{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:48.800522Z",
     "iopub.status.busy": "2025-04-15T03:12:48.800258Z",
     "iopub.status.idle": "2025-04-15T03:12:49.098600Z",
     "shell.execute_reply": "2025-04-15T03:12:49.098152Z",
     "shell.execute_reply.started": "2025-04-15T03:12:48.800502Z"
    }
   },
   "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": "2025-04-15T03:12:49.100286Z",
     "iopub.status.busy": "2025-04-15T03:12:49.100169Z",
     "iopub.status.idle": "2025-04-15T03:12:51.251035Z",
     "shell.execute_reply": "2025-04-15T03:12:51.250365Z",
     "shell.execute_reply.started": "2025-04-15T03:12:49.100273Z"
    }
   },
   "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": "2025-04-15T03:12:51.253538Z",
     "iopub.status.busy": "2025-04-15T03:12:51.253398Z",
     "iopub.status.idle": "2025-04-15T03:12:51.268553Z",
     "shell.execute_reply": "2025-04-15T03:12:51.268024Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.253523Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pair quality scores: 10019\n"
     ]
    }
   ],
   "source": [
    "OUT_DATA_DIR = \"/app/suno/data/dpo/diffv2_v2_t3/\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "NPZ_DIR = \"/app/suno/data/dpo/diff2_v2\"\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/up_diff2_v2/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": "2025-04-15T03:12:51.270463Z",
     "iopub.status.busy": "2025-04-15T03:12:51.270326Z",
     "iopub.status.idle": "2025-04-15T03:12:51.744644Z",
     "shell.execute_reply": "2025-04-15T03:12:51.743855Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.270450Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (19306, 89)\n",
      "unique users 7161\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    \"/home/tony/Data/Preference/up_diff2_v2/interesting_clips_upv2_u2_20250427_full.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": "2025-04-15T03:12:51.747038Z",
     "iopub.status.busy": "2025-04-15T03:12:51.746891Z",
     "iopub.status.idle": "2025-04-15T03:12:51.766197Z",
     "shell.execute_reply": "2025-04-15T03:12:51.765556Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.747021Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    17858\n",
      "True      1448\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": "2025-04-15T03:12:51.767024Z",
     "iopub.status.busy": "2025-04-15T03:12:51.766856Z",
     "iopub.status.idle": "2025-04-15T03:12:51.796856Z",
     "shell.execute_reply": "2025-04-15T03:12:51.796294Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.767007Z"
    }
   },
   "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": "2025-04-15T03:12:51.797639Z",
     "iopub.status.busy": "2025-04-15T03:12:51.797488Z",
     "iopub.status.idle": "2025-04-15T03:12:51.928980Z",
     "shell.execute_reply": "2025-04-15T03:12:51.928306Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.797624Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31392\n",
      "10376\n",
      "21016\n",
      "pre-downloaded df (19306, 90)\n",
      "downloaded df (19306, 90)\n",
      "vae downloaded df (19306, 90)\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": "2025-04-15T03:12:51.929789Z",
     "iopub.status.busy": "2025-04-15T03:12:51.929625Z",
     "iopub.status.idle": "2025-04-15T03:12:51.954220Z",
     "shell.execute_reply": "2025-04-15T03:12:51.953690Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.929773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    19306\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": "2025-04-15T03:12:51.954979Z",
     "iopub.status.busy": "2025-04-15T03:12:51.954831Z",
     "iopub.status.idle": "2025-04-15T03:12:51.981015Z",
     "shell.execute_reply": "2025-04-15T03:12:51.980445Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.954965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name         \n",
      "False       chirp-v4-up-u-d-2-2    9653\n",
      "True        chirp-v4-up-u-d-2-2    9653\n",
      "Name: count, dtype: int64\n",
      "(19306, 91)\n",
      "(19306, 91)\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": "2025-04-15T03:12:51.981746Z",
     "iopub.status.busy": "2025-04-15T03:12:51.981600Z",
     "iopub.status.idle": "2025-04-15T03:12:52.018043Z",
     "shell.execute_reply": "2025-04-15T03:12:52.017481Z",
     "shell.execute_reply.started": "2025-04-15T03:12:51.981732Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19306, 91)\n",
      "(19306, 91)\n",
      "preference  model_name         \n",
      "False       chirp-v4-up-u-d-2-2    9653\n",
      "True        chirp-v4-up-u-d-2-2    9653\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": "2025-04-15T03:12:52.018792Z",
     "iopub.status.busy": "2025-04-15T03:12:52.018645Z",
     "iopub.status.idle": "2025-04-15T03:12:53.329974Z",
     "shell.execute_reply": "2025-04-15T03:12:53.329205Z",
     "shell.execute_reply.started": "2025-04-15T03:12:52.018778Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unpacked pair quality scores: 115026\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": "2025-04-15T03:12:53.330917Z",
     "iopub.status.busy": "2025-04-15T03:12:53.330747Z",
     "iopub.status.idle": "2025-04-15T03:12:54.693644Z",
     "shell.execute_reply": "2025-04-15T03:12:54.693005Z",
     "shell.execute_reply.started": "2025-04-15T03:12:53.330900Z"
    }
   },
   "outputs": [],
   "source": [
    "clip_diffs = []\n",
    "clip_ratios = []\n",
    "loudness_diff = []\n",
    "spec_decay_diff = []\n",
    "last_spec_decay_diff = []\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    mean_neg_scores = []\n",
    "    mean_pos_scores = []\n",
    "    neg_loudness = []\n",
    "    pos_loudness = []\n",
    "    neg_spec_decay = []\n",
    "    pos_spec_decay = []\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_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 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_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",
    "    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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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": "2025-04-15T03:12:54.694561Z",
     "iopub.status.busy": "2025-04-15T03:12:54.694395Z",
     "iopub.status.idle": "2025-04-15T03:12:55.331193Z",
     "shell.execute_reply": "2025-04-15T03:12:55.330538Z",
     "shell.execute_reply.started": "2025-04-15T03:12:54.694544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = clip_ratios\n",
    "y = clip_diffs\n",
    "# Create the 2D histogram (heatmap)\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Create a 2D histogram\n",
    "bin_edges = np.linspace(-0.5, 0.5, 101)  # 30 bins from -1 to 1\n",
    "hist, x_edges, y_edges = np.histogram2d(\n",
    "    x,\n",
    "    y,\n",
    "    bins=[bin_edges, bin_edges],  # Same bins for both x and y\n",
    "    range=[[-0.5, 0.5], [-0.5, 0.5]],  # Ensure range is from -1 to 1 for both axes\n",
    ")\n",
    "\n",
    "# Create a heatmap using pcolormesh for better control\n",
    "X, Y = np.meshgrid(x_edges[:-1], y_edges[:-1])\n",
    "plt.pcolormesh(X, Y, hist.T, cmap=\"viridis\", shading=\"auto\")\n",
    "\n",
    "# Add a color bar\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(\"Counts\", rotation=270, labelpad=20, fontsize=12)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Clip quality diff ratios\", fontsize=12)\n",
    "plt.ylabel(\"Clip quality diff ratio difference with prev\", fontsize=12)\n",
    "plt.title(\"2D Histogram (Heatmap) of Correlated Data\", fontsize=14)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"2d_histogram.png\", dpi=300)  # Save to file (optional)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:55.332105Z",
     "iopub.status.busy": "2025-04-15T03:12:55.331934Z",
     "iopub.status.idle": "2025-04-15T03:12:56.243729Z",
     "shell.execute_reply": "2025-04-15T03:12:56.243100Z",
     "shell.execute_reply.started": "2025-04-15T03:12:55.332089Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Differences in quality between positive and negative vs previous chunk\n",
      "0.05 ---> -0.15037139567039562\n",
      "0.1 ---> -0.11168777459538343\n",
      "0.2 ---> -0.07008723953582391\n",
      "0.5 ---> 0.0002422866856955086\n",
      "0.8 ---> 0.07110026634429183\n",
      "0.9 ---> 0.11269540584247982\n",
      "Differences in quality between positive and negative for the same chunk\n",
      "0.05 ---> -0.19485272635169665\n",
      "0.1 ---> -0.14009521014043613\n",
      "0.2 ---> -0.08457164642140753\n",
      "0.5 ---> 0.0010031879394531418\n",
      "0.8 ---> 0.08150214722684171\n",
      "0.9 ---> 0.1292981064465564\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "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(-1, 1, 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]:\n",
    "    print(percentage, \"--->\", np.quantile(sorted(clip_diffs), percentage))\n",
    "\n",
    "# Second subplot for clip_ratios\n",
    "ax2.hist(clip_ratios, bins=np.linspace(-1, 1, 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]:\n",
    "    print(percentage, \"--->\", np.quantile(sorted(clip_ratios), percentage))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:56.244623Z",
     "iopub.status.busy": "2025-04-15T03:12:56.244451Z",
     "iopub.status.idle": "2025-04-15T03:12:56.721454Z",
     "shell.execute_reply": "2025-04-15T03:12:56.720784Z",
     "shell.execute_reply.started": "2025-04-15T03:12:56.244607Z"
    }
   },
   "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": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:56.722291Z",
     "iopub.status.busy": "2025-04-15T03:12:56.722129Z",
     "iopub.status.idle": "2025-04-15T03:12:56.795578Z",
     "shell.execute_reply": "2025-04-15T03:12:56.794857Z",
     "shell.execute_reply.started": "2025-04-15T03:12:56.722275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19306, 104)\n",
      "(19178, 104)\n",
      "(19178, 104)\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": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:36.043975Z",
     "start_time": "2024-05-16T13:58:56.910958Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:56.796433Z",
     "iopub.status.busy": "2025-04-15T03:12:56.796268Z",
     "iopub.status.idle": "2025-04-15T03:12:56.816727Z",
     "shell.execute_reply": "2025-04-15T03:12:56.816136Z",
     "shell.execute_reply.started": "2025-04-15T03:12:56.796417Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 9589\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": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:40.799375Z",
     "start_time": "2024-05-16T13:59:36.394236Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:56.817489Z",
     "iopub.status.busy": "2025-04-15T03:12:56.817334Z",
     "iopub.status.idle": "2025-04-15T03:12:56.898570Z",
     "shell.execute_reply": "2025-04-15T03:12:56.897922Z",
     "shell.execute_reply.started": "2025-04-15T03:12:56.817474Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    19178\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    9589\n",
      "True     9589\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-v4-up-u-d-2-2    19178\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-v4-up-u-d-2-2    9589\n",
      "True        chirp-v4-up-u-d-2-2    9589\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    19178\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": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:56.899391Z",
     "iopub.status.busy": "2025-04-15T03:12:56.899230Z",
     "iopub.status.idle": "2025-04-15T03:12:56.940644Z",
     "shell.execute_reply": "2025-04-15T03:12:56.940070Z",
     "shell.execute_reply.started": "2025-04-15T03:12:56.899374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    7101\n",
       "2.0    2488\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:56.945288Z",
     "iopub.status.busy": "2025-04-15T03:12:56.944916Z",
     "iopub.status.idle": "2025-04-15T03:12:57.217719Z",
     "shell.execute_reply": "2025-04-15T03:12:57.217114Z",
     "shell.execute_reply.started": "2025-04-15T03:12:56.945270Z"
    }
   },
   "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": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:57.218535Z",
     "iopub.status.busy": "2025-04-15T03:12:57.218372Z",
     "iopub.status.idle": "2025-04-15T03:12:57.502772Z",
     "shell.execute_reply": "2025-04-15T03:12:57.502202Z",
     "shell.execute_reply.started": "2025-04-15T03:12:57.218519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-12.667264050483139\n",
      "-12.314121071270474\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": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:57.503795Z",
     "iopub.status.busy": "2025-04-15T03:12:57.503638Z",
     "iopub.status.idle": "2025-04-15T03:12:57.742663Z",
     "shell.execute_reply": "2025-04-15T03:12:57.742091Z",
     "shell.execute_reply.started": "2025-04-15T03:12:57.503780Z"
    }
   },
   "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": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:57.743410Z",
     "iopub.status.busy": "2025-04-15T03:12:57.743252Z",
     "iopub.status.idle": "2025-04-15T03:12:57.985174Z",
     "shell.execute_reply": "2025-04-15T03:12:57.984624Z",
     "shell.execute_reply.started": "2025-04-15T03:12:57.743394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j4/zxxx8AhX7xdu7cGZPJxLffflvk6zIyMsjLyyvyudDQULKysti8ebO5vm+99ZbFPunp6bz11ls0a9aMpk2bMmbMGLKzs+/4cxQlKCiI7Oxs9Hr9bffbtm0bOp2OZ555xrzN1dWVnj17cvDgQa5evVrs+wAlWjhXlA45AyNuy9PTk2vXrpGYmFhotuGixMbGcvbsWcLCwnB2dub06dPs27eP1q1bm0c/WXPx4kWGDx/Os88+yxNPPMEnn3zCgAEDmDhxInPmzOG5554DYNmyZbz++uts27YNrfbfHtxgMPDqq6/SrFkzRowYwdatW5k0aRLu7u7MmTOHxx9/nPbt27N27VpGjx5N48aNzbMcZ2dn88ILLxAXF8ezzz5LlSpVOHjwILNnzyY+Pp63337bItZNmzaRk5PD008/jYuLC97e3kV+ptOnT9O/f39CQ0MZOnQoLi4uXLx4kT///NMi7v79+7N79266dOlC7969yczM5LfffuPUqVNUr14dk8nEa6+9xt69e+nZsyf169dn165dzJgxg7i4uEKXYPbs2cO3335Lr1698PHxISgoiISEBJ5++mk0Gg29evXC19eXX375hbfffpuMjAzz+lnWvP3222zZsoXHHnuMJk2asGfPHvr163fb10D+2mDr16/nyJEjTJkyBYD77ruPGTNmsGTJErKyssyn94ODgwEYP348mzdvpnv37rz44ovExMSwatUq/v77b9asWWOxVtj58+cZPnw4zzzzDE8//TS1atWyuZ5fffUVmZmZPPPMM2g0Gj766COGDBnC9u3bze914sQJevXqhZOTE8888wxBQUFcunSJHTt28MYbbwDcdY5tNXnyZDZu3MiECROYPn06HTt2pGfPnhaXO0oiNzcXoNAfKu7u7kD+YIZbjRkzhqysLHQ6HU2bNmXUqFEWl5RmzJjBuHHjCA8PN88sfuslptdff52qVavy5ptv8vfff/PFF1/g6+vLyJEjbYr/Zjdu3CArK4usrCz279/Ppk2baNy4cbFn5Y4fP07NmjXx8PCw2B4eHm5+vkqVKubtJpOJ5ORkDAYDFy9eZObMmeh0OrmH614yCcW88MILpilTpigdxm1dvHjRtGDBAtOCBQtMX3zxhenXX381Xbx40ZSXl1do33nz5pkWLlxoSklJMW9LSEgwbdy40bRhwwbzto0bN5pCQkJMly9fNm9r27atKSQkxPTnn3+at+3atcsUEhJiCg8PN125csW8fe3ataaQkBDTnj17zNtGjx5tCgkJMS1ZssS8LTU11RQeHm4KDQ01ff311+btZ8+eNYWEhJjmzZtn3rZw4UJT48aNTefPn7f4TDNnzjTVr1/fFBsbazKZTKbLly+bQkJCTE2aNDElJiYWm7/o6GhTSEjIbffdsGGDKSQkxBQdHV3oOaPRaDKZTKYffvjBFBISYlq0aJHF80OGDDGFhoaaLl68aN4WEhJiqlevnun06dMW+44dO9bUsmVLU1JSksX2N954w9S0aVNTdna21RiPHz9uCgkJMb377rsW2998881CuSyqvqNHjzY1bty40HFfeOEFU5cuXSy27d+/3xQSEmLasmWLxfZffvml0PaC75tffvnFYl9b6/nAAw9YfN9u377dFBISYtqxY4d5W69evUwREREW34sm0781MpnuLsd348yZM6bp06ebIiMjTSEhIab27dubli5darp+/XqJXv/dd9+ZQkJCTF9++aXF9jVr1phCQkJMjz32mHnbH3/8YRoyZIjpiy++MG3fvt20dOlS0wMPPGAKCwszHTt2zOL1jRs3No0ePbrQ+82bN88UEhJiGjNmjMX2QYMGmR544IGSfuwiLV261BQSEmL+6tOnj7net9OlSxdT7969C20/ffq0KSQkxLRmzRqL7devX7d4n9atW1v8nBH2J5eQirF06VJ69OhBREQELVq0YODAgZw7d85in5ycHCZOnEjz5s2JiIhgyJAh5nWQIP+u/dDQ0Ht6ajElJYXhw4fTpEkTmjVrxtixY8nMzLzta1588UXzqd6Cr48++oiePXtSq1YtLl++zLvvvsuTTz5JWFgYLVu2ZNKkSRanrqtVq2ZxNsLPzw9nZ2euXLlSbMx16tQxT/4HmG8QfvDBBy2WRyjYXtTw8puXPPDy8qJWrVq4u7tbzFZcu3ZtvLy8LF6/bds2mjZtipeXF0lJSeavyMhIDAYD+/fvt3if9u3bl2i0QcH9HT/++KPFpYubff/99/j4+BS5ZETByIlffvkFnU7Hiy++aPH8//3f/2Eymfjll18stt9///3UqVPH/NhkMvH999/Trl07TCaTxWeMiooiPT2dY8eOWf0cBcPjb33/Pn36WH3Nndq2bRuenp60bNnSIs6Cm8hvXWG9atWqtGrVqtAxbKln586dLb5vC85gFHyPJCUlsX//fnr06FFoqY6CGt1tju9GcHAwo0ePZufOnSxatIjg4GDmzZvHQw89xMCBAzlx4sRtX9+mTRuCgoKYMWMG33//PVeuXOGbb75hzpw5ODk5cePGDfO+TZo0Yd68efTs2ZOHH36Yfv36sX79ejQaDbNmzbIp7meffdbicbNmzUhJSbmry2FdunQhOjqaWbNmmeewujl+a27cuFHkfWwFZ6VuPYa3tzfR0dEsWbKEoUOH4uPjc8f3BYo7I5eQirFv3z569epFWFgYBoOB2bNn07dvX77++mvzPSBTp05l586dfPjhh3h6ejJ58mQGDx5scdPXvTZixAji4+OJjo5Gr9czduxYxo8fX+wPmKeffpqhQ4eaH7u7u+Ph4UHnzp158MEHKVeuHJ6enuY5ZHbt2kVqaqr5uLeefoX8mYhL8gPp5tOzgHmyv8qVK1tsL3iPWxtCV1fXQk2Fp6cnlStXLjSE0tPT0+L1Fy9e5OTJk7Ro0aLI2G6dr6Kkw787d+7MF198wbhx45g1axYtWrTg0UcfpWPHjubLX5cuXaJWrVq3nUX5ypUrVKxYsVB+Cy653Nog3hpfUlISaWlprFu3jnXr1pXoM976/lqtttAlgNq1a1t9zZ26ePEi6enpVmuRmJho8bioWthaz1u/9wqamYLvkYJGJiQkxGrcd5vjlJQUi/s03Nzc8PT0JCkpyWIW73LlylG+fPkij+Hk5MTDDz9Mu3bt+Pbbb3nnnXf48ccfeeCBB257s6+rqytLly7l9ddfNw+Hd3FxYeTIkSxZsqTYUTU1atTg4Ycf5vvvv8dgMJT4Jv1bm8GChj81NbXInyUlERQUZL4f5bHHHuOdd97h5ZdfZtu2bbe9jOTm5ma+lHaznJwc8/M3c3FxITIyEoC2bdvSokULnnvuOfz8/Gjbtu0dxS5sIw1MMT7++GOLx9OnT6dFixYcO3aM+++/n/T0dDZu3MjMmTPNPyynTp1K586dOXToEP7+/uY5E+6//34AunXrxvTp04H8v9pmzJjBhg0bcHZ25tlnnzX/ALlTZ8+eZdeuXWzYsMF8TXrcuHH069ePUaNGUalSJauvdXNzs7quka+vL/379wfyrwdv376d8uXLs337dvM+N9+TcjNTCaYbsvZDz9r2W495N683Go20bNmSV155pch9a9asafG4pKNc3NzcWLVqFXv37uXnn39m165dfPPNN6xbt45PPvnEbqOxbo2v4OzPE088Qbdu3Yp8TWhoqF1isZXRaMTPz4+ZM2cW+fytTWpRtbC1niX9Hrudu83xkCFD2Ldvn/lxwc+Jnj17WjSoN8+5c6srV66wefNmNm/eTExMDEFBQbz88svmMxG3U7duXb766ivOnDlDamoqderUwc3NjWnTppl/dt1O5cqV0ev1ZGdnl7j5uJufFyXVoUMH1q9fz/79+wudqbtZQEAAcXFxhbbHx8cDxa8z16RJEwICAti6das0MPeINDA2Sk9PB/79C+3o0aPo9XpzJw75fxUHBgZy6NAhXnzxRebPn8+QIUPYtm0bHh4eFj9wN2/ezMsvv8z69es5dOgQb731Fk2aNDFPnPTWW29x5cqVQsMDb+fgwYN4eXlZ3FAXGRmJVqvlyJEjPProo1Zfu3XrVrZs2UJAQABt27Zl4MCB5hv5blaxYkUyMjLYu3dviX64qV316tXJysqyqGNp0Wq1tGjRghYtWjBmzBiWLFnCnDlz2Lt3L5GRkVSvXp3Dhw+j1+stbk69WVBQELt37yYjI8Pil0PB5cyCvzit8fX1pXz58hiNxjv6jEFBQRiNRi5dumRx1uXWy6mloXr16uzevZsmTZrc8XDo0q5nwc3eN68Uf6u7zfHo0aMtzgoW/ML84IMPzGcBbo6lwI0bN/jhhx/YuHEje/bswdnZmUceeYRJkyYRGRlZogncCmg0GurWrWt+vHPnzhJ/npiYGFxdXVU3B0rBpZ+Cn93W1KtXj7179xb6N3b48GEA6tevX+x75ebmFvs+ovTIPTA2MBqNTJ06lSZNmphPJSckJODs7FxoLgs/Pz/i4+PR6XTmZsfPz4+AgACLdZAKZjGtWbMmXbt2pWHDhhaLJQYEBBQ6vV2chISEQn+lOjk54e3tbf5roiiPPfYYH3zwAStWrKBfv37873//Y/DgwYX+GnrzzTd56KGH+PTTT/Hy8uK9996zKT416tSpEwcPHjQPN75ZWlqa1aGixUlJSSm0reAHYcHp6vbt25OcnGwe8n2zgty3bt0ag8FQaJ9PP/0UjUZD69atbxuHTqejQ4cOfPfdd0X+Ei5uSveC49/aSNu6RldJdOrUCYPBwKJFiwo9l5eXV6J7yUq7nr6+vtx///1s3LiR2NhYi+cKanS3OW7YsCGRkZHmr4J7mJo2bWqx/eYGZvz48URFRTFixAgSExN56623+OWXX5gzZw4tW7a0qXm51Y0bN5g7dy4BAQEWw6uL+hwnTpxgx44dtGzZ0uKsSrly5e7ZvX/W8rthwwY0Gg0NGjSw2Pfs2bMWQ7Y7duyIwWCwuPyXm5vLpk2baNSokfnncFZWVpFDvb/77jtSU1Np2LBhaX0kUQw5A2ODiRMncvr0aVavXl1qx7z1lHJAQIDFNf7hw4ff9vXjx4+3mNPk4MGDdxzLzfMfhIaGEhAQwEsvvcTcuXNp1qwZPj4+GAwGWrRogY+PDzdu3ODgwYNMmzaNd999947fVw369u3Ljh07GDBgAN26daNBgwZkZ2dz6tQpvvvuO3788cc7miJ84cKFHDhwwHyTZGJiIqtXr6Zy5co0bdoUyJ8s68svv2TatGkcOXKEpk2bkp2dze7du3nuued45JFHaNeuHc2bN2fOnDlcuXKF0NBQfvvtN3788Uf69OlT6N6UogwfPpy9e/fy9NNP89RTT1GnTh1SU1M5duwYu3fvtrh8cav69evz2GOPsXr1atLT04mIiGDPnj1cvHjR5pwU54EHHuCZZ55h6dKlHD9+nJYtW+Ls7MyFCxfYtm0bb7/9drETqNmjnuPGjeO5556jW7duPPPMM1StWpUrV67w888/87///Q+4uxzfia+++orOnTvz1FNP3dGs2DcbNmwYFStWpE6dOmRkZLBx40YuX77MsmXLLM5IvP7667i5uREREYGfnx9nzpxh/fr1uLm5MWLECItjNmjQgN27dxMdHU3FihWpWrWqzXEWLDuwYsUKmjdvbnW/xYsX8+eff9KqVSsCAwNJSUnh+++/56+//uLFF1+kRo0a5n1XrVpV6JiNGjWiY8eOzJ49m8TERGrUqMHmzZu5cuWKxR9pFy9e5KWXXqJz587Url0brVbL0aNH2bJlC0FBQVaXWRClTxqYEpo0aRI///wzn3/+ucVNpf7+/uj1etLS0izOwiQmJlq9l+Rmt964qdFobLr+O2zYMPOskTfHdOtfI3l5eaSmppYopgIFP2h0Oh0XLlzg6NGjGI1GPD09adOmDc2aNePvv/+mV69eDr8In7u7OytXrmTp0qVs27aNL7/8Eg8PD2rWrMmQIUNKvHr4rdq1a8eVK1fYuHEjycnJ+Pj48MADD1gcU6fTsXz5chYvXsxXX33F999/T4UKFWjSpIm5wdVqtSxevJh58+bxzTffsGnTJoKCghg1ahT/93//V6JY/P39+eKLL1i4cCE//PADa9asoUKFCtSpU6fQL56iTJ06FR8fH7Zu3cqPP/5I8+bNWbZsWbFrX92JSZMm0bBhQ9auXcucOXPQ6XQEBQXxxBNP0KRJk2Jfb4961qtXj/Xr1zN37lzWrFlDTk4OgYGBFiPc7jbHtvr1119L7ZJNw4YN2bRpE+vWrcPNzY2mTZsya9asQpdOHnnkEbZu3cqnn35KRkYGPj4+PProowwePNiiSYD8S+Djx4/nww8/5MaNG3Tr1s3mBiYrKwuNRoO/v/9t93vooYe4fPmy+d+ai4sLoaGhTJs2zeo9SbeaMWMGH374IVu2bCE1NZXQ0FCWLFlicZm8UqVKdOjQgT179vDll1+i1+sJCgqiV69eDBgwAB8fH5s+n7gLCgzddihGo9E0ceJEU1RUVKE5JUwmkyktLc3UoEED07Zt28zbCuYZOXjwoMlkyp83ISQkpNDcEEXNA/Paa68VOW+CLc6cOWMKCQkx/fXXX+Ztu3btMoWGhpquXbtW4uMcOHDAFBISYjp+/LjVffbt21dozg8hhCgtPXr0MA0ZMkTpMIQKyT0wxZg4cSJbtmxh1qxZlC9fnvj4eOLj4803hnl6etKjRw+mT5/Onj17OHr0KGPHjiUiIsK8KmtQUBAajYaff/6ZpKSkYudjudmsWbMYNWqUTTEHBwfTqlUr3nnnHY4cOcIff/zB5MmT6dKli3kEUsGiZwXTpV+6dImFCxdy9OhRYmJi+PHHHxk9ejT333+/efjlzp072bhxI6dOnSImJoaff/6ZCRMm0KRJE1lVWghR6jIyMjhx4oR5fSYhbiaXkIqxZs0aoPAEXtOmTaN79+4AjB07Fq1Wy9ChQ8nNzSUqKooJEyaY961UqRJDhgxh1qxZjBkzhq5du5qHURcnPj6+2DU4ijJz5kwmT55Mnz590Gq1tG/fnnHjxpmf1+v1nD9/3nwzmrOzM7t372bFihVkZWVRpUoV2rdvb3FpyNXVlS+++IJp06aRm5tLlSpVePTRR0s0nbwQQtjKw8OjyGUMhADQmEylOOBeCCGEEOIekEtIQgghhHA40sAIIYQQwuFIAyOEEEIIhyMNjBBCCCEcjjQwQgghhHA4ZX4YdWJiOrcbZ6XRaChf3tW8kmyxMrNwOnmcvND6UP7uZsB85ZX/IzQ0lJEjR9/VcdROq9WSmZlTqivM2osmPh7X/20m58lumGyYtfi/QM25kdgcj+TFsdmzfhoN+PkVP1t2mR9GnZBw+wZGq/23gSlqvyVLFrFs2RKLbTVr1mTTpi3mxzk5OcyePZPvv99Gbm4uLVpEMmbMOPz8/AA4cGA//fr1ZefOX/H0/He5gVdf/T9CQu6+gSnu/YtiMplYsmQRmzdvJD09nUaNGjN27DiqV/93KvDU1FRmzJjGL7/sRKPR8vDDjzBy5Gibpi7XaP5tYIzGMv2tJoQQohRoNODvX3wDI5eQSiA4OJjvv9/B99/v4Iet3/DpmHFo/pmJF2DWrBns2rWT99+fyfLl0cTHxzNixBv3LL47ef/PPotmzZrVjB37Dp99tgp3d3cGDRpATk6OeZ+3336Ls2fPsmjRUubOnc+ff/7BlCkT7f1xFKVJScZly2Y0KclKh6I6as6NxOZ4JC+OTQ31kwamBHQ6J/z9/fH39yfAyYkqP/+IJjUFgPT0dL78cjNvvjmCBx5ozn333ce7707m8OFDHDlymNjYK/Trl7/YYps2UTRpEs6ECf/OiGsymfjww9k89FAUjz7aliVLFtkUW3HvXxSTycTq1Z/zyiuv8tBDbQkJCWHSpPeIj4/n5593AHDu3Dl+//03xo9/l7CwcCIimjBq1Ft899024uOv30EWHYPu0kW8X+mD7lLpr7Ls6NScG4nN8UheHJsa6icNTAlcunSR9u0f5vHHOzHm/WnE3rSW0fHjf5OXl0fz5g+at9WqVYvKlatw5MgRKlWqzAcfzAZg8+YtfP/9DkaM+PeS0VdfbcHd3Z0VK1YxbNgbLF++lD17dpufnzBhHK++an214eLevyhXrlwhISHB4jWenp40bBhmbnqOHDmMp6cn993XwLxP8+YPotVq+euvv4rNmRBCCGFPZf4m3rsVFhbGxIlTqFGjJgkJ8SxbOJ9e+/byRZ//wx1ITEzA2dnZ4t4WAD8/PxITE9DpdHh7ewPg6+tbaL86derSv/9rAFSvXoN169ayb99eHnywBQD+/gG3vcG4uPe39pr8eCzvkfHz8yMhIdG8j6+vr8XzTk5OeHl5WT2uEMJxGI1GDIY85d5fq+FGjRrkajUY9LmKxSHuzN3UT6vVotXq0Gg0dxWDNDDFaNmylfn/Q0JCCK9Uic7PP8P3u3byZK3ad338unVDLB77+/uTlJRkfjxkiKzCKoQoXTk52SQnxwMK3ljvVY6ERYsxepWDRNsXrBUKu8v6ubi44eXli5OT8x2HIA2Mjbwq+FDT15fL1+IA8PPzR6/Xk56eZnEWJDExET8//2KP5+RkWQKNRoPJVMIh3Xf4/gXbk5ISCbhp+FtiYiKhoaHmfW5upADy8vJIS0sr0edyVCY3d/RhjTC5uSsdiuqoOTcSW8kZjUaSk+NxcXHDw8P7rv8KvmM5OWhvGDD6VgZXV2ViEHfuDutnMpkwGPLIyEghMfEaFStWvePvQWlgbJTh7s6l7Gw6VasGQP369+Hk5MS+fXt5+OFHAbhw4TzXrl0lPDwcAGfn/A7TYCh5Y1JSJXn/WwUFBeHv78++fXsJDa2X/7kyMjh69C+eeuppAMLDG5Gens7ff//NfffdB8D+/fswGo2EhYWV+udQC0NIKCk/7lI6DFVSc24ktpLLv2xkwsPDGxcXBRsHZxcIqYdOuQjE3bir+rmi0+lISoojL0+Ps7PLHR1FGphizJkzk9atH6JKlSrEx8ezZMkitFodHTt2AvJvfu3atRuzZs3Ey8ub8uU9mDFjGuHhjQgPbwRAlSpV0Gg07Nq1k6ioVri6upV4LpX58+dy/XockydPLfL5krw/QPfuTzB48DDatXsYjUbD88+/wEcfLaN69eoEBgaxePFCAgICeOihdgDUrl2byMiWTJnyLmPHvkNeXh7vvz+NDh06EhBQ8W5SKoRQAcXOvAgBaDR3P4ZIRiEVIy7uOmPGjKZbtycYPXoEFdzcWN+mNX56vXmf4cNH0apVa0aOfJNXXnkJPz9/Zs6cY36+YsVKDBgwkPnz5/LII215//2im5GiJCTEc+3atdvuU9z7A1y4cIGMjAzz4z59XubZZ59nypRJvPji82RlZbFgwWJcbzoV+N5706lZsxYDBrzK0KGDaNw4gnHjJpQ4dkfk9Ndh/Kv64/RX0UPQ/8vUnBuJzQFlZeF0+BBkZSkdibgTKqifzMRbzEy8hfaPu4b7imiye7+MsVLl0gu0jHK0mXidjhzC55HWJG//hbzwxkqHoypqzo3EVnJ6fS6JiVfx86ticepeo7m3Z2VMmVnoTh4nL6Qe2DC7t1CJrCycTp244/pZ+z6Eks/EK5eQhBDiP06jAb1GS2buvRtWXd7FBZ2T3AFzr6WlpTJnzgf89tsutFoNbdq0Y9iwEbe9rSEnJ4cFCz7kxx+/R6/P5YEHHmT4wGHcfDPBtWvXmDVrGn/+eQB393J06vQY/fsPKjRQpTRJAyOEEP9xGo2GzNw8fjpxnawc+zcx5VydaFvHF3etNDD32sSJ75CYmMCcOQvJy8tj2rSJzJjxHu+++57V18yfP5vff/+VyZOnU768B3PmzODtieNYPnAoAAaDgVGjhuHr68eSJZ+QkJDAe+9NwMnJif79B9nts0gDI4QQAoCsnDwy7kEDcycGD+5H7drBAHz33Tc4OTnRtWtPXnllgPnSV1paGnPnzuS333ah1+fSuHFTXn99BNWqVQfg2rWrzJ49gyNHDpGXp6dy5UAGDRpKixZRJYrh44+XsmvXTnr2fIZPPllGenoaHTp04Y03RrJ27eesW7cao9HIU089S58+fc2vS09PZ+HCD/n1153k5uqpV68+Q4a8aZ4H7MqVGObPn82xY0e5cSObGjVq0b//IO6/v7n5GD17Ps4TT3QjJuYyP/30I56envTp05cnn+xe4hxeuHCevXt/56OPVlCvXv7o0tdfH8nIkcMYPPh1/P0LryqdkZHBV1/9jwkTptC06f0AjB07gV69enL0/DnqhdRj3749XLhwng8/XISvrx9164byyisDWLx4Pv/3f/3MI3FLm9zEayOjnz/ZL7+KsQzPhfJfllc3lKRf9pJXN1TpUFRHzbkxhISSvEudsak5b4rS6TAE1wU3txK/5Ntvv0anc2L58s8YNmwE69atYuvWL83PT536LidPHuf992ezZEk0JpOJkSOHkZeX35TNnv0+en0uCxcu57PP1vLaa0Nwd7ft/o0rV2LYs+d3Zs2az4QJ7/H11/9j5MjXiY+/zoIFS3nttSEsX76YY8eOml/zzjujSU5OYubMeXz88UpCQurx+uuvkZaWCkBWVhYPPtiSuXMX8cknq2jevAWjR79ZaADH2rWrqFfvPqKjV9Gt21PMmjWdS5cumJ8fPLgf7733rtXYjx49goeHp7l5AWjW7AG0Wq1FvDc7efI4eXl5NGv2bzNVo0ZNKlWqzOHMDHBz49ixv6hdu47F7O4PPNCCzMxMzp8/W6K83glpYGzl5ITR3x9K4brehAnjePNNmWlXVdzdMdSrD+7qmHRMVVSaG40G9OXKc6N+AzTl1BUboNq8KU6jAXc30Jb811ClSpUYOvRNqlevSfv2nejR4xnWr18NwOXLl/j1118YPXocjRpFULduCBMmTCY+/jq//PIzAHFx1wgLa0RwcB2CgqrSsmUrGjduYlPYJpORsWPHU6tWbaKiWhMR0YzLly8ydOhwqlevSZcuT1C9eg3+/PMAAIcPH+L48WNMnvw+9erdR7Vq1Rk8+HU8PDz56acfgfwZ2bt27UHt2nWoVq06r776GkFBQfz2206L927RIpLu3Z+iatVqvPBCH7y9K5jfJz8/lW870WhSUiI+Pj4W25ycnPD09CIpKbHI1yQmJv6zXI3lTbW+vr4kpqWBVktiYmKhpWcKmpnExKKPWxrkElIxDAYDS5cu5ptvviIxMZEAPz+63teA/3v7HfCuAOTPLLhkySI2b95Ieno6jRo1ZuzYcVSvXgOA2NgrPPZYJ9asWW+eOK40Fff+RVmyZBHLli2x2FazZk02bdpifjxlyiT27dtDfHw87u7laNSoEUOHvkGtWrVK/TOohfbyJcrNnkHWm6Mw/nPaWeRTa240Gg05587hM+cDjKPeIi+omtIhWVBr3hRnMKK5cg38/KCEE+rdd19Di5FSDRuGsXbt5xgMBi5ePI9Op+O++xqan/f2rkD16jW4ePE8AD17PsvMmdPYv38PzZo1p02bdtSpU9emsCtXDqRcufLmx76+vuh0WrQ3NWK+vn6kpOTPZH7mzCmys7Pp0uVhi+Pk5ORw5UoMkH8G5pNPlrF7968kJiZgMBjIyckhLs7yDExw8L+xajQafH39SE5ONm97551JNn2Wu2I0oUlPg9yce/eet5AGphiffvoJGzasZ+LEKQQHB3P899+YMGM65erU4dl++YswfvZZNGvWrGbSpCn/TAq3gEGDBrBhw5cW86rYy52+f3BwMIsXLzc/1uksb6irX/8+OnXqTJUqVUhNTWXp0sUMGtSfrVu/LbRvWaFNTsJ91QpuvPyK/LK5hZpzo0tKwnvNSlJe6Qdqa2BUnDdFmYxok5MweFeAO5uI1WaPP96VBx54kN27f2Xfvr2sXBnN4MGv07PnsyU+RlHLvxQ10qZg2ojs7Cz8/PyZP39poX08PPLPaixc+CH79+9l0KDXqVq1Gq6urowbNxq93vJ+pKLe+3aL/d7q1oYH8peISU9PK7S4bwE/P79/lqtJtzgLk5SchL+zM+QZ8PPz4/jxYxavKzij4+dX9HFLg1xCKsbhw4dp06YtrVq1JjAwiEdbtSaqcmWOnjwJ5J/9WL36c1555VUeeqgtISEhTJr0HvHx8fz88w4AHnssf9be5557miZNwnn11f+zeI8VKz6lfft2tG3bimnT3kN/0yR5xSnJ+1uj0znh7+9v/rr11GKPHj1p2rQZgYFB1K9/HwMHDuHatWvExsaWOD4hhCgtf/9t+Uvy2LGjVKtWHZ1OR40atTAYDPz997/3cqSmpnDp0kVq1vz3rHGlSpXp2rUnU6d+wLPPvmBxD409hIbWIykpEZ1OR9Wq1Sy+KlSoAMBffx2mc+fHadOmLcHB+feSXLtW+j9nGzYMJyMjnRMnjpu3/fnnAYxGIw0aNCzyNaGh9XFycuKPP/aZt126dIG463GE1cq/qbpBgzDOnTtDcvK/6+ft37+X8uXLU7Pm3S96bI3NDcz+/fsZMGAAUVFRhIaGsn37dqv7jh8/ntDQUD799FOL7SkpKQwfPpwmTZrQrFkzxo4dS2ZmpsU+J06c4PnnnycsLIw2bdqwfPlylNCoUSP27dvLxYsXADh57ix/xMfTsln+3dhXrlwhISGB5s0fNL/G09OThg3DOHIkf+bNlSvzr9EuXryM77/fYTFL7oED+4mJuczSpR8zceIUtm79H1u3/s/8/JIli+jSpaPV+Ery/tZcunSR9u0f5vHHO/H2229x9ar1FUWzs7PYsuVLgoKCqFxZJvATQtx7cXHXmD9/NpcuXeCHH7axceM689mTatWq06pVG95//z0OHz7E6dOnmDRpPAEBFWnV6iEA5s6dxd69u4mNvcLJkyf4888D1Khh30vizZo1p0GDMMaMGcG+fXu4ejWWv/46zNKlCzlx4m8Aqlatzs6dOzh9+iSnT59i4sS372jiz8mTx7NkyQKrz9esWYvmzSOZMWMKf/99lCNHDjF79gwefri9eQRSfPx1nn++h7kR9PDw4LHHnmT+/Dn8+ecBTpw4ztSpk2h4XwMa1spvTh544EFq1qzF5MnjOX36FHv37mb58sV07/40Li72O71m8yWkrKwsQkND6dGjB4MHD7a63w8//MDhw4epWLHwujkjRowgPj6e6Oho9Ho9Y8eOZfz48cyaNQvIH7bVt29fWrRowcSJEzl16hRjx47Fy8uLZ555xtaQ78rLL/clMzOT7t2fRKfTYTAYeCMsjC7tHsYIJCYmABQ6/ebn50dCQv4ptIIzGxUqVMDf3/IGK09PL0aPHotOp6NWrVq0atWaffv20b17z39e40PVqlWtxleS9y9KWFgYEydOoUaNmiQkxLNs2RL69n2JL77YRPny/17fXb9+LXPnziE7O5uaNWuyaNEyuw2JE0Ioq5zrvbmr4E7fp2PHLuTk5PDqq33QanX07PmsxTDiMWMmMHfuTEaPfh29Xk+jRk344IO55ksvRqOB2bPfJz7+OuXKlad58xYMHfqm+fU9ez5Op06P0bdv/7v7gDfRaDTMnDmXZcsWMXXqRFJSkvH19aNx4yb4+OTf+DpkyBtMmzaJAQP+D2/vCvTq1afQH/UlERd3zeJenKJMmDCZ2bNnMGzYQPNEdq+/PtL8fF5eHpcuXeTGjRvmbUOGvIlGo+Xtt0f9M5FdC4YPHAoJ8UD+7QczZnzIzJnTGDDgZdzd3enYsXTzWBSbv4vatGlDmzZtbrtPXFwckydP5uOPP6Z/f8sPcPbsWXbt2sWGDRvMqxqPGzeOfv36MWrUKCpVqsSWLVvQ6/VMnToVFxcX6taty/Hjx4mOjr7nDcwPP3zHt99+zdSp06ldO5hTRw4zc84svH/ZyeNP3X0swcHBFveT+Pv7c/r0afPjZ599jmeffe6u3+dWLVu2Mv9/SEgIYWFhdOnSkR9++I6uXf/9gdCpUxcefLAF8fHxrFz5GaNHjyA6esU9ubdHCcaAimQNfROjLFhZiJpzkxdQkeTBb0ARfzApTc15K2AymSjv4kTbevcuxvI6MPraNqLTycmJYcOGM2LEmCKf9/Lyuu2NrG+8Mcrqczdu3CApKYmIiKZW9+nbt3+hX8pvv/1uof0WLFhm8bhcufK8/vpIi0bhZlWqBDJvnuWgih49nrZ4vGHD1kKv+/TT1bd936J4eXnfdtK6KlUC+fXXAxbbXF1dGT58NMOHj/53Y24uRq3OXL/Klaswc+a8Yt+/NJV6u200Ghk5ciR9+/albt3Cd3cfPHgQLy8vc/MCEBkZiVar5ciRIzz66KMcOnSIZs2aWZx6ioqKYvny5aSmpuLt7V3aYVv14YezeemlvnTokH8fS926IVxNTiZ6zSoef+oZ85C1pKREAgL+nQQoMTGR0NDi530ofPOXBluWp7rb9y/g6elF9eo1uHz58i3bPfH09KR69RqEhzeiTZuW/PTTj3Ts2LnEx3YkxiqBZI57V+kwVEnNuTFUCSRx7AR8XHSgsjW31Jy3AiYTOGPMz989e08TBhVdjv7zzwM0bdqMJk2aKR2KY3BxwRgYqGgIpX4T7/Lly3FycqJ3795FPp+QkFBovLiTkxPe3t7Ex8eb97n1UkvB44SEhNIO+bZu3LiBVvvvsD1Nbi5O6WkYDfl3fgcFBeHv78++fXvN+2RkZHD06F+EhzcCMF9yMRhKfrd4SZXk/UsiKyuLmJjLhfJ+s4LGKje35DcZOxpNRjrOv+1Ck5GudCiqo+bcaDLScf99F6SrMza15u1mJlP+yJl79WXKM+TnxGBQ+qMDEBkZxQcfzFU6DMdhUL5+pdrAHD16lBUrVjBt2rR7uqqpPbVu3YaPP17Orl2/EBt7hZ++2sLK9eto989NsxqNhueff4GPPlrGzp0/cfr0KcaPf5uAgAAeeqgdAD4+vri5ufH777+SmJhIug0/ZNeuXUP//q9Yfb4k7w/Qv/8rrF27xvx4zpyZ/PHHAWJjr3D48CGGD38drVZHx475Z5piYmL45JOP+Pvvv7l69SqHDx9i1KjhuLq6EhVVsmm3HZHu3FkqdOuC7pz9Zo90VGrOjcu5swT1fFyVsak5b4rKyUF35jTklGwekQULljFs2HA7ByVKzMb62UOpXkI6cOAAiYmJtG3b1rzNYDDw/vvvs2LFCnbs2IG/vz9JSUkWr8vLyyM1NdV8CcTf37/QmZaCx7c7Q2APo0aNYdGiBUyb9h7JyUkE+PjyTJ069O3dx7xPnz4vk52dzZQpk0hPT6dx4wgWLFhsvk/EycmJkSNHs3z5UpYsWURERBOWL/+kRO+fkpJMTEzMbfcp7v0hvyFJSfl3/H9c3HXGjBlNamoKPj4+NG7chM8++9x8U5mrqwsHD/7J6tWfk5aWhp+fH02aNCU6eoXV+QKEEEKIe6VUG5gnn3ySyMhIi219+/blySefpHv3/BtDIyIiSEtL4+jRozRsmD/ufM+ePRiNRsLDwwFo3LgxH374IXq93nz55ffff6dWrVr39P4XgPLlyzNy5GhGjsy/eUkbdw33FdFkOztTcEFIo9Hw2muDeO0166tuduvWg27delhsmzhxSqH9Ct6nwIABAxkwYOBtYyzJ+3/99TaLx9Onz7jtMQMCKjJ//qLb7iOEEEIoxeYGJjMzk0uXLpkfx8TEcPz4cby9vQkMDCw0GZqzszP+/v7Urp0/Xjw4OJhWrVrxzjvvMHHiRPR6PZMnT6ZLly5UqlQJgMcff5yFCxfy9ttv8+qrr3L69GlWrFjBmDFF33kuhBDCNrYMFhCitJXG95/NDczRo0ctbtCdNm0aAN26dWP69OklOsbMmTOZPHkyffr0QavV0r59e8aNG2d+3tPTk48//phJkybRvXt3fHx8GDhw4D0fQl0krQ6jpydoy+ZU+v91JidnDFUCMTnJXDe3UnNuTM7O5FUJBBXOUaS2vBXME2Iw5AEKToeg0WBydslf1FE4nrusX+4/ayjpdHd+IUhjKuNteEJCOrf7hFqthvLlXTEajbfdT9wZjSb/B2ZmZs4dzSwpRHG0Wg3JufkjIXxcdPJ9VgyTyURS0nWMxjy8vf3QaGRFGXHvmEwmcnNzyMhIxt3dA2/vwvdUajTg7+9ZxKstyWKOQgjxH6LRaPD29iUx8RpJSXFKhyP+o9zdPfDy8i1+x9uQBsZG2vh4XDeuI6fHMxhvmjjuTkyYMI709HRmz5a5B9RC9/cxvJ/rQeqajRjua6B0OKqi5ty4HD9G4AtPkbFuE8Z69ykdjgU15s3JyZmKFauSl6fcnE7aM6fxHDGU9JnzMNYpPOmpULe7qZ9O51TskgclIQ1MMTIzM1m0aAE//bSD5OQkQmsH8061qtQ1/jt5j8lkYsmSRWzevJH09HQaNWrM2LHjqF69BgCxsVd47LFOrFmzntDQeqUe448/bmfjxi84fvxvUlNTS/Q+W7b8j3fffcdim4uLC3v2/DuFdHGfqyzS5OnRXY1Fo+APdrVSc240ej1OV2PBhpXc7xW15k2j0eDsbL+F9orjpNdTfv9+cvV68hSMQ9wZNdRPLn4WY9Kkd9m7dw+TJ7/HunUbadGkCS//9BNxN81T89ln0axZs5qxY9/hs89W4e7uzqBBA8i5RxP8ZGdn07hxBEOHvm7T6zw8PPj++x3mr6+//s7ieaU/lxBCCGGNNDC3cePGDXbs2M6wYW/QtGkzqlevzmsv9KaGhwdffJ2/sJbJZGL16s955ZVXeeihtoSEhDBp0nvEx8fz8887AHjssfzZbZ977mmaNAnn1Vf/z+J9Vqz4lPbt29G2bSumTXsPvY1/RT722OP06zeA5v/MDlxyGvz9/c1ffn7/3kxVks8lhBBCKEUamNswGAwYDAaLRSUBXHU6Dh47BsCVK1dISEiwaB48PT1p2DCMI0cOA7ByZf6KoYsXL+P773cwc+Yc874HDuwnJuYyS5d+zMSJU9i69X9s3fo/8/NLliyiS5eOdvl82dlZdO7cgU6dHuWNN4Zy9uwZ83Ml+VxCCCGEUqSBuY3y5csTHt6Ijz5aRnz8dQwGA1/t2cOhpCQSUlMASEzMv5R06/T6fn5+JCQkApgn96tQoQL+/v4Wswl7enoxevRYatWqRevWbWjVqjX79u0zP1+hgg9Vq1Yt9c9Wo0ZNJkyYyJw5c5kyZRpGo5GXX+5NXNy1En+usshQO5iUzV9jqB2sdCiqo+bc5NYO5sqGraqMTc15U5LkxbGpoX7SwBRj8uSpmEwmOnR4hAcfbMaaDevp0KETmlKayC44OBid7t9j5a8V9W+D8Oyzz7F06Uel8l43a9SoEY899gShofVo2rQZM2fOoUIFHzZu3FDq7+VITB6e6Fu2wuRR/BwE/zVqzo3Jw5PsyFbgqc7Y1Jo3JUleHJsa6icNTDGqVavGRx9F89tve/jmm+/5fPFSjFdjqVq5MgB+fvmLS97cdAAkJibi71/8oodOTrcOBNMoMsW3s7Mz9erV4/Ll/GUi7vZzOSrt1VjKT3kX7dVYpUNRHTXnRnc1Fr+pE1UZm5rzpiTJi2NTQ/2kgSkhd/dyBAQEkH7tGr8fO8pD9z8AQFBQEP7+/uzbt9e8b0ZGBkeP/kV4eCMA84KUBoOx8IFVwmAwcObMafz98+e2KcnnKou08dcpN2822vjrSoeiOmrOjVP8dXwWzEFzXX2xqTlvSpK8ODY11E/mgSnG77//hslkombNmly+fJkPZ75PbS8vnmzfAcifS+H551/go4+WUb16dQIDg1i8eCEBAQE89FA7AHx8fHFzc+P333+lUqVKuLi44FnCU91r167hp59+vO1lpNTUVK5du0p8fDwAFy5cAPLPovj7559JeeedsVSsWIkhQ4YBsGzZEsLCwqlWrTrp6WmsWPEpV69epVu37iX+XEIIIYRSpIEpRkZGBgsWzCUuLg5vb28ebhHJSBdnnJycKDif0qfPy2RnZzNlyiTS09Np3DiCBQsW4+qav1Cak5MTI0eOZvnypSxZsoiIiCYsX/5Jid4/JSWZmJiY2+6zc+fPFpPSjRkzCoB+/QYwYMBAAK5du2Yx82FaWhqTJ08kMTEBLy8v6te/j+joFdS+6Yas4j6XEEIIoRRZzNHGxRy1cddwXxFNdu+XMVaqXHqBllGOtpij05FD+DzSmuTtv5AX3ljpcFRFrbnRajVkHfiDah0fIuXHXejD1HWJU615U5rkxbHZs34lXcxR7oGxkcndHX14I0zu7kqHIuzA6ONLdq/eGH3ubpGxskjNuTH4+pL63IuYfNUXm5rzpiTJi2NTQ/3kDIyNZ2CEbRztDIxwPFqthuTc/LXJfFx08n0mhIOTMzD2kpeHNiEB8vKUjkTYQ3Y2uhPHITtb6UjUR8W50WRn43JSnbGpOW+Kkrw4NhXUTxoYG2kTE3CPXo42MaH4nYXDcTp9Et/WzXE6fVLpUFRHzblxOX2S6m1boDulvtjUnDclSV4cmxrqJw2MgiZMGMebbw5TOgwhhBDC4cgw6mJkZmayaNECfvppB8nJSYTWDuadalWpe9M+EyaMY+vWLRava9EikoULlwAQG3uFxx7rxJo16wkNrVfqMS5Zsojvv9/GtWvXcHZ2pn79+xg0aAhhYeG3fd26dWtZseJTEhMTCAkJYdSoMTRsGGZ+fsqUSezbt4f4+Hjc3cvRqFEjhg59g1q1apX6ZxBCCCFsIQ1MMSZNepezZ88wefJ7BARU5Nsv1vLy+nVsfPElAm4aRh0Z2ZJ3351sfnzrCtb2VKNGDUaPHktQUFVycm6watVKBg0awP/+9xU+Vu4Q/+67bcye/QFjx75DWFgYq1Z9zqBBA9i8eYt5Acf69e+jU6fOVKlShdTUVJYuXcygQf3ZuvVbi/WbhBBCiHtNLiHdxo0bN9ixYzvDhr1B06bNqF69Oq+92Ifqnp588fVXFvu6uLjg7+9v/vLy8jI/99hjnQB47rmnadIknFdf/T+L165Y8Snt27ejbdtWTJv2Hnq93qY4O3XqQvPmD1K1alWCg+vw5psjycjI4NSpU1Zfs2rVCrp168GTT3aldu1g3n77Hdzc3Pnf/74079OjR0+aNm1GYGAQ9evfx8CBQ7h27RqxsWV47RKNBpOLS/5t8MKSinNjUnFsas6boiQvjk0F9ZMzMLdhMBgwGAwWZ1OMFSvhUr0GB09bNgcHDhzg4Yfb4OXlxf33P8DAgUOoUKECACtXrubFF59n8eJlBAfXMa+NlP+6/fj7+7N06cdcvnyJt94aSWhoKN279wTyLw9t3bqFr7/eVqKY9Xo9mzZtwMPDk5CQUKv7HD9+nJdffsW8TavV0rx5c44cOVzka7Kzs9iy5UuCgoKoXLnsTuCXF9aIhBi5Qbsoas5Nblgjzl64jo+LDlQ2jFrNeVOS5MWxqaF+0sDcRvny5QkPb8RHHy2jdu3a+Pr6sW3btxw5cphq1aqZ94uMbEm7dg8TGBhETEwMCxbMY8iQgXz66Up0Oh0+Pj4AVKhQwbw2UQFPTy9Gjx6LTqejVq1atGrVmn379pkbmAoVfKhatWqxsf7yy07GjBnFjRs38PcPYPHipeb3vVVKSjIGg8F8qaiAr68fFy6ct9i2fv1a5s6dQ3Z2NjVr1mTRomUWDZgQQgihBLmEVIzJk6diMpno0OERHnywGWs/X0HnkBC0N/2V16FDJ9q0aUvduiG0bduOuXMXcOzYUQ4c2F/s8YODgy3uJ/H39ycpKdH8+Nlnn7vtQo4F7r//ftas+YLo6BVERrZk9OgRFse5U506dWHNmvUsX/4J1avXYPToEeTk5Nz1cdVKd+okFR5upcrhuEpTc26cT5+kavvW6E6dUDqUQtScNyVJXhybGuonDUwxqlWrxkcfRfPbb3v45pvvWfXhPAxZWQTd5jJK1apVqVDBh8uXLxd7fCenW0+CabiTyZHd3ctRvXp1wsMbMWHCRHQ6J778cnOR+1ao4INOpyvU4CQlJeLnd+sZIk+qV69B06bN+OCD2Vy4cJ6ffvrR5vgcheZGNs5/HUZzQybXupWac6PNzsbt6BHIvqF0KIWoOW9Kkrw4NjXUTxqYEnJ3L0dAQABp6en8evUqDz3Ywuq+cXHXSE1NISAgvxkouORiMBitvqa0mUxGcnNzi3wuf6h1ffbt22veZjQa2bdvL+Hh1hfCK2iscnNtu8lYCCGEKG1yD0wxfv/9N0wmEzVr1uTy5ct8OPN9ant58WT7DgBkZWWxdOliHn74Efz9/bl8+TJz586hWrXqtGjREgAfH1/c3Nz4/fdfqVSpEi4uLnh6Fr/OA8DatWv46acfrV5Gys7O4qOPltOmzUP4+weQkpLC+vVruX79Oo8+2t68X//+r9C27cM8++xzAPTq1ZsJE8Zx33330aBBGKtXf052djZPPNEVgJiYGL7/fhsPPhiJj48P16/HER39Ma6urkRFRd1pOoUQQohSIQ1MMTIyMliwYC5xcXF4e3vzcItIRro44+TkhJH80TunT5/mq6+2kJ6eTkBARR58sAUDBw42j15ycnJi5MjRLF++lCVLFhER0YTlyz8p0funpCQTExNj9XmtVseFCxf46qvhpKQk4+1dgQYNGvDxx58SHFzHvF9MTAwpKcnmxx06dCQ5OZnFixeRmJhAaGgoCxYsxs8v/8ZeV1cXDh78k9WrPyctLQ0/Pz+aNGlKdPSKQjf/CiGEEPearEZt42rUmhs30F68gLFGTUxubqUXaBnlaKtRa1KScf7lZ/StH8JUoehRXP9Vas2NVqsh9Xoi7rt+xvXhdhi8KigdkgW15k1pkhfHZs/6lXQ1amlgbGxghG0crYERjker1ZCcawDAx0Un32dCOLiSNjByE6+NNJmZOB/YhyYzU+lQhB1orl/HffECNNevKx2K6qg5N7r461RYqs7Y1Jw3JUleHJsa6icNjI00Gem4/PQjmox0pUMRdqC7FovHhLHorpXh5RLukJpz43Q1Fv+J49BeVV9sas6bkiQvjk0N9ZMGRgghhBAORxoYIYQQQjgcaWCEEEII4XBkHhgzDRpN8aMXTO7u5IWFYXJ3l1XgS8SxkmT09CKnQyeMnl5Kh6I6as6NwcubzEc7YvJSX2xqzpuSJC+OTQ31+88Po9ZooFw5F7RaORllL0ajkaysXBmmLuxChlELUbaUdBj1f/4MjMkEWVm5aEp6OkWvh/R08PSEf9Y4ErdnMpkcp3nR69GkpmLy9pb63krNudHr0aalgr8v6FT2Y03NeVOS5MWxqaB+Np922L9/PwMGDCAqKorQ0FC2b99ufk6v1/PBBx/w+OOP07hxY6Kiohg1ahRxcXEWx0hJSWH48OE0adKEZs2aMXbsWDJvmVflxIkTPP/884SFhdGmTRuWL19+hx+xeCYTGI2mEn1pjx3FL6QG2mNHS/ya//qXwzQvgNPxY/jfVxun48eUDkV11Jwb1+PHqB1WB93f6otNzXlTkuTFsamhfjY3MFlZWYSGhjJhwoRCz924cYO///6b1157jU2bNrFgwQLOnz/Pa6+9ZrHfiBEjOHPmDNHR0SxZsoQDBw4wfvx48/MZGRn07duXwMBANm3axKhRo1iwYAHr1q27g48ohBBCiLLG5nOtbdq0oU2bNkU+5+npSXR0tMW2d955h6eeeorY2FgCAwM5e/Ysu3btYsOGDYSFhQEwbtw4+vXrx6hRo6hUqRJbtmxBr9czdepUXFxcqFu3LsePHyc6OppnnnnmDj6mEEIIIcoSu9+5mpGRgUajweuf0QEHDx7Ey8vL3LwAREZGotVqOXLkCACHDh2iWbNm5tWcAaKiojh//jypqan2DlkIIYQQKmfXBiYnJ4eZM2fSpUsXPDw8AEhISMDX19diPycnJ7y9vYmPjzfv4+/vb7FPweOEhAR7hiyEEEIIB2C32/X1ej3Dhg3DZDIxceJEe73NPZfXIIyEszGYypVXOhRhB1Jf69Scm5wGYZw9eYkKFdQ3p4ia86YkyYtjU0P97NLA6PV6Xn/9dWJjY/nss8/MZ18g/0xKUlKSxf55eXmkpqYSEBBg3ufWMy0Fj289M3PP6XSYZOKlskvqa52ac1MQm04HapsHRs15U5LkxbGpoH6lfgmpoHm5ePEin376KT4+PhbPR0REkJaWxtGjR83b9uzZg9FoJDw8HIDGjRtz4MAB9Hq9eZ/ff/+dWrVq4e3tXdoh20R37gzeT3dFd+6MonEI+5D6Wqfm3DifO0vgc93RnlVfbGrOm5IkL45NDfWzuYHJzMzk+PHjHD9+HICYmBiOHz9ObGwser2eoUOHcvToUWbOnInBYCA+Pp74+Hhyc3MBCA4OplWrVrzzzjscOXKEP/74g8mTJ9OlSxcqVaoEwOOPP46zszNvv/02p0+f5ptvvmHFihW8/PLLpfjR74wmIwOXn3egychQOhRhB1Jf69ScG21GOuV2qjM2NedNSZIXx6aG+tl8Ceno0aP07t3b/HjatGkAdOvWjcGDB7Njxw4AnnzySYvXrVixgubNmwMwc+ZMJk+eTJ8+fdBqtbRv355x48aZ9/X09OTjjz9m0qRJdO/eHR8fHwYOHChDqIUQQggB3EED07x5c06ePGn1+ds9V6BChQrMmjXrtvvUq1eP1atX2xqeEEIIIf4DZAVDIYQQQjgcaWBsZAisSvq0mRgCqyodirADqa91as6NPqgq8e99gDEoSOlQClFz3pQkeXFsaqifxmRypKX2bJeQkO5QiwkKIWyj1WpIzjUA4OOiw6i2YdRCCJtoNODv71nsfnIGxkaa5CRcv1iLJjmp+J2Fw5H6Wqfm3GiTk/HYuE6Vsak5b0qSvDg2NdRPGhgb6S5fwmtQP3SXLykdirADqa91as6N8+WLVB7SH+0l9cWm5rwpSfLi2NRQP2lghBBCCOFwpIERQgghhMORBkYIIYQQDkcaGBuZypVH3/R+WUG1jJL6Wqfm3BjLledG0/sxlSundCiFqDlvSpK8ODY11E+GUQshHJoMoxaibJFh1EIIIYQos6SBsZHTkUMEVPTC6cghpUMRdiD1tU7NuXE9cog6gRXQHT6kdCiFqDlvSpK8ODY11E8aGCGEEEI4HGlghBBCCOFwpIERQgghhMORBkYIIYQQDkeGUdvqxg20sVcwBgaBm1spHliogtTXOpXmRqvVkJKWidPVWDxqVMPo4qp0SJZUmjfFSV4cmx3rV9Jh1NLACCEcmswDI0TZIvPA2In24gU8X3sF7cULSoci7EDqa52ac+N06QKVBvdTZWxqzpuSJC+OTQ31kwbGRtrUFNw2rkebmqJ0KMIOpL7WqTk3upQUPDetR5OSonQohag5b0qSvDg2NdRPGhghhBBCOBxpYIQQQgjhcKSBEUIIIYTDcVI6AEdjrFSZzBFvYaxUWelQhB1Ifa1Tc27yKlUm6c3RoMLY1Jw3JUleHJsa6ifDqIUQDk2GUQtRtsgwajvRpKfhvGM7mvQ0pUMRdiD1tU7NudGkp1Hu5x9VG5ta86YkyYtjU0P9pIGxke78OSo82x3d+XNKhyLsQOprnZpz43L+HIHP90B7Tn2xqTlvSpK8ODY11E8aGCGEEEI4HGlghBBCCOFwpIERQgghhMORBsZGJhdXDDVrYVLbireiVEh9rVNzbkyuruTWrAWuKoxNxXlTkuTFsamhfjKMWgjh0GQYtRBliwyjFkIIIUSZJQ2MjXTHjuJXvxa6Y0eVDkXYgdTXOjXnxuXvo9RqGKzK2NScNyVJXhybGuonDYyNNIY8tImJaAx5Soci7EDqa52ac6PJy0OXlAh5KoxNxXlTkuTFsamhftLACCGEEMLhSAMjhBBCCIcjDYwQQgghHI7NDcz+/fsZMGAAUVFRhIaGsn37dovnTSYTc+fOJSoqivDwcF566SUuXLhgsU9KSgrDhw+nSZMmNGvWjLFjx5KZmWmxz4kTJ3j++ecJCwujTZs2LF++3PZPZwd5teuQ/PUP5NWuo3Qowg6kvtapOTe5wXWI2fI9hmD1xabmvClJ8uLY1FA/mxuYrKwsQkNDmTBhQpHPL1++nJUrV/Luu++yfv163N3d6du3Lzk5OeZ9RowYwZkzZ4iOjmbJkiUcOHCA8ePHm5/PyMigb9++BAYGsmnTJkaNGsWCBQtYt27dHXzEUubhQd79zcHDQ+lIhD1Ifa1TcW5M5T240ewBVcam5rwpSvLi2FRQP5sbmDZt2vDGG2/w6KOPFnrOZDKxYsUKXnvtNR555BHq1avHjBkzuH79uvlMzdmzZ9m1axdTpkyhUaNGNGvWjHHjxvH1118TFxcHwJYtW9Dr9UydOpW6devSpUsXXnzxRaKjo+/y4949bewVyr8zBm3sFaVDEXYg9bVOzbnRxV7B/92xqoxNzXlTkuTFsamhfqV6D0xMTAzx8fFERkaat3l6etKoUSMOHjwIwMGDB/Hy8iIsLMy8T2RkJFqtliNHjgBw6NAhmjVrhouLi3mfqKgozp8/T2pqammGbDNtQjzlli5EmxCvaBzCPqS+1qk5N04J8VRYtghNvPpiU3PelCR5cWxqqF+pNjDx//zw8PPzs9ju5+dHQkICAAkJCfj6+lo87+TkhLe3t/n1CQkJ+Pv7W+xT8LjgOEIIIYT475JRSEIIIYRwOKXawAQEBACQmJhosT0xMdF8BsXf35+kpCSL5/Py8khNTTW/3t/fv9CZloLHt56ZEUIIIcR/T6k2MFWrViUgIIDdu3ebt2VkZHD48GEiIiIAiIiIIC0tjaNH/10/Yc+ePRiNRsLDwwFo3LgxBw4cQK/Xm/f5/fffqVWrFt7e3qUZss2Mvn5kv/wKRl+/4ncWDkfqa52ac2Pw9SOlzyuY/NQXm5rzpiTJi2NTQ/00JpPJprXnMzMzuXTpEgBdu3ZlzJgxNG/eHG9vbwIDA1m2bBnLly9n+vTpVK1alblz53Ly5Em++eYbXF1dAXjllVdITExk4sSJ6PV6xo4dS8OGDZk1axYA6enpdOzYkZYtW/Lqq69y+vRpxo4dy5gxY3jmmWds+oAJCenY9gmFEI5Eq9WQnGsAwMdFh9Eo/+CFcGQaDfj7exa/n60NzN69e+ndu3eh7d26dWP69OmYTCbmzZvH+vXrSUtLo2nTpkyYMIFatWqZ901JSWHy5Mns2LEDrVZL+/btGTduHOXLlzfvc+LECSZNmsRff/2Fj48PL7zwAv369bMlVMAODUxWFk5nTpFXJwTKlSvFAwtVkPpap9LcaLUaUlLScTlzinL31cfo5q50SJZUmjfFSV4cmx3rZ7cGxtGUdgPjdOQQPo+0Jnn7L+SFNy69AwtVkPpap9bcaLUasg78QbWOD5Hy4y70YY2UDsmCWvOmNMmLY7Nn/UrawMgoJCGEEEI4HGlghBBCCOFwpIERQgghhMORBsZGJo0Wo4cnJo2kriyS+lqn5tyYtPmxoVVhbCrOm5IkL45NDfWTm3iFEA5NhlELUbbITbxCCCGEKLOkgbGR7uQJfFo9gO7kCaVDEXYg9bVOzblxPnWCag89qMrY1Jw3JUleHJsa6icNjI00OTdwOnkCTc4NpUMRdiD1tU7NudHeuIHrqRNwQ32xqTlvSpK8ODY11E8aGCGEEEI4HGlghBBCCOFwpIERQgghhMORYdQ20qSm4Lz7d/QtIjF5Vyi9AwtVkPpap9bcaLUaUuMTcdvzO26tW2Hw9FY6JAtqzZvSJC+OzZ71k8Uc/yHzwAhRtsk8MEKULTIPjJ1o4uJwnzsLTVyc0qEIO5D6Wqfm3Oiux+Ezf7YqY1Nz3pQkeXFsaqifNDA20sVdxeO9iejiriodirADqa91as6N07Wr+E2bhPaa+mJTc96UJHlxbGqonzQwQgghhHA40sAIIYQQwuFIAyOEEEIIhyMNjI2MXt7kPN4Vo5e6hmqK0iH1tU7NuTF4VyDjsScxeasvNjXnTUmSF8emhvrJMGohhEOTYdRClC0yjNpecnPRxl6B3FylIxH2IPW1Ts25yc1Fp+LYVJs3JUleHJsK6icNjI2cTvyNX+P6OJ34W+lQhB1Ifa1Tc25cT/xNrWYN0B1XX2xqzpuSJC+OTQ31kwZGCCGEEA5HGhghhBBCOBxpYIQQQgjhcKSBEUIIIYTDkWHUtjIaQa8HZ2fQSv9X5kh9rVNpbrRaDck39KDX41PeDSMapUOypNK8KU7y4tjsWL+SDqN2KtV3/S/QasHVVekohL1Ifa1Tc24KYtNqQW3zwKg5b0qSvDg2FdRP2l4b6c6exrtrZ3RnTysdirADqa91as6N89kzBPXogvaM+mJTc96UJHlxbGqonzQwNtJkZuLy+69oMjOVDkXYgdTXOjXnRpuZgfvu31QZm5rzpiTJi2NTQ/2kgRFCCCGEw5EGRgghhBAORxoYIYQQQjgcaWBsZAiqRvrs+RiCqikdirADqa91as6NPqga1z+Yh7FqVaVDKUTNeVOS5MWxqaF+Mg+MEMKhabUaknMNAPi46DCqbRi1EMImJZ0HRs7A2EiTmIjb55+hSUxUOhRhB1Jf69ScG21iIl6rVqBJTFA6lELUnDclSV4cmxrqJw2MjXRXLuP55hB0Vy4rHYqwA6mvdWrOjfOVy1QcORRtTIzSoRSi5rwpSfLi2NRQP2lghBBCCOFwpIERQgghhMMp9QbGYDDw4Ycf0q5dO8LDw3nkkUdYuHAhN98rbDKZmDt3LlFRUYSHh/PSSy9x4cIFi+OkpKQwfPhwmjRpQrNmzRg7diyZMmOjEEIIIbBDA7N8+XLWrFnD+PHj+eabbxgxYgQfffQRK1eutNhn5cqVvPvuu6xfvx53d3f69u1LTk6OeZ8RI0Zw5swZoqOjWbJkCQcOHGD8+PGlHa7NTOXLkxsZhal8eaVDEXYg9bVOzbkxlvcgu0VLVcam5rwpSfLi2NRQv1IfRt2/f3/8/PyYOnWqeduQIUNwdXVl5syZmEwmWrVqxcsvv0zfvn0BSE9PJzIykunTp9OlSxfOnj1L586d2bBhA2FhYQD88ssv9OvXj507d1KpUqUSxyPDqIUo22QYtRBli2LDqCMiItizZw/nz58H4MSJE/zxxx+0bt0agJiYGOLj44mMjDS/xtPTk0aNGnHw4EEADh48iJeXl7l5AYiMjESr1XLkyJHSDtk2RiPk5OT/V5Q9Ul/r1Jwbic3xSF4cmwrqV+oNTL9+/ejcuTOdOnWiQYMGdO3alT59+vDEE08AEB8fD4Cfn5/F6/z8/EhIyJ/DISEhAV9fX4vnnZyc8Pb2Nr9eKU5HjxBQLQCnowo3UsIupL7WqTk3rkePUKdWJXR/qS82NedNSZIXx6aG+jmV9gG//fZbtm7dyqxZs6hTpw7Hjx9n2rRpVKxYkW7dupX22wkhhBDiP6jUG5gZM2bQr18/unTpAkBoaCixsbEsXbqUbt26ERAQAEBiYiIVK1Y0vy4xMZF69eoB4O/vT1JSksVx8/LySE1NNb9eCCGEEP9dpX4J6caNG2g0GottOp3OPIy6atWqBAQEsHv3bvPzGRkZHD58mIiICCD/Ppq0tDSOHj1q3mfPnj0YjUbCw8NLO2QhhBBCOJhSPwPTtm1blixZQmBgoPkSUnR0ND169ABAo9HQu3dvFi9eTI0aNahatSpz586lYsWKPPLIIwAEBwfTqlUr3nnnHSZOnIher2fy5Ml06dLFphFIQgghhCibSn0YdUZGBnPnzmX79u3my0RdunRh0KBBuLi4APkT2c2bN4/169eTlpZG06ZNmTBhArVq1TIfJyUlhcmTJ7Njxw60Wi3t27dn3LhxlLdxzHmpD6POzUWbEI/RPwD++TyiDJH6WqfS3Gi1GpIzstElxOMVWBmjk7PSIVlSad4UJ3lxbHasX0mHUZd6A6M2Mg+MEGWbzAMjRNmi2DwwZZ32wnm8+vZGe+G80qEIO5D6Wqfm3DhdvEDlfn1UGZua86YkyYtjU0P9pIGxkTYtFdetX6JNS1U6FGEHUl/r1JwbXWoKHl/9D02q+mJTc96UJHlxbGqonzQwQgghhHA40sAIIYQQwuFIAyOEEEIIh1Pq88CUdYZKVch4ewKGSlWUDkXYgdTXOjXnJq9yFRLHjEdTWX2xqTlvSpK8ODY11E+GUQshHJoMoxaibJFh1HaiSU3BZds3aFJTlA5F2IHU1zo150abmkK579QZm5rzpiTJi2NTQ/2kgbGR7uIFvHs/i+7iBaVDEXYg9bVOzblxvniBwJefR3vhgtKhFKLmvClJ8uLY1FA/aWCEEEII4XCkgRFCCCGEw5EGRgghhBAORxoYG5lc3cgLrYfJ1U3pUIQdSH2tU3NujG5u5ITUAzf1xabmvClJ8uLY1FA/GUYthHBoMoxaiLJFhlELIYQQosySBsZGur+O4Fc7CN1fR5QORdiB1Nc6NefG5egRaodUU2Vsas6bkiQvjk0N9ZMGxkYakxFtRjoak1HpUIQdSH2tU3NuNMb82DCqMDYV501JkhfHpob6SQMjhBBCCIcjDYwQQgghHI40MEIIIYRwODKM2lZZWTidOUVenRAoV64UDyxUQeprnUpzo9VqSElJx+XMKcrdVx+jm7vSIVlSad4UJ3lxbHasX0mHUUsDI4RwaDIPjBBli8wDYyfamMt4jH4TbcxlpUMRdiD1tU7NuXGKuYz/mBGqjE3NeVOS5MWxqaF+0sDYSJuUiHv0R2iTEpUORdiB1Nc6NedGl5RIhc8+QpOovtjUnDclSV4cmxrqJw2MEEIIIRyONDBCCCGEcDjSwAghhBDC4UgDYyOjfwBZ/Qdh9A9QOhRhB1Jf69Scmzz/AFL6DcQUoL7Y1Jw3JUleHJsa6ifDqIUQDk2GUQtRtsgwanvJyMBp/17IyFA6EmEPUl/rVJwbTWYGbgf2qTI2NedNUZIXx6aC+kkDYyOnc2fw6fIoTufOKB2KsAOpr3Vqzo3L2TNUfaI9urPqi03NeVOS5MWxqaF+0sAIIYQQwuFIAyOEEEIIhyMNjBBCCCEcjjQwNjLpnDD6+WHSOSkdirADqa91as6NyckJg68fOKkwNhXnTUmSF8emhvrJMGohhEOTYdRClC0yjFoIIYQQZZY0MDbSnTiO7wON0J04rnQowg6kvtapOTcuJ49TPTJClbGpOW9Kkrw4NjXUzy4NTFxcHCNGjKB58+aEh4fz+OOP89dff5mfN5lMzJ07l6ioKMLDw3nppZe4cOGCxTFSUlIYPnw4TZo0oVmzZowdO5bMzEx7hGsTTW4Ougvn0eTmKB2KsAOpr3Vqzo0mJweXC+chR4WxqThvSpK8ODY11K/UG5jU1FSee+45nJ2dWb58OV9//TWjR4/G29vbvM/y5ctZuXIl7777LuvXr8fd3Z2+ffuSc9MPnxEjRnDmzBmio6NZsmQJBw4cYPz48aUdrhBCCCEcUKnfPrx8+XIqV67MtGnTzNuqVatm/n+TycSKFSt47bXXeOSRRwCYMWMGkZGRbN++nS5dunD27Fl27drFhg0bCAsLA2DcuHH069ePUaNGUalSpdIOWwghhBAOpNTPwOzYsYOGDRsydOhQWrRoQdeuXVm/fr35+ZiYGOLj44mMjDRv8/T0pFGjRhw8eBCAgwcP4uXlZW5eACIjI9FqtRw5cqS0QxZCCCGEgyn1Buby5cusWbOGmjVr8vHHH/Pcc88xZcoUNm/eDEB8fDwAfn5+Fq/z8/MjISEBgISEBHx9fS2ed3Jywtvb2/x6pRhq1SZl7SYMtWorGoewD6mvdWrOTW6t2sSu3oixtvpiU3PelCR5cWxqqF+pX0IymUw0bNiQN998E4D77ruP06dPs3btWrp161bab3fPmTy90Ld7ROkwhJ1Ifa1Tc25Mnl5kPfQwri46UNk8MGrOm5IkL45NDfUr9TMwAQEBBAcHW2yrXbs2sbGx5ucBEhMTLfZJTEzE398fAH9/f5KSkiyez8vLIzU11fx6pWjjrlFuxlS0cdcUjUPYh9TXOjXnRhd3Dd+Z09BcU19sas6bkiQvjk0N9Sv1BqZJkyacP3/eYtuFCxcICgoCoGrVqgQEBLB7927z8xkZGRw+fJiIiAgAIiIiSEtL4+jRo+Z99uzZg9FoJDw8vLRDtok27hrlZ06Xf3RllNTXOjXnxinuGr6z31dlbGrOm5IkL45NDfUr9QamT58+HD58mCVLlnDx4kW2bt3K+vXref755wHQaDT07t2bxYsX8+OPP3Ly5ElGjRpFxYoVzaOSgoODadWqFe+88w5Hjhzhjz/+YPLkyXTp0kVGIAkhhBCi9O+BCQ8PZ8GCBcyePZuFCxdStWpVxo4dyxNPPGHe59VXXyU7O5vx48eTlpZG06ZN+eijj3B1dTXvM3PmTCZPnkyfPn3QarW0b9+ecePGlXa4QgghhHBAdllGsm3btrRt29bq8xqNhmHDhjFs2DCr+1SoUIFZs2bZIzwhhBBCODhZC8lGRu8K3OjxNEbvCkqHIuxA6mudmnNjqFCB9O5PY6pQQelQClFz3pQkeXFsaqifxmQyqWvMYSlLSEinbH9CIf7btFoNybkGAHxcdBhVNoxaCGEbjQb8/T2L3U/OwNjqxg20587CjRtKRyLsQeprnYpzo7lxA+fz51QZm5rzpijJi2NTQf2kgbGR06kT+D0YgdOpE0qHIuxA6mudmnPjcuoENVo2QXdSfbGpOW9Kkrw4NjXUTxoYIYQQQjgcaWCEEEII4XCkgRFCCCGEw5EGRgghhBAOR4ZRCyEcmgyjFqJskWHUQgghhCizpIGxke7MaSp0ehjdmdNKhyLsQOprnZpz43zmNFUffxTt6VNKh1KImvOmJMmLY1ND/aSBsZEmKxPnP/ajycpUOhRhB1Jf69ScG21WJm5/7EeTlaV0KIWoOW9Kkrw4NjXUTxoYIYQQQjgcaWCEEEII4XCkgRFCCCGEw5EGxkaGatVJW7gMQ7XqSoci7EDqa52ac6OvVoNr85dirK6+2NScNyVJXhybGuon88AIIRyazAMjRNki88DYiSYhAbePl6FJSFA6FGEHUl/r1JwbbWIC3tHL0STEKx1KIWrOm5IkL45NDfWTBsZGutgYPMeMQBcbo3Qowg6kvtapOTfOV2IIeHsk2itXlA6lEDXnTUmSF8emhvpJAyOEEEIIhyMNjBBCCCEcjjQwQgghhHA40sDYyOThQe5D7TB5eCgdirADqa91as6N0cOTrDbqjE3NeVOS5MWxqaF+MoxaCOHQZBi1EGWLDKO2F4MBTXoaGAxKRyLsQeprnZpzI7E5HsmLY1NB/aSBsZHTsb/wD66K07G/lA5F2IHU1zo158b12F8Eh1ZHd1R9sak5b0qSvDg2NdRPGhghhBBCOBxpYIQQQgjhcKSBEUIIIYTDkQZGCCGEEA5HhlHbSq9Hk5qKydsbnJ1L8cBCFaS+1qk0N1qthuTMG2jTUvH298Woc1I6JEsqzZviJC+OzY71K+kwamlghBAOTeaBEaJskXlg7ER7/hxeLz6D9vw5pUMRdiD1tU7NuXG6cJ4qfZ5VZWxqzpuSJC+OTQ31kwbGRtr0NFy/+xZteprSoQg7kPpap+bc6NJSKf/DNjRp6otNzXlTkuTFsamhftLACCGEEMLhSAMjhBBCCIcjDYwQQgghHI40MDYyVA4kY+JUDJUDlQ5F2IHU1zo15yavSiAJE6ZgrKK+2NScNyVJXhybGupn92HUy5YtY9asWfTu3Zu3334bgJycHKZPn84333xDbm4uUVFRTJgwAX9/f/PrYmNjeffdd9m7dy/lypWja9euDB8+HCcn2+Z4kGHUQpRtMoxaiLJFFcOojxw5wtq1awkNDbXYPnXqVH766Sc+/PBDVq5cyfXr1xk8eLD5eYPBQP/+/dHr9axdu5bp06ezefNm5s2bZ89wS0STkozLls1oUpKVDkXYgdTXOjXnRpuSQvmtX6oyNjXnTUmSF8emhvrZrYHJzMxk5MiRTJkyBW9vb/P29PR0Nm7cyFtvvUWLFi1o2LAhU6dO5eDBgxw6dAiAX3/9lTNnzvDBBx9Qv3592rRpw7Bhw1i1ahW5ubn2CrlEdJcu4v1KH3SXLioah7APqa91as6N86ULVOn/EtqL6otNzXlTkuTFsamhfnZrYCZNmkSbNm2IjIy02H706FH0er3F9uDgYAIDA80NzKFDhwgJCbG4pBQVFUVGRgZnzpyxV8hCCCGEcBB2WTTk66+/5u+//2bDhg2FnktISMDZ2RkvLy+L7X5+fsTHx5v3ubl5AcyPC/YRQgghxH9XqTcwV69e5b333uOTTz7B1dW1tA8vhBBCCFH6DcyxY8dITEyke/fu5m0Gg4H9+/ezatUqPv74Y/R6PWlpaRZnYRITEwkICADyz7YcOXLE4rgJCQkA5n2UYnJzRx/WCJObu6JxCPuQ+lqn5twY3d250TAc3N2UDqUQNedNSZIXx6aG+pX6MOqMjAxiY2Mtto0ZM4batWvz6quvUqVKFVq0aMGsWbPo0KEDAOfOnaNTp06sW7eOxo0bs3PnTgYMGMCvv/6Kn58fAOvWrWPGjBns3r0bFxeXEscjw6iFKNtkGLUQZUtJh1GX+hkYDw8PQkJCLLaVK1eOChUqmLf36NGD6dOn4+3tjYeHB1OmTCEiIoLGjRsD+Tfs1qlTh1GjRjFy5Eji4+P58MMP6dWrl03NixBCCCHKJkVm4h07diwPPfQQQ4cO5YUXXsDf35/58+ebn9fpdCxZsgStVsszzzzDyJEj6dq1K0OHDlUiXAtOfx3Gv6o/Tn8dVjoUYQdSX+vUnBuXvw4TXLMiuiPqi03NeVOS5MWxqaF+dhmFdKuVK1daPHZ1dWXChAlMmDDB6muCgoJYvny5vUOzncmEJjcXuS5VRkl9rVNxbjQqjk3NeVOU5MWxqaB+shaSEEIIIRyONDBCCCGEcDjSwAghhBDC4dh9NWqllfow6uxsdBcvYKhRE9xl/oIyR+prnUpzo9VqSEnNwPnSBcrXCcboqrK5YFSaN8VJXhybHetX0mHU0sAIIRyazAMjRNlS0gZGLiHZSHv5Eh5vDEZ7+ZLSoQg7kPpap+bcOMVcImD4EFXGpua8KUny4tjUUD9pYGykTU7CfdUKtMlJSoci7EDqa52ac6NLSsJ7zUo0SeqLTc15U5LkxbGpoX7SwAghhBDC4UgDI4QQQgiHIw2MEEIIIRzOPVlKoCwxBlQka+ibGAMqKh2KsAOpr3Vqzk1eQEWSB78BFdUXm5rzpiTJi2NTQ/1kGLUQwqHJMGohyhYZRm0nmox0nH/bhSYjXelQhB1Ifa1TW240mvwvyI/N/fddkK6O2G6mtrypheTFsamhftLA2Eh37iwVunVBd+6s0qEIO5D6Wqem3Gg0oNdo0Wvyf4S5nDtLUM/H0Z07a9HYqIGa8qYmkhfHpob6yT0wQgiHo9FoyMzNA8DV1emm7ZibGmeMcvlYiDJMzsAIIcqQ/MYmMzcPjZpOwwghSp00MEIIIYRwONLA2Mjk5IyhSiAmJ2elQxF2IPW1Ts25MTk7k1clEJzVd1VczXlTkuTFsamhfjKMWgjhcG4eOu3r6kRSTl6h/5ch1UI4JhlGLYQQQogySxoYG+n+PoZvo3ro/j6mdCjCDqS+1qk5Ny7Hj1Gz6X3o/j4KgFaTP1JJDffxqjlvSpK8ODY11E8aGBtp8vTorsaiydMrHYqwA6mvdWrOjUavx+lqLOjz0GpAq9ORlGNAr9Eq3sSoOW9Kkrw4NjXUTxoYIUSZotFoyMrNY+/5RBlOLUQZJg2MEKJMuqE3KB2CEMKOpIERQjgUzT/3twgh/ttkGLWNNBnpOB0+RF6jxpg8ih/mJRyL1Nc6NeSmYKmAG3kG9EbQYsLX1YnkxGTcjhzCvVkzUt3KkZ1nZPeZBNqEBCg+nFoNeVMjyYtjs2f9SjqMWhoYIYTDKJj/5fDlFOoHeeOi1eLrqrOYByZVb1BVAyOEsI3MA2Mn2quxlJ/yLtqrsUqHIuxA6mudmnKTk2d5f4vuaix+UyeiiVU+tlupKW9qInlxbGqonzQwNtLGX6fcvNlo468rHYqwA6mvdWrOjVP8dXwWzFFlbGrOm5IkL45NDfWTBkYIIYQQDkcaGCGEEEI4HGlghBBCCOFwpIGxkdHHl+xevTH6+CodirADqa91as6NwdeX1OdexORbODaNJn/0klJTx6g5b0qSvDg2NdRPhlELIRxGwTDqfecTaVzDt9hh1G1DAyjn4kRGTh7lXZxwNhnl54EQKifDqO0lOxvdieOQna10JMIepL7WqTg3muxsXE4Wjk2j0ZCRY2DPOQXXRVJx3hQleXFsKqifNDA2cjp9Et/WzXE6fVLpUIQdSH2tU3NuXE6fpHrbFuhOFR1bjoLrIqk5b0qSvDg2NdRPGhghhBBCOBxpYIQQQgjhcEq9gVm6dCk9evQgIiKCFi1aMHDgQM6dO2exT05ODhMnTqR58+ZEREQwZMgQEhISLPaJjY2lX79+NGrUiBYtWvD++++Tl5dX2uEKIYQQwgGVegOzb98+evXqxfr164mOjiYvL4++ffuSlZVl3mfq1Kn89NNPfPjhh6xcuZLr168zePBg8/MGg4H+/fuj1+tZu3Yt06dPZ/PmzcybN6+0w7WdRoPJxQXFxmQK+5L6Wqfi3JhUHJua86YoyYtjU0H97D6MOikpiRYtWvD5559z//33k56eTosWLZg5cyYdO3YE4OzZs3Tu3Jl169bRuHFjdu7cyYABA9i1axf+/v4ArFmzhpkzZ7J7925cXFxK/P4yjFqIssPWYdTt6lXEYISdJ+NoE1pRVqYWwgGoZhh1eno6AN7e3gAcPXoUvV5PZGSkeZ/g4GACAwM5dOgQAIcOHSIkJMTcvABERUWRkZHBmTNn7B2yEKKM02jkD38hHJ1dGxij0cjUqVNp0qQJISEhACQkJODs7IyXl5fFvn5+fsTHx5v3ubl5AcyPC/ZRiu7USSo83MrqcE3h2KS+1imZm4LZdK1xPn2Squ1boz15okTH0mu06DXae9LEyPdU0SQvjk0N9XOy58EnTpzI6dOnWb16tT3f5p7S3MjG+a/DaG7I5EtlkdTXunudm/yzJBrARC5aMnPz8HR1BgrP6aLNzsbt6BFybtwowXE1ZObmX3LycdFh78nI5XuqaJIXx6aG+tntDMykSZP4+eef+eyzz6hcubJ5u7+/P3q9nrS0NIv9ExMTCQgIMO9z66ikgscF+wghyq6CsyTJuQbyNDoyc/PYcy6RLH0eINd+hBB2aGBMJhOTJk3ihx9+4LPPPqNatWoWzzds2BBnZ2d2795t3nbu3DliY2Np3LgxAI0bN+bUqVMkJiaa9/n999/x8PCgTp06pR2yEEJlCs6S3Ny0KDmbrhBCfUr9EtLEiRP56quvWLRoEeXLlzffs+Lp6Ymbmxuenp706NGD6dOn4+3tjYeHB1OmTCEiIsLcwERFRVGnTh1GjRrFyJEjiY+P58MPP6RXr142jUASQjg2aVqEENaUegOzZs0aAF588UWL7dOmTaN79+4AjB07Fq1Wy9ChQ8nNzSUqKooJEyaY99XpdCxZsoR3332XZ555Bnd3d7p168bQoUNLO1ybGarXIPWjzzBUr6F0KMIOpL7WqTk3+uo1ubr0U9xq3D62/Jt27+0lKDXnTUmSF8emhvrZfR4Ypck8MEI4noL5XnaevE67+pXMc7kU/P++8wk2zwPTtl5Fyrk4ka03oDeCFpPMCyOECqlmHpiyRnP9Ou6LF6C5fl3pUIQdSH2tU3NudPHXqbB0AZrrcVb30Wg0ZOQY+PNiMnlGI/fqTIya86YkyYtjU0P9pIGxke5aLB4TxqK7Fqt0KMIOpL7WqTk3Tldj8Z84Du3Vq8Xum5N3b++rUXPelCR5cWxqqJ80MEIIVdFoIE+jxSjDpYUQtyENjBBCVTQaDVn6vPzLPDLfvxDCCmlghBBCCOFw7LqUQFlk9PQip0MnjJ5exe8sHI7U1zp75+ZuTrYYvLzJfLQjJi/11U2+p4omeXFsaqifDKMWQijm1vWOAFwwkZZnLHIo9N0Moy7qtT4uWhlGLYTKyDBqe9Hr0SQkgF6vdCTCHqS+1pVybopa7ygzN++fhsb22LSJdxdbwYrXpX7bjXxPFU3y4thUUD9pYGzkdPwY/vfVxun4MaVDEXYg9bWutHNT1HpHd8r1+DFqh9VB97ftseU3Lv82U3qNtlSbGPmeKprkxbGpoX7SwAghFKXkekcFZ4FuXvH6js8CCSHuKbmJVwjxH5Z/Fkin1SArXgvhWOQMjBBCCCEcjjQwQghxE61G5s8TwhHIJSQb5TUII+FsDKZy5ZUORdiB1Nc6Necmp0EYZ09ewsfHG4x3fhxnnRatTkcuGpw0plKZgkHNeVOS5MWxqaF+0sDYSqfDJBMvlV1SX+vUnJuC2HQ6MN75fSxOOg1ZuXnk/TNHTKlMk6XmvClJ8uLYVFA/uYRkI925M3g/3RXduTNKhyLsQOprnZpz43zuLIHPdUd7Vn2xqTlvSpK8ODY11E8aGBtpMjJw+XkHmowMpUMRdiD1tU7NudFmpFNupzpjU3PelCR5cWxqqJ80MEIIYYVGbugVQrWkgRFC3HMaDeRptBjvYvZdeyuY5K60Z+YVQpQOaWCEEPdMwRkNjUZDlj6PPKNRxac4NHe3PpMQwq6kgbGRIbAq6dNmYgisqnQowg6kvtbdbW5uPqNR2vRBVYl/7wOMQUGlfuy7Jd9TRZO8ODY11E+GUdvI5O/Pjb79lA5D2InU17qiclNwYqJgtHHB2RWTyWSxLf+/+Wc0AFxdS/dHj9HPn9SXX8XX1QlKeTkA7T+fSVOCeWFuzQfI95Q1khfHpob6yRkYG2mSk3D9Yi2a5CSlQxF2IPW17tbc3HqPSMHjm1d0tudZl5tpk5Px2Liu1Oum1YBWpyMpp/Aq1fmrWGtuatCKvmdGvqeKJnlxbGqonzQwNtJdvoTXoH7oLl9SOhRhB1Jf627OTcGZlpvvESl4fPOKzrfuYy/Oly9SeUh/tJdKt24aTf7EdnvPW65SXXSzdms+8ve7OW83Nzz/dfJvzbGpoX7SwAghbJJXzBmVsrii841/PtO/Z10KN2s3K+rMU94tDY8Q4u5IAyOEsMkNfeEzKkUNiy44K1FWaDWQi4bkXAN5/zQmOXqDlcUfC595uqG33vAIIWwnDYwQohRYDosu+GWfqjeoeq4XW2g0GjJyDOw5l0iWPg/QWCz+WBTtLU1cWTw7JYRSpIGxkalcefRN75cVVMsoqW/hm1MLtunLe3Cj6f3klfMowTHyf9n/eTE5v6mxcxNjLFeeG03vx1SunF3fByybkILFHzNyDIXOqhTcAJzi5EZuswcw/oe/p4oi/9YcmxrqJ8OobWSoU5eUb39UOgxhJ//1+hbcu5GZm0d5FyecMWIy5TckaTVqs2vRF9QP8salhA1JTt69OeOgr1OXmK0/2GUY9Z0quAH4oM6XyK9+wGAETsbd4bHy/1sai2OrxX/935qjU0P95AyMEP9BRZ1lyd9++5tT71VDUpbcKKKhKsi/VnvzPDlFT0osSxoIUTRpYGzkdOQQARW9cDpySOlQhB2Uhfpaa04KaLWWI2IKfonefCNuzi2jbgBcjxzi+ZbBuP11+F59lBJzPXKIOoEV0B0+pHQohfidOkatyt64/vM9dfP9QTkaLXn/1MBak3KvhqLfa2Xh39p/mRrqJw2MEGVIUfOT3Pp8nkZHWm7+zajZ+rybfnEWfSPuzaNuxN0ruD/o4KUUUrL1ZOQa0Pxz2e7WuWasnZEpQ32MEHdMfioJ4aCKvtm26EtARS2imJNnNP8yLXouk8KjbkTpud3lOGszGN/p5aTizsoJ4YikgRHCQdz8l3dxZ1punp/kbqfzl6G/91Z+s1H4jMw/z5boctLN3yu3XjKUJkaUFdLACOEACq87dPubbW+en6Ss3kNRFt3t/DkaDeh0GvNsyfnNy7+XDOV7QJQlMozaRnkh9UjccxBjYJDSoQg7UGN9zX+R5+Sv5OzjojM/V9TNtvDv/CR5Wi2+rjpKQ25IPbas+5G6dUNxLpUjlp7ckHpc/O1PPGtWVzqUQlJq1OHy7j/JrRQEF1Nvu2/BZbvDl5Pzh6tri/4b8+YVsgteByZy0XJDb0BvBC0mXF2dLC4Zqoka/62JklND/eQMjK3c3DDWDgY3N6UjEfagsvoWnHm5+S/yW6ftv1c325rc3MioWhOTSnJzM5ObG/patVVTt5sZXF3JqxVsU95ud3/MzStk52m15stDeRodmbl592zywLumsn9rwkYqqJ80MDbSXryA52uvoL14QelQhB2orb4Fl38sfylZjha6VzfbOl26QIuJb+J86aJdjn83nC5doNLgfqqp2808rl4mYNCrOF26UCrHu3mF7Gy9gYxcy9o7ylw9avu3JmyjhvqpuoFZtWoV7dq1IywsjKeeeoojR44oHRLa1BTcNq5Hm5qidCjCDtRa35L8UrL3zba6lBRqff8/dCrLDeTH5rlpPZqUFKVDKcQ1PQ2PjevRlXJsN0+Q54g3Wqv135ooGTXUT7UNzDfffMO0adMYNGgQmzdvpl69evTt25fExESlQxNC/AcVveq0EEIpqm1goqOjefrpp+nRowd16tRh4sSJuLm5sXHjRqVDE0LY6NZVmR1NwaguxzvPIUTZpcpRSLm5uRw7doz+/fubt2m1WiIjIzl48KBNxyr1n5k6HXh6gk4nf42VRSqrb8F8HuVdnPJv3tQWjD7Jbwo83JzQasFosvz/ovbXaKy/9nbPFfy/5p/caHS6Eh+3vIsTTlrQOeXfiJw/w6/pruIo8vMVxOaku6Pc3PY5DXi7O5Ojz8NoBA93Z5vyXM7dxZy3u4njjnNz0/7/Hkfp72xU929N2MiO9Svp8TQmk/rWN42Li6N169asXbuWiIgI8/YZM2awf/9+vvjiCwWjE0IIIYTSVHsJSQghhBDCGlU2MD4+Puh0ukI37CYmJuLv769QVEIIIYRQC1U2MC4uLjRo0IDdu3ebtxmNRnbv3m1xSUkIIYQQ/02qvIkX4OWXX2b06NE0bNiQ8PBwPvvsM7Kzs+nevbvSoQkhhBBCYaptYDp37kxSUhLz5s0jPj6e+vXr89FHH8klJCGEEEKocxSSEEIIIcTtqPIeGCGEEEKI25EGRgghhBAORxoYIYQQQjgcaWCEEEII4XCkgbkHBgwYwEMPPURYWBhRUVGMHDmSuLg4pcNSlZiYGMaOHUu7du0IDw/nkUceYd68eeTm5iodmqosXryYZ599lkaNGtGsWTOlw1GFVatW0a5dO8LCwnjqqac4cuSI0iGpyv79+xkwYABRUVGEhoayfft2pUNSlaVLl9KjRw8iIiJo0aIFAwcO5Ny5c0qHpTqrV6/m8ccfp0mTJjRp0oRnnnmGnTt3KhqTNDD3wIMPPsiHH37Itm3bmDdvHpcvX2bYsGFKh6Uq586dw2QyMWnSJL7++mvGjBnD2rVrmTNnjtKhqYper6djx44899xzSoeiCt988w3Tpk1j0KBBbN68mXr16tG3b99Cs3j/l2VlZREaGsqECROUDkWV9u3bR69evVi/fj3R0dHk5eXRt29fsrKylA5NVSpXrsyIESPYtGkTGzdu5MEHH2TQoEGcPn1auaBM4p7bvn27KTQ01JSbm6t0KKq2fPlyU7t27ZQOQ5U2btxoatq0qdJhKK5nz56miRMnmh8bDAZTVFSUaenSpQpGpV4hISGmH374QekwVC0xMdEUEhJi2rdvn9KhqN79999vWr9+vWLvL2dg7rGUlBS2bt1KREQEzs7OSoejaunp6Xh7eysdhlCp3Nxcjh07RmRkpHmbVqslMjKSgwcPKhiZcGTp6ekA8rPnNgwGA19//TVZWVmKLu+j2pl4y5oPPviAVatWkZ2dTePGjVmyZInSIanaxYsX+fzzzxk9erTSoQiVSk5OxmAw4OfnZ7Hdz89P7mEQd8RoNDJ16lSaNGlCSEiI0uGozsmTJ3n22WfJycmhXLlyLFy4kDp16igWjzQwd2jmzJksX778tvt88803BAcHA9C3b1969uxJbGwsCxYsYPTo0SxduhSNRnMvwlWMrXkCiIuL45VXXqFjx448/fTT9g5RcXeSIyFE6Zs4cSKnT59m9erVSoeiSrVq1eLLL78kPT2d7777jtGjR/P5558r1sRIA3OH/u///o9u3brddp9q1aqZ/9/X1xdfX19q1apFcHAwbdq04dChQ2V+dW1b8xQXF0fv3r2JiIhg8uTJ9g5PFWzNkcjn4+ODTqcrdMNuYmKirJkmbDZp0iR+/vlnPv/8cypXrqx0OKrk4uJCjRo1AGjYsCF//fUXK1asYNKkSYrEIw3MHSpoSO6E0WgE+E8MEbYlTwXNS4MGDZg2bRpa7X/jFq27+V76L3NxcaFBgwbs3r2bRx55BMj/t7V7925eeOEFhaMTjsJkMjF58mR++OEHVq5cKX8s2MBoNCr6e0waGDs7fPgwf/31F02bNsXLy4tLly4xd+5cqlevXubPvtgiLi6OF198kcDAQEaPHk1SUpL5uYCAAAUjU5fY2FhSU1OJjY3FYDBw/PhxAKpXr0758uUVju7ee/nllxk9ejQNGzYkPDyczz77jOzsbLp37650aKqRmZnJpUuXzI9jYmI4fvw43t7eBAYGKhiZOkycOJGvvvqKRYsWUb58eeLj4wHw9PTEzc1N4ejUY9asWbRu3ZoqVaqQmZnJV199xb59+/j4448Vi0lWo7azkydP8t5773Hy5EmysrIICAigVatWDBw4kEqVKikdnmps2rSJMWPGFPncyZMn73E06vXWW2+xefPmQttXrFhB8+bNFYhIeZ9//jkff/wx8fHx1K9fn3HjxtGoUSOlw1KNvXv30rt370Lbu3XrxvTp0xWISF1CQ0OL3D5t2jRphG8yduxY9uzZw/Xr1/H09CQ0NJRXX32Vli1bKhaTNDBCCCGEcDj/jZsMhBBCCFGmSAMjhBBCCIcjDYwQQgghHI40MEIIIYRwONLACCGEEMLhSAMjhBBCCIcjDYwQQgghHI40MEIIIYRwONLACCGEEMLhSAMjhBBCCIcjDYwQQgghHI40MEIIIYRwOP8Pnu9vOTyDF+MAAAAASUVORK5CYII=",
      "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": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:57.985997Z",
     "iopub.status.busy": "2025-04-15T03:12:57.985842Z",
     "iopub.status.idle": "2025-04-15T03:12:58.330317Z",
     "shell.execute_reply": "2025-04-15T03:12:58.329752Z",
     "shell.execute_reply.started": "2025-04-15T03:12:57.985982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cHsOxfft2xo4dS6tWrRzGAuXn/PnzDBo0iMaNG9OqVSsmTpzIpk2biI6OZtu2bQW+12V9+vShT58+9vtGo5GPPvqI7t27c8cdd9C4cWOeeuop/vrrrxt+DteOQbn33ns5cuQI27dvJzo6mujoaPr06cOZM2eIjo5m4cKFedrYuXMn0dHRrFmz5obvV1hHjhzhnnvuYeDAgaxfvx6z2Vws7ebk5BS4rlafPn34/fffOXv2rH3fr5352Wq18umnn9oL3meffZZTp07ddJ7KlSvfdI/EmjVrUBSlSAX21S4P+E9PT7/hthEREQCkpaXd1HsJURjSgyIE2K/mCgwMBGDRokXce++9dOnSBZPJxNq1a3nppZeYM2cOd9999w3bGzduHMHBwQwePJisrKwCt8vJyeHZZ5/l3Llz9OnThwoVKvDdd98VqpgoSEZGBv/3f/9H586defzxx8nMzOSbb77hxRdf5P/+7/+oV69eodsaPXo0EyZMwNvbmwEDBgC2K8iqVKlC06ZN+f777/Oc/li9ejU+Pj43vGS9KOrVq8fAgQNZuXIlgwYNIiwsjG7dutGjRw+qV69+U22uXLmSL7/8ElVVqVWrFgMHDnTofRgwYADp6emcP3/ePm7p2jEX8+bNQ1EUXnjhBTIyMvjss88YMWKEfQmM0mIymfjxxx9p0qSJfexQYaSlpWE2m0lISOCLL74gIyMjz6kjsC2PkZaWhslk4siRI0yfPh0fHx8aNmxYnLshhAMpUEpInz59qFu3Lm+88Yaro4h8ZGRkcPHiRYxGIzt37mTWrFl4enpyzz33APDTTz/h6elp37537950796dBQsWFKpACQgIYOHChTc8XfLVV19x8uRJpk+fTseOHQHo2bOnU6ceAgIC+O233xxOP/Xs2ZOOHTuyePFiJk6cWOi27r//fqZPn05QUFCeTF27duXtt9/m2LFj1KpVC7jyi/KBBx4o0lieG/H392fIkCEMHjyYbdu2sWLFChYtWsTcuXNp1qwZjz32GA899JDDMbueJk2a0LFjRyIjI4mPj+fLL79kxIgRpKen89RTTwG2MSqLFi0iLS2twOORm5vLqlWr7J+1v78/7777LocPH84z2LQkbd68mZSUlEKd3rlaz5497VMXeHt7M3DgQB577LE82+3du5cnnnjCfr9GjRp8+umn9oJeiJJQrk/xzJkzhx49etCkSRNatWrFoEGDOH78uMM2ubm5jBs3jhYtWtCkSROGDh1qX4cHrlxuWBJdnSkpKUyYMIEHH3yQhg0bcvfdd/POO+/k6YKNi4ujX79+NGrUiFatWjFlypRCd4EbjUYeffRRoqOjOXDggMNzmzZtomfPnjRp0oSWLVsydOjQMnP56XPPPWc//fLyyy/j4+PDzJkz7YMLr/5Fl5qaSnp6OnfccQf79+8vVPs9e/Ys1FiOP/74g7CwMIcrQby8vOzrNd0MvV5v/4VptVpJSUnBbDZTv379QucvjI4dO+Lh4cHq1avtj23evJnk5GQeeeSRYnufqymKQsuWLXn//ff5888/GTduHEajkZiYGNq0acOYMWNITU29YTvLly/n2Wef5b777uPJJ59kxYoVREVF8eGHH5KTk1PoPN27d3coBO+8806AG04fUNzWrFmDwWCwF7mFNWnSJD777DPGjBlDrVq1yM3NzXfSydq1a7NgwQJmzZrFiy++iLe393V7BoUoDuW6B2X79u307t2bBg0aYLFY+OCDD+jbty9r1661D2qcOHEiGzduZPr06fj5+TFhwgSGDBlin4ekJMXHxxMfH09MTAy1a9fm7NmzjB07lvj4ePtcLRaLhf79+xMaGsry5cvt2xsMBl555ZUbvsd7771HhQoVOHjwoMPjZ86cYdCgQTz//PNMnTqV9PR0Jk2axNChQ697eeOt4u2336ZGjRro9XpCQ0OpUaOGw7n/DRs28Omnn3LgwAGHmXMLe8VOYbvZz549S7Vq1fK0W6NGjUK9viArV67k888/58SJE5hMpiLnKgx/f3/uuece1qxZw/DhwwHb6Z2KFSvax/QU5PJCnJf5+flhMBi4ePGiw+MBAQEFDkT29fWlV69edOvWjU8//ZTZs2ezfPlyevXqRUBAQJH2xd3dnd69ezNmzBj27t1rLzRuJDw83OG+v78/ULpjMzIzM1m/fj1t2rQhKCioSK+9eqBtp06dePjhhwGIiYlx2M7X19e+qvv999/P6tWrGTRoECtXrnS44keI4lSuC5T58+c73J88eTKtWrVi3759NGvWjPT0dFasWMHUqVPt52UnTpzIww8/zK5duwgNDeWZZ54BoFmzZgB069bNPieGqqq89957fPPNNxgMBnr16mWfb6IwoqKiHCbRqlq1KsOHD2fkyJGYzWbc3NzYvHkzR48eZcGCBYSGhlKvXj1eeuklpk6dypAhQ657lcnGjRv5888/mTFjBn/88YfDc/v27cNqtTJ8+HD7L+4XXniBQYMGYTKZ7BO63aoaNmxov4rnWn///TcDBw6kWbNmjBkzhrCwMAwGAytWrCj0wM/LE/mVBovF4tBb89133zFq1Cjuv/9++vbtS0hICHq9njlz5hT7X/Zdu3Zl3bp17Ny5k6ioKH777TeefPLJGw70bNOmjcP9SZMm0bx58zzjVhYtWkSLFi3ybWPPnj2sWLGCH374gbS0NBo1asRjjz1mP91UVJUrVwYoVA/MZQXtZ2lOL/Xrr78W+uqd6wkICKBly5asXr06T4FyrQceeIDXXnuNtWvXSoEiSky5LlCudfnUyeW/vvbu3YvJZLL/5QBQq1YtwsPD2bVrF3369GHGjBkMHTqUdevW4evr63BqYOXKlTz//PN8/fXX7Nq1i1GjRtG0aVNat24N2CZbOnv2bL4TMRUkIyMDX19f+0KEu3btIioqymGCsTZt2jB27FiOHj2aZ16HyxITE3nrrbfsYy+udfvtt6MoCitWrKB79+5kZWXx3Xffcdddd93yxcmN/PTTT3h4eDB//nyHAm/FihXF/l4REREcPnwYVVUdelEujwu4WkBAQL5/mcfFxTks8vjTTz9RpUoVZs6c6dDm1TMkF8X1eo3atm1LcHAwq1evplGjRmRnZxdq/MyCBQsc7teuXZuAgIA8j1/7yy8pKYnvvvuOb7/9liNHjhAYGEi3bt147LHHnB7zcbl4Cw4Otj92K8xxs3r1ary9vfNcYXQzcnJyCnUVj9FoxGq1FmpbIW6WFCiXWK1WJk6cSNOmTe3/0CUmJmIwGOzdtpeFhISQkJCAXq+3FzMhISF5trs8SyfY5lxYsmQJW7dutRcoYWFhWK3WQme8ePEin3zyicNgtcTExDyzn16+f203+mWqqjJq1Ch69epFgwYN8h1XUqVKFT7//HOGDx/OmDFjsFgsNGnShLlz5xY6761Kr9ejKIrDufjY2FjWr19f7O/Vrl07Nm/ezLp16+zjB7Kzs/n666/zbFulShX++ecfjEajvXDasGED586dcyhQLvemXF307N69m127duU5JVEYXl5eBZ6ycHNzo1OnTqxZs4Zjx44RFRVVqL+ory76C/P4uXPnmDBhAhs3bsRisdjHjN1///2FmovmahcvXnQoQsBW+H/xxRcEBQVx++232x/38vLS9C/hixcvsnXrVjp16lTgoOS4uDiys7MdepaSkpIICQlx2C42NpatW7dSv359+2NpaWl4eXnl+aPk8lVKV28rRHGTAuWScePGceTIEb788stia/Pa6bTDwsJISkqy33/11VcL3VZGRgb9+/enVq1a+U6yVBSLFy8mMzOT/v37F7hNQkICb731Fl27dqVz585kZmby8ccfM2zYMBYsWHBL/GV5s9q3b8+CBQt48cUX6dy5M0lJSXz55ZdUrVqVQ4cOFet79ezZk6VLlxITE8O+ffsICwvju+++y7dX6/HHH+enn37ixRdfpGPHjpw+fZrVq1dTtWpVh+3uvvtufv75ZwYPHszdd99NbGwsy5cvp3bt2jc1sPH2229n2bJlfPLJJ1SrVo3g4GCHS1G7du3K4sWL2bZtGyNGjCj6h1AIp0+fZv/+/fTr148ePXo4NZZm6dKl/Prrr9xzzz2Eh4cTHx/Pt99+S1xcHO+9955DwXP77bfzww8/MGnSJBo0aHBTPRWXp8W/ekmBguzYsYMdO3YAtuIjKyuLTz75BLCdRr58KvmyH374AbPZfN3TOzExMWzfvt3hu9ulSxdatWpF3bp1CQgI4OTJk6xYsQKz2ezw79L27dt55513ePDBB6lWrRomk4l//vmHn3/+mfr165fYYGghQAoUAMaPH8/vv//OkiVLqFSpkv3x0NBQTCYTaWlpDr0jSUlJhIWF3bDdy6dhLlMU5abOTWdkZPDiiy/i4+PDrFmzHP6aCQ0NZc+ePQ7bX77KqKCMf/31F7t27cozBqNHjx506dKFKVOmsHTpUnx9fXnttdfsz7///vu0b9+e3bt3l+nZI1u1asW7777LvHnzmDhxIpGRkYwYMYKzZ88We4Hi5eXFwoULmTBhAkuWLMHT05MuXbrQrl07XnzxRYdt27Zty6hRo1iwYAETJ06kfv36zJ49mylTpjhs1717dxITE/nqq6/YvHkztWvX5v3332fdunVs3769yBkHDx5MXFwcn332GZmZmTRv3tyhQKlfvz516tTh2LFjJfYLq1GjRvz222/FMq1606ZN+ffff/nmm29ISUnBy8uLhg0b8u677+aZA+Spp57iwIEDfPvttyxcuJCIiIgiFyiXi8LC/Jvx119/MXPmTIfHPvroI8A2++u1Bcrq1asJCQkpsOepIE8++SS///47mzZtIjMzk+DgYFq3bk3//v0d/rCKioqiRYsWrF+/noSEBFRVpWrVqgwePJi+ffsWufdKiCJRyzGr1aqOGzdObdOmjXrixIk8z6elpam33367um7dOvtjx44dU6OiotR///1XVVVV/eeff9SoqCj14sWLDq99+umn1XfeecfhsYEDB6oxMTFFypienq727NlTffrpp9WsrKw8z//+++9q3bp11cTERPtjy5cvV5s2barm5ubm2+bZs2fVQ4cO2W+bNm1So6Ki1HXr1qnnzp1TVVVVJ02apD722GMOr7tw4YIaFRWl/vPPP0XaB1F0f/31lxoVFaX+9ddfro5SKI8++qj6zDPPuDqGJk2ZMkVt165dgT+PQoj8let5UMaNG8f333/PtGnT8PHxISEhgYSEBPs8CH5+fvTo0YPJkyfz119/sXfvXkaPHk2TJk3sPQgREREoisLvv//OxYsXyczMLPT7T5s2zaGH4loZGRm88MILZGVl8e6775KRkWHPeHl8RJs2bahduzavvfYaBw8eZNOmTUyfPp3evXvb/7rZs2cPDz30EBcuXABsl0ZGRUXZb5dn4qxataq9B6l9+/b8999/zJw5k5MnT7Jv3z5ef/11IiIiChx4K8qn//77jwMHDtC1a1dXR9Gkbdu2MWjQIOltEKKIyvUpnsuLYF29jgnYLnm8vHLr6NGj0el0DBs2DKPRaJ8M6rKKFSsydOhQpk2bxuuvv07Xrl3tlxnfSEJCAufOnSvw+X379rF7924AOnTo4PDc+vXriYyMRK/XM3v2bMaOHcsTTzyBl5cX3bp1Y9iwYfZts7Oz88yHcSOtWrVi2rRpfPbZZ8yfPx9PT08aN27MvHnzCj1bpyjbDh8+zL59+/j8888JCwuzz6EhHJXE1V9ClAeKqpbiBftCiEK5PLDyevOAuNqMGTOYNWsWNWrUYNy4cTRv3tzVkYQQZYgUKEIIIYTQnHI9BkUIIYQQ2uRUgTJ37lyio6N599137Y/daHE9cG5xOyGEEEKUfTddoOzZs4fly5fnmYxs4sSJbNiwgenTp7N48WLi4+MdJha7vLidyWRi+fLlTJ48mZUrV970NNxCCCGEKHtuqkDJzMxk5MiRvPPOOw6rhl5eXG/UqFG0atWK+vXrM3HiRP7991927doFYF/c7v3336devXq0b9+el156iaVLlzqsGiuEEEKI8uumLjMeP3487du356677uLTTz+1P36jxfUaN25804vb5ScpKZ0bDfFVFAUfH48irXlTHF588QWio6MZOfL6q4KWNTqdjszM3FJdzbWsUxIS8PhuJbmPdkMtxGyk5SmPlrI4Q+v7UVz5nGlH65+RKBxFgZAQv0JtW+QCZe3atezfv59vvvkmz3M3Wlzv8jZFXdyuIKpKIQoUx+2vNnv2J8ydO9vhserVq/Ptt9/b7+fm5vLBB1P5+ed1GI1GWrW6i9dff9O+0Nbff++gX7++bNy4GT8/x/0uTL7rSU1NZfbsT/jrry2cP3+eoKAg7r77XgYOHIyfn+0AHz58iAUL5rNr17+kpKRQuXI4jz32OE899XSB7V7OnJ/Fi7/k9tvrk5uby8SJEzhwYD8nTpygbdt2fPDBR9fNe73PWtw8NTSM7L79Lt1xbRbQVh4tZXGG1vejuPI5047WPyNR/IpUoJw7d453332Xzz//HA8Pj5LKVKpq1arFp5/Os9+/vBLsZdOmvcfmzZuYMmUqvr5+TJkykREjXmbBgkUlni0hIZ6EhHiGD3+VmjVrce5cHBMnvkNCQjzvv/8BAPv37yc4OJh33plExYqV2L17F+++Ox6dTk+vXk/m226jRo35+effHB779NOZbN++jdtus63karVa8PDwoFevp1i//teS3VFxXUpKMoY/fsfU7m7UwCBXx9FUHi1lcYbW96O48jnTjtY/I1H8ilSg7Nu3j6SkJPssq2Ab9Lpjxw6WLl3K/Pnzb7i43s0sbleS9Hq3PD06l6Wnp7Nq1UomTpxM8+a2ybLGjp1Ajx6PsmfPbkJDQ+09Ee3btwGgS5dHGDfuHcC23P306R+watW3GAwGevR4nAEDBhU6W+3adZg69UP7/SpVqjB48FDefPN1zGYzbm5udO3azeE1kZGR7Nmzm99++7XAAsVgMDjss8lk4vffN9Cr11P2VYq9vLwZPfotAHbv3qXpJefLOv3pUwS8+CzJv/6BWQP/MGspj5ayOEPr+1Fc+ZxpR+ufkSh+RRok27JlS1avXs2qVavst/r169OlSxf7/xsMBrZu3Wp/zfHjx4mLi7OvXdO4cWMOHz5MUlKSfZstW7bg6+tL7dq1i2eviuD06VM88MB9dOnSkTfeGOUw9fyBA/sxm820aNHS/liNGjWoVKkye/bsoWLFSvaejJUrv+fnn39jxIgrY07WrPkeLy8vFi1ayksvvcy8eXP4668rn82YMW/yv/+9UKS8GRnp+Pj45lkp2XGbDIfByzfyxx+/k5qayiOPPFqkLEIIIURJKVIPiq+vL1FRUQ6PeXt7ExgYaH/88uJ6AQEB+Pr68s477zgsrnf14nYjR44kISEhz+J2xUFVVaxWCxaLitXqhtVqzTMu4vbbb2fs2PFUq1aNxMRE5s2bQ9++z/LVV9/g4+NDYmICBoMBHx8frFaL/XXBwcEkJiagKNjHggQGBtr/32q1oKoqtWvX4X//s50zjYyMZPnyZWzbttU+JXhISAgWi8Wh7etJSUlm3rw5dOvWvcDX7N69i59//omPPvq40O2uXPktLVu2IiwsLN/XqKpq/zyvx9b5omIyGbFa5SRxYeh0OnQ6vb3nSgghhE2xLxZ4o8X1CrO4nbPMZhOpqRcxmXJwc3PDw6MiZrMlz5U8TZo0tv9/REQ4kyZNpmfPx1m7djWdO3cmN9e2qnFWVobD66xWKyaTkaysDHJzs+3b6PXKVdtYqF69hsNrg4KCSEiItz/2wgsv5Nt+fjIzM3nllZepVq0affo8ne9rjh8/ziuvDOe5556jYcMGhWo3Pj6ev/7aytix4wrc3mw2YbGYb9ieTqfDzU1PcnK8TLxXBO7unvj7B+PmZnB1FCGE0AynC5TFixc73Pfw8GDMmDEORcm1IiIimDdvXoHPO0NVVZKSzqPT6QgICMVgMODh4YXBkLcH5Vre3r5Uq1aN+Ph4vL19CQ+PwGQyYbGo9t4RsPVkVKpUGW9vXzw8vOyv9fb2tW+j0+nx8vJyeMzNzQ2dTu/wWGFkZmYSE/Mafn5+fPDBR/kOUD5+/BivvPIy3bs/xsCBgwvd9q+/fklAQAAPPPBggb8g3dwM6PVuN8ytKLYiJSiogvSgFIKqqlgsZjIyUkhKOk+FCpH59qSonl6YGjRC9fRyQcq8tJRHS1mcofX9KK58zrSj9c9IFL9i70FxNbPZhKpaCQgIw93dE51OQafTA8oNC5SsrCxiY2Pp1KkLOp2e226rj5ubG3//vYP77usAwMmTJzh//jyNGjVGp9PbiwVV5dL72Nh+0Sh5HlMUx8duJCMjgyFDBuHu7s6HH87AyyvvD+exY0cZMKAfnTs/wtChLxW6bVVVWb36ezp37oK7u2eB2xU29+UCxWBwlwKl0DzQ6/VcvHgBs9mEwZD3NKclKpqU9ZtckC1/WsqjpSzO0Pp+FFc+Z9rR+mckil+ZXSxQUW68ax9+OJV//vmbuLiz7N69i1dfHY5Op+ehhzoCtvElXbt2Y9q0qezYsZ39+/czduzbNGzYiIYNGwFQuXJlFEVh06aNJCdfJCsrq9AZZ8z4iLfeGl3g8xkZGQwa1J/s7GzefnscmZmZJCYmkpiYiMViGw9y9OgR+vXrS8uWd/H008/Yn09OvmhvZ+/e/+je/RHi4y84tL99+zbOnj1L16498n3/48ePcejQQVJTU8nIyODQoYMcOnSw0PsnCqcw31UhhChvylwPSlFcuBDP66/HkJqaQlBQEI0bN+WLL5YQFBRs3+bVV19DUXSMHPnKpYnaWvP662/Yn69QoSIDBgxixoyPGDv2bTp37mK/zPhGEhMTOH/+fIHPHzx4gL17/wPg0Uc7OTy3Zs2PhIdH8Ouvv5CcnMwPP6zhhx/W2J+vXDmctWvXAZCTk8PJkyfzjAv57ruVNGrUmBo1auT7/kOHDubcuTj7/Sef7AnAzp178t1elAy3/3YT2PE+Un5cj7lBI1fH0VQeLWVxhtb3o7jyOdOO1j8jUfwU9RaekzwxMe9U9yaTkaSkc4SEVMZgcEenu3aq+9K6WkItd7OpXj7Fk5mZK6d4iuDa7+y13PbsIuj+drb5Hxo2Lv2AGs6jpSzO0Pp+FFc+Z9rR+mckCkdRIDS0hKa6v5VZUDBZSmdNHoNeh14pf0WKK507F8fChZ+xc+ffJCUlERoayoMPPswzz7yAwWAbALxz5998/fWXHDiwj8zMTCIjq/LUU3144IGOBbabmprCuHFvcezYEdLSUgkKCqZNm3b07z8YH5+8A4f37NnF0KH9qVGjFgsXflli+yuEEGVZOSpQbMXJicTMEi9SDHodNUJ90Ot1yKIRpefUqZOoqsrIkaOJiIjkxIljTJnyLtnZ2QwZMhyAvXv3UKtWHXr3fpbg4BD+/HMT77wzBh8fX1q3bptvu4qio23b9vTrN5DAwCBiY8/wwQdTSEtLY+zYdx22TU9P5513xnDHHc24ePFivu0JIYS4sXJUoNiYLFaM5tJd2bgwfvhhtX3sy7FjR9DpdNStextNm95p3yY3N5dt27Zw+vRprFYLlSpVpmXLu/D3t80am5GRwdatf3LhwnmsVgu+vn40b96CyMiqhcrw77//cOrUSW67rT7//vsPRmMutWrVoVWr1uzdu4e9e/9DVVVuv70+jRo1sb/OaDSyfftfJCRcwN/fnx9//Ik+fV6gTh3b5H1nz8YyY8YH7Nu3l5ycbKpVq0H//oNp1qyFvY3HHuvCI490Izb2DBs2rMfPz49nn+3Lo492z5OzIC1b3kXLlldW0o6IiOT06VOsXLnCXqA884zjzL09ez7Jjh1/sXHjbwUWKP7+/nTr9pj9fqVKlenW7XGWLVucZ9upUyfSocND6HQ6Nm3aWOjsQgghHMnlAxpy9OhhdDodXbp0pUWLu9i7d4/DVTObNv1OYmIiHTo8SOfOj6Kq8PPP6+zja7Zu3YzVaqFTpy506/YYzZq1KPLkX+npacTGnuHBBzvSvv29HDlyiJ9/XkdmZiYPP9yZZs2a888/O0hIiLe/5rfffiEnJ5u7776X1q3bUrNmbYYPH0haWipgu3y7ZcvWfPTRJ3z++VJatGhFTMwreQYIL1++lLp1b2PBgqV06/Y406ZN5vTpk/bnhwzpx7vvji3S/mRkZORZXTv/bQq/NEBiYgIbN/5G48ZNHR5fu/Z74uLO8vzz/ytSxhsx14nm4h/bMNeJLtZ2b5aW8mgpizMK2g/buC7F4eaKSYeL63N2pp2ycqxF4UmBUkImjH2Ll18u2uy4Pj6+tGjRioCAQGrVqs1tt9Vn3z7bVTxpaamcPn2KNm3aUbFiJYKDQ7j77nvIysrk1KmTAGRmZlChQkWCgoLx8/OnSpWqVKpUuUgZVFWlbdv2BAYGUbVqNSpVCictLcWeq06daAICAu1X91y4cJ7ExATuued+goND8PHx5YUX/oevrx8bNqwHoE6dKLp27UHNmrWpUqUq//vfQCIiIvjzT8cehlat7qJ798eJjKzC008/S0BAIDt3/m1/vmLFSoSE5L+wY35iY8+wYsVX1+2FWb/+Fw4e3M/DD3e5YXtjxozmvvta07VrR3x8fIiJedP+3Jkzp5k9eyZvvTXhuusk3RQvLyx160E+c+C4hJbyaCmLM/LZD0UBk6Ij2WhxuJkUXekXKcX1OTvTTlk51qLQynWBYrVYWLX0M0b9ryeDHr+f0f17searL7j6wiZVVflu6XxGPNeVQY/fzwdvvcyFuDP25xMvnON/j7bj9PEjTucJC6vgcD84OJgff/yBxx/vzoMP3s+CBQuYMeMje++Fh4cnAQEBpKamAFCnTjQxMSNp2rQh3323slBjIFRVZciQgTRt2pCdO3fi6+uHwWDg+++/o2nThrz+egyTJk3ijjsa0bRpQ5o2bYjFYiEnxzbF/8WLSZhMJr78chHffPMVP//8I0880ZVz5+I4ezYWsPWgzJw5nd69H+Ohh+6mQ4e2nDp1kgsXHHtQatWqY/9/RVEIDg4hOTnZ/thbb41nwIAhhfosExLiefXVodxzz/088ki3fLfZufNvJk0ax2uvvUHNmrVu2OawYa/w+edLmTx5GmfPnmXGDNtK0xaLhXHj3qRv335UrVqtUPmKQnfmNL4vD0F35nSxt30ztJRHS1mckd9+KIpCptHMhoPxrN0dx9rdcWw4GE+m0VzqazcV1+fsTDtl5ViLwit3Y1Cu9uO3X7Lxx+94fvhowqtU59TRQyz4eBJe3j7c18U25mDdt1+yfu0KXnjpdUIrhrNq6WdMHzuC8TMXYXDPO+V8ccrNzSU+Pp5Bg4bi7+/H77//xv79Bxg+fBhLly7Ps/0PP6yldu06JCYmkpqayvfff0vz5i257bb6Bb7H0qVLHP6x0+lsNesDDzzIXXe1Ztu2LRiNRtq2vZsxY97EaDTi6+trvzrJZDLh7e1Nx45d7Gvx1KhRF7PZjK+v7VKyWbOms2PHNgYPHk5kZBU8PDx4880YTCbHeVmu7XlQFCXP+kmFkZiYwNChA6hfvyGvvfZGvtv8++8/xMS8zNChr9CxY+dCtRsSEkpISCjVqlXHzy+AwYNf5LnnXsTDw4ODB/dz5MghPvzwfYBLi1OqtG/fgg8+mMkddzQr8n5cpku+iNfSReQ8/yLWKoUbT1SStJRHS1mccb39yMo1k5Hr2rWtiutzdqadsnKsReGV6wLl2MG9NGrRmoZ3tgIgtGJltm/6lRNHDgC23oX1q/+PTo/3oXEL2wDKF4a/wavPduXfvzbTvN19vN7vCQAmvNwXgKj6jXljykz7eyxatJAlSxZhMpl44IGHGDHiNfslr9e6elwH2NbgefbZ53jwwYdIS0vlwIF9tGnTnpdeGsK5c+cIDg4iNTWVwMBA/vxzE1u3bmXq1A947LFutGjRkvT0NA4fPlhggXLo0EGWLPmCJUuW88AD9zo85+npiaenJ/7+/hiNRvR6HTt2bOftt8c5bBcaGkp2djY6nYKfny86nY7w8HCHeVD++283Dz/chfbt7wFsPSrnz8cBdxR8cG5SQkI8Q4cOIDq6LqNHj7EXXFfbufNvYmJeZsCAoUUahHs1VbUVTiaTkeDgYBYtciwYv/32G3bu3ME770yhcuWIm3oPIYQoz8r1KZ5adetzcM9Ozp+1nbI5c+IoR/b/R/2mtqtLEi+cIzX5IvUaXbmSxtvHl5pR9Th+aC8Ao6fOAeCV8R8ydeFKBo26Movs33/vIDb2DHPmzGfcuHdYvfo7Vq/+zv787Nmf0KnTQ/b7mZkZbN++ldTUFI4fP8qBA/vsxYW/fwDVqlVn27atKIqC2Wxi48YNeHv74OPjx4QJ4+jVqxcpKSkApKSkcO5cHAEBQfnue3Z2NqNHj2LUqDcIDb3xuI41a1bj6enF/fd3cHg8PDySsLAK/Prrz5w7d46srCwOHNjHnDmzOHhwPwCRkVXZuPE3jhw5xJEjhxk37o2bmshtwoS3mT17ZoHP24qT/lSsWIkhQ4aTkpJMUlIiSUmJ9m127vyb114bzmOP9eLuu++1P395QC/Axo0beOqpK9P/b926mbVrv+f48aOcOxfHli2bmTp1Eg0aNKJy5XB0Oh01a9Z2uAUFBeHu7kHNmrXzXT9JCCHE9ZW7HhSD/kpN9ugTfTDlZPH24KfR6XRYrVZ6PNOPdvfbioasNNv4h9DQENzdrrwuICiY9NRk3N10BAfbLg0ODAwkLCzM4T38/PyJiRmNXq+nRo0atG3bju3bt9O9+2OXXhNEZGSkvd3ataMwmy2sXr0KRVG47bb61K1bz/58s2Yt+eij6URHR7Nhw69UrFiZDh0e5I03Xuexx3pStWpVdu7cAdh+qTZtegctWrSyv/7rr5dRp04UTZrcwbRp79OoUSPuvvueQn1uq1atpGPHjnh65l1U8IEHOvLPPzvYtm0r3t5eLFz4BVWqVLNfNj106MtMmjSeAQNeICAgkN69nyUzM7NQ73u1CxfO59sjctmOHduIjT1DbOwZunV72OG5zZttg21//HENOTk5LF68gMWLF9ifb9y4KTNnzgVsheLp06fsz3l4eLJ69SpmzPgAo9FEhQoVad/+Hp5++rki74MQQojCKUcFimqfQO2yX376kR2bfmXcO5OoUas2Rw4dZPoH71O3RiSdOj9CznlvAGqG+TpMzevnaUBRIKqiH36WNACqhXgTVfHKNjoFatWqhV5/ZQXg0NBQjhy5Mpi2V68n6dXrySuv0elo0aIVd93VJk96k8nE22+/ga+vL3Pnfo6vr20G02XLlpKVlcXzz/dFr9dTtWp1PvnkEzp27Ex0dF37681mM9nZ2VSqVJmNGzewY8d2li372uE9atasxT33OJ7qadv2bnbv3s2JE8eZMGEiQJ4rXgwGAy1b3mWf6r5t2/sdekgqVw7n449nO7ymR4+eDve/+WZ1nn2+dhbWywVEQR5+uMsNr8Z5442xvPHG2CK107Tpncye/fl1X3Otvn3707dv/yK9piDWsApkDXsF6zWDqF1FS3m0lMUZWt+P4srnTDta/4xE8StHBQroUS/N7moz6+MPeeH5vnR52LYQX/3oaBIvnGfJwvn0eLQr4RVsPSKZKclEVqxof13KxSSio+viqdfhcak9D70Oz6vaVsg76BMUbmbpI5PJxKhRIzl37hxz5nxmL04AduzYzp49u2nZ8k6H1zz99JN07Pgw48fbZjo9dy6O8PBwKlcOZ8mSxcTGnqF9+9YOrxk58hWaNGnKvHmOv4xXrfqW6Oi63HbbbUXOLpxnrRxO5ptjXR3DTkt5tJTFGVrfj+LK50w7Wv+MRPErVwWKrTa4UiDk5OSgKI5Fg+1Uj4qqqoSHRxAaGsq2bX8RFWWbHCgjI4O9e//j8cd7oqqqvQgxmy03VXzcyOXi5PTpU8ydO5/AwECH50eOHMWgQVcuvU1ISGDw4AFMnvwe9es3sD9epUpVqlwa+f78833p1s1xcGjPnj149dWRtGvX3uHxrKwsfvnlJ4YMeamY90wUlpKRjtvuXZgbNUb1LdwiW+Ulj5ayOEPr+1Fc+ZxpR+ufkSh+5XqQbLt27Zk/fx6bNv1BXNxZfvttPUuWLLaf5lAUhaeeeprPPpvLxo0bOHLkMG+//QZhYWHcfbdtm6CgYDw9PdmyZTNJSUmkp6cX+v2XL19G//4vArbTClePFwFbcfLaa6+yf/8+3n13MhaLlcTERBITEzGZTABUrlyZ2rXr2G/Vqtnm4YiMrELFipUAiI+/QPfuj7B3r23St9DQUIfX1K5tm3+kUqXKREREOmT4+ed1WCwWOnXqVKTPVhQf/fFjBHbrhP74MVdHAbSVR0tZnKH1/SiufM60o/XPSBS/ctWDcq3XXnudTz6ZyaRJ75KcfJGwsDB69HiMfv0G2Ld59tnnyc7O5p13xpOenk7jxk2YOfNTPDxsc6C4ubkxcmQM8+bNYfbsT/I9RVKQlJRkYmNjC3w+ISGejRt/B6BXr8cdnps7dz533lm4uTXMZjMnT54kJyenUNtfbdWqldx77334+V1/unghhBCiOJXrAsXHx4eRI2MYOTKmwG0URWHgwMEMHDi4wG26detBt249HB4bN+6dPNtd+z4DBgxiwIBBBbYbHh7Bzp17Cny+sK8pTDsFPb9wYd4F8YQQQoiSVq5P8QghhBBCm6RAEULjVDcDlsrhqEVcmbqkaCmPlrI4Q+v7UVz5nGlH65+RKH6KWhKXnpSSxMR0rk1vMhlJSjpHSEhlDAZ3dDoFHx+PS2ujuCZneXF5HpTMzNybmim2vLr2OysEgE6nkGy0sHZ3nH0tHl8PNzo1CifIXS8/Y+KWpCg4zCt2PeV6DIoQQpRFioLDIqCqqsofaOKWU65O8Vz+oS2N25gxb/LKKzJ3iHCefv8+ghvVRb9/n6ujANrKo6UszijO/VAU8Fay8TGdt9+8lWyuqldcls+ZdsrKsRaFV656UAyYUay59vuZmZl8/Olc1m/4g4vJF6kXHcWoES/T4PYrM6aqqsrM2fP4ZuX3pGek06RRQ95+/TWqVa0CwNm4czzQpTvffPkF9aKj7K/T4fyfKyaTiU8+mcmff24iNjYWX18/WrRowbBhwwm7arrnTp0e4ty5OIfXDh36Es8/3zffdlNTU5k9+xP++msL58+fJygoiLvvvpeBAwfj53el623btr/49NNZHD16BC8vLzp3foTBg4fmM0Outmzc+BsrV37D0aOHMRpN1KhRkxde6Ocwz8zixQvYuHEDp06dxMPDgwYNGjJw4FCqVq1+3XYXLVrA2bNnMJvNREZWpVev3jz0UP5zxLz//kS+++5bhg17hZ49n7rp/VHMJvTn4lDMpptuozhpKY+WsjijOPdDURR0pjSsh38BYya4+6CL6oBi8L7pySSLK58z7ZSVYy0KT9u/aYqVgmLNRU06BhbbF/ytSZ9w9ORZJr3yHBVCAln921ZeHDCE7+a8S8VQ2yrA879ey9Kv1/Luqy8SUSmMmYu+pd/AwXw351083A2ol1fKvXgSNd5q+3+9AVSL04lzcnI4ePAAL77Yn6ioKNLS0pg6dQrDhw9j6dLlDtsOHDjY4VJnHx/vAttNSIgnISGe4cNfpWbNWpw7F8fEie+QkBDP++9/AMDhw4cYNmwwffv+j/Hj3yUhIZ53352A1Wrh5ZdHOL1vJWnXrn9p1qwF/fsPxtfXjx9+WE1MzMvMnbuQqCjb+kT//ruT7t0fp27d27BYLMydO4uXXx7CkiX/V+Dqw35+/jzzzAtUq1Ydg8HAn39uYtKk8QQFBeeZZG/jxg3s27eX0NCwEt9fIfJlzETNzcCJjhMhXKocFSiXWEyoZiM5uUZ+3fwPH785iDvq1QBgUK+H+f2vf1m++heG9emKqqosXvUz/Xo+zD3N6gPw7vDnuLvPCNZv3k7Hds148LmRADw2ZAwAd9aPYuH7r9vfbtGihSxZsgiTycQDDzzEiBGvYTDkHYX+ww+rCQ4OQa/Xc/jwQXQ6PXXr1uPTT68skGc0GnnwwY5Mm/Y+s2Z9TM2aNe2/GL29vQkNDWXXrp3s378Pi8VMjRq18PT0JDb2DF27Xileateuw9SpH9rvV6lShcGDh/Lmm69jNpv59tv/47///qNSpUp4eXnw11+badmyNYMGDWH06Bj8/f0ICQmhTZv2Dr+AExISOH78KOPHj8dsNtOu3d307z/E/gt/3bq1/N//Lef06VN4eXnRtOmdvPTSq/ZVj3fu/JthwwYwffonfPrpDE6ePE6dOtGMHv32dXs2rvXSS6863O/ffzCbNm3kzz832QuUDz6Y4bDN6NFj6dKlA4cOHaBx46b5ttu0qeN6Rz17Psm6dWvYs2eXQ4GSkBDP9OnvM23aDF57bXihcwtRrC6dbnbq3I4QLlSuxqBczWKxYrFacXd3rNE83Q38u982lXLshUQSk9No2bie/Xk/Hy8aRNVg98HjACybZitG5r0znA2L3mP66Cuz0P799w5iY88wZ858xo17h9Wrv2P16u/sz8+e/QmdOj1kv3/06GHc3Nzo0qUrzZo1Z9euncTFXZlp9rfffiEtLQVFUeje/TFCQkJZt24tqqqycOHntGt3FzExtkUFO3V6BF9fXw4e3F+ozyMjIx0fH1/76Zvz58/h7e3Do492JzKyKn/8sYFDhw5gNpupXTsaPz9//vjjd/vr09PT2LjxNypVqsxHH33KuHET2bNnFx9++J59G7PZzIsvDmDhwi+ZOHEq58+f4913x+bJMnfuJwwZMpzPPluMXq9n0qTx9ufOnYujTZs72bnz70LtF4DVaiUrKxN//4Jnw83MzAC47jZXU1WVv//ezunTp2jcuInDe02Y8DZPPtmHmjVrFTqjEMXJikKuxUqOxUquxYpV+lHELajcFig+3p40qluTOct/ID4pBYvFyuoNf7H70HESk1MBSEpOAyAk0PGXVkigv32boADbysKBfr6EBgUQ4Odj387Pz5+YmNHUqFGDdu3a07ZtO7Zv325/PjAwiMjIK2vfBAUF06TJHfj7B1C7dhShoaHExdnGlly4cJ7z58+xadNmHnqoI5Urh9O8eUvc3d25//77mTTpPfr0eYb77ruf77//jgULPqdx46b23onrSU5OZt68uXTvfqWXpVmz5hw5cpgtW/6kYcNGXLx4kT/++AOA7OwsGjRoTEpKMtnZWQDs3r2LqlWrU6NGTSIiImjQoBEvvTSSdevWkptrG/fTufOjtGrVmoiISOrXb8Dw4SP4668tZGVlOeTp128QTZrcQY0aNXn66Wf577899jbc3NyoWrUanp6eN9yvy5YtW0x2djb33tsh3+etVisffzyNBg0aUbNm7eu2lZGRQYcObbn77pa89tpwhg8fSbNmLe3PL136BXq9nscf71XofDdiqVmLlJVrsWik4NFSHi1lcUZx7oeiKJitKqcvZnH4fDqnL2ZhtqoOV/W4Kp8z7ZSVYy0Kr/yd4rnKpFde4K2PvuC+52LQ63TUq1WVju2asf/o6WJpv1atWuj1evv90NBQjhw5Yr/fq9eT9Or1pP1+cHCIw+u9vLzJzs4GbAv+rV69mszMTO699x4WL14A2Hol2rZtx513NuPgwf3cc8+9REXVZeLECQwd+hJhYWH2Iic/GRkZvPTSYGrWrEn//gPtj7dqdRfe3j5MnPgO2dnZKIrCE088yZEjh1EUnf20TXZ2Dl5e3ly8mERubg5paSl8/PHHJCYmoqoqVquVc+fiqF69BgcPHuDzz+dy9Ohh0tPTUVXbmJ0LF85To0bNqz63Ovb/DwkJBWxFVKVKlQgLq8CXX64o5BGwLXa4YME8Jk2aVmCx9sEHUzh+/BiffPLZDdvz9vZmwYIvyc7O4u+/dzBz5oeEh0fQtOmdHDx4gP/7v+V8/vkSp34ZXEv19cPUum2xtecsLeXRUhZnlMR+mM1WjBYrBrPV6baKK58z7ZSVYy0Kr1wXKFUqh7Fw8giycnLJzMohLDiAEVPmElnJ9ksxJMjWc5KUkkZYcID9dUkpadStWeWG7ee92kW57ih6nc6xQ8v2S07FZDLx0UfTycjI4LPPFhIQEOCwnbu74+ReDRo0wGw2Exd39rr5MjMzGTJkIN7ePkybNt1hbIxOp+Ppp5+hd+8+JCYmsHLlN9x2WwOWLl1MZGTkVae1bftjNpuoVasOt912OzVq1MVsNtvbqlixEtnZ2bz66hCaN2/FmDHvEBgYxIUL53nllSGYrxmVf/XndvkX/eVipih+/fUnpkyZwIQJU2jWrEW+23zwwRS2bNnMzJlzqVCh4g3b1Ol0REbajn2dOtGcOnWCJUsW0rTpnezZ8y/JyRfp0aOzfXuLxcLMmdP5+utlfPPN6iLvA4DuXBxe8+eS3bcf1srhN9VGcdJSHi1lcYbW96O48jnTjtY/I1H8yu0pnqt5e3oQFhxAakYmW/7dzz0tGgEQWTGU0CB/tu0+aN82Iyub/w6foFFd21/8hku/TC1W5/9KyY/FYmHUqJEkJiby6KOPEhQUiL+/v8Pt8umOgIAAEhMTOHToEDqdjuDgEBISEvJtNyMjg0GD+mMwGPjww4/tqzNfS1EUwsIq4ObmxqZNf1CpUiXq1q2XZ7uQkFDS0lLx8fEhPDycyMgq9pvBYODUqZOkpqYyYMAQGjVqQrVq1UlOvlh8H9Q1fvllHRMnjmfs2He56642eZ5XVZUPPpjCH3/8zkcffUp4eMRNvY/VasVoNALw4IMP88UXy1iwYKn9FhoaxpNP9skzKLcodAnxeH/8AbqE+JtuozhpKY+WsjhD6/tRXPmcaUfrn5EofuW6B+XPnftQVZXqEZU4fS6eDxasoEZkJbre3xqw/XJ++pH7mPPVD1QNr0BExVBmLvmOsOBA7m3ZGIDgQD883Q38+c8+KoYE4eFuwD+gcNOVL1++jA0b1jNnTv6nFiwWC198sZCEhAQ++mgmO3f+zapV39K4cRMiIiIxmUz8/vtvpKSkcvfd9xASEsr333/Hli1b6NDhAY4fP0py8kVUVaV790cYP/5d6tdvYC9OcnJyeOedSWRmZpKZmQlAUFCQ/f2/+GIBd93VGp1Ox44dO/jnn3+YMmWqw2mryxo0aMxvv/3Mvn3/YbXq0evdOHnyODt2bOOVV2KoWLESBoOBFSu+4tFHe3DixDEWLrzxKZVrJSTE89JLA3nzzXHcdlv9fLf5+ed1vPvuGF56aQS33VafpEuXgnt4eOLraxszNG3aFH79dR2TJk3D29vbvo2vry8eHraCb8KEtwkLq8CAAUMA29wpdevWIzzc9tlv3fonP/30AyNG2AZKBwQEEhAQ6JDFzc2NkJCQIl2FJIQQojwWKHqDfTx7Ro6J6Qu+4UJiMgF+PnRocwfDnu2Bu+eVeTD6PtGFHKOFcbOWkJ6RRdPbo5jzzqt4etsGwxrcYNTA3sz+8ntmffk9TW+PYuG0twoVJSUlmdjY2AKfT01NZe/evQD06vW4w3Pdu3enTp0oTCYzf/yxkSVLFmMyGQkODqFRo0ZERdUhIyOd2rWjOHLkMCdPniQnJ4dz5+KYN282e/f+B8CjjzpOMrZmzY/2///zz83Mn/8ZJpORwMBARo16g3vuuTffrMHBwdx3XweOHz/G+PHjSUxMIDw8kvvusw1MDQoKYvToMcyd+wnffPMVUVF1GTx4OKNGvVKoz+oys9nM6dOnyMnJKXCb77//FovFwgcfTOGDD6bYH+/YsTNvvDEWgFWrvgFg6ND+Dq8dPXoMDz/cBbCNjbn6tFt2djbTpk0hPj4eDw8PqlWrzttvT+C++x4o0j4I4QzdpRmxL381ZRp7UVYVabHAL7/8kmXLlnH2rG1sQ506dRg0aBDt27cHoE+fPg5XqQA88cQTjB9/5TLRuLg4xo4dy7Zt2/D29qZr1668+uqrNzU7aVEXC7x2JtmSpOo8MOHm8n841q1bi7e3N+3a3QPAkSOH2L17F927P55nzIuzZLHAm3OjxQLd9uwi6P52JP/6B+aGjUs/oIbzaCmLM/Lbj/wWCwz2cadX40C8zBlcHv9lNfiTpXrZ/63R63W45ZzjyG+LycxIxcc3gDr39sHsWRmL5eZORRfX5+xMO2XlWJd3JbZYYKVKlRgxYgTVqlVDVVVWrVrF4MGDWblyJXXq2K686NmzJ8OGDbO/5upZOS0WC/379yc0NJTly5cTHx9PTEwMBoOBV14p2l/SN8OEG+hKa6nu0v+rxmw2c/DgfiIiqqDTKRw/foy4uLMOU7GfOXOGO+5oVuzFiSg51qBgsns/g7UQl4yXBi3l0VIWZxR2P9z0OvTGdNRjv6DmFs809sWZryTbKSvHWhRekXpQ8tO8eXNGjhzJ448/Tp8+fahbty5vvPFGvttu3LiRAQMGsGnTJkJDbVfKLFu2jKlTp7J169Y8V6PcSFF7UFzdm1HSLBYzv/zyE0lJSVgsZgICAmncuAnVqtUolfeXHpSbc6MeFFE+5deDUsHfkyeiFdwOfIeak47i4YuuflcyDZXsP3Ml0YMiRHEpsR6Uq1ksFtatW0dWVhZNmlyZSXP16tV8//33hIWFcc899zBo0CB7L8quXbuIioqyFycAbdq0YezYsRw9epTbbrstz/uIwtPr3QpcuE7cwrKz0Z86iaVadShgnaBSpaU8WsriDK3vR3Hlc6YdrX9GotgVuZ//0KFDNGnShAYNGjBmzBhmzZpF7dq22Tc7d+7M+++/z6JFi+jXrx/fffcdI0eOtL82MTHRoTgB7PcLuhxWiPLO7cghgtu1wO3IIVdHAbSVR0tZnKH1/SiufM60o/XPSBS/Iveg1KhRg1WrVpGens5PP/1ETEwMS5YsoXbt2jzxxBP27aKjowkLC+O5557j9OnTVK1atViDa92YMW+Snp7OBx985OooQgghxC2nyAWKu7s71apVA6B+/fr8999/LFq0yOFKncsaNbJNeHbq1CmqVq1KaGgoe/bscdgmMdE2/0RYWPEuS1+YoTWZmZl88slMNmz4jeTki0RH12XkyBhuv/3K/BpjxrzJ6tXfO7yuVau7mDVrNgBxcWfp3Lkjy5Z9TXR03WLdB5PJxCefzOTPPzcRGxuLr68fLVq0YNiw4YSFVbBv16nTQ5w75zid/dChL/H8830LbPvMmTNMnz6Nf//9F5PJyF13tea1114nJMRxuv1Nm/5g3rzZHDlyBHd3d+64404puopZSQ5uFEKIW5XT86BcPZPmtQ4cOABcKT4aN27M7NmzSUpKsv8i3LJlC76+vvbTRM66PImY0ZiLu3v+s6NeNn78WI4dO8qECe8SFlaBH35Yw8CB/fjmm5UO057fdVdrxo6dYL9f1MG8NysnJ4eDBw/w4ov9iYqKIi0tjalTpzB8+DCWLl3usO3AgYPp1u3KYn8+Pt4FtpudncXgwf2pUyeaOXPmAfDpp7MYPnwoX3yxxH6Fz/r1vzBhwjiGDBlGs2bNsVgsHD16tAT2tHwzGm2Xvuv15W9aIlEMFJ3DvCiKosjaxaJMKNK/iNOmTaNdu3ZUrlyZzMxM1qxZw/bt25k/fz6nT59m9erVtG/fnsDAQA4dOsSkSZNo1qwZdevaehbatGlD7dq1ee211xg5ciQJCQlMnz6d3r17F9svfZ1Oj5eXLxkZyQB4enri5eWW5yqenJwcfvvtV6ZN+9A+yLdfv/788cdGvv76KwYNGgzY/ro1GAwEBwc5vI/VagGgc+eOADz5ZE8Amja9g7lzP7s0eZLKF18sYMmSxZjNJh544EFefXUEbm6Fu9TZx8ebWbM+dXhs5MhRPPvs08TFxVKpUuVLj6p4eXkVmPFa//67k7i4OJYsWWafWXXs2HHcc097tm3bSosWLTGbzbz//hSGDRtO167d7K+tXr16ge3als1RMZmMchVPIaiqitGYS0ZGMl5evgVf+q0oqO7uUIwLEDpFS3m0lMUZN7sfenfc9ArexvNcnhcFnRuKzoJeV4yfSXF9zs60U1aOtSi0IhUoSUlJxMTEEB8fj5+fH9HR0cyfP5/WrVtz7tw5tm7dyqJFi8jKyqJy5co88MADDBo0yP56vV7P7NmzGTt2LE888QReXl5069bNYd6U4uDvb7tOPiMjmZwcNzw9dZjNFqxXrZeTlZWFxWJBVS1kZWXYHzcY3Ni582/7Y2aziX/++Zv7778HPz8/mjZtyosv/s++YN+cOXPp378fH374IdWr18BgMJCVlYHZbOLvv3cQGBjA9OnTOXs2lrFjx1K9ejW6dHkEgM8//5x1637k66//r9D7lpSUgKIo6PU6e0ZVVVm48HM++2wuFStW5P777+fxx3sWOPldenqabUl2s9FhP21T2m+nQYP67N+/n/j4eMxmI08+2ZOkpCTq1KnDwIGDqFmzZr7t6nQ63Nz0JCfHOywWKK7Py8vX/p3Nj7lBIxJjE0sx0fVpKY+WsjjjpvdDb0AxZqIe22SbFwVQfMPQVb0D5aoCReHy7LNXHivKDLTF9Tk7005ZOdai8IpUoEycOLHA5ypXrsySJUtu2EZERATz5s0rytsWmaIoBASE4OcXhKpa8PDwwmBw7EHx9valYcOGLF68hLp1byM4OISfflrHvn37iIysgre3rWehbdv2dOjwIBEREcTGxjJr1gxiYmJYsOAL9Ho9lSvbFpmrUKEyVapUs7fv5mbA39+f0aPfQq/XU6/e7fz663p27drNE088BUBYWAWqVKlqf68byc3NZe7cuTz44EOEhV05BdWr11PUrVuPgAB/du/ezaxZM0hNTeOVV0bk286ddzbD09OL+fPnM3jwEFQV5s6di8ViITU1DW9vX5KSbAv5LVy4kJdffpXw8HCWLFnMSy+9xLffrsqzorLtc7cVKUFBFaQHpZD0ejeZNE84TTVmoeZe+kPLw/HfE71OQdEppBgtDoua+ri7YaDszw8lbl1l+l9GnU53abI2fb63CRMmAdCx44PcdVcLvvpqOQ8+2BGdTmffpmPHTtxzz31ERdXl3nvv56OPZrF//z527tx5aRud/b2ubltRFGrVqu3w/mFhYSQnJ9vvP/lkb+bM+azAfFffLBYrr78eA6iMHv2Ww3PPPPMczZu3IDq6Hj179uLll0fw9ddfYTZb8m0rJCSM996byqZNf9C2bWvuvrstGRmZ1K1bD73eth+X9e3bjw4dHuT22xswbtw7KIrC+vXrr5vVYHCXWyFvhSlO9IcPEXhfW/SHtXF5pZbyaCmLM0pyPxSdgtli5c9jCazdHcfa3XFsOBhPptGMUsjTJcWVz5l2ysqxFoVXrkflValShc8+W0B2dhYZGZmEhYUREzOSyMjIAl8TGRlJYGAQZ86coUWLltdtP+8pFuWmrtgwmUyMGjWSc+fOMWfOZ/ZxIwVp0KABZrOZuLizVK+e/yyyrVrdxfff/0BycjJubnr8/Pzp0OEeIiJs+x4aahvYfPXpHHd3dyIjIzh//lyR90HcPCUnG8N/u1Fysl0dBdBWHi1lcUZp7Ed2rsU+I21RFVc+Z9opK8daFF6Z7kEpLC8vb8LCwkhLS2Pr1i20b39PgdteuHCe1NQUwsJsE8wZDLYBryU1hfTl4uT06VPMnj2XwMDAG77m0KFD6HQ6goNDbrhtUFAQfn7+bN++jYsXL9K+/d0A1Kt3G+7u7pw6ddIhS1xcHJUrh9/k3gghhBCFU657ULZs+RNVValevfqleUE+oHr16jzyyKOAbSDtnDmfct999xMaGsqZM2f46KMPqVKlKq1atQYgKCgYT09PtmzZTMWKFXF3d8fPr3DrDCxfvowNG9YzZ85n+T5vMpl47bVXOXjwAB99NBOLxWqfNyYgIACDwcDu3bvZu3cPzZo1x9vbhz17djNt2ns8/HAn/P39AYiPv8CAAf9j/Ph3qV+/AQDffbeKGjVqEBQUzJ49u5k6dQq9e/ex97j4+vrSo8fjzJ79CRUrVqJy5cosWrQQgA4dHri5D1wIIYQopHJdoGRkZDBz5kdcuHCBgIAA7r33fgYPHmrvFdHpdBw5coQ1a74nPT2dsLAKtGzZikGDhtgvi3Zzc2PkyBjmzZvD7Nmf0KRJU+bN+7xQ75+SkkxsbGyBzyckxLNx4+8A9Or1uMNzc+fO5847m+HubuCnn9YxZ85sTCYj4eER9O7dh6effsa+rdls5uTJk+Tk5NgfO3XqJDNnfkRqairh4RH07fs/evfu4/Aew4e/gpubnrfeGk1ubi716zdgzpzP7IWPEEIIUVKcXs3YlfJbzfha5Wk1Y1eT1YxLhpKSjOGP3zG1uxs1MOjGLyhHebSUxRn57UehVjP2q4hH7bbkHvgJNSfd1pZ/JdxqteHAHytITUkmIDCIam2f4v8OQ3ya7Y8UXw83OjUKJ8hdX6if1eL6nJ1pp6wc6/KuVFYzFkKUDjUwCOMj3W68YSnRUh4tZXGG1vejuPI5047WPyNR/GSQrBAap8TH4/XpTJT4eFdHAbSVR0tZnKGF/bD1gCoOt8tXIRdXPmfa0cJnJEqXFChCaJz+fBy+Y0ajPx93441LgZbyaCmLM1y9H4oC3ko2PqbzDjdvJRtFKb58zrTj6s9IlD45xSOEECVMUchnUjQVLi3rV9gJ00qKoijoTGlYD/8CRtuU+bj7oIvqgGIoeOFRIUqSFChCCFGCLvdO6ExpDo+b3QOIN7pjVcHDZCEQMCs6FAXXDeg3ZtqnzJcl+YSrSYEihBAlKL/eCcXDB2utDmw5oSMpPZewo4k8BeSYzHgrNzfjtBBlTTkqUBQURX7oS5b8zVUSrH7+5D7YEaufNuaf0VIeLWW5oat6Jy6PPs022qafd3Pz4niLe9D5Oy7CqVPA2+PKP9Ne7noUxWr7SbOdNyqV6MX1OTvTzi11rEWxKPMFiqqqWK3WS4uyyS/Qkmabb0YKweJkrVGTtMVfuTqGnZbyaCmLM1IqV2H12Fl0qn5lGQlFgSC3HFqHZWO6tAqxtyd4KDqyLCpWixWdxYp7KeQrrs/ZmXbKyrEWhVcOChTIyjK6fBBaeaGqqkyIV9xMJpTUVNSAALg0y7Hk0WAWJ+jMJryy09CZw1A83NDpQFF06I3pmA/9RHambRI2r7BwaNiauNRs0lLS8bP4Uwfbn14l+mNXXJ+zM+2UkWMtCq9cXGasqmC1qnIrhZsUJ8XP7cA+Qm+riduBfa6OAmgrj5ayOKPCqSP0e7INnocPcjHXQrLRQrrZggqYsjMwZqVhzErDlGsbw2KyWDFarJhLaJHSaxXX5+xMO2XlWIvCK/M9KEIIcavIMVnYcOgCWTlmIoO9uS9CLbVxJkJoTbnoQRFCiFtFVq5t4Gy2yeLqKEK4lBQoQgghhNAcKVCEEEIIoTkyBkUIjTPf3oDEY7Go3j6ujgJoK4+WsjjjQvVoFq7eSfPbqsKBBFfHyaO4Pmdn2ikrx1oUnhQoQmidXo+qpcmptJRHS1mKStHh5Q6+Hm6AG25+Hujc9K5Olb/i+pydaedWPtbipsgpHiE0Tn/8KAE9u6I/ftTVUQBt5dFSliLRu+PhptA8OJO7w3N5wHSYbjF9qHBmP+5u2vtnubg+Z2fauWWPtbhp0oMihMYpGRm4//4bSkaGq6MA2sqjpSxFojegmDIxH/qZ7LRUlJPn8PlzC9bkeNw8wlydLo/i+pydaeeWPdbipmmvVBdCiHLClGObhM2ck+nqKEJojvSgCCGEE2xr9jlOppbvkg+KYt9Opl4T4sakQBFCiJukKOCtZKMzpTk8bjX4k4WXvUixopBrsaJemppeb1XxKu2wQtxipEARQuMs4ZGkT5qKJTzS1VEAbeVxdRZFUdCZ0rAe/gWMl07TuPugi+qAYvBGVVUURcFsVTl9MYvsdNuif8EEUv2qLpbcIF9in38Yv/DKkOSKPbm+4vqcnWnH1cdalD4pUITQODU0lJy+/Vwdw05LeTSTxZiJmmsbvFnQ6Ruz2bbAH9gW+7uayc+bpAea4xsSrMkCpbg+Z2fa0cyxFqVGBskKoXFK8kU8/m85SvJFV0cBtJVHS1mupSgKOp2SZ3xKftwycwjctBslOaXo72N7M5TLtyK3cHVjV9q5epHC4vqcnWlHy8dalAwpUITQOP2Z0/gP7of+zGlXRwG0lUdLWa5mRSHVZCHZaCHdbOHa8bLX8kxKo9onK9HFni3S++gvFRG5Fis5l25G643ereDMV7eTa7FivVTuFNfn7Ew7Wj3WouTIKR4hhChOl8ac/HkikaT0XCKDvbkvQnXokSi2t9IpqKpKXEo2aSn5j28pXOS842S8/HTUsKqF6gESoiRIgSKEECUg22ghI9dMtslS4u9lshQ8vqUorh4nYzDffDtCFAcpUIQQoqxTdHi5K5fW/QFvDzd00jEiNE4KFCE0TvX2wXRHM82s4qqlPFrK4gyLu4HMOpGo3l5QzB0uOoMHngYdzYMzyPazNW7QmQgy5ILO9rndaHBtcX3OzrRTVo61KLwiFShffvkly5Yt4+xZ20CuOnXqMGjQINq3bw9Abm4ukydP5ocffsBoNNKmTRvGjBlDaGiovY24uDjGjh3Ltm3b8Pb2pmvXrrz66qu4uUmtJER+LLXrkPLjelfHsNNSHi1lcXDVSsVeBj2Kcv2qI7tSEEfHv0i1WjXhcPFG0ekN6IwZ9nV/APQBIXhXfATM6aiqCjo3FJ0FfQHdKsX1OTvTjmaPtSgxRaoKKlWqxIgRI6hWrRqqqrJq1SoGDx7MypUrqVOnDhMnTmTjxo1Mnz4dPz8/JkyYwJAhQ1i+fDkAFouF/v37ExoayvLly4mPjycmJgaDwcArr7xSIjsohBClyr5Ssa3HwtcLPHRKgb/8HVx1KqYwhU1RXF73B8Di44diysR6bBPW3EwU3zB0Ve9AkfM+QkOKdJnxvffeS/v27alevTo1atTg5Zdfxtvbm127dpGens6KFSsYNWoUrVq1on79+kycOJF///2XXbt2AbB582aOHj3K+++/T7169Wjfvj0vvfQSS5cuxWg0lsT+CXHLc9uzi7AK/rjt2eXqKIC28mgpi93VKxXv+hbL0Y3oLKbrXsTjezqeRk+OxevAfpoHZ3J3eC6Ng7LwuE6vRnFQjVmouRmopuzrbldcn7Mz7WjyWIsSddPzoFgsFtauXUtWVhZNmjRh7969mEwm7rrrLvs2tWrVIjw83F6g7Nq1i6ioKIdTPm3atCEjI4OjR4/e/F4IIYTGXO6xMOUWfqVinSmrSIWNEGVZkQd+HDp0iF69epGbm4u3tzezZs2idu3aHDhwAIPBgL+/v8P2ISEhJCQkAJCYmOhQnAD2+5e3EUKI8uxKYRPg6ihCuFSRC5QaNWqwatUq0tPT+emnn4iJiWHJkiUlkU0IIUQJcZgi39VhhMhHkQsUd3d3qlWrBkD9+vX577//WLRoER07dsRkMpGWlubQi5KUlERYWBhg6y3Zs2ePQ3uJiYkA9m2EEEKUrKunyLdarOitKl4uziTEtZxei8dqtWI0Gqlfvz4Gg4GtW7fanzt+/DhxcXE0btwYgMaNG3P48GGSkq4s17llyxZ8fX2pXbu2s1GEKJPMUXVJ+utfzFF1XR0F0FYeLWVxRmblYI58Mgq1Tq1Seb+rp8g/fD6dC2k5cJ3p8Yvrc3amnbJyrEXhFakHZdq0abRr147KlSuTmZnJmjVr2L59O/Pnz8fPz48ePXowefJkAgIC8PX15Z133qFJkyb2AqVNmzbUrl2b1157jZEjR5KQkMD06dPp3bs37u7uJbF/Qtz6PD2x1iydX1yFoqU8WsriBNXghrFSKHh6lOr7Xp4i/4bT4xfX5+xMO2XkWIvCK1IPSlJSEjExMTz00EM899xz/Pfff8yfP5/WrVsDMHr0aO6++26GDRvG008/TWhoKDNmzLC/Xq/XM3v2bHQ6HU888QQjR46ka9euDBs2rHj3SogyRHfqJH4DX0R36qSrowDayqOlLM7wTEwl4sMv4XSsq6Pkq7g+Z2faKSvHWhRekXpQJk6ceN3nPTw8GDNmDGPGjClwm4iICObNm1eUtxWiXNOlpuC54muyBw5BC8u3aSmPlrI4wy0rl8A/dmJKTXN1lHwV1+fsTDtl5ViLwpP55YUQoggUxbZ2je3/y/b1LwqX1um5an8V5brDVYQoNlKgCCFEISkKeCvZ6EyXejp0etywUBbnwdbrFBSdQorRgpvJQiCQbrLgpugwYJUiRZQ4p6/iEUKI8kJRFHSmNKyHf8G6dxXW43+gWE2UxeleFZ2C2WLlz2MJbDpimw5ix8lkMo3mMt9zJLRBChQhNM5asRKZI0ZhrVjJ1VEAbeVxWRZjJmpuBhiziqe5AB/in+iAWkF780Fl51pI8AnijycGkOQX7FRbzhwvLX3vROmQUzxCaJy1YiWyXhvt6hh2WsqjpSzOMAb4kNDrQQIrhsFhV6fJKyM4jE1PDsLXw7lfGc4cr7JyrEXhSQ+KEBqnpKdh+O1XlHRtXOGhpTxayuIMfXYuPv8egvQMV0fJl3tWBjX//RP3TOfyOXO8ysqxFoUnBYoQGqc/cZzAXt3Rnzju6iiAtvJoKYszvBJSqT5+HsrJ066Okq/gc6d5ctxAAs6dRnfpKiadznYrynAUZ45XWTnWovDkFI8QQohCMegVgtxy8DJmALbLeKwGf7Lwkqt6RLGTAkUIIUSh6HU69MZ01GO/oOZmgrsPuqgOKAZvVKlQRDGTAkUIIUSRqMYs1NwM5GJjUZJkDIoQGqe6e2CpXgPVvXQXkiuIlvJoKYszrG56ciuFgEYXTTUb3LlYqQpWg3P5nDleZeVYi8KTHhQhNM5Stx4Xt+92dQw7LeXRUhZnZIWHcPTT16kTXRvi97g6Th6JVWvz6ey1VPD3dKodZ45XWTnWovCkQBFCiAJcve6O7b6c1BCitMgpHiE0Tr9vLyH1aqDft9fVUQBt5SnJLIoCJkVHstFiv6WaLKi2qsVWrBRTweITm0D0s2NQ9mtwljagwsnDDH+mPcHHDjrVjjPHS0vfO1E6pAdFCI1TLGZ0SUkoFrOrowDaylOSWRRFIdNoZsPBeLJybe1XCvSiU3UwWVRUixWdxYq7Ylv193KxcjMli2JVcUvLxKSBzzQ/OosZn7RkFIvFqXacOV5a+t6J0iEFihBCXEdWrpmMSwWKyWLFbLFy+mIm2enpBFoDqKVCrlXFarECoLeqeLkysBBlhBQoQghRRGaLitFixaKqqKpKXEo2aSnpAAQTSHWZE0QIp0mBIoQQTjJZrBgv9aCYLv1XCOEcGSQrhMaZa9Ymee0vmGvWdnUUQFt5tJTFGVkVAjk+aQhqzequjpKvpPDqLJy8mNQq1Z1qx5njVVaOtSg86UERQut8fTE3a+HqFFdoKY+WsjjB6ulOdvUq4OPt6ij5Mnl5c7ZuIyp4OTcPilPHq4wca1F40oMihMbp4s7i89br6OLOujoKoK08WsriDPfkdCp+/j3EnXd1lHz5JZ7n/s/fxyfBuXzOHK+ycqxF4UmBIoTG6RIT8J4zC11igqujANrKo6UsznBPzyZ09R8oSRddHSVfPqkXafH9YjyTk5xqx5njVVaOtSg8KVCEEEIIoTkyBkUIIUShKVw1MZ1M/S9KkBQoQgghCsWgV1B0CjmXZtJVLFY8bmruXCFuTAoUITTOGhxC9vMvYg0OcXUUQFt5tJTFGSZfTy4+dBe+wUGQ4uo0eWX5B/F3xycwBYU4zKTr5aejhlW9tIjijSenc+Z4lZVjLQpPChQhNM4aWYWMKR+4OoadlvJoKYszcoP9Ode/O3UiKsNxV6fJKy2sMj/1f4NqoT6AxT6TrsFctEnpnDleZeVYi8KTQbJCaF1WFm57dkFWlquT2Ggpj5ayOEFnNOF5LBays10dxZGiw8tdT6BqosaZw/hajM4NO3HmeJWRYy0KTwoUITTO7ehhgu5vh9vRw66OAmgrj5ayOMP7fDK1RkxHOXrC1VHsdAYPPA06mgdn0iH3EE8NfYw7Lu7DQ2dBr7u5KsWZ41VWjrUoPClQhBBC5KHTG9AZMzAf+pmcQ78BYD3zDzqLSS7eEaVCxqAIIYQokCknA0tOJgBmo8ZOQYkyTXpQhBBCCKE5UqAIoXGqosPq64eqaOPHVUt5tJTFGaqiYPHyAJ0290NVFMye7nCTY0+utHPzx6usHGtReHKKRwiNszRoSNJx7SyQpqU8WsrijMwqYRz88l3q3F4XNu13dZw8MquEseXDgYRUinCqHWeOV1k51qLwpBQVQgghhOYUqUCZM2cOPXr0oEmTJrRq1YpBgwZx/LjjrEJ9+vQhOjra4fb22287bBMXF0e/fv1o1KgRrVq1YsqUKZjNZuf3RogySH/oIEFtm6M/dNDVUQBt5dFSFmd4n0ui1rD34fAxV0fJl/e5JO4YvxiPM+edaseZ41VWjrUovCKd4tm+fTu9e/emQYMGWCwWPvjgA/r27cvatWvx9va2b9ezZ0+GDRtmv+/l5WX/f4vFQv/+/QkNDWX58uXEx8cTExODwWDglVdeKYZdEqJsUXJzcDt0ECU3x9VRAG3l0VIWZ+hMFjzPXMCUm+vqKPnSmSz4nLuIYnTuD0lnjldZOdai8IpUoMyfP9/h/uTJk2nVqhX79u2jWbNm9sc9PT0JCwvLt43Nmzdz9OhRFixYQGhoKPXq1eOll15i6tSpDBkyBHd395vYDSGEEEKUJU6NQUlPTwcgICDA4fHVq1fTokULOnfuzLRp08i+avrmXbt2ERUVRWhoqP2xNm3akJGRwdGjR52JI4QQQogy4qav4rFarUycOJGmTZsSFRVlf7xz586Eh4dToUIFDh06xNSpUzlx4gQzZ84EIDEx0aE4Aez3ExISbjaOEEI4TVG4tDKv7b9OXlUrhHDCTRco48aN48iRI3z55ZcOjz/xxBP2/4+OjiYsLIznnnuO06dPU7Vq1ZtPKkQ5ZalWndRFy7FUq+7qKIC28hRnFkUBbyUbnSnt8iMobr64u+mghIeGZIcGcPr156lUNRJStDcINDs0gL0DuqCvFOJUO84cLy1970TpuKkCZfz48fz+++8sWbKESpUqXXfbRo0aAXDq1CmqVq1KaGgoe/bscdgmMTERoMBxK0KUZ2pAIMaHHnZ1DDst5SnOLIqioDOlYT38CxgzUTx80NfqgJu+5GdjsHh7kN68NpUC/Ev8vW6GxduDi41qEuLjdeONr8OZ46Wl750oHUX6yVNVlfHjx/PLL7/wxRdfUKVKlRu+5sCBA8CV4qNx48YcPnyYpKQk+zZbtmzB19eX2rVrFyWOEOWCcuECXh9NQ7lwwdVRAG3lcSaLooBOp9hvl0/tYMxEzc1ANWYVc9qCGVIzCV2xHuITS+09i8KQmkmVdTtwS0678cbX4dTx0tD3TpSOIhUo48aN4/vvv2fatGn4+PiQkJBAQkICOTm2y75Onz7NrFmz2Lt3L7Gxsaxfv56YmBiaNWtG3bp1AduA2Nq1a/Paa69x8OBBNm3axPTp0+ndu7dcwSNEPvQXzuH77jj0F865OgqgrTw3m0VRwKToSDZa7LdUkwUrrhl04pGaScUlP6JciHfJ+9+IR2omNb7bgttF5woUZ747WvreidJRpFM8y5YtA2yTsV1t0qRJdO/eHYPBwNatW1m0aBFZWVlUrlyZBx54gEGDBtm31ev1zJ49m7Fjx/LEE0/g5eVFt27dHOZNEUKIkqQoCplGMxsOxpOVa5vbI8TPg0dqqLgpMjJWCC0oUoFy6NCh6z5fuXJllixZcsN2IiIimDdvXlHeWgghil1WrpmMSwWKt4cbuKgHRQiRl6zFI4QQQgjNkQJFCI2z+geQ26UrVv+AG29cCrSUp8SzKDq83PX4erjh6+GGl0FPSZwBMnt5kNqqIaq/Nq/iMXt5kNC0NhYnr+Jx5nhp6XsnSsdNz4MihCgd1uo1SJu/yNUx7LSUp0Sz6N3xcFNoHpxBtp8FAF8v8NAp6It5BrecsABiX3uGOtUi4fT2Ym27OOSEBXDgf50IcXIeFGeOl5a+d6J0SIEihNYZjegSE7CGhoEWrnTTUp6SzKI3oJgyMR/6mey0VAC8wsLRNWxd7L0oitmCW2IKGE3F23AxUcwWDOlZKCFOrjrvzPHS0vdOlAo5xSOExrkd3E9I43q4Hdzv6iiAtvKURhZTTgbGrDSMWWmYcjNL5D184pKI/t87KIeOlEj7zvKJS6Ll6M/xOH3eqXacOV5a+t6J0iEFihBCCCE0RwoUIYQQQmiOFChCCCGE0BwpUIQQQgihOXIVjxAaZ67fkIQzCWAwuDoKoK08WsrijIzIMPZ/PZnat9eFPw+6Ok4eGZFhbPp4MMER4U6148zxKivHWhSeFChCaJ1OBx4erk5xhZbyaCmLM3QKqsHNtj9apFNQdcWQz5njVVaOtSg0jf40CCEu0x87QkDXh9Ef08YlqFrKo6UszvC6kEz1Nz+B4yddHSVfXheSafjBN7ifTcjznIJt8UWd7sqtoHlinDleZeVYi8KTHhQhNE7JzMR9y2aUzJKZg6OotJRHS1mcoc814bPvOKbMLFdHyZc+10TgkbMk5eQ6Pq5TUHQKKUYLFqvV/riPuxsGrKiqYzvOHK+ycqxF4UmBIoQQ4qYoOgWzxcqfpxJISrMVL94ebtxTtwJB7nrUaysUIYpAChQhhLhEuXRDUSiBNQHLrOxcCxm5Tk6DL8Q1pEARQgjAXW87XZFjUVEtVvRWFefW7hVCOEMGyQqhcZaIKqR/MANLRBVXRwG0lac4s+j1OswWK6cvZnL4fDoX0nLIM4iihOQE+3F20OOoEZVL5f2KKifYj8O978MUFlSo7XWK48DZy4NmnTleWvreidIhPShCaJwaEkLO08+6OoadlvKURBazRcVosWKyWG+8cXG9p68XKR2iCAsuXAFQ2sy+XpxvU58Qf58bbuvupkOn13Mx1wLYCjz7oFknjpeWvneidEgPihAapyQl4bnkC5SkJFdHAbSVR0tZnOGWkU3gL9vgYrKro+TLLSObSpv3ok+78RU0bnodWUYzGw5dYO3uODYcjCfTaEZRFKeOV1k51qLwpEARQuP0Z8/g98pQ9GfPuDoKoK08WsriDM+L6UR88n8oZ8+5Okq+PC+mE7V0PYaEwhdQWZcGzmZdNXjWmeNVVo61KDw5xSOEKBeUS+MibP8v1+gIoXVSoAghyjxFAZOiI9No+2tebxvFibfHlX8CvQx6FMXiqohCiGtIgSKEKPMURSHTaGbDwXiycs1UCvTkvlpetK6QbR8M6+sFHjrFVrwIIVxOChQhNE718cF4VxtUnxtfQVEatJSnqFmycs1k5JoxWVTcjOmYD/1MdkYaAF5h4egati5wHZmSZPEwkHl7TQw+3pBS+u9/IxYPAyl1IrB6OrdYnzPfHS1970TpkAJFCI2z1KpD6qofXB3DTkt5nM1iys7AmGUrUEy5AcUVq8iyKwZx8p1B1KlZHc7udFmOgmRXDGLPK48RUinMqXacOV5a+t6J0iFX8QihdVYr5Oba/qsFWsqjpSzOsKooJrN296O48jlzvMrKsRaFJgWKEBrntncPYVXCcNu7x9VRAG3l0VIWZ/jGJnBbz1Eo+w66Okq+fGMTaDtsFp4n4pxqx5njVVaOtSg8OcUjhBDi5ik6vNwVfC9dEeXlrkfGGYviIAWKEEKIm6IzeOBp0NE8OINsP9sl2l4GM8EGb9zdpINeOEcKFCGEEDdFpzegM2bYroZKSwXA4B+Am/8juEk3inCSFChCCCGcYsq56mood72L04iyQgoUITTOXPc2knYdwBrq3CWexUVLeYqSRadgnzlWa7PGZoaHcGjem9SIrgPbtDdQNjM8hL8mvoB/1UpOtePMd0dL3ztROqRAEULr3N2xhke4OsUVWsrj7o4aEYHumtnVVFVFVa/cVxQIcsuhdVg2JqtVc7PGqm56zKGB4G5wdZR8qW56jEF+qIZC/sq4auCst4fblUGzznx3tPS9E6VCRjEJoXG6kyfw7/sMupMnXB0F0FYe/akTBPbtje/R7fiYzttv3rps9HoFnc52UxQdemM65kM/kb3rWyxHN6KzmFwya2x+PBNSiXxvEZyKdXWUfHkmpFJv3loM55NuuO2VgbOZ3B2eS+uwbILcclAU5747WvreidIhBYoQGqdLS8Vj9Sp0lwYhupqW8ujS0nD7fjXW/37BuncV1r2rUA//AsZ0Uk1Wko0Wko0W0s0WVK7MHGvKzXR1dAdu2bkEbN2Dkpbm6ij5csvOJWznUfSZ2Tfc1mHg7K5vMR/6Cb0xHVCc+u5o6XsnSkeRCpQ5c+bQo0cPmjRpQqtWrRg0aBDHjx932CY3N5dx48bRokULmjRpwtChQ0lMTHTYJi4ujn79+tGoUSNatWrFlClTMJvNzu+NEKJ8MmWj5mbYbqYszFaVP48msnZ3HGt3x7HjxEVUVUUzXSblwOWBs6bsDFdHEbeoIhUo27dvp3fv3nz99dcsWLAAs9lM3759ycrKsm8zceJENmzYwPTp01m8eDHx8fEMGTLE/rzFYqF///6YTCaWL1/O5MmTWblyJR9//HHx7ZUQotzLNlrIuLQ4YLZJOwNihRCFU6QCZf78+XTv3p06depQt25dJk+eTFxcHPv27QMgPT2dFStWMGrUKFq1akX9+vWZOHEi//77L7t27QJg8+bNHD16lPfff5969erRvn17XnrpJZYuXYrRaCz2HRRCCCHErcepMSjp6ekABATYVgHdu3cvJpOJu+66y75NrVq1CA8Ptxcou3btIioqitDQUPs2bdq0ISMjg6NHjzoTR4gyyVKxMhlvjMFSsbKrowDaymOtVBnT66+ihvi7OopTcgN8uPB0R9SKFVwdJV+5AT6cePQuzMHOfc7OfHe09L0TpeOmLzO2Wq1MnDiRpk2bEhUVBUBiYiIGgwF/f8cvcUhICAkJCfZtri5OAPv9y9sIIa5QK1Yk+6VXXR3DTkt51IoVsQwbAHtXQe6tO9bBFOBDYo/7CKoQCodcnSYvU4APZx5qRkiQcwWKM98dLX3vROm46R6UcePGceTIET788MPizCOEuIaSmoL7uh9QUlNcHQXQVh4lNQXdT+sh48ZXl2iZPisXv+37IFWbV/Hos3IJ3n0cXSGu4rkeZ747WvreidJxUwXK+PHj+f333/niiy+oVOnKzIKhoaGYTCbSrrlULikpibCwMPs2117Vc/n+5W2EEFfoT50k4Jle6E+ddHUUQFt5dCdP4v7cAJRzN56fQ8u8ElOpOmkBymltzoPilZhK/dmrcS/EPCj5URQdOp2C4cxpAp7pheHMaXRF/O2jpe+dKB1F+oqoqsr48eP55Zdf+OKLL6hSpYrD8/Xr18dgMLB161b7Y8ePHycuLo7GjRsD0LhxYw4fPkxS0pUv+pYtW/D19aV27dpO7IoQQgitMXh44mHQoc+OQ2+0/TGqNybioeQUuUgR5UuRxqCMGzeONWvW8Mknn+Dj42MfM+Ln54enpyd+fn706NGDyZMnExAQgK+vL++88w5NmjSxFyht2rShdu3avPbaa4wcOZKEhASmT59O7969cXd3L/YdFEKUA4qCcmmOEwUuTbUOvhpde6c80RvcITedc//8gnnPQW4D4vf+TmiD21E8vQGrqyMKjSpSgbJs2TIA+vTp4/D4pEmT6N69OwCjR49Gp9MxbNgwjEYjbdq0YcyYMfZt9Xo9s2fPZuzYsTzxxBN4eXnRrVs3hg0b5uy+CCHKKZPFitli+0Wn0xnwdFNoHpxBtp+tKNHa2jvlUXZWOpYs20DmnGxtzeQrtKlIBcqhQzceXu7h4cGYMWMcipJrRUREMG/evKK8tRDllurhiTm6LqqHp6ujABrL4+mJJao2cdlmUs7bpj0IrhRI9ctTrV+aFt0rLBxdw9aanUjWatCTU6UiOg8PV0fJl9WgJ7NyMKq7c+vL2tsp7KKDV9HU906UClnNWAiNs0TXJXnTdlfHsNNSHmvdemRu+JHM3xZjzLAVI6ZLPSmXp1oHMOUGuCxjYWRVDuHYxyOpE1ULLux2dZw8siqH8M/bfQi56qIIZ9rx8S368dDS906UDhmiJIQQQgjNkQJFCI3T/7eHkJoR6P/b4+oogLby6P/bg190Y7xOnXd1FKf4nEmg7lNvoOw76Ooo+fI5k8BdL3+K54mzxdLOzRwvLX3vROmQAkUIjVNUK7qMdBRVG1c7aCqP1YqSkQmq6uokTlFUFX12Llg18JnmQ1FV3HKMYHXuc7a3cxPHS1PfO1EqpEARQgghhOZIgSKEEEIIzZECRQghhBCaIwWKEBpnrh1F8q9/YK4d5eoogLbyWOpEkbFuFTmVQ2+8sYZlVQri2NThqLVruDpKvrIqBbHz9SfJjaxQLO3czPHS0vdOlA4pUITQOm9vzA0bg7e3q5PYaCmPtzfWBrejehhcncQpVncDObUiwcvL1VHyZXU3kFG1AqqHc8uRXGnnJo6Xlr53olRIgSKExuliz+Ab8wq62DOujgJoK48u9gyeo8diSEx1dRSneFxMo/Kcb+HsOVdHyZfHxTRqL9uAISG5eNq5ieOlpe+dKB1SoAihcbqLSXgt+AzdxZtb6r64aSmPkpSE+xdLccvIcnUUpxgycghetwXlonMFQEkxZOQQ/sce9GnOraFzuR23jCxQdCiKgk5nu91oGQItfe9E6ZCp7oUQQpQqnZsBDzcFjOdQL82JYjX4k4XXrT6ljShGUqAIIYQoVTq9G4opE+uxTVhzM8HdB11UBxSDt71gEUIKFCGEZikKKNf0/auqKn9llxGqMQs1NwONLjItXEwKFCE0zhoaRlb/wVhDw1wdBSi9PIoC3ko2OlOa4/tfdSrAGhpG7v+ex+zvA9y6VYvRz4vELu3wDwmGFFenycvo50XsfU0wB/i6rB2t/RyIkicFihAaZw2PIHPCJFfHsCutPIqioDOlYT38CxgvDc685lSAGhFB7tjRmH5bDBm37pU8xiA/LrzwCP7hleCYq9PkZQzy4/hj7QgJDSyWdgJCit6O1n4ORMmTq3iE0LqMDNx2bIOMDFcnsSntPMZM1NwM1NyMK4XKVVn0f/+LLsdYOllKiC7HiNfBk5CpzauRdDlG/I6fQ5ed67p2tPZzIEqcFChCaJzb8aMEdeqA2/Gjro4CaCuP/thRfB7ticf5W/vSU+/4FGq+PhPl+ElXR8mXd3wKTd7/Gve4hGJpx+Nc0dvR0vdOlA4pUIQQQpQ6BUBRbIOgbzQJiiiXZAyKEOLWYp/gS36v3ap0lw5crsWK1WJFsVjxkGt5xDWkQBFC3Dr07rjpFbyN5wEVxWybeVUvlcotRVEUVFUlLiWbtJR0vPx01LCqly4pv3WvxhLFSwoUITRO1bthDQlB1Wvjx9WlefQGFGMm6rFNqLmZ6M7noAYHgdutfbZa1Sm2S6U1coyvpeoUjL5eqHrnPudr2zFZrBgtVgxm641fq7GfA1Hybu2faiHKAcvt9Uk6cALL7fVdHQXQRp7LE3xZq4Vi+u8PcqpVclmW4pAZGcahL8ah3hbl6ij5yowM46/3+5FbPdxl7WjheydKlxQoQgghhNAcKVCE0Dj9wQMEN2+E/uABV0cBtJVHORGHofXDeJyJd3UUp3jHJVF74CSUQ9q8hNY7Lolmby/E4/R5l7Wjpe+dKB1SoAihcYoxF/3JEyhG5ybJKi6aymM0o5w8g85sdnUSp+jMFttcLkZtTjinM1vwSkhFMTn3OTvTjqa+d6JUSIEihBBCCM2RAkUIIYQQmiMFihBCCCE0RwoUITTOUqMmKcu/xVKjpqujANrIo2Cb7IvIMExLZ5NbMdhlWYpDdlgAJ9/+H2r1qq6Okq/ssAD+G9oVY+VQl7Wjhe+dKF0y440QGqf6+WO6935Xx7Ar9TyX12vh0gykQK5VxWqxovfxRH93a6x/nAdjdullKmYWLw8ya1QBP19XR8mXxcuD5NuqEeLt6bJ2tPZzIEqe9KAIoXG6C+fxfm8iugvOXeJZXEozjxWFXIuVnEs3o1XFCsSlZHP4fDqJJ+PRT52FW3J6iWcpSe6pmYQt/wkuOLdacElxT82k2pq/cLuY5rJ2tPZzIEqeFChCaJzuwnl8pk7WzD/MpZVHURTMVpXTF7M4fD6dw+fTuZCWA6pqnyKdxBT0H87GkHLrFygVvvoFJV7DBcrabbglF0OBcpPtaO3nQJQ8OcUjhNA0s/lSMYJt7RYhRPkgPShCCCGE0JwiFyg7duxgwIABtGnThujoaH799VeH50eNGkV0dLTDrW/fvg7bpKSk8Oqrr9K0aVPuvPNORo8eTWZmpnN7IoQQQogyo8ineLKysoiOjqZHjx4MGTIk323atm3LpEmT7Pfd3d0dnh8xYgQJCQksWLAAk8nE6NGjefvtt5k2bVpR4whR5lkDAsnp0RNrQKCrowDaymPx9cLSrRMWHy9XR3GK2duDlHZN8Q7whxRXp8nL7O3BhebRWHyd+5ydaUdL3ztROopcoLRv35727dtfdxt3d3fCwsLyfe7YsWNs2rSJb775hgYNGgDw5ptv0q9fP1577TUqVqxY1EhClGnWatVJ//QzV8ew01IeU8UQLDMmYdz0LaQkuzrOTcsJDeDsy09Rp2oknNru6jh55IQGcOj5hwipGOKydrT0vROlo0TGoGzfvp1WrVrx4IMPMmbMGJKTr/zD8e+//+Lv728vTgDuuusudDode/bsKYk4QtzacnLQHT8GOTmuTmKjoTyK0QQnTtv+ewtTTGbczyVCjjYXwlNMZjzjU5z+nJ1qR0PfO1E6ir1Aadu2LVOmTGHhwoWMHDmSHTt28L///Q+LxQJAYmIiwcGOsz66ubkREBBAQoI2L7ETwpXcDh8kpGUT3A4fdHUUQFt5PM5cwL1tZzzP3tr/dvicu0idQZNRjhxzdZR8+Zy7SPMxX+Bx5oLL2tHS906UjmK/zLhTp072/788SPb++++396oIIYQQQtxIiV9mXKVKFYKCgjh16hQAoaGhXLx40WEbs9lMampqgeNWhBBCCFG+lHiBcv78eVJSUuzFR5MmTUhLS2Pv3r32bf766y+sVisNGzYs6ThCCA1TFNDpFHS6K+vvCCHKpyKf4snMzOT06dP2+7GxsRw4cICAgAACAgKYOXMmDz74IKGhoZw5c4b333+fatWq0bZtWwBq1apF27Zteeuttxg3bhwmk4kJEybQqVMnuYJHiHJMUcCk6Mg0mgFwd1MJcHEmIYTrFLlA2bt3L88884z9/uX5Trp168bYsWM5fPgwq1atIj09nQoVKtC6dWteeuklh7lQpk6dyoQJE3j22WfR6XQ88MADvPnmm8WwO0KUPeaGjUmId24NlOJUUnkURSHTaGbDwXiycs1EBntzX4Rqq1wKkFMrEmPsHrJv8cuMM6pWYN/KqdRpcBts0t4g0IyqFfjj05cIqRTpsna09nMgSl6RC5QWLVpw6NChAp+fP3/+DdsIDAyUSdmEEPnKyjWTkWsm22RxdRQhhAvJWjxCaJz+6BECO96H/ugRV0cBtJXH/Ww8bo88jUdcoqujOMXrfDI1YmbAsROujpIvr/PJNH7vK9zPxrusHS1970TpkAJFCI1TsjIx/LMDJUsb61VpKY8ux4hu5x50uUZXR3GK3mjC+/AplKxsV0fJl95owv/EeXQ5zn3O121H0aEoin2QtG2g9FVPa+h7J0pHsc+DIoQQQhSFzuCBh5sCxnOoqmp/3GrwJwsvrnpIlCNSoAghhHApnZsBxZSJ9dgmrLmXekjcfdBFdUAxeDsULaL8kAJFCCGEJqjGLNTcDABkFhwhY1CE0DhLlaqkzZqLpUpVV0cBtJXHVCEY80cTMYYFujqKU3JC/Il96UnUyAhXR8lXTog/B597EFOF4BtvXELtaOl7J0qHFChCaJwaFEzu471Qg5z75VBctJTH4ueNtUdnLL7ero7iFLOPJ6l33wFB2pyazuzjSXyLulj8nPucnWlHS987UTqkQBFC45TERDznz0VJ1MaltFrKo0/NQLdwOfq0W/vKDkN6FsE//AlJF2+8sQsY0rOo/Ptu9KkZLmtHS987UTqkQBFC4/Rxsfi9PgJ9XKyrowDaymNITMHtzYm4J6W6OopTPJIzqDxvJUrceVdHyZdHcgZ1vvodQ2KKy9rR0vdOlA4ZJCuEcAlFwWFBQFkcUAhxNSlQhBCl7tqFAQH0OgWrXLshhLhEChQhRKm7dmFAgBA/D5pWD77u4oBCiPJDxqAIoXGqry/Gu+9F9fV1dRSgePNcXhgwI9dMtrHoiwNavTywtr8Li6eH01lcyexpIKNxFKqPj6uj5MvsaeBivapYvZz7nJ1pR2s/B6LkSYEihMZZatYm9etVWGrWdnUUoPjy6BTw9nDD99LNy12ProidJ8bwMMxLZ2OsHOJUFlfLqRDEqTH9oGY1V0fJV06FIPYO64YxPMxl7Wjt50CUPDnFI4TWWSwoWZmo3j6g17s6TbHkURQIcsuhdVg2JqsVAC+DmWCDN+5uRfi7yWKF9Ay41MYty2pFl5UDlqL3IpUKqxV9rsn2eZdQOwqAolwZLH3tqT6t/RyIEic9KEJonNu+/witFYnbvv9cHQUorjwKemM65kM/kb3rW7J3fYv50M+4GdNxK0I3iufJONzr3YXXKW1enltYvrGJ1Ov9Jsr+Q66Oki/f2ERavzIbz5NxJdKO/lIxkmuxknPplmuxOgya1trPgSh50oMihHAZU3YGxqw02/+7y1/F5ZWiU1BVlbiUbNJS0gHw8tNRw6pe6lGRxQLLIylQhBBCaILJYsV46fSPwZz/6SRFUdBd1cumqiqy2HHZJAWKEEKIW4aXOQnVdOWUntXgTxZeUqSUQVKgCCGE0LzLg2etp/9C1R2xPejugy6qA4rBG1UqlDJHBskKoXHmereTuP845nq3uzoKUMJ5FB1e7nrbZccG/Q3nbMupVhnj7t/JrlKx+LOUosyIEA4uHItat46ro+QrMyKEre/9j5xqlV3WjuW228n57y/UyEDU3AzU3Aww3tqLRIrrkx4UIbTOYEANDXV1iitKKI/O4IGnQUfz4Ayy/Sz4eoGHTkF/vat63PQQEmz77y1M1euxBPiCweDqKPlS9XpMft5Of85OtWMwQGgInNeDRq/GFsVLelCE0DjdieP493kC3Ynjro4ClFwend6AzpiB+dDPZO/6FsvRjegspuv2ohjOJeL2/FDcL1ws1iylzTMhhSoTP4eTZ1wdJV+eCSnc/sn3GM4luqwd3YnjGJ7tjxLnXAZx65ACRQiN06Wn4fHTj+jS01wdBSj5PKYc26XHptwbd9/rs3LQ/bIRfVZOiWQpLW7ZRvx37EdJT3d1lHy5ZRsJ+e+E059zUdtRuHLVji49Hf3Pv0HmrX2sReHJKR4hhBCao9cpKDqFFKMFi9WKl9lCgKtDiVIlBYoQolQoypUrMRRZsVjcgKJTMFus/HkqgaS0XOrEpRABstp1OSIFihCixCkKmBQdmUYzAO5uqvw1LAolO9dCRq6ZHNMtvt6SKDIpUITQOEulcDLGTcRSKdzVUYCby6MoCplGMxsOxpOVayYy2Jv7IlSn/xo2hQRgfnsEpiAAk1NtuVJuoA/nn+tCcKWKkHLQ1XHyyA304ViPtphCnCsrnWknK7QiOW+/jhrq7lQGceuQQbJCaJxaoQLZA4egVqjg6ihA4fIoCraBjZduiqKgUyAr10xGrplsU/FcJ2oJ9MPa7xnMgb7F0p6rmPx9SHq0PYSFuDpKvkz+Ppy9vymWQD+XtZMdHIqx/wsQ5FwGceuQAkUIjVNSknH/fiVKSrKrowA3zqMo4K1k42M6b795G88T5JaDu1vx/pOjy8hCWfMz+oz/b+/e46Mo7/2Bf56Z2WuyuSdA5CpKUC7hVjlCVPCCfSlq8UbP0VJaaos9Kq36q60vBVFeQluqVbStp/RYodqWCtqjeAOrVIUKWkAugQCBXAghyeay953Zmef3x2aXbLJJNtkkMxu+79drX7C7s89+Z5+dzXeeeS7+Pi13oEneADI+2w80G2OkVnuSN4C8L49B8Ph0K8fsboH01ruAO7kYSOqgBIUQgxMrK5D5vW9DrKzQOxQA8eOJbTERICguaGXboB18E9rBN8GPfQBR8cJhMyU8S2wizGcbYVr6MMz1xkjeesvqdGHE2o1gVdV6hxKX1enCpevfSXq+mWTKcZyphn3pA2C1qT3nDUkc9UEhhCSlfQdYUWCwggGyNzwdOQBIFlgk1rNZYgkh5zVKUAghSWnfATbXYcHNYziktk0koglM8YZniXW1wJZfCGHybBoxSgjpFF3iIYT0iWgHWLnzDrA9mSWWEHJ+63GCsmfPHixduhQlJSUoKirC9u3bY57nnOO5555DSUkJJk+ejMWLF+PUqVMx2zQ3N+Ohhx7CtGnTMGPGDDz66KPweukHi5B4uNUGZVIxuNWmdygAjBWPZjZBmzgemtmYi+wlSjVJ8F94AbjVqncocakmCe4R+Ul/zsmUo1osUCdeCqR4XZPE9ThB8fl8KCoqwooVK+I+//vf/x4bN27EE088gU2bNsFms2HJkiUIBoPRbR5++GEcP34cL7/8Mn73u9/hiy++wPLly3u/F4QMYuq4IjR/+AnUcUV6hwLAWPHII4Yg9N4mBC/I1zuUpPiH5aD8Vz8GLr5Q71Di8g/Lwd5H/wvyiCG6ldM86iJ43/87+KjkYiCpo8cJylVXXYUf//jHuO666zo8xznHhg0bcO+99+Laa6/F+PHj8Ytf/AJ1dXXRlpYTJ07gk08+wapVq1BcXIwZM2bgsccew9atW3H27Nnk94gQQsjgwATYzGJ45JdZBHVZOr/0aR+U6upq1NfXY9asWdHHHA4HiouLsXfvXgDA3r17kZGRgUmTJkW3mTVrFgRBwFdffdWX4RAyKEgH9iNveB6kA/v1DgXAuXhMB7+KmYRND9by0zBdOB22k2f0CaCPpFXV4ZI7HgE7WKp3KHGlVdWh5P4XYC0/PWDlCCYLrCYBl+V4MacwiCvO7kX6mEshHEsuBpI6+nQUT319PQAgNzd2NsTc3Fw0NDQAABoaGpCTkxMbhCQhMzMz+npCSBucg8kywLnekYAxwIoAmCzDpjSAK7UAGJjJgSx7uG9A+Ex3gNZN4RxMVgDo/9kkg3FACKlQDVDH8UTiS/Y72JNyBNEEQfZER345muRBUdckcTTMmBCSMMYYhFC4Q7t24iNw4RiYPRv2i6/G5fl++BUVNjOHRRChMkTX2qGmedJbkZFfIZkSk/NNn17iyc8Pd1RzOp0xjzudTuTl5QEA8vLy0NgYOxNgKBRCS0tL9PWEkBQg+8GDHnBVOTfHyb4t0Mp3gGkKgipHQNUQUDXIGv1xIYT0TJ8mKMOHD0d+fj527doVfczj8WD//v2YOnUqAGDq1KlwuVw4ePBgdJt//etf0DQNkydP7stwCCEDKHKmq8o+cM5R0+xHWa0bZbVunHUFDHGJihCSOnp8icfr9aKysjJ6v7q6GqWlpcjMzERhYSEWLVqE3/72txg1ahSGDx+O5557DgUFBbj22msBAGPHjsUVV1yBxx9/HCtXroSiKHjqqadw4403YsgQGj5GSHuhi4vQ+M/PoY4arXcoAAB+8Vj41z8MnmMBY6zTyzeKqkFWtej/+0Nw+BAoH25BoPpLwOfpl/cYCL6hOTj+3MMYedGFwBdH9A6nA9/QHHzx+N2wD0/uNzqZcgIXDIHyjzfA/UcAHoh5jjEGoc3pNuec8uFBoMcJysGDB7Fo0aLo/dWrVwMAFixYgDVr1uCee+6B3+/H8uXL4XK5MH36dKxfvx4WiyX6mrVr1+Kpp57Ct7/9bQiCgHnz5uGxxx7rg90hZBCy2aCOv0TvKKI0mw3BEQXhdXZUDaLGodeUbdxiAi+6CLzuKyCFF7nVzBKCQ4cCNmNO1KaZJfgKc2GzJDdJWjLlcIsJKLoIOFIOFgjPq8UYAwdDi6JCbXMZMc0swQSNkpQU1+MEZebMmTh69GinzzPGsGzZMixbtqzTbbKysvCrX/2qp29NyHlJqKqE/ZlfwPfgT6CNGKlrLIwxqJXV0Nb8GRXzpkHOzUAOsjBap78EprpGiA+vgGnWKCCFJxi1OF0o3LwJWDVR71DisjhdGPnObri/fYtu5VjrmyE+vAK+G8ZDzQz/6RI0DqZxfHayAU53OGmxWyTMHV+AbLMIThlKSqO1eAgxOKGpEbZXN0BoMsYy86ypGbkf7QVcPsiq1m+XbxIhun0Q//IGJE8KN58AMHkDyN6+G6ypWe9Q4jJ5Axi28xBEd3KfczLlSF4fhD9vgfN0Y7RvU02zH5xz+GUVnta1oHzBUFIxEuOgYcaEEEJSRkjr/75NxBioBYUQQgghhkMJCiGEEEIMhxIUQgxOyy+A74EHoeUX6B0KAIDn5eLM/NmQM+x6h4JQlgPqfy+BkpmudyhJkTPsqL/1avC83O431oGcYUfl9TMQynLoVs5gqWuSOEpQCDE4bVghvI89AW1Yod6hAAD4sKGoWXgN5Cz9/1CEcjOh/mwZQjkZeoeSFDkrHXXfugEYZsy5oOSsdJz6xmyEcjN1K2ew1DVJHCUohBgc87hh+uwTMI9b71DCPB6kl56CGJD1jgSCPwC2cw8Ef1DvUJIiBmTYDx4HPF69Q4lLDMjILKuG4A90v3E/lTNY6pokjhIUQgxOLD+BrAU3Qiw/oXcoAADhZAWKnt4AW12z3qHAXNMA051LYKl1dr+xgdnqmjHm8d+BnazQO5S4bHXNKH52M8w1DbqVM1jqmiSOEhRCCCGEGA4lKIQQQggxHJqojRDSKcbC09ufu9/Z0oCEENK3KEEhxOC4ZII6rBBcGtjFZhgDFCbAK5+bOtwscWRLEuRsBzRR/wZYLongQwvARVHvUJKiiQKU3ExAMuZPsiYKCGalg0vJfc7JlDNY6pokTv9fGEJIl9RLJ6Bx/xGol07o9/diDBAE1noT4FdC+OhIHbbur8HW/TXYc7IR6vhxOLDuQfguyOv3eLoTHDUMyhfbERhpzOG5ifJdkIey9Y+DXzJO71Di8l2Qh89XL0Fw1DDdyhksdU0SZ8x0nRAy4BgD7MwPQXFFHgGT0hFSNXhaF2DzK6p+ARJCzivUgkKIwYmHDyGneDzEw4f69X0YYxAUF7SybdAOvgl+7AOIshtSu0s5QulRTLr/GdhPJzfktC9YKs7ANONaWCvP6h1KUuynGzDue0+BlZbpHUpc9tMNmPmzP8BScUa3cgZLXZPEUQsKIQbHQgrEMzVgIWVg3lD2ggc94SaVuPGEYG5yQzDASrIspILV1oGpqd2yI6gaTM4WKKFQ9xvrQFA1WJo9YKHkPudkyulJXQutnbuF1tyacw7Oe/yWRGeUoBBCCBk0zJKAbCkAm+wBEM5KNFMGfLBRkpJiKEEhhBAyaEiiAFF2g5/YBh70AuY0COOuAzPZwSlDSSmUoBBCCBl0uOwDD3pAM/ekLuokS4jBqReORfMbW6FeOHbg35wJsJlFpFskpFsk2EwitAtH4eij34a/IGvg42lHLsyDsukPCA7N1TuUpPgLsnDyqaXgY0bpHUpc/oIs7P/xbZALkxtankw5g6WuSeKoBYUQg+PpDiizrxi4N2QMjDEw0QyLxHBZjgd+R7hjYroNsDhs8E8cA7WlaeBi6oRms4J/7WvQPqkCgj69w+k11WqGb/QIID1N71DiUq1mtIwbjlybVbdyBktdk8RRCwohBiecqUHaqicgnKnp9/fSwBBUNQRUDTKTwBQvQkc/gH/fFvj3bYF6fAeE6moM+8t2mJs9/R5PdyRnC8TVz0FqdHW/sYGZmz0o2PgOcMaYQ2jNzR6MfvMzSM4W3coZLHVNEkcJCiEGJ9TXwf78MxDq6/r1fRhjCGkclY0+lNW6cdYVADiHEvBA9rkg+1xQgl6wBieG/N+nMLv0P4uVmt0QX/wDTC36J0vJMLt8yN/yD7AGp96hxGV2+TDy/S8gNbt1K2ew1DVJHF3iIYTECIU0yKoGxQDznBDSLSbAZmZIt4T/nNnMIhjouzsYUIJCCCEkJQkmC6wmIaaflM3MYRZEaIjMgkJSFSUohAwCrHXmzAiaOZOcDwTRBEH2hPtJucL9Wky5Q8GmXqlzZKQvUIJCiMFp2Tnw37UIWnZO3OcZAxQmwCufmyY9zSzBBK1fkhSenQXn3KlQ0pIb0dEXVIcd6jcXIJRuRyqfLytpVjRdexnSsrOAZr2j6UhJs+LMrAlQHXbdyumqriP9pMLv4UgqRmIc1EmWEIPTRoyE59kXoI0YGfd5xhi8cggfHanD1v01+OhIHbxyKKZFJf7rAEFg0Vt320cNL0T1929BMDejp7vS55SCHKhrV0LJz9I7lKQEczNQ8993AsML9Q4lrmBuBo5961ooBfGT5IEoZ7DUNUkcJSiEGJ3fD/FIKeD3d7mZLxiCJxiCL9j9gnORVpcmWY3e3CE1sTYIfwCWqjoIsv4L27GgAnb0OJg8QAsp9hNBDsFSWQv4A3qHEpcgh2CvcYIFk/uckylnsNQ1SRwlKIQYnHTsKHKunAnp2NE+K7N9q8vW/TXYc7IxvFZJdy0vx8sx/ie/gb22sc/i6S1L9VmYrrkV1tP1eoeSFHttIy5athbseLneocRlr23EjKf+BEt1cvO0JFPOYKlrkjjqg0LIINR+uXkgfsfZSKsLAPiV7pexJyQVMCA6I3Ik4U7keCDGQgkKIYNMvOXmAVpynpwfxNaEJKhq0FQNgqrBzBhaFBWqdu7L358dyUnfoASFkEGmw3LzAC05T84bTGDgnKOm2Q9XsxsZWRIKQxo+q2iA0xUEANgtEuaOL0C2WaTjwcAoQSHE6BgDN5u77RvSXmS5eSDS5C1Em7kZYxB6uw49Y9AkEdwI69gzBm42oXUPUxZngCaJPa7jgdJX8SVVTg/rWlFjZ0T2B9Xo5UySGqiTLCEGF5pUjIbqBoQmFfe+ENEMSWSwy7VIU2phl2uRLQVglnr+E8AnXoIDGx+Hd0RB7+PpI4ELL4BS/iX8Y4bpHUpSvCMKUPq3n4NPvETvUOLyjijAp+vuQ+DCC3QrZ7DUNUlcn7egrFu3Di+88ELMY2PGjMF7770HAAgGg1izZg3eeecdyLKMkpISrFixAnl5eX0dCiGGN2AzwIomMNkLfuIT8KAXzJIGcex1kEQ6RyGEGFO//DpdfPHF+PTTT6O31157Lfrc008/jY8++gi//vWvsXHjRtTV1eG+++7rjzAIMTTGADvzI02pjd7szN+h9VssO4qsa66AWJb8MOPIZR8uJ7ES8bFyXPyz38F2Rv9hxuaqs5C+ficsKT701HamERc+9CxwzJjDjG1nGjH16ddgrkpumHEy5QyWuiaJ65c+KKIoIj8/v8Pjbrcbmzdvxtq1a3H55ZcDCCcsN9xwA/bt24cpU6b0RziEGBJjDILigla2DZC9nXZkZQE/TAf2gwX8ra+LbXVJqj9Jb+IOBGA/VQtR0f96viArEA4egSBfCaSlbpc6UQnBVn4aSsCYE7WJSgiOqnrUJTlJWjLlDJa6Jonrl1quqKhASUkJLBYLpkyZgoceegiFhYU4ePAgFEXBrFmzotuOHTsWhYWFlKCQ85fsBQ96Eur6F2l1ERRX20fBpPRwf5JgN69vLSTSadZmxrll6k0iGKO5UAghxtDnCcrkyZOxevVqjBkzBvX19XjxxRdx11134a233kJDQwNMJhMyMmLX8MjNzUV9PTXbEdKdDq0uQML9STiAoMbDc0MIJlglFrNMfboNsAgM4kA2xxBCSCf6PEG56qqrov8fP348iouLMXfuXLz77ruwWvVf/ZSQQaG11QVAYkM2GYMGROeGyBmahdHtlqm35RdCmDzbqCNdCSHnmX7vwp+RkYHRo0ejsrISeXl5UBQFLpcrZhun0xm3zwohBFBHjkLL+legjhyVdFnt54aILFMv+1xQIpO6dYOPGI5Ty+5AwACrGctDcqD8bi3k/Gy9Q0lKIDcDVQ9/C3zEcL1DiSuQm4HD37sB8pDkVjNOppzBUtckcf2eoHi9XlRVVSE/Px8TJ06EyWTCrl27os+Xl5ejpqaG+p8Q0pnsbIS+cStYTk5M59gYTIDNLCLdIsFmFsMdaRG+JMQifU76SlYGWv5jAkJp+reIaul28PnzoKbb9A4lKaE0K1yzi4Es/ZO+eEJpVjRMvxhaul23cpKq6zbHR7pFgt0iDWjHctI7fX6J5+c//znmzp2LwsJC1NXVYd26dRAEAfPnz4fD4cBtt92GNWvWIDMzE+np6Vi1ahWmTp1KCQohcTAGqPX1EP/6Z7hvvRMYMgTW9umGaIalTX8SuxWwMAE+NdzfBABEjaPP/oTXO5G3dSd8k0ZCyUjrq1J7RWx2Q/ifDZBSfO4uk8uL3M93AKOn6x1KXCaXFwW7j0KZn6VbOb2ta8FkgdUkxPS3MgkKsqUAONP3+0u61uctKLW1tXjwwQfx9a9/HT/60Y+QlZWFTZs2IScn3KT36KOPYs6cOXjggQdw9913Iy8vD+vWrevrMAgZFBhjkCurkLfyMez651f47HgDQhqP7XcimsAUb7g/yb4t4Cd2AKqMmhY/ymrdKKt146wrgL6aAY7VnsUFf/oAlubELgn1J5OzBdKTa2FqcnW/sYFZmr0Y+se3wGqTm2ekv1iavRi7+ROYnC26ldPbuhZEE4RIf6t9W+DftwWho+9DlN1I9SUSBrs+b0F59tlnu3zeYrFgxYoVWLFiRV+/NSGDml8OQZNVdPajGulPogQzw/db+5tE/k/I+SxyfACAia7vpASa7YYQQsj5J7p4ZjhZ6bdlJkivUYJCSIphODfhWuQ+ISRxgskCi8QA+Ux01mbNlAEfbJSkGAglKIQYnJqRifKZcxGwO5AuMjCBIaBy8P7oAJsA7nCgZdo4hGzmAXzX+FS7Fdp1V0G16z+iKBkhmxmur10Kq8MBNOsdTUchmxnOSWOS/pyTKacv61qQwv22tBOfQAu2LjNRdD0EU1rMMhPUqqIvWsqUEIMLjR6Dt554Ec3DRkAUBYRUDZWN3n7pAJuQ0SNw6v/9FwL5WQP3np1QhuUh9PK6pOfn0FsgPwtVj34XGD1C71DiCuRn4dAPb4YyLLlV55Mppz/qOrJ4JlQFkshgl2u7XbyTDBxqQSGkD7RfwK9Pz7wUBbbmRvhM59pJQirXrwOsokB0ecFUFVwUB/a92wupgLMx/G8KY6oKscUDKMktxtdfmKpC8gWBvOQ+56TK6eO6jq5LxRggmcFkL/iJT8AjExZ2sngnGTjUgkJIDzEGCAKL3kSRIcQENMlq9KYwoc/OvCylh/D9/yxBQcWxvikwSezIMUz8wS+RdtqpdyiwVpyBuXgObFXGHJ6bqLTTToxf/ATYEWPUcXtpp524/Ce/h7XijG7l9GVdi60HZ1DVEGgd7cZxrkWFBz3Rta6IfqgFhZAeYAxQmACvHIo+JgoMigbsOFIHXzAEu0XC3PEFyDaLdOZFiAExgYFzHl2byqFm4GKEW1XoiDUOSlAI6QHGGLxyCB+1JiMAkOuwYNroHPhkFZ5gqIvXxl4G6nTaekLIgIjMFRSieYIMiRIUQnrBFwxFkxG7peNhJLQmI0LrRVTGACt8EOQ2s2AKIiSmQglnLp2uShx52GaWYDGJYCy1+1sQQkgiKEEhpA8IDLCbRYBzZNlNECURjUEVkQZjUWCwcjd42Tbw1mvbLD0fGDkdQS28Zg5TNVgFEYIggLHw6xhjyBRlAMCMfBlitg8WgUGkmTAJIYMcJSiEJMkkCsgzBTC7wA9F1WAzhZDGPXj7uBct3nBykeuw4OYxHJLSOqwRACzp0IDodfCMXDsuEhikQE10BJAgijCNvwCHNz4Fl/8Qco63QJg8W9ehj/zSIhz4w0/hCejfiTAwuhBy6U74//0e4EpunRg9eYbnofTVVRh7aRGw84je4XTgGZ6Hz55ZiqyRhbqVM1jqmiSOEhRyXuuL4cGSyCDJ7vBiZB4XTBmZkDJuhhJCu8tAnayhExlFIEhA0I0zX26Du/UHOCu/ECMmzoIiqZA9HihB/ZMCiCI0uxWQ/XpHAogC4EhH9FpaqhKE8Geq97DtzggCVJsl/HnrVY5Odd32Ui1N3DawUvyoJiRMEABRFGJu3f2OMQbYmb/HEzMJLJxwpLfebCYxPLrH37pYX8ADMAE2s3huG7OY8JT0fp8bXk8LvJ4WBPweoLwCF67eCGtdU4Il9DMDxWOuqYd011KYz+g/5DkZ1romjFr5P0B5hd6hxGWta8LE59+AuaZet3L6u67bzosSuXEwtCj9M30A6R61oJCUJwiAhQWgBWKbfQVrJoKCFVonHfQZYxAUF7SybeE5DzqZmKltK4sgMORIAczO90NpLTjdhph+IYLJAqtJwGU5Hvgd4Q6tNjOHRRChMvR4DR3m9cLx1QlIN16W4Cv6l5HiEfxBCDt2Qrx+ImAbyAn/+5YUUJC+rwyK1wAtZHFIAQU5pZVo8gd1K6c/67rtvCha64geQeNgGsdnJxvgdAdp+gAdUIJCUh5jArRAC6p3/x1+T3iUjC09A8MvuwXMagfQzRBC2Qse9LSeQQkdRt/Y4AOT3eH7ogioQWhlH5x7r/zCmH4hgmiCIHvCl3xaL9VY8oaBTbkCQZVHfwAHeg0dQkh87edFAYCMLAmFnMPfZvqA9qPzALrs058oQSGDht/jgteTROc50RxdjyM6XZMgQuQy/Ec+hCp7ITkKYBk1HarshewLJyhKMDNucUrAE91GlTM7/ADmIAuj6ZeNEMOI9AeL/L8tsyQgWwrAJnvQdjo3WgW5/1CCQgavaGtIuGmj2zMd0dRhPQ6Wng8+cjqq651wNTchZ6iE0SN7/0vU1Q8gIcS4JFGAKLvBT2yj9XoGCCUoZFASTBZYJAbIZ6I/HIme6URXOAUASzqAc4mFHkkFLxyK6u/cgGB2OiwD/u4dtY1Hb0peFkKrHoWcqwCarHc4vRbMTseZexYgt3Ao0Gy8YcbB7HQcWzgHal6WbuUYpa7b/j5Qf9n+RaN4yKAkSCYwxQte9gG0g29CK9sGQXHFn14+sqKpUbvn5+bAOe8yKA673pGEGSgeNTMd2uJvQs1I0zuUpCgOOxpvmA3k5ugdSlyKw44zc4qhZiaXlCZTzmCpa5I4SlBIymm/mjBjrNMzmejZTicrk2pgHVY0NZymFmR9sh+SN6B3JGEGikd0+yBsfhuix6d3KEmRvAFkfvwl0GTMCcgkbwAFnx+B6E7uc06mHF3qus10AT2ZKoD0DUpQSEphDLALfqQpZ6M3e6gOZkHt8fTvjDGENI7KRh/Kat043ewHb308euuf3egRVn0ao37zBqxOV/cbDwAjxWOqa4S07FGY65v1DiUpVqcLw5/7M1j1ab1DicvqdGH8H9+Hqa5Rt3IGuq7PTRfgxZzCIC7LDcAsqIb4TThfUB8UYnht5yFhjIEpbgSPvA+ttVUkMrKG9XJ9mlCotfVEC7eftJ0LgYYCE3J+aj9dgCl3KNjUK/UO67xCCQrRVXdTzUdmexWU1rN1QQLjIVSdbYDH1QwAnY6saTszZCL9S+LNhUBDgQk5v0WmC1DSHHqHct6hBIXopkPygY4jbdrP9srS8yGMnA6V8y6H67afGZKpGiwJNs7SUGBCSE/ETt7G0X58D03m1juUoBDdJDrVPABA8YW3URLr/d++NcTmEHChxiEIAiIzy8Yd0WNA3G6D9+LhUM0mvUMBYKx4NKsZ2rTJ0CxmvUNJimo2wTduFCS7DWjWO5qOVLMJrjFDoVmT+5yTKccIdR3TKgvErNejahwCAwrMMiQ5trMzTebWO5SgkAHT/nJO9P9dTjXPwFl4pA1XtR73CYm0hlg1DiaKcIc0qK1r6JgkIKNP9qyfjR2D409+D/7mJug/sBcx8ehNvqAAof/7E4KfbAEMEE9v+Ydm4+TP78fFY8cANXv1DqcD/9Bs7PvJQuQOLdCtHL3rOpH1enIdFtw8RqPJ3PoIJSikT7RPPoDYZs14l3MgiJCgIjrlUtyp5sN9Tk43+eFxuXvdJ0S0WCEJgNJUgaAcXsCPWS0wp3OIyS4hTwgZ9Dpdr6fd5RwGmsytr9AvM0kaY4DChOiS5JFbSBDazFciQFBc4Me2QTv0d2iH/g5e/k8wTTnXgTUy1fyx1snVDr4JXr4DTFOifU562yck0iM/ePg9uP79Olz/fh3ysY8gqIph52eLYAcOo/g/n0B6ZZ3eoQAwVjzWE9UwD58M28kavUNJSnplHSYseBjswGG9Q4krvbIOV977HKwnqnUrxyh1HWmVlVUNqmBKeChyZNmNc/M3DXjoKYdaUEjSGGPwKyH8q9wJf2vrRIbNhP+4KA8eRQPAYRIZrIwh6PdEzywE0Ya4vRhk37mJ1RLsc5Kotgv4dbbIHyGEJCLRocham34qEWlmCSZo1C+lC5SgkF5pe0lHEBiypQCmZXqgtPbvsJlEpHEr3j7uRYtXxvAcO665QENlow9+d7h51KFmYBwQHQYc7nAGBDVO85AQQlJG26HI8TrSKm36qQCA3SJh7vgCZJtF6pfSBUpQSI9FLul45RCA8DLkmYoboaPvw+8NJx+mjExIGTdDCQGeYAh+JdyyElLPDQ9uPzGaqHGIAM1DQghJSZ12pAXgl1V4giEdo0s9lKCcR+J1ZG0/Zj+R8frtL+kUZtlQMoRDCXjPXT4xi63rWLDwOhYmEYypseW063QWSUZoHhJCSCrqtCMtnWT1CiUo54n2rR4A4o7ZjzdeXxAAxoQ2ZTHkSN7oJZ10WwgWgcWshXNuHQsP/A4V6TZ02CYikpBQMhIfv3gsSp+9H15Rg0XvYBAbj96CI4ZA/uRtBMp3AT6P3uH0mndYDo795qcYdfFYYM8RvcPpwDssB7tXfhvpI4boVk4q1XWHk6w2J2tA+BKPKMRO8EaTuXVECcp5gjEGrxzCR0fq4GttZuwwZj/OeH1BACwsAC1wLokRRBEmLkMr+wB+jwu2/EIIk2fH9Epv33ks3jYkQVYL5KG54EaZ58NA8XCzCRgzErz6CyCFFzTmJgny0DzAaoQUtCNukhAoyEJakpPzJVNOqtZ1+5M1ALBIKoaZPGBKIPpbS5O5dUTDjM8zvmAIntZbZMRNdMy+7G0zUVrkJoIHWlC9++849o+NOPaPjTi77wMwVYYqhy/pKJEJieKIdh7rYhvSjcpqjHxhM6wNLd1vOxAMFI/prBPi/T+DuU7/ZCkZ1oYWXPDsa0BlcsN4+4u1oQVFL78H01mnbuWkal3HnKzt2wL/vi3QTv4TQqAJvKx1SoWybRAUV8rMbj1QdE1QXn31VVx99dWYNGkS7rjjDnz11Vd6hpNSGEO3Y+rbbpPQF7/NRGlpSvhmU+pgFlTIPje8nhZ4PS0I+I3dvDrYsBYXsj87AMkX1DsUAMaKR/T4Ib6xFaLXr3coSZF8QWT9899gLa7uN9aB5AtiyO6jED3Jfc7JlJPqdR05WZN9LigBb3hCN8XfuoRHuEmo/VwpghD7Oy+KgCgKMTdhEDcz6HaJ55133sHq1auxcuVKFBcX45VXXsGSJUvw3nvvITc3V6+w+lTHTqkdF5GK9xhjHG1zR8Y0cN72PqAA8MrnOp3azSIkLbZtMCQw+Fq3EVu/4HbLuSq3mUUwpkWHxUEyg8le4MQn0OTwASOk5YGNnA4Wp+8IIYSQnms/2kdQNZgFER5Vi86VIjDAYhKjLd2iAORIQWj+2JZLwZYFmVmjl4bi9WXpbtV4o9ItQXn55Zdx55134rbbbgMArFy5Eh9//DE2b96M73//+3qFBSCxyuxuG8YAu+CHILtbHxAgmi3QZP+57VofU4NtLqgyAYLZiqDfF05dmACL1YpgwIdzLxNgNVvgamqCHNJgNonIzM8CCwXOTS0vCODMDHfrNjaLCUNzMnDFkACCofAX3m4FLEyATw3POyKqGswMCAS80ALhuEXJTvOQEEJIH2o/2icrLw2jGaA0VUaX4rBZTMjIyYCnuQmyosFutUBK5zj1xTvwusJJSlpWHsbMnA+T5or2ZeFmB/ywxywzYoMPLPK3CIBmyYBfi/1lN2LSokuCIssyDh06hB/84AfRxwRBwKxZs7B3b+ILZfXH5TrGgBAT4A+da51Ik0SI7baxch+Ycq7CuSkDAXaug5MgMLCgB3LFv8BDAYjWLEgXXAK5ch80JdxEKVizIBVeAnZ6HzQlEH7MlgUMGY/GI58jEPAhLSMXBRdOQuPRPQj4w/04Io8Jp3dD83ohZeVBzChGoOpc2aItG+Zh46PbCNn5kNImwXxmD0LecDnm7HwgfRIauR2+oAJHmg02ztCohe8DgEO1wgaGtIxcaCzcsc2Wng0wIfpY+/t6b6P3+/d5jFwCHA7Ys/JgNsJ+tImHZRfo+jlaWAhwOGDNyIEj25Gy3webSwMcDkCUDBljKCsPcDhgSbLsZMoZLHXd9jFzWjYsmgizIw+C4gOr+hxa6+9z5DdbqA7/hrPsfAhjJ0GSzBBM4c7UJksahJA/+tsvSFaIF86Cl9miLTEmkcGqeqC0/i1ilnTYxvwHRNUdk5C0/xsWpkHr48F6Pfm7zbgO09idPXsWV155Jf7yl79g6tSp0cd/8YtfYM+ePfjb3/420CERQgghxEAGcfcaQgghhKQqXRKU7OxsiKIIpzN2qJnT6UReXp4eIRFCCCHEQHRJUMxmMyZMmIBdu3ZFH9M0Dbt27Yq55EMIIYSQ85Nuo3i+853v4JFHHsHEiRMxefJkvPLKK/D7/bj11lv1CokQQgghBqFbgnLDDTegsbERzz//POrr63HJJZdg/fr1dImHEEIIIfqM4iGEEEII6QqN4iGEEEKI4VCCQgghhBDDoQSFEEIIIYZDCQohhBBCDGdQJyh79uzB0qVLUVJSgqKiImzfvj3mec45nnvuOZSUlGDy5MlYvHgxTp061W25r776Kq6++mpMmjQJd9xxB7766qt+2oOudbV/iqLgl7/8JW666SZMmTIFJSUl+MlPfoKzZ892Wea6detQVFQUc/v617/e37vSqe7q8Kc//WmHeJcsWdJtualQhwA67Fvktn79+k7LNFIdvvTSS7jtttswdepUXH755fjhD3+I8vLymG2CwSBWrlyJmTNnYurUqbj//vvR0NDQZbm9PXb7Wnf719zcjKeeegrXX389Jk+ejDlz5mDVqlVwu91dlNr773V/SKQOv/Wtb3WId/ny5V2Wmyp1WF1d3elx+O6773ZarlHq8LXXXsNNN92EadOmYdq0aVi4cCF27NgRfd7Qxx8fxD7++GP+zDPP8A8++ICPGzeOb9u2Leb5l156iU+fPp1v27aNl5aW8qVLl/Krr76aBwKBTsvcunUrnzBhAn/99df5sWPH+GOPPcZnzJjBGxoa+nt3Ouhq/1wuF1+8eDHfunUrP3HiBN+7dy+//fbb+YIFC7os8/nnn+c33ngjr6uri96cTmd/70qnuqvDRx55hC9ZsiQm3ubm5i7LTJU65JzH7FddXR1//fXXeVFREa+srOy0TCPV4Xe/+12+efNmXlZWxktLS/k999zD58yZw71eb3Sb5cuX86uuuorv3LmTHzhwgN9555184cKFXZbbm2O3P3S3f0ePHuX33Xcf//DDD3lFRQXfuXMnnzdvHr///vu7LLc33+v+kkgd3n333fyxxx6LidftdndZbqrUYSgU6nAcrlu3jk+ZMoV7PJ5OyzVKHX744Yf8448/5idPnuTl5eX8mWee4RMmTOBlZWWcc2Mff4M6QWmr/Y+/pml89uzZfP369dHHXC4XnzhxIn/77bc7Lef222/nK1eujN5XVZWXlJTwl156qX8CT1C8P27t7d+/n48bN46fPn26022ef/55fvPNN/d1eH2iswTl3nvv7VE5qVyH9957L1+0aFGX2xi5Dp1OJx83bhzfvXs35zx8zE2YMIG/++670W2OHz/Ox40bx/fu3Ru3jN4euwOh/f7F88477/AJEyZwRVE63aY33+uBEm8f7777br5q1aqEy0j1Orzlllv4z372sy7LMXIdfu1rX+ObNm0y/PE3qC/xdKW6uhr19fWYNWtW9DGHw4Hi4mLs3bs37mtkWcahQ4diXiMIAmbNmtXpa4zE4/GAMYaMjIwut6uoqEBJSQmuueYaPPTQQ6ipqRmgCHtn9+7duPzyy3H99ddjxYoVaGpq6nTbVK7DhoYG7NixA7fffnu32xq1DiOXNjIzMwEABw8ehKIoMfUxduxYFBYWYt++fXHL6M2xO1Da7188Ho8H6enpkKSu58nsyfd6IHW2j2+99RZmzpyJ+fPn41e/+hX8fn+nZaRyHR48eBClpaUJHYdGq0NVVbF161b4fD5MnTrV8MefbjPJ6q2+vh4AkJubG/N4bm5up9ffmpqaoKpq3Ne0vyZrNMFgEGvXrsWNN96I9PT0TrebPHkyVq9ejTFjxqC+vh4vvvgi7rrrLrz11ltdvk4vV1xxBa677joMHz4cVVVVeOaZZ3DPPffgr3/9K0RR7LB9KtfhG2+8gbS0NMybN6/L7Yxah5qm4emnn8a0adMwbtw4AOGky2QydUiac3Nzo8doe705dgdCvP1rr7GxEb/5zW+wcOHCLsvq6fd6oHS2j/Pnz0dhYSEKCgpw9OhRrF27FidPnsQLL7wQt5xUrsPXX38dY8eOxbRp07osy0h1ePToUXzzm99EMBiE3W7Hiy++iIsuugilpaWGPv7O2wTlfKIoCpYtWwbOOVauXNnltldddVX0/+PHj0dxcTHmzp2Ld999F3fccUd/h9pjN954Y/T/kY5o1157bfTMZTDZvHkzbrrpJlgsli63M2odrly5EseOHcNrr72mWwz9qbv983g8+MEPfoCxY8fivvvu67Iso36vO9vHtglXUVER8vPzsXjxYlRWVmLkyJEDHWavdVeHgUAAb7/9Nn74wx92W5aR6nDMmDF488034Xa78f777+ORRx7Bn/70pwGNoTfO20s8+fn5AACn0xnzuNPp7HQ9oOzsbIii2KPX6E1RFPzoRz9CTU0N/vd//7fHZ9AZGRkYPXo0Kisr+ynCvjVixAhkZ2ejoqIi7vOpWIcA8MUXX+DkyZO9SjCMUIdPPvkkPv74Y7zyyisYOnRo9PG8vDwoigKXyxWzvdPpjB6j7fXm2O1vne1fhMfjwfe+9z2kpaXhxRdfhMlk6lH53X2vB0J3+9hWcXExAHQabyrWIQC89957CAQC+MY3vtHj8vWsQ7PZjFGjRmHixIl46KGHMH78eGzYsMHwx995m6AMHz4c+fn52LVrV/Qxj8eD/fv3Y+rUqXFfYzabMWHChJjXaJqGXbt2dfoaPUWSk4qKCvzxj39EdnZ2j8vwer2oqqrq9MtqNLW1tWhubu403lSrw4jXX38dEyZMwPjx43v8Wj3rkHOOJ598Etu2bcMrr7yCESNGxDw/ceJEmEymmPooLy9HTU0NpkyZErfM3hy7/aW7/YvEtmTJEphMJvz2t7/ttgUsnu6+1/0pkX1sr7S0FAA6jTfV6jBi8+bNuPrqq5GTk9Pj99GzDtvTNA2yLBv/+OvTLrcG4/F4+OHDh/nhw4f5uHHj+Msvv8wPHz4cHcXy0ksv8RkzZvDt27fzI0eO8HvvvbfDUKlFixbxjRs3Ru9v3bqVT5w4kW/ZsoUfP36cP/7443zGjBm8vr7eUPsnyzJfunQpv/LKK3lpaWnMULdgMNjp/q1Zs4Z//vnnvKqqin/55Zd88eLFfObMmboNU+1qHz0eD1+zZg3fu3cvr6qq4jt37uQLFizg8+bN63IfU6UOI9xuNy8uLuavvfZa3DKMXIcrVqzg06dP559//nnMd9Dv90e3Wb58OZ8zZw7ftWsXP3DgAF+4cGGHYY7XX389/+CDD6L3Ezl2B0J3++d2u/kdd9zB58+fzysqKmK2CYVCcfcv0e+1UfaxoqKCv/DCC/zAgQO8qqqKb9++nV9zzTX8rrvuiiknVesw4tSpU7yoqIjv2LEjbjlGrcO1a9fy3bt386qqKn7kyBG+du1aXlRUxD/99FPOubGPv0HdB+XgwYNYtGhR9P7q1asBAAsWLMCaNWtwzz33wO/3Y/ny5XC5XJg+fTrWr18fc4ZTVVUV0/P6hhtuQGNjI55//nnU19fjkksuwfr163Vpluxq/+677z784x//AADccsstMa/bsGEDZs6cCaDj/tXW1uLBBx9Ec3MzcnJyMH36dGzatKlXZwx9oat9fOKJJ1BWVha9tlpQUIDZs2dj2bJlMJvN0dekah2uWbMGALB161ZwzjF//vy4ZRi5Dv/85z8DCE/k1dbq1atx6623AgAeffRRCIKABx54ALIso6SkBCtWrIjZ/uTJkzGTmyVy7A6E7vbv0KFD2L9/PwDguuuui9nmww8/xPDhwwHE7p8oigl9rwdKd/sYOQPfsGEDfD4fhg0bhnnz5nXop5GqdRixefNmDB06FCUlJXHLMWodOp1OPPLII6irq4PD4UBRURH+8Ic/YPbs2QCMffwxzjnv0xIJIYQQQpJ03vZBIYQQQohxUYJCCCGEEMOhBIUQQgghhkMJCiGEEEIMhxIUQgghhBgOJSiEEEIIMRxKUAghhBBiOJSgEEIIIcRwKEEhhBBCiOFQgkIIIYQQw6EEhRBCCCGGQwkKIYQQQgzn/wMhEvsNVEGdbwAAAABJRU5ErkJggg==",
      "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": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:58.331364Z",
     "iopub.status.busy": "2025-04-15T03:12:58.331202Z",
     "iopub.status.idle": "2025-04-15T03:12:58.346494Z",
     "shell.execute_reply": "2025-04-15T03:12:58.345944Z",
     "shell.execute_reply.started": "2025-04-15T03:12:58.331348Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:58.347201Z",
     "iopub.status.busy": "2025-04-15T03:12:58.347050Z",
     "iopub.status.idle": "2025-04-15T03:12:58.581054Z",
     "shell.execute_reply": "2025-04-15T03:12:58.580493Z",
     "shell.execute_reply.started": "2025-04-15T03:12:58.347187Z"
    }
   },
   "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": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:58.582071Z",
     "iopub.status.busy": "2025-04-15T03:12:58.581679Z",
     "iopub.status.idle": "2025-04-15T03:12:58.604833Z",
     "shell.execute_reply": "2025-04-15T03:12:58.604303Z",
     "shell.execute_reply.started": "2025-04-15T03:12:58.582054Z"
    }
   },
   "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>32</th>\n",
       "      <td>bd1f5754-60e9-4d15-b851-a24e3646611d</td>\n",
       "      <td>003c8d92-a4a3-41de-b274-dfc219eb969c</td>\n",
       "      <td>10.975900</td>\n",
       "      <td>False</td>\n",
       "      <td>-12.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>59a470d5-4436-4e94-abb1-5b32ae8c7eaa</td>\n",
       "      <td>003c8d92-a4a3-41de-b274-dfc219eb969c</td>\n",
       "      <td>14.151700</td>\n",
       "      <td>True</td>\n",
       "      <td>-11.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>6f7e81d4-9cd8-429f-9b8b-f7bc11cb933b</td>\n",
       "      <td>06392013-dc85-4083-8339-b24f59d357c6</td>\n",
       "      <td>18.584700</td>\n",
       "      <td>False</td>\n",
       "      <td>-9.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>ade81732-3ef6-4278-b9e4-82ad51d83877</td>\n",
       "      <td>06392013-dc85-4083-8339-b24f59d357c6</td>\n",
       "      <td>23.666083</td>\n",
       "      <td>True</td>\n",
       "      <td>-9.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>1fa9d6f6-8c3a-4e76-bb31-53cc1b9b1f80</td>\n",
       "      <td>07afbe8a-4ade-4c69-8031-636f8b7fde66</td>\n",
       "      <td>15.769983</td>\n",
       "      <td>False</td>\n",
       "      <td>-15.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>c80ca764-e962-4ef4-af3d-2e2d054d0b28</td>\n",
       "      <td>07afbe8a-4ade-4c69-8031-636f8b7fde66</td>\n",
       "      <td>24.389150</td>\n",
       "      <td>True</td>\n",
       "      <td>-15.809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id                            request_id  pair_quality  preference  loudness_abs\n",
       "32   bd1f5754-60e9-4d15-b851-a24e3646611d  003c8d92-a4a3-41de-b274-dfc219eb969c     10.975900       False       -12.124\n",
       "33   59a470d5-4436-4e94-abb1-5b32ae8c7eaa  003c8d92-a4a3-41de-b274-dfc219eb969c     14.151700        True       -11.180\n",
       "720  6f7e81d4-9cd8-429f-9b8b-f7bc11cb933b  06392013-dc85-4083-8339-b24f59d357c6     18.584700       False        -9.186\n",
       "721  ade81732-3ef6-4278-b9e4-82ad51d83877  06392013-dc85-4083-8339-b24f59d357c6     23.666083        True        -9.402\n",
       "898  1fa9d6f6-8c3a-4e76-bb31-53cc1b9b1f80  07afbe8a-4ade-4c69-8031-636f8b7fde66     15.769983       False       -15.023\n",
       "899  c80ca764-e962-4ef4-af3d-2e2d054d0b28  07afbe8a-4ade-4c69-8031-636f8b7fde66     24.389150        True       -15.809"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:58.605765Z",
     "iopub.status.busy": "2025-04-15T03:12:58.605614Z",
     "iopub.status.idle": "2025-04-15T03:12:59.041415Z",
     "shell.execute_reply": "2025-04-15T03:12:59.040861Z",
     "shell.execute_reply.started": "2025-04-15T03:12:58.605751Z"
    }
   },
   "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[\"stereo_width_diff\"] = df[\"stereo_width\"].diff()\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"stereo_width_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"stereo_width_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['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": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:59.042244Z",
     "iopub.status.busy": "2025-04-15T03:12:59.042084Z",
     "iopub.status.idle": "2025-04-15T03:12:59.774009Z",
     "shell.execute_reply": "2025-04-15T03:12:59.773416Z",
     "shell.execute_reply.started": "2025-04-15T03:12:59.042230Z"
    }
   },
   "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": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:12:59.774811Z",
     "iopub.status.busy": "2025-04-15T03:12:59.774653Z",
     "iopub.status.idle": "2025-04-15T03:13:00.566861Z",
     "shell.execute_reply": "2025-04-15T03:13:00.566248Z",
     "shell.execute_reply.started": "2025-04-15T03:12:59.774796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "# take the relative centroid diff\n",
    "df[\"spectral_centroid_diff\"] = df[\"spectral_centroid\"].diff() / df[\"spectral_centroid\"]\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95, 98]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Spectral Centroid Difference ratio --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:00.567706Z",
     "iopub.status.busy": "2025-04-15T03:13:00.567543Z",
     "iopub.status.idle": "2025-04-15T03:13:00.660480Z",
     "shell.execute_reply": "2025-04-15T03:13:00.659792Z",
     "shell.execute_reply.started": "2025-04-15T03:13:00.567689Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    19178\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    9589\n",
      "True     9589\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-v4-up-u-d-2-2    19178\n",
      "Name: count, dtype: int64 preference  model_name         \n",
      "False       chirp-v4-up-u-d-2-2    9589\n",
      "True        chirp-v4-up-u-d-2-2    9589\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    19178\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": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:00.661350Z",
     "iopub.status.busy": "2025-04-15T03:13:00.661177Z",
     "iopub.status.idle": "2025-04-15T03:13:00.959219Z",
     "shell.execute_reply": "2025-04-15T03:13:00.958606Z",
     "shell.execute_reply.started": "2025-04-15T03:13:00.661333Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36666666666666664\n",
      "1.8\n",
      "5.2\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\"]][\"total_shimmer_score\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"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": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:00.960044Z",
     "iopub.status.busy": "2025-04-15T03:13:00.959887Z",
     "iopub.status.idle": "2025-04-15T03:13:01.578483Z",
     "shell.execute_reply": "2025-04-15T03:13:01.577774Z",
     "shell.execute_reply.started": "2025-04-15T03:13:00.960029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 996 duplicated prompts 498 unique requests\n",
      "Found 250 request_ids with duplicate prompts but not highest play counts in their group\n",
      "['5428b6ab-176e-4457-8335-a0c722e229f6', 'e454666b-be06-4687-8490-c9be40ddace0', '5ba2e727-8444-460e-8cd6-cc1a7f65d214', '24d9a298-417e-4365-b8ed-fbf35c396139', '58ebe06a-6ec4-4903-88df-21e6c7235d78', '659c60c7-6561-45fb-a690-7fed3822c1c0', 'c0bc9b52-be8a-494c-8bab-4d2e16b800d7', '5101bfe7-b0a3-44bc-97da-d201e2e10acf', '73acb08f-5a47-4151-9083-b4bf1c37938d', '302d2cf2-8ad8-43d9-8716-1736125497eb']\n",
      "Before dedup user gen requests 19178\n",
      "After dedup user gen requests 18678\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": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.035167Z",
     "start_time": "2024-05-16T13:59:40.801098Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:01.579373Z",
     "iopub.status.busy": "2025-04-15T03:13:01.579204Z",
     "iopub.status.idle": "2025-04-15T03:13:01.626144Z",
     "shell.execute_reply": "2025-04-15T03:13:01.625516Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.579356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 8423 positive 6212\n",
      "total pair requests 9339 selected pair requests 5556 frac 0.595\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",
    "    # & (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": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.250737Z",
     "start_time": "2024-05-16T13:59:41.036434Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:01.626958Z",
     "iopub.status.busy": "2025-04-15T03:13:01.626799Z",
     "iopub.status.idle": "2025-04-15T03:13:01.689801Z",
     "shell.execute_reply": "2025-04-15T03:13:01.689149Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.626943Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 5556 clips 11112 total khrs 0.575; N gpus for 1000 iters 0.695; 4 gpus for x iters 173.625; n unique users 4415 n pro users 3379\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.277006Z",
     "start_time": "2024-05-16T13:59:41.252105Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:01.690798Z",
     "iopub.status.busy": "2025-04-15T03:13:01.690641Z",
     "iopub.status.idle": "2025-04-15T03:13:01.709979Z",
     "shell.execute_reply": "2025-04-15T03:13:01.709399Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.690783Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (1433, 116)\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": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:01.725674Z",
     "iopub.status.busy": "2025-04-15T03:13:01.725535Z",
     "iopub.status.idle": "2025-04-15T03:13:01.737853Z",
     "shell.execute_reply": "2025-04-15T03:13:01.737358Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.725660Z"
    }
   },
   "outputs": [],
   "source": [
    "# def modify_model_name(model_name, metadata):\n",
    "#     if (\n",
    "#         model_name.startswith(\"chirp-v3p5-engine-t\")\n",
    "#         or model_name.startswith(\"chirp-v3p5-engine-s\")\n",
    "#         or model_name.startswith(\"chirp-v4\")\n",
    "#         or model_name.startswith(\"chirp-v3p5-h-s-31\")\n",
    "#     ):\n",
    "#         if \"param_experiment\" in metadata:\n",
    "#             exp = metadata.get(\"param_experiment\", \"\")\n",
    "#             if exp:\n",
    "#                 return f\"{model_name}_{exp}\"\n",
    "#     return model_name\n",
    "\n",
    "# metrics_check_df_slice = df_slice.copy()\n",
    "# metrics_check_df_slice[\"model_name\"] = metrics_check_df_slice.apply(\n",
    "#     lambda row: modify_model_name(row[\"model_name\"], row[\"metadata\"]), axis=1\n",
    "# )\n",
    "# get_preference_counts(metrics_check_df_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:01.738672Z",
     "iopub.status.busy": "2025-04-15T03:13:01.738523Z",
     "iopub.status.idle": "2025-04-15T03:13:01.755599Z",
     "shell.execute_reply": "2025-04-15T03:13:01.755060Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.738658Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web        5480\n",
      "android    3258\n",
      "ios        2374\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-15T03:13:01.756313Z",
     "iopub.status.busy": "2025-04-15T03:13:01.756168Z",
     "iopub.status.idle": "2025-04-15T03:13:01.962305Z",
     "shell.execute_reply": "2025-04-15T03:13:01.961617Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.756299Z"
    }
   },
   "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[40], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# df_slice.to_csv(\"/home/tony/Data/Preference/13b_v0/interesting_clips_v3p5_s_8_20240828_slice.csv\")\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mBREAK\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BREAK' is not defined"
     ]
    }
   ],
   "source": [
    "# df_slice.to_csv(\"/home/tony/Data/Preference/13b_v0/interesting_clips_v3p5_s_8_20240828_slice.csv\")\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": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.962765Z",
     "iopub.status.idle": "2025-04-15T03:13:01.962957Z",
     "shell.execute_reply": "2025-04-15T03:13:01.962868Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.962858Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5556\n"
     ]
    }
   ],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.963736Z",
     "iopub.status.idle": "2025-04-15T03:13:01.963925Z",
     "shell.execute_reply": "2025-04-15T03:13:01.963834Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.963825Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5500 56\n",
      "(11000, 116) (112, 116)\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": 43,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.964525Z",
     "iopub.status.idle": "2025-04-15T03:13:01.964687Z",
     "shell.execute_reply": "2025-04-15T03:13:01.964613Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.964605Z"
    }
   },
   "outputs": [],
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.965323Z",
     "iopub.status.idle": "2025-04-15T03:13:01.965497Z",
     "shell.execute_reply": "2025-04-15T03:13:01.965422Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.965413Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.966220Z",
     "iopub.status.idle": "2025-04-15T03:13:01.966381Z",
     "shell.execute_reply": "2025-04-15T03:13:01.966309Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.966301Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11000/11000 [00:00<00:00, 30943.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "570 hours of 11000 clips, 0.6875 nodes, 171.875 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": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.966856Z",
     "iopub.status.idle": "2025-04-15T03:13:01.967010Z",
     "shell.execute_reply": "2025-04-15T03:13:01.966938Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.966931Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (112, 116)\n",
      "total chunks: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done! val: wrote 231000 semantic tokens and 29568000 vae latents. \n",
      "Total slices of data: 308. Per node: 9.6. \n",
      "Passed quality check: 308, Failed quality check: 72. \n",
      "Total chunks with prev chunk as vae ctx: 220.\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": 50,
   "metadata": {},
   "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": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.967628Z",
     "iopub.status.idle": "2025-04-15T03:13:01.967783Z",
     "shell.execute_reply": "2025-04-15T03:13:01.967712Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.967704Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (11000, 116)\n",
      "total chunks: 55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 55/55 [01:14<00:00,  1.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done! tr: wrote 26773500 semantic tokens and 3427008000 vae latents. \n",
      "Total slices of data: 35698. Per node: 1115.6. \n",
      "Passed quality check: 35698, Failed quality check: 6766. \n",
      "Total chunks with prev chunk as vae ctx: 26558.\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": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-29T19:46:47.549860Z",
     "start_time": "2024-01-29T19:46:47.548015Z"
    }
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.968654Z",
     "iopub.status.idle": "2025-04-15T03:13:01.968847Z",
     "shell.execute_reply": "2025-04-15T03:13:01.968750Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.968741Z"
    }
   },
   "outputs": [],
   "source": [
    "# verify\n",
    "metas_val = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_val.jsonl\"))\n",
    "print(len(metas_val) / 2)\n",
    "mm_semantic_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_semantic_val.bin\"), dtype=np.uint16, mode=\"r\"\n",
    ")\n",
    "mm_vae_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    ")\n",
    "\n",
    "\n",
    "mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "print(mm_vae_val.shape)\n",
    "\n",
    "mm_semantic_val = mm_semantic_val.reshape(-1, SEMANTIC_MEMMAP_SIZE)\n",
    "print(mm_semantic_val.shape)\n",
    "\n",
    "assert len(metas_val) == mm_vae_val.shape[0] == mm_semantic_val.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.969184Z",
     "iopub.status.idle": "2025-04-15T03:13:01.969336Z",
     "shell.execute_reply": "2025-04-15T03:13:01.969265Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.969258Z"
    }
   },
   "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": 59,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.969791Z",
     "iopub.status.idle": "2025-04-15T03:13:01.969942Z",
     "shell.execute_reply": "2025-04-15T03:13:01.969870Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.969863Z"
    }
   },
   "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": 60,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.970419Z",
     "iopub.status.idle": "2025-04-15T03:13:01.970565Z",
     "shell.execute_reply": "2025-04-15T03:13:01.970497Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.970490Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.971122Z",
     "iopub.status.idle": "2025-04-15T03:13:01.971275Z",
     "shell.execute_reply": "2025-04-15T03:13:01.971202Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.971195Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.equal(torch.tensor(mm_semantic_val[idx]), torch.tensor(mm_semantic_val[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.971800Z",
     "iopub.status.idle": "2025-04-15T03:13:01.971959Z",
     "shell.execute_reply": "2025-04-15T03:13:01.971887Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.971879Z"
    }
   },
   "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": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.972630Z",
     "iopub.status.idle": "2025-04-15T03:13:01.972789Z",
     "shell.execute_reply": "2025-04-15T03:13:01.972716Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.972709Z"
    }
   },
   "outputs": [],
   "source": [
    "def validation_on_metas(input_metas):\n",
    "    total_bad = 0\n",
    "    total_good = 0\n",
    "    for idx in range(len(input_metas)):\n",
    "        if idx % 2 == 0:\n",
    "            pos_idx = idx + 1\n",
    "            if input_metas[idx].get(\"tags\") != input_metas[pos_idx].get(\"tags\"):\n",
    "                # print(test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"), test_metas[idx].get(\"tags\"), test_metas[pos_idx].get(\"tags\"))\n",
    "                total_bad += 1\n",
    "            else:\n",
    "                total_good += 1\n",
    "    print(total_good, total_bad)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.973430Z",
     "iopub.status.idle": "2025-04-15T03:13:01.973584Z",
     "shell.execute_reply": "2025-04-15T03:13:01.973511Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.973503Z"
    }
   },
   "outputs": [],
   "source": [
    "metas_tr = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_tr.jsonl\"))\n",
    "validation_on_metas(metas_tr)\n",
    "print(len(metas_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.974126Z",
     "iopub.status.idle": "2025-04-15T03:13:01.974275Z",
     "shell.execute_reply": "2025-04-15T03:13:01.974206Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.974199Z"
    }
   },
   "outputs": [],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    },
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.974828Z",
     "iopub.status.idle": "2025-04-15T03:13:01.974978Z",
     "shell.execute_reply": "2025-04-15T03:13:01.974910Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.974903Z"
    }
   },
   "outputs": [],
   "source": [
    "!cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.975280Z",
     "iopub.status.idle": "2025-04-15T03:13:01.975427Z",
     "shell.execute_reply": "2025-04-15T03:13:01.975360Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.975352Z"
    }
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "# Basic file copy\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/make_dataset_diff_upsample_v2_r2.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": 68,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.976074Z",
     "iopub.status.idle": "2025-04-15T03:13:01.976229Z",
     "shell.execute_reply": "2025-04-15T03:13:01.976157Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.976149Z"
    }
   },
   "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": 69,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.976812Z",
     "iopub.status.idle": "2025-04-15T03:13:01.976965Z",
     "shell.execute_reply": "2025-04-15T03:13:01.976895Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.976888Z"
    }
   },
   "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": 70,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.977535Z",
     "iopub.status.idle": "2025-04-15T03:13:01.977687Z",
     "shell.execute_reply": "2025-04-15T03:13:01.977616Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.977608Z"
    }
   },
   "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": 71,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.978267Z",
     "iopub.status.idle": "2025-04-15T03:13:01.978432Z",
     "shell.execute_reply": "2025-04-15T03:13:01.978353Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.978345Z"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.978977Z",
     "iopub.status.idle": "2025-04-15T03:13:01.979127Z",
     "shell.execute_reply": "2025-04-15T03:13:01.979058Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.979050Z"
    }
   },
   "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": 73,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.979458Z",
     "iopub.status.idle": "2025-04-15T03:13:01.979600Z",
     "shell.execute_reply": "2025-04-15T03:13:01.979534Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.979527Z"
    }
   },
   "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": 74,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.980193Z",
     "iopub.status.idle": "2025-04-15T03:13:01.980346Z",
     "shell.execute_reply": "2025-04-15T03:13:01.980276Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.980269Z"
    }
   },
   "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": 75,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.980858Z",
     "iopub.status.idle": "2025-04-15T03:13:01.981228Z",
     "shell.execute_reply": "2025-04-15T03:13:01.981148Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.981139Z"
    }
   },
   "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": 76,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.981735Z",
     "iopub.status.idle": "2025-04-15T03:13:01.981893Z",
     "shell.execute_reply": "2025-04-15T03:13:01.981820Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.981813Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.982453Z",
     "iopub.status.idle": "2025-04-15T03:13:01.982606Z",
     "shell.execute_reply": "2025-04-15T03:13:01.982536Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.982529Z"
    }
   },
   "outputs": [],
   "source": [
    "t = rng.draw(4)[:, 0].to(torch.bfloat16)\n",
    "print(t)\n",
    "# Replace 1% of t with ones to ensure training on terminal SNR\n",
    "t = torch.where(torch.rand_like(t) < 0.5, torch.ones_like(t), t)\n",
    "print(t)\n",
    "t = torch.repeat_interleave(t, repeats=2, dim=0)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.983092Z",
     "iopub.status.idle": "2025-04-15T03:13:01.983240Z",
     "shell.execute_reply": "2025-04-15T03:13:01.983171Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.983163Z"
    }
   },
   "outputs": [],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.983752Z",
     "iopub.status.idle": "2025-04-15T03:13:01.983905Z",
     "shell.execute_reply": "2025-04-15T03:13:01.983832Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.983824Z"
    }
   },
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.984429Z",
     "iopub.status.idle": "2025-04-15T03:13:01.984592Z",
     "shell.execute_reply": "2025-04-15T03:13:01.984520Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.984512Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-26T00:15:31.409372Z",
     "iopub.status.busy": "2025-04-26T00:15:31.408926Z",
     "iopub.status.idle": "2025-04-26T00:15:36.613514Z",
     "shell.execute_reply": "2025-04-26T00:15:36.612947Z",
     "shell.execute_reply.started": "2025-04-26T00:15:31.409355Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# # with open(f\"/home/tony/Data/Preference/up_diff2_v1//full_pair_quality.json\", \"r\") as f:\n",
    "# #    result = json.load(f)\n",
    "# result = {}\n",
    "# print(len(result))\n",
    "# for job_idx in range(24):\n",
    "#     with open(f\"/home/tony/Data/Preference/up_diff2_v1/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",
    "# with open(f\"/home/tony/Data/Preference/up_diff2_v1/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.986986Z",
     "iopub.status.idle": "2025-04-15T03:13:01.987148Z",
     "shell.execute_reply": "2025-04-15T03:13:01.987073Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.987066Z"
    }
   },
   "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": 83,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.987732Z",
     "iopub.status.idle": "2025-04-15T03:13:01.987882Z",
     "shell.execute_reply": "2025-04-15T03:13:01.987811Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.987804Z"
    }
   },
   "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": 84,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.988359Z",
     "iopub.status.idle": "2025-04-15T03:13:01.988508Z",
     "shell.execute_reply": "2025-04-15T03:13:01.988436Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.988429Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.989063Z",
     "iopub.status.idle": "2025-04-15T03:13:01.989209Z",
     "shell.execute_reply": "2025-04-15T03:13:01.989142Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.989135Z"
    }
   },
   "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": 86,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-04-15T03:13:01.989973Z",
     "iopub.status.idle": "2025-04-15T03:13:01.990132Z",
     "shell.execute_reply": "2025-04-15T03:13:01.990061Z",
     "shell.execute_reply.started": "2025-04-15T03:13:01.990053Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] > 2)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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