{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:42:42.319320Z",
     "iopub.status.busy": "2025-10-27T22:42:42.319193Z",
     "iopub.status.idle": "2025-10-27T22:42:42.333009Z",
     "shell.execute_reply": "2025-10-27T22:42:42.332595Z",
     "shell.execute_reply.started": "2025-10-27T22:42:42.319303Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.040680Z",
     "start_time": "2024-05-16T13:58:19.777010Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:42:42.334531Z",
     "iopub.status.busy": "2025-10-27T22:42:42.334414Z",
     "iopub.status.idle": "2025-10-27T22:42:44.721000Z",
     "shell.execute_reply": "2025-10-27T22:42:44.720282Z",
     "shell.execute_reply.started": "2025-10-27T22:42:42.334518Z"
    }
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from preference_data_preparation_diff import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from suno_utils.utils.s3 import download_s3_files\n",
    "from suno_utils.utils.text import read_json, read_jsonl, write_json, write_jsonl\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from suno_utils.audio import Audio\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.082172Z",
     "start_time": "2024-05-16T13:58:21.041926Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:42:44.723609Z",
     "iopub.status.busy": "2025-10-27T22:42:44.723472Z",
     "iopub.status.idle": "2025-10-27T22:42:52.836163Z",
     "shell.execute_reply": "2025-10-27T22:42:52.835376Z",
     "shell.execute_reply.started": "2025-10-27T22:42:44.723594Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output dir is /app2/suno/data/dpo/diff4_dorado_t1_v4/\n",
      "Total pair quality scores: 56823\n",
      "Total hoot cer scores: 38285\n"
     ]
    }
   ],
   "source": [
    "OUT_DATA_DIR = \"/app2/suno/data/dpo/diff4_dorado_t1_v4/\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "print(\"Output dir is\", OUT_DATA_DIR)\n",
    "NPZ_DIR = \"/app2/suno/data/dpo/dorado_t1\"\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/dorado_t1/full_pair_quality.json\", \"r\") as file:\n",
    "    full_pair_quality = json.load(file)\n",
    "print(\"Total pair quality scores:\", len(full_pair_quality))\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/dorado_t1/hoot_cer.json\", \"r\") as file:\n",
    "    clip_id_to_cer = json.load(file)\n",
    "print(\"Total hoot cer scores:\", len(clip_id_to_cer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:53.962528Z",
     "start_time": "2024-05-16T13:58:21.105919Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:42:52.837051Z",
     "iopub.status.busy": "2025-10-27T22:42:52.836885Z",
     "iopub.status.idle": "2025-10-27T22:42:55.222007Z",
     "shell.execute_reply": "2025-10-27T22:42:55.221240Z",
     "shell.execute_reply.started": "2025-10-27T22:42:52.837034Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (99524, 91)\n",
      "unique users 33529\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    \"/home/tony/Data/Preference/dorado_t1/interesting_clips_dorado_t1_20251025.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": "2025-10-27T22:42:55.222951Z",
     "iopub.status.busy": "2025-10-27T22:42:55.222781Z",
     "iopub.status.idle": "2025-10-27T22:46:42.901454Z",
     "shell.execute_reply": "2025-10-27T22:46:42.900894Z",
     "shell.execute_reply.started": "2025-10-27T22:42:55.222934Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "99524it [03:47, 437.22it/s]\n"
     ]
    }
   ],
   "source": [
    "# find all the hoot jsons in the json dir\n",
    "# clip_id_to_cer = {}\n",
    "JSON_DIR = \"/app2/suno/data/dpo/dorado_t1_json/\"\n",
    "for _, row in tqdm(df.iterrows()):\n",
    "    clip_id = row[\"s3_id\"]\n",
    "    if clip_id in clip_id_to_cer:\n",
    "        continue\n",
    "    hoot_json_path = os.path.join(JSON_DIR, f\"{clip_id}_hoot.json\")\n",
    "    if not os.path.exists(hoot_json_path):\n",
    "        clip_id_to_cer[clip_id] = 1.0\n",
    "        continue\n",
    "    with open(os.path.join(JSON_DIR, f\"{clip_id}_hoot.json\"), \"r\") as f:\n",
    "        data = json.load(f)\n",
    "    for data_dict in data:\n",
    "        if \"hoot_cer\" in data_dict:\n",
    "            clip_id_to_cer[clip_id] = data_dict[\"hoot_cer\"]\n",
    "            break\n",
    "\n",
    "# add the cer to the df\n",
    "df[\"cer\"] = df[\"s3_id\"].map(clip_id_to_cer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:42.902190Z",
     "iopub.status.busy": "2025-10-27T22:46:42.902039Z",
     "iopub.status.idle": "2025-10-27T22:46:44.010837Z",
     "shell.execute_reply": "2025-10-27T22:46:44.010337Z",
     "shell.execute_reply.started": "2025-10-27T22:46:42.902174Z"
    }
   },
   "outputs": [],
   "source": [
    "# update the hoot cer cache\n",
    "with open(\"/home/tony/Data/Preference/dorado_t1/hoot_cer.json\", \"w\") as file:\n",
    "    json.dump(clip_id_to_cer, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:44.011761Z",
     "iopub.status.busy": "2025-10-27T22:46:44.011400Z",
     "iopub.status.idle": "2025-10-27T22:46:45.112210Z",
     "shell.execute_reply": "2025-10-27T22:46:45.111717Z",
     "shell.execute_reply.started": "2025-10-27T22:46:44.011745Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile 5: -0.034\n",
      "Percentile 10: -0.021\n",
      "Percentile 15: -0.015\n",
      "Percentile 85: 0.014\n",
      "Percentile 90: 0.019\n",
      "Percentile 95: 0.030\n",
      "\n",
      "Debug info:\n",
      "Total positive preference samples: 49762\n",
      "cer_diff range: -0.917 to 0.935\n",
      "cer_diff mean: -0.001\n",
      "cer_diff median: 0.000\n",
      "cer_diff std: 0.048\n",
      "Positive cer_diff samples: 20365 (40.9%)\n",
      "Negative cer_diff samples: 21013 (42.2%)\n",
      "Zero cer_diff samples: 8384 (16.8%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.fillna({\"cer\": 1})\n",
    "df[\"cer_diff\"] = df[\"cer\"].diff()\n",
    "df = df.fillna({\"cer_diff\": 0})\n",
    "# plot the positive preference and negative preference cer\n",
    "positive_pref = df[df[\"preference\"]]\n",
    "negative_pref = df[~df[\"preference\"]]\n",
    "\n",
    "# Create figure with 2 subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# First subplot: CER distribution\n",
    "ax1.hist(\n",
    "    positive_pref[\"cer\"], bins=50, alpha=0.2, color=\"green\", label=\"Positive Preference\"\n",
    ")\n",
    "ax1.hist(\n",
    "    negative_pref[\"cer\"], bins=50, alpha=0.2, color=\"blue\", label=\"Negative Preference\"\n",
    ")\n",
    "ax1.set_xlabel(\"CER\")\n",
    "ax1.set_ylabel(\"Frequency\")\n",
    "ax1.set_title(\"CER Distribution - Positive vs Negative Preference\")\n",
    "ax1.legend()\n",
    "\n",
    "# Second subplot: CER diff for positive preference only\n",
    "ax2.hist(\n",
    "    positive_pref[\"cer_diff\"],\n",
    "    bins=50,\n",
    "    alpha=0.7,\n",
    "    color=\"green\",\n",
    "    label=\"Positive Preference\",\n",
    "    range=(-0.75, 0.75),\n",
    ")\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_xlabel(\"CER Diff\")\n",
    "ax2.set_ylabel(\"Frequency\")\n",
    "ax2.set_title(\"CER Diff Distribution - Positive Preference Only\")\n",
    "\n",
    "# Add percentile lines BEFORE the legend\n",
    "percentiles = [0.05, 0.10, 0.15, 0.85, 0.90, 0.95]\n",
    "colors = [\"red\", \"orange\", \"blue\", \"blue\", \"orange\", \"red\"]\n",
    "# Fix: Use sorted() instead of .sort() which returns None\n",
    "sorted_positive_cer_diff = sorted(positive_pref[\"cer_diff\"].values.tolist())\n",
    "for i, (p, color) in enumerate(zip(percentiles, colors)):\n",
    "    percentile_value = np.percentile(\n",
    "        sorted_positive_cer_diff, p * 100\n",
    "    )  # Fix: multiply by 100 for np.percentile\n",
    "    print(f\"Percentile {p*100:.0f}: {percentile_value:.3f}\")\n",
    "    ax2.axvline(\n",
    "        x=percentile_value,\n",
    "        color=color,\n",
    "        linestyle=\"--\",\n",
    "        alpha=0.8,\n",
    "        linewidth=2,\n",
    "        label=f\"{p*100:.0f}th percentile: {percentile_value:.3f}\",\n",
    "    )\n",
    "\n",
    "# Add legend AFTER the percentile lines\n",
    "ax2.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Debug: Check the actual distribution of cer_diff\n",
    "print(\"\\nDebug info:\")\n",
    "print(f\"Total positive preference samples: {len(positive_pref)}\")\n",
    "print(\n",
    "    f\"cer_diff range: {positive_pref['cer_diff'].min():.3f} to {positive_pref['cer_diff'].max():.3f}\"\n",
    ")\n",
    "print(f\"cer_diff mean: {positive_pref['cer_diff'].mean():.3f}\")\n",
    "print(f\"cer_diff median: {positive_pref['cer_diff'].median():.3f}\")\n",
    "print(f\"cer_diff std: {positive_pref['cer_diff'].std():.3f}\")\n",
    "\n",
    "# Check if there are any positive values\n",
    "positive_cer_diff = positive_pref[positive_pref[\"cer_diff\"] > 0]\n",
    "negative_cer_diff = positive_pref[positive_pref[\"cer_diff\"] < 0]\n",
    "zero_cer_diff = positive_pref[positive_pref[\"cer_diff\"] == 0]\n",
    "\n",
    "print(\n",
    "    f\"Positive cer_diff samples: {len(positive_cer_diff)} ({len(positive_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")\n",
    "print(\n",
    "    f\"Negative cer_diff samples: {len(negative_cer_diff)} ({len(negative_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")\n",
    "print(\n",
    "    f\"Zero cer_diff samples: {len(zero_cer_diff)} ({len(zero_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:45.112915Z",
     "iopub.status.busy": "2025-10-27T22:46:45.112766Z",
     "iopub.status.idle": "2025-10-27T22:46:45.131363Z",
     "shell.execute_reply": "2025-10-27T22:46:45.130895Z",
     "shell.execute_reply.started": "2025-10-27T22:46:45.112900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    93847\n",
      "True      5677\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"is_public\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.199480Z",
     "start_time": "2024-05-16T13:58:53.963687Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:45.132014Z",
     "iopub.status.busy": "2025-10-27T22:46:45.131875Z",
     "iopub.status.idle": "2025-10-27T22:46:45.726665Z",
     "shell.execute_reply": "2025-10-27T22:46:45.726079Z",
     "shell.execute_reply.started": "2025-10-27T22:46:45.132000Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169854\n",
      "56208\n",
      "113646\n",
      "pre-downloaded df (99524, 94)\n",
      "downloaded df (98914, 94)\n",
      "vae downloaded df (98912, 94)\n"
     ]
    }
   ],
   "source": [
    "df[\"upsample_clip_id\"] = df[\"metadata\"].apply(lambda x: x.get(\"upsample_clip_id\", \"\"))\n",
    "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": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.467253Z",
     "start_time": "2024-05-16T13:58:56.207647Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:45.727401Z",
     "iopub.status.busy": "2025-10-27T22:46:45.727249Z",
     "iopub.status.idle": "2025-10-27T22:46:45.763436Z",
     "shell.execute_reply": "2025-10-27T22:46:45.762988Z",
     "shell.execute_reply.started": "2025-10-27T22:46:45.727385Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    98912\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.592883Z",
     "start_time": "2024-05-16T13:58:56.470781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:45.764087Z",
     "iopub.status.busy": "2025-10-27T22:46:45.763943Z",
     "iopub.status.idle": "2025-10-27T22:46:45.787935Z",
     "shell.execute_reply": "2025-10-27T22:46:45.787466Z",
     "shell.execute_reply.started": "2025-10-27T22:46:45.764072Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name       \n",
      "False       chirp-carp-up-c-1    49456\n",
      "True        chirp-carp-up-c-1    49456\n",
      "Name: count, dtype: int64\n",
      "(98912, 95)\n",
      "(98912, 95)\n"
     ]
    }
   ],
   "source": [
    "## for 13b this is easy for now\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "print(df.shape)\n",
    "# df = df[df[\"model_name\"].isin([\"chirp-v4-up-u-d-2-2\"])]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:45.788509Z",
     "iopub.status.busy": "2025-10-27T22:46:45.788372Z",
     "iopub.status.idle": "2025-10-27T22:46:45.876757Z",
     "shell.execute_reply": "2025-10-27T22:46:45.876207Z",
     "shell.execute_reply.started": "2025-10-27T22:46:45.788495Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "task\n",
      "upsample        98880\n",
      "fixed_infill       32\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"task\"].value_counts())\n",
    "df = df[df[\"task\"] != \"fixed_infill\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.909539Z",
     "start_time": "2024-05-16T13:58:56.595736Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:45.877453Z",
     "iopub.status.busy": "2025-10-27T22:46:45.877299Z",
     "iopub.status.idle": "2025-10-27T22:46:45.995288Z",
     "shell.execute_reply": "2025-10-27T22:46:45.994726Z",
     "shell.execute_reply.started": "2025-10-27T22:46:45.877438Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(98880, 95)\n",
      "(98880, 95)\n",
      "preference  model_name       \n",
      "False       chirp-carp-up-c-1    49440\n",
      "True        chirp-carp-up-c-1    49440\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": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:45.996005Z",
     "iopub.status.busy": "2025-10-27T22:46:45.995856Z",
     "iopub.status.idle": "2025-10-27T22:46:46.182510Z",
     "shell.execute_reply": "2025-10-27T22:46:46.181953Z",
     "shell.execute_reply.started": "2025-10-27T22:46:45.995990Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unpacked pair quality scores: 679422\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": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:46.183240Z",
     "iopub.status.busy": "2025-10-27T22:46:46.183085Z",
     "iopub.status.idle": "2025-10-27T22:46:52.721823Z",
     "shell.execute_reply": "2025-10-27T22:46:52.721246Z",
     "shell.execute_reply.started": "2025-10-27T22:46:46.183224Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize lists to store metrics\n",
    "clip_diffs = []\n",
    "clip_ratios = []\n",
    "loudness_diff = []\n",
    "spec_decay_diff = []\n",
    "last_spec_decay_diff = []\n",
    "pos_spec_decay_values = []  # New list for positive decay values\n",
    "neg_spec_decay_values = []  # New list for negative decay values\n",
    "clip_id_to_mean_ear_score = {}\n",
    "clip_id_to_mean_shimmer_score = {}\n",
    "total_clip_ratios = []\n",
    "\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    # Initialize lists for current request\n",
    "    mean_neg_scores = []\n",
    "    mean_pos_scores = []\n",
    "    pos_scores = []\n",
    "    neg_scores = []\n",
    "    neg_loudness = []\n",
    "    pos_loudness = []\n",
    "    neg_spec_decay = []\n",
    "    pos_spec_decay = []\n",
    "    neg_shimmer = []\n",
    "    pos_shimmer = []\n",
    "    neg_clip_id = None\n",
    "    pos_clip_id = None\n",
    "\n",
    "    # Process each clip pair\n",
    "    for i, (clip_id, pair_quality) in enumerate(pairs_of_qualities.items()):\n",
    "        if pair_quality is None:\n",
    "            continue\n",
    "\n",
    "        if i % 2 == 0:  # Negative clip\n",
    "            mean_neg_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"]))\n",
    "            neg_scores.extend(pair_quality[\"ear_v2_quality_scores\"])\n",
    "            neg_loudness.append(pair_quality[\"abs_loudness_factor\"])\n",
    "            neg_spec_decay.append(pair_quality[\"spectrum_decay\"])\n",
    "            neg_shimmer.append(pair_quality[\"shimmer_score\"])\n",
    "            if neg_clip_id is None:\n",
    "                neg_clip_id = clip_id\n",
    "        else:  # Positive clip\n",
    "            mean_pos_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"]))\n",
    "            pos_scores.extend(pair_quality[\"ear_v2_quality_scores\"])\n",
    "            pos_loudness.append(pair_quality[\"abs_loudness_factor\"])\n",
    "            pos_spec_decay.append(pair_quality[\"spectrum_decay\"])\n",
    "            pos_shimmer.append(pair_quality[\"shimmer_score\"])\n",
    "            if pos_clip_id is None:\n",
    "                pos_clip_id = clip_id\n",
    "\n",
    "    # Skip if we don't have both positive and negative samples\n",
    "    if not (mean_pos_scores and mean_neg_scores):\n",
    "        continue\n",
    "\n",
    "    # Calculate ratios and differences\n",
    "    ratios = [\n",
    "        (pos - neg) / (pos + 0.0001)\n",
    "        for pos, neg in zip(mean_pos_scores, mean_neg_scores)\n",
    "    ]\n",
    "    pos_diffs = [\n",
    "        (pos - prev_pos) / (prev_pos + 0.0001)\n",
    "        for prev_pos, pos in zip(mean_pos_scores, mean_pos_scores[1:])\n",
    "    ]\n",
    "    neg_diffs = [\n",
    "        (neg - prev_neg) / (prev_neg + 0.0001)\n",
    "        for prev_neg, neg in zip(mean_neg_scores, mean_neg_scores[1:])\n",
    "    ]\n",
    "\n",
    "    # Calculate loudness and spectrum decay differences\n",
    "    loudness_diff.extend(\n",
    "        [\n",
    "            (pos_l - neg_l) / (pos_l + neg_l + 0.0001)\n",
    "            for pos_l, neg_l in zip(pos_loudness, neg_loudness)\n",
    "        ]\n",
    "    )\n",
    "    spec_decay_diff.extend(\n",
    "        [\n",
    "            (pos_s - neg_s) / (pos_s + neg_s + 0.0001)\n",
    "            for pos_s, neg_s in zip(pos_spec_decay, neg_spec_decay)\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # Store individual decay values\n",
    "    pos_spec_decay_values.extend(pos_spec_decay)\n",
    "    neg_spec_decay_values.extend(neg_spec_decay)\n",
    "\n",
    "    # Calculate last spectrum decay difference only if we have values\n",
    "    if pos_spec_decay and neg_spec_decay:\n",
    "        last_spec_decay_diff.append(\n",
    "            (pos_spec_decay[-1] - neg_spec_decay[-1])\n",
    "            / (pos_spec_decay[-1] + neg_spec_decay[-1] + 0.0001)\n",
    "        )\n",
    "\n",
    "    # Store clip ratios and differences\n",
    "    if len(ratios) > 1:\n",
    "        for i in range(1, len(ratios)):\n",
    "            clip_ratios.append(ratios[i])\n",
    "            clip_diffs.append(pos_diffs[i - 1] - neg_diffs[i - 1])\n",
    "\n",
    "    # Store mean scores\n",
    "    if pos_clip_id is not None and neg_clip_id is not None:\n",
    "        clip_id_to_mean_ear_score[neg_clip_id] = np.mean(neg_scores)\n",
    "        clip_id_to_mean_ear_score[pos_clip_id] = np.mean(pos_scores)\n",
    "        clip_id_to_mean_shimmer_score[neg_clip_id] = np.mean(neg_shimmer)\n",
    "        clip_id_to_mean_shimmer_score[pos_clip_id] = np.mean(pos_shimmer)\n",
    "\n",
    "        # Calculate total clip ratio\n",
    "        total_clip_ratios.append(\n",
    "            (np.mean(pos_scores) - np.mean(neg_scores)) / (np.mean(pos_scores) + 0.001)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:52.722593Z",
     "iopub.status.busy": "2025-10-27T22:46:52.722439Z",
     "iopub.status.idle": "2025-10-27T22:46:55.430308Z",
     "shell.execute_reply": "2025-10-27T22:46:55.429788Z",
     "shell.execute_reply.started": "2025-10-27T22:46:52.722577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spectrum Decay Difference Percentiles:\n",
      "5th percentile: -0.1505\n",
      "10th percentile: -0.0995\n",
      "15th percentile: -0.0702\n",
      "85th percentile: 0.0702\n",
      "90th percentile: 0.0999\n",
      "95th percentile: 0.1514\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 15))\n",
    "\n",
    "# Calculate means for legend\n",
    "mean_spec_decay_diff = np.mean(spec_decay_diff)\n",
    "mean_last_spec_decay_diff = np.mean(last_spec_decay_diff)\n",
    "mean_pos_decay = np.mean(pos_spec_decay_values)\n",
    "mean_neg_decay = np.mean(neg_spec_decay_values)\n",
    "mean_diff = np.mean(np.array(pos_spec_decay_values) - np.array(neg_spec_decay_values))\n",
    "\n",
    "# Calculate percentiles for spec_decay_diff\n",
    "percentiles = [0.05, 0.1, 0.15, 0.85, 0.9, 0.95]\n",
    "percentile_values = np.percentile(spec_decay_diff, [p * 100 for p in percentiles])\n",
    "print(\"\\nSpectrum Decay Difference Percentiles:\")\n",
    "for p, v in zip(percentiles, percentile_values):\n",
    "    print(f\"{p*100:.0f}th percentile: {v:.4f}\")\n",
    "\n",
    "# Plot histograms of differences\n",
    "ax1.hist(\n",
    "    spec_decay_diff,\n",
    "    bins=100,\n",
    "    range=(-0.5, 0.5),\n",
    "    alpha=0.3,\n",
    "    label=f\"All Spectrum Decay Differences (mean={mean_spec_decay_diff:.3f})\",\n",
    "    color=\"blue\",\n",
    ")\n",
    "ax1.hist(\n",
    "    last_spec_decay_diff,\n",
    "    bins=100,\n",
    "    range=(-0.5, 0.5),\n",
    "    alpha=0.3,\n",
    "    label=f\"Last Spectrum Decay Differences (mean={mean_last_spec_decay_diff:.3f})\",\n",
    "    color=\"red\",\n",
    ")\n",
    "\n",
    "# Add labels and title for differences plot\n",
    "ax1.set_xlabel(\"Normalized Spectrum Decay Difference\", fontsize=12)\n",
    "ax1.set_ylabel(\"Count\", fontsize=12)\n",
    "ax1.set_title(\n",
    "    \"Distribution of Spectrum Decay Differences\\nBetween Positive and Negative Samples\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax1.legend(fontsize=10)\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot histograms of raw values\n",
    "ax2.hist(\n",
    "    pos_spec_decay_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.3,\n",
    "    label=f\"Positive Sample Decay (mean={mean_pos_decay:.1f})\",\n",
    "    color=\"green\",\n",
    ")\n",
    "ax2.hist(\n",
    "    neg_spec_decay_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.3,\n",
    "    label=f\"Negative Sample Decay (mean={mean_neg_decay:.1f})\",\n",
    "    color=\"orange\",\n",
    ")\n",
    "\n",
    "# Add labels and title for raw values plot\n",
    "ax2.set_xlabel(\"Spectrum Decay Value\", fontsize=12)\n",
    "ax2.set_ylabel(\"Count\", fontsize=12)\n",
    "ax2.set_title(\n",
    "    \"Distribution of Raw Spectrum Decay Values\\nFor Positive and Negative Samples\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax2.legend(fontsize=10)\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Calculate and plot the difference between positive and negative values\n",
    "diff_values = np.array(pos_spec_decay_values) - np.array(neg_spec_decay_values)\n",
    "ax3.hist(\n",
    "    diff_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.7,\n",
    "    label=f\"Positive - Negative Difference (mean={mean_diff:.1f})\",\n",
    "    color=\"purple\",\n",
    ")\n",
    "\n",
    "# Add labels and title for difference plot\n",
    "ax3.set_xlabel(\"Difference Value (Positive - Negative)\", fontsize=12)\n",
    "ax3.set_ylabel(\"Count\", fontsize=12)\n",
    "ax3.set_title(\n",
    "    \"Distribution of Differences Between\\nPositive and Negative Spectrum Decay Values\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax3.legend(fontsize=10)\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:55.431033Z",
     "iopub.status.busy": "2025-10-27T22:46:55.430884Z",
     "iopub.status.idle": "2025-10-27T22:46:56.104801Z",
     "shell.execute_reply": "2025-10-27T22:46:56.104272Z",
     "shell.execute_reply.started": "2025-10-27T22:46:55.431017Z"
    }
   },
   "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": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:56.105719Z",
     "iopub.status.busy": "2025-10-27T22:46:56.105385Z",
     "iopub.status.idle": "2025-10-27T22:46:56.430071Z",
     "shell.execute_reply": "2025-10-27T22:46:56.429596Z",
     "shell.execute_reply.started": "2025-10-27T22:46:56.105702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile  |  Quantile Value\n",
      "-------------------------------\n",
      "  5th      |   -0.144\n",
      " 10th      |   -0.105\n",
      " 20th      |   -0.065\n",
      " 50th      |   -0.000\n",
      " 80th      |    0.061\n",
      " 90th      |    0.097\n",
      " 95th      |    0.128\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Plot histogram (PDF)\n",
    "counts, bins, patches = plt.hist(\n",
    "    total_clip_ratios,\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    color=\"skyblue\",\n",
    "    edgecolor=\"black\",\n",
    "    alpha=0.7,\n",
    "    label=\"Histogram (PDF)\",\n",
    ")\n",
    "\n",
    "# Plot CDF on the same axis\n",
    "sorted_ratios = np.sort(total_clip_ratios)\n",
    "cdf = np.arange(1, len(sorted_ratios) + 1) / len(sorted_ratios)\n",
    "plt.plot(\n",
    "    sorted_ratios, cdf * counts.max(), color=\"red\", linewidth=2, label=\"CDF (scaled)\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Clip quality diff ratios\", fontsize=12)\n",
    "plt.ylabel(\"Count\", fontsize=12)\n",
    "plt.title(\"Distribution of Clip Quality Difference Ratios\", fontsize=14)\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "percentages = [0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95]\n",
    "print(\"Percentile  |  Quantile Value\")\n",
    "print(\"-------------------------------\")\n",
    "for idx, percentage in enumerate(percentages):\n",
    "    quantile_value = np.quantile(sorted_ratios, percentage)\n",
    "    quantile_value_rounded = round(quantile_value, 3)\n",
    "    print(f\"{int(percentage*100):>3d}th      |  {quantile_value_rounded:>7.3f}\")\n",
    "    # Draw the vertical line\n",
    "    plt.axvline(\n",
    "        quantile_value,\n",
    "        color=\"k\",\n",
    "        linestyle=\"dotted\",\n",
    "        linewidth=1,\n",
    "        alpha=0.8,\n",
    "        label=f\"{int(percentage*100)}th percentile\"\n",
    "        if idx == 0\n",
    "        else None,  # Only label first to avoid duplicate legend\n",
    "    )\n",
    "\n",
    "plt.xlim(-0.5, 0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:56.430904Z",
     "iopub.status.busy": "2025-10-27T22:46:56.430757Z",
     "iopub.status.idle": "2025-10-27T22:46:57.236625Z",
     "shell.execute_reply": "2025-10-27T22:46:57.236058Z",
     "shell.execute_reply.started": "2025-10-27T22:46:56.430889Z"
    }
   },
   "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": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:57.241225Z",
     "iopub.status.busy": "2025-10-27T22:46:57.240818Z",
     "iopub.status.idle": "2025-10-27T22:46:57.479241Z",
     "shell.execute_reply": "2025-10-27T22:46:57.478657Z",
     "shell.execute_reply.started": "2025-10-27T22:46:57.241207Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(98880, 108)\n",
      "(98606, 108)\n",
      "(98606, 108)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df.dropna(\n",
    "    subset=[\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\",\n",
    "        \"spectrum_decay\",\n",
    "    ]\n",
    ")\n",
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:36.043975Z",
     "start_time": "2024-05-16T13:58:56.910958Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:57.479942Z",
     "iopub.status.busy": "2025-10-27T22:46:57.479792Z",
     "iopub.status.idle": "2025-10-27T22:46:57.508108Z",
     "shell.execute_reply": "2025-10-27T22:46:57.507618Z",
     "shell.execute_reply.started": "2025-10-27T22:46:57.479927Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 49303\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": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:40.799375Z",
     "start_time": "2024-05-16T13:59:36.394236Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:57.508776Z",
     "iopub.status.busy": "2025-10-27T22:46:57.508634Z",
     "iopub.status.idle": "2025-10-27T22:46:57.759887Z",
     "shell.execute_reply": "2025-10-27T22:46:57.759311Z",
     "shell.execute_reply.started": "2025-10-27T22:46:57.508761Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    98606\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    49303\n",
      "True     49303\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-carp-up-c-1    98606\n",
      "Name: count, dtype: int64 preference  model_name       \n",
      "False       chirp-carp-up-c-1    49303\n",
      "True        chirp-carp-up-c-1    49303\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    98606\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row.get(\n",
    "            \"continue_at\", row[\"duration\"]\n",
    "        )\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id],\n",
    "            row.get(\"continue_at\", row[\"duration\"]),\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:57.760608Z",
     "iopub.status.busy": "2025-10-27T22:46:57.760459Z",
     "iopub.status.idle": "2025-10-27T22:46:57.889323Z",
     "shell.execute_reply": "2025-10-27T22:46:57.888818Z",
     "shell.execute_reply.started": "2025-10-27T22:46:57.760593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    35613\n",
       "2.0    13690\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:57.890040Z",
     "iopub.status.busy": "2025-10-27T22:46:57.889891Z",
     "iopub.status.idle": "2025-10-27T22:46:58.096560Z",
     "shell.execute_reply": "2025-10-27T22:46:58.095994Z",
     "shell.execute_reply.started": "2025-10-27T22:46:57.890024Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    98606.000000\n",
      "mean        22.980142\n",
      "std          2.222306\n",
      "min          6.751258\n",
      "25%         21.786222\n",
      "50%         23.145135\n",
      "75%         24.440009\n",
      "max         32.534470\n",
      "Name: mean_ear_score, dtype: float64\n",
      "count    49303.000000\n",
      "mean         0.004236\n",
      "std          1.878538\n",
      "min         -9.842578\n",
      "25%         -1.132884\n",
      "50%         -0.004254\n",
      "75%          1.128703\n",
      "max         12.583730\n",
      "Name: mean_ear_score_diff, dtype: float64\n",
      "count    49303.000000\n",
      "mean        -0.003187\n",
      "std          0.083242\n",
      "min         -0.809305\n",
      "25%         -0.049983\n",
      "50%         -0.000183\n",
      "75%          0.047703\n",
      "max          0.486015\n",
      "Name: mean_ear_score_diff_ratio, dtype: float64\n",
      "count    98606.000000\n",
      "mean         0.506718\n",
      "std          1.112691\n",
      "min          0.000000\n",
      "25%          0.066667\n",
      "50%          0.211111\n",
      "75%          0.548148\n",
      "max         50.425000\n",
      "Name: mean_shimmer_score, dtype: float64\n",
      "count    49303.000000\n",
      "mean         0.016936\n",
      "std          0.734265\n",
      "min        -14.625000\n",
      "25%         -0.133333\n",
      "50%          0.000000\n",
      "75%          0.155556\n",
      "max         20.931759\n",
      "Name: mean_shimmer_score_diff, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df[\"mean_ear_score\"] = df[\"s3_id\"].map(clip_id_to_mean_ear_score)\n",
    "print(df[\"mean_ear_score\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff\"] = df[\"mean_ear_score\"].diff()\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff_ratio\"] = df[\"mean_ear_score\"].diff() / (\n",
    "    df[\"mean_ear_score\"] + 0.1\n",
    ")\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff_ratio\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score\"] = df[\"s3_id\"].map(clip_id_to_mean_shimmer_score)\n",
    "print(df[\"mean_shimmer_score\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score_diff\"] = df[\"mean_shimmer_score\"].diff()\n",
    "print(df[df[\"preference\"]][\"mean_shimmer_score_diff\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score_diff_ratio\"] = df[\"mean_shimmer_score\"].diff() / (\n",
    "    df[\"mean_shimmer_score\"] + 0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:58.097299Z",
     "iopub.status.busy": "2025-10-27T22:46:58.097143Z",
     "iopub.status.idle": "2025-10-27T22:46:58.497360Z",
     "shell.execute_reply": "2025-10-27T22:46:58.496862Z",
     "shell.execute_reply.started": "2025-10-27T22:46:58.097284Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"pos\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.25,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"neg\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.25,\n",
    ")\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Distribution of Loudness (abs) for Positive and Negative Preferences\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:58.498274Z",
     "iopub.status.busy": "2025-10-27T22:46:58.497927Z",
     "iopub.status.idle": "2025-10-27T22:46:59.076164Z",
     "shell.execute_reply": "2025-10-27T22:46:59.075676Z",
     "shell.execute_reply.started": "2025-10-27T22:46:58.498257Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loudness (web): -13.534226336355704\n",
      "Mean loudness (mobile): -13.408969142259414\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare data\n",
    "web_loudness = df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"]\n",
    "mobile_loudness = df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"]\n",
    "bins = list(np.linspace(-20, -5, 100))\n",
    "\n",
    "# Plot histogram (PDF)\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(\n",
    "    web_loudness,\n",
    "    label=\"web\",\n",
    "    bins=bins,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    mobile_loudness,\n",
    "    label=\"mobile\",\n",
    "    bins=bins,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.title(\"PDF: Loudness (abs) for Positive Preferences by Source\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "\n",
    "# Plot CDF\n",
    "plt.subplot(1, 2, 2)\n",
    "web_sorted = np.sort(web_loudness)\n",
    "web_cdf = np.arange(1, len(web_sorted) + 1) / len(web_sorted)\n",
    "plt.plot(web_sorted, web_cdf, label=\"web\")\n",
    "\n",
    "mobile_sorted = np.sort(mobile_loudness)\n",
    "mobile_cdf = np.arange(1, len(mobile_sorted) + 1) / len(mobile_sorted)\n",
    "plt.plot(mobile_sorted, mobile_cdf, label=\"mobile\")\n",
    "\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.title(\"CDF: Loudness (abs) for Positive Preferences by Source\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "\n",
    "plt.tight_layout()\n",
    "print(\"Mean loudness (web):\", web_loudness.mean())\n",
    "print(\"Mean loudness (mobile):\", mobile_loudness.mean())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:59.076859Z",
     "iopub.status.busy": "2025-10-27T22:46:59.076708Z",
     "iopub.status.idle": "2025-10-27T22:46:59.381404Z",
     "shell.execute_reply": "2025-10-27T22:46:59.380899Z",
     "shell.execute_reply.started": "2025-10-27T22:46:59.076843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df[\"loudness_diff\"] = df[\"loudness_abs\"].diff() / df[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:59.382205Z",
     "iopub.status.busy": "2025-10-27T22:46:59.381975Z",
     "iopub.status.idle": "2025-10-27T22:46:59.959838Z",
     "shell.execute_reply": "2025-10-27T22:46:59.959354Z",
     "shell.execute_reply.started": "2025-10-27T22:46:59.382189Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "\n",
    "# Calculate mean_shimmer_score_diff and its ratio\n",
    "df[\"mean_shimmer_score_diff\"] = df[\"mean_shimmer_score\"].diff()\n",
    "df[\"mean_shimmer_score_diff_ratio\"] = (\n",
    "    df[\"mean_shimmer_score\"].diff() / df[\"mean_shimmer_score\"]\n",
    ")\n",
    "\n",
    "# Prepare percentiles for both diff and diff_ratio\n",
    "diff_data = df[df[\"preference\"]][\"mean_shimmer_score_diff\"].dropna()\n",
    "diff_ratio_data = df[df[\"preference\"]][\"mean_shimmer_score_diff_ratio\"].dropna()\n",
    "diff_percentiles = np.percentile(diff_data, lookup_percentiles)\n",
    "diff_ratio_percentiles = np.percentile(diff_ratio_data, lookup_percentiles)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Left plot: mean_shimmer_score_diff\n",
    "hist_range = (-1, 1)\n",
    "axes[0].hist(\n",
    "    diff_data,\n",
    "    label=f\"pos, mean: {np.mean(diff_data):.2f}\",\n",
    "    bins=np.linspace(*hist_range, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "diff_textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(diff_percentiles)\n",
    "    ]\n",
    ")\n",
    "axes[0].text(\n",
    "    0.05,\n",
    "    0.98,\n",
    "    diff_textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    transform=axes[0].transAxes,\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "# Only plot lines within the histogram range\n",
    "for percentile in diff_percentiles:\n",
    "    if hist_range[0] <= percentile <= hist_range[1]:\n",
    "        axes[0].axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "axes[0].set_xlim(hist_range)\n",
    "axes[0].set_title(\n",
    "    f\"Mean shimmer score diff\\n{lookup_percentiles[-1]}th: {diff_percentiles[-1]:.2f}\"\n",
    ")\n",
    "axes[0].legend()\n",
    "\n",
    "# Right plot: mean_shimmer_score_diff_ratio\n",
    "hist_ratio_range = (-2.5, 2.5)\n",
    "axes[1].hist(\n",
    "    diff_ratio_data,\n",
    "    label=f\"pos, mean: {np.mean(diff_ratio_data):.2f}\",\n",
    "    bins=np.linspace(*hist_ratio_range, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "diff_ratio_textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(diff_ratio_percentiles)\n",
    "    ]\n",
    ")\n",
    "axes[1].text(\n",
    "    0.05,\n",
    "    0.98,\n",
    "    diff_ratio_textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    transform=axes[1].transAxes,\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "# Only plot lines within the histogram range\n",
    "for percentile in diff_ratio_percentiles:\n",
    "    if hist_ratio_range[0] <= percentile <= hist_ratio_range[1]:\n",
    "        axes[1].axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "axes[1].set_xlim(hist_ratio_range)\n",
    "axes[1].set_title(\n",
    "    f\"Mean shimmer score diff ratio\\n{lookup_percentiles[-1]}th: {diff_ratio_percentiles[-1]:.2f}\"\n",
    ")\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:46:59.960573Z",
     "iopub.status.busy": "2025-10-27T22:46:59.960415Z",
     "iopub.status.idle": "2025-10-27T22:47:01.299406Z",
     "shell.execute_reply": "2025-10-27T22:47:01.298917Z",
     "shell.execute_reply.started": "2025-10-27T22:46:59.960557Z"
    }
   },
   "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\"].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": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:01.300081Z",
     "iopub.status.busy": "2025-10-27T22:47:01.299935Z",
     "iopub.status.idle": "2025-10-27T22:47:01.317643Z",
     "shell.execute_reply": "2025-10-27T22:47:01.317192Z",
     "shell.execute_reply.started": "2025-10-27T22:47:01.300066Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:01.318361Z",
     "iopub.status.busy": "2025-10-27T22:47:01.318135Z",
     "iopub.status.idle": "2025-10-27T22:47:01.611205Z",
     "shell.execute_reply": "2025-10-27T22:47:01.610732Z",
     "shell.execute_reply.started": "2025-10-27T22:47:01.318346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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37o26detyinn+/HmEhoZCq9WiatWqGDRoEAYNGmT4vG/fvkhJScGZM2ewdOnSYmP8/PPPyMnJQd++fcEwDDZv3oxx48bhjz/+MGnvtOfdvHkTAAx3LItUqlQJlStXLnaHelO1bdvWcOeybdu2mD59Oo1PI2VCCYwNDRgwAMHBwS8N3CXmpVQqkZaWBpVKhX///Rdr166Fvb09WrduDQD49ddfYW9vbzi+f//+6NmzJ6KiosqUwLi5uWHr1q2lPo75/vvv8fDhQ6xcudKw+OB7771n0i9gNzc3/Pnnn0aPt9577z28/fbb2LFjBxYuXFjmWO3atcPKlSvh4eHxUpu6d++OWbNm4f79+4ZHCGq1Gr/88gs6dOjwyrFEXM9bKpXi3XffxapVq156BFO7dm1MnToVNWrUMHsCI5FI8NFHH+Gjjz7CtWvXsH//fhw+fBg7d+5E3bp10bt3b3Tr1q3Mj68CAwPRuHFj+Pv7IyMjAwcOHMDChQuRkpKCKVOmAADCwsJQo0YNnDlzpsTzSUpKwm+//QY3NzcAhWNoRo8ejdOnTxu+l01VtNVIcQteymQywxIL5uDq6ooPP/wQoaGhkEgkuHz5Mnbt2oXr169j//79cHZ2NltdpGKiR0il2LhxI3r16oWwsDCEh4dj9OjRiIuLMzqmoKAAc+fORbNmzRAWFoZx48YZ9kECnt0Kz8rKslq7MzIyMGnSJDRq1AhNmjTBjBkzkJOT88oypZ1H0a374r6e3/6gvBk8eLDh8c6nn34KJycnrFmzBpUqVQIAo+QlMzMT2dnZaNy4seGv0dK89957ZRpL8vfff0Mmk6FTp06G9xwcHPDee++xPKNnhEKhIXnR6XTIyMiARqNB/fr1y9z+snj77bdhZ2eHw4cPG947ffo00tPT8c4777yyrCXO21pCQkIwd+5cnD59GsuWLYObmxvmz5+PiIgITJ48uUxbd2zYsAHDhg1Du3bt0Lt3b3z33XeIiIjA1q1bkZycXOa2dO7c2ZC8AECTJk0AgNMyCyXJz88HUPx4Lzs7O8Pn5jBo0CB88cUX6NatGzp27IjPP/8cixcvxsOHD7Fr1y6z1UMqLroDU4qLFy+if//+aNCgAbRaLVasWIEhQ4bgyJEjhsGaCxcuxMmTJ7Fy5Uq4uLhg/vz5GDt2rNH6KdY2efJkyOVyREVFQa1WY8aMGZg1axaWL19eYpnSzqNz586IjIw0KjN9+nSoVKqXNrMsT2bNmgV/f38IhUJIpVL4+/tDIHiWu584cQLr16/HrVu3jMZllHXGUVmf+T9+/BjVq1d/Ke6Ls1HYOnDgAL799ls8ePAAarWadbvKwtXVFa1bt8bPP/+MTz75BEDh46NKlSoZxhSVxFLnXVZpaWlGq2A7OjrCycnppY1NXVxcjJLZ59nZ2aFbt254++23sXv3bixZsgSHDx9Gp06d4OPjw6o9DMNg8ODBOH36NC5cuFDmO0hVqlQxel2UzHD5wygjI8Poe8Xe3t7o/Isbn1RQUFBi/5hLt27dsGTJEpw9e9awHhUhJaEEphQvLh61ePFihIeH48aNG2jatCmys7Oxf/9+LFu2zDDifuHChejcuTOuXr0KqVSKgQMHAgCaNm0KAOjRo4dhDRi9Xo+lS5di3759EIvF6Nevn8nTKO/fv49Tp05h3759hmfZM2fOxPDhwzF16lTDnYfnlXYeoaGhsLe3N/oBlpaWhgsXLmDBggUmtdfSQkJCXnqmX+Ty5csYNWoUmjZtitmzZ0Mmk0EsFmP//v2lDkwtUrRgoTVotVqjuz0//fQTpk+fjnbt2mHIkCHw8vKCUCjExo0bzfqXOVD4GOnYsWP4999/ERgYiD///BPvv/++UTJYHvXu3dtoHZaidUkiIiKMjlu0aFGJ08fv37+P/fv349ChQ5DL5ahduzZ69+6NZs2acWpTUTKSmZlZ5jIl3eXjMvh13LhxuHjxouF10c+k51fcfjFhksvlCAkJYV0XW5UrV2bVL+T1RQkMS0Ur1xb99RMTEwO1Wo3mzZsbjgkICICPjw+uXr2KAQMGYPXq1Rg3bhyOHTsGZ2dnoyTgwIED+Oijj7B3715cvXoV06dPR6NGjdCiRQsAhXc4Hj9+jB07dpS5jVeuXIGrq6vRL+3mzZtDIBDg2rVrhq0DnlfaeRS3h9LBgwdhb29v9GiAb3799VfY2dlhy5YtRrfN9+/fb/a6qlatirt370Kv1xvdjSjasuF5bm5uxf5lnZSUZDS999dff4Wfnx/WrFljFPP5laLZeNVdp8jISHh6euLw4cNo2LAh8vLyynT3gM15W8JXX31lNJOoqP+ioqKMjntxqnZ2djaOHj2K/fv3Izo6Go6OjujcuTP69Olj8p5iRcnl89OHrbnG0LRp04y+v4o2oi0a9H39+nWjZOXp06dITk62+GM/vV6Px48fcx4sTV4vlMCwoNPpsHDhQjRq1AiBgYEACpczF4vFLw3o8/Lyglwuh1AoNCQ7Xl5eLx0XFBRkWKCrRo0a+O6773Du3DlDAiOTyaDT6Vi1MzU19aV1FUQiEdzc3F66bf58mVedR3H27duHrl27Wvy2siUJhUIwDGP0iCExMRHHjx83e11vvfUWTp8+jWPHjhkGs+bl5WHv3r0vHevn54d//vkHKpXKkFidOHECT548MUpgiv4qfz45iI6OxtWrV1k/2gAKx6aU9EhCJBKhS5cu+Pnnn3H//n0EBgaWacd1NudtCY0bNy72/eeT9ecplUrMmTMHv//+O/Lz8xEaGooFCxagc+fOcHJyYlV3RkYGXFxcjO6eqNVqfPPNNxCLxUZ3cIoGQr+4i7wllLRqce3atVGzZk3s3bsX/fr1M7R79+7dYBjG6I+V7OxspKSkwNvbu8x7uz0vLS3tpZ9Tu3btQlpa2kuPqgkpDiUwLMydOxf37t0z6wCzF9d6kMlkRgNiJ02a9Mrys2bNMhpYeeXKFbO17VWuXLmC+/fvlzjlky9atmyJqKgoDB06FF27doVCocCuXbtQrVq1UreQYOu9997Dzp07MW3aNNy4cQMymQw//fRTsQlgnz598Ouvv2Lo0KF4++23ER8fj8OHD6NatWpGx7Vq1Qq//fYbxowZg1atWiExMRF79uxBrVq1XjmluyT16tXD7t27sW7dOlSvXh2enp5Gi5F1794dO3bswIULFzB58mSzn3d5kJGRgdOnT6Nfv37o3bs3ateuzTnWn3/+ifXr16Njx47w9fVFZmYmfv75Z9y9excTJ040mu1Tr149AMCCBQsQEREBoVCILl26sKovMTERbdu2NXpMzdbUqVMxatQofPzxx+jSpQvu3r2LnTt3ok+fPkaL2P3+++/47LPPXnr0dvDgQSQlJRkG/F66dAnr1q0DULj6c9WqVQEArVu3RufOnREYGAiJRIJ///0XR44cQZ06ddC3b19ObSevF0pgymjevHn466+/8N1336Fy5cqG96VSKdRq9Ut/NSkUimKnIr7oxb1FGIZh9Ux7woQJLy2lLpVKkZaWZvSeRqNBZmZmiW1iex5Fa4yU1/1nyio8PBxffvklNm3ahIULF8LX1xeTJ0/G48ePzZ7AODg4YOvWrZg/fz6+++472Nvbo1u3bnjrrbcwdOhQo2MjIyMxffp0REVFYeHChahfvz42bNiAJUuWGB3Xs2dPpKam4vvvv8fp06dRq1YtfPXVVzh27JjRGIeyGjNmDJKSkrB582bk5OTgjTfeMEpg6tevj9q1a+P+/fulzj7ict7lQaVKlfD333+XaeXl0gQGBiIgIACHDh1CWloaxGIx6tSpYzSlvEiHDh0wYMAAHDlyBIcOHYJer2edwBQlrWX52VOS1q1bY82aNVizZg3mz58PT09PjBgxAmPGjClT+f379xt97124cMGwSGPjxo0NCUy3bt1w5coV/Prrr1CpVPDx8cHQoUMxcuRIVlt8kNeYnrySTqfTz507Vx8REaF/8ODBS59nZWXp69Wrpz927Jjhvfv37+sDAwP1V65c0ev1ev0///yjDwwM1KelpRmV/fDDD/ULFiwwem/UqFH6adOmmdTm2NhYfWBgoP769euG906dOqUPCgrSJycnF1umLOdRRKlU6kNDQ/U7duwwqZ2k0Pnz5/WBgYH68+fP27opZfLuu+/qBw4caOtmkGJ89913+tDQUL1cLrd1UwixuPI9faAcmDt3Lg4dOoTly5cbpl7K5XLD7VEXFxf06tULixcvxvnz5xETE4MZM2YgLCzMMNCvatWqYBgGf/31F9LS0kpdj+V5y5cvx9SpU1m1OSAgAJGRkfjiiy9w7do1/PPPP5g/fz66dOlimIH09OlTdOrUybAUeVnOo8jRo0eh1WrL/Bc4qTiuX7+OW7duoXv37rZuCinGhQsXMGDAAFrJlrwW6BFSKXbv3g0ARvvPAMZTLmfMmAGBQIDx48dDpVIhIiICs2fPNhxbqVIljBs3DsuXL8dnn32G7t27l/n5tFwux5MnT1i3e9myZZg/fz4GDRoEgUCADh06YObMmYbP1Wo1Hjx4gLy8PMN7pZ1Hkf3796N9+/YWH2hIyo+7d+/ixo0b+PbbbyGTydC5c2dbN4kUg+vsM0L4iNHraQctQmzpwoULGDhwILZv3855XRFLW716NdauXQt/f3/MnTsXb7zxhq2bRAh5zVECQwghhBDeoTEwhBBCCOEdSmAIIYQQwjuUwBBCCCGEdyiBIYQQQgjvVPhp1ApFNl41TJlhGDg52bHeb8gchg79GEFBQZgyZZrV67YmgUCAnJwCTrvmEmOMXA67nw6g4N0e0HNcbdUcMSwRqzzXa2p9XMvbqn8JsSWGAby8St9fq8LPQkpNfXUCIxA8S2CKO27DhnX45psNRu/VqFEDP/54yPC6oKAAK1Ysw2+/HYNKpUJ4eHN89tlMeHl5AQAuX76E4cOH4OTJ03BxebZ2yrBhHyMw0DIJTGZmJpYuXYS//z4JhhGgbdt2mDJlGhwdHUssU9p5AMCTJ0+waNECXL58CQ4ODuja9R2MGzfhpS0RijDMswRGp6vQ32qEEELMgGEAqbT0BIYeIZVBQEAAfvvtT8PXli3bjD5fvnwpTp06iSVLlmHTpijI5XJMnvypjVpb6PPPp+P+/ftYt24jVq1ajX///QcLFsx9ZZnSzkOr1WLChDFQq9WIitqOefMW4PDhQ1i/fq2lT4f8h8lIh+TQATAZ6TaNYYlY5bleU+vjWt5W/UsIH1ACUwZCoQhSqdTw5eHhYfgsOzsbBw8ewMSJk/HGG81Qt25dzJkzH9HRV3HtWjSSkh5j+PDCzRZbtoxAo0YhmD372Yq4er0eK1euQKtWEWjfvjU2bFhncnvj4uJw9uwZzJo1Bw0ahCAsrBGmTp2OX389Brk8pdgypZ0HAJw/fxZxcXFYsGARgoKC0aJFJEaPHoMffvgearXa5HaT0gnjH8Ft6CAI4x/ZNIYlYpXnek2tj2t5W/UvIXxACUwZxMc/QocObdGt29v4/PPpRkv737p1ExqNBs2avWl4z9/fH5UrV8G1a9dQqVJlfPXVCgDAgQOH8Ntvf2Ly5GePjH7++RAcHBywfftOTJjwKTZt2ojz588ZPp89eyaGDfuYVXuvXYuGi4sL6tatZ3ivWbM3IRAIcP369WLLlHYehXGvoVat2kaPlMLDm0OpVOL+/VhWbSSEEEJMUeEH8ZqqQYMGmDt3AapXr4HUVDm++WYDhgwZjB9++BFOTk5QKFIhFouNxrYAgJeXFxSKVAiFQri5uQEAPD09XzquVq3aGDFiFACgWrXq+P77Pbh48QLefDMcACCVylgPMFYoUuHp6Wn0nkgkgqurKxSK1BLLvOo8ACA1NRWenl5Gnxe9LikuIcT29Ho9dDqtTSYrEPIigUAAgUAIhmFMikMJTClatIg0/H9gYCAaNGiALl064ffff0X37j1Njl+7dqDRa6lUirS0NMPrceMmvLL8l1/Ox9GjPxtenzlzweQ2EUIqDo1GjczMNKjV+bZuCiEGEok9XF09IRKJOcegBIYlFxdXVKtWHQkJCQAALy8p1Go1srOzjO5eKBQKeHmVvqX9i7N3GIaBXl/2v5JGjRqNAQMGGb3n5WWcBAGARqNBVlZWiW0qy3lIpVLcuBFjVC4tTWEoTyxPb+8AdYOG0Ns72DSGJWKV53pNrY9reZPr1euhUCRDIBDAzU0KoVBk8l+9hJhCr9dDq9VAqcyAQpEMb29fzt+TlMCwlJubi8TEBHTp0hUAUKdOXYhEIly8eAFt27YHADx8+ADJyU8QEhICABCLCzNMrdb8t289Pb1eeqwTEtIQ2dnZuHnzJurWrQsAuHTpInQ6HRo0aFBsnLKcR0hICLZs2YS0NIWhzvPnz8PZ2Rk1awaY/dzIy7SBQcg4fsrmMSwRqzzXa2p9XMubWq9Go4Zer4ObmwwSiT3nOISYlx2EQiHS0p5Co1FDLJZwikKDeEvxv/8twz//XEZS0mNER1/FpEmfQCAQolOntwEALi4u6N69B5YvX4ZLly7i5s2bmDNnFkJCGiIkpCEAoEqVKmAYBqdOnUR6ehpyc3PLXP/q1avwxRczWLW5Zs2aaN68BRYsmIOYmOu4evUKlixZhI4dO0Em8wYApKQ8Rc+e7yAm5nqZz+PNN5ujZs2amDnzc9y9ewdnz57BunWr0adPX0gk3L4BCSGWxzD0o56UL+b4nqTv6lI8fZqCzz6bhh493sG0aZPh5uaObdu+g4fHs0GykyZNRWTkW5gyZSKGDh0MLy8pli37n+Fzb+9KGDlyNFavXoV27VpjyZKFZa4/NVWO5ORk1u3+8svFqFHDHyNHDsP48WMQGhqGmTNnGz7XaDR4+PAh8vOfPRcv7TyEQiFWrlwDoVCAwYMHYObMGejatRtGjRrDun2EG9H1aEh9pRBdj7ZpDEvEKs/1mlof1/K26l9C+IBW4i1lJV5iGlqJ17xE167Co91bSP/jb2hCQm0WwxKxynO9ptbHtbyp9arVKigUT+DlVcXoNj3DwKpjYfR6Pf18JUZK+t4Eyr4SL42BIYSQ1wjDAGpGgByVxmp1OklEEIP+SCwPkpOTsXz5Ivz772U4ODji7be7YsSIMSVuBwMAWVmZ+N//vsKZM6cgEDBo2bINJkyYbLQ1TWzsPaxYsQS3b9+Eu7sHevV6D/37DyoxpjlQAkMIIa8RhmGQo9LgxO0U5BZYPolxtBOhdbA3PCRC2tDVxrRaLaZOnQBPTy9s2PAtUlNT8eWXsyESiTBiRMlDAebO/QIKRSr+97+10Gg0WLRoLpYu/RJz5nwJAMjJUWLixLFo0uQNTJ78GeLiYrFo0Tw4O7vg3XdNX26kJJTAEELIayi3QAOlFRIYLsaOHW6Y2fjrr0chEonQvXtvDB060vDoKysrC6tWLcOZM6egVqsQGtoYn3wyGX5+1QAAyclPsGLFUly7dhUajRqVK/tgzJjxCA+PKFMbtmzZiFOnTqJ377749ttvkJ2dhY4du+DTT6dgz57v8P33u6DT6dCnTz8MGjTEUC47Oxtr167E6dMnoVKpERxcB+PGTTSs+fX4cSJWr16BGzdikJ+fh+rV/TFixBg0bdrMEKN37254550eSExMwIkTx+Hi4oJBg4aYnAxcvHgeDx8+wMqV6+Dp6YXatYMwdOhIrF+/Gh9/PNwwY/Z5Dx8+wIULZ7F583YEBxfOav3kkymYMmUCxo79BFKpDL/9dgxqtRqffTYLYrEYNWsG4N69u/j++50WTWBoEC8hPKKpHYS0vy9AUzvIpjEsEausGAbQBQUj4/RF6IKCYY2hHKaeJ9fytujf8uKXX45AKBRh06ZtmDBhMr7/ficOHz5o+Hzhwjm4c+cWlixZgQ0boqDX6zFlygRoNIVJ2YoVS6BWq7B27SZs27YHo0aNg4ODYwm1Fe/x40ScP38Wy5evxuzZX+LIkZ8wZconkMtTsGbNRowaNQ6bNq03Wh/riy+mIT09DcuWfY0tW3YgMDAYn3wyCllZmQAKl+J4880WWLVqHb79dieaNQvHtGkTX5qssWfPTgQH10VU1E706NEHy5cvRnz8Q8PnY8cOx5dfzmF1PjduXEfNmrWMlt54441w5OTk4MGD+8WWiYm5BmdnF0PyAgBNmrwBgUBgOO+YmGsIDQ0zSoCaNQtHfPwjZGVlsWojG5TA2NDs2TMxceKrV9olxIiDA7TBdQAHExZwM0cMS8Qqg6LxG+lCCVJrBiJdKIGaEVg+iTH1PLmWt3L/lieVKlXC+PETUa1aDXTo8DZ69eqLvXt3AQASEuJx+vTfmDZtJho2DEPt2oGYPXs+5PIU/P33XwCAp0+T0aBBQwQE1ELVqr5o0SISoaGNWLVBr9dhxoxZ8PeviYiItxAW1gQJCY8wfvwkVKtWA126vINq1arj338vAwCio6/i1q0bmD9/CYKD68LPrxrGjv0Ezs4uOHHiOIDC1de7d++FmjVrwc+vGoYNG4WqVavizJmTRnWHhzdHz5594Ovrhw8/HAQ3N3dDPYX9U5n1AqIKheKlbWaebQejKLZMWprCaANjoHABVhcXV8NCpoXHGMctel10jCVQAlMKrVaLdevWoGvXTggPb4p33umMTZs2Gj3L1ev1WL9+LTp0aIPw8KYYOXIY4p/bPTYp6TEaNQrBnTu3LdLG0uovyfff70GXLp3w5ptNMHDgB4Y1YZ4XHR2N4cOHoHnzNxAZGY4hQwYbTb0m1iVIiIfzp2MhSIi3aQxLxCqLovEbl/66gvyPhuDSX1eQo9JYfEaNqefJtby1+7c8qVu3vtF1rV+/ARIS4qHVavHo0QMIhULUrVvf8LmbmzuqVauOR48eAAB69+6Hbdu2YNSoj7Fly0bExt5j3YbKlX3g6OhkeO3p6YkaNfwhEAiee88LGRmFK5/Hxt5FXl4eunRpi/btIw1fT54k4fHjRACFd2DWrFmJ/v17o1OnVmjfPhKPHj3E06fGd2ACAmob/p9hGHh6eiE9Pd3w3hdfzMPIkWNLbPukSeMN9X/44Xusz50PKIEpxdat32Lfvr2YNm0G9u8/iPHjP8G2bVHYs2eX4Zht26Kwe/cuzJjxBbZt2wkHBweMGTMSBQUFVmkjl/p//fUYVqz4CsOHj8SuXd+jdu0gjBkz0ihbjo6OxrhxoxAe3hw7duzCjh270Lfv+0b/eIl1CdLT4LBzOwTpaaUfbMEYlojFhl6hQP1f90Nfwl+N5mbqeXItb6v+rQi6deuOvXt/QseOnXH/fiyGDh2Affv2sIpR3FYvxc3WKVoiIi8vF15eUkRF7TL62rVrPz74YCAAYO3alfj77xMYPnwM1q7djKioXahZsxbUauPxSMXVzWYzzunTZxrqX7ZsFYDCzXlf3Gbm2XYwXi/FAPBS4gQUriOWnZ1luHtTeIxx3KLXL64Ub070m6gU0dHRaNmyNSIj34KPT1W0a9cBb74ZjpiYwmd/er0eu3Z9h6FDh6FVq9YIDAzEvHlfQi6X46+//gQAdO1auGrv+++/h0aNQjBs2MdGdWzfvhUdOrRB69aRWLToS6jV6jK3ryz1F2fnzu3o0aMX3n23O2rWDMDnn38Be3sH/PTTQcMxy5cvRb9+H+Cjj4YgIKAWatTwR4cOHWnVXUKIxd28ecPo9Y0bMfDzqwahUIjq1f2h1Wpx8+azsSeZmRmIj3+EGjX8De9VqlQZ3bv3xsKFX6Ffvw+NxtBYQlBQMNLSFBAKhfD19TP6cnd3BwBcvx6Nzp27oWXL1ggIKByPkpycZPa2yGTehrorV64CAKhXrwHi4mKNko1Lly7AyckJNWrULDZO/fohUCqzcfv2LcN7//57GTqdDvXq1Tccc/XqFcP4o6K41apVh6ur60sxzYUSmFI0bNgQFy9ewKNHDwEAd+/ewdWrV9CiReFI9sePHyM1NRXNmr1pKOPi4oL69Rvg2rXC1TN37Ci8W7N+/Tf47bc/jVa3vXz5EhITE7Bx4xbMnbsAhw//hMOHfzJ8vmHDOnTp0qnE9pWl/hep1WrcunXLqIxAIECzZs0MZdLSFIiJuQ5PT08MHjwA7dq1wtChH+HKlX/L1G+EEGKKp0+TsXr1CsTHP8Tvvx/D/v3fo3fvfgAAP79qiIxsiSVLvkR09FXcu3cX8+bNgkzmjcjIVgCAVauW48KFc0hKeow7d27j338vo3p1/1fUaLomTZqhXr0G+Oyzybh48TyePEnC9evR2LhxLW7fvgkA8PWthpMn/8S9e3dw795dzJ37OadFPufPn4UNG9awKvPGG2+iRg1/zJ8/C/fu3cWFC+ewadN69Oz5nuEP05s3Y/DBB70gl6cAAGrU8EezZs2xdOkC3LwZg2vXrmLFiqVo27YDpFIZAKB9+04Qi8VYtGge4uLu4/jx3/DDD7vRt29/1ufFBk2jLsVHHw1BTk4OevZ8F0KhEFqtFmPGjEPnzl0AAApFKoCXb5N5eXkhNbXw1lzRACh3d3dIpcaDrlxcXDFt2gwIhUL4+/sjMvItXLx4ET179v6vjAd8fX1LbF9Z6n9RRkY6tFrtS2U8Pb3w8GHh8+PExMLntRs3rscnn0xCUFAQfv75MEaOHIYffvgR1apVL7FNhJDyz9HOOj/+udbTqVMXFBQUYNiwQRAIhOjdu5/RlNzPPpuNVauWYdq0T6BWq9GwYSN89dUqw6MXnU6LFSuWQC5PgaOjE5o1C8f48RMN5Xv37oa33+6KIUNGmHaCz2EYBsuWrcI336zDwoVzkZGRDk9PL4SGNjIMah037lMsWjQPI0d+DDc3d/TvPwg5OTms63r6NJn143yhUIilS1di2bJFGDnyIzg4OKBTJ+M+yM/PR3z8I6O7KbNnz8eKFUsxYcJow0J2n3wyxfC5s7MzVqxYgxUrlmDo0AFwc3PH4MFDLTqFGqAEplS///4rfvnlCBYuXIyaNQNw584dLF++FDKZDN26vWty/ICAAAiFQsNrqVSKe/eeDTbr1+999Ov3vsn1sFU0SLlnz954993uAIDg4Dq4ePECfvrpIMaNo9lTtqCTeSN3/ETo/tuU01YxLBGLjVx3L5zpNQS57pZ7vv48U8+Ta3lL9K9er4eTpHBxOWtxkoig15d9/AZQOAZkwoRJmDz5s2I/d3V1xRdfzCux/KefTi3xs/z8fKSlpSEsrHGJxwwZMuKl5Obzz+e8dNyaNd8YvXZ0dMInn0wx+gX/vCpVfPD11xuM3uvVy3iQ7b59h18qt3XrLqPXL9ZbVpUrV8GyZV+X+HmjRk1w+vRlo/dcXd0Mi9aVpFat2li3bjOnNnFFCUwpVq5cgcGDh6Bjx8JxLLVrByI5+QmioragW7d3DdPY0tIUkMlkhnIKhQJBQaWv3fDygDCG1WqVXOp3d/f4bytz4zs0aWkKQ7yiO0VFi0kV8fevieTkJ2VuHzEvXRUf5MycY/MYlojFRo60Ev4aMAHOVrqLYOp5ci1vif7V6wExdPCQCEs/2Gx1lq9tBP799zIaN26CRo2a2LopxAQ0BqYU+fn5EAiMp2gKBALDM8uqVatCKpXi4sULhs+VSiViYq4jJKQhABgW99Fq2f0FUhZlqf9FYrEYderUMSqj0+lw8eIFQxkfn6qQybwNY3+KxMc/MgwII9bHKLMhPnMKjDLbpjEsEYsNcW4Oql2/BHEu+1vvXJh6nlzLW6p/9frCmTPW+ipPyQsANG8ega++WmXrZhATUQJTirfeaoktWzbh1Km/kZT0GH/+eRzffbcDrVu3AVD4zPODDz7E5s3f4OTJE7h37y5mzfocMpkMrVoVHuPh4Ql7e3ucPXsaCoUC2dll/2G0Z89ujBgxtMTPy1I/AIwYMRR79uw2vO7ffyAOHNiPw4d/QlxcHBYuXIC8vDy88053Q9yBAwdhz55d+OOP3xAfH49169bg4cMH6N7dss81ScmEcffh3qMLhHHFr5pprRiWiMWGe9IjDPhiCNyTSl/vyBxMPU+u5W3Vv7a2Zs03mDBhkq2bQco5eoRUiqlTP8O6dWuwaNGXSE9Pg0wmQ69evTF8+EjDMYMGfYS8vDwsWDAP2dnZCA0Nw5o162FnZweg8DHRlCnTsGnTRmzYsA5hYY2wadO3Zao/IyPdMKC2JKXVDxQOys3IeDaXv2PHTkhPT8f69eugUKQiKCgIa9asN1oLoH//AVCpVFi+/CtkZmYiMDAI69ZthJ+fX5naTgghhFgKo6/g24Ompma/8valQMDAyckOOl35ekZbUTBM4SO3nJwCTlMFiTHRtavwaPcW0v/4G5qQUJvFsESsshAIGKSrtLi4/w98MK43dq3ehzd6tYOHRGjR7y9Tz5NreVPrVatVUCiewMurCsRiWr+JlB+v+t5kGEAqdSk1Bj1CIoSQCq6C/51KeMgc35OUwBDCI3qRGNoqPtCLXt723poxLBGLDZ1QhCwvb+iE1nkKbup5ci1var1FSzSoVNbZ1oSQsir6nhSa8G+YHiHRIySLokdIxJyKHiEdiU6CskADZzsRujT0sfgjJD7LzFQgL08JZ2cPSCR2Ft/4kpBX0ev1UKkKoFSmw8HBGW5uL6/lVNZHSDSIlxBSbjEMjH7hFvfLV/DfMc8vSqrXl7+pu7bi6lq4AqxSmV7KkYRYj4ODs+F7kytKYGxo9uyZyM7OxooVtB4BKRvhzRtwe78XMnfvh7ZuPZvFsESsFzEMoGYEyFE9W9JcKGCgAwOvB3fx0RfD8cuiTRA09kNagRbAs4zFSSKCGOa7q2rqeXItb47+ZRgGbm5ecHHxgFarKb0AIRYmFIpYb4NQHEpgSpGTk4N169bgxIk/kZ6ehqCgYEyZMs2wCydQ+Nfehg3rcODAfmRnZ6Nhw1DMmDHTsF9QUtJjdO36Nnbv3ougoGCzt7G0+ovzzz+XsX37Vty6dQupqXIsX77SsLaNKXGJZTEaNYRPksBoyr5juSViWCLWS7EZBjkqDU7cTkFuQeEvXi8XOzSq4QmBVgNXRQrEOh1yVRqcuSdHbn7hMY52hcvke0iEZhu8aup5ci1vzv4VCAQQCGgmEqk4aBBvKebNm4MLF85j/vwv8f33+/Hmm+EYNWo4UlKeGo7Zti0Ku3fvwowZX2Dbtp1wcHDAmDEjUVBgnYFzXOrPz89DYGAQpk+fYda4hJhbboEGyv++8lTaEo7RGo4pSnYIIRUbJTCvkJ+fjz///AMTJnyKxo2boFq1ahg5cjR8ff3www97ARTepdi16zsMHToMrVq1RmBgIObN+xJyuRx//fUnAKBr18J9lN5//z00ahSCYcM+Nqpn+/at6NChDVq3jsSiRV9CrS77X1tlqb84LVpEYsyYcWjTpq1Z4xJCCCHWQAnMK2i1Wmi1Wkgkxrdd7e3tcfXqFQDA48ePkZqaimbN3jR87uLigvr1G+DatWgAwI4dhbuIrl//DX777U8sW/Y/w7GXL19CYmICNm7cgrlzF+Dw4Z9w+PBPhs83bFiHLl06ldjGstTPhaXiEkIIIeZACcwrODk5ISSkITZv/gZyeQq0Wi2OHPkZ165FIzVVDgBQKFIBAJ6exlPBvLy8kJpauNuzh4cHAMDd3R1SqRRubm6G41xcXDFt2gz4+/vjrbdaIjLyLVy8eNHwubu7B3x9fUtsY1nq58JScYlptDUDkHHgCLQv7BJu7RiWiMVGRtUa2DF/C7J8rTMey9Tz5FreVv1LCB9QAlOK+fMXQq/Xo2PHdnjzzSbYs2cXOnZ8Gwxjnq4LCAgwLDYFAFKpFGlpzxKEfv3ex8aNm81SF+E/vbML1C0ioXcufY0ES8awRCw21I5OiG/QFGpHZ6vUZ+p5ci1vq/4lhA8ogSmFn58fNm+Owpkz53H06G/YsWMXNBqN4a6Il5cUAIySDgBQKBSQSl9eoOdFItGLE8EYVjMnTK3f2nGJaQRPkuC0YA4ET5JsGsMSsdhwSn2KVjtWwVGebJX6TD1PruVt1b+E8AElMGXk4OAImUyGrKwsnDt3Fi1btgYAVK1aFVKpFBcvXjAcq1QqERNzHSEhDQEAYnHhMuBarc7s7SpL/eUpLjGNQJ4Cx69XQCBPsWkMS8RiwzE9FS32b4FDunUeZ5p6nlzL26p/CeEDWgemFGfPnoFer0eNGjWQkJCAlStXoEaNGnjnnXcBFK5V8cEHH2Lz5m9QrVo1+PhUxfr1ayGTydCqVeG6Kh4enrC3t8fZs6dRqVIlSCQSuLiU7Zbwnj27ceLE8RIfI5WlfgAYMWIoWrdui3793gcA5ObmIiEh3vD548ePcefObbi6uqFKlSpljksIIYTYAiUwpVAqlVizZhWePn0KNzc3tGnTDmPGjDPcVQGAQYM+Ql5eHhYsmIfs7GyEhoZhzZr1sLOzA1D4mGjKlGnYtGkjNmxYh7CwRti06dsy1Z+RkY7ExMRXHlNa/QCQmJiIjIxnS4nfvHkDw4cPMbxeseIrAEC3bu9g7twFZY5LSHn04vYCtLUAIRUPJTCl6NChIzp06PjKYxiGwahRYzBq1JgSj+nRoxd69Ohl9F5RovC8KVOmGb0eOXI0Ro4cbXL9R44cM3rdpElT/PvvNZPjElLeSEQCCIRCo+0FzL21ACHE9iiBIYRHdB6eyOs/EDoP7pugmSOGJWKxke/qjqvteqDA1f2lz0RCgdH2AubYWsDU8+Ra3lb9SwgfMHpzbRZSTqWmZr/yry6BgIGTkx10OvrrzBIYpnAPlpycAuh01MGk7AQCBukqLY5EJ0H53/YA3q72aBEow+8xyVDmq196XdwxznYidGnoAw+JkL4HCeEBhgGk0tLHidIsJEL4JC8Pwtu3gLw828awRCwWhAX5kMbHQliQb50KTT1PruVt1L+E8AElMITwiOjeHXi+1Qyie3dsGsMSsdjwjL+PEeN7wv3RfavUZ+p5ci1vq/4lhA9YJTAbN25Er169EBYWhvDwcIwePRpxcXFGxwwYMABBQUFGX7NmzTI6JikpCcOHD0fDhg0RHh6OJUuWQKMx3kH2woUL6NGjB+rXr4/27dvjxx9/5HiK5dfs2TMxceIEWzeDEEII4R1Wg3gvXryI/v37o0GDBtBqtVixYgWGDBmCI0eOwNHR0XDce++9h/HjxxteOzg4GP5fq9VixIgRkEql2LNnD1JSUjBt2jSIxWJMnDgRAJCQkIARI0agX79+WLZsGc6dO4eZM2dCJpMhMjLS1HNmJScnB+vWrcGJE38iPT0NQUHBmDJlGurVq284ZvbsmTh8+JBRufDw5li7dgMAICnpMbp2fRu7d+9FUFCw2duo1+uxYcM6HDiwH9nZ2WjYMBQzZsxEtWol7xPzzz+XsX37Vty6dQupqXIsX74SrVsbr++yYcM6/PbbMSQnJ0MsFqNOnboYM2YcGjQIMfs5EEIIIWywSmC2bNli9Hrx4sUIDw/HjRs30LRpU8P79vb2kMlkxcY4ffo0YmNjERUVBalUijp16mDChAlYtmwZxo4dC4lEgj179sDX1xfTp08HULhf0D///IOtW7daPYGZN28O7t+Pxfz5X0Im88bRoz9j1Kjh2LfvALy9KxmOa968BebMmW94/eIO1pa0bVsUdu/ehXnzFvy34NwajBkzEvv2HSxxzZb8/DwEBgbh3Xd7YPLkT4s9pnr16pg2bQaqVvVFQUE+du7cgTFjRuKnn36GB82KIGbG/Ld2y7PXzCuOJoS87kwaA5OdnQ0ARrsrA8Dhw4fRrFkzdO3aFcuXL0fecwPQrl69isDAQEilUsN7ERERUCqViI2NNRwTHh5uFDMiIgJXr141pbms5efn488//8CECZ+iceMmqFatGkaOHA1fXz/88MNeo2MlEgmkUqnhy9XV1fBZ165vAwDef/89NGoUgmHDPjYqu337VnTo0AatW0di0aIvoVary9xGvV6PXbu+w9Chw9CqVWsEBgZi3rwvIZfL8ddff5ZYrkWLSIwZMw5t2rQt8Zi33+6CZs3ehK+vLwICamHixClQKpW4e/dumdtHzIxhoJdICn/b2zKGmWMxDKBmBEhXaQ1fmWotdCghLsNAIxKb5xzK2ECTzpNreXNeK0IqGM7rwOh0OixcuBCNGjVCYGCg4f2uXbvCx8cH3t7euHPnDpYtW4YHDx5gzZo1AIDU1FSj5AWA4bVcLn/lMUqlEvn5+bC3t+fabFa0Wi20Wu1Ld1Ps7e1x9eoVo/cuX76Mtm1bwtXVFU2bvoHRo8fB3d0dALBjxy4MGPAB1q//BgEBtYxW8b18+RKkUik2btyChIR4TJ8+BUFBQejZszeAwsc4hw8femkhuiKPHz9GamoqmjV70/Cei4sL6tdvgGvXotGx49vm6Aqo1Wr8+OM+ODu7IDAwyCwxCXuaBg2Rmphq8xjmjsUwDHJUGpy4nYLc/6ZMe7nYoVENz2J/ectr1cWSff/A29U6PwtMPU+u5c15rQipaDgnMHPnzsW9e/ewa9cuo/f79u1r+P+goCDIZDIMHjwY8fHxqFatGveW2oCTkxNCQhpi8+ZvULNmTXh6euHYsV9w7Vo0/Pz8DMc1b94Cbdq0hY9PVSQmJmLNmq8xbtxobN26A0KhEB4eHgAAd3f3lxIzFxdXTJs2A0KhEP7+/oiMfAsXL140JDDu7h6Gna+Lo1AU/nDz9DTeIdrLywupqaZvdPf33yfx2WdTkZ+fD6lUhvXrNxrOhxBzyy3QGNZ8cbSjdTYJISXj9Ahp3rx5+Ouvv7Bt2zZUrlz5lcc2bFi4c/GjR48AFN5JSU01/oui6HXRuJmSjnF2drba3Zci8+cvhF6vR8eO7fDmm02wZ88udOz4NhjmWdd17Pg2WrZsjdq1A9G6dRusWrUGN27E4PLlS6XGDwgIgFAoNLyWSqVIS3uWePTr936JGzlaQ9OmTbF79w+IitqO5s1bYNq0yUbtI9YlvHsH7m0jIbzLfVqtOWJYIhYbHvH3MWTie3B/FGuV+kw9T67lbdW/hPABqwRGr9dj3rx5+P3337Ft2zajuxAluXXrFoBnyUloaCju3r0LheLZL8GzZ8/C2dkZtWrVMhxz/vx5ozhnz55FaGgom+aahZ+fHzZvjsKZM+dx9Ohv2LFjFzQazSvvivj6+sLd3QMJCQmlxheJXvwrk2G13LmXV+EdnReTCoVCAanUq7girDg4OKJatWoICWmI2bPnQigU4eDBAybHJdww+XkQX48Gk899YTNzxLBELDZEBfmoHHcbwoICq9Rn6nlyLW+r/iWED1glMHPnzsWhQ4ewfPlyODk5QS6XQy6XIz+/cDXM+Ph4rF27FjExMUhMTMTx48cxbdo0NG3aFMHBhdOHIyIiUKtWLUydOhW3b9/GqVOnsHLlSvTv398w1qRfv35ISEjA0qVLcf/+fezcuRO//PILBg8ebN6zZ8HBwREymQxZWVk4d+4sWrZsXeKxT58mIzMzAzJZYXJRNOZFq9WZvV1Vq1aFVCrFxYsXDO8plUrExFxHSEhDs9en1+ugUqnMHpcQQghhg9VD5t27dwMoXKzueYsWLULPnj0hFotx7tw5bN++Hbm5uahSpQo6dOiA0aOf7aYsFAqxYcMGzJkzB3379oWDgwN69OhhtG6Mn58fNm7ciEWLFmH79u2oXLkyFixYYPUp1ABw9uwZ6PV61KhRAwkJCVi5cgVq1KiBd955FwCQm5uLjRvXo23bdpBKpUhISMCqVf+Dn181hIe3AAB4eHjC3t4eZ8+eRqVKlSCRSODiUvo+DwCwZ89unDhxvMTHSAzD4IMPPsTmzd+gWrVq/02jXguZTIZWrZ6t6zJixFC0bt0W/fq9b2h3QkK84fPHjx/jzp3bcHV1Q5UqVZCXl4vNmzehZctWkEplyMjIwN69hev2tG/fgVNfEkIIIebCKoG5c+fVz2GrVKmC7777rtQ4VatWxaZNm155TLNmzXDw4EE2zbMIpVKJNWtW4enTp3Bzc0ObNu0wZsw4w10VgUCAe/fu4eefDyE7OxsymTfefDMco0ePNdxREolEmDJlGjZt2ogNG9YhLKwRNm36tkz1Z2SkIzEx8ZXHDBr0EfLy8rBgwTxkZ2cjNDQMa9asN1oDJjExERkZ6YbXN2/ewPDhQwyvV6z4CgDQrds7mDt3AQQCIR4+fIiff56EjIx0uLm5o169etiyZSsCAmqVrfMIIYQQC6HdqGk3aoui3ajNi8lIh/jvv6B+qxX07txmg5kjhrljlWXn6eff+/vcHcgunoYyoiWaNq5l8d2oTT1PruXNea0I4Yuy7kZN8xQJ4RG9uwdU7/SweQxLxGKjwMUNt1t0gLeLdWYlmnqeXMvbqn8J4QPajZoQHmFSUuCwfg2YlBSbxjBHrMK7cwwEAob1tgGO6al446ftcEizziJvpvYZ1/LmvFaEVDSUwBDCI8LkJDjPngFhcpJNY5ga68WtA165bUAxnFKfon3UMjimPmXbZE5M7TOu5c15rQipaOgREiHE6l7cOuBV2waYg+C/jSIFz/3JptfradwbITxGCQwhxGaKtg6w5LYBEpEAAqEQaQVaAM8yFieJCGLQ4H1C+IoSGEJIhSYSCpCr0uDMPTly85/ts9Q62BseEiGrla8JIeUHJTAGDBiGfpCZn2UeCbyudC6uKOj4NnQurjaNYYlYbKicXHC3aUuonMq2ICQA5BZoDVO02TL1PLmWt1X/EsIHr30Co9frodPpIBAIQL9sLaNwjR1KDs1B518TWTu+t3kMS8RiI9OnGn74fDW8Xa0zjdrU8+Ra3lb9SwgfUAKjB3JzVayncZKyo8GSZqRWg8nMhN7NDfhvNWibxLBELBYEGjUcM9PAOMqsU6Gp58m1vI36lxA+oGnUKExidDo9fVnoi5IX8xHdugFp3ZoQ3bph0xiWiMWG14O7+HRQK3jG3bVKfaaeJ9fytupfQviAEhhCCCGE8A4lMIQQQgjhHUpgCCGEEMI7lMAQQgghhHde+1lIhPCJpl4DpN5PhN7RyaYxLBGLjdSawfhq11l4yDysUp+p58m1vK36lxA+oASGED4RCqE3dVEzc8SwRCwW9EIhVI7O0AuF1qnQ1PPkWt5G/UsIH9AjJEJ4RBgXC7f3ukMYF2vTGJaIxYb744foN2ckXBMfWqU+U8+Ta3lb9S8hfEAJDCE8wiiVkPz1Jxil0qYxLBGLDXFuDgKunoU4N8cq9Zl6nlzL26p/CeEDeoRECHktCRiAYRgI/vszjlaMJoRfKIEhhLx2JCIBBEIh0gq0AAqzFieJCGLoKIkhhCcogSGEvHZEQgFyVRqcuSdHbr4GjnYitA72hodESBuPEsITlMAQwiNaH19kL1oGrY+vTWNYIhYbSlkVHBs+AzneVUyKk1ughbJAU+pxpp4n1/K26l9C+IASGEJ4RC+VIn/IcJvHsEQsNvLcPfFP537wdrW3Sn2mnifX8rbqX0L4gGYhEcIjTHoa7H7YAyY9zaYxLBGLDbvsDNT/62fYZWVYpT5Tz5NreVv1LyF8QAkMITwiTIiH65jhECbE2zSGJWKx4Zr8GO+unAHn5MdWqc/U8+Ra3lb9SwgfUAJDCCGEEN6hBIYQQgghvEMJDCGEEEJ4hxIYQnhE7+gEdeOmJu1ObI4YlojFhtreEYlBIdDYO1ilPlPPk2t5W/UvIXxA06gJ4RFtrdrI+OW4zWNYIhYbGX7+2LbkO6tNozb1PLmWt1X/EsIHdAeGEEIIIbxDCQwhPCK6dhUyb1eIrl21aQxLxGJDdu8GPu8eAq+7N6xSn6nnybW8rfqXED6gBIYQQgghvEMJDCGEEEJ4hxIYQgghhPAOJTCEEEII4R2aRk0Ij2gCg6E4fwU6n6o2jWGJWGykVa+Fdet/hn2N6lapz9Tz5FreVv1LCB9QAkMIn9jbQ1czwPYxLBGLBa3EDplVqsFbYmedCk09T67lbdS/hPABPUIihEcEjx7CZdRQCB49tGkMtrEYBhAIGMMXwzAm1euanIh3/vcZXJ4kmBSnrEztM67lzXmtCKloKIEhhEcEmRmw378XgswMm8ZgE4thADUjQLpKa/jKVGuhA/ckxi47Ew1OHoEkO4tzDDZM7TOu5c15rQipaOgREiHEohiGQY5KgxO3U5BboAEAeLnYoVENz8LshhBCOKAEhhBiFbkFGij/S2Ac7ehHDyHENPQIiRBCCCG8QwkMITyiq1QZOZOnQ1epsk1jWCIWGzmeMvzddyRyvWRWqc/U8+Ra3lb9Swgf0H1cQnhEV6kycqfOsHkMS8RiI9fLG6feHw1vV3ur1GfqeXItb6v+JYQP6A4MITzCZGdB/OcfYEyYfWOOGJaIxYYkR4maV85AnKO0Sn2mnifX8rbqX0L4gBIYQnhE+CAO7v16QvggzqYxLBGLDbekR3h/7ii4Pn5klfpMPU+u5W3Vv4TwASUwhBBCCOEdSmAIIYQQwjuUwBBCCCGEdyiBIYRH9BI7aGv4Q2/CJobmiGGJWGxoxRKkVfaDTiyxSn2mnifX8rbqX0L4gFUCs3HjRvTq1QthYWEIDw/H6NGjERdnPLisoKAAc+fORbNmzRAWFoZx48YhNTXV6JikpCQMHz4cDRs2RHh4OJYsWQKNRmN0zIULF9CjRw/Ur18f7du3x48//sjxFAmpOLTBdZB2MRra4Do2jWGJWGyk1aiN9RuOIN2/tlXqM/U8uZa3Vf8SwgesEpiLFy+if//+2Lt3L6KioqDRaDBkyBDk5uYajlm4cCFOnDiBlStXYseOHUhJScHYsWMNn2u1WowYMQJqtRp79uzB4sWLceDAAXz99deGYxISEjBixAg0a9YMP/30EwYNGoSZM2fi1KlTZjhlQgh5mYAp3LfJeNdsW7eKEFISVgnMli1b0LNnT9SuXRvBwcFYvHgxkpKScOPGDQBAdnY29u/fj+nTpyM8PBz169fHwoULceXKFVy9ehUAcPr0acTGxuKrr75CnTp10LJlS0yYMAE7d+6ESqUCAOzZswe+vr6YPn06AgIC8OGHH6Jjx47YunWrWU+eEL4R3oiBVx1/CG/E2DSGJWKxIY27g08GtoTn/dtmiScRCSAQCpFWoDXaNVvNCMAwpp8n1/K26l9C+MCkMTDZ2dkAADc3NwBATEwM1Go1mjdvbjgmICAAPj4+hgTm6tWrCAwMhFQqNRwTEREBpVKJ2NhYwzHh4eFGdUVERBhiEPK6YrQaCBQKMFpN6QdbMIYlYrGt1ykrHYxWa5Z4IqEAuSoNTtx5iiPRSTgSnYQTt1OQo9KAYRiTz5NreVv1LyF8wHkrAZ1Oh4ULF6JRo0YIDAwEAKSmpkIsFsPV1dXoWC8vL8jlcsMxzycvAAyvSztGqVQiPz8f9vbWWT6cEPJ6yS3QGnbMJoSUb5wTmLlz5+LevXvYtWuXOdtDCCGEEFIqTo+Q5s2bh7/++gvbtm1D5crPdkmVSqVQq9XIyjLet0OhUEAmkxmOeXFWUtHr0o5xdnamuy+EEEIIYZfA6PV6zJs3D7///ju2bdsGPz8/o8/r168PsViMc+fOGd6Li4tDUlISQkNDAQChoaG4e/cuFAqF4ZizZ8/C2dkZtWrVMhxz/vx5o9hnz541xCDkdaWpWQvpR36HpmYtm8awRCw2MnxrYOviHcj0q2GV+kw9T67lbdW/hPABq0dIc+fOxc8//4x169bBycnJMGbFxcUF9vb2cHFxQa9evbB48WK4ubnB2dkZCxYsQFhYmCH5iIiIQK1atTB16lRMmTIFcrkcK1euRP/+/SGRFC5K1a9fP+zcuRNLly5Fr169cP78efzyyy/YuHGjec+eEL5xdoamaTPbx7BELBbUDk54HNwQ3g5WuiNr6nlyLW+j/iWED1jdgdm9ezeys7MxYMAAREREGL6OHj1qOGbGjBlo1aoVxo8fjw8//BBSqRSrV682fC4UCrFhwwYIBAL07dsXU6ZMQffu3TF+/HjDMX5+fti4cSPOnj2Ld999F1FRUViwYAEiIyPNcMqE8Jcg6TGcvvgMgqTHNo1hiVhsOMuT0e7br+AkT7ZKfaaeJ9fytupfQviA1R2YO3fulHqMnZ0dZs+ejdmzZ5d4TNWqVbFp06ZXxmnWrBkOHjzIpnmEVHiCVDkcN65FQZ++0PlUtVkMS8RiwyFDgWaHduBx555Wqc/U8+Ra3lb9Swgf0F5IhBBCCOEdSmAIIYQQwjuUwBBCCCGEdyiBIYRHdJ5eyPtoKHSeXjaNYYlYbOS7eeDy232R7+ZhlfpMPU+u5W3Vv4TwAeeVeAkh1qfz9YNyyQqbxygtFvPfzs6F/2/+LZ2zvX3w64jP4e1qnWnUpvYZ1/LmvFaEVDR0B4YQPsnNhejaVSA317YxXhGLYQA1IzDs6Jyp1kIH8yYxovw8VL5/E8L8PLPGLZGpfca1vDmvFSEVDCUwhPCIKPYuPNq9BVHsXZvGeFUshmGQo9LgxO0UHIlOwpnYVGh0usLMxkw8EuIwZFI/uMfHmS3mq5jaZ1zLm/NaEVLRUAJDCLGI3AINlAUa5Km0tm4KIaQCogSGEEIIIbxDCQwhhBBCeIcSGEJ4RM8IoHN2gZ7h/k/XHDEsEYtVvQIBChycoBeYf4ZTsfWZeJ5cy9uqfwnhA5pGTQiPaBuEQBFn2sZ+5ohhiVhspAbUwbLd56w2jdrU8+Ra3lb9SwgfUFpPCCGEEN6hBIYQHhHeuQ2PyDcgvHPbpjEsEYsNz0exGD6uB9wfxlqlPlPPk2t5W/UvIXxACQwhPMIU5EN05zaYgnybxrBELDaEqgLIEu5DqCqwSn2mnifX8rbqX0L4gBIYQgghhPAOJTCEEEII4R1KYAghpASC/zaltMSGlIQQ09A0akJ4RFu9BjK374G2eg2bxrBELDYyq/hh74xVyPPxs1gdEpEAAqEQaQVaCHz8kBu1CwL/mhAxgF7PLhbXfrJV/xLCB5TAEMIjejd3qDp1tnkMS8RiQ+XsintvtIa3s+XWgREJBchVaXDmnhy5+Ro4Vm+C1g7O8GAY6FlmMFz7yVb9Swgf0CMkQniEefoUDquWg3n61KYxLBGLDcc0OZrv2wyHNLnF68ot0EKfnIy629dBmMLtPLn2k636lxA+oASGEB4RPn0C5y/nQvj0iU1jWCIWG06KFLT+7ms4pqZYpT6XtBS02LoSomRu58m1n2zVv4TwASUwhBBCCOEdSmAIIYQQwjuUwBBCCCGEd2gWEiE8onN1Q0G37tC5utk0xoux4O4OgaBwrRRrrJlS4OyKW83bQ+XsYvG6ACDfyRX3IjpA7ObOqTzXPjfntSKkoqEEhhAe0dXwR9aW7TaPUUTv74+0qO+Qo9IAKi0AQChgoINlk5isKn74cepyeLtabhr18zIq++Lo5yvRpboPp/Jc+9yc14qQioYSGEL4RKWCIFUOnVQGSCS2i/EfRq1GflIyTir0UOoKn0h7udihUQ1PwIJ3YgRqFVxSn0LgwC2hYF+fGs5ZqYBKCkgc2Afg2udmvFaEVDQ0BoYQHhHdvgmv0DoQ3b5p0xhFhLduwr9JPTjcvQ1lgQbKAg3y/rsTY0leD+9h/NAO8Hhwz+J1AYB3/D0MGdgGdhz7jGufm/NaEVLRUAJDCCGEEN6hBIYQQgghvENjYAghpIyY/3anFjz3p59er2e9uSMhxHSUwBBCSBkV7U4NPMtYnCQiiKGjJIYQK6MEhhAe0dQPgTxBDojFNo1RRNsgBLEPnkJ+Uw6odSbHKyt5QB0s/uEyvDycrVJfsn8wthyLQXhgFZy58xS5+RoAgKOdCK2DveEhEb5yh2qufW7Oa0VIRUMJDCF8IhAAdna2j/FiLIEAgPUSGAgE0IolMHqWY+H6dJLC+nILtFAWaFiX59Tn5rxWhFQwNIiXEB4R3r8Ht+6dIbzPffqwOWIUEcTeQ9VeXeCe+NDkWGy4Jz7Ah59/DLeEB1apz/PxQ3T59ENI4mI5lefa5+a8VoRUNJTAEMIjTE4OJGdPg8nJsWmM52M5nDsDcX6uybHYEOflovqNyxDlWadeSX4ufKIvQsCxz7j2uTmvFSEVDSUwhBBCCOEdSmAIIYQQwjuUwBBCCCGEdyiBIYRHtFX9kL1iNbRV/Wwao4jO1xcpX32NbFkVk2Oxke3tgyNjZkNZyTr1Zsqq4O9JC6Cu6supPNc+N+e1IqSioWnUhPCI3ssL+R8OsnmMZ7GkyOo/EPnRSQDbqcUmyHfzwNX2veDtam+V+vJcPXCny3uQenoBScmsy3Ptc3NeK0IqGroDQwiPMAoF7L/bBkahsGmMZ7FS4bpzO+wz002OxYZ9ZjpCf98Pu8w0q9TnkJWOoCN7IUzj1mdc+9yc14qQioYSGEJ4RPg4AS4Tx0H4OMGmMYoIEhPhPWU8XORPTI7FhktKErqsnQvnp9ap103+BG8tnwnx40RO5bn2uTmvFSEVDSUwhBBCCOEdSmAIIYQQwjuUwBBCCCGEdyiBIYRH9E5OUDWPgN7JyaYxno+VF94CantHk2OxoXZwxKN6TaBxsE69KntHJDV8AzqOfca1z815rQipaGgaNSE8og2ojcyDR20eo4iuVm083n8EGVaeRp3h64/vvvzWatOo06rWwJH/fYcWNWVADPtp1Fz73JzXipCKhu7AEMInOh1QUFD4X1vGsEQslvUK1Srr1avTQaAyoT6u/WSr/iWEByiBIYRHRDHXIPOTQRRzzaYxigivX0Mt/0qQxd02ORYbsvu3ML1PE3jF3rJKfZUf3MaQTvVhf+M6p/Jc+9yc14qQioYSGEIIIYTwDiUwhBBCCOEd1gnMpUuXMHLkSERERCAoKAh//PGH0efTp09HUFCQ0deQIUOMjsnIyMCkSZPQqFEjNGnSBDNmzEBOTo7RMbdv38YHH3yABg0aoGXLlti0aROH0yOEEEJIRcR6FlJubi6CgoLQq1cvjB07tthjIiMjsWjRIsNriURi9PnkyZMhl8sRFRUFtVqNGTNmYNasWVi+fDkAQKlUYsiQIQgPD8fcuXNx9+5dzJgxA66urujbty/bJhNCCCGkgmGdwLRs2RItW7Z85TESiQQymazYz+7fv49Tp05h3759aNCgAQBg5syZGD58OKZOnYpKlSrh0KFDUKvVWLhwISQSCWrXro1bt24hKiqKEhjyWtME14Xi6i3opMX/+7JGDIYBGIYBAOjq1sODyzegSFIDVpwoo6hRG19v/g1Ofj5WqS+lWm3s+v5vhAYFAXfZbyDJtc/Ncb0JqagsMgbm4sWLCA8PR8eOHTF79mykpz/bqfbKlStwdXU1JC8A0Lx5cwgEAly7VjjS/urVq2jSpInRnZuIiAg8ePAAmZmZlmgyIfwgkUDnUxV44a6mtWIwDKBmBEhXaZGu0iKTEULt4wud2IT2cKATS5AtrWy1enViMXJklbn3O9c+N8f1JqSCMnsCExkZiSVLlmDr1q2YMmUKLl26hGHDhkGr1QIAUlNT4enpaVRGJBLBzc0NcrnccIxUKjU6puh1amqquZtMCG8IHj6A65CBEDx8YJMYDMMgR6XBidspOBKdhOiT/6LysIFwTea2SzNXrk8S0HPpJLgkxVulPvfkRLSdMx7iRw85lefa5+a43oRUVGZPYLp06YK2bdsiKCgI7dq1w8aNG3H9+nVcvHjR3FUR8toRZGXC7vBBCLK434k0R4zcAg2UBRro0jPgeuQn2CmzOMfiwk6ZhTpnf4dEmW2V+uxzslDz72MQcuwzrn1ujmtFSEVl8WnUfn5+8PDwwKNHjwAU3klJSzN+hqzRaJCZmWkYNyOVSl+601L0+sU7M4QQQgh5/Vg8gUlOTkZGRoYhOQkLC0NWVhZiYmIMx5w/fx46nQ4hISEAgNDQUFy+fBlqtdpwzNmzZ+Hv7w83NzdLN5kQQggh5RzrBCYnJwe3bt3CrVuFS3gnJibi1q1bSEpKQk5ODpYsWYKrV68iMTER586dw+jRo1G9enVERkYCAAICAhAZGYkvvvgC165dwz///IP58+ejS5cuqFSpEgCgW7duEIvF+Pzzz3Hv3j0cPXoU27dvx0cffWTGUyeEEEIIX7GeRh0TE4OBAwcaXhet99KjRw/MmTMHd+/excGDB5GdnQ1vb2+0aNECEyZMMJpRtGzZMsyfPx+DBg2CQCBAhw4dMHPmTMPnLi4u2LJlC+bNm4eePXvCw8MDo0ePpinU5LWnrVQFys9nQ1upik1jFMmVeuPp9C+Q4+Vtciw2cry8ceLD8ciVWqfebE9vXBw6EW6VKgMpetblufa5Oa8VIRUN6wSmWbNmuHPnTomfb9mypdQY7u7uhkXrShIcHIxdu3axbR4hFZq+UiXkTZhk8xhF8jxlUIydiNyYZCBfXXoBM8n1lOFs76HwdrW3Sn05HlJEfzASLbxlQEoy6/Jc+9yc14qQiob2QiKER5jMDEiOHQWTmWHTGEUkyiw4/3YUEivPQpIos1D74gmr1WunzEK1M8ch4LgOFdc+N+e1IqSioQSGEB4RPnoIt4H9IOS4Hom5YhRxSUpAtY/7w+1Jgsmx2HB7koD3Fk6AS5J16vV4moiOX4yCJP4hp/Jc+9yc14qQioYSGEIIIYTwDiUwhBBCCOEdSmAIIYQQwjuUwBDCI3o7e2iCgqG34z77xhwximgldsgPDIZWYmdyLLb1yv0CrFavRmKH9Oq1oLPjVh/XPjfntSKkomE9jZoQYjvaoGCknzJtXzFzxCiSUaMW4v48hzQrT6NOq14L36w+YLVp1Kl+AdgXdRQtAmVADPtp1Fz73JzXipCKhu7AEEIIIYR3KIEhhEeE16/Bq2ZVCK9fs2mMIp6xNxEUXA3S+7dMjsWG9P4tTH4/HJ6xN61SX6W42xjUNQx2N65zKs+1z815rQipaOgREiE8wuh1ECizweh1No1hiKXTQ6jMBqMzPRa7enWwy8sBo2O/rD+n+vQ6SHJzCs+TMf5MwAAMw0Dw3J+Der0eer1xeS59bs5rRUhFQwkMIYRwJBEJIBAKkVagBfAsY3GSiCCGziiJIYSYFyUwhBDCkUgoQK5KgzP35MjN1wAAHO1EaB3sDQ+JEHrKYAixGEpgCCHERLkFWigLNLZuBiGvFRrESwiPaGoFIv2Pv6GpFWjTGEUyqtVE3C9/Id2vpsmx2Ej3q4kty/cgo5p16k319cePGw6goFZtTuW59rk5rxUhFQ3dgSGETxwdoQkJtVoM5r8Bqs9eG49g1do7ID+wGjRWXgdGY++A5IC68La3zjowGjsHKALrQe/gCIDDDthcr5s5rjchFRTdgSGERwSJCXCeNhGCRO67MJc1BsMAakaAdJXW8JWp1kL33DQcp6dJqPz5ZLikJHFuDxcuKUnouPFLOD21Tr2u8idovmoORI+59TvX62aO601IRUUJDCE8IkhTwCFqMwRpCovHYBgGOSoNTtxOwZHoJByJTsKZ2FRodLrC7AaAfWY6PLdtgX1mOuf2cGGfmY4mv3xvtXods9JR76ddEKWlcSrP9bqZ43oTUlHRIyRCyCvlFmgMA1Qd7ehHBiGkfKA7MIQQQgjhHUpgCCGEEMI7lMAQwiM6qQy5I8ZAJ5XZNEaRfA8vKIaNRp67l8mx2Mhz98KFdwYg38M69ea4eeJ678HQeEk5lefa5+a8VoRUNPRAmxAe0flURc78RTaPUSRHVhlPZ38JpZWnUStllfHHx1Pg7WqdadTZ0so4P3oGWvjIgLRk1uW59rk5rxUhFQ3dgSGET5RKiC5dAJRK28b4jygvBw7/XIQ4L8fkWGyI83JQ9XY0RFaqV5yXC+8bV8DkcOwzrn1uxmtFSEVDCQwhPCKKi4VHl/YQxcXaNEYRt4SH8H+3I9wTH5ociw33xIcYPH0A3BKsU69X0kO8O64v7OLucyrPtc/Nea0IqWgogSGEEEII71ACQwghhBDeoQSGEEIIIbxDCQwhPKIXiqDz8oJeyH0CoTliPIslhMbTPLHY1StCjqsH9EKhVerTCUXIc+NeH9c+N+e1IqSioX8VhPCItl59KG49sHmMImkBwbh7LRapVp5GnVozCCu3n7TaNOqUGoH47sAFtAiUATHsp1Fz7XNzXitCKhq6A0MIIYQQ3qEEhhAeEd6+Bc83GkJ4+5ZNYxTxeHAPtVo0gufDeybHYsPz4T2MGtkFHg+sU680PhbvfdgOdnfK1mcCpnA3b4Gg8Et0h1ufm/NaEVLR0CMkQniEURVA+PABGFWBTWMUEahVkDx6AKFaZXIsNoRqFTyTEyCwUr0itQpuSfFQqFQA8+pjJSIBBEIh0gq0APQAADeVmlOfm/NaEVLRUAJDCCFmJBIKkKvS4Mw9OXLzNXC0E6GTWmPrZhFS4VACQwghFpBboIWy4L/EpZS7NoQQ9mgMDCGEEEJ4hxIYQnhE618TGXt+hNa/pkViMAwMA08ZpvTbBllVq+PRd/uQ6VOdc3u4yPSpjt2z1yOrqnXqTatSDb8s2QJVDX9O5VX+NZG19wDr62aO601IRUWPkAjhEb2LK9Rt2lkkBsMAakaAHFXhYw+hgIGulGcfaidn5IS1hcrK68ConJwRF9YC3k7WWQdG5eiMxKaRqO7iCiCXdfmiPtfr9JzKEUJeRndgCOERwdNkOC5dCMFT9ouplRaDYRjkqDQ4cTsFR6KTcCY2FRqdrjCzKYGDIgWy5YvhqEjh3B4uHBUpiNy9Dg5Wqtc5TY5GW7+GiGO/C58mw2EJ++tmjutNSEVFCQwhPCJ4mgynZYtNTmBeFSO3QANlgQZ5Km2psRwVcsj+twROaXLO7eHCKU2Ot77fAEeFdep1Tpej8fY1EKU85VRe9DQZjl8t4pTAmHq9CamoKIEhhBBCCO9QAkMIIYQQ3qEEhhBCCCG8QwkMITyic3NHfq/3oHNzt2mMIioXV2T06IMCFzeTY7FR4OKG6y27QOXiapX68pxdca/dO9By7DOtuzsKevdl3efmvFaEVDQ0jZoQHtFVr4Hs9ZttHqNIdhU/JK3+BllWnkadVdkXhz5dBG9X60yjzqzki79mLEOLajIghv2AWk21GlBu2Awdy2nU5rxWhFQ0dAeGED7Jz4cg7j6Qn2/bGP8RqgogfhAHoZU3GxSqCuDxJN5q9QpVBXB9/AgMxz5juPa5Ga8VIRUNJTCE8Ijo7m14vRkG0d3bNo1RxP1hLGpHNobno1iTY7Hh+SgWo0d1hftD69QrS7iPvgPaw+7eHU7lJXdvw+ONUNZ9bs5rRUhFQwkMIYQQQniHEhhCCCGE8A4lMIQQQgjhHUpgCCGEEMI7NI2aEB7RhIRCnpJl8xhFFIH1cDMxHXIrT6OW166HLw9es9o06uSAutj05120COQ2jbogJBSK1GzW06jNea0IqWjoDgwhhBBCeId1AnPp0iWMHDkSERERCAoKwh9//GH0uV6vx6pVqxAREYGQkBAMHjwYDx8+NDomIyMDkyZNQqNGjdCkSRPMmDEDOTk5Rsfcvn0bH3zwARo0aICWLVti06ZN7M+OkApGGHsP7m+3hTD2nk1jFHGLj0ONdzrAPeGBybHYcE94gEHTPoRbfJxV6vN8/ADvjH0Pkvvc+kwcew+undqw7nNzXitCKhrWCUxubi6CgoIwe/bsYj/ftGkTduzYgTlz5mDv3r1wcHDAkCFDUFDwbMGpyZMnIzY2FlFRUdiwYQMuX76MWbNmGT5XKpUYMmQIfHx88OOPP2Lq1KlYs2YNvv/+ew6nSEjFweTmQPzPJTC5OaUfbMEYRUT5eXD89xLE+bkmx2JDnJ8L3zvXIMrPs0p9kvw8VLp5FYJcbucpyM2B+DL7PjfntSKkomE9BqZly5Zo2bJlsZ/p9Xps374do0aNQrt27QAAS5cuRfPmzfHHH3+gS5cuuH//Pk6dOoV9+/ahQYMGAICZM2di+PDhmDp1KipVqoRDhw5BrVZj4cKFkEgkqF27Nm7duoWoqCj07dvXhNMlhBDrY5ii/zIQCApf6PV66NkNiSGEPMesY2ASExMhl8vRvHlzw3suLi5o2LAhrly5AgC4cuUKXF1dDckLADRv3hwCgQDXrl0DAFy9ehVNmjSBRCIxHBMREYEHDx4gMzPTnE0mhBCLkogEEAiFAIBstRbpqsIvNSMwJDaEEPbMOgtJLpcDALy8vIze9/LyQmpqKgAgNTUVnp6exo0QieDm5mYon5qaCl9fX6NjpFKp4TM3N+vufEsIIVyJhALkF2gBAKfupUKuT4KjnQitg73hIRFCT7dhCOGEZiERwiNav2rIWvsNtH7VbBqjiLJyVTxetQFZlauaHIuNrMpV8dMnC6G0Ur0Z3lVx4rOvoPLl1mcq32o4NmUJnrhXgrJAg9wCTZnKmfNaEVLRmDWBkclkAACFQmH0vkKhMNxBkUqlSEtLM/pco9EgMzPTUF4qlRru2BQpel0Uh5DXkd7DEwV9+kHv4Vn6waXEgKcnBALG8MVweJ5R4OqOzF59UeDizrk9XBS4uCOmVVcUuFqn3nwXN8S2fxc6Dw9O5XUeHrjT9h3ku7C7e2yO601IRWXWBMbX1xcymQznzp0zvKdUKhEdHY2wsDAAQFhYGLKyshATE2M45vz589DpdAgJCQEAhIaG4vLly1Crny2MdfbsWfj7+9PjI/JaY1JTYb/lGzAvJPhsYzh8+w20CoVhPEa6SotMtRY6sEti7DPS4LF1Exwy0ko/2IwcMtLQ+Oge2FupXsfMNNQ9+B2ECm79LlSkIuTQTjhmsmuvOa43IRUV6wQmJycHt27dwq1btwAUDty9desWkpKSwDAMBg4ciPXr1+P48eO4c+cOpk6dCm9vb8OspICAAERGRuKLL77AtWvX8M8//2D+/Pno0qULKlWqBADo1q0bxGIxPv/8c9y7dw9Hjx7F9u3b8dFHH5nx1AnhH2FSIlw+mwxhUqJJMZynT4bq4SOcuJ2CI9FJOBKdhDOxqdDodGAzstQp5QmqzJwKZ/kTzu3hwln+BJ2+WQinFOvU65qajBZfz4M46TGn8uKkx2i9bgFcU9mt4muO601IRcV6EG9MTAwGDhxoeL1o0SIAQI8ePbB48WIMGzYMeXl5mDVrFrKystC4cWNs3rwZdnZ2hjLLli3D/PnzMWjQIAgEAnTo0AEzZ840fO7i4oItW7Zg3rx56NmzJzw8PDB69GiaQk2ImeUWaKD8bzyGox3tLEII4Q/WP7GaNWuGO3fulPg5wzCYMGECJkyYUOIx7u7uWL58+SvrCQ4Oxq5du9g2jxBCCCGvAZqFRAghhBDeoQSGEB7ROztD1aoN9M7OpsVo3RY6ZxeT26N2dIKyZRuoHZ1MjsW23vuhza1Wb4GDExKbREDnxK3fdU7OeNSoBQoc2LXXHNebkIqKHnoTwiPamrWQufegyTGyfzgItUoLRCeZFCvLtwbid+5HRkwykK8uvYCZZFStgT1zNsDb1d4q9aX7VMcvS79Fi5oyIIbdQFwAUNUMwJGFm6Fk2UfmuN6EVFR0B4YQPtFqwWRnAVqtbWP8h9FqIcjOAmOGWGzrleQqrVYvo9VCnKPk3mdaLSQ5HNprxmtFSEVDCQwhPCK6cR3SAF+Iblw3KYanf1XYmRCjiOf92wiuUx3SuNsmx2JDGncbUz5oDs/71qm30sM7GNytEexvxpR+cDHsb8ZgVK+mqPSw5AkQxTHH9SakoqIEhhBCCCG8QwkMIYQQQniHEhhCCCGE8A4lMIQQQgjhHZpGTQiPaOrUQ+rNOOhN2NRUU6ce0m7HocDBBbgpN6k9aTUDcSf6HhQJeYDGpFCsKPwD8b9tf8Gliswq9aVUr40dP55H4+CawB0F6/L5wXXxzZ4zUIjYTfs2x/UmpKKiOzCE8IlYDL1UCojFJsaQmRbjP3qRGFovKXQi02OxoROJkevmCb2V6tWJxMh39+TeZ2Ix8tw92feTOa43IRUUJTCE8IjgQRxcB/SF4EEcq3IMAwgEDAQCBqJHD+DyYV+IHj4wuT0uj+Ph99H7cEuKNzkWG25J8ejz5Ti4PLZOve5PEtDh85EQc+wz8cMH6DZ7NNyfJBjeEzCFe8cVXZfiNgHner0JeR1QAkMIjwiys2D36y8QZGeVuQzDAGpGgHSVFukqLZSKdEiOHQWTVfYYJZHkZMPl92OQ5GSbHIttvYGXTlqtXvvcbFQ/9yeELPr9ecLsLNS8cAL2uYXtlYgEEAiFSCvQGq6LmhG8lMRwud6EvC5oDAwhFRzDMMhRaXDidgpyCzSQxabiAwBavR7F/tlPLE4kFCBXpcGZe3Lk5mvgaCdC62BveEiE0Ov1tm4eIbxACQwhr4ncAg2UBRo4q6w42pa8Um6BFsoCuh6EcEGPkAghhBDCO5TAEMIj2so+UM5dCG1lH84xsr0q4fyo6VBXrmJye3KllZA8awFypJVMjsVGjrQSfv9oMnKtVK+pfaauXAV/D5uGbC927TXH9SakoqIEhhAe0Xt7I2/UWOi9vTnHyHH3wvU+H0Mr4x6jSJ6nFGnDxyDXQ2pyLDZyPaS4+O5A5Hlap15T+0wr88aVXoOR4+7Fqpw5rjchFRUlMITwCJORDsmhA2Ay0jnHsFdmwf+vXyDIyDC5PZLsTLj8fBB22Zkmx2LDLjsTwWd+g8RK9ZraZ4KMDNT6+xjslexmE5njehNSUVECQwiPCOMfwW3oIAjjH3GO4f40Ee3mTYAkgXuMIi5PEuE38iO4JieaHIsN1+RE9PpqMlyeWKdeU/tMkvAIXRZ+Cven7NprjutNSEVFCQwhhBBCeIcSGEIIIYTwDiUwhBBCCOEdSmAI4RG9vQPUDRpCb+/AOYZaYo/UWnWhs2e3M3JxtHZ2yKsfAo2d6bHY0NjZI7lmMLR2dlapz9Q+09nbIyWgDtQSduXNcb0JqahoJV5CeEQbGISM46dMiqHwq4kD3xxEi9oyICbZpFgZ1WvhwbGTSI9JBvLVJsViI71aALas2AtvV+skTqb2map2EHav/RFKln1kjutNSEVFd2AIIYQQwjuUwBDCI6Lr0ZD6SiG6Hs05RqW4W/i4Yz3Yx1wzuT1e924iuGYlyGJvmhyLDVnsTUzr3Rhe96xTr6l9Zh9zDWO6haBS3C1W5cxxvQmpqCiBIYRP9HowKhVgwo7FjF4PoVptUozn2yMwsT1c6xVpzHQOZWByn+n1EKnVYNiWN8P1JqSiojEwhFRADAMwDPPf/zM2bg0hhJgfJTCEVDAMA6gZAXJUGgCAUMBAB0piCCEVCyUwhFQwDMMgR6XBidspyC3QwMvFDo1qeBZmNoQQUkHQGBhCeERTOwhpf1+ApnZQqcfmFmigLNAgT6U1ej/Vtyb2bTmCglqBJrcno3oA7h8/i7RqASbHYiOtWgA2fv0jMqpbp15T+6ygViB2bDiEVN+arMqxud6EvG4ogSGETxwcoA2uAzhwX9hMY2ePdP/a0JsQo4jWzh4FQXWgtfJCdlo7e6RWq2W1ek3tM72DA9Jq1Ga/4J8ZrjchFRUlMITwiCAhHs6fjoUgIZ5zDNeUJEQumwFxIvcYRZyTH6PK5PFwefrY5FhsuDx9jC5rZsM52Tr1mtpn4sR4tP3fTLimJJV4jOC/gdcCwbMvYaLp15uQiooSGEJ4RJCeBoed2yFIT+McwzE7A8FH90GYnm5ye+yyMuCxZwfsszJMjsWGfVYGQv84ADsr1WtqnwnT01H/1/1wzM4o9nOJSACBUIi0Ai3SVc++tOkZJl9vQioqGsRLCCE2JhIKkKvS4Mw9OXLzC2ePOdqJ0EmtsXHLCCm/KIEhhJByIrdAC2XBc0kLTRwjpET0CIkQQgghvEMJDCE8opN5I3f8ROhk3pxj5Lh74er7w6GRykxuT56HF1LHfIJcD6nJsdjI9ZDiTK8hyPPwskp9pvaZRirDpfeGIcedXXs1Mm/kTTDtehNSUVECQwiP6Kr4IGfmHOiq+HCOke1VCZeGTYbGhBhFcmWVkfLZbORIK5kci40caSX8NWACcmWVrVKfqX2mqeKDsx9PRLYXu37SVvFB7hdzTbrehFRUlMAQwiOMMhviM6fAKLM5x5Dk5aDK1QsQmBCjiDhXCcezpyHOzTE5Frt6c1Dt+iWIc5VWqc/UPhMos1E1+iIkeez6iVFmQ3TatOtNSEVFCQwhPCKMuw/3Hl0gjLvPOYZn0iN0nTgAkgdxJrfHNfERarzXDe6PH5ociw33xw8x4IshcE18ZJX6TO0zyYM49J42CJ5J7NoribsPt+6dTbrehFRUlMAQQgghhHcogSGEEEII71ACQwghhBDeoQSGEB7Ri8TQVvGBXiTmHEMrFEEprQS9yPR1LHUiEdSVfaAzoT3c6hUjy8sbOjOcQ1mY2md6kQjZ0krQCtmV14tNv96EVFS0Ei8hPKKtWw9p0bdNiiGvEYjde0+hRaAMiEk2KVZ6zSDcu3wDiphkIF9tUiw2FP6BWL3lD3i7Wmc3alP7rKBOPXz73V9QsuwjVZ16yLh+BzqdnnWdhFR0lMAQwnPMf7sYP3tN688TQio+eoRECI8Ib96AZ8NgCG/eAFCYvKgZgdEOxplqLXSv2ERH9vAu3n8vEna3bpjcHo+4O6jdpB68Htw1ORYbXg/uYtyQdvCIu2OV+kztM7tbN/Dxh60ge8iun+xu34BHgyCIb9+EQMCAclNCnqE7MITwCKNRQ/gkCYym8FEEwzDIUWlw4nYKcv/bBNDLxQ6NaniipN92Qq0GzqlPkaLRmLxZoECjgTg5CQKN9R4fFdarhqsiBQKNdXZrNrXPGI0GLqlPIdSWvb0SkQBCnQ6CJ0nIzs1HgUoLJ4kIYuigpydKhNAdGEIqgtwCDZT/feWptLZuDjEDkVCAfHXhtTx1LxUnbqcgR6WhR4SE/IcSGEIIKefyVBrDHTZCSCGzJzCrV69GUFCQ0VenTp0MnxcUFGDu3Llo1qwZwsLCMG7cOKSmphrFSEpKwvDhw9GwYUOEh4djyZIl0FjpVjEhhBBCyj+LjIGpXbs2oqKiDK+FQqHh/xcuXIiTJ09i5cqVcHFxwfz58zF27Fjs2bMHAKDVajFixAhIpVLs2bMHKSkpmDZtGsRiMSZOnGiJ5hLCG9qaAcg4cATamgGcY6T5VMfPK3agln9N4KFpmzBm+VbHw72HkSGqYlIctjKq1sCO+Vug9a1ulfpM7TOVf03sW7INaT7s2vt8OQnrWgmp2CzyCEkoFEImkxm+PD09AQDZ2dnYv38/pk+fjvDwcNSvXx8LFy7ElStXcPXqVQDA6dOnERsbi6+++gp16tRBy5YtMWHCBOzcuRMqlcoSzSWEN/TOLlC3iITe2YVzDJWDE56ENoPOhBhF1I7OyG0eAbWjk8mx2NXrhPgGTaF2dLZKfab2mc7ZBY8bvgGVA7t+4lqOkNeBRRKYR48eISIiAm3btsWkSZOQlJQEAIiJiYFarUbz5s0NxwYEBMDHx8eQwFy9ehWBgYGQSqWGYyIiIqBUKhEbG2uJ5hLCG4InSXBaMAeCJ0mcY7gonqLppmUQmRCjiKM8Gd6L5sIp9anJsdhwSn2KVjtWwVFu2kJ8ZWVqn4meJKH5tyvgomDXT1zLEfI6MHsCExISgkWLFmHz5s2YM2cOHj9+jP79+0OpVCI1NRVisRiurq5GZby8vCCXywEAqampRskLAMPromMIeV0J5Clw/HoFBPIUzjGcMhQI3f0NRKmm/3tySFdAunYlHNNTSz/YjBzTU9Fi/xY4pCusUp+pfSZKlaPp3k1wymDXXq7lCHkdmH0MTMuWLQ3/HxwcjIYNG6J169b45ZdfYG9vnWW/CSGEEFKxWXwataurK2rUqIH4+HhIpVKo1WpkZWUZHaNQKCCTyQAU3m15cVZS0euiYwh53TEM89/KrLQmCCHk9WTxBCYnJwcJCQmQyWSoX78+xGIxzp07Z/g8Li4OSUlJCA0NBQCEhobi7t27UCie3TI9e/YsnJ2dUatWLUs3lxBeyFaXbdsAQgipqMz+CGnJkiVo3bo1fHx8kJKSgtWrV0MgEKBr165wcXFBr169sHjxYri5ucHZ2RkLFixAWFiYIYGJiIhArVq1MHXqVEyZMgVyuRwrV65E//79IZHQRELymvPyQub7A3A6TY+U6KRStw0oTq6LO2537g2hhweQYVpzClzdkd5vAPJd3U0LxFK+qzuutuuBAivVa2qfaT08ENOxF3Jd3E0qJ2CK7r49O0av19PWAuS1ZPYEJjk5GRMnTkRGRgY8PT3RuHFj7N271zCVesaMGRAIBBg/fjxUKhUiIiIwe/ZsQ3mhUIgNGzZgzpw56Nu3LxwcHNCjRw+MHz/e3E0lhHd0ftWgWL4aKdFJUBZo4GjH/p9wlrcPTk1eiBa+MiDDtFk8yspV8WTZ18iOSQbyrbcfUnalqjgydi68Xa0zrs7UPlP7VsPxTxdAybKPni/nKRJAIBQirUAL4FnGQvsjkdeV2ROY//3vf6/83M7ODrNnzzZKWl5UtWpVbNq0ydxNI4T/8vIgib0PYYEduP7zFRXkw+NBAhg/09dQERbkw+7OLQgLHAAISz3eXIQF+ZDGP4CwtnUeK5vaZ0xeHjwf3kO+R2Vo7MqedD1fTuRqj1yVBmfuyZGbX7gyuaOdCK2DveEhEUJPGQx5zdBeSISUYwwDCASM4Ut07y6qtQ6HZ0Ic55jSxDj0HtIFdrF3TW6f+6P7CGjbHJ7x902OxYZn/H2MGN8T7o+sU6+pfWYXexcDRr4DaSK761ZcudwCrWHjTtofibzOLLKVACHEdAwDqBkBclTPfkk5aLRws2GbCCGkvKAEhpByimEY5Kg0OHE7xfCXdu2kDFS1cbsIIaQ8oASGkHIu97/HBQCQr9bZuDWEEFI+0BgYQviEYaCTSFhNm36RnmGgFYtNimHO9nCtVyMy0zmUgcl9xjDQiMXQsy3PtRwhrwFKYAjhEUXturgd9xTyWnU5x3hasw6+/fUG8uuHlIv2cCGvVRdL9v0DRW3r1Gtqn+XXD8Haw9fwtGYdq5Qj5HVACQwhhBBCeIcSGEJ4xP1RLPw7tYSHCdOWvRLi0GN4d0ju3SkX7eHCI/4+hkx8D+6PYq1Sn6l9Jrl3B++P6QkvltPfuZYj5HVACQwhPCIsKIBDzDWICvI5xxCr8iGNvQlBPvcY5mwPF6KCfFSOuw1hQYFV6jO1zwT5+fC+fwtiFbvyXMsR8jqgWUiElCPMf3vdFP4/DdwkhJCSUAJDSDnx4sJ1QgFDO00TQkgJKIEhpJx4ceE6LjtNk9fPiztU0+7U5HVBY2AIKWeKFq7LU2lf+iy7ii8SNkQhq7Iv5/gZlXzxx6xVUPlVN6WZZmsPF1mVfbF/yjJkV7FOvab2mcqvOo7M+B8yKrFrb2nlJM/tUJ2uKvxSMwLKeclrgRIYQnhE5eKG7K7dUeDCfUekfGdXPGj1NnTu7uWiPVwUuLjhdosOUFmpXlP7TOfujti3OiHf2dWs5URCAXJVGpy48xRHopNw4nYKclQaGj9FXguUwBDCIw5pqfD8Zi0c01M5x3DKUKDBD99CKE8pF+3hwjE9FW/8tB0Oadap19Q+E8pTELZ/K5wyFBYpV7RDNe1OTV4nlMAQwiOOqU9Red5MOKU+5RzDRfEUb65fDHHyk3LRHi6cUp+ifdQyOFqpXlP7TJz8BG9tWgIXBbv2ci1HyOuAEhhCCCGE8A4lMIQQQgjhHUpgCCGEEMI7lMAQwiMqJxdkt+8ElZML5xj5ji54FN4GWhd2M2Is1R6u9d5t2tJq9ZraZ1oXV8Q1a418R3bt5VqOkNcBJTCE8Eh21WpIiNqNTJ9qnGNkVPHDb19ugLqGf7loDxeZPtXww+erkV3VOvWa2mfqGv44PHcdMqr4WaUcIa8DSmAIsRGGAQQCxvBVlrU7GI0aQkUqBBo153oFGjXsM9IANfcY5mwPFwKNGo6ZaWCsVK/JfaZWwyEjjX0/cSj3bGXe57+3WLaXEB6gBIYQGyja96ho9dR0lRaZam2pex95xt1FUMPa8Hpwl3Pd3o/uYUDPN2F/+ybnGOZsDxdeD+7i00Gt4BlnnXpN7TP72zcxvF8LeD+6Z9Fyxa3MS6vzkoqK9kIixAZe3PcIAO19RExWtDLvmXty5OYXfl852onQOtgbHhIh9LRJEqlAKIEhxIaK9j0CCn/REGIORSvzElKR0SMkQgghhPAO/clHCCEV3LOBvc/e0+v1oCdKhM8ogSHESpj/fokU/j+3cS5pAcG4fesRUuOyAbWOU4ynNYKw9fC/eKNuNeCWnFMMc7aHi9Sawfhq11l4yDysUp+pfZZftz7W77+EdJY/cp8vJ2Nda6HnB/YCzzIWJ4kIYugoiSG8RQkMIVZQNOsoR1U4LkEoYEqdcVQcvVAInYsr9MJczgmDXiiE2skeEAo5lTd3e7jWq3J0ht4M51DW+kzqM6EQKidn6PNZTqPmWu45NLCXVFQ0BoYQK3h+1tGR6CSciU2FRqdjPePINfEhqvXvBffHDzm3xSPpEd6e+jEkcfc5xzBne7hwf/wQ/eaMhGuideo1tc8kcffRfcZQeCQ9skq54hQN7FUWaAwz3wjhM0pgCLGAkhapK5p1lKfScoorzs2B88k/Ic7N4dw2u7wc+F4+DUGOknMMc7aHa70BV89arV5T+0yQo0T1f8/ALo9de7mWI+R1QI+QCDGzFx8XAdwfGRFCCCkeJTCEmBktUkcIIZZHCQwhFkKL1JHy7MWp1TStmvANjYEhhEdyvKvgyYKlUMqqcI6RJa2MM+NnQe1TtVy0hwulrAqODZ+BHG/r1Gtqn6l9quLE6JnIkla2SrnSFLdnEu2XRPiGEhhCeCTf3RPpg4chz92Tc4xcN0/c7P4htF7SctEeLvLcPfFP537It1K9pvaZ1kuKa+/0R64bu/ZyLVeaoqnVJ+48xZHoJJy4nYIclYbz+kSE2AIlMISYqKQZR5Zgl5UBt/3fwy47g3MM++xM1Pr9JwjS08tFezjVm52B+n/9DLss69Rrap8J0tMRdPwQ7LMzrVKurIqmVtO0asJHlMAQwtLzCYtQyEDDCAy34dNVWmSqtRabceSc/BhVJ4yEa/JjzjHcUx6j9aIpkCTGl4v2cOGa/BjvrpwBZyvVa2qfSRLj0emraXBPYdderuUIeR3QyEJCWChuRV21DjhJM44IIcSqKIEhhIUXp0gXJSu5Ki3NOCKEECuin7SEcFA0RZqSFVJR0I7VhG/opy8hr/D8DtKFr237WEhj74DcRk2htnfkHENl74CndUOhc3QE8mzfHi7U9o5IDAqBxt7BKvWZ2mc6R0c8CW4IFcv2Pl/Okj+sacdqwkeUwBBSgvK4JUBmtZp4eOg3ZMQkAxx3KE6r6o9Da/aiRYAMiEm2eXu4yPDzx7Yl38Hb1d4q9ZnaZ6qA2jiycg+ULPvo+XLerGstu5J2rG5bxxt2EpFhx2q6I0PKE0pgCCkBbQlAXjdF06qB4u/K0B0ZUp7QNGpCSlE03sWUXaTNxevuDdT19YDs3g3OMSrfv4lhbQJhfz26XLSHC9m9G/i8ewi87lqnXlP7zP56NCZ0qoPK929apZw50GJ3pLyjOzCEPOf5MS/0g5oQ47syNNCXlCeUwBDyn+LWeLHleBdCyhMa6EvKG0pgCPlPSWu80HgXQkoe6Ns62BseEqFhoC8h1kIJDHltvDgluvCvyJenSNMaL4SU7PlHSoTYEv2EJhXSy+u3ACo9A6Xq2bN8e4nIaBM7PjwyyqhRC/dO/YO0dCGg4xZD7heA73f8jpDaQUBshs3bw0Va9VpYt/5n2NeobpX6TO2zgtpB2PrtMcidvTiXY1fSel4eF2P8hwFA42SIZVACQ3jn5Tspxj8gS1q/5fk9i4oeDz1/O5wPj4y0Ejuo/X2hzeG+7opWYocsaXXo7U1fQ8Uc7eFab2aVavCW2FmtPlP6TG9vj0yf6tCy7COu5azlxXExxf1hANA4GWIZNI2alCvP7/Rc+GX8urjdn9NVWmgEgueOExjGshyJTsKR6CSciU2FRqcz7FlUNB266HZ4eZgiXRYuTxLgM244XJMTOcdwe5qIVgsnQxz/qFy0hwvX5ES887/P4PIkwSr1mdpn4vhH6LhkKtyesusnruWs5cWp1mfvK5CRp8aJO8/+7dH0a2Ip5TqB2blzJ9q0aYMGDRqgT58+uHbtmq2bRMzoxWTlxeQkU61FwQvJSqZahyyV1ig5OX0vFflaPdJVOkM5HZhytX6LuUiys+B+4AfYZWdyjuGgzELtPw5BmJlRLtrDhV12JhqcPAJJdpZV6jO1z4SZGQg+cRgOSnbt5VrO2or+ECjuD4PcAs1zj5mK/8NEIGDK841PUk6V20dIR48exaJFizB37lw0bNgQ27Ztw5AhQ3Ds2DF4eZXXp8Gvh9Ie4ZSl3ItjUgB2j3le3P35+dkRfHgURMjroqyPmZztRBC/9EPEeDwNjaUhzyu3CUxUVBTee+899OrVCwAwd+5c/PXXX9i/fz+GDx9u49bx04uJR3E/DIpLTp7/IVJc4gEU98PnxRk+xuVeTFaAl5OTollAz896eNXMoKLjaPYQIeXHi9Ovi/vDxM1RglZ1KkGp0aFojZniEp3ikpwXf45x/QOL8E+5/EmvUqlw48YNjBgxwvCeQCBA8+bNceXKFVaxLPFHeGm/5Et+z7bHqGGceDhLRBBD/8pjBAxgLy68wwEUJh4aHfDPozQUqAunnTjZC9HEX4qc/374vFimuHJuDmIE+bhCLBJAoil8kikWMhAwgJujGBIBA1cHkdFrAGV6r7wdY87Yzo4SwMUFzk528HSUcIrj4mQPuLiAEQrhZm9aG19sj7X60dnJrrBeR4lVrqNjGfuspNiMRgi4uMDFyR6qYq5bSXGeL6cvh9+PbI4RCwWQCAWGf+dFrwHAQSJAvlqD6IQMFPz3GKroZ0TRey/+nCny4s+xF3+GFXeM+X7Oci1nzWMsF9tSiWFZf28z+nK4+tDTp0/x1ltvYc+ePQgLCzO8v3TpUly6dAk//PCDDVtHCCGEEFsr14N4CSGEEEKKUy4TGA8PDwiFQigUCqP3FQoFpFKpjVpFCCGEkPKiXCYwEokE9erVw7lz5wzv6XQ6nDt3zuiREiGEEEJeT+VyEC8AfPTRR5g2bRrq16+PkJAQbNu2DXl5eejZs6etm0YIIYQQGyu3CUznzp2RlpaGr7/+GnK5HHXq1MHmzZvpERIhhBBCyucsJEIIIYSQVymXY2AIIYQQQl6FEhhCCCGE8A4lMIQQQgjhHUpgCCGEEMI7lMBUUBkZGZg0aRIaNWqEJk2aYMaMGcjJySlTWb1ej6FDhyIoKAh//PGHhVvKb2z7OSMjA/Pnz0fHjh0REhKCVq1aYcGCBcjOzrZiq/lh586daNOmDRo0aIA+ffrg2rVrrzz+l19+QadOndCgQQN069YNJ0+etFJL+Y1NP+/duxcffPABmjZtiqZNm2Lw4MGlXhdSiO33c5EjR44gKCgIo0ePtnAL+YcSmApq8uTJiI2NRVRUFDZs2IDLly9j1qxZZSq7bdu2YjarJMVh288pKSlISUnBtGnT8PPPP2PRokU4deoUPv/8cyu2uvw7evQoFi1ahDFjxuDAgQMIDg7GkCFDXlqdu8i///6LSZMmoXfv3jh48CDatm2LMWPG4O7du1ZuOb+w7ecLFy6gS5cu2L59O/bs2YMqVarg448/xtOnT63ccn5h289FEhMTsWTJEjRp0sRKLeUZPalwYmNj9YGBgfpr164Z3jt58qQ+KChIn5yc/MqyN2/e1EdGRupTUlL0gYGB+t9//93SzeUtU/r5eUePHtXXq1dPr1arLdFMXurdu7d+7ty5htdarVYfERGh37hxY7HHT5gwQT98+HCj9/r06aP/4osvLNpOvmPbzy/SaDT6sLAw/YEDByzUwoqBSz9rNBp937599Xv37tVPmzZNP2rUKGs0lVfoDkwFdOXKFbi6uqJBgwaG95o3bw6BQPDK25Z5eXmYNGkSZs2aBZlMZo2m8hrXfn6RUqmEs7MzRKJyu66kValUKty4cQPNmzc3vCcQCNC8eXNcuXKl2DJXr15FeHi40XsRERG4evWqJZvKa1z6+UV5eXnQaDRwc3OzVDN5j2s/r127Fl5eXujTp481mslL9BOzAkpNTYWnp6fReyKRCG5ubpDL5SWWW7RoEcLCwtCuXTtLN7FC4NrPz0tLS8O6devQt29fSzSRl9LT06HVauHl5WX0vpeXF+Li4ootk5qa+tIq3V5eXkhNTbVYO/mOSz+/aNmyZfD29jb65UyMcenny5cvY9++fTh48KAVWshflMDwyLJly7Bp06ZXHnP06FFOsY8fP47z58/jwIEDnMpXJJbs5+cplUqMGDECAQEBGDt2rMnxCLGmb775BkePHsX27dthZ2dn6+ZUGEqlElOnTsX8+fNf+gOJGKMEhkc+/vhj9OjR45XH+Pn5QSqVIi0tzeh9jUaDzMzMEh8NnT9/HvHx8WjatKnR++PGjUOTJk2wY8cO0xrPI5bs5yJKpRJDhw6Fk5MT1q5dC7FYbHK7KwoPDw8IhcKXBjgqFIoS90KTSqUv3W151fGEWz8X2bJlC7755htERUUhODjYks3kPbb9nJCQgMePH2PUqFGG93Q6HQCgbt26OHbsGKpVq2bZRvMEJTA84unpWaaMPCwsDFlZWYiJiUH9+vUBFCYoOp0OISEhxZYZPnz4S89au3Xrhs8++wytW7c2vfE8Ysl+BgqTlyFDhkAikWD9+vX01+sLJBIJ6tWrh3PnzhkeZ+p0Opw7dw4ffvhhsWVCQ0Nx/vx5DB482PDe2bNnERoaaoUW8xOXfgaATZs2YcOGDdiyZYvR+C9SPLb9XLNmTRw+fNjovZUrVyInJweff/45KleubJV284KtRxETyxgyZIi+e/fu+ujoaP3ly5f1HTp00E+cONHweXJysr5jx4766OjoEmPQLKTSse3n7OxsfZ8+ffRdu3bVP3r0SJ+SkmL40mg0tjqNcufIkSP6+vXr63/88Ud9bGys/osvvtA3adJEL5fL9Xq9Xj9lyhT9smXLDMf/888/+rp16+q3bNmij42N1X/99df6evXq6e/cuWOrU+AFtv28ceNGfb169fTHjh0z+t5VKpW2OgVeYNvPL6JZSMWjOzAV1LJlyzB//nwMGjQIAoEAHTp0wMyZMw2fq9VqPHjwAHl5eTZsJf+x7ecbN24gOjoaANC+fXujWMePH4evr6/1Gl+Ode7cGWlpafj6668hl8tRp04dbN682XDL/cmTJxAInk2ibNSoEZYtW4aVK1dixYoVqFGjBtauXYvAwEBbnQIvsO3nPXv2QK1WY/z48UZxxo4di3Hjxlm17XzCtp9J2TB6vV5v60YQQgghhLBBKR8hhBBCeIcSGEIIIYTwDiUwhBBCCOEdSmAIIYQQwjuUwBBCCCH/b7cOSAAAAAAE/X/djkBXyI7AAAA7AgMA7AgMALAjMADAjsAAADsCAwDsCAwAsBPaI7b7Vs36DwAAAABJRU5ErkJggg==",
      "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": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:01.611993Z",
     "iopub.status.busy": "2025-10-27T22:47:01.611846Z",
     "iopub.status.idle": "2025-10-27T22:47:01.644408Z",
     "shell.execute_reply": "2025-10-27T22:47:01.643975Z",
     "shell.execute_reply.started": "2025-10-27T22:47:01.611977Z"
    }
   },
   "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>30</th>\n",
       "      <td>69ff6a1c-70b1-446f-a050-2440eceaf062</td>\n",
       "      <td>000f2598-363a-4426-a207-0fde02491e49</td>\n",
       "      <td>18.205400</td>\n",
       "      <td>False</td>\n",
       "      <td>-11.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4cbe006f-92e5-414d-aa9d-4f05ff60f747</td>\n",
       "      <td>000f2598-363a-4426-a207-0fde02491e49</td>\n",
       "      <td>22.904550</td>\n",
       "      <td>True</td>\n",
       "      <td>-12.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>df5ac1aa-9898-4291-9e4c-2492b41cc805</td>\n",
       "      <td>00af91b4-f925-4d95-8a1a-33c3b81a2796</td>\n",
       "      <td>18.386267</td>\n",
       "      <td>False</td>\n",
       "      <td>-12.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>d0cb9090-2e1c-42cc-a4b8-e87cc98e1933</td>\n",
       "      <td>00af91b4-f925-4d95-8a1a-33c3b81a2796</td>\n",
       "      <td>23.314900</td>\n",
       "      <td>True</td>\n",
       "      <td>-10.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>61af5b4f-327b-4b15-b1be-b8b03db783b5</td>\n",
       "      <td>0168faec-a336-4cc2-83e5-676fe81ca395</td>\n",
       "      <td>19.128533</td>\n",
       "      <td>False</td>\n",
       "      <td>-12.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0b9ce839-0dca-44db-8de3-ff21f7fe001b</td>\n",
       "      <td>0168faec-a336-4cc2-83e5-676fe81ca395</td>\n",
       "      <td>24.823400</td>\n",
       "      <td>True</td>\n",
       "      <td>-11.064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id                            request_id  pair_quality  preference  loudness_abs\n",
       "30   69ff6a1c-70b1-446f-a050-2440eceaf062  000f2598-363a-4426-a207-0fde02491e49     18.205400       False       -11.458\n",
       "31   4cbe006f-92e5-414d-aa9d-4f05ff60f747  000f2598-363a-4426-a207-0fde02491e49     22.904550        True       -12.754\n",
       "408  df5ac1aa-9898-4291-9e4c-2492b41cc805  00af91b4-f925-4d95-8a1a-33c3b81a2796     18.386267       False       -12.027\n",
       "409  d0cb9090-2e1c-42cc-a4b8-e87cc98e1933  00af91b4-f925-4d95-8a1a-33c3b81a2796     23.314900        True       -10.809\n",
       "814  61af5b4f-327b-4b15-b1be-b8b03db783b5  0168faec-a336-4cc2-83e5-676fe81ca395     19.128533       False       -12.339\n",
       "815  0b9ce839-0dca-44db-8de3-ff21f7fe001b  0168faec-a336-4cc2-83e5-676fe81ca395     24.823400        True       -11.064"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:01.645031Z",
     "iopub.status.busy": "2025-10-27T22:47:01.644892Z",
     "iopub.status.idle": "2025-10-27T22:47:02.528958Z",
     "shell.execute_reply": "2025-10-27T22:47:02.528458Z",
     "shell.execute_reply.started": "2025-10-27T22:47:01.645017Z"
    }
   },
   "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": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:02.529677Z",
     "iopub.status.busy": "2025-10-27T22:47:02.529525Z",
     "iopub.status.idle": "2025-10-27T22:47:03.050132Z",
     "shell.execute_reply": "2025-10-27T22:47:03.049638Z",
     "shell.execute_reply.started": "2025-10-27T22:47:02.529661Z"
    }
   },
   "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": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:03.051063Z",
     "iopub.status.busy": "2025-10-27T22:47:03.050702Z",
     "iopub.status.idle": "2025-10-27T22:47:03.386656Z",
     "shell.execute_reply": "2025-10-27T22:47:03.386080Z",
     "shell.execute_reply.started": "2025-10-27T22:47:03.051047Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    98606\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    49303\n",
      "True     49303\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-carp-up-c-1    98606\n",
      "Name: count, dtype: int64 preference  model_name       \n",
      "False       chirp-carp-up-c-1    49303\n",
      "True        chirp-carp-up-c-1    49303\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    98606\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": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:03.387367Z",
     "iopub.status.busy": "2025-10-27T22:47:03.387218Z",
     "iopub.status.idle": "2025-10-27T22:47:05.486484Z",
     "shell.execute_reply": "2025-10-27T22:47:05.485908Z",
     "shell.execute_reply.started": "2025-10-27T22:47:03.387352Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 6516 duplicated prompts 3258 unique requests\n",
      "Found 1609 request_ids with duplicate prompts but not highest play counts in their group\n",
      "['9ce94119-bb38-47bb-8f97-d198d11f8425', 'fdc32963-1a54-4d2a-9472-5be270379f16', '4d77a17a-595b-4642-8151-ded178077aa0', '0c83c91e-31a6-4d3c-b80e-d552121004cb', '2d88c9d2-ffc9-4182-8ea2-a3ea7759f3a1', '7f607c85-6f90-4baa-acdd-b5702744c63f', '20b1f50b-c817-4453-b731-4dfaf06dfa2d', '6076a316-0327-4ac2-8701-b9de90ccc043', 'bcaf8953-a223-45a3-8823-fc0d191975d5', '48ea94cb-599b-41b8-ad18-15dfa3471f79']\n",
      "Before dedup user gen requests 98606\n",
      "After dedup user gen requests 95388\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": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:05.487224Z",
     "iopub.status.busy": "2025-10-27T22:47:05.487071Z",
     "iopub.status.idle": "2025-10-27T22:47:05.521482Z",
     "shell.execute_reply": "2025-10-27T22:47:05.520991Z",
     "shell.execute_reply.started": "2025-10-27T22:47:05.487209Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    95388.000000\n",
      "mean       436.112467\n",
      "std        619.110360\n",
      "min         20.000000\n",
      "25%        109.000000\n",
      "50%        243.000000\n",
      "75%        513.000000\n",
      "max      20495.000000\n",
      "Name: user_n_clips, dtype: float64\n",
      "count    95388.000000\n",
      "mean         4.592947\n",
      "std          4.917493\n",
      "min          2.000000\n",
      "25%          2.000000\n",
      "50%          2.000000\n",
      "75%          6.000000\n",
      "max         52.000000\n",
      "Name: user_n_clips, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"user_n_clips\"].describe())\n",
    "df[\"user_n_clips\"] = df.groupby(\"user_id\")[\"s3_id\"].transform(\"count\")\n",
    "print(df[\"user_n_clips\"].describe())\n",
    "# BREAK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.035167Z",
     "start_time": "2024-05-16T13:59:40.801098Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:05.522166Z",
     "iopub.status.busy": "2025-10-27T22:47:05.522022Z",
     "iopub.status.idle": "2025-10-27T22:47:05.624988Z",
     "shell.execute_reply": "2025-10-27T22:47:05.624454Z",
     "shell.execute_reply.started": "2025-10-27T22:47:05.522151Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 43553 positive 14473\n",
      "total pair requests 47694 selected pair requests 13086 frac 0.274\n"
     ]
    }
   ],
   "source": [
    "normal_pos_play_count = 3\n",
    "# this is lower, cause a concat is probably already ensuring that it is good\n",
    "concat_pos_play_count = 2\n",
    "# this is a filter on the concated clip\n",
    "concat_total_play_count = 3\n",
    "\n",
    "neg_filter_selection_mask = (\n",
    "    (~df[\"preference\"])  # get basics aligned\n",
    "    & (df[\"reaction_play_count\"] >= 1)  # has to be played once\n",
    "    # & (df[\"play_count\"] <= 3)  # if it is actually bad, shouldn't be listened often\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    # & (df[\"duration\"] <= 60)  # can't be badly long\n",
    "    & (df[\"has_continue_and_start_continue_at\"].isna())  # won't have any continues\n",
    "    & (df[\"norm_play_frac\"] <= 2.1)\n",
    "    # & (df[\"sum_total_play_duration_5\"] >= 31)  # this cut doesn't matter as much tbh\n",
    "    # & (df[\"dislike_count\"] >= 1) # this is kinda strict\n",
    "    #     & (\n",
    "    #         (df_slice[\"is_in_playlist\"] == False)\n",
    "    #         & (df_slice[\"concat_in_playlist\"] == False)\n",
    "    #     )  # can't be part of a playlist -- otherwise there are some like signal in it?\n",
    ")\n",
    "pos_filter_selectin_mask = (\n",
    "    (df[\"preference\"])  # get basics aligned\n",
    "    & (\n",
    "        df[\"good_continue_at\"]\n",
    "    )  # if continue, needs to continue off a certain percentage\n",
    "    & (df[\"reaction_play_count\"] >= 1)\n",
    "    & (df[\"play_rel_diff\"] >= 0)  # this is more like quality assurance\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    & (df[\"dislike_count\"] == 0)  # can't have dislikes\n",
    "    & (df[\"flag_count\"] == 0)  # can't have issues\n",
    "    & (\n",
    "        (\n",
    "            (df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= concat_pos_play_count)\n",
    "            & (df[\"concat_play_counts\"] >= concat_total_play_count)\n",
    "        )\n",
    "        | (\n",
    "            (~df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= normal_pos_play_count)\n",
    "            & (\n",
    "                df[\"norm_play_frac\"] >= 2.1\n",
    "            )  # this is a bit of a luxury cut...not for now...\n",
    "            & (df[\"sum_total_play_duration_5\"] >= 31)\n",
    "        )\n",
    "    )\n",
    "    & (abs(df[\"mean_ear_score_diff\"]) >= 1)\n",
    "    & (abs(df[\"mean_ear_score_diff_ratio\"]) >= 0.05)\n",
    "    # & (df[\"source\"] == \"web\")\n",
    "    & (df[\"cer_diff\"] < 0.05)\n",
    "    # & (df[\"mean_shimmer_score_diff\"] < 1.0)\n",
    "    # & (df[\"norm_play_frac\"] >= 1.9)\n",
    "    # & (df[\"user_n_clips\"] >= 10)  # user needs to have genereated at least 20\n",
    "    # & (df[\"duration_rel_diff\"] < 10) # positive isn't just longer\n",
    "    # & ((df[\"task\"] == \"\") | (df[\"task\"] == \"extend\"))\n",
    "    # & (\n",
    "    #     (df[\"upvote_count\"] >= 1)\n",
    "    #     | (df[\"reaction_play_count\"] >= 5)\n",
    "    #     | (df[\"concat_play_counts\"] >= 5)\n",
    "    # )\n",
    "    # & (df[\"pos_diff_preference\"] == 2)\n",
    "    # & ((0 < df[\"similarity\"]) &  (df[\"similarity\"] <= 0.99))\n",
    "    # & (\n",
    "    #     (df[\"cer_diff_preference\"] < 0.25) & (df[\"cer\"] < 0.8)\n",
    "    # )  # cut on hoot cer difference and abs cer\n",
    "    # & (df[\"pair_quality\"] > 0.31)  # bottom 5%\n",
    "    # & ((df[\"total_shimmer_score\"] < 1) | (df[\"shimmer_score_diff\"] < 0.4))\n",
    "    # & (df[\"stereo_width_diff\"] > -0.2)  # cut off bottom 5%\n",
    "    # & (df[\"spectral_centroid_diff\"] < 0.25)  # crop off the top 5%\n",
    ")\n",
    "print(\n",
    "    \"negative\",\n",
    "    sum(neg_filter_selection_mask),\n",
    "    \"positive\",\n",
    "    sum(pos_filter_selectin_mask),\n",
    ")\n",
    "\n",
    "neg_filter_requests = df[neg_filter_selection_mask][\"request_id\"].unique()\n",
    "pos_filter_requests = df[pos_filter_selectin_mask][\"request_id\"].unique()\n",
    "# looking for very strong signal here:\n",
    "# listen to the positive/negative more than once\n",
    "# disliked one of the clips\n",
    "unique_requests = set(pos_filter_requests).intersection(neg_filter_requests)\n",
    "print(\n",
    "    \"total pair requests\",\n",
    "    df[\"request_id\"].nunique(),\n",
    "    \"selected pair requests\",\n",
    "    len(unique_requests),\n",
    "    f\"frac {len(unique_requests) / df['request_id'].nunique():.3f}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.250737Z",
     "start_time": "2024-05-16T13:59:41.036434Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:05.625836Z",
     "iopub.status.busy": "2025-10-27T22:47:05.625682Z",
     "iopub.status.idle": "2025-10-27T22:47:05.707124Z",
     "shell.execute_reply": "2025-10-27T22:47:05.706563Z",
     "shell.execute_reply.started": "2025-10-27T22:47:05.625820Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 13086 clips 26172 total khrs 1.423; N gpus for 1000 iters 1.636; 4 gpus for x iters 408.938; n unique users 11035 n pro users 10782\n"
     ]
    }
   ],
   "source": [
    "df_slice = df[df[\"request_id\"].isin(set(unique_requests))].copy()\n",
    "print(\n",
    "    f\"{os.path.basename(OUT_DATA_DIR)} requests\",\n",
    "    df_slice[\"request_id\"].nunique(),\n",
    "    \"clips\",\n",
    "    df_slice.shape[0],\n",
    "    f\"total khrs {sum(df_slice['duration'] / 3600 / 1000):.3f};\",\n",
    "    f\"N gpus for 1000 iters {df_slice.shape[0] / 8 / 2 / 1000:.3f};\",\n",
    "    f\"4 gpus for x iters {df_slice.shape[0] / 8 / 2 / 4:.3f};\",\n",
    "    f\"n unique users {df_slice['user_id'].nunique()}\",\n",
    "    f\"n pro users {df_slice[df_slice['is_pro_user']]['user_id'].nunique()}\",\n",
    ")\n",
    "# up t7 requests 37735 clips 75470 total khrs 3.964; N gpus for 1000 iters 4.717; 4 gpus for x iters 1179.219; n unique users 19093 n pro users 16217\n",
    "# up t17 requests 49262 clips 98524 total khrs 5.187; N gpus for 1000 iters 6.158; 4 gpus for x iters 1539.438; n unique users 24093 n pro users 20326\n",
    "# up v2 t2 requests 10772 clips 21544 total khrs 1.154; N gpus for 1000 iters 1.347; 4 gpus for x iters 336.625; n unique users 6527 n pro users 6027\n",
    "# up v3 t10 requests 27102 clips 54204 total khrs 2.938; N gpus for 1000 iters 3.388; 4 gpus for x iters 846.938; n unique users 13932 n pro users 12187\n",
    "# up v4 t1  requests 3201 clips 6402 total khrs 0.343; N gpus for 1000 iters 0.400; 4 gpus for x iters 100.031; n unique users 2363 n pro users 2321\n",
    "# up v4 t7  requests 31797 clips 63594 total khrs 3.384; N gpus for 1000 iters 3.975; 4 gpus for x iters 993.656; n unique users 16398 n pro users 15325\n",
    "# up v5 t2  requests 20030 clips 40060 total khrs 2.108; N gpus for 1000 iters 2.504; 4 gpus for x iters 625.938; n unique users 12395 n pro users 9156\n",
    "# up v6 t11  requests 43842 clips 87684 total khrs 4.588; N gpus for 1000 iters 5.480; 4 gpus for x iters 1370.062; n unique users 24777 n pro users 16668\n",
    "# v2 v1 t0  requests 3688 clips 7376 total khrs 0.381; N gpus for 1000 iters 0.461; 4 gpus for x iters 115.250; n unique users 3261 n pro users 2141\n",
    "# v2 v1 t1-5  requests 5406 clips 10812 total khrs 0.565; N gpus for 1000 iters 0.676; 4 gpus for x iters 168.938; n unique users 4655 n pro users 3180\n",
    "# v2 v1 t1-6   requests 9134 clips 18268 total khrs 0.952; N gpus for 1000 iters 1.142; 4 gpus for x iters 285.438; n unique users 7658 n pro users 5242\n",
    "# v2 v1 t1-7  requests 12274 clips 24548 total khrs 1.282; N gpus for 1000 iters 1.534; 4 gpus for x iters 383.562; n unique users 10036 n pro users 6818\n",
    "# v2 v1 t1-17  requests 14235 clips 28470 total khrs 1.497; N gpus for 1000 iters 1.779; 4 gpus for x iters 444.844; n unique users 11018 n pro users 8181\n",
    "# v2 v1 t1-18  requests 10007 clips 20014 total khrs 1.044; N gpus for 1000 iters 1.251; 4 gpus for x iters 312.719; n unique users 7998 n pro users 5831\n",
    "# v2 v3 t1   requests 13157 clips 26314 total khrs 1.430; N gpus for 1000 iters 1.645; 4 gpus for x iters 411.156; n unique users 8409 n pro users 8193\n",
    "# v2 v3 t2   requests 22601 clips 45202 total khrs 2.465; N gpus for 1000 iters 2.825; 4 gpus for x iters 706.281; n unique users 13991 n pro users 13639\n",
    "# v2 v3 t3  requests 48158 clips 96316 total khrs 5.253; N gpus for 1000 iters 6.020; 4 gpus for x iters 1504.938; n unique users 26195 n pro users 25433\n",
    "# v2 v3 t4  requests 62132 clips 124264 total khrs 6.772; N gpus for 1000 iters 7.766; 4 gpus for x iters 1941.625; n unique users 32164 n pro users 31090\n",
    "# v2 v3 t8  requests 65469 clips 130938 total khrs 7.135; N gpus for 1000 iters 8.184; 4 gpus for x iters 2045.906; n unique users 33619 n pro users 32454\n",
    "# v2 v3 t9 requests 33031 clips 66062 total khrs 3.666; N gpus for 1000 iters 4.129; 4 gpus for x iters 1032.219; n unique users 21173 n pro users 20503\n",
    "# v2 v3 t10  requests 36607 clips 73214 total khrs 4.066; N gpus for 1000 iters 4.576; 4 gpus for x iters 1143.969; n unique users 23179 n pro users 22385\n",
    "# v2 t3 t11  requests 43078 clips 86156 total khrs 4.788; N gpus for 1000 iters 5.385; 4 gpus for x iters 1346.188; n unique users 26646 n pro users 25611\n",
    "# v2 t3 t14  requests 45156 clips 90312 total khrs 5.018; N gpus for 1000 iters 5.644; 4 gpus for x iters 1411.125; n unique users 27724 n pro users 26626\n",
    "# v2 t3 t20  requests 48822 clips 97644 total khrs 5.425; N gpus for 1000 iters 6.103; 4 gpus for x iters 1525.688; n unique users 29583 n pro users 28314\n",
    "# v2 t3 t22  requests 173478 clips 346956 total khrs 18.892; N gpus for 1000 iters 21.685; 4 gpus for x iters 5421.188; n unique users 67818 n pro users 64118\n",
    "# v2 t4 t1   requests 5563 clips 11126 total khrs 0.611; N gpus for 1000 iters 0.695; 4 gpus for x iters 173.844; n unique users 4514 n pro users 4478\n",
    "# v2 t4 t3  requests 4667 clips 9334 total khrs 0.513; N gpus for 1000 iters 0.583; 4 gpus for x iters 145.844; n unique users 3884 n pro users 3851\n",
    "# v2 t4 t4  requests 5563 clips 11126 total khrs 0.611; N gpus for 1000 iters 0.695; 4 gpus for x iters 173.844; n unique users 4514 n pro users 4478\n",
    "# v2 t4 t5   requests 6589 clips 13178 total khrs 0.718; N gpus for 1000 iters 0.824; 4 gpus for x iters 205.906; n unique users 5204 n pro users 5160\n",
    "# v2 t4 t9  requests 4836 clips 9672 total khrs 0.534; N gpus for 1000 iters 0.605; 4 gpus for x iters 151.125; n unique users 3827 n pro users 3799\n",
    "# v2 t4 t16  requests 11003 clips 22006 total khrs 1.217; N gpus for 1000 iters 1.375; 4 gpus for x iters 343.844; n unique users 7822 n pro users 7724\n",
    "# v2 t4 t17 requests 15605 clips 31210 total khrs 1.724; N gpus for 1000 iters 1.951; 4 gpus for x iters 487.656; n unique users 10557 n pro users 10374\n",
    "# v2 t4 t18  requests 21479 clips 42958 total khrs 2.385; N gpus for 1000 iters 2.685; 4 gpus for x iters 671.219; n unique users 13858 n pro users 13589\n",
    "# v2 t4 t19  requests 19816 clips 39632 total khrs 2.206; N gpus for 1000 iters 2.477; 4 gpus for x iters 619.250; n unique users 13045 n pro users 12793\n",
    "# v2 t4 t20  requests 17780 clips 35560 total khrs 1.979; N gpus for 1000 iters 2.223; 4 gpus for x iters 555.625; n unique users 12041 n pro users 11810\n",
    "# v2 t4 t21  requests 69490 clips 138980 total khrs 7.583; N gpus for 1000 iters 8.686; 4 gpus for x iters 2171.562; n unique users 33013 n pro users 32231\n",
    "# v2 t4 t23  requests 20702 clips 41404 total khrs 2.273; N gpus for 1000 iters 2.588; 4 gpus for x iters 646.938; n unique users 10816 n pro users 10610\n",
    "# v2 t4 t24  requests 9801 clips 19602 total khrs 1.079; N gpus for 1000 iters 1.225; 4 gpus for x iters 306.281; n unique users 2464 n pro users 2442\n",
    "# v2 t4 t25   requests 14973 clips 29946 total khrs 1.649; N gpus for 1000 iters 1.872; 4 gpus for x iters 467.906; n unique users 5062 n pro users 4978\n",
    "# v2 t4 t26   requests 20952 clips 41904 total khrs 2.274; N gpus for 1000 iters 2.619; 4 gpus for x iters 654.750; n unique users 4129 n pro users 4071\n",
    "# v2 t4 t27  requests 7953 clips 15906 total khrs 0.870; N gpus for 1000 iters 0.994; 4 gpus for x iters 248.531; n unique users 1940 n pro users 1919\n",
    "# v2 t4 t28   requests 7937 clips 15874 total khrs 0.890; N gpus for 1000 iters 0.992; 4 gpus for x iters 248.031; n unique users 2152 n pro users 2135\n",
    "# v2 t4 t39  requests 12626 clips 25252 total khrs 1.418; N gpus for 1000 iters 1.578; 4 gpus for x iters 394.562; n unique users 3352 n pro users 328\n",
    "# carp t1 v1  requests 944 clips 1888 total khrs 0.109; N gpus for 1000 iters 0.118; 4 gpus for x iters 29.500; n unique users 872 n pro users 867\n",
    "# carp t1 v2  requests 5046 clips 10092 total khrs 0.585; N gpus for 1000 iters 0.631; 4 gpus for x iters 157.688; n unique users 4309 n pro users 4255\n",
    "# dorado t1 v1  requests 3012 clips 6024 total khrs 0.345; N gpus for 1000 iters 0.377; 4 gpus for x iters 94.125; n unique users 2605 n pro users 2592\n",
    "# dorado t1 v2  requests 5448 clips 10896 total khrs 0.620; N gpus for 1000 iters 0.681; 4 gpus for x iters 170.250; n unique users 4557 n pro users 4512\n",
    "# dorado t1 v3 requests 10232 clips 20464 total khrs 1.120; N gpus for 1000 iters 1.279; 4 gpus for x iters 319.750; n unique users 8375 n pro users 8290"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.277006Z",
     "start_time": "2024-05-16T13:59:41.252105Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:05.707811Z",
     "iopub.status.busy": "2025-10-27T22:47:05.707665Z",
     "iopub.status.idle": "2025-10-27T22:47:05.728674Z",
     "shell.execute_reply": "2025-10-27T22:47:05.728182Z",
     "shell.execute_reply.started": "2025-10-27T22:47:05.707795Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (3678, 124)\n"
     ]
    }
   ],
   "source": [
    "test_mask = (df_slice[\"preference\"]) & (\n",
    "    (df_slice[\"is_in_playlist\"]) | (df_slice[\"concat_in_playlist\"])\n",
    ")\n",
    "print(\"positive in playlist\", df_slice[test_mask].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:05.729431Z",
     "iopub.status.busy": "2025-10-27T22:47:05.729289Z",
     "iopub.status.idle": "2025-10-27T22:47:06.456851Z",
     "shell.execute_reply": "2025-10-27T22:47:06.456375Z",
     "shell.execute_reply.started": "2025-10-27T22:47:05.729416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said -1 means stero to mono; 1 means mono to stereo\n",
    "# only cut off the left side\n",
    "df_slice[\"stereo_width_diff\"] = df_slice[\n",
    "    \"stereo_width\"\n",
    "].diff()  # / df_slice[\"stereo_width\"]\n",
    "# df_slice[\"stereo_width_diff\"] = df_slice[\"stereo_width\"]\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"stereo_width_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"stereo_width_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[(df_slice[\"preference\"]) & (df_slice[\"source\"] == \"web\")][\n",
    "        \"stereo_width_diff\"\n",
    "    ],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[(df_slice['preference']) & (df_slice['source'] == 'web')]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Stereo Width Difference --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:06.457526Z",
     "iopub.status.busy": "2025-10-27T22:47:06.457380Z",
     "iopub.status.idle": "2025-10-27T22:47:06.902527Z",
     "shell.execute_reply": "2025-10-27T22:47:06.902032Z",
     "shell.execute_reply.started": "2025-10-27T22:47:06.457512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16666666666666666\n",
      "0.7666666666666667\n",
      "2.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[~df_slice[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"neg, mean: {np.mean(df_slice[~df_slice['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"total_shimmer_score\"].dropna(), [50, 75, 90]\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    print(percentile)\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.yscale(\"log\")\n",
    "plt.title(f\"Shimmer score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:06.903244Z",
     "iopub.status.busy": "2025-10-27T22:47:06.903089Z",
     "iopub.status.idle": "2025-10-27T22:47:07.152689Z",
     "shell.execute_reply": "2025-10-27T22:47:07.152212Z",
     "shell.execute_reply.started": "2025-10-27T22:47:06.903228Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df_slice[\"loudness_diff\"] = df_slice[\"loudness_abs\"].diff() / df_slice[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:07.153366Z",
     "iopub.status.busy": "2025-10-27T22:47:07.153222Z",
     "iopub.status.idle": "2025-10-27T22:47:07.169381Z",
     "shell.execute_reply": "2025-10-27T22:47:07.168930Z",
     "shell.execute_reply.started": "2025-10-27T22:47:07.153351Z"
    }
   },
   "outputs": [],
   "source": [
    "# tr_metas_t1_v7 = read_jsonl(os.path.join(\"/app2/suno/data/dpo/diff2_v2_d3_v10\", f\"metas_tr.jsonl\"))\n",
    "# known_train_ids = set()\n",
    "# for prev_tr_meta in tr_metas_t1_v7:\n",
    "#     known_train_ids.add(prev_tr_meta[\"id_x\"])\n",
    "# print(len(known_train_ids))\n",
    "# print(df_slice.shape)\n",
    "# df_slice = df_slice[~df_slice[\"id\"].isin(known_train_ids)].copy()\n",
    "# print(df_slice.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:07.170152Z",
     "iopub.status.busy": "2025-10-27T22:47:07.170009Z",
     "iopub.status.idle": "2025-10-27T22:47:07.184308Z",
     "shell.execute_reply": "2025-10-27T22:47:07.183886Z",
     "shell.execute_reply.started": "2025-10-27T22:47:07.170137Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[\"created_at\"] = pd.to_datetime(df_slice[\"created_at\"], utc=True)\n",
    "# cutoff_date = pd.to_datetime(\"2025-05-12\", utc=True)\n",
    "# print(df_slice.shape, df_slice[df_slice[\"created_at\"] >= cutoff_date].shape)\n",
    "# df_slice = df_slice[(df_slice[\"created_at\"] >= cutoff_date)].copy()\n",
    "# print(\"after date cut\", df_slice.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:07.185020Z",
     "iopub.status.busy": "2025-10-27T22:47:07.184773Z",
     "iopub.status.idle": "2025-10-27T22:47:07.202658Z",
     "shell.execute_reply": "2025-10-27T22:47:07.202197Z",
     "shell.execute_reply.started": "2025-10-27T22:47:07.185006Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web        11768\n",
      "android     8594\n",
      "ios         5810\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:07.203267Z",
     "iopub.status.busy": "2025-10-27T22:47:07.203132Z",
     "iopub.status.idle": "2025-10-27T22:47:13.323934Z",
     "shell.execute_reply": "2025-10-27T22:47:13.323343Z",
     "shell.execute_reply.started": "2025-10-27T22:47:07.203253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROD\n"
     ]
    }
   ],
   "source": [
    "# fetch lyrics\n",
    "home_dir = os.path.expanduser(\"~\")\n",
    "snow_password_path = os.path.join(home_dir, \".aws\", \"snow_pw.txt\")\n",
    "if os.path.exists(snow_password_path):\n",
    "    # !pip install snowflake\n",
    "    from snowflake.core import Root\n",
    "    from snowflake.snowpark import Session\n",
    "\n",
    "    with open(snow_password_path, \"r\") as fp:\n",
    "        fp_lines = fp.readlines()\n",
    "        snow_password = fp_lines[0].strip()\n",
    "        snow_username = fp_lines[1].strip()\n",
    "\n",
    "    CONNECTION_PARAMETERS = {\n",
    "        \"account\": \"fu90569.us-east-2.aws\",\n",
    "        \"user\": snow_username,\n",
    "        \"private_key_file\": \"/home/tony/.aws/rsa_key.p8\",\n",
    "        \"role\": \"ACCOUNTADMIN\",\n",
    "        \"database\": \"SUNO_PROD\",\n",
    "        \"warehouse\": \"SUNO_PROD_LARGE\",\n",
    "        \"schema\": \"PROD\",\n",
    "    }\n",
    "\n",
    "if not os.path.exists(snow_password_path):\n",
    "    raise Exception(\"you are not authorized to access snowflake -- please setup\")\n",
    "\n",
    "snow_session = Session.builder.configs(CONNECTION_PARAMETERS).create()\n",
    "\n",
    "snow_root = Root(snow_session)\n",
    "snow_schema = snow_root.databases[\"SUNO_PROD\"].schemas[\"PROD\"]\n",
    "print(snow_schema.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-27T22:47:13.324662Z",
     "iopub.status.busy": "2025-10-27T22:47:13.324511Z",
     "iopub.status.idle": "2025-10-27T22:48:14.664578Z",
     "shell.execute_reply": "2025-10-27T22:48:14.664067Z",
     "shell.execute_reply.started": "2025-10-27T22:47:13.324646Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 26172\n",
      "Length of the ID query string: 1020707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [01:01<00:00, 61.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Shape of df_snow_test:\n",
      "Rows: 26159\n",
      "Columns: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False    24368\n",
       "True      1804\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v4_clip_ids = list(str(s) for s in df_slice[\"id\"].unique())\n",
    "snow_batch_size = 100_000\n",
    "snow_results = []\n",
    "\n",
    "for clip_ids_chunk in tqdm(\n",
    "    [\n",
    "        v4_clip_ids[i : i + snow_batch_size]\n",
    "        for i in range(0, len(v4_clip_ids), snow_batch_size)\n",
    "    ]\n",
    "):\n",
    "    id_query_str = \",\".join(\"'\" + x + \"'\" for x in clip_ids_chunk)\n",
    "    print(f\"Number of clip IDs in this chunk: {len(clip_ids_chunk)}\")\n",
    "    print(f\"Length of the ID query string: {len(id_query_str)}\")\n",
    "\n",
    "    session_query = snow_session.sql(\n",
    "        f\"\"\"select ID, PROMPT_TEXT\n",
    "        from DDB_CLIP_META_HEAVY\n",
    "        where ID in ({id_query_str})\n",
    "        order by p_hour desc;\"\"\"\n",
    "    )\n",
    "    temp_df_snow_test = pd.DataFrame(session_query.collect())\n",
    "    snow_results.append(temp_df_snow_test)\n",
    "print(len(snow_results))\n",
    "df_snow_test = pd.concat(snow_results)\n",
    "df_snow_test = df_snow_test.rename(columns=lambda x: x.lower())\n",
    "# df_snow_test = df_snow_test.rename(columns={\"song_id\": \"str_id\"})\n",
    "print(\"Shape of df_snow_test:\")\n",
    "print(f\"Rows: {df_snow_test.shape[0]}\")\n",
    "print(f\"Columns: {df_snow_test.shape[1]}\")\n",
    "df_slice[\"id\"] = df_slice[\"id\"].astype(str)\n",
    "df_slice = df_slice.rename(columns={\"prompt_text\": \"prompt_text_old\"})\n",
    "df_slice = df_slice.merge(df_snow_test, on=\"id\", how=\"left\")\n",
    "(df_slice[\"prompt_text\"] == df_slice[\"prompt_text_old\"]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-10-27T22:48:14.665385Z",
     "iopub.status.busy": "2025-10-27T22:48:14.665148Z",
     "iopub.status.idle": "2025-10-27T22:48:16.116534Z",
     "shell.execute_reply": "2025-10-27T22:48:16.115783Z",
     "shell.execute_reply.started": "2025-10-27T22:48:14.665369Z"
    }
   },
   "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[49], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# df_slice.to_pickle(\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#     f\"/home/tony/Data/Preference/up_v2_d3/interesting_clips_ahi_d3_20250519_slice.pkl\",\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# print(df_slice.shape)\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[43mBREAK\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BREAK' is not defined"
     ]
    }
   ],
   "source": [
    "# df_slice.to_pickle(\n",
    "#     f\"/home/tony/Data/Preference/up_v2_d3/interesting_clips_ahi_d3_20250519_slice.pkl\",\n",
    "# )\n",
    "# print(df_slice.shape)\n",
    "BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need to kick out the ones has gpt prompt -- these are pairs with different text inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.116942Z",
     "iopub.status.idle": "2025-10-27T22:48:16.117123Z",
     "shell.execute_reply": "2025-10-27T22:48:16.117040Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.117031Z"
    }
   },
   "outputs": [],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.117690Z",
     "iopub.status.idle": "2025-10-27T22:48:16.117855Z",
     "shell.execute_reply": "2025-10-27T22:48:16.117776Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.117768Z"
    }
   },
   "outputs": [],
   "source": [
    "train_requests, val_requests = train_test_split(\n",
    "    sorted(list(final_filtered_requests)), test_size=0.01, random_state=42\n",
    ")\n",
    "print(len(train_requests), len(val_requests))\n",
    "\n",
    "train_df = df_slice[df_slice[\"request_id\"].isin(set(train_requests))].copy()\n",
    "val_df = df_slice[df_slice[\"request_id\"].isin(set(val_requests))].copy()\n",
    "train_df = train_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "train_df = train_df  # .reset_index()\n",
    "val_df = val_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "val_df = val_df  # .reset_index()\n",
    "\n",
    "print(train_df.shape, val_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.118390Z",
     "iopub.status.idle": "2025-10-27T22:48:16.118547Z",
     "shell.execute_reply": "2025-10-27T22:48:16.118476Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.118468Z"
    }
   },
   "outputs": [],
   "source": [
    "BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.118984Z",
     "iopub.status.idle": "2025-10-27T22:48:16.119141Z",
     "shell.execute_reply": "2025-10-27T22:48:16.119066Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.119058Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.119419Z",
     "iopub.status.idle": "2025-10-27T22:48:16.119562Z",
     "shell.execute_reply": "2025-10-27T22:48:16.119495Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.119488Z"
    }
   },
   "outputs": [],
   "source": [
    "total_duration = 0\n",
    "for i, row in tqdm(train_df.iterrows(), total=len(train_df)):\n",
    "    # we need to alternate between preference: neg, pos\n",
    "    # print(i, row)\n",
    "    try:\n",
    "        assert row[\"preference\"] == (i % 2 == 1)\n",
    "    except:\n",
    "        print(i, row)\n",
    "    total_duration += row[\"duration\"]\n",
    "print(\n",
    "    f\"{round(total_duration / 60 / 60):,} hours of {train_df.shape[0]} clips, {train_df.shape[0] / 8 / 2 / 1000} nodes, {train_df.shape[0] / 8 / 2 / 4} steps\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.119822Z",
     "iopub.status.idle": "2025-10-27T22:48:16.119963Z",
     "shell.execute_reply": "2025-10-27T22:48:16.119897Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.119890Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    val_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=True,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.120531Z",
     "iopub.status.idle": "2025-10-27T22:48:16.120674Z",
     "shell.execute_reply": "2025-10-27T22:48:16.120607Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.120600Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_npz = np.load(\"/app/suno/data/dpo/diff2_v2/506a8426-551f-45d9-9638-b1fa673cc031.npz\")\n",
    "# for key in test_npz.keys():\n",
    "#     print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.120928Z",
     "iopub.status.idle": "2025-10-27T22:48:16.121063Z",
     "shell.execute_reply": "2025-10-27T22:48:16.121000Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.120993Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    train_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=False,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.121488Z",
     "iopub.status.idle": "2025-10-27T22:48:16.121636Z",
     "shell.execute_reply": "2025-10-27T22:48:16.121568Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.121560Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.121929Z",
     "iopub.status.idle": "2025-10-27T22:48:16.122067Z",
     "shell.execute_reply": "2025-10-27T22:48:16.122004Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.121996Z"
    }
   },
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-29T19:46:47.549860Z",
     "start_time": "2024-01-29T19:46:47.548015Z"
    }
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.122470Z",
     "iopub.status.idle": "2025-10-27T22:48:16.122616Z",
     "shell.execute_reply": "2025-10-27T22:48:16.122549Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.122542Z"
    }
   },
   "outputs": [],
   "source": [
    "# verify\n",
    "metas_val = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_val.jsonl\"))\n",
    "print(len(metas_val) / 2)\n",
    "mm_semantic_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_semantic_val.bin\"), dtype=np.uint16, mode=\"r\"\n",
    ")\n",
    "mm_vae_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    ")\n",
    "\n",
    "\n",
    "mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "print(mm_vae_val.shape)\n",
    "\n",
    "mm_semantic_val = mm_semantic_val.reshape(-1, SEMANTIC_MEMMAP_SIZE)\n",
    "print(mm_semantic_val.shape)\n",
    "\n",
    "assert len(metas_val) == mm_vae_val.shape[0] == mm_semantic_val.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.123074Z",
     "iopub.status.idle": "2025-10-27T22:48:16.123216Z",
     "shell.execute_reply": "2025-10-27T22:48:16.123151Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.123143Z"
    }
   },
   "outputs": [],
   "source": [
    "# # load codec for decoding\n",
    "# from suno_utils.tasks.dac_vae_100hz_peaq import (  # NOTE: works for 25hz as well\n",
    "#     preload_models as preload_codec_models,\n",
    "#     decode as codec_decode,\n",
    "#     encode as codec_encode,\n",
    "#     get_embedding_rate,\n",
    "#     load_model as load_codec_model,\n",
    "# )\n",
    "\n",
    "# CODEC_FILEPATH = \"s3://suno-data/christian/25hz_vae_peaq_kl_0.005.pth\"\n",
    "# preload_codec_models(CODEC_FILEPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.123662Z",
     "iopub.status.idle": "2025-10-27T22:48:16.123810Z",
     "shell.execute_reply": "2025-10-27T22:48:16.123740Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.123733Z"
    }
   },
   "outputs": [],
   "source": [
    "# # decode some audio\n",
    "idx = 108\n",
    "# # ensure even index\n",
    "assert idx % 2 == 0\n",
    "# print(metas_val[idx])\n",
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[idx])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(metas_val[idx + 1])\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[idx + 1])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.124212Z",
     "iopub.status.idle": "2025-10-27T22:48:16.124361Z",
     "shell.execute_reply": "2025-10-27T22:48:16.124290Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.124283Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.124709Z",
     "iopub.status.idle": "2025-10-27T22:48:16.124848Z",
     "shell.execute_reply": "2025-10-27T22:48:16.124783Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.124776Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.equal(torch.tensor(mm_semantic_val[idx]), torch.tensor(mm_semantic_val[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.125295Z",
     "iopub.status.idle": "2025-10-27T22:48:16.125440Z",
     "shell.execute_reply": "2025-10-27T22:48:16.125371Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.125364Z"
    }
   },
   "outputs": [],
   "source": [
    "# original_npz_path = f\"/app/suno/data/dpo/7b_npz/{test_metas[idx]['id']}.npz\"\n",
    "# original_npz_path = \"/app/suno/data/dpo/7b_npz/729c3011-f672-4ccd-8d82-1cbf2b52ff69.npz\"\n",
    "# original_arr = np.load(original_npz_path)[\"v2_raw\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.125839Z",
     "iopub.status.idle": "2025-10-27T22:48:16.125978Z",
     "shell.execute_reply": "2025-10-27T22:48:16.125913Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.125907Z"
    }
   },
   "outputs": [],
   "source": [
    "def validation_on_metas(input_metas):\n",
    "    total_bad = 0\n",
    "    total_good = 0\n",
    "    for idx in range(len(input_metas)):\n",
    "        if idx % 2 == 0:\n",
    "            pos_idx = idx + 1\n",
    "            if input_metas[idx].get(\"tags\") != input_metas[pos_idx].get(\"tags\"):\n",
    "                # print(test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"), test_metas[idx].get(\"tags\"), test_metas[pos_idx].get(\"tags\"))\n",
    "                total_bad += 1\n",
    "            else:\n",
    "                total_good += 1\n",
    "    print(total_good, total_bad)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.126273Z",
     "iopub.status.idle": "2025-10-27T22:48:16.126413Z",
     "shell.execute_reply": "2025-10-27T22:48:16.126346Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.126339Z"
    }
   },
   "outputs": [],
   "source": [
    "metas_tr = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_tr.jsonl\"))\n",
    "validation_on_metas(metas_tr)\n",
    "print(len(metas_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.126921Z",
     "iopub.status.idle": "2025-10-27T22:48:16.127067Z",
     "shell.execute_reply": "2025-10-27T22:48:16.127000Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.126992Z"
    }
   },
   "outputs": [],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.127478Z",
     "iopub.status.idle": "2025-10-27T22:48:16.127619Z",
     "shell.execute_reply": "2025-10-27T22:48:16.127554Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.127547Z"
    }
   },
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    },
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.128002Z",
     "iopub.status.idle": "2025-10-27T22:48:16.128142Z",
     "shell.execute_reply": "2025-10-27T22:48:16.128076Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.128069Z"
    }
   },
   "outputs": [],
   "source": [
    "# !cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion_infill.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.128469Z",
     "iopub.status.idle": "2025-10-27T22:48:16.129241Z",
     "shell.execute_reply": "2025-10-27T22:48:16.128543Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.128536Z"
    }
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "# Basic file copy\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/make_dataset_diff_dorado_v1.ipynb\",\n",
    "    os.path.join(OUT_DATA_DIR, \"make_dataset.ipynb\"),\n",
    ")\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/preference_data_preparation_diff.py\",\n",
    "    os.path.join(OUT_DATA_DIR, \"preference_data_preparation_diff.py\"),\n",
    ")\n",
    "print(\"Cache kept!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.129839Z",
     "iopub.status.idle": "2025-10-27T22:48:16.130021Z",
     "shell.execute_reply": "2025-10-27T22:48:16.129944Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.129935Z"
    }
   },
   "outputs": [],
   "source": [
    "# df[df[\"preference\"] & (df[\"shimmer_score_diff\"] > 3)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()\n",
    "\n",
    "# df[df[\"preference\"] & (df[\"pair_quality\"] < 0.1)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"pair_quality\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.130457Z",
     "iopub.status.idle": "2025-10-27T22:48:16.130608Z",
     "shell.execute_reply": "2025-10-27T22:48:16.130538Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.130531Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_pair_df = df[df[\"request_id\"] == \"621c8b02-a905-48f1-a2d5-a4e8423d1505\"]\n",
    "# print(\n",
    "#     test_pair_df[\n",
    "#         [\n",
    "#             \"s3_id\",\n",
    "#             \"total_shimmer_score\",\n",
    "#             \"pair_quality\",\n",
    "#             \"request_id\",\n",
    "#             \"preference\",\n",
    "#             \"prompt_text\",\n",
    "#         ]\n",
    "#     ]\n",
    "# )\n",
    "# negative_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[0]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"negative\")\n",
    "# negative_audio.get_segment(0, 30).play()\n",
    "# positive_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[1]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"positive\")\n",
    "# positive_audio.get_segment(0, 30).play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.130903Z",
     "iopub.status.idle": "2025-10-27T22:48:16.131048Z",
     "shell.execute_reply": "2025-10-27T22:48:16.130981Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.130974Z"
    }
   },
   "outputs": [],
   "source": [
    "# total_dict = {}\n",
    "# total_dict.update(pair_quality_dict)\n",
    "# total_dict.update(pair_quality_1_dict)\n",
    "# total_dict.update(pair_quality_2_dict)\n",
    "# total_dict.update(pair_quality_3_dict)\n",
    "# len(total_dict)\n",
    "# with open(\n",
    "#     os.path.join(\"/home/tony/Data/Preference/up_v1\", \"pair_quality.json\"), \"w\"\n",
    "# ) as fp:\n",
    "#     json.dump(total_dict, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.131344Z",
     "iopub.status.idle": "2025-10-27T22:48:16.131488Z",
     "shell.execute_reply": "2025-10-27T22:48:16.131420Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.131413Z"
    }
   },
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.131766Z",
     "iopub.status.idle": "2025-10-27T22:48:16.131918Z",
     "shell.execute_reply": "2025-10-27T22:48:16.131849Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.131841Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_arr = np.load(\"/home/tony/Data/test_npz/diffusion_input_tensor([ 18, 182]).npy\")\n",
    "# test_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.132393Z",
     "iopub.status.idle": "2025-10-27T22:48:16.132540Z",
     "shell.execute_reply": "2025-10-27T22:48:16.132473Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.132465Z"
    }
   },
   "outputs": [],
   "source": [
    "# mm_vae_val = np.memmap(\n",
    "#     os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    "# )\n",
    "\n",
    "# mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "# print(mm_vae_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.133007Z",
     "iopub.status.idle": "2025-10-27T22:48:16.133150Z",
     "shell.execute_reply": "2025-10-27T22:48:16.133084Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.133077Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(test_arr[0].T / 2.5)\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(test_arr[1].T / 2.5)\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.133546Z",
     "iopub.status.idle": "2025-10-27T22:48:16.133688Z",
     "shell.execute_reply": "2025-10-27T22:48:16.133621Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.133614Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[18])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[19])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.133971Z",
     "iopub.status.idle": "2025-10-27T22:48:16.134111Z",
     "shell.execute_reply": "2025-10-27T22:48:16.134045Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.134039Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.134510Z",
     "iopub.status.idle": "2025-10-27T22:48:16.134662Z",
     "shell.execute_reply": "2025-10-27T22:48:16.134592Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.134584Z"
    }
   },
   "outputs": [],
   "source": [
    "t = rng.draw(4)[:, 0].to(torch.bfloat16)\n",
    "print(t)\n",
    "# Replace 1% of t with ones to ensure training on terminal SNR\n",
    "t = torch.where(torch.rand_like(t) < 0.5, torch.ones_like(t), t)\n",
    "print(t)\n",
    "t = torch.repeat_interleave(t, repeats=2, dim=0)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.135065Z",
     "iopub.status.idle": "2025-10-27T22:48:16.135213Z",
     "shell.execute_reply": "2025-10-27T22:48:16.135144Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.135137Z"
    }
   },
   "outputs": [],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.135522Z",
     "iopub.status.idle": "2025-10-27T22:48:16.135663Z",
     "shell.execute_reply": "2025-10-27T22:48:16.135597Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.135590Z"
    }
   },
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.136122Z",
     "iopub.status.idle": "2025-10-27T22:48:16.136265Z",
     "shell.execute_reply": "2025-10-27T22:48:16.136198Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.136191Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge jsons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.136732Z",
     "iopub.status.idle": "2025-10-27T22:48:16.136875Z",
     "shell.execute_reply": "2025-10-27T22:48:16.136808Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.136801Z"
    }
   },
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/dorado_t1/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# # result = {}\n",
    "# print(len(result))\n",
    "# for job_idx in range(8):\n",
    "#     with open(f\"/home/tony/Data/Preference/dorado_t1/full_pair_quality_{job_idx}.json\", \"r\") as fp:\n",
    "#         current_result = json.load(fp)\n",
    "#         result.update(current_result)\n",
    "# print(len(result))\n",
    "\n",
    "# with open(f\"/home/tony/Data/Preference/dorado_t1/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.137267Z",
     "iopub.status.idle": "2025-10-27T22:48:16.137416Z",
     "shell.execute_reply": "2025-10-27T22:48:16.137347Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.137340Z"
    }
   },
   "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": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.137810Z",
     "iopub.status.idle": "2025-10-27T22:48:16.137954Z",
     "shell.execute_reply": "2025-10-27T22:48:16.137887Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.137880Z"
    }
   },
   "outputs": [],
   "source": [
    "# import torch\n",
    "# semantic_codes_chunk = torch.ones((1, 100))\n",
    "# semantic_skip_phase = 0\n",
    "# semantic_skip_factor = 4\n",
    "# mask = torch.ones_like(semantic_codes_chunk, dtype=torch.bool)\n",
    "# indices = (\n",
    "#     torch.arange(semantic_codes_chunk.size(1)) + semantic_skip_phase\n",
    "# ) % semantic_skip_factor == 0\n",
    "# mask[:, indices] = False\n",
    "# mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.138264Z",
     "iopub.status.idle": "2025-10-27T22:48:16.138398Z",
     "shell.execute_reply": "2025-10-27T22:48:16.138335Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.138329Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.138790Z",
     "iopub.status.idle": "2025-10-27T22:48:16.138929Z",
     "shell.execute_reply": "2025-10-27T22:48:16.138863Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.138857Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] < 0.5)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-10-27T22:48:16.139307Z",
     "iopub.status.idle": "2025-10-27T22:48:16.139450Z",
     "shell.execute_reply": "2025-10-27T22:48:16.139383Z",
     "shell.execute_reply.started": "2025-10-27T22:48:16.139376Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] > 2)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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