blackJack.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computer Vision Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import distance as dist\n",
    "import cv2\n",
    "import numpy as np\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import copy\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {},
   "outputs": [],
   "source": [
    "cards = {\n",
    "    # Jokers in no light\n",
    "    '00279': ['Joker', None, 0],\n",
    "    '00390': ['Joker', None, 0],\n",
    "\n",
    "    # Spades in no light\n",
    "    '00526': ['Ace', 'Spades', 1],\n",
    "    '00665': ['2', 'Spades', 2],\n",
    "    '00790': ['3', 'Spades', 3],\n",
    "    '00929': ['4', 'Spades', 4],\n",
    "    '01079': ['5', 'Spades', 5],\n",
    "    '01211': ['6', 'Spades', 6],\n",
    "    '01408': ['7', 'Spades', 7],\n",
    "    '01543': ['8', 'Spades', 8],\n",
    "    '01680': ['9', 'Spades', 9],\n",
    "    '01824': ['10', 'Spades', 10],\n",
    "    '01962': ['Jack', 'Spades', 10],\n",
    "    '02088': ['Queen', 'Spades', 10],\n",
    "    '02230': ['King', 'Spades', 10],\n",
    "\n",
    "    # Diamonds in no light\n",
    "    '02366': ['Ace', 'Diamonds', 1],\n",
    "    '02472': ['2', 'Diamonds', 2],\n",
    "    '02631': ['3', 'Diamonds', 3],\n",
    "    '02744': ['4', 'Diamonds', 4],\n",
    "    '02921': ['5', 'Diamonds', 5],\n",
    "    '03093': ['6', 'Diamonds', 6],\n",
    "    '03211': ['7', 'Diamonds', 7],\n",
    "    '03376': ['8', 'Diamonds', 8],\n",
    "    '03495': ['9', 'Diamonds', 9],\n",
    "    '03632': ['10', 'Diamonds', 10],\n",
    "    '03724': ['Jack', 'Diamonds', 10],\n",
    "    '03844': ['Queen', 'Diamonds', 10],\n",
    "    '03957': ['King', 'Diamonds', 10],\n",
    "\n",
    "    # Clubs in no light\n",
    "    '04088': ['King', 'Clubs', 10],\n",
    "    '04217': ['Queen', 'Clubs', 10],\n",
    "    '04317': ['Jack', 'Clubs', 10],\n",
    "    '04425': ['10', 'Clubs', 10],\n",
    "    '04521': ['9', 'Clubs', 9],\n",
    "    '04609': ['8', 'Clubs', 8],\n",
    "    '04733': ['7', 'Clubs', 7],\n",
    "    '04873': ['6', 'Clubs', 6],\n",
    "    '04965': ['5', 'Clubs', 5],\n",
    "    '05097': ['4', 'Clubs', 4],\n",
    "    '05209': ['3', 'Clubs', 3],\n",
    "    '05329': ['2', 'Clubs', 2],\n",
    "    '05449': ['Ace', 'Clubs', 1],\n",
    "\n",
    "    # Hearts in no light\n",
    "    '05553': ['King', 'Hearts', 10],\n",
    "    '05653': ['Queen', 'Hearts', 10],\n",
    "    '05749': ['Jack', 'Hearts', 10],\n",
    "    '05841': ['10', 'Hearts', 10],\n",
    "    '05933': ['9', 'Hearts', 9],\n",
    "    '06041': ['8', 'Hearts', 8],\n",
    "    '06153': ['7', 'Hearts', 7],\n",
    "    '06245': ['6', 'Hearts', 6],\n",
    "    '06349': ['5', 'Hearts', 5],\n",
    "    '06417': ['4', 'Hearts', 4],\n",
    "    '06549': ['3', 'Hearts', 3],\n",
    "    '06685': ['2', 'Hearts', 2],\n",
    "    '06869': ['Ace', 'Hearts', 1],\n",
    "\n",
    "    # Desk in no light\n",
    "    '06904': ['Desk', 'Desk' , 0],\n",
    "    '07064': ['Desk', 'Desk' , 0],\n",
    "\n",
    "    # Desk in light\n",
    "    '07324': ['Desk', 'Desk' , 0],\n",
    "    '07344': ['Desk', 'Desk' , 0],\n",
    "\n",
    "    # Hearts in light\n",
    "    '07544': ['Ace', 'Hearts', 1],\n",
    "    '07630': ['2', 'Hearts', 2],\n",
    "    '07738': ['3', 'Hearts', 3],\n",
    "    '07834': ['4', 'Hearts', 4],\n",
    "    '07934': ['5', 'Hearts', 5],\n",
    "    '08026': ['6', 'Hearts', 6],\n",
    "    '08126': ['7', 'Hearts', 7],\n",
    "    '08234': ['8', 'Hearts', 8],\n",
    "    '08342': ['9', 'Hearts', 9],\n",
    "    '08442': ['10', 'Hearts', 10],\n",
    "    '08546': ['Jack', 'Hearts', 10],\n",
    "    '08658': ['Queen', 'Hearts', 10],\n",
    "    '08754': ['King', 'Hearts', 10],\n",
    "\n",
    "    # Clubs in light\n",
    "    '08862': ['Ace', 'Clubs', 1],\n",
    "    '08970': ['2', 'Clubs', 2],\n",
    "    '09074': ['3', 'Clubs', 3],\n",
    "    '09186': ['4', 'Clubs', 4],\n",
    "    '09282': ['5', 'Clubs', 5],\n",
    "    '09378': ['6', 'Clubs', 6],\n",
    "    '09490': ['7', 'Clubs', 7],\n",
    "    '09610': ['8', 'Clubs', 8],\n",
    "    '09722': ['9', 'Clubs', 9],\n",
    "    '09810': ['10', 'Clubs', 10],\n",
    "    '09926': ['Jack', 'Clubs', 10],\n",
    "    '10026': ['Queen', 'Clubs', 10],\n",
    "    '10142': ['King', 'Clubs', 10],\n",
    "\n",
    "    # Diamonds in light\n",
    "    '10250': ['King', 'Diamonds', 10],\n",
    "    '10342': ['Queen', 'Diamonds', 10],\n",
    "    '10426': ['Jack', 'Diamonds', 10],\n",
    "    '10546': ['10', 'Diamonds', 10],\n",
    "    '10646': ['9', 'Diamonds', 9],\n",
    "    '10890': ['8', 'Diamonds', 8],\n",
    "    '10734': ['7', 'Diamonds', 7],\n",
    "    '11066': ['6', 'Diamonds', 6],\n",
    "    '11158': ['5', 'Diamonds', 5],\n",
    "    '11258': ['4', 'Diamonds', 4],\n",
    "    '11366': ['3', 'Diamonds', 3],\n",
    "    '11486': ['2', 'Diamonds', 2],\n",
    "    '11598': ['Ace', 'Diamonds', 1],\n",
    "\n",
    "    # Spades in light\n",
    "    '11714': ['King', 'Spades', 10],\n",
    "    '11814': ['Queen', 'Spades', 10],\n",
    "    '11930': ['Jack', 'Spades', 10],\n",
    "    '12050': ['10', 'Spades', 10],\n",
    "    '12158': ['9', 'Spades', 9],\n",
    "    '12258': ['8', 'Spades', 8],\n",
    "    '12370': ['7', 'Spades', 7],\n",
    "    '12478': ['6', 'Spades', 6],\n",
    "    '12610': ['5', 'Spades', 5],\n",
    "    '12718': ['4', 'Spades', 4],\n",
    "    '12877': ['3', 'Spades', 3],\n",
    "    '12978': ['2', 'Spades', 2],\n",
    "    '13088': ['Ace', 'Spades', 1],\n",
    "\n",
    "    # Jokers in no light\n",
    "    '13220': ['Joker', None, 0],\n",
    "    '13312': ['Joker', None, 0]\n",
    "}\n",
    "\n",
    "Train_overlap = {\n",
    "\n",
    "    '00001': [['Desk', 'Desk' , 0]],\n",
    "    '00165': [['King', 'Hearts', 10]],\n",
    "    '00277': [['King', 'Hearts', 10], ['7', 'Hearts', 7]],\n",
    "    '00445': [['King', 'Hearts', 10], ['7', 'Hearts', 7], ['8', 'Clubs', 8]],\n",
    "    '00653': [['3', 'Hearts', 3]],\n",
    "    '00753': [['3', 'Hearts', 3], ['5', 'Hearts', 5]],\n",
    "    '00849': [['3', 'Hearts', 3], ['5', 'Hearts', 5], ['Ace', 'Clubs', 1]],\n",
    "    '01049': [['3', 'Hearts', 3], ['5', 'Hearts', 5], ['Ace', 'Clubs', 1], ['Queen', 'Clubs', 10]],\n",
    "    '01305': [['3', 'Hearts', 3], ['5', 'Hearts', 5], ['Ace', 'Clubs', 1], ['Queen', 'Clubs', 10], ['6', 'Diamonds', 6]],\n",
    "    '01481': [['Ace', 'Diamonds', 1]],\n",
    "    '01545': [['Ace', 'Diamonds', 1], ['King', 'Diamonds',  10]],\n",
    "    '01641': [['Ace', 'Diamonds', 1], ['King', 'Diamonds',  10], ['5', 'Spades', 5]],\n",
    "    '01765': [['Ace', 'Diamonds', 1], ['King', 'Diamonds',  10], ['5', 'Spades', 5], ['10', 'Clubs', 10]],\n",
    "    '01941': [['10', 'Hearts', 10]],\n",
    "    '02027': [['10', 'Hearts', 10], ['King', 'Clubs', 10]],\n",
    "    '02176': [['6', 'Spades', 6]],\n",
    "    '02251': [['6', 'Spades', 6], ['3', 'Clubs', 3]],\n",
    "    '02344': [['6', 'Spades', 6], ['3', 'Clubs', 3], ['10', 'Spades', 10]],\n",
    "    '02464': [['6', 'Spades', 6], ['3', 'Clubs', 3], ['10', 'Spades', 10], ['8', 'Diamonds', 8]],\n",
    "    '02611': [['6', 'Hearts', 6]],\n",
    "    '02704': [['6', 'Hearts', 6], ['10', 'Diamonds', 10]],\n",
    "    '02794': [['6', 'Hearts', 6], ['10', 'Diamonds', 10], ['6', 'Clubs', 6]],\n",
    "    '02986': [['4', 'Spades', 4]],\n",
    "    '03091': [['4', 'Spades', 4], ['5', 'Diamonds', 5]],\n",
    "    '03172': [['4', 'Spades', 4], ['5', 'Diamonds', 5], ['2', 'Clubs', 2]],\n",
    "    '03301': [['4', 'Spades', 4], ['5', 'Diamonds', 5], ['2', 'Clubs', 2], ['Queen', 'Spades', 10]],\n",
    "    '03577': [['9', 'Spades', 9]],\n",
    "    '03685': [['9', 'Spades', 9], ['3', 'Diamonds', 3]],\n",
    "    '03748': [['9', 'Spades', 9], ['3', 'Diamonds', 3], ['Jack', 'Hearts', 10]],\n",
    "    '03850': [['9', 'Spades', 9], ['3', 'Diamonds', 3], ['Jack', 'Hearts', 10], ['7', 'Diamonds', 7]],\n",
    "    '03919': [['9', 'Spades', 9], ['3', 'Diamonds', 3], ['Jack', 'Hearts', 10], ['7', 'Diamonds', 7]],\n",
    "    '04132': [['8', 'Spades', 8]],\n",
    "    '04198': [['8', 'Spades', 8], ['Jack', 'Clubs', 10]],\n",
    "    '04351': [['7', 'Spades', 7]],\n",
    "    '04414': [['7', 'Spades', 7], ['7', 'Clubs', 7]],\n",
    "    '04528': [['7', 'Spades', 7], ['7', 'Clubs', 7], ['Queen', 'Hearts', 10]],\n",
    "    '04705': [['4', 'Diamonds', 4]],\n",
    "    '04780': [['4', 'Diamonds', 4], ['Ace', 'Hearts', 1]],\n",
    "    '05005': [['4', 'Diamonds', 4], ['Ace', 'Hearts', 1], ['Jack', 'Spades', 10]],\n",
    "    '05152': [['4', 'Diamonds', 4], ['Ace', 'Hearts', 1], ['Jack', 'Spades', 10], ['2', 'Hearts', 2]],\n",
    "    '05224': [['4', 'Diamonds', 4], ['Ace', 'Hearts', 1], ['Jack', 'Spades', 10], ['2', 'Hearts', 2], ['5', 'Hearts', 5]],\n",
    "    '05395': [['9', 'Hearts', 9]],\n",
    "    '05467': [['9', 'Hearts', 9], ['4', 'Hearts', 4]],\n",
    "    '05545': [['9', 'Hearts', 9], ['4', 'Hearts', 4], ['Queen', 'Diamonds', 10]],\n",
    "    '05692': [['2', 'Diamonds', 2]],\n",
    "    '05782': [['2', 'Diamonds', 2], ['9', 'Clubs', 9]],\n",
    "    '05869': [['2', 'Diamonds', 2], ['9', 'Clubs', 9], ['9', 'Diamonds', 9]],\n",
    "    '05950': [['2', 'Diamonds', 2], ['9', 'Clubs', 9], ['9', 'Diamonds', 9], ['Ace', 'Spades', 1]],\n",
    "    '06202': [['4', 'Clubs', 4]],\n",
    "    '06268': [['4', 'Clubs', 4], ['2', 'Spades', 2]],\n",
    "    '06333': [['4', 'Clubs', 4], ['2', 'Spades', 2], ['King', 'Spades', 10]],\n",
    "    '06469': [['4', 'Clubs', 4], ['2', 'Spades', 2], ['King', 'Spades', 10], ['Jack', 'Diamonds', 10]],\n",
    "    '06597': [['3', 'Spades', 3]],\n",
    "    '06673': [['3', 'Spades', 3], ['8', 'Hearts', 8]],\n",
    "    '06801': [['Desk', 'Desk' , 0]]\n",
    "\n",
    "      }\n",
    "\n",
    "Train_flat = {\n",
    "    '00001': [],#Desk\n",
    "    '00157': [['8', 'Clubs', 8]],\n",
    "    '00293': [['8', 'Clubs', 8], ['5', 'Hearts', 5]],\n",
    "    '00529': [['8', 'Clubs', 8], ['5', 'Hearts', 5], ['Ace', 'Spades', 1]],\n",
    "    '00673': [['8', 'Clubs', 8], ['5', 'Hearts', 5], ['Ace', 'Spades', 1], ['Jack', 'Clubs', 10]],\n",
    "    '01265': [],#Desk\n",
    "    '01481': [['4', 'Clubs', 4]],\n",
    "    '01573': [['4', 'Clubs', 4], ['Ace', 'Clubs', 1]],\n",
    "    '01741': [['4', 'Clubs', 4], ['Ace', 'Clubs', 1], ['Ace', 'Diamonds', 1]],\n",
    "    '02053': [['4', 'Clubs', 4], ['Ace', 'Clubs', 1], ['Ace', 'Diamonds', 1], ['10', 'Diamonds', 10]],\n",
    "    '02213': [['4', 'Clubs', 4], ['Ace', 'Clubs', 1], ['Ace', 'Diamonds', 1], ['10', 'Diamonds', 10], ['6', 'Clubs', 6]],\n",
    "    '02629': [['8', 'Spades', 8]],\n",
    "    '02901': [['8', 'Spades', 8], ['2', 'Spades', 2]],\n",
    "    '02945': [['8', 'Spades', 8], ['2', 'Spades', 2]],\n",
    "    '03041': [['8', 'Spades', 8], ['2', 'Spades', 2], ['King', 'Diamonds', 10]],\n",
    "    '03313': [['8', 'Spades', 8]],\n",
    "    '03401': [['8', 'Spades', 8], ['2', 'Spades', 2]],\n",
    "    '03477': [['8', 'Spades', 8], ['2', 'Spades', 2], ['King', 'Diamonds', 10]],\n",
    "    '03717': [['King', 'Spades', 10]],\n",
    "    '03885': [['King', 'Spades', 10], ['7', 'Spades', 7]],\n",
    "    '04445': [['6', 'Spades', 6]],\n",
    "    '04573': [['6', 'Spades', 6], ['Queen', 'Clubs', 10]],\n",
    "    '04657': [['6', 'Spades', 6], ['Queen', 'Clubs', 10]],\n",
    "    '04821': [['6', 'Spades', 6], ['Queen', 'Clubs', 10], ['3', 'Clubs', 3]],\n",
    "    '05261': [['Ace', 'Hearts', 1]],\n",
    "    '05337': [['Ace', 'Hearts', 1], ['10', 'Clubs', 10]],\n",
    "    '05821': [['King', 'Clubs', 10]],\n",
    "    '05913': [['King', 'Clubs', 10], ['Jack', 'Spades', 10]],\n",
    "    '06115': [['7', 'Clubs', 7]],\n",
    "    '06187': [['7', 'Clubs', 7], ['4', 'Hearts', 4]],\n",
    "    '06375': [['7', 'Clubs', 7], ['4', 'Hearts', 4], ['10', 'Spades', 10]],\n",
    "    '06891': [['5', 'Diamonds', 5]],\n",
    "    '06975': [['5', 'Diamonds', 5], ['5', 'Spades', 5]],\n",
    "    '07115': [['5', 'Diamonds', 5], ['5', 'Spades', 5], ['Jack', 'Diamonds', 10]],\n",
    "    '07303': [['4', 'Diamonds', 4]],\n",
    "    '07355': [['4', 'Diamonds', 4], ['3', 'Spades', 3]],\n",
    "    '07431': [['4', 'Diamonds', 4], ['3', 'Spades', 3], ['King', 'Hearts', 10]],\n",
    "    '07919': [['8', 'Hearts', 8]],\n",
    "    '07975': [['8', 'Hearts', 8], ['Queen', 'Diamonds', 10]],\n",
    "    '08343': [['8', 'Hearts', 8], ['Queen', 'Diamonds', 10], ['2', 'Clubs', 2]],\n",
    "    '08667': [['9', 'Spades', 9]],\n",
    "    '08735': [['9', 'Spades', 9], ['2', 'Hearts', 2]],\n",
    "    '08811': [['9', 'Spades', 9], ['2', 'Hearts', 2], ['Jack', 'Hearts', 10]],\n",
    "    '09419': [['5', 'Clubs', 5]],\n",
    "    '09551': [['5', 'Clubs', 5], ['10', 'Hearts', 10]],\n",
    "    '09903': [['5', 'Clubs', 5], ['10', 'Hearts', 10], ['4', 'Spades', 4]],\n",
    "    '10255': [['Queen', 'Hearts', 10]],\n",
    "    '10355': [['Queen', 'Hearts', 10], ['2', 'Diamonds', 2]],\n",
    "    '10443': [['Queen', 'Hearts', 10], ['2', 'Diamonds', 2], ['9', 'Hearts', 9]],\n",
    "    '10971': [['9', 'Clubs', 9]],\n",
    "    '11055': [['9', 'Clubs', 9], ['6', 'Diamonds', 6]],\n",
    "    '11823': [['9', 'Clubs', 9], ['6', 'Diamonds', 6], ['9', 'Diamonds', 9]],\n",
    "    '12107': [['8', 'Diamonds', 8]],\n",
    "    '12183': [['8', 'Diamonds', 8], ['3', 'Hearts', 3]],\n",
    "    '12263': [['8', 'Diamonds', 8], ['3', 'Hearts', 3], ['7', 'Hearts', 7]],\n",
    "    '12871': [['Queen', 'Spades', 10]],\n",
    "    '12951': [['Queen', 'Spades', 10], ['7', 'Diamonds', 7]],\n",
    "    '13123': [['Queen', 'Spades', 10], ['7', 'Diamonds', 7], ['3', 'Diamonds', 3]],\n",
    "    '13415': [['6', 'Hearts', 6]],\n",
    "    '13483': [['6', 'Hearts', 6], ['9', 'Diamonds', 9]],\n",
    "    '13667': [['6', 'Diamonds', 6], ['6', 'Hearts', 6], ['9', 'Diamonds', 9]],\n",
    "\n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_image(image, image_name='image'): \n",
    "    cv2.imshow('image',image)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()\n",
    "    for i in range(1, 5):\n",
    "        cv2.waitKey(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image_grad(image):\n",
    "    image = np.float32(image) / 255.0\n",
    "    # Calculate gradient \n",
    "    gx = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=1)\n",
    "    gy = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=1)\n",
    "    \n",
    "    mag, angle = cv2.cartToPolar(gx, gy, angleInDegrees=True)\n",
    "    gray = cv2.cvtColor(mag,cv2.COLOR_BGR2GRAY)\n",
    "    blur = cv2.GaussianBlur(gray,(5,5),0)\n",
    "    retval,thresh = cv2.threshold(blur,0.20,1,cv2.THRESH_BINARY)\n",
    "    return thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def valid_contours(min_, max_, min_area, max_area, contours):\n",
    "    valid_conts = []\n",
    "    for i in np.arange(len(contours)):\n",
    "        if min_ < cv2.arcLength(contours[i], False) < max_ :\n",
    "            if min_area < cv2.contourArea(contours[i], False) < max_area :\n",
    "                valid_conts.append(i)\n",
    "    return valid_conts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_contours(image):\n",
    "    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n",
    "    retval,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)\n",
    "    im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "    return contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "def order_points(pts):\n",
    "    xSorted = pts[np.argsort(pts[:, 0]), :]\n",
    " \n",
    "    leftMost = xSorted[:2, :]\n",
    "    rightMost = xSorted[2:, :]\n",
    " \n",
    "    leftMost = leftMost[np.argsort(leftMost[:, 1]), :]\n",
    "    (tl, bl) = leftMost\n",
    " \n",
    "    D = dist.cdist(tl[np.newaxis], rightMost, \"euclidean\")[0]\n",
    "    (br, tr) = rightMost[np.argsort(D)[::-1], :]\n",
    " \n",
    "    return np.array([tr, br, bl, tl], dtype=\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_contours(folder, filename):\n",
    "    path = os.path.join(folder, filename + '-lbl.png')\n",
    "    image = cv2.imread(path)\n",
    "    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n",
    "    retval,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)\n",
    "    im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "    return contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "def affine_transform(folder=None, filename=None, contour=None, image=None):\n",
    "    if image is None:\n",
    "        path = os.path.join(folder, filename + '.png')\n",
    "        image = cv2.imread(path)\n",
    "        \n",
    "    epsilon = 0.01*cv2.arcLength(contour, True)\n",
    "    approx = cv2.approxPolyDP(contour, epsilon, True)\n",
    "    if len(approx) is not 4:\n",
    "        rect = cv2.minAreaRect(approx)\n",
    "        approx = cv2.boxPoints(rect)\n",
    "    else:\n",
    "        approx = np.reshape(approx, (4,2))\n",
    "    \n",
    "    approx = order_points(approx)\n",
    "    h = np.array([ [0,0],[449,0],[449,449],[0,449] ],np.float32)\n",
    "    transform = cv2.getPerspectiveTransform(approx, h)\n",
    "    warp = cv2.warpPerspective(image,transform,(450,450))\n",
    "    return warp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Deck(object):\n",
    "    def __init__(self):\n",
    "        self.cards = []\n",
    "\n",
    "    def load_deck(self, cards, folder):\n",
    "        # Initiate SIFT detector\n",
    "        sift = cv2.xfeatures2d.SIFT_create()\n",
    "        \n",
    "        for key in cards:\n",
    "            card = Single_card()\n",
    "            card.filename = key\n",
    "            card.rank = cards[key][0]\n",
    "            card.suit = cards[key][1]\n",
    "            card.value = cards[key][2]\n",
    "            card.contour = train_contours(folder, key)\n",
    "            if card.contour:\n",
    "                card.card = np.array(affine_transform(folder=folder, contour=card.contour[0], filename=key))\n",
    "                card.sift = sift.detectAndCompute(card.card,None)\n",
    "            else:\n",
    "                card.card = np.array([])\n",
    "            self.cards.append(card)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Single_card(object):\n",
    "    def __init__(self):\n",
    "        self.rank = None\n",
    "        self.suit = None\n",
    "        self.value = 0\n",
    "        self.contour = None\n",
    "        self.card = None\n",
    "        self.height = 0\n",
    "        self.width = 0\n",
    "        self.sift = None\n",
    "        self.filename = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "class black_jack_round(object):\n",
    "    def __init__(self):\n",
    "        self.round = []\n",
    "        self.filename = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "class training_set(object):\n",
    "    def __init__(self):\n",
    "        self.set = []\n",
    "\n",
    "    def load_training_set(self, training_set):\n",
    "        for key in training_set:\n",
    "            black_jack = black_jack_round()\n",
    "            for i, card_ in enumerate(training_set[key]):\n",
    "                card = Single_card()\n",
    "                card.rank = card_[0]\n",
    "                card.suit = card_[1]\n",
    "                card.value = card_[2]\n",
    "                black_jack.round.append(card)\n",
    "            black_jack.filename = key\n",
    "            self.set.append(black_jack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sing_cards ='CV-BlackjackStudent/GOPR0317.MP4-lbl/'\n",
    "lap_cards ='CV-BlackjackStudent/GOPR0318.MP4-lbl/'\n",
    "ind_cards ='CV-BlackjackStudent/GOPR0319.MP4-lbl/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('CV-BlackjackStudent/GOPR0319.MP4-lbl/08343.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh = preprocess_image_grad(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "imS = cv2.resize(thresh, (960, 540))   \n",
    "show_image(imS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh = cv2.convertScaleAbs(thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_conts = [contours[i] for i in valid_contours(250, 2000, 500, 100000, contours)]\n",
    "#drawn = cv2.drawContours(image.copy(), valid_conts, -1, (0,255,0), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "imS = cv2.resize(drawn, (960, 540))   \n",
    "show_image(imS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(valid_conts)):\n",
    "    warp = affine_transform(image=image, contour=valid_conts[i])\n",
    "    show_image(warp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck = Deck()\n",
    "deck.load_deck(cards, sing_cards)\n",
    "len(deck.cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('CV-BlackjackStudent/GOPR0319.MP4-lbl/07431.png')\n",
    "thresh = preprocess_image_grad(image)\n",
    "thresh = cv2.convertScaleAbs(thresh)\n",
    "\n",
    "im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "valid_conts = [contours[i] for i in valid_contours(250, 2000, 500, 100000, contours)]\n",
    "\n",
    "#for i in range(len(valid_conts)):\n",
    "img1 = affine_transform(image=image, contour=valid_conts[1])\n",
    "\n",
    "card = Single_card()\n",
    "card.filename = '07431'\n",
    "card.rank = 'Back'\n",
    "card.suit = 'of Card'\n",
    "card.value = 0\n",
    "card.contour = [valid_conts[1]]\n",
    "if card.contour:\n",
    "    card.card = np.array(affine_transform(folder='CV-BlackjackStudent/GOPR0319.MP4-lbl', contour=card.contour[0], filename=card.filename))\n",
    "    card.sift = sift.detectAndCompute(card.card,None)\n",
    "else:\n",
    "    card.card = np.array([])\n",
    "deck.cards.append(card)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 89.  79.   0. ...   0.   0.   0.]\n",
      " [  0.   0.   0. ...  75.   0.   0.]\n",
      " [ 31.  35.   0. ...   0.   0.   0.]\n",
      " ...\n",
      " [ 31.  69.   0. ...   0.   0.   0.]\n",
      " [ 14. 105.  50. ...   0.   0.   9.]\n",
      " [ 48.  49.   0. ...   0.   0.   0.]]\n"
     ]
    }
   ],
   "source": [
    "print(deck.cards[2].sift[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set_overlap = training_set()\n",
    "training_set_overlap.load_training_set(Train_overlap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set_flat = training_set()\n",
    "training_set_flat.load_training_set(Train_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "warp = affine_transform(image=thresh, contour=valid_conts[5])\n",
    "show_image(warp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<__main__.Single_card object at 0x10b827208>]\n"
     ]
    }
   ],
   "source": [
    "image_cards = training_set_flat.set[3].round\n",
    "print(image_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.join(ind_cards, training_set_flat.set[3].filename + '.png')\n",
    "image = cv2.imread(path)\n",
    "\n",
    "thresh = preprocess_image_grad(image)\n",
    "thresh = cv2.convertScaleAbs(thresh)\n",
    "\n",
    "im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "valid_conts = [contours[i] for i in valid_contours(250, 2000, 500, 100000, contours)]\n",
    "\n",
    "for i in range(len(valid_conts)):\n",
    "    warp = affine_transform(image=image, contour=valid_conts[i])\n",
    "    show_image(warp)\n",
    "# imS = cv2.resize(image, (960, 540))   \n",
    "# show_image(imS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_(image):\n",
    "    image = np.float32(image) / 255.0\n",
    "    # Calculate gradient \n",
    "    gx = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=1)\n",
    "    gy = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=1)\n",
    "    \n",
    "    mag, angle = cv2.cartToPolar(gx, gy, angleInDegrees=True)\n",
    "    gray = cv2.cvtColor(mag,cv2.COLOR_BGR2GRAY)\n",
    "    blur = cv2.GaussianBlur(gray,(5,5),0)\n",
    "    gray = np.float32(gray) * 255.0\n",
    "    retval,thresh = cv2.threshold(gray,20,255,cv2.THRESH_BINARY)\n",
    "    #thresh = np.float32(thresh) * 255.0\n",
    "    return thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255.0\n"
     ]
    }
   ],
   "source": [
    "warp = affine_transform(image=image, contour=valid_conts[1])\n",
    "warp_ = affine_transform(image=image, contour=valid_conts[2])\n",
    "pros_im = preprocess_(warp)\n",
    "show_image(pros_im)\n",
    "print(pros_im.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17546548.0\n"
     ]
    }
   ],
   "source": [
    "diff = cv2.absdiff(preprocess_(warp),preprocess_(warp_))  \n",
    "show_image(diff)\n",
    "print(np.sum(diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "card:Jack Hearts / diff:6794985.0\n",
      "card:2 Diamonds / diff:787950.0\n",
      "card:Queen Diamonds / diff:3871665.0\n",
      "card:Ace Diamonds / diff:3128085.0\n"
     ]
    }
   ],
   "source": [
    "cards_detected = []\n",
    "for i in range(len(valid_conts)):\n",
    "    diffs = []\n",
    "    warp = affine_transform(image=image, contour=valid_conts[i])\n",
    "\n",
    "    for j in range(len(deck.cards)):\n",
    "        if len(deck.cards[j].card) > 0:\n",
    "            diff = cv2.absdiff(preprocess_(warp),preprocess_(deck.cards[j].card))  \n",
    "            diff = cv2.GaussianBlur(diff,(5,5),5)    \n",
    "            flag, diff = cv2.threshold(diff, 200, 255, cv2.THRESH_BINARY)\n",
    "            diffs.append(np.sum(diff))\n",
    "        else:\n",
    "            diffs.append((warp.shape[0]*warp.shape[1])**2)\n",
    "    \n",
    "    indx = np.argmin(diffs)\n",
    "    if diffs[indx] <= 500000:\n",
    "        cards_detected.append(deck.cards[indx])   \n",
    "    print('card:' + str(deck.cards[indx].rank) +\" \" + str(deck.cards[indx].suit) +\" / diff:\" + str(diffs[indx]))\n",
    "    show_image(warp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(img):\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    blur = cv2.GaussianBlur(gray,(5,5),2 )\n",
    "    thresh = cv2.adaptiveThreshold(blur,255,1,1,11,1)\n",
    "    blur_thresh = cv2.GaussianBlur(thresh,(5,5),5)\n",
    "    return blur_thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracyDiff(folder, training_set):\n",
    "    total_c_cards = 0\n",
    "    total_t_cards = 0\n",
    "    total_f_cards = 0\n",
    "    iterations = len(training_set.set)\n",
    "    for i in range(iterations):\n",
    "        sys.stdout.write(\"\\rTraining Iteration: \" + str(i+1) + \"/\" + str(iterations)) \n",
    "        \n",
    "        image_cards = training_set.set[i].round\n",
    "        \n",
    "        path = os.path.join(folder, training_set.set[i].filename + '.png')\n",
    "        image = cv2.imread(path)\n",
    "\n",
    "        thresh = preprocess_image_grad(image)\n",
    "        thresh = cv2.convertScaleAbs(thresh)\n",
    "\n",
    "        im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "        valid_conts = [contours[i] for i in valid_contours(250, 2000, 500, 100000, contours)]\n",
    "        \n",
    "        cards_detected = []\n",
    "        for j in range(len(valid_conts)):\n",
    "            diffs = []\n",
    "            warp = affine_transform(image=image, contour=valid_conts[j])\n",
    "            for k in range(len(deck.cards)):\n",
    "                if len(deck.cards[k].card) > 0:\n",
    "                    diff = cv2.absdiff(preprocess_(warp),preprocess_(deck.cards[k].card))  \n",
    "                    diff = cv2.GaussianBlur(diff,(5,5),5)    \n",
    "                    flag, diff = cv2.threshold(diff, 200, 255, cv2.THRESH_BINARY)\n",
    "                    diffs.append(np.sum(diff))\n",
    "                else:\n",
    "                    diffs.append((warp.shape[0]*warp.shape[1])**2)\n",
    "\n",
    "            indx = np.argmin(diffs)\n",
    "            if diffs[indx] <= 5000000:\n",
    "                cards_detected.append(deck.cards[indx]) \n",
    "        c_cards, t_cards, f_cards = correctCount(image_cards, cards_detected)\n",
    "        total_c_cards += c_cards\n",
    "        total_t_cards += t_cards\n",
    "        total_f_cards += f_cards\n",
    "    return (total_c_cards, total_t_cards, total_f_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correctCount(image_cards, detected_cards):\n",
    "    c_cards = 0\n",
    "    f_cards = len(detected_cards)\n",
    "    t_card = len(image_cards)\n",
    "    for i in range(len(detected_cards)):\n",
    "        if detected_cards[i].rank == 'Back':\n",
    "            f_cards-=1\n",
    "        for j in range(t_card):\n",
    "            if detected_cards[i].suit == image_cards[j].suit and detected_cards[i].rank == image_cards[j].rank:\n",
    "                c_cards +=1\n",
    "    return (c_cards, t_card, f_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Iteration: 61/61"
     ]
    }
   ],
   "source": [
    "correct_cards, total_cards, found_cards = accuracyDiff(ind_cards, training_set_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3524590163934426\n"
     ]
    }
   ],
   "source": [
    "print(correct_cards/ total_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43 122 260\n"
     ]
    }
   ],
   "source": [
    "print(correct_cards, total_cards, found_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('CV-BlackjackStudent/GOPR0319.MP4-lbl/07431.png')\n",
    "thresh = preprocess_image_grad(image)\n",
    "thresh = cv2.convertScaleAbs(thresh)\n",
    "\n",
    "im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "valid_conts = [contours[i] for i in valid_contours(250, 2000, 500, 100000, contours)]\n",
    "\n",
    "#for i in range(len(valid_conts)):\n",
    "img1 = affine_transform(image=image, contour=valid_conts[1])\n",
    "show_image(img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('CV-BlackjackStudent/GOPR0317.MP4-lbl/03211.png')\n",
    "thresh = preprocess_image_grad(image)\n",
    "\n",
    "thresh = cv2.convertScaleAbs(thresh)\n",
    "\n",
    "im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "valid_conts = [contours[i] for i in valid_contours(250, 2000, 500, 100000, contours)]\n",
    "  \n",
    "#for i in range(len(valid_conts)):\n",
    "img2 = affine_transform(image=image, contour=valid_conts[1])\n",
    "show_image(img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66\n"
     ]
    },
    {
     "data": {
      "image/png": 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tjA/EtKNW9jh2eJ6Tx48g5SMEQcAXvvAkZ556Ctcr4ZfKOI6LSOeGvQXVmJtc16Xb7er3cUwUhnx46iv8tZ17UUJkDmVbiZZqgIcoinAch263h9Fx9Lir7DCiGS8nPZfiOg6u5+JIJ93VkV/cZno40qpVQRIngx9pGmnNJbsUPYa6PD0v7JcDLWiQgoyYYMiwIbRCjCZSFNs+HsZpIMOaiP0+QSWWVlnQEnL5xhFO631OiLDqst/bfbxRuXm/9oxNK9QIsixGprVxb5etVGriTHdCWjmzsgQi85t+PfCSYQA2EQQyYgxkanyc6EM8Zh+4IeZKqYzwhGGI67qcOnUqs1NfvnyZs2fPsre3l9ritXPXlnLNwpdSUq/XqVarTE9PMz09TaVSYWpqikajkdVp2gzWoaPCziM7rXlvD3iR0dhl2jBOHS1qNePU+SKOTd2Q3+lj12XX5ziCiWYDMdHMlVNkIEVV3sZLEASZL8R8T5KYVmuXjY1+OnYR/SCkkxJu4wcwTNl1JDubm9n5DTPuxtSkANcrZeasWq3G9PQ05XKZ3528xFxc5ROlp/m23ZODw2+WGcjgwzCvOI75/Oc/n/W5OL6m72Xfp1ouM9FsMjU1Rb1ez209BaMRjLaba5yplHiMIDo6ITY9GEmHUkKfuR4s7ct8WvYPXT/WPDIJh1yXRS2m+HZEU3JMIeuFzltgOAIyBlDU3q9HhEcyH/RGBrt8O60atz0325xqcDNc9358Mf9OgRKZVUcoBucazBgWZXhl0g0YdrF1QoCUoBLFN8AH/NJhADBsDjBgzA6xGm1mMXkMQTK7R4bNKXpR2vZyc3I0iqLM/mwYx97eHmfPnqXf7+f2xpstjkDmKK5UKtTrdZrNJpVKhVKplKUv7hYChphEUXIaZWKy+1lklqPAPkls+zDGqbjjmM+4ttgwKq9tdpFSUq1WqdfrI/tp+hQnEKVE39b8kiTh4YcfRilFt9NN+yPoB306bb2Vt93psL6xSafTYWVlhWeffZZ2u43v+1x+c4NT50ucfjDiwpfO4fs+SqmMYEvp4Fdr2dwxbd7cXMqEhFFjFccx/V6Pfq+HShLK5XJmrnMKTrqidGr3X6XEdxTR2Q+uJyGniQZSP8ONyI+rQIxo6Y2YUIqwn5nJ/izOx6JAMY4ujGvX0DMx2lhi1zGYv4Yd3Vi/xqeBbMStj+w0t8WcTa35vCMLTIWB1ET0P5MGMI4A2t9HOU+LB4mKJh27PLN90Zb8y+VybmEbomCblwwhM2k8z8vtI7cZj2EeZvdMFEUZMbPrMFJtkiQZwzDhDeytlOa3eWeYmB0+wd45Y+MQyAiovS2zuKBsIiLMYlEqkyCNij5qvIo4L74bZ4cdp0onDDYAmDEqwsTERI5QZGEpoiTTGgY2fD0u6+vrvP/976f1gT/nU++YZf7XFqlUKkxOTbGwsEClXKXc7uBXKll/XNfl+PHj1Go1JicncwxcKUW322Vra4tOu02UnjC3dysN4cOYKApEL8Mhw3N/FL7N71EaYPGkaapb7FvWUDlcX8IfVU6xzOK8GtcPyOscZhNAsTyTvojf65Y9lG70+6+FyY077JGNobLLzm9/FoC01brCWOaFg8GxOZmec4mTIpZePLykGMA4KdKAHJFuYLdMJX5NvTKEZ7tgVJLiefS+ZVuqH9c+W6LTB6ryiydJJcBKSkTMM2PCGuVbMNs/TRrbdGJMHKDPJBRxVQytYLQec37AxlVxF4zJWySk5gCW/dzkscEsQiMZFzWUYltNX007otSXkyMUWGoywwvUwKiTwMZEE4T9HI5NfY1mnf/jH/8j4jjm4x//OD8z/yus3enT/eQVNtZWmZiY4rbbbsP1NNP1PZ8EwfHjxymXy6mWMAgbkSQJm5ub7O7uIlNmVUu1wFqtlhsDuy+jIGeqGMMY8/0dPBtixIUixIi6dXSgYSZg5xrblhGMfxRcby3t+77QulEMaxQUCX32HIuhpZK5UuMJ/+jyFflkaRlpBTfKRHJrqmAEUoV0RaacraWURSfXqetG4CXDAEZO5sJEGc3hDZFAT86cqjRwyIjBQ/us5Q0t0FH1j5LARmkdhiAXzQH2RLDLLOY3xMZmEIb4Zr4RKxyD2eFiMxJTtn3S1xAoO8aP6VM5DXlht8VoEIaQG9OZ0VJMWWbPvR2GwUjhlUrFcr6bnU8iy58oSGIdukD3WWsh4yTi4hgU8WZwJIQgDPskSYTneTz++FtYX1/jNz72UTpND6cdsbGxhnQEJ+VtVMoVYj8hEZJarZZpVqYOo9UZTS9RinLqc6hWq5l5aUioGSPkpDMiV8fQ21S4se3Zo+fsKM1suKZx030UEc3VUxCi9mMCXyuoERrnKBiPy/0ykZlQ9itnsH1YIB0jLNnMZJjgF41HCvI7NS3fgxAiS3w9GjTE0AqC09cDLwkGYMh4cTGPMxkYaVzb1W3Cm5CogaMuSeLUoqmHbTC5BzULU9+IduVV2EKb1Q1MfIsh2QWYXMXdRKP6akudxhwyivjZeW2JZ1QbczhmGNekRC4Mw4y5hGGYYxZ2yA3jkDdmNkMwR2131H0SuK6T2dsNY1Mqr70UHeimjGL8JFOuPu1bQilSxpiQqDgbAqUSkiSiVCrx+OOPEYZ9fvPD/5nld7+M+U+sZY5Dv1xGJQq3VMo0PRtv3W6X3d1dtre36Xa7VMtlms0ms7Ozmf2/uE1X/6UmGvKmmoEgo+fwOEhpV7pd9fqCy7g09nweHp/RWniR0Bltw2Zq5nf2vVC3wcMNaUU6Q04TsRnqOMYz0opQrE8IvV06FWaiKM58e3b5ruvpMUsp+6gwHftBkSGQjl1O47tB3pkn+qm/LP6fyQeQ/tmDjY0oi3h7rsfZs2c5c+Y0Wxvr1JsNJqcmqdVqlEolKpUK5XKZatlPI+f5NCcmaDQbQ1K20RLiOLF2Gem2JMng1N0QocXyM2TPRMbIIM/88/NlYO+1049aqKM0oFGmmdw7K49t6ikyHFtDGExoPRKlkosQlSz9KIlDl2kIl2nvoKz8PQ52XKYkk6LNny5nsKgNYbe369oB6oyPxhz4Mmal7e1tpJQcOXKESsUnCBJLQ5IkCTh+mYX5BRrVOhKY/uhVdl9zAO+re7R296hXG0jLGVw0kbXbbVZWVjLGV280mJmZYXp6OmtfcRyFWe1iMJ+FIsObjdNxTFsz9zwBvBEzTF4T0drXsDBkxh5GSbdFBmB/5p4bxjBUr5VuHyJu1lv6I3v2NWsEI4RIKRz6oY4Jtrm5zdrqJrVajfn5eSrVcjr3BwfzTBkCgRQaZ6M0oKFxGMMkBukG23BHlVHUxgbv8lr41wMvGQZgwyhEakk1wZESR8KX/+xPuXbtCqduu41mvUqn1WJvd4cojgmDUH/GIa12hygMKZd9SqXBwSazoGv1OpNT0zQnJqjX60xMTjIzM0MplfwMEbbNJoZZJIkiivX+d2XZCG0GkDEciygoa8DtCSCyvLbKbRH4ApHWz2006byiECtp1OcoyXxQ9vBY7GdmSYdoiBk5jjtiUZo2DojqIIb/YOGZT+NAN9qIzcCDIMjCQtvS0OrqKqdPn+b+++/j+PFjWQhwo1UEQcjOzg6oQeTT5hd3uPQ3jnH7FyW9bpd6cwLHOiRm99XsEBNCh5nwfR+vVMqC6mV5CljNMCH0QZ5sO6AlQevneeJsdkM5JlChGJgwjDYxjOH0m6koq29AvsdLsQOGnntqhCEjmdvPX4Q5Iq9Z54lb8bei0I6s4wNp3q7ZXiEDBjJ4J9JTtGEY0en02NjY4NLlS8xMz1CrV/E8h1LJQyBJVBoKJFdBnhkNtc1eH4zQAgptHayjfMoBPvPrdKxp7muElxwDGNkpPRtTFVohhWJ6aoLWzgYnjizwsV//dZIggDhGRRGOgpLn0phoMjUzi18uIxyJ57gIBFE/YHpyMrPXNkslanFCvL3DtUuXOdPao9Xr0ep22G616PR6eYKZEvQoCnBcl3qjSbVWY3JyiqmZGWZmZylXqvjp7h5jGzeRQaUYmHWCMAKh3UHG9p0jIjliUryEJp9GmgtI0t+JWbAMppatTY0CkTKaTMW/3thY74qEoGiqGsV8yAxWgjg2TntBFBmCLnGc/HmDoonFMGfHcdja2uLAgQM0m00++cnf55Of/AMOHz7Mq1/9amq1WpYvTqOsum4a8VUITv2nLRbfMMGV+g6vXPPxrVba/pZ2u02n08l2B9UbDbxSKQtZbvqYI2aCzAScJxo20cubGWRq5tra2WZtbY2yX8bzStl2YyEEiXV/g8FmDscpgVTKMIoXu9F0UJ5ICdxIs6OlDQ8R7UJa+/t4gWJY+y+CyuiCyOEQSDfnFDQMIVAJxApaez3W17dYXV1ja3uVRPXxfHCdl1Eu13BcD0e4ICKQkSV45Ns3ikHZa0fY+LfbXfiuUSsRorg+in02ZQqk+PrJ90uGAYySNiCvZkohcIQg6gWEvR6zU9O019aouy6ddoc3v+lNPPLgg3z605/m6aefYnNzAwe9U6XT7/Dtb34T99x7L77v89WvfpVPP/EEK4tLVDwPodQgaFsYakkrtee6rkej3qDb7dEPAij5TE9N0vTL1JtNKqnpqSQUwcY6S5sbSCEp+T5lr4RXKhEGURZcrdNu001vL0tIcH0fhECWSiRC0A0CgiiikdqVJyYmmJ2d5cDBOWq1Gs1mUztMU7OJkDqIWKlU1rhLdEiIJMkzlFEnmM3pVvNbkd8bbePfhKctwhCxIz+Jx5mysEbYpC9u8TPpi2pyvg8RoO37zWYdIQTb29s88MADXLp4kafOnOGTOzu88Y1vpFqtUfJcXCk5+8wzoJQOOy0kM1NTHN6coBE1+dWXPcN7Vu7PGI8h/N1ul263i+M42WHBRr2eOb9tNV3fWqW/F3Gg+2FrfwKBh1Aim/QqFsQxrCxtc/bs81TKVTyvxLFjx6hXZtH3DqWhsY25IosfPx72G6tRz3P4hty8sDWyrJxxa9nS7uzvo8Z4VHuLZdqCRS6fspsgQA0iDIdBQmuvz+LVLVaW11hZ7tHZnCDYddle3kKEW0T9MtPTUzQadcAhifX9IypJEDIBMbz1fD88Z9/tdqmCVghawLX1Nyu/bQoVmNvoRlb7ouAlwwBGgcS22+k7A0qug1SKrY0N5uZmkUCn36c5MUE/DJmen+et73wnfzkM+OhHP8IX/+RJXKm3vT199lk2trc5fPgwL7/rLlaWl7l69Sq+0rdmJUnCzNwcr370UUqex+/94Se57777OHr0KKVSiTAMWV5Z4fNf+AKd1hbNxiFKnqCzt816p8Pyyip33nEHhxYO84ef/EN6/R4lV8fKmZiYYmJignK5TLfbJQwDGo0q1VqVer2RxdQplyv0e0G2fz+OY9jZY3tnj7Vz5wjjmG7Qpx+FBHFMN+zTCwKq1Sq1Wo2K7zM1NZXdDuZ5JXy/TK3WoFavU6nUmJyaZnJyOCwGDIh5JimnEmYURQgGcYNsqUZAGoJ5mGjvB5mNFTDajW2+KsIoP4QB2wQkhI5Gury8zAP338/uzg7PPPMMly9f5vjx43iey+rKMhvrqzgSpIBExfR7XVAxnfYeP3jpTn5//nnuFW/A+Bey4HX9fioYuNRT4i+L5qKMjps4OCrzG5FhLdc7QGKHQtfnSGK2t3bZ3NjGdTu4jkut2mR6apdyuUS5okNi6OISpLjO3vBCtfvZ6G2wNVGpE41Ms5/WOMrkMy7tfm3ZL709pXReiY6PIkhi6HVDtrdabKztsLG+S2cvQYV1whCinmBteTfVLBxKpQqOo3A8RzNmiziPY56j2jeUZl/+PA4vg45lZ5Vv3Oo2Fl4yDGCkxMegj4lSqASiIMJzXer1BstLK5y8524cIej1OiyuLNGKQla2trh89Qr3vvZRbr/3lcxMTTE7M8viCy/wkY98hE/+0af5Wz/2Y7ywuMK3vektNOt16vU6J06cYHFxkQMHDrC2vsa9Dz7I7s4Ov/Xf/zu1Wo31tTUOHz3K93//9xMlMR/72Md45uxZ3v72t6OUYv2Tn2RqaoL77r2Hl991B3Ecs7OzQxJDrdagWi0ThiGXLl5ke3ubqekJJicnKZVKnDlzhj/8vc8gpUOElJDOAAAgAElEQVTJ81Eq3e8uJROTkzQnJhBS0um06fW6VNIYOjNVn+rkBI7rUq1q53d3Z5swjnHTe5QT1yVs77K34bEWhDzd69ILAvphSDs1dSVKDW0NBdITzg2qtToTE1P4fpnJyWmqtXpmdkmSBN8KGlcqlTLV2JhFpBCZY7V4i1iCdiZrZjMIzaCnvJFqB/PE3gVjpOuidCmEYH5+nmeffZaXvexlnD59mvX1dRYWFkDB+fPntUnFcUApmvUGjUaTyclJwljfpvb61aP8aOVn+IUTf5c4jtnd3WVnZ4cgCPB9Pzvz4blOqsLrXWgD44Ww2pw3zyhlHLpiQKiUi8RJJdWYzbUeW5u7rC/32NtSQIggwhXrxP0yswcmOX7LIcplX9NjESMIUcSWxDi00nLPRplzbBhJwEeksU02akTecen3S3ejfoUhs5tg4CzHQeBn82tns8+VC9usLnbZWA+I+zVkPI1SmsasXgvY2dpABU1U2KA5UWJiuqwlbhRJ0gURDrS0IRzb5qA8s8hrJuP7a9IP3Iapz8gyl35DqD8vIQYAeTvgKGlBKoWQkl63g1J67/W15TWE0NElTz9zjl/4pQ/wN3/8b3Pk2Ak+/Zk/ZmVlhTtvv52d3TZTExP89R95Lx/+8If58tNnefm99/HIww+xtbXFU6dP4ywv8/KXv5x+v88HP/Qhtra2+I7HHuOhVz+CEILPf/7zXLjwPNeuLtIPe7zyla/kLd/xHSwtLXH16lXe9KY3cerUKZ588knOnTvPo48+yu///u9z9MhxbrnlFhYXF1ldXWV5aYlHX/Moc3NzrKyscO3aNQ7MzfH4W7+TP/7UHxOFEUkc0ajVeOf3vYuLFy6wsbVFt9ulNjPNHXfczumvfpmLFy9SS28ZC8OQialJbr/jTo4sLGRSqed5RHFMq91mfX2dtbU1lpaXWVxcRCmYmZ7m5MmTVKs1NtY3kFKQBPr6yGazSUM6lPoRjh9RVzG9vS1W11fo9HuEcUw/CIixNIcoIo4iojCk027TbrW0FlKu4Hk+wnEJogjplXAcl3KlwsTUBL7vMzend9I0m00c1yNBX/6jAOl6gMjCYmdzRoG5g1mml74nifYlBP2AZrPJ+val3GGuKNTnAdqtdjrvpA5qF4ZsbG4yM3cgDcOd8P5j/xtb4R67/d3sToRSqZQd+PI8T+/pUgmDw4ajfSIGtISZMgEhtNajJAIHpQRxmNBudVm8usrKyibra7tEoUSK1Cew0aLffYG91h5CxszNzVKv1/HLDkLEgEJmdxinbFRpTcTEmrHX3P4+mkJaimaLYZt+0S4+DgfF+vbTDsa1R6n8eRddgGU6iROiMKTb0fdYXLu6wnNnX9CBAiMH4ioqqqX5Ejp7PTrdNhfVIp12wLETB6jU5vV6cgRCRiSYK2EV0rHbnDe/jZLSbVzv3++C+cj6vp8m/GLhJcEAbHtiDlKzQmYGQu808VyXdqvDsSMLtHd3SRJ45X338/yVq7z1u/8KSIcEwasfeQ1nTp/m6WfPUfZ9HCk1gZmeZq/d5hUH53n+8hWefPILvO1tb6NSqfDM2Wc5evQo3/eDP8jP/pt/w29+9KPUqlWOHzvG/Pw8r3/9GwjCPivLK6ysrvLlL39FX8XY6/HIw4/gSId7XnEvc7PzfOK3f5s3v/nNHF44ggBmZmY4f/4cjgPNZp3d3d2MmCRJwrXlZabnZji8cJgoitjZ3uHa0hL9MOSv/fW/QafT5plnnmZ5aQnH9ekHEf3+Do6U3Hnnndz9iru5cPEiFz53ka2UYRw8eJD7HniAI0eO0Gg0OPWyl2XtXU4Zwate9So2N7c4c+Ypjh07xtz0NGEYsri4SHlvj/Oba3zH8dv5yNXnwXXp9/rZ3mnf95mcmKCeOkQnJyepT09k118qkToO0+GUrgNCnxfo9/t0O112L19kMwxZkRLhukRJQqvXoxdFtIM+3X4Px6/wjne8kxMnTyIShYozN1ve9JI+E0JS9sv0jv0GE+XD3PuGdeIthef5KCXodvssLS1loUAUOsSEXy7TarVwvVK2uJuyyie6n+VUoEOEeJ6nJf80DHWSxKnvRGTmHznCmae0xzd1+Ov0EkliiD8eQRDR64esrbdYXe2ystShvSdJ4jrgooRDN5YEfYHjxvjlFlJMIKWH43hIDyAmiRO0bjVgAJCggywk2aKTcrBbbbDsRtvwdR8KS7T42zAJS5i7nm1/P8ZjmEnOXl7QQjKyq4yBSmQvksQjietsb/VYX29x7VqP3V2POE613LgGcUnjiATCGiSSjXVBkvSRbptyvcfsbI1q1YQ3d1AqBhGTEOn5rfQmFcngfhDjCrbI92BnUG5djDFljcGJ1m9kJvh8PfCSYAAKNWTDLTogId1mKCU1vw5KcPrM07zh9a8nihIee/w7efbZ5/jIx36Lhx5+iFtvu5W5uTkefPAR7rrrHj70oQ9x4sQJjp+4jT/81Kdpt9tcuHSNO++8k9e97lt55plz7O3ucvzoEc4+d45Wq8W3f/tjdFotVJLQ7/XwSlWq9UkmSy7lap25+cMolbC+tsqZM6f5jV//zzz08MN0u12Ugte+5ls4c/osy0vrlMtlnjn7NI4Q3HXXHQRhRLvdYWNzg6tXr7K3u0uz2eT++x7kT//0T+l0Opy6/XYmZ+eYOnCQzZ0dZmZm2Gl1mJiZ49jJ27jrnnsQQNDvs7y8zKGFYyBcXNfn1Q8+zOkzpznz1BnOPneOqalpjhw+ShhFLC8v84Y3vIHDhw8zPTXLxvom7XaXw4ePMDMzw+bmJmfPnuX221/G7Q/cyx29HiJO+K57bid6+gITr3s1nXaHP/vSl3j+/HnKjqDX2uXpC+eBQVjuJEkIo4g4SZicmuLWW2+j1+1y7rlzBP0+UjokKsYveZnGkl1a45Vp+j7HDx3ixMmTLG1s8aXPfg4HwZGjxwhifZ+x3t0/Yk4prW87yke17uO2V57muT8WOFIHA3zh8mXq9TpRFKPiGOGVCPt9uv0Av1zW5q10T38cx3yruIdneZ6P3vIV3rv3Rmq1mnXiVy/YjMALhsxWeeefPvSmbRU6hDXKJVGuDmi3vsGVF5Z44YUVNtZaaUYfhYfAIQbCULG+1qHb66BUiSBwODg/wcSUo6/vFAqtO6UEX68ybT4j77Adty9olDaedqGwfvPpi9/HlZsJdiPCpOccuyNogZV4UJ6UqfSfmtakoNuJ2Vrb5fLlZZaWlthY7RPHpSx7okqoRAsMUroIWUZISRjGbG13iVimF7U5deoUMzMT1CcUXkk7lfUWbc1oFaQ76Oy2jfb0ZMxsH3ObGGPkH5i5xHV8CTcGLwkGgBoELTMqnZYeNVIcxyHsB/RTx9vK6ipO2afGJF8+/RRnTp9mcW2DI0eO8PBrXoPjOPzXj32cCxcuMDU1SbPZpFqt0Gq3mZqd5e/8xE8gpaTf7/PVr36Vj338t2i1WhxZWKDemGBuZg7H8flv//W/Mj01xcnjJ2hO1PA8h14vYK/dpd8P6HQC1tfXeeihh/iOx/8K6+vrfO7zX+C1r30th44ssLm1xSsfuJ/PfvazHDp2lHvuuxdHCn7v936P1t4e9VoN1/W5++57qVarOFLS7Xa5+1X3sba+zsZel0uLq8weOEB1epaNdofQ8Xj+/PP0u12uXXmBb3n965mdmSGMYn7ugx9COSX+/K7v5jv/9Pe49eRJXnHPvayurnLp0mXe+c530UqjYz7xxBM88cR/4vWvfz0PPPggcay49bYd/uAPfp8TJ05w++13sba2ymc/9yT3vupVnD9/gd3dXR555BG++IUvckJJvu1Nj7HX+S98x+Nv4eTJk0RBwFNnzvDZz3yGpcVrJIm+Z/etb30rj772taytrRH2+1y9eoXJehXQAdyiKKLb0Y7x2dlZ5g8coNXpsLh4jY3VVRqux87mJn0EDUdSdhzifgBSpkvPvk1usGkgCAJ2F7+LQD7JR//9LfzQ//Fx2s/eQaPeYPHaNX3rl9BXVOorNcs06noXkeu4SKmdvyaEuNNXvOPyvbz/lj/gn5d+MBceI2eHBi2hWfN7INKR7fgBBxVDGCvCfky33eXatU2WV9a4cmmH7dUGYXtSbwcVHiqpgCqBkiBiut0evb09yqJOb9tHRD6Nag0HLV0mogeijxL9rG1aO7G0JgomoczWbNmrtUg/+GoFOEvl5hyIAvEvHm7OHP05sb6YRmRJRoFtV7fbK6kgqZDEeuzWl3b46p8vsry8ys5OiyisIlRzQGuEj/B0HLAwSSDyUFEar6ob0+3pU9+99irTMz1uPTXDzGxDn/h2QmBXiyFKIWQf6NqtzPdA5B3Jdl+NCWmwsSJvbBvwiiT7najRV9u+GHhpMAAhspj8+qf+jJJBxEuv5OnJKSUzBw7wve9+t959kiTZQSClFJsbG9SqVb79sccIw4CtjU163S5hEHDq1CmCQB8s6na7PPPMM3zlK1/BL5V482Nv4fDCgrbrOtpO7Po+7X6f5a1NSp5HtVKhWq1SKpVY3dxifW1N345VrnBlcYk4jHjsscf51Kc+RfDkF1leXs7CIVy9soRX8lAo2q097r//Pl5+11382Ze+xLVrVymVSuzt7TE3N0el2WDh+DFmIjh+7BgnbrmFKFGUqjXuvPtuzl+4yOTcQZpTM4TCYafbA9dn4dG3cEuywWLicPD2VzA1UWF1fYPNrW1WVlf5xG//Dq+69z6Wl8+ztLrO4WMnOHriJOfOX6BWraZ3/Xb5kz/5IkIITp48ySOPvI7DRxY4fPQ41xaXmZw9yHFK1CoVVv7wCfZaXRZXN5iem2djfY1b77iTydk5vvC5z3HgwAH+6IknqE3NcG1ljX/7sz9LEkVIoJpGN436IQuHF3j53XfzinvuYXZ+npLn8edf+hK/8eEPZ8HWZian2NveZqJeBynwPJc4GTJo57RHz/PYXt1m6slvoZ98mGf+4F3c/aafpvPsP0IpRavVotmYIEkU/SCg1++TRDFuqWSKw+yUsm3N/3vy3fyTzn/gZ+p/My+pkhImoc9iDEnKWk3IWJZtz+2EfdbWd1laWmZ1dZWd7TZxPKNNO1IzgES5oNyULhjqKdjdbRGGMX6lTbleoV6vMDExgVvKn8pVKgERI0QyFKxu0MgBcdfmjhsXM83usFx/TVOzISratvcPDJcWpHtbMBll+LSZr9S+kt2dXZaWlrhyeYuLl5bpdvSdE5JKbsdWHCYkcZgKnegDnipO8aPFiyAIuHr1Kq12jVojxPUSDh48qC/tscoaiYsRDO5GfB22liCKadOCR0VHfrHw0mAADKuBSikcIQcTUDqZmSiM49wpW8fzcFJ77JE03rwQeu/27Pw8QkpUok8RhwBSUC41uf/RR3jgNY/mJLkojgmVQpRKvO8n/6E2BQiBFII4inA9D4XWWFzXRQlBt6NPG+uboeDxd70LRzqU0jYFQcD29jZ+GqZCCEGn00EAkwcOc+z2u6jXaly6eIFbb72VSq1GnCiCWNEPQ5a294jjbaQUVMsVHvuu7+bKhYvs7e5w9YUX2N3ZJg5DTp06xX/pH+KVa5/juTDm5w5/G+/eu8Sr7n2Aex95DefOn+PM+fPceuut/PC3/iVc12V1dZU/P/MUi4uLXLx4Ed/3OXnLLUxPT7GwcJjtdpvnP/skQb/PkcOHabe1hPPFr3yZF65eYa/Vxjlzls1jJzl6+92c+epXWV9b49vf+j1MNZvIcp0zZ88zv9CBUpUDh2b47rd9FwePTNDZUUQqYWp2jsXFa/zZC9d45dQMk9JDVershRHzx07glat0Ox3m5xdo9wLcMCaMTJyh0SKkWTDV3TruccH3/vEPsywu0L/wU7TjrzB7WOCd9kFKPL+MXy5Tqda0IztOiERElJA50icnJzMhpV6v82+qP8ZKssVUpKjLcr5epYiN2cK0MTVNiHS7p1JlUCWCMCToB1y9ss3ZZ66ysryhJfuuh0h8pPRTQuyilItShuAIEB4iqrKznbCz3SaMe3QCyezcNEeP1mlOlSiVXRyvkm5G7YPqAyFxBDokRJL+WWZXO2gZeR6buTrHEK+czJtpQ3nH5TifgM6fN0kpIMb4AESWxnwK4SLE4O6Nfs8l6idcvLTO6dNn2duW7O24gJfW2yCKKoPyEweBJIm1+UYIH0cofbSCEGSJOA5otxVB2KMXXGBjs8vtt5ep1gWNaYHr6jMgQiUIFaZ9BUWEYDhezygfixE0BogfxFqyQc8e7c35BlwJjLhRj/s3E+666y71H//Dh3IxY8xWQTsSI+jJUwxZbKBoQho16Ya2jBXK1hqlyOU3ixoGk9/g3j6roNBbyRIjLRqThLFHF+LLFCoHk78Qg8bul90HKfU1hMbRJ6WkUqkQBIE2vwjBajfifU+s8uuPHcMXijAMdAiLMAQhqFer+H6Zfr9HECVZgDUbJ0G/j+NIJIK93V16vS7zC4cIw5CtnT0q1SrdXo+rH/ogh979V/F9nyuXLzM1OUkUhmxvbRH29aUpx44d48srP8vh4AfoXrjA7GvuZyM9vXtgbo7O3i7nzj7DJz7+cZIgwPc8HnzwQdZ3d5lfOMwDDz/E9OwsvSDKxtxuqwHj6Hb+5QSLcy9w5MItXP7bTzM/P8/Wxgbvfe97+cDv3saPvfUanq8l5qPHTlBvNKg3JgijkDCJePRbHsvd+QDDd1D81s7neLz5SHFaW20SoAZ7/AUSFZchKbG8rG3TS4vbnD+3Qr8XakKX1CCcgcRP55+TmoDS+xFEAiIAp5dpFV55j1J1h+npSY4eO8bCkSmmZis0Jnx9DaUMgB6J6uvQJELhOAlSJhjmJBjYlgc2eH1QLfVeZu/s92NBDV6PdGjm/bnpYUOVkXxsE1O2lAxzBaE8UH5GQBdf2GNzrcPFiy9w6dJlkrBK1J8crKW4Dkk1G5cktphz5rsxDdangBFRqtmFKLlLvVHW18zO17jtjlmmpiapViq4bg8pO1nHHdlDYOap1YkiimyNJk07arttis40FIig1Wrxg3/17V9SSj0wfgD2h5eEBiBEPkTxSMJsEb3Mklbkmmna/S6JGcUwiiq8feouSRKSVDMwbVRKZXfNGiJvLkYvRujLxZA3wZtE/mJwoy56rpvtMjGMsNheO49SiiAM8dI26ZPMYdbPQCkaUvHBN0wTdFp87x9t8L++vMHDB8uotE07nS50ekNtKTJAmfbLLZep+T6tTg+loFytI6SkWq1z64/8TZJ+j0sf/AC3/PB7Mmlwev5QhhulFNOfepjj33+Ma7/WZP77DnD0+PGsPxMzs7z6dW/gtX/pW+l2Ozz99NNUq1WufupT9OOYuYOHUrwOX6dp27VJhYevfNfn2N3d5ePiN/mepXdR+dVZFt99Fdf1ePcbnua2O2c5fmuJi8+kGmCU0G63dVwYMYhNVJwnZl4JIXi8+Qj/z+pv8hNz76AISZIgcJB4INx07CXtHUG/E3PxuS2effYFel0IWhVQNe3MTHySxEOHBxAgUi01kyjT08OJl0nuUVAijDzCXsju1iJbGx3mDtY5fHSOubk5/HIJIV0cUSJRCRBD0tOSbjovM+nV7qu52nCktjXMgItMeawDF4bwmmCfONd77LXLwKTRB7uyA3PKR8UlWnt7bGxs8Pyza6ws7bG700ZFVVAVHFmyapQkmdlIoTAH75TFjNIWC32ATClpqDKIMnu7EXu7q2xtSza3r3LrrbeysLDAxARUq4akxmn6DLWMv4oyjz9EHi8jhUUBQu5f3o3AS4IBDNuzcobdXErtuBm+K8B+P0rSsIm/nWZox8GIPLYqZgdyy/JYJ2ltX4ZdppQSh4Fpyr5IxfwZKdNEuDSB0AyDMczFZgyO4xBZfbL7YkImm7588HXTXNoL+Xuf3+Rf3tcgVaKtiZcyzxTHRbxJ67teMnqnhX1Bi/TL3PLD7+HKh/8jC9/zTmQantneu3/Pu9/BC//4IrPfu8DFf3qOQ3/vMLXmLACxMjvCEjzf56GHH6bX7fLcc89RrVbZ3d2mUq1luBhnT3VcV8f6KZVwPA+/UqE1t0P/H24y969O4AiBXyqxsRRz5ITg//qlMr/2f/t4JW22i+IoCxxoyjexgMxtcObuYYCfmHsHf+vqz/Lzx/9upsEa3EnpoPUnPRZRHLO1tcv2Ro+VlRW2t7eJQ48oKlv79zV+Td36bzDORkDMCTXpwzAMCcMQby0giHaQbkTZLyOdEl4JHeROaNlaKQXm3oVs4g8thSEc55htYc3l1t8Y5WBQlsrtSlKJyhiAUZ5k5otRmPMT0pFIIYliCHoRW1t7XLzwAktLbbbWe8SxAlwEDiAt/IlsHExnLXY1xLD0CpHZt+xkMQmdTp/VlQ4lr0y71eXo0Sbz83Uc18XzxECSZ8iSMxYnpn5VeD4Kd98I681LhAHkuZ2JiT4galjvIGZwvSEWQdbITs0nxo8uJDaRzWq0COYg7/AkL74XahAGwUwU4Qzvxy0yIUO0BeCWSrn8dowdux1Fx/goc4c0Jiar7eP6IoTg5ITkpx8q8+xOyD//sx3+47ceyGlH9p8hZDl8WG1OUIikqL7qz4Nvfwcxihd++ec5+D1vR1ZrmnlLPXYT/2AaJULm77+VRCnO/aOv0nhfA+nWdPTOrt6tNDU1RbVa5Y1vehOXLl1it9NBeqUhbS6nGaX47na76b2+m5TLVcrVKlEc899u/U1+cf3D/PTST3GJi5x5ssO/ft8E/+JXr/KL/+wkrgdJL6Ld6SAdF1KtUAjF3t4O58+fR0rJ7OwsBw8ezMKP/8KJv88vrn6cV1fu5OWlUwOTSuwhRJWgH9Pr9Vhb3eLZM0ssL+7QarXod1wkFUrOJPpEMKikhKCkpU9SmuwkeseJ7qn+E0kmuUvpgWiQJHpn1cZal+3tbfZ2u2yt9zl2fIbZAzWmpmu4bgkpYxIj/admJCW0AHA94jIQWshJrENzT2Hvh8rNxXTghviNXifp0/SAHZjL0CUoD5XosxTra7ssXtliZWWFixcv022XCPpu6jh3AReUk5lhhXJAWdFyKfKo1BRrxcjKOolEJalKIiCOFb1uyNUrS6yubFAp38LUVBkfh5LnplqKHOBpBA6L/X4x8GLTj4KXCAMYQFFqNiYZwxWFEJaPKj/ZlFI6WBPWoKo4SyqyHOTTpOVKJawJIXJpihN8RMvTdoghYm33TYEO1jZGvRunjZi05p1hKMZSmvXB8hMMmc6s33dO+fz6Gw8Sxoof+qM1/tVD0xytDV+LOba/QgfmG9VHG078L+8B4NIv/2L2HcD380S88S/u1ul+9AXmf2aC6mFtNtrc3OTcuXMIIWg0GqkZCQy+izF4zPiZ6znDMGRjY4O9vTa1ahUB9Ht93n32O/E8j4++7A94z9YP4JVK/Nw/nmf+mOJ733eO9/+TW0jMjBGCtbVVrl29xsbmJteuLuL7ZVp7PcJQcfDgHBMT2nz5I3NvQyWCf7j4c/yrQ+9LCYAkCCLW1zdZW1vj0sVFlq602dnUt7c5jg+qhErMZgRQKkHFKbEvznejG9iCtlJa9hGagUjhIoWLQLGz3WFv9wK9/jY7u00WDs9x4MAcXgmkK1P7RFpnylSGdHAz1pZ0rudfwdxjzQm9blNzhdXY/JxSmRBnxlMar5Yy7yyZOI2kGwSB9kFtbrK+vsbe3g5xHOEIn5Lj6XYmQOxAog966WDCBWFNpWb/FAexigfCmJlN1lrSIaI1nUmSGCHh2NETzM8f5MjRCrV6CceRCBmhhMKwEQHZ9tmsYgsfZt7mtL8xPDiJU4ZyHZPSjcA3wI/8jQMh9PY+3/epVCo0Gg0ajQbNZpPJ9K/ZqNFsVJmoV2jWfOoVj0bFo+4LGr6g6XvUfZdaycV3HTxnQKhEoa6cfTeLnJkgBUihECRI688RKvdbDoYt+1NCpbdQaTOG+TOTRshUajPp9zE9FReTHdkzS1PIV0xnYJxN0ZXwa996gMt7ET/39O7I+oufdnn7/Zl0oBnBlV/7EDtf+UquLLutSimOv/8oVz9wns3ViwSBDuVw5513UqlUuHjxIhsbGyCENhUpRZQk2fc41aocIXBSJ3wYBJR9n2azQWDuBci2dTr80NL38P9OfUC3RSnWlwW/9M9O8QN/94qOXRS7JFGJne2IK1c2WVpssb3psL3psrqUsHSlz+4O9HqCKNKSPpT5Fwd/nM+0z4AoEUWCnd0OSysbXL6yxAtXltndDYgilyQpoZSvnb1JCZV+kjgp+0kgiUHFCGKEMJ/pnDKEKUlIEgHKBeWlUrJHEjlEgSToCYIexJEgiSVJ7KCUi9Y4JOk5e0tKsuY0egtpxiBSezlieG4Ux98UNV6jGOS3N2/I9G8wD42qMdAgHMehVq8xPT1NvV6nXK4g0Lv1tKnYmH/ITEfFus0OpTRRXhMfsQ6yW8MUNJp1jh07xpEjRzhyZIGJiYks/PuLlc6H1/WwRm7+Rl0x+7XCdTUAIcQHgMeBVaXU3emzaeA/ASeAS8A7lVJbQvf63wJvATrADyml/uy6rUgHp1TyOb9ygZ//wC/hHW5wst1ABmF2EMf3S3glj3K1graRx2xtbuJIwczMNOVKGdd1CFVCEATIOGFmZgavVGJicoKSX0a6TnbVn5fuRfd9n7LnU6vVmJiY1IfS0AsrDCOEdLKY8VmTzSQXqZQCmeot0VwaFI7rEKt0B5Ad4VOS7lnWKqWxcRoiPpAEjEYz0C4GKvJAVrHGK9++wjPjgyhO0Ncc9HnNQZ/fvdpluRvzV0/V03rREhD5RSxIhqQ6ZZqSCU6WTVMpjv/ADwJw5SO/yaHHvxNZruTNcGk/T/7YKwG49J4ljv38QYQQbP33V+HHd3D19k8wP68Xm9kpNopBGeIfhSFJHDM7PYWXai3PPfcsiVCa9gnFj7ffy11H7uLOzis5Vz0LwK//7CbLHM0AACAASURBVAl+9P88x2c+sUZrr8vFC5e4dm2JoJ8QBg5JFBL2t4miGMeDbrfF3NwcU9NTICSuK3ld4wH+ydK/4+/XfoRWq01rT4cDVwlI6WQ+Bik9VOyShNKaYilhVmQB9ZSx06T4LG52kDImSVLfiAQh9E6fStWnXquzcPgAhw41mZmdolJJDzLZgqYeKMvUN1pLLUrw+Tln8lkCjNHMlDF7Fglr2m8h0r4O5o5SECfWfFUJSkU4jvbzHDw4y0RzhqmZBtWazwsXttla76VCXYSUChxr51Zm1bFs/2N4k2aw+k/jJCShh1uSNJo1jh6f4o67D3HgwAFqtQrC2QXRTeezZtRKmfsaBvYHozlZ7AaRXhE/WLODcS6CefaNYAI3YgL6IPDvgQ9Zz34S+KRS6l8LIX4y/f0PgMeAU+nfQ8D708/rgiO0q6VVjZmnQu2hU6x/ywz88O/gxuAISb/ToY9iO4rwfB3meK7RZKJZx3UdojDAE3BooobjNAiTBN/3cV2XikgI27vEcUw/jtmJFXGSEMYJjueRWJd8G4YT9APKfplKtUKpXKbT7dJqd2i12wRhiO+XmJxoUkpDL/c6eoE3m5N4pRKe7yFdDyWgF4R41sXh9XqdSqVCray3YYZhqMusVGg2JyhXdRRO4fr64vEoolrW0SuFEFQqFRQQRpHeNYKex1LK7OSlvZ10IMWplOEOjF2KATH5zlt1XKKtfszf+cwGv/C6SZTSl5OYegQClWj8ZZoUA3VSoZBOavk19aSioBCCo29/OwjB0u/8NtMPP4I7MYkWJyXmbK9SiqPvP0BraZ3Nfxrzsp8u89SvTFA/+0YuznyJe++9N5MaYUBcbM3O933a7TatVgshRCad6YtgPNw0wFy5XGZX7PHs1NP87Qt/j1+585dpxz1+6V/eyfFTy7z5Xb/FT/3oa+hsNSCpIKImUSwJkoSolZAEu6yvtLjlFg8RNymVJeWqRErBPzv04zzVep6js8eJQoXnlXBFjY3lmE4rSU8ahwjlIKwjs4ZQagepLZsOYMiUQoJKtx2iwPcdPN/n6NEFbr31Vg4eKlNvSrySsNJatu7U5CKEZbXPtIH9wJb684ROQM6xbaSEYW1Svxy6c0KAIwfndLRfyux0kziuT61WolxeYGZ6hunmNqvLe1y9epWNtb2MAGuBXoeK0MKaMfUMLnxXqblngHeN0+x+cSfGceHQwgGOHV/g0OEGR49Naq0j6SPogwjS8mL0OYDMiGzhRmV9y/A3Bs2jTMTm8/+Xg2BKqSeEECcKj98GfEv6/VeBP0IzgLcBH1K6lV8QQkwKIQ4ppZauV49ZtIdaVcRPPcx0u8Z7L9zDR395lovNPd539hVUQo9+r8va6irL166yubHBytIS5555jqnJJkePHGZleYXPfOoJ+v1+JumWyz7lShnHkRinUrfbwXEkzWaTer1BHJGdEA6CgGqtRnNykkQIAhSuEPR3dykJwS3zB7P2ag3Cw/d9up0OvX4/DZJWYnJqIrtHoNNpE8cxQdAh7rfp7G7RShKuhRFKOEjHI4hj1jY22drZQUpJuezjl4w9M6HTatHr9ZientHag+uB49Dp9YgShVcq6e1+qWbT6bSIwgihBOVKhamZKer1OtV6nXq9gXRd4vQUpOd5+oY0z6Ner7PQLPMbbz7EO39nib92R4O/fLSEQuB6HlIoUIMtkmEY6vsMXDe9HhNyoRDSZWDOSMQqJkoS5h9/C7tnnkYtL/H/cffe8ZaUVb7396mqnfM+OfbpnOmmAzQ0DShIUgmCoIgyzjiiAtc0MzqD4VXHq3P96BgQw9VRQEAMCChxlJy7ETr36T6n+8Q+Oe28d4Xn/lFVO5w+zThz7/u+Xp/PB07vvSs8VfXUCr+11m+FV67GxYPLeeMIIs0NRL4Hu74K311xNxtpYUfGR6lUqqlmnR98VBTFrvx0evg21DcgpWR8fLzcVtGyLPs+Kko5ffYHq27lwoG38mDLb9E8Xvp6JF/52Ju58XPP8k83tKFIr81K6ygf09SZnp4lndPtfgwBP5GoH80bKfMarQ2t5Ktj3+MjDX9DMBjAq4URxhzTZMlmsxh6CSkNsIwy3l6hZHAyfqhNhqAK/rMcxS4Ux1p1PIVoNEgs6aOjs5nORS34giU0TwmE4Sh8m9DMqXoqQ0qiOsAgToTzynN4g1GGc+wd522/8L7V3mLNkFUQrgBFpTJPaSAU8KgCRfXQ3JYgFA4hlRxgkksLcqmMc2y17NkrTlzYvS7X66imI0cxEYrN9YM08Xgt/GHoWtrIylWdBII6qpZ34lImisyAE1S3L9equtQKzFT50jXzeQMvZOHkj/n//q+O/2oQuMkV6lLKESFEo/N9GzBYtd2Q890bKgAhnOo2y6C+vp5PZM92Xk7Jlf1LbIZBofCzxYcZCmW48dAGmrq6ylihKhRUxV5kmirwejSbXyafp5DPUygUKBaLZLNZCtksgwMDaIpCfbKObCqF5lQK9/X30djYQHNzM5lMhrb2dgDuueduzj33HFq3ncb+/fvxejwsXbqUxoYG8oUCmpO/n0qlMAyDgN9PLp9leHiIRx9/jOnpaTyaI5BUOzOgqb6JsFO1XMgVyKQyFIpFPF5vGTZKWQamQ4ehaRqxRIJEMkosFmRiYoK5dBpdN4jGYsRCYaIhH0m/RsAfwDQNZCSE5lY5Y+AXJYJmDtJFJkYH0U0ToWpoPj+RaIwZ06KQzZGam0UISESifMavofQLPr+vjpbiOKuYQrckBV0nV8hhOLAXgKpo6LpuW7XFIn6/H0VVAYFpmeQLRfLFIj6/n3gySVdXF+ec9xZ03SB1rI/S8ePUbzvT9iZEbUrrbWf9ikZfkPHXRii1R9h3awvr/nYaJVDVGLsKapBSUiwWy9z9dXV1mKZJOp0mm82WBZRumKhOOmcwGCSdzfJA430ohp8Lxt7CQy39SEvja5/ayA8feJHP3bCOiQEfAs0WzwqoIoBX86MQQEgvQmioqijDB0idf2j8IMP6GHfp93PT6g8QDqeZnkwzMDDA+FgJo2hSyuer5IC/nONvX5QC5QCmC5bZFqqT04klDYRSdK4lwKKlzbS0xWhsrCMcU5FCR1JAYgcohaIjhAmiuqaiQlPtWsRvlJRQLdBcyKZ68/memesBVMOc1duecCYJ5TRRYdODW7hKCserNZGWRKBRV5cgHlVRFInfG2JkMMtIPuXQxdhxAbe5lF2sicPMaXfYs5wLkIC0dJAGiqbg92q0diRZtrLOJlGsiyCUNIgCQjER0nQ687l1BRXI6WSC2lV2rl6QJyjKynbVo2z5nzSu8qeP/9NZQAtd6YKzFEJ8EPggQEtLi+3emXYKmpvIKcsZBPa1Xt23orz/vV3dDAcz3NS9CZ8lKLoLSkqg5JxZAW+IgD9CSFGocxbhqk2nl7k/FGE3ehBIzgaEtFCAdDrN5OQkIyOjrD99O70jkwzPZMjndXweyQu/+DWzszOEA36KuRzhcIhiocDy5ctZtWoVQ0NDIOCqq69hfGyM3z/6KFK1q3O3bD2deCxOvlCgobER07TI5+0q2bvvvhujWLLnJxU2rFvHxlM3ki8UmJqbYXh4mFKhwJZTN9PR0YlSVTzm9Xo5ePAAA4P9nHHGNtLpNL/4xb0sX76cpcu6KBazTE2OcvjwYcbHJ1BVlcbGRiLhGNlMjonxCWZn59AA07QI+v0oQuBVVXyWxayi8ES8hZ4Nl7H5lZ/Yz0xKVFVDUVUMw8QfDBAKh2kIhokHQuhFh4hM0TAjXrxNjTS3t5MzLXbt2oVhWJx33vkku7rQ2zsYvv8+tGCQpvMvcNcJAD89+1Iu139B89YAn314hFuzD+GLnsGrX29iw0fHaxafG0fx+XyVbClHkCUSCYaGhlAVrVz1rOsGUsLM7KwDdalYisHDzY9wc89N3L/2CPs9R/jYu9Zj6T6+/cAT/LfLtiORBHyCuoYIyfogi7paaWtvwuOz8HjdBvY2a6kiFDp9jdzScgMf6PsMty39Js1NRaJRD0OxcSYn8hwfSFUJRpfb3661wBIVkjXpCH4qf4UwEapFIOQhmYjT3tHGkmUx6puCeL0aFhkEOYQo2Ng0Ni+QojgegGNei5o7Wbmf84V1NX5PjUB2MW9bMdnellJOTsDJ6KmGDssgiSt4qfYCnPqJBaWIRAjDvnZXGZkZVI9Ge2eUcMhDMp4lGPAwNDRELltEmnbXN7ug1IOwwLIcZSdcj8hRWJ4SQtVpbGqgta2ZppYQHUuieL0qKGmkyABZW8kL6dxLN5gry3dTytq1WZm+rAGFquMisuZZ1+7r1uiY/z8qgDEX2hFCtADjzvdDQEfVdu3A8YUOIKX8IfBDgLVr10q7Jq/2OZdtEnehVF3wVY4yeKT9GFO+Au89uqZmH2dHwMnDmRcwcyZh7yHtF8A2MuwouwiGaOyK0LR4KW7I1TRs7m+f11NmHfdpCpl0mr6+Pvr7++2ev1JBBsJYpsGRvmFGR44TTtSzcd1aFMDr83N8bIztO3awcuVKpBQ8/fTTHB8fZ/W6dUxOTKLrRdKzsyxbsYJMJsdjjz3Gpi2b6Wjr5KWXX8a0BPliiaefftoOeCsKjY2NXHnllWzatBlV1dg59Ar1dU001DeD1JicnCAYDHLRxW/n0KFDNDY00NTchGVaeL1eIsEwd999F5mZWVYsX45XVTnc3e3ENSIoikLEnCPy/Pd5/uLPc4P3KO88fQ1HDh/m8YcfZmZqGpHNk83kKCqTTKsKRdMkly9gmCYo9nNcdcopLFu7jvpIlLjfhyLAcGh1Wy69FFM36b/rZ3S+57ryo5qQWR4MXMtz2R4OLjrE5/wDnPO1Ih/82FZ2f7uDlddN4Y/r7tpCURTm8jOoPhvzN3QDIQRzc3P4fX67qKj8wlUUhGmaIAUepxfAq9ePsfWPLRRax8jmBPlsnk9cs4zP3PYyX/3EEqKJCC1t9dQ3xmlqDhGNqVjCxBKlsvGCBGFVzvXjri/xqaEv8uX6f6JVxkFYKGKO9Gyx3CcB06SS8+9avyquKLENaSfLTEoURRII+ahvDNLY2EhnZyuxOHh9ElU1AQMhdBAV3N8WUrJW3p+EB+hk0EMl6Gv/rc5mcWNLknlehKx6lx3L3j3uybOFKrOqlqG2ILcTG6QlUXD7eKu2h90UwtR9ZDIppEyTz+BAgzj3wYWS7GnYcVX7O1/Ag8ev0tSUYFFXM5GYSiCgOvfchtLcBjw4sZp5eXknvYbaj5U5LBTrmT9OSifzXxj/VQXwIHA98FXn7wNV398khPg5dvB37k/C/53/VZM71qaRycpG88bFQ4sRQlBSLH7f0sest8S7j66siqaf3HrBeSFFlQCQSCzTKG+nzMuUlVJWiMgETrhIpXnxMpoXL6vspygoHg8IgSYloWCQfDpNem4ORVUYHR1lbGaGwrEhfD4fSryO2fFxgs1thDx+kvEYPp+Xl/cfIpmIU9/ZydDEJNFIhLbOTny+AFPTs5y5fQfbnB4ETzzxBD+9/U4CwSDrTzmFs7Zv59RNW/jDE39A1y0WLVrK5OQkjzz6GFu3bqWhqYn7fnM/b3nLW5iYnKbnyDFO23YW3d3ddC5bxVnbzgDLYm5ujl//6lccOXwYr6ZiGfDgmT5MYxlXPzXFup5dpPv6CQT8RL0qhqmjqB62nn4aG7dsoaW1lWAwyO0/uYP9Bw4wPTLCkje/mYN79qLPzhHUPGSLJQyh2N28NI22d19L+uhRzFSK6IYNJAnwmS9+Eiug0f3RDfxs1RV4zvfwrid+zeeHV+NJLGH3tztYf/MoAihYBUpPPEs2ZFLadIiguhXLMjh4cD9CAU11aaBV/AEfwWAAo2gQikcwHcI1wzCIREJktyu0jDTz9sNb+PGSP9B/dIzvf2k9289PYxkhWlo6SSSjBIN+pDSQZfoyqoBmcG1CgcK/tP49L2Z2sTW5Hs3TSCQcJx6r49ixY0xMTFDKWxgFo6w0FKE5XcDtY1gYSGEihGHHnAIqnV0x2hclbErt5iiaP42qFbErfi2kyAPFinXqWK5Vi7v2o3MN0j1r2cp/g1iAAylZZdvKhq4q0AiOPqutl6l+3904SBnOqzq8glUjBaS0294gJEIFQab8PvvDAQLBEP5gDC3QwfjoLP09c8zN5DHNApbpQRrYcJ4QtgUvKkZEQ2OYZGOApcvq6eyMI9Qiipa3BbAoIdQSQnWRB1dWVeH7Vda8e11vJLTL9R0LyMHq4SqA+bQz/5Xxp6SB3oMd8K0XQgwBn8cW/L8QQvwNMAC4JCgPY6eA9mCngb7/T5lEtUNb7Sq5D19gwzSKsFOfhKY5LlMlQ8BnWVw+sQLTNCloBo+0HCOj6bz72MryURe6+bUNrue5viebb42VU9myYg05WTUONl4C8oWCvWUggCUlTR1dtHQtKQcjW9o77BxjRUFKC9My0TSNN1/y1vI8s5kM+WyeibEx/B476DzY18/rew6jqipti5YzOpVi6fLlLFm5ltcOHaGjvY3RmRTPv/IqoWCQUqnI+vXrqWtsIxSN8c53vdcO4voirNuYoLvnCC+/vof9vf00L16KPxDgwKFudrzt7Vy/uAtVEfz744/z0s5XWbF8OetfuZ1CKk3DBdeRa1/NupGXicRinHXWdoanpzjY308pVkedP0RDUwtTz71AMplkenKGJYu6SGWy5PI5VK8Xq1iqeVahxbZyL46PMb1/H//8ua/z9X/+DJf82zQjl4zx2c5X+em5l+E9V+GaJ+7jS2Or0DPrOPSTBtoG/4bf/90s09kjFBsFjaP1ZFJz5DJp8tks0WgMTVEIBuzeykf39rBsw0oOvLSbtWdtQVVVTNNCUfOgCOL1Hv546RFW9NZRnxIMBcaZmUrQ3BLlgmtvp/vFj6Mohg1JYKFUYecSC1nuDwtIE1OW2BJcjCHmGPAcZ0nzMvz+GJaIEQgXmTiuMDlqw0cSUNCc2gZn3QqJFDpev0IsFiWeCLJ0eYK2Dru9ptdnglpyhD7YtSiuB1AZbuqxvYqrbf7qd0CUhdx84jc77dRN66zEMxU7DxXLwdbnpyRbUoIThHdtO1tQ1nogCFGh1i4Lw0o6KdXfO9BTRecaIEv4AyodnY1EI2GE5WN4aIzRkRGkCKIoQVTFjglIDEwKeL0e/P4AnYuStHTEqK8P4PEUkaKAJR0FoOgo0iifWwjsQq/y9GtrCgQnlydUbVct8BeMkVR99/9VFtC7T/LTeQtsK4Eb/3cmVMO14ywnRdj5wQKJR/MxOHKcH/7whwQDNivfsmXLaG5qJBGPEU8kiAWDvGtqFToWt65+Ha+l8pHBTbgt+1yKAztX+ORVuf/hXGv2KyN5zm8nuspumpxwXwwn5gGACYZZ5TpLi3yhVFY0qqqgaV4icR+xugbAzoNetWULhmGUaxDOuOgi9GKeTDqNPx6lWCiwav0GzrvgIjo62imVdI4c6eGZl14il8vZLJgdHXg8Hnp7enjppZfxeLzoUlC0oLWpmW3JOl565RXm8nmWLFlM+5KlDPX1Mzg8gscX4IwLt7Nu7QpSpskn8ufwgzNi9IwNs7e7m+3bt1NXV8fx/n5eO7AfU1UwpaRg6DZrqtf2koyykKh18QF8jU00xRN87Z8+SbK1jdGhPjAs7rSu4FPav/P3+hn8/E1X8PfhP/C+L/hZ/eU0A3/dxVWhdzF1yYX88t1XcM+NN3N13zec3HulfF89Xi8ej00u5vF5nfoNKuvDeaKBgB9pSYodOnUzScKhKPloibr6BPufuZmNb/kyB575TIXN0mGVdI2CmrYpbqgKO1dKx+An+Xv5q+h11DfEUDVJKZ8lk8pjmgaGUbIjYsJN57WbjwilSCAUoK7BR119kESdj1BYsdk9Rd62UkUFkxfCcgjEZNUcKP+jWshW1rQ7qpVDdcGf3QfZvR6XK8eyD2J/o1B+74SiVKA3B/dwGz+5Tkn1cNl27d/n9yeQICsqo3wVZUdDIlQLj1BQVQ/S9NPYHCWXm2N2DqwSmHlAmiAVUEw01SIU8tLUVEd9fYzGxpiN+aOXvTvFrZmr8Ygq97V8u6jICHd+ouq7heRNdTB+oVEdJ/g/Mf4s6KDXrl0r773nbtzoO1C1aCvBL1Wz+wHv2rmT3oMHWbqok9npKSI+O7/eNI1y4Y8bTNMtHVOC6vfy3PV1aEWLbb/MEU/W4fUHCEciRGMxAsEgmseDqmkoimZXBpumjRVbdqWwu4ihQlPhCpLqQCO8sTIRwkZ3/9T0upNCWCcZCx1PVVU01W5jp2oahq6Xs5dyuRyaouAPBBCKQiqdZnZ21ubRmZpibGwMv9/PxPgo01NTnL3tDI4dO8a+fftIpVKcsulUJicnaW5oJhqLMptJc29gM99/UwsNAQ+HDh7kF3fewezUFBGPB6ukc9k1VzMyMsLRmRluvvlmCqadtXOy4nQpJa/rf6SOOoyf/Tu+Cy/hwXvv4P0f+ii+QIAvep7lxuJmEvj5h11P8P5fLuFHbbvZKNbj8T3OOb9/ltlvf5gHo1/mpXcqhCNxhFCIxOMsXrIERfNgFAz8sRgTfcPEWhrw+ry87fJrnFo/21MrFkrkcjbH0KkPruDA1f34AjbuHEocIpdqolSMUM5csWePpJrZVeDy0dhWrxdF+rhh6It8I/5PFAtFjh6eob93mkwmY1c/W16E9JefrS8AXh80tzawbNkyEskwyQaNQND1nA2EkgWl5OxjxwKEYlbWR1lou9N0BbgLvTjxkcoGUFZstX/d65JSIK1Khz830G6n7Nr9PWSVYrT1jCh7AUoV3XRZGVUvBFH9zghcKueF3xGngQ4qQgG9qJJPaQwNjtLT00Nq2mJsqIRw6CU8gTyBqE5LcwtLl3bR0BogGveUDVG3bsKes6RS0e8om3neU23cw427zEMZFnzfq27pAqM6UHzlVZf8308H7Q5FuPnirnZVHcsHcOEf4PjQEM3hIKO9PezZ+TKKgEKxYPcPqBLAHo9mCz6PBxMw7itgqSr721sY/+Z5+NNznH3vQfwROyc+r+vohs5cKkuhUCCVSpEvFPF4fTQ0NZOsqycWi1Pf2EQ4GiMWt9v1uViloih2f1ns3AWXWROcFDNHKbnFLvOVx8nG/Fz3NxonUyaGYWCYzhJ1MnMo2dlSUgh0KTHy+fJzSCYSmFISi8fp6uqyG6BralnxLd9wChdecbl97FIJXTfI5wvoDuX1Vl1nbnyMv90P7+pq4Mrrruexhx4iMzNDMZtl/7EBOru6ENk8Q2OTJJN1uKyN1XMv3x8h2eo7nVeyLzN9TheLjx/nrfEGVI+Hr33hH/nEJ/6RYMjPuwP3c+u2izkWHGX/QI7lx17Ca8VIX/MrYv9yM2futjh0tiAQLpJ73U7DTaXSBEJhNK+XQqFAtKUeS1oceuE1Lrn0KvtZOhCI16fh8UYBSc91wwQLXhI7I4xvmSU1tQwpJet2fIu9z91UI7lENc88iv1Z2vAQ0kAKwQ86Pss/jXybjyauo7UzjFB0xidyZIopDF3BMjzlEHC0Pk4iEaZjUZiWTj/hiIrXqyMVw1lnBoISAieOIBwvpHodCagUBbqfa1ZTrcUt5m8yv6DLFrolvcTx4+PoukEkHCUajREK+fB4NCS2FV2dBlpWi/NegVrYpwqpxX0naj/PH3YsxsQlZfN4/KiREK2tzXg8GqPDKYq5MTLpLMVinki9QnNrlEVdDSxd0YZUM0hRLBeCIWxZpDiFcrXKr/Lv6rVbc/Oq57YAvFN1V0/YfqHr/AvqCFZxl+1CDfvbSlTd8TGlXYiUzedpioQo+XyYpQKhSIS3X3IxsWiMxx9+mJGRETyaRtAfREpJvlDgwksuZvuOHaTTae68804KF34PXVH53U+vRtvVS+Cnr+ENBKhvaCAajxPy+4kkYqhePz5/EF8ggC/oxyMs0tPjHO3eb1vP+UoPUE3TiESjhMNRgsEg0VicUChMIBi0eUqch2dYZtmVz+fzeDweDMOiwlxKeV0ttJjmu5WqqiIdCGihBjrV287/vhzLqMGsK7ilDTcoGJYFpQqMUSyWas8DeL3ecnN3IeyerD89R2GyBF98VecHH/soApsOuVAsYhgGbcuX2cVQhk2RXU1aV60MVFXlGeMpclaORDBJRvWw7vr3M3z7T/jHL32Nx357H6vXb+Tulst43NtHcpWHd/Yu4oXCEOGij0suOcbo1G08UPwrbnp0H987N8qK74+Q+kILAb8fVQi8Hq/dZtJZfIs3r+HWz36Bm7/02cpFUiuIzKDF3Mo06763mH0f7gUh2ff8jWw4+1/Z89xHWXg4D1hUsblKCzD4StuNPDj7DG+On4lpRjDJMDntI5cxyWdL5XsRi/upa4iSrIsSDHnweAR2Pr9ZNpjsmLGoOW/Za/0PEena4aZZyupjzDNeLMtC10vMzs7R09OLYZg01DVCu4rH47Mrr93JuU6InH8eqCieed4FtWLRvnVvcB3lgztIgmKheIqEY6B6Q0hyTM8WkZPTGLOzJBqb6exqoa7Bgy9QwJAlTKuEoliOAndkkrAq8M+8i6jEU2rnaiE5mXG20GfkiUriT8uS+s+NPxMFUNH+FWOkOo9WYucXKxSLBaIBP7likfr6ekq6hdfn55RT1pOob2TFmjV4A34KuRz/8tWvAtDR2cGDjz7Gzr17ue6667jw7W9nf/dhBAraX9+H1+cluLqd1GfOIrtnjv1f+DXNLW286U1vpq2zs2yt/OpXv+LY0aOomkY8FseSFrOzszalQyhES0sLxWQSI5mk4PczJSUzqRQlXUc3TQq6TrGkk9cLeP1+whG7ubTdtD5IPB4nHArhDwTxeLwEAkGbtlfV8Hp9dhcnq5ZArWQYmIbLT69S/cLMT92bv1jL31cpgYUtKRZc5POP4b4Vhpty60BddV741hkJjswUuGXnLP92TgOavQsivwAAIABJREFUx4fq8dEQCC2o3BaCx96snY+UEsNjMDc3x0szL1DauJSX776bTalZZjtS3POTH/COa69n8eLFfOcdu7jkJwVUVefGF57lWn0XqifG8Yt/xUXP/hUtb7X43bunsCI6ymArhmk5nqPdkEcTgpu+9FmMUon777ibK//meveyyvcYoBTV2ffhXjofbWLo/HFMj8me5z5KorGbupbd9Oy+uvZ+SqsMCSnC5Zo3sWmPBW+Pn0nONNCVLG3eBELrYGhgjOPHJ9BUm0Mo2eilbVGcZF0Azacj1RJCKaGICl20K7Qq1rasElyyPJfKvITTKOZEw0NWCf9y5bFUEUJDEXZFdqlYYHx0hvHxScZH5sjnikyPl0jNSFatCpKs8xEIC1TNSfh24wjldSSRoiJsq+d7gnyUtepg/tqdn1UkLTsVVlVL+Py2IWmaYQyrEfVoBqGlaW1L0NrWQCAQwLSKSHSEcKGbhb0MsdDk5o3a2FaV6hXl/+HGD6o9rvnr7P8NuP7PJAawRv7ynnsAMKUAaZXxNHf5gcTn85LP53joF/eyZvkyhnuP8sIzz6Biccnb3sYZl1yClJL+gX4E0N7eikdVUVWFQ92H+Nldd7F08VISsQRbt26lvb3d5vvx+jjW22OzRkYi3PabO5j79Om07c8x/PXHyqRd559/PvF4nHQqzfPPPU9rayudnZ389re/JZFIcNHFF5PJ2uX9qqpiCUFDQwPhcBiPRyOXs6GliYlxxibGGB8fZ/j4cZvtNBYjGong9/vLPEGKqjKby5Er5Mnk8uQLRUolA0tKorEY4UiEeCJBPB4nEonQ0GB3fnIDclLaBV1SViAooKw8ylZ22RuoPBO3ZB6qYBh5ovX3RuON4iHXPzXJLafGWBHz/KdiHPNdYEVRuGfmLi4IX0j/3gECLz7P7niSgSP7uPzq6/jdvz/Mix9q4wt963l55+v8smTyjlETMb2WiL+BuhdvYveZZ1I4ewxluBnGY+XzKIrCJZdfVfaEfvw//pUdF57Pig1rqyc07y+s+84y9tx8pHwP1p15K/teuGnB+wnYHPNVDWPAzvm3LLh56Ot8Ifh+ensGGBoYKRf8LV+xnJbmZkJhV4GaIIpIl/5cWjb7bA1E4TLUOoLGtTJdbw+lbFGXBWoZnxc1+9i/a6jCj2XazdQnxmY42jvM1NQsx48fp5gHTQkQjzXQ0d5JXUOMzq4kkZgfgcCSBqbM2fEKQCgSRTVwydfK0BBVkI/rIc8bC8uxih1u33MVVXjK74JhGBSLJcbGxkilUrS3t5CsS9heqAKSEm6VtJTlI1WOLd8AqrEntfBvbxDnq8Bx1fQmnNQj+N+NAfyZKAAnCIxthYh5ARNFSFRFweO1KR7+7VvfZO3q1UyOHOfJJ58koCkYQqF58WJu+NCHCEciDA8PMTg4QDKRwOvx0Fhfzx133EEymSQajXLNNe9GVVXyuRzPP/cc9ck6VqxYwZ7XXuPuu+9i6dIlbH/7edx5fo627iJTt/yWLVvs+/zcM8+haRp1dXWoqsrQ0BDnnnsuibo6BgYGWLFiBbph8PDDDwPg9/tZvnw5hWKBqclJQLJkaRft7e3Mzs7y7LPPMj42Zl+vaT/YQDBIoqGeaDKB5vFQ0ktYEsLhCPUNDWWrXC+VUMDuAwwIVcUw7TaWJja+HwgGqa+vJxqNEgqFbIUTjxMMhhyl4FZqyqr/apUFAEJgWrZCqe40xjxlUT3eKHaxf0bnt/05PrUhuuDvC1pc8xQR2HM5bg2BCjP75ph7/jnGfH6mUpOkCiaLlyzluUsDLPn5MA/ffR/yfdfwjmMN7Dz6CO9deRuZV99L68wMj964AQwVbaAFKWzq34vffkW1ncnY0DCTY+Os3bzRmVD5f2UvSwrJum8tY89/O1IWZOt3fJM9z3y0vF0NzFXVMN4WurYykNKOI904+K98LH8px4fH8Hg0vB4vHZ1tNDTYgerqLB9wufxlFV25/Vk6RWCyRohW32OFGo55UVEf5aygsqfuBK8JUsjb5Ikjx6c43D3M3Gyaubk5zJIXRYbwe2NEo1GSdWFWrmmhoSlKIBBAKAYGs7i590IxUVSdSlOnE2dYA7vXfP/GCsD+pCLQqtaPC1vpmKZJIOjD47HTyi3LBEUHUfGQavn3q+9H5X5WQ0DzZ1IzlyoI1jlCrcKosv6lE39yt3ffKVVVueIdF/0lKIA18t577sG1Wl0rptJ43XII1jwUCwV+94t7icWipOZmSUajNNfX0Xd8mNVr17Jq3Sk2L7xQKOQLPPX0UwwODrB29Rosy+SBBx5g5cqVXHzhhRw4cADLstiwYQMHDxygsaGBpUuW8t3vfJvx8TGkaZJMJlm7di3LTlvHHW9Ks/iwydaX4eDBg+ilEkNDQ2w69VR2nH02CEHfsWPce++9+P1+3nPddSxatAghBI888ggPPfQQmqZw5ZVXkkjG6OnpwdB12lpbef211+g+cBC/0Ghe1MlZZ5+NVBTGJyfxer2Ew2EWLVpEqVTitltvRddL+L0amWwWTbM7Z9VVdagKhkJ4PB7SmYwjtCWRaLSMs2cyGaSUTE9Po+u6TS2rKGheLx6/H4/Xiy8YIpZI4g8E8Xp9RCIRPB4PgUAAn8/n9Eeu9CpwG9rbed/gwlDVi7ZmvTkC5pWJIrftT/NvZydr1kUFfrC3nR/ymo+pqqrKr1O/ZJNnMz/96u1c29TA/XNpvOhsOu1MHn70MQ5e14r67V34cgqzb30T7+yvxydbaPOeivLCe9h96ibS24dRhhvxzjbytsuurD4jLv/ONz71BT54yyeIRKPl6wRbRljSTm1M7okR7w5z9KohkJJE035ijYc4tucdtdc4Txjb8ZdKK0PLknx06Dt8xnstiqKgaRp+vw+PV6u0/RR2SmYF0pGoooqiAQspdWrSUecJSCnFggqgItSU8j42rOLF0v1MTc4wMztL/7FRDncfp1go2fUFVgBhBVGFH03TCEd9tHdFae9soq21FV9AQfVmQehIS4IogZJ3FJlDgifmK4AT5VVFodV6WIIqYkFXgS0QjHVpxe0MVfcZgJvxVAmayxP2rflcQzZg85c5/6JqeguMecF2ag0nadV6PW7CiaZpXHb5BX8BCmDNGvnze+6mkgVSwSFtbh67CMzr9TA7NcmDv/41m9avY+fLL2MU83zgr/+avGnS29tLz+AQwVCIpuZm1qxZg98XYM+ePQwODtLc1MTgwABTU1NEw2GWLV+GrpeYmZmxUyINg3A4jIogm80Q9Pvx+XyAnQfu9foQwKxa4jeXShYfNvH/aA9Hjhxh85YtmJZFY2MjXYsX89TTTzM3N8fll19OqVTiiSeeYPnyZTQ22qyUc3Nz7Nmzh317dhMNh9m8aRNrVqzC7/URjNuC+rWDB9i6dSv1iSTDw8Ps2rWLjvZ29r72GpOjx0lPT1EyTK648koMy+L5F19gdi7N1NQ0pmly1llncdVVV6GqKjOzc8xMzzCXmmNkZITdu18nFotx4YUX8tBDDzE8PEwsFkNKSS6Xo7GxkZbWVrx+O3gnFIE/HEbzeMjn82RzOUq6TqFgZ/7ohoHm9RJPJAmHIyQSdSSS9STr6giFwmVmTHdUKwS3UChdMvnQc1N8bWuMpE8pF0+5a6ES8HN5808MYAcCAUZHR3nUfJi+H/RhlEq8u6ONieUreeKxB9nbM4BhCcxfv4OGT++kKVDHUHgZ55UihLQVHFde5Yw//JxnznkTuZt+x9VTP69aqZXgpPtyfvMfv8jHv/r5qi2cgF/1+v7eUg58qKeMw6/d/l32PTe/XKa26UflPjnnlfBcej9j+ixXJrbX7ikqdN/le+u+P+VtsCEhMc+ro0qQyQoRnP3RTQl1Z2j32HXTJot5QWZWob9vkLGxMUaGp5kYL9jMKkIgrABIH1h2TMXjk4RiJi1t9SxdupRo3Eddo4bX63o+RRDZcpaQcOddhb+fTF7Vfl9hUHVTeGtScJkHgdU8vWoDpUqiL+DEinn/kpIqRVTx6OzkDvf3Woy/sn3le4c8o2wcWNJOQ68eqmr3KLn8srf8BSgAxwMQslIEBY5V5zbmkza/eyGX4Zvf+DpXvOV8nn3uOV575SWampvpXLKEltZW6lpa6T5yhCO9vZRKOhs3bOTUU0+ls7OTaDiMEHbT7MHBQV774y6GhoYIBoMoikJLUxP1dXXs37sPAWzZvLnsHpZKJXtRSsq8Mrt7DzLwlc2c9miBSE+WrsWLSSYTtLW1U9RLPP/880xOTuLz+ZibmyM1l8bv9xMMBgmFgvj9fo729lDIZdm2bRt//ONrDA4N0ba4i+XLlxOKxWhtaaGjvZ2XXnqJQj5Pa0MDxVyOgCbw+7w88LvfcdHFFyNUlV/+6le89ZK3Mzk5yQMPPEAkHKa1rY3BwUEmxieIxWIsW7ac8847j5aWFhRFYWpqip/efjtLlizh7LPPRgjBwEA/u3fvZt/e3QSDQTweD9lMBr/fa3sXwSDRSIRQMEgoGMbj89t4vNeLJQRCVSlZkrxD2awoCoZhlJlCC4UCfqcGI1lXT319A5FojHg8Tigc4YG+LBnd4j0rbTzeLcoqlQxM04IyrcCJmGggEGBoaIg7b7+d5BVJRn89yrpV6+icmeKXu/dyeGCAukSMVM6AK1dRN+ch2m+we2UzZx9ppi1aR5e1g9JL17B//XrmPvkHzp/578SMDiohulpY65v/+EU+9pXP2b+IE+05Aaz9zgr239RdWfNnfYsDz3/I6Qhmb7VQXMXNirKcWpQZI4OJRZ0asc9X9q4oS6T5CsA+rhMUrqF3tq+lLAKqMG0h7J67slrMSS8CJ+irCOamdcaGCxzt7WN0dJTZmQLFnGYfRwBWACwfdpcyiVB1hDdDfX2MtvZ2kvUhOhfHiEYDaJqGqumgZpGWgWHaDVUQTgvGeQrgBAilSom5v9uUF7UKYCHFV91TonabBfjD5g1RdePLUyjDetXzPEmsQAhHcVfO7a6h8jwdWLZ6H9cDuOIvwgNYu1b+8m47CGzJqhQrKatgIAj4fRSLOX5w662sX9RJIZflmd8/TjiZ4K8+8LcsXrmKw0ePMjM3h6ZpFEsm+/btJxgMsGnTqTTU1TE9PU0kHKZUKjIxPkZPby/hcJhly5YRj8dJJhKMjY6yb98+pqemWLp0Kam5FKZpsKijk3AgxOzMDE899RQbNmxgzZo1jI6NMWVkePTdPk55MEX4cIZsLsfExASJRIJFixaxefNmXn39NV599VUCgQAdHR2E/La1Wt/UiN/nR1E1ug8fYWp2huaWFto7OtiyZQuBQICHH3mY1tYWDu/bx1NPPsklF17Axo2nMDE2yl0/+xlenwe/z09X1yJOO+00PB4Px44dIxaLsWTJEurq6ujr6+fVV3eRzeYZGhqitbWNM888g9HxCZ577jk2bdpEd3c30WiUtWvXoGo2E9L4+DjpdIpVK1dSF4/z4AMPcOxQt+0dmTa85DbeURQV07IolEoYpkWyrh4EjI2NOx3XYuUWfuFwmEgkQjAUQvF4kIqCqQhKpoUuJY/MhpkZHWbRzCEuvfwKFi9dju5W6IrabkhudbdLsf3ZW25BSpv9s+GdDagvqjz04KPsaGrg4PQswUQAExXpjdH9xZV0PThH3ZES+4P1nDaTpCXcSkod5K0/+wrZH9/Or5Z9hCsn7oJq68xZoxK7zuK+H93J1R9+vzOjStzEFUKBYT+dD7Vy8AM95Xl3rnmA/v2XOp+qq6CrGTbt45UDkQL+58Tj/G3DBVXWfAVyKH+ugZRAVHlU5XTOalUlZY2hK6WGZVXaNApp010bhkmxWOL4wBxHDk4wcnyM2dlZTF1FiHDFspY+hPTh0lhLUcBS5vAHNILBIPWNIRYvS9DUXE8sFsMfBEXLYlmG08vDsKmWxYmK/mReUo0Bv4ACqN6gGjZaeCz8WzWU6Qpj18J3M65OTOt8IwUwT/GI2s/z/7rnVBSFK664+P9+BbCuWgFQWyDi/qcIgc/rpe/YMZ559CG2nLKW7ld3sv/VnWRNk3e882o2n38BxZJO7+EjHOvrZ/Pp22hubmZubo5f3Hsv69auJRgMMjQ0gKoqjI+No6oKGzZsYNGiTlugKQqGrhMKBpmemuKOn96OYRhccvHFHD16jPHxCbxeLx0dHWzcuBEhBMf6+/D57SrNAwcOEL/0FJ7ZmONNz3qZe66H7TvOQlqSZCJJOBJBCMGLL75Id3c3Hq+Xkm5XH7qNXiLRCOFIhKamJqfxiYJRKvDEE09g6joN9fWkUrME/H5am1sQ0iKXy5LOZEil7L6+E+PjTE5Oous68XjcYR2VpFIphoaGuPiiiyiViggBJb3Erp076evrY+vWraxYsYJCoUBDQwOP//73pNNpLr/8cvbv20dLUyNmSee1l1/m4osuYs2qNZSKBfbv389vfvMbxsfHQbHpHm644QZWr15NtmDzp3z1n79s9/kNBMCSKJZFKpOxG+5oGiXDwDIMNI8H4fFx2VXvxPL5ePjFP3Jk4zX8/JIuMiXdiWmcaNEJYXf3Onb0KL/8xS9IpVJEo1GWLFmCEIKp06b5zUceJOgL8Zntp/Hk2BQzlBDBeiwU9nyymY23TaFaCvu0BG8JpdmQuYXgeceIfOASRu77Lo8n/p4rJ+8sn7O6g5UQgju/cRsbztzKKds218yr+u/iezo5+q7+8tzXbP8O3a/8NUYpeMK78UbQxx2TT/C++je7W56gACo6qFYBSFnxZGTVsd13zcXUpdSQ0oF8hIo0/WB5mBif5PjxMUaHMxw5MIWuO9XFlg+sQBnWw/KAtJvTK6oKooQlsiiK3Y85EIZYnaRrcSdNTU3U1fsIhXU7A0dKUPJIpYhQTPcKEbI6+aAyZFk7Vu6TUqV8ca9XLmzVL5hCusB37vfV3oOboeRCPNUQWzngb1VBb8KldF5AKdUoKFn+2YWsnIOWi2avuuqtfxmVwLbLadVou+oHIESlSfT4+Cjx+nMYmxgjo5e45O2X8qYLLmCiqJNOpThl3Xq2bDkNxevHtExef/11orEYTS0t9Pcfo1gqce2115LNZnn4oYf43UMPc8UVV9DY2Mgrr7zC8OAg4UCQjRs3sv3MsygWCoyNjAKgaSp+vx+Px0MoEiaRTNLY1mr/5vVy6tatDA4OEv1ZH3uKPQze0sXa6QjrUklKxRK5UglFVTll82YiySSHjhzmzdu3E4/GcbNIwF5YxWLJCQCZ+EMRdpz7JjKZNFPj4xRLJQrFAi/v3Ek8EiYUCqIbBv5AgL6+PkzTZNuZZ+H3+5menqZvYIi2tjZWrVnHRZe8lQP77UK2aDRMf28P7Z2ddCxaRG9vL6/v2UMymeS000/nvPMvZGxsjP7+QZqaWpiZmeHQ/n0MDwyyr/sIi1etZSqTo3XZCs664CIikQitra38+Ec/YiabY/+hw9z1szuZmpjAp6o2qV2hiKUohKIRtpyxjR07dtDW0YFuGNx5++0c7e0llc6Sm57Cl0ggR45x8xm9XPGol5vXRdla7ykHMyVVpMnOC18qlSiVSuUAXzAYZHp6ml9/4D7O+bsd7Ln7IF/btYdF9fWc09HCC3MplFKK028LsOddcYIzks1P5PidDLGo6w78s29h7tu/p+XyN/PO//lv/HLFdVw5+bMF1/F7P/FhpsYm2PPSq5yyrfJe2rLJfrrH3tXPyh8vYfCi4+Rachx4/iaaFz/DWP8ZWJZWtY+AMq/8iZDHe+vexEf6v8f3uj4C1L4rVWd17pOc9w1Vqb7zBV1VrAXFaaKiUihZGKUS4+NTDA0dZ2bCoFA07G0UFYSKlIrjSUiEUMGJGVTLL7AtWF0vMDWdRvMo5HI5DDNBVyiO4sYhFGFzF5VdITcYe6I1LeZHi2uus4xxLbxf1bxq9pO1HlkFpnGLVeenpLpQTu09tXd3YLqyEK9q11qtXHCXSTVs5QaSqxXCyeMh/5nxZ6MAqrVmRfPJcqTEzTRpbmpC9Xk4cGg//vok2X6Vex/6HQdHRknU1XF8cIThoWFaO7s46+xzmJmdJhAIUCwWSdYl6T3Wy4o1a3jmhRdYvXo1V197Lbt27uS+3/yG5uZmzjrrLFYsWUJ/Xz+jIyMEAwHmZmfJZGzsVVEVNJ/GKZs2gEfFEhJL2oyI3d3dvPDii5iWRTgcpk0LsfFuEPERnm2b4OGNU1zU28RpegfBYJANGzbQtXQJpZKOJQTFol12LhWlnH0hkXhUDVM3eOjf/0A6naaQSlOXTNDS1EjboiWkUim8kSTL21upr6+jpa2DocFBCsUidXV1eL1eisUioVCIXC6HlLBh4wYOHjxIJp2hq2sJ2VyWmZlZTt201e43LCW5bI69u/dSdLp7xSNxQqEoJamQKln4InFyJQN8AYbGxphIZVi3eSvxeJwr3nUtI8eHONDdDYrKuvUbed973kOyvo69u3eDprFs6VLG52YZmhiHYolYJEpzYwt7Xt9LXSCIKJUY6T1CfTRKQyzKb05toWhafH7nFG/t8LMu4Sk3x5BSIk2TYqFANBQiPTeH1+slHo1iGQbHh4YRUvL0/3iGYCjM279/CTu/8ioPD41zQWsDCU+cJ9Ip1v10Fm+4nj3vCHLa40UC60/hs0/cy40NjRTfdwifPsD15+YYvn+Q38dv4YqJ2+etZEGysYFkYwPf+vQXuf7vbiJRn4R5lt2hvzqCEII1313J/pu6GTm2A4DOVY8wcOjiE7wGe8fqs9hr47ZFH+bBmZcJKQHeHN1Qhn3c96diOcpqp+CErBRRPqp7XgUpvYAHIQWGDhOjOWamivT2TtHfN4NZ8iOMiGPhCqSpYZqeitWLrQDK2UUCpBBYFhg6UFRAVRjKw+hQivSspJA3iMfjNDU1ofmCCFGsqHhRAqWAlHbGmeuzVO5VtTlgX+D87Jpq5eF6OzX59lV3ozZd1t1dONlvFUvfbjo0P35QNYeq48yPXdUq3Er2XHnbhXTJfxAQ/8+MPxsI6Bd33Y1FJYsBqMH/VUWxIaCjPbzw7FOsWtpJPpvB1EvEYjE0X4inn36arkVLiSUSnLJ5C4cPH6Fr8WLy+TwvvvgSK1auoKW1hZKhM+NU8BqGwWOPPcaHbrgBj8+Haejs272H3a+/hldVCQWDDA4O0t7ezrJlKwhHIkxOTTExM81sKsUZZ2yjvb3dTiMTCj6vB01VyWQyvP766xw+fJjm5maikQiFQoHjx4/zujGE9anTCP7oII39gk1bNrN69WoCwRCGUcvxbZom0sH8PB772FgW+/fuZf/efaTTKbuFpGli6Dp6Pk82m2XliuVceullWFIyOTnJ888/z7p16+jr66O9vZ2dO1+muamJLZs3MzMzjcfroa/vGN2HDrJy2QqklGiqQiwWw7JsjyQQsIN1mtM+UVEU0pk0wslI6Ovr45VXXnHSbC/i0Uce4cWnnqJQKPC5Wz7LzNwcR4YG2bFjB3v37qVUKrFyzWoamhpJ57O8/sKLPPnb36HncgQQvPUdl/H8iy9QVL187JN/RykcR9d1SqaJQHBgxuC3gwX+4RQ7IKopNlngnt27ueeee1BVlRUrVhCJRNi581V27dqF5jSGj0ZiBINBzvzy6Rz8Zj+KqrIpFmTGFIwW8yANtFAdr/xDPZ/qPwMjZ/GjX73I9esTRHvfQ3Ttb/B/5hZ6f/N58sosiwo7cK3EWrhX8q1Pf5GPf9Wmk7ADuhWcWAhB85N2R9WRc+1akHjTftLTyzAMf/koouY9dQTqvODjRwa/y20dNzlndZrF8AaCokrY2HNTqj6rqCKEwE8mk2FyYpb9e44zPDBLLpejWDDADCDMSuGcaapYllYRik7WEK6nISyEYjinlnaevVooK6pQ2CCaKNHQ0MCiRV00tYaIxjW8PjuDTFLEkpnaS6gS4LazUC2IK0GBk8E584+10OcyhFbz23x3Y14A4g2O6x6z8v0bwFrVWnve/oZpcvXVb/vLgIBcV7cGjxQVJlBpWaiqSkkvcuToUdYtX0RPTw9Br4e9e/axfOVqioUC9c0NbNt+NpZQWH/qqZiWRSgW57J3XOVQuNrEXg1NdhtKXde56p3XMJvKoCg5isUirZ1ddC5ZxtDgAHMzM3iCtvBete4Uu1l7KEKkvoHW1lZ8Ph+6YSKF3ZmoVNDR9RwzMzOk8yU6Fi1BURUO9fRilop0dnRwdWIFA98dIBBYzNx5fn77Fp3BZ17nzMwKG/fX9cqi1iovVKlUogR4NI2169ezfv0peL3O705zCBV4+sknefrJJ/n2t77Ftm3b2LBhAx1tbRw9ehShKPQPDvCe976XYDDEa6/9kZGRERRFYfDYAHNzWeobGmlpaeH40BBzc3PlzB9N08jmcrz81FMkk0kSiQQTU5P09PYSCAQ444wzOOecc+jp6eH+++/D7/eRK+ZJJBMcGxqib2iQ0YkJvKEQW047jUAggGVZFCyT8akpdu/cRbFkEPT6yKRSCI9G2+IlHO4fQkfYfEe6brdtlLAu4WFtwsMrkyVuO5DhrvOa8Hm9NnsmNjeTYdgNU9LpdNn60zTbSjUteO0r3SQ643Rd18ze742jqiqdgQA+ReHw1AD/z96L8Qf9fHr1k/zLe85nQs/ynb238unFb2L8XQdoe/52ZEbnyMVPEDM7SBorqH1jBR/76ud49dkXWbxyGXVNDRUL2XmuI+eOArY3cPCmw8yOrUUKwdJT76T3tfc6gd8q215WFWhRMWq/33kTH+z/Nt9qvwGfop0gN05Ilax9AalmAwWBaSiYhuRY7xA9R/roP5pmdrpoZ+woPjA9oDvpvUKApYBU7D7dmrBbLTpwicSBsqQbVJbOuVwvRZLL5skX58hmTGam8yxKxWlsCZNIRolEImiagcer4VrVrtAVotayXkjgzsflT7Zd9XC9SxeFqDbGhRuN/w/GQlD2QjGGE2FLs2bjAAAgAElEQVS46thMxcup7gamiL8UMjhZ6ZhjOcUXbicuWZW2putFGhsaaKyvY3j4OEODg8yOjxEKR2lpbObM7Wex/tRTyVsWulFCUVS7obxjmVkWIK0aJ1HRvMQSdWWIyeOzrS5FCCLROFNTM6SzWVpbW0llM0ghiDfU0+htxXSKn1BU3MwHIQRer4/29nYWLVrkXJ/JjnPORnXcx97uw7z66h/ZuGEjO1rXctEek4O+49y67Qjv2ZlnabKz/KAtBwYTVW6nYVmYhmFXMuuFsiDxahpeRaW9o4N169aRz+WYmZpmZnqazZs3U3KgKZ/PZ2PkQmHLadvKhXZP/P73NE9OcuDAAR555BFUZ9Gl02mKxSLLly9HOp+vv/56YrEYqAqpVIr+/n66u7vJZDKsWrWK3sPdFAtFtp5xBuFwlO7eXqZnZ9hx9tlsPu00Ck4sxOPxkMmlCAcDqIpAYhIIRrCwO5GlszmaGhsp6AaaafdyNS0L4TYQFYLT6r1sO6eOjzw7wS3rQ2ia3Zze47EFfS6XxzCMsuAtp1ZKu+uXlYa+H4yz9MON9P/Pafp1iHoEb25up/fQ6wz0HOCrV9/AZxY/zbXja/n4u8/hv//8Ca5q2gUj7yN4yiOsv/STHLn/Foa8L9FWPK1GQINk01mns+flV/ntnb/kfZ/8EGUlISpi5cCN3az99ioOfagHyys5+sf3seasf+Xgcx93jlIZbkyh1rK1+GHnTXxj7H4+3nQZzMeXRRXf0zzr3z2qe48MXTI9NcXcTIHBwSHGxyfRdTv1UFVVJyis1ATAhaKgyHmB6JrMpuqrqPJeHGtaUVRU1UupaDA9PYc3WCCvezAt29CSSDRP9f4SobhUFbJyPFGhraCM47tKp7KvO5+FUjUFNgNB1c074V7Oh24WsuAXEvgnKqA38E6kzR48/9hvFPP4z4w/CwUgwUn7qjAAyeqFIrEzAyyTgYFB/JqHSDhGU6yO/PgkUpe8//1/Q9HvJ5PLU7AsFE3DtCinu9Xo2uqH6WpbR8hWP6T65hYaW1rtKll3rpaFFIJ0Nl+uEXCbM7vFGaqioBsmQlH+F3fnHR7ZWd/7z3vadM2Meu+r7X1ddu31LrZxw7jgQjEBc3EcegvkBgJpNxBySQESQjCmdxsDXhvbGGPjXtbeXiSttOpadWlmpKnnnPf+cc4UaddAwn3y8OTdRxrtmdPe9ivfX0NVFHchSYfA2TZTU1PMTM1w3733ce89P6Zr9Wqi5RW8rq+W9gua+GXDKK+UTfChkfPxZ4uh6/lUzB7DwMzlSGfSJNPpou0gl8NQVTpXreJkdzdHX34Fn9/PsSNH+MQnP4nX6yGdybC4tFg082XA0BxoKRytwOfzMzs/jy0Ejc1NlAX8rF+3jp/+9KfUN9Rx9eteh2VZdPeecg15OWZnZwtMoqamhkw2x1XXXkd1VZUzu1KQTqfp6enhlVdeYWjI8YDp7+9HWia7d15ATU0Nd955J9FgiP7eXh54cB9lkQijLx9iVVcXhi+IqqiY+QAhls+hlJIv7SrHsiwelOXMd16Md/RlwuEwS0tJpqamnAI0bpFyRdVQNQMhBNls1nGb/coMetCg5bYyxr+9xDNZyU3BMBvrWug++jJ3nq6hqb2Cv2p/ir99y2tZNLN84Z5/42M1O5m67QSNh36MGJvm5HWPEDE7qMitKixhgWDTBdvZdMF2vv/Fu7n1PW93KsAVJG9nzZ38YC8gaP9hMwNvGqH72Y8QrT9EbLIL2/QWNIFlBMCltcKVVP+09gZGc7OEVD8B4Vm27kv+U4CRitKsk+rDsm3isST9A9NMTiwwPT3NwmIS0y5Dah6kqmKjI3DcfqWUWNhIS3VKQFICm6wgnAXoSkqn+BPFvaUIA0V4QIKZlSSTGdR4lmzWRFFUJ9GdLBJuRJGoF56T37/kg8GK0r/4HSTmwv4vgY7ORS/yDikFOC8/gnlJvYT4n+USumIuSodnWdRwwW6wnGkUrv2fZAQuDnyxFXBSnMlTNBXV0LHTGTShsbQQJ5MxOe/8zcyn0qAoZG2JlALTchalU2BCYktlmeyhlPwtz/HM/ITbUmK5Oe5LJ03RNDya5haYLln0JYvCMk0yloltmYUCGZqiUFVbRzAcIVgWpqGhCZ/Ph6rrzMzN8fAjv0BKyVrT5BGxj+ErgyysM+j80hQRnOCxvARvWSaBgB+P14OiqAjbQgEyS0m2bd9Of08PMzMzVFZWcv/DD7H3ssvwBYMIyywUpLGkRDN0hoaGefnQITKuttPS1sbkxBnKouV09/XT3rWals5V/PrpZ9j/0n62bt3Kxk2b8Pl91DU0kEgkePHFF3n5wAGymRxer49Dhw6TTqdZv349Lz7/PGY2R1tLK5Gwg7+3NbcwPj7C4aNHWXjiCRZmZ8mllhzvpICf88/bQTQSxjJNvD4vadPEsm2Qrrtwfi7cT03TnFQVp/dTNdjNVOdFHFGiNGZ60dwUGKriFCfRDQ+Gx4OmO1vAKY1oY6YsBu+eR3gFNa8JYpVtglWb6BrpQ2gGw4O93HY8SHYT/FPHS3z8rZdzz9AxhpLP82ftF5OZuI61j38Lqce49+o/4oq5fyIkq0uIg+S2D/4xIPn8n3+aD/79JwoMorgXYPDNw3Td1c7otWeIsRkktG/9HoOHbsuv1JLNU9xD+aMNRgUSeOfAF7m79f1nb7iSIK8CbcORNm0pyeZyzC/EWIjFWFxaIps1SwrjOBW0hG2j2HaB6JbSSIcoSvJ1h/NbR1GE21+whZu5yIWIbDsHtummPvHT2VlBXWOQioooAb8HhFOVa7lb5/Ko5ldrZwmBefiIoraykkGWnp8vHSvz50u39GTpfeVy+KZwLiW07Jzwk1j2Zz4gduX7u48ofP7+5P8PhAEIUaywBcuNL/nBtixJMm06JeUUhVg8huIx8EciBCMR0raFYdtI3OIdQrogge3exy6BAEShAr1wF2DB99h5gxUTpJS8a1Fay7+fQskCkxIhi8manORSRqFfCuAPBLj6+utYWFhgfHyCsooKotGok2vHMBwi5vVieDwOAxqweP5t4xyMTvL2M1sIp3UMQyeXyzrQhiv5KEIgbKcEniIEazdsYv+LL7J6zTpsRaG/7zTtq7tQdR3LNsm71iaTSYbHRpmLLbCuazXSspiZmaO/b4DF2CINDQ2EKsLMzi3g8wXYs/dSJiYmOH6ym6WlJQYGBhgcHKShoYHXvvZKLtq1i56eHl566SU6Ozs5ePAg4UiEDevXO1rAoYNUVlbS0NDAeeefx549lzAxOspdX/kPAl4vQhGEQyEWYjGymQxGIIBlO9XZNEVzCYxr4iwQFgVbShYTCeLxOEIIWiaPENbbeKThteje51Dd/EfO3CoFOCgPCamuBiiEwEyaTD+2yNF3PMWG2CXYzasQS3Fan32J2M4reOHn9/Kpq9/IV+tf4Y3KesqaNvJ3P/olH7kmRuLXdxBYfT9vvOtGTtx2ANNcQ7nZWlxTrtT6oc/+BZ//80/z4c9+klIClH+Hvj8ZINjnx4jpZMM5ho+8kdUX/ws9LiS0sp2LEH6t7QN8dOTrfK7xHUUPkxWnlWreKAJVCAKBEO3t7UQjSwwODjJxZo5MRsHMWY5jggWKNFFtu6Tge2m6DnAKxFvLniMUF/eXeSN10VNHCommqUSjEZqaG2hsDFJd53WhPFnQ5vNj+NsQkHNJ3qV7vNRm4JzqMgLnIMsC/lghnJZCeCV9Xkmoz/Xsc71X/rh0katCjfRXgZUQ4n8OA0DiulKd3VlFONigoigoqsBMpwmXR5memcFUFG5+2x9h2jYvHTrEBRdeiGmfjZUJ4eQB1HUdgJHhQYaHB9F1jcqKSiLRcgKBIIbhQdMdYl2o3uUmYsqnIzj7/ZSCjWEZpudikdi2o9YJgZKPTFSgrq6e+oZG1q7fWJAiTMsqwBTpbJaFeLwAja1d8LNRW4VpxXmocZpTlTFuPN1Ohel3CBlFNpWybfxeL9suvIDFZJKsbdPW2srs/BwHDh3i/PPPR1OUwpjrmsYFO3dywXnnk04msbImPp+PY63tPPzQQ+heP6tWraK/v59kMkltbS3RiiqWkkn6+gYIBoPceee72LhxI9IyOXnyJPPz86xatYrxiQk8Hg+madLW0UHHqk5mZmd56aX9DA0PU1kVJR6Pc/DQIcxMBls46nVFOEK4LEJqcZGmjlUgJZZdJN4FsiHBBITl1LudnJqivLKSkbExFE2j3KNy3rF7eHJmCuNdX0Q+/m3UyX7XjRA0VXPXnyP1qu74CyGwcpL1C7uxhc3JsmfYwB6yr70ZXybNnrIoi8lF2v71IMEbN/ON+sO89y0Xc3hmlseTX+JPa7dhnfoE6578LiI5wA9v/hQ3z36nsDzy7cP/8EniczHu+swX+Ojn/qogVOR/Ym0JkLDmy6vo+ZM+up/+EBWNrzA/sQHbNH7TlioQiM81voM/Hvw37mp+X+nOQFGc4DGHATipH/JifCDoob29lkyDJBIJMxSdoL9vlngs49T4VRSEpbiajZO8TVGlay910rk4JEpZJs06dQ9cEicsVMXGtJxaCJEyHzV1FTTUN7CqqwN/ZAnDl3Y1CBOBtWzwRN7TSRSPLMPTyTMlWdijiDwktBxoJi/NL1cTivARKxnsCmG15LfEhYpXXHNuwn+OY2dphK+ebff3bX8QDCDP8SioYipO0QxX1UKgKg4Bnxgfo7KmhpTHwOvz4gsEkIpCW0Mjhm6Qs80i55b5rICORqCpjrT7/e99k82bN1FbGWVipI+ZvixLqSTJVIpkJovQDHSPl4rqOqrq6vD6fETdcpCaphW8S7LZLLYpnchUJChFCUiIvJZRUvs1nx1Q5HmDIwlpedVQqK4rm0DXVQytuMGlLEpAe6abuGiqAcuyeKJuiNPRBNecbCC4pDjpn71ebBs8/iDRmhpO9/cjh4bYtHkTE5OTDA0N0tHZUcjPkzeAIwWKx4vmVVE1jerGJlSfn/6RUbxlYa649vVUV1ViuwZoVVW57vob+PKXv8wzzz5Pc0sb/X296LpOe2cXzz37LK/ZewlDg4M8/qtf0btjGx2dnSBg564L0TWFublpgn4POy/eyXhfDxNjYwgJfq8XJPhCZSQzWRRVAzNXhD1dFb3gMiwcG0woFCKRSLiBdBnS6QyZjPOj3PVhdE0n8/r3YgwcRFuaBkA3DDRNd+ClFVK4s34EG+J7mPAMMGuMsD5xCbmdl+MxTa4sr+bIgWep+eFRIm/exL7tvfxpxUUcmJnlhbm7ePfuLcjnd/C2136FqXsmeST6UW6Z+/ayxV8WDfPR//uXHH/5EDUNdVTW1RRglDzx6b7zFC0/ayLRucgs2wBYc/EX6X7mA6+6qUptmHc3v5+7Zh9hh7+LHYFVxTVJKU5OibupjVRTGD6F1s4qwuVBgsEK5mYXGR0dJTaXQtoaUirkSy4qwsmjb9s2Ir/2pVIiPjuFcBz5yM1OamUL672trYHVaxooKysjGg0hjQylNYjz+0YWQPKzW5E+Fj2QnLPzLLH4U8okFZZrAbDSd38Fbs8KgrzSLbTUVlM4dPZ9VjIt59HLgelz2RGkC9X9vu0PggEsb6K4MEVRXbVsi9jCEh7DIJ1Kkcxlee6lFzlx7Bg3vPFNVJVXkHZ96BWlyIlZ8ZdhGGiKwFxaom94kAMv76c8GKC6upry8nKaamoIRcrRPF5S6TSTPcfI5XIsJtNOrhtVxfD5aOnopLKqmnC0klAwRM40yUdWmqblaA2uUc15A0ebKar6AuHCU478VGwr8UrcsRAueCqEQJEWaBqXzrZy2Swcrpnmp7Uj3Hl8HXbGJpVKu9GVNrrh4czkFO1LKYRQGRocpqOjE10zwM05X3w25KTjblnT2Mid73sf8/PzqKpKsLycJdf2oOScBR4I+Elns2TSaTxeL11r1mDbtpMme8c2zGya6elpLMti/wsv8MpLL3Gq7xSpdIZIJEhNbQ1bNm6ipaWFoM+HbVmYEhTdYHZ+Hk+wDGnbBZXYMfw5cI8oGS/TtvEoCjnTZHJqynHX9XrJZrMsLi4WDPlCCLyPfwfZtJrhy99F51NfdwLxbKf0pVJi8F+56WozbdRm2jgY/iVbFi4HTcPc+zo2P3of6258Bz/52uf4zDs/xnPhUaIeL2+4aQv/8Iv9fGTnPNNvPkz1Hbt5y81/xPff/DZunS2mk8hTmg3bN3PslcP89Bs/5I6Pf+is+R++YRRj3iAwHGCpeYnuZz7Amt2fp/vpD50N/+ThHorQwp0VV3MqO0bazuJVjBUEzcnDlU86JqVTh9khzpqbSDDKbCSBZWWR5hzpRUE6Z4JQcYi75QD7BWy91ADsQirYLnG0kZhoukk0GiUcDtLSXklNfdBxIRUJBE48htMJJyLeqXJRINFnEdniXi8lrMLVFmSR7BeVneIYn3WPczchlp/tosgrzjkX1n9uhnAOxeKcrWjMLgomv2/7A2QAjmomSiAVR/p1plzzeVFzGaSUREMhZicmOXzwILt27WLJkmDbWO7Auh7DWIAqANtkbKifuvIIVZrKcGIRI5OlobmZTCzO4RMnQUoMjwcUlZxlksOR8P3BIMGQk6OnobEBq8zLzPwE3XOzLOUsFE0nmcliSUFFVS2VVVVU1dQ7QWqahtfrLahy2WzOVXulm+s0vwHd/guxLPCn4E1QUDSFu4aLi2DLXA1b5mpAwNc2HSWtWry7ZxPnnXcBqqo6tXrdcUln0ySXUuTcDKfuQ0AIVNV181NV0ukMhuGhtrbW8XYyTacsJUrBlTZj2uzYuZOe7h7u/dlPqawopzwaJRwK8ZN776G/7xTtra001tXT0tjAoUOH6Ghqorq6iiNHDrJ9/Xqmzozzi333M9B7ClVKygIByioqiSUSjE/PcFltLRnLIfJCOIFwy/aMLEqJp/r6Cob4UKgMwxUYbMtCFfmkZAJtdpSGX3wJAgF6d76V5iMPEcgmnIz3eTuAaZ5zI29ZuJy4Mc2A/whbYpeSu8KpGfDGpnZm56c5c/f32fnOj/G3657mDZWbmZnRuDf+73zsk/eSPlzD267+NpPfH+cX0T/n5ulvUkpM1m/bzLptm/nxXd/ihre/Cc0wnKlxv8+VZ8mVZ1n7r110v7+H7qc/QFXLs2ieBGd6rlqmIRXEYXc9CaDLU4+UkicTR7gkuN7tm7P2Ci7HQoC0sWUOKR2Dr6brRKJevN4yNL2ZSFmQ6Yklp7B6JoOZs508PW46F2kLcLUCxzsH8oXppS1RVAvDEEQrwqxdu5q6+moqqhT8QTexn51FSNv5yYv9ZwnZxR2Rd/ss2BNkMV/UsuGQLDuWb8p/gqAWbCmFdVFkNAXtosTmeK62nPgvtzCcC+4pXX8FiPD/gxXgD4oBCBzf2+UD4MBBmqrjDWrE4ou0VNcSn5kjNRfDpxnUlldg2Y5njjBzBSzcLrmvpqgoispAbw+6ohBAMD40jF8KrrviajZv24Zl28wvLPDwww/z7LPPYWWzeAwdSxVcdcU1XHr5ZSQSCT7/uc+x/6nn8KoC2y0mYVkW4eoqWjo7yUmLqflpTh18iUQygyVFIVpWNwzKwmGam1qoqKigta0NoSiuVw7kTIucawuQMo8FOpNt21ahR3k5K48z5rUKgNt71oF71V2rj5BRLf6kZwOKm9kRCYqq4fMXmUJBMZZO/hzTsrGyuUKxjPwngMfjwTBcCTJnsWXLdnbsuAAhnFiN3hMnGD8zwU23vJFXXt7Pqe5uzt+ylYmpSdasXc/kxARev4+29g6+/rVvcsstt3L++RfS1tbG+dt3MDoywuTMPP5ImJaGRjpa24i7lcjyG2vZulEEiqpjA7Pz845B2LZJpzPkcibxWAxNUdFUDVXVENKJLFcUBWlbtD/9dYSicOr8N9J08lGM9OIyo/BKyQsgnKtmS+xy5o0zSCwqcg2YV95MKJvhTS2rmFuMs/dfx1h/7W7+bt0z/EXoNfzN/vvxL1h8+K8fxj8yz9tuTjNwXzeL6gTtqdcs2+Q33/FHCCH4wic/w4c+/YkCscqf0/3+HhRToe6XtYxetQsQrNn9BU4+de5C9CvJ0O7AOj4x/h0+XffWAhEtrdfgHJEgLaTtaLRSpNA8UFUTdDTCYAiP4WF0bJSlxQzkNMe4K3CMvdIpGOSgsSY2GWyRQtVUwlEf1bVRmtuqaGiIUFZmoGlJTNvJ4YRiI1YYkF0KUehQXqcovm0JIV1B0PM1xkUpJyhdR1Iuy7m/Eq4pBKTK5WS36B5K4Z6OIbcIP5HX3HG0ojw/y1+SZ5BF3WnFvc/RpJQr69P8l9ofDAModbss+NK63ymKszhnZqeZmZujTFPBsrByJrrXS1koTMaFf1TF9QKS+Xwkxci5WCzG1NQZhFCYmp9nLpHA7/FyZnGRJmkzMTtDxra5/JZb6diyjd6eHsKBAC+8vJ8f3HMv3rIQ27ZtI5MzMU0LTfeRyVpYOQvLtliaj/Pycy+Qk2D4/dhCEAyGqaiowO/3YxgGUZ+XMkWQmhmnb3SAV557kqxpo/t8VFTXUF5VW7A7eH0+EMJxNRXC6bNpFjIL5g1u+aa4G6IoBAreeWoDAD9o6yFgalwx2oohFadylShmR8xLvooATVPQEGBoRWnHVXsty8I0TZLJZMF2IKTEMAwCAR9P/vrXDPSfYvOmTTzx6ydRbJuKiipisTjpVIaUnnaMu0tZKspr8PkCaJrOK8eOMzkxwab1G4mUlzMwMoWSlVRVVjOfWESJhAv9LHUYcOa4KDSEy8rIZjLoulMqMZVMkvdyKXX3y9d40HQNj9eLaVm0vnwvsbq1zGzcwdoDPypIwysludIxL8/VklISvFL2KNvjV4BukHntjUSffoRdl17HA9/6PJ986/v4SvNBPnbeLszNFv/8w3v56OXXMf2WY7S99TzOfPsz3Ff9dt4w+c2znvGB//NxPv/Jz/CRT3+ipL/uOGg2Zy6boOqFSqYunOHE0x9k3Z7Pc+KpD+YF7qL0L4okMg9//H3dW3nH8L/yjeb3u8TeJaFCQF43LawxGylsFE3Bpwsitg9d82LmJIupORA2mYTAKhSlwTGESqtAcIXr5eP3+ymviFJVFaa6urpQM7t0boUL0xS9cIorO+9Om0cFVo6ZKDzPLtEKVsKqooQCF4WfV5PYi3XWVmoAK56dP2cly3XnIn9Nab8Kxwu/ilJ+4W1XMiRxtqvof6X9QeQCWr9unfzh975bkNzzE2FJZ9JVxfFUOX26j8G+PkK6xoGnn+XMyAjBSJgPfPCDBFrbSaVSjs1VFjms7eLsuq7TffQgR155kapAiNNHjzE+MoJm22SE4OI9l7D3mqvRDIPuwVEikQhVldVIM4ehCP75c/9IS2MjF+7cycLCAjsvvICZmWmSS0s0NzU50JGus2/fPk4cP8Yle3YzOztDOpUmFArR0NDAU08+yaFDh+ha3UUkUkY6k6HvdD+pdBrD46W+qZXyiioqausor6gA1aD/9GkWFhbweDxU1lQTDoepra+nprYWw+PB4/GSt5tYpoUtBdmcU2RbU1UsuVyjsm2bH3X0EjR1rhxtxcgH7nA25p2fi2IThd9nGcEoBsI5dVazpJaSLC4ssLS4yNTkOLU1NTz+2GOEQiH6u3uQ0kkJHY1GmJmZxsxlMTNZKqoq2XXRbo6e6CYcibL3dddQXluDbRcDBpe9q3DcQC3L5sEHH+CVl19GVXUqK6uYmZ6mt6eHpcUkuq4jUfEHgqhu+gJfMIhQFITmRCXnpX4jWMbA1pv4243hwvPynmElowMl47CkxpnTJqhPryqcoe9/CmvDFn76g7t4wx0f4at1h7l5qovvnzxG4qVF/nTP9ZDLEP7EjQz86FMk1VmaUxct658iBJnkEi/8+lkuvfYKR8osmR8poWJ/ObPr57F8EkUxqe14ioney0oIxjncJl2I5O8nfszN0Ytp9dQWCKHMG1HJ14fWEK7dQAJWVsfKGszPLDE8PMzcTJwzw4ssxrIuBKQizCBINzmcYoKaxROAhvoGWtqqqakLUF3rRG4LxUQqMWyShXFW1OVCukPUS/9XHP+Va9XRF4q5kPKpoQvfn8Oouvz6EtyNswn9byObssBgVvahcPeiJlLQBvIazIrguRXvnX93y5bccsvvVxP4D0YDKFU+84tMcbmcJSU+TcXj8TB1Zpy2HTvIZtJkLZNsLkf1H3+X1HuuQl61wRnDfP6RkihBRVGYmRwnnU5T29hGPwp//O53Mz42ylRyiVvfehu2ppHO5Ni4pYqJ8TPsf/FFMuk0tTU1bN22g+NHjnC8u4fLr7gCW/dg6V72n3iZsfkFtm7cSCKZpKe/n8q6Ojx+P1FRhaIonDx5ku5Tp9h90UWsWbuWF196iUNHjrN3716amtt49LHHuPiiS9iybRuvvHKA2bExyGZQDQPdzhHyqGi6gEySxEyK2PQY3YccqCaVSpFOp8nmLPyBEHX1TdQ3NhEIBolEK4iUlzuxBG7aCsu2efPQmsIm+07HSSoyXl4z1oRXnns5CFmU4NwBRXHLd0qZD6JyAoQyOTeiWwo8gRCeQIhy26Zj/QZ0VWH1pq2oAgxVRUiJmcuxGI8zOTnJ4MAAL77wAslsmpG5OWKmSVU0QmV9nfscszCfBS3RthCAqqgOHDEyjK470dN+v5dcNks8HkfXDGxboqpg5jIIw0ARCqnFBIqmoqgaGXedIAT23BzGwD8yv/pv+NKwyUdaNXzqq2PEUkp8Zoj6XJBFbYGENkNtuoPceRejCMmtje1MTIxR/tXHiNyxidErk3xs3U7uOvY41S9Krv/0Lyjvj9Pyrx/k+L+ZBO1aKjNrnHsD3mCQPa+7kp7j3fj8Aepbm/KsB6RgascCSIWu77bQe9sQE6cuZ+2ef6H3ufee013U2SbOHf533U1I4K/Gv89f1r2lSBwVJ+M39yQAACAASURBVPJWuC6bQpjkiZdhGAhdRxEhFK2esrIy7MwU0pp31mPadDzaXHFBVQW6V6e8PEBzcz0NDeUEw6BqEltmwM4hySLFCgYvi2/sMKdXz/ZZWJ4u8V4GD8niulkJ6zmwp8TMFSEnXTdQVa0wt3mKLwrvYBXWYuGcZYMrz9GHEs2dIn85F7EvaDmuWiBLxiGvxf6PMgKXTkrpsbyrkyLBq+ksJpZAioKkuaesjfS+9yLveQllYAqrvZrlxUIcjqooCmY2QywWQ8HC49FRFMHmHdv52SOP8IN77+WCXbtob1uFQMWor6f72DEGBgdpam5GMXSm5ufpAOKLizz6y18xMzPDrW96E0uLcfr6+qmtqSFcXskrB17h+ImTtDQ3kkqnUBWFTZs2kc5kONnTQzhaQWv7KnKm5KX9L7J501ZWda7m2OFjNNQ1YLR5icfjTE1PEw2HqXMzWg4NDeHzGni9Hnp7e5memqKvv5/2jg7Ky6sIR6MY2CxMTzA3I+jtPl6IQPZ4PETKo9TU1lJZWUVZMISqabx3ehvxXIYHmvtZ0nK86fTqwsbIY/55d1xkHiZa7tutKA78BLiB/a4fuC1d+iQwcyZmzrleVRSSdq4AO2nBEC2RKJ1r13PF668jY2XJZXOMT06Sy+aYmZlxjdNaIXArX2NYcQOL8r77pmmSSqXw+4MsJhbJ5XIYhuPt5Pi9O7i2beaQLrE3rZwDlSiq4wkk8gFhENbgE+0aP5uy6fALNoVePZ1AXqoLWhE8tpfTwQN0LG5FIsldeT21v36EW15zDffe/U/8/R9/lOfKxrh0axunyxb4pyfu48N7X8vCnd9iwxuv5vQP/oxh/7M0py52oSinn53r1tB3oofxoVHqW5vyG6Wg9fa8ZZCN/76K4+85RfdTH6Zh7YOMnby2BApxmjxLLBX8Rd2tHE0NsMnfjsBx6ClKyi7kULjMBJlCNyAS1VGEh8S8D9NKEFswiePaBGwdKW0Mv0pZ1KChyU9Ds5+KagVFX0KoKZdF2EDOWW3SLQovFQe5L0C5y7WvUqizCKoXwaGzUJgVxtVSLcDM5UgmnfQuUkI0Wo5h+FxBqZjnP4/v54WeEsyn5M3OrU2vfObKY3lnhpX9dM4p7cMKe8jv0f5gGACcjcHZ0pl2Bafk3uDAIIFQiLGhIUzbRtV05KYmtC/8AvXpbjLbW4m++UvIoJfEHZeSOa8doUgUYROfmWR2apaIN8DczDRDg6f5wuf/BW8oyJbt22lqbODk0SM88NOfs7AQQ9V1rr7mGgyvl1P9/Zy/YweLqRSxRIKjx0+wfuNGYrEYzz//PM0NdRiGzqm+PtauX8+GDRsKbogzMzPMzs4yPRtnYHgMqRlIy2ZgcJBMOs2GDRu47LLLCAQcV9Tv//CHzM7Osm3bNvbs3k08HmdmdpbvfPsbmLkcmqbR1bWK1NISFeXlvOXmm3jx+Rc48fJLACRTKQKBMhobG/GFQkzPzJCzbNKZDA1NTUxUVaF7dRYXF0kkEuRMk3B5JX6vl8pgiBNNGX7x+hzNqRD/a3oTtm07GTWFimk6KS0sM1d0lxSOom2681fAMxVcD5KSRS8dbcJy91O+KpK0gJxT+q+0Pmp5eTmq7sFeAfsIioF6abfaWDqdJh6P09raimEYzM7O093dzfjYOMlkimwmi+FGWedjOXAZSN7ryTQtV6oShUAep4uCG2uc/k5n4d4Jk/c0L986BWzeJTK69NKxuI2cmmHEf5zO5DbMS69BSrh18gypWIyJu3/EBf/rw/xo50lu7NjIw70nmHxplts//WPKjylEvng7x75uEjTrqc6uxTQtTMuitrUV27a4/1s/4opbrsfr9zskT3HG9Pj7TxMcCWD5TEZOXouimFS0vMjU4K5lY7hMSkVgoLPF386HR+7in5vvRLhE34nSz2POjpBmu146qqbi11VUJYDoMNA1wZhmks2aZCwLKQW6oVJeEaS6LkxTcy1lYR+qZiGFiZSme1/XjbQA8zjHKPjy28shH5FfaqVQYAmDQ7iaa+FL8pqALQXSjU+QUhKLxRkeHqanxylz6vcH6OxYS11dAFXV0HQVSQ6Z93ACEK7II/Mee2LZk1aujdL2m+Cd/Pel81J6zfLj/w0agBCiCfg2UItjC7lLSvkFIUQ58COgFRgEbpVSzgvnDb8AXAMkgdullAd+l5fJp4Mo9R1WAN3dqB7dAAQdLS08+sADAITKykh/7EqyH7oay7aZ/d77wDUXKYtpwv/wM4wDg8xdt4HJ+DjnbV5H34ljeMwsuipYtaqD29/8JmK2ZG5+geSSRUtLK7quk02n2X3RRXg8HoQQdLS1EY/HefTRR3nggfvp6upiw4YN+H1e0qk0mm7w5K9/zYZ1a2ltbSWbzRKJhIlEypxMo7qOqij4fT4ef/xxpqam2H/gAOVV1aTTaYaHR7j99jt44oknmJmZYXJylrKyMixTctWVr8Mf8JLNZamurCSXzXBmbAzDMFiKx1GQKJqO1+Nh956LaGtrY2BolI7ODqdATShEXX09E2fO8JP77iOxMIemKgXtQFFVItEorR1dbD/qw7Il3+Z5TnysE19vgrrvDlMWDhMIBKiqclJhh8rKKK+sQLp5lnKWE9Vp2xLLsp3IUmcaXWmpJFOkcLZ7ITpa5NV0ZdkGybl5mF6tGV5/Ye34g2W84eZbC5BfPpupomhI22Zubo7ZmRmOHz/OM888y/iZCSYmJkilUghFcW0qHlRVw7LOnWqgyoD3NGsMpyTfGDP5VKcbX5A/QZQEqAmBbntoX9pGv/8wjek16NJDdtdlKFJy07aLMOPzbP2bA6x/y4X87OJe/rxxJw+NHWV6/wy3Xf0ErUd6CXz9Y3z37nF2H/88iUSChdgCsXiMtm0bsSR8+W8+y7s+9XGHZFoOoYzXLyIEdH23lVNvHWJqcBfrdn+e7uffg7Q8BSk1D2yUyp3/3HQnzy92c0FwdYHMSOGkOJHSdr1lLCCLxNmbXp9Gbb0PTY8SilgEynKcPn2aXC7nCDctzbS3l9HUBKo6iyQH5JCYy5lSSbNZno7hNwLvJRh/3tOtaKwtRAy4xwWKoqMoGtPTMwwNjTM0NM7IyCwew4PXl0LXZ5CEiEailEV8DrMSTgYqR1BX3WcK8lHKRX60gjjL383GtszAu4ypLM8hVChD+f+BAfxWI7AQog6ok1IeEEKEgFeAG4DbgTkp5WeFEH8ORKWU/1sIcQ3wfhwGcAHwBSnlBb/pGevXrZP3fO/75NWf0jqrALriZMA8vP8AP/3ZT3nbjTfwja9/HQ246c1vZuPFF2NaloNzuxChLfOjbqLrOpmBYRY/+DU64hovdJo8lOtFN3REWYgb3/RmWtdvwLQhsZTl+eeeY2ZmBq/fj2madLpF1YUQDtYJHDt6lLnZWbq6umhrbSUYCKCqKg/9/OfMzkzT0tJCTU0NyXiMTCbN4uIiCwsLBIMhTp8+ja7r7Nq1i2AwRCqVKmgA4bIIiUSCY8eOMTo6SjweJ56I09jYQEVVOYqqoGsKp3p7OX7sGMlkkrVr13L55ZeTy5mEXC+Y2bl5ysJRNmzcAFLyq18+RiadpjxaxgMP7COTiKHrOjnLYseO8+jsWo1uGCSSaVQ3l3wqlWF2boEkOY7/aRs1j8/S2mPT2NiIYRhMz8wwMjqKP1RGOBKmpqGB2lrHg8mRtj0FTyHLtLAsu1RTdzeiWLaxS6UfKeU5oIriOb9p7QqR935xNqOqqoWUIvmgMEVxUll3d3dz6PAR7r9/H5OTk04uJp8fEPzTl+5+VXVeIrlrxOTaapU6z4rv8u9WkGZhwnuaef0MaxMXFc5Tx0cQZo4nj+5n+55r+HZXH1U5Px19EZ54sJv3vnYPnIkS+dItfOe+DJGnbmTxVIB4bIn6+gbWrVtHNBrm7s/+He/7m/+DaZqAjaKaCOE4QKz5aicn7zgFQMuGfQwfv/4cfYGVfvMfH/0mH6q9nmotsqxfTt+c4jF510ohdYTlY2nRCUAcHBzkyJEjWJZFZWUF7e3NNDTWUV1d4dr2ck6cAWYB515u4HWl+CJSvkwDKDXSOgyXArDupo9EymIK9XyylDx2LvAhbYXe3l76+08zOzPH9PSs4zBiGNTWNtLU1ExdXR1VNRF0bwqEG7tTUtBHURQUcuRdVvOCzDKNQJ4brT9XkFjp/lg2GEIsO89hAAo3veHy/96i8EKI+4F/c3/2SinPuEzi11LK1UKIr7h//8A9vyd/3qvds8gA3Bz4xZdDIFEUic/j48lfP8XwwADNtbU886vH8OgaN9/+DtpWryGZThbulyccUkpUTUFXVfpPHOfQC0/TWlvBsVcOMN57itd1XMglL2TJravj0HUthJobae/odJ6NYC4W4+t3f43WlhY6Ojo4ffo0kWiU2bk5xsfGaGxooLW1lcrKSgxdJ5vJUFVRjmmavPzyy+zbt49IyEtlZSWqotPmahDHjh2nqqaWrq7VNDW1kMvlmJ+bA2kzOTGBpmlMTU1RXV3Npk2b8Pm9xBMJHvvVr2htbQFsvB4PXq+HTCaDz+clEo4wPx/jV48/zvU33MDQ+BlWr13L2vXrEcDXvnoX61avpqWxnlRyEY8Kp0+f5sCBA+zdeynzCzGee/55DI+HiYlJstksN95wE+eddx7jZ84QCATw+HyMzkzww6syWF/Zj/eZMfbu3Utf32nGxsaIlJfjDwbx5FN0CMHs/AKaqiE0lbq6OmpqamhoaKCqqgrdMLCkk2/JlmCaDoOwTCfmQIKbPOzcbWVwzErPmVdjLHkjteUW0dF1vQANjYyMcP+++/nOd76D4fHyxa9+9yz8/FwM4eQSnEhIbqpd8V0JA8i3Ce9pQmYFATPsfiVQF2ZRJke595lHufTG27l3wxi3TK2jd3aW537eyx2Rd7J47N2Eu4/ywP1Zpv7qampra2lvb6e+vpZwJIyC5IkH7ueqW28FkUEKE1xhypPQ8E34iXUlULQsFXUHmR4+v7RX5zAqOt/++/RDvLf6muIR6cIzhbq2ACpC6liWEzMyOzPD+NgYAKFQiPLyCkLBMnw+n5tXa3kiOAp5ffL/V8hXE8t/v9Lr56x5KE3KhuJopi7MowjNsfEIgbQhHjOJL6Q4fvwEg4PDZDMW2Uw+tbOGpoWormqgpqaW5rYaapp0dN3VUpUcQskUxk1T0ihKrgBJOSUwSxJIlvTibHorlsGk+WTnvwkmKuSqsuHm35MB/KdsAEKIVmAr8CJQkyfqLhOodk9rAEZKLht1jy1jAEKIO4E7Aepq61g2scueajsFpyUsLCyQzWYxAn7m52ZpbW2lvrWNnFtBS7o2A1sAQrpGP0ceOH7yJLaZI2vD5MwsSlmIe0Zf4fGtVZTVSCZ+cRhlJsNHztQT9QaYvn49P0kPsH3rVlqamzl+8iSWZREMhth10W68hsHDDz/Mk088ydatW5mdnXWKzwf87N69m4rySm647joMXZCIJxgcGGRmZgbTNHn77bezcdNmEqkM07NzaIZBQ0cH++7fx/q168ikknhCZSAls7EYxGIEy0Lc9KY3UllZycGDB5389jmbwaFRwmVlTM3GSKdSbNy6nYNHj2OrCnsbGkAKZufmqKlrpG9wiAd//hDl4RDrVndRW1uL5gnw/R/dQy6XIxotZ9eui5iZnnOKwjzyCMeOH6OmtpqjR49xZmKCpsYm7lBvo2n328jusfnHTQOYX+hja9laLrzwQgzDYHR0lH0P3M/ExAR+vxfD8FAWDuHPJVESc8wP9ZHN5dxcPSniySVypkmksopoZSWRyipq6uqJllcRCoSROO6fTgyC5RhEXThC4Gzy/JpR8phpSXoLIZxkfAWHApzllg98yts2VFWltraWd7/73WzYuIFPfeqviguy1Mi3gtkArA3A2oBgfwx+OWPziY6iey0rtJXadDsA495TRHI1+K0y7GgFsrySW5eSMNhP5c+epfx/XcrJNae5Jns+nznxZa5ddT6rWr7Ilje8kWfX6djv/gbDX/wMZARWnUK0PMQV1/8JAycPI5VFWtY0F56bDplkyhI0PVbL2N4ppkYuoKbtOaaGzsO2NUqLQRb76fT1XeVXccfgv/LVlvcV+l+U0YsDZNsWQoCqKlRURgmHAyAc92BV0VCEgmVnl5H9EorgCvAOAXTG115xRvF3wUZQ+r5KSQ9sJxDNXQ3YOLUKhBCkU1n6+88wNjLN6OgksVgOBQOB7mqsCrm0zqSZIr4wQSqdRvVWURYOEIlEkDJFzsoUHAWkBMdu7VZGW2EP+I1AzYqBcCMg/tva78wAhBBB4D7gQ1LK+KupxZy7v2drNVLeBdwFjgZQVOycRZXPSy6wkRZ4NEEoGOTk0SN4Nm0kEo0WIBrd0AvGGSeKcNl7owrB5OQkFYagLBxh23k72LBhPYrHIJVKkcpkONHTx2jwDP/RZpHJznKpluCtD8wi5fOkbxN0bO5AVVWGh0fJptPoqsrFF1/Mk088QW9PD3t2X8K61Ws4cuQI83PzmDkT27LISYhEytm0OYpAsDC/QFNzGzlLYisKZeEwXr8fhOBNb7mN48eOMXCqDwHs2bOHlrY2R2Jy4xkUVcVXVs74sZNYlonXG2TreRdiWiZzc3NU19QQjkYJBIN4vV6koqDoOhawbu06J1FcOkVTcyup1BI+v5/XXHoZfr8faTtl/FpaW+lavZpMJkuoLEQkGmbP3tfw4M9/TiIR54WXXuDhuRidnZ28N76d/5hI8fLHtzB+qB/tvh462tu59vWv5+GHfs7qrlV0drSjqgo9PT0cfnk/c9Mz6LpOMOgnHC4jHImger1UhQIo0mZxaoLZ8VHiS0lsJB6vl+rqaqLRKNFoOXV1DSiajiMpOZ4itmWTzWQpRgqXGs3ykJGT60YinPoJeZKSFx5sm1wuh1fzcskll7B169b8Wl32+RvWPjvKJDvKBP84YPOuJoWQ/qqnUpfqZMGYIq0uUZ6tA8DcvgdhK9wam2XfDz/LtrYtHFg/QGegjAflAtEz3+CWvV9j4wt/wborvTz4i08wdfenQckgFJuKCp2WznWMjhxgqKefplXtDoVy98TwZWfouK+Z0zePMDm4i/bN93D60K3n3rL52AEhuLvlfXxm4l7+ovYWEMLJc2iDUASqWxTGxixcq6o2iipcqdhGuG6TUgrsvJfPCrZTOh+/qZV+fy6J2qHBTtp4cJQKS6pgq6QzWebnk4yPzDExGicZF8hsCEkApLMH8naCbFLDysL0mQxDZXNUVYEuKvH4PKiqgWubRgqJLZzkiM6bmUis5f0oWTvLcX+KY+DaLs7Vt3P3+b/BCOw+XMch/t+TUv7EPTwphKgrgYCm3OOjQFPJ5Y3A+G97Rl6WKEZjl+J9kM1mWYjNEi0vZ3R4CMPrpaGjE03T3GycLmYoQJRcp0hJcnEJDZtwOEoymSSeXOJnD+yjuqoaW0oGBge4490fIJFI0D804uSwP3wY+08vZGpimuPHH6d+qpo3HBes7Z0neWWMp3ZEGZ0Y5yMf/SjVVVWkkkkOHTiIpmkMDQ4VqmMZhsH0tFMzN5tOk81lEU8/w86LL8YbDKIYCrH4It09Pbxy8BA1VVVUVlW7kIufRCJBIBBA0w1M28JSBJt2bGfz9m1uCmOTTCYLSJpdKTfPQJNZC7IWQvOwZdt5CGlTWddIfG6GudkZ4gsLRMprMLwhkqmMI6lJwQM/f7jgKZOzcoTDYcc4qhkkEkk2bmzkyiuuJh5PkMvkWB9sovu9j7HpoovI7N3K01doNB9Mct31N/DYY49xsqcHv9/Hqo4O3v722+k+eoxHH30UXdVJLCQY7xt0Av00jUw2CwJsIciaJpbqFAEyfD42bd5MOBploqyHVCrF/Py8k/ZZNwhGI9Q3NtHQ0EywLEy0ohJ/IFhIbmbbNmbOQioSS+bX2vLALuGq3slUEkVRaG5uPqe0D+f25BCiuFE/2u6Qs8dnJV4VdkXFWdcLISg3awGYM86gCIVopg2En+TOd3Lh6SNUjjxL9w8niE21Y39Rx/yW4Mv6D3jtrouYtz/P+hvfS9/OHvrv+A72/V9HUyrwBaChtRNFy/HIj+7jktddicfvKbxz/03DqFmV9V/p5NgH3oiqZ1m780uceOrDK/ojKEDtUvKJmpt5KPYKV4W2On11Je7SOrX5feeCtw6wpIgCnON6E7vnndtdk7w32bJxlyWaR16uVkqOu88scSApeDkJBZnzYFsGo0OTDA6MMTqwSCImsW0/iu1DyBDYARwjooWiOgKcmbOZmppjMTtEbV01iuKhutZDuNwROvPMTEqBaTqxMnl/B8fX4VzyfCkxd981/8rugJTGK6xcN8VPld+3/S5eQAL4GnBSSvnPJV/tA94OfNb9vL/k+PuEED/EMQLHfhP+f+6W55ZOzhpd0RgfGWFibIJAwI/Pa5DO5mju7MTr9bKYThat/FDgIk4KCRgcHcJnqJRXVHDopRcY6juFtCxCmkF5YzM1tY3sf/kAHq+XmooqxsfH2bJlC90nugmHw7zt9ttp6+hgIZ7g6ZPdHO8+ydDzx9msV1J/57ex68vhbRexfddF7BCC6clp5ufnGRwcxDRNdm7YTM7MMTszQzqXZT6dIauq6IpCNpdF9XrYuHkL2887n1wux3PPPsvRY8cZGxuja1UniqIwNTXFSy++wOTkBIrHwPB6QVG59vXXsWPH+Q6e7a4Vyy5KG06eIUlsaYnx4WEW5uZJLS3R3tZKdUML01MTTM3Ns27tWjo72rn//vvZtHUrGzduYDGxSDKTZmlpierqatatW4eiKCwuLjI0OEgiHmfw9ADjY2OsWr2aM1NTjB8cI/SgSevWrWS3lzH1jStpeHSKN3s2k1xMkMnlmJ6fZ+OWzbxm7142rV3LYjzO0UOH+cmPf4yZTjtqupljx9atvPWOd6LrOrFEgp/8+F4ee/xX+Pw+LNORNkOhEKFIBMXOYSowt5RgEoElBEsZm5O9vYxMzvCaSy/lDTfd4sCFpl1wMYblxNy2bQzDwLIkPT09XPq6ZXvhty/dFXTr0krnPw9OSlr9gg2h4r3yz1UUhUqrAYBD4Z/TuHAB5mw5p6ayTMfa0exyrm6OceSvDzI6swXxBS8H3hfil7u/zmWXX8IlyttJ3vAE+296nDlsmoavpUnz4g/6ufKmtwEW//7Xn+Rdf/kXLiQKGDYnPnCKiqMREh2LdD/zYeq7fsX06T3Ydj4FSMm7uv+uCW9lycowkZun3VO9jOiL4gWOm2Ve7bCXj7OqKAXbx/KxK9HIYEW64+X4f+m75eOFlnvOKCCctW9bNtNTcWLzOXp7RxgZmiSdCGCZAXceDGxLR9r5lO2OTcoxyAqyWZvsfA7LjJFJ9bBmXQ2dWjWapuHxeJ1rRM59voXEhBLhQpH/teI15zr+n7XZ/rb2u2gAFwF/BBwVQhxyj30Ch/DfI4R4JzAM3OJ+9xCOB1AfjhvoO363Vyk1KOUH3zEQIsA0HXeyoZEBTvQeJeAPkM6kHelfFiPllBUZkhQFkskkmeQSHt3AjC2imxAKh2mqq+fiq6/m2Weeo6e3D4/Xi1f3Ut/cRG/PKTTdS2t7Jye7e9n34EO87vXXs3XrNuqbmnjp5f0Mj46xuO9jZNJZlKNDlF33f7FW13P80loODpwimUxy1ZVX0tq1ClVVmZ+bx/D7KAuHsaQka1nYOJvBlDbpZJKZmRliiQRSQF9fHz3dJ1mMxzFzOexMmspolPMv2c3qNWvwhyNUVVWRkU5SOgqSEICjpiu2RFNVOjs6WdPVhSIl3SdOMj42hqbr6LpBRVU1Hq+XhXicm2+9hdHRUSYnJwv1AhZiMSanp5mdm6e5qZnm5iZm5+aZn5ujLBzBHwjg9XoJBoN0dHaiqiqHDx/myLFjXHFkK1pNBZ977TgNnz3EwoEBpicn0XTBmg0baLMsxmMxmjZuYK+0aXIN65/8sz+jde0ahkZH6e3upr+/n5H+frxCI6wZKJpONmuSmJwlOT6NLSUnfToWEksoBMvKuPCivezavoPDPb0k5h3GpxuuZ1JeymQ5MS6ms3DqPZS23+a/XeKYQn4i8le8rkZhJgt/d8riz9pVNFdCVKSObXsQwnGn7Bq7kZnMDNPeY4yNWMzMpMgshRi0IvzJZptvzudo/KDBmY/PY4yFeCKWYT75bbZd8jVW//yTnJ5cx/GP3Ub5oXvR3JoNqqbx7k/9LV/+27/kPX/11+RTI4NkfuMCNU9XoqZVzshLad36febGthGfWnOWt1W+nwHVw3Q6zgvJXm4r3+32Nd9zUaTtKwh4Ee4o5Ow8x1iWHv9diZ0o2NqLDMFhCrZtk07lmJ6Oc2Ys4UjziynsrBdsDU1TXQKuFCBkWaI9KIoCqopUNJLJNCPDYwRDkkBAEA5HqKj0oyo2ihCuRgmUeASdy3X1NwaD/RZW8TsJIf+J9geVC6h0wTmfFgoCTWj86kdPMC/OkFpcpDxchjcUZsu27fhDAfbtu49Tvd20tXVy7bXXEwqVAY4GkErFOXb0CAOHDtBUXU33oUOMjgyzbu0azrv4Yhq71nDmzCQ1tXWMjo3zy8d+RWwxwZVXXsnE5CyrurpYvW4dZ86cob9/gNWrVxMqjxZU1KL66xgjVVUlEAggpmIYH/sWYjrG0tv3kr52W8HrJJ+fpyCNlSw2XdcLaqNhaFg5E2FZeAyd+clJThw/TufqLmwEU7NznBoYwB+JsmbNGqLRaCHyN9/yC0ZTVTTFqeM6OjrK7NQ02WyW+flZfF4PdTXVZDJpnnrqSaLRCBs3bmDDhg0cPnyYqakZNm/ZwvHjJ+nq6uKB++9nz549dLa3Mzw0SMDnY3p6imeffYaa2lpCoRAV5RWEwxHi8ThLS0t4dZ3amhoW/BY/ujzJa/ZluGrV+STSSRYSCYZG/x937x0mR3mme/8q0ykc4AAAIABJREFUdA7T3dOTs2Y0kmaUJZRAASGCyAgDJtqAbWxj7LWPzwav1/Z6v8U2a3sdwdgGbJIIhgUEQoAIEgIJlPNoNDmHnu6ezqmqvj+qw4wkvHvOfudc/lxzzTXTqbrep973fdL93M8AJpOJhYsWIWkq77/5Bl6Xi3fefpuBnh7qa2r5zO23E/D7efbJJ7FbrVx/8200zpyJmO30FpwY5ZlnnuHA/v243G5Wr1lLKKnQ1dPHsgvXs2TFCqLpDGlFYWqiaOoGpysAA/v3H+CBB37Id3/4y7Pm6yeX7k9/fOY2VviYwP2dGW6ukGgwmxBUSz4HMdg/ychQmN6eMapu7mZ4n0DXVheoOp24LGr8zbJ2nuvrIxhsZeBvbNgftBGa42NJ0MbFxs8SeefTdH5mGeY5Jprim3AWWRANCQRBJRmPs+1PT3LtZ2+BbOFVzm5ueWgmJ77Ujga0rv4FJ7MhIX0sOry0IAN9vF/of5Df1nz5HDLQELTpSdr/TIbTXp8mt+mFUfq/Yh5po0dRcgEl/XVFsZBMCkSjMUZH/Jw+GWBsOEooFCYZB0ktRsSUZS0V0VQRVc2GswQNnfoi+5ViCsREfow2u4K72EhZWRn1DdVU1liwO+Usci2uv1dIZBFF5Km4/+wh6CgsLZ9Enp53OhdkVBDk//sw0P8TR2tLi7b5Sb1Bhq40p0wcBR785u+46MY1PP/bF1l3y3n4xsexOotYumg1FbUeFDVTKDoCJnzj7N+3h/37P2LO7DlIqkax3cZQXzeJWJTq8lIsJhMNs1uYO38Br297i3RGwe5w4nK5kE1G+vsHMBgtNDU343S5cLncRKMxFDSd2tloJJlMEsry2MRiCTwejw7PylIn5ws2NA0prVD57D68758munIm6rduQLMaUbPolrPw75peeJPjzYlGIrzy0kucajtJdDLErDlzaGltpaSiig8+/JBZc1tZvnIl0Xh8mtU2bXPLJsXU7PfJsoQoCsiabsGYjTJbX3uNk0cO4XQW0TxnNnX19Rw8eJh4IkkynaGstJR5c+eipNMMDQ0xOjLC8NAQ4ckA8WiEK6+8kvLyckRBIhgMEo1Gp8UzFUUhOBmkuMSLVmrjPzamuHVfKebxFG9uewODQea6q65kbHSUP23erMfJPR7uuvtuTnd3EwwGWXjeecQjMfx+P5FYjOq6Wso8LtqPHeGVJ54kHAjgdLu5+pprOdTWRXdPD5/64hepqq0lksoVlp0NLxWEXNtQgUcffYynn36KXz2yecrGnZPp9Me58RWeKSiA6fchl/iU8nPjd30prijx4lbS+AMButrHGe4PMTIU1BlXFYE130uw+1+rICNn8xQyNc4ot8xt46meIYbHLiT4jUksj1pJNflYEHGw3nAHFU+38PoDd+NxNdJsWYnJnC1aEzM8/IPvsuG6q2hs0ak/cvBL86iJui3VtN3ZSfXsN8mkHIz3nI9etne2AgCBrw38jn+pvBWnaC3I8ixbdnoI55z7zrRK3qnfUfg/J2cRA4IgZTH5gCAiZCk8QCASFvD5ooyMjNDd1c/ogEo0kjW6NDNknKDmuH50yCi5BjbZvgX5AQhpBDGdD1sJUgiDKYPZYsHrdTG7xUtpuRNXkRuLTUMTI2hCPK8AxDOMgz+/554d6vpEJSAaueH6i/+6FMBUAWiahozMD+78Cd/6/d/wrVu+j6YIOIqtWEtauf6za3jyl3v5h1+uyysAHfOrW9ayATpOt7P9tS2M+n1oioJZEvjK5z/P6dMd7N13EJfLjcdbgiCKZBSF4mIvdfV1mMxWEokUH3y0B0VRuOiiDZSWlqGJAvF4nFOnTxMIBKiuqcHlcpFIpolEdO6Z0dFR/H4/kiTh8XiwWq3Y7XYqKioQRZF4YJLi372H+8MOoutayHzzGgSLaXrIITd5UhqiWcBqMiMC27ZuZXhwkAvXrmVwcBCDo4j6hgac3mL986KI2WRCQIdOqlMmj4buoRSKefRfk6gvLCWZYKCvl5eeew6jyYjX6+X6G27EZneQzCgoWu4TQt5aVlNp0skkO9/ZzvjYCC2zZ3P06BFOtbXj9XopLi6mtLSU8vJyAgGdnsFR5MRb4iUQmqStvR33VfMZ+3QdF+8ycGLzu1gtFjZddx3/9q//itvjoaW1laJiDw6PC00Q6R8ZxeFw0tgwg/LKcqKxGKePHOaNrVtRhgZRVRVniZf1F23g0LF2oskk1955J0XeEuJZrpdce86phyAImEwmVFXje9/7Z3bufI8HH/tTQX5nrZWsNHIKLvvMmYiNgnLXk6GCUFAAmbSBtkmNP/mCXBvTFcDoUAT/eIwcJl3EyaIvBjm5uYRU0ACanD2PwDdXfcSjpzIY5CBdyxYjdsukMjFqJI2bjZ8j/NEX6f/UPEzzDczOXINBNiAZVJBCDPV1EY/EaGydTb5Be3aMMzfX03lzH7Ixgsk+TixQq2+MORlowrQN+unADm5xr50iywIY48x1fe5jOjhUp56YthzI7ciCIOi4fqYqABlRMCCIOpJtqD9BX0+AoaFhBvpHScXNKJksJEs1gmIFTcq2QhXRtELNQbZtzZSxKIiikn9NkMIIUkL39u0GKqtNFHut1NfX4/GaMJhTSIZ0ntpaEv4z+79A+pbzyKYabmcqgLwxIRq46VOX/DWwgU698QWu91zD9U2/u5h3x97gvJta0TSNiopKtj8Vp2G2E4Oc4evX/QdWm8LKS+u59NaleRNN0DRSySSaqnDhypX0dJymqqqKUCjEcCDIeDzGaDTCXevW0TKnlXQ6zc5du3jjre2sXLmagYEBVEXj1ltuI5PJ0NXdzgnHvzFXvo+iIiehUIgPP/gAl8uNxWJlwYIFeEtKmNfaSnByklA4TE11NelUiqNHjzIxPs7g4CC9vb0YW43UbNhIQ3Mtad8Y2qkB5vxqB6LbQeSB20gWWYj+vRXPAyn8d5sJ/Vs/b2zbRl9PD0aDgYOHDlNeUUEyFmPv3r2kVIVkJoPRZMJmyZFYaUSjURKJxLS2j6Ikkcny4WuKgizqXoGERioSZnBgCFkW6TjVjr3Ixdq1axEsNn0zy/YiyLWxNMgy/mCAtCZgsbvYtu0tjEYj11//KWw2K4ODg+zYsQODwcDGjRtZt24dxaVeilxFaJKE2WwmEAjQ9UwX+/bsouL2VexbIbL5N28zf8kSDCYTiiiy66OPcRY5ufjSjVy5cSlxNdugRhRBVUnGYowPDlFmMKGiUV1djT8UYcw/TtO8hRQVuUkrKvnK1ezCmrrYckcgEMQ3PoZhSkOYqZt64f8cfz7k49BnHFPfKyAhICFiQERCFESiMQ1XSOVyX5SD7T10Lp2PrbsdMtlmPZqEppo5+EsXgiCw8N4+Tj5ZR3pSp3P48a41yFKSryzfy+PvDxGJVRK40MzoWJIfOX5O/aKl3NT/OcofmMUT7z3Kwp5/ppbFGGWZmoZGNE3jx3/3D3zhW/8Ti82cRewIdNzcy6w/NDBw8QjhSiuta3/ByffvnTqyaZvTLe61hNQYKTWDV3b+ry1/pm/wn/AOCoahgKopFPYNXZkrikYsHCUaiXNgXx9dHT5SqRTpNKCayIb2QdXQMtmSq3OE6rKjy1+OMI38UANNzx9kFAhPZujNTODzTRKaFKit9+AttVDkNmerzjMgJ6dFNc6dAyisq9wTZxodGlP6JQhnJpz+946/KA9AvzcF2mFBE3glvJnri29h89gjxN9MU2atIKNqXHTRBhwOO2Sjk7lhxGJptj66jwO7RsikNGbMT+CqSjK7qYxtr7yMb2KCmrpaZjW3kEgmWXLecppnz+bQ0WMcOHSQ3uF+ij1e6qrrUdIqq9es0UndJibwVts4Fn2KE+9ruM47AZqLBncTC5z3IGkmTp06hdlspqGhgUQiQXd3N/V1daRTKYaHh5k5cyaJhN4j111UhSbEObIzwpGBF6mtreWiDRsYHRnFaDLhNVgI32mnJPozhqxfQ3SYsMyVSG0Y4a32rbS2tjJr1mzMJjMJRUGURDRJQslksvk3rUB0lrU2z51A0gvmZFFAVTJYNJW3t2/nhWefY8nSpZTVVKMC6y7bSCKRzFvPqoYOwVUUPtrxHuOjo1SWlGCzWkgnEhQ5nRQXu4lEIvT09lJTU0ORy4XD4cBqtzPh9+PzTzA6Osrw8DAmg5GG+nriiRjNzc1U1FSz1zHO1rJB7uidgXpyjHg8gYaAzWYjEokwODDAvo/2EJmcpLK8nGWLFiJkMny4+wPOW76MQChKe2c3a664hgWLFhFOps7azAsQQpBkGYPByPbtb/PrX/2CeCzGTx9++qz3FiR3tsX/5w4BCTT9N7e4T58apaN9jGAgwMjICErSAYqT6PWtWLf0IsS17OYlZ88hgiaz/sFDfPB380lFZHQaAn3r+OYFH/LocRuJtJH+n4vYHneREka5eLKE0pCHxdu+zhM7bVzW+Twms57w1oQ0ihbnwe/+P3z5+3+PKAh540vTNFp/PZPj97ZRPWcriXA5EwOLslZzQS5TZfCFgV/zu+ovMzVE9kkewCftPwVbOPdouiQFTSrkAFQZVbEQntSyBIBjDHRlCE+SZYw1oqgmQM5ei5iXJ4CqZsnutClrRJjq2U0Nf2mIUgbIZMEEcQTZhyQrmM0myirtlFfaqK4toaKyEoNBwWBI5uz6KfLIjV8fj+4FnDmPdK/x3DLSsaY3/TWFgPJBCS3b3QsJURB4wP+PpJQklS/PxFteTGJOjDbHQWTJwBW2TcwxL0JmCnc3YJQlzEYDv33wl5jNZooSXra+HETDgsPi47YvzGYyNkppVT3ByRBmi5VUJo2nuBhnUTGKqnL46FE0QeXAkf0YZBm3qxSfb4ILNi1ifvX5dAYP0D15BCeVlNpL8Zc/RSQ9QnPyPmrsyxgaGKeivBxJFDl27Cjz5s9nYGCQEm8JkwMeNPMQA6cUxEgToqwwqr1J/9hxzGYzyWSSRQsWsXy53tM3lUrheOpDjC/spttgJ2j+DHbFg/urCqklsXxVay6ZCOQpsz+poCR3aFmlK4kCNoOBU22nGBwYRBIlKioq8QX8VNfVUFpVpXMuZVkzJYOBeDzOf2x+BqvVyqLWOYSDk8QCAcLhMJKsK55UKoWzqIh0Oo3PP0GR28WybNVwOBLm1VdfxT/uQ1VVFi5aQHNzMwPDQyiqirekhIGBAXpmifguLqPqkW6krkkURaGsxMvMxkbKPC6MRiNFDge7d+/m+Ref57Zbb+PgwaPYiktYsXY9lTW1xLIhtk9SAAajkXA4yuOPP86Lzz2D3e7ggYeeOEtW0x7DGR7BGetpKqpFk0GTSSc1He01GaLrVICBHr2vbjKZhIwbVIcepwYSC92QFDAciWdBA3oOIbf5rX9wH+/euwiYUgAnpPnbNXt4uqefyck59PzYhOcfQatQuEAqp8yfwuY6yIf3vcNl3c9gtkgIYgZBzPDBG+8w0jfA9XffwVQTs+IDL+PLxlGNKnPW/IITO742fV/OJTyzsngs8DZ3etafNdcKbz+T3vmM1/9MyEgQRARNJsftE4tmCPhT9HT5ONl2konxSTKJYlBsiIIIgoySsaBpchapJSFgyI9PUbMhp/zXiWjo6CB9PBnQcoVsKohpcjQYiHEEwziSrGAymaiuc1JV66C6poSSkhJEKYlsiJ0ziQug5RoyCULBQyG3JvVCuTycVNAT3LmOhwgSN17/f5EK4v/0caY7LmQz+t8uvZ+RkRG2mbehZUTOEy/gevdNWK0WhhPDPDv5CJ2pU1TJdVxhv55yQzUyAvFoDEXNkE4lKHZH8RRtwSxoVDbOYdsrbvr6RAShh+s+XUvaFaS7t4dINEp1jU4vkUynmTdvHhvWX8zY2BivvPYKNruJ7c/sZOiaNhxSMee5rkMyGhgN9xDsWIDNuIYJNYrP/AATFfvxhzexuOImBKC7qxOrzYbFamJEaic26GDFBi/x+DDj4+OM7hVplO9GMsSYuUIgKg6y7/ROFs04X98cbl2F4bNr2f/qVhrfepS6k5NkXj6P0ONXooYlTF+JIy/J5JXB1O5ZZ8URcxBIciEM0BQNRdRwOJ0gDTE6PEx9bQ0Ws5GO9lOU19SgpdMImr5AM5kMNouFz37ubgL+AEO9vVhsdgRNo7Ovj3Q6jcvlwlVUhMVmxyYKONwuij3FdHd2s337dtLpNAsXLmDFectYtHgxqUya06fb6ezsxOl00t3dzbLzzqPEl2DfN3Yjrqqn78dN3P1BOWI0xZtvbOPjD3YhSxJOUUJBpayshERMb5RTV1aBxWzOIn/OmG9TNv9cD+b+/n4mfD6dME6bHovN/Z0+RyGH4vpkW7bwfaqiEIslGBgYYHh4BN+IQiSSbcspimiClE1K6uc0HQyhWE3EbvZif9ZfOGXWS37n3sVc+OsD7P1BM9FBSxbBIvDAB0v4xkqR17qcWL97kvZ7Z2D/rchrLQNcaXRTG72GO9Zu4fEdN3P18GZkQc+dnX/JhaSTaV545HE23fWZ/BhGzh+j7rVqolVRTvI15qz+JW3vf7UgnzNGfadnPff0/4afV92FWTy7Ic1/dhQgo9PlWICJqvlnBUHDYJCw2kwYjRJGo4ygiigpvSWlqhYsbX1P0RO++ZwC51I1hVChvtlOXUP6N+vXovcLkGUJt8dFZWUpZWV2ioocU/pVnD1vCqCA7HVkX8oFm4TCF+UzE2Qr2ck/+1fUD0DTcsiBnLYTdTooQcsXH8XjccrLKzAZDNjtNtLpNB6plNucX8yeI9tWQo3TkT7N876niK+NUeev5tjOQewmI0o6RXV9JRddXsHp/iEMZgt9/QdJD2tMjJrpPVnMSUGlZYGHG+9ZSlrTm8gcPnyYdWsupNjrxeefQFEUDAYD0cgkb7y1FZ/Px8yZs/DW11BZU43ma8EqbqAzeBif/wW8RUX0VD5PWgoSCN1IbelGfPjYvHkr4+M+ampquPjSiykuNrN790GeefFt1l27mqDPym8n/4kNyo+YsVgvfnKXlbBj0Rjjt65k8dKlWNIxpJEgnm8/i5o2MFT7BTK9FoyfSmG4WietmjYBp05mQM5aH6qmEEvrLRmdo0WEJ/wcPHSQC9asJpVO0XXqFDNmzCCjZlA1PW+QzGRIiSJWm5VZrS2YDEaGBgd5f+8+GmfMYPHixZSVlekNeCQJRckQCoWYbDtJ3cwmaqqr2bBhPaqqcvzkSQYHBykt8VJSVobdZCbgm6CyopL6hgaWLF1KR2cn/Q8OkG6wM9Yg0//TFcz80gTB9l5ckkwsmaC1voF4KMrkhJ+KyipKiosJK+o0JXimF5BrBdnW1kZ/X68eGtGmu+A5qplcYRR5Cep/C7gYsjvVFMWgaaTSaVJJlclAEL/PR9AfJBo2kozLBZZSNdcERcxDA+WohuNZP6pTInqdG8dTAbQ8S7bGO/cuAFQu+vVh3r1vPjn7+ad7FgPwlfNGef633YQSFUSXm+l6T2andIiNX/wXrnqiFqQnefHWV9k09rgOvDDKbLr7dh594GesuGgtrUsWANB35QAALb+axQnuQzTEKSptJzA4H03IybUgg4dr9HX5eugAG52L/8t7gZCFWE93VrNBoVxcXCsoAaPJRHGxFYvZg8NhIxCI0nUqzMR4hEQiQTyeQdP0ZjMIoKoGUPT7LYpiHjQy1efQUMk1ktcEvTBMZylWkWXdW1JVFaNZw+UtorTMTXNzM6XlFqx2DdnAGWc8Wwl84vinfargFuRrHNRCOOq/e/zFhICefuIJCgLTkJCy2lrRQwXhME8//TR1NXVcuP5CREnMduURdfyCltObAkYDyLLEnt272PvRHi5euIAtL75I3BwgsdRIeJ5Ai9CMu8tNRaoCu7OIMd84iqpitTtQVY0FCxehqiq73uxg/44JAn6Vuhkil908j8a5lZjNZmRZzqNqYrEYk5OTJBIJjh0/ytGjhwiHJ1m4YAkVpaVMjI9TWulhJNFHqjiEFLZg1FSMi3dhMIo0SZ/CE11Db1c/breHYGiSt1K/wm51cInzXgZibRxRtiILFmb2fp3UkAfR3cf66+bkG5xPqy6Npyj65RuYP+gkuOxCJrouQJqvYNyURKjVMQ4Gg0G3ULK1A8lknGQ8xtjgICePH2P3zh0YjEY8nmLmLlrC2vUXYXPYUVSVTBYznZmSXAZAEDAZ9Ybs4XBYr22QZUyyDKqClq20NRgMvPXmmwwPDXHr7bfS39+PURYxyAZOHD9GU2Mjo0ODvPzyyyxatIhPf/rTel5DFkikk3S0nwLAY3fwox/+AMPSCtJ/u47i2//EJasv5OTwOMFonKtvvo3yikpCyeQnLr5MrleDZOD+++/ng53vYZBl0CR+9Jsn9WGhIeaLFQuN6P/TxtxToY2qBKpMJJxgcHAQvz/EQHeM0cE4iqLovQ/UYlAdoBnQNzwRlGy4QtPVjKbJRO90Y94WRRzOIGQhi4IARkea1T88yjtfnVnwWgxhBFOAv104zssTFoprmnj1rmHu7VjHE89u52ueL2HJvEm4ei9DK400R6+aNoR///vv8z9+9N1p9rjzlB3vfi+dN3czZ83PObHjq9OHzXT7/UsDD/NQ9T1TnvkkueU8rcL/U4+CAsgajYKAphnQVCOqqpO5xWMKo4NpBvp9jI2NMTo6SXhSRFWzTJqqBS1jmxLiyeUAsogtMVtFrCpoqpZtU5mjeFARpBiSrCLJMqVlVprnFFFS6qGmuhrJGAchikZGVyBiCiFbQ5C7/qljzfEh5WsGznjPVK8jhyTLf1aQuP5T/70cwF+MBzDVxRMQp/Sh1Qc8Pj6OphYSmxlFt4azcyH3aXJWAkgoSopipwOPw45RFkkOpbG9plK0y82q6xfhd0d5z7WLmCnBQtd8ogei3LBmHaFQCLNZbwIzf1UFV928gu1vv42gmjj60TC/vf8wsqzQONfIZZ+ei8VhyVfCer1eGhsbuOyyyzi87yN2vv8+h/btRhUlbMfNzKiqxjToomlWM85qK4d7BVKKTEcqgTrj26RnD+LiazRWLGX/5lV4vaU4V3mwjVdxd8u/YzKZOO09xCve7wACwvZv4phchLdRoGxWPC9N1SIT/LsrEQQROZai5qdPIr/by2TyEgInl6EtixO4cJyYPUx4Uu9ZYDYZMQqAqmIQRaxmC5lMhsb6eq64/AoUVSUYDOrWmWTQm6mLot4fVhDyfYfTGZ2G2GbTS+0VRcl2h8oRZGlMBoOM+nxMBIN0dHbidrkYGhggEglTVlGB3eHgzWNHsdmtdHaeZtu2rRw7doyMmmbR4kXMa20lk8lgt1lZs3IlH297E+POblLfv4LnV1dT809juO1lOJ1OUqoyDVEx1QPIWVWiKBIIBPBP+EgmEhjsjrz7Pu3IWqe52TZ1/ubj1tq5E5iCICFKIjabjaqqKlxFHoRMEFHV6yX8/iBqOtv8MIcaydk1+XPqm4DtsRBqmUzyCjvmreHsvIdkSOLdr85j3U+Os+ObTaiqiiRoGAwyWzPrOL9ujJBF5KanTXRfOsHVd1/AT//4IGvNblYOXEnjU4/z4hfuYNPY4/nr/sYPv8NP/u6f+eaPvpt/LjQrQmhWhNot1ZzkazSe9wSde287h7h0KT1UfQ939/2aR2pzSKJPykVxztfPDMUpyhTDIx+q0z9vMhkpLXOgaSDJaV25JmOkU5msopQQMAN69byqSWhZXp0cuEYU9NCSJmpoQgZBzM5dQUElidFkxOGwUlXlpbamGJvNgiiooGVAUCiAgv9rBnauavhcuYLpcpmiCP6L4IM/+71/OR7Ak+jCUpAECRERUBBFvUHDRx9/zOGDh1m1ahXNs5pRVIWpayS3MARBw2gQSKeTvL/zHezpNBZV5e3XX6Ohtpp1l1xCUUkJ7390kI7OThREgsFJmmY2c/r0aZwNLkJLw/icPkrEEi7WLqfCUM5T74/TWhSgaUYt31v/Hb798Xcowo3ZYuGt50/y8TuDJOICsxeYWHihm+HRfpLxOJKgIYmQiMWpKC/HarMhyDIfHzhAIBykqqKSa6+8Fo+nmEQqQVf0BF2hI9gMTrwmG72OR5GNAjWBz1BruBCPR28Ks+W1LUyGw1xy2WU4PDbagx+za+QZytVllBy/C6fTRO2ySVJqiEAgwGQwiMloxGgy4bTbqd7VjWPzHkLuWgaSmxDSMm3nv49tsUrjjBm4zBb6+/vZseM9GuobWLR8BVaHA4PJRDqjkEjrCyszJckcnJxk3OdDFAQcDgder5eioiIMRqMOx1PS+qYqihhlGVkUSSYSJGIxBAGKipyoyRT79+1jy8svsvqC8+lsO8maNWtob28HNObNb2VsbBSH3cbAwADHjx4lMjYOySSqKHDJxo0YrE72Hz6K9L1LsVYVc0VXPaaURME4KKwdVVWzxHcGPvxwNz//2U8JBgKYjBYEQeSHDz2hex7ZDWH6giwolFwinWlUJNoZYWwjgqrj+DUNMmkN/1gG32iUcd84vb19TPpNxMIyqpprXiIjYtKhj6qKhoigmUAojCdTLZHc4MT6h3EEQUWQ0ohSEsGgsOb+AU7+rJn6Jie1tbUUFxdjsaexffhbfBUzODnUyxufs3Pd6By2/PEgX/N+BltkP9GSNxm+sISZ8csAPdo8PjzKk7/8LV+//58KQ9I05jzUTMfN3SjFk9i9XQSHW/JhlanFjQA/HXuFb5RefdYecOYGnztyBkOhboXshl8APWiqhKYZQcuifDQZTbUSiyWJx+P0907QcWqccChGIBBASZkRFGdBmagGVE2eZiDkUHR6uiCJSiobKlSw2FXq6suprq6itt5NabmIIKgoqoIgRUBKoHMCqbk2NFPkoCe/c7mF6cVu0y3/aXBlLbfH5UJkeuHbpk0X/f/fA9DIMQqq+iLSQBP0GJ+GxEuvJqiuMFBUVERZWZnOQqiix7LJGUk5q0xFEGRuQBnhAAAgAElEQVSSyQRdXT1cPGcW7e3tBCfGaUvEkF0ubr/jM1TX1CAbjXi8pfQPDDBnTiuXbryMo8eOUWmuIjAWJBKN07SgiZc/6mPxcvhgj4Pnqh9ibft6frTyB1x5+lpau+Zx3sVVXHDlDGw2G6IgMDI0jKQ5eemR00TCGjZbgvWbqqmsdNPT14uqqlxx6eUEg0EGh4fZsWOnTssQCjAyPkJleSXzWipISUaSA5+iZL6XTqGbsGUHtTY37cbfYL7IzujbXmKTKzCgkTxtQNjfQl/KT1fmR5jdMrYaHykhRnXsYtxd1+GZGyGa6WTH9u04BsaI3lRNc3MjDQ0hyiQLM//+FDvKjpDa+nUmu6sxXyaQVFU+OnCAo22nKK+qQpQkJiMxSivKSaXTBIJBvWewKGIwmXA6HBR7PMyeNQtZlhkdGcHv9yMbZGpqazBbrXrIKpEgBUiCgMVqZfv2N+nq7KSytIyWlhbWX7iO0eEhmpuaaG9rY8aMGfh8YwR8Pk4dP0Zb20kuveQSbr/lZoI+PSezc89uXJWVjI5PklI1rjzmoTE5i3ha4dWGbhJShit66jFn5PzCEgUNs9GIpgl0d3YyNjqKxWLJW/NTET7AtE0itzHlF6pANmqsz000La8P9O/TQ0eCoCJJIqIo4S4xIRsNmOwaolGhryNBJp0ik1bz1CFgQBDEbA5C1CmLs7NdEMAwlMHwuA/NIBC9pQzLSwMI8QwOs4O2nyxm5twi7Bv3UTG6CJPJBFKSxNpPYQMuGB+A34wT1kZQ/7Ycf/txfvEfx7gv/G0W3bCG098fhVnzqEoupKSihK/f/4989M5O5iyai8NVBMDJL7VlFcECTn7eRE3rVgZPXM5U7ZeT4zdKr2Yo7ccuWXCKlv90byhQrUxNwEIWiaArYkkDLUWuqTyajIaCxSZitRmQ5WIsFjs+n5/u7jghP0QnY9k8gx5KkTSRAhFcGlXL5L9HIwFCGgGwF5mZNbuMGY01lJSUYHNoaATy16mSBi2V9yTyKdzCheszRJvuG5zLEJ/mQ0wJ/+iMvxri/wc5gL8IBVA4pmtATYC774ny0C+dfPErldx5WwBBlvSmL2dWCuaSx5qGoIKaShMPhYmGw8QiEQQN3G4PtVWVgEBNbQ2RWIze3l5C0RjH204STyTIKAqerOV66MgxRFnmquXzePvoIK0VAnPeXcOJ+hGilXDwQ4EXr/4XlOI4s+MtXHPkeix+K40zZjCzWcZVZqDtxHFiYeg4kuLVJ/sRBFi40szy890Uud3U1NXR2dlJLBajrq5Oj5sDk8EAB48f1pE2tqV4bbOYt3g+Ckkip9aSiCTRMiH2mf8nZrNK9ZxLWG5cRH/PCI2NjRSXeCkrLycciuALjvB68l72JEI4xhcx/7SJoo2fJ/3uH1h0++3EYzEGfBMM3F7CrDnfYdT9LKWjzyM9JXJJyZ2Y4l6kG/3U3uxGkiROnDpNbW0tktFAOp1mbGyMYq+XZCrFoYMHGR0eZktPDza7nVmzZlFdXY2GxoEDB7DZbMybNw+TwYCiKNkcisq8+fNZumQJkeAkvT3dhCcnkQSByspKotFoFs6qYLPZcLtduN1uyrxeXntlC06nkwtWr2bp8uVE40kisRhFxfo9TGeTv5d31aGKKu9XDNNdFOLOky0AeStV02BkZGQanJaplCTn8LbPXrNnJi7PUBiaiECuqbqCpukcTTabBSELdoiHAijpKKFwhEg4jm5JZhEfIqDmOHly6JIcAaIeFHc+1UtqjolUkQf3uEhxsYvKMg81/hs4Vv0HFvjvQBAyWQMLUudfyfkn96AGxvD/8362/oOFpvsq+feHH+Wrd2+h9PAg0aGnaL9olJmxSwBYduH57Hl3F53HTnHLV+7Kj/Xk59tofmImp28XaFr2BJ0f33GWzARBoNLg4TH/O8y31LHE0piTHFPRMNM9KTgbI1+4J0K2k1iuf7CqaGiahICMIIpYbRaqqooxGIxEImEyiRixkA4J1vOI6LnEKUaoKOjKR1NVNDGN0SRgsViorvHSMKOcklI7VitIchJRSuld7VQFkWzDoumXOGUgTIELTJfL1PnClMeF16Z4m2d6l/+bx19ECKhlTov21ON/RBBUxOwPaGiCyue/FOfhB518/ssjeD0KY+N2XG6JG66XmTNbxjmFXlcQBIzZ1nK9PZ0c3LWLGqeT7tOnOHX0IGXVVSxfsYKYKBMIR9AQmb9gIa0LF+m0BugMnPv376epqYlUWtEpmZubqaqpYXRsHKPRyKlT7TjsDhobdTrq3bt3Y3WXcnQiQ19ylJ6LXyZUP0C5VsbFH1+Bd9TLrLo5pJIJPB4XkVCYZCTFQFeIZ35znEwGikszrL+hmsXL5mIxGgkGg2x55RWKvR6aGhsJBPwcOHKIkfExBFHgvNZWYsEAXq+XmWubODC8H1kwMKuugnbDw2RSArvvcXLeDdfz6qnn+MkXH6Jz/DQrWi7g1M9+i3ptE6GHtoOmkfbMQyy9gjkXWfjo4G8xqCepbLia4Oh2EN2cOqFy/iFY2C8Sn7+B0cGVKMsncd9oYNIQYGBggIrKSkbHxnDa7VRVVWE0Gmk/dYoT+17HP9yBXHYeF1xwAVXV1Zz+w7+g2VxUX3E3TqeTZDqFIAqYJRmn3U4qFuHQgX0cP3gIWRSRZb13a8OMOo4cOYJRlqivqaHIbuXD3btJJJNYLRZWnb8KT3k17ac7Ka2dweVXXUMomYULnjHPNUnj/Yoh+p0R7ulZSEdHJw888CM6Ok4jy4ZsglDkxw8/nfUwp+PWz7VuBHJFjDkv4Iz3aFnisVzMWpNAs4KmJ81TKYW+rhAT43H6+vrp7x9A00xoijWfpEQzgGIGLcd7o7dM1dcASAYFg0nFVWxAXtZEly3N17x1mCzJvBF51Ps08ycvzV4zIGQABan7FEOhAHsOfEjwHy5iw1Atj768n/ucN1H19NVsflnmvNA9VCYX5of0x39/mM98/Z68TARBwDhpxOw3k5g1xozlj3B6VyFBfKbcvjbwCD+rugvO2OD/XB3AOQRfeK+moSoS6ZSMJBl0tk/NgqZaiUQi+Hw+Ott8dLSNk06niccTKBk7mmrOhnyysXjUbCMmBdmcpri0iIqKCmY0VlBVY8VkykJ1xRiiFC6MTUyCmMmG76ZcXn78hUsV8h7I2VDtqXUA0+SSyxMIenDpU//NJPBfhgJoadGefvxxfQJD9gboPzl0xquvvoZkNnF66QLusBdhzOiFL36/wpvbFba/nUYU4cqNRi6/RGbvx9sR/BMUWax8uO1VwgE/7lIvd999Nymnm67ublJpvdDoyPGTiAYZfzBIMp7A7SqiuXkWlZVVTPj9TPj9mK1WVqxYwcjoKAh6cdOxY8cZ9/lYvXoNlVVVJJMpurq7Ka+owmyx0H66m3Z/nN6JJL76Nno2vopqTXNJ7DIuadtIbDjC3NZWfD4fIyMj1NXUceLACE/+8iCZjEyRK8qtX12Mu8RKPB5l94cfYDXK+MZG8fX3UFFRQVrV6AmGUTSNUoeDhqaZLF25nBcefoxIKghJkf3eU9zyFTs9h20cP5BAUwx8+7Z/ZXZdK1u3bqV1zizKKkp4dttPqDkaxhJzoJSvp3TpOpqXpRkY6OG9t7dS5u7DZExS3XAJwlthWj8cImOqpTezCUkyMnz1EWZcVoPBYODUqVOMdnzI8otu5PWnH0BD5DnPZ6lJDPLlFeXwwWPUXegkYLgdb3UVgiDw8nPP45+YoMRl5/2dOyGVRsmkaWpq5Nprr0EQwWgwUF5Wyvj4CK9u2cLIyTYsBiMT4RC33n47/ZE4R44cYe3Ga1i6bAXhcyiAfHwXFZPRiGqUeTi8ndOMcfLax/Kbv4DIv/3myQJD5JQw0DnL+afGd0HP02X/6gb6FCtX0D0CDb2gCUBVIB4xkU4ZaG9vp7u7i2gYAr4pSU/NhKDaCgpAUEBIAiqqquLx2qiqdjFzVgVerxeHw86kFuTR8RG+UV2sn0NMEDT30m87yvzgJeQqibUsh704NsjEYB9D6SRHrill9Uglmzcf5j7jFyh96Twef8PA9WOP5sedyWT45Xd+xNfv/8eCLIA5v55D271tVM99hYFj15x1H3KPP9f/EA9Vfw45X6n7v34IQgEiqmkymmLOnh801QiaJfs+6O0M0Nvpwx/wMzIyQiphJpM25O8TmoCqCGQyaWSjQGW9lfrGCiorKyircGC2xoAs666QyKN88rkhoWDRC2fMu5yiy83B3G8O3jkVrFAIH8FUZZjLASCIbLrur4QNVPcABKTs9agUqlk1TeOtt97l9ILZfLayli2hIDeYHZyZZRdRiUVFtr4Z5bVtfhJxC601PST6nsFjHmDVqpVcevkVTCQzFJeVY7Y7OHLsOJ1d3SgChCMRVi1fQSQS4dTJNkKTISqrq4nH43T39bJhwwZmzZ6t9ybOZLBYrfQN9HPw8BE0AWbNms3s2XOw2u0MDg4SDoZobGykvb2dlpYWugYm+KCtm75YnP4L3sDf0oZFMvEF/xdpmWwhPhrnyNHDHD16lI2XXUZtVSO//v42hvoATeH8Sx1suvkCdn/wLkc+/IDqqirIZJAMRtIZhYGgH3OpF1/AT/zEAHd865uEA0FO7P+Ydw+8xbIVi1l00RIi9S/Q15nkwA6Rvv4JvnbtN5lR0sxHuz9iw0UX4fBYONr1LqGXP8AWsJIpXUnMPZdJaQfuYgdNTTWM9r1GOuWjofkqzG9Hqdx6goipjqB8G6qkwZfHkGcovPLEPyOJEtvm/5ArTSc40naSayN7aVlfgrXxLlIjj9GzM0PZiksJGYrp7epkTvNMLCYT0ckQmzc/hcfjpqVlNuPjY9htFi7fuJF9e/fwpz/9CVc0gSYIhFIJbrnjDo4PjdLT18/GTTfRMHM2yczZmz9kKQLQ0THhcJTnnnuOJ574I0vf/SqS1cjJjb9HS8ED2STwmQogd0zjCKJQx3KuY6pVWyjIm/J+TYaMG02x0t/fz+DAMBO+JH3dEZKJtA4T1cwIql0nQxMEIIOqJRAlCiGKphIam8owGGRESQQpjCBFub8vxNVeC/OdGgg6ZchB1xssmrwYXQFMSbYmYqgH9rCl7TCl37gRV8rMlkeP8dmKS2jcfBdPv6iyMvQVylJz80vw3791f14J5IIgDZtn0H1zNzOW/YGR9suIBSumjz/793ujz/K9spvOuk//ZZqNaQpAQlVlCiE5mVy0WxAEAuNpJsbijI2N0dPTT3BCIB7NheYEUGTQdIi03Wlm9oIy6mdU4HK5sDlEEP1oWibrAaQQpURW2Qt5r6/gAeSC/VM2dS2Xt8y+L1uLoKrnIhycLqsCD1BOAfw1eABzWrSnnygogNwPgMlkIhZL8M4775FctpASu4MxJcPFJhsjmTR70nH8qsJMychMWWam2cypk0c4+PHHVNrt9B87wlBHO5LZxkCwhnHtfCaV+ZilHhrKOlm+ME1Hx3FWrVnDwoWL0JQMx4+f4NChQxQ5iygvr6S0rAxBEhgZGWF4eJiBvj4CgQCpVIKW1lY2XHYZ5eUV9PQP0H66A9/EBGazhZUrV1FSUoKmaTidTkwmE5lMmmBoErfHQywe59ixExzsGWc8bSJSEaD3ypdRvFGaA81s+GgD5bF6ZsyqYrhnnBktFWTSKd544TBbnulHUyWslgHKmvwoJLC4XNx51924SspIJBKkkBgeGWT/3t2cPHGUlavWsnDhUjLxBE///KeM9Q5RuayK1Z+Zx6BpCz3tCXZuTVHiqOCKJdewet7aPJRzPDzAnvd+T8lRAVF1kqq7C0tVhiVrPRw4tA81PYYS/wjZWE6JaxmNDx/DMiQwVHkPmZCFwev6eaksyYmYjbuvMFPdtZltQ7PYIO7EJqSorTGhGRZx4IUPsKlxjDOWEpDcDA30EYvFuGD1+fR0dzF7VjNlJV4MksBwfz9PP/groqkkNfX1zF+5it0HD+Mqq+Dya6/HaHGdc1HlIHeioGE2Wzhw4CBbtrzCzp07MJnM5BZpy5ufY7k0l9mdXgR1urFxLiUg/Bl6g7NCQkLhPLnYtYCMpjhANRFPxAmHoowOR+ho9+Gb8BPw+0FxIKSLyXMDiXEEOYzDaaauro66hnK8pVZKymz6+VHRxEmQYtlrhaeH0ix3CTRl2ZtjYpg2xx4WBtdPQdtkrzWTRvrgXV4f6ePjb83lmpFGXn/kJF+13Efpi4t4fJvIprFH8pZtcCLAYz95kP/xg2/nNzgxJVF8sBjfigmaVv2C9l1fOUsBCIJAe3KIGtmLWfwzjZTPlKuQN9un3BsJVZXy90VD0nMCuTxqxkY8quHzjdPT00/PaR++8TCKkkHNCGiqjNnkoKK8nNJyF7PnleHx2nRLXU6AYRJVzSoAIYMgFCrupxsE+QuaEsc/Myk8Nf7PJ00fClnOXOhIBxFce+1fgQJobW3Vnvjj40iCkHWZ9PCOil40lE5meOyxP3DFVVdQVVWFklaYyiWSS4qIApiMMs+/+zY+i4lIfQMRf4DMiaO49n9MbKCfeAbuvfdeekdH+NMLL3DBhRdh97Ty1MsCk7EGbFaJFXM7aKzxU1VpIx5LkIjHiMdj2O12hodHSKVSNDc3YzQa8fv9HDp0iEAwiNliZeasWSxcvAS3x4Pb7SGjKCSTSYaHh4nF9EUYiUQo9XpxOByUl5YhyxJGg4He/j6OnjyB2+Ohqr6JD3snKUmlean6XeaGT1CjbsDramK4vZeZdTOY1dyM025HVFT6Onz85F92MT6aoXG2gQ03zaNpXhUZdNungCGAYDDAli3/QV9fD1VVtWzadCPvvPEGbbveJxYIMf/6RTSe72HE+SKHtzs5fMhHtbuWz2/4Eh67h2Q8zt69exma7GCmNUVZu5m0o5xM1acpmTdBWXURyeQkIz0voShRauovpH4nGJ/ey87X/5aSf7XR8ayR0I4jLBeMhBUzDx0I0pZxsbLn9yx2DbB2dTEjY8UovSkU/wg1F1yFtWYOBw7s59iRwyxZtACjUWbba68xOtTHhvUX4ayoZdeHe2heuIQrrrqKSCwzzaWetjg1FYNBxmAw8dxzz/Hqq68wMjKCLBvziURJknngocfR0Ni65jQN/UXM7tLDKGfHd7Xs3D1zk5/63WevcL26NPe6BIpVhzQCigKTAZWJcYX29nba209DxoGadGcVm4rFpuEuBq+3iKaZTZRVODFZMhjMmazCUUCIg5DKX1POS+lLwNsTGndV6WsuJocJGMaojDee0Y4RpOF+uns7OdpxgkPfWcLi990ce3+E+4z34Ni/nud/H2PT6GP5YR/56ADVDbV4y0rQLXER90E3k7MmqVz2PH1HrvvEve7uvl/z+5ovTZGsNk1s2hSZ6kl7fXafy9PTZSxkAZlZEahWlLRMJpMhFIzSfmKI0SE/4+PjxKJpZNGK11NJc/NMSsuLKK8xYzILaKggxkEO6/9rOfkWOoCJhQmRu9js+PX7f9YcmXKo6tQuCtq0seavPTuHRVFnk73m2v8eHfRfhgJoadUe/+MfMYhithJOycLqNIwGI+mUwquvvsqKVSsoL68gnUqDpk5HXKh6zYDZLPPHxx7DLkBNZQWvv/gnymvrWXHzTWxNq6SMJsqTSa4osmEIhti5YweReJzzV6+mxOtl39697PmoHXPROvafagagoXKCC88borvjQ1wuF00zZzIxMYEgCDQ0NOjVh/EkPt8Epzs68QeDpDIKLpebmpoa3G43TqcTT7EHh8OOx+NhbHiE0GSIwOgoAb+f/v4+jp04gd3hwGIyYDQaUVWV4uJi3OskTu4MYlk1SSh4OZYlXZw/tAyjaqS8sZiZ5Y35Ca8AY0MBdrzUxrtvDWO9uxTzRcV8I1KDxaKfM0cpq2kae/d+TDgc4ODB/SiKwo033oySSLF7+5t0fHyY8qWV1C2vxnze+xzbo3FgdwwhY+aGpTcT7A+wb98+JEGgbIaLRXVGyo7KZGzFCDM+DxXtWIo0ZClDNPA2aiaKw7WIzJrrWHHJr+i0z2Pib68hcp2Mrd/H8MuvExCKOOBYSE9EZV3Pz7l2hUBZmQnZtJKTL79NKh7jRNKBu6aBhYvm884779Dd2c7GjRsZj6To6u1n3pJlrFx5Pol4+pybv6ZpSIKGyWRCFGV+8Yufs2XLK4iihCTlSAUFREnmxw89nlUiCnFTmt2LhnBGTCw9VlZQAp+wuM9OZBYe5+Q/vY5aAtUCGLIhAZFEHCIRkdMdp+nq7CYW1giMK6iKXv3rKbZQW+ukrNxLQ0MDTrcRQUoiyIkpUNUUkDlLDoIgkAHu71T5QrVKhQkyYoojzg9YEFh3lszEaATj7nd5pq+Dse+spyRtpf0Xw3zNfB8lLy3lia0a1489Qk4tHvnoAAc/3Mdnv/7F/MZW/l4po+eP07T6d/Qfu5JYqHTa9eSO7408w3fLbsh/97mOgsVdMG8KYaApCVV0eo1cQkZQLaAaEASIx5N0tQ8yNuqnu7uTaDSG2WSjoqyS1rlzcHscuItNyIbcuVJo4tSqchUta7DqHo82pZr3TDPhz0M3FXUKIV3BfZiWcyr8FZFE6a9JATyOnMXh5pj2RFHEaDDS39fPibYTrFy5EovFSiYzvTsRgFHUqQ2ik37ee+sttGQCgwC73nkHWRRYd8EqDHYHp06fxmqzctmll2FzOtnb0UlfZRUDziJkTcNw9AhlY0PUFnuxWu3EYxGikTCTk5N4S+sZGrPw0tvFRBJeSotTLJtzhJqyIDarBYPBgGw0YjabMZksZ5GxDQ0N4ff78fl8ZDIZlGSSZCyGkkxiNppQMzqO2W6zoaoqFpsNt9uNIAj0N+3GOO7FOVZLzDFBeGYHqiWFMtFIOLqedJ2PZq/MzJFm+mPdEBPouXohV1POC+ZxijQD7hd8HHyqm3hIZcn5Hi7c1EIo4aOstASTychLL73E3LlzaW1t4cSJ42x9fQtVVTUsXLgEGXj98Sfx9Y9QvriC1uu9xEv3cGS3wp4dIewGJ3eu+TyXr78SVVUZHR/maM82LG/3YooZyNRdR/GSFooqw8hyhr6ObUz6DyHKHlZbL8f28C5CoWI6Pvs5/l/u3jtMjqvM9/9UVXd17umenINmRtKMck62ZTnbcpAtjC2CASfAsCwLLMuyC5dll7vLpotZMjhhGxwxDrIcZCvnMAqjyTnHzrkr3D96Qk9rbLj3d5fn+XEejTrU6VOnTtV5v29+vQ9b0GM+ErvfpbFzhNOmRUy6a7l28Feszhliy5ZCQuFqRi8Ook2OYajbQFS0cOlSG47sfNauW09OcRnx5GwCuExiJhtSEczd3b3853/+B42NF2cLfE9p8w0Gme//+Ik0aXN2r0TNCkdXD5Ljs7CquYD5AGCaGMye93IV0bQEkOogoeup1M+pZ0ZEVWSSSROTk5OMj00w1O+jrWUIURCQDFBams2iuiJcbgd5ubnIZi1VjUqYTj+Q4lJTHkowY+lMm9s0DXi0V+QTRRo5MiSEOL3WFmrDKy4nwIk4yeP7eb2jCc+3rmdZSw773mznS8bPYj95BS89obBz7MmZsZ//+a/Z+cAnMBgMM2BZ8WoZvbf3s/iKn9B6eDZ1dJollvv7fspjZZ+bIryzR6clig+6tzNKrGkQYDrNvDB16eaZNU4mVfzeCF6vn+bmZgL+EBaTldKScpYurcdqM2EwKuioMwCAmJgh+FNx22kkPv0ZuBwAPkTHQyrDa9oTI8wFs7QDU5lBRe6866Y/AwBYskR/+olfpwpgCLOcvSAIWM1Wurq66Ozu5Iorrpx1oWM2AlUkVTrRaBAZGern8Ft7KMjNZaynmzOnTiKqKvd/5jPUrUutkyqKjIyOcuDQIS5cuEBBfj6qphGJRKhduJDqmmoutLTSrWjI265FcLuxRSIYjh9lQ0U5sqbzxu430HWdu+66k0g4Qihq561DTlp6i3E5ItSW9ZJrbyTbJWOxWCgsLETTUvlezGYLWVlZmI1GAoEAg/39TExMkIgncTqdlBQVYbfbsFgtmGUZu81Kbk4OiWSSSCzK8MgIPp+P4eFhRgaH6OrsRCGBfWUC25oEQiyLuG81gawSzFuy2dhiRIqB2SBQmVtBqNLN6xYPUTQWXj2E4k1wKMeDK1vFtXCIpWuqqaioRDaZcblcWK1WEgmFPXveoKO9FafDyY3X30zQ4+e9372AZ3CUnMW51O/MIZB/hPYzFk4fCXDdqlt4aPvnkQQjPp+PsUA3/QOHGN52K3X/vB/VfQN56+3ULc+mqekcUd8BdC1EaeEmkt87TeWQg847v8zEZ90kyxN0/ddPyDOa2adXc0KroMhzlk/a97DtynxiCTundveRbzRStO5abMu2zQRSZXJPaKnc7ZJkYO/e9/i3f/s+iqIgyyaYCrERhBQA/M//emxKxTiXeKcTnahF5eiqAQo8Vla05k59q8+j1L2cAOhTKoLU++lgMWHKI2cqz42eKj8aCoUZGhymtbVjJiVKeXkxNTWVWCxmrFYrgqig6wl0kjPzTFf/zTuPdBBC54RfwJsUuClHIyFG6bd2Uh1aetm+FRQF7dDb7PVOMvaF9VS1Z3F6dy8PGT6JOHArnX93FUtCd870/19//8985XvfnJmPdcCGalHJv+YZvEPLCI4tvMw2ENMS9CYnqTUVpZ+ZWbUKczUBM5z+bNN0HTWdzmkGUv6GU6olVScaiTI0NEQ8nsBoNOJyuSgoKMAgSaCnGcgFlZTb7Ox6TdtwZj/9sQAwd6KCLs4GEU7NLRMAMiW4HTtv/vMAgGee+PUUt6xN2+AQAEmQGBsbY2xijLq6eqZ1femBE6KuIgpglkXGRod55anHWL16Nb2NjZw+dQqTJJBUVUpqFlJXV0dReTntnZ2Mjo2xetUqbDYbXp8vZYjqaEfTNOrrUy6SNVWVrF27Fn8wRHsgiGnpSjrsdvmgW7kAACAASURBVEyiSF4wQNXEOILfj6IomIxGrFYrzc3NuHNrOH2piDPNhYiiwKLyFm7aquKw6QTDUXxeL/FYDJPJzPJlK6mtrQVSbq8GSUIQU8moJEGYKbwSiaTyiqdSF0wXCNFJJuKEw2Ham5tpbDhLf08PmjVBYtkIhrIkashBJHgLifoAFVYTS4eWETH6KTLnUVW3iNefbmM8kSD5YBbJn3TQ/WYYQdC5fkcxdzy0ia6uLryTfkpLS8nOcqGpKq+9/hojo0NMeie4Zuu1uJ3ZNJ0+zul3DmLJs7DpoTXEFrzN0bd0Wi+FqStdxtfv+Rbvlals97h4rP19TGf3srTNjGjMQ8+/kdJN5dQuNdPf187ZU48jiTGkkQqubLJgb5No2fUlxh+0omRFOPy5h1H9Uc7U3UusYg31/qPck3uAZctzMVjq6XyvAz2pkn/DpzHYUhGrM9G/aJjNZgRB4plnnuGXv/z5VHK/lO49HQC+98NfzQBAOqHJVC0JQFJW2b21i/wJK1saihAu2/DzcYCzYn+K8M/qs1NpoQ3ouoyiKMRicTweH8NDw1PeIwL5eW6KivKQTUZkoxFBUNBR0HVlxtidmuesSiKzXu8015pOC6K6wH92S/xleRKrpNGUdYal/g1zZj7d37j3DV6bHMF5/00YVJm9z7bwReMjuA5t4KWndXaOPD6zVj/41r/wlX/62xkPGWeTi3B5CEPhILpmIB7OuYzjPRPtZrV1QYZ7ZDohnMcgn3azFF2fSvc9BS6qBFMlNQUEUHWUpEI0Gp0i8qmEhlabLRWRpKtTIJmSpvRpnf/sLZxdk8zYjw9tadehz8DanB6ZKq3MmIE7P3LL//8BoL5+if6bp6YKbwgqCKmtY5AMKEmF4ZFhjLKR/Lx80GddzGbconQVdBXZAN2dHVxqOEVxQQGnDhygt7OdukULWbl+A55giKysLCRJ4u1397J27VpEUaS3t5ely5Zx6tQpFi5exPDwCJOTk2y7+moMokAimeTEqdNUL6jBYrGSTCax2WwoikIymcSenc2EDmdsNrT8AtyhEIWeSRzDwwS8XkxmC0lFQVVVYnGBpLiGo+er8Adg9QoDO25KkEgM093dTEPDeRKJBAZJorKykvz8fOKxGBMeDzarFYvVOgMEuqYRDodREgkcDgcb1q9naX0dVoOEQZQQElHOnz1Lw5kzCOgoepQJfZDQ0j40AyS9VVQ+ejtYFbL+Rcc24CDypkKiRUSrFLj4N2bECpnKPgOnv9VEIqCSU6By86dqyCt04bA7CIRC5OXkoCkqbZ1tHD12EK/Px6LaOpYsWsKhN15hsm2A4qUubq+w8MYDt9P0ypMMD2ms2nYb7hWrqOqLURUQGBi8SPDCEXJ7zSiuahIF1+Is7eLIsfdYtSILXehjfDSG9S2VHaMLGCzaSP9/3kl0g0785BmG3znA2Ip72DcuUx1t4mPW37NquRNNqqD/wCBqUsW15gaclXXIsolAIMgPfvC/OHBgP7IsI4oGpnMyiqKE0Sjzjz/4BVOkOYPTnEtkMlVACVlj99ZuisesbDyfP9MnYwTmmpFT9DqdH5/m31MuihrxeIJYLD7jP54yZBtmDKJCmpF3dm+n2R6YlQpmT5LZf277py4DX6tQ8dh68MrjLPGvm7e/8b03eL6vk5KHd6AaZA4/1cUXDA8g9l5D37e3szi8HUEQCAdCnDt+hitv2JoaQ4eaxxfTcX8rNVf8nI7Dn50zrg4kUfhs3694rHw69buQuXKpS5lVH8wcVfWpisd6qu90Tn1xyuaYKnY9W7d5WrqYMdxr6Q4n2hyOP9NN9f8EANKjm6drn8wZLS1D6Qe5xf5ZAMCS+nr92aeemXoQtRntvjRl6W7raKOkpASr2ZrKbkhauP7U5jQadHRd4f1338ZutaCHgrz31h7UcJDbbr0VRTbRcP4csVicbdu2MTw8TENDA7fddhtdXV2Mj4/jcGbh8/sRRYn6JUvw+7w0Nzehajr+YJCPf+yTdHV1sXv3blwuFytWrJgh0IqiYDQYGB4epqKiApvNxtjYGO7ahXQoKuMLFiBIElVGI2s1GG1pw+v1cuz4SSqrKln3yZ3I/RZefjPOmVMaOdkC9+2ysmqlhGzUGRwaQlNVCgsLMZvNGA0GBE1ncnKSvr4+6urqmJiY4MyZM7S0tGCQJBbV1LBq+TIKC/KwG430drTT0tSEx+Mh6vUQCoUYGuxDtcaQFyaQV6soUQfR2G0ki+MsczopGSjD1+oj8oKAkGUkvCuL/rut5HfFGXlmkJGTYYpKRW7ZtQzJGaa2thaXy8XAwACiJDA8MsTh/e8h+ke5MzdKRJAocA7SM5jFiZBM/r1uRkynOfuewEQ0l6s//zfoFgu2A62slZw09b5Fdo+C1WtBLd9FzgIXjQ2/YHKsmeI6K+58O/mBpRS91I9Fq6P96w/h/YgReyxO5WiA33cn+X2fTn60j09IT7NmuQODpYLBg6OExkd4sXGcI639SJKBVO3X1LMlSUaMRiPfe/RnKakhk2imEZ1Z/2zSpP3UsywAcVnj9a19XHWmgHzPbP4bfVpLxJR0wixEzGz4GZDIUCvMqG3mEvb5dcxz7WWXSyYf3Kb15iDwD50id+Rp5BZ0kp3Mx6o45swVACWJ4cA7PNfbxqV/2syVA0WcfrqPb3SOMF59hMindlGRWI8AXDx9nrzCfApKikAHQ9CIZdhCZOkgFsc4ockFl+n3vzT4JI8Wf2rO/KYJZDrxT786LSW/zSWkU/0EHcSMpZirpNERpoh++tHZOI65QPB/Rk8zVFnpRH7m2UgrEJM+Az3V788CAFKRwE8z/aCqUzdTRCQej9M30EdpaSkWkwVN1TCIUhoipng2g6Siqiov/OZJli1dQmhwkNdeeYUcl4MNGzawcNlSxscnOHP2HCMjI2y98kq8Ph+tbW1s2bIFSZLw+fxYbTYKi0oZGRlh7963+cpXvoKq6pw9ew6jycy+ffvwTEyQX5BPRXkF2dluYrEoLpeL/Px89u/fz+bNm1P55IGRSS8VFRWsWL2Gzu5udIOZMdnAW14PWpaDPNHAJoNMVpYVVZaIooInCL4gJr2Il36vcaFRRZZh6xU+Nq23UFGeNfPQjAwPoyoK1dXVaJqG0WAgFotz8sRx+nv78ExOYLPbuG7rVmpqakjG4nR3d9Pe1JjKp+N20tbcRE9HG+1trVhMJqhIIi2MYqmWMCvr8ASq0ZZMsGVsBYYJmcGXJhBbLRgWmGj7lgOx2kLO2RgN/9IGMYUFiy1svqMQVY2jKAoDAwPkeQbIW7+JnpFxzp47Q01REVuyJIr0QwRDZo73ycSuLiZSe5ALR5J092ZzxcN/TVZBETldHjh8HndxmETDIFnDZqJFtzPqH+OKzdAbH8YXuIDDZWad63Zyn2ohFKyk8Rt34b9NInsyRv6Yn1+3abw/rLE4dp6HCt6hrNTInnf7yZuQUDWdX50eZiyc0u8aDDKSwci//NfPU9zhZU9tJteZWRtgrkQgCAIT7hgHV4+w7lIeZSO2mQ2eatMEZP49kslxfpDL4/yBU3Ojof9YAJgpjJ5WPWs0Ac+PCHyiZoQeawurfVfNS/SkY/t4qa2RxTdcx/kVEt0/HOGz8qcxd15P/z/cRn3kVgCaz13ClZNNYUkRuq5jGjNT/nolg1/dj5o0k4i454yr6zr39/+Mx8s+l5qhwBRHrzNNTKcUWrO/mf5uStqYoz6CqdTzU30z1iYFrPplNybdwKxz+X2Zb00uvzfzA8AHtbmq79T9+bMBgGefms0/Pi1GSYJEQkmQTCaRjUYMkoGpom4zN3M6EZZshHg8xu+ff5bqinJG+ns5un8/t19/HcUV5TQ0XaKzsxtNh00bN1JSXIzX5yMUDhMOBnE6s4jFYlitVhRNQJIkTGYjFZVV2O1OGs6dwyibmZyYYGR4mI9+9KPY7XaGBvuRJInnnv8tRtnIli1b+M2zvyEairBocR2f/eJf4s7JwReNsefNPQiCyJVbr6KvfwAdKC4pwWg0MDo6SrzAzbhk4lwihqJpVETjbMh1oxsNhCUdPZ4EbwCH7uTwISOHjyUYG1O49mojV19lYWGNRCQS4cKFC1RUVFBZWYmmahxzPkNiTxHtvY3gM/LRnXeR53bj9Xg4eeIYSjyK3WbDIOqMDQ/R39WFx5Pyi0YQUHUdR76TJTvKCecMoAgawfEtBC0WKksEFvYuJtQdIfKCjmYS8d1hZuATDgq7VRq/1Yoa1qlZLLNtZwmimKSkpIRAMMiF8+fxTIyjSCJ9g31U5eVzZ34SWb1ILC6xu9uKcEucWG4Ph1+XMJauofqa7dgsNhI/eA570E/ZhkKyT2jokhlp4WdYfGsZoiXGaO97jPXupzj3Sir2x7G8E6DxgS8w9pAZq6xhP9/Ks28eYa9lHUXxLm6X9nLrFgMnz/qIdcZxigaevRTkL7/3KACZkr0gzqqE5t30aRJAZhMEgbHsKIdXjbK2MXcKDKbHEC7rm/46/f7D9m2mampav/+BADBforU542XmpNHoj+r8akjiL+qGEHSB7ER+xpxS/aVzx3ml8QyjX7sSKzL9Pxzjm71JXv3+S6wPzeYVeuGxZ7njEx9BllN2mPJXqui7s5vqzU/SefTTGZIA/O3Ib/nL3FsoNLguA0A9A4ynPa1m+s1cr5D2f9o3aU4oKVZbm7LJ6vAhgVzpLfP+zN8/w5bxIfd0Ol1E6j5Og4HInX8ORuD6+jr9mSefnDJsGaaKHqfEbm0qKZNhagHEjDqYgj5VM0AS6WhroautmVyrzKnjx+i+dBFR0yisqqKyupo161JGrOd/8xwlpSWsXLsGo9GIz+NhYHAIn99PWVk5sWicS02NDA0NYrHbWbRwERULaikvL0eUDDQ0NJCbm8vExARVFWX4fF5ami+xdOlShoeHuXjuAlWVVez61GfILSgkrms89eyztLV1ct2117Fy7Vq6u7uprq6mubkZTVFSEo7FwtjYGIWFhTizshgeGcHr9ZKTl0fIbud4IspwPI5FELguy40NjYlECLPNikUXkQNx9u8Z4vCJQiYmBGqrJT71MTOB1a/hPF9HpLaHhBwiogQJnIMay3KWVKzENzHGqVOnMKBTUlyEpGskEnEMksB3X3yKR668gUffe4OVkotoPEEkEsVoMmEymTBlC9g3+VFdQUjk44tsgcow68PVGDpkxp8IIfhlQtcYGP1mMSZdYfjbLURbwGyOc+s9RXT3N1BTW8vQ0BDl5eWMTkzQ1NFCQtfYUlPNCsM4TuEcYxM2jk9aEG6M0a22cP6gSPaya1l0x73IcY2dhkomBhrpObyH7AaFZNEGiq/ajnuBTjzWQU/j0wiiyPLkNtzfb6CncCM9v7wJX0GIQz96nJNvHSN+y18jmqx8LPIYd1+hohlrmbzgIz7uQdz0cbBPBYLNAwDpXOKs59DUc/ohRHskN8rRlWOsP59L8ahtpn/66/9Nm5FMMryR5ipIhLlYMPN+mqOe6kM60M3qxBXge90GPlkziFsScCcKplQwaVGxuo5x72u8Nj7E6Bc3ohxLUNJSxG1n9/PCT97n7rHHZsb/6T//kM994y9Sez0psvCXdXR+9QQmq4ewp4JZ//rU2P8x/jpfyb11zlplAkC6qg2YrSOSYqNniH7mWqc+z/r5p370h+/HH09TZ+07c6FgLlORGWU8fT2C+GfiBpqSAJ5CEJgqip3SxE5HA898ZrqIcqoJ6KBrGCUJg8HAiaMHCAX9OAWF119/Hd3vRRcEYkB2dg7LVqxMReOqOovrFtPb308wFMIoyxiNBrKysujv6ycvNxejSaasrJwJjwd/IIBssnLdddfhcrmmsghGCYVCNJw9RXd3N4lEgp6eHrxeL6Iu8sjnP8+aLVcRTyToHR3hpz//BduuvpZ169bhCQSw2WzYLBa8Xi+BgB/ZKKNpKhaLlUAwiNPlIjc3l8HBQeLxOOWVlRiNqZJ3p06dwpibzago0m6zIhokijSNWiWJOduJaJKRFA23As1nQxw4FKdm1yXO/mwr128zse0KA6/sfpoxrZf199UiWcEWzWZsb4TYaILyslJ0TSURi/DMwbe4ffVmHn37dywtrWJFZS0l9mxMJhOqquLxeJicSFXRCvj9fGnjxzhkeIe3k6eIR7Kx16whN19jZe8KJg/4iR3SEA0yI9/Kw7/aiOHICL0/GkeI6Vy5LY9PfXETSSXJvn3v097RTllxEYJR4lTjRYqzXCyVQix2tpJURM72WBndnI2v7DjnDgnoWatZe98jmIxmtgRcCKMddL/yPM5eCSV/CQVb7qGwTkDVejl76IeEwxFGX4vyQO8i+hfdzMDjV+M1TrD/0V/SfH6I4DVfYIlxiO3ibq5dL+MNVOE53YFudCKu/wjI5rmc+RxJYJbwXmYonGfP6bqOz5Fg7+Zhlrdms7Bn1nPp/3X7Q6qGmX4fMO9pAEi/jqawSLsaYl3RKMWR6ssAAF1H9Ixz8she/NuX4QsJtO2Z4G/7DLz2/SdZG/w8xfGV6LrOf33n3/jyP3x9Zg6LflJP/9+9Q0HtfrpOfJx0AIhpCQaTHhbIBZcBwLxS1RTxnIkuFmbtxZk2lFkJ4I+XltLXKXMt5/Pn/yAJIF1imaF5GZJDKg7gzyAQLJUO+tdT4pvGNKnXdX1GHSTo+gz3P+P/P6X/N8syPp+Hg/vfxW414x/q59D+/ZTluKhfuZLSykqqqlIPpWdykqA/QCAcxuf3s2z5ckZGRkgmExw8eJAVy5YTjUUJBIPs2LGDeDyOUZYZGR7lvff3UVZWRl5ePl6fj0QiRn5eLtnZ2SiKQmFhIadPn6a/d4AHHnyQrIJizpxr4I233qayspLbb7+TWDyOx+slEglx6uRJBvr6ue+TnyQ3z000EmZ8fBJNB4NJRlU0hv5tBZJZp+QrrRQWFhKLxRgfH6OivAxRgGAwyG+fTXG2S+qXYLTZ6EegWxAQFlRRJMvUiSL5JgMRWUrZS7xB/F1BTp5M8O4BO7Iss36VzH332BjoOcqEbRDbUgE9S8F/LsqBV9u5piQVEGSQJBwOB0NBH+d62mgfGaAmv4Q1lbV8PG8bPiGEW7NzcOwsR2MX6ZWG0QWdMrcVsbgfASMB7xUYF8HiUBXOVhfjzwTRJwwkr7XR9/UcbAaB3r9tItIco6TMz+rNbrZdv5ZXnnuOzhPHkQwStopKQkYTuqaxI1+g3DmIpiu83e1C2lbEeN5h9u+WWLT9PgpXradMs7PGZ2RypIGJ595FDpkJ52zgzZYmeiYOsLLOzoaVDtpPxdl2Posy0zY6/uNjRLbJmPv8BLu9/KjdQCyZ5MHkT7llPSRxM3hgEsXgRKrfhuAumfN8ztum1dAzfaa59NTBac7VmxVn36ZhVjXnUN2fNf9Qf5SeeW7/D+I0PxxoUtLOzBwzJZ203zYEBQ5FguysnKQ4tmC6E6SpoAxnj/LihZO4776aEd1AfC88cLGNF3/8RirLqK4TCYY5+t5Brt9xMwD5RwoxxCSs3/5HOo48eNkMPzfwC35e9tmZ9csEgD9GN58i0hnBemkAkfqYuYKz6zDf+/nOM91mi7tPnYh0Lj9ThcSM4Xe6358EAARBMAMHAROptDIv6br+PwRBqAKeA7KBs8AndV1PCIJgAn4NrAEmgXt0Xe/5sHOksoGmisJPB8CknR+4/EGbzrwoCgJmk4lzZ0/S39dNUZadvW++wZJFtWy/8Qbe2r+fsw0NBH1BrrvuOgKBIM0tLVQsWMDatWuZmJzEaDSArtPe1obNakNDT3nSGAzk5eVRUlLK2YYGhoeH2blzJ8lkkurqakpLSjh95jRDQ0MUFhYSDIYYGBhk6ZJlrNu8hazcfJ574QX6Bwa44fqbcDqzUFSV3r4+FtbUYLPZePyJX9Le3ESeyYjNZqN2cQ3r1m9CttrwhyLIVhu9j1cRaDNhXzNB0UaIOgdx5zrIc7t4c/du3nv9NWw2Gy5BxGK2YDWb0YBJfwCvmkQzGCgoKWX9NdcScjlpdzpRTTJFgsASdAKxEOYsBw7RSGHSzMu/7eXwSZFJv50lC7wsrhyiMGeMwtVO/HmT6AVJTF47ahuMn/dTnFeYyp2u61wtL+fR5lc51HeRmtwilskFXFm+hkXuSia0AO0McyRyjouTzaCqLFpkB9coyXAOfmklC3OyKRkpI3QkRuRtnUSdxOh9WcSWSwx/u4VEl4AhNsqtN4pULCnlcMM5ShYuYcPGTfQMjfLG6y9jEEU+XpNLnnoIf8DCiVELyt1GLvQ30XrBTP1dD+CoWsjAvrP0PrOb7avd1I660A1uuqTNHGnbg8k+yM1bLeTmLaK8vZCKl6Oc//oXmPy4GWMwitY6yt4hkSPjOh+PPcZNy0JYbC4GDvlRQnGkTfeCs2jOcz4fsb1s/2W4ZAqCgMeV4P31g9x2oBxzwjD3N5lGzcsp/AdblufZY5nzTe07pv7mN0CnE6XpKYVUOGU9SpVaTFm8fI530/TeNVxq4OUzR7jw3Q0suGBDe0fk/re+w/OvWrl75AkAOprasNqtFJeXArDo5/V0ffkYRlOIsLd0znz3BBu42bFyikmfVhXNXv7c6/vj1iT1IZ32zNVCZK5V+ufZsVK/m5vTaNouQ5p6J1PldrndYloFlnL7lf4kACAANl3XQ4IgGIHDwF8CXwF+p+v6c4Ig/Aw4r+v6TwVBeARYruv65wRBuBe4U9f1ez74DCkAeCbNCDzdPszQIgqAriGJIrLRyHt73yQY9LOstIgnfvVLfEEvV1xxBV1N7YyPj/OlL3+ZoqJiYgmFw0eOEI7HsVjM+P1+fD4PXq+H4qIitl29jXgygdls5sCBA5y/cIF77rmXkZERDhw4gMvlYseOHVRWVmKzWunr76O5uRmbzU53dzeXGpv5zGc+w5LVa3G43Dz/0kt0dnbyyOf/ArPZTCQSIZFIMDIyQk5ONq2XLrD31d+TazEj6ArhkB/JbMeelUVFXR3s3oWa7aHui34cspvJ3jjhUyWMnxUwyAJKcRunen+KZItilyUEVUONx1FFkbUbN5G7ZDHZubnIdieiJJFQUwFCqq6CKOIzmjiuKoypCoQjbLXbyMt2EFSTRDxe6B/n0sk+GttW0j1ox2mPc+OWYRYu8OMolBjK60GuEEn6VcytTnwXQkSCESKRCJULKnnr+BH6Q14Ug8Dq8ho2ly3jquylWBUjA4FRhspivDW8n8HgMLl2B+a8QTBG0JQVxHLz2eCvxzhkYvz5AIkBncByjYG/y8PWGmLoJR/JnhhL14l87HM3ILucM9G/k5PjvPr754kGvGytKmGpdgRNU2jqt9GxykWX9Rhnj+iUbLqT6mtuxt/cT9N//oZrl1qp82QhaU5GHDdSe1c1erKXcN+LyFIelc0FFL0F57/9EL4dMqaJMP62Sd7oF2j3JfiS9DO21OsoWh6D+3rQjVlIG3eB2f6Bz/+cPZgGAOmcqSAINFV5aaye5IajpThD8tSPM/XW/FEAMJNQbYboXE4gpzn+zJYOBCkCJs51wUwzuu4LxLDltrM+sWyOEXraDiGODfPu/j2Yyopou6GcwBMh/qo9xss/eIq7x1Ig0N7UhmySKV9QAcDinyyh/+/fJL/6KN2nds1Zxy8P/5p/KNyJQ7RmuFP+cZz55SCtz7EP/CEJ4DJJQ9A/GGC1WWKePpeUJDIXAOaacQSma1bctfNPqAISBMFKCgA+D+wGCnVdVwRB2AR8R9f1GwVBeHvq/TFBEAzACJCnf8iJpr2AMrn8D2vilIFNEgSMRgP797+NZ3KShTlZvPXG60QmJxAQMCJRt2wZN+64g57+QZpbWvEFg2y/7Tas1lSaiaQSZ2xsnO6ubtrb2qlcUMGOHTtSidNUlUQizoEDB4hGo+Tk5EylDNZxu7MJhUPouk5xcQnnzp2jrbWDLVdcyWce/hwJReHw8eOcPn2GW7ffTlFREaFwGE3X8Pm8RCNRqqoqsWkqxw4fZv+e3RhEAauQRJZlvCEVXyKG4HSQ1HVyC0spKytj7dq1uLOyCAaDnDpxjMMHD+KSDJRb6lie8xmiPQUggKPeg239GPZCCW/Ix6THg2iQsNptaHqq3J2SUBB0kESRaDRMdnY2Fkmkvb+PhmgEecsWNIOBrNFR1risSA4HksWMv7WNocZ+WppKudhVhShAbUkjLvkoOSUSC++sQKxWEfwSyQ6BrncHqK1aiMPhJBKNcrjxLE3DvSRQWZJbQbUxh7tqtuKyOunyDfDjC88QqdQw2UxUFmWRdHYQCdmIs5yiCjeLWhYRbIwT3J2EJTZGP+4kutrIyN+3EWtPUFoq8fGvXEF2oXPKhqQxPDzE8eOHaGy8wBZjgC0L4sjGBO93OOkrU5moaOXc2RwWbL2Fsg1XMvjiCbYvLiXScIjcRo1E2VVYaq7EUuslNPA8SnSI3NhSVv40wtCKW2j90UowKph7Jvl9S4ITExqbEgf58qoL6EI2g+/3oxuzMGzaBWbHzLOcTiwu+25278353FTlpbHGw82HyrFH0tInC7MqjHQf+fTPc8aFVAqWec6ZSQznG2eKhM3Vk8zhdFNgc1i+gD8hcwuLL7NBCIIAfi+H332VlvvqUQWZ6FNxvvr2f/D8a1Y+OvokAK/95nfcsOMWZLOMo9tJ3skCDD/6Bu2HH0w/M7qg84TnAJ92X8UMsZ4HBD7MeyfdnpBux5kGgPlA4zIJ4IOCwmbUgGSMdbmtIm2V0o5PWUMFkTt3/gkKwgiCIAFngBrgx8C/Acd1Xa+ZOl4G7NF1fakgCI3ATbquD0wd6wQ26Lo+kTHmw8DDAEWFhWt2v/b61EZNFUfQhdTSp0KxdYTpPHozNoFUxLBBFIlGApw6cRg1FiHmm+DcwQOo8Rh3f/TjhEJhDh07Tt/gADfeegcb1q/HYLHws5/9jJKSEoxGI8GQ8RCIRwAAIABJREFUn1UrV+N0OPF4PfT0dlNQUEA8Hqe0pJixsTESicRMhk5d10kmkwSDQdzZOQSDQTo7O9m8eTPhcJgz5y6yftNmbr9jB21t7Rw5eoz6JavJL1hIS1OEG27OTyWD0zUGBgaw2qy43W7OHT7McHcXlQV2SivzkEU7QezospG4qtHU0kptbQ2DgwMgpUoL1i9axODAAJNjY+Tl5ZGIxVB1kM0W7FlOdCkVTKd4LARPugm32VECEqZqH9Y6P/bFUUxmAavVyqlTJ2hvb2fXvfdSWlzExTOneO6Zp8nPzWHTxvUcOXIYVdMoX7+RkZpafHYHJgQWhrwUOB1YCvKJ+/xE+4bpOS/z2jtZ6KKVisIAq2obWbjSgFCukKwJkQwpxNpUCv0LGOgYoKOjg5tuvpFXD+7lQl9HKse/buK+xdexvnI1AC+ce5lGtYtglYzdZsFiC2Eq9KMllxF35rBKWYzxhI3IMRV1TGL0c06818nE3x1j7OlxinIF1l9fxjvHXuLcuQYkyYDZbKKkJBeTychmG9yy2IMgaBxsc9G+1Utz/wS+aCkbHvwqToubMo+Mfv49rG9fwpA0oNbcRdbmanSxE3/vi5iEHGpOO7E31nD+J3cQXSZh65hk34UQbw/DKvUM31h6BNHgZuJiiKgPDJt2oZum8vd/iC55rq56VjXQWOuheYGXmw+VY4tM5biZ6f8B9RBmzzKtcLhs32eqHzLHSAeB9F+nNDDp+XGY8ZVvtDVwuG0Zny+WMn8BgHT+FCc7m2ncWU3vviSuCRuPHPtX2n6+iyXBHei6zk/+5w/4wt/9FYIgUP3kQvofOUH5qpdoP/wgOqBN0Y7P9v+CX5Q9POPnn+lZMz05fXoNhPmUO5lrM531c5ogp0lDGcbneUeac2y6rvM8qjVNTwMAIeOepQBXEER2/oklABfwCvBt4IkMAHhT1/VlgiBcAm7MAID1uq5PftC400ZgAFFPXaw2lQlvxulTF6afrJQ+UtcQ0JGNRnyeEV555WXqKktpa75Ex6nTAHzigYdZu3YdGpBUNfbuP8TQ8BAOpxOzyURNTTWCAOfON1BYWIgsy/j9fs6cOUO2201paWkq8liSaGhowGIxU1VVhcPhIBaLpTyBwqkc/3l5eRgMBjweDwlVJBaL8cDDDyGKEucuNvHGa5189Rt30NMZ58jBMYYGVEAnO0ejsiZJQaGRyqpCrFYzdmsSBJWB3j7cLgslxVZMpjjxuEI4amB4BMKRlJjo93npHxggJy+PkuJiEvE4RpMFg8GA0WrBbLEgG03IciqfjKqqqOiIU2K7KAjEJwR8bSIdv9MJDihk1cWpvlqmaI3Cd/7uywT8fmwmI9deey1Wq5XW1laiiThOp5Oevj5KSkpStX116DFbEeuXoJaUkB+NsMJiwmC3Ek0maD1wlMigQm/flbT1mDAbg3x6p0BF1QiJXA9jBf2YzCZy4vmUj5dz8fA59r72e2KJGEGrhUE5ZYm8b81WrindTLZmB4tMe2mQA8MnuThwhrxsOybXOKJJIZBcSo27iqKuUnyvx1HaJCI1Ai1fM+M7PYT2vobaF2BCPUpEGEMTQTIYMJlMFBY4yLIYub/OTZHciqII7B3JJnBVjJOt3cgLbqH6qhtwG5xUd4cJNO3FtX+EpLscQ/kt2FcbiIy+hBLuI8+3gMXPubjwnQfx32LCNBziYqOHNwegONHK39fuwemQGT4PoQkdacm1kFf1B7nV+dr5RZO0Vfi55VA5tqiBy8lzBhcMCH8gFcQ0N/pBc8gMnkon+DNfMAte6Dpn3Pt55/xW/rYswxNmaj5iexMvHn6H2K61tB9RuX9gLY6xB/F/64tURrew58XXALj57jswRGRqnqom/vi36Dr1CRBmI3cnlAAm0YhLtM0BgNlrneXwP/D6dH3GYDuropn+fRoIZLifzjfO3PZhAJABVOn9mHWz/cjOG/+0XkCCIPwPIAL8Df+vVEAZAKALs6g8K3gJadz/VApWXcNsNuGZGOTV373Mmvpajh46xEBTM3aHg/se/BxxTSccj7NkyTJOnj6DIAgUFBQgCeBw2BkYGODo0aOsXLkyxeHrCuPj4wT8frLdbsLhMIFgkEgkkkpHYbXOZGKMx5MIgojP56OgIJ9oNEosHseelc3pU6dYs349N2/fjj8Y4cWXXqKjeRVFxQZC3/sqTrWA+4Z+CcgEfCq9XVGOHhrCM6Fgt+usWCWyarUDg8nOxMQYqqpSUlJMNBYmHpsk2xkh223AbjMRDAlEY2Z0sglHFVRSBas9wQDRSBRNE5BlGavVgtVqxe50YDQaU5JMIgG6jsloIBoO88NHH2XdmvWsq9vC2CEHrW9GUZOgZXdSuGUIT6KPkw2Huf7GG1m5ahWdXV28+eab9Pb2AgLVC2pYt24dnZ2d+MJhVm7fTqisnGZBwAWUKXFyTUZkp4PIxCRab4T333NzqdWEJCrsuDbExnUSYskgbbZmNF1B6Ugw8UofHafb0VH59COP8OvTx2kd7sdmd/DFWz/Namsl2X6dgE2nzTzJC12/wxMapajYCLZh0LOJO2uooZKsgwWMvhBEjwsMfcbKyDaRxJ5JlL0J4tFOQqZeBJuK0WTiL/76r+ltu8iF0we5vsjFcksnsuzjeGc2Q/UmznMer7CexTfeRba7iJreJNG2Azjf6UKz5ED5bTjXO4mMPY8aGSZvopSal/O58O/3E9xqxNrj49B5L3uHRdbox/ja0lNIkon+w0GS5nKERVeBzf0HASCTiHSWBWis8XDl2UKy/fJMvxkCpU+nGJgvyjmj6WnEmwxCNmNwnR5/9jVTgpiRYKa+7re2s6+/lGvsVkpN6dcxNafJcTzH3ud4hYOLONCbBb585od0/HgXS0J3EYtE2ffGO9x8907QBRb/cgHa01+m58wuElHn1PQEnvEe4pPuq/4gAIiiOK/aK/0vU602py8ZwPcH29yYpkzj/pxlTlc5IaLrqVKSH9n531wRTBCEPCCp67pPEAQL8A7wfeBTwMtpRuALuq7/RBCELwDL0ozAd+m6/tEPO0d9Xb3+7FNPIwipJZkKXJ+j8UoZv6cMR1OxAYKuY5KNNJw5jn9sGIdF5uj77zE2OEBN3VIqaxZhcjgQJQnJKFNXV4/ZbOa99/bitNqoqKigvbWZi42N5BcUoCoKqqIQDgYJ+P1UVVVRW1uDx+PF5/Oi6RoOhwPJaCQUClGQX4jT6aS9o4Pi4mKGh4eJxuIoqpaKns1ysWrNWiqrqghGojz2+OPklmXzkdt28eOaW1FRKPdsJKdpLY5IIVV5dTidToxGA4l4jOGhIVRVxW7PZnRI4sDeASJhI5qq4swKEQtdYGjwKM4sM5s2b2HD1msxykaMUgSrJU5edqrmbVKVCcVziSk2YnGFUDiE3+8nEomgJBLouk5RXj6xaJQXX3yeDWvXcu3VVxH0+/nNM0/TfqmRpVVV2LPdxJIJPIMKm0rvJ9SRQ2BER8qfwLlwgvdbf8W4f5C1a9bR09uHQZZxT0lSxSXF7HlzDyaTia1bt5I0Gpk0m+nILyBolJF9PhbEwyxcWo+maTS/d5hju2MMebYgGUxcsdSD5nuc4qUmyj9ZQ8QQQxmVWGG+BptQjKaD1+/ljf1vcLrxDGaTmUeu/QSbhRKMSZ0Jl8rPu1+mcfQcSSVGflkEW46GqtdTVLSIBd21TL4SIX4BwuXQ/+1s/BcmSLwQwqQluenh5ZQsLAAgnohy7sQ++rpa2OCycGVOM5IY53xvNuc3hWkZHSDqvJpld3wCq26kstOH8toLOAdEEnkLcW6+E820j+jECeSkjXWPuxmvu522ny7F4I8S7Pfw4waFGq2R+wv2U1FgYOBIiISpHHHxVrC5L99ETKtlLq850FYZoLXSz/aDZYhayqg5a5Cd5b/n0gJh6l/KkJpJ1OajGx9ESzKjm6fnOt3OuN/HNLqeJq+djxbMo/oKh9j3xm+IVuVzwZLDzReX0//QV1mm309F9Ap+9N1/Zfs9O6lcWE35myWES8LkPPKvtB99YOb6/FqEbw49x49L759XZ595fZlgN/06LQlkGulhPlnrw9oH2xJmBmMewAV0PZWzShAEPvKR/34AWA48RSqBtgi8oOv6dwVBWMCsG2gD8Ald1+NTbqNPA6sAD3CvrutdH3aO+ro6/Zknn0acsiVdZqzKeID0qfROBklAQufVV15h3ZLFtLQ08/7rv6eyooIdu3ZRVb2ACW+AxsZGookkW7ZswW63oyoKbS2t9PX2EQmFZpBf01RURcFsTFXk8noncbvdM0FPZrMZgFgiQSKRRJZNOJxO/H4/gUAAQRCQzWZGR0cpr6gkOzuH/qFBzGYz19x0C7FYjEtNzRxw/4Ja43quzLobk8mEQZZJqiqaIcGg1sIJ7SUCuV3EbJM4tAJKk0uRTi+gRl6LWbTS29aMRYRzRw/S2d6G0+okkrCgGFZicqzCaLTgchspLouQU5QkO9dONBrG7XZjsZnIz7GQmxXGYowhChqxhIVo0saxY63s33+Ye++9h/z8PKLBAF//669x06bNbNtyBacvXSCZTKLqAt3d3SyqW4woCuTnp7JdvvDCC1xzxS1UONfQ+7aL2KgNQ56XSHYTxRtiPPfyE5SUlLBjxw4mJiaoqanhxRdeJicnh40bN9LT20t5ZSWKxcwlo0yjaMCYTOIcG6Yyx4XVlYUxnqDvpJ/fPm8knsyjfpGZu+8RyK0IM5l3lqjswZR0UOhfgS1WgCjoBEJ+Xtn7GgdPHkZUdNZZqvnixjuxGs08eXEPB8QzSG4wiApFCyPIZhnFvow1gU2o78pETmsoDiM9X3cSzdWZ/I9epL4w9Zvz2XjHMkSjiKokaTp/guZzx1jqMHFTQTeCkKB12M6ppQmO9nmoufVz5NQsptRvRD7yFtbj3chhM8KyT2KpsxP3P4caHqZwoID84+u49Nh2EqUiYuMYPz4Rxa108XDe29QW6gydiBL1i0ibPzYPIKSrJ+ZmMW2t9NNeHmD7oVLEVIaDy8o/pu39D/B5md/wmXlsrnHzg7ni6f5ns/ch9F3D7nGBv6/SLjtOPMaxV5/h8EM1KI+J3BldRMtXvsFdk08B8O/f+C5f+5dvATp1P64n8dQ3GWm/mpivbGaM6TxCHzb/uXOf/3qnAWAuZz5X8rl8HdLHvnxNPpTmzTkmMi097Lz7zyIQrE5/9smnEYTLdV56JqxOiZy6rqb03LrGL3/+M7auW8PZhrP0Nl3g4c9/nrIF1UxMevD4/MTjcVRdp7m1hcKiIpbU1XHi+AkS8QRrVq/B6XTS0d7O2XPnyMnJIREOMz4+jqqqBAMBfD4PggCyLCPLJnLz8sjJzcXpdFJUXDyjX08kEjOunoFQAEVRSagqSUWlpHIBa9auQxBFDh85Qlt7B/X19axevRokCVGSGBwcZHBwkMrKBeTk5KAoCrqm4Q2M0q9eYqDyCMPmSySsPuzhHNRLJoKvgmnAhh4TCQTDjGjwqc/cz8o1azh+/CLBQB7xsJOujhAGA7hzRUqqohQUibjcNooKCzEZjSRiAXrajrFqRQG5eVmoqoHRcZ2v/tU/InjC7Nq1i7LaBQRDIU6faSAWj5Obl8fGjRtSEksySXt7O4KgEwqH8Hg85ObmMjQ4SE/TGCuyP4rYvYRkBMLmTkquGUd0+Th5/giLFy+mpKSEd955h8KiIsrLyzFIEgZNwyCKdHV2ciYcxXr7Dkw5uRTFo5SKOtkFBViMRgJdcPKgyr73FQoLBLZeIbFuWwhvwVmiJi8WxUlZZC1P/vuLvP76q8hWGTnXimAWsQlmdhRv5pZF67HJZn7Q9DIdpj6ErCQ2m0JhTQIdN5acJSw9t47A4TixCwL+6yyM3m3Fe3aC8AsTOE0a2x9ZQ3apC4ChgS6O79uNXYvzsZIgDvMI53uzaS4VOJrswr36o5Stu5L8uJmsE4cwHWrC4jWg1d+LbUke8dBvUUNDFPXlk31xG02PXwNGhUj7JD88HWO1cJoHKo7hspgYOORDkfMR1+1EN5jm7J9MAAAQdJ3314/gdcS5bX8pkjKri06nB6KYHnuvp3mu/H8DgA+jOQ3ZB6gK1vPzjgIeKVaxGzLmLgj0/f4Z3t+ax7jkYMVbxcTv/wdWqY9QFlvPv3/ju3zh21/BYrVS95N6Br/5NrGYCyU263X1UP8v+EXpQx86l0yD+7zXO2WInVanzQSh/V8AwGXqn6nXzKbr+gwACALsvPtPbAP472j1dXX6s0/9Os26nm5AypAGpu0AQqowind4iFPHjmKXDVw610BP8yUwSNTVL2HRsuUMjYzgCwYJhkLceOt2ysvLCQUC9PX14XRmcfTIUUyyzIqVq6muqcXr8RDw+XDY7bjdbgyCgMNux2KxkEwm0XSdcCTC8NAAExMTNLW00t/Xh3dyEk1Tqampoa5uMWZLylVU01Q0yUBMURj3eFlcV8+yZctJJhUOHTnCiRMnqVm8mIKCAlasWIHTmUU0GkMQBBRFYXhgAJMsk5+fR1PTJQR0SoqLybOaeP/99zn27h7MJhO6QUGrirH4q+X0ZZ0jIUTJipVS670e01ARBdoCyosrAVAUJWXH+LxEz6e8NJz3cbHXg6bKVC3IYsPmLFyuOK+/9DjDl5opdWaRt7CYquUlrFm/kLz8LEJhlf4eH6PDcZyOXJKKwvDwMC2tTYyMjKQK5JhMlBQXU129gO7ubvr7+sjLzaW0tBSXy4XZYsEkZtO1TyTYmos1UYKjIoFj0TidiXcYGGmj41IjVlHAZDJxxabNLFy2gpPNbTgLSli5Zi1ibgEnE1F61CSCCKvNZpyyhCCCGBQ49KrK4YMqiqIg6K8Tiu/j+i9WULO5kLA3zuHHWmk7Mo6eFDBaTQhZGrpBp8Ccw6cW3siti9fjF5O8opzm8Mh76KjkFEWR7QmSxhLq1M2Y3sohdFJBMRjp+7qTYI6K59EB7N4oq67739y9d3Rk13Xm+7uxbmWgqlDIOXfOgd1sdjOJUaRIiqQomaYlj+2xJNvjINleHo9lW2PPaGmex55nSZY8EqlAimISUzN3s9mRnRPQaOQMVBVQOd/w/igAjW42Zb/11vtjeNbqVUDjonDvrXPP3ufb3/6+Btbe0gFYDPad4/zpozQYSe6unkJRU/SNl7OvM86FSILqG3+d2jVbqEiKON5+Gf/7s+iaA2n9b6DWniYb3oc9X8aql1dw4e+fILNRxroQ5jtHU+T1LN+q+xFVXpG5SwUSUTfilgcRVPs1DM3FhfxKuXL/lhni7gJ3v1+HrIvLnrFlYtRLi6Dw7woCyx7YZQve9cf1suuzgQ9YN7eLd6MidhF2lF1DEU/EmN73Ku9sLGP0QyfbsnUkvvo3PDD/JGDxj3/1LX7vr/4EgPrX6rH97V8zePiJpXP6rYl/4R9qnsAuXKmPwKJwnMVVvQ0fc60fxfrNq35+/QCwSGYp3ZMrdRJYZCRxVYy53k7gCnz04EOfBC2g7hXWz54sbeMsFk0tSvzkK/fHvKrerigyIhaHDxxAtXTGR0a4dOoE8UiYDdu2sXXrVi4PDtHa0cHo1BS6YaC5XXz44XE2btpMeXk5c3NzuJxubDY7oqxSW1eH3+djbGyMsbFRIuEwqWQSQSj5DTc2NpYWEiCfy6HIMjXV1dRUVmFTVZ6Rj+LZn+DvbjnJxt8ex1fmoampkfLyMlwuF95AkHw+z+T0NNt37qSxpY2iaXL0+HFOnz6DacGdd91Fc0sLliVw8eJFaqur8Xq9xONRzp45Q2tbGw67ndmJcd564w2kfIFHH30Ut9vNxct9lAUCZAoFREmirXMFqs2GZVlk0mkuh88S1yZIrLzApOsMD/7mf+e1r3+bjd98An+umvpEC4IiMNcc4kT0A94deYNdO29CUBo53mNgWF6cdpmaqhSrVspU+BXK/W5eeukFKirK2bC5le6VQZwuG4U8jI8lmZnKEp2PMzU1g6aqKIpCJpOhoqKCdDZHLBbF5/Njs9mYj0aZmZkhWBFgYnycFa1rkcfqmTzpQEo34akqIjdNc3zuWW568HbWrl1LxrgiD7I8UzMEgXGjwEmrSNw08eayBNJx/BV+YrNzvP2zPkb760knqxCs03h9Z9n2mMDKO4IYRYuLr80xdCDJb/3B15mdneDNd39BaG6aDQ0r+fKa+6kxNaaEJD9Jv0Pv/DlsNomymgiiVobiqmNdz83MPZfHDAnEbnUw8aDK3HcnsMaybLu1ko13dAEwNTHM4X17qRbyPFgzg6KkGJ/18HptirN5O8177qeqdTVNA2FsP3sJx7xEoXE7ri2NFDLvYSYnWfV6I8mWhxj+dhfiVIqTPXO8N5LjmzU/otlfJNJrkIh6kbY8CIq2dL8Wx/Kv3902TcpZ5O79tcimeP0awcfAE9ftM/gV2e71suvF4AQlOud5/0HWze/i1YjI5YzAHzUuk08QgFyWN5/9V+Y31TLYW85fvPdznv95mofCT/LOS3tJJpI88GufpfP73Ux+6ThGeYpMrHppYX09cYo73euvnMPCP1G8el593EK8nDhq/hssoI8blmWV3sm6fjBdzjwq1SBK91MUxU9OAHj6qR8tTAB5GTXtSraBsIwSuhAAZExeefmXNAXKOXfmNOePHcNut/P5X/91XC4X5y5eRLLZyOTz1Dc1Mz49hddbhsvtIRKZL8k3pErsnqraOsLhMIcPHUYURRoa6tm0aTPFQgHDMHA6ncTj8VItwG5nJjSLpqpkMxkmhkdQFZl/3nOe32Qnf+x6AW/BhjqQwr0/gncwg29eYl37KurqG1AdDiZDYTSniy07duEPBinoBmfPneXd9/ZTX19PZWUlGzduxOl0UiwWGR0dRVEUPB43sihy9tQp5ufm2HHDDbi9XsLhMIODg2zbtg2P10s0GmV0omRO4/V4Sn0Mmkoul2N8fJypyUnGRke59c1bmf7rAdydFhc8exmUjqNHwD/SSMcv7mDFmR148ZEW0nxovU8yGOGu372XcAr6Luc5cSZJKpVHlrJs3GDD5QxRX+entbWZfe+9hebIs3JNPW3tNaSTWUKzSd556zSzM3ECgSC1dXWcPHmS3t5eBFFkw/p1dHZ2YhoGjQ0NOLMZfvb004RCIX77y19hKiZy6d0C9YV7EHI27HUG9fcUcTYayO4rJvCWZWFaJnZ7yfrxRz/8Ic+//x51N+6gcvcu3KKAP52ms64KPV+g/9QI+39pZz5chygmqaw5wJZHUqy7uwYsEVuoDXWuAUuXGR69xN79z5FIxblrxW6eaN2Dx5Q5wwQ/mX+ZuUwIp9vEUxUDpZxaaTuBN9qIv1vEcEiM/5GbmXiC3GsxAlaRO35rHZ6Ai3QqwZH9b5AJTfBYbZaAa4pU0sGLpsxhW4quB7+Cu6KahskMjp8+i2sScnWr8WxeSSH/HmZqklVvtjG/+lHGv9GINZzg3fMxhmYifKvtaZyCROhskpxzFUL3bhCvR0MsFYkPrQ9hCrD1fABb4ZpM9zqSyNcd10BA14WLlr3Ptfj64ugpP0J3bAsiIn87LPP1JgNVXHbehoHx3uu8mg1xXm6jOeHF8bv/gwfnnqSQz/PaMy/xmccfxnfWj3HjadL5AHq+1Jk9U4ziEG24RTssC2CLdcHrsYKuvC5bn2Cpd+n/9bCWcvpffdgnNwB0L+wASobc4lIH3WKELd0hmcWIbKCqKpJo8ssXXiRgVzl74jijAwO4y8vZedMuOjs78Xg8PPfCC9TW1ZEp6NTVNzA0No4/UMGG9Rvp7e1l9dr1zEejHDtxHFVV2bx5K8FgidKZTCbIZrPouk5tbQ35fJ5EIkGwqoq+vj5aWlqIRCL4yspw2O0MD/TzZyte44/3b6amuoqO1mamhgc5eOAAfRfOk7by5GSduTaJ+M1BUo0qGRdoOZH6fBl3OTeyXezAm9GIhaLse/99hkdHCAQCbNmyhebm5pIEs2rj2NGjqJpGrpAnGAjQ3NyMpRtEo1EqK0uSvIZpEYvFMIySGf3FnoslemY8zsb1G1i1ahU1VUFEUSA2P89rr75KQ201yViMdCJOe1sbl3p6mJuL0LK5mvyqCMPe06SrZ1DnvbhP1tP180+xcnInHsVLXI0yWtmH3G0yIyY4czGDrK4hlxcwjRSq3I8kTBOsENi5YyvRaIRocoL7HtiJy10yPo9GDBIxk1gky3wowhvP/pxcLseqFSvYcNPN9E9Ok9JN7rzvARwuD/nilW23noG5kwpzJ2USl2Xs1QZN95kIVSm+/Z1v8uGHR7GpNkQkWJQXF2QUpwtHbQ21D9yJp7kBT6FAlyZT7nVDzk7vhyZ7nzNwaLBha5GN90xAYx8IJupcA2qoBTMvMzh6iTf3v0gul+Wza27n4bptyJbJu0YvL4VfxxByOL1ZnIE8bm0zzcNrSD0rU5wTSWyzMf5ZhbnvTSJNZtnzcAsdWxrI5TKcPLIPffwSD9dFsGtxJma9POudp1/QWPno71NuDxAcnaH8h68hGhpW3Y3Y12TIze/DmXBR1/MZ+v/nbqx0gZMXwnwwEOdbLU9R6TKYOZElbVuF0LENS3F8ZAkSgEMbZjFF2HUieNXPPg7/v7ps91G2y7W/f+0ie3VZ9crr+fIjrIpuRRQEvjGk8HiNQYv96nO49OyT9Fg5LnhW8LW3X+fVp6Z5MPQjvvd3/8juu2+ja+1K2n7chvW//oKBo08s/e3fnPgOP6j7j8sWeijZP37krK/56toC5b+XAnr1EKyrl/+PvbfL2EGCIPDQZz8JfgDdK6ynf/zTpQ2UuEgBXcT/F1Q/JUofyqL+//jYGBdPn8Shyrz5yksU8nnqmlq4++67SSSTNDY24g9WMTc/z8+e/QWrV6/htttup1AsMDg0jCwreMrKOfLhMVasWoXf58PldBKNRikUCgBomlbqBUgkaWhoIJlMYlhg6DouLyD4AAAgAElEQVR2TSMcDiNJEj5fOV6vh1QiQWg2RFHPMzY2ypqVK6itrWXg/EWKqRSVmsbBgwfp6emhUChgmDoOpxO3x4Pd6yZbKNC9fj1aZTnpWpXZLoGT5jCXpBkKgoG9IFE1phA8q+OYMfHmNFRRQVVtmKbJ/Hx0idWh6zoulwuHw0FrUzM33XRTyVhHlink88zPz3P50iXSqSQOzYZRLJKMx1AkCVVR0At5YrEoslzynJ2enWblypV4PR4mJydK2v2zs8xHE3R0dLCv5w18twmwMk7IM4hntpK6n9xI3em1dETXlxLHqiLpNWF+fu55WtZson/ITihSCmoVFTpB3wQOR5rGunIykWly+Vm61rWyamMnuQJc6h0jkvbSvXob2ZyBaVofgR4EQcC0TARRQJYVjh8/wT//w/fwpztplrbSJG0mbc0zapzkgvkWOSWGYtNQVBVJUZEkCUmSEESRL/3FnxEWTAYUHZcs0azIVNkVLB3CoyYv/EBnbhZaO4vc9rlZtBWXMJU8aiKIY7YDM6dyub+Xvftfwm9zck/nLu6uWMekGeHdwlkOzh1GtRdxlifRPB7aUrfhOFpD7N0imW6V0P12JkdimG+G2X1/My1rayiYOQ699wpCdJrP1cRwOeaIRD382J5kpnUjTTtuo1xwEnx1L/5jYYouP7YtezCM5xAKRToPbGHwm1+i0CwxcjzMi6cjPFR2gPvrLzN7Ok866YYtj4DqYLluT14xOLY2gmgK3HAiAMAiIr04ruVsLH4e8PGw0/LjxI8EgWuOF+BM+UHWx3YC8L1xibsCBnXa1fDU2Is/4YVby0ge9PKHB5+h/5+2sy7xBb7959/kD7/5F7Q/3cb8XefJ1CXJJkpS0v8j/DJf9d29lDAhgCSJyLJcmg/CQlCwrtcHcC3Udf3ru3YsBb9lAeDfuk/Wsh3Yw4/c/ckIAD996sdL0OJy0/fFsSj+BiBJEnZJ4tTJE8yODCEJUBUoQxRF7C4PhmkSjkQwTYv6hkYGhoaQbRqiKOPz+wkEg0SjUXy+ALF4nEMfHuOxxx4jm04Ti8VwOBzYbDYEQSCfz1MoFMhkMrjdbgRBIJVKYVvA1mOxGG1tbWSz2VJh2emgvr4eu8NOPBbjyJEjVFVX0drUxOT4GF63i/rqGuZmZxkfHmb/W28zOjhYgplcTlwuF77KIIIkkaMkjl1ZV8uNu26kPFDSIcqJJpNijMvCDPukXs5Jk+Qo4LRsdKWDbE020Kz7WVHWjFdzLE1Q0zBIp1L09PQyPDhMMpmisa6eoN/HfCSCXbPR13cJX3k5gUCA2elpLMukqOuYpkFtXR2JRJJQaJb29vYlaQxBFEuw2Owsl3p7qauvZ/XKlTgdDp569gfEpXl2/eEKTjneoejK4J9oYOUvPk3nwDZco0EyVppiZZqR6ksMMo1pr2Zyykc6LSMY81jZyzxyTwfTxTnOnTvB537nN1i30o8ilWis6bxGMmMnlbGjmwvYLRayIiMIEs899xzf+973cDgcV+HXKg5ale2slz+DmyBpMUKf/T3mbaPklQSiJPGVr/9ZqfFwwUhcB+Zkk8s2k5xs0ayotNgVRFEgMW3wyx+ajFy2qG8sctfjs7jaxzE98yiJII7ZNvS0xOkLJ9l/5C0cksp/2vYYm50NzJhR/ndyLwPJy9g0k7KaGI6yZprHt6P/3Ed+Cma+6GKuzWL+qWmqBYONd7YRaCzj4pnDzAxc4B5vgmb/BPmcjeeSAscagrTc/GnKy6qpevsA1W+OUHQ6kLfcjim8iFgo0npsF0P/7QsUKwQuHJ7lWN8k/yH4NuvLI0weSZO3tSCsvQMkeWlJK4oGb+yZxp6TuOVg1XUsFSnVOq9h0izvpr0evFJ6zpdLLVw9lh9/snw/a+M7kU2Zy2l4elbiL5v1q47PvPVLXp8YonfdJn73+ffY//0hHgw9xbf//G/547/7S+xTdhzNF4nqlRhFJ4Zp8jtT3+Wv7Q+TjCYQRRGn04nL5cZuLwVDBHNhd3Al1H2EDroUrK5XD/mYa7omcv6qddm0rtznRx6+55MRAH785FMgXJkAHz2vK1t9WZbRE3H27duH3TJJJGIk41HWbdiAx+vlwsUeQGDLtm2YlsnY5CRFXWflytUIgkBPbx+iKLFp0yZmQyFOXzjP+rXrSs5jC16+ixNUFEVsNhuKJFMoFIjMRejv78dut5NZEIdra2sr2Una7WTTKQqFAoqi4Pf7sSyLwaEhLMtiRXcXIyPD+Hw+ampqMBfYODabDVEUCQ0M8s4be7l84Ty5bI7KqiCBYBCHy0V4fo6hkREQwO1xozocKKqKt7ycYGUlgcogNpsNSZRIppLEEwmSySTzZoqEs8hgbYr+xgwZj4WhCvjSKpURlfZBjbaUjyqHDz1bQBSEhcK3QDQaRVEUKisrmY9GsSwLj8eLz+cjlUwyNjrGpb5empqaaGlrYWh4iObmZqqqqnj2mWewa3ZuumkP0WiUvkuX6OzsJLBAn+3p6WFgYICJiQk+dd8tVK8OEG69SI/3EHkjjed8HV0/vY3Oc7vRDCdiOVwsO8WUK0xgy3b6hyE8U8ACWjodNLbYaWq143CBTTEocyYod+XQizqXB6f51j88z8VLM6TSuWUMDwFFVlFsGrKsINtsqGrps1DRqNS7uan819DDEpLHwLUrjdJSRPHpH4VKBIucCMOKyZRkoagCa+w2XIqElbc4+obOvpctFNninsfDNNzcg2VPI2U8aNOd6AmFU2ePc/DD9wjY3PzJlsdo0fz0G9P8KPoy0WIIb2USzSvSkN+D69lOspcN0k0qo1/SiJ9NYO6b5ZZHVtK4qprBvnOMnXibHe48K4JjhKNu/rctx2xdK933fp7ySIqydw8RPBYm17kNW6OBkT1AfU8Tha4vMvO79Uyfneflc3M84XmVG/wTTH+YISM1Ya25A8TSc2LIFntvnsaVlrn5UOUSHXKxs3ZxXJvVXq/Tdjnr6HrSDNdbq075DrBhfhcABRP+26jMV+p1ypfRRwv7X+d5M8SAt5Xfef0lpv9+M5V9N/PM937I73/jP9P9/Tasn/wBZ978LaZnZ5mdneX92Bk6c9VUV9VQV1dHVVVVqS4pGiWRSPNKw92iXWPpJJdDQ8vpPMtKIksXJCwFkiWq7TXXuFggXr4nWu4i8PBnPyE7gKd+9ORV+z7xI0dduWybLDM1Psrw5csopsnxY4eZmhhn9549VNfUMDg4TEtrKzt23UhR18kbBu+99x6KYiMWiyFIEm1t7bhdbmRV4eTp02zZuAlFUUilMsiyjCiWtn7FYpFMJsPkxARTU5OkF7poI5EITpeLHTt2UlNTg93uQO0bJtlUha7rS+5dkiQRrK3hF88+y8aNG+no6KB/cJDa2lrcHg/ZbHZpAi1CD6IoYpcV4pEIw309hKdnSMxFmAuHiMeiBIMVyIrC8PAwfr8fWZZQVJn6+npSmQzh2RDJVApRhEQyRSwRx+5wgGmiqio2l4vqqmrKvOXU1tah2rQSpdbKMOZOcCIwy5Arxqwri2JJBHJ2WofsNAwr1Mg+Gp2VFAoFkskETnup7yGdTlNVXUWxUGR2dhZJkojF4qRSaVatWkU0GqW9rQ2/30csFuOF559n8+bNpb6LQp5QKITbrpJNJil3uSjm8rzz5ptksmm237Cd4MpWDoXfI3CLm9zaYYR5herhFWx85RGCp1oRdBGrFnI7DeJ+nfNjCXp7MiQSGTLZMOOzB0lm+pFlna7OII8/uoPVK+qw220cOznC6fOTHDk5SSZXCsoIpVb73/vTPyupV5rLFrACFIdsZM9r5C7aEJ0malMB964UStVCIXpZ9mcCUdGiVzXJKgINqkyLXUVAYKbf4IV/NYhGLHbeHmfjIz0I3hhCQUOb6UaPODhx6igfnn6fWxs3cl/zDlyiwJPpt7mQ7MGQMvjrMti9HrpOPEDmJTtGUWT6CRcTpMkfiLKq1c3GuzrpPXeUyMBpHgzMEfCEiSWcfFfUEe5+FF9jG+XjYVq++w5S3sJYvxPB/T5ivkDt1OcY+6tbSI6neflkhNuVN7mj4jKJcZ1wpBZx9Z1YooQum7x68xRlCZVdR4PXDQDXW+AWx68KAL9qnUpLCS55Ti8FAkEQ+PsRia/UXekjsCwL48h+notcZijbzbpHvk+n9h+Y/qXA2q1b8WhevCNz7CucofdclonxCQYezLL1QB0tra10d69AVSWcbm1BENIEDATBXLrOkovh4rUs7hYWFvUSpr3swj8SDcCyFuxwF+bNUhIqIIhXW0Iudmd/9pNhCr/SevKH/7qsCLTQYLE0LLCu+HLaFYnB/n5G+vqo9Pl489WXCIVCVNfU8Cdf+xrR+RjT09OYgGKz0TfQj02zU+7zE43FcDidtLS2kc8X8AcrOHjoEF3tHdTX1yNJCslkknw+j67rS9tWu6YxOjrM+fPnS93CmsbExATpTIZNmzaxaiiC+9495I+eR79p/dIEFkUR1WZjcHCITDbDmrVricZiqKpKIBDAMk2wLCxBxDQtigLIoogiiqiihF0WkUURuyhiGQZ6MY8sy4wMD3P61CkOHTxIPBalsjLI9m1byWaz9Jw7QzgUJp/NoKgKhlniwYOAIEoYokQuX0BeaGxTNQ11AfKyqSqrV6/G7/eXrkGUMU0Twy0z5kkypM1xJhghXJbHFCzKUwprw35akh7iB4ZJDM/S1tpOU1MzmqaRz+c5c+Yc6XSaBx54gKqqKmZnZzl75gzbt22jUMjjdDq43HeJV3/5Ijs2b6azrY2R4WEO7H+fZCrOAw8+QFqwcfFSH7vvvo+16zeQypcW2ryQZEo7z4TzFEOuD7CFnNT1rmbDaw/h620gnkvSl+nnDX0/IaeKaK/G7WjGtIpk87PE0meQ7An85Rq337qCO/Z0Ul5mZzaU4o39g3RsuRfDEDD1a8uaXDU/zYJAYUgluc+FPisjOk3cuzJo7XmkQJFrJRpSIoRlGFQgoMk0qjIeRSY2ZXJyv8Gxd0023pRk3e0TODtGkYp27DPd5CIq7+7by+DABbrK6vly973EhASvxg9z0byE3VvA489RLa+j4rmdZHtNMnUKI0/YiLw4g2M+x6d+Yy1ZPczAuSPcYI6zsnKCoq7wbEyj/+adVK/ZgieWo/WfX8cWK1JcuxGp7DjuiA2n9TDTX7+BmYEEe0/N8uWyZ+m0zzHflydu2wItm9EFi7f2TNMy5qZr0POR5/3jgsG1AeDfM5a/xynf+2yM7l76fn9U5EhM4OvN5lJmLg9f5tkDr3HZuYHPDb1J8htrSLxYw/jQCNu23knQ2Ydry0X+7k/sCCL0fT7JbYe66ejowOcvo7auchl8ZQD60rmKwqJTV2lOCMtwnaXawXV4/ou/b5rmkp/FVQ2xonD17mGZttJDnwhT+O4V1gvP/wLDMCgUCiw6pS1hhguFKGnhLrg0lfNnz3L0/f2sX7OGV5/+KclYjLqaGh587DFc/gDJVIroQrYfjUZZvX4Dmt1O0TDo7+9n3fqN6LrO2NQkfp+Pc2fOIssKGzZsXBJ8m4/FmAuHcTmdCx+MRTKZIBmPs2PHDgqFAq+99ho9ly7x6ckc9q8+hvzkK6gbV5KfnMWciSDrOVbtsDj+gUTSaaOsrYV8lR8hUEZNczOivdS5aVynwcSyTGRZQpYlDAxyySSiKOBxOJmcmORyby9rVq+msb6+1DVsWciiBHqRYqHI5Z4ehoYG8ZWVs37dOtwuNzogKmopyBUKmKaJw+lEkmVMyySXy1Io5AmHw5w5c4aLFy9gmiYb9Er0DZVLEM53zTCr5/Mc96vceHKEVDKJTdPYfMMWLLvCZTXCmcoIp9wzpNQCkiFSN2+nYlZmdSRA+HA/9eXV3H3nHXi9HgrpFN/+1n8nHQ7z+OOP09XVxSuvvkI0EWXLtu30jk4hOz1svGEnFVU15PXrZ5KL2ZndrpFIpPjGX32DiyOnqb3JRvC+NJ5mi0CslhXP3kv9uXUE5+uYJ86INMmHjkFi9nKQA4ho6FaM3Z9ega9SpipoUh/IE3AXcdgMklmZUFxhel4jk1+2lV9MYsSFZhbLwtIhN6SQ3OdGn1YRVBPHjiS2jixyVXGR5waWiY5FRoQBm0zaJlGpyLTZVawiHHnN4PRBE80VZ+cXTuPviKLrRU69kGbyfIp0NoYq5NhWs5LfWn0H5wqDvJP5kNHcME5/iqCvjLrLuzCeqyZvl4jcoTFmpZEOhlmzo5qalQ7OH3iVDUKUDTUTpNJ29uZULt95C9Xd66h65xg1+0YoOt3Y1qiYhSEqIjuI/MGX0G3w0hvDPCS/yDrvNPM9OaLKFsym1bxx2zztQy7ahz0f+byWj18lPfFvjSUmmFTkfNkRNszftLByQEwX+MGkyFfqTWyICIJE8u1neXNyiAH3JtY99H3cs5/nxD/3krM28LjrJm584f/mN29bi2XpTD48wtbDO6hp8NK1uqT/ZXcoQA6T7BJ8tbiTL12HhbBsB1AKAIswtlDK6oXlOyRzqfi8iAYIywPKR2rLpTn3iYCAvN4y67577sPv9/PpT99LWVlZyWKQK6qfgiAgUqrKYxq8+8Ze4nNzCMUCh9/fB5aF1+Nh9Zp12B0O+gYG8AcCTM/MkEwmKQoi3d3ddK9cSTyeIFARZNXq1ViiQHRuHq/Hw/DwCIODQzQ0NfHKxDR/eOse5lNp8vNz+Hw+FFkkEong8Xjwer1omkYoFMLv9zM9M8PhvW9R3txAe0d7qYDkclEpJrFnpnGOvMdBo5u2tVvpn5rH6fUTCFZhFgoUk2nMVAp9ZBJ9eBJjbAormwPDQHQ6yEgW52NhzEovE4kokXQcU7XR3t7J3ffeS3VNLbKikM5kCEcijI2NoWka7e3tSKLIuTNnGB8dwzJM7A4nXq+H1oUi7kwohKqqWJbF+Pg40Wi0ZLIjy3g9LiqDQaren8X9mXXM/edfcqAtx+HBc4w8upPdZ2c4tKONakEhKpqkLRNVN3DkinSLDtxzKZKjE+zctIWuri7OnTvHkUOHWLduHatWr6SAzt989++w72km3WXjkj9O0Ski5k3cAxn8h2M8VruLMtXPqQ/Psfve+2huaUVQ7QuIzEchgtKriaZpfPDBIf7pH/8XiUQCu80OXAESJVFGURRUzY6iqIheC+/WFJ7bZ3FVWFSOt7Du5w/RPbADKa+Sr7KY6c4xLua4MJEgmTRQVIHado3WlRo+n0VDVZHGQB5FtsgURMYjGnNJmURW/GjWKyxkiDrkx23kL9jJnXaBZGFryePclUatEcBSWBRDjBeKhEWDAS1NwKVQK0uUKTIDZ7KcPiAxMZRhy/29dO6O4Sy3EEZXkZ/wcOjIXpKhYX5/3YM0uf3szRzh7eRBTLFAeXWKpsxWvO+uI3teIrpHY3qLRPTlSTZ1l1O/1kvPibe4WRyn0T9NNqvxHVclts88iqY5aXjhABXHJig01aPUjuFIe5GbvkT0gTYO7B9HDl/k92reJtafYz4e5M0vb6Z52rcUDK6FgESul/0v+/6axizryn8DFpZgYmGiiwXGnAO0JFazBChbIt8cFfhajQu7IpHPW+QOPcMzjhnmJpvp+uL3ufi7X0W13sSUP83f3NiG/fd/yh8+vIZCbYrYthlum9xOR3sn1TUVSLKOohgLrDsLxDwWJWtUUbCQhOXNictrAovBonRehlGSac9k0iWWoWEiCCIetweny1WChkWxxJI0l3kJCwIPfxKkIPy+gHXTjTeTz+X4whe+wIqV3aWMFn0p+7fMUidwSbcmw89/8mMq/H7qggFcDjuvvfpqybA9EMTldjM2MUFdXR3FYhHVpjE9N4fNbmfr1q1UVVVTKOropolhmej5ApOTk/j9ASKROY5fvEhUcyDaNE6NTyIA+gJG51BkKl0uuqorqSkvQ8ikaauvw2W3c+TQISYmJrj33nvx+soJhcO0h9/jkLmCNeG3mW26E59dwIwMUi2n0MwMciGJYJmYokReLSPrqCbjrMW0eSg6fJiWSGR8nFdfeIkahwt9Po4nlsGbt3CYAi5FxRQECgJE7TJ6TYDVt+3GVu6jZ3SQ8729CALctGs3DXX16LrBxESJwqkoCiMLDWbBYHBBwqFIbU0tgwP9FAo5hoeHKDsdxryxmU1PTyE/soHwW2co9of4y99r4G//cRxDFSj6bJirKyk2uEkYWU5ODDKUjiN0NqI11zNjFihaJk5RxhPLUB3LUGdz8MGLL+G0wKEqGKk0SrGIzaZi2QRSHpnVv3Mnx+xznPTMQtCBJio06UF25jvpKNbQYAawG8pSwd6yTCRJRFFUnnrqxzz5o6dKRXxZAWtB+ExYKP4qCorNjk2zIyzUXhbpn4IoIqrwxT9/nKijj7GKNzDTcQIjzWx87mGqejoRTZlctclwW5oRI0vfZBpdt3C4JVpWarR2KwR8Jm3VObwOg1xBJJqWGA7ZSObEpZ3uYlZomRICIoIhUBx1kN7npDArIkhg31wg3xgjaabp6+tjcHAI3ZSQg378n2rFV1dGp8tGpaaSS5kcehkun8/TtTHB9sd7kB1ZpIkuYiMm+97biz2d596WG1gVqOWp5Bv0ZQcQtAwVVSZ1U9tQnllFwRKZu0Nj1ErjuBBj10PNTE73UDdxgm01E1gWPFesZvKB+3H5K6nef5qGV3rINgVQ68M4CpUI3V8kcUs9b74zxKrMEe6vOMXsmTx779uC19bOmt6rhewWyZAfVwMooStXc/GtpZ9bmJQSR0EQMLG44DvKmrldC+8hYuk2fh4x2exSqSwaDPRFcM4cpyd6gr6Kzay5//uMvPgQmUPnkeRf4/vVd3DmP/0V/9efrkd0pRCaYmwJbqGxsRpvmYzboyxg/AZIOUp1AWHBr8RYdu7mskK3AIKEQCnLLxYM8nmd8bEJLlzoQZZEJFmku3sF1dXV2DQFRZGWmFSmdYXt9PDDnwAxOJ8vYO28YTd+v4+HHnqA2poaTEvHssyS8xcgWKVKuabKFPN5fvidf+amm27irVdeYtOmTQSDQfL5AmfPnKWqpga3x0MimaSmthYDgflYjFgiQUVFBRYlDf+Va1bT2NSEpZeidjqdJRwOU15eDqLIM+8foFWzUVNTg2UZlJWVEQgEiMViiKJITU0N0WiUubk52tvbiczP88qrr5OTJJyBCqZyOQZmZtFFGUFRMC2QBQi63bRUVhBwOWkO+Am63bgcduyqQi6XIzo/x+TEBL0XziMYBk5VxEERRyGKWy7S4Laodgt47BISJgYyeUElawsgV3bQO5Ph3OUxKmobWdHejUNWCF3qh8kQtmSG4tg0ZiZbyizcTlJuB+7mepztzUxmU2jlXgRFoaGh1B09Pz+PHk8zFpkhHA6zsrub7s5OCoUCuq4TDAYxTZPXX38dURRobGxkemQMr6Vim8kgTSQITBewxYoIJuRlC2VlNZ7OWuR1tYTVLGnRICTk+eGxg8ScKlm3E1EUqVY0HOkCwbksD916FxXeMnQdliPxWaFAXMxwRh3lQ3WAETlMVEyTm01ROBTCfqmA7WwOMVwAo/QAKoqKarOh2uwIkowoSVf43gsBQBAEvvq1PwWzZJ95vWGKOmltgpj7EhO+/cjzRap7ulnz2n34hxpAFkg0mFwIxjk/myaZ07E5RBq7bdQ2KXS1GzRX5XHZLHRTYDysEU6oJNIaum4DU8KyIJPJ0X9piunRKOnxPLWRVgJ6EAGJEWmaKXGOqDiHLiZQVRl3hZfqPbUoXQ4CfpVOhw1Rh/E+i3eey9G5bZwVt45iDyQRZiuIDyq8+8Z+6k0Hn26+AbtL4keJVwkVwmhlKRrEJiqe/RT6FMR22BhqNygcDbNjWwXp9Hn80SF2146T0TXeUysYuf9+PKJG7TtnCJwYwtpQhsAULt/jxB/dw5mTk7gmjvK5wGGmLuYYq+wgWH7Hde/xVTuCa4vIi7yjhR2AaZVgn0UhyRJSrzNQfpLO+R0IloNCziQajXIxlOG4peJ85zT5fJ61foFo+VmGXevo/sIP6P3ynyCbr7DN9TW+uPdf+OpDm0inLSY/e5l7Bx+gpt5LTV05ds2BJJuIYgoLfWEHoCOKxYVTXjCxEhZZTwICMgIysiwTiyaJRhP09lzi/PkLSJKIqso0N7dQUVFBY1M9Pl85oighigKGWYINRVHigYd2fQICQHnA2rxpO2vWrOHRRx8uXaRRAKwl7X8RFgqjAj3nzzM2PIgmS7z3xuuIosidd91FIBBElCReeOUVdu7cydTsTKkJqr2deDzO6dNnWbFiBTfu3k0qnV5aBLBANy2MBbtHwzCIx+Ok02nq6urQdZ2RwQFGRkbIZrMoioLP58Pn8yEIAvPz82iahkPTOHf2LP19fVRXV3PH3XdTVVNDIpFiYmoSzW6nsaEBzVmy/8vlcoyMjDA4OEgoFCKWSFBbW0t1UzM5y2KuUGAoPEconiRZKJSyVIB8jhqHnSqPi1w4QpO/HM2m0N3ezsjwEP0Dg9TV1dO9ciVlLgcUs4jZefTwIC4rSZ3TQNFTCJaBINvISy7iho2Cu4HRuEmgsQvdgLHBEZwFHa8uwPAkudEJjEwWo6iTlyV0h42qDWtIlDkwnA4aujo419vD6OgoGzZsQJVlLlw4Ty6bpqurs+Q3bLdjV1UuX75EKBxCEATKXA5qa2s4f+AwF1/9gAbTSX2xjEa5AtkQKVoGRpUH755VGHVeEg0eQg6ZaSPN2WSIvvQ8WVNHEUXqbG6anWWUR/M880/fIzQyiaqoSKJUAhQdEtRrFLe4KK5yUmhTsBQBOWnhuQhlPQL2kIQSA1GHr37t61jGxweA0riW034168MQ8iTtk0xW7COp9uOYtLP6tXuoubgSVzhAzmcx79c5ocQZjKcxAX+ljTVbvDTUCzTXRvG5ioTnipw8k2Lv2/MMj+Yx8k6EYtnCOQhYQsdhscMAACAASURBVAYvElVmFa16E4ookXNnMVfnqFpbiccvI9sKpLQcA1qGnE2nzaHglyVySdj3XAFdmmbdXaP42iIIc15ifTIf7D1Ktxrk4c4buVwc5eeJNylISdxlRTp6P4X4RiOGQ2T01zRmj87Q6BTxNURZlThGR/k8eUnlR00bsbZsxZM26P7OfsxqUPwJNPcaMg//Nrmiyei+vTxW/j7ZcIHZuRVY7SUcfzHQXysRsXjvlxMvTRPMJf2AknFKafUo5eNDZUexn9rM5b5+5sJ5pidMchVucrsbcTx1GUw7HYEERsUbTDu7afjtn9H3pV/HoJbv1N5D+n/+GV9//AYGvvg+d3/4aZpaAguEBxFZSS3g/iAIRQQhv3TeolSyujVMCywRERWsUsI3NjLN9ESY0dFxQqEQogiSLGC329E0jaaWOurrawgEAnjLPFhWqS9H1w0efezm//MDQHm537pxxx66u7v5/Oc/h64XMIzCEvwjUKqwK5KIKAoc3rcPmyQSmZrg8Pv7ySRT/N4f/D51TS2YpsmhDz/k7NmzOLweysrKqKqqYmRkhEwmi2bXCFQEUWw2MtkcyUSKYDDILbffBpQmkCRJ2Gw2isUikUik1BVomUtdtIosk0qliEajZLNZEguce7fLhcfj4fiRI2zevJnG5mbmo1GKuklzS8uC/3CKvv7LjI+Pk0gksCyLuro6GhobqaioKMlAF4uMDA3z1JNP4rLbkGWZ7Zs309rSwsTYOAMDA5QFArS1t+PwBzg7NEQ4lWYqm2c4HCFrmliCgGSBQ5bwqjItAT8VioygF2mqqkKwLCzToqa2hmCwktDMBInIDHYyVCo5tHwEv5hCM7NgGegmpCyNnOJBrVlBgjKGp+cYHw3RWdeEMRdFmA4jjM+SCUcQizqyplF02khIYGuppXvPjQgODUuRGBofYXR0hFQiDsUimAZDfb3Ex8dRTBPZpnLvp+9lOm8yODTE3Z9/nEq3j0w0g9gbgpMTCJMxhPl0yR/Za8dsDyJuaSJX7ebF80d5/vxR6KhF8LoQNAUrm4dYGnE4jDwVx1YwUS0JUVZK+LNLphiQyNYLRNdbpFostHInTkOjK13HhkQ7lYUyfLobxZSvqstdm5cudvYsPV/WtXCGgCkWSTrGmQruIy0P4R0oY/Vr91LZ14E94SVZITDhLHI4M8N0MYduFjHJ09Coc/+9LpqbVGyqyMmTFkePWRw/kSWZyS39HZdXxOGUaPLU0BhvRAnJiDZwbrGwrTOQPUVQkuSkAvMyjDmgxqlQqcqQFzi2t8jEWIptj1ygoiMM8y6mTxmcePcUt1asZkt1B/sLxzmQPgFykUqXSvMvH8Yck0lssjHQVkS/MM/6tSremX1s9c3hdCZ5IbiN8G23YcvrdP3LPiR7Grkigy24jexnfp1UOot45Glucl4kPlxkTt4NtSsXFtZrV48rgde0QDdKr6JYMk0xLRHLKu3mCjmYD+mEV7/D6W9JpJMimaQDrBIUk72/C+3tOcSEjlPJU93wAv2NG+l68F+59NtPIHGZf6n/B07+x7/lB3+/noGHD3BP7/10dnbj9kh4/QUkebFGkUcQckuf9/JudQEZwVIo5E3i8TiXe4cZHZkmNp8in9OX+lREQUaWZPwVLmrqKqmqrKayyo+7TAEsDMPkkcdu/D8/AJSV+aw777yPzZs3ccvNN6EXcgu0uYXO38XmMKlUPHn56Z/htGukZ6c5euQIerHILbfcyi233U42nyeZyRAIBglUljp+X3/9daqrquhobyeRSNDV1cX49DTpdJp0tsjQ0BAPfvazIAikF6SYF/sARLGk982yTsbkgkVkNpsll8thFkuG8W6XC9MwOPjBB2TSae684w4SiQRT09NMTE3j8XrxB4PU1dfjdrvJZrNkcjksyyKTSqEbJaP76urqklyyTS05ehUKFLI5XC4XmXSal19+GU3T2L59O9XV1Rw9epRAIEBNdTW/fOklGurrae/oYHBwkORCU5cgCJimSSaTIZ/PlxrZCgVMWQa7E8Hh5MLoGBlFJWNaFPRSw4tTVXBrNrpqqqn1uLFhoBgGTkXFoWmMjY0xOTbEji2bkNJz1LpMfKpOZuwcbjGP3aZgmCYpXSYplTEZzZFW/YQTBsVsgfVNXdhmYxSmI4R6+7GyeSwgL4G3uYYhssxbBrc98ThquZ+8BSwwOeDq4q9lmTgcdoxCkf/yl/+Fkwc/pMEdoEpwsMoqZ7VVTjkqKiJxUSckF+lz6QxUScTLVOL1HvJVLgyHiiUJSFmdLQ1d1MgummQvbllDXSgopsQcc0qC885hTnsGiSgJdMEgUPTQmAuyPtFKUy6I3dCQTXFh/38lZJgL8MRSw5QlIVjK0nXlMhZzM0Ui8Tn6U+9T6DpGZUOamolG1r90PxWDrRiGSJ+Y44w9SVQrkhSLiHIBf9Di9tsUtm+VkCS4PKhz4LCCLgco87vwV7hKhW/RwIgkSR+UyQ+qWHkBbW0abVUGtTGHKZsUJYEhm0jeqdBhV7EJIuOXTE4fjdC0aYTW7ZOYCZnpEwZn3rnIQ/XbaPBV8Hz6HfoLw2j2Io2hNZS9sglDERh7zMbciVm6WzJ40ue5vXKYnGZnX+V6pm/eQ+D0MHX7LiJUZ1AqyxDXf5FkZz25fb9gt3aK3ESOkHUj1K76yDpiIWBYIpYFhmFiIQEquiGRzWaZn0vTdyHExFgIuW6eVY/IvPtHVQhCqf4jWApYMsVuN5ZbRj6cRxVFmjv/lVHfKpq/9BMGvrSVKtvn+K/7nubLD24iWZ+n01pFdaWHti4fDqdWurdCDoQMlgWmuVADWGCJmQYYRYvQbIRLly4xOWiSnFMwdHmBHWQiCKVaAoKBJUXxlNnw+/00NFXR2FqBppUo3I88tvP/UwCQ/+1D/v8flmVht9tpbWkqcWH1YimCLzLpAAQBu6SQiMVQECnEk6STafzlfjSbDcMyCYVnqWttYX44ziuvvYzfH2DL1q3Y7XbiiQTjk5MU83nefucdtu/cSXNzC2fOnGVyfJTpiTGqqmtwOzRyuTzZbIYXX9/L7OwsLo+Hz3zmM3iWVDU1NIcDy7KQxAUbPssinU7zxquvcfL4cYxikd6eHlasXklHRwc337oHt8eDgYS0EFicbjeyXNIjLxW9WdBCKo2CYVIwSppE2GzkDAPF6cTt8TI3FyEUDhOsrKSiomJJBM5bXo4pCGRzORoaGtA0bam4ufh1Jp1mfGyMWCxGPB5HFSwmB/oQBgep9/vx+QOUlfuoqatDURWApWAoCBKCKpBIJMim06guF6bbz4sfnsPX2ET/wCzJok66WEO+UMBhU/FoNioddipkkXR4lvzcBA/ccxeWYXBpfJjtN66iRuti4Pkh3Jk0tX47DVVe7GqGDUUdU9aQUkdJmrVkBQ/Zgp1iNEMhFMfoH8GYCUMuD1hkXU4Mh53W2QQpr5c5QadXiHPGiiGKo8iSjCwrmLKKqqoomh0pJyGFJVyROAFDwo9KW1alPSXTreYR8jqWIlGoLSfT4GOqq5yBSoEJMcugYYFRSwW1lEl2umx+qhUXNYobh6ggI5JQMoxoIU57Bpi0zTGjRpEtCX/Rw7pEG6syTfgLfty6g2LBQi/qzEzF6Ls4SzgUJxr1knvnNiaN8v+HuzcPjuy6zjx/9+25AshMAIm9UIXaUMViLWSRxeImUhZNk5IoyRRlyfK+75Y9XsLqHndMTMzYPW55PI7w1i1LspvWZsuyRFGSxX2RuNVeBRR2VGHNBHLf3nrnj5dAoSS329GO6HDrRlQkKvPhId99951z7jnf9x3OyghP4QLTgAGOiRoto/WukX7HqwxnKwzN7KPzyUdY/lgPTQIm9Bor0SYFa4NIR45jJ02+9x0WmS4HVQ1YP2WxUTZYK2n4gRcWNCXIDQM5E6H75SRBXSE41IRbmoztdtj19hgF5RAzS4fpiRnsvl1l5NC7+eaLJV5PrDD+vj3sa4yy/KbNzPQi9/+wixKFueqXkMcKbHoZ9It385VnH2L1cYu19QY9r04wYnyTWzI5bDXCZw/cRdC1TP/nn6H78hnO9QboJ76fxl0PYL/4Be6Sr+GsNsjJe/F79iMVbStWQ1UFfqDh+yqlUp35+Xk2clXmp4s06i7Bhsr6WzEG7nSQrsramRhIBSRol6tIBO6xFK0jUa785Y9yqHcF+QfHMO7bhTL6cX7srgi/1fMu4r/6e/zuT47w9B2f5f1vvZ/+gSyZTIZYXMGKGMggIAh8hAKIMIh0XR/X8ak36lRrVVxfw/NDVJiiqshAEnhtCWgUfFejWVOoKD52r0AROgIdpPqvtr3/JnYAHR1d8md+5hd5/Pvfg6oqeHaz7QBupIA0VcXUdOamrvLmq69iSg/peTz44IP09/dTt5usr6+zsrlBrVajUqlgOw7LKyvU63UymQyGYRCLxXjooYdIJpOYhsmr3/wWX/jCF+jp6WXvvv10dnUxv7CA53mcOnWK5ZUVJicnOXHiBCdPniSQEi/w8byAoF0v2DKwly5dwqnVSSYSXLxwgeXlZX7rd36bIAjwfB/XlwRCwXPD4hBCQSjfGcluDbGjMYeUIQ9C0zQunj/PhXPn2b9vH6dPn2ZlZYVcLsfQ0BCTExO89tq3eNc730k6nabZqFNvdzgDiMVixGIxlq5dY2VlhVgsRjabJZfL8daZMyAEH/qhHwlJaoqgWq1uG39d18MWlpq2TYfXNI2JiQlmZ2c5dOgQCgLf8xjo7+f111+nWinQaDRYX11ldP9+VNPktSsTaOluCrZLDYHUdFRVRQO8wgZqq8FtA710xixWZqf5nne+m4P7D2C3WqhuA8veJNZYxrILmE4JxbcBga9HaZpp5nJNPvF3z5Bft0nJKCmps8eI0qnqqELgIilIyaaQzCmSvKZgqwotRYE2GxtCZ/zLv/obIfSwvftTWg5aqY41vY55bRNrOodaDbf6bjpGcX8PheEkkyM6E1qdPE0qvo0mFHrVOEN6B2NmmrQaR1cUbNXlmpVjMbLBucQMq3qOQEqipQjRN5NolyyM2QhqMQJeBwTtBiZSRfgG+OYNDoSw8WmvLQlGzybGyCo93/sWYxGNW597O32T48QKGcpxyXLE5S2ljNvh0zei8+BDKgf2tDD0gFpL5VrOoFTXabbCkCRwFPyiQuXFBM3LEVAgdqRB4nQFrcujYsBUREVGFPZEDHRfYWWhxcraPAffPoUMXGa+0aD2Ron37TnFAkt8pfYywrBJ13oZ/NKD2D0Wubs1ChOr3N55jju6VohnGnzpyGMU9h0gNbnK4CsvoY0aqOOP0TpxOx0vfJxxbY7NKYdi5glExwBCCBoNqNUCFhfXuTo5RangUNuw8D0NIRWktJB+lOSIzR2/dZ1v/Py+Hc+gIAg0pFRwf6wb/a83wPU4eexzXIrvZdfJ55n/WJzfH/gLLnz4D/nMnx1g4Xvf5OGVd5JJd5Ptt0h3G0CI4/dpIgnJmFuRXmGzwOLiIsuzLpurAY7t4zouMlAQ7bSVoko0s0W2P8PAwAAju7vpG45sF70f/4E7/9dPAaVSafn7v/cxbjtxnMB3kb6HFBDIAKWdBjLVkI37zNNfYWpygoPDA3zxi18kGo0ytncv44eP0JPNIoxQgqFUKnF9eZmxffsYHR0lHo8jgGefeYY333yT7u5uenp6whpBTw+NRoP5+YVQ4iEWR9N1YrE4kWgMx3WpN5t0d3fjui6O63LP/fdhGAaNRiNE97RlI6avTtHb3UOz0eDc+XOcuvsuerNZHNclCJRtpc4tLZotLPA/zY68ufCoKAq6prF0/TrPP/sse/bs4fSdp7Z1e+bn5zl86BCvvfYaKysrPPzww8SjUTRNw/d9VldWmJ2dZXJigmQyiWGEEsxDQ0P09PSgmiaaptJs2SHxTVW2+Q5Gu5mLoii4rkuz2cRxHFzXpVarcenSJU6fPk2mK8XFixe57cQJisUCszNXyWQy1KtVCpsb9Pdl2SxsomsqTrPJQF+WL/7tZ1lcXKQj8MDzyGZ7ee9738urUzNEu9K87V3vxfYktZZP3bbJ1+osbGyyUauxuFmk0mri+gGqIohoOrWNPKvT06j1Gm6lhhn4RDTBUIfOYKfFrUMxbumP0pPQSJoCX0LDgcWKZHojIFeWNMvQ1YQj6UFEtYawbaSmISMR/IE+/MF+gu40sjOJb5rb8tJbQ24Vgr0ArdzEWCwSubqGuVTEWCuDHxBEDDb3plkYT7OYMTgXabAqy7iBBzaIdQU1p6AvG6g1A9fScbodqieXqR1dx+uwUVwVazpFdKKb+JkBjLUESksL15Fig+IiRNt5GTXQmnSlYmGascsie9DGG7tEK3qV5Ixg/KsPkF7YhWZHWU3YzCo202aT/lsk73hIYWhQErUklbpKvqyzVtRpOeEa9goa1VdjtKYs/IqKtb9J9JYG2j6btahPPqLSGTfJ6hobqw2uX1tn5M7LuI0a179VQ78ouHdkP286l3nLvkJCmPReOk7n+T2svcck16hzsPkmj/ZdojzQwyuH3kZpYBcj//BNYs1ZjON34j7wAZSFC9y29FmKszZn5/fz7EyMtbU1fMdE9fsQMjTMSAMZRLafx+Roi8x4nZkvdbchw0qYthMCQYB7bwJlzSaxWCG774us947QnZwg9w+38Bc/fjs//cVJLj5xiVNfeoiRPQmGRpIkk0m6Uh0I1UYobV5TWx7bdRxarRYr83UKeZuVlSVyuXV8D3xHIhSJaRpk+zPs3TtGNpulKx1Bt5w2JNTn8R84/b9+Cghg92iY/gn8AGQo5yt35EMUAUuL81TLJTrjMSYnJ3HtFi3PYbNYYHTvHnoHBtANE9t1WV1d5cylCzQuXaCvr5fZmWn6unt48G33k0wmmJ6eIZFI0N/XR6vZxDAMjh69dTvNs766hmHozM7Ps7a2hmYYDPR107QdXn7lJXRL55577kFVBYHn4wc+kWiEWDKOois03RazC3OM7d9Lb28vqhB4Nxn30Ph/h0gWOzEN7SN3CGf5nkdfXx+WZTE7O8vdp+5CVVUymQwCmJ2d5cSJE2QyGZ5/9lkMw2Bubo58Ps/4+DhHjhzh1KlTFAoFCpubjI2NMTM3x9LKCneePk2hUCAaVQmkJJ5MsLS0hK7rdHZ2YhgGiqJQLod9luMY7FXTXNJtisUirutSqlQwTDNEUPX3YWqChbk51tfWWF9fpTuTZnFxgf6+Pq5evsSeXSPsGh5manISTdcxYzF6s1kqdqjAeuz+t6MoIYIioitEdJ3uRILxvuxNyBshJIZh8OzXv8Gffe5vaJUqWJaFkAJXUfAUhSlfYbbq8mIpQL+mIqMRiEWRphE2R9nCb5sBWoeDatrE3/MwCU0hJWxSskjMK5By14n7b6HiQhWchoWDRYkUxYqFW7Cxr28i1goEzTYUMBGnMdBP9d5uvOxhgnSawDTxhSDu+vTlHfyZBp1zAaVcjkTRY39Loc912eM2iAY+NUNjbSnKG/kkl692U+4OsFNhsCQSKv5YHU9VUKoavubRGF+jcTBP43AeP+GglXX0tQjFN3rIT9qkN2KUywkGrt9BZ+dDpDImlceqNHQfaAGSAVRSRp2NxFmetqdIf62fA19/Gx3Lg/iKYDNe57LZQO13ufdRwdt+cxPDkNRbKtfzBsWaTt1W6M0bdC0Y1F5MUChoRMZMbj1swVQvi13QeafK8P0Gm75LdG2M+9Oj+EGOpWyRYGiC05luXrHPccFbI7d+mP7PncAdsFg/lmO0vsgtqTUKK6/zzOIuQKF+sYN4ViH204/w/WqNW6c+RVBzefJJycvTh4C2gd/x7JVnI5RmYnTsbpLa32D+6Z6d4j4YL5eBgPJDWeqTH8ScrqLsu8bIf/x7fuXjU9zjfIj/7eg8/+6TRSqRVWq1NIODg+haBCvuoelb6zWEtasaxOIWI7uTDA8bROISVIdqqUapWEXVBMmuOAfGdzE03Es0ZqHqHr6wkQRI5Z9Dpv3Lxr8JByBhG4MtPRdfbhVMwglTBGiGQb3RwJeSRDzOxPlVkJITJ07wgZ/8CaKxOMVqg7krE8zMzyOlZGR4gFq1xqWL5/nWN7/FHcdPYNtNFhbmKRY2aTUaREyTffv2kUiETaNX2imfN994gztO3kHg+wgE+fV1Ojo76Rsc5Nd/7ddIJELZ6TD9I8LUUNs4T01MMDo6yubmJjMzM5w8cRuO64EI0yZesEUJv3n3parq9o5nK5cJO3DQbTislJJUVxfra2tsbm6STqfxfZ90Ok0QBNRqNfr6+njjjTe4OjmJFYnwC7/4i/T09OC6LivLy1iWxf6DB1nfgp8ODaGqKvVWk8GBwTA/Wa2yf/9+Njc3QyQUIQsxFovRa/bSXXZZjthMnL1KprubZEcHG/l8KMOsKASeS3c6zfW5WdxWk6mJSfJr65iJKHt2j5Lt6WEjn0M3DHq6upClCslkB5nuHkq1Bm7LIdPbh+/7BMF/e6nKtkx4s95gYmICx3FRVRW/jQbZeoiFH6AID9FoovoBlCqoqoquhykouZN8pChIVcVFMF+u8WJxg7VKiardouVqQAZdVemKROlNdtAVjbGrM85AH8T6W/SOJ+iyLSJ+FStoIKSDK1ZpiTIld4PSrMDeaGJfL0ChRtrxGQoEewKDdV9lWk/yppqlSYym0BGBgfQjEKhElzy6r7W4xW9wyN1kt1fCkh6ODrOdceZTFmfGoqxlQXFSxM+kEGUd7ZqOUgyQjktpV47cwQpvHS0QdAYY0mC43sfJxh4GvTQDTppIoCOAmNNLfPNhhHgYhqHyE5KiKNE0iuRjZ4hXF0hPDGD+6fewsJnG1gMuR2osZ1z6D9k88C7ouTUgCASr79rkWs6gXldwShr1f0zSeTYKvsDb38K6zyXWmyTv30t/1GDgQSjcniNnXWCv2EXw5ThH0iPUfmSKSfsqrnRYuHKKwvP3UXwojvnqa7wteZ7omMLXDn2YVrDKwHOvc6azD+fUBzm59wI/vPo1pl8P+Oyzx5nN99+0loSQlOcs7JLG0Z9d5NyfDt2A9rbhpObXiwQ9CpUPj7LwiUfZ+6mAZu8uzkX/nMzv/AJ/849/xeO/vgtpp9rPdYhevBkvduPVjEgEHj39Ji03wnU2KDfyRBMJunpTZIfiRDsEiuIQ4LU5Uv9j7Se/ffybSAH19mbl5z73BTo7kjitFkG7qCpEgEBiCIiZJhfefJ1XXnmFe04c5S8//l/wW3V+5999lP79+3B8iRdIKvU60/NzdCYTDA/0cX1hgWefeYa+7h6qlSp33nM307OzeE0HTdMwjQimaXL58mXq9TqqqrJnzx5SqRT5fJ5arYZt2+i6zr5D41RrNU7ecYrOri6klLiuiytvpG4MTSO/ts65s2c589ZbOG3EjWHFSHR0Ek0kiCXiWNH4dm49EomgaVqoPR61iMViIa8gGgXYZgDKIEBVQnG4qauTfPXpp3nssccYGBzk+vXrnDlzjo18nr1795JIJOns7KKrq4uFhQXOnDnD0NAQe8b2MDg4AMDCwgKJRIJIJMLa2hp79uxhs1SkXq8zMjLC0tISruuSzWZptVrk83ni8Th/9rkIv/aEzrWnGqwn38KNSI4eOUpCs7g2OcP+od3kr14jZgd0qlHMQMX0FKKGhaGHshOu9AlEQNmrcXVphkajTLGyQWSok+E9I1xYXqDiNHnXj/0UPgGtQL1Bgf+2IYRE13VWl5b57d/6bQqbxTCP35YsDggdgaHqaIaBakZQdQOhqmjtfL9QlO3i/A0NFsEvf+Q3t2sAW057i3y087jwe2wV7iSu7+F4Hk3XZbNWY61SYblUoNJqUW2FgYwiBKaiEkXHaHoouSb9aovBqM0t3QF7Uh4J00NXJHVHI1+yKJR0Kus+aqmBajsIx6Pla9SlxaKf4ZrXTSGIUwgSOKh4CHQR0Bk47PLyZIMa426FHt/GMWEpo3H2cJLlkRirvYKmEaApCr1agt1WkqSlUkkWON81x0Ikjys8Yr7Jwfogext9HKgP0uUl0QKlLXR2A+3iqTaOUWbDPIO3miN7aZSxF0+jNqOsRJosJRy67vK4650+nR3gB4LrGwabZY3CmkXtWzFaExZeQcPY1UI73CB3u4Oa1ei3BL6sUnDOU9qYIf9Kg+PuHi4rkyy4K0TsBEP/eAolkaZ1R573GF+hNZTkmfs/DA2foem34NaH6NVK7Fv8MvUNjz/6+Elmc0M3aipSYKVcDjyxxvk/HWIL1ht+7iHxERq0fmQU46l5xge+QvDD51n7D6P88eMf5NnEMP+H/ed8LPVRhF4Pi+tsMRtupHxVoYIUNJuNkFi6UaDZbNLZ2UEimaCnJ4OmbdWlfKQMdckAnnj/d4EY3PDwiPz7L36ZwHPxfa+tkxI2SVYEGKqCqii8/Mw3ePnFF3nkwfu5OnGZqckrrK2tIgiIGFFc38cMAgJFwQ5cPOkTEEat/88f/AEvvvQSzVaLJ554grnFa1TKFXaNjBKNxjh/7jye75NKpSiXy3ieh++72wXYRCLBCy++iJSSY7fdHkIVHZfebB8HDh5kY2ODzc1NLF1ndNcuNvIbfP1rX2d+dpYPfvBDjB+/Dd/38ZXw9vu+xHEcWu1mM77v43surWYorRwEAY7jbENNq9UqOoJoNIomJBv5PPlcjnqjwel77+XY8eMkOjpRVRXX81A1nSCQzM7M4LouXV1d2LbNtWsLFAoFVFVl18gIR269lUajwebm5rYG09LSEt3d3fT29jIzM4OiKIyMjGzXOX79/9rkrvHzvHD+AL/yYZuu7j6Km0Vy+SIDA2NUKi1eePENUl0jVBoGhbJHy9UpVX0CqROgoqlaGxLXwmmW0AMXXVbYNRgjm7ZYy88wtn+E43feii4CNCEwRYAqBREnQG/56LbEaARYDR/NA7faoFmtowgFXwY0XYeyGysZmAAAIABJREFU06Ds1FmobrLSKFLybDa9JgXfxpY+HiC1sIk8Ut6UkkMo/MpHfiPkTOyAASNuOKKdxn/rVW4Zim0egAB5Q/tFAr4PnhfWqgqbFTbWGyxMF6mUq9QbdXzHQlE6EboCRkAQk/iWj4zaSMNGam1j4mnEXZ24qzGiCA7GbUY7GoylK6SjNpoIsD2FQkWnmJM4FZegYOPXAlwpaKpRnO5B1L4RtLFR1B4VaakoToC1uom5UiQ6uYZyfZO68Cl06lw4GOPMPpNCJITrxlWDLs2iO25QTm2yFt/gWjyPp3jEvQiHa8Mcq+wm4ybpcuMhLLY9XKVJLbJITcxiTruMPXMnHSv9SF9luauBPRhw9AMuA3sDfAkrBYO5OZPNbyZoXYnirRmogzbySIPCPT7ZXpWI1qQir7A+f4nitxocEUO86L1FOajSs7Kb7ktH6bpnhaPWa1w/fYxLB+8hkiuQFQo9PR3snnkKWa7zh39xjInlYYQQHP+l67h1lQv/ua/9zX1oQzWFkAitSev793B8uUXQeBL3p16j+Zt38ftPudz3aZ2PZX9+2wHAljJsW+65DXNXFAXPCwUxfT/Y1gDS2sJwW0NKiWhLUL//u6En8NDQsHz6q1/Hdx0cx76J7q2IsEVkiALSKBcKvPX6a1yfvgoyoNPU6EmncWthymJ+airs5WvbDI/u4v0f+iDxjiTrxQJPPvkks7OzaJEIP/ChDzHYP0gmnaHRaPDv//3/jqIoHBwf5/Dhw8TicSqVMsVikXQ6TSQS4cKFCziOw3333Ueys5NGs8lmocjc/AKmZXH48C1YlsXzzz/P448/wZkzZ3juueeIxmL89M/9HD09vXh+gB+EMnd+EGxLTostrrgQqG2ns9UiTxGCjfwGZ15/Hdu2kYHHZj5PqVDglqNHecfDD4cpsmbrhvpgG0+ut8lrrVaLpevXqdcqxONxDMOgVqtx/fp1NgsFAilJpVJ0ZzLkNzawLIvRkRHW1taYm5sDIBKJEIvFGB0dZXR0FNd1mZufxw8CMuk02d5epB/w/HPPUS8XiEcjFPIb2LbN0aNHOH7iBA3H5mtPP00yGuHggQNYps4rzz7D6y+/hBWL8fCjj1KuN7h4ZYIf+JlfIJpIYDty55LAdiSeFxrQpiNotRRyGw2e+cYbTM+to+qdqEYKoSZQFBOEEiKuQq4o4IBsAg1UUUcRdXTFJkKLhO4TUxVSuk6fleDR0/djORLLCbBciQgkavu7BAJcTeBoAluHpimoRRRcDeoGOJqCJySBELg7JB2lVJCBjpShvotj+1TLDqUNj4WFBfL5PJVSQL2qI4MQRSKCCPiJ0Fttbf3bbOMgaPeulWoIZdz6O6qHVDwwXYJIi6CjAJaNSHgIPUBRocNUGY5F6YuanOzy2Wdu0iFaJKmhSQ8pBbatUt0MKC25OOWAZl3gGlG8RBJ/ZBi/L4timOALrIUCxvUCysIGdbtJLqlw7mCUy6MmpajA0xXimkFnXMNNNLiWWWEzXkJRoK/VxfHKboZaGYbtDDHPQkqJpzWo6As0/AU6z0XY88JJoptpWhGH+pjN7sd8Uvt8fB0WFmIsT8RZ/1oSZ0mHiKRyWxnzUY/uPofAnGX+4utor6h0xUxetM9gtGL0TI4x0hfhlr2vM338JDN7TtCXLxEd2MuJiU+iVKq88EKKqWMjnPvzAdy6jxChWkEkqtHTbzB2IMnQ0BDXLJ1S0GD5ub8g9kPPcvwvf5Dr7+ug4gQ8mrzjJnG4LXLbDvHn8J+40TVNfntgsnWkEHzwu6EncG9vVn7hC18kFo3gOHZb+K0tCyXCLb6CQFVEKAeh60i7RbPZZHN1hdnpGaavTGA36uzq6yOT6sB3HKZmpljP53CkpFirQrOFrmnhQ6xqPPze93LfA28jXy7x1Je+FMoeHz+OYRq4nofnedh2k3w+F9YURkbY2NjkrbfOohk6tXqN++67n9N3383a2jozMzO8de4s8Xic8fHD7N6zB8/3mZ2dZXZuHsOMomk68WQHQlWJJ5KcuO227daK/s4b3Y4MlLYy59WJCWYmr3LL4UMMDQ5w+eJF3nzzTWYXFxkYGOD+Bx7gliNHqDVbrL30PM3cKnue+KFwZ+EHbQazgu95FItFcrkcjuMQj8cxI22lzPauQzMMIpEIzWYTz3WJRiLEolHisRie57G+vk6pVEJVVbrSaQYGBtBUFbvV4mtfeZqVlRXuPnkcRVFYW16h1WqRHchy1+m7aLZaXL1yhc1cjlRXJ7tGhtF0hWazycz0NH19fbz11lmWl1f4id/6KJ7r43n/LZhsKKtrGDrL15f4/f/795idmUNRFHwRRk2aooSkPt1ENy0Uw0DVdVTNQFV1VNVE0UxAJ5AWUokCFlImQOlgaPgQti1xHHDcG45IVcN/EQviMUHMhHRC0B2HuAUZTdIhJLFAkvAhaYPpSbRAogbhwpaArwocVcEWgpJqcq1aZq1aZTZXZeJamYbtYUuJ61n4Tie0nUZ7Agjhim3AhNS2Wa3AtjDa9qFqHdQW2Ww3wyODdGcjDO2KEYmEzFKHEg1/nZrTYLlcYr1SpNxsULdtdOHRY0CvpXBrUnI81iSlOHTSuuEomlDbCGgUAqobAQ3HwLfi2JkegsEBMCJoVRdrvoSYW6fh2sxmFM7tj7DUo7GRVFA0FUtV0LoCNrtzlOIVXMsh6yYZsNMcK+9mV7ObhBvB1RqUtWnc0gKZC1l2fesEZi1OM+lgnHQZfaeL1gXXVixe+9sYlfNRKJjUuqooP+DRd8xD6Zrk+vkzcB5QXc47U0SdOAcvHOH2Oy+ycesA5469g0wLeuJxjk3+FW/5SZ7+5C7mriQxDJ10d5yRPUkOHM7Q0ZHEMDRsUeeP10q8ffELzD7wD5z44sOM/2SM5ev30KnEt9fyt+sEhe+L7+gN8O3pxq3xofd/FzSE6ejoku9/4oM88sj3MToyjO97BH670LHjerf6K0gZINrbKFWApipomoqpaqhCEHhhhDxx7hwLs7NI1yWT6mJ0dBdCCFbm55m4MkFubY1CqUSghPrbx44d49DRW7k6PU2xXMJ2bL7vkUe4cuUKrmcTjUbZ2NigUCzy+OOPk06nOfPWOXp6ezAtk438Bmu5NdKpDAMDg0SjUZ566ilGRka47bbbWFhcZnV1lUKxSGc6g1ANDhw6RDKdCnuOqiot2w4ZwYQ33mhv9SzDoFIqk1tdZunaNRKxGLFolEwmQ29fH0JVKVcq1Dc26R0b4/wf/UecSoVIbxahaaiRKEokgqIbqJEIRiyO0HT0WAzV0BCaga8qtGybAIEvJW7gI2WAIgSGGSUei6PrOtFoNCxmex6+lKFzUVVazSZTk1fJra8R0QTDg4NErAjzC/PUm3UK5bDBvBL4lIpFNFXliSee4NNP/hWvv/EG99x9N/v27eeN199kZGwv3/PY+2i0PPxt9dvvbP6u6yq+53PpwkX+3Uc/iiJCUTcp1DCKAhShYFkRNCuCZhhohhkqgLZ5Fa12XwRd17fPHRaRBb/0i7+C1maDh5H2zTWA8Ht8uxCEJNhRIwDa80WY+nMVmk2B6yrUapJaXaFeU6lUBdWaQ6vl02j6OG4QkoLaTYMigC4kUeHTqUqSKgxaMBCBlKHQbSqkLYGhCvQ23NyTkqrjU3R8NlyXigzwuzIkBoYgJlDjPrbWRphpLTS9ztYOQxEhDFvZvr5vd8ICgbLtFFueS9VtUbcd8rUq14tbDqSGJV26tIDxmMfxuMtey6ZD8UI+jy9xW1AvBJRz0KwLGl6MVmcvWB0YVYGXr1JvNplOSc7uMVnI6timQGgCLe5R76hTyBbQTEnKi3KkuptDlTE6pIV0r8PKHOmJXnaduRVV6ujjLsk7bYJRh5eeMll5rQO92Yk74KGfaNH5yFVWLn8TMQnLLJPzioxUMjzQBZfffZpGdh/R8gJ3rr2JvtFgYvl2rF2PsKX8iWghRNgs5j+tthj61p/gnTrLL9x+gD+aPMKHOt62Y/2wPa++HwJFyuUyq6uraJpGR0cHyWSSWFtDbOeu4Ae/G5rCp9Ld8h3veIRGo4GmwH333cPRo7eSSqfxpU/gBwTejl6ccssitL1nmyxmCLEt97s1ttiv2A6taoViocjk5UusLy4QuC7ZTIZ4LEbdtllfX2d1dZVSuYwVs0gkE0RjIWa6ZdepVqp0dHbQaDZ54MEH0XWTqalpnn/+eTo7O3nHO96BaZpMTc1w8eJFLMtiZGiI7kyGlm3TaDaRgOsFdKRSZPv7iCSTrOc32CiWSPf0MH7oMI7rthUDQYVtLSRNVcitrfHkk08yMzVFEASYuo5pGLRaLQYHh8lms/S88QKBEOiP/xAHDx5keHiYRrN507zoeqh/o6oKoTRBOG/Xri0ikWQyGeLxOEEQ0GrZBK6HCAKULV2TwAc/oFYuMXnpEqZucGDPbkrr66wtXaNRLDA/eRU18Di8fx8qUC+XCGybqK4ROC6u41De3CRwHVRVxTCMEPEjJaZloapaiK9XlFCxU9fDV9NENS0UPXRgTc9ncWWVMxcvgWbgKQotH1pBgFR1hK6B0JBWBEXTUMwIatvYK7qO144ytgrAUobzIYFf+oVfwtC0G4V4KbfZGf+UA5Dt35PbJL6tjOZOBIgSpn/aTT2EVBGYKES2dzUb+Sb59VCddn5+nvJmQLWoIYMta6G200FbjUMUkHp4LtF2QopNIBw0TcewdLKDXUSTESKxJIpq0mg2qTVaNJoBYb+jAAOJoUDCgH4L0qZkMAZ9JvTroROKCIneXpdSCHwFfBVaOtQsgW1A04CGoeCpEluHQAnnLVB2zscNZyKEwAsCbMeh5XmsV6usVqqU6xVsp0pCNhkwXG61bMajNlnNwxAB+BKnLmnVoLwmqJajeE6MimOxrgoWOuH8qM5KRsPVBIomsRN1Kj1lbMtFv2bRe1Fl2JbcLTKcmDhOcrMbPa4Qvd3Bznq88UIHm7NxZNLH+PBVSjyPu9Bgxl9k0FEw3ncCwxqgKz3C6cufRpUuF5ePoGbvbV+fjxQeXy2o1F76M/JvrNPz0T7ea/zETXOgadp23e/6tWtMTU+TTCQYGBhgYGCAVCqF67o3dgbAB78rHEAqIx9+9L0oghDbbzfQNJW+/n72jI0xtmc3w8NDKO0mytL38XyPQPptstgNnPxO3XBB2+ZJianr6IqCUFUCJJYbTnTu+hKzs7PMzMzQajToiETpyqRQdZW1tTWWlpdxHJuRkSEOHz5MEAQ8/8KzqKrKgw8+SKPRIBaLcfbsOXTT5I6Td1Kp1lhZWQ3JVUJQ2NxkuQ29VDUV27ExrQimZeEqgtE9e8kOj9A3MIjteHi+f0MnhtD4K1JiGKH2eKVUxt/qO+wHNKtVisUivh+ikpaWlshvbHD63nsZP3QIRQ3/ZsNuQbuQPDExQT6fpzOZQNc10uk03d3d/L8f+09heioe58Rtt3H4lltwfS+kqrd3I7lcDs/zyPb2ErEsPv/5z1MsFMh0dBKLRulOdbC2usLa9SWikQhzczOM7t7NHXfeSSaVQlcF586f5ezZMxRnF1CaLcZuuYUTd53mqZdfJp3J8J4f/nFUTUNuN86+uUu0EAICH11TuPDWGb78D1/m/NmzREwTBQVNhPWjiKYR0Q0sVcE0wjVgKQKzrRRjEqAFflhnkgGqlChIlPZrLBptt/eV25IkQlFCx6RqCE1D6AaoGoppIcwIQtcQVgQlEgHNQLEsiJgobQckjAjoUVBVhKqDaiKUKAILRdFAKDheBNexWFtbY3FxkdWlKmvXWvheu7NUoIYGn7CYvh0uhNafAA9f1gho0dHRQXdPmv3jfQwMp0gkEkSjFpIaUpTY6lQlFAehhPj/EJ3SnmcJrgeOJ/E9aDShVpfYtqBcE5QrUG9AoyHDOqcPEQEdKnToMGRJBiOSlAHduqRTBVPQPj/4CngquLqgaYBjhI6kaYCrgqOBo7brZu2gz/E83CCg1GySr9Uo1itUa2UMr0JWaXE40uSQZdOl+iS00FEENlRXFdYXTFaqMdaFzvlhnckRg3JMwTEEQnHxoy2qqRrRmuCwMHnHlQEOf/M40WoHehe0hgIuT8cpOyre/ZOUO5/FSQjytWUe8A2W7zpBV1cfJ659GbclmSi+HZHeBUDBlbz43F9T3Zzl7F1Z/mDwZ7fXs6IotJpN6vU6S0tLTE9P093dzfBwGNjF43Fc1yVow8+FEHzgf1YNQIQqVW8Cy1LKR4UQo8CngRRwBviwlNIRQpjAp4ATwCbwhJRy4Z87d1cqI7/n4fegKQoy8ENOjiJuRFbtLVHgOViWxcDAAIdvOczg0BCZdBrTNNu5bm/bO4YB183XthX/ivbia19XiDDR9dCRSEmtVmFzc5P5mRmmJibQkWTSKdxWC9dxqFTKpFJdxOIx5ubm2L17N/0DAzSaTV599VVM0yTTneHMmTOkOzs4efIk9UaDUrnMwGAf6+vr7N+7l1wuxxtnzjCyew+79u5DKionT9+Dj6DRbJHL5cjncsQMk4G+Pmy7ycsvv8z09DQHDhzg7W9/Oz3dPXiui6Zq6JqOIhQmrlxhPZejXKlQrddptVqcuON2hkeG8aXEtCJcOHeOufl5yht5IlYETQmLpOVSkWQySU9PyGYeHx9neNcoumnhA6qm8+nPfIZms0lPdze2bXP81qN0dnbi2TZvvvk6U1cnuP2223BtGyElsWiEiYkJFhcXOXL4ELceuYWvfu0rTE5O0mdLVMPg1EMPEUskeOXCRfbu28/dj74T23Ha6R+x4+7dyIlu5f//+hOf4vOf/zyu46GqGlIoN6V/NE3HiEaJxhMIRUUzTFRVC22lEPi+j6ZpNyF5fBk2mPnZn/oZopEIWzLhcCO63zluIvQREOxkcbeN1tbak22FSrbqXFvRfGBs54OFZ0Jg0Wg0qJRKXJ/dYGEy7PPcqJbAkaioaCLAEA4qPio2urTRhIchmlhqE01xSVgKcVNgqC5KYIdQTd+FwEMG3s7LCJ2bEKCooXNTVIRuIEwjdG6ahmJFEFYEoekokVjo+CLRcHdmWQjdRBgGaGpIdmyfByVE8wGgfSevY+suBwF4HtiuxHVDmadave14yoJ6FYQDiieIBpASkk5dMmxCjwEJVRLXwNqigAiBp4CrCFqaStVUKErBpuMzXywztTFHyaljmQ1GE1V2xxrcGm8xYHoYvqRa1plbsZi3Td5IR5nsNynFVBQkEekw6sCR5Sy3XTrC87/5Enf81SmKBcH0kS/S9DYZy+oEb3uAI9dfwggaXFm9Cy17C194/uOsvnGdn/+N391ee6VSiWKxyMrKKuvr62R7s+w/sJ9YLIZhmARb9q19/Af+Z3UEE0J8BLgNSLYdwGeBv5NSfloI8afAeSnlnwghfg44IqX8GSHEB4D3SCmf+OfO3dGZknfe/fbtB1vTQj0W09C3IVKijXhQhMBzXWzHwfPDZueKIkinUmS6M5w4cZyhwQGSidi2Zs1Wr+Htost236FwwalbuF9oR95hOkRRVXRVRVMUVCFolcvk19aYm55iaW4Op9kkEY0Sj0TQNIVqtQqqYGBwkGSqC1VVWVlaolQq4TgOA/19ZLO9nD13ho3NDUZ376Y3myXZleHylSucOHUno7t38+I3X8N1Pc6fPYvve4zvO4DjOjTrNQzD2JafHh8f59ixY8zPz1MolrEiEa4tXmNhYYHv+77v44033qQzleLIkSOM7d9LIMPcNBLWVlbI5daxG3UaxTKB6+G5YRosCALi8TjpbJZCsYiqhUxg1/dZW1+n3rB592OPoSgKju3guS6FzU1y6+uUS5tEoxG6Ojrpy2b51mvfpNFocPrUKaanrvKtV1/hR370R3j66ado1Rsc37OfqueR3bWLzUoFJ5AcO3knA6O7cd0t46TcxJreSl3pusa1xUX+vz/8I86fP49lRkDV2ogfgSoUzEi4DoRhhudQNUzT3E71KJq2nQ7bisIkkkCGMLwf/9EfJx6NhnMXhCtki4Aj2k5muy4gt/YJN9cI2k/azqepjdZpnyNQCHwF6Yc8BEURCKIoWEgZEEifjVWbQs4ln8+xsDBHuWRjt8D3wmAJKQg8HRmERWLdhK6sSmfaZP/+/QyP9BOJN1H1UFxQSh+JjaSx/a3CNsZi+7lg61nYHsGO6/unx86uvmEN7+aipdiyN2FXn/CeBl67YOGG7wcBgWMjXQc8j8Bp4TcaSLtF0GwgPRfZahK0muGrbYfyMa5D4LntpgA+MpDfFgSKEBGmqgglrBMFwqAhdRxPpeSY1PwInhrH19LoZhpdjxG3YmRjUdKmQl9EJ2MKUgYYAvT25flImoGPEzg4ssXfPnKJfV/u5prpMJn0yBuhHRrOC8aUFRIHOzjasU5UNPi9T9SZul4jtS/KI3d/mLXVNfL5Der1OsPDw4yPj2+TFXcOieSJ7//XdQT7FzGBhRCDwCPA/wl8RIQr+wHgg+1DPgn8LvAnwLvbPwN8HvhjIYSQ/4yn+XaYk++HxS8Z+OiaFqKChEDX9bChAhpW5EbBLgh86o0W1flrTE1NI5B0dnayd+8exsbGyGQydHaG6QnP8/DaUNPtv9nO7SpbeKytwp3nYbsuDqEYnR6NMTQ2xsiePUR1Dd/zuD43z6VLl6hVyzSFgq5oFCo1chsb+H6ArmsYugESpq5e5YXnnqNQ2CDwPS5duEgkFuXnfvmXKRU3GBseplGtERECIX2Gs73Mzc6ysjBLtrcXr17Dq0Oms5O4rlHOrfM3n/wk1XqdPWN78YKAjVyeSMRiZmaacrmI77v8/d99nmg0immGkrK3Hj1KsVikWi4xdXWS1YVFRkdHaTRqnDp1irW1NVqtFvPTVzEMk2RnF81albMXLqBqBo+9930MDw2yvLzM7Ow0t9xyC4l4lAMH9vFf/vTPGB8f59CBcf7rk/8VBNz/tvuYm5/n7NmzpFIpmo0G999/P0EgyWR6qDda6IbBzMI1xvbuJZXJ7CB9tWkzO6HBbfE9gJWlFdbW1lDVsLuS1zY+SltvRUq5zfJVNS0sfOsGmqqFMNzAx7adEG1lhI4ARUERO+CUO+oCNyL5bzNsbfy/4Ob3dxxx45MdyIZwuYW6L1v5EyFAERJF2G0DGdDRZWIaSVDr1FsJJArVskOz4bdTOEqbLBhedyRqMdCfpncgSW9vhljMALWBlC5tJgJh0xLvRmmoLUK2fX1th7Dl8G4mwv3T19i+wK1J2fEeN3/W3gGEn+rtnyPhM/jPjJ1BwFYtIXyRBOKGaxKAv6M7mEBpo6TCNGrg6wSuSasJQa1Gs1BncyrH2voGtUoFp+ohgiYadVRhk4hJojE4MDbMru4UmiwgaDA1OE8079C9oiObTdyGi/AD0n8Ikz/hEb0gOflGlIjehaXHyUSTpCJHiV5SiOmCupbn+NvPUHl9lvHhAV44/xmG4rfjui6WZRGxLEzDaPOibjjUf0pJ4H9k/EulIP4Q+A0g0f5/GihJud2ccgkYaP88AFxvf0lPCFFuH7+x84RCiJ8CfgrAsqLbEdjWVlsSRjee297yBAHSYluPxvX9diSoouk6iuIjFEFc6QAZmoG5uUWmpmZotUJyVSQSobOjg9Fduzh+/Di97Ry2kDcaM8vAu1Fk5sYUu56H127WjJRU7DCSivcPcM/QSJvuHT6E5WKRfC7H1MVLTF65gvQcujo76M30MTQ4yvrKImury2xubGA3W7z0j99geGSEv/3M36DrOpZlYbdarC/Mk45axCyDYm6d2bl57FbIVr7r3nvRdZ1YdJRcLsfSwhzRaIz+7gw93T2M7NrF977tbUgpmZ6eYX5+niNHj4YduUyT1198iasTlzl999149Qarq6tksz288MILjI6Octttt5HOZKg1mkBYXB8/dIg3z57nc5/9LIlolF2joxQ3NvjS3/89gwMDYU+CoUHOnj/H6Ogov/qRj/CpT32ST/zlJzBMg5PHj2EYOs89/xxra6tomkEQSB5912OkUikajQa7xsboSmfCgrkMCIuc7XvRLkCHuzoJqFy+fJnl5RUiVpimod1MQyBQ2k4hoG1oVa3tONrGQyhoqoJU2jtAP0SYyTbBR1GUGylFtlJPbWLX1nuEkXP7gB2LZmf0L25+VHdcTxiIwE7hPykhkF7IiG8fqxugCoVsfxIrOkgslmN9rUihUKJer+L7CooeR1Mj6LpOti/KvgN9ZPtT4TOj2kgcEF57ntqa8ztKK0IRIblha64hTF9t4dXZEjNrO4Rt+MVNl3XThQrEDqO8o5n7jlrdzt//72Uk5LbzFDe7ona1/abvIIOtvVobJhvAtgMMUDSfaEIQSyp0dEVIdvYSn22wslIgnytRqeS2wQk9B/cyNNjP8PAwXelOAlEnkA6H/Duh00PZ7/P8/YukihaHJ7vQPEnWA8bhyj2vcLBwEAWFkpQUQ1BXez0PMMQAw32f4IWvXuV7H30fi4vXUBWFdCpFRzKJ3s5k7ISIt2/Iv3r8dx2AEOJRICelfEsIcf/27H/nkP+Cz268IeWfA38OYQpoq6ixRYraWfIThLnJlm3TtO0bRiAIMNryxO1zEolEoL31E74AFDQjSocRIQh8Gk2by5cnOHv2PJ7nbWvc9Pf3s3fvXkYGBxjZNUIymUARAsdxcBxnR3CzAwInBH47h8yW0woC1HiCnmiMzK5R7ntvKHEdMQxa5Qob+RyRqxm0eIrB3T7JziTJRIym3aJYqOK25ZuXr18P/254D+js6uRd734XrudxbXmJdCqNZUU4d+48V6emOHbsGCdP3onjOMzPz3PxwnlKpRK+79PR0YEg4JMf/89hekDXScTj7N69G2TA7bef2Fb7rFarNJtNvvHMs2xsbPChH/wh+vr7WM/nyec36Mv2sXfPGC8+9zyf/8xnyfZlcVstKqUSa/9/e2ceJMe1EJN2AAAgAElEQVR13/fPr7vn2pmdPbHAYhfXEicJ8BYpHqIuWpIlS3al5JRUrrJSlVRSSf6wk0rZVqWSKlflH6dSKSWuxE4iOXLsRD4kK9ZFyaQOi+J9gzgIkjiIcwHsAew5M328/PHe6349OwAhEuRuzPkVFtPT093v169f/47v+/1+b3KSnmqVz33+8+zevZulpQXu/+AD3Pa+23XIaKtJvV7nY7/4MX7w7e/y8He/x9aJ7VSCAidPvMHWrVvpH9lAMwyJjILvNPlrhfPCwgIv79+frtSWYGOlPZTno/wA5XnEostnxOb5SaJRekUmcNIF4cVY5KnVn5jvKt2/8h3JD3pb0i8NnrTvbe5t0L9n+S7aA8gEZJJDL8QPCCSg5vmUyv3EsaJcqeAHQuPUHHESkahlKtUK69bXmbhhhOENZXrqMXE8R6ia+F6IiC0jEIMkeWiqXfjasg7mHcxu0sJEYizSKyg+52itUKwyyQS15ADZlfMrWWx81tegKwXn+tQ5Pp3LQcy8nr1AhNV4XqAQtEGnx4zPEHWWl4fNWr6KVmuKer2HkZF17Nw5zpYtmygUA2I1BzQQiY0jo5XpRx/dmFrmj3zoDOsvVLjxcD97Lt4DwMHhx9g9/X4EO86yMOF7b/k1do1Pc/z4CVqtFuvXr2fz5s1pJF6qMK9z0M61eAD3AZ8RkU8CZaCO9gj6RSQwXsA4cNYcfxrYBJwWkQDoA2berBFR+kH54qehdKlG98zMmIpBqbSqpgKWl5fTWvVKKV3Zs1BIM2DF05BSIIIvOotSiVApVvDKps55krAwt8CzzzzLT//2JxQKBeq9dfbt28vevXsZHBykVquBmU/QxcliEjOwfGmDG8wgDETSiKWlMMQrBGwYH2Pzls2UPY8kjJibneXY0aOcO3eWVgTKKzG8YZAdu/bS09PDkVcOc+H8eWamLvLoTx9j7003sWnjOCePneDsWQ1/VKpVzp89y6uHDzI2NkbYXObs6dMsLi3y4Q9/hP6BAXp761y+fJmZmRl2795NvV7Xyha9kM3U1BS+79NqtZibm2N6eobLl+eZPHuOWrVGuVRh29YJZmZmOXHiBJ7nUSqVGOgf4NZbbmVhYYEzk5OEccSTTz7J448/TqOxxOYtW9g2McGmsTFUHDI7M8Nf/sU3OPT005QRNo2OUgkC5mYvsfPGGylUKoRRtELQ5l5s83n86DGmp6cpFkva+0oxfPNiieAFAcrTC737ZuH3wCwCbzOxc+QB6EgfT5zkG/OXCnU7bkWlkI7CzVh2hJrqLNTaDrvyuyFoeEha+IUEvwD1AQ8JCiyHPgstYXmpRaPRZHC0l9HxgA2bipRrIfgxSkV6wpckgz61VM4LfaOYMgWlNOzg9HnGkxH8jguh0v+uds8ZnJQKfsmku3ITf1Dpd92eQil3cn3lGHE/E5WNm0zJmNILAjYzXCldY6tWUwwNeQRBjWazzOJCgaGhGmNjQwwMVCmWPJIk0iHQXozC5uskaEgt4+2jPx6lVUx48q6L+Ilw99PD7Jm6h2MDL7Fl7iaCpJTjvdlsMjOjawBZQ7Zaraa5KalhYLK+RWSF8nsr9KYKQCn1ReCLhokPAf9KKfVrIvKXwGfRkUBfAP7anPIt8/0J8/uProb/g47UKBaLbpuY9tL5gTiODV6aPXTf91Gel4aGKqXLJTfimCgMtXtuoJlI9KeQ6HkFz0PZtTf9gGKxBCiK5YrJzI157sX9/PSxxwnDkCgM6eutMzg4yL59+9i5c2daKz/wzcOIY8KoBWKtOotTmlXDkoRWDEthlLpw0ltj8623MHHb7TxYKlL0fJYuXebS7CwnTxyn1j/E5cUGI5UaW27YydxyyJmLJ/F9n52793LbHXczODxIvV5nenqayQtTjKwfZdfuPTSbDcrlAoWCMD11lpNvvMbYxo0M9FWAkIWFywixhp2KMDt7kZmZGaq1PjasH2J0dD1nz59hqdkgKBQ5efIkSimGRtZx7977qPRU6anqv0KhwML8PM8+8wxHjx6lUCwyPLKB0Y3jlMs9tBp6jecfPfJDnnz8STYCLc9n2w3bmZqa5ugbb/Chj39cD3CzNKae/M0EgN0uFrVL/NSTTzE9PU3FQIjKJCUp0/e+7yOej18oIEFBZwD7BRCP0MwxiLP4izKCweL9vqcXpNflHJReMKo9skyBpzqj4lcX/NaIyca6+92uJ2CFl/JCFImZJPboH+ihp1oBiUBCGsstlpZD1q9fz/r1g/T2FfEDfY54MZ4kxpK3MJbSRlfcNrGonMzhnHNgIaHsu0qPzyzudDsd++4cX4Zbuxmw2b0728pVpuQEvysDxEA/oJ9f5nVpwywPSziwk0pS+Cp7LjG9vVXKlQJBUGTdyAjVWg/9/XWqvULMHDq/UDlzKRgvaeUIKLY87n1yHQDNQswz75um3Bxn+/MlXht4jtBrIkrYcfEuFhYWuHjxIsvLywD09vZSr9dTiz/tb+f+2hXgW6G3Uw76t4E/E5F/B7wAfMXs/wrwJyLyOtry/9ybXknyD9WSOznsewJKL6iSQa6y8jjfT7F4nWRj3F302sKe5xEnSmP2ojNBgyBIDSLfL9BoRSbr06e3PmgUj25zuRny5FNP89NHf0az2SRJYmq9NYaHh9mzZw933XUntVqN2JSSsJNazcbiyvtWWqPHIoRENJa1hyO+UBwZZs/GDdx0/32ICMVCgThssby0xKsHDnBg/36OHHuDJIrwPaFUKlEuF2k2GywuLjA9fZGhoSE+/JGP8NxPHuX1o6/ysQcfZKC/n0ceeYQw1MLihhsm+NnPfobnedx///2Mb9rK7Nw8zz77LGcnJ/mt3/pt1q0b5dLly+zavZOlxUUef+IJHjt3jo1jY5w6fZrTZ85w8803MzQ0xIGDB7n3vvvYsWMHx0+c4Jlnn2V+bo6bduxk944d7LtxL5MnTzN55Ah9gwOEKuHo2TNs3LKZnr5+XSXNGRg25rndKNj/0ku8vH8/FopD9MStMti/5weI72mhYl4WpRRxokNFPWclNptgmCQxRa+QQkz2d7fdFM5IYYb8sj3S9umO5fTTDra2MWwFrDve0vmoxMBCiZAooVDwCArC0HAZ8QaJY73OcG9vnd7eMqVyCBKmuTIo0A65Z6xfq1CzTF7Betp6cIpRGum9uWiP0lCXrTll97l3bpVppryVA2GYZ5IK6c7IsVUc7d6f3ZY2oZ/bn3O0zPkpHKTSyf20gyWmUFIExYANYwOMjA6mno7vg17Ra+WzM6aKVgLOPveZV0KfBx4fASAqJ5y/cYzqvMeNL/QxOzvF7Owsly5dolbtZXiophEHYwRYKNKd89CP6V1WAEqpnwA/MdvHgLs6HNMAfvXnvG6K/WtcVHeu7/sGbtHLL1otaB+w1f52TgBIhYV7jH3BbeRIkujVdJRSJMvNdCLZJjqJsQRBiGJdCsHzJC0drBAq1RI9tToiOvnq4tQMpx7+Id/97vfo7+9jZGSEXbt2csu+fVSrVXpqdW3dGhgpzWo2et2OS08kDUdVUYSYaJRWqD0gv1hh7513cfvd9xK3WsxcvMDx11/n1Kk3OHfuLKrVoLdWY2R4mPXr13Ps6HHmLs/TWGpy9PVjjI9t5O677qZSKbOwuEC1WuXGvXs5fPgwzzz3HKdOneKXPvXL3HP3vTz/4gsMDgxRKBaJk4QDrxzG9zw+8KEP0j8wyMmTp3jhpZfYsmULm8bHaTWbBIHPc88+w7aJbdx0003s2rOHpcVFvv/tb/PYExe4de9efv0Lv86ffvWrSBDQCEMuXbrMbXfcQalcIYxiIxRUKjTyZaC1N3jwwEFmZ2cpGhfZvhgeNnta8BUkKkY8vdi273l4vo94WVRPrBTiaaUhTluu95kT/nZ8XsO4To1oV3GY73py1fxmxlong86Gl7reb5Ik2ssUoVKpsG7dOowVRRAUCAIfz0tIlxTVN6SzuPVMSSbIjUIALRyzqB+Dzuc0QCZRr1UA5Q07d0o4b5tf+Xxz3hUMRPfzqjyofEuKDE5pv54ORffS+/NEsPnf7pxR5vlg5jcyhdKulNx9xZbPA09s5NDA4+zf0kCKm0iOLSJK0VevMTy8jnpvD76HDj9WbXklsAK9e6u0NhaEUdAMw9Tysp9W8MdJgooTs7hHlpGaOpXmZba90u4ZWGXSHoFgt0OTV2BfKpcH364RGya0JMIzGWRiJ6s9D/ELFIMiRQtFxDGT56c5dfpveeihh0ElejJRxVQqFSYmJrjlllsYGhpidHSDjr4A7TXEsVlIOkknLfVrmpGIsGhi5EtD67hxeIR9995PMQgoeBAItJoNzk2e4+DBA6xrxQTFMr5f5tzkDD2VBcJWk8X5eaYuXEA8xdjYGP39/ezeNsGxVw6xuLzEgx98AL8QoETRX6/x9FOnCIKAsdFRHnr821SrNfrLJV57eT9333Yrw/199D3wAYqlEt/4xte577772L59O321Hj7xiY/xX7/0n+gpFnnggx+gOjTIxMQECws6t+G2W24hKBRpJQ0DqWcVUd1n5fs+YRjys58+ysLCAuVyReP49oUU32Tp+hRKJQqlok5CEjFwtS4rrKOFwFe6v8TL8GwRITKZ1tYTSPu+7bN92353lUUmYe09dTjJebbpY1/RgMqUhySIQKkEpVJAFpFj4QkdC4/FvB0UXDdhxbxKL41qs8YFVE7wWNPEHtNepVJWPK/2frF9kHoarteQUzCZFe/Cf/a67p8LIbnt2vkDleGC+W43BqS9hjUeXXhKn5/1gc7RSDmxB5CQGQg5E0FW9kOCVj43nL+TyXPneP2112jQIvr9myj8OGC8OU4hCLQscOBFZWWh2We92LdDa0IBADm3G8hKJGOsYjNphyKXCg1CFIXpNmK9BK09PfOQk8RYe5BlI+qW0a9BnA4GawVagSOiSzSLJ2m4p1s4LLYPKudzCsViiVJJzy14qRWpOHX6LMeOv0HYapEkMb29NQYGBti9ezc7duxgbGxMJ1mZKCCVJERhqKes4hjfkxRaipSdGItZDkMz7vQCJj3r1vG+D38Y3/coFYt4ccL8/Dxnjh/nyKHDXFoMiYMK1Z4Kyu8hTAIuXbzE/NwiCwsL/OlX/5RW48s0m03iMGSgVqPuezz5zW+y6847uOdTn+LymVNcmjrPH/z+l/jkZ36FLdsmmJm6yMcf/AivHjtGomJ2bNnK0ddf4/TJE5x64zhPfO+73HTrrQz3DXDuzCS33XYH9YEhWmFMglmgxY3Dd6wu3/d448Rpzpw5g62vj6dtft/zNfzjmdBgk1AYoauC2nGmrGehdLZvogDrgTqCZXl5OYUx7DNNhXsHCe16CXZ37juZtZ9ajym04Rol7n5zAdMfKZSeKgPblFukzhhCyhVHKh0znaxpBBLcSVi18tgc3LPyGismisnDMFkhPef4q1ixysHD7J2o9E5d3N14LU5/dOaHFe1lk/ygrE4Su/ZXetBK3lTeI7CXzuYuOlfvtGMsiiLm5uaYPH+e8xcvUq/X2fGlBlu3bsUfKfOdj57grpfWM36hlziOdUb4/Dxnzp6lUqlQq9Xoq/dfufOukdaOAjDWuxW4ifMi2sHsWlVJFKUWerFQSDF/q+yVAs8LUjzVdYetO5292KSKxX2gOvvUYMcqxvc9kkTh+wo/CAjciB8Tq+tGlVh+RLTWF9EOOCiCYomgWCJBkfge56ZneP1vHiZ+6Pv09fbSX6+zaXyMfXv3MTqq64DY/kjDV1XmhAvWarRYspdaPlGiiKOWVmTlKhP7bmXXrbcjStFcXOTS1BRnTp3i1cOHmFts0lMfom9ghDvetwEfxdLCAguzs5w5fYrLs9Mshk0W5xeYunCeX/nlT/Pxjz3I888/z3e+8RcsLS0RJvBLn/40n/rs3+fc5CTNVpPx0VG2T0zwxutH6a9WGRkapjm/QByGbNu2DeUHRElCIvbFXfnS2bj8gwcPpAZDHMcgAZ74eJ4W+H5QTEODRTyD+XupwZkaFp6uImUFY3ukkTKGQ8fx6lrqbdUy2w7MmfMdhe8Vrp1HSax3q3KWsuVbGcw+hTFM21dtLWdV233W2rWwpCPEVP65aCF4NRzCTKpb4eycncIyziUtDxmMohwLOlNMVibYva6CdtvOLqz/01VFHI8ndy/5DG/N8sqwXwvJ5fZljgDtrLTPS4A2GJeXl5mdnWVhYYEwDKlWq2zYsEEHrMw1+IW/Ws/CQMy3PnSU7YdqDO5XXFya4eSuRUZeauD7PpVyzxX6/dppzSiA1P1KEl39USmU8kBpzFKJwvPsZJ1+BraGvvUcxCyX6Fpy1qpvh1Dsp039J8krBf1poSAdxZMkCa0ohBCWG01zDT1BbZWRrSljV5Fa4b6ak1x4KUkSCsWAvoKuy++JML/U4vn9h3lh/yHiKKLVbOD5OjnkrrvuZM/uXawbXqdLH6N5azabOlpKLNaatWvDbEERxiHLlh8JKI9sYPv6Ufa8/16Kvk8g4KOQKOToq69y7swZQs9nrNbLJs+jVq2wZ9cuzl84x1f+6I9ZvDTD9PQ0yfwyNTxqQcBT3/4eLz3xNB//zKcZvOUWyq2QaG6eQhRT7ulhfNsE586dYzGKGduyhSXxiaKWgXIycl+gYjFgenqaJx9/UuctBAVEPMLEeIOicwB02SoPT3yUFxAERQrFosH8HSDAvMux25b1CpKsPtAK766NN7ut2oScFf6u0dGuELILXFlUZxE0ncnOWSk8lGQQBk7f5ZrIG/lm88r5vU5LOZ6yshHZBd1oGD2XY++3rW9SIe/ykY/8sbcgbQxb5WavmvW74+GoTt3cpjgdYa7M9/RdFSvE8+eIOc59JJnBYuaLzHXbx4eVU9ZQbDQauqJvqcTly5d54YUX0jGXhpt/PWF/o0HznirBF7YS/pM36Lvzho7K6a3QmlAAepiEZnLLJ0l0ESnfU9r1NR2srbFEwzXpZJ25hoMDum68JWv5ec6x2W/ORF/OTdaQg5uU4044a94hDKMcjmiFfhzHlMvl9HsKWylTGleZqpOelw1EB4ayOQVB0adYriBKsbTc4oc/+ikPPfQwSRzTCpv4vke9Xmf37t1MTEywadMmhgYG6OmpEJt1FcJWU89DYD2dxO0ABNFRRi07Oan7tn/bDazbtp3bfJ/eSpVms8ni5VlOnjhBiwp+uZ/56DIbt+5i3yd2Ui6VQCXErZCZqYs88v2H+Nqf/C9aiY6U8KKY4bFNzLdCpheX2LZjB6Hv62CXVAHnJx+t0my1Is6fv8iRI69SKpXwAx06XPB1nL9nrH/PC/ADDQMlBiaK0+Uc/ZzF7/Y5aM8zjmJtiSWkwQLpBK7lyRlfnbDuTp6mVTCdqNP81DVTqs8cg0NlYzprP0nHdSpSFW33sdJbyXjKQj5TUtZXSB1pcy6ZhW92ZCLbtr3yXvOvZj5JrNN5ue1EP8OOqkwyDtNnovK/u4EjqUhv4zE9r115mqzjnBIiP04srO37PuVymeHhYYIgoLe3l2azmeY0Zd6nlgE9PT1U98PyL8T4/+NOxr/sMzg4SKVcWXmfPyetCQXgBzpWW0lMFMUUAx9B6QgOC84haVleT3yUsZzt0LPPSafz6ydk4R/PEb52AiWNqDDwUvsLbk2A9smmbO7BHKUUnm8mpw1mqZSOVEnihGajlUYfxUmiC8wVi4iNt45ijV07140BxMcPfJcbM6mnB2ghKBhhruGgsBXx3LPP8/TTT6MSxeDAAGNjG7n11lvYuHEjg4ODlEplfX1jXdhoJHS3YqDQzPU27naCQoUxS2aupdg/wA13DLNdhA8kMUtzc1yenuLVAy9z4cIM85cu0Vet0dMzwL33P0ilXKbVbHLixAkWl5cZ2byVuYVFLi8tM7FhlFg8oiQisXCDs6yheVj4XsD8/GXOnDmjMf6gqNc9xmR7orM5xXhtSiQ3ZRnFsZ4087KkPfezHfqx+/xrmGhzX3Yr9OUKv3c690oehktX+t0K2jxcskJMk5daZspyhT7qIOwc/t1j8rBMyo1zLiv6dCXvbYZYx2NUG4/Suc9U+3nt+HsHteBY+FbV5EGulSTS7lsbvsXxh1T++PbxBbqkTb1ep1wuMzg4mM4jZpGKWVRjmlz6qIcfBJTHy9rzv4rXeK20JhTA0sI8y7MXGBgcpG9kmKWlZaI4odUK045J8BxIVCd5e14WLpgfC27ymCCBr22XDi+aVRB+h0ElosALCMMEzwtS1zpJEjzsgzffxXPmIRQiPkHg5y1AIIoiWlGk78Lg9KVSKU3wse6tpcD3HZzax/dMbRxj5XmqQKFQBhR6waDEukGcOX2WU6dO02w2iaKIYrFAtVplZGSEPXt2s23bthR3xPAWtlooY3nEcZzFaYsGCTyEZrMJblhc4FPdsIE7N25EfF9X4fQDomaLy1NTXDw/ycXjx1kqFlmKIp569VWmpqcYHR1lz803sxxGbcXfyK6Nzt8oFgKOHz/OE489bqC2gvHGLPyjk74kKOD5vs4ANoogMl6WBL4Rkpb3lcLJvoC2T5qtpl4mMwh0noEJ5U1FhStEZKW9moY3d7JaHUu9/bzOwjd35fQeOkkslf5I9n6odGisFGAd2lopkNuFv/XWViqIq51/pXZXKgrleCj5caGcT02Zt3B1b8ptY2VbWRsdTnVayauTNrUh+f7t9Aw936NcKVPpqWieTKPtBmrGj1qhTNI6aW+D1oQCQCWcOXqEU68pWipB/IBSuYctWydYP7oRgDCOWVpq0Gg2UUqHQNk6Hol5qZMkH7vt+35aThql0ollIBW4qNjkBayc1S8UCigw2jbb74kYIZA9GLAx2NnqUdGK6CANbXiulSZC2Gzm+LIT23EcEypFsVhMJ8kF9BxJGqrqpy+DiA5TSxeVL2gey9V6zg09OznFydM/Rqkf0Wo2iKKQOIrYuHEjd95+O7t376avr0693pveYxiGRFFo7MhEL+2EMpEjJgY6AeKIEEVTmiigMDTApnVDbL15L77nUygEqVKPlWK5FeqJcfFzb43l187xiMCLL77IwYOHqFQqWb6Hryd/EcFLs319neFrwkFjQdf9t7kfRihbC9+tPOV6BgDz8/P4vk9vby9eoWiOaYcqHL7bt60n4pzQLlusj3ElodGGODhHu5i15CALayNk95S/R/cG3hqWnAnRKwrLa7Dw7f43h76UsXgzIalW/G4tdFdRQebX5jP03Sa1Iejw3MZOZsRd+R5sS+nFDCUu3NpGSXIFDe626Xy6CqA9RPmt0BpRAEAc67VnPSEo+CRRg0P7n+PQyy/QU6sxtmkT60Y2UK/1EcYxrVZEK4wIWyF4fvpAXTwXsmqFWllkAyLNLjUx9641kmYRi4dfyKz41FIxi2bYY92Q1diJ3bUCwCoj+7vbnht7rBWSIgpbRK1G2mbUamgL1BbM0w4EnlGAFkLSpwuJ483oSpB+dl9ExCoiKBQR8SiVKyRJQhiGTM3O8e3v/YBvfuu7RFGLzZs3sW/vXsbGxti0aZx6b41SsQAKoxB07XaxcAIZjJb2l0oIY4hiCCSh2Wzh2M8IEHh6fifd71hwViFenJrhzJlJWmFMf3/N5IhAwQuwcJ3NArfKMTEWdltwZvoslFLEkanxb9pqzzpeXmpQKbeoVTHiw0vzUPQ1OrzccvUJ1U4Cd4XiaN+3AmbS/Fhr8WoCNJ0Uda/njOefl34ewd5p35t5HJ33W+FvhDf5+9GC10xlt/0uzuPPWdqO95W2mB6nL9LxWbnnpt5LOzPO16vAfG64s9sHVzreGjC6mb8rCsC8op7o6JmooYVfwROUQGN5kZMnjjF59ixRFBEUi1SrVfr6+hheN4ISjyjWk7HNVmyWzTPYfyqkI/Pia0s5jsMUOvCM1W47V4mO+U8QlAnrdAVDEsdpuGrBhKC68A9kL1jQdm0Ady7BfciJUYKB7wFeLmPYnu95nqlZk7+GhUU8W/NILH7tkSTaM/A8XQqgUNC5E0mc4YzFQplKpaohIDOptdyMePypZwlbj9MysE+lUmbdumE2b93Mzp072L17V1bHKYZWq5UpucQk1KANosS+4PaerVBLEuwayG7fJUql/Xf48GEmz00yMDBAuVwGhFacECdKZ/GKmIifQHsDRhEkSpmJfy+N80ayPIrEeNGu52b71vM85uYWKBRK9PUNIGUfPIVylLjkpIeBJNuErVUdV4Imkg4rjHWmzAp1i7RdUfivwIjzQuvNrO524ffzKIxOkFe+vZUlINLaPPb3nAVvf5DU27EmvFgJL6YNlV0xhaqM9yDuFV0vyBgxSRuvnYV2PmdJH9Lmj3S4/07USem1y4pO7QNm0vvt0RpRADoBJUnM5IeJVvGUoEu2AkmDOAx15cckYfHSJeZnZzl57BitVotiWS94Mj6+mfXrNyBSYHFpiUarRTOKieJ8h2rBqlIYKceN84DbXxbrPsdJoqtPOvsQDb/YwnWeCCS6lIQVRAqD3jovsNuObiROhaBbDTBVQGhPommgoyAI0lXP9NxDkIakuusmpGFoSukl/5x7j5Wi1Qz1YFZJCjf5XoBX8ikWy+mxC/NNXjl0jMMHjvK15W+wvLSICNRqNbZvv4GtW7cwPj7OyMgI9XqfVoBxqNdciGKd1m5gI8+BEFyrVCk97yN+gFLw3AsvkYjH+tGNxJG26rMqrLp/I1NEzno81qCwfaZLHWgF5JnJ4NDL+jXN+jbtR1HEubOTeOIzumGMJCHNxEScsiVXeQ/boZhUJIkdA5nFmo4JlIsgrLymrMS7V1Cb0GmfFH0zD6DdkHH3pd/Nf8rhJd3nrJtg5+TsZ6dJUb294k5tA8bqdybZ2+4lUyQqO86Zn1CotIQGWCzdtt9JIb5J/+bM/M6F6iy5xmM7BGnP6SRn3IhCe51cCQr5O+MBZB2jreV0bwriSAKBBxJFqVXui0Cs6CtXiEKZ0zUAAAkrSURBVICo0eDIK69w9OgJSuUeNm/aRF9fP0m5RKO1nCZpNZOAKAGIQcUEni7NageX6+K50TmuZnb5bv+eg3QM2Xh/LQzj1JX3jOJw200kC5hzayTZ2iWJc32XNwvz2OUvrdB3cw463VMKm6RwVJC7Ty/nwSiiWEGsE/Eq5SqFoIiIrlnyypEjHDx0iDAMqVQqjI2NMT4+zs7tE2zesplKWUcw2GJ5OpFLt5M4L0QSxxTKZeJEMT09zfkLFxgcXGegMF2I18IgnnjESuErlYV7GrL9ajOxsf3adky7F2e9t+npGXp6qszPz+vV3YoFisViFkGVszjtBZ3xgPOzAxdYpDovc8TsEPuvI1lEQ5zxAeSKIFoF7gqR9rHd2TIVh9eVARaYN/Lq52Y37R5nefA8s+Ka2HkL21M23yb/ntllMq9ErlKxoeJi2tBd40IwecHeCW66EvSS5ycT6rEx2NzjXJ5cmLddAdpEOyvYrVD3TZADqRITY7R4jod95bmFa6U1owAsKQBXYxqrAaVIwhAxN+/5ZlD7PiqJqJZ7iPEol3yaUsEvlphZSmh6LYrlMj3VOuVAC+Fy3GJpuUEU6Xj4MGqlcJFtywCARKmbtbIiZd4S0i9caKx/vSsT0jmF4GUrTcV2QJhrxImu3thuCXgielITEOfB513NjDe7FnK7leGWsbCQUoqdt7m2IkIhCHTf2+J1SpdJdqtlFn2fKI6QOKZQ8CgUFNWqj+cJU1NTzMzMcOjQIeYX5kiShGq1yuDgAMNDw9x0441s2bKVarWaE1Czs7McPfEG3//B3zA5eZ5KuYofFFAIXqAljA9pRUcBPI11Ge+BbFJUqRXK3RoaKu27fO6IVdhnJyeJkoRWFLFl6xbWr9/AyMgInlfQZcCtErCjxAN3EfiVzynD8NufSyogAFvOwTWM7HVEMu+nXYBpAag0FGjHQmpUWaPGJsxl3nD7WGofN25b1nsGN0qHrDNzykty1283WHD6Qm/7aQCFbSNOlCnY52eGjNNCFEUpD56nUu/PXc8h9Z7NfJc9x33m2aJA2Xtgs+6jKErfqfZ3s1AsEBSCtHxLKwxpNJopEmDhWdczFxEajQbNVkSz2UqhU9u2fSctP41GgzAMWV5eTnMGLly8yNula14U/p0kEZkHjqw2H200TNsylmuE1iJfXZ6ujbo8XTutRb7WIk+7lFK9b35YZ1orHsAR9TZWtn8nSESeXWs8wdrkq8vTtVGXp2untcjXWuXp7Zz/9mcRutSlLnWpS/9fUlcBdKlLXerSe5TWigL476vNQAdaizzB2uSry9O1UZena6e1yNffOZ7WxCRwl7rUpS516d2nteIBdKlLXepSl95lWnUFICKfEJEjIvK6iPzOu9juH4nIBRE54OwbFJGHReQ18zlg9ouI/GfD434Ruf0d4mmTiPxYRA6LyEER+Y3V5ktEyiLytIi8ZHj6XbN/m4g8ZXj6cxEpmv0l8/118/vW682Tw5svIi+IyHfWEE8nRORlEXnRRmisgXHVLyJfF5FXzNi6Z5XH1C7TP/ZvTkR+cw30078wY/yAiHzNjP1VHVMi8huGn4Mi8ptm3/Xrp3xizLv7hy6EeBSYAIrAS8CN71LbDwC3Awecff8e+B2z/TvA75ntTwIPofNb3g889Q7xNArcbrZ7gVeBG1eTL3PtmtkuAE+Ztv4C+JzZ/4fAPzXb/wz4Q7P9OeDP38Fn+C+B/wN8x3xfCzydAIbb9q32uPpj4B+Z7SLQv9o8Obz5wCSwZZXH+RhwHKg4Y+kfrOaYAvYCB4AedMj+I8CO69lP79iDvcYbvAf4gfP9i8AX38X2t5JXAEeAUbM9is5PAPhvwOc7HfcO8/fXwC+sFb7MQHweuBudEBO0P0fgB8A9Zjswx8k7wMs48EPgI8B3zKBfVZ7M9U+wUgGs2vMD6kawyVrhqY2PjwGPrTZPaAVwChg0Y+Q7wMdXc0wBvwp82fn+b4Dfup79tNoQkO10S6fNvtWi9UqpcwDmc8Tsf9f5NC7lbWiLe1X5MlDLi8AF4GG013ZJKRV1aDflyfx+GRi63jwBX0K/DLYuxtAa4Al0QYS/EZHnROQfm32r+fwmgIvA/zRw2ZdFpLrKPLn0OeBrZnvVeFJKnQH+A3ASOIceI8+xumPqAPCAiAyJSA/awt/Edeyn1VYAnSo8rcWwpHeVTxGpAd8AflMpNXe1Qzvsu+58KaVipdStaKv7LmDPVdp9x3kSkV8CLiilnnN3ryZPDt2nlLod+EXgn4vIA1c59t3gK0BDnX+glLoNWETDBqvJk25I4+mfAf7yzQ7tsO96j6kB4JeBbcBGoIp+hldq9x3nSSl1GPg9tNH1fTREHl3llJ+bp9VWAKfRGs3SOHB2lXgBOC8iowDm84LZ/67xKSIFtPD/30qpv1orfAEopS4BP0Hji/0iYkuJuO2mPJnf+4CZ68zKfcBnROQE8GdoGOhLq8wTAEqps+bzAvBNtMJczed3GjitlHrKfP86WiGshTH1i8DzSqnz5vtq8vQgcFwpdVEpFQJ/BdzLKo8ppdRXlFK3K6UeMNd/jevYT6utAJ4BdpiZ9iLaHfzWKvLzLeALZvsLaAze7v91M8v+fuCydcGuJ4mIAF8BDiul/uNa4EtE1olIv9muoF+Uw8CPgc9egSfL62eBHykDSF4vUkp9USk1rpTaih4zP1JK/dpq8gQgIlUR6bXbaHz7AKv4/JRSk8ApEdlldn0UOLSaPDn0eTL4x7a9WjydBN4vIj3mPbT9tNpjasR8bgb+Hrq/rl8/Xc9Ji7c40fFJdLTLUeBfv4vtfg2N9YVozfkP0RjeD9Fa9ofAoDlWgP9ieHwZuPMd4ul+tMu2H3jR/H1yNfkCbgZeMDwdAP6t2T8BPA28jnbhS2Z/2Xx/3fw+8Q4/xw+RRQGtKk+m/ZfM30E7ntfAuLoVeNY8w/8LDKwBnnqAaaDP2bfaPP0u8IoZ538ClNbAmHoUrYheAj56vfupmwncpS51qUvvUVptCKhLXepSl7q0StRVAF3qUpe69B6lrgLoUpe61KX3KHUVQJe61KUuvUepqwC61KUudek9Sl0F0KUudalL71HqKoAudalLXXqPUlcBdKlLXerSe5T+H2XsM8fD1OtPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.image.AxesImage at 0x62cbafb00>, None)"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initiate SIFT detector\n",
    "sift = cv2.xfeatures2d.SIFT_create()\n",
    "kp1, des1 = sift.detectAndCompute(img1,None)\n",
    "kp2, des2 = sift.detectAndCompute(img2,None)\n",
    "\n",
    "# queryKP,queryDesc=detector.detectAndCompute(QueryImg,None)\n",
    "# # BFMatcher with default params\n",
    "# bf = cv2.BFMatcher()\n",
    "# matches = bf.knnMatch(des1,des2, k=2)\n",
    "\n",
    "# BFMatcher with default params\n",
    "bf = cv2.BFMatcher()\n",
    "matches = bf.knnMatch(des1,des2, k=2)\n",
    "\n",
    "# Apply ratio test\n",
    "good = []\n",
    "for m,n in matches:\n",
    "    if m.distance < 0.75*n.distance:\n",
    "        good.append([m])\n",
    "\n",
    "# cv2.drawMatchesKnn expects list of lists as matches.\n",
    "img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good, None,flags=2)\n",
    "print(len(good))\n",
    "plt.imshow(img3),plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracySift(folder, training_set):\n",
    "    total_c_cards = 0\n",
    "    total_t_cards = 0\n",
    "    total_f_cards = 0\n",
    "    iterations = len(training_set.set)\n",
    "    for i in range(iterations):\n",
    "        sys.stdout.write(\"\\rTraining Iteration: \" + str(i+1) + \"/\" + str(iterations)) \n",
    "        \n",
    "        image_cards = training_set.set[i].round\n",
    "        \n",
    "        path = os.path.join(folder, training_set.set[i].filename + '.png')\n",
    "        cards_detected = card_detector(path, deck) \n",
    "       \n",
    "        c_cards, t_cards, f_cards = correctCount(image_cards, cards_detected)\n",
    "        total_c_cards += c_cards\n",
    "        total_t_cards += t_cards\n",
    "        total_f_cards += f_cards\n",
    "    return (total_c_cards, total_t_cards, total_f_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Iteration: 61/61"
     ]
    }
   ],
   "source": [
    "correct_cards, total_cards, found_cards = accuracySift(ind_cards, training_set_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8032786885245902\n"
     ]
    }
   ],
   "source": [
    "print(correct_cards/ total_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 61 cards are not part of the mask. but are present in the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98 122 163\n"
     ]
    }
   ],
   "source": [
    "print(correct_cards, total_cards, found_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "99 122 235"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [],
   "source": [
    "def card_detector(path, deck):\n",
    "        image = cv2.imread(path)\n",
    "        \n",
    "        thresh = preprocess_image_grad(image)\n",
    "        thresh = cv2.convertScaleAbs(thresh)\n",
    "\n",
    "        im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "        valid_conts = [contours[i] for i in valid_contours(250, 2000, 500, 100000, contours)]\n",
    "        \n",
    "        # BFMatcher with default params\n",
    "        bf = cv2.BFMatcher()\n",
    "        \n",
    "        cards_detected = []\n",
    "        for j in range(len(valid_conts)):\n",
    "            diffs = []\n",
    "            warp = affine_transform(image=image, contour=valid_conts[j])\n",
    "\n",
    "            kp1, des1 = sift.detectAndCompute(warp,None)\n",
    "            for k in range(len(deck.cards)):\n",
    "                if(deck.cards[k].sift == None):\n",
    "                    diffs.append(0)\n",
    "                else:\n",
    "                    \n",
    "                    matches = bf.knnMatch(des1,deck.cards[k].sift[1], k=2)\n",
    "                    # Apply ratio test\n",
    "                    good = []\n",
    "                    for m,n in matches:\n",
    "                        if m.distance < 0.75*n.distance:\n",
    "                            good.append([m])\n",
    "\n",
    "                    diffs.append(len(good))\n",
    "            indx = np.argmax(diffs)\n",
    "            if diffs[indx] > 15:\n",
    "                Flag_Safe_Add = True\n",
    "                for l in range(len(cards_detected)):\n",
    "                    if cards_detected[l].suit == deck.cards[indx].suit and cards_detected[l].rank == deck.cards[indx].rank:\n",
    "                        max_match = max(cards_detected[l].matches, diffs[indx])\n",
    "                    \n",
    "                        if max_match is diffs[indx]:\n",
    "                            cards_detected.pop(l)\n",
    "                            break\n",
    "                        else:\n",
    "                            Flag_Safe_Add = False\n",
    "                if Flag_Safe_Add:           \n",
    "                    card = copy.deepcopy(deck.cards[indx])\n",
    "                    card.contour = valid_conts[j]\n",
    "                    card.matches = diffs[indx]\n",
    "                    cards_detected.append(card) \n",
    "        return cards_detected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 905,
   "metadata": {},
   "outputs": [],
   "source": [
    "cards = card_detector('CV-BlackjackStudent/GOPR0319.MP4-lbl/08811.png', deck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 906,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = cv2.imread('CV-BlackjackStudent/GOPR0319.MP4-lbl/08811.png')\n",
    "for i in range(len(cards)):\n",
    "    # compute the center of the contour\n",
    "    M = cv2.moments(cards[i].contour)\n",
    "    cX = int(M[\"m10\"] / M[\"m00\"])\n",
    "    cY = int(M[\"m01\"] / M[\"m00\"])\n",
    "    text = 'card:' + str(cards[i].rank) +\" \" + str(cards[i].suit)\n",
    "    cv2.putText(im, text, (cX - 40, cY + 20), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 59, 174), 3)\n",
    "    cv2.drawContours(im, [cards[i].contour], 0, (0,255,0), 2)             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 907,
   "metadata": {},
   "outputs": [],
   "source": [
    "imS = cv2.resize(im, (960, 540))   \n",
    "show_image(imS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## surf\n",
    "## orb\n",
    "## kaze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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