{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:39:24.282058Z",
     "iopub.status.busy": "2025-06-02T23:39:24.281924Z",
     "iopub.status.idle": "2025-06-02T23:39:24.294494Z",
     "shell.execute_reply": "2025-06-02T23:39:24.294078Z",
     "shell.execute_reply.started": "2025-06-02T23:39:24.282041Z"
    }
   },
   "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": "2025-06-02T23:39:24.295094Z",
     "iopub.status.busy": "2025-06-02T23:39:24.294961Z",
     "iopub.status.idle": "2025-06-02T23:39:26.966297Z",
     "shell.execute_reply": "2025-06-02T23:39:26.965756Z",
     "shell.execute_reply.started": "2025-06-02T23:39:24.295081Z"
    }
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from preference_data_preparation_diff import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from suno_utils.utils.s3 import download_s3_files\n",
    "from suno_utils.utils.text import read_json, read_jsonl, write_json, write_jsonl\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from suno_utils.audio import Audio\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.082172Z",
     "start_time": "2024-05-16T13:58:21.041926Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:39:26.967058Z",
     "iopub.status.busy": "2025-06-02T23:39:26.966850Z",
     "iopub.status.idle": "2025-06-02T23:39:52.497387Z",
     "shell.execute_reply": "2025-06-02T23:39:52.496780Z",
     "shell.execute_reply.started": "2025-06-02T23:39:26.967043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pair quality scores: 227075\n"
     ]
    }
   ],
   "source": [
    "OUT_DATA_DIR = \"/app2/suno/data/dpo/diff2_v2_d3_v24/\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "NPZ_DIR = \"/app2/suno/data/dpo/diff2_v2_d3\"\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/up_v2_d3/full_pair_quality.json\", \"r\") as file:\n",
    "    full_pair_quality = json.load(file)\n",
    "print(\"Total pair quality scores:\", len(full_pair_quality))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:53.962528Z",
     "start_time": "2024-05-16T13:58:21.105919Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:39:52.498126Z",
     "iopub.status.busy": "2025-06-02T23:39:52.497974Z",
     "iopub.status.idle": "2025-06-02T23:39:58.133442Z",
     "shell.execute_reply": "2025-06-02T23:39:58.132852Z",
     "shell.execute_reply.started": "2025-06-02T23:39:52.498111Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (438838, 90)\n",
      "unique users 70336\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    \"/home/tony/Data/Preference/up_v2_d3/interesting_clips_ahi_d3_20250608.pkl\"\n",
    ")  # , engine='python')\n",
    "print(\"Preference data shape\", df.shape)\n",
    "print(\"unique users\", df[\"user_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:39:58.135119Z",
     "iopub.status.busy": "2025-06-02T23:39:58.134946Z",
     "iopub.status.idle": "2025-06-02T23:39:58.153308Z",
     "shell.execute_reply": "2025-06-02T23:39:58.152830Z",
     "shell.execute_reply.started": "2025-06-02T23:39:58.135103Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    411529\n",
      "True      27309\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": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:39:58.153974Z",
     "iopub.status.busy": "2025-06-02T23:39:58.153829Z",
     "iopub.status.idle": "2025-06-02T23:39:58.417893Z",
     "shell.execute_reply": "2025-06-02T23:39:58.417442Z",
     "shell.execute_reply.started": "2025-06-02T23:39:58.153960Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"upsample_clip_id\"] = df[\"metadata\"].apply(lambda x: x.get(\"upsample_clip_id\", \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.199480Z",
     "start_time": "2024-05-16T13:58:53.963687Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:39:58.418678Z",
     "iopub.status.busy": "2025-06-02T23:39:58.418536Z",
     "iopub.status.idle": "2025-06-02T23:40:00.952184Z",
     "shell.execute_reply": "2025-06-02T23:40:00.951584Z",
     "shell.execute_reply.started": "2025-06-02T23:39:58.418664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "773281\n",
      "252203\n",
      "521078\n",
      "pre-downloaded df (438838, 91)\n",
      "downloaded df (433378, 91)\n",
      "vae downloaded df (433232, 91)\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": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.467253Z",
     "start_time": "2024-05-16T13:58:56.207647Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:00.952922Z",
     "iopub.status.busy": "2025-06-02T23:40:00.952763Z",
     "iopub.status.idle": "2025-06-02T23:40:01.039311Z",
     "shell.execute_reply": "2025-06-02T23:40:01.038863Z",
     "shell.execute_reply.started": "2025-06-02T23:40:00.952906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    433232\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.592883Z",
     "start_time": "2024-05-16T13:58:56.470781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:01.039961Z",
     "iopub.status.busy": "2025-06-02T23:40:01.039818Z",
     "iopub.status.idle": "2025-06-02T23:40:01.082402Z",
     "shell.execute_reply": "2025-06-02T23:40:01.081936Z",
     "shell.execute_reply.started": "2025-06-02T23:40:01.039947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name         \n",
      "False       chirp-ahi-up-1         204881\n",
      "            chirp-v4-up-u-d-2-3     11735\n",
      "True        chirp-ahi-up-1         204881\n",
      "            chirp-v4-up-u-d-2-3     11735\n",
      "Name: count, dtype: int64\n",
      "(433232, 92)\n",
      "(433232, 92)\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": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.909539Z",
     "start_time": "2024-05-16T13:58:56.595736Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:01.083036Z",
     "iopub.status.busy": "2025-06-02T23:40:01.082898Z",
     "iopub.status.idle": "2025-06-02T23:40:01.503436Z",
     "shell.execute_reply": "2025-06-02T23:40:01.502869Z",
     "shell.execute_reply.started": "2025-06-02T23:40:01.083022Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(433232, 92)\n",
      "(433232, 92)\n",
      "preference  model_name         \n",
      "False       chirp-ahi-up-1         204881\n",
      "            chirp-v4-up-u-d-2-3     11735\n",
      "True        chirp-ahi-up-1         204881\n",
      "            chirp-v4-up-u-d-2-3     11735\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": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:01.504157Z",
     "iopub.status.busy": "2025-06-02T23:40:01.504006Z",
     "iopub.status.idle": "2025-06-02T23:40:02.221818Z",
     "shell.execute_reply": "2025-06-02T23:40:02.221230Z",
     "shell.execute_reply.started": "2025-06-02T23:40:01.504142Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unpacked pair quality scores: 2723926\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": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:02.222579Z",
     "iopub.status.busy": "2025-06-02T23:40:02.222423Z",
     "iopub.status.idle": "2025-06-02T23:40:25.284928Z",
     "shell.execute_reply": "2025-06-02T23:40:25.284355Z",
     "shell.execute_reply.started": "2025-06-02T23:40:02.222563Z"
    }
   },
   "outputs": [],
   "source": [
    "clip_diffs = []\n",
    "clip_ratios = []\n",
    "loudness_diff = []\n",
    "spec_decay_diff = []\n",
    "last_spec_decay_diff = []\n",
    "clip_id_to_mean_ear_score = {}\n",
    "total_clip_ratios = []\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\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_clip_id = None\n",
    "    pos_clip_id = None\n",
    "    for i, (clip_id, pair_quality) in enumerate(pairs_of_qualities.items()):\n",
    "        if i % 2 == 0:\n",
    "            mean_neg_scores.append(\n",
    "                np.mean(pair_quality[\"ear_v2_quality_scores\"] if pair_quality else 0)\n",
    "            )\n",
    "            neg_scores.extend(\n",
    "                pair_quality[\"ear_v2_quality_scores\"] if pair_quality else [0]\n",
    "            )\n",
    "            neg_loudness.append(\n",
    "                pair_quality[\"abs_loudness_factor\"] if pair_quality else 0\n",
    "            )\n",
    "            neg_spec_decay.append(pair_quality[\"spectrum_decay\"] if pair_quality else 0)\n",
    "            if neg_clip_id is None:\n",
    "                neg_clip_id = clip_id\n",
    "        if i % 2 == 1:\n",
    "            mean_pos_scores.append(\n",
    "                np.mean(pair_quality[\"ear_v2_quality_scores\"] if pair_quality else 0)\n",
    "            )\n",
    "            pos_scores.extend(\n",
    "                pair_quality[\"ear_v2_quality_scores\"] if pair_quality else [0]\n",
    "            )\n",
    "            pos_loudness.append(\n",
    "                pair_quality[\"abs_loudness_factor\"] if pair_quality else 0\n",
    "            )\n",
    "            pos_spec_decay.append(pair_quality[\"spectrum_decay\"] if pair_quality else 0)\n",
    "            if pos_clip_id is None:\n",
    "                pos_clip_id = clip_id\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",
    "    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",
    "    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",
    "    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",
    "    assert pos_clip_id is not None\n",
    "    assert 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",
    "    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": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:25.285696Z",
     "iopub.status.busy": "2025-06-02T23:40:25.285543Z",
     "iopub.status.idle": "2025-06-02T23:40:29.745293Z",
     "shell.execute_reply": "2025-06-02T23:40:29.744772Z",
     "shell.execute_reply.started": "2025-06-02T23:40:25.285681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate mean and standard deviation\n",
    "mean_spec_decay_diff = np.mean(spec_decay_diff)\n",
    "std_spec_decay_diff = np.std(spec_decay_diff)\n",
    "mean_last_spec_decay_diff = np.mean(last_spec_decay_diff)\n",
    "std_last_spec_decay_diff = np.std(last_spec_decay_diff)\n",
    "\n",
    "# Plot histogram\n",
    "plt.hist(\n",
    "    spec_decay_diff,\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.7,\n",
    "    label=\"All Spec Decay Diff\",\n",
    ")\n",
    "plt.hist(\n",
    "    last_spec_decay_diff,\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    "    color=\"orange\",\n",
    "    label=\"Last Spec Decay Diff\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_spec_decay_diff,\n",
    "    color=\"r\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"M: {mean_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_spec_decay_diff + std_spec_decay_diff,\n",
    "    color=\"g\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"M + Std: {mean_spec_decay_diff + std_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_spec_decay_diff - std_spec_decay_diff,\n",
    "    color=\"g\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"M - Std: {mean_spec_decay_diff - std_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.axvline(\n",
    "    mean_last_spec_decay_diff,\n",
    "    color=\"purple\",\n",
    "    linestyle=\"dashed\",\n",
    "    linewidth=1,\n",
    "    label=f\"Last M: {mean_last_spec_decay_diff:.2f}\",\n",
    ")\n",
    "plt.legend()\n",
    "plt.title(\n",
    "    f\"Spectrum Decay Difference (pos - neg) (Mean: {mean_spec_decay_diff:.2f}, Std: {std_spec_decay_diff:.2f})\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:29.746011Z",
     "iopub.status.busy": "2025-06-02T23:40:29.745861Z",
     "iopub.status.idle": "2025-06-02T23:40:30.691809Z",
     "shell.execute_reply": "2025-06-02T23:40:30.691314Z",
     "shell.execute_reply.started": "2025-06-02T23:40:29.745997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3Ttddd52uueaas1r/vHnz1KRJk1yX9e/fX99++61jGRdffLEqVqyoV199VVu3btWll16qFi1aqFatWvmaXXfLli2SlOs91FWqVFG1atX066+/Kjk5WbGxsb7B0EUXXZRj/caNGxsvMW/UqJHtsk8++UTz5s3TTz/9pBMnTigzM9O3LGsyo3/6+0AnS8WKFbV3717Vq1cv2+vh4eGKj4+3Leufjh07Jsl8iWxUVJR+/PFH2+V16tTJ9jwlJUXbt29XpUqV9Nprr+VYP+se0V27dmV73U3fmNSsWVMxMTHZXsuaEK5OnTo5jp+swcE/69q7d6+mTZumtWvX6sCBA0pLS8u2/ODBgzkuy27WrFmOSYjCwsLUrFkz/frrr9q6dWuOQV3ZsmUl5Ryc5Ca/x/PZlNW23C7pbdq0qaKjo3P9Q02dOnWMf9jK7XO1e/duHT9+XJUqVcr1jzJZf3D457GWGzfvc27y26bdu3crNTVVrVq1ynZrhpT9mDkbAtUH0pnbI959910tXLhQO3bsUFJSUrYoPjefaQAo6BgIAwWA1+vVQw89pMWLF+uqq67S6NGjXZeVlpamY8eOKTw8/KzHbcTFxWn+/PmaMmWKPvvsM99EUOeee64GDRqkG264IU/lZN1PWKFChVyXV6pUSb/++qtOnjyp2NhY3/q5nSkNCwtTuXLlbOuyO7v65ptv6qmnnlJ8fLwuueQSnXPOOb4z3TNnzlR6enqu2+U2kMkaiNsty+sMvln/8E5NTc3T+nlx4sQJWZalAwcO2J4xlqRTp075/t9t35jk1jdZV0SYlv2973777Tdde+21Sk5OVsuWLdWxY0fFxsYqLCxM3377rb799tscgwXJ/jjLOjayJnn7u9OnT0vK/eqHf8rv8exGVtl5OUOdW9ty+xx4PB5VqFAh1zLt9iVLbuVl/SFnx44d2rFjh+22dpPhZXH7Pucmv23KOhbsvjdMV2u4lTUI/fuVJoHsA0kaO3as5syZo3PPPVedOnVSxYoVfX/omDp1ar7KAoDCgoEwEGJer1f/93//p0WLFumKK67QhAkT/IrI2LBhgzIyMlS/fv18T7YVCFWqVNGECRPk9Xq1bds230RAY8aMUZkyZXyXu5pkDQZymxVYkg4dOiRJvstzs9bPbTZnr9ero0ePqnLlyrmWlduZ6oyMDL300kuqWLGi3n///Wz/uLUsS6+//rrjPgRD1j+E83PJu5OsPqxfv36eZkEuqH0jSTNmzNDx48f19NNP54iXGjVqlO1VDXbHWdbxlNukaFnvwd8HJ3byezy70bBhQ0VGRmrz5s35OrP898/OP88eWpalw4cP51qW0xUeuS3PKqdr166aMmVKntqXG7fvc27y26asYyG37xrT6/745ptvJJ15j7MEsg8SExP11ltvqU6dOpo3b162KzMOHTpk/AMZABRm3CMMhNDfB8Hdu3fX008/7eq+4L+X98orr0hSngacwRQWFqZ69epp0KBBevbZZyWdiWj5+3JJ2S6/y5J1CXFu/5j7448/tHfvXlWvXt33j9isy5G/++67HOv/8MMP+c5MPXr0qJKSktS0adMcZ3h+/PFH39nAsy0hIUFS3i4dzavY2FjVqlVLu3bt0okTJxzXd9M3pvc6kPbs2SPpzP2pf2dZlr7//nvb7b777rscbfN6vfruu+/k8Xhyvdx99+7dioyM1L/+9S/HduX3eHYjJiZGPXr00OnTp/Xmm28a183IyPDtb1bbsgZbf7dp0yalpqbmuv9u1KpVS7Gxsdq8ebOrqway5Pd9zvpO/fvl+27bdP755ys6OlqbN2/OcWVG1jETSKdPn9b06dMlST169PC9Hsg+2Lt3ryzLUps2bXLcnrB+/Xr/dgAACjAGwkCIZF0OvWjRIl1++eV65pln/BoEHzlyRPfff7/Wrl2rCy64QNdff30AW5s3O3bsyPWsV9Zrf7+nLuuy7T/++CPH+p07d1ZcXJzee++9bJcrWpaliRMnKiMjQz179vS9ftlll6lkyZJasGCB7x+I0pl/8D///PP53o/y5curRIkS+umnn7Jdpnn8+HGNHTs23+UFSvPmzRUWFqYffvghoOX2799fKSkpeuSRR7JdAp1l7969vrxRN31jeq8DKeuM5oYNG7K9/uqrr2r79u222/3666+aP39+ttfmz5+vX3/9VZdeemmOs75paWn6+eef1aBBgxwDh9zk93h2695771V8fLxeeeUVzZo1K9c/PGzdulX9+/f3XRJ95ZVXKiIiQjNmzMh2CXRaWpomTpwoSQFpm3TmNoCseLWnnnoq14Hn9u3bHc+q5vd9zjr+/vzzT7/blBV/lZiYmOMPDv/73/8Cen/w/v379d///le//PKLWrZsmS02LZB9kDVZ4Pfff5/tmPnzzz99f8QEgKKIS6OBEHnxxRe1cOFClSxZUjVr1tTLL7+cY53OnTvnmGBJOnOPZsmSJeX1epWcnKydO3dq/fr1Sk1NVbNmzfTss8/m6R/ogfb111/rmWeeUbNmzVSzZk2VLVtWe/fu1cqVKxUdHa1+/fr51m3VqpXefPNNjRo1Sl26dFFMTIyqVKniywB+4oknNHz4cF133XXq1q2b4uPjtXr1av30009q1KiRbrvtNl9ZpUuX1v/93//p0UcfVa9evdS9e3fFxcVp1apVioyMVKVKlfI1WVdYWJj69eunN998U1dffbU6duyo5ORkrVq1SlWrVvVN1HS2lSlTRhdffLE2bNig1NTUHJP1uNW3b19t2rRJCxcu1Hfffac2bdqoUqVKSkxM1K5du7Rp0yZNmjRJ1apVc9U3pvc6kPr27av33ntPd911l7p166ayZctq48aN+vnnn3XppZfq888/z3W7tm3bauzYsfriiy9Uu3Zt7dixQ5999pnKlSunhx9+OMf6GzZsUFpaWo6zcXbyezy7dc455+jNN9/UkCFD9OSTT2rGjBlq3bq1KlSooOTkZP3www/68ccfFRsb67ttokaNGhoxYoQmTJigq666St26dVNMTIw+++wz7d69W5dddlmOS2/9cdddd+nnn3/W7Nmz9cUXX6h58+YqX768Dhw4oO3bt2vr1q2aN2+e8V7b/L7P//rXv1SpUiUtWbJEUVFRqly5sjwej/r376+4uLh8t2n48OFas2aNJk+erA0bNujCCy/Uzp079cUXX6ht27b66quv8tUnmZmZeuGFF3z/n5SUpG3btum7775TZmamLrvsMk2YMCHbd1gg+yBrpumPP/5YvXv3VqtWrZSYmKjPP/9crVq1yvbHRQAoShgIAyGSFW1x6tQp3+XM/1S1alXbgbB05mxGqVKldO655+qKK65Qt27ddMkll/h1j7E/2rVrp3379mn9+vX65JNPdOrUKVWuXFndu3fXbbfdpgsuuMC3bocOHXT//ffrf//7n6ZPn6709HS1aNHCNzjq1q2bKlasqGnTpunTTz9VSkqKqlatqjvvvFODBg3KMQi87rrrVLp0aU2bNk0LFy5UXFycOnXqpBEjRqhjx46qUaNGvvblvvvuU5kyZbRw4UK9/fbbqlChgq644goNHTpUV155pd995Vbfvn117733asWKFb4IIX95PB5NmDBB7du31//+9z99/vnnOnXqlOLj43Xeeedp5MiRat26tW/9/PaN03sdKBdeeKHeeOMNTZ48WZ988onCw8PVtGlTvfPOO1q5cqXtQLhJkya644479Pzzz2v27NkKCwtT586ddf/99+caUfPBBx8oMjJSvXv3znPb8ns8u1WvXj0tWbJE8+fP1/Lly7VixQolJSWpZMmSqlWrlu655x716dMnW4zXLbfcoho1amjGjBn64IMPlJ6erpo1a+rBBx9U//798/VHJCdRUVF67bXXtGDBAi1atEiffPKJ0tLSVKFCBdWqVUt9+/b13QJgJ7/vc3h4uKZOnaqJEydq8eLFOnnypCTpqquuUlxcXL7bVKlSJc2dO1fPPPOMvvrqK61fv17169fX9OnTtXbtWlcD4az7cCMjIxUbG6tq1aqpT58+uuKKK3KdDT/QfTB+/HhVrVpVH3/8sebMmaMqVaro5ptv1qBBg/Txxx/na38AoLDwWJZlhboRABAsv/32m7p06aJu3bpp8uTJoW6O39LT03X55ZerRo0avnsH4c4333yjAQMGaOjQoRo2bFietjl+/Lg6duyorl27avz48UFuIQAACBbuEQZQJBw/fjxHxMfp06d9g5XOnTuHolkBFxkZqeHDh2v16tUBn5gHzqZPny6v16t77rkn1E0BAAB+4NJoAEXCunXr9PDDD+uSSy7Rueeeq6NHj2rt2rXat2+fWrVqFbDLiAuC7t27a//+/b4MVJw9ZcuW1VNPPWUbxwUAAAoHBsIAioQLLrhAbdq00XfffacjR45Iks477zzdfffdGjhwYMjumw6WQEyuhPy7+eabQ90EAAAQANwjDAAAAAAoVorWKRIAAAAAABwwEAYAAAAAFCsMhAEAAAAAxQoDYQAAAAAwsDIPhroJCDAmy3Kpb7XBSkk6HepmAAAAAIVCTFwJzf391VA3wzXvwXaSlRy6BnhiFVbpy9DVX8QQn+RSStJpnUpKCXUzAAAAAJwNVrJknQx1KxAgDIQBAAAAwIFXliRvCFtgcV9rANGXAAAAAIBihYEwAAAAAKBY4dJoAAAAAHCQaXklK5SXRnsZvAUQZ4QBAAAAAMUKA2EAAAAAQLHC2XUAAAAAcHBm1mgrhC0IZd1FD2eEAQAAAADFCmeEAQAAAMCBV16FNkc4lHUXPZwRBgAAAAAUKwyEAQAAAADFCpdGAwAAAICDTMuSLCbLKio4IwwAAAAAKFYYCAMAAAAAihUujQYAAAAAB+QIFy2cEQYAAAAAFCucEQYAAAAAB15ZskJ4VtbDGeGA4owwAAAAAKBYYSAMAAAAAChWuDQaAAAAABxwaXTRwhlhAAAAAECxwkAYAAAAAFCscGk0AAAAADjItCxZFpdGFxWcEQYAAAAAFCsMhAEAAAAAxQqXRgMAAACAA68U0ouTPSGsuyjijDAAAAAAoFjhjDAAAAAAOMgkR7hI4YwwAAAAAKBYYSAMAAAAAChWuDQaAAAAABxkWlIIY4SZLCvAOCMMAAAAAChWGAgDAAAAAIoVLo0GAAAAAAfkCBctnBEGAAAAABQrDIQBAAAAAMUKl0YDAAAAgAOvPPKG8ALlMC6ODijOCAMAAAAAihXOCAMAAACAA6915oGigTPCAAAAAIBihYEwAAAAAKBY4dJoAAAAAHCQGeLJsiwmywoozggDAAAAAIoVBsIAAAAAgGKFS6MBAAAAwAGXRhctnBEGAAAAABQrDIQBAAAAAMUKl0YDAAAAgAOv5ZHXCuXlyVwaHUicEQYAAAAAFCucEQYAAAAAB0yWVbRwRhgAAAAAUKwwEAYAAAAAFCtcGg0AAAAADjIVJm8IzyNanMMMKHoTAAAAAFCsMBAGAAAAABQrXBoNAAAAAA6sEOcIe5g1OqA4IwwAAAAAKFY4IwwAAAAADjLlUWZIz8pyRjiQOCMMAAAAAChWCv1A+K233lKnTp3UsGFDXXvttfrhhx/ytN2SJUtUp04d3XnnnUFuIQAAAACgICnUA+GlS5dq/PjxGjJkiBYuXKi6detq4MCBSkxMNG73+++/66mnnlLz5s3PUksBAAAAFGaZVljIHwicQt2b06dP13XXXafevXvrggsu0OjRo1WiRAm9++67tttkZmZqxIgRGjZsmKpXr34WWwsAAAAAKAgK7UA4LS1NP/30k9q0aeN7LSwsTG3atNH3339vu92LL76o8uXL69prrz0bzQQAAAAAFDCFdiB89OhRZWZmqnz58tleL1++vA4fPpzrNuvXr9eCBQv0xBNPnI0mAgAAACgivPLIq7AQPvI3a/Tbb7+tK6+8Us2aNVOzZs3Up08fffHFF77lqampGj16tFq2bKmmTZtq2LBhOcZR+/fv1+DBg9W4cWO1bt1aTz31lDIyMrKt880336hnz55q0KCB/v3vf+u9995z38lnUaEdCOdXcnKyHnjgAT3xxBOKj48PdXMAAAAAIGjOOeccjRgxQu+9957effddtWrVSkOGDNGOHTskSePGjdNnn32myZMna/bs2Tp48KCGDh3q2z4zM1O333670tPTNXfuXE2YMEELFy7UlClTfOvs3btXt99+u1q2bKn3339fN910kx555BF9+eWXZ31/86vQ5giXK1dO4eHhOSbGSkxMVIUKFXKsv3fvXu3bt0933HGH7zWv1ytJuvDCC/XRRx+pRo0awW00AAAAAJwFnTp1yvb83nvv1TvvvKONGzfqnHPO0bvvvquJEyeqdevWks4MjLt3766NGzeqSZMm+uqrr/TLL79o+vTpqlChgurVq6e7775bEydO1NChQxUVFaW5c+eqWrVqevDBByVJtWrV0oYNGzRjxgy1a9furO9zfhTaM8JRUVGqX7++1qxZ43vN6/VqzZo1atq0aY71//Wvf+nDDz/UokWLfI9OnTqpZcuWWrRokc4555yz2XwAAAAAhUimPCF/SGeudP37Iy0tzbntmZlasmSJTp06paZNm2rz5s1KT0/PNt9SrVq1VKVKFW3cuFGStHHjRiUkJGQ7ydi2bVslJyfrl19+8a2TNZD++zpZZRRkhfaMsCTdcsstGjlypBo0aKBGjRpp5syZSklJUa9evSRJDzzwgCpXrqzhw4crOjpaCQkJ2bYvXbq0JOV4HQAAAAAKovbt2+vkyZO+50OHDtWwYcNyXXfbtm3q27evUlNTVbJkSb344ou64IILtGXLFkVGRvrGQ1nKly+vQ4cOSZIOHz6c40rbrOdO6yQnJ+v06dMqUaKEfzsbRIV6INy9e3cdOXJEU6ZM0aFDh1SvXj29/vrrvjfjjz/+UFhYoT3pDQAAAKCACH2W75m6V61ale3VqKgo2y3OP/98LVq0SElJSfr44481cuRIzZkzJ6itLCwK9UBYkm688UbdeOONuS6bPXu2cdsJEyYEo0kAAAAAEBSxsbF5XjcqKkrnnXeeJKlBgwb68ccfNWvWLHXr1k3p6ek6ceJEtrPCiYmJqlixoqQzZ3Z/+OGHbOVlzSr993X+OdP04cOHFRsbW6DPBkuF+B5hAAAAAEDeeb1epaWlqUGDBoqMjMw239KuXbu0f/9+NWnSRJLUpEkTbd++PdvkxKtXr1ZsbKwuuOAC3zpr167NVsfq1at9ZRRkhf6MMAAAAAAE25kc4fxl+QaSJ591T5o0Se3bt9e5556rkydPavHixfr222/1xhtvKC4uTr1799aECRNUpkwZxcbGauzYsWratKlvENu2bVtdcMEFeuCBB3T//ffr0KFDmjx5sm644Qbf5dh9+/bVW2+9paefflq9e/fW2rVrtWzZMk2bNi3Qux9wDIQBAAAAoIhJTEzUyJEjdfDgQcXFxalOnTp64403dMkll0iSHnroIYWFhemuu+5SWlqa2rZtq8cee8y3fXh4uF555RU9/vjj6tOnj2JiYtSzZ0/dddddvnWqV6+uadOmafz48Zo1a5bOOeccjR07tsBHJ0mSx7IsK9SNKIyuLjNAp5JSQt0MAAAAoFAoGRej94/PCnUzXFv+68XKtE46rxgk4Z5S6lxzXcjqL2o4IwwAAAAADrwKU2YIp1jyML1TQNGbAAAAAIBihYEwAAAAAKBY4dJoAAAAAHCQaYUp0+LS6KKC3gQAAAAAFCucEQYAAAAAB16FyRvC84ihrLsoojcBAAAAAMUKA2EAAAAAQLHCpdEAAAAA4CDT8ijT8oSsfo9CV3dRxBlhAAAAAECxwkAYAAAAAFCscGk0AAAAADjIVJgyQ3gekRzhwKI3AQAAAADFCmeEAQAAAMCB1wqT1yJHuKigNwEAAAAAxQoDYQAAAABAscKl0QAAAADggMmyihZ6EwAAAABQrDAQBgAAAAAUK1waDQAAAAAOvJIyLU/I6g8LXdVFEmeEAQAAAADFCgNhAAAAAECxwqXRAAAAAODAqzB5Q3geMZR1F0X0JgAAAACgWOGMMAAAAAA4yLTClGmF7jxiWAjrLoroTQAAAABAscJAGAAAAABQrHBpNAAAAAA48Mojr0IX5hvKuosizggDAAAAAIoVBsIAAAAAgGKFS6MBAAAAwAGzRhct9CYAAAAAoFhhIAwAAAAAKFa4NBoAAAAAHGQqTJkhPI8YxjnMgKI3AQAAAADFCmeEAQAAQsnjcF7C8p6ddgAw8loeea0Q5giHsO6iiDPCAAAAAIBihYEwAAAAAKBY4dJoAAAAAHDgDfFkWeGcwwwoehMAAAAAUKwwEAYAAAAAFCtcGg0AAAAADrxWmLxW6M4jhrLuooiBMAAAQCgRjwQAZx1/VgAAAAAAFCucEQYAAAAAB5nyKFOekNaPwOGMMAAAAACgWOGMMAAAAAA4YLKsooXeBAAAAAAUKwyEAQAAAADFCpdGAwCAvPEY/n5emCKATPshnf19CVZ7isr7BRQQmQrthFWZIau5aOKMMAAAAACgWGEgDAAAAAAoVrg0GgAAAAAcMGt00UJvAgAAAACKFc4IAwAAAICDTCtMmSE8KxvKuosiehMAAAAAUKxwRhgAgLwgiiY4QhFl5E+ZwTgOgnX8cFwCgC0GwgAAAADgwJJH3hDmCFshrLso4tJoAAAAAECxwkAYAAAAAFCscGk0AAAAADhg1uiihd4EAAAAABQrDIQBAAAAAMUKl0YDAJCloEUkuW2PUySRW0Uljsef/ilox4GJP8dIUXmvgQDyWh55rdDN3BzKuosizggDAAAAAIoVzggDAAAAgINMhSkzhOcRQ1l3UURvAgAAAACKFQbCAAAAAIBihUujAQAAAMABk2UVLZwRBgAAAAAUK5wRBgAgSzAiiYIVYROsiCSTUMQ5hWI/3SpocVf+xIEFY1+CFclU0GLPABQKDIQBAAAAwIFXYfKG8ILaUNZdFNGbAAAAAIBihYEwAAAAAKBY4dJoAAAAAHCQaXmUGcKZm0NZd1HEGWEAAAAAQLHCGWEAAAAAcECOcNHCQBgAUPgUtLiUUEQHuazTE2b+h5TltZxalXu54eGutnNbX8i4fT9DEQNVXCKt3H7m/YkvA1DoFbBvMgAAAAAAgoszwgAAAADgwLLC5LVCdx7RCmHdRRG9CQAAAAAoVhgIAwAAAACKFS6NBgAAAAAHmfIoUyHMEQ5h3UURZ4QBAAAAAMUKZ4QBAEWLP9FKQYgrCho/IpIKEn/inEzbut3O323PdnuMClrMkT9xRcHYF+KRgGKNgTAAAAAAOPBaktcK3R8aC1vsekFXwP5UCAAAAABAcHFGGAAAAAAceEOcIxzKuosiehMAAAAAUKwwEAYAAAAAFCtcGg0AAAAADrzyyBvCLN9Q1l0UMRAGAARPsKKMTIIVweJyW7cRP/6U64+CFr3ktj2e8HD7hQ7HXkHrA5NgHV8h4c/3RaDrC1adAAoMLo0GAAAAABQrnBEGAAAAAAeZlkeZIcwRDmXdRRFnhAEAAACgiJk2bZp69+6tpk2bqnXr1rrzzju1a9eubOv0799fderUyfYYNWpUtnX279+vwYMHq3HjxmrdurWeeuopZWRkZFvnm2++Uc+ePdWgQQP9+9//1nvvvRf0/fMXZ4QBAAAAwEFhyxH+9ttvdcMNN6hhw4bKzMzUs88+q4EDB2rJkiUqWbKkb73rrrtOd911l+95TEyM7/8zMzN1++23q0KFCpo7d64OHjyokSNHKjIyUvfdd58kae/evbr99tvVt29fTZw4UWvWrNEjjzyiihUrql27dn7udfAwEAYAAACAIuaNN97I9nzChAlq3bq1fvrpJ1188cW+10uUKKGKFSvmWsZXX32lX375RdOnT1eFChVUr1493X333Zo4caKGDh2qqKgozZ07V9WqVdODDz4oSapVq5Y2bNigGTNmFOiBMJdGAwAAAEAhkZycnO2RlpaWp+2SkpIkSWXKlMn2+ocffqiWLVvqiiuu0KRJk5SSkuJbtnHjRiUkJKhChQq+19q2bavk5GT98ssvvnVat26drcy2bdtq48aNbnbvrOGMMAAgNPyJMgpSuW6jaIxRPcb6ghTPEqy+DYZiElETtEgm43udadjM3B7j8e5yX6xM+/b8VbC7ZW4Vk2MPgeOVR94QTliVlSPcvn17nTx50vf60KFDNWzYMPO2Xq/GjRunZs2aKSEhwff6FVdcoSpVqqhSpUratm2bJk6cqN27d2vq1KmSpMOHD2cbBEvyPT906JBxneTkZJ0+fVolSpRwucfBxUAYAAAAAAqJVatWZXseFRXluM3o0aO1Y8cOvf3229le79Onj+//69Spo4oVK+rmm2/Wnj17VKNGjcA0uIAqRH8yBgAAAIDiLTY2NtvDaSA8ZswYff7555o5c6bOOecc47qNGzeWJP3222+SzpzZPXz4cLZ1sp5n3Vdst05sbGyBPRssMRAGAAAAAEeWPGcujw7Rw1L+Lsu2LEtjxozRp59+qpkzZ6p69eqO22zZskXS/x/kNmnSRNu3b1diYqJvndWrVys2NlYXXHCBb521a9dmK2f16tVq0qRJvtp7tjEQBgAAAIAiZvTo0frggw80adIklSpVSocOHdKhQ4d0+vRpSdKePXv04osvavPmzfr999+1YsUKjRw5UhdffLHq1q0r6cykVxdccIEeeOABbd26VV9++aUmT56sG264wXcmum/fvtq7d6+efvpp7dy5U2+99ZaWLVumm2++OVS7nieu7hFOSkpSXFxcoNsCAAAAAAiAd955R5LUv3//bK+PHz9evXr1UmRkpNasWaNZs2bp1KlTOvfcc9WlSxfdeeedvnXDw8P1yiuv6PHHH1efPn0UExOjnj17Zssdrl69uqZNm6bx48dr1qxZOuecczR27NgCHZ0kSR7LsuynBbTRoEEDtWvXTt27d1enTp1UqlSpYLStQLu6zACdSkpxXhEAirPCNHuxzv6s0UGbtbYw9Xso+qCQzRbs9tgzzdJc6GaNDoZCdhwUBSXjYvT+8VmhboZrt667SymZp0NWf0x4Cb158ZSQ1V/UuDojfMstt+ijjz7S/fffr+joaHXo0EHdunVTx44dC/QN0QCAIDD849XtwNK5yrMfX2Gs0/QPalP/OAygjQOHIPV7MPrWWKfD4MftQM9tmZJ/x6YrDgMyt++JP30XlM9YkAa6xuPd5THyV8H2yxhEA4Weq4Hw8OHDNXz4cP3www9atmyZPv74Y33yySeKiYlRx44d1b17d7Vv3z5PU3kDAAAAQEHntcLktUJ3xU0o6y6K/MoRbtSokRo1aqSRI0fq+++/19KlS/Xxxx9r2bJlio2N1bp16wLVTgAAAAAAAsKvgfDfNW3aVOXKlVOZMmU0ffp0JScnB6poAAAAAAACxu+B8N69e7Vs2TItW7ZMW7duVVhYmFq2bKlu3boFon0AAAAAEHJeyyOvdfbnqPh7/QgcVwPhP/74Q8uWLdPSpUv1008/yePx6KKLLtKoUaPUtWtXxcfHB7qdAAAAAAAEhKuBcMeOHeXxeNS4cWP93//9ny6//HJVqlQp0G0DAAAAACDgXA2EH3jgAXXr1k3nnntuoNsDAAgWf6JLTFEhxhgRl/m68iO+xZ/IkyDEu/gTQ+M2n9if+B/X+bHGOCdDv7vNYJbkMV0m6MdxcNbjuTyRxsV+RQDZVenU74UoHsifeC4gP7zyyKsQXhodwrqLIlcD4VtvvTXQ7QAAAAAA4Kxw/Wey/fv3++4JbtGihS8q6ciRIxo7dqx+/vnngDUSAAAAAIBAcXVG+JdfftENN9wgr9erRo0aac+ePcrIyJAkxcfHa8OGDTp16pTGjRsX0MYCAAAAQCgwa3TR4mog/MwzzyguLk7z58+XJLVp0ybb8g4dOmjZsmX+tw4AAAAAgABzNRBet26dhgwZovj4eB09ejTH8ipVqujAgQN+Nw4AAAAACgLOCBctru4RtixLJUqUsF1+5MgRRUVFuW4UAAAAAADB4uqM8IUXXqgvvvhCN9xwQ45lGRkZWrJkiRo3bux34wCgyHKK9DBFlwQjDiQEsUJ+cRuN48d+BCVSx5/jwBB/4zEl4zhFK7ntP2O0kqt/bvzVniBE4wQrSsxlncGIR3LidDxbLpvkT3SXiam9xjr9iYEKxvFViGKpgKLO1Sd88ODB+vLLL/XYY49px44dkqTExEStXr1at956q3bt2qXBgwcHtKEAAAAAECpZl0aH8oHAcfUn2g4dOmj8+PEaN26cb8Ks+++/X5ZlKTY2Vk899ZQuvvjigDYUAAAAAIBAcH2t0jXXXKMuXbpo9erV+vXXX+X1elWjRg21bdtWsbGxgWwjAAAAAAABk++BcEpKii699FINGjRIt912mzp37hyMdgEAAABAgRHqy5O5NDqw8n2PcExMjMLDwxUTExOM9gAAAAAAEFSuJsvq0qWLPv74Y1mmmRzPkrfeekudOnVSw4YNde211+qHH36wXXf+/Pnq16+fLr74Yl188cW6+eabjesDAAAAAIoeV/cI9+jRQ6NHj9aAAQN07bXXqmrVqrnmCtevX9/vBposXbpU48eP1+jRo9W4cWPNnDlTAwcO1EcffaTy5cvnWP+bb75Rjx491KxZM0VFRen111/XrbfeqiVLlqhy5cpBbSuAYsifCI0gxBWZ40fM9QUjOsixTEMfuN0XjyFy6K+NXbXHGB1k4hQ1E+FuKg/jfjr8Edvy2veBJyzwx6WpPsm8L5YnSFE0xj4y9K3HcBxkGvrVj+PSbVyR43auv4PcR0G5jkgyF+qyNX4gIqnIsiR5FbrLk0N/CrJo8VguTuvWrVv3/xeQy5e+ZVnyeDzasmWLf61zcO2116phw4YaNWqUJMnr9apDhw7q379/nuKbMjMzdfHFF2vUqFG65ppr8lX31WUG6FRSiptmAyguCtVA2PxTEJQMXSeuB8L2+1LoBsLh7o6DIjUQNtTptK1rbq94czkQdhSEgXCw+JOJHJSBcLAw2HWlZFyM3j8+K9TNcK331w/oVGZqyOovGR6tdy95OmT1FzWu/tQ8fvz4QLcj39LS0vTTTz/p9ttv970WFhamNm3a6Pvvv89TGSkpKcrIyFCZMmWC1UwAAAAARQCTZRUtrgbCPXv2DHQ78u3o0aPKzMzMcQl0+fLltWvXrjyVMXHiRFWqVElt2rQJRhMBAAAAAAWQ6xzhLImJidq3b58kqWrVqrnem1sQvfrqq1q6dKlmzZql6OjoUDcHAAAAAHCWuB4Ir1mzRs8880yO+4Dr1aunESNGBP0sa7ly5RQeHq7ExMRsrycmJqpChQrGbd944w29+uqrmj59erb7nQEAAAAgN1waXbS4mvXi008/1cCBA3Xo0CHddtttGjt2rMaOHet7bdCgQfr0008D3dZsoqKiVL9+fa1Zs8b3mtfr1Zo1a9S0aVPb7V577TW99NJLev3119WwYcOgthEAAAAAUPC4OiM8efJk1a5dW2+99ZZiY2OzLfvvf/+r66+/XpMnT9a///3vgDTSzi233KKRI0eqQYMGatSokWbOnKmUlBT16tVLkvTAAw+ocuXKGj58uKQzl0NPmTJFkyZNUtWqVXXo0CFJUsmSJVWqVKmgthVAMcSson7NnG2cqdo0o7TDxNBGprgi00zCptmCDTyRDo01lWuawdk0c6/DzNnGGadNszSb+sfQrx6nGZpNs1hHRtpvZ+oDp/fL0CbTrMjGGa7dzkQtyRTZ5PG4LNfh82ecpdmwbUhmmC9MnBIB+N0AzhpXA+G9e/dq+PDhOQbBkhQbG6v//Oc/evbZZ/1unJPu3bvryJEjmjJlig4dOqR69erp9ddf910a/ccffyjsbz9Kc+fOVXp6uu66665s5QwdOlTDhg0LensBAAAAFE5cGl20uBoI/+tf/9KRI0dslycmJqpmzZpu25QvN954o2688cZcl82ePTvb85UrV56NJgEAAAAACjBX9wjff//9mjt3rpYvX55j2aeffqp58+Zp5MiRfjcOAAAAAAqCrDPCoXwgcFydEZ49e7bKlSunYcOGqVKlSqpRo4Ykac+ePTp48KBq1qypWbNmadasWb5tPB6PXn755cC0GgAAAAAAl1wNhLdv3y5JOvfccyXJlyMcHh6uc889V6mpqb51snhcTiQCAAAAAEAguRoIc68tAAAAgOLEsjyyQnh5cijrLopcDYQBoEjxJ87CaVvbzdz/mJliTdyW67hdUPbTj5wjY7RScP6hYIwVchtz5NRWt8eJoVxPpMNPvyk2x9AeKz3DVZ2mOCK/mKKwnBiijkxXuBmjlQzHj1994PY98SfmyG0sWmGLDgpGewraPgLFmLt/2QAAAAAAUEhxRhgAAAAAHHjlkVchzBEOYd1FEWeEAQAAAADFCgNhAAAAAECxwqXRAAAAAODAa3nkDeHMzaGsuyjijDAAAAAAoFhxdUbYsizNmzdPCxYs0N69e3XixIkc63g8Hv38889+NxAAsnEb22FSyOIsTLEnrqOVHGJNXEetmMr1K77FZUxUZGSQyjX8nLqMI3JkigdKS/ejXENMlClWKDrKVXWOPWDaz0zDZ9f0OXGMK3J5HJgitjLso4z8ivwy9IGxPQ7fe8bvGVP/BeM72g9uvy//2th+WSH73UBgkCNctLgaCD/99NOaMWOG6tWrp6uuukplypQJdLsAAAAAAAgKVwPhRYsWqUuXLnr++ecD3R4AAAAAAILK1UD49OnTatOmTaDbAgAAAAAFEpNlFS2uJstq3bq1fvzxx0C3BQAAAACAoHM1EH7ssce0adMmvfLKKzp69Gig2wQAAAAAQNDk6dLopk2byvOPGQ0zMzP1/PPP6/nnn1d0dLTCwrKPqT0ejzZs2BC4lgIAAABAiDBrdNGSp4Fw165dcwyEASAkTJEVDhFAQanTdZHm2A63cUVneztJkimixVipuU5j9EuY4b02lWuI/zlTrv22xvaYYplMdTr1nSmmxtQHMSXsl3kdjmdTuaZ9MS0zlenUHlPskCnqydS3DvFJpvfa+Nk19YHhuPSY+keSZeoj02fX1Fan78tgfNc6bGeKZXIbg+QYkWRskMvvfmKXgEIhTwPhCRMmBLsdAAAAAACcFa7+pDd16lRt377ddvmOHTs0depU140CAAAAgILE+mvW6FA9uDQ6sFwPhLdt22a7fMeOHXrxxRddNwoAAAAAgGBxlSPs5NixY4o03S8FAAAAAIWIJeepJoJdPwInzwPhdevW6ZtvvvE9//TTT/Xbb7/lWC8pKUlLly5VQkJCYFoIAAAAAEAA5Xkg/M033/ju+/V4PPrkk0/0ySef5LruBRdcoEcffTQwLQQAAAAAIIDyPBC+7bbbdMMNN8iyLLVp00ajR49Wly5dsq3j8XgUExOj6OjogDcUQBHiT2xHMLZzao/LqBBj3E6QuI5BctpHU7ku4/U8TrfQmCJjwu3b6zHti2G7M8tNEUmGn0xTRIup75z6INPQB6boIH+4ve7P1FZTv6enm8t1G4dlWOZx+neKMbbK0J50l3FXTkyfBePnz+VxKUmmhKkgRQC5jUg622X+VbD7bVFoeeWRV6GbsCqUdRdFeR4IlyhRQiVKnMklXLFiheLj4xUTExO0hgEAAAAAEAyuJsuqWrVqoNsBAAAAAMBZkaeBcKdOnRQWFqZly5YpMjJSnTp1ksfhUjiPx6Ply5cHpJEAAAAAEEpWiLN8yREOrDwNhFu0aCGPx6Owv+5vyXoOAAAAAEBhk6eB8IQJE4zPAQAAAKAo81oeeUN4VjaUdRdFTHkHAAAAAChWXE2W1bZtWzVv3tz3qFu3bqDbBaCw8ydawu22hkiPUEQZGTnEjxjb67Z/3EYrScaIFrd964lyik8yRJuYooNMMTWmCCTJdRSUSkTZLrJMdUY4vJcZpvgkw7au43bkOj7Jk+kuysjxPckw5PiYlnkNy9IzzHVG2LfJdGuYX3E8wWCKrcpw6AOTIHxHO1fprt+DFpEUpAgpAGePq4HwZZddpu+++04ff/yxJCk2NlZNmzb1DYwbNmyoSKdsRAAAAAAoJCzLfdR6oOpH4LgaCI8ePVqSdPz4ca1fv17r16/Xd999pylTpigzM1NRUVFq1KiRZs+eHdDGAgAAAADgL1cD4SxlypTRZZddpssuu0x//PGHVq1apenTp+vXX3/V+vXrA9VGAAAAAAACxvVAeOfOnb6zwRs2bNAff/yhuLg4NWnSRL169VLz5s0D2U4AAAAACBlyhIsWVwPhVq1a6fjx4ypfvrwuuugi3XLLLb5Js8gXBgAAAAAUZK6m/Tt27Jg8Ho/OP/981apVS7Vq1dJ5553HIBgAAAAAUOC5OiO8Zs0abdiwQRs2bNCXX36pV199VZJUt25d38zRzZo1U3x8fEAbCwBG/kQ2BaFcU9yH5Eeck6Fc1zFRTtuZYljcckoXMP1x1bTMFMvk9AdbQ5SPZYorMkXqRNr3rWnZmRUMU4Sa0lvCDfvpNO2oYV/CUtLtmxNhOC79iOrxmD4rxv4xxCc5RXeZ4pUM8Vwe0/GTZt93TjyGz4qV7rJcp+81Uxydyygjf+osVIhdKrK4NLpocTUQLleunDp37qzOnTtLklJSUrRx40atX79ey5Yt08yZM+XxePTzzz8HtLEAAAAAAPjLr1mjJflmiM56/P7775LE2WAAAAAARYbX8sgbwrOyoay7KHI1EJ4zZ47WrVunDRs2KDExUZZlqVq1amrevLluv/12NW/eXOeff36g2woAAAAAgN9cDYTHjRun2rVrq2vXrrrooovUvHlzVapUKdBtAwAAAAAg4FwNhL/55hvFxcUFui0AAAAAUCBZlvN8g8GuH4HjahpQBsEAAAAAgMLK78myABQQxSSuwXVsh7nQs1+nE0ObTBFJltcQeWKKK3KIFfKY+ijCEG9jiJoxRRWdKdew3PBncSsmyn47h/30lrTf1pNuH8eTWcp+O8sUPeXw530r0rCt4WPt8ee4NLTJa2iPJ8N+u7B00344fD+lGaKMPPbHtCfTj+8903GbluauTNOxl2mIenJqj1tOvwumSDWn9rqtszAJVlwfgLOGgTAAAAAAODhzaXQoc4RDVnWRxJ+zAAAAAADFCgNhAAAAAECxwqXRAAAAAODAsjwhvjQ6dHUXRa7PCO/fv1+jRo1S165d1aJFC61bt06SdOTIEY0dO1Y///xzwBoJAAAAAECguBoI//LLL+rZs6eWLVumatWqKSkpSRkZZ2Z1jI+P14YNGzRnzpyANhQAAAAAQsUqAA8EjqtLo5955hnFxcVp/vz5kqQ2bdpkW96hQwctW7bM/9YByLtgxVK4jYgwtcePuCLjdqa4D1N7HPvOvlxjnS7340zBhm0Ny9xGJBn3Q5KiDZFEpmgXUwRStKGtkqwIQ7mG9nqj7Os0likpI9a+TVa4u/fTirDfLjPSoUzD4vA0+38SpcfY72dkivl496QbIrgM/woLM2xnZj72wkzHbWq6oVj3sVVGps9YuqE9pu+DMId/jqXbR0h5DJ8/yxRz5PTdbtjWr3JNDNua6nQdcefP72YxiSwEijJX31br1q3T9ddfr/j4eHly+YGqUqWKDhw44HfjAAAAAAAINFdnhC3LUokSJWyXHzlyRFFRhrMHAAAAAFCIMFlW0eLqjPCFF16oL774ItdlGRkZWrJkiRo3buxXwwAAAAAACAZXA+HBgwfryy+/1GOPPaYdO3ZIkhITE7V69Wrdeuut2rVrlwYPHhzQhgIAAAAAEAiuLo3u0KGDxo8fr3HjxvkmzLr//vtlWZZiY2P11FNP6eKLLw5oQwEAAAAgZEI9dTPTRgeUq4GwJF1zzTXq0qWLVq9erV9//VVer1c1atRQ27ZtFRsbG8g2AgAAAAAQMK4HwpJUsmRJde7cOVBtAYoHp2gJt7ELhnKN0RKmGAx/uGyPZI67cB2R5Ed7XMdkeOy/Yk2RJ5KM8UAmnih38UmO9Znaa6jTMsQuWTHu45MsQ3vSyhnqNEQZnSnXEHUUZb/Ma4hB8hq6NjPa3J6wDMNnwXDomaKV0mLNx15Yun2bTO0JM8RLmf6xEXba4TvIcNxaDhFctkWmpJlXMEUvGaKM3EaUOX4Pu417MkUHOX3veQ19YMrRMvHj9891RFKQ2kNEElD4ubpHePXq1Xr22Wdtlz/33HNas2aN60YBAAAAQIHy16zRoXqIWaMDytVA+KWXXtIff/xhu/zAgQN6+eWXXTcKAAAAAIBgcTUQ3r59uzEeqWHDhtq2bZvrRgEAAABAQWJZoX/kx7Rp09S7d281bdpUrVu31p133qldu3ZlWyc1NVWjR49Wy5Yt1bRpUw0bNkyHDx/Ots7+/fs1ePBgNW7cWK1bt9ZTTz2ljIzst4l888036tmzpxo0aKB///vfeu+991z18dnkaiCclpam9PR04/LTp0+7bhQAAAAAwL1vv/1WN9xwg+bPn6/p06crIyNDAwcO1KlTp3zrjBs3Tp999pkmT56s2bNn6+DBgxo6dKhveWZmpm6//Xalp6dr7ty5mjBhghYuXKgpU6b41tm7d69uv/12tWzZUu+//75uuukmPfLII/ryyy/P6v7ml6uBcO3atfXpp5/musyyLH3yySeqVauWXw0DAAAAALjzxhtvqFevXqpdu7bq1q2rCRMmaP/+/frpp58kSUlJSXr33Xf14IMPqnXr1mrQoIHGjRun77//Xhs3bpQkffXVV/rll1/0zDPPqF69eurQoYPuvvtuvfXWW0pLOzPx4Ny5c1WtWjU9+OCDqlWrlm688UZ17dpVM2bMCNGe542rgfCNN96o7777TnfddZe2bdumjIwMZWRkaOvWrbr77ru1ceNG9e/fP9BtBQAAAICQCOVEWb4JsyQlJydne2QNSJ0kJSVJksqUKSNJ2rx5s9LT09WmTRvfOrVq1VKVKlV8A+GNGzcqISFBFSpU8K3Ttm1bJScn65dffvGt07p162x1tW3b1ldGQeUqPunqq6/W3r179dJLL+nTTz9V2F8xFl6vVx6PR3fccYd69uwZ0IYCRUawIhcM5VrGGAxXfw/7a9PgzF5ojEgybmiISHJbpmSOGTFFJPlTpymGxRSRZBJTwlCf+TiwYqLtF5pijiLt+yCzlH3MkSRlRhu2jbGvM72k/TKvQ3yS19C1GSUM8S2Gt9pUphzu97IMx0GYIXHHFIHkcUjqiTht36jIk+6+vzJk30ERTtE3hs9f2Mm8/eMv54buv/eUZn9rmIkxIinT/e+CsVxTDJQfdbqOMvLj9891RBJQBLVv314nT570PR86dKiGDRtm3Mbr9WrcuHFq1qyZEhISJEmHDx9WZGSkSpcunW3d8uXL69ChQ751/j4IluR77rROcnKyTp8+rRIlDP/+CCHXOcJDhw7VVVddpU8//VR79+6VJNWoUUOdO3dWjRo1AtZAAAAAAMAZq1atyvY8Ksr8h2VJGj16tHbs2KG33347WM0qdFwPhKUzA9+BAwcGqi0AAAAAUDCFOsv3r7pjY2PztdmYMWP0+eefa86cOTrnnHN8r1eoUEHp6ek6ceJEtrPCiYmJqlixom+dH374IVt5WbNK/32df840ffjwYcXGxhbYs8GSy3uE/+7kyZP6448/tH///hwPAAAAAMDZZ1mWxowZo08//VQzZ85U9erVsy1v0KCBIiMjtWbNGt9ru3bt0v79+9WkSRNJUpMmTbR9+3YlJib61lm9erViY2N1wQUX+NZZu3ZttrJXr17tK6OgcnVGODU1VVOnTtWCBQt07Ngx2/W2bNnitl0AAAAAUGC4yfINdP35MXr0aC1evFgvvfSSSpUq5bunNy4uTiVKlFBcXJx69+6tCRMmqEyZMoqNjdXYsWPVtGlT3yC2bdu2uuCCC/TAAw/o/vvv16FDhzR58mTdcMMNvkuy+/btq7feektPP/20evfurbVr12rZsmWaNm1aIHc/4FwNhB9//HEtWrRInTt31kUXXeSbeQwAAAAAEHrvvPOOJOVI8xk/frx69eolSXrooYcUFhamu+66S2lpaWrbtq0ee+wx37rh4eF65ZVX9Pjjj6tPnz6KiYlRz549ddddd/nWqV69uqZNm6bx48dr1qxZOuecczR27Fi1a9fuLOylex7Lyv/fNZo3b67u3btrzJgxwWhToXB1mQE6lZQS6mYAeePHzNDmYoN0n0wQ2hu8WaMNs/P6U6dhW2aNLk6zRtsvM88abb8sWLNGh2XYbxeWZr8s4lSGsT3Gcg2zRnsy7Mv1pJrrlNcwu3HKaVfbWemGN8VpBmfDzNDGWaMN30/+zBptTikoQrNGByvloRgrGRej94/PCnUzXGv0v4lKznA5W30AxEZE6YdrR4Ss/qLG1Rlhj8ejCy+8MNBtASAFbdDqVtAGu2ebH/vhekBrGsxGOnz9mv4Ba2qPaZAcYV+nVcI8uPaaBsLh9m3NKGlfbmZJc79mRNuXmx5r/znJiLHfzuvQ7ZmG3cyIMWxoeLuc6jQJM4zXPKZlhn+/R5xyqNRw7FmGOJ6oJMOANd00ajd/Nj1p9jtq+kOLxzSYNS2TJMMg2hi9ZIhW8hi+201/SJFkbq/pu8KPaziNA2zjhoVo8FiY2oqCwZLjHzCDXj8CxtW/uC+77DKtXr060G0BAAAAACDoXA2E77zzTv3+++969NFHtXnzZh05ckTHjh3L8QAAAAAAoKBxdcFWly5dJEk///yzFixYYLses0YDAAAAKAosyyMrhDnCoay7KHI1EB4yZIg8pntSAAAAAAAooFwNhIcNGxbodgAAAAAAcFYEZHrapKQkZbqdXRAAAAAACgMrhA8ElOtQhx9//FGTJ0/W+vXrlZ6erjfeeEOtW7fWkSNH9PDDD+vmm29Wy5YtA9lWoPAoYBFIJgUxC9iv/F3bQv3YT5fbeqINOblOsSaGqCOVMGT8GKJdrGhTfJI509cbY79tRoz9+5VWxn67TEM8kiSll7RfnmlorikCKb20sUqlx9m/L1a4y2VR9ss8qebPSVi6fR9EnLRfZopdMuYaS4o6YWiPoVzT+xmeZoi0inToA0NOtSfDPv7GY4pscmLK2DV9dg3fp67jiJz4EZFk4va3wfIa3k+HuCJTVrCpPa6zi51+p4hXAoo0V/9S/e6779SvXz/99ttvuuqqq+T9W75dfHy8kpOTNW/evIA1EgAAAABCKWuyrFA+EDiuBsLPPfecatWqpaVLl+ree+/Nsbxly5batGmT340DAAAAACDQXA2Ef/zxR/Xq1UtRUVG5zh5duXJlHT582O/GAQAAAAAQaK7uEY6IiMh2OfQ/HThwQCVLlnTdKAAAAAAoUEI9aRUTZgWUqzPCjRs31scff5zrslOnTum9997TxRdf7FfDAAAAAAAIBlcD4bvuukubN2/W4MGDtWrVKknStm3b9L///U+9evXSkSNHdOeddwa0oQAAAAAABIKrS6MbN26sV199VY8//rhGjhwpSZowYYIkqUaNGnr11VdVt27dwLUSkMwxB6GIOChgEUmuoyX82Q9DvwctlslUriHmyBjJZIgckiSPKcoowlCuKdYk0iHDxhS9ZNgXK8Z+O2+MfZ0Zseb2WIZ+zyhp3x5TBFJaafMxkmG4wyYjxn6ZKQLJW9aQ/+MgLNp+2+gS9svS0wzHT6y5zsxUQzRVCftlkUftlzmFk5miqcJPG96zYH0PG469sDT7frfCDd8HTpFo4YbvhAxDDJLpuyLdlD3lEK1k+i4x7YvLqKczbTIvDgbXv2NuEY+EfPP89Qhl/QiUfA+ELcvSyZMn1axZM3388cfasmWLfv31V1mWperVq6tBgwbOPzAAAAAAAIRIvgfC6enpatGihe69914NGjRI9erVU7169YLRNgAAAAAAAi7fA+GoqChVqFBBUVGGa6cAAAAAoChh1ugixdXNgT179tT777+vtLS0QLcHAAAAAICgcjVZVp06dbRixQpdccUV6tmzp6pWraoSJUrkWK9Lly5+NxAAAAAAQo4zwkWKq4Hwfffd5/v/559/Ptd1PB6PtmzZ4q5VAAAAAAAEiauB8KxZswLdDsCZ25iDgha75FRnEGKZjFFGIWiPP2WaYpAsr2FfTBFJpqgUSZYpJsoURmOaSyHKIa7IEINkRdl/dXtL2JebWcJ+u/RS5lCd9Fh3EUmpZezLzMx5IVH2beMNMUjl7W/NCY+yf7/KxqUY6wwPs9823OPyT/GGGKiklGjjpinp9semFWHfnsyS9svC0h2SHUwJZYYu8EYaom+M30Hm5pgigKwI+/4JS013KNglUyRRhvt4LiNTNJzhu83y4xY213FFBS2SqKD9GwBAgeFqINyiRYtAtwMAAAAACi7Lc+YRyvoRMK4GwlnS0tL0008/KTExUc2aNVN8fHyg2gUAAAAAQFC4vj5x1qxZatu2rfr166dhw4Zp27ZtkqQjR46oZcuWWrBgQcAaCQAAAABAoLgaCL/77rsaN26c2rVrpyeffFLW3+6XiY+PV6tWrbR06dKANRIAAAAAQsmyQv9A4LgaCE+fPl2XXXaZJk2apI4dO+ZYXr9+fe3YscPvxgEAAAAAEGiuBsK//fab2rdvb7u8bNmyOnbsmNs2AQAAAAAQNK4myypdurSOHj1qu/yXX35RxYoVXTcKyLdgxSO4Lde0XTDiiJz4E1dkiD2xvC730xSlIhmjQoybmeKKDHWaIpnObGvYlxKG+BtTuQ51emPsy7Ui7duTUco+PimttH2dmdHmPk+Jt19uikFKj7NfdrqqOd4munSq7bKIiEzbZefF2/8+xUaY42RiI0/bLosJt29vpMe+PX+ets+QOhAZa2zPEcN+Jifbd7x12v4YMcUcOfEaUr8iT9pfs+fJNEQgOXw9eQwxPp50+/6RKf7H6fpC07aZhjpN312mOh3aY5nqNLU1WL+NLn//jPshh6g/t4hIQiBZco58C3b9CBhX/zpu37695s+frxMnTuRYtmPHDv3vf/9Tp06d/G4cAAAAAACB5uqM8D333KPrrrtOV1xxhTp27CiPx6NFixbp3Xff1SeffKKKFSvqzjvvDHRbAQAAACA0yBEuUlydEa5cubLee+89tWvXTsuWLZNlWXr//ff12WefqUePHpo/fz6ZwgAAAACAAilPZ4RXrFihBg0aqHLlyr7XypcvryeffFJPPvmkjhw5Iq/Xq/j4eIWZ7qUDAAAAACDE8jRqHTp0qL799lvf88suu0wrVqzwPY+Pj1eFChUYBAMAAAAokjxW6B8InDyNXEuVKpVtYqx9+/bp1KlTQWsUAAAAAADBkqdLoxs1aqRXXnlFiYmJios7k4XxxRdf6PDhw7bbeDwe3XzzzQFpJODIbZSRP1yW6xQPYZmiQoxRRobtDEk9jtFBhr41butHDIbHdHVJlCG/xdQHEYavu0hDmZI5EsXQB1ZJQwRStLlOK8q+3PRY+33JKGXazr5fU8uY36/0UoZlpQ3LzrWPK4ouZY4yKhljv7x2Ofvfn7pxf9ouOzfqmLHOdMu+/+LC7KOVDmTYd0JKpn2sV0aM+XvkyKmStstMh6U3zPBZcDijYJqLxZNhvyzMtCzN/nskPNUcb2OMSHIZtWaMQJLMcUaGOq1UwzHttd9Py7DMqU5jnkooooNMvxl+/P4BwN8lJyfr5MmTKlWqlGJjzVGEdvI0EH7sscc0cuRIvfTSS5LODHIXL16sxYsX227DQBgAAABAkUGOcEjt3btXr732mj7//HMdPHjQ93rlypV16aWXatCgQapWrVqey8vTQPi8887T3LlzlZqaqsTERHXq1EkPPfSQLrvssvzvAQAAAAAAebRy5UoNHz5cp0+fVo0aNdS5c2eVKlVKJ0+e1Pbt2zV//nx9+OGHevbZZ3XppZfmqcw8DYRnzZqldu3a6fzzz1eVKlU0dOhQtWrVSlWrVvVnfwAAAACgcCBHOCT27Nmj++67T9WrV9fYsWPVuHHjHOts2rRJDz/8sO655x59+OGHql69umO5ebrJcfz48dq8ebPv+Ysvvqht27blo/kAAAAAAOTP66+/rujoaM2YMSPXQbAkNW7cWDNnzlR0dLRee+21PJWbp4Fw6dKllZiY6HtumSaRAAAAAAAgAFavXq2rr75a5cuXN65Xvnx5XX311Vq9enWeys3TpdEtW7bUCy+8oC1btvhmjV60aJE2bdpk3O6RRx7JUyMAAAAAoEBjsqyQOHjwoGrXrp2ndWvXrq25c+fmad08zxo9btw4ff3110pMTJTH49HXX3+tr7/+2nYbj8fDQBj54xRHFIqIJAO3UUb+lGvaT2NEkqmtDjEiwYhI8kTZx8mcWcFluZGGrzTTfkQ4REgZ2mtF29dpRRqilcLN++iNsn+vTRFJaXH226WXtK8ztayxOUora39MZ5axz82JKGG/rHr5o8Y6G5T9w3ZZ6Qj7KKNGJffYLisbdspYp8n+jHK2y6pEHrNdlhxVwnbZSUO0kiTFlUi1XXbimCFaKdOUrWSsUpGGLopItT8OwjLslxmPd6erzEzfB6bvrzRDlFGmQyeY4owyHKKXXDBGxsnhe9r0/Z7ux7+aXUYvhSQCKRQxUQDOmqioKKWnp+dp3fT0dEU6xWL+JU8D4fLly2vSpEm+53Xr1tUzzzyjK6+8Mk+VAAAAAACQX3Xq1NH333+vfv36Oa67YcMGJSQk5KlcV6fRxo8fr6ZNm7rZFAAAAAAKH6sAPIqhHj166KOPPtKePfZXfElncoY/+eQTdevWLU/luhoI9+zZM19hxQAAAAAA5Ne1116r3r176/PPPzeu9/nnn6tnz57q06dPnsrN06XR/fv3V1hYmN544w1FRERowIABjtt4PB7NnDkzT40AAAAAAOCfIiMj9fjjj2vr1q169dVXtW/fPklS1apV1b59e9WtW1fSmTFrfuRpICxJ3r9NHJGX+CQilgAAAAAUGaG+PLmYDq+8Xq8efvhhLVy4UB6PR2F/TTDo9Xr17LPP6pprrtGTTz6pcNPEqLnI00B49uzZxucAAAAAAATaK6+8okWLFqlPnz666aabVLNmTVmWpd9++00zZszQ/PnzVb16dQ0ZMiRf5eb5jDCQZ6YoI38iDtxGJAUpdskUEeE2AskfxjpN2zn99SwY5bqMR3Is1xTLZIpIcuqDcPv3zIq2n6I/M8b+K9a0TJLSDRFJ6aUM0UrR9n2bXsq+voxY85+ZM0vbxyCVirfP2zm3zAnbZU3j9xnrbFhyr+2ykmH2sUIXR++3XXYg0z7KSJLKGsqN8thH2OxMq2S7LNMwHUeG13zsHU4yvGmGiKSIk/Z1xhw2VinDbirilLv4pKijhigjh+8YT6p9ZIYnw/D9broyzRSt5MAy/aZk2H9OTN97TjF2CkYkUWGLHHLb3mD9mwTFk+U58whl/cXQwoULdfnll2v06NHZXv/Xv/6lMWPGKCkpSYsWLQrOQHj/fvt/VJhUqVLF1XYAAAAAAPz555+65ZZbbJe3aNFCy5cvz3e5eRoId+rUSR4XZ3C2bNmS720AAAAAAJCkihUr6ocffrDNEf7hhx9UsWLFfJebp4HwuHHjsg2EvV6vZs2apf379+vKK6/U+eefL0natWuXFi9erKpVq+Z71i4AAAAAKKg81plHKOsvjq666iq98sorKl++vG699VaVL19eknT48GG9+eabWrhwoW6//fZ8l5ungXCvXr2yPX/55ZeVmpqqTz75ROXKlcu2bNiwYbr++ut1+LDDTUgAAAAAABgMGTJEu3bt0ptvvqk33nhDpUuXliSdOHFCHo9H//73v/N9f7DkcrKsuXPn6uabb84xCJak+Ph4XXfddZo9e7arkTkAAAAAANKZHOEpU6bo22+/1eeff+6bv6pKlSq69NJL1aJFC1fluhoIHzt2TCkpKbbLU1JSdOzYMVcNyq+33npLb7zxhg4dOqS6devq0UcfVaNGjWzXX7ZsmZ5//nnt27dPNWvW1IgRI9ShQ4ez0lYAAAAAhRQ5wiHVokUL14Pe3LgaCDdu3FgzZ85U+/bt1aBBg2zLfvzxR82ePds4GA2UpUuXavz48Ro9erSvTQMHDtRHH33ku3b877777jsNHz5c9913nzp27KgPP/xQQ4YM0XvvvaeEhISgt7dICUYEkFOMgds6Ddu5jRzySygipEzbOUWX5DOc/P9vaCjXqc4Iw1eTaZmJYT+sGEPsktO24fb7klnCvq2meKQzy+3fs7RY+zpTy9qXmVbGfllGafNxGV3WPlaoUlyy7bJqpY7bbxdpH60kSfHh9uXWiky0XVYjIs52WZTHvkxJOm2I3DnmLWm7bHeq/SQdGZb9e73vZGlje1JP2h+bniT74yvc/m/Vjukb4WmGiKRM+2Xhp+2PIY/XflnYSft4JEmSoU6lG+KKMk3ReQ6dYGivsVwDx4gkt4IRK+RQrik+MGiCEaEIoFhz9S/KUaNGqX///rr22mvVuHFj1axZU5L066+/atOmTSpTpoweffTRQLYzV9OnT9d1112n3r17S5JGjx6tzz//XO+++64GDx6cY/1Zs2apXbt2uu222yRJ99xzj1avXq05c+ZozJgxQW8vAAAAACDvLMvS/Pnz9b///U979+7V8eO5/5F969at+SrX1UD4ggsu0IcffqhXX31Vq1at0tKlSyWduU57wIABuu2221xNYZ0faWlp+umnn7LdhxwWFqY2bdro+++/z3WbjRs36uabb872Wtu2bV3lTgEAAAAAguu5557Tq6++qgsvvFDXXHON4uLsr/zKD5fXGEoVKlTQQw89pIceeiggDcmvo0ePKjMzM8cl0OXLl9euXbty3ebw4cOqUKFCjvWZ4RoAAAAACp4FCxaoS5cumjJlSkDLdT0QBgAAAIDiwqMQ5wiHruqQSklJUZs2bQJebhBmPDo7ypUrp/DwcCUmZp8wJTExMcdZ3ywVKlTIcfbXtD4AAAAAIHQuvvjifN//mxeFdiAcFRWl+vXra82aNb7XvF6v1qxZo6ZNm+a6TZMmTbR27dpsr61evVpNmjQJZlMBAAAAAC48+uij+vrrrzVnzhylpzskDeRDob40+pZbbtHIkSPVoEEDNWrUSDNnzlRKSop69eolSXrggQdUuXJlDR8+XJI0YMAA9e/fX2+++aY6dOigpUuXavPmzcwY7Uaw4hqCwBSR5BQBYYwOMvWBH/vpur2mthq2c4xHMsYgGfYz3BBb5VSnKSLJ1J5oQwxSpKFMh/Z4DTFI3ij7bb0RhvfS4RDJiLHfNs0wR4Rl2M2MWMMxG2OIoZEUV/K07bLzS9tHGdUr9aftsvgIc5RR2bBTtsvSDR24LtX+R7KsQ78nemNslx3KsO/4VK99x285Xtl22f5DZc0NMkQkhaXaHyPh9m+XIuy7VZIUecoQkZRifwxFJNv3u8cUgWSKKpLkSTP8o8cUSWSIwlKGOcrICkbkjqk9oYgjCkWskD8xicQgoSCwPM4ZdMGuvxi6+eablZKSorFjx2r8+PGqWLGiwnL5N+jKlSvzVW6hHgh3795dR44c0ZQpU3To0CHVq1dPr7/+uu9S5z/++CNbJzVr1kwTJ07U5MmT9eyzz6pmzZp68cUXyRAGAAAAgAKoZcuWskx/THSpUA+EJenGG2/UjTfemOuy2bNn53itW7du6tatW7CbBQAAAADw07hx44JSruuBcHJysmbMmKHPP/9c+/fvl3QmR/jSSy/VzTffrNjY2IA1EgAAAABCyvrrEcr6ETCuBsIHDhzQDTfcoN9//13/+te/1KxZM0nS7t27NXXqVL3//vt66623VKlSpYA2FgAAAABQfCxcuDBP6/Xs2TNf5boaCE+cOFGHDx/WtGnT1KFDh2zLvvjiC91zzz2aNGmSnnrqKTfFAwAAAEDBwhnhkHjooYdkWZY8uUyY+vd7h8/KQPjLL7/UTTfdlGMQLEkdOnRQ//79NX/+fDdFAwAAAAAgSVqwYEGO1zIzM/Xnn39q3rx5Onr0qB555JF8l+tqIJySkqLy5cvbLq9QoYJSUlLcFI3Cwm3EgR+RQ/7EILkp86+CTRu7qtMfjrFDLlim+BFJHrdRRqb3JNKPvjPEMplikCxD1JNl2g9JlqFOb6R9nRml7JdlGpKenJZbkfbL0uPs+92KsX+vo0uZc/lKR9vn8aQZooMyZd+3YQ5/2k6Xff9Feuw/m2mGcjemVjXWuT+9nO2yw+n2c1/sPlnBdtmOQxVtl4WFmyNhwpLs+yAqyX4707LoJHOd4aft+y/itCmuyH6RJ80+nssYrSRJmYb2phtiv/zJmjTUabkt1/Sd6EeskPE73LRdKCKbnH43iUgCkIv69evn+nqjRo3UpUsX3XLLLfr44499t+vmlat/jdaqVUtLlixRWlpajmXp6elasmSJatWq5aZoAAAAAChwPFboH8ipc+fO+uCDD/K9naszwoMGDdK9996ra6+9Vv369VPNmjUlnZksa+7cudq2bZuee+45N0UDAAAAAJAn+/bt0+nT9leu2XE1EO7WrZtSUlI0adIkPfbYY74bly3LUvny5TVu3DhdfvnlbooGAAAAAECS9O233+b6+okTJ7R+/XrNnj1bHTt2zHe5rnOEe/XqpauuukqbN2/OliPcoEEDRZjuKQQAAACAwoZZo0Pipptusp01Ojw8XF27dj17k2X5No6IUJMmTdSkSRN/igEAAAAAIIdZs2bleM3j8ah06dKqUqWKYmPtJ7M0ydNAeN26dZKkiy++ONtzJ1nrAwAAAACQX8EaU+ZpINy/f395PB5t2rRJUVFRvud2sk5db9myJWANRQFTwGKFXAtSlEMwop4kSaa4J1MEULj9Mk+UQ46PZd9eY5yTqa2GKCNJUqThq8lUpylayRBzZJUwfxVahv7LLGGKErPvu/SS5igsU3xSRkn7ZYYkI3ki7NsTHmY+1i3Lvg9qxiTal2u4juuU13zsJWba/4X3WKZ9J2QaAhFM8UiS9NPJKrbLth6rZLvs8An7tqYl2e9nxAFzH0SeNCw7YVh2yr7fI0+a3+uIU/ZxPOEp9nFFYadyJklk8Zw2RA6lphrbY4xIcoh/s2NlGMr0h+Ezb/xO9PrxW+QyztApPjAo8UrEI6Gw49LoAmnFihV68skntXLlynxtl6eBcNbp6Ki//sGc2+lpAAAAAAACKTExUS+++KJ++uknnTp1KsfypKQk/fnnn7ryyislSddff7369evnWG6eBsItWrQwPgcAAACAoizUWb7FNUf44Ycf1qpVq1SnTh2VK5fzyi6v98zVJlnLYmJi8lSuq8myBgwYoDvuuEOtW7fOdfnatWv10ksvceYYAAAAAODa+vXrNWzYMN1xxx25Ll+8eLFGjBiR77Gnq5s5v/32Wx0+fNh2+ZEjR/I8oRYAAAAAALk5efKkzjvvPNvllml+BgPX8UmmybJ+++03lSpVym3RAAAAAFCwWJ4zj1DWXwxdfPHFio+Pt11eoUIFV7fu5nkgvHDhQi1cuND3/OWXX9b8+fNzrJeUlKRt27apffv2+W4MAAAAAABZnC55bt26te0tuyZ5HginpKTo6NGjvucnT55UWC4RKCVLllTfvn01ZMiQfDcGBi7jEQpcnX7ELpmiHJxiIGzLdIjeMMUDuY1IcmyrKR7IFGdhiPgxcrqcxBRJ5DrOyRwdZGyTKbrN0AemZd5I87GXaYhXMsUneSPd/+XWMiVTGZJovOXsP2NWun17IsLNn+nTGZG2y747Vt12WfWSx2yXlYowx+YcybCPJDI5lBZnu+xERgnjttuPVbRdlphkf6VTWqJ9ueEn7N/M6GPG5igi5+SYPiWO2n9OIlLs38+wVPN7HX7KPlrIk2ZYlm74PvUG53fKGINk+r50+O633LbXUG5Q4ojOFByccgGggJg2bZpmzZqlJUuWqGzZspLO3Df8/PPPKykpSR06dNCwYcMUEZG/i53zvHa/fv1801B36tRJDz/8sC677LJ8VQYAAAAAhRI5wiGxcuVKtWjRwjcIPnHihIYMGaK4uDhdcMEFevXVVxUZGamhQ4fmq1xXk2WtXLmSQTAAAAAAIKh+/fVX1a9f3/d8xYoVOn78uN544w298soruu666/Thhx/mu1zXk2VlSU5OVnJysi+/6e+qVKnib/EAAAAAgGIqLS1NZcqU8T3/6quvVLt2bd9M0g0bNsw2l1VeuR4Iv/3225oxY4b27t1ru86WLVvcFg8AAAAABYbHOvMIZf3FUZUqVfTjjz/q2muvVUpKir766iv17t3btzw5OVkxMTH5LtfVQPidd97RmDFj1LZtW/Xu3VvPPfecbr75ZkVHR+u9995ThQoV1L9/fzdFAwAAAAAgSbr66qv13HPP6cSJE9qzZ4+Sk5N19dVX+5Zv3rxZtWrVyne5rgbCc+bMUdu2bfX666/r6NGjeu6559ShQwe1bt1at912m3r37q1jx465KRoAAAAACh4mywqJgQMH6vDhw1q8eLGioqI0evRo1alTx7e8bt262c4Q55WrgfCePXt8M0hHRp6J1EhPP5PnERcXp//85z96++23deutt7opHrkpaPEIDlFHbrZzihUKRvSEKR5J8iMiyVSuP++loT3mOt33ncf0Xudzmvo8C7PfF6uEfYyPJ8MQHRTtMupJksdl/3kjDMePQ9d5TEk0pmilNPv3KzPKvn9Op9r3qySFhdlvWyrKvkE7k8rbLqsUk2ysc11yDdtlER779mRY9n1wIsUcn3TiWEnDQvs+ijhpX2fUCfsio4/aL5Ok8DT7Yy/ylH0fRB2zjxUKTzFEDsn8OQpLMWR3pRmWpZy2X5ZLFGM2hkgi0/eTZdnvp1N0nlGwYpBMXP5uBC2yCQDOovDwcD300EN66KGHcl1+2223uSrX1WgmLi5OmX/9iMTGxiomJkZ//vmnb3mpUqV0+PBhVw0CAAAAACCYXA2Ea9eura1bt/qeN27cWO+8844OHDigP/74Q/PmzVPNmjUD1UYAAAAACC3r/0+YFYqHm0uj161bp//+979q27at6tSpo+XLl2db/uCDD6pOnTrZHgMHDsy2zrFjxzR8+HA1a9ZMzZs310MPPaSTJ09mW2fr1q3q16+fGjZsqA4dOui1117Lf2PPMlcD4auuuko7duxQWlqaJGnYsGHauXOnLr30UnXq1Em7d+/WPffcE8h2AgAAAADy4dSpU6pTp44ee+wx23XatWunr776yvd49tlnsy0fMWKEfvnlF02fPl2vvPKK1q9fr1GjRvmWJycna+DAgapSpYree+89PfDAA5o6darmzZsXtP0KBFc3+PXu3TvbDckXXXSRlixZopUrVyo8PFyXXHKJzj///IA1EgAAAACQPx06dFCHDh2M60RFRalixYq5Ltu5c6e+/PJLLViwQA0bNpQkPfLIIxo8eLAeeOABVa5cWR988IHS09M1btw4RUVFqXbt2tqyZYumT5+uPn36BHyfAiXfZ4RTU1M1a9YsrVu3Ltvr1atX10033aQbb7yRQTAAAACAosUqAI8g+Pbbb9W6dWt17dpVjz32mI4e/f8zOX7//fcqXbq0bxAsSW3atFFYWJh++OEHSdLGjRvVvHlzRUVF+dZp27atdu/erePHjwen0QGQ74FwdHS0Jk6cqN27dwejPQAAAAAAG8nJydkeWberutGuXTs99dRTmjFjhu6//36tW7dOgwYN8k2MfPjwYcXHx2fbJiIiQmXKlNGhQ4d861SoUCHbOlnPC/IEyq4uja5du7b27dsX6LbALVO8TUGLXQoSY5SRKZbJoX8sr8uYKFO5bqOnJHPMjynixyEmysQy7IsxdMgUiRLuvg88mfb7aUXZf6VZhvZY5vQkZZaw7z9vuCHSynRcZpgrDTOkuxiXnTZENhmOgzQr2tiezAz7/juVYr9teLj98XM0xRBVJCk9w769ScdjbJdZhggpT6ZDv5+yrzP6iP22EafsyzTFJ0WcNv95PzLZvv8iTtkfCKaIpLB0c3SQ55ThH1SmiKRUw3Zew3eiQ5SRlWGIe8o0lGuq04kpdigUv6suf+fdRgACKNjat2+fbbKqoUOHatiwYa7K6tGjh+//sybL6ty5s+8scVHmaiB87733avjw4WrZsqXatGkT6DYBAAAAQMESxMuT81y/pFWrVmV7+e+XJPurevXqKleunH777Te1bt1aFSpU0JEjR7Ktk5GRoePHj/vuK65QoUKOM79Zz/95prggcTUQnjNnjsqWLauBAweqWrVqqlatmqKjs58R8Hg8evnllwPSSAAAAACAFBsbG7Sy//zzTx07dsw3yG3atKlOnDihzZs3q0GDBpKktWvXyuv1qlGjRpKkJk2aaPLkyUpPT1dkZKQkafXq1Tr//PNVpkyZoLXVX64Gwtu3b5cknXvuucrMzNRvv/2WYx2P6RJOAAAAAChEfHm+Iaw/v06ePKk9e/b4nv/+++/asmWLypQpozJlymjq1Knq2rWrKlSooL179+qZZ57Reeedp3bt2kmSatWqpXbt2unRRx/V6NGjlZ6erieeeEI9evRQ5cqVJUlXXnmlXnzxRT388MMaNGiQduzYoVmzZun//u//ArLfweJqILxy5cpAtwMAAAAAEECbN2/WgAEDfM/Hjx8vSerZs6cef/xxbd++XYsWLVJSUpIqVaqkSy65RHfffXe2y60nTpyoJ554QjfddJPCwsLUpUsXPfLII77lcXFxeuONNzRmzBj16tVL5cqV05133lmgo5MklwNhAAAAAEDB1rJlS23bts12+RtvvOFYRtmyZTVp0iTjOnXr1tXbb7+d7/aFkh/T1wIAAAAAUPhwRrgocBvl4E+Mj7HYAhbX4Md+GqOX3NbpVKbh/nqPKZLI7X35DlFGngjD14QpnsTUHqf3xNAmK8JdH3gM8VJhGebjMizVfj/DI0z7ab8sPM1cZ3qsYVtDVI+MXwemfjW3x3vEPiIps4Q5/sZOimUfgSRJnjT7PggzRSQZ+iDipEN8kiEdKPqoodxT9v0XlWS/LPq4IRpIUli6/c6EpZmWGeKTTPFIkvuIJBPTd4XT70K6oY8Mn2vLtJ3b73a5/x0z/jY6RUgVtKijYhLNCKBo44wwAAAAAKBY4YwwAAAAADgpIDnCCIw8nREeOnSo1q9f73u+bt26HMHKAAAAAAAUBnkaCK9YsUL79+/3PR8wYIC+/vrroDUKAAAAAAqSrBzhUD4QOHkaCFeuXFlbtmzxPbcsSx63E/MAAAAAABBCebpHuHv37nrzzTe1bNkyxcXFSZImTZqkadOm2W7j8Xj0wQcfBKaVAAAAAAAESJ4GwsOHD9d5552nb775RomJifJ4PIqJiVHZsmWD3Dz4zZ+IJFM8gstyXccROZUbHm6/MAj74chUp9epTsN1L6b4JANjBJITQzyJTP1uumrEIbLJKNPUP/aLLFN7TPvowGNqj2WKVjKXG3nSfpkp4sckzJDQEpVkeC8lZdqnJ8kyvJ/eSEO8jcNhEJlkiMMyRiTZLwt3SP8JMyyPNEQkRZ40RGydNi0zx+aEn7J/sz3p9tt6Ugw7YooykszxSRmGSCKXEUmWqUzJHJFkih0y/d44xBUFQ6GLQArW7yNQ2BWwjzLcy9O/jsPDw9WnTx/16dNHklS3bl3dcccduvLKK4PaOAAAAAAAAi1Pf+7r2bOnVq1a5Xs+fvx4XXjhhUFrFAAAAAAAwZKngfC2bdt09OhR3/OHHnpIP//8c9AaBQAAAAAFilUAHgiYPA2Eq1SpojVr1ijzr3tqmDUaAAAAAFBY5eke4b59+2rixIn68MMPFR0dLY/Ho4cfflijRo2y3cbj8WjDhg0BaygAAAAAAIGQp4Hwbbfdprp16+qbb77R4cOHtWjRIjVs2FDVq1cPdvsAAAAAIOQ81plHKOtH4OQ5U6Vt27Zq27atJGnhwoXq06cPs0YXFG4jDoIUnWCKiDDFJzlFS7iOXjLth1MfyBQPZCjXFA/kR1SPW8aIEYf2GKOXTOWaopXSHfJ/IiPtl5kOS2PUkynKyHxshaXbHydhGfbLrHD7/bBSjVUamaKDTLlMprgir6HLJcljSqkx7Is30r49YQ6pOablpgipyJOG7yCHj3zkKcN7nW5fbniq/XYRyaYIJHODPKcd8p7smD6bTnFFafZ1GqOOMk3xSYbPSZCijKx0h/00cP17Y/hNKXDxScQjASjmXIWLbt26NdDtAAAAAICCK9QTVhWwv6cVdnkaCO/fv1/SmUmz/v7cSdb6AAAAAAAUFHkaCHfq1Ekej0ebNm1SVFSU77mTLVu2+N1AAAAAAAACKU8D4XHjxsnj8Sjyr/v2sp4DAAAAQHHAZFlFS54Gwr169TI+BwAAAACgsGDKQAAAAABAsZKnM8JTp07Nd8Eej0dDhgzJ93aFntuoHn9iDNyWG6ToBLexEx5T3I6Uh6gjN5UGqd9NaSBO/eP2toMwd5FNxngkydxeU1v9iZAytdcQV2S8ZijTUKYcImxMu2LonzBDpE646f2SFJ5m315vhH2dppgjE1O0khNTJJFl+Fg7xSeZ9sX0Vkcl2W/oFJ9kijOKOGXf4LA0Q53p9svCTjnEI5kid0zxQKaYo1Q/srtMEUnBumXKVKdLTr9TpkinAheDZBKM302gOGPW6CLF9UA46x5h6x//YPV4PLIsq/gOhAEAAAAABVqeBsL/zA0+cOCABg8erNq1a+umm27S+eefL0natWuXZs6cqZ07d2ratGmBby0AAAAAAH5ydTHc6NGjdd5552nixIlq2LChYmNjFRsbq0aNGmnSpEmqUaOGxowZE+i2AgAAAEBoWAXggYBxNRBeu3atWrVqZbu8VatWWrNmjetGAQAAAAAQLK4GwtHR0dq4caPt8u+//17R0dFu2wQAAAAABUpWjnAoHwicPN0j/E9XXnmlZs+erdKlS+vGG29UjRo1JEl79uzR7NmztXjxYvXv3z+gDQUAAAAAIBBcDYRHjBiho0ePas6cOXrrrbcU9lcMiNfrlWVZ6tGjh0aMGBHQhhYawYoqCFb0UhC4j5Yw5764jWXyp+9M+2Jsj7FOVx+7v4q1L9cYP2XYznI4Zj0ylGuKQTptiGiJjDTWaWSIUrGi7fs2LM0+TsYKNx9b4Rn2x4nXUGe4IT7Jn7/qmqKOIk7b70tmpP0yUwyUJIWnG+KcDIeIcT8d+sDUf2EZ9huHpRriigzbSZLHcHx5TtsfQx5DXJHHEK2kNIf4JNNnLMNQbnq6/TKHmCMr1aFNtu0xfMYMcUTGiCgnLn9zHX+njL8Nhqgsw+fIWGew/u3gNtIRAIoBV/8ij4qK0jPPPKOBAwdq1apV2rdvnySpatWqat++verWrRvQRgIAAABASIV6wioujQ4o96emJNWtW5dBLwAAAACgUClY19QCAAAAABBkfp0RBgAAAIBigUujixTOCAMAAAAAihUGwgAAAACAYoVLowsKpxiDEEQkuY6BcFmm4z66jXowlOs6ksmhXCNTHIpkjDbxhBnqNMWTRBg+6oa4GMdtTdy2VZIyDO+LIXrJk2KIfYkyRDZFGvJ/ZI4rCrPsY2rCUu3jZLwOdXqjzcvtWBH2fRcWYfgs+BVhY7/IYzjePZkOdRraFJZmiDnKMCxzOPaM2xoiuIxRRm7jiCTjZ8VKNUSUmaQb9kNy/o6y28wQ02b8zAfruz9IZfr1u2FfaODLlIhIAgLMY/kXfxiI+hE4rr55H3vsMX333XeBbgsAAAAAAEHn6lTP4sWLNX/+fFWtWlVXXXWVrrrqKtWsWTPATQMAAACAAoLJsooUV2eE16xZo2effVa1a9fWq6++qm7duunaa6/VW2+9pSNHjgS6jQAAAAAABIyrgXBUVJS6deuml19+WV9//bUee+wxRUVFaezYsWrfvr1uv/12LV26VKlu718CAAAAACBI/J4sq0yZMurbt6/69u2r/fv36+mnn9ZHH32kVatWqVSpUuratav69++vunXrBqK9AAAAAHDWMVlW0RKQWaP/+OMPffjhh/rwww+1Y8cOlS1bVt27d1dkZKQ++OADLVy4UI888oj69esXiOoAAAAAAHDN9UD4xIkTWrZsmT788EN99913Cg8P16WXXqq7775bHTp0UORfESfDhw/Xfffdp5deeomBsIk/8RGuY3zMsQqWN0ixQ/YVmpeHIEIqKJxiagy7aRkiSDzGiCRDdIkp5kiSlWYfD2SKWfFER9lvF+4QDWSKmzHESxljX0zbmZZJ8shwbIYbYsYM+xlmipqR5DEs90YZyk2174MIU/+4jMyRJE+6IeLH0AdOUUbGOt3GHDm81zId76b2miKJTNuZYpf8YWiPleEQn2TiT8yWHcffIpdxTqbvS4ffMLd1OhRqvywUEVIAUMy5GggPGTJEq1atUnp6uho3bqxHHnlEPXr0UJkyZXKsGxUVpa5du2r58uV+NxYAAAAAQoJZo4sUVwPhLVu2aODAgbrmmmvyFJt0ySWXaNasWW6qAgAAAAAgoFwNhFeuXJmv9ePj49WiRQs3VQEAAABA6HFGuEhxddNlvXr1tHjxYtvlS5cuVb169Vw3CgAAAACAYHE1ELYsS5ZhYpXMzEx5nCYkAQAAAAAgBFzPGm030E1OTtZXX32lcuXKuW4UAAAAABQknr8eoawfgZPngfDUqVP14osvSjozCL7//vt1//3357quZVnq379/YFpYXPgTHeQ2VsGPOCJjLIVTNI7rSgMfIeUUkWHaF9d94E/0lOFKC3NUiKF/wv2JpTLsS6YfcR+m/jPF1BivRLGPxfE4xcn8FQeXK1PMmCHix3KKbMo0RFOlGt5rtxFJDvFJxhgkQ9ST65gjSVaEoW9TDBFbJqZ4JMn8+XQbkWT4LJg+t47lGjiWGwwuf4ucvofdxiC5/f52FIzfXOKRAOCsy/NAuGHDhurXr58sy9Lbb7+tSy65JMeM0R6PRzExMapfv766dOkS6LYCAAAAAOC3PA+EO3TooA4dOkiSUlJS1LdvXzVu3DhoDQMAAACAAoNZo4sUV/cIjx8/PtDtAAAAAADgrMjTQHjRokWSpKuvvloej8f33Mk111zjslkAAAAAAARHngbCDz74oDwej7p3766oqCg9+OCDjtt4PB4GwgAAAACKBI915hHK+hE4eRoIr1ixQpIUFRWV7TkAAAAAAIVNngbCVatWNT5HADjF/4QgIsltuaYoDFPURSg4tsfQ78aIJNP7ZYjbcWTa1BStZIq3MUXCOLbHcBzI0AeWuQ+MEUAOsUO2wqPtlzlF6jhEC9kyxOZ4oqPM255yGc9lWmY4Dpz61WOInzKWa4ruMm0nyZNsiEgyxX6Z4njSHd5rkwz798RKM7XV9F3hPjrIuK1DJJG50uDEILmtz/Xvhqlcf+KK3MYg+RMBSLwSUDAwWVaREqRREgAAAAAABVOezggPGDAg3wV7PB7NnDkz39sBAAAAABBMeRoIWy4uC3SzDQAAAAAUWAxxiow8DYRnz54d7HYAAAAAAHBWcI8wAAAAAKBYydMZYZPk5GQlJyfLm8vsn1WqVPG3eAAAAAAIOXKEixbXA+G3335bM2bM0N69e23X2bJli9viix+naIRgxSCdZSGJVjL2rSHWRE7tDUGchSlKxRDRYorbMcazOPBEGr5CDNFBTtEultc+0slYp6ncVJfxNk7bmuKBTJFEpjL9YXo/Td8jXofjwPR+mpjmi3CaS8K0L6YUJNPx7hSfZDqGTO01HQcZ9sez4+fPdAyZllmGqCeHz5/pu9i47dmOXfKHP3FFwYgyIh4JAM46V6Ord955R2PGjFGNGjV0zz33yLIs3XTTTRo8eLAqVKigunXr6sknnwx0WwEAAAAA8JurgfCcOXPUtm1bvf7667ruuuskSR06dNC9996rpUuX6uTJkzp27Fgg2wkAAAAAoWMVgAcCxtVAeM+ePerYsaMkKTIyUpKU/tflZnFxcfrPf/6jt99+O0BNBAAAAAAgcFzdIxwXF6fMv+5rio2NVUxMjP7880/f8lKlSunw4cOBaSEAAAAAhBiTZRUtrs4I165dW1u3bvU9b9y4sd555x0dOHBAf/zxh+bNm6eaNWsGqo0AAAAAAASMq4HwVVddpR07digt7cysp8OGDdPOnTt16aWXqlOnTtq9e7fuueeeQLYTAAAAAICAcHVpdO/evdW7d2/f84suukhLlizRypUrFR4erksuuUTnn39+wBpZLAQrHskUyRCCSCbXsRwO27rlT2yH222dYpeMkU2mmB/Ddpb8iEgy1ZlmiKIxRbuYomYcWG7rNETYKNzcHo/xs2Lo2wjT++UUmeZyX0wRP/68J6ZtTZFExlgh83FpGb6/jJFgKaftC3WKK3KKdLLbzG0MmdP3iCEGKVjf4cGIMwpJRBIABFqoJ6ziqzSgXOcI/1P16tV10003Bao4AAAAAACC4uyfEgQAAAAAIIRcnRGuW7euPKbL5P6yZcsWN8UDAAAAQIHCrNFFi6uB8JAhQ3IMhDMzM7Vv3z4tX75c559/vi9nGAAAAACAgsTVQHjYsGG2yw4ePKg+ffoQnwQAAAAAKJACfo9wpUqV1LdvX7300kuBLhoAAAAAQsMqAA8ETMBmjf67mJgY/f7778EouuAzxVmYooxMy/ypMwQRSSbBitAwRZcY43+c+t1l//kT9WRsr4lpX0zJLg77aHlcHpuGGBrX+yiZY3xMTHWmG+KIJFku22tqqWPcjinGxyHu6azLNBwjpraaorAk43ttitGyDNFUHoeYKNcxSCbBig4yfOaDsh8qZDFIwfpd9adcAECBEfCB8Pbt2zV79mwujQYAAABQdIT6rGwh+ltkYeBqINypU6dcZ41OSkpSUlKSSpQowaXRAAAAAIACydVAuEWLFrkOhMuUKaPq1aurR48eKlu2rL9tAwAAAAAg4FwNhCdMmBDodgAAAABAgUWOcNFSwGZcAQAAAAAguFydEZ46dWq+t/F4PBoyZIib6gAAAAAACBjXA+Gse4Stf0R8mF4vFgNht7EKTjE9xSCuwSlyyBTbYdzWn74zbWt4z/yJGPGEBeG99icKJMMUjWOIFTL0gWXMc/KDIW7HY4ojcopkchlFYzwKnMo0xfwY4oo8EfZf68ZIHVP/SJIhksjYVlM0lUO/G2PRHGKQ3JQpyfV77TZqLVgxR6Fg+h4OWuwSv7kAziZmjS5SXA2Ev/jiCw0ePFi1a9fWTTfdpPPPP1+StGvXLs2cOVM7d+7UtGnTVLly5YA2FgAAAAAAf7n6E/bo0aN13nnnaeLEiWrYsKFiY2MVGxurRo0aadKkSapRo4bGjBkT6LYCAAAAQEh4LCvkDwSOq4Hw2rVr1apVK9vlrVq10po1a1w3CgAAAACAYHE1EI6OjtbGjRttl3///feKjo522yYAAAAAAILG1T3CV155pWbPnq3SpUvrxhtvVI0aNSRJe/bs0ezZs7V48WL1798/oA0FAAAAgJBhsqwixdVAeMSIETp69KjmzJmjt956S2F/zd7p9XplWZZ69OihESNGBLSh/3Ts2DE98cQT+uyzzxQWFqYuXbro4YcfVqlSpWzXf+GFF/TVV1/pjz/+UHx8vDp37qy7775bcXFxQW0rAAAAAKDgcDUQjoqK0jPPPKOBAwdq1apV2rdvnySpatWqat++verWrRvQRuZmxIgROnTokKZPn6709HQ99NBDGjVqlCZNmpTr+gcPHtTBgwc1cuRIXXDBBdq3b58ef/xxHTx4UFOmTAl6ex2FIOKnoPEnXiNY0RyuY5n86He3++K2rU71eUwRSaboF5fRSpIk076YtjWmRBneL0MckSQp3OX76Uc0jmlCDNO+WGlp9mUaopWc4pPM/edyP5363RTHY6rTn+8DUyyaMc7J/WfMKATf727ba3xP/IlwCwbikQCg2HM1EM5St27dszLo/aedO3fqyy+/1IIFC9SwYUNJ0iOPPKLBgwfrgQceyDW2KSEhQS+88ILveY0aNXTPPffo/vvvV0ZGhiJM/0AEAAAAUKx5rDOPUNaPwCk8pwv/5vvvv1fp0qV9g2BJatOmjcLCwvTDDz/kuZzk5GTFxsYyCAYAAABQ5Kxbt07//e9/1bZtW9WpU0fLly/PttyyLD3//PNq27atGjVqpJtvvlm//vprtnWOHTum4cOHq1mzZmrevLkeeughnTx5Mts6W7duVb9+/dSwYUN16NBBr732WrB3zW+FciB8+PBhxcfHZ3stIiJCZcqU0aFDh/JUxpEjR/TSSy+pT58+wWgiAAAAAITUqVOnVKdOHT322GO5Ln/ttdc0e/ZsPf7445o/f75iYmI0cOBApaam+tYZMWKEfvnlF02fPl2vvPKK1q9fr1GjRvmWJycna+DAgapSpYree+89PfDAA5o6darmzZsX9P3zR4E6FTpx4kTHvx4sXbrU73qSk5N1++23q1atWho6dKjf5QEAAAAo4grhrNEdOnRQhw4dci/OsjRr1izdcccd6ty5syTp6aefVps2bbR8+XL16NEjT7ekfvDBB0pPT9e4ceMUFRWl2rVra8uWLZo+fXqBPulYoAbCt956q3r27Glcp3r16qpQoYKOHDmS7fWMjAwdP35cFStWNG6fnJys2267TaVKldKLL76oyMhIv9sNAAAAAIXJ77//rkOHDqlNmza+1+Li4tS4cWN9//336tGjh+Mtqf/+97+1ceNGNW/eXFFRUb512rZtq9dee03Hjx9XmTJlzup+5VWBGgjHx8fnuOQ5N02bNtWJEye0efNmNWjQQJK0du1aeb1eNWrUyHa7rNP2UVFRevnllxUdHR2wtgMAAAAougrKZFnJycnZXo+Kiso2CM2rrFtKy5cvn+318uXL6/Dhw5Lydkvq4cOHVa1atWzrVKhQwbesSA6EMzMz9dNPP+n333+XJFWrVk3169dXuCk2JQBq1aqldu3a6dFHH9Xo0aOVnp6uJ554Qj169PDNGH3gwAHddNNNevrpp9WoUSMlJyfr1ltvVUpKip555hklJyf7DqL4+Pigtzlkiki0UrAYI4f84U80h8uYEcvr7v107AOX0UseuY8Ocr2psQ/8+OUyxfy4jXpyYJnijDyGOg3LrLR01+1xzW0EmWQ+Dlx/ThziwgztNS0zlhukqB5jXFFh4vRbRNQRAGTTvn37bJNVDR06VMOGDQthiwon1wPh9957T88++6wSExN9/2DzeDyKj4/Xvffeq//85z8Ba2RuJk6cqCeeeEI33XSTwsLC1KVLFz3yyCO+5enp6dq9e7dSUlIkST/99JM2bdokSfr3v/+drawVK1bk+CsGAAAAABQ0q1atyvbczdlgSb5bShMTE1WpUiXf64mJib6I3LzcklqhQgXfGeQsWc+zzgwXRK4GwnPnztXjjz+uevXqaejQoapZs6Ykaffu3Zo3b54effRRpaen6/rrrw9kW7MpW7asJk2aZLu8WrVq2rZtm+95y5Ytsz0HAAAAgDwrIJNlxcbGBqS4atWqqWLFilqzZo3q1asn6cxl15s2bfKN4/JyS2qTJk00efJkpaen++ZfWr16tc4///wCe1m05DI+6bXXXlPz5s01f/589e3bV61atVKrVq10/fXX63//+5+aNm2q119/PdBtBQAAAADk0cmTJ7VlyxZt2bJF0pkJsrZs2aL9+/fL4/FowIABevnll7VixQpt27ZNDzzwgCpVquSbRfrvt6T+8MMP2rBhQ45bUq+88kpFRkbq4Ycf1o4dO7R06VLNmjVLt9xyS8j2Oy9cnRE+fPiwbr311lxnXI6MjFSPHj30zDPP+N04AAAAAIA7mzdv1oABA3zPx48fL0nq2bOnJkyYoEGDBiklJUWjRo3SiRMndNFFF+n111/PNqmw0y2pcXFxeuONNzRmzBj16tVL5cqV05133lmgo5MklwPhevXqaffu3bbLd+/e7buuHAAAAAAKu4Iya3R+ON0e6vF4dPfdd+vuu++2XcfpllRJqlu3rt5+++38NzCEXF0a/eijj+qjjz7SzJkzdfr0ad/rp0+f1owZM/TRRx9p1KhRAWskAAAAAACB4rGMGR25u/LKK3X8+HEdOnRI4eHhvlnGDh48qMzMTFWqVEmlS5fOXpHHow8++CAwrS4Ari4zQKeSUkLdDGf+RCQRveQ+LiVIdZoYo4xCEQ9mOH6c2mPcl2D0T7BitPwRjM+YP59pt9uGIPomWJ9N1/zog5Dsi9v2FrDjAEDBUzIuRu8fnxXqZrjWafBUnTydFrL6S5WI0spXh4as/qLG1aXRZcuWVdmyZXXeeedle71q1aoBaRQAAAAAFCgFZNZoBIargfDs2bMD3Q4AAAAAAM4KVwNhAAAAAChuQjlZFgIrTwPhdevWSZIuvvjibM+dZK0PAAAAAEBBkaeBcP/+/eXxeLRp0yZFRUX5ntuxLEsej8cX3AwAAAAAQEGRp4HwrFlnZneLiorK9hwAAAAAigXLOvMIZf0ImDwNhFu0aGF8jrPAbSxFQYusKGSRTG6jS/yJ43Eb82OsMxTHgeH99CcSJhhxMk5lGmO0MjPttzPERDnvh6HcoERI+RHxY+iDoClgcU5uP7dBi0fyZz+Lyu8NAAAGBW/kAQAAAABAEOXpjPD//d//5btgj8ejcePG5Xs7AAAAAChoPFZoZ41mxurAytNA+Jtvvsl3wabJtAAAAAAACJU8DYRXrlwZ7HYAAAAAQMFl/fUIZf0IGO4RBgAAAAAUK3keCKempmrUqFGaPXu2cb1Zs2bpscceU3p6ut+NAwAAAAAg0PJ0abQkzZs3TwsXLtTSpUuN61166aV65plnVKdOHfXr18/vBqIYcYrecBvpEYJYpmBFogQtaiUI/InjOescjhFjPJApJsrldk5cl2v4nFghSEAKGtN+huAz5Fe8VChi5QpYNBUAFBQe75lHKOtH4OT5F3bZsmXq0qWLqlevblyvRo0auvzyy7VkyRK/GwcAAAAAQKDleSC8fft2XXTRRXlat2nTptq2bZvrRgEAAAAAECx5vjQ6PT1dkZGReVo3MjJSaWlprhsFAAAAAAUKs0YXKXk+I1ypUiXt2LEjT+vu2LFDlSpVct0oAAAAAACCJc8D4TZt2uj9999XYmKicb3ExES9//77atOmjd+NAwAAAAAg0PI8EB40aJBSU1N10003adOmTbmus2nTJt18881KTU3VbbfdFrBGAgAAAEAoeazQPxA4eb5HuHr16po8ebLuu+8+9e3bV9WrV1dCQoJKlSqlkydPaseOHdqzZ49KlCihZ599VjVq1Ahmu4uf4hBL4RQTEow+8KfMUMSaFCLBiqnxhHkCX6g/x4HbbQvZZ9r0fgblPfGjTn/a6vq4dft+FrbvkUJ23AIAYCfPA2HpTEbwBx98oNdee02ff/65li9f7ltWqVIlXXvttRo0aJBjxBIAAAAAFCqWdeYRyvoRMPkaCEtStWrVNHr0aElScnKyTp48qVKlSik2NjbgjQMAAAAAINDyPRD+u9jYWAbAAAAAAIBCxa+BMAAAAAAUB6GesIrJsgKrkM3SAQAAAACAfxgIAwAAAACKFS6NhjsFLcooWOW6jTYx1VnY4lJCwG00TkETiv3whAXnc+Q2IilY++m2XKftXL9nofhcE2UEAGeX9dcjlPUjYPgXOQAAAACgWGEgDAAAAAAoVrg0GgAAAAAcMGt00cIZYQAAAABAscIZYQAAAABwYllnHqGsHwHDGWEAAAAAQLHCGWHAJBTxJAUtEsUUCxOkmChjTE0hiqYKRdST2zqd4pGCsi9Ox3oI3s+z/p4FK96toH2PAABQwDAQBgAAAAAHTJZVtBSs0ycAAAAAAAQZA2EAAAAAQLHCpdEAAAAA4MT66xHK+hEwnBEGAAAAABQrDIQBAAAAAMUKl0YDZ1uwYk2CFXPktr3BioUpYBFJhSnOyRSRFIqop5DwJ7KpoEUSFbT2AEARx6zRRUvB+lcaAAAAAABBxhlhAAAAAHDitc48Qlk/AoYzwgAAAACAYoWBMAAAAACgWOHSaAAAAABwQo5wkcIZYQAAAABAscIZYaCoCEXMkUmwYmiC0V5/Yo4KWmyVsUg/3pMCFgUVtNgqIokAACgWGAgDAAAAgAOPQpwjHLqqi6QC9id+AAAAAACCizPCAAAAAODEss48Qlk/AoYzwgAAAACAYoWBMAAAAACgWOHSaAAAAABw4LFCPFkWV0YHFANhAMFR0CKSQlFfsCKk3ArWe+I2rihYEVJEIAEAAAdcGg0AAAAA/6+9O4+qqt7/P/4CBOeJwbpOOQWaOOBQV8IJvWWiUZZRqWnq0kyxlZhTZrkwTKWW5bW8jte6pnkz+2GprbLymomtDC9LG1ET05LBCZArIPv3B3G+nRCO4OHsw9nPx1p75fmcvffnvXefpbx5f/Znw1KoCAMAAACAI8bvm5n9w2moCAMAAAAALIVEGAAAAABgKUyNBgAAAAAHvAxDXoZ585PN7NsTUREGAAAAAFgKFWEAqC68lqliVX3tEgAAZij+fTOzfzgNP4UAAAAAACyFRBgAAAAAYClMjQYAAAAAB1gsy7NQEQYAAAAAWAqJMAAAAADAUpgaDQAAAACOGL9vZvYPpyERBoCaxoxXJFUHT7kOAABQ4zA1GgAAAABgKVSEAQAAAMARwyjZzOwfTkNFGAAAAABgKVSEAQAAAMABL6NkM7N/OA8VYQAAAACApZAIAwAAAAAshanRAAAAAOAIi2V5FCrCAAAAAABLIREGAAAAAFgKU6MBAAAAwAGv4pLNzP7hPFSEAQAAAACWQiIMAAAAALAUpkYDAAAAgCOsGu1RqAgDAAAAACyFijAAAAAAOGL8vpnZP5yGijAAAAAAwFJIhAEAAAAAlsLUaAAAAABwwMsw5GXiglVm9u2JqAgDAAAAACyFRBgAAAAAYClMjQYAAAAAR3iPsEehIgwAAAAAsBQSYQAAAABwxJBUbOJWyYLwihUrFBISYrcNGTLE9v2VK1e0cOFC3XHHHQoLC1NsbKyysrLsznHmzBlNmjRJ3bp1U58+fbRkyRIVFRVVLhA3xdRoAAAAAPBAt956qzZs2GD77OPjY/tzQkKC9u7dq+XLl6thw4aKj4/XtGnTtGXLFknS1atXNXnyZAUGBmrLli3KyMjQ7Nmz5evrqxkzZrj8WpyNijAAAAAAeCAfHx8FBQXZNn9/f0lSTk6Otm3bpjlz5qhPnz4KDQ1VQkKCUlJSdPjwYUnSF198obS0NC1btkydOnVS//799dRTT2nTpk0qKCgw8aqcg0QYAAAAABwofY+wmVtlnTx5UhERERo0aJDi4uJ05swZSdKRI0dUWFio8PBw277t27dX8+bNbYnw4cOHFRwcrMDAQNs+ERERys3NVVpa2o3dTDfA1GgAAAAAqCFyc3PtPvv5+cnPz6/Mfl27dtXixYvVtm1bZWZmauXKlRo1apR27NihrKws+fr6qlGjRnbHBAQEKDMzU5KUlZVllwRLsn0u3acmIxEGAAAAgBqiX79+ysvLs32eNm2aYmNjy+zXv39/2587duyobt26aeDAgdq1a5fq1KnjkljdGYkwAAAAADhiyOT3CJf85z//+Y9d87WqwdfSqFEjtWnTRunp6QoPD1dhYaEuXbpkVxXOzs5WUFCQpJLqb2pqqt05SleVLt2nJuMZYQAAAACoIRo0aGC3XW8inJeXp1OnTikoKEihoaHy9fXVgQMHbN8fP35cZ86cUffu3SVJ3bt3148//qjs7GzbPl9++aUaNGigDh06OPWazEBFGAAAAAA8zJIlSzRw4EA1b95cGRkZWrFihby9vTVs2DA1bNhQDzzwgF566SU1btxYDRo00KJFixQWFmZLhCMiItShQwfNmjVLzzzzjDIzM7V8+XKNGjXqupNvd0YiDAAAAACOGIbJU6Mr1/dvv/2mGTNm6MKFC/L391fPnj21detW2yuU5s2bJ29vb02fPl0FBQWKiIjQ888/bzvex8dHq1at0gsvvKCYmBjVrVtX999/v6ZPn+7UyzKLl2GY+X+z5opu/Jgu5+SbHQYAAABQI9RrWFf/7+KbZodRZff3S9DlvCum9V+vfm1t/8880/r3NFSEAQAAAMCR4t83M/uH07BYFgAAAADAUkiEAQAAAACWwtRoAAAAAHDAyzDkZeLySmb27YmoCAMAAAAALIVEGAAAAABgKUyNBgAAAABHath7hFExKsIAAAAAAEshEQYAAAAAWApTowEAAADAEaZGexQqwgAAAAAAS6EiDAAAAACOUBH2KFSEAQAAAACWQiIMAAAAALAUpkYDAAAAgCPFv29m9g+noSIMAAAAALAUEmEAAAAAgKUwNRoAAAAAHPAyDHmZuHKzmX17IirCAAAAAABLoSIMAAAAAI7wHmGPQkUYAAAAAGApNTYRvnDhguLi4tSjRw/16tVL8+bNU15e3nUdaxiGJk6cqJCQEH3yySfVHCkAAAAAwJ3U2ER45syZSktL04YNG7Rq1Sp9/fXXWrBgwXUdu3HjRnl5eVVzhAAAAAA8RrFh/ganqZGJ8LFjx7Rv3z4tWrRI3bp1U69evTR//nx9+OGHOnv2bIXHfvfdd1q/fr0SEhJcFC0AAAAAwJ3UyEQ4JSVFjRo1UpcuXWxt4eHh8vb2VmpqarnH5efnKy4uTgsWLFBQUJArQgUAAAAAuJkauWp0VlaW/P397dpq1aqlxo0bKzMzs9zjFi9erLCwMA0ePLi6QwQAAADgSVg12qO4VSKcmJioNWvWVLjPzp07q3TuPXv2KDk5Wdu3b6/S8QAAAAAAz+BWifD48eN1//33V7hPq1atFBgYqHPnztm1FxUV6eLFi+VOeU5OTlZ6erp69+5t1x4bG6tevXrprbfeurHgAQAAAAA1glslwv7+/mWmPF9LWFiYLl26pCNHjig0NFRSSaJbXFysrl27XvOYSZMmaeTIkXZtw4cP19y5czVw4MAbDx4AAACABzN5arSYGu1MNXKxrPbt26tv37567rnnlJqaqkOHDik+Pl5RUVG66aabJElnz57VkCFDbItnBQUFKTg42G6TpObNm6tVq1amXQsAAAAAwLXcqiJcGYmJiYqPj9fYsWPl7e2tu+66S/Pnz7d9X1hYqBMnTig/P9/EKAEAAAB4BBbL8ig1NhFu0qSJXn755XK/b9mypX744YcKz+HoewAAAACA56mRU6MBAAAAAKiqGlsRBgAAAACXKTZKNjP7h9NQEQYAAAAAWAqJMAAAAADAUpgaDQAAAACOGMUlm5n9w2moCAMAAAAALIVEGAAAAABgKUyNBgAAAABHDKNkM7N/OA0VYQAAAACApVARBgAAAABHeI+wR6EiDAAAAACwFBJhAAAAAIClMDUaAAAAABxhsSyPQkUYAAAAAGApJMIAAAAAAEthajQAAAAAOGLI5KnR5nXtiagIAwAAAAAshUQYAAAAAGApTI0GAAAAAEdYNdqjUBEGAAAAAFgKFWEAAAAAcKS4uGQzs384DRVhAAAAAIClkAgDAAAAACyFqdEAAAAA4AiLZXkUKsIAAAAAAEshEQYAAAAAWApTowEAAADAEaZGexQqwgAAAAAAS6EiDAAAAACOFBslm5n9w2moCAMAAAAALIVEGAAAAABgKUyNBgAAAAAHDKNYhlFsav9wHirCAAAAAABLIREGAAAAAFgKU6MBAAAAwBHD5FWjeY+wU1ERBgAAAABYCokwAAAAAMBSmBoNAAAAAI4YhrnTk5ka7VRUhAEAAAAAlkJFGAAAAAAcKS4u2czsH05DRRgAAAAAYCkkwgAAAAAAS2FqNAAAAAA4wmJZHoWKMAAAAADAUkiEAQAAAACWwtRoAAAAAHDAKC6WYeLKzWb27YmoCAMAAAAALIVEGAAAAABgKUyNBgAAAABHWDXao1ARBgAAAABYChVhAAAAAHCk2CjZzOwfTkNFGAAAAABgKSTCAAAAAABLYWo0AAAAADhiGJJh4rt8WSzLqagIAwAAAAAshUQYAAAAAGApTI0GAAAAAAeMYkOGiSs3m9m3J6IiDAAAAACwFBJhAAAAAIClMDUaAAAAABwxik1eNdrEvj0QFWEAAAAAgKVQEQYAAAAAB1gsy7NQEQYAAAAAD7Vp0yZFRkaqS5cuGjlypFJTU80OyS2QCAMAAACAB9q5c6cWL16sqVOnavv27erYsaMmTJig7Oxss0MzHYkwAAAAADhSuliWmVslbdiwQQ899JAeeOABdejQQQsXLlSdOnW0bdu2arhBNQuJMAAAAAB4mIKCAh09elTh4eG2Nm9vb4WHhyslJcXEyNwDi2VVUd2GdcwOAQAAAKgxavrPz/Ua1XWL/nNzc+3a/fz85OfnV2b/8+fP6+rVqwoICLBrDwgI0PHjx6sv0BqCRLiKtvyy2uwQAAAAALjI5lP/MDsE5eXlqU+fPiooKLC1TZs2TbGxsSZGVTORCAMAAABADeDr66sDBw7YtV2rGixJTZs2lY+PT5mFsbKzsxUYGFhtMdYUPCMMAAAAADWAn5+fGjRoYLeVlwj7+fmpc+fOdolzcXGxDhw4oLCwMFeF7LaoCAMAAACAB3r88cc1e/ZshYaGqmvXrtq4caPy8/M1YsQIs0MzHYkwAAAAAHigoUOH6ty5c3rttdeUmZmpTp06ae3atUyNluRlGIZhdhAAAAAAALgKzwgDAAAAACyFRBgAAAAAYCkkwgAAAAAASyERBgAAAABYCokwnOrChQuKi4tTjx491KtXL82bN095eXnXdaxhGJo4caJCQkL0ySefVHOkcFeVHUMXLlxQfHy87r77bnXt2lUDBgzQokWLlJOT48KoYaZNmzYpMjJSXbp00ciRI5Wamlrh/rt27dKQIUPUpUsXDR8+XHv37nVRpHBHlRk/W7du1aOPPqrevXurd+/eGjdunMPxBs9X2b+DSn344YcKCQnRk08+Wc0RArgWEmE41cyZM5WWlqYNGzZo1apV+vrrr7VgwYLrOnbjxo3y8vKq5gjh7io7hjIyMpSRkaHZs2frgw8+0OLFi7Vv3z49++yzLowaZtm5c6cWL16sqVOnavv27erYsaMmTJig7Ozsa+7/zTffKC4uTg8++KDef/99DRo0SFOnTtWPP/7o4sjhDio7fg4ePKioqCi9+eab2rJli/7yl79o/PjxOnv2rIsjh7uo7Bgq9csvv2jJkiXq1auXiyIFUIYBOElaWpoRHBxspKam2tr27t1rhISEGL/99luFx3777bdG3759jYyMDCM4ONj4+OOPqztcuKEbGUN/tHPnTqNz585GYWFhdYQJN/Lggw8aCxcutH2+evWqERERYfzjH/+45v5PPfWUMWnSJLu2kSNHGs8991y1xgn3VNnx82dFRUVGWFiYsX379mqKEO6uKmOoqKjIiImJMbZu3WrMnj3bmDJliitCBfAnVIThNCkpKWrUqJG6dOliawsPD5e3t3eF04Ty8/MVFxenBQsWKCgoyBWhwk1VdQz9WW5urho0aKBatWpVR5hwEwUFBTp69KjCw8Ntbd7e3goPD1dKSso1jzl8+LD69Olj1xYREaHDhw9XZ6hwQ1UZP3+Wn5+voqIiNW7cuLrChBur6hhauXKlAgICNHLkSFeECaAc/JQIp8nKypK/v79dW61atdS4cWNlZmaWe9zixYsVFhamwYMHV3eIcHNVHUN/dO7cOb3++uuKiYmpjhDhRs6fP6+rV68qICDArj0gIEDHjx+/5jFZWVkKDAwss39WVla1xQn3VJXx82eJiYlq1qyZXSIE66jKGPr666/17rvv6v3333dBhAAqQiIMhxITE7VmzZoK99m5c2eVzr1nzx4lJydr+/btVToeNUN1jqE/ys3N1eTJk9W+fXtNmzbths8HAOVZvXq1du7cqTfffFO1a9c2OxzUALm5uZo1a5bi4+PL/NIXgOuRCMOh8ePH6/77769wn1atWikwMFDnzp2zay8qKtLFixfLnfKcnJys9PR09e7d2649NjZWvXr10ltvvXVjwcMtVOcYKpWbm6uJEyeqfv36WrlypXx9fW84bri3pk2bysfHp8yiNNnZ2WWqvqUCAwPLVH8r2h+eqyrjp9S6deu0evVqbdiwQR07dqzOMOHGKjuGTp06pdOnT2vKlCm2tuLiYknSbbfdpt27d6t169bVGzQAGxJhOOTv739dv7kMCwvTpUuXdOTIEYWGhkoqSXSLi4vVtWvXax4zadKkMs/IDB8+XHPnztXAgQNvPHi4heocQ1JJEjxhwgT5+fnpjTfeoDpjEX5+furcubMOHDhge7SiuLhYBw4c0OjRo695TPfu3ZWcnKxx48bZ2r788kt1797dBRHDnVRl/EjSmjVrtGrVKq1bt85uPQNYT2XHULt27bRjxw67tuXLlysvL0/PPvusbr75ZpfEDaAEi2XBadq3b6++ffvqueeeU2pqqg4dOqT4+HhFRUXppptukiSdPXtWQ4YMsS18FBQUpODgYLtNkpo3b65WrVqZdi0wR1XGUG5ursaPH6/Lly/rxRdfVG5urjIzM5WZmamrV6+aeTlwgccff1xbt27V9u3bdezYMb3wwgvKz8/XiBEjJEmzZs3Syy+/bNv/scce0759+7R+/XodO3ZMK1as0JEjRypMfOC5Kjt+Vq9erVdffVUJCQlq0aKF7e+ait51Ds9WmTFUu3btMj/zNGrUSPXr11dwcLD8/PzMvBTAcqgIw6kSExMVHx+vsWPHytvbW3fddZfmz59v+76wsFAnTpxQfn6+iVHCnVV2DB09elT//e9/JUl/+9vf7M61Z88etWzZ0nXBw+WGDh2qc+fO6bXXXlNmZqY6deqktWvX2qYl/vrrr/L2/r/f+fbo0UOJiYlavny5XnnlFbVp00YrV660/RIO1lLZ8bNlyxYVFhZq+vTpdueZNm2aYmNjXRo73ENlxxAA9+FlGIZhdhAAAAAAALgKv6ICAAAAAFgKiTAAAAAAwFJIhAEAAAAAlkIiDAAAAACwFBJhAAAAAIClkAgDAAAAACyFRBgAAAAAYCkkwgAAAAAASyERBgA3FxkZqTlz5tg+Hzx4UCEhITp48KCJUTnPmDFjNGbMGNvnX375RSEhIXrvvfdMiykkJEQrVqywfX7vvfcUEhKiX375xW6/tWvXatCgQerUqZOio6MlSUVFRVq6dKn69++vjh076sknn3Rp7KU8bZwAAOBMtcwOAACsKj09XWvXrtX+/fuVkZEhX19fBQcH65577lFMTIzq1KljdohuY+/evUpNTVVsbKzZodh88cUXWrZsme69917FxsaqadOmkqRt27Zp3bp1Gjt2rG677TY1b968WuPYtGmT6tatqxEjRlRrPwAAeBISYQAwweeff66nnnpKfn5+io6OVnBwsAoLC3Xo0CEtW7ZMaWlpio+Pv+axvXv3Vmpqqnx9fV0ctWu0aNFCqampqlXr//6J2rt3rzZt2mRaIhwdHa2oqCj5+fnZ2pKTk+Xt7a0XX3yxTPtNN92kefPmuSS2zZs3q2nTpmUSYU8fJwAA3AgSYQBwsVOnTunpp59W8+bNtXHjRjVr1sz23ahRo3Ty5El9/vnn5R7v7e2t2rVruyBSc3h5ebnd9fn4+MjHx8euLTs7W3Xq1LFLgkvbGzVqVKV+DMPQlStXnDIbwNPHCQAAN4JnhAHAxdauXavLly/rxRdftEuCS91yyy0aO3Zsucdf69nPMWPGaNiwYTpy5Igefvhhde3aVZGRkdq8efN1xVRQUKCEhAT99a9/VVhYmJ544gn99ttvZZ6VnTNnjiIjI8scv2LFCoWEhNi1bdu2TY899pj69Omj0NBQDR06VG+//bbDWP78jPCcOXO0adMmSSXP7pZuhmEoMjJSU6ZMKXOOK1euqGfPnlqwYEGVrvvP/vyMcGl8ly9ftsVTus/Bgwf1008/2dorekY3MjJSkydP1r59+zRixAh17dpVW7Zsue77FxkZqZ9++klfffWVrb/S563Le0Z4165dtr7uuOMOzZw5U2fPnrXbJzMzU3PnzlW/fv0UGhqqiIgITZkypcwz0gAA1FRUhAHAxT777DO1atVKPXr0cOp5L168qEmTJumee+5RVFSUdu3apRdeeEG+vr568MEHKzz22WefVVJSkoYNG6YePXooOTlZkyZNuqF4Nm/erFtvvVWRkZGqVauWPvvsMy1cuFCGYWjUqFHXfZ6YmBhlZGRo//79Wrp0qa3dy8tLw4cP17p163ThwgU1adLE9t2nn36q3Nxc3XvvvRWeu6rXvXTpUm3dulWpqalatGiRJOm2227T0qVLtWrVKl2+fFkzZsyQJLVv377Cc504cUJxcXGKiYnRQw89pLZt20q6vvs3b948xcfHq169enriiSckSYGBgeX29d5772nu3Lnq0qWLZsyYoezsbL355pv65ptv9P7779sq2bGxsUpLS9Po0aPVokULnTt3Tvv379evv/6qli1bOrw/AAC4OxJhAHCh3NxcnT17VoMGDXL6uTMyMjRnzhw9/vjjkmRLrF555RVFR0eX+6zo999/r6SkJD366KN6/vnnJZVM0Y6Li9MPP/xQ5Xj+9a9/2U3xHT16tCZMmKANGzZUKhEOCwtTmzZttH//ftvKzKXuu+8+rVq1Srt27dIjjzxia09KSlKLFi3Us2fPcs97I9cdHR2tAwcO6Ntvv7WL6dZbb9W7776r8+fPl4m1PCdPntTatWvVt29fu/bruX+DBw/W8uXL1bRpU4f9FRYWKjExUcHBwdq0aZNt2nTPnj01efJk/fOf/9T06dN16dIlpaSkaNasWZowYYLt+MmTJ1/X9QAAUBMwNRoAXCg3N1eSVL9+faefu1atWoqJibF99vPzU0xMjLKzs3X06NFyj9u7d68k2b3CSFKF07Ovxx+TuJycHJ07d0633367Tp06pZycnBs6d6m2bduqW7du2rFjh63twoUL2rdvn4YPHy4vL69yj62u666sli1blkmCJeffvyNHjig7O1uPPPKI3bPDAwYMULt27WzPpdepU0e+vr766quvdPHixcpfEAAANQAVYQBwoQYNGkiS8vLynH7uZs2aqV69enZtbdq0kSSdPn1a3bt3v+Zxp0+flre3t1q3bm3X3q5duxuK59ChQ1qxYoUOHz6s/Px8u+9ycnLUsGHDGzp/qejoaMXHx+v06dNq0aKFdu/ercLCQocV0uq67soqb6qxs+/fmTNnJMk29fqP2rVrp0OHDkkq+QXKzJkztWTJEt15553q1q2bBgwYoPvuu09BQUGV6hMAAHdFRRgAXKhBgwZq1qyZfvrpJ7NDqZLyKqxXr161+5yenq5x48bp/PnzmjNnjlavXq0NGzZo3LhxkqTi4mKnxRQVFaVatWrZqsJJSUkKDQ11eUJbVddaIdqV9+9axo0bp48++kgzZsxQ7dq19eqrr2ro0KH69ttvq7VfAABchUQYAFxs4MCBSk9PV0pKilPPm5GRocuXL9u1/fzzz5JK3s1bnhYtWqi4uFjp6el27cePHy+zb6NGjXTp0qUy7aXVxlKffvqpCgoK9MYbb+jhhx9W//79FR4eXuXXAlU0xblJkyYaMGCAduzYodOnT+ubb765rudzK3PdrlaZ+1fRvfmj5s2bSypZnOvPTpw4Yfu+VOvWrTV+/HitX79eH3zwgQoLC7V+/foqXA0AAO6HRBgAXGzixImqV6+e5s+fr6ysrDLfp6ena+PGjZU+b1FRkd555x3b54KCAr3zzjvy9/dX586dyz2uX79+kqS33nrLrv1aMbRu3Vo5OTn6/vvvbW0ZGRn6+OOP7fYrfeeuYRi2tpycHG3btq0SV/R/6tatK0nXTMKlkunRaWlpWrp0qXx8fBQVFeXwnJW5blerzP2rW7duufflj0JDQxUQEKAtW7aooKDA1r53714dO3ZMAwYMkCTl5+frypUrdse2bt1a9evXtzsOAICajGeEAcDFWrdurcTERD399NMaOnSooqOjFRwcrIKCAqWkpGj37t0aMWJEpc/brFkzrVmzRqdPn1abNm20c+dOfffdd4qPjy93xWhJ6tSpk4YNG6a3335bOTk5CgsLU3Jysk6ePFlm36FDhyoxMVHTpk3TmDFj9L///U+bN29W27Zt7RbkuvPOO+Xr66snnnhCDz/8sPLy8vTvf/9bAQEByszMrPS1lSbyixYtUkRERJlkt3///mrSpIl2796tfv36KSAgwOE5K3PdrlaZ+9e5c2dt3rxZr7/+um655Rb5+/urT58+Zc7p6+urmTNnau7cuRo9erSioqJsr09q0aKFbdr1zz//rHHjxmnIkCHq0KGDfHx89MknnygrK+u6fsEAAEBNQCIMACYYNGiQkpKStG7dOu3Zs0ebN2+Wn5+fQkJCNGfOHD300EOVPmfjxo310ksvadGiRdq6dasCAwO1YMGC6zpXQkKCmjZtqh07dmjPnj264447tHr1avXv399uv6ZNm+rvf/+7XnrpJS1btkwtW7bUjBkzdPLkSbtEuF27dnrttde0fPlyLVmyRIGBgXrkkUfk7++vefPmVfra7rrrLo0ZM0YffvihkpKSZBiGXVLm5+enoUOH6u23377u1xZV5rpdrTL3b+rUqTpz5ozWrl2rvLw83X777ddMhCVpxIgRqlOnjtasWaPExETVq1dPgwcP1jPPPGN7h/DNN9+sqKgoHThwQElJSfLx8VG7du20fPly3X333dV+7QAAuIKX8cd5VwCAGmnMmDE6f/68PvjgA6eeNyQkRNOmTVNsbKxTz1sdEhIS9O6772r//v22qdQAAADXwjPCAIAa78qVK0pKStLdd99NEgwAABxiajQAoMbKzs7Wl19+qY8++kgXLlzQY489ZnZIAACgBiARBgDUWGlpaZo5c6YCAgI0f/58derUyeyQAABADcAzwgAAAAAAS+EZYQAAAACApZAIAwAAAAAshUQYAAAAAGApJMIAAAAAAEshEQYAAAAAWAqJMAAAAADAUkiEAQAAAACWQiIMAAAAALAUEmEAAAAAgKX8f6V5bjOhNFqLAAAAAElFTkSuQmCC",
      "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": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:30.692507Z",
     "iopub.status.busy": "2025-06-02T23:40:30.692357Z",
     "iopub.status.idle": "2025-06-02T23:40:32.120266Z",
     "shell.execute_reply": "2025-06-02T23:40:32.119758Z",
     "shell.execute_reply.started": "2025-06-02T23:40:30.692493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05 ---> -0.13874279747315943\n",
      "0.1 ---> -0.09978963229980861\n",
      "0.2 ---> -0.05967323494712444\n",
      "0.5 ---> 0.004753915021233241\n",
      "0.8 ---> 0.06843589391115126\n",
      "0.9 ---> 0.10564971376140435\n",
      "0.95 ---> 0.1393988264078789\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(total_clip_ratios, bins=np.linspace(-0.5, 0.5, 100))\n",
    "for percentage in [0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95]:\n",
    "    print(percentage, \"--->\", np.quantile(sorted(total_clip_ratios), percentage))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:32.121002Z",
     "iopub.status.busy": "2025-06-02T23:40:32.120847Z",
     "iopub.status.idle": "2025-06-02T23:40:45.283439Z",
     "shell.execute_reply": "2025-06-02T23:40:45.282927Z",
     "shell.execute_reply.started": "2025-06-02T23:40:32.120987Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Differences in quality between positive and negative vs previous chunk\n",
      "0.05 ---> -0.13923960572588498\n",
      "0.1 ---> -0.10312127495953863\n",
      "0.2 ---> -0.0646334301316078\n",
      "0.5 ---> 0.0009429718343611192\n",
      "0.8 ---> 0.06695529425447996\n",
      "0.9 ---> 0.10576935498039443\n",
      "0.95 ---> 0.14241861478224402\n",
      "Differences in quality between positive and negative for the same chunk\n",
      "0.05 ---> -0.19194959571549972\n",
      "0.1 ---> -0.13464766692517485\n",
      "0.2 ---> -0.07932831323135427\n",
      "0.5 ---> 0.006204315751487727\n",
      "0.8 ---> 0.08784245715347849\n",
      "0.9 ---> 0.1371498840207766\n",
      "0.95 ---> 0.1846474979009251\n"
     ]
    },
    {
     "data": {
      "image/png": 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SRNKF72VMTIz+/PNP3X333brqqqu0e/duvfDCC0pMTNRTTz3ldD9yj3tliqsyMjI0atSoAnFVsZgusH79ejMwMNDcs2eP0zodO3Y077zzTuv1/PnzzcDAQIc6MTExZq9evQq896abbjJHjBjhULZx40azdevW5tdff+1Q/sYbb5iBgYHmt99+a5UFBgaarVu3Ng8ePOhQd9KkSWaXLl3MlJQUh/JHH33U7Nixo5menm6apmnu3LnTDAwMNHv27GlmZmZa9VasWGEGBgaa+/fvN03TNM+fP29269bNvOmmm8zTp087rNNut1v/Hzp0qHn77bc7rMtut5sDBw40b731Vqvs73//e4H9Lo7c9u7cudMqi4mJMQMDA824uDirLDMz0+zSpYv58MMPF7m+H3/80QwMDDSnTZvmUP7YY4+ZgYGB5vz5862yCRMmmDfddFOBdRT2eece37z+7//+z7z55psdymJiYsyYmBjr9dGjR83AwEBz/fr1Vtn06dMLrN80TfPIkSNmYGCguWbNGofyBx54wLzpppscPpfCBAYGmoGBgeb3339vlf3+++9mu3btzNGjR1tlxT2X9uzZU6DtpmmaOTk5ZocOHcw5c+aYpnnhfLjuuuvMMWPGmG3atDFTU1NN0zTNZcuWma1bt7bOr6+//toMDAw03333XYf1ffHFFw7lqampZqdOnczJkyc71EtMTDQ7duzoUD5hwgQzMDDQXLBggUPdO++80+zTp0+Rx8s0L3xfAwMDzQ8//NAqO3v2rNmlSxeHa8Czzz5rBgYGOnyHU1NTre9QTk6OaZqm+eCDDxZ6Xcgr9xp09OhRq6xXr14O502u/N8Pu91udu3atcD3ID4+3qF9JTmGhcnMzLT2KdfRo0fN4OBgh2Nd3OtNVlaWGR4ebt5xxx0O9d58800zMDCw0H3PLzAw0Gzbtq3566+/WmW53/dVq1ZZZcU9v1977TUzMDDQ/Oijj6w6GRkZ5m233VbgmlTY93/RokVmUFCQ+fvvv1tlzr7bue3Pe/157LHHzPDwcPP8+fNW2YkTJ8zWrVs7HOPiXoMLs3XrVjMwMND89NNPHcrvv/9+h2tXcc7bwuR+/l27djXPnj1rleeejytWrLDKcq/rb7zxhsM6ivv7mHt9zPtZm6ZpTps2zQwNDXX4jPIf61GjRplt27Y1f/vtN6vszz//NMPCwsxBgwZZZYVd+02z4Hf2o48+umgcUZgzZ86YYWFhZv/+/c2MjAyHZXmv78X9DSzsWmKal/e7mvveadOmmYGBgeadd95pdu7c2fzhhx8K7A+xErESsRKxErESsVKuyhormWbhv0nLly83AwMDzXfeeccqy8rKMgcOHGiGhoZacU/u9atDhw5mcnLyRbdlmqYZGhpqTpgwoUB57nX1ySefdCgfPXq0ed1111mvjx07ZrZp08Z85ZVXHOrt37/f/Nvf/lagPL+OHTua06dPL7JOcT/P3OvLwoULrbLTp0+bISEhZlBQkPnBBx9Y5YcPHy7wGb/88stmaGio+fPPPztsa+7cuWabNm3M48ePO21jZYqrNm3aZKamppoxMTFO46qLqXCPbeby9vbWuXPnSm19mzdvVosWLXTNNdcoJSXF+nf99ddLUoHs/bXXXuvw3LVpmvrPf/6jbt26yTRNh3VERkbq7NmzDt0sJalv374OPQ06deok6cJdEunCna5jx45pyJAhBTLTNptN0oVeAjt37lTPnj2VmppqbfPkyZOKjIzUL7/8oj///FOS5Ovrq4MHD+qXX34phSN24TO44447rNeenp5q166d1X5ntmzZIkkFHu/Jf3egpPL2njh79qxSUlJ03XXX6ejRozp79uxlrTtX8+bN1b59e4dupqdOnbIy57mfS1HCwsIUHBxsvW7YsKFuvvlmbdu2TTk5OZd0LuVnGIbCwsKsO6eHDx/WqVOnNGLECJmmaT0K9c0336hVq1bW+bV582bVrl1bXbp0cdhu27Zt5e3tbX0Ptm/frjNnzqhXr14O9QzDUPv27Qu92/WPf/zD4XXHjh2LNb25JF1xxRUOd6R8fHx055136ocfflBiYqKkC+dVSEiI9T2SpFq1amngwIH6/fffdejQIUkXvgd//PGH9uzZU6xtl5TNZtNtt92mLVu2OFyjNm3apCuvvNK6630pxzAvT09PGcaFS3ROTo5Onjwpb29vNW/eXD/88EOB+he73uzdu1fJycm6++67Her16dNHtWvXLvb+R0REONyda926tXx8fKztlOT83rp1q6688krdfPPN1vpq1KhRaC+avN//tLQ0paSkKCwsTKZpFno8iqNnz55KTk52eBzhww8/lN1ut3pRleQaXJjrr79efn5+io+Pt8pOnz6t7du3O/TUutzz9s4773SYefG2225T/fr1retxLk9PzwIDPRf397F58+Zq06aNw77k5OToww8/VLdu3Rw+o7xycnL05Zdfqnv37tZdTunC9/7222/Xt99+q9TU1BLtb+45+/nnnys7O7vY7/vyyy917tw5jRgxosC4Lvmv75f6G1iUkqwz97GM33//XatWrSp0IHRiJWKl/IiV/kKsdAGx0l+IlUquPGIlZ7744gvVr19ft99+u1Xm4eGhwYMHKy0trcCj3rfeeqv8/f0vaT/zu/vuux1ed+rUSadOnbLilY8++kh2u109e/Z0+Pzq1aunq6+++qLnrq+vr/773/8WeVxK+nn279/fYf3NmzeXl5eXQ+/Pa665Rr6+vg6/UZs3b1bHjh3l6+vrsC8RERHKyckp9JH6XJUprjp79qyGDx+uI0eOOI2rLqZCPrYpXThBAgICSm19v/76qw4fPqzw8PBClycnJzu8zt9FOCUlRWfOnNGbb76pN998s9B1pKSkOLzOnQ4+V+6Pcu7YC7kfam5318L89ttvMk1TL774ol588UWnbb/yyis1ZswYjRo1Sj169FBgYKAiIyN1xx13OMzEVRINGjQocNLXqVNH+/fvL/J9v//+uwzDKND99ZprrrmkduT69ttv9dJLLykhIaHAM+dnz54t0Y9aUe644w4988wz+v3339WoUSNt3rxZ2dnZDl/Molx99dUFypo1a6b09HQrICjpuVSYTp06acGCBcrIyNA333yj+vXrq23btmrdurW++eYbdenSRd9++63DBfPXX3/V2bNnL/o9yP2jwlkQn/ePdOnCj3j+H6s6deoUOjZIYa6++uoC51qzZs0kXTif6tevr+PHjxf6aFfueXX8+HEFBgbq/vvv1/bt29W/f39dffXV6tKli26//XYrUCsN0dHRWrFihT799FP17t1b586d05YtWzRw4EBrP0p6DPOz2+1auXKl1qxZo2PHjjmMDZL3MZlcF7veHD9+XFLB89PDw8MhoXExhT2yUKdOHWs7JblW/v7772ratGmBzz7/tSO3/fPnz9enn35a4LwqaeIl1w033KDatWsrPj7e+k7Ex8erTZs2at68uaSSXYML4+7urltvvVXvv/++srKy5Onpqf/85z/Kzs52SJ5d7nmb/3O12Wy6+uqr9fvvvzuUX3nllQUGzy3J72N0dLReeOEF/fnnn7ryyiv11VdfKTk52eE6k19KSorS09OtY5pXixYtZLfb9b///U+tWrW66H7muu6669SjRw8tWLBAy5cv13XXXafu3burd+/eRQ4O/Ntvv0lSsbZ1qb+BpbXO999/X9KFRzuctZdY6QJipb8QKzkiVrqAWOkCYqWSK49YyZnff/9dV199tZUgzdWiRQtJf31euYp6xLeknJ0rp0+flo+Pj3755ReZpqlbb7210Pfnn7wnv8cff1wTJ07UjTfeqLZt2yoqKkp33nmnw3lWks+zsOtL7dq1C/2Nql27tsNYkL/++qv279/v9JpX1LW2MsVVM2fOVFZWluLi4koUc+ZVIZNnf/zxh86ePVvoReFS2e12BQYGOh1MuEGDBg6v899Bzx0s7+9//7v69OlT6DryD9qa/4ueyzTNYrU573b/7//+T127di20Tu5xuvbaa/XRRx/pk08+0Zdffqm3335bK1as0PTp0x0y0cVVHtMUO7s7mX/w1N9++0333nuvrrnmGk2cOFFXXXWVPDw8tGXLFi1fvrzYgxkWR69evRQbG6v33ntPDzzwgN59910FBwdfdkCb61LOpcJ07NhR2dnZ2r17t7755hvr7lnHjh31zTff6PDhw0pJSXG4+2i32xUQEKC5c+cWus7ci27uOTpnzhyHsY1y5T83XDGltTMtWrTQ5s2b9fnnn2vr1q36z3/+ozVr1mj06NEOA5pfjtDQUDVq1EibNm1S79699dlnnykjI8MhGVLSY5jfwoUL9eKLL6pfv34aO3as6tSpI8MwNHPmzEKvIaVxvSkOZ+3O3U5pnd955eTkaNiwYda4Btdcc428vb31559/auLEiZf8/ff09LTGJJs6daqSk5P13XffWWND5d2f4lyDnenVq5fefPNNffHFF+revbs2b96sa665xuGP9fI4b6WCv21SyX4fe/bsqeeff16bNm3Svffeq02bNql27dq64YYbSqV9xf1NsNlsmj9/vhISEvTZZ59p69atmjRpkpYtW6Y333xTtWrVuuy2FOe65qy9zs7Jklwr//a3vykhIUFxcXHq2bNnge84sVLB7RIrESvlR6xUOGIlR8RKzpVXrFQanPWAvxQXO1fsdrtsNpsWL15c6Oft7e1d5Pqjo6PVqVMnffTRR/ryyy+1dOlSLV68WC+99JKioqJK/Hk6O+cudi7m7kuXLl103333FVo3N0l/uVwdV918882Kj4/Xq6++qjlz5jj9jItSIZNn77zzjiQpMjKy1NbZtGlT/fTTTwoPDy9Wd/L8/P39VatWLdntdkVERJRKm3IzywcOHHC6ztw6Hh4exdpu3bp11a9fP/Xr10/nzp1TTEyMXnrppUsKCC9Vo0aNZLfb9dtvvzkEUUeOHClQ19fX1yHznSv/nYRPP/1UWVlZeuWVVxzuBFysS6wzRZ0DdevW1Y033qj33ntPvXv31nfffadJkyYVe92Fzdrzyy+/yMvLywq4insuFdXOkJAQeXh46Ntvv9W3336r4cOHS7rwh8Fbb72lnTt3SpJDQNi0aVPt2LFDHTp0KPIHJve8CwgIKLXzvSi//vqrTNN02N/cu5GNGjWSdOEO0M8//1zgvbnnVd7zwtvbW9HR0YqOjlZWVpYefvhhLVy4UCNHjnQ6BXdJrws9e/bUypUrlZqaqvj4eDVq1EihoaHW8ss9hh9++KE6d+6smTNnOpSfOXPGGmy2JHKPz6+//upwZyk7O1vHjh275F4X+ZXkWtmoUSMdOnSowGefexcr14EDB/TLL79o9uzZuvPOO63ywmbHu5TPMS4uTjt27NDhw4dlmqZDD4SSXoMLc+2116p+/fqKj4+3BgXPHWw5r0s5b3Plv+6Ypqlff/21WMF3SX4fmzRpopCQEG3atEkxMTH6z3/+o+7duxfZ28vf319eXl5Ov7+GYVh36fP2Asj7iF7+34RcoaGhCg0N1aOPPqr33ntPjz/+uOLj453+5uUG7wcPHiy050tJ5bYx/+Nw+Xv8XYrc5NnWrVs1ffp0TZ8+3WE5sVLBOsRKxEr5EStdQKxUPMRKhSuPWKkwjRo10v79+2W32x0SHYWdz+WtadOmMk1TjRs3LrRnfXFcccUVGjRokAYNGqTk5GT16dNHCxcuVFRUVIk+z8vVtGlTpaWlXdJnV5niqu7duysyMlITJ05UrVq1CsRVxVHhxjzbsWOH/v3vf6tx48bFmq2nuHr27Kk///xT69atK7AsIyNDaWlpRb7fzc1NPXr00IcffugwJW+u4nQdz69t27Zq3LixVq5cWSAoys0GBwQE6LrrrtObb76pEydOFLndkydPOiyrVauWmjZtWmB6+bKW2wNh1apVDuX5p5SXLnzhzp496zCl9IkTJwrMmJabVc6bJT979qzWr19/SW308vKSpEKDUenC4wiHDh3SnDlz5Obmpl69ehV73bt373YYh+N///ufPvnkE3Xp0kVubm4lOpeKameNGjXUrl07vf/++zp+/LgV+HXq1EkZGRlauXKlmjZtqiuuuMJ6T8+ePZWTk1NgxjRJOn/+vLWdrl27ysfHR4sWLSp0PKFLOd+Lkv8zT01N1caNG9WmTRvrTmRUVJT27Nmj3bt3W/XS0tK0bt06NWrUyBp3J//3wNPTUy1atJBpmkWOjeTl5eX0fChMbrAZFxenrVu3Fnhs7XKPoZubW4E7oZs2bbqkMSMkKTg4WP7+/lq7dq3DNSEuLq5E+30xJTm/IyMj9eeffzpMY56ZmVngOp0bMOU9HqZpauXKlQXWf7Hvdn4RERGqW7eu4uPjtWnTJoWEhDh0mS/JNdgZwzB022236bPPPtO7776r8+fPF5iZ8lLP21wbN2506MK/efNmJSYmFqtHWEl/H6Ojo5WQkKD169fr5MmTRT6yKV04J7p06aJPPvnEYWyfpKQkvf/+++rYsaP1aE5uEJZ3fI20tDRt3LjRYZ2nT58u8P3IHbuiqN+8yMhI1apVS4sWLVJmZqbDskvpeVBYe3Nycgo9lpdq+PDhWrt2rZ577jmrjFjpAmIlYqWLtZNYiVipJIiVClcesVJhbrjhBiUmJjqMtXr+/HmtWrVK3t7eDjPklpS3t/dlfaa33nqr3NzctGDBggLnoGmaBb5jeeXk5BRIDgUEBOiKK66wzruSfJ6Xq2fPntq9e7e2bt1aYNmZM2d0/vx5p++tbHHVnXfeqcmTJxeIq4rLpT3PvvjiCx05ckQ5OTlKSkrSrl279OWXX6phw4Z65ZVXLnqnvSTuuOMObdq0SVOnTtWuXbvUoUMH5eTk6MiRI9q8ebOWLFmidu3aFbmOcePGadeuXRowYID69++vli1b6vTp09q3b5927NjhMJBicRiGoWnTpunBBx/UnXfeqb59+6p+/fo6cuSIDh06pKVLl0q6MN7JPffco969e2vAgAFq0qSJkpKSlJCQoD/++EPvvvuupAtd6K+77jq1bdtWdevW1ffff29NgVue2rRpo9tvv11r1qzR2bNnFRYWpp07dxZ6lzE6Olpz587VQw89pMGDBysjI0NvvPGGmjdv7hBUdenSRR4eHnrggQd0991369y5c3rrrbcUEBBgDZJaErlTAM+YMUORkZEFgr6oqCjVrVtXmzdv1g033FCiMWUCAwM1fPhwh+nXJenhhx+26hT3XGratKl8fX21du1a1apVS97e3g4/WJ06ddKrr76q2rVrW+PBBAQEqHnz5vr5558LDAx+3XXXaeDAgVq0aJF+/PFH67j+8ssv2rx5s5566inddttt8vHx0bRp0zR+/Hj17dtX0dHR8vf31/Hjx7VlyxZ16NBBU6ZMKfFxd6ZZs2Z66qmn9P333ysgIEDr169XcnKyYmNjrTojRozQBx98oPvvv1+DBw9WnTp1tHHjRh07dkwvvfSS9SMzfPhw1atXTx06dFBAQICOHDmi119/XVFRUUWOndG2bVu98cYb+ve//62rr75a/v7+Tp/9z61/9dVX61//+peysrIKJEMu9xjeeOONevnll/Xkk08qLCxMBw4c0HvvvVeiMTfy8vDw0COPPKIpU6Zo6NChio6O1rFjx7Rhw4ZLXqczxT2/Bw4cqNdff13jxo3TkCFDVL9+fb333nvWtT/3zug111yjpk2bavbs2frzzz/l4+OjDz/8sNCg52Lf7cKOyy233KIPPvhA6enpmjBhQoE6xb0GF6Vnz55atWqV5s+fr8DAQGu8jlyXet7mqlOnju655x717dtXycnJWrFiha6++upCBxTOr6S/jz179tTs2bM1e/Zs1a1bt1h3Kh955BFt375d99xzj+655x65ubnpzTffVFZWlsO07126dFHDhg311FNP6ciRI3Jzc9P69evl5+fn0MsmLi5Ob7zxhrp3766mTZvq3LlzWrdunXx8fIpMGPr4+OjJJ5/U5MmTddddd+n222+Xr6+vfvrpJ2VkZGj27NkX3Ze8WrVqpdDQUL3wwgs6ffq06tSpo/j4+CIDzZKqVauWbrvtNi1ZssRKnhArESsRKxErESsRK0lVK1bKb+DAgXrzzTc1ceJE7du3T40aNdKHH35o9XQtTnzkTNu2bbVjxw4tW7ZMV1xxhRo3blzoeIHONG3aVI888oief/55/f777+revbtq1aqlY8eO6eOPP9aAAQOsnq75nTt3TlFRUerRo4dat24tb29vbd++Xd9//70mTpwoqWSf5+UaPny4Pv30Uz3wwAPq06eP2rZtq/T0dB04cEAffvihPvnkE6cTMVTGuComJkapqan617/+pdq1axf6NIgzLk2ezZ8/X9KFL2TdunUVGBioSZMmqW/fvpf1ZSiMYRh6+eWXtXz5cr3zzjv66KOP5OXlpcaNG2vw4MHF6m5Zr149vfXWW3r55Zf10Ucf6Y033lDdunXVsmVLPf7445fUrq5du2rFihV6+eWX9dprr8k0TTVp0sThD56WLVtq/fr1WrBggeLi4nTq1Cn5+/vrb3/7m0aPHm3VGzx4sD799FN9+eWXysrKUsOGDfXII484/eKWpZkzZ8rPz0/vvfeePvnkE3Xu3FmvvvqqoqKiHOr5+flpwYIFmjVrlp577jk1btxYjz32mH799VeHgPCaa67R/PnzNW/ePM2ePVv16tXTP/7xD/n7+5foMYFct956qwYPHqwPPvhA7777rkzTdPjR8PT0VHR0tNasWVPswW9zXXvttQoNDdXLL7+s48ePq2XLloqNjXXo6l3cc8nDw0OzZs3SCy+8oGnTpun8+fOKjY0tEBCGhYU5dGfu1KmTfv7550IHfn366acVHBystWvX6l//+pfc3NzUqFEj/f3vf1eHDh2ser1799YVV1yhV199VUuXLlVWVpauvPJKderUqUCgebmaNWumf/7zn5ozZ45+/vlnNW7cWP/6178cxk2oV6+edZfg9ddfV2ZmpoKCgrRw4ULdeOONVr2BAwfqvffe07Jly5SWlqYGDRpo8ODBGjVqVJFtGD16tI4fP64lS5bo3Llzuu6664oMCKULSYSFCxfq6quvtgKRvC7nGD7wwANKT0/Xe++9p/j4eP3tb3/TokWL9Pzzzxf5vqIMHDhQOTk5Wrp0qebMmaPAwEC98sorTgd3vVTFPb9r1aqlFStWaMaMGVq5cqW8vb115513KiwsTA8//LAVGHp4eGjhwoWaMWOGFi1apBo1auiWW27RoEGDCnw/L/bdLkx0dLTeeust2Wy2QntRFfcaXJQOHTroqquu0v/+978CfzxIl37e5nrggQe0f/9+vfrqqzp37pzCw8M1depU6+5yUUr6+9igQQOFhYXpu+++U//+/eXh4XHRbbRq1UqrV6/W888/r0WLFsk0TYWEhOi5555zCFY9PDy0YMECTZ8+XS+++KLq16+voUOHytfX12Ecruuuu07ff/+94uPjlZSUpNq1ayskJERz58696B84/fv3V0BAgF599VX9+9//lru7u6655hrde++9F92PwsydO1dTpkzRq6++Kl9fX911113q3Lmzhg0bdknryy83VjIMQ9u2bdM111xDrESsRKxErESsJGKlqhYr5VezZk2tWrVKc+fOVVxcnFJTU9W8eXPFxsZe9vdr4sSJmjJliubNm6eMjAz16dOnRMkz6UKyulmzZlq+fLlefvllSRdipC5duqhbt25F7tc//vEPffnll/rPf/4j0zTVtGlTKwEplezzvFxeXl5atWqVFi1apM2bN2vjxo3y8fFRs2bN9PDDD190opnKFldJF64dZ8+etRJogwYNKtb7bGZpj5AIFCEoKEgPPfSQw53FimrmzJl6++239eWXXxbrD1Bcmm7duqlVq1ZatGiRq5uCCmL58uWKjY3VF198UeKZmaqbXbt2aciQIXrxxRd12223ubo5AEoBsRLyI1ZCfsRKQPmrcGO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AAAAAAACAEyTPAAAAAAAAACdIngEAAAAAAABOMOYZAAAAAADlyM3tr34sdrspu910YWsAXAzJMwAAAAAAyoG3u4fspilf378m4bLb7Tp5Mo0EGlCBkTwDAAAAAKAc1HRzl2GzKf7oQaVkpsu/hpeim7SSYdhIngEVGMkzAAAAACiEYdhkGDbrdd5H7YDLkZKZrhMZaa5uBoBiInkGAAAAAPkYhk1+ft4yDBJmAFDdkTwDAAAAgHwu9DozrMfrJKmZT11FNmjq4pYBAMobyTMAAAAAcCLv43X+NbwuUhsAUBWRPAMAMF06AAAAADhB8gwAqjGmSwcAAACAopE8A4BqjOnSAQAAAKBoTB0DwIFh2OTubsjd3WA69mokdzyX3AGRAZSur7/+Wg888IAiIyMVFBSkjz/+2GG5aZp68cUXFRkZqZCQEN1777365ZdfHOqcOnVK48aNU4cOHdSpUydNmjRJ586dc6jz008/6Z577lG7du0UFRWlxYsXF2jLpk2bdNttt6ldu3bq3bu3tmzZUur7CwAAUJXwlzEAS+6U7H5+teTnV8vhUT4AwKVLS0tTUFCQpk6dWujyxYsXa9WqVZo2bZrWrVsnLy8vDR8+XJmZmVadxx9/XIcOHdKyZcu0cOFCffPNN5oyZYq1PDU1VcOHD1fDhg21YcMGjR8/XgsWLNCbb75p1fnuu+80btw43XXXXdq4caNuvvlmjR49WgcOHCi7nQcAAKjkSJ4BsOSdkv31Q3u07Y/fXN0kAKgSoqKi9Oijj+qWW24psMw0Ta1cuVIPPvigunfvrtatW2vOnDk6ceKE1UPt8OHD2rp1q2bMmKH27durU6dOmjx5sj744AP9+eefkqR3331X2dnZmjlzplq1aqVevXpp8ODBWrZsmbWtlStXqmvXrrrvvvvUokULPfLII/rb3/6m119/vXwOBFDB0QMfruLmZljnnru7IcOwubpJAPLgFwFAAbmP8J3Jzrx4ZQDAZTl27JgSExMVERFhldWuXVvt27fX7t27JUm7d++Wr6+v2rVrZ9WJiIiQYRjas2ePJCkhIUGdOnWSp6enVScyMlI///yzTp8+bdUJDw932H5kZKQSEhLKaveASoMe+HCFvJM35Z57F/55k0ADKhAmDAAAAHChxMRESVJAQIBDeUBAgJKSkiRJSUlJ8vf3d1ju7u6uOnXqWO9PSkpS48aNHerUq1fPWlanTh0lJSVZZYVtB6jO8vbAT8lMVzOfuops0NTVzUIVl3/yJklM4ARUQCTPAAAAAOD/y+2B71+DnmcoP7nnHYCKicc2AQAAXKh+/fqSpOTkZIfy5ORkq5dYvXr1lJKS4rD8/PnzOn36tPX+evXqFehBlvs673ry18m7HQAAABRE8gwAAMCFGjdurPr162vHjh1WWWpqqv773/8qLCxMkhQWFqYzZ85o7969Vp2dO3fKbrcrJCREkhQaGqpvvvlG2dnZVp3t27erefPmqlOnjlVn586dDtvfvn27QkNDy2r3AAAAKj2SZwAAAGXs3Llz+vHHH/Xjjz9KujBJwI8//qjjx4/LZrNpyJAheuWVV/TJJ59o//79Gj9+vK644gp1795dktSiRQt17dpV//znP7Vnzx59++23euaZZ9SrVy9deeWVkqTevXvLw8NDTz31lA4ePKj4+HitXLlSw4YNs9oxZMgQbd26Va+99poOHz6sl156SXv37lVMTEz5HxQAAIBKgjHPAAAAytjevXs1ZMgQ63VsbKwkqU+fPpo1a5buv/9+paena8qUKTpz5ow6duyoJUuWqEaNGtZ75s6dq2eeeUZDhw6VYRi69dZbNXnyZGt57dq1tXTpUj399NPq27ev/Pz8NGrUKA0cONCq06FDB82dO1fz5s3TCy+8oGbNmunll19WYGBgORwFAACAyonkGQAAQBnr3Lmz9u/f73S5zWbT2LFjNXbsWKd16tatq+eff77I7bRu3Vpr1qwpsk7Pnj3Vs2fPohsMAAAAC49tAgAAAAAAAE7Q8wwAUICbm+O9FbvdlN1uuqg1AAAAAOA6JM8AABZvdw/ZTVO+vl4O5Xa7XSdPppFAAwAAAFDtkDwDAFhqurnLsNkUf/SgUjLTJUn+NbwU3aSVDMNG8gwAAKCc8CQAUHGQPAMAFJCSma4TGWmubgYAAEC1w5MAQMVD8gwAAABAtWMYNhmGzXqdv5cP4Co8CQBUPCX+hfj666/1wAMPKDIyUkFBQfr4448dlpumqRdffFGRkZEKCQnRvffeq19++cWhzqlTpzRu3Dh16NBBnTp10qRJk3Tu3DmHOj/99JPuuecetWvXTlFRUVq8eHGBtmzatEm33Xab2rVrp969e2vLli0l3R2gWjMMm9zdDesfQSMAAKgODMMmPz9v+fnVsv7l7+UDuFrukwAnMtKsJBoA1yjxX8ppaWkKCgrS1KlTC12+ePFirVq1StOmTdO6devk5eWl4cOHKzMz06rz+OOP69ChQ1q2bJkWLlyob775RlOmTLGWp6amavjw4WrYsKE2bNig8ePHa8GCBXrzzTetOt99953GjRunu+66Sxs3btTNN9+s0aNH68CBAyXdJaBaImgEAADV1YVeZ4bijx7U64f26PVDe7Ttj99c3SwAQAVV4sc2o6KiFBUVVegy0zS1cuVKPfjgg+revbskac6cOYqIiNDHH3+sXr166fDhw9q6davefvtttWvXTpI0efJkjRgxQuPHj9eVV16pd999V9nZ2Zo5c6Y8PT3VqlUr/fjjj1q2bJkGDhwoSVq5cqW6du2q++67T5L0yCOPaPv27Xr99df19NNPX9LBAKqTvEFj7p2sZj51FdmgqYtbBgAAUD7yjvHpX4ObiACAwpXqM1rHjh1TYmKiIiIirLLatWurffv22r17tyRp9+7d8vX1tRJnkhQRESHDMLRnzx5JUkJCgjp16iRPT0+rTmRkpH7++WedPn3aqhMeHu6w/cjISCUkJJTmLgFVXt7u4GeyMy/+BgAAAAAAqpFSTZ4lJiZKkgICAhzKAwIClJSUJElKSkqSv7+/w3J3d3fVqVPHen9SUpLq1avnUCf3dd715K+TdzsAAAAAAADA5WK2TQAAAABVGjNrAgAuR6kmz+rXry9JSk5O1hVXXGGVJycnq3Xr1pIu9CBLSUlxeN/58+d1+vRp6/316tUr0IMs93Vub7PC6iQnJxfojQYAAACg+sqdJMkwSJgBAC5Nqf6CNG7cWPXr19eOHTusstTUVP33v/9VWFiYJCksLExnzpzR3r17rTo7d+6U3W5XSEiIJCk0NFTffPONsrOzrTrbt29X8+bNVadOHavOzp07Hba/fft2hYaGluYuAQAAAKjEmFkTAHC5Spw8O3funH788Uf9+OOPki5MEvDjjz/q+PHjstlsGjJkiF555RV98skn2r9/v8aPH68rrrjCmn2zRYsW6tq1q/75z39qz549+vbbb/XMM8+oV69euvLKKyVJvXv3loeHh5566ikdPHhQ8fHxWrlypYYNG2a1Y8iQIdq6datee+01HT58WC+99JL27t2rmJiY0jguAAAAAKoQJklCVeDmZsjd/a9/eR9HBlB2SvzY5t69ezVkyBDrdWxsrCSpT58+mjVrlu6//36lp6drypQpOnPmjDp27KglS5aoRo0a1nvmzp2rZ555RkOHDpVhGLr11ls1efJka3nt2rW1dOlSPf300+rbt6/8/Pw0atQoDRw40KrToUMHzZ07V/PmzdMLL7ygZs2a6eWXX1ZgYOAlHQgAQNHyjw9jt5uy200XtQYAAKD68Hb3kN005evr5VBut9t18mQaMRlQxkqcPOvcubP279/vdLnNZtPYsWM1duxYp3Xq1q2r559/vsjttG7dWmvWrCmyTs+ePdWzZ8+iGwwAuCwEawAAAK5V081dhs2m+KMHlZKZLknyr+Gl6CatZBg24jGgjDHbJlBNMMsULhXBGgAAQMWQ+/gxgPJF8gyoBphlCqWBYA0AAABAdUTyDKgG8s4yldtzqJlPXUU2aOrilgEAAAAAULGRPAOqkbw9h/xreF2kNgAAAAAAIHkGAAAAoMpgnFdUN8yIDpQ9kmcAAAAAqgTGeUV1wozoQPkheQYAAACgSmCcV1QnzIgOlB+SZwAAAACqFMZ5RXXCjOhA2aM/MwAAAAAAAOAEPc8AAJeMAWoBAAAAVHUkzwAAJcYAtQCAioCZNQEA5YHkGVAFEUiirDFALQDA1ZhZEwBQXkieAVUMgSTKEwPUAgBchZk1AQDlheQZUMUQSAIAgOqEmTWBgvI+ecKYtMDlI3kGVFEEkgAAAED1Uti4tIxJC1w+kmcAAAAAAFQB+celZUxaoHSQPAMAAABQKeSdFIkJkQDnGJcWKF0kzwAAAABUeEyKBABwFZJnAIBSlb8nAIPUAgBKQ/5JkZgQCQBQXkieAQBKRWED1EoMUgsAKF25j6MxIRJQfNzcBC4PyTMAQKnIP0CtJAapBQAAcCFubgKlg+QZUMnlHThXYvBcuB4D1AIAAFQM3NwESgfJM6ASY+BcAAAAABfDzU3g8pA8Ayqx/APnSmLwXAAAUCXQux4AUFGQPAOqgLx3khg8FxURg9QCAEqC3vUAgIqE5BkAoMwwSC0AoDgK62VG73qgbHFzEyg+kmdAJZM3uOTxBVR0DFILALiYonqZ0bseKH3c3ARKjuQZUInwCAMqKwapBQA4wxiuQPni5iZQciTPgEokf3BJYAkAAKoKepkB5Yubm0DxkTwDKqHcHzoCSwAAAAClhXHQgMKRPAMAAABQbgqbHACAazEOGlA0kmcAAJfI+8cSdzUBoHpg/FagYmIcNKBoJM+ACow7s6iKCruzyV1NAKgemBwAqNgYBw0oHMkzoILiziyqqvx3NrmrCQDVD5MDAAAqE5JnQAXFnVlUddzZBICqj170QOXHJAIAyTOgwuPOLAAAqIzoRQ9UbkwiAPyF5BkAoELgriYAVH55e5q5uRn0ogcqMSYRAP5C8gwA4FLc1QSAqsFZTzN60QOVW2FDbXDTE9UNyTOggmBMEFRX3NUEgKoh/3it9DIDqh5ueqK6InkGVACMCQIwgQAAVDbObvzlXs/pZQZUPdz0RHVF8gyoAJhZEygcjwQAQMXEjT+gervYo5zEbKhqSJ4BLnCxO7USY4KgeuORAACo2LjxByBXYXEbMRuqGpJnQDnjTi1wcTwSAAAVCzf+ADiTP24jZkNVRPIMKGfcqQWKj9mdAKD85U+U2Ww2+frW5MYfgCLlj9uI2VCVkDwDyhh3aoHSwaOcAFD2iuohz40/AMVBzIaqiOQZUIZ4RBMoPTzKCQClr7CbfM56yHPjD0BxFBWzeXi4KSfHbtWlNxoqC5JnQBniEU2g9PEoJwBcurzJsqIexyRRBuBy5b2O0BsNlR3JM6AcEIACZYNADACKz1mPeG7yAShr9EZDZUfyDChFzsY3A1A2eJQTAJy72COZPI4JoLzRGw2VFckzoJQwvhngOhd7lJM7mACqupLMkJl7zSRRBsCV6I2GyoTkGXAZ8gaqRQ2wC6D8FHYX026368yZDJmmmaeMIAxA5VSSRBlxCYCKrri90fLGcsRxKG8kz4BiKm6gyqMPgGvlv4vZ0Lu2bryqmerW9XaoxyMBACqDy02UEZcAqEwK641WWCzHjVGUN5JnQCEuJVDlbi5QseR9LIlHAgBUBiTKAOCC/Nez4t4YJaGGskLyDMinqLHLigpUCVKBiu1SHgm4UEbQBeDy5U+M5UeiDACKVtSNURJqKGskz1DtXWwmKolAFahqivtIgETQBaBoF0uKSc4TY3bTlGFzfC/xBwAUT1G90yRiO5Qukmeo0i7nLi+BKlD1lVbQRcAFVH6FxQz5v9vFfayysKSYVHhiLP/wD8QfAHDpSjOhlh/xXvVW6ZNnq1ev1tKlS5WYmKjWrVvrn//8p0JCQlzdLLhAcQLakgSzAKqnSwm6ihNwXahH0IWKoarGT8XpBVYYp0mwPN/tkj5WWdweZAz/AABl65Jiu0L+biTeq94qdfIsPj5esbGxmj59utq3b68VK1Zo+PDh2rx5swICAlzdPFyC0g56pYID+vM4BICSuqRBawm6UEFVhvjpUuKBkvQCK87NNGff7ZImxfKWAQAqhqJiu8L+biyLeC8/4r+KrVInz5YtW6YBAwaoX79+kqTp06fr888/1/r16zVixAgXt65qyB+8FvaFvtSEV36lHfQ6G9CfYBZAaShq0FpXBV2Xg4Ct+qjo8ZOziXuKGw8UtxdYcZJghf0xRRwBAFXTxf5uLPV4L18Z8V/FVmmTZ1lZWdq3b59GjhxplRmGoYiICO3evbtE6yokB1MqDMMmW76Vm2bB7V1qWWmuq7CyQh97zPeFvtyEV2FlXyf+rjPZmZKkK2v6KNj/imKXudsMedgutMXt/6/3ipq15GEz5OdZ0+G1pFItK+v1u2KblX39VXGfKvv6q+I+5b4u7PqTt8zr/09ScLHrWUANb4X4X3nRoOtyrrPFSdiV9W9M6a+/7IK/sooTXKGyxE+GYZQ4HigqFihuWWHf9dx6RdWpDNcqV2yT9Ve8bVb29VfFfars66+K+1RUndKK9/KXFTf+u5wyZ/HfqVNpZRJDVaX4SZJsZnmmNUvRn3/+qRtuuEFr165VWFiYVT5nzhx9/fXXeuutt1zYOgAAgIqH+AkAAKDkCg4QBQAAAAAAAEBSJU6e+fn5yc3NTcnJyQ7lycnJqlevnotaBQAAUHERPwEAAJRcpU2eeXp6qm3bttqxY4dVZrfbtWPHDofHEAAAAHAB8RMAAEDJVdoJAyRp2LBhmjBhgoKDgxUSEqIVK1YoPT1dffv2dXXTAAAAKiTiJwAAgJKp1Mmz6OhopaSkaP78+UpMTFSbNm20ZMkSHjsAAABwgvgJAACgZCrtbJsAAAAAAABAWau0Y54BAAAAAAAAZY3kGQAAAAAAAOAEyTMAAAAAAADACZJnAAAAAAAAgBMkz2A5deqUxo0bpw4dOqhTp06aNGmSzp07V6z3mqap++67T0FBQfr444/LuKVVT0mP/alTp/TMM8+oR48eCgkJ0Y033qgZM2bo7Nmz5djqymn16tXq1q2b2rVrp/79+2vPnj1F1t+0aZNuu+02tWvXTr1799aWLVvKqaVVU0mO/7p163TPPffo2muv1bXXXqt77733op8XnCvpuZ/rgw8+UFBQkEaNGlXGLQQqJ+In1yKGKj/EUK5D/OQ6xE/IRfIMlscff1yHDh3SsmXLtHDhQn3zzTeaMmVKsd67YsUK2Wy2Mm5h1VXSY3/ixAmdOHFCEyZM0Pvvv6/Y2Fht3bpVTz31VDm2uvKJj49XbGysRo8erbi4OLVu3VrDhw9XcnJyofW/++47jRs3TnfddZc2btyom2++WaNHj9aBAwfKueVVQ0mP/65du9SrVy+tXLlSa9eu1VVXXaX/+7//059//lnOLa/8Snrscx07dkyzZ89Wp06dyqmlQOVD/ORaxFDlgxjKdYifXIf4CQ5MwDTNQ4cOmYGBgeaePXussi1btphBQUHmH3/8UeR7f/jhB7Nr167miRMnzMDAQPOjjz4q6+ZWKZdz7POKj48327Zta2ZnZ5dFM6uEu+66y5w+fbr1Oicnx4yMjDQXLVpUaP2xY8eaI0aMcCjr37+/+c9//rNM21lVlfT453f+/HkzLCzMjIuLK6MWVl2XcuzPnz9vDhw40Fy3bp05YcIE88EHHyyPpgKVCvGTaxFDlR9iKNchfnId4ifkRc8zSJJ2794tX19ftWvXziqLiIiQYRhFdk1NT0/XuHHjNGXKFNWvX788mlrlXOqxzy81NVU+Pj5yd3cvi2ZWellZWdq3b58iIiKsMsMwFBERod27dxf6noSEBIWHhzuURUZGKiEhoSybWiVdyvHPLz09XefPn1edOnXKqplV0qUe+5dfflkBAQHq379/eTQTqJSIn1yLGKp8EEO5DvGT6xA/IT9+ISBJSkpKkr+/v0OZu7u76tSpo8TERKfvi42NVVhYmLp3717WTayyLvXY55WSkqJ///vfGjhwYFk0sUo4efKkcnJyFBAQ4FAeEBCgI0eOFPqepKQk1atXr0D9pKSkMmtnVXUpxz+/uXPn6oorrnAIYnBxl3Lsv/nmG7399tvauHFjObQQqLyIn1zr/7V3B6/w7WEcx59L/BZWNAsLJEqYSYg/wAIlioWdIkRqWChKWM32JKkJMybFxoqFyGa2ZhYsplioKSERUTiyoPnexS9zDb/jGq75mrnvV83mO+ecnvM0i888Z84cMlRikKH0IT/pQ37CawzPUpxhGOL1et/dZnNz81PH9vv9EgwGZW1t7VP7p7rv7P1LpmlKf3+/FBcXi9Pp/PLxgJ/I4/HI5uamLC0tya9fv3SXk9JM05TR0VFxuVxvvpQC/xfkJ73IUMB/g/yUOOSn1MfwLMV1d3dLW1vbu9vk5+eLzWaT6+vrmPWnpye5ubmxvJ0gGAzK8fGx1NbWxqwPDg5KTU2NLC8vf634JPedvX9mmqb09vZKVlaWuN1uycjI+HLdqSo7O1vS09Pf/MHn1dXVmyujz2w225srpO9tD2uf6f8zn88nHo9HFhcXpbS09DvLTEnx9v7k5EROT09lYGAguhaJREREpLy8XLa2tqSgoOB7iwY0Iz/pRYb6WchQ+pCf9CE/4TWGZykuJyfnQ5Pvqqoqub29lb29PXE4HCLyO9xFIhGpqKj44z59fX1v7uVuaWmRsbExqaur+3rxSe47ey/yO/T19PRIZmamzM7OcjXpX2RmZordbpdAIBC9TSYSiUggEJCOjo4/7lNZWSnBYFC6urqia9vb21JZWZmAilPLZ/ovIuL1emVubk58Pl/Mf9rg4+LtfVFRkayvr8esTU9Py/39vYyPj0tubm5C6gZ0Ij/pRYb6WchQ+pCf9CE/4Q3dTyzAz9HT06NaW1tVKBRSOzs7qqGhQQ0PD0ffPz8/V42NjSoUClkeg6dFfU68vb+7u1Pt7e2qublZHR0dqYuLi+jr6elJ12n8eBsbG8rhcKjV1VUVDofV5OSkqqmpUZeXl0oppUZGRpRhGNHtd3d3VXl5ufL5fCocDquZmRllt9vVwcGBrlNIavH2f35+XtntdrW1tRXzGTdNU9cpJK14e/8aT4sCrJGf9CJDJQYZSh/ykz7kJ7zEL88QZRiGuFwu6ezslLS0NGloaJCJiYno+4+Pj3J4eCgPDw8aq0xN8fZ+f39fQqGQiIjU19fHHMvv90teXl7iik8iTU1Ncn19LTMzM3J5eSllZWWysLAQ/en12dmZpKX98xDi6upqMQxDpqenZWpqSgoLC8XtdktJSYmuU0hq8fZ/ZWVFHh8fZWhoKOY4TqdTBgcHE1p7sou39wA+jvykFxkqMchQ+pCf9CE/4aW/lFJKdxEAAAAAAADAT8SYFAAAAAAAALDA8AwAAAAAAACwwPAMAAAAAAAAsMDwDAAAAAAAALDA8AwAAAAAAACwwPAMAAAAAAAAsMDwDAAAAAAAALDA8AwAAAAAAACwwPAMAAAAAAAAsMDwDAAAAAAAALDA8AwAAAAAAACwwPAMAAAAAAAAsPA3BpHph1UtFsMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:45.284181Z",
     "iopub.status.busy": "2025-06-02T23:40:45.284020Z",
     "iopub.status.idle": "2025-06-02T23:40:51.703839Z",
     "shell.execute_reply": "2025-06-02T23:40:51.703256Z",
     "shell.execute_reply.started": "2025-06-02T23:40:45.284166Z"
    }
   },
   "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": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:51.704610Z",
     "iopub.status.busy": "2025-06-02T23:40:51.704459Z",
     "iopub.status.idle": "2025-06-02T23:40:52.820506Z",
     "shell.execute_reply": "2025-06-02T23:40:52.819918Z",
     "shell.execute_reply.started": "2025-06-02T23:40:51.704593Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(433232, 105)\n",
      "(381626, 105)\n",
      "(381626, 105)\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": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:36.043975Z",
     "start_time": "2024-05-16T13:58:56.910958Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:52.821259Z",
     "iopub.status.busy": "2025-06-02T23:40:52.821104Z",
     "iopub.status.idle": "2025-06-02T23:40:52.883818Z",
     "shell.execute_reply": "2025-06-02T23:40:52.883298Z",
     "shell.execute_reply.started": "2025-06-02T23:40:52.821244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 190813\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": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:40.799375Z",
     "start_time": "2024-05-16T13:59:36.394236Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:52.887236Z",
     "iopub.status.busy": "2025-06-02T23:40:52.886860Z",
     "iopub.status.idle": "2025-06-02T23:40:53.885616Z",
     "shell.execute_reply": "2025-06-02T23:40:53.885029Z",
     "shell.execute_reply.started": "2025-06-02T23:40:52.887220Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    381626\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    190813\n",
      "True     190813\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-ahi-up-1         360040\n",
      "chirp-v4-up-u-d-2-3     21586\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-ahi-up-1         180020\n",
      "            chirp-v4-up-u-d-2-3     10793\n",
      "True        chirp-ahi-up-1         180020\n",
      "            chirp-v4-up-u-d-2-3     10793\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    381626\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": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:53.886342Z",
     "iopub.status.busy": "2025-06-02T23:40:53.886187Z",
     "iopub.status.idle": "2025-06-02T23:40:54.831422Z",
     "shell.execute_reply": "2025-06-02T23:40:54.830899Z",
     "shell.execute_reply.started": "2025-06-02T23:40:53.886326Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    133343\n",
       "2.0     57470\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:54.832143Z",
     "iopub.status.busy": "2025-06-02T23:40:54.831993Z",
     "iopub.status.idle": "2025-06-02T23:40:55.367401Z",
     "shell.execute_reply": "2025-06-02T23:40:55.366825Z",
     "shell.execute_reply.started": "2025-06-02T23:40:54.832129Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    381626.000000\n",
      "mean         23.593549\n",
      "std           2.604508\n",
      "min           6.784946\n",
      "25%          22.279327\n",
      "50%          23.997029\n",
      "75%          25.367710\n",
      "max          32.419675\n",
      "Name: mean_ear_score, dtype: float64\n",
      "count    190813.000000\n",
      "mean          0.164305\n",
      "std           1.973267\n",
      "min         -14.734264\n",
      "25%          -1.066930\n",
      "50%           0.132262\n",
      "75%           1.358529\n",
      "max          12.546717\n",
      "Name: mean_ear_score_diff, dtype: float64\n",
      "count    190813.000000\n",
      "mean          0.003635\n",
      "std           0.086764\n",
      "min          -1.383854\n",
      "25%          -0.045768\n",
      "50%           0.005533\n",
      "75%           0.055784\n",
      "max           0.548265\n",
      "Name: mean_ear_score_diff_ratio, 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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:55.368143Z",
     "iopub.status.busy": "2025-06-02T23:40:55.367992Z",
     "iopub.status.idle": "2025-06-02T23:40:55.945348Z",
     "shell.execute_reply": "2025-06-02T23:40:55.944837Z",
     "shell.execute_reply.started": "2025-06-02T23:40:55.368127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"pos\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"neg\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:55.946069Z",
     "iopub.status.busy": "2025-06-02T23:40:55.945916Z",
     "iopub.status.idle": "2025-06-02T23:40:56.797121Z",
     "shell.execute_reply": "2025-06-02T23:40:56.796606Z",
     "shell.execute_reply.started": "2025-06-02T23:40:55.946054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.901619377579612\n",
      "-11.850077617546217\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[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"],\n",
    "    label=\"web\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"],\n",
    "    label=\"mobile\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.legend()\n",
    "print(df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"].mean())\n",
    "print(df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"].mean())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:56.797861Z",
     "iopub.status.busy": "2025-06-02T23:40:56.797707Z",
     "iopub.status.idle": "2025-06-02T23:40:57.461616Z",
     "shell.execute_reply": "2025-06-02T23:40:57.461100Z",
     "shell.execute_reply.started": "2025-06-02T23:40:56.797846Z"
    }
   },
   "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(-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": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:57.462338Z",
     "iopub.status.busy": "2025-06-02T23:40:57.462182Z",
     "iopub.status.idle": "2025-06-02T23:40:58.106970Z",
     "shell.execute_reply": "2025-06-02T23:40:58.106467Z",
     "shell.execute_reply.started": "2025-06-02T23:40:57.462322Z"
    }
   },
   "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[\"shimmer_score_diff\"] = df[\"total_shimmer_score\"].diff()\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"shimmer_score_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"shimmer_score_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['shimmer_score_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(\n",
    "    f\"Shimmer score difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:58.107699Z",
     "iopub.status.busy": "2025-06-02T23:40:58.107546Z",
     "iopub.status.idle": "2025-06-02T23:40:59.185813Z",
     "shell.execute_reply": "2025-06-02T23:40:59.185291Z",
     "shell.execute_reply.started": "2025-06-02T23:40:58.107684Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BksAcPnyYf//9l1GjRtGlSxfL48PCwhg5ciR79uzJdhZzQSgo0QdGEHLhySefBLD0/fj888/p3LkzDRo0oGbNmrRv395mf4cH+8Bk9iHZv38/Y8aMoWHDhlb9kGy5du0affr0oXbt2jRs2JBJkyaxc+dOwsPD2bdvX7bPlemNN96wWr4iIyODWbNm0b59e+rVq0ft2rV5/fXX2bt3b46vw4N9YJo1a8bZs2fZv38/4eHhlqUyoqKiCA8PZ/ny5VnqOHjwIOHh4fzwww85Pl9unT17lqZNm9K7d2+2bdtmt6Us0tLSsl3T7I033uCPP/7gypUrlmN/cNZsk8nEggULLAnxW2+9xaVLl/IdT6lSpXKVjGzevJn4+HgGDRqELMukpqZiMply/TzJycn4+PhYJeBarRY/Pz+KFStmVQ6yTuSZOb2EGJUoFCZxBkYQciFzJJqvry8AK1asoFmzZrRu3Rq9Xs+PP/7IgAEDWLRoEU2aNMmxvrFjx+Lv78/7779PampqtuXS0tJ46623uHr1Km+88QbFixfnu+++y1WykZ3k5GS+/fZbWrVqxauvvkpKSgrr1q2je/fufPvtt5bpAHJj5MiRjB8/Hnd3d3r16gWYR7+FhoZSt25dvv/++yyXVzZv3oyHh4dljTB7qFq1Kr1792bjxo306dOHoKAg2rVrR4cOHShXrly+6ty4cSNff/01iqJQoUIFevfuTevWrS37e/XqRVJSEteuXbP0m/Lw8LCqY8mSJUiSxLvvvktycjJLly5l6NChluVFCsuePXvw9PTk+vXr9OnTh8jISNzd3Xn55ZcZOXJkjonFE088wZIlS5g5cybt2rVDkiQ2b97MsWPHmDlzpqVc9erVcXd3Z9asWfj4+FC+fHkuXbrEp59+So0aNXK9BIkg5IdIYBzojTfeoEqVKmL1ZhVKTk7mxo0bZGRkcPDgQebNm0exYsVo2rQpAD///LPVL9EuXbrQvn17li1blqsExsfHh+XLl+d4Oeabb74hMjKSmTNn0rJlSwA6duxYoEsbPj4+bN++3erXdceOHWnZsiUrV65k0qRJua7rueeeY+bMmfj5+WWJqW3btowaNYrz589ToUIFAPR6PVu2bOH555/PU1+inHh7e9O3b1/ef/999u3bx/r161mxYgWLFy+mfv36vPLKK7zwwgtW79nD1KlTh5YtWxISEkJsbCxff/01Q4cOJSkpiddffx0w95FZsWIFt2/fzvb9SE9PZ9OmTZbX2tvbm4kTJ3LmzJks0xTYU2RkJEajkT59+vDKK68wZMgQ9u/fz8qVK0lKSmLGjBkPfXyfPn2Ijo5m4cKFLFiwAAA3Nzdmz55tmR4CzNMlfPbZZ3z88cdWiWpERASzZ88Wa6IJhUpcQsrBokWL6NChA3Xq1KFhw4b06dOHCxcuWJVJT09n7NixNGjQgDp16tCvXz/LOkhwb0jl7du37R5fYmIi48ePp0WLFtSsWZMmTZowYcIEkpKSrMrFxMTQo0cPatWqRcOGDZk6dWqOp9kXLFhA586dqVWrFo8//niW/ZmXE2zdEhIS7Hqcj9rbb79tubwzaNAgPDw8mDt3LiVKlACw+iK8desWSUlJ1KtXjxMnTuSq/o4dO+aqL8mff/5JUFCQ1UgWNzc3y1pZ+aHRaCxfqCaTicTERAwGA9WrV891/LnRsmVLXF1drSZE3LVrFzdv3uTll1+22/PcT5IknnzyST799FP++usvxo4dS0ZGBsOHDyciIoLRo0dz69atHOtZs2YNb731Fs8++yyvvfYa69evp3Llynz22WekpaXlOp727dtbJYqZn6OcpkYoqNTUVO7cuUObNm34+OOPef755/n444/p1KkTP/74I5GRkQ99vIuLC+XKlaNFixbMmDGDTz/9lOrVq/PBBx9w+PBhq7L+/v5Uq1aNQYMGMW/ePPr168c///xjNZpPEAqDSI9zsH//frp06UKNGjUwGo3MmDGDbt268eOPP1o6Xk6aNIkdO3Ywc+ZMvLy8GD9+PH379rXMw1KYYmNjiY2NZfjw4VSsWJErV64wZswYYmNjLXPVGI1GevbsSWBgIGvWrLGU1+l0DB48ONu69Xo9L7zwArVr17a5oOOLL75Io0aNrLaNGDGCjIyMLNfEnc2oUaMICwtDo9EQGBhIWFiYVd+D33//nQULFnDy5EkyMjIs23M74ihzCHJOrly5QtmyZbPUGxYWlqvHZ2fjxo188cUXXLx4Eb1en+e4csPb25umTZvyww8/MHDgQMB8+ahEiRKWPkXZyVwkNZOXlxc6nY4bN25YbX+wn8b9PD096dy5M+3atWPBggUsXLiQNWvW0LlzZ3x8fPJ0LC4uLnTp0oXRo0dz7Ngxmwm9LQ8OI/b29gYolB8z98tMsB/s6Nu6dWu++eYbDh8+/NBLa+PGjePIkSNs3LjR0u5btmxJq1atmDhxouUSWFRUFG+++SZTp06lRYsWgPmsXOnSpRkxYgQ7duzIsY+XIOSXSGBy8Pnnn1vdnzJlCg0bNuT48ePUr1+fpKQk1q9fz7Rp02jYsCFgTmhefPFFDh8+TGBgoGXOhfr16wPQrl07y7wgiqLwySefsG7dOnQ6HZ07d7bMuZEblStXtpporEyZMgwcOJAPPvgAg8GAVqtl165dnDt3jmXLlhEYGEjVqlUZMGAA06ZNo2/fvtl+AfTv3x8wn2mxpVixYlZnIm7cuMG+ffuYMGFCruNXq5o1a1pGIT3o77//pnfv3tSvX5/Ro0cTFBSETqdj/fr1ue6Y+ig7NxqNRquzPd999x0jRozgueeeo1u3bgQEBKDRaFi0aJHdzwy0bduWrVu3cvDgQSpXrsz27dt57bXXcuyIGhERYXV/8uTJPPHEE1n6zaxYsYIGDRrYrOPo0aOsX7+en376idu3b1OrVi1eeeUVy+WsvCpVqhRArs7gZMruOAt7+q3ixYtz9uzZLD8k/P39gYcfQ0ZGBuvXr6d79+5W8et0Oho1asSqVavIyMjAxcWFDRs2kJ6ebrm0mimzM/PBgwdFAiMUGpHA5FHmpZnMX3DHjh1Dr9dbdVarUKECwcHBHD58mDfeeIM5c+bQr18/tm7diqenp9WX/saNG3nnnXdYu3Ythw8fZsSIEdStW5enn34aMJ/RuHLlykMnq3pQcnIynp6eluvPhw8fpnLlylaTsEVERDBmzBjOnTuXZW6L/Nq0aRPFihWz68RdavTzzz/j6urK559/bpX8rV+/3u7PVbp0ac6cOYOiKFZnYTKXi7ifj4+PzV/2MTExhIaGWu7//PPPhIaGMnfuXKs6759dOi8edtapUaNG+Pv7s3nzZmrVqmW5rJGTZcuWWd2vWLEiPj4+WbY/uGBqQkIC3333HRs2bODs2bP4+vrSrl07XnnllQL3OclM7jKTAMj9GbdH7bHHHuOvv/7i+vXrlC9f3rI9c7mR+4/hQZmXFG2t3WYwGDCZTJYRTQkJCSiKkqVs5uVpR63/JhQNIoHJA5PJxKRJk6hbt67lj2F8fDw6nc5yajhTQEAAcXFxaDQaS7ITEBCQpVzmTKdgnnfiq6++Ys+ePZYEJigoKE/DH2/cuMH8+fPp1KmTZVt8fHyWGWQz7z94qr4g1q1bR6tWrXLdUdJZaTQaJEmy+uMcHR3Ntm3b7P5czzzzDLt27WLr1q2WTrx37txh7dq1WcqGhobyzz//WH4dg/lS19WrV60SmMyzMfcnRUeOHOHw4cP5mjnVzc0t20siWq2Wl156iR9++IHz589TuXLlXK3Snt3oley2X716lfHjx7Njxw6MRqOlv9pzzz2Xq7l47nfjxo0sX/DJycl8+eWX+Pn58dhjj1m2u7m5ZelvpgYtW7Zk8eLFrFu3znJmGMyfUa1WyxNPPGHZFhMTw507dyxnpjL/Tv3666/079/f8vqlpKTw+++/U758ectnvFy5ciiKwpYtW6wmMsw8E2mvH0eCYItIYPJg7NixnD17lq+//tpudT44ZXlQUJBVB9ghQ4bkuq7k5GR69uxJhQoVLEnRo3Lo0CHOnz/PJ5988kif1xEaN27MsmXL6N69O61atSIhIYGvv/6aMmXKcPr0abs+V8eOHVm1ahXDhw/n+PHjBAUF8d1339lMEl999VV+/vlnunfvTsuWLbl8+TKbN2+mTJkyVuWaNGnCL7/8wvvvv0+TJk2Ijo5mzZo1VKxY8aFDurPz2GOPsXr1aubPn0/ZsmXx9/e3+tJs27YtK1euZN++fQwdOjTvL0IuXL58mRMnTtCjRw86dOhQoL48q1at4rfffqNp06YEBwcTGxvLhg0biImJ4ZNPPrFKiB577DF++uknJk+eTI0aNXB3d88yF0xOMpcduH/JhuwcOHCAAwcOAOZEKzU1lfnz5wPmS9SZl6mrVatGhw4dWL9+PUajkfr167N//362bt1Kz549LZ3RAYYPH87+/fstbVej0fDuu+8yc+ZMOnXqRJs2bTCZTKxbt45r167x6aefWh7brl07vvjiC0aNGsWJEyeoVKkSx48fZ926dVSqVMlqxJIg2JtIYHJp3Lhx/PHHH3z11VeULFnSsj0wMBC9Xs/t27etzq4kJCRYJnN6mAeHGUqSlK/r48nJyXTv3h0PDw/mzZuHTqezivHo0aNW5TNHSeUmxtzInD+kevXqdqlPzRo2bMjEiRNZsmQJkyZNIiQkhKFDh3LlyhW7JzBubm4sX76c8ePH89VXX1GsWDFat27NM888Q/fu3a3KNmrUiBEjRrBs2TImTZpE9erVWbhwIVOnTrUq1759e+Lj4/nmm2/YtWsXFStW5NNPP2Xr1q3s378/zzG+//77xMTEsHTpUlJSUnjiiSesEpjq1atTqVIlzp8/X2ijj2rVqsX27dvzPOOsLXXr1uXQoUOsW7eOxMRE3NzcqFmzJhMnTrQ6LoDXX3+dkydPsmHDBpYvX07p0qXznMBkJo25+Szu3buXuXPnWm2bNWsWAH379rUkMGD+wRUcHMyGDRv47bffCA4O5sMPP8zVsge9e/cmJCSEFStWMG/ePDIyMggPD2f27NmWzroAfn5+rF+/nlmzZvH777+zZs0afH196dChA4MGDcrz2S9ByBNFeCiTyaSMHTtWiYiIUC5evJhl/+3bt5XHHntM2bp1q2Xb+fPnlcqVKyuHDh1SFEVR/vnnH6Vy5crKjRs3rB7btWtXZcKECVbbevfurQwfPjxPMSYlJSkdO3ZUunbtqqSmpmbZ/8cffyhVqlRR4uPjLdvWrFmj1K1bV0lPT8+x/vXr1yv16tXLdn9ycrJSu3ZtZeXKlXmKW8i/vXv3KpUrV1b27t3r6FBypU2bNsqbb77p6DBUaerUqcozzzyTq8+iIAj3iHlgcjB27Fi+//57pk+fjoeHB3FxccTFxVnmgvDy8qJDhw5MmTKFvXv3cuzYMUaOHEmdOnWoXbs2YO6IKUkSf/zxBzdu3CAlJSXXzz99+nSGDRuW7f7k5GTeffddUlNTmThxIsnJyZYYM/toREREULFiRYYNG8apU6fYuXMnM2fOpEuXLpZfSEePHuWFF17g+vXrlrpjYmI4efIkMTExGI1GTp48ycmTJ7PE/9NPP2E0Ggvt17Xg3P79919OnjyZZRVjwWzfvn306dNHnK0QhDwSl5BysHr1agCrtWTAPKwzs9PayJEjkWWZ/v37k5GRYZkwK1OJEiXo168f06dP58MPP6Rt27aWYdQ5iYuL4+rVq9nuP378OEeOHAHIsmjatm3bCAkJQaPRsHDhQsaMGUOnTp1wc3OjXbt2lmHSYO4Y+uCcILNnz2bjxo2W+5lfQA8OXV2/fj3NmzfP0kFZKNrOnDnD8ePH+eKLLwgKCuLFF190dEiqVBij1wShKJAUpZAnJBAEwe4yO34+bB4UR5szZw7z5s0jLCyMsWPHWo18EQRBKCiRwAiCIAiC4HREHxhBEARBEJxOnhOY69evM3ToUBo0aEDNmjVp3bo1//77r2W/oijMmjWLiIgIatasydtvv51l4bDExESGDBlC3bp1efzxxxk5cmSWjqGnTp3i9ddfp0aNGjRu3JglS5bk7wgFQRAEQfjPyVMCc+vWLV577TV0Oh1Llizhxx9/ZPjw4VYLoy1ZsoSVK1cyZswY1q5di5ubG926dSM9Pd1SZujQoZa1eRYuXMjff//NqFGjLPuTk5Pp1q2bZQ6DYcOGMXfuXL755hs7HLIgCIIgCM4uT31gpk2bxsGDB7OdiVZRFBo1asQ777xDt27dAPPaQU899RRTpkzhpZde4vz587z44ousW7fOsljen3/+SY8ePdixYwclSpTg66+/ZubMmezatcsytHDatGn89ttvbN26taDHLAiCIAiCk8vTMOrt27cTERFB//79OXDgACVKlOD111+nY8eOgHk9mLi4OKv1Sry8vKhVqxaHDh3ipZde4tChQ3h7e1ut9PvUU08hyzJHjx6lefPmHD58mMcff9xqXoSIiAiWLFnCrVu3rM745CQhIYmHpWiSJOHh4Zqn9YbspXv3dwkPD+eDD4Y/8ud2JFmWSUlJL/QVeZ2dFBeH63cbSW/TDsVOMyb/F2KxRe3xFRZnP+7CiN9edTr7a+vMJAkCArxyLJenBCYqKorVq1fzzjvv0KtXL/79918mTJiATqejXbt2loUBH1zCPSAgwDJ1fXx8fJaF0rRaLT4+PpbHx8fHZ1nLJHPxwfj4+DwlMIpCDgmMddkHLVw4n8WLF1ptK1euHBs2fG+5n56ezowZ0/jll61kZGTQsOFTfPjhx5bX4e+/D9CjRzd27NiFl5f1XCk5xZeTW7dusXDhfPbu3c21a9fw8/OjSZNm9O79Pl5e5gZw5sxpli37nMOHD5GYmEipUsG88sqrvP5614fWffLkCWbPnsnx48fRaGSaNXuOIUM+wN3dHTD3ZfrooxGcPXuWW7cS8ff3p3HjpvTt2x9PT0+bdeb0egv3KIFB3OnW4+4dEcvDqD2+wuLsx10Y8durTmd/bYuCPPWBURSFxx57jMGDB1OtWjU6depEx44dWbNmTWHFpwoVKlTgl1+2W26ff/6l1f7p0z9h584dTJ06jSVLlhEXF8fQoYMeSWxxcbHExcUycOAQ1q7dwJgx49m9+y/Gjbs3kd6JEyfw9/dnwoTJfPvtRrp1e4+5c2ezZs3qh9bbu3cPQkNDWbHiK+bOXcCFC+cZPfpjSxlZlmnSpCkzZ85m48bNjBkznv379zJp0vhCPeaiQkq8icv3G5ESbzo6FFXFYova4ysszn7chRG/vep09te2KMhTAhMUFGRZcj1T+fLliYmJsewHrFZTzryfeQYlMDCQGzduWO03GAzcunXL8vjAwEDLGZtMmfcz63mUNBotgYGBlpufn59lX1JSEps2bWTw4KE88UQDqlWrxpgx4zly5DBHjx4hJuYKPXqY+wM1bhxB3bo1rZIARVGYOXMGTZpE0Lx5UxYunJ+n2CpWrMS0aZ/RuHETQkNDeeKJBrz/fj/+/HMHBoMBgLZt2/HBByOoV+9xQkJCeOmlVrRu3Ybt23/Ltt4///wTrVbLiBEfUa5cGI89Vp2RIz9m27bfuHz5MgDe3t68+monqlV7jODgYBo0eJJXX+3EoUMH83QMgm2ay5fw6f4WmsuXHB2KqmKxRe3xFRZnP+7CiN9edTr7a1sU5CmBqVu3LhcvXrTaFhkZSenSpQEICQkhKCiIPXv2WPYnJydz5MgR6tSpA0CdOnW4ffs2x44ds5TZu3cvJpOJmjVrAlC7dm3+/vtvq2ntd+/eTVhYWJ4uH9nL5cuXeP75Z2nduiUffTTCamr/kydPYDAYaNDgScu2sLAwSpYsxdGjRylRoiSffjoDgI0bv+eXX7YzdOi9Pi8//PA9bm5urFixigEDBrFkySL27r33+o0e/THvvfdunuJNTk7Cw8Mzy0rX1mWSH/pa6vUZ6HQ6q9V9XV2LAXD48CGbj4mLi2X79m3Urft4nuIVBEEQhLzKUx+Yt956i9dee42FCxfSsmVLjh49ytq1axk3bhxg7hD75ptvsmDBAsqWLUtISAizZs2iePHiPPfcc4D5ckyjRo343//+x9ixY9Hr9YwfP56XXnqJEiVKANC6dWvmzZvHRx99xHvvvcfZs2dZsWIFH374oV0PXlEUjEYjJpMWk8lks0/GY489xpgx4yhbtizx8fEsWbKIbt3e4ptv1uHh4UF8fBw6nQ4PDw9MJqPlcf7+/sTHxyFJWPqi+Pr6Wv5tMhlRFIWKFSvx3nvm66whISGsWbOaffv2WKZdDwgIuBujkdxITLzJkiWLaNeufbaPOXLkML/88jOzZs3Otky9eo8zffo0li//gtdee507d+4we/ZnAMTFXbd63MiRI9ixYwfp6Wk0avQMH3/8v2zrNfeBUdDrMzCZxIXlhzHJEmllyvDou5cLgiCoX54SmJo1azJ37lxmzJjBvHnzCAkJYeTIkVarEL/33nvcuXOHUaNGcfv2berVq8fSpUtxdXW1lJk2bRrjx4/nrbfeQpZlnn/+eT7++N5lFS8vLz7//HPGjRtH+/bt8fPzo0+fPnTq1MkOh2xmMOi5desGimJApyuBwWC0ORKpTp3aln+XLh3M5MlT6NjxVX78cTOtWrUiPd28KnVqarLV40wmE3p9BqmpyaSn37GU0Wik+8oYKVcuzOqxfn5+xMXFWra9++67Nuu3JSUlhcGDB1G2bFneeKOrzcdcuHCBwYMH8vbbb1OzZo1s6y1VqgQjR45k3ry5zJs3B1mW6dChA/7+/hgMBqvH9e7dmzfeeIOoqCgWL17Ep59OYfDgITbrlWUZrVbDzZuxlktcQja83YmfvwDF2w3f9Du4uro5OiJBEATVyPNq1E2bNqVp06bZ7pckiQEDBjBgwIBsy/j6+jJ9+vSHPk+VKlWynW+moBRFISHhGrIs4+3tj4uLGzqd7TMwD3J396Rs2bLExsbi7u5JcHBp9Ho9RqNiObsC5jMhJUuWwt3d0/LF4+7uibv7vdE5sqzBzc3NaptWq0WWNVbbciMlJYXhw4fh5eXFjBmzrBLGTBcunGfw4EG0b/8KvXu/n2Odbdq0o02bdiQkJODm5oYkSaxdu5awsPJW8WX+u2rVxyhevATdu79Lr159CAzMOvRQksxJjJ9fcXEGJifp6chpRm55eHHzZhzFi4dYXdJ7lJRibuhr1EIpps4kSu3xFRZnP+7CiN9edTr7a1sU5DmB+S8wGPQoigkfnyCKFXNDo9FgMkm5SmBSU1OJjo7mpZdaI8saqlWrjlar5e+/D/Dss80BiIy8yLVr16hVqzayrLEkE4piTloySZIESFm2SZL1tpwkJyfTt28fXFxc+OyzObi5Zf3AnT9/jl69etCq1cv065d9cmlLUFBxADZt2oiLiwsNGz6VbXyZr6HBYLRZJjOB0elcRAKTE50LVK6CZ0Y6N25cw2g0IMsuOT+uEBgrh5O4badDnjs31B5fYXH24y6M+O1Vp7O/tkVBkUxgMklSzr9mP/tsGs8804RSpUoRFxfHwoXzkWUNL7zQEjBf7mrbth3Tp0/D29sHDw9PPvlkMjVr1qJmzVoAlCpVCkmS2LlzBxERjXB1LWaZSyUnc+bMIjb2OuPHT7K5Pzk5mT59epKWlsaECZNJSUmxrCvl5+eHRqPh3Lmz9OzZnYYNn6Zr1zctI7o0Ghk/P/OcPMeO/cuoUR+xcOESihc390Vas2Y1tWrVwt3dnb179zJr1gz69Rtgmctm166dJCQk8Nhjj+Hu7s758+eZOXMGtWvXITi4dK6OT8iZdP/kOYIgCAJQxBOY3Lh+PZYPPxzOrVuJ+Pn5Ubt2Xb788ivLFz/AkCHDkCSZDz4YfHciu6f58MOPLPuLFy9Br159mDNnFmPGjKJVq9aMHTshV88fHx/HtWvXst1/6tRJjh0zL6bZps1LVvt++GELwcGl+e23X7l58yY//fQDP/30g2V/qVLB/PijeWmGtLQ0IiMjrfqlHD/+L4sWzSc1NZVy5cIYOfJ/tGrV2rLf1dWVjRvXM336p+j1GZQoUZJmzZ7lnXfyNmpKyEZqKtqzZ9CHhTk6ErT/HsG35bMkbtmGoUYtR4eThdrjKyzOftyFEb+96nT217YoyNNaSM4oPj7rUgJ6fQYJCVcJCCiFq6vrA0sJPMpfu0qRm4028xJSSkq6uISUk9RUtGdOcad8BRJSEwkIKIVO55hLSNqjh/F77hlu/vYnhpq1HRLDw6g9vsLi7MddGPHbq05nf22dmSRBYKCdlxL4r5M0MsZHOGe0BhmMues8LNjH1asxLF++lIMH/7ZMsNiixYu8+ea76HQ6AA4e/Ju1a7/m5MnjpKSkEBJShtdff4Pnn2/50LojIrLOfzNmzESee65Flu1Hjx6mX7+ehIVVYPnywumsLgiC8F8mEhgLCSMKiel3MD6CjEIjSfi6uiEjIRbaeHQuXYpEURQ++GAkpUuHcPHieaZOncidO3fo23cgAMeOHaVChUp06fIW/v4B/PXXTiZMGI2HhydPP93oofWPHDmaBg0aWu57emb9FZGUlMSECaOpV69+llmpBUEQhNwRCcwDjIqC8VGsTJ2P4bA//bTZ0vfm/PmzyLJMlSrVrGa+TU9PZ9++3Vy+fBmTyUjJkqV48smn8PY2z7qbnJzMnj1/cf36NUwmI56eXjzxRANCQsrkKoZDh/7h0qVIqlWrzqFD/5CRkU6FCpVo2PBpjh07yrFj/95dM6s6tWrVsTwuIyOD/fv3Ehd3HW9vb7Zs+Zk33niXSpUqA3DlSjRz5szg+PFjpKXdoWzZMHr2fJ/69RtY6njllda8/HI7oqOj+P33bXh5efHWW91o06Z9rl/DJ598iiefvLdaeunSIVy+fImNG9dbEpg337Tuw9Ox42scOLCXHTu255jAeHp6ERDw8OUupk2bRPPmLyDLMjt37sh17IIgCMI9jplUQsi3c+fOIMsyrVu3pUGDpzh27CinT5+y7N+58w/i4+Np3rwFrVq1QVHgl1+2Wvr47NmzC5PJyEsvtaZdu1eoX78BWq0uTzEkJd0mOjqKFi1a0rhxM86ePc0vv2wlJSWFF19sRf36T/DPPweIi4u1PGb79l9JS7tDkybNePrpRpQvX5GBA3tz+/YtwDw8/cknn2bWrPl88cUqGjRoyPDhg7N0YF6zZhVVqlRj2bJVtGv3KtOnT+Hy5UjL/r59ezBx4pg8HU9ycjLe3t65KJPzMhYzZkzlpZee5b333uSHH77jwS5mP/74PTExV3jnnfdyDqxYMQzhVcHGnD6PmqFSODf+3IehUrijQ7FJ7fEVloIct7k/mmS5OWKwW2G8b/aqs6i2KWciEhgHGv3xRwwa1D9Pj/Hw8KRBg4b4+PhSoUJFqlWrzvHj5lFIt2/f4vLlS0REPEOJEiXx9w+gSZOmpKamcOlSJAApKckUL14CPz9/vLy8CQ0tQ8mSpfIUg6IoNGrUGF9fP8qUKUvJksHcvp1oiatSpXB8fHy5etW8yOf169eIj4+jadPn8PcPwMPDk3fffQ9PTy9+/30bAJUqVaZt2w6UL1+R0NAyvPdeb0qXLs1ff1mfoWjY8Cnat3+VkJBQunZ9Cx8fXw4e/Nuyv0SJkjmeAblfdHQU69d/89CzONu2/cqpUyd48cXW2ZYB6N69F+PGTeazz+bRuHEzZsyYyrp131j2R0VdZuHCufzvf+Mfuk6VhSyDm1u+ztbZnZsbxipVzfGokdrjKyz5PG5JAtlVg0GH5aZx1VglNI8kqSmM981edRbVNuVExCWkHBiNRlYtXcr2rT9z80YC/oFBNH/pRV575x3L/ByKorByyRK2fvc9KclJVKtRk77DhlG6TCgA12Ou8nb79sxd8SUVKlcuUDyZk8pl8vf3Z9GiBaxevZro6Ci0Wi0XL0bSv/9AgoKK4+paDB8fH3r06JZllfAOHV6hd++++Pv7Y0t6ejozZkzjl1+23h0e/hStWrXG09PL0uG1bt2alvKTJ0+++/+puLm5kZZmXkLhxo0E9Ho9X3+9And3d3x9fZk9ezZXr8Zw5Uo0YD4D88UXi9mzZxcJCfEYjUbS09O5ft36DEyFCpUs/5YkCX//AG7evLfc/f/+Ny7Xr2VcXCxDhvSjadPnePnldjbLHDz4N5Mnj2XYsI8oX76CzTKZ3n67u+XflStXIS0tjdWrV/Lqq50xGo2MHfsx3br1oEyZsrkLMCMd+fp1CLD9/jxKctRl3Gd8QurgYZhCc3e58VFSe3yFJb/HLUkSesXExds3yTAZcdNoKe/nj9EFq0EFOkmDKd1YaAMNCuN9s1edRbVNORORwOTg25Ur+XHDRoaM+h9lw8pz5tRJPpswEQ8PT9p06ni3zFd8v/Zbhoz6HyVLBbNi8WI+HjiQRau/xqWQT/+np6cTGxtLnz798Pb24o8/tnPixEkGDuzPqlVrrMr27v0+7dp1IDU1hZiYK9y8eYPvv9/AE088SbVq1bPUPX36J+zatZOpU6fh6enF1KmTmD9/Lq+/3sWq3Ouvd7m7SGcTwDy537Ztv1j+6On1etzd3WnZsrVlLaSwsCoYDAZLJ9d582Zy4MA+3n9/ICEhobi6uvLxx8PR663XS3rwzIUkSTbXsMpJfHwc/fr1onr1mgwb9pHNMocO/cPw4YPo128wLVu2yvNzVKtWneXLl5KRkUF6ejqnTp3g7NnTfPbZpwB3FxBVaNy4ATNmzKVevfrWFRiMyAnx4IAV2B8k37yB26oVpL3TXZV/zNUeX2Ep6HFnmIxkGI24yBoMionI2zdJN5oXYnWRNYR5+6GVpCyXQu2lMN43e9VZVNuUMxEJTA5O/vsvTz7TiCeefhqAEsGl2PHLr5w+cQIwn33Z9M03dH7nbRo+8wwAQ0eP4rUXX2L3n3/SpHlz3m5vvjzR9823AKhRpw7TFy20PMeKFcv56qsV6PV6nn/+BYYOHWY5w/Gg+/uVgHkNpLfeepsWLV7g9u1bnDx5nIiIxgwY0JerV6/i7+/HrVu3kGUZd3d3AgMDgUDLWYC//97PmTOnsiQwSUlJbNq0kUmTpvDEE+aOtGPGjKdDhzZER0dblXVzc8Pb2/tu3VkFBgZy584dZFnCy8sTWZYJDg62mgfm33+P8OKLrWnc2LzOVmpqKteuxQD1bNZZEHFxsfTr14vw8CqMHDna5vpCBw/+zfDhg+jVq1+eOgnf7+zZ03h5eePi4oJWq2XFCuuEcsOGdRw8eIAJE6ZSqpSYuVhwvMyERhCcgQourqtb1Ro1OHzgb6IvXwbgwtmzHD9yhMcbmofKXouJ4WZCAnXq3/v17OHpSfhj1Tj17zEAZn7xOQCT5sxm1Y8/8L8pky1l//77ANHRUSxa9Dljx05g8+bv2Lz5O8v+hQvn89JLL1jup6Qks3//Hm7dSuTChXOcPHncknx4e/tQtmw59u3bgyRJGAx6duz4HXd3D7RaLcuXf0GjRg155ZW2LF68kNjY61y9GoOPj1+W4z558gQGg4EGDZ60bAsLC8PfP4Do6CirsuvWfcvo0aN4443X2bRpY5Zfa8HBIQQFFee3337h6tWrpKamcvLkcRYtmsepU+ZEMCSkDDt2bOfs2dOcPXuGsWM/ytdEd+PHj2LhwrnZ7jcnLz0pUaIkffsOJDHxJgkJ8SQkxFvKHDz4N8OGDeSVVzrTpEkzy/7MDscAO3b8zuuvd7Dc37XrTzZv3sSFC+eIjo5i48Z1rFy5jFdeMa+gLssy5ctXtLr5+fnh4uJK+fIVba5fJQj2krXDrvRIp+wUhMIgzsA8QGP+pFvuv/b226Sl3qFHp87IsozJZOLt3r1o/qJ5UrNbd/tfBAQGornvcf7+ASTeuIFGli19TPz8/AgKCrr3PICXlzfDh49Eo9EQFhZGo0bPsH//ftq3fwUAX18/QkJCLPVWrFgZg8HI5s2bkCSJatWqU6VKVcv++vWfZNasmYSHh/P7779RokQpnn/+BdLS0qlSpSqRkRc4ePAfli37nAMH9vHGG29azVuydu1qKlWqTEJCPDqdzrLuUSYfH2+Sk5Mt93v3fh8XF93dSzkKU6ZM5M6dVHx8rB/3/PMt+eefA+zbtwd3dzeWL/+S0NCylmHh/foNYvLkcfTq9S4+Pr506fKWZU2nvLh+/dpDV2w+cGAf0dFRREdH0a7di1b7du0ydwbesuUH0tLSWLlyGStXLrPsr127LnPnLgbMieTly5cs+7RaLRs2rGX27BmAQunSofTtOyjbvjWCUFgkyXr9LEkCSSeTody71CpLmf8RBOclEhgLBQ0yvq7Wv4R/3rKFP37+mYlTplK+QgXOnD7N9E+mUrZUaVq3aYOPSzEA/FzdCCh2b4FGF40GSZIIKOZO+t06fVyKWZWRkKhQoQIazb1VmwMDAzl79qzlfufOr9G582uW+7Is06BBQ556KiLLEej1ekaN+ghPT08WL/4CT09Py76uXd8E4PHH6/PKK53uXh4az7x5T+PiYp6e3mAwcOfOHUqWLJXt+kvu7h5Uum9Y4Xvv9bTaf+fOHVasWM6WLb9abdfpdDz55FOWpQQaNXrO6gxLqVLBzJ690OoxHTp0tLq/bt3mLPE8OIttZoKRnRdfbJ3jaKKPPhrDRx+NyVM9D84vkxvduvWkW7ee2RfQajEVLwna3K9MXlhMQcVJ7T8Y0wOdyNVC7fEVlgePO3N0kd4qWZFQZIWLN80ddgE8tDpCfXxwufu3RyfLDjkjUxjvm73qLKptypmIBOY+itHEgx/jWTOm88473Wh5dxr58AqVuXYlhuWfL6VNqzYE+QUAkBiXQAn/IMvjbsQnEB5eBdkI8t2/JbIJ5PsvLyuKjeG0+eswp9frGTHiA65evcqiRUutkhdbatSogcFgICbmCuXKmRcLvHo1huDgYEqVCiYqKgq9Xk9S0m2rszAJCQkPHaZcvXoNlixZREZGhiUxEvLJxQVTcDDoMxwdCaZSwaR8PMbRYWRL7fEVlgeP+8HRRWBOVkJ8fNAr9/q3uGm0eOgkwnxdMZoUtLKMh06ynBm21He3zsyTmopi3/XbCuN9s1edRbVNORPRB+Y+ipL5Ab13S0tLQ7rbCz/zZr6UZP53cHBpAgMD2bdvr2V/UlISx479S82aNVHuS1IMBqNVPfaSmbxcvnyJhQsX4+vrm+NjTp8+jSzL+PsHWLaFhpaheXNzf5uqVauh1WrZv3+fZX9k5EWuXbtKzZo1s9R3r95TeHt7i+TFHoxGpOQkUEGnSik5Cd1fO83xqJDa4yss2R13ZmfcDKMRvY1RerIkIUsGjKZrZBijMSnXkSUDsnTvK0EjSWi0MgadYpkrRnbV2HVumMJ43+xVZ1FtU85EJDA5eOaZxnz++RJ27vyTmJgrbN++ja++WknTps0A86+T11/vytKli9mx43fOnj3DqFEfERQURJMm5jJ+fv4UK1aM3bt3kZCQQFJS7j8Qa9aspmdP8/wiL77Y2qq/CpiTl2HDhnDixHEmTpyC0WgiPj6e+Ph49Ho9AEeOHGHVqpWcOXOa6OhofvrpR6ZP/4QXX3zJMgNtbOx12rd/mWPHzJPieXl50bZtO6ZPn8aBA/s5ceIEY8aMombNWtSsaV5afseOP9i4cT3nzp3l8uXLfPvtN3zxxVI6dXoNwQ7S09GcOwsZjj8Do7lwHt92L6G5cN7Rodik9vgKS0GP26QYMCp6jIp5ugKdLOOi0eCi0VBMo8V492zO6cR4Lt6+iV4xWfWvcXT8hVlnUW1TzkRcQsrBsGEfMn/+XCZPnsjNmzcICgqiQ4dX6NGjl6XMW2+9w507d5gwYRxJSUnUrl2HuXMX4Hp3DhitVssHHwxnyZJFLFw4nzp16rJkyRe5ev7ExJtZhi3fLy4ulh07/gCgc+dXrfYtXvw5jz9eHxcXHT//vJVFixai12cQHFyaLl3esPSLAXP/l8jISNLS0izbhgwZhiTJfPDB4LsT2T3Nhx/emzNFq9Wydu03TJ/+KYqiEBpahsGDP6B9+w4IguA8JGS0soZQbxcMJvPXQuZlJaNiEkOrBVWSlMKaoUgl4uOTslyz1eszSEi4SkBAKVxdXfHwcL07qZhjYixKMjvxpqSk52uYdJGSmor2zCnulK9AQmoiAQGl0Okcc2lOe/Qwfs89w83f/sRQs7ZDYngYtcdXWDKPO3HbToy1apunT9AqnEqMtyQdnjoXwvz8OHMjnnSDeZufazGqBXmScOc86cYM3HWeBLmHEJ8aTZohHYBiWld8XEM5Enub5IwMXDQawn0D0eqx22e3MN43e9VZVNuUGkgSBAZ65VhOnIERBEFwckbt3T4qBRwebbx7Scn873sjlFw0GlxkjZg7RlAVkcAIglpJEorOBYcsE/wARavDWCoYJY8rlz8qao+vsEguLhiDg4lOv8OtxHjLiCN7NJkHLyvJkoSHTiLdYL8zp4XxvtmrzqLappyJSGAEQa3c3DA+Vt08jDrvc/rZlbHaY9w4csqxQTyE2uMrLMZqjxF/8jS37l4ycpFtzxkkATpZw92TKmhzscK5JMlIkgGTcp0MYzo6jQ5ZKov5a8M+SUxhvG/2qrOotilnIhIYQRAEJyaBJXGxNSGd5u6Zk8w5XwBcNFq0spSrs3uZl5U0iuPPBArC/cQw6vtkTsH9qG6jR3/M4MEDHH3YglrduYPm+DG4b2SYo2hOHMe/VhU0J447OhSb1B5fYdGePE5gtXAqXjlPRT83Qn1cskxI9+CcL+Z5X+KQJBP5WRHJPLHd/esq5T/+wnjf7FVnUW1TzkScgbmPTichSdaTPqWkpDB79jy2bdvOjRs3qFq1CiNGDKNGjXurNyuKwty581m3bgNJSUnUqVObUaM+omxZ84rPV65c4fnnX2Tdum+oWrWK5XG5OIubI71ez/z5c/nrr51ER0fj6elFgwYN6N9/IEEPTIG9c+efLFmykLNnz+Li4kK9eo8zY8asbOtWFIWFC+ezceN6kpKSqFWrNiNHfmxZyRpg6dLF7Nq1kzNnTqPV6vjzz78KflCPwI4d29m4cR3nzp0hI0NPWFh53n23h9U8OytXLmPHjt+5dCkSV1dXatSoSe/e/ShTply29f7002YmTRprtc3FxYXt23fbLP/pp5P47rsN9O8/mI4dX7feqShI+gzUMDxOMujRXI1BMugdHYpNao+v0OgNyFdiMGVcJ8PohSy5IkuhVhPSZTJZddA15PmpJCQkWcKoUzDe92dSJ2kwpRvz1UwL432zV51Ftk05EZHAWJiTF0VJAe59Ov/3v1GcO3eByZM/onjxQDZv/pnu3Xvw3XdfUaKEeemAzz//ilWrvmbixI8oXboUc+cupUePnnz33Ve4urrerRMgFUXJnMTOPie/0tLSOHXqJN2796Ry5crcvn2badOmMnBgf1atWmMpt23br4wfP5a+fftTv/4TGI1Gzp0799C6v/xyGatXf824cRMIDi7NggVzef/9Xqxbt8kyx41er+e5556nZs1abNq00S7H9CgcPnyI+vUb0LPn+3h6evHTT5sZPnwQixcvp3Jlc5J56NBB2rd/lSpVqmE0Glm8eB6DBvXlq6++fejq0R4eHnz99XrL/ewm/tqx43eOHz9GYGCQzf2CkFv3JqQrvHWzMmckv5yUSPLdSTJdZA1h3n5opfwtgSIIBSESmCxMKHcXQktLS+e333Ywe/Yk6tUzT5/fp887/PHHX6xZs4H+/d9DURRWrlxLjx5v0LTp0wBMnDiSJk3asm3bn7Rs+SwtWpgnmHvllXcAePzx2ixfPtfyjCtWLOerr1ag1+t5/vkXGDp0GDpd1p7vP/20GX//ADQaDWfOnEKWNVSpUpUFC+4tYJiRkUGLFi2ZPv1T5s2bTfny5Xn88Sf49NOpDBw4mLZt23P48EFOnDiO0Whg164/KVasGNHRUbRte28COkVR+Prrr+je/T2aNGkKwLhxE2nevCl//LGdW7cSqVw5nMceq8alS5HcuBGPyWQiLS2NXbt2cPVqDF5eXkRENLb6go6Li+PChXOMGzcOg8HAM880oWfPvpaEYOvWH/n22zVcvnwJNzc36tZ9nAEDhlhWrT548G/69+/FzJnzWbBgDpGRF6hUKZyRI0c99MzIgwYMGGJ1v2fP99m5cwd//bXTksDMmDHHqszIkWNo3bo5p0+fpHbtutnWLUnSQ9eLMr8Oscyc+SnTp89h2LCBuY5bEHIjc+gz5K7Dbl5kmMTEdoI6iD4wD2E0GjEajVnW9SlWzJVDh8xT7kdHXyU+/gZPPvm4Zb+Xlyc1alTlyJFjAKxevQiAJUs+4/ffNzJz5gRL2b//PkB0dBSLFn3O2LET2Lz5OzZv/s6yf+HC+bz00guW++fOnUGr1dK6dVvq13+Cw4cPEhNzb6be7dt/5fbtRCRJon37VwgICGTFimXExsYiSTLt27ehb98+/Pbbr9SoURtPT09OnTqR5divXLlCfHw8DRo8ed9xeVG9eg2OHj0CwPHjxyhevCRt2rTH3z8Ao9HAn3/+ToUKlXj55fZ4eXnz559/WB6flHSbHTu2U7JkKWbNWsDYsZM4evQwn332iaWMwWCge/deLF/+NZMmTePatatMnDgmS3yLF8+nb9+BLF26Eo1Gw+TJ4yz7rl6NISLicQ4e/DvL47JjMplITU2xLK1gS0pKMsBDy4B5Re4OHVrRvv1LjBgxmAsPTEVuMpkYP34Ur732BuXLV8h1jIKQk/uHPlf0c6OinxulvV1y3WFXEJyJSGAewsPDnVq1HmPRoi+JjY3HaDSyefMvHDlynPj4BMC8OjNAQICf1WMDAvyJj78BgJ+fLwC+vt4EBgbg43PvC9DLy5vhw0cSFhbGM880plGjZ9i/f79lv6+vHyEhIZb7fn7+1KlTD29vHypWrExgYCAxMTEAXL9+jWvXrrJz5y5eeKElpUoF88QTT5KcbP7iXbRoAQ0aPEG/fv0pWbIUgwcPICysguXsxv0SEuIBrBZ7NB9XgOXYQ0NDqVKlKt7ePpQpUxZFUQgMDCIsrDw+Pj7UqFGbxMSb3LmTCsCRI4cpU6YcYWHlKV26NDVq1GLAgA/YuvVH0tPNs3+2atWGhg2fpnTpEKpXr8HAgUPZu3c3qampVnH06NGHOnXqERZWnq5d3+Lff49a6tBqtZQpU5ZixYo95N21tnr1Su7cuUOzZs1t7jeZTMyePZ0aNWpRvnzFbOspU6YsI0b8jylTpvO//43DZFLo3ftdYmOvW8qsWvUlGo2GV1/t/PCgXF0xVqwEKlgY01i+Aokbf8So0oRL7fEVFmP5CqT/shlDhTLAg0OfC95h15b710sq6OR2hfG+2avOotqmnIm4hJSDyZM/5n//m8Kzz7ZHo9FQtWolWrZ8lhMnTtul/goVKqDR3LtuHRgYyNmzZy33O3d+jc6d7y2O+GBC4ebmzp07dwDzgoybN28mJSWFZs2asnLlMgDL/m7d3uPOnRTq1Xucdu1e4YUXmvPrr79QpkyIJQnKi/sTHxcX1yzbMi8L3bmThpubOzduJJCensbt24nMnj2b+Ph4FEXBZDJx9WoM5cqFcerUSb74YjHnzp0hKSnJcjnv+vVrhIWVv+91q2T5d+blmps3b1KyZEmCgopb9UHJyS+/bGXZsiVMnjzdZjIHMGPGVC5cOM/8+UsfWlf16jWpXv3eat01atSiS5dX+O67Dbz3Xm9OnTrJt9+u4Ysvvsp5UTyNBsXTyzwPjIMpnl7on27k6DCypfb47CVzpKTlvrc3psaNUNLOg/FeOzEWsMOuzee2sV5SQSe3K4z3zV51FpU25cxEApOD0NDSLF8+h9TUO6SkpBAUFMjQoaMJCQkGzGckABISbhIUdK/fQ0LCDapUyf6Xeiat9sG34OGd4eQHrmeb/5gp6PV6Zs2aSXJyMkuXLsfHx8dSpkyZMH755RfKly/P8ePmS18uLi6EhJTm2rWrlCkTwoMyk4IbNxIICrrXhyUhIYHw8HCbsTy47d7fWfPxGAx6KlSoRLVqjxEWVgWD4d4f1hIlSnLnzh2GDOnLE080ZPToCfj6+nH9+jUGD+6L4YGRAPe/bpl/0DOTnbz47befmTp1POPHT6V+/QY2y8yYMZXdu3cxd+5iihcvkaf6tVotlSqFEx0dBcDRo4e4efMGHTq0spQxGo3MnTuTtWtXs27d5nsPzshAjo8H34dfsnoU5KsxuH2+mDvdemAqFezocLJQe3z2IEmgcdVguG8SOflaDLrJ89G8+zyU8C3k57ee2A4o8OR2hfG+2avOotCmnJ24hJRL7u5uBAUFcutWErt3H6Bp0wgAQkJKERjoz759/1jKJien8O+/J6lVyzzUOrNDrtGY9y/Y3DAajYwY8QHx8fG0adMGPz9fvL29Lbd69R7HxcWFS5ci8fHxIT4+Dr1eT0xMDKVKBRMXF5elztKlSxMYGMj+/fvuO65kjh37l5o1a+UrzoCAQG7fvoWHhwfBwcGEhIRabjqdjkuXIrl16xa9evWlVq06lC1bjps3b+T7dcnJr79uZdKkcYwZM5GnnorIsl9RFGbMmMqff/7BrFkLCA4unefnMBqNXLhwjsBAc0LYosWLfPnlapYtW2W5BQYG8dprb2TpNIzBgBx7DQyO7zApx8XiPnsGclyso0OxSe3x2YMsS7i4KChyKkYpBaOUghR7Ge2nn6GJK7zPyYOMlhFPekx3z+7cPzdMXrraFMb7Zq86i0KbcnYigcnBX3/tZ9eufURHx7B79wG6dRtAWFgZ2rZ9ETB/cLt2fZVFi1bw+++7OHPmPCNHTiQoKIBmzcxfiv7+vhQr5spff+0jPv4GSUnJuX7+NWtW07Nn92z3G41Gvvjic06cOM4nn0zD3z+ATZs2cOzYUW7evEFs7HVOnz7JSy+1YuHC+ej1Bvbu3cOHHw5DURRKlizBzZs3kCRo3/5ltm/fZjmu11/vytKli9mx43fOnj3DqFEfERQURJMmzSzPf/XqVU6fPsW1a1dRFIWLFy9w+vSpLH1WAGrUqE18fBzHj//LhQvniYq6zM6dfzBjxlTAfBZGp9Oxfv03XLkSza5dO1i+/OGXbGyJi4vl9dc7cOLEsWzL/PLLViZMGE3fvgOpVq06CQnxJCTEW/oLAUyfPpVfftnC6NETcHd3t5RJT783sdz48aNYuPDeiLJly5awf/9erlyJ5vTpU4wb9z+uXbtGq1ZtAfDx8aV8+YpWN61WS0BAQJ5GUQlFkXlCOr3pKmmGKNIMUZi4eXePYzro3j83jEF3d0FJV43oLyw8EuISUhay1YcvOTmFmTMXcf16HD4+3jRv3pj+/XtYjUzq1q0raWnpjB07jaSkZOrWrcGiRdMpVszcB0Snc2HEiIEsXLiMefO+oG7dmixfPj9X0SQm3iQ6Ojrb/bdu3eLYMfMXdefOr1rta9++PZUqVaZkyVL07z8QT09P5s+fS2pqKiVKlKBVq1YoiomKFSsTHx9HZGQkycnJXL0aw5YtP/Dqq525c+cOEyaMIykpidq16zB37gLLHDAACxfOY/Pm7y33Bw8eCMDixZ9TpUoVq3j8/f159tnmXLhwnnHjxhEfH0dwcAjPPmvuOOvn58fIkaNZvHg+69Z9Q+XKVXj//YGMGDE4V69VJoPBwOXLl0h7yAy233+/AaPRyIwZUy0JFEDLlq346KMxAGzatA6Afv16Wj125MjRvPhia8DcN+f+y2ZJSbeZOnUCN24k4OXlTXh4FRYu/Nyq/44g5Nb9fV4y/289IZ1jz849ODeMmBdGeJQk5T/eyuLjk7LMEKnXZ5CQcJWAgFK4urri4eGKyWSyORNvYVIUGb1ecfhEq1u3/oi7uzvPPGOe7+Xs2dMcOXKY9u1ftdnPpSAkydxPJiUlHZPpP930Ci41Fe2ZU9wpX4GE1EQCAkqh0zlmRJL26GH8nnuGm7/9iaFmbYfE8DBqjy8/JMl8NkN/t2+XVpZxdzUQf+ccGXc77Hofv0xQo07E/vk1SdXDcNd5EuQewvWUSNIM5gQ+N9vyW6aYthj+xcI4Enub5IwMXDQawn0D0erJ1ee7MN43e9X5X2xTzkKSIDDQK8dy4gzMffR6hUd7Ve3RJy8Gg4FTp05QunQosixx4cJ5YmKu8MILL1nKREVFUa9efbsnL0IeaTWYAgJBW3izq+aWyc+fO13exJTNKC1HU3t8+SFJEnrFxMXbN8kwGfFxcaWyq7vVxSLF3xfTu29g8vfJth41K4z3zV51/hfb1H+NSGDuY04m/ttnBSQJoqOjOHLkMEajAR8fX559trlVB9VmzZ5zYISChYsrptAyqhhGbQotQ/Jnc3Mu6CBqj68gMkxGMoxGDKasZ4eNZYIxLZmNMSUSDI5f9DOvCuN9s1ed/+U29V8hEpgiRqPRWp1tEVTMZIL0dJBV0CPyzh00lyIxli0HD1kHymHUHl9huZMGF09CCQmyrj7iEBKZo5LubVOUbM42F8b7Zq86i2qbciLiGoEgqFVaGtrTJ81JjINpz57G/5kGaM/aZwJHe1N7fIVFe/oC2ppPoTt90dGhAKCRJDRaGcN9o5IeNjKpMN43e9VZVNuUMxFnYBxo9OiPSUpKYsaMWY4ORRAEFZIwr/gM9l+UsTBoJBmDYiLy9k3S7y74KEYmCYWlSCcwufkwpaSkMH/+XH7/fTs3b94gPLwKH3wwnMceq24pM3r0x1ZDiQEaNnyKefMWAhATc4VWrVqyevVawsOthxYXlF6vZ/78ufz1106io6Px9PSiQYMG9O8/kKCg4lZld+78kyVLFnL27FlcXFyoV+/xhyZPdevWtLl9wIBBvPXWO/z99wF69Ohms8zKlV9bvUZC/ok/+UWTJIGHq0Q5X1dMioKLRqvaRRkz10fSyTIS9/rtCEJhKpIJTObaQxkZ6Tku+Ddu3BjOnz/H+PETCQoqzk8//UDv3j1Yt26j1bTyTz31NGPGjLfcf3AF68KSlpbGqVMn6d69J5UrV+b27dtMmzaVgQP7s2rVGku5bdt+Zfz4sfTt25/69Z/AaDRy7ty5h9b9yy/bre7/9dcuxo0bbZm3pVat2lnKLFgwl/3791Gt2mN2OkLBaDJ/EYhRYUWNeeI6o3INvVGPVvZAkko6bNI6Wx5cH0kry3joJDQqTLKE/54imcDIsgY3N0+Sk2+i0cgUK6ZFUUxZOpmlpaWxfftvTJ/+GXXq1AGgR4+e/PnnDtau/YY+fd4HzGdydDod/v7WK1Kb7n7xtGrVEoDXXusIQN269Vi8eOndjm0KX365jK++WonBoOf551swZMhQtNrc9cjz8HBn3rwFVts++GAEb73VlZiYaEqWLIXBYODTT6fSv/9A2rZtZylXrlw5S4y2PHg8f/yxnccfr09wcClMJiMajWxVxmDQ88cfv9OpU2ebrydk/nhU0OszxDwwOTHoMWo1JKfexqWYG7LswOHUkoTi4qLKX/+A+uMrAJNl6n4bizI6+LgfXB9JllyRpVBkKZfJdmHEb686/8Nt6r+iSCYwAN7e5rH9KSm38PIqhsFgxPTAMMXU1FSMRiOKYiQ19d4U8zqdloMH/7ZsMxj0/PPP3zz3XFO8vLyoW7cu3bu/Z1lQcdGixfTs2YPPPvuMcuXC0Ol0pKYmYzDo+fvvA/j6+jBz5kyuXIlmzJgxlCtXltatXwbgiy++YOvWLaxd+22ujy0hIQ5JktBoZFJTkzlx4gSxsbEYDBm89lpHEhISqFSpEr1796F8+dzNEHvjxg127drJyJEfWb0W9/vjjz+4desWzz33XLZlZFlGq9Vw82as1WKOQjYCfZEkGT9v/5xXry5Ehhq1iI+Od9jz50Tt8RUWQ62qGO9cR+/gYdT31kfKW5JdGO+bveosqm3KmRTZBEaSJHx8AgAFV1cXdLqsZwzc3T2pWbMmK1d+RZUq1fD3D+Dnn7dy/PhxQkJCcXf3BKBRo8Y0b96C0qVLEx0dzbx5cxg+fDjLln2JRqOhVCnzHCvFi5ciNLSspX6tVoe3tzcjR/4PjUZD1aqP8dtv2zh8+AidOr0OQFBQcUJDy1ieKyfp6eksXryYFi1eICjIfIkrIcG80Nvy5csZNGgIwcHBfPXVSgYMGMCGDZusVq7OzrffrsPDw4MXXnjRaimB+23dupUnn2xIuXLZJ0WZM/H6+RUXZ2BySavVOTR5EQRBUKMif1FdlmVkWZPtbfz4yQC0bNmCp55qwDffrKFFi5ZWj2vZ8iWaNn2WypWr0KzZc8yaNY8TJ45z8ODBu2Vkm88lSRIVKlREp3OxbAsKCuLmzZuW+6+91oVFi5Y+NMbMm9Fo4sMPhwMKI0f+z7I9U7duPWjevAWPPVaDsWMnIEkS27Zty1Xdmzd/R8uWL+Hm5m5zf1xcHHv37qFdu/a5qk+ncxG3HG7FLl4k6IVn0Z4944iPhhXNmdP4PtsIzRl1DilVe3yFRXvqAprHG6M9fcHRoeRLYbxv9qqzqLYpZ1LkE5ichIaGsnTpMv76ay8//fQLK1d+jcFgICQkJNvHhISE4OvrR1RUVI71a7UPngTL31BDvV7PiBEfcPXqVebPX4yn570zNoGBQQBWl4tcXFwICSnNtWtXc6z74MF/iIyMpF279tmW+f777/Dx8eGZZ5rkOXbBNintDrp/jyCl3XF0KKqKxRa1x1do0tKQDh1FuuP4uYLyozDeN3vVWWTblBMRCUwuubm5ExQUxO3bt9mzZzeNGzfNtuz169e4dSuRoKBAAHQ6c4dco7FwForMTF4uX77EwoWL8fX1tdpftWo1XFxcuHQp0uoxMTExlCoVnGP93323kapVq1G5crjN/Yqi8P33m2jVqrXlWAVBEAShMIkEJge7d//FX3/t4sqVaPbu3UOPHt0oV64cL7/cBjB39P3ss+kcPXqEmJgr7Nu3l0GDBhAaWoaGDZ8GwM/Pn2LFirF79y4SEhJISkrK9fOvWbOanj27Z7tfr9czbNgQTpw4zsSJUzAaTcTHxxMfH49erwfA09OTDh1eZeHC+ezZs5vIyItMnjwBgObNn7fU1b79y2zfvs2q/uTkZH799ZeHnn3Zv38fV65coW3bDrk+LkEQBEEoiDx14p0zZw5z51ovbhUWFsbWrVsBcwfSKVOm8NNPP5GRkUFERASjR48mMDDQUj4mJoYxY8awb98+3N3dadu2LUOGDLG6lLJv3z6mTJnC2bNnKVWqFL1796Z9++y/QAtTcnIyc+fO4vr16/j4+NCs2XO8/34/y5kGWZY5e/YsP/zwPUlJSQQFFefJJxvSp09fy1wwWq2WDz4YzpIli1i4cD516tRlyZIvcvX8iYk3iY6OznZ/XFwsO3b8AUDnzq9a7Vu8+HMef7w+AAMHDkar1fC//40kPT2d6tVrsGjRUry9vS3lIyMjSU62Hj3088/m97ZFi5bZxvDddxupVas2YWFhuTomQRAEQSgoSclDh4s5c+bw888/s2zZMss2jUaDv795SPLo0aPZsWMHkydPxsvLi/HjxyNJEmvWmCdUMxqNtG3blsDAQIYNG0ZsbCzDhw+nY8eODB48GICoqChat25N586defXVV9mzZw+TJk1i0aJFNGrUKM8HGB+fZHsRsbtkWcLDwxWTyfa8JYJ9ZY5CSklJF6OQciAl3kT35x/on2mC4uuX8wOKSCy2qD2+3JAkrEabybJMMQ898WnnyTBm4K7zJMg9hOspkaTdHTLtkWwkaPdZrjcszx0vF5tlcrPNXmWKaYvhXyyMI7G3Sc4wr6LuotEQ7huIVk+Wz3xhvG/2qvO/0KaclSRBYKBXjuXyPIxaozGPlHlQUlIS69evZ9q0aTRs2BCASZMm8eKLL3L48GFq167Nrl27OHfuHMuWLSMwMJCqVasyYMAApk2bRt++5jMWa9asISQkhBEjRgBQoUIF/vnnH5YvX56vBEYQnJXi60fGy+1yLvgIqCkWW9QeX04kCTSuGgz3LRwhyeQ4fF7x80F5tS2Kg+eBya/CeN/sVaezt6miIM99YC5dukRERATPPvssQ4YMISYmBoBjx46h1+t56qmnLGUrVKhAcHAwhw8fBuDw4cNUrlzZ6pJSREQEycnJlmntDx8+bEmA7i+TWYcgFBVSbCxuC+YixcY6OhRVxWKL2uPLiSxLuLgoKHIqRikFo5SCIt1Bk0MSI8fGI302Dzk24RFGm7PMtZFcNBpcZE22ix8UxvtmrzqdvU0VBXlKYGrWrMnkyZNZunQpY8aM4cqVK3Tp0oXk5GTi4+PR6XRWfSoAAgICiIuLAyA+Pt4qeQEs93Mqk5ycTFqa8/3CEIT80lyLwXP0SDTXYhwdiqpisUXt8eXMvO6R3nSVNEMUaYYojEockmR66NpHckwsmqEfo4lRx5fs/WsjVfRzo6KfG+V8XfFwlZDle7fMnKww3jd71en8beq/L0+XkBo3bmz5d5UqVahVqxZNmzZly5YtOS6KKAiCIDxc5rpHgO21j1TuwbWRAFw1Lmg0ZTHqNGTOJKGTNJjSxWrVQsEUaBi1t7c35cqV4/LlywQGBqLX67l9+7ZVmYSEBEufmcDAQOLjrdeWyLyfUxlPT0+RJAmCIDiBe2sj6TFhRFEULiclcjoxnou3b6JXTGJ5DKHACpTApKSkEBUVRVBQENWrV0en07Fnzx7L/gsXLhATE0Pt2rUBqF27NmfOnCEh4d712t27d+Pp6UnFihUtZfbu3Wv1PLt377bUIQiCIDifDJOJDKORDJM48yLYR54uIU2dOpWmTZsSHBxMbGwsc+bMQZZlWrVqhZeXFx06dGDKlCn4+Pjg6enJhAkTqFOnjiX5iIiIoGLFigwbNowPPviAuLg4Zs6cSZcuXSxzpnTu3JlVq1bxySef0KFDB/bu3cuWLVtYtGiR3Q/emoQkiWG9hU/86sotk5c36S1aYvLyzrlwEYrFFrXHZ8v9w6bzezZC8fbC1OoFTN65W+xVbQrjfbNXnc7YpoqaPCUw165dY/DgwSQmJuLv70+9evVYu3atZR6YkSNHIssy/fv3t5rILpNGo2HhwoWMGTOGTp064ebmRrt27ejfv7+lTGhoKIsWLWLy5MmsWLGCkiVLMmHChEIbQq0oCiaT6e6Ci+LL9VEwz7kjksWcmMLKc3vlN44OA1BXLLaoPb4HSRK4u8vIcmY/FxMajfTQDru2GMuHYvpuNUYnHUZdGO+bvep0tjZVFOUpgfnss88eut/V1ZXRo0dbJS0PKl26NEuWLHloPQ0aNGDTpk15CS3fFAVSUzPE9dhHSFEUMWlgbuj1SLduofj4gKPXmFJTLLaoPb4HSJKELBswmWIBA5LkioR/3v8O6fUQFw86vXP+/iqM981edTpZmyqKxFpImJMYk0kRt0d0E8lL7mhPHiewWnm0J487OhRVxWKL2uPLngFFMaAo+esXoj1+Fm3JSuiOn7NzXPZnmRvmvnlhCuN9s1edztumio48z8QrCIIgCLl1/9wwBpMWWZLw0EmkG8QvGaFgRAIjCIIgFJoH54bRaXTIUlnE149QUKIFCYIgCIUuc24YjeKMnXUENRJ9YARBEBxIknDODriC4GDiDIwgqJThsRrEn49GcfdwdCiqisUWtcdni6SRUCRQuPtLMh8jIQ01wjHcvISeOLi7BIGzkCQJU42a3Lh4BcXdA0nCLh387dUWnLFNFTUigREEtdJoUNQyiZaaYrFF7fE9QJIkTIpCYkY6elM67loZf10+vr01GnD3hpQbYHCOBEZCQpIljDoFo0YDxczvm06SMaUbC57E2KstOFmbKorEJSRBUCnNhXP4dGyL5oLjh8iqKRZb1B5fdkyKCaNJwZjPb23NuUvIL3RAc+6SnSMrPJIkWdZGunToALrWL3L1yEG7rY9kr7bgrG2qKBEJjCColJScjMsf25GSkx0diqpisUX18Ukgy5LlZq+JM6XkFORftyMnp9qlvkcpw2RCuX0b/z93YnpgEeCCsFdbUHubEsQlJEEQhEKVddkAyO/SAYIg3CMSGEEQhEL04LIB5m35XDpAEAQLkcAIgiA8EuZlA8zEn15BKCjRB0YQVMoYHELS5GkYg0McHYqqYrFF7fEVFlPpkhjnfIIxpISjQ8mXtFKlODVmNOnBpexWp73aQlFtU85E/AwQBJVSAgNJ69bD0WEA6orFFrXHV1hMQf4ofd7DlBIJhjRHh5Nn+oAAot98AxeNxm512qstFNU25UzEGRhBUCnp5g1cv12DdPOGo0NRVSy2qD2+wiLduIX01TdIN245OpR80SYmUnLTJrSJiXar015toai2KWciEhhBUClN1GW83++BJuqyo0NRVSy2qD2+TJJk36UDNJevoHmrF9rLMfap8BFzi46m+uChFIuKtlud9moLztKmijJxCUkQBKGQ3b9sAOR/6QBBEO4RCYwgCEIhenDZACD/SwcIgmAhEhhBEIRHIHPZACDfSwcIgnCP6AMjCCqluHugr1dfFavhqikWW9QeX2FR3N1QnqyP4uHm6FDyxejuTmKd2hjd7Re/vdpCUW1TzkScgREElTJWrETilm2ODgNQVyy2qD2+wmKsHIbxr18wOOkw6tTy5fl7/Tq7DqO2V1soqm3KmYgzMIIgCIIgOB2RwAiCSmmPHiaouDfao4cdHYqqYrFFbfHdv/p0Ya53pD18Aq3GD93hk4X2HIXJ69gxnitfEc9/j9mtTnu1BbW1KSErcQlJEATBjrKuPi1WnrZFJ8tIsvnSkVaWxasj5JlIYARBEOzowdWnxcrT1iRktLKGUG8XJG9XAIK9XHBzlUg3iNFZQu6JS0iCIAiFwrz6tKIYHR2IqkiSjCQZMCnX0ZtiATApCciSAbtNUSwUCeIMjCAIgvDIGRUDJsV8mS3z/4KQFyKBEQSVMlSuQsLeQ5iCSzs6FFXFYova4ysshioVMJz+B72fnnsLFTgPfXg5Lv29Fjk0xG512qstFNU25UxEAiMIalWsGKbyFRwdhZmaYrFFpfHZc+FGm4q5QsUQcNJ5YJRirhjKh+KicbFfpfZqCyptU8I9og+MIKiUfCkSr97dkS9FOjoUVcVii9rikzQSigZMGsx/ZQupA68mMhr5jR5oIq8USv2FTXsphuI9x9g1fnu1BbW1KSErkcAIgkrJtxIptn4t8q1ER4eiqlhsUVN89y/eGJ+WSrIhg8K6vCMl3kb++lvkxNuFUn9hkxOT8Pr2Z0v85hFc0n1z6OSjTju1BTW1KcE2cQlJEAShEGQu3igWbsyZhIQkSxh1CkbTve06SYMp3Yh4CQVbRAIjCIJQAJKE1RwvYr6XvJMkCUVRiEm5TarePCJJJ8uEevqgubtPEB4kEhhBEIR8yjrrLkiSgixm3s2TzMntSnpqMZjMPRtkScJDJya3E7InEhhBUClTiZKkDB2BqURJR4eiqlhscVR8kiSh0RkxKrEod+cykSRXNFLAIzkTYyoZhGnUcIwlAwv9uQqDsUQAN4Z1w1SquGVyuwxjOgA6jQ5ZKov5ayr3SYy92oLa27wgEhhBUC1TiZKkDhvp6DAAdcVii6Piy+ywezM9Bb3J/MXrrvXEX/tozhqYSgZhGj0Ck5MOozaWDOTmiO646zzN9xUDRkUPgEbJXwJor7ag9jYviFFIgqBaUtJtdNt/Q0py/AgTNcVii6Pjs3TYfcSddqXbyUg/b0O6nfzIntOepNspuG3ba9f47dUWHN2mhJyJBEYQVEpz8QK+ndujuXjB0aGoKhZb1B5fYdFcuIzmxVfQXohydCj5orsYTfCrg9BcuGy3Ou3VFopqm3ImIoERBEEQBMHpiARGEARBEASnIxIYQRAEQRCcjhiFJAgqpbi4YiwXhuLi6uhQVBWLLWqPr9C4uqBUCENxteNiiI+Q4qJDH1YaHhJ/5vICAIqi5Dgrr73aQpFtU05EJDCCoFLGKlW5sf+Io8MA1BWLLY8qPrXNumuoWhHjmYMYnHQYtb5qeS7/s84yjPp+tpYXyM3SAvZqC2pv84JIYARBEHLF1qy7YEIjZt0tFJnLC1xOSiRZr8dF1hDm7YdWLC0g3CX6wAiCSmmOHyOgahia48ccHYqqYrHlUcRnvpRhwGSKxWSKwWSKQVHikTA57EyM9thpNCUqoj12xiHPX1Aux89RrlJLtMdOZ1smw2Qiw2gkw2TMVZ32agtqb/OCOAMjCKolGQ3ICQlIRkPOhYtQLLY82vgMlmUDHP4n1GBEik9AMuTuy111DEY0CYlgx/jt1RbU3uYFcQZGEARBEAQnJBIYQRAEQRCcjkhgBEEQBEFwOqIPjCColKF8RW7++CuG8hUdHYqqYrFF7fEVFmPFchh2/YyhfNZhyM5AXyGU6K2L0VYsZ7c67dUWimqbciYFOgOzePFiwsPDmThxomVbeno6Y8eOpUGDBtSpU4d+/foRHx9v9biYmBh69OhBrVq1aNiwIVOnTsVgsO4otW/fPtq1a0f16tVp3rw5GzZsKEioguB8PD0x1G8Anir4clJTLLaoPb5Coni6Q8MnzP93QoqnO+lP1LBv/PZqC0W0TTmTfCcwR48eZc2aNYSHh1ttnzRpEr///jszZ85k5cqVxMbG0rdvX8t+o9FIz5490ev1rFmzhilTprBx40Zmz55tKRMVFUXPnj1p0KAB3333HW+99RYff/wxO3fuzG+4guB05JgrePzvQ+SYK44ORVWx2FJY8UkSyLJ5JlhHT1pni3zlGvKQj5CvXHd0KPmiuRJLwEezkK9cs1ud9moLam/zQj4TmJSUFD744AMmTJiAj4+PZXtSUhLr169nxIgRNGzYkOrVqzNp0iQOHTrE4cOHAdi1axfnzp3j008/pWrVqjRu3JgBAwawatUqMjIyAFizZg0hISGMGDGCChUq0LVrV1q0aMHy5csLfMCC4Czk+DjcF81Djo9zdCiqisWWwogvc+I6Dw8jHh5G3N2NaLXqSmLkuBvIM+ejibvh6FDyRRN/E98Fa5DtGL+92oLa27yQzwRm3LhxNG7cmKeeespq+7Fjx9Dr9VbbK1SoQHBwsCWBOXz4MJUrVyYwMNBSJiIiguTkZM6dO2cp07BhQ6u6IyIiLHUIgiAUtqwT18WDZAJJurukAIgJeAufTpZx0WhwkTXi5Ras5LkT748//siJEydYt25dln3x8fHodDq8vb2ttgcEBBAXF2cpc3/yAlju51QmOTmZtLQ0ihUrltewBUEQ8sk8cZ0kac2XkWS4uzSP+RegCi8t/RdIyGhlDaHeLhhMWmRJwkMnkW4QywgIZnlKYK5evcrEiRP54osvcHUVK3QKglC0KIrCbX06aUbzwonuWhl/nfhCLQySJCNJBkzKdTKM6eg0OmSpLOavLfGaC3lMYI4fP05CQgLt27e3bDMajRw4cIBVq1bx+eefo9fruX37ttVZmISEBIKCggDzmZSjR49a1Zs5Sun+Mg+OXIqPj8fT01OcfRGKDJN/AHfe6Y7JP8DRoagqFlseZXxGRcFoUiz/diRTgB+m3t0wBfg6NI78MgX4cKtbe0wBftmWMSoGjIoejZK7M132agtqb/NCHhOYJ598ks2bN1tt+/DDDylfvjzvvfcepUqVQqfTsWfPHlq0aAHAhQsXiImJoXbt2gDUrl2bhQsXkpCQQECAuWHs3r0bT09PKlasaCnz559/Wj3P7t27LXUIQlFgCgkleeoMR4cBqCsWW9QeX2ExhZbCNHcaxpRIMKQ5Opw8M4SUJP7TD3DX2W+osr3aQlFtU84kT514PT09qVy5stXN3d0dX19fKleujJeXFx06dGDKlCns3buXY8eOMXLkSOrUqWNJPiIiIqhYsSLDhg3j1KlT7Ny5k5kzZ9KlSxdcXFwA6Ny5M1FRUXzyySecP3+eVatWsWXLFt5++217H78gqFdqKtqjhyE11dGRqCsWW9QeX2FJvQMHjyCl3nF0JPkipabhcuS0+TjsxV5toai2KSdi96UERo4cSZMmTejfvz9du3YlMDCQOXPmWPZrNBoWLlyILMt06tSJDz74gLZt29K/f39LmdDQUBYtWsTu3btp06YNy5YtY8KECTRq1Mje4QqCamnPncHvuWfQnjvj6FBUFYstao+vsGjPXERbvwnaM5GODiVfdGcvEdr0bbRnLtqtTnu1haLappxJgZcSWLlypdV9V1dXRo8ezejRo7N9TOnSpVmyZMlD623QoAGbNm0qaHiCIAiCIPwHicUcBUEQBEFwOiKBEQRBEATB6YgERhBUSpFkTJ5eKJLjP6ZqisUWtcdXaGQZxcsLZCc9blnC5Olu1/jt1RaKbJtyIgXuAyMIQuEw1qhJwgV1LCSnplhsKez41LpsgKFmFYyJl9E76TDqjBqVuXh5m12HUdurLai9zQsigREEQciWpJFQJPO8r2LZAEFQF3FuTBBUSnP6FH6NnkBz+pSjQ1FVLLYURnySJGFSFBIz0olPSyXZkIHaprDXnjqPpkZDtKfOOzqUfNGdukhow9ftGr+92oLa27wgzsAIgmpJ6WloT59CSnf8pQE1xWJLYcZnUkwYTYrDlw2wKS0d6cQppLQMR0eSL1J6Bi6nL0Jaeq4fI8sS9//2VhQTJtO9/fZqC2pv84JIYARBEAQnIKNBq9EgeRhQ7k8mFR2pyUarJEYoGkQCIwiCIKieLGuQJAO3M66QbjSfsdHJLvi4hCJJWkBkMEWNSGAEQRAEp6E36ckwOuclM8G+RAIjCCplLFuOWyvWYCxbztGhqCoWW9QeX2ExlgvBuHEVhnIhjg4lXwzlgrm66hMkO8Zvr7ZQVNuUMxEJjCColOLjS8YLLzo6DEBdsdiSn/gkyTzS6L5auH+yF8kJhkwrvt4oL7+I4qTzwJh8vEht2ciu88DYq62qvc0LYhi1IKiWdP06brOmI12/7uhQVBWLLXmNT5LA3V3Gw8NouXl7y3h63rvv7m5Eq5GQ1DiD3V3y9XikKTOQr8c7OpR80VxPwPezL+0av73aqtrbvCASGEFQLc31q3hOHIvm+lVHh6KqWGzJa3ySJCHLBkymWEymGExKArLGgFGKxSjF3L3FIUmKqs/EyFdj0Xw0Hs3VOEeHki+aa/EEjF+IfDXWfnXaqa2qvc0L4hKSIAhFmgFFMSChRVEUbmekkmY0X4px13rir1Xh3C+CIAAigREEQbAwKgpGk2L5tyAI6iUuIQmCIAiC4HREAiMIKmXy9iG9dVtM3j6ODkVVsdii9vgKi+LjhalDG0w+Xo4OJV9MPp4kv9wUxY7x26stFNU25UzEJSRBUClTuTBuf77C0WEA6orFFrXHV1iMYaGY1i7H6KTDqA3lSnN9+SS7DqO2V1soqm3KmYgzMIKgVhkZyDFXIEMFs46qKRZb1B5fYcnQQ/QV8/+dUYYezZVY+8Zvr7ZQVNuUExEJjCColPbUCQJqV0V76oSjQ1FVLLaoPb7Coj1xFm3Z6uhOnHN0KPnicvIC5Wq0QXvirN3qtFdbKKptypmIBEYQBEEQBKcjEhhBEARBEJyOSGAEQRAEQXA6IoERBEEQBMHpiGHUgqBShuo1iYuKA53O0aGoKhZbChKfJIGK12t8KEPNKhhSr6HPuAIm5xstk1GjEuev7sDd3dduddqrraq9zQsigREE9ZJlcHV1dBRmaorFlnzEJ2kkFAkU7p6KVvGijdnKPG6DDCZHB5MPsgyuLub/27VOO7RVtbd5QVxCEgS10pw/i0/bF9Gct98Q0/9CLLbkNT5JkjApCokZ6cSnpZJsyMCcyjgXzdlINM1aoT17ydGh5Ivu3GWCW/dBczayQPWYVxc33zQX7NNW1d7mBZHACIJqSSkpuOzehZSS4uhQVBWLLfmNz6SYMJoUp124UUpJRdrxF1JKqqNDyRcp5Q5ufx3Kd/wSEpIsYdQpGHRg0IEm445d2qra27wgLiEJglAESJL5V/q9+054uUjIQpIkFEXhclIiyXo9LrKGSk6ajAp5JxIYQRD+0yQJ3N1lZNlw31YTGo2E5Ky9dwUrGSYTGUajo8MQHjGRwAiC8J9m7h9hQFFiURRzEiPJrkj4izMxguDERAIjCCplLB1K0ow5GEuHOjoUVcViS07xSRoJo8mAgjmBkdE656ijBxhDSmFcNAtjaElHh5IvhpASxM78EFNIqQLVo5NlXDQaXGQNptAQkj8reFtVe5sXRAIjCKqlBASQ1vUtR4cBqCsWWx4W3/0jjvSmdADctTL+OufvK6EE+qF0fxNTSiQY0hwdTp6ZAnxJevNl3HWe+Xq8hIxW1hDq7YLBpEWWJNx1HqS/+TaKsWDvr9rbvCBGIQmCakkJCRT76kukhARHh6KqWGzJTXyWEUdOPOroQVL8TaSlK5ATbjo6lHyRExLxWvE9Unz+4pckGUkyYFKuk2GMxqhcQ064juvKgrdVtbd5QSQwgqBamitReA3uh+ZKlKNDUVUstqg9vsKiib6KpucANFHXHB1Kvmijr1N84GQ00VcLVI9RMWBU9JgUA1JUFB4D+xa4LRTVNuVMRAIjCIIgCILTEQmMIAiCIAhORyQwgiAIgiA4HZHACIJKKR4eZDwVgeLh4ehQVBWLLWqPr7AoHu4ojZ9G8XB3dCj5oni4cefpOvaN38MT/dMFbwtFtU05EzGMWhBUylihErc2/eToMAB1xWKL2uMrLMZK5TBu/wGDkw6j1lcsQ8zm+fkeRm2LUrkiSd9vxWgs2PLcRbVNORNxBkYQ1MpkgvR08/8dTU2x2KL2+AqLsx+3yQTpGfaN316vibO/tkWASGAEQaW0x44SFBqE9thRR4eiqlhsUXt8hUV79BRa95Lojp52dCj54vLvWSqUaoz26Cm71SkdOYp/cECB20JRbVPORCQwgiAIgiA4HZHACIIgCILgdEQnXkEQ/nMkCctK02LFaUH4bxIJjCAI/ymSBO7uMrJsuLvFhEYjISESGUH4LxEJjCColKFKNRIOn8QUGOToUFQViy33xydLErJswGSKBQxIkisS/v/JMzGGapUwXDqG3iMVMDo6nDzLqFqeyH+/wzU41G51Ko9V4+bR0xj8AwpUj9rbvCASGEFQLxcXTMGlHR2FmZpiscVmfAYUxcB/+s+ciw5CSkNKJBicL4HBRYexdHHQ6exYpwtK6dJQwHlgVN/mBdGJVxDUSo68iHe3N5EjLzo6FFXFYova4yssmotRyB3fRnMx2tGh5Is28gol3h6J5qL9VnyWLkTi+U7XAreFotqmnEmeEpivv/6a1q1bU7duXerWrUunTp3YsWOHZX96ejpjx46lQYMG1KlTh379+hEfH29VR0xMDD169KBWrVo0bNiQqVOnYjAYrMrs27ePdu3aUb16dZo3b86GDRsKcIiC4Jzk27dw3bwJ+fYtR4eiqlhsUXt8hUW6lYS8/jvkW0mODiVf5FvJeH7/O5I947+ViMv3BW8LRbVNOZM8JTAlS5Zk6NChbNiwgfXr1/Pkk0/y/vvvc/bsWQAmTZrE77//zsyZM1m5ciWxsbH07dvX8nij0UjPnj3R6/WsWbOGKVOmsHHjRmbPnm0pExUVRc+ePWnQoAHfffcdb731Fh9//DE7d+600yELgiAIguDs8pTANGvWjMaNG1OuXDnCwsIYNGgQ7u7uHD58mKSkJNavX8+IESNo2LAh1atXZ9KkSRw6dIjDhw8DsGvXLs6dO8enn35K1apVady4MQMGDGDVqlVkZGQAsGbNGkJCQhgxYgQVKlSga9eutGjRguXLl9v72AVBEARBcFL57gNjNBr58ccfSU1NpU6dOhw7dgy9Xs9TTz1lKVOhQgWCg4MtCczhw4epXLkygYGBljIREREkJydz7tw5S5mGDRtaPVdERISlDkEQhPs9OOfLf3G0kZB3kiQhy+abaBL/TXnunn/69Gk6d+5Meno67u7uzJs3j4oVK3Ly5El0Oh3e3t5W5QMCAoiLiwMgPj7eKnkBLPdzKpOcnExaWhrFihXLa8iC4JSMJUqR/NFojCVKOToUVcVyP8ucL+WLox83imLli4O7Ea1WIkOPeeaX//CXl6lUcYwT/4exlHMO9TWWDCThf70wlSpul/okJCgdTMroMehDS2G6O7hJJ2kwpRtRlDzEptI2L9yT5wQmLCyMTZs2kZSUxM8//8zw4cP56quvCiM2QSjSlBIluDNgiKPDANQVy/2kzDlfikuYhnQGjKDEI8kBIEuYuHua+T/6E9xUIhBlxGBMKZFgSHN0OHlmLBFA4qC3cNd52qU+SZJQShTndM/uJOv1kBiPi6whzNsPrSSh5CGDUWubF+7J8yUkFxcXypYtS/Xq1RkyZAhVqlRhxYoVBAYGotfruX37tlX5hIQEgoLMvw4CAwOzjErKvJ9TGU9PT3H2RShSpFuJuGz9CelWoqNDUVUsNiXeQP7hV5SbN0AxoigKt/XpxKelkmzIAPLw09uJSIm3kb7/CSnRWUchJeG+ZSdS4u2cC+dWYiIeW7diunmTDKORDFP+5sdRfZsXCj4PjMlkIiMjg+rVq6PT6dizZ49l34ULF4iJiaF27doA1K5dmzNnzpCQkGAps3v3bjw9PalYsaKlzN69e62eY/fu3ZY6BKGo0FyKxOfNzmguRTo6FFXFYot0MZpinQYgR96bD8WoKBhNCsa8XDdwMprIaDTtuqCNdNZ5YGIo1WUYGjvGL12MJPzd7rhdvlygetTe5oU8JjDTp0/nwIEDREdHc/r0aaZPn87+/ftp3bo1Xl5edOjQgSlTprB3716OHTvGyJEjqVOnjiX5iIiIoGLFigwbNoxTp06xc+dOZs6cSZcuXXBxcQGgc+fOREVF8cknn3D+/HlWrVrFli1bePvtt+197IIg/Bf9N68WCXmkkzW4aDS4yBrRJP6j8tQHJiEhgeHDhxMbG4uXlxfh4eF8/vnnPP300wCMHDkSWZbp378/GRkZREREMHr0aMvjNRoNCxcuZMyYMXTq1Ak3NzfatWtH//79LWVCQ0NZtGgRkydPZsWKFZQsWZIJEybQqFEjOx2yIAj/JZJGQtGY/61oMP8s+4/2eREeTkJGI5kbQ6i3K3o/N2RJwkMnkW74756JK6rylMBMmjTpoftdXV0ZPXq0VdLyoNKlS7NkyZKH1tOgQQM2bdqUl9AEQSiCJEnCpCgk6TNwAxLT09AaMvDXiS+rokiSZCTJ3OdFb4olwxiNTqNDlspi/roT7eK/5D+8ypkgODfFtRiG8Cooro7vvK6mWGwxuurICA/D4OKC9B/u85JFMVeUalVQirk4OpJ8UVxdyAgPg2Ku9qu0mCv6KuUxuMgYFT0aJX9n49Te5gWRwAiCahnDq3Bz535HhwGoK5YHJ64DMFQpT9SerwGw47rGqmeoUgHjv3swOOkwan2VMKL2fG23YdQAVKtC7P716Av4eqipzQu2iQRGEASnYZm4Ts5cANaERiOZJzATBKFIKfAwakEQCofm36MElC+N5t+jjg5FNbFYJq4zxWIyxaAo8UiYcPn3DGFlnsXl3zMOje9R0x49hca3DLqjpx0dSr5kvm/ao6fsV+nhfylVOqLAbUEtbV7InjgDIwgqJSkm5OQkJMXk6FBUFYuZAUUxYPkTZjIhJ6eCqQj1fwEwmZCSksCklvclj0zK3ffNjvGbTMhJKQVuC+pr88KDxBkYQRAEQRCcjkhgBEEQBEFwOuISkiAIgvCfZ+4/Ze7srShKnlamFtRJJDCCoFKGipW5+dufGCpWdnQoqorFFkPlckT9vhx9pbJF6o+aoXIYhgN/YAh1znlg9JXKEvX7crSVw+xXaZVKxP75NfrypQCQkJBkCaNOwXi3O4tO0mBKNz40iVF7mxdEAiMI6uXujqFmbUdHYaamWGxQ3N3IqBXu6DAePXc3qFsJxUnngVHci5FRKxytzs1+lbq7o69dFeXu6yFJEoqicDkpkWS9HhdZQ5i3H9q72x9Wj5rbvCD6wAiCasnRUXgOH4wcHeXoUFQViy2aqKsEfvAp2uhrjg7lkZKjriL3HYom6qqjQ8kXbfQ1Aj/4FNme8V+Owmfw5CxtIcNkIsNoJMNkzFU1am/zgkhgBEG15BsJuC1binwjwdGhqCqWTJKEZeVpOSERn883ICfccmhMj5qccBN5wefICYmODiVf5IRbd9+3m/arNP4GnkvXFrgtqLHNC9bEJSRBEJyKpJFQJPOyfDKIlacFoYgSCYwgCE4jc/XpxIx09KZ03LWyWHlaEIoocQlJEASnY1JMGE0KRjEWVhCKLHEGRhBUyhQYRGrP9zEFBjk6FIfFcv/K0+b7ti8XGYP8SezdGWOgX5H6o2YK8sc0sA/GIH9Hh5IvxkA/Ent3xmTP+IsHkvx+F4yBfgWqRk2fP8G2ovRZFwSnYgouTcr4yY4OA3BMLFlXnobsVp82lS5BwsQBjzQ+NTCVLolp+kRMTjqM2li6OAkTB+Cu87RfpSGluTV5KMYCvh5q+vwJtolLSIKgVsnJaA/sg+RkR0fikFgeXHn6/tWnHzwTIyWn4rr/X6Tk1EcWnxpIyamwZ7/THnehvG/JybjsO5KlTp0s46LR4CJryFW3bzV9/gSbRAIjCCqlvXAOv5eao71wztGhODgW88rT5pvtOTy05y4R8kIPdOeL1pwdmnORaCNaoD13ydGh5IvufBQhL/RAcy7SfpWeOU9Q87ctbUFCRitrCPV2oaKfG+V8XfFwlXIcvKamz59gm7iEJAiC6lm+bMSIaSGPJElGkgyYlOtkGNPRaXTIUlnMX3+iE7gzEwmMIAiqdf+cLyDmfRHyz6gYMCp6NIpoP/8VIoERBEGVHpzzBRDzvgiCYCESGEFQKUWjxRQQgKJx/MfUkbFkzvkCZDvvi6LVYAzwBa3mEUamAloNSmAAirMed2G8b3aqU02fP8E28c4IgkoZH6tOwsmLjg4DUFcsthiqVyby7BagaP1RM1QPx3j9HAYnHUad8VhFIs9use8w6prVuXbxdzIK+Hqovc0LReuzLgiCiuV20jpBEAQQw6gFQbU0p07i/0QtNKdOOjqUQo8lc9I6Dw+j5ebmZkBrY9I6W7Qnz1Om3ivoTl4olPjUSnvyHJrKddGePO/oUPJFd/ICZeq9gvakHYcqHz9JiVov59gWzPMM3bs9mC+r6fMn2CbOwAiCSkkZ6WgiLyJlpDs6lEKP5f5J68Bwd5srEv65OhMjpWegu3gFKUNfKPGpVnoG0vmLSOkZjo4kX6QMPbqLV8Ce8adnoL0YlW1bkJCQZAmjTsFourddJ2kwpRvJ7Galps+fYJtIYARBUBHzhHVm4s+TYH+SJKEoCpeTEknWm5McF1lDmLcf2rv7BOcg/kIIgiAIRU6GyUSG0fbMzoJzEH1gBEEQBEFwOuIMjCColDGsPIlrNmAMK+/oUB5pLPlZNsBQPpSYbz9DHxZSpP6oGcuXwfjTOgzlSzg6lHzRh4UQ8+1nyOXL2K/SimHEb5iHPiykQNWo6fMn2FaUPuuC4FQUL2/0zZ5zdBjAo4mlIMsGKN6epD37ZGGFplqKtydKi2dRnHQeGMXbgzvPPmnfeWC8vUl/7imUAr4eavr8CbaJS0iCoFLy9Wu4fzIJ+fo1R4dSKLFIEvcNY5UtywbEp6USn5ZKsiGD3C62J1+Lw2/KUjTX4u0WnzOQr8Uhj52CfC3O0aHki+ZaPH5Tlto3/qvX8Jq0sMBtQU2fP8E2kcAIgkrJ16/hMW2KKv6A2juWB+d9yZzzRVEUjKa7tzyMBtFci8f/k8/RXE+wS3zOQr4WhzxuqtMmbprrCfh/8rmdE5jreE9ZVOC2oKbPn2CbuIQkCMIjlznvi6LEoigGJDn3c74IgiCASGAEQXAQSSNhNBlQMCCjzXV/F0EQBBAJjCAIDiBJkqXPi96UjrtWxl8nJhATHh2dLOOiMa9Y7SJr8jLoTVAJkcAIgkqZfHxJ69ARk4+vo0MptFhMiinP/V1s1uPrTdKrLTD5etkpMueg+Hpjev1VTL7ejg4lX0y+XiS92gLFnvH7+ZLa8cVs24KEjFbWEOrtgsFk/gqUJQkPnUS64V47VNPnT7BNJDCCoFKmsuVIWrDU0WEA6orFFmO50sQuGgOAi2NDeaSM5UIwrVyM0UmHURvKBhO7aIx9h1GHleXm0okYsnk9JElGkgyYlOtkGM3rHOk0OmSpLOavRHMSo/Y2L4hRSIKgXmlpyBfOQ5oKvpjUFIstaeloL0QhpRWxhffS0uHcBfP/nZB0932za/xpaWjOX86xLRgVA0ZFj1HRY7Ksv2Vdj6rbvCASGEFQK+2ZUwQ8WQftmVOODkVVsdiiO3WBso93RHc60tGhPFLaU+fRhtdDd+qCo0PJF93pSMo+3hHtqfP2q/TEaUrWaVPgtqD2Ni+IBEYQBEEQBCckEhhBEARBEJyOSGAEQRAEQXA6IoERBEEQBMHpiGHUgqBShpq1iYu97egwgILHIsvm4av37kt2nThMX7sq52/sAYrWHzVD7WoYjDfRO+kw6oxa4Zy/sce+w6jr1uLK7UNkFPD1UNPnT7CtKH3WBUFwAFkGd08NSHrLNkmS0cgS9k1jBEEoSsQlJEFQKc25s/i2fBbNubOODqVAsUiSDJKeWxlRxKedJz7tPMn6GCTJZLfFG7VnIyn9/Hvozl6yS33OQnPmIpqnn0d7NtLRoeSL7uwlSj//HpozF+1X6emzBD37ZoHbgpo+f4Jt4gyMIKiUlJqC7p8DSKkpjg7FLrHoTRlkGDMA0Mr2nS9XSrlDsb+PIaU632WUgpBS7yDtPYCUcsfRoeSLlJpGsb+PkZRqx/hTUnE58G+B24KaPn+CbSKBEQRBEATMfbMyL0yIxdHVTyQwgiAIQpEmo0Gr0SB5GFDuLiwquYseFmqXp3do0aJFdOjQgTp16tCwYUP69OnDhQvWU1inp6czduxYGjRoQJ06dejXrx/x8fFWZWJiYujRowe1atWiYcOGTJ06FYPBei2Kffv20a5dO6pXr07z5s3ZsGFDPg9REARBELInyxokycDtu/20bmVEgWRjfSRBVfKUwOzfv58uXbqwdu1ali1bhsFgoFu3bqSmplrKTJo0id9//52ZM2eycuVKYmNj6du3r2W/0WikZ8+e6PV61qxZw5QpU9i4cSOzZ8+2lImKiqJnz540aNCA7777jrfeeouPP/6YnTt32uGQBcE5GEPLcHveYoyhZRwdiqpiscVQJpjrC0djKFPS0aE8UsYypTF+uRBDmWBHh5IvhjIlub5wNMYype1Xabky3Fg8IV9tQW/Sk2HMQG/KQClbluQFS1Xb5oU8XkL6/PPPre5PmTKFhg0bcvz4cerXr09SUhLr169n2rRpNGzYEDAnNC+++CKHDx+mdu3a7Nq1i3PnzrFs2TICAwOpWrUqAwYMYNq0afTt2xcXFxfWrFlDSEgII0aMAKBChQr8888/LF++nEaNGtnp0AVB3RQ/f9Jf7ezoMAB1xWKL4u9DcscXHB3GI6f4+6B07YTipPPAmPzM75td54Hx9+NO55cwFfT18Pcjo2NnFKPJPnEJdlegi3xJSUkA+Pj4AHDs2DH0ej1PPfWUpUyFChUIDg7m8OHDABw+fJjKlSsTGBhoKRMREUFycjLnzp2zlMlMgO4vk1mHIBQFUnw8xT5fjPTAJdiiHostcvwNvJeuQ46/6ehQHik57gbS/CXI8TccHUq+yPE3ze9bnB3jj4vHY/E3BW8LcfG4Ll2k2jYvFCCBMZlMTJo0ibp161K5cmUA4uPj0el0eHt7W5UNCAggLi7OUub+5AWw3M+pTHJyMmlpzvcrQxDyQxMTjdeHQ9HERDs6lDzHIknmER2yLNltrpeH0URfJ2jYdLRXYgv9udREvnINTb9haKKvOzqUfNFeiSVo2HTkK9fsV2nUFXyHTilwW5Cio/EYPkQVnz/BtnyPQho7dixnz57l66+/tmc8giA4OUkCd3cZWc7sBGlCoxGz7gqCYF/5OgMzbtw4/vjjD7788ktKlrzXUSowMBC9Xs/t29brRyQkJBAUFGQp8+CopMz7OZXx9PSkWLFi+QlZEIRHRJIkZNmAyRSLyRSDosQjYb9ZdwVBECCPCYyiKIwbN45ff/2VL7/8ktDQUKv91atXR6fTsWfPHsu2CxcuEBMTQ+3atQGoXbs2Z86cISEhwVJm9+7deHp6UrFiRUuZvXv3WtW9e/duSx2CIDgDw//bu/P4KKq0X+C/c6qqswcSkgBhEwIEJyyCzOWKcRlcr4iC4uCMgiiiuKEzzqvzMsqifITroFdRZoYrM4w44oigviriAnNxdGTcXmQNYUlIAgGy0+mEpGs5949ON1k6W3dXV3X6+X4+zSfpVJ88lXqqebrqLBBCgxC61YEQQnqgbhUwy5YtwwcffIAXXngBCQkJKC8vR3l5ua9fSlJSEm699VasXLkS//73v7F//34sWrQI48eP9xUfubm5GD58OJ544gkcOnQIX375JV566SXccccdcDg804vffvvtKCkpwfPPP49jx47hzTffxLZt2zB37tyQ7jwhdiYSE+G+cgpEYghHaPSAWPwxEuNR/7NJEInxVocSViIxAcY1U2BE6H4L33FLCF2jSYlomHJJ8LmQmAT1Z1fZNudJN/vAvPXWWwCA2bNnt3h+xYoVuOWWWwAAixYtAuccCxcuhNvtRm5uLpYsWeLbVpIk/OlPf8LSpUsxa9YsxMXFYcaMGVi4cKFvm0GDBmHt2rVYsWIFNmzYgH79+mH58uU0hJpEFX3YcJzd9L7VYQCwVyz+6MOH4NSWlwAAirWhhJU+fAiMT7ZAj9Bh1GrWIJza8lJoh1GPyELl+3+AGuTfQ4zIQu3m/4JOw6htq1sFTH5+fqfbxMTEYMmSJS2KltYGDBiA1157rcN2Jk2ahPfff7874RHSs+g6WH0dRHwCIEkRGQtjQFj67uo6mLMOIiE2yioYHXA6AUTobTpdB6trAHrHhbZNpwuIYcGdN7oOOOuB2Djrzz/iFy32QIhNyQf2IS1rIOQD+6wOpcNYmg+Z9g6bZhKDkABDguddxuQOvMq+wxh2wdVw7D9q6u+xG3lfPuSUIVD2HbY6lIA49h/FsAuuhryv8w/HXbZnPzIHXhZ0LrC9+5A6NNMW5x/xjxZzJIQErO2QaYAxDs4BZ0MjGo1GxMscqYqwMEpCSE9EBQwhJGCMMUiKDl2UQQit6bkYSKwPDAC6IaALKl4IIaFHBQwhJGCMMRhCoLqxDqrRCACIlxORKlPRQggxFxUwhJCgGcKAbniKFrriQggJBypgCLEp7cIcVBwsgGhaLNUusTAG36y6dpldV80ZjsLDH8PolRRVb2pazghop49AVaoRiSOR3D/JQuHhjxGb1q/zjbtqzE9wqmAH3AmOoJoRo3NQc/g4jKRkcO7JcyEEqD63j2g61wmJLIoC0WpRU8s0xWLbdY4UBUZairUxWEFRgF5pQJ0L0CKvgIEie46bEsKx74oCIy01qHlxGBhYjANa/zjflUUAUJgEo1GnIsYmaBg1ITbFCwuQPHsWeGGB1aH4YpGOF4JzDUI0rXMEe6xzJBWUoN8v/wNyYXStHCwVlIDf/AtIBSVWhxIQufAE+v3yP0Ib/7FCpM56NKhcYIwBRwvguHUGSvfuRqGzGidcZ6EJ63OdnEcFDCE2xWudiPl0G3its/ONTeKd40Vy1TbFUgsmMRhcgyFpEEw3fY6XruBOFxI++QrcWWd1KGHFnLXgH30C7nRZHUpAuLMOCZ98BeasDV2jZ52I2/bPoHKBgUOqdSH5s88xUDRgcC8HBiQrSIhhdkh30oRuIRFC/Gp+u4jFeW5PxMbqNMcL6fEY42DMk/OqUQa3fgKKpICzIfD8t0k5bwdUwBBC/GKMgXMNhlEGZlQAAISoAYNBc7yQqKELDbpQIQm69GI3dAuJENIJrekBCEEL2xFC7IEKGEJsSu+XCdey56D3y7Q6FBj9+6JxxW8gMvtaHYpfemYGKp5dCL1/utWhhJWRmQF91XLomRlWhxIQvX86Kp5dCCOU8Q/oj7PP/Tr4XAhVO8Q0dAuJEJsSGRk498DDVofh0bcPtEfmgLFYqyPxy8jog7MP/cLqMMLOyEiD+NVDMOqOBzVs2Cp6RirOPvQLxCuJoWu0bwZcD8+GHuzfI1TtENPQFRhCbIrVVMPxwXtgNdVWhwJUOyG9+xlQfdbqSPxi1U4kvL8DvMa6EVtWYNVnwd55H6w6Mveb13iOGwtlXlXXIPa9z4PPhVC1Q0xDBQwhNiUVF6HXvXdBKi6yOhTwohOInfMbsOP2nGdFLjqJfvc8BbnolNWhhJVUdBLS7XdDLjppdSgBkYtOod89T0EKZfyFRehz1xPB50Ko2iGmoQKGENJ1NBCDEGIT1AeGENIuJjEIBkBqeoLDFhPXEUIIXYEhhPjFGIMhBGrcjahp9HRkPKeroEm8CCF2QFdgCLEpERsHdcw4iNg4S+MwhAERE4PGsSOhx8RYGkt7RJwnPhFrz/hMExsLMX4sRFxk7reI9Rw3xIZwdFtcLNzjRgWfC6Fqh5iGChhCbEofmY2aHV9aHQYAQM2+ACd2vh7a4a4hpGUPw4mdrwMAQriuse1po4ZB//4LaBE6jNqUvLowG+VfvgU12L9HqNohpqFbSIQQQgiJOFTAEGJT8r49SBuYBnnfHqtDgWNvPob1uxzynjyrQ/FL2XMIw/pdDsfefKtDCSt5Tx6kuL5Q9hyyOpSAmJJXu/ciM+1/BJ8L7bTjWSPs/IP6tFuHbiERYldCgLndgB0WTBQAc6v2iMUfIZriszqQMLNTjgTCjLwKVS60aoeBgXEGXRHQmy0JpjAJRqMesYcgklEBQwjxYczzCdPzNX20JMSLMQYhBIpra+BSVQCAg0sYmpwCuelnJLyogCEkSjUvVrzfx8UxMKY1fS/AJQYGFnUXNghpj9sw4NZ1q8MgoAKGkKjEGBAfz8G51vxZyArg1ssghAbGYiCxPp5PnpZFSggh/lEBQ4hNaSOyUfXPb6APuSDkbXs6ImowjDIA3isuMQBS4XTXo0FvQLyciFTZU7qoI4eg+F9vQhkxLOSxhIKaPRTF/3oT2gWZUfWmpmUPg7b3a6h9I/N2nyl5deFInPlmM9SBfezRDjFNNJ3rhESWuDjooy40+Zdo8BYwountQBcCuiGgN7unL+JioV44DIoSwgnHQqkpvqgTFwvkDAcidB4YU/IqLg7ahVkQwf492mlH4RwOybO2hoNLtDyYhWgYNSE2xUuKkfirh8FLik1pn0kMQgKMpkdH6xzJJaeQvvA5SMWlpsQSLKm4FOkLn4NcEl0rB0vFpeDzF9r2uHTGlLwqKkbvh5cFnwut2mHgkLmEQckODE+Jw/CUOFzQOwYJMTSU2ipUwBBiU7y6CnFvbgCvrgp5283XOapoqEdFQz1cmhvtjT3lVU4k/+1DsKqakMcSCrzqLJL/9iF4ldPqUMKKVdWA/+UN8KqzVocSEFPyqrIaCRveDz4XWrXDGAdjGgxxBm79BNz6CejiNDjTQMu0W4NuIRESBdqOOPJ8bQgDuuEpWnQaBkpIp3ShQReeYdSSoMLFSlTAENLD+R9xZEBqGiJNCCGRiAoYQnq49kYcMaTSZHWEkIhFBQwhNmWkZ6B+4a9hpGeEqEUNQnivwnTv1NfTU1D92GwYGfYcUqpn9EH1Y7Ohp6dE1ZuakdEHxpOPQbfpcemMKXnVNx21v74benqKPdohpommc52QiGL0z0TdU0utDgMAoGdmoGrxg4hXEq0OxS+jKb5oY2T2hfHcEhgROozalLwakAnn0oXQg/17hKodYhoahUSITTFXLZR/fQnmqu3+axmarZYb/G0iVluH2K/+G6y2Lui2zGD3+MzCauvAdn4VsfttynGrrYXjy++Db7Mb7TRfoZruyoYPFTCE2JRUcAy9Z0yFVHCsW6/zdtpNSNCRkKAjPl6HLDOAeUcjodujPpWCExhw00OQjhV174VhIh8rxoCbHoJScMLqUMJKOlYE6appkI+ZM1eQ2UzJqyMFSJ86P/hc6EI7zVeo1hRAUwAeI1EREyZ0C4mQHqZNp10WA8b7AJzBaNqGeza0LkhCeoDWK1TT6tThRQUMIT2Wp9MugwwhBJxqIxp0z/38eJkjVaE3WEJCgVaotgYVMIREuPYmqWvNu8aR92tCCIlkVMAQYlNCVqD3z4SQlXa38T9JHYMsM7jdIQxGkaD1TwcUe75lCEVuik+yOpTwUmSIAZkQNj0unTIjrxQZemZG8LnQjXa8CzzS4o7hFaFZT0jPp/8kB1V7DnW4jb9J6oAYgKWGNBb3T4aj6MAHth1GreWMQNGBDwBE15ualjMSevEBaBE6jNqUvBqTg9OHPoU72L9HF9ppvsCjZsjgjCFBYWjU6ApnOETTuU5ID6bhfAFDpzUh4dBygcdGKJICzobAcw5SEWM2GkZNiE1JBw8gddwoSAcPdLgdkxiEBBhND0hN/WAYC2jItD+Og0cxJOcmyAcOB9+YCeQDRzAk5yY4Dh61OpSwkg8chjQ4B/KBI1aHEhBT8mrfAfQbdV3wudCNdrwLPBpC63RbEjr0UY0Qm2KaCulUKZimtr8NYzCEQI27EarRCACIkxgcCgAJMESIhkyrOuRT5YBqzzdopmpN8UXZSBBVAztZCmbT49IpM/JK1SCVlgWfC6Fqh5iGChhCegBDGL4RRoYEAAJOt2fYNA2ZJoT0RHQLiZAeyjtsmoZME0J6IroCQ0iEaT7vSyjWOSKEkEhEBQwhNqUPy0LNe1uhD8vyPdd23hcDksTATJ59Qh02ECc/WAMpa4ipvydQWtZgnPxgDdRhA6PqTU3PGgJ9x4fQslKsDiUgpuTViGEo3/oa1GED7dEOMU23byF99913WLBgAXJzc5GdnY3t27e3+LkQAi+//DJyc3MxduxYzJ07F8ePH2+xTU1NDR5//HFMmDABEydOxKJFi1BX13LFz0OHDuGXv/wlxowZgyuuuAKvvfZa9/eOkAgmEpOg5V4GlpzsW+mWc+6b98UwSiFEBRgM06/EiKQENOROgEhKMPX3BMru8ZlFJCVAXJkbsfttynFLSoL7sonBtxlEO81Xp6YVqs3T7QKmvr4e2dnZWLJkid+fv/baa3jjjTewdOlSbNq0CXFxcZg3bx4aGxt92/zmN7/B0aNHsX79evzpT3/C999/j8WLF/t+7nK5MG/ePGRmZuLdd9/FE088gVdffRVvv/12ALtISGSSTpei18plSKgp8bOytGfeF4HwjJCQSsuQ+swfwEvPhOX3dRdvik8qLbM6lLDipWfAFy0Dj9D9NiWvTpYieenq4HMhgHa8E9vFx2u+c9Zz3nIqYkzQ7QLmiiuuwK9+9Stcc801bX4mhMCGDRvwwAMP4Oqrr8aoUaPw/PPPo6yszHel5tixY/jyyy+xfPlyjBs3DhMnTsRTTz2FrVu34swZTxJ/8MEHUFUVzz33HEaMGIGpU6di9uzZWL9+fZC7S0jk4OXlUF54AeJ0Pgyj1PMQFWBceFaWluA5g8PwziiVVyPlpTfAyypN/12BkMoqkfLSG5DKq60OJax4WSX4/34Jkk2PS2dMyasz5Uh6cX3wuRBAO80ntvOds0YZONeov5oJQjoK6cSJEygvL8fkyZN9zyUlJWHcuHHYvXs3AGD37t1ITk7GmDFjfNtMnjwZnHPs3bsXAPDjjz9i4sSJcDgcvm1yc3NRWFiIs2fPhjJkQiKADiE8K0tD6L6VpSsa6uHS3KAZPwmxFyGanbOI0Dl6IkBIC5jy8nIAQJ8+fVo836dPH1RUVAAAKioqkJracp0WWZbRq1cv3+srKiqQlpbWYhvv9952CIlmNESaEBLtoqnDPiG2194Qad+XdBWaENtjvn+ImUJ6BSY9PR0AUFnZ8n5mZWWl7wpKWloaqqqqWvxc0zScPXvW9/q0tLQ2V1q837e+MkNIT+EdIu3t+BeT2QvGPbMh0nudX+coTH1eWjNSk+G8cxpEau+w/+6uMFJ7wXnnNBipyVaHElYitTeMe2bDSO1ldSgBMSWv+qSgbs704HMhgHYYPGuQNV+bTEie9cpI6IW0gBk4cCDS09Oxa9cu33Mulwt79uzB+PHjAQDjx4+H0+nE/v37fdv8+9//hmEYGDt2LADgoosuwvfffw9VPb8GzNdff42hQ4eiV6/IPFEJ6Yxn6GWzIdKD42H839VwZqajoqHe0j4v2qD+KF+9CPrgzLD/7q7QB2eifPUiaIP6Wx1KWOmDM2G8ttq2x6UzpuTVkMGoeXVJ8LkQQDueq6aeZTy852yNuxGGENSJ1wTdLmDq6uqQl5eHvLw8AJ6Ou3l5eSgtLQVjDHPmzMEf//hH7NixA/n5+XjiiSeQkZGBq6++GgCQlZWFyy67DE8//TT27t2LH374Ac8++yymTp2Kvn37AgCmTZsGRVHwu9/9DkeOHMHHH3+MDRs24O677w7hrhNiLcbQaq4I7xtc0xDpc3XAgTzo9ec8/V0s7PPCzjVAySsAzjVY8vs71RQfs2t8ZjnXABzIs+9x6YQpeXXuHOS8Y8HnQhDt+PqoGQKGMIKLg7Sr2wXM/v37MX36dEyfPh0AsGLFCkyfPh2rV68GAMyfPx933nknFi9ejJkzZ6K+vh7r1q1DTEyMr41Vq1Zh2LBhuOuuu3DfffdhwoQJeOaZZ3w/T0pKwp///GecOHECt9xyC1auXIkHH3wQs2bNCnJ3CbGH1reLvA9ZOX/5mR0+BnncZCj5hVaHC+VwEQZfegfk/AKrQ/FLyS/E4EvvgHK4yOpQwkrOL4A81h45EghT8irvMPpOmhl8LoSqHWKabnfinTRpEvLz89v9OWMMjz76KB599NF2t+nduzdeeOGFDn/PqFGjsHHjxu6GR0hEYIxBUnTooqxpqCXAWAwk1ge1qhsNegOSdRVxFsdJCCF2RaOQCLEAYwyGEKhurINqeGapjpcTkSoLGiJNCCFdQAUMIRYyhAHd8BQqVLAQ0nN510cCPLPW0+kePCpgCAmT9uZ46egFwuGwZNh021gA4VDsEYs/jDXFZ3UgYWanHAmEGXkVqlwIUTsMDIwz6IqA3tSfV2ESjEadipggUQFDSBgwBkgxErSmIdCMd17EaOMuhH7uDNS644Bm7SgT99hsFJz+J+KVREvjaI86bhQKTv8TQHS9qdkpRwJhSl6NH4vSim/hDvbvEaJ2GGMQQqC4tgYuVYWDSxianAK56XkSuGg61wmxDOcMDoeAqtXDEAKcSZC4QnNDEBIl3IYBtx6e1eOjRUgnsiMkGrWez8XzaD3HCwdnGlTjFBq0EuiiHIwZnpk72yEfKoA08QpbDF1W8o9j4JV3QT5kfSz+yPkFGHjlXVDyj1sdSljZKUcCYUpe5eUj/bJfBJ8LoWqHmIauwBASBO98Lpy3XHFWkmToevPnDEgSgxA6dKFCF11YobahAWz3XrBzjaENOgCsoRExew8DDfa8TcHOeeJjDdb/rcLKRjkSCFPy6lwDHHsOBZ8LoWqnicI5HJIEB5eirquWWaiAISQIzaf/98ygCwAxkJVUCK28xRwvDKl0y4iQKMPAIXMJg5Id0AwZnDEkKAyNGvV/CRYVMISEhNasWPGcVkKcf45ONUKiE2McjGkwxBm49UYokgLOhsDznkBFTDDoXZUQs7BmPVzowgshUU0XGnShQhLnp1LwzgsD0NwwgaAChpBuam8+F9+X3p9zwLuMG2+xQdfoQwZA//t6aEMGBB1zsLQh/XH6L8sBG8TijzZkAE7/ZTm0If2j6k3NTjkSCFPyaugQVL7+PLQhQa5GHap2WvE3LwxAc8MEIprOdUKC1rbTLoOsMDRq5y8Gc3i+dqqNaNA9nRPjZY5UpXvvTCKlF8Rt0yFsMMeH0TsZddOvsu08MCLFE1+0sVOOBMKUvErpjYYZ18AI9u8RqnZaaT0vDACaGyZANIyakG5o3mnXMEohRAUAA7WqGxUN9ahoqIdLcwNotqZRgOsa8bIKsP+zBrysMuT70V1SWRV6rXkLvKzC6lD84mWV6LXmLUhlVVaHElZ2ypFAmJJXZ8qQ+OobwedCqNpph3deGLeuw23Q/DCBoAKGkIBoTZ10PW88wRYr/vDSMki/eQpSaVlI2guGdKocaU+vBrdBLP5IpWVIe3o1pFPlVocSVnbKkUCYklcnT6HXoheDz4VQtdMO37BqGlodMLqFREiAGAN1ziWEdEvrYdUAaGh1gKiAIaSbmMQgmKefSyCdcwkh0av1sGoANLQ6QFTAENKB5iOOAIBzDkPoqHE3QjUaA+qcSwgh3mHVAHxDq0n3UAFDSDv8LxPgXRIgtP1d/BHJSTBuvB5GsvUjf4zkBNRdnwuRnGR1KH4ZyYmouz4XRnKC1aGElZ1yJBCm5FWvZJz7X5cHnwuhaoeYhgoYQprh3HOJF/BceZEkFYZRdn6WXR6+JQH0YYNg/Ndb0G0wRFYbOhCnN/7etsOo9WGDcHrj7wEADotjCSc75UggTMmrrKGoevtlaMH+PULVDjENFTAkarW+PcQYEJ/AIZjqfQaSzKCrGoymdY445PD1eVFVoLwCUFTrOwurGvjZWiAtxuJA2qGq4JXVMHolAYrVwYSRnXIkEGbklaqCV1QBCQ5ACeK/uFC10w3NZ+elmXk7R8OoSVTy3h5KSNB9j/h4A5KswekuQUXDMdRppwAYcPqZ4yUc5ANHIPcbAeXA0bD8vo44Dh7D0JE3QD5wxOpQ/FIOHMXQkTfAcfCY1aGElZ1yJBCm5NW+g+g/7KrgcyFU7XSBd3ZewwEYDgbDwSDFSDQ+oBN0BYZEJX+rSHtXjNaEBrfuhsw9NyO8c7x4vyaEkFDiTILEOMDqoTMDnDE45Hg0qgy6Tu857aEChkQ5WjGaEGIt79Bq1TiNBo1WrO4qescmhBBCbKD1itWkY1TAkKjReoQRY3rT100b0HsGIcRGmnfqBahjb2tUwJCowDkQnygBrUcYAfCuaG+3WXW1MdnQqougohwQaucvMJF79HAUHN+OuN5plsbRHnXMSBQc3w6REBtVb2p2ypFAmJJX40aj9MSXcMcEeS6Hqp0AeDv16oqAbpx/XmESjEadipgm0XSukyjibwZdMBVn3SVQDTfi5AT0lvvCqbrRoHvmebDdrLqSBMQnA3VVgGbxf06SBJGc4InJjrzxRRs75UggzMgrSYJITgx+XpxQtRMAxjyTZRbX1sCleo6rg0sYmpwCuelnhIZRkx7I3xDpuDgNssSgGSrcuhuq4XlTMGMV6VCRjhaBX38rpKNFVocC5VgJ+t/6mC1i8Uc6WoT+tz4G5ViJ1aGElZ1yJBCm5NWRY+gz/cHgcyFU7QTBbRhw67rnYeiWxWFXVMCQHsc7RFqIMhhGKQyjFAIVYDDCMoNuqDBXHfjn/wB31VsdCpirHvH/7xswV53VofjFffFZ/7cKJzvlSCBMyataF2L/sSv4XAhVO0FQOIdDkjwPLlE3vVboFhLpkZjEoBsahBUz6BJCSBAYOGQuYVCyA5rh+W+aM4YEhaFRs9eVYitRAUN6HMYYDCF8K0YDNuzfQggh7fDOC2OIM3DrnvcwmhumLSpgSI9lCINm0CWERCzvvDAAaG4YP6gPDCE2ZQzoB/2V56EP7Gt1KNAGZKD8+cdhDOhndSh+6QP7ovz5x6ENyLA6lLCyU44EwpS8GjQANat+G3wuhKqdEPPODcM5i/q74nQFhvQIzSep45z1iM5uRnoqxIPzYdQdt2QoZ4tY0lLgvHcm4pVES+Noj5GWCue9M60OI+zslCOBMCWv0tNQd98sGMH+PULVToj4mxsm2ueFoSswJOIwBt8nEM4ZZJkhIVFCXKKGuEQNsQk6JIkh0ssYVnUW7G9vg1WdtToU8OqzSNz0iS1i8YdVeeLj1faMzyx2ypFAmJJXVdWI+/vW4HMhVO2EiHdumNI6Jwqd1TjhOgtNRNbIylCjAoZEFMYAKUbyLTlvOBiEgwNcw1l3CSoajsGlloKxyD+xpeKTkO5aALm41OpQIBefRt8FyyAVn7Q6FL/k4lL0XbAMcvFpq0MJKzvlSCBMyavjxUi976ngcyFU7YSId2RSv0QZg3s5MCBZQUJMdN9GoltIxNbazqjL4HAIqFo9jKbrppxJkLgCTXgmqZO5w6pwCSHEFK1HJnlHJTGmgPPz6w1E03pJVMAQ2/LOqMu51vxZSDLgdJ9Go+4GAHAlAYz1i/hbRoQQ0hnvyCRZSJC5hPh4rcXSAoYho77eiIoihgoYYivNr7g0n1FXCE8Rw3gMGFJhQPcNL9SF1m57hBDSE3EmgXEduiiDMJreH5kMSeoLxnhUrJdEBQyxjdZXXBjzrBjdqEXnjLoiPg7if/4UIiHO6lAg4mPRMHE0RLz1sfgjEuKa4ou1OpSwslOOBMKUvEqIh/unY4LPhVC1YxLPBz0BZ2O9b0FahccgNUb4ftbTUQFDbIMxBklp+kQhNDAWA4n1Qa2dV4w2kT5yKPR/fQbNBkNk1RFDcPKz12w7jFobcQFOfvYaAECxOJZwslOOBMKUvMoegfIdG6AG+/cIVTsm8y5ICwAS8/SF4Zyh+RgdIQwYhr9XRzYqYIhl2nbQ5TCEjurGOqhGI+LlRKTKosUJSjPqEkKIfxwSZEkCS2jZLwZCQb1L73FFDA2jJpbw3i5KSNB9j/h4HbLkmetAN0TUFyvyjwchSylQfsyzOhQ49uQjK/USyD8etDoUv5Qf85CVegkce/KtDiWs7JQjgTAlr/57DwYkjw8+F0LVThhxLoExDc6mKSUqGo7hrLsEYCo4l3rcDL50BYaETevZciVF890uAuC7ZRTp87cQQoiVVMMzpQRwfv6Y5qOVespIJSpgiCna3h4C4uI5BFObfs7BOVBVX4dG34rRnltGhBBCQqP1aCXvSCXOpRa3mSJx/hgqYEhItClYHBxas17wjDPPbLmNJVANN+LkBPSO7QsDoP4thBBiktajlRw8Dulxntv3kT5/DBUwpNua3woCPMVLbCzA2Pn5WLjM4Gysh0az5RJCiOW8gyEE97xXC1EOo9n8MZxngDEpouaPoQKGdAvnQHyiBDTdCvJgUCRAVZsuUfIYcJYKTZxBg+a5PUSz5XafNioLWv4PUFNUWD2ng5p9AYq+3wTHkGGWxtEeddQwFH2/CXpmRlS9qdkpRwJhSl79JBund/8X1L697NGOzTAwgDEYXIfR9KGTAZB55L03R9O5TgLU/PYQ5xxgKs66PbeCACBWikeK3B+i6YTwTjbnnfIaoNlyAxIbAwwfCNhgjg8RGwNt2CA4lBhL42hXU3xRx0Y5EghT8io2FnrWYIhg/x6hasdmfLeU3I1tJsCTJA7GPIVwJMwdQ8OoSQucA5LEfQ9Z5i1WfxYOzwmgGm64dc9DExq8J0RFQz1cmhuR+GnQbqTjJ8Bn3wfpuPUrQMtFpci4fymk4yesDsUv6fhJZNy/FHJRZK7KHCg75UggTMmrwiKk3Pu74HMhVO3YlPeWkm4IQDDIkoTYBA1xiZ5HQqIESWK+odd2HH5t6wLmzTffxJQpUzBmzBjcdttt2Lt3r9UhRbS2xUnbYiUhUfIlsOehIyFOgPF66KwOgp2DxOF3qLNO87eEFKtxgm98B7zGaXUo4DW1SHrnUzAbxOIPr3Ei6Z1PwWtqrQ4lrOyUI4EwJa+qaxC/6ePgcyFU7USA1vPHON0lgKRBxHDfh1fDwSDFSLYqaGxbwHz88cdYsWIFHnroIbz33nsYNWoU5s2bh8rKSqtDiwj+rqS0Lk4SkyXEtyhWDEhyy0mQXOopcK429WcpgS7KwZhBfVkIIaSH8c4fowsDEuMA83xw1VkdGK9HbCyAmJYFjZVFjG37wKxfvx4///nPceuttwIAli1bhp07d2LLli247777LI4udFqP6AEMCNE6IwTQrGDw3KNsWXsyZkAI3vQ1EBfPfHOueJ7jkLhA1bkSuL3DmOW+cKon0ah7Otp6hzZrQvNNguQdLeTtz0J9WQghpGdjjIMxDapx2jcQI06Og8zjAN4A3TDAGYNDjkejyqDr1lx1t2UB43a7ceDAAdx///2+5zjnmDx5Mnbv3t2ttsyoDjkH2l68MrrwXMvv/RUaMldg6GqLsfiSJMMwNAjheQ2XFGhG81FAgCwp0HXVU+o0FSu17nJoumc7RYpBUkyqZ1IjcLCmOFp/DQAOHgMhwfd18+daf2/1Nlb/fjNjlOVYICkJshKLGCnW0hgNpSkWObbbbYcjRrkpPocSCzkCj3Wg27TOETvGGEheBdU2l3y5wPz8TULVjp3+jmYda4lJkLlnI5krTVNlVEMINxhTwJkDgOzr+BsqXf1/mwkbDvo+c+YMLr/8cvz973/H+PHjfc8///zz+O677/DOO+9YGB0hhBBCrGbbPjCEEEIIIe2xZQGTkpICSZLadNitrKxEWlqaRVERQgghxC5sWcA4HA7k5ORg165dvucMw8CuXbta3FIihBBCSHSyZSdeALj77rvx5JNPYvTo0Rg7dixef/11nDt3DrfccovVoRFCCCHEYrYtYG644QZUVVVh9erVKC8vx4UXXoh169bRLSRCCCGE2HMUEiGEEEJIR2zZB4YQQgghpCNUwBBCCCEk4lABQwghhJCIQwUMIYQQQiJO1Bcw3333HRYsWIDc3FxkZ2dj+/btLX4uhMDLL7+M3NxcjB07FnPnzsXx48c7bffNN9/ElClTMGbMGNx2223Yu3evSXvQsY72T1VV/P73v8e0adNw0UUXITc3F0888QTOnDnTYZuvvPIKsrOzWzyuv/56s3fFr86O329/+9s2sc6bN6/Tdu1y/IDO97H1/nkf69ata7dNOx3DtWvX4tZbb8X48eNxySWX4MEHH0RBQUGLbRobG7Fs2TJMmjQJ48ePxyOPPIKKiooO2w303A21zvavpqYGzz77LK677jqMHTsWV155JZYvX47a2toO2w00t0OtK8dv9uzZbWJdvHhxh+3a5fgBne/jiRMn2j0Pt23b1m67djmGGzduxLRp0zBhwgRMmDABs2bNwhdffOH7uW3PPxHldu7cKV588UXx2WefiZEjR4rPP/+8xc/Xrl0rLr74YvH555+LvLw8sWDBAjFlyhTR0NDQbptbt24VOTk5YvPmzeLIkSPiqaeeEhMnThQVFRVm704bHe2f0+kUc+fOFVu3bhXHjh0Tu3fvFjNnzhQzZszosM3Vq1eLqVOnirKyMt+jsrLS7F3xq7Pj9+STT4p58+a1iLWmpqbDNu10/ITofB+b71tZWZnYvHmzyM7OFsXFxe22aadjeM8994gtW7aIw4cPi7y8PDF//nxx5ZVXirq6Ot82ixcvFldccYX4+uuvxb59+8TPf/5zMWvWrA7bDeTcNUNn+5efny8efvhhsWPHDlFUVCS+/vprce2114pHHnmkw3YDyW0zdOX43XnnneKpp55qEWttbW2H7drl+AnR+T5qmtbmPHzllVfERRddJFwuV7vt2uUY7tixQ+zcuVMUFhaKgoIC8eKLL4qcnBxx+PBhIYR9z7+oL2Caa/2fg2EY4tJLLxXr1q3zPed0OsXo0aPFRx991G47M2fOFMuWLfN9r+u6yM3NFWvXrjUn8C7y959fa3v27BEjR44UJ0+ebHeb1atXi5tuuinU4QWtvQLmgQce6FY7dj1+QnTtGD7wwANizpw5HW5j12MohBCVlZVi5MiR4ttvvxVCeM65nJwcsW3bNt82R48eFSNHjhS7d+/220ag5244tN4/fz7++GORk5MjVFVtd5tAcjsc/O3fnXfeKZYvX97lNux8/ITo2jG8+eabxX/+53922I5dj6EQQvz0pz8VmzZtsvX5F/W3kDpy4sQJlJeXY/Lkyb7nkpKSMG7cOOzevdvva9xuNw4cONDiNZxzTJ48ud3X2InL5QJjDMnJyR1uV1RUhNzcXFx11VV4/PHHUVpaGqYIu+/bb7/FJZdcguuuuw5LlixBdXV1u9tG+vGrqKjAF198gZkzZ3a6rV2PoffWSa9evQAA+/fvh6qqLY5JVlYWMjMz8eOPP/ptI5BzN1xa758/LpcLiYmJkOWO5xrtTm6HS3v79+GHH2LSpEm48cYb8cILL+DcuXPttmHn4wd0fgz379+PvLy8Lp2HdjuGuq5j69atqK+vx/jx4219/tl2Jl47KC8vBwD06dOnxfN9+vRp9/5fdXU1dF33+5rW94XtprGxEatWrcLUqVORmJjY7nZjx47FihUrMHToUJSXl2PNmjW444478OGHH3b4OitcdtlluOaaazBw4ECUlJTgxRdfxPz58/H2229DkqQ220fy8QOA9957DwkJCbj22ms73M6ux9AwDDz33HOYMGECRo4cCcBTlCmK0qao7tOnj+8cbS2Qczcc/O1fa1VVVfjDH/6AWbNmddhWd3M7HNrbvxtvvBGZmZnIyMhAfn4+Vq1ahcLCQrz66qt+27Hr8QO6dgw3b96MrKwsTJgwocO27HQM8/Pzcfvtt6OxsRHx8fFYs2YNhg8fjry8PNuef1TAEACeDr2PPvoohBBYtmxZh9teccUVvq9HjRqFcePG4Wc/+xm2bduG2267zexQu2Xq1Km+r72d5K6++mrfp56eZsuWLZg2bRpiYmI63M6ux3DZsmU4cuQINm7caFkMZups/1wuF+6//35kZWXh4Ycf7rAtO+Z2e/vXvBjLzs5Geno65s6di+LiYgwePDjcYQals2PY0NCAjz76CA8++GCnbdnpGA4dOhTvv/8+amtr8emnn+LJJ5/E3/72t7DG0F10C6kD6enpAIDKysoWz1dWVra7JlNKSgokSerWa6ymqioee+wxlJaW4i9/+Uu3P4EnJyfjggsuQHFxsUkRhs6gQYOQkpKCoqIivz+PxOPn9f3336OwsDCgAsQOx/CZZ57Bzp078frrr6Nfv36+59PS0qCqKpxOZ4vtKysrfedoa4Gcu2Zrb/+8XC4X7r33XiQkJGDNmjVQFKVb7XeW22brbP+aGzduHAC0G6sdjx/QtX385JNP0NDQgOnTp3e7fSuPocPhwJAhQzB69Gg8/vjjGDVqFDZs2GDr848KmA4MHDgQ6enp2LVrl+85l8uFPXv2YPz48X5f43A4kJOT0+I1hmFg165d7b7GSt7ipaioCH/961+RkpLS7Tbq6upQUlLSbjLbyenTp1FTU9NurJF2/JrbvHkzcnJyMGrUqG6/1spjKITAM888g88//xyvv/46Bg0a1OLno0ePhqIoLY5JQUEBSktLcdFFF/ltM5Bz1yyd7Z83tnnz5kFRFPzxj3/s9AqaP53ltlm6sn+t5eXlAUC7sdrp+AHd28ctW7ZgypQpSE1N7fbvseoY+mMYBtxut73Pv5B1B45QLpdLHDx4UBw8eFCMHDlSrF+/Xhw8eNA3Cmft2rVi4sSJYvv27eLQoUPigQceaDMUbM6cOeKNN97wfb9161YxevRo8e6774qjR4+Kp59+WkycOFGUl5fbav/cbrdYsGCBuPzyy0VeXl6LoXyNjY3t7t/KlSvFN998I0pKSsQPP/wg5s6dKyZNmmTJMNyO9s/lcomVK1eK3bt3i5KSEvH111+LGTNmiGuvvbbD/bPT8ROi8xwVQoja2loxbtw4sXHjRr9t2PkYLlmyRFx88cXim2++aZGD586d822zePFiceWVV4pdu3aJffv2iVmzZrUZxnndddeJzz77zPd9V87dcOhs/2pra8Vtt90mbrzxRlFUVNRiG03T/O5fV3PbDvtXVFQkXn31VbFv3z5RUlIitm/fLq666ipxxx13tGjHrsdPiK7lqBBCHD9+XGRnZ4svvvjCbzt2PYarVq0S3377rSgpKRGHDh0Sq1atEtnZ2eKrr74SQtj3/Iv6PjD79+/HnDlzfN+vWLECADBjxgysXLkS8+fPx7lz57B48WI4nU5cfPHFWLduXYtPSCUlJS16jt9www2oqqrC6tWrUV5ejgsvvBDr1q2z5NJnR/v38MMP4x//+AcA4Oabb27xug0bNmDSpEkA2u7f6dOn8etf/xo1NTVITU3FxRdfjE2bNgX0iSNYHe3f0qVLcfjwYd993YyMDFx66aV49NFH4XA4fK+x8/EDOs9RANi6dSuEELjxxhv9tmHnY/jWW28B8Ex21tyKFStwyy23AAAWLVoEzjkWLlwIt9uN3NxcLFmypMX2hYWFLSZ/68q5Gw6d7d+BAwewZ88eAMA111zTYpsdO3Zg4MCBAFrunyRJXcrtcOhs/7yf3jds2ID6+nr0798f1157bZs+InY9fkDXchTwXH3p168fcnNz/bZj12NYWVmJJ598EmVlZUhKSkJ2djb+/Oc/49JLLwVg3/OPCSFEyFojhBBCCAkD6gNDCCGEkIhDBQwhhBBCIg4VMIQQQgiJOFTAEEIIISTiUAFDCCGEkIhDBQwhhBBCIg4VMIQQQgiJOFTAEEIIISTiUAFDCCGEkIhDBQwhhBBCIg4VMIQQQgiJOFTAEEIIISTi/H/mQjdN0GLhIgAAAABJRU5ErkJggg==",
      "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": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:59.186549Z",
     "iopub.status.busy": "2025-06-02T23:40:59.186396Z",
     "iopub.status.idle": "2025-06-02T23:40:59.204675Z",
     "shell.execute_reply": "2025-06-02T23:40:59.204204Z",
     "shell.execute_reply.started": "2025-06-02T23:40:59.186534Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:59.205430Z",
     "iopub.status.busy": "2025-06-02T23:40:59.205286Z",
     "iopub.status.idle": "2025-06-02T23:40:59.849705Z",
     "shell.execute_reply": "2025-06-02T23:40:59.849193Z",
     "shell.execute_reply.started": "2025-06-02T23:40:59.205415Z"
    }
   },
   "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": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:59.850417Z",
     "iopub.status.busy": "2025-06-02T23:40:59.850264Z",
     "iopub.status.idle": "2025-06-02T23:40:59.897331Z",
     "shell.execute_reply": "2025-06-02T23:40:59.896886Z",
     "shell.execute_reply.started": "2025-06-02T23:40:59.850402Z"
    }
   },
   "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>104</th>\n",
       "      <td>4d729e9b-0454-4451-89c9-f96d39e44483</td>\n",
       "      <td>009bf538-b93b-4e8a-ae1b-a22dee7bc2e8</td>\n",
       "      <td>16.806883</td>\n",
       "      <td>False</td>\n",
       "      <td>-10.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>5ddd6e9d-28e9-4ade-b38a-89d212f75821</td>\n",
       "      <td>009bf538-b93b-4e8a-ae1b-a22dee7bc2e8</td>\n",
       "      <td>21.086633</td>\n",
       "      <td>True</td>\n",
       "      <td>-12.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>44314cf9-a592-4a11-b43e-f6eb4487e6f4</td>\n",
       "      <td>00c2af8b-aa73-476e-b20a-1b3a14fcc4aa</td>\n",
       "      <td>17.918667</td>\n",
       "      <td>False</td>\n",
       "      <td>-11.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>8ef722eb-9d77-4b3b-95f2-982599d3e0b3</td>\n",
       "      <td>00c2af8b-aa73-476e-b20a-1b3a14fcc4aa</td>\n",
       "      <td>24.147550</td>\n",
       "      <td>True</td>\n",
       "      <td>-10.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>a64f361a-1386-4759-a399-78c9f995332d</td>\n",
       "      <td>01072cf7-6c5a-4e29-8e20-58d32b66c46c</td>\n",
       "      <td>19.446900</td>\n",
       "      <td>False</td>\n",
       "      <td>-11.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>77eed37e-747a-4755-bdac-5218368d2f5e</td>\n",
       "      <td>01072cf7-6c5a-4e29-8e20-58d32b66c46c</td>\n",
       "      <td>24.504717</td>\n",
       "      <td>True</td>\n",
       "      <td>-11.072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id                            request_id  pair_quality  preference  loudness_abs\n",
       "104   4d729e9b-0454-4451-89c9-f96d39e44483  009bf538-b93b-4e8a-ae1b-a22dee7bc2e8     16.806883       False       -10.444\n",
       "105   5ddd6e9d-28e9-4ade-b38a-89d212f75821  009bf538-b93b-4e8a-ae1b-a22dee7bc2e8     21.086633        True       -12.267\n",
       "1888  44314cf9-a592-4a11-b43e-f6eb4487e6f4  00c2af8b-aa73-476e-b20a-1b3a14fcc4aa     17.918667       False       -11.488\n",
       "1889  8ef722eb-9d77-4b3b-95f2-982599d3e0b3  00c2af8b-aa73-476e-b20a-1b3a14fcc4aa     24.147550        True       -10.079\n",
       "2554  a64f361a-1386-4759-a399-78c9f995332d  01072cf7-6c5a-4e29-8e20-58d32b66c46c     19.446900       False       -11.204\n",
       "2555  77eed37e-747a-4755-bdac-5218368d2f5e  01072cf7-6c5a-4e29-8e20-58d32b66c46c     24.504717        True       -11.072"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:40:59.898013Z",
     "iopub.status.busy": "2025-06-02T23:40:59.897866Z",
     "iopub.status.idle": "2025-06-02T23:41:01.504622Z",
     "shell.execute_reply": "2025-06-02T23:41:01.504108Z",
     "shell.execute_reply.started": "2025-06-02T23:40:59.897999Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\"Spectral Centroid\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:01.505333Z",
     "iopub.status.busy": "2025-06-02T23:41:01.505180Z",
     "iopub.status.idle": "2025-06-02T23:41:02.381276Z",
     "shell.execute_reply": "2025-06-02T23:41:02.380751Z",
     "shell.execute_reply.started": "2025-06-02T23:41:01.505319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7jEceeYT169dbTQpZu3Yt0dHRNGnShKtXrxbzWZVdEsCUYX/++SceHh48+uij+uOoXOnp6aVUK8elp6dTrlw5p+e7ceNG3n33Xe6//35mz55t9cf+6aefZuvWreTk5Di9XEeYzWaMRmOe61kQVVVtTmuPSpUq8fDDD1u999JLL/Hiiy+yYsUKatWqZfXN9/o65M5G8/X1ten9oiiu+6a0OPN8iuPesFV6ejoJCQl2r1OVnx9//JG9e/fyxhtv0KtXLwCeeOIJRo4cycKFC3nkkUf0tbbWr18PWMYH5q6p1bt3b/r27cuGDRv0AKakRUVF8e233zJ69GgGDhwIQLdu3ejatSuzZs1i3bp1Nzw+v61eHnnkEdq2bcuaNWusHtW/+eabVKpUCVVV6dq1q3NPxIXIGJgyLCkpicDAwHx/SV3/C3D+/Pn89ttvHDlyhFWrVrFw4ULWr1+vPyq4VmpqKps2beL9999n4cKFfPTRRxw8eDBPupycHP2xzMKFC/nggw/47rvvSEpKIjk5mWXLlgGwa9cu5s+fz/z58/WxGps2bWLx4sUkJyfz+++/07FjR1566SXAEpiNHDmSe+65h8aNG9O2bVumTZtGZmamQ+00d+5cAgICmDZtWr49FW3atKFdu3b66+zsbObNm0fHjh318t988019sHKusLAwJk+ezKZNm+jatSuNGzemS5cubNmyRU8zf/583nzzTQDuvfdewsLCCAsLIyYmxiqPr776ii5dutCkSRO2bt0KwMGDB3n66adp3rw5ERERDBgwIM9KyQWNgVm/fj0dOnQgPDycXr168eeffzrUdtfy8vLizTffJCAggMWLF1tN5792rMXYsWPp27cvAKNGjSIsLEwffzNmzBgAevXqRVhYGGPHjtXz2L9/PwMHDuS2226jadOm9O3blz179ljVIXfc0/Hjx3nxxRe54447rAKpL7/8kh49ehAeHs6dd97J888/z4ULF6zy6NevH127duX48eP069ePpk2b0qZNG5YuXZrnnLOyspg/fz73338/TZo0ITIykuHDh3P27Fk9jdlsZsWKFfr1a9WqFePHjycpKanQNs0da3H27FkGDRpERESEXT8HY8eO5aOPPtKvQe6//K5LLlvuK2dITEykY8eO9O/fn6+//rpIe7/l3gddunSxer9z585kZWWxefNm/b3U1FQ8PT3z9HaGhITg5eUFWCZbtGzZEoAFCxbo7XZ9W126dImhQ4cSERHBXXfdxcyZMx2eLbpx40YMBgOPPfaY/p6npye9evVi7969ee5TW+ROALl+4keVKlVQVfnzLT0wZZivry8XL14kISEhz0q/+Tl//jzHjh0jPDwcg8FAdHQ0X331FY8++qh+fHp6Op988gmKotCkSRO8vb05c+YMmzdvJjs7W5+lZDab+eabbzh37hyhoaE0bdoUo9HI2bNn9W9d99xzD7/++iv16tWjbt26gGWtmFxms5kffviBunXrMmzYMP39jRs3kpmZyeOPP05AQABRUVGsXr2aixcvWg2OtsXp06c5efIkPXv2tFqzpiBms5lnn32WPXv28Oijj1KvXj2OHj3KypUrOX36NAsXLrRKv2fPHn788UeeeOIJypcvz6pVqxg5ciS//PILFSpUoGPHjpw+fZpvvvmGV155RZ96fu2KxX/88Qfff/89ffr0oUKFClSrVo1jx47Rp08fypcvz9NPP42bmxvr16+nX79+rF69+oaPBz/55BPGjx+v/3E6d+4czz77LP7+/lSpUsWu9rte+fLl6dChA59++inHjx+nQYMGedI89thjVKpUicWLF9OvXz+aNGmiX/c6deqwfv16Ro4cSfXq1alZsyYAO3bsYNCgQTRu3Jjhw4ejKAqff/45AwYMYM2aNXmm4Y8aNYpatWrx/PPP64HUokWLmDt3Lg888AC9evUiMTGR1atX06dPH7744gurP2hJSUk8/fTTdOzYkQceeIAffviBWbNmERoaStu2bQHLY4tnnnmGHTt20KVLF/r3709aWhq///47R48e1es+fvx4NmzYQI8ePejXrx8xMTF60L927dpCH+/l5OTogduYMWP0P7K2/Bw89thjXL58md9//10PlG+kKPeVvSpWrMiYMWP4/PPPeemll/Dz8+PBBx+kV69e3HrrrXbllZ2djcFgyNOWueOnoqOjefTRRwG48847+e677xg/fjxPPfWU/gjpp59+4uWXXwYsP38TJ05k4sSJdOzYkY4dOwJYBX+5j63Cw8MZPXo0O3bs4IMPPqBGjRr5jrspzKFDh6hdu3ae30O59/ahQ4ds+vlMTk4mJyeHuLg4Vq5cSWpqqh6MieuU7lZM/219+/bVpk6dWuDnZ86c0RYsWKAtWLBA++STT7Rt27ZpZ86c0XJycvKknTdvnjZv3jzt0qVL+nvJycnawoULtW+//VZ/b9OmTdr777+vpaenWx2/ceNGbcmSJZrRaNQ0TdMOHDigzZs3L9/NH81ms6Zpmpaenq7NmzdP++OPP/Kk+emnn7R58+Zpu3btyvNZRkZGnveWLFmihYWF6RtH5p5TaGhonrTX2rRpkxYaGqotX778hulyffHFF1rDhg213bt3W72/du1aLTQ0VNuzZ4/+XmhoqNaoUSPtzJkz+nuHDh3SQkNDtVWrVunvLVu2TAsNDdXOnTuXp7zQ0FCtYcOG2rFjx6zeHzp0qNaoUSPt7Nmz+nuXLl3SIiIitD59+ujv/fHHH1poaKjextnZ2VrLli21hx9+WMvKytLTrV+/3mqj0Btp166dNnjw4AI/X758uRYaGqpt2rTJ6jzmzZuXp17XbhiqaZr22Wef5dnw02w2a/fdd5/2v//9T793NM1yH7Rv31576qmn9Pdyr/kLL7xglW9MTIx2yy23aIsWLbJ6/8iRI9qtt95q9X7fvn210NBQbcOGDfp7WVlZWuvWrbURI0bo73366acF3ju59dy9e7cWGhqqffXVV1afb9myJd/3rzdmzBgtNDRUmzVrVp7PbP05mDRpUoE/B9dfF1vvK2fbv3+/Nn78eO3222/XQkNDtW7dummrV6/WkpKSbDr+gw8+0EJDQ/P8XM6aNUsLDQ3VnnnmGf29nJwcbfLkyVqjRo30DXJvueUWbc2aNVbHJiQk5GmfXLnXZcGCBVbvd+vWTevevbutp22lS5cuWv/+/fO8f+zYMS00NFRbu3atTfncf//9+nk1a9ZMe/vttzWTyXTDcm35ub8ZSR9UIZYsWULPnj2JiIigZcuWDB06lJMnT1qlycrKYtKkSbRo0YKIiAhGjBih74ME/z4GsHc6ac2aNenVqxd16tQhPj6ev/76iy+//JLly5fnqQNYuhVzVwO+evUqEydOZNGiRYwdO5ZXXnmF1NRUTpw4QZ06dQDLILfcfzVr1mTPnj088cQTNG/enO7du6MoitU345iYGMaNG8e9995LeHg4Xbt2ZefOnTccX3LLLbfkeS/3GyhYeoQSExOJiIhA07R8H2XdSGpqKoDNs6A2btyo9xglJibq/+666y6API9qWrVqpX8TB2jYsCE+Pj52TW2/4447qF+/vv7aZDLx+++/06FDB6vxAxUrVqRr167s2bNHP6/rRUdHk5CQQO/evfHw8NDf7969u9PGneS2ZVpamlPyO3ToEKdPn+bBBx/kypUrepunp6fTsmVLdu/ejdlstjqmd+/eVq9/+uknzGYzDzzwgNV1Cw4OplatWnmuW7ly5azG+Hh4eNCkSROr6/bjjz9SoUIF/XHYtRRFASz3i6+vL61bt7YqN3dQvS3T28Eym/B6zvw5gKLdV0UVHh7OpEmT2LZtG7NmzcLf358pU6YQGRnJSy+9VOgWJ127dsXX15dXX32V33//nZiYGNavX8+aNWsArB6rGQwGatSoQWRkJDNnzuTtt9+mXbt2TJ06lU2bNtlV7+uvy2233aY//rVXZmam1c9krtwhALY+Ip8+fTrLli1jwoQJ1KtXj6ysrGJZBPVmII+QCrFr1y769OlDkyZNMJlMzJkzh4EDB/Ltt9/q41CmTZvGb7/9xjvvvIOvry9Tpkxh+PDhhQ7askWlSpXo3LkzJpOJ+Ph4Tp48yb59+9i4cSO9e/e2elTh7++v//dLL71EXFwcY8aMITo6mt27d/Pqq69Sv359oqOjrVbTzZWTk0NERAQdOnRg9uzZ+Pv7Wz1nPXnyJJqmMXnyZGrVqkV0dDSvvPIKn3zySb7TBFVVzTewiI2NZd68efz88895xhHY+ws2t7vW1j+2Z86c4cSJEwV2yV67DxWQb5evv7+/XcHo9TO0EhMTycjI0APJa9WrVw+z2cyFCxfyfXyT+4egVq1aVu+7u7s7ZTAl/NuWzpoaf/r0aQB9fEx+UlJSrO7f69vs9OnTaJpW4HT467fkqFy5sh6E5PL397daKuDs2bPUqVMnz7HXOnPmDCkpKTbfLwXVrXLlynned+bPARTtvso9/to/lOXKlaN8+fJ5Nmr19fW1Cr6u5enpyYMPPsgDDzzA2rVrmTlzJl9//TWdOnWiatWqBdY9JCSERYsWMXr0aP73v/8Blp/t119/nTFjxliN+Xvvvff48MMP+eGHH/R7tHPnzvTr149JkyZxzz333PCaXlvX6zen9ff3t2lsU368vLzyjKMD9LFBBbXZ9a5dn6ZLly507twZuPHPz3+VBDCFeP/9961ez5gxg5YtW3LgwAHuuOMOUlJS+Oyzz5g1a5b+S27atGl07tyZffv2ERwcTP/+/QHLN3GwfFvOXQNG0zTefPNNPv30U9zd3endu3e+0yUNBgOVKlWiUqVKBAQEsGnTJo4fP86dd96ZJ+2JEyfYunUrn376KWlpaSQmJjJ69GhGjhxJ5cqVue222/LtGenWrRtBQUH8/fff+bbF3Xffzd13362/Dg4OJiIiosABpAaDIc8fEZPJxFNPPaWPUahbty7lypXj0qVLjB07Ns838cLkjr05evSoTenNZjOhoaF5pgnnuv4PTUFTVDU79quy9RdXWXHs2DEgb5DkqNy2Gj16dL73HeQdlH79wHWz2YyiKCxdujTfa3L98c6aWmw2mwkKCmLWrFn5fn79H8D8eHh45Blw6eyfA2fo1auX1aD/4cOHM2LECCIjI63STZ8+nR49euSbx4kTJ/jss8/46quviIuLo0GDBvTq1cumacx33HEHmzZt4ujRo6Snp9OwYUMuX74MQO3atfV0a9asoUWLFnkC7HvvvZfp06dz/vx5m+5dZ08/DwkJ4dKlS3nezw0Ar90rz1b+/v7cddddfP311xLA5EMCGDvljgbP/bYYHR2N0Wi0WvOiXr16VK1alX379tGvXz/mz5/PiBEj2LhxIz4+PlZ/0DZs2MBTTz3Fxx9/zL59+xg7dizNmzfXezTGjh3L+fPnrRYpyv1BuL7XIfebw969e/Hz86NJkyZs3LgRd3d37rnnHlRVJSEhAU3TbPq2npSUhMlkuuEPenZ2tl3f1I8ePcrp06eZOXOmvroxwO+//25zHteqU6cOderUYfPmzaSlpRVal5o1a3L48GFatmyZJ7hylL35BAYG4u3tzalTp/J8dvLkSVRVLXCwX+632DNnzlj1ChiNRmJiYmzaQPRG0tLS2LRpE1WqVKFevXpFyitX7r3m4+Pj8NowNWvWRNM0qlevnm8Pg6N57t+/H6PRWOBA3Jo1a7Jjxw6aN2/u1EDUnp8DW++votxXAG+99ZbVTKLc67Z8+XKrdNc+DgXL78TvvvuOzz77jP3791OuXDk6d+7MI488csO91/JjMBisgtzt27cDWN038fHx+QZ4RqMRQH+k7ayfb1s1bNiQnTt3kpqaajWQd//+/UD+j9NtkZmZadf2M/8lMgbGDmazmWnTptG8eXN9ZcT4+Hjc3d3zTOkLCgoiLi4Og8GgBztBQUGEhIRYjVUICwtj+PDh1K5dm27dutG4cWN9E8aYmBiCg4Pz/NLJ7ZK/frPFCxcucPnyZeLj4wkMDCQlJYVTp05Ro0YNPDw88Pf3p1y5cpw4cSLfru+MjAyr15mZmfn2xuR+o46NjSUqKoo2bdoU2na5cr+JXtuDoWkaH374oc15XG/kyJFcvXqV1157Ld/xONu2beOXX34B4IEHHuDSpUt8/PHHedJlZmY6tL5O7kwJW3/JGAwGWrduzebNm62et8fHx/PNN99w2223FTijqnHjxgQGBrJu3Tqr7uoNGzYUecn+zMxMRo8ezdWrVxkyZIjT/gA0btyYmjVr8sEHH+T7qO/6Hc7zc99992EwGFiwYEGe3i9N07hy5Yrd9brvvvu4cuWKPk35+jzBcr+YTKY8s9PA8ofS0Ta35+cg9/4qrKyi3FdgGf/RqlUr/V9uAHPte61atdK/QKWmpvLSSy8RGRnJ+PHjURSFqVOnsm3bNt544w27g5frJSYmsmzZMsLCwqwCmDp16rB9+3ara24ymfj+++8pX768PmbN1nZzlk6dOmEymfR1asDyBe/zzz+nadOmVr/HY2NjOXHihNXx+f1OjomJYceOHTRu3Lj4Ku7CpAfGDpMmTeLYsWP6wDJnuH6vn5CQEP1G3rJlCyEhIdx1110cOHAAk8nEhQsXOH78OH5+ftxyyy2MHz+er7/+Wv/24e3tTUxMDFlZWXz++ecAVt23devWRVEUPvnkE2699VYCAwPJysri8uXLxMTEMGjQID1tlSpV2Lp1K5cuXaJKlSrk5ORw7tw5mjRpQvny5RkyZAiNGjWiatWqREVF4eXlRVBQ0A2nfNetW5eaNWsyc+ZMLl26hI+PDz/88EORfsl07tyZI0eOsHjxYg4ePEjXrl31lXi3bt3Kjh079O0PHn74Yb7//nsmTJjAzp07ad68OSaTSV+JdtmyZTRp0sSu8hs1agTA22+/TefOnXF3d6ddu3Y3XKzsueeeY/v27TzxxBM88cQTGAwG1q9fT3Z2tj4VND/u7u4899xzjB8/ngEDBtC5c2diYmL4/PPP7RoDc+nSJb788kvAMoD0xIkTbNy4kbi4OP73v//lGURbFKqqMnXqVAYNGkTXrl3p0aMHlSpV4tKlS+zcuRMfHx8WL158wzxq1qzJc889x+zZszl//jwdOnSgfPnyxMTEsGnTJh599FF98TBbdevWjS+++ILp06cTFRXFbbfdRkZGBjt27ODxxx+nQ4cO3HnnnTz22GMsWbKEQ4cO0bp1a9zd3Tl9+jQbN27k1VdfpVOnTna3iT0/B7n319SpU4mMjMRgMORZLyWXo/eVI65evcq2bdvo3bs3vXr1KnBsja369u1Ls2bNqFWrFnFxcXz88cekp6ezePFiq0dwgwYN4uWXX+bRRx/l0UcfxcvLi2+//ZYDBw7w3HPP6b1pXl5e1K9fn++//57atWsTEBBAgwYN8izLX5ixY8eyYcMGNm/efMMVx5s2bUqnTp2YM2cOCQkJ1KpViw0bNnD+/HneeOMNq7Rjxoxh165dVmOyHnzwQVq2bEnDhg3x9/fn9OnTfPbZZ+Tk5PDiiy9aHb979252794NoA+Izw2y77jjDn24ws1OAhgbTZ48mV9//ZXVq1dbjZMIDg7GaDSSnJxs1QuTkJBASEhIofleP9hMURT9W1nr1q05fvw4p0+fJjo6GrPZjK+vL02aNOH222/H09OTUaNGMXDgQD788EMaNmxIeHg4hw4dIjExEU9PT+69916Cg4PJyckhKSmJatWq0bp1a3bv3s3JkyeJjo7W91e6vnv/gQce4MiRIxw9epQTJ07g5eVFlSpVMJvN9O/fn4iICEaNGsW2bdvYtm0bJpOJO++884YBjLu7O4sXL2bq1KksWbIET09POnbsSJ8+ffKsDGuP559/nrvuuotVq1axdu1akpKS8PPzo2nTpixcuJB7770XsPwxfffdd1mxYgVffvklP/30E97e3lSvXp1+/fo59HgiPDycUaNGsW7dOrZu3YrZbGbz5s03DGAaNGjARx99xOzZs1myZAmaphEeHs5bb71V6Fodjz32GCaTiffff58333yT0NBQfY0UWx06dIjRo0ejKArly5enSpUqtGvXjkceeSTPmizO0KJFC9avX8/ChQtZvXo16enphISEEB4ebrXw140MHjyY2rVrs2LFCt59913AMmapdevWtG/f3u46GQwGli5dyqJFi/jmm2/48ccfCQgIoHnz5lZfLCZPnkzjxo1Zt24db7/9NgaDgWrVqvHQQw/RvHlzu8sF+34O7rvvPvr168e3337LV199haZpBQYwRbmv7FWpUiW2bNmS78wbRzRq1IiNGzfqAV2rVq147rnn8gTmDz30EBUqVOC9997j/fffJzU1lTp16jBp0qQ8gffUqVOZMmUK06dPx2g05ruvUGFy96CzZZuQN998k3feeYevvvqKpKQkwsLCWLx4sU0BxeOPP86vv/7K1q1bSUtLIzAwkNatW/PMM8/k+aL7xx9/sGDBAqv3cn/+hw8f/p8JYGQdmEKYzWZt0qRJWmRkpHbq1Kk8nycnJ2uNGjXSNm7cqL934sQJLTQ0VF9DZc+ePVpoaKiWmJhodWx+68A8++yz2pgxY+yu57x587Rff/1V0zRNO378uBYaGqr9/fff+udbt27VwsLCtIsXLxaaV+4aH/mt4XDx4kXtvvvu055//vl816MRQoibScuWLbUZM2aUdjVEPmQMTCEmTZrEV199xezZs/UphXFxcfqcfl9fX3r27MmMGTP4448/iI6OZty4cUREROjPgKtVq4aiKPz6668kJibatb7G7NmzGT16tF11rlevHm3atOH1118nKiqKPXv2MGXKFLp06UKlSpUAyyOETp06ERUVpR8XFxfHoUOH9GXUjx49yqFDh/TNwi5dukS/fv2oUqUKY8aMITExUW8PIYS42Rw7dozMzEyrR+ui7JBHSIVYu3YtYNlf5VrXTiUcN24cqqoycuRIsrOziYyMZMKECXraSpUqMWLECGbPns0rr7xCt27d9GnUhYmLi3NoD41Zs2YxZcoUBgwYgKqq3HfffVabnBmNRk6dOmU1cHfdunVW3ZJ9+vSxOtfff/+dM2fOcObMGavp1IDVs1whhLgZNGjQgL/++qu0qyEKoGiaHQtaiDJr/vz5hIeH6/u8CCGEEDczCWCEEEII4XJkDIwQQgghXI4EMEIIIYRwORLACCGEEMLlSAAjhBBCCJdz00+jTkhI4UbDlC0rkXqWyu6vTz/9P8LCwnj55Zt7l1FVVUlLy7JrB2dxY0pcHJ5fbiDr4e5oNqz4XBJ5ObNOZbFMR8uy97iSKkeIskpRICjIt/B0N/sspPj4GwcwqvpvAJNfusWLF/Lee9b7tNSuXZvPP/9Kf52VlcWcObP48ceNZGdn07JlK1555TV9Sf0//9zN4MED+e23bfj6/rsc9aBB/yM0tOgBTGHl50fTNBYvXsiGDZ+RkpJC06bNGDfuNWrW/Hcb+mXL3mPbtq0cPXoENzd3tmyxf8doRfk3gDGbb+pbTQghhBMoCgQHFx7AyCMkG9SrV48ff/xZ//f++yutPp89+022bv2NmTNnsXTpcuLi4njppedLrH6OlL9y5XLWrl3DuHGvs3LlR3h7ezNs2BCysrL0NEajkQ4d7qNXr0eL+xSEnZSrV/D4agPKVft3YS6uvJxZp7JYpqNl2XtcSZUjhKuTAMYGBoMbwcHB+r8KFSron6WkpPDFFxt44YWXuPPOFtx6661MnDiF/fv3ERW1n9jY8wwebNklt23bSJo3D2fChH9XxNU0jXfemcM990TSsWM7Fi9eaFfdCis/P5qmsWbNap5+ehD33NOO0NBQJk9+g7i4OH799Wc93bPPDqNv337Ur1+0XWaF8xnOnsH/6QEYzp4pM3k5s05lsUxHy7L3uJIqRwhXJwGMDc6ePcN9993Lgw8+wKuvjrVa2v/QoYPk5OTQosVd+nt16tShcuUqREVFUalSZd56aw4AGzZ8xY8//sxLL/37yOibb77C29ubDz/8iFGjnmfp0iX88ccO/fMJE15j0KD/FVi3wsrPz/nz54mPj7c6xtfXl8aNmxQY9AghhBBlyU0/iLeomjRpwqRJU6lVqzbx8XG8995iBg58kk8++Zzy5cuTkBCPu7u71dgWgKCgIBIS4jEYDPj7+wMQGBiYJ139+g145plnAahZsxbr169j166d3HVXSwCCg0NuOMC4sPILOsZSH+sxMkFBQcTHJxTWJEIIF6NpGmazqVQmKwhxPVVVUVUDiqIUKR8JYArRunUb/b9DQ0Np0qQJXbp04qeffqBbtx5Fzr9Bg1Cr18HBwSQmJuqvR4wYVeQyhBD/XTk5RpKSEjEaM0u7KkLoPDy88PMLxM3N3eE8JICxk6+vHzVr1uLcuXMABAUFYzQaSUlJtuoFSUhIICgouND83NysL4GiKGia7d+SHCk/9/3ExARCrplumZCQQFhYmM1li9KjeXljbNIUzcu7zOTlzDqVxTIdLcve45xZjqZpJCRcRFVV/P2DMRjcivytV4ii0DQNkymH1NSrJCRcpGLF6g7fkxLA2Ck9PZ2YmHN06dIVgFtuuRU3Nzd27drJvfd2BOD06VNcvHiB8PBwANzdLRGmyeT87ltbyr9etWrVCA4OZteunYSFNQQgNTWV6Oi/eeQRmXHkCkyhYVzdvLVM5eXMOpXFMh0ty97jnFlOTo4RTTPj7x+Ch4eX3XkKUTw8MRgMJCZeIifHiLu7h0O5SABTiLffnsXdd99DlSpViIuLY/HihaiqgU6dHgAsg1+7devO7Nmz8PPzp3x5H958czrh4U0JD28KQJUqVVAUha1bfyMysg2enl6UK1fOpvLnz5/L5cuXmDJlWr6f21I+QI8eDzF8+Cjat78XRVF44om+LFv2HjVr1qRq1WosWvQuISEh3HNPe/2YCxcukJycxMWLFzCbTRw5chiAGjVq2lx/IUTpUxSZryHKFmfck3JXF+LSpcu88soYund/iDFjXsLfP4CVK1dToUKgnubFF0fTps3dvPzyCzz99JMEBQUza9bb+ucVK1ZiyJChzJ8/lw4d2jFzZv7BSH7i4+O4ePHiDdMUVj7A6dOnSU1N1V8PGPAUvXs/wdSpk+nX7wnS09NZsGARnp6eeprFi9/l8ccfZfHihaSnp/P444/y+OOPcvDgAZvrL4qH29/7Ca4ejNvfRZ815qy8nFmnslimo2XZe1xJlSOEq5OVeAtZiVcUjazEWzzcovZRocPdXNm0hZzwZmUiL2fWqSyW6WhZ9h7nzHKMxmwSEi4QFFTFqpteUSjRsTCapsnvV2GloHsTbF+JVx4hCSHEf4iigOppwGjHZIGiclcMmLNMEsSUARcvXmT27On89defeHuX44EHuvLMM8PyTCi5VnJyEm+//Ra//74VVVVo27Y9o0a9ZDWU4PjxY8yZM5PDhw8SEFCBnj0fpU+fAcV6LhLACCHEf4iiKBg1M6eSr5BtNhV7eR6qgTp+FXBTFNnQtZSZTCZGjx5FYGAQixd/QHx8PG+8MQE3NzeeeWZYgcdNmvQ6CQnxvP32u+Tk5DB9+iTefPMNJk58A4C0tFReeGE4t99+Jy+99AonTx5n+vTJ+Pj48vDDRV9upCASwAghxH9QttlEtqn4AxhHDB8+mLp16wHwww/f4ebmRrduvXj66SH6o6/k5GTmzp3F779vxWjMplmz23juuZeoUaMmABcvXmDOnDeJitpHTo6RypWrMmzYSFq2jLSpDu+/v4StW3+jV6/H+OCD90hJSeb++7vw/PMvs27datavX4PZbOaRR3ozYMBA/biUlBTeffcdtm37jexsIw0b3sKIES/oa36dPx/D/PlzOHAgmszMDGrVqsMzzwzjjjta6Hn06vUgDz3UnZiYc/zyy2Z8fX0ZMGBgkYOBXbv+4PTpU7zzzkICA4No0CCMp58ewqJF8/nf/wbrM2avdfr0KXbu3M6yZR/SsOGtADz33Mu8/PIohg9/juDgEH78cSNGo5FXXhmPu7s7devW49ixo6xf/1GxBjAyiFcIF5TTIIzELTvJaVD0dXuclZcz6+RomZaxHSVTVnEdV1LllHXff/8tBoMbS5euZNSol1i//iO+/voL/fNp0yZy5MghZs6cw+LFy9E0jZdfHkVOTg4Ac+bMxGjM5t13l7Jy5TqefXYE3t72zZ48fz6GP/7YzuzZ85kw4Q2+/fZLXn75OeLiLrNgwRKefXYES5cu4sCBaP2Y118fw5UricyaNY/3319FaGhDnnvuWZKTkwDLUhx33dWauXMX8sEHH9GiRUvGjHkhz2SNdes+omHDW1m+/CO6d3+E2bNncPbsaf3z4cMH88YbE+06nwMH/qZu3fpWq7DfeWdL0tLSOHXqRL7HREdH4ePjqwcvALfffieqqurnHR0dRbNmEVYBUIsWLTl79gzJycl21dEeEsCUogkTXuOFF2SlXeEAb29MDW8Bbycs4OasvJxZJwfKzB3boXoaUNViCGQcPT97jyupcsq4SpUqMXLkC9SsWZv77nuAnj0f4+OP1wBw7txZtm3bwpgxr9G0aQQNGoQyYcIU4uIus2XLrwBcunSRJk2aUq9efapVq07r1m1o1qy5XXXQNDPjxo2nTp26REbeTUTE7Zw7d4aRI1+kZs3adOnyEDVr1uKvv/4EYP/+fRw6dIApU2bSsOGt1KhRk+HDn8PHx5dfftkMWFZf79atJ3Xr1qdGjZoMGvQs1apV4/fff7Mqu2XLVvTo8QjVq9egb98B+PsH6OVY2qeyTYulXishIYHAwECr93KDmYSE/LeRSUxMsNrAGCwLsPr6+pGYmHBNGut8c1/npikOEsAUwmQysXDhArp27UTLlnfw0EOdWbp0idWzXE3TWLToXe67rz0tW97BkCGDOHvNjrCxsedp3jxcX0fF2QorPz979vzJqFHDue++e2nePJxffvk5T5rFixfSo8dDtGp1J23btmbIkEH8/Xf+G0SKkqWeO4vP88NRz50tM3k5s06OlJk7tiNHM+uBjDODGEfPz97jSqqcsu7WWxtbzZRq3LgJ586dxWQycebMKQwGA7fe2lj/3N8/gJo1a3HmzCkAevXqzcqV7/Pss//j/feXcPz4MbvrULlyVcqVK6+/DgwMpHbtOqiqes17QVy9atn+5fjxo2RkZNCly7107NhG/3fhQiznz8cA/LNkxTv06dOLTp3uoWPHNpw5c5pLl6x7YOrVa6D/t6IoBAYGceXKFf2911+fzJAhwwus+4svjtTL79v35lygVAKYQqxY8QGffvoxY8aM47PPvmDkyOdYuXI569at0dOsXLmctWvXMG7c66xc+RHe3t4MGzaErKysEqmjI+VnZmYQGhrG2LHjCkxTq1YtxowZx8cff84HH6ykatWqDBs2hCtXEgs8RpQM9Uoi3h99iOqEa+GsvJxZp6KUmRvIGDWzU6cKO3p+9h5XUuXc7B58sBsff/wl99/fmRMnjvP00/349NN1duWR31Yv+c3WyV0iIiMjnaCgYJYvX2P1b82az3jiif4AvPvuO2zZ8guDBw/j3XeXsXz5GurWrY/RmFNo2fZsxjl27Gt6+bNmzQUsG/Zeu9ce/NtDEhQUlCcPIE/gBJCTk0NKSrLee2NJY51v7uvrNw12JglgCrF//37atm1HmzZ3U7VqNTp0uI+77mpJdLTl2Z+maaxZs5qnnx7EPfe0IzQ0lMmT3yAuLo5ff7X0anTtalm19/HHH6V583AGDfqfVRkffriC++5rT7t2bZg+/Q2MRqPN9bOl/Py0bt2GYcNG0L79vQWmeeCBLrRocRfVq1enXr36vPDCy6SmpnL06FGb6yeEEI64fsHMAweiqVGjJgaDgVq16mAymTh48N+xJ0lJVzl79gy1a9fR36tUqTLduvVi2rS36N27r9UYmuIQFtaQxMQEDAYD1avXsPoXEBAAwN9/76dz5wdp27Yd9epZxqNcvBjr9LqEhFTUy65cuQoAjRo14eTJ41bBxu7dOylfvjy1a9fNN5/GjcNJTU3h8OFD+nt//fUnZrOZRo0a62n27durjz/KzbdmzVr4+fnlydNZJIApRNOmTdm1aydnzpwG4OjRI+zbt5fWrS0j2c+fP098fDwtWtylH+Pr60vjxk2IirKsiLlqlaW3ZtGi9/jxx5+tVsn988/dxMScY8mS95k0aSpff/0lX3/9pf754sUL6dKlU4H1s6V8ZzAajXz++af4+PgSGnpzDBIUNx/ZpvDmcenSRebPn8PZs6f56aeNfPbZenr16g1YtjNp06YtM2e+wf79+zh27CiTJ48nJKQibdrcA8DcubPZuXMHsbHnOXLkMH/99Se1atW5QYlFd/vtLWjUqAmvvPISu3b9wYULsfz9936WLHmXw4cPAlC9ek1+++1njh07wrFjR5k06VWHFvmcMmU8ixcvsOuYO++8i9q16zBlyniOHTvKzp07WLp0ET16PIqHh2UxuYMHo3niiZ7ExV0GoHbtOrRo0Yo335zKwYPRREXtY86cN7n33vsIDrZsBtyxYyfc3d2ZPn0yJ0+eYPPmH/nkk7U89lgfu8/LHjKNuhBPPTWQtLQ0evR4GIPBgMlkYtiwEXTu3AWAhIR4IG83WVBQEPHxlq653AFQAQEBBAdbD7ry9fVjzJhxGAwG6tSpQ5s2d7Nr1y569Oj1zzEVqF69eoH1s6X8otiy5TdeeWU0mZmZBAeHsGjRkjwDuoQoDdeuJqsoYPBUMSkaihlktZHCeaiGMl1Op05dyMrKYtCgAaiqgV69eltNyX3llQnMnTuLMWOew2g00rRpc956a67+6MVsNjFnzkzi4i5Trlx5WrRoyciRL+jH9+r1IA880JWBA58p2gleQ1EUZs2ay3vvLWTatElcvXqFwMAgmjVrrg9qHTHieaZPn8yQIf/D3z+APn0GkJaWZndZly5dtBqLYwuDwcCbb77DrFnTGTLkKby9venUyboNMjMzOXv2jFVvyoQJU5gz501GjRqqL2T33HMv65/7+PgwZ84C5syZydNP98PfP4Ann3y6WKdQgwQwhfrppx/4/vtvmTZtBnXr1uPIkSPMnv0mISEhPPjgw0XOv169ehgM//6ABwcHc+zYv4PNevd+nN69Hy9yOY664447WLv2E65evcKGDZ8zZsxLfPjhR8X6XFMUzhxSkfSRL2AOqVhm8nJmnQqjryZbtSIZL7yIUr0KRtXySBVFKZYIxtHzs/e44i5H0zTcFcviciXFXVExa/atOePm5saoUS/y0kuv5Pu5n58fr78+ucDjn39+dIGfZWZmkpiYSETEbQWmGTjwmTzBzauvTsyTbsGC96xelytXnueee9nqD/y1qlSpyrx5i63e69nTepDtp59+nee4FSvWWL2+vlxbVa5chVmz5hX4efPmt7Nt259W7/n5+euL1hWkfv0GLFy4zKE6OUoCmEK8884cnnxyIPffbxnH0qBBKBcvXmD58vd58MGH9WlsiYkJhISE6MclJCQQFlb4o5a8A8LsW62yqOUXxtu7HDVr1qRmzZqEhzfl4Ye78sUXG/jf/54uct7CceYqVUl7bWKZysuZdSpM7kDdGH8fak6c9E/MUrz9Lo6en73HFXc5mgbmLBNuJbgXklkrW9sI/PXXn9x22+00b357aVdFFIGMgSlEZmYmqmr9g66qqv7Mslq1agQHB7Nr107989TUVKKj/yY8vCmAvriPyeT8vUdsKd+ZNM1Mdna20/MV9lFSU3D/fStKakqZycuZdbKVOTkJ961bUFL+LbO4/iw7en72HlcS5WiaZeZMSf0rS8ELQKtWkbz11tzSroYoIglgCnH33W15//2lbN26hdjY8/z882ZWr15Fu3btAcs3wSee6MuyZe/x22+/cOzYUcaPf5WQkBDuuceSpkKFQLy8vNi+fRsJCQmkpNj+i2ndurU880zBvR22lA/wzDNPs27dWv11eno6R44c1temOX/eMtDtwoULgGU64Pz5c4mK2k9sbCwHDx5k4sTxXL58mY4d77O9AUWxMJw8QUD3LhhO5r96Zmnk5cw62UIBvE6dIqBLZ9QTJ/T3VIOCoipOD2QcPT97jyupcsqyBQveY9SoF0u7GqKMk0dIhRg9+hUWLlzA9OlvcOVKIiEhIfTs2YvBg4foaQYMeIqMjAymTp1MSkoKzZpFsGDBIjw9PQHLY6KXXx7D0qVLWLx4IRERzVm69AObyr969QoxMTE3TFNY+QAxMTFcvfrvXP6DBw8wePC/+3fMmfMWAA8++BCTJk1FVQ2cPn2ab755katXr+DvH0CjRo14//0V1KtX36a6C1EcLONfLAN2836mkKNpmNGKbUsBIUTZoGg3+fag8fEpN+y+VFWF8uU9MZvNZa6b82agKJZHbmlpWQ5NFRT5c4vaR4UOd3Nl0xZywpuVibycWacbUVUFs4eCWdWI27aFxp26cGXrNnKa/1umWbPMRjJk47T7ztHzs/c4Z5ZjNGaTkHCBoKAquLt72JyXEMXtRvemokBwsG+hecgjJCGEuMnd5N9ThQtyxj0pAYwQLkhzc8dUpSqam3vhiUsoL2fWqTC5T4cUd3fMVauieOQtUwFU1dJj44zHSY6en73HObOc3CUasrNLZlsTIWyVe08aDI6PZJFHSPIIqVjJIyThTIoCBi8DJhVMmpnYlCSq+wXkSadpGu6qioJCttGEm6JizipbU3lLSlJSAhkZqfj4VMDDw9Op+0MJYS9N08jOziI19Qre3j74++ddU8zWR0gyiFcI4TJyB+nams6smTmfnky1cn64KfatsXSz8POzrACbmnqlkJRClBxvbx/93nSUBDClaMKE10hJSWHOHFmPQNjHcPAA/o/3JGntZ5hubVQm8nJmnWzldegQgX0HkLThc0yNG+ebJsds3wqwBXH0/Ow9ztnlKIqCv38Qvr4VMJlybpCDECXDYHCzexuE/EgAU4i0tDQWLlzAL7/8zJUriYSFNeTll8fou3CCpUts8eKFbNjwGSkpKTRt2oxx416jZs1aAMTGnqdr1wdYu/ZjwsIaOr2OhZWfnz17/uTDD1dw6NAh4uPjmD37HX1tm1wJCQnMm/c2O3bsIDU1hYiI5owZ88oN8xUlQ8kxYrgQi5Jj+87lxZ2XM+tkc5nGHAyxsSjG4v/D7Oj52XtccZWjqiqqKjORxM1DBvEWYvLkiezc+QdTprzB+vWfcdddLXn22cFcvnxJT7Ny5XLWrl3DuHGvs3LlR3h7ezNs2BCyskpm4Jwj5WdmZhAaGsbYsePy/VzTNF54YRQxMTG8/fZc1qxZT5UqVRkyZDAZGenFdSpCCCGETSSAuYHMzEx+/nkTo0Y9z2233U7NmjUZMmQo1avX4JNPPgYsf+jXrFnN008P4p572hEaGsrkyW8QFxfHr7/+DEDXrpZ9lB5//FGaNw9n0KD/WZXz4YcruO++9rRr14bp09/AaLT9m5ct5eendes2DBs2gvbt783387Nnz/D331GMG/cajRo1pnbtOowb9xpZWZls3Pi9zfUTQgghioMEMDdgMpkwmUx4eFh3u3p5ebFv317AsgR/fHw8LVrcpX/u6+tL48ZNiIraD8CqVZZdRBcteo8ff/yZWbPe1tP++eduYmLOsWTJ+0yaNJWvv/6Sr7/+Uv988eKFdOnSqcA62lK+I3L3O/Lw+Hc1X1VV8fDw0M9dCCGEKC0SwNxA+fLlCQ9vyrJl7xEXdxmTycS3335DVNR+4uPjAEhIiAcgMNB6KlhQUBDx8QkAVKhg2bY+ICCA4OBg/P399XS+vn6MGTOOOnXqcPfdbWnT5m527dqlfx4QUIHq1asXWEdbyndE7dp1qFy5CgsWzCU5ORmj0ciKFR9w6dIl4uLiHc5XOIepbj2ubvgWU916ZSYvZ9bpRq6dBJxVtw5Xv/8OU/3iLRMcPz97jyupcoRwdRLAFGLKlGlomsb993fgrrtuZ926Ndx//wMoinOarl69evpiUwDBwcEkJv4bePTu/ThLlixzSln2cHd3Z9astzlz5gz33BNJq1Z3snv3Llq3jsyzO7coeZqPL8bWbdB8Cl8roaTycmad8pO7BxKGf+8/s48PxrvvRvO9cZnKP8fn/nOEo+dn73ElVY4Qrk4CmELUqFGDZcuW8/vvf/Dddz+yatUacnJy9F6RoKBgAKugAywzeIKD8y7Qcz03t+sngtm3VkVRy7+RW2+9lXXrPuG3337nxx838+67i0lKukq1agX3CImSoV6IpfzUiagXYstMXs6sU35y13bR+Pfnw/3CBcqPn4AaW3CZBkXB4KaieKgYvAyongaHghhHz8/e40qqHCFcnQQwNvL2LkdISAjJycns2LGdtm3bAVCtWjWCg4PZtWunnjY1NZXo6L8JD28KWHozAEwms9PrZUv5ReXr60uFCoGcPXuGgwcPcs897ZySr3CcGneZcvPmoMZdLjN5ObNOtnKLi6fc7NmolwouU1UUTJoZDJCjaORoZodWo3X0/Ow9rqTKEcLVyTowhdi+/Xc0TaN27dqcO3eOd96ZQ+3atXnooYcBy7fCJ57oy7Jl71GzZk2qVq3GokXvEhISwj33WNZVqVAhEC8vL7Zv30alSpXw8PDAt5Au71zr1q3ll182F/gYyZbyAZ555mnatbuX3r0fByA9PZ1z587qn58/f54jRw7j5+dPlSpVAPjppx+pUKEClStX4fjxY7z11kzuuacdLVu2sr8hhShF+sq8aE7ZF0kIUfokgClEamoqCxbM5dKlS/j7+9O+fQeGDRuh96oADBjwFBkZGUydOpmUlBSaNYtgwYJFeHpaZvC4ubnx8stjWLp0CYsXLyQiojlLl35gU/lXr14hJibmhmkKKx8gJiaGq1f/XUr84MEDDB48UH89Z85bADz44ENMmjQVgPj4OObMeeufx1EhdO36IIMGPWNTvYUoDgrw39sMQAiRH9nMUTZzLFaymWPxcIvaR4UOd3Nl0xZywpuVibycWafrqSoYPA3kqOhjYMyaxpXft9Hw/ge4sm0bORF5yzRrmtWGj2ZNQzGDIRu770dHz8/e40qqHCHKKls3c5QxMEK4IHOFQDL69MdcoWiboTkzL2fW6VqW2UfWwUuunAoVyBgwAHOQ7WXmzkiyl6PnZ+9xJVWOEK5OemCkB6ZYSQ+MKCpVVTB7KGCwfv/63pX8XJ9G0zTcVBXFBKZMk/zMC1EGSQ+MEDezjAwMhw9BRkbZycuZdbKRkpGB4eBBm8tUFAWj2YzRkZlIjp6fvceVVDlCuDgJYIRwQW7HjhB4dwvcjh0pM3k5s0628jp2nMA77sTtcPGX6ej52XtcSZUjhKuTAKYUTZjwGi+8MKq0qyGEEEK4HJlGXYi0tDQWLlzAL7/8zJUriYSFNeTll8fQqFFjPc2ECa/x9ddfWR3XsmUr3n13MQCxsefp2vUB1q79mLCwhk6vo6ZpLF68kA0bPiMlJYWmTZsxbtxr1KxZq8BjPvhgGT//vJnTp0/h6elJ06bNGDnyOWrXrqOnGTTof+zZ86fVcT17PsKrr77u9HMQQggh7CEBTCEmT57IiRPHmTLlDUJCKvLdd9/w7LOD+fTTDVSsWElP16pVayZOnKK/vn4H6+K0cuVy1q5dw+TJU/9ZyG4Bw4YN4dNPv7BaC+Zae/b8yaOP9qZRo0aYTCYWLJjH0KFD+OyzDXh7l9PTde/ek2efHaa/9vLyKvbzEUIIIQpj1yOkJUuW0LNnTyIiImjZsiVDhw7l5MmTVmn69etHWFiY1b/x48dbpYmNjWXw4ME0bdqUli1bMnPmTHJycqzS7Ny5k+7du9O4cWM6duzI559/7uApOi4zM5Off97EqFHPc9ttt1OzZk2GDBlK9eo1+OSTj63Senh4EBwcrP/z8/PTP+va9QEAHn/8UZo3D2fQoP9ZHfvhhyu47772tGvXhunT38BoNNpcR03TWLNmNU8/PYh77mlHaGgokye/QVxcHL/++nOBx7377mIeeuhh6tWrT2hoGJMmTeHixQscPHjQKp2Xl5fVefn4+NhcN1GMFAXNw8PxnQmLIy9n1un6rEuhTKeVZe9xJVWOEC7Orh6YXbt20adPH5o0aYLJZGLOnDkMHDiQb7/9lnLl/v3W/uijjzJy5Ej9tbe3t/7fJpOJZ555huDgYNatW8fly5cZM2YM7u7uvPDCCwCcO3eOZ555ht69ezNr1ix27NjBa6+9RkhICG3atCnqOdvMZDJhMpny9KZ4eXmxb99eq/f+/PNP7r23LX5+ftxxx50MHTqCgIAAAFatWkO/fk+waNF71KtX32oV3z//3E1wcDBLlrzPuXNnGTv2ZcLCwujRoxcAixcv5Ouvv+LbbzfmW8fz588THx9PixZ36e/5+vrSuHEToqL2c//9D9h0rikpqQD4+/tbvf/999/x/fffEhQUxN1338PTTw+2up6idOQ0aUp8THyZysuZdcqVuwO1SYX81uDNaNKY+CuJTi2zII6en73HlVQ5Qrg6uwKY999/3+r1jBkzaNmyJQcOHOCOO+7Q3/fy8iIkJCTfPLZt28bx48dZvnw5wcHB3HLLLYwaNYpZs2YxfPhwPDw8WLduHdWrV2fs2LEA1KtXjz179rBixYoSDWDKly9PeHhTli17j7p16xIYGMTGjd8TFbWfGjVq6OlatWpN+/b3UrVqNWJiYliwYB4jRgxlxYpVGAwGKlSoAEBAQADBwcFWZfj6+jFmzDgMBgN16tShTZu72bVrlx7ABARU0He+zk9CguUXVmCg9c7TQUFBxMcn5HdIHmazmVmz3qRZswjq12+gv9+pU2eqVKlCSEgIx44dY968tzl9+jSzZ79tU75CFFXuHkZCCHG9Is1CSklJAfJ+a//6669p0aIFXbt2Zfbs2WRcsy7Bvn37CA0NtfpDHhkZSWpqKsePH9fTtGzZ0irPyMhI9u3bV5TqOmTKlGlomsb993fgrrtuZ926Ndx//wMoyr9Nd//9D9C2bTsaNAilXbv2zJ27gAMHovnzz92F5l+vXj0Mhn9X6AoODiYx8d/Ao3fvxwvcyNFZZsx4gxMnjjN9+kyr93v27EWrVq1p0CCUzp27MHnyG/zyy2bOnTtXrPURhTMcPULAvW0wHC36lFln5eXMOtnK8+gxAlq1xnD4sF3HObIar6PnZ+9xJVWOEK7O4UG8ZrOZadOm0bx5c0JDQ/X3u3btStWqValYsSJHjhxh1qxZnDp1igULFgAQHx+fpxci93VcXNwN06SmppKZmVmiA0lr1KjBsmXLychIJzU1jZCQEMaMefmGvSLVq1cnIKAC586ds3q0kx83t+svgYI9iyMHBVnaKTExwarXKyEhgbCwsEKPnzFjGlu3bmHZsuVUqlT5hmmbNGkCwLlzZ616oETJUzIzcP97P0pm0Rctc1ZezqyTrdTMTNz370fJyLT5GAUwuKkoCih2rMbr6PnZe1xJlSOEq3M4gJk0aRLHjh1jzZo1Vu8/9thj+n+HhYUREhLCk08+ydmzZ6lZs6bjNS1l3t7l8PYuR3JyMjt2bGfUqOcLTHvp0kWSkq4SEmIJLnLHvJhMZqfXq1q1agQHB7Nr1059inZqairR0X/zyCOPFnicpmnMnDmdX375maVL36datYIDslxHjli+2QUH5/94UAhnURTL4yNFcf4O1Lmr8SoaGBT7vjAIIcoOhwKYyZMn8+uvv7J69WoqV77xt/amTZsCcObMGWrWrElwcDBRUVFWaeLjLeM4cnsQgoOD9feuTePj41Pi03i3b/8dTdOoXbs2586d45135lC7dm0eeuhhANLT01myZBH33tuB4OBgzp07x9y5b1OjRk1atmwNQIUKgXh5ebF9+zYqVaqEh4cHvr6F7/MAsG7dWn75ZXOBj5EUReGJJ/qybNl71KxZ859p1O8SEhLCPfe019M988zTtGt3L717Pw5YHht9//33vP32XMqVK6+3d24bnzt3jo0bv6N16zYEBPhz7NhRZs9+i+bNb7PqcRPC2fTNGzUzHu4GNE3Ls4mjEELYFcBomsaUKVP46aefWLVqlU2PEQ4dOgT8G5w0a9aMxYsXk5CQQFCQZeDp9u3b8fHxoX79+nqaLVu2WOWzfft2mjVrZk91nSI1NZUFC+Zy6dIl/P39ad++A8OGjdB7VVRV5dixY3zzzVekpKQQElKRu+5qydChw/XZS25ubrz88hiWLl3C4sULiYhoztKlH9hU/tWrV4iJiblhmgEDniIjI4OpUyeTkpJCs2YRLFiwyGoNmJiYGK5evaK/zp0Gfv2U7okTp/DQQw/j7u7Ozp1/sGbNajIyMqhUqTLt23fg6acH21RvIRylKApGzczFjFSqutkW6Ash/nvs2o164sSJfPPNNyxcuJA6df5dsdXX1xcvLy/Onj3L119/Tdu2bQkICODIkSNMnz6dypUrs3r1asAyNblbt25UrFiRl19+mbi4OEaPHs0jjzxiNY36wQcf5IknnqBnz5788ccfvPHGGyxZssTuWUiyG3Xpkt2oi4dy9QruW37FePc9aAEVykRezspHVRVy3OF8WhI1/AMKTGfWNC6dO0OtP//C2K4dWoW8ZRa0Y7VZ01DMYMjG5vvS0fOz97iSKkeIssrW3ajtCmAKGhQ6ffp0evTowYULF3j55Zc5duwY6enpVKlShQ4dOjB06FCrBdDOnz/PxIkT2bVrF97e3nTv3p0XX3zRakDrzp07mT59OsePH6dy5coMHTqUHj162FpVnQQwpUsCGGEvewKY/IITW9I4EsAIIUpGsQQwrkgCmNIlAUzxUC5fxuuzj8ns+ShaxYplIi9n5WNPAHP55HHqfLuRzMceRatUKd80BQUwqhkMRjCZbOyBcfD87D2upMoRoqyyNYCR3aiFcEGGi7H4TBiH4WJsmcnLmXWylfuFi/i88gqG2At2HadPpfZQbV4PxtHzs/e4kipHCFcnmzkKIf5zZCq1EK5PemBK0eLFC+nd+5HSroYQQgjhciSAKURaWhpvvTWTzp3vp2XLO3jyyX4cOBBtlSY9PZ0ZM6bRqVMHWra8g549u/Hpp9a7VTdvHs4vvxS8O3RRrV+/ji5dOnHXXbfTv/8TREf/fcP0mzdvok+f3tx9d2tatbqT3r0f4Ztvvs6TZujQZ2jXrg3Nm4dz5Ih9y7ULIYQQxUUeIRVi8uSJnDhxnClT3iAkpCLfffcNzz47mE8/3UDFipZBg7Nnv8Xu3buYOnU6VatWZceOHcyY8QYhISG0bduu2Ov4ww8bmTPnLcaNe50mTZrw0UerGTZsCBs2fJVnk8dc/v7+DBw4iNq16+Du7s7Wrb8xadJ4AgMDadXKsgBfRkYGzZpF0LHjfUyZMqnYz0PYzuzrR9b9D2D29SszeTmzTrYy+fmS1bkzZv/iL9PR87P3uJIqRwhXJ7OQbjALKTMzkzZtWjJnzlzatLlbf/+JJx6jdetIhg0bAcAjj3Tnvvs6MWjQM/mm6dKlExcu/DuwrkqVqnz77UYWL17Ir7/+Qt++/Vm06F1SUpJp1SqS11+fQPny5W0+x/79n+DWWxszduw4wLJP1QMP3Efv3o/z1FMDbc7niSceJTLyboYOHW71fmzsebp2fYC1az/WtyuwlcxCEvZSVQWTO8QU4zTq3M9kKrUQZY/MQnICk8mEyWTSV9TN5eXlxb59e/XX4eHN+O23X7l8+RKaprF79y7Onj3DXXdZdtRevdqyX9TEiVP48cef9dcAMTHn+PXXn5k7dz7vvDOfv/76k+XL39c//+qrL2nePLzAOhqNRg4dOmS1aaSqqrRo0YKoqP02naemaezc+QenT5+mefPbbDpGlDKjESU+HozGspOXE/KxbCOggsHGqUFGI0pcnHPawZayHDk/e48rqXKEcHESwNxA+fLlCQ9vyrJl7xEXdxmTycS3335DVNR+4uPj9HRjxrxC3bp16dSpIy1a3Mbw4c8yduw4brvtdsCyFxJYViwODg7WX4Olt2TSpKnUr9+A5s1vo3PnruzatVP/3MfHh9q1axdYx6tXr2AymfI8KgoMDCIhIb6AoyxSUlJo3boFLVrcxqhRwxk9+hU96BJlm9uhAwTfWhe3QwfKTF7OyEdRFHL+2fvIlunN3ocOE1y7Dm7RRW+Hwjh6fvYeV1LlCOHqZAxMIaZMmcakSeO5//4OGAwGGja8hfvvf4BDhw7qadatW8Pff0fx9tvzqFKlKn/9tYcZM6YRElLRqmckP1WrVrN6XBQcHMKVK4n66/bt76V9+3udf2JYArS1az8hIyOdXbt2MmfOLKpXr87tt99RLOUJUdYogKpa/kvTNFnMUggXIgFMIWrUqMGyZcvJyEgnNTWNkJAQxox5merVqwOWcTILFsxj9ux39HEyoaGhHD16mA8/XFFoAHPt9glg6UK353l8QEAFDAYDiYkJVu8nJiYQFBR8w2NVVaVmzZoAhIU15NSpk3zwwfsSwIj/BAVwc1NR3BRMRhNuigFzlkmCGCFchDxCspG3dzlCQkJITk5mx47t+uyinJwccnJyUFXr/m5VNVgtjuXm5obZbHJ6vdzd3bnlllusHjuZzWZ27dpJeHhTu/IymzWMxmxnV1GIMin3cVWW2URMejJGzYxi67K8QohSJz0whdi+/Xc0TaN27dqcO3eOd96ZQ+3atXnooYcByxiV2267nXfemYOnpxdVqlRhz549fPvt17zwwkt6PlWrVmPXrp00bRqBh4cHfn62TXX8+efNLFgwl88//6rANH369GfChNe49dZbadSoCWvWrCYjI4OHHuqmp3n99XFUrFiJESNGAfDBB8u49dZGVK9eg+zsbH7/fSvfffcNr7zyqn5MUlISFy9eIC7OMt7n9OnTAAQFBRMcfOPeHSHsVZqxQ04xfLkQQhQvCWAKkZqayoIFc7l06RL+/v60b9+BYcNG4O7urqeZPv1N5s+fy6uvvkJychJVqlRh2LAR9Or1qJ7m+edfZM6cWWzY8DkhIRX59tuNNpefGzgU5P77O3HlyhUWLVpIQkI8YWFhLFiwiKCgfwf2Xrx4EVX9t8MtIyOD6dPf4PLlS3h6elK7dh2mTJnG/fd30tP89tuvTJz4uv76lVdGAzB48BCGDBlqU/1F8chp1IT4EzFo5Wyfbl/ceRUlH8vsIwOKgl3L+mc0upX4C7Fodiw74ChHz8/e40qqHCFcnawDI7tRFytZB0bYIncHaoOigAImpWhrvNib5kzSFWr5VMDNKGvCCFHaZB0YIW5ihpPH8X+0G4aTx8tMXkXNRwFUg4Ki2v4syfPkSfwfehjD8aK3Q2EcPT97jyupcoRwdfIISQgXpKSm4vHrzyipqWUmr6LmoygKJuzr/VBT0/DYvBklpejtUBhHz8/e40qqHCFcnfTACCHEPxSldAcTCyFsJwGMEEJgeYSleKj6YGIhRNkmAYwQQmB5hGXUzLIejBAuQgIYIVyQqWp1UqbPwlS1epnJy5l1spWxalVS5szGVL1asZfl6PnZe1xJlSOEq5Np1DKNuljJNGphC1VVMHsoYLC8duYUaVunUdfxDUTDsh+STKcWovTINGq7KfoAPvnnvH+WkQXC2ZQriXh+sg7lmo0/SzsvZ9bJVoYrV/Bcuw4lsfjLdPT87D2upMoRwtX95wMYTdMwm82oqoKqqvLP6f+Uf3q35NusMxnOncVv2GAM586WmbycWSdbeZyLwe/ppzGcKf4yHT0/e48rqXKEcHX/+XVgNA3S07Nl0F4x0jRNHs8JIYRwqv98AAOWIEZ6CIQQQgjX8Z9/hCSEEEII1yMBjBAuSCtXHuNtdzhl52Fn5eXMOtnKXK4cxjvvRCtfrsh5FfYQ2dHzs/e4kipHCFf3n59GLYQofaU9jTom+Sp1/AMxaRqaScMg06iFKDUyjVoIIWyk/rORpBlNZv4L4SIkgBHCBblF7SOkoh9uUfvKTF5FzceRuME76m9CyvvgttexMu3h6PnZe1xJlSOEq5MARghRqlQVDJ4qGKTrQwhhOwlghBClRlFA9TSQo4KGjDkRQthO1oERQpQaRVHIKaOj7HPXtiyj1RPiP08CGCGEuIaC5bGW5maZEmXOMkkQI0QZJNOohXBFmZmosecxV60GXl5lIy8H8rl++nQuW6c/X4i7RI2UNMzV8i/T3qnWmqbhpqqoZsj+Z4d6fWdqR9vJ3uNKqhwhyihbp1FLACOEKDVFDWCKI42mabirKjlmWRNGiNIg68AIcRNTz5zG99mnUc+cLjN5ObNOtvI4exbf/w1EPe28MnPH5Vy/Joyj52fvcSVVjhCuTgIYIVyQmnQVr88+Rk26WmbycmadbGW4moTX+vWoV4q/TEfPz97jSqocIVydBDBCCCGEcDkSwAghhBDC5cg0aiFEiVMUy1gTRbEMM5EhskIIe0kAI4QLMleqTNpLYzFXqlxm8rI1H331Xc2Mh7sBTdMcXoXXWKkiaeNewVy56O1QGEfbyd7jSqocIVydTKMWQpQoVVXIcYeLGalU9c1/qmRpTqO+9n3FDIZsmUYtREmSadRC3MSUlGTcf96EkpJcZvKyNx+T2VSk8gDUlBTcf9qEklz0dsiPwr9bCjjaTvYeV1LlCOHqJIARwgUZTp0koHcPDKdOlpm8nFknW3meOk1At24YTji/TAUwuKkoHiqK4vj52XtcSZUjhKuTMTBCCJEPRVEwms0oGhgUpfADhBAlSnpghBBCCOFyJIARQgghhMuRAEYIF6R5eGKqXQfNw7PM5OXMOtlcpqcHprp10Tw9ir8sB8/P3uNKqhwhXJ1MoxZClKjcadTn05Ko4R+Qb5qyMI069zOZSi1EyZJp1EIIIYS4aUkAI4QLMhyIJuiWOhgORJeZvJxZJ1t5HTxIUK1aGP4u/jIdPT97jyupcoRwdXYFMEuWLKFnz55ERETQsmVLhg4dysmT1msOZGVlMWnSJFq0aEFERAQjRowgPj7eKk1sbCyDBw+madOmtGzZkpkzZ5KTk2OVZufOnXTv3p3GjRvTsWNHPv/8cwdPUYibj2LKQU1IQDHlFJ64hPJyZp1sLjPHhBqfgJJT/GU6en72HldS5Qjh6uwKYHbt2kWfPn34+OOPWb58OTk5OQwcOJD09HQ9zbRp0/jll1945513WLVqFZcvX2b48OH65yaTiWeeeQaj0ci6deuYMWMGGzZsYN68eXqac+fO8cwzz9CiRQu+/PJLBgwYwGuvvcbWrVudcMpCiNImq6oIIYrKroXs3n//favXM2bMoGXLlhw4cIA77riDlJQUPvvsM2bNmkXLli0BS0DTuXNn9u3bR7Nmzdi2bRvHjx9n+fLlBAcHc8sttzBq1ChmzZrF8OHD8fDwYN26dVSvXp2xY8cCUK9ePfbs2cOKFSto06aNk05dCFHSLBs5qpgUGRArhCiaIo2BSUlJAcDf3x+A6OhojEYjrVq10tPUq1ePqlWrsm/fPgD27dtHaGgowcHBeprIyEhSU1M5fvy4niY3ALo2TW4eQgjXpCgKpn92n5bFbYUQReFwAGM2m5k2bRrNmzcnNDQUgPj4eNzd3fHz87NKGxQURFxcnJ7m2uAF0F8XliY1NZXMzExHqyzETSOnbn2ufPsTOXXrl5m8Cssnt/cFg/Mil6x6dbny82ZyGhS9HQrjaDvZe1xJlSOEq3N4L6RJkyZx7Ngx1qxZ48z6CCFs4eNDzh0tylZeheSjKAo5Tl6UyVy+PDktnNQOhXG0new9rqTKEcLFOdQDM3nyZH799VdWrlxJ5cqV9feDg4MxGo0kX7e1fUJCAiEhIXqa62cl5b4uLI2Pjw9eXl6OVFmIm4oae57yr7+CGnu+zOTlzDrZyj02lvJjxqKeL/4yHT0/e48rqXKEcHV2BTCapjF58mR++uknVq5cSY0aNaw+b9y4Me7u7uzYsUN/7+TJk8TGxtKsWTMAmjVrxtGjR0lISNDTbN++HR8fH+rXr6+n+eOPP6zy3r59u56HEP91anwc5Za8ixofV2bycmadbOUWn0C5BQtQLxd/mY6en73HlVQ5Qrg6uwKYSZMm8dVXXzF79mzKly9PXFwccXFx+rgUX19fevbsyYwZM/jjjz+Ijo5m3LhxRERE6MFHZGQk9evXZ/To0Rw+fJitW7fyzjvv0KdPHzw8LPuZ9O7dm3PnzvHmm29y4sQJPvroI77//nuefPJJp568EEIIIVyTXWNg1q5dC0C/fv2s3p8+fTo9evQAYNy4caiqysiRI8nOziYyMpIJEyboaQ0GA4sXL2bixIk89thjeHt70717d0aOHKmnqVGjBkuWLGH69Ol8+OGHVK5cmalTp8oUaiGEEEIAdgYwR44cKTSNp6cnEyZMsAparletWjWWLl16w3xatGjBF198YU/1hBBCCPEfIXshCeGCzIFBZDz1NObAoDKTlzPrZKucwEAyBg/CHFz8ZWpBjp2fve3iaDuWRvsLUZoUTXPyvMYyJj4+hZv7DIVwDaqqYPZQwABmTSM2JYnqfgH5pi3s85JKY9Y0FDO4GQF3y/c9c5ZJfqcIUYwUBYKDfQtNJz0wQrii9HTcovbBNfuQlXpezqyTjZT0DNz2lkSZCsa0VLS9f6FkZNh3qL3t4mg7lkL7C1GaJIARwgW5HT9KhQ5343b8aJnJy5Z8nL17gNfx41SIjMTtSNHboSAKlkX43I8eJahtGwzH7CvL3vZ19Ho4854QwhVIACOEKHbFsY1ASVAAg5uKyV1zuboLcbOTAEYIUexytxHQcK3BI4qiYDSbiU1P0esum1AKUTZIACOEEIUwmU364y/FXZEgRogyQAIYIVyQpqiYfXzRlKL/CDsrL2fWyWaqitnXF00tgTJVA2ZfX3IUBcWOCMbednG0HUul/YUoRTKNWghR7K6dQg2uM436+s9zp1UbssFsll8sQhQHmUYthBBCiJuWBDBCuCDDkcNUaHMnhiOHy0xezqyTrbyOHKXC7bdjOHSo2MsyHDpE0O13YDhs3/nZ2y6OtmNptL8QpUkCGCFckJKViduRwyhZmWUmL2fWyfYys3A7dBglM6v4y8rMwu3wYZRM+87P3nZxtB1Lo/2FKE0SwAghhBDC5UgAI4QQQgiXIwGMEKJEyNIpQghnkgBGCBdkqlWbpA/XYapVu8zkVVA+xbmNQHatmiR9vB5TndqFpi0qU53aXF2/DlNt+8qyt30dvR7OvCeEcAWyDowQolhdvwYMyDowQoiCyTowQtzElEuX8J47G+XSpTKTlzPrZCu3y5fxfmsWysXiL1O5eIlys2ah2nl+9raLo+1YGu0vRGmSAEYIF2S4dAGfNyZhuHShzOTlzDrZyv3iJXwmTsRwofjLNFy4gO/ESah2lmVvuzjajqXR/kKUJglghBBCCOFyJIARQgg7KQqyI7UQpUwCGCGEsIMCKB4qqqdBghghSpFbaVdACGE/s58/WQ92w+znX2bycmadbGXy9yOrezfMAcVfpjnAn8xu3dACAsjRzGgauCkKhU3ktLddHG3H0mh/IUqTTKMWQhQrVVXQPBS0m2QatZumYNI0SwBjlOnUQjibTKMW4maWnY0aex6ys8tOXvnkU5yL2AEo2dmo553UDoXJzsZw/jyqyYiiKravLGxv+zp6PZx5TwjhAiSAEcIFuR0+SFCzW3A7fLDM5JVfPoryT28FxdNL4XX4CEGhYbgdKHo7FMbtwEGCwxpC9AHMaDbvjWBv+zp6PZx5TwjhCiSAEUIUi+LufRFC/LfJIF4hhNOpqiVoyZEBaEKIYiI9MEIIp1I9VHLcLb0v0vcihCguEsAIIZwqR9OISUu29L5IBCOEKCYyjVoIV2Q2g9EI7u6gFvF7iLPyMptRTTnklHPndFoS9fwCQQEtnyydNo066QrVvcsXWHenTqP28dPbyawotu9KbW/7Ono9nHlPCFGKbJ1GLWNghHBFqgqenmUrL1UFN09QwaAoqAYFDYptBpJeprPawZ6yNM2yIq8tPUz21tHRcyrJthCiDJAwXQgXZDhxDP9unTGcOFZm8jKcOIbfQw9gOH4MVVEwoVmmGxcjzxMn8e/UCcOxordDYQzHjullKYDBTUXxUAsNYuxtX0evhzPvCSFcgfTACOGClLQ0PLZvQ0lLKzN5KWlpuG/fhpJa9DrZSk1Lw2NryZSppP5blqIoGM1mFM3S23SjJ/H2tq+j18OZ94QQrkB6YIQQwkE2P0YSQjidBDBCCOEAex4jCSGcTx4hCSGEA+x5jCSEcD7pgRHCBZmq1SBlznxM1WqUmbxM1WqQ+vZ8TDWqF7lOtsquVo2UdxeUSJmmGtUdKsve9nX0ejjznhDCFcg6MEIIp1FVhRx3OJ+WRA3/gALTOW0dmBJIc6PPzZpm+3owQgib2LoOjPTACOGClIQEvFavRElIKDN5KQkJeK5agZIQX+Q62cqQkIjXihUo8cVfphIf71BZ9ravo9fDmfeEEK5AAhghXJDh/Dl8XxiB4fy5MpOX4fw5fJ4fgeFcTJHrZCuP8+fxHTa8RMo0nItxqCx729fR6+HMe0IIVyABjBBCCCFcjgQwQgghhHA5EsAIIYQQwuVIACOEC9LKlye7VSRa+fJlJi+tfHmMrSLRfIpeJ1uZy5cnu03JlKn5OFaWve3r6PVw5j0hhCuQadRCCKeRadRCiKKSadRC3MzMZsjKsvx/WcnLmXUqi2U6Wpa9x5VUOUK4OAlghHBBbtFRhNQIwS06qszk5RYdRVC1YNyiil4nW3lHHyAkMAi3/cVfptv+KIfKsrd9Hb0ezrwnhHAFEsAIIZxK9jUUQpQECWCEEM5lkBBGCFH8JIARQjiFolgCFw0NRWIYIUQxszuA2b17N0OGDCEyMpKwsDA2bdpk9fnYsWMJCwuz+jdw4ECrNFevXuXFF1+kefPm3H777YwbN460tDSrNIcPH+aJJ56gSZMmtG3blqVLlzpwekIIUbwUkIBNiFLgZu8B6enphIWF0bNnT4YPH55vmjZt2jB9+nT9tYeHh9XnL730EnFxcSxfvhyj0ci4ceMYP348s2fPBiA1NZWBAwfSsmVLJk2axNGjRxk3bhx+fn489thj9lZZiJtOTsNbSdh3CHNwSJnJy3TLrSQcOkJOpWDIyihyvWyR2TCMhKNHMIcUvR0Kk9Po1jxlKYDBTUVRQMk05btkg73t6+j1cOY9IYQrsDuAadu2LW3btr1hGg8PD0IK+IVy4sQJtm7dyqeffkqTJk0AeO211xg8eDCjR4+mUqVKfPXVVxiNRqZNm4aHhwcNGjTg0KFDLF++XAIYIQA8PDBXrVa28vLwwFytGqhaiQUwmocH5uCKJVKWfn7XUBQFo9mMooFBUch3WS1729fR6+HMe0IIF1AsY2B27dpFy5Ytuf/++5kwYQJXrlzRP9u7dy9+fn568ALQqlUrVFUl6p/pl/v27eP222+36rmJjIzk1KlTJCUlFUeVhXAp6ulT+A3sj3r6VJnJSz19Cr/+fTGcKnqdbOVx5gx+ffuilkCZ6qlTBZZleYyk5Psoyd72dfR6OPOeEMIVOD2AadOmDTNnzmTFihW8/PLL7N69m0GDBmEymQCIj48nMDDQ6hg3Nzf8/f2Ji4vT0wQHB1ulyX0dHx/v7CoL4XLU5CQ8v/4CNbnoAb2z8lKSkvD84guUEvySYUhKxnPDF6hXi79M9WpSvmXlPkbKcddQPQ15ghh729fR6+HMe0IIV2D3I6TCdOnSRf/v3EG8HTp00HtlhBA3H0UB1eO/OZI19zHS+bRkqpXzw62gR0lCCKcq9mnUNWrUoEKFCpw5cwaw9KQkJiZapcnJySEpKUkfNxMcHJynpyX39fU9M0KI0qcoCqb/+N/sHLOptKsgxH9KsQcwFy9e5OrVq3pwEhERQXJyMtHR0XqaP/74A7PZTHh4OADNmjXjzz//xGg06mm2b99OnTp18Pf3L+4qCyGEEKKMszuASUtL49ChQxw6dAiAmJgYDh06RGxsLGlpacycOZN9+/YRExPDjh07GDp0KLVq1aJNmzYA1KtXjzZt2vD6668TFRXFnj17mDJlCl26dKFSpUoAPPjgg7i7u/Pqq69y7NgxvvvuOz788EOeeuopJ566EK7LVKkKqa9OwFSpSqnnpSigeqqYq1UhdeJEzFWKXidbGStXInXiREwlUKapShWHyrK3fR29Hs68J4RwBYpm58PanTt30r9//zzvd+/enYkTJzJs2DAOHjxISkoKFStWpHXr1owaNcrq0c/Vq1eZMmUKP//8M6qqct999/Haa69Rvnx5Pc3hw4eZPHkyf//9NxUqVKBv374MHjzY7hOMj0/Jd20GIYRzqKqC2UMBg+W1WdOITUmiul9AgccUlsYZeZR0Xc4kXaGWTwXcjGA2yy8dIRylKBAc7Ft4OnsDGFcjAYy4GSlJV3HfsR1jy1Zo/gGlmlduAKOkXMX999/JatWK8walRIKGSzFnqRUVjbF1a7SAvGmdGcDUMIP777/nW9aNAhh729fR6+HMe0KI0mRrACN7IQnhggxnTuPfvzeGM6fLTF6GU6fxf/QxDKeLXidbeZw5aynzVPGXqZ+fnWXZ276OXg9n3hNCuAIJYIQQQgjhciSAEUIU2X9zBRghRGmSAEYI4bDcGUgYJIQRQpQsp6/EK4QofpqnFzlhDdE8vUo1L0VRyPlnlLzm5UnOLc6pk600z3/K9PIs/rK8HCvL3vZ19Ho4854QwhXILCQhhMNUVUHzUNAM/74n06hlGrUQRSGzkIQQxUoeHwkhSpMEMEK4IMPfUQTVrYbh76hSyyv38ZGGpbfBsD+KoMpVcIsqep1s5R19gKDKVTDsL/4yc8/P3rLsbV9Hr4cz7wkhXIEEMEK4IEUzo6amoGjmMpOXYjajpqSAueh1stk/ZSolUKbiYFn2tq+j18OZ94QQrkACGCGEcBIFy6M1IUTxkwBGCCGcwKAoGNxUFA9VghghSoBMoxZCCCdQFYUczYyiWYKZm3yCpxClTnpghHBBOfVDubJpCzn1Q8tMXjlhoVzZto2c0KLXyVaZ9etbygwr/jL187OzLHvb19Hr4cx7QghXID0wQriicuXICW9WtvIqV46ciGagaZCSVPT8bKCV87aUWRJyz8+R4+xpX0evhzPvCSFcgPTACOGC1Jhz+Ix5ATXmXJnJSz13Dp/nn0c9V/Q62co95nyJleno+dnbvo5eD2feE0K4AglghHBBamIC3suXoSYmlJm81PgEvN9bippQ9DrZyi0x0VJmfPGXqZ+fnWXZ276OXg9n3hNCuAIJYIQQwolkKrUQJUMCGCGEcBIFZCq1ECVEAhghhMPkb7Q1RVEwms3kaGZUVVpHiOIkAYwQLsgcHEL6M8MwB4eUSl6qCobrNnI0VwwhffhwzCFFr5OtcoKDLGVWLP4y9fMrpKzre2HsbV9Hr60z7wkhXIGi3eSrLcXHp3Bzn6EQJUtRwOBlwKSib+R4LbOmEZuSRHW/gALzKCyNM/IozbqYNQ3FDIZsMJvlF5AQ9lAUCA72LTSd9MAI4YpSU3HbvRNSU0s8r+t3obbKZ+dOFGfUyUZqWhpuO53UDoX55/zsLsvea+XotXXmPSGEC5AARggX5HbyOBW6dMTt5PEyk5fbseNUaH8vhuNFr5OtPE+cpEL7e3E7Vvxl5p6fvWXZ276OXg9n3hNCuAIJYIQQNlMUSw+MDE8VQpQ22UpACGETRQHV04AJDcWgQD7jX4QQoqRID4wQwiaKomDUzMSmp+Q7eFcIIUqSBDBCuCDN4IY5KAjNUPROVHvzMplN+efj5oY5OAjcSq5jV3MzYA4OQiuBMnPPz96y7G1fR6+tM+8JIVyBTKMWQthEVRVy3OF8WhI1/AMKTCfTqGUatRBFIdOohRBCCHHTkgBGCBdkOHyIwDubYjh8qMzkZTh4kMAm4RgOFr1OtvI6cuSfMg8We1n/np99Zdnbvo5eD2feE0K4AnlYKoQLUrKzMJw+hZKdVWbyUrKyMZw86ZQ62V1mVnaZK0vBsuUCdravo9fDmfeEEK5AAhghhHAyBXBzU1HcFFQP6egWojhIACOEEE6Wu92CWTPjLlPOhSgW8tVACCGEEC5HAhghXJCpTl2urvscU526ZSYvU726XP3iC0x1i14nW2XVqW0ps17xl6mfn51lmerWJfnjDTa3r6PXw5n3hBCuQNaBEULYRNaBsT8PWQ9GCPvJOjBC3MTUSxcp9+Y01EsXy0xe6oWLlHvjDdSLRa+TrdwuXbKUeaH4y9TPz86y1IsX8Z5pe/s6ej2ceU8I4QokgBHCBamXLlJ+1gynBTCF5aXYsP20evEi5adNL9EAxv3S5RIr09HzM1y8SLm3ptsVwDhybZ15TwjhCmQWkhDihnJ3oVYUUMzm0q6OS1EA1WBD9CeEsJsEMEKIG7JMCTajolj+Igub5U6nzv1vIYTzSAAjhCiQpfdFxaxoGBRFBsQLIcoMGQMjhAsy+weQ2fNRzDeYDeSMvPQeBAVy0DCjFTgexlwhgMzHHsMcUPQ62coU4G8ps0Lxl6mfn51lmQMCyHz0MTQb28XRa+vMe0IIVyDTqIUQBVJVBbOHAgbLa1eYulzW6iJTqYWwj0yjFuJmlpmJevIEZGaWnbwyM1FPOKlONlJKskxHy8rMxGDPcY5eD2feE0K4AAlghHBBbkcPE3RXBG5HD5eZvNwOHSYovCluh4teJ1t5HT1mKfNQ8Zepn5+dZbkdPkxQRDMMR2w7ztHr4cx7QghXIAGMEEIIIVyOBDBCCCGEcDkSwAghRAmQZWCEcC4JYIQQohjlxi2KuyJBjBBOJNOohRAFkmnUzstDNYPBqGA2m+V3khA3INOohRCijFAAg5tKjrum7yslhCgaCWCEcEGG48cIeOBeDMePlZm8DEePEtCuPYajR4tcJ1t5Hj9RYmU6en6Go0ep0P5ezIcPE5OWjFEz33BfJEevhzPvCSFcgd0BzO7duxkyZAiRkZGEhYWxadMmq881TWPu3LlERkYSHh7Ok08+yenTp63SXL16lRdffJHmzZtz++23M27cONLS0qzSHD58mCeeeIImTZrQtm1bli5dav/ZCXGTUtLTcN+zGyU9rfDEJZSXkpaO+65dKOnpRa6TrdT0f8pMK/4y9fOzs6xr2yXHbCo8vYPXw5n3hBCuwO4AJj09nbCwMCZMmJDv50uXLmXVqlVMnDiRjz/+GG9vbwYOHEhWVpae5qWXXuL48eMsX76cxYsX8+effzJ+/Hj989TUVAYOHEjVqlX5/PPPGT16NAsWLGD9+vUOnKIQQgghbjZ270bdtm1b2rZtm+9nmqbx4Ycf8uyzz9KhQwcA3nzzTVq1asWmTZvo0qULJ06cYOvWrXz66ac0adIEgNdee43BgwczevRoKlWqxFdffYXRaGTatGl4eHjQoEEDDh06xPLly3nssceKcLpCCHspgIw5FUKUNU4dAxMTE0NcXBytWrXS3/P19aVp06bs3bsXgL179+Ln56cHLwCtWrVCVVWioqIA2LdvH7fffjseHh56msjISE6dOkVSUpIzqyyEKICqgsFTBYOMOBVClD1ODWDi4uIACAoKsno/KCiI+Ph4AOLj4wkMDLT63M3NDX9/f/34+Ph4goODrdLkvs7NR4j/MlONmiS/+x6mGjWLJS9FAdXTQI4Kmo39L6ZaNUletgxTzaLXyVbZNapbyqxV/GXq52dnWfa2i6PX1pn3hBCuwO5HSEKI0qdVCCTrkd5Oz0tR0GfI5Ni5WIkWGEjW473RNA1SSqan1FShAlmPO6cdCpN7fo4ep2kaJF1B4car8jp6bZ15TwjhCpzaAxMSEgJAQkKC1fsJCQl6D0pwcDCJiYlWn+fk5JCUlKQfHxwcnKenJff19T0zQvwXKfHxeL3/HooTeiRz81IT4i29Lu6geqrY++BIiYvDa8kSlH96UkuCW3xCiZXp6Plde5xBUTC4qSgeaoFBjKPX1pn3hBCuwKkBTPXq1QkJCWHHjh36e6mpqezfv5+IiAgAIiIiSE5OJjo6Wk/zxx9/YDabCQ8PB6BZs2b8+eefGI1GPc327dupU6cO/v7+zqyyEC7JEBuD7ysvYYiNcWJe5zFqZmLSki29L3ZGMIaY8/i+8CKG8+eLXCdbucfGWsqMKf4y9fOzs6xr20VVFHI0MzmaGVXNv4EdvbbOvCeEcAV2BzBpaWkcOnSIQ4cOAZaBu4cOHSI2NhZFUejfvz+LFi1i8+bNHDlyhNGjR1OxYkV9VlK9evVo06YNr7/+OlFRUezZs4cpU6bQpUsXKlWqBMCDDz6Iu7s7r776KseOHeO7777jww8/5KmnnnLiqQsh8mPLWiXCcbmr8t6oF0YIUTi7x8BER0fTv39//fX06dMB6N69OzNmzGDQoEFkZGQwfvx4kpOTue2221i2bBmenp76MbNmzWLKlCkMGDAAVVW57777eO211/TPfX19ef/995k8eTI9evSgQoUKDB06VKZQCyFcnqIoGM1mFA0MisJNvh2dEMXG7gCmRYsWHDlypMDPFUVh1KhRjBo1qsA0AQEBzJ49+4blNGzYkDVr1thbPSGEEEL8B8heSEK4IM3Hh+x72qP5+JSZvDRfH7LvvdcpdbKV2ae8pUzf4i9TPz87y7K3XRy9Hs68J4RwBYp2k/dfxsenyNb1QthAVRVy3OF0yhXq+QWCAtp1X3HMmkZsShLV/QIKzMcZaUqqnNKqi1nTUMxgyAazWX5BCXEtRYHgYN9C00kPjBCuyGRCSUkGkxMG3F6Xl0FRUA0KSgGzZG6YT7KT6lQWy3S0LHuPc/TaOvOeEMIFSAAjhAtyO/A3wfWq43bgb6flZYi25KUqCiY0zHbugOQW9TfBVari9nfR62Qr7wMHLWVGFX+Z+vnZWZa97eLotXXmPSGEK5AARgghhBAuRwIYIYQQQrgcCWCEEEII4XIkgBFCCCGEy5HdqIVwQTm3NCL+4Ek0J+wNZrq1EVdOnoaAALs3cLSqU+NGxJ8+hcnfHzLTi1wvW2Tc0pD406fQAgKKvazc87O3LHvbxdFr68x7QghXIAGMEK7I3R3NSTuzKx4eZJcPwqAoYOfMozx1CgkBTYNMp1TNtjJvsPaK08sKCXH8OFvbxdFr68R7QghXII+QhHBB6qmT+PV7DPXUSafkFdD7UdzOnLJ/7Zdr8zl5Er9HHsVwsuh1spXH6dP4PfIoagmUmXt+9pZlb7s4em2deU8I4QokgBHCBakpyXj+8D1qSnKR81KSk/H8/nu0pCTMaA7vkKwmJeP53XeWRdtKiCE5Bc/vvkNNKv4yc8/P3rJu1C6KQp72dvTaOvOeEMIVSAAjhBClQAUUDxXV0+Bw0CjEf5kEMEIIUcIUwOCmkqNo5GhmFIlghLCbBDBCCFHCFEUhR9P0R3YSvwhhPwlghHBBpspVSZ00DVPlqkXOy1ylKqnTpmOqWqVodapahdTp0zFXKXqdbGWsUpnU6UWvuy1yz8/esm7ULrk9MYqHqgcxjl5bZ94TQrgCRdO0m3ov9/j4FG7uMxSiaFRVweyhgAHMmkZsShLVC5iaXNjnzkpTUuWUhbqYNQ3FDIZsMJvll5UQigLBwb6FppMeGCFckHL1Ch5fbUC5esU5eW34HOVK0fJSrlzB4/Oi52MPw9WrJVamo+dn73GOXltn3hNCuAIJYIRwQYazZ/B/egCGs2eKnJd65gz+A/pjOF20vAynz+Dfrz+GM0Wvk608zp6zlFnEuttCPz87y7K3XRy9ts68J4RwBRLACPEfpiigesgIUiGE65EARoj/MEVRMMmwCyGEC5IARoj/KEUB1VOV3wJCCJckv7qEcEGalzfGJk3RvLwdziN3LRLN2wtj06Zo3l5Fq1NuPl5Fy8ceZi/n1N0WjraTve3i6LV1xj0hhCuRadRC/EddO306V2lPKS7pcspCXWQatRDWZBq1EEK4CAVZjVcIe0kAI4QLcvt7P8HVg3H7e3+R8lEAt337Ca4QiNu+ouWl57O/aPnYw/vvaKfU3RaOtlNh7XL9aryOXltn3RNCuAq30q6AEMIBmoaSnY2jz0dzB/Ca1KLn5aw6lfkyHS2rkOMURcFoNqNoYFCUYitHiJuNBDBC/AflDuAVQghXJY+QhBCiDLCMg5GBMELYSgIYIYQoZbnjYHLcNVQP+bUshC3kEZIQLiinQRiJW3ZiqlW76Hk1DCNx9y5Mdeo4JZ+c2rUhJ7vI9bJFZoP6Tqm7LRxtJ1vaJXcczPm0ZKqFhqJt20VOjVr2lePEe0IIVyABjBCuyNsbU8NbHDo0z1MKb29Mt95q+e+ijIvJzUfTIKVkAhjN2xvTrVVKpCyrdnLkOBvaJcdsAh8/y7W1d02YItwTQrgi6asUwgWp587i8/xw1HNn7TrOMvvIgOqpkhvHqGfP4jN0GOpZ+/LKUycn5WMP93MxJVamo+dn73Hq2bOUHzXM7mvr6D0hhKuSAEYIF6ReScT7ow9RryTadZyiKBg1s2UG0j8RjJqQiPfKlagJ9uWVp065+SQWLR97uF254pS628LRdrK3XdTERLwcuLaO3hNCuCoJYIQQQgjhciSAEUKIMkQmUgthGwlghPgPkj+SZZNBUVANcnWEsIXMQhLCBZlDKpI+8gXMIRXtPlYBVIOCBmhomCtVJP3FFzFXsj8vqzrl5lOxaPnYIyck2Cl1t4Wj7WRPu6iKgrFiCOkvvACVKtlXThHuCSFckaJpN/d64vHxKbI1iBD/UFUFs4cChvw/N2sasSlJVPcLcOhzZ6UpqXLKUl1yP6/m64+bqqKYwJRpkt9f4j9HUSA42LfQdPIISQgXpKSm4P77VpTUFNuPUSyzkK5/QKGkpOC+ZQtKiu155Zu/k/Kxh5qaWmJlOnp+9h6npqbCr7+Rk5xk19YCjtwTQrgyCWCEcEGGkycI6N4Fw8kTNqXPXf/F5A5cN8bCcPwEAQ90xnDctrwKrFNuPieKlo89PE+eckrdbeFoO9nbLobjJwjsbH872ntPCOHqJIAR4iZk6W259r8t67/EpqegIc8khBCuTwbxCnGTye1tAdCyTSgeBksQYzZjMptKuXZCCOEcEsAIcZPJ7W0BcFdUjJoZFUXmTrsYRf8fIUR+5BGSEC5Ic3PHVKUqmpv7DdMpNsQtmrsbpqpV0dyL9n0mNx8KqZMzOavuxVmWve2Sm1718kDxUPNuvlnQcTbeE0LcLKQHRggXZLq1EYn7D98wjQIoHpa/fqoZCnp4ZGrcmMRjRy0vijBnNzcfs6ZBSpLD+dgj85Zb/q17MbNqJweOs7Vdrk2vms0YVAWTqfDrYss9IcTNRHpghLhJKYpi2bQRDdVNQVHleYQrUQCDm2pXL4wQ/yUSwAjhggwHDxDYtCGGgwcKTJP7Ny83kDGj5fuH0BAdTWCDUAzR0UWr0z/5uEUXXCdn8zp0yCl1t4Wj7WRvu+jpDxzAaDZj1Mw2rQdjyz0hxM1EHiEJ4YKUHCOGC7EoOcb8P8d6u4Ab5mXMwRAbi2LMKVqd/smHAupUHJxV9+Isy952cbicQu4JIW42EsAIcRNSFAWTrPcihLiJySMkIYQQQrgcCWCEEKKMu3ZlZSGEhdMDmPnz5xMWFmb1r1OnTvrnWVlZTJo0iRYtWhAREcGIESOIj4+3yiM2NpbBgwfTtGlTWrZsycyZM8nJKf5n3EK4ClPdelzd8C2muvWKnlf9elz9/jtM9YuWl55PvaLXyVZZdes4pe62cLSd7G2X68tRAcVDRfU03DCIceY9IYQrKJYxMA0aNGD58uX6a4PBoP/3tGnT+O2333jnnXfw9fVlypQpDB8+nHXr1gFgMpl45plnCA4OZt26dVy+fJkxY8bg7u7OCy+8UBzVFcLlaD6+GFu3cU5evr4Y7777nxeOj5vJzUcrwXVgzD4+/9a9mFm1kwPH2dou15ajaBoGN5Ucs4Zm1jAoiiWf/I5z4j0hhCsolkdIBoOBkJAQ/V9gYCAAKSkpfPbZZ4wdO5aWLVvSuHFjpk2bxt69e9m3bx8A27Zt4/jx47z11lvccssttG3bllGjRvHRRx+RnZ1dHNUVwuWoF2IpP3Ui6oXYoucVG0v58RNQY4uWl7PysYf7hQslVqaj52fvcdemv3YKfGFLKjvznhDCFRRLAHPmzBkiIyO59957efHFF4n95wc3Ojoao9FIq1at9LT16tWjatWqegCzb98+QkNDCQ4O1tNERkaSmprK8ePHi6O6QrgcNe4y5ebNQY27nO/n9gyXUC9dptzs2aiX8s/L7nwuFy0fe7jFxTul7rZwtJ3sbReHyynknhDiZuP0R0jh4eFMnz6dOnXqEBcXx7vvvkufPn34+uuviY+Px93dHT8/P6tjgoKCiIuLAyA+Pt4qeAH017lphBD5s+xErWJSAZlGfVNRkIG8QlzL6QFM27Zt9f9u2LAhTZs2pV27dnz//fd4eXk5uzghxDUURcGkFbZ0nXA1+rYCCiiZ/+5qVYQhS0K4vGKfRu3n50ft2rU5e/YswcHBGI1GkpOTrdIkJCQQEhICWHpbrp+VlPs6N40QIq/c3hcM8jX9ZqMoir6tgKoqqJ6GQmclCXGzK/YAJi0tjXPnzhESEkLjxo1xd3dnx44d+ucnT54kNjaWZs2aAdCsWTOOHj1KQkKCnmb79u34+PhQv3794q6uEC7BXCGQjD79MVewDJBXFFBV9Z/eF/u+lpuDAskYMABzUGDR6pSbT2DR8rFHToUKTqm7LRxtJ3vb5UblWB4jqeRoefdIuv6eEOJmp2gFzclz0MyZM2nXrh1Vq1bl8uXLzJ8/n0OHDvHdd98RGBjIhAkT2LJlC9OnT8fHx4epU6cCWE2j7tatGxUrVuTll18mLi6O0aNH88gjjzg0jTo+PkW6WcVNTVVB9TCQg4ZiUAoMYMyaRmxKEtX9AgrMq7A0zsijLJVTlupS2OeapuGmqphNGmYFMGkYjGA2yy84cXNRFAgO9i00ndPHwFy8eJEXXniBq1evEhgYyG233cbHH3+sT6UeN24cqqoycuRIsrOziYyMZMKECfrxBoOBxYsXM3HiRB577DG8vb3p3r07I0eOdHZVhXBdGRkYzpzGXLs2agUfjIrGhdQUqvoW/kOfb16nTmGqUweKMk7tn3zMtWs7noedlIwMDDGxlrp7exdvYde2kz1l2dsuBZST+xjpUnoqlcr75H189M89YapVu/jbQogywOkBzNtvv33Dzz09PZkwYYJV0HK9atWqsXTpUmdXTYibhtuxI1TocDdXN2/FeEcEmqJh1kyFH5hfXoePUCEykivbtpHdrKnjdfonn4RtW6FeHYfzsYfXseME3v8AV7ZtIyeiWbGWdW072VOWve1SWDkmc/7XOfeeuLJpCznhttdPCFcleyEJIYQQwuVIACOEEC5I1oUR/3USwAghhIvR14XxUCWIEf9ZxbKZoxCimCkKmoeHc76COysvZ9apLJbpaFn2HmdD+twBvYrGvxs8lkb7C1GKJIARwsUoCmh3NOdq/BU0o7nI+eU0a0r8lUTLiyKsOZCbj7kEd6POaNL437oXM6t2cuA4W9vF4XKaNCU+Jr7whELcJOQRkhAuxrJdgBmzwbLyrnzhFkL8F0kAI4QLyd0uwO34UfxatUI5ehhFLVoEYzh8mIBWrTEcPlwm8rGH59FjJVamo+dn73EOl3P0CAH3tsFw9IhdxwnhqiSAEcKFKIpCjqZBRgbu+/djTs/AXMStG5WMTNz370fJyHROPplFy8ceaqZz6m4LR9vJ3nZxuJzMDNz/3o+SmWHXcUK4KglghBBCCOFyJIARwsXIkBchhJAARgiXkTv+BYOEMEIIIdOohXAR+vgXwFS7FkmrPsRUu1aR83VWXno+tYpeJ1tl16zhtHYojKPtZG+7OFxOzVokLVuJqWbJtb8QpUkCGCHKuPymSWsVKpDdo4dT8rfKqwjrwOTmo5XgOjCmgACntUNhHG1ze9vFnnIUQFUtl00LqED2Q93trp8QrkoeIQlRhlkeGxkweBpQFEUf/6JcuoT3vPkoly4VvQwn5ZWbj3rpcpHrZCu3uDintUNhHG0ne9vF1nIUwM1NRfGy3B9q3GW8Fy1AuVxy7S9EaZIARogyzPLYyLJondkDffyLIfYCPq+8giH2QpHLcFZeufmoF2KLXCdbuV+46LR2KIyj7WRvu9haTu4jxSyziRzMuF26gM+EcRgullz7C1GaJIARooxSlH96YP4JYmLTU9CKuOaLuPnoGzu6y+Bu8d8iY2CEKIMUBctjI7d/NurTNExmU2lXS5RBiqKQYzZjkK+j4j9GAhghyiBFUchBK9KgWvHfce0MNUU2xxL/ERKzC+GCzP5+ZHXujNnfr8zklZuP5lf0OtnK5OfrtHYojKPtZG+7OFqO5udH1gMPlGj7C1GaFE27ub/ixcenyJdY4XJUVcHsoYDB8tqsacSmJFHdLyDf9IV97qw0N1s5ZakuRc3DrGkoZjBkg9ksv/SE61IUCA72LTSd9MAIUUbkDtq1idGIEhcHRmPRC3ZWXs6sU1ks09Gy7D2uKOXEl3D7C1GKJIARogy4dr0X1YafSrfoAwTXroNb9IEil+2svPR8DhS9TrbyPnTYae1QGEfbyd52cbicAwcIqVsXw8GSa38hSpMEMEKUAdeu96J6Giy9MaVdKeGScnvx7OrRE8IFySwkIcqI3CBG1cDdU8WsALLui7CRvkqzu4LBoKC4q2iAOcsk4wDFTUkCGCHKmNwpsZp8exZ2yN1oQlPB7KFgVjQwaRiUf9YSEuImI4+QhCgluV38ln/X7HNUqrUSri5H0/RVm+UxkriZSQ+MEKUgd9Bu7t+WHDQUg4KiaagGBbPGDbcNyAlvQvyFWLTy5YtcF2fllZuPqVw5SE8tcr1skdHoVqe1Q2EcbSd728UZ5ZjSU//dYkABJVMeI4mbj/TACFHCcntcjJqZHDSMmK/5xmx5fGQubOyLwWBZsMxgKHqFnJWXM+tUFst0tCx7j3NSOYqiYDSbydHMljEx0hMjbjISwAhRgnJ7XlRPFYV/HxfZu8+R4fhx/B96GMPx40Wuk7PycmadbOV58mSJleno+dl7nDPLUQA3NxXFyzJFX4IYcTORAEaIEpTb82LSLOMTVIOCotr/V0VJScVj82aUlKI/qnFWXno+qSXz+AhATU1zWjsUxtF2srddnFlObo9eltlEDmZUB+41IcoqCWCEKGEqlsBFNaiYsDwukm/Gojjp42E8VLnXxE1DBvEKUUJUFQyeKpoKJlnfRZSg3PEwioZMqxY3DQlghChG+hRpRUP1MJCj3nh2kRDFSUGmVYubhzxCEqKY5A7YNbmD6u2GyYnBi6l6NVLmzMZUvVqZyUvPp1rR62QrY9WqTmuHwjjaTva2S3GVc+1jJFWVWUnC9UkPjBDFJHdrgAsZqVR1K3xreHtoISFkPvOM8/MqwqOF3Hw0TYOUJKfUrTA5wUFOa4fCONrm9rZLcZWj/L+9uw+Kqvr/AP6+9+4iksX4xFhf9fdt8AciIIL0RYmeKLFRMymxJyuTsiwrs2m0Rismi3LQ0lJDUSbTfmSizRTWpP5+Wo1U5vCQQiCTgI/Fo7ACwu79/P647GUvrOwDu8sufl4zjuzds+eeh713zz33nHMFAUZZhk4SYfIDdJD4MQPMp3EPDGNuoPS+iIAkODxF2q746+sx6H9yINTXe01crkyTvaSGBo/t09n8Ofo5d+7HPBbm7OUmdJAMgbthmA/jBgxjLiIIykBd87gXE5G6nLurSVXVuOHppyFVVXtNXGo81X1Pk738zpx1WTnY4mw5OVountiP0Q2NasY8jW8hMeYkc8OESPlbGiQBOgEwKo0Wo4g+3ZJhzJ0EKA1uy68of12ZL+EGDGMOUh/A6Kcs2S5fMSk9LiCYZBk6QQABPNeIeS1JEJQVevUCJCMBgtJ44TExzJdwA4YxB6iPAhCAdpIBAvSS8kNgFGH3wxgZ609iZ4PbKMvQ60QYZQLJxGvEMJ/CDRjGHGCeWaSTRIgkKD8EIgGdU6TNS7e7G10XgI7//Ad0XYDXxKXGE9D3NNlLDnBdOdjibDk5Wi6e2g/Q9agBXg2a+SJuwDDmIPPtIghAB8n4u8WAG4e4dpq0LaaQEDT+3/+6Pq4+NL7M8cgenEZ9ZVywy8rBFmfL3NFy8dR+rDHfHuVOGOYLuAHD2FWYZxOZu9SvdoXqjmnSjHmaCCiL3PFYGOYjeBo1u+aZrzotX0uSMqvIqFf+l/wliAE6SINEeENPu66gECOvGwJdQaHXxKXGU9j3NNlrcPEfLisHW5wtJ0fLxVP7sWRepdcoEEzU86nV3Y8RxrwBN2DYNU15wKKkDMwVLV4P1sEoEs63NIMkQBaBdtmk3jpibCAxj4UhECS99nEDlscIN2KYN+FbSOyaY3kSFgdJ6BAIookgDZIgADCCQJ0DGwFZfXK0APAMIzagdX/cgB5Ko6VdIMDEs5SYd+EeGHZNUadBD5IsZgwpV50mgUAiIIjWLzMtZ2wwNlBZPm7ACFIa9NzzyLwQ98CwAaP7yriClatFQRCUZ8AA0Iti54m5syEjKL0vjDHlcQOWbRYB2rEw3BHD+hs3YJjP674yLrWbIPhJ6CAZfoKknHnNJ1sBEGUZok7sXL/F8k3fYQwbj7riIsj/+pfXxGWOx3jTTUDHlT6nyx5tIf/tsnKwxdlycrRcPLUfWyRBgCgpq0oLRJB0ojLAvfNwkdtNkOU+74Yxp3EDhnkVy54T8xXe1a74LBsu5pVxiQA/UUQHyTjf0oz/uiEQJiKIBJhAEEmAICqL0fXH+i0u4+8POTjY9XH15bLaHA+Rxxow5O8POWiUR/bldJk7Wi6e2o8N5tV6ga7bSjoIkEEQJRHiIAm4IgNQjlXukWGexmNgmNcwj08x6qGZFSQOkpSpzBK6ZgpJgrJtkAQTZMidY1ckAIKfAEgCiJQBuASCqBMgigIkSVDHuPjy+i1iZSWuX5gKsbLSa+JyZZrs5Vdd7bF9Ops/Rz/nqf04yjyIXZREmEhWZuf5oXN5AUl9Erv5f8bczasbMLt27UJiYiIiIyORkpKC4uLi/k4S6wPL++fd/1YaJkrPyNnLTcpaFJ2Dbc0nS3GwDlKA8k/WCzCKyqBbURLVhooyGNe8rL85/s4pop1jXAbCIFyxoRH+X34JsaHRa+JS42nse5rsJTVeclk52OJsOTlaLp7aj6O0jx1QemQutBrQIZtAus4LDXW9JO2Ua15HhrmD1zZg9u/fj/T0dLzwwgvYt28fxo8fj9TUVNTV1fV30gYUWycWe66orC0Ep/aeqD0mUHtMLHtPzNvFAB2kwRKEzp4T86JaMhEEUenK7iAZHSTjimzC+dZmddCtbNHNzbOEGPMck2xSp17DYr0kI2RIkrnXE5rjnRsyzFW8tgGTnZ2NefPm4cEHH8S4ceOQlpYGf39/5Obm9nfS+t3VGhXqmBCL9202LjpPLKIoaD5j2ejo6iIWbDRMum7tiAFdvSVSgA7SYB1MInV2O3f2nkjK/k0CwUgy2kmGDILU2WAx3/qxNq3Zl2//MDbQaHpnAOh0IgR/CbJe6Tk1iQSYe1EtGjLKuUZ7XnGk54Z7dq5tXjmIt729HSdPnsSzzz6rbhNFEfHx8SgoKHAoLnd8uc0DTW3PXnFFmJ7vC3pRGeNhIqBD7gqjt1jmXhIAk/I5AoAOZb0T82cBADKUWzWSCPIDJEkHmGT18wKUga+yrPSIEDrDWKRHAGAkGaIgQtTpYDIqt3uIrExPEJSw9VdaMWJwAEwkA4IIURC6elEA6CVd55RNASbq2m65T3OY3kqttzCuiKNf9yNJwPXXA5Ky+F6f9tMtLqfz0xmPIEmeKze9XpN2p+OxIy3dy8nuOOwsF0/vx13fffNxK5OM+o5WjNAFAIJyLiFZhiSJECQdJJOsLGEgy9Dpus4rXec1APrOa+zO81cPtt7vkeL+OWcP1P24a/C2vb/bAnnhsop///03br/9duTk5CA6OlrdvmbNGhw7dgxfffVVP6aOMcYYY/3Na28hMcYYY4xdjVc2YIYOHQpJknoM2K2rq8OIESP6KVWMMcYY8xZe2YDx8/NDeHg48vPz1W2yLCM/P19zS4kxxhhj1yavHMQLAE899RSWL1+OiIgITJw4EZ999hlaW1vxwAMP9HfSGGOMMdbPvLYBM2PGDNTX12PDhg2oqalBWFgYsrKy+BYSY4wxxrxzFhJjjDHGWG+8cgwMY4wxxlhvuAHDGGOMMZ/DDRjGGGOM+RxuwDDGGGPM53ADxorNmzfj4YcfRlRUFGJjY+36DBFh/fr1SEhIwMSJE7FgwQJUVlZqwjQ2NuLVV19FTEwMYmNj8cYbb+Dy5ctuyEHvHE3H2bNnERoaavXfd999p4az9n5eXp4nsqThTDk//vjjPdL+5ptvasKcP38eixYtQlRUFKZOnYoPPvgARqPRnVm5Kkfz2NjYiHfeeQfTp0/HxIkTceedd2L16tVobm7WhOuvOty1axcSExMRGRmJlJQUFBcX9xr+u+++w7333ovIyEjcd999OHLkiOZ9e45HT3Mkj7t378ajjz6KW265BbfccgsWLFjQI/yKFSt61FVqaqq7s3FVjuRv7969PdIeGRmpCePrdWjtnBIaGopFixapYbylDo8dO4bnnnsOCQkJCA0NxcGDB21+5tdff0VycjIiIiIwbdo07N27t0cYR49rhxHrYf369ZSdnU3p6ek0efJkuz6TmZlJkydPpgMHDlBpaSk999xzlJiYSG1tbWqY1NRUmj17NhUWFtKxY8do2rRptGzZMndl46ocTYfRaKR//vlH8+/jjz+mSZMmkcFgUMOFhIRQbm6uJpxl/j3FmXKeP38+rVy5UpP25uZm9X2j0UizZs2iBQsWUElJCR0+fJji4uJo7dq17s6OVY7msaysjJYsWUKHDh2iqqoqOnr0KCUlJdGLL76oCdcfdZiXl0fh4eG0Z88eOnXqFK1cuZJiY2OptrbWavjjx49TWFgYbd26lSoqKujDDz+k8PBwKisrU8PYczx6kqN5XLZsGe3cuZNKSkqooqKCVqxYQZMnT6aLFy+qYZYvX06pqamaumpsbPRUljQczV9ubi7FxMRo0l5TU6MJ4+t12NDQoMlfeXk5hYWFUW5urhrGW+rw8OHDtG7dOvrhhx8oJCSEDhw40Gv46upqioqKovT0dKqoqKDPP/+cwsLC6Mcff1TDOFpezuAGTC9yc3PtasDIsky33norZWVlqduampooIiKCvv32WyIiqqiooJCQECouLlbDHDlyhEJDQzUnJXdzVTruv/9+ev311zXb7Pniu5uz+Zs/fz6tXr36qu8fPnyYxo8frznJfvHFFxQTE0NXrlxxTeLt5Ko63L9/P4WHh1NHR4e6rT/qcO7cuZSWlqa+NplMlJCQQJmZmVbDv/zyy7Ro0SLNtpSUFFq1ahUR2Xc8epqjeezOaDRSdHQ07du3T922fPlyWrx4sauT6hRH82fr3DoQ6zA7O5uio6Pp8uXL6jZvqkMze84Ba9asoZkzZ2q2LV26lBYuXKi+7mt52YNvIbnA2bNnUVNTg/j4eHXb9ddfj6ioKBQUFAAACgoKcMMNN2i6SePj4yGKouu71XrhinScOHECpaWlmDt3bo/30tLSEBcXh7lz52LPnj0gDy8z1Jf8ffPNN4iLi8OsWbOwdu1atLa2qu8VFhYiJCREs5BiQkICDAYDKioqXJ+RXrjqu2QwGDBkyBDodNr1LD1Zh+3t7Th58qTm2BFFEfHx8eqx011hYSGmTp2q2ZaQkIDCwkIA9h2PnuRMHrtrbW2F0WhEYGCgZvtvv/2GqVOnYvr06XjrrbfQ0NDg0rTbw9n8tbS04K677sIdd9yBxYsX49SpU+p7A7EOc3NzMXPmTAQEBGi2e0MdOsrWMeiK8rKH167E60tqamoAAMOHD9dsHz58OGprawEAtbW1GDZsmOZ9nU6HwMBA9fOe4Ip07NmzB8HBwYiJidFsf+mllzBlyhQMHjwYP//8M9LS0tDS0oInnnjCZem3xdn8zZo1CzfddBOCgoJQVlaGjIwMnD59Gp988okab/dVoM2vPVl/5rT0tQ7r6+uxadMmPPTQQ5rtnq7DhoYGmEwmq8fOX3/9ZfUz1urC8liz53j0JGfy2F1GRgaCgoI0Pwi33XYbpk2bhtGjR+PMmTNYt24dnnnmGXz55ZeQJMmleeiNM/m7+eab8d577yE0NBTNzc3Yvn07Hn74YeTl5WHUqFEDrg6Li4tRXl6Od999V7PdW+rQUVc7HxoMBrS1teHSpUt9/s7b45ppwGRkZGDr1q29htm/fz+Cg4M9lCLXsjd/fdXW1oZvv/0Wzz//fI/3XnjhBfXvCRMmoLW1Fdu2bXPJj5+782f5Qx4aGoqRI0diwYIFqK6uxtixY52O1xGeqkODwYBnn30WwcHBWLJkieY9d9Yhc86WLVuwf/9+7NixA4MGDVK3z5w5U/3bPAD0nnvuUa/ovVl0dLTmwbzR0dGYMWMGcnJysHTp0v5LmJvs2bMHISEhmDhxoma7L9ehN7hmGjALFy5EcnJyr2HGjBnjVNwjR44EANTV1SEoKEjdXldXh/HjxwNQWqf19fWazxmNRly6dEn9fF/Ym7++puP7779HW1sb5syZYzNsVFQUNm3ahPb2dvj5+dkM3xtP5c8sKioKAFBVVYWxY8dixIgRPW7PmK8EXVF/gGfyaDAY8PTTT+O6667Dxo0bodfrew3vyjq0ZujQoZAkCXV1dZrtdXV1V33u2YgRI3pchVuGt+d49CRn8mi2bds2bNmyBdnZ2TbTPmbMGAwdOhRVVVUe/fHrS/7M9Ho9wsLCUF1dDWBg1WFLSwvy8vLw0ksv2dxPf9Who6wdg7W1tRgyZAj8/f0himKfvxP2uGbGwAwbNgzBwcG9/nP2BD169GiMHDkS+fn56jaDwYCioiL1KiM6OhpNTU04ceKEGuaXX36BLMs9WuXOsDd/fU1Hbm4uEhMTe9zCsKa0tBSBgYEu+eHzVP4s0w50nUgnTZqE8vJyzQF59OhRDBkyBOPGjetz/jyRR4PBgNTUVOj1emzevFlzNX81rqxDa/z8/BAeHq45dmRZRn5+vuYK3dKkSZPwyy+/aLYdPXoUkyZNAmDf8ehJzuQRALZu3YpNmzYhKyurxxRjay5evIjGxkaXNajt5Wz+LJlMJpSXl6tpHyh1CCgXfe3t7Zg9e7bN/fRXHTrK1jHoiu+EXVw2HHgAOXfuHJWUlKhThUtKSqikpEQzZXj69On0ww8/qK8zMzMpNjaWDh48SH/++SctXrzY6jTqOXPmUFFREf3++++UlJTUb9Ooe0vHxYsXafr06VRUVKT5XGVlJYWGhtKRI0d6xHno0CHavXs3lZWVUWVlJe3atYuioqJo/fr1bs9Pd47mr6qqij755BP6448/6MyZM3Tw4EG6++676bHHHlM/Y55GvXDhQiotLaUff/yRpkyZ0q/TqB3JY3NzM6WkpNCsWbOoqqpKM23TaDQSUf/VYV5eHkVERNDevXupoqKCVq1aRbGxseqMr9dee40yMjLU8MePH6cJEybQtm3bqKKigjZs2GB1GrWt49GTHM1jZmYmhYeH0/fff6+pK/M5yGAw0Pvvv08FBQV05swZOnr0KCUnJ1NSUpLHZ8U5k7+PP/6YfvrpJ6qurqYTJ07QK6+8QpGRkXTq1Ck1jK/XodkjjzxCS5cu7bHdm+rQYDCov3MhISGUnZ1NJSUldO7cOSIiysjIoNdee00Nb55G/cEHH1BFRQXt3LnT6jTq3srLFa6ZW0iO2LBhA/bt26e+Nt8u2bFjB+Li4gAAp0+f1iwC9swzz6C1tRVvvvkmmpqaMHnyZGRlZWmucjMyMvDOO+/gySefhCiKSEpKwsqVKz2TKQu20tHR0YHTp09rZuEASu/LqFGjkJCQ0CNOnU6HXbt24b333gMAjB07FitWrMC8efPcmxkrHM2fXq9Hfn4+duzYgZaWFtx4441ISkrSjPORJAmffvop3n77bTz00EMYPHgwkpOT7eoWdgdH83jy5EkUFRUBAKZNm6aJ69ChQxg9enS/1eGMGTNQX1+PDRs2oKamBmFhYcjKylK7mi9cuABR7OosjomJQUZGBj766COsW7cO//73v7Fx40aEhISoYew5Hj3J0Tzm5OSgo6Ojx/dryZIlePHFFyFJEsrLy/H111+jubkZQUFBuPXWW/Hyyy+7rbesN47mr6mpCatWrUJNTQ0CAwMRHh6OnJwcTW+mr9chAPz11184fvw4tm/f3iM+b6rDEydOaMa5paenAwCSk5Px/vvvo6amBhcuXFDfHzNmDDIzM5Geno4dO3Zg1KhRWL16NW677TY1jK3ycgWByMPzXBljjDHG+uiaGQPDGGOMsYGDGzCMMcYY8zncgGGMMcaYz+EGDGOMMcZ8DjdgGGOMMeZzuAHDGGOMMZ/DDRjGGGOM+RxuwDDGGGPM53ADhjHGGGM+hxswjDHGGPM53IBhjDHGmM/hBgxjjDHGfM7/A8vYUJ7m5tDFAAAAAElFTkSuQmCC",
      "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": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:02.382021Z",
     "iopub.status.busy": "2025-06-02T23:41:02.381865Z",
     "iopub.status.idle": "2025-06-02T23:41:03.892507Z",
     "shell.execute_reply": "2025-06-02T23:41:03.891916Z",
     "shell.execute_reply.started": "2025-06-02T23:41:02.382006Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    381626\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    190813\n",
      "True     190813\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-ahi-up-1         360040\n",
      "chirp-v4-up-u-d-2-3     21586\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-ahi-up-1         180020\n",
      "            chirp-v4-up-u-d-2-3     10793\n",
      "True        chirp-ahi-up-1         180020\n",
      "            chirp-v4-up-u-d-2-3     10793\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    381626\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": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:03.893273Z",
     "iopub.status.busy": "2025-06-02T23:41:03.893122Z",
     "iopub.status.idle": "2025-06-02T23:41:19.574660Z",
     "shell.execute_reply": "2025-06-02T23:41:19.574072Z",
     "shell.execute_reply.started": "2025-06-02T23:41:03.893258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 43910 duplicated prompts 21955 unique requests\n",
      "Found 11359 request_ids with duplicate prompts but not highest play counts in their group\n",
      "['1a11bc0c-6cf7-4b21-bb49-2826906e3fc2', 'b5561740-4a75-40e7-a928-819af56001c1', 'f7a00497-1edd-4189-8850-c50835c7172c', 'e9ae9395-38eb-41c7-abb2-180b8f28df33', '51a13cb4-ce0d-49fc-b3f6-1a23685c891d', '72da94a3-24d2-4d4b-8991-593b9458931c', '7f15ad05-b0d0-49af-b0a2-df02e5e8ae87', 'd996fee9-cc45-4479-92eb-a2d1e3049872', '2e1687d1-a0de-4e3d-81e0-564584f2a5cc', '18079e18-2cc1-4c56-960c-75952d7139a7']\n",
      "Before dedup user gen requests 381626\n",
      "After dedup user gen requests 358908\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": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.035167Z",
     "start_time": "2024-05-16T13:59:40.801098Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:46:01.122112Z",
     "iopub.status.busy": "2025-06-02T23:46:01.121817Z",
     "iopub.status.idle": "2025-06-02T23:46:01.635911Z",
     "shell.execute_reply": "2025-06-02T23:46:01.635291Z",
     "shell.execute_reply.started": "2025-06-02T23:46:01.122095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 137305 positive 64464\n",
      "total pair requests 179454 selected pair requests 49670 frac 0.277\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)\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[\"norm_play_frac\"] >= 1.9)\n",
    "    # & (df[\"user_n_clips\"] >= 100)  # 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": "2025-06-02T23:46:35.930886Z",
     "iopub.status.busy": "2025-06-02T23:46:35.930519Z",
     "iopub.status.idle": "2025-06-02T23:46:37.139394Z",
     "shell.execute_reply": "2025-06-02T23:46:37.138773Z",
     "shell.execute_reply.started": "2025-06-02T23:46:35.930869Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 49670 clips 99340 total khrs 5.537; N gpus for 1000 iters 6.209; 4 gpus for x iters 1552.188; n unique users 28814 n pro users 26925\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 t3 t24  requests 49670 clips 99340 total khrs 5.537; N gpus for 1000 iters 6.209; 4 gpus for x iters 1552.188; n unique users 28814 n pro users 26925"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.277006Z",
     "start_time": "2024-05-16T13:59:41.252105Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:20.793526Z",
     "iopub.status.busy": "2025-06-02T23:41:20.793374Z",
     "iopub.status.idle": "2025-06-02T23:41:20.847821Z",
     "shell.execute_reply": "2025-06-02T23:41:20.847286Z",
     "shell.execute_reply.started": "2025-06-02T23:41:20.793511Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (14791, 119)\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": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:20.848522Z",
     "iopub.status.busy": "2025-06-02T23:41:20.848367Z",
     "iopub.status.idle": "2025-06-02T23:41:22.016279Z",
     "shell.execute_reply": "2025-06-02T23:41:22.015760Z",
     "shell.execute_reply.started": "2025-06-02T23:41:20.848507Z"
    }
   },
   "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\"] = (\n",
    "    df_slice[\"stereo_width\"].diff() / df_slice[\"stereo_width\"]\n",
    ")\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\")][\"stereo_width_diff\"],\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": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:22.017009Z",
     "iopub.status.busy": "2025-06-02T23:41:22.016855Z",
     "iopub.status.idle": "2025-06-02T23:41:22.935335Z",
     "shell.execute_reply": "2025-06-02T23:41:22.934840Z",
     "shell.execute_reply.started": "2025-06-02T23:41:22.016993Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.03333333333333333\n",
      "0.23333333333333334\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": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:22.936041Z",
     "iopub.status.busy": "2025-06-02T23:41:22.935891Z",
     "iopub.status.idle": "2025-06-02T23:41:23.419952Z",
     "shell.execute_reply": "2025-06-02T23:41:23.419437Z",
     "shell.execute_reply.started": "2025-06-02T23:41:22.936026Z"
    }
   },
   "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": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:23.420934Z",
     "iopub.status.busy": "2025-06-02T23:41:23.420519Z",
     "iopub.status.idle": "2025-06-02T23:41:23.436204Z",
     "shell.execute_reply": "2025-06-02T23:41:23.435747Z",
     "shell.execute_reply.started": "2025-06-02T23:41:23.420919Z"
    }
   },
   "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": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:23.436857Z",
     "iopub.status.busy": "2025-06-02T23:41:23.436704Z",
     "iopub.status.idle": "2025-06-02T23:41:23.450715Z",
     "shell.execute_reply": "2025-06-02T23:41:23.450295Z",
     "shell.execute_reply.started": "2025-06-02T23:41:23.436842Z"
    }
   },
   "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": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:23.451281Z",
     "iopub.status.busy": "2025-06-02T23:41:23.451146Z",
     "iopub.status.idle": "2025-06-02T23:41:23.498526Z",
     "shell.execute_reply": "2025-06-02T23:41:23.498042Z",
     "shell.execute_reply.started": "2025-06-02T23:41:23.451268Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web        66692\n",
      "ios        17848\n",
      "android    14800\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:41:23.499284Z",
     "iopub.status.busy": "2025-06-02T23:41:23.499148Z",
     "iopub.status.idle": "2025-06-02T23:41:23.787358Z",
     "shell.execute_reply": "2025-06-02T23:41:23.786116Z",
     "shell.execute_reply.started": "2025-06-02T23:41:23.499271Z"
    }
   },
   "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[54], 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": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:50:07.102988Z",
     "iopub.status.busy": "2025-06-02T23:50:07.102598Z",
     "iopub.status.idle": "2025-06-02T23:50:08.052603Z",
     "shell.execute_reply": "2025-06-02T23:50:08.052050Z",
     "shell.execute_reply.started": "2025-06-02T23:50:07.102970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49670\n"
     ]
    }
   ],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:50:08.053633Z",
     "iopub.status.busy": "2025-06-02T23:50:08.053466Z",
     "iopub.status.idle": "2025-06-02T23:50:08.860864Z",
     "shell.execute_reply": "2025-06-02T23:50:08.860258Z",
     "shell.execute_reply.started": "2025-06-02T23:50:08.053618Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49173 497\n",
      "(98346, 120) (994, 120)\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": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:50:08.861609Z",
     "iopub.status.busy": "2025-06-02T23:50:08.861448Z",
     "iopub.status.idle": "2025-06-02T23:50:08.877313Z",
     "shell.execute_reply": "2025-06-02T23:50:08.876860Z",
     "shell.execute_reply.started": "2025-06-02T23:50:08.861593Z"
    }
   },
   "outputs": [],
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:50:08.878070Z",
     "iopub.status.busy": "2025-06-02T23:50:08.877926Z",
     "iopub.status.idle": "2025-06-02T23:50:08.891928Z",
     "shell.execute_reply": "2025-06-02T23:50:08.891500Z",
     "shell.execute_reply.started": "2025-06-02T23:50:08.878055Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:50:08.893244Z",
     "iopub.status.busy": "2025-06-02T23:50:08.893089Z",
     "iopub.status.idle": "2025-06-02T23:50:20.404942Z",
     "shell.execute_reply": "2025-06-02T23:50:20.404275Z",
     "shell.execute_reply.started": "2025-06-02T23:50:08.893230Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 98346/98346 [00:03<00:00, 29925.55it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5,481 hours of 98346 clips, 6.146625 nodes, 1536.65625 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": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:50:20.405763Z",
     "iopub.status.busy": "2025-06-02T23:50:20.405602Z",
     "iopub.status.idle": "2025-06-02T23:51:19.947642Z",
     "shell.execute_reply": "2025-06-02T23:51:19.947067Z",
     "shell.execute_reply.started": "2025-06-02T23:50:20.405747Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (994, 120)\n",
      "total chunks: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:18<00:00,  3.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done! val: wrote 3790500 semantic tokens and 485184000 vae latents. \n",
      "Total slices of data: 5054. Per node: 157.9. \n",
      "Passed quality check: 5054, Failed quality check: 1104. \n",
      "Total chunks with prev chunk as vae ctx: 3830. \n",
      "Total seeds: 990.\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": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-02T23:51:19.948385Z",
     "iopub.status.busy": "2025-06-02T23:51:19.948228Z",
     "iopub.status.idle": "2025-06-02T23:51:20.849809Z",
     "shell.execute_reply": "2025-06-02T23:51:20.849273Z",
     "shell.execute_reply.started": "2025-06-02T23:51:19.948370Z"
    }
   },
   "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": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-02T23:51:20.850547Z",
     "iopub.status.busy": "2025-06-02T23:51:20.850385Z",
     "iopub.status.idle": "2025-06-03T01:23:49.487506Z",
     "shell.execute_reply": "2025-06-03T01:23:49.486931Z",
     "shell.execute_reply.started": "2025-06-02T23:51:20.850531Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (98346, 120)\n",
      "total chunks: 492\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 492/492 [30:22<00:00,  3.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done! tr: wrote 369561000 semantic tokens and 47303808000 vae latents. \n",
      "Total slices of data: 492748. Per node: 15398.4. \n",
      "Passed quality check: 492748, Failed quality check: 115984. \n",
      "Total chunks with prev chunk as vae ctx: 370424. \n",
      "Total seeds: 98076.\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": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:49.488248Z",
     "iopub.status.busy": "2025-06-03T01:23:49.488086Z",
     "iopub.status.idle": "2025-06-03T01:23:50.363131Z",
     "shell.execute_reply": "2025-06-03T01:23:50.362564Z",
     "shell.execute_reply.started": "2025-06-03T01:23:49.488232Z"
    }
   },
   "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": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:50.364050Z",
     "iopub.status.busy": "2025-06-03T01:23:50.363885Z",
     "iopub.status.idle": "2025-06-03T01:23:50.716184Z",
     "shell.execute_reply": "2025-06-03T01:23:50.715693Z",
     "shell.execute_reply.started": "2025-06-03T01:23:50.364034Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2527.0\n",
      "(5054, 750, 128)\n",
      "(5054, 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": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:50.716856Z",
     "iopub.status.busy": "2025-06-03T01:23:50.716707Z",
     "iopub.status.idle": "2025-06-03T01:23:50.730455Z",
     "shell.execute_reply": "2025-06-03T01:23:50.730015Z",
     "shell.execute_reply.started": "2025-06-03T01:23:50.716841Z"
    }
   },
   "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": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:50.731053Z",
     "iopub.status.busy": "2025-06-03T01:23:50.730911Z",
     "iopub.status.idle": "2025-06-03T01:23:50.744214Z",
     "shell.execute_reply": "2025-06-03T01:23:50.743778Z",
     "shell.execute_reply.started": "2025-06-03T01:23:50.731038Z"
    }
   },
   "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": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:50.744846Z",
     "iopub.status.busy": "2025-06-03T01:23:50.744707Z",
     "iopub.status.idle": "2025-06-03T01:23:52.597498Z",
     "shell.execute_reply": "2025-06-03T01:23:52.596899Z",
     "shell.execute_reply.started": "2025-06-03T01:23:50.744833Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:52.600086Z",
     "iopub.status.busy": "2025-06-03T01:23:52.599666Z",
     "iopub.status.idle": "2025-06-03T01:23:52.644478Z",
     "shell.execute_reply": "2025-06-03T01:23:52.644050Z",
     "shell.execute_reply.started": "2025-06-03T01:23:52.600069Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "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": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:52.645077Z",
     "iopub.status.busy": "2025-06-03T01:23:52.644930Z",
     "iopub.status.idle": "2025-06-03T01:23:52.661003Z",
     "shell.execute_reply": "2025-06-03T01:23:52.660563Z",
     "shell.execute_reply.started": "2025-06-03T01:23:52.645063Z"
    }
   },
   "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": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:52.661725Z",
     "iopub.status.busy": "2025-06-03T01:23:52.661583Z",
     "iopub.status.idle": "2025-06-03T01:23:52.676795Z",
     "shell.execute_reply": "2025-06-03T01:23:52.676356Z",
     "shell.execute_reply.started": "2025-06-03T01:23:52.661711Z"
    }
   },
   "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": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:23:52.677423Z",
     "iopub.status.busy": "2025-06-03T01:23:52.677282Z",
     "iopub.status.idle": "2025-06-03T01:24:36.047589Z",
     "shell.execute_reply": "2025-06-03T01:24:36.046817Z",
     "shell.execute_reply.started": "2025-06-03T01:23:52.677410Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "246374 0\n",
      "492748\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": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.048558Z",
     "iopub.status.busy": "2025-06-03T01:24:36.048372Z",
     "iopub.status.idle": "2025-06-03T01:24:36.624903Z",
     "shell.execute_reply": "2025-06-03T01:24:36.624315Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.048540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16104"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.625707Z",
     "iopub.status.busy": "2025-06-03T01:24:36.625542Z",
     "iopub.status.idle": "2025-06-03T01:24:36.645707Z",
     "shell.execute_reply": "2025-06-03T01:24:36.645145Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.625691Z"
    }
   },
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    },
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.646456Z",
     "iopub.status.busy": "2025-06-03T01:24:36.646305Z",
     "iopub.status.idle": "2025-06-03T01:24:36.664978Z",
     "shell.execute_reply": "2025-06-03T01:24:36.664461Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.646441Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 10813\n"
     ]
    }
   ],
   "source": [
    "!cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion_infill.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.665701Z",
     "iopub.status.busy": "2025-06-03T01:24:36.665556Z",
     "iopub.status.idle": "2025-06-03T01:24:36.703056Z",
     "shell.execute_reply": "2025-06-03T01:24:36.702498Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.665687Z"
    }
   },
   "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_r3.ipynb\",\n",
    "    os.path.join(OUT_DATA_DIR, \"make_dataset.ipynb\"),\n",
    ")\n",
    "print(\"Cache kept!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.703810Z",
     "iopub.status.busy": "2025-06-03T01:24:36.703658Z",
     "iopub.status.idle": "2025-06-03T01:24:36.718731Z",
     "shell.execute_reply": "2025-06-03T01:24:36.718217Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.703796Z"
    }
   },
   "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": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.719436Z",
     "iopub.status.busy": "2025-06-03T01:24:36.719283Z",
     "iopub.status.idle": "2025-06-03T01:24:36.734160Z",
     "shell.execute_reply": "2025-06-03T01:24:36.733651Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.719422Z"
    }
   },
   "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": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.734873Z",
     "iopub.status.busy": "2025-06-03T01:24:36.734726Z",
     "iopub.status.idle": "2025-06-03T01:24:36.749206Z",
     "shell.execute_reply": "2025-06-03T01:24:36.748696Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.734860Z"
    }
   },
   "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": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.749913Z",
     "iopub.status.busy": "2025-06-03T01:24:36.749767Z",
     "iopub.status.idle": "2025-06-03T01:24:36.764273Z",
     "shell.execute_reply": "2025-06-03T01:24:36.763753Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.749899Z"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.764997Z",
     "iopub.status.busy": "2025-06-03T01:24:36.764850Z",
     "iopub.status.idle": "2025-06-03T01:24:36.779230Z",
     "shell.execute_reply": "2025-06-03T01:24:36.778716Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.764984Z"
    }
   },
   "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": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.779960Z",
     "iopub.status.busy": "2025-06-03T01:24:36.779810Z",
     "iopub.status.idle": "2025-06-03T01:24:36.794360Z",
     "shell.execute_reply": "2025-06-03T01:24:36.793851Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.779946Z"
    }
   },
   "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": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.795038Z",
     "iopub.status.busy": "2025-06-03T01:24:36.794894Z",
     "iopub.status.idle": "2025-06-03T01:24:36.809475Z",
     "shell.execute_reply": "2025-06-03T01:24:36.808950Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.795025Z"
    }
   },
   "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": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.810160Z",
     "iopub.status.busy": "2025-06-03T01:24:36.810018Z",
     "iopub.status.idle": "2025-06-03T01:24:36.824401Z",
     "shell.execute_reply": "2025-06-03T01:24:36.823896Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.810146Z"
    }
   },
   "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": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.825412Z",
     "iopub.status.busy": "2025-06-03T01:24:36.824956Z",
     "iopub.status.idle": "2025-06-03T01:24:36.845037Z",
     "shell.execute_reply": "2025-06-03T01:24:36.844631Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.825396Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.845904Z",
     "iopub.status.busy": "2025-06-03T01:24:36.845632Z",
     "iopub.status.idle": "2025-06-03T01:24:36.864553Z",
     "shell.execute_reply": "2025-06-03T01:24:36.863997Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.845889Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.4746, 0.5586, 0.9883, 0.0396], dtype=torch.bfloat16)\n",
      "tensor([0.4746, 1.0000, 0.9883, 0.0396], dtype=torch.bfloat16)\n",
      "tensor([0.4746, 0.4746, 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": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.865208Z",
     "iopub.status.busy": "2025-06-03T01:24:36.865063Z",
     "iopub.status.idle": "2025-06-03T01:24:36.881217Z",
     "shell.execute_reply": "2025-06-03T01:24:36.880777Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.865194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.4688, 0.4688, 1.0000, 1.0000, 0.9688, 0.9688, 0.0312, 0.0312])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.881842Z",
     "iopub.status.busy": "2025-06-03T01:24:36.881703Z",
     "iopub.status.idle": "2025-06-03T01:24:36.896287Z",
     "shell.execute_reply": "2025-06-03T01:24:36.895836Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.881829Z"
    }
   },
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.896856Z",
     "iopub.status.busy": "2025-06-03T01:24:36.896718Z",
     "iopub.status.idle": "2025-06-03T01:24:36.912670Z",
     "shell.execute_reply": "2025-06-03T01:24:36.912232Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.896842Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0000, 0.4688, 1.0000, 1.0000, 1.0000, 1.0000, 0.0312, 0.0312],\n",
       "       dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge jsons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.913302Z",
     "iopub.status.busy": "2025-06-03T01:24:36.913162Z",
     "iopub.status.idle": "2025-06-03T01:24:36.927553Z",
     "shell.execute_reply": "2025-06-03T01:24:36.927115Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.913288Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v2_d3/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_d3/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_d3/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.928164Z",
     "iopub.status.busy": "2025-06-03T01:24:36.928027Z",
     "iopub.status.idle": "2025-06-03T01:24:36.942649Z",
     "shell.execute_reply": "2025-06-03T01:24:36.942201Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.928151Z"
    }
   },
   "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": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.943258Z",
     "iopub.status.busy": "2025-06-03T01:24:36.943121Z",
     "iopub.status.idle": "2025-06-03T01:24:36.957513Z",
     "shell.execute_reply": "2025-06-03T01:24:36.957076Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.943244Z"
    }
   },
   "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": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.958110Z",
     "iopub.status.busy": "2025-06-03T01:24:36.957974Z",
     "iopub.status.idle": "2025-06-03T01:24:36.972201Z",
     "shell.execute_reply": "2025-06-03T01:24:36.971764Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.958097Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.972923Z",
     "iopub.status.busy": "2025-06-03T01:24:36.972674Z",
     "iopub.status.idle": "2025-06-03T01:24:36.986943Z",
     "shell.execute_reply": "2025-06-03T01:24:36.986498Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.972909Z"
    }
   },
   "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": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-06-03T01:24:36.987530Z",
     "iopub.status.busy": "2025-06-03T01:24:36.987392Z",
     "iopub.status.idle": "2025-06-03T01:24:37.001691Z",
     "shell.execute_reply": "2025-06-03T01:24:37.001254Z",
     "shell.execute_reply.started": "2025-06-03T01:24:36.987516Z"
    }
   },
   "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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