model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IRV-E5YFo7UB",
    "outputId": "d0ff565b-a7ff-4e7f-8b44-48f6f7216fc6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n",
      " activity\t\t\t\t    mcm2022\n",
      " beast\t\t\t\t\t   'MOHAMMED RISHIN.gdoc'\n",
      " certificate.pdf\t\t\t    Mohammed-Rishin.pdf\n",
      " Classroom\t\t\t\t   'My Drive'\n",
      "'Colab Notebooks'\t\t\t   'object detection'\n",
      "'Copy of IMG_2891.heic'\t\t\t   'OFF STAGE ITEMS.gsheet'\n",
      " d22\t\t\t\t\t   'ON STAGE ITEMS.gsheet'\n",
      " DATAFILE4.jpg\t\t\t\t    oven.pdf\n",
      "'DBMS Test-1 Marks (1).gsheet'\t\t    Pics\n",
      " F3823F60-626C-4E13-9CE0-C0A98065606D.png   project_helmet\n",
      " Files\t\t\t\t\t    Resume.gdoc\n",
      " GROUP.gsheet\t\t\t\t   'SRS_Rollno-08 (1).docx'\n",
      " helmet_detection\t\t\t   'SRS_Rollno-08 (2).docx'\n",
      " IMG_7878.PNG\t\t\t\t   'SRS_Rollno-08 (3).docx'\n",
      " kia\t\t\t\t\t    SRS_Rollno-08.docx\n",
      "'Ladder Ashli (1).gslides'\t\t   'Untitled document.gdoc'\n",
      "'Ladder Ashli (2).gslides'\t\t   'Untitled document.pdf'\n",
      "'Ladder Ashli (3).gslides'\t\t   'Untitled folder'\n",
      "'Ladder Ashli.gslides'\t\t\t   'Untitled presentation.gslides'\n",
      "'Ladder Ashli.pptx'\n"
     ]
    }
   ],
   "source": [
    "# code for google colab\n",
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')\n",
    "\n",
    "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
    "! ls /mydrive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1gAw9USqT5FP"
   },
   "source": [
    "# New Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UkemvtFHpB6k",
    "outputId": "bd73f50a-1ad3-4e06-b0d3-f2fe455a15b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with_helmet  without_helmet\n"
     ]
    }
   ],
   "source": [
    "# ls\n",
    "# dataset face_detector\n",
    "# ls dataset\n",
    "# with_helmet without_helmet\n",
    "# ls face_detector\n",
    "!ls /mydrive/helmet_detection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "z0YEWBJ-ZzQ2"
   },
   "outputs": [],
   "source": [
    "# edit this directory location\n",
    "import os\n",
    "DIRECTORY = \"/mydrive/helmet_detection\"\n",
    "CATEGORIES = [\"with_helmet\", \"without_helmet\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "33ctgYSvZ-JN",
    "outputId": "dd9b68d9-89f9-4436-d555-8c60155dc574"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mydrive/helmet_detection/with_helmet/IMG_9989.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9987.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9985.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9991.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9990.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9986.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9993.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9988.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9994.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9992.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9995.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9996.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9997.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9998.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_9999.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0001.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0002.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0004.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0003.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0005.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0006.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0007.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0008.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0009.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0010.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0011.jpg\n",
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      "/mydrive/helmet_detection/with_helmet/IMG_0013.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0014.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0015.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0016.jpg\n",
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      "/mydrive/helmet_detection/with_helmet/IMG_0018.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0019.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0020.jpg\n",
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      "/mydrive/helmet_detection/with_helmet/IMG_0035.jpg\n",
      "/mydrive/helmet_detection/with_helmet/IMG_0036.jpg\n",
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      "/mydrive/helmet_detection/without_helmet/augmented_image_86.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_85.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_84.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_83.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_82.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_81.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_80.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_79.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_78.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_77.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_76.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_74.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_73.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_72.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_71.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_70.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_7.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_69.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_68.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_67.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_66.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_65.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_64.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_63.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_62.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_61.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_60.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_59.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_58.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_57.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_56.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_55.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_54.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_53.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_52.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_51.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_50.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_5.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_48.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_45.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_44.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_43.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_42.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_41.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_40.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_4.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_39.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_38.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_37.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_36.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_35.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_32.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_33.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_319.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_318.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_317.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_315.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_311.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_314.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_310.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_309.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_31.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_308.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_306.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_307.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_304.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_303.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_30.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_3.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_299.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_298.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_301.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_297.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_296.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_294.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_293.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_292.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_291.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_290.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_285.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_284.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_283.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_282.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_281.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_276.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_273.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_272.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_277.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_270.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_27.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_269.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_267.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_265.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_263.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_262.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_260.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_258.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_257.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_256.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_253.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_252.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_251.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_250.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_25.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_249.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_246.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_245.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_244.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_243.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_241.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_240.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_239.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_238.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_237.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_236.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_235.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_234.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_233.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_232.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_231.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_230.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_23.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_229.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_227.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_226.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_224.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_223.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_222.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_221.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_220.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_22.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_219.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_216.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_215.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_214.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_211.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_210.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_21.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_209.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_208.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_206.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_205.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_204.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_203.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_201.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_200.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_2.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_20.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_199.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_198.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_197.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_194.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_193.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_192.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_191.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_19.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_189.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_187.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_188.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_186.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_185.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_184.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_183.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_182.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_181.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_180.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_18.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_179.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_178.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_177.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_176.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_175.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_174.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_173.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_172.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_171.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_170.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_17.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_169.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_168.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_166.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_165.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_164.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_163.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_162.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_161.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_160.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_16.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_158.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_157.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_156.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_155.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_154.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_153.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_152.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_151.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_150.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_149.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_148.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_147.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_145.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_144.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_143.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_142.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_141.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_140.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_14.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_139.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_138.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_137.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_136.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_135.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_134.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_133.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_132.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_131.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_130.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_13.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_129.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_128.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_127.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_126.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_124.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_125.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_123.JPG\n",
      "/mydrive/helmet_detection/without_helmet/augmented_image_122.JPG\n"
     ]
    }
   ],
   "source": [
    "for category in CATEGORIES:\n",
    "    path = os.path.join(DIRECTORY, category)\n",
    "    for img in os.listdir(path):\n",
    "      img_path = os.path.join(path, img)\n",
    "      print(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "qWT-Tn05pFGl"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.layers import AveragePooling2D\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from imutils import paths\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "pNlRDwuwQJ_n",
    "outputId": "53048a70-b00a-459c-cee4-6add88737795"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2.12.0'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "LoBz1JtCpIPY"
   },
   "outputs": [],
   "source": [
    "# initialize the initial learning rate, number of epochs to train for,\n",
    "# and batch size\n",
    "INIT_LR = 1e-4\n",
    "EPOCHS = 20\n",
    "BS = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "5Q-TMsy_pJNs"
   },
   "outputs": [],
   "source": [
    "# edit this directory location\n",
    "DIRECTORY = \"/mydrive/helmet_detection\"\n",
    "CATEGORIES = [\"with_helmet\", \"without_helmet\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7JYBzEd3Za7t"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4wDaW9x3pJLJ",
    "outputId": "5e8c82e6-cd13-43c4-a998-2eb8ac2e9540"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading images...\n"
     ]
    }
   ],
   "source": [
    "# grab the list of images in our dataset directory, then initialize\n",
    "# the list of data (i.e., images) and class images\n",
    "print(\"[INFO] loading images...\")\n",
    "\n",
    "data = []\n",
    "labels = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "UTdLIsYepJJA"
   },
   "outputs": [],
   "source": [
    "for category in CATEGORIES:\n",
    "    path = os.path.join(DIRECTORY, category)\n",
    "    for img in os.listdir(path):\n",
    "    \timg_path = os.path.join(path, img)\n",
    "    \timage = load_img(img_path, target_size=(224, 224))\n",
    "    \timage = img_to_array(image)\n",
    "    \timage = preprocess_input(image)\n",
    "\n",
    "    \tdata.append(image)\n",
    "    \tlabels.append(category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "l1z3AG9-pJGc"
   },
   "outputs": [],
   "source": [
    "#Label Binarizer is an SciKit Learn class that accepts Categorical data as input and returns an Numpy array.\n",
    "# perform one-hot encoding on the labels\n",
    "lb = LabelBinarizer()\n",
    "labels = lb.fit_transform(labels)\n",
    "labels = to_categorical(labels)\n",
    "\n",
    "# convert labels into numbers\n",
    "data = np.array(data, dtype=\"float32\")\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "dkO5G8U7pJEM"
   },
   "outputs": [],
   "source": [
    "(trainX, testX, trainY, testY) = train_test_split(data, labels,\n",
    "\ttest_size=0.20, stratify=labels, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "y-7av1n7pJB5"
   },
   "outputs": [],
   "source": [
    "# construct the training image generator for data augmentation\n",
    "aug = ImageDataGenerator(\n",
    "\trotation_range=20,\n",
    "\tzoom_range=0.15,\n",
    "\twidth_shift_range=0.2,\n",
    "\theight_shift_range=0.2,\n",
    "\tshear_range=0.15,\n",
    "\thorizontal_flip=True,\n",
    "\tfill_mode=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7biCD6wvpZmc",
    "outputId": "248deae3-037c-4fae-dc40-15d1a7aa275f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
      "9406464/9406464 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# load the MobileNetV2 network, ensuring the head FC layer sets are\n",
    "# left off\n",
    "baseModel = MobileNetV2(weights=\"imagenet\", include_top=False,\n",
    "\tinput_tensor=Input(shape=(224, 224, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "jjBlWyJppZkj"
   },
   "outputs": [],
   "source": [
    "# construct the head of the model that will be placed on top of the\n",
    "# the base model\n",
    "headModel = baseModel.output\n",
    "headModel = AveragePooling2D(pool_size=(7, 7))(headModel)\n",
    "headModel = Flatten(name=\"flatten\")(headModel)\n",
    "headModel = Dense(128, activation=\"relu\")(headModel)\n",
    "headModel = Dropout(0.5)(headModel)\n",
    "headModel = Dense(2, activation=\"softmax\")(headModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "ejbUdnKrpZh_"
   },
   "outputs": [],
   "source": [
    "# place the head FC model on top of the base model (this will become\n",
    "# the actual model we will train)\n",
    "model = Model(inputs=baseModel.input, outputs=headModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "LecCKs0epZfb"
   },
   "outputs": [],
   "source": [
    "# loop over all layers in the base model and freeze them so they will\n",
    "# *not* be updated during the first training process\n",
    "for layer in baseModel.layers:\n",
    "\tlayer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wH13aZjHpZcw",
    "outputId": "fcade814-0cbd-4cd2-87ef-dd96cf228b0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] compiling model...\n"
     ]
    }
   ],
   "source": [
    "# compile our model\n",
    "print(\"[INFO] compiling model...\") \n",
    "# INIT_LR = 1e-4\n",
    "# EPOCHS = 20\n",
    "# BS = 32\n",
    "# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "#     initial_learning_rate=INIT_LR,\n",
    "#     decay_steps=10000,\n",
    "#     decay_rate=0.9)\n",
    "# opt = Adam(learning_rate=lr_schedule)\n",
    "opt = Adam(learning_rate=INIT_LR,)\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
    "\tmetrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ythWFfkPpZak",
    "outputId": "14541352-54ef-4d48-94bb-d0d27e038193"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] training head...\n",
      "Epoch 1/20\n",
      "11/11 [==============================] - 20s 688ms/step - loss: 0.6393 - accuracy: 0.7043 - val_loss: 0.4368 - val_accuracy: 0.9263\n",
      "Epoch 2/20\n",
      "11/11 [==============================] - 5s 403ms/step - loss: 0.4387 - accuracy: 0.8783 - val_loss: 0.3022 - val_accuracy: 0.9684\n",
      "Epoch 3/20\n",
      "11/11 [==============================] - 4s 351ms/step - loss: 0.3254 - accuracy: 0.9233 - val_loss: 0.2204 - val_accuracy: 0.9684\n",
      "Epoch 4/20\n",
      "11/11 [==============================] - 5s 466ms/step - loss: 0.2251 - accuracy: 0.9623 - val_loss: 0.1703 - val_accuracy: 0.9684\n",
      "Epoch 5/20\n",
      "11/11 [==============================] - 4s 352ms/step - loss: 0.1870 - accuracy: 0.9768 - val_loss: 0.1418 - val_accuracy: 0.9789\n",
      "Epoch 6/20\n",
      "11/11 [==============================] - 4s 356ms/step - loss: 0.1428 - accuracy: 0.9768 - val_loss: 0.1264 - val_accuracy: 0.9789\n",
      "Epoch 7/20\n",
      "11/11 [==============================] - 5s 446ms/step - loss: 0.1173 - accuracy: 0.9855 - val_loss: 0.1046 - val_accuracy: 0.9895\n",
      "Epoch 8/20\n",
      "11/11 [==============================] - 4s 332ms/step - loss: 0.0980 - accuracy: 0.9884 - val_loss: 0.0931 - val_accuracy: 0.9895\n",
      "Epoch 9/20\n",
      "11/11 [==============================] - 4s 352ms/step - loss: 0.0892 - accuracy: 0.9855 - val_loss: 0.0868 - val_accuracy: 0.9895\n",
      "Epoch 10/20\n",
      "11/11 [==============================] - 5s 486ms/step - loss: 0.0750 - accuracy: 0.9971 - val_loss: 0.0854 - val_accuracy: 0.9895\n",
      "Epoch 11/20\n",
      "11/11 [==============================] - 4s 338ms/step - loss: 0.0699 - accuracy: 0.9884 - val_loss: 0.0788 - val_accuracy: 0.9895\n",
      "Epoch 12/20\n",
      "11/11 [==============================] - 5s 454ms/step - loss: 0.0633 - accuracy: 0.9855 - val_loss: 0.0746 - val_accuracy: 0.9895\n",
      "Epoch 13/20\n",
      "11/11 [==============================] - 4s 330ms/step - loss: 0.0594 - accuracy: 0.9913 - val_loss: 0.0661 - val_accuracy: 0.9895\n",
      "Epoch 14/20\n",
      "11/11 [==============================] - 4s 334ms/step - loss: 0.0452 - accuracy: 0.9942 - val_loss: 0.0646 - val_accuracy: 0.9895\n",
      "Epoch 15/20\n",
      "11/11 [==============================] - 4s 372ms/step - loss: 0.0406 - accuracy: 0.9971 - val_loss: 0.0642 - val_accuracy: 0.9895\n",
      "Epoch 16/20\n",
      "11/11 [==============================] - 4s 347ms/step - loss: 0.0391 - accuracy: 0.9971 - val_loss: 0.0606 - val_accuracy: 0.9895\n",
      "Epoch 17/20\n",
      "11/11 [==============================] - 5s 452ms/step - loss: 0.0294 - accuracy: 1.0000 - val_loss: 0.0548 - val_accuracy: 0.9895\n",
      "Epoch 18/20\n",
      "11/11 [==============================] - 4s 367ms/step - loss: 0.0387 - accuracy: 0.9972 - val_loss: 0.0535 - val_accuracy: 0.9895\n",
      "Epoch 19/20\n",
      "11/11 [==============================] - 4s 353ms/step - loss: 0.0315 - accuracy: 0.9943 - val_loss: 0.0544 - val_accuracy: 0.9895\n",
      "Epoch 20/20\n",
      "11/11 [==============================] - 5s 442ms/step - loss: 0.0285 - accuracy: 1.0000 - val_loss: 0.0564 - val_accuracy: 0.9895\n"
     ]
    }
   ],
   "source": [
    "# train the head of the network\n",
    "print(\"[INFO] training head...\")\n",
    "H = model.fit(\n",
    "\taug.flow(trainX, trainY, batch_size=BS),\n",
    "\tsteps_per_epoch=len(trainX) // BS,\n",
    "\tvalidation_data=(testX, testY),\n",
    "\tvalidation_steps=len(testX) // BS,\n",
    "\tepochs=EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jw8lqakupkkg",
    "outputId": "31fb6c07-6fc2-40f9-c4ea-3ebbb4f0832a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] evaluating network...\n",
      "3/3 [==============================] - 1s 335ms/step\n"
     ]
    }
   ],
   "source": [
    "# make predictions on the testing set\n",
    "print(\"[INFO] evaluating network...\")\n",
    "predIdxs = model.predict(testX, batch_size=BS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "iDi9MFezpkiO"
   },
   "outputs": [],
   "source": [
    "# for each image in the testing set we need to find the index of the\n",
    "# label with corresponding largest predicted probability\n",
    "predIdxs = np.argmax(predIdxs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eUgoEeFspkgH",
    "outputId": "2c733477-4cc0-45ed-abba-7283e401bb8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "   with_helmet       0.98      1.00      0.99        47\n",
      "without_helmet       1.00      0.98      0.99        48\n",
      "\n",
      "      accuracy                           0.99        95\n",
      "     macro avg       0.99      0.99      0.99        95\n",
      "  weighted avg       0.99      0.99      0.99        95\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# show a nicely formatted classification report\n",
    "print(classification_report(testY.argmax(axis=1), predIdxs,\n",
    "\ttarget_names=lb.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XxdJ_e_ypZYC",
    "outputId": "6bc745c8-648d-4639-dcfe-5dcb0584aced"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] saving helmet detector model...\n"
     ]
    }
   ],
   "source": [
    "# serialize the model to disk\n",
    "print(\"[INFO] saving helmet detector model...\")\n",
    "model.save(\"helmet_detector.model\", save_format=\"h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 477
    },
    "id": "5g8mFwHSpvKX",
    "outputId": "9daea3c3-9ec0-40d7-946e-6c8d9eaf3bb7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jq6++ysqVKxkxYkSlVjcRGKQbS4hy/vSnP3HffffRs2dPbrzxRoxGI2vXruWXX37hV7/6FfPnz2/oIl7UX//6V5YtW8arr77Khg0b6N+/P6dOneKLL75g9OjRfP3117UelLpnz55KY0fKtGrVimnTpvHee+8xYMAAHn/8cZYsWcIVV1zhWWdHp9Px8ccfe/4Xf+LECXr27Em3bt247LLLSE5OJi8vj++++46MjAz+8Ic/eNL+4Q9/4MSJEwwYMMCzWOFPP/3EsmXLSElJ4ZZbbrlo+R9++GEWL17MwIEDmThxIpGRkWzevJk1a9YwYcIE5syZU6vPoyoDBgzg97//PW+//TZdu3ZlwoQJnnV2oqOjqx3PdL7Dhw/zww8/EBsby7hx46pNd/PNN/Pwww8zd+5c3n77bWJiYvjvf//LkCFDmDJlCnPnzmXIkCEopdi/fz9Llixhz549nkCjNml///vf8/HHH3P//fezdOlSkpOT2bp1K+vXr2fMmDF89913tfqs4uPjueWWW5g1axY9evTgmmuuITc3l//973+YzWZ69OjB1q1bKxxzzTXX8Oyzz/LSSy/RqVMnzzo7p0+fZs2aNfTr16/SYoshISH89re/5a233gLcXdEiQDXkVDAhfOViU88v5OOPP1bdu3dXISEhqlmzZmrcuHFq+/btF5xiW93U8/PTKqXU4cOHFaB++9vfXrRsZVOWq5sqXt2U4PT0dPWb3/xGxcbGKrPZrLp3765mzJihvvzySwWov//97xf8DM7P/0KP7t27V8j3vvvuU61atVJGo1E1a9ZMXX/99Wrjxo0VzpuTk6NefPFFNXToUNWyZUtlMplUfHy8Gjx4sJo5c2aF6eizZ89Wt9xyi2rbtq0KDQ1V4eHhqkuXLmrKlCnqzJkzNaqHUu4p23379lVhYWEqMjJSjRgxQq1cuVJ9/PHHVa5jdCnTrV0ul3r77bdVx44dlclkUgkJCeqBBx5QFovlgucrb8qUKQpQf/rTny6advLkyQpQb7zxhmdbVlaWeuKJJ1T79u1VUFCQioyMVN27d1dTpkxRhYWFFY6vTdrVq1erQYMGqeDgYBUeHq5Gjx6ttm3bVqvfRXmFhYVqypQpqk2bNiooKEglJSWpBx54QGVlZV3wd7pgwQJ17bXXqujoaGUymVRSUpIaN26cWrp0aZXpt27dqgCVkJBQ5Xo/IjDIvbGECCDPPPMMr7zyCosWLeLaa69t6OII4XMzZsxg0qRJPPvss/z5z39u6OKIBiLBjhB+6OTJk7Rs2bLCth07dniW5T9x4gRms7mBSidE/XA4HFx++eXs3r2bw4cP13g2nPA/MmZHCD90xRVX0LZtW7p27UpoaCj79+9nwYIFuFwu/vWvf0mgI/zamjVrWLlyJStWrGDHjh089NBDEugEOGnZEcIPvfjii3z99dccOXKE/Px8oqKi6NevH4899liV06+F8CcvvPACL774IjExMdx444384x//qPFihMI/SbAjhBBCCL8m6+wIIYQQwq9JsCOEEEIIvybBjhBCCCH8mgQ7QgghhPBrMvW8VE5OTpX3Y/FW8+bNyczMrPPzNkZSV/8VSPWVuvqvQKpvINTVYDAQHR1ds7Q+LkuT4XA4qryjrzc0TfOc298nvUld/Vcg1Vfq6r8Cqb6BVNeakm4sIYQQQvg1CXaEEEII4dck2BFCCCGEX5NgRwghhBB+TYIdIYQQQvg1CXaEEEII4dck2BFCCCGEX5NgRwghhBB+TYIdIYQQQvg1CXaEEEII4dca1e0ifvnlF7799lsOHz5MTk4Ojz32GH369LngMbt27eLTTz/l+PHjNGvWjBtvvJEhQ4bUT4GFEEII0eg1qpYdq9VKamoqv/vd72qU/syZM/z1r3+lS5cuvPrqq1x33XW89957bN261bcFFUIIIUST0ahadnr27EnPnj1rnH7JkiXExcXxm9/8BoCkpCT27NnDggUL6NGjh49KKYQQQoBSCqcTnA6FywU6HegNGnr9uZtxBjqnU2GzKpRShITqG6wcjSrYqa39+/fTrVu3Ctu6d+/OjBkzqj3GbrdXuLu5pmkEBwd7XtelsvMFwpde6uq/GlN9lUthtSmCTBqaru7L05jq6muNpa7KpXCUCxh8RdMUlmwruTlOHA6F06E8z2UBi8OhcDoVTgcX31f6ujp6fVngo6E3gKHca/ezVrqtNJ1Bw1Duddn2CscZNAz60mDqAt9/X15bp1NhK1FYrS5sVoW1pPTZqrCVuNzP1nPPjtI/t7FxBvoPC6/z8tRUkw52LBYLkZGRFbZFRkZSXFyMzWbDZDJVOmbevHnMmTPH8z4tLY3p06fTvHlzn5UzPj7eZ+dubKSu/qs+66uUIi/XTs5ZKzlnrWSftZJztgRLtg2nU6HTQVi4kbAIIxGRJsIjjIRHlD5HGgkJNXj1D30gXduL1VUp9x96h92Fw+7CbnfhcCj3c9m2SvtdOOzn0pQdUyFN6TanU9VTTQFyfXZmnY4KwZrTSWndfFM/vV7DYNRhNGoYDDoMxtKHQcNo1PGL4cS5/UZdaRr3vkrvDTpcTkVxsYOSYifFRQ6Ki52e1+Wf7fbaR6SaBkaTiYSEBB98EjXTpIOdSzF+/HjGjBnjeV/2D2JmZiYOxwXC9EugaRrx8fFkZGSgVH3+oOuf1NV/+bK+SimKi1zk57rIy3WSX/ooyHPidFZ/nMsFebl28nLtnDxeVGm/TgfBoTpCPA+9+zlMR3CojqAgrcpgqClf27LugvNbLKp8XfreZAomP7/ovGMqt2zUF50O8GFDk8GgQ6dTnhYXQ7lWFncrChX3eV5X0SJT+r6sdUbTae5uLYf7WjjKtQpVvhYKh4MKn7fToXA4y64PpdtL0zkrXwunU+F0OrGW+O7zqo6mgSlII8iscz8HaZjM7t+VKUhHkLn0OUjDFKRhNLl/b6dOnarTchgMhho3VDTpYCcqKorc3IqRem5uLsHBwVW26gAYjUaMRmOV+3z1j5tSqsn9w3mppK6Xrri4mJycHHJycigqqvwH3FtKubsJXC733xNN5/7jUpMWkPDwcPLz873K22EHq9V1rtm7tClcVfPHVNNw/wNq1jz/sAaV/sPpsIPd7sJuU+6HXWErfe2wK/d/pvOqL4+mA6NJw2h0n6/sYTLpiIwKo6ioCJ1ONWgXj7tFpdwfS8e57p5z3TDn/hC66iEo0XTuP+q6ct+dstc6neb5TlV4XZpG07u367Sy75523rl836V2oe+xE3AqwF76qG8690NndL+s6q9U2W9YucCl3F2ALlf5bQrldO8zGYMoLi4p/c0r9/7S1+5jK27XtHPjjQxlXWmegK58V5uG7rwxSU6g2OF+UFh19UJDQ+ncuXPdf2411KSDnXbt2rFly5YK27Zv30779u0bqERCXJjT6SQ3N9cT1JQ9LBYLJSUN8F+0xq6goQsghKgL8fHxEuyUKSkpISMjw/P+zJkzHDlyhLCwMGJjY5k5cybZ2dk89NBDAFxzzTUsXryY//73vwwdOpSdO3eyfv16nnrqqYaqghDurplyrTTlA5rc3NwLtgYZ9KEY9BEYdKE+bc5vCBq4xwiUtqiYTBpGkw6DsX4HyXpaTOylY1Ec7tYgR7mBp42JXuf+37ZOX/7Z/b9rz3ud+31NW+rKhISE+KQVsbEKpPo2trqeP762vjWqYOfgwYO8+OKLnveffvopAIMHD+bBBx8kJyeHrKwsz/64uDieeuopPvnkE77//nuaNWvGfffdJ9PORb1wOBxYLJYKwUzZa6vVWu1xmmbAqI/AaIjAqI8s9xyOTnM3XpdNYS0/bqCqGR2esQNVjDOoNLvDgKcJGjg3nuCCs0/c6YLNoeRa8s91n1zoWKciOFhHeKSe8MjS5wg9oeE69PrGHcGVjdk5kX7KPci2urETFcZYlE9T/XiLstd6fdkYB42goNIxD+XHOJTbbvLRrLOyuiYkJHDq1KmA6HoOpPoGUl1rSlPySQDuAcrlp6TXhUD6wjVkXW02W+VWlBwLDh+OrFRKkZd3kVYaXWgVAU0kel0weoNWbvCs+1F+QG3ZgL7GQL7H/imQ6gqBVd9AqavRaAyMAcoicLhcLvLz86tsRSksrGZEXD1wt9JEVghqTPpITKZwQsNMVQYyIaHu/7U3lmBGCCH8nQQ7olGxWq1YLBays7MrdRE5L9BSo9OZMeojMBkiPV1EOl3Vs+7qgqbpCQsNJyIylNBQfcVgJsw9g0iCGSGEaBwk2GmilFLk5OTU+dpA3nA6nRXGVF1IdYN4LzSgTtN0GPXhGPRlAc25FpWQkCCiYvRENTMQHaMnMkaPyeSbW78FShOxEEL4Cwl2mqCcnBxWrFjB8ePHG7ooPhFkCsZkikRPBDrODeQ16EPRNB16A0RFuwObqBg90c0MmIOlJUUIIUTVJNhpQhwOB5s3b2bz5s24XC50Oh0hISENXSwPvV5/wa6m8xn0JoLNkeh1EShnBMoRjlEfgU53bkFITYPwSD3RzfTulpsYA+EROp/NUBFCCOF/JNhpIo4cOcLKlSs9K0anpKQwZMiQBl+7oExNu3aUS3H8iI09O0qwlpSmc+FehMUIIWE6omP0ni6piCg9BoMENkIIIS6dBDuNXEFBAatWreLAgQOAe8ntwYMH06ZNmybXbZN1xs6uLSXkWdytP0aTVtpiYyC6mXucTVCQb8bZCCGECFwS7DRSLpeLbdu28eOPP2K329E0je7du9OvX79q7/vVWBUWOPllawkZJ9zrGBmM0L6zmdR2QY1+kTkhhBBNnwQ7jdCpU6dYvny5Z2ZTfHw8Q4cOrfHiSY2F3abY/0sJh/ZbUaVdVSmtTXToaibILC04Qggh6ocEO41ISUkJ69atY+fOnQAEBQUxYMAAunTp0qS6rFwuxbFDNvbuLMFmdY/LiW1hoEuPYCKi9A1cOiGEEIFGgp1GQCnFnj17WLNmDcXFxQB06tSJAQMGNKrZVjVxJsPOri1F5Oe6AAgN19GlRzBxCYYmFbAJIYTwHxLsNLCzZ8+yfPlyTp48CUBMTAxDhw4lMTGxgUtWOwV5ThZuOMaxwwWAe/Bxhy5mUtqa0Mk0cSGEEA1Igp0GYrfb2bhxI1u2bMHlcmEwGOjbty89evRAr286XT02m4t9u6wc2W9FKfe6OKltTbTvYsYkM6uEEEI0AhLsNIBDhw6xcuVK8vPzAUhLS2Pw4MFEREQ0cMlqzuVSHD3oHpdjt7nH5bRKC6NNJx1h4RLkCCGEaDwk2KlHeXl5rFy5ksOHDwMQHh7O4MGDad26dQOXrHZOn7Lzy5ZiCvLd43LCI3R06RlC98tbyf2ihBBCNDoS7NQDp9PJzz//zMaNG3E4HOh0Onr27EmfPn0wGn13Z+66lp/rZNfWYjIz3DcfNQVpdOhqplVrE3q9tOYIIYRonCTY8bFDhw4xZ84csrOzAWjZsiVDhw6lWbNmDVyymrNaXezbWcLRgzb3uBwdpLULon3nIIw+urO4EEIIUVck2PGRoqIi1q5dy+7duwEwm80MGjSIjh07Npkp2C6n4vABK/t3WbHb3V1T8YlGOnc3ExredAZRCyGECGwS7PhIenq6J9Dp2rUr/fv3x2w2N3Cpau7MKTs7fy6msMA9Liciyr1eTmyLptPtJoQQQoAEOz7Trl07Tp06Rf/+/TGZTE1m0K7N6mLX1mLSj7jvYxVkLh2Xk2ZCk/VyhBBCNEES7PiIpmkMGTKEhIQETp061dDFqZFT6TZ2/FSMtcQdmKW1D6JDVzNGowQ5Qgghmi4JdgTWEhc7fy7m5HF3a05YuI7ufUKIiZWvhxBCiKZP/poFMKUUJ4+7x+bYrApNgzYdg2jfxYxeL605Qggh/IMEOwGqpNjF9p+KOH3CvWZORKS7NScqRr4SQggh/Iv8ZQswSinSj9jYtaUEu12h6aB9ZzNtOwahk9YcIYQQfkiCnQBSVOhi++YizwrIkdF6evQJISJK1swRQgjhvyTYCQBKuW/a+cu2YpwO0OmgQ1czrTsEoZPp5EIIIfycBDt+rrDAybZNxZw9427NiW7mbs0Ji5DWHCGEEIFBgh0/pVyKwwds7NlejNMJej10vCyYtLayOKAQQojAIsGOH8rPc7JtYxE5Z50ANIsz0L13MKFh0pojhBAi8Eiw40dcLsWhvVb27izB5QKDATr3CKZVa1OTufmoEEIIUdck2PETeRYnWzcWkZvjbs2JSzBw2RUhBIfoGrhkQgghRMOSYKeJczkV+3eXsP8XK0qB0ajRpWcwSalGac0RQgghkGCnSbNkO9i6sYj8XBcA8YlGuvUKxhwsrTlCCCFEGQl2mqgzp+xsXF2IUmAK0uh2eTAJydKaI4QQQpxPgp0mav/uEpSCFi0NdO8dQpBZWnOEEEKIqshfyCaoIM9JdqYTNOjWSwIdIYQQ4kLkr2QTdOyQDYAWCQaZbSWEEEJchPylbGJcTsXxI+5gp1XroAYujRBCCNH4SbDTxGSctGOzKszBGnEJMuRKCCGEuBgJdpqYowfdrTrJaSa5Y7kQQghRAxLsNCFFBU6yTrvvXt4qzdTApRFCCCGaBgl2mpBjh92tOrEtDITITT2FEEKIGpFgp4lwuRTHS4OdlDbSqiOEEELUlAQ7TcSZUw5KihWmII34lsaGLo4QQgjRZEiw00QcO2QFIDnVhE4vA5OFEEKImpJgpwkoLnJx+lTpwOTW0oUlhBBC1IYEO03A8cM2UBDTXE9YhAxMFkIIIWpDgp1GTinl6cJKkRWThRBCiFqTYKeRy8xwUFykMBo1EpJkYLIQQghRWxLsNHJlN/1MSjWiN8jAZCGEEKK2JNhpxKwlLjJO2AG56acQQghxqSTYacSOH7ahFETF6ImIkoHJQgghxKWQYKeRcg9MlhWThRBCCG9JsNNInc10UFjgQm+AlskS7AghhBCXSoKdRurYQXerTmIrEwajDEwWQgghLpUEO42QzeriVLp7YLJ0YQkhhBDekWCnEUo/asflgogoPZHRMjBZCCGE8IahoQtwvkWLFjF//nwsFgspKSncddddtG3bttr0CxYsYMmSJWRlZREREUHfvn257bbbMJmaZouIUopjB8tWTDahadKFJYQQQnijUbXsrFu3jk8//ZQJEyYwffp0UlJSePnll8nNza0y/Zo1a5g5cyY33XQTf//737nvvvtYv349n3/+eT2XvO7knHWSn+dCp4fElKYZsAkhhBCNSaMKdr777juGDx/O0KFDSUpKYvLkyZhMJpYvX15l+r1799KhQwcGDhxIXFwc3bt3Z8CAARw4cKCeS153yqabt0w2YjRJq44QQgjhrUbTjeVwODh06BDjxo3zbNPpdHTr1o19+/ZVeUyHDh1YvXo1Bw4coG3btpw+fZotW7YwaNCgavOx2+3Y7XbPe03TCA4O9ryuS2Xnq+l57XbFyWNla+uYm1QXVm3r2pQFUl0hsOordfVfgVTfQKprTTWaYCcvLw+Xy0VUVFSF7VFRUZw8ebLKYwYOHEheXh7PPfccAE6nkxEjRnDDDTdUm8+8efOYM2eO531aWhrTp0+nefPm3leiGvHx8TVK98u2bJxOiIox0aVbUpP8ota0rv4gkOoKgVVfqav/CqT6BlJdL6bRBDuXYteuXcybN4+7776bdu3akZGRwccff8ycOXOYMGFClceMHz+eMWPGeN6XBRSZmZk4HI46LZ+macTHx5ORkYFS6qLpt2/JAyAxRU9GRkadlsXXalvXpiyQ6gqBVV+pq/8KpPoGSl0NBkONGyoaTbATERGBTqfDYrFU2G6xWCq19pSZPXs2V111FcOHDwegVatWlJSU8P7773PDDTeg01UekmQ0GjEajVWez1dfCqXURc9tyXaQm+NEp4PEFGOT/YLWpK7+IpDqCoFVX6mr/wqk+gZSXS+m0QxQNhgMtG7dmp07d3q2uVwudu7cSfv27as8xmq1VurqqSrAaQrKBibHJxkJCmqadRBCCCEao0bTsgMwZswY/vnPf9K6dWvatm3L999/j9VqZciQIQC88847xMTEcNtttwHQq1cvFixYQFpamqcba/bs2fTq1atJBT0Oh+LE0dKBya1lurkQQghRlxpVsNO/f3/y8vL44osvsFgspKamMmXKFE83VlZWVoWWnBtvvBFN05g1axbZ2dlERETQq1cvbr311gaqwaU5ecyGwwEhYTqaxTWqSyKEEEI0eY3uL+vIkSMZOXJklfteeOGFCu/1ej033XQTN910Uz2UzHfKurBayYrJQgghRJ1rOn09fio/10nOWSeaBsmp0oUlhBBC1DUJdhrY0dL7YLVoacQcLJdDCCGEqGvy17UBOZ2K9KPu1ZxbtZFWHSGEEMIXJNhpQKeO27HbFMEhGnEtGt3wKSGEEMIvSLDTgI4dcndhtWodhKaTgclCCCGEL0iw00AK8p2czXSCBslp0oUlhBBC+IoEOw2kbLp5XLyB4BC5DEIIIYSvyF/ZBuByKo4fLl0xuU1QA5dGCCGE8G8S7DSAjJN2bFZFkFkjLkEGJgshhBC+JMFOAyjrwkpOM6GTgclCCCGET0mwU8+KCp1kZjgA9+0hhBBCCOFbEuzUs7JWndgWBkLD9A1cGiGEEML/SbBTj1yucgOTpVVHCCGEqBcS7NSjM6cclBQrTEEaLRKNDV0cIYQQIiBIsFOPylZMTko1odfLwGQhhBCiPkiwU0+Ki1ycPiUDk4UQQoj6JsFOPTl+2AYKYprrCY+QgclCCCFEfZFgpx4opThWOjC5VWtZMVkIIYSoTxLs1IPMDAfFhS6MRo2WSTIwWQghhKhPEuzUg6OlA5MTU4zoDTIwWQghhKhPEuz4WHGRg4wTdkBu+imEEEI0BAl2fGzvLxaUC6Ji9EREycBkIYQQor5JsONDSin27LQAMt1cCCGEaCgS7PjQ2UwHuTk29AZIbCXBjhBCCNEQJNjxoaMH3dPNk1qZMBhlYLIQQgjRECTY8RGb1cWp46Vr68jAZCGEEKLBSLDjI5mnHbhc0Kx5EFExMjBZCCGEaCiGhi6Av0psZSIiykBEeAxouSilGrpIQgghRECSlh0fiojUk5AY0tDFEEIIIQKaBDtCCCGE8GsS7AghhBDCr0mwI4QQQgi/JsGOEEIIIfyaBDtCCCGE8GsS7AghhBDCr0mwI4QQQgi/5lWw8/XXX5OdnV1XZRFCCCGEqHNeraA8a9YsZs2aRadOnbjqqqvo168fwcHBdVU2IYQQQgivedWy8+6773LbbbdRUFDAe++9xz333MObb77Jzz//jMvlqqsyCiGEEEJcMq9admJiYhg7dixjx47l2LFjrFmzhrVr17J+/XrCw8Pp378/gwYNol27dnVVXiGEEEKIWqmzG4G2atWK2267jdtuu43du3ezYMECFi9ezOLFi4mPj+eqq67i6quvJjIysq6yFEIIIYS4qDqdjWWz2Vi7di3ffPMNP/30Ezqdjp49e5KcnMzcuXP5/e9/z8aNG+sySyGEEEKIC/K6ZUcpxfbt21m9ejWbNm2ipKSE1NRU7rjjDgYOHOhpycnJyeEf//gHn376KX369PG64EIIIYQQNeFVsDNjxgzWr1+PxWIhOjqaESNGMHjwYJKTkyuljY6OZtiwYfzzn//0JkshhBBCiFrxKthZunQpffr0YfDgwXTr1g1N0y6YvmPHjtx///3eZCmEEEIIUSteBTsffPABZrO5xunj4uKIi4vzJkshhBBCiFrxaoCyw+Hg6NGj1e4/duwYBQUF3mQhhBBCCOEVr4KdGTNm8P7771e7//333+c///mPN1k0Wcpux7V1A0Wrf2joogghhBABzatgZ9euXfTq1ava/b169WLHjh3eZNFkqS3rcb3zErmfvINSqqGLI4QQQgQsr4KdvLw8IiIiqt0fHh5Obm6uN1k0WdplvcFkwnEqHY4eaOjiCCGEEAHLq2AnKiqKw4cPV7v/0KFDFwyG/JlmDka7zL2ekGvT6gYujRBCCBG4vAp2evfuzbJly9i8eXOlfZs2bWL58uUBvYCg1nsQAGrTapTcGFUIIYRoEF5NPZ84cSI7duzgtddeIzU11bOY4PHjxzly5AhJSUlMnDixTgraFGndeqEFh6Kys+DQHmjbuaGLJIQQQgQcr1p2QkJCePnll7nxxhtxOBz8+OOP/PjjjzgcDm688UZefvllQkND66qsTY5mCiL4ysEAqE1rGrg0QgghRGDy+t5YZrOZiRMnBnQLzoWEDLqGomXfo35ai7r5d2g6fUMXSQghhAgodXrXc1GZuWdfCAmD3BzYt6uhiyOEEEIEHK9bdmw2Gxs2bODw4cMUFRXhOm8grqZpAX0/LM1oRLv8StSa/6E2rUbreFlDF0kIIYQIKF4FO5mZmbz44otkZmYSEhJCUVERYWFhnqAnPDy8VvfO8ldan6vcwc5P61C33otm8DrGFEIIIUQNefVX9z//+Q9FRUW8/PLLxMXFMXnyZP70pz/RoUMHFi5cyKJFi3jmmWdqdc5FixYxf/58LBYLKSkp3HXXXbRt27ba9IWFhXz++eds3LiRgoICmjdvzm9/+1suv/xyb6pWp7QO3SA8EvJzYc826Fr9qtNCCCGEqFte3y7immuuoW3btuh07lMppTAajYwdO5auXbsyY8aMGp9v3bp1fPrpp0yYMIHp06eTkpLCyy+/XO0qzA6Hg5deeonMzEweeeQR3nzzTe69915iYmK8qVad0/R6tF4DAFAbZYFBIYQQoj55FexYrVbi4uIACA4OBqCoqMizv3379uzZs6fG5/vuu+8YPnw4Q4cOJSkpicmTJ2MymVi+fHmV6ZctW0ZBQQGPP/44HTt2JC4ujs6dO5OamnrplfIRzwKDW39E2e0NXBohhBAicHjVjRUbG8vZs2cB0Ov1xMTEsH//fvr27QtAeno6JpOpRudyOBwcOnSIcePGebbpdDq6devGvn37qjzmp59+ol27dnz00Uds3ryZiIgIBgwYwLhx4zwtTeez2+3YywUbmqZ5AjVN02pU1poqO5+maWjtOkN0M8g5C7t+RuvZr07zamjl6+rvAqmuEFj1lbr6r0CqbyDVtaa8Cna6du3K5s2buemmmwAYMmQIX3/9NQUFBSilWLVqFYMHD67RufLy8nC5XERFRVXYHhUVxcmTJ6s85vTp02RmZjJw4ECefvppMjIy+PDDD3E6nZ4ynW/evHnMmTPH8z4tLY3p06fTvHnzGpXzUsTHxwOQM/haCr6eiXnnZpqNHu+z/BpSWV0DQSDVFQKrvlJX/xVI9Q2kul6MV8HOuHHjOHDgAHa7HaPRyPjx48nJyWHDhg3odDoGDhzIb37zm7oqayVKKSIiIrj33nvR6XS0bt2a7Oxsvv3222qDnfHjxzNmzBjP+7LINzMzE4fDUafl0zSN+Ph4MjIyUEqhOveCr2dStH4F1iOH0YL8Z6ba+XX1Z4FUVwis+kpd/Vcg1TdQ6mowGGrcUOF1N1ZsbKznvclk4r777uO+++6r9bkiIiLQ6XRYLJYK2y0WS6XWnjJRUVEYDIYKXVaJiYlYLBYcDgeGKqZ4G41GjEZjlefz1ZdCKeV+pLaF2BaQdRrXtk3oeg/0SX4NqayugSCQ6gqBVV+pq/8KpPoGUl0v5pIHKFutVu666y6+/fbbOimIwWCgdevW7Ny507PN5XKxc+dO2rdvX+UxHTp0ICMjo8JChqdOnSI6OrrKQKehaZqGVhrgqM0yK0sIIYSoD5cc7AQFBaHX6wkKCqqzwowZM4alS5eyYsUK0tPT+fDDD7FarQwZMgSAd955h5kzZ3rSX3PNNRQUFDBjxgxOnjzJzz//zLx587j22mvrrEx1TbvCPSuL7ZtRxUUXTiyEEEIIr3nV/NG3b19+/PFHrrnmmjoZ9d2/f3/y8vL44osvsFgspKamMmXKFE83VlZWVoV8YmNjeeaZZ/jkk094/PHHiYmJYdSoURVmdDU6yWkQnwgZJ1DbNqD1G9rQJRJCCCH8mqa86ND75Zdf+OijjwgPD2f48OE0b968yqnmrVu39qqQ9SEzM7PClPS6oGkaCQkJnDp1qkK/qevbmaj5s6DbFej/8Hyd5tlQqqurPwqkukJg1Vfq6r8Cqb6BUlej0Vg/A5RffPFFz+vdu3dXm2727NneZON3tN6D3MHOL1tQhflooeENXSQhhBDCb3kV7ATy3cy9oSUkQ1IqpB9B/bwebdA1DV0kIYQQwm95FeyUDRwWtaddMRCVfgS1aTVIsCOEEEL4jFf3xhKXruxeWezZgcqzNGhZhBBCCH/mVcvOu+++e9E0mqZJd1cVtLgESG0HR/ajflqHNnR0QxdJCCGE8EteBTu7du2qtM3lcmGxWHC5XERERNTpOjz+Rus9EHVkv3uBQQl2hBBCCJ/wKtj55z//WeV2h8PBDz/8wIIFC3juuee8ycKvaVcMRH35Mez/BZWdhRYTe/GDhBBCCFErPhmzYzAYGDlyJN27d+ejjz7yRRZ+QYtpDm07gVKon9Y2dHGEEEIIv+TTAcopKSkXXH9HnBuorDbJvbKEEEIIX/BpsLN9+3YZs3MRWq8BoOng8D5UZkZDF0cIIYTwO16N2ZkzZ06V2wsLC9m9ezeHDx/m+uuv9yYLv6dFRkOHrrBnO2rzWrRRNzZ0kYQQQgi/4lWw8+WXX1a5PTQ0lBYtWjB58mSGDx/uTRYBQes9CLVnO2rTKpBgRwghhKhTXgU7cs+ruqFdfiVq5ntw/DAqIx0tPqmhiySEEEL4DVlBuRHQwiKgUw8A1KY1DVsYIYQQws94Fexs376dmTNnVrv/888/Z+fOnd5kETC03gMB96wspVQDl0YIIYTwH14FO3PnzuXs2bPV7s/Ozmbu3LneZBEwtB79wGCAU8fhxNGGLo4QQgjhN7wKdo4dO0a7du2q3d+mTRuOHTvmTRYBQwsJha5XALLmjhBCCFGXvAp2HA4HDofjgvutVqs3WQQU6coSQggh6p5XwU5ycjIbN26scp9Sig0bNpCUJDOLakrr3gdMQZCZAUcPNHRxhBBCCL/gVbAzcuRI9u7dyxtvvMGxY8dwOp04nU6OHj3KG2+8wb59+xg5cmRdldXvaUFmtMt6A9KVJYQQQtQVr9bZueqqqzh9+jRz585lw4YN6HTu2MnlcqFpGjfeeCNDhgypi3IGDK33INTmNe7HjXei6WR1ACGEEMIbXgU7ADfddBODBg1i48aNnDlzBoAWLVrQu3dv4uPjvS5gwOnWC8zBkJ0Fh/ZA284NXSIhhBCiSfM62AGIj49n7NixdXGqgKcZTWg9+qF+XI7atAZNgh0hhBDCK171kRw6dIjFixdXu3/x4sUcOXLEmywCktZnEADqp7Uol7OBSyOEEEI0bV4FO7NmzWLHjh3V7t+5cyezZs3yJovA1Kk7hIRBbg7s29XQpRFCCCGaNK9bdjp27Fjt/k6dOnHw4EFvsghImsGI1qs/ILOyhBBCCG95FewUFxej1+ur3a9pGkVFRd5kEbC03mVdWetQF1i4UQghhBAX5lWwk5CQwLZt26rdv3XrVlq0aOFNFoGrfVcIj4TCfNhT/WcshBBCiAvzKtgZNmwYW7Zs4ZNPPqGwsNCzvbCwkBkzZrB161aGDRvmdSEDkabXo10xAAC1UbqyhBBCiEvl1dTzUaNGceTIEb7//nsWLlxIdHQ0ADk5OSilGDRoENddd12dFDQQaVcMQi3/HrX1R5TdjmY0NnSRhBBCiCbHq2BH0zQeeOABrrrqKjZs2OBZVLB379707duXLl261EkhA1bbThDVDCxnYddP0KNfQ5dICCGEaHLqZFHBrl270rVr10rbXS4XW7ZsoVevXnWRTcDRdDq03gNR//sGtXE1mgQ7QgghRK3VSbBzvr1797J69Wp+/PFH8vPzmT17ti+yCQha70HuYGfbRpS1BC3I3NBFEkIIIZqUOgt20tPTWbNmDWvWrCEzMxOz2Uz37t2lVcdbqe0gtgVknUZt34zWe2BDl0gIIYRoUrwKdrKzs1m7di1r1qzhyJEjmEwmbDYbt9xyC7/61a8wGHzScBRQNE1zd2UtnIvavBok2BFCCCFqpdbRSFFRET/++CNr1qxh9+7dmEwmevXqxc0330xcXByPPvooLVu2lECnDmm9r0ItnAvbN6OKi9CCQxq6SEIIIUSTUeuI5J577gGgZ8+e/OEPf6BXr16YTCYAMjIy6rZ0wi0pFeKTICMdtW0DWr+hDV0iIYQQosmo9aKCdrud0NBQ4uLiaNGihSfQEb5T1pUFssCgEEIIUVu1btl54403WL16NWvWrOG7774jPj6eAQMGMGDAgAveJ0t4R+s9CDV/FvyyBVWYjxYa3tBFEkIIIZqEWgc7iYmJ3HLLLdxyyy3s2bOH1atXs3jxYubOnUtcXBwA+fn5dV7QQKclJLu7s9KPoH5ejzbomoYukhBCCNEkeDWKuGPHjnTs2JG77rqLLVu2sGrVKnJycvjggw/49ttvueKKK+jVq5espFxHtN6DUOlHUJtWgwQ7QgghRI3UyZQpvV7PFVdcwRVXXEFxcTEbNmxg9erVfP/99yxYsEAWFawjWu9BqHn/gT07UHkWtIiohi6SEEII0ejVOtjJzc0lMjKy2v3BwcEMGTKEIUOGkJ2dzbp167wqoDhHax7vXmTwyH7UT+vQho5u6CIJIYQQjd4lTT1v06YNl19+OZdffjmtW7euNm1MTAxjxozxqoCiIq33INSR/ahNq0CCHSGEEOKiah3sPP7442zZsoVly5bx5ZdfEhkZSY8ePejVqxeXXXYZwcHBviinKKVdMQD15b/hwG5UdhZaTGxDF0kIIYRo1God7JSNzQE4duwYP//8M1u2bOHNN99E0zQ6dOjgafVJTEys8wIHOi2mObTtDAd+Qf20Fm3E9Q1dJCGEEKJR82qAcqtWrWjVqhXjxo2jqKiIrVu3smXLFr799lv++9//EhcXR8+ePbn88svp0qULRqOxrsod0LTeA1EHfnHPypJgRwghhLigOruBVUhICP3796d///4AHDhwwNPqs2TJEiZMmMCECRPqKruApvUagJr1IRzeh8rMcA9cFkIIIUSVfHa3zrZt29K2bVsmTpxIbm4uRUVFvsqqUVJKsTG9gJhCI+1C6/bcWmQ0dOwGu7ehNqxAG3NL3WYghBBC+JFa3xurvKysLPbs2VNh25EjR3jnnXf4+9//zsaNGwGIjIwkISHBm6yanGWHcnl5ZTqvL9uHw6Xq/Pxa/2EAqMXzUJazdX5+IYQQwl94Fez8+9//5ssvv/S8t1gsvPjii2zYsIHdu3fz+uuvs2HDBq8L2RQNTIkgyqznZG4JPxy01Pn5tT6DIa09lBSjvvh3nZ9fCCGE8BdeBTsHDx6kW7dunverVq3CZrPx2muv8d5779GtWzfmz5/vdSGboiCDjgld3dPCv9iRhd3pqtPzazodutvvB02H2rQatXtbnZ5fCCGE8BdeBTsFBQUVVlP+6aef6Ny5M/Hx8eh0Ovr06cOJEye8LmRTNbJdFHFhQWQVOVhyILfOz6+ltEEbMhIA18x/oRz2Os9DCCGEaOq8CnYiIiLIzMwEoLCwkP3799O9e3fPfpfLhctVty0aTYlJr2NSvxQA5uw6i9VR95+FNu4OCI+EjHTU/76p8/MLIYQQTZ1XwU63bt1YuHAh3333He+88w5KKfr06ePZn56eTrNmzbwuZFM2tltLmocayC52sPiApc7Pr4WEod10FwDqu9mos5l1nocQQgjRlHkV7Nx2220kJSXxn//8h+3bt/PrX/+auLg4AOx2O+vXr6dr1651UtCmymTQcXPp2J05u85S4ovWnX5DoH0XsFlxzf6gzs8vhBBCNGVerbMTFRXFn//8Z4qKijCZTBgM506nlOK5554jNlbu3TSsTRRzdp0lo8DO9/tyuKFz3bZ2aZqG7rb7cE37I2z5EbVjM1q3K+o0DyGEEKKp8qplp0xISEiFQAfAZDKRmppKWFhYXWTRpBl0Gjd3cwd9X/2STZHdWed5aIkpaFePBcD1+fsom7XO8xBCCCGaIq+CnR07dvDtt99W2LZs2TLuv/9+Jk+ezIwZMy5pgPKiRYt48MEHuf3225kyZQoHDhyo0XFr165l4sSJvPrqq7XO09cGp0bQMtxEvtXJgr05PslD+9UtEBUDmRmoRXN9kocQQgjR1HgV7Hz55ZccOXLE8/7YsWN88MEHRERE0LlzZxYuXFgpGLqYdevW8emnnzJhwgSmT59OSkoKL7/8Mrm5F566febMGf7zn//QqVOnS6mKz+l1Grd0c3dffb07m0KbD1p3zCFoE+8GQC2cizpzqs7zEEIIIZoar4KdEydO0KZNG8/7VatWERwczLRp0/jTn/7E8OHDWbVqVa3O+d133zF8+HCGDh1KUlISkydPxmQysXz58mqPcblcvP3220ycONEzQLoxGpgSQXKkiQKbi/l7fNS6c8UA6NQdHHZ3d5aq+1tVCCGEEE2JVwOUS0pKCA4O9rzfunUrPXr0ICgoCHDfDHT16tU1Pp/D4eDQoUOMGzfOs02n09GtWzf27dtX7XFz5swhIiKCYcOGsXv37gvmYbfbsdvPLb6naZqnDpqm1bisNVF2vrJng17j1sua8+rqE3yzJ5sxHWMID9LXeZ7a7ffhnPp72PkTbN2AdvmVdZpHdfmWf/ZngVRXCKz6Sl39VyDVN5DqWlNeBTuxsbEcPHiQYcOGkZGRwfHjxxkzZoxnf0FBAUajscbny8vLw+VyERUVVWF7VFQUJ0+erPKYPXv2sGzZshqP05k3bx5z5szxvE9LS2P69Ok0b968xuWsrfj4eM/rG+Pjmbcnl/2ZBSw9buX+QW0ucOQlSkjAcuOvyf/iY7QvP6LF8FHozMEXP64OlK+rvwukukJg1Vfq6r8Cqb6BVNeL8SrYGThwIHPmzCE7O5v09HRCQ0Pp3bu3Z/+hQ4d8erfz4uJi3n77be69914iIiJqdMz48eMrBGRlkW9mZiYOh6NOy6dpGvHx8WRkZFToTrqpcxSvrCzg883HGZZkIsLs1WWokho8GpYuwJl5mpMf/gP9jb+t8zzKq66u/iiQ6gqBVV+pq/8KpPoGSl0NBkONGyq8+it7ww034HA42LJlC7GxsTzwwAOEhoYC7ladXbt2MXr06BqfLyIiAp1Oh8ViqbDdYrFUau0BOH36NJmZmUyfPt2zrezC3nLLLbz55puVIluj0Vhta5OvvhRKqQrn7pMYSpuYIA5mW/nql7P8tqcPxhmZgtDdMhnXP19GLfka15XD0BKS6j6f85xfV38WSHWFwKqv1NV/BVJ9A6muF+NVsKPX67n11lu59dZbK+0LCwvjgw9qt5qvwWCgdevW7Ny503PbCZfLxc6dOxk5cmSl9C1btuRvf/tbhW2zZs2ipKSEO++8s9EuaKhpGrdd1pw/r0hnwd4cru8YQ1Rw3bfuaD36wmW9YfsmXDPfQ/fIn6UPVwghRMCpk0UFwT1YOT09nfT0dEpKSi75PGPGjGHp0qWsWLGC9PR0PvzwQ6xWK0OGDAHgnXfeYebMmYB74cJWrVpVeISGhmI2m2nVqlWlhQ4bk14tQ2nfzIzVqZj7y1mf5aO7ZTIYTbBnO2pTzQeLCyGEEP7C62jgwIEDfPbZZ+zZs8ezgKBOp6Njx47ccccdFaam10T//v3Jy8vjiy++wGKxkJqaypQpUzzdWFlZWX7ROqFpGrd1b84Ly46zaL+FcZ1iaBZS88HcNc6neTza6Amob2aivvg3qtsVaMEhdZ6PEEII0VhpyosOvf379/PCCy9gMBgYOHAgiYmJgHv9nbVr1+JwOHjhhRdo27ZtnRXYVzIzMytMSa8LmqaRkJDAqVOnquw3VUox5X/H+CWzmOvaR3FPb9+MnFd2G64Xfg9nTqFdPRbdzXfXeR4Xq6s/CaS6QmDVV+rqvwKpvoFSV6PRWOMByl51Y82aNYuYmBj+8Y9/MHnyZEaPHs3o0aOZPHkyb775JtHR0Xz++efeZOHXNE3j1svc44oWH8gls7Bugy1PPkYTulvvBUAt+w6Vftgn+QghhBCNkVfBzv79+xkxYkSVM6WioqK4+uqr2b9/vzdZ+L3L4kPp1iIEh0vx5U7fjd3Rul4Ol/cHlwvXZ++hLuGeZUIIIURT5FWwo2kaTmf193hyuVx+Mb7G124rbd354aCF0wU2n+Wju/l3EGSGA7tR66u//YYQQgjhT7wKdjp06MDixYvJzMystC8rK4slS5bQsWNHb7IICJ3jQuiREIpTwewdPmzdiWmONuZmANTcGajCAp/lJYQQQjQWXs3GuvXWW5k6dSoPP/wwffr08ayWfPLkSTZv3oxOp6tyDR5R2W2XxbL1VCHLD+cyoUszWkaYfJKPdvVY1LplcOo46uv/oN1+v0/yEUIIIRoLr1p20tLSeOWVV+jRowebN29m7ty5zJ07l59++okePXrw5z//uca3cQh0HWKDuaJlKC4Fs3dk+SwfzWBEd/t9AKiVi1BHZEyVEEII/+b1OjtJSUk8/vjjuFwu8vLygHO3ffjqq6+YPXs2s2fP9rqggeDWy5qz+WQhq47mMaFrM5Ijg3ySj9ahG1rfwagNK3F99h66p19F09Xt3deFEEKIxqLOVlDW6XRERUURFRWFTldnpw0obZuZ6ZsUhkvBLB+27gBoN90FwSFwZD9q1RKf5iWEEEI0JIlKGpmymVlrj+ZzJOfSb7txMVpkNNr1twOg5v0HlZ/rs7yEEEKIhiTBTiOTGm1mQKtwFPXQujNkNCSnQVEBau4Mn+YlhBBCNBQJdhqhWy6LRQPWHy/gULYPW3f0enSls7HU2qWoA7/4LC8hhBCiodR6gPKhQ4dqnDY7O7u2pxdAq8ggBqVGsOpIHjO3Z/HskCSf5aW16Yg24GrU2h/cg5Wf/TuaXgYrCyGE8B+1DnaefvppX5RDnOeWbrGsOZrHphMF7D9bTLtmwT7LS7vxt6gtP0L6EdTyBWhXj/VZXkIIIUR9q3Wwc//9sghdfUiMMDEkLYJlh/KYuS2LqcOSfZaXFh6JdsNvUP99F/XNZ6grBqJFxfgsPyGEEKI+1TrYGTJkiA+KIapyc9dYVhzO4+dThezOLKJT8xCf5aUNGoFa8z/3VPQvP0ab/KjP8hJCCCHqkwxQbsTiw00Mbx0JwMztPp6ZpdO7V1bWNNTGlag9232anxBCCFFfJNhp5CZ2jcWgg+0ZRew8XeTTvLTUdmiDRwHg+uw9lMPu0/yEEEKI+iDBTiMXF2ZkRJsoAGZuz0Qp5dP8tHF3QHgkZKSj/vetT/MSQggh6oMEO03AhK7NMOo0dp0pZluGj1t3QsPQJtwJgPpuFursGZ/mJ4QQQviaBDtNQGyIkWvbRQHusTs+b925chi06ww2K64Zb6FcLp/mJ4QQQviSBDtNxIQuzTDpNfZmFfPzyUKf5qVpGrrf/gFMJtizHbX8e5/mJ4QQQviSBDtNRHSwgdHto4F6at1p0RLtxjsBUF/NQGWc8Gl+QgghhK9IsNOE3NA5BrNB40B2CRtPFPg8P23IaOjUHWw2XP/+O8rp9HmeQgghRF2TYKcJiTQbGNPBvbLx59uzcPm6dUenQ3fnHyA4FA7vQy2a69P8hBBCCF+QYKeJub5TDMEGHYdzrPx4PN/n+WkxzdFumQyAmj8LdazmN4IVQgghGgMJdpqYiCA9Yzu5x+7UR+sOgHblUOjRD5wOd3eWXRYbFEII0XRIsNMEje0YQ6hJx7FcGysP5/k8P03T0P36AfdigyeOor6d6fM8hRBCiLoiwU4TFGbSM76Te+zOe5tOcySnxOd5ahFR6O54AAC1eB7qwG6f5ymEEELUBQl2mqjxnZtxWYsQShwuXlqRjqXY4fM8tcuvROs3FJTL3Z1l9X2QJYQQQnhLgp0myqDTeGJQIi3DjWQWOXhl1QlsTt+vdKzdOhmiYyEzAzVnhs/zE0IIIbwlwU4TFh6k59khyYSZdOzNKuadHzN8v9hgSJh7OjqgVnyP+mWLT/MTQgghvCXBThOXGGHiyUGJ6DVYeSSPL3ed9XmeWuceaENHA+D6+C1Uke8XOBRCCCEulQQ7fuCy+FDu6d0CgM+2ZbHuWD3M0LrxTohLAMtZ1Ocf+Dw/IYQQ4lJJsOMnRraL5lcd3Ovv/H3dKQ6c9e3gYS3IjO6uP4GmQ/24HNdP63yanxBCCHGpJNjxI5Muj+PyhFBsTsXLK9M5W+Tbxf+0Nh3RRo4HwPWff+LM8X0XmhBCCFFbEuz4Eb1O47GBLUmONJFd7ODllSewOnw7Q0v71W2QlAoFeeS884rPB0gLIYQQtSXBjp8JNel5dnASEUF6DmaX8Ob6Uz69pYRmNLq7s/QGin9ciVq3zGd5CSGEEJdCgh0/FB9u4qmrEjHoYN2xfD7fnuXT/LTkNHTX3waAa9b7qLOZPs1PCCGEqA0JdvxUl7gQHugTD8AXO8+y8nCuT/PTrr0BU8duUFyE65O3UC7fL3AohBBC1IQEO35seJsobujsvofW2z9msDer2Gd5aXo9MY+8CCYT7N6GWvG9z/ISQgghakOCHT93R/fm9EkKw+5SvLIyncxC383QMia2QjdhEgBq7gxUxgmf5SWEEELUlAQ7fk6v03ikf0vSooOwlDh5aUU6xXbfdTFpQ0ZDp+5gs7lvFup0+iwvIYQQoiYk2AkAwUYdzwxOIsqs54jFyhvrTuJ0+WaGlqbTue+dFRwCh/ehFs31ST5CCCFETRkaugBNgcPhoKio6JKOLS4uxmaz1XGJai8I+OvgWA5kl6AU7D+ZRcuIoDrNw1NXQxDqsb/CyWOgaXDmNJo5uE7zqi2lFAaDgdDQ0AYthxBCiPonwc5FOBwOCgsLCQ8PR6erfUOY0WjEbvftSsY1FQGEhTs5XVAafJmMRJjr7itQvq4qPBzMQVBUAA4rhDVHu4TPry4VFhZitVoJCqrbIE8IIUTjJt1YF1FUVHTJgU5jFB6kJzrYHeCcKbRTbPfNmBpN06BZc9DrwWaF3Gyf5FMbISEhWK3Whi6GEEKIeuYff8F9zF8CnTIxwQbCTHoATuXbsTt9M2BZ0xsgJs79JteCKvHd1PcalUfTGjR/IYQQDcO//oqLGtE0jbgwI0EGHS6lOJlv992A5dAwCI0AFJw9I4sNCiGEqHcS7AQonaaREGZEr9OwO12cLrD77iaeMbFgMILdBjm+vXWFEEIIcT4JdgKYQa8jIdyEpmkU2Z1kFTmqTNe3b18++OCDS85H0+uhmbs7a93KFSQmJpKb69vbVwghhBBlZDaWn5owYQKdO3dm2rRpF0xnNuhoEWoko8BGbokDk14j8rwZWt9//z0hISFelUcLDkGFR3rey2KDQggh6ou07AQopRQOh7slJyxIT0yIO8DJLLRTZKsYiDRr1ozg4DpYJye6tDsLIKfhZ2cJIYQIDBLs+KGHH36Y9evX89FHH5GYmEhiYiKzZ88mMTGRZcuWMXLkSNLS0ti4cSNHjhxh0qRJDO13BSP7XcY9t47nu/+twFZuhtb53ViJiYnMnDmT3/3ud7Rp04YBAwawZMmSi5ZL0+kgIsr9pigPVVgAwIIFCxg6dChpaWn07duX9957r8JxM2bMYMCAAbRu3Zru3bszefJkz77vvvuO4cOH06ZNG7p06cLNN998yQtACiGE8E/SjVVLSin3ujE1Te9youpqUUFTUI2mT0+bNo1Dhw7RsWNHHnvsMQD27t0LwCuvvMLzzz9Pq1atiIyM5OTJkwwbNownn3wSo9HIxzO/4MnfTyblux+4omMael3V+b3xxhs8++yzPPvss3z88cc89NBD9O/fn7CwsAuWTTOVW9Dv7Gm2bdvKfffdxyOPPMLYsWPZvHkzU6ZMITo6mptvvplt27bx/PPP89Zbb3HFFVdgsVjYsGEDAKdPn+bBBx/kmWeeYdSoURQUFLBhwwbfDbQWQgjRJEmwU1s2K66HJtY4eV0uYad75wsIMl80XUREBCaTCbPZTFyce2DwgQMHAHj88ce56qqrPGmjo6Pp0qWL5/0LzzzFih8Ws3LZ/0hKupOEcFOVeUycOJFx48YB8NRTT/HRRx+xZcsWBg0aVLPKmIPB5eL99z9g4BW9eHjy3RAaRps2bdi/fz/vvfceN998MydOnCAkJISrr76asLAwkpKS6Nq1KwBnzpzB4XAwevRokpKSAOjUqVPN8hdCCBEwJNgJMJdddlmF94WFhbz++ussXbrUEzyUlJRwJuMUxXYX6bk2qmonKR9UhISEEB4eTmZmZs0L0jwBDDoOHD3GNYP6Q1YG5JtR0bH07t2bDz/8EKfTyVVXXUVSUhJXXnklQ4YMYejQoYwaNYrg4GA6d+7MwIEDGT58OIMHD2bw4MFcd911REVFXdqHI4QQwi9JsFNbpiB3C0sN1em9sUze39Pp/FlV06ZNY/Xq1Tz33HOkpqZiNpu55557MOFEr2nYnC6cLkWJw4VSytONZjQaK5xH07RadR9pmoYWFgEmE5hDQKcDawlkpKMs5wYvh4WFsWjRItatW8eqVav429/+xuuvv873339PZGQks2bNYvPmzaxcuZKPP/6Y6dOn891339GqVSsvPiUhhBD+RAYo15KmaWhB5oZ51OJ2B0ajEVcNVivevHkzN910E6NGjaJTp07ExcWRnp6OQaeRHBVEsNH9FSmwOjldUPcrLbdr147NO3ZCyxQIjwQ0Nm3eTOukJHSWbJTTicFg4KqrruLZZ5/lhx9+ID09nbVr1wLu69G7d28ee+wxFi9ejNFoZOHChXVaRiGEEE1bo2zZWbRoEfPnz8disZCSksJdd91F27Ztq0z7ww8/sGrVKo4fPw5A69atufXWW6tNHyiSk5PZsmULx48fJzQ0tNrAJy0tjYULFzJixAg0TeO1117zpDXoNFqGm9CVBlkFNicljrq93cO9997L6NGjefPttxk7diw/bdzIjK/m8cojD0O+hR8WL+JotoV+g4cQFR3N0qVLcblctGnThp9//pk1a9YwePBgYmNj+fnnn8nOzqZdu3Z1WkYhhBBNW6Nr2Vm3bh2ffvopEyZMYPr06aSkpPDyyy9Xu+LuL7/8woABA5g6dSovvfQSzZo146WXXiI7O7DXcbn33nvR6XQMGTKEbt26ceLEiSrTTZ06lcjISK6//nruvPNOT/oymqah0yDKbMCg03CUtuzk25x1MuupW7duvPfee3z77bcMHz6cv/39TR5//Akm3nU3mIKICA1h4aKFTJx4E4MHD+Y///kP//znP+nQoQPh4eFs2LCBX//61wwaNIhXX32V559/nmHDhnldLiGEEP5DU41snu6UKVNo06YNv/vd7wBwuVzcf//9jBo1yjP750JcLheTJk3irrvuYvDgwTXONzMzs8qxNXl5eURERNT4POer0zE7DczpUmQW2ikoXXQw2KijRZgJg+7cOJ66rKtSCgrzIecsOEtvZRFkhuhYNPOlLXLo7fUEdwCYkJDAqVOnAmKaeyDVV+rqvwKpvoFSV6PRSPPmzWuUtlG17DgcDg4dOlShZUGn09GtWzf27dtXo3NYrVYcDsdF13sRtafXabQIMxIXakTTNIrtLo5brBTafHPrB88g5sQUiGpWcRDzmVMou80n+QohhPAvjWrMTl5eHi6Xq9LU4aioKE6ePFmjc3z22WfExMRUCJjKs9vtFVofNE3z3AqhNgOAA5WmaUSYDZiNOjIK7NgcLk7l24gyG4iPNPDkk0/y1VdfVXnsDTfcwPTp02ufp04HUTEQHuGeqZWfB0UFUFyICo9Ei4wBvb5WdfBG2fGB8n0JpPpKXf1XINU3kOpaU40q2PHW119/zdq1a3nhhRcwmapeDG/evHnMmTPH8z4tLY3p06dX2xRWXFxcaZp1bXl7fGNkNEJakIkz+VZyimxYShyUOIt49Ikneeihh6o8Jjw83LvPwmiE+ERUdCzOs6dxFRZAngVVkI8+JhZ9VDRoF26sNJlMJCQkXHoZyomPj6+T8zQVgVRfqav/CqT6BlJdL6ZRBTsRERHodDosFkuF7RaL5aILxX377bd8/fXXPPfcc6SkpFSbbvz48YwZM8bzvizyzczM9NwYszybzebVOBR/GrNTlWbBesx6E6cL7JTYnVi1EJrHRRARVPVXq04+C53OvShhWBHkZIHNijPrNE5LNkQ3QwsNr/ZQm83GqVOnvMpe0zTi4+PJyMjw6/7wMoFUX6mr/wqk+gZKXQ0GQ43H7DSqYMdgMNC6dWt27txJnz59APeA4507dzJy5Mhqj/vmm2/46quveOaZZ2jTps0F8zAajdW2Lvjzl8KXQk16WkXpOFPooMjm4EyBnWK7i9gQY7X31qoLWnAIypx8bhCzww6ZGag8ywUHMdfVdVZKBdR3JpDqK3X1X4FU30Cq68U0qgHKAGPGjGHp0qWsWLGC9PR0PvzwQ6xWK0OGDAHgnXfeYebMmZ70X3/9NbNnz+b+++8nLi4Oi8WCxWKhpKSkgWoQmAw6jVbRIcQEu+PnfKuT9Dxrna/Lc74LDmLOzEA5K7fWCSGECCyNqmUHoH///uTl5fHFF19gsVhITU1lypQpnm6srKysCoOu/ve//+FwOHjjjTcqnGfChAlMnFjzG3YK72kaxIQYCTbqOF1gx+5UpOdaaRZiJMqs9+lgubJBzCosAnJLBzEX5kNxESomFkLDZbCeEEIEqEa3zk5DkXV2vFe+rk6X4kyh3TMtPcSoJy7M6FmTx9eUtQTOngFb6X3ng0MgJo784mJZZ6eWAqm+Ulf/FUj1DZS6Ntl1doT/0Os04sOMNC9dk6fI7uR4rpUiH63Jcz4tyAwJyRDdzN3kVFwEp46hzmaiXPVTBiGEEI2DBDuiSn379uWDDz6oUdrExEQWLVpUabumaUSaDSRFmDDpdThdipP5NrKK7PXyvw1N09xr8LRsBeZgcLng9Alcf30SdeKoz/MXQgjROEiwI3wuyKAjKdLkmY5uKXaQnmfD7vTt4OUymtEELRKhWZx78cHD+3D9+U+4vpmJCpAuRiGECGQS7Ih6odM04sKMxIe576Judbg4arGSkW+j2F43NxW9EE3T0MIjoXUH6N4HnA7Ud7Nw/flh1ME9Ps1bCCFEw5Jgxw/997//5fLLL8flqthyMmnSJB555BGOHDnCpEmT6N69O+3atWP06NGsWrWqzvLfvXs3N910E23atKFLly488cQTFBYWAhAWpCf9l5+4//YbuLZPNwb3voxx48axcfdhcksc7Ny5iwkTJtC+fXs6dOjAyJEj2bZtW52VTTOa0D34DNo9T0B4JJw6jmv6k7g+fx9VUlRn+QghhGg8JNipJaUUJQ5XzR/2WqS9yKOmrR9jxowhJyeHtWvXerbl5OSwYsUKxo8fT2FhIcOGDWP27NksXryYIUOGMGnSJE6cOOH151NUVMTtt99OVFQUCxYs4F//+herV6/mmWeeAdw3e7138t1cNaA/3y9ZwozZX/GrCbfgKL2j+n0PPkhM83i++fY7Fi5cyIMPPojBULcrJGiahq73QHTT/onWfzgohVr2Ha6pv0ft+KlO8xJCCNHwGt06O42d1am4eXbN7sBe12bf3B6z4eJTt6Oiohg6dChff/01gwYNAmDBggXExMQwYMAAdDodXbp08aR/4oknWLRoEUuWLGHSpElelXHevHlYrVb+8Y9/EBISAsBLL73EnXfeyTPPPIPBYCAvL4+rr76aDm1aA9CnWyfyrU5yrQ5OnzrJLb+djLF5MkFGHUOvSSHU5JuYXAuLQJv0R1Tfq3B9+k84ewbXWy+i9RuCNvFutHDvpqgLIYRoHKRlx0+NHz+e77//HqvVvc7MvHnzGDt2LDqdjsLCQqZNm8bgwYPp1KkT7dq1Y//+/XXSsrN//346derkCXQAevfujcvl4uDBg0RHRzNx4kRuv/12fvvb3/Lhhx+SlXmGqGADrSKD+N3dk3n1xSk8Mvk3fPjeu/y0ez9HLVayi+w4XL4Z16N17onuxXfQrr4eNB3qxxW4nn8A14aVfr1GhRBCBApp2amlIL3G7Jvb1zi90WDE7qibGT9B+povyDdixAiUUixdupTu3buzYcMGXnjhBQCmTZvG6tWree6550hNTcVsNnPPPfdgs9nqpJwX8/e//53f/e53LF++nG+//ZZXX32Vzz//nF69evHUE49z0403sOSHH1i6dDkfv/sWz7/6JlcNv4bsYgdhJj2RZj1mg65OV0TWgsxoN/8O1WcQrk/ehhNHUR++jtqwEt0d96PF1GzhKiGEEI2PtOzUkqZpmA26mj+MtUh7kUdt/ribzWZGjRrFvHnz+Oabb2jTpg3dunUDYPPmzdx0002MGjWKTp06ERcXR3p6ep18Pu3atWP37t0UFZ0b7Ltp0yZ0Ol2Fm7R27dqV3//+93z77bd06NCBr7/+2rOvTZs23H/vvcz5YhajRo9i2XdfYTa4v6oFNicn8mwcz7WRW+LAWcetPVpae3TPvoF2/W1gMMCOzbiefwjX8gUoV/1MlRdCCFG3JNjxY+PHj2fp0qXMmjWL8ePHe7anpaWxcOFCdu7cya5du3jwwQcrzdy6VDfccANBQUH88Y9/ZM+ePaxdu5bnnnuOG2+8kebNm3Ps2DH+8pe/sHnzZtLT01m5ciWHDx+mbdu2FBcX88wzz7Bu3TrS09PZtGkT27dto1OH9iRFBpEcGUREkPseWzani8xCO0csVjIL7Vjr8IajmsGIbswt6J57E9p0BGsxaua/cL32NOpU3QSFQggh6o90Y/mxgQMHEhUVxcGDBysEO1OnTuWRRx7h+uuvJyYmhgcffJCCgoI6yTM4OJjPPvuM559/nuuuuw6z2cx1113H1KlTPfsPHDjAl19+SU5ODnFxcdx55538+te/xuFwkJOTwx//+EeysrKIiYlh1KhRPProo4B7ccK4MBPNXKp0QLMTu9NFbomD3BIHwUYdkUEGQk1108WltWyF7om/olZ8j/rqP3BgN65pf4Axt6DufNDr8wshhKgfciPQUnIjUO/Vd12VUhQ7XOSWOD03HAX3fbkigvSEmfSY9FqFwOdSr6c6m4nrv+/CTvfUdGNqW5wjxkGPvmimIK/r0pgFyk0FQerqzwKpvoFS19rcCFRadkSTpWkaIUY9IUY9dqeLPKuTPKsTp0uRU+wgp9iBzjPGyv18qTO6tGbN0f3hedTGVahZH2A/cgA++BsEh6BdMRCt/zBo06lOB00LIYSoGxLsiAv66quvePLJJ6vcl5SUxPLly+u5RFUz6nU0C9ERHWyg0OYiz+qgxKFwKUWR3UlRaYPTqawivlyTTYfmZjrEBtOxeTCJ4aYaBSmapqH1HQxdehK6cSV5//sGss6gVi9BrV4CcQloVw5Fu3IYWrM4H9dYCCFETUmwIy7ommuuoWfPnlXuMxqN9Vyai9NpGuFBesKD9CilsDndXV0lDhdWhwsFHM21cjTXypIDuQCEm3S0Lw18OsYG065ZMMHG6sfua+GRRN5xL4VDx6D27UStW4b6aS2cOYX6Zibqm5nQoZs76OnVH80cXE+1F0IIURUJdsQFhYWFERYW1tDFuCSaphFk0AgynAtcwlQwT18Vxt6sYvZkFnMgu4R8m4ufThby00n3/bt0GqREBdExNtjT+hMfZqzU+qPpdO6gpkM31G33on5ej1q/DPZsh707UHt3oGa+5w54rhzmTquTCZBCCFHfJNgRAcWo19EvOZx+yeEA2J2KI5YS9mQWewKgzCIHh3OsHM6xsnC/BYDIID0dmruDn07Ng4mOdVY4rxZkRrtyKFw5FHU2E/XjctS6ZXDmJGr9ctT65RDTHK3fULT+w9BatKzvqgshRMCS2VilZDaW95pCXWtyPc8W2T2Bz56sEg5ml1Qa2KzXaXRrEUK/pDD6JocTE1z5/w1KKTi0193NtWk1FBee29mmozvouWIgWkjjbjkLlJkdIHX1Z4FU30Cpa21mY0mwU0qCHe81hbpeyvW0O10cyrGWBj/uICi72OHZrwHtY4PplxzGlcnhJISbKp1D2W2orRvcrT27toAqXQTRYETr2c/dzdW5B5pe7031fCJQ/uEEqas/C6T6BkpdZeq5EHXIqNfRoXT8zvWl2xzBUXz70yF+PJ7H3qwS9ma5u8E+2ZJJSlQQ/ZLD6JcUTlp0kHsWl9GE1nsQ9B6EsmSjNqx0j+85cRS1abW75ScyGq3vEHeLT2JKg9ZZCCH8iQQ7QtSSpmm0ig7hxi7NuKFzDGeL7GxIL+DH4/nsOF3EUYuVoxYrs3ecpUWYkX5J7hafDs2D0WkaWlQM2rXjUdeMg2OHUOuWojauhNwc1JJ5qCXzIDkN7bLeaF0uh9YdGmWLjxBCNBUS7IiL6tu3L3fffTeTJ09u6KI0Ss1CjIxuH83o9tHkW51sOuEOfLacKuR0gZ1v9uTwzZ4cosx6+iaF0y85jG4tQjHqNUhpg5bSBnXTJNjxE651y2DHJjh+GHX8MGrBFxAcCp26o3W9HK1LT7kDuxBC1JIEO35qwoQJdO7cmWnTpnl9ru+//56QkJA6KJX/Cw/SM6x1JMNaR1LicLHlZCE/Hs9n04kCLCVOFh+wsPiAhVCjjl6JYVyZHMblLcMwG4zQsx/6nv1Q+bmoHZth58+oX7ZCYT78vA718zoUQMtWpYHP5dCuM5qx8hghIYQQ50iwE6CUUjidTgyGi38FmjVrVg8l8j9mg44rW4VzZatw7E7FzjNF/Hg8nw3H88kpcbLqSB6rjuRh0mv0SAjlyuRweieGER4eidZ/OPQfjnI54cgB1M6fUbt+hsP74eQx1MljqCVfgynIvX5P18vdjziZ0i6EEOeTFc780MMPP8z69ev56KOPSExMJDExkdmzZ5OYmMiyZcsYOXIkaWlpbNy4kSNHjjBp0iS6d+9Ou3btGD16NKtWrapwvr59+/LBBx943icmJjJz5kx+97vf0aZNGwYMGMCSJUtqVDan08mjjz5Kv379aNOmDYMGDeLDDz+slG7WrFkMHTqUtLQ0evbsyTPPPOPZl5ubyxNPPEH37t1p3bo1w4YN43//+98lflr1w6jX6JkQyv194vn3DW356zWtGNcphvgwIzanYmN6Af9Yf4rfzN3Pcz8c45vd2WzPKCTXqiCtPbqxt6J/+jV0b3yKds/jaAOGQ2QM2KywYzPq8/dxPXMfzin34PrsPdS2jaiS4oauthBCNArSslNL7haRmqfXNIXDUTdT//R6anQPp2nTpnHo0CE6duzIY489BsDevXsBeOWVV3j++edp1aoVkZGRnDx5kmHDhvHkk09iMpmYM2cOkyZNYtWqVSQmJlabxxtvvMGzzz7Ls88+y8cff8xDDz1E//79L7rassvlIiEhgX/9619ER0ezefNmnnjiCeLi4hg7diwAn3zyCdOmTePpp59m6NCh5Ofns2nTJs/xd9xxB4WFhbz99tukpKSwb98+9E1oAK9O0+jUPIROzUO4s2dzjlqsrD+ez4/HCzhisbL9dBHbTxd50ocH6UmJNJEcGUSrqCBatepFq25XEh6khxNH3K0+O3+GA7shMwO14nvUiu9Bb3B3c5V1eSWmyI1KhRABSYKdWnI6YeHc3AbJe9SNkdSg14mIiAhMJhNms5m4OPcNKQ8cOADA448/zlVXXeVJGx0dTZcuXTzvn3jiCRYtWsSSJUuYNGlStXlMnDiRcePGAfDUU0/x0UcfsWXLFgYNGnTBshmNRk8ABtCqVSt++ukn5s+f7wl23nrrLe655x7uvvtuT7oePXoAsHr1arZu3cqKFSto06YNACkpTXeatqZppEabSY02c+tlzTmVb+PH4/nszizmWK6VjHw7+VYnO88Us/NMxZaaKLOeVpFBJMcOpNX1w0gOVrQ6vZ/QPaXBT9Zp2LMdtWc7as4MiIpB69ITuvRCa9PR/V5uXyGECAAS7ASYyy67rML7wsJCXn/9dZYuXcqZM2dwOByUlJRw4sSJC56nU6dOntchISGEh4eTmZlZozLMmDGDWbNmceLECUpKSrDb7Z6AKysri4yMDAYOHFjlsbt27SIhIcET6PibhHAT4zs3Y3zpe6vDxYk8G0ctVo7lWjmea+VYro3TBXYsJU4sJRVbgSCSmOARJF99Ha2MDpJz00k+voPkPesIsWSj1i6FtUvdA50NBoiJg9gWaLHuZ2JbuO/YHtsCwiOlJUgI4Rck2Kklvd7dwlJTdbmqcF301Jw/q2ratGmsXr2a5557jtTUVMxmM/fccw82m+2C5zn/jueaptVopc5vvvmGP//5zzz33HNcccUVhIaG8n//939s2bIFALPZfMHjL7bf3wQZdLSOMdM6pmK9i+0u0vOsHLO4g5/jue7XmUUOsovdj20AxEHkcOg7nFiji2RHLslnjxB/9ghR1jyiSgqIOnSUqD07Mbvc31PPVTQFQbM4tOYtyGnVGldIODRrfi4oauS3uRBCiDIS7NSSpmk16koqYzBoKFX//zs2Go24XK6Lptu8eTM33XQTo0aNAtwtPenp6T4r16ZNm+jVqxd33nmnZ9vRo0c9r8PCwkhOTmbNmjUMGDCg0vGdOnXi1KlTHDx40G9bd2oi2KijXbNg2jULrrC9yO7keK6tNAhyB0LHLFayix1k2XVkEc2WmGiI6VnpnGblIMpZRGRJHtHFOUTa8om2FRCZmU/0iZ1E2guItuUTacsnyOWAkFBoVtYy1AKalT7HtoAWCWgGY6U8hBCiIUiw46eSk5PZsmULx48fJzQ0tNrAJy0tjYULFzJixAg0TeO1116rUZB0qdLS0pgzZw4rVqwgOTmZuXPnsm3bNpKTkz1pHnnkEZ5++mliY2MZOnQohYWFbNq0ibvuuosrr7ySvn37cs899zB16lRSU1M5cOAAmqYxdOhQn5W7qQgx6j23tiivwOr0dIEdy7WSWVjWDeYgp9iBzako0QxkGCLICIuAsKQL5hPsKCHKlk+UrYAoaz5RR/KJ2neAKNtWIm0FBOHC0DwOQ3wi+pZJGBOTMTRvgUGvR68Dg05Dr9MwaBoGvYZe0zDoajYAXwghakuCHT9177338vDDDzNkyBBKSkp44403qkw3depUHnnkEa6//npiYmJ48MEHKSgo8Fm57rjjDnbu3Mn999+Ppmlcf/31/Pa3v2XZsmWeNBMnTsRqtfLBBx/w5z//mZiYGK677jrP/rLtDzzwAMXFxaSmpvL000/7rMz+ICxIT6e4EDrFVV4cUilFscNFbomTnGJHaQDkDoTcDyeFTh2ZecXkFDuwuxTFBjPFBjOnQmqwmvNJ4GQJcPRiKdFppYFQafDjCYrKBUdGvUaUWU9UsIGYYAPRwQaizaXPwXqigw2Y9DLwWghxjtz1vJTc9dx7TaGu3l5PCJw7CpcpX1+Xy0WR3UVOiYPcYic5JRWDo9wSB5ZiJzabDYfVhsNux+lw4nApnJoOp6bDoelx6PS4NN8tFxBq0hFtdgdD54IifbmgyP0INeoqtCYF0rUNpLpCYNU3UOoqdz0XQviEpmmEmvSEmvQk1SJmVE6ne+XnI/vhyH7U4f2oE0dxuHAHQTo9Dk2PU9PjiInDmdQaZ1IqzpYpOOOTcBrMOF0Ku0thc7qwlLZC5RQ7yCntinM/nNhdikKbi0KbjfS8Cw+0N+k1okoDoJhgPdHBRpJiS3DZigg16twPk54Qo45Q07nXOuluE6JJkWBH1Kknn3ySr776qsp9N9xwA9OnT6/nEonGQNPr3XdyT06DQdcAoGxW9McPnwuAjuyHjBOQkQcZB2Bz2cEaxCehpbaDtHZordpAZBQkRoA5uELLjFLuQCe7xIGldFaau2vO6XldFiAV2lzYnIozhXbOFJZrkdyXc9H6hJQGQiEmvScgCjXpKrwOMZZtq/gcZtKj10mwJER9kmBH1KnHH3+c++67r8p94eHh9Vwa0ZhppiBo09G9wGEpVVQARw+iyoKfI/shOwtOHUedOg7rl1GhUV5vgNAwCA2H8AgIDSckLIKQ0HCSwsIhNBwtrHRfQjiEutNoej1Wh8vTBVe+hcihDyLTUkChzUmh3eluJbK7KLQ5sTnduRfZ3d15FDlqXW+9BrGhRuJCjTQPNRIXaij32khsqBGDBENC1CkJdkSdio2NJTY2tqGLIZooLSQMOnVH69Tds03l5cDhA6gj+9wB0IljUJAHdhs4HZBncT9OlaY/75xVjlgICcUQGk5sWASxZQFRaDhaWASRScnkhukgIhoioyEiGq10XSm7U1HkCYDOe7Y5KSoNiiruP7et2OHCqeB0gZ3TBVWPb9NpEB1soEVpANQ81EiLsLLXBpqHGAkyyABsIWpDgh0hRKOmRURD995o3XtX2K6sVijMcwc+BfmownwoyC/dlg+F+ajS57I0FBe6Dy4qdD8yM9znKjsnUGUnVkgYREaji4wmvPThCYTKXifEQEjoBafPO12KnBIHZwrsnu6zzEI7Zwrd2zIL7dhdirNFDs4WOSCz6pu5Rpn1FVqD4sJKW4VCDAQbdRVmsRlLn3WaTO0XgUuCHSFEk6QFBUFQc4hxz8aoyZ9x5XS6g59ygdG5gMj9HGQroeRMBuRmQ67F3XpUVOB+nDp+4ZYjg7E0CIqCyBi0SPczkVFokTHoIqJpFhZOs5AQOsWEop23QqlLKXJLnOeCoCqCohKHq3SNJCf7zpbU6jMzlAY+ZdP6g4yH0JSr3HZ3kGQ8b9p/+X06zf1ZaxpouN8DnmCqbF/ZIO5z6c/t0zTQ4d6hK3cug969VpTZoCPEqCPYoCPYWO5h0GHSaxK0iVqTYEcIETA0vd4diEREndtWfr+m0bzclF2llDsQyrVAbra7S630NbkWVG62uwstN9vdUuSww9kz7gcVA6Equ9NMJjCHQHAomIMhJJQIczCRwaG0Cw6B4JDS/SHQwv063xBCphZMpsvAGYeBM1bILHJwptBOVpEDu9OFw6VwVLE2qHt7uZKUOC/xk2w4eg1P4OMOgvQV3p8fJHnem/Tk6/OxFdmJCNLLuKgAI8GOEEJUQ9M0CItwPxJbXbD1SNltkJvjebgDo9LX5bZTVAA2q/sgm839yLNUPNcF8gkrfaSdK6Q7UDKHuLvRWraC1LaQ0g5ncmucJjMOp8Kh3IFO2WunC6JimpFxJhOH0z2tvywYKpvm7359LkiyuxQoUCiUAldpYV2l75V7tztQBMriKpdSFfad215uP+4xUcUOF8WlA8CL7S7P+5LS6M2poMDmosB2KSu9Hzv3OZp0RAQZiDLriTDriQwyEGnWux+e1+7ncJlB1+RJsCOEEHVAM5o8N0mFC3erKYcDrMXu1qCSYvdYouJiVHEhlBRBcblHSRHK8758+iJwOt0RRtn+nCzUiaOwaTUAOk2HrmUyptS2kNrOPX0/KRXNYHIvPBcfQZQqbBILz7mUoqSaQOj898V2Z7nXpfsdLgrtYCm24fIETDZO5l88bw0ID9ITEaQvDY4MRAbpiTIb3IGS2b3PpNedWwW8dMXv82+Pote5t+lLX9cnV2mgWfbsdJ1773Ap7KVBb/lnm9PlDnydCluF/a7S/eeOLdvvcCpspfvL0reKDOKBvvH1Wt/yJNgRVerbty933303kydPbuiiCOF3NIMBDO4ZYBW21+IcSin3jLTygVFBHurYQdSRA+5p+5azcOKoOwBau9TdYmQwQFIaWlp7Cnv0RkU3R7Voiabz3YrWdUGnaYQY9YQY9TS7hOPLVhU+cfIk+SUOcq1Ockuc5Fod7ueS0merk7zS26TkWp0UWJ0oIM/qJM/qJD2v7uqkQem4KCoEQ2Xv9eXe6zTtXLDiAqdSVQYtTlXWUrYPp8uF03UuuGnIkNbpatiAWoIdIYRogjRNA1OQ+xEZfW57t16e18qS7VmwUR0uXbeoqMCzLXv5AnfCoGBIaeNu+Ulth5bWDprF+eVAYJ2mEWE2EGE2kBx58fROlyLf6iwNjtxBUJ4nQDoXLOVbneW6/twPR2lQ4nCd67orT0G58VUNGwzoNDDq3PeeM+p1GHUaJr37vaHsta50n+f1uWdT6TFGvVZpv0mvI8LcsMG0BDtCCOGntKgY6NEXrUdfoLQ1KDOjdMHGA5hOHsW6/xd3l9q+nah9O93pwD1OqbTry716dVv3MgABRq/TiCq9xxoEXfJ5VGmri7NcQOQ4/73LncbhUrhc58ZWuYMlhU5zz34rmxWn09ytP2Wvy/bpdRot4uLIzspEA3S6srRUeY6yZ38mwY4f+u9//8sbb7zB5s2b0enOLT42adIkoqOj+cMf/sCLL77Izz//TFFREe3ateOpp57iqquuuqT8/vWvf/HFF19w7NgxIiMjGTFiBM8++yyhoaGeNJs2bWL69Ols2bKFoKAgevTowbvvvktUVBQul4v33nuPzz77jJMnTxIbG8sdd9zBH//4R68/CyHEOZqmQVwCWlwCWt/BxCUkcPJEOurkcdThfaUtPgcg/Yh7baKdP6F2/nSuzSEy2h0EhYZBSJh7EciQMAgNdT+HhKGV7nM/3NvLFmUMZJqmYSgdz3PpIVPN80qICcFkNTWJ8Vj1QYKdWlJK4XDUbon4uroTuMFgqFGz8pgxY3juuedYu3YtgwYNAiAnJ4cVK1bw6aefUlhYyLBhw3jyyScxmUzMmTOHSZMmsWrVKhITE2tdLp1Ox7Rp02jdujUHDx5kypQpvPTSS/zlL38BYOfOndx8883cfPPNvPjiixgMBtatW4fL5Z5N8Ze//IWZM2cydepU+vTpw5kzZzhw4ECtyyGEqD1Np4fEFLTEFBg4AiidWZZ+xN0CdLjsvmXp52aUlarqz2i1U+xDygVBoWFoIaEV3hMShmYOdo8p0utBb3S/NhjctwUpey57bTCcS6PT+WWXm6g7EuzUksPh4P/+7/8aJO/7778fYw3+hxQVFcXQoUP5+uuvPcHOggULiImJYcCAAeh0Orp06eJJ/8QTT7Bo0SKWLFnCpEmTal2uskHMRqORhIQEnnjiCZ566ilPsPN///d/XHbZZZ73AB06dACgoKCAjz76iJdeeomJEycCkJqaSp8+fWpdDiFE3dCMJkhrj5bWHoa6t6mSIveNWgsL3PcwKyxdaLGoEIpKtxUVltte4B40rVTpFPtssGR78qhxoFSjAmtVBELnPRuMZMbF44yIgbh4tLgEaJ4AMc3d6y81IspmhczTkHkKlZUBZzLcn2dkNEQ1g6hmaNExpa9j0AzScnYxEuz4qfHjx/PEE0/wyiuvEBQUxLx58xg7diw6nY7CwkJef/11li5dypkzZ3A4HJSUlHDixIlLymvVqlW88847HDx4kPz8fJxOJyUlJRQXFxMcHMyuXbsYM2ZMlcfu378fq9XKwIEDvamuEMLHNHMIpLZzv67hMcrlcgc8ZcFPaSCkSgMk97ZywZK1BBwO96rV5Z8ddvc0+/LbK2Sk3GkcdrBWX56Sw/vOHVL2Qm+AZnGe7j3Pc/MEiI3zSSChlIL8XPf4qcwM921Lyr/Ozb74Ocq/CY+EqJjSIKgZWlQzClJb49L0nu2Ehgd065cEO7VkMBi4//77a5zeaDTWaTdWTY0YMQKlFEuXLqV79+5s2LCBF154AYBp06axevVqnnvuOVJTUzGbzdxzzz3YbLZal+n48ePceeed/PrXv+aZZ54hLCyMTZs28eijj2Kz2QgODsZsNld7/IX2CSGaNk2nK70rfVjF7V6eVylVMfDxBEXnv7aXPjvBYSfCaSf3wF7UmZNw5pQ7sHDY4cxJOHOywj3SSisAMbFVBEItoXkLNFP1o2+UwwHZmRWCGJV5qrTFJsM9KPxCgkOhebw7n+YJ7s8w1wKWs+5Zdpaz7ofD4Q6c8nPh+GH3wo1UcY83o6k08IlBi2oG0c3OtRJFxUB4BJjMYDaDyVzpViZNnX/Vph5omlajrqQytUlbl8xmM6NGjWLevHkcOXKENm3a0K1bNwA2b97MTTfdxKhRowAoLCwkPT39kvLZvn07LpeLqVOnEhQUhN1uZ/78+RXSdOrUiTVr1vDYY49VOj4tLQ2z2cyaNWu47bbbLqkMQojAomma+z5ktWh10TSN8IQECkpvBQKlLU+Ws3DmFOrMKfdzZkZpIHTK3dJUevsPtXub+5jyJ42OhealXWLRzdy3EMksDaKyM90L4lRfIPcxsfFozeNLA5t4d2DTvEWNWmKUUu57upUGPiqnNACyZBNUXEBJxkl312FBnntNprKg6/zzVHVyvQGCzKWPIPfyBKXPmimo3L7z9pvMaKUBU4U0wSFoYREXu0w+I8GOHxs/fjx33nkne/fu5YYbbvBsT0tLY+HChYwYMQJN03jttdc8g4VrKzU1Fbvdzr///W9GjRrF+vXr+c9//lMhzUMPPcTVV1/N008/za9//WtMJhNr167lV7/6FTExMTz44IO8/PLLGI1GevfuzdmzZ9m3bx+33nqrV/UXQogL0XQ6941kY5qjdbyswj6llPs2HuUCITLLvS4uhJws96rVpVP2KylbVTsuAS22BTRPQIuLh9h4dxeZ0eRd+TXN3SITHgHJaZ4Ws0r3eLPb3EGPJRtlOQvlgiJPgFTWjegsvV9a+RvgnudiY6uq3J/SFv2zb3hRW+9IsOPHBg4cSFRUFAcPHmT8+PGe7VOnTuWRRx7h+uuv9wQbBQWVv9A10aVLF6ZOncq7777LX//6V/r27cvTTz9dYdp4mzZtmDlzJn/9618ZM2YMZrOZnj17Mm7cOAAefvhh9Ho9f/vb3zh9+jRxcXH8+te/9qruQgjhDU3T3AOCI6PR2nWusM9zg9jygZDlrDt9WUtNXDxERLsDqgamGU3nWo4uklY57GC1urvZrFawlUBJifvZWoKyup+rSqNsZftKHzar+/YmNqv7/m0NSFMyCR+AzMzMKsfW5OXlERFx6U1vdTlmp7FrCnX19nrCuWXnT5VrDvdngVRfqav/CqT6Nsa6KqXqfIC00WikefPmNUrb8CGnEEIIIfxaQ88Ek24scUFfffUVTz75ZJX7kpKSWL58eT2XSAghhKgdCXbEBV1zzTX07Nmzyn0NNdNMCCGEqI1GGewsWrSI+fPnY7FYSElJ4a677qJt27bVpl+/fj2zZ88mMzOT+Ph4br/9di6//PJ6LLH/CgsLIyws7OIJhRBCiEaq0Y3ZWbduHZ9++ikTJkxg+vTppKSk8PLLL5Obm1tl+r179/KPf/yDYcOGMX36dHr37s1rr73GsWPH6rnkQgghhGiMGl2w89133zF8+HCGDh1KUlISkydPxmQyVTs25Pvvv6dHjx6MHTuWpKQkbrnlFlq3bs2iRYvqueRCCCGEaIwaVbDjcDg4dOiQZ6VfcN9Ru1u3buzbt6/KY/bt21chPUD37t3Zv39/nZXrUhfcE41LY5mCKYQQon41qjE7eXl5uFwuoqKiKmyPiori5MmTVR5jsViIjIyssC0yMhKLxVJlervdXmEtGE3TCA4O9rw+X0hICPn5+YSHh6NrBItDiUtXVFREUFCQ11Mgy45v6KmU9SWQ6it19V+BVN9AqmtNNapgpz7MmzePOXPmeN6npaUxffr0Cy5MVFxczOnTp93LbteydaCkpOSSy9rUNPa6hoeHEx8fX2fnq8tzNQWBVF+pq/8KpPoGUl0vplEFOxEREeh0ukqtMhaLpVJrT5moqKhKg5dzc3OrTT9+/HjGjBnjeV8W+WZmZuJwOKotW1BQ9Xe3rY6macTHx5ORkeH3XShNoa5KKU6dOuX1eZpCXetSINVX6uq/Aqm+gVJXg8FQ4xWUG1WwYzAYaN26NTt37qRPnz6Ae7zMzp07GTlyZJXHtG/fnh07dnDdddd5tm3fvp127dpVmd5oNFa7PoyvvhSX0iLUVEld/Vcg1Vfq6r8Cqb6BVNeLaXSDUMaMGcPSpUtZsWIF6enpfPjhh1itVoYMGQLAO++8w8yZMz3pR48ezbZt25g/fz4nTpzgiy++4ODBg9UGR0IIIYQILI2qZQegf//+5OXl8cUXX2CxWEhNTWXKlCmebqmsrKwKg646dOjAH/7wB2bNmsXnn39OQkICjz/+OK1atWqgGgghhBCiMWl0wQ7AyJEjq22ZeeGFFyptu/LKK7nyyit9XCohhBBCNEWNMthpCAaD7z4KX567sZG6+q9Aqq/U1X8FUn39va61qZ+mZPSSEEIIIfxYoxug7E+Ki4t58sknKS4ubuii+JzU1X8FUn2lrv4rkOobSHWtKQl2fEgpxeHDhwNi6p/U1X8FUn2lrv4rkOobSHWtKQl2hBBCCOHXJNgRQgghhF+TYMeHjEYjEyZMqHbFZn8idfVfgVRfqav/CqT6BlJda0pmYwkhhBDCr0nLjhBCCCH8mgQ7QgghhPBrEuwIIYQQwq9JsCOEEEIIv+bfN86oB4sWLWL+/PlYLBZSUlK46667aNu2bbXp169fz+zZs8nMzCQ+Pp7bb7+dyy+/vB5LXHvz5s1j48aNnDhxApPJRPv27bnjjjto2bJltcesWLGCd999t8I2o9HIZ5995uvieu2LL75gzpw5Fba1bNmSN998s9pjmuJ1BXjwwQfJzMystP2aa67h7rvvrrS9KV3XX375hW+//ZbDhw+Tk5PDY489Rp8+fTz7lVJ88cUXLF26lMLCQjp27Mjdd99NQkLCBc9b2998fblQfR0OB7NmzWLLli2cOXOGkJAQunXrxm233UZMTEy157yU30J9uNi1/ec//8nKlSsrHNO9e3eeeeaZC563MV7bi9V14sSJVR53xx13MHbs2Cr3Ndbr6ksS7Hhh3bp1fPrpp0yePJl27dqxYMECXn75Zd58800iIyMrpd+7dy//+Mc/uO2227j88stZs2YNr732GtOnT6dVq1YNUIOa+eWXX7j22mtp06YNTqeTzz//nJdeeok33ngDs9lc7XHBwcH84x//qMeS1p3k5GSee+45z3udrvpG0KZ6XQH+8pe/4HK5PO+PHTvGSy+9xJVXXlntMU3lulqtVlJTUxk2bBh/+9vfKu3/5ptvWLhwIQ8++CBxcXHMnj2bl19+mTfeeAOTyVTlOWv7m69PF6qvzWbj8OHD3HjjjaSmplJQUMCMGTN49dVX+etf/3rB89bmt1BfLnZtAXr06MEDDzzgeX+xm0Y21mt7sbq+//77Fd5v2bKF9957j759+17wvI3xuvqSBDte+O677xg+fDhDhw4FYPLkyfz8888sX76ccePGVUr//fff06NHD0+0fcstt7Bjxw4WLVrEPffcU59Fr5Xz/zf04IMPcvfdd3Po0CE6d+5c7XGaphEVFeXj0vmGTqercdmb6nUFiIiIqPD+66+/pkWLFn5xXXv27EnPnj2r3KeU4vvvv+eGG26gd+/eADz00ENMnjyZTZs2MWDAgCqPq+1vvj5dqL4hISEV/rAB3HXXXUyZMoWsrCxiY2OrPW9tfgv15UJ1LWMwGGpV7sZ6bS9W1/PruGnTJrp06UKLFi0ueN7GeF19SYKdS+RwODh06FCFH4FOp6Nbt27s27evymP27dvHmDFjKmzr3r07mzZt8mVR61xRUREAYWFhF0xXUlLCAw88gFKKtLQ0br31VpKTk+ujiF7LyMjg3nvvxWg00r59e2677bZq/yD4y3V1OBysXr2a6667Dk3Tqk3XlK9rmTNnzmCxWLjssss820JCQmjbti379u2rMti5lN98Y1ZUVISmaYSEhFwwXW1+C43JL7/8wt13301oaChdu3bllltuITw8vMq0/nJtLRYLW7Zs4cEHH7xo2qZ6XS+VBDuXKC8vD5fLVSkyjoqK4uTJk1UeY7FYKjWHRkZGYrFYfFTKuudyuZgxYwYdOnS4YBdNy5Ytuf/++0lJSaGoqIhvv/2WZ599ljfeeINmzZrVY4lrr127djzwwAO0bNmSnJwc5syZw/PPP8/rr79OcHBwpfT+cF0BNm7cSGFhIUOGDKk2TVO+ruWVXZvaXLdL+c03Vjabjc8++4wBAwZcMNip7W+hsejRowd9+/YlLi6OjIwMPv/8c1555RVefvnlKrtr/OXarly5ErPZXGFMT1Wa6nX1hgQ7olY++ugjjh8/zrRp0y6Yrn379rRv377C+z/96U/873//45ZbbvF1Mb1Svsk4JSXF8w/D+vXrGTZsWAOWzLeWL19Ojx49LjhgtSlfV+HmcDj4+9//DlDlIPTymupvoXzLXKtWrUhJSeH3v/89u3btolu3bg1YMt9avnw5gwYNqnbMWZmmel294d8jknwoIiICnU5X6X+BFoul2n7QqKgocnNzK2zLzc1tMv2mH330ET///DNTp06t9f/iDQYDaWlpZGRk+Kh0vhMaGkrLli2rLXtTv64AmZmZbN++neHDh9fquKZ6XcuuTW2u26X85hubskAnKyuLZ5999qJdWOe72G+hsWrRogXh4eHVltsfru3u3bs5efLkJQUrTfW61oYEO5fIYDDQunVrdu7c6dnmcrnYuXNnhf/5lte+fXt27NhRYdv27dtp166dT8vqLaUUH330ERs3buT5558nLi6u1udwuVwcO3aM6OhoH5TQt0pKSsjIyKj2H72mel3LW758OZGRkbWeLt9Ur2tcXBxRUVEVrltRUREHDhyo9vd7Kb/5xqQs0MnIyOC5556rdvzKhVzst9BYnT17loKCgmq/p0392gIsW7aM1q1bk5qaWutjm+p1rQ0JdrwwZswYli5dyooVK0hPT+fDDz/EarV6xjy88847zJw505N+9OjRbNu2jfnz53PixAm++OILDh48yMiRIxuoBjXz0UcfsXr1av74xz8SHByMxWLBYrFgs9k8ac6v65w5c9i2bRunT5/m0KFDvPXWW2RmZta65aAhfPrpp/zyyy+cOXOGvXv38tprr6HT6Rg4cCDgP9e1jMvlYsWKFQwePBi9Xl9hX1O+riUlJRw5coQjR44A7kHJR44cISsrC03TGD16NF999RWbN2/m2LFjvPPOO0RHR3tmZwFMmzaNRYsWed5f7DffkP6/vXsJiWoP4Dj+1W75vqBU2tvK0h6cFLOgRRFGUIFRlEEJUhKBptCilxi9NCkqqEXQqrKXza5FGq0yAoUpzIwgbcqETE2mafLVaONdXDy3UbMop8m5v89Gzn/OzPz/czzDb875P4Zrb29vL2fPnuXVq1fk5ubidrvN87i3t9d8jYHt/d654CvDtbW7u5urV69SV1dHa2srtbW1nDp1ipiYGBYtWmS+xmg5tsO1tV9nZydVVVXfvKozWo6rN6nPzi9YtmwZTqcTi8WCw+EgNjaW/Px8Mx33f6n2i4+PJy8vj9LSUm7evMmkSZPYu3fvHz8Xy7179wA4cuSIR3l2drb5RTCwre3t7Vy8eBGHw0FYWBizZs2isLCQqVOn/q5q/zS73c65c+f49OkTf//9NwkJCRQVFZnDtP3luParra2lra3NHHL7tdF8XG02G0ePHjW3S0pKAFixYgU5OTmsX7+ez58/c/HiRTo7O0lISCA/P9+jv0NLSwtOp9Pc/t4570vDtXfz5s08evQIgH379nk87/DhwyxYsAAY3N7vnQu+Mlxbd+7cSWNjIxUVFXR0dBAVFYVhGGzZsoWxY8eazxktx/Z7/8fw7xxBfX193wwro+W4elNAX19fn68rISIiIuItuo0lIiIifk1hR0RERPyawo6IiIj4NYUdERER8WsKOyIiIuLXFHZERETErynsiIiIiF9T2BGR/6X79++Tnp6OzWbzdVVExMs0g7KIeMX9+/e5cOHCNx8vLCwcNesO/Qir1cqZM2e4fPkywcHBXLp0iTdv3gyaeVxEfj+FHRHxqvT09CEXj42JifFBbbynvr6e6dOnExwcDEBdXR0LFy70ca1EBBR2RMTLkpKSmD17tq+r4XU2m81c6d7lctHQ0MCGDRt8XCsRAYUdEfGx1tZWdu/eTUZGBoGBgZSVlfHx40fi4uLIysoatKDqs2fPsFgsvH79mjFjxjB//ny2bt06aDFSu93OrVu3ePLkCZ8+fSIyMpLExES2b9/OX3/999XX09PDlStXePDgAS6XC8Mw2LVr1w8tivj14oo2m43FixfjdDqx2Wx8+fKF6OhonE4nQUFBBAUF/eInJSI/SwuBiohX9PfZOXToEDNmzPB4LCAggIiICOC/sDN9+nS6urpYvXo1PT09lJWVERgYyOnTp82Vp58+fUpxcTETJ04kNTUVl8tFeXk5brebkydPmrfL7HY7Bw8epLOzk9TUVKZMmYLdbqeqqorCwkLCwsLM+s2cOZOwsDCWLFlCa2srZWVlLF26lD179ny3jenp6T/0WWzatOmH9xWRkacrOyLiVcePHx9UNnbsWK5fv+5R1tzczPnz54mKigIgMTGR/Px8bt++TWZmJgDXrl0jPDycoqIiwsPDAUhJSWHfvn1YLBZ2794NwI0bN3A4HJw4ccLjFtqWLVsY+PsuPDycgoICAgICAOjr66O8vJzOzk5CQ0OHbVtBQQEAVVVVWK1WcnNzAbh+/TqRkZGsXbsWgOjo6B/4pETEWxR2RMSrsrKymDRpkkdZYODgWS9SUlLMoAMQFxfHnDlzqK6uJjMzkw8fPtDQ0EBaWpoZdABmzJiBYRhUV1cD4Ha7sVqtJCcnD9lXqD/U9Fu1apVH2bx587hz5w7v378fdEVqIMMwALh37x4LFy7EMAzcbjfNzc2sWbPGfFxEfEthR0S8Ki4u7oc6KA8MRP1llZWVALx//x6AyZMnD9pvypQp1NTU0N3dTXd3N11dXYP6+nzL+PHjPbbDwsIA6OjoGPZ57e3tuN1uAJ4/f87GjRtxOp00Njaa7+90Ohk3bpw5QktEfENhR0T+14a6ygQMut010P79+80ABlBSUkJJSYm5feDAAQBWrFhBTk7OCNRURH6Wwo6I/BHevXs3ZNmECRMAzL9NTU2D9mtqaiIiIoLg4GDGjRtHSEgIjY2NXq1vbm4uLpcLq9VKZWUleXl5AJSWlhIREcG6desAPG7NiYhvaLkIEfkjWK1W7Ha7uf3y5Uvq6+tJTEwEIDIyktjYWCoqKjxuMTU2NlJTU0NSUhLw75WalJQUHj9+PORSECM1ADUhIQHDMOjq6mLu3LkYhoFhGLS1tZGcnGxuDxwSLyK/n67siIhXVVdX8/bt20Hl8fHxHqOUYmJiOHTokMfQ84iICNavX2/uk5GRQXFxMQUFBaxcuRKXy8Xdu3cJDQ31GNq9detWnj59ypEjR0hNTWXq1Kl8+PCBqqoqjh07ZvbLGQkvXrxg1apVALS0tOBwOIiPjx+x1xeRX6ewIyJeZbFYhizPzs72CDvLly8nMDCQO3fu4HQ6iYuLY8eOHURGRpr7GIZBfn4+FosFi8ViTiq4bds2jyUpoqKiOHHiBKWlpTx8+JCuri6ioqJITEwc0cn9HA4HLS0tZripq6sjJCSEadOmjdh7iMiv06SCIuJTX8+gnJaW5uvqiIgfUp8dERER8WsKOyIiIuLXFHZERETEr6nPjoiIiPg1XdkRERERv6awIyIiIn5NYUdERET8msKOiIiI+DWFHREREfFrCjsiIiLi1xR2RERExK8p7IiIiIhfU9gRERERv/YPL+MOwR/iQVMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the training loss and accuracy\n",
    "N = EPOCHS\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_acc\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.savefig(\"plot.png\")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}