{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Lens Flare 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 condition (in this case, <b><i>a lens flare caused by a bright light source</i></b>), can affect an object detection model, and how that impact can be measured. The overall structure is:\n",
    "\n",
    "- **Evaluation Guidance: Computing Mean Average Precision (mAP):**\n",
    "    - An overview of the evaluation strategy.\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 absolute mAP metric of the detector on the perturbed datasets.\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.\n",
    "\n",
    "To run this notebook in Colab, use the link below:\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Kitware/nrtk/blob/main/docs/examples/maite/nrtk_lens_flare_demo.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2e1964-46e2-4497-88ff-274eaca17960",
   "metadata": {},
   "source": [
    "## Evaluation Guidance: Computing Mean Average Precision (mAP)\n",
    "\n",
    "This notebook evaluates the robustness of an Object Detector when exposed to NRTK (Natural, Physics-based) perturbations, using mean Average Precision (mAP) as the performance metric. The baseline case, where no perturbations are applied (identity augmentation), typically yields an mAP close to 1.0 and serves as the reference point. Unlike standard evaluations that focus on whether the detector can correctly identify and localize objects, our goal here is to measure how well the detector maintains its performance under realistic perturbations. \n",
    "\n",
    "By comparing the absolute mAP scores of the baseline against perturbed datasets, we can assess the detector's sensitivity to environmental variations and quantify its robustness in generating reliable predictions beyond controlled conditions."
   ]
  },
  {
   "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.\n",
    "\n",
    "**Note for Colab users**: After setting up the environment, you may need to \"Restart Runtime\" in order to resolve package version conflicts (see the [README](https://github.com/Kitware/nrtk/blob/main/docs/examples/README.md#run-the-notebooks-from-colab) for more info)."
   ]
  },
  {
   "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": 2,
   "id": "2561179f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning package installation...\n",
      "Installing nrtk's extras...\n"
     ]
    }
   ],
   "source": [
    "import sys  # noqa: F401\n",
    "\n",
    "print(\"Beginning package installation...\")\n",
    "!{sys.executable} -m pip install -qU pip\n",
    "\n",
    "print(\"Installing nrtk's extras...\")\n",
    "!{sys.executable} -c \"import nrtk\" || !{sys.executable} -m pip install -q \\\n",
    "  \"nrtk[maite,albumentations]>=0.25.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "340ef422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing required packages...\n"
     ]
    }
   ],
   "source": [
    "print(\"Installing required packages...\")\n",
    "# Add temporary numpy<2.0 constraint to avoid dependency conflicts\n",
    "!{sys.executable} -m pip install -q \"matplotlib\" \"torchvision\" \"torchmetrics\" \"ultralytics\" \"numpy<2.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f00b03b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing a fresh install of opencv-python-headless...\n"
     ]
    }
   ],
   "source": [
    "# opencv-python depends on libGL, which is unnecessary for running the notebook\n",
    "# and can cause failures in some environments (e.g., GitLab CI). To avoid this,\n",
    "# we uninstall all opencv variants and install opencv-python-headless instead.\n",
    "\n",
    "# OpenCV must be uninstalled and reinstalled last due to other packages installing OpenCV\n",
    "# Add temporary numpy<2.0 constraint to avoid dependency conflicts\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\" \"numpy<2.0\" --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c18e6bd1-1c56-447a-b1e4-16df26269ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected status of NRTK extras and their dependencies:\n",
      "\n",
      "[Pillow]\n",
      "  - Pillow                    ✓ 10.4.0\n",
      "\n",
      "[albumentations]\n",
      "  - albumentations            ✓ 2.0.5\n",
      "\n",
      "[diffusion]\n",
      "  - torch                     ✓ 2.8.0+cu128\n",
      "  - diffusers                 ✓ 0.34.0\n",
      "  - accelerate                ✓ 1.9.0\n",
      "  - Pillow                    ✓ 10.4.0\n",
      "\n",
      "[graphics]\n",
      "  - opencv-python             ✗ missing\n",
      "\n",
      "[headless]\n",
      "  - opencv-python-headless    ✓ 4.11.0\n",
      "\n",
      "[maite]\n",
      "  - maite                     ✓ 0.8.2\n",
      "  - fastapi                   ✓ 0.116.1\n",
      "  - pydantic                  ✓ 2.10.4\n",
      "  - pydantic_settings         ✓ 2.7.0\n",
      "  - uvicorn                   ✓ 0.34.0\n",
      "\n",
      "[notebook-testing]\n",
      "  - datasets                  ✓ 4.0.0\n",
      "  - matplotlib                ✓ 3.9.2\n",
      "  - numba                     ✓ 0.60.0\n",
      "  - tabulate                  ✓ 0.9.0\n",
      "  - torch                     ✓ 2.8.0+cu128\n",
      "  - torchmetrics              ✓ 1.6.2\n",
      "  - torchvision               ✓ 0.23.0+cu128\n",
      "  - transformers              ✓ 4.53.1\n",
      "  - ultralytics               ✓ 8.3.85\n",
      "  - jupytext                  ✓ 1.16.7\n",
      "  - albumentations            ✓ 2.0.5\n",
      "  - xaitk-jatic               ✓ 0.7.0\n",
      "\n",
      "[pybsm]\n",
      "  - pybsm                     ✓ 0.12.0\n",
      "\n",
      "[scikit-image]\n",
      "  - scikit-image              ✓ 0.24.0\n",
      "\n",
      "[tools]\n",
      "  - kwcoco                    ✓ 0.8.7\n",
      "  - Pillow                    ✓ 10.4.0\n",
      "\n",
      "[waterdroplet]\n",
      "  - scipy                     ✓ 1.13.1\n",
      "  - shapely                   ✓ 2.0.7\n",
      "  - geopandas                 ✓ 0.14.0\n",
      "\n",
      "\n",
      "For details about installing NRTK extras, please visit:\n",
      "    https://nrtk.readthedocs.io/en/v0.25.0/installation.html#extras\n",
      "\n"
     ]
    }
   ],
   "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  # type: ignore\n",
    "from PIL import Image\n",
    "\n",
    "from nrtk.impls.perturb_image.generic.albumentations_perturber import AlbumentationsPerturber\n",
    "from nrtk.utils._extras import print_extras_status\n",
    "\n",
    "print_extras_status()"
   ]
  },
  {
   "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": 6,
   "id": "57facb8b-4cb6-476b-a321-f48d70e773ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25.0\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": [
    "import nrtk\n",
    "\n",
    "print(nrtk.__version__)\n",
    "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",
    "\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 Lens Flare Perturbation: Examples and Guidance\n",
    "\n",
    "Lens flare (i.e. \"Sun flare\") is created by unwanted scattering of light from bright sources within the optics of the camera. These flares are present in most outdoor images with the sun present, and since they are not actually a part of the image, they should be ignored in an object detection model. However, because the flare is typically very bright and extend across many parts of the image, they can easily obscure objects and confuse a model which is not trained to ignore them.\n",
    "\n",
    "While the flare is a complex phenomenon which depends on the camera's lens, it has two main components: The first is a bright glow or haze near the bright object which reduces the image contrast. This can happen even if the bright object is just out of frame. The second component is a series of circles or geometrical shapes which represent the internal reflections of the light in the optical components. Most often, these shapes are colocated along a line emanating from the bright source.\n",
    "\n",
    "The [AlbumentationsPerturber](<project:../../_implementations/nrtk.impls.perturb_image.generic.albumentations_perturber.rst#module-nrtk.impls.perturb_image.generic.albumentations_perturber>) serves as an interface from the perturbations available from [Albumentations](https://albumentations.ai/). We can simulate a lens flare on our input image by using the [RandomSunFlare](https://explore.albumentations.ai/transform/RandomSunFlare) perturber from Albumentations with the following parameters:\n",
    "\n",
    "- `flare_center`: An (x, y) tuple of floats representing the location in the image to draw the flare\n",
    "- `src_radius`: The radius of the primary sun flare\n",
    "- `src_color`: A tuple of integers representing the RGB color of the flare\n",
    "- `circles`: A list of additional circle overlays. Each includes an alpha value, center, radius and color.\n",
    "- `p`: The probability of applying a perturbation to the image\n",
    "\n",
    "If `p` is 0, the output image will be unchanged.\n",
    "\n",
    "These parameters are all optional. If `src_radius` is left empty, it will default to a value of 400. For any other parameter that isn't provided, Albumentations will select some random default value. See the [documentation from Albumentations](https://albumentations.ai/docs/api-reference/albumentations/augmentations/pixel/transforms/#RandomSunFlare) for more information on these parameters and their defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
    "\n",
    "params = {\n",
    "    \"p\": 1.0,\n",
    "}\n",
    "\n",
    "for idx in range(0, 8):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    factor = (idx - 1) / 2\n",
    "    radius = (idx + 1) * 100\n",
    "    params[\"src_radius\"] = radius\n",
    "    perturber = AlbumentationsPerturber(perturber=\"RandomSunFlare\", seed=idx, parameters=params)\n",
    "    ax[row, col].set_title(f\"Lens Flare Radius: {radius}\")\n",
    "    ax[row, col].imshow(perturber(img_nd_bgr)[0][:, :, ::-1])\n",
    "    _ = ax[row, col].axis(\"off\")\n",
    "\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 comparisons against the perturbed images.\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": 8,
   "id": "55600de0-089f-4dd6-ac4d-7896b8df5494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics 8.3.85 🚀 Python-3.10.18 torch-2.8.0+cu128 CUDA:0 (Quadro RTX 5000, 15928MiB)\n",
      "Setup complete ✅ (40 CPUs, 31.0 GB RAM, 463.5/914.7 GB disk)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 51.9ms\n",
      "Speed: 6.9ms preprocess, 51.9ms inference, 129.4ms 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)"
   ]
  },
  {
   "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.tasks.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.tasks.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 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": 9,
   "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 YOLODetectionTarget:\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[DatumMetadataType] = [{\"id\": 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": 10,
   "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",
    "    \"\"\"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",
    "        \"\"\"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",
    "        \"\"\"Processes a batch of images using the YOLO model and converts the predictions to `YOLODetectionTarget`s.\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": 11,
   "id": "1dcbb5d0-b899-4355-9922-9d52fb866b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our single image, its baseline detections, and 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": 12,
   "id": "aa7a398f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nrtk.impls.perturb_image.generic.albumentations_perturber import AlbumentationsPerturber\n",
    "from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation\n",
    "\n",
    "perturber = AlbumentationsPerturber(perturber=\"RandomSunFlare\", parameters={\"p\": 0.0}, seed=3)\n",
    "identity_augmentation = JATICDetectionAugmentation(perturber, 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": 13,
   "id": "e33407c5-f1d7-4c7a-9a31-2d500f97edcc",
   "metadata": {},
   "outputs": [],
   "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",
    "    \"\"\"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",
    "        \"\"\"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",
    "        \"\"\"Converts an ObjectDetectionTarget into a dictionary format compatible with the Torch's `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: Sequence[od.TargetType], targets: Sequence[od.TargetType]) -> None:\n",
    "        \"\"\"Updates the wrapped Torchmetrics metric with the given predictions and targets.\n",
    "\n",
    "        Args:\n",
    "            preds (Sequence[od.TargetType]): A batch of predictions in the format expected by the Torchmetrics metric.\n",
    "            targets (Sequence[od.TargetType]): 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",
    "        \"\"\"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 lens flare probability of 0.0, 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": 14,
   "id": "a0af9f36-c69b-400f-82ec-f1fd7e9d98ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 49.53it/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.tasks 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 (`0.0` is no haze)\n",
    "- **sweep_high**: the maximum perturbation value (we use something high to see where accuracy will drop to zero)\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": 15,
   "id": "48b5e334-d8eb-415c-b263-4c4bd3618b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 30 perturbation augmentations\n"
     ]
    }
   ],
   "source": [
    "SWEEP_LOW = 30\n",
    "SWEEP_HIGH = 900\n",
    "SWEEP_COUNT = 30\n",
    "VISUALIZATION_INDICES = [4, 12, 24]\n",
    "\n",
    "##\n",
    "## end user-settable parameters\n",
    "##\n",
    "\n",
    "perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)\n",
    "augmentations = [\n",
    "    JATICDetectionAugmentation(\n",
    "        AlbumentationsPerturber(perturber=\"RandomSunFlare\", parameters={\"p\": 1, \"src_radius\": int(p)}, seed=3),\n",
    "        augment_id=str(p),\n",
    "    )\n",
    "    for _, p in enumerate(perturbation_values)\n",
    "]\n",
    "\n",
    "print(f\"Generated {len(augmentations)} perturbation augmentations\")"
   ]
  },
  {
   "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",
    "The location of the sun-flare will have a high impact on our results. If the area of the image obscured by the flare contains no objects, it would not interefere with detection. Because we select the locations randomly, this results in a high variation in mAP depending on the random seed used to place the flare. To get more meaningful results, we will repeat the evaluation for 10 different random seeds.\n",
    "\n",
    "Any augmentation indices specified above will be rendered in this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4951dfc-c2cc-434a-8fd2-1786ba3acf96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 18.80it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 19.61it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 17.92it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 15.87it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 14.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #4: Flare Radius 150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 13.10it/s]\n",
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      "100%|██████████| 1/1 [00:00<00:00,  8.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #12: Flare Radius 390\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  8.03it/s]\n",
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      "100%|██████████| 1/1 [00:00<00:00,  5.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #24: Flare Radius 750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  4.99it/s]\n",
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     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 3000x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perturbed_metrics = list()\n",
    "_, ax = plt.subplots(len(VISUALIZATION_INDICES), figsize=(30, 12))\n",
    "for idx, a in enumerate(augmentations):\n",
    "    # reset the metric object for each dataset\n",
    "    mAP_metric.reset()\n",
    "    src_radius = a.augment.get_config()[\"parameters\"][\"src_radius\"]\n",
    "    result, _, _ = evaluate(model=yolo_model, dataset=single_image_dataset, augmentation=a, metric=mAP_metric)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        print(f\"Perturbation #{idx}: Flare Radius {src_radius}\")\n",
    "        datum = single_image_dataset[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(a(batch)[0][0], (1, 2, 0))\n",
    "        # Plot image\n",
    "        ax_idx = VISUALIZATION_INDICES.index(idx)\n",
    "        ax[ax_idx].imshow(model(aug)[0].plot())\n",
    "        ax[ax_idx].set_title(f\"Flare Radius: {src_radius}\")\n",
    "        _ = ax[ax_idx].axis(\"off\")\n",
    "    perturbed_metrics.append(result)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "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 the mAP scores compared against the detections in the unperturbed image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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(\"mAP@50 on Lens Flare perturbed dataset\")\n",
    "plt.xlabel(\"Lense Flare Radius\")\n",
    "plt.ylabel(\"mAP @ 50% IoU\")\n",
    "_ = plt.plot(perturbation_values, map50_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8720fd-32dc-493d-ab5c-81c313615504",
   "metadata": {},
   "source": [
    "### Evaluation Interpretation\n",
    "\n",
    "**General things to know about mAP calculation**:\n",
    "- The mAP metric calculation does not take into account the images that do not have any detection or any ground truth. In such cases, it returns the default initialization value, -1. It means it couldn't compute the metric. (Source: [link](https://github.com/Lightning-AI/torchmetrics/issues/1184#issuecomment-1589210748)). Hence, in this notebook example, we clip values to a range of `[0, 1]`.\n",
    "- When having images with no ground truth, the order of those images in the batch can change the mAP calculation. To be more precise, if empty images are at the end of the batch, they will be ignored in the computation. But the empty images that are placed before the last non-empty image are taken into account. (Source: [link](https://github.com/Lightning-AI/torchmetrics/issues/1774#issuecomment-1590681234)).\n",
    "\n",
    "The general trend is that mAP decreases as the flare radius increases, as the flare is able to obscure a greater number of objects. However, the most important factor in affected mAP will be the location of the flare and whether it could obscure any of the objects in the image. It is possible even with a smaller lens flare for it to significantly impact mAP, if the location of that flare happens to obscure multiple objects. As the size of the flare increases, the probability of each object being obscured also increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efad3f96",
   "metadata": {},
   "source": [
    "### Additional Plots\n",
    "\n",
    "For further insight, we can plot the mAP per class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2207bb64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections.abc import Iterable\n",
    "\n",
    "#\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",
    "\n",
    "def _get_classwise_mAP(  # noqa: C901, N802\n",
    "    perturbed_metrics: Iterable[dict[str, Any]],\n",
    ") -> dict[str, Any]:\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: N806\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_value = m[\"map_per_class\"][this_class_idx].item()\n",
    "                # Clip value to 0 if negative\n",
    "                if class_map_value < 0:\n",
    "                    class_mAP[class_idx].append(0)\n",
    "                else:\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",
    "    return class_mAP\n",
    "\n",
    "\n",
    "#\n",
    "# plot\n",
    "#\n",
    "plt.title(\"mAP per class for Lens Flare perturbed dataset\")\n",
    "plt.xlabel(\"Lense Flare Radius\")\n",
    "plt.ylabel(\"mAP @ 50% IoU\")\n",
    "for class_idx, class_mAP_list in _get_classwise_mAP(perturbed_metrics).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": "8dce2974",
   "metadata": {},
   "source": [
    "The above plots shows several interesting results:\n",
    "\n",
    "- Person, car, truck, and motorcycle classes are generally more robust to lens flare perturbations than the car class.\n",
    "- The person and car classes show more consistent performance/degradation across the perturbation range, while motorcycle and truck classes show a drastic variation.\n",
    "- At extreme lens flare perturbations (>600), all classes except the person class experience degraded performance, which is expected due to the positioning on the large-radius lens flare in the image frame.\n",
    "\n",
    "Note: 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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c89a0638",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"mAP values per area for Lens Flare perturbed dataset\")\n",
    "plt.xlabel(\"Lense Flare Radius\")\n",
    "plt.ylabel(\"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",
   "id": "1326271f",
   "metadata": {},
   "source": [
    "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 lens flare perturbations than small ones, which makes sense when observing how objects tend to get obscured in the perturbation examples shown in the [Examples and Guidance](#NRTK-Lens-Flare-perturbation:-examples-and-guidance) section above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
   "metadata": {},
   "source": [
    "## End of Notebook"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nrtk-QgDWFgV4-py3.10",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}