{
 "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": "2025-07-26T22:20:48.000827Z",
     "iopub.status.busy": "2025-07-26T22:20:48.000714Z",
     "iopub.status.idle": "2025-07-26T22:20:48.169198Z",
     "shell.execute_reply": "2025-07-26T22:20:48.168780Z",
     "shell.execute_reply.started": "2025-07-26T22:20:48.000813Z"
    }
   },
   "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": "2025-07-26T22:20:48.169933Z",
     "iopub.status.busy": "2025-07-26T22:20:48.169729Z",
     "iopub.status.idle": "2025-07-26T22:20:53.555188Z",
     "shell.execute_reply": "2025-07-26T22:20:53.554543Z",
     "shell.execute_reply.started": "2025-07-26T22:20:48.169919Z"
    }
   },
   "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_old 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": "2025-07-26T22:20:53.556506Z",
     "iopub.status.busy": "2025-07-26T22:20:53.555871Z",
     "iopub.status.idle": "2025-07-26T22:20:53.573801Z",
     "shell.execute_reply": "2025-07-26T22:20:53.573311Z",
     "shell.execute_reply.started": "2025-07-26T22:20:53.556489Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-07-26 22:20:53.571834 1753568453.5718374 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": "2025-07-26T22:20:53.574702Z",
     "iopub.status.busy": "2025-07-26T22:20:53.574330Z",
     "iopub.status.idle": "2025-07-26T22:20:53.936127Z",
     "shell.execute_reply": "2025-07-26T22:20:53.935608Z",
     "shell.execute_reply.started": "2025-07-26T22:20:53.574688Z"
    }
   },
   "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": "2025-07-26T22:20:53.936907Z",
     "iopub.status.busy": "2025-07-26T22:20:53.936693Z",
     "iopub.status.idle": "2025-07-26T22:20:54.808279Z",
     "shell.execute_reply": "2025-07-26T22:20:54.807802Z",
     "shell.execute_reply.started": "2025-07-26T22:20:53.936893Z"
    }
   },
   "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": "2025-07-26T22:20:54.809005Z",
     "iopub.status.busy": "2025-07-26T22:20:54.808789Z",
     "iopub.status.idle": "2025-07-26T22:43:11.536180Z",
     "shell.execute_reply": "2025-07-26T22:43:11.535632Z",
     "shell.execute_reply.started": "2025-07-26T22:20:54.808990Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start gather data from 2025-05-06 23:20:00\n",
      "Bots Action: 602,612,608 rows\n",
      " ---- Execution time: 306.09 seconds\n",
      "Reactions: 602,435,168 rows\n",
      " ---- Execution time: 300.31 seconds\n",
      "Discord Info: 756,774 rows\n",
      "subscription_status\n",
      "active      741931\n",
      "past_due     14843\n",
      "Name: count, dtype: int64\n",
      " ---- Execution time: 9.41 seconds\n",
      "Playlist Clips: 12,250,034 rows\n",
      " ---- Execution time: 22.94 seconds\n",
      "Filtering for model: chirp-auk-t0\n",
      "Total Clips: 9,616,771 rows\n",
      " ---- Execution time: 554.81 seconds\n",
      "Before user_n_clips filtering: 9,616,771 rows; \n",
      "After user_n_clips filtering: 6,893,403 rows; \n",
      "Before request_id filtering: 6,893,403 rows; na rows: 39,671\n",
      "Generation requests: 3,356,536 rows; na rows: 39,671\n",
      "After request_id filtering: 6,701,043 rows; na rows: 39,671\n",
      "Before is_deleted filtering: 6,701,043 rows; na rows: 39,671\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tony/Work/glockenspiel/suno_analytics/suno_analytics/preference_data_selection_old.py:288: 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: 4,879,359 rows; na rows: 39,671\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=20, 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": "2025-07-26T22:43:11.536913Z",
     "iopub.status.busy": "2025-07-26T22:43:11.536737Z",
     "iopub.status.idle": "2025-07-26T22:43:12.747790Z",
     "shell.execute_reply": "2025-07-26T22:43:12.747301Z",
     "shell.execute_reply.started": "2025-07-26T22:43:11.536899Z"
    }
   },
   "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": "2025-07-26T22:43:12.748611Z",
     "iopub.status.busy": "2025-07-26T22:43:12.748305Z",
     "iopub.status.idle": "2025-07-26T22:43:24.852895Z",
     "shell.execute_reply": "2025-07-26T22:43:24.852369Z",
     "shell.execute_reply.started": "2025-07-26T22:43:12.748596Z"
    }
   },
   "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": "2025-07-26T22:43:24.853656Z",
     "iopub.status.busy": "2025-07-26T22:43:24.853464Z",
     "iopub.status.idle": "2025-07-26T22:43:31.681121Z",
     "shell.execute_reply": "2025-07-26T22:43:31.680640Z",
     "shell.execute_reply.started": "2025-07-26T22:43:24.853642Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total clips: 4879359\n",
      "gen: 4839688 (99.19%)\n",
      "concat: 26704 (0.55%)\n",
      "edit_crop: 6119 (0.13%)\n",
      "edit_speed: 4850 (0.10%)\n",
      "edit_fade: 1019 (0.02%)\n",
      "rendered-project: 979 (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: 1766\n",
      "Average clips per hour: 2762.94\n",
      "Max clips in an hour: 3862\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": "2025-07-26T22:43:31.681826Z",
     "iopub.status.busy": "2025-07-26T22:43:31.681662Z",
     "iopub.status.idle": "2025-07-26T22:43:43.031818Z",
     "shell.execute_reply": "2025-07-26T22:43:43.031314Z",
     "shell.execute_reply.started": "2025-07-26T22:43:31.681811Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of upvoates: 47,633,249 rows\n",
      "number of flagged reports: 1,064,990 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": "2025-07-26T22:43:43.032633Z",
     "iopub.status.busy": "2025-07-26T22:43:43.032358Z",
     "iopub.status.idle": "2025-07-26T22:43:56.827702Z",
     "shell.execute_reply": "2025-07-26T22:43:56.827203Z",
     "shell.execute_reply.started": "2025-07-26T22:43:43.032619Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactions fraction by pro user:\n",
      "False: 315468622 (52.37%)\n",
      "True: 286966546 (47.63%)\n",
      "------------\n",
      "Clip generated fraction by pro user:\n",
      "True: 4360874 (89.37%)\n",
      "False: 518485 (10.63%)\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": "2025-07-26T22:43:56.828372Z",
     "iopub.status.busy": "2025-07-26T22:43:56.828211Z",
     "iopub.status.idle": "2025-07-26T22:43:56.847682Z",
     "shell.execute_reply": "2025-07-26T22:43:56.847304Z",
     "shell.execute_reply.started": "2025-07-26T22:43:56.828359Z"
    }
   },
   "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": "2025-07-26T22:43:56.848351Z",
     "iopub.status.busy": "2025-07-26T22:43:56.848109Z",
     "iopub.status.idle": "2025-07-26T22:44:05.789538Z",
     "shell.execute_reply": "2025-07-26T22:44:05.789035Z",
     "shell.execute_reply.started": "2025-07-26T22:43:56.848339Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clips in a splaylist:\n",
      "False: 4797897 (98.33%)\n",
      "True: 81462 (1.67%)\n",
      "------------\n",
      "Clips has stem children:\n",
      "False: 4879359 (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": "2025-07-26T22:44:05.790227Z",
     "iopub.status.busy": "2025-07-26T22:44:05.790057Z",
     "iopub.status.idle": "2025-07-26T22:44:07.219846Z",
     "shell.execute_reply": "2025-07-26T22:44:07.219348Z",
     "shell.execute_reply.started": "2025-07-26T22:44:05.790213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clips that have children: 352062 \n",
      "clips that are parents: 137702 \n",
      " Average continues from clip =  2.56\n",
      "web: 4198208 (86.62%)\n",
      "ios: 430612 (8.88%)\n",
      "android: 218006 (4.50%)\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": "2025-07-26T22:44:07.220525Z",
     "iopub.status.busy": "2025-07-26T22:44:07.220367Z",
     "iopub.status.idle": "2025-07-26T22:44:10.529698Z",
     "shell.execute_reply": "2025-07-26T22:44:10.529172Z",
     "shell.execute_reply.started": "2025-07-26T22:44:07.220511Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total uploads: 0\n",
      "clips without request id: 39671\n",
      "clips without request id: 39671 clip_type\n",
      "concat              26704\n",
      "edit_crop            6119\n",
      "edit_speed           4850\n",
      "edit_fade            1019\n",
      "rendered-project      979\n",
      "Name: count, dtype: int64\n",
      "Clips without request id (concat, uploads...) frac = 0.00547\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": "2025-07-26T22:44:10.530480Z",
     "iopub.status.busy": "2025-07-26T22:44:10.530221Z",
     "iopub.status.idle": "2025-07-26T22:44:10.755796Z",
     "shell.execute_reply": "2025-07-26T22:44:10.755309Z",
     "shell.execute_reply.started": "2025-07-26T22:44:10.530465Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-auk-t0: 4839688 (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": "2025-07-26T22:44:10.756541Z",
     "iopub.status.busy": "2025-07-26T22:44:10.756299Z",
     "iopub.status.idle": "2025-07-26T22:44:14.542355Z",
     "shell.execute_reply": "2025-07-26T22:44:14.541856Z",
     "shell.execute_reply.started": "2025-07-26T22:44:10.756527Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-filter model type clip_df shape: (4839688, 40)\n",
      "post-filter model type clip_df shape: (4839688, 40)\n",
      "chirp-auk-t0: 4839688 (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": "2025-07-26T22:44:14.543036Z",
     "iopub.status.busy": "2025-07-26T22:44:14.542883Z",
     "iopub.status.idle": "2025-07-26T22:44:15.950862Z",
     "shell.execute_reply": "2025-07-26T22:44:15.950362Z",
     "shell.execute_reply.started": "2025-07-26T22:44:14.543024Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": 2973260 (61.43%)\n",
      "cover: 1001418 (20.69%)\n",
      "artist_consistency: 347891 (7.19%)\n",
      "extend: 194620 (4.02%)\n",
      "artist_cover: 164859 (3.41%)\n",
      "upload_extend: 128205 (2.65%)\n",
      "artist_extend: 29345 (0.61%)\n",
      "cover_extend: 34 (0.00%)\n",
      "playlist_condition: 22 (0.00%)\n",
      "artist_cover_extend: 22 (0.00%)\n",
      "underpainting: 8 (0.00%)\n",
      "overpainting: 4 (0.00%)\n"
     ]
    }
   ],
   "source": [
    "print_out_value_counts_nicely(clip_df, \"task\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:44:15.951612Z",
     "iopub.status.busy": "2025-07-26T22:44:15.951373Z",
     "iopub.status.idle": "2025-07-26T22:44:52.284732Z",
     "shell.execute_reply": "2025-07-26T22:44:52.284243Z",
     "shell.execute_reply.started": "2025-07-26T22:44:15.951598Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "concat reactions: 55039 unique concat clips: 26688\n",
      "total concats (26704, 43)\n",
      "check \n",
      "        reaction_play_count  reaction_upvote_count  reaction_dislike_count\n",
      "count         26688.000000           26688.000000            26688.000000\n",
      "mean              7.276941               0.471073                0.021583\n",
      "std              59.466890               4.271177                0.160512\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            6972.000000             361.000000               11.000000\n",
      "All concats 26704\n",
      "total concats with plays 26688\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": "2025-07-26T22:44:52.285477Z",
     "iopub.status.busy": "2025-07-26T22:44:52.285236Z",
     "iopub.status.idle": "2025-07-26T22:44:53.368486Z",
     "shell.execute_reply": "2025-07-26T22:44:53.367974Z",
     "shell.execute_reply.started": "2025-07-26T22:44:52.285463Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "26688it [00:01, 25643.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total concat unique clips are: 52218 with error: 473, duplicate 2357 \n",
      " uploads are in concats 5 frac 5.000\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": "2025-07-26T22:44:53.369335Z",
     "iopub.status.busy": "2025-07-26T22:44:53.369008Z",
     "iopub.status.idle": "2025-07-26T22:44:53.597250Z",
     "shell.execute_reply": "2025-07-26T22:44:53.596751Z",
     "shell.execute_reply.started": "2025-07-26T22:44:53.369320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    4.839688e+06\n",
      "mean     7.786837e+01\n",
      "std      9.384569e+01\n",
      "min      1.000000e+00\n",
      "25%      2.800000e+01\n",
      "50%      4.800000e+01\n",
      "75%      9.200000e+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": "2025-07-26T22:44:53.601167Z",
     "iopub.status.busy": "2025-07-26T22:44:53.600966Z",
     "iopub.status.idle": "2025-07-26T22:45:05.840504Z",
     "shell.execute_reply": "2025-07-26T22:45:05.840022Z",
     "shell.execute_reply.started": "2025-07-26T22:44:53.601153Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has upvoted upvoted\n",
      "False    4532612\n",
      "True      307076\n",
      "Name: count, dtype: int64 upvoted\n",
      "False    0.93655\n",
      "True     0.06345\n",
      "Name: proportion, dtype: float64 upvote_count\n",
      "False    0.936509\n",
      "True     0.063491\n",
      "Name: proportion, dtype: float64 upvote_count\n",
      "False    0.998683\n",
      "True     0.001317\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": "2025-07-26T22:45:05.841260Z",
     "iopub.status.busy": "2025-07-26T22:45:05.841014Z",
     "iopub.status.idle": "2025-07-26T22:45:35.639262Z",
     "shell.execute_reply": "2025-07-26T22:45:35.638772Z",
     "shell.execute_reply.started": "2025-07-26T22:45:05.841246Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downvoted fraction by category:\n",
      "False: 4332847 (89.53%)\n",
      "True: 506841 (10.47%)\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": "2025-07-26T22:45:35.639935Z",
     "iopub.status.busy": "2025-07-26T22:45:35.639773Z",
     "iopub.status.idle": "2025-07-26T22:45:37.667490Z",
     "shell.execute_reply": "2025-07-26T22:45:37.666998Z",
     "shell.execute_reply.started": "2025-07-26T22:45:35.639922Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_continued fraction by category:\n",
      "False: 4837853 (99.96%)\n",
      "True: 1835 (0.04%)\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": "2025-07-26T22:45:37.668173Z",
     "iopub.status.busy": "2025-07-26T22:45:37.668010Z",
     "iopub.status.idle": "2025-07-26T22:45:37.714227Z",
     "shell.execute_reply": "2025-07-26T22:45:37.713797Z",
     "shell.execute_reply.started": "2025-07-26T22:45:37.668160Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has upvoted in exp 0.06345\n",
      "has downvoted out of exp 0.10473\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": "2025-07-26T22:45:37.714956Z",
     "iopub.status.busy": "2025-07-26T22:45:37.714706Z",
     "iopub.status.idle": "2025-07-26T22:45:38.203380Z",
     "shell.execute_reply": "2025-07-26T22:45:38.202888Z",
     "shell.execute_reply.started": "2025-07-26T22:45:37.714942Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "part_of_concat fraction by category:\n",
      "False: 4820880 (99.61%)\n",
      "True: 18808 (0.39%)\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": "2025-07-26T22:45:38.204130Z",
     "iopub.status.busy": "2025-07-26T22:45:38.203891Z",
     "iopub.status.idle": "2025-07-26T22:48:36.703685Z",
     "shell.execute_reply": "2025-07-26T22:48:36.703188Z",
     "shell.execute_reply.started": "2025-07-26T22:45:38.204117Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72492974\n",
      "has_action fraction by category:\n",
      "False: 4483395 (92.64%)\n",
      "True: 356293 (7.36%)\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": "2025-07-26T22:48:36.704364Z",
     "iopub.status.busy": "2025-07-26T22:48:36.704199Z",
     "iopub.status.idle": "2025-07-26T22:48:39.150366Z",
     "shell.execute_reply": "2025-07-26T22:48:39.149879Z",
     "shell.execute_reply.started": "2025-07-26T22:48:36.704350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flagged fraction by category:\n",
      "False: 4799863 (99.18%)\n",
      "True: 39825 (0.82%)\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": "2025-07-26T22:48:39.151086Z",
     "iopub.status.busy": "2025-07-26T22:48:39.150844Z",
     "iopub.status.idle": "2025-07-26T22:48:39.184540Z",
     "shell.execute_reply": "2025-07-26T22:48:39.184120Z",
     "shell.execute_reply.started": "2025-07-26T22:48:39.151072Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deleted fraction by category:\n",
      "False: 4659501 (96.28%)\n",
      "True: 180187 (3.72%)\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": "2025-07-26T22:48:39.185214Z",
     "iopub.status.busy": "2025-07-26T22:48:39.184988Z",
     "iopub.status.idle": "2025-07-26T22:48:41.464851Z",
     "shell.execute_reply": "2025-07-26T22:48:41.464357Z",
     "shell.execute_reply.started": "2025-07-26T22:48:39.185201Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of edits per clip: 2.615429917550059 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": "2025-07-26T22:48:41.465660Z",
     "iopub.status.busy": "2025-07-26T22:48:41.465385Z",
     "iopub.status.idle": "2025-07-26T22:48:42.023862Z",
     "shell.execute_reply": "2025-07-26T22:48:42.023361Z",
     "shell.execute_reply.started": "2025-07-26T22:48:41.465646Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total clips: 4,839,688\n",
      "Must be positive: 617,952 (12.77%)\n",
      "Definitely not negative: 4,134,532 (85.43%)\n",
      "Must be negative: 705,156 (14.57%)\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": "2025-07-26T22:48:42.024633Z",
     "iopub.status.busy": "2025-07-26T22:48:42.024392Z",
     "iopub.status.idle": "2025-07-26T22:48:52.341145Z",
     "shell.execute_reply": "2025-07-26T22:48:52.340632Z",
     "shell.execute_reply.started": "2025-07-26T22:48:42.024618Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liked requests: 472,442\n",
      "Not liked requests: 2,300,464\n",
      "Requests with preference paired generations: 332,498\n",
      "Percentage of total unique requests: 13.62%\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": "2025-07-26T22:48:52.341982Z",
     "iopub.status.busy": "2025-07-26T22:48:52.341717Z",
     "iopub.status.idle": "2025-07-26T22:48:56.473969Z",
     "shell.execute_reply": "2025-07-26T22:48:56.473450Z",
     "shell.execute_reply.started": "2025-07-26T22:48:52.341967Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disliked requests: 482,441\n",
      "Not disliked requests: 2,215,661\n",
      "Requests with preference paired generations: 257,694\n",
      "Percentage of total unique requests: 10.56%\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": "2025-07-26T22:48:56.474783Z",
     "iopub.status.busy": "2025-07-26T22:48:56.474508Z",
     "iopub.status.idle": "2025-07-26T22:48:56.515837Z",
     "shell.execute_reply": "2025-07-26T22:48:56.515405Z",
     "shell.execute_reply.started": "2025-07-26T22:48:56.474767Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total selected pairs of requests: 501,263\n",
      "Percentage of total unique requests: 20.54%\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": "2025-07-26T22:48:56.516533Z",
     "iopub.status.busy": "2025-07-26T22:48:56.516309Z",
     "iopub.status.idle": "2025-07-26T22:48:56.568942Z",
     "shell.execute_reply": "2025-07-26T22:48:56.568455Z",
     "shell.execute_reply.started": "2025-07-26T22:48:56.516518Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in preference counts:\n",
      "0: 3,532,342 (72.99%)\n",
      "-1: 705,156 (14.57%)\n",
      "1: 602,190 (12.44%)\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": "2025-07-26T22:48:56.569710Z",
     "iopub.status.busy": "2025-07-26T22:48:56.569467Z",
     "iopub.status.idle": "2025-07-26T22:49:04.181824Z",
     "shell.execute_reply": "2025-07-26T22:49:04.181308Z",
     "shell.execute_reply.started": "2025-07-26T22:48:56.569695Z"
    }
   },
   "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": "2025-07-26T22:49:04.182615Z",
     "iopub.status.busy": "2025-07-26T22:49:04.182370Z",
     "iopub.status.idle": "2025-07-26T22:49:06.399063Z",
     "shell.execute_reply": "2025-07-26T22:49:06.398628Z",
     "shell.execute_reply.started": "2025-07-26T22:49:04.182601Z"
    }
   },
   "outputs": [
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       "    }\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": "2025-07-26T22:49:06.399804Z",
     "iopub.status.busy": "2025-07-26T22:49:06.399559Z",
     "iopub.status.idle": "2025-07-26T22:49:06.422883Z",
     "shell.execute_reply": "2025-07-26T22:49:06.422489Z",
     "shell.execute_reply.started": "2025-07-26T22:49:06.399790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference 0: 412,334 (41.13%)\n",
      "Difference 1: 332,498 (33.17%)\n",
      "Difference -1: 257,694 (25.70%)\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": "2025-07-26T22:49:06.423539Z",
     "iopub.status.busy": "2025-07-26T22:49:06.423324Z",
     "iopub.status.idle": "2025-07-26T22:49:06.447167Z",
     "shell.execute_reply": "2025-07-26T22:49:06.446775Z",
     "shell.execute_reply.started": "2025-07-26T22:49:06.423525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value 1.0: 412,334 (82.26%)\n",
      "Value 2.0: 88,929 (17.74%)\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": "2025-07-26T22:49:06.447839Z",
     "iopub.status.busy": "2025-07-26T22:49:06.447616Z",
     "iopub.status.idle": "2025-07-26T22:49:08.708668Z",
     "shell.execute_reply": "2025-07-26T22:49:08.708158Z",
     "shell.execute_reply.started": "2025-07-26T22:49:06.447826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique request_ids: 501,263\n",
      "Number of unique ids: 1,002,526\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": "2025-07-26T22:49:08.709465Z",
     "iopub.status.busy": "2025-07-26T22:49:08.709209Z",
     "iopub.status.idle": "2025-07-26T22:49:08.728637Z",
     "shell.execute_reply": "2025-07-26T22:49:08.728252Z",
     "shell.execute_reply.started": "2025-07-26T22:49:08.709451Z"
    }
   },
   "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": "2025-07-26T22:49:08.729359Z",
     "iopub.status.busy": "2025-07-26T22:49:08.729092Z",
     "iopub.status.idle": "2025-07-26T22:50:38.808423Z",
     "shell.execute_reply": "2025-07-26T22:50:38.807908Z",
     "shell.execute_reply.started": "2025-07-26T22:49:08.729346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of interesting clips: 1,002,526\n",
      "Unique request and clip counts in interesting_clips:\n",
      "Request IDs:    501,263\n",
      "Clip IDs:       1,002,526\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": "2025-07-26T22:50:38.809164Z",
     "iopub.status.busy": "2025-07-26T22:50:38.808982Z",
     "iopub.status.idle": "2025-07-26T22:50:40.027843Z",
     "shell.execute_reply": "2025-07-26T22:50:40.027353Z",
     "shell.execute_reply.started": "2025-07-26T22:50:38.809150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference counts and fractions by batch index:\n",
      "--------------------------------------------------\n",
      "Batch Index: 0\n",
      "  Preference False: Count: 243,473 Fraction: 48.57%\n",
      "  Preference True: Count: 257,790 Fraction: 51.43%\n",
      "\n",
      "Batch Index: 1\n",
      "  Preference False: Count: 257,790 Fraction: 51.43%\n",
      "  Preference True: Count: 243,473 Fraction: 48.57%\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": "2025-07-26T22:50:40.028577Z",
     "iopub.status.busy": "2025-07-26T22:50:40.028344Z",
     "iopub.status.idle": "2025-07-26T22:50:40.090858Z",
     "shell.execute_reply": "2025-07-26T22:50:40.090401Z",
     "shell.execute_reply.started": "2025-07-26T22:50:40.028562Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-auk-t0: 1002526 (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": "2025-07-26T22:50:40.091577Z",
     "iopub.status.busy": "2025-07-26T22:50:40.091345Z",
     "iopub.status.idle": "2025-07-26T22:50:40.119961Z",
     "shell.execute_reply": "2025-07-26T22:50:40.119485Z",
     "shell.execute_reply.started": "2025-07-26T22:50:40.091563Z"
    }
   },
   "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 23:21:55.161000\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": "2025-07-26T22:50:40.120699Z",
     "iopub.status.busy": "2025-07-26T22:50:40.120444Z",
     "iopub.status.idle": "2025-07-26T22:50:41.284556Z",
     "shell.execute_reply": "2025-07-26T22:50:41.284054Z",
     "shell.execute_reply.started": "2025-07-26T22:50:40.120685Z"
    }
   },
   "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": "2025-07-26T22:50:41.285176Z",
     "iopub.status.busy": "2025-07-26T22:50:41.285057Z",
     "iopub.status.idle": "2025-07-26T22:50:41.789012Z",
     "shell.execute_reply": "2025-07-26T22:50:41.788518Z",
     "shell.execute_reply.started": "2025-07-26T22:50:41.285163Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio of preferred clips to total clips for each model:\n",
      "------------------------------------------------------------\n",
      "chirp-auk-t0                   10.36% ± 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": "2025-07-26T22:50:41.789683Z",
     "iopub.status.busy": "2025-07-26T22:50:41.789525Z",
     "iopub.status.idle": "2025-07-26T22:50:43.260982Z",
     "shell.execute_reply": "2025-07-26T22:50:43.260472Z",
     "shell.execute_reply.started": "2025-07-26T22:50:41.789670Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 501263\n",
      "differing counts: 0\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 501263, total 501263.\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": "2025-07-26T22:50:43.261747Z",
     "iopub.status.busy": "2025-07-26T22:50:43.261493Z",
     "iopub.status.idle": "2025-07-26T22:50:44.375701Z",
     "shell.execute_reply": "2025-07-26T22:50:44.375259Z",
     "shell.execute_reply.started": "2025-07-26T22:50:43.261732Z"
    }
   },
   "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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",
      "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": "2025-07-26T22:50:44.376466Z",
     "iopub.status.busy": "2025-07-26T22:50:44.376229Z",
     "iopub.status.idle": "2025-07-26T22:50:44.395093Z",
     "shell.execute_reply": "2025-07-26T22:50:44.394730Z",
     "shell.execute_reply.started": "2025-07-26T22:50:44.376453Z"
    }
   },
   "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": "2025-07-26T22:50:44.395743Z",
     "iopub.status.busy": "2025-07-26T22:50:44.395532Z",
     "iopub.status.idle": "2025-07-26T22:50:48.352721Z",
     "shell.execute_reply": "2025-07-26T22:50:48.352206Z",
     "shell.execute_reply.started": "2025-07-26T22:50:44.395730Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clips in interesting_clips:\n",
      "1,002,526\n",
      "Number of clips in user_interesting_clips:\n",
      "1,001,872\n",
      "Ratio of preferred clips to total clips for each model:\n",
      "------------------------------------------------------------\n",
      "chirp-auk-t0                   10.35% ± 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": "2025-07-26T22:50:48.353536Z",
     "iopub.status.busy": "2025-07-26T22:50:48.353248Z",
     "iopub.status.idle": "2025-07-26T22:50:48.624263Z",
     "shell.execute_reply": "2025-07-26T22:50:48.623838Z",
     "shell.execute_reply.started": "2025-07-26T22:50:48.353522Z"
    }
   },
   "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": "2025-07-26T22:50:48.624991Z",
     "iopub.status.busy": "2025-07-26T22:50:48.624760Z",
     "iopub.status.idle": "2025-07-26T22:50:48.732985Z",
     "shell.execute_reply": "2025-07-26T22:50:48.732494Z",
     "shell.execute_reply.started": "2025-07-26T22:50:48.624977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of user_interesting_clips:\n",
      "Total requests: 1,001,872\n",
      "Unique clips: 500,936\n",
      "Fraction of total clips: 20.53%\n",
      "Time Validation:\n",
      "Earliest timestamp: 2025-05-06 23:20:00.810000\n",
      "Latest timestamp:   2025-07-16 23:21:55.161000\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": "2025-07-26T22:50:48.733743Z",
     "iopub.status.busy": "2025-07-26T22:50:48.733500Z",
     "iopub.status.idle": "2025-07-26T22:50:48.752822Z",
     "shell.execute_reply": "2025-07-26T22:50:48.752458Z",
     "shell.execute_reply.started": "2025-07-26T22:50:48.733728Z"
    }
   },
   "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": "2025-07-26T22:50:48.753465Z",
     "iopub.status.busy": "2025-07-26T22:50:48.753242Z",
     "iopub.status.idle": "2025-07-26T22:50:50.961267Z",
     "shell.execute_reply": "2025-07-26T22:50:50.960761Z",
     "shell.execute_reply.started": "2025-07-26T22:50:48.753452Z"
    }
   },
   "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": "2025-07-26T22:50:50.961947Z",
     "iopub.status.busy": "2025-07-26T22:50:50.961823Z",
     "iopub.status.idle": "2025-07-26T22:51:15.498010Z",
     "shell.execute_reply": "2025-07-26T22:51:15.497510Z",
     "shell.execute_reply.started": "2025-07-26T22:50:50.961935Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1001872, 57)\n",
      "Model Name Value Counts and Fractions:\n",
      "chirp-auk-t0: 989956 (98.81%)\n",
      "chirp-auk-t0_mask_control_slider: 11907 (1.19%)\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": "2025-07-26T22:51:15.498680Z",
     "iopub.status.busy": "2025-07-26T22:51:15.498523Z",
     "iopub.status.idle": "2025-07-26T22:51:19.456791Z",
     "shell.execute_reply": "2025-07-26T22:51:19.456281Z",
     "shell.execute_reply.started": "2025-07-26T22:51:15.498666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 500936\n",
      "differing counts: 11916\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 0.503, (0.494, 0.512), counts 5985, total 11907.\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 489020, total 489020.\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 0.497, (0.488, 0.506), counts 5922, total 11907.\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, 488.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 1025.6, (-843, 21544)\n",
      "chirp-auk-t0_cfg_steps_10: 983.8, (-16515, 10020)\n",
      "chirp-auk-t0_mask_control_slider: 1027.4, (-841, 21542)\n",
      "chirp-auk-t0_n_tag_2: 1047.2, (1000, 28926)\n",
      "chirp-auk-t0_tag_cfg_1: 978.5, (-13748, 1000)\n",
      "chirp-auk-t0_tag_cfg_3: 978.5, (-11049, 1000)\n",
      "chirp-auk-t0_temp_s_95: 959.1, (-22904, 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",
      "  chirp-auk-t0 (2 comparisons)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "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": "2025-07-26T22:51:19.457580Z",
     "iopub.status.busy": "2025-07-26T22:51:19.457332Z",
     "iopub.status.idle": "2025-07-26T22:51:23.146543Z",
     "shell.execute_reply": "2025-07-26T22:51:23.146038Z",
     "shell.execute_reply.started": "2025-07-26T22:51:19.457566Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first gen\n",
      "len pos models: 300691\n",
      "differing counts: 5441\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 0.498, (0.485, 0.512), counts 2708, total 5435.\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 295250, total 295250.\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 0.502, (0.488, 0.515), counts 2727, total 5435.\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, 494.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 1019.9, (-1276, 25405)\n",
      "chirp-auk-t0_cfg_steps_10: 1002.6, (-6785, 25422)\n",
      "chirp-auk-t0_mask_control_slider: 1018.7, (-1285, 25412)\n",
      "chirp-auk-t0_n_tag_2: 1024.6, (1000, 31884)\n",
      "chirp-auk-t0_tag_cfg_1: 978.1, (-24083, 1000)\n",
      "chirp-auk-t0_tag_cfg_3: 978.1, (-24083, 1000)\n",
      "chirp-auk-t0_temp_s_95: 978.1, (-22617, 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(\"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": "2025-07-26T22:51:23.147309Z",
     "iopub.status.busy": "2025-07-26T22:51:23.147063Z",
     "iopub.status.idle": "2025-07-26T22:51:24.508469Z",
     "shell.execute_reply": "2025-07-26T22:51:24.507956Z",
     "shell.execute_reply.started": "2025-07-26T22:51:23.147295Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is continue\n",
      "len pos models: 25324\n",
      "differing counts: 475\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.520, (0.475, 0.565), counts 247, total 475.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 24849, total 24849.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.480, (0.435, 0.525), counts 228, total 475.\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, 1148.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 993.1, (976, 1010)\n",
      "chirp-auk-t0_mask_control_slider: 1006.9, (990, 1024)\n"
     ]
    },
    {
     "data": {
      "image/png": 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++ODlnnPIIYcUitv//e9/M2fOnGrp04VJ3mRBe4s11lhjmfNtt912adu2bT766KOljhkzZkwhodyrV68abarZtm3bdOzYMW+99VbGjRuXmTNnFrVlyQMPPFDt2VZVVWX69OkZNWpUtc0j69Spk4svvnixhPaivvvd76Z58+bLvN6zzz5bSMTvtttuNUrzd+3aNY0bN86cOXPy4osvLnf8skyePLmw0eWGG26Yzp07L/echg0bplu3bvn3v/+dmTNn5o033ljmeTUpBP/73/8uvF74M7I8i/ZsHzlyZI4++uilju3QoUM222yzZc636C+KPvjgg8U+r6ioqLZx6uGHH16jdS7J132/AMCqTZEbAICi+s53vpOtt946zz//fJ599tmUl5enTZs2eeKJJwobGK5oivv111/P/PnzkySNGzdOp06dlnvOJptsUiiMzp8/P6+99lp69OhR+HxhoTNZ0LJiecrKytK1a9f8/e9/X+qYl156qfB60qRJueCCC5Y7b/K/jR2rqqoyadKkoha5n3vuuTz33HPLHNO6detcfPHF+d73vrfMccsrqibVn9Hrr79e42e00IwZMxb7hcWKWPT6n332WY2v/9577xVeT5o0aZlF7po8h0U3cfz73/+eF154YbnnzJw5s/B60V9ALMnGG2+83PlatGhReL2wP/qixo4dm88//zzJgo0mF/Y//zK+7vsFAFZtitwAABTdfvvtl+effz7z58/Pgw8+mGOOOSb3339/kgXF4hUtcn/88ceF12uttdYSN7X7ojp16qRdu3YZP378YnMkKWxguXDOmljeuMmTJxdev/7663n99ddrNO+iZsyYscLn1KaysrI0adIkLVu2zCabbJIdd9wx//d//7fclh7JgnYmy7PoMxo5cmRGjhy5wmv85JNPvnSRe9HrT5gwodBeZkUs799oRZ/D0vphL8vCX4wsTU1+UbJoK5N58+Yt9vmUKVMKr9u1a7fU1ic18XXfLwCwalPkBgCg6PbYY49cdNFF+fTTT3Pfffdlv/32y1NPPZUk2XLLLdOhQ4cVmm/27NmF10vaqG5pFh276BzJgk0EV3TO5Y1bNHn6ZS1MrBdL//79q/Vm/ipqUgivjWe0pIJsTRXj36gmLViWlJyuzTXU5BdBy7Poz8yX/aXCQl/3/QIAqzZFbgAAiq5p06b5/ve/n7/+9a95/fXXc/nllxcKk/vvv/8Kz7doH+hPP/20xuctOvaLvaQXLdrVdM7ljVu0CN67d++cc845NZr322TRZ/TrX/86Rx111Eq7/i677JLrrruuqNdfdB0LC+733nvvMjdSXVkW/ZlZ9JdCX0Yp3C8A8M217K3cAQDga7JoMfuee+5JsiDhuscee6zwXItuCjlp0qRUVVUt95zKyspMmjRpiXMk1VtK1LTf76LzLUnr1q0Lrxdt9cD/LPqMysvLV+r1V+a/0aKbkq6M51ATiz6rSZMmfaUEfSncLwDwzaXIDQDASrHddtulTZs21Y59//vf/1KbKnbq1Cl169ZNsqCFQk16Xb/22muF9GndunUX2yhwk002KbxedDPCpamqqsrLL7+8zDFdunQpvB41alSNivHfNos+oxdffLHo119088SxY8d+5YRybaxjZTyHmthkk00KrVc+/fTT5X7/L0sp3C8A8M2lyA0AwEpRt27d7LPPPtWOfZlWJcmC9iebb7554f2999673HPuuuuuwusuXbos1lO4Z8+ehdf//ve/M3369GXON3z48OUmubfccsusvvrqSRYkX//xj38sd53fNjvssENhA8NRo0bltddeK+r1O3TokA022CBJMnfu3GrfJ8X0ve99r/D67rvvzueff75S1rEsDRo0qPZz8mU26VyoFO4XAPjmUuQGAGCl+dnPfpa77rqr8LX99tt/6bkOPfTQwus//elPyyyOjhkzJnfccUfh/Y9+9KPFxmy//fZZa621kixIqV522WVLne/zzz/PgAEDlrvGBg0a5Mgjjyy879evXz766KPlnrfQt6HFSdu2bbPvvvsmWZCOP/PMM2u8KWFlZWWmTZv2lddw3HHHFV7//ve/r9FfBixUW602dt9996y77rqFOc8///waJ/9nz55dtAT60UcfXXj90EMP5aGHHvpS85TK/QIA30yK3AAArDSrr756tthii8LXwpYjX8Y+++xTaDkyd+7c/PSnP83w4cMXG/fss8/muOOOK/QP3myzzbL33nsvNq5u3br5xS9+UXh/11135eKLL14sYVpeXp4TTzwxr732WurXr7/cdR599NHZaKONkiQfffRRDjrooDz88MOprKxc4vhp06bljjvuyAEHHJAbbrhhufOvCk499dRCK5vXX389Bx98cJ5++umljp80aVJuuumm7LHHHvnb3/72la+/7777Ztttt02yoID6k5/8JH/+859TUVGxxPGzZs3KAw88kN69e+fCCy/8ytdPFnz/nX/++YWfiXvuuSfHH3983nrrraWeM3bs2Fx22WX53ve+lwkTJtTKOpZnu+22q9ZH/4wzzsigQYOWuAlrZWVlhg8fnp///OeFTSYXKpX7BQC+meqt7AUAAEBtaNCgQa644oocfvjhmTZtWsrLy3PkkUemc+fOhf7aY8eOrZbwbtWqVQYOHLjU4vQBBxyQJ598Mg8//HCSZNiwYbn//vvTs2fPtGjRIhMnTsyIESNSUVGR9u3b5/vf/35uvvnmZa6zSZMmue6663LUUUdlwoQJKS8vz6mnnpo11lgj3bp1S+vWrVNVVZUZM2bkzTffzLvvvlsogC8svK7q2rZtm2uvvTbHH398Pv7447z99ts59thj07Zt23Tp0iUtW7bM3Llz8/HHH2fcuHG1XuCsW7dufv/73+eYY47Jq6++mlmzZuW8887LZZddlm7duqVt27apW7duZsyYkbfffjvjx48v/NJk9913r7V1bLfddjn//PNz/vnnZ/78+fn3v/+dp556KhtuuGE6deqUJk2a5LPPPkt5eXlee+21WkmxfxkXX3xxPvzww4wePTrz58/PNddckxtuuCE9evRIu3btUlVVlY8++ihjxowptP1ZUkq7VO4XAPjmUeQGAGCVscEGG+S2227L6aefnldffTXJgg0ml9S6ZLPNNsvvf//7rLPOOsuc87LLLkujRo0Kfb5nzJiRv//979XGdOzYMYMGDapxirhDhw65++67c9555+XRRx9NVVVVPv744/zzn/9c6jmrr756Nt544xrNvyro0qVL7r777px99tl57rnnkixIvj/22GNLPad169aFlhdf1RprrJHbb789/fv3z1133ZV58+Zl1qxZy0yUN2rUKJtttlmtXH+hH/7wh1lnnXVy3nnn5Z133klVVVXGjRuXcePGLfWcjTbaKM2bN6/VdSxL06ZNc8stt+Tiiy/O3Xffnfnz52fOnDlLfVYNGzZMnTpL/qPiUrhfAOCbR5EbAIBVyvrrr5+77747jzzySP7+979n9OjRhcRny5Yt07Vr1+y+++7ZfffdU1ZWttz56tevnwEDBmS//fbLnXfemRdffDFTp05N8+bNs84662TPPffMQQcdlCZNmqzQOlu0aJGrrroqb7zxRh566KGMGDEiEyZMyPTp01OnTp2svvrqWWeddbLppptmu+22y3e/+900bNjwSz2TUvWd73wnN910U0aNGpVHHnkkL7zwQiZNmpRPPvkkdevWTYsWLbLuuutm8803z/bbb59tttmmsGllbWjUqFH69euX4447Lg888ECGDx+ed955J9OnT09lZWWaNWuWDh06pHPnztl2222z4447pmnTprV2/YW23Xbb/O1vf8vjjz+ef/3rX3n55ZczZcqUzJo1K40aNUrr1q3TsWPHdO/ePTvuuGPhLxeKqVGjRrnwwgtz1FFH5f77789zzz2XDz74IDNmzEj9+vXTpk2bdOrUKdttt1322muvZT6nUrhfAOCbpayqprt5AAAAAADAN4yNJwEAAAAAKFmK3AAAAAAAlCxFbgAAAAAASpYiNwAAAAAAJUuRGwAAAACAkqXIDQAAAABAyVLkBgAAAACgZClyAwAAAABQshS5AQAAAAAoWYrcAAAAAACULEVuAAAAAABKliI3AAB8w4wYMSKdOnVKp06d0rt375W9HErMWWedVfj+ueeee1b2cgAAvnaK3AAAAAAAlCxFbgAAoOTdc889hfTyWWedtbKXAwBAESlyAwAAAABQsuqt7AUAAADV9ezZM6+//vrKXgYAAJQESW4AAAAAAEqWIjcAAAAAACVLuxIAAPiGGTFiRI444ogkyTbbbJNbbrllqWPfeuut3H333fnPf/6Td999N7Nnz05ZWVmaNm2atdZaK506dco222yTXXbZJc2bN6+1NU6cODH33HNPnnnmmbz33nuZPn16GjdunLXXXju9evXKD3/4w6y//vpLPPeRRx7JL37xiyRJ3bp1c+utt6ZHjx5LvVZFRUV+9KMf5ZVXXkmSfO9738vgwYOTJGeddVbuvffeauPvvffexY4ly3+Wzz33XB5++OGMHDky5eXlmTNnTlq0aJFOnTpl5513zsEHH5xGjRot87l06tSp8Hphy5nx48fn9ttvz9NPP51JkyalrKws7du3z0477ZSjjz46LVu2XOaci3r88cdz9913Z8yYMfn444/TsmXLbLDBBtlvv/3yf//3f6lXz3/iAQDfPv4fEAAAlKhrrrkm1113XebPn7/YZ9OmTcu0adPyyiuv5J577sk+++yTyy+//Ctfs7KyMtdcc01uuOGGfP7559U+mzFjRmbMmJGxY8dm2LBh+elPf5pTTz01ZWVl1cbtscceOeigg3L33Xdn/vz5OeOMM3L//fenadOmS7zmlVdeWShwt27dOv379//K97GoiRMn5swzz8zzzz+/2Gfl5eUpLy/P008/ncGDB+fKK6/MVlttVeO5b7/99lxyySWpqKiodvz111/P66+/njvvvDPXX399tthii2XOM3v27Jx22ml58sknqx3/6KOP8tFHH+XZZ5/NnXfemauuuqrGawMAWFUocgMAQAm6+eabM2jQoML7NdZYI926dUubNm1SVlaW6dOn5+23385bb721xCL4lzF//vycdtppefTRRwvH2rZtmy5duqRly5aZPXt2Ro8enffeey/z5s3L//f//X+ZNm1aLrzwwsXmOuecczJy5Mi88847mTBhQs4///wlFuGfffbZDB06NElSVlaW/v37V0s+9+rVK40bN8748ePz3HPPJUk6duyYXr16LTbXuuuuu9ixt956K0ceeWTKy8sL19h0002z4YYbplGjRvnoo4/ywgsvZPbs2Zk8eXKOPvroDBkyJNtuu+1yn9c999yT888/P0my/vrrZ/PNN0+jRo0yfvz4vPjii6mqqsr06dPzs5/9LA8//HCaNWu2xHnmzp2bE044IS+88ELhWJs2bbLVVlulSZMmee+99zJy5MiMHDkyffr0Sfv27Ze7NgCAVYkiNwAAlJh58+bluuuuK7zv27dvjj766NSvX3+xsdOnT88TTzyRadOmfeXrDho0qFDgbtOmTc4999zstttuiyW1H3744fz2t7/NzJkzc+edd6ZXr17Za6+9qo1p3LhxLr/88vz4xz/O3Llz8+CDD2bHHXfMvvvuWxjz8ccf51e/+lWqqqqSJIcffnh23HHHavPst99+2W+//XLPPfcUitxdu3bNueeeu9z7mTNnTk4++eRCgXvHHXfMb3/726yzzjrVxs2aNSuXX355br/99lRUVOSXv/zlMovSC5133nlp2bJlLr300sXW/cILL+TEE0/MrFmzUl5enptvvjl9+vRZ4jx//OMfCwXusrKynHrqqTnuuONSt27dwpi33347p556al566aVC6h0A4NvCxpMAAFBixo8fn48//jhJ0qNHjxx//PFLLHAnSYsWLXLQQQfluOOO+0rXnDBhQqEPdosWLXLbbbflBz/4wWIF7iTZc889q6XMBw0aVChUL2qLLbbIKaecUnh/wQUXZMKECYX3Z599diZPnpwk2XjjjXPGGWd8pXv4oqFDh+att95Kkuy2224ZPHjwYgXuJGnatGnOP//8HHDAAUkWtDC5/fbba3yNLxa4k2TrrbfO6aefXnj/0EMPLfH8mTNnZsiQIYX3ffr0yYknnlitwJ0sSIoPHTo0bdq0ydy5c2u0NgCAVYUiNwAAlJhZs2YVXq/IpoVfxbBhwwptT0466aQlFoMXte2222b77bdPsqAlyKuvvrrEcT/96U/Ts2fPJAsKumeccUbmz5+f22+/PU888USSpGHDhhk4cGAaNmxYW7eTuXPn5k9/+lOSpEGDBunXr1/q1Fn2fx6ddtpphaL+gw8+uNxrHHrooencufNSP99vv/0KG0W+/fbb1f5dF3rwwQfz6aefJknatWuX448/fqnztWzZstovDQAAvi0UuQEAoMSstdZahdcjRozI22+//bVfc9END/fZZ58anbNo3+qRI0cucUydOnXyu9/9Li1atEiSvPjiiznnnHMyYMCAwpgzzjgjG2+88ZdY9dKNGTMmU6dOTbKgr3erVq2We07btm3TsWPHJMm4ceMyc+bMZY7fY489lvl506ZN06FDhyRJVVVVPvjgg8XGjBgxovB6r732SoMGDZY551577bXUVD8AwKpKT24AACgxa621Vrp165aXXnopM2fOzIEHHpj99tsvu+22W3r06JHVVlutVq/38ccf55133kmS1K9fv1orkmV58803C68nTpy41HHt2rXLhRdemJNPPjnJgg0bF9pxxx3Tu3fvL7HqZXvppZcKrydNmpQLLrigRud98sknSRYUpSdNmrTMvtw1KcwvLO4nWWKSe+zYsYXX3bp1W+58TZs2zcYbb6wvNwDwraLIDQAAJejiiy/OkUcemSlTpmTOnDm5/fbbc/vtt6devXrp3Llztt5662y//fbp1avXYv2bV9TCjRmT6m0+VsTC4vDS/OAHP8ghhxySv/zlL4VjrVq1Sv/+/Vf4WjWxsNd3krz++ut5/fXXV3iOGTNmLPPz5W1MmaRa6nrevHmLfb7ohqGLJviXZa211lLkBgC+VbQrAQCAErThhhvm/vvvT+/evasVU+fNm5cxY8Zk6NChOfbYY7PzzjtXKxx/Gctry1ETC/t5L8sXW4Z069YtrVu3/srXXpJi3NOSNuVcUXPmzCm8rmlCv7aT/AAA33SS3AAAUKJat26dc845J2eeeWZeeuml/Oc//8moUaPy4osvFlpffPTRRznnnHPy+uuv55xzzvlS12ncuHHhddOmTZfaX/ur+M9//pMhQ4ZUO/bEE0/kgQceyL777lvr11u0ENy7d+8v/Wy+bo0bNy4U5BduQLk8NR0HALCqkOQGAIAS16BBg2yzzTY56aSTMmTIkAwfPjxDhgzJlltuWRhzyy23ZPTo0V9q/kUT1rNmzar1IurMmTNz5plnFpLRCzd3TJILLrhgiRsyflWLJsSnTJlS6/PXlpYtWxZeL6uv+aJqOg4AYFWhyA0AAKuY+vXrZ8cdd8xNN91UbfPDf/7zn19qvjXXXLNaP+hRo0Z95TUu6vzzzy8UsjfccMPcfffd6dmzZ5IFBfAzzjij1luDdOnSpfB61KhRqaqqWsFVF8cmm2xSeL3oZplLM3v27IwbN+5rXBEAwDePIjcAAKyiGjRokO9+97uF91OnTv3Sc33ve98rvL7tttu+yrKque+++/LXv/41yYLi/MCBA9O4ceP87ne/S/PmzZMkI0eOzHXXXbfMeRo2bFh4PXfu3OVed8stt8zqq6+eJJk0aVL+8Y9/fNlb+FotLPYnycMPP7zce/vb3/6WioqKr3tZAADfKIrcAABQYmbMmJHKysoajV20dcWirS9W1DHHHJO6desmSR577LHcc889NT63vLx8icfff//9XHDBBYX3p59+ejp37pwkadeuXbXPrrvuumUmmVu0aFF4PXny5OWuqUGDBjnyyCML7/v165ePPvpouectVKwWJ/vss0+hf/jEiRMX61u+qI8//jhXX311UdYFAPBNosgNAAAl5oknnsjuu++eG264IRMmTFjimIqKitx666159NFHC8d23HHHL33NddZZJz/72c8K73/zm9/k0ksvzbRp05Y4ft68eXn66adzxhln5IADDljs8/nz5+eMM87I7NmzkyTbbbddjj766Gpj9thjjxx44IGF+c4444zChppftNFGGxVev/zyy/nwww+Xe09HH3104byPPvooBx10UB5++OGl/gJh2rRpueOOO3LAAQfkhhtuWO78taFZs2b56U9/Wnh/9dVX549//ONi7VveeeedHHPMMZk8eXLq169flLUBAHxT1FvZCwAAAFbce++9l9/97nf53e9+l7XXXjudOnUqJLWnTJmSl19+OdOnTy+M32effdKjR4+vdM0+ffrkgw8+yL333puqqqrceOONueWWW7L55ptnnXXWSaNGjTJ79ux88MEHef311zNnzpwk1VPWC1177bWF3t4tWrTIgAEDlthX+5xzzsnIkSPz7rvv5r333stFF12UAQMGLDauTZs26d69e0aNGpXPP/88++23X3bYYYe0adMmdeosyPZ06NAhP/nJTwrnNGnSJNddd12OOuqoTJgwIeXl5Tn11FOzxhprpFu3bmndunWqqqoyY8aMvPnmm3n33XcLBfBtt932Kz3LFXH88cfnmWeeyYsvvpiqqqoMHDgww4YNyzbbbJPGjRvn3XffzciRIzN//vx07do1HTp0KLSAAQD4NlDkBgCAEtO4ceOUlZUVNkv88MMPl5pcrlOnTn70ox/lN7/5zVe+bllZWQYMGJDNNtss11xzTWbMmJG5c+dm1KhRS92MsqysbLHi+osvvlitx/ZFF12Utm3bLvH8Jk2a5PLLL8+Pf/zjzJs3L/fee2922mmn7LnnnouNPfvss3PkkUdm9uzZ+eSTT/LQQw9V+3ybbbapVuROFhS+77777px33nl59NFHU1VVlY8//niZm3Suvvrq1Tb0/Lo1aNAgQ4YMyamnnpqnnnoqyYIWMF+8v+7du+fqq6/OFVdcUbS1AQB8EyhyAwBAidljjz3y9NNP5+mnn86LL76Y119/Pe+//34++eSTJAtaXKy33nrZcssts//++2fDDTes1ev37t07BxxwQO6///48++yzee211zJt2rRUVFSkSZMmadu2bTbaaKNss8022WmnnbLWWmsVzp01a1bOOOOMQruNQw45JLvtttsyr9elS5f06dMnv//975Mk5513Xrp161Zt3iTZYost8sADD+TWW2/NiBEj8v7772fOnDmLtfb4ohYtWuSqq67KG2+8kYceeigjRozIhAkTMn369NSpUyerr7561llnnWy66abZbrvt8t3vfrfaRpfF0LRp01x//fX5+9//nnvuuSdjxozJ9OnTs8Yaa2SDDTbIPvvsk3333VerEgDgW6msamH8AwAAAAAASoyNJwEAAAAAKFmK3AAAAAAAlCxFbgAAAAAASpYiNwAAAAAAJUuRGwAAAACAkqXIDQAAAABAyVLkBgAAAACgZClyAwAAAABQshS5AQAAAAAoWYrcAAAAAACULEVuAAAAAABKliI3AAAAAAAlS5EbAAAAAICSpcgNAAAAAEDJUuQGAAAAAKBkKXIDAAAAAFCyFLkBAAAAAChZitwAAAAAAJQsRW4AAAAAAEpWvZW9AKC0dbx64MpeAgAAAMBXMv6Uvit7CXwFktwAAAAAAJQsRW4AAAAAAEqWIjcAAAAAACVLkRsAAAAAgJKlyA0AAAAAQMlS5AYAAAAAoGQpcgMAAAAAULIUuQEAAAAAKFmK3AAAAAAAlCxFbgAAAAAASpYiNwAAAAAAJUuRGwAAAACAkqXIDQAAAABAyVLkBgAAAACgZClyAwAAAABQshS5AQAAAAAoWYrcAAAAAACULEVuAAAAAABKliI3AAAAAAAlS5EbAAAAAICSpcgNAAAAAEDJUuQGAAAAAKBkKXIDAAAAAFCyFLkBAAAAAChZitwAAAAAACzT4MGDc9BBB6V79+7p1atXTjrppIwfP36Z59x55535yU9+kq233jpbb711jjrqqIwePbramKqqqlx11VXZfvvt06VLlxx11FF55513VmhtitwAAAAAACzT888/n8MOOyx33nlnhg4dmnnz5uXYY4/NnDlzlnrOiBEjsvfee2fYsGH585//nLXWWivHHHNMPvroo8KYIUOG5JZbbsn555+fO++8M6uttlqOPfbYfP755zVeW1lVVVXVV7o74Fut49UDV/YSAAAAAL6S8af0XdlLWCkqKipSUVFR7ViDBg3SoEGD5Z47bdq09OrVK7feemu23nrrGl1v/vz52XrrrXPuuedm//33T1VVVXbYYYccffTROfbYY5MkM2fOzHbbbZcBAwZk7733rtG89Wo0CgAAAACAWlM5aeOVvYQM/svJGTRoULVjffr0ycknn7zcc2fOnJkkad68eY2v9+mnn2bevHmFcyZMmJDy8vJst912hTHNmjVL165dM2rUKEVuAAAAAACW7oQTTsjRRx9d7VhNUtyVlZW55JJL0qNHj2y8cc2L9ZdffnnWXHPNQlG7vLw8SdKqVatq41q1apUpU6bUeF5FbgAAAACAb6Gatib5on79+mXcuHG57bbbanzOH//4x/ztb3/LsGHD0rBhwxW+5rIocgMAAAAAFFllKlf2ElLnS5xzwQUX5F//+lduvfXWtGvXrkbn3HDDDfnjH/+YoUOHpnPnzoXjbdq0SZJMnTo1a665ZuH41KlTq41bni9zHwAAAAAAfItUVVXlggsuyGOPPZabb745HTp0qNF5Q4YMybXXXpvrr78+W2yxRbXP2rdvnzZt2uS5554rHJs1a1ZefvnldO/evcZrk+QGAAAAACiy+VUrP8m9IsXhfv365a9//WuuvfbaNGnSpNBPu1mzZmnUqFGS5Mwzz0zbtm3Tt2/fJAtalFx99dUZOHBgvvOd7xTOady4cZo0aZKysrIcccQRue6667Luuuumffv2ueqqq7Lmmmtm1113/VruAwAAAACAb6Hbb789SdK7d+9qx/v3758DDzwwSTJx4sTUqfO/5iF//vOfM3fu3JxyyinVzunTp09OPvnkJMlxxx2XTz/9NOeee24++eSTbLnllrn++utXqG93WVVVVdWXuiuAJB2vHriylwAAAADwlYw/pW/Rr/n5xI5Fv+YXNVxr/MpeQq2Q5AYAAAAAKLLKyB7XFhtPAgAAAABQshS5AQAAAAAoWdqVAAAAAAAUWWUqV/YSVhmS3AAAAAAAlCxJbgAAAACAIptfZePJ2iLJDQAAAABAyVLkBgAAAACgZGlXAgAAAABQZJXRrqS2SHIDAAAAAFCyJLkBAAAAAIpsviR3rZHkBgAAAACgZClyAwAAAABQsrQrAQAAAAAoMhtP1h5JbgAAAAAASpYkNwAAAABAkc2vkuSuLZLcAAAAAACULEVuAAAAAABKlnYlAAAAAABFVrmyF7AKkeQGAAAAAKBkSXIDAAAAABTZ/Nh4srZIcgMAAAAAULIUuQEAAAAAKFnalQAAAAAAFNl83UpqjSQ3AAAAAAAlS5EbAAAAAICSpV0JAAAAAECRVa7sBaxCJLkBAAAAAChZktwAAAAAAEU2P2UrewmrDEluAAAAAABKliI3AAAAAAAlS7sSAAAAAIAiq6xa2StYdUhyAwAAAABQsiS5AQAAAACKzMaTtUeSGwAAAACAkqXIDQAAAABAydKuBAAAAACgyLQrqT2S3AAAAAAAlCxJbgAAAACAIquskuSuLZLcAAAAAACULEVuAAAAAABKlnYlAAAAAABFZuPJ2iPJDQAAAABAyVLkBgAAAACgZGlXAgAAAABQZPPlj2uNJwkAAAAAQMmS5AYAAAAAKLLKKhtP1hZJbgAAAAAASpYiNwAAAAAAJUu7EgAAAACAIpsf7UpqiyQ3AAAAAAAlS5IbAAAAAKDI5lfJH9cWTxIAAAAAgJKlyA0AAAAAQMnSrgQAAAAAoMgq5Y9rjScJAAAAAEDJkuQGAAAAACiy+Slb2UtYZUhyAwAAAABQshS5AQAAAAAoWdqVAAAAAAAU2fwq+ePa4kkCAAAAAFCyJLkBAAAAAIqs0saTtUaSGwAAAACAkqXIDQAAAABAydKuBAAAAACgyObLH9caTxIAAAAAgJKlyA0AAAAAQMnSrgQAAAAAoMjmV8kf1xZPEgAAAACAkiXJDQAAAABQZJXyx7XGkwQAAAAAYJkGDx6cgw46KN27d0+vXr1y0kknZfz48cs8Z9y4cTn55JOzyy67pFOnTrnpppsWG3PNNdekU6dO1b722GOPFVqbJDcAAAAAAMv0/PPP57DDDssWW2yR+fPn54orrsixxx6bhx56KI0bN17iOZ9++mnat2+fPfbYI/3791/q3BtttFGGDh1aeF+3bt0VWpsiNwAAAABAkc2vKlvZS1ghN9xwQ7X3AwYMSK9evfLKK69k6623XuI5Xbp0SZcuXZIkAwcOXOrcdevWTZs2bb702hS5AQAAAAC+hSoqKlJRUVHtWIMGDdKgQYPlnjtz5swkSfPmzb/yOt59991sv/32adiwYbp165a+fftm7bXXrvH5itwAAAAAAEU2/xuwXeLgwYMzaNCgasf69OmTk08+eZnnVVZW5pJLLkmPHj2y8cYbf6U1dOnSJf3798/666+f8vLy/OEPf8hhhx2WBx98ME2bNq3RHIrcAAAAAADfQieccEKOPvroasdqkuLu169fxo0bl9tuu+0rr2GnnXYqvO7cuXO6du2anXfeOQ8//HAOOeSQGs2hyA0AAAAA8C1U09Yki7rgggvyr3/9K7feemvatWtX62taffXVs9566+W9996r8TmK3AAAAAAARVZZtfLblayIqqqqXHjhhXnsscdyyy23pEOHDl/LdWbPnp33339/hTaiVOQGAAAAAGCZ+vXrl7/+9a+59tpr06RJk5SXlydJmjVrlkaNGiVJzjzzzLRt2zZ9+/ZNsmBjy7feeqvw+qOPPsrYsWPTuHHjrLvuukmSSy+9NDvvvHPWXnvtTJ48Oddcc03q1KmT//u//6vx2hS5AQAAAACK7Juw8eSKuP3225MkvXv3rna8f//+OfDAA5MkEydOTJ06/7uvyZMnZ//99y+8v/HGG3PjjTdmm222yS233JIkmTRpUk4//fRMnz49LVu2zJZbbpk777wzLVu2rPHayqqqqqq+7I0BdLx64MpeAgAAAMBXMv6UvkW/5m1v9iz6Nb/oJxuOWNlLqBWl9esCAAAAAABYhHYlAAAAAABFNr+qbGUvYZUhyQ0AAAAAQMmS5AYAAAAAKLJK+eNa40kCAAAAAFCyFLkBAAAAAChZ2pUAAAAAABTZ/Cr549riSQIAAAAAULIUuQEAAAAAKFnalQAAAAAAFFllylb2ElYZktwAAAAAAJQsSW4AAAAAgCKz8WTt8SQBAAAAAChZitwAAAAAAJQs7UoAAAAAAIpsvvxxrfEkAQAAAAAoWZLcAAAAAABFVllVtrKXsMqQ5AYAAAAAoGQpcgMAAAAAULK0KwEAAAAAKDIbT9YeTxIAAAAAgJIlyQ0AAAAAUGSVVfLHtcWTBAAAAACgZClyAwAAAABQsrQrAQAAAAAosvkpW9lLWGVIcgMAAAAAULIUuQEAAAAAKFnalQAAAAAAFFlllfxxbfEkAQAAAAAoWZLcAAAAAABFZuPJ2iPJDQAAAABAyVLkBgAAAACgZGlXAgAAAABQZDaerD2eJAAAAAAAJUuSGwAAAACgyOZLctcaTxIAAAAAgJKlyA0AAAAAQMnSrgQAAAAAoMgqU7ayl7DKkOQGAAAAAKBkSXIDAAAAABSZjSdrjycJAAAAAEDJUuQGAAAAAKBkaVcCAAAAAFBklVU2nqwtktwAAAAAAJQsSW4AAAAAgCKbL39cazxJAAAAAABKliI3AAAAAAAlS7sSAAAAAIAis/Fk7ZHkBgAAAACgZClyAwAAAABQsrQrAQAAAAAoskr541rjSQIAAAAAULIkuQEAAAAAimy+jSdrjSQ3AAAAAAAlS5EbAAAAAICSpV0JAAAAAECRVWpXUmskuQEAAAAAKFmS3AAAAAAARVZZJX9cWzxJAAAAAABKliI3AAAAAAAlS7sSAAAAAIAimx8bT9YWSW4AAAAAAEqWJDcAAAAAQJFVVkly1xZJbgAAAAAASpYiNwAAAAAAJUu7EgAAAACAIquskj+uLZ4kAAAAAAAlS5IbAAAAAKDIKmPjydoiyQ0AAAAAQMlS5AYAAAAAoGRpVwIAAAAAUGTzq7QrqS2S3AAAAAAALNPgwYNz0EEHpXv37unVq1dOOumkjB8/fpnnjBs3LieffHJ22WWXdOrUKTfddNMSx/3pT3/KLrvski222CKHHHJIRo8evUJrU+QGAAAAAGCZnn/++Rx22GG58847M3To0MybNy/HHnts5syZs9RzPv3007Rv3z59+/ZNmzZtljjmb3/7W/r375+f//znuffee9O5c+cce+yxmTp1ao3Xpl0JAAAAAECRVVaVVv74hhtuqPZ+wIAB6dWrV1555ZVsvfXWSzynS5cu6dKlS5Jk4MCBSxwzdOjQ/PCHP8xBBx2UJOnXr1/+9a9/5e67787xxx9fo7WV1pMEAAAAAKBWVFRUZNasWdW+KioqanTuzJkzkyTNmzf/Std/5ZVXst122xWO1alTJ9ttt11GjRpV43kkuQEAAAAAiqzyG7Dx5ODBgzNo0KBqx/r06ZOTTz55medVVlbmkksuSY8ePbLxxht/6et//PHHmT9/flq1alXteKtWrZbb73tRitwAAAAAAN9CJ5xwQo4++uhqxxo0aLDc8/r165dx48bltttu+7qWtkIUuQEAAAAAvoUaNGhQo6L2oi644IL861//yq233pp27dp9peuvscYaqVu37mKbTE6dOjWtW7eu8Tx6cgMAAAAAFFllylb614qoqqrKBRdckMceeyw333xzOnTo8JWfQYMGDbLZZpvlueee+99zqazMc889l+7du9d4HkluAAAAAACWqV+/fvnrX/+aa6+9Nk2aNEl5eXmSpFmzZmnUqFGS5Mwzz0zbtm3Tt2/fJAs2lnzrrbcKrz/66KOMHTs2jRs3zrrrrpskOfroo/OrX/0qm2++ebp06ZKbb745n376aQ488MAar02RGwAAAACgyL4JG0+uiNtvvz1J0rt372rH+/fvXyhIT5w4MXXq/K95yOTJk7P//vsX3t9444258cYbs8022+SWW25Jkuy1116ZNm1arr766pSXl2eTTTbJ9ddfv0LtSsqqqqqqvuyNAXS8euDKXgIAAADAVzL+lL5Fv+aPhx9f9Gt+0e3b/nFlL6FW6MkNAAAAAEDJ0q4EAAAAAKDIKqvkj2uLJwkAAAAAQMmS5AYAAAAAKLJS23jym0ySGwAAAACAkqXIDQAAAABAydKuBAAAAACgyCqjXUltkeQGAAAAAKBkKXIDAAAAAFCytCsBAAAAACiyyirtSmqLJDcAAAAAACVLkhsAAAAAoMgkuWuPJDcAAAAAACVLkRsAAAAAgJKlXQkAAAAAQJFpV1J7JLkBAAAAAChZktwAAAAAAEUmyV17JLkBAAAAAChZitwAAAAAAJQs7UoAAAAAAIqsMtqV1BZJbgAAAAAASpYkNwAAAABAkdl4svZIcgMAAAAAULIUuQEAAAAAKFnalQAAAAAAFJl2JbVHkhsAAAAAgJIlyQ0AAAAAUGSS3LVHkhsAAAAAgJKlyA0AAAAAQMnSrgQAAAAAoMi0K6k9ktwAAAAAAJQsRW4AAAAAAEqWdiUAAAAAAEVWpV1JrZHkBgAAAACgZElyAwAAAAAUWWUkuWuLJDcAAAAAACVLkRsAAAAAgJKlXQkAAAAAQJFV2niy1khyAwAAAABQsiS5AQAAAACKrEqSu9ZIcgMAAAAAULIUuQEAAAAAKFnalQAAAAAAFJmNJ2uPJDcAAAAAACVLkhsAAAAAoMhsPFl7JLkBAAAAAChZitwAAAAAAJQs7UoAAAAAAIrMxpO1R5IbAAAAAICSJckNAAAAAFBkVVUrewWrDkluAAAAAABKliI3AAAAAAAlS7sSAAAAAIAiq4yNJ2uLJDcAAAAAACVLkRsAAAAAgJKlXQkAAAAAQJFVVWlXUlskuQEAAAAAKFmS3AAAAAAARVYpyV1rJLkBAAAAAChZitwAAAAAAJQs7UoAAAAAAIqsqmplr2DVIckNAAAAAEDJkuQGAAAAACiyKhtP1hpJbgAAAAAASpYiNwAAAAAAJUu7EgAAAACAItOupPZIcgMAAAAAULIkuQEAAAAAiqxSkrvWSHIDAAAAAFCyFLkBAAAAAChZ2pUAAAAAABRZVdXKXsGqQ5IbAAAAAIBlGjx4cA466KB07949vXr1ykknnZTx48cv97yHH344e+yxR7bYYovss88+efLJJ6t9ftZZZ6VTp07Vvo499tgVWpskNwAAAAAAy/T888/nsMMOyxZbbJH58+fniiuuyLHHHpuHHnoojRs3XuI5L774Yvr27ZvTTz89O++8cx588MH8/Oc/zz333JONN964MG6HHXZI//79C+8bNGiwQmtT5AYAAAAAKLKqqrKVvYQVcsMNN1R7P2DAgPTq1SuvvPJKtt566yWeM2zYsOywww756U9/miQ59dRT8+yzz+bWW2/NBRdcUBjXoEGDtGnT5kuvTbsSAAAAAIBvoYqKisyaNavaV0VFRY3OnTlzZpKkefPmSx3z0ksvpVevXtWObb/99nnppZeqHXv++efTq1ev7L777jnvvPPy8ccfr9B9SHIDAAAAABTZNyHJPXjw4AwaNKjasT59+uTkk09e5nmVlZW55JJL0qNHj2ptR75oypQpad26dbVjrVq1ypQpUwrvd9hhh+y2225p37593n///VxxxRU57rjjcscdd6Ru3bo1ug9FbgAAAACAb6ETTjghRx99dLVjNemH3a9fv4wbNy633XbbV17D3nvvXXi9cOPJXXfdtZDurgntSgAAAAAAvoUaNGiQpk2bVvtaXpH7ggsuyL/+9a/cfPPNadeu3TLHtm7dulpqO0mmTp26WLp7UR06dMgaa6yRd999t8b3ocgNAAAAAFBkVd+ArxVab1VVLrjggjz22GO5+eab06FDh+We061btwwfPrzasWeffTbdunVb6jmTJk3K9OnTV2gjSkVuAAAAAACWqV+/fnnggQcycODANGnSJOXl5SkvL89nn31WGHPmmWdm4MCBhfdHHHFEnnrqqdx444156623cs0112TMmDE5/PDDkySzZ8/OpZdempdeeikTJkzIc889l5NOOinrrrtudthhhxqvTU9uAAAAAIAi+yZsPLkibr/99iRJ7969qx3v379/DjzwwCTJxIkTU6fO/3LVPXr0yOWXX57f//73ueKKK7LeeuvlD3/4Q2Gzyrp16+aNN97Ifffdl5kzZ2bNNdfMd7/73fziF7+oUW/whcqqqqpWNJkOUNDx6oHLHwQAAADwDTb+lL5Fv+bGd19Y9Gt+0RsH/XZlL6FWaFcCAAAAAEDJ0q4EAAAAAKDY9NeoNZLcAAAAAACULEluAAAAAIAiK7WNJ7/JJLkBAAAAAChZitwAAAAAAJQs7UoAAAAAAIqsysaTtUaSGwAAAACAkiXJDQAAAABQZDaerD2S3AAAAAAAlCxFbgAAAAAASpZ2JQAAAAAAxaZdSa2R5AYAAAAAoGQpcgMAAAAAULK0KwEAAAAAKLKqqpW9glWHJDcAAAAAACVLkhsAAAAAoNgkuWuNJDcAAAAAACVLkRsAAAAAgJKlXQkAAAAAQJFVVZWt7CWsMiS5AQAAAAAoWZLcAAAAAADFZuPJWiPJDQAAAABAyVLkBgAAAACgZGlXAgAAAABQZDaerD2S3AAAAAAAlCxJbgAAAACAYrPxZK2R5AYAAAAAoGQpcgMAAAAAULK+UruSZ599NkOHDs1///vfzJw5M5WVlYuNKSsry6uvvvpVLgMAAAAAsIqx8WRt+dJF7kcffTSnnXZaKisrs/baa6djx46pW7duba4NAAAoUT2/0z7HdN8yXdu2S9MGDfPO9I8z5MUXcv/rry3zvLWbNsuFO++abdt3yOy5c3PP2Fdy2bNPZX7V/5pW9vxO+5y9w/eyUatWmThzZv7wwojcPfaVavP07tItx/XYKm0aN8nYKeU5/8l/ZPRHk76WewUAYOX60kXuP/zhD2nYsGGuvfba9OrVqzbXBAAAlLgt11o7r00pz+D/vJApn87OLut1zOW77ZmZn1fkH++MX+I5dcrKcsO+B6R8zpwc/Jfbs2aTJrn8B3tmXmVlLn/u6SRJ+9VXzw37Hpjb/vtyTnv0b9muwzrp//0fZPLsWXnqvXeTJHtv1Cm/2WGn/PYfj+eljybm6G5b5ub9Dsqut9yYqZ9+WrRnAACwTDaerDVfuif322+/nb333luBGwAAWMy1/3k+Vw5/Ni9O+jDvzZiRm14elX+/+05232CjpZ6zwzrrZsOWrXL6o3/L2CnlefLdd3Llc8+kd5duqV9nwX+6HLZ517z/yYxc8vSTeevjabll9Et5+M03ckz3LQvzHNt9y9wx5r+5a+wreXPatJzzj8fy6by5OWTTLb72+wYAoPi+dJG7RYsWadSoUW2uBQAAWIU1a9gw0z//bKmfd2+3dl6fOiVTPp1TOPbv995Js4YNs1Gr1gvGrLV2nv1/ie2Fnnr3nfRot3aSpH6dOtl8zbZ55v33Cp9XJXnm/ffSfa21avFuAAD4pvjSRe7dd989zz33XObNm1eb6wEAAFZBe220cbZo2zZ3vTpmqWPaNGmSKXPmVDu28H2bxo0L/7toEXzhmGYNG6Zh3XpZY7XVUq9OnUyZM3uxMW0aN6mNWwEAqB1V34CvVcSXLnKffvrpadasWU477bR8+OGHtbkmAABgFbJt+w753a575DdPPJZx06au7OUAALCK+dIbT+6zzz6ZN29eXn755Tz++ONZffXV07Rp08XGlZWV5fHHH/9KiwQAAErTNt9pnyH/t38u+vc/c+9rry5zbPns2enatl21Y63/X4K7/P8lusvnzEnr1RovNmbm55/n8/nz8vGnlZlXWZnWX0htt27cOOVfSHcDALBq+NJJ7qqqqtStWzdrrbVW1lprrTRp0iRVVVWLfVVWVtbKQidMmJBOnTpl7NixSx1zzz33ZKuttqqV631dzjrrrJx00kkrexkrRU3+DVcltfVv/cV5evfunYsvvniZ5+yyyy656aabvvK1AQC+ip7faZ8b9jkgv3v2qfz5lf8ud/yoSR+mU6vWabXaaoVj23dYNzM//zxv/r8E+KiJH2a7DutUO2/7ddbNi5MW/HXp3MrKjJn8UbUxZUm267BORk2cWAt3BQBQS6rKVv7XKuJLJ7n/8Y9/1OY6asVee+2VnXbaaWUvo9adddZZ+eSTT3LttddWOz59+vRceOGF+ec//5k6derkBz/4Qc4+++w0aaLX4JcxYcKEfP/73899992XTTbZZGUvZ6muueaa1Kv3pX90AQCKYtv2HXL9PgfkppdezMNvvlFIZM+dX5kZ/2/zyR903DBnbLdDdrt1aJLkqffezZvTpmbgD/bKgGf+nTaNG+f0XtvnltEvpWL+/CTJn8a8nN5du+dX390xf3l1TLZr3yF7bdQpxz5wT+HaN4wamct32yP//WhSXv5oUo7u1iON69VfZj9wAABK1ypVKWvUqFEaNWq01M8rKirSoEGDIq7o6/XLX/4y5eXlGTp0aObOnZvf/OY3OffcczNw4MCVvbRV2sr+PmrRosXXfo2VfY8AQOk7sPOmaVy/fk7aumdO2rpn4fjwCe/nJ/fcmSRp1rBhNmjZsvBZZVVVjn3w3ly08665+5AfZ868ubln7Ku5cvgzhTETPvkkxz5wT87Zcecc1a17Js2alV8/8fc89d67hTEPjXs9LVdbLadt+920btI4Y8vLc9T9dy+2YSUAwMpUtQpt/LiyfeOK3JWVlbnhhhty5513ZuLEiWndunUOPfTQ7LPPPkmS999/P5dccklGjx6dddddN/369Uv37t2TLGhXcskll+Q///lPkgWJ18cffzyHH354rrvuunz44Yd57bXX0rt372y00UZJkvvvvz/16tXLj3/84/ziF79IWdnSY/pDhw7NPffck/fffz/NmzfPzjvvnDPOOKOQnF54vfvvv79wzk033ZRhw4YtNfk+evToHH/88TnmmGNy/PHHL/b5Nddck3vvvTdJ0qlTpyTJsGHD0rp16zz11FO56667ssUWWyRJzjnnnBx//PE588wz07Zt22U+54XP6rLLLsuAAQMyadKk7LTTTrn00kvzyCOP5JprrsnMmTOz33775de//nXq1q2bJLnvvvsybNiwvP3222ncuHG23Xbb/OY3v0mrVq2SJDNmzMgFF1yQZ555JnPmzEm7du1ywgkn5KCDDlpsDfPnz88555yTUaNG5cYbb8zaa6+9zDV/8sknufzyy/P4449n5syZWXfdddO3b9/svPPOSZJHH300V199dd59992sueaaOfzww3PMMccUzt9ll13ywx/+MO+++24eeeSRNG/ePD/72c9y6KGHJkm+//3vJ0n233//JMk222yTW265pZCk32KLLfKnP/0pDRo0yD/+8Y+8/vrrufjii/PSSy9ltdVWyw9+8IOcddZZXypJ/8gjj+QPf/hD3n333ay22mrZZJNNcu2116Zx48aLje3du3c6d+6cs88+O0kyderUnH322Xn22WfTunXrnHrqqUt8dpdeemmeeOKJVFRUZPPNN89vfvObdO7cOcnSf1YAAL6sMx9/NGc+/ugyx9w99pXcPfaVasc+nDkzxzxw7zLPG/HBhOxz+y3LHHPL6Jdyy+iXarRWAABKW42L3IMGDUpZWVkOO+ywtGjRIoMGDarReWVlZfn5z39e4wUNHDgwf/nLX/LrX/86W265ZSZPnpy333678PmVV16ZX/3qV1l33XVz5ZVXpm/fvvn73/++1PYN7733Xh599NEMGjQoder8rwX5vffem4MPPjh/+ctfMmbMmJx77rlZe+2188Mf/nCZ93L22Wenffv2ef/999OvX79cdtllOf/882t8f4t67rnncvLJJ+eMM84oFFq/6Jhjjslbb72VWbNmpX///kmS5s2b54EHHsjqq69eKHAnyXbbbZc6depk9OjR2W233ZZ7/c8++yy33HJLrrzyysyePTt9+vRJnz590qxZs/zxj3/M+++/n5NPPjk9evTIXnvtlSSZN29efvGLX6Rjx46ZOnVqBgwYkLPOOitDhgxJklx11VV56623MmTIkKyxxhp577338tlnny127YqKipx++un54IMPctttt6XlIgmeJamsrMxxxx2X2bNn57LLLss666yTN998s/BvOmbMmJx66qnp06dP9tprr4waNSr9+vVLixYtcuCBBxbmGTp0aE455ZSceOKJefTRR3P++edn6623TseOHfOXv/wlhxxySG666aZsuOGGqV+/frV/q6ZNm2bo0AV/Sjtnzpwce+yx6d69e+66665MnTo155xzTi688MIMGDBguc9+UZMnT07fvn1zxhlnZNddd83s2bPzn//8J1U1/HXeWWedlcmTJ2fYsGGpV69eLrrookydOrXamF/84hdp2LBhhgwZkmbNmuWOO+7IkUcemUcffbSQDF/azwoAAAAAfJOtcJF7r732+tqK3LNmzcqwYcNy7rnn5oADDkiSrLPOOtlqq60yYcKEJAuKvt/73veSJKecckr23nvvvPvuu9lggw2WOOfcuXPzu9/9brEi6lprrZXf/OY3KSsrS8eOHfPGG2/kpptuWmaR+6ijjiq8bt++fU499dScd955X6rI/dhjj+XMM8/MxRdfXCggL0mTJk3SqFGjVFRUpE2bNoXjU6ZMWeye6tWrl+bNm6e8vLxGa5g7d27OP//8rLPOgk15dt999zzwwAN55pln0qRJk2y44Ybp2bNnhg8fXljjwQcfXDi/Q4cOOfvss3PwwQdn9uzZadKkST788MNssskmheJ7+/btF7vu7Nmzc/zxx6eioiLDhg1Ls2bNlrvWZ599NqNHj87f/va3rL/++oXrLzR06ND06tWr8L22/vrr580338wNN9xQrci944475rDDDkuSHHfccbnpppsyYsSIdOzYsfA8W7RoUe1ZJ0njxo1z0UUXFVp43HnnnamoqMill15aSFufe+65OfHEE/PLX/4yrVu3Xu49LVReXp558+Zlt912y3e+850k/0vtL8/bb7+df//73/nLX/6SLl26JMli31P/+c9/Mnr06Dz33HOF9f/qV7/K448/nkcffbTwC5al/awAAAAA8DXQrqTW1LjIPWzYsCQptJRY+L42jR8/PhUVFdl2222XOmbR4t/CQuS0adOWWuRee+21l1i069q1a7XWJN26dcvQoUMzf/78DBkyJIMHDy589tBDD2XttdfOs88+m8GDB2f8+PGZNWtW5s+fn88//zyffvppVltkB/jlGT16dP71r3/l6quvzq677lo4/uGHH2bvvfcuvD/hhBNy4okn1njeFbXaaqsVCtxJ0rp163znO9+p1m6jdevWmTZtWuH9mDFjMmjQoLz22muZMWNGIW08ceLEbLjhhvnxj3+cU045Ja+++mq++93vZtddd02PHj2qXbdv375p165dbr755mX2UF/U2LFj065du0KB+4vGjx9faDeyUI8ePTJs2LDMnz+/0G5l0e+fsrKytG7derHU85JsvPHG1XpUv/XWW+nUqVO1diI9evRIZWVl3n777RUqcnfu3Dm9evXKPvvsk+233z7bb799dt999zRv3ny557711lupV69eNt9888KxDTbYIKuvvnrh/euvv545c+akZ8+e1c797LPP8t577xXeL+1nBQAAAAC+yWpc5N5mm21y9dVXp6ysLF27ds0222xT64tp2LDhcscs2kJiYZG6srJyqeNXpPi80I9+9KPsueeehfdrrrlmJkyYkBNOOCE//vGPc9ppp6V58+YZOXJkzj777MydOzerrbZaysrKFmsxMW/evMXm79ChQ1q0aJG77rorO+20U+Ge1lxzzdx3332Fccsqcn6x+LzwWjNmzFgshbw0X2zxUlZWtsRjC5/vwhYd22+/fS6//PKsscYamThxYo499tjMnTs3SbLTTjvln//8Z5588sk888wzOeqoo3LYYYflV7/6VWHOnXbaKQ888EBGjRqVXr161WitNS2GL8+S7q8mbUG+zPdRTdWtWzdDhw7Niy++mGeeeabQQubOO++sllb/smbPnp02bdrkllsW71u5aIr+67xHAAAAAL6gaul7A7JiVqjx7rXXXpsjjjgiW2+9dY466qhce+21GTly5BILuV/Geuutl0aNGmX48OG1Mt+yjB49utr7l19+Oeuuu27q1q2bFi1aZN111y181atXL6+88kqqqqpy1llnpVu3bll//fUzefLkanO0bNkyU6ZMqVY0HTt27GLXXmONNXLzzTfnvffey6mnnlooENerV6/adRf2Sq5fv/5ihfzu3bvnk08+yZgxYwrHhg8fnsrKykLbito2fvz4TJ8+Pb/85S+z1VZbZYMNNlhiCrply5Y54IADcvnll+c3v/lN7rjjjmqf//jHP07fvn1z0kkn5fnnn6/RtTt16pRJkyZV68++qI4dO+bFF1+sduzFF1/MeuutV0hxL8/CXzbMnz9/uWM32GCDQkJ60evVqVNnqWnzZSkrK8uWW26ZU045Jffdd1/q16+fxx9/fLnndezYMfPmzav2fTB+/Ph88sknhfebbbZZpkyZkrp161b7/lp33XUltwEAAAAoeTVOcicLNrh7/vnnM3LkyAwfPjzDhw9PWVlZGjVqlB49eqRnz57Zdttts/nmm3+pjesaNmyY4447Lpdddlnq16+fHj16ZNq0aRk3blyNE7819eGHH6Z///459NBD8+qrr+bWW2+tljb+onXXXTdz587NLbfckl122SUjR47Mn//852pjevbsmQsuuCBDhgzJHnvskaeeeipPPfVUmjZtuth8rVq1ys0335wjjjgiffv2zRVXXLHUzTO/853v5Omnn8748ePTokWLNGvWLBtssEF22GGH/Pa3v02/fv0yd+7cXHjhhdl7773Ttm3br/ZwlmLttddO/fr1c8stt+THP/5x3njjjVx77bXVxlx11VXZbLPNstFGG6WioiL/+te/lthKpnfv3pk/f35OOOGEDBkyJFtttdUyr73NNttkq622yimnnJKzzjor66yzTsaPH5+ysrLsuOOOOeaYY3LwwQfnD3/4Q/baa6+89NJL+dOf/pTzzjuvxvfXqlWrNGrUKE899VTatWuXhg0bLrVf+D777JOrr746Z511Vvr06ZNp06blwgsvzH777bdCrUqSBb9gee655/Ld7343rVq1yssvv5xp06alY8eOyz23Y8eO2WGHHQq94evWrZtLLrmkWvJ9u+22S7du3fLzn/88Z5xxRtZbb71Mnjw5Tz75ZHbddddqm5cCAHydvtNs9Zy8zbbp1X6dtGnSOB/Nnp37XxubP7wwPHOX8teZzRs2yqnbbpcd1lk3azdrlmmffpq/v/Vmrhz+TGZWVBTGbdd+nZzW67vp1Kp1Pp07N/eMfSWXP/d05v+/AErP77TPMd23TNe27dK0QcO8M/3jDHnxhdz/+mtFuXcAAL4+K1TkPuqoo3LUUUelqqoqr732WkaMGJHhw4cX2iw888wzKSsrS+PGjbPVVlulZ8+e6dmzZzbbbLMaX+Okk05K3bp1c/XVV2fy5Mlp06ZNfvSjH63wjS3P/vvvn88++yyHHHJI6tatmyOOOKKwAd+SdO7cOb/+9a8zZMiQXHHFFdlqq61y+umnVyuMb7DBBjnvvPMyePDgXHfddfnBD36QY445JnfeeecS52zTpk1uvvnm9O7dO7/85S8zcODAJaaOf/jDH+b555/PQQcdlDlz5mTYsGHp2bNnLr/88lx44YU58sgjU6dOnfzgBz/IOeec89UfzlK0bNkyAwYMyBVXXJFbbrklm222WX71q1/lZz/7WWFM/fr1c8UVV+SDDz5Io0aNsuWWW+aKK65Y4nwLv5eOP/74XH/99Yv17v6ia665JpdeemlOP/30fPrpp1l33XXTt2/fJAvSyr///e9z9dVX57rrrkubNm1yyimnVNt0cnnq1auXc845J3/4wx9y9dVXZ6uttlpii49kQWuPG264IRdffHEOPvjgrLbaavnBD36Qs846q8bXW6hp06Z54YUXcvPNN2fWrFlZe+21c9ZZZ2WnnXaq0fn9+/fPOeeck8MPPzytW7fOL37xi1x99dWFz8vKyvLHP/4xv//97/PrX/86H3/8cVq3bp2tttpqhQvyAAA1cduBP8zdY1/J3WNfqXZ8g5YtU6esLGf/87G8O316Nm7VOv2/v1tWq18//Z9+colztW3SJG2bNMklTz+ZN6dNzXearZ6Ldt41bZs2zc//9mCSpHPrNrlhvwNy7Qsj8su/P5y2TZvmop13TZ06dQrzbrnW2nltSnkG/+eFTPl0dnZZr2Mu323PzPy8Iv94Z/zX+0AAAJagzMaTtaasqiYNiZejqqoqY8eOzfDhwwtJ75kzZ6asrCxlZWV59dVXa2OttaZ3797p3Llzzj777JW9FCh5Ha8euLKXAAB8wyytyL0kx/XYKodt0TXfu/mGGs+/54Yb54rd98zm116d+VVV+WWv7bP9Outm/zv+VBizy/odM2jP/8vWQ67L7P/XHvCLbtjngEyZMye/euLRGl8bAFg1jT+lb9Gvud6Qy4p+zS9657gzVvYSasUKJbmXpqysLJtuumk23XTT7LHHHnnmmWcybNiwjBs3rjamBwAAVlHNGjTMjM8+W7FzGjbMrIqKQiuSBnXr5vMv7BP0+bx5aVSvfjZfs21GfDBhqfO8+fG0JX4GAPC1k+SuNV+5yP3RRx9lxIgRha8PPvggSdK4cePssMMO2Xrrrb/yIllxP/3pTzNy5MglfnbCCSfkxBNPLPKKlu2BBx5Yav/stddeOw899FCRV1R7Pvzww+y9995L/fyhhx7K2muvXcQVAQDUrpO22iY/26pn4X2jevXSvd1aOX+nXQrHdr/1pnw4a2a189Zt3iJHdu2eS5bSqmRJ1mi0Wk7eetv8ecz/NpL/93vv5OhuPbLPxp3z0LjX06Zxk5y8zYI9fdZssvj+OEmy10YbZ4u2bXP2Px6r8bUBAPhmWuEi99SpUwu9uEeMGJH33nsvVVVVadasWbbccsv85Cc/ydZbb53NNtvsS20+WQxL67O8Krn44ovz2VISMc2bNy/yapZvl112SdeuXZf42dI25CwVa665Zu67775lfg4AUMr+9N/ReWjcG4X3V+6+Vx55c1wefet/f9n50exZ1c5p26Rphu53YP725hu545X/1ug6TRs0yA37HpBx06bmqhHPFY4//d67GfDMv3PRzrtm4A/2TMX8+Rn0/PBs8532qVxCd8Zt23fI73bdI7954rGMmzZ1RW8XAIBvmBWqHu611155++23kySrr756ttpqq/z4xz/ONttsk0022SRlZWVfyyJZcW3btl3ZS1ghTZs2TdOmS07ZlLp69epl3XXXXdnLAAD42sz4/LPM+Px/AYvP5s3L1E/n5N0Z05c4fs0mTXLbgYfkxYkf5jdP/L1G12hSv36G7ndQZldU5MSH7s+8yspqn98wamRuGDUyazZpkhmffZ72q6+eM7+7Q97/whq2+U77DPm//XPRv/+Ze1/7Zu0dBAB8y1SppdaWFSpyjx8/PnXq1Mmuu+6aI444Il27dk39+vW/rrUBAACrmLZNmua2Aw/JmMmTc+bjj9aoFWXTBg1y034HpWL+/Bz31/tSMX/+UsdOnj07SbLPxp3z4cxPMqZ8cuGznt9pn+v3OSC/e/ap/LmG6XEAAL75VqjI3bt37zz//PN57LHH8thjj6VRo0bp2rVrttlmm2yzzTaK3gAA8C3UuH79NF7kvwNOeeSvSZLWjRsXjk379NO0adwktx/0w3zwySe55Okn03K11QqfT5kzJ8mCIvitBxySvo89nNEfTUrTBg1y8/4HZbV69XP63/+Wpg0apGmDBoU5F7YjOa7HVvn3u++ksqoqu2+wUU7capuc/PBfC59v275Drt/ngNz00ot5+M03CmubO7+yWgodAIDSs0JF7rPPPjtJMn369Dz//PMZMWJEnn/++VxzzTVJkoYNGxaK3j179lT0BgCAb4HjemyVX/Tcbpljdhg6JNu275D1WqyR9VqskeeOPaHa5x2vHpgkqVenTjZo2TKr/b99WTZrs2a6t1uwSfe/jvzpYnN+MPOTJMlO666fn2/dMw3q1s3YKeU54a/35cl33ymMPbDzpmlcv35O2rpnTtr6f5tkDp/wfn5yz51f7sYBAL6KmvxJGzVSVlW1hJ1YVtC0adMKBe/nn38+b731VsrKytKwYcN069YtN910Uy0sFfgmWvgfpAAAAAClavwpfYt+zfX+v8uLfs0veufEX67sJdSKFUpyL03Lli2z5557Zs8998zcuXPzj3/8I4MGDcq4ceMyYsSI2rgEAAAAAMCqQ5K71nzlIvf8+fMzevToQpJ71KhR+eyzz7IwIL7GGmt85UUCAAAAAMCSrHCRu7KyMv/9738zYsSIjBgxIqNGjcqnn35aKGo3b9483/3ud9OzZ8/07NkzG2+8ca0vGgAAAAAAkhUsch933HF58cUXM2fOnEJRu1mzZvne975XKGp37tw5ZWVlX8tiAQAAAABWCdqV1JoVKnI/9dRTady4cXbYYYdCUXvTTTdNnTp1vq71AQAAAADAUq1QkfuOO+7I5ptvnrp1635d6wEAAAAAWPVV6YZRW1aoyN21a9evax0AAAAAALDC9BkBAAAAAKBkrVCSGwAAAACAr67MxpO1RpIbAAAAAICSJckNAAAAAFBskty1RpIbAAAAAICSpcgNAAAAAEDJUuQGAAAAAKBkKXIDAAAAALBMgwcPzkEHHZTu3bunV69eOemkkzJ+/Pjlnvfwww9njz32yBZbbJF99tknTz75ZLXPq6qqctVVV2X77bdPly5dctRRR+Wdd95ZobUpcgMAAAAAFFlZ1cr/WhHPP/98DjvssNx5550ZOnRo5s2bl2OPPTZz5sxZ6jkvvvhi+vbtm4MPPjj33Xdfvv/97+fnP/953njjjcKYIUOG5JZbbsn555+fO++8M6uttlqOPfbYfP755zVemyI3AAAAAADLdMMNN+TAAw/MRhttlM6dO2fAgAH58MMP88orryz1nGHDhmWHHXbIT3/602ywwQY59dRTs+mmm+bWW29NsiDFPWzYsPzsZz/Lrrvums6dO+d3v/tdJk+enMcff7zGa1PkBgAAAAD4FqqoqMisWbOqfVVUVNTo3JkzZyZJmjdvvtQxL730Unr16lXt2Pbbb5+XXnopSTJhwoSUl5dnu+22K3zerFmzdO3aNaNGjarxfdSr8UgAAAAAAGpHVdnKXkEGDx6cQYMGVTvWp0+fnHzyycs8r7KyMpdcckl69OiRjTfeeKnjpkyZktatW1c71qpVq0yZMiVJUl5eXji2tDE1ocgNAAAAAPAtdMIJJ+Too4+udqxBgwbLPa9fv34ZN25cbrvttq9raStEkRsAAAAA4FuoQYMGNSpqL+qCCy7Iv/71r9x6661p167dMse2bt16sUT21KlTC+nuNm3aFI6tueaa1cZ07ty5xmvSkxsAAAAAoNiqvgFfK7LcqqpccMEFeeyxx3LzzTenQ4cOyz2nW7duGT58eLVjzz77bLp165Ykad++fdq0aZPnnnuu8PmsWbPy8ssvp3v37jVemyI3AAAAAADL1K9fvzzwwAMZOHBgmjRpkvLy8pSXl+ezzz4rjDnzzDMzcODAwvsjjjgiTz31VG688ca89dZbueaaazJmzJgcfvjhSZKysrIcccQRue666/LEE0/k9ddfz5lnnpk111wzu+66a43Xpl0JAAAAAECxrWCSemW7/fbbkyS9e/eudrx///458MADkyQTJ05MnTr/y1X36NEjl19+eX7/+9/niiuuyHrrrZc//OEP1TarPO644/Lpp5/m3HPPzSeffJItt9wy119/fRo2bFjjtZVVVVWV2OMEvkk6Xj1w+YMAAAAAvsHGn9K36NfseOUVRb/mF40/7fSVvYRaoV0JAAAAAAAlS7sSAAAAAIAiK9Nfo9ZIcgMAAAAAULIkuQEAAAAAik2Su9ZIcgMAAAAAULIUuQEAAAAAKFnalQAAAAAAFJt2JbVGkhsAAAAAgJIlyQ0AAAAAUGRlkty1RpIbAAAAAICSpcgNAAAAAEDJ0q4EAAAAAKDYqspW9gpWGZLcAAAAAACULEluAAAAAIBis/FkrZHkBgAAAACgZClyAwAAAABQsrQrAQAAAAAosjLtSmqNJDcAAAAAACVLkRsAAAAAgJKlXQkAAAAAQLFpV1JrJLkBAAAAAChZktwAAAAAAEVm48naI8kNAAAAAEDJUuQGAAAAAKBkaVcCAAAAAFBs2pXUGkluAAAAAABKliQ3AAAAAECxSXLXGkluAAAAAABKliI3AAAAAAAlS7sSAAAAAIAiK9OupNZIcgMAAAAAULIUuQEAAAAAKFmK3AAAAAAAlCxFbgAAAAAASpaNJwEAAAAAis3Gk7VGkhsAAAAAgJKlyA0AAAAAQMnSrgQAAAAAoMjKtCupNZLcAAAAAACULEluAAAAAIBik+SuNZLcAAAAAACULEVuAAAAAABKlnYlAAAAAADFpl1JrZHkBgAAAACgZElyAwAAAAAUWZkkd62R5AYAAAAAoGQpcgMAAAAAULK0KwEAAAAAKDbtSmqNJDcAAAAAACVLkhsAAAAAoMhsPFl7JLkBAAAAAChZitwAAAAAAJQs7UoAAAAAAIpNu5JaI8kNAAAAAEDJkuQGAAAAACg2Se5aI8kNAAAAAEDJUuQGAAAAAKBkaVcCAAAAAFBkZdqV1BpJbgAAAAAASpYiNwAAAAAAJUu7EgAAAACAYtOupNZIcgMAAAAAULIkuQEAAAAAik2Su9ZIcgMAAAAAULIUuQEAAAAAKFnalQAAAAAAFFmZdiW1RpIbAAAAAICSJckNAAAAAFBskty1RpIbAAAAAICSpcgNAAAAAEDJ0q4EAAAAAKDIbDxZeyS5AQAAAABYphdeeCEnnnhitt9++3Tq1CmPP/74cs/505/+lD333DNdunTJ7rvvnvvuu6/a5/fcc086depU7WuLLbZY4bVJcgMAAAAAFFuJJbnnzJmTTp065aCDDkqfPn2WO/62227LwIEDc9FFF2WLLbbI6NGjc84552T11VfPLrvsUhjXtGnTPPLII4X3ZWVlK7w2RW4AAAAAAJZpp512yk477VTj8Q888EAOPfTQ7LXXXkmSDh065L///W+GDBlSrchdVlaWNm3afKW1KXIDAAAAAHwLVVRUpKKiotqxBg0apEGDBrUyd8OGDasda9iwYf773/9m7ty5qV+/fpIFCfGdd945lZWV2XTTTXP66adno402WqFr6ckNAAAAAFBsVSv/a/Dgwdlyyy2rfQ0ePLhWbm/77bfPXXfdlTFjxqSqqir//e9/c9ddd2Xu3Ln5+OOPkyTrr79+Lrnkklx77bW57LLLUlVVlR/96EeZNGnSCl1LkhsAAAAA4FvohBNOyNFHH13tWG2kuJPkpJNOSnl5eQ499NBUVVWlVatW2X///XP99denTp0F2evu3bune/fuhXO6d++evfbaK3/+859z6qmn1vhaitwAAAAAAEW24tsr1r7aak2yJI0aNUr//v1zwQUXZOrUqWnTpk3uuOOONGnSJC1btlziOfXr188mm2yS9957b4WupV0JAAAAAABfi/r166ddu3apW7du/va3v2XnnXcuJLm/aP78+XnjjTdWeCNKSW4AAAAAAJZp9uzZ1RLWEyZMyNixY9O8efOsvfbaGThwYD766KP87ne/S5K8/fbbGT16dLp27ZpPPvkkQ4cOzbhx4zJgwIDCHIMGDUq3bt2y7rrr5pNPPskNN9yQDz/8MIcccsgKrU2RGwAAAACg2KpW9gJWzJgxY3LEEUcU3vfv3z9JcsABB2TAgAEpLy/PxIkTC59XVlZm6NChefvtt1OvXr307Nkzt99+e9q3b18Y88knn+S3v/1tysvL07x582y22Wb585//nA033HCF1lZWVVVVYo8T+CbpePXAlb0EAAAAgK9k/Cl9i37NLqddWfRrftHoK09b2UuoFXpyAwAAAABQsrQrAQAAAAAosjL9NWqNJDcAAAAAACVLkhsAAAAAoNgkuWuNJDcAAAAAACVLkRsAAAAAgJKlXQkAAAAAQLFpV1JrJLkBAAAAAChZktwAAAAAAEVWJsldayS5AQAAAAAoWYrcAAAAAACULO1KAAAAAACKTbuSWiPJDQAAAABAyZLkBgAAAAAoMhtP1h5JbgAAAAAASpYiNwAAAAAAJUu7EgAAAACAYtOupNZIcgMAAAAAULIUuQEAAAAAKFnalQAAAAAAFFmZdiW1RpIbAAAAAICSJckNAAAAAFBskty1RpIbAAAAAICSpcgNAAAAAEDJ0q4EAAAAAKDYtCupNZLcAAAAAACULEluAAAAAIAiK5PkrjWS3AAAAAAAlCxFbgAAAAAASpZ2JQAAAAAAxaZdSa2R5AYAAAAAoGRJcgMAAAAAFFlZlSh3bZHkBgAAAACgZClyAwAAAABQsrQrAQAAAAAoNt1Kao0kNwAAAAAAJUuSGwAAAACgyMokuWuNJDcAAAAAACVLkRsAAAAAgJKlXQkAAAAAQLFpV1JrJLkBAAAAAChZitwAAAAAAJQs7UoAAAAAAIqsTLuSWiPJDQAAAABAyZLkBgAAAAAoNknuWiPJDQAAAABAyVLkBgAAAACgZGlXAgAAAABQZDaerD2S3AAAAAAAlCxJbgAAAACAYpPkrjWS3AAAAAAAlCxFbgAAAAAASpZ2JQAAAAAARWbjydojyQ0AAAAAQMmS5AYAAAAAKLYqUe7aIskNAAAAAEDJUuQGAAAAAKBkaVcCAAAAAFBkNp6sPZLcAAAAAACULEluAAAAAIBik+SuNZLcAAAAAACULEVuAAAAAABKlnYlAAAAAABFVla5slew6pDkBgAAAACgZClyAwAAAABQsrQrAQAAAAAotqqVvYBVhyQ3AAAAAAAlS5IbAAAAAKDIyiS5a40kNwAAAAAAJUuRGwAAAACAkqVdCQAAAABAsVXpV1JbJLkBAAAAAFimF154ISeeeGK23377dOrUKY8//vhyz/nTn/6UPffcM126dMnuu++e++67b7ExDz/8cPbYY49sscUW2WefffLkk0+u8NoUuQEAAAD4/9m78zg7x/t//K+TZbLIRiKW2LdEkQWxNaSWflv7UhqUKEqtRX1sba2tnRalraKLPaWonVqqVAQhNJUglpDEMlnJwsxk5veHn2lGgmRM5vTOPJ+PRx6Pc9/3da7zvidzwnnNNe8LaGaluvL/WRSzZ89O7969c/rppy/U+BtvvDEXX3xxjj766Nxzzz350Y9+lDPPPDOPPPJI/Zjnnnsuxx9/fPbcc8/ccccd2XbbbXPkkUfmlVdeWaTatCsBAAAAAOALDR48OIMHD17o8XfeeWeGDBmSHXbYIUmy8sor59///neuuuqqbLPNNkmSa6+9NltuuWV+8IMfJEmOPfbYPPnkk7n++utz1llnLfRrWckNAAAAANACVVVVZebMmQ3+VFVVNdnc7dq1a3CuXbt2+fe//53q6uokyahRo7L55ps3GDNo0KCMGjVqkV5LyA0AAAAA0Nzqyv/nyiuvzEYbbdTgz5VXXtkktzdo0KDceuutGT16dOrq6vLvf/87t956a6qrqzNt2rQkyeTJk9OjR48Gz+vevXsmT568SK+lXQkAAAAAQAv0wx/+MAceeGCDcxUVFU0y9xFHHJHKysoMGTIkdXV16d69e3bbbbdcffXVadWqaddeC7kBAAAAAJrZom78uDhUVFQ0Waj9We3bt8+5556bs846K1OmTMmyyy6bYcOGZamllsoyyyyTJOnRo8d8q7anTJky3+ruL6NdCQAAAAAAi0Xbtm2z/PLLp3Xr1rn33nuz9dZb16/k7t+/f5566qkG45988sn0799/kV7DSm4AAAAAAL7QrFmz8tZbb9UfT5gwIWPGjEnXrl2z4oor5uKLL857772XCy64IEnyxhtv5MUXX0y/fv3ywQcf5I9//GNeffXVnHfeefVzDB06NPvvv3/+8Ic/ZPDgwbn33nszevTonHXWWYtUm5AbAAAAAKC51f0P9CtZBKNHj87QoUPrj88999wkye67757zzjsvlZWVeeedd+qv19bW5o9//GPeeOONtGnTJptuumluuummrLTSSvVjNtxww1x00UW55JJL8stf/jKrrbZarrjiiqyzzjqLVFuprq5gX03gf8oal11c7hIAAAAAvpLXf3R8s7/mlrtd2Oyv+VmP33FCuUtoEnpyAwAAAABQWNqVAAAAAAA0s5L+Gk3GSm4AAAAAAArLSm4AAAAAgOZmJXeTsZIbAAAAAIDCEnIDAAAAAFBY2pUAAAAAADQzG082HSu5AQAAAAAoLCu5AQAAAACaW62l3E3FSm4AAAAAAApLyA0AAAAAQGFpVwIAAAAA0Nx0K2kyVnIDAAAAAFBYVnIDAAAAADSzkpXcTcZKbgAAAAAACkvIDQAAAABAYWlXAgAAAADQ3Or0K2kqVnIDAAAAAFBYVnIDAAAAADQzG082HSu5AQAAAAAoLCE3AAAAAACFpV0JAAAAAEBz066kyVjJDQAAAABAYQm5AQAAAAAoLO1KAAAAAACaWalOv5KmYiU3AAAAAACFZSU3AAAAAEBzqy13AUsOK7kBAAAAACgsITcAAAAAAIWlXQkAAAAAQDOz8WTTsZIbAAAAAIDCspIbAAAAAKC5WcjdZKzkBgAAAACgsITcAAAAAAAUlnYlAAAAAADNzcaTTcZKbgAAAAAACstKbgAAAACAZlaykLvJWMkNAAAAAEBhCbkBAAAAACgs7UoAAAAAAJqbjSebjJXcAAAAAAAUlpXcAAAAAADNrFRb7gqWHFZyAwAAAABQWEJuAAAAAAAKS7sSAAAAAIDmZuPJJmMlNwAAAAAAhSXkBgAAAACgsLQrAQAAAABobrqVNBkruQEAAAAAKCwruQEAAAAAmlnJxpNNxkpuAAAAAAAKS8gNAAAAAEBhaVcCAAAAANDctCtpMlZyAwAAAABQWFZyAwAAAAA0t9pyF7DksJIbAAAAAIDCEnIDAAAAAFBY2pUAAAAAADSzko0nm4yV3AAAAAAAFJaV3AAAAAAAzc1K7iZjJTcAAAAAAIUl5AYAAAAAoLC0KwEAAAAAaG7alTQZK7kBAAAAACgsITcAAAAAAIWlXQkAAAAAQHOrLXcBSw4ruQEAAAAAKCwruQEAAAAAmlnJxpNNxkpuAAAAAAAKS8gNAAAAAEBhaVcCAAAAANDctCtpMlZyAwAAAADwhZ555pkcdthhGTRoUHr37p2HHnroS59z5513Zpdddkm/fv0yaNCgnHLKKZk2bVr99dtuuy29e/du8GeDDTZY5Nqs5AYAAAAAaG4FW8k9e/bs9O7dO9/5zndy1FFHfen4kSNH5qSTTsopp5ySrbfeOu+9917OOOOMnHrqqbn88svrx3Xq1Cn3339//XGpVFrk2oTcAAAAAAB8ocGDB2fw4MELPX7UqFHp1atXhg4dmiRZeeWVM2TIkFx11VUNxpVKpSy77LJfqTbtSgAAAAAAWqCqqqrMnDmzwZ+qqqommbt///55991389hjj6Wuri6TJ0/OAw88MF9QPnv27Gy99dYZPHhwDj/88Lz66quL/FpWcgMAAAAANLf/gXYlV155ZYPWIUly1FFH5eijj/7Kc2+00Ua58MILc+yxx6aqqio1NTXZeuutc9ppp9WPWX311XPOOeekd+/e+fDDD/OHP/whe++9d+65554sv/zyC/1aQm4AAAAAgBbohz/8YQ488MAG5yoqKppk7nHjxuXss8/OkUcemUGDBqWysjIXXHBBTj/99JxzzjlJkgEDBmTAgAH1zxkwYEB22GGH3HzzzTn22GMX+rWE3AAAAAAAza223AV8Emg3Vaj9WVdeeWU23HDD/OAHP0iS9OnTJx06dMj3vve9HHvssenZs+d8z2nbtm3WXXfdvPXWW4v0WnpyAwAAAADQpD766KO0atUwfm7dunWSpO5zWrXMnTs3r7zyyiJvRGklNwAAAAAAX2jWrFkNVlhPmDAhY8aMSdeuXbPiiivm4osvznvvvZcLLrggSbL11lvn1FNPzY033pgtt9wy77//fs4555z07ds3yy23XJLk8ssvT//+/bPqqqvmgw8+yDXXXJNJkyZlr732WqTahNwAAAAAAM2s9D+w8eSiGD16dIYOHVp/fO655yZJdt9995x33nmprKzMO++8U399jz32yKxZs3LDDTfk/PPPT+fOnbPZZpvlhBNOqB/zwQcf5NRTT01lZWW6du2a9dZbLzfffHPWWmutRaqtVPd5a8MBFsIal11c7hIAAAAAvpLXf3R8s7/m9l/7SbO/5mfd99I55S6hSVjJDQAAAADQ3Kw9bjI2ngQAAAAAoLCE3AAAAAAAFJZ2JQAAAAAAza1Wu5KmYiU3AAAAAACFJeQGAAAAAKCwtCsBAAAAAGhuddqVNBUruQEAAAAAKCwruQEAAAAAmpuV3E3GSm4AAAAAAApLyA0AAAAAQGFpVwIAAAAA0Ny0K2kyVnIDAAAAAFBYVnIDAAAAADS3Wiu5m4qV3AAAAAAAFJaQGwAAAACAwtKuBAAAAACgudXVlruCJYaV3AAAAAAAFJaV3AAAAAAAza3OxpNNxUpuAAAAAAAKS8gNAAAAAEBhaVcCAAAAANDcarUraSpWcgMAAAAAUFhWcgMAAAAANDcbTzYZK7kBAAAAACgsITcAAAAAAIWlXQkAAAAAQHPTrqTJWMkNAAAAAEBhCbkBAAAAACgs7UoAAAAAAJqbdiVNxkpuAAAAAAAKy0puAAAAAIDmVltb7gqWGFZyAwAAAABQWEJuAAAAAAAKS7sSAAAAAIDmZuPJJmMlNwAAAAAAhWUlNwAAAABAc7OSu8lYyQ0AAAAAQGEJuQEAAAAAKCztSoCv5PUfHV/uEgAAAACKp1a7kqZiJTcAAAAAAIVlJTcAAAAAQDOrq6stdwlLDCu5AQAAAAAoLCE3AAAAAACFpV0JAAAAAEBzs/Fkk7GSGwAAAACAwhJyAwAAAABQWNqVAAAAAAA0tzrtSpqKldwAAAAAABSWldwAAAAAAM2ttrbcFSwxrOQGAAAAAKCwhNwAAAAAABSWdiUAAECzq6yszAsvvJDJkycnSXr06JF+/fpl2WWXLXNlAADNxMaTTUbIDQAANJvZs2fntNNOy7333ptSqZSuXbsmSWbMmJG6urrsuOOOOeuss9KhQ4cyVwoAQFEIuQEAgGZz9tln59///neuvPLKbLHFFmndunWSZO7cuRk+fHh+/vOf5+yzz84vfvGLMlcKALB41dl4ssnoyQ0AADSbBx98MOeee2623HLL+oA7SVq3bp1BgwblnHPOyQMPPFDGCgEAKBohNwAA0Gxqa2vTtm3bz73etm3b1FrVBADAIhByAwAAzeYb3/hGTjvttLz00kvzXXvppZdyxhlnZOutty5DZQAAzayurvx/lhClurol6G4AAID/aTNmzMjxxx+fJ554Il27ds0yyyyTJJk6dWo++OCDDBo0KBdffHG6dOlS5koBABavby01tNwl5IFZ15a7hCZh40kAAKDZdO3aNVdffXVee+21jBo1KpMnT06S9OjRI/3798+aa65Z5goBAJpJrbXHTUXIDQAANLupU6dm1113TZs2DT+S1NTU5Pnnn8/AgQPLVBkAAEWjJzcAANDshg4dmhkzZsx3/sMPP8zQoeX/1V0AAIrDSm4AAKDZ1dXVpVQqzXd++vTp6dChQxkqAgBoZnW15a5giSHkBgAAms1RRx2VJCmVSjn55JNTUVFRf23u3Ll5+eWXM2DAgHKVBwBAAQm5AQCAZtO5c+ckn6zkXmqppdK+ffv6a23btk3//v2z1157las8AIBmU2fjySYj5AYAAJrNueeemyTp1atXDjrooHTs2LHMFQEAUHQ2ngQAAJrdUUcdVR9w//73v88HH3xQ5ooAACgqITcAAFBWv/vd7zJjxoxylwEA0Lzqasv/Zwkh5AYAAMqqrk4/SgAAGk/IDQAAAABAYdl4EgAAKKt77703PXv2rD+uq6tLqVQqY0UAAItfXa3fZmsqVnIDAADN7uqrr65/vMIKK6R169ZJkrlz5+b4448vV1kAAHyOZ555JocddlgGDRqU3r1756GHHvrS59x5553ZZZdd0q9fvwwaNCinnHJKpk2b1mDMfffdl29/+9vZYIMNsvPOO+exxx5b5NqE3AAAQLO75pprcssttzQ4N3fu3Bx33HEZM2ZMmaoCAGhG5d50chE3npw9e3Z69+6d008/faHGjxw5MieddFL23HPP3H333bnkkkvy73//O6eeemr9mOeeey7HH3989txzz9xxxx3Zdtttc+SRR+aVV15ZpNq0KwEAAJrdlVdemYMPPjidO3fOt7/97dTU1OTYY4/N66+/nmuvvbbc5QEA8BmDBw/O4MGDF3r8qFGj0qtXrwwdOjRJsvLKK2fIkCG56qqr6sdce+212XLLLfODH/wgSXLsscfmySefzPXXX5+zzjproV/LSm4AAKDZ9e3bN5dddll+8pOf5OGHH87RRx+dN954I9dee22WXXbZcpcHANAiVFVVZebMmQ3+VFVVNcnc/fv3z7vvvpvHHnssdXV1mTx5ch544IEGQfmoUaOy+eabN3jeoEGDMmrUqEV6LSu5AQCAsth8881zwQUX5Ec/+lHWWGONXHfddVlmmWXKXRYAQLP4e+0tXz5oMfv1r3+dyy+/vMG5o446KkcfffRXnnujjTbKhRdemGOPPTZVVVWpqanJ1ltvndNOO61+zOTJk9OjR48Gz+vevXsmT568SK8l5AYAAJrFUUcdtcDzSy+9dLp06dLgA89nP2wBAND0fvjDH+bAAw9scK6ioqJJ5h43blzOPvvsHHnkkRk0aFAqKytzwQUX5PTTT88555zTJK/xKSE3AADQLDp37rzA81tuuWUzVwIAQPJJoN1UofZnXXnlldlwww3r+2336dMnHTp0yPe+970ce+yx6dmzZ3r06DHfqu0pU6bMt7r7ywi5AQCAZnHuueeWuwQAAJrJRx99lNatWzc49+lxXV1dkk/6dj/11FP5/ve/Xz/mySefTP/+/RfptWw8CQAAAADAF5o1a1bGjBmTMWPGJEkmTJiQMWPGZNKkSUmSiy++OCeeeGL9+K233jp///vfc+ONN+btt9/OyJEj84tf/CJ9+/bNcsstlyQZOnRoHn/88fzhD3/Ia6+9ll//+tcZPXp09ttvv0WqrVT3aWwOAADQTLbZZpuUSqXPvf7www83YzUAAHyZESNGZOjQofOd33333XPeeefl5JNPzsSJE3PdddfVX7vuuuty8803Z8KECencuXM222yznHDCCfUhd5Lcd999ueSSSzJx4sSsttpqOeGEEzJ48OBFqk3IDQAANLs///nPDY5ramry0ksv5YknnsjBBx+cQw89tEyVAQBQNHpyAwAAze6AAw5Y4Pkbbrgho0ePbuZqAAAoMj25AQCA/xlbbbVVHnjggXKXAQBAgQi5AQCA/xn3339/unXrVu4yAAAoEO1KAACAZrfbbrs12Hiyrq4ukydPztSpU3P66aeXsTIAAIpGyA0AADS77bbbrsFxqVTKMsssk0022SRrrrlmmaoCAKCISnV1dXXlLgIAAAAAABrDSm4AAKCsPv7441RXVzc416lTpzJVAwBA0Qi5AQCAZjd79uxcdNFFue+++zJ9+vT5ro8ZM6b5iwIAoJBalbsAAACg5bnwwgvz1FNP5YwzzkhFRUV+8Ytf5Oijj07Pnj1z/vnnl7s8AAAKRMgNAAA0u0cffTSnn356vvWtb6V169bZeOONc8QRR+S4447LXXfdVe7yAAAoECE3AADQ7GbMmJGVV145ySf9t2fMmJEk2WijjfLss8+WszQAAApGyA0AADS7lVZaKRMmTEiSrLHGGrnvvvuSfLLCu3PnzuUsDQCAginV1dXVlbsIAACgZfnTn/6UVq1aZejQoXnyySdz2GGHpa6uLjU1NTn55JNzwAEHlLtEAAAKQsgNAACU3cSJE/Of//wnq6yySvr06VPucgAAKBAhNwAAUFbvvvtuevbsmVatdFMEAGDR+b9IAACgrHbYYYdMnDix3GUAAFBQQm4AAKCs/HIpAABfhZAbAAAAAIDCEnIDAABlddhhh6Vr167lLgMAgIKy8SQAAFBWn34kKZVKZa4EAIAispIbAAAoi1tuuSU77bRTNthgg2ywwQbZaaedcsstt5S7LAAACqZNuQsAAABanksvvTR/+tOfst9++6V///5JklGjRuWcc87JpEmTcswxx5S3QAAACkO7EgAAoNltttlm+dnPfpaddtqpwfm77747P//5zzNixIgyVQYAQNFoVwIAADS7mpqarL/++vOdX2+99TJ37twyVAQAQFEJuQEAgGa366675qabbprv/F/+8pfsvPPOZagIAICi0q4EAABodj//+c9zxx13ZIUVVki/fv2SJC+++GImTZqU3XbbLW3a/Hf7oFNOOaVcZQIAUAA2ngQAAJrdK6+8kq997WtJkrfeeitJ0q1bt3Tr1i2vvPJK/bhSqVSW+gAAKA4ruQEAAAAAKCw9uQEAgGZVXV2dr33taw1WbAMAQGMJuQEAgGbVtm3brLDCCqmtrS13KQAALAGE3AAAQLM77LDD8stf/jLTp08vdykAABScntwAAECz22233TJ+/PjU1NRkxRVXTMeOHRtcv/3228tUGQAARdOm3AUAAAAtz3bbbVfuEgAAWEJYyQ0AAAAAQGHpyQ0AAAAAQGFpVwIAADSLTTbZJPfff3+WWWaZDBw4MKVS6XPHPv30081YGQAARSbkBgAAmsUpp5ySTp06JUl+8pOflLkaAACWFHpyAwAAAABQWFZyAwAAZVFbW5vx48dnypQp+ezam4EDB5apKgAAikbIDQAANLtRo0bl+OOPz6RJk+YLuEulUsaMGVOmygAAKBrtSgAAgGa36667ZrXVVsuPfvSjLLvssvNtQtm5c+cyVQYAQNEIuQEAgGbXv3///O1vf8uqq65a7lIAACi4VuUuAAAAaHn69u2b8ePHl7sMAACWAHpyAwAAzWLs2LH1j/fff/+cf/75mTx5ctZZZ520adPwo0mfPn2auzwAAApKuxIAAKBZ9OnTJ6VSab6NJj/16TUbTwIAsCiE3AAAQLOYOHHiQo/t1avXYqwEAIAliZAbAABodldeeWW6d++ePffcs8H5W2+9NVOnTs2hhx5apsoAACgaG08CAADNbtiwYVljjTXmO7/22mvn5ptvLkNFAAAUlZAbAABodpWVlVl22WXnO7/MMsuksrKyDBUBAFBUQm4AAKDZrbDCCnnuuefmOz9y5Mj07NmzDBUBAFBUbcpdAAAA0PLstddeOeecc1JTU5PNNtssSTJ8+PBceOGFOeigg8pcHQAARWLjSQAAoNnV1dXloosuynXXXZfq6uokSbt27fKDH/wgRx11VJmrAwCgSITcAABA2cyaNSuvvfZa2rdvn9VWWy0VFRXlLgkAgIIRcgMAAAAAUFg2ngQAAAAAoLCE3AAAAAAAFJaQGwAAAACAwhJyAwAAAABQWEJuAAAAAAAKS8gNAAA0qQkTJqR37945+OCDy10KAAAtgJAbAAAAAIDCEnIDAAAAAFBYbcpdAAAA0HJNnDgxV1xxRR5//PFMmzYtyyyzTAYNGpSjjjoqK664YoOx77//fn7/+9/nn//8Z959991UVFRk2WWXzcCBA3PCCSekc+fO9WOrqqpyww035M4778wbb7yRUqmUddddNwcffHC23Xbb5r5NAAAWIyE3AABQFm+88Ub23XffTJ06NVtvvXXWXnvtvPrqq/nrX/+aRx99NDfeeGNWX331JMmcOXOyzz77ZOLEifn617+e7bbbLtXV1ZkwYULuvPPOHHzwwfUhd1VVVQ4++OA8/fTTWXfddbPnnnumuro6jz32WI444oiceuqp2W+//cp56wAANCEhNwAAUBann356pk6dmrPOOitDhgypP3/DDTfkrLPOyhlnnJE///nPSZLhw4dnwoQJOeCAA/KTn/ykwTyzZs1K27Zt64+vuOKKPP300zniiCPyox/9KKVSKUkyc+bMHHDAATnvvPPyzW9+M8stt1wz3CUAAIubntwAAECzmzRpUkaMGJG11lor3/3udxtc22effbLGGmvkqaeeyjvvvNPgWvv27eeba6mllkpFRUWSpLa2NjfddFNWWWWVBgF3knTq1ClHHnlkqqur8/e//30x3BUAAOVgJTcAANDsxowZkyQZOHBggyA6SVq1apWBAwfm9ddfz5gxY7LCCitk4MCBWXbZZfP73/8+Y8eOzTe+8Y1ssskmWXPNNRs8/4033siMGTPSs2fPXH755fO97tSpU5Mkr7/++mK8OwAAmpOQGwAAaHYzZ85MkvTo0WOB15dddtkG4zp37py//OUvueyyy/Loo4/mscceS5KssMIKOeSQQ/K9730vSTJ9+vQkyauvvppXX331c19/zpw5TXIfAACUn5AbAABodp06dUqSTJ48eYHXKysrG4xLkhVXXDHnnXdeamtr8/LLL+eJJ57Iddddl7POOitdu3bNTjvtVD/+W9/6Vi677LLFfBcAAPwv0JMbAABoduuuu26S5Nlnn01dXV2Da3V1dXn22WcbjJtXq1atsu666+aQQw7JL3/5yyTJI488kiRZc80106lTp4wePTrV1dWL8xYAAPgfIeQGAACa3YorrphNN900r776am699dYG14YNG5bXXnstm222WVZYYYUkn7QfWdCq70/PtWvXLknSpk2b7LPPPpk4cWLOP//8BQbdr7zySqZMmdLUtwQAQJmU6j67bAIAAOArmDBhQrbddtv07NkzX//61xc4Zo011sh2222XfffdN9OnT88222yTtdZaK6+++moeeeSRLLPMMrnxxhuz+uqrJ0n+9Kc/5cILL8yGG26Y1VZbLd26dcvbb79dv4L7hhtuyAYbbJAkqaqqymGHHZZ//etfWWWVVbLxxhune/fuee+99/LKK69k7NixGTZsWPr3798sXw8AABYvITcAANCkPg25v8gmm2yS6667LhMnTszll1+exx9/PNOmTcvSSy+dLbfcMkcddVR69epVP/61117LzTffnGeffTaTJk3K7Nmzs9xyy2XjjTfOD37wg6y11loN5p87d25uvfXW3HHHHXnllVdSVVWVHj16ZM0118y2226bXXfdNR07dlws9w8AQPMScgMAAAAAUFh6cgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAABAYQm5AQAAAAAoLCE3AAAAAACFJeQGAAAAAKCwhNwAAAAAABSWkBsAAJrYr3/96/Tu3Tu9e/fOr3/962Z73W222ab+dSdMmNBsrwsAAOXUptwFAACwZNt///3z9NNPNzh3xRVXZLvttlvoOc4///z84Q9/aHDuqKOOytFHH90kNbJg22yzTSZOnNio537e38+83w9N+Xf44Ycf5tFHH81jjz2WsWPHZsqUKZk5c2a6deuWHj16ZMCAAfnGN76Rr3/962nTZvF+DBo9enTuuuuuvPDCC3nrrbfywQcfpFQqpWPHjll++eWz6qqrZv3118+GG26Y/v37L/Z6AACWdP5vCgCAZve3v/1toUPuuXPn5q677lrMFVFU1dXVueGGG/Lb3/4206dPn+96ZWVlKisrM2bMmNx4441ZffXVc+KJJ2abbbZp8lrefffdnHbaaXnssccWeL2qqirTp0/P2LFj88ADDyRJOnfunBEjRqR169ZNXg8AQEsh5AYAoNk9+uijmTFjRrp27fqlY//1r3+lsrKyGarii2y++eZZY401Fnp83759F2M1n5g5c2aOOOKIjBgxosH5ddZZJ6uvvnq6du2ayZMnZ8yYMXnnnXeSJG+88UYOP/zwHHTQQTnxxBNTKpWapJaJEydmv/32y6RJk+rPdenSJX379k3Pnj3Ttm3bTJ8+PW+88UZee+21zJ07N8knK9Dr6uqapAYAgJZKyA0AQLNZa621Mm7cuFRXV+eee+7Jvvvu+6XP+dvf/jbf82l+u+yyS/bYY49yl1Fvzpw52X///fPSSy/Vn/vmN7+Z448/PquvvnqDsXV1dXnmmWdy9tlnZ+zYsUmSP/zhD5k1a1bOOuusJqnnJz/5SX3A3alTp/z0pz/NzjvvnLZt2843dubMmXn00Udz991355///GeTvD4AQEtm40kAAJrNDjvsUB/6zRtef56ZM2fmoYceSpKsu+66WWeddRZrfRTHL37xiwYB94knnpjLL798voA7SUqlUjbZZJP89a9/zTe/+c3688OGDcudd975lWt58cUX89RTT9W/1u9+97vsscceCwy4k09C8J133jlXXnllHnzwQa1KAAC+IiE3AADNZplllsmWW26ZJBk1alTefPPNLxx/33335aOPPkqS7Lbbbou5Oopi+PDhufXWW+uP999//xx88MFf+rw2bdrkl7/8ZdZbb736c2eeeWY++OCDr1TPv/71r/rH/fr1y8CBAxf6uSuvvHKTtUwBAGiptCsBAKBZ7bbbbnnkkUeSJHfccUeOPfbYzx376WrvNm3aZOedd84LL7ywSK9VV1eX+++/P3//+9/z4osvZsqUKUmS7t27p1+/fvnmN7+Zb33rW4sUMj711FO55ZZb8txzz2Xy5Mnp2rVrVlllley4447ZY4890qFDh0Wq8VPDhw/Pfffdl5EjR6aysjKzZ89Ot27d0rt372y99dbZc8890759+0bNvaS55ppr6h8vt9xy+fGPf7zQz62oqMjPf/7zfOc730ldXV1mzpyZYcOG5ZBDDml0Pe+991794xVXXLHR83yZxx57LA8//HCee+65VFZWZubMmenQoUNWXnnlbLDBBhk8eHAGDx6cNm0+/2NeU70n9t9//zz99NNJkmuvvTabbrpp3n///dx222156KGH8s4772Tq1KlZaqml8uyzz873/E9bFj366KMZPXp0pk6dmrq6uiyzzDLp379/tt9++2y33XYL9d588cUXc8cdd+T555/PhAkTMmvWrLRp0yZdunTJiiuumHXXXTebbrppvvGNb6Rjx45fOh8AUDxCbgAAmtXWW2+drl27ZsaMGbnzzjtzzDHHLDDImjBhQn04NmjQoHTv3n2RXufNN9/Mcccd16Clxadmz56dt99+O3fffXfWW2+9XHrppVl55ZW/cL6ampqcdtpp+etf/9rgfGVlZSorKzNy5MjceOON+fWvf71Idb7zzjs58cQT6wPDBc39xBNP5Morr8yvfvWrbLzxxos0/5JmwoQJefzxx+uPhwwZssjB5XrrrZeBAwfWf81vuummrxRyt2r131+QnTBhQqPn+TyvvvpqTj755IwePXq+ax9++GFeeumlvPTSSxk2bFh22GGH/OpXv1rgPE39npjXQw89lJ/85CeZMWPGl44dMWJEfvazn+Wtt96a79rEiRMzceLE3HPPPenfv38uu+yyLLfccgucp6amJmeddVaGDRs237W5c+fWv39eeOGF3HzzzTnssMNy3HHHLfQ9AQDFIeQGAKBZVVRUZPvtt8/NN9+ciRMn5plnnskmm2wy37g77rgjdXV1SZJdd911kV7jtddey3777ZepU6fWn1tnnXWy7rrrplQq5aWXXsorr7ySJPnPf/6TvffeO9dff/0C+zl/6qSTTsrdd99df9ylS5dsuumm6datW955552MGDEi48aNy6GHHpptttlmoes84IADUllZmeSTfs5f+9rXstZaa6V9+/Z577338swzz2TWrFl5//33c+CBB+aqq67KZptttkhfjyXJZ38YsPPOOzdqnl122aV+rk+D1V69ejVqrlVWWaX+8ejRozN8+PBsvvnmjZrrs0aMGJHDDz88s2bNqj+34oorZoMNNki3bt0ye/bsvPHGG3n55ZdTXV2djz/+eIHzLI73xKeef/75XH755amurk63bt0ycODALL300pkyZUrGjBnTYOx9992XE044IdXV1UmS9u3bp1+/funVq1datWqVN998M6NGjUpNTU1GjRqVIUOG5NZbb02PHj3me90LLrigQcC93HLLpW/fvllmmWVSW1ub6dOnZ9y4cXnjjTe+9B4AgGITcgMA0Ox222233HzzzUk+CbMXFHJ/uiFgly5dsu222y703FVVVfnxj39cH+Z17949F110UbbYYosG45544on83//9X6ZNm5bJkyfn+OOPz7Bhwxa4WeAdd9zRIODeb7/9csIJJzRoH/L+++/nhBNOyFNPPZUbb7zxS+ucPXt2jj766PqAe6uttsqpp57aIDBNPtl886KLLspNN92Uqqqq/N///V/uu+++dO7ceaG/JkuSkSNH1j9eeuml5/t6Lay+ffvON29jQ+6tt946559/fmpra1NbW5vDDz88Bx54YHbbbbesuuqqjZoz+WSV/zHHHFMfcK+00ko5/fTTs9VWW803dsaMGbnvvvsyfvz4+a4tjvfEvH79619n7ty5OeaYY3LIIYc0GF9VVVX/+NMV6dXV1SmVSjnwwANz+OGHp0uXLg3me/vtt3PSSSdl5MiReeedd3LKKafkqquuajBm2rRpueGGG5IkrVu3ztlnn53ddtttgb8V8v777+eBBx7Q7gcAlmBCbgAAmt2AAQOy2mqr5c0338wDDzyQ0047rUEA9dxzz9WHddtvv33atWu30HPfddddGTt2bJKkbdu2ufrqq/O1r31tvnGDBg3K73//++yzzz6pqanJf/7zn9xzzz3zbXBZW1ubSy65pP54jz32yKmnnjrffD179syVV16Z7373u3n55Ze/tM4//vGPee2115Ik3/zmN3PZZZc1aHvxqU6dOuWMM87IRx99lNtvvz2VlZW56aabcuihh37pazSlO++8c4HtMj7PaaedtljqmDhxYv3jtddeu9HzrLXWWmnVqlVqa2vnm3dRrbrqqvne976X6667LkkyZ86c/OY3v8lvfvOb9OrVK/369cv666+ffv36pW/fvqmoqFioeS+++OJMmzYtSdKrV68MGzZsgSuak6Rr167Ze++9F3itqd8Tn1VTU5Njjz02hx9++HzX5r3XX/ziF/UbyZ588sn5/ve/v8D5Vl555Vx99dXZa6+9Mm7cuPzzn//MCy+8kH79+tWP+XS1d5LssMMO2X333T+3vp49e2b//ff/wnsAAIpNyA0AQFnsuuuuufTSSzNz5sw89NBD2Wmnneqv3XHHHQ3GLYp52xfsvffeCwzzPtW3b9/stddeuemmm5J80pv5s4He448/nnfeeSfJJ60VTjzxxM+dr3379jnppJNy0EEHfWGN1dXV9atQKyoqcuaZZy4w4J7XcccdV9/C5a677mr2kHv48OEZPnz4Qo9fXCH3vD2fu3bt2uh5WrdunaWWWioffvjhfPM2xsknn5yampr676VPfdoK5d57703yyd/317/+9ey5557ZbrvtPne+9957L/fdd1/98RlnnPG5AfeXaer3xGf17NnzS3uajx07Nk899VSS5Gtf+1oOOOCALxzfsWPHHHHEEfWbit51110NQu6ZM2fWP15mmWW+cC4AYMn3xf8nDQAAi8muu+5a31pg3lC7qqqqPtxbZZVVstFGGy30nDNnzmyw2njPPff80ufstdde9Y///e9/Z/bs2Q2ujxgxov7x4MGDs/TSS3/hfFtsscXnbpT3qdGjR2fKlClJks0333yhNtVcbrnlssYaayT5pO3Dp+FsSzNvb+oOHTp8pbnm3bBy3tC0Mdq0aZMzzjgjN998c7bbbrvPbfFRVVWVRx99NEceeWT23nvvTJo0aYHjnnzyyfqVyqutttoCW5QsjMXxnvisb33rW2nT5ovXTz322GP1j3fccccFthX5rHl7z8/bpiZJVlhhhfrHf//73+vfTwBAy2QlNwAAZdGrV68MHDgwTz/9dJ588slUVlZm2WWXzcMPP5wPPvggyaKv4n755Zczd+7cJJ8EmL179/7S56y77rrp2LFjZs+enblz52bs2LHZcMMN66/Pu3Fe//79v3S+UqmUfv365cEHH/zcMaNGjap//O677+ass8760nmT1H9d6urq8u677zZrX+5zzz03e+yxR7O93udZaqml6h/PmTPnK801b3jbqVOnrzTXpwYMGJArrrgiH374YZ555pmMHDky//nPf/LSSy/Nt1r8+eefz3e/+9389a9/ne8HI/N+jyyoZ/3CWhzvic9af/31v3TO559/vv7xiBEjPjfcn9enG88mqf9tik/169cvK6ywQt55551MmjQpO+64Y/bYY49ss802i9QSBgBYMgi5AQAom1133TVPP/105s6dm7vuuisHHXRQ/va3vyX5JCxe1JD70/7FyScrPRdmtWirVq2y/PLL5/XXX59vjiT1m/V9OufC+LJx77//fv3jl19+eaF6eH/WV22vUVTztij5Kl+DuXPnNlgV/lVanyxI586ds80222SbbbZJ8klg+9JLL+XOO+/MsGHD6gP6ysrKnH766fnd737X4PnzrkxeeeWVG13H4nhPfNaX/XZD0vB7/p///OeXjv+sT3/A86m2bdvmggsuyA9/+MPMnj0706ZNyzXXXJNrrrkm7dq1y/rrr5+BAwdmq622yoYbbrhQ9w0AFJd2JQAAlM23v/3t+pYTd9xxR6ZMmZLHH388SbLRRhstcrjX2FYW846dd46k4WrfhZ3zy8Y1RauRT1fntjS9evWqf/zqq682ep5x48bVbzr52XkXh1KplPXWWy+nnHJKbrvttiy77LL11x599NG8/fbbDcbP+304b1uVRbU43hOfNe+msZ/nq7aDWdD3+yabbJI777wzu+22W4MaPv7444wcOTK/+93vsu++++bb3/52Hnrooa/0+gDA/zYruQEAKJtOnTpl2223zd13352XX345F110UX0f4i/b7G5BGtvKYt6x886RNAwYF3bOLxs3b4C4//7752c/+9lCzUuy4YYb5tZbb03yyQrj8ePHZ9VVV13keV588cUGx4vS+/2rWmONNXLyySfn+OOPrz83cuTIBj/Umff78Mt6Yn+RxfGeaIx5v+cvv/zyfPOb3/zKcyafrHI///zzc/rpp2fkyJEZOXJknnvuubzwwgv56KOPkiRvvvlmjjzyyJx88sk58MADm+R1AYD/LVZyAwBQVvOG2bfddluSpF27dvn2t7+9yHPN2zbh3XffbdDT9/PU1tbm3XffXeAcSbLMMsvUP/5sX+DPM+98C9KjR4/6x5MnT16oOfnEZ/tT33XXXY2a584776x/3KtXr8W+kvuzttxyywbH87bzSNJgM9IJEyY0+nUWx3uiMeb9nq+srPzK831Wx44ds+WWW+bYY4/NtddemxEjRuTSSy/NOuusUz/m4osvznvvvdfkrw0AlJ+QGwCAstpiiy0atG5Ikm233bZRmyr27t07rVu3TvJJi4WF6XU9duzY+pWyrVu3Tp8+fRpcX3fddesfz7sZ4Oepq6vLCy+88IVj+vbtW//4+eefX6jgkU+svPLKGTRoUP3xX/7yl0Ve6fyf//wnzzzzTP3xPvvs02T1Lax27do1OP7sRonzbnI6YsSIRr/O4nhPNMa83/PPPffcV57vy7Rv3z7f/va3c91119UH7NXV1fXtkACAJYuQGwCAsmrdunV23nnnBuca06ok+aT9yfrrr19/fPvtt3/pcz5tfZF8EsR9tv/xpptuWv/4n//8Z6ZPn/6F8z311FNfupJ7o402SpcuXZJ8srr2kUce+dI6+a+DDz64/vF7772XX/7ylwv93Orq6px66qn1P1jo1KlThgwZ0uQ1fpmxY8c2OF5xxRUbHG+xxRZp0+aT7pJvvvlmo8PZxfGeaIytt966/vHf//73ZvsNhm7dumXDDTesP553Q08AYMkh5AYAoOwOP/zw3HrrrfV/5l2pu6jmDSxvuOGG+cLEeY0ePTrDhg2rP957773nGzNo0KCssMIKST7pU3zhhRd+7nwff/xxzjvvvC+tsaKiIgcccED98ZlnnrlIbRRaeouTLbbYInvssUf98XXXXZc//OEPX/q8mpqa/PjHP85//vOf+nOnn356/Q8cGuu2227L/fffv9Ar8ufOnZtf//rX9cdt27bN5ptv3mDMcsstl+23375BnY39e2/q90Rj9O3bt77VzEcffZQTTzwxVVVVC/XcqqqqzJgxo8G5adOmLfRrz9tmaN72QwDAkkPIDQBA2XXp0iUbbLBB/Z9P2ys0xs4771zfXqG6ujo/+MEP8tRTT8037sknn8whhxxSv9Hleuutlx133HG+ca1bt84xxxxTf3zrrbfm7LPPzscff9xgXGVlZQ477LCMHTs2bdu2/dI6DzzwwKy99tpJPlmN/J3vfCf33XdfamtrFzh+6tSpGTZsWHbfffdcc801Xzr/ku7UU09N796964/PP//8HH300XnjjTfmG1tXV5dnnnkme+65Zx588MH680OGDMkuu+zylWt5/fXXc8wxx2SHHXbIVVddlYkTJ37u2HHjxuXQQw/NE088UX9u7733XmB7nuOPPz7dunVLkkycODFDhgz53BXdH3zwQYYNG5YLLrhgvmtN/Z5orFNPPbV+Vfi//vWv7Lfffl/Y2ueNN97IFVdckW222Wa+FifXX399dt1119x4442f2+N71qxZ+dWvfpV///vfST55L3+VH6ABAP+72pS7AAAAaEoVFRX55S9/mf322y9Tp05NZWVlDjjggPTp06e+v/aYMWMarGbt3r17Lr744s8Np3ffffc89thjue+++5Ik1157bf72t79l0003Tbdu3fLOO+9kxIgRqaqqykorrZRtt902f/7zn7+wzqWWWiq//e1v8/3vfz8TJkxIZWVljj322Cy99NLp379/evTokbq6usyYMSPjxo3L+PHj6wPwzTbbrCm+VIvkzjvvzOjRoxd6/PLLL59DDz30C8fcfPPNeeihhxZ6zh/96EfZdtttk3yy0eD111+fI488Mk8//XSS5MEHH8yDDz6Y3r17Z4011kjnzp0zderUvPTSS5k0aVKDuQ488MCcdNJJC/3aC+P111/PRRddlIsuuijLL798evfunWWWWSYVFRWZMWNGXn311bz22msNnjNgwIAcd9xxC5xvhRVWyCWXXJIjjjgis2fPzoQJE/KDH/wgvXr1ygYbbJCuXbtm9uzZefPNNzN27NhUV1fXf33mtTjeE42xzjrr5Je//GWOO+64zJkzJy+88EK++93vZpVVVsnXvva1dO3aNVVVVZkyZUpefvnlL/3thrFjx+bMM8/MWWedlVVWWSVrr712ll566dTU1KSysjLPPfdcg37thxxySP1vZQAASxYhNwAAS5w111wzN954Y3784x/npZdeSvJJILagNg3rrbdeLrnkkqyyyipfOOeFF16Y9u3b1/c0njFjRoNVwUmyxhpr5PLLL8+99967UHWuvPLK+etf/5rTTz89DzzwQOrq6jJt2rQ8+uijn/ucLl26ZJ111lmo+ZvS8OHDM3z48IUe36dPny8NuSdPnrxILTg+27KiS5cuueaaa3L99dfnd7/7Xf31l19++XM3WFx99dXzf//3f9luu+0W+nW/TP/+/bPWWmtl3Lhx9efefffdL+zN3qZNm+y777457rjjvrDn9eabb56bbropJ510Uv3378SJEz93tfjnzbU43hONsfXWW+fmm2/OT37yk/q2MW+99Vbeeuutz31Or169svzyyzc4t9RSS9U/rqury/jx4zN+/PgFPr9t27Y57LDDctRRRzXBHQAA/4uE3AAALJFWX331/PWvf83999+fBx98MC+++GKmTp2a5JO+vP369cu3vvWtfOtb30qpVPrS+dq2bZvzzjsvu+66a/7yl7/kueeey5QpU9K1a9esssoq2X777fOd73ynQfi2MLp165ZLL700r7zySu65556MGDEiEyZMyPTp09OqVat06dKlfqXrFltska9//etp165do74mS6KKioocdNBB2WuvvfLwww/nsccey9ixYzN16tTMmjUrXbt2TY8ePdK/f/9svfXWGTRoUP2Gjk1lu+22y3bbbZe33norI0aMyPPPP5/XX389EyZMyAcffJC5c+emY8eOWWaZZbLOOutko402yg477JCePXsu1Px9+vTJHXfckYceeigPPfRQRo0alcmTJ2fOnDnp1KlTVlpppfTt2zdbb711ttxyy8+dp6nfE43Vp0+f3HbbbXniiSfy0EMP5bnnnsv777+fDz/8MBUVFVl66aWz+uqrp1+/fhk0aFAGDBgwXz0HHXRQ/t//+3958skn8/zzz+fll1/OxIkTM2vWrJRKpXTp0iVrrLFGNttss+y2227p1avXYrsfAKD8SnULuzsKAAAAAAD8j7HxJAAAAAAAhSXkBgAAAACgsITcAAAAAAAUlpAbAAAAAIDCEnIDAAAAAFBYQm4AAAAAAApLyA0AAAAAQGEJuQEAAAAAKCwhNwAAAAAAhSXkBgAAAACgsITcAAAAAAAUlpAbAAAAAIDCEnIDAAAAAFBYQm4AAAAAAApLyA0AAAAAQGEJuQEAAAAAKCwhNwAAAAAAhSXkBgAAAACgsITcAAAAAAAUlpAbAAAAAIDCalPuAoDimzLlw9TVlbuKxuvQoSJz5lSVuwwAAAAopKJ/ri6Vku7dO5e7DL4CITfwldXVpdAhd1L8+gEAAKCcfK6mnLQrAQAAAACgsITcQIvnp80AAADQeD5XU25CbqDFK3LfMAAAACg3n6spNyE30OK1alUqdwkAAABQWD5XU25CbqDFa9++bblLAAAAgMLyuZpyE3IDAAAAAFBYQm4AAAAAAApLyA20eLW1toEGAACAxvK5mnITcgMt3kcfVZe7BAAAACgsn6spNyE30OK1aeOfQgAAAGgsn6spN9+BQItXUdGm3CUAAABAYflcTbkJuQEAAAAAKCwhNwAAAAAAhSXkBlq8uXNry10CAAAAFJbP1ZSbkBto8T7+uKbcJQAAAEBh+VxNuQm5gRavbdvW5S4BAAAACsvnaspNyA20eP5jDAAAAI3nczXlJuQGAAAAAKCwhNwAAAAAABSWkBto8Wpq5pa7BAAAACgsn6spNyE30OJVVfmPMQAAADSWz9WUm5AbaPEqKmyQAQAAAI3lczXl1qbcBQA0t8kzP87kWVX1x+3bt81HH1XXH/dYqiI9OrUrR2kAAABQOG3atLaam7IScgMtzm0vvpOrhr/1udcP2XyVHLrFas1XEAAAAACNJuQGWpw9+q6QrdbsniR5c8rsnHrfy/n59r2zWveOST5ZyQ0AAABAMQi5gRanR6d287UjWa17x/RZrnOZKgIAAIDiqq7WqoTysvEkAAAAANBoQm7KTcgNAAAAADRau3aaRVBeQm4AAAAAoNFatxYxUl6+AwEAAAAAKCwhNwAAAAAAhSXkBgAAAAAaraqqptwl0MIJuQEAAACARqupqS13CbRwQm4AAAAAoNHat29b7hJo4YTcAAAAAECjtWpVKncJtHBCbgAAAAAACkvIDQAAAABAYQm5AQAAAIBG++ij6nKXQAsn5AYAAAAAGq22tq7cJdDCCbkBAAAAgEbr0KGi3CXQwgm5AQAAAIBGK5XKXQEtnZAbAAAAAIDCEnIDAAAAAFBYQm4AAAAAoNHmzKkudwm0cEJuAAAAAKDR6urqyl0CLZyQGwAAAABotI4dK8pdAi2ckBsAAAAAgMIScgMAAAAAUFhCbgAAAAAACkvIDQAAAAA02uzZVeUugRZOyA0AAAAANFqpVCp3CbRwQm4AAAAAoNE6dGhb7hJo4YTcAAAAAAAUlpAbAAAAAIDCEnIDAAAAAI1WV1fuCmjphNwAAAAAQKPNmVNV7hJo4YTcAAAAAECjtWpVKncJtHBCbgAAAACg0dq3b1vuEmjhhNwAAAAAABSWkBsAAAAAgMIScsP/oGeeeSaHHXZYBg0alN69e+ehhx5qcL2uri6XXnppBg0alL59++b73/9+3nzzzfrrEyZMyE9+8pNss8026du3b7bbbrtcdtllqapquBHE2LFjs++++2aDDTbI4MGDc9VVVzXH7QEAAABLkNraunKXQAsn5Ib/QbNnz07v3r1z+umnL/D6VVddleuuuy5nnHFG/vKXv6RDhw45+OCD8/HHHydJXn/99dTV1eWss87KPffck1NOOSU333xzfvWrX9XPMXPmzBx88MFZccUVc9ttt+XEE0/M5ZdfnmHDhjXLPQIAAABLho8+qi53CbRwbcpdADC/wYMHZ/DgwQu8VldXl2uvvTaHH354tttuuyTJBRdckC222CIPPfRQdtxxx2y11VbZaqut6p+z8sor54033shNN92Uk046KUly5513prq6Ouecc04qKiqy9tprZ8yYMfnjH/+YIUOGLP6bBAAAAJYIbdq0Sk1NbbnLoAWzkhsKZsKECamsrMwWW2xRf65z587p169fnn/++c993ocffpiuXbvWH48aNSobb7xxKioq6s8NGjQob7zxRmbMmLF4igcAAACWOBUV1tFSXr4DoWAqKyuTJN27d29wvnv37pk8efICnzN+/Phcf/319au4k2Ty5MlZaaWVGozr0aNH/bV5A/Ev06HDf4Pympq5qaqam4qK1mnTpnX9+erquamunpt27dqkdev//nytqqomNTW1ad++bVq1KtWf/+ij6tTW1qVDh4qU/ns6c+ZUp66uLh07/vc1k2T27KqUSqV06NC2/lxdXTJnTlVatSqlffv/nq+trctHH1WnTZtW9efbt2+bdu3a5OOPa9K2beu0bfvf2ot2T/P+z8XcubXuyT25J/fkntyTe3JP7sk9uSf35J7c02K9p7ZtWzeov8j3RDGV6urqdIaH/2G9e/fOFVdcUd+a5Lnnnss+++yTxx9/PD179qwfd8wxx6RUKuWSSy5p8Pz33nsv++23XzbZZJOcffbZ9ecPOuigrLTSSjnrrLPqz40bNy477rhj7r333qy55poLXePkyR+mqP+SjH3vw+x//fO5br8B6bNc53KXAwAAAIXTsWNFZs+uKncZjVYqJT16yASKTLsSKJhll102STJlypQG56dMmVK/EvtT7733XoYOHZoBAwbk5z//eYNrPXr0mG/l96fHn50HAAAA4PPMnasfN+Ul5IaCWWmllbLssstm+PDh9edmzpyZF154IQMGDKg/92nAvd566+Xcc89Nq1YN3+79+/fPs88+m+rq/+6A/OSTT2b11VdfpFYlAAAAQMv28cc15S6BFk7IDf+DZs2alTFjxmTMmDFJPtlscsyYMZk0aVJKpVKGDh2a3/72t3n44Yfz8ssv58QTT0zPnj3rW5q899572X///bPCCivkpJNOytSpU1NZWVnfzztJdt5557Rt2zY//elP8+qrr+bee+/NtddemwMPPLAs9wwAAAAU07y9saEcbDwJ/4NGjx6doUOH1h+fe+65SZLdd9895513Xg455JDMmTMnp512Wj744INstNFGufrqq9OuXbskyb/+9a+MHz8+48ePz1ZbbdVg7pdffjlJ0rlz51xzzTU566yzsscee2TppZfOEUcckSFDhjTTXQIAAABLgrZtW6e6em65y6AFs/Ek8JXZeBIAAABaLhtPUm7alQAAAAAAUFhCbgAAAACg0WpqtCqhvITcAAAAAECjVVUJuSkvITcAAAAA0GgVFa3LXQItnJAbAAAAAGi0Nm2E3JSXkBsAAAAAgMIScgMAAAAAUFhCbgAAAACg0aqrbTxJeQm5AQAAAIBGE3JTbkJuAAAAAKDR2rVrU+4SaOGE3AAAAABAo7VuLWKkvHwHAgAAAABQWEJuAAAAAAAKS8gNAAAAADRaVVVNuUughdMVHr6C8ePH57nnnsu7776badOmpUOHDll66aXTu3fvDBgwIO3bty93iQAAAACLVU1NbblLoIUTcsMieuedd3LLLbfk9ttvz7vvvpskqaurazCmVCqldevWGTRoUIYMGZJvfOMbKZVK5SgXAAAAYLFq375tPvqoutxl0IIJuWEhTZ06NZdddlluvfXW1NTUZNVVV80uu+yS9ddfP927d0+3bt3y0UcfZcaMGXnjjTcyatSoPPXUU3nsscey6qqr5oQTTsi2225b7tsAAAAAaFKtWlnYR3kJuWEhbbfddmnVqlX233//7LLLLll33XW/9DmzZ8/OAw88kFtuuSVHHXVUTjrppHz/+99f/MUCAAAAQAsh5IaFNHTo0Bx00EHp0qXLQj+nY8eO2X333bP77rtn+PDhmTlz5mKsEAAAAABaHiE3LKRjjz32Kz1/8803b5pCAAAAAP6H6MdNubUqdwEAAAAAQHHV1taVuwRaOCE3AAAAANBoHTpUlLsEWjjtSmARHHLIIYv8nFKplN///veLoRoAAACA8iuVyl0BLZ2QGxbB448/vsjPKfmXHgAAAAAWGyE3LIKHH3643CUAAAAAAPMQcsMi6NWrV7lLAAAAAPifMmdOdblLoIWz8SQAAAAA0Gh1dXXlLoEWTsgNi2D27Nn5f//v/2XvvfdOdfXn/5Syqqoq++yzT7797W/no48+asYKAQAAAJpXx44V5S6BFk7IDYvgtttuy9tvv53jjz8+bdu2/dxxFRUVOf744/Pmm2/mr3/9azNWCAAAAAAti5AbFsHDDz+cNddcMwMHDvzSsRtvvHHWWWedPPjgg81QGQAAAAC0TEJuWARjx47NxhtvvNDjN9xww7zyyiuLsSIAAAAAaNmE3LAIPvzww3Tr1m2hx3ft2jUffvjh4isIAAAAoMxmz64qdwm0cEJuWARLLbVUpk+fvtDjZ8yYkaWWWmrxFQQAAABQZqVSqdwl0MIJuWERrL766nn22WcXevyzzz6b1VdffTFWBAAAAFBeHTq0LXcJtHBCblgEW221VV577bXcc889Xzr23nvvzbhx4/KNb3xj8RcGAAAAAC2UkBsWwX777ZcuXbrkZz/7WW677bbPHXf77bfnpz/9abp165Z99923GSsEAAAAgJalTbkLgCLp0qVLLrnkkhx++OH56U9/mssvvzwDBw7M8ssvnyR577338vTTT+edd95Ju3btcskll6RLly5lrhoAAABg8amrK3cFtHRCblhEm2++eW6++eb84he/yLPPPpu//e1v840ZOHBgfvrTn6ZPnz5lqBAAAACg+cyZU1XuEmjhhNzQCH369Mn111+ft956K88991wqKyuTJMsuu2w23HDDrLLKKmWuEAAAAKB5tGpVSm2t5dyUj5AbvoJVVllFoA0AAAC0aO3bt83s2VZzUz42ngQAAAAAoLCE3AAAAAAAFJaQGwAAAABoNP24KTchNwAAAADQaB99VF3uEmjhhNwAAAAAQKO1aSNipLx8BwIAAAAAjVZR0abcJdDCCbmhidTU1GTGjBmpqakpdykAAAAA0GL4MQt8BXPnzs11112X2267LePGjUtdXV1KpVLWXnvt7L777tlvv/3Spo23GQAAAAAsLtI3aKRZs2bl4IMPzgsvvJBWrVplhRVWSI8ePTJ58uSMGzcu559/fh544IFcc8016dixY7nLBQAAAFgs5s6tLXcJtHBCbmikyy67LKNGjcpOO+2UH//4x1lxxRXrr02aNCkXX3xx7rnnnlx22WU5+eSTy1gpAAAAwOLz8cdat1JeenJDI913331Zf/31c9FFFzUIuJNkxRVXzMUXX5z11lsv9957b5kqBAAAAFj82rZtXe4SaOGE3NBI06dPzxZbbPGFY7bYYovMmDGjmSoCAAAAaH5CbspNyA2NtOqqq2bKlClfOGbq1KlZZZVVmqkiAAAAAGh5hNzQSEOHDs29996bV199dYHXX3755dx777054IADmrkyAAAAAGg5bDwJjbTaaqtls802y3e+853stttu2WijjdKjR49Mnjw5I0eOzB133JFBgwZl1VVXzTPPPNPguQMHDixT1QAAAABNq6ZmbrlLoIUr1dXV1ZW7CCiiPn36pFQq5dO3UKlUqr+2oHPzGjNmzOIvsBlNnvxhivovydj3Psz+1z+f6/YbkD7LdS53OQAAAEAzK5WSHj1kAkVmJTc00pFHHvm5ITYAAABAS1FR0TpVVVZzUz5Cbmiko48+utwlAAAAAJRdmzZCbsrLxpMAAAAAABSWkBsAAAAAgMLSrgS+gnfeeSe//e1v8+STT+b9999PdXX1fGNKpVJeeumlMlQHAAAAsPhVV2tVQnkJuaGR3n777ey111754IMPstZaa6Wqqiorrrhi2rVrl7fffjs1NTXp06dPOne2Oy8AAACw5BJyU27alUAjXX755Zk5c2b+9Kc/5c4770yS7LHHHrnvvvvyyCOPZJtttsmcOXNy2WWXlblSAAAAgMWnXTvraCkvITc00pNPPpmtttoqm2yyyXzXevbsmUsuuSRJ8qtf/aqZKwMAAABoPq1bixgpL9+B0EjTpk3LGmusUX/cpk2bzJkzp/64oqIiW2yxRR599NFylAcAAAAALYKQGxpp6aWXbhBqd+vWLRMnTmwwpnXr1vnwww+buzQAAAAAaDGE3NBIq622Wt5666364759++aJJ57I22+/nSSZOnVqHnjggay88srlKhEAAABgsauqqil3CbRwQm5opC233DIjRozIBx98kCQ54IADMmvWrOyyyy75zne+k29961uZPHly9t9//zJXCgAAALD41NTUlrsEWjghNzTSvvvum+uuuy6tWn3yNtp0003zy1/+MiuuuGJeffXVdO/ePT/72c/y3e9+t8yVAgAAACw+7du3LXcJtHBtyl0AFFWnTp3Sr1+/Bue23377bL/99mWqCAAAAKD5tWpVKncJtHBWckMjDR06NJdcckm5ywAAAACAFk3IDY304osvprZWzykAAAAAKCchNzTSGmuskYkTJ5a7DAAAAICy+uij6nKXQAsn5IZG2m+//fLII49k3Lhx5S4FAAAAoGxqa+vKXQItnI0noZFWXnnlbLLJJvnud7+bIUOGZIMNNkiPHj1SKs2/2cLAgQPLUCEAAADA4tehQ0XmzKkqdxm0YEJuaKT9998/pVIpdXV1+eMf/7jAcPtTY8aMacbKAAAAAJrPF0Qi0CyE3NBIRx555BcG2wAAAADA4ifkhkY6+uijy10CAAAAALR4Np6ERnrmmWcyadKkLxzzzjvv5JlnnmmmigAAAACa35w51eUugRZOyA2NNHTo0Nx2221fOOaOO+7I0KFDm6kiAAAAgOZXV1dX7hJo4YTc0EgL8w94bW2tvt0AAADAEq1jx4pyl0ALJ+SGxWj8+PHp3LlzucsAAAAAgCWWjSdhEZxyyikNjh9++OFMnDhxvnG1tbV555138uyzz2arrbZqrvIAAAAAoMURcsMiuP322+sfl0qljBkzJmPGjFng2FKplA022GC+YBwAAAAAaDpCblgEDz/8cJJP+nFvt912OeCAAxa4sWTr1q3TpUuXdOzYsblLBAAAAGhWs2dXlbsEWjghNyyCXr161T8+99xzs+666zY4BwAAANDSlEql1NXVlbsMWjAhNzTS7rvvvsDzdXV1GT9+fNq1a5cVVlihmasCAAAAaF4dOrS1mpuyalXuAqCoHnzwwZx44omZMWNG/bkJEyZkl112yfbbb59tttkmxx13XObOnVvGKgEAAABgySbkhka66aabMmbMmHTt2rX+3LnnnptXX301m266aXr37p37778/f/3rX8tYJQAAAAAs2YTc0Ejjxo1L3759649nzpyZxx57LDvssEP+9Kc/5ZZbbsmaa64p5AYAAACWaNpxU25CbmikGTNmpEePHvXHI0eOTE1NTXbcccckSdu2bbPFFlvkrbfeKleJAAAAAIvdnDn6cVNeQm5opE6dOmX69On1xyNGjEirVq2y8cYb159r06ZN5syZU4bqAAAAAJpHq1alcpdACyfkhkZaY4018uijj2batGn54IMPcvfdd2e99dZr0KN70qRJ6d69exmrBAAAAFi82rdvW+4SaOGE3NBI+++/f95///0MHjw43/jGN1JZWZl99tmnwZgXXnghffr0KVOFAAAAALDka1PuAqCovvWtb+W0007LrbfemiTZcccds8cee9Rff/rppzNz5sxsueWW5SoRAAAAAJZ4pbo6+58CX83kyR8Wdiflse99mP2vfz7X7TcgfZbrXO5yAAAAoHDat2+bjz6qLncZjVYqJT16yASKTLsSaKTLL788zzzzzBeOefbZZ3P55Zcv8tzPPPNMDjvssAwaNCi9e/fOQw891OB6XV1dLr300gwaNCh9+/bN97///bz55psNxvz2t7/N3nvvnX79+jXYDHNekyZNyqGHHpp+/fpl8803z/nnn5+amppFrhcAAABouYoccLNkEHJDI11++eUZMWLEF4555plncsUVVyzy3LNnz07v3r1z+umnL/D6VVddleuuuy5nnHFG/vKXv6RDhw45+OCD8/HHH9ePqa6uzre//e35+oR/au7cufnhD3+Y6urq3HzzzTnvvPNy++2357LLLlvkegEAAICWq00bESPlpSc3LEbV1dVp3br1Ij9v8ODBGTx48AKv1dXV5dprr83hhx+e7bbbLklywQUXZIsttshDDz2UHXfcMUnyox/9KEly2223LXCeJ554IuPGjcsf//jH9OjRI+uuu26OOeaYXHTRRTnqqKNSUVGxyHUDAAAALU9FRZvU1FSVuwxaMCE3fAWlUulzr1VVVeXZZ5/NMsss06SvOWHChFRWVmaLLbaoP9e5c+f069cvzz//fH3I/WVGjRqVddZZJz169Kg/N2jQoJxxxhkZN25cvva1ry10TR06/DcQr6mZm6qquamoaJ02bf4b8FdXz0119dy0a9cmrVv/9ye8VVU1qampTfv2bdOq1X+/nh99VJ3a2rp06FCReb/Mc+ZUp66uLh07NgzhZ8+uSqlUSocObevP1dUlc+ZUpVWrUtq3/+/52tq6fPRRddq0aVV/vn37tmnXrk0+/rgmbdu2Ttu2/629aPdUUfHff9rnzq11T+7JPbkn9+Se3JN7ck/uyT25J/fknpr0nio//DiVMz/Ox1U1qautS6dO7VNTM7d+fOc2rdJ9qYpC3RPFZuNJWATbbrtt/eOJEyemS5cu6dx5/o0JamtrM23atHz88cfZa6+9ctZZZzX6NXv37p0rrriiftX2c889l3322SePP/54evbsWT/umGOOSalUyiWXXNLg+bfddlvOOeecPPvssw3On3rqqZk0aVKuueaa+nNz5sxJ//798/vf//5zV5IviI0nAQAAoOX4/ZNv5qrhb33u9UM2XyWHbrFa8xX0Fdl4svis5IZFMO/PhEqlUurq6rKgnxO1adMma621VjbbbLMcccQRzVkiAAAAwGK1R98VstWa3ZMkb06ZnVPvezk/3753VuveMUnSYyktUGleQm5YBI888kj94z59+uSAAw7IUUcd1aw1LLvsskmSKVOmNFjJPWXKlPTp02eh5+nRo0defPHFBucmT57c4DUAAAAAPqtHp3bp0aldg3Orde/oN6QpG1ufQiM9/PDDOeCAA5r9dVdaaaUsu+yyGT58eP25mTNn5oUXXsiAAQMWep7+/fvnlVdeyZQpU+rPPfnkk+nUqVPWWmutJq0ZAAAAABYXK7mhkXr16lX/uKamJm+88UZmzpyZTp06ZfXVV0+bNo1/e82aNStvvfXf3lYTJkzImDFj0rVr16y44ooZOnRofvvb32bVVVfNSiutlEsvvTQ9e/as79udJJMmTcqMGTMyadKkzJ07N2PGjEmSrLLKKllqqaUyaNCgrLXWWjnxxBNzwgknpLKyMpdcckm+973vpaLCrxUBAAAAUAxCbvgKpk+fnosuuih33313Pv744/rz7du3z0477ZQf//jHWXrppRd53tGjR2fo0KH1x+eee26SZPfdd895552XQw45JHPmzMlpp52WDz74IBtttFGuvvrqtGv3318Vuuyyy3L77bfXH++2225JkmuvvTabbrppWrdund/97nc544wzMmTIkHTo0CG77757fvSjHy1yvQAAAABQLqW6Be2aB3yp6dOnZ8iQIRk/fny6du2a9ddfPz179kxlZWVGjx6d6dOnZ9VVV82wYcPSrVu3cpe7WE2e/GGK+i/J2Pc+zP7XP5/r9hugdxgAAAAsoiXhc3WplPToUcza+YSV3NBIv/nNbzJ+/PgcfPDBOfLII9OxY8f6a3PmzMlvfvObXHXVVfnd736Xk08+uYyVAgAAAMCSy8aT0EgPP/xwNtlkk5xwwgkNAu4k6dChQ44//vhssskm+fvf/16mCgEAAABgySfkhkZ6//33M2DAgC8cM2DAgLz//vvNVBEAAAAAtDxCbmikzp07Z+LEiV84ZuLEiencWU8nAAAAAFhchNzQSAMHDsz999+fJ598coHXhw8fnvvvvz+bbLJJM1cGAAAAAC2HjSehkY466qg89thjOfjggzN48OAMHDgw3bt3z5QpU/L000/nn//8Z9q3b58jjzyy3KUCAAAAwBJLyA2NtPbaa+fqq6/OKaeckn/84x/5xz/+kVKplLq6uiTJKqusknPPPTdrr712mSsFAAAAgCWXkBu+go033jgPPvhgRo4cmTFjxmTmzJnp1KlT1l133Wy00UYplUrlLhEAAAAAlmhCbviKSqVSNt5442y88cblLgUAAAAAWhwhNzSBmpqavPHGG/UruVdfffW0aePtBQAAAACLmxQOvoLp06fnoosuyt13352PP/64/nz79u2z00475cc//nGWXnrpMlYIAAAAAEs2ITc00vTp0zNkyJCMHz8+Xbt2zUYbbZSePXumsrIyo0ePzi233JKnn346w4YNS7du3cpdLgAAAAAskYTc0Ei/+c1vMn78+Bx88ME58sgj07Fjx/prc+bMyW9+85tcddVV+d3vfpeTTz65jJUCAAAAwJKrVbkLgKJ6+OGHs8kmm+SEE05oEHAnSYcOHXL88cdnk002yd///vcyVQgAAAAASz4hNzTS+++/nwEDBnzhmAEDBuT9999vpooAAAAAoOURckMjde7cORMnTvzCMRMnTkznzp2bqSIAAAAAaHmE3NBIAwcOzP33358nn3xygdeHDx+e+++/P5tsskkzVwYAAAAALYeNJ6GRjjrqqDz22GM5+OCDM3jw4AwcODDdu3fPlClT8vTTT+ef//xn2rdvnyOPPLLcpQIAAADAEkvIDY209tpr5+qrr84pp5ySf/zjH/nHP/6RUqmUurq6JMkqq6ySc889N2uvvXaZKwUAAACAJZeQG76CjTfeOA8++GBGjhyZMWPGZObMmenUqVPWXXfdbLTRRimVSuUuEQAAAACWaEJu+IpKpVI23njjbLzxxuUuBQAAAABaHCE3NIFp06Zl7Nix9Su5+/Tpk6WXXrrcZfEFZlfNzY0jJ+Qvz09Mkvzor//Odwf0yr4brZSOFa3LXB0AAAAAC0vIDV/BhAkTcvbZZ+exxx6r78WdfLK6+xvf+EZ+8pOfZKWVVipjhSzI7Kq5+eGwF/JK5czU/v9/bdPm1OSq4ePz2LgpuXJIP0E3AAAAQEEIuaGR3nrrreyzzz6ZMmVKVl111Wy44Ybp0aNHJk+enOeffz6PPPJIXnjhhdx8881ZeeWVy10u87hx5IQGAfenauuSVypn5saRE/KDzVctT3EAAAAALBIhNzTSRRddlKlTp+bMM8/Md7/73QabTNbV1WXYsGE588wzc+GFF+ayyy4rY6V81u0vvjNfwP2p2rpPrgu5AQAAAIpByA2NNHz48GyzzTYZMmTIfNdKpVL23nvvPPbYYxk+fHgZquOLTJ5V9ZWuAwAAAPC/o1W5C4Cimjt3btZaa60vHLPOOutk7ty5zVQRC6vHUhVf6ToAAAAA/zuE3NBI6623XsaNG/eFY1599dWsv/76zVQRC2v3viukVWnB11qVPrkOAAAAQDEIuaGRjj322Pzzn//MLbfcssDrw4YNyxNPPJFjjz22eQvjS+270UpZZ9lO8wXdrUrJOst2yr4brVSewgAAAABYZHpyw0K6/PLL5zu36aab5rTTTssf/vCHbLjhhunRo0cmT56c5557Lm+++WYGDRqU4cOHZ8MNNyxDxXyejhWtc+WQfrlx5IT85fmJmTanJkt3aJPvDuiVfTdaKR0rWpe7RAAAAAAWUqmurq6u3EVAEfTp06dRzyuVShkzZkwTV/O/ZfLkD1PUf0nGvvdh9r/++Vy334D0Wa5zucsBAACAQlkSPleXSkmPHsWsnU9YyQ0L6dprry13CQAAAADAZwi5YSFtsskm5S4BAAAAAPgMG08CAAAAAFBYQm4AAAAAAApLyA0AAAAAQGEJuQEAAAAAKCwhNwAAAAAAhSXkBgAAAACgsITcAAAAAAAUVptyFwBLijlz5uSuu+7K66+/nlKplDXXXDM77bRT2rdvX+7SAAAAAGCJJeSGJvDiiy/m8MMPz9SpU7P00kunqqoqM2fOzKWXXprf//73WXfddctdIgAAAAAskbQrgSZwxhlnZJ111sk//vGPPPnkk3n22Wdzww03pK6uLmeeeWa5ywMAAACAJZaQGxbBPffcs8DzL7/8cg499NAst9xy9ec22mij7LDDDnnppZeaqzwAAAAAaHGE3LAITjrppBx22GF57733Gpxfbrnl8o9//KPBudmzZ2fEiBFZfvnlm7FCAAAAAGhZhNywCG6//fZMmzYtO+ywQ2644Yb680cddVT+/Oc/Z/vtt89xxx2XI488MltvvXVefvnlHHnkkWWsGAAAAACWbEJuWARrr712br755hxzzDG5+OKLs/fee+e1117LHnvskRtvvDF9+vTJm2++mUmTJmXQoEEZNmxYdt1113KXDQAAAABLrDblLgCKplQqZejQodluu+1yxhlnZLfddsuhhx6aww47LL/61a/KXR4AAAAAtChWckMjrbjiivn973+fc845JzfddFN23XXXPPfcc+UuCwAAAABaFCE3fEU777xz7rnnnqy//vrZb7/9cuaZZ2bmzJnlLgsA4P9r787D5KwKtHE/1Vv2ICEkQ8IewSAQQNlBEIj5GBiGBMHgqGGJILI4gMsAoo4DCuIyQkAcGL7BAK4whC3KkkSMEgk7AwSdD0mQANkISZos3emu3x/8aIwBpDudvFTVfV8XV7rOebv6OV529VVPnz4vAADUBCU3dNKTTz6Zf/3Xf81nPvOZ/Ou//mueeuqpbLzxxrnkkkty1VVXZfr06Tn88MMzZcqUoqMCAAAAQNVTckMnTJ06NR/72McyefLkLF68OL/85S9zzDHH5Ne//nWSZP/998/tt9+eQw89NJ/73Ofyz//8z1m0aFGxoQEAAACgiim5oRMuu+yyDBs2LFOnTs3Pf/7zTJ06NcOGDcull17acU3Pnj1z7rnn5ic/+UmeffbZ/P3f/32BiQEAAACguim5oRPmzJmT/fffP3379k2S9OnTJ/vvv3/mzJmz1rUjRozIzTffnPHjx2/omAAAAABQM5Tc0AlbbbVVZsyYkZUrVyZJVq5cmfvuuy9bbLHFm15fX1+fz3zmMxsyIgAAAADUlIaiA0AlOeOMM3L66afnwAMPzDbbbJPZs2dnyZIlmTBhQtHRAAAAAKAmKbmhEw455JD87Gc/y4033piXXnopo0aNytFHH50RI0YUHQ0AAAAAapKSGzppxIgRSm0AAAAAeJdwJjcAAAAAABXLTm7oJi+//HIef/zxLF++PJtvvnl23nnnlEqlomMBAAAAQFVTckMnTJo0KcOHD8/w4cM7xsrlci655JJcd911aWtr6xjfeuut8+1vfzs77bRTEVEBAAAAoCY4rgQ64Zxzzsk999yzxti3vvWt/Nd//Vf69++fo48+OieddFL22GOPPPvssxk/fnzmzZtXUFoAAAAAqH52csM6mDdvXq6//voMGzYsP/rRjzJw4MCOuR/96Ee56KKLcu211+Zf/uVfCkwJAAAAANXLTm5YB7///e/T1taWz3/+82sU3Ely3HHH5f3vf39+85vfFJQOAAAAAKqfkhvWwetHkey6665vOr/LLrvkhRde2ICJAAAAAKC2KLlhHfTo0SNJ0rt37zed79WrV9rb2zdkJAAAAACoKc7khk6aOXNmLr/88iTJ7NmzkyRz587NsGHD1rp23rx5ec973rMB0wEAAABAbVFyQyfNnDkzM2fOXGNs2rRpb1pyP/HEE9lmm202VDQAAAAAqDlKbuiEiRMnvun4gAED1hp78skn09ramv333399xwIAAACAmqXkhk7Yc8893/G1O+64Y6ZOnboe0wAAAAAAbjwJAAAAAEDFspMb1kFra2v+/Oc/Z9myZSmVStlkk00ydOjQomMBAAAAQM1QckMXTJ48OT/5yU/yyCOPpK2tbY2597znPTn88MNz0kknZfDgwQUlBAAAAIDa4LgS6IT29vaceeaZ+fznP58HHnggq1evTrlcTrlczmabbZb3vve9efXVV3P99dfniCOOyIwZM4qODAAAAABVTckNnTBx4sT86le/ysEHH5xJkyblwQcfzKRJkzJy5Mg0Nzfn+9//fh588MFccsklaWxszKmnnpo///nPRccGAAAAgKql5IZOuOmmm7L99ttnwoQJGT58ePr27Zvhw4fn0ksvzd/93d/l29/+dpqamvKP//iPmThxYtra2nLVVVcVHRsAAAAAqpaSGzrhueeey7777pu6ujW/derr67PPPvvkwQcf7BgbNmxYDjrooPzud7/b0DEBAAAAoGYouaETmpqasnTp0jedW7Zs2Vo3odx6662zYMGCDRENAAAAAGqSkhs6Yccdd8zdd9+dl156aY3xl156KXfffXe23377NcZfeeWV9OnTZ0NGBAAAAICa0lB0AKgkJ554Yk4++eQceeSR+djHPpahQ4fm+eefz4033pjm5uZ88pOfXOP6//mf/8l73/vegtICAAAAQPVTckMnHHDAATnvvPPyne98J1dffXVKpVLK5XLq6+vz2c9+NkcccUTHtc3Nzdlxxx2z3377FZgYAAAAAKqbkhs6ady4cTn00EPzm9/8JgsXLszGG2+c/fbbL5tvvvka1/Xt2zcXXHBBQSkBAAAAoDYouaELBg0alKOPPrroGAAAAABQ89x4EgAAAACAiqXkBgAAAACgYim5AQAAAACoWEpuAAAAAAAqlpIbAAAAAICKpeQGAAAAAKBiKbkBAAAAAKhYSm4AAAAAACqWkhu6ySGHHJKJEycWHQMAAAAAaoqSG7rJ3Llzs3Tp0qJjAAAAAEBNUXIDAAAAAFCxlNwAAAAAAFQsJTe8Cz3wwAM55ZRTsv/+++d973tf7rnnnjXmy+VyLr300uy///4ZMWJEjj/++MyePXuNa1555ZV8/vOfzwc+8IHsvvvuOe+88/Lqq6+ucc3TTz+df/qnf8rOO++cAw88MFdfffX6XhoAAAAAdKuGogNAJbr88svfdHzmzJlrzZVKpZx22mmdev7ly5fnfe97Xz760Y/m9NNPX2v+6quvznXXXZeLL744m2++eS699NKMHz8+kydPTo8ePZIkX/jCF7JgwYL813/9V1pbW3Peeeflq1/9ar773e8mSZqbmzN+/Pjss88++frXv54//vGPOe+889K/f/+MHTu2U3kBAAAAoChKbuiCtyu5Z86cucZYV0ruAw88MAceeOCbzpXL5UycODGf/exnM3LkyCTJJZdckn333Tf33HNPDj/88DzzzDOZPn16brzxxuy8885JkvPPPz8nn3xyvvSlL2Xw4MG59dZb09ramm9+85tpamrKdtttl1mzZuW//uu/lNwAAAAAVAwlN3TBlClT1nhcLpczcuTIHHfccRk3btx6/drPP/98FixYkH333bdjrF+/ftlll13yyCOP5PDDD88jjzyS/v37dxTcSbLvvvumrq4ujz/+eD7ykY/k0Ucfze67756mpqaOa/bff/9cffXVWbJkSTbaaKN3nKlXrzeeY/XqtrS0tKWpqT4NDfUd462tbWltbUuPHg2pr3/jpKSWltVZvbo9PXs2pq6u1DG+cmVr2tvL6dWrKaU3hrNiRWvK5XJ6937jaybJ8uUtKZVK6dWrsWOsXE5WrGhJXV0pPXu+Md7eXs7Kla1paKjrGO/ZszE9ejRk1arVaWysT2PjG9krbU1NTW+8tLe1tVuTNVmTNVmTNVmTNVmTNVmTNVmTNa23Nb2uZ8/GjjVU8pqoTEpu6IKhQ4e+6Xi/fv3ecq67LFiwIEmyySabrDG+ySabZOHChUmShQsXZsCAAWvMNzQ0ZKONNur4/IULF2bzzTdf45qBAwd2zHWm5F6xoiXl8ppjLS2v/XD5a6tWrX7T5/jLH4x//dxvZvnytcfL5fKbjre3v/n46tXtHV935crWjmyv/wD8a5WyptWr1x63JmtKrMmarCmxpsSaEmuyJmtKrCmxJmuypqT71vT63F9nqpQ1lUpJnz493nSeyuDGkwAAAAAAVCwlN1SYTTfdNEmyaNGiNcYXLVrUsRN74MCBefnll9eYX716dZYsWdLx+QMHDuzY+f261x+//jwAAAAA8G6n5IYKs/nmm2fTTTfNjBkzOsaam5vz2GOPZbfddkuS7Lbbblm6dGmeeOKJjmt+//vfp729PSNGjEiS7LrrrnnwwQfT2vrGnxrdd9992WabbTp1VAkAAAAAFEnJDd3koosuysiRI7vluV599dXMmjUrs2bNSvLazSZnzZqVF154IaVSKePGjcuVV16ZKVOm5A9/+EO+9KUvZdCgQR1ff9iwYfnQhz6Ur3zlK3n88cfz0EMP5YILLsjhhx+ewYMHJ0mOOOKINDY25stf/nL+93//N5MnT87EiRNzwgkndMsaAAAAAGBDcONJ6CZjxozptud64oknMm7cuI7HF110UcfXuPjii3PSSSdlxYoV+epXv5qlS5fmgx/8YP7zP/8zPXq8cZOE73znO7ngggty3HHHpa6uLqNGjcr555/fMd+vX79cc801+bd/+7ccddRR2XjjjXPqqadm7Nix3bYOAAAAAFjfSuVyuVx0CKCyLVy4LJX6SvL0vGX51PWP5LpP7pbhg/sVHQcAAAAqSjW8ry6VkoEDKzM7r3FcCQAAAAAAFUvJDQAAAABAxVJyAwAAAABQsZTcAAAAAABULCU3AAAAAAAVS8kNAAAAAEDFaig6AFSDV199NbNnz86KFSuy++67Fx0HAAAAAGqGndywDp5//vl89rOfzZ577pmjjz4648aN65h76KGHcthhh+X+++8vMCEAAAAAVDclN3TRCy+8kLFjx+Y3v/lNDjnkkOy6664pl8sd87vssksWL16cO+64o8CUAAAAAFDdlNzQRRMmTMiSJUty3XXX5bLLLst+++23xnxDQ0N23333PPzwwwUlBAAAAIDqp+SGLpo+fXo+8pGP5AMf+MBbXjNkyJDMmzdvA6YCAAAAgNqi5IYuWrJkSYYOHfq215TL5bS0tGygRAAAAABQe5Tc0EUDBw7MnDlz3vaaP/7xj9lss802UCIAAAAAqD1KbuiifffdN9OmTcvTTz/9pvMPPvhgfv/73+fAAw/cwMkAAAAAoHY0FB0AKtVnP/vZ3HnnnfnkJz+Z8ePHd+zqvvfee/PII4/k2muvzcYbb5zx48cXnBQAAAAAqpeSG7po8803zzXXXJOzzjorl156aUqlUsrlck455ZSUy+UMGTIkl156aQYNGlR0VAAAAACoWkpuWAe77LJL7rrrrkybNi2PPfZYlixZkr59+2bEiBE55JBD0tTUVHREAAAAAKhqSm5YRw0NDfnIRz6Sj3zkI0VHAQAAAICa48aT0EXjxo3LpEmT3vaaW265JePGjdswgQAAAACgBim5oYtmzpyZ559//m2veeGFF/LAAw9soEQAAAAAUHuU3LAerVixIg0NTgUCAAAAgPVF+wad8MILL6zxeNmyZWuNJUlbW1teeuml3HnnnRk6dOiGigcAAAAANUfJDZ1w8MEHp1QqJUlKpVImTpyYiRMnvuX15XI5X/rSlzZUPAAAAACoOUpu6ITRo0enVCqlXC5n0qRJGT58eHbYYYe1rqurq8tGG22UvffeOwcccEABSQEAAACgNii5oRMuvvjijo9nzpyZo446KuPGjSswEQAAAADUNiU3dNHUqVOLjgAAAAAANa+u6AAAAAAAANBVdnLDOmhubs4NN9yQ++67L/Pnz09LS8ta15RKpdxzzz0FpAMAAACA6qfkhi56+eWXc+yxx+a5555L375909zcnH79+qW1tTUrV65MkgwaNCgNDb7NAAAAAGB9cVwJdNGECRPy3HPP5Vvf+lYeeOCBJMlxxx2XRx99ND//+c8zYsSIDB06NHfccUfBSQEAAACgeim5oYvuvffe7LPPPjnyyCNTKpXWmBsxYkSuvvrqzJ07N5dffnlBCQEAAACg+im5oYsWLFiQHXbYoeNxfX19Vq1a1fF4o402ygEHHJBf/vKXRcQDAAAAgJqg5IYu6tevX1avXt3xuH///nnppZfWuKZv375ZtGjRho4GAAAAADVDyQ1dtMUWW2Tu3Lkdj9///vfnvvvuy+LFi5MkK1euzLRp07LZZpsVFREAAAAAqp6SG7pov/32y4wZM7JixYokydixY7No0aIceeSR+dznPpd/+Id/yHPPPZejjjqq4KQAAAAAUL2U3NBFxx57bC688MKOknvUqFH50pe+lBUrVuSuu+7KwoULc/zxx2f8+PEFJwUAAACA6tVQdACoVIMGDcphhx22xtiJJ56Y4447LosXL84mm2ySUqlUUDoAAAAAqA1Kbuhm9fX1GThwYNExAAAAAKAmKLmhG7S3t2fhwoVZvXr1m84PGTJkAycCAAAAgNqg5IZ1cMstt+T//t//m2eeeSZtbW1vek2pVMpTTz21gZMBAAAAQG1QckMXXXPNNfnOd76ThoaG7L777tl0003T0OBbCgAAAAA2JI0cdNH111+fwYMH56c//Wn+7u/+rug4AAAAAFCT6ooOAJXq5ZdfzqhRoxTcAAAAAFAgJTd00dZbb52lS5cWHQMAAAAAapqSG7ro+OOPz5QpUzJ37tyiowAAAABAzXImN3TRmDFjsmjRohx77LH5p3/6pwwfPjx9+/Z902v32GOPDZwOAAAAAGqDkhvWQXNzc5qbm3PZZZe97XWzZs3aQIkAAAAAoLYouaGLLr300vzHf/xHBgwYkMMOOyybbrppGhp8SwEAAADAhqSRgy666aabsvXWW+fGG29Mnz59io4DAAAAADXJjSehi5YuXZoPf/jDCm4AAAAAKJCSG7po++23z/z584uOAQAAAAA1TckNXXTKKadkypQpefLJJ4uOAgAAAAA1y5nc0EVLly7Nvvvum2OPPTZHHnlkhg8fnr59+77ptaNHj96w4QAAAACgRii5oYvOOeeclEqllMvl3HjjjUmSUqm0xjXlcjmlUknJDQAAAADriZIbuuiiiy4qOgIAAAAA1DwlN3TRmDFjio4AAAAAADXPjScBAAAAAKhYSm4AAAAAACqW40rgHRo+fHjq6upyxx13ZJtttsnw4cPXutHkmymVSnnqqac2QEIAAAAAqD1KbniH9thjjyRJr1691ngMAAAAABRHyQ3v0HXXXfe2jwEAAACADc+Z3AAAAAAAVCwlN3TRIYcckokTJ77tNTfccEMOOeSQDZQIAAAAYMNY3tKW/5wxJ5+76X+SJJ+76X/ynzPmZHlLW8HJqEWOK4Eumjt3bpYuXfq21yxdujQvvPDCBkoEAAAAsP4tb2nLZ372WP64oDnt5dfGFq9YnatnzMm9/29R/mPsLundVF9sSGqKndywHi1btixNTU1FxwAAAADoNj9+6Pk1Cu7XtZeTPy5ozo8fer6YYNQsO7mhEx544IE1Hs+dO3etsSRpa2vLSy+9lNtuuy1bb731BkoHAAAAsP7d/PiLaxXcr2svvzb/6X222rChqGlKbuiET33qUymVSkmSUqmUSZMmZdKkSW96bblcTqlUyuc///kNmBAAAABg/Vr4ass6zUN3U3JDJ5x22mkplUopl8u54oorsscee2SvvfZa67q6urpstNFG2XvvvTNs2LACkgIAAACsHwP7NGV+81sX2QP7OLqVDUvJDZ1wxhlndHw8c+bMfPSjH83o0aOLCwQAAACwgY0ZsVmunjHnTY8sqSu9Ng8bkpIbuui6664rOgIAAADABvdPH9w89/6/RWvdfLKulGy/ad/80wc3Ly4cNamu6ABQqV588cXMmDEjK1as6Bhrb2/PVVddlWOPPTbHH398fv3rXxcXEAAAAGA96N1Un/8Yu0tO2merbNzrtT20G/dqyEn7bJX/GLtLejfVF5yQWmMnN3TRpZdemmnTpuW3v/1tx9iVV16ZCRMmdDx+4IEH8pOf/CQjRowoIiIAAADAetG7qT6f3mer7L/tgHzq+kdy2Ud3zvDB/YqORY2ykxu66OGHH84+++yTxsbGJEm5XM4NN9yQbbfdNr/+9a/zi1/8Ir169co111xTcFIAAAAAqF5KbuiiRYsWZciQIR2PZ82alZdffjmf/OQn83d/93fZeeedM3LkyPzP//xPgSkBAAAAoLopuaGL2tvbUy6/cXeFmTNnplQqZe+99+4YGzx4cBYuXFhEPAAAAACoCUpu6KIhQ4bk8ccf73h8zz33ZNNNN822227bMbZgwYL079+/iHgAAAAAUBPceBK6aNSoUfnhD3+Yz33uc2lqaspDDz2UT3ziE2tc88wzz2TzzTcvKCEAAAAAVD8lN3TR+PHj87vf/S533XVXkuR973tfzjjjjI75uXPn5vHHH8/JJ59cVEQAAAAAqHpKbuiivn375uc//3n++Mc/JkmGDRuW+vr6Na6ZMGFCdt555yLiAQAAAEBNUHLDOtp+++3fdHzo0KEZOnToBk4DAAAAALVFyQ3raMGCBbnrrrvy7LPPZsWKFfnGN76RJHn55Zfz/PPPZ/vtt0/Pnj0LTgkAAAAA1amu6ABQyW644YYccsghueCCC3L99dfnv//7vzvmFi1alLFjx+bWW28tMCEAAAAAVDclN3TR1KlTc8EFF2T77bfPlVdemY9//ONrzG+33XZ53/vel3vuuaeghAAAAABQ/RxXAl10zTXXZMiQIZk4cWJ69+6dJ598cq1rtt9++zz44IMFpAMAAACA2mAnN3TRrFmzcuCBB6Z3795vec3gwYOzaNGiDZgKAAAAAGqLkhu6qFwup6Hh7f8YYtGiRWlqatpAiQAAAACg9ii5oYu22WabPPTQQ285v3r16jz44IPZfvvtN2AqAAAAAKgtSm7ooiOOOCJPPfVULr/88rXm2tra8q1vfSt//vOfM3r06A0fDgAAAABqhBtPQhd98pOfzNSpU3PFFVfktttu6ziW5J//+Z/zxBNPZO7cudlvv/1y9NFHF5wUAAAAAKqXndzQRY2Njbnmmmty8skn55VXXsn//u//plwu584778ySJUty0kkn5corr0ypVCo6KgAAAABULTu5YR00NTXlrLPOyplnnpk//elPWbJkSfr27Zthw4alvr6+6HgAAAAAUPWU3NANSqVShg0bVnQMAAAAAKg5jisBAAAAAKBiKbkBAAAAAKhYSm4AAAAAACqWkhsqVHNzc77xjW/koIMOyogRI3Lsscfm8ccf75hfuHBhzjnnnOy///7ZZZddMn78+MyePXuN51i1alW+/vWvZ6+99spuu+2WM844IwsXLtzAKwEAAACArlNyQ4U6//zzc9999+WSSy7Jbbfdlv322y8nnHBC5s2bl3K5nNNOOy1//vOf84Mf/CA333xzhg4dmhNOOCHLly/veI5vfvObmTZtWr7//e/nuuuuy/z583P66acXuKoNY2Hzqjw9b1menrcssxe99r/H7EXLO8YWNq8qOCEAAAAA71SpXC6Xiw4BdM7KlSvzgQ98ID/4wQ/y4Q9/uGP8qKOOyoc+9KGMHj06hx56aG6//fZst912SZL29vbst99+Ofvss3PMMcdk2bJl2WefffKd73wnhx56aJLkmWeeyWGHHZaf/exn2XXXXd9xnoULl6WSXkmuum92rp7x3FvOn7TPljl53603XCAAAACoUE/PW5ZPXf9Irvvkbhk+uF/RcbqkVEoGDqzM7LymoegAUM0WLVqUBQsWZPjw4d36vKtXr05bW1t69OixxniPHj3y8MMP57DDDut4/Lq6uro0NTXloYceyjHHHJMnnngira2t2XfffTuuGTZsWIYMGZJHH320UyV3r15Nf5GtLS0tbWlqqk9DQ33HeGtrW1pb29KjR0Pq69/4I5KWltVZvbo9PXs2pq6u1DG+cmVr2tvL6dWrKaU3hrNiRWvK5XJ6937jaybJ8uUtKZVK6dWrsWOsXE5WrGhJXV0pPXu+Mf7xPbbMAcM2SX19XRobX8u5enVb2trLaW1Znb97T681nr8S1tTeXs7Kla1paKhLU9MbL+1tbe1ZtWp1Ghvr09j4RnZrsiZrsiZrsiZrsiZrsiZrsiZrsqbuWNPrevZs7FhDJa+JymQnN3TCDjvskNNPPz2nnXZax9jkyZMzefLkXH755Wtdf/nll+eKK67IrFmzuj3Lsccem8bGxnznO9/JwIEDc/vtt+ecc87Jlltumdtvvz2jRo3KiBEj8m//9m/p1atXrr322nz3u9/N/vvvn2uuuSa33XZbzj333DzxxBNrPO/RRx+dvfbaK1/84hffcZZK28n913r3bsry5S1FxwAAAICKYyc37wbO5IZOKJfL+evfC/3pT3/KlClTNniWSy65JOVyOQcccEB23nnnXHfddTn88MNTV1eXxsbGTJgwIbNnz86ee+6ZXXfdNffff38OOOCAlEp+O/nX2trai44AAAAAQBc5rgQq1JZbbpnrr78+y5cvT3NzcwYNGpQzzzwzW2yxRZJkp512yi233JJly5altbU1AwYMyDHHHJOddtopSTJw4MC0trZm6dKl6d+/f8fzLlq0KJtuumkhayrKqlWri44AAAAAQBfZyQ0Vrnfv3hk0aFCWLFmS3/72tznkkEPWmO/Xr18GDBiQ2bNn54knnuiY32mnndLY2JgZM2Z0XPunP/0pL7zwQqfO464Gf3mGFwAAAACVxU5uqFDTp09PuVzONttsk+eeey6XXHJJtt122xx11FFJkl/+8pcZMGBAhgwZkj/84Q/55je/mZEjR2b//fdP8lr5/dGPfjQXX3xxNtpoo/Tt2zcXXnhhdtttt5osuVtb24qOAQAAAEAXKLmhQi1btizf+9738tJLL+U973lPRo0albPOOiuNja/diXjBggW5+OKLO44fOfLII3Pqqaeu8RznnXde6urq8rnPfS4tLS3Zf//987Wvfa2I5QAAAABAlyi5oUIddthhOeyww95yfty4cRk3btzbPkePHj3yta99TbENAAAAQMVSckMn3XDDDZk8eXLH48WLFyfJmxbOr8/x7rZ6taNKAAAAACqVkhs6afHixW9aXv/pT3960+tLpdL6jsQ6amlRcgMAAABUKiU3dMLTTz9ddATWg6amekU3AAAAQIWqKzoAQNEaGuqLjgAAAABAFym5YT0699xz8/73v7/oGAAAAABQtZTcsJ6Vy+WiIwAAAABA1VJyAzWvtdV53AAAAACVSskN1DwlNwAAAEDlUnIDNa9Hj4aiIwAAAADQRUpuoObV13spBAAAAKhUmh0AAAAAACqWv9GHTthll106df3q1avXUxIAAAAAIFFyQ6dssskmRUdgPWhp8csIAAAAgEql5IZOmDp1atERWA9Wr24vOgIAAAAAXeRMbqDm9ezZWHQEAAAAALpIyQ3UvLq6UtERAAAAAOgix5VAJ0yaNKlLnzd69OhuzQEAAAAAvEbJDZ1wzjnnpFR6bddvuVzu+PitvH6NkhsAAAAA1g8lN3RSfX19DjzwwOyyyy5FR6GbrFzZWnQEAAAAALpIyQ2dcOihh2bq1KmZOnVq5syZk6OOOiqjR4/OgAEDio7GOmhvLxcdAQAAAIAucuNJ6ITvf//7mT59es4999w0NjbmkksuyQEHHJAzzjgjv/71r9Pe3l50RLqgV6+moiMAAAAA0EV2ckMnbbTRRhk3blzGjRuXJ598MjfeeGMmT56ce+65JwMHDsyYMWNy1FFHZeutty46Ku/Q3zhaHQAAAIB3MTu5YR3suOOO+drXvpbp06fn29/+drbbbrv853/+Zw477LD89re/LToeAAAAAFQ9JTd0g6ampuy5557Zc889s8kmm6S9vT2rVq0qOhYAAAAAVD3HlcA6WL16daZMmZKbbropv/vd79LW1padd945p512Wvbdd9+i4/EOrVjRWnQEAAAAALpIyQ1d8Ic//CE33nhjbr/99ixevDgbb7xxPvnJT+ajH/1ott9++6Lj0UnlcrnoCAAAAAB0kZIbOuGGG27ITTfdlFmzZqWuri777bdfjj766Bx88MFpaPDtVKl6927K8uUtRccAAAAAoAu0ctAJF1xwQRoaGnLQQQdlzJgxGTx4cJLkqaeeetvPGzFixIaIBwAAAAA1R8kNnbR69epMmzYt06ZNe8efM2vWrPWYCAAAAABql5IbOmHMmDFFRwAAAAAA/oKSGzrhoosuKjoC64HzuAEAAAAqV13RAQCKViqVio4AAAAAQBcpuYGa16tXY9ERAAAAAOgiJTe8Q+PHj8/jjz/epc9dvnx5rrrqqtxwww3dnAoAAAAAapszueEdWrx4ccaOHZvdd989o0ePzqhRo9KvX7+3/ZxHH300t956a+64446sWrUqF1988QZKCwAAAAC1oVQul8tFh4BKcfPNN+fyyy/P3LlzU1dXl2222SY77rhjNtlkk/Tv3z+rVq3KkiVL8uyzz+aJJ57Iq6++mvr6+hx22GE588wzM2TIkKKXsF4sXLgslfxK0qtXU1ascPNJAAAA6Kyn5y3Lp65/JNd9crcMH/z2mwHfrUqlZODAyszOa+zkhk4YM2ZMRo8enXvvvTf//d//nfvvvz+33nrrWtfV1dXlfe97X0aOHJljjjkmgwYNKiAt75SCGwAAAKByKbmhk0qlUj784Q/nwx/+cJLkmWeeyUsvvZRXXnklPXr0yIABA7Lddtv9zaNMePeoqyulvb2Ct6IDAAAA1DAlN6yjYcOGZdiwYUXHYB307NmY5cvt5gYAAACoRHVFBwAAAAAAgK5ScgMAAAAAULGU3EDNcx43AAAAQOVScgM1b+XK1qIjAAAAANBFSm6g5jU0eCkEAAAAqFSaHaDmNTU1FB0BAAAAgC5ScgMAAAAAULFsX4QuKpfL+f3vf59HH300CxYsSJJsuumm2XXXXbP33nunVCoVnBAAAAAAqp+SG7rg3nvvzYUXXpjnn38+yWuFd5KOYnuLLbbI+eefnwMOOKCwjLxzbW3tRUcAAAAAoIuU3NBJN954Y7761a+mvb09u+yyS/bee+9sttlmSZIXX3wxv//97/PYY4/llFNOyYUXXpijjjqq4MT8LatWrS46AgAAAABdpOSGTpgzZ07+9V//NRtttFG++93vZt99933T62bMmJHPf/7z+drXvpbdd989W2655QZOSmc0NtantbWt6BgAAAAAdIEbT0InXHvttSmXy/nhD3/4lgV3kuyzzz754Q9/mLa2tlx77bUbLiBd0thYX3QEAAAAALpIyQ2dcN9992XvvffOLrvs8jevHTFiRPbZZ5/87ne/2wDJAAAAAKA2KbmhE+bNm5cddtjhHV+/ww47ZN68eesxEQAAAADUNiU3dEJdXV3a2t752c1tbW2pq/Nt9m63erXzuAEAAAAqlfYNOmHzzTfPI4888o6vf+SRRzJ06ND1mIju0NKi5AYAAACoVEpu6IQPfehDeeyxxzJt2rS/ee2vf/3rPProoznggAM2QDLWRVOTG08CAAAAVColN3TCCSeckF69euXss8/OTTfdlHK5vNY15XI5//3f/52zzjorvXv3zvHHH7/hg9IpDQ1KbgAAAIBK1VB0AKgkAwcOzL//+7/njDPOyPnnn58JEyZkzz33zGabbZYkefHFFzNz5szMmzcvDQ0NmTBhQjbddNOCUwMAAABA9VJyQycdeOCB+fGPf5xvfOMbeeSRR3Lrrbeudc1uu+2WL3/5y9lpp50KSAgAAAAAtUPJDV2w00475Sc/+UnmzJmTRx55JAsWLEiSbLrpptltt92y1VZbFZyQzmhtdeNJAAAAgEql5IZ1sNVWW71toX3uuefmlltuyVNPPbUBU9FZSm4AAACAyuXGk7CevdnNKXl36dHD7/sAAAAAKpWSG6h59fVeCgEAAAAqlWYHAAAAAICKpeQGAAAAAKBiKbmBmtfSsrroCAAAAAB0kZIbqHmrV7cXHQEAAACALmooOgBUkl122aVT169ebYdwJejZszErV7YWHQMAAACALlByQydssskmRUdgPairKxUdAQAAAIAuUnJDJ0ydOrXoCAAAAADAX3AmNwAAAAAAFUvJDetRS0tLmpubi47B3+A8bgAAAIDKpeSGTjjkkEMyceLENcamT5+eiy666E2vv+qqq7LHHntsiGisg/b2ctERAAAAAOgiJTd0wty5c7N06dI1xh577LG1im8qS69eTUVHAAAAAKCLlNxAzSuVik4AAAAAQFcpuQEAAAAAqFhKbgAAAAAAKpaSG6h5K1a0Fh0BAAAAgC5ScgM1r1wuFx0BAAAAgC5qKDoAVJrbbrstjz32WMfj5557Lkly0kknrXXt63O8u/Xu3ZTly1uKjgEAAABAFyi5oZPmzJmTOXPmrDU+ffr0N72+VCqt70gAAAAAULOU3NAJU6ZMKToCAAAAAPAXlNzQCUOHDi06AgAAAADwF9x4Eqh5zuMGAAAAqFxKbqDmOTcdAAAAoHIpuYGa16tXY9ERAAAAAOgiJTcAAAAAABVLyQ0AAAAAQMVScgM1r1wuOgEAAAAAXaXkBmreihUtRUcAAAAAoIuU3EDNq6srFR0BAAAAgC5ScgM1r2fPxqIjAAAAANBFSm4AAAAAACqWkhsAAAAAgIql5AZqXnt7uegIAAAAAHSRkhsqVHNzc77xjW/koIMOyogRI3Lsscfm8ccf75h/9dVX82//9m854IADMmLEiBx22GH5yU9+ssZzrFq1Kl//+tez1157ZbfddssZZ5yRhQsXbuilFG7lytaiIwAAAADQRUpuqFDnn39+7rvvvlxyySW57bbbst9+++WEE07IvHnzkiQXX3xxpk+fnm9/+9uZPHlyjjvuuFxwwQWZMmVKx3N885vfzLRp0/L9738/1113XebPn5/TTz+9qCUVpqHBSyEAAABApdLsQAVauXJl7rrrrnzxi1/MHnvska222ipnnHFGttpqq/z4xz9OkjzyyCMZPXp09tprr2y++eYZO3Zshg8f3rHbe9myZbnppptyzjnnZJ999slOO+2Ub37zm3nkkUfy6KOPFri6Da+pqaHoCAAAAAB0kWYHKtDq1avT1taWHj16rDHeo0ePPPzww0mS3XbbLVOnTs3RRx+dQYMG5f7778+zzz6bc889N0nyxBNPpLW1Nfvuu2/H5w8bNixDhgzJo48+ml133fUd5+nVq+kvsrWlpaUtTU31aWio7xhvbW1La2tbevRoSH39G79fa2lZndWr29OzZ2Pq6kod4ytXtqa9vZxevZpSemM4K1a0plwup3fvN75mkixf3pJSqZRevRo7xsrlZMWKltTVldKz5xvj7e3lrFzZmoaGujQ1NaSxsT69ezelra09q1atTmNjfRob38heiWt6nTVZkzVZkzVZkzVZkzVZkzVZkzVZ0/pc0+t69mzsWEMlr4nKVCqXy+64BhXo2GOPTWNjY77zne9k4MCBuf3223POOedkyy23zJ133pmWlpZ85StfyaRJk9LQ0JBSqZQLL7wwo0ePTpLcdtttOffcc/PEE0+s8bxHH3109tprr3zxi198x1kWLlyWSn4l6d27KcuXtxQdAwAAACrO0/OW5VPXP5LrPrlbhg/uV3ScLimVkoEDKzM7r7GTGyrUJZdckvPOOy8HHHBA6uvr8/73vz+HH354nnzyySTJddddl0cffTRXXnllhgwZkgcffDBf//rXM2jQoDV2b/Pab4EBAAAAqExKbqhQW265Za6//vosX748zc3NGTRoUM4888xsscUWWblyZf793/89l19+eT784Q8nSYYPH55Zs2blmmuuyb777puBAwemtbU1S5cuTf/+/Tued9GiRdl0000LWlUxVq1aXXQEAAAAALrIjSehwvXu3TuDBg3KkiVL8tvf/jaHHHJIVq9endbW1pRKa54pVV9fn9dPKNppp53S2NiYGTNmdMz/6U9/ygsvvNCp87irwV+e4QUAAABAZbGTGyrU9OnTUy6Xs8022+S5557LJZdckm233TZHHXVUGhsbs+eee+bb3/52evbsmSFDhuSBBx7IpEmTcs455yRJ+vXrl49+9KO5+OKLs9FGG6Vv37658MILs9tuu9Vkyd3a2lZ0DAAAAAC6QMkNFWrZsmX53ve+l5deeinvec97MmrUqJx11llpbHztTsTf+9738r3vfS9f+MIXsmTJkgwZMiRnnXVWPv7xj3c8x3nnnZe6urp87nOfS0tLS/bff/987WtfK2pJAAAAANBppfLrZxcAdNHChctSya8kvXs3ZfnylqJjAAAAQMV5et6yfOr6R3LdJ3fL7SKeOwAAHOlJREFU8MH9io7TJaVSMnBgZWbnNc7kBmre6tWOKgEAAACoVEpuoOa1tCi5AQAAACqVkhuoeU1N9UVHAAAAAKCLlNxAzWtoUHIDAAAAVColNwAAAAAAFUvJDQAAAABAxVJyAzWvtdWNJwEAAAAqlZIbqHlKbgAAAIDKpeQGal6PHg1FRwAAAACgi5TcQM2rr/dSCAAAAFCpNDsAAAAAAFQsJTcAAAAAABVLyQ3UvJaW1UVHAAAAAKCLlNxAzVu9ur3oCAAAAAB0kZIbqHk9ezYWHQEAAACALlJyAzWvrq5UdAQAAAAAukjJDQAAAABAxVJyAwAAAABQsZTcQM1bubK16AgAAAAAdJGSG6h57e3loiMAAAAA0EVKbqDm9erVVHQEAAAAALpIyQ3UvFKp6AQAAAAAdJWSGwAAAACAiqXkBgAAAACgYim5gZq3YkVr0REAAAAA6CIlN1DzyuVy0REAAAAA6CIlN1DzevduKjoCAAAAAF2k5AYAAAAAoGIpuQEAAAAAqFhKbgAAAAAAKpaSG6h5y5e3FB0BAAAAgC5ScgM1r1QqFR0BAAAAgC5ScgM1r1evxqIjAAAAANBFDUUHAAAAAAAqx8LmVVn46mtHf85etHyNf5NkYJ+mDOzbo5Bs1KZSuVwuFx0CqGwLFy5LJb+S9O7d5FxuAAAAeIeuum92rp7x3FvOn7TPljl53603XKB1VColAwf2KzoG68BObqDmVXJBDwAAABvaUSM2ywHDNul43KNHY1atau14PLBPUxGxqGFKbqDmrVhhFzcAAAC8UwP79niT40h6FpIFEjeeBEhdXanoCAAAAFCxvK+maEpuoOb17NlYdAQAAACoWN5XUzQlNwAAAAAAFUvJDQAAAABAxVJyAzWvvb1cdAQAAACoWN5XUzQlN1DzVq5sLToCAAAAVCzvqymakhuoeQ0NXgoBAACgq7yvpmj+HwjUvKamhqIjAAAAQMXyvpqiKbkBAAAAAKhYSm4AAAAAACqWkhuoeW1t7UVHAAAAgIrlfTVFU3IDNW/VqtVFRwAAAICK5X01RVNyAzWvsbG+6AgAAABQsbyvpmhKbqDm+WEMAAAAXed9NUVTcgMAAAAAULGU3AAAAAAAVCwlN1DzVq9uKzoCAAAAVCzvqymakhuoeS0tfhgDAABAV3lfTdGU3EDNa2pygwwAAADoKu+rKZqSG6h5DQ1+GAMAAEBXeV9N0ZTcAAAAAABUrIaiAwCVr1QqOsG6q4Y1AAAAQFEq+X11JWfnNaVyuVwuOgQAAAAAAHSF40oAAAAAAKhYSm4AAAAAACqWkhsAAAAAgIql5AYAAAAAoGIpuQEAAAAAqFhKbgAAAAAAKpaSGwAAAACAiqXkBgAAAACgYim5AQAAAACoWEpuAAAAAAAqlpIbAAAAAICKpeQGAAAAAKBiKbmBqtbW1pbDDjus6BgAAABQsVpaWvL888/n+eefT0tLS9FxYC0NRQcAWJ/q6+szYMCArFixIr169So6DgAAAFSM+fPn5xvf+EamTZuWfv36pVwup7m5OQcddFDOO++8DB48uOiIkCQplcvlctEhANan8847L0899VQOPfTQ9O7du2N83LhxBaYCAACAd7fjjz8+BxxwQMaOHZs+ffokSV599dX89Kc/zb333puJEycWnBBeYyc3UPXK5XJ22GGHzJkzp+goAAAAUDFeeumlnHjiiWuM9enTJ+PHj88vfvGLglLB2pTcQNW76KKLio4AAAAAFadHjx554IEHsscee6wxPnPmzDQ1NRWUCtbmuBKg6i1btiz//u//nhdeeCE//OEP8//+3//L008/nX/4h38oOhoAAAC8az322GP54he/mIaGhgwZMiRJMnfu3LS1teWSSy7JrrvuWmxA+P8puYGqd9ZZZ2W77bbL5MmTc/vtt2flypUZO3ZsbrnllqKjAQAAwLtauVzOE088kRdffDFJstlmm2WnnXZKqVQqOBm8oa7oAADr2+zZs3PqqaemoeG1E5p69uwZv98DAACAv61UKmX69OkZNWpURo0alZ133jmlUik/+MEPio4GHZTcQNVrbGxc4/HKlSuV3AAAAPAO3X333e9oDIrixpNA1dtrr71y5ZVXZtWqVbnvvvty7bXXZtSoUUXHAgAAgHe16dOnZ/r06Zk3b14uuuiijvFly5YVmArWZic3UPX++Z//OXV1denbt2++973v5QMf+EBOPfXUomMBAADAu1qPHj3Sv3//1NXVpV+/fh3/bb/99pkwYULR8aCDG08CAAAAAG/p6aefzvDhw4uOAW9JyQ1Urcsvv/xt508//fQNlAQAAACqw9ixY/Ozn/2s6BiwBmdyA1Xr1VdfTZLMmzcvM2bMyMEHH5xSqZSpU6dmn332KTgdAAAAVJ5Vq1YVHQHWouQGqta//Mu/JElOPPHETJo0KYMHD06SzJ8/P+eee26R0QAAAKAi9ezZs+gIsBY3ngSq3vz58zsK7iQZNGhQ5s2bV2AiAAAAqBx/uXv7pz/9aZJk0aJFRcWBtSi5gao3ePDgXHbZZXnxxRfz4osvZsKECWuU3gAAAMBbO+uss/KXt/V75ZVXMn78+AITwZqU3EDVu/jii/PMM89k9OjRGT16dP70pz/l4osvLjoWAAAAVIRtttkmF154YZKkubk5J598cj7xiU8UnAreUCr/5a9hAAAAAAD+ytlnn53tttsu9913X0aOHJnjjjuu6EjQQckNVK2ZM2dmzz33zJQpU950/pBDDtnAiQAAAKByNDc3d3y8atWqfOYzn8nee++dU045JUnSt2/foqLBGpTcQNU6//zzc+GFF+ZTn/rUWnOlUikTJ04sIBUAAABUhuHDh6dUKqVcLnf8+7pSqZRZs2YVmA7eoOQGAAAAAKBiNRQdAGB9efrpp992fvjw4RsoCQAAAADri53cQNU6+OCD33KuVCq95VndAAAAwBteP7bkrzmuhHcLJTcAAAAA8JaWL1/e8fHKlStzyy23pK2tLZ/+9KcLTAVvqCs6AMD69vjjj2fFihVJksmTJ+db3/pW5s2bV3AqAAAAqAy9e/fu+G/AgAE54YQTcueddxYdCzoouYGqd/7556epqSmzZ8/O97///TQ0NOS8884rOhYAAABUpGeeeSaLFy8uOgZ0cONJoOrV19envr4+v/nNb/Lxj388J5xwQkaPHl10LAAAAKgIe+yxR8eZ3G1tbSmXy/nKV75ScCp4g5IbqHotLS1ZuHBhpk2bli984QtJXvuhDAAAAPxtkyZN6vi4oaEhAwcOTH19fXGB4K8ouYGqd/zxx+fQQw/Nvvvumx133DHPPfdcNtpoo6JjAQAAQEUYOnRo0RHgbZXK5XK56BAAG1JbW1va2trS1NSUJPnlL3+Zv//7vy84FQAAALw7LVq0KJdddln+8Ic/ZNWqVR3jN998c4Gp4A1uPAnUnPr6+o6CO0muuuqqAtMAAADAu9uXv/zlDB06NIsXL84ZZ5yRQYMG5cADDyw6FnRQcgM1zx+0AAAAwFt78cUXc/LJJ6epqSkHH3xwJkyYkBkzZhQdCzoouYGa9/odogEAAIC1NTY2JkmampqyePHiNDQ0ZPHixQWngje48SQAAAAA8Ja23nrrLF68OP/4j/+Yj33sY+nbt2923HHHomNBBzeeBGre6NGjM2nSpKJjAAAAwLveQw89lKVLl+ZDH/pQGhrsn+XdwXElQM0766yzio4AAAAAFeG5557LQQcdpODmXcVObqBqnXbaaW973vbll1++AdMAAABA5RszZkxuvvnmomPAGvzKBahaI0eOLDoCAAAAVBX7ZXk3spMbqBmvv9y93e5uAAAA4K1NmjQpo0ePLjoGrMGZ3EDVmz9/fk466aTssssu2WWXXfKZz3wm8+fPLzoWAAAAVIwXXnght9xyS+rq6vLCCy8UHQfWoOQGqt5Xv/rVfPCDH8xvf/vb/Pa3v80HP/jBfPWrXy06FgAAAFSE2267LWPGjMndd9+dO++8M0cddVTuuOOOomNBB8eVAFXvyCOPzC233PI3xwAAAIC1HXroobn66quzxRZbJEmef/75fPrTn86vfvWrgpPBa+zkBqpeuVzOggULOh4vWLDAjTIAAADgHerVq1dHwZ0km2++eXr16lVgIlhTQ9EBANa3E088MWPGjMmHPvShJMn06dPzpS99qeBUAAAAUBkOPPDATJgwIcccc0zK5XJuuummHHTQQWlubk6S9O3bt+CE1DrHlQBV7fVd3EuWLMn999+fJNlrr72y3XbbFZwMAAAAKsPw4cPfcq5UKmXWrFkbMA2sTckNVLVyuZwjjjgit99+e9FRAAAAAFgPnMkNVLVSqZTBgwfn5ZdfLjoKAAAAVJy2trYcdthhRceAt+VMbqDq9enTJ6NHj86BBx6Y3r17d4yfe+65BaYCAACAd7/6+voMGDAgK1ascLNJ3rWU3EDV23777bP99tsXHQMAAAAq0pZbbpmPf/zjOfTQQ9fYPDZu3LgCU8EblNxA1Rs5cuRaN8l4+umnC0oDAAAAlaVcLmeHHXbInDlzio4Cb8qNJ4GqN2bMmNx8881/cwwAAACAymMnN1C1Fi1alAULFmTlypX5wx/+kNd/p9fc3Jzly5cXnA4AAADe3WbOnJk999wzU6ZMedP5Qw45ZAMngjen5Aaq1u23354f/ehHmT9/fj772c92jPfr1y+f/vSnC0wGAAAA73633npr9txzz1x77bVrzZVKJSU37xqOKwGq3hVXXJHTTjut6BgAAAAArAdKbqAmtLe3Z8GCBWlra+sYGzJkSIGJAAAAoHLMnz8/zz///Brvq/fYY48CE8EbHFcCVL2bb745F1xwQRobG1MqlZK89mdVM2bMKDgZAAAAvPtdeeWVueaaa7LFFlukrq4uyWvvq2+88caCk8Fr7OQGqt7IkSNz1VVXZdttty06CgAAAFSckSNH5he/+EU23njjoqPAm6orOgDA+rbxxhsruAEAAKCLBg4cqODmXc1ObqDq/cd//Ed69OiRI444Ij169OgY79u3b4GpAAAA4N3t6aefTpLcddddWbZsWY444og0NTV1zA8fPryoaLAGJTdQ9d7sh26pVMqsWbMKSAMAAACV4eCDD37LuVKplClTpmzANPDWlNwAAAAAwFuaMmVKdt9992y00UZJkiVLluThhx/OQQcdVHAyeI0zuYGa8OKLL+a2227Lbbfdlnnz5hUdBwAAACrGZZdd1lFwJ0n//v1z2WWXFZgI1qTkBqrePffck9GjR+eXv/xlfvWrX2X06NGZOnVq0bEAAACgIpVKpbS1tRUdAzo0FB0AYH274oor8vOf/zxbbbVVkmTOnDk588wz3/ZsMQAAAOA1ffr0ycMPP5wPfOADSZKHHnooffr0KTgVvEHJDVS9tra2joI7Sbbaaqu0t7cXmAgAAAAqxxe/+MWcfvrp2XbbbZMks2fPzhVXXFFwKniDkhuoeptsskl+8Ytf5KMf/WiS5KabbsqAAQMKTgUAAACVYbfddsvkyZPz6KOPdjzu379/saHgL5TK5XK56BAA69Nzzz2XL3zhC3nqqaeSJDvuuGO+/e1vZ8sttyw4GQAAAADrSskN1IxXX301SZwbBgAAAFBF6ooOALC+/exnP8srr7ySPn36pE+fPlm8eHF+/vOfFx0LAAAAgG6g5Aaq3o9//OO85z3v6Xi88cYb58c//nFxgQAAAADoNkpuoOq92alMbW1tBSQBAAAAoLspuYGqt+mmm2by5MkdjydPnpxBgwYVmAgAAACA7uLGk0DVe+aZZ3LqqaemtbU1SdKzZ8/84Ac/yNZbb11sMAAAAADWmZIbqAltbW159tlnkyTbbLNN6uvrO+b+/Oc/Z4sttigqGgAAAADrQMkN1LwxY8bk5ptvLjoGAAAAAF3gTG6g5vldHwAAAEDlUnIDNa9UKhUdAQAAAIAuUnIDAAAAAFCxlNxAzXNcCQAAAEDlUnIDVW/VqlVrjS1atKjj44985CMbMg4AAAAA3UjJDVS9s846a43d2q+88krGjx/f8fi0004rIhYAAAAA3UDJDVS9bbbZJhdeeGGSpLm5OSeffHI+8YlPFJwKAAAAgO5QKjuMFqgBZ599drbbbrvcd999GTlyZI477riiIwEAAADQDZTcQNVqbm7u+HjVqlX5zGc+k7333junnHJKkqRv375FRQMAAACgmyi5gao1fPjwlEqllMvljn9fVyqVMmvWrALTAQAAANAdlNwAAAAAAFQsN54Eqt6LL76YlpaWJMlDDz2U66+/fo2jTAAAAACoXEpuoOqdeuqpKZfLmTdvXs4+++w8/PDDOe+884qOBQAAAEA3UHIDNaFHjx759a9/nbFjx+Z73/teZs+eXXQkAAAAALqBkhuoei0tLWlpacnvfve77LXXXkXHAQAAAKAbKbmBqnf44Ydnv/32y4svvpgPfOADmT9/fnr16lV0LAAAAAC6QalcLpeLDgGwvi1dujR9+/ZNXV1dXn311TQ3N2fw4MFFxwIAAABgHTUUHQBgQ+jVq1fmzJmTVatWdYwpuQEAAAAqn5IbqHrTpk3LV77ylSxZsiS9e/fO0qVLs9lmm2Xq1KlFRwMAAABgHTmTG6h6l156aX72s59l2LBhuf/++3PxxRfn//yf/1N0LAAAAAC6gZIbqHp1dXUZOnRo2trakiRHHnlk7r///oJTAQAAANAdHFcCVL2Ghtde6gYPHpy77747Q4cOzZIlSwpOBQAAAEB3UHIDVW/cuHFZsmRJzjzzzJx99tlZunRpzjvvvKJjAQAAANANSuVyuVx0CAAAAAAA6Ao7uYGacO+992bOnDkd53InyQknnFBgIgAAAAC6g5IbqHqf//zn88wzz2SHHXZIfX19kqRUKhWcCgAAAIDuoOQGqt6TTz6ZO+64o6PgBgAAAKB61BUdAGB9Gzp0aFpaWoqOAQAAAMB64MaTQNX73//933zlK1/Jnnvumaampo7x008/vcBUAAAAAHQHx5UAVe+73/1uGhsbs2rVqrS2thYdBwAAAIBupOQGqt6zzz6bO++8s+gYAAAAAKwHzuQGqt4222yT5ubmomMAAAAAsB7YyQ1UvZ49e2bMmDHZb7/90qNHj47xc889t8BUAAAAAHQHJTdQ9d773vfmve99b9ExAAAAAFgPSuVyuVx0CIAi/ehHP8pxxx1XdAwAAAAAusCZ3EDNmzRpUtERAAAAAOgiJTdQ8/xBCwAAAEDlUnIDNa9UKhUdAQAAAIAuUnIDAAAAAFCxlNxAzXNcCQAAAEDlKpW1O0CVa2lpyfz585MkgwYNSlNT0xrzTz/9dIYPH15ENAAAAADWkZIbqFrz58/PN77xjUybNi39+vVLuVxOc3NzDjrooJx33nkZPHhw0REBAAAAWEdKbqBqHX/88TnggAMyduzY9OnTJ0ny6quv5qc//WnuvffeTJw4seCEAAAAAKwrZ3IDVeull17KiSee2FFwJ0mfPn0yfvz4juNLAAAAAKhsSm6gavXo0SMPPPDAWuMzZ85c61xuAAAAACqT40qAqvXYY4/li1/8YhoaGjJkyJAkydy5c9PW1pZLLrkku+66a7EBAQAAAFhnSm6gqpXL5TzxxBN58cUXkySbbbZZdtppp5RKpYKTAQAAANAdHFcCVLVSqZTp06dn1KhRGTVqVHbeeeeUSqX84Ac/KDoaAAAAAN1AyQ1UvbvvvvsdjQEAAABQeRqKDgCwvkyfPj3Tp0/PvHnzctFFF3WML1u2rMBUAAAAAHQnJTdQtXr06JH+/funrq4u/fr16xjfbLPNcuqppxaYDAAAAIDu4saTQNV7+umnM3z48KJjAAAAALAeOJMbqHp/WXCPHTu2wCQAAAAAdDclN1BTVq1aVXQEAAAAALqRkhuoKT179iw6AgAAAADdyJncQNVbtWpVevToscbYokWLsskmmxSUCAAAAIDuYic3UPXOOuus/OXv81555ZWMHz++wEQAAAAAdBclN1D1ttlmm1x44YVJkubm5px88sn5xCc+UXAqAAAAALqD40qAmnD22Wdnu+22y3333ZeRI0fmuOOOKzoSAAAAAN1AyQ1Urebm5o6PV61alc985jPZe++9c8oppyRJ+vbtW1Q0AAAAALqJkhuoWsOHD0+pVEq5XO7493WlUimzZs0qMB0AAAAA3UHJDQAAAABAxXLjSQAAAAAAKlZD0QEA1rfXjy35a44rAQAAAKh8Sm6g6j388MMdH69cuTK33HJL2traCkwEAAAAQHdxJjdQk4455pj84he/KDoGAAAAAOvImdxAzXnmmWeyePHiomMAAAAA0A0cVwJUvT322KPjTO62traUy+V85StfKTgVAAAAAN3BcSVA1Zs7d27Hxw0NDRk4cGDq6+sLTAQAAABAd1FyAwAAAABQsRxXAlS9RYsW5bLLLssf/vCHrFq1qmP85ptvLjAVAAAAAN3BjSeBqvflL385Q4cOzeLFi3PGGWdk0KBBOfDAA4uOBQAAAEA3UHIDVe/FF1/MySefnKamphx88MGZMGFCZsyYUXQsAAAAALqBkhuoeo2NjUmSpqamLF68OA0NDVm8eHHBqQAAAADoDs7kBqre1ltvncWLF+cf//Ef87GPfSx9+/bNjjvuWHQsAAAAALpBqVwul4sOAbChPPTQQ1m6dGk+9KEPpaHB7/kAAAAAKp3jSoCa8txzz+Wggw5ScAMAAABUCSU3UFMmTpxYdAQAAAAAupGSG6gpTmgCAAAAqC5KbqCmHH/88UVHAAAAAKAbKbmBmvDCCy/klltuSV1dXV544YWi4wAAAADQTZTcQNW77bbbMmbMmNx999258847c9RRR+WOO+4oOhYAAAAA3aBUdkAtUOUOPfTQXH311dliiy2SJM8//3w+/elP51e/+lXByQAAAABYV3ZyA1WvV69eHQV3kmy++ebp1atXgYkAAAAA6C52cgNV7/vf/37q6+tzzDHHpFwu56abbkp7e3tOPPHEJEnfvn0LTggAAABAVym5gao3fPjwt5wrlUqZNWvWBkwDAAAAQHdScgMAAAAAULGcyQ1Utba2thx22GFFxwAAAABgPVFyA1Wtvr4+AwYMyIoVK4qOAgAAAMB60FB0AID1bcstt8zHP/7xHHrooendu3fH+Lhx4wpMBQAAAEB3UHIDVa9cLmeHHXbInDlzio4CAAAAQDdz40kAAAAAACqWndxA1Zo5c2b23HPPTJky5U3nDznkkA2cCAAAAIDupuQGqtatt96aPffcM9dee+1ac6VSSckNAAAAUAUcVwIAAAAAQMWykxuoCfPnz8/zzz+ftra2jrE99tijwEQAAAAAdAclN1D1rrzyylxzzTXZYostUldXl+S140puvPHGgpMBAAAAsK6U3EDVu+mmm3L33Xdn4403LjoKAAAAAN2srugAAOvbwIEDFdwAAAAAVcqNJ4Gq9fTTTydJ7rrrrixbtixHHHFEmpqaOuaHDx9eVDQAAAAAuomSG6haBx988FvOlUqlTJkyZQOmAQAAAGB9cCY3ULWmTp2aJJkyZUp23333bLTRRkmSJUuW5OGHHy4yGgAAAADdxJncQNW77LLLOgruJOnfv38uu+yyAhMBAAAA0F2U3EDNKZVKaWtrKzoGAAAAAN1AyQ1UvT59+qxxPMlDDz2UPn36FJgIAAAAgO7ixpNA1XvkkUdy+umnZ9ttt02SzJ49O1dccUVGjBhRcDIAAAAA1pWSG6gJS5YsyaOPPpok2W233dK/f/9iAwEAAADQLZTcAAAAAABULGdyAwAAAABQsZTcAAAAAABULCU3AAAAAAAVS8kNAAAAAEDFUnIDAAAAAFCxlNwAAAAAAFQsJTcAAAAAABXr/wNemuxib24ERwAAAABJRU5ErkJggg==",
      "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": "2025-07-26T22:51:24.509284Z",
     "iopub.status.busy": "2025-07-26T22:51:24.509015Z",
     "iopub.status.idle": "2025-07-26T22:51:26.985196Z",
     "shell.execute_reply": "2025-07-26T22:51:26.984667Z",
     "shell.execute_reply.started": "2025-07-26T22:51:24.509270Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is cover\n",
      "len pos models: 98588\n",
      "differing counts: 3608\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.508, (0.492, 0.525), counts 1833, total 3606.\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 94980, total 94980.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.492, (0.475, 0.508), counts 1773, total 3606.\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, 590.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 997.4, (-1004, 5776)\n",
      "chirp-auk-t0_mask_control_slider: 1003.2, (-1005, 5781)\n",
      "chirp-auk-t0_n_tag_2: 1038.7, (1000, 17621)\n",
      "chirp-auk-t0_temp_s_95: 960.7, (-16576, 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": "2025-07-26T22:51:26.985935Z",
     "iopub.status.busy": "2025-07-26T22:51:26.985754Z",
     "iopub.status.idle": "2025-07-26T22:51:28.984440Z",
     "shell.execute_reply": "2025-07-26T22:51:28.983924Z",
     "shell.execute_reply.started": "2025-07-26T22:51:26.985921Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is artist\n",
      "len pos models: 41525\n",
      "differing counts: 1257\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.504, (0.476, 0.532), counts 633, total 1256.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 40268, total 40268.\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 0.496, (0.468, 0.524), counts 623, total 1256.\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, 697.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 1022.2, (991, 1845)\n",
      "chirp-auk-t0_cfg_steps_10: 952.9, (-691, 1000)\n",
      "chirp-auk-t0_mask_control_slider: 1024.9, (994, 1850)\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": "2025-07-26T22:51:28.985250Z",
     "iopub.status.busy": "2025-07-26T22:51:28.984976Z",
     "iopub.status.idle": "2025-07-26T22:51:29.004330Z",
     "shell.execute_reply": "2025-07-26T22:51:29.003937Z",
     "shell.execute_reply.started": "2025-07-26T22:51:28.985235Z"
    }
   },
   "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": "2025-07-26T22:51:29.005011Z",
     "iopub.status.busy": "2025-07-26T22:51:29.004792Z",
     "iopub.status.idle": "2025-07-26T22:51:29.113856Z",
     "shell.execute_reply": "2025-07-26T22:51:29.113350Z",
     "shell.execute_reply.started": "2025-07-26T22:51:29.004998Z"
    },
    "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": "2025-07-26T22:51:29.114598Z",
     "iopub.status.busy": "2025-07-26T22:51:29.114369Z",
     "iopub.status.idle": "2025-07-26T22:51:30.423000Z",
     "shell.execute_reply": "2025-07-26T22:51:30.422506Z",
     "shell.execute_reply.started": "2025-07-26T22:51:29.114584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is upload\n",
      "len pos models: 11744\n",
      "differing counts: 392\n",
      "chirp-auk-t0_mask_control_slider_win_over_chirp-auk-t0, win ratio 0.510, (0.461, 0.560), counts 200, total 392.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 11352, total 11352.\n",
      "chirp-auk-t0_win_over_chirp-auk-t0_mask_control_slider, win ratio 0.490, (0.440, 0.539), counts 192, total 392.\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, 1150.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELOs\n",
      "chirp-auk-t0: 996.5, (980, 1013)\n",
      "chirp-auk-t0_mask_control_slider: 1003.5, (987, 1020)\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": "2025-07-26T22:51:30.423839Z",
     "iopub.status.busy": "2025-07-26T22:51:30.423545Z",
     "iopub.status.idle": "2025-07-26T22:51:31.368094Z",
     "shell.execute_reply": "2025-07-26T22:51:31.367594Z",
     "shell.execute_reply.started": "2025-07-26T22:51:30.423825Z"
    }
   },
   "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": "2025-07-26T22:51:31.368911Z",
     "iopub.status.busy": "2025-07-26T22:51:31.368647Z",
     "iopub.status.idle": "2025-07-26T22:51:32.341834Z",
     "shell.execute_reply": "2025-07-26T22:51:32.341313Z",
     "shell.execute_reply.started": "2025-07-26T22:51:31.368896Z"
    }
   },
   "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": "2025-07-26T22:51:32.342676Z",
     "iopub.status.busy": "2025-07-26T22:51:32.342397Z",
     "iopub.status.idle": "2025-07-26T22:51:32.365766Z",
     "shell.execute_reply": "2025-07-26T22:51:32.365356Z",
     "shell.execute_reply.started": "2025-07-26T22:51:32.342661Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive proportion of data meeting criteria: 43.44%\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": "2025-07-26T22:51:32.366450Z",
     "iopub.status.busy": "2025-07-26T22:51:32.366218Z",
     "iopub.status.idle": "2025-07-26T22:51:34.185265Z",
     "shell.execute_reply": "2025-07-26T22:51:34.184756Z",
     "shell.execute_reply.started": "2025-07-26T22:51:32.366436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Name Value Counts for Preferred Clips:\n",
      "----------------------------------------------------------------------\n",
      "Model                               Count        Fraction\n",
      "----------------------------------------------------------------------\n",
      "chirp-auk-t0                      217,593         100.00%\n",
      "----------------------------------------------------------------------\n",
      "Total                             217,593         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": "2025-07-26T22:51:34.186063Z",
     "iopub.status.busy": "2025-07-26T22:51:34.185809Z",
     "iopub.status.idle": "2025-07-26T22:51:34.697727Z",
     "shell.execute_reply": "2025-07-26T22:51:34.697219Z",
     "shell.execute_reply.started": "2025-07-26T22:51:34.186049Z"
    }
   },
   "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": "2025-07-26T22:51:34.698480Z",
     "iopub.status.busy": "2025-07-26T22:51:34.698247Z",
     "iopub.status.idle": "2025-07-26T22:51:34.738028Z",
     "shell.execute_reply": "2025-07-26T22:51:34.737615Z",
     "shell.execute_reply.started": "2025-07-26T22:51:34.698466Z"
    }
   },
   "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 (435186, 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": "2025-07-26T22:51:34.738733Z",
     "iopub.status.busy": "2025-07-26T22:51:34.738480Z",
     "iopub.status.idle": "2025-07-26T22:51:34.793528Z",
     "shell.execute_reply": "2025-07-26T22:51:34.793044Z",
     "shell.execute_reply.started": "2025-07-26T22:51:34.738720Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Unique Users:\n",
      "--------------------\n",
      "135,982\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": "2025-07-26T22:51:34.794273Z",
     "iopub.status.busy": "2025-07-26T22:51:34.794040Z",
     "iopub.status.idle": "2025-07-26T22:51:34.813077Z",
     "shell.execute_reply": "2025-07-26T22:51:34.812696Z",
     "shell.execute_reply.started": "2025-07-26T22:51:34.794260Z"
    }
   },
   "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": "2025-07-26T22:51:34.813604Z",
     "iopub.status.busy": "2025-07-26T22:51:34.813488Z",
     "iopub.status.idle": "2025-07-26T22:51:34.829542Z",
     "shell.execute_reply": "2025-07-26T22:51:34.829190Z",
     "shell.execute_reply.started": "2025-07-26T22:51:34.813588Z"
    }
   },
   "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": "2025-07-26T22:51:34.830194Z",
     "iopub.status.busy": "2025-07-26T22:51:34.829969Z",
     "iopub.status.idle": "2025-07-26T22:51:34.844652Z",
     "shell.execute_reply": "2025-07-26T22:51:34.844308Z",
     "shell.execute_reply.started": "2025-07-26T22:51:34.830182Z"
    }
   },
   "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": "2025-07-26T22:51:34.845173Z",
     "iopub.status.busy": "2025-07-26T22:51:34.845067Z",
     "iopub.status.idle": "2025-07-26T22:51:36.770603Z",
     "shell.execute_reply": "2025-07-26T22:51:36.770102Z",
     "shell.execute_reply.started": "2025-07-26T22:51:34.845162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129145\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": "2025-07-26T22:51:36.771346Z",
     "iopub.status.busy": "2025-07-26T22:51:36.771109Z",
     "iopub.status.idle": "2025-07-26T22:51:38.658738Z",
     "shell.execute_reply": "2025-07-26T22:51:38.658259Z",
     "shell.execute_reply.started": "2025-07-26T22:51:36.771332Z"
    }
   },
   "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": "2025-07-26T22:51:38.659431Z",
     "iopub.status.busy": "2025-07-26T22:51:38.659242Z",
     "iopub.status.idle": "2025-07-26T22:51:38.791998Z",
     "shell.execute_reply": "2025-07-26T22:51:38.791524Z",
     "shell.execute_reply.started": "2025-07-26T22:51:38.659418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "web: 356436 (81.90%)\n",
      "ios: 49196 (11.30%)\n",
      "android: 29554 (6.79%)\n"
     ]
    }
   ],
   "source": [
    "print_out_value_counts_nicely(final_interesting_clips, \"source\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:51:38.792786Z",
     "iopub.status.busy": "2025-07-26T22:51:38.792597Z",
     "iopub.status.idle": "2025-07-26T22:51:42.878929Z",
     "shell.execute_reply": "2025-07-26T22:51:42.878447Z",
     "shell.execute_reply.started": "2025-07-26T22:51:38.792772Z"
    }
   },
   "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": "2025-07-26T22:51:42.879562Z",
     "iopub.status.busy": "2025-07-26T22:51:42.879439Z",
     "iopub.status.idle": "2025-07-26T22:51:42.898067Z",
     "shell.execute_reply": "2025-07-26T22:51:42.897705Z",
     "shell.execute_reply.started": "2025-07-26T22:51:42.879549Z"
    }
   },
   "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": "2025-07-26T22:51:42.898636Z",
     "iopub.status.busy": "2025-07-26T22:51:42.898481Z",
     "iopub.status.idle": "2025-07-26T22:51:42.932497Z",
     "shell.execute_reply": "2025-07-26T22:51:42.932101Z",
     "shell.execute_reply.started": "2025-07-26T22:51:42.898624Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-auk-t0: 435186 (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": "2025-07-26T22:51:42.933096Z",
     "iopub.status.busy": "2025-07-26T22:51:42.932930Z",
     "iopub.status.idle": "2025-07-26T22:51:42.948550Z",
     "shell.execute_reply": "2025-07-26T22:51:42.948204Z",
     "shell.execute_reply.started": "2025-07-26T22:51:42.933083Z"
    }
   },
   "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": "2025-07-26T22:51:42.949030Z",
     "iopub.status.busy": "2025-07-26T22:51:42.948923Z",
     "iopub.status.idle": "2025-07-26T22:51:42.963039Z",
     "shell.execute_reply": "2025-07-26T22:51:42.962696Z",
     "shell.execute_reply.started": "2025-07-26T22:51:42.949019Z"
    }
   },
   "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": "2025-07-26T22:51:42.963668Z",
     "iopub.status.busy": "2025-07-26T22:51:42.963440Z",
     "iopub.status.idle": "2025-07-26T22:54:58.217507Z",
     "shell.execute_reply": "2025-07-26T22:54:58.217007Z",
     "shell.execute_reply.started": "2025-07-26T22:51:42.963655Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "match model (435186, 62)\n",
      "(428924, 62)\n",
      "(428924, 62)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                                           | 0/5 [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": [
      " 20%|███████████████████████                                                                                            | 1/5 [00:45<03:00, 45.10s/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": [
      " 40%|██████████████████████████████████████████████                                                                     | 2/5 [01:27<02:11, 43.68s/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": [
      " 60%|█████████████████████████████████████████████████████████████████████                                              | 3/5 [02:04<01:20, 40.43s/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": [
      " 80%|████████████████████████████████████████████████████████████████████████████████████████████                       | 4/5 [02:42<00:39, 39.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 28924\n",
      "Length of the ID query string: 1128035\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [03:09<00:00, 37.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "Shape of df_snow_test:\n",
      "Rows: 419418\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: 0.7411\n",
      "Number of unique requests passing play duration criteria: 196163\n",
      "Fraction of unique requests that pass play duration criteria: 0.9147\n",
      "Number of unique request IDs: 121,709, Total 214462\n",
      "Start --------------------------\n",
      ": 255486 (59.56%)\n",
      "cover: 82626 (19.26%)\n",
      "artist_consistency: 35452 (8.27%)\n",
      "extend: 25650 (5.98%)\n",
      "artist_cover: 16618 (3.87%)\n",
      "upload_extend: 9628 (2.24%)\n",
      "artist_extend: 3460 (0.81%)\n",
      "overpainting: 2 (0.00%)\n",
      "underpainting: 2 (0.00%)\n",
      "End --------------------------\n",
      ": 151556 (62.26%)\n",
      "cover: 43140 (17.72%)\n",
      "artist_consistency: 23166 (9.52%)\n",
      "artist_cover: 9916 (4.07%)\n",
      "extend: 9742 (4.00%)\n",
      "upload_extend: 4212 (1.73%)\n",
      "artist_extend: 1684 (0.69%)\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": "2025-07-26T22:54:58.218263Z",
     "iopub.status.busy": "2025-07-26T22:54:58.218021Z",
     "iopub.status.idle": "2025-07-26T22:54:59.419503Z",
     "shell.execute_reply": "2025-07-26T22:54:59.419016Z",
     "shell.execute_reply.started": "2025-07-26T22:54:58.218249Z"
    }
   },
   "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": "2025-07-26T22:54:59.420199Z",
     "iopub.status.busy": "2025-07-26T22:54:59.419999Z",
     "iopub.status.idle": "2025-07-26T22:54:59.438926Z",
     "shell.execute_reply": "2025-07-26T22:54:59.438562Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.420185Z"
    }
   },
   "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": "2025-07-26T22:54:59.439499Z",
     "iopub.status.busy": "2025-07-26T22:54:59.439339Z",
     "iopub.status.idle": "2025-07-26T22:54:59.453672Z",
     "shell.execute_reply": "2025-07-26T22:54:59.453326Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.439487Z"
    }
   },
   "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": "2025-07-26T22:54:59.454364Z",
     "iopub.status.busy": "2025-07-26T22:54:59.454087Z",
     "iopub.status.idle": "2025-07-26T22:54:59.468188Z",
     "shell.execute_reply": "2025-07-26T22:54:59.467850Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.454351Z"
    }
   },
   "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": "2025-07-26T22:54:59.468831Z",
     "iopub.status.busy": "2025-07-26T22:54:59.468598Z",
     "iopub.status.idle": "2025-07-26T22:54:59.482571Z",
     "shell.execute_reply": "2025-07-26T22:54:59.482221Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.468818Z"
    }
   },
   "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": "2025-07-26T22:54:59.483122Z",
     "iopub.status.busy": "2025-07-26T22:54:59.482970Z",
     "iopub.status.idle": "2025-07-26T22:54:59.496680Z",
     "shell.execute_reply": "2025-07-26T22:54:59.496337Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.483110Z"
    }
   },
   "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": "2025-07-26T22:54:59.497155Z",
     "iopub.status.busy": "2025-07-26T22:54:59.497049Z",
     "iopub.status.idle": "2025-07-26T22:54:59.511061Z",
     "shell.execute_reply": "2025-07-26T22:54:59.510715Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.497144Z"
    }
   },
   "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": "2025-07-26T22:54:59.511619Z",
     "iopub.status.busy": "2025-07-26T22:54:59.511465Z",
     "iopub.status.idle": "2025-07-26T22:54:59.525653Z",
     "shell.execute_reply": "2025-07-26T22:54:59.525304Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.511607Z"
    }
   },
   "outputs": [],
   "source": [
    "# print_out_value_counts_nicely(final_subset_auk_clips_df, \"source\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:54:59.526392Z",
     "iopub.status.busy": "2025-07-26T22:54:59.526066Z",
     "iopub.status.idle": "2025-07-26T22:54:59.539779Z",
     "shell.execute_reply": "2025-07-26T22:54:59.539443Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.526380Z"
    }
   },
   "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": "2025-07-26T22:54:59.540491Z",
     "iopub.status.busy": "2025-07-26T22:54:59.540189Z",
     "iopub.status.idle": "2025-07-26T22:54:59.554472Z",
     "shell.execute_reply": "2025-07-26T22:54:59.554131Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.540478Z"
    }
   },
   "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": "2025-07-26T22:54:59.555069Z",
     "iopub.status.busy": "2025-07-26T22:54:59.554859Z",
     "iopub.status.idle": "2025-07-26T22:54:59.770248Z",
     "shell.execute_reply": "2025-07-26T22:54:59.769821Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.555056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "task\n",
       "                       2973260\n",
       "cover                  1001418\n",
       "artist_consistency      347891\n",
       "extend                  194620\n",
       "artist_cover            164859\n",
       "upload_extend           128205\n",
       "artist_extend            29345\n",
       "cover_extend                34\n",
       "playlist_condition          22\n",
       "artist_cover_extend         22\n",
       "underpainting                8\n",
       "overpainting                 4\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": "2025-07-26T22:54:59.775896Z",
     "iopub.status.busy": "2025-07-26T22:54:59.775691Z",
     "iopub.status.idle": "2025-07-26T22:54:59.794630Z",
     "shell.execute_reply": "2025-07-26T22:54:59.794265Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.775882Z"
    }
   },
   "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": "2025-07-26T22:54:59.795274Z",
     "iopub.status.busy": "2025-07-26T22:54:59.795020Z",
     "iopub.status.idle": "2025-07-26T22:54:59.808880Z",
     "shell.execute_reply": "2025-07-26T22:54:59.808531Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.795262Z"
    }
   },
   "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": "2025-07-26T22:54:59.809566Z",
     "iopub.status.busy": "2025-07-26T22:54:59.809311Z",
     "iopub.status.idle": "2025-07-26T22:55:04.531123Z",
     "shell.execute_reply": "2025-07-26T22:55:04.530634Z",
     "shell.execute_reply.started": "2025-07-26T22:54:59.809553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cover usage: 63204 out of 117785 (0.5366) \n",
      " -------------->\n",
      "chirp-auk-t0: 1001418 (100.00%)\n",
      "\n",
      " --------------------------\n",
      "infill usage: 0 out of 117785 (0.0) \n",
      " -------------->\n",
      "\n",
      " --------------------------\n",
      "artist usage: 36561 out of 117785 (0.3104) \n",
      " -------------->\n",
      "chirp-auk-t0: 347891 (100.00%)\n",
      "\n",
      " --------------------------\n",
      "image/video usage: 0 out of 117785 (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": "2025-07-26T22:55:04.531927Z",
     "iopub.status.busy": "2025-07-26T22:55:04.531637Z",
     "iopub.status.idle": "2025-07-26T22:55:07.836610Z",
     "shell.execute_reply": "2025-07-26T22:55:07.836113Z",
     "shell.execute_reply.started": "2025-07-26T22:55:04.531913Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "any task usage: 78911 out of 117785 (0.67) \n",
      " -------------->\n",
      "chirp-auk-t0: 1520640 (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": "2025-07-26T22:55:07.837346Z",
     "iopub.status.busy": "2025-07-26T22:55:07.837135Z",
     "iopub.status.idle": "2025-07-26T22:55:10.255377Z",
     "shell.execute_reply": "2025-07-26T22:55:10.254869Z",
     "shell.execute_reply.started": "2025-07-26T22:55:07.837332Z"
    }
   },
   "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": "2025-07-26T22:55:10.256252Z",
     "iopub.status.busy": "2025-07-26T22:55:10.255901Z",
     "iopub.status.idle": "2025-07-26T22:55:10.275325Z",
     "shell.execute_reply": "2025-07-26T22:55:10.274940Z",
     "shell.execute_reply.started": "2025-07-26T22:55:10.256238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38874\n"
     ]
    }
   ],
   "source": [
    "print(len(special_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:55:10.275917Z",
     "iopub.status.busy": "2025-07-26T22:55:10.275755Z",
     "iopub.status.idle": "2025-07-26T22:55:10.356786Z",
     "shell.execute_reply": "2025-07-26T22:55:10.356309Z",
     "shell.execute_reply.started": "2025-07-26T22:55:10.275904Z"
    }
   },
   "outputs": [],
   "source": [
    "potential_special_bot_mask = clip_df[\"user_id\"].isin(special_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:55:10.357490Z",
     "iopub.status.busy": "2025-07-26T22:55:10.357306Z",
     "iopub.status.idle": "2025-07-26T22:55:10.794431Z",
     "shell.execute_reply": "2025-07-26T22:55:10.794007Z",
     "shell.execute_reply.started": "2025-07-26T22:55:10.357477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    38874.000000\n",
       "mean        25.102408\n",
       "std         25.522280\n",
       "min          1.000000\n",
       "25%          8.000000\n",
       "50%         21.000000\n",
       "75%         32.000000\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": "2025-07-26T22:55:10.795115Z",
     "iopub.status.busy": "2025-07-26T22:55:10.794910Z",
     "iopub.status.idle": "2025-07-26T22:55:11.230179Z",
     "shell.execute_reply": "2025-07-26T22:55:11.229756Z",
     "shell.execute_reply.started": "2025-07-26T22:55:10.795101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "91182204    973\n",
       "73147386    510\n",
       "86420847    453\n",
       "75632151    422\n",
       "31759494    411\n",
       "           ... \n",
       "50055167      1\n",
       "71426126      1\n",
       "82416663      1\n",
       "62733262      1\n",
       "83918311      1\n",
       "Name: count, Length: 38874, 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": "2025-07-26T22:55:11.230847Z",
     "iopub.status.busy": "2025-07-26T22:55:11.230642Z",
     "iopub.status.idle": "2025-07-26T22:55:11.253780Z",
     "shell.execute_reply": "2025-07-26T22:55:11.253422Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.230832Z"
    }
   },
   "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": "2025-07-26T22:55:11.254337Z",
     "iopub.status.busy": "2025-07-26T22:55:11.254179Z",
     "iopub.status.idle": "2025-07-26T22:55:11.739760Z",
     "shell.execute_reply": "2025-07-26T22:55:11.739339Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.254325Z"
    }
   },
   "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": "2025-07-26T22:55:11.740670Z",
     "iopub.status.busy": "2025-07-26T22:55:11.740234Z",
     "iopub.status.idle": "2025-07-26T22:55:11.759813Z",
     "shell.execute_reply": "2025-07-26T22:55:11.759447Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.740655Z"
    }
   },
   "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": "2025-07-26T22:55:11.760393Z",
     "iopub.status.busy": "2025-07-26T22:55:11.760238Z",
     "iopub.status.idle": "2025-07-26T22:55:11.774685Z",
     "shell.execute_reply": "2025-07-26T22:55:11.774334Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.760380Z"
    }
   },
   "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": "2025-07-26T22:55:11.775250Z",
     "iopub.status.busy": "2025-07-26T22:55:11.775091Z",
     "iopub.status.idle": "2025-07-26T22:55:11.789589Z",
     "shell.execute_reply": "2025-07-26T22:55:11.789240Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.775237Z"
    }
   },
   "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": "2025-07-26T22:55:11.790282Z",
     "iopub.status.busy": "2025-07-26T22:55:11.790012Z",
     "iopub.status.idle": "2025-07-26T22:55:11.804429Z",
     "shell.execute_reply": "2025-07-26T22:55:11.804080Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.790268Z"
    }
   },
   "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": "2025-07-26T22:55:11.805140Z",
     "iopub.status.busy": "2025-07-26T22:55:11.804848Z",
     "iopub.status.idle": "2025-07-26T22:55:11.818613Z",
     "shell.execute_reply": "2025-07-26T22:55:11.818267Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.805127Z"
    }
   },
   "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": "2025-07-26T22:55:11.819101Z",
     "iopub.status.busy": "2025-07-26T22:55:11.818993Z",
     "iopub.status.idle": "2025-07-26T22:55:11.832510Z",
     "shell.execute_reply": "2025-07-26T22:55:11.832172Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.819089Z"
    }
   },
   "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": "2025-07-26T22:55:11.833157Z",
     "iopub.status.busy": "2025-07-26T22:55:11.832922Z",
     "iopub.status.idle": "2025-07-26T22:55:11.846508Z",
     "shell.execute_reply": "2025-07-26T22:55:11.846161Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.833143Z"
    }
   },
   "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": "2025-07-26T22:55:11.847073Z",
     "iopub.status.busy": "2025-07-26T22:55:11.846920Z",
     "iopub.status.idle": "2025-07-26T22:55:11.860984Z",
     "shell.execute_reply": "2025-07-26T22:55:11.860627Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.847060Z"
    }
   },
   "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": "2025-07-26T22:55:11.861478Z",
     "iopub.status.busy": "2025-07-26T22:55:11.861368Z",
     "iopub.status.idle": "2025-07-26T22:55:11.875722Z",
     "shell.execute_reply": "2025-07-26T22:55:11.875376Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.861467Z"
    }
   },
   "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": "2025-07-26T22:55:11.876395Z",
     "iopub.status.busy": "2025-07-26T22:55:11.876124Z",
     "iopub.status.idle": "2025-07-26T22:55:17.124008Z",
     "shell.execute_reply": "2025-07-26T22:55:17.123502Z",
     "shell.execute_reply.started": "2025-07-26T22:55:11.876382Z"
    }
   },
   "outputs": [],
   "source": [
    "unique_clips_in_playlist = playlist_clip_df[\"clip_id\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:55:17.124755Z",
     "iopub.status.busy": "2025-07-26T22:55:17.124543Z",
     "iopub.status.idle": "2025-07-26T22:55:19.790984Z",
     "shell.execute_reply": "2025-07-26T22:55:19.790463Z",
     "shell.execute_reply.started": "2025-07-26T22:55:17.124741Z"
    }
   },
   "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": "2025-07-26T22:55:19.791761Z",
     "iopub.status.busy": "2025-07-26T22:55:19.791535Z",
     "iopub.status.idle": "2025-07-26T22:55:21.260606Z",
     "shell.execute_reply": "2025-07-26T22:55:21.260117Z",
     "shell.execute_reply.started": "2025-07-26T22:55:19.791747Z"
    }
   },
   "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": "2025-07-26T22:55:21.261330Z",
     "iopub.status.busy": "2025-07-26T22:55:21.261117Z",
     "iopub.status.idle": "2025-07-26T22:55:21.280157Z",
     "shell.execute_reply": "2025-07-26T22:55:21.279799Z",
     "shell.execute_reply.started": "2025-07-26T22:55:21.261316Z"
    }
   },
   "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": "2025-07-26T22:55:21.280731Z",
     "iopub.status.busy": "2025-07-26T22:55:21.280572Z",
     "iopub.status.idle": "2025-07-26T22:55:21.294724Z",
     "shell.execute_reply": "2025-07-26T22:55:21.294369Z",
     "shell.execute_reply.started": "2025-07-26T22:55:21.280718Z"
    }
   },
   "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": "2025-07-26T22:55:21.295289Z",
     "iopub.status.busy": "2025-07-26T22:55:21.295175Z",
     "iopub.status.idle": "2025-07-26T22:55:21.309432Z",
     "shell.execute_reply": "2025-07-26T22:55:21.309088Z",
     "shell.execute_reply.started": "2025-07-26T22:55:21.295277Z"
    }
   },
   "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": "2025-07-26T22:55:21.310068Z",
     "iopub.status.busy": "2025-07-26T22:55:21.309841Z",
     "iopub.status.idle": "2025-07-26T22:55:21.323530Z",
     "shell.execute_reply": "2025-07-26T22:55:21.323189Z",
     "shell.execute_reply.started": "2025-07-26T22:55:21.310055Z"
    }
   },
   "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": "2025-07-26T22:55:21.324179Z",
     "iopub.status.busy": "2025-07-26T22:55:21.323948Z",
     "iopub.status.idle": "2025-07-26T22:55:21.337699Z",
     "shell.execute_reply": "2025-07-26T22:55:21.337366Z",
     "shell.execute_reply.started": "2025-07-26T22:55:21.324166Z"
    }
   },
   "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": "2025-07-26T22:55:21.338251Z",
     "iopub.status.busy": "2025-07-26T22:55:21.338142Z",
     "iopub.status.idle": "2025-07-26T22:55:24.421808Z",
     "shell.execute_reply": "2025-07-26T22:55:24.421294Z",
     "shell.execute_reply.started": "2025-07-26T22:55:21.338239Z"
    }
   },
   "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": "2025-07-26T22:55:24.422738Z",
     "iopub.status.busy": "2025-07-26T22:55:24.422361Z",
     "iopub.status.idle": "2025-07-26T22:55:24.441876Z",
     "shell.execute_reply": "2025-07-26T22:55:24.441484Z",
     "shell.execute_reply.started": "2025-07-26T22:55:24.422722Z"
    }
   },
   "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": "2025-07-26T22:55:24.442517Z",
     "iopub.status.busy": "2025-07-26T22:55:24.442400Z",
     "iopub.status.idle": "2025-07-26T22:55:24.457371Z",
     "shell.execute_reply": "2025-07-26T22:55:24.457021Z",
     "shell.execute_reply.started": "2025-07-26T22:55:24.442505Z"
    }
   },
   "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": "2025-07-26T22:55:24.458006Z",
     "iopub.status.busy": "2025-07-26T22:55:24.457790Z",
     "iopub.status.idle": "2025-07-26T22:55:24.472054Z",
     "shell.execute_reply": "2025-07-26T22:55:24.471710Z",
     "shell.execute_reply.started": "2025-07-26T22:55:24.457994Z"
    }
   },
   "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": "2025-07-26T22:55:24.472618Z",
     "iopub.status.busy": "2025-07-26T22:55:24.472464Z",
     "iopub.status.idle": "2025-07-26T22:55:24.486642Z",
     "shell.execute_reply": "2025-07-26T22:55:24.486299Z",
     "shell.execute_reply.started": "2025-07-26T22:55:24.472606Z"
    }
   },
   "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": "2025-07-26T22:55:24.487125Z",
     "iopub.status.busy": "2025-07-26T22:55:24.487019Z",
     "iopub.status.idle": "2025-07-26T22:55:25.404556Z",
     "shell.execute_reply": "2025-07-26T22:55:25.404041Z",
     "shell.execute_reply.started": "2025-07-26T22:55:24.487114Z"
    }
   },
   "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": "2025-07-26T22:55:25.405341Z",
     "iopub.status.busy": "2025-07-26T22:55:25.405110Z",
     "iopub.status.idle": "2025-07-26T22:55:26.027587Z",
     "shell.execute_reply": "2025-07-26T22:55:26.027090Z",
     "shell.execute_reply.started": "2025-07-26T22:55:25.405327Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len pos models: 217593\n",
      "differing counts: 0\n",
      "chirp-auk-t0_win_over_chirp-auk-t0, win ratio 1.000, (1.000, 1.000), counts 217593, total 217593.\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": "2025-07-26T22:55:26.028325Z",
     "iopub.status.busy": "2025-07-26T22:55:26.028099Z",
     "iopub.status.idle": "2025-07-26T22:55:26.252181Z",
     "shell.execute_reply": "2025-07-26T22:55:26.251760Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.028310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_name\n",
       "chirp-auk-t0    4839688\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": "2025-07-26T22:55:26.252844Z",
     "iopub.status.busy": "2025-07-26T22:55:26.252655Z",
     "iopub.status.idle": "2025-07-26T22:55:26.271506Z",
     "shell.execute_reply": "2025-07-26T22:55:26.271146Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.252830Z"
    }
   },
   "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": "2025-07-26T22:55:26.272034Z",
     "iopub.status.busy": "2025-07-26T22:55:26.271926Z",
     "iopub.status.idle": "2025-07-26T22:55:26.285687Z",
     "shell.execute_reply": "2025-07-26T22:55:26.285340Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.272022Z"
    }
   },
   "outputs": [],
   "source": [
    "# infill_clip_df[infill_clip_df[\"part_of_concat\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:55:26.286166Z",
     "iopub.status.busy": "2025-07-26T22:55:26.286059Z",
     "iopub.status.idle": "2025-07-26T22:55:26.299950Z",
     "shell.execute_reply": "2025-07-26T22:55:26.299605Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.286155Z"
    }
   },
   "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": "2025-07-26T22:55:26.300430Z",
     "iopub.status.busy": "2025-07-26T22:55:26.300324Z",
     "iopub.status.idle": "2025-07-26T22:55:26.314532Z",
     "shell.execute_reply": "2025-07-26T22:55:26.314184Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.300419Z"
    }
   },
   "outputs": [],
   "source": [
    "# all_pairs[\"flagged\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:55:26.315096Z",
     "iopub.status.busy": "2025-07-26T22:55:26.314943Z",
     "iopub.status.idle": "2025-07-26T22:55:26.329064Z",
     "shell.execute_reply": "2025-07-26T22:55:26.328718Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.315084Z"
    }
   },
   "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": "2025-07-26T22:55:26.329757Z",
     "iopub.status.busy": "2025-07-26T22:55:26.329480Z",
     "iopub.status.idle": "2025-07-26T22:55:26.343609Z",
     "shell.execute_reply": "2025-07-26T22:55:26.343265Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.329744Z"
    }
   },
   "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": "2025-07-26T22:55:26.344079Z",
     "iopub.status.busy": "2025-07-26T22:55:26.343973Z",
     "iopub.status.idle": "2025-07-26T22:55:26.358605Z",
     "shell.execute_reply": "2025-07-26T22:55:26.358258Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.344068Z"
    }
   },
   "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": "2025-07-26T22:55:26.359083Z",
     "iopub.status.busy": "2025-07-26T22:55:26.358979Z",
     "iopub.status.idle": "2025-07-26T22:55:26.372725Z",
     "shell.execute_reply": "2025-07-26T22:55:26.372385Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.359073Z"
    }
   },
   "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": "2025-07-26T22:55:26.373284Z",
     "iopub.status.busy": "2025-07-26T22:55:26.373135Z",
     "iopub.status.idle": "2025-07-26T22:55:26.387248Z",
     "shell.execute_reply": "2025-07-26T22:55:26.386906Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.373272Z"
    }
   },
   "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": "2025-07-26T22:55:26.387818Z",
     "iopub.status.busy": "2025-07-26T22:55:26.387667Z",
     "iopub.status.idle": "2025-07-26T22:55:26.402042Z",
     "shell.execute_reply": "2025-07-26T22:55:26.401694Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.387806Z"
    }
   },
   "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": "2025-07-26T22:55:26.402508Z",
     "iopub.status.busy": "2025-07-26T22:55:26.402402Z",
     "iopub.status.idle": "2025-07-26T22:55:26.416748Z",
     "shell.execute_reply": "2025-07-26T22:55:26.416401Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.402497Z"
    }
   },
   "outputs": [],
   "source": [
    "# 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": 142,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-26T22:55:26.417244Z",
     "iopub.status.busy": "2025-07-26T22:55:26.417136Z",
     "iopub.status.idle": "2025-07-26T22:55:26.432320Z",
     "shell.execute_reply": "2025-07-26T22:55:26.431958Z",
     "shell.execute_reply.started": "2025-07-26T22:55:26.417233Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auk og (243418, 90)\n",
      "Saving done! to 20250726\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 = \"20250726\"\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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