44YYbnPu6wzF88sknMWvWLFxzzTXIzc3F1KlTcfPNN2Pp0qVROfr1GLID00bBYBB5eXlYu3atc59lWVi7di2Kioq6MLP4XXDBBcjOzo5qS21tLTZt2uS0paioCNXV1diyZYtTZt26dbAsCwUFBQCAESNG4O9//zsaGxudMmvWrMEll1zi+WldrTXmz5+Pd955B8uWLcOFF14Y9Xh+fj4CgUBUG3fv3o3y8nKMGDHCyX/nzp1Rf5xr1qxBeno6Bg8e7JRZt25dVOw1a9Y4MTqTZVloaGjoFm277LLLsHLlSvzxj390fvLz83Httdc6/070NsY6ceIE9u/fj+zs7G7RvpEjRzpjJyL27t2LAQMGAOge7zMRb775Jvr27YsJEyY493WHY1hXVwchRNR9SilnGrXvj2GHhgD3MKtWrdL5+fn6zTff1KWlpfo///M/9ahRo6JGmPtFbW2t3rZtm962bZvOycnRL730kt62bZs+ePCg1tqeGjdq1Cj97rvv6u3bt+t///d/b3Zq3NSpU/WmTZv03//+d3311VdHTY2rrq7W48aN0w888IDeuXOnXrVqlS4sLOyU6Y2PPvqo/upXv6o/+eSTqGmOp06dcso88sgjesKECXrt2rV68+bNesaMGc1Ocbztttt0SUmJ/tvf/qYvu+yyZqc4Llq0SJeWlupXXnmlU6Y4Ll68WK9fv17v379fb9++XS9evFjn5ubqjz76KOHb1pKms5C0Tvw2Lly4UH/yySd6//79+h//+Ie+5ZZb9JgxY5yp1Inevk2bNulhw4bpX/ziF3rv3r36rbfe0oWFhfpPf/qTUybR32e0tmebTpgwQT/11FNnPJbox3Du3Ln68ssvd6ZR//nPf9ZjxozRTz75pFPGz8eQHZg4vfzyy3rChAk6Ly9P33DDDXrjxo1dnVKz1q1bp3Nycs74mTt3rtbanh63ZMkSPW7cOJ2fn69vvvlmvXv37qgYR48e1bNnz9YjRozQI0eO1A899JCura2NKlNSUqK/+93v6vz8fH355Zd32pTy5tqWk5OjV6xY4ZSpq6vTjz32mB49erQuLCzUP/zhD/Xhw4ej4hw4cED/27/9my4oKNBjxozRCxcu1I2NjVFl1q1bp//1X/9V5+Xl6UmTJkU9h1d+8pOf6CuvvFLn5eXpyy67TN98881O5yXR29aS2A5Morfx/vvv11//+td1Xl6evvzyy/X9998ftUZKordPa63/8pe/6ClTpuj8/Hw9efJk/bvf/S7q8UR/n9Fa6w8//FDn5OSckbfWiX8Ma2pq9OOPP64nTJighw8fridNmqSfeeaZqOnOfj6GQusmS+4RERERJQCOgSEiIqKEww4MERERJRx2YIiIiCjhsANDRERECYcdGCIiIko47MAQERFRwmEHhoiIiBIOOzBERESUcNiBISIiooTDDgwRERElHHZgiIiIKOGwA0NEREQJ5/8Bj4dg5u1DGUUAAAAASUVORK5CYII=",
      "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": "2025-06-24T04:02:50.261816Z",
     "iopub.status.busy": "2025-06-24T04:02:50.261666Z",
     "iopub.status.idle": "2025-06-24T04:02:50.905581Z",
     "shell.execute_reply": "2025-06-24T04:02:50.905060Z",
     "shell.execute_reply.started": "2025-06-24T04:02:50.261801Z"
    }
   },
   "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": "2025-06-24T04:02:50.906305Z",
     "iopub.status.busy": "2025-06-24T04:02:50.906153Z",
     "iopub.status.idle": "2025-06-24T04:02:51.659644Z",
     "shell.execute_reply": "2025-06-24T04:02:51.659071Z",
     "shell.execute_reply.started": "2025-06-24T04:02:50.906290Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    359232\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    179616\n",
      "True     179616\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-ahi-up-2         336089\n",
      "chirp-v4-up-u-d-2-4     20418\n",
      "chirp-ahi-up-d-4-16      2490\n",
      "chirp-ahi-up-d-4-22       235\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-ahi-up-2         167889\n",
      "            chirp-v4-up-u-d-2-4     10209\n",
      "            chirp-ahi-up-d-4-16      1377\n",
      "            chirp-ahi-up-d-4-22       141\n",
      "True        chirp-ahi-up-2         168200\n",
      "            chirp-v4-up-u-d-2-4     10209\n",
      "            chirp-ahi-up-d-4-16      1113\n",
      "            chirp-ahi-up-d-4-22        94\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    359232\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": "2025-06-24T04:02:51.660337Z",
     "iopub.status.busy": "2025-06-24T04:02:51.660189Z",
     "iopub.status.idle": "2025-06-24T04:02:57.376136Z",
     "shell.execute_reply": "2025-06-24T04:02:57.375553Z",
     "shell.execute_reply.started": "2025-06-24T04:02:51.660322Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 52889 duplicated prompts 26445 unique requests\n",
      "Found 13844 request_ids with duplicate prompts but not highest play counts in their group\n",
      "['d9ef9de3-e62a-429a-b060-f41898ed4a71', '59c7c91b-1181-472a-b69d-b7e8f6f43587', '2f6b4684-b9ff-4e25-b7a1-d8e959e7fde6', 'c3535951-2230-4c6f-ae63-adc83fdc77c5', '120557f9-7f12-4660-b37e-9be9d7773e74', '24235e90-0ec8-41a7-996f-86384231a2af', 'a78dfe84-8e99-4bf4-a80b-2d1f4ec02872', 'fb180344-703d-4535-9a0f-4dbf9d096e8e', 'f0ef0379-9c32-4663-b0c2-db87cdf21515', 'bf1d4373-3908-4aef-bf50-3ab48f95fc3e']\n",
      "Before dedup user gen requests 359232\n",
      "After dedup user gen requests 331544\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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    331544.000000\n",
      "mean        218.593719\n",
      "std         382.111760\n",
      "min           2.000000\n",
      "25%          46.000000\n",
      "50%         100.000000\n",
      "75%         240.000000\n",
      "max       15787.000000\n",
      "Name: user_n_clips, dtype: float64\n",
      "count    331544.000000\n",
      "mean         15.867336\n",
      "std          21.074730\n",
      "min           2.000000\n",
      "25%           4.000000\n",
      "50%           8.000000\n",
      "75%          18.000000\n",
      "max         196.000000\n",
      "Name: user_n_clips, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"user_n_clips\"].describe())\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": "2025-06-24T04:02:57.376910Z",
     "iopub.status.busy": "2025-06-24T04:02:57.376751Z",
     "iopub.status.idle": "2025-06-24T04:02:57.567772Z",
     "shell.execute_reply": "2025-06-24T04:02:57.567193Z",
     "shell.execute_reply.started": "2025-06-24T04:02:57.376894Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 147172 positive 13587\n",
      "total pair requests 165772 selected pair requests 12982 frac 0.078\n"
     ]
    }
   ],
   "source": [
    "normal_pos_play_count = 3\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 = 3\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\"] >= 2.1\n",
    "            )  # this is a bit of a luxury cut...not for now...\n",
    "            & (df[\"sum_total_play_duration_5\"] >= 31)\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": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.250737Z",
     "start_time": "2024-05-16T13:59:41.036434Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:02:57.568509Z",
     "iopub.status.busy": "2025-06-24T04:02:57.568352Z",
     "iopub.status.idle": "2025-06-24T04:02:57.670579Z",
     "shell.execute_reply": "2025-06-24T04:02:57.670008Z",
     "shell.execute_reply.started": "2025-06-24T04:02:57.568494Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 12982 clips 25964 total khrs 1.458; N gpus for 1000 iters 1.623; 4 gpus for x iters 405.688; n unique users 3389 n pro users 3307\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 t4 t40  requests 12982 clips 25964 total khrs 1.458; N gpus for 1000 iters 1.623; 4 gpus for x iters 405.688; n unique users 3389 n pro users 3307"
   ]
  },
  {
   "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": "2025-06-24T04:02:57.671294Z",
     "iopub.status.busy": "2025-06-24T04:02:57.671143Z",
     "iopub.status.idle": "2025-06-24T04:02:57.692313Z",
     "shell.execute_reply": "2025-06-24T04:02:57.691825Z",
     "shell.execute_reply.started": "2025-06-24T04:02:57.671278Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (4054, 132)\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": "2025-06-24T04:02:57.692990Z",
     "iopub.status.busy": "2025-06-24T04:02:57.692842Z",
     "iopub.status.idle": "2025-06-24T04:02:58.455018Z",
     "shell.execute_reply": "2025-06-24T04:02:58.454505Z",
     "shell.execute_reply.started": "2025-06-24T04:02:57.692975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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 = [5, 10, 20, 50, 80, 90, 95]\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": "2025-06-24T04:02:58.455951Z",
     "iopub.status.busy": "2025-06-24T04:02:58.455579Z",
     "iopub.status.idle": "2025-06-24T04:02:58.939327Z",
     "shell.execute_reply": "2025-06-24T04:02:58.938827Z",
     "shell.execute_reply.started": "2025-06-24T04:02:58.455935Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n",
      "1.3666666666666667\n",
      "3.466666666666667\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]\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": "2025-06-24T04:02:58.940036Z",
     "iopub.status.busy": "2025-06-24T04:02:58.939885Z",
     "iopub.status.idle": "2025-06-24T04:02:59.194221Z",
     "shell.execute_reply": "2025-06-24T04:02:59.193729Z",
     "shell.execute_reply.started": "2025-06-24T04:02:58.940020Z"
    }
   },
   "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_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": "2025-06-24T04:02:59.194905Z",
     "iopub.status.busy": "2025-06-24T04:02:59.194758Z",
     "iopub.status.idle": "2025-06-24T04:02:59.239453Z",
     "shell.execute_reply": "2025-06-24T04:02:59.238996Z",
     "shell.execute_reply.started": "2025-06-24T04:02:59.194890Z"
    }
   },
   "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": "2025-06-24T04:02:59.240074Z",
     "iopub.status.busy": "2025-06-24T04:02:59.239931Z",
     "iopub.status.idle": "2025-06-24T04:02:59.260218Z",
     "shell.execute_reply": "2025-06-24T04:02:59.259788Z",
     "shell.execute_reply.started": "2025-06-24T04:02:59.240060Z"
    }
   },
   "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": "2025-06-24T04:02:59.260810Z",
     "iopub.status.busy": "2025-06-24T04:02:59.260671Z",
     "iopub.status.idle": "2025-06-24T04:02:59.282678Z",
     "shell.execute_reply": "2025-06-24T04:02:59.282203Z",
     "shell.execute_reply.started": "2025-06-24T04:02:59.260796Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web    25964\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:02:59.283285Z",
     "iopub.status.busy": "2025-06-24T04:02:59.283150Z",
     "iopub.status.idle": "2025-06-24T04:02:59.499301Z",
     "shell.execute_reply": "2025-06-24T04:02:59.498713Z",
     "shell.execute_reply.started": "2025-06-24T04:02:59.283271Z"
    }
   },
   "outputs": [
    {
     "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[50], 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;66;03m# print(df_slice.shape)\u001b[39;00m\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": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:42.937319Z",
     "iopub.status.busy": "2025-06-24T04:05:42.936966Z",
     "iopub.status.idle": "2025-06-24T04:05:43.869618Z",
     "shell.execute_reply": "2025-06-24T04:05:43.869078Z",
     "shell.execute_reply.started": "2025-06-24T04:05:42.937301Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12982\n"
     ]
    }
   ],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:43.870573Z",
     "iopub.status.busy": "2025-06-24T04:05:43.870416Z",
     "iopub.status.idle": "2025-06-24T04:05:43.953398Z",
     "shell.execute_reply": "2025-06-24T04:05:43.952854Z",
     "shell.execute_reply.started": "2025-06-24T04:05:43.870558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12852 130\n",
      "(25704, 133) (260, 133)\n"
     ]
    }
   ],
   "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": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:43.954065Z",
     "iopub.status.busy": "2025-06-24T04:05:43.953919Z",
     "iopub.status.idle": "2025-06-24T04:05:43.967885Z",
     "shell.execute_reply": "2025-06-24T04:05:43.967443Z",
     "shell.execute_reply.started": "2025-06-24T04:05:43.954049Z"
    }
   },
   "outputs": [],
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:43.969072Z",
     "iopub.status.busy": "2025-06-24T04:05:43.968796Z",
     "iopub.status.idle": "2025-06-24T04:05:43.981735Z",
     "shell.execute_reply": "2025-06-24T04:05:43.981309Z",
     "shell.execute_reply.started": "2025-06-24T04:05:43.969056Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:43.982352Z",
     "iopub.status.busy": "2025-06-24T04:05:43.982216Z",
     "iopub.status.idle": "2025-06-24T04:05:44.991685Z",
     "shell.execute_reply": "2025-06-24T04:05:44.991118Z",
     "shell.execute_reply.started": "2025-06-24T04:05:43.982339Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25704/25704 [00:01<00:00, 22123.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,443 hours of 25704 clips, 1.6065 nodes, 401.625 steps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "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": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:44.992412Z",
     "iopub.status.busy": "2025-06-24T04:05:44.992257Z",
     "iopub.status.idle": "2025-06-24T04:05:51.713213Z",
     "shell.execute_reply": "2025-06-24T04:05:51.712486Z",
     "shell.execute_reply.started": "2025-06-24T04:05:44.992396Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (260, 133)\n",
      "total chunks: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:10<00:00,  5.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done! val: wrote 978000 semantic tokens and 125184000 vae latents. \n",
      "Total slices of data: 1304. Per node: 40.8. \n",
      "Passed quality check: 1304, Failed quality check: 364. \n",
      "Total chunks with prev chunk as vae ctx: 952. \n",
      "Total seeds: 260.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "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": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:51.714160Z",
     "iopub.status.busy": "2025-06-24T04:05:51.713986Z",
     "iopub.status.idle": "2025-06-24T04:05:51.733095Z",
     "shell.execute_reply": "2025-06-24T04:05:51.732548Z",
     "shell.execute_reply.started": "2025-06-24T04:05:51.714143Z"
    }
   },
   "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": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:05:51.733843Z",
     "iopub.status.busy": "2025-06-24T04:05:51.733694Z",
     "iopub.status.idle": "2025-06-24T04:14:40.916162Z",
     "shell.execute_reply": "2025-06-24T04:14:40.915573Z",
     "shell.execute_reply.started": "2025-06-24T04:05:51.733828Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (25704, 133)\n",
      "total chunks: 129\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 129/129 [08:05<00:00,  3.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done! tr: wrote 96379500 semantic tokens and 12336576000 vae latents. \n",
      "Total slices of data: 128506. Per node: 4015.8. \n",
      "Passed quality check: 128506, Failed quality check: 31518. \n",
      "Total chunks with prev chunk as vae ctx: 94376. \n",
      "Total seeds: 25704.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "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": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T12:10:46.371748Z",
     "iopub.status.busy": "2025-06-24T12:10:46.371310Z",
     "iopub.status.idle": "2025-06-24T12:10:47.760447Z",
     "shell.execute_reply": "2025-06-24T12:10:47.759967Z",
     "shell.execute_reply.started": "2025-06-24T12:10:46.371727Z"
    }
   },
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>time_used</th>\n",
       "      <th>metadata</th>\n",
       "      <th>user_id</th>\n",
       "      <th>status</th>\n",
       "      <th>discord_message_id</th>\n",
       "      <th>prompt_id</th>\n",
       "      <th>request_id</th>\n",
       "      <th>is_generated</th>\n",
       "      <th>s3_id</th>\n",
       "      <th>upvote_count</th>\n",
       "      <th>batch_index</th>\n",
       "      <th>model_name</th>\n",
       "      <th>prompt_text</th>\n",
       "      <th>daily_theme_id</th>\n",
       "      <th>is_deleted</th>\n",
       "      <th>image_s3_id</th>\n",
       "      <th>is_public</th>\n",
       "      <th>dislike_count</th>\n",
       "      <th>flag_count</th>\n",
       "      <th>play_count</th>\n",
       "      <th>skip_count</th>\n",
       "      <th>title</th>\n",
       "      <th>slug</th>\n",
       "      <th>p_date_x</th>\n",
       "      <th>p_hour_x</th>\n",
       "      <th>created_session_id</th>\n",
       "      <th>allow_comments</th>\n",
       "      <th>continued_parent</th>\n",
       "      <th>duration</th>\n",
       "      <th>source</th>\n",
       "      <th>clip_type</th>\n",
       "      <th>task</th>\n",
       "      <th>edited_clip_id</th>\n",
       "      <th>is_pro_user</th>\n",
       "      <th>is_in_playlist</th>\n",
       "      <th>has_stems</th>\n",
       "      <th>user_n_clips</th>\n",
       "      <th>upvoted</th>\n",
       "      <th>downvoted</th>\n",
       "      <th>has_continued</th>\n",
       "      <th>part_of_concat</th>\n",
       "      <th>has_action</th>\n",
       "      <th>flagged</th>\n",
       "      <th>deleted</th>\n",
       "      <th>n_edits</th>\n",
       "      <th>pos_preference</th>\n",
       "      <th>neg_preference</th>\n",
       "      <th>diff_preference</th>\n",
       "      <th>request_count</th>\n",
       "      <th>preference</th>\n",
       "      <th>reaction_play_count</th>\n",
       "      <th>reaction_pro_play_count</th>\n",
       "      <th>total_start_s</th>\n",
       "      <th>total_clip_s</th>\n",
       "      <th>concat_play_counts</th>\n",
       "      <th>concat_in_playlist</th>\n",
       "      <th>concat_likes</th>\n",
       "      <th>concat_dislikes</th>\n",
       "      <th>str_id</th>\n",
       "      <th>p_date_y</th>\n",
       "      <th>p_hour_y</th>\n",
       "      <th>web_total_play_cnt_0</th>\n",
       "      <th>web_total_play_duration_0</th>\n",
       "      <th>ios_total_play_cnt_0</th>\n",
       "      <th>ios_total_play_duration_0</th>\n",
       "      <th>android_total_play_cnt_0</th>\n",
       "      <th>android_total_play_duration_0</th>\n",
       "      <th>web_total_play_cnt_5</th>\n",
       "      <th>web_total_play_duration_5</th>\n",
       "      <th>ios_total_play_cnt_5</th>\n",
       "      <th>ios_total_play_duration_5</th>\n",
       "      <th>android_total_play_cnt_5</th>\n",
       "      <th>android_total_play_duration_5</th>\n",
       "      <th>sum_total_play_cnt_0</th>\n",
       "      <th>sum_total_play_duration_0</th>\n",
       "      <th>sum_total_play_cnt_5</th>\n",
       "      <th>sum_total_play_duration_5</th>\n",
       "      <th>web_non_creator_listen_duration_0</th>\n",
       "      <th>web_non_creator_listen_duration_5</th>\n",
       "      <th>ios_non_creator_listen_duration_0</th>\n",
       "      <th>ios_non_creator_listen_duration_5</th>\n",
       "      <th>android_non_creator_listen_duration_0</th>\n",
       "      <th>android_non_creator_listen_duration_5</th>\n",
       "      <th>sum_non_creator_listen_duration_0</th>\n",
       "      <th>sum_non_creator_listen_duration_5</th>\n",
       "      <th>norm_play_frac</th>\n",
       "      <th>level_0</th>\n",
       "      <th>p_date</th>\n",
       "      <th>p_hour</th>\n",
       "      <th>creation_source</th>\n",
       "      <th>is_hidden</th>\n",
       "      <th>display_tags</th>\n",
       "      <th>tags</th>\n",
       "      <th>is_onebox</th>\n",
       "      <th>is_instrumental</th>\n",
       "      <th>user_n_upsample</th>\n",
       "      <th>cer</th>\n",
       "      <th>cer_diff</th>\n",
       "      <th>upsample_clip_id</th>\n",
       "      <th>is_up</th>\n",
       "      <th>pair_quality</th>\n",
       "      <th>total_shimmer_score</th>\n",
       "      <th>loudness_factor</th>\n",
       "      <th>spectral_character</th>\n",
       "      <th>spectral_centroid</th>\n",
       "      <th>bass_ratio</th>\n",
       "      <th>mid_ratio</th>\n",
       "      <th>high_ratio</th>\n",
       "      <th>stereo_width</th>\n",
       "      <th>total_clips</th>\n",
       "      <th>clips_per_second</th>\n",
       "      <th>loudness_abs</th>\n",
       "      <th>spectrum_decay</th>\n",
       "      <th>original_duration_s</th>\n",
       "      <th>has_continue_and_start_continue_at</th>\n",
       "      <th>good_continue_at</th>\n",
       "      <th>duration_rel_diff</th>\n",
       "      <th>play_rel_diff</th>\n",
       "      <th>pos_diff_preference</th>\n",
       "      <th>mean_ear_score</th>\n",
       "      <th>mean_ear_score_diff</th>\n",
       "      <th>mean_ear_score_diff_ratio</th>\n",
       "      <th>mean_shimmer_score</th>\n",
       "      <th>mean_shimmer_score_diff</th>\n",
       "      <th>mean_shimmer_score_diff_ratio</th>\n",
       "      <th>loudness_diff</th>\n",
       "      <th>pair_quality_diff</th>\n",
       "      <th>spectral_centroid_diff</th>\n",
       "      <th>stereo_width_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>408712</th>\n",
       "      <td>42502214</td>\n",
       "      <td>3572213d-4f51-4ed1-bcdc-375a45edc113</td>\n",
       "      <td>2025-06-30 04:37:18.141000+00:00</td>\n",
       "      <td>2025-06-30 04:38:11.914000+00:00</td>\n",
       "      <td>12.413110</td>\n",
       "      <td>{'lang': 'English', 'tags': 'A high-energy roc...</td>\n",
       "      <td>77230334</td>\n",
       "      <td>complete</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0008f464-4dac-4d9e-a84c-5c0ecb8dac89</td>\n",
       "      <td>True</td>\n",
       "      <td>3572213d-4f51-4ed1-bcdc-375a45edc113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>chirp-ahi-up-2</td>\n",
       "      <td>[Verse 1]\\nKejujuran sejak lagi di panggung pe...</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>image_3572213d-4f51-4ed1-bcdc-375a45edc113</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Civilizatation  (Cover) (Cover) (Remastered)</td>\n",
       "      <td>None</td>\n",
       "      <td>2025-06-30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>185.00</td>\n",
       "      <td>web</td>\n",
       "      <td>upsample</td>\n",
       "      <td>upsample</td>\n",
       "      <td>a90c173a-0064-4aae-88d5-5a54cdd968ac</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3572213d-4f51-4ed1-bcdc-375a45edc113</td>\n",
       "      <td>2025-07-13</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.543218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.543218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.543218</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.543218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A high-energy rock anthem with a driving rhyth...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.307869</td>\n",
       "      <td>0.135701</td>\n",
       "      <td>a90c173a-0064-4aae-88d5-5a54cdd968ac</td>\n",
       "      <td>True</td>\n",
       "      <td>21.132117</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>-9.291</td>\n",
       "      <td>bassy</td>\n",
       "      <td>2009.247</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.528</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>0.319</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-9.291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>-7.56</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>21.258617</td>\n",
       "      <td>-2.826928</td>\n",
       "      <td>-0.132355</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>0.927778</td>\n",
       "      <td>0.954286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.025986</td>\n",
       "      <td>0.430104</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408713</th>\n",
       "      <td>23427723</td>\n",
       "      <td>ee1c0840-1407-4352-ba29-4df4802f871f</td>\n",
       "      <td>2025-06-30 04:37:18.141000+00:00</td>\n",
       "      <td>2025-07-10 09:07:22.909000+00:00</td>\n",
       "      <td>11.117350</td>\n",
       "      <td>{'lang': 'English', 'tags': 'A high-energy roc...</td>\n",
       "      <td>77230334</td>\n",
       "      <td>complete</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0008f464-4dac-4d9e-a84c-5c0ecb8dac89</td>\n",
       "      <td>True</td>\n",
       "      <td>ee1c0840-1407-4352-ba29-4df4802f871f</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>chirp-ahi-up-2</td>\n",
       "      <td>[Verse 1]\\nKejujuran sejak lagi di panggung pe...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>image_ee1c0840-1407-4352-ba29-4df4802f871f</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Civilizatation  (Cover) (Cover) (Remastered)</td>\n",
       "      <td>None</td>\n",
       "      <td>2025-06-30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>185.00</td>\n",
       "      <td>web</td>\n",
       "      <td>upsample</td>\n",
       "      <td>upsample</td>\n",
       "      <td>a90c173a-0064-4aae-88d5-5a54cdd968ac</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ee1c0840-1407-4352-ba29-4df4802f871f</td>\n",
       "      <td>2025-07-13</td>\n",
       "      <td>16</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1561.334634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1545.801387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1561.334634</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1545.801387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.355683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A high-energy rock anthem with a driving rhyth...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.288340</td>\n",
       "      <td>-0.019529</td>\n",
       "      <td>a90c173a-0064-4aae-88d5-5a54cdd968ac</td>\n",
       "      <td>True</td>\n",
       "      <td>20.881600</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>-10.049</td>\n",
       "      <td>bassy</td>\n",
       "      <td>1931.335</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.582</td>\n",
       "      <td>-0.253</td>\n",
       "      <td>0.283</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-10.049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.440225</td>\n",
       "      <td>-0.818392</td>\n",
       "      <td>-0.039843</td>\n",
       "      <td>0.161111</td>\n",
       "      <td>-0.811111</td>\n",
       "      <td>-5.034483</td>\n",
       "      <td>0.075430</td>\n",
       "      <td>-0.011997</td>\n",
       "      <td>-0.040341</td>\n",
       "      <td>-0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408724</th>\n",
       "      <td>21783494</td>\n",
       "      <td>b3c3e12e-b2c9-4425-8239-a1da830e04fc</td>\n",
       "      <td>2025-07-04 21:43:35.894000+00:00</td>\n",
       "      <td>2025-07-05 01:45:43.748000+00:00</td>\n",
       "      <td>18.778496</td>\n",
       "      <td>{'lang': 'English', 'tags': 'Genre: Alternativ...</td>\n",
       "      <td>15132405</td>\n",
       "      <td>complete</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>000f6a47-c367-4cec-b053-6d368c280454</td>\n",
       "      <td>True</td>\n",
       "      <td>b3c3e12e-b2c9-4425-8239-a1da830e04fc</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>chirp-ahi-up-2</td>\n",
       "      <td>Verse 1\\nI left the screen on / let it breathe...</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>image_b3c3e12e-b2c9-4425-8239-a1da830e04fc</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Verse 1 (Remastered)</td>\n",
       "      <td>None</td>\n",
       "      <td>2025-07-04</td>\n",
       "      <td>21.0</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>189.00</td>\n",
       "      <td>web</td>\n",
       "      <td>upsample</td>\n",
       "      <td>upsample</td>\n",
       "      <td>245dfe96-b1ff-434e-91a7-c560da48976f</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>b3c3e12e-b2c9-4425-8239-a1da830e04fc</td>\n",
       "      <td>2025-07-13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.199626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.780401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.199626</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.780401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.099367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Genre: Alternative rock with dynamic contrast\\...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.348506</td>\n",
       "      <td>-0.651494</td>\n",
       "      <td>245dfe96-b1ff-434e-91a7-c560da48976f</td>\n",
       "      <td>True</td>\n",
       "      <td>22.139167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18.190</td>\n",
       "      <td>bright</td>\n",
       "      <td>1798.740</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-18.190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>-10.72</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>23.559361</td>\n",
       "      <td>1.288472</td>\n",
       "      <td>0.054459</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-15.600000</td>\n",
       "      <td>0.447554</td>\n",
       "      <td>-0.026434</td>\n",
       "      <td>-0.663146</td>\n",
       "      <td>-0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408725</th>\n",
       "      <td>63964654</td>\n",
       "      <td>37e0d0a3-280c-44eb-9dd6-c40b359f5c78</td>\n",
       "      <td>2025-07-04 21:43:35.893000+00:00</td>\n",
       "      <td>2025-07-06 23:23:27.874000+00:00</td>\n",
       "      <td>13.233990</td>\n",
       "      <td>{'lang': 'English', 'tags': 'Genre: Alternativ...</td>\n",
       "      <td>15132405</td>\n",
       "      <td>complete</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>000f6a47-c367-4cec-b053-6d368c280454</td>\n",
       "      <td>True</td>\n",
       "      <td>37e0d0a3-280c-44eb-9dd6-c40b359f5c78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>chirp-ahi-up-2</td>\n",
       "      <td>Verse 1\\nI left the screen on / let it breathe...</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "      <td>image_37e0d0a3-280c-44eb-9dd6-c40b359f5c78</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Verse 1 (Remastered)</td>\n",
       "      <td>None</td>\n",
       "      <td>2025-07-04</td>\n",
       "      <td>21.0</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>189.00</td>\n",
       "      <td>web</td>\n",
       "      <td>upsample</td>\n",
       "      <td>upsample</td>\n",
       "      <td>245dfe96-b1ff-434e-91a7-c560da48976f</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>18</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37e0d0a3-280c-44eb-9dd6-c40b359f5c78</td>\n",
       "      <td>2025-07-13</td>\n",
       "      <td>16</td>\n",
       "      <td>8.0</td>\n",
       "      <td>544.152307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>522.382788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>544.152307</td>\n",
       "      <td>6.0</td>\n",
       "      <td>522.382788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.763930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Genre: Alternative rock with dynamic contrast\\...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.266003</td>\n",
       "      <td>-0.082504</td>\n",
       "      <td>245dfe96-b1ff-434e-91a7-c560da48976f</td>\n",
       "      <td>True</td>\n",
       "      <td>21.367933</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-15.202</td>\n",
       "      <td>bright</td>\n",
       "      <td>2865.708</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.334</td>\n",
       "      <td>1.027</td>\n",
       "      <td>0.197</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-15.202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.324797</td>\n",
       "      <td>-1.234564</td>\n",
       "      <td>-0.055054</td>\n",
       "      <td>0.038889</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>-0.196553</td>\n",
       "      <td>-0.036093</td>\n",
       "      <td>0.372323</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408726</th>\n",
       "      <td>55803978</td>\n",
       "      <td>c94d63a0-5e84-41fa-8f79-caf7b6429c3c</td>\n",
       "      <td>2025-07-03 16:08:22.399000+00:00</td>\n",
       "      <td>2025-07-03 16:27:38.130000+00:00</td>\n",
       "      <td>15.859948</td>\n",
       "      <td>{'lang': 'English', 'tags': 'Modern Emotional ...</td>\n",
       "      <td>13931115</td>\n",
       "      <td>complete</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>000f8492-f5df-414a-8e77-6465d4b5846e</td>\n",
       "      <td>True</td>\n",
       "      <td>c94d63a0-5e84-41fa-8f79-caf7b6429c3c</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>chirp-ahi-up-2</td>\n",
       "      <td>[Modern pop]\\n\\n[K-pop]\\n\\n[Nu K-Pop]\\n\\n[Nu-p...</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>image_c94d63a0-5e84-41fa-8f79-caf7b6429c3c</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Humour Me, Dude (Remastered x4*) (Edit)</td>\n",
       "      <td>None</td>\n",
       "      <td>2025-07-03</td>\n",
       "      <td>16.0</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>177.72</td>\n",
       "      <td>web</td>\n",
       "      <td>upsample</td>\n",
       "      <td>upsample</td>\n",
       "      <td>93dd1fc3-8103-4dbe-a343-ce4b3184a26b</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c94d63a0-5e84-41fa-8f79-caf7b6429c3c</td>\n",
       "      <td>2025-07-13</td>\n",
       "      <td>16</td>\n",
       "      <td>10.0</td>\n",
       "      <td>100.395548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.378573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>100.395548</td>\n",
       "      <td>4.0</td>\n",
       "      <td>76.378573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.429769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Modern Emotional Cinematic Nu K-Pop Empower. C...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.275560</td>\n",
       "      <td>0.009557</td>\n",
       "      <td>93dd1fc3-8103-4dbe-a343-ce4b3184a26b</td>\n",
       "      <td>True</td>\n",
       "      <td>23.419333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>-14.067</td>\n",
       "      <td>bright</td>\n",
       "      <td>2524.419</td>\n",
       "      <td>-0.556</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.306</td>\n",
       "      <td>0.381</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-14.067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>-11.28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>23.344613</td>\n",
       "      <td>1.019816</td>\n",
       "      <td>0.043499</td>\n",
       "      <td>0.206667</td>\n",
       "      <td>0.167778</td>\n",
       "      <td>0.811828</td>\n",
       "      <td>-0.080685</td>\n",
       "      <td>0.087594</td>\n",
       "      <td>-0.135195</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           index                                    id                       created_at                       updated_at  time_used                                           metadata   user_id    status discord_message_id prompt_id                            request_id  is_generated                                 s3_id  upvote_count  batch_index      model_name                                        prompt_text daily_theme_id  is_deleted                                 image_s3_id  is_public  dislike_count  flag_count  play_count  skip_count                                         title  slug    p_date_x  p_hour_x created_session_id  allow_comments continued_parent  duration source clip_type      task                        edited_clip_id  is_pro_user  is_in_playlist  has_stems  user_n_clips  upvoted  downvoted  has_continued  part_of_concat  has_action  flagged  deleted  n_edits  pos_preference  neg_preference  diff_preference  request_count  preference  reaction_play_count  \\\n",
       "408712  42502214  3572213d-4f51-4ed1-bcdc-375a45edc113 2025-06-30 04:37:18.141000+00:00 2025-06-30 04:38:11.914000+00:00  12.413110  {'lang': 'English', 'tags': 'A high-energy roc...  77230334  complete               None      None  0008f464-4dac-4d9e-a84c-5c0ecb8dac89          True  3572213d-4f51-4ed1-bcdc-375a45edc113             0            0  chirp-ahi-up-2  [Verse 1]\\nKejujuran sejak lagi di panggung pe...           None        True  image_3572213d-4f51-4ed1-bcdc-375a45edc113      False              0           0           1           0  Civilizatation  (Cover) (Cover) (Remastered)  None  2025-06-30       4.0               None            True             None    185.00    web  upsample  upsample  a90c173a-0064-4aae-88d5-5a54cdd968ac         True           False      False            20    False      False          False           False       False    False     True      NaN           False            True               -1              2       False                  1.0   \n",
       "408713  23427723  ee1c0840-1407-4352-ba29-4df4802f871f 2025-06-30 04:37:18.141000+00:00 2025-07-10 09:07:22.909000+00:00  11.117350  {'lang': 'English', 'tags': 'A high-energy roc...  77230334  complete               None      None  0008f464-4dac-4d9e-a84c-5c0ecb8dac89          True  ee1c0840-1407-4352-ba29-4df4802f871f             0            1  chirp-ahi-up-2  [Verse 1]\\nKejujuran sejak lagi di panggung pe...           None       False  image_ee1c0840-1407-4352-ba29-4df4802f871f      False              0           0          12           0  Civilizatation  (Cover) (Cover) (Remastered)  None  2025-06-30       4.0               None            True             None    185.00    web  upsample  upsample  a90c173a-0064-4aae-88d5-5a54cdd968ac         True           False      False            20    False      False          False           False        True    False    False      NaN            True           False                1              2        True                 11.0   \n",
       "408724  21783494  b3c3e12e-b2c9-4425-8239-a1da830e04fc 2025-07-04 21:43:35.894000+00:00 2025-07-05 01:45:43.748000+00:00  18.778496  {'lang': 'English', 'tags': 'Genre: Alternativ...  15132405  complete               None      None  000f6a47-c367-4cec-b053-6d368c280454          True  b3c3e12e-b2c9-4425-8239-a1da830e04fc             0            1  chirp-ahi-up-2  Verse 1\\nI left the screen on / let it breathe...           None        True  image_b3c3e12e-b2c9-4425-8239-a1da830e04fc      False              0           0           2           0                          Verse 1 (Remastered)  None  2025-07-04      21.0               None            True             None    189.00    web  upsample  upsample  245dfe96-b1ff-434e-91a7-c560da48976f         True           False      False            18    False      False          False           False       False    False     True      NaN           False            True               -1              2       False                  2.0   \n",
       "408725  63964654  37e0d0a3-280c-44eb-9dd6-c40b359f5c78 2025-07-04 21:43:35.893000+00:00 2025-07-06 23:23:27.874000+00:00  13.233990  {'lang': 'English', 'tags': 'Genre: Alternativ...  15132405  complete               None      None  000f6a47-c367-4cec-b053-6d368c280454          True  37e0d0a3-280c-44eb-9dd6-c40b359f5c78             1            0  chirp-ahi-up-2  Verse 1\\nI left the screen on / let it breathe...           None       False  image_37e0d0a3-280c-44eb-9dd6-c40b359f5c78      False              0           0           9           0                          Verse 1 (Remastered)  None  2025-07-04      21.0               None            True             None    189.00    web  upsample  upsample  245dfe96-b1ff-434e-91a7-c560da48976f         True            True      False            18     True      False          False           False       False    False    False      NaN            True           False                1              2        True                  9.0   \n",
       "408726  55803978  c94d63a0-5e84-41fa-8f79-caf7b6429c3c 2025-07-03 16:08:22.399000+00:00 2025-07-03 16:27:38.130000+00:00  15.859948  {'lang': 'English', 'tags': 'Modern Emotional ...  13931115  complete               None      None  000f8492-f5df-414a-8e77-6465d4b5846e          True  c94d63a0-5e84-41fa-8f79-caf7b6429c3c             0            1  chirp-ahi-up-2  [Modern pop]\\n\\n[K-pop]\\n\\n[Nu K-Pop]\\n\\n[Nu-p...           None        True  image_c94d63a0-5e84-41fa-8f79-caf7b6429c3c      False              0           0          10           0       Humour Me, Dude (Remastered x4*) (Edit)  None  2025-07-03      16.0               None            True             None    177.72    web  upsample  upsample  93dd1fc3-8103-4dbe-a343-ce4b3184a26b         True           False      False            14    False      False          False           False       False    False     True      NaN           False            True               -1              2       False                 10.0   \n",
       "\n",
       "        reaction_pro_play_count  total_start_s  total_clip_s  concat_play_counts concat_in_playlist  concat_likes  concat_dislikes                                str_id    p_date_y p_hour_y  web_total_play_cnt_0  web_total_play_duration_0  ios_total_play_cnt_0  ios_total_play_duration_0  android_total_play_cnt_0  android_total_play_duration_0  web_total_play_cnt_5  web_total_play_duration_5  ios_total_play_cnt_5  ios_total_play_duration_5  android_total_play_cnt_5  android_total_play_duration_5  sum_total_play_cnt_0  sum_total_play_duration_0  sum_total_play_cnt_5  sum_total_play_duration_5  web_non_creator_listen_duration_0  web_non_creator_listen_duration_5  ios_non_creator_listen_duration_0  ios_non_creator_listen_duration_5  android_non_creator_listen_duration_0  android_non_creator_listen_duration_5  sum_non_creator_listen_duration_0  sum_non_creator_listen_duration_5  norm_play_frac  level_0 p_date  p_hour creation_source is_hidden display_tags  \\\n",
       "408712                      1.0            NaN           NaN                 NaN               None           NaN              NaN  3572213d-4f51-4ed1-bcdc-375a45edc113  2025-07-13       16                   1.0                  39.543218                   0.0                        0.0                       0.0                            0.0                   1.0                  39.543218                   0.0                        0.0                       0.0                            0.0                   1.0                  39.543218                   1.0                  39.543218                                0.0                                0.0                                0.0                                0.0                                    0.0                                    0.0                                0.0                                0.0        0.213747      NaN    NaN     NaN             NaN       NaN          NaN   \n",
       "408713                     11.0            NaN           NaN                 NaN               None           NaN              NaN  ee1c0840-1407-4352-ba29-4df4802f871f  2025-07-13       16                  11.0                1561.334634                   0.0                        0.0                       0.0                            0.0                  10.0                1545.801387                   0.0                        0.0                       0.0                            0.0                  11.0                1561.334634                  10.0                1545.801387                                0.0                                0.0                                0.0                                0.0                                    0.0                                    0.0                                0.0                                0.0        8.355683      NaN    NaN     NaN             NaN       NaN          NaN   \n",
       "408724                      2.0            NaN           NaN                 NaN               None           NaN              NaN  b3c3e12e-b2c9-4425-8239-a1da830e04fc  2025-07-13       16                   2.0                  20.199626                   0.0                        0.0                       0.0                            0.0                   2.0                  18.780401                   0.0                        0.0                       0.0                            0.0                   2.0                  20.199626                   2.0                  18.780401                                0.0                                0.0                                0.0                                0.0                                    0.0                                    0.0                                0.0                                0.0        0.099367      NaN    NaN     NaN             NaN       NaN          NaN   \n",
       "408725                      9.0            NaN           NaN                 NaN               None           NaN              NaN  37e0d0a3-280c-44eb-9dd6-c40b359f5c78  2025-07-13       16                   8.0                 544.152307                   0.0                        0.0                       0.0                            0.0                   6.0                 522.382788                   0.0                        0.0                       0.0                            0.0                   8.0                 544.152307                   6.0                 522.382788                                0.0                                0.0                                0.0                                0.0                                    0.0                                    0.0                                0.0                                0.0        2.763930      NaN    NaN     NaN             NaN       NaN          NaN   \n",
       "408726                     10.0            NaN           NaN                 NaN               None           NaN              NaN  c94d63a0-5e84-41fa-8f79-caf7b6429c3c  2025-07-13       16                  10.0                 100.395548                   0.0                        0.0                       0.0                            0.0                   4.0                  76.378573                   0.0                        0.0                       0.0                            0.0                  10.0                 100.395548                   4.0                  76.378573                                0.0                                0.0                                0.0                                0.0                                    0.0                                    0.0                                0.0                                0.0        0.429769      NaN    NaN     NaN             NaN       NaN          NaN   \n",
       "\n",
       "                                                     tags is_onebox is_instrumental  user_n_upsample       cer  cer_diff                      upsample_clip_id  is_up  pair_quality  total_shimmer_score  loudness_factor spectral_character  spectral_centroid  bass_ratio  mid_ratio  high_ratio  stereo_width  total_clips  clips_per_second  loudness_abs  spectrum_decay  original_duration_s has_continue_and_start_continue_at  good_continue_at  duration_rel_diff  play_rel_diff  pos_diff_preference  mean_ear_score  mean_ear_score_diff  mean_ear_score_diff_ratio  mean_shimmer_score  mean_shimmer_score_diff  mean_shimmer_score_diff_ratio  loudness_diff  pair_quality_diff  spectral_centroid_diff  stereo_width_diff  \n",
       "408712  A high-energy rock anthem with a driving rhyth...       NaN             NaN             14.0  0.307869  0.135701  a90c173a-0064-4aae-88d5-5a54cdd968ac   True     21.132117             2.533333           -9.291              bassy           2009.247       0.684      0.528      -0.211         0.319          2.0             0.067        -9.291             0.0                  NaN                               None              True              -7.56          -19.0                 -2.0       21.258617            -2.826928                  -0.132355            0.972222                 0.927778                       0.954286            NaN          -0.025986                0.430104                NaN  \n",
       "408713  A high-energy rock anthem with a driving rhyth...       NaN             NaN             14.0  0.288340 -0.019529  a90c173a-0064-4aae-88d5-5a54cdd968ac   True     20.881600             0.633333          -10.049              bassy           1931.335       0.671      0.582      -0.253         0.283          1.0             0.033       -10.049             0.0                  NaN                               None              True               0.00           10.0                  2.0       20.440225            -0.818392                  -0.039843            0.161111                -0.811111                      -5.034483       0.075430          -0.011997               -0.040341             -0.036  \n",
       "408724  Genre: Alternative rock with dynamic contrast\\...       NaN             NaN             24.0  0.348506 -0.651494  245dfe96-b1ff-434e-91a7-c560da48976f   True     22.139167             0.000000          -18.190             bright           1798.740       0.043      0.296       0.661         0.048          1.0             0.033       -18.190             0.0                  NaN                               None              True             -10.72           -1.0                 -2.0       23.559361             1.288472                   0.054459            0.027778                -0.433333                     -15.600000       0.447554          -0.026434               -0.663146             -0.235  \n",
       "408725  Genre: Alternative rock with dynamic contrast\\...       NaN             NaN             24.0  0.266003 -0.082504  245dfe96-b1ff-434e-91a7-c560da48976f   True     21.367933             0.000000          -15.202             bright           2865.708      -0.361      0.334       1.027         0.197          2.0             0.067       -15.202             0.0                  NaN                               None              True               0.00            7.0                  2.0       22.324797            -1.234564                  -0.055054            0.038889                 0.011111                       0.285714      -0.196553          -0.036093                0.372323              0.149  \n",
       "408726  Modern Emotional Cinematic Nu K-Pop Empower. C...       NaN             NaN             16.0  0.275560  0.009557  93dd1fc3-8103-4dbe-a343-ce4b3184a26b   True     23.419333             0.533333          -14.067             bright           2524.419      -0.556      0.250       1.306         0.381          2.0             0.067       -14.067             0.0                  NaN                               None              True             -11.28            1.0                 -2.0       23.344613             1.019816                   0.043499            0.206667                 0.167778                       0.811828      -0.080685           0.087594               -0.135195              0.184  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:40.916951Z",
     "iopub.status.busy": "2025-06-24T04:14:40.916775Z",
     "iopub.status.idle": "2025-06-24T04:14:41.845483Z",
     "shell.execute_reply": "2025-06-24T04:14:41.845004Z",
     "shell.execute_reply.started": "2025-06-24T04:14:40.916933Z"
    }
   },
   "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": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:41.847376Z",
     "iopub.status.busy": "2025-06-24T04:14:41.847204Z",
     "iopub.status.idle": "2025-06-24T04:14:41.906246Z",
     "shell.execute_reply": "2025-06-24T04:14:41.905689Z",
     "shell.execute_reply.started": "2025-06-24T04:14:41.847361Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "652.0\n",
      "(1304, 750, 128)\n",
      "(1304, 750)\n"
     ]
    }
   ],
   "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": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:41.906867Z",
     "iopub.status.busy": "2025-06-24T04:14:41.906725Z",
     "iopub.status.idle": "2025-06-24T04:14:41.919347Z",
     "shell.execute_reply": "2025-06-24T04:14:41.918911Z",
     "shell.execute_reply.started": "2025-06-24T04:14:41.906852Z"
    }
   },
   "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": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:41.919920Z",
     "iopub.status.busy": "2025-06-24T04:14:41.919784Z",
     "iopub.status.idle": "2025-06-24T04:14:41.932045Z",
     "shell.execute_reply": "2025-06-24T04:14:41.931617Z",
     "shell.execute_reply.started": "2025-06-24T04:14:41.919906Z"
    }
   },
   "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": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:41.932665Z",
     "iopub.status.busy": "2025-06-24T04:14:41.932526Z",
     "iopub.status.idle": "2025-06-24T04:14:43.907462Z",
     "shell.execute_reply": "2025-06-24T04:14:43.906842Z",
     "shell.execute_reply.started": "2025-06-24T04:14:41.932650Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:43.908244Z",
     "iopub.status.busy": "2025-06-24T04:14:43.908079Z",
     "iopub.status.idle": "2025-06-24T04:14:43.950818Z",
     "shell.execute_reply": "2025-06-24T04:14:43.950393Z",
     "shell.execute_reply.started": "2025-06-24T04:14:43.908226Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.equal(torch.tensor(mm_semantic_val[idx]), torch.tensor(mm_semantic_val[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:43.951452Z",
     "iopub.status.busy": "2025-06-24T04:14:43.951312Z",
     "iopub.status.idle": "2025-06-24T04:14:43.966117Z",
     "shell.execute_reply": "2025-06-24T04:14:43.965693Z",
     "shell.execute_reply.started": "2025-06-24T04:14:43.951438Z"
    }
   },
   "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": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:43.966747Z",
     "iopub.status.busy": "2025-06-24T04:14:43.966605Z",
     "iopub.status.idle": "2025-06-24T04:14:43.982081Z",
     "shell.execute_reply": "2025-06-24T04:14:43.981645Z",
     "shell.execute_reply.started": "2025-06-24T04:14:43.966733Z"
    }
   },
   "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": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:43.982691Z",
     "iopub.status.busy": "2025-06-24T04:14:43.982550Z",
     "iopub.status.idle": "2025-06-24T04:14:46.531388Z",
     "shell.execute_reply": "2025-06-24T04:14:46.530756Z",
     "shell.execute_reply.started": "2025-06-24T04:14:43.982677Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64253 0\n",
      "128506\n"
     ]
    }
   ],
   "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": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:46.532193Z",
     "iopub.status.busy": "2025-06-24T04:14:46.532022Z",
     "iopub.status.idle": "2025-06-24T04:14:46.608547Z",
     "shell.execute_reply": "2025-06-24T04:14:46.608067Z",
     "shell.execute_reply.started": "2025-06-24T04:14:46.532174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4420"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:46.609271Z",
     "iopub.status.busy": "2025-06-24T04:14:46.609110Z",
     "iopub.status.idle": "2025-06-24T04:14:46.629623Z",
     "shell.execute_reply": "2025-06-24T04:14:46.629168Z",
     "shell.execute_reply.started": "2025-06-24T04:14:46.609255Z"
    }
   },
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:46.630269Z",
     "iopub.status.busy": "2025-06-24T04:14:46.630123Z",
     "iopub.status.idle": "2025-06-24T04:14:46.990528Z",
     "shell.execute_reply": "2025-06-24T04:14:46.989958Z",
     "shell.execute_reply.started": "2025-06-24T04:14:46.630255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 11020\n"
     ]
    }
   ],
   "source": [
    "!cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion_infill.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:46.991390Z",
     "iopub.status.busy": "2025-06-24T04:14:46.991225Z",
     "iopub.status.idle": "2025-06-24T04:14:47.054201Z",
     "shell.execute_reply": "2025-06-24T04:14:47.053734Z",
     "shell.execute_reply.started": "2025-06-24T04:14:46.991375Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cache kept!\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "\n",
    "# Basic file copy\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/make_dataset_diff_upsample_v2_r4.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": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.054900Z",
     "iopub.status.busy": "2025-06-24T04:14:47.054757Z",
     "iopub.status.idle": "2025-06-24T04:14:47.070181Z",
     "shell.execute_reply": "2025-06-24T04:14:47.069763Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.054887Z"
    }
   },
   "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": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.070849Z",
     "iopub.status.busy": "2025-06-24T04:14:47.070701Z",
     "iopub.status.idle": "2025-06-24T04:14:47.085862Z",
     "shell.execute_reply": "2025-06-24T04:14:47.085436Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.070835Z"
    }
   },
   "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": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.086542Z",
     "iopub.status.busy": "2025-06-24T04:14:47.086400Z",
     "iopub.status.idle": "2025-06-24T04:14:47.101055Z",
     "shell.execute_reply": "2025-06-24T04:14:47.100627Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.086527Z"
    }
   },
   "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": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.101732Z",
     "iopub.status.busy": "2025-06-24T04:14:47.101586Z",
     "iopub.status.idle": "2025-06-24T04:14:47.116424Z",
     "shell.execute_reply": "2025-06-24T04:14:47.115998Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.101718Z"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.117091Z",
     "iopub.status.busy": "2025-06-24T04:14:47.116953Z",
     "iopub.status.idle": "2025-06-24T04:14:47.131410Z",
     "shell.execute_reply": "2025-06-24T04:14:47.130988Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.117077Z"
    }
   },
   "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": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.132037Z",
     "iopub.status.busy": "2025-06-24T04:14:47.131899Z",
     "iopub.status.idle": "2025-06-24T04:14:47.146290Z",
     "shell.execute_reply": "2025-06-24T04:14:47.145875Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.132023Z"
    }
   },
   "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": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.149295Z",
     "iopub.status.busy": "2025-06-24T04:14:47.149134Z",
     "iopub.status.idle": "2025-06-24T04:14:47.163818Z",
     "shell.execute_reply": "2025-06-24T04:14:47.163390Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.149281Z"
    }
   },
   "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": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.164420Z",
     "iopub.status.busy": "2025-06-24T04:14:47.164281Z",
     "iopub.status.idle": "2025-06-24T04:14:47.179087Z",
     "shell.execute_reply": "2025-06-24T04:14:47.178664Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.164406Z"
    }
   },
   "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": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.179737Z",
     "iopub.status.busy": "2025-06-24T04:14:47.179588Z",
     "iopub.status.idle": "2025-06-24T04:14:47.211628Z",
     "shell.execute_reply": "2025-06-24T04:14:47.211185Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.179723Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.212289Z",
     "iopub.status.busy": "2025-06-24T04:14:47.212109Z",
     "iopub.status.idle": "2025-06-24T04:14:47.245995Z",
     "shell.execute_reply": "2025-06-24T04:14:47.245514Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.212275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.4746, 0.5586, 0.9883, 0.0396], dtype=torch.bfloat16)\n",
      "tensor([1.0000, 1.0000, 0.9883, 0.0396], dtype=torch.bfloat16)\n",
      "tensor([1.0000, 1.0000, 1.0000, 1.0000, 0.9883, 0.9883, 0.0396, 0.0396],\n",
      "       dtype=torch.bfloat16)\n"
     ]
    }
   ],
   "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": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.246618Z",
     "iopub.status.busy": "2025-06-24T04:14:47.246472Z",
     "iopub.status.idle": "2025-06-24T04:14:47.266607Z",
     "shell.execute_reply": "2025-06-24T04:14:47.266181Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.246604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0000, 1.0000, 1.0000, 1.0000, 0.9688, 0.9688, 0.0312, 0.0312])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.267235Z",
     "iopub.status.busy": "2025-06-24T04:14:47.267098Z",
     "iopub.status.idle": "2025-06-24T04:14:47.281806Z",
     "shell.execute_reply": "2025-06-24T04:14:47.281371Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.267222Z"
    }
   },
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.282428Z",
     "iopub.status.busy": "2025-06-24T04:14:47.282289Z",
     "iopub.status.idle": "2025-06-24T04:14:47.302734Z",
     "shell.execute_reply": "2025-06-24T04:14:47.302317Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.282414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.0312, 0.0312],\n",
       "       dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge jsons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.303358Z",
     "iopub.status.busy": "2025-06-24T04:14:47.303220Z",
     "iopub.status.idle": "2025-06-24T04:14:47.317767Z",
     "shell.execute_reply": "2025-06-24T04:14:47.317341Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.303345Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v2_d4/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# # result = {}\n",
    "# print(len(result))\n",
    "# for job_idx in range(8):\n",
    "#     with open(f\"/home/tony/Data/Preference/up_v2_d4/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_d4/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.318539Z",
     "iopub.status.busy": "2025-06-24T04:14:47.318273Z",
     "iopub.status.idle": "2025-06-24T04:14:47.333038Z",
     "shell.execute_reply": "2025-06-24T04:14:47.332607Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.318524Z"
    }
   },
   "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",
    "# print(len(result_loundess))\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": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.333651Z",
     "iopub.status.busy": "2025-06-24T04:14:47.333515Z",
     "iopub.status.idle": "2025-06-24T04:14:47.348001Z",
     "shell.execute_reply": "2025-06-24T04:14:47.347577Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.333637Z"
    }
   },
   "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": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.348616Z",
     "iopub.status.busy": "2025-06-24T04:14:47.348479Z",
     "iopub.status.idle": "2025-06-24T04:14:47.362974Z",
     "shell.execute_reply": "2025-06-24T04:14:47.362547Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.348602Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.363602Z",
     "iopub.status.busy": "2025-06-24T04:14:47.363462Z",
     "iopub.status.idle": "2025-06-24T04:14:47.377979Z",
     "shell.execute_reply": "2025-06-24T04:14:47.377547Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.363587Z"
    }
   },
   "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": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-24T04:14:47.378607Z",
     "iopub.status.busy": "2025-06-24T04:14:47.378464Z",
     "iopub.status.idle": "2025-06-24T04:14:47.392908Z",
     "shell.execute_reply": "2025-06-24T04:14:47.392416Z",
     "shell.execute_reply.started": "2025-06-24T04:14:47.378594Z"
    }
   },
   "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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