{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Random Translation Perturbations\n",
    "\n",
    "## Introduction\n",
    "This notebook is part of the NRTK demonstration suite, demonstrating how perturbations can be applied and their impact measured via MAITE evaluation workflows.\n",
    "\n",
    "## Layout\n",
    "This notebook demonstrates how a particular augmentation (in this case, <b><i>Random Translation of the Image</i></b>), can affect an object detection model, and how that impact can be measured. The overall structure is:\n",
    "\n",
    "- **Traditional vs. relative mAP:**\n",
    "    - An overview of the nuances of what we'll be evaluating.\n",
    "- **Setup:**\n",
    "    - Notebook initialization, loading the supporting python code. Depending on if this is the first time you've run this notebook, this may take some time.\n",
    "    - Loading the source image, which will be used throughout the notebook.\n",
    "- **Image perturbation examples:**\n",
    "    - The NRTK perturbation is demonstrated on the source image.\n",
    "- **Baseline detections:**\n",
    "    - The object detection model is loaded and run on the unperturbed image. These will serve as \\\"ground truth\\\" for comparisons against the perturbed images.\n",
    " \n",
    "At this point, we have the fundamental elements of our evaluation: the model, our reference image, and a mechanism for creating the perturbed test images. Next we adapt these elements to be used with the MAITE evaluation workflow:\n",
    "\n",
    "- **Wrapping the detection model**\n",
    "- **Wrapping the reference image as a dataset**\n",
    "- **Wrapping the perturbation as augmentation objects**\n",
    "- **Wrapping the metrics**\n",
    "\n",
    "After the evaluation elements have been wrapped, we can run the evaluation:\n",
    "\n",
    "- **Preparing the augmentations:**\n",
    "    - We specify the range of perturbation values to evaluate and optionally specify which ones we'd like to visualize.\n",
    "- **Evaluation of augmented data:**\n",
    "    - Each augmentation is run through MAITE's evaluation workflow, computing the mean average precision metric relative to the unperturbed detections.\n",
    "- **Evaluation analysis:**\n",
    "    - We plot and discuss the mAP@50 metric from each of the perturbed images, as well as per-class and per-area results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2e1964-46e2-4497-88ff-274eaca17960",
   "metadata": {},
   "source": [
    "# Evaluation guidance: traditional vs. relative mAP\n",
    "\n",
    "This notebook will be evaluating the perturbed images using mean average precision (mAP) **relative** to detections from the unperturbed image. Traditional mAP scores the computed detections to ground-truth annotations vetted by an analyst; the mAP metric indicates how well the detector does compared to that analyst and thus measures the detector's \"absolute\" performance (\"absolute\" in the sense that the assumption is no detector can do better than the analyst.)\n",
    "\n",
    "In contrast, in this notebook, we're not concerned with the **absolute** ability of the detector to find objects of interest. Rather, we're interested in how the **perturbations** affect the detector *relative to the unperturbed image*. It's expected that the detector won't find every target in the unperturbed image; instead, we're measuring the **change in the detections** (or classifications) caused by the perturbations.\n",
    "\n",
    "To support relative mAP, we'll be computing detections on the unperturbed image and using those as our \"ground truth\" dataset, and using the MAITE dataset class slightly differently than usual. For example, there's no on-disk json file of reference annotations with an associated data loader; instead, we'll be taking the computed detections and manually copying them over into the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9268412-0d13-4fed-ae78-f6ee02c94c9c",
   "metadata": {},
   "source": [
    "# Setup: Notebook initialization\n",
    "The next few cells import the python packages used in the rest of the notebook.\n",
    "\n",
    "**Note:** We are suppressing warnings within this notebook to reduce visual clutter for demonstration purposes. If any issues arise while executing this notebook, we recommend that the first cell is **not** executed so that any related warnings are shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "796743f8-2f1e-42aa-ad6b-f71e721602f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "# warning suppression\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2561179f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys  # noqa: F401\n",
    "\n",
    "print(\"Beginning package installation...\")\n",
    "!{sys.executable} -m pip install -qU pip\n",
    "\n",
    "print(\"Installing required packages...\")\n",
    "!{sys.executable} -m pip install -q \"matplotlib\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"torchvision\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"torchmetrics\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"ultralytics\" --no-cache-dir\n",
    "\n",
    "# OpenCV must be uninstalled and reinstalled last due to other packages installing OpenCV\n",
    "print(\"Doing a fresh install of opencv-python-headless...\")\n",
    "!{sys.executable} -m pip uninstall -qy \"opencv-python\" \"opencv-python-headless\"\n",
    "!{sys.executable} -m pip install -q \"opencv-python-headless\" --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c18e6bd1-1c56-447a-b1e4-16df26269ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "from collections.abc import Sequence\n",
    "from typing import Any\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# some initial imports\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = \"jpeg\"  # Use JPEG format for inline visualizations\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "from nrtk.impls.perturb_image.generic.translation_perturber import RandomTranslationPerturber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242e9bfa-fcea-4a1d-a8e0-9455b28777a2",
   "metadata": {},
   "source": [
    "# Setup: Source image\n",
    "\n",
    "In the next cell, we'll download and display a source image from the __[VisDrone](https://github.com/VisDrone/VisDrone-Dataset)__ dataset. The image will be cached in a local `data` subdirectory.\n",
    "\n",
    "### A note on image storage\n",
    "\n",
    "Typically in ML workflows, batches of images are processed as tensors of the color channels. Both our perturber (NRTK) and object detector (YOLO) accept numpy `ndarray` objects, and we will use [matplotlib.imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) to view them. The complication is that although YOLO inferences on `ndarray`, [it expects the color channels to be in BGR](https://docs.ultralytics.com/modes/predict) order. If we naively view the same data YOLO inferences on, the colors will be wrong; if we naively inference on what we view, the detections will be wrong. (Our NRTK perturbation is agnostic to the channel order.)\n",
    "\n",
    "In this notebook, we'll convert the channel order to BGR when we load, and convert back whenever we explicitly call `imshow`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57facb8b-4cb6-476b-a321-f48d70e773ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(540, 960, 3)\n"
     ]
    },
    {
     "data": {
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lUyRSwxybc8Z28npXR6bCZdJuLNjybaRd2P8APrXI+IpmjisVSFJXEQj+dN2QpPT8KiqrxOzCRvV9465JDcf2W3G1klO3aOowR2+tJPFnxVcBgNgWJ1G0cE4B7ZqtpUgk0rT51Cjy3KhVXAwy44HatC/UR61HPk5liUY7cH/61FLWCMqsWqjSGG3Xyb5yql2nCsdo5XzP8DVye3jYXTFF5twBx2wf8KZcK0cOoogLEAygep+U/wBKVruWWTHkECezLfRhnj9a1MtUZ2qRpbaza7F+Xyl4+hx/SktdZW6WJPJQCVzGwLkY4HTj3qHXZ2lNldMNrSQbtv45rEjvI7e0tz2U5Ct2bcf6CsK1Z0ldHVWk40os6C+lsrSI5tzvP3f3h49CazrS+VJmYgzBzhSp2hTz3rJm1Ar+7mfzc8EL0xnpmnQXyTCQMxRTg5x6dq462LxFSSm29DylTgtkX5vFF7HbRK5IjWZm9VY5/nzW3YeIFQ3l1NNvDAMIySQpOOn4H9K54rC5ZWSNlXggYA9M4/EVQjka3v3jYfupQVVdwPy9vp1rOnVnvEtSaPS1lYadaSpNGg35DNj5l9P0qu94baQyNOu5+Pl5Ax7VzNtdKunLYrLmIjeuRg9MdazYtWKSxRyBmV8j94eT2/A11rFuXwornPULPxXFGw8yU+ap7jII5FdDZeJ7WZGEjtvB5+XGBXiZvJPM2ltxzxjqa6/Q3PkKroDIVyc8HHWumhKU1eRKm2dhqviiBZdkF0yIAckDvWHrHiKLUre3hMvCOTu5DH6+1YeqwKczBZGLDP7s8VyzX78ggq+cYIq6rnDWIueSZ2ltc/Z79GEoVAMlpJc5z0xn3xTf7Sls9Tu91wAHyQoXIz259sdPeuJhvPNmMkoVoyVQAnuPSrU07SBmZ84G3C9SAP64FctTEyittR+0Z2Fv4iNrbpEwBKSiRmVyMgYGKsWvi6KUwpNH5m1jht3DZzgH8xXm8s88sLJGp84/Mcc5Pp+tatuxSLyEBcggHHcgY/z9a5ZYuslqCmux2+vaod0DwvGm2NS0asGGcnjNYureIxax+UsjLI68ngAdemPpXOx30nkXQuIH3hcIWzlST1+nWsW7uGufLxIBJtYHb3HQ5/KtvrTnGyVmU56aHpGl61bCLzbm9JLcncen+FWBJB9uaf7S+xlBCmU7R1ycZryyO+PlIvmAIzYcY9B1ru9FulvLIA8tGgXk8kds1vRqtvlkFyLXZotUW1iS4Eim5Aba2ccGkt4Es7dYIydq+pyaw9Vhm0tYzHOu4ybgdvQgGtSzuPM0+OSRwzMmScYrSq0l7yubQvYj1idxBarJIFTep+Xg4q9prwrbfLC0qg8MX6cA1x+vapb6hpwSFHRlVAwY5zntU2matdafp/kQRQlcsx3Zz0Aq031FJRWx10zRGT5IVjAwdpAPOP8AA02Pi6gUbQGkGQAB9az7S5lvbcTyoFdyMqCcDGB/So7vUjpCwXBh81kcnZuxn8efWuBTk61r9TblXJc7Voo1jwFAGegqnJGoFsMDHU/lWKfFcrR/8g9hnuJgf6VnN45EkkSCwkzkIP3g5PT0r0DnPQBpto9vEfKXIXnAx161A+m2Ue+V0UIvzN7YqFPElgd6eegZDtK5GQR17+1cRf6vPbxXMcd7H5Vzu85TyQ5z/wDWrOdRRB3RU8UQxQauzWt35kbfvNgOQv1xWFBfyRXSh1EibgcEcEdaknuXurhpA4ztKZUBe2DwPbis4oYnA3jcSOo4riaUm3Yjc0tRhgExeElYJPnUHqB6e+KrtFAEJWIsSOcsDn3zQ14JW5+RRwpAHUf/AK6rTTPncrhhnBHelFSegDi3yhANnrn0PvVh7mGPAdcqFwCQDj8PzrJeVn+QE9PyqLexU/MQByf6Vr7G+40i8Zo/MWWNjk54AwOvWlecFSGbJz0HaqIkJMe48A9WJxj0qSUxNhwzKe+0Vbpq47FlJ4LgEHhBwoPHPvTIp5bliiABE4Zg2AeeKoqBFubdk/wjv9auWUkykLsPlBsFSDj1z+dP2aSuFi2FlSBlcHORwT973qs0pK5AwR0AqzcXYu52kwke4/w9Bxjj8qqMSM4fjnqOprOMdSRkTEuGYfKTxnnJ9KQN5shTdtIyQcdBUZlEbMS6k/7PrVeCbDOWDFScED9K3SGW49uwl2UhDjBHU19S/CFt3wt0Y/8AXf8A9HSV8sPHJPGXVWODyPavqX4PoE+FmjKCSB5/X/rvJVQtcqO53NFFFaFhXE63BKfEU0oX5Mpzx/dFdtXhPi7WL+L4v3VkL+4W0MkS+SrkKMwqf5nP41MldAavilBG9vPsZgrENtXccEZ6fgK8ik0G1VzjUjjPT7LJkfpXtmu2zXWmTKjsrlMqynBBx1zXh90+r28aSXF3cosnKkyNzVJtsm6W48aHanrfyE+1o1TN4biRA5ubhgRkEWp/q1X/AAtLJca/bRTTTvHLGwxI5IJxn1rL1C0vbGcpNcTMpkZFw5OcHHrRd3sO1jpPB+h+RrMV1FLPuQ4KvEFBH1yfSvR2WBHKvcwqc/dZ8GuM+HIDpcbgSyyA5JycEf8A1qx/EOnLY+Ohd4DRzXak/wCycA/41Mlzbg3ys9Kt5bVbxFS7gZ842rICaljGy8Uf7ZFcvo1mLPxRdx4XGSy4HQbv/r1082VvM9w4NYVFaxcdTIW9s9RuLu2Ebs1nJ8wdcDIJHFWNE1GPVYt0cbIoJTa2O6g9qwZzLaa7rotx+9PzjHPUqf5E1e8LSQAzLapsjEi4UjGDhgf5VpyJMhSbKGgSPM8pZAhDvDt3Z7Z/pXI6vJdNo9nPACJpXdcKucYI9a7DSR9n1G8hYFdl4SuRjIJcf1FcvrSTnRjbQnbPFdOOuDtOOn/fNKr8J04Wb9ojQ0u4nfRLbfuWQXKCTcozjoauax9ql1jTPJm2wiFmcFcglf17msXRGmi0Cc3LMXiIc5OTgNW/K6Tizu4iQpLqAfRgOKVD4AxCftmW7ncZtQ+ZvnjwvPTMZ/qDV1VVp48AcwMo+mf/ALKsm6uxb30HmDFvNbrvf0OCB+hNUrbWFihsS9wm+NmEijumOP5CtHJI535jfEk6JZ2bIy7o02nBHGQDz+dcddSPNIPlLSliu0HP0xU19eNLLcIHMgY5Q+gGar2cqtqtm4bhJosL6jcK5ZLnldiqS5kl2LIjtYZC0rvcurBZI0O1euCN3f8ADFMmliErx2yLFEo34DFiR7579KobkWaRMlWDsODkg568+9BlxCqsx8xSQEHOTkcfTp+VTKL2MTWimlEgkVCrlsnJ46ccfrVe6jYSLOCgKfLJg579arW9xIYSd5zG27gjgk4qfzvLkDKiqjcsp5J9ax5HGWgWLNvdkoVPHynG31x/hUM7nAZVJBwVHo3GarWlwYrsSLkhTlevau8jkV47Z5FAKlWJdu+D7V00qSvcLXMvQbeSWYzzwFlAxGDxk+vP0rstNnPmFAz9CGD4496zo5hgFQSCMcOetX7KYCcoeGwMgnnNdkVyqyKSsSXgLxgqNp28kAc5Ncje6Z9oBnZ8ueQV4JHrj68cV2nLxMhA4PHtWXPCBIpYKwVdhOecj0q730Y+W5wEkVxbzqssTosY+XI4JPH+NPmuwy5BG/IG0DqTiug1a1nu7F1lkTz1bKqpHzDPTtziuTm82K58uWJkeNuVIwemc1xV6WtzOUdTRQiKMMCBsUkHjk9x79qrs/mMEY7TGd4J/i9aqGRmhBZzuGQo6HrToJ9rSSDDHBwCM5HH5f8A1q4/ZtXYrFm9uVkWQqSy7TtI479MVlwzlVWTaTt3DB6N/nNK793OWYZIHrj/ABqmZUjTClgB7+vatqVKyGkW1ktEjBmilJxgPG4x+RH9a3dGdi223uZGt5WCuVOxlOCRu/DOMGuSExY4JC56ADtW/p832HTLmaHDPG0anIz1J/wNdDVrDaN22iVTbx3TmQNOx/e8nGz+WavI8UttthC+XtIGONtcgLlJpMyXLE552nn9aswa4sMJt41wp4JPJPbk1FWrKT2LhNLRmtqWixNbWVlDNvPnyIzE9lIxz+FQJokk8cjRujKAygg5zzSi7tLgw3FzJGoXOYySQScjr+NXLWW2ii+WYeSp5aOVcAds12SUpax1CE4Ws9xqo9uPLbI2Eg/nUF5YXupQRC2RpPnweRitqJ4JgzJLkEkg5znk1JbTBNUhttyhCPNyWxyOPxrmpqTlrH5nTKouSyMU6VqcSAGCQYHPGaz9P029W5s3mtnCNcpyU5ABGSfTvXp5IZevXmq0sihwNwznpmumxynl91aX1xqU0tsEl3lmURupPzH0zUN9HcQLLm3dW3/IpU5wODXRyeH9IukSaKUxM5ADLJjsT/Q1Qv8ARWtpVlh1RlEhLeW5685xWc4q2p3zqwlGz/IxoI4DZswEnn7OBjA75quqCFdzgYPQt3HtVi6nZH3uG8wjBz6CqTyG5YRYwAOp6Vxq8n5HBLV6BGYt4cMv+6RkD8KasKrOMKCuDtGepqZbOAmNkkdT/F8v5/1qZYYwpSOaTcDuCkcim5pbCKJtmBWR2AJ9+tRczu5RiCW9OMZxWgAY5h5jqybTgk8jPX6UrtHtCRmPA4GD1/zimqgzMm3qoQ7TtG3OO2T/APXpkR3bY4x9c9PrWmyxyOqvHlcbSVPANPjtkt/kVlBbkjOOabq2QXKXlQgq6rkqORt4J96sw27RqIWDBn+YKPQ9KatuWmURyADy2JJOAW7Z/GpMmNmKsGKAjJ6UO7QAIIi2H4UZU8cioJ4FMSxhu52sc5BpHuB5e3p9B+uaheZjbn5hwwK470RUr3EV5rcLsQsC+Mnjiq6MiXSJjaAecHOa1Q2+3EZO5Bk4zj9azzZrCiyu+SSTtXqB6mtlLoxpk6zs9y4YjGBmvqj4Rv5nwv0dhjH74DA7CaQV8plwfmAJXpkDg19U/B1Svwq0UEY/15/8jyVUVrcuJ3NFFFaFBXgfjrStRf4o6hfQ2czxLJburqpwcRJn+Ve+V5p4rvoofEt2jISw2chQf4FqZSUVqC0dx7fvLUHaT8hFeX6vod9eaWttHaStLDKdh4AK5IHP0xXf2mtwiAq6EYJxx2qp/bMI48ts/QVPtEtSXG5xei+HtVtL3Tp3tQnkygud68KeD39Kval4bvb6W5j2JsN0ZoX39FJzg/ma6NtbQDIiP4mom11s/wCrA/4Ean2ut0i3ra5B4U0O50O4lMroyyhRhMnBGfb3qTxB4dOoalK5nCxybGxt5Vl7jn0xTP7cfeGGwYINNvtfaVg4lRTjoCKftPIUved2XUtBDqUl95nzN1BxxwAf5ZrXuyDLuBByobj/AD7VxEusqwxJPnPvUEniizgwxvfmAxw/OPTrWc7yQ4J9EdtdWkA1GWcRKJZUGXxyflx/SmQBIZRsUKMhsAe4/wAa5F/FjEo6iRtpGMkDOP6VLot+954hkmckBkI2FiQuF7f98/rTSbdxtWRYv7wJ4nktXZAUlDqM84JVjxWRPBBqGsXtqzyKom3blU4z8359ap+M5PI8dGRRkS2wPHX7pH9K0E1jTrO1iE11GuBnbnJ/KujlUlZiU5RfumVps6HSNUkjjO6JXUox4O0VLe6hJF4a0ya3ZUUSguoGe2SP0rOsLyG1bV4XJMV20ghZRkHOcGsmfUpTpKWLR/uozv3dCTjAqeRQVkrFc0ql2y7dazNqc0aTSeVEqeX8p4xWU0jROv77PUYOaPs9xEwMgRGIyoY5PrxjipILC4v0ljSRFSNS5L8His3HXUwauyrNOxYP0A6jrRHNtvopFbhWVsj2NO2GCRA7CXzFwSRx/nIproMA4LY5PGNtJWTsIl1WJItVvoyQJFuJACe/zHiq6zJkJJ2IyM8mrGvqTrupdAv2l2LY9TmqkYUIFZQSASOcfrVOI7Fu1kMb5OAJBjB7DtU1wcRxtySM5I79P61mwqSwBcgE+nWtiyaZSyJNLGwXjyiRnnp/OspRXMmTYitraa+vEggT53bqTwBjnNei2+kJeKLbKbIzu67R26/mKztOml09Gjub2SSXq25uVHYfyplzqIWSO4kuZ2RWOYwGbfnA6Z4rojGxaVjoJdM8sLAJhlTv3RYPGavJFEJPOEYWU8eYAA351j21+k0An8xmVxuVBkEAAZGD345+tayP5gBLAEjuQKsGTo6bnAJz6GsuO8N60gls5YjEMrvTAY81cBHnZByM44P+fSsvWb14onitrmKG4QhiZSMbenf3xQPdkbXd0tzLJNGrb+EACr2J4zn8a5bWHgn1SRLYM86jDBiG3Hpwc9ea7W4WGYRlo8EYO/cSPfj3HFYjWWmLNJJDZvyflMcgUqfxByc9/wBKJJSWpnf3uU4gnZGBuIOMkY6f/XohclCTs2hQpXPOa7a907SJVWU2tyziImTZIAS3I7g54x6c5rLWLw9FZtM321HJyifKSwGd3PA/u9vX8OedLTQVzl5VLIoJC5B57jFVmiD+oYdMjI+ldVar4Yn3rc3GoxKFP31TqegGD64qkU8Ny3OF1HUgxOAps0YHt/f5pwTBM51lKzHI3jpux1HtWvC8h8P3pjB3vcwqOwPyyVqt4f0oaDqVzaT3txPAsflxy23lYy4BwMndxn6VkFHi8PuZUaNmvU3BuDxGe3/Aq1cXu0O6MlPMaUrnkd85FTiGeW2aSKPcqMA5HYnpSpdRwSKYo8jOTkZq3b7pd7C32qxyXZcDmoclFXY20kC295DFDOy5WTAB6gE9qjmh86d9+3fwDj1xV0GN5UBmcqGBwRwfpSW9pbzXRMdyu7OVQ8GnRXtH7u44yiots27a58rTm8txvROmc4OKpxeI7sS4nt4JlXOMrg+n0/SnTWkPlxxxxq128m3G0g9MnDZwe3HuKzzBLA7NNGyBhgblxnLVdJToO3NuVUpqortGzNr928a7I7WLHON7EjHp0FZhurm482VnRWQA7ljGeT6nNSy7Tp1vGCpZpDnH3hW1Y6VpC+bFNLLJIcKULAdWIA47nFd0Kq5Xz7nM4WkuQ5YxuLYRpcrJIPm2eZjHykAAfjUk0k0SKs0cmUDPt5xgnGT+X5V1N7pthcDEdumR0LHJ/Pr+tZc2jushNvJPEDj5Fbcv+Nc08PNwbtodn1ylP3Gzm5YJZZGXJ3jnb7U7EUBCSPiXu6njHpU1y03muvOVYgnGCQKzLn55OhwPQ15yTbsYk6z+YSC5Kp0IwKRZFjy+7c3sen1qm4Z8rAhJxlq1LHw7qEkKSi3mkjcZAVDg1bikOxQa7d2yMADqOtWYYnlIlk2qu3jPGf8APrUU1nJBfIkkEqDG5k2ncAPXNNnuQOeckfdXH86HHTQGiWUGOUorBwcHHcVK8kc0YYhRKDkKvHHp71jq58zOCuenNXrBliuDM0ZcYwS3IBNEoaXFYvRK6xY8ox7gASQemc/n0pk8qKXXIy3O4DAJzmpvO+UjJKnGAarO0flEMFL9s9azi29xFSSTeG4J2jke1JayKGKsBtIxg81FOrAk7SuRn1qO3gaWZfnWPuN2a6LaFLY25WDKqDZgkElQBnioJGiLbjnbjG3uaZIwS1HBaQHLNngD0xVYzKcfMwOMn0NZRiImZYvsZhRjGcg4PQ/WvqP4QHPws0U/9dv/AEdJXyxk4GOVHWvqb4PHd8K9GO0L/r+B/wBd5K1grMqO53NFFFalhXzh8UvEtzYfEbVrWNFKp5OCfeFD/Wvo+vlf4vqT8U9ZIH/PD/0RHUyV0deDpxqTal2MU+MtSxhSB+X+FRN4r1Nv+Wgz61T060t57jZcs4j2liY+q4Gcng8Vq2ei2Murm2lFwIzEJFDOFb05yB3qORndKlh4aNFR/EWpuuWnVAeg55qtJrWouP8Aj5Jx6CrF1ZxJdzxpGPLQ4VmmUjGPWo7cW8DoZRHIisHZRIDkemB/jRyER9i9oFVtSvmGDcPjPsKYZ7qQZ86VgO+410Y1bS7XUWniEQUxFQIos8hgR2Xk81l3etMVkhtWZIGkZypGA2T3GfrRyrubQcW9IWM6aC4iCNPHKocZUuCN30zTrMRmcrIAQyMBkZ5wcfrWhd6q+qwxifYHjJG5yO/PAAzUEEe6eJA29HbB2IRj8TRZF8z5NVZl77SPKXk/MoJx9K6DwxdMl+0vzbVTcV2gZ42+vvXMeWFCKzIrn5cM3vV+1tbdCrXMjRwNjcS5AHI/+vRZdDxZ3ua3ji4SbV7S7t3Rna02Ng52nnP6GucMenCzieYnzXwWO7kDPOBW1Pc2AgdLLTiYyhUTyny0OT6nk8Vzsq20XPm5wV+VRubkAn265qnFysrl0JcjbLiTwR2iMo3Pg4DZOPSq5uAu4FFII6EZOKri4RpflDIuTt3HLH8uKs+fGsgZpGdSOfl+bp3rKUVF73MJ1NX5iX19NdhDEhKKo6DOMCq0V1JsdC+A3B9SKtgxjJCMgJycKAeahmS3QsyRtvHvwDT509DC5ULvGSATn68YpGDSMNzBQTzt7CrccDyzRqYxukO0c/59KJIFNwy2+GU45PBx7jNaxRrGHcn8RRJ/bE7KMlgj/min+tVFt1Nn5g5O9VJ+oP8AhWvfxRnUFe5RnDW8P3cjJMajt6Y/SqmBb294HhwsckYAYn5vvcgj/PNVyt7BbsRrFHudmTejDIC8eWdw/wDritvTbV7WUSuiMwU7GBDAHjB4+p/KoNKgVpprh1j+zKoBL5ILHHyj1/8A1Vtw2sl3Op82Fdi4ZS+36Y6+lHLqTcz7aCdtSMzsNhzkZJ3E9zXR2sK+YzgkswC4+n/66pi0lt5tivGSvcc+9XLZbgTqPJUxgEFvNC4Prz1/CqSBy0JbfTFi1d71pDu24CYwBkDP8qt3bX6vE9i1vxkOJlyCOMYrN0/U3vrq5WG1dFiIXMpIDEZyRx7VppLcgqpijz1Pz8f+g0PQFqOtftMca/a3jaXdljGML1PT86i1HQ9O1G4866t0kkxjJz0p8rz/ADkwIeO0nuPans8xA/0f/wAiCkOw0xiNNg+6BgfSs/U4LizCFg0W5gThc7hVmUy7sfZiR67xXPSJeo0KuJG/0gs7Ej5lx0+nWqVgsTyTE8M+F65Jxis/WbeSS38y33CQnDxqThvcAfrT726kjt5po4miaM4B698ZNVZtUOnxJLLG+AevGRipcbhYyRotxdRmWFfMVfvfNjHGT1+tQ2cAstThu5G+SKXLqgJ4BHT8/wBK17thfNFf2ds4TaXYDHJPUgfgaii2XFtMyoWDnPUeg/wpRXK7oTimrHa2GvwXdlPdQRXBhgeMEsi5JOQMe3Brmdftnn0o3kMTFWuXkCkjIURxjP8AOtezuDB4YvGaFyvnRDO4dgxrIW7uTotqs0O6OQyAHPTlQO/4fhW9XETqKzMaeGhT1ic1bxobZpGiVmBx8w6H2p0l2wKBhhemOwHtWjeQyLGZ3TCMdqhQPXqcVmyQytLGjxSbnxhSvJGcfzricLvUvl1Hb4oQjfMjHkNwfwqWzWL7ad0KTRsEBxySSTnAPfPpUWo6Xe6aEa8t5Y1k+7vXms6S4R2XaSrDjIHvVRi4suLsrHf+HtMu7q2luba0iu4reSTcHZvNHQAL3zxjkUTag11bXahY44wCoill3SAg4P8ADg/mawdD8UXOiK3lTXCzElo3RuM9SGU8EE/jT4r77WrS7VMpcsxBHU9c1tKEJ2ctWbOrJXsTTNvhtI42lMiNyCAApJHQ4roZNKuo7j7TGVkXfHII8YJKq2OenVs1k6MFudXsopIdoedA2+TAIyO/bvXfwpM9lJcpFGQk/lhN5z0yD0/ziqb1sRyOm3ZnnElxfW0EccnmxARQxAMh+8zZY/XtXXy+XDbxiNk3FSxQdQB61eudMj1m2jl+xhHIDD95gqeoyCOorn9UsdVS4ZBBCv7vYxEmc/p70K97EzkpbqzOaurcXKNclNgLDO33ycfzrFuoPKR3Q9F4z610Mum6lHaG3WNAm4MDn0B/xrHutPvRGwkQFSO3ek6cSEzJtlmjnV1IM3UYGaurquoafJutrqWMj+45GP6UyaBklADEMkaA47HaDVZopAudpI7msGveKOhsfiDfxyCS/s7e7Krt87bskx6bhUU3ijSNRnk/tHQojGW+SW1bypVHvj5WP4VzrwzBcsMKR3NMSDzOARyatDOmTQtH1KTdo+tRiRultfr5TfQN90/pVO50m90y6a3vIJIX27sbcgj1B6Ee4rNjtXSQ5GDj5eeDXZ+Hbv8AtGH+wL+bfHKCLeR+sMnbHse4qZK6IehzjzARFRJzn6EGqhkR5CXJZgODmp76CSGeaCRWRo3Kuu3nIPNZZBjkztJHTFZxiCRcMks8bqSSO+Bg/nVUNbLOgkDfL1O7vUIkMZbBI3dKlXE38Ydh1PTNWkOxbeTanBVlbsCTxVWdPMkXyTgAYJ7UskYPyIzH2POKiiIRjuIJUdCeKEuwIlW3n8lWWRXDcYB5r6s+DalfhToqsMEGfIP/AF3kr5TW+RQBtO0dADyPoa+rPg1j/hVGi4OR+/5/7byU436lI7uiiirKCvlr4uRyH4p6yygY/cdSP+eEdfUtfM3xUSI/EfX1e0WSR/IKSEj5f3MdRN2R14OfLUb8jzvyHz99Fzx98U5bYyMF8+Iseg3EmtczW5aXbYQKrwiMbiBtPPI96ruPNMDkwRiEAfL/ABf5/rWKk30PQddd0UjbLG+1phnGcKhOamWyll+REnfP8KxbeRU0gR5Y5VlKMoxgKT1qUXUinK3ExPJG1QtN83QzWIjy3b1IF0yTy9/2ZiPKMwLSAZX8KfcWX2OOYtFbbotpK5Zsg+nT/IpwlYqCRKVRSvzSHGPTA7VFHukJEYgBAJy3XjtRaXUzeKj3J2jUThQTHGUY5jjzz2HSnSJblWMa3LFclWkfpwMd+xz2rX0zw/PqUTSy3LxoG27VUA9K0h4SskV9xeRx91ncnH4VXJfW5zSxdlaxy90VWR1CRrghiyrluue3SpnvALOJlPlEymPcy/d4BBOa19E0d9TspZluJF+bbIF6Hj171m6jpaJdS20U3mgEEHPccEfhVqD6GftJVGkkX4vCyvb/AG3UtSVomXO5HyMfU1hajbWUEQ+xzGVA4BJXtz3pEje2sbu1lDKXw8ak8EBuaqfaJHiihkiYKHG9cYJGR0J6VVmnqRUTi+Vod9kgd0mLfOoOV/Gmz5RNyxEBjjJWpZDbwxJL8jyE42+mOmcd+BWlb2lw9nHcSWzsj4EcEURYkdQzE8KOuM0nT5ncxaMuKTfGCjFiOWVQc+nBqws1urqOGLjnPX8a1tOhjsl+2zlFmwfKh8zJz9Bxiq/2VbhhFDAvmOcLk4rN0ri5SqfskMyXizASxOP3IXBPODSASGSWS2jmfc3SNPqeePeu0j8F280SqNXVZiP9Wtsx59M8Vy6wHTrho76aS1VwHjJjPzrk847dK6Ix6G9JSqPkuT3DTLPY4iMjtYrmOVc4Ckgn69KatkbyOcxyRhHbDbJMlPmA5B6Z5qWTTZdXNjLD8yJEVLk4AwxOfxz0q/IsYifT7BGkZWLy7QWyxIPOPf8AwpNuOxLXK2jOvZDEkUMUY+ywjhQ2Mn1PFJE2r/aTJbSoqnHyv2xx2FT3WnXTQjYQZNwDpg7kJ5GcitC2tyiKCecUridrDrI3yo815J58p7IPyFb1hDfJo8U1xpsgOwZJUgL6VUthHFLGDP5UoIdGwDggjqPpXY288d8HhOsI4Cea0QxyFIOOPpVR0Mnucnp5ZrdCEOSpOAOuTj+tTfbTJdR23kTxmEEuXGFYnoB9KrwQiHTGjhYnCYTua1ILGOO2jMl/beYsYJXJJJx06UblJ2I5fmZhjgox/IZ/pSgkqD7UrAecoJ+8pAGOTkY/rTIjmMGoT1KYx889fWqrpnqKtvkHIFQOjZICkn0UZP5CqEYWppaXkDaey3HnucsVIVAowRzjuf5Vzt5Z6tDcXnkFPIuAVYsQfl69CK7CW0ja6W4JIYDrnAx70y/09nhkgZgkjKRlTkDPSncDlImn0gLFGfNt1k2JN2bnpV68tZJnZ7AxB05m2seB6cjHHtVN/DereWsLalugV94XLYz64z15Nb9hps2mzLNxkgBxjG71P9aTsG6LltYW8XhP7PdalD59zKs20vtIIXhfyOa5e9llhsLK1L52B2JBDc+acc/gK6XVLRLuDfBHI0j/ADs6KCVIODhT1Jzz34qk8Umn20UKkvKIuFIwCSxPPTFa3U1ZKxEIuL3v5HLKHhtre3imYlWO0+pOe341uS+fF5PnxOjAjG9cHiqaJNdXEkiqu8fNlz39qngn1q4hW3mc3MWAEUqMhs+uM+1YtNOyNbdGbt1rqXmnXc1wnlrEgA8psHLMOcnODwenqa4zUCb9DOXUiM4wW3Y9OfwNbN7BLZWt3bXITzIp4weAUJG8kH1qPTrQQ21q0m24gvmCyJEDlRvwee3HGRVp20Gk9kc7LZXbWkU8UR8l2CiQDuSR+lNvLST+0FSyR2eRSNqjGCOvP4Gu61TamiGOCPy7R3CwqRypDHqfQAVhaXNaT+KLQRBt8RcuGPysQpPX0+9+lEkrrlJjdJ8xn6E1xBrERkjcmLDFHPbOc/lXqFjr1nfSNCJFEsPyspHzLgEdu3vXC6zbS2/iE3cZXF00WIx95FxtJIPUdaraOpbVLrY5XLlcjuOT/SlJSjvuKyZ6vaO8D7pESR5VxksTjjAIJ69qp3/z3kpPr/SuVtvEU2mMpkJETnI43xk8np/D0PSnX/isxOJHgiTzcuoklZDt7Hle9KMu5tLDzlZxd0S3dh9pv9zXN3Aix71mXZ5KbQ+5WGcknagH1PtWHfy3AllAiyoXjK96dN4itHkF09haedyN/mgsMe+2qV74lmm+SH93uIAKTZx+lKUuxcYThrON0WdNs9El1C4bVJrtG84KgiXKKoABJ469azNRs4FvZ0sZzcWoP7t2XZuH0ofW5bSa4ijeYYmcjbI2PvegqI6tDM7FZ/Mlxkgxn+ZNK2pjJNrYpzWUkgy5OewHIFRCAwBmKYPBHHoa0hqNvtG9tr9+DUM11bXNq5jLfL3x3qiEmQ2UsN1IFd3Sc5wpxswPc1b0UXMuu2WwLzcxbCrZ43D+lQwy+XbDcAEBbJCg5Bx0rX8GwwXXi/TBEzhVdpcbeDhSf8KT1LUU07lTxVKv/CX6tEQwTzyQTnH+c1zM0aA7nYknptrc1u+e51C7uHlV0eVmQcnqTx/Ks8G0cLJ5Wwjr71m7pmVmincCKWMNGwB4GO9ViQmAoJIHUDFWriFDKZQQqnnC/wCFRGEu22Njhj26U00NDEJK7mcK3uaLlQXVkcNkdu1XYreP7K8cq/vs8MRW7pFhZtYYu0LK7gKpBBVeMmsqlZU1diucijKoCumccivrn4NMG+E+iEDA/f8AH/beSvlzX7G3sdQ2WqSGAoCGfufyr6j+DJB+E2hkLt4n4/7byVrCSklJFRZ3dFFFWUFfNfxUn0iL4lauLm1mlnHk7iHwP9SmMfhivpSvm34l6dLf/FXVY49wLeT82BgDyY+9DE79DkI7/RlTzBpTdcBTITzVw6hZfZlMWlp8w5DHOKi1jSE0orDFdi7kc/MEbDJ9Rz1zWdHDdTQsgtpHdWxtAJ+nSloJXLgvIoGgcRl2MgDI2MFceuMg5qtqN4Z7kv5Kxx8BF3FgOOetWP7F1oKrx6PdSFQQyiJgCPr7EA1Z16AaZs0g2csZlCzpI+PmPOff1FSk7nX7SmqTi17xhI7NIF2jr2UVdhYiCX96F3YDc5J5Bx7U2CzlZwVQn1oW3uFaSPyHJ74GMVUdXYwsjsfDNyGtJtu35SuSxxxzz+lakepxrJJ50kBx90xHOa4K3lNqzYVVKJysq7sE9T/n3q1p8YeSQebIsQ+YgP1zznI/HjtVr3U0yLKVze03xXN4fsHs7dNxkd3UbRnAOO/4VjNc/are8uZIZbcKPMCjj05GffrWt4dCJdxNG+UkWdQp6qVZcZ+oqXWzKmpuAwHm6fOoOM/OACMflSTsJNxejOas9StxOizW8Tbjjcclh9D2/KtPW5lECWqxQRW04GZVIkbGfbpXH2zatczxRzS3KxFhlmVsLVqa21NrwwqZHiSQYYIdppvUbd3qdHoOoW/hq41lUt5bwSkC0uBF8pwThirDuDmq99qV7fXBdnlnyqndO+0A4GRtUdM1baJ5YoMhsKpDZ68Ow/kBTZoEhjj44kkEYGcc4J/pSbsJIzonljgeNpE8t9v/ACz5G0nofxqzpV15GpRyLMYmXlZMjg/j+NPNsBhOACeBSrD5Qb9x5qlSpX6jFTcqx2F38RtO8loY1v0uozkCBBtLemccrmuasJv7W1JXv5DI5g3Pnjoent1ohhaMKdgHHTFSqiLIHSPYRkfJx1od27ohJqSlF2sW7+d4lMFjHsBHDqAOD3A/qazNL0qcC4C6kbSTKucE5cAnI6+4rSt2YziIFV3feXGSy9OPfOOar6lZT6dtkuImZMkfIM7geMcHjrn8KNXuapPclluF0C+Y3N28wmIDywuS33ehzwRnirNvdWE10kCSOZduSpQ8YAJ56dCPyNQ2WnzLF+/YHJJxjOPbPetJI7oyMxnHlCIR7Vi6KD/nmhWRF2yoJoVvHVJt8rsB5GQCuBkEE8etQ3F0jvFHFePBdmRotqkMduMHoMdf5VXv/C1tfXjTyyzpJ907TjpU2meFLGyvUuA07uvTe2avm0J5dTZF1Fp9tHJIB5SnLZOPl6Hn6VrS6/4UWISfumOMD943X0x/WoJoY2TyymeMYxWaNJtmlcQW0AnVN/zoACMgEZ6Z5pKSSBxuxkNxeXGsQXIV4rbnEbEgOMggj1GD1q7CpWPHpxWboM0MsXlyNP8AalXZLFJ0TBI47YPsfWtnywEOFwO1T1uU0kQuMjmqF9ePYWrzwOySAYBHbPFaLKcYx1qvKgYFdvNMFoZaxiXTFhVy+YtuScEjGKrWV5a24NldRH+0DITGUzt2tyTxx2PatdY8DGKz30qL7b9pUssnfB60x82t2a+jR+bq9umzed2QNuQDjgn2BxmtjxOZ4rSBbm589vNysixBeMfMpP1II+hrmvIlXDQ3EsDjo8ZwRULWt2yJFPqN1PGpJVJWBAPr0oJJ4pzbEvktnsD78VleIZo3uoblJgu5dshzg5HqPzrSSJkOMnIpjWtvJKon2gDlugO0detJ7WRM48ytc5/E4s7eS3Ak3Md4XBDKSadFYzKZYGkFraS4BkEhBUjOCSc9yeldFc2mnpclLGXzY14JUqQD1wMegIFc/wCJJpLeC3ALKjPhmAzg8Ef1oi3E1pU+dqB0keiWlxZ/Zk1SxmJ5YGb5jxjnIrHvPDup6Zb4+1QuoYGPMgJPOcAE89uKxr3wzq0NzCsZMkJO8zxjCA45ye3GPzNYSNdDUEhFwxcSKFJJwDng0XOqFFttQZ2+qWpmtQsj+QYjnhckfT3rB0QWum3LXNxB5lw8hlgmaXAVQrBlYe5I/Kuoki+3WEcczN5pVXbnBJx6VjPYaZEAskkreTlW/dhlGWJ/PJx+FXE45zio2l3H6pc2z3kDiVnCQoyuoB3d6zZbnTrfUp723MxikgyUjTbsJJUc/StMR2M0K7dS+aN8q7w/dGMbemMU6SwtZXZrRonfbyyr60qibTuTSrU27FLUJIZNPsJfJRSjBB5Rz5gKjGfQ8/rWLrM011YRSSl8B/KjDNkhVHT9elXbzTLmJdkWAN+/gcgjpWJezXTsEmLMgcuoxxkgAkflUPbQ7sPZzu3Yu36Wy6XI6k+YsixhWP3h649KgtJH+0W0DRJsaQDcY1LZz0z1xT7C0vtcu7e0cSLDI2fM8skLgE/0Nat3oltpN/YurlpGlGBtAHy8kmhNrYyn7seVvUypdQQuYHjgBZ8bxEA3X1qhaxssks2xWDcjvjmtBjpUrsYrZw6jd5jkrg/rmq1nD9kaSG5EQb5lw59sA/rQpc61ITUUyn80hZhGeSeacbVhHu8yNG643c1fNnNaQZbyQpG/7/JHsKrWfk3khSRH3OwAwenPetbQsZxc3KyLE7ObVIwu0BDkjjjritz4eIYNQ1K/ZsraWErr6A8D+hrOlVbrUm2r+8U7FRemAMf1rd0uAad4Q8T3Y4MqRwDOOpzkD/voVCXUbbtY4B5GLAbjj3NaEShrQjzXxuyqbiV+uPWqUKmWRY1B3b8k46CrzAb1OM4yMjgcetS9Qk2yJsQIicMwOeRxUYkMOcqpOPur2prPIzMVGSTwOuKYpYBiq5OBuOPyrOxCJ7GNpLpY5XG4ngs2K6C3uXW4kh8tfNjThjyBz2rEsGklO7y1IXILd+lWW5jkHO5lIHzd65ay5pWZD3Kur5EqxpIJFAwwDZIPevqn4SoyfC/RVYYISXjGP+Wr1836FpUUN1MlyZA4QYI4DZ6/lX038OfLHgLTBEu1MSYH/bR60o1U5+zXRFQetjqaKKK6zQK+dfiheyJ481hFjhHlmLDuuSMwxnIJ4r6Krwn4h2cc3jnUmdAwbyuMf9MkpNXEzzzQrywGrPNdyMjFQVVYs+Y+CMHsK04Z7z7V5yz75V2kfKAcKcip49LijlDRQoreoXmtKGzK88ZpSXMrF058krmNNqfiKect5sgfcSHVMEfQmtrUo4dUisZJ03TRwbJd2OT1/wAanEKhcnk08ouOQAKo1rYiVVJWSt2MuO1t4VVI4gFHYZqZLYZwkZFXFNuhJaVF9t1PF3bIcKxb/dUn+lI5yn/ZsRVjJHGSe5FJ9hUx7YyqJ3CjFXWvlAwsUhHuAP5mmi844hA/33H9KAKWjaZHp13JJKd6FmaMAdN1aOowx3flmIFGU8nbk4qs11Ox6xL/ALqk0xpJHHzTv+BA/kKYhxtUiXO3ntmmNbxK4eSRc9hnioysbcHc/wDvMTShI0O4Iqke2KAK4ZIpp98iFWIMYA59/wBapX2my3mCpWMFWU89j3/DtXRQaVeXaLLFbuUYZDngH8TUh0G4/juraP1Bk5/SpbXUL2MRYAiqGwxHenM6Qru2Z+gz+laD2Qjdk3o4U43r0NQXNwtjA0mFGMnewyBx3xzVwjGT1dkVGzdmyjbyPOWjcP5kbEMGXHuKtJbySOIo4yWcgADuazV1S5u3u7iKwR/3RSOfJBYA5HP+etaOhX9zLZxXEimOWJ8K2eW29G6cGqmknZbBUir6FfXob+xW3sJtO+yyXMmxJznLAY6kH68f0qumiXX2qKSTUPMy27ywWO3aemDXX3ur6DqlmlrNdXgvkk3pI0I4bBGB+vpWPpmmxw6sLq5leZGPKdPX/GldMpOSXvF+RXt4AAu9zyfaqWu6nNpFrFNbfLKjhTnng9eO/StS+urSwcpbsEVUXayjcxA4Jx79D9KzL2ODUixdRtfnAGKHy8q7kN9LD9NvTfWUE8mN7gluc85rSgy04zzjmqFrbxW0axoeFGBmrsZ2nINSIuxuDKzegrF1KaQMgVXKnO4qOnH+fyrTyCNwxnvUEi7qFoNOzuQ6RPJLau0saq/HSPbxj+daBf8Ac/Q1VjwvFSFsjGfxoAfIwKKailPIb1FDkAYLCoS6kZzTEKcLL7Go3GGI9KRiM5JqMzxDILjPfmgCWNgylcH24pMblPByPampCzRG5SeAQISJAcls8Ywegpn2mAj/AFgoAcxyN2CGFZupayVibSVt0Y3imIzDrHkZP8hWoiedYTXisvkwsFY55yemB1rF+yhZWnZy/wC8LqVXoD2NJsaTauP023urYztPIrebJuAB+77Cp720jvIDHMgdeuKje9RV4Vj9arSX8rcKcc9cUWBOxYGtalaW9rZTXz2tqu5WlAEhcnscnoBj8zXOroqpqC3D3RYSSnZhMc9s9a0bq7imj8uaHzGBBGTgZp8Vhe6lHa28NrOx3q0awDeG5PU5HT17UNMqM2tiYvLZKzqyuwQgKRyQOccVjzCNdZTzJRFbFMlTIAGY5yeuetdNd2Vppl9dRPG8vkZi+0yRnET4BIBGRnkcZrF1K6ju44kmilNwN3mLKg2gZ+Ug9c461ootK5hK0rrYbHHp6xtCAjozAkZ37j2Aqzp4trGS5X5Y4zhlG0gKOhyfy/OsG2jtrOWSWW3dzt/dmI7SjdiCPxqVL21guBNbA7jz++U5DE89OMcfrU1ajtZImhQ5W25XNSDVLO7vr63ugfJSMNEVf75BHTA6GlvJZtQCfY9NEkwjAMu0bGwcfKvfjP5Vz/2oqZpU2iWV3DkL/CcdPSobG71OxRUtYhNEMkJJEGxn0JBxUK9zq9x7ux0C614k06xhQWAaIFgVFrjoOOV+tQXGp3+r+dPLZrC1v8zFE2qVIx0PccdPWs8eI76BZMaakcx6SKihlPqMKKqalrE9zMm+aeT91tPzuwwccfMT6dqoOSHSRHchfsyptUEnB+Xn86hupZZ2ZvLY7l2NxkgDv+dJ9oXIP/oQzV600/VdRj8yy0+6mTON0cDMufqKmKaMoR5dyu80d1NEfKWNYI0Vl/v464/rQk8JvTNbwhFCZIxgZArYXwp4pZedGmx6lBn+dZ97putadEwu9LmjjHVmgbH5g1VmXdGat35V0HBkDljuZMf59a66aRovhbHkgvfXxJJA5C8f+yCsCz0ye6sHvZhFbWa5CyOv+sb0UZ5+tdN42YW3hvQLVYgjeWZiifw5GT+poVyeZN2RwTt5TnaccdRUlqiS7iD8xOODziomuFb+AH6iupez02KJRLDOt1tUNsYKpGPpUPRXY2ZenWkDzMJod8aIdzAkAmte6itdQtX2xrFMQFXAzkLgcmpLe1sbaxmw8kchUSHLZGMD+dYrS+VKp+YE4J55rzJTlOo5Rexi32LX2GKDTfLiYyTlwW2joOmB9K1bVbGwsiq7HY/M0kg+bPT8KyICXuFjL8E7cr29P1qa8P2ZdjoWbHOeBWc1KWje4tSaW7l/hAZV6g8ivo34XSNN8OdKdup87p/12evmCEyLGsQUEOd555r6c+FWf+Fa6RkYP77j/ts9dOFgozfoXBanZUUUV6BqFeEfELUlg8e6lCY8keVzz/zyT2r3evnr4mOi/EHVNzKD+66n/pklAmZP9qMPu7B/wEn+tNOoSn/lq/8AwEAf0NZn2iEcmRfzpjajbr/Hn6CnYRqfa3PVnYe7H+lCylm+4Cffn+dZseoQyZwSAO7cVMNShiPCkj+9SGa0TOgPbPpxThKXbAOR69qxjqvmKQYyF7DPWmLqcuNpVQvsKLAbe/j8cVIAxrEN7I23AC+uCOacbqRiMuPz/wDrUWA2wuCNxFIyq44k2gdcGsRbmVizBjgn0PP6U4E45z15+U0AbcaW8fPm5P8AvU9pLcMO57f5xWGCMdOfp/8AXprPyTyD6ZGKdhGpquqLbQgCV2VYS5QMcgcAAc+/6ViaHd3E0b3F3I5UnYNzcDnOf1xVKPS72/1P7PbfPNMdqBpQM/iRgV2978MtX+wJdz6hZIWw1xAzGNFA7Bun1yO1RKDa0E3pYx59SkgCqjDHOeKo3OsyeWQ4DLkEgqCCPyqlZ2zoGQupUOcbSTxn1q4bVSPm5B9aqMbKwLQg07WmQBZxiFd3IXjGSelWYdX+0qxizySCpXAHuMU2O1XpjAqdYNowpbHoKoHq7lYXkNqoY3Eoui+1V2grtx1z61cGpt5iBpXWPIDMvUD1qnJpjSzq5WQ46ACrCWsyAMYCQDkg8Z9qlKxtUq86SfQ6vxT/AGbpmkRpKzz6hqKoRM27jAAyMgeoPHTPpXOWW6MKnmHgdzVnxL4qvPF89pEulrbpaSEqwbPse3sPyotbKRRyACe+c09DJslkuio2p19c0RyzEDMmBn1qVLEd8H86tJbog42A+pFAEIY8ETHI5yCalzHIAWkZSOvJ5/WpUKoP9ZH/AN8inblP/LQH6KP8KQFRrWaUHyVlYHjK8j86eNkYAuJAjAYwvJP5VNI645kYDPYUwIvJy/P0pgN+026KRDD5rHg+YcfoKoSSqgaSUbUXJYg4wO9X2xnh3B+tZ988Ygl37nBUjazdePrTAt6lpVvpixmFJ/8ASszFnlDAHj8vpWdsP9wfiarW2urqEm2SR3QKCE+6Ij3Uc8jgdeeatmW3PJHX/aovcHoyraanqMV1caaXi+xnkARgkbhuxn/9dWyg25GM1TEVik3mC2+c981cjaLBYIpXvk9P0oEyxpl1aW17LHeWsc9rJCfMGG3LgjkEdPrVHRrRfB94ZL+Bbm2vHZVjJz5sRVTlCeQQSBn2NLJdWiT26Topt3lVZgGIynccdqS+14a1qMkUlrAYbclbcquBs4x1/Cla5S2CWS3mnlNorLAWOxJCCyj0OKqzRhfmB49Kkm1CGwhMkkaKq8DauT9BU/2/dHuCjH+7TIHWWkQ6hHIr3aq6/fiA+Zf84rWjtY/DWnC8W0lQbSEeEs0zdj93oK4+512wW6L3EADjA8wR+nrS3Ou3UmmnUhLB9mD7VQkhyM4zkdPpXOq8uZx5GXKiuVNSLcvimGOH7PDDe28BfzHTBAJ/vcjqPasi4vLO7meSC6Yv1ZZARj6UjeKomT5U2HsftJx+tRTeIrSWFUeIlgQSxudwP4Vp7WXWL/Az5OzJXgkEQk25U9wen1qAxsf+WbH8Knl1SW605Ll5US2Mgj2Fssc9TnpT4LxjCgCR4AwMtmrUr9CkmtzPdSoJMbDH1qa2vb5rd/IkOyMAsM1pLb311ayyw2gmiQHeVb86yba4tYIZFDsySqhcDqmD0Jx0z6U2iJpPV7E1yl8rBnm3biQrBuH6dPXrWbPFMHYSRuGB5BGOabMytKgWd2jjJZQrHAzyccf5xWg13cSgyuYznqR1osWlH7KM62s3ur63tgGDTSrGPqTivYbDQdT0fZDoF7EtsJcOWbleedyngkfrXE2fiPTtL8JNt8mXU5Z94ULh4sHCsCRwRjNWrX4jQ6wsGm61psFwJJAqTthME8ZPp+BqtEDTZu+KNW8djV500nT0FmuBHKNrs49cE4H0xXJ3GofEhm/ewXJXuFt0P8hXVa9c6zoKS3yXEctmWDrFPF5YEeOit0zzjHOaLrxEltZrdS2t35RAJKorYz6gNmjcOVvZHGRDXL9ltdV0meNGIHmx2+wKSepBG38eK6Xx3oF/qLIbJt8cEIj8rzNpOO+OhqTSvGGn6lq9rZxrch5ZABvi2jjn19qz9c1XVH1bUZEuUjsYZPJSPA3Oe5xjJ71EpKKbIUdbo85FvMHYeU+UPzcdK2Uu5Li2Wa4KuxYnPTjp3+lTGZouUx6n5aqCOQ7gCsgck7cYNcUq3tE0VzaWLjSnyQzT58wBCAew6VnXDv5yF2BOcUsj+SQojZTjlD1zVW5m8wqWXBHaopw1FFGuigSRtG+WYcHOM1HetISFMwIU9M81QgeZ8mKJmKjjYDwPWhYZ55A6Au2M4zR7P3rtisaKGPcoYsruMg46ivpz4WHd8N9JOc/64Z/7bPXzXaSxGPyrldkcbeYQw5JIA4z7V9I/CchvhppLAsQTOfm6/wCvkq8P8bKhudpRRRXYaBXy/wDFt8fE/WBn/nj/AOiY6+oK+Yfi1ErfE7WGLqCfJ4J/6YpQBxO/ik345JNTRwozAGVfwzVhbCDOWnY/RaY1YrQBpG46VofM4CkjA6CnxpaRY2o5+tWElgx/qh+OaA32KgWQk4zjtgVYgEqZO3J7cipRPHn5YB+tSJdkttWA59QlIQ3dKRhEBbPTzP8ACnxwXAB4Qk9yxJ/nUiTygACPp9Kk8+fHpn/aH+FADUtJiP4P1qdbGU4+7/3yKjM8397/AMf/AMKeJpCPvrn/AIEaBMn+yeWAXdVHvtFSra8ZDDB5zuH+FVd82Mt0HJ/dn+tNkuC4xnK+4AoQmyaWx8w8ygD03VfMsb6c8c3zBAsRkLFhz068Z6VnafZzapex2luEEjnjccAD1ruLPwIotjHdXDtuIJCjaOPzq9SU11OLjt7WMYDg/hU6tar/AAgn6V1F38PnxusbvJx92XufqK5u+0XUdKJ+1W0ir2kXlT+IpWEmIJ4gfliJ+iinfaQBhYjmqYkYqdoJpAx/iwCKCi413IBxFj6mmefcNkBUBzjk/wD16hecOfUgelItwcMcYOaQE7ecBkyovsFpFeQpvMx+gGKga4Yj2PrQrsy7QB+VIZYMq45dycf3qaZxjATFRooJ+dcAdT0pyYwVPTsaYEqysR8q8elWUhuCA0gES46uar2szQyBlGaLy/gWbfJKgZuQjNlvwFIcYuTtHctf6OD95pmHp8q/41MssrJ8hTZnlQg/X1qiquD/AKsnd2NTwvMyY8ogDqM4zTEx/kiQ4BXcewWs67tRG5RsHdnt0rUWJpCGf5QOmDzQ9vHIw3BnbPBJoA5n7KluXfZnAzhRyaLC7h1C186NWVSSCrdQRW5qFjBJp81udsYm+TJBPOCAOMnGSKW38Laf4csYoYLs3M8mGmJTAzjt/hTHbS5hzPHAFaR1RCQCzHAHuTU8HlwyMqyyzea5AyFCDA/hPXB9xU2twRzWPllQQzAMMdq5+3tdLOPMuLkEKFMkc5LZ9cD+VTKVi4QUka08MdxERjIb9Kp21kbORjlyOoH+Famk3dpcmS1iBYWrEIx6snbOe/OD71qT2dvfbIrhmS3zmV1YLtXB7noOlF9CeVKVpPQ4bWNQ86G3eJJIv4xu4JyDkEdsf1rYsxIbGEyIyuY13A/SpNU0e3MGzTDK9tn5Gdt2cDHB/wAa07JSLdd0Ijz1UdBSi76l1XGyjE5+502OV9xQHPXiqcmkxY4jXHXpXZ/ZoSSdg5qF9OiPQ/nVGJxculRumDEuR0NVxp8YIxFgiu0k0t1Py7CD3qF9M28tAH91OKAOW+xLtB8vFSJHtGBXQtaWoBTyWEgHTdmqUsEJyvlbT65pjHrrL6fpsS2dqysVPmN527zGB4wp6de1cvCJpN1vJE0caqSVwflPB79ck/nWsdRtW8qORAgh3BSwxnnk5/ClnvoNjAS4VRk4bJ+gFF7noxwLcU7/APAMdrcpjIIBOPbPpU8G+1lErQiULnCMTgnHFdZ4cvoIGI8iN2uIW4OGJAJBUDsePrUAtCihp7WeAM20B2GfbIotocDjZ6HJJBBMAZZNrdcMpxn61PJDawBZMIzA5Xmurkj8LCZ0uVms5oxjzIpPMU57lex+lc3qOkwf2hjT9Qju4HXcJFUrs7YIPQ1LO+jVhCOtP59yvd6hcalEkFy0ssafMqs5OBWjey61baLYols62zKcHBbI46/TAqjZxRajFfXIlaIWlsv3QPmb0+mRWhp9/dW2npHbsBuwdzru471lVqulG6MK9T3bpWKlnPc2aFZ9pLNuDA9/ekntzuW7DE+cT97+I9zVmdjNHcXFsjTqhCTs8fCsemPQ8dqyjuYcZIXqK5FzP3no2cVydHEnmREgELwVHX86bLJ5bblHzAAZxxmi5he1VIxLG0m3I8pskfWqk7FU2ktvbkj0oUbsLWY15jNKsrAAk4JI6VHLblpQFdWGOOeBS28isxWTCehJ4NNlVS4O7jODgda3WjsCNfR3ns7SeQbg5UpyMcZ7etU5ZmkvIniXAUDGRjPv+dS263MUAjLb1B3At2H+FRXZWKQTE7nYYAHAArBL322IleeQJIWjjJHOSeh9a+mfg7n/AIVVopY5J885zn/lvJXyvHKZcrI2A3THU19WfCUg/DHRyoIH77r/ANdnroox5XYuJ2tFFFdBYV8yfFi2kk+JurskZbPk8/8AbFK+m6+cfipfNb/EPVYmjIVhEBIv/XFKDow1OFSdpuxxMVjJBH5kkLDd/EVOKlVQTjpVlr3+0THFc6lJc4PCTSnaCPbpU4ktYl8tS08gBz5S9MDPXp0pm08E0ruSRJY6NdXkUstpDLNHGMu6xcL+J4qqi4u2h81mfqEVQMVpaRrFzPHNpC3axWkuGPmgMoOf55FKyaNBq7RXE6F33BZDKHj2dcFlIORzjODSkmcezaZmmNmZlKyAA4OWwTU6QHA+VAPeSuv1Xw5ZX1il3pWpWM8kMIH2cXAZnUDJI5zn2NcikTjrCnNBIojCnloPxOalBA/5bRD6JmhYmUZZVX/gFWEtmYbi4x/uUCGI4x/r3Of7kVSDOMkXTAc5IxUTy2kcximvPKIHRmC1HLPbFlWO9icHjmUGnYQskyucLvC+jNmmAZOB19BVwWTLGS3X020gsmC5Gd1MRTmW8S0na0WQXG0bdhw2Nwzj3xVrRPiJrmkadNLfs12kMqRhWG1wCGJz+Qpkk4sS89wzYVeo549MVO/2PWrICeMTQt8wzlWUjj6gjmncV0eheHviDouuW8cjyfZWd/LHm/L8+M4rV1PxHpOnh43m+0OG2siDIznBGTxXjbeFreSyitLa6CRicybpjnbuAGeOuAD2rf1ZHKW48xrsqDCGiQsXdep4GeQVb6s1NaktLoWtWlstUV5rDTltWU87XzvH06CsHyZQMsjjIyPl6it/SfD+v3DBoNLkhQ/8tLpvLX8up/KrmreH5dIs4Tc3STTMSHCjAUdgM84pNDTORZWTHUH6U9FcsQFbnqeauXK26RbppVjAPBZsU6PUNPPIvLfP/XQVBZR8p9xTbyvNTRW8j4wCPWtGN4Zv3kTpIOm5SDUgABoAofZH3ArkjPQ1a+yoF4+90zU4I9aML7UAV4oUhfls7iAOM4rTl0bw/BfpfvcMs7RiOHzThSx6nH0PHH50zToxJqNvGH2ZcHcOoA5JpPFT22n6pa2ENwWgkjWZVOAQxYlgQAO2Dz7UXGnZ6DSFRiPMD+470se3b2zUaSB0OApAqSMfL6UA3cUFfUUHA7ipBgjpSEe1AjL1QSSWpEe0svzYJ64NZunXNzKJXnZi7vklj3wB/SujaJSclQR7io5LdUOQqgHkAUW1uO+ljC1JZHtJAASdp4B61xcc9zbSnEJjyfmGN2fTrnpXpbQow5UH8KqTaZbs2TCn/fIoKjNx2Od8OIYllnKFXc4+oHf6nrWlq00/kKnkvJbFlaURthiAc7RxxnHXmtOGziiHyxqB7CrNvBvkEQGTnCj1zQS5Nu7OesdVtRcPaW0E0MDBp4YpTvcDAyMgD09O9b6DitxrONNMJS1gld+RIEAO0nrnr0rGC1MZXB9xcYppGewpxoqyRMcYpuOadRjjNIZE8aN1UE+uKzruyjdtyuqE/wB7pWowGxiWwR0GOtZt+DLaTRpt3lCACO+KBozpPAInJe11NSAxPK59+CDXMaxpdzpd6IbtkkcruBU53DPetGwsroMDLMbOXcF3xv5a5PTJHFJrWm366jE93eRzsUG0eZvbHXHHv60jvVNxa95P5aljSxHBZJ51msp3EHaPmGOKZe6tZzQ4tWnWU5DI+Ttx9ahfUYTZRxrZQW97ASxuA5zKM9Cp4J5/SopmeeFbR5w9y1wiLg9UIPOcZwOKm7R0qFSKu0ihCiyyTRsjnbknGeaW0nMME5t4jJ5jBRzg4x+ddB4XS0DXM099HblpTmN35YA8cdvrV7ToBNcSRw2tuNzNIA7diegPemc1Wo1DRnI2tlcRRPv/AHMUpG5Vyc+matNM8LhEJ2qcjB6+9bWseUhieKSIkE7kU9DWfY2EOqTTtI5hA5UK2cn3zXDiKqvrsjz6km9y7o+oRWmn3MQjDCU7iMn5mHrWDNb7bwjIRJGGT1xxVjy5rQPGwIIc/P61BICUD+ZtY9QB0Hr+dZQu5Np7maH3NsYZgqyqzKuAyjr9ay7ssJX5L/KDuq0zuuc7W75zVOVsFsA9efYV0U0+pSRLaWwkgdnVGLfdYnkUvleVJhW3nofTNU4rkwlhng989Ku/Z40VWjk8z+IA4w1XK6eoWBZCHaPP8OWYnGB7VXLEAs7ZA44PGKke2LMHjAQHO5M5IPp/Wq05CrjcAVPQU4xXQEh8M8fnf3ADkHGcV9XfCWQS/DHR3XofO/8ARz18lQh5HyhAr6y+D4I+FmjAjB/f/wDo+StIx965SWp3FFFFalBXjHjr4aXWv+OLzVotUtII5vKzE4bd8qKv/stez180fFbWtcs/iXq0VrqF3Dap5O1EchRmFCcfjmmhpXaVzRX4N3Hn721WwdQ24Ahvbj+f51ow/Cy5Tj+2baFB0EC4J+pPT8q8p/4SnXwf+Qte/QymtPRPEGpXUl19r1e8UpAXiBnIBYYzn8M8Uua50zoSSSc0zu5fhncxztBHf2WycfKrMzYAUAknH0/Oqi/B6/Chf7S044Hct/hXJ3Hi+bMUgu7tnTleSM5/HpWQviLXppZSmtXSDnAeZuh9KakZ18PKn1ud1F8JdXmENzbanp0IBJBLtk89eBXSD4faioGLvTsj/bb/AArxxNe12H5V1e8CpztExxWude1Xyku49SumGDmOW5YA8HnjFNXlsZKNldnqK+A9S/ju9PI9nb/Clm8F30FvJK91Z7UUscOc4H4V5RpPinUlu1km1C9lj8w7lMjEBf8A63X8K7lr2V0KvcyFWGCC5wRSQVItML74W6xfXb3Am0/a4BCvKcrwOOnrmsy5+E+ttI1tC+necU3584gAZx1xVLVYtcmvA+n61PFEFAEZncYx9M5rPGm+L5ZGlXV5i5GNwuHU49KdzOx6bD4H1ZIY1eazLhAGIl7457UreCdV7PaH/tr/APWrBS+vo7OCFppWmSNVkcyH5mA5/WnrPfKm97t19txP9aQWZc1HwNqi2/zG2bcwQASA5LHArIl+G/iiSZ5Db2GWYni4FOkkvbk7WvJsAhvvdCDkVyl1H4tiu5Vj1G6ePedjfaiMjPHfimmKzOlPwz8UZ4tbM/S4Wug8B+Gdf0XV7q9lggCeWbcbZlJ3Bue/sK82kPi/zCY7q9VD0BvCT+eRSQzeK7VN8c9yJBKHwZyVPXORnnNFxcp9C6gmuTjFt9miUryS4ZgfpwP1rn7nRNbuIJ0e3EjSKQZJZkz7AAHC8/WvJ2vfF94wMmoyQKONqMRWhpkWqxSBp9TuZT6NM5H86Vw5TZ1vwLrt7Ja28VsjSKWkZfPQfLjHr61mf8Ks8Qnrp8H/AIER/wCNN1+11XUIYjY3skUyEhmErKSPTI98Vz8mj+K9gU38rAc8XRyaEyjodF0a70Y3NteRrGZCssYRwwKMODx9K2QigdM1h+GtPvbGCb7e5Z3YFcvuIAFbjHtSGAAPYYpGVQOgpwGF560qjcwoAq3MNxHbvLbSMk45QqehrGg0m9u9S+2XE7h8gjcdxzx6107j5cHvT7eLgmlYZDHDs464FWguIj07U3HJ+tSsCEPuaYhbcfvDn0pJx+9NOg+8T7U2X/Wtz+FFgJ5VzacegqK3XcjjuOasnm0x/s1XtT+8I9RSQFBhtkPsasXCh4Q2ORUdwm2Y1Mnz2+PwpgUQcUxbg2t5DMH2bXGWPansOSKguYlliKuAR6UARXUi3N0mqLeSKu7yxAr/ACgD/Z/+tVyCdX4yDnnrUcGlwt8ylgRxtzx7UpjMT4A5FKKsDuSMdr1HFeQXnnCGQO0D7HHofSnmcQPHK1q05LogiDY3ksABn6kVva7Y2FiHks7JYHnk3SPGCFY4A57dh+VWo6XEYIoPSms4Vscj3oLAjgipACKqXM0MJjWWQRiVtocjIBwTz+VTM9QM8D3cDPMiPHuaMOOGJGPUetDNaSvNaXKSaZJJGYrbUbC73Ekr5i5P4HFY+rKdGgkjltIobphtVkAyc9D/ADP5V00loZUVpLe3fIPHB/p/WuZ8XWtnY2SXMMYinllC7CRjCjPb1Jpcs4/Edsqt004mdc6LqcoaSJlkjk+YIw+73xV/R/CHiHUVW4tLaDfESQgkTcO3QnPvW9Z3LCyh3oFcxgsBzg4pBeiOTzYHZJwcLIpxjPvSs29DmeIqJNX0Kdt4E1mBGc6c5ZeeGBP8+ayrt5IJUJcxSRsRtBwyn6dRXTTeKPECpLbyXRIx9/A3H23Vy2oX09+4jlAUAhtnTB9QevNc9Wco+7JfM53IZcBbnTDcGeITxcMp3b5CxOeehAFUbWRk27ciRWyrA9R1pZf3aYDFRngg5xQ5eEsrSKSejAVhN80SXruS3txLLIimQsx5yTx3rPaVhHJtAww53c1JI6HyWZgSeuewqtLJHKrgZwCSD7VVOmkJIkMTLaJLG6kZwR3H+cVVnlYRszD5TwMVOLV2tmYSsI1+ZV7Y9ao7TKqgZwTgnsK3jYofaqjsGkdFXGAdv86mQtuaIxhWPAIPektraI3e0lhCuA3P61q3GjQ29tHOZ0ct2Xgqf8BilUnFaMTZQEdyhJI+pJ7fSsxm+dwwz9K05PNjP7wmTtkdMVQaF2LELgA/lVU2+o0LBIUjDY2gdxX1h8HiD8K9FI6Hzz/5Hkr5KVG38k4HJFfWfwax/wAKn0THT9//AOj5K0S1Gju6KKKsoK8H+Ilok3jrUmIBJ8r/ANFJXvFeI/EOYL4zvlEYyPLy3c/u1oA4ptBtZRkxjPtTB4etcY2YrXifzRkjBHelndI7G5uHYK0KghM8uSQAB+efwpFRTlJJHmVwVYhZWYPENmAPQ042kpvUt7eCXzmUfI2GJyM9vat+wkthrPktpUMhmm3GSZshF6ng11SJpg1iD7agEaRN5rIvzEhfX6mko9z1JJRuuTU861WxFrKCpYAgDazAnOOenFO0+0tbqItc3Mq7Gx5aJn5cdck4HNM1BZ5dQmSVmdo3MfToFOKSPTriQfLC9El72mwRr0IRSe/kNima3kmhidhG5KjgEn0/yK7PS7oI4tZI2DbA4cvv3dj24+lc3b6RJ0kV0AI+bHT8a6nS9IYz+bLM8rYALMc4HpRFWOLE141NIqyNeNfMYDIC9zU80qxr5cRGe+DTpGit0EcIG7+VQxQiRtzDgfrVHIOgj48xh780ksjSvjsOlEsuflXp396lhQRw+YcFj/KgBjDyBjqahWMM2SOKczGSTP8AkU8/IoA60ARuB0HShY1Yc05Fy3PenPheBQBEUDHpUhQIOOtIg5zTicmgAUjOKcTmm44zQpyaAJVGKTqaceF96ZznA60APb0HSnxfez2FR1PB0zQAr/eqxGMRA496rHJbPcmrzYSD3xigCsn3ufWpnI8oYPeoV6+9OckIoOPwoAkhxkn2pspBlNNjdgT8hP402Rz5h+X9aANBBm1H+7VOE4mX3qeGb9wq7e3ciqO6QOu1Rwc8mgCS+GJAw70ls3ykU28aUrkrHx/dbNQQSyKchB+JoAWUYc1Ax+Ur3xUk7yl8+WAPZqhLE9VI7UAyzZyjcPRhilugA+R3qlbuULK2eDxxV6Z4pEyG5xnoaYkzKu7hI7d1k+4cAnnI5B496rpfXXmQobq6ks8kpFIysF465xk/nUt9Es0TJnqMVVEupTpFBctujiJKnPX8O1BcZJJ3VzZEiuox1pBjuBVeIMAKmyRQQIyR9QCB7VWub6CEJbpArTKGYysA2QeNoyOO9XQwZNpxWNrMf2R7WdUZmmBHTgbWH9DSN6ElGomzTsdRY2TK0KHzRskAA4OTyMdOPzrlfF2nR3V6YLW682GNA4DNnyycYBz7CrNrrFusitbyQAkqNvmAg56g/SsoR5mu53ff5uXYbhwQcDr/APrxW88RSlT5eptHBVqdWThK8X5lqfU2EDYUpHGACPwqTQozPrywyOGidWkI67sdB/8Aq9Kp6fbi5S4V1wRwc49eOn0FT2tnPbXCywTMk8efLzyORgisY6PmOefWKL95FuuhLLKbZVYkKOFYDtWZdW43pcvl45QQGQZGR2zVdRPI7edNkISMtzz34/rUTS4HlBCpXg45z71yYmNR6y2ewTpShBSa0ZVnXAdwQGwOBTobaeS0a6RQUi4OB096ifiQKTuU9/SrCXCCJljYiMgAjd19qxd1FJGBTnkWQncuHzgc5qCO38mZfMbfG4z8rVLNAscqyw5BzyM9hVOZZGIwByxIwK3hsMsO6IWG9vJbgKGzx/8ArqBrlgBGuSAMAY5NTx2RnhKKH+0bSdmOuP8A61XbbQpPO3wSq3lkZDHaV44Oe3/1qOaEdGBlWksn2kJtYs524A5rUkm82IbCF+nU+uahnhWMPdvcGS4LEArJ0OeTkde9VYnSORQ7FUxyMZzSajJ3XQdiK4dd6sN+AeRnimeY77gx2gmrcrRmBQHXarZAPFUjE7k4xkDOM1qgGSbkOUY4z1r60+DWP+FUaLjp+/8A/R8lfKCSZTZ5YAB5PcfSvrD4NkH4U6IRnH7/AK/9d5KtFI7qiiiqGFeAfFK6ktPHc771SEvGJC3p5S9K9/rwb4mPb/8ACY6lHcDg+Uyvn7pES0Wb2Gmr6nNtcBIBcKd6MAQV7g1SvZxdTRBOQoycfr/T86ZJOxt5IERCUiPyo+T0GMf4+oqo2qWqwlZPvNyyhSWGe3HtTtrqejl0Y+0527W7kkzqIJVWWMtIjR5DZIyMHp7GmXU9zb6BPPI+6RVSGNyc5wck/oKgiuI3ZVttOnfPRvL4/M1W1WaZgLSaIxK6PgZzk44+nIps769alaUuZOXSx0kECXI+0tGgeQlm2jvmrCQYPAFUPD1yZ9JjLdV4/QVrBh1qUeBUVpNCxwrwG6d+KuCaG3gWO3QhgMewqoXOMKMn+VJl8cBR9eaCCZCN3z5Pc1O9wDHtRdtUVVwRl8/QYqVUXqcn60xEiHnJGac0ueP0qLy1A6DFBRcj5QSKAHCeNDjIJ9uaDMzkkRsfrxSjAFOA4oAFeTHKqB7GjOTSbu1NkcRQyStnCKWOOvFICXOOKK57w9dQXl1e3Ecj+ZIQzI38I5rfzQOUXF2Y4sTSpwc5puT+NKKCSQtxSLnrnk03r1p4PFADxT1JHeoxTs0DJl5p3NMBxgVNHG0gyBxQAMV2KB1xzSIjSOFRSxPAAGSakmjOc4AGKNPvbyx1eya2t4nhnnS3eVmw8e48lfw/KmhMtJomqOflsZ/xXFD6HqcfLWMwHsua9DPh3T541NwbqUkZO+5c/wBab/wi2lrzELqJvWO5kX+tOxPMeaywywriWJ4yem9SKgB5rV8W6ld2WtLoNtbSX6GHzvMmmAaL5iM7j2wDxg1mpbSNyCAD0z1pMpO4y5cySF2XBNVfMVOWYKPc1evI22b3KgRpyevAri21axvbtrKW1t7iWSbCyzM22NOAMBSD6nPvQM6U3VuRjzo8/wC+KYzKwyrAj2NZyaK0Uoxa6GVXA5Eh9u9MutHbc5isbMDkhrO4MR59mOKLCujSRgJzk/eAqyPmWsBNSjjuFguG8mVSBiUgHP8AnmuihiDqT5iihgiCQAjGKg8pQ2QP1q/JAFH3wfeqxUZ5NAyMHBpTxmlKjOM8UrIu371AiBnCjmrKWkWt6QsfmmOSCRh8p5IPX8wT+IqtJGp6t1rAvp7nTLhp4J9sJH7wZxnkUjSna49fAEiXCJ5Zjj3cyLIrED1IzTr3wl/Z7LKl6sqRyRsfkPzdeAc1bl1mW3iiYkM0q7lSJyx7HtmsjWPELT2ctqz+Zll3gdk79gc9KV01Y09k1qmaAWMs0yFTu+9j24q6lrFPGpdthPQ1m6eFS3VVwUxxU41SJLeRJVybaTAAHUH/ADirtaNjDWUiHU9FWCOS4t5z5jEkqwHfristGtiginQLJu5kRcE8d63JdXgdAJLG9Uj1tzXO3bQz3DvAJFCjI3jB64x+ZrmxUZVIJX2KqVJ1ElJ7GbewQ2b7vOWVCrYIBBU9s5x/Wq0N5hHzGpkZtwkHYkenTHNF7GQDvIDHP51WjljgIV1BzxkHge9TD4TOw0ySAt84G5gpY9KaPM+0KASWwOnPWld4pVCxqdxOB2BNEcTeYgYhAwzuBrRJWKsWoZpTOJlOCjAcmtJb8tDMpdomZgCABtYDsfzrOlh8s7g275Rwpz+dLCoaPc6bmT5lQr8p/H8qxcVITGXRYTbWlBVhnAGPwpoa186FXXy0ON7jr3/+t+VOs7ZdRv0t5JTGZOAxHG7HTrXR6r4Zs5lLafcxLKFwYWcY3cDHtnBrWMbIDkDCTA8plQ/OBtHX649KImWQkSSHjofWnDYtsy5bfuwR2Ax+v/1qZ5EpUMqMMjrVjHyDcD8+0NgjjA49q+rPg0CPhPogPX9//wCj5K+VoLYTBA8p78HqK+rPhBH5Xwt0ZPTz+v8A13koi1ew0dxRRRVjCvnL4yXUlr4n1aaFBvRoAWZAR/q045r6Nr54+LVoL/xfq1u8jIjeScjtiNaL2A8t0LVppNR8qdi/mgjOenBOK7HwXoayx3jSSo2yQJiQ4xx19a5az0OC2WG7EkssiMCFC4HXmtGTVJ9IvzcxhQskRCo2SCR3wD1pSvujSmnO8U9T0Y2NnbqTNdJGqjjjH6muS8YpZDVLB4CX+Ut8w+8MjGP1/Suetr+5u9JW0laIpcT7TOxzIgyMj3BJFUFvbu7khWV3laP5FZmJwvTAqfeNaNFRnectjoNHZorfyxgRgnYB6ZrZjkJIArMs4SkajGWrThTbgd+9Wc83eTZZQ46fnTucU1QaeBkUyBFOTk1MCOtRYAqQUCHHmkAxzUF1dxWkJllLYBwAqkkn2AqW1njubZLmM5jddykjFAyYjHHems2Fo3ZGfWmE5pCFHWknUNE6E9VI4oBxSb+CxoGmcb4UmS01GWzkgIuCWG8nBx/dwfpXbZ44riL6VJPFNvewZEAnjjeUcAt35+ldiZN6blb5fUd6bVipy5ncsDliT0p1NXhAO55NL2pEjgacvrUQPOM8+lSqOOaAHd6eKYOlLnigRKvJzUquyqAGIGagU47VqanF5U8EW1QVt4920Ac7cn+dD0GjPllYj7x/OtDSVhZ9NlmkCRR3MtxIzdAEQYP5msmVuDW4li7+CftMEcjyEMhCrnhpVLH8kFUiZHoSeJNEcfLqlp+MoH86cviHRmQN/aloARnmZf8AGvGAqZwG+b071Gdqg7iAQOBVWRB1HiaSC48YG9trhJIJNNlG9DuBKlieR6bhXNRXLNzk/nWxZWZk8NzXsgZBClzGuV6hlQ5H4g/nXMwPhaTRUTTvZytrC+f4TmuGvMEpcouCZ3z/AOOkfzrq71ybCPHYmuRu5MzNCGw3nq232ZQD/wCg0kU9jdbUGJ6mgagQepqltAUZpNozjOK3MRCtvJqsk86KwZVzuHQdP5108Eo2+1cfOwguVZjlWRlOfUcj+VbFlcyHBzlMVjJamkTpVO+Fl7ryKrt1otZPmXPQ8USLhyD24qEWMPI4oU0lN4HHNMQrjNYmvlYtPMhjRiGGC/3VOeprZZuKx9ZVZLR43G9D1XOOM0Ac/pviG60aFhDFFJJIxLPJk8ZPA54H5VJFqf8AbOqNJd2yCZ1KmRMgnHrUZsbO0vG8+7uYoZFDRSRJu3r15p0MFtCWutOnnudpAcvHt25P/wBahrWxaXu83Q29OjNuWg6hOVJ/unpVJrWWTVIYDhfPnXDHOOTjmp7o/a7QSwthgMrtPUelW7aRLm60+Uq+5pkb5RkBgRnNRdha6uV9U16Kz1G8s3tplaORo35BwQT0yOhrnHBmuJZgCu9y2Pqa7fxVosetW76pCdt5ari4VVyZYhwHx6jofbBrz/7TNbTyIFLoGOG29qU9VYyrOpOCSJ7mL92XfG0ggZOMnFT6zoZ0oW0cxMrvErrgYCk9VrR0vF0IYHXaHdWTI65PIrR8aT7dTSFU3AjselRypRKimkrnESWksSyhLcsAclgPuj/IqtNG+0NuJ47dvauglv8AyQRJbs0bcHgjIxgj86wJJcyAAbR2z/KhNNFOwsDPs2eYVJxge1T+fJHFtU55496jctPO0shUNxkqOKSNgbhUJ/dngnHApNCJJU8yJ3Vl+TjGDluetMheFHPznkdQevtV0svlPjLnICkDrWWWQE/ugCTwR2ojJsCaRfOeSVAF2jcQD7gf1pircMVUBgC3BI4P0/OrUMUTaW3LedLKiBFXomSSc+ucV0thYfbpo5ntl8uHK4d/kyMAAYzlVA69zWiimgOekiaK13sUGQRu6kEdiByOvevpz4NMX+FGiMxyf3+f+/8AJXz1fX9uupPjaqJE0Y8lQVVvbOfz+lfRfwlk834ZaRJgDcZzgHP/AC3koiknoCZ2tFFFWUFeF/EW3EvjfUDsB/1WT6/ulr3SvE/HrAeNtQGRn93x/wBs1ppXZMnZHEPasTzgAVg6hZm5vhGg+WIZb6np+ldXdSrFAzN0AyfpVG1tTHbNJIMSzEs3tn/AU5iizlGtLiPZHEjECTcMDoTjn9BWnYaSyEO4561trAN2ccmp1j21JRHHCEGB1qeMeuM0AZ+lPAA6UAKMdO9KCegoxxTl4FMQnP1p2e1NJ96cCBgnn2oA0LaxgurJonbFxKDsI5KAd8e+amvre0i09I4VSN7bbHjpvGP6YrCXXo9ONxtKTXaqcKAQRxn6dB1zUUfiGy1O0tS0mb6XLsm3AQcjGe/amkrBfoXC3FN3ZOKiEgboakB+tSA4noBVTUYftVlJb7nQvjBQ4PUZH0IyKtqO9Nxkk4oEUhYxCFIREgjT7qlQcVejUBcHmm44qRBxQMlz3pe2KaOlOHXmgAUAHgdetSA8VGPvU/PFADgad3pi9af0oEWbSLzryCEf8tJFT8zip9U1eCXXLpDkEg7Px5FLooxqPnY/1ETyj6hTj9SKy9QhR1mkXhhIMP3wDn+QpSa2ZcFd2EkcEV2Hhzxlp+ly2+hXCmO5kjR7ctwsoYcgHsdwPX1riXb5DXUaHeaRqGm29pf2iTyQj5WZOUI44bsePWqROl3dHdyahZyczWKO3uFY/rUa3enKd6aYgb2RAay5jp90oEu449aSI6fbhtjMN3oP5VpaNr9TC7uZXinxjpl27aJAwa8KsJY158pQuTuI4BzgY9686hb5RzXS69JoGk2tzHp1vHHdy5Msmd0jZ4GT7k1zEGdhYkDAyazbNrLoWrnJ036PXHfK+tSea20IOM98KMfzrspQW06UDswNcFquXvSFdFOSDvOBwBxQmVFJu0nZHQglsYGadK28DEKofVc1zFmLRbyEXE8tvCTib5dxX3HHNat//Ysthm11uRZ4o2CJ5ZXec5G5v610QXMrmM7RlZaiaqpWyZ8/dIP64NbOnS5hH+zXATSbXQi4MxPU5PH512umENFtHfkVjJ3NXBRSs7nRwPletW5juCuP4hzWdCfl61fT57U+qHNQBFnrTGJ3A9qceDTG+7TEBOQaz7yMyRsMZ4rVhtLi7gne1heeSFQ3lJyzc44rKuNP19/EFraR2z28MsDy4bbuYDhhj8R+dOwilHcWVotil9G9woDPhRyASRtPtx+lX5b3SzafZvICQ7cxhQoK56EVhato7vceahdX+6RnFZv9i3JblnP/AAI1anHeS1RLjK1ovQ24JICCtuHyPvFhhSfb0FOgEsNzmOUpIG3qnZx3H1qxZWRNkPlAePg+49amlt/MhDIv72M5U1lJqUrmkbpWN+yupNsN/ACmSd0TDOD0Kn1BH6GsnW9CSxuTc2sQe1uMvHublD3Q8dv5Yq1pF2jjaTiGQYYd1P8A9atjclxFPYyttimHySHkRuB8r/T19qLCuzltDf8A4n1rEI4w+1i6EBsDH6EVW19odQ1WW2ikxIsgQhicA461Y8M2dxpmuXUF/HsuIlYOxPVs44PpzmrFzqej6bZXEr2Aea6YmS4Z8FuMALz29qTjpYd7s4e+tbmxkEdxbmNyN3XIx+FV4Vt7h0hlYIc4DkD5SccnpxSavf3t86iSGXyowRGTHzj1JxzVaKyE3WZCD/Fk1EaVthqMepoJpwn81V2sYMFtpyG+h7097AwCC5ONhkxJGOGTBxn6U2zt44wI5J1ZX4K4GMdc59R1rR1bSNLhtEa0vQ104B2Rn5WHf6U/Z2NIqn3MGW2mS4mWQMWLZwevr/KtJrOxi0yS7UN5jKDFlshT6Yp9ja3EFsJ03F+c7kyMemevas6dWQDcCu92bbnIxnjH60ctiGtSOR8qi7dqMeueCf8AJq1BLcR2a/6S8cRcrlJASFI5G306Hr3rR06yjv5rYzMqQQDKKwHztnOD7cVfvb3SEshDHaxSqHG9B8u3nrnvyaSsiXoctO0cHyx73UjPK4zX1D8Gzu+FOinGP9fx/wBt5K+XtR3TXbOsm4btu7+n4V9R/B1Snwq0VT1Hn/8Ao+SnFaglqdzRRRVlBXhHj5H/AOFg6q5Py/ucf9+kr3evCPiTOLbxlqTnBz5QC9yfKTAqo7ikctcN5tzHbg5A+dz7DoKsEs7bVBb0A5qKztZApeQbriY5IH8q0mZbJDFHgyt99x29hUt3YLQqY28Y5pDSk0gzjjrQIeBgAU5Rmoim5ged3YipVBUfe5pgOPBxQSAPakOc9ajO9jweKAFHJyaHf5cZpOR1qCVjikBW1jUobKJ7iF1EpXD27AFZGHAIwPQ965mwEsmpS3GAgkJfYOi5OcCprto5b6VnOWDkevGBj+tX7IwoBhXPuENFwNa3B2jIzVxc8YqrFNGFGAw/CpxcRAc7gT3INAE4PNKcdKh+0Jj5MsfYUgllbOISv+8aBE3FSDpxiqyicn70Y/DNP8uUnJmIHsooGTnNOHAqt5Umcids+4FPAmAA3ofqKAJ14p1QAzZ/gPtzSiVl+/Gw/wB3mgROppd3NQrcRngtg+/FODrj7woGbWl5i06+uOmdkQJ78lz+ifrWLpenavq+vSWoWP8As/yXbey8A4459cmtS7n/ALP8Ipcs7bWMkgXPHYDj3w1XvBN/L/wiNs5QCS6kd9x5JAYgZ/AUWuaxVoOfyOZeIxKYifmQ7SCckEetYt3p13JKWtLmaFicny3K/jxWndlrTxZqdm3KySeajeuRkj8M/pWpawAQtIw5PSmyZpJ6M4h73xLbOyJq15gHAzIW/nUbar4okO06tdc+jYrrrqxBYsnUnODWa1gzXUUgfaUzlD0IoRFjKtLbULmRXvbuafacgSOW5r0Kw8EQXlirXV28Uh6xiPp+vPUVzw2xRl/7ozkda2LfV7mPwre6vYss0zQvJtB6PnnI7HqfemknowTad0JqlnYwWTx2kpkYDLHII4JHb6V5vrdhvcyKoOeSCK7rT72TUtEhnu8i8eA+Yp9ATg/jyfxrHuIFdelJqwXfU8/8mSPgREf7rkU1lkPBWb/v5XZ/2dGx5WnLpSGVPk4JouBxkFh5koDIw57tXXWOY51XoCuKtPpipLlY/lHIOabIhjaMtjgnBFCdxM1Yjgdea0LQgOVzw4xWF9rkUqRESD3q7DdPhZBG3qOaGCLpBBZSOhqNsYpbiZmfesL4YZ5qo0s5z+7UfU0AyzaTyRanZLFO8LSXCR7l7gnkH2xXXX90bZTIspDKpG7qcdx7V5vdl3dFn+WLcCxR8EfQ9jTTqV1cXk/mai9zDFj7OGXlgT/ERxu+tWnZAot6o1bk/aZJJW6scmqMZZVdpgBtapre4kL7TGuH4OWrM1ZJ4Z9rZAI4IPBqGNG5ayICsikFGHPuKfcRmCQgfdPI+lchDdXFs37uRlHpnium0y+/tOxeKTH2iHn/AHlqWhldHNrdk/wzHj2b/wCvXQW063EJBP7yMdM/w/8A1v5VjSwrPbtERh15VsdPQ1HZXDrKjsuLiM856Z/+v0ppiZ0mpFptGmu0i33MEWGdT8zIvt3xn8q8jNuLnci4j53ENn5j/nvXrcdwLa0hltSFzJuCkZ2+qn/PSuK8U6PBY3cd3aD/AEG8y0W3kRt3T8O3tT3EiHTLK91eYWdhFK87LghRkD3J7D613ug+F7XSPCcuna/Hb3MErNKcRkGP8evYciqfhTxno+h6abSR4YWLEpxtYj0Y9znNWdS8Uz6tFJHYskSSArvZeSORkYNDdhNnmuvrBoepS22nzW1xbSoCkkYG4Kexx3qla3ds+IpDIXz+7KAZ56jn36e/1rrdG8JWkGpmW/hiktNhCRJk5J/vZPb61VuvBFql3Mymdo2ctGwcDaMnjGPpzmpcrK5cfedi74a15LGK/wBRSe4kJiMaWoBCbsYyRyB24/Guf+yRyTJI8BAXAYZ4Jx/+uta6tJI1lkY4VMSFMY56ZFZ6aHqOtWRazdIlV85eTbkgY49etDlFpNGs4RglZ3ZYTYo8tfk/3RiqS6QztsSVHbGVMnc1s6jp/wDZdyts1wkxKBsjrj3qsoUD5cAUpRUkZ2EtvDdvPao1z5qTchwrdDzX0L8L7M2Hw60u2L79hmw3qDM5/rXh1lfHcFc/OOh7OPQ+9e++AnWTwXp7qCAfM4Pb941EUhWszo6KKKoYVn3Wg6Pe3JubvSbG4nPWWW3R26Y6kZ6CtCigDPXQdGRtyaTYKfUWyD+lNPh3QyedG0//AMBU/wAK0qKAMz/hHND/AOgNp3/gKn+FH/CO6H/0BtO/8BU/wrTooAzP+Ed0Mf8AMG07/wABU/wpf+Ed0T/oD6f/AOAqf4VpUUAZv/CO6J/0B9P/APAVP8KP+Ed0P/oDaf8A+Aqf4VpUUAZv/CO6H/0BtP8A/AVP8Ka3hnQWGG0TTSPe0j/wrUooAyB4V8OqcjQNLB9RZx/4U8eGtBHTRNNH/bqn+FalFAGZ/wAI5of/AEBtO/8AAVP8KX/hHdEP/MG0/wD8BU/wrSooAzf+Ed0T/oD6f/4DJ/hR/wAI7on/AEB9P/8AAVP8K0qKAM3/AIR3RP8AoD6f/wCAyf4Uf8I9ov8A0B9P/wDAZP8ACtKigDN/4R7Rf+gPp/8A4DJ/hS/8I/ov/QIsP/AZP8K0aKAM7/hH9F/6BFh/4DJ/hR/YGjf9Aiw/8Bk/wrRooAzj4f0U9dIsD/27J/hSDw7oY6aNp4/7dU/wrSooAz5tB0e4gWCbSbGSFRgRvboVA9MEU6HRtLt4kig02zijQYREgVQo9AAOKvUUBfoZr+HdEkl819G09pP75tUJ/PFPGh6SBgaXZY9Ps6f4VfooAzzoWjnrpVif+3dP8KafD2ik5Oj6fn1+zJ/hWlRQBnDw/ooORpFgCP8Ap2T/AAog0DRrVZVt9IsIlm4kEdsi7/rgc/jWjRQBn/2Do2FH9k2GFXYB9mThfTp0pn/CN6F/0BdO/wDAVP8ACtOigDL/AOEa0H/oCab/AOAqf4Uo8OaGOmjacP8At1T/AArTooAzf+Ee0Q9dH0//AMBk/wAKYfDOgN10PTT9bSP/AArVooAyx4b0IDA0XTgB6Wqf4U7/AIR7RP8AoD6f/wCAyf4VpUUAZ39gaMQAdIsMDp/oyf4Un/CO6J/0BtP/APAVP8K0qKAMl/C/h+UYk0LTHHo1pGf6U1PCXhuPOzw9pK5xnFlGP6VsUUAZY8NaCpyuiaaD7Wqf4USeGtBmAEuiaa4HTdaIcfpWpRQBj/8ACJeG/wDoXtJ/8Ao/8KdF4X8PQvvi0LTI2xjKWkYOPyrWooAzP+Ec0POf7G07/wABU/wpP+Ea0Hdu/sTTdx7/AGSPP8q1KKAM3/hHtE/6A+n/APgMn+FEnh3RJYfJk0bT3i3btjWqFc+uMda0qKAMRvBnhZ23N4a0csO5sYif/QamTwzoEYATQ9NUDoFtIxj9K1aKAM4aBow6aRYD/t2T/ClOg6Oeuk2B/wC3ZP8ACtCigLGW/hvQpARJounMCMHdaof6UR+GtCiQJHommoo6KtqgA/StSigDMPhzQ2OTo2nH62qf4Uz/AIRbw9/0AdL/APAOP/CtaigDJHhbw8Omg6X/AOAcf+FaNvbwWkCwW0McMK52xxqFUc54A96looAKKKKAP//Z",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_dir = \"./data\"\n",
    "os.makedirs(data_dir, exist_ok=True)\n",
    "img_path = os.path.join(data_dir, \"visdrone_img.jpg\")\n",
    "if not os.path.isfile(img_path):\n",
    "    url = \"https://data.kitware.com/api/v1/item/623880f14acac99f429fe3ca/download\"\n",
    "    _ = urllib.request.urlretrieve(url, img_path)  # noqa: S310\n",
    "\n",
    "img_pil = Image.open(img_path)\n",
    "img_nd_bgr = np.asarray(img_pil)[\n",
    "    :,\n",
    "    :,\n",
    "    ::-1,\n",
    "]  # tip o' the hat to https://stackoverflow.com/questions/4661557/pil-rotate-image-colors-bgr-rgb\n",
    "plt.figure()\n",
    "plt.axis(\"off\")\n",
    "print(img_nd_bgr.shape)\n",
    "_ = plt.imshow(img_nd_bgr[:, :, ::-1])  # explicitly changing BGR to RGB for imshow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe2e2a3-a3a2-4101-9872-bae618effab0",
   "metadata": {},
   "source": [
    "# NRTK Random Translation perturbation: examples and guidance\n",
    "\n",
    "The Random Translation perturbation is by a random sampling from 0 (no translation) to a `max_translation_limit` in both the x and y dimensions. For the purposes of this notebook we will keep the limit the same in both the x and y dimensions\n",
    "\n",
    "- `max_translation_limit == (0.0, 0.0)` (the minimum value): returns the original image. No pertubation in either direction\n",
    "- `0.0 < max_translation_limit <= (img_height, img_width)`: Allows translation of between 0 and max_translation_limit in either direction.\n",
    "- `max_translation_limit > (img_height, img_width)`: Throws an error as it is possible to completely remove the original image via translation\n",
    "  \n",
    "`max_translation_limit` is a metadata parameters included in the `perturb` call of the augmentation as opposed to an instance level parameter of the `RandomTranslationPerturber` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a370d2a-2489-4c48-b29a-ea8ad3ef98fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 4, figsize=(10, 4))\n",
    "for idx, limit in enumerate([0, 50, 100, 200, 250, 300, 400, 500]):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    bp = RandomTranslationPerturber(seed=2, color_fill=(0, 0, 0))\n",
    "    ax[row, col].set_title(f\"max_translation_limit: {limit}\")\n",
    "    ax[row, col].imshow(bp(img_nd_bgr, additional_params={\"max_translation_limit\": (limit, limit)})[0][:, :, ::-1])\n",
    "    _ = ax[row, col].axis(\"off\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30eb653b-6be9-4d58-8d69-db4b64c3d81c",
   "metadata": {},
   "source": [
    "# Baseline detections\n",
    "\n",
    "In the next cell, we'll download a [YOLOv11](https://docs.ultralytics.com/models/yolo11/) model, compute object detections on the source image, and display the results. As discussed above, these detections will serve as the \"ground truth\" for our relative mAP evaluation later.\n",
    "\n",
    "*Note that here, we're using YOLO's built-in visualization tool, which automatically adjusts for BGR / RGB order.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55600de0-089f-4dd6-ac4d-7896b8df5494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics 8.3.85 🚀 Python-3.9.21 torch-2.6.0+cu124 CUDA:0 (Quadro RTX 5000, 15929MiB)\n",
      "Setup complete ✅ (40 CPUs, 31.0 GB RAM, 393.9/914.7 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 58.5ms\n",
      "Speed: 7.1ms preprocess, 58.5ms inference, 230.4ms postprocess per image at shape (1, 3, 384, 640)\n"
     ]
    }
   ],
   "source": [
    "# Import YOLO support\n",
    "import ultralytics\n",
    "\n",
    "ultralytics.checks()\n",
    "print(\"Downloading model...\")\n",
    "model = ultralytics.YOLO(\"yolo11n.pt\")\n",
    "print(\"Computing baseline...\")\n",
    "baseline = model(img_nd_bgr)\n",
    "baseline[0].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39050929-bf48-44b1-9247-5cac7f44cd8c",
   "metadata": {},
   "source": [
    "# MAITE Evaluation workflow preparation\n",
    "\n",
    "We'll use the [MAITE Evaluation workflow](https://jatic.pages.jatic.net/cdao/maite/generated/maite.workflows.evaluate.html) to evaluate the performance of the perturbed data against our baseline detections. We'll need to \"wrap\" our model, data, and perturbations into callable objects to pass to the `maite.workflows.evaluate` function:\n",
    "\n",
    "- We'll wrap the **model** to make predictions on input data when called.\n",
    "\n",
    "- The wrapped **dataset** will return our test image when called. Note that this will be the original, unperturbed image; we'll apply our perturbations via...\n",
    "\n",
    "- ...the **augmentation** object, which applies the perturbation to the image inside the evaluation.\n",
    "\n",
    "- Finally, the **metric** object will define our precise scoring methodology.\n",
    "\n",
    "The evaluation workflow in this notebook is slightly unusual. Typical ML workflows apply many different augmentations / perturbations to much larger datasets, and only call `evaluate` once to get a statistical view of performance. But since the goal of this notebook is to drill down into how perturbation affects performance, we've essentially flipped the process, calling `evaluate` (and thus our wrapped objects) many times, once per loop on our single image perturbed to a known degree, and then observing how the metrics respond."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da908c7f-b499-4361-8e2b-cf7647b45de6",
   "metadata": {},
   "source": [
    "## Some helper classes\n",
    "\n",
    "The following cell adds two classes to allow us to use YOLO detections with the MAITE evaluation workflow:\n",
    "\n",
    "1. The `YOLODetectionTarget` helper class that stores the bounding boxes, label indices, and confidence scores for a single image's detections.\n",
    "\n",
    "2. The `MaiteYOLODetection` adapter class that conforms to the MAITE [Object Detection Dataset](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Dataset.html) protocol by providing the `__len__` and `__getitem__` methods. The returned item is a tuple of (image, `YOLODetectionTarget`, metadata-dictionary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "346f4207",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "import torch\n",
    "from maite.protocols.object_detection import DatumMetadataType\n",
    "\n",
    "from nrtk.interop.maite.interop.object_detection.dataset import JATICObjectDetectionDataset\n",
    "\n",
    "##\n",
    "## Helper class for containing the boxes, label indices, and confidence scores.\n",
    "##\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class DetectionDatumMetadata(DatumMetadataType):\n",
    "    \"\"\"Dataclass for detection datum-level metadata\"\"\"\n",
    "\n",
    "    id: int | str\n",
    "    max_translation_limit: tuple[int, int]\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class YOLODetectionTarget:\n",
    "    \"\"\"\n",
    "    A helper class to represent object detection results in the format expected by YOLO-based models.\n",
    "\n",
    "    Attributes:\n",
    "        boxes (torch.Tensor): A tensor containing the bounding boxes for detected objects in\n",
    "            [x_min, y_min, x_max, y_max] format.\n",
    "        labels (torch.Tensor): A tensor containing the class labels for the detected objects.\n",
    "            These may be floats for compatibility with specific datasets or tools.\n",
    "        scores (torch.Tensor): A tensor containing the confidence scores for the detected objects.\n",
    "    \"\"\"\n",
    "\n",
    "    boxes: torch.Tensor\n",
    "    labels: torch.Tensor\n",
    "    scores: torch.Tensor\n",
    "\n",
    "\n",
    "##\n",
    "## Prepare results for ingestion into maite dataset by puttin them into detection object\n",
    "## Images must be channel first (c, h, w) in maite dataset objects\n",
    "##\n",
    "imgs = [np.transpose(img_nd_bgr, (2, 0, 1))]\n",
    "dets = []\n",
    "metadata: list[DetectionDatumMetadata] = [{\"id\": 0, \"max_translation_limit\": (0, 0)}]\n",
    "for _detection in baseline:\n",
    "    boxes = baseline[0].boxes.xyxy.cpu()\n",
    "    labels = baseline[0].boxes.cls.cpu()  # note, these are floats, not ints\n",
    "    scores = baseline[0].boxes.conf.cpu()\n",
    "\n",
    "    dets.append(YOLODetectionTarget(boxes, labels, scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cd8fb3-2ea6-492c-ab84-f3432b9d3440",
   "metadata": {},
   "source": [
    "## (1) Wrapping the detection model\n",
    "\n",
    "The first object we'll wrap will be the detection model. The cell below defines a class adapting YOLO for the [MAITE Object Detection Model](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Model.html) protocol. The `__call__` method runs the model on images in the batch and is called by the MAITE evaluation workflow later in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1131aa7-3716-479f-838b-0464b74106e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import maite.protocols.object_detection as od\n",
    "import ultralytics.models\n",
    "from maite.protocols import ArrayLike, ModelMetadata\n",
    "\n",
    "\n",
    "class MaiteYOLODetector:\n",
    "    \"\"\"\n",
    "    A wrapper class for a YOLO model to simplify its usage with input batches and object detection targets.\n",
    "\n",
    "    This class takes a YOLO model instance, processes input image batches, and converts predictions into\n",
    "    `YOLODetectionTarget` instances.\n",
    "\n",
    "    Attributes:\n",
    "        _model (ultralytics.models.yolo.model.YOLO): The YOLO model instance used for predictions.\n",
    "\n",
    "    Methods:\n",
    "        __call__(batch):\n",
    "            Processes a batch of images through the YOLO model and returns the predictions as\n",
    "            `YOLODetectionTarget` instances.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model: ultralytics.models.yolo.model.YOLO) -> None:\n",
    "        \"\"\"\n",
    "        Initializes the MaiteYOLODetector with a YOLO model instance.\n",
    "\n",
    "        Args:\n",
    "            model (ultralytics.models.yolo.model.YOLO): The YOLO model to use for predictions.\n",
    "        \"\"\"\n",
    "        self._model = model\n",
    "        # Dummy model metadata type to pass type checking\n",
    "        self.metadata = ModelMetadata(id=\"0\")\n",
    "\n",
    "    def __call__(self, batch: Sequence[ArrayLike]) -> Sequence[YOLODetectionTarget]:\n",
    "        \"\"\"\n",
    "        Processes a batch of images using the YOLO model and converts the predictions to `YOLODetectionTarget`\n",
    "        instances.\n",
    "\n",
    "        Args:\n",
    "            batch (Sequence[ArrayLike]): A batch of images in (c, h, w) format (channel-first).\n",
    "\n",
    "        Returns:\n",
    "            Sequence[YOLODetectionTarget]: A list of YOLODetectionTarget instances containing the predictions for each\n",
    "            image in the batch.\n",
    "        \"\"\"\n",
    "        # Convert images to channel-last format (h, w, c) for YOLO model\n",
    "        batch_transposed = [np.transpose(batch[i], (1, 2, 0)) for i in range(len(batch))]\n",
    "\n",
    "        yolo_predictions = self._model(batch_transposed, verbose=False)\n",
    "        return [\n",
    "            YOLODetectionTarget(\n",
    "                p.boxes.xyxy.cpu(),  # Bounding boxes in (x_min, y_min, x_max, y_max) format\n",
    "                p.boxes.cls.cpu(),  # Class indices for the detected objects\n",
    "                p.boxes.conf.cpu(),  # Confidence scores for the detections\n",
    "            )\n",
    "            for p in yolo_predictions\n",
    "        ]\n",
    "\n",
    "\n",
    "# create the wrapped model object\n",
    "yolo_model: od.Model = MaiteYOLODetector(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cef0788-137f-41c1-a9e4-80e7d08b47d1",
   "metadata": {},
   "source": [
    "## (2) Wrapping the dataset\n",
    "\n",
    "MAITE pairs images and their reference detections (aka targets, ground truth) into **datasets**. Typical ML workflows have many images per dataset; when these do not all fit in memory simultaneously, a *dataloader* object is used which can page images and annotations in from disk. For this notebook, however, each invocation of `evaluate` will use the same single-image dataset (our reference image with its baseline detections.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dcbb5d0-b899-4355-9922-9d52fb866b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our single image, its baseline detections, and an empty metadata dictionary\n",
    "# switch image to channel first\n",
    "single_image_dataset: od.Dataset = JATICObjectDetectionDataset(imgs, dets, metadata, dataset_id=\"visdrone_ex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7963f7fa-052c-476d-944e-5bbdae3181b5",
   "metadata": {},
   "source": [
    "## (3) Wrapping the perturbations as augmentations\n",
    "\n",
    "The `evaluate` function will perturb the image from the dataset using instances of the class defined below, one instance per perturbation value. Note that the object doesn't perform any augmentations until called by the `evaluate` workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa7a398f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation\n",
    "\n",
    "bp = RandomTranslationPerturber(seed=3)\n",
    "identity_augmentation = JATICDetectionAugmentation(bp, augment_id=\"identity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81020be7-76ba-43e3-b231-7d2bd7c30387",
   "metadata": {},
   "source": [
    "## (4) Wrapping the metrics\n",
    "\n",
    "We'll compare the detections in each perturbed image to the unperturbed detections using the Mean Average Precision (mAP) metric from the `torchmetrics` package. The following cell creates a mAP metrics object, wraps it in a MAITE [MAITE Object Detection Metric](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Metric.html) protocol-compatible class, and then creates an instance of this class, which will be called by `evaluate`.\n",
    "\n",
    "This code is copied directly from the [MAITE object detection tutorial](https://jatic.pages.jatic.net/cdao/maite/tutorials/torchvision_object_detection.html#metrics) (with the exception of setting `class_metrics=True`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e33407c5-f1d7-4c7a-9a31-2d500f97edcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error: no \"view\" rule for type \"image/png\" passed its test case\n",
      "       (for more information, add \"--debug=1\" on the command line)\n"
     ]
    }
   ],
   "source": [
    "from maite.protocols import MetricMetadata\n",
    "from torchmetrics import Metric as TorchMetric\n",
    "from torchmetrics.detection.mean_ap import MeanAveragePrecision\n",
    "\n",
    "##\n",
    "## Create an instance of the MAP metric object\n",
    "##\n",
    "\n",
    "tm_metric = MeanAveragePrecision(\n",
    "    box_format=\"xyxy\",\n",
    "    iou_type=\"bbox\",\n",
    "    iou_thresholds=[0.5],\n",
    "    rec_thresholds=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "    max_detection_thresholds=[1, 10, 100],\n",
    "    class_metrics=True,\n",
    "    extended_summary=False,\n",
    "    average=\"macro\",\n",
    ")\n",
    "\n",
    "##\n",
    "## This wrapper associates the MAP metric object with methods called by the evaluate\n",
    "## workflow to accumulate detection data and compute the metrics.\n",
    "##\n",
    "\n",
    "\n",
    "class WrappedTorchmetricsMetric:\n",
    "    \"\"\"\n",
    "    A wrapper class for a Torchmetrics metric designed to simplify its usage for object detection tasks.\n",
    "\n",
    "    This class facilitates the conversion of object detection targets and predictions into the format\n",
    "    expected by Torchmetrics metrics, allowing for easier integration with existing pipelines.\n",
    "\n",
    "    Attributes:\n",
    "        _tm_metric (Callable): The Torchmetrics metric to be wrapped, which takes lists of dictionaries\n",
    "            containing torch.Tensor objects representing predictions and targets.\n",
    "\n",
    "    Methods:\n",
    "        to_tensor_dict(target):\n",
    "            Converts an `ObjectDetectionTarget` into a dictionary format compatible with the Torchmetrics\n",
    "            metric's `update` method.\n",
    "\n",
    "        update(preds, targets):\n",
    "            Updates the wrapped Torchmetrics metric with batches of predictions and targets in their native format.\n",
    "\n",
    "        compute():\n",
    "            Computes the final metric values using the wrapped Torchmetrics metric.\n",
    "\n",
    "        reset():\n",
    "            Resets the state of the wrapped Torchmetrics metric.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        tm_metric: TorchMetric,\n",
    "    ) -> None:\n",
    "        \"\"\"\n",
    "        Initializes the WrappedTorchmetricsMetric with the given Torchmetrics metric.\n",
    "\n",
    "        Args:\n",
    "            tm_metric (Callable): A Torchmetrics metric instance that expects predictions and targets as lists of\n",
    "                dictionaries containing torch.Tensor objects.\n",
    "        \"\"\"\n",
    "        self._tm_metric = tm_metric\n",
    "        # Dummy metric metadata type to pass type checking\n",
    "        self.metadata = MetricMetadata(id=\"0\")\n",
    "\n",
    "    @staticmethod\n",
    "    def to_tensor_dict(target: od.ObjectDetectionTarget) -> dict[str, torch.Tensor]:\n",
    "        \"\"\"\n",
    "        Converts an ObjectDetectionTarget into a dictionary format compatible with the Torchmetrics metric's\n",
    "        `update` method.\n",
    "\n",
    "        Args:\n",
    "            target (od.ObjectDetectionTarget): An object detection target instance containing boxes, labels, and scores.\n",
    "\n",
    "        Returns:\n",
    "            dict[str, torch.Tensor]: A dictionary with keys `boxes`, `scores`, and `labels`, each mapping to a tensor.\n",
    "        \"\"\"\n",
    "        return {\n",
    "            \"boxes\": torch.as_tensor(target.boxes),\n",
    "            \"scores\": torch.as_tensor(target.scores),\n",
    "            \"labels\": torch.as_tensor(target.labels).type(torch.int64),\n",
    "        }\n",
    "\n",
    "    def update(self, preds: od.TargetBatchType, targets: od.TargetBatchType) -> None:\n",
    "        \"\"\"\n",
    "        Updates the wrapped Torchmetrics metric with the given predictions and targets.\n",
    "\n",
    "        Args:\n",
    "            preds (od.TargetBatchType): A batch of predictions in the format expected by the Torchmetrics metric.\n",
    "            targets (od.TargetBatchType): A batch of targets in the format expected by the Torchmetrics metric.\n",
    "        \"\"\"\n",
    "        preds_tm = [self.to_tensor_dict(pred) for pred in preds]\n",
    "        targets_tm = [self.to_tensor_dict(tgt) for tgt in targets]\n",
    "        self._tm_metric.update(preds_tm, targets_tm)\n",
    "\n",
    "    def compute(self) -> dict[str, Any]:\n",
    "        \"\"\"\n",
    "        Computes and returns the final metric values using the wrapped Torchmetrics metric.\n",
    "\n",
    "        Returns:\n",
    "            dict[str, Any]: A dictionary containing the computed metric values.\n",
    "        \"\"\"\n",
    "        return self._tm_metric.compute()\n",
    "\n",
    "    def reset(self) -> None:\n",
    "        \"\"\"Resets the state of the wrapped Torchmetrics metric, clearing any accumulated data.\"\"\"\n",
    "        self._tm_metric.reset()\n",
    "\n",
    "\n",
    "##\n",
    "## This is our instance variable that can compute the MAP metrics.\n",
    "##\n",
    "\n",
    "mAP_metric: od.Metric = WrappedTorchmetricsMetric(tm_metric)  # noqa: N816"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0102dfde-cb9b-46a7-8e4a-d12ccd948864",
   "metadata": {},
   "source": [
    "# Running the evaluation\n",
    "\n",
    "We now have all the wrappings required to evaluate our range of perturbations:\n",
    "- The `yolo_model` object, wrapping the YOLO model\n",
    "- The `single_image_dataset` object, providing our source image and its baseline detections\n",
    "- The `augmentation` object, which when instantiated, applies a single perturbation value to its input\n",
    "- The `mAP_metrics` object, defining the metrics to compute at each perturbation value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a7fef1-a3cc-4e63-8a2b-6133274e4b1c",
   "metadata": {},
   "source": [
    "## Evaluation sanity check: ground truth against itself\n",
    "\n",
    "Here we quickly check the evaluation workflow by creating an *identity augmentation* (with a brightness perturbation factor of 1.0, leaving the image unchanged) and scoring it.  The detections should also be unchanged from the baseline and thus give an mAP of 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0af9f36-c69b-400f-82ec-f1fd7e9d98ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 39.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity check: overall mAP (should be 1.0): 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from maite.workflows import evaluate\n",
    "\n",
    "# call the model for each image in the dataset (in this case, just the source image),\n",
    "# scoring the resulting detections against those from the dataset\n",
    "sanity_check_results, _, _ = evaluate(\n",
    "    model=yolo_model,\n",
    "    dataset=single_image_dataset,\n",
    "    augmentation=identity_augmentation,\n",
    "    metric=mAP_metric,\n",
    ")\n",
    "\n",
    "print(\"Sanity check: overall mAP (should be 1.0):\", sanity_check_results[\"map\"].item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1a72bb-dbf1-431e-892e-132771cd7b92",
   "metadata": {},
   "source": [
    "## Preparing the data\n",
    "\n",
    "Now we'll prepare the augmentation instances for the evaluation. In the cell below, you can set three parameters for sweeping the set of perturbation values:\n",
    "- **sweep_low**: the minimum perturbation value (must be >= 0)\n",
    "- **sweep_high**: the maximum perturbation value\n",
    "- **sweep_count**: how many perturbations to generation\n",
    "\n",
    "You can also optionally select perturbations to visualize:\n",
    "- **visualization_indices**: a list of perturbation indices *p*, 0 <= *p* < sweep_count. These instances will be rendered along with their corresponding detections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48b5e334-d8eb-415c-b263-4c4bd3618b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 30 perturbation datasets\n"
     ]
    }
   ],
   "source": [
    "SWEEP_LOW = 0.0\n",
    "SWEEP_HIGH = 100\n",
    "SWEEP_COUNT = 30\n",
    "VISUALIZATION_INDICES = [3, 9, 21]\n",
    "\n",
    "##\n",
    "## end user-settable parameters\n",
    "##\n",
    "perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)\n",
    "augmentation_datasets = []\n",
    "for idx, p in enumerate(perturbation_values):\n",
    "    dset_metadata: list[DetectionDatumMetadata] = [{\"id\": 0, \"max_translation_limit\": (round(p), round(p))}]\n",
    "    augmentation_datasets.append(\n",
    "        JATICObjectDetectionDataset(imgs, dets, dset_metadata, dataset_id=f\"augment_{idx}\"),\n",
    "    )\n",
    "# create new pertruber for each dataset to preserve seed value\n",
    "print(f\"Generated {len(augmentation_datasets)} perturbation datasets\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549d0d4d-eabd-4f54-a487-7cfbc99916c9",
   "metadata": {},
   "source": [
    "## Calling evaluate on the augmented data\n",
    "\n",
    "We loop over all the augmentations, calling `evaluate` on each one and building up a list of resulting metrics for analysis.\n",
    "\n",
    "Any augmentation indices specified above will be rendered in this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a4951dfc-c2cc-434a-8fd2-1786ba3acf96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 36.36it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 48.48it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.76it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 51.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #3: translation perturbation value (10, 10)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 26.46it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 47.21it/s]\n",
      "Error: no \"view\" rule for type \"image/png\" passed its test case\n",
      "       (for more information, add \"--debug=1\" on the command line)\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.78it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.89it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 46.00it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 46.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #9: translation perturbation value (31, 31)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 25.59it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.07it/s]\n",
      "Error: no \"view\" rule for type \"image/png\" passed its test case\n",
      "       (for more information, add \"--debug=1\" on the command line)\n",
      "100%|██████████| 1/1 [00:00<00:00, 42.49it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 48.54it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.16it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.43it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.36it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.35it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.50it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.23it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.43it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #21: translation perturbation value (72, 72)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 26.40it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 48.60it/s]\n",
      "Error: no \"view\" rule for type \"image/png\" passed its test case\n",
      "       (for more information, add \"--debug=1\" on the command line)\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.26it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.32it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.39it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 47.25it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.73it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 50.83it/s]\n"
     ]
    }
   ],
   "source": [
    "perturbed_metrics = list()\n",
    "for idx, d in enumerate(augmentation_datasets):\n",
    "    # reset the metric object for each dataset\n",
    "    mAP_metric.reset()\n",
    "    perturber = RandomTranslationPerturber(seed=3)\n",
    "    augmentation = JATICDetectionAugmentation(perturber, augment_id=str(idx))\n",
    "    result, _, _ = evaluate(model=yolo_model, dataset=d, augmentation=augmentation, metric=mAP_metric)\n",
    "    perturbed_metrics.append(result)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        print(f\"Perturbation #{idx}: translation perturbation value {d[0][-1]['max_translation_limit']}\")\n",
    "        datum = d[0]\n",
    "        batch = ([datum[0]], [datum[1]], [datum[2]])\n",
    "        # Extract the image from the augmentation and switch it to channel last\n",
    "        aug = np.transpose(augmentation(batch)[0][0], (1, 2, 0))\n",
    "        _ = model(aug)[0].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0303b6a-f587-43c8-ae9f-0057a7884241",
   "metadata": {},
   "source": [
    "# Evaluation analysis\n",
    "\n",
    "Now we can plot how the metrics (for example, mAP @ IoU=50) vary with perturbation level, keeping in mind this is a **relative** mAP against the detections in the unperturbed image.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09b6804b-c98c-46fb-af98-ab5598892ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map50_list = [m[\"map_50\"].item() for m in perturbed_metrics]\n",
    "plt.title(\"relative mAP@50\")\n",
    "plt.xlabel(\"Translation perturbation factor\")\n",
    "plt.ylabel(\"relative mAP @ 50% IoU\")\n",
    "_ = plt.plot(perturbation_values, map50_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8720fd-32dc-493d-ab5c-81c313615504",
   "metadata": {},
   "source": [
    "## Evaluation interpretation\n",
    "\n",
    "The metric shown, mAP@50, is the average precision of detections across all classes when the bounding box IoU is at least 0.5 (for more details, [see here](https://lightning.ai/docs/torchmetrics/stable/detection/mean_average_precision.html).) After translation is introduced, there is immediately a precipitous drop in performance. After a `max_translation_limit` of around 20, the mAP drops to 0. This is potentially due to the large amount of black space resulting from the newly translated image. (Note the relative mAP is guaranteed to be 1.0 when the perturbation is 0.0, i.e. when the image is unchanged, the two detection sets are identical.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0f9b97-ce54-4d21-be91-160f4a5d3054",
   "metadata": {},
   "source": [
    "## Additional plots\n",
    "\n",
    "For further insight, we can plot the mAP per class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65d28ff5-838e-467a-a703-81432a408b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Each instance of the metrics object has, potentially, a different set of observed classes.\n",
    "# Loop through them to accumulate a unified set of classes to ensure consistent plotting across\n",
    "# all thresholds.\n",
    "#\n",
    "\n",
    "unified_classes = set()\n",
    "for m in perturbed_metrics:\n",
    "    for class_idx in m[\"classes\"].tolist():\n",
    "        unified_classes.add(class_idx)\n",
    "\n",
    "#\n",
    "# dictionary of class_idx -> list of per-class mAP, or 0 if not present at that threshold\n",
    "#\n",
    "\n",
    "class_mAP = {class_idx: list() for class_idx in unified_classes}  # noqa: N816\n",
    "\n",
    "#\n",
    "# populate the lists across the perturbation values\n",
    "#\n",
    "\n",
    "for m in perturbed_metrics:\n",
    "    this_perturbation_classes = m[\"classes\"].tolist()\n",
    "    for class_idx in unified_classes:\n",
    "        if class_idx in this_perturbation_classes:\n",
    "            # the index of the class in this individual metric instance\n",
    "            this_class_idx = this_perturbation_classes.index(class_idx)\n",
    "            class_mAP[class_idx].append(m[\"map_per_class\"][this_class_idx].item())\n",
    "        else:\n",
    "            class_mAP[class_idx].append(0)\n",
    "\n",
    "#\n",
    "# plot\n",
    "#\n",
    "\n",
    "plt.title(\"Relative mAP per class\")\n",
    "plt.xlabel(\"Translation perturbation factor\")\n",
    "plt.ylabel(\"relative mAP @ 50% IoU\")\n",
    "for class_idx, class_mAP_list in class_mAP.items():  # noqa: N816\n",
    "    plt.plot(perturbation_values, class_mAP_list, label=baseline[0].names[class_idx])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aef482b-1a2b-4b2e-b522-7032776320d2",
   "metadata": {},
   "source": [
    "This plot shows several interesting results:\n",
    "\n",
    "- Person, car, and truck classes all follow the same general pattern with car being generally the most robust.\n",
    "- Truck is the most interesting category as it is the only category to have the mAP increase as well as decrease. This is potentially an attribute of the randomization in the perturber, the very few number of truck instances, and the location of the truck instances being somewhat in the corners.\n",
    "\n",
    "Any conclusions about classification accuracy should be considered in light of these caveats. In particular, the foreground positioning of the detected non-car objects suggests that instead of looking at per-class results, we drill down by bounding box area. Fortunately, the metrics class supports this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23ec21e7-e682-4d08-ad1d-6c42d850402f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"relative mAP values per area\")\n",
    "plt.xlabel(\"Translation perturbation factor\")\n",
    "plt.ylabel(\"relative mAP\")\n",
    "for k in (\"map\", \"map_small\", \"map_medium\"):\n",
    "    plt.plot(perturbation_values, [m[k].item() for m in perturbed_metrics], label=k)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
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    "The `map` line covers all sizes; `map_small` and `map_medium` are the mean average precision for objects (smaller than 32^2 pixels, between 32^2 and 96^2 pixels) in area, respectively. (There are no detections in the `map_large` category.) (Here, the mAP value is averaged over a **range** of IoU thresholds, between 0.5 and 0.95.) We see that medium objects, regardless of class, are generally much more robust to the transformation perturbations than small ones. This is most likely due to the smaller objects being more likely to be translated out of the image than medium sized objects.\n"
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