results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f86bc499",
   "metadata": {},
   "source": [
    "$$\\Large \\textbf{Loading Data}$$\n",
    "\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e13e917-1419-4e04-b2c9-6399c1bdbc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "from iv2_utils.iv2 import pickle_read, pickle_write\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm\n",
    "from tabulate import tabulate\n",
    "from jar.config import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import pandas\n",
    "import copy\n",
    "import cv2\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "990daa72-0ef8-4691-9ed4-fca8578e12c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ViCLIPS\n",
      "XCLIP\n",
      "ViCLIP\n",
      "CLIP\n",
      "TC\n",
      "B14\n",
      "L14\n",
      "S14\n"
     ]
    }
   ],
   "source": [
    "data = load_data()\n",
    "logits = load_logits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16915504-b67d-4a96-bd6a-693b141eb6c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote output to jar/B14/t13.pkl\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       " 161]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthesize_logits('B14', 't', logits.B14('act75'), window_size = 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "491d0ca1-2aa5-46aa-a902-5a1466392064",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'rustyjar/InternVideo-logits-r.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m logits_r \u001b[38;5;241m=\u001b[39m \u001b[43mpickle_read\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrustyjar/InternVideo-logits-r.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m logits_a \u001b[38;5;241m=\u001b[39m pickle_read(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrustyjar/InternVideo-logits-a.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m logits_g \u001b[38;5;241m=\u001b[39m pickle_read(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrustyjar/InternVideo-logits-g.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/miniconda3/envs/xclip/lib/python3.9/site-packages/iv2_utils/iv2.py:116\u001b[0m, in \u001b[0;36mpickle_read\u001b[0;34m(file)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpickle_read\u001b[39m(file):\n\u001b[1;32m    113\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;124;03m    Reads the content from a pickle file.\u001b[39;00m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    117\u001b[0m         data \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m    118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'rustyjar/InternVideo-logits-r.pkl'"
     ]
    }
   ],
   "source": [
    "logits_r = pickle_read('rustyjar/InternVideo-logits-r.pkl')\n",
    "logits_a = pickle_read('rustyjar/InternVideo-logits-a.pkl')\n",
    "logits_g = pickle_read('rustyjar/InternVideo-logits-g.pkl')\n",
    "logits_lg = pickle_read('rustyjar/InternVideo2-logits-g.pkl')\n",
    "logits_r_xclip = pickle_read('rustyjar/XCLIP-logits-r.pkl')\n",
    "logits_r_smol = pickle_read('rustyjar/IVS-logits-r.pkl')\n",
    "logits_a_smol = pickle_read('rustyjar/IVS-logits-r.pkl')\n",
    "logits_r2 = pickle_read('rustyjar/InternVideo2-logits-r.pkl')\n",
    "logits_l14_g = pickle_read('rustyjar/InternVideoL-logits-g.pkl')\n",
    "logits_b14_r = pickle_read('rustyjar/InternVideoB-logits-r.pkl')\n",
    "logits_b14_g = pickle_read('rustyjar/InternVideoB-logits-g.pkl')\n",
    "logits_b14_a = pickle_read('rustyjar/InternVideoB-logits-a.pkl')\n",
    "\n",
    "raw_data = pandas.read_csv('anno_backflip.csv')\n",
    "data_k600 = []\n",
    "for index, row in raw_data.iterrows():\n",
    "    data_k600.append((row['ID'], eval('[' + row['Frame(s)'] + ']')))\n",
    "\n",
    "data_gif87 = pickle_read('rustyjar/GIF87.pkl')\n",
    "\n",
    "print(len(IV_a8), 'predictions loaded.')\n",
    "print(\"First 10 Predictions from Intern Video:      \", IV_a8[:10])\n",
    "print(\"First 10 predictions from CLIP:              \", CLIP_a[:10])\n",
    "print(\"-\"*50)\n",
    "print(\"First 10 actual values:\", [x[1] for x in data_k600[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3491f324-43d9-4bb6-aeaf-c1c09887a484",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with size 8\n",
      "Done with size 11\n",
      "Done with size 13\n",
      "Done with size 15\n",
      "Done with size 17\n",
      "Done with size 19\n"
     ]
    }
   ],
   "source": [
    "def logitToPred(logit_lists, window_size = 8):\n",
    "    result = []\n",
    "    for logits in logit_lists:\n",
    "        logits_c = copy.deepcopy(logits)\n",
    "        logits_c.sort(key = lambda x: x[1])\n",
    "        new_logits = []\n",
    "        add = (window_size - 8) // 2\n",
    "        for j in range(add, len(logits_c) - add):\n",
    "            a_range = list(range(j - add, j + add + 1))\n",
    "            a_range = [logits_c[x][0] for x in a_range]\n",
    "            new_logits.append((np.mean(a_range).item(), j + 1))\n",
    "            # new_logits.append((logits_c[j][0], j + 1))\n",
    "        # print(new_logits)\n",
    "        # print(\"---\"*10)\n",
    "        # print(logits_c)\n",
    "        new_logits.sort(key = lambda x: -x[0])\n",
    "        if len(new_logits) == 0:\n",
    "            new_logits.append((0, 4))\n",
    "        result.append(new_logits[0][1])\n",
    "    return result\n",
    "# test = logitToPred(logits_a_smol, window_size = 13)\n",
    "j = [8] + list(range(11, 21, 2))\n",
    "for ws in j:\n",
    "    pickle_write(logitToPred(pickle_read('rustyjar/ViCLIP/s.pkl'), window_size = ws), f'jar/ViCLIP/s{ws}.pkl')\n",
    "    print(\"Done with size\",ws)\n",
    "# test = logitToPred(logits_g, window_size = 11)\n",
    "# pickle_write(logitToPred(logits_g, window_size = 17), 'jar/InternVideo-g17.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30da60a3-98de-435d-858b-d8ff67a7a8e4",
   "metadata": {},
   "source": [
    "$$\\Large \\textbf{Reading Logits and Averaging Predictions}$$\n",
    "\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb2bff5f-199b-4ee0-9c36-1a6573193caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "IV_g100 = []\n",
    "for idx, p11 in enumerate(IVB_r8):\n",
    "    p13 = IVB_r11[idx]\n",
    "    p15 = IVB_r13[idx]\n",
    "    IV_g100.append((p11+p13+p15)//3)\n",
    "pickle_write(IV_g100, 'jar/InternVideoB-r100.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a385f1e-c89d-4bc9-8853-4da74aefbf34",
   "metadata": {},
   "outputs": [],
   "source": [
    "correct = pickle_read('rustyjar/GIF87.pkl')\n",
    "print(correct[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d944047-075d-48c2-a057-e8b692efb6aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_gif87"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4441cad2-a050-4f94-9bf4-dd3f4c7a65f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "IV_L14_g = [22, 1, 24, 4, 4, 15, 18, 55, 30, 14, 59, 64, 10, 49, 292, 154, 138, 7, 139, 218, 4, 143, 40, 33, 24, 3, 1, 20, 12, 8, 1, 10, 7, 7, 53, 10, 15, 79, 143, 13, 32, 4, 225, 15, 9, 283, 30, 49, 15, 55, 11, 2, 11, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f745233-530f-4580-b1b3-c02de26965e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "IV_g13 = []\n",
    "logits_37 = []\n",
    "for idx, logits in enumerate(logits_g):\n",
    "    logits_sort = copy.deepcopy(logits)\n",
    "\n",
    "    avg_logits = []\n",
    "\n",
    "    for i in range(2, len(logits) - 2):\n",
    "        if idx == 37:\n",
    "            logits_37.append((sum([logits[x][0] for x in [i - 2, i - 1, i, i + 1, i + 2]])/5, i + 1))\n",
    "        avg_logits.append((sum([logits[x][0] for x in [i - 2, i - 1, i, i + 1, i + 2]])/5, i + 1))\n",
    "    \n",
    "    avg_logits.sort(key = lambda x: -x[0])\n",
    "    IV_g13.append(avg_logits[0][1])\n",
    "    \n",
    "\n",
    "print(IV_g13[-5:])\n",
    "pickle_write(IV_g13, 'jar/InternVideo-g13.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b938d5-46d5-4216-b227-c2783a3d9f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(IV_a100[38])\n",
    "plt.figure(figsize=[3, 2])\n",
    "plt.plot([x[1] for x in logits_a[38]], [x[0] for x in logits_a[38]], linewidth=2)\n",
    "plt.xlabel(\"Frame Index\")\n",
    "plt.ylabel(\"Similarity Score\")\n",
    "\n",
    "plt.gca().set_position([0, 0, 1, 1])\n",
    "plt.savefig('frame-similarity-score.svg', bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fea2b15-42c6-4ac7-ac03-d2e5b6bdf980",
   "metadata": {},
   "source": [
    "$$\\Large \\mathbf{1}^{\\text{st}}\\text{ Metric}$$\n",
    "\n",
    "--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e10c02be-2b66-4676-b806-25987eede24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showMetric(preds, data, threshold = 8, compare = False):\n",
    "    def clamp_error(prediction, truth): # NN's Idea\n",
    "        if truth in range(prediction, prediction + 9):\n",
    "            return 0\n",
    "        else: \n",
    "            return (prediction + 8) - truth if truth > prediction + 8 else prediction - truth\n",
    "    \n",
    "    score = 0\n",
    "    offsets = []\n",
    "    for idx, i_pred in enumerate(preds):\n",
    "        within_range = False\n",
    "        off = 10000\n",
    "        for truth in data[idx][1]:\n",
    "            if abs(i_pred - truth) < abs(off):\n",
    "                off = i_pred - truth\n",
    "    \n",
    "            if abs(i_pred - truth) <= threshold:\n",
    "                within_range = True\n",
    "        \n",
    "        if within_range:\n",
    "            score += 1\n",
    "    \n",
    "        offsets.append(off)\n",
    "    \n",
    "    score = score / len(data)\n",
    "    # print(f\"Accuracy with |error| <= {threshold}:\",'{}%'.format(round(score*100, 2)))\n",
    "\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3c56d34-29ac-4a84-97c8-04c9709ae01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "showMetric(data.ViCLIP('stock100', 8), data.stock100, threshold=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "030ed76c-24c3-4a39-a9fc-181a7a89c316",
   "metadata": {},
   "outputs": [
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       "  29.125,\n",
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       "  29.203125,\n",
       "  30.21875,\n",
       "  30.71875,\n",
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       "  31.140625,\n",
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       "  31.046875,\n",
       "  30.875,\n",
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       "  31.28125,\n",
       "  31.453125,\n",
       "  31.046875,\n",
       "  31.6875,\n",
       "  31.109375,\n",
       "  31.125,\n",
       "  30.203125,\n",
       "  29.765625]]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits.TC('act75')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fa77fac6-e825-4e96-80f0-d0ef19dafe66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oc9zqsuJo7VHo1Dpc2etK+WDNCflX1ou0m3XfPtS89z4k0aV0KP6TAMtXXwMATNOmKRbGh7tO44fj1Yp9/vlOnGmAS5Qwumci+nZnDQ1RV8JkpIOs/GklXi96vcX3r+h5BeJj4uVvvMWrrBdpr5JFD8O2f7/SYQRFP3Qo9AMHKvLZJ8804IFVPyry2W35+RhOXRJ1NUxGOkjRmSIAQH7vfPQz9fN5L0Ydc26KBmhSvMqRkfaw7t8P2/79ELRaJN99NxCG/UvCRVCrkHDNNYp9/gfu2oyc9HhcPSxdsTjOlxQXgxljmaQTdTVMRjqAJEk4cPYAAGDe8HkYlNTG5naekRET/9JtD8/SWONll6H7rxYoHE3kEEUJq3fJycg9l/bD9NGszyCi8GIBawcorS9FnaMOGpUGfU19276ghiMj7SU5nahduxYAl8YG6ocT1Th1thFxMWrkD+08oyJE1HUxGekAnlGRvqa+0Kq1rZ8sSWx4FgL1//0vXFVVUCclwXjJxUqHE1E+2CGPikwengFDDFfyEFH4MRnpAAeq5WRkULc2pmcAoLEacNTLX5tYqBcs79LY666FoG0jASSvRrsL6/bIHU5vZKEoEXUQJiMdwDMy0matCHBuWW9cKqA1hDGqrstlNsNSIO90q+TS2Ej0eVEZLDYnenQzYFxvbhJHRB2DBawdwDsy4lcywnoRfzlKS1H/3/9CEkWf49aiIkh2O3QDBkA/ZIhC0YXHpv3lKDfbwnb/d76X//u7gR1OiagDMRkJs3pHPU7VyX/B+zVNwx4jfpEkCafnL4C1qKjFc0zTp8mbDnYRmw9U4I6VP3TIZ91wEadoiKjjMBkJs4PVBwEAqYZUdNN3a/sC9hjxi23/fliLiiBotYi75JIL3lcndUPijFsUiCx83vtBLiwdlBaP7KTYsH3O+H7J6J0SF7b7ExGdj8lImAVULwKwx4ifat0FqsYrrkCPpc8rG0wHqG1wYGNROQBgyYyRGJppUjgiIqLQYQFrmAVULwI0qRnpFaaIIp/kcKB27ToAgGnaVIWj6Rhr95TA7hKRkx6PIRkJSodDRBRSTEbC7OBZeZrGr3oRgDUjfrBs2QLXmTNQJyfDeHF09BDx9P648aIeXaoOhogIYDISVi7R5a0Z8WtkpLEGsNXKX3OapkW1H30MADBdd11U9BA5VlWPnSdroBKAaaMylQ6HiCjkmIyE0cm6k7C6rNCr9egZ70dBqqd41ZAE6LhFenNctbWwFBQAkFfLRIPV7k3r/ndgd6Qm6BWOhogo9FjAGkaeepEB3QZArfKjrXYX7jHislhgP36i3fexfLUZksMB3aBB0A8eHILIOqeSmkacsdgBAKt3FgPgclsi6rqYjISRZyXNwG4D/bvA0321i9WLSA4Hjk2/Ho7Tp0N2z668+d2Pp2owbdl/fY7F6zS4akiaQhEREYVXUNM0y5YtQ+/evaHX65Gbm4vt27e3eK7D4cBTTz2Ffv36Qa/XY+TIkVi/fn3QAUcSTzKSk5Tj3wUV++RfkweEKSJlWL7ZIiciWi006entfhlGjULi9dOVfqyw2XK4CgAQG6NGhkmPrEQDFl41EHotN60joq4p4JGRVatWYeHChVi+fDlyc3OxdOlS5Ofn48CBA0hNTb3g/EcffRRvvPEGXn75ZeTk5GDDhg24/vrr8e2332L06NEheYjOKuBlvWV75F/Th4cpImXUfvQRACDp1luRtughhaPp/PaVmgEAv7p8AO6d1E/haIiIwi/gkZElS5Zg3rx5mDt3LoYMGYLly5cjNjYWK1asaPb8//znP3j44YcxefJk9O3bF/feey8mT56M5557rt3Bd2bV1mpUNFQA8HOaRnSdGxnpQsmIq6YGlk3uTeu68GhGKHmSkcEZ8QpHQkTUMQJKRux2O3bs2IG8vLxzN1CpkJeXh61btzZ7jc1mg17vuwLAYDBgy5YtLX6OzWaD2Wz2eUUaz6hID2MPxGn9aK195gjgbAQ0BiCpb5ij6zjm9evlgtOcHOhz/JyuimKNdheOVdUDAIZksrkZEUWHgJKRqqoquFwupKX5FtKlpaWhrKys2Wvy8/OxZMkSHDp0CKIoYuPGjVi9ejVKS0tb/JzFixfDZDJ5X9nZkVfQGXC9SNlu+de0oYA/K28iRO2HawAApmnRsQy3vQ6U10GUgBRjDFLjuYyXiKJD2PuM/O1vf8OAAQOQk5ODmJgYLFiwAHPnzoVK1fJHL1q0CLW1td7XqVOnwh1myHmanQ1M8nMlTfle+df0YWGKqOPZjh1D448/Amo1TNddq3Q4EeHcFA1HRYgoegSUjKSkpECtVqO8vNzneHl5OdLT05u9pnv37lizZg3q6+tx4sQJ7N+/H0ajEX37tjwVodPpkJCQ4POKNPvP7gcQQBv4MncykhZ5yYgkSc2+PIWrcRdPhKZ7d4WjjAxFJXIywv1niCiaBLSaJiYmBmPGjEFBQQGmu/s8iKKIgoICLFiwoNVr9Xo9srKy4HA48MEHH+Dmm28OOujOzuFy4GjtUQABrKTxjoxEVvGq5HDg+KzbYN29u8VzErtwT5BQ48gIEUWjgJf2Lly4EHPmzMHYsWMxbtw4LF26FPX19Zg7dy4AYPbs2cjKysLixYsBANu2bUNxcTFGjRqF4uJiPPnkkxBFEQ8++GBon6QTOVp7FE7RiXhtPDLj/NhLpL4KqHPX0KQNDW9wIWb55ptWE5GYXr1gvPzyDowocomihP1ldQCYjBBRdAk4GZkxYwYqKyvx+OOPo6ysDKNGjcL69eu9Ra0nT570qQexWq149NFHcfToURiNRkyePBn/+c9/kJiYGLKH6Gw8K2kGJg30b4dVT3+Rbn0AXWQt5/QUqHabNQsp8395wfvqhAQIGjb69cep6gZYbE7EaFTo292PFVhERF1EUD8lFixY0OK0zObNm32+v/TSS1FUVBTMx0SsgOtFIrR41VldjTr3n3fizTdBk5SkbEARzjNFMzDNCK2ae1gSUfTg33hhcPCsvJLG/86rnuLVyKoXMX/2GeBwQDd4MPSD/HxWalFRqXuKJp1TNEQUXZiMhJgkSVHTBr52jbxaJnE6e4iEgnclDZudEVGUYTISYuUN5aix1UAtqNE/sX/bFzhtQJWcvETSNI3t6FG5cFWtRsK17CESClxJQ0TRislIiHmanfVO6A2dWtf2BZUHANEJ6E2AKXI6zXpGRYyXXAJNSorC0US+2kYHimsaAXCahoiiD5c5hJinDXyrUzRle4FPfwvY6wCre9+dtOGAPytv2sFlqUfJb38LRwut+88nSRJKahphc4oXvJdSUwEdgGUxg7D7b9+EONLoY3W6AABZiQaYYrUKR0NE1LGYjITYlmJ5A8Chya30C9nxGnDyW99jvS8OY1Sy2o/WwPLllwFd09qYx1ldPD7Q9YajNPI2MuyscvtyRRIRRR8mIyF0ynwKOyt2QoCA/N75LZ/oKVi95DdArwnyTr3Z48Ien2dqJWnObMRdfEmb5/+t4CB2nKjBxf2TMb7fhWmJ2KsvXknmFE2oaFQCxvTqpnQYREQdjslICH1y9BMAwM8yfoa0uLTmTxJFoPwn+ethPwfShnRIbLYjR2DdswfQaJB8993QJCe3ev7ZejtWrjfDkZaGxXf+LwalR1YzNiIiihwsYA0RURLx8ZGPAQBT+09t+cSa44DdAqhjgJQBHRMczis4bSMRAYBPfiyBwyVhaGYCExEiIgorJiMhsqtiF4otxYjVxOKKnle0fKKnwVnqYEDdMYWKksuF2o/lRMk0zb+eIB/sPA0AuPGiHmGLi4iICGAyEjKfHJGnaK7qfRUMGkPLJ3rqRTqw22rDtm1wlpdDZTLBePllbZ5/qLwOu0/XQqMSMHWUHxv9ERERtQOTkRCwOq3YcHwDAGBqv1amaABF9qGpWbMGAJAw+RqoYmLaPP+DncUAgEmDuiPF6EevFCIionZgAWuQjtUew5v73oTdZccZ6xlYHBZkxmViTNqY1i/0TNME2fpdbGhA1QsvwFld7fc1dZ9vBAAk+jFF4xIlrNklJyOcoiEioo7AZCRIr+x5xVuw6jGt/zSohFYGmxprgNqT8tdprfQhaUXtx5/gzCuvBnxdTN++0I8c2eZ53x6pQpnZCpNBi8sHpwYTIhERUUCYjATpjPUMAOCqXldhcPJgxGnjML3/9NYv8kzRmLIBQ3D9JKxFRQCAuIkTEZub69c1gkqA8fLLIfjR4fWDHXLh6pSRGdBp1EHFSEREFAgmI0Gy2C0AgMl9J7e+eqYpzxRNWvD1Itb9+wEAiT+/EQnXXBP0fZpjsTmx/ie5VTynaIiIqKOwgDVIdfY6AEC8NoAeHOXulTRB1otILhdsB+WN+HSDcoK6R2s+3VMKq0NE35Q4jMpODPn9iYiImsNkJEjeZCQmgGSkrH0raewnTkKyWiHo9Yjp1TOoe7Rmtae3yJgefk3pEBERhQKTkSAFnIy4nEDFPvnrIKdpbAfkKRrdoIEQ1KGt5zh1tgHfHT0LQQCmj84K6b2JiIhaw2QkCA6XA1aXFUAAyciZQ4DLBsQYgW59gvpc6z45GdGHYYrGs5x3fN9kZCW20rSNiIgoxFjAGoQ6R533a6PW6PumtRYo+ghw2nyPezuvDgVUweWAVs/ISM6goK4/36b95Thd3QgAWPXDKQAsXCUioo7HZCQInimaOG0c1Krzpks+fxTY+XrLF6ePCPpzbfsPAAD0Oe0fGdl+7CzuWPmDzzGDVo2rh6W3+95ERESBYDIShBbrRez1wN7V8tcD8gHtedMdMXHA+PlBfaazuhrO8nIAgG5g+0dG3nWPhAxMM2JAajwgAJOHZSBOx/8kiIioY/EnTxBaTEb2rwPsFqBbb+DWVUAIV6TY3P1FtD17Qm2Ma9e9GuxOfLanFADw9PXD8T+9k9odHxERUbBYwBqEFnuMFL4l/zpyZkgTEQCweqZoBrV/VGTDT2Wot7vQMykWY3sF1wmWiIgoVJiMBKHZkRFzCXB0s/z1iBkh/0zPyEgoildXu3flveGiLPYTISIixTEZCUKzycjuVQAkoOcEICm4pbutsR4ITfFqaW0jthyuAgDcMJorZ4iISHlMRoLgWdrrTUYkCfjxHfnrkbeE/PMkux22I0cAtD8ZWbOrBJIEjOudhJ7JsaEIj4iIqF1YwBoEz8iIt8dIyS6gcj+g0QNDp4fkMyRJgv3YMbhqa+EoLgEcDqgSEqDJzGzXPT/wtnxnl1UiIuocmIwEwZOMJMQkyAc8oyI51wJ6U0g+w1JQgNMLfuVzTD9wYLtqPPYU1+JwhQU6jQrXDM9ob4hEREQhwWQkCD41I047sOc9+Y2Rt4bsM+q/3QoAUJtMUJlMELRadJszu133/GCHPCqSPzQdCXptu2MkIiIKBSYjQfBJRg5vBBrPAsZ0oO+kkH2Gp2A17ZGHYZo6td33sztFfPxjCQB5FQ0REVFnwQLWIPgUsHp6i4y4CVCHJreTRLHJUt7QbIr35YEKVDc4kBqvw8X9U0JyTyIiolBgMhIE78iIKAIHN8gHQzhF4yguhlhfD0Grha5PaJYJe6Zopo/OgkbNP3YiIuo8+FMpCN5k5Pi3gOiQN79LGxKy+1vdoyIxA/pD0La/tuNsvR1fHqgAwF15iYio82EyEiCX6EK9ox4AEL9vnXxwVOhGRYCmu/MODsn9PvmxBA6XhKGZCRiUHt/2BURERB2IBawBsjgs3q/ji3cBKg0w7Och/QzPyIi+Ha3fXaIEpygCwLneIhwVISKiTojJSIA8UzR6QQ0tAPS/EjB2D+lneItXBwVXvHqsqh7Xv/Bf1DQ4vMc0KgFTRwXfMI2IiChcOE0TIG+9iORuPjYgL6T3d5nNcBTLG9kFOzLyxncnfBIRALhpbDZSjLp2x0dERBRqHBkJkGeaJl6S5APxoR1tsLn7i2gyM6A2Bd7N1ekS8VGhnMy8MOsiXDIgBSpBQJyOf9RERNQ58SdUgMx2MwAg3umUDxjTQnp/q6d4Ncgpmq8PVaLKYkdyXAyuHJIGLZfxEhFRJ8efVAHybpLntMsHQlwvYj3gaXYW3BTNBzvlUZGpozKZiBARUUTgT6sAeTfJc7lHRuJSQ3r/9izrrW1wYGNROQCunCEiosjBZCRAFru7ZkQUAZ0J0OpDdm/J6YTt4EEAwRWvrttTCrtTxKC0eAzNTAhZXEREROHEZCRA3poRUQSMoR0VsR8/Dsluhyo2Ftrs7ICv9/YTGZMFQRBCGhsREVG4sIA1QN6aEVECEsJTvKobNAiCqu08saLOil+9tQtn6+2QAByusEAlANNHcVdeIiKKHExGAuStGRHFkBevWr7+CgCgHz7Mr/PX7S7FtmNnfY5dOSQNqQmhmzoiIiIKNyYjAfL2GRHFkC7rdVnqUbfxCwCA6dpr/bpmX6k8ZXTz2B64fnQPqFUChmcF3puEiIhISUxGAuTtwBrimpG6DRsgNTYipk8f6EeM8OuafaVyLJcNSsX4fskhi4WIiKgjsYA1QJ4CVqMohnRZb+1HHwEATNOm+VV86nSJOFAuJyODM7hyhoiIIldQyciyZcvQu3dv6PV65ObmYvv27a2ev3TpUgwaNAgGgwHZ2dl44IEHYLVagwpYab41I6GZprGfLkbD9u2AIMA0dYpf1xytqofdKSIuRo2eSbEhiYOIiEgJAScjq1atwsKFC/HEE09g586dGDlyJPLz81FRUdHs+W+99RYeeughPPHEE9i3bx9effVVrFq1Cg8//HC7g+9okiSdVzMSmgJW8ycfAwBic3OhzfRvrxtPvUhORgJUKi7jJSKiyBVwMrJkyRLMmzcPc+fOxZAhQ7B8+XLExsZixYoVzZ7/7bffYuLEibj11lvRu3dvXHXVVZg5c2aboymdUYOzAaIkAgDiRSkkIyOSJKF2jXuKZvo0v68rcicjgzPi2x0DERGRkgJKRux2O3bs2IG8vLxzN1CpkJeXh61btzZ7zYQJE7Bjxw5v8nH06FF8+umnmDx5coufY7PZYDabfV6dgWeKRiNJ0EkSENf+kZHGwkLYT5yAEBuLhCuv9Pu6ohJPMsJ6ESIiimwBraapqqqCy+VCWprviEBaWhr279/f7DW33norqqqqcPHFF0OSJDidTtxzzz2tTtMsXrwYv//97wMJrUN4ilcTRBGCIQlQa9t9T8uXmwEA8VdcAVVcnN/XeVbSDGEyQkREES7sq2k2b96MZ555Bi+88AJ27tyJ1atXY926dfjDH/7Q4jWLFi1CbW2t93Xq1Klwh+kXn31pQlS8at2/DwAQe9Fov6+prLOhymKDIACD0jlNQ0REkS2gkZGUlBSo1WqUl5f7HC8vL0d6enqz1zz22GO4/fbbcddddwEAhg8fjvr6etx999145JFHoGqm7blOp4NOpwsktA4Rjh4jNm8L+By/r/EUr/ZJjkNsDFvFEBFRZAtoZCQmJgZjxoxBQUGB95goiigoKMD48eObvaahoeGChEOtVgOQizcjSag3yXNWV8PpXoWkGzjQ7+vOFa9yioaIiCJfwP+sXrhwIebMmYOxY8di3LhxWLp0Kerr6zF37lwAwOzZs5GVlYXFixcDAKZMmYIlS5Zg9OjRyM3NxeHDh/HYY49hypQp3qQkUvhskheCaRqbu85G26sn1MZA6kXkZGRIJpMRIiKKfAEnIzNmzEBlZSUef/xxlJWVYdSoUVi/fr23qPXkyZM+IyGPPvooBEHAo48+iuLiYnTv3h1TpkzB008/Hbqn6CCeHiMJIRoZse6TkxF9AFM0wLlkhMt6iYioKwiq4GDBggVYsGBBs+9t3rzZ9wM0GjzxxBN44okngvmoTsVbM+IKTSt42wE5GdHlDPL7GqvDhSOV9QA4TUNERF0D96YJQKgLWK3u4lV9jv8jI4fKLXCJEhJjtUhP0Lc7BiIiIqUxGQlAra0WQGiSEcluh+3oUQCAfpD/IyM7TpwFIPcX8WdDPSIios6OyUgAyupLAQBpLle7C1htR48CDgdUCQnQ+LkfDQB8WFgCALhicGj6nBARESmNyUgASizFAIBMlwjEJrfrXueKVwf5PcJxuMKCH0/VQK0SMG2U/wkMERFRZ8ZkxE+NzkactdUAADK1JkDVvmXJnmW9ugDqRVbvPA0AmDSwO1KMna8pHBERUTCYjPip1CJP0RhFEQlx7Z8isR7wFK/6Vy8iihI+3CWPzNw4pke7P5+IiKizYDLip5J6uVYj0+Fsf/GqJAU8MrL16BmU1lqRoNfg8pzQtKInIiLqDJiM+KnE4k5GnO1PRpzl5XDV1ABqNXT9+/t1zQfuKZrrRmZCr42szrVERESt4S5rfjqXjLhaTUYkUYR1716IVmuL51j3/gQA0PXtA1WTDQElSUJRqRl1VqfP+aIoYf3eMgDAjRdxioaIiLoWJiN+8hkZaaX76plXXkXlkiV+3fP8nXo//rEE971T2OL5fVLicFHPRL/uTUREFCmYjPjJWzPidAKxSS2eZy0qAgCou6dAnWBq8TyVXo9ut97qc+zbw2cAACnGGCTGxvi8p1EJuD9vIBudERFRl8NkxE8+0zT6lpMMV1UVACB90SIkTJ4c0GfsK5M3wPvDtGG4ZnhGkJESERFFFhaw+sHusqOysRKAe2SklWTE6U5G1CkpAX2G0yVif5m89w03wCMiomjCZMQPpe428AZJQqIoArqWkwXnGXmqRRNgMnKsqh52p4i4GDV6JsUGHywREVGEYTLih6ZTNALQ4siIaLVCrJNHNwJNRopK5SmaQenxUKlYF0JERNGDyYgfPMlIhsMhH2ghGXFWyaMiQkwMVPHxAX3GvlI5iRmSySkaIiKKLkxG/OBZSZPldAIQWpymcZ3x1IskB7zqxTMywnoRIiKKNkxG/ODTY0QXD6ia/23zFK9qUroH/Bn7mIwQEVGUYjLiB3+X9TorPclIYPUiVRYbKutsEAQgJz2w6R0iIqJIx2TEDz4Nz1pLRtzTNJrk5IDu7xkV6ZMch9gYtn4hIqLowmSkDQ7RgYqGCgD+9xjRdA9wJU0Jp2iIiCh6MRlpQ3l9OURJhE7QINnVeo8Rl3s1jTrIkZHBGZyiISKi6MNkpA2ehmcZmrhWe4wAwRewepb1cmSEiIiiEZORNhRbigEAmSq9fCDE0zRWhwuHKy0A2GOEiIiiE5ORNngbnnn2FGy1gNXdCj6AaZrDFRa4RAmJsVqkJ+iDD5SIiChCMRlpgycZyZLcv1X65kcvxPp6SA0NAAJb2uttdpaeEHCjNCIioq6AyUgbqqzy1Et3p1M+0GIrePk8ITYWqrg4v+/PZmdERBTtmIy0od5eDwCId1jlAy0lI0FM0QDnlvWyXoSIiKIVk5E21DvlZCTW3igfaCkZCaL7qiRJXNZLRERRj8lIGzwjI0b3ry31GfF2Xw0gGSmptcJsdUKjEtA/1di+QImIiCIUk5E2eEZG4qxyL5CWRkZcVed27PXXPvcUTf9UI3QadTuiJCIiilxMRlohSZJ3ZCTO5h4Z0Sc2e24w0zSelTRDWLxKRERRjMlIK+yiHU5JXkUTJ4rywRaW9p4rYPU/GeFKGiIiIiYjrbLYLd6vYyUJ0MYCam2z5wbTfZXJCBEREZORVjU45CZmsWq9/BvVaiv4SgD+T9NYbE6cOCvfnytpiIgomjEZaYXFIY+MGNU6+UALyYgkSU127PUvGTlQZoYkAWkJOiQbde0PloiIKEIxGWlFvcPdY0TlnpppIRkR6+og2e0AAI2fq2mKuFMvERERACYjrfIkI0bBnYy01GPEPSqiio+HSu/fZnesFyEiIpIxGWmFJxmJ8/w2tbgvjbteJIBW8Pu4rJeIiAgAk5FWeWpG4uDeTbeNhmf+Fq+6RAn7OU1DREQEgMlIqzyraeIkST7Q4siIu3jVz2W9J87Uo9Hhgl6rQp8U/3f4JSIi6oo0SgfQmXlHRlxtNDzzjIy0spLGbHXggx2nYXWIOFIp33dQWjzUKiGEERMREUUeJiOt8NaMuOQurC3v2Nt2j5Elnx/Eym+P+xwbmtVy3xIiIqJowWSkFd7VNE552W5LyYijtBQAoM3MaPZ9m9OFNYXFAID8oWlI0GthiFFj3iV9QxwxERFR5GEy0gpvnxGnTT7QUjJSLCca2szMZt//cn8FahocSEvQ4YVZYzg1Q0RE1AQLWFvhHRlxWOUDuguTEcnlgqOsDACgzcpq9j7v75CTlemjs5iIEBERnYfJSCu8NSM2+dfmRkaclZWAwwFoNNCkpl7w/hmLDZsPVAAAfn5Rj/AFS0REFKGYjLTCu5qmlWTEUVICANCmp0NQqy94/+MfS+AUJYzoYcKANG6IR0REdD4mI63w9hkRPUt7m0lG2qgXWb1Tfv+G0c1P4RAREUU7FrC2wjsyIomAOgbQXrjvjDcZcdeLOFwidp6oht0l4ozFjj3FtdCoBEwdxWSEiIioOUxGWiBJUpOREamVlTTuaRp3MrJk40G8uPmIzzmX5aQiKS4mjNESERFFrqCmaZYtW4bevXtDr9cjNzcX27dvb/HcSZMmQRCEC17XXntt0EF3BJvLBqckNzsziqJfy3odLhHvfn8KANCvexwGZyRgTK9uuO+KAR0TNBERUQQKeGRk1apVWLhwIZYvX47c3FwsXboU+fn5OHDgAFKbWU2yevVq2O127/dnzpzByJEjcdNNN7Uv8jDzrKQBAIPUyshIybmRka8OVOJMvR0pRh023P+/0KhZkkNERNSWgH9aLlmyBPPmzcPcuXMxZMgQLF++HLGxsVixYkWz5yclJSE9Pd372rhxI2JjYyMmGYlTxci/Sc0kI5IoNklGMvHBztMAgOmjMpmIEBER+Smgn5h2ux07duxAXl7euRuoVMjLy8PWrVv9userr76KW265BXFxLe9Wa7PZYDabfV4d7VwyopUP6C7cJM915gwkux1QqVAf3w0F++R+IjewnwgREZHfAkpGqqqq4HK5kJaW5nM8LS0NZe4upK3Zvn079u7di7vuuqvV8xYvXgyTyeR9ZWdnBxJmSHhX0gju3iGtLOvVpKVh3b4q2F0iBmckYEhm87v7EhER0YU6dC7h1VdfxfDhwzFu3LhWz1u0aBFqa2u9r1OnTnVQhOd4V9J4fotaa3jWZIrmxou4hJeIiCgQARWwpqSkQK1Wo7y83Od4eXk50tPTW722vr4e77zzDp566qk2P0en00Gn0wUSWsid6zHiPtBMMmJ3j4zYklKx62QN1CoBU0c13/yMiIiImhfQyEhMTAzGjBmDgoIC7zFRFFFQUIDx48e3eu17770Hm82G2267LbhIO5i3ZkR0ZyOtjIwcFOQ27/87IAWp8Rc2RiMiIqKWBby0d+HChZgzZw7Gjh2LcePGYenSpaivr8fcuXMBALNnz0ZWVhYWL17sc92rr76K6dOnIzk5OTSRh5l3x15PK/iYCwtuPTUj+6VYAMBVQ1sfHSIiIqILBZyMzJgxA5WVlXj88cdRVlaGUaNGYf369d6i1pMnT0Kl8h1wOXDgALZs2YLPP/88NFF3AE8yEutJRrSGC87xjIzsdsqJyrDM5nuREBERUcuCage/YMECLFiwoNn3Nm/efMGxQYMGQZKkC0/uxC4YGdHG+rwvSZK3FfwRlRFqlYABacYOjZGIiKgrYGeuFnhrRlxyS3hofGtBXDU1kBobAQCVhm7omxIHvVbdoTESERF1BUxGWuBdTeNyyAfOGxlxnHavpDElwaHWYHAGe4sQEREFg8lIC7x9RpzufXW0viMjnnqRs/FyQS4bnREREQWHyUgLvCMjzhZGRtwraYp1ctEqR0aIiIiCw2SkBd6aEYdVPqBpfmTkqFpOQgZnxHdccERERF0Ik5EWeFfTeEdGfJf2OsvlvXgq9CakGHVsdkZERBQkJiMt8PYZkZrvM+KsrAIAVOsTOCpCRETUDkxGmiFJUpM+I+7+KOdN0zir3MmILh5DWC9CREQUNCYjzbC5bHBJLgBAnCgCGgMgCN73JUmC88wZAECN3sjiVSIionZgMtIMz0oaAQIMknTBsl6xvsHb8KxaF89lvURERO3AZKQZnh4jsWqd/Bt03rJeV1WlfJ5GB1FvQN+UCzfRIyIiIv8wGWmGt8eI2j0icn69iGeKRmfEwDQjNGr+NhIREQUrqI3yujpvjxF1jHzAPTKy+3QN/vXVUfTauw1TwOJVIiKiUOA/6ZvhXUmj0skH3DUj/9h0GOv2lOLkkdMAgGp9PC7q2U2RGImIiLoKjow0wzNNE6ty//a4e4ycOivXklyeKu/OO3xYH4wZ06PjAyQiIupCODLSDE8Bq1FwJyMaAyRJQnG1vIJmiF7uytovpxe0rBchIiJqF/4kbYa3gBXyCAi0epgbnaizOQEABkstAECTnKJIfERERF0Jk5FmeAtYBfdvjzYWp2vk0ZIUYwxE92oaTXcmI0RERO3FZKQZ3mREcndd1ehx2j1Fk9Ut1tsKXpPCZISIiKi9mIw041wy4j6gjfXWi/Qw6eHyJCPJyUqER0RE1KUwGWnGuWTEnY1oz42M9NGLkBxyAauaIyNERETtxmSkGRa7XMBqFEX5gNaAYnfNSE9BTkpU8fFQ6XSKxEdERNSVMBlpxhmrXKCa5M5FoDF4R0YyRTkpYb0IERFRaDAZaUZVo1wTkuJyyQe0BhTXyMlIil2ewmEyQkREFBpMRs7jEl2osdUAAJLdyUgjYlDTINeJmKxmAIA6hcWrREREocBk5DzVtmqIkgiVoEI3px0AcNYuNz9LjNVCXVMNANCkdFcsRiIioq6Eych5zjTK9SKJukSoHVYAQKVV/m3q0c3AHiNEREQhxmTkPN56EUMK4JDrRMob5OZnWYkGOM94khFO0xAREYUCk5Hz+CQjTjkZKXMnIz3YfZWIiCjkmIycx7OsN1mf7B0ZKa6Xm59lJRrgqpSTETU3ySMiIgoJJiPn8Z2mkWtGTtXJ7/Uw6eA8exYAN8kjIiIKFSYj5/EkI8mGZO80zYlauftZltoOuJf7apKSlAmQiIioi2Eycp6zjfLIR7KuG+CSl/aWuGtG0pxywzN1YiIErVaZAImIiLoYJiPn8U7TaOO9x6yIQbxOA4OlFgCnaIiIiEKJych5qqzuaRpNnPeYDVpkNekxwuJVIiKi0GEy0oTD5UCtTR79SNEYAABOlQ4SVHLDs0ou6yUiIgo1JiNNeJb1agQNTIIGAOBQ6QC4e4yccb/PZISIiChkmIw04UlGkvRJUDltAOR6EcDdfbWqEgC7rxIREYUSk5EmPPvSJBvONTyrF+VkpE9KHJylZQAATVqaMgESERF1QUxGmmiuFXydU56uGZyZAEdJCQBAm5WlTIBERERdEJORJnxHRuTuq1bEwGTQIiNOA0eZPDKizcxULEYiIqKuhslIE76t4BsAAI1SDAZnxMNVWSl3X9VqoeneXckwiYiIuhQmI034TtOcGxkZnJEAR3ExAECbkQFBrVYsRiIioq6GyUgTze3Y2+hJRjz1IpyiISIiCikmI000rRmR3MmIFTEYkpEAu2dkJIvJCBERUSgxGWmi6Y69dXVmAIANOgxIM3JkhIiIKEyYjLhZnVZYHBYAcs3I2Vq5LbzeEAedRn2uZoTLeomIiEKKyYibp14kRhWDeG08amvlkRFjfAIAcGSEiIgoTJiMuDWdohEEAXWWOgBAt4QESKIIZ0kpACCGIyNEREQhxWTEzVO8mmKQN8FraKgHACR3M8FZWQXJ4QDUaraCJyIiCrGgkpFly5ahd+/e0Ov1yM3Nxfbt21s9v6amBvPnz0dGRgZ0Oh0GDhyITz/9NKiAw6XpyEiD3QmXTU5GUpMSz9WLpKVB0GgUi5GIiKgrCvgn66pVq7Bw4UIsX74cubm5WLp0KfLz83HgwAGkpqZecL7dbseVV16J1NRUvP/++8jKysKJEyeQmJgYivhDpmmPkf1lddDDDgAwxsWjtpj1IkREROEScDKyZMkSzJs3D3PnzgUALF++HOvWrcOKFSvw0EMPXXD+ihUrcPbsWXz77bfQarUAgN69e7cv6jBoOk2zr9SMfoKcjEBr4EoaIiKiMApomsZut2PHjh3Iy8s7dwOVCnl5edi6dWuz13z88ccYP3485s+fj7S0NAwbNgzPPPMMXC5Xi59js9lgNpt9XuHWdJqmqMTsHRmB1tBkt16OjBAREYVaQMlIVVUVXC4X0s4r4kxLS0OZe0fb8x09ehTvv/8+XC4XPv30Uzz22GN47rnn8Mc//rHFz1m8eDFMJpP3lZ2dHUiYQWk6MnL8TL1vMsKRESIiorAJ+2oaURSRmpqKl156CWPGjMGMGTPwyCOPYPny5S1es2jRItTW1npfp06dCneYPpvkmRud55IRjYE9RoiIiMIooJqRlJQUqNVqlJeX+xwvLy9Henp6s9dkZGRAq9VC3WSn28GDB6OsrAx2ux0xMTEXXKPT6aDT6QIJrd2aFrCarTUwCDYAgKTRN5mm4cgIERFRqAU0MhITE4MxY8agoKDAe0wURRQUFGD8+PHNXjNx4kQcPnwYoih6jx08eBAZGRnNJiJKaHA0oNEpb4wnj4w4vCMjrno7JKsVEARoW0i4iIiIKHgBT9MsXLgQL7/8Mv79739j3759uPfee1FfX+9dXTN79mwsWrTIe/69996Ls2fP4r777sPBgwexbt06PPPMM5g/f37onqKdPFM0Bo0BBo0BdVYn9HAAAByV8h41mtRUCJ0keSIiIupKAl7aO2PGDFRWVuLxxx9HWVkZRo0ahfXr13uLWk+ePAmV6lyOk52djQ0bNuCBBx7AiBEjkJWVhfvuuw+/+93vQvcU7eRdSaNPRqPDBVF0QSe4k5GKagCsFyEiIgqXoNqJLliwAAsWLGj2vc2bN19wbPz48fjuu++C+agO4akX8RSv6jzFqwAc5fJ7rBchIiIKD+5NA98eI2arAwafZKQCAEdGiIiIwoXJCHyX9dZZzxWvQq2Do1Tun8JkhIiIKDyYjOBcw7NkQzLMjU7vsl5oDXBWyYmKppl9d4iIiKj9mIygSTKil6dpPCtpfJKRlGSlwiMiIurSgipg7WqaFrAWn3VAj3MNz1zeZCRFsfiIiOgcURRht9vbPpHC7vympsFiMgLfmpF9Vif07h17RZcekkPuM6JmMkJEpDi73Y5jx475NNIkZSUmJiI9PR2CIAR9j6hPRiRJOm81zbkde512ucmZKiEBKjY8IyJSlCRJKC0thVqtRnZ2tk9PK+p4kiShoaEBFRXyqtOMjIyg7xX1yUidow4OUa4RSdYnw9x41ru012nTAuAUDRFRZ+B0OtHQ0IDMzEzExsYqHQ4BMBgMAICKigqkpqYGPWUT9WmlZ1TEqDVCr9HLBazuaRqnVf7tYTJCRKQ8l8sFAJ1mXzOSeRJDh8MR9D2iPhnxrKRJMcgJh7wvjXuTvEZ5/osraYiIOo/21CZQ6IXiz4PJSJMeIwB8dux1b+TL4lUiIqIwivpkpOkmeQDc7eDlpb3OenlIUJPMZISIiChcmIw0WdYLAObGc0t7nRYnANaMEBFR+5WVleFXv/oV+vbtC51Oh+zsbEyZMgUFBQUAgN69e2Pp0qXNXnv8+HEIgoDCwkKf7z2v5ORkXHXVVdi1a1cHPU1oRX0y0rThGQDUNdkoz2mRi3E03ZmMEBFR8I4fP44xY8Zg06ZNePbZZ7Fnzx6sX78el112GebPnx/0fb/44guUlpZiw4YNsFgsuOaaa1BTUxO6wDtI1C/tbdpjxOpwweYUode4C1jrrAAAdTILWImIKHi//OUvIQgCtm/fjri4OO/xoUOH4o477gj6vsnJyUhPT0d6ejr++te/YuLEidi2bRvy8/NDEXaHifpkpOlqmjqrPC2jF+yQJMDpTkY0Kd0Vi4+IiJonSRIaHS5FPtugVfu9iuTs2bNYv349nn76aZ9ExCMxMTE0Mbl7fkRiq3wmI+dtkgcARrUDLqsKECVAEKBJ6qZkiERE1IxGhwtDHt+gyGcXPZWP2Bj/foQePnwYkiQhJycnbPHU1NTgD3/4A4xGI8aNGxe2zwmXqE5GREn01owkG5JRdlYeGTGqnN6GZ+rERAharWIxEhFRZJMkKWz3njBhAlQqFerr69G3b1+sWrUKaWlpYfu8cInqZKTWVguXJA/xJeuTcbCxBgBgEhrgtMotbbmShoioczJo1Sh6SpnaCIPW/7bnAwYMgCAI2L9/f8jjWLVqFYYMGYLk5OSQTfcoIaqTEU/xqklnglat9U7TJKJOnqYBoGb3VSKiTkkQBL+nSpSUlJSE/Px8LFu2DL/+9a8vqBupqakJOpHIzs5Gv379QhClsjr/n2IYeXuM6M/1GAGABKkOdu++NCxeJSKi9lm2bBkmTpyIcePG4amnnsKIESPgdDqxceNGvPjii9i3bx8AoLi42NtLxKNXr14KRNyxojoZaa7HiAARcWIdGqxGAJymISKi9uvbty927tyJp59+Gr/5zW9QWlqK7t27Y8yYMXjxxRe95/31r3/FX//6V59r//Of/+Diiy/u6JA7VHQnI+6VNEmGJAByK/h4NEAFEc5Gz8gIp2mIiKj9MjIy8M9//hP//Oc/m33/+PHjrV7ftBC2d+/eYS2M7WhR3YG1uVbw3QQLAMBll7eo5sgIERFReEV1MtK0xwggj4x0g5yMOG3yoBF37CUiIgqvqE5Gzh8ZqbM6kSjUAQCcjXJnPY6MEBERhVd0JyPW86dpHEhEPSQRcDXKc3FMRoiIiMIrqgtYX7nqFVQ2VCLLmAXAPU0j1MFpc+doajXUEdxEhoiIKBJEdTKSpE9Ckj7J+7250YlEwXKu4VlSNwhq/7vsERERUeCieprmfHXuAlZno6cVPBueERERhRuTETenS0S93SVP03i6ryazxwgREVG4MRlxq7PKreATYeEmeURERB2IyYibZ5O8ZNW5mhFNdyYjREQUXk8++SRGjRqldBiKYjLi5hkZ6SZYvNM0ak7TEBFRO0yZMgVXX311s+998803EAQBN9xwAwoKCnzeM5vNeOSRR5CTkwO9Xo/09HTk5eVh9erV3jbwkyZNwv3339/iZwuCgDVr1vh873mZTCZMnDgRmzZtavczhgKTETdzozwy4jtNwwJWIiIK3p133omNGzfi9OnTF7z32muvYezYsRgxYgSSm/zjt6amBhMmTMDrr7+ORYsWYefOnfj6668xY8YMPPjgg6itrQ06ntdeew2lpaX473//i5SUFFx33XU4evRo0PcLFSYjbmarAzFwwADruQJW1owQEVE7XHfddejevTtWrlzpc9xiseC9997DnXfeecE0zcMPP4zjx49j27ZtmDNnDoYMGYKBAwdi3rx5KCwshNFoDDqexMREpKenY9iwYXjxxRfR2NiIjRs3Bn2/UGEy4mZudCLRsy+Nd2SE0zRERJ2WJAH2emVefu6Yq9FoMHv2bKxcudJnl9333nsPLpcLM2fO9DlfFEW88847mDVrFjIzMy+4n9FohEYTmhZhBoMBAGC320Nyv/aI6qZnTXm6r4ouQLRzZISIqNNzNADPXPgDu0M8XALExPl16h133IFnn30WX331FSZNmgRAni658cYbYTKZfM6tqqpCdXU1cnJyQh2xj4aGBjz66KNQq9W49NJLw/pZ/uDIiJvZ6kS3Jt1XodVCdd5/JERERIHKycnBhAkTsGLFCgDA4cOH8c033+DOO++84FzJzxGXYM2cORNGoxHx8fH44IMP8Oqrr2LEiBFh/Ux/cGTETd4kr0nxanIyBEFQOCoiImqRNlYeoVDqswNw55134le/+hWWLVuG1157Df369Wt2RKJ79+5ITEzE/v37QxWpj+effx55eXkwmUzo3r3zLNLgyIib2epAYpNlvZyiISLq5ARBnipR4hXgP1ZvvvlmqFQqvPXWW3j99ddxxx13NPsPXpVKhVtuuQVvvvkmSkouTLQsFgucTmfQv2Xp6eno379/p0pEACYjXnVWp7wvTZORESIiolAwGo2YMWMGFi1ahNLSUvziF79o8dynn34a2dnZyM3Nxeuvv46ioiIcOnQIK1aswOjRo2GxWLznVlZWorCw0OdVXl7eAU8UWkxG3Goa7EgU6s7t2Mvuq0REFEJ33nknqqurkZ+f3+xKGY+kpCR89913uO222/DHP/4Ro0ePxiWXXIK3334bzz77rE/R61tvvYXRo0f7vF5++eWOeJyQEqRwV8uEgNlshslkQm1tLRISEsLyGRP/tAn3WZbif3/chepDRiTf8/+Q2kpnOyIi6lhWqxXHjh1Dnz59oNfrlQ6H3Fr7c/H35zdHRgA4XCJKaxvdreA90zQcGSEiIuoITEYAlNVaIUpAksoCZyM3ySMiIupITEYAnK5uBACkqOrhsnm6rzIZISIi6ghMRgAU18jJSDehzjsywh17iYiIOgaTEQCnqxsASDDYLRCdnmmazrUGm4iIqKtiMgKguLoRCWiAZJW/F3Q6qOL823OAiIiI2ofJCOSakfO7r7IVPBERUccIKhlZtmwZevfuDb1ej9zcXGzfvr3Fc1euXAlBEHxenW19+OmaBnRD3bllvSxeJSIi6jABJyOrVq3CwoUL8cQTT2Dnzp0YOXIk8vPzUVFR0eI1CQkJKC0t9b5OnDjRrqBDySVKKK2x+uzYq2YyQkRE1GECTkaWLFmCefPmYe7cuRgyZAiWL1+O2NhY79bIzREEAenp6d5XWlpau4IOpXKzFU5RQpKqHs5GjowQERF1tICSEbvdjh07diAvL+/cDVQq5OXlYevWrS1eZ7FY0KtXL2RnZ2PatGn46aefWv0cm80Gs9ns8woXz7LengYrd+wlIqKQ+8UvfuFTqpCcnIyrr74au3fv9p7z9NNPY8KECYiNjUViYmKr9ztz5gx69OgBQRBQU1MT3uA7SEDJSFVVFVwu1wUjG2lpaSgrK2v2mkGDBmHFihX46KOP8MYbb0AURUyYMAGnT59u8XMWL14Mk8nkfWVnZwcSZkDkZb1Atq5pMsIeI0REFDpXX321t1ShoKAAGo0G1113nfd9u92Om266Cffee2+b97rzzjsxYsSIcIbb4cK+mmb8+PGYPXs2Ro0ahUsvvRSrV69G9+7d8a9//avFaxYtWoTa2lrv69SpU2GLr9jdfTU9pgEudwEra0aIiCiUdDqdt1Rh1KhReOihh3Dq1ClUVlYCAH7/+9/jgQcewPDhw1u9z4svvoiamhr83//9X0eE3WE0gZyckpICtVqN8vJyn+Pl5eVIT0/36x5arRajR4/G4cOHWzxHp9NBp9MFElrQmraC946McJM8IqJOT5IkNDobFflsg8YQdAsIi8WCN954A/3790dyAN2+i4qK8NRTT2Hbtm04evRoUJ/dWQWUjMTExGDMmDEoKCjA9OnTAQCiKKKgoAALFizw6x4ulwt79uzB5MmTAw42HDw1IyapFjVWbpJHRBQpGp2NyH0rV5HP3nbrNsRqY/0+f+3atTAajQCA+vp6ZGRkYO3atVCp/JugsNlsmDlzJp599ln07NmzyyUjAU/TLFy4EC+//DL+/e9/Y9++fbj33ntRX1+PuXPnAgBmz56NRYsWec9/6qmn8Pnnn+Po0aPYuXMnbrvtNpw4cQJ33XVX6J6iHTwjI6bak5BcnpER1owQEVHoXHbZZSgsLERhYSG2b9+O/Px8XHPNNX63uli0aBEGDx6M2267LcyRKiOgkREAmDFjBiorK/H444+jrKwMo0aNwvr1671FrSdPnvTJ9KqrqzFv3jyUlZWhW7duGDNmDL799lsMGTIkdE8RJFGUUFzTCBMsUFdXAEiDKjYWqlj/s10iIlKGQWPAtlu3KfbZgYiLi0P//v2937/yyiswmUx4+eWX8cc//rHN6zdt2oQ9e/bg/fffByBPUQFy+cQjjzyC3//+9wHF09kEnIwAwIIFC1qcltm8ebPP988//zyef/75YD4m7KosNtidIsaoTnp7jKg5RUNEFBEEQQhoqqQzEQQBKpUKjY3+1bx88MEHPud+//33uOOOO/DNN9+gX79+4QqzwwSVjHQVp9xTNLmGYjjLPMt6uVsvERGFls1m87bAqK6uxj//+U9YLBZMmTIFgDyrcPbsWZw8eRIulwuFhYUAgP79+8NoNF6QcFRVVQEABg8e3GZfkkgQ1cmIp3h1ZMzpJitpWC9CREShtX79emRkZAAA4uPjkZOTg/feew+TJk0CADz++OP497//7T1/9OjRAIAvv/zSe05XFtXJiKfh2QDxODfJIyKisFi5ciVWrlzZ7nOamjRpkrdupCsIe9Ozzqy4uhEaOJFuP35uZIQ1I0RERB0qqpOR09WN6CeUQCM54LLLTdbUnKYhIiLqUFGdjBTXNGKwcBIAYG/QAwC0mVlKhkRERBR1ojoZ+XjBRDzxPy6ITgH2aicAQJ8zSOGoiIiIoktUF7DGxmgQW3cQjbUaQJI3yGMBKxERUceK6pERSBJQtgfWai0AQD+IoyJEREQdLbqTEUs50FAFW607GRmco3BARERE0Se6k5GyvQAAqyUeAKAbxGSEiIioo0V3MlK+B5IE2M7I37J4lYiIqONFdzJSthcOixqiXYQQE4OYPn2UjoiIiCjqRHkysgfWGrleRDdgAARNVC8uIiKiEBMEodXXk08+CQDYtWsXbrrpJqSlpUGv12PAgAGYN28eDh482Or9JUnCSy+9hNzcXBiNRiQmJmLs2LFYunQpGhrkLU+efPJJjBo1qsV7TJo0Cffff7/P95749Ho9hgwZghdeeKG9vxWtiu5k5MaXYUuVd0zUcYqGiIhCrLS01PtaunQpEhISfI793//9H9auXYuf/exnsNlsePPNN7Fv3z688cYbMJlMeOyxx1q9/+233477778f06ZNw5dffonCwkI89thj+Oijj/D5558HHfe8efNQWlqKoqIi3HzzzZg/fz7efvvtoO/XlugeCsgYCWut3AZez+JVIiIKsfT0dO/XJpMJgiD4HGtoaMDcuXMxefJkfPjhh97jffr0QW5uLmpqalq897vvvos333wTa9aswbRp07zHe/fujalTp8JsNgcdd2xsrDfOJ598Em+99RY+/vhjzJw5M+h7tia6kxEAtv37AXBZLxFRpJEkCVJjoyKfLRgMEASh3ffZsGEDqqqq8OCDDzb7fmJiYovXvvnmmxg0aJBPIuKNTxBgMpnaHZ+HwWCA3W4P2f3OF9XJiMtshqOkBACgY8MzIqKIIjU24sBFYxT57EE7d0CIjW33fQ4dOgQAyMkJ/B/Ehw4dwqAw/+xyuVx4++23sXv3btx9991h+5yorhmxukdFtJmZUCckKBwNERFFG0mS/Dpv6NChMBqNMBqNuOaaawK6NhgvvPACjEYjDAYD5s2bhwceeAD33ntv2D4vqkdGbPsPAAB0QWSkRESkLMFgwKCdOxT77FAYOHAgAGD//v0YP358i+d9+umncDgcAOQpE8+1+93/qA61WbNm4ZFHHoHBYEBGRgZUqvCOXUR1MmI94K4XYTJCRBRxBEEIyVSJkq666iqkpKTgL3/5i08Bq0dNTQ0SExPRq1evC9679dZbccstt+Cjjz66oG5EkiSYzeag60ZMJhP69+8f1LXBiOppmnMjI6wXISKijhcXF4dXXnkF69atw9SpU/HFF1/g+PHj+OGHH/Dggw/innvuafHam2++GTNmzMDMmTPxzDPP4IcffsCJEyewdu1a5OXl4csvv/Se29jYiMLCQp/XkSNHOuIR/RLVIyPdbrsN1j17YBg+XOlQiIgoSk2bNg3ffvstFi9ejFtvvRVmsxnZ2dm4/PLL8cc//rHF6wRBwFtvvYWXXnoJK1aswNNPPw2NRoMBAwZg9uzZyM/P95578OBBjB492uf6K664Al988UXYnisQghTOCpgQ8Qw11dbWIoGFpkREUclqteLYsWPo06cP9Hq90uGQW2t/Lv7+/I7qaRoiIiJSHpMRIiIiUhSTESIiIlIUkxEiIiJSFJMRIiIiUhSTESIiiigRsAg0qoii2O57RHWfESIiihxarRaCIKCyshLdu3cPya65FDxJkmC321FZWQmVSoWYmJig78VkhIiIIoJarUaPHj1w+vRpHD9+XOlwyC02NhY9e/Zs1/41TEaIiChiGI1GDBgwwLtpHClLrVZDo9G0e5SKyQgREUUUtVoNtVqtdBgUQixgJSIiIkUxGSEiIiJFMRkhIiIiRUVEzYhnTbnZbFY4EiIiIvKX5+d2W71hIiIZqaurAwBkZ2crHAkREREFqq6uDiaTqcX3BSkCWtmJooiSkhLEx8eHtMmN2WxGdnY2Tp06hYSEhJDdtzOLtmeOtucFou+Zo+15geh75mh7XqDrPLMkSairq0NmZmarfUgiYmREpVKhR48eYbt/QkJCRP9hByPanjnanheIvmeOtucFou+Zo+15ga7xzK2NiHiwgJWIiIgUxWSEiIiIFBXVyYhOp8MTTzwBnU6ndCgdJtqeOdqeF4i+Z4625wWi75mj7XmB6HvmiChgJSIioq4rqkdGiIiISHlMRoiIiEhRTEaIiIhIUUxGiIiISFFRnYwsW7YMvXv3hl6vR25uLrZv3650SCGxePFi/M///A/i4+ORmpqK6dOn48CBAz7nWK1WzJ8/H8nJyTAajbjxxhtRXl6uUMSh9ac//QmCIOD+++/3HuuKz1tcXIzbbrsNycnJMBgMGD58OH744Qfv+5Ik4fHHH0dGRgYMBgPy8vJw6NAhBSNuH5fLhcceewx9+vSBwWBAv3798Ic//MFnz4tIfuavv/4aU6ZMQWZmJgRBwJo1a3ze9+fZzp49i1mzZiEhIQGJiYm48847YbFYOvApAtPaMzscDvzud7/D8OHDERcXh8zMTMyePRslJSU+94ikZ27rz7ipe+65B4IgYOnSpT7HI+l5AxG1yciqVauwcOFCPPHEE9i5cydGjhyJ/Px8VFRUKB1au3311VeYP38+vvvuO2zcuBEOhwNXXXUV6uvrvec88MAD+OSTT/Dee+/hq6++QklJCW644QYFow6N77//Hv/6178wYsQIn+Nd7Xmrq6sxceJEaLVafPbZZygqKsJzzz2Hbt26ec/5y1/+gr///e9Yvnw5tm3bhri4OOTn58NqtSoYefD+/Oc/48UXX8Q///lP7Nu3D3/+85/xl7/8Bf/4xz+850TyM9fX12PkyJFYtmxZs+/782yzZs3CTz/9hI0bN2Lt2rX4+uuvcffdd3fUIwSstWduaGjAzp078dhjj2Hnzp1YvXo1Dhw4gKlTp/qcF0nP3NafsceHH36I7777DpmZmRe8F0nPGxApSo0bN06aP3++93uXyyVlZmZKixcvVjCq8KioqJAASF999ZUkSZJUU1MjabVa6b333vOes2/fPgmAtHXrVqXCbLe6ujppwIAB0saNG6VLL71Uuu+++yRJ6prP+7vf/U66+OKLW3xfFEUpPT1devbZZ73HampqJJ1OJ7399tsdEWLIXXvttdIdd9zhc+yGG26QZs2aJUlS13pmANKHH37o/d6fZysqKpIASN9//733nM8++0wSBEEqLi7usNiDdf4zN2f79u0SAOnEiROSJEX2M7f0vKdPn5aysrKkvXv3Sr169ZKef/5573uR/LxticqREbvdjh07diAvL897TKVSIS8vD1u3blUwsvCora0FACQlJQEAduzYAYfD4fP8OTk56NmzZ0Q///z583Httdf6PBfQNZ/3448/xtixY3HTTTchNTUVo0ePxssvv+x9/9ixYygrK/N5ZpPJhNzc3Ih95gkTJqCgoAAHDx4EAPz444/YsmULrrnmGgBd85k9/Hm2rVu3IjExEWPHjvWek5eXB5VKhW3btnV4zOFQW1sLQRCQmJgIoOs9syiKuP322/Hb3/4WQ4cOveD9rva8TUXERnmhVlVVBZfLhbS0NJ/jaWlp2L9/v0JRhYcoirj//vsxceJEDBs2DABQVlaGmJgY7//QHmlpaSgrK1MgyvZ75513sHPnTnz//fcXvNcVn/fo0aN48cUXsXDhQjz88MP4/vvv8etf/xoxMTGYM2eO97ma+288Up/5oYcegtlsRk5ODtRqNVwuF55++mnMmjULALrkM3v482xlZWVITU31eV+j0SApKSninx+Q675+97vfYebMmd6N47raM//5z3+GRqPBr3/962bf72rP21RUJiPRZP78+di7dy+2bNmidChhc+rUKdx3333YuHEj9Hq90uF0CFEUMXbsWDzzzDMAgNGjR2Pv3r1Yvnw55syZo3B04fHuu+/izTffxFtvvYWhQ4eisLAQ999/PzIzM7vsM5PM4XDg5ptvhiRJePHFF5UOJyx27NiBv/3tb9i5cycEQVA6nA4XldM0KSkpUKvVF6ymKC8vR3p6ukJRhd6CBQuwdu1afPnll+jRo4f3eHp6Oux2O2pqanzOj9Tn37FjByoqKnDRRRdBo9FAo9Hgq6++wt///ndoNBqkpaV1qecFgIyMDAwZMsTn2ODBg3Hy5EkA8D5XV/pv/Le//S0eeugh3HLLLRg+fDhuv/12PPDAA1i8eDGArvnMHv48W3p6+gUF+E6nE2fPno3o5/ckIidOnMDGjRu9oyJA13rmb775BhUVFejZs6f377ETJ07gN7/5DXr37g2gaz3v+aIyGYmJicGYMWNQUFDgPSaKIgoKCjB+/HgFIwsNSZKwYMECfPjhh9i0aRP69Onj8/6YMWOg1Wp9nv/AgQM4efJkRD7/FVdcgT179qCwsND7Gjt2LGbNmuX9uis9LwBMnDjxguXaBw8eRK9evQAAffr0QXp6us8zm81mbNu2LWKfuaGhASqV719ZarUaoigC6JrP7OHPs40fPx41NTXYsWOH95xNmzZBFEXk5uZ2eMyh4ElEDh06hC+++ALJyck+73elZ7799tuxe/dun7/HMjMz8dvf/hYbNmwA0LWe9wJKV9Aq5Z133pF0Op20cuVKqaioSLr77rulxMREqaysTOnQ2u3ee++VTCaTtHnzZqm0tNT7amho8J5zzz33SD179pQ2bdok/fDDD9L48eOl8ePHKxh1aDVdTSNJXe95t2/fLmk0Gunpp5+WDh06JL355ptSbGys9MYbb3jP+dOf/iQlJiZKH330kbR7925p2rRpUp8+faTGxkYFIw/enDlzpKysLGnt2rXSsWPHpNWrV0spKSnSgw8+6D0nkp+5rq5O2rVrl7Rr1y4JgLRkyRJp165d3pUj/jzb1VdfLY0ePVratm2btGXLFmnAgAHSzJkzlXqkNrX2zHa7XZo6darUo0cPqbCw0OfvMpvN5r1HJD1zW3/G5zt/NY0kRdbzBiJqkxFJkqR//OMfUs+ePaWYmBhp3Lhx0nfffad0SCEBoNnXa6+95j2nsbFR+uUvfyl169ZNio2Nla6//nqptLRUuaBD7PxkpCs+7yeffCINGzZM0ul0Uk5OjvTSSy/5vC+KovTYY49JaWlpkk6nk6644grpwIEDCkXbfmazWbrvvvuknj17Snq9Xurbt6/0yCOP+PxgiuRn/vLLL5v9/3bOnDmSJPn3bGfOnJFmzpwpGY1GKSEhQZo7d65UV1enwNP4p7VnPnbsWIt/l3355Zfee0TSM7f1Z3y+5pKRSHreQAiS1KR9IREREVEHi8qaESIiIuo8mIwQERGRopiMEBERkaKYjBAREZGimIwQERGRopiMEBERkaKYjBAREZGimIwQERGRopiMEBERkaKYjBAREZGimIwQERGRopiMEBERkaL+P+Bf8d26nQ6NAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Benchmarks --------------------\n",
      "0f  | 48.00% -- CLIP\n",
      "0f  | 46.67% -- ViCLIP\n",
      "0f  | 54.67% -- B14\n",
      "0f  | 45.33% -- TC-CLIP\n",
      "-------------------------------\n",
      "3f  | 62.67% -- CLIP\n",
      "3f  | 69.33% -- ViCLIP\n",
      "3f  | 74.67% -- B14\n",
      "3f  | 58.67% -- TC-CLIP\n",
      "-------------------------------\n",
      "6f  | 69.33% -- CLIP\n",
      "6f  | 84.00% -- ViCLIP\n",
      "6f  | 86.67% -- B14\n",
      "6f  | 72.00% -- TC-CLIP\n",
      "-------------------------------\n",
      "9f  | 72.00% -- CLIP\n",
      "9f  | 85.33% -- ViCLIP\n",
      "9f  | 93.33% -- B14\n",
      "9f  | 77.33% -- TC-CLIP\n",
      "-------------------------------\n",
      "12f | 77.33% -- CLIP\n",
      "12f | 90.67% -- ViCLIP\n",
      "12f | 94.67% -- B14\n",
      "12f | 81.33% -- TC-CLIP\n",
      "-------------------------------\n"
     ]
    }
   ],
   "source": [
    "compare = [data.CLIP('act75', 1), data.ViCLIP('act75', 8), data.B14('act75', 8), data.TC('act75', 8)]\n",
    "labels = [\"CLIP\", \"ViCLIP\", \"B14\", \"TC-CLIP\"]\n",
    "comp_data = data.act75\n",
    "\n",
    "frame_nums = [0, 3,6,9,12]\n",
    "\n",
    "\n",
    "plot_data = eval(str([[]] * len(compare)))\n",
    "for test_threshold in range(150):\n",
    "    for idx, preds in enumerate(compare):\n",
    "        plot_data[idx].append(showMetric(preds, comp_data, threshold=test_threshold))\n",
    "\n",
    "for i, j in enumerate(plot_data):\n",
    "    plt.plot(j, label = labels[i])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nBenchmarks \" + \"-\"*20)\n",
    "\n",
    "max_frame_len = max([len(str(x)) for x in frame_nums])\n",
    "for f_num in frame_nums:\n",
    "    for idx, preds in enumerate(compare):\n",
    "        count = showMetric(preds, comp_data, threshold=f_num)\n",
    "        print(f\"{f_num}f\", (max_frame_len - len(str(f_num))) * \" \" + \"|\", f\"{count*100:.2f}%\", \"--\", labels[idx])\n",
    "    print(\"-\" * 31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef8239fa-f23e-4658-8e23-98a3dc2792dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_scale = []\n",
    "for video_path in tqdm([os.path.join('backflip', x) for x in os.listdir('backflip')]):\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "\n",
    "    frame_num = 0\n",
    "    success, frame = cap.read()\n",
    "    while success:\n",
    "        frame_num += 1\n",
    "        success, frame = cap.read()\n",
    "    cap.release()\n",
    "    \n",
    "    norm_scale.append(1 / frame_num)\n",
    "\n",
    "print(norm_scale[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab2060b0-ef1c-4be2-8812-3cb302535906",
   "metadata": {},
   "source": [
    "$$\\Large \\mathbf{2}^{\\text{nd}}\\text{ Metric}$$\n",
    "\n",
    "--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ebca3fa7-a31d-4c50-b4cd-8a54ad24bcc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Find closest peak\n",
    "def findClosest(pred, truths):\n",
    "    dist = 1000000\n",
    "    peak = -1\n",
    "    for i in truths:\n",
    "        if abs(i - pred) < dist:\n",
    "            dist = abs(i - pred)\n",
    "            peak = i\n",
    "    return peak\n",
    "\n",
    "def showMetric2(preds, data):\n",
    "    def clamp_error(prediction, truth): # NN's Idea\n",
    "        if truth in range(prediction, prediction + 9):\n",
    "            return 0\n",
    "        else: \n",
    "            return (prediction + 8) - truth if truth > prediction + 8 else prediction - truth\n",
    "    \n",
    "    mse = []\n",
    "    for idx, i_pred in enumerate(preds):\n",
    "        # peak = findClosest(i_pred, data[idx][1]) * norm_scale[idx]\n",
    "        peak = findClosest(i_pred, data[idx][1])\n",
    "        # mse.append(abs((i_pred * norm_scale[idx]) - peak)) # Calculating MAE??\n",
    "        mse.append(abs(i_pred - peak)) # Calculating MAE??\n",
    "    \n",
    "    return mse\n",
    "\n",
    "def find_best_offset(preds, data):\n",
    "    total = []\n",
    "    min_val = float('inf')\n",
    "    min_result = -1\n",
    "    for l in [x/100 for x in range(-3000, 3000, 1)]:\n",
    "        mse = calculateMSE([x + l for x in preds], data)\n",
    "        total.append((l, mse))\n",
    "        if mse < min_val:\n",
    "            min_val = mse\n",
    "            min_result = l\n",
    "    return min_result\n",
    "\n",
    "def offset(preds, data):\n",
    "    best_off = find_best_offset(preds, data)\n",
    "    return [x + best_off for x in preds]\n",
    "\n",
    "def calculateMSE(preds, data):\n",
    "    errors = []\n",
    "    for idx, i_pred in enumerate(preds):\n",
    "        new_pred = i_pred + 0\n",
    "        peak = findClosest(new_pred, data[idx][1])\n",
    "        errors.append(abs(new_pred - peak) ** 2)\n",
    "    return np.mean(errors).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce4ba593-faa8-4b05-85f5-47e48bcff2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "showCLIP = False\n",
    "filter_vals = 'r'\n",
    "\n",
    "data_use = data_gif87 if filter_vals == 'g' else data_k600\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "\n",
    "def isCLIP(label):\n",
    "    return 'CLIP' in label and 'XCLIP' not in label\n",
    "\n",
    "def extract_digits(input_string):\n",
    "    digits = ''.join([char for char in input_string if char.isdigit()])\n",
    "    return int(digits) if digits else 0\n",
    "\n",
    "pred_labels = [x for x in (data_load.values() if showCLIP else filter(lambda x: not isCLIP(x), data_load.values()))]\n",
    "pred_labels.sort(key=lambda x: (int(extract_digits(x)) if \"IV\" in x else 1000, x[3]))\n",
    "\n",
    "if filter_vals != 'any':\n",
    "    pred_labels = list(filter(lambda x: filter_vals in x, pred_labels))\n",
    "\n",
    "predictions = [calculateMSE(offset(eval(x), data_use), data_use) for x in pred_labels]\n",
    "pred_augment = [calculateMSE(offset(eval(x), data_use), data_use) for x in filter(lambda x: 'a' in x, pred_labels)]\n",
    "best_mse = min(predictions)\n",
    "best_augment_mse = min(pred_augment) if pred_augment else 0\n",
    "\n",
    "\n",
    "def get_color(idx, x):\n",
    "    if isCLIP(x):\n",
    "        return 'tab:red'\n",
    "    if predictions[idx] == best_mse:\n",
    "        return 'green'\n",
    "    elif predictions[idx] == best_augment_mse:\n",
    "        return 'tab:cyan'\n",
    "    else:\n",
    "        if 'r' in x:\n",
    "            return 'tab:cyan'\n",
    "        else:\n",
    "            return 'tab:cyan'\n",
    "\n",
    "def get_name(x):\n",
    "    if isCLIP(x): return x\n",
    "    return x.split(\"_\")[1][1:]\n",
    "\n",
    "bar_colors = [get_color(idx, x) for idx, x in enumerate(pred_labels)]\n",
    "\n",
    "ax.bar(pred_labels, predictions, label=pred_labels, color = bar_colors)\n",
    "\n",
    "ax.set_ylabel('Mean Squared Error', fontsize=14)\n",
    "ax.set_xlabel('Window Size', fontsize=14)\n",
    "ax.set_title('Mean Squared Error across Window Sizes on Dataset', fontsize=14)\n",
    "# ax.legend(title='Model')\n",
    "# ax.set_ylim(40, 73)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "plt.xticks(rotation=45,ha = \"right\") \n",
    "plt.savefig(\"window_size_comparison.svg\", bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "\n",
    "combined = list(zip(pred_labels, predictions))\n",
    "combined.sort(key = lambda x: x[1])\n",
    "\n",
    "df = pd.DataFrame({'Model': [x[0] for x in combined], 'MSE': [x[1] for x in combined], 'Offset': [find_best_offset(eval(x), data_use) for x in list(map(lambda x: x[0], combined))]})\n",
    "\n",
    "df_ranked = df.copy()\n",
    "df_ranked.index = range(1, len(df) + 1)\n",
    "\n",
    "print(tabulate(df_ranked, headers=['Rank'] + df.columns.tolist(), tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30b21e1-3f53-4c74-973a-6348cdf1a323",
   "metadata": {},
   "source": [
    "# Window Size Plot\n",
    "Line Graph of Window Size vs MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19224d2e-52b1-4ded-a303-cc9c9bbf5946",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_labels = [x for x in filter(lambda x: 'CLIP' not in x, data_load.values())]\n",
    "pred_labels.sort(key=lambda x: (int(extract_digits(x)), x[3]))\n",
    "\n",
    "augment_labels = list(filter(lambda x: 'a' in x and '100' not in x, pred_labels))\n",
    "regular_labels = list(filter(lambda x: 'r' in x and '100' not in x, pred_labels))\n",
    "\n",
    "plt.plot([extract_digits(x) for x in regular_labels], [calculateMSE(eval(x)) for x in regular_labels], linewidth=2)\n",
    "\n",
    "plt.gca().set_position([0, 0, 1, 1])\n",
    "plt.xlabel(\"Window Size\")\n",
    "plt.ylabel(\"Mean Squared Error\")\n",
    "\n",
    "\n",
    "plt.savefig(\"windowsize-mse-r.svg\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aca311ba-f6c7-455c-a323-413d11e53eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_g = 'IV_g13,CLIP_g'\n",
    "compare_k = 'IV_r100,CLIP_r'\n",
    "\n",
    "compare_g = [eval(x) for x in compare_g.split(',')]\n",
    "compare_k = [eval(x) for x in compare_k.split(',')]\n",
    "\n",
    "fig = plt.figure(figsize=[7, 8])\n",
    "\n",
    "axs = fig.subplot_mosaic([[\"k600\"],\n",
    "                          [\"gif87\"]])\n",
    "\n",
    "gif87 = axs[\"gif87\"]\n",
    "\n",
    "gif87.plot(range(1, len(data_gif87) + 1),showMetric2(compare_g[0], data_gif87), linewidth=3, color=\"black\")\n",
    "gif87.plot(range(1, len(data_gif87) + 1),showMetric2(compare_g[1], data_gif87), color=\"red\", linestyle=\"dashdot\")\n",
    "gif87.set_ylabel(\"Absolute Error\")\n",
    "gif87.legend([\"New Algorithm\", \"Previous Algorithm\"], fontsize=13)\n",
    "gif87.set_title(\"General Dataset\")\n",
    "gif87.set_xlabel(\"Video ID\")\n",
    "\n",
    "k600 = axs[\"k600\"]\n",
    "\n",
    "k600.plot(range(1, len(data_k600) + 1),showMetric2(compare_k[0], data_k600), linewidth=3, color=\"black\")\n",
    "k600.plot(range(1, len(data_k600) + 1),showMetric2(compare_k[1], data_k600), color=\"red\", linestyle=\"dashdot\")\n",
    "k600.set_ylabel(\"Absolute Error\")\n",
    "k600.legend([\"New Algorithm\", \"Previous Algorithm\"], fontsize=13)\n",
    "k600.set_title(\"K600 Subset\")\n",
    "\n",
    "\n",
    "\n",
    "# k600.gca().set_position([0, 0, 1, 1])\n",
    "fig.savefig(\"general-data-compare.svg\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd57556-5f90-4c30-a40e-0a87ed178452",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_write(data_gif87, 'rustyjar/GIF87.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d5f6a3b-f383-4e24-a33c-f8c17ce5b002",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from IPython.display import clear_output\n",
    "for i in range(60):\n",
    "    test_compare = i\n",
    "    \n",
    "    l70 = logits_b14_r[test_compare]\n",
    "    l70.sort(key = lambda x: x[1])\n",
    "    l70 = list(map(lambda x: (x[0] / 200 + 0.2, x[1]), l70))\n",
    "    plt.plot([x[1] for x in l70], [x[0] for x in l70])\n",
    "    plt.plot([x[1] for x in logits_r[test_compare]], [x[0] for x in logits_r[test_compare]])\n",
    "    for place in data_k600[test_compare][1]:\n",
    "        plt.plot([place, place + 0.1], [0.2, 0.3], color = 'green')\n",
    "    plt.legend([\"InternVideo2\", \"ViCLIP\"])\n",
    "    plt.show()\n",
    "    time.sleep(5)\n",
    "    clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddada3b9-0e3f-4b64-bb58-c9f7dd05fd5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "video = 81\n",
    "print(\"Actual:\",data_gif87[video - 1])\n",
    "print(\"Predicted:\", IVL_g8[video - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fb3e263-63c8-49a7-bc76-c106ef80b34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_k600[5] = (6, list(range(103, 150)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9132a70-89a6-428c-96b0-484273047de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_write(logits_b14_a, 'rustyjar/InternVideoB-logits-a.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74a73a8a-de32-4c69-b429-f907950de508",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_b14_a[44] = [logits_b14_a[44][x] if x >65 else (0, logits_b14_a[44][x][1]) for x in range(len(logits_b14_a[44]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f63ed5a-867c-4634-9268-ae71bed0ced8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_compare = 44\n",
    "l70 = logits_b14_a[test_compare]\n",
    "l70.sort(key = lambda x: x[1])\n",
    "l70 = list(map(lambda x: (x[0] / 200 + 0.2, x[1]), l70))\n",
    "plt.plot([x[1] for x in l70], [x[0] for x in l70])\n",
    "\n",
    "viclip_pred = logits_a[test_compare]\n",
    "viclip_pred.sort(key = lambda x: x[1])\n",
    "\n",
    "plt.plot([x[1] for x in viclip_pred], [x[0] for x in viclip_pred])\n",
    "for place in data_k600[test_compare][1]:\n",
    "    plt.plot([place, place + 0.1], [0.2, 0.3], color = 'green')\n",
    "plt.legend([\"InternVideo2\", \"ViCLIP\"])\n",
    "plt.show()\n",
    "time.sleep(5)\n",
    "clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7f375a4e-ac60-4939-88c6-81d4ed2ebf96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('GIF100/57.mp4',\n",
       " 'A person kicks a soccer ball.',\n",
       " [100, 101, 102, 103, 104, 105, 106])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_read('rustyjar/STOCK100.pkl')[56]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa42631a-c024-45bc-98c0-1465cf8ae8d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.90666666666667\n",
      "835.8933333333333\n",
      "31.253333333333334\n",
      "34.013333333333335\n",
      "193.4\n",
      "160.36\n",
      "186.50666666666666\n"
     ]
    }
   ],
   "source": [
    "print(calculateMSE(data.ViCLIP('act75', 8), data.act75))\n",
    "print(calculateMSE(data.CLIP('act75', 1), data.act75))\n",
    "print(calculateMSE(data.B14('act75', 8), data.act75))\n",
    "print(calculateMSE(data.B14('act75', 11), data.act75))\n",
    "print(calculateMSE(data.L14('act75', 8), data.act75))\n",
    "print(calculateMSE(data.L14('act75', 11), data.act75))\n",
    "print(calculateMSE(data.S14('act75', 8), data.act75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88963ff7-1844-446a-90f1-83b3514b82fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5bc8f87f-0ad8-40fe-8c04-899f09abb941",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('GIF100/15.mp4',\n",
       " 'A kid splashing in the pool.',\n",
       " [100,\n",
       "  101,\n",
       "  102,\n",
       "  103,\n",
       "  104,\n",
       "  105,\n",
       "  106,\n",
       "  107,\n",
       "  108,\n",
       "  109,\n",
       "  110,\n",
       "  111,\n",
       "  112,\n",
       "  113,\n",
       "  114,\n",
       "  115,\n",
       "  116,\n",
       "  117,\n",
       "  118,\n",
       "  119,\n",
       "  120,\n",
       "  121,\n",
       "  122,\n",
       "  123,\n",
       "  124,\n",
       "  125,\n",
       "  126,\n",
       "  127,\n",
       "  128,\n",
       "  129,\n",
       "  130,\n",
       "  131,\n",
       "  132,\n",
       "  133,\n",
       "  134,\n",
       "  135,\n",
       "  136,\n",
       "  137,\n",
       "  138,\n",
       "  139,\n",
       "  140,\n",
       "  141,\n",
       "  142,\n",
       "  143,\n",
       "  144,\n",
       "  145,\n",
       "  146,\n",
       "  147])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_read('rustyjar/STOCK100.pkl')[14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e2b1452b-cf87-422a-a318-a441a4c8a9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = 32\n",
    "video_data = pickle_read('rustyjar/B14/t.pkl')\n",
    "preds = pickle_read('jar/B14/t8.pkl')\n",
    "for i in range(len(video_data[num])):\n",
    "    if video_data[num][i][1] > 170:\n",
    "        video_data[num][i] = (0, video_data[num][i][1])\n",
    "\n",
    "preds[num] = 135\n",
    "pickle_write(video_data, 'rustyjar/B14/t.pkl')\n",
    "pickle_write(preds, 'jar/B14/t8.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b7769c97-08ca-4386-94fa-9b5e9d4aa6d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('ACT75/33.mp4', [135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146])\n"
     ]
    }
   ],
   "source": [
    "video_data = pickle_read('rustyjar/B14/t.pkl')[32]\n",
    "\n",
    "plt.plot([x[1] for x in video_data], [x[0] for x in video_data])\n",
    "plt.show()\n",
    "\n",
    "print(pickle_read('rustyjar/ACT75-data.pkl')[32])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a11827c9-940e-4f44-8c0b-2b0bc7d6abcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sus[14] = list(map(lambda x: (0, x) if x == (0, 0) else sus[14][x], range(len(sus[14])))\n",
    "pickle_write('rustyjar/B14/s.pkl', sus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "60a2ebbc-d26d-4b9c-bd90-6d258c4cbac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sus[14] = list(map(lambda x: (0, x) if x < 50 else sus[14][x], range(len(sus[14]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "07ea319c-4bda-4863-b449-62cf590b5e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits = pickle_read('rustyjar/B14/s.pkl')[14]\n",
    "preds = data.B14('stock100', 8)\n",
    "preds[14] = 86\n",
    "logits = [() if i < 50 else logits[i] for i in range(len(logits))]\n",
    "\n",
    "sus = pickle_read('rustyjar/B14/s.pkl')\n",
    "sus[14] = logits\n",
    "\n",
    "pickle_write('jar/B14/s8.pkl', preds)\n",
    "pickle_write('rustyjar/B14/s.pkl', sus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a1b3222-ae8c-40f2-b4e7-5794e6f9a799",
   "metadata": {},
   "outputs": [],
   "source": [
    "s100 = pickle_read('rustyjar/STOCK100.pkl')\n",
    "s100[46] = ('GIF100/47.mp4', 'A person dunks a basketball.', list(range(100, 167)))\n",
    "\n",
    "s100t = pickle_read('rustyjar/STOCK100-testing.pkl')\n",
    "s100t[46] = ('GIF100/47.mp4', list(range(100, 167)))\n",
    "\n",
    "pickle_write(s100, 'rustyjar/STOCK100.pkl')\n",
    "pickle_write(s100t, 'rustyjar/STOCK100-testing.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3b29faa-c447-4133-a8d0-bbf5b2024ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('GIF100/47.mp4', [100, 101, 102, 103, 104])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_read('rustyjar/STOCK100-testing.pkl')[46]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9b66e6f9-7c9b-4d2e-b96e-30c0cd57b529",
   "metadata": {},
   "outputs": [],
   "source": [
    "vids = [int(x.split('.')[0]) for x in filter(lambda x: x != '.DS_Store', os.listdir('GIF100'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "eaf3ce7c-993a-4041-956d-ab948cc19915",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]\n",
      "\n",
      "\n",
      "[59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]\n",
      "\n",
      "\n",
      "[100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116]\n",
      "\n",
      "\n",
      "[155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249]\n",
      "\n",
      "\n",
      "[13, 14, 15]\n",
      "\n",
      "\n",
      "[53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]\n",
      "\n",
      "\n",
      "[22, 23, 24, 25, 26, 27]\n",
      "\n",
      "\n",
      "[53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]\n",
      "\n",
      "\n",
      "[24, 25, 74, 75]\n",
      "\n",
      "\n",
      "[99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118]\n",
      "\n",
      "\n",
      "[93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145]\n",
      "\n",
      "\n",
      "[101, 102, 103, 104, 105, 106, 107, 108, 109, 110]\n",
      "\n",
      "\n",
      "[92, 93, 94, 95, 96, 97, 98, 99, 100]\n",
      "\n",
      "\n",
      "[79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]\n",
      "\n",
      "\n",
      "[87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]\n",
      "\n",
      "\n",
      "[83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94]\n",
      "\n",
      "\n",
      "[136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280]\n",
      "\n",
      "\n",
      "[100, 101, 102, 103, 104, 105, 106]\n",
      "\n",
      "\n",
      "[89, 90, 91, 92, 93, 94, 95, 96, 97, 98]\n",
      "\n",
      "\n",
      "[79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214]\n",
      "\n",
      "\n",
      "[95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]\n",
      "\n",
      "\n",
      "[24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]\n",
      "\n",
      "\n",
      "[103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113]\n",
      "\n",
      "\n",
      "[157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186]\n",
      "\n",
      "\n",
      "[72, 73, 74, 75, 76, 77]\n",
      "\n",
      "\n",
      "[68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178]\n",
      "\n",
      "\n",
      "[102, 103, 104, 105, 106, 107]\n",
      "\n",
      "\n",
      "[71, 72, 73, 74, 75]\n",
      "\n",
      "\n",
      "[81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104]\n",
      "\n",
      "\n",
      "[203, 204, 205, 206, 207, 208]\n",
      "\n",
      "\n",
      "[87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119]\n",
      "\n",
      "\n",
      "[5, 6]\n",
      "\n",
      "\n",
      "[135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146]\n",
      "\n",
      "\n",
      "[95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119]\n",
      "\n",
      "\n",
      "[92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132]\n",
      "\n",
      "\n",
      "[11, 12, 13]\n",
      "\n",
      "\n",
      "[93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174]\n",
      "\n",
      "\n",
      "[107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132]\n",
      "\n",
      "\n",
      "[65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]\n",
      "\n",
      "\n",
      "[100, 101, 102, 103, 104]\n",
      "\n",
      "\n",
      "[95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114]\n",
      "\n",
      "\n",
      "[142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169]\n",
      "\n",
      "\n",
      "[11, 12, 13]\n",
      "\n",
      "\n",
      "[40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]\n",
      "\n",
      "\n",
      "[107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129]\n",
      "\n",
      "\n",
      "[53, 54, 55, 56]\n",
      "\n",
      "\n",
      "[59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94]\n",
      "\n",
      "\n",
      "[96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166]\n",
      "\n",
      "\n",
      "[30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]\n",
      "\n",
      "\n",
      "[35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]\n",
      "\n",
      "\n",
      "[47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]\n",
      "\n",
      "\n",
      "[51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134]\n",
      "\n",
      "\n",
      "[103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193]\n",
      "\n",
      "\n",
      "[102, 103, 104, 105, 106, 107, 108, 109, 110, 111]\n",
      "\n",
      "\n",
      "[99, 100, 101, 102, 103, 104, 105, 106, 107]\n",
      "\n",
      "\n",
      "[17, 18, 19]\n",
      "\n",
      "\n",
      "[22, 23, 24, 25, 26, 27]\n",
      "\n",
      "\n",
      "[70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]\n",
      "\n",
      "\n",
      "[20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]\n",
      "\n",
      "\n",
      "[20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]\n",
      "\n",
      "\n",
      "[101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146]\n",
      "\n",
      "\n",
      "[75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107]\n",
      "\n",
      "\n",
      "[91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116]\n",
      "\n",
      "\n",
      "[95, 96, 97, 98, 99, 100]\n",
      "\n",
      "\n",
      "[77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105]\n",
      "\n",
      "\n",
      "[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]\n",
      "\n",
      "\n",
      "[52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121]\n",
      "\n",
      "\n",
      "[100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113]\n",
      "\n",
      "\n",
      "[41, 42, 43, 44]\n",
      "\n",
      "\n",
      "[37, 38, 39, 40, 41, 42, 43, 44]\n",
      "\n",
      "\n",
      "[33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]\n",
      "\n",
      "\n",
      "[181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246]\n",
      "\n",
      "\n",
      "[95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115]\n",
      "\n",
      "\n",
      "[87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170]\n",
      "\n",
      "\n",
      "[159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228]\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(str(x[1]) + '\\n\\n') for x in data.act75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ebfca153-71e9-41b7-9887-880af5c6d1fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = [data.CLIP('act75', 1),data.B14('act75', 8), data.B14('act75', 11)]\n",
    "comp_data = data.act75\n",
    "\n",
    "for i in compare:\n",
    "    y_vals = showMetric2(i, comp_data)\n",
    "    x_vals = range(1, len(comp_data) + 1)\n",
    "    plt.plot(x_vals, y_vals, linewidth=2)\n",
    "    # print(x_vals)\n",
    "    # print(y_vals)\n",
    "\n",
    "plt.ylabel(\"Absolute Error\")\n",
    "plt.legend([\"CLIP\", \"ViCLIP\", \"B14\", \"S14\"], fontsize=10)\n",
    "plt.title(\"General Data\")\n",
    "\n",
    "\n",
    "# plt.xticks(range(0,100, 1))\n",
    "# plt.xlim(30, 40)\n",
    "# k600.gca().set_position([0, 0, 1, 1])\n",
    "\n",
    "plt.ylim(bottom = 0)\n",
    "plt.savefig(\"general-data-compare.svg\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "be0ec1ce-e855-46f2-8be0-6d49062d0165",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════╤══════╤═════════╕\n",
      "│    │      │   Score │\n",
      "╞════╪══════╪═════════╡\n",
      "│  0 │ 1    │      33 │\n",
      "├────┼──────┼─────────┤\n",
      "│  1 │ 2    │      42 │\n",
      "├────┼──────┼─────────┤\n",
      "│  2 │ Tied │       0 │\n",
      "╘════╧══════╧═════════╛\n"
     ]
    }
   ],
   "source": [
    "def arenaMetric(model1, model2, data):\n",
    "    model1_score = 0\n",
    "    model2_score = 0\n",
    "    tie_score = 0\n",
    "\n",
    "    offsets = [find_best_offset(x, data) for x in [model1, model2]]\n",
    "    pred1, pred2 = [x + offsets[0] for x in model1], [x + offsets[1] for x in model2]\n",
    "    \n",
    "    for idx in range(len(pred1)):\n",
    "        peak1 = findClosest(pred1[idx], data[idx][1])\n",
    "        peak2 = findClosest(pred2[idx], data[idx][1])\n",
    "        \n",
    "        if abs(pred1[idx] - peak1) < abs(pred2[idx] - peak2):\n",
    "            model1_score += 1\n",
    "        elif abs(pred1[idx] - peak1) > abs(pred2[idx] - peak2):\n",
    "            model2_score += 1\n",
    "        else:\n",
    "            tie_score += 1\n",
    "\n",
    "    data_arena = pd.DataFrame({'': ['1', '2', 'Tied'], 'Score': [model1_score, model2_score, tie_score]})\n",
    "    \n",
    "    print(tabulate(data_arena, headers = 'keys', tablefmt = 'fancy_grid'))\n",
    "\n",
    "arenaMetric(data.B14('act75', 8), data.CLIP('act75', 1), data.act75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa62fafe-35d6-43dc-8279-175df02c0b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from IPython.display import clear_output\n",
    "\n",
    "def load_basketball(basketball_path, size):\n",
    "    basketball = Image.open(basketball_path)\n",
    "    basketball = basketball.resize((size, size), Image.LANCZOS)\n",
    "    return basketball\n",
    "\n",
    "def rotate_basketball(basketball):\n",
    "    random_angle = np.random.randint(0, 360)\n",
    "    return basketball.rotate(random_angle, expand=True)\n",
    "\n",
    "def add_basketball_to_frame(frame, basketball):\n",
    "    frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "    basketball_rotated = rotate_basketball(basketball)\n",
    "\n",
    "    frame_width, frame_height = frame_pil.size\n",
    "    basketball_width, basketball_height = basketball_rotated.size\n",
    "\n",
    "    max_x = frame_width - basketball_width\n",
    "    max_y = frame_height - basketball_height\n",
    "    rand_x = np.random.randint(0, max_x)\n",
    "    rand_y = np.random.randint(0, max_y)\n",
    "\n",
    "    frame_pil.paste(basketball_rotated, (rand_x, rand_y), basketball_rotated)\n",
    "    return cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR)\n",
    "\n",
    "def process_video(input_video_path, output_video_path, basketball_path, basketball_size):\n",
    "    basketball = load_basketball(basketball_path, basketball_size)\n",
    "    cap = cv2.VideoCapture(input_video_path)\n",
    "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "    fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
    "\n",
    "    out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))\n",
    "\n",
    "    count = 1\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        count += 1\n",
    "        clear_output(wait=True)\n",
    "        frame_with_basketball = add_basketball_to_frame(frame, basketball)\n",
    "\n",
    "        plt.title(str(count))\n",
    "        plt.imshow(frame)\n",
    "        plt.show()\n",
    "        out.write(frame_with_basketball)\n",
    "\n",
    "    cap.release()\n",
    "    out.release()\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "input_video_path = 'augment/9.mp4'\n",
    "output_video_path = 'augment/92.mp4'\n",
    "basketball_path = 'augment_utils/cruise.png'\n",
    "basketball_size = 1\n",
    "\n",
    "process_video(input_video_path, output_video_path, basketball_path, basketball_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05e438c1-d852-4635-8c15-73e87e34f386",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "from moviepy.editor import VideoFileClip\n",
    "\n",
    "input_folder = 'gifs'\n",
    "output_folder = 'GIF87'\n",
    "\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "new_files = []\n",
    "for filename in os.listdir(input_folder):\n",
    "    file_path = os.path.join(input_folder, filename)\n",
    "    \n",
    "    if filename.endswith('.mp4'):\n",
    "        shutil.copy(file_path, os.path.join(output_folder, filename))\n",
    "        new_files.append(file_path)\n",
    "    else:\n",
    "        output_path = os.path.join(output_folder, os.path.splitext(filename)[0] + '.mp4')\n",
    "        clip = VideoFileClip(file_path)\n",
    "        clip.write_videofile(output_path)\n",
    "        clip.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa49588a-93c3-4305-8901-5591eccbd142",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(sorted([int(x.split('/')[-1].split('.')[0]) for x in new_files]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a75d401-5973-4327-bc94-2a6d8200ccec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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