{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Preference Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:04.757824Z",
     "start_time": "2024-05-26T00:11:04.555293Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:08:42.016823Z",
     "iopub.status.busy": "2025-07-28T14:08:42.016577Z",
     "iopub.status.idle": "2025-07-28T14:08:42.188965Z",
     "shell.execute_reply": "2025-07-28T14:08:42.188629Z",
     "shell.execute_reply.started": "2025-07-28T14:08:42.016809Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup tailscale if you haven't\n",
    "# https://tailscale.com/kb/1031/install-linux\n",
    "!sudo tailscale up --accept-routes=true\n",
    "\n",
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:08.392310Z",
     "start_time": "2024-05-26T00:11:04.759383Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:08:42.190493Z",
     "iopub.status.busy": "2025-07-28T14:08:42.190294Z",
     "iopub.status.idle": "2025-07-28T14:08:48.500589Z",
     "shell.execute_reply": "2025-07-28T14:08:48.500090Z",
     "shell.execute_reply.started": "2025-07-28T14:08:42.190480Z"
    }
   },
   "outputs": [],
   "source": [
    "# make sure sqlalchemy is >=2\n",
    "# pip install psycopg2-binary\n",
    "# pip install \"sqlalchemy>=2\"\n",
    "import os\n",
    "import datetime\n",
    "from collections import defaultdict, Counter\n",
    "import json\n",
    "from urllib.parse import quote\n",
    "import time\n",
    "\n",
    "import boto3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import tqdm\n",
    "from botocore.exceptions import ClientError\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "from suno_analytics.preference_data_selection import (\n",
    "    gather_data,\n",
    "    gather_data_with_snowflake,\n",
    "    plot_clip_distribution,\n",
    "    parse_metadata_for_basics,\n",
    "    get_concat_clip_ids,\n",
    "    validate_preference_data,\n",
    "    run_bot_detection,\n",
    "    print_out_value_counts_nicely,\n",
    "    merge_concat_clips_with_reactions,\n",
    "    plot_clip_basic_distributions,\n",
    ")\n",
    "\n",
    "\n",
    "# setup some pandas display stuff\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)\n",
    "\n",
    "\n",
    "def get_secret():\n",
    "    secret_name = \"app-user-main-db-secret\"\n",
    "    region_name = \"us-east-2\"\n",
    "    # Create a Secrets Manager client\n",
    "    session = boto3.session.Session()\n",
    "    client = session.client(service_name=\"secretsmanager\", region_name=region_name)\n",
    "    try:\n",
    "        get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n",
    "    except ClientError as e:\n",
    "        raise e\n",
    "    secret = get_secret_value_response[\"SecretString\"]\n",
    "    return json.loads(secret)\n",
    "\n",
    "\n",
    "my_secrets = get_secret()\n",
    "\n",
    "# alternative...\n",
    "engine = sqlalchemy.create_engine(\n",
    "    \"postgresql://suno:%s@suno-main-postgres-prod-analytics.cnfvffydbwvc.us-east-2.rds.amazonaws.com/suno_main\"\n",
    "    % quote(my_secrets[\"password\"]),\n",
    ")\n",
    "\n",
    "\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",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate some info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:08.550447Z",
     "start_time": "2024-05-26T00:11:08.397196Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:08:48.502677Z",
     "iopub.status.busy": "2025-07-28T14:08:48.502422Z",
     "iopub.status.idle": "2025-07-28T14:08:48.519162Z",
     "shell.execute_reply": "2025-07-28T14:08:48.518786Z",
     "shell.execute_reply.started": "2025-07-28T14:08:48.502664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-07-28 14:08:48.517426 1753711728.5174296 2025-05-06 23:20:00\n"
     ]
    }
   ],
   "source": [
    "# there are 4 hr time difference between eastern time and utc\n",
    "# cutoff_date = \"2024-08-26 21:00:00\"  # v4-t3 out\n",
    "# cutoff_date = \"2024-09-12 21:00:00\"  # covers beta out\n",
    "# cutoff_date = \"2024-09-22 00:00:00\"  # pre fe exp out\n",
    "# cutoff_date = \"2024-09-26 12:00:00\"  # s29 out\n",
    "# cutoff_date = \"2024-10-09 15:20:00\"  # 30b t5 out\n",
    "# cutoff_date = \"2024-10-31 16:00:00\"  # 30b t6 out\n",
    "# cutoff_date = \"2024-11-12 13:00:00\"  # 30b t6-2 out\n",
    "# cutoff_date = \"2024-11-19 16:00:00\"  # v4 out\n",
    "# cutoff_date = \"2024-12-16 20:40:00\"  # v4 s32 out\n",
    "# cutoff_date = \"2025-01-28 15:30:00\"  # diff v4 out\n",
    "# cutoff_date = \"2025-01-30 01:45:00\"  # diff v4 out with cfg...\n",
    "# cutoff_date = \"2025-02-21 22:15:00\"  # diff v5 out\n",
    "# cutoff_date = \"2025-03-06 19:00:00\"  # diff v6 out\n",
    "# cutoff_date = \"2025-03-24 00:00:00\"  #  diff v7 out\n",
    "# cutoff_date = \"2025-03-24 23:15:00\"  #  diff v2 data collection out\n",
    "# cutoff_date = \"2025-05-01 00:00:00\"  # auk out\n",
    "cutoff_date = \"2025-05-06 23:20:00\"  # auk-og out\n",
    "# cutoff_date = \"2025-06-06 00:00:00\"  # auk-og out\n",
    "# cutoff_date = \"2025-06-24 00:00:00\"  # partial data\n",
    "# cutoff_date = (\n",
    "#     (datetime.datetime.now() - datetime.timedelta(hours=4))\n",
    "#     .astimezone(datetime.timezone.utc)\n",
    "#     .strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "# )\n",
    "print(datetime.datetime.now(), time.time(), cutoff_date)\n",
    "\n",
    "target_model_name = \"chirp-v3p5-engine-t-6\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:11:09.349857Z",
     "start_time": "2024-05-26T00:11:08.551408Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:08:48.520746Z",
     "iopub.status.busy": "2025-07-28T14:08:48.520644Z",
     "iopub.status.idle": "2025-07-28T14:08:48.806068Z",
     "shell.execute_reply": "2025-07-28T14:08:48.805547Z",
     "shell.execute_reply.started": "2025-07-28T14:08:48.520735Z"
    }
   },
   "outputs": [],
   "source": [
    "df_all_tables = pd.read_sql_query(\n",
    "    \"SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'\",\n",
    "    engine,\n",
    ")\n",
    "# should have all the basic table names here\n",
    "assert df_all_tables[\"table_name\"].nunique() >= 61"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query the DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:08:48.807878Z",
     "iopub.status.busy": "2025-07-28T14:08:48.807773Z",
     "iopub.status.idle": "2025-07-28T14:08:49.748312Z",
     "shell.execute_reply": "2025-07-28T14:08:49.747838Z",
     "shell.execute_reply.started": "2025-07-28T14:08:48.807866Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROD\n"
     ]
    }
   ],
   "source": [
    "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)\n",
    "\n",
    "# from snowflake.snowpark.functions import col\n",
    "# !pip install \"snowflake-connector-python[pandas]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:08:49.750172Z",
     "iopub.status.busy": "2025-07-28T14:08:49.749962Z",
     "iopub.status.idle": "2025-07-28T14:29:01.387816Z",
     "shell.execute_reply": "2025-07-28T14:29:01.387239Z",
     "shell.execute_reply.started": "2025-07-28T14:08:49.750159Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start gather data from 2025-05-06 23:20:00\n",
      "Bots Action: 614,905,345 rows\n",
      " ---- Execution time: 330.30 seconds\n",
      "Reactions: 613,421,608 rows\n",
      " ---- Execution time: 318.44 seconds\n",
      "Discord Info: 760,935 rows\n",
      "subscription_status\n",
      "active      746092\n",
      "past_due     14843\n",
      "Name: count, dtype: int64\n",
      " ---- Execution time: 8.39 seconds\n",
      "Playlist Clips: 12,525,042 rows\n",
      " ---- Execution time: 17.74 seconds\n",
      "Filtering for model: chirp-auk-t0\n",
      "Total Clips: 9,611,181 rows\n",
      " ---- Execution time: 342.46 seconds\n",
      "Before user_n_clips filtering: 9,611,181 rows; \n",
      "After user_n_clips filtering: 9,597,708 rows; \n",
      "Before request_id filtering: 9,597,708 rows; na rows: 49,851\n",
      "Generation requests: 4,668,987 rows; na rows: 49,851\n",
      "After request_id filtering: 9,308,166 rows; na rows: 49,851\n",
      "Before is_deleted filtering: 9,308,166 rows; na rows: 49,851\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tony/Work/glockenspiel/suno_analytics/suno_analytics/preference_data_selection.py:489: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  valid_requests = request_deletion_status[\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After is_deleted filtering: 6,751,708 rows; na rows: 49,851\n"
     ]
    }
   ],
   "source": [
    "# gathered_data = gather_data(engine, cutoff_date)\n",
    "gathered_data = gather_data_with_snowflake(\n",
    "    snow_session, cutoff_date, filter_play_count=1, filter_user_n_clips=2, filter_model_name=\"chirp-auk-t0\"\n",
    ")\n",
    "# gathered_data = gather_data_with_snowflake(\n",
    "#     snow_session, cutoff_date, filter_play_count=1, filter_user_n_clips=20, filter_model_name=\"chirp-ahi-up-2\"\n",
    "# )\n",
    "# gathered_data = gather_data_with_snowflake(\n",
    "#     snow_session, cutoff_date, filter_play_count=1, filter_user_n_clips=20, filter_model_name=\"chirp-auk-t1\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:29:01.388784Z",
     "iopub.status.busy": "2025-07-28T14:29:01.388379Z",
     "iopub.status.idle": "2025-07-28T14:29:02.682040Z",
     "shell.execute_reply": "2025-07-28T14:29:02.681505Z",
     "shell.execute_reply.started": "2025-07-28T14:29:01.388769Z"
    }
   },
   "outputs": [],
   "source": [
    "bots_action_df = gathered_data[\"bots_action_df\"]\n",
    "reaction_df = gathered_data[\"reaction_df\"]\n",
    "total_clip_df = gathered_data[\"total_clip_df\"]\n",
    "playlist_clip_df = gathered_data[\"playlist_clip_df\"]\n",
    "discord_info_df = gathered_data[\"discord_info_df\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:29:02.682793Z",
     "iopub.status.busy": "2025-07-28T14:29:02.682575Z",
     "iopub.status.idle": "2025-07-28T14:29:18.150464Z",
     "shell.execute_reply": "2025-07-28T14:29:18.149929Z",
     "shell.execute_reply.started": "2025-07-28T14:29:02.682779Z"
    }
   },
   "outputs": [],
   "source": [
    "# parse out the necessary metadata early\n",
    "total_clip_df[\n",
    "    [\"continued_parent\", \"duration\", \"source\", \"clip_type\", \"task\", \"edited_clip_id\"]\n",
    "] = pd.DataFrame(\n",
    "    total_clip_df[\"metadata\"].map(parse_metadata_for_basics).tolist(),\n",
    "    index=total_clip_df.index,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:29:18.151287Z",
     "iopub.status.busy": "2025-07-28T14:29:18.151049Z",
     "iopub.status.idle": "2025-07-28T14:29:26.734724Z",
     "shell.execute_reply": "2025-07-28T14:29:26.734272Z",
     "shell.execute_reply.started": "2025-07-28T14:29:18.151273Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total clips: 6751708\n",
      "gen: 6701857 (99.26%)\n",
      "concat: 32876 (0.49%)\n",
      "edit_crop: 7998 (0.12%)\n",
      "edit_speed: 6448 (0.10%)\n",
      "edit_fade: 1270 (0.02%)\n",
      "rendered-project: 1259 (0.02%)\n",
      "total without model: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of hours: 1798\n",
      "Average clips per hour: 3755.12\n",
      "Max clips in an hour: 5238\n",
      "Min clips in an hour: 1\n"
     ]
    }
   ],
   "source": [
    "# filter on versions\n",
    "clip_df = total_clip_df.copy()\n",
    "total_clip_counts = clip_df.shape[0]\n",
    "print(f\"total clips: {total_clip_counts}\")\n",
    "print_out_value_counts_nicely(clip_df, \"clip_type\")\n",
    "# check the number of audio uploads\n",
    "upload_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"upload\"].copy()\n",
    "stem_clip_df = total_clip_df[total_clip_df[\"clip_type\"] == \"stem\"].copy()\n",
    "print(\"total without model:\", (total_clip_df[\"model_name\"] == \"\").sum())\n",
    "\n",
    "# Call the function\n",
    "plot_clip_distribution(total_clip_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceed with feature engineering and cleaning up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:29:26.735449Z",
     "iopub.status.busy": "2025-07-28T14:29:26.735224Z",
     "iopub.status.idle": "2025-07-28T14:29:38.307747Z",
     "shell.execute_reply": "2025-07-28T14:29:38.307224Z",
     "shell.execute_reply.started": "2025-07-28T14:29:26.735435Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of upvoates: 48,486,504 rows\n",
      "number of flagged reports: 1,078,915 rows\n"
     ]
    }
   ],
   "source": [
    "upvoted_df = reaction_df[reaction_df[\"reaction_type\"] == \"L\"].copy()\n",
    "print(f\"number of upvoates: {upvoted_df.shape[0]:,} rows\")\n",
    "upvoted_ids = upvoted_df[\"clip_id\"]\n",
    "\n",
    "flagged_df = reaction_df[reaction_df[\"flagged\"]].copy()\n",
    "print(f\"number of flagged reports: {flagged_df.shape[0]:,} rows\")\n",
    "flagged_ids = flagged_df[\"clip_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:29:38.308522Z",
     "iopub.status.busy": "2025-07-28T14:29:38.308290Z",
     "iopub.status.idle": "2025-07-28T14:29:51.907101Z",
     "shell.execute_reply": "2025-07-28T14:29:51.906600Z",
     "shell.execute_reply.started": "2025-07-28T14:29:38.308509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactions fraction by pro user:\n",
      "False: 321268494 (52.37%)\n",
      "True: 292153114 (47.63%)\n",
      "------------\n",
      "Clip generated fraction by pro user:\n",
      "True: 5780467 (85.61%)\n",
      "False: 971241 (14.39%)\n"
     ]
    }
   ],
   "source": [
    "# this is probably the right way to figure out the pro user group\n",
    "pro_users = set(discord_info_df[\"user_id\"].unique())\n",
    "reaction_df[\"is_pro_user\"] = reaction_df[\"user_id\"].isin(pro_users)\n",
    "clip_df[\"is_pro_user\"] = clip_df[\"user_id\"].isin(pro_users)\n",
    "\n",
    "# this is very interesting....\n",
    "# reaction check\n",
    "print(\"Reactions fraction by pro user:\")\n",
    "print_out_value_counts_nicely(reaction_df, \"is_pro_user\")\n",
    "print(\"------------\")\n",
    "# clip check\n",
    "print(\"Clip generated fraction by pro user:\")\n",
    "print_out_value_counts_nicely(clip_df, \"is_pro_user\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:29:51.907806Z",
     "iopub.status.busy": "2025-07-28T14:29:51.907590Z",
     "iopub.status.idle": "2025-07-28T14:29:51.927641Z",
     "shell.execute_reply": "2025-07-28T14:29:51.927260Z",
     "shell.execute_reply.started": "2025-07-28T14:29:51.907792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stem parent ids: 0\n"
     ]
    }
   ],
   "source": [
    "# find out the stem parent ids\n",
    "stem_parent_ids = set(\n",
    "    stem_clip_df[\"metadata\"].apply(lambda x: x.get(\"stem_from_id\", \"xxx\"))\n",
    ")\n",
    "print(\"stem parent ids:\", len(stem_parent_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:12.019778Z",
     "start_time": "2024-05-26T00:22:57.637371Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:29:51.928352Z",
     "iopub.status.busy": "2025-07-28T14:29:51.928063Z",
     "iopub.status.idle": "2025-07-28T14:30:01.378778Z",
     "shell.execute_reply": "2025-07-28T14:30:01.378282Z",
     "shell.execute_reply.started": "2025-07-28T14:29:51.928340Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clips in a splaylist:\n",
      "False: 6641492 (98.37%)\n",
      "True: 110216 (1.63%)\n",
      "------------\n",
      "Clips has stem children:\n",
      "False: 6751708 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "# add clip is in playlist feature\n",
    "clip_df[\"is_in_playlist\"] = clip_df[\"id\"].isin(playlist_clip_df[\"clip_id\"].unique())\n",
    "print(\"Clips in a splaylist:\")\n",
    "print_out_value_counts_nicely(clip_df, \"is_in_playlist\")\n",
    "clip_df[\"has_stems\"] = clip_df[\"id\"].astype(str).isin(stem_parent_ids)\n",
    "print(\"------------\")\n",
    "print(\"Clips has stem children:\")\n",
    "print_out_value_counts_nicely(clip_df, \"has_stems\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:17.912428Z",
     "start_time": "2024-05-26T00:23:12.021726Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:30:01.379536Z",
     "iopub.status.busy": "2025-07-28T14:30:01.379274Z",
     "iopub.status.idle": "2025-07-28T14:30:03.389890Z",
     "shell.execute_reply": "2025-07-28T14:30:03.389362Z",
     "shell.execute_reply.started": "2025-07-28T14:30:01.379523Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clips that have children: 446148 \n",
      "clips that are parents: 180137 \n",
      " Average continues from clip =  2.48\n",
      "web: 5655987 (84.28%)\n",
      "ios: 702816 (10.47%)\n",
      "android: 352322 (5.25%)\n"
     ]
    }
   ],
   "source": [
    "# parse the metadata for histories and types\n",
    "clip_history_df = clip_df[~clip_df[\"continued_parent\"].isna()].copy()\n",
    "# these are the direct parent's ids -- not grandparents\n",
    "has_continued_children_ids = clip_history_df[\"continued_parent\"]\n",
    "print(\n",
    "    \"clips that have children:\",\n",
    "    len(has_continued_children_ids),\n",
    "    \"\\nclips that are parents:\",\n",
    "    has_continued_children_ids.nunique(),\n",
    "    \"\\n\",\n",
    "    \"Average continues from clip = \",\n",
    "    round(\n",
    "        len(has_continued_children_ids) / len(has_continued_children_ids.unique()), 2\n",
    "    ),\n",
    ")\n",
    "# Get value counts\n",
    "print_out_value_counts_nicely(clip_df, \"source\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:22.092835Z",
     "start_time": "2024-05-26T00:23:17.914456Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:30:03.390545Z",
     "iopub.status.busy": "2025-07-28T14:30:03.390416Z",
     "iopub.status.idle": "2025-07-28T14:30:07.472604Z",
     "shell.execute_reply": "2025-07-28T14:30:07.472080Z",
     "shell.execute_reply.started": "2025-07-28T14:30:03.390532Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total uploads: 0\n",
      "clips without request id: 49851\n",
      "clips without request id: 49851 clip_type\n",
      "concat              32876\n",
      "edit_crop            7998\n",
      "edit_speed           6448\n",
      "edit_fade            1270\n",
      "rendered-project     1259\n",
      "Name: count, dtype: int64\n",
      "Clips without request id (concat, uploads...) frac = 0.00487\n"
     ]
    }
   ],
   "source": [
    "print(\"total uploads:\", (clip_df[\"model_name\"] == \"\").sum())\n",
    "print(\"clips without request id:\", (clip_df[\"request_id\"].isna()).sum())\n",
    "# the nans are concats, we want to drop them for now\n",
    "concated_clips = clip_df[\n",
    "    (clip_df[\"clip_type\"] == \"concat\") | (clip_df[\"clip_type\"] == \"concat_infilling\") | (clip_df[\"clip_type\"] == \"stem_mix\")\n",
    "].copy()\n",
    "non_request_clips = clip_df[clip_df[\"request_id\"].isna()].copy()\n",
    "print(\n",
    "    \"clips without request id:\",\n",
    "    non_request_clips.shape[0],\n",
    "    non_request_clips[\"clip_type\"].value_counts(),\n",
    ")\n",
    "# need to kick them out...\n",
    "clip_df = clip_df[~clip_df[\"request_id\"].isna()]\n",
    "print(\n",
    "    f\"Clips without request id (concat, uploads...) frac = {concated_clips.shape[0] / total_clip_counts:.5f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:22.833148Z",
     "start_time": "2024-05-26T00:23:22.094796Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:30:07.473359Z",
     "iopub.status.busy": "2025-07-28T14:30:07.473134Z",
     "iopub.status.idle": "2025-07-28T14:30:07.770939Z",
     "shell.execute_reply": "2025-07-28T14:30:07.770438Z",
     "shell.execute_reply.started": "2025-07-28T14:30:07.473345Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-auk-t0: 6701857 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "# check the model conts\n",
    "print_out_value_counts_nicely(clip_df, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:26.317699Z",
     "start_time": "2024-05-26T00:23:22.835074Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:30:07.771642Z",
     "iopub.status.busy": "2025-07-28T14:30:07.771452Z",
     "iopub.status.idle": "2025-07-28T14:30:13.007022Z",
     "shell.execute_reply": "2025-07-28T14:30:13.006513Z",
     "shell.execute_reply.started": "2025-07-28T14:30:07.771629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-filter model type clip_df shape: (6701857, 40)\n",
      "post-filter model type clip_df shape: (6701857, 40)\n",
      "chirp-auk-t0: 6701857 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "print(\"pre-filter model type clip_df shape:\", clip_df.shape)\n",
    "clip_df = clip_df[\n",
    "    (clip_df[\"model_name\"] != \"chirp-v3-5\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-v3-0\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-v3-5-tau\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-v3-5-upload\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-v3-5-short\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-v4\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-v4-tau\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-up\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-auk\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-ahi\")\n",
    "    & (clip_df[\"model_name\"] != \"chirp-v4-h-t-6-cfg-null\")\n",
    "]\n",
    "print(\"post-filter model type clip_df shape:\", clip_df.shape)\n",
    "print_out_value_counts_nicely(clip_df, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:30:13.007894Z",
     "iopub.status.busy": "2025-07-28T14:30:13.007531Z",
     "iopub.status.idle": "2025-07-28T14:30:13.340744Z",
     "shell.execute_reply": "2025-07-28T14:30:13.340245Z",
     "shell.execute_reply.started": "2025-07-28T14:30:13.007880Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": 4384513 (65.42%)\n",
      "cover: 1237384 (18.46%)\n",
      "artist_consistency: 446588 (6.66%)\n",
      "extend: 235313 (3.51%)\n",
      "artist_cover: 186988 (2.79%)\n",
      "upload_extend: 178959 (2.67%)\n",
      "artist_extend: 32016 (0.48%)\n",
      "cover_extend: 34 (0.00%)\n",
      "playlist_condition: 24 (0.00%)\n",
      "artist_cover_extend: 22 (0.00%)\n",
      "underpainting: 10 (0.00%)\n",
      "overpainting: 6 (0.00%)\n"
     ]
    }
   ],
   "source": [
    "print_out_value_counts_nicely(clip_df, \"task\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:30:13.344516Z",
     "iopub.status.busy": "2025-07-28T14:30:13.344295Z",
     "iopub.status.idle": "2025-07-28T14:30:59.023397Z",
     "shell.execute_reply": "2025-07-28T14:30:59.022897Z",
     "shell.execute_reply.started": "2025-07-28T14:30:13.344503Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "concat reactions: 62934 unique concat clips: 32854\n",
      "total concats (32876, 43)\n",
      "check \n",
      "        reaction_play_count  reaction_upvote_count  reaction_dislike_count\n",
      "count         32854.000000           32854.000000            32854.000000\n",
      "mean              7.170268               0.425336                0.019541\n",
      "std              54.320768               3.896081                0.151440\n",
      "min               1.000000               0.000000                0.000000\n",
      "25%               1.000000               0.000000                0.000000\n",
      "50%               3.000000               0.000000                0.000000\n",
      "75%               6.000000               0.000000                0.000000\n",
      "max            6999.000000             361.000000               11.000000\n",
      "All concats 32876\n",
      "total concats with plays 32854\n"
     ]
    }
   ],
   "source": [
    "concated_clips = merge_concat_clips_with_reactions(concated_clips, reaction_df)\n",
    "# TODO: why so many clips are concats without plays??? -- oh probably they concat multiple times?\n",
    "print(\"All concats\", concated_clips.shape[0])\n",
    "concated_clips = concated_clips[concated_clips[\"reaction_play_count\"] > 0]\n",
    "print(\"total concats with plays\", concated_clips.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:38.974479Z",
     "start_time": "2024-05-26T00:23:34.385507Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:30:59.024134Z",
     "iopub.status.busy": "2025-07-28T14:30:59.023904Z",
     "iopub.status.idle": "2025-07-28T14:31:00.297547Z",
     "shell.execute_reply": "2025-07-28T14:31:00.297032Z",
     "shell.execute_reply.started": "2025-07-28T14:30:59.024120Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "32854it [00:01, 26653.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total concat unique clips are: 64141 with error: 675, duplicate 2808 \n",
      " uploads are in concats 6 frac 6.000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "concat_clips_ids = get_concat_clip_ids(concated_clips, clip_df, upload_clip_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:40.236690Z",
     "start_time": "2024-05-26T00:23:39.995713Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:31:00.298357Z",
     "iopub.status.busy": "2025-07-28T14:31:00.298071Z",
     "iopub.status.idle": "2025-07-28T14:31:00.645941Z",
     "shell.execute_reply": "2025-07-28T14:31:00.645445Z",
     "shell.execute_reply.started": "2025-07-28T14:31:00.298343Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    6.701857e+06\n",
      "mean     5.872986e+01\n",
      "std      8.555654e+01\n",
      "min      1.000000e+00\n",
      "25%      1.400000e+01\n",
      "50%      3.200000e+01\n",
      "75%      6.900000e+01\n",
      "max      1.820000e+03\n",
      "Name: user_n_clips, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# set user number of clips generated\n",
    "clip_df[\"user_n_clips\"] = clip_df[\"user_id\"].map(clip_df[\"user_id\"].value_counts())\n",
    "print(clip_df[\"user_n_clips\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:44.826860Z",
     "start_time": "2024-05-26T00:23:40.238330Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:31:00.646663Z",
     "iopub.status.busy": "2025-07-28T14:31:00.646449Z",
     "iopub.status.idle": "2025-07-28T14:31:14.001568Z",
     "shell.execute_reply": "2025-07-28T14:31:14.001073Z",
     "shell.execute_reply.started": "2025-07-28T14:31:00.646649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has upvoted upvoted\n",
      "False    6305779\n",
      "True      396078\n",
      "Name: count, dtype: int64 upvoted\n",
      "False    0.9409\n",
      "True     0.0591\n",
      "Name: proportion, dtype: float64 upvote_count\n",
      "False    0.940862\n",
      "True     0.059138\n",
      "Name: proportion, dtype: float64 upvote_count\n",
      "False    0.998821\n",
      "True     0.001179\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# add upvoted column\n",
    "clip_df[\"upvoted\"] = clip_df[\"id\"].isin(upvoted_ids)\n",
    "print(\n",
    "    \"has upvoted\",\n",
    "    clip_df[\"upvoted\"].value_counts(),\n",
    "    clip_df[\"upvoted\"].value_counts(normalize=True),\n",
    "    (clip_df[\"upvote_count\"] >= 1).value_counts(normalize=True),\n",
    "    (clip_df[\"upvote_count\"] > 1).value_counts(normalize=True),\n",
    ")\n",
    "# clip_df = clip_df.drop(columns=['upvote_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:48.404433Z",
     "start_time": "2024-05-26T00:23:44.857788Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:31:14.002296Z",
     "iopub.status.busy": "2025-07-28T14:31:14.002080Z",
     "iopub.status.idle": "2025-07-28T14:31:44.969129Z",
     "shell.execute_reply": "2025-07-28T14:31:44.968623Z",
     "shell.execute_reply.started": "2025-07-28T14:31:14.002283Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downvoted fraction by category:\n",
      "False: 6056740 (90.37%)\n",
      "True: 645117 (9.63%)\n"
     ]
    }
   ],
   "source": [
    "disliked_ids = reaction_df[reaction_df[\"reaction_type\"] == \"D\"][\"clip_id\"].unique()\n",
    "\n",
    "clip_df[\"downvoted\"] = clip_df[\"id\"].isin(disliked_ids)\n",
    "print(\"downvoted fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"downvoted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:23:56.590022Z",
     "start_time": "2024-05-26T00:23:48.405668Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:31:44.969876Z",
     "iopub.status.busy": "2025-07-28T14:31:44.969642Z",
     "iopub.status.idle": "2025-07-28T14:31:46.684364Z",
     "shell.execute_reply": "2025-07-28T14:31:46.683877Z",
     "shell.execute_reply.started": "2025-07-28T14:31:44.969863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_continued fraction by category:\n",
      "False: 6699720 (99.97%)\n",
      "True: 2137 (0.03%)\n"
     ]
    }
   ],
   "source": [
    "# add continued column -- uuid and str are not compatible X.x\n",
    "clip_df[\"has_continued\"] = (\n",
    "    clip_df[\"id\"].astype(str).isin(set(list(has_continued_children_ids)))\n",
    ")\n",
    "print(\"has_continued fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"has_continued\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:31:46.684993Z",
     "iopub.status.busy": "2025-07-28T14:31:46.684871Z",
     "iopub.status.idle": "2025-07-28T14:31:46.741610Z",
     "shell.execute_reply": "2025-07-28T14:31:46.741169Z",
     "shell.execute_reply.started": "2025-07-28T14:31:46.684980Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has upvoted in exp 0.0591\n",
      "has downvoted out of exp 0.09626\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"has upvoted in exp\",\n",
    "    round(clip_df[\"upvoted\"].value_counts(normalize=True)[True], 5),\n",
    ")\n",
    "print(\n",
    "    \"has downvoted out of exp\",\n",
    "    round(clip_df[\"downvoted\"].value_counts(normalize=True)[True], 5),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:04.704306Z",
     "start_time": "2024-05-26T00:23:56.591353Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:31:46.742252Z",
     "iopub.status.busy": "2025-07-28T14:31:46.742065Z",
     "iopub.status.idle": "2025-07-28T14:31:47.377691Z",
     "shell.execute_reply": "2025-07-28T14:31:47.377188Z",
     "shell.execute_reply.started": "2025-07-28T14:31:46.742240Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "part_of_concat fraction by category:\n",
      "False: 6679007 (99.66%)\n",
      "True: 22850 (0.34%)\n",
      "------------\n",
      "Model distribution for part_of_concat clips:\n",
      "chirp-auk-t0: 100.00%\n"
     ]
    }
   ],
   "source": [
    "# add concat column\n",
    "clip_df[\"part_of_concat\"] = clip_df[\"id\"].astype(str).isin(concat_clips_ids)\n",
    "print(\"part_of_concat fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"part_of_concat\")\n",
    "\n",
    "print(\"------------\")\n",
    "print(\"Model distribution for part_of_concat clips:\")\n",
    "for model, fraction in (\n",
    "    clip_df[clip_df[\"part_of_concat\"]][\"model_name\"]\n",
    "    .value_counts(normalize=True)\n",
    "    .items()\n",
    "):\n",
    "    print(f\"{model}: {fraction:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:09.954233Z",
     "start_time": "2024-05-26T00:24:04.705554Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:31:47.378550Z",
     "iopub.status.busy": "2025-07-28T14:31:47.378202Z",
     "iopub.status.idle": "2025-07-28T14:35:05.010156Z",
     "shell.execute_reply": "2025-07-28T14:35:05.009653Z",
     "shell.execute_reply.started": "2025-07-28T14:31:47.378537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74041489\n",
      "has_action fraction by category:\n",
      "False: 6176735 (92.16%)\n",
      "True: 525122 (7.84%)\n"
     ]
    }
   ],
   "source": [
    "# verify bots action are all non-empty\n",
    "bots_action_df.fillna(0, inplace=True)\n",
    "action_mask = (\n",
    "    bots_action_df[\"download_audio_count\"]\n",
    "    + bots_action_df[\"download_video_count\"]\n",
    "    + bots_action_df[\"download_audio_wav_count\"]\n",
    "    + bots_action_df[\"share_count\"]\n",
    "    # will remove share cause it can be negative, just can be...\n",
    ") >= 1\n",
    "has_action_ids = set(i for i in bots_action_df[action_mask][\"clip_id\"].unique())\n",
    "print(len(has_action_ids))\n",
    "clip_df[\"has_action\"] = clip_df[\"id\"].isin(has_action_ids)\n",
    "print(\"has_action fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"has_action\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:14.579841Z",
     "start_time": "2024-05-26T00:24:09.955568Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:05.010793Z",
     "iopub.status.busy": "2025-07-28T14:35:05.010667Z",
     "iopub.status.idle": "2025-07-28T14:35:07.962814Z",
     "shell.execute_reply": "2025-07-28T14:35:07.962311Z",
     "shell.execute_reply.started": "2025-07-28T14:35:05.010780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flagged fraction by category:\n",
      "False: 6653625 (99.28%)\n",
      "True: 48232 (0.72%)\n"
     ]
    }
   ],
   "source": [
    "# add downvoted column\n",
    "clip_df[\"flagged\"] = clip_df[\"id\"].isin(flagged_ids)\n",
    "print(\"flagged fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"flagged\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:07.963529Z",
     "iopub.status.busy": "2025-07-28T14:35:07.963314Z",
     "iopub.status.idle": "2025-07-28T14:35:13.151142Z",
     "shell.execute_reply": "2025-07-28T14:35:13.150644Z",
     "shell.execute_reply.started": "2025-07-28T14:35:07.963515Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deleted fraction by category:\n",
      "False: 6440164 (96.10%)\n",
      "True: 261693 (3.90%)\n"
     ]
    }
   ],
   "source": [
    "clip_df[\"deleted\"] = clip_df[\"is_deleted\"]\n",
    "print(\"deleted fraction by category:\")\n",
    "print_out_value_counts_nicely(clip_df, \"deleted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:13.152058Z",
     "iopub.status.busy": "2025-07-28T14:35:13.151650Z",
     "iopub.status.idle": "2025-07-28T14:35:15.624743Z",
     "shell.execute_reply": "2025-07-28T14:35:15.624240Z",
     "shell.execute_reply.started": "2025-07-28T14:35:13.152045Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of edits per clip: 2.562079018799638 median 2.0\n"
     ]
    }
   ],
   "source": [
    "edit_id_counts = clip_df[\"edited_clip_id\"].value_counts()\n",
    "clip_df[\"n_edits\"] = clip_df[\"id\"].map(edit_id_counts)\n",
    "print(\n",
    "    \"number of edits per clip:\",\n",
    "    clip_df[\"n_edits\"].mean(),\n",
    "    \"median\",\n",
    "    clip_df[\"n_edits\"].median(),\n",
    ")\n",
    "# print_out_value_counts_nicely(clip_df, \"n_edits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:15.382315Z",
     "start_time": "2024-05-26T00:24:14.581073Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:15.625491Z",
     "iopub.status.busy": "2025-07-28T14:35:15.625270Z",
     "iopub.status.idle": "2025-07-28T14:35:16.362246Z",
     "shell.execute_reply": "2025-07-28T14:35:16.361736Z",
     "shell.execute_reply.started": "2025-07-28T14:35:15.625478Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total clips: 6,701,857\n",
      "Must be positive: 861,545 (12.86%)\n",
      "Definitely not negative: 5,773,445 (86.15%)\n",
      "Must be negative: 928,412 (13.85%)\n"
     ]
    }
   ],
   "source": [
    "# This is probably the most important cell of this notebook -- what are good labels, and not having good label makes it a bad label\n",
    "must_be_positive_mask = (\n",
    "    (clip_df[\"upvoted\"])\n",
    "    | (clip_df[\"has_action\"])\n",
    "    | (clip_df[\"part_of_concat\"])\n",
    "    | (clip_df[\"is_in_playlist\"])\n",
    "    | (\n",
    "        clip_df[\"n_edits\"] >= 10\n",
    "    )  # has more edit operations (upsample, cover, extend, etc)\n",
    ")\n",
    "must_be_not_negative_mask = (\n",
    "    (~clip_df[\"downvoted\"]) & (~clip_df[\"deleted\"]) & (~clip_df[\"flagged\"])\n",
    ")\n",
    "must_be_negative_mask = (\n",
    "    (clip_df[\"downvoted\"]) | (clip_df[\"flagged\"]) | (clip_df[\"deleted\"])\n",
    ")\n",
    "total_clips_count = clip_df.shape[0]\n",
    "must_be_positive_count = sum(must_be_positive_mask)\n",
    "definitely_not_negative_count = sum(must_be_not_negative_mask)\n",
    "must_be_negative_count = sum(must_be_negative_mask)\n",
    "\n",
    "print(\n",
    "    f\"Total clips: {total_clips_count:,}\\n\"\n",
    "    f\"Must be positive: {must_be_positive_count:,} ({must_be_positive_count/total_clips_count:.2%})\\n\"\n",
    "    f\"Definitely not negative: {definitely_not_negative_count:,} ({definitely_not_negative_count/total_clips_count:.2%})\\n\"\n",
    "    f\"Must be negative: {must_be_negative_count:,} ({must_be_negative_count/total_clips_count:.2%})\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:31.095990Z",
     "start_time": "2024-05-26T00:24:15.383572Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:16.362975Z",
     "iopub.status.busy": "2025-07-28T14:35:16.362777Z",
     "iopub.status.idle": "2025-07-28T14:35:24.989069Z",
     "shell.execute_reply": "2025-07-28T14:35:24.988551Z",
     "shell.execute_reply.started": "2025-07-28T14:35:16.362962Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liked requests: 660,026\n",
      "Not liked requests: 3,185,996\n",
      "Requests with preference paired generations: 463,675\n",
      "Percentage of total unique requests: 13.71%\n"
     ]
    }
   ],
   "source": [
    "mask = must_be_positive_mask & must_be_not_negative_mask\n",
    "total_unique_requests = clip_df[\"request_id\"].nunique()\n",
    "liked_requests = clip_df[mask][\"request_id\"].unique()  # requests with at least 1 like\n",
    "unliked_requests = clip_df[~mask][\"request_id\"].unique()  # requests without like\n",
    "has_liked_requests = set(liked_requests).intersection(\n",
    "    set(unliked_requests)\n",
    ")  # the request must have 1 like and one without like\n",
    "print(f\"Liked requests: {len(liked_requests):,}\")\n",
    "print(f\"Not liked requests: {len(unliked_requests):,}\")\n",
    "print(f\"Requests with preference paired generations: {len(has_liked_requests):,}\")\n",
    "print(\n",
    "    f\"Percentage of total unique requests: {len(has_liked_requests) / total_unique_requests:.2%}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:24.989824Z",
     "iopub.status.busy": "2025-07-28T14:35:24.989600Z",
     "iopub.status.idle": "2025-07-28T14:35:30.431049Z",
     "shell.execute_reply": "2025-07-28T14:35:30.430536Z",
     "shell.execute_reply.started": "2025-07-28T14:35:24.989810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disliked requests: 645,772\n",
      "Not disliked requests: 3,096,925\n",
      "Requests with preference paired generations: 360,350\n",
      "Percentage of total unique requests: 10.65%\n"
     ]
    }
   ],
   "source": [
    "# introduce a negative preference count\n",
    "has_disliked_half_requests = clip_df[must_be_negative_mask][\n",
    "    \"request_id\"\n",
    "].unique()  # requests with at least 1 dislike\n",
    "not_have_disliked_requests = clip_df[~must_be_negative_mask][\n",
    "    \"request_id\"\n",
    "].unique()  # request without dislike\n",
    "has_disliked_requests = set(has_disliked_half_requests).intersection(\n",
    "    set(not_have_disliked_requests)\n",
    ")  # the request must have 1 dislike and one without dislike\n",
    "print(f\"Disliked requests: {len(has_disliked_half_requests):,}\")\n",
    "print(f\"Not disliked requests: {len(not_have_disliked_requests):,}\")\n",
    "print(f\"Requests with preference paired generations: {len(has_disliked_requests):,}\")\n",
    "print(\n",
    "    f\"Percentage of total unique requests: {len(has_disliked_requests) / total_unique_requests:.2%}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:31.099372Z",
     "start_time": "2024-05-26T00:24:31.097244Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:30.431787Z",
     "iopub.status.busy": "2025-07-28T14:35:30.431575Z",
     "iopub.status.idle": "2025-07-28T14:35:30.496874Z",
     "shell.execute_reply": "2025-07-28T14:35:30.496394Z",
     "shell.execute_reply.started": "2025-07-28T14:35:30.431774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total selected pairs of requests: 701,286\n",
      "Percentage of total unique requests: 20.73%\n"
     ]
    }
   ],
   "source": [
    "requests = has_liked_requests.union(has_disliked_requests)\n",
    "print(f\"Total selected pairs of requests: {len(requests):,}\")\n",
    "print(\n",
    "    f\"Percentage of total unique requests: {len(requests) / total_unique_requests:.2%}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:31.239254Z",
     "start_time": "2024-05-26T00:24:31.100389Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:30.497567Z",
     "iopub.status.busy": "2025-07-28T14:35:30.497379Z",
     "iopub.status.idle": "2025-07-28T14:35:30.621676Z",
     "shell.execute_reply": "2025-07-28T14:35:30.621175Z",
     "shell.execute_reply.started": "2025-07-28T14:35:30.497553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in preference counts:\n",
      "0: 4,932,906 (73.61%)\n",
      "-1: 928,412 (13.85%)\n",
      "1: 840,539 (12.54%)\n"
     ]
    }
   ],
   "source": [
    "# this used to be a terrible bug...X.x\n",
    "assert mask.shape[0] == clip_df.shape[0]\n",
    "clip_df[\"pos_preference\"] = mask\n",
    "clip_df[\"neg_preference\"] = must_be_negative_mask\n",
    "# note that this is along the same row, so a positive clip can't be negative\n",
    "clip_df[\"diff_preference\"] = clip_df[\"pos_preference\"].astype(int) - clip_df[\n",
    "    \"neg_preference\"\n",
    "].astype(int)\n",
    "print(\"Difference in preference counts:\")\n",
    "value_counts = clip_df[\"diff_preference\"].value_counts()\n",
    "total = value_counts.sum()\n",
    "for value, count in value_counts.items():\n",
    "    fraction = count / total\n",
    "    print(f\"{value}: {count:,} ({fraction:.2%})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:37.322031Z",
     "start_time": "2024-05-26T00:24:31.240829Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:30.622525Z",
     "iopub.status.busy": "2025-07-28T14:35:30.622189Z",
     "iopub.status.idle": "2025-07-28T14:35:41.988497Z",
     "shell.execute_reply": "2025-07-28T14:35:41.987975Z",
     "shell.execute_reply.started": "2025-07-28T14:35:30.622511Z"
    }
   },
   "outputs": [],
   "source": [
    "# filter out 8 stems for now?\n",
    "clip_df['request_count'] = clip_df.groupby('request_id')['request_id'].transform('count')\n",
    "# creation of interesting_clips\n",
    "interesting_clips = clip_df[(clip_df[\"request_id\"].isin(requests)) & (clip_df['request_count'] == 2)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:41.989259Z",
     "iopub.status.busy": "2025-07-28T14:35:41.989046Z",
     "iopub.status.idle": "2025-07-28T14:35:45.080254Z",
     "shell.execute_reply": "2025-07-28T14:35:45.079809Z",
     "shell.execute_reply.started": "2025-07-28T14:35:41.989245Z"
    }
   },
   "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>request_id</th>\n",
       "      <th>pos_preference</th>\n",
       "      <th>neg_preference</th>\n",
       "      <th>diff_preference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00002403-8db0-4a4e-8f8c-3cf549913431</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00002403-8db0-4a4e-8f8c-3cf549913431</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000261e-bc74-4416-a210-c754bb8e25c6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000261e-bc74-4416-a210-c754bb8e25c6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00009320-c446-47a8-9af4-324a9749e417</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00009320-c446-47a8-9af4-324a9749e417</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             request_id  pos_preference  neg_preference  diff_preference\n",
       "0  00002403-8db0-4a4e-8f8c-3cf549913431           False           False                0\n",
       "1  00002403-8db0-4a4e-8f8c-3cf549913431            True           False                1\n",
       "2  0000261e-bc74-4416-a210-c754bb8e25c6           False            True               -1\n",
       "3  0000261e-bc74-4416-a210-c754bb8e25c6           False           False                0\n",
       "4  00009320-c446-47a8-9af4-324a9749e417           False            True               -1\n",
       "5  00009320-c446-47a8-9af4-324a9749e417           False           False                0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interesting_clips = interesting_clips.sort_values(\n",
    "    by=[\"request_id\", \"diff_preference\"]\n",
    ").reset_index()\n",
    "interesting_clips[\n",
    "    [\"request_id\", \"pos_preference\", \"neg_preference\", \"diff_preference\"]\n",
    "].head(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:45.080957Z",
     "iopub.status.busy": "2025-07-28T14:35:45.080753Z",
     "iopub.status.idle": "2025-07-28T14:35:45.106001Z",
     "shell.execute_reply": "2025-07-28T14:35:45.105588Z",
     "shell.execute_reply.started": "2025-07-28T14:35:45.080944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference 0: 578,547 (41.25%)\n",
      "Difference 1: 463,675 (33.06%)\n",
      "Difference -1: 360,350 (25.69%)\n"
     ]
    }
   ],
   "source": [
    "# this is a mix now\n",
    "value_counts = interesting_clips[\"diff_preference\"].value_counts()\n",
    "total = value_counts.sum()\n",
    "for value, count in value_counts.items():\n",
    "    fraction = count / total\n",
    "    print(f\"Difference {value}: {count:,} ({fraction:.2%})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:45.106753Z",
     "iopub.status.busy": "2025-07-28T14:35:45.106453Z",
     "iopub.status.idle": "2025-07-28T14:35:45.134330Z",
     "shell.execute_reply": "2025-07-28T14:35:45.133926Z",
     "shell.execute_reply.started": "2025-07-28T14:35:45.106741Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value 1.0: 578,547 (82.50%)\n",
      "Value 2.0: 122,739 (17.50%)\n"
     ]
    }
   ],
   "source": [
    "diff_series = interesting_clips[\"diff_preference\"].diff()\n",
    "value_counts = diff_series[1::2].value_counts()\n",
    "total = value_counts.sum()\n",
    "for value, count in value_counts.items():\n",
    "    fraction = count / total\n",
    "    print(f\"Value {value}: {count:,} ({fraction:.2%})\")\n",
    "# 1 is pos, not neg pair or nothing, neg; 2 is pos / neg (hence the larger difference)\n",
    "# there are only two values for this positive pair\n",
    "assert diff_series[1::2].nunique() == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:38.960130Z",
     "start_time": "2024-05-26T00:24:37.323369Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:45.135084Z",
     "iopub.status.busy": "2025-07-28T14:35:45.134786Z",
     "iopub.status.idle": "2025-07-28T14:35:48.395123Z",
     "shell.execute_reply": "2025-07-28T14:35:48.394608Z",
     "shell.execute_reply.started": "2025-07-28T14:35:45.135071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique request_ids: 701,286\n",
      "Number of unique ids: 1,402,572\n",
      "Validation passed!\n"
     ]
    }
   ],
   "source": [
    "# assign the labels now\n",
    "interesting_clips[\"preference\"] = interesting_clips.index % 2 == 1\n",
    "# get df of requests -- let's move on!\n",
    "print(f\"Number of unique request_ids: {interesting_clips['request_id'].nunique():,}\")\n",
    "print(f\"Number of unique ids: {interesting_clips['id'].nunique():,}\")\n",
    "validate_preference_data(interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:43.332222Z",
     "start_time": "2024-05-26T00:24:43.166461Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:48.395865Z",
     "iopub.status.busy": "2025-07-28T14:35:48.395667Z",
     "iopub.status.idle": "2025-07-28T14:35:48.415566Z",
     "shell.execute_reply": "2025-07-28T14:35:48.415175Z",
     "shell.execute_reply.started": "2025-07-28T14:35:48.395851Z"
    }
   },
   "outputs": [],
   "source": [
    "# # listen to some pairs\n",
    "# test_requests = interesting_clips[\"request_id\"].sample(10)\n",
    "\n",
    "# for i in range(1):\n",
    "#     rows = interesting_clips[interesting_clips[\"request_id\"] == test_requests.iloc[i]]\n",
    "#     assert rows.shape[0] == 2\n",
    "#     # Audio.from_s3(f\"s3://suno-data-uploads/studio/uploads/{row['s3_id']}.mp3\").play()\n",
    "#     # sort by likes\n",
    "#     rows = rows.sort_values(\"upvoted\", ascending=True)\n",
    "#     print(rows.iloc[0][\"prompt_text\"])\n",
    "#     print(rows.iloc[0][\"metadata\"])\n",
    "#     for _, row in rows.iterrows():\n",
    "#         print(row[\"id\"], row[\"preference\"], row[\"upvoted\"])\n",
    "#         Audio.from_s3(\n",
    "#             f\"s3://suno-data-uploads/studio/uploads/{row['s3_id']}.mp3\"\n",
    "#         ).play()\n",
    "#         with open_from_s3(\n",
    "#             f\"s3://suno-data-uploads/studio/uploads/{row['s3_id']}.npz\", as_binary=True\n",
    "#         ) as f:\n",
    "#             # read numpy array\n",
    "#             npz_a = np.load(f)\n",
    "#             if \"v1_raw\" in npz_a:\n",
    "#                 a = np.load(f)[\"v1_raw\"]\n",
    "#             elif \"v3.0_raw\" in npz_a:\n",
    "#                 a = np.load(f)[\"v3.0_raw\"]\n",
    "#             else:\n",
    "#                 print(\"npz_a\", npz_a)\n",
    "#                 raise ValueError\n",
    "#             print(a.shape)\n",
    "#     print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further cuts and selections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:35:48.416287Z",
     "iopub.status.busy": "2025-07-28T14:35:48.416029Z",
     "iopub.status.idle": "2025-07-28T14:38:35.941089Z",
     "shell.execute_reply": "2025-07-28T14:38:35.940566Z",
     "shell.execute_reply.started": "2025-07-28T14:35:48.416274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of interesting clips: 1,402,572\n",
      "Unique request and clip counts in interesting_clips:\n",
      "Request IDs:    701,286\n",
      "Clip IDs:       1,402,572\n"
     ]
    }
   ],
   "source": [
    "# need the reaction play counts\n",
    "# Filter reaction_df for relevant clip_ids\n",
    "partial_reaction_df = reaction_df[\n",
    "    reaction_df[\"clip_id\"].isin(set(interesting_clips[\"id\"]))\n",
    "].copy()\n",
    "\n",
    "# Calculate total play counts\n",
    "total_play_counts = (\n",
    "    partial_reaction_df.groupby(\"clip_id\")[\"play_count\"].sum().reset_index()\n",
    ")\n",
    "total_play_counts = total_play_counts.rename(\n",
    "    columns={\"clip_id\": \"id\", \"play_count\": \"reaction_play_count\"}\n",
    ")\n",
    "\n",
    "# Calculate pro user play counts\n",
    "pro_play_counts = (\n",
    "    partial_reaction_df[partial_reaction_df[\"is_pro_user\"]]\n",
    "    .groupby(\"clip_id\")[\"play_count\"]\n",
    "    .sum()\n",
    "    .reset_index()\n",
    ")\n",
    "pro_play_counts = pro_play_counts.rename(\n",
    "    columns={\"clip_id\": \"id\", \"play_count\": \"reaction_pro_play_count\"}\n",
    ")\n",
    "\n",
    "# Merge with user_intersting_clips\n",
    "interesting_clips = interesting_clips.merge(total_play_counts, on=\"id\", how=\"left\")\n",
    "interesting_clips = interesting_clips.merge(pro_play_counts, on=\"id\", how=\"left\")\n",
    "\n",
    "print(f\"Number of interesting clips: {len(interesting_clips):,}\")\n",
    "# Get unique counts for request_id and id\n",
    "unique_request_ids = interesting_clips[\"request_id\"].nunique()\n",
    "unique_clip_ids = interesting_clips[\"id\"].nunique()\n",
    "\n",
    "# Print the results in a formatted manner\n",
    "print(\"Unique request and clip counts in interesting_clips:\")\n",
    "print(f\"{'Request IDs:':<15} {unique_request_ids:,}\")\n",
    "print(f\"{'Clip IDs:':<15} {unique_clip_ids:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:43.737067Z",
     "start_time": "2024-05-26T00:24:43.563216Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:35.941759Z",
     "iopub.status.busy": "2025-07-28T14:38:35.941623Z",
     "iopub.status.idle": "2025-07-28T14:38:37.254631Z",
     "shell.execute_reply": "2025-07-28T14:38:37.254132Z",
     "shell.execute_reply.started": "2025-07-28T14:38:35.941746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference counts and fractions by batch index:\n",
      "--------------------------------------------------\n",
      "Batch Index: 0\n",
      "  Preference False: Count: 341,940 Fraction: 48.76%\n",
      "  Preference True: Count: 359,346 Fraction: 51.24%\n",
      "\n",
      "Batch Index: 1\n",
      "  Preference False: Count: 359,346 Fraction: 51.24%\n",
      "  Preference True: Count: 341,940 Fraction: 48.76%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "preference_counts = interesting_clips.groupby(\"batch_index\")[\n",
    "    \"preference\"\n",
    "].value_counts()\n",
    "total_counts = preference_counts.groupby(level=0).sum()\n",
    "\n",
    "print(\"Preference counts and fractions by batch index:\")\n",
    "print(\"-\" * 50)\n",
    "for batch_index in [0, 1]:\n",
    "    print(f\"Batch Index: {batch_index}\")\n",
    "    for preference in [False, True]:\n",
    "        count = preference_counts[batch_index, preference]\n",
    "        fraction = count / total_counts[batch_index]\n",
    "        print(f\"  Preference {preference}: Count: {count:,} Fraction: {fraction:.2%}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:44.304573Z",
     "start_time": "2024-05-26T00:24:43.973218Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:37.255322Z",
     "iopub.status.busy": "2025-07-28T14:38:37.255128Z",
     "iopub.status.idle": "2025-07-28T14:38:37.335678Z",
     "shell.execute_reply": "2025-07-28T14:38:37.335194Z",
     "shell.execute_reply.started": "2025-07-28T14:38:37.255308Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-auk-t0: 1402572 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "print_out_value_counts_nicely(interesting_clips, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:44.533983Z",
     "start_time": "2024-05-26T00:24:44.305722Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:37.336445Z",
     "iopub.status.busy": "2025-07-28T14:38:37.336186Z",
     "iopub.status.idle": "2025-07-28T14:38:37.370280Z",
     "shell.execute_reply": "2025-07-28T14:38:37.369780Z",
     "shell.execute_reply.started": "2025-07-28T14:38:37.336432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Validation:\n",
      "--------------------\n",
      "Interesting Clips:\n",
      "  Earliest: 2025-05-06 23:20:00.810000\n",
      "  Latest:   2025-07-16 23:22:34.455000\n",
      "\n",
      "All Clips:\n",
      "  Earliest: 2025-05-06 23:20:00.810000\n",
      "  Latest:   2025-07-16 23:22:47.690000\n"
     ]
    }
   ],
   "source": [
    "print(\"Time Validation:\")\n",
    "print(\"-\" * 20)\n",
    "print(\"Interesting Clips:\")\n",
    "print(f\"  Earliest: {interesting_clips['created_at'].min()}\")\n",
    "print(f\"  Latest:   {interesting_clips['created_at'].max()}\")\n",
    "print(\"\\nAll Clips:\")\n",
    "print(f\"  Earliest: {clip_df['created_at'].min()}\")\n",
    "print(f\"  Latest:   {clip_df['created_at'].max()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:50.674838Z",
     "start_time": "2024-05-26T00:24:46.366677Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:37.371144Z",
     "iopub.status.busy": "2025-07-28T14:38:37.370797Z",
     "iopub.status.idle": "2025-07-28T14:38:39.067599Z",
     "shell.execute_reply": "2025-07-28T14:38:39.067095Z",
     "shell.execute_reply.started": "2025-07-28T14:38:37.371130Z"
    }
   },
   "outputs": [],
   "source": [
    "# make sure we sort here before proceed\n",
    "interesting_clips = interesting_clips.sort_values(by=[\"request_id\", \"preference\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:51.600621Z",
     "start_time": "2024-05-26T00:24:50.676186Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:39.068337Z",
     "iopub.status.busy": "2025-07-28T14:38:39.068135Z",
     "iopub.status.idle": "2025-07-28T14:38:39.765003Z",
     "shell.execute_reply": "2025-07-28T14:38:39.764510Z",
     "shell.execute_reply.started": "2025-07-28T14:38:39.068324Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio of preferred clips to total clips for each model:\n",
      "------------------------------------------------------------\n",
      "chirp-auk-t0                   10.46% ± 0.01%\n"
     ]
    }
   ],
   "source": [
    "# Calculate the ratio of preferred clips to total clips for each model\n",
    "clip_df_model_counts = clip_df[\"model_name\"].value_counts()\n",
    "preference_ratio = (\n",
    "    interesting_clips[interesting_clips[\"preference\"]][\"model_name\"].value_counts()\n",
    "    / clip_df_model_counts\n",
    ")\n",
    "\n",
    "# Print the results in a formatted manner\n",
    "print(\"Ratio of preferred clips to total clips for each model:\")\n",
    "print(\"-\" * 60)\n",
    "for model, ratio in preference_ratio.items():\n",
    "    n = clip_df_model_counts[model]\n",
    "    uncertainty = (ratio * (1 - ratio) / n) ** 0.5\n",
    "    print(f\"{model:<30} {ratio:.2%} ± {uncertainty:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:56.302599Z",
     "start_time": "2024-05-26T00:24:51.601863Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:39.765781Z",
     "iopub.status.busy": "2025-07-28T14:38:39.765508Z",
     "iopub.status.idle": "2025-07-28T14:38:41.869427Z",
     "shell.execute_reply": "2025-07-28T14:38:41.868920Z",
     "shell.execute_reply.started": "2025-07-28T14:38:39.765767Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 701286\n",
      "differing counts: 0\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 701286, total 701286.\n"
     ]
    }
   ],
   "source": [
    "get_preference_counts(interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:57.443236Z",
     "start_time": "2024-05-26T00:24:56.306473Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:41.870170Z",
     "iopub.status.busy": "2025-07-28T14:38:41.869960Z",
     "iopub.status.idle": "2025-07-28T14:38:43.056402Z",
     "shell.execute_reply": "2025-07-28T14:38:43.055916Z",
     "shell.execute_reply.started": "2025-07-28T14:38:41.870156Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:180: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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AWAGVlpbm5JNPzu2335733nvvax3rySefzLRp03L77bfnjDPOyJVXXpmjjjoqrVu3zt133539998/55577iLnueSSS3LYYYdl7Nix2WabbXL00Ufnww8/TPL5je8hhxySLbfcMvfcc09uvPHGzJgxIyeeeGKNY4wZMyZNmzbNHXfckWHDhtVa32233ZZbbrklp59+eu6///707NkzxxxzTN56660kyRNPPJFvfOMbOfzww/PEE0/k8MMPX+I1v/TSS7nwwgtz/PHH56GHHsqNN95Yr/krxowZk7Kysvz2t7/N2WefnVtvvTW//e1v67z/Qov7DJfk0UcfzbHHHptevXpl7NixGTlyZLp27VrvGgAAWPG431+6+/263KfffPPN6dSpU8aOHZtjjjkmU6ZMyRFHHJE99tgj999/fy677LI899xz+dnPfpbk83+oHzhwYLp165YnnniiOlg5//zzM378+Fx22WW5//77s+eee+ZHP/pRdd1J8sknn+SGG27IBRdckN///vdp27Ztnfe7+eabc8kll+T222/Pf//731x88cXV7b/5zW9y/vnnZ+DAgXnggQfy61//OhtuuGF1+wknnJAZM2bkhhtuyH333ZetttoqhxxySD766KMkyamnnpq1114799xzT+67774cccQRadq0aa2faWVlZY444og8//zz+cUvfpEHH3wwp5xySkpL6/5PhuPGjcubb76ZUaNG5Ve/+lX+8pe/5Oqrr06STJs2Laecckr69++fBx98MLfddlt23333GoEUwNIQbACsoHbfffdsscUWueKKK77WcdZYY4389Kc/zaabbpof/OAH2WSTTfLJJ5/k6KOPzsYbb5yjjjoqTZs2zXPPPVdjv4MOOijf/e53s9lmm+W8887L6quvnnvuuSdJcvvtt2fLLbfMySefnM022yxbbrllfv7zn+epp57Kv//97+pjbLzxxjnttNOy6aabZtNNN621vptuuilHHHFE9t5772y66ab5yU9+kk6dOmXkyJFJkvbt26esrCyrrbZa2rdvnxYtWizxmv/73/+mefPm+fa3v5311lsvW265ZQYPHlznz2ydddbJWWedlU033TT77rtvBg0atFTjyC7uM1ySa6+9Nn369Mnxxx+fzTbbLJ06dcpRRx1V7xoAAFgxud+v//1+Xe7Td9hhhxx++OHZcMMNs+GGG+a6667LPvvsk0MPPTQbb7xxtt1225x99tkZO3ZsPv3006yxxhpZddVV07Rp07Rv3z5rrLFG3n333dx3330ZMWJEtt9++2y44YYZMmRItttuuxpvaX/22Wc577zzsu2222bTTTfNhx9+WOf9hg0bli5dumSrrbbKQQcdlCeffLK6/Zprrslhhx2WQw45JJtsskm6du2aQw89NMnnb59MmDAhV1xxRbp06ZKNN944p59+elq1alU9Z9+7776bHj16ZLPNNsvGG2+cvfbaK506dar1M/3nP/+ZCRMm5Morr8xOO+2UDTbYILvuumt69er1ld/DlzVr1iw///nP841vfCPf/va3c/zxx+e2225LZWVlpk+fngULFmT33XfP+uuvn/Ly8hx00EF16tcBLE6TQhcAwFc79dRTc8ghh3zla9110bFjxxpP27Rr1y7f+MY3qpfLysqyxhprZMaMGTX269atW/XfmzRpks6dO2fy5MlJPn+1+amnnqqxzUJTpkzJJptskiTZaqutFlvbnDlzMm3atGy77bY11m+77bY1XqWurx49emTdddfNbrvtlp133jk777xzdt999zRv3rxO+2+99dY1xtXdZpttcsstt6SioiJlZWV1rmNxn+GSTJo0KQMGDKjzuQAAKD7u9+unLvfpnTt3rrHPK6+8kldffTUPPPBA9bqqqqpUVlZm6tSp2WyzzRY5z2uvvZaKiorsueeeNdbPnz8/a6yxRvVy06ZNa0z4Xtf9mjdvXuMNjA4dOlR/PzNmzMi0adOy44471voZvPrqq5k7d266d+9eY/0nn3ySKVOmJEkOO+yw/PSnP83vfve79OjRI3vuuWeN833RpEmTsvbaa1d/p0ujvLy8Rl+rW7dumTt3bv773/+mU6dO2XHHHbPPPvukZ8+e6dmzZ7773e+mdevWS30+gESwAbBC++Y3v5mePXvm0ksvTb9+/Wq0lZSULPL67oIFCxY5RpMmNf9TX1JSUuu6+kx0N3fu3Oy666459dRTF2lr37599d/rGiQ0tJYtW2bMmDF5+umn88QTT+SKK67IVVddlXvuuSetWrX62sev62f/day66qoNejwAAFY87vcb3pdrmjt3bvbff//quTO+aJ111qn1GHPnzk1ZWVnuvffeRR5sWm211ar/vuqqq9YIWuq6X23fz8LvepVVVlnc5eXjjz9O+/btM2rUqEXaVl999STJcccdl+9973t57LHH8ve//z1XXHFFLrvssuy+++6L7LOs+x1lZWW55ZZb8vzzz+cf//hHRo0alcsuuyx33313Nthgg2V6bmDlZigqgBXcKaeckkceeSTjx4+vsX7NNdfMBx98UKOzM2nSpAY77wsvvFD99wULFmTixInVr5dvtdVWef3117Peeutlo402qvHnizfsS9KyZct06NAhzz//fI31zz//fDp27Pi16m/SpEl69OiR0047Lffff3/eeeedGq93L86ECRNqLL/44ovZaKONqjsna665Zo3J/d56663MmzdvkeMs7jNcks033zzjxo2r07YAABQv9/t1t6T79NpsueWWeeONNxa5jo022ijNmjWrdZ8tttgiFRUVmTlz5iL7fDHYaaj9vqhly5ZZb731vrIvsNVWW+WDDz5IWVnZIudYc801q7fbZJNNcuihh+bmm2/OHnvsUesE8snnb1u89957NYYYq69XX301n3zySfXyCy+8kNVWW606OCopKcl2222X448/PmPHjk3Tpk3z8MMPL/X5ABLBBsAKr7y8PPvss88iT+R07949M2fOzA033JApU6Zk9OjRefzxxxvsvL/5zW/yl7/8JW+++WbOP//8zJo1K/3790+SHHjggZk1a1ZOPvnkTJgwIVOmTMnjjz+eM888MxUVFfU6z5AhQ3LDDTfkwQcfzOTJk/PLX/4yr7zySr3mxPiyRx55JLfddlsmTZqUd955J2PHjk1lZWWdX69+9913M3z48EyePDm///3vc/vtt9eoZ4cddsjo0aPzr3/9Ky+99FLOPff/a+/eQRrL4jiO/4xTKigEBUWi2Fha+EBBsLLwAVqI4COFYqVJYcBnIaTxgaKjkmgaX4Vgr4VaKGghoqIhBEEhEGMhEkUCmiDsFrvIOOsjO8zuzMXvp7yHc++fwy3+8D/nfwZfvYzvvTX8SGdnp9bW1jQ1NaWLiwudnZ3J4/HEtwAAAAAwDPL9+H2Up7+mvb1dx8fHcjqd8vv9CgQC2traktPpfHNOTk6Oampq1N3drY2NDQWDQZ2enmpubk7b29s/fd73bDab5ufntbS0pEAgIJ/P9/x/lJaWKj8/Xx0dHdrd3dXl5aWOjo40MTEhr9erx8dHOZ1O7e/vKxQK6fDwUF6v99WWW5JUVFSkgoIC2e127e3tKRgMPp/0iFcsFtPAwIDOz8+1s7Oj6elpNTc3y2Qy6eTkRLOzs/J6vbq6utLGxobC4XDcG74A4C20ogIAA7Db7VpfX3/xLDc3V4ODg5qbm5Pb7VZFRYVaW1u1urr6U77pcDjk8Xjk9/tlsVjkdrufdwClp6drZWVFY2NjamtrUywWU0ZGhsrKyl70942H1WpVJBLR8PCwwuGwcnNz5XK5lJ2d/cOxJycna3NzUzMzM4pGo7JYLBofH3/Ra/g9tbW1enx8VH19vRITE2W1WtXQ0PA83tPTo/7+fjU1NSktLU39/f3y+Xz/eM97a/iR4uJiff36VS6XSx6PR0lJSSosLIxvAQAAAGAo5Pvx+ShPf01eXp6Wl5c1OTmpxsZGSVJWVpYqKyvfnTc0NCS3263h4WFdX18rJSVF+fn5Ki8v/0/mfauurk7RaFQLCwsaHR1VSkrK870dCQkJ8ng8mpycVF9fn25vb2U2m1VQUCCz2SyTyaS7uzv19PTo5uZGqampqqiokN1uf/N709PTGhkZUVdXlx4eHmSxWORwOOKOt6SkRBaLRU1NTYrFYqqurpbNZpP01wmUg4MDLS4uKhKJKCMjQ729vf/qcnIAeE3CH983bAQA4BNraWlRXl6eBgYGfnUoAAAAAP5Gnv576u3t1f39vVwu168OBcAnQysqAAAAAAAAAABgGLSiAgB8GldXV6qqqnpzfG1t7X+MBgAAAAAAAD+CVlQAgE/j6elJoVDozfHMzEx9+ULNHwAAAAAA4HdGYQMAAAAAAAAAABgGd2wAAAAAAAAAAADDoLABAAAAAAAAAAAMg8IGAAAAAAAAAAAwDAobAAAAAAAAAADAMChsAAAAAAAAAAAAw6CwAQAAAAAAAAAADIPCBgAAAAAAAAAAMAwKGwAAAAAAAAAAwDD+BJzXrLRAcljMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clip_basic_distributions(interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:58.153310Z",
     "start_time": "2024-05-26T00:24:57.858363Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:43.057137Z",
     "iopub.status.busy": "2025-07-28T14:38:43.056943Z",
     "iopub.status.idle": "2025-07-28T14:38:43.076420Z",
     "shell.execute_reply": "2025-07-28T14:38:43.076049Z",
     "shell.execute_reply.started": "2025-07-28T14:38:43.057124Z"
    }
   },
   "outputs": [],
   "source": [
    "# FUCK THIS FOR NOW\n",
    "# MAX_PREFERENCE_PER_USER = 400\n",
    "# grouped_interesting_clips = interesting_clips.groupby([\"user_id\"])\n",
    "# user_top_df = (\n",
    "#     interesting_clips.sort_values(\n",
    "#         [\"preference\", \"upvote_count\", \"part_of_concat\", \"is_in_playlist\"], ascending=False\n",
    "#     )\n",
    "#     .groupby(\"user_id\")\n",
    "#     .head(MAX_PREFERENCE_PER_USER)\n",
    "# )\n",
    "# print(user_top_df.shape, interesting_clips.shape)\n",
    "\n",
    "# user_top_requests = user_top_df[\"request_id\"].unique()\n",
    "# user_intersting_clips = interesting_clips[\n",
    "#     interesting_clips[\"request_id\"].isin(user_top_requests)\n",
    "# ].copy()\n",
    "# print(user_intersting_clips.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:58.890520Z",
     "start_time": "2024-05-26T00:24:58.154350Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:43.077109Z",
     "iopub.status.busy": "2025-07-28T14:38:43.076861Z",
     "iopub.status.idle": "2025-07-28T14:38:48.616613Z",
     "shell.execute_reply": "2025-07-28T14:38:48.616102Z",
     "shell.execute_reply.started": "2025-07-28T14:38:43.077097Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clips in interesting_clips:\n",
      "1,402,572\n",
      "Number of clips in user_interesting_clips:\n",
      "1,401,600\n",
      "Ratio of preferred clips to total clips for each model:\n",
      "------------------------------------------------------------\n",
      "chirp-auk-t0                   10.46% ± 0.01%\n"
     ]
    }
   ],
   "source": [
    "# subselect interesting clips\n",
    "interesting_clips_masks = (interesting_clips[\"model_name\"].str.contains(\"v3p5|v4|v5|auk|ahi\")) & (\n",
    "    interesting_clips[\"reaction_play_count\"] > 0\n",
    ")\n",
    "# make sure we have pairs\n",
    "extra_compare_mask = interesting_clips[interesting_clips_masks][\"request_id\"].isin(\n",
    "    interesting_clips[interesting_clips_masks][\"request_id\"]\n",
    "    .value_counts()\n",
    "    .index[interesting_clips[interesting_clips_masks][\"request_id\"].value_counts() == 2]\n",
    ")\n",
    "user_intersting_clips = interesting_clips[\n",
    "    interesting_clips_masks & extra_compare_mask\n",
    "].copy()\n",
    "\n",
    "print(\"Number of clips in interesting_clips:\")\n",
    "print(f\"{interesting_clips.shape[0]:,}\")\n",
    "print(\"Number of clips in user_interesting_clips:\")\n",
    "print(f\"{user_intersting_clips.shape[0]:,}\")\n",
    "# Calculate the ratio of preferred clips to total clips for each model\n",
    "preference_ratio = (\n",
    "    user_intersting_clips[user_intersting_clips[\"preference\"]][\n",
    "        \"model_name\"\n",
    "    ].value_counts()\n",
    "    / clip_df_model_counts\n",
    ")\n",
    "\n",
    "# Print the results in a formatted manner\n",
    "print(\"Ratio of preferred clips to total clips for each model:\")\n",
    "print(\"-\" * 60)\n",
    "for model, ratio in preference_ratio.items():\n",
    "    n = clip_df_model_counts[model]\n",
    "    uncertainty = (ratio * (1 - ratio) / n) ** 0.5\n",
    "    print(f\"{model:<30} {ratio:.2%} ± {uncertainty:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:59.204547Z",
     "start_time": "2024-05-26T00:24:58.891851Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:48.617257Z",
     "iopub.status.busy": "2025-07-28T14:38:48.617134Z",
     "iopub.status.idle": "2025-07-28T14:38:48.900527Z",
     "shell.execute_reply": "2025-07-28T14:38:48.900094Z",
     "shell.execute_reply.started": "2025-07-28T14:38:48.617244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the number of preferences per user\n",
    "preferences_per_user = user_intersting_clips[\"user_id\"].value_counts()\n",
    "\n",
    "# Determine the maximum number of preferences\n",
    "max_preferences = preferences_per_user.max()\n",
    "\n",
    "# Choose bins using Sturges' rule, but ensure a minimum of 15 bins and a maximum of 30\n",
    "n_bins = max(30, min(100, int(np.ceil(np.log2(len(preferences_per_user)) + 1))))\n",
    "\n",
    "# Calculate bin edges using a linear scale\n",
    "bin_edges = np.linspace(preferences_per_user.min(), max_preferences, n_bins)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(preferences_per_user, bins=bin_edges, edgecolor=\"black\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"Number of preferences per user\")\n",
    "plt.ylabel(\"Number of users (log scale)\")\n",
    "plt.title(\"Distribution of User Preferences\")\n",
    "plt.grid(axis=\"both\", linestyle=\"--\", alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:24:59.207707Z",
     "start_time": "2024-05-26T00:24:59.205659Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:48.901199Z",
     "iopub.status.busy": "2025-07-28T14:38:48.901013Z",
     "iopub.status.idle": "2025-07-28T14:38:49.050561Z",
     "shell.execute_reply": "2025-07-28T14:38:49.050064Z",
     "shell.execute_reply.started": "2025-07-28T14:38:48.901185Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of user_interesting_clips:\n",
      "Total requests: 1,401,600\n",
      "Unique clips: 700,800\n",
      "Fraction of total clips: 20.76%\n",
      "Time Validation:\n",
      "Earliest timestamp: 2025-05-06 23:20:00.810000\n",
      "Latest timestamp:   2025-07-16 23:22:34.455000\n",
      "Earliest timestamp: NaT\n",
      "Latest timestamp:   NaT\n"
     ]
    }
   ],
   "source": [
    "print(\"Summary of user_interesting_clips:\")\n",
    "print(f\"Total requests: {user_intersting_clips.shape[0]:,}\")\n",
    "print(f\"Unique clips: {user_intersting_clips.shape[0] // 2:,}\")\n",
    "print(\n",
    "    f\"Fraction of total clips: {user_intersting_clips.shape[0] / total_clip_counts:.2%}\"\n",
    ")\n",
    "print(\"Time Validation:\")\n",
    "print(f\"Earliest timestamp: {user_intersting_clips['created_at'].min()}\")\n",
    "print(f\"Latest timestamp:   {user_intersting_clips['created_at'].max()}\")\n",
    "model_to_test = target_model_name\n",
    "print(\n",
    "    f\"Earliest timestamp: {user_intersting_clips[user_intersting_clips['model_name'] == model_to_test]['created_at'].min()}\"\n",
    ")\n",
    "print(\n",
    "    f\"Latest timestamp:   {user_intersting_clips[user_intersting_clips['model_name'] == model_to_test]['created_at'].max()}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:49.051240Z",
     "iopub.status.busy": "2025-07-28T14:38:49.051053Z",
     "iopub.status.idle": "2025-07-28T14:38:49.070506Z",
     "shell.execute_reply": "2025-07-28T14:38:49.070148Z",
     "shell.execute_reply.started": "2025-07-28T14:38:49.051226Z"
    }
   },
   "outputs": [],
   "source": [
    "def parse_for_instrumental(x):\n",
    "    if \"make_instrumental\" not in x:\n",
    "        return False\n",
    "    out = x.get(\"make_instrumental\", False)\n",
    "    return out\n",
    "\n",
    "\n",
    "# from suno_analytics.preference_data_selection import parse_for_tag, parse_for_one_box\n",
    "# user_intersting_clips[\"tags\"] = user_intersting_clips[\"metadata\"].apply(parse_for_tag)\n",
    "# user_intersting_clips[\"is_onebox\"] = user_intersting_clips[\"metadata\"].apply(parse_for_one_box)\n",
    "# user_intersting_clips[\"is_instrumental\"] = user_intersting_clips[\"metadata\"].apply(parse_for_instrumental)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:00.893917Z",
     "start_time": "2024-05-26T00:25:00.485775Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:49.071173Z",
     "iopub.status.busy": "2025-07-28T14:38:49.070922Z",
     "iopub.status.idle": "2025-07-28T14:38:52.163851Z",
     "shell.execute_reply": "2025-07-28T14:38:52.163345Z",
     "shell.execute_reply.started": "2025-07-28T14:38:49.071161Z"
    }
   },
   "outputs": [],
   "source": [
    "user_compare_mask = (\n",
    "    user_intersting_clips[\"created_at\"] >= cutoff_date\n",
    "    # & (\n",
    "    #     (user_intersting_clips[\"model_name\"].str.startswith(\"chirp-v3p5-engine-t\"))\n",
    "    #     | (user_intersting_clips[\"model_name\"].str.startswith(\"chirp-v3p5-engine-s\"))\n",
    "    # )\n",
    "    # & (~user_intersting_clips[\"is_pro_user\"])\n",
    "    # & (~user_intersting_clips[\"is_onebox\"])\n",
    "    # & user_intersting_clips[\"is_instrumental\"]\n",
    ")\n",
    "# # this is fucked up sometimes one box doesn't give prompt to one generation\n",
    "extra_compare_mask = user_intersting_clips[user_compare_mask][\"request_id\"].isin(\n",
    "    user_intersting_clips[user_compare_mask][\"request_id\"]\n",
    "    .value_counts()\n",
    "    .index[user_intersting_clips[user_compare_mask][\"request_id\"].value_counts() == 2]\n",
    ")\n",
    "\n",
    "user_compare_mask = user_compare_mask & extra_compare_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:01.140472Z",
     "start_time": "2024-05-26T00:25:00.895575Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:38:52.164707Z",
     "iopub.status.busy": "2025-07-28T14:38:52.164383Z",
     "iopub.status.idle": "2025-07-28T14:39:25.414666Z",
     "shell.execute_reply": "2025-07-28T14:39:25.414162Z",
     "shell.execute_reply.started": "2025-07-28T14:38:52.164694Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1401600, 57)\n",
      "Model Name Value Counts and Fractions:\n",
      "chirp-auk-t0: 1385923 (98.88%)\n",
      "chirp-auk-t0_mask_control_slider: 15668 (1.12%)\n",
      "chirp-auk-t0_cfg_steps_10: 3 (0.00%)\n",
      "chirp-auk-t0_n_tag_2: 2 (0.00%)\n",
      "chirp-auk-t0_temp_s_95: 2 (0.00%)\n",
      "chirp-auk-t0_tag_cfg_3: 1 (0.00%)\n",
      "chirp-auk-t0_tag_cfg_1: 1 (0.00%)\n"
     ]
    }
   ],
   "source": [
    "user_intersting_clips_3p5 = (\n",
    "    user_intersting_clips[user_compare_mask].reset_index().copy()\n",
    ")\n",
    "\n",
    "\n",
    "def modify_model_name(model_name, metadata):\n",
    "    if (\n",
    "        model_name.startswith(\"chirp-v3p5-engine-t\")\n",
    "        or model_name.startswith(\"chirp-v3p5-engine-s\")\n",
    "        or model_name.startswith(\"chirp-v4\")\n",
    "        or model_name.startswith(\"chirp-v3p5-h-s-31\")\n",
    "        or model_name.startswith(\"chirp-auk\")\n",
    "        or model_name.startswith(\"chirp-ahi\")\n",
    "    ):\n",
    "        if \"param_experiment\" in metadata:\n",
    "            exp = metadata.get(\"param_experiment\", \"\")\n",
    "            if exp:\n",
    "                if exp == \"mask_control_slider\" and not metadata.get(\"control_sliders\", None):\n",
    "                    return model_name\n",
    "                return f\"{model_name}_{exp}\"\n",
    "    return model_name\n",
    "\n",
    "\n",
    "user_intersting_clips_3p5[\"model_name\"] = user_intersting_clips_3p5.apply(\n",
    "    lambda row: modify_model_name(row[\"model_name\"], row[\"metadata\"]), axis=1\n",
    ")\n",
    "user_intersting_clips_3p5 = user_intersting_clips_3p5.sort_values(\n",
    "    by=[\"request_id\", \"preference\"]\n",
    ")\n",
    "print(user_intersting_clips_3p5.shape)\n",
    "model_counts = user_intersting_clips_3p5[\"model_name\"].value_counts()\n",
    "model_fracs = model_counts / model_counts.sum()\n",
    "\n",
    "print(\"Model Name Value Counts and Fractions:\")\n",
    "print_out_value_counts_nicely(user_intersting_clips_3p5, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:25.415384Z",
     "iopub.status.busy": "2025-07-28T14:39:25.415173Z",
     "iopub.status.idle": "2025-07-28T14:39:30.091050Z",
     "shell.execute_reply": "2025-07-28T14:39:30.090535Z",
     "shell.execute_reply.started": "2025-07-28T14:39:25.415370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 700800\n",
      "differing counts: 15677\n",
      "chirp-auk-t0_cfg_steps_10_win_over_chirp-auk-t0, win ratio 0.333, (-0.200, 0.867), counts 1, total 3.\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.501, (0.494, 0.509), counts 7857, total 15668.\n",
      "chirp-auk-t0_n_tag_2_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 2, total 2.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 685123, total 685123.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_cfg_steps_10, win ratio 0.667, (0.133, 1.200), counts 2, total 3.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.499, (0.491, 0.506), counts 7811, total 15668.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_tag_cfg_1, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_tag_cfg_3, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_temp_s_95, win ratio 1.000, (1.000, 1.000), counts 2, total 2.\n",
      "tournament players: ['chirp-auk-t0', 'chirp-auk-t0_cfg_steps_10', 'chirp-auk-t0_mask_control_slider', 'chirp-auk-t0_n_tag_2', 'chirp-auk-t0_tag_cfg_1', 'chirp-auk-t0_tag_cfg_3', 'chirp-auk-t0_temp_s_95']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:02<00:00, 471.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 1025.9, (-884, 35093)\n",
      "chirp-auk-t0_cfg_steps_10: 983.9, (-10042, 13424)\n",
      "chirp-auk-t0_mask_control_slider: 1026.9, (-882, 35090)\n",
      "chirp-auk-t0_n_tag_2: 1047.3, (1000, 46960)\n",
      "chirp-auk-t0_tag_cfg_1: 978.5, (-15537, 1000)\n",
      "chirp-auk-t0_tag_cfg_3: 978.5, (-16376, 1000)\n",
      "chirp-auk-t0_temp_s_95: 959.1, (-37167, 1000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7XKB+9dVXM2HChIoU6NREbTKlVUSLFi1mOV+nTp2yzDLL5KOPPprpmEGDBpWTwptuuukcHRS5zDLLpE2bNnnrrbfy5ptv5vPPP69q+48HHnig4t6WSqWMGTMmL7/8csWBiPXq1csll1wyXVJ6Wj/5yU/SrFmzWa733HPPlZPp22233Ryl6jt06JBFF100EyZMyEsvvTTb8bPy8ccflw9vXHXVVbP66qvP9jONGzfOuuuum6effjqff/553njjjVl+bk6KuU8//XT5+dQ/I7MzbQ/zAQMG5OCDD57p2BVWWCFrrbXWLOeb9oc977///nTv19bWVhwG+stf/nKO9jkjC/r7AgALP4VqAACqarnllstGG22UF154Ic8991xGjhyZVq1a5W9/+1v5UL65TVMPGTIkkydPTpIsuuiiadeu3Ww/s8Yaa5SLm5MnT87rr7+e9ddfv/z+1GJlMqX9w+zU1NSkQ4cOeeKJJ2Y65j//+U/5+YcffpgLL7xwtvMmXx9WWCqV8uGHH1a1UP3888/n+eefn+WYpZZaKpdcckm23HLLWY6bXWE0qbxHQ4YMmeN7NNXYsWOn+6HD3Jh2/S+//HKO1x82bFj5+YcffjjLQvWc3IdpDyZ84okn8uKLL872M59//nn5+bQ/RJiR1VZbbbbzNW/evPx8ar/waQ0ePDhfffVVkimHJ07tBz4vFvT3BQAWfgrVAABU3a677poXXnghkydPzoMPPphDDjkkf/3rX5NMKfjObaH6008/LT//0Y9+NMOD2r6pXr16WXbZZTN06NDp5khSPpRx6pxzYnbjPv744/LzIUOGZMiQIXM077TGjh0715+Zn2pqarLYYoulZcuWWWONNbLFFlvkZz/72WzbYyRTWoPMzrT3aMCAARkwYMBc7/Gzzz6b50L1tOsPHz683Kplbszu12hu78PM+kPPytQfbszMnPywY9q2IJMmTZru/VGjRpWfL7vssjNtIzInFvT3BQAWfgrVAABU3Q477JCLL744X3zxRfr06ZNdd901zzzzTJJkgw02yAorrDBX840fP778fEaHr83MtGOnnSOZcjDe3M45u3HTJkDn1dTkeLVcdtllFb2Kv405KWbPj3s0o6LqnKrGr9GctDOZUYJ5fu5hTn6YMzvT/pmZ1x8MTLWgvy8AsPBTqAYAoOqaNm2abbbZJg899FCGDBmSq666qlxc3G233eZ6vmn7In/xxRdz/Llpx36zt/K0hbc5nXN246YtZHft2jVnn332HM37QzLtPTrjjDNy0EEHFbb+1ltvnZtuuqmq60+7j6lF8/vvv3+Wh4MWZdo/M9P+YGdefBe+LwCwYM36iHEAAFhApi1I33fffUmmJE132GGHuZ5r2oMOP/zww5RKpdl+pq6uLh9++OEM50gq2zPMaf/baeebkaWWWqr8fNq2CXxt2ns0cuTIQtcv8tdo2oM2i7gPc2Lae/Xhhx9+qyT7d+H7AgALlkI1AACF6NSpU1q1alVxbZtttpmngwLbtWuX+vXrJ5nSjmBOej+//vrr5RRo/fr1pzv8bo011ig/n/aAvZkplUp55ZVXZjmmffv25ecvv/zyHBXUf2imvUcvvfRS1def9kDAwYMHf+uk8PzYRxH3YU6sscYa5TYmX3zxxWx//8/Kd+H7AgALlkI1AACFqF+/fnbeeeeKa/PS9iOZ0kpk7bXXLr++//77Z/uZe+65p/y8ffv20/XY7dixY/n5008/nTFjxsxyvn79+s02Ub3BBhtkiSWWSDIlgfrUU0/Ndp8/NJtvvnn5UL6XX345r7/+elXXX2GFFdK2bdskycSJEyt+n1TTlltuWX5+77335quvvipkH7PSqFGjij8n83Lw5FTfhe8LACxYCtUAABTm17/+de65557yY7PNNpvnufbdd9/y8z//+c+zLHAOGjQod955Z/n1fvvtN92YzTbbLD/60Y+STEmLXnnllTOd76uvvsrll18+2z02atQoBx54YPn1BRdckI8++mi2n5vqh9AuZJlllskuu+ySZEpK/dRTT53jg/bq6uoyevTob72Hww47rPz82muvnaOE/lTzq23F9ttvn5VWWqk85/nnnz/HCfzx48dXLQl+8MEHl58//PDDefjhh+dpnu/K9wUAFhyFagAACrPEEktknXXWKT+mtu+YFzvvvHO5fcfEiRPzq1/9Kv369Ztu3HPPPZfDDjus3E93rbXWSpcuXaYbV79+/Rx//PHl1/fcc08uueSS6ZKeI0eOzJFHHpnXX389DRs2nO0+Dz744Pz4xz9Oknz00UfZc8898+ijj6aurm6G40ePHp0777wzu+++e26++ebZzv99cMIJJ5TbwgwZMiR77bVX/vWvf810/IcffphevXplhx12yCOPPPKt199ll12yySabJJlSBP35z3+eO+64I7W1tTMcP27cuDzwwAPp2rVrLrroom+9fjLl99/5559f/jNx33335fDDD89bb701088MHjw4V155ZbbccssMHz58vuxjdjp16lTRV/6UU05J9+7dZ3iwaF1dXfr165ejjz66fHDiVN+V7wsALDgNit4AAADMD40aNcpvf/vb/PKXv8zo0aMzcuTIHHjggVl99dXL/aYHDx5ckbRecsklc/XVV8+0wLz77rvnn//8Zx599NEkSe/evfPXv/41HTt2TPPmzfPBBx+kf//+qa2tzfLLL59tttkmt9566yz3udhii+Wmm27KQQcdlOHDh2fkyJE54YQT0qJFi6y77rpZaqmlUiqVMnbs2Pzvf//Lu+++Wy5iTy2eft8ts8wyufHGG3P44Yfn008/zdtvv51DDz00yyyzTNq3b5+WLVtm4sSJ+fTTT/Pmm2/O9yJl/fr1c+211+aQQw7Ja6+9lnHjxuW8887LlVdemXXXXTfLLLNM6tevn7Fjx+btt9/O0KFDyz/42H777efbPjp16pTzzz8/559/fiZPnpynn346zzzzTFZdddW0a9cuiy22WL788suMHDkyr7/++nxJk8+LSy65JCNGjMjAgQMzefLk3HDDDbn55puz/vrrZ9lll02pVMpHH32UQYMGlVvozCgt/V35vgDAgqFQDQDA90bbtm3zl7/8JSeddFJee+21JFMOTZxRG5C11lor1157bVZcccVZznnllVemSZMm5b7XY8eOzRNPPFExpk2bNunevfscp3lXWGGF3HvvvTnvvPPy+OOPp1Qq5dNPP83f//73mX5miSWWyGqrrTZH838ftG/fPvfee2/OOuusPP/880mmJNCffPLJmX5mqaWWKreP+LZatGiR22+/PZdddlnuueeeTJo0KePGjZtlsrtJkyZZa6215sv6U+2zzz5ZccUVc9555+Wdd95JqVTKm2++mTfffHOmn/nxj3+cZs2azdd9zErTpk1z22235ZJLLsm9996byZMnZ8KECTO9V40bN069ejP+y73fhe8LACwYCtUAAHyvrLLKKrn33nvz2GOP5YknnsjAgQPLycuWLVumQ4cO2X777bP99tunpqZmtvM1bNgwl19+eXbdddfcddddeemll/LJJ5+kWbNmWXHFFbPjjjtmzz33zGKLLTZX+2zevHmuu+66vPHGG3n44YfTv3//DB8+PGPGjEm9evWyxBJLZMUVV8yaa66ZTp065Sc/+UkaN248T/fku2q55ZZLr1698vLLL+exxx7Liy++mA8//DCfffZZ6tevn+bNm2ellVbK2muvnc022ywbb7xx+SDG+aFJkya54IILcthhh+WBBx5Iv3798s4772TMmDGpq6vL4osvnhVWWCGrr756Ntlkk2yxxRZp2rTpfFt/qk022SSPPPJI+vbtm3/84x955ZVXMmrUqIwbNy5NmjTJUkstlTZt2mS99dbLFltsUf4bBNXUpEmTXHTRRTnooIPy17/+Nc8//3zef//9jB07Ng0bNkyrVq3Srl27dOrUKTvttNMs79N34fsCAPNfTWlOT6gAAAAAAIAFwGGKAAAAAAAUSqEaAAAAAIBCKVQDAAAAAFAohWoAAAAAAAqlUA0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUArVAAAAAAAUSqEaAAAAAIBCKVQDAAAAAFAohWoAAAAAAAqlUA0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUArVAAAAAAAUSqEaAAAAAIBCKVQDAAAAAFAohWoAAAAAAAqlUA0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUArVAAAAAAAUSqEaAAAAAIBCKVQDAAAAAFAohWoAAAAAAAqlUA0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUArVAAAAAAAUSqEaAAAAAIBCKVQDAAAAAFAohWoAAAAAAAqlUA0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUArVAAAAAAAUSqEaAAAAAIBCKVQDAAAAAFAohWoAAAAAAAqlUA0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUArVAAAAAAAUSqEaAAAAAIBCKVQDAAAAAFAohWoAAAAAAAqlUA0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUArVAAAAAAAUqkHRGwC+27art3fRWwAAvuceH/FK0VsAAL7n6i37RtFb+MGTqAYAAAAAoFAK1QAAAAAAFEqhGgAAAACAQilUAwAAAABQKIVqAAAAAAAKpVANAAAAAEChFKoBAAAAACiUQjUAAAAAAIVSqAYAAAAAoFAK1QAAAAAAFEqhGgAAAACAQilUAwAAAABQKIVqAAAAAAAKpVANAAAAAEChFKoBAAAAACiUQjUAAAAAAIVSqAYAAAAAoFAK1QAAAAAAFEqhGgAAAACAQilUAwAAAABQKIVqAAAAAAAKpVANAAAAAEChFKoBAAAAACiUQjUAAAAAAIVSqAYAAAAAoFAK1QAAAAAAFEqhGgAAAACAQjUoegMAAAAAAD80dR+uVtja9ZZ9o7C1Z0aiGgAAAACAQilUAwAAAABQKK0/AAAAAACqrC51ha29MKaXF8Y9AQAAAADwAyJRDQAAAABQZZNLxSWqF8aisEQ1AAAAAACFWhiL5wAAAAAA32t1KRW9hYWKRDUAAAAAAIVSqAYAAAAAoFBafwAAAAAAVFldijtMcWEkUQ0AAAAAQKEkqgEAAAAAqmxyyWGK05KoBgAAAACgUArVAAAAAAAUSusPAAAAAIAqq4vWH9OSqAYAAAAAoFAS1QAAAAAAVTZZorqCRDUAAAAAAIWSqAYAAAAAqDI9qitJVAMAAAAAUCiFagAAAAAAZujFF1/MkUcemc022yzt2rVL3759pxvz1ltv5cgjj8wGG2yQddddN3vuuWdGjBgxV+to/QEAAAAAUGWTS9+N1h8TJkxIu3btsueee+aYY46Z7v1hw4bl5z//efbcc88cd9xxadq0ad588800btx4rtZRqAYAAAAAYIY6d+6czp07z/T9a665JltssUVOPfXU8rUVV1xxrtfR+gMAAAAAoMrqCnzU1tZm3LhxFY/a2tq5/w51dfnHP/6RlVdeOYceemg23XTT7L333jNsDzI7CtUAAAAAAD8gPXr0yAYbbFDx6NGjx1zP88knn2TChAnp2bNnNt988/zpT3/Kdtttl2OOOSYvvPDCXM2l9QcAAAAAQJVNTnE9qo844ogcfPDBFdcaNWo01/PU1dUlSbbZZpscdNBBSZI11lgjL730Uu64445svPHGczyXQjUAAAAAwA9Io0aN5qkw/U0tWrRIgwYN0rZt24rrbdu2zYABA+ZqLq0/AAAAAACYa40aNco666yTt99+u+L6O++8k+WWW26u5pKoBgAAAACossnFdf6YK+PHj8+wYcPKr4cPH57BgwenWbNmad26dQ499NCceOKJ2WijjdKxY8c888wz+fvf/57evXvP1To1pVLpO3JLgIXRdvX2LnoLAMD33OMjXil6CwDA91y9Zd+o+prvDP9R1decauXlP5jjsf37988BBxww3fXdd989l19+eZLknnvuyR/+8Id8+OGHWWWVVXLsscdm2223nas9KVQD34pCNQCwoClUAwALWhGF6qEFFqrbzEWhulr0qAYAAAAAoFB6VAMAAAAAVNnk1BS9hYWKRDUAAAAAAIVSqAYAAAAAoFBafwAAAAAAVFldqegdLFwkqgEAAAAAKJRENQAAAABAlTlMsZJENQAAAAAAhVKoBgAAAACgUFp/AAAAAABUmdYflSSqAQAAAAAolEQ1AAAAAECV1ZUkqqclUQ0AAAAAQKEkqgEAAAAAqkyP6koS1QAAAAAAFEqhGgAAAACAQmn9AQAAAABQZZNliCu4GwAAAAAAFEqiGgAAAACgyupKDlOclkQ1AAAAAACFkqgGAAAAAKiyyZGonpZENQAAAAAAhVKoBgAAAACgUFp/AAAAAABU2eSSDPG03A0AAAAAAAolUQ0AAAAAUGV1MsQV3A0AAAAAAAqlUA0AAAAAQKG0/gAAAAAAqLLJqSl6CwsViWoAAAAAAAolUQ0AAAAAUGWTSzLE03I3AAAAAAAolEQ1AAAAAECV1elRXUGiGgAAAACAQilUAwAAAABQKK0/AAAAAACqbLIMcQV3AwAAAACAQklUAwAAAABU2eSSDPG03A0AAAAAAAolUQ0AAAAAUGV1MsQV3A0AAAAAAAqlUA0AAAAAQKG0/gAAAAAAqLLJpZqit7BQkagGAAAAAKBQEtUAAAAAAFU2WYa4grsBAAAAAEChFKoBAAAAACiU1h8AAAAAAFVWV5Ihnpa7AQAAAABAoSSqAQAAAACqzGGKldwNAAAAAAAKJVENAAAAAFBlk0s1RW9hoSJRDQAAAABAoRSqAQAAAAAolNYfAAAAAABVVidDXMHdAAAAAACgUBLVAAAAAABVNrkkQzwtdwMAAAAAgEJJVAMAAAAAVFldaorewkJFohoAAAAAgEIpVAMAAAAAUCitPwAAAAAAqsxhipXcDQAAAAAAZujFF1/MkUcemc022yzt2rVL3759Zzr23HPPTbt27dKrV6+5XkehGgAAAACgyianXmGPuTFhwoS0a9cu55133izHPfnkk3nllVey9NJLz9P90PoDAAAAAOAHpLa2NrW1tRXXGjVqlEaNGk03tnPnzuncufMs5/voo49y0UUX5eabb84RRxwxT3tSqAYAAAAAqLK6Uk1ha/fo0SPdu3evuHbMMcfk2GOPneu56urqcsopp+TQQw/Nj3/843nek0I1AAAAAMAPyBFHHJGDDz644tqM0tRzomfPnmnQoEEOOOCAb7UnhWoAAAAAgB+QmbX5mFuDBg1K7969c99996Wm5tslxBWqAQAAAACqbG4PNVwY/fvf/84nn3ySrbbaqnxt8uTJueKKK9K7d+889dRTczyXQjUAAAAAAHNt1113TadOnSquHXroodl1112zxx57zNVcCtUAAAAAAFVWV/puJKrHjx+fYcOGlV8PHz48gwcPTrNmzdK6deu0aNGiYnzDhg2z1FJLpU2bNnO1jkI1AAAAAAAzNGjQoIqDEi+77LIkye67757LL798vq2jUA0AAAAAwAx17NgxQ4YMmePxc9OXeloK1QAAAAAAVTY5NUVvYaHy3WiEAgAAAADA95ZENQAAAABAlX1XDlOsFncDAAAAAIBCSVQDAAAAAFSZHtWVJKoBAAAAACiUQjUAAAAAAIXS+gMAAAAAoMocpljJ3QAAAAAAoFAS1QAAAAAAVTZZorqCuwEAAAAAQKEkqgEAAAAAqqwuNUVvYaEiUQ0AAAAAQKEUqgEAAAAAKJTWHwAAAAAAVeYwxUruBgAAAAAAhZKoBgAAAKruq6+S83+b/HdIMnRYsuWmSfdLph9XW5vceGvywJPJqNFJqyWTow5I9uwy6/nvfzTpdVfyzvCk6aLJ9lsm55749fulUnLLncldDyYjPkpaNEv23y05suuU98+4LOnz2PQHnbVduZSHbp3nrw1QVldymOK0FKoBAACAqptclzRulPxyz+TJp2c+7sTzk1GfJhefmqy0XPLxJ1OKzLPS687klruSU45M2q+ZfPFl8v6HlWMuvT559t/JqUclq7VJxn6WjPns6/fPPDY56fCvF5o8Odnt0GSHLef6qwIwBxSqAQAAgKpbdJHk/G5Tnr88KPl83PRjnumfvPhK8sTtSfMlplxb7keznnfs58l1Nyc3XpZsusHX19u1/fr5W+8kd/w1eaBXssqKU64t/415F2865TFV32eSzz5Pdt9xTr4dAHNLoRoAAABYKD31bLJWu+Tm25MHnkgWaZJs/ZPkuEOTJo1n/JnnXkzqSslHI5MuXZPxXyTrrZWcenTyo6WnjPn7c8nyrZN/PJ8cdkpSypSi9slHfl0Q/6Z7H54yZrllF8hXBX6AJjs+sIK7AQAAACyUhn+QvPRq8ubbyQ0XJ2ccmzz+z+TC3876M6W65A9/njL+2guSMZ8nh3ZLaid+PWbER8lj/0guPzO57PTktSHJCefOeM6PRyXPvJDsNZu+2ADMO4VqAAAAYKFUV5fUJLny7KT9GknnTZLTjk76PJ58+dXMPzNxUk3OOi7ZbONk3bWSq89N3h2evPDy12Nqa2ty+ZnJhh2SjddLLjot6f9yTd4eNv2cfR6b0gZkm80X2FcFfoDqSjWFPRZGCtUAAADAQqnVkskyrSp7RbddMSmVavLhyJl/JknarvT1tZbNkxbNpqSop45pUL+UVVaYZt7/P37qmKlKpeTeR5Jdfpo0avitvg4As6BQzQ/G8OHD065duwwePHimY+67775suOGGVdzV3Dv99NNz1FFHFb0NAACABW79tae03Rg/4etr7wxP6tUrZdlWM/7MeutM+efb7319bcxnyadjv+4vvf46yaTJNRn2/jTz/v/xrb/Rg/rF/yTD3q/Jnjt9q68CMJ261CvssTBaOHcFBdlpp53y+OOPF72N+W5mxe0xY8akW7duWX/99bPhhhvmzDPPzPjx4wvYIQAA8EP0v3eSwW8mYz9LPh835fngN79+v8u2Uw43POvyKWNffCW58qZkjx2/PkzxyaeTnbp+/ZlVVki22ayUS29IXh6UvDE0OePSZJUVp7T4SKYcirjmaqWcdUXy2hvJf4ck512ddNqwMmWdJPc8nLRfs5TV2izIOwFAg6I3AAuTJk2apEmTJjN9v7a2No0aNarijhask08+OSNHjswtt9ySiRMn5swzz8y5556bq6++uuitAQAAPwBHnJaM+PDrXql7/GrKPwf/s5QkWWzR5Oark4uvS/Y+fErReoetkuN/9fUc48Ynbw+rSVIqX7v8zOSy7smRpyU19ZKNOiQ9r0wa/v8qSL16yU2XTZm363HJooskm2+cnHp05f4+HzelEH7GsQvi2wMwrZpSqVSa/TD47qirq8vNN9+cu+66Kx988EGWWmqp7Lvvvtl5552zzTbb5IYbbshtt92WgQMHZqWVVsoFF1yQ9dab8mP1++67L5deemn+/e9/J0luuOGG9O3bN7/85S9z0003ZcSIEXn99dfTtWvX/PjHP06S/PWvf02DBg2y//775/jjj09Nzcwb0t9yyy2577778t5776VZs2bZaqutcsopp2SxxRarWO+vf/1r+TO9evVK796989RTTyWZko7+7LPPcuONNyZJBg4cmMMPPzyHHHJIDj/88OnWvOGGG9K9e/eKa717985SSy2VnXbaKffcc0/WWWfK3417+umnc/jhh+ef//xnlllmmTm639vV23uOxgEAzKvHR7xS9BYAgO+5esu+UfU1T/zPflVfc6pr1r2jsLVnRusPvneuvvrq9OzZM0cddVQeeeSRXHXVVVlqqaXK719zzTU59NBD06dPn6y88srp1q1bJk2aNNP5hg0blscffzzdu3dPnz59ytfvv//+1K9fP3fffXfOOuus9OrVK3ffffcs91ZTU5OzzjorDz30UC6//PL069cvV1555Tx/1+effz6HHHJITjzxxBkWqZPkkEMOyY477pjNN988//rXv/Kvf/0r6623Xl5++eUsscQS5SJ1knTq1Cn16tXLwIED53lPAAAAADC3tP7ge2XcuHHp3bt3zj333Oy+++5JkhVXXDEbbrhhhg8fnmRK4XbLLbdMkhx33HHp0qVL3n333bRt23aGc06cODG/+c1v0rJly4rrP/rRj3LmmWempqYmbdq0yRtvvJFevXpln332men+DjrooPLz5ZdfPieccELOO++8nH/++XP9XZ988smceuqpueSSS7LTTjM/1WOxxRZLkyZNUltbm1atvj5tZNSoUdN9pwYNGqRZs2YZOXImx2cDAAAAMF/UlWb+t/J/iBSq+V4ZOnRoamtrs8kmm8x0TLt27crPpxZuR48ePdNCdevWracr6CZJhw4dKtp8rLvuurnlllsyefLk9OzZMz169Ci/9/DDD6d169Z57rnn0qNHjwwdOjTjxo3L5MmT89VXX+WLL77IIossMsffc+DAgfnHP/6R66+/Pttuu235+ogRI9KlS5fy6yOOOCJHHnnkHM8LAAAAAEVQqOZ7pXHjxrMd07Bhw/LzqYXmurq6mY6fmwLyVPvtt1923HHH8uull146w4cPzxFHHJH9998/J554Ypo1a5YBAwbkrLPOysSJE7PIIoukpqYm32wbP6O2JCussEKaN2+ee+65J507dy5/p6WXXrqiPUmzZs1musellloqo0ePnm6tsWPHViSvAQAAAJj/6kq6Mk/L3eB7ZeWVV06TJk3Sr1+/Bb7WN/s4v/LKK1lppZVSv379NG/ePCuttFL50aBBg/z3v/9NqVTK6aefnnXXXTerrLJKPv7444o5WrZsmVGjRlUUqwcPHjzd2i1atMitt96aYcOG5YQTTsjEiROTTGndMe26zZs3TzKlOP/NYvx6662Xzz77LIMGDSpf69evX+rq6tK+fftvdW8AAAAAYG4oVPO90rhx4xx22GG58sor06dPnwwbNiz/+c9/ZnvI4bwYMWJELrvssgwdOjQPPfRQ/u///i8HHHDATMevtNJKmThxYm677ba899576dOnT+64o/KE1Y4dO2b06NHp2bNnhg0blj//+c955plnZjjfkksumVtvvTVDhw6d7YGQyy23XIYMGZKhQ4dm9OjRmThxYtq2bZvNN98855xzTgYOHJgBAwbkoosuSpcuXbLMMsvM200BAAD4Ft7/IDnrimTbfZN1t0t+un9yw5+S2olfj+l+S7JG55rpHutvP/N5X/9f0u2CZKu9pszbpWvS+57KMR9/kpx8YbLDL5I1t0wuvWH6eQ44fsZrH3HafPn6AD9oWn/wvXPUUUelfv36uf766/Pxxx+nVatW2W+//eb7Orvttlu+/PLL7L333qlfv34OOOCA7LvvvjMdv/rqq+eMM85Iz54989vf/jYbbrhhTjrppJx22tf/RdO2bducd9556dGjR2666ab89Kc/zSGHHJK77rprhnO2atUqt956a7p27ZqTTz45V199derXrz/duH322ScvvPBC9txzz0yYMCG9e/dOx44dc9VVV+Wiiy7KgQcemHr16uWnP/1pzj777G9/cwAAAGbhgOOT3XdIdt+x8vrQYUmpLrng5GTF5ZI3307OvTL54svk1KOmjDl432TfXSpbJh58UrLO6jNf779DkiVbJFecnfxo6eTlQcl5VyX16yW/2GPKmIm1ScvmyZFdk94zyTpdf1EyceLXa4/5LNn90GSHLefu+wMkyeQ4THFaNaVvNsQFZqtr165ZffXVc9ZZZxW9lcJtV2/vorcAAHzPPT7ilaK3AMxnMytUz8jNtyd3/DV58o4Zv//6/5LdD63JbdeXsmGHOd/DhdckQ99Nel074/2tvmpy5rGznuPWu6ckvp++L1l07o83AhYi9ZZ9o+prHvXSL6u+5lQ3rv9/ha09MxLVAAAAwELr8/FJsyVm/v49DyUrrzB3ReokGTebeefEvQ8nO22tSA3Mm7qSRPW0FKoBAACABarHbckf/vz16y+/Sl55Lbn4uq+vPXhr0vobx+W8Ozz5833JKb+e8bxffZU81Df51c/nbj8vD0oefSr5/RVz97lpDRycvPl2TS4+zV9UB5gfFKphHtx2221FbwEAAOA7Y99dkx22+vr1qRcn220x5THV0ktWfuajkcnhpybbb5nss/OM5+37TDJ+QrLbDnO+lzeGJkefmRx1UPKTjeb8c99078PJam1Kab/GvM8BwNcUqgEAAIAFqvkSUx5TNW485XDDlZaf8fiPRyUHnpCsu1Zy4ckzn/eeh5POmyZLtZyzffzvneSQk6YUvn99wJzufnoTvkgeeSo59pB5nwOgrlSv6C0sVNwNAAAAYKHx0cgphxmutVpy6elJvZlULoZ/kPR/Odmzy5zN++bbyUEnJLtun5xw2Lfb4+P/SGonJjtv9+3mAeBrEtUAAADAAjV+wpQU8lRXnzvlnyM/+fpay+bJqNFTitStl01OPSoZPebr91t9ozXIvY9MubZFx+nXe/Lp5JqeySP/v2vjG0OTg0+c0urjoH2+Xrd+/SnrTjX4zSn/nPBF8umYKa8bNkxWXfkbaz+cbLNZ0qLZHH19gBmqi8MUp6VQDQAAACxQt9yZ/K7XrAsyfe8o5YX/JMPer8mw95Mt96p8f/A/vz60sK4u6fNosvsOU4rN3zRufPL2sJokUz7zxD+T0WNq8uCTyYNPfj2u9bKl/O3Or1/v8auv9/jfIVMOavzmmLeHJQNerckfr3KIIsD8VFMqlfybFZhn29Xbu+gtAADfc4+PeKXoLQAA33P1ln2j6mse/OLBVV9zqls2uqWwtWdGj2oAAAAAAAqlUA0AAAAAQKH0qAYAAAAAqLK6kgzxtNwNAAAAAAAKJVENAAAAAFBldaWaorewUJGoBgAAAACgUBLVAAAAAABVVheJ6mlJVAMAAAAAUCiFagAAAAAACqX1BwAAAABAlTlMsZJENQAAAAAAhZKoBgAAAACosrqSDPG03A0AAAAAAAolUQ0AAAAAUGV6VFeSqAYAAAAAoFAK1QAAAAAAFErrDwAAAACAKquL1h/TkqgGAAAAAKBQEtUAAAAAAFX2XTlM8cUXX8zNN9+cQYMGZeTIkfnd736XbbfdNkkyceLEXHvttXn66afz3nvvpWnTpunUqVO6deuWZZZZZq7WkagGAAAAAGCGJkyYkHbt2uW8886b7r0vv/wyr732Wn7961/nvvvuS/fu3fP222/n17/+9VyvI1ENAAAAAMAMde7cOZ07d57he4svvnhuueWWimvnnHNO9t5774wYMSKtW7ee43UUqgEAAAAAqqzI1h+1tbWpra2tuNaoUaM0atToW889bty41NTUZIkllpirz2n9AQAAAADwA9KjR49ssMEGFY8ePXp863m/+uqrXHXVVenSpUuaNm06V5+VqAYAAAAAqLIiE9VHHHFEDj744Ipr3zZNPXHixBx//PEplUq54IIL5vrzCtUAAAAAAD8g86vNx1QTJ07MCSeckBEjRuTWW2+d6zR1olANAAAAAFB1RSaq56epRep33303vXv3TosWLeZpHoVqAAAAAABmaPz48Rk2bFj59fDhwzN48OA0a9YsrVq1ynHHHZfXXnstPXr0yOTJkzNy5MgkSbNmzeYqta1QDQAAAADADA0aNCgHHHBA+fVll12WJNl9991zzDHH5KmnnkqS7LrrrhWf6927dzp27DjH6yhUAwAAAABUWV2+G60/OnbsmCFDhsz0/Vm9NzfqzZdZAAAAAABgHklUAwAAAABU2fflMMX5RaIaAAAAAIBCSVQDAAAAAFSZRHUliWoAAAAAAAqlUA0AAAAAQKG0/gAAAAAAqDKtPypJVAMAAAAAUCiJagAAAACAKpOoriRRDQAAAABAoRSqAQAAAAAolNYfAAAAAABVVtL6o4JENQAAAAAAhZKoBgAAAACosrpIVE9LohoAAAAAgEJJVAMAAAAAVFmdHtUVJKoBAAAAACiUQjUAAAAAAIXS+gMAAAAAoMpKWn9UkKgGAAAAAKBQEtUAAAAAAFXmMMVKEtUAAAAAABRKohoAAAAAoMr0qK4kUQ0AAAAAQKEUqgEAAAAAKJTWHwAAAAAAVeYwxUoS1QAAAAAAFEqiGgAAAACgykqlonewcJGoBgAAAACgUArVAAAAAAAUSusPAAAAAIAqq4vDFKclUQ0AAAAAQKEkqgEAAAAAqqxUkqielkQ1AAAAAACFkqgGAAAAAKiyOonqChLVAAAAAAAUSqEaAAAAAIBCaf0BAAAAAFBlpVLRO1i4SFQDAAAAAFAoiWoAAAAAgCorOUyxgkQ1AAAAAACFkqgGAAAAAKgyiepKEtUAAAAAABRKoRoAAAAAgEJp/QEAAAAAUGV1Wn9UkKgGAAAAAKBQEtUAAAAAAFVWKhW9g4WLRDUAAAAAAIWSqAYAAAAAqLKSHtUVJKoBAAAAACiUQjUAAAAAAIXS+gMAAAAAoMq0/qgkUQ0AAAAAQKEkqgEAAAAAqqxU9AYWMhLVAAAAAAAUSqEaAAAAAIBCaf0BAAAAAFBlDlOsJFENAAAAAEChJKoBAAAAAKrNaYoVJKoBAAAAACiURDUAAAAAQJXpUV1JohoAAAAAgEIpVAMAAAAAMEMvvvhijjzyyGy22WZp165d+vbtW/F+qVTKddddl8022yzt27fPQQcdlHfeeWeu11GoBgAAAACoslKpuMfcmDBhQtq1a5fzzjtvhu/37Nkzt912W84///zcddddWWSRRXLooYfmq6++mqt19KgGAAAAAGCGOnfunM6dO8/wvVKplN69e+fXv/51tt122yTJb37zm3Tq1Cl9+/ZNly5d5ngdiWoAAAAAgCorlWoKe9TW1mbcuHEVj9ra2rn+DsOHD8/IkSPTqVOn8rXFF188HTp0yMsvvzxXcylUAwAAAAD8gPTo0SMbbLBBxaNHjx5zPc/IkSOTJEsuuWTF9SWXXDKjRo2aq7m0/gAAAAAAqLZSTWFLH3HEETn44IMrrjVq1Kig3UyhUA0AAAAA8APSqFGj+VKYbtWqVZLkk08+ydJLL12+/sknn2T11Vefq7m0/gAAAAAAYK4tv/zyadWqVZ5//vnytXHjxuWVV17JeuutN1dzSVQDAAAAAFRZqVT0DubM+PHjM2zYsPLr4cOHZ/DgwWnWrFlat26dAw44IDfddFNWWmmlLL/88rnuuuuy9NJLZ9ttt52rdRSqAQAAAACYoUGDBuWAAw4ov77sssuSJLvvvnsuv/zyHHbYYfniiy9y7rnn5rPPPssGG2yQP/7xj2ncuPFcrVNTKn1XavfAwmi7ensXvQUA4Hvu8RGvFL0FAOB7rt6yb1R9zTZ/ubTqa0419OdnFrb2zOhRDQAAAABAoRSqAQAAAAAolB7VAAAAAABVVirVFL2FhYpENQAAAAAAhZKoBgAAAACotlLRG1i4SFQDAAAAAFAoiWoAAAAAgCrTo7qSRDUAAAAAAIVSqAYAAAAAoFBafwAAAAAAVJvDFCtIVAMAAAAAUCiJagAAAACAqnOY4rQkqgEAAAAAKJRENQAAAABAtelRXUGiGgAAAACAQilUAwAAAABQKK0/AAAAAACqTeuPChLVAAAAAAAUSqIaAAAAAKDaSjVF72ChIlENAAAAAEChFKoBAAAAACiU1h8AAAAAAFVWcphiBYlqAAAAAAAKJVENAAAAAFBtEtUVJKoBAAAAACiURDUAAAAAQLWVaorewUJFohoAAAAAgEIpVAMAAAAAUCitPwAAAAAAqqzGYYoVJKoBAAAAACiURDUAAAAAQLVJVFeQqAYAAAAAoFDfKlH93HPP5ZZbbsmrr76azz//PHV1ddONqampyWuvvfZtlgEAAAAA+H4p1RS9g4XKPBeqH3/88Zx44ompq6tL69at06ZNm9SvX39+7g0AAAAAgB+AeS5U/+53v0vjxo1z4403ZtNNN52fewIAAAAA4AdkngvVb7/9dnbddVdFagAAAACAueUwxQrzfJhi8+bN06RJk/m5FwAAAAAAfoDmOVG9/fbb5/nnn8+kSZPSoMG3OpMRAAAAAOCHRaK6wjwnqk866aQsvvjiOfHEEzNixIj5uScAAAAAAH5A5jkKvfPOO2fSpEl55ZVX0rdv3yyxxBJp2rTpdONqamrSt2/fb7VJAAAAAIDvFYnqCvNcqC6VSqlfv35+9KMfVVyb0TgAAAAAAJiZeS5UP/XUU/NzHwAAAAAA/EA5BREAAAAAoNpKNUXvYKEyz4cpAgAAAADA/DDHieru3bunpqYmv/jFL9K8efN07959jj5XU1OTo48+ep43CAAAAADwfVPjaL8Kc12o3mmnnRSqAQAAAACYb+a4UN27d+8kSevWrSteAz9sNfXrF70FAOB7bvvWHYreAgDwPfdkXdE7YI4L1RtvvHGuv/761NTUpEOHDtl4440X5L4AAAAAAL6/tP6oMMeF6iS58cYbc9NNN6VRo0ZZb731svHGG6djx47p0KFDGjSYq6kAAAAAACDJXBaqTz/99LzwwgsZMGBA+vXrl379+qWmpiZNmjTJ+uuvn44dO2aTTTbJ2muvnXr16i2oPQMAAAAA8D1SUyqV5jpkXiqV8vrrr6d///7p169fXnrppXz22WdTJqypyaKLLpoNN9wwHTt2TMeOHbPWWmvN940DC4efNtyv6C0AAN9zpcmTi94CAPA992Td3VVfc5Ubrq76mlO9fWy3wtaemXkqVH9TqVTK4MGD069fv3Li+vPPP09NTU1qamry2muvzY+9AgshhWoAYEFTqAYAFrQiCtVtri+uUD30uIWvUD1fGkvX1NRkzTXXzJprrpkddtghzz77bHr37p0333xzfkwPAAAAAMD32LcuVH/00Ufp379/+fH+++8nSRZddNFsvvnm2Wijjb71JgEAAAAA+P6a60L1J598Uu5N3b9//wwbNiylUimLL754Nthgg/z85z/PRhttlLXWWsuBigAAAAAAM1KqKXoHC5W5KlTvtNNOefvtt5MkSyyxRDbccMPsv//+2XjjjbPGGmukpsbNBQAAAABg7sxVoXro0KGpV69ett122xxwwAHp0KFDGjZsuKD2BgAAAADw/VQqegMLl7kqVHft2jUvvPBCnnzyyTz55JNp0qRJOnTokI033jgbb7yxwjUAAAAAAHNtrgrVZ511VpJkzJgxeeGFF9K/f/+88MILueGGG5IkjRs3LheuO3bsqHANAAAAADAjEtUVakql0re+JaNHjy4XrV944YW89dZbqampSePGjbPuuuumV69e82GrwMLopw33K3oLAMD3XGny5KK3AAB8zz1Zd3fV12xzzW+rvuZUQ088qbC1Z2auEtUz07Jly+y4447ZcccdM3HixDz11FPp3r173nzzzfTv339+LAEAAAAAwPfUty5UT548OQMHDiwnql9++eV8+eWXmRrUbtGixbfeJAAAAADA90mN1h8V5rpQXVdXl1dffTX9+/dP//798/LLL+eLL74oF6abNWuWn/zkJ+nYsWM6duyY1VZbbb5vGgAAAACABWvy5Mm54YYb8sADD2TUqFFZeumls/vuu+eoo45KTU3NfF1rrgrVhx12WF566aVMmDChXJhefPHFs+WWW5YL06uvvvp83yQAAAAAwPfKdyBR3bNnz9x+++254oorsuqqq2bQoEE544wzsvjii+eAAw6Yr2vNVaH6mWeeyaKLLprNN9+8XJhec801U69evfm6KQAAAAAAivXyyy9nm222yZZbbpkkWX755fPwww9n4MCB832tuSpU33nnnVl77bVTv379+b4RAAAAAAAWvNra2tTW1lZca9SoURo1alRxbb311stdd92Vt99+O6usskpef/31DBgwIKeffvp839NcFao7dOgw3zcAAAAAAPCDU2Drjx49eqR79+4V14455pgce+yxFdcOP/zwjBs3LjvuuGPq16+fyZMn58QTT8wuu+wy3/c014cpAgAAAADw3XXEEUfk4IMPrrj2zTR1kjz66KN58MEHc/XVV2fVVVfN4MGDc9lll5UPVZyfFKoBAAAAAKqspsBE9YzafMzIb37zmxx++OHp0qVLkqRdu3YZMWJEevToMd8L1U5BBAAAAABgOl9++WVqamoqrtWvXz+l0vyvsktUAwAAAABUW6lm9mMKttVWW+X3v/99WrduXW79ccstt2TPPfec72spVAMAAAAAMJ2zzz471113XS644IJ88sknWXrppbPvvvvm6KOPnu9rKVQDAAAAADCdpk2b5qyzzspZZ521wNdSqAYAAAAAqLYCD1NcGDlMEQAAAACAQklUAwAAAABUWY1EdQWJagAAAAAACiVRDQAAAABQbRLVFSSqAQAAAAAolEI1AAAAAACF0voDAAAAAKDKHKZYSaIaAAAAAIBCSVQDAAAAAFSbRHUFiWoAAAAAAAolUQ0AAAAAUG0S1RUkqgEAAAAAKJRCNQAAAAAAhdL6AwAAAACgymq0/qggUQ0AAAAAQKEUqgEAAAAAKJRCNQAAAAAAhVKoBgAAAACgUA5TBAAAAACoNocpVpCoBgAAAACgUBLVAAAAAABVViNRXUGiGgAAAACAQklUAwAAAABUm0R1BYlqAAAAAAAKpVANAAAAAEChtP4AAAAAAKg2rT8qSFQDAAAAAFAoiWoAAAAAgCqrkaiuIFENAAAAAEChJKoBAAAAAKpNorqCRDUAAAAAAIVSqAYAAAAAoFBafwAAAAAAVJnDFCtJVAMAAAAAUCiJagAAAACAapOoriBRDQAAAABAoRSqAQAAAAAolNYfAAAAAADVpvVHBYlqAAAAAAAKJVENAAAAAFBlNRLVFSSqAQAAAAAolEQ1AAAAAEC1SVRXkKgGAAAAAKBQCtUAAAAAABRK6w8AAAAAgGrT+qOCRDUAAAAAAIWSqAYAAAAAqLIaieoKEtUAAAAAABRKohoAAAAAoNokqitIVAMAAAAAUCiFagAAAAAACqX1BwAAAABAlTlMsZJENQAAAAAAhZKoBgAAAACoNonqChLVAAAAAAAUSqEaAAAAAIBCaf0BAAAAAFBtWn9UkKgGAAAAAKBQEtUAAAAAAFVWU/QGFjIS1QAAAAAAFEqiGgAAAACg2vSoriBRDQAAAADADH300Uc5+eST07Fjx7Rv3z4777xzXn311fm+jkQ1AAAAAADTGTt2bPbff/907NgxPXv2TIsWLfLuu++mWbNm830thWoAAAAAgCqr+Q60/ujZs2eWXXbZXHbZZeVrK6ywwgJZS+sPAAAAAIAfkNra2owbN67iUVtbO924p556KmuvvXaOO+64bLrpptltt91y1113LZA9KVQDAAAAAFRbqbhHjx49ssEGG1Q8evToMd0W33vvvdx+++1ZeeWVc/PNN2f//ffPxRdfnPvvv3++3w6tPwAAAAAAfkCOOOKIHHzwwRXXGjVqNN24UqmUtddeOyeddFKSZM0118ybb76ZO+64I7vvvvt83ZNCNQAAAABAtRXYo7pRo0YzLEx/U6tWrdK2bduKa23atMnjjz8+3/ek9QcAAAAAANNZf/318/bbb1dce+edd7LccsvN97UUqgEAAAAAmM6BBx6YV155Jb///e/z7rvv5sEHH8xdd92Vn//85/N9La0/AAAAAACqrKbA1h9zqn379unevXt++9vf5ne/+12WX375nHnmmdlll13m+1oK1QAAAAAAzNBWW22VrbbaaoGvo1ANAAAAAFBt34FEdTXpUQ0AAAAAQKEkqgEAAAAAquy70KO6miSqAQAAAAAolEI1AAAAAACF0voDAAAAAKDatP6oIFENAAAAAEChJKoBAAAAAKrMYYqVJKoBAAAAACiUQjUAAAAAAIXS+gMAAAAAoNq0/qggUQ0AAAAAQKEkqgEAAAAAqk2iuoJENQAAAAAAhZKoBgAAAACoshqJ6goS1QAAAAAAFEqhGgAAAACAQmn9AQAAAABQbVp/VJCoBgAAAACgUBLVAAAAAABVVlMSqZ6WRDUAAAAAAIWSqAYAAAAAqDaB6goS1QAAAAAAFEqhGgAAAACAQmn9AQAAAABQZTVaf1SQqAYAAAAAoFAS1QAAAAAA1SZRXUGiGgAAAACAQilUAwAAAABQKK0/AAAAAACqzGGKlSSqAQAAAAAolEQ1AAAAAEC1SVRXkKgGAAAAAKBQEtUAAAAAAFWmR3UliWoAAAAAAAqlUA0AAAAAQKG0/gAAAAAAqDatPypIVAMAAAAAUCiJagAAAACAKnOYYiWJagAAAAAACiVRDQAAAABQbSWR6mlJVAMAAAAAUCiFagAAAAAACqX1BwAAAABAlTlMsZJENQAAAAAAhZKoBgAAAACoNonqChLVAAAAAAAUSqEaAAAAAIBCaf0BAAAAAFBlNXVF72DholANAMyRVdZZMcdcf0jabdgmY0d+nj6/eyx3X/3gLD9z1DUHZq1O7bLSWivkvdffz683PH2mY1u3XSY3vnh56ibXZY9Wh5avX9n33HTovOZ04/s/8lLO2fU38/6FAAAAWGgoVAMAs7Xo4ovkskfOzMtPvZrrj/5jVll7hZzU88iMHzshj/zxb7P87GO9/pHVN141bdZZcaZj6jeonzP+77gM+tfrWXPT1Sreu3Dvq9Og0df/ybLEkovn9wOuyNP39v92XwoAAKBIDlOsoFANAMzW1j/fLA0aNcjVv/p9Jk2cnHdfG542HVbOnsfvNMtC9Y0n3pokab7UErMsVB904b55b8iIvPzUoOkK1Z9/Or7i9Zb7dsqXE77KM/f0+xbfCAAAgIWJwxQBgNlaY5MfZ9AzgzNp4uTytQFPvJIVVl8uTZsv9q3mXnfLtbLFnh3T/dg/zdH4HQ7eKv+86/l8OeGrb7UuAABAkWpKxT0WRgrVAMBstVymeT79eGzFtamvWyzbbJ7nXbxl05x8869z1aE3ZcLnX8x2fLuN2maVtVfMo396ap7XBAAAYOGjUA0AFObE3x+ep+54Nq/+6/U5Gr/DwVtl6KvvZsiLby3gnQEAAFBNelQDALM1+qMxabF0ZXJ66utPPxw7o4/MkXW3Wiub7rxB9j7pZ1Mu1NSkfv16efSLP+faX/fM473+UR7bZNHG2XKfTrn1grvneT0AAICFRmkh7cFRkO9Monr48OFp165dBg8ePNMx9913XzbccMMq7mrunX766TnqqKOK3kYh5uTX8Ptkfv1af3Oerl275pJLLpnlZ7beeuv06tXrW68NMNXgfm9m7c3XSP0G9cvX1t+2fd57/f2MGzN+Fp+cteM3Pye/3vC08qP3+Xdl/GcT8usNT8uzfV6sGLv5XpukYeMG+dufn5nn9QAAAFg4fWcK1XNip512yuOPP170Nua7mRU8x4wZk27dumX99dfPhhtumDPPPDPjx897seCH7rtSSL/hhhty/PHHF70N4Afmqdv/lUm1k3JSzyOy0prLp/Pem2b3Y3fIvdc9Uh7zk103ys2vXl3xudZtl0mbDiulxbLN0qhJo7TpsFLadFgpDRpOKXi/9/qIvPPf4eXHqBGfplRXyjv/HT5dAXyHg7fKc3/9dz4fPW7Bf2EAAIAFzGGKlb5XrT+aNGmSJk2azPT92traNGrUqIo7WrBOPvnkjBw5MrfccksmTpyYM888M+eee26uvvrq2X+YeVb076PmzZsv8DWK/o7AwmfCZ1/kjJ0uzTHXH5Lf9b80Y0d9nv+7+L488se/lccs2mzRrLD6chWfO7HHEenQec3y69//+4okSddVj81H746c4/WXX+1HWWez1XP6DrP+GyUAAAB8Ny10ieq6urr07Nkz2223XdZee+1sueWWuemmm8rvv/fee+natWs6dOiQXXbZJS+//HL5vW+2/rjhhhuy66675u67787WW2+d9u3bJ5nSOuHCCy/MhRdemA022CAdO3bMtddem9Js+sLccsst2XnnnbPuuuumc+fOOf/88ysSzFPXm1avXr2y9dZbz3TOgQMHZpNNNskf/vCHGb5/ww035P7778/f/va3tGvXLu3atUv//v3z1ltv5ZlnnsnFF1+cDh06ZMMNN8zZZ5+dhx9+OB999NEsv8e09+rvf/97tt9++3To0CHHHXdcvvjii9x///3Zeuuts9FGG+Xiiy/O5MmTy5/r06dP9thjj6y33nr5yU9+km7duuWTTz4pvz927Nh069Ytm2yySdq3b5+f/vSnuffee2e4h8mTJ+eMM87IDjvskBEjRsx2z5999lnOPffcdOrUKeuss05+9rOf5e9//3v5/ccffzxdunTJ2muvna233jp/+tOfKj6/9dZb5/e//33OOOOMrLfeetlyyy1z5513lt/fZpttkiS77bZb2rVrl65duyb5OtF+0003ZbPNNssOO+yQJBkyZEgOOOCAtG/fPh07dsw555wzz4n2xx57LDvvvHN5roMOOigTJkyY4dhvtv745JNPcuSRR6Z9+/bZeuut88ADD8zw3p111lnZZJNNsv766+eAAw7I669/fXDZzP6sAEzr7VeHpdtW5+dnix+QX6xydO66qvLfN0/2/md+2nC/imunbHthftpwv+keMytSP9n7n9mj1aHTXR/+xgf5acP98tLfXp1/XwgAAKBIpQIfC6GFLlF99dVX5+67784ZZ5yRDTbYIB9//HHefvvt8vvXXHNNTjvttKy00kq55ppr0q1btzzxxBNp0GDGX2XYsGF5/PHH071799Sr93Vd/v77789ee+2Vu+++O4MGDcq5556b1q1bZ5999pnp3mpqanLWWWdl+eWXz3vvvZcLLrggV155Zc4///x5+q7PP/98jj322JxyyinZd999ZzjmkEMOyVtvvZVx48blsssuS5I0a9YsDzzwQJZYYomss8465bGdOnVKvXr1MnDgwGy33XazXf/LL7/MbbfdlmuuuSbjx4/PMccck2OOOSaLL754/vCHP+S9997Lsccem/XXXz877bRTkmTSpEk5/vjj06ZNm3zyySe5/PLLc/rpp6dnz55Jkuuuuy5vvfVWevbsmRYtWmTYsGH58ssvp1u7trY2J510Ut5///385S9/ScuWLWe517q6uhx22GEZP358rrzyyqy44or53//+V/41HTRoUE444YQcc8wx2WmnnfLyyy/nggsuSPPmzbPHHnuU57nlllty3HHH5cgjj8zjjz+e888/PxtttFHatGmTu+++O3vvvXd69eqVVVddNQ0bNqz4tWratGluueWWJMmECRNy6KGHZr311ss999yTTz75JGeffXYuuuiiXH755bO999P6+OOP061bt5xyyinZdtttM378+Pz73/+e7Q9Opjr99NPz8ccfp3fv3mnQoEEuvvjiih8eJMnxxx+fxo0bp2fPnll88cVz55135sADD8zjjz9eTmjP7M8KAAAAACxoC1Whety4cendu3fOPffc7L777kmSFVdcMRtuuGGGDx+eZErhdsstt0ySHHfccenSpUvefffdtG3bdoZzTpw4Mb/5zW+mK4T+6Ec/yplnnpmampq0adMmb7zxRnr16jXLQvVBBx1Ufr788svnhBNOyHnnnTdPheonn3wyp556ai655JJyEXhGFltssTRp0iS1tbVp1apV+fqoUaOm+04NGjRIs2bNMnLknP1V6okTJ+b888/PiiuumCTZfvvt88ADD+TZZ5/NYostllVXXTUdO3ZMv379ynvca6+9yp9fYYUVctZZZ2WvvfbK+PHjs9hii2XEiBFZY401ygX05Zdffrp1x48fn8MPPzy1tbXp3bt3Fl988dnu9bnnnsvAgQPzyCOPZJVVVimvP9Utt9ySTTfdNEcffXSSZJVVVsn//ve/3HzzzRWF6i222CK/+MUvkiSHHXZYevXqlf79+6dNmzbl+9m8efOKe50kiy66aC6++OJyO4y77rortbW1ueKKK7LooosmSc4999wceeSROfnkk7PUUkvN9jtNNXLkyEyaNCnbbbddlltuyl+Zb9eu3Rx99u23387TTz+du+++u5yC/ubvqX//+98ZOHBgnn/++fL+TzvttPTt2zePP/54+YckM/uzAgAAAAAL2kJVqB46dGhqa2uzySabzHTMtAW8qcXE0aNHz7RQ3bp16xkW3jp06JCampry63XXXTe33HJLJk+enJ49e6ZHjx7l9x5++OG0bt06zz33XHr06JGhQ4dm3LhxmTx5cr766qt88cUXWWSRReb4ew4cODD/+Mc/cv3112fbbbctXx8xYkS6dOlSfn3EEUfkyCOPnON559YiiyxSLlInyVJLLZXlllsuiy22WMW10aNHl18PGjQo3bt3z+uvv56xY8eWU78ffPBBVl111ey///457rjj8tprr+UnP/lJtt1226y//voV63br1i3LLrtsbr311ln2FJ/W4MGDs+yyy5aL1N80dOjQcuuOqdZff/307t07kydPTv36Uw7tmvb3T01NTZZaaqnp0sczstpqq1X0bH7rrbfSrl27cpF66np1dXV5++2356pQvfrqq2fTTTfNzjvvnM022yybbbZZtt9++zRr1my2n33rrbfSoEGDrL322uVrbdu2zRJLLFF+PWTIkEyYMCEdO3as+OyXX36ZYcOGlV/P7M8KAAAAAPPfwnqo4az84Q9/yNVXX50DDjggZ5111nyde6EqVDdu3Hi2Y6ZtxzC10FxXVzfT8XNTQJ5qv/32y4477lh+vfTSS2f48OE54ogjsv/+++fEE09Ms2bNMmDAgJx11lmZOHFiFllkkdTU1EzXrmHSpEnTzb/CCiukefPmueeee9K5c+fyd1p66aXTp0+f8rhZFSq/WUCeutbYsWOnSwPPzDfbpdTU1Mzw2tT7O7XdxWabbZarrroqLVq0yAcffJBDDz00EydOTJJ07tw5f//73/PPf/4zzz77bA466KD84he/yGmnnVaes3PnznnggQfy8ssvZ9NNN52jvc5pQXt2ZvT95qTFxrz8PppT9evXzy233JKXXnopzz77bLkdy1133VWRGp9X48ePT6tWrXLbbbdN9960afYF+R0BAAAA+G4bOHBg7rjjjjnuBDC3FqpGtCuvvHKaNGmSfv36LfC1Bg4cWPH6lVdeyUorrZT69eunefPmWWmllcqPBg0a5L///W9KpVJOP/30rLvuullllVXy8ccfV8zRsmXLjBo1qqLwOXjw4OnWbtGiRW699dYMGzYsJ5xwQrnI26BBg4p1p/YObtiw4XTF+PXWWy+fffZZBg0aVL7Wr1+/1NXVLbCD8IYOHZoxY8bk5JNPzoYbbpi2bdvOMI3csmXL7L777rnqqqty5plnVhxYmCT7779/unXrlqOOOiovvPDCHK3drl27fPjhhxX9yqfVpk2bvPTSSxXXXnrppay88srlNPXsTP2BwbSHR85M27Zty0nladerV6/eTFPfs1JTU5MNNtggxx13XPr06ZOGDRumb9++s/1cmzZtMmnSpIrfB0OHDs1nn31Wfr3WWmtl1KhRqV+/fsXvr5VWWkmCGgAAAKAopVJhj9ra2owbN67iUVtbO9Otjh8/PqecckouvvjiOeoCMC8WqkJ148aNc9hhh+XKK69Mnz59MmzYsPznP//J3XffPd/XGjFiRC677LIMHTo0Dz30UP7v//4vBxxwwEzHr7TSSpk4cWJuu+22vPfee+nTp0/uuOOOijEdO3bM6NGj07NnzwwbNix//vOf88wzz8xwviWXXDK33nprhg4dmm7dus0weT3VcsstlyFDhmTo0KEZPXp0Jk6cmLZt22bzzTfPOeeck4EDB2bAgAG56KKL0qVLlyyzzDLzdlNmo3Xr1mnYsGH5Hvztb3/LjTfeWDHmuuuuS9++ffPuu+/mzTffzD/+8Y8ZtmXp2rVrjj/++BxxxBH597//Pdu1N95442y44YY57rjj8uyzz+a9997LP//5zzz99NNJpvQuf/755/O73/0ub7/9du6///78+c9/ziGHHDLH32/JJZdMkyZN8swzz2TUqFH5/PPPZzp25513TqNGjXL66afnjTfeSL9+/XLRRRdl1113nau2H8mUH5L8/ve/z6uvvpoRI0bkiSeeyOjRo9OmTZvZfrZNmzbZfPPNc9555+WVV17JoEGDcvbZZ1ck0Dt16pR11103Rx99dP71r39l+PDheemll3LNNdfk1Vdfnau9AnzT4i0Wy+m9j8n9n/wp9428OSf94Yg0WWzWf0Pq+Bt/lV6vX5cHP+udu0b8Ieffe3JWaNe6Ysy6W62da56+MH1G35I73vt9Dr3056lXv/I/W7bYa5Pc9O/L88DYW3Pb/27I3if9bL5/PwAAgO+jHj16ZIMNNqh4TNsK+ZsuvPDCdO7cOZ06dVpge1qoCtVJctRRR+Xggw/O9ddfn5122iknnnjidC0u5ofddtstX375Zfbee+9ceOGFOeCAA8qHys3I6quvnjPOOCM9e/bMz372szz44IM56aSTKsa0bds25513Xv7yl79k1113zcCBA2dZKG3VqlVuvfXWDBkyJCeffPJMk7z77LNPVlllley5557ZdNNNy8nhq666Km3atMmBBx6Yww8/POuvv34uvPDCebgbc6Zly5a5/PLL89hjj2WnnXZKz549K1p6JFNSyb/97W+zyy675Je//GXq1auX3/72tzOc76CDDspxxx2Xww8/fLo09IzccMMNWXvttXPSSSelS5cuueqqq8pJ87XWWivXXnttHnnkkey88865/vrrc9xxx1UcpDg7DRo0yNlnn50777wzm2++eY466qiZjl1kkUVy8803Z8yYMdlrr71y/PHHZ9NNN80555wzx+tN1bRp07z44os5/PDDs/322+faa6/N6aefns6dO8/R5y+77LIsvfTS+eUvf5ljjz02++yzT5Zccsny+zU1NfnDH/6QjTbaKGeccUZ22GGHnHTSSXn//ffnuqgO/DBd2ffcbHfAjP+ddHrvY7PSmsvnjB0vzTm7/SbrbLZ6Trjp8FnO9+ZLb+fqX92UX63TLWd2uTQ1Ncllj5yZevWmtPRq037FXPzgafn34//JURudnkt+fl02/dkGOfTSn5fn2Gj7dXN672Py0B/65vB1T8kNx/4pexy/U3Y5avv598UBAAAWoJpScY8jjjgiAwYMqHgcccQRM9znww8/nNdeey3dunVbwPdjThr0fs907do1q6+++nxv+A0/RD9tuF/RWwAWsCv7npsnev8zT/b+Z8X1FVZvnZtf/W2O3uTMvDlgaJJkw592yMUPnpafr3x0Rn/w6RzNv8o6K6bHS7/Jge2OzwdDP8rBF+2X9bddJ8du+vX/Tm/SZf2cdfsJ2af14fli3Jc5vfexadCwfi7e/9rymF2P3j77dNslv2hz9Lf/0sBCpTQHrdkAAL6NJ+vmf0eH2dli1yurvuZUT//1lDka98EHH2TPPffMn/70p6y++upJFlxtdaFLVAMA3w1rbrJaPv90XLlInSQv/e3VlOpKWWPjVedojiaLNs72B26ZD4Z+lJHvjUqSNGzcILVfTqwY99UXtWm8SKP8eP02X4/5avoxrVZYMsusNGeHCgMAADBr//3vf/PJJ59kjz32yJprrpk111wzL7zwQm677basueaac3TW25xqMN9mYqHyq1/9KgMGDJjhe0cccUSOPPLIKu9o1h544IGcd955M3yvdevWefjhh6u8o/lnxIgR6dKly0zff/jhh9O6deuZvg9Qbfudtlv2P3238utGizTKGh1XzTHXHVy+9qv23dJimeYZ8/FnFZ+tm1yXz0ePS4tlm89yjZ2P3C6/uuwXWaRpk7z3+vs5fcdLM2nilP/AGfDEwOx+3E7Zct9Oefru59Ni2eb55dl7Jkla/mjKvAOeHJgjr+qaJ7ZaO6/8479pveqy2euEn5XHfPTuyG95FwAAABaw70Cfi0022SQPPvhgxbUzzjgjbdq0yWGHHZb69evPt7V+kIXq2267regtLHCXXHJJvvzyyxm+t6BO5vw2tt5663To0GGG7zVo8N3+bbr00kunT58+s3wfYGHy8B+ezNP3PF9+fXrvY/PMff3zbJ8Xytc+GTFnbT1m5m9/+VcG9H01Sy7bPHud9LOcffvxOWGL8zLxq4kZ0Hdg/nj6/+X43/0qp/U6OhO/mpg/X3Jf1tl8jZTqpvyX3CN//Ft+1GaZXPTXU9OgYf2M/+yL9Lnh0Rxw3t7lMQAAAHw7TZs2zWqrrVZxbdFFF03z5s2nu/5tfbcrgMzUMsssU/QW5krTpk3TtGnTorexQDRo0CArrbRS0dsAmGOffzo+n386vvz6qy9qM2bkZxnx1kcV4z79aEyaL71ExbV69etl8ZZN8+mHY2a5xoTPvsiEz77IiP99mMH938x9I2/OT3bbKP+487kkyb3XPpJ7r30kLX/UIuM+HZdlVm6VQy/9eT54++s93HzmX3LL2benxbLNM3bkZ1lv63WSJB8M/WiGawIAACxMamRsKihUAwDz5LV+b2TxFk3z4/VXyZsvvZ0kWW+rtVNTryaDX/jfHM9TU1OT1NSkYeOG07039UDGrfb9ST4eNir/+//rTFVXVyqnu7fcr1P++/wbGTvq83n9SgAAAMzGgupWoVANAFRosljjLNK0Sfn1pb+4LknSYpmvW0eNHflZ3nt9RF587D854feH5/qj/5j6Devn6OsOzj/ufL5cYF6ydYv85vGz85tDbsyQF9/KsqssnS333jQD+g7MmJGfpdXyS2bfU3ZJ7Re1efHRl8vz733Sz/LiE6+kVFfKZrttnH1P3TWX7H9t6v5/W48lllw8m+/ZMQP/+VoaNmmY7Q/cMlvsuUlO3uaCatwiAAAA5jOFagCgwt4n7Zyu5+41yzFdVz02H707MpcfcEOOvu6QXPH42SnVlfLM/f1z4wm9yuMaNGyQFVZfLo0XaZwkqf1yYtbebPXsftyOadqiacZ8NDav/mtwTtji3IwZ+fXBjBvtsG72P2P3NGzcMEMHvpvz97gqLz7+n4o9bNd1ixx+xS9TU5O81u/NnLLthRny4lvz7T4AAAAsUM7XqVBTKpXcEWCe/bThfkVvAQD4nitNnlz0FgCA77kn6+6u+pqdu/ym6mtO9c+HTy1s7ZmRqAYAAAAAqDbx4Qr1it4AAAAAAAA/bBLVAAAAAABVViNRXUGiGgAAAACAQilUAwAAAABQKK0/AAAAAACqraT3x7QkqgEAAAAAKJRENQAAAABAlTlMsZJENQAAAAAAhZKoBgAAAACoNonqChLVAAAAAAAUSqEaAAAAAIBCaf0BAAAAAFBlNSW9P6YlUQ0AAAAAQKEkqgEAAAAAqq2u6A0sXCSqAQAAAAAolEI1AAAAAACF0voDAAAAAKDKHKZYSaIaAAAAAIBCSVQDAAAAAFSbQHUFiWoAAAAAAAolUQ0AAAAAUG16VFeQqAYAAAAAoFAK1QAAAAAAFErrDwAAAACAKqvR+aOCRDUAAAAAAIWSqAYAAAAAqDaHKVaQqAYAAAAAoFAS1QAAAAAAVVZTV/QOFi4S1QAAAAAAFEqhGgAAAACAQmn9AQAAAABQbQ5TrCBRDQAAAABAoSSqAQAAAACqTaC6gkQ1AAAAAACFUqgGAAAAAKBQWn8AAAAAAFRZjcMUK0hUAwAAAABQKIlqAAAAAIBqk6iuIFENAAAAAEChJKoBAAAAAKqtrugNLFwkqgEAAAAAKJRCNQAAAAAAhdL6AwAAAACgymocplhBohoAAAAAgEJJVAMAAAAAVJtEdQWJagAAAAAACiVRDQAAAABQbRLVFSSqAQAAAAAolEI1AAAAAACF0voDAAAAAKDa6orewMJFohoAAAAAgEJJVAMAAAAAVFmNwxQrSFQDAAAAAFAoiWoAAAAAgGqTqK4gUQ0AAAAAQKEUqgEAAAAAKJTWHwAAAAAA1ab1RwWJagAAAAAACiVRDQAAAABQbd+BRHWPHj3yxBNPZOjQoWnSpEnWW2+9nHzyyWnTps18X0uiGgAAAACA6bzwwgv5xS9+kbvuuiu33HJLJk2alEMPPTQTJkyY72tJVAMAAAAA/IDU1tamtra24lqjRo3SqFGjims333xzxevLL788m266af773/9mo402mq97UqgGAAAAAKi2uuKW7tGjR7p3715x7Zhjjsmxxx47y899/vnnSZJmzZrN9z0pVAMAAAAA/IAcccQROfjggyuufTNN/U11dXW59NJLs/7662e11Vab73tSqAYAAAAAqLKaAg9TnFGbj9m54IIL8uabb+Yvf/nLAtmTQjUAAAAAADN14YUX5h//+Ef+7//+L8suu+wCWUOhGgAAAACg2gpMVM+pUqmUiy66KE8++WRuu+22rLDCCgtsLYVqAAAAAACmc8EFF+Shhx7KjTfemMUWWywjR45Mkiy++OJp0qTJfF1LoRoAAAAAgOncfvvtSZKuXbtWXL/sssuyxx57zNe1FKoBAAAAAKqtbuFv/TFkyJCqrVWvaisBAAAAAMAMSFQDAAAAAFTbd+AwxWqSqAYAAAAAoFAS1QAAAAAA1SZRXUGiGgAAAACAQilUAwAAAABQKK0/AAAAAACqTeuPChLVAAAAAAAUSqIaAAAAAKDa6iSqpyVRDQAAAABAoRSqAQAAAAAolNYfAAAAAADVVqoregcLFYlqAAAAAAAKJVENAAAAAFBtJYcpTkuiGgAAAACAQklUAwAAAABUW51E9bQkqgEAAAAAKJRCNQAAAAAAhdL6AwAAAACg2hymWEGiGgAAAACAQklUAwAAAABUm0R1BYlqAAAAAAAKJVENAAAAAFBtEtUVJKr/H3t3HqbXfP+P/zlZJotsJFRo7CKKLIglpbVWq9RWS9VS0uqCqvpaq/YWpT6q2tLSKlXUri2qVJWKWEOR2NckKouESGLW3x9+uZuRkEjuzDmZeTyuy3Xd97nPnPt1Ms+JzHPOvA8AAAAAAIVSVAMAAAAAUChLfwAAAAAAtLampqInKBVXVAMAAAAAUChXVAMAAAAAtDY3U2zBFdUAAAAAABRKUQ0AAAAAQKEs/QEAAAAA0Nos/dGCK6oBAAAAACiUK6oBAAAAAFpbkyuq5+aKagAAAAAACuWKagAAAACAVtbc3FT0CKXiimoAAAAAAAqlqAYAAAAAoFCW/gAAAAAAaG1uptiCK6oBAAAAACiUK6oBAAAAAFpbsyuq5+aKagAAAAAACuWKagAAAACA1tbUVPQEpeKKagAAAAAACqWoBgAAAACgUJb+AAAAAABobW6m2IIrqgEAAAAAKJQrqgEAAAAAWlmzmym24IpqAAAAAAAK5YpqAAAAAIDWZo3qFlxRDQAAAABAoRTVAAAAAAAUytIfAAAAAACtrcnSH3NzRTUAAAAAAIVyRTUAAAAAQGtrbip6glJxRTUAAAAAAIVSVAMAAAAAUChLfwAAAAAAtLJmN1NswRXVAAAAAAAUyhXVAAAAAACtzc0UW3BFNQAAAAAAhXJFNQAAAABAK7NGdUuuqAYAAAAA4ENdeeWV2WabbbLBBhtkzz33zBNPPFH191BUAwAAAAAwX7feemvOPPPMHHroobnxxhszaNCgjBw5MlOmTKnq+yiqAQAAAABaW3NTcf99DL/73e+y1157ZY899shaa62VU089NV27ds31119f1T8ORTUAAAAAQDtSV1eXGTNmtPivrq5uvvs99dRTGTFiRGVbhw4dMmLEiDz22GNVncnNFAEAAAAAWtnfm64t7L1//vOf58ILL2yx7bDDDsvhhx/eYttbb72VxsbG9O3bt8X2vn375sUXX6zqTIpqAAAAAIB25Jvf/GYOOuigFttqa2sLmuZ9imoAAAAAgHaktrZ2oYrpZZddNh07dpznxolTpkxJv379qjqTNaoBAAAAAJhHbW1t1ltvvYwaNaqyrampKaNGjcqwYcOq+l6uqAYAAAAAYL4OOuigHHvssVl//fUzePDg/P73v8+sWbOy++67V/V9FNUAAAAAAMzXjjvumKlTp+aCCy7IpEmTsu666+aSSy6p+tIfNc3Nzc1VPSLQrnyu8z5FjwAAtHHNjY1FjwAAtHF/b7q26BHaPWtUAwAAAABQKEU1AAAAAACFUlQDAAAAAFAoRTUAAAAAAIVSVAMAAAAAUChFNQAAAAAAhappbm5uLnoIAAAAAADaL1dUAwAAAABQKEU1AAAAAACFUlQDAAAAAFAoRTUAAAAAAIVSVAMAAAAAUChFNQAAAAAAhVJUAwAAAABQKEU1AAAAAACFUlQDAAAAAFAoRTUAAAAAAIVSVAMAAAAAUChFNQAAAAAAhepU9AAAwNJv0qRJefzxxzN58uQkSb9+/TJkyJAsv/zyBU8GALQFdXV1ufPOOzNmzJgW/94YNmxYtt1229TW1hY8IQCLq6a5ubm56CEAgKXTzJkzc9JJJ+XWW29NTU1NevfunSSZPn16mpub88UvfjGnnXZaunXrVvCkAMDS6pVXXsnIkSPz5ptvZsiQIenbt2+SZMqUKXn88cez4oor5je/+U1WXXXVgicFYHEoqgGARfaDH/wgDz/8cE488cSMGDEiHTt2TJI0NjZm1KhROf300zN8+PCcccYZBU8KACytDjrooHTr1i0/+clP0qNHjxavzZgxI8ccc0zee++9XHrppQVNCEA1KKoBgEU2fPjwXHzxxdlwww3n+/ojjzySb33rW3nooYdaeTIAoK0YMmRIrr322gwcOHC+rz/zzDPZa6+98vjjj7fyZABUk5spAgCLrKmpKZ07d/7Q1zt37pympqZWnAgAaGt69uyZ8ePHf+jr48ePT8+ePVtxIgCWBEU1ALDIttpqq5x00kl5+umn53nt6aefzimnnJKtt966gMkAgLZizz33zLHHHpvLLrss48aNy+TJkzN58uSMGzcul112WY4//vjsvffeRY8JwGKy9AcAsMimT5+eo446Kvfdd1969+6d5ZZbLkkyderUvP3229liiy3y05/+NL169Sp4UgBgafbrX/86l19+eSZPnpyampokSXNzc/r165cDDzww3/jGNwqeEIDFpagGABbbCy+8kDFjxmTy5MlJkn79+mXo0KFZc801C54MAGhLXnvttRb/3hgwYEDBEwFQLYpqAGCxPfTQQxk2bFg6derUYntDQ0Mee+yxDB8+vKDJAAAAWBpYoxoAWGwHHHBApk+fPs/2d955JwcccEABEwEA7cXEiRNz/PHHFz0GAItJUQ0ALLbm5ubKepFzmzZtWrp161bARABAezF9+vTcdNNNRY8BwGLqtOBdAADm77DDDkuS1NTU5LjjjkttbW3ltcbGxjzzzDMZNmxYUeMBAG3AXXfd9ZGvv/baa600CQBLkqIaAFhkPXv2TPL+FdXLLLNMunbtWnmtc+fOGTp0aPbcc8+ixgMA2oBDDz00NTU1+ahbbM3vN7sAWLooqgGARXbmmWcmSVZeeeUcfPDB6d69e8ETAQBtzfLLL5+TTz4522233XxfHzt2bHbfffdWngqAarNGNQCw2A477LBKSf3rX/86b7/9dsETAQBtxXrrrZennnrqQ19f0NXWACwdapr9bQ4AVNGGG26Ym2++OQMGDCh6FACgDXj44Yczc+bMfOYzn5nv6zNnzsyTTz6ZTTbZpJUnA6CaLP0BAFSVn4EDANW08cYbf+Tr3bt3b1FSv/HGG1lhhRXSoYNfIgdYmvhbGwAAAGgzdtxxx4wfP77oMQD4mFxRDQBU1a233poVVlih8ry5uTk1NTUFTgQAtCd+uwtg6eSKagBgsV1yySWVx/3790/Hjh2TJI2NjTnqqKOKGgsAAIClhKIaAFhsl156aa699toW2xobG3PkkUdm7NixBU0FAADA0sLSHwDAYrv44oszcuTI9OzZM5///OfT0NCQ733ve3nxxRdz+eWXFz0eAAAAJaeoBgAW2+DBg3PBBRfk0EMPTefOnXPdddfl1VdfzeWXX55+/foVPR4A0I64NwbA0snSHwBAVWy++eb5yU9+ku9+97t5/fXXc8UVVyipAYBW52aKAEunmmZ/gwMAi+Cwww6b7/YxY8Zk1VVXzbLLLlvZduGFF7bWWABAOzFjxow88MADWX311bPmmmtWtk+cODErrLBC5ebOACwdLP0BACySnj17znf7lltu2cqTAADtwRFHHJHhw4dnv/32y+zZs7PHHntk/PjxaW5uznnnnZcddtghSdK/f/+CJwVgUSiqAYBFcuaZZxY9AgDQjjz88MP59re/nST5+9//nubm5jz00EO58cYb86tf/apSVAOwdLJGNQAAAFB677zzTnr37p0kuffee/O5z30u3bp1y1ZbbZVXXnml4OkAWFyuqAYAFts222yTmpqaD339rrvuasVpAIC2qH///nnsscfSu3fv3HvvvTnvvPOSJG+//XZqa2sLng6AxaWoBgAW24EHHtjieUNDQ55++uncd999GTlyZEFTAQBtyQEHHJCjjz463bt3z0orrZRNN900SfLQQw9l4MCBBU8HwOKqaW5ubi56CACgbbryyivz5JNPWs8aAKiK//znP3njjTcyYsSILLPMMkmSf/7zn+nZs2c22mijgqcDYHEoqgGAJea1117LLrvskkcffbToUQCANmROlfFRS48BsHRxM0UAYIm5/fbb06dPn6LHAADaiGuvvTY77bRTNthgg2ywwQbZaaedcu211xY9FgBVYI1qAGCx7brrri2uaGpubs7kyZMzderUnHzyyQVOBgC0FT/72c9y2WWXZb/99svQoUOTJGPGjMmPf/zjTJgwIUcccUSxAwKwWCz9AQAstgsvvLDF85qamiy33HLZZJNNsuaaaxY0FQDQlmy22WY58cQTs9NOO7XY/pe//CWnn356Ro8eXdBkAFSDK6oBgMV22GGHFT0CANDGNTQ0ZP31159n+3rrrZfGxsYCJgKgmqxRDQBU1XvvvZcZM2a0+A8AYHHtsssuueqqq+bZ/qc//Sk777xzARMBUE2W/gAAFtvMmTNz7rnn5rbbbsu0adPmeX3s2LGtPxQA0Kacfvrpuemmm9K/f/8MGTIkSfLEE09kwoQJ2XXXXdOp0/9+afz4448vakwAFpGlPwCAxXbOOedk9OjROeWUU3LMMcfkpJNOyn//+99cc801Oeqoo4oeDwBoA5599tl86lOfSpK8+uqrSZI+ffqkT58+efbZZyv7zX2DZwCWHq6oBgAW21ZbbZWzzz47m266aTbccMPceOONWXXVVXPTTTflr3/9a37zm98UPSIAAAAlZo1qAGCxTZ8+PQMGDEiS9OjRI9OnT0+SbLTRRnn44YeLHA0AaGNeeeWV3HvvvZk9e3aSxPV3AG2DohoAWGyf/OQn8/rrrydJ1lhjjdx2221Jkrvvvjs9e/YscjQAoI146623cuCBB2aHHXbIIYcckkmTJiVJTjjhhJx11lkFTwfA4lJUAwCLbY899si4ceOSJIccckiuvPLKbLDBBjnzzDMzcuTIgqcDANqCM888M506dco///nPdO3atbJ9xx13zL333lvgZABUg5spAgCL7Wtf+1rl8YgRI3LbbbflqaeeyiqrrJJBgwYVNxgA0Gb8+9//zqWXXpoVV1yxxfbVVlstEyZMKGgqAKpFUQ0AVNUbb7yR/v37Z+WVVy56FACgDZk5c2aLK6nnmDZtWmprawuYCIBqsvQHAFBVO+64Y8aPH1/0GABAG7PxxhvnpptuarGtqakpl1xySTbddNNihgKgalxRDQBUVXNzc9EjAABt0NFHH52vfe1refLJJ1NfX59zzjknzz//fKZPn56rrrqq6PEAWEyKagAAAKD0Bg4cmL/97W/5wx/+kGWWWSYzZ87M9ttvn69+9atZYYUVih4PgMVU0+yyJwCgii6++OJ85StfSa9evYoeBQBoQyZMmJD+/funpqZmvq+ttNJKBUwFQLUoqgGAqprzT4v5fRMJALCo1l133dx3333p27dvi+1vvfVWRowYkbFjxxY0GQDV4GaKAEBVXHvttdlpp52ywQYbZIMNNshOO+2Ua6+9tuixAIA2orm5eb4/CJ85c2a6dOlSwEQAVJM1qgGAxfazn/0sl112Wfbbb78MHTo0STJmzJj8+Mc/zoQJE3LEEUcUOyAAsNQ688wzk7z/21rnn39+unXrVnmtsbExTzzxRAYNGlTUeABUiaIaAFhsV111VU4//fTstNNOlW3bbrtt1llnnZx++umKagBgkT399NNJ3r+i+tlnn03nzp0rr9XW1mbQoEE5+OCDixoPgCpRVAMAi62hoSHrr7/+PNvXW2+9NDY2FjARANBWXHHFFUmS448/Pj/4wQ/So0ePgicCYEmwRjUAsNh22WWXXHXVVfNs/9Of/pSdd965gIkAgLbmzDPPbFFSz5gxI3feeWdeeOGFAqcCoFpqmpubm4seAgBYup1++um56aab0r9//wwZMiRJ8sQTT2TChAnZdddd06nT/36J6/jjjy9qTABgKXbEEUdk+PDh2W+//TJ79uzssssuGT9+fJqbm3Peeedlhx12KHpEABaDpT8AgMX27LPP5lOf+lSS5NVXX02S9OnTJ3369Mmzzz5b2a+mpqaQ+QCApd/DDz+cb3/720mSv//972lubs5DDz2UG2+8Mb/61a8U1QBLOUU1ALDY5qwdCQCwpLzzzjvp3bt3kuTee+/N5z73uXTr1i1bbbVVzjnnnIKnA2BxWaMaAFgs9fX1+dSnPtXiymkAgGrr379/HnvsscycOTP33ntvPv3pTydJ3n777dTW1hY8HQCLyxXVAMBi6dy5c/r375+mpqaiRwEA2rADDjggRx99dLp3756VVlopm266aZLkoYceysCBAwueDoDF5WaKAMBiu/baa/P3v/89P/nJT9KnT5+ixwEA2qgnn3wyEydOzIgRI7LMMsskSf75z3+mZ8+e2WijjQqeDoDFoagGABbbrrvumldeeSUNDQ1ZaaWV0r179xav33jjjQVNBgC0NxtuuGFuvvnmDBgwoOhRAPgYLP0BACy27bbbrugRAACSJK7HA1g6KaoBgMV22GGHFT0CAAAAS7EORQ8AAAAAAED75opqAGCRbLLJJrn99tuz3HLLZfjw4ampqfnQfR988MFWnAwAAICljaIaAFgkxx9/fHr06JEkOeGEEwqeBgDgfR/1w3MAyqum2V0GAAAAgDZi2LBhueWWWzJgwICiRwHgY1BUAwBV0dTUlFdeeSVTpkzJB/95MXz48IKmAgDam4cffjiDBw9ObW1t0aMA8DEoqgGAxTZmzJgcddRRmTBhwjwldU1NTcaOHVvQZABAW3HmmWfOd3tNTU26dOmSVVZZJdtuu2369OnTuoMBUBWKagBgse2yyy5ZbbXV8t3vfjfLL7/8PGtD9uzZs6DJAIC2Yv/998/TTz+dpqamrL766kmSl156KR07dswaa6yRl156KTU1NfnjH/+YtdZaq+BpAfi4FNUAwGIbOnRobr755qy66qpFjwIAtFGXXXZZHnnkkZx55pmVGzq/8847+cEPfpCNNtooe+21V4466qi89957ufTSSwueFoCPq0PRAwAAS7/BgwfnlVdeKXoMAKANu/TSS3PEEUdUSurk/d/aOvzww3PJJZekW7duOfTQQ/Pkk08WOCUAi6pT0QMAAEuncePGVR7vv//+OfvsszN58uQMHDgwnTq1/CfGoEGDWns8AKCNmTFjRqZMmTLPsh5Tp07NjBkzkiS9evVKfX19EeMBsJgU1QDAItl1111TU1PT4uaJJ5xwQuXxnNfcTBEAqIZtttkmJ5xwQo477rhssMEGSZL//Oc/Ofvss7PddtslSZ544omsttpqBU4JwKKyRjUAsEjGjx+/0PuuvPLKS3ASAKA9ePfdd3PmmWfmpptuSmNjY5KkY8eO2W233XL88cene/fulR+Or7vuukWOCsAiUFQDAIvt4osvTt++ffPlL3+5xfbrrrsuU6dOzSGHHFLQZADA0mzcuHEZOHBgOnT43y223n333bz22mtJkgEDBmSZZZYpajwAqsjNFAGAxXbNNddkjTXWmGf72muvnauvvrqAiQCAtmC33XbLW2+9lSTZdttt89Zbb2WZZZbJoEGDMmjQICU1QBuiqAYAFtukSZOy/PLLz7N9ueWWy6RJkwqYCABoC3r16pXXX389yfvLjvmlcIC2y80UAYDF1r9//zz66KMZMGBAi+2PPPJIVlhhhYKmAgCWdp/73Oey3377Zfnll09NTU322GOPFsuAzO2uu+5q5ekAqCZFNQCw2Pbcc8/8+Mc/TkNDQzbbbLMkyahRo3LOOefk4IMPLng6AGBpdfrpp2f77bfPq6++mjPOOCN77rmn5T4A2ihFNQCw2L7+9a9n2rRpOfXUU1NfX58k6dKlS77+9a/nm9/8ZsHTAQBLs8985jNJkqeeeioHHHBAevToUfBEACwJNc0WeAIAquTdd9/NCy+8kK5du2a11VZLbW1t0SMBAG3EO++8k8bGxvTp06fF9mnTpqVTp04KbIClnJspAgBVs8wyy2Tw4MEZOHCgkhoAqKojjzwyf/3rX+fZftttt+XII48sYCIAqklRDQAAAJTeE088UbkXxtw22WSTPPHEEwVMBEA1KaoBAACA0qurq0tDQ8M82xsaGjJ79uwCJgKgmhTVAAAAQOltsMEG+dOf/jTP9quvvjrrrbdeARMBUE1upggAAACU3iOPPJKDDjooG2ywQTbffPMkyahRo/Kf//wnv/3tb7PxxhsXPCEAi0NRDQAAACwVxo4dm0suuSTjxo1Lly5dss466+Sb3/xmVltttaJHA2AxKaoBAACANuPXv/519tlnn/Tq1avoUQD4GKxRDQAAALQZF110UaZPn170GAB8TIpqAAAAoM3wi+MASydFNQAAAAAAhVJUAwAAAABQKEU1AAAAAACFUlQDAAAAAFAoRTUAAADQZmy88cbp0qVL0WMA8DHVNLsdLgAAAFBykyZNyuOPP57JkycnSfr165chQ4Zk+eWXL3gyAKqhU9EDAAAAAHyYmTNn5qSTTsqtt96ampqa9O7dO0kyffr0NDc354tf/GJOO+20dOvWreBJAVgcrqgGAAAASusHP/hBHn744Zx44okZMWJEOnbsmCRpbGzMqFGjcvrpp2f48OE544wzCp4UgMWhqAYAAABKa/jw4bn44ouz4YYbzvf1Rx55JN/61rfy0EMPtfJkAFSTmykCAAAApdXU1JTOnTt/6OudO3dOU1NTK04EwJKgqAYAAABKa6uttspJJ52Up59+ep7Xnn766ZxyyinZeuutC5gMgGqy9AcAAABQWtOnT89RRx2V++67L717985yyy2XJJk6dWrefvvtbLHFFvnpT3+aXr16FTwpAItDUQ0AAACU3gsvvJAxY8Zk8uTJSZJ+/fpl6NChWXPNNQueDIBq6FT0AAAAAAALMnXq1Oyyyy7p1KllldHQ0JDHHnssw4cPL2gyAKrBGtUAAABA6R1wwAGZPn36PNvfeeedHHDAAQVMBEA1KaoBAACA0mtubk5NTc0826dNm5Zu3boVMBEA1WTpDwAAAKC0DjvssCRJTU1NjjvuuNTW1lZea2xszDPPPJNhw4YVNR4AVaKoBgAAAEqrZ8+eSd6/onqZZZZJ165dK6917tw5Q4cOzZ577lnUeABUSU1zc3Nz0UMAAAAAfJQLL7wwBx98cLp37170KAAsAYpqAAAAYKny61//Ovvss0969epV9CgAVImbKQIAAABLlYsuuijTp08vegwAqkhRDQAAACxV/HI4QNujqAYAAAAAoFDWqAYAAACWKhMnTswKK6yQjh07Jnn/CuuampqCpwJgcbiiGgAAACi9Sy65pPK4f//+lZK6sbExRx11VFFjAVAlimoAAACg9C699NJce+21LbY1NjbmyCOPzNixYwuaCoBq6VT0AAAAAAALcvHFF2fkyJHp2bNnPv/5z6ehoSHf+9738uKLL+byyy8vejwAFpOiGgAAACi9wYMH54ILLsihhx6azp0757rrrsurr76ayy+/PP369St6PAAWk5spAgAAAEuNO++8M0cccUTWWGON/P73v89yyy1X9EgAVIGiGgAAACilww47bL7bx4wZk1VXXTXLLrtsZduFF17YWmMBsARY+gMAAAAopZ49e853+5ZbbtnKkwCwpLmiGgAAAACAQnUoegAAAAAAANo3S38AAAAApbfNNtukpqbmQ1+/6667WnEaAKpNUQ0AAACU3oEHHtjieUNDQ55++uncd999GTlyZEFTAVAtimoAAACg9D5YVM9x5ZVX5sknn2zlaQCoNmtUAwAAAEutz3zmM/nb3/5W9BgALCZFNQAAALDUuv3229OnT5+ixwBgMVn6AwAAACi9XXfdtcXNFJubmzN58uRMnTo1J598coGTAVANimoAAACg9LbbbrsWz2tqarLccstlk002yZprrlnQVABUS01zc3Nz0UMAAAAAANB+uaIaAAAAWKq89957qa+vb7GtR48eBU0DQDUoqgEAAIDSmzlzZs4999zcdtttmTZt2jyvjx07tvWHAqBqOhQ9AAAAAMCCnHPOOXnggQdyyimnpLa2NmeccUYOP/zwrLDCCjn77LOLHg+AxaSoBgAAAErv7rvvzsknn5wddtghHTt2zMYbb5zvfOc7OfLII/PnP/+56PEAWEyKagAAAKD0pk+fngEDBiR5fz3q6dOnJ0k22mijPPzww0WOBkAVKKoBAACA0vvkJz+Z119/PUmyxhpr5Lbbbkvy/pXWPXv2LHI0AKqgprm5ubnoIQAAAAA+ymWXXZYOHTrkgAMOyP33359vfetbaW5uTkNDQ4477rgceOCBRY8IwGJQVAMAAABLnfHjx+epp57KKquskkGDBhU9DgCLSVENAAAALFXeeOONrLDCCunQwYqmAG2Fv9EBAACApcqOO+6Y8ePHFz0GAFWkqAYAAACWKn45HKDtUVQDAAAAAFAoRTUAAACwVPnWt76V3r17Fz0GAFXkZooAAADAUmVOlVFTU1PwJABUiyuqAQAAgKXCtddem5122ikbbLBBNthgg+y000659tprix4LgCroVPQAAAAAAAvys5/9LJdddln222+/DB06NEkyZsyY/PjHP86ECRNyxBFHFDsgAIvF0h8AAABA6W222WY58cQTs9NOO7XY/pe//CWnn356Ro8eXdBkAFSDpT8AAACA0mtoaMj6668/z/b11lsvjY2NBUwEQDUpqgEAAIDS22WXXXLVVVfNs/1Pf/pTdt555wImAqCaLP0BAAAAlN7pp5+em266Kf3798+QIUOSJE888UQmTJiQXXfdNZ06/e82XMcff3xRYwKwiNxMEQAAACi9Z599Np/61KeSJK+++mqSpE+fPunTp0+effbZyn41NTWFzAfA4nFFNQAAAAAAhbJGNQAAAFBq9fX1+dSnPtXiymkA2hZFNQAAAFBqnTt3Tv/+/dPU1FT0KAAsIYpqAAAAoPS+9a1v5bzzzsu0adOKHgWAJcAa1QAAAEDp7brrrnnllVfS0NCQlVZaKd27d2/x+o033ljQZABUQ6eiBwAAAABYkO22267oEQBYglxRDQAAAABAoaxRDQAAAABAoSz9AQAAAJTSJptskttvvz3LLbdchg8fnpqamg/d98EHH2zFyQCoNkU1AAAAUErHH398evTokSQ54YQTCp4GgCXJGtUAAAAAABTKFdUAAADAUqGpqSmvvPJKpkyZkg9edzd8+PCCpgKgGhTVAAAAQOmNGTMmRx11VCZMmDBPSV1TU5OxY8cWNBkA1WDpDwAAAKD0dtlll6y22mr57ne/m+WXX36eGyv27NmzoMkAqAZFNQAAAFB6Q4cOzc0335xVV1216FEAWAI6FD0AAAAAwIIMHjw4r7zyStFjALCEWKMaAAAAKKVx48ZVHu+///45++yzM3ny5AwcODCdOrWsNAYNGtTa4wFQRZb+AAAAAEpp0KBBqampmefmiXPMec3NFAGWfopqAAAAoJTGjx+/0PuuvPLKS3ASAJY0RTUAAABQehdffHH69u2bL3/5yy22X3fddZk6dWoOOeSQgiYDoBrcTBEAAAAovWuuuSZrrLHGPNvXXnvtXH311QVMBEA1KaoBAACA0ps0aVKWX375ebYvt9xymTRpUgETAVBNimoAAACg9Pr3759HH310nu2PPPJIVlhhhQImAqCaOhU9AAAAAMCC7Lnnnvnxj3+choaGbLbZZkmSUaNG5ZxzzsnBBx9c8HQALC43UwQAAABKr7m5Oeeee26uuOKK1NfXJ0m6dOmSr3/96znssMMKng6AxaWoBgAAAJYa7777bl544YV07do1q622Wmpra4seCYAqUFQDAAAAAFAoN1MEAAAAAKBQimoAAAAAAAqlqAYAAAAAoFCKagAAAAAACqWoBgAAAACgUIpqAACgql5//fWss846GTlyZNGjAACwlFBUAwAAAABQKEU1AAAAAACF6lT0AAAAQPs1fvz4/OIXv8i9996bt956K8stt1y22GKLHHbYYVlppZVa7Pvmm2/m17/+df71r3/ljTfeSG1tbZZffvkMHz48Rx99dHr27FnZt66uLldeeWVuueWWvPTSS6mpqcm6666bkSNHZtttt23t0wQAYAEU1QAAQCFeeuml7Lvvvpk6dWq23nrrrL322nnuuedy/fXX5+67784f//jHrL766kmSWbNm5Stf+UrGjx+fT3/609luu+1SX1+f119/PbfccktGjhxZKarr6uoycuTIPPjgg1l33XXz5S9/OfX19bnnnnvyne98Jz/84Q+z3377FXnqAAB8gKIaAAAoxMknn5ypU6fmtNNOy957713ZfuWVV+a0007LKaeckt///vdJklGjRuX111/PgQcemBNOOKHFcd5999107ty58vwXv/hFHnzwwXznO9/Jd7/73dTU1CRJZsyYkQMPPDBnnXVWtt9++3ziE59ohbMEAGBhWKMaAABodRMmTMjo0aOz1lprZa+99mrx2le+8pWsscYaeeCBBzJx4sQWr3Xt2nWeYy2zzDKpra1NkjQ1NeWqq67KKqus0qKkTpIePXrk0EMPTX19ff7+978vgbMCAGBRuaIaAABodWPHjk2SDB8+vEWZnCQdOnTI8OHD8+KLL2bs2LHp379/hg8fnuWXXz6//vWvM27cuGy11VbZZJNNsuaaa7b4+JdeeinTp0/PCiuskAsvvHCe9506dWqS5MUXX1yCZwcAwMelqAYAAFrdjBkzkiT9+vWb7+vLL798i/169uyZP/3pT7ngggty991355577kmS9O/fP9/4xjfy1a9+NUkybdq0JMlzzz2X55577kPff9asWVU5DwAAqkNRDQAAtLoePXokSSZPnjzf1ydNmtRivyRZaaWVctZZZ6WpqSnPPPNM7rvvvlxxxRU57bTT0rt37+y0006V/XfYYYdccMEFS/gsAACoFmtUAwAArW7ddddNkjz88MNpbm5u8Vpzc3MefvjhFvvNrUOHDll33XXzjW98I+edd16S5B//+EeSZM0110yPHj3y5JNPpr6+fkmeAgAAVaSoBgAAWt1KK62UTTfdNM8991yuu+66Fq9dc801eeGFF7LZZpulf//+Sd5fymN+V1/P2dalS5ckSadOnfKVr3wl48ePz9lnnz3fsvrZZ5/NlClTqn1KAAAshprmD16+AAAAsBhef/31bLvttllhhRXy6U9/er77rLHGGtluu+2y7777Ztq0adlmm22y1lpr5bnnnss//vGPLLfccvnjH/+Y1VdfPUly2WWX5ZxzzsmGG26Y1VZbLX369Mlrr71WuZL6yiuvzAYbbJAkqaury7e+9a38+9//ziqrrJKNN944ffv2zX//+988++yzGTduXK655poMHTq0Vf48AABYMEU1AABQVXOK6o+yySab5Iorrsj48eNz4YUX5t57781bb72VZZddNltuuWUOO+ywrLzyypX9X3jhhVx99dV5+OGHM2HChMycOTOf+MQnsvHGG+frX/961lprrRbHb2xszHXXXZebbropzz77bOrq6tKvX7+sueaa2XbbbbPLLruke/fuS+T8AQD4+BTVAAAAAAAUyhrVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAUGU///nPs84662SdddbJz3/+81Z732222abyvq+//nqrvS8AACyuTkUPAABA27b//vvnwQcfbLHtF7/4RbbbbruFPsbZZ5+d3/72ty22HXbYYTn88MOrMiPzt80222T8+PGL9LEf9vmZOw/V/By+8847ufvuu3PPPfdk3LhxmTJlSmbMmJE+ffqkX79+GTZsWLbaaqt8+tOfTqdOS/bboCeffDJ//vOf8/jjj+fVV1/N22+/nZqamnTv3j0rrrhiVl111ay//vrZcMMNM3To0CU+DwDA0sC/iAAAaHU333zzQhfVjY2N+fOf/7yEJ2JpVV9fnyuvvDK/+tWvMm3atHlenzRpUiZNmpSxY8fmj3/8Y1ZfffUcc8wx2Wabbao+yxtvvJGTTjop99xzz3xfr6ury7Rp0zJu3Lj87W9/S5L07Nkzo0ePTseOHas+DwDA0kRRDQBAq7v77rszffr09O7de4H7/vvf/86kSZNaYSo+yuabb5411lhjofcfPHjwEpzmfTNmzMh3vvOdjB49usX2gQMHZvXVV0/v3r0zefLkjB07NhMnTkySvPTSS/n2t7+dgw8+OMccc0xqamqqMsv48eOz3377ZcKECZVtvXr1yuDBg7PCCiukc+fOmTZtWl566aW88MILaWxsTPL+leDNzc1VmQEAYGmmqAYAoNWstdZaef7551NfX5+//vWv2XfffRf4MTfffPM8H0/r+9KXvpTdd9+96DEqZs2alf333z9PP/10Zdv222+fo446KquvvnqLfZubm/PQQw/lRz/6UcaNG5ck+e1vf5t33303p512WlXmOeGEEyoldY8ePfKDH/wgO++8czp37jzPvjNmzMjdd9+dv/zlL/nXv/5VlfcHAFjauZkiAACtZscdd6wUd3MX0B9mxowZufPOO5Mk6667bgYOHLhE52PpccYZZ7QoqY855phceOGF85TUSVJTU5NNNtkk119/fbbffvvK9muuuSa33HLLYs/yxBNP5IEHHqi810UXXZTdd999viV18n6RvfPOO+fiiy/OHXfcYdkPAIAoqgEAaEXLLbdcttxyyyTJmDFj8vLLL3/k/rfddltmz56dJNl1112X8HQsLUaNGpXrrruu8nz//ffPyJEjF/hxnTp1ynnnnZf11luvsu3UU0/N22+/vVjz/Pvf/648HjJkSIYPH77QHztgwICqLT8CALA0s/QHAACtatddd80//vGPJMlNN92U733vex+675yrrjt16pSdd945jz/++Md6r+bm5tx+++35+9//nieeeCJTpkxJkvTt2zdDhgzJ9ttvnx122OFjFYUPPPBArr322jz66KOZPHlyevfunVVWWSVf/OIXs/vuu6dbt24fa8Y5Ro0aldtuuy2PPPJIJk2alJkzZ6ZPnz5ZZ511svXWW+fLX/5yunbtukjHbmsuvfTSyuNPfOIT+f73v7/QH1tbW5vTTz89e+yxR5qbmzNjxoxcc801+cY3vrHI8/z3v/+tPF5ppZUW+TgLcs899+Suu+7Ko48+mkmTJmXGjBnp1q1bBgwYkA022CCf/exn89nPfjadOn34t3nV+prYf//98+CDDyZJLr/88my66aZ58803c8MNN+TOO+/MxIkTM3Xq1CyzzDJ5+OGH5/n4Ocv/3H333XnyySczderUNDc3Z7nllsvQoUPzhS98Idttt91CfW0+8cQTuemmm/LYY4/l9ddfz7vvvptOnTqlV69eWWmllbLuuutm0003zVZbbZXu3bsv8HgAQDEU1QAAtKqtt946vXv3zvTp03PLLbfkiCOOmG8Z9frrr1cKri222CJ9+/b9WO/z8ssv58gjj2yxPMQcM2fOzGuvvZa//OUvWW+99fKzn/0sAwYM+MjjNTQ05KSTTsr111/fYvukSZMyadKkPPLII/njH/+Yn//85x9rzokTJ+aYY46plH7zO/Z9992Xiy++OP/3f/+XjTfe+GMdv615/fXXc++991ae77333h+7fFxvvfUyfPjwyp/5VVddtVhFdYcO//tF1ddff32Rj/NhnnvuuRx33HF58skn53ntnXfeydNPP52nn34611xzTXbcccf83//933yPU+2vibndeeedOeGEEzJ9+vQF7jt69OiceOKJefXVV+d5bfz48Rk/fnz++te/ZujQobngggvyiU98Yr7HaWhoyGmnnZZrrrlmntcaGxsrXz+PP/54rr766nzrW9/KkUceudDnBAC0LkU1AACtqra2Nl/4whdy9dVXZ/z48XnooYeyySabzLPfTTfdlObm5iTJLrvs8rHe44UXXsh+++2XqVOnVrYNHDgw6667bmpqavL000/n2WefTZI89dRT2WefffKHP/xhvusbz3HsscfmL3/5S+V5r169summm6ZPnz6ZOHFiRo8eneeffz6HHHJIttlmm4We88ADD8ykSZOSvL++8ac+9amstdZa6dq1a/773//moYceyrvvvps333wzBx10UH7zm99ks802+1h/Hm3JBwv9nXfeeZGO86UvfalyrDnl6Morr7xIx1pllVUqj5988smMGjUqm2+++SId64NGjx6db3/723n33Xcr21ZaaaVssMEG6dOnT2bOnJmXXnopzzzzTOrr6/Pee+/N9zhL4mtijsceeywXXnhh6uvr06dPnwwfPjzLLrtspkyZkrFjx7bY97bbbsvRRx+d+vr6JEnXrl0zZMiQrLzyyunQoUNefvnljBkzJg0NDRkzZkz23nvvXHfddenXr9887/uTn/ykRUn9iU98IoMHD85yyy2XpqamTJs2Lc8//3xeeumlBZ4DAFA8RTUAAK1u1113zdVXX53k/UJ6fkX1nJvc9erVK9tuu+1CH7uuri7f//73K4Vc3759c+6552bEiBEt9rvvvvvy//7f/8tbb72VyZMn56ijjso111wz3xvg3XTTTS1K6v322y9HH310i6U43nzzzRx99NF54IEH8sc//nGBc86cOTOHH354paT+zGc+kx/+8IctSs/k/RtKnnvuubnqqqtSV1eX//f//l9uu+229OzZc6H/TNqSRx55pPJ42WWXnefPa2ENHjx4nuMualG99dZb5+yzz05TU1Oampry7W9/OwcddFB23XXXrLrqqot0zOT9q+2POOKISkn9yU9+MieffHI+85nPzLPv9OnTc9ttt+WVV16Z57Ul8TUxt5///OdpbGzMEUcckW984xst9q+rq6s8nnNleH19fWpqanLQQQfl29/+dnr16tXieK+99lqOPfbYPPLII5k4cWKOP/74/OY3v2mxz1tvvZUrr7wySdKxY8f86Ec/yq677jrf3854880387e//c3SOQBQcopqAABa3bBhw7Laaqvl5Zdfzt/+9recdNJJLUqkRx99tFK4feELX0iXLl0W+th//vOfM27cuCRJ586dc8kll+RTn/rUPPttscUW+fWvf52vfOUraWhoyFNPPZW//vWv89y0sampKeeff37l+e67754f/vCH8xxvhRVWyMUXX5y99torzzzzzALn/N3vfpcXXnghSbL99tvnggsuaLGExBw9evTIKaecktmzZ+fGG2/MpEmTctVVV+WQQw5Z4HtU0y233DLfpSc+zEknnbRE5hg/fnzl8dprr73Ix1lrrbXSoUOHNDU1zXPcj2vVVVfNV7/61VxxxRVJklmzZuWXv/xlfvnLX2bllVfOkCFDsv7662fIkCEZPHhwamtrF+q4P/3pT/PWW28lSVZeeeVcc801872yOEl69+6dffbZZ76vVftr4oMaGhryve99L9/+9rfneW3ucz3jjDMqN0c97rjj8rWvfW2+xxswYEAuueSS7Lnnnnn++efzr3/9K48//niGDBlS2WfOVddJsuOOO2a33Xb70PlWWGGF7L///h95DgBA8RTVAAAUYpdddsnPfvazzJgxI3feeWd22mmnyms33XRTi/0+jrmXAthnn33mW8jNMXjw4Oy555656qqrkry/VvEHS7l77703EydOTPL+MgXHHHPMhx6va9euOfbYY3PwwQd/5Iz19fWVq0Fra2tz6qmnzrekntuRRx5ZWQ7lz3/+c6sX1aNGjcqoUaMWev8lVVTPvQZy7969F/k4HTt2zDLLLJN33nlnnuMuiuOOOy4NDQ2VLM0xZ1mRW2+9Ncn7n+9Pf/rT+fKXv5ztttvuQ4/33//+N7fddlvl+SmnnPKhJfWCVPtr4oNWWGGFBa7xPW7cuDzwwANJkk996lM58MADP3L/7t275zvf+U7lRpl//vOfWxTVM2bMqDxebrnlPvJYAMDS4aP/NQwAAEvILrvsUvk1/bmL6bq6ukpBt8oqq2SjjTZa6GPOmDGjxVW/X/7ylxf4MXvuuWfl8X/+85/MnDmzxeujR4+uPP7sZz+bZZdd9iOPN2LEiA+9+dscTz75ZKZMmZIk2XzzzRfqRpGf+MQnssYaayR5fwmFOQVrezP3Ws3dunVbrGPNfRPGuYvPRdGpU6eccsopufrqq7Pddtt96HIZdXV1ufvuu3PooYdmn332yYQJE+a73/3331+5Yni11Vab73IfC2NJfE180A477JBOnT76Gqh77rmn8viLX/zifJfo+KC512Kfe8mXJOnfv3/l8d///vfK1xMAsPRyRTUAAIVYeeWVM3z48Dz44IO5//77M2nSpCy//PK566678vbbbyf5+FdTP/PMM2lsbEzyfgm5zjrrLPBj1l133XTv3j0zZ85MY2Njxo0blw033LDy+tw3gxs6dOgCj1dTU5MhQ4bkjjvu+NB9xowZU3n8xhtv5LTTTlvgcZNU/lyam5vzxhtvtOo61WeeeWZ23333Vnu/D7PMMstUHs+aNWuxjjV3AdujR4/FOtYcw4YNyy9+8Yu88847eeihh/LII4/kqaeeytNPPz3PVduPPfZY9tprr1x//fXz/HBj7ozMbw33hbUkviY+aP3111/gMR977LHK49GjR39oQT+3OTdTTVL5rYY5hgwZkv79+2fixImZMGFCvvjFL2b33XfPNtts87GWVwEAykNRDQBAYXbZZZc8+OCDaWxszJ///OccfPDBufnmm5O8X/h+3KJ6znq+yftXXC7MVZsdOnTIiiuumBdffHGeYySp3IBuzjEXxoL2e/PNNyuPn3nmmYVa0/qDFnepiqXV3Mt9LM6fQWNjY4ursxdnGZH56dmzZ7bZZptss802Sd4vXZ9++unccsstueaaayol+6RJk3LyySfnoosuavHxc18hPGDAgEWeY0l8TXzQgn7LIGmZ+X/9618L3P+D5vyQZo7OnTvnJz/5Sb75zW9m5syZeeutt3LppZfm0ksvTZcuXbL++utn+PDh+cxnPpMNN9xwoc4bACiWpT8AACjM5z//+cryDTfddFOmTJmSe++9N0my0UYbfeyCblGXhZh737mPkbS86nZhj7mg/aqxbMecq2Tbm5VXXrny+Lnnnlvk4zz//POVGyl+8LhLQk1NTdZbb70cf/zxueGGG7L88stXXrv77rvz2muvtdh/7hzOvUTJx7UkviY+aO4boX6YxV1aZX5532STTXLLLbdk1113bTHDe++9l0ceeSQXXXRR9t1333z+85/PnXfeuVjvDwAsea6oBgCgMD169Mi2226bv/zlL3nmmWdy7rnnVtblXdAN3OZnUZeFmHvfuY+RtCwJF/aYC9pv7hJw//33z4knnrhQxyXZcMMNc9111yV5/0rfV155JauuuurHPs4TTzzR4vnHWQt9ca2xxho57rjjctRRR1W2PfLIIy1+MDN3Dhe0RvRHWRJfE4ti7sxfeOGF2X777Rf7mMn7V5ufffbZOfnkk/PII4/kkUceyaOPPprHH388s2fPTpK8/PLLOfTQQ3PcccfloIMOqsr7AgDV54pqAAAKNXchfcMNNyRJunTpks9//vMf+1hzL0HwxhtvtFjj9sM0NTXljTfemO8xkmS55ZarPP7gOrkfZu7jzU+/fv0qjydPnrxQx+R9H1yv+c9//vMiHeeWW26pPF555ZWX+BXVH7Tlllu2eD730hhJWtxg8/XXX1/k91kSXxOLYu7MT5o0abGP90Hdu3fPlltume9973u5/PLLM3r06PzsZz/LwIEDK/v89Kc/zX//+9+qvzcAUB2KagAACjVixIgWyyAkybbbbrtINwpcZ5110rFjxyTvL1ewMGs/jxs3rnLFaseOHTNo0KAWr6+77rqVx3Pf4O7DNDc35/HHH//IfQYPHlx5/Nhjjy1Uecj7BgwYkC222KLy/E9/+tPHvuL4qaeeykMPPVR5/pWvfKVq8y2sLl26tHj+wZv/zX3jztGjRy/y+yyJr4lFMXfmH3300cU+3oJ07do1n//853PFFVdUSvL6+vrK0kIAQPkoqgEAKFTHjh2z8847t9i2KMt+JO8vJbL++utXnt94440L/Jg5y0gk75dpH1wPeNNNN608/te//pVp06Z95PEeeOCBBV5RvdFGG6VXr15J3r/K9R//+McC5+R/Ro4cWXn83//+N+edd95Cf2x9fX1++MMfVn440KNHj+y9995Vn3FBxo0b1+L5Siut1OL5iBEj0qnT+ys1vvzyy4tcsC6Jr4lFsfXWW1ce//3vf2+13yTo06dPNtxww8rzuW9SCQCUi6IaAIDCffvb3851111X+W/uK2Y/rrlLxyuvvHKeQnBuTz75ZK655prK83322WeefbbYYov0798/yfvr9p5zzjkferz33nsvZ5111gJnrK2tzYEHHlh5fuqpp36sJQna+3IhI0aMyO677155fsUVV+S3v/3tAj+uoaEh3//+9/PUU09Vtp188smVHxosqhtuuCG33377Ql8Z39jYmJ///OeV5507d87mm2/eYp9PfOIT+cIXvtBizkX9vFf7a2JRDB48uLJsy+zZs3PMMcekrq5uoT62rq4u06dPb7HtrbfeWuj3nnvJnrmX8gEAykVRDQBA4Xr16pUNNtig8t+cpQoWxc4771xZqqC+vj5f//rX88ADD8yz3/33359vfOMblZs3rrfeevniF784z34dO3bMEUccUXl+3XXX5Uc/+lHee++9FvtNmjQp3/rWtzJu3Lh07tx5gXMedNBBWXvttZO8f1XwHnvskdtuuy1NTU3z3X/q1Km55pprsttuu+XSSy9d4PHbuh/+8IdZZ511Ks/PPvvsHH744XnppZfm2be5uTkPPfRQvvzlL+eOO+6obN97773zpS99abFnefHFF3PEEUdkxx13zG9+85uMHz/+Q/d9/vnnc8ghh+S+++6rbNtnn33mu9TNUUcdlT59+iRJxo8fn7333vtDr6x+++23c8011+QnP/nJPK9V+2tiUf3whz+sXJ3973//O/vtt99HLpPz0ksv5Re/+EW22WabeZYL+cMf/pBddtklf/zjHz90zet33303//d//5f//Oc/Sd7/Wl6cH4IBAEtWp6IHAACAaqqtrc15552X/fbbL1OnTs2kSZNy4IEHZtCgQZX1pseOHdviqtK+ffvmpz/96YcWzLvttlvuueee3HbbbUmSyy+/PDfffHM23XTT9OnTJxMnTszo0aNTV1eXT37yk9l2223z+9///iPnXGaZZfKrX/0qX/va1/L6669n0qRJ+d73vpdll102Q4cOTb9+/dLc3Jzp06fn+eefzyuvvFIpsTfbbLNq/FF9LLfcckuefPLJhd5/xRVXzCGHHPKR+1x99dW58847F/qY3/3ud7Ptttsmef/meX/4wx9y6KGH5sEHH0yS3HHHHbnjjjuyzjrrZI011kjPnj0zderUPP3005kwYUKLYx100EE59thjF/q9F8aLL76Yc889N+eee25WXHHFrLPOOlluueVSW1ub6dOn57nnnssLL7zQ4mOGDRuWI488cr7H69+/f84///x85zvfycyZM/P666/n61//elZeeeVssMEG6d27d2bOnJmXX34548aNS319feXPZ25L4mtiUQwcODDnnXdejjzyyMyaNSuPP/549tprr6yyyir51Kc+ld69e6euri5TpkzJM888s8DfMhg3blxOPfXUnHbaaVlllVWy9tprZ9lll01DQ0MmTZqURx99tMX65d/4xjcqvx0BAJSPohoAgDZnzTXXzB//+Md8//vfz9NPP53k/VJrfkserLfeejn//POzyiqrfOQxzznnnHTt2rWyxu/06dNbXJ2bJGussUYuvPDC3HrrrQs154ABA3L99dfn5JNPzt/+9rc0Nzfnrbfeyt133/2hH9OrV68MHDhwoY5fTaNGjcqoUaMWev9BgwYtsKiePHnyx1rO4oPLP/Tq1SuXXnpp/vCHP+Siiy6qvP7MM8986E0DV1999fy///f/st122y30+y7I0KFDs9Zaa+X555+vbHvjjTc+cq3yTp06Zd99982RRx75kWtAb7755rnqqqty7LHHVvI7fvz4D71q+8OOtSS+JhbF1ltvnauvvjonnHBCZQmWV199Na+++uqHfszKK6+cFVdcscW2ZZZZpvK4ubk5r7zySl555ZX5fnznzp3zrW99K4cddlgVzgAAWFIU1QAAtEmrr756rr/++tx+++2544478sQTT2Tq1KlJ3l+ndsiQIdlhhx2yww47pKamZoHH69y5c84666zssssu+dOf/pRHH300U6ZMSe/evbPKKqvkC1/4QvbYY48WBdrC6NOnT372s5/l2WefzV//+teMHj06r7/+eqZNm5YOHTqkV69elStOR4wYkU9/+tPp0qXLIv2ZtEW1tbU5+OCDs+eee+auu+7KPffck3HjxmXq1Kl5991307t37/Tr1y9Dhw7N1ltvnS222KJyk8Jq2W677bLddtvl1VdfzejRo/PYY4/lxRdfzOuvv5633347jY2N6d69e5ZbbrkMHDgwG220UXbcccessMIKC3X8QYMG5aabbsqdd96ZO++8M2PGjMnkyZMza9as9OjRI5/85CczePDgbL311tlyyy0/9DjV/ppYVIMGDcoNN9yQ++67L3feeWceffTRvPnmm3nnnXdSW1ubZZddNquvvnqGDBmSLbbYIsOGDZtnnoMPPjif+9zncv/99+exxx7LM888k/Hjx+fdd99NTU1NevXqlTXWWCObbbZZdt1116y88spL7HwAgOqoaV7YO34AAAAAAMAS4GaKAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChFNUAAAAAABRKUQ0AAAAAQKEU1QAAAAAAFEpRDQAAAABAoRTVAAAAAAAUSlENAAAAAEChOhU9ALD0mzLlnTQ3Fz3FouvWrTazZtUVPQYkkUfKRR4pC1mkTOSRMpFHymJpz2JNTdK3b8+ix2j3FNXAYmtuzlJdVCdL//y0LfJImcgjZSGLlIk8UibySFnIIovL0h8AAAAAABRKUQ20e37qS5nII2Uij5SFLFIm8kiZyCNlIYtUQ01zsygBi2fy5KV7jWoAAACg/aqpSfr1s0Z10VxRDbR7HTrUFD0CVMgjZSKPlIUsUibySJnII2Uhi1SDohpo97p27Vz0CFAhj5SJPFIWskiZyCNlIo+UhSxSDYpqAAAAAAAKpagGAAAAAKBQimqg3WtqcidIykMeKRN5pCxkkTKRR8pEHikLWaQaapqbmyUJWCyTJ78Tf5MAAAAAS6OamqRfv55Fj9HuuaIaaPc6dfJXIeUhj5SJPFIWskiZyCNlIo+UhSxSDVIEtHu1tZ2KHgEq5JEykUfKQhYpE3mkTOSRspBFqkFRDQAAAABAoRTVAAAAAAAUSlENtHuNjU1FjwAV8kiZyCNlIYuUiTxSJvJIWcgi1VDT3NzcXPQQwNJt8uR34m8SAAAAYGlUU5P069ez6DHaPVdUA+1e584dix4BKuSRMpFHykIWKRN5pEzkkbKQRapBUQ20e/6HSpnII2Uij5SFLFIm8kiZyCNlIYtUg6IaAAAAAIBCKaoBAAAAACiUohpo9xoaGoseASrkkTKRR8pCFikTeaRM5JGykEWqQVENtHt1df6HSnnII2Uij5SFLFIm8kiZyCNlIYtUg6IaaPdqa930gfKQR8pEHikLWaRM5JEykUfKQhaphk5FDwDQ2ibPeC+T362rPO/atXNmz66vPO+3TG369ehSxGiQTp06uhqB0pBHykIWKRN5pEzkkbKQRapBUQ20Ozc8MTG/GfXqh77+jc1XySEjVmu9gQAAAADaOUU10O7sPrh/PrNm3yTJy1Nm5oe3PZPTv7BOVuvbPcn7V1QDAAAA0HoU1UC7069Hl3mW9litb/cM+kTPgiaC/6mv9+tylIc8UhaySJnII2Uij5SFLFINbqYIACXiH3iUiTxSFrJImcgjZSKPlIUsUg2KagAokS5d/LIT5SGPlIUsUibySJnII2Uhi1SDohoASqRjR/9rpjzkkbKQRcpEHikTeaQsZJFqkCIAAAAAAAqlqAYAAAAAoFCKagAokbq6hqJHgAp5pCxkkTKRR8pEHikLWaQaFNUAUCINDU1FjwAV8khZyCJlIo+UiTxSFrJINSiqAaBEunbtXPQIUCGPlIUsUibySJnII2Uhi1SDohoASqRDh5qiR4AKeaQsZJEykUfKRB4pC1mkGhTVAAAAAAAUSlENAAAAAEChFNUAUCKzZ9cXPQJUyCNlIYuUiTxSJvJIWcgi1aCoBoASaWpqLnoEqJBHykIWKRN5pEzkkbKQRapBUQ0AJdKtW23RI0CFPFIWskiZyCNlIo+UhSxSDYpqACiRGjfLpkTkkbKQRcpEHikTeaQsZJFqUFQDAAAAAFAoRTUAAAAAAIVSVANAicya5W7ZlIc8UhaySJnII2Uij5SFLFINimoAKJHmZnfLpjzkkbKQRcpEHikTeaQsZJFqUFQDQIl07+5u2ZSHPFIWskiZyCNlIo+UhSxSDYpqAAAAAAAKpagGAAAAAKBQimoAAAAAAAqlqAaAEpk5s67oEaBCHikLWaRM5JEykUfKQhapBkU1AJRITU1N0SNAhTxSFrJImcgjZSKPlIUsUg2KagAokW7dOhc9AlTII2Uhi5SJPFIm8khZyCLVoKgGAAAAAKBQimoAAAAAAAqlqAaAEmluLnoC+B95pCxkkTKRR8pEHikLWaQaFNUAUCKzZrlbNuUhj5SFLFIm8kiZyCNlIYtUg6IaAEqkQwd3y6Y85JGykEXKRB4pE3mkLGSRalBUA0CJdO3qbtmUhzxSFrJImcgjZSKPlIUsUg2KagAAAAAACqWoBgAAAACgUIpqACiRpia3y6Y85JGykEXKRB4pE3mkLGSRalBUQ4F+/etfZ5111smPfvSjyrb33nsvp556ajbddNMMGzYshx9+eCZPntzi4yZMmJBDDjkkQ4YMyeabb56zzz47DQ0NLfYZPXp0dtttt6y//vrZfvvtc8MNN8zz/ldeeWW22WabbLDBBtlzzz3zxBNPLJkTBRba7Nn1RY8AFfJIWcgiZSKPlIk8UhaySDUoqqEgTzzxRK6++uqss846Lbb/+Mc/zt13353zzz8/V1xxRd58880cdthhldcbGxvzzW9+M/X19bn66qtz1lln5cYbb8wFF1xQ2ee1117LN7/5zWy66aa5+eabc+CBB+bEE0/MvffeW9nn1ltvzZlnnplDDz00N954YwYNGpSRI0dmypQpS/7kgQ/VqZP/NVMe8khZyCJlIo+UiTxSFrJINUgRFODdd9/N0UcfnTPOOCO9e/eubH/nnXdy/fXX57jjjsvmm2+e9ddfPz/+8Y/z2GOPZcyYMUmS++67L88//3zOOeecrLvuuvnsZz+bI444IldeeWXq6uqSJFdffXU++clP5rjjjsuaa66Z/fbbLzvssEMuu+yyynv97ne/y1577ZU99tgja621Vk499dR07do1119/fWv+UQAfUFvbqegRoEIeKQtZpEzkkTKRR8pCFqkGRTUU4LTTTstnP/vZjBgxosX2J598MvX19S22r7nmmllppZUqRfWYMWMycODA9OvXr7LPFltskRkzZuT555+v7LP55pu3OPYWW2xROUZdXV2eeuqpFu/ToUOHjBgxIo899lg1TxUAAAAAFsiPO6CV/fWvf83TTz+d6667bp7XJk+enM6dO6dXr14ttvft2zeTJk2q7DN3SZ2k8nxB+8yYMSOzZ8/O9OnT09jYmL59+87zPi+++OLHPqdu3WorjxsaGlNX15ja2o7p1KljZXt9fWPq6xvTpUundOz4v5+R1dU1pKGhKV27dk6HDjWV7bNn16epqTndutWm5n+bM2tWfZqbm9O9+//eM0lmzqxLTU1NunXrXNnW3JzMmlWXDh1q0rXr/7Y3NTVn9uz6dOrUobK9a9fO6dKlU957ryGdO3dM587/m31pO6e5f5Ld2NjknJbCc5rzHm3pnOZwTkvXOXXs2KHF+7aFc2qLn6f2cE5z5m1L59QWP0/t5Zw6d+6YTp06tKlzStre56m9nNOcfzu2pXOawzktXec09/cxS/s5URxFNbSiiRMn5kc/+lF++9vfpkuXLkWPUzWzZtWl+QM3+K2re/9/EB/03nsN82xLPvzGC7Nm1c13+8yZ825vbm6e7/ampvlvb2hoqrzv7Nn1ldnm/E/sg5aWc2pomHe7c1p6zmnuLM6xtJ9TW/w8tZ9zapjv/Ev3ObXFz1PbP6cuXd7/tqUtndMczmnpO6cuXTqloaEpSds5p7k5p6XrnOZcbDNHWzinD3JOS8c5ze/7mGTpOaeammSZZdpOT7O0UlRDK3rqqacyZcqU7L777pVtjY2Neeihh3LllVfm0ksvTX19fd5+++0WV1VPmTIlyy+/fJL3r4x+4oknWhx38uTJSdJinznb5t6nR48e6dq1azp06JCOHTvOc+PEKVOmzHMlNtC6PuwfT1AEeaQsZJEykUfKRB4pC1mkGqxRDa1os802y5///OfcdNNNlf/WX3/97LzzzpXHnTt3zqhRoyof8+KLL2bChAkZOnRokmTo0KF59tlnW5TM999/f3r06JG11lqrss8DDzzQ4r3vv//+yjFqa2uz3nrrtXifpqamjBo1KsOGDVtCZw8sjLl/dQ2KJo+UhSxSJvJImcgjZSGLVIMrqqEV9ejRIwMHDmyxrXv37unTp09l+x577JGzzjorvXv3To8ePXLGGWdk2LBhlZJ5iy22yFprrZVjjjkmRx99dCZNmpTzzz8/X/3qV1Nb+/7aT/vss0+uvPLK/OQnP8kee+yRBx54ILfddlsuvvjiyvsedNBBOfbYY7P++utn8ODB+f3vf59Zs2a1uNobaH2dO3ec76+vQRHkkbKQRcpEHikTeaQsZJFqUFRDyZxwwgnp0KFDvvvd76auri5bbLFFTj755MrrHTt2zEUXXZRTTjkle++9d7p165bddtst3/3udyv7DBgwIBdffHHOPPPMXH755VlxxRVzxhlnZMstt6zss+OOO2bq1Km54IILMmnSpKy77rq55JJLLP0BAAAAQKuraW7+4C3QAD6eyZPfmedmikuLcf99J/v/4bFcsd+wDPpEz6LHgXTvXjvfG4hAEeSRspBFykQeKRN5pCyW9izW1CT9+ukEimaNagAokYYGvy5HecgjZSGLlIk8UibySFnIItWgqAaAEqmr8w88ykMeKQtZpEzkkTKRR8pCFqkGRTUAlEhtrbtlUx7ySFnIImUij5SJPFIWskg1KKoBoEQ6dfIPPMpDHikLWaRM5JEykUfKQhapBkU1AAAAAACFUlQDAAAAAFAoRTUAlEh9vZuQUB7ySFnIImUij5SJPFIWskg1KKoBoET8A48ykUfKQhYpE3mkTOSRspBFqkFRDQAl0qVLp6JHgAp5pCxkkTKRR8pEHikLWaQaFNUAUCIdO/pfM+Uhj5SFLFIm8kiZyCNlIYtUgxQBAAAAAFAoRTUAAAAAAIVSVANAidTVNRQ9AlTII2Uhi5SJPFIm8khZyCLVYKVz2pVXXnkljz76aN5444289dZb6datW5Zddtmss846GTZsWLp27Vr0iEA719DQVPQIUCGPlIUsUibySJnII2Uhi1SDopo2b+LEibn22mtz44035o033kiSNDc3t9inpqYmHTt2zBZbbJG99947W221VWpqaooYF2jnunbtnNmz64seA5LII+Uhi5SJPFIm8khZyCLVoKimzZo6dWouuOCCXHfddWloaMiqq66aL33pS1l//fXTt2/f9OnTJ7Nnz8706dPz0ksvZcyYMXnggQdyzz33ZNVVV83RRx+dbbfdtujTANqZDh38kIzykEfKQhYpE3mkTOSRspBFqkFRTZu13XbbpUOHDtl///3zpS99Keuuu+4CP2bmzJn529/+lmuvvTaHHXZYjj322Hzta19b8sMCAAAAQDumqKbNOuCAA3LwwQenV69eC/0x3bt3z2677Zbddtsto0aNyowZM5bghAAAAABAoqimDfve9763WB+/+eabV2cQgI/Bum6UiTxSFrJImcgjZSKPlIUsUg0dih4AAPifpqbmBe8ErUQeKQtZpEzkkTKRR8pCFqkGRTUAlEi3brVFjwAV8khZyCJlIo+UiTxSFrJINVj6gzbtG9/4xsf+mJqamvz6179eAtMAzGvyjPcy+d26yvOuXTu3+LW5fsvUpl+PLkWMRjskj5RVTU3RE8D/yCNlIo+UhSxSDYpq2rR77733Y39Mjb9dgVZ0wxMT85tRr37o69/YfJUcMmK11huIdk0eAQCAoiiqadPuuuuuokcA+Ei7D+6fz6zZN0ny8pSZ+eFtz+T0L6yT1fp2T/L+FazQWuQRAAAoiqKaNm3llVcuegSAj9SvR5d5llJYrW/3DPpEz4Imoj2TR8pq1qz6Be8ErUQeKRN5pCxkkWpwM0UAAKDUmpubix4BKuSRMpFHykIWqQZFNW3azJkz87nPfS777LNP6us//Kd7dXV1+cpXvpLPf/7zmT17ditOCADAgnTvbtkZykMeKRN5pCxkkWpQVNOm3XDDDXnttddy1FFHpXPnzh+6X21tbY466qi8/PLLuf7661txQgAAAABAUU2bdtddd2XNNdfM8OHDF7jvxhtvnIEDB+aOO+5ohckAAAAAgDkU1bRp48aNy8Ybb7zQ+2+44YZ59tlnl+BEAAAAAMAHKapp095555306dNnoffv3bt33nnnnSU3EAAAH9vMmXVFjwAV8kiZyCNlIYtUg6KaNm2ZZZbJtGnTFnr/6dOnZ5lllllyAwEA8LHV1NQUPQJUyCNlIo+UhSxSDYpq2rTVV189Dz/88ELv//DDD2f11VdfghMBAPBxdev24TfFhtYmj5SJPFIWskg1KKpp0z7zmc/khRdeyF//+tcF7nvrrbfm+eefz1ZbbbXkBwMAAAAAKhTVtGn77bdfevXqlRNPPDE33HDDh+5344035gc/+EH69OmTfffdtxUnBAAAAAA6FT0ALEm9evXK+eefn29/+9v5wQ9+kAsvvDDDhw/PiiuumCT573//mwcffDATJ05Mly5dcv7556dXr14FTw0AwNyam4ueAP5HHikTeaQsZJFqUFTT5m2++ea5+uqrc8YZZ+Thhx/OzTffPM8+w4cPzw9+8IMMGjSogAkBAPgos2bVFT0CVMgjZSKPlIUsUg2KatqFQYMG5Q9/+ENeffXVPProo5k0aVKSZPnll8+GG26YVVZZpeAJAQD4MB061KSpyaValIM8UibySFnIItWgqKZdWWWVVZTSAABLma5dO2fmTFdqUQ7ySJnII2Uhi1SDmykCAAAAAFAoRTUAAAAAAIVSVAMAAKVmzUvKRB4pE3mkLGSRalBUAwAApTZ7dn3RI0CFPFIm8khZyCLVoKgGAABKrVMn37ZQHvJImcgjZSGLVIMUAQAApVZb26noEaBCHikTeaQsZJFqUFTTbjU0NGT69OlpaGgoehQAAAAAaNf8uIN2pbGxMVdccUVuuOGGPP/882lubk5NTU3WXnvt7Lbbbtlvv/3SqZMvCwAAAABoTRo52o133303I0eOzOOPP54OHTqkf//+6devXyZPnpznn38+Z599dv72t7/l0ksvTffu3YseFwCA/19jY1PRI0CFPFIm8khZyCLVoKim3bjgggsyZsyY7LTTTvn+97+flVZaqfLahAkT8tOf/jR//etfc8EFF+S4444rcFIAAOb23nuWaqM85JEykUfKQhapBmtU027cdtttWX/99XPuuee2KKmTZKWVVspPf/rTrLfeern11lsLmhAAgPnp3Llj0SNAhTxSJvJIWcgi1aCopt2YNm1aRowY8ZH7jBgxItOnT2+liQAAWBi++aVM5JEykUfKQhapBkU17caqq66aKVOmfOQ+U6dOzSqrrNJKEwEAAAAAiaKaduSAAw7Irbfemueee26+rz/zzDO59dZbc+CBB7byZAAAAADQvrmZIu3Gaqutls022yx77LFHdt1112y00Ubp169fJk+enEceeSQ33XRTtthii6y66qp56KGHWnzs8OHDC5oaAICGhsaiR4AKeaRM5JGykEWqQVFNu7H//vunpqYmzc3N+dOf/pRrr7228lpzc3OS5O67787dd989z8eOHTu21eYEAKClujrf/FIe8kiZyCNlIYtUg6KaduPQQw9NTU1N0WMAAPAx1dZ29A0wpSGPlIk8UhaySDUoqmk3Dj/88KJHAABgEXTq5JtfykMeKRN5pCxkkWpwM0UAAAAAAAqlqAYAAAAAoFCW/qBdmThxYn71q1/l/vvvz5tvvpn6+vp59qmpqcnTTz9dwHQAAMxPfb1fJaY85JEykUfKQhapBkU17cZrr72WPffcM2+//XbWWmut1NXVZaWVVkqXLl3y2muvpaGhIYMGDUrPnj2LHhUAgLn45pcykUfKRB4pC1mkGiz9Qbtx4YUXZsaMGbnssstyyy23JEl233333HbbbfnHP/6RbbbZJrNmzcoFF1xQ8KQAAMytSxfX11Ae8kiZyCNlIYtUg6KaduP+++/PZz7zmWyyySbzvLbCCivk/PPPT5L83//9XytPBgDAR+nY0bctlIc8UibySFnIItUgRbQbb731VtZYY43K806dOmXWrFmV57W1tRkxYkTuvvvuIsYDAAAAgHZLUU27seyyy7Yopvv06ZPx48e32Kdjx4555513Wns0AAAAAGjXFNW0G6uttlpeffXVyvPBgwfnvvvuy2uvvZYkmTp1av72t79lwIABRY0IAMB81NU1FD0CVMgjZSKPlIUsUg2KatqNLbfcMqNHj87bb7+dJDnwwAPz7rvv5ktf+lL22GOP7LDDDpk8eXL233//gicFAGBuDQ1NRY8AFfJImcgjZSGLVIOimnZj3333zRVXXJEOHd6P/aabbprzzjsvK620Up577rn07ds3J554Yvbaa6+CJwUAYG5du3YuegSokEfKRB4pC1mkGjoVPQC0lh49emTIkCEttn3hC1/IF77whYImAgBgYXToUFP0CFAhj5SJPFIWskg1uKKaduOAAw7I+eefX/QYAAAAAMAHKKppN5544ok0NVkzCQAAAADKRlFNu7HGGmtk/PjxRY8BAMDHNHt2fdEjQIU8UibySFnIItWgqKbd2G+//fKPf/wjzz//fNGjAADwMTQ1NRc9AlTII2Uij5SFLFINbqZIuzFgwIBssskm2WuvvbL33ntngw02SL9+/VJTM++C/8OHDy9gQgAA5qdbt9rMmlVX9BiQRB4pF3mkLGSRalBU027sv//+qampSXNzc373u9/Nt6CeY+zYsa04GQAAH+Uj/tkGrU4eKRN5pCxkkWpQVNNuHHrooR9ZTgMAAAAAxVBU024cfvjhRY8AAAAAAMyHmynSbjz00EOZMGHCR+4zceLEPPTQQ600EQAAC2PWrPqiR4AKeaRM5JGykEWqQVFNu3HAAQfkhhtu+Mh9brrpphxwwAGtNBEAAAujubm56BGgQh4pE3mkLGSRalBU024szF+aTU1N1rEGACiZ7t1rix4BKuSRMpFHykIWqQZFNczllVdeSc+ePYseAwAAAADaFTdTpE07/vjjWzy/6667Mn78+Hn2a2pqysSJE/Pwww/nM5/5TGuNBwAAAABEUU0bd+ONN1Ye19TUZOzYsRk7dux8962pqckGG2wwT7kNAAAAACxZimratLvuuivJ++tTb7fddjnwwAPne7PEjh07plevXunevXtrjwgAwALMnFlX9AhQIY+UiTxSFrJINSiqadNWXnnlyuMzzzwz6667bottAACUX01NzULdGBtagzxSJvJIWcgi1eBmirQbu+22WwYNGjTP9ubm5rz88suZOHFiAVMBALAg3bp1LnoEqJBHykQeKQtZpBoU1bQbd9xxR4455phMnz69su3111/Pl770pXzhC1/INttskyOPPDKNjY0FTgkAAAAA7Y+imnbjqquuytixY9O7d+/KtjPPPDPPPfdcNt1006yzzjq5/fbbc/311xc4JQAAAAC0P4pq2o3nn38+gwcPrjyfMWNG7rnnnuy444657LLLcu2112bNNddUVAMAlIwlLykTeaRM5JGykEWqQVFNuzF9+vT069ev8vyRRx5JQ0NDvvjFLyZJOnfunBEjRuTVV18takQAAOZj1qy6okeACnmkTOSRspBFqkFRTbvRo0ePTJs2rfJ89OjR6dChQzbeeOPKtk6dOmXWrFkFTAcAwIfp0KGm6BGgQh4pE3mkLGSRalBU026sscYaufvuu/PWW2/l7bffzl/+8pest956LdasnjBhQvr27VvglAAAfFDXrp2LHgEq5JEykUfKQhapBkU17cb++++fN998M5/97Gez1VZbZdKkSfnKV77SYp/HH388gwYNKmhCAAAAAGifOhU9ALSWHXbYISeddFKuu+66JMkXv/jF7L777pXXH3zwwcyYMSNbbrllUSMCAAAAQLukqKZd2XfffbPvvvvO97VNNtkkDz30UCtPBADAgjQ1NRc9AlTII2Uij5SFLFINlv6g3bjwwgsXWEQ//PDDufDCC1tpIgAAFsbs2fVFjwAV8kiZyCNlIYtUg6KaduPCCy/M6NGjP3Kfhx56KL/4xS9aaSIAABZGp06+baE85JEykUfKQhapBimCudTX16djx45FjwEAwFxqa61YSHnII2Uij5SFLFINimralZqamg99ra6uLg8//HCWW265VpwIAAAAAPDjDtq0bbfdtsXz3//+97nhhhvm2a+pqSlvvfVW3nvvvey5556tNR4AAAAAEEU1bVxz8//uOltTU5Pm5uYW2+bo1KlT1lprrWy22Wb5zne+05ojAgCwAI2NTUWPABXySJnII2Uhi1SDopo27R//+Efl8aBBg3LggQfmsMMOK3AiAAA+rvfeayh6BKiQR8pEHikLWaQaFNW0G3fddVd69epV9BgAAHxMnTt3TH19Y9FjQBJ5pFzkkbKQRapBUU27sfLKK1ceNzQ05KWXXsqMGTPSo0ePrL766unUyZcDAEAZ+eaXMpFHykQeKQtZpBo0c7Q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",
      "text/plain": [
       "<Figure size 1800x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common models in comparisons:\n",
      "  chirp-auk-t0 (2 comparisons)\n"
     ]
    },
    {
     "data": {
      "image/png": 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XLlzQeN7WrVuRlpaGbt26AQAGDRqUZSq4/NbEv6ts2bJo1qwZLl++jOPHj2c5aZ4+fRqpqakoXbo0mjdvDj8/v1y3p1QqMW7cOHG9Bg0aYMiQIahUqRLCw8Nx+PBhnD9/XjwpHjt2TCMRSU1Nxddff434+HiULFkSgwYNQsOGDVG2bFmkpaUhKCgIfn5+OHfunMbrLlu2DElJSRg1ahTCw8OzDNIEkO8pjtTnGjMzMzRo0CBfz83O5cuXMXv2bAiCADMzM4wcORKNGzeGoaEh/Pz8sHHjRrx58warVq2ChYVFrtP+Xb16FXfv3oWzszOGDh2KKlWq4M2bN9i5c6fY2rVx40aULl0au3btQvPmzdGvXz/Y2dkhNDQUmzZtwp07d/DXX3/h4MGDGDRoUI6vFRAQgJMnT+LTTz/FyJEjoVAokJCQgMOHD+PEiRP4999/8f3336N9+/b44YcfUK9ePQwdOhRVq1bFmzdvsGvXLly5cgUPHjzA+vXrMX369Cyv4e/vj3HjxiEtLQ1GRkYYMmQIWrdujRIlSsDf3x+bNm1CUFAQ9uzZgxIlSmDGjBk5xqvr83VhO4aAjNxHfSwAQOfOndGzZ0+ULVsW//77L7Zv344jR47gyZMnucai7fP72rVrxeOsVatW6NatGypUqAATExNERUXB39//w8ZoCPkUHx8v1KxZU1AoFMLIkSOzLG/fvr2gUCiEQ4cOiWUNGzYUFAqFcPHiRY11r169KigUCkGhUAgbNmzIsq3Vq1eLy7PTqlUrcXmvXr2EuLi4LOscP35cXGf79u353NsMgYGB4jZmzZqV43pjx44V1zt27FiO20pNTc1xGyEhIcLnn38uKBQKYfr06dmuM2vWLEGhUAitWrV6b+wDBgwQFAqF0LNnTyEqKirbda5cuSI4OjoKCoVCOHDgwHu3mZ2hQ4fm+lnlZNCgQeLzXr9+rbFs1apVgkKhEFxdXYV79+5l+/ygoCChadOmgkKhEKZNm5Zl+fu+Q+q4hw4d+t5YX7x4ketyLy8v8X08ePBgluWDBw8WFAqF0K9fPyEtLS3H7bx58+a9seTm3Llz4j57e3trLPvxxx8FhUIhODk5CW/fvs1xGxMmTBC3ce7cuSzLExMThb59+4rr5PT+fux7Jgj/HefZHXvXr1/XiGHdunVZ1lGpVMLIkSMFhUIh1KxZM8tx8Mcff4jPf/ToUY6xJiUlCUlJSVnK1c9dvXp1rvuaF5l/a9Tb8/T0FBQKhVCnTp0sv3EDBw4UFAqFsGDBAkEQNN+Pw4cPZ9n+7t27xeUzZ84UVCpVlnXUx51CoRB++OEHjWV///23uOzd3/PM0tLSsv09zu2zzI/Q0FAxjoEDB37UtgRBEFJTU4VmzZoJCoVCqFevnvDw4cMs62T+ralbt262v6eZv4uTJ08W0tPTNZanp6cL/fv3FxQKheDs7CzUrl1bWLp0aZbtJCYmiu9Vt27dso058/lvwIABQmJiYpZ1Jk+eLCgUCqFGjRpCgwYN3htTgwYNsv1t6tOnj7idv/76K8vyt2/fCp07dxYUCoXg6OgoPH78ONd4dXW+LozHkCAIwooVK3LNfVJTU8XfLPW/wMDALOsVxPk9t3NgixYtxO9ybvJ7zsr3nYtKliwpNon7+flBqVSKy94doa6mrvV8t7o2t/6d+bVs2bJs+6d069YN5cuXz/b1C0JsbCz8/Pwwbtw4MfN3dnbOsZmgUqVKuV5x2NraYtSoUQAyppERMjWJ5Jevr694VbZixYocrx6bN28u9nn62Cbe/Mrc5y4mJkZ8nJCQgD179gAApk6dKnYQf5ednR0mTJgAADh79qxGs3BBq1KlSq7LmzRpIjZrv1s7BmTUrAEZ34/c+tvk1g8xL9TN7La2tmjYsKHGMnVTUmpqqjiK811hYWHid7lDhw5o27ZtlnVKlCiBxYsXvzeWj33P8qNWrVrZTpkik8nwxRdfAMiogX23RkP9uahvBJETU1NTmJqaflSMH6JNmzawsLBAcnIyzp49K5YHBgaKTX15bSJUH1NWVlaYN29etrOITJ48GdWqVQMA/PHHHxrNzur3Csh9MKihoaFWp5N78+aN+Lhs2bIfvb1z586JtU/jx4/PttuXnZ0dZs6cCQBISkrK9beyRIkSWLRoUZZWPLlcjv79+wPI+I2zsrLKtnawRIkS6NmzJ4CMvtO5tYLIZDIsXbo02xpjda2sUqlESkrKe2N6+/Ytnj59qrH87t274ojwfv36oVmzZllex9LSEosWLQKQ0eVl7969OcYL6P58XZiOodTUVBw+fBhARp/g7Ka3MzIywtKlS3PNFXRxflcf7+/Lz/J7zvqgW2aqf3ASEhLg7+8vlqv7cVpbW2uccNRJ6LtfJHX/ThMTE9SuXftDQgGQMajH0dEx22UymQw1a9YEkPEl+1hHjx4VJ4x2cHCAm5sbBg4ciEuXLsHIyAi9e/fGli1b8lydHR8fj8DAQDx58gSPHz/G48ePxR+Q+Pj4j7pNlvpEXrVq1ffeMlL9md6/fx/p6ekf/Jr5lbmDfUJCgvj45s2b4o9tbgMBgP9iT0tL0/rk/ZlFR0fj33//FT+3x48fiwd/dpPjq/sWXrp0CdHR0VqJ6e3bt2J/pa5du2b5UbS3txdPqjmN8rxx44Z4QZnbj7Gjo2OOx11O8vue5Ue3bt1ynI6tVq1a4uN3fwfUn0tMTAzOnz//UTFog3pgGKD5makfV6lSJU/9m8PCwsTuQJ06dcoxMTQ0NBTv1hITE6NxTGXuH6s+eUoh829FQdyFyNvbG0DG+SK3u+x17NhRnB1C/ZzsNGnSJMeTceZjpl27djmeKzKvl9t5wMHBIcf+0Zm3kdeY3n2tzDO05Pbe1K9fX4wjt1lddHm+VitMx9CDBw/ESpZevXrl+Jtla2uLpk2b5hiLLs7v6uP91KlTGv1xP9YHDXOqX78+tm3bBiAjmVTXRqkTy8y1ncB/2fKDBw+QnJwMU1NTpKam4u7duwAy+vq9b/RXbtRXFjlR97PK/GOlDZ9++imGDx/+3iv94OBgbNu2DZcuXcrSIfhdb968QeXKlT8onvv37wPIGK2b13uVp6WlISYmpkBqEfIi82eS+X1Txw4g2yvsnERERBRMYDm4desWdu3aBW9vb427c70rc42MWs+ePXHz5k28fPkS7du3R7t27dC0aVO4uroW2G0sPT09xQEG3bt3z3ad7t27w9/fH35+fnj16pU45Zla5n5FOdU0Z17+voTxY96z/MjtdyDzCffd34HWrVujVKlSiI2NxaRJk8SZBVxdXVGjRo0P7ntekHr27IlDhw7Bx8cHISEhqFChgjiIKqfP+V2ZP9f3zZqQue/ukydPxLvW1a9fH5UrV0ZgYCCWLVsGDw8PtGvXDq6urqhdu/ZH/Y7nR+Y+cwVxQlS/N5UqVcq1X6GxsTFq1KgBHx8fPH78OMf1cqvpzzxqOrf11AkukPu5qyBeK/N67w4eUr83RkZG7x0AXLduXTx79gwvX77MdtYbQLrzdWE5hjJ/b95X4Va7dm2xIuFduji/9+zZE+vWrYOfnx/atGmDjh07onHjxqhfv/5H9b/9oMTT1dUVMpkMgiDg1q1bYjOWugZT3bSuVrNmTZQoUQJJSUm4c+cOGjVqhHv37olzN35sM/v7rnjVI+wK4hZpmTvFq1QqhIeH46+//sKBAwfw9OlTDBs2DPv378/x4Lpy5QqmTp2a5x/L5OTkD471Q2vVCvLK5n0yJxuZk4OoqKgP2t7HvF/v8+50VvmNo2/fvuII0ri4OBw5ckRs+vjkk0/Qpk0bDBky5IMvNID/mtnVNfLZ6dq1K1auXAmlUoljx45hypQpGsszd3koU6ZMrq/3vh+fj33P8iO3ZvDMo2zf/R0oU6YM1q9fj2nTpiEsLAw3btzAjRs3AGRcDDVu3Bh9+vTR6gjt93F1dYWdnR2Cg4Ph4eEBNzc3vHz5EjKZLM8nzcyf6/tOPJlHv2a+WDAyMsKGDRswZcoUPHv2DPfu3RObYU1NTeHq6oqePXuic+fOWk3YM38vMzf/fyj1PublhJy5hjwnuZ2TMtdw5bZe5u9s5i5t+XmtzNvIa0zvHh/q96Z06dLvnZJH/b0RBAGxsbHZjqLW5fk6s8JyDGXexvt+P3Mbha6L8/uECRMQFhaGI0eOICoqCnv27BG7Gtjb26N9+/YYPHhwvkfLf1DiWbp0adjb2+Px48diLWfmEerv1ngaGRmhdu3a8PHxga+vLxo1aqQxoXxRmjhefeciNUdHRzRv3hytW7fGl19+iZiYGEyfPh1//PFHlh/e6OhoccJjMzMzjBo1Cs2aNcMnn3wCc3Nz8erQ29tbTOY/po+n+sfK0dERP/74Y56fZ2Nj88GvmR8qlUqc+9Hc3Fzjy5v5h/bo0aN5noOsoGoO3+Xt7S0mUJUrV8bIkSNRv359VKxYESVKlBDj+/XXX7Fu3boct/P111+jf//+8PDwgLe3N/755x8kJSXh1atX2L59O3bv3o1vvvkm11GsOXn+/LnYivDo0aM8XQWfOHECkydP1sodwwrqPdMFV1dXnDt3DmfPnsWVK1fg6+uL0NBQxMfH49y5czh37hyaNWuGtWvXFkjTbn7JZDJ069YNGzZswPHjx/H69WsAGRf5H3Kh8jGfd/Xq1eHh4YFLly7h4sWL8PX1xcuXL5GcnIxr167h2rVr2L59OzZv3qy1lhMbGxuUKVMGb968waNHj6BUKgsk0S3sd86jD1eYjqGC2IYuzu9GRkZYtmwZRo4ciZMnT+L69eu4f/8+0tLS8OTJEzx58gTbt2/Hjz/+mO1YgJx88Izhrq6uePz4MaKjo/Hs2TMEBQVBpVLBzMxM7KORWf369cXEE/ivP6iRkZFOJnfXtsaNG2PYsGHYtm0bHjx4gCNHjqBfv34a65w9exaxsbEAMubzbNKkSbbbyu1KOj/UNYiJiYk6nXswr/z8/MTBQPXq1dM4cWSu0bCystJaQplXBw8eBJDRDHTw4MEcr1Tz8tnZ2dlh3Lhx4vQk9+7dw+nTp3HgwAGkpKRg4cKFqFu3brbHUW4+5I5EgYGBuHXrlkarQ+YpgNQTUeckt6vugnzPdMHExATdu3cXaz8CAwNx5coV7Nq1C//++y+uXbuGn3/+GXPnzpUkvp49e2LDhg14+vSp2P9NPQAlLzJ/ru+rJcy8PLt+gXK5HG3bthVPNuqWnz179ojz686fPx+//fZbnuPLLzc3N/z5559ITEyEj48PGjdu/MHbUu9jXmpP1d15PnaqrKJC/d68ffs2y5R371K/fzKZLM8TsetSYTiGMr8vUVFRqFq1ap628S5dnt+rV68utvSmpKTg1q1b8PDwwPHjx8XpH8+dOycODHufDxpcBGg2j9+6dUuswXw3gVBT14L+888/SE1NFUdj1axZU2OASVE2duxYsZ/ib7/9lmUSYvVowdKlS+eYdAKa/Ruzk9erpMydtLXd9/FD7NixQ3z87mT5mfsSvTtJrxTUn13Dhg1zbR5532f3LiMjI7i4uOCbb77BTz/9BCCjljvzyMu8EARB7K/k4OCAVatW5frvp59+EufMVDfPq2W+A8X79ie35dp6z3SlcuXKGDp0KA4fPixe+Jw+fVqyeKpWrSr2K0tJSYGxsTE6duyY5+fb29uLj9U14znJvDzz83JSvnx59OnTBwcOHBAHcl2+fFmrXV/UgzcAzd+SD6Hex6CgoFwvptLS0sQBtYXxYl4b1O9N5n3Pifp78+mnn+qsv29+FIZjKPP3Jrf7xwO5/zZKdX43MTFBkyZNsHz5cnGWh+Tk5Bz7omanQBJP9R1IgKzN7GrqCeITExNx5MgRccTyx/bvLExKly4t3n0nJCQkywldPZosJSUlx/4rSUlJ7625Uh/Q7ya271JPUyMIAnbu3Pne+HXJ09NTTK7KlSuncRIBMkZgqps0d+7c+VFdDnKjTr7e916qP7vcpmt6+PAh/vnnnw+OJXONTX4H2ty4cUNsOurZsye6dOmS67+uXbuKg7bOnDkj9rcGMhJFdT+r3L6L6jtt5UQX75kumJubi4MAsvtc8vodKgg9e/aEsbExjI2N0a5du3zVKtnY2Iijjk+fPp3j4A2lUineztLS0lJjRoD3MTIyErtOpaeniy08agX5XrVs2VIcHX3p0qV81fira0rV1MeeIAi5jtY/e/aseO76mBrWoiRzJUlu742fn594sZlbxYrUpD6GnJycxJrT48eP53huCwsLw7Vr13KMpTCc3xs1aiQ+zs8564MTTxsbG3E07I0bN8TMPKfE09zcXMz0t2zZIpYXpf6defHFF1+ICdOmTZs0+iqqRxUmJSVlW3OiVCrx7bffatxCKzvqzu1RUVFZRiBm1qxZM/HqbuvWrTnO26j26NEjjXuTa0Nqaiq2bNkiXinJ5XIsXbo0y9VxqVKlxCTez88Py5Yty7WzeWRkJP744498x6N+LwMDA3NNbj/99FMAGbWvL1++zLI8Ojpa3KecHD9+PNepLDL/yFSqVCnXbWW3bbX27dvn6Tnq6UXi4uI05tC0tbVFixYtAGScaLObYig5ORnz5s3LdfsF8Z7pwl9//ZXrMRcXFyfWXmT3uai/Q69evdJOgJkMGTJEHNDz7h2Y8vp8IOO9X7JkSbbrrF27Vkwg3r3ftLo/Z05SU1PFSggzM7MsNd0F+V7JZDKsXLlS/L2dO3cu9uzZk+vvhHq/p0yZonEstm3bVmwm3LBhg9j3PLOQkBB8//33ADIGyLx7sayv6tSpI85u8ccff2Q7jVRcXBzmz58PIGNw0If0UdcVqY8hY2Nj8bvj7++vkQ+ppaenZ7kF6ru0fX5X3/kot/Oil5eX+Dg/56wP7uMJZNRWvnr1CmFhYRkbMzTMtb9m/fr14e/vL/atMDAwyDFRLaqsrKzQr18/7Ny5E4GBgfDw8BD7kHTq1AmrVq1Camoq5syZA39/fzRt2hTm5uZ4+vQpdu3ahQcPHsDFxSXX5mX1rAEqlQrfffcd3N3dNfpEqk/4APDTTz+hX79+ePv2Lb7++mucOHECnTt3RpUqVWBgYKBx26s7d+5g5MiR4pXUh8o8XYRKpUJsbCzCwsJw584dnD17VmwWMDY2xvz588Uk511Tp07FzZs38c8//2Dnzp3w8fFB//794ejoCDMzM8TExODp06f4+++/cfXqVSgUiiz9at/HxcVFHLG3fPlydO/eXZzGxNDQULwFZ8+ePXHp0iUkJiZi6NChGDNmjHgV6+fnh+3btyMyMhLOzs453nJt5syZ+OGHH9CuXTs4Ozvjk08+gYmJCSIjI/H3339j3759ADJO2OrbMOZFUlKSWHtcq1atPP8AtG7dGkZGRkhLS8OxY8c0bnowZ84cXL9+HUlJSZg6dSoGDRqEtm3birfM3LJlC54+fYratWvn2FxUEO+ZLnh6emL8+PFo0qQJmjZtCoVCAUtLSyQkJODx48fYs2eP+Bs3cODALM93dnZGUFAQLl68iP3798PFxUWs2TM3N9fZ1GR5MXDgQHh4eMDPzw9HjhzB69evMXjwYFSqVAkRERE4fPgw/vzzTwAZMy2ob86g5u3tjXXr1sHV1RUtWrSAg4MDrKyskJycjH///Rf79+8X5yzs27dvlv6Azs7OuHHjBu7du4dNmzahefPmYuJoamqa74GN9vb2+PXXX/HVV18hMTERixYtwv79+9GpUyfUrl0bVlZWSEpKwuvXr+Hl5YXz589ne7FubGyMxYsXY9y4cYiPj8egQYMwatQoNG7cGHK5HH5+fti0aZM428bMmTO1evvlwmbJkiXo168f0tLSMHr0aLi7u6NVq1Yat8xUn9fVt+3UVx97DAHAxIkTcfr0aYSGhmLlypUICAhAjx49NG6Zee/ePTg5OeXa3K7N83t8fDzGjx8POzs7tG/fHnXq1IGdnR3kcjkiIiJw6dIlsbLHxsYmy61Ic/PRiWfmmfBr1KiR64jP+vXrY/fu3eLfCoWiUHZA/lijRo3Cvn37kJaWhk2bNqF79+4wMDCAra0tFixYgG+//RYpKSnYvHkzNm/erPHczp07o3///uKo9uw0atQI9erVw507d3Dy5EmcPHlSY3nmq/VPPvkE+/fvx5QpU/D48WNcunQp13urvntP2Q/xvqRJJpOhcePGmD17dq4jr42NjbFt2zbMmTMHf/75JwICAsS7Y2TnQ+6U0rlzZ2zcuBGBgYHYsWOHRl8xOzs78QqxY8eO6N27N44cOYLw8PAsV7pyuRxz5swR72SVk8jISOzbt09MMt9lYWGBVatWoUKFCnneh3PnzolNPvnpr2RhYYEmTZrgypUr8PLyQmRkpDizwKefforffvsNkyZNQmJiInbt2oVdu3ZpPH/SpElQqVS4d+9etvdYL6j3TBfS0tJw5coVXLlyJcd1Bg4ciGHDhmUpHzVqFM6ePYvU1FR89913Gst69eqFFStWFHi8H0oul2PDhg0YP348bt++jevXr+P69etZ1vvss8+wefPmbH8PVCoVfHx84OPjk+PrtGnTBv/73/+ylA8ePBj79+/H27dv8dNPP4n9moGMe16/+x3LixYtWmDfvn1YuHAhbt++Ld6YICelS5fGpEmTsvxetGzZEsuXL8f8+fORkJCA1atXY/Xq1RrryOVyTJ06Ndf7tOujGjVqYMOGDZg6dSri4+Oxbds2cS7vzIYMGZLt565PCuIYsrCwwJYtWzBixAhERERkex7v3bs33NzcMGfOnBxj0cX5PTg4GNu3b89xebly5bBu3bp8bfujEs93m8nfV3v57nJ9a2ZXs7W1Ra9evXDw4EE8e/YMZ8+eRadOnQAAffr0QdWqVbF161bcvn0bcXFxKF26NBwdHdG7d2907txZnEMwJwYGBti6dSu2bNmCS5cu4dWrV0hKSsqxSrxq1ao4duwYTp8+jT///BP37t1DdHQ0lEolSpcujapVq6J+/fpo165dvvpz5YWJiQksLCzE2xHWrl0bbdq0ee+tFNXMzc2xZs0a+Pr64tixY/D19UV4eDhSUlJgbm6OypUro06dOmjRokW+JppXK1myJPbv34+NGzfCy8sLr1+/znGes+XLl6NRo0Y4ePAg/P39kZaWhnLlysHV1RVDhw5FnTp1sGbNmhxf6+TJk7h8+TJu3bqFwMBAREZGIi4uDiVLlkS1atXQrFkzDBo0KN9zon1IM3vm9a9cuYL09HScPHlS44KnadOm8PDwwKZNm3Dt2jWEh4fD0tISTk5OGDp0KD7//HMsXboUgOZk15l97HumC3PmzEGTJk1w/fp1PHr0CBEREYiOjoZcLoetrS2cnZ3Rt2/fHPuj16hRAwcOHBCP6cjISJ309/xQpUuXxp49e3DixAmcPHkS/v7+iImJQcmSJaFQKNCxY8cszYNqI0eOhIODA/7++2/4+/sjPDxcrAW0trZGnTp10LNnzxxrP2xsbPDHH39g48aNuHnzJkJDQzX6F38oR0dH7Nu3D97e3rhw4YL4OxETEwNTU1OUK1cOTk5OaNmyJdq1a5fthRKQcaHg5uaGHTt2wMvLCyEhIVCpVChfvjwaNWqEoUOH5nmybn3TrFkznDt3Djt27MCVK1cQGBiI1NRUWFtbo379+hg4cKBejdnIzcccQ2r29vY4efIkNm/ejPPnz+P169fi8/v374+uXbvm6RaX2jq/29nZ4Y8//sDVq1fh5+eH4OBgREVFITExERYWFqhevTpatWqFAQMG5LvSRyZoa9QGEem9L774At7e3qhfv/57789MRET0wYOLiKh4CwsLE+flzXx7OCIiopww8SSibOU2ejk5ORlz5swRR13mZxJmIiIqvj6qjycR6a9vv/0WiYmJ6NSpE2rVqoXSpUsjISEB9+/fx969e8XEtG/fvsW23xsREeUPE08iytH9+/dznc6jXbt2753Pk4iISI2Di4goWw8ePMC5c+dw/fp1hIWFITo6GoIgoGzZsqhbty569eqV4xysRERE2WHiSUREREQ6wcFFRERERKQTTDyJiIiISCeYeBIRERGRTjDxJCIiIiKdYOJJRERERDrBxJOIiIiIdIKJJxERERHpBBNPIiIiItIJJp5EREREpBNMPImIiIhIJ5h4EhEREZFOMPEkIiIiIp1g4klEREREOsHEk4iIiIh0goknEREREekEE08iIiIi0gkmnkRERESkE0w8iYiIiEgnmHgSERERkU4w8SQivXbkyBE4ODggKChI6lDyzN3dHV27dn3vekFBQXBwcMCRI0d0EJV25HVfiUg/MPEkIp3Zs2cPHBwc0K9fP6lDKTBXrlzBmjVrpA6jWEpKSsKaNWtw48aNbJfHxsZi3rx5aNSoEerVqwd3d3c8ePBAx1ESUWZMPIlIZzw8PGBnZ4e7d+/i5cuXOnnNHj164O7du7Czs9PK9q9cuYK1a9dqZdvvo34ve/ToIcnrSy0pKQlr166Fj49PlmUqlQpjxozByZMnMXToUMyYMQPR0dFwd3fHv//+q/tgiQgAE08i0pHAwED4+flhzpw5sLKygoeHh05eVy6Xw8TEBDKZTCevp0symQwmJiaQy+W5rpeYmKijiAqPM2fOwM/PD8uXL8ekSZMwZMgQ7Nq1C3K5nDXURBLSq8QzNjYWhw8fxpw5czB8+HAMGDAA48aNw+rVq3H79m2pwyMq1jw8PGBpaYkWLVqgQ4cOOSaeb968wYwZM+Di4gJXV1fMmjULAQEBWfoyBgQEYPbs2WjTpg1q166Npk2bYs6cOXjz5o3G9rLr49m6dWuMHTsWvr6+6Nu3L2rXro02bdrg2LFjGs9NS0vD2rVr0b59e9SuXRsNGzbEoEGD4OXlBQCYPXs29uzZAwBwcHAQ/73PlStXMHToUDg7O8PFxQV9+vTJ9v14+vQp3N3dUbduXXz++efYvHmzxvLs+njOnj0bzs7OePXqFUaPHg1nZ2dMnz4dwH/9Ke/fv4+BAweiTp06aN26Nfbt2/femAHg7du3+P7779GtWzcx9i+//BIBAQEa6+XUr/bGjRtwcHDIsWlc7dq1a6hbty6mTZuG9PT0bNcJCgpC48aNAQBr164V33t1Unn27FlYW1ujffv24nOsrKzQqVMnXLhwAampqXnaZyIqWIZSB1AQwsLCsHr1anh4eKB8+fKoU6cOatSoARMTE8TExODGjRvYtm0bKlasiEmTJqFz585Sh0xU7Hh4eKBdu3YwNjZG165dsW/fPty9exd16tQR11GpVBg/fjzu3r2LQYMGoVq1arhw4QJmzZqVZXt///03AgMD0bt3b5QrVw5PnjzBwYMH8fTpUxw8ePC9NZwvX77E1KlT0bdvX/Tq1QuHDx/G7NmzUatWLdjb2wPISGg2btyIfv36oU6dOoiPj8f9+/fx4MEDNG3aFAMGDEB4eDi8vLzwww8/5Ol9OHLkCObOnQt7e3uMHTsWFhYW8Pf3x19//YVu3bqJ68XExODLL79Eu3bt0KlTJ5w9exYrV66EQqFAixYtcn2N9PR0jBo1CvXr18esWbNgamqqsd0xY8agU6dO6NKlC06fPo0FCxbAyMgIffv2zXW7gYGBOH/+PDp27IhKlSohMjISBw4cwNChQ+Hp6QkbG5s8vQe5uXTpEqZMmYLOnTtj2bJlOdbmWllZYcGCBViwYAHatWuHdu3aAYCY+Pv7+6NmzZowMNCsX6lduzYOHDiAFy9e5OkigYgKmKAHGjduLHz//ffCkydPclwnKSlJ8PDwEPr37y9s2bJFh9ER0b179wSFQiF4eXkJgiAIKpVKaN68ubBkyRKN9c6ePSsoFArh999/F8uUSqUwbNgwQaFQCIcPHxbLk5KSsrzOyZMnBYVCIdy8eVMsO3z4sKBQKITAwECxrFWrVlnWi4qKEpycnIQVK1aIZd27dxfGjBmT674tXLhQUCgU73sLBEEQhNjYWMHZ2Vno16+fkJycrLFMpVKJj4cOHSooFArh6NGjYllKSorQtGlTYfLkyWJZYGBglvdl1qxZgkKhEFauXJnl9dXb3bZtm8Z2e/ToITRu3FhITU3NNf6UlBRBqVRqlAUGBgpOTk7C2rVrxbLs3nNBEITr168LCoVCuH79ukZMXbp0EQQh4/OvVauW8O2332Z5nexERUUJCoVCWL16dZZl9erVE+bMmZOl/PLly4JCoRCuXr363u0TUcHTi6Z2T09PzJw5E9WrV89xHVNTU3Tt2hUHDhxA7969dRgdEXl4eMDa2hoNGzYEkNE3sXPnzjh16hSUSqW43l9//QUjIyP0799fLDMwMMCQIUOybDNzLV5KSgqio6NRt25dAMjTyOXq1avD1dVV/NvKygpVq1ZFYGCgWFaqVCk8efKkwAajeHl5ISEhAWPGjIGJiYnGsndraM3MzDQGDRkbG6N27doa8eVm0KBB2ZYbGhpiwIABGtsdMGAAoqKi3vu+GRsbizWISqUSb968gZmZGapWrYqHDx/mKa6cnDx5El9//TUGDBiARYsWZampzK/k5GQYGxtnKVeXpaSkfNT2iejD6EVTe5kyZbS6PhF9OKVSCU9PTzRs2FCjz1+dOnWwbds2eHt7o1mzZgCA169fo1y5cihRooTGNj755JMs23379i3Wrl2LU6dOISoqSmNZXFzce+OqUKFCljJLS0vExMSIf0+ZMgUTJkxAhw4doFAo0KxZM/To0QOOjo7v3X52Xr16BQBiU35ubG1tsySjlpaWePTo0Xufa2hoCFtb22yXlS9fHmZmZhplVapUAQAEBwejXr16iIiI0FhuYWEBU1NTqFQq7Ny5E3v37kVQUJDGRUPp0qXfG1dOgoKCMGPGDHTs2BHz5s3Lsvzt27dIS0sT/zY1NYWFhUWu2zQ1Nc22H6e67N3En4h0Qy8Sz+zEx8fjt99+g4+PD1QqFVxcXDBx4kRYWVlJHRpRsXL9+nVERETA09MTnp6eWZZ7eHiIiWd+fPXVV/Dz88OoUaNQo0YNmJmZQaVS4csvv4QgCO99/vtGggOAm5sbzp07hwsXLsDLywuHDh3Cjh07sHDhQq3PRZqX+HKSuWbyQ7z7eSxfvhy9e/fGhg0b8Ouvv6JPnz6YOnUqLC0tYWBggGXLlmm85zn1r1WpVNmWlytXDuXKlcOVK1dw79491K5dW2P55MmTNaZM6tWrF1asWJHrPpQrVy5LAg0A4eHhADIScCLSPb1NPOfNmwdTU1NMmTIFaWlpOHjwIGbMmIGtW7dKHRpRseLh4YGyZcti/vz5WZadO3cO586dw8KFC2FqaoqKFSvixo0bSEpK0qj1VNcUqsXExMDb2xuTJ0/GpEmTxHJtzM9YunRp9OnTB3369EFCQgKGDh2KNWvWiIlnfqZpUtfcPnnyBJ9++mmBx5oX4eHhSExM1Kj1VL9v6rlOt2/frvEcdTems2fPomHDhli2bJnG8tjYWI2WpFKlSgHIWvMcHBycbUwmJibYuHEjhg8fji+//BK7d+/WqBWeNWsWYmNjxb/VSWNu772joyNu3boFlUqlkYTfvXsXJUqUQNWqVXN8LhFpj94knr///juGDx8u/hDdu3cPZ8+eFWsNqlatqtGviYi0Lzk5GX/++Sc6duyIjh07Zllevnx5nDx5EhcvXkTnzp3RrFkzHDx4EAcPHsTw4cMBZNSSqacsUsupNnDHjh0FGv+bN280EqqSJUvik08+QUhIiFimTpBjY2PFhCsnzZo1Q8mSJbFx40Z8/vnnGs29giDoZK7R9PR0HDhwACNGjACQ0fR84MABWFlZoVatWgCAJk2aZPtcuVyepTb59OnTCAsL00ik1Qn2zZs3UaNGDQAZXS4OHjyYY1wWFhbYsmULhg4dihEjRmDv3r3idpycnLJ9Tub3/l0dO3bE2bNnxe8fAERHR+PMmTNo1apVtv0/iUj79CbxfPXqFfr164dFixahZs2aaNKkCcaMGYO2bdsiPT0dx48f/6DmPCL6cBcvXkRCQgJat26d7fJ69erBysoKJ06cQOfOndG2bVvUqVMH33//PV69eoVq1arh4sWLYr9LdWJmbm4ONzc3bNmyBWlpabCxsYGXl1eB34+9S5cuaNCgAWrVqoXSpUuLF7RDhw4V11Ena0uWLEGzZs0gl8vRpUuXbLdnbm6OOXPm4Ntvv0Xfvn3RtWtXlCpVCgEBAUhOTsb3339foPFnp3z58ti8eTOCg4NRpUoVnDp1Cv7+/li8eDGMjIxyfW7Lli3x22+/Yc6cOXB2dsbjx4/h4eGBypUra6xnb2+PevXqYdWqVYiJiYGlpSVOnTqV45ycalZWVti+fTsGDRqEL774Avv27ct1iiZTU1NUr14dp0+fRpUqVVC6dGnY29tDoVCgQ4cOqFevHubMmYOnT5+iTJky2LdvH5RKJSZPnpz3N4yICpTeJJ7z58/HnTt3MHfuXDRs2BD/+9//cOLECfz9999QKpXo2LGjxsmCiLTvxIkTMDExQdOmTbNdbmBggJYtW8LDw0OsXdy4cSOWLl2Ko0ePwsDAAO3atcPEiRMxaNAgjRrCn376CYsXL8bevXshCAKaNm2KzZs34/PPPy+w+N3d3XHx4kV4eXkhNTUVFStWxFdffYVRo0aJ67Rv3x7u7u7w9PTEiRMnIAhCjoknAPTr1w9ly5bFpk2bsG7dOhgaGqJatWr44osvCizu3FhaWmLFihVYsmQJDh48CGtra8yfP19jJoGcjBs3DklJSfDw8MCpU6dQs2ZNbNy4ET/99FOWdVeuXIn58+dj06ZNKFWqFPr27YuGDRuKNa05sbGxwe+//47BgwdjxIgR2L17d65985csWYLFixdj+fLlSEtLw6RJk6BQKCCXy7Fp0yb88MMP2LVrF1JSUlC7dm0sX74c1apVe/8bRURaIRPy0gu/CElPT8fmzZtx/PhxzJw5M8eaFiIqOs6fP4+JEydi7969qF+/vtThFFnu7u548+YNTp48KXUoRFRM6cU8npkZGhpi/Pjx2LBhA3bs2IEpU6ZkO7KRiAqn5ORkjb+VSiV27doFc3NzsVmbiIiKJr1pag8ICMA333yD58+fw8HBAcuWLcOOHTtw+PBhDBw4EKNGjcLgwYOlDpOI3mPx4sVITk6Gs7MzUlNT8eeff8LPzw/Tpk3TmDSeiIiKHr1pau/duzfc3NzQv39//PXXX7hw4QJ27doFIGMk47JlyxAYGIgDBw5IHCkR5cbDwwPbt2/Hy5cvkZKSgk8//RSDBg1iH+0CwKZ2IpKa3iSezs7OOHbsGD799FMolUq0a9cOFy9e1Fjn2rVrHNlOREREJBG9STzHjRuHxMREdOnSBdevX4eBgUG2Iy2JiIiISBp6k3jGxMRgw4YNePbsGRwdHTFmzBiYm5tLHRYRERER/T+9STyJiIiIqHDTi+mUXr9+na/1w8LCtBQJEREREeVELxLPvn37Yv78+bh7926O68TFxeHgwYPo2rUrzp49q8PoiIiIiAjQk6b2N2/eYMOGDTh8+DBMTExQq1YtlC9fHiYmJoiJicGzZ8/w5MkT1KpVCxMmTECLFi2kDpmIiIio2NGLxFMtOTkZly9fxq1bt/D69WskJyejTJkyqFmzJpo1awaFQiF1iERERETFll4lnkRERERUeOlFH08iIiIiKvyYeBIRERGRTjDxJCIiIiKdYOJJRERERDrBxJOIiIiIdMJQ6gAKUmpqKs6fP487d+4gMjISAGBtbQ1nZ2e0adMGxsbGEkdIREREVHzpzXRKL1++xKhRoxAeHo66deuibNmyAICoqCj8888/sLW1xebNm/Hpp59KHCkRERFR8aQ3ieeIESNQokQJ/PDDDzA3N9dYFh8fj5kzZyIlJQVbt26VKEIiIiKi4k1vEs+6devijz/+yPHuRI8ePUL//v3xzz//6Dgy7YiKioN+fHJERESUG5kMKFvWQuowCoTe9PG0sLBAcHBwjolncHAwLCz040MDAEEAE08iIiIqUvQm8ezXrx9mzZqFCRMmoFGjRrC2tgYAREZG4vr161i/fj2GDh0qcZRERERExZfeNLUDwKZNm7Bz505ERkZCJpMBAARBgLW1NYYPH47Ro0dLHGHBiYxkUzsREVFxIJMB1tb60WqrV4mnWmBgoMZ0SpUrV5Y4ooLHxJOIiKh40KfEU+8mkF+7dq04d6ezs7OYdCYnJ2Pt2rUSR0dERERUfOld4vnbb78hMTExS3lSUhJ+++03CSIiIiIiIkAPE09BEMT+nZkFBATA0tJSgoiIiIiICNCjUe1ubm6QyWSQyWTo0KGDRvKpVCqRmJiIgQMHShghERERUfGmN4nn3LlzIQgC5s6di8mTJ2vM2WlkZAQ7Ozs4OztLGCERERFR8aZ3o9p9fHzg4uICQ0O9yamzxVHtRERExQNHtRdiDRo0EJPOMWPGIDw8XOKIiIiIiAjQw8Qzs5s3byIlJUXqMIiIiIgIep54EhEREVHhodeJp52dnd739SQiIiIqKvRucFFaWhqMjIyyXRYdHQ0rKysdR6QdHFxERERUPHBwUSE2bdo0ZJdLR0ZGYtiwYRJERERERESAHiaer1+/xjfffKNRFhERAXd3d1SrVk1ncWzatAkODg5YunSpWJaSkoKFCxeiYcOGcHZ2xuTJkxEZGamzmIiIiIikpHeJ5+bNm+Hn54fly5cDAMLCwjB06FAoFAr88ssvOonh7t272L9/PxwcHDTKly1bhkuXLuGXX37Brl27EB4ejkmTJukkJiIiIiKp6d3IGysrK2zbtg2DBw8GAFy+fBk1a9bEypUrYWCg/Tw7ISEBM2bMwJIlS7B+/XqxPC4uDocPH8bKlSvRuHFjABmJaOfOnXHnzh3Uq1dP67ERERERSUnvEk8AqFChArZt24YhQ4agSZMm+PHHHzXu3a5NixYtQosWLdCkSRONxPP+/ftIS0tDkyZNxLLPPvsMFStW/KDE08hILj5WKgUolSrI5QaQyzPfo14FpVKAoaEBDAz+K09PV0GlEmBkJEfmtyUtTQVBEGBs/N+2M8qVEARkKU9NVUIm04zlv3IZjIz+S/QFIWM7BgYyGBr+V65SCUhPV0Eul0Eu/6+c+8R94j5xn7hP3Cfuk/7Ri8TTzc0t28QyKSkJly5dQsOGDcUyHx8frcXh6emJhw8f4tChQ1mWRUZGwsjICKVKldIoL1u2LCIiIvL9WuovZmYZX/qs66anq3LcRnZSU/NeLgg5lQvZlqtU2ZdnHMjZlXOfuE/cJ+4T9wngPgHFd590VHemE3qReM6dO1fqEBASEoKlS5di27ZtMDExkTocIiIiokJHLxLPXr16SR0CHjx4gKioKPTu3VssUyqVuHnzJvbs2YOtW7ciLS0NsbGxGrWeUVFRKFeunBQhExEREemUXiSemb1+/TrX5RUrVtTK6zZq1AgeHh4aZXPmzEG1atUwevRoVKhQAUZGRvD29kaHDh0AAM+fP8fr1685sIiIiIiKBb1LPFu3bp3rQCJ/f3+tvK65uTkUCoVGmZmZGUqXLi2W9+nTBytWrIClpSXMzc2xZMkSODs7M/EkIiKiYkHvEs9jx45p/J2WlgZ/f39s374dX3/9tTRB/b+5c+fCwMAAU6ZMQWpqKpo1a4bvvvtO0piIiIiIdEXv7tWek8uXL2Pr1q3YtWuX1KEUCN6rnYiIqHjgvdqLoKpVq+LevXtSh0FERERUbOldU3t8fLzG34IgIDw8HGvXrsWnn34qUVREREREpHeJp6ura5bBRYIgoEKFCli1apVEURERERGR3vXxfPfORAYGBihTpgw+/fRTGBrqT57NPp5ERETFgz718dS7xLO4YOJJRERUPOhT4qk/VYDvePr0KV6/fo20tDSN8jZt2kgUEREREVHxpneJZ2BgICZOnIjHjx9DJpNBXaGr7veprQnkiYiIiCh3ejed0tKlS1GpUiX8/fffMDU1haenJ3bv3g0nJye9mcOTiIiIqCjSu8TTz88PU6ZMgZWVFQwMDCCTyeDq6opp06ZhyZIlUodHREREVGzpXeKpUqlQsmRJAECZMmUQHh4OALCzs8OLFy+kDI2IiIioWNO7Pp729vZ49OgRKleujLp162LLli0wMjLCwYMHUblyZanDIyIiIiq29K7Gc/z48VCpVACAKVOmICgoCEOGDMGVK1fwzTffSBwdERERUfFVLObxfPv2LSwtLbPc0ago4zyeRERExYM+zeOpdzWemZ08eRKJiYkoXbq0XiWdREREREWRXiee8+fPR1RUlNRhEBERERH0PPEsBr0IiIiIiIoMvU48iYiIiKjw0OvEc/PmzbCxsZE6DCIiIiKCHiee6r6dd+/eZT9PIiIiokJA7yaQj4+Px8KFC3Hq1CkolUoAgFwuR6dOnfDdd9/BwkI/piMgIiIiKmr0rsbz22+/xd27d7Fhwwb4+vrC19cXGzZswP379zF//nypwyMiIiIqtvRuAvl69ephy5YtcHV11Sj39fXFl19+iTt37kgTWAHjBPJERETFAyeQL8RKly6dbXO6ubk5SpUqpdXX3rhxI/r06QNnZ2c0btwYEyZMwPPnzzXWSUlJwcKFC9GwYUM4Oztj8uTJiIyM1GpcRERERIWB3iWe48ePx4oVKxARESGWRURE4Mcff8SECRO0+to+Pj4YMmQIDh48iO3btyM9PR2jRo1CYmKiuM6yZctw6dIl/PLLL9i1axfCw8MxadIkrcZFREREVBjoXVN7z5498fLlS6SlpaFChQoAgJCQEBgZGaFKlSoa6x49elSrsURHR6Nx48bYvXs33NzcEBcXh8aNG2PlypXo2LEjAODZs2fo3LkzDhw4gHr16uV522xqJyIiKh70qald70a1t23bVuoQRHFxcQAAS0tLAMD9+/eRlpaGJk2aiOt89tlnqFixIu7cuZOvxNPISC4+VioFKJUqyOUGkMtlmcpVUCoFGBoawMDgv/L0dBVUKgFGRnJkvoV9WpoKgiDA2Pi/bWeUKyEIyFKemqqETKYZy3/lMhgZ/VehLggZ2zEwkMHQ8L9ylUpAeroKcrkMcvl/5dwn7hP3ifvEfeI+cZ/0j14lnkqlEg0bNoSDg4PW+3O+j0qlwrJly+Di4gKFQgEAiIyMhJGRUZbYypYtq9E1IC/UX8zMMr70WddNT1fluI3spKbmvVwQcioXsi1XqbIvzziQsyvnPnGfuE/cJ+4TwH0Ciu8+ZU5Yizq96uMpl8sxcuRIxMTESB0KFi5ciCdPnuDnn3+WOhQiIiKiQkGvEk8AsLe3R1BQkKQxLFq0CJcvX8aOHTtga2srlltbWyMtLQ2xsbEa60dFRaFcuXK6DpOIiIhIp/Qu8fzqq6/w/fff49KlSwgPD0d8fLzGP20SBAGLFi3CuXPnsGPHDlSuXFljuZOTE4yMjODt7S2WPX/+HK9fv85X/04iIiKiokjvRrU7OjqKj2WZOkUIggCZTAZ/f3+tvfaCBQtw8uRJrFu3DlWrVhXLLSwsYGpqCgD47rvvcPXqVSxfvhzm5uZYsmQJAGD//v35ei2OaiciIioe9GlUu94lnj4+Prkub9CggdZe28HBIdvy5cuXo3fv3gAyJpBfsWIFPD09kZqaimbNmuG7777Ld1M7E08iIqLigYknSY6JJxERUfGgT4mnXkynFBAQAIVCAQMDAwQEBOS6buameCIiIiLSHb2o8XR0dISXlxfKli0LR0dHyGQyZLdb2u7jqUus8SQiIioe9KnGUy8Sz+DgYFSsWBEymQzBwcG5rmtnZ6ejqLSLiScREVHxwMSTJMfEk4iIqHjQp8RTL/p4vuvff//FjRs3EBUVBZVK89ZVkyZNkigqIiIiouJN7xLPgwcPYsGCBShTpgysra015vKUyWRMPImIiIgkondN7a1atcKgQYMwZswYqUPRKja1ExERFQ9sai/EYmJi0KlTJ6nDICIiKhBhYaEICwvNcbmNjS1sbGx1GBHRh9O7xLNjx464du0aBg0aJHUoREREH23Hjm1YuXJFjsunT5+NmTPn6jAiog+nF03tO3fuFB8nJSVh+/btaNmyJRQKBQwNNXPrYcOG6To8rWBTOxFR8ZC5xvPJk0cYP3401q/fDHv7jNs0s8ZT/+lTU7teJJ6tW7fO03oymQwXLlzQcjS6wcSTiKj4uXv3Dtq2bY7z56+iTp16UodDOqJPiadeNLVfvHhR6hCIiIiI6D0MpA6AiIiIiIoHvUs8J0+ejE2bNmUp37x5M6ZMmSJBREREREQE6GHiefPmTbRo0SJLefPmzeHr6ytBREREREQE6GHimZiYCCMjoyzlhoaGiI+PlyAiovdTqVT4+ecf0L9/DwwY0BOHDx/Icd1ffvkRfft2Q7Nmrnjy5JFYnp6ejjlz/ofhwwdh7twZSE9PBwCkpKRg4sTRiI2N1fp+EBER5UbvEk+FQoFTp05lKT916hSqV68uQUREmiZNGoOQkNcaZWfPnsK//77Avn1HsHnzDuzduwvPnz/L9vktW7bBunVbYGtbQaP8xg1vWFiUwo4d+2Bubo4bN7wBAL//vgW9e/dHqVKltLNDREREeaQXo9ozmzBhAiZPnozAwEA0atQIAODt7Q1PT0/8+uuvEkdHlL2LF8+hW7eekMvlKFXKEm3atMP582cxZsyELOvWq+eS7TYMDQ2RkpIMAEhJSYaRkRGePn2CV6/+xdixE7UaPxERUV7oXY1n69at8dtvv+Hly5dYuHAhVqxYgdDQUGzfvh1t27aVOjyibIWFhWrUYNraVsz1FnnZcXNrCDOzkhg+fBBKljSHi4sr1qz5GVOnTi/ocImIiD6I3tV4AkDLli3RsmVLqcMgEi1ePB/Pnj0FAAQHB2LGjKkwNMzoi7x8+coCeQ0DAwPMmvWt+PfBg3vRvHkLKJVKLFjwDdLSUtG7d3/Ur+9WIK9HRESUX3qZeBIVNvPmLRIfT5o0Bt98swAVKlQUy2xsbBEaGgInpzoAgNDQ1x91C7zQ0BB4e3vhp5/WYOnS79C9ey84OtbAmDEjsHv3wQ/fESIioo+gd03tREVRq1Zt4eFxDEqlErGxMbhw4RzatGn/wdv79deVmDx5GgwMDJCUlAyZTAaZzADJyUkFGDUREVH+sMZTAnv27MHWrVsREREBR0dHzJs3D3Xq1JE6LJJQhw6d4e//EAMH9oZMBgwYMASffZYxC8O1a1dw7dpVzJ49DwDwww9L4e3thejoKEybNhlmZmY4cOCYuK0//zyD6tUVqFbtMwDA0KHD8f33S5GenoYvvhil830jIiJSkwmCIEgdRHFy6tQpzJw5EwsXLkTdunWxY8cOnDlzBmfOnEHZsmXzvJ3IyDjwkyMiKl7u3r2Dtm2b4/z5q6hTp57U4ZCOyGSAtbWF1GEUCL2v8VQqlXj8+DEqVqwIS0tLqcPB9u3b0b9/f/Tp0wcAsHDhQly+fBmHDx/GmDFj8ryd1NQ08JpBv4WHhyIsLCzH5TY2Nihf/sP7gRJR0ZOWlib+n5qaKnE0pCsymUzqEAqM3iWeS5cuhUKhQL9+/aBUKjF06FD4+fmhRIkS2LBhAxo2bChZbKmpqXjw4AHGjh0rlhkYGKBJkybw8/PL17bWrVuNlJSUgg4RGd9t/fmCF2WXLl2Ar69PjstdXRugVas2OoyIcifoTSuEPp3k9I16mrV9+3bj4sXzEkdD2dFGpZCJiQmWLFlQ4NuVgt4lnmfPnkX37t0BAJcuXUJQUBBOnz6N48eP4+eff8b+/fsli+3NmzdQKpVZmtTLli2L58+f52tbcrkB5PKMsWGCIEClEmBgINM4YeRUrlIJEARBfH7mcrlcBkNDeZbXEwR1Upp7WVEvL0yxyOW5n/zlchlMTDQP4cK+TwVVXphiUZerVCqoVP+dcHI+zlQQBGQpVypVkMkyLkazlstgYCB7b3l+fwuyK5fJAENlOpCaiizXoQIgIJv3IIdyQUDWbeRUrt6GVOVFZJ+M0zJuEmGclgzT1CS92Ce9+pyMjJAmN9RYkNs5N6+/Ee+uU5TpXeL55s0blCtXDgBw5coVdOzYEVWrVkWfPn2wc+dOiaMrOGPHTtLKVZVMxtqOwqJPn4HvbWr/mCmXqGAJgn7UeBoYyFAqLhrChvUQIiKlDofecScmFlsADIuJRT25kdThUCayctaQjR6DWAsrjYvQAtm2Hp2X9S7xtLa2xtOnT1GuXDn89ddfWLBgAQAgOTkZcnnWmjxdKlOmDORyOaKiojTKo6KiYG1tna9tGRsb6cVJjnJmZ1cZdnaVpQ6DihkDAxmMjY2hin4DhIdLHQ69wyg2JuP/6GgYpysljoY0yOUwMDbOOH4KPPEs0M1JSn/qbv9f79698dVXX6Fr166QyWRo0qQJAOCff/5BtWrVJI3N2NgYtWrVgre3t1imUqng7e0NZ2dnCSMjIiIi0j69q/GcPHky7O3tERoaio4dO8LY2BgAIJfLMXr0aImjA0aMGIFZs2bByckJderUwY4dO5CUlITevXtLHRoRERGRVuld4hkSEoKOHTtmKe/Vq5cE0WTVuXNnREdHY/Xq1YiIiECNGjWwZcuWfDe1ExERERU1epd4tm7dGvXr10f37t3RoUOHQjF357uGDh2KoUOHSh0GERERkU7pXR/Pw4cPo3bt2vjtt9/QrFkzTJgwAWfOnOFEu0REREQS07vEs2bNmpg1axYuX76MzZs3w8rKCvPnz0eTJk0wZ84cqcMjIiIiKrb0LvFUk8lkaNSoEZYsWYLt27ejUqVKOHbsmNRhERERERVbetfHUy00NBQeHh44efIknjx5gnr16mH+/PlSh0VERERUbOld4rl//36cPHkSt2/fRrVq1dCtWzd07doVdnZ2UodGREREVKzpXeK5fv16dOnSBd9++y0cHR2lDoeIiIiI/p/eJZ6XL1/Wq3uaEhEREekLvRtcxKSTiIiIqHDSu8STiIiIiAonJp5EREREpBNMPImIiIhIJ5h4EhEREZFO6N2odgA4c+YMTp8+jZCQEKSlpWksO3r0qERRERERERVvelfjuXPnTsyZMwfW1tZ4+PAhateujdKlSyMwMBDNmzeXOjwiIiKiYkvvajz37t2LxYsXo2vXrjhy5AhGjx6NypUr49dff0VMTIzU4REREREVW3pX4xkSEgJnZ2cAgKmpKRISEgAAPXr0gKenp5ShERERERVrepd4WltbizWbFSpUwJ07dwAAQUFBEARBwsiIiIiIije9a2pv1KgRLl68iJo1a6JPnz5Yvnw5zp49i/v376Ndu3ZSh0dERERUbOld4rl48WKoVCoAwJAhQ1C6dGn4+fmhdevWGDBggMTRERERERVfepd4hoaGokKFCuLfXbp0QZcuXSAIAkJCQlCxYkUJoyMiIiIqvvQu8WzTpg2uXbuGsmXLapS/ffsWbdq0gb+/v0SREREVHbJy1mCv+ELI2Cjjf2troKy1tLGQBlk5fh55oXeJpyAIkMlkWcoTExNhYmIiQUREREWHIABCyZKQTZyErL+kJDWD+/eAkx4wGDsWBk61pQ6H3iGULAmOY86d3iSey5cvBwDIZDL88ssvKFGihLhMqVTi7t27cHR0lCo8IqIiQRAExMpMILPghXphFG9mKf4fa2ElcTT0LkEAZ9B5D71JPB8+fAgg4wN//PgxjIyMxGXGxsZwdHTEyJEjtfLaQUFBWLduHa5fv47IyEiUL18e3bt3x7hx42BsbCyuFxAQgEWLFuHevXuwsrLC0KFDMXr0aK3ERET0oQRBYK1NIaVOagRBgErFD4mKHr1JPHft2gUAmDNnDr755huYm5vr7LWfP38OQRCwaNEifPrpp3j8+DHmzZuHpKQkzJo1CwAQHx+PUaNGoXHjxli4cCEeP36MuXPnolSpUhxtT0RERMWC3iSeauomd11q3ry5xn3gK1eujBcvXmDfvn1i4nnixAmkpaVh2bJlMDY2hr29Pfz9/bF9+3YmnkRERFQs6EXiOWnSpDyvu3btWi1G8p+4uDhYWlqKf9+5cweurq4aTe/NmjXD5s2bERMTo7FuXhgZycXHSqUApVIFudwAcrksU7kKSqUAQ0MDGBj8V56eroJKJcDISI7M47DS0lQQBAHGxv9tO6NcCUFAlvLUVCVkMs1Y/iuXwcjovxtjCULGdgwMZDA0/K9cpRKQnq6CXC6DXP5fOfeJ+8R94j5xn7Luk3o76v3Th33Sx89JG/ukL/Qi8bSwsJA6BA0vX77E7t27xdpOAIiMjESlSpU01rO2thaX5TfxVH8xM8v40mddNz1dleM2spOamvdyQcipXMi2XKXKvjzjQM6unPvEfeI+cZ+4T0DGPqn3S53MAEV/n7LDfdIsz2ayniJLLxJPbTWvr1y5Eps3b851nVOnTuGzzz4T/w4LC8OXX36Jjh07on///lqJi4iIiKgo0ovE813p6enw8fHBq1ev0LVrV5ibmyMsLAzm5uYoWbJknrczcuRI9OrVK9d1KleuLD4OCwvDsGHD4OzsjMWLF2usZ21tjcjISI0y9d/qmk8iIiIifaZ3iWdwcDC+/PJLhISEIDU1FU2bNoW5uTk2b96M1NRULFq0KM/bsrKygpVV3uZJUyedtWrVwvLly2FgYKCxvF69evjll1+QlpYmTvX0999/o2rVqvluZiciIiIqigzev0rRsnTpUjg5OcHHx0fjTkXt2rXD9evXtfKaYWFhcHd3R4UKFTBr1ixER0cjIiICERER4jrdunWDkZERvvnmGzx58gSnTp3Czp07MWLECK3ERERERFTY6F2N561bt7Bv3z6N0eMAYGdnh7CwMK28ppeXF16+fImXL19qTKsEAI8ePQKQMQBq69atWLRoEXr37o0yZcpgwoQJnEqJiIiIig29SzxVKhVUqqwjykJDQ/PVvzM/evfujd69e793PUdHR+zdu1crMRAREREVdnrX1N60aVPs2LFDoywhIQFr1qxBixYtJIqKiIiIiPQu8Zw9ezZu376Nzp07IzU1FdOnT0fr1q0RFhaG6dOnSx0eERERUbGld03ttra2OH78ODw9PfHo0SMkJiaib9++6NatG0xNTaUOj4iIiKjY0rvEEwAMDQ3Ro0cPqcMgIiIiokz0LvH09vbGuXPnEBwcDJlMhkqVKqFDhw5wc3OTOjQiIiKiYk2vEs/58+fj4MGDsLS0RJUqVSAIAvz8/LBnzx4MHjwY8+bNkzpEIiIiomJLbxLPc+fO4ciRI1i2bBl69eoFmUwGIGN6pSNHjmDBggVo0qQJ2rRpI3GkRERERMWT3oxqP3z4MEaMGIHevXuLSScAGBgYoG/fvhg+fDgOHTokYYRERERExZveJJ4PHz5Eu3btclzevn17PHjwQIcREREREVFmepN4vnnzBjY2Njkut7W1xdu3b3UXEBERERFp0JvEMy0tDUZGRjkul8vlSEtL02FERERERJSZ3gwuAoBffvkFJUqUyHZZUlKSjqMhIiIiosz0JvF0c3PDixcvcl3H1dVVR9EQERER0bv0JvHctWuX1CEQERERUS70po8nERERERVuTDyJiIiISCeYeBIRERGRTjDxJCIiIiKdYOJJRERERDqhl4mnr68vpk+fjgEDBiAsLAwAcOzYMfj6+kocGREREVHxpXeJ59mzZzFq1CiYmpri4cOHSE1NBQDEx8dj48aNEkdHREREVHzpXeK5fv16LFy4EEuWLIGh4X/TlLq4uODhw4cSRkZERERUvOld4vnixYts71BkYWGB2NhYrb9+amoqevToAQcHB/j7+2ssCwgIwODBg1G7dm20aNECmzdv1no8RERERIWF3iWe1tbWePXqVZbyW7duoXLlylp//R9++AHly5fPUh4fH49Ro0ahYsWKOHLkCGbOnIm1a9fiwIEDWo+JiIiIqDDQu8Szf//+WLp0Kf755x/IZDKEhYXhxIkT+P777zFo0CCtvvaVK1fg5eWFWbNmZVl24sQJpKWlYdmyZbC3t0eXLl3g7u6O7du3azUmIiIiosJCb+7VrjZmzBioVCp88cUXSEpKwtChQ2FsbIyRI0fC3d1da68bGRmJefPm4bfffoOpqWmW5Xfu3IGrqyuMjY3FsmbNmmHz5s2IiYmBpaWl1mIjIiIiKgz0LvGUyWQYP348Ro0ahVevXiExMRGfffYZSpYsqbXXFAQBs2fPxsCBA1G7dm0EBQVlWScyMhKVKlXSKLO2thaX5TfxNDKSi4+VSgFKpQpyuQHkclmmchWUSgGGhgYwMPivPD1dBZVKgJGRHLL/ipGWpoIgCDA2/m/bGeVKCAKylKemKiGTacbyX7kMRkb/VagLQsZ2DAxkMDT8r1ylEpCeroJcLoNc/l8594n7xH3iPnGfsu6Tejvq/dOHfdLHz0kb+6Qv9C7xnDNnDr755huYm5ujevXqYnliYiIWL16M5cuX53lbK1eufO8AoFOnTsHLywsJCQkYO3bsB8edX+ovZmYZX/qs66anq3LcRnZSU/NeLgg5lQvZlqtU2ZdnHMjZlXOfuE/cJ+4T9wnI2Cf1fqmTGaDo71N2uE+a5ZkT1qJO7xLPY8eOYfr06TA3N9coT05OxvHjx/OVeI4cORK9evXKdZ3KlSvj+vXruHPnDmrXrq2xrE+fPujWrRu+//57WFtbIzIyUmO5+m91zScRERGRPtObxDM+Ph6CIEAQBCQkJMDExERcplQqcfXqVVhZWeVrm1ZWVnl6zrfffouvvvpK/Ds8PByjRo3Czz//jLp16wIA6tWrh19++QVpaWkwMjICAPz999+oWrUq+3cSERFRsaA3iaerqytkMhlkMhk6dOiQZblMJsPkyZO18toVK1bU+NvMzAwA8Mknn8DW1hYA0K1bN/z222/45ptvMHr0aDx58gQ7d+7EnDlztBITERERUWGjN4nnzp07IQgChg8fjjVr1mjUIhoZGaFixYqwsbGRLD4LCwts3boVixYtQu/evVGmTBlMmDABAwYMkCwmIiIq/MLCQhEWFgoAePLkkcb/AGBjYwsbG1tJYiPKL5kgvDtEpWgLDg5GhQoVYGCgd1OUaoiMjMsyuIiIiPTPDz8sw8qVK3JcPn36bMycOVeHEZGuyWSAtbWF1GEUCL1LPNWSkpLw+vVrpKWlaZQ7OjpKFFHBYuJJRFQ8ZK7xzA5rPPWfPiWeetPUrhYdHY05c+bg6tWr2S5/9/7pREREuvbLLz/i2rWrCA0Nwfbte2Bv7yAuS01Nxdq1P8PH5zqMjY1RvboC8+cvzrINT88T+OOP/eLfERFhqFvXBcuW/YjXr4Px3XdzkZSUhPbtO2LYsJEAgH//fYH161fj++9/1v5OEmVD7xLPpUuXIjY2FgcPHsSwYcOwdu1aREZGYv369Zg9e7bU4REREaFlyzYYPHgYJkz4MsuyDRvWQCaTYd++I5DJZIiKisxmC0CXLt3RpUt38W939/5o374jAODIkT/Qu3c/tG/fCUOH9kPfvgNQooQZVq/+CdOnc1ArSUfvEs8bN25g3bp1qF27NmQyGSpWrIimTZvC3NwcGzduRMuWLaUOkYiIirl69VyyLU9KSsLJkydw9KgnZP8/a3jZsu+f6/nBg/t48+YNmjVrAQAwNDRESkoy0tPTIQgCZDIDHDt2GG5ujVCxol3B7QhRPundCJzExERx7k1LS0tER0cDABQKBR4+fChlaERERLkKDg5CqVKlsHPndowa5Y4JE76Er6/Pe5938uRxdOjQGYaGGfVJffsOxNWrlzFu3AgMHDgUCQnxuHz5Avr3H6TtXSDKld7VeFatWhUvXrxApUqV4ODggAMHDqBSpUrYv38/ypUrJ3V4REREOVIqlQgNDUGVKlUxfvxkPH4cgK+/nohduw7Cyqpsts9JSkrChQt/YuPG7WKZtbU1Vq1aK/797bezMGnSV7h92xfHjh2CkZExxo2bBFvbClrfJ6LM9K7Gc9iwYYiIiAAATJo0CVevXkXLli2xa9cuTJs2TeLoiIioODp9+iS++GIwvvhiMDw9T+S4no2NLQwMDNC+fScAgELhiAoV7PDs2dMcn3Pp0nlUrVoNVatWy3b55csXYGdXCfb2Dvjllx/xzTcL0L17L2zZsuHjdoroA+hdjWePHj3Ex05OTrh06RKeP3+OChUq5PuWmURERAWhU6eu6NSp63vXK126NOrXd4OPjzcaN26G16+DERISjCpVqub4nJMnj6Nr1x7ZLouLi8Mff+zHqlVrAADJycmQyQwgk8mQlJT4YTtD9BH0dh7Pd6WkpGD37t0YNWqU1KEUCM7jSURUdP3ww1J4e3shOjoKpUpZwszMDAcOHAOQ0c9zxYrFiIl5C5nMACNGfImWLdsAAFasWIxmzZqLg4hevfoXo0YNw/Hjp2FmVjLb12nTpj3q13cDAJw4cRQHDuyBoaER5syZB0fHmrrZYfoo+jSPp14lntHR0fjnn39gZGSExo0bQy6XIy0tDXv37sWmTZuQnp6OGzduSB1mgWDiSUREVDzoU+KpN03tvr6+GDduHOLj4yGTyeDk5ITly5dj4sSJkMvlmDRpEnr16iV1mERERETFlt7UeLq7u6N8+fIYO3Ysjh49iu3bt+PTTz/F119/jY4dO0odXoFjjScREVHxoE81nnqTeDZs2BB79uxB9erVkZycDGdnZ6xZswZt27aVOjStYOJJRERUPOhT4qk3Te0xMTEoU6YMAMDU1BSmpqZQKBQSR6U9/39DCyIiItJz+nTO15vEEwCePn0qzuEJAC9evEBiouZ0EY6OjroOSyvKltWPKx8iIiIqPvSmqd3R0REymQzZ7Y66XCaTwd/fX4LoiIiIiEhvEs/g4OA8rWdnZ6flSIiIiIgoO3qTeBIRERFR4aZ392onIiIiosKJiScRERER6QQTTyIiIiLSCSaeRERERKQTTDyJiIiISCf0agJ5AIiMjMT3338Pb29vREdHZ5nXk/N4EhEREUlD7xLP2bNnIyQkBBMmTED58uWlDoeIiIiI/p/ezePp7OyMvXv3okaNGlKHQkRERESZ6F0fzwoVKmR720yiwkipVOLy5ctITU2VOhQiIiKt07saz2vXrmH79u1YuHAhKlWqJHU4RO/l7OwMPz8/qcMgIgn5+vri8OHDePToERISElCyZEkoFAr07dsXrq6uUodHVGD0LvF0c3NDUlISlEolTE1NYWRkpLHcx8dHosiIsjdq1Ch8/fXXcHJykjoUIpLAH3/8gR9++AHdunVDjRo1YGFhgbi4OAQEBODkyZOYMWMG+vbtK3WYRAVC7wYXzZ07V+oQiPLF0dERY8eORdeuXVGhQgUYGPzXA2bYsGESRkZEurB+/Xps3boVderUybKsR48emDp1KhNP0ht6V+NJVNS4u7tnWy6TybBz504dR0NEuubs7Izr16/DxMQky7Lk5GQ0btyY3XFIb+hF4hkfHw9zc3PxcW7U6xERERUGY8aMQfny5TFt2jRYWVmJ5dHR0fj5558RGhqKzZs3SxghUcHRi8SzRo0auHbtGsqWLQtHR0fIZLIs6wiCAJlMxgnkqVCKiYnBlStXEB4eji+//BJhYWEQBAG2trZSh0ZEWhYeHo5p06bBz88P5cqVg4WFBeLj4xEeHg4XFxf89NNPnJea9IZeJJ4+Pj5wcXGBoaEhbty4kW3iqdagQQMdRkb0fn5+fhg3bhyqVauGgIAA+Pn5wdvbGzt37sT69eulDo+IdCQwMFBjVLuDgwMqV64sdVhEBUovEk8g44DlAUpFUb9+/TB27Fi0bdsWbm5uuHnzJpKSktCuXTtcu3ZN6vCIiIgKjN5MIN+uXTu0bt0ac+bMwfHjxxEaGip1SER58u+//6Jt27YAINbWlyhRgpPKExHS09MxZ84cqcMgKjB6k3ju2LEDvXr1QlBQEObNm4dWrVqhffv2mD9/Pjw9PREZGSl1iETZqlChAgICAjTKHjx4wBsgEBFUKhWOHTsmdRhEBUZv5vFs2LAhGjZsCABISUnB7du34ePjAx8fHxw9ehTp6emoVq0aPD09JY6USNPYsWMxbtw4jBo1CmlpaThw4AC2bt2K//3vf1KHRkQ6sHz58hyXKZVKHUZCpH1608czO6mpqbh9+zauXr2KAwcOIDExkaPaqVC6cuUK9u7di+DgYNja2mLIkCFo1aqV1GERkQ7UqlULbdq0QcmSJbMsUyqV8PDw4LmL9IZeJZ6pqan4559/cOPGDdy4cQN3796Fra0t3Nzc4OrqigYNGqBixYpSh0lERCTq1q0bZs6cic8//zzLspSUFNStWzdLdxyiokpvmtqHDRuGu3fvolKlSnBzc8PAgQM59xkVWjdv3szTem5ublqOhIik1rZtW0RFRWW7TC6Xo1evXjqOiEh79KbGs1atWihXrhzatm2LBg0awM3NDWXKlJE6LKJsvZtQJiYmQqlUwtjYGKmpqZDL5ShZsiR8fHwkipCIiKjg6U3imZiYCF9fX/j4+ODGjRvw9/dH1apV4ebmhgYNGqBBgwYatyIjKiz27NmD+/fvY/r06ShbtiyioqKwatUq1KxZE0OGDJE6PCIqRFxcXHD79m2pwyD6YHqTeL4rPj4et27dwo0bN+Dj44OAgABUqVIFJ0+elDo0Ig3NmzfHuXPnYGJiIpYlJyejXbt2+OuvvySMjIgKG2dnZ/j5+UkdBtEH05t5PN9lZmaG0qVLo3Tp0rC0tIShoSGePXsmdVhEWSiVSoSEhGiUhYSEcBoVIsoit1tCExUFejO4SKVS4f79++KI9tu3byMpKQk2NjZo2LAh5s+fL87zSVSY9OvXDyNGjMDQoUNRsWJFvH79Gnv27EH//v2lDo2IiKhA6U1Tu4uLC5KSkmBtbS1OJt+wYUN88sknUodGlCtBEHD48GGcPHkS4eHhKF++PLp06YK+ffuydoOINLCPJxV1epN47t+/Hw0bNkTVqlWlDoWIiEgr2MeTijq9aWofOHCg1CEQ5dmTJ09gb28PALlODO3o6KirkIioCBg3bpzUIRB9FL2p8SQqSjLXWuSUXMpkMt4mj6gYWb9+PT7//HM4OTnhxo0bmDhxIgwNDbFmzRreTIL0BhNPIiKiQqBFixbw9PSEubk5hgwZgg4dOqBkyZLYt28fDh06JHV4RAVCb6dTIiIiKkri4uJgbm6O+Ph4PHr0CEOGDEGfPn3w77//Sh0aUYHRmz6eREXJxIkT8zRife3atTqIhogKg3LlyuHWrVt49uwZXFxcIJfLkZCQAAMD1hGR/mDiSSSBGjVqSB0CERUyEydOxPDhw2FkZIT169cDALy9veHg4CBxZEQFh308iYiIComkpCQAQIkSJQAAUVFRUKlUKFeunJRhERUY1ngSSezKlSuoUqUKPv30UwQHB2PJkiUwNDTEN998A1tbW6nDIyIdUiqVuHTpEsLCwmBjY4OWLVvCwsJC6rCICgxrPIkk1rFjR/z++++wtbXFlClTYGRkBDMzM0RERGDDhg1Sh0dEOnLr1i1MmDABZcuWRcWKFRESEoLIyEisW7cO9evXlzo8ogLBGk8iiUVGRsLW1hbp6enw9vbGpUuXYGxsjM8//1zq0IhIhxYtWoSvv/5a44YoBw8exMKFC3HixAkJIyMqOBwqRyQxU1NTREZGwsfHB9WqVYO5uTlkMhnS09OlDo2IdOjVq1fo37+/RlmfPn0QGBgoUUREBY81nkQS69GjB/r27YvU1FRMnjwZAHD//n1UrlxZ4siISJeaNGmCv/76Cy1atBDLrl27hqZNm0oYFVHBYh9PokLAy8sLhoaGaNiwIQDg3r17iI+PR+PGjSWOjIh0Ze7cufD09ESjRo1QsWJFvH79GtevX0fXrl1hbm4urjdnzhwJoyT6OEw8iYoAFxcX3L59W+owiEiL8ppQLl++XMuREGkPE0+iIsDZ2Rl+fn5Sh0FERPRR2MeTqAjIy+01iajo+/fff3H69GlERERg/vz5eP78OVJTU+Ho6Ch1aEQFgqPaiYiICoGLFy+ib9++ePbsGY4dOwYAiI2NxQ8//CBtYEQFiDWeREREhcDPP/+MTZs2wcXFBW5ubgCAmjVrwt/fX+LIiAoOazyJigB2xSbSf6GhoXBxcQHwX/caQ0NDqFQqKcMiKlBMPImKgHHjxkkdAhFpWZUqVeDj46NRdvPmTVSrVk2iiIgKHke1E0lg586deVpv2LBhWo6EiAqLv//+G1999RV69+6N/fv3Y9iwYThy5Ah++ukncY5foqKOiSeRBNzd3d+7jkwmy3OCSkT6ISAgAAcPHkRwcDBsbW0xcOBA1KhRQ+qwiAoME08iIqJC4NatW6hfv36W8tu3b4t9P4mKOiaeRIVAfHw8rly5gtDQUFSoUAHNmzfXuEUeEem/nO5Q1qBBgyx9P4mKKk6nRCSxhw8f4ssvv0SpUqVQqVIlBAcHY8mSJdiyZQtq1qwpdXhEpCPZ1QNFR0fDwIDjgEl/sMaTSGJDhgxB+/btMXz4cLFs165dOH36NPbu3SthZESkC25ubpDJZIiLi4OFhYXGsoSEBPTp0weLFi2SKDqigsXEk0hiDRo0gLe3N+RyuVimVCrRqFEj3Lx5U8LIiEgXfHx8IAgCxowZg82bN4vlBgYGKFu2LKpWrSphdEQFi03tRBKztrbGnTt3NAYV3L17F9bW1hJGRUS60qBBAwDAlStXULp0aWmDIdIyJp5EEhs7dixGjx6NHj16wM7ODsHBwfDw8MC3334rdWhEpEP79u3D559/DicnJ9y4cQMTJ06EoaEh1qxZI95Ck6ioY1M7USFw8+ZNnDhxAqGhobC1tUX37t15oiEqZlq0aAFPT0+Ym5tjyJAh6NChA0qWLIl9+/bh0KFDUodHVCCYeBJJKD09HYMGDcLu3bthYmIidThEJCH1dErx8fFo2bIlbty4AblcDldXV/j6+kodHlGBYFM7kYQMDQ0RGRkpdRhEVAiUK1cOt27dwrNnz+Di4gK5XI6EhAROp0R6hd9mIomNGTMGy5cvR3x8vNShEJGEJk6ciOHDh2P58uUYOXIkAMDb2xsODg4SR0ZUcNjUTiQxNzc3JCQkQBAElCxZUqN2g3crISpekpKSAAAlSpQAAERFRUGlUqFcuXIAcr6tJlFRwcSTSGK5JZfqaVaIiICcb6tJVFSwjyeRxMLDw9G1a9cs5Z6enhJEQ0SFGeuKqKhjH08iic2fPz/b8oULF+o4EiIq7GQymdQhEH0U1ngSSUQ9mEgQhCwDi169egVDQx6eRESkX3hmI5KIq6urWHvx7mTxBgYGmDhxohRhERERaQ0TTyKJXLhwAYIgoH///vjjjz/EcgMDA1hZWXFCeSLKgn08qajjqHYiIqIiwtfXF66urlKHQfTBWONJJLH4+Hj8/vvvePDgARISEjSW7dy5U6KoiEjX3N3dsx08ZGxsjIoVK6JTp05o3LixBJERFRwmnkQSmzFjBsLCwtC+fXtx0mgiKn7q1KmDw4cPo3PnzqhQoQJCQkJw+vRp9OjRA4mJiZg4cSKmT5+OwYMHSx0q0QdjUzuRxFxdXXH58mWYm5tLHQoRSWj48OH43//+hzp16ohld+/exU8//YQdO3bA29sbCxcuxJkzZySMkujjcB5PIolVqFABaWlpUodBRBK7f/8+atasqVFWs2ZN3Lt3DwDQqFEjhIWFSREaUYFh4kkksSFDhuDrr7+Gl5cXAgICNP4RUfFRvXp1bNq0CSqVCgCgUqmwefNmVK9eHQAQFhYGCwsLKUMk+mhsaieSmKOjY7blMpkM/v7+Oo6GiKTy5MkTTJgwATExMShXrhwiIyNhYWGBdevWQaFQwNfXF0FBQejZs6fUoRJ9MCaeREREEomJiYGlpaX4d3p6Ou7cuYPw8HCUL18e9erV413MSK8w8SQqJCIiIhAaGooKFSrA2tpa6nCISAdcXFxw+/ZtAMAXX3yB33//XdqAiLSMl1FEEouOjsaMGTPg5eUFY2NjpKWloUmTJvjxxx9hZWUldXhEpEUmJiYIDQ2Fra0t7t69K3U4RFrHGk8iiU2bNg0AMHv2bJQvXx4RERFYsWIFlEolfvnlF2mDIyKtWrt2LdavX4+SJUsiLi4ux8FDPj4+Oo6MSDuYeBJJrEmTJrhw4YLG5PEJCQlo27YtvL29JYyMiHQhNDQUQUFBGDVqFDZv3pztOg0aNNBxVETawaZ2IomZmJggNjZWI/GMi4uDsbGxhFERka7Y2trC1tYWP/30ExNM0nucx5NIYl27dsXo0aNx5swZ3L17F6dPn8a4cePQrVs3qUMjIh2Kj4/PMn+vv78/jh8/LlFERAWPTe1EEktPT8eGDRvg4eEhDjLo1q0bxo4dCyMjI6nDIyIdad26NQ4dOqQxqDA6Ohp9+/bFxYsXJYyMqOAw8SQiIioE6tevj1u3bmmUCYKA+vXri1MuERV1bGonkti1a9fw/PlzjbJnz57By8tLooiISAqVKlXKMnrd19cXdnZ2EkVEVPCYeBJJbPHixTAzM9MoMzMzw+LFiyWKiIikMHr0aEyePBnr1q3DqVOnsH79ekyZMgVffvml1KERFRg2tRNJLPOdSzJzdnaGn5+fBBERkVQuXryIffv24fXr16hYsSIGDBiAtm3bSh0WUYHhdEpEErOxsYG/vz9q1KghlgUEBKB8+fISRkVEUmjdujVat26d4/JNmzZhzJgxOoyIqGCxqZ1IYoMGDcKUKVNw4sQJ/PPPPzhx4gSmTp2KQYMGSR0aERUyGzZskDoEoo/CGk8iibm7uwPIOKG8fv0adnZ2GDx4MIYNGyZxZERU2LB3HBV17ONJVAScPHkSXbt2lToMIpJYTn3CiYoKNrUTFQHz58+XOgQiIqKPxsSTqAhgwwQREekDJp5ERYBMJpM6BCIqBHgRSkUdBxcREREVAqmpqXjx4gUSEhJQsmRJVK1aFcbGxhrrcG5fKuqYeBIREUkoKSkJixcvhqenJ1JSUlCiRAkkJSXBxMQE3bp1w9y5c7Pc3YyoqGJTO1ERwOY1Iv01f/58hIeHY/fu3bh//z78/Pzw4MED7N69G+Hh4fjuu++kDpGowHA6JSKJnT9/Pttb4q1fvx7jx48HAISEhKBChQq6Do2IdMDV1RWXL1+Gubl5lmVxcXFo2bIlbt26JUFkRAWPNZ5EElu0aBF8fX01yjZt2oRjx46JfzPpJNJfJiYmiI6OznZZdHQ0TExMdBwRkfawjyeRxFavXo0pU6Zg69atsLe3x9atW3Hw4EHs3r1b6tCISAcGDRqEL774Au7u7nB0dESpUqUQFxeHgIAA7Ny5E4MHD5Y6RKICw6Z2okLg8uXLWLRoEbp16wYPDw/s3r0bFStWlDosItKRw4cP48iRI3j06BESExNhZmYGBwcH9O7dG3369JE6PKICw8STSALx8fFZyg4dOoQtW7Zgy5YtqFSpEgBk2+eLiIioqGLiSSQBR0fHLJPCqw9FmUwGQRAgk8ng7+8vRXhEJAF1i8e7evTogePHj0sQEVHBYx9PIglcuHBB6hCIqJAJCgrKtjwkJETHkRBpDxNPIgnY2dnluCw6OhpyuRyWlpY6jIiIpLJ8+XIAQHp6uvhYLTAwUOx6Q6QPOJ0SkcQWLlyIO3fuAABOnz6Nzz//HE2bNsXZs2elDYyIdCI2NhaxsbEQBEF8HBsbi7i4OFSvXh2//vqr1CESFRj28SSSWLNmzXD+/HmYmpqid+/eGD9+PMzNzbFs2bJs+3sRkX7atWsX3N3dpQ6DSKtY40kkscTERJiamiI6OhrBwcFo164dGjdujNevX0sdGhHpUOakc8GCBdIFQqRFTDyJJFapUiV4eHhg7969aNiwIYCMpjcjIyOJIyMiqZw4cULqEIi0goOLiCQ2a9YszJ49G0ZGRvjtt98AAJcuXULt2rUljoyIpMJecKSv2MeTqBBKS0sDANZ6EhVT3333HRYuXCh1GEQFjoknUSHy7h2NeOciouLjxYsXqFq1apbyv//+G02aNJEgIqKCx8STSGLBwcGYN28ebt26hdTUVI1lvHMRUfHRrl077N27F+XKlRPLvL29MW3aNHh7e0sYGVHB4eAiIoktWrQIpUqVwv79+2FmZoajR4+idevWWLRokdShEZEOjRw5EqNGjRJbPnx8fDBt2jSsWrVK4siICg5rPIkk1rBhQ1y8eBElS5aEq6srfH198ebNGwwZMgSnTp2SOjwi0qHVq1fDx8cHEyZMwLRp07By5Uo0a9ZM6rCICgxrPIkkZmBgAGNjYwAZfTrfvn0LCwsL3p+ZqBiaMmUKqlWrhvHjx+PHH39k0kl6hzWeRBIbOXIkRowYgc8//xz/+9//kJKSAlNTU7x8+RJ//PGH1OERkRb17NkTMplMoyw9PR2hoaEa92g/evSorkMj0gomnkQSCwkJgUqlgp2dHaKjo/HTTz8hISEBkydPxmeffSZ1eESkRXlNKHv16qXlSIh0g4knEREREekE71xEVAj4+/vjwYMHSExM1CgfNmyYRBERka4dO3Ysx2U9e/bUWRxE2sQaTyKJ/fzzz9i+fTscHBxgamoqlstkMuzcuVPCyIhIl95NLiMjI/H27VvY29uzjyfpDdZ4Ekls//79OHr0KPtzEhVz2dV4bt68Genp6boPhkhLOJ0SkcQsLS01Rq8SEamNHDkSu3btkjoMogLDxJNIYjNnzsTChQvx8uVLxMfHa/wjouLtzp07WaZbIirK2NROJLFSpUrBy8tLow+XIAiQyWS8VztRMfLunJ5JSUkICgrC9OnTJYyKqGBxcBGRxNq2bYvu3bujS5cuGoOLAMDOzk6iqIhI194dQGRmZoYaNWrgk08+kSgiooLHxJNIYq6urrh58yab04iISO+xqZ1IYp07d8aFCxfQtm1bqUMhIokFBwfDz88Pb968QeZ6Ic7pS/qCiSeRxMLDw/H111+jVq1asLa21li2du1aiaIiIl07c+YMZs6ciWrVquHp06eoXr06njx5AhcXFyaepDeYeBJJzMnJCU5OTlKHQUQSW7t2LVasWIHOnTvDzc0Nx44dw6FDh/Ds2TOpQyMqMOzjSVQEbNq0CWPGjJE6DCLSIhcXF9y6dQsymQxubm64efMm0tPT0aJFC3h5eUkdHlGB4DyeREXAhg0bpA6BiLSsdOnSePv2LQDAxsYG/v7+iIiIQEpKirSBERUgNrUTFQFsmCDSf506dYKXlxe6du2Kvn37wt3dHXK5HJ07d5Y6NKICw6Z2oiLAxcUFt2/fljoMItKhW7duIT4+Hs2bN+d0a6Q3WONJRERUyNy6dQv169eXOgyiAsc+nkRERIXM6NGjpQ6BSCuYeBIVAewRQ0RE+oBN7UQSS01NxYsXL5CQkICSJUuiatWqMDY21ljHz89PouiISAq82CR9xcFFRBJJSkrC4sWL4enpiZSUFJQoUQJJSUkwMTFBt27dMHfuXJiZmUkdJhERUYFh4kkkkRkzZuDNmzeYOnUqatSoAUNDQyiVSjx8+BBr1qyBpaUlfvzxR6nDJCId8vX1xfHjxxEWFgYbGxt0794dbm5uUodFVGCYeBJJxNXVFZcvX4a5uXmWZXFxcWjZsiVu3bolQWREJIW9e/fip59+Qrdu3VCxYkWEhITg5MmT+PrrrzF48GCpwyMqEOzjSSQRExMTREdHZ5t4RkdHw8TERIKoiEgqW7ZswdatW1GvXj2xrGfPnvjqq6+YeJLeYOJJJJFBgwbhiy++gLu7OxwdHVGqVCnExcUhICAAO3fu5ImGqJhJSEiAk5OTRlnNmjWRmJgoUUREBY9N7UQSOnz4MI4cOYJHjx4hMTERZmZmcHBwQO/evdGnTx+pwyMiHfr5559hYGCASZMmQS6XQ6lUYt26dUhPT8fXX38tdXhEBYKJJxERUSHQs2dPPHnyBGZmZihfvjzCw8ORmJgIhUKhsd7Ro0clipDo47GpnUhi3bp1g4eHR5byHj164Pjx4xJERERSGD58uNQhEGkdE08iiQUFBWVbHhISouNIiEgq6enpePz4Mb766isOLCS9xsSTSCLLly8HkHHCUT9WCwwMRKVKlaQIi4gkYGhoiCNHjmDGjBlSh0KkVbxXO5FEYmNjERsbC0EQxMexsbGIi4tD9erV8euvv0odIhHpUKdOnXDixAmpwyDSKg4uIpLYrl274O7uLnUYRCSxcePG4dq1a7C3t0eFChVgYPBf3dDatWsljIyo4LCpnUhimZPOBQsWYMGCBdIFQ0SScXJyyjKPJ5G+YY0nUSHi4uKC27dvSx0GERGRVrDGk6gQ4XUgUfHy5MkT2NvbAwACAgJyXM/R0VFXIRFpFRNPokKke/fuUodARDrUv39/+Pn5AciYQD47MpkM/v7+OoyKSHvY1E4ksRcvXqBq1apZyv/++280adJEgoiIiIi0g9MpEUlszJgxiIiI0Cjz9vbG//73P4kiIiIi0g42tRNJbOTIkRg1ahT27t0Lc3Nz+Pj4YNq0aVi1apXUoRGRDoWFheGXX37BgwcPkJCQoLHswoULEkVFVLDY1E5UCKxevRo+Pj6YMGECpk2bhpUrV6JZs2ZSh0VEOuTu7o4SJUqgW7duKFGihMaytm3bShQVUcFi4klUSMyfPx/Hjx/H2rVr8fnnn0sdDhHpmIuLC65fvw5jY2OpQyHSGiaeRBLo2bMnZDKZRll6ejpCQ0M17tF+9OhRXYdGRBIZMGAAfv75Z1SsWFHqUIi0hn08iSQwfPhwqUMgokIgc9/Ndu3aYfz48RgyZAjKli2rsV6bNm10HRqRVrDGk4iISCKtW7d+7zoymYyDi0hvMPEkktixY8dyXJbThNJEpH9CQ0NhYmKCMmXKiGVv375FSkoKbGxsJIyMqOCwqZ1IYr///rvG35GRkXj79i3s7e2ZeBIVI5MnT8bixYs1Es+QkBAsWLAABw4ckDAyooLDxJNIYtnVeG7evBnp6em6D4aIJPP8+fMs92R3dHTEs2fPJIqIqODxzkVEhdDIkSOxa9cuqcMgIh2ytLREZGSkRllkZCTMzMwkioio4DHxJCqE7ty5k2W6JSLSby1btsScOXMQFhYGIONORvPmzcvTACSiooJN7UQSe3dOz6SkJAQFBWH69OkSRkVEujZt2jTMnj0bLVq0gImJCVJTU9GmTRv+FpBe4ah2Iom9O0m8mZkZatSogU8++USiiIhISlFRUQgODoadnV2W+TyJijomnkRERESkE2xqJyoEgoOD4efnhzdv3iDzteCwYcMkjIqIiKhgMfEkktiZM2cwc+ZMVKtWDU+fPkX16tXx5MkTuLi4MPEkIiK9wsSTSGJr167FihUr0LlzZ7i5ueHYsWM4dOgQ5+4jIiK9w+mUiCT2+vVrdOrUSaOsZ8+eOHHihEQRERERaQcTTyKJlS5dGm/fvgUA2NjYwN/fHxEREUhJSZE2MCIiogLGpnYiiXXq1AleXl7o2rUr+vbtC3d3d8jlcnTu3Fnq0IiIiAoUp1MiKmRu3bqF+Ph4NG/enHcvIiIivcIaT6JC5NatW6hfv77UYRAREWkF+3gSFSKjR4+WOgQiIiKtYeJJRERERDrBxJOoEGGXayIi0mccXEREREREOsHBRUSFgK+vL44fP46wsDDY2Nige/fucHNzkzosIiKiAsWmdiKJ7d27F2PHjoVcLoerqysMDQ0xYcIE7N27V+rQiIiIChSb2okk1rp1a6xatQr16tUTy/755x989dVXuHTpknSBERERFTDWeBJJLCEhAU5OThplNWvWRGJiokQRERERaQcTTyKJDRw4EL/99huUSiUAQKlUYsOGDRg4cKDEkRERERUsNrUTSaxnz5548uQJzMzMUL58eYSHhyMxMREKhUJjvaNHj0oUIRERUcHgqHYiiQ0fPlzqEIiIiHSCiSeRhNLT0/H48WN89dVXMDExkTocIiIirWIfTyIJGRoa4siRIzAyMpI6FCIiIq1j4kkksU6dOuHEiRNSh0FERKR1HFxEJLFx48bh2rVrsLe3R4UKFWBg8N/14Nq1ayWMjIiIqGCxjyeRxJycnLLM40lERKSPWONJRERERDrBGk8iCTx58gT29vYAgICAgBzXc3R01FVIREREWscaTyIJODs7w8/PD0DOyaVMJoO/v78uwyIiItIqJp5EREREpBOcTomIiIiIdIJ9PIkkFhYWhl9++QUPHjxAQkKCxrILFy5IFBUREVHBY+JJJLHp06ejRIkSGD16NEqUKCF1OERERFrDxJNIYg8ePMD169dhbGwsdShERERaxT6eRBKzt7dHZGSk1GEQERFpHUe1E0kgc9/NFy9ewMPDA0OGDEHZsmU11mvTpo2uQyMiItIaJp5EEmjduvV715HJZBxcREREeoWJJ5HEQkNDYWJigjJlyohlb9++RUpKCmxsbCSMjIiIqGCxjyeRxCZPnoywsDCNspCQEEyZMkWiiIiIiLSDiSeRxJ4/f57ltpmOjo549uyZRBERERFpBxNPIolZWlpmGdUeGRkJMzMziSIiIiLSDiaeRBJr2bIl5syZIza3h4WFYd68eXkagERERFSUcHARkcTi4+Mxe/ZsnD9/HiYmJkhNTUWbNm2wYsUKmJubSx0eERFRgWHiSVRIREVFITg4GHZ2dlnm8yQiItIHTDyJiIiISCfYx5OIiIiIdIKJJxERERHpBBNPIiIiItIJJp5EREREpBNMPImIiIhIJ5h4EhEREZFOMPEkIiIiIp34PwJJ2xH+prNcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_preference_counts(user_intersting_clips_3p5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:02.332947Z",
     "start_time": "2024-05-26T00:25:02.010694Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:30.091782Z",
     "iopub.status.busy": "2025-07-28T14:39:30.091574Z",
     "iopub.status.idle": "2025-07-28T14:39:34.484901Z",
     "shell.execute_reply": "2025-07-28T14:39:34.484376Z",
     "shell.execute_reply.started": "2025-07-28T14:39:30.091768Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first gen\n",
      "len pos models: 449379\n",
      "differing counts: 7721\n",
      "chirp-auk-t0_cfg_steps_10_win_over_chirp-auk-t0, win ratio 0.500, (-0.193, 1.193), counts 1, total 2.\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.502, (0.491, 0.513), counts 3872, total 7715.\n",
      "chirp-auk-t0_n_tag_2_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 441658, total 441658.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_cfg_steps_10, win ratio 0.500, (-0.193, 1.193), counts 1, total 2.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.498, (0.487, 0.509), counts 3843, total 7715.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_tag_cfg_1, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_tag_cfg_3, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_temp_s_95, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "tournament players: ['chirp-auk-t0', 'chirp-auk-t0_cfg_steps_10', 'chirp-auk-t0_mask_control_slider', 'chirp-auk-t0_n_tag_2', 'chirp-auk-t0_tag_cfg_1', 'chirp-auk-t0_tag_cfg_3', 'chirp-auk-t0_temp_s_95']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:02<00:00, 489.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 1019.8, (-2340, 28882)\n",
      "chirp-auk-t0_cfg_steps_10: 1001.3, (-8196, 27279)\n",
      "chirp-auk-t0_mask_control_slider: 1021.1, (-2339, 28877)\n",
      "chirp-auk-t0_n_tag_2: 1023.0, (1000, 34586)\n",
      "chirp-auk-t0_tag_cfg_1: 978.3, (-30674, 1000)\n",
      "chirp-auk-t0_tag_cfg_3: 978.3, (-21691, 1000)\n",
      "chirp-auk-t0_temp_s_95: 978.3, (-27963, 1000)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common models in comparisons:\n",
      "No models found with multiple comparisons\n"
     ]
    }
   ],
   "source": [
    "print(\"first gen\")\n",
    "first_gen_slice_df = user_intersting_clips_3p5[\n",
    "    (user_intersting_clips_3p5[\"continued_parent\"].isna())\n",
    "    & (user_intersting_clips_3p5[\"task\"] == \"\")\n",
    "].copy()\n",
    "if first_gen_slice_df.shape[0] > 0:\n",
    "    get_preference_counts(\n",
    "        first_gen_slice_df,\n",
    "        title_name=\"first generation\",\n",
    "    ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:02.574659Z",
     "start_time": "2024-05-26T00:25:02.334214Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:34.485563Z",
     "iopub.status.busy": "2025-07-28T14:39:34.485440Z",
     "iopub.status.idle": "2025-07-28T14:39:35.907254Z",
     "shell.execute_reply": "2025-07-28T14:39:35.906741Z",
     "shell.execute_reply.started": "2025-07-28T14:39:34.485550Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is continue\n",
      "len pos models: 30731\n",
      "differing counts: 536\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.521, (0.478, 0.563), counts 279, total 536.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 30195, total 30195.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.479, (0.437, 0.522), counts 257, total 536.\n",
      "tournament players: ['chirp-auk-t0', 'chirp-auk-t0_mask_control_slider']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 1154.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 992.9, (979, 1008)\n",
      "chirp-auk-t0_mask_control_slider: 1007.1, (992, 1021)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common models in comparisons:\n",
      "No models found with multiple comparisons\n"
     ]
    }
   ],
   "source": [
    "print(\"is continue\")\n",
    "get_preference_counts(\n",
    "    user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"extend\")],\n",
    "    \"is extend\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:35.907995Z",
     "iopub.status.busy": "2025-07-28T14:39:35.907796Z",
     "iopub.status.idle": "2025-07-28T14:39:38.508236Z",
     "shell.execute_reply": "2025-07-28T14:39:38.507705Z",
     "shell.execute_reply.started": "2025-07-28T14:39:35.907982Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is cover\n",
      "len pos models: 123168\n",
      "differing counts: 4404\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.501, (0.486, 0.516), counts 2206, total 4402.\n",
      "chirp-auk-t0_n_tag_2_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 118764, total 118764.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.499, (0.484, 0.514), counts 2196, total 4402.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_temp_s_95, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "tournament players: ['chirp-auk-t0', 'chirp-auk-t0_mask_control_slider', 'chirp-auk-t0_n_tag_2', 'chirp-auk-t0_temp_s_95']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:01<00:00, 589.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 999.7, (-1288, 3744)\n",
      "chirp-auk-t0_mask_control_slider: 1000.4, (-1300, 3745)\n",
      "chirp-auk-t0_n_tag_2: 1038.9, (1000, 11982)\n",
      "chirp-auk-t0_temp_s_95: 961.0, (-9764, 1000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common models in comparisons:\n",
      "No models found with multiple comparisons\n"
     ]
    }
   ],
   "source": [
    "print(\"is cover\")\n",
    "get_preference_counts(\n",
    "    user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"cover\")],\n",
    "    \"is cover\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:38.508998Z",
     "iopub.status.busy": "2025-07-28T14:39:38.508779Z",
     "iopub.status.idle": "2025-07-28T14:39:41.899508Z",
     "shell.execute_reply": "2025-07-28T14:39:41.898975Z",
     "shell.execute_reply.started": "2025-07-28T14:39:38.508984Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is artist\n",
      "len pos models: 54659\n",
      "differing counts: 1624\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.511, (0.486, 0.535), counts 829, total 1623.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 53035, total 53035.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_cfg_steps_10, win ratio 1.000, (1.000, 1.000), counts 1, total 1.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.489, (0.465, 0.514), counts 794, total 1623.\n",
      "tournament players: ['chirp-auk-t0', 'chirp-auk-t0_cfg_steps_10', 'chirp-auk-t0_mask_control_slider']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:01<00:00, 692.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 1020.0, (990, 1830)\n",
      "chirp-auk-t0_cfg_steps_10: 952.5, (-663, 1000)\n",
      "chirp-auk-t0_mask_control_slider: 1027.4, (997, 1837)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common models in comparisons:\n",
      "No models found with multiple comparisons\n"
     ]
    }
   ],
   "source": [
    "print(\"is artist\")\n",
    "get_preference_counts(\n",
    "    user_intersting_clips_3p5[\n",
    "        (user_intersting_clips_3p5[\"task\"] == \"artist_consistency\")\n",
    "    ],\n",
    "    \"is artist\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:41.900278Z",
     "iopub.status.busy": "2025-07-28T14:39:41.900058Z",
     "iopub.status.idle": "2025-07-28T14:39:41.919707Z",
     "shell.execute_reply": "2025-07-28T14:39:41.919325Z",
     "shell.execute_reply.started": "2025-07-28T14:39:41.900264Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"is infill\")\n",
    "# get_preference_counts(\n",
    "#     user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"infill\")],\n",
    "#     \"is infill\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:41.920409Z",
     "iopub.status.busy": "2025-07-28T14:39:41.920164Z",
     "iopub.status.idle": "2025-07-28T14:39:42.063869Z",
     "shell.execute_reply": "2025-07-28T14:39:42.063369Z",
     "shell.execute_reply.started": "2025-07-28T14:39:41.920397Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upsample\n"
     ]
    }
   ],
   "source": [
    "print(\"upsample\")\n",
    "upsample_slice_df = user_intersting_clips_3p5[\n",
    "    (user_intersting_clips_3p5[\"task\"] == \"upsample\")\n",
    "].copy()\n",
    "if upsample_slice_df.shape[0] > 0:\n",
    "    get_preference_counts(\n",
    "        upsample_slice_df,\n",
    "        title_name=\"upsample\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:42.064568Z",
     "iopub.status.busy": "2025-07-28T14:39:42.064383Z",
     "iopub.status.idle": "2025-07-28T14:39:43.417412Z",
     "shell.execute_reply": "2025-07-28T14:39:43.416915Z",
     "shell.execute_reply.started": "2025-07-28T14:39:42.064554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is upload\n",
      "len pos models: 16429\n",
      "differing counts: 520\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.487, (0.444, 0.529), counts 253, total 520.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 15909, total 15909.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.513, (0.471, 0.556), counts 267, total 520.\n",
      "tournament players: ['chirp-auk-t0', 'chirp-auk-t0_mask_control_slider']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 1147.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 1004.7, (989, 1018)\n",
      "chirp-auk-t0_mask_control_slider: 995.3, (982, 1011)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common models in comparisons:\n",
      "No models found with multiple comparisons\n"
     ]
    }
   ],
   "source": [
    "print(\"is upload\")\n",
    "get_preference_counts(\n",
    "    user_intersting_clips_3p5[(user_intersting_clips_3p5[\"task\"] == \"upload_extend\")],\n",
    "    \"is upload extend\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up SHIT\n",
    "\n",
    "to get the right play conts, we need the right df..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:08.095035Z",
     "start_time": "2024-05-26T00:25:07.782738Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:43.418155Z",
     "iopub.status.busy": "2025-07-28T14:39:43.417959Z",
     "iopub.status.idle": "2025-07-28T14:39:44.704583Z",
     "shell.execute_reply": "2025-07-28T14:39:44.704072Z",
     "shell.execute_reply.started": "2025-07-28T14:39:43.418141Z"
    }
   },
   "outputs": [],
   "source": [
    "def unpack_dict(x):\n",
    "    if v := concat_clips_ids.get(str(x)):\n",
    "        return v\n",
    "    else:\n",
    "        return {\n",
    "            \"total_start_s\": None,\n",
    "            \"total_clip_s\": None,\n",
    "            \"concat_play_counts\": None,\n",
    "            \"concat_in_playlist\": None,\n",
    "            \"concat_likes\": None,\n",
    "            \"concat_dislikes\": None,\n",
    "        }\n",
    "\n",
    "\n",
    "# TODO: concat play count / play duraiton needs to be somehow counted as well\n",
    "extra_cols = user_intersting_clips[\"id\"].apply(unpack_dict)\n",
    "extra_cols_df = pd.DataFrame.from_records(extra_cols.values, index=extra_cols.index)\n",
    "user_intersting_clips[\n",
    "    [\n",
    "        \"total_start_s\",\n",
    "        \"total_clip_s\",\n",
    "        \"concat_play_counts\",\n",
    "        \"concat_in_playlist\",\n",
    "        \"concat_likes\",\n",
    "        \"concat_dislikes\",\n",
    "    ]\n",
    "] = extra_cols_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:44.705350Z",
     "iopub.status.busy": "2025-07-28T14:39:44.705130Z",
     "iopub.status.idle": "2025-07-28T14:39:46.063903Z",
     "shell.execute_reply": "2025-07-28T14:39:46.063395Z",
     "shell.execute_reply.started": "2025-07-28T14:39:44.705336Z"
    }
   },
   "outputs": [],
   "source": [
    "def unpack_dict(x):\n",
    "    if v := concat_clips_ids.get(str(x)):\n",
    "        return v\n",
    "    else:\n",
    "        return {\n",
    "            \"total_start_s\": None,\n",
    "            \"total_clip_s\": None,\n",
    "            \"concat_play_counts\": None,\n",
    "            \"concat_in_playlist\": None,\n",
    "            \"concat_likes\": None,\n",
    "            \"concat_dislikes\": None,\n",
    "        }\n",
    "\n",
    "\n",
    "extra_cols = user_intersting_clips[\"id\"].apply(unpack_dict)\n",
    "extra_cols_df = pd.DataFrame.from_records(extra_cols.values, index=extra_cols.index)\n",
    "user_intersting_clips[\n",
    "    [\n",
    "        \"total_start_s\",\n",
    "        \"total_clip_s\",\n",
    "        \"concat_play_counts\",\n",
    "        \"concat_in_playlist\",\n",
    "        \"concat_likes\",\n",
    "        \"concat_dislikes\",\n",
    "    ]\n",
    "] = extra_cols_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:08.361849Z",
     "start_time": "2024-05-26T00:25:08.199583Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:46.064680Z",
     "iopub.status.busy": "2025-07-28T14:39:46.064462Z",
     "iopub.status.idle": "2025-07-28T14:39:46.089019Z",
     "shell.execute_reply": "2025-07-28T14:39:46.088596Z",
     "shell.execute_reply.started": "2025-07-28T14:39:46.064667Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive proportion of data meeting criteria: 45.54%\n",
      "Negative proportion of data meeting criteria: 100.00%\n"
     ]
    }
   ],
   "source": [
    "pos_too_much_data_mask = (\n",
    "    (user_intersting_clips[\"preference\"])\n",
    "    & (\n",
    "        (\n",
    "            user_intersting_clips[\"reaction_play_count\"] >= 3\n",
    "        )  # single play is super catchy\n",
    "        | (\n",
    "            user_intersting_clips[\"concat_play_counts\"] >= 3\n",
    "        )  # or the concat play is super catchy\n",
    "    )\n",
    "    # & (user_intersting_clips[\"user_n_clips\"] >= 40)\n",
    "    # & (user_intersting_clips[\"continued_parent\"].isna())\n",
    ")\n",
    "neg_too_much_data_mask = (\n",
    "    (~user_intersting_clips[\"preference\"])\n",
    "    & (user_intersting_clips[\"reaction_play_count\"] >= 1)  # single play is super catchy\n",
    "    # & (user_intersting_clips[\"user_n_clips\"] >= 40)\n",
    "    # & (user_intersting_clips[\"continued_parent\"].isna())\n",
    ")\n",
    "# Calculate and print the proportion of data that meets our criteria\n",
    "pos_proportion = pos_too_much_data_mask.sum() / user_intersting_clips.shape[0] * 2\n",
    "print(f\"Positive proportion of data meeting criteria: {pos_proportion:.2%}\")\n",
    "neg_proportion = neg_too_much_data_mask.sum() / user_intersting_clips.shape[0] * 2\n",
    "print(f\"Negative proportion of data meeting criteria: {neg_proportion:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:08.523643Z",
     "start_time": "2024-05-26T00:25:08.367348Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:46.089822Z",
     "iopub.status.busy": "2025-07-28T14:39:46.089504Z",
     "iopub.status.idle": "2025-07-28T14:39:48.790720Z",
     "shell.execute_reply": "2025-07-28T14:39:48.790211Z",
     "shell.execute_reply.started": "2025-07-28T14:39:46.089809Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name Value Counts for Preferred Clips:\n",
      "----------------------------------------------------------------------\n",
      "Model                               Count        Fraction\n",
      "----------------------------------------------------------------------\n",
      "chirp-auk-t0                      319,134         100.00%\n",
      "----------------------------------------------------------------------\n",
      "Total                             319,134         100.00%\n"
     ]
    }
   ],
   "source": [
    "final_good_enough_requests = set(\n",
    "    user_intersting_clips[pos_too_much_data_mask][\"request_id\"].unique()\n",
    ").intersection(\n",
    "    set(user_intersting_clips[neg_too_much_data_mask][\"request_id\"].unique())\n",
    ")\n",
    "final_interesting_clips = user_intersting_clips[\n",
    "    user_intersting_clips[\"request_id\"].isin(final_good_enough_requests)\n",
    "].copy()\n",
    "# Get the value counts of model_name for preferred clips\n",
    "model_counts = final_interesting_clips[final_interesting_clips[\"preference\"]][\n",
    "    \"model_name\"\n",
    "].value_counts()\n",
    "\n",
    "# Print the results in a nicely formatted way\n",
    "total_count = model_counts.sum()\n",
    "print(\"Model Name Value Counts for Preferred Clips:\")\n",
    "print(\"-\" * 70)\n",
    "print(f\"{'Model':<30} {'Count':>10} {'Fraction':>15}\")\n",
    "print(\"-\" * 70)\n",
    "for model, count in model_counts.items():\n",
    "    fraction = count / total_count\n",
    "    print(f\"{model:<30} {count:>10,d} {fraction:>15.2%}\")\n",
    "print(\"-\" * 70)\n",
    "print(f\"{'Total':<30} {total_count:>10,d} {1:>15.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:09.198504Z",
     "start_time": "2024-05-26T00:25:08.885507Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:48.791451Z",
     "iopub.status.busy": "2025-07-28T14:39:48.791238Z",
     "iopub.status.idle": "2025-07-28T14:39:49.582452Z",
     "shell.execute_reply": "2025-07-28T14:39:49.581937Z",
     "shell.execute_reply.started": "2025-07-28T14:39:48.791437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation passed!\n"
     ]
    }
   ],
   "source": [
    "validate_preference_data(final_interesting_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:49.583154Z",
     "iopub.status.busy": "2025-07-28T14:39:49.582960Z",
     "iopub.status.idle": "2025-07-28T14:39:49.632164Z",
     "shell.execute_reply": "2025-07-28T14:39:49.631738Z",
     "shell.execute_reply.started": "2025-07-28T14:39:49.583141Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in final_interesting_clips for model chirp-v3p5-engine-t-6:\n",
      "0\n",
      "done (638268, 62)\n"
     ]
    }
   ],
   "source": [
    "# Get the number of rows for final_interesting_clips with the specific model\n",
    "row_count = final_interesting_clips[\n",
    "    final_interesting_clips[\"model_name\"] == target_model_name\n",
    "].shape[0]\n",
    "\n",
    "# Print the row count in a nicely formatted way\n",
    "print(f\"Number of rows in final_interesting_clips for model {target_model_name}:\")\n",
    "print(f\"{row_count:,}\")\n",
    "print(\"done\", final_interesting_clips.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For faster processing once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T00:25:10.265241Z",
     "start_time": "2024-05-26T00:25:09.934743Z"
    },
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:49.632799Z",
     "iopub.status.busy": "2025-07-28T14:39:49.632625Z",
     "iopub.status.idle": "2025-07-28T14:39:49.719362Z",
     "shell.execute_reply": "2025-07-28T14:39:49.718867Z",
     "shell.execute_reply.started": "2025-07-28T14:39:49.632787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Unique Users:\n",
      "--------------------\n",
      "458,933\n",
      "--------------------\n",
      "Series([], Name: count, dtype: int64)\n",
      "(0, 52)\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of unique users\n",
    "total_unique_users = clip_df[\"user_id\"].nunique()\n",
    "\n",
    "# Print the result in a nicely formatted way\n",
    "print(\"Total Unique Users:\")\n",
    "print(\"-\" * 20)\n",
    "print(f\"{total_unique_users:,}\")\n",
    "print(\"-\" * 20)\n",
    "\n",
    "# This can take a while cause we have a lot of users...\n",
    "# query = \"\"\"\n",
    "# SELECT *\n",
    "# FROM auth_user\n",
    "# \"\"\"\n",
    "# user_df = pd.read_sql_query(query, engine)\n",
    "# user_df.head()\n",
    "\n",
    "test_user_id = 3\n",
    "print(\n",
    "    clip_df[clip_df[\"user_id\"] == test_user_id][\"created_at\"]\n",
    "    .apply(lambda x: str(x)[:10])\n",
    "    .value_counts()\n",
    ")\n",
    "print(clip_df[clip_df[\"user_id\"] == test_user_id].shape)\n",
    "query = \"\"\"\n",
    "SELECT *\n",
    "FROM auth_user\n",
    "WHERE id=3\n",
    "\"\"\"\n",
    "# 3 keenan\n",
    "# 6 martin\n",
    "# 8 tony -- that's me!\n",
    "# 186417 georg\n",
    "# test_user_df = pd.read_sql_query(query, engine)\n",
    "# test_user_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find some weird generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:49.720074Z",
     "iopub.status.busy": "2025-07-28T14:39:49.719879Z",
     "iopub.status.idle": "2025-07-28T14:39:49.739326Z",
     "shell.execute_reply": "2025-07-28T14:39:49.738943Z",
     "shell.execute_reply.started": "2025-07-28T14:39:49.720061Z"
    }
   },
   "outputs": [],
   "source": [
    "# not_known_bot_gens_mask = clip_df[\"model_name\"] != \"chirp-v3p5-engine-b\"\n",
    "# # run_bot_detection(clip_df[not_known_bot_gens_mask], reaction_df, write_to_file=True, cut_off_freq=0.95)\n",
    "# run_bot_detection(\n",
    "#     clip_df,\n",
    "#     reaction_df,\n",
    "#     write_to_file=True,\n",
    "#     cut_off_freq=0.95,\n",
    "#     min_generations_for_no_reaction=4,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alpha testing user selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:49.740041Z",
     "iopub.status.busy": "2025-07-28T14:39:49.739776Z",
     "iopub.status.idle": "2025-07-28T14:39:49.755771Z",
     "shell.execute_reply": "2025-07-28T14:39:49.755410Z",
     "shell.execute_reply.started": "2025-07-28T14:39:49.740029Z"
    }
   },
   "outputs": [],
   "source": [
    "# Alpha testing user selection\n",
    "# we focus on the folks who are good good\n",
    "\n",
    "# # 0526 is v2 -- prod\n",
    "# # 0529 is v4 -- still good IMO, more data\n",
    "# early_v3p5_data = pd.read_csv(\"/home/tony/Data/Preference/13b_v0/interesting_clips_20240529.csv\")\n",
    "\n",
    "# print(\"uqniue users for vp5\", early_v3p5_data[\"user_id\"].nunique())\n",
    "\n",
    "# early_v3_data = pd.read_csv(\"/home/tony/Data/Preference/7b_v0_interesting_clips.csv\")\n",
    "\n",
    "# print(\"uqniue users for v3\", early_v3_data[\"user_id\"].nunique())\n",
    "\n",
    "# early_v2_data = pd.read_csv(\"/home/tony/Data/Preference/3b_v0_interesting_clips.csv\")\n",
    "\n",
    "# print(\"uqniue users for v2\", early_v2_data[\"user_id\"].nunique())\n",
    "\n",
    "# intersection_user_ids_super = set(early_v3p5_data[\"user_id\"].unique()).intersection(set(early_v3_data[\"user_id\"].unique())).intersection(set(early_v2_data[\"user_id\"].unique()))\n",
    "\n",
    "# intersection_user_ids_v3_on = set(early_v3p5_data[\"user_id\"].unique()).intersection(set(early_v3_data[\"user_id\"].unique())).difference(intersection_user_ids_super)\n",
    "\n",
    "# print(len(intersection_user_ids_super), len(intersection_user_ids_v3_on))\n",
    "\n",
    "# super_user_df = user_df[user_df[\"id\"].isin(intersection_user_ids_super)].copy()\n",
    "# print(super_user_df.shape)\n",
    "# v3_onward_user_df = user_df[user_df[\"id\"].isin(intersection_user_ids_v3_on)].copy()\n",
    "# print(v3_onward_user_df.shape)\n",
    "# super_user_df.to_csv(\"/home/tony/Data/Preference/alpha_users/super_user.csv\", index=False)\n",
    "# v3_onward_user_df.to_csv(\"/home/tony/Data/Preference/alpha_users/v3_onward_user.csv\", index=False)\n",
    "# print(\"Done!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User generated clips lifetime filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:49.756416Z",
     "iopub.status.busy": "2025-07-28T14:39:49.756255Z",
     "iopub.status.idle": "2025-07-28T14:39:49.770846Z",
     "shell.execute_reply": "2025-07-28T14:39:49.770496Z",
     "shell.execute_reply.started": "2025-07-28T14:39:49.756404Z"
    }
   },
   "outputs": [],
   "source": [
    "# query = \"\"\"\n",
    "# SELECT *\n",
    "# FROM bots_userstats\n",
    "# WHERE total_clips>=100\n",
    "# \"\"\"\n",
    "# user_stats_df = pd.read_sql_query(query, engine)\n",
    "# print(user_stats_df.shape)\n",
    "# user_stats_df[\"total_clips\"].describe()\n",
    "# top_users = user_stats_df[user_stats_df[\"total_clips\"] >= 100][\"user_id\"].unique()\n",
    "# print(len(top_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:49.771517Z",
     "iopub.status.busy": "2025-07-28T14:39:49.771259Z",
     "iopub.status.idle": "2025-07-28T14:39:52.177962Z",
     "shell.execute_reply": "2025-07-28T14:39:52.177448Z",
     "shell.execute_reply.started": "2025-07-28T14:39:49.771505Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "331785\n"
     ]
    }
   ],
   "source": [
    "# be careful for single clip filters\n",
    "top_users = clip_df[clip_df[\"user_n_clips\"] >= 4][\"user_id\"].unique()\n",
    "print(len(top_users))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-21T19:35:17.755108Z",
     "iopub.status.busy": "2024-06-21T19:35:17.754937Z",
     "iopub.status.idle": "2024-06-21T19:35:17.774581Z",
     "shell.execute_reply": "2024-06-21T19:35:17.774106Z",
     "shell.execute_reply.started": "2024-06-21T19:35:17.755091Z"
    }
   },
   "source": [
    "# Snow flake access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:52.178658Z",
     "iopub.status.busy": "2025-07-28T14:39:52.178457Z",
     "iopub.status.idle": "2025-07-28T14:39:52.945350Z",
     "shell.execute_reply": "2025-07-28T14:39:52.944852Z",
     "shell.execute_reply.started": "2025-07-28T14:39:52.178645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROD\n"
     ]
    }
   ],
   "source": [
    "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": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:52.946041Z",
     "iopub.status.busy": "2025-07-28T14:39:52.945851Z",
     "iopub.status.idle": "2025-07-28T14:39:53.136500Z",
     "shell.execute_reply": "2025-07-28T14:39:53.135998Z",
     "shell.execute_reply.started": "2025-07-28T14:39:52.946027Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "web: 506466 (79.35%)\n",
      "ios: 80650 (12.64%)\n",
      "android: 51152 (8.01%)\n"
     ]
    }
   ],
   "source": [
    "print_out_value_counts_nicely(final_interesting_clips, \"source\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:53.137298Z",
     "iopub.status.busy": "2025-07-28T14:39:53.137106Z",
     "iopub.status.idle": "2025-07-28T14:39:53.179696Z",
     "shell.execute_reply": "2025-07-28T14:39:53.179258Z",
     "shell.execute_reply.started": "2025-07-28T14:39:53.137284Z"
    }
   },
   "outputs": [],
   "source": [
    "def analyze_clip_data_with_snowflake(\n",
    "    final_interesting_clips, target_model_name, top_users, snow_session, min_play_cut=5\n",
    "):\n",
    "    # select the df we want to squery for play counts\n",
    "    subset_v4_clips_df_full = final_interesting_clips[\n",
    "        final_interesting_clips[\"model_name\"] == target_model_name\n",
    "    ].copy()\n",
    "    print(\"match model\", subset_v4_clips_df_full.shape)\n",
    "\n",
    "    pre_play_duration_mask = (\n",
    "        subset_v4_clips_df_full[\"preference\"]\n",
    "        & (subset_v4_clips_df_full[\"user_id\"].isin(top_users))\n",
    "        & (\n",
    "            (subset_v4_clips_df_full[\"reaction_play_count\"] >= min_play_cut)\n",
    "            | (subset_v4_clips_df_full[\"concat_play_counts\"] >= min_play_cut)\n",
    "            | (\n",
    "                subset_v4_clips_df_full[\"upvote_count\"] >= 1\n",
    "            )  # positive signal leakage (strongest)\n",
    "        )\n",
    "    ) | (\n",
    "        (~subset_v4_clips_df_full[\"preference\"])\n",
    "        & (subset_v4_clips_df_full[\"user_id\"].isin(top_users))\n",
    "    )\n",
    "    subset_v4_clips_df_all = subset_v4_clips_df_full[pre_play_duration_mask].copy()\n",
    "    print(subset_v4_clips_df_all.shape)\n",
    "\n",
    "    # Filter for pairs\n",
    "    pair_request_mask = subset_v4_clips_df_all[\"request_id\"].isin(\n",
    "        subset_v4_clips_df_all[\"request_id\"]\n",
    "        .value_counts()\n",
    "        .index[subset_v4_clips_df_all[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "    subset_v4_clips_df = subset_v4_clips_df_all[pair_request_mask].copy()\n",
    "    print(subset_v4_clips_df.shape)\n",
    "\n",
    "    # Get clip IDs and query Snowflake in batches\n",
    "    v4_clip_ids = list(str(s) for s in subset_v4_clips_df[\"id\"].unique())\n",
    "    snow_batch_size = 100_000\n",
    "    snow_results = []\n",
    "\n",
    "    for clip_ids_chunk in tqdm.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 *\n",
    "            from ML_SONG_SUMMARY_INFO\n",
    "            where p_date = DATE(SYSDATE() - INTERVAL '3 HOUR')\n",
    "            and p_hour = hour(SYSDATE() - INTERVAL '3 HOUR')\n",
    "            and song_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",
    "\n",
    "    # Process Snowflake 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",
    "\n",
    "    # Merge data and calculate normalized play fractions\n",
    "    subset_v4_clips_df[\"str_id\"] = subset_v4_clips_df[\"id\"].astype(str)\n",
    "    subset_v4_clips_df_test = subset_v4_clips_df.merge(\n",
    "        df_snow_test, on=\"str_id\", how=\"left\"\n",
    "    )\n",
    "    subset_v4_clips_df_test[\"norm_play_frac\"] = (\n",
    "        subset_v4_clips_df_test[\"sum_total_play_duration_5\"].fillna(0)\n",
    "        / subset_v4_clips_df_test[\"duration\"]\n",
    "    )\n",
    "\n",
    "    # Create visualization\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "    # First subplot: Total play duration\n",
    "    pos_play_time = subset_v4_clips_df_test[subset_v4_clips_df_test[\"preference\"]][\n",
    "        \"sum_total_play_duration_5\"\n",
    "    ]\n",
    "    neg_play_time = subset_v4_clips_df_test[~subset_v4_clips_df_test[\"preference\"]][\n",
    "        \"sum_total_play_duration_5\"\n",
    "    ]\n",
    "\n",
    "    pos_play_time.hist(\n",
    "        bins=np.linspace(0, 400, 100),\n",
    "        alpha=0.5,\n",
    "        label=f\"pos (mean={pos_play_time.mean():.2f}, median={pos_play_time.median():.2f})\",\n",
    "        ax=ax1,\n",
    "    )\n",
    "    neg_play_time.hist(\n",
    "        bins=np.linspace(0, 400, 100),\n",
    "        alpha=0.5,\n",
    "        label=f\"neg (mean={neg_play_time.mean():.2f}, median={neg_play_time.median():.2f})\",\n",
    "        ax=ax1,\n",
    "    )\n",
    "    ax1.legend()\n",
    "    ax1.set_xlabel(\"Total play duration in seconds\")\n",
    "    ax1.set_ylabel(\"counts\")\n",
    "    ax1.set_title(\"Play duration comparison\")\n",
    "\n",
    "    # Second subplot: Normalized play fraction\n",
    "    pos_norm_play_frac = subset_v4_clips_df_test[subset_v4_clips_df_test[\"preference\"]][\n",
    "        \"norm_play_frac\"\n",
    "    ]\n",
    "    neg_norm_play_frac = subset_v4_clips_df_test[\n",
    "        ~subset_v4_clips_df_test[\"preference\"]\n",
    "    ][\"norm_play_frac\"]\n",
    "\n",
    "    pos_norm_play_frac.hist(\n",
    "        bins=np.linspace(0, 10, 100),\n",
    "        alpha=0.5,\n",
    "        label=f\"pos (mean={pos_norm_play_frac.mean():.2f}, median={pos_norm_play_frac.median():.2f})\",\n",
    "        ax=ax2,\n",
    "    )\n",
    "    neg_norm_play_frac.hist(\n",
    "        bins=np.linspace(0, 10, 100),\n",
    "        alpha=0.5,\n",
    "        label=f\"neg (mean={neg_norm_play_frac.mean():.2f}, median={neg_norm_play_frac.median():.2f})\",\n",
    "        ax=ax2,\n",
    "    )\n",
    "    ax2.legend()\n",
    "    ax2.set_xlabel(\"Normalized play counts (play duration/duration)\")\n",
    "    ax2.set_ylabel(\"Log counts\")\n",
    "    ax2.set_yscale(\"log\")\n",
    "    ax2.set_title(\"Normalized play duration comparison (Log scale)\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Apply filters and analyze results\n",
    "    play_duration_mask = (\n",
    "        subset_v4_clips_df_test[\"preference\"]\n",
    "        & (subset_v4_clips_df_test[\"norm_play_frac\"] >= 0.95)\n",
    "        & (subset_v4_clips_df_test[\"sum_total_play_duration_5\"] >= 10)\n",
    "        & (subset_v4_clips_df_test[\"user_id\"].isin(top_users))\n",
    "        & (\n",
    "            (\n",
    "                subset_v4_clips_df_test[\"reaction_play_count\"] >= min_play_cut\n",
    "            )  # used to be 3 -- increase to 5\n",
    "            | (\n",
    "                subset_v4_clips_df_test[\"concat_play_counts\"] >= min_play_cut\n",
    "            )  # used to be 3 -- increase to 5\n",
    "            | (\n",
    "                subset_v4_clips_df_test[\"upvote_count\"] >= 1\n",
    "            )  # positive signal leakage (strongest)\n",
    "        )\n",
    "    ) | (\n",
    "        (~subset_v4_clips_df_test[\"preference\"])\n",
    "        & (subset_v4_clips_df_test[\"norm_play_frac\"] <= 3.1)\n",
    "        & (subset_v4_clips_df_test[\"sum_total_play_duration_5\"] >= 10)\n",
    "        & (subset_v4_clips_df_test[\"user_id\"].isin(top_users))\n",
    "    )\n",
    "\n",
    "    # Calculate and print statistics\n",
    "    frac_pass_play_duration = (\n",
    "        play_duration_mask.sum() / subset_v4_clips_df_test.shape[0]\n",
    "    )\n",
    "    print(\n",
    "        f\"Fraction of clips that pass the play duration cut: {frac_pass_play_duration:.4f}\"\n",
    "    )\n",
    "\n",
    "    unique_requests_pass_play_durations = subset_v4_clips_df_test[play_duration_mask][\n",
    "        \"request_id\"\n",
    "    ].unique()\n",
    "    print(\n",
    "        f\"Number of unique requests passing play duration criteria: {len(unique_requests_pass_play_durations)}\"\n",
    "    )\n",
    "\n",
    "    fraction_requests_pass = (\n",
    "        len(unique_requests_pass_play_durations)\n",
    "        / subset_v4_clips_df_test[\"request_id\"].nunique()\n",
    "    )\n",
    "    print(\n",
    "        f\"Fraction of unique requests that pass play duration criteria: {fraction_requests_pass:.4f}\"\n",
    "    )\n",
    "\n",
    "    # Final filtering and analysis\n",
    "    subset_v4_clips_df_pass_duration = subset_v4_clips_df_test[\n",
    "        play_duration_mask\n",
    "    ].copy()\n",
    "    play_duration_mask_request_mask = subset_v4_clips_df_pass_duration[\n",
    "        \"request_id\"\n",
    "    ].isin(\n",
    "        subset_v4_clips_df_pass_duration[\"request_id\"]\n",
    "        .value_counts()\n",
    "        .index[subset_v4_clips_df_pass_duration[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "    final_subset_v4_clips_df = subset_v4_clips_df_pass_duration[\n",
    "        play_duration_mask_request_mask\n",
    "    ].copy()\n",
    "\n",
    "    unique_request_count = final_subset_v4_clips_df[\"request_id\"].nunique()\n",
    "    print(\n",
    "        f\"Number of unique request IDs: {unique_request_count:,}, Total {subset_v4_clips_df_test['request_id'].nunique()}\"\n",
    "    )\n",
    "\n",
    "    print(\"Start --------------------------\")\n",
    "    print_out_value_counts_nicely(subset_v4_clips_df_test, \"task\")\n",
    "    print(\"End --------------------------\")\n",
    "    print_out_value_counts_nicely(final_subset_v4_clips_df, \"task\")\n",
    "\n",
    "    return final_subset_v4_clips_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:53.180360Z",
     "iopub.status.busy": "2025-07-28T14:39:53.180187Z",
     "iopub.status.idle": "2025-07-28T14:39:53.198905Z",
     "shell.execute_reply": "2025-07-28T14:39:53.198528Z",
     "shell.execute_reply.started": "2025-07-28T14:39:53.180348Z"
    }
   },
   "outputs": [],
   "source": [
    "# for x in final_interesting_clips[final_interesting_clips[\"model_name\"] == \"chirp-v5-stem-v0\"][[\"id\"]].values:\n",
    "#     print(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:53.199587Z",
     "iopub.status.busy": "2025-07-28T14:39:53.199348Z",
     "iopub.status.idle": "2025-07-28T14:39:53.242104Z",
     "shell.execute_reply": "2025-07-28T14:39:53.241679Z",
     "shell.execute_reply.started": "2025-07-28T14:39:53.199574Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-auk-t0: 638268 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "print_out_value_counts_nicely(final_interesting_clips, \"model_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:53.242827Z",
     "iopub.status.busy": "2025-07-28T14:39:53.242553Z",
     "iopub.status.idle": "2025-07-28T14:39:53.259212Z",
     "shell.execute_reply": "2025-07-28T14:39:53.258863Z",
     "shell.execute_reply.started": "2025-07-28T14:39:53.242814Z"
    }
   },
   "outputs": [],
   "source": [
    "# # short cut\n",
    "# end_cutoff_date = pd.to_datetime(\"2025-06-14\", utc=True)\n",
    "# final_interesting_clips[\"created_at\"] = pd.to_datetime(final_interesting_clips[\"created_at\"], utc=True)\n",
    "# final_interesting_clips = final_interesting_clips[final_interesting_clips[\"created_at\"] >= end_cutoff_date].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:53.259877Z",
     "iopub.status.busy": "2025-07-28T14:39:53.259615Z",
     "iopub.status.idle": "2025-07-28T14:39:53.273883Z",
     "shell.execute_reply": "2025-07-28T14:39:53.273532Z",
     "shell.execute_reply.started": "2025-07-28T14:39:53.259865Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_auk_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-auk-t1\", top_users, snow_session, min_play_cut=5\n",
    "#     # final_interesting_clips, \"chirp-auk-t1\", top_users, snow_session, min_play_cut=10\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:39:53.274505Z",
     "iopub.status.busy": "2025-07-28T14:39:53.274304Z",
     "iopub.status.idle": "2025-07-28T14:44:46.762933Z",
     "shell.execute_reply": "2025-07-28T14:44:46.762422Z",
     "shell.execute_reply.started": "2025-07-28T14:39:53.274494Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "match model (638268, 62)\n",
      "(592154, 62)\n",
      "(592154, 62)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                                           | 0/6 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|███████████████████▏                                                                                               | 1/6 [00:51<04:19, 51.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|██████████████████████████████████████▎                                                                            | 2/6 [01:37<03:13, 48.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████████████████████████████████████████████████████████▌                                                         | 3/6 [02:21<02:18, 46.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|████████████████████████████████████████████████████████████████████████████▋                                      | 4/6 [03:06<01:31, 45.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 100000\n",
      "Length of the ID query string: 3899999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|███████████████████████████████████████████████████████████████████████████████████████████████▊                   | 5/6 [03:56<00:47, 47.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 92154\n",
      "Length of the ID query string: 3594005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [04:45<00:00, 47.60s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "Shape of df_snow_test:\n",
      "Rows: 579123\n",
      "Columns: 27\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of clips that pass the play duration cut: 0.7469\n",
      "Number of unique requests passing play duration criteria: 272229\n",
      "Fraction of unique requests that pass play duration criteria: 0.9195\n",
      "Number of unique request IDs: 170,061, Total 296077\n",
      "Start --------------------------\n",
      ": 373714 (63.11%)\n",
      "cover: 103608 (17.50%)\n",
      "artist_consistency: 46792 (7.90%)\n",
      "extend: 31358 (5.30%)\n",
      "artist_cover: 19390 (3.27%)\n",
      "upload_extend: 13432 (2.27%)\n",
      "artist_extend: 3854 (0.65%)\n",
      "overpainting: 4 (0.00%)\n",
      "underpainting: 2 (0.00%)\n",
      "End --------------------------\n",
      ": 222668 (65.47%)\n",
      "cover: 55018 (16.18%)\n",
      "artist_consistency: 30994 (9.11%)\n",
      "artist_cover: 11798 (3.47%)\n",
      "extend: 11704 (3.44%)\n",
      "upload_extend: 6020 (1.77%)\n",
      "artist_extend: 1918 (0.56%)\n",
      "underpainting: 2 (0.00%)\n"
     ]
    }
   ],
   "source": [
    "# for this we eat the quality cost and get a bit more data\n",
    "final_subset_auk_og_clips_df = analyze_clip_data_with_snowflake(\n",
    "    final_interesting_clips, \"chirp-auk-t0\", top_users, snow_session, min_play_cut=3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:46.763847Z",
     "iopub.status.busy": "2025-07-28T14:44:46.763470Z",
     "iopub.status.idle": "2025-07-28T14:44:48.050264Z",
     "shell.execute_reply": "2025-07-28T14:44:48.049762Z",
     "shell.execute_reply.started": "2025-07-28T14:44:46.763833Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_upsample_diff_v1_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-v4-up-u-7\", top_users, snow_session, min_play_cut=5\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.050985Z",
     "iopub.status.busy": "2025-07-28T14:44:48.050786Z",
     "iopub.status.idle": "2025-07-28T14:44:48.069979Z",
     "shell.execute_reply": "2025-07-28T14:44:48.069613Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.050972Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_upsample_diff_v1_df.to_pickle(\n",
    "#     f\"/home/tony/Data/Preference/up_v2_d3/interesting_clips_diff_v1_20250528.pkl\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.070494Z",
     "iopub.status.busy": "2025-07-28T14:44:48.070384Z",
     "iopub.status.idle": "2025-07-28T14:44:48.084637Z",
     "shell.execute_reply": "2025-07-28T14:44:48.084284Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.070484Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_auk_infill_30b_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-auk-infill\", top_users, snow_session, min_play_cut=3\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.085294Z",
     "iopub.status.busy": "2025-07-28T14:44:48.085059Z",
     "iopub.status.idle": "2025-07-28T14:44:48.099589Z",
     "shell.execute_reply": "2025-07-28T14:44:48.099240Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.085282Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_stem_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-v5-stem-v0\", top_users, snow_session, min_play_cut=1\n",
    "# )\n",
    "# final_subset_upsample_ahi_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-ahi-up-1\", top_users, snow_session, min_play_cut=5\n",
    "# )\n",
    "# final_subset_upsample_ahi_clips_df_2 = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-v4-up-u-d-2-3\", top_users, snow_session, min_play_cut=5\n",
    "# )\n",
    "# final_subset_auk_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-auk-t1\", top_users, snow_session, min_play_cut=5\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.105419Z",
     "iopub.status.busy": "2025-07-28T14:44:48.105127Z",
     "iopub.status.idle": "2025-07-28T14:44:48.120178Z",
     "shell.execute_reply": "2025-07-28T14:44:48.119814Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.105406Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_upsample_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-v4-up-u-7\", top_users, snow_session, min_play_cut=5\n",
    "# )\n",
    "# final_subset_s32_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-v4-h-s-32\", top_users, snow_session, min_play_cut=5\n",
    "# )\n",
    "# final_subset_t6_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-v4-h-t-6\", top_users, snow_session, min_play_cut=5\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.120852Z",
     "iopub.status.busy": "2025-07-28T14:44:48.120587Z",
     "iopub.status.idle": "2025-07-28T14:44:48.134999Z",
     "shell.execute_reply": "2025-07-28T14:44:48.134649Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.120841Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_upsample_ahi_clips_df = analyze_clip_data_with_snowflake(\n",
    "#     final_interesting_clips, \"chirp-ahi-up-2\", top_users, snow_session, min_play_cut=5\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.135656Z",
     "iopub.status.busy": "2025-07-28T14:44:48.135406Z",
     "iopub.status.idle": "2025-07-28T14:44:48.150343Z",
     "shell.execute_reply": "2025-07-28T14:44:48.149995Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.135644Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(\"auk\", final_subset_auk_clips_df.shape)\n",
    "# print(\"ahi\", final_subset_upsample_ahi_clips_df.shape)\n",
    "# print(\"ahi sneaked\", final_subset_upsample_ahi_clips_df_2.shape)\n",
    "# print(\"diff stem\", final_subset_stem_clips_df.shape)\n",
    "# cut at 5\n",
    "# s-32 (1221770, 95)\n",
    "# t-6 (409626, 95)\n",
    "# upsample (211348, 95)\n",
    "# vs cut at 10\n",
    "# s-32 (712578, 96)\n",
    "# t-6 (258662, 96)\n",
    "# upsample (80826, 96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.150832Z",
     "iopub.status.busy": "2025-07-28T14:44:48.150726Z",
     "iopub.status.idle": "2025-07-28T14:44:48.165776Z",
     "shell.execute_reply": "2025-07-28T14:44:48.165417Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.150821Z"
    }
   },
   "outputs": [],
   "source": [
    "# print_out_value_counts_nicely(final_subset_auk_clips_df, \"source\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.166445Z",
     "iopub.status.busy": "2025-07-28T14:44:48.166203Z",
     "iopub.status.idle": "2025-07-28T14:44:48.180720Z",
     "shell.execute_reply": "2025-07-28T14:44:48.180361Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.166433Z"
    }
   },
   "outputs": [],
   "source": [
    "# total_ahi_df = pd.concat([final_subset_upsample_ahi_clips_df, final_subset_upsample_ahi_clips_df_2])\n",
    "# total_ahi_df = final_subset_upsample_ahi_clips_df.copy()\n",
    "# print(\"total ahi\", total_ahi_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.181375Z",
     "iopub.status.busy": "2025-07-28T14:44:48.181137Z",
     "iopub.status.idle": "2025-07-28T14:44:48.195864Z",
     "shell.execute_reply": "2025-07-28T14:44:48.195508Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.181362Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_subset_auk_clips_df.to_pickle(\n",
    "#     \"/home/tony/Data/Preference/auk_t1/interesting_clips_auk_t1_20250502.pkl\",\n",
    "# )\n",
    "# print(\"auk_t1\", final_subset_auk_clips_df.shape)\n",
    "# total_ahi_df.to_pickle(\n",
    "#     \"/home/tony/Data/Preference/up_v2_d3/interesting_clips_ahi_d3_20250502.pkl\",\n",
    "# )\n",
    "# print(\"ahi_d3\", total_ahi_df.shape)\n",
    "# print(\"Saving done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task usage stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.196517Z",
     "iopub.status.busy": "2025-07-28T14:44:48.196273Z",
     "iopub.status.idle": "2025-07-28T14:44:48.498006Z",
     "shell.execute_reply": "2025-07-28T14:44:48.497554Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.196505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "task\n",
       "                       4384513\n",
       "cover                  1237384\n",
       "artist_consistency      446588\n",
       "extend                  235313\n",
       "artist_cover            186988\n",
       "upload_extend           178959\n",
       "artist_extend            32016\n",
       "cover_extend                34\n",
       "playlist_condition          24\n",
       "artist_cover_extend         22\n",
       "underpainting               10\n",
       "overpainting                 6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[\"task\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.498766Z",
     "iopub.status.busy": "2025-07-28T14:44:48.498515Z",
     "iopub.status.idle": "2025-07-28T14:44:48.518322Z",
     "shell.execute_reply": "2025-07-28T14:44:48.517934Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.498752Z"
    }
   },
   "outputs": [],
   "source": [
    "# plot_clip_distribution(clip_df[(clip_df[\"task\"] == \"cover\") & (clip_df[\"model_name\"] == \"chirp-v4-h-s-32\")& (clip_df[\"created_at\"] > \"2025-04-01 00:45:00\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.519080Z",
     "iopub.status.busy": "2025-07-28T14:44:48.518770Z",
     "iopub.status.idle": "2025-07-28T14:44:48.534241Z",
     "shell.execute_reply": "2025-07-28T14:44:48.533882Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.519065Z"
    }
   },
   "outputs": [],
   "source": [
    "# bad_clip_df = clip_df[(clip_df[\"task\"] == \"cover\") & (clip_df[\"model_name\"] == \"chirp-v4-h-s-32\")& (clip_df[\"created_at\"] > \"2025-04-01 00:45:00\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:48.534907Z",
     "iopub.status.busy": "2025-07-28T14:44:48.534669Z",
     "iopub.status.idle": "2025-07-28T14:44:54.973373Z",
     "shell.execute_reply": "2025-07-28T14:44:54.972864Z",
     "shell.execute_reply.started": "2025-07-28T14:44:48.534895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cover usage: 122079 out of 362048 (0.3372) \n",
      " -------------->\n",
      "chirp-auk-t0: 1237384 (100.00%)\n",
      "\n",
      " --------------------------\n",
      "infill usage: 0 out of 362048 (0.0) \n",
      " -------------->\n",
      "\n",
      " --------------------------\n",
      "artist usage: 65705 out of 362048 (0.1815) \n",
      " -------------->\n",
      "chirp-auk-t0: 446588 (100.00%)\n",
      "\n",
      " --------------------------\n",
      "image/video usage: 0 out of 362048 (0.0) \n",
      " -------------->\n",
      "\n",
      " --------------------------\n"
     ]
    }
   ],
   "source": [
    "n_pro_created = clip_df[clip_df[\"is_pro_user\"]][\"user_id\"].nunique()\n",
    "task_mask_cover = clip_df[\"task\"] == \"cover\"\n",
    "task_mask_artist = clip_df[\"task\"] == \"artist_consistency\"\n",
    "task_mask_infill = (\n",
    "    (clip_df[\"task\"] == \"infill\")\n",
    "    | (clip_df[\"task\"] == \"infill_intro\")\n",
    "    | (clip_df[\"task\"] == \"infill_outro\")\n",
    ")\n",
    "task_mask_image = (clip_df[\"task\"] == \"image_to_song\") | (\n",
    "    clip_df[\"task\"] == \"video_to_song\"\n",
    ")\n",
    "\n",
    "\n",
    "def print_task_usage_stats(clip_df, task_mask, task_name, n_pro_created):\n",
    "    n_created = clip_df[task_mask][\"user_id\"].nunique()\n",
    "    print(\n",
    "        f\"{task_name} usage: {n_created} out of {n_pro_created} ({round(n_created / n_pro_created, 4)})\",\n",
    "        \"\\n\",\n",
    "        \"-------------->\",\n",
    "    )\n",
    "    print_out_value_counts_nicely(clip_df[task_mask], \"model_name\")\n",
    "    print(\"\\n\", \"--------------------------\")\n",
    "\n",
    "\n",
    "print_task_usage_stats(clip_df, task_mask_cover, \"cover\", n_pro_created)\n",
    "print_task_usage_stats(clip_df, task_mask_infill, \"infill\", n_pro_created)\n",
    "print_task_usage_stats(clip_df, task_mask_artist, \"artist\", n_pro_created)\n",
    "print_task_usage_stats(clip_df, task_mask_image, \"image/video\", n_pro_created)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:54.974051Z",
     "iopub.status.busy": "2025-07-28T14:44:54.973914Z",
     "iopub.status.idle": "2025-07-28T14:44:59.463720Z",
     "shell.execute_reply": "2025-07-28T14:44:59.463194Z",
     "shell.execute_reply.started": "2025-07-28T14:44:54.974038Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "any task usage: 158100 out of 362048 (0.4367) \n",
      " -------------->\n",
      "chirp-auk-t0: 1848982 (100.00%)\n",
      "\n",
      " --------------------------\n"
     ]
    }
   ],
   "source": [
    "task_mask_any = (\n",
    "    (clip_df[\"task\"] == \"infill\")\n",
    "    | (clip_df[\"task\"] == \"infill_intro\")\n",
    "    | (clip_df[\"task\"] == \"infill_outro\")\n",
    "    | (clip_df[\"task\"] == \"cover\")\n",
    "    | (clip_df[\"task\"] == \"artist_consistency\")\n",
    "    | (clip_df[\"task\"] == \"extend\")\n",
    "    | (clip_df[\"task\"] == \"upload_extend\")\n",
    "    | (clip_df[\"task\"] == \"upsample\")\n",
    ") & (clip_df[\"is_pro_user\"])\n",
    "print_task_usage_stats(clip_df, task_mask_any, \"any task\", n_pro_created)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:44:59.464496Z",
     "iopub.status.busy": "2025-07-28T14:44:59.464268Z",
     "iopub.status.idle": "2025-07-28T14:45:02.641503Z",
     "shell.execute_reply": "2025-07-28T14:45:02.640977Z",
     "shell.execute_reply.started": "2025-07-28T14:44:59.464483Z"
    }
   },
   "outputs": [],
   "source": [
    "special_users = set(clip_df[clip_df[\"is_pro_user\"]][\"user_id\"].unique()).difference(\n",
    "    clip_df[task_mask_any][\"user_id\"].unique()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:02.642252Z",
     "iopub.status.busy": "2025-07-28T14:45:02.642036Z",
     "iopub.status.idle": "2025-07-28T14:45:02.662122Z",
     "shell.execute_reply": "2025-07-28T14:45:02.661737Z",
     "shell.execute_reply.started": "2025-07-28T14:45:02.642239Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "203948\n"
     ]
    }
   ],
   "source": [
    "print(len(special_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:02.662842Z",
     "iopub.status.busy": "2025-07-28T14:45:02.662550Z",
     "iopub.status.idle": "2025-07-28T14:45:02.795841Z",
     "shell.execute_reply": "2025-07-28T14:45:02.795333Z",
     "shell.execute_reply.started": "2025-07-28T14:45:02.662830Z"
    }
   },
   "outputs": [],
   "source": [
    "potential_special_bot_mask = clip_df[\"user_id\"].isin(special_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:02.796537Z",
     "iopub.status.busy": "2025-07-28T14:45:02.796347Z",
     "iopub.status.idle": "2025-07-28T14:45:03.571777Z",
     "shell.execute_reply": "2025-07-28T14:45:03.571343Z",
     "shell.execute_reply.started": "2025-07-28T14:45:02.796524Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    203948.000000\n",
       "mean          8.838650\n",
       "std          14.082759\n",
       "min           1.000000\n",
       "25%           2.000000\n",
       "50%           4.000000\n",
       "75%          10.000000\n",
       "max         973.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[potential_special_bot_mask][\"user_id\"].value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:03.572483Z",
     "iopub.status.busy": "2025-07-28T14:45:03.572278Z",
     "iopub.status.idle": "2025-07-28T14:45:04.355471Z",
     "shell.execute_reply": "2025-07-28T14:45:04.355034Z",
     "shell.execute_reply.started": "2025-07-28T14:45:03.572470Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "91182204    973\n",
       "73147386    510\n",
       "86420847    453\n",
       "75632151    424\n",
       "31759494    411\n",
       "           ... \n",
       "22777491      1\n",
       "46966755      1\n",
       "48290058      1\n",
       "87216452      1\n",
       "98395004      1\n",
       "Name: count, Length: 203948, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[potential_special_bot_mask][\"user_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:04.356361Z",
     "iopub.status.busy": "2025-07-28T14:45:04.355959Z",
     "iopub.status.idle": "2025-07-28T14:45:04.377403Z",
     "shell.execute_reply": "2025-07-28T14:45:04.377040Z",
     "shell.execute_reply.started": "2025-07-28T14:45:04.356347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: count, dtype: int64)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[task_mask_image][\"user_id\"].value_counts().head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:04.378116Z",
     "iopub.status.busy": "2025-07-28T14:45:04.377835Z",
     "iopub.status.idle": "2025-07-28T14:45:04.986867Z",
     "shell.execute_reply": "2025-07-28T14:45:04.986426Z",
     "shell.execute_reply.started": "2025-07-28T14:45:04.378104Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "17457824    878\n",
       "19585217    853\n",
       "15836361    701\n",
       "41410106    637\n",
       "87187524    615\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[task_mask_cover][\"user_id\"].value_counts().head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:04.987586Z",
     "iopub.status.busy": "2025-07-28T14:45:04.987376Z",
     "iopub.status.idle": "2025-07-28T14:45:05.007255Z",
     "shell.execute_reply": "2025-07-28T14:45:05.006881Z",
     "shell.execute_reply.started": "2025-07-28T14:45:04.987572Z"
    }
   },
   "outputs": [],
   "source": [
    "# v4_clip_ids = list(str(s) for s in clip_df[\"s3_id\"].unique())\n",
    "# snow_batch_size = 100_000\n",
    "# snow_results = []\n",
    "\n",
    "# for clip_ids_chunk in tqdm.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 *\n",
    "#         from ML_SONG_SUMMARY_INFO\n",
    "#         where p_date = DATE(SYSDATE() - INTERVAL '2 HOUR')\n",
    "#         and p_hour = hour(SYSDATE() - INTERVAL '2 HOUR')\n",
    "#         and song_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",
    "\n",
    "# # Process Snowflake 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_snow_test[\"clip_id\"] = df_snow_test[\"str_id\"]\n",
    "# run_bot_detection(\n",
    "#     clip_df,\n",
    "#     df_snow_test[df_snow_test[\"total_play_time\"] >= 5],\n",
    "#     write_to_file=True,\n",
    "#     cut_off_freq=0.95,\n",
    "#     min_generations_for_no_reaction=10,\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.007942Z",
     "iopub.status.busy": "2025-07-28T14:45:05.007675Z",
     "iopub.status.idle": "2025-07-28T14:45:05.022509Z",
     "shell.execute_reply": "2025-07-28T14:45:05.022157Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.007929Z"
    }
   },
   "outputs": [],
   "source": [
    "# total_clip_df[\"is_pro_user\"] = total_clip_df[\"user_id\"].isin(pro_users)\n",
    "# run_bot_detection(\n",
    "#     total_clip_df,\n",
    "#     reaction_df,\n",
    "#     write_to_file=True,\n",
    "#     cut_off_freq=0.95,\n",
    "#     min_generations_for_no_reaction=6,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Infill test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.023168Z",
     "iopub.status.busy": "2025-07-28T14:45:05.022934Z",
     "iopub.status.idle": "2025-07-28T14:45:05.037655Z",
     "shell.execute_reply": "2025-07-28T14:45:05.037307Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.023156Z"
    }
   },
   "outputs": [],
   "source": [
    "# def get_infill_type(x):\n",
    "#     if max(x[\"infll_start_context\"], x[\"infll_end_context\"]) <= 30:\n",
    "#         return \"short\"\n",
    "#     elif max(x[\"infll_start_context\"], x[\"infll_end_context\"]) <= 60:\n",
    "#         return \"mid\"\n",
    "#     else:\n",
    "#         return \"long\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.038332Z",
     "iopub.status.busy": "2025-07-28T14:45:05.038059Z",
     "iopub.status.idle": "2025-07-28T14:45:05.052759Z",
     "shell.execute_reply": "2025-07-28T14:45:05.052407Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.038320Z"
    }
   },
   "outputs": [],
   "source": [
    "# clip_df_infill_task_mask_infill = (\n",
    "#     (clip_df[\"task\"] == \"infill\")\n",
    "#     | (clip_df[\"task\"] == \"infill_intro\")\n",
    "#     | (clip_df[\"task\"] == \"infill_outro\")\n",
    "# ) & (clip_df[\"created_at\"] >= \"2024-11-06 02:00:00\")\n",
    "# clip_infill_df = clip_df[clip_df_infill_task_mask_infill].copy()\n",
    "# ##\n",
    "# user_intersting_clips_3p5_task_mask_infill = (\n",
    "#     (user_intersting_clips_3p5[\"task\"] == \"infill\")\n",
    "#     | (user_intersting_clips_3p5[\"task\"] == \"infill_intro\")\n",
    "#     | (user_intersting_clips_3p5[\"task\"] == \"infill_outro\")\n",
    "# ) & (user_intersting_clips_3p5[\"created_at\"] >= \"2024-11-06 02:00:00\")\n",
    "# user_intersting_clips_3p5_infill = user_intersting_clips_3p5[\n",
    "#     user_intersting_clips_3p5_task_mask_infill\n",
    "# ].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.053447Z",
     "iopub.status.busy": "2025-07-28T14:45:05.053186Z",
     "iopub.status.idle": "2025-07-28T14:45:05.067584Z",
     "shell.execute_reply": "2025-07-28T14:45:05.067230Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.053435Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_slice_series = clip_infill_df[\"metadata\"].apply(pd.Series)\n",
    "# df = pd.concat([clip_infill_df, test_slice_series], axis=1, join=\"inner\")\n",
    "# print(df.shape)\n",
    "# df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# df[\"infll_start_context\"] = df[\"infill_start_s\"] - df[\"infill_context_start_s\"]\n",
    "# df[\"infll_end_context\"] = df[\"infill_context_end_s\"] - df[\"infill_end_s\"]\n",
    "# df[\"infill_type\"] = df[[\"infll_start_context\", \"infll_end_context\"]].apply(\n",
    "#     lambda x: get_infill_type(x), axis=1\n",
    "# )\n",
    "# clip_df_model_counts = df[\"infill_type\"].value_counts()\n",
    "# print(clip_df_model_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.068246Z",
     "iopub.status.busy": "2025-07-28T14:45:05.067992Z",
     "iopub.status.idle": "2025-07-28T14:45:05.082320Z",
     "shell.execute_reply": "2025-07-28T14:45:05.081970Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.068233Z"
    }
   },
   "outputs": [],
   "source": [
    "# plt.hist(df[\"infill_context_start_s\"], bins=np.linspace(-10, 300, 100))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.083027Z",
     "iopub.status.busy": "2025-07-28T14:45:05.082715Z",
     "iopub.status.idle": "2025-07-28T14:45:05.097146Z",
     "shell.execute_reply": "2025-07-28T14:45:05.096792Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.083014Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_slice_series = user_intersting_clips_3p5_infill[\"metadata\"].apply(pd.Series)\n",
    "# df = pd.concat([user_intersting_clips_3p5_infill, test_slice_series], axis=1, join=\"inner\")\n",
    "# print(df.shape)\n",
    "# df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# df[\"infll_start_context\"] = df[\"infill_start_s\"]  - df[\"infill_context_start_s\"]\n",
    "# df[\"infll_end_context\"] = df[\"infill_context_end_s\"] -  df[\"infill_end_s\"]\n",
    "# df[\"infill_type\"] = df[[\"infll_start_context\", \"infll_end_context\"]].apply(lambda x:  get_infill_type(x), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.097696Z",
     "iopub.status.busy": "2025-07-28T14:45:05.097591Z",
     "iopub.status.idle": "2025-07-28T14:45:05.112281Z",
     "shell.execute_reply": "2025-07-28T14:45:05.111938Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.097685Z"
    }
   },
   "outputs": [],
   "source": [
    "# plt.hist(df[\"infill_context_start_s\"], bins=np.linspace(-10, 300, 100))\n",
    "# plt.show()\n",
    "# plt.hist(df[\"infill_context_end_s\"], bins=np.linspace(-10, 300, 100))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.112949Z",
     "iopub.status.busy": "2025-07-28T14:45:05.112699Z",
     "iopub.status.idle": "2025-07-28T14:45:05.140132Z",
     "shell.execute_reply": "2025-07-28T14:45:05.139708Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.112937Z"
    }
   },
   "outputs": [],
   "source": [
    "# model_counts = df[df[\"part_of_concat\"]][\"infill_type\"].value_counts()\n",
    "\n",
    "# # Print the results in a nicely formatted way\n",
    "# total_count = model_counts.sum()\n",
    "# print(\"Model Name Value Counts for Preferred Clips:\")\n",
    "# print(\"-\" * 70)\n",
    "# print(f\"{'Model':<30} {'Count':>10} {'Fraction':>15}\")\n",
    "# print(\"-\" * 70)\n",
    "# for model, count in model_counts.items():\n",
    "#     fraction = count / total_count\n",
    "#     print(f\"{model:<30} {count:>10,d} {fraction:>15.2%}\")\n",
    "# print(\"-\" * 70)\n",
    "# print(f\"{'Total':<30} {total_count:>10,d} {1:>15.2%}\")\n",
    "\n",
    "# # Calculate the ratio of preferred clips to total clips for each model\n",
    "# preference_ratio = (\n",
    "#     df[df[\"part_of_concat\"]][\"infill_type\"].value_counts() / clip_df_model_counts\n",
    "# )\n",
    "\n",
    "\n",
    "# # Print the results in a formatted manner\n",
    "# print(\"\\n Ratio of preferred clips to total clips for each model:\")\n",
    "# print(\"-\" * 60)\n",
    "# for model, ratio in preference_ratio.items():\n",
    "#     n = clip_df_model_counts[model]\n",
    "#     uncertainty = (ratio * (1 - ratio) / n) ** 0.5\n",
    "#     print(f\"{model:<30} {ratio:.2%} ± {uncertainty:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other ppl's clip in playlists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:05.140854Z",
     "iopub.status.busy": "2025-07-28T14:45:05.140607Z",
     "iopub.status.idle": "2025-07-28T14:45:10.495649Z",
     "shell.execute_reply": "2025-07-28T14:45:10.495129Z",
     "shell.execute_reply.started": "2025-07-28T14:45:05.140842Z"
    }
   },
   "outputs": [],
   "source": [
    "unique_clips_in_playlist = playlist_clip_df[\"clip_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:10.496389Z",
     "iopub.status.busy": "2025-07-28T14:45:10.496174Z",
     "iopub.status.idle": "2025-07-28T14:45:13.867743Z",
     "shell.execute_reply": "2025-07-28T14:45:13.867209Z",
     "shell.execute_reply.started": "2025-07-28T14:45:10.496375Z"
    }
   },
   "outputs": [],
   "source": [
    "total_clip_id_to_user_id = (\n",
    "    total_clip_df[total_clip_df[\"id\"].isin(unique_clips_in_playlist)]\n",
    "    .set_index(\"s3_id\")[\"user_id\"]\n",
    "    .to_dict()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:13.868608Z",
     "iopub.status.busy": "2025-07-28T14:45:13.868301Z",
     "iopub.status.idle": "2025-07-28T14:45:15.337013Z",
     "shell.execute_reply": "2025-07-28T14:45:15.336499Z",
     "shell.execute_reply.started": "2025-07-28T14:45:13.868593Z"
    }
   },
   "outputs": [],
   "source": [
    "playlist_clip_df[\"clip_user_id\"] = playlist_clip_df[\"clip_id\"].apply(\n",
    "    lambda x: total_clip_id_to_user_id.get(x)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:15.337868Z",
     "iopub.status.busy": "2025-07-28T14:45:15.337565Z",
     "iopub.status.idle": "2025-07-28T14:45:15.357597Z",
     "shell.execute_reply": "2025-07-28T14:45:15.357186Z",
     "shell.execute_reply.started": "2025-07-28T14:45:15.337854Z"
    }
   },
   "outputs": [],
   "source": [
    "# v4_users = clip_df[clip_df[\"model_name\"].str.contains(\"v4\")][\"user_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:15.358144Z",
     "iopub.status.busy": "2025-07-28T14:45:15.358031Z",
     "iopub.status.idle": "2025-07-28T14:45:15.374858Z",
     "shell.execute_reply": "2025-07-28T14:45:15.374491Z",
     "shell.execute_reply.started": "2025-07-28T14:45:15.358132Z"
    }
   },
   "outputs": [],
   "source": [
    "# discord_info_df[discord_info_df[\"user_id\"].isin(v4_users)][[\"user_id\", \"subscription_status\", \"extra_credits_balance\", \"display_name\", \"handle\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:15.375530Z",
     "iopub.status.busy": "2025-07-28T14:45:15.375264Z",
     "iopub.status.idle": "2025-07-28T14:45:15.390318Z",
     "shell.execute_reply": "2025-07-28T14:45:15.389972Z",
     "shell.execute_reply.started": "2025-07-28T14:45:15.375518Z"
    }
   },
   "outputs": [],
   "source": [
    "# bad_ids_dict = run_bot_detection(\n",
    "#     total_clip_df,\n",
    "#     reaction_df,\n",
    "#     write_to_file=False,\n",
    "#     cut_off_freq=0.5,\n",
    "#     min_generations_for_no_reaction=10,\n",
    "#     return_bad_user_ids=True\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:15.391001Z",
     "iopub.status.busy": "2025-07-28T14:45:15.390708Z",
     "iopub.status.idle": "2025-07-28T14:45:15.405082Z",
     "shell.execute_reply": "2025-07-28T14:45:15.404739Z",
     "shell.execute_reply.started": "2025-07-28T14:45:15.390988Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(bad_ids_dict[\"bad_pro_user_ids\"]))\n",
    "# print(len(pro_users))\n",
    "# good_pro_users = set(pro_users).difference(bad_ids_dict[\"bad_pro_user_ids\"])\n",
    "# print(len(good_pro_users))\n",
    "# with open(\"/home/tony/Work/good_pro_user_2024_11_22.json\", \"w\") as fp:\n",
    "#     json.dump([int(x) for x in sorted(good_pro_users)], fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:15.405770Z",
     "iopub.status.busy": "2025-07-28T14:45:15.405483Z",
     "iopub.status.idle": "2025-07-28T14:45:15.420237Z",
     "shell.execute_reply": "2025-07-28T14:45:15.419886Z",
     "shell.execute_reply.started": "2025-07-28T14:45:15.405757Z"
    }
   },
   "outputs": [],
   "source": [
    "# selected_indices = total_clip_df[\"prompt_text\"].apply(lambda x: bool(re.search(r'Wir ziehen durch die Straßen und die Clubs dieser', str(x), re.IGNORECASE)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:15.420717Z",
     "iopub.status.busy": "2025-07-28T14:45:15.420609Z",
     "iopub.status.idle": "2025-07-28T14:45:19.876115Z",
     "shell.execute_reply": "2025-07-28T14:45:19.875602Z",
     "shell.execute_reply.started": "2025-07-28T14:45:15.420706Z"
    }
   },
   "outputs": [],
   "source": [
    "final_interesting_clips[\"model_name\"] = final_interesting_clips.apply(\n",
    "    lambda row: modify_model_name(row[\"model_name\"], row[\"metadata\"]), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:19.876776Z",
     "iopub.status.busy": "2025-07-28T14:45:19.876652Z",
     "iopub.status.idle": "2025-07-28T14:45:19.896405Z",
     "shell.execute_reply": "2025-07-28T14:45:19.896041Z",
     "shell.execute_reply.started": "2025-07-28T14:45:19.876763Z"
    }
   },
   "outputs": [],
   "source": [
    "# requests_with_vol = final_interesting_clips[final_interesting_clips[\"model_name\"].str.contains(\"chirp-v4-h-s-32-u-4-6\")][\"request_id\"].unique()\n",
    "# print(len(requests_with_vol))\n",
    "# vol_final_interesting_clips = final_interesting_clips[final_interesting_clips[\"request_id\"].isin(requests_with_vol)].copy()\n",
    "# get_preference_counts(\n",
    "#     vol_final_interesting_clips,\n",
    "#     title_name=\"subset test\",\n",
    "# )\n",
    "# # vol_final_interesting_clips.to_pickle(\n",
    "# #     \"/home/tony/Data/Preference/13b_v32/interesting_clips_exp_20250219_full.pkl\",\n",
    "# # )\n",
    "# print(\"vol exps\", vol_final_interesting_clips.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find a song with matching lyrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:19.896983Z",
     "iopub.status.busy": "2025-07-28T14:45:19.896872Z",
     "iopub.status.idle": "2025-07-28T14:45:19.911836Z",
     "shell.execute_reply": "2025-07-28T14:45:19.911483Z",
     "shell.execute_reply.started": "2025-07-28T14:45:19.896971Z"
    }
   },
   "outputs": [],
   "source": [
    "# # takes ~ 3 mins\n",
    "# lyrics_session_query = snow_session.sql(\n",
    "#     f\"\"\"select *\n",
    "#     from CLIP\n",
    "#     where REGEXP_LIKE(prompt_text, 'kwaśna.*')\n",
    "#     \"\"\"\n",
    "# )\n",
    "# lyrics_matched_df = pd.DataFrame(lyrics_session_query.collect())\n",
    "# print(lyrics_matched_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:19.912502Z",
     "iopub.status.busy": "2025-07-28T14:45:19.912251Z",
     "iopub.status.idle": "2025-07-28T14:45:19.926864Z",
     "shell.execute_reply": "2025-07-28T14:45:19.926514Z",
     "shell.execute_reply.started": "2025-07-28T14:45:19.912491Z"
    }
   },
   "outputs": [],
   "source": [
    "# lyrics_matched_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find a specific user's creations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:19.927548Z",
     "iopub.status.busy": "2025-07-28T14:45:19.927252Z",
     "iopub.status.idle": "2025-07-28T14:45:19.941819Z",
     "shell.execute_reply": "2025-07-28T14:45:19.941461Z",
     "shell.execute_reply.started": "2025-07-28T14:45:19.927535Z"
    }
   },
   "outputs": [],
   "source": [
    "# user_session_query = snow_session.sql(\n",
    "#     f\"\"\"select *\n",
    "#     from CLIP\n",
    "#     where user_id=62804651\n",
    "#     \"\"\"\n",
    "# )\n",
    "# user_matched_df = pd.DataFrame(user_session_query.collect())\n",
    "# print(user_matched_df.shape)\n",
    "# # user_matched_df.to_csv(\"/home/tony/Data/for_minz_20250202.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:19.942309Z",
     "iopub.status.busy": "2025-07-28T14:45:19.942204Z",
     "iopub.status.idle": "2025-07-28T14:45:21.332337Z",
     "shell.execute_reply": "2025-07-28T14:45:21.331823Z",
     "shell.execute_reply.started": "2025-07-28T14:45:19.942298Z"
    }
   },
   "outputs": [],
   "source": [
    "final_interesting_clips = user_intersting_clips[\n",
    "    user_intersting_clips[\"request_id\"].isin(final_good_enough_requests)\n",
    "].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:21.333075Z",
     "iopub.status.busy": "2025-07-28T14:45:21.332864Z",
     "iopub.status.idle": "2025-07-28T14:45:22.263431Z",
     "shell.execute_reply": "2025-07-28T14:45:22.262911Z",
     "shell.execute_reply.started": "2025-07-28T14:45:21.333062Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 319134\n",
      "differing counts: 0\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 319134, total 319134.\n"
     ]
    }
   ],
   "source": [
    "get_preference_counts(\n",
    "    final_interesting_clips,\n",
    "    title_name=\"subset test\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.264160Z",
     "iopub.status.busy": "2025-07-28T14:45:22.263952Z",
     "iopub.status.idle": "2025-07-28T14:45:22.560796Z",
     "shell.execute_reply": "2025-07-28T14:45:22.560361Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.264146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_name\n",
       "chirp-auk-t0    6701857\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_df[\"model_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.561406Z",
     "iopub.status.busy": "2025-07-28T14:45:22.561284Z",
     "iopub.status.idle": "2025-07-28T14:45:22.580873Z",
     "shell.execute_reply": "2025-07-28T14:45:22.580510Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.561388Z"
    }
   },
   "outputs": [],
   "source": [
    "# infill_clip_df = final_interesting_clips[(final_interesting_clips[\"task\"] == \"infill\") & (final_interesting_clips[\"model_name\"] == \"chirp-v4-6b-t-03\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.581372Z",
     "iopub.status.busy": "2025-07-28T14:45:22.581263Z",
     "iopub.status.idle": "2025-07-28T14:45:22.596269Z",
     "shell.execute_reply": "2025-07-28T14:45:22.595920Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.581361Z"
    }
   },
   "outputs": [],
   "source": [
    "# infill_clip_df[infill_clip_df[\"part_of_concat\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.596939Z",
     "iopub.status.busy": "2025-07-28T14:45:22.596680Z",
     "iopub.status.idle": "2025-07-28T14:45:22.610965Z",
     "shell.execute_reply": "2025-07-28T14:45:22.610615Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.596927Z"
    }
   },
   "outputs": [],
   "source": [
    "# all_pairs = clip_df[clip_df[\"request_id\"].isin(infill_clip_df[\"request_id\"].unique())].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.611601Z",
     "iopub.status.busy": "2025-07-28T14:45:22.611373Z",
     "iopub.status.idle": "2025-07-28T14:45:22.626402Z",
     "shell.execute_reply": "2025-07-28T14:45:22.626044Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.611589Z"
    }
   },
   "outputs": [],
   "source": [
    "# all_pairs[\"flagged\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.626884Z",
     "iopub.status.busy": "2025-07-28T14:45:22.626778Z",
     "iopub.status.idle": "2025-07-28T14:45:22.641335Z",
     "shell.execute_reply": "2025-07-28T14:45:22.640986Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.626873Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_clip_df.to_pickle(\n",
    "#     \"/home/tony/Data/Preference/auk/interesting_clips_exp_20250422_auk_t1.pkl\",\n",
    "# )\n",
    "# print(\"auk exps\", test_clip_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.641972Z",
     "iopub.status.busy": "2025-07-28T14:45:22.641732Z",
     "iopub.status.idle": "2025-07-28T14:45:22.656200Z",
     "shell.execute_reply": "2025-07-28T14:45:22.655848Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.641960Z"
    }
   },
   "outputs": [],
   "source": [
    "# subset_request_ids = user_intersting_clips_3p5[user_intersting_clips_3p5[\"model_name\"].isin([\"chirp-v4-6b-t-21_a_c_c_1\", \"chirp-v4-6b-t-21_a_c_c_2\"])][\"request_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.656910Z",
     "iopub.status.busy": "2025-07-28T14:45:22.656601Z",
     "iopub.status.idle": "2025-07-28T14:45:22.671112Z",
     "shell.execute_reply": "2025-07-28T14:45:22.670771Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.656898Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_subset_df = user_intersting_clips_3p5[user_intersting_clips_3p5[\"request_id\"].isin(subset_request_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.671813Z",
     "iopub.status.busy": "2025-07-28T14:45:22.671502Z",
     "iopub.status.idle": "2025-07-28T14:45:22.685760Z",
     "shell.execute_reply": "2025-07-28T14:45:22.685413Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.671801Z"
    }
   },
   "outputs": [],
   "source": [
    "# test_subset_df.to_pickle(\n",
    "#     \"/home/tony/Data/Preference/auk/interesting_clips_exp_20250422_auk_t1_sara_cfg.pkl\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.686418Z",
     "iopub.status.busy": "2025-07-28T14:45:22.686168Z",
     "iopub.status.idle": "2025-07-28T14:45:22.700872Z",
     "shell.execute_reply": "2025-07-28T14:45:22.700528Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.686406Z"
    }
   },
   "outputs": [],
   "source": [
    "# from collections import defaultdict\n",
    "# from suno_utils.audio import Audio\n",
    "# audio_loundesses = defaultdict(list)\n",
    "# loundess_models = [\"chirp-v4-up-u-d-2-3\", \"chirp-ahi-up-1\", \"chirp-v4-up-u-7\"]\n",
    "# for test_model in loundess_models:\n",
    "#     subset_clip_df = final_interesting_clips[(final_interesting_clips[\"model_name\"] == test_model)][\"s3_id\"]\n",
    "#     print(subset_clip_df.shape)\n",
    "#     for index, s3_id in tqdm.tqdm(enumerate(subset_clip_df.unique())):\n",
    "#         if index > 50:\n",
    "#             break\n",
    "#         try:\n",
    "#             audio = Audio.from_s3(f\"s3://suno-data-uploads/studio/uploads/{s3_id}.mp3\", n_channels=2)\n",
    "#             loudness = audio.loudness\n",
    "#             audio_loundesses[test_model].append(loudness)\n",
    "#         except:\n",
    "#             pass\n",
    "# plt.clf()\n",
    "# for test_model in loundess_models:\n",
    "#     plt.hist(audio_loundesses[test_model], label=f\"{test_model}, mean {round(np.mean(audio_loundesses[test_model]), 2)}\", alpha=0.5, bins=np.linspace(-20, -10, 50))\n",
    "# plt.legend()\n",
    "# plt.title(\"loudness war\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.701469Z",
     "iopub.status.busy": "2025-07-28T14:45:22.701280Z",
     "iopub.status.idle": "2025-07-28T14:45:22.715633Z",
     "shell.execute_reply": "2025-07-28T14:45:22.715293Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.701456Z"
    }
   },
   "outputs": [],
   "source": [
    "# final_interesting_clips[\"created_datetime\"] = pd.to_datetime(final_interesting_clips[\"created_at\"])\n",
    "# final_interesting_clips[\"hour\"] = final_interesting_clips[\"created_datetime\"].dt.strftime(\"%H\")\n",
    "# subset_request_ids = final_interesting_clips[final_interesting_clips[\"model_name\"].str.contains(\"tech\")][\"request_id\"].unique()\n",
    "# subset_final_interesting_clips = final_interesting_clips[final_interesting_clips[\"request_id\"].isin(subset_request_ids)].copy()\n",
    "# for fixed_hour in sorted(final_interesting_clips[\"hour\"].unique()):\n",
    "#     print(\"Fixed hour\", fixed_hour)\n",
    "#     get_preference_counts(\n",
    "#         subset_final_interesting_clips[subset_final_interesting_clips[\"hour\"] == fixed_hour],\n",
    "#         title_name=\"subset test\",\n",
    "#     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T14:45:22.716283Z",
     "iopub.status.busy": "2025-07-28T14:45:22.716036Z",
     "iopub.status.idle": "2025-07-28T14:45:22.730627Z",
     "shell.execute_reply": "2025-07-28T14:45:22.730276Z",
     "shell.execute_reply.started": "2025-07-28T14:45:22.716271Z"
    }
   },
   "outputs": [],
   "source": [
    "# subset_request_ids = user_intersting_clips_3p5[user_intersting_clips_3p5[\"model_name\"].isin([\"chirp-auk-t1-d6\"])][\"request_id\"].unique()\n",
    "# subset_request_ids = user_intersting_clips_3p5[user_intersting_clips_3p5[\"model_name\"].isin([\"chirp-auk-t1-d6\"])][\"request_id\"].unique()\n",
    "\n",
    "# subset_dur_user_intersting_clips_3p5 = user_intersting_clips_3p5[user_intersting_clips_3p5[\"request_id\"].isin(subset_request_ids)].copy()\n",
    "\n",
    "# subset_dur_user_intersting_clips_3p5[\"model_name\"].value_counts()\n",
    "\n",
    "# subset_dur_user_intersting_clips_3p5[subset_dur_user_intersting_clips_3p5[\"model_name\"] == \"chirp-auk-t1-d6\"][\"duration\"].describe()\n",
    "\n",
    "# subset_dur_user_intersting_clips_3p5[subset_dur_user_intersting_clips_3p5[\"model_name\"] == \"chirp-auk-t1\"][\"duration\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-28T16:08:42.195055Z",
     "iopub.status.busy": "2025-07-28T16:08:42.194755Z",
     "iopub.status.idle": "2025-07-28T16:08:51.063821Z",
     "shell.execute_reply": "2025-07-28T16:08:51.063298Z",
     "shell.execute_reply.started": "2025-07-28T16:08:42.195039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auk og (340122, 90)\n",
      "Saving done! to 20250728\n"
     ]
    }
   ],
   "source": [
    "# print(\"auk\", final_subset_auk_clips_df.shape)\n",
    "# print(\"ahi\", final_subset_upsample_ahi_clips_df.shape)\n",
    "# print(\"ahi sneaked\", final_subset_upsample_ahi_clips_df_2.shape)\n",
    "print(\"auk og\", final_subset_auk_og_clips_df.shape)\n",
    "todays_save_date = \"20250728\"\n",
    "# final_subset_auk_og_clips_df.to_pickle(\n",
    "#     f\"/home/tony/Data/Preference/auk_t0/interesting_clips_auk_t0_{todays_save_date}.pkl\",\n",
    "# )\n",
    "# total_ahi_df.to_pickle(\n",
    "#     f\"/home/tony/Data/Preference/up_v2_d4/interesting_clips_ahi_d4_{todays_save_date}_oq.pkl\",\n",
    "# )\n",
    "# print(\"ahi_d4\", total_ahi_df.shape)\n",
    "# final_subset_auk_clips_df.to_pickle(\n",
    "#     f\"/home/tony/Data/Preference/auk_t1/interesting_clips_auk_t1_{todays_save_date}.pkl\",\n",
    "# )\n",
    "# print(\"auk_t1\", final_subset_auk_clips_df.shape)\n",
    "# # final_subset_auk_infill_30b_clips_df.to_pickle(\n",
    "# #     f\"/home/tony/Data/Preference/30b_t7/interesting_clips_30_infill_t1_{todays_save_date}.pkl\",\n",
    "# # )\n",
    "# # print(\"auk_30b_infill\", final_subset_auk_infill_30b_clips_df.shape)\n",
    "print(f\"Saving done! to {todays_save_date}\")\n",
    "\n",
    "# auk (4743256, 90)\n",
    "# ahi (367122, 90)\n",
    "# ahi sneaked (22902, 90)\n",
    "# auk og (116910, 90)\n",
    "# ahi_d3 (390024, 90)\n",
    "# Saving done!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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