Skip to content
Snippets Groups Projects
Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb 2.02 MiB
Newer Older
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWyPGNkCGhIX"
   },
   "source": [
    "# Part I : Create Your Own Dataset and Train it with ConvNets\n",
    "\n",
    "In this part of the notebook, you will set up your own dataset for image classification. Please specify \n",
    "under `queries` the image categories you are interested in. Under `limit` specify the number of images \n",
    "you want to download for each image category. \n",
    "\n",
    "You do not need to understand the class `simple_image_download`, just execute the cell after you have specified \n",
    "the download folder.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8rckz3ZuGhIc",
    "outputId": "6f615f06-759a-4eea-839e-658155df8d36"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import urllib\n",
    "import requests\n",
    "from urllib.parse import quote\n",
    "import array as arr\n",
    "\n",
    "\n",
    "# Specifiy the queries\n",
Mirko Birbaumer's avatar
Mirko Birbaumer committed
    "queries = \"brad pitt, johnny depp, leonardo dicaprio, robert de niro, angelina jolie, sandra bullock, catherine deneuve, marion cotillard\"\n",
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000
    "limit = 1\n",
    "download_folder = \"./brandnew_images/\"\n",
    "\n",
    "\n",
    "class simple_image_download:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def urls(self, keywords, limit, download_folder):\n",
    "        keyword_to_search = [str(item).strip() for item in keywords.split(',')]\n",
    "        i = 0\n",
    "        links = []\n",
    "        while i < len(keyword_to_search):\n",
    "            url = 'https://www.google.com/search?q=' + quote(\n",
    "                keyword_to_search[i].encode(\n",
    "                    'utf-8')) + '&biw=1536&bih=674&tbm=isch&sxsrf=ACYBGNSXXpS6YmAKUiLKKBs6xWb4uUY5gA:1581168823770&source=lnms&sa=X&ved=0ahUKEwioj8jwiMLnAhW9AhAIHbXTBMMQ_AUI3QUoAQ'\n",
    "            raw_html = self._download_page(url)\n",
    "\n",
    "            end_object = -1;\n",
    "\n",
    "            j = 0\n",
    "            while j < limit:\n",
    "                while (True):\n",
    "                    try:\n",
    "                        new_line = raw_html.find('\"https://', end_object + 1)\n",
    "                        end_object = raw_html.find('\"', new_line + 1)\n",
    "\n",
    "                        buffor = raw_html.find('\\\\', new_line + 1, end_object)\n",
    "                        if buffor != -1:\n",
    "                            object_raw = (raw_html[new_line + 1:buffor])\n",
    "                        else:\n",
    "                            object_raw = (raw_html[new_line + 1:end_object])\n",
    "\n",
    "                        if '.jpg' in object_raw or 'png' in object_raw or '.ico' in object_raw or '.gif' in object_raw or '.jpeg' in object_raw:\n",
    "                            break\n",
    "\n",
    "                    except Exception as e:\n",
    "                        print(e)\n",
    "                        break\n",
    "\n",
    "                links.append(object_raw)\n",
    "                j += 1\n",
    "\n",
    "            i += 1\n",
    "        return(links)\n",
    "\n",
    "\n",
    "    def download(self, keywords, limit, download_folder):\n",
    "        keyword_to_search = [str(item).strip() for item in keywords.split(',')]\n",
    "        main_directory = download_folder\n",
    "        i = 0\n",
    "\n",
    "        while i < len(keyword_to_search):\n",
    "            self._create_directories(main_directory, keyword_to_search[i])\n",
    "            url = 'https://www.google.com/search?q=' + quote(\n",
    "                keyword_to_search[i].encode('utf-8')) + '&biw=1536&bih=674&tbm=isch&sxsrf=ACYBGNSXXpS6YmAKUiLKKBs6xWb4uUY5gA:1581168823770&source=lnms&sa=X&ved=0ahUKEwioj8jwiMLnAhW9AhAIHbXTBMMQ_AUI3QUoAQ'\n",
    "            raw_html = self._download_page(url)\n",
    "\n",
    "            end_object = -1;\n",
    "\n",
    "            j = 0\n",
    "            while j < limit:\n",
    "                while (True):\n",
    "                    try:\n",
    "                        new_line = raw_html.find('\"https://', end_object + 1)\n",
    "                        end_object = raw_html.find('\"', new_line + 1)\n",
    "\n",
    "                        buffor = raw_html.find('\\\\', new_line + 1, end_object)\n",
    "                        if buffor != -1:\n",
    "                            object_raw = (raw_html[new_line+1:buffor])\n",
    "                        else:\n",
    "                            object_raw = (raw_html[new_line+1:end_object])\n",
    "\n",
    "                        if '.jpg' in object_raw or 'png' in object_raw or '.ico' in object_raw or '.gif' in object_raw or '.jpeg' in object_raw:\n",
    "                            break\n",
    "\n",
    "                    except Exception as e:\n",
    "                        print(e)\n",
    "                        break\n",
    "\n",
    "                path = main_directory + keyword_to_search[i]\n",
    "\n",
    "                #print(object_raw)\n",
    "\n",
    "                if not os.path.exists(path):\n",
    "                    os.makedirs(path)\n",
    "\n",
    "                filename = str(keyword_to_search[i]) + \"_\" + str(j + 1) + \".jpg\"\n",
    "\n",
    "                try:\n",
    "                    r = requests.get(object_raw, allow_redirects=True)\n",
    "                    open(os.path.join(path, filename), 'wb').write(r.content)\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    j -= 1\n",
    "                j += 1\n",
    "\n",
    "            i += 1\n",
    "\n",
    "\n",
    "    def _create_directories(self, main_directory, name):\n",
    "        try:\n",
    "            if not os.path.exists(main_directory):\n",
    "                os.makedirs(main_directory)\n",
    "                time.sleep(0.2)\n",
    "                path = (name)\n",
    "                sub_directory = os.path.join(main_directory, path)\n",
    "                if not os.path.exists(sub_directory):\n",
    "                    os.makedirs(sub_directory)\n",
    "            else:\n",
    "                path = (name)\n",
    "                sub_directory = os.path.join(main_directory, path)\n",
    "                if not os.path.exists(sub_directory):\n",
    "                    os.makedirs(sub_directory)\n",
    "\n",
    "        except OSError as e:\n",
    "            if e.errno != 17:\n",
    "                raise\n",
    "            pass\n",
    "        return\n",
    "\n",
    "    def _download_page(self,url):\n",
    "\n",
    "        try:\n",
    "            headers = {}\n",
    "            headers['User-Agent'] = \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.87 Safari/537.36\"\n",
    "            req = urllib.request.Request(url, headers=headers)\n",
    "            resp = urllib.request.urlopen(req)\n",
    "            respData = str(resp.read())\n",
    "            return respData\n",
    "\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            exit(0)\n",
    "            \n",
    "response = simple_image_download\n",
    "response().download(queries, limit, download_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CRHl9UX6GhIs"
   },
   "source": [
    "Please check carefully the downloaded images, there may be a lot of garbage! You definitely need to \n",
    "clean the data.\n",
    "\n",
    "In the following, you will apply data augmentation to your data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3SX21FtcGhIu"
   },
   "outputs": [],
   "source": [
    "# General imports\n",
    "import tensorflow as tf\n",
    "tf.compat.v1.enable_eager_execution(\n",
    "    config=None, device_policy=None, execution_mode=None\n",
    ")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Shortcuts to keras if (however from tensorflow)\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard \n",
    "\n",
    "# Shortcut for displaying images\n",
    "def plot_img(img):\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "# The target image size can be fixed here (quadratic)\n",
    "# the ImageDataGenerator() automatically scales the images accordingly (aspect ratio is changed)\n",
    "image_size = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rN_Mp1rmGhI1",
    "outputId": "6417b1f9-e7d4-4d56-a213-191f9d17524a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11 images belonging to 8 classes.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9X5McydLmh/3cIyIzq7obwMyZc959Sa6MpNEkmT6sPoWuZaaPoQvJdKELUSRNxuXu+54/gxkA3V2VmRHhrguPrAZm8GfO7Erci5MwAN1VlVlVmenh7o8//ri4O//Y/rH9Y/vPb9P/tT/AP7Z/bP/YPr/9wzj/sf1j+890+4dx/mP7x/af6fYP4/zH9o/tP9PtH8b5j+0f23+mW/7ak//n/8v/KaDcLwC68qtH/LOP/pZHftPjIoDQ3ZDxR1VJCVzAcbIqSEIkobnjnsEVfIMpI5qQppScURHAEQM8gSfMG6jEspUaIhrv5BqvF8ANkYSI42KI+vFpcHPw8VuWcVac2jrrdWNbN57fP7Ffr2zblcvzI//z//j/5v3Pb/nw4Sesdmp1ejfEBbdO653nfaPkTBZhUufaBBunpKQTJRemlMGvGOAo2Seu6mx01suFKSkpKWlJsBv1unP9cGW3zrvrM+8uT2SgSCJrZp7OaK/QO23vrO1KLonv3nzHVjdqq9S9YlbJKc6pUJinQikKasxFSCWjy2v+8q9vqXtFU+L7P5w4zxPnaWbbwN0R4HQqrFtl2yvbvtF6p1un1itt3UmaOJ/OvHnzCs0JEK5t53R/x93DHfenE2aGJOW7H34gdWe7rPzlX/8DqUzUWnn68MR/+W//hHlj36788HDPlDNTUk40eKywGQml24K7IL6TPePWqbZh3XHvuHXOacYUXIVkQnVo7nQxJjVyEsqcuZ9nUKGZsz53nrbK41b5P/5f/++fvfG/apziv8Wxfsmgvv4a/9JzXzlcGFO8RPA4ijiiKY7pgnvcsGFQMg5ndFPEFBD02N8JYxIQsTiWQ/wD3hOiiqggYsg4He6AGS4gCmKCyO2XsYgoiODumBmtNfZ9i791Z9tWrs/PfPjpJ57fv2e/XPAaF1sAldive8e9U1RIHt++uzLNM5ISqonrtbJdd3q7UuuVlBJlyrz64Q7pTqqCNbjuO7ijT0ACr4Y7NDPEjckNb6BTouTE+e6EbYrXhrrRbUcAa5W6rbGAmOPdMHe6xXG1dsSUXIRrc2ytcAFRo8xKEmhrZUeYNJHSxGVdua5XHlcZCxzMs5ItUyusV2dvjtAx25B8JSVFkiA4tldsqzyulWmamJaJvq70Dt47D/d3NIvrN58Wau2YN5o1Hp+vnOYZnybmU6GcFElG243kGoskGVoYoHomSca94VTmMqNZkCxIT6x7ZWuN5k51oXWoa6e3HSSMs1Zn787XgtevGueXd5RP/vvscx9tXzTEX+36jZrr8FxiAhiHXxKJFfTljY730WHEhDfzjw3W8WGcouBiw0gTjoM74hkRRV1w6Z9+IY8oQYax35aMjwwTiRvNDOphnNtK3Vf29crl6ZEPP73l+uEDdbvirWNu42iOu2HecZyiGp4ZoVlizhMpZyRl2tPOdd1Zt511uzKXzMlmTkVQAzXw5uy1Ya0j5shJSS7kseIkYFaoDgkhJ+V0PtFVMG00MVq74nR6q7S9hqGLQvf4nhrXsDehmZBIrN2oBkZnXjIlC1kFa522N/bUSDmz7TuPl2cMo4hScmJZZpIkOtCbU3ssBK1ueFJyTqSsLCXR90a9buzXjYdXD5SU2J+v4IKKcHc+s+4VRDCH1sfiZ8bzZQVXkhYsJZjDOI3O5CmuvwsdG/ePkim4x303TTNlFnIRvMf9YxiXZrQucT81o9aGA92g+biFvmITXzdO/cg4P2M4n33kWwb2hWP9ls0/MobDm+EGkhGUJDpsIsJSd0EkPpOLIx43pNLproCBN3DBRDFNTB7HdHGSFlQdEcOJG9BxwNBcwvhU0RQXxN1wUxRFffjnbrS9cXl65vnxke16YXt65sPbt7x/+yM//su/Z3v6QO9GxKlhoN2N3gwRJaXEPJ9o3ajN2a+d9cMj3TvNOn3taEqcz5lXr+6Rsdpf/vwWsQZmpOYUhzRNPJzOpDmBCiZC+vCBkpwpObZEGJ4U7pYz6XTCu3F53EH2WFhqJU8FdUjO8PaOCnTvcVod1toxTYgIYht2bchUuDufWO7PrK3z489PPF/e4hgiTkqOqGOb8a//7hLnwZ2G0/eOd6ca7L1T5sKyzEzLiafnlcd3T8i+M7kyufD23QfyMrOcF/7wx+8hCdOUOZ9mLpeVhiMYHy5PlDxhOGtzrnVH3Hn15jUJJXWBi8MUFqUGrT+DgTAxpcRUEnlK0DvWE07m0Spughl0b+zWcFFMMoYevuaL21eN08W+uvOnz/kn//3q5990jK/vJxqhrLmhYwUTEoxAVXR4RTmOOjJTcVQ7Sf0lzzyelbHvMKjkgkkYoKiFoQOSdHhTQUmxcImiCJgj4nETakIQcKFXY9t21m3lerlyvTyxPT+zfnjk/U8/8+HdO67Pjxj9cLzgYO6YOUkVRwGlS6Z6o/bGXleaNcw73Qw1oXuj207bdhKZJIlJBaEjCvdzoXcnqXAqRpkSooIJXLwhY+nb6sZpPrHMJ7bLxg9/esM8FSZ5osjOus9c9xXHoBtS4/iCIRgdJ0uk7dvex6ePKGKSxKLKUoTaVvba2Vuntopb7C8ZqkZ+LyWhOoE5tu/sdKo71R2pxPtvjb9dd04pc0qF7+Y7ni4re1txv3D/6g0lP9D6KwxHszCnQhZoLVNbYV8cR1m3nVzSyH+d0laqK8WURTMlL2CObzskBRzvwrN19l3J3aE39uq0DurEewpAGfdVLGhqCZOOS/2ibXw95/yclXzL4L4Vmf5HHMNl5IeEOXEYp8vtwKL6cqMjDCeKahiSMl4uEQarCC6Kisb/471NDhv3cbwj340/HrdcGKT77f0iRg6Ao9XOvu9s68a+bdR1Y1+vXJ8eefrwgevzhdZreGOJxYUeea8ASZVmAfxUc2qN0KjvG9Ur7iP8loQ7Eaa1DdcMWkZO7GgSyqRkB1Uh00YuZYg7SmdSRcrEtu3MU+G8LGQVljJxmmc4N5LfMe2JtBXMK14brpVsCeiIN2ykCeC0HgtWQGqJJcffkoVtrfR+pA8eIBsei647iJLngmnBzLFmiLQIm3HcLDxS7bR15zzNtBnOeYFtZ2uNoivT+sxUEm290MwoJTMtM5lCa4naCtud41bZa2Pd+1jMYd131IXiiQSchkGag+aEuYPB7g1rRjfBm9G70c1vdwpAkhQRo8f17XEknI/SpV9sXzVObV+3l/9/P6ZuIzeawwg0gRawGuGtN+RcIk8cppiHGbnnWMEEECVxgDiCaxiVSIAuIjo84ct7RwiiN+M8FgQfYfOBJJvHhelmbOuF69Mz6/WZ+vRMX3e2y8bbtz/x87u3tH0jl4nrtqEi5JJg7xRNFAIoqdvOVhvr+kS7Vmzf6PsHmkQUkCUxTxohbm+0VvEUGXlTpQC44zXCd+9O7Rf2dcWt0/vOooU392dOeebHKXG6e+B8fuDh1RuowPPO/bLwajZ6P3PdhbpeaG2j7xtbB7cd7zvQaQbdnWnZsRp3YrHEq/NMmRMUwZ9X1IRzyuTTHKGxG3NOkDLkRD6dMBFaiyTNu5OpZGnsGN2g90g01n1nb5XeL9ydTtwtCz/cf8/z0zN9/ZFz3akKp9dvSK9ecU555IawnO95+/Yn3r9/h+lGzpmsYHaFpiSUllbyslBEUTGWaaZrp9LYpYe1tY5bw9xGmB6AlQLZhap5/Gw8tYpaOxLPz25fNc79S0HtFwHWlyD6V3uKfPLYx4HnF48n8ovHAuCxwxYCHgmUjAQk6IKLYWKkMtEdujlGw2UAPBLATTiejkqKGERH8k4YaLgAv31OHQYcqbXcAKXIzcOTe+9Ya7RaWS+P7NsTdb/Q6zPX5/dsl2dUFLNA79Qa8/DSap0sxr4b2w4/1061yCv3baNdV7xXlIrk+CwiRiFRxLEk1FLgyPNUI+RM4NJZdCZJJhF5smZDVcnzHaf5zGla+P6+ICWRp8L9mwcKSnalMFG50g32PbM+v6W3Ru/GT5cr1sI4r+0JbYKY0E+vUeskc3IXTrPiSdlS5ofXJ2ozWjU22wFDcTQVNBckZ9JUaL1RW2N2aCXRWqW1Sh1otpmx9joWhAin131jr5W6bdzNsGTob39imZVrXVn3K99990fO5zN35zP3i6N/uON8yvz1px95vl4Qh+/OM8kVc3i3XTjfPXNOE0uGjJBc0aKIKxnI5jSRkSIYkzguAeJhFudChKLKSRXXjJM+b2N8K6zVX7iOXxoP3HKyzz33S4P85DUv/3z+NfKZZzxyTr+95sgNj7xxRA4fHfdWqI0nBiL7Un8M7ye3x+zmXW+x8UsoLUcOG446jhGGLhzGavTW6Xul1pXeKtYb7mGMtW7UuuPWwDpmRtaMDMBp3yvbZqyb87z5yCUbddvo64q4scyJaZoiVDdBhRGejrtzfJ+kypSFkqBk56wTRQpZEiknkjolT0zLPct8YpkW+rngyZGSON+fmCWTSRSfqBS6Oa1m1hxG0ruhU6LXHW87T01ILfJ3myaSNxJG9vgMpoktTZyWTquduu3slFtYSyqkXNCUkZypbWNviYzSS8Zap/dKI/Lt1hvXqlRzmkEzpzXDrbPvG5mEd4kyFTnKG62jZKw3kkIqSinC3d1MeUrxHZtFiaUsgWi7sbfGJAlJOUpUEliEdI08W+O+sYHtIVF6CVNV1DoqkMWZ00D1v7J91TiXUm6W8iuvJ182rN+y+Vd++9Vrb1hTgDJ+VE088nJ98XdoGiuVC5iCRUyfcw5g1xxNYL0TOWkergUwaIySyvDFxwkUXkonh8ey4zxIwiyMq9VO3aOIvu9Xaq0DdRVcnNZ2Ht+9RdtKotPNIY8bojf+5edn9trpo/Sx143eG5jTtkZJibuHO374/geSppHXPkHbkW4UF1KCpMJcMvOUmYowz/A6PzCnKWqLOZOzskzCNAeJoeRCLhnPQBbm6Z4sMyoZJyMe6UNvzv4AtW3U7cobCq02em1c7DXiBbEMu5BYo0ZcRiEoJXxeeNrqiAaudHrUSs2xlEgpkzSgpLUX9tZYTx2a4RaGV3ultsrWKo/rFobZoxba9g3bK73u7NdOFdgLpJIpvSGXyn79F54fz1yvD9y9esXp7o5pOfHD9/esOzw9bfxP//3/wD/98B33pxOnMrFXo6pjc8JaJSPMqiATSQ1xWCUWZ+tGIaIZJKEqWA/UX6STNfAO/YoBfd1zyuf3POqAnzOrbwK0/qsfPr/Prx4c7yfD/Q0jcQcbr5WbCQEasH7SAI3cCCRVARoHo0DFbiUaRMg5h9G5QwE5QIsbcq3gaaBMIKrjMzi9NVqNv7VVqrVbWLZuG5fHyEF9XykKOSWaw751Ltedx6cLP797pI2La+6YdxTnvkzofcGA909X9u1fmKbCskyc54VpnphnpahQSqaUzGlaeJgKS0ksp8S5nCmayeqQEkkzJWVKSSRVUsqU5S5C5qRknRGZgETHUFfEG64Nk1d0q7TzgvUW0ULrrLVG7dMF3xq7J1ycUmYsZ0wM0437BDZn/OFESoXejF4NV8PcRjlJufqJ6s5uBrWDxXNbrWG0e2VaVup4/32v1CnR6s52Eba90rqxb86758YyZx6WhXVr6OMVNePy4cLdqwfuXt3x6m5hFiH3wh9fv2HbOrVe2CdDpgkx5cEKD8vC5I3iO9NcSKoRUtfKenHaDqggp4VSJs46sV+v1G1jXzd0TpSlUE7zF03l66WUEUb+Rrv56oP+5ad+yxNhdPJioIchhpccIecRin4MCY+H3ALSPgzsBvAeIesByqqiRJ1TPooYhPSC3rrBQdnzUd90i/NlwfLx3jEff61R9531cqVe1+GRhU4AR9e18XRZeXy+sq77KCtAykrR8CJJUxAczMFakCccSs4subBoZtbEeUqUSZlK4jyduZ8mlpyZT5mSZ5IoRR0kR76ZMkWIHDQlSppQDWaUSonIgkB2VYPGSEq4JCwJ2YFesWRYNuaUb+et58Leg0ShOWEaxtkxbMo3IBxJWOpY6oEXuNPdaCYUd9r4a1ojlDUnS6akTtEKKux7o2qjiLAr1BTIu6iw185178EwEqeoM6VEM2XbneSGXnYgMUvGNVFUePXqxOO1UbtxqTv5eiELPOTMQxJ0cpYpc3r9ilISqrA8X3iWzq6GTJny5p4yz0FTfKdsT8pqnXzOlIcz06u7L97zXzVOM/uy0XymrPkbn/zlq76+feS8P627Ds+JgKcbZc+P3FMi1PUBtvgwqkBiEy4HFB2r/FExdU24BNqm/lK0CTfZQQy3hkqONHYYodv4PmYRSlsND2AN65VtW7k8PrI9X5iSULvQu7PtlfePFx6frrx/urBulaxQsnKaJ+ZlIqXEtnfq3nFziiaWaebutPBwd89DKsyaWVLm9cNMKVCycL/ccc4zU87kpQRhAgkwQ3IYmwrJHNWEpkzSCZWEmmIS9EAE3C0ox6IEIUtxieqmquIparM+jBMR+gKtBsvJtIEJnUTzhT69FMOa1Th3AqbBunHCUfbeI5TF2XD23lmbk3NhVqeliaywaWXTyirClhJ7L5SUyFNm3SqVnb12VjPcKt+9PtMks3ummHC9dNp+JfXEfFpIU+K77+/g0Xi6bnx4/zP22LHemDK8TpW7u4XzH+54+Dd/YjoVcnJ4+zM/952rdtLDzN0/vWG6OzGdJ9Z/Vda3ynWv5LvM9N0905++++Kt/3XjPFCPzxjKx4DOx5t//MLf2vPy1cT15UnVSK2xzrgt0JQQT7FK8mLwigRSebCRNEChuCEyeZRBHCiuh0kHquvgJLJHEdkERI0Mg28stOGxU3eqCOaO987KyrVfWOsF30buszd873hv4UUxnq8bz5eNnx9X/vb+keu2s9YdxQeLZeLh9T3uUS7Y3HCC5H8/Z+7PE3dL4dWceD3PnMvEaZp4tRRKSeSsTPNCSYWUMlOaESkIgyWlObxjFtRsRB4KlkcerrCveDZQMCpZFFUFCtBHxKFomTF3xJw8zRw8qoyRUiCqnYaLkcwpPWO81HN7FTx1XCPFMFGCptvomvDe6bVicnzmRrOdjlMJzvNUJpY0MaeFre9Ua9gyc2mVa634tPP04cK+7bx/vCI58eo+M59O1L5Ta2ej0qsxLxemOVHuClPOnErmohlrwuWy85f2M/79K3xRftA7ykMgyyIRqpflDqmJRcAnxZeELzOn+4WyV8rjTBu59deSzm/Q936542cO9IUyyOdx2i8f5ksW+sv1IHLDUcccTBI/Qlw3jo6QA1k94tZjLYmc1T861svx/YhvgfCU8buM2ueRZ4sMEv7IOYP83Wmtsa0bfW9IJ5gt3qm28/T4RN0qrRrNGh+erjxdN57Xyt46zYJ4P0+ZZZlZTjPltLCtjdYaqkEPm5NyP2den2fO88TDPPFwPrGUiSUXyhzdIblkpjKRNIUnVA2DRGKdGuiieorSyii9qHQYDQ9RSTIwIakMgscn0FjEKpoivJcIfY86XzLoGic+WEItCP0OCXspUlnUms07fXBhQSje0Q59IOoqcisalDzAlJ7oklF1Uot0xEd01AUWdSQJdVb6Kd5zqztPzxtZM+d5Ibnh3sE7IjbCXWWxGdEJsegs6ebU2rj2yk8fhFez8Nf7idOHR5ZpJpGo76/U607fGlsV/MdH5LJhp430dMUuG611mjX6O0HbLxzgR9tv59byS/P52m+/flS++MKvmvEvnr8xayMvEkEVrMdltkFM5qDkHeRFeTlKmObH4bHePOitI+wgJ3CEx84nTCQdSK5AtFiAdaPWynbdoHa0DTI4jWo7799/YNvaYPnsvHu6cll3rptRW3SHaBLOy8zpdGI+LeRl4bpf6W7kpJxL4lwyr08Lb+4WziVzP2UezmemXMIoM+RcKGViylH0FlFUo7QSebm9RB4oSXzUcBW0xbnyFKj2oLLlYeAImHnUhgl2j0qJlMMNzYpZC/bVyPmiA0iDfumBlt8WVML7mkVTwgHuBX9aoUf64Uk/8vCRj8sA5dwdbQ0Futgohw0aJYaK0KbEfnK6O9d15fmyklW5mydOJRGZcMOboVVQlaivLg1ESBpocLfGWne6Nf6a4NWU+P67n/FyZiKzvn1mf96wa6WaQL0ik9KXC9obtjbqWjEarDv8fP3inf9V4yzyy6f984SGzyG3v0Blfw3SvrzaPrfjr9JWf2nfSooTdgFCKqMrRWLRd4OOk9LNdzLuizBMGd0FHjdr1wAKFCdJHrnsy5uLDocqx00lqI2uFh8Usm1nX6/stWK9YtbY64JdL/glEEPMabXx558/cO2NS935+d2F1hpziVD1T3/6AzqdIC2QlOWcmWZjQXh9KpyniYd54fvTiSUrS4K70x1pADopCyUVcspMKXME7Im4oSP20mFcCYaR+cg/o3bawcJjS0qoZBh9jdHnaqhnjE7TnVmMFAHz6O6JVjL1GU3hUd0qyTNg2GGcPu6nLIzdPnrc6Ulxi0JZ0swJofREcqX1DXBKiuYCUUWLYa8yuQutwbY2Lls0EhRv0b0yTazLzPO+8e7yzNPlyn/7X/0RLfHdet2DO23C5WmjlCc0J9IyRW3UndY6tTX++rPhvfHPD69h2blPmfa0orWBG5fayZ7Q3fH1yrrWQPP3ipli3nC2zxhUbF81zt5e+Hv+ecv7u7bPF1++sH3GnQ6IZ/RsxiobiGni1q/SR8yGhqXKAefYi7+UURZAB2L4sV/VWy50aw4dH+cgyft4inETmR3UuahHWnesOcmd697Yrxv1+ZEPHz7w9HRhfd55uq6s2467ccqJh/PC69f3dDK9OfSdSYOhklU5T4VXd/ecS+ac4ZQ1UNicyCmP9qnMNAXpPUmUSl7aAyzyc9GA+ImfnVF/lGBAZVJYSXKSKAxvq97iLFhQ0dJB8BePNMMDDzC3AY4pngpJDPGO1xotVp4IE21hhD0iFZMoV+XBEz4MVLNEA4I5Kh3Rjrni0hGL2igEdzqRKS6Rz4qTcqL0RMcH+8rJKkxZyUnYDK5m/PnDB+5PE+clMw2TcIy9N65bR1VZurOcE1kVyxNbr6zNeX9tvH1/YTaYlyXyyBwxSRah5EJKiiZhqx2rwt4YzRJfuNHH9nXj7J8h5X5sW1+LR7+Qi/49+30mAh6fIViRLsRqDhxGFfHUqGce+ScjtOMwPx23bCCO+umhuZVI5KO3ldjz408VbWJO743eO61FuxTNIzxyo+0763WlXp55enri8enKvjUuzzutVVTgPGfOy8x5OXHtgrceheo0BX82KUuZg8VTErM25pKiez9P5BSMn5wTUymjZS0hMjynCIoNmmKYqh7fRUDd45yMLhsfCg/x+gjt1Vq04KEkz4Fki+KS4xj0yCeBG907RfE9mgkStQ8QbVAg3aJIHdygULdQjYXVfUQ/w1jVDNGGaMe90aXSWzCvju6ipEru0KTTxUmj5zO7oaIk6eQEpcQ53btRzfnp+YJjZF1Ypol4Z6d5Z9vqOE/CMs+knEm5sFmnmvC8G28fLzxk5SEn7tIJPJooskIph3Equu4gnWYRKehIy760fZ2E8LVnv2Bgn2aIX97lW1DT517rA/wxiYvp7rgxcqnDyQ3vZqEmIHQQj77FVBBJI9QbTCB1xMatKkKjc8P1c6gnHAUaIRFsqX4Lb80tmDJDtkPdwBrWdsQaT0/v+fntT7x7/8Tj8xOXdWfvmb11VIVXdwvfvTrTTPjr+2cklQjRNJGkcn9/4rTMlGmm9Z2mii6Z6eGeOU9MWhB6RKeZqL/6oO95vp2X45gymrY/rguno3PEoznbhjcM8n8YXVIdjeQJ1YJJJfyj4blgZIRo/i59R6xHy1QCNJOX70l1i95ShOYT1joujWYS5RIf+V0aUY0qJUiJmDWkd6R3PO+Ru4166rDxkCeh3wIm0fDMZkLJRhIjiTC1hXne6b7TfOe6O8qK9crDH14D0ZaIG2kRzIzH9Zn8pDzcn3nz+jVrM5rB8678f/72FvWKYvzb70aKoZm5JEqJ75NSYVkn+h7nbKcj7iT7nYBQ+QJF73NQ0C8942/Aeb/46JdeE3U1BlgTXypCoPEaPXDY8Rp5KewcAARE466M2FVHeeIAJwY9EhHH/OhA4eatA4wKxNG6BfLWGr3XkM+QihMX3fbG9emJp8cPPF0OPRxj2zeSQimJMpXomG/O3oz7kklJKDlxd7fw8HAKr5oyD/PE3TLx3as7ljyRiRUYeoR3rVM7kBXNGZLeKGKRbgaYIu7Bmx4o6BHCC4f0yksNOUm0xqkGjBTHCCIEBGVNLPSYBNBWR69tCYCnG96NrtGTGTS9oD6622BCVSRBToXdDCV0mZKmjyjRCW9AEjxB9wq375BordEsaqo6yOUuhmelu1J6hha1VncdulEjLRG4drDN+Mu7K3eLMmfhfF64Xhtba6HUsFY8XUmTM6cMrXNdr3zQzL/8ZFjdmfTEqzwzawYcs0ZOwlQcNQ+VCUmso+7+Fdv8unHmG33vy97w6499xfh+Ad9+KxP1YfERoo79DtDoJTX85Nh+M9WXUImjbjm8IQPk8E93jZ+H0cvxVv6CMd60gfq4wSy0fpxGp2FW2bbKel1ZL1euWx1on7HXuGC5JCQltjqO404e4WrJQc07LROn08RZM+clDPXudBf9mOahdHB4Of/4c0dNUiVoZVEjHiUkDQM8CPvQj1VuRHDRiO2HUNktT2W8F4jZgbDd8qcIf23ks6PMYlFWscGaiq4diw4gj75MpN/IIWI+FkJHUkbNXkLko29WM60XDvUJvA9Dj+9/lIWMAJWSBcPqIGWLhxTLUUJCnOZg1Xl/3VFJJMlRHtGIwNwjZ9R1p2TjPN3FAtV31t15f4Es8H7dSFPCsyBuWOvYYHsGq0vIqkiXsUD9zpxTfxHXfnqcj3Ovj41Lfvn07TVfMtYvG+YvkFsZv8jBA4pj3iD5j3e73akyQtFDqGlA7xoXJ+kBCgX23z/6kuXwpTfLHzeBM/o2Q5WuBeUTcWNlC1L23nh898TzhyeuTxcu+8beOlsztvXK99/dkeZCE+XpupFUWKbMm+9eMeWJkgrLaWGeC8uUeTidOJfT8JgB+Igako2cyqhlJgqgpKhfShlN5tD7RqJH7jPlgdZG+G91h25BcSyDJ5rA2sv3P3pEZdzM0jOMeiZG0O4wpIfekkuE7a5gpvg6PBngZLR4MKl2p+gczdPNmQnmlFk0iSNRPinThNcdlWAHWZqRPRBmdA+NJ1e0phB8EwlbLE4XJTdFDaQFODSXMggmHki7Oc2c91twmeOzFIokZFJqN/at8rR21sed12+MKSeWKbFdO4+a0Vl416JccN0rxXYmjEkFasNa3GtTSUw9wuL2Ff/1VeNsH7ujTy3wk18+Na7x22fctX/FDL9YevnFdoRmN3UShpDHyJvCix5LwS35CADCHTwK6soREtnt/cS5KfzFAjDgjYPooHEz9q64K27Bm/VWoRrUBD5Re7BSfvqX/5m3b9/y89OFlTDCtu3klNE84ZpYLS5ylFJOzMspapYpB0ulRLdIKRNpXiBlGqG1o04sMGKhQpeCUHA0hXfpR8mXpB6dIij0hOlgS4miKXOgZTnlUGDB6VSQghPgkktwkm9G+dFieXTnCBkTx+mI9/C4KngSJM3gPqQ7a2gyWecw2dB6kkGMgH6pYdzi1H7FPQUxXnYyikiO2mkJ9HiaCjlP0SdqQfvrtFFGinOQkzOpM88JNIN0ahPUo6jRHbYaLW7XtCMl4wJ5SlG3bp1eO4/v94hoTpnufbSZNf71x79id9/xej5znydUKgmnVyFJZy4wZ6WnxGU32v5lm/gNxPfj519Z0Zf2+uyPv3zwi0f5Smd4LNof5YFIdJr7oS0ro9nkhSF0K5Ic7B64ASIQ9LxbuOwfiy6NTPXjyH6E0zaQxpAFaVhrg4es0Iy2b1yvF96/e8flcmWtUedstWMG0zTRiBJMB045cZ5n7pbl9pllKNDlQfOKrvoESWnemTRCyqgNHuEo3HR2I/YcHuTIyUOTyEa+pSIDPNFPUobD5pA4bzLkNWKFGyJehMTJLSk5yi4aHjVSqjYQcxCx6G20gR1YeGq/5V1xfbob6Vh5e4v/NfR7j+ZAExuGmUgpGEGmgqmEYmLdoUFuGbUa50QDoS1JaUWjxxRhdmHJgg0CRPPoC92as9Y+ejbjvkpjoesO+27kbPQ5WtFEldadD5dnHsqZOU/Mg3yvEsctk5KzknLiTMNwdvudxmm9fe3pl5v2q09//s3l9s9vP54SsH8axWpSQpiGp4j3Uj0OclDZIapWPdBIkcF0GX2hY9U/Sgt6K60cK3zclDKkTNzCm/bWqLWOrvtrkL5VoD6xP/7M09u/8ePPjyE2tVf6uiMVshSWh1c87u/x5pScOc8PvDqfefVwz95q1Mi0RLmkTKQ8hSasJtBE7+0WknUxoAc4JDlCeFE+AX/cg5F35JEVBAspyxRGpRqqd24NP6gLmoOG2DtCR9Hgs3pFvN8ilqRBpZRUQuXBBTPB9i0iDgFoYZhDZtJ6x1vDu42GBkEcqu24zrFw+GAxiWDJI0IBINE1IVkpkvA64kMX8gI9O70aeZsCoBJDtVBKobtjybleGnksNnfL0YXjXHt4was5j3tjlk62qI0m9Vh/YTC9jNY7r1+9Apy9Gx+uV16drkxTYc4SUioOhc75dEc5FeZTIekFFaX571ZC+LVhfR3okV/9+MLR+cbrf/XIZ/YZ6Fq1jiRHpAXbJ03gsfplVw700aTebtSwxxGi3vrDxvsM/dHj0wIv3ub4PRKr+GvGXje2/cq+X+l9w7tjXbhcGx9+euLtn9/y7vmZn5+feL5eqVUp8wQY18tbPHVKUu5zYnrI9FS4rJnvvsuUZaZME4stodJmTnWLm9Oj4dg0dFTdWwAxB6DjfoOc5fD8MspAmiI6SNGXqQqaYgmKyCHOh43ahGq5hbLdGyKVg8onWQarCtLQ43HfQxLGwkN265hFBKJ9ow3yPpRoqzuiJHk5+wc7ywVkEjxFq5ztPTizKFPP7ELcA1MiJxBbolm7b+St4MxoutKALpllFabcQAopn2gGNSdaKUzamSdjqUZZd572ndo777txX4XZQIbY9xEgiULrnXXd2bcWtWHrdOm8vzxSxPguCUUniiSaweN15Vp3yiUHWLg7df+CafBNQOjTovvLJr/5t189/tmnv2byHz/q3NpP5CXnCSlDeal1+su+N3q2+O35j/s0ceUFXgpU7uXJccscfaSBfmO90VqQCHoLceWQJzGeHp95erxwfbpyuVzYa6X1TusWiu1uWKvIHMV5DbAR0wjXWutk66O61+g94RVyUXrbMEZ5ocfnFf+I+SMHgPqCvB6e9JD1DEvQ49tyAxMGkg0v9Dk1+2gRsxtmoAPtPd4PCa93awzwoCqaBPGfAwfwOI7QBtkhlA8ZXj6osmmEoeM9NLy4oxGC33LqI5Q3NGfEIlxv0khdMRK9hqSIqpF0J5cZ146qc39ubHtm3Te811HbdaxHKL/itN6pA4lWCfG3A6CONdrZ9hYCbUrgFGKs+8Y1J6w1dCokIaik6gf8wXFDye8V+BrabS836ifW83Uf+tXtizHtb9lvqOlpdHGIO4xxCDryrSPPCnpe5KfjOr+8ToafdLlhV8f9ZUeKht4atA+U181obaPVnV43rO1gjJELGz+9/YkP7z5webzy9PTE3loovtdKG21V6k7JAylWpV2dLB2mxvXaSFmYi+JZ2XfQZpxS0Pqqa4wCGDenin+E3kqQ8C1CVD1EykQDoPOXxcoE0jh/x+3h7jfhs7DvdmvOQQb98YhCDkLI0L5Vh2SC6fDK1ujqo3E8LsDB4BJaILmkccyEaohKZ504EmjRhKeIaqRb8KblAP0koohugeqqkmx8pim6YbwnkIyooSlTJpBR+ppPief1ij8LvYFrNB6oKUWcqzjvLtHYfWhMlfH5jwCqudGr83S9ULKSs5CSsNaNy6b01tHiZIU5C8ssg7CfEfMYa/F7dWvRj+LhzxjU32deX1ohPvf410EnVwHXWyuX6wi9ZAj43qJWua3K0WLMWIXttmLFMUZDsR6C0mPfMQ8ljhFq3r011vVCXVfaWrHdEE/UfeXDhw/8+d//L7z/8T1Pj8/0baM1o3bHWhvOzCkKUNA0MZ0X7l+dUM2YGdPpFMfbnMfeKPlKyRv7lpjLPaIzWRJFJtLxeYuQcmLKJdQHGdKM6TTU36LGG2wU59C7dSHYVhZKDhCDoORoqO4voafn9FG04bglRHOIk2moHXY1vDaSKKmcmPHwhOZYvSApEFTpbfS4j/PqM07DqDc8AHGc6D8VVcop02SPRakTOe6IPEyi2O9dSDmGCpUUAE2pldYFLYnkjT6AuJJnygRTi/Mw9wD17LywXuF6zTjGpfWgEopyN8d33/YeoyEIiGpnRwaf2TxHyDzkUdU6sya+e5h5+NMD6XRCpzPtwzPv3z0iP3/44n3+bVHplyvyVYP5/PY55Pb3HGfseUsIfURiEvS6dMxEsU/eRuRjEsLLAaJFTG7rjejL70fec1Rgbrt5nOxD66bXTm+GdwFx9q3y+P6RD+8/8PT8xLpdBwk+BLGitppIGgJcqhmRROA86VZvDe8cOV/3HiriJKzLGPDj6E0MOgzJeo0pV51YtG513Z0wxhhXwY35+lFoeLzn+N90FPFFRjlsjKJwHWEbIIdXPtDwA/Eei+EAlJI43luAaD7eW8Oou+0j99fgz4781NOEU0EiXDc5SAUTnoIsIWh8Th/7Skg0u3jk1Shi6QUY0/BY2TMmgTZLEnLJzMtMsy0QcYS6dWSOqGnzRr+sbLWxVaMOQezTncLWoh7bo0ziqkO+Je4iFUUVSoIlC6clcX5YyHdn0umeTZy97lyev2yC3xhk1H9lSyMr+ZL5/MbHPveq3+CHx6Fehv1EyKO3wNPwIydzDrf4SR76klrdUIiPIvYD6TwChbjpHMfGKLreO323UJtrURrR5GzbFob54ZHL5ZnrtgYoNTon3Ll1j5TsUVuUGBkoA0FWGEwjDUPxHiMH3LCWBsMkFNpDrV3Jmqi9RR+jOkpBpAxy+XarTKlkDiWIsK+DcX0kUjbWvOOECJIy3g/20EtaI0IQ1MeJ9QE8+UBWX8QgjWYV6/F5b0LgqWC14SNF0SR4VeiKaR4Xr4XQ2IEZaSYNSpx6pueOupBM6R4Kf+hoVjh6U7mBDGgWJnIoIFoYbM6JhZl1LaGhpBl6JaPklKjJuFqjWmfdGntP5Ek5nROejLoZdQXbBUsJK2kg/LE45RRlmlNRzrNyOk3kuxm9W5Bt47pMlOnj1PHT7RvG+SW09kuPfsvA/t4885evPxAZw8eFEPGh0i5EY9hhb2MlPQz0RvaGY3yCjYt4LDYyEI6opYVKgI5QtFpjbzt73el7KJ1br4hGLW99fuanP7/l8fGJ5/U51PZqi7FzEr2Up9PCMk8sU6aOLpnkHqHxAtNUhhE43Su+dvJoklYVysmYkpMK4UVS3LBu0eiUUDynqPuheJ1QDZGuJImiQYR3d3oOxhRmZE94kpHfDeKGSwxo8qhFSvPgp41SVYTGbZy5AS25ICZBUveOEaMJfFwzE0UwEkbS8GIGdO3opKQ8I71iKWM60actZqK408tGJmQ6JWXMV9wjUhLP5FnI4ly26PMEwbrilnE6Pmh8JR+0OY9xCnROyxTnSzPz6xl6ptXM+alzv4dWsAxpnF6F7QpLOZNLR62xrZW1N/oO1xzu4lwK82nmu+/veXMqZHH6+0ds3eHxyuXDyvq40dcvt6V8nYTAV/pZvrr9Lpjom/sFUJFwF1RaIHnpCL0FSC9ACYyVfQSrH0muHKJfx+JzDG71G6LIWHRvrnqwQwKlrYcgFdEy9fPP7/jxp3e8/fknOkFX603Z7WgvVc5TYiqhine/nLlKHDM1o+57eJZuaBpeTTU6XKZY1ZecmFNmThMllxDkGl4quk0C+Tzy2vgKOfI/lUGlG4iuvRAIRBRKCJ8dIM1xpsw7qA30dZyUSFTpH8Fox2kzGE3sRxgbfZaHQuJLtBLhbMwoFsT7zUNGn2f04iaf8XTERgXpW1AAiwzVfMNsi3A+KS6RHhgVTzX0b1NGe4TMtTW6hV7xAfRZP1rdQritu4QiYUrcu8QwJs+IZ9ZrDF8Sh5JiSPBpSXzQ5xjwuzekwmrCtVS2fY/arinb1bmsG64VkyvPW+V67Vyvv3NWyu81Mv/iL3/Xnr9+5mY4KeqbKjeJA7+VfezFJo+w5hb2fhqWH57y4/e9IYHAcdc5jncf2qw1QjUCxTNX3r37wLv373h8eoy8tEPvQhs3pCZlKqF2F/2ZmSSd7kF239YrvWZabeRZoGUkhTyjlOjXPJXCfEiRaAnVApEIHzXIB45GIe4IOY8J32MswyHG7cJHZHWNgbDCS656nAxrBLf1yMlfjNPGeRM80GrhFrXora195O/KkWeM8gkDBR6k+lbHByO8oYzg0OdBnlBgwm3DtdPpwZaixaSukd6EcZaIityinzNlkga7qNXoXAl1z3Hth3YTFuT81iAtCzkVTiflfN3xLlgXrpeN3sM43aDMhXmZYwDSurLvQbTYxWKCWm3szdir4825bu02R+fSo4No71+2sW/Q935hHPiv7OfXfNmX1/hH//5qjy8ARJ8c76MXxqWNnDCneYRIg9A1Qi9DmAbUrwraZTQFMzpGuBltjk7tEJUWu7WXHf2gMsJLG0BJiDtV9hoq40bkUn1r/Ou/+3f89Je/sK9PPF1XLuvGuu4kd5a7yCvMla05rW/0/rcQOm6VdV/RPfxVUmW6F/5wes0fzq84fX/P/XLm1fmeP716zfm8DP5oQokRDohF+UEP4a0RGqvg05ghSij4deIc2AB0kiiFCPV9KB7kNNBdidJIGEunS6FvcVMOfGx0xQxsWAkjHyoA8dkORM2xI60gxLwgFgZF6RJgVegRR155DCtOWiAlekpouo/FbzdyLrHQqbBNDc2FlDO2t6DwOEyl07xQBaa1sK0r215xEea7GSeEvS/rJdQTzdmeNtxgOp25W+65uwvj3/aGaBhp7c7eK6kr2oW7uwWIobh1226u4Kk2/vy48rwGur13p3pn90b3dJv18qXt68T3vn9iPv+pgtWvHecjesAIPX/9avfoy8MF8TQkLmIu5DFn2gFXv00jPjxmRFXCy3wUvTGhfDBejlX/MPpuRm31puZurQ8iQmW7XtnXC+v1wuVy5fIcK2gf4wXYGq3F+5aSERWeV6e2YxpVfNo8FU7nhdd3E9+dX/H6/MA/f/eK+7vX3J3OlPkU389jnHnUNsMQXkiKUZpwiVI/Lmw2BgQFAHw4KPBoeu7Dq2qK0Pfa9hFwCJN4NKy74LTROA346PBQEBnSJCNCkfbiVY9GgaNw/zLcON3OshHlnNt11nRjcNnw2hCobzwvpClFp405rVsMP5KMeiC3QWIoWDqjaSdpxSx6Xnt3eocmWwBI6mjOeI3FdlkyzTbWa/SqdnE0B71xHuBNa32IgW9se+N+WchZOJ8nVglpFXfjujeetoZ7xjBqV5oL1cb4wLhJv2gL3/Ccn1r1541UPvPTl3/5smHKy7+ffdFHOeNQjhstC5AOFsfAfIe3C3bswQAaKOIIW2/IpEDEWKPf8UB3x/e99W22Gk3VLeZ6RItTY9u2mMG576xbZdsbvYVhdgeqYWPoTU4Dw7RGraHQENTW6O1c5on705mHuzOvHu54dT5xd16Y5zlKEAzmjemNExxTrF+uzLiVwYkR8x5uRH3MhPTBgukxVCeNmqYMnRtrNcJvFZoKZTRsM0AygHSMozjOdeujL1FhjL6I8xttZJFQ5pFvHpHMR8bJR/HTEfYK4/qNxXUQJBBGjfNIOWJBkmAoxHXWKKVARjzyeQU0J6Qavne2tUZ9uCRKmSKSiIQfaccIx3WE1dEYP5UYQFxrH+lLiIbPmgINnhLdMom4Nr0Hsb1YTOGsLmPYktBGc4D83mbrIsea/JHh/HKTL5vbsdMXM8mPdv3sa37Fb41/nZtICEgK0vcAfQR5GYeQNLqChpSlekgeHJQxl7Bv8UBOOWQcx/HdRgmlVbZ1pe47vR6atEKtxuPjlcv1ynXduO47W+uBArl8MuQ1IXiJZEW7UQbFTTtMy8RSMqeUOZ9O/Om7V/zz9284FeX+PFPKxLqFglzWaEtrJRTpEwVsG+PklK3tR1oY08xGB4mLsnujdaOvjX17jjMqinvkcEeeO+cxQ0WE82lhmkJuAxttaq4sp3koJiTa9RoLgippPoVinkGnR1MJypRjbmrgo0dPbIxdsBvZIe5WGdxg3BDbI5qyaMYmKeSh2KiJUk43L2RR1xrXMggG27ri28ZSCq9ePaDpyvv1icfnC1oKy/mOP/zwipwjgtou71Et1Gr8L//ur2HQKrx59SpGOnTHriu9O63GuMbkz9zfTyznidOpkLqhaXToSMZl8IN7pjs066xeg2H0e43Tk94M6IBQPrt94eFvYkFf4RXejvCrHDfe0D3CVh9ZqhD5pQQTm9vcQ4vVwVUDfTw+7gBIDAIVvYFDBxc3aHCtOvvW2dY1JA1boH1Oou7Ghw/PPH54Zl23uNnGOxx5Rx//G8RKO6Q7VIgWpizcn2fuljN3pzu+f3jD/XJmTgEApVRQzZQcAJgJNNuZUUyMPVko9nWntpXHp+fb+133mMsiDnuDJ1vZW6U97+zbJfJJjRAs5RJ6t8IQqJ54ON3xtG3MU+J8nllyImtikoRZQtxCnqU1GLKakkrUQD2kSw9oqElHCBL/VjvVX8CRrV0i/xr83yQh3anLTNIQqs4yoTIjPUHrUerSaKxWEWx056CHN/AIgTXGU9xNztpDt2i7K6SLsz5feH5+4u3bfxmzOk+ck1Kt0t34pz8+sJqwbo33P30YIx13rBtrHXdicZ5rw6+hgbHME6WEisI8L6MRvoyqb4SzDacjNFfqV0zg68b5K6/3hSP9Cjj62kF/06u+ut3yUpeXvPSWTAVSGeHtOIHOreMCuHU+QNAWjuMd4FEAQkSHf4+R59565C1uN6ZS68blsrKtG6222/scFPHbRxrvY4Po3CyYI4xCeMkp9IRK4TTPTLkMacsyqIWxYPQDnOqNrIp3oWvl0pVqRrUYP9h65MmX/Rk1B4OtCk+2Bsf3GrVa4wDCnFyiRJMwaploZSZrplujtQip9TxDgqR51C+PERN2yyN91IkDrQsBNvMoQ5lHjrjWPXpch8zLpV5CAb1HlFEkkTVT7u+YSlDxlmyDuRTMG9MYKZES5BIh88DBOORLXELqJGFk1Ze/JZBu752t7mztOrSgOpsGUSGXxPf/5nvkWsO7jbVbiEiseYxZ1KTsW0f3jkh81lQKU4kGeUiYR8Nd99D1i1hGx+f88n3+jUFGn9vz197s46d+8cNvs0P5xQ/y8VO/fkwH8BMUNQYHeJCsxyCcAHv6bd9b65QEkuvjIgZIcujeygvlL6RtgqHToghv1mn0WKU9xKSfHp9Y1xVrnUy6AS8f51KHBzViv+p+a+JNU36RtZwSS47aHJLRNEcoOOh1Wx+LQzekBUGgWeVfnq/s3ukY353vuW6V67qx9if6FvNBW0tcW6X3jtVOOji4WnEVpmp46XjbsWnClsppWugN6i5430NNYR5Dj9jAGt4iYsiiodJwtLWJ41ki/65Gu66s1th747LtfHi+sLcQz3qqO1vb2frOXo1FMnMu3P3wB+5OE6e58HqZUSpJElmDYK8pk1PhnO5vw5WCZBFCX10aWsKAUyjykojcvxQhbYI3p+2NZ7+ybo3r2nk4zXz/5oE3P/wB+/E9vRnLeUI/FFQbZUqsNXR9S8k8XS7RON6CJJLPZ85zYZpmzJXahR1CTtOdJmAeNfn/iPmcv7Ke+PkTgEl+ZVtfOMgvHv8Umf0tUHC8xMkYbjpyxaEKLoeQFKGtIwOyPwAGQhX8kIQM7xlLYZKXtzdh5Il9DGdtrL2z9yjEJ4tREEa/daj0XhGMOSvzAE+C1RrbkQOhL8BVThEiZimkPJNS6JuWnCl5IpUZTRlzhS70gew2c553w/xK3xv7pfFkla3vbG3j7V9/Yp6ifNO7U7dOq429X2OAkUNLcJqji0MadLuirZOpLNOZ0zIzLxO06xC7SvSts10FesNTC5QXwJ2c01DNN/Za8RboqGui7pW6Vy7PVy69svfG1nbePT6z1sa1NnYGAto61YVn2dEE97qRlxg18aA5RKNz4rwUpuUuPKdmtr5T5uiBnSBq0a3S+wbMkabURCIxqXAGHqYJW0I58boaz5eVnZ37u3veXS8871fm/2emSwZNPLz5ga06/j7xtG+UDGJGfd5GqxrsImy7QlUmKzwsD2SLaeDbvrL5Tifqsf0A2OTL3usbzdafwrxfxX4+89xhTJ8+//ELf/ncp5jsrzY/nteXL+bgoyvfRQZyOTK+W+0vvOSoYL4cSyQQ3o+M8yN9vVHfDCEvGwpvR+/mrbnTQ+NURMilkNLg9o4ZmxnIKixj2jTEAlBUY/6IKznPpDwFfzTnMXU63fihzvD0o3ukW6iR11bZew0K40GLK1PcvFNoyVqZsdqobWdK+daGpW5od7Q7tT2HukI3ppQijmgOUm89oCIphgMj1APdHTnfkKjD8EC16xbyLS7UvbLvjefrlatVduvsvbG3xl4re21svdFaoJ/NBTQE2OTZ0K2FAWqJfLcUtraQt05KhSkXNmss86BGkoAIuXuP9jw3hy63ERWLKkvKrDlTSqjVt9pYu3M+n+NzbZ2//e0tOs3kaWK5OyMCOSslF7Ytwl39qOVVCA3lKRWKhtRawwcnO2aqukS+2QeI9TWT+no/Z9ZfGcm3jOdbMe+vP8yXwuTPh8bBUpnoaeznFvKLMrRajx2EEJU6inufeZNx241a2lhK/ID5D6pbx3rFx8h19x4j6y0anQNUdCRpTIgu0auHGUWEkwqzCkuJTgoDUGPSTNGEuDBNJ3KZ0VTIabCAUr5R3245nNvQ0gk1iN07uzbOmkkIBeX1m9fMc2EqCdEJNYmxBXVlWRZymcjTzPPzc4THLrTtkevlyuX5Gu/XofWQ73SP4qZKCs9mjiYj54zkCU0FyXlIoMSQpm0L8CzQzDDE521l8071EN9qZiEZYkbbriHK1Y2OkpJCEtanhvsKKM9popTCPM+sXej2zFQmzsuCXh+5mxfuppmTTszTFN68jbzOANPIAVvFU+KUC2vZmaaoE/fW2LZoCdy3Sqs7f7OfKMtEWSbuagwcyiqcl4XLZcc7Q+8otIaKCktJnKaFpZyi1axHs72Pe8YR2hgQHAypL5vn143Td176tL61fft1L8LOfFzY+ruO4w7iFdKRHzYWXUA1Gn1HUR2APnRuBujjNyP1m1fSA0w6+jjHyAF34SB2m9tAXSMfzSlqlTJ0dzjkXFIIZiVxzqr8Nz98zz+/uudhmujW+dvlwuO+8X67gvQhPQKZyJ20hL55jE54EX6O8oVEf6A7d0XwXihT1NYePJPkRErOm9ffkXLULEs6DX1ZSP4D+TxBGjNm9k7nirUn3CvzktDlHtWFy+XCZb3y5w/PZP9AEeF+Wnjz6p7zPCPLiTxP5LmQT2emZRlrh7HvP0VpaVu5Xjb23WjNWFuljanW3UFNOKfM3Tljp3zD8kqeySWjKaPEeYt9HFMN4sFeuT6vPNkTP6lzfihsdw/U0z3XNHHPAyedScvg3/ZOr4bOc7TbXRZS2mI8YhaW+cR07dAqf/nLj8yilKx0TWzPT6QL9O0SSgoMSqANjSk94sNId/757oH/7Z/+yH/9xz/xT98/4B6h/tt3wt+eKs0C5zh0hP33CnzhytdEb7+4229+8O/DbcOGotYWAsMedTrRURoJOt5N/UAP2OgFaeNjTzR+/zSsdo4q+PHd5QYoMfgKAR6lMWH6vCwxQ1OGYPBUyCJ8d3/i9fnEwzKDKj7NTNsGzxO1Xkd9L0bPWW9YFawP0GaUFm7KIs5tMFESYcmd7MoszmRKSULJEuJntVOrU9Vvin/iE7Zdo0TUG/vTBes74jt1vUb9DVA1Wm+oQEkZevBRn64bqYQmbZkKMk/keaEM7i8e/afu0MzYauVxvbJvndrGmMOBqIoKUwqUesqKlmlo5SaWMpPzFMY5ZEa7NbZeaRitO7sY0p2tVda+UbfGkz2xXyt3eaJuO9dlYT7lF5xB9dbhE2UjpSQo4ug0Q1pxEZoZU5GboFttQXcUF3ptGOmj7jm/EXVU49q/Pp+4nyfOc+ZujtRkTc7jlJDUI/phyOG4f1ZC9ti+gdZ+WRnsY4P50m9fA3ZfXvLLV/x6p49/PUJRHdmkiAY4cYNYlUOfNvsxGEc4mn0/Sjk/oQce4MaXFqPDOF8aK8I4z8vC3elE3XfMjJJjwO0pZ16dZs7LxDLP5Hmm5QlZKzuZy7NQZaX1LYzTKl4Z4wNDAU7Nb+Ws5MSo+FF7nnOnHKUiiTH1U0lYDUR07wG0rFulNsd9YpUespatIz0WMk0dWffoUzVHtZJLyG2cpolaA6y5ro28XZHknO/uYJ5JcxAk0pCuDJlRoY1a5tO6sW1HPimDeaSjdKTMSVmmzHSabqH8eTqFcWpBPIdWU6+sbWO3KBNVhSzK866wdWrvXPeVi69YntjXjWmZON+dSNNELoXT6TSUAhOpBD8558GAKjOScyhhGMEKSoPIYS9UyN5j9qel0LntbnRGBUBihMar08IylVHDZnTPJPLIy12GPKi9lJm+tH2dW3vo23/BUP6+J2P7Cjj12eMcDuuTB9JobRonO2prsdIZElQ5HTXBNhQOch5zQ8K07XCe4jdPK5a4CWIOwCm8ZsxH0ayD+haczJxn7s733J3P1JRwM9483CFuTJKYpwVTZfVOq1eeemf1hmtIeqSpcDedI29LGckF0RPChLqGgPIYKyg+M41G5AqktMVQn25srVIrrFdYLxtPzxee15VHqXy4dK67UR32+gQqzMs933//J0rJqCW+P3/H809veXz7N7578x33ZaFM0bBdU9yUbXKa7+w+WrNSgCNTybgYXeL8l1JIUsAyH55jgjM4SymUtFBS5rRMzHOM8Vu9sl56cGM180EbOc0kLTFjhCg7zaJkFE+KZeVUhIeqPNTEasJWG7W2aGRIxt4r26Vz2kpENyWRp0wqBc5nHt8VSrkjnxO8/4n5VHhlCx9+umAGW29sl3esNTp23l0+cLecSQoineZG7ZHuzCrczYXXdwsP96/YDd49r+QSoOrenKfdqWNOTICYFuyo30t8/xzv7zcZF/x98eo3tk+Mc+SNnoJXGqIVh0IbvBAT4KbUfiTe8vJzipfG7MYDz5WK+VF++YjggOBkOOqgqdOz42WAUKpITmTJnM9zUPyM0EDdQVtj88bW4gL11m/F/zaIAxMJzQzFhUb1xhQQLe7O3vcYJSeCmtH2zr5X6rrx4fEnYgRe4brtXLadrVVkSoPoHWWkeUh/lPOJu1f3MQuzVpZloU4fwhiSMmmiaCKrs5xCcGvvwranYC1pQXqJkQw92DluERfLmCgtDnnUJFWgaEKzkoaRrHWntvCIl71RO9TBfEx6eFHl1TRxypm7aUIHW0hzcGCzOvdTZpLEPuUxyKgSQ4Gjs2bJARClXEYaNJoNSsZrePxqDcGYlBgRmBXNQq+dGRAT2ir4HEi974b66D0X4X7KvL6befVwhrywm/K4deq7K5KE1uH5OrSkRo55IPD2ewGhL8XDt8P9JzTAv2uzMbRnGGq6lRxGqMsAb46i9O2Ro6xyoGQHOf549iDKfw6j1rFnlE2iU1iQHKPxJKXo25wKuwer6No7+8hJ1hb1MDPobYyYw5FO5IUpLjgWpZLmRuGlfNW931QblKgJtr1Rt8Z+vYZAWFFsGGI+kM22IWKkUijd0JLg4Z5Xd2fSaAafU6JNmToXlhyNxkUjJ5tLQiWxtSg5lJRDt9bS6HOMnMy74WO2ylFHjmlfJYr1WWgjtOuEYvrWOmt3nhtcqnHZjedtI1r54FwSb5aZ+6nwelmYk1CyMpfMNKVRFw6jz1npEKWYmw4xzGVmmmZyCuO83SM58lHMqD3mfGacqWgo5CVBupBSxLe9+y1/xQKddYn8/zwX7s8nHu7vIE3sJng1nmu9AXC1CnVMVxN/oZ1+TZ7n657zfy3j+8omEPKSFj7R9KacMULbMXnDo9jvg0Wko3XJCK9Ij3w6iRCK4yAejcndemiZUnEa4o1UDel1ADg70gz1HqWTMuFAl0FVI2QTf94uty5ka5UU6wGtw9YrTniCkipu05gOHdqmkmKEQJESXO8S+jruRt8bRTpSlDnd8ccf/vfM08w8nViy0BCaCFWMy7t3WO3cf/d9yG+48cxKbi06NOaZ7fKO85T443/1b6ClQGJL4TRnljkmla3PTp4IgxCFvWMSHt6khXRM77daXy4RCUxZWUrh4XTix+uV53Xnbz9deCiF6Xzm/g8n/pv/4r/g6oXHDf6nf/33fHj7Vy6P73m8VP7901vEG/dJeFUm7kvhu3nm+9f3LFNhnhJlKZFbThOzTAhjqFOZKPOZlEto27ag2GXNLPPMvE5kT9QWXUW1NV6/emC1HbfOm7uJRU9Yd56uz9zd3aFJaUvlUp1snZScH/7wB374/nu+f/MmzpXDZdTHGcqXrYchRzB2VA70q+SBbygh/Mdt38R5v/jBvrwq3HLAYYGhRl7Haplunu3W1+lH3qxDdS9KE93tRki4tYgJCIcHE3wYljvs2of6HWAZhidLSelb4/p8wdrOvl5p1eg9Bs6qRYjXOmOuZwy8qbXiOCmlUfsMECaP+SgqyvGNEjJyMsFdSTrxMN1hObo7shsZp4hRpIRoVE50cc4CvTd0TkgPvdu75LTd6bthyZjSa8wr7nskCVNGp8J5ucN9x1oj9cakL5/LrYUcpcUQ2FBbj86fnCamPHFKiWnKlDlTzjNvpsy5dl6fOrMFL5UO/f1bkmfuuvJvM6zfvaI+nGjdoFbUnBOKWI0F0Xcuz89cnyFJ4tWbO073wiQZJBTyighTHo3pSW/3QIwnrMHO0ihhictQunB8inH2SYQ39w8IOVQPinLnCZWM3S/89PMTc47pb//05g1/eHjN6/kBPNTijaPElsB11ENDgFxdya63Jokvbb+RvvcfsX31GF8iJnx9/4MXG0eQW0M1KFlughqjlWgwXEe7UsDf439ekNcwTOfWGfzRm7lHbsqNbeKBbvYgJLS9U9edul9pdac1xy0K0pNE18SqEd5GX72jRL9n5CCD0J30Zpg68rboOz64wrEfomieMY9+Sa07SUJDQOVlbowMSQ5xsLbjrYNAljaGDEUpJ+t8+8oigucIP0VDfc/d4rYeOkMxzS10ggRjdKUFGQMlaaakiSUl0hjck3Ig2HNxeu7kIRfaBVrdSXQmT5Q5UedzMGsGr1nNWcgx9sJ2rA9FA4toBxRrFrl+jvtA5Jh5EyzniKAOMCLC5qTRwxlpkQ5i/tDW1WACiWRwKAp3Esbsp4U5B9vo4XTm4RQEiDkHlTCIL4dkxKgvyDH4mZsUzpEofWn77cb5pWP8PY//pziGRxtXzzZysAg+o14vIbsxii3dnCBLOTDdckkZBndQInyEm2G07Rb6ygAVDMF6Cl5pa9S6sq8rl6cnLh8e2a8r27YPdkyj146Y8M/THW/mhZISP65P1B65ZE4gGnzdrQsyFNtzKuQyhXq7gfYjUoh8WKwhPjxymTA8RtyJBzdXQ9CrbRv7ZWe97NR2odkeAlfbkKMsTtGEd6W1RNJGmhNpydBinEDdDa8w5YySkAzJBq1OXlrykgyFPYt2OEmFnGfm0ng1zVg6yk/CaZ4jnlk6VDuEFKEw9H4SMuXRkOxc120IcAmnMpHLfcwXTQlJoxYqmb/+5d9zfX5me7owPSxj+XP6dgGrSJ7w+T48vHeaN3CPCeJzGbXa8LpP140sMTKw1sbpHCLeivCHdGI6LfBwz18f3jGlxPd3d9zPMWS3+x7NDKM/uFpENnGzVZrIEKdzkkSzxpdlZr8V1pav2c7fn5D+3Y74C2+hJYI9CKqZiKIuqMmQPAz1cApjPGBovh4JuKkM1tDIVw96igvDF4TIlPJSm/Ieo+Q9BrWmoiynhdffveG//K/+yOOHzPWSaPbA5WmlXjce5oXvloVTSdw/JJJOmMPjtvHv3/3ItXeqFu4f7piXGVW9EQ2k+5gCBqKBTEofYtJClF6O/sc8oRLEcKrh+w77Suo9OlxKIelCXXbcx0j3NMX3QpDSxwyYmNB19rgt7vJCpuBmPNnbYRzj87WKCWzutO5xnnKCnNBeKPPE6XwO8rk52jqSO6ShvEDoNKkLOQdVUTV0fLP7QG0DbFMV5lyIqDXUC9IUQtnuzp/+9M/R8tU79fo+BiPLQIkPTGA6hWK8GMUKpjXed85ozuRlobjx+P6Rc4rFcG+VhxwItXVjOi3My4Jq5g9v3jAleL1MzGUJsZgeaoNigrmRpVJ1NIabfxRxdLrEa8x/Xa48tm94zhF2/h12KF/57euvHdsX3ktuTw9AhxG6HTs54B17ic+i+XoEeXYQDIaXPNTsDxHl48/xAh+6RC/PBbIbujbR6JtL4Xx3x/ffvSZrJ6eOpJnk77jUziln7kvhfpl4OJ3JZaa7M11XHvtOqpUVZZkn5rlQpozmo4QzRkYMFonIMSadobaeItzshy7PyKvs43ksCc0pbr60UL0ESd/HZDYhmNs5pkpbI4S6PKKLSaMOavjwVmmEzT6I8R5hc4wrG0T4TNZOS5npdKKtPkJnGULYhEqAh95wQknjuBCat2mE84eMjKBkOeaOMkphPhQvQk8p54LnibXXmBMzdJHyvCB5DtHnPuRDVRGNxufaYsHKJTOzoI8XSnaWkljmhSlnclJ2FbqO9KaH1u2clfMUoe9tLo/dkqW4JiOFOvSVjwb/FzWP3+k59XN1zi++Wj7znHz07+eP8slzH7WHfOEVt06RJjGpKicFbzeFPJPRqEwi2zFlOdTn4kqPm18P9s1RVombXF05tGtuuaAoWWCPND84mRJcypwm/umHH5gTZDVO96/R2uH5yn1OfDcvfHe6I//wgE6Z6ka6PHFNiXndeLeunE8z5/PC+f5EyvISxjLiPhNwZcrLUAlwugbpAfOozcbJQczIaHjTJZPLgqZMyoUmPdDk3gehvSNah5EqlIRqlFfMHemC7RHml9Md1mORUgVqRCSaCzpPoXEkaaQCnZ4y0/1DtPS1YB3FwiCUnGEAb5lApSO3j/5KkWjjQwYPeIhVqwfApg60htUeoboZJZ0oeYHpHp0EyaHLpNMMkmgN+qhfdxI9legl/fCe3vYgTywzj++feLXAm3Phj9//gTlnzDqrwrN3at8pW2eaE3fTxN18ovdDsTHO3Ugqo894NNgf4uSx4JT4+bDlL2zf0BD6VCr+Swbz8sgv14Ffk/Pi0b/v908fOzxdGIq5RBsUgPiQzBTwaG6+yTEaSAHRIRpFRqL2gUxyU5KTPuqP6BBkTqScWfJE69NtirZ3Q9XIxXj9cEfbnnh+TNydF5ZlJpeJucycp4W7OXIVF6G68/35gW2PWu1lvXCtK3NPVJYx5m6cz+aRR2p8FrLgSekoNEeJ8fWSFBFD6TF8yEp8h6zDFQr0HjNaRGLwrOehKuDEOPfwvNI9gKIxudslQkkd7W3iSm6CzgtaFko6j2sRoFqVTk/gWbnvC7w+jfugse4XRIyFhC5njlkm0W0TtV9NOUZJAK0Jex2tVt4QywGWNaX2nd4aXjuppGOuMEVk9KAqKgXxFAah0bJnaQx9moX7uxM/vPmBpz7x89MzT9cr/82/+Z7/zZ/+iR8eHliAn//yI89PF2y98PO0ozmoeMv9wjlP5HRiq3t4ZBG2Xm/6xHGFjtKeD1GAIM24BxZyfNfPbV/n1v6mLPEXnu0Xz/kvnvm84f3SpL/8moB65BevDY/oDHJCmG3kU0coK0fGOV7rL59Nx/HNQSN4jP0zaCmk1uOitDxyWh0hWke9k+fgiuajFJOUlPMt5Es5kTRF+cYddWFKmdM0c393ZtfoqBcJEMv0+Kbyyf+3ry1REFdPJM1DK9ZCwEyiUfzoJ73N2gw2RgBiIoNJpYjkG4Di+E0/8+iicyVKSh1kRCRJM5qid1U1SOU+pEMO+QeFyJdThNzSJ+beEJwiZYTIUdwwU0zj2qkf81aApLfnVELYQ9yRUeI69IZyCs6xJG76vzoU+byPay+j3c4GJcVGipSC0ucO1hrfvVr4/nTmu/M9RRw7PVO6UVD+1gKNN1dkFba8sS6RxxoEZuEdfEwYH+fdJZoK7HY9/cgdfn9Y+8uh8/7Zn28Fis+/1n9pbL/2k7/FNI9/BSGPjy3HPzdbjbwGCHqc6xAqhmACAChkvUktepLInSK1Y5GJNHIByy0K8k5MUG5xE+89jFPdI2WblVyUSYExMTqVTBcgRYlERemjLkjrZE2cloXvinBxZykZTSnG1uG4RmeEDFAqcsz4JgpjHMPRahbsHR1SbuY1pnrT6OP0JE/IAB9srPIQBtclgXXc2zFFgSMIOxTavQ29WC0UDX0hFY0uoBGHSlJ8ZYSgTi1GGuMUshdSmkcEXRDtqBjZOl0TUaES8ITcPluoK3azyKPH4vuxEVNC/yjCggiTk4x6pB3X2ehE2csHt7U1pxpUCQqdWcPbzvfzK16XiYdcmE6Z9OYVD2WiLo33P/4Haq80Glzgki48LQvTNMftZYJ5Q8iYC0oLoIgAGvtYZpO8LBK/Wwlh95fBnp/tHvnVb19+zbeej+1LOerHuxzDikZcT+RktzXIGX2eMhBaC/6ix8V2A7rebgATbg3Z4tBLHQSEAfowhgedFK2F3GPCccMwEjUt5Nm4f/UG/mlla527aWI7zVy3lZY6nkOMCxesd9bnZ8iFpcz8cHfPz9slRgOqht6tOF2MngxL4ZFMnc6QZhGhDO+gOlBbQpZFMuFJLWPaSKMIGQDkCKs00+qGDapgnPGOYpgco/MILZ6h4eqmCDM5TZQ0jXI+JHpM35YwgqIZT2V47B4jGkWQJPgctdBujSKZLJk5K5bGtbPQaaIHdOwdeh5hqWXMo+/TkzPN06AJKs2DE+2mlGl5af4ecjO9N2rfqHsbpY1QmY+/Qu+NuylzfnXHH797w/1SmNQoZrx58x37Xeft4zPl+S26hmL7m/OJZQ4JlGxj4JVY5LgWNfXVhWRtRAFCSgOM68dsGOOgj35u+8Ygoy/DvL/cPm9QH2Osn77q82b4LaxXXlq2JAw0pC3jPeLvUSQ5QJJYWPSjz+Ijl4o89SMQashWxvoW2qvRZpTJco9rx5KTWg+jZwhuJUinheXhO+r7d0zTxHKa6bWGvKgqtY2ipStmcdOqhnL6TQz51iU+PucAMEKUIegWeHgmV3mR9hwtNoPbH5724LjqjfKAWB7RREESqHWkG7oIvceE5yYBAIWurHAMz025kHUK0rtGunCbKXMQNyw+p6ZE1glpjZumvSiUKeqLTW7JQwDSfrt8aajfMaaDMcYyiB6ffUzPzjIa6xUog2uiqJY4b0PN3Xe/CYBbj0FFCKOBO4gkWOM8ZU7THW/uH7hbTiwlumIea+W5Vp7qBZNAdbMWTvNESYJ7qAqqRIh94B7dhNYbcxKmLJRS0JLoFrNc+1qx7r8fEPolDMNv+u3lkS8DSJ9Ddr/1zHh+3IFC4ByfEnr8JtxwRAsuIw/95EayMcF63GDHkFWCwM1tLKAMJfBM4g70imtjyEXH0hBaFaR5Yr5/xfPjE7kUpmXm+fkxmqwl0Voj5WCzxHvZ4KMOGY0U6O/HZyIMU3mZOTJWWQ8AzGQguzYUzYcY8wCfR9gU37GLkySDp6AfpjB27YbmHsSLBm3MlIkwP0JpQUglk9IU+ab2YZzHefUjrQM3dOTYSpDKbfTSeg7+sPrHI+/j8/tg0iRNjCFjiCdkDD4RRrlFFJMU2qI3/aI+WEpBb+xqt5EOB9ndW4S1htwG6PahkiDWuZuCs/v67oHzNFNSQsVY+5UP25UP2weaGKkop3lmmXLMFfWg6h1T2pBxLhHMjNO5cH8qnB9O5Glir53Hx41Lf2SPVeKL29fR2o+flm8bzm96DbysuL/jNYHQOjbEokVjKM+B3JpbNLgeXnYAGzcxMCHAknF8HSoKQpzc3glXrNGhIhISITlL6LhqYtcAhfCoA3qP10w5CAGSFc+G+B43Tyns+x4NvVPmnIXrhyfW1ln7FZ8KWqZoMu7XG2zAuBmRoJ7JQPjCg7yERkF1j+9npjcXGsYZCI16kP/FHW8NU6F7o/uV9pOF7q0b5umFGaYaQFNW5vmOKZWhnO9Bflew7GgalEgXautIifw0TQ/06zPWKuJRje/WaHVjKkvsL/2moiiksQ6NHCMbhyS6JgEtICHB4ikU5JXhLdMok/SKDK0eTS8Tx6SFdGUHqlro1a4b+2XjVDLf3z/wb1694Q8P3w/9Jdis8n7/C399esff3v2V6tGA/uqUmDOYJEzmMcYhIpmCYr4Bxqsl87/7P/yX/PO//RN//G//a8pSWH9+x9v/8X/m//b/+Avv3n+gXj980Qa+apwpfVsJ4XPbVzz1b3qeI9T5wr4vxekIRY+AFuBFMPpAxCLX+1iUTz567Us4/MuGMx9dLbFfStE13zXjmslDYDq0aGPwap+g3N0xXS/MWthz4tkq79rGeV5givam0ylqb5d95/1e2R2sVnY3zho80D5QVzvqf+a3BccENMQV0V5vhhnfN93AnsNrItEaZRY9YrYb1RrdG5Wdbe83L60lDQ8uMbDJieigX2Go96k7nkPjSDWkS9xstI0Jfauhy3q6H2SOwfZxp7vQvVOtjy6cRrah/ZSjBGH0G5BzGyOfYtzEgYIeCLbrR9ffIacJl0HK7w33nU4N6c4Uk8lSUrQ7klckJR6Wex4eXnF390DWFEr1bjxvO8/7xqVurLXGJDOFSeMYMsp2bhZ8P4LBoTLy+MFUSpqYpxOpTLSyk6bTaPz/ujP7BkPoZcdPTOUL1vXZh/3TZ75qmL9Cdr/0Hj6iqXGBXgSCwsCGUblbdAPIy8c4TO+W38mLOX4S/h4/jjaulCS0ZlPC9MUAfABTpgqSSacTZVooeUJT4tp3HtvK3d0DUqKV6pRnMEOSsuN8qKEt05uNDt6XBSPiyyCX+yDoW/IbshoO62Aw+ei8kaF+fszrIAyngzcLZbxeadJp2tlaQzWNG4kAb1B8byEJOvpLs070lBFpwRH1mHHm3jFrMeiphkxJ70rLidYq0j2AKpGblnAYp6FeUcvIlD7Jfo4ifYBTOvi8HElqRADCuA7jxhFQCTWLY1Zptx4RArFwxQSy4Cbn0Wa2LBPztMQ8l0i2ce88XVau285Wd1rvLFMJlfdBQBEdNfXm4zNr0COPdAmhNmPfGvtaSZbYNmNrMZoD969Gkd8YAfhlU/mSZ7s9/5UHvrbnNw5LGOZopObF00UINxDGcXMmB44ulY/2MTsAIhmhbrA7Qn/2aNQNknd0jTg5Z6ZpDgS4dmgbTYTuY6XXAD3y+RXT3cp0vsCUufRn0ib80/RPTKczU84UhP0UZOksMF2vgJElKIZJhTkLiyamHuUGqRbyJknxOW60NKpDPlhDOhaTG8iSNEpFKK03eotBTH3daG4RChaltX1wQ6OZ3CSAk7pvNKt4byGFMl2QrPRinGUhlxNpuWdKlb1e2dYr61OnCTSF9cd/4ZTvmcoJvXPK3V3o96jy+OE9UjemXlE9xSnPIDJx3OqJMsYIOq7RTOAe7X5qQ7ZVGIN0AYzeXgTS9ubUzdl3Z9s6Oz2Q4ZQpc+H+zSt8mmhPz/jW2LcP7EuBZWI35z/8y4+8+/k913VFJPSOSg6h75Qn0hhytD1FbdPGWto8psl5hX/58wce187b9xs5LVwvGz//+BM/Pz7S6v77pTH7R5IKX7Oob3tV/+qv33r5x5vgY+TfCEXdx9TmA6o8/h7UguOYh9YQA1Q4aHJ9vHYEGBoGqhKCyeKdpNBywPTuQmsbrWYYurV9SIkITiqFeTlxvrsj58K+Vz70J/7l5x/5Z/6IznM0TPcWLVxj1qMH1IkPsreh0T3RYvVXN5I7yZSyO5YmGAQCpIbU4pjzF91cgqYE6tjIxbptofLnDc+x8qtmprKEJu22c3leaR6zQLbeYlKYO5M5tSZSSTAJjffkdCFfL+z9SuuhjNd22NWpEpIsj+lnxN/i+8b33/+b0M6dlG27kLGQoBRwa7TV8CX4wyJKkZiV6TSsjZJZODZE9mjb8Q5eRi04ACGzUBDct429hkBYtx56sRa9m5LnuMYo/bpREbac2fOCThN77/y0X3naVrYtcsicC66JD1tjuotpbPNUsDrKPoPkUN1po8f1x58vPF423v38jDJRq/H8vLKv+3BwX04dvz0r5RuG9FvCXQD5tkv8jdshUeIvv8c7cBt9Oyh7LwXe0X1xxLVDDxYYZIXjOHLsPi70CMUGCFFKAs/UmtlyQnrCxXAPRhEes1rylJnmiayJzSpr2/np8T330wnpxpQ06qQWcia1NRAPlstYYBzG5OMGPQj3joAbqTfkBY5FJAbDogGgRJP4mGGpY4yDxyCmmCwWoatotKqVknDbaR7j06tBc2e3PmZyRt4u3kgWp7XJSpadrJ3n/UJ3pbuyXistgSUFT6g2vFfa8xNFF+rpxHwO9XhJQk+ZTgveriladrLmaJtLMkT1PfpP7WB2SeT6eijvgxyq8z1FyaQ1Wq2j73bIjOIDTAxSQyyohHaQClqUrsKOs1rn2nf2ttN6izzUIpr03tlbZbbRTZMjsvUWUUs36B5Dm+xq7Htlu27ghdZh3zrpqG/+Xs95xNIfb780sl8d+ltJ5a8O+JmXfeaxm2KlvLzoyPqCFRQ3didCVBXGeKLwn8pA/IbhHUyzjuIeciV6aMQMUKm3xiSh2tdTI4uQs9N9Zl2nEJRqDbdp9FruSA5VunnOTFJYfWerlT//+FekOt/dveKf33xPy8LWjLU1rtcLOQvTaSLpHLNb3NGh8N49hLkggwvJDekhau0plO/cxhQ0Orc6r2uMgBjfNcjYGYoMNcJE8sKSZ5yVvRt7Z8hkjnYmzYOil7luhteKmCFpI6NM7jy3Kz0VOol//+c/M59nlvOJ+9PD0NtV8nTHh+dnntZnlivc5zMyJ65SQfYbq4feEc4knenesCZY12iyJsJ+EcW1R+0oJTy1YAglpfdM69FbW/c9ppeZjUFSUb/tzdjtSjdj0s7D6xN308QyFWqFD89PfLhc6PWK95As6Vp4/LAzTZ1lTvx0+RmTRi4Zn6YgV7RoQogZnLD3Tu7QxNk0yjeR9wtzibv3K7K13zDOX8rvyTds7ysVki9qU3+7OvPpcTj62m08/5HwdZBjDxzvoz385g1jofIbOJQG4ncMNnIbJRhV2iDJq4QnSO5kLSzTHdN8pbUGAsFbUdwncNitcbU10E/NOMZ1vfI/XP4Dd9PP7Gsj3Z/pONda2bYVlUKRJWatCIj16LzoIW3RLHJS0FFIr5h2dDDlzTvmbegUBfFANXQXbiAIUbgnJ2AiuKCCD2bLkgtv0kSrG61V9v06nh9MHIlRCmaV3kLOQywYTO6Oq3F+ODPnmEdS3Em2I3Q0RZFfSyLNBHMIKC3ohkcnULdKT41NC7NMkbsRr8kWC23Fw30HDHyblGaq9F5ofadbjFQ4OMMBLg1soTvPbUUEpqS8vn/FMpcgCpjy89/e8tO6cWkbOsOUY+hygN7OtjX2plhfqe2RN6cFyDAptSX2GtOrSRnrO3iAUhClIBcZEqM3nPmz29eNU3/x+zcM6WvPf7O0+ZnnDzv61WZ+0NrDGQzj9I94ikc09sIAOmopcJRYDkDJj5rgeOoAiI5jRo/KKBukGIiTUyalQDiRgZKO97mFT0MRrnfjum88rY0PspJNef2HP5KmjGsQGpJoyEdq8HZDEsVGSGe3z2XEsB/TWKDSIC6Yx5CjYwyeCEhOuEbHyW0pEyFJxqWEcRq4BxqakgaJRxJdN7DRYuaRA3o5VmfBWjCogrQhY6xAZzmfyPbRtRENCRWdY2xDSrhGzIIQJHaxW5bSW1D3bEhq+vh+gQiDY2OoFDf2lOuo20oAeM0qzSq970FYAG6jHYmcvLWGjj7UqUwx2S1nrBmXVnlcY9AwOhZw45bq2CBbtGrUFor2kmx0prws/OGrRxRjI4YbhAszHUymL5vE143zC7nq5+1MfsNrvrLXb3bJY6am97GipxG0BtiTAPko1wReap/+kW7QzbgV/+W7HMi8DykUFdJAUR2naw7RY81MqtQU7WmHlw0ZlMRWYPfOtu+8XzfeP620+oE//+0t/101Xr265/xQQks2FRYdfFFiXewH/IcHk0iCyO4jR1ZJoRt00OyitkAabKM0z8EfddjNKBJK6ZkJ0WkMdQW3oOSEqs1EF6OqcU2F1ttgwEA6hVwHdULrmHvCiF5GPvvq4RXtsmF7A4RcFkpWlnKiTKfIf9tGTxF+m0cz81iP4sYf0VCVCtY4+nLakJ1xr4TW9lgUUxhmF8VcaN6iycA3bBAXUh4N9OYxdLj1gMqBnCdSKgjKVjc+XJ75+fmRvW4UjQXGhPE5Qs3RhvyNIKxdyYSYuXaPtMMd64002takK836jaooJniWkdN/fvs6CeE3m9jLTf0fvf0mACoCUpyoocU9ecswOYAdjiJ8PH5MdgolvqNlavhPIUKlQ1CIELXqLTRnZlMs5wFG+A2IwAW3QbKuG3s33v3tZ/78H/6V/9f/8B+4vn1PWzfm+5mSJLR8WqW6sVql1co/f/cDb+aZUymIdZTgappGUzLGbShvNJXXGDeoAxoxBjBRgtI2PPxKo/YjdC24KE2VTXzM0HR6d3xMz3YXKjuP9cplu9L2pyi441y3CiWRcmGZ7zmVQJ3N/CYQ3azR3Kkp0Ut4Ps0LTBPltERPrQ3vXiYM2E1oboj4IDQIdtRAU4S/IardyT1FDVthZ48ykoM1jbF6gXFj3TA3dm94Gu11oqj3m3GG9KnRcyysboZ146effuLnn37k8f1P0CpSBhNplNoCGCxxTQg9W+8ZUshvLjmmjbfW2bYYZeFD/kbSYEDJ0MF1J++/N6y93ah/5/afCpj9zAEFXgrPMHoVj1kpURg/pDGPFRf8hTXDR+ENnwS74WFvDwyjH+9hvY95kpXH9098eP+e9XLh8vTI9frEtl3Z1wu2VR5/+pHL+/dMODYF6BISmQFeOcbPj29pcs/r+Z5liQL4oe4ewl2xsDsyeiojLjBCX6doFOfdB0Y9GD4mBwLtNHOaaVAN1YMx06F5w1ssKuGR+g3NFITn7cp125ARbivENG9C42ffG6kIiNLFyTqHdpC1oLAtE4VgCq0W2j7WY7ZL8iENNhYEkRh2G/rBwlFmDn0dGVPCnGo11P3EwfvoCnrpRmpug+l3DcKFR5OCj/lwRwhvw1XVVpFeUXrIlA5l/KenZ9brStv2kDXIIQkjeKQNDF3hkcvfnIUPZFmPBAJGB/9xcw3u7TCrsbJ8Xo4gtm8Y599TR/k92+84gETfiQk3Q1KFF0K83jombobpxMnhwBEOC/TwOgdQNGqfeuQnN8X4kEB5enzk+emJv/3r37g8X9jWlfX6zPv3P3F5fuTp8ZF+ueL7Ba8r350Sm5zZtsbz4z7G8QVu+Nef/sxub3j1+sQyL2QE3zcYt5MAC5GqdIEuDpLiBqPdBLFk1HllND1X90HEcKoJzSLzUQvBqr13rrVjNRQDima27SlyNOuclzPXdWPbK9My03UgbClqw25G31c0TfjI85ZSmGTCMbb9ynxayDnz019/ovYKBFp5UigIkwjJc6gvJKE1JyNkUVI6ws8oSbQxvqL1feg3CUKn6cADxs3fzUMv1rcXI5IyzlsssM0DrUWhtj3ayerGum3Azl4b7x+fuF6vtH2P/SxKdAO/j1tQCOE0TdGOKMcCfuTDw+T0oCOC5OFExn0XNMxoD/zS9pu7Uv5TbH/v0T6r6encLlB0HHSMNFq7DvAnLpwk6MgY4Xargsa+9uIzfeiWChqFfCCyUieVMTejV7Z94/npmaenR+q60dpQ40udac685j76D1foq/HhWjF3ukF1Bw32y7Zemc8hfjyVE359JqWZmYIP9De5RUOyFoRE7yuSnYTiTIge5aLEfJoxh92ND63S3AMcySVCWsC9cblc2LbKZatYU3IqMUmsXePmt0YeQswpBS1PBiqaSuZyjQ6TKWWaBAC1t53rc+X08MD51StOGvnrvnUuWDBgbOf6dsPeLOQs6N6Zcco0M5eFazOmnFl0otmVIkLKglWnETzgtq1IyqNLSJFpGAeKj0G9tXnUoiX6S/e0o3mKRQyYciENIa7TesfT5cr7y5X//s9/5nK98OHpkb/95W+s60pvjZSFTiVpD03hDIjTWkdKCY3hnMmj/c57DDMWT8NBHGoPTveXjik1RVMP7rD8TvW938ob+Jow7v/vtpegVW4/H+40DM99hE7IyKkGkvoRn9YDpw8ytw+AOh4c+UF8QzOLEQV3Z17/4Q3r0yP7JujaqDbhJSHLgi2Z+uSs0pG90rxRR62yDdAk6UhvrWN1Q2qPRWaEdX54v5sMnTNpurGRNnW6pBijrhOac3jE1nlqTmPoDIkMKluEhOte6a3B+J466qUFIUnCE0yEVGWShFuPBS84c2S34M3SuF7Wkac1kmRsr9TrSl/3AfRAcqeua3jvljlVG4Yl1OyYdFrb+PnyzMOiSDnTa2fJMR4wFx2jNySmgrcaHUgpUyRu3YMxhB+UZLmJt4UiQwiCifqt1quSmHLBfeXxsvHXv/yNfVup+4bt+/CMMZZDsZtR9UNfSiSAIg2dZP2EDnoU8WQIgg8jGiPnb/GcACockwk+t32j2Vp+8ft/HltEAnrgPtzwV39hAQW/NPIibqfk5Tsc6XS0d/ow3CPnO3JZv4W1rQdRfVom7l7dI76j0rCemKbQxEmTwORcvdJbRZ5WjCvVQiajD5mMuQSJ23un1zVEtZJh2kkDebWDyTOuQk759vkjzFVImVJmTIXajefuYZx+IPedXmswZixGqot5KN4B2WPeR7TN/X95+9NmSZIsTQ97jqqaufvdYo/Myq2qq6oXDKaxEAOCEEJkSBEIKQIhfgz/E0X4kcI/QAEhFIAkBmujMdPTXV3VVdW5xnYXX8x0OfxwjprfyLxxIyq7h5aSN3wxNzc306N6lvd9j3FNR4VRIkNve68mxFxrY2zG5C8ef9OaybRsEpIrZbcn50IXfUwiTLP152wh0aopUegoFKnMTWkl83q7RcOKYWOK6xKEEGEcIqFB0IomMRC9pU0ZdHQ+r4UcotaP1YotPj03sc93b8rvrQSbjGpt3OwO/Po3v0dLJtB4cLJivRoIITDXxiAWNoSIua2udiiufB9h8cr6QmBTuxoNTm4nDz3GdAIDGPDlXdsf1CvlH2991HuefcjzY9s/Dw45Cla1I84Sda0fV5aTo5kuXNygRr5qhXlqVsDubRZKdZyqJSjytCXPO3bbK7ZXV8yHibw/cNjeGDSOxpgqc2u0YcPqJCPbHU32NGkGEghwcXrG9bQnxICGaIxMj0fXYhxECaaFk4Isq3kJZtByyKDJZVKEQuBqbnx7c+DyZqa2YudTJ4pWw5WWwno1sBlGzlan1veyFmQyV9lC9oDMhRAV8VW5qcV+qxyIxWLa2WF0QQZSTAxhRdCIZGgaKUGoURjGFWOI5GwIm1USypx5dX2DhkAujf1UmA43zE04tMCDC8gIexLh9JzE7JDIwuG7A5oNqpjyAGKZ0FwbCfFOZrZCmUJGMu1itdix1EoNSo3C7jBzeb3lm1evuNzu6Nq6Y8Tde1uB18OGzWbNZr3icDBvISAMLSDVMMi7oKyTsIqRsTUOrVHU9CeiYAk9K3jj/pp3a3hbrO772/3A9/eslfe9e/d7eud7+r6D3XrbvYFbP0mOKVcP9r36jbRjTXOpzPvBOmDBGOxY/CpCDCMhmlTjkNrS/h2seW4YBgdqB8ZxYt5MnJydQiughe1hS9GClHnJJporFUjB9H9O1mvmlknRQAImJueubGuuoChIsHgpep02dG2klO1xAPVEQ0qRMSTq4TUVA8DGAKEItGA6P1czWWe2YUeugynpSfSY2ya31WpFSGr0xPXKFN6HFevNGlqh1sJ+mpn3N6BKrAGmhmZdEnKIlUY0ztbNrDXmlomyIgQ4TRvamMitIkMmRFOhn8sNLT1kt93T6g3jGIhVCa1CNu1cRot1q+BgDW/UFCIaIxL7YBBfZa10ZqoKThUMwlSs/NOpJE2tjjrNxmhJVVivk/NpA6qBcVjb6tvUllKnHZaWyc3Lds0kWMDsp/nkENXc5KbVciC8NYjv3O4Hvuv9K9yPf+1uI/2QzUNAFsVLjuUOfSvz9f1fLott0mMTz9B2l0Q7KLdz+oLNoiaZLzRv1ju2GXKjpEQoA2FVkJahZSZVwnRApS5djAN246zjtjAMydTYg83mPUpueAmhmQSJYvpDEsw9F1cykBSOWXonPA9DYj2MRLUyAgFWaTQ5EGubxpSBXGjFOmFVTCAsI0gaiOOKYbWxGk4K6OkJMgzEFBlGw/SWnGG7p9WMluZKQMmQL57XtHuk1DZZI6ROxA4gY2IIgdoCDSUGU5jQVpinLXO54Ga/o0wTjx6emXawKqko47BCBqXWDoJvtOLHD2LZXy/5iBrJ2c0UMLZLFSv8z9UA7R4FATbesyOecB5pB0QIQkqDeRIVE9N2+U1DUZl7HbQjowz8bmGTH99bgjRtXjq6f6zfa5z5R5nPP35k2g3y9gu9Z0gMuqS7G85YceZEp5CpyJLcOQIVbrUx8NJJwBIfVmnsuCP7k4vFbNRGkoSuEoiVYmrN1GKr3mp1xkEKdc6U/R7JhdTUJG9CI4rSqIzBqEaXL19Sf/I5GiNNg7Vp98ljHqwmGEQIKRG1MEhkfbJGipi8R40MQ+LBJpHCivmwY7vbknPm2fkDTlJgCCafUbNSpsZ0lbl8dck+T9yUA/umnDx6zsVnn/H4T/8MQoHQePjoocvAwCoFElAOM/G7S+bfBOqUiRqJcaCUTCkzElbEVtA8c/3tNflmS51nyyAfTPVeA2xVDT3VMrEVsjRKgKyWzRYa28OBMa6IKjA1Hm8GYgowrhi3lWk+sNtPhDSawr8E9nULmogMDPHEdWQ9RoyWIKyq7Oc9c55oxbL+XWnL4nvLI6xWppYv0ZpMhWBNiqIkTk/OjLHUIE/NgPsE98A8Z1Eac1WvIQtFwuJJJddq+tGdreUdhmbGcvdB75NdWD77jlfv++Rb76m1Okcx5kzoLPpAbC5KLEr1ckPAsJ8S3DBFfJ++mvbMmpVoWrCEirjiu3RirzrqRYxyJDrRaqPMQiOaZ3uYoVknsMdPnpBSZHsz8ur6knPvEXJolhG9OWS+e/WCf/YnhTEkyu1pVoSSrJ4XRYmMpGAr5kmMzLvivEtlxJMnKfHwwSkxwDRNDCGxHhObIbIKAzU36qhMsRLHSJ4q81ShCiFuiG8K+X/4FZBBKq9PNgwXJ4wXJ4yfPCOcnbI6PSGlB5xcPAGUGKHOle3VFTdv3kBRa8W3P9BevCBPhbKfCSHS5uqAAmu6SwqsV4k0Dmx8tQ4pEZOp1O22W8YL6wDWsLpo04bMBUmJYT2wCSdkgRojs0RaGxnTihQGlEJV7/TVGlutHGrjep7Z7g5cb7dcXl+6ep5FRDEIqyGxWSXGcXBB8OCyJBZA2pTfEPXGzJ5dr7hhYmFKT41Z1GTEbHXhXdGEy569c8zf3yvlewb4wcbzvVffl0j6g48r9JRtL5ocY1IPOaS/q54YEk/hSof0fe87RFwd3vVfOqihOTokKC16Kh1zp2qshvoIFmv0lgYxRIbVyBBPyXmmlELabVkFm1F3s8nxz7WymybmmsktkdRZ/g4zbIopxgnI6Jk9sazkZDRoAx1g/UuGYE2R8pwdUWTNeYdhYBUNM1sdMK+hUcZGGZRUEo1Ay1D3W0QLIg29ntF9QfeFmlbUQzOIYIsM1abh2KDMjWGuDEUp80zIlZALiUCSRIgGKh/iQAtCqc1KELF30RZCCsgQaMOA1MRcTDEvYGWNmAZH5liMKAEkBsKQfIINNgokEcXaIc46u86TrSVVYa6F3WFiOhzMRee4YgZ6ieMI7LDb62GJayuZ8Jl7Wh4W9Uww3Kpy9GHUQTFOksC9PbhffPY96nvHNO8flKm9B/XwD95f7AZFj24M6x1d6MsCiI5NtfWuMx5i//CtiNceLZlfggX5HXHk0h1aj75y6N2m5kaQsqTEe/QrKbI+PSHFxGG/Y9xuGWJgCIkwJGpT6qRs91sOOTOMgW3ds8qQWqCmCE4TE3lotLGmsAo2gIP1yWzB0CVKYaMDAzb7D8PAOI7GWZxnC61jJAyJIEqMSkiNFgptUHQViHkgZyFnW4EGGolGKopcV7i5YvrqinkwinBuDQ6meStFgWxAcHUpFEfIXAicnV/AuRDGEdkMFJTdNBHCxHAysLrYuCq+GUMGRhpTjgS15EoKifE0MUg2qHFMgHlPRRUZ1sSaCbXAMKItUIoyx+axMLTRWvRVVXZlZndzjajy6MED8jRBNXLaXJWpFmKBTRuJauitoNaGsAMajuJc4mqPpv54a5gucaWErrKhi7E3aRy03Wue97u1d7UZgw80pmXtund7+/0fTgHv+nzFXdYYCV3ankYQxbpYR5o4W0XUWPM9i6I9orTVUpe25BZrHkENznzA8LBG1g4gyZoHxUhMRjcSP6uwHv0rKpffvLb0e20MQYhpMOWDem2Qurhisx6Z9oW9FOI4sEZN5Q6hTHvqEEhR0JhprGgq5DYj1SaQUAI5Y650bIgM9Bb1/dZXoNRqLRSCEKISazZ9nigwCHEGohIbJIkMklg1p69hIYR6+LAKFTk1zGutjTJHd98U2SgMyUHeI4xrdIi0dUPLAVrmcZ1QDiZsHYMpIuAlrZJZpUgKazbrU4YQSShpgIQgmgzwUYopCqKMAiF6F2qM7Fxr5VBnoqxoBA570za6vrrh17/6O8r+wDrC+jyyux64mTK7XFnT2E+KNmU1HHi0OWUdYRMjF+s1qd+bFigNSyCF1hdEHz/LcGJRDMRph1iYkqUtCb53bfeDEN65XB7fePeh7/Om7/vSD9xNOou8t2Lv+Z9jSva4qn3v+O5dvBWLyxGE3J1xU/XzY8eedXWweQrEGklxRUmTUdg0oBrJzORa2B+88U1r9HBXm6It++pv2cMXl9cIsEqJhHpjoEBuJhsZgSaF0Fy3NRylWsRB+XXJSwopWWOk2izdX5uB29MwOhBZkZRwwpj97mSxk7RmGVIJRI0e2phHYewVH4DNstFSGgRTnTM9JzWweAjWO2UVYYzUhGU9g5grGldL3Vmw9hZZGyLVFSmUzbiyCUi904iIAwoiarOT5QW6RyTYebor2zwAbFiZJEtlmma22y2hZCuNAUMKxOzroIjTznDQSCFg0jJDiCSf3GtVWlVKca6tHj0yy8l0SL5w5xiku9I/MiFUvwct+qHdvMNI7zGwu9ZGueMz8r19fvCa9HxQW+RIkB73yfJf/2QnnXcVPjtsj0H94OK9LLxtnnqbA6KhPQIG1VJVKy+0ES1KHUwASqm0Fsi1sj3s2O+3aJ5R767VcqXOBakzg0MEc1V+9eVXlFp5eHrG0CphsNrmvmYjb7TKhkwsphu7WikHLNEh2qjFGCT44B2HSJDBXcVEqSZFOSaDrmlo7uYaXcsymnYNQitErH29ZYotEaLRxK0IZhDMM6024y/WalDEWiitUrXS2kyNGXSGHLzN/GzLyxCI6+AyKQlK9Zqlt18IgYCyXq2NzqVW/mEYQAxHXS1V7413rU7ZHHzQxd5EPYxolVqVSQuHaSLPE0mz0bg0EqMwOog/ESFGWjTqWmsZ0cImJZIYs6iUmXyAXJS5WFjVi+8LaQJlkWzV4yhuujhv1tX73abyvua537OYWxZv29tu7w8M76393+fA3vem3PHURJZrM5U8M1ahYKiMI7OEJUHUMbbWQyQsh62Oe2wiRKydXPSINTikD3ottZGIrOvA3JQq5ei+BDPW+VDZbQ/EYaBMkwt1KS0bfzMiNDXxqWkq3JQdrzYrXl0/YDw5QV3pbhUqoRm+9HBzQMR0c4NUJBgUbp4L46oar7VZC0EkEIcTzs/XhvipyuCcwtYqxGYuMAHRxKwHa54UYaiNooYwmtrs6KEAIS+QtDGuiCtBS6XM2TSJBCKJcshoK/b7UgOcDJ8iYxRiCsQkMNjKoq5uKNUSQKk1Zo0UUfbthqCJiJCiUOdsHkRI1oc1GtBdY6SWbJxS78ZmncyVuUzk2phIfPX6BS+v3iChsBk3tGZKBoNEztYDqxS43hVOonC2Gvjk4QkfXzziYnPKOhihOtfG/tDYNSjeEMnGRh+ajpEWLAxqZieGATYhN+2keO5D1r43W3vfWvnuTX7w4O5P3lPiufszt56qk6GPL9pKaex4WV5ekBjOCuir6VvnuPzOjmS1bwiivriK/6uuMO5uHhhUzh3i5m0chiFystqwH1doOJjLqaamZ5xEK1zX2silEWnkPPHm6pqHw8CYlKRKaI2MxVp5KgzJpD1Sza53am5VDJVApaVKGpK1gIiROG5QDuCE31IzxhIOxGIJCxkSKiPUTK2FUhvZZUByMRfXyMqg2gghMGhmYKC1SmmzuZtOYcuhulJgvx6OTU2JOIh16osBDS5DpqbO0Fx7V6eZJCbXor7sWNTv6oY4GCMYAIBQXb7Fyxe1WJOyBurqeXNT9lp5dX3J1faapoUQ1zYAHTyRoomhTXNe+rhEAglrxNsw4n1tXYnDJvkUjHPaXQ/Rfs6304Q9yGtLHlKxcXWfoMF7lBB+aB93b/IPsdp37Pf9feR7i68F00nCW/to19+xAM9uIr2+tHhvy+Wzr7r1GNyQdSlpHONP/3AQl0lslDZT1SQ7mjQkVtabET0/Y3e9pqStu9HSozsTD1Ox/pBVWQ9CnSdevnnN87NT2y/ZAMkqJA3M+0w6qZbxawWREYBcK2luVnsdKuv1CkmD0avCaJIdQWlV0GbGqXEgNu+MNUYkraAoJWfyoVG0Mmtlm4s58wGX3jAKWMoTQ+wrz2TumSSGEKnB4unenq+vIGkYiIO4kp8YsglbACKRGIt1aY97hjB4G/uurKhUBzlbuUNNClMbKtbi0WJMJddCbibiJeXAXAr72rhuysvLN9zsbtBWUI9rQ7WyVkqJIQamfLCYHl1YLaKWsGq5UJrFtsH7tCCyNGrqwJeFuK521+2cvU8N3mfHcyTxHlN4j4bQ8W259Zfvvfqu528DEv6Qfe/bX33yMdn9pgVa9fbsa4sZ3bnXYJoxC67Wp+qmJv1hM348prr7zNcpW5YhsNd7IkJNVTzniTkfmOaJ0kw+UdydGteJJCfcXF4wbSfmuSDT3tPoalnGFigtUmplGCFPB14cZr69OOPRxRnncU1tnk1uMESFtKZKQuZsaTANLt410iSZePRsNDCiNcM9TBO1VtbDhpBWdg1aQIbqnocyqDKENWerDfM6s50mbqaJN/nKtGsVQlHDAldl2l1T8tYGWFSG0FiNifWQWGElhzFGUhiWFTUk7ax4Ty6x1B+N2QGjwGa1omigERa0FjS0wDqdgkI9zFB2BsIvjWm6hlZMmBooxcS36jSzbY3reeLvX7/m9avXTPOEJOV0PVn/lsCycq5XI5+tT4hpxWZY8cWjh6xTIkYbbTHYv9TKKNEBB4KDs5cV0e6NkrVSdUJRI19Eg4A2BRVDCN2HX7/frWW456P3CSzY9nZEqt97pHe9ZQ/fceDb3xiDCyxpMO2YJdq+FV/6oVQ7COHoYnRP9lg6sZ2PjpRd+GOruw7mEwfLuxJB6M1m7RxUrOmODMqwSozrgfEwMMdIkb56moyjOpJJMW5gjLAvW9Y5kubAGNPCqBlKJZYZQiNKRaLLb4RAI5r4VS6WYQVEInG9glwQhdVqRc0W44nXblXs/EMvjgORwBAT60F5cHJq4H21MGLKhblmxLVgza0L1oVNG1ErQ4yMURkSpN5PJAgSKl3QVHoDKFyHR4KHBsoQInQFh2NVH9VG1gyINfytQsF6uFiDXKO1zR4q5NaYFK7zzOvtjr//+gX73UyjEaNAMaxvozHERHL19o8ebjjbbDhdb/jJxQW73UytMEZhFZKVWLKSnZFStdlvXMaOraRVIWhcVnwR69vpEBeaY31/dMxpyf7bpnT7r3vT+rbZvdsEv2/MessQ3/HePc8No34kWSsWP3Rf/C13tMeNfQzKrTN03CPqZN3O4dS3ItNbAbJ4GwMDjQc3TssvNsvcRYts0zgwjIlhTAtvr6nJhjQBHF6oYqWZ1RhpTOQ6M5cRxHiH0oRYG0OZvZCtjFI9hnbZy9bQ3CgLzSwRVivCNAEwjgNTybTaOXCu1+sYY0NGGb0uxcgaeHjqDYRUyGXmTd2hUtAAJTRCEMYY2YTIGAKrIKytcmLqAMmTSWJuXfPJLYhSyXZPiHQ+t2LoqqoGCGgeD3b0eGnZBr9gbjiNIvUYK9fGPFdyNdreQeFmzlzudnz34g2hYa410XWGbEJIwaROx3Hg06ePeHxxxvnJhgfrE75+ccXhUBgJnA4JmpHm98DclFlNNDvQOZ3RYl4UDVbGcg1RsnoNtlnGpNc937Xda5zbtnuHcXArifLuCPK+SuddGdzb7x2PeZwK3v430pWSEiDSKJK9FuiIf2cTSMBdWcfUYnGf5VSs+xYYXC6kW792EQsTX10s44lAHEeGWhhXA7lEK/arAZqRiMbAZvOAm3AFVTjUyqSNrEpTK6dELayTskqJ89MVDy9OeHy+JgTrw1KmmTwmSJGpgsx7smayjDx0yQzRQqbCbAS1NFi/ypgSOq5JJw1yptCQXhKq5mL1njChNGK0DmhZG6NGqxFuEjVGcq28evGdFeNP15w8f8x8mBhi4GQzWANaTKF+iM2lUUw90NQH1F3ZZNdQKhXrL2pC1666J3Y/WzMOKmqsoK6E2FzrFgL72qxTWp653FtDIIlwAPZzpuTKKkX22z03NwdaE4xwMrLaXDDXa6IGJHaJ08TpuOaf/uJnPL04Yz0MbLeNy5sKbWKoyskwkJxpkrJyaI2hNgNtBGUIpvYfx0RcJTaPzkCNGzzvDlzdTExzZcqVqdps9A/I1pZ3Gs99hnX3Jh+wz13vvytu9ewXXYDJ9FrpcElfJntiQh0I0F3auEhGdJU+/1W3PocH+F0sbJkmQiAOkVRstj0cElq7+kBbDL5WNaLwMDKs1kxzQYplHIc0mFsogXEwnGmTgIQVw7BmTGvWac0qDojC5f6G/cECtSEOzAHvNdKRxQIqrgwfaDGxP0zW3j0O7Mrk3ZoDrVmvUTCVA2kKxSalmKyJcHfrTR8BzseBtUPsxnGgSueoDrRwRLsEUVQKLTQ0VEoJtBqoGq39XxAkJlod6GGCKQYKTSJZC3OrzLUQXMWtU3Txe9fUAAu5Wp+ZPHvGXBr7ZlnW1ThwMmwo8zcc9gcrAwmghVZ2oMoqBU42idgSj9cjHw0j4VAYzgorAjeH2XvPeE8bKYgYxmITA0MQSjDFhyEEhgirdWT96JTVg1NOP/1omWR3L96Qvn7NfjezJ3MI1WuwPzLmjO9d+e7f7k4i3X+Ut8sr93xWXdtFLADvDJN+xr308fZRjkltlgLxsSCjyx7+bGGtHD/bUS0xRlKKpDQQY6KGxqJcrljzIbU2BGm1YrXecDjM5FwQEeNzit3YNNjxrG/kahG+OhtPGENEW2M/J1pTsgtZTdIIg2Dt/2xVV6A0kzuOIq5qntAQyejS6qGJxYNQaaUizWK6pupxtB6vgWesN+PA2rtep2SlBfPLkwsuWzYXwdTaQ6UF60Demuk5LXq/zfic4jFzsJSuJ1HUDK81krM+eoBhYs4mljW3Zu5rqUbnw+LY0mBYRVbeC6aUaqRqsdLRGIV1UE5k4GwcebBeM0ji+XrDRydrhqqEXGmSyYdsneB6+aQ0vGRrfVpRqnZFe6MFrlaR8wcbNs8ecPrZR9CUvDuQqnK43CGlEYp1p5trY/6x2Nood0m+32dy73Zwf/x292drswseos3wnW2lwVzcPu+r2o3TDjoQRTUYYTZAcOEn6ats/z4RSOopRSPKWl1AGIIQBoE2kMc1Ma4s/S4+g9fmXaErw+mKs/QQjYFcTPpkF/YMQUhR0RZZjYlxndisVpw+ecDIyMjAEAZWMZEkcLY+4dWb1ybfeFNom5lUDbp2OpxbeScq25yhVAPUDlY0F0BXJ95LFFpolupzUWma0EqjqQlNx+B1yxhImogiDGdnxlZRgEjRG7pxVgk0KTQqMQSDzTdlrlbGqYNPurmQ58YhN4hCHCLjYHxQO53IpDuyu6CtqfMkFdFsMpnNOJK7YjpG0zxzKMXut8DpsOJ0dUJMAy/f7JjLASSzGpWTlHhydsrPPnrMhSQ2aWAzrHj2YM3z8w1Pztacb1ZM28LVZeblq8bhYE2dZBjZiHCCsmmN9cpEuqsIGoqt8CKsTkbOnz7g5NNnrL74OZRGvr6m7mbWX79Gp4LkTKO30fiRK6fc0zvwPsN5927/ECP93mY5cNRTIgZAsNqVupFJVztAPDdoa2Rwyk+nji0oSLV9+lmG1vGW6q3efYX1mZ5YCSla89YgC/IDLLk7rgfiEBg3I2lcsdvtKLWy3d5YoyK1pIFJURo0L8bBhLGbCTAnhEECYYw8ODtjmjOHqbKtlSSV9QSh3BBiRFJExsBUK61mHmxOKYY8JkljNyuByOnpaM2TiMRxoE47GoWGxWtjGhgS3lbPM9OrCMV+n5KIzYANeFzZ+opnpoSKMuVGDXa9YjQ/LMZGGmzFVIR9gbm7z5pYpRPGZjje3A7knGlVmVpgKpbsOeSZ3WQAgynPNI1shoGTceTh5oxhGMlVeX3zNa1WViHwaLPi6eaE5+fn/NGDJzyMiSEmxjTy9GTgdBwZJLHbB67nzG6u7A8G7g8IY4Nh3JACxJhpARALlRLJNKKk97axCc+Ep13ovFlcvlDGbGA5x/ju7T1dxv4wl/Qfa3tfieZ4CrcRtN65YiF0dsdMlt+xuEdwq7Zp2VLpLNnbjJvbdZ2uknZrohGHtvWyCkGWi65BCGlAYiA24xienZ8zTxPXl2+o82QZw6aenPL4t5+HE4DtcIaZXQ2JgMlkHFBbRWaF6AgcNeBBq756qydlFDRvUR1QEapAHEabHFSpcbIkTgNJCVKCaL3T1NvsdTC+Ex6RNHqG1aYsQwt5IkjNk8nNkzxi7rfGZOyg5uUk3xcJ3jOzmeqBx/lHyRMDrh9KZSqF/ewY2VIorTCuRlbjyGa1YrNeIyRLFpXCiHCWBkgjz05OeHpywqPVhouUrDYeE6uY7DMtMlfYHyr7XCjN7rEZYAC1zHgleg7DQRXEJUGqWWkZ2qxILq6MndFcrHzV1H+3Z6LvMaM/SBrz/3/b+8zTVA0WXEE/UxFzaWXBZdD1Scy0bDBEp4h01kqn+YSusODG0qrNdAExsahgCSNVQ4+ImgrgkBI1JfKQyC14cBQJ4wpaRVTZrAKPnz8nxMS037F9/dr6R5bqanF2zjXjsMHmtR0r65TSGIOQhugxbWHORhyexxWhVULLDBXGscFK2JZKTBu0KldvLnly8ZQgcLm74uOnHxMkkHP2NhITWmDz8IIUTXy57vZGc0uBQFmMToPCsEGbWisHCYYqoixJjtqUrKZdG4xUSouD9VLJhVyrkdLHEYaBedozz3tS3qNlhloIZfCMeaUxc5MndnNmPxXK/mCAfYFnp6dcrNecrlYMaU3NIK2wDsrjcY11Ekt8dH7Kw80pD+KadRjQCC0KU4EJ0Ipd0ykz1QphZIiJgcBaI3NpFu9iGOQhRlZxoGh0fWBj6+RLJZ82uLqEVtGbG8rNjjplSlbmGqwTmlprjXdtf4Di+4esZ+/ZRz5gnw9837pJCxKV2ox6HfG4sdM28bKIQgrBZSTMYJsc27OJl1yMySAmPyFizAQckiAd19nLLNZwKA2BzeaEFKIV4FWso3IpxHG9lGUkwcenZzx89pz16QW//dVfc335mu31JUMQxtWa9eoECcPCkmkirswHVKU5O389JqQGZgnsVNjnmVaBDOMo1HmP5hUn8SHDYBqwgwTGixOGcUOcL5iS1SZPVwN6FSnVuo7J5pRSlTzNbC9f8uTJc8Zxw1yt/6c6a0clUJopvmscmNXaHVTBVrTaKDEQxGqWJopnBdBxveHmzRtazQZcUGu5kDWQCQZBLQ3dzxS1FvI3057dYW9xbCmMKTIOxoc9WyVWg8XwxEqeC5WZzy7OWK0HkxEJgWEcWCWLFfclkFslt8yeASnYPdZCU7teKQ4kNcD7gFBLoFTlkCujrr3LQDCYZ00EjZyWyPbLS+brienVjV2PeWL76jU3b/bMswmj7at6We3dI/09xnkLovH97a1F9ccZnL7n/fs+r+okVvO9FsNvegQTdHe2u7J0N8KqxUvCdimlLALUHRkU6A2Eb2GGjoYchBgD4zjS1Up0LkwSPIvqKB4UjZBCQkR49OQRN2+eGNWpFtZjZFytWK9OGDcnJEzwubcxt5Rld9ut6L1SaxOBqoELHGY3lxkOe1uNDgeiWAsDSaOp7UkgpGjqehgSR70OXGqhlEwohjYqmLp70Zl5mkmrwShTh4k2FHKzUkZrkGuhtEZBTBW+KTWZNEgIIDER00gYBsbNCrnZ0kqmqbuMISGhkRVqqbS5oDkzt0quhcNsannaGlGstcJ6HDlZrRljYojWX5QlroM0jgwhGR82dMRStOZ9YmzW2pRiUnmglRSsFBODMBBJYSSFxHocKc76KS0Ti1h2ujYj3a9H0jCyWkVCKujU2H9zSQl2XQ/bAzUbda02k9fUf4hxqtxd57zPaD5onx+89KFR5i1zlu7eBaL7/CpWXzTZ+4bISIecSycC+8raXNRXWk8MHRnrHYoVxItJUo/zlFhl1DRuo8lIum5sSsn6VB4i9RDI0wFRT/yoNQGKQXj06Jzyk49YDaaCfnZh0iKrcc148thUDnJDrq7wjJUjiSx2i1hN11brxO5wgGpq6TMT5eaaXCqbkzNUK8O4hvGEqVgLhERkvRnQotzsJ2qozDozTzuGm4FUBakNXY0UmZlyZtrOXJx/Ag3m1zuKzLZy1kKbClkrWRslWgNabY2qkSIWVIzDSFxtGDYb1g9OSW+uKIcDpRYkDMb/SIFdu2KaM+UwoXNmKoVSrRzSqjGMhziY0PMwshnWnsCKpBQoubNgImUYmaIZJ9HbOmCi1SF5clAFkWS0vlJII6xiYpTEyECKpwzrEzZPLmg6UnJl3u+J2wZlQsuB1XDC8PgR48MHnJ+O1O0b6vaK3etvyGKtEec6mzfQXPjLS27aBd3u2N7TZazP/O/b7o9N3wK1H5exW9sPcRJyzzM7N39NFUJGxRqnpsGTEk1QMSnJSMCAX4NldVtdCty1NQYJx05dYsB5VTHAQDBL7dSx44obIJjKnoiAyynGGEm7PUPaI02Zqymwa61Oi4oMJyc8fPaINHZAeGNcbVifXPD8sz+nXGfm19ccLv8lqVrbhxiMvmUIJ1NDCGoTxcOTMw555pAn8nygzDvKIbM9u2DWxnhWefzoY1QStQbPCidAaQPkcaQMAzkEXl/dcH5+xmqzYXs1I/vGANQ2sXvxDahQs7m4w8kJp08f8/rlDfubG3bbHa/fvEHGgbAaWOkZUxCGzcizB084e/CQYVwhY+LiYk9KWw6HnRlGrpQpMx0mDvsDebeD6mUwVYYQSGlkCJHVsDKtpJgYQ7T+LtmkKGnRRbZhszohZowNIm3pl1I9aWdrdjTR52QiYoPOrIlsdGCskbPnH7P5+Cec/5NfQBioh0x5dU3+25fUyzfU6zdsHj1l/KNPGb54znixob14zfzdS+ZrYTd9y1StX45SEbEiyklKDv/7kcbZB+M9e9xnPu/57D9k335u3d30vGzPrmqfFc2NJLCsqEGwmNIYR5aG8YSuYWobSzSqHX2tSDyeoWrvBmLnbb1GbBIb1ysn+sI8zehs3700NfPzSStrw77ZnFBpbM4ecfH4Jzx4/Dm7+op2NTkwIBPEkicJkx1Rl1hU/73rcQAxzuO6mgZsC6CtUXIh5LyIlkkIEBO1VhcaVKbSKAQYRkqZKWIKA/PcyNgPN2GyvdWIiYzDKSqJac60aNe2aSOmSBgHZFxRVU1jaTS9WU1KCZWWlTgOpDkRUK7fvEZLRksh7w9oNpV41WLZ0GiJtzEIQ0ys0mDXw1X0UwgGFKhK0WZsJIExJIgNrYbTrRK6UCLVJ2Hwrm4iRAmkFj2SUVZh5OTxMzYf/4TxJ8+RcY0eCvV0x+5Vw1K7meHiguHBGePFGXGzIpxkdDMzDKfIPKBYY1/1WnoAkimS/ngNIbkFQnjvSva+979fobhv+5B9A0tyZuHOqdLlbAUhteTNWLuae7NYUJPJ9ouii84t0FUPPBkTqS4xaZjbvjVV5xrapBC6MHUACXFZWfM006i0bEoIPbuMNGIaSKMhh4oqpxdPefTsZ5w/+IR6ldnxDU0zoqZsFzEcLBJovTuP805XklAxgayTah2diwCtUUuhzAXVDDLaCaRILnmJzfdzQVUI48qkRmIkhEieGyUaHzOrIvOEilDSmvVwakp2Nze0IGYQEdYna8L6BBlH5lIYl/KGUnRGSyXPJoUZk0ArvHn1LZRK1EbeH5BciKpUrVb3jdY3ZhUjKVh3taC4nqyRoVUbtVbT/sW8oBgCLRhzpFYLCY7rVHFDCZ4A8nqyJFq1iWZYr1g/esr6o4+Izx4RN6cwN+rqwP7Xr5DrPTIeiI/OSOcb0jqBOF53GBmG9SIl2m7pEUcR77niqKp3bPfHnLca1t9j4G8b0AeGj2+Lh8mdD9964XuvB3cvbfmzZa9LXC4/SnVhH4iC1EAMkKTQqtfsmhj0rhn301BRVtOqUZZ+KzUosRjZl9gFrWwVJnb4oCJRGGQkxGQt46IQp0SMFaGgWqj1YDjclIibEzbrCx4++4LHH/8RWlcm/jxNpmgvoyklFEV0JkRjVQSJiCQIQpXCOkbGcSSNiTlPzGVi+vYr5OIRmjMvUTZnj1ifXnC+Slxvb2wGl8g0ZdBmEiibU3TYUMNoGj2bkTAEylVmnl6QS2EKO9KjJ0w58/LNKyQGNuOazZOn5NcvSZuR8eSMB+tThs0FKpFvvruhvrwmpsDJuLYu4dOeqcyEkmnzgZpn2n5voUhPtA3JFBaJ1mumh0BeC7WerGoymWKljtYEbYGEsUJqE+Zm46Nr0JoKoUCK1CTsamVXzF1fBeEEYZN2rA6v4Pohp7ufEFqhXk/Mv3/JqxffEoYdm1+sCH90QlhnZPsSXs/o9ga216zPdlwMkTiNlG1knmwQhyhIrVYD1R/bn/O2ysBSE/zATZY/x+O95/0/ZNMmR7zAEkMcj+mVTPDVFOnM+6UY40usJQmCu/AlehNdOsIjIzTH2Lt0RvDiu9p5HPteyJHKloRhTIxl5RNbNmZEVUpxwHNIjCcXnD/8hLMHz1mtz9i/KdR5ps4zoQMRFhe+2YRZTTtXlnJQV4ALjCkZcbgObOuBXCbKVeH19RW7iy2nDx+xWq+YDgdSNPbEerNmng/MOZvwsySaRGSVmIPVMne1cSjNapRUrndbDqVws5+s+VEwXaN0esp4csa4OSUNa2ullyvXby5J65EUI/t94/ryW/K8o0w78n5rLfhaNZnL1Aw6GALRaXlD6I0Ll2DCKGetGX+y1kUvNzfvY9oc8KC9jmy3PAY4eXTO5nTD5nzDZp0ohwN5vyfvrjhdbThdnXJ+/pzx0QptW7Z/9ze0NlBu9uy//Y6Sv2Lz8Iz1J89IP3lKWA0QIKZrymaPnERW6wvOdplw05jaljzvrZlRa8ey3j0WdX8jI/lhouZeo5IfPLjz6W1F7O+98f0Hd7zXnwevjBxLJt8/N/UsbS9BdJNTbXTI3u1JR8RU3Xp5JWpHH1XLxS9oF8uc2gHCok3UzzNgbl5KiWEYTNzLyw21iT8341ydnHD28GNOzh4zDht25TVtnmlzNjcdXeqednz1xkAsfND+vapiBhcS0GhV4LCn7Pfs9jPznGmlcHF+waFUxtEznUMiF6G0SohrSsOA+zFyqAXVyi5ndqVZTxhVrg875lI5TLP1EwkVlcLJ6Ya0XpOGFeI9Oct8YHd9yUk7Q0OkVrh+8TVl3qFlok4HizFRhihIAolKIpEkEiUxxrgk6qxVhkuwqfWwqc0A8SZrGZibNbsV95oCnkMIBiU8fXjOxdOHPHj+kNPNiml7zf76kulN4fzBY87OHnHx+AtiHdCSOXz39+StMG1v2L7+hqHuiOsVw7Mz4qNzZD1ABJGCrK5hE0mbDZurFSoj48uRpntydUqcTbULOeOu7f6V852NPfUHxtJzMd97cMdO73jhg0qdt/K+LtkYQ7IfaNokR8O3XexfgRCjC0EpJhQ1+PGaF+Jcgrlgwi7BdHxqTdBc41XME9YqhNttG9R06bo8RU9JG3Ml0WrlQGaerQ3AfluoJbBaX/DgyRc8+egXrMYNSZLJmUwHwjw54LsnpQSNJv1psW3/Gqu9Rnfra2ikFAlx5CREJAlpDqxWgXmeKd99xa++/oo2rtmcn7N99sRoYjIwyMC025KnPYKw21e221fkaWclIg2IDgQaV/stJVfyYWKO1og258rqbEOtUOaKBuFwdcnu+orD62+o1y8sRKiN+eolNOvJsiGaux6EcYho9DhME2NaeSNfU68LNEYJiCSTpKmZMmdyq0yqzCT2Gjm0wD5bsicFZeNVL4IQBnj0yWOeffEZT7/4nNOTNYc3L9i/+obdK+X02edsHn7M6eM/os2Fuj+gL18yvL4hyIHp8g1tNzHvXjPdfMO6nKDlFGUkbwRqsmrAm9eEl9/SXl5y+foNX91cMddmCaxRGQRWw7uH/P2llDuwRYvTqHe9/pb5vL3pPe/d9Ul91zt+BlJBzKWJLSxdpAyPIG95uSImA4K6+fZ9biGD+m+SLm4jpgsbXBXXpasXD7r1SUiqu9cVaY0Q3P2ko5UsGVXmicN2x25/4Ppmy2b9lHH9jAePPmUcNsQwWK4GJYky+Ml3iUdFTTcWW+EX4LRni91pB1WiDkSNDFHQYSCJso6RORpNKeRCCxWZbth9Z7QoiSMSV5Q5s7p4wHCyoeQtbc6QGysHGDSpFDKyT6ZGlw9cv9qxOTknnV9w890L8rAixRENicPlKw7bG/L2kqrFJ5LI2Ew1T0Nk9A5eIZiwdlfsi2GwVggBywK36DXo3rG7odXCjhADYwgmVzmbeJrRtAtNGyPCOiZWKbBJ1mikVcgzHFJgzkKpQm2BXNWUJ7QSViaANsgpYQhInJh2G64vb9h994LWMkPZM1w8JJ2eESm0q5e0q9eU3/09N9+85s3lDa+vdrTJEFZzgk8vhAePT7l4dPFOi3iPbu3bcdz3zOMH+99tfHe9esdretdR5Y5H9kzFGHyq1d3VDpPWt11VNz6PzPyHeT6dHsvd+o6+CCsLN9M+e2u+ENxg8FS4T1bq0myCAw9sv1orh8OBl68uubnZsTvMfPHFH7E+fcT69IG1EgjWgSwmI0IPMVgfkOMFuvNq6PLbHYSvpqskGknS0JQM1hgSMTZisQRadsHkupuph2zGOawoU/aJrJIPV7TD5DpEpvBnOoN7SnMkzzRR50wVK/qX6wNlGEnRmgzn62vyYUebd2iZDYs8rBhDQKOYVm4y4xQRUozGJxVIQyJUc+2Ns2qyOaJKacWaPNUjxU1ihGr6TEWESRvSCmNoKJHVGNiMiZPViObKvD1wc3nDPCv5es98M3HYZerNnhJv0NUlw3pl6K8BWEVkbXF6roXp6orpsOckNFaPrhnOzkkR6vVL6vUbpq9fcPlyy5vtge1+Nu5saIg0Hj3e8PjTCx785NMfWEff7jXOUdf3vX3clrFyj2/6g7jzjqX3hxb7w/19n1nNaw+AhrZgUEN1n8+zct1eLTOGJYaG5ILZBn5vauB2NDndzMokNSvR07W1VcRd6YUBrLeoZIJ1IluSEKb7midbMX//5Sv+q3/xF7x6fc35+oJ/5z/4z3j6yRfIOHh3a3OPx/M16/M1m5M1l9+1ZdVX8YjZf0t1eKIJjMnCRU0EE6JWa2cnIcEYGCQwNFNkX8+B/T6TW2FqFcZmiS9pzMzkF9fsvzZBryM5IBp3ts3U6ZJ9G9AWoEVO0gptV1xPO+r1DaM33J3zzlZFCUQy1MYqDlyMg9WDA2RRVnFcjDNotbY2qiTUy8fq+rJ9MjUWkIaINuMdC0KrwlxhHk84DJFvX70hHA6chcbzzRlPztecn1h2fPvimpurPfrVl8T1BfNhx7y7IV9/x+b8itXZV5w+/ZaTs3NW6xWnq4GUhXKzY1eU63lmut5R95ny5prxdMOwGRmCUKcrymHL/vKG7a6wy8qumeTKaqxcrCsf/fkXnH72T1g/+3ffaTL3x5zd9//hO/d9jL7c3O3Eeh3qHe+92/F9+zuj2N4WRxrVKnRwuJgyXJS4eKxG/TFDCzVafCnmgFo1WBwsb12szb1Vk9sAazcQPLlUOdaCuq8LIIGsFre20nj95oq//tVv+O3v/p6/+Fd/xc31xMOHT/nf//P/lMfPnzOuV4Qw0GVQBDyZE8kNLnfXnKwiq8Ha5xlaSWkto1ksVo7OX/VA1BJerrojpm8jweqD4uwJw8VEcius2oxqXD4/U2hjT1ptrL2CJ2vmydTvVuM5Y4wmL1orq/WADCZsdtgExgGGBA/WG8LqxJoatdm+O0ZONxvSuKapQQ6ju+YhGG3NMz+EaoqGoqClWckLIbTgnNoAySdPNcX+Z5/9lE9/8nPy+px/8T/+Bf/6X/1PfHd5RW6FlgYeTnC2Uq5zZmrKVCFrsgbINUM9EOI1IQpx9TvOT044XW94cn7OSgTmQr3ecbWdyVOl5MbNqx3hcksIJokndYI6U+tEK1bKqd4YeTMqD4fA6sE56ewRrJ+9c8S/B4RwD/D9HsNZPv9Bn/1x+wQ59kM8ZjL7CttjzsWx/d6huvilKc6h5i5pz4x2l5ajK2xqC77aKktNVf25uqs7zZk6VeZ95nd//zW/+vXv+PVvf8/vvvqG548/4fnzn/DFz3/J+uSUkBILmc1j8pgS4n1JDnOxrtQihDj4OZlGkcWZLPVVkz7wxJHHpDHoAvkLnS8aMIUHhNiE2DrrxoODkljEQlui1EppjdwytOrk6YQkUygIrZHGREzWxmIVNob7jcIYIjIMBgwnUEpFYmBcRdNMcnwzrXqDYGw1lF4Ca0dgp2dd7czdzQ0CaqqHIEiMnD96woNPPqOdPOTB779G08hNaXxzs+fics8+w/na2vzNRZmzMhUTrW5Uqzu6V6bSOFvdcDKuuTnbcxLtesmsTFOhZFOSuMkG8tBmTKVRC4nCGKujugC1zueiFv/HcUUYNhBO3jni36NbW35gFG8P9HcY1Xts7b633/9eX6q6mJTXKBVui3oFCTZesZvYOrtEFeVgpRjU+o44OL2GZoX9phY+DtE+JyBErAdl11QIdEB9a+oolMLldsfu6sCbVzf8v/6b/46//tWv+fa7F0yl8h/9h3/CP/23/z0+/uQL4mqwGBGx/j2edR1XK8aTDXF9wv4Ag1ZSE9bB9GsIShjU1BPCQEyDCWp5QyIVF9KK3j2rl3aaLrE5IRLGwEBgpUKpuiSXkmwI0cjhodpvK62yLRNpgFoa2gJx9Gx0TMRaGMaRYTWS2gPrtUJljAOtmeLaMK6p1dRqawhIa7a6h0Sp85IwTMOpV8kaMnuLPMX6y3gVYJF0czhirc2kSlcbLp5/xKPnH8PJQ548e87m7IKXr1/x1eUVqpc8WGcen1ZWg2FwRcVWZW0UrOTVWTWtKNubA1Fmvh4yZ0FYhcBKBiKNVpurNChzqZRquhMX2jgPwkeryEkwCZY6zUxGs2YQCGGFyAaRd4eO99c574EWvdt43v/+fTnbvskPHrz9Qmdp9ORPh9/1VuDCkZCtsPTZREDUdWkk0GQ0etbSYMb6mKAQq2vZOMslNCFIWpAM5vpVDoeZ/X7Pzc2WX/3md7x8ecW3373mv/iv/z/M88QwrPizP/13+Xf/vf81v/zln5CGkRAi/eRMot8uUFyPnDx+yMNPPuLhxx8h29eUMlN1jaghZGILlgxRmxRCHAgOAm8dBCwQMRGq1gpSZpZekcGhb0Qrj7S8XNq03pi0piRSs8mv0kjzyOlqNFJxNhGtXs7R2mxSWZmIddMVFZhRUo2glUOppLghxsCQ4HBjjKc0BJJulklP20x0Ff4iICFZJl4ti90JfOa++/1qMKzXjBcPuXj2EePpGbra8PkXP+VnP/8lpVb+5q/+ku92O67zzOU08ej0lFVMBn1sBn2svaLQTFpkEIMOWqWtkqvFuxloMtO0GlUPKK1Qa2YtGMl7FfjT549Yk5hy4fdXNxSdiQGaVETOkHCKxB9pnB9mPfe+9EHvf7j7e+sgC2pGFsdVbs2sTiNZSindkUUxVQPBaFweu+Ku6eLPLv6tPbXZ/Kg1ZHmZyjwfuL664er6hlev3vCv/upXvHx1ycs3l1xdXpGGgbPzE/7o53/G02cfc3p2gSxoF0sad9cYMHbLyQmnjx5y/vgxu+maPO0pOROGBJja20JK8J4jiUgKiRpM1Exd7rLrIlks269QM+0atZLQQo3DAOBBgjM7xGJ4GmsMoNFqo0VlHCNWqKhoNLDFEBNaG1UNGhm0+m2yxNQixKYdwyzLdTCwhdKI3ifUxb0kmAB2K4uLaz+p3081RYX1hvX5BevzC+K4oqWBB48e8/TZR7x8+RJCMm0l51CGFDlJAy3aKtyV7QUbH/2s8NerYtxXm0ppsS3HKs3zjaooppd7PiYenJ2xUuEwZ072B8YQiUFNHpQ1yLCM47u292gI3ZGgkTsf/vCj9+z0IStn3/QHD249lW6YrlDQmQbqMYssl9cTKWCyF+bSmifcaF2CxI3ThI7tWwwUZAfqSmmhWZZ2roXr3RXfffuSFy/f8NU33/H//W//gjfXl+ymPa3AsDnjwcUT/uk//Wc8fvKM1XpNiNEIwOBsDm5NAonh9IyzJ0958NHHHF58zSG/5jBNjFRIiSSDY2sjgUDCgdshUqK5ebU2pBiMSEJkWI8e+lRqmwkxUFujZTWwt9dTDVlsCNYQExpNK2fVQRbRrtsqBapWpjpDiBZzeu+R1mz2Ct6ECMHkQ0qx2qRWTJjNWjk0LQStRJQ6rJBaCN6JGzW9nSlXI0EL1r0smjGgShoG1ufnnD59ysnDR7TVGuLAo8dP+PijT3n14hUxrcl5u2SoNQjzuKKNyjrYpIcea982qhSKnb/6ZB5FwNUdTdgsMJXmkjVGU9wMkYv1wMnZGWNrSAycpNGMMwIpoWxQTPP3Xdv9Med9VqRv/XPn2/rWg3sPc+cLP/zY8RWTouwCS8Hjwk6lcgyqsMywZqSW5Wue2DGbazRvsNNQUmjeEdmBDcFjWkkm/9gada7sDntevLrkr3/9Jf/V/+O/5OWrF1xur3h9uaMIzt2EcRg4Oz/lj37xc87Oz4kxWTKpT3wiqBbHfyqzRtJqw3jxgM3D55A25KwcdntolgaNa2WQwbmnkaoBLVb7W/q8CDBEkppzK81UBDoJYJ5mi6tqg7TGhLBnZhGiNyDKOtGBUDFC6Zq7q9FCh2ZdulIyV1paBTkOtgElYwoN2oRYXfirVmIYURWm0khBkBYIDQb1Foah0WpFo2VlYzWEEABi3kOIYpPSo4c8/tnPePzH/xbjyUP2IZFVIUQePXrG8+ef8+zZZ3z59W/I854WGteHnWdozZhSGEiSGMPosElsom69nKUQEiqQQ6NW76CtDZGC0qgiTDLw5b5Rypb0t7/jNBqe+s0+s1XL4cwo8/4NuvuWsDoD/uRO27g/5vzgkPOHy9oHOqnvPMx9nzfnrNcyg6+UeM3SXD7Pqx6BBM5esHNrS1zm3p17CXZT1F3N2y5Xq5XDlNnebPnyd1/y6s1LXry+5Le//44vv/6G65sr9nnHXLCGqsF6jlw8fMLTZz9hc7JhjNYx2mQ07Rd2NRXFmgvlaplIGQbiuCHGFRIGajvQaqSGRqnVDMJ8czdsc2XtuMEV/YLP+rr83uMF7rhdQWs1IbLmHdhqsIx09GQVUCKWbURQrTSCx/Eep6k1SVp+l+OQxUntZPX6r/VMiaiv4pUUzHhbE6TOi6en3nLQEAkr5rL386s0FWptJpB2s+IsV1IcGYbEoYmxjILw6PETPvlsz09//ku+e/U107ynEwhyqexkohBYxcroyRvDYVt8GXGBWFXQ6u6vxafNr5cuYBHrhXLdlKCV317tOI12LS9z5U0pbPcKbxrjr37H6a6xfv4G/vyf3znO32OcP8rE7tz+AD6L7/++HWxGJUSk1SVm06Mj601ObWBG7ZC3Djg+qsR3F9c5YPgybK86oDpPmaubHV9/+4J/8d//BV9+/XveXF7x8tUV16+vmctM1kJucSmByph49OQjnn/0BeMwkoI3Yu0dy1QJrc/SGEi8WHf1OETiakMaN6S0opUtrSo1KKVU2soBheqZZePEudHZjBOIEFu34aM7r21ZHUIIlLlAT8hoo5WMabJa386mSk0C1fR7WgOR5I5dv/AKzriAWzExYvXQbGGHYiWFiHXmrrXBEM04K8BhAVagxpUVESSN5HYwqY9arI18zqZfWyNP9hOiVooKs923ADx68hgFfv7LP+Yv/uf/lpubN36uYvGyNmawem5saFQvPQVWYnG6qBClT3yeJPTEYfVSVIc6l6bcNKVURa4nTrwB57Y1dq2SauNGG+O//A0P37zi/PVv4D+7e4i/R1T6DzfOd8EI7ocX/CFRqO0fHCQgUqwZzxJ/WmflGrwblRraR8GFfm21DQ5QMKOsPaLq9HRzX1rj5es3vH59ye9/8yVfv3jDt9+94C//5f/C66uXzNNMmW0WtclAkGADL2iltYGLp095+tlnJI0g0Vw0UbIIVCE1qGqDpKKUOtPGRFqtefjsU64//hy9ueT11y9oSWhBmHNlowE0Iq3H0O4xBDWXHEGqr1YBbxab/Ppl6MkasZXAmDqm2L5kuxtIi6Z7Uw2Ij4DmyjAkVCJhiECkaaG1QK7FmyQBDUKJSDEu7CEUqpg6es3ZSOva0GGF5cONIqfNV2R3a1QNwztES/6UOTAdbjgcJm72M+PDETkZ0XXioJGpVXJTBomEGFivB548OOdkPOEyDOR6IKZO6THgw6SVUg9ctx0xRWKMnMcNq2DoqrU3MRaxloWiPSFkOLXa6pLvyAo7heu5cCLBAPcopZnXv9PA9b9+Cb++pI2R/9P/+e5x/sG6tR9uQPL+Z987lNzx6AevyNvvdy5cjx97zIl3Uxa1VHs3WWmWglhmdO0r7HHlAouHpjlzmDNXuy2//f2XfPXlt/zNv/4Nr95ccXVzzcvXbzjMB2thUFk6K6saOMLOw87rZLPi4mwDsWeGbdBJs/i4RhZIroqdc632Oy6ePuLpTz9F59e8+Pqv6GmIKIq24hMJRwifhsUlN+3cekyK3RLRNrD8kd+4XFXFoH/SB21B9dgxR11UWZu1uVt4zyEt17jWQqw2YbSaoFlsNsueVhMiAykWqsemwSVjRBtRYQjDIvOSwmCiba1Ry0QcvHdKa5S50qoQhxWf/+KPefT8E9LqzISsm4cLFl2wWo08e/4RX3zxU2qd+PLrG0+OqfV2pcvAGQ+0VSiqaJ1IQRglcDqMrIVF9cK8lt5G0FMrPdhXG3+ZSPMSUBCrpUqFLMK+FlOJfyfz673G2Rfru947bnKH0d1pcHc//MEbd3/j20ds3u9RvUZpqrKW9LlNkeuxgDRL7jhJbDHOpS6CqXEfDpnXl1e8vrzim5cv+Nvf/Ja///uv+Zt//Xdsd1vmPDO3asahVhvtRN4FedYvh8BmNXB6srbsonQ0jiyxbfMssfY2eM2MswXh5ME558+fsnvzjEoftBURRWtxVodjjPw3m5dg6X6let03vHVNj1OWZ1XFldcRa0ERXKeou7q+fx98rQo1WsyqbuS+PNOKIlptHVRrhKsKmQmaAS8kOjbYv7tHKFFNX7hU6z2aYqK0TFVzZYmDu8KVnKu19EsrfvL5z3j45DlpPOXQev3TJyURxmHgyeOnfPbp51xfv+bLr3/tOQqlN9hRd/3VV0Nqo2CtBYdg8jA1qPFLg4su+pVW9RCKXitfrq6RBaSRYqCFaGNXTdu3ucf0ru1e40xx+P7S9UPDkVvG+d7tB3B3e+0HL+qtv307/ojms6d9v/Ec7fWG1GP5Qzy2tJNMx+OFXtPqq1ykVGGaC3/zu+/4i7/4X/if//Jf8rd//Vcc8sFEkgtmFKJkDQac97oWwZIzRiXzVUmEmII1J1qvLLkS+4ovi4FGVXLAARVCbpldbTRJrFeJg6zZ6RklPEbza1NCT4maK0UCYYgMqVAVcgEtyVaMKOZDRe8sbQDV46Bd7qaQhgBVUBe30pBc1zXT1QiTt0tvTZmmA2VS1wFK5DZZbbAKdRcpsaDSiLUae0gKOSqpZoyRnJCKc2kiaa5O+o7EMtl1lcA6JraaLfbUQJ0NibM7TGz3mYMG6kZ49pNPOX38nLA5o84m9GX3NxJCYFyNPHv6hD/6o1/w+vIV/8Nf/neYjBsQkinZZ7+fIRFwYbhaaATmCq/KDG3PGE1m5Ww8MSJ/tURcz1uZZKsTvCUwbCKnm8SjByeIuKL8duLlm2taqbeH9Q+296ycx1Xlttno2zvd+cm7n/3jJZiOR1qmPHtdj5NFl/egu5P9dQXVQq7KPFf+9te/45tvv+Orr77i919+xXffveDFi1fsdgcKxaB5nkCx1aMuxwsanOJtbqUV1q0GuUwS7gq5ZJGf29FTD9JbkNv6r1VoohAi64sLHjx/zmef/pT9iz2arxbaUdNKycW1aRKiVhcUd6lrC/7DKyruh3qsakldE9qSEIyNVXGlA2yS81XZ0ICmmA/Wsa1V4ya2ZuWl1szzyDXbihht7Yja+2mtHURhk0aIFn6k0EhDYggDowzEap3Migo5b6EK0iKi1nu0lMo8+6q5XjM+fIyK9XFpMdG5rz0Q6xjc1XrgwaMHXDx4yGZ1Ts1XQHMFh+hoEHf1zbJ8fFWPyS2zPTdoh8yUtzZZo8SmDCkyeKyqGC54kMLjZ4/5+PljfvHFF8QYudnt+fq7lxz+9d+xv9kzH6Z3jvH3GGe79fjd5rE8u8dqP9Qs37E+v2tHm/vNU7QZ6/jqMeGDTVAlF2Phz5lcMvNc2O9m/uqv/5bf//5L/u7v/o4XL19wOByY5plcChqOMQWLy6r0ZPvbPy449SlYpvSWS9BLDN2bvv0zLEGlRjh2baJalSaB8fSUs8ePePzRc765+hUlH+NtPPNZc7NY12fvfk61HSeB5V7acmExeLDEmMbuaQC1QHM6nnrm0tYg1FvdW6XCSj89Bmxq7fmaNqffCdDPS1BNSJRloKcYCaLEYC5f6rVLn8yMAjajLS2usU0CjVZMbmRzfs7FZ58R0spX+4BSluve3XcRRwSdnnJ6ds7p6TnXl1vzvpq3h/QZvKcw0J7Bd+OkIWKTWq2FudZezSOpUsX+TxbMEqgMofLg8QUfffIRn//85wwpcXVzQ1iP/P6rF+RcmKb5nUP8Pdjad5iUfG+E3R5p3O3m3hmD3nXoe9897qOdyiXtdt2bo3q7MzB6oN/g25eXvHr5ml//7W95/eYVu+2O7c2Wr7/6jqvrGy6vbpimyQnFlaKNUN35EnFpTQ9TVGzAS4E60IEQDC6hQaLOhVaMFFxRX0HEEwr2S01L1+QuQxCCRFqz1gZZhc3FGQPPOPzyc17+/r9lum5UTZYR7bN7axAy0EirtemyqtKmibDaWHvBYiszodFiNjYJkSEO5GWGsxWAaYY8GYG5d0qLwWp64lz1nghpzVY3jGuakmNRgyK1+MRjLfSKzJ7VDpysoxnG3FgFcXXCyqxKTdbwV2aj7zWtTNbhCbQheWKzWfHTP/slf/5//M9ZnT2jjKdkDVS1bFCEhWhrq6dy9uCcx8+e8ZNPf8pu+5p59r6aagkeCT134MLirVqsi6kqttYBLrrozYpACZGDFiz22TGKdb5+eDrwk88+5/Nf/imPfvnnrNcrzneXnD15wK9/+zXTXLi62b9znL8HIfTuTNL9aSL9gH3uOdZbL9w9QXRuiOIpOS+0a7MuVvNcuby+ZJ5m78aV+fuvvuXb717yN3/zG64uXzNPB+ZpYp5na6RT2sJd7OX14+PQaYZO8IUQrESteMOhZWIwde/aAvt9YbfLVndTa+jTWTQ2EGyFIYAmWzUMCF+ZizKOK8bzU+KTR8TNQ8JwiXrE1AXLHMTjOkLR9DfU+KqGJGnGEnPXewgJGWwyoFbC3FvSFftfi5U5qquIqU1MogVqoUwHaM1aHybrz9mNUKSZAPcCsB8QFVqZnThtspWxVlcpHLwlhh4nnPkAtZGZydlqh0UabVLT0g2J3TwxtYKsEunBA4psyFUosyF6jIlkk7QlrRqb0zMePnnC808/5e9+/Zfk3Jv0+iRPoypECcS+mt4ajCp9QdDveSLqeG37/9Aq01yZcuH//l/8C07/m7/i7OS/5E/+/Jc8frjm0UbZHvbk0tD27h64fwB878fGi+//3Lv3uO8dF4q+BRy2VnmVm+2By6stv/7N79htt+wPE7vDxHcvX/P6zSVffvU1++0NOc9GsO03QLu3cDtmdaPXYzLriKk36KBIryV6zOIZZESoJVPm2ZJV4FKX4ZgaC2r1Vzn+j2dGawFdRcI4Ek9OGU8fMm5eo/OVZ4i9ttln8X7DPCEhwdBT3e00IWMlqcmi2O+yOo5oc9fNgAkxJtfs9bpnzVCLrSStLnGy9OvjAzOIZXslmKEFSXZdqxpsUe2aRTWuqcRIyM4mWhJlNqlUirWtr80aAJXiLQfhUK1RL2llSuwtUqv91pBMKaHpcZJFDKAwrlacnJyYSoRdaZuIetaVLlBuCasl7dqHo4NaOj5Hlvt9DMl64qxOjcPXr4jhkkG+5iCZp0/OeHYx8t3ra64PM/meMf4ePudR8PauQ/zgNXnH6+947a3X5R2vv3P/WzOXS0221rjZ7fnym5f89rdf81/8P//fXL55w263ZSoTzbGlpTbLsqmtQV2m7+3Kn8d/bvyFtqi/H2uknjST5qp4gSqYm6TWf0PrRJ23tFpt4EVLFjWpnsoXlzRyd7C3GlShTEo9DcgwEjcPOXv6KXqzZ/vNDbUpMUCMkVoK5kAEWusJDDMMg5pVslZqng0LO1jTWNMyKyZFqUJqwWrDKZKCQEs2BrRQ/DdY01jDDNvIrEg8hhIBIYTkcpm2Urdand2SetBOVIi4lIqCB4a+8tpqUovf8QZtr+Q2MbdKAfYtkHWFpHNqXPm9naEVQljZdakFbWEpU1mX7mgwSm1Ei09oVF9BjWBtHePEbboDWJbMg282war2QpNPLGKTogB5quRSEMkgyr/8V/+KISZSiGjxFpI/VhpzfrdX+9YmIne//g7H9nbS5/sffVdC6Icxq5JL5eZmz//4P/01v/v9l/zdb3/LpYsuHQ57Li9vjOKkmL6PNzg9kiUMplVdk9YSmL1q2b0UuwjGtz+CFrrEpiIkmqFHQvBap4AquVV2ZWabDzRn9Rvrw+KiqhggHJZ3RPeEpmgRihxobSCMax5+/AntT/+EUTLXX/0NXZUhiAltGRBAKF4uQYQUB1e8N4qTDoEQEjGEZba31c6MRkXQNtP1csFgfEgjjSuk2LVrlQW9g9rE1UnuKW5QseSMhGKiXloYQ0CSnaM0JUYWxT2TLY0ogZRWIEoLlZBnAytIhVbIpRqWdlb+g//sP+eLP//3OX3wgBIcKNF6pdsGvXTZVBpBI+shcbJacbY5tXvXa8bB8wiIZbwbFpYszBeIrbNOLCHWBdl6XnhJw2hbJgKJSivVSOZBKdc7T67h2equUHH39ofFnHJ88H2PV7FUelNdANjNg+de2DVWBG+9rqo+6+Gp+duvewDumbrbr7dmWdfr6x3/8q9+xVdffc2XX37Jdr+jFmMblFxvoV38GO3tX9AL9N2bFc/Cvg1dlAWXenvNvH1ZRMy8eqKgy5qUnJnnmVzqESiufW0WJ30LS5Ol3nUbGzRVbVJYn55y8vQZ+xdPiWlDkkJQpZV8BIo3sZKJ9PJMWNA2CrZquzvbPTbx/XDGjrXgsBPsfWbEi/DiyTCbTfqXBmLwGEyUFFaopCXWs5XcL7qD8YNLX/YVKsRA8+8OQVwPShzlhas8WMmm1MaUG88//ymPPvqENK4oBFTt+h4hUT7x91vl7KIUhMHjSePzxiU0CQFHC/mYRJfIvqqSpGd/PfwQX/UJ5j31RIL49QnHc7FxyzKGLVcp71zY4H3YWv3eB2/HwLe2bmCWVCnWUKZUivdWrNXqcfa42evFGAl9v1qr6dVke1xrW45Va3UgQF2OnXNmnjI3Nzu++fYF2/2W3W7ncvdHA+/GUusxlrTMRG+7EEymBF2SS9KN81bsCLdKM4Zhw6+vz4Agot4dy2NRbZR5Yt7vmWZTJdeemMHA1BL6IKm2aqewyHRoDBSvsZ6cnrB+9ozVi48YVg9YxS2iM2U+EAcTCdOKiYBFWQajlQPsj7maXS/JDHMhNEtY0E42spUYrTTUX2oRW2G1RxXmzoYhLmiXgLFoRKxPSWn9uhY0GszQp0Orobob7eRIUx7AASQe47XWqE6rK7Wxy5WnP/mMh0+fE8a1yWFqb3Fhd88MtRuXhwtidz35fVeMxxo0IlJp0TijrRaqC5ulMPrq1t4q84QlvgdVzyF4rL9sUSyTrYYFVz1O60tC8/s2dmu71zj/L//X/5styc3hUvVoUPUt47LHbxmnG1rzlQ83FhsnurgL3W+/+7Hv6zPSbYEt9dWzFNPusWxjvzjSb+9yzK52gNcDjfdZlmSJzYYR1RFdjKf5mDkmg0Qt/EjNXJLgsaYhajyHdxzPbK+vef3iBfvtNeXiHB1Gu2lirmVbsHeCtA4SSF6uaX4jLVZtJHQ4IT39CfnyNwzS2Jyem6PUoNRMdAEuSLRcemaDOoPWDKFySI20Gl0uO7iLZzQqKR2YahIoKsbt7K3rwIyoi2qZW2y1TdVIazNKNkXAZgbe4ooaV10uwEooqlQVmgYf425UhwOlGR2s6kzOs/Vn8XLN6vycB58/Zzw5I6SRFqIp3FVrd7iUQ7zmU90SYnIvhUptmTAqcVSoSm2HhXygOtCpdImRUO0+ZFHmkokSWEUTM7OxaPpFpTajvyFdmdUmv5SsBFeOHdC6q9XTVT/KOP/yf/nXPnhNGNncQms42+783/dzF7bWvtR3o/Q/2p3CnmC5/c+t09Xbz76/79FIjwV+g3n1laFjWe0xvH0oCzzVZTS7Qp0heHosobeuZTd2QaiEFJeZtCkGX/NMZV+1QdjudlxeXTIf9rSS3cXrruYxfl4M+jZKBYFllleGzYazp0/5yZ/9KS/+p2/Rg7V8D/H2lbPWd5IiWsoyU8dg9dOqVsWNY1+RmoO4ffJrrX8tWpsx9T3RI15L0nZbk9C7iWt/VvG00GKItmIdk2uWMDlidcGSRrUV5v2B0rA6bzHuam0NiaaIf/b4CY///f+Q00ePiKvRDdHodq0UwrCkTTnKovj3LI97pOi4Ymxla+4V9F3MSHEvJCxZ9S4UfuQAR6+lRo857djR9ZxUrB8oPtl6EdXO4McmhH71q1/fArl8z6i+9xhYjMSfHA3re4+PA+ld293v3vXq7cmoD7DOzHBVWtuvx4yKuxI9TmpE0hKjaeiGc1zFe9mkEcwlVlkCejTQqhWtQxPCEDy2Nrjcdrfn9eUV035HK8VWDSyhpHIEwAN0HIMlRKz/SldyV1WG0xPOP3rG+E//Cdd/899xOGwppZJCpRcGDE9qMLKSJ78CQpRkbrW3VUjN4UNa7Or1EanmCmoTmhSjnWGF3dA9mW7AHle3qgszB882hz4reEa0d3czgwg2gPvQEUFbpbQDh8NErgYAmVsj+2Qf4mh9TB895U//2X/E6ePHhNWaGTPOWk05IY5puRZvZwe827njJ9VDDFFZmCZgXooxjGyqMZZfsLLPUus20bQeVgZVDxEw995/aVrChD5WfanosbDokgO5a7vXOKc7cH/3G9Xd24/5zAcdVx1WpT0mDIsmjxlpoYsla5/lAWme4OjG1l0qbR7/iTtAbpxuoCziA8LcKiFYf8i4AGWbq5VbaUQlcr3d8+r1G7bbLXnO1NKc1HuMu/psbar1Vt4xtYGeDDHNnc3ZhjoEtFTi5jmSDrT6Bq+jGCjA40+A1fqUw/6Gkg8cpMFo5Y3KRFFrLhxlIOhAjIkUEqXtcbyM2W5VaMWEuzxZRa0EB37Y4ujGKWLKeT4gmx7J34lg2j++1Ekty2BtLZgagwh1FKZ9ZsoTeTLXsGE8121pPBzXPPriM9LmnBoGaoGq3sioVaIkT7SEJbZV92xytfaAIBBHkETVSgs28Sp4+ce8m+iIYtRwxD3etrHnKvNAqcd9QlCXGLKyWsOuT84zdWnYG30C5Li63LG9H/j+j7C9O+S96zvvXenvPnpw908CTW+vIu6TLMOlZ/CkT/wsJYRb7rLQQd+e8tNjZs6ydXGZHdW/31Zu04vpdS+NUOY90/6aPE3mommjSFe5W45A9Ru95JoaS1zdegNgR+SEzYaHP/8FIVQuf3tjH7BUL57iBYrXC21WCS3QcueUisW9CQjqbrlSqQtk2LzNAM66sJyxg/lDpMPYVO0re+ZR1L9T1PRzneFh6Nwez/q05G3ItRQDa+RMzaYFqwWTtQvHrPcn/84/4yd//u+zPntov00NapfzTG6VrCD16D7bj7FySVDXFm7Vyk0hoSGgUtyLwUsggjWUs+z50a3RZaKhh1PiZOuefl/uvHv0Wozy1qoRybu6/5L17YPw7u09xvnh2/IV/0B7fp8h37pU9EDNRL66IRpwIrjLiXgB2tEvi2/lRzrqDWEDyqPKgLeT523jtVU03PqZRxcKOXIg+6u1TJTJyzvNOy+jb1146TM8DudzTxJ3Q7VVW12an36KPPjiC9q05fqr3wF7c+CCf67Lhwh2TSQRWkOLr1SCPZY+wMz7EJUlWQa2img9uvjdQyGI108dUeUME8PXejbY/FdE4tGoBY76TXZ/LGZttOIJxKKoU76Ccy1FAnG14eN/68/56I//LVabM4JTs1CllbIIVt9uUNVBAd1aVBtVK6VVC3MsGL51//t9PC4QPZEofdS5i960eWmkoUEXVFQvoaEWz+dWaM3i0xCiZ3uPMe8PCv23tveUUu5790du95zMH3wov2pDGoBgAX2ZidE6ImuzOmFVx4WKa9MIKL7ChmgraU9WWGRJUHPRlpDA0+R9GsCzdVTTGAoxEeOARF8dtSGtEnKjzDM1T2acnjh4a+VNaooK6n6QG0pqjVAbmiv7XWYOYpjTw4En//YvGc8Gym7L7m//R7RNdu6tO+SVXGZC3DCEATlsF+HkWiHO1j6AVgitommDpBUhKiH40l0cPSWBJoVF2fAwU7xbmUSPx3r4UFg0fomJyEAvKy0156DHuK4FpB1otZKXmqiVeHQ0V3F18YjP/4N/zi//2f+Gh598yrhaoS1YZrlYC8gRYQ1YNr5akmxopuwATDSC/959OzDvdmjOps1bPeYMMLfjuG9aF48gqBnyUt6pZZEpCZJsYmvWvU1b9TyLG7YEmjQXBTi6x+JaTu/a3tMr5YeG9OH2qj981pMOP+oId7yiNrwrxWcscZSVAcep2eQf1RI/5qdEe0znUx27dB3bOtD9uiVDCbdX7Q5S6K6ctWmw8F9u1fI80+3i01qttXrEGP8dlaS1EJqaMXoaKwtoiiiNUg7sL68RNcTJfr/j6bMLhkeP+Ow//o/59Vd/S9lVhJnoA6BqNU5mNKPQqGhRi/dU0GoqCuI8Rlqj1QypkzmEFhzwAZ4S9eROhcHAd4gkYrDGxCqgMdPAYm+JBKlGhxOs+a+GZdXVZqukyb3Y/whIFM/4DmyefcL6o8+4+KM/ZmqBV6+veHWzYxxPDVkUI8WPJc3dIgdVGJTQDaqZay01EvaB2JY9aHqg+zraMqr11v09egWdpF+9vNIThj1pINKx2b4yOg2u9RXedYbEx1fPbbxr+1Furb5rjzs+cIeJftjWM3nv+4yqKbhxdE8bIM1T1+oJgSV2tM8sMYLgCm/HfW7D+7Dd3lrw+/csN+32N0vok/CyNVVKsZWqq971CUGB6nA/0xc+ln/E9XNaK+TDAWlKyYXD/sB02LDZrDj59DPS5gEtH0CLQ8ocxO6zMyoOyj+63EdiZnB3XzFisXfwYlnEzaA9Brb9gg9aL8h3nCkWf/Vac5+RRZrFqXLr+nXDzAZqb04PsUSUuoFGTp99zMmnP2X1+BlTruwvr5lb4+S0MKxGxvVIKeJC1nrrPll2OXSAu5gxmpC183n8Nyj997PElTY8bsm73Dr3t/I4cvuzHA2V22Oug2KOBt1j1x/t1tZ2P/D9h9uHG98f4tzKXce99dLS00VASH5hG3jPjYjXH2/Bp6IMBBFiiwyrwQaWt/9r0lyJAB96mMSkj816q2iqS/7BVuvOgw5+85sbihmn41yll0kcooYlN6SaInpQGBRS28PBgOyrtAKppGRB0mFXqIyUizXpyS+sXcB2ook1lNXq0pY5AxmGQJfS7B6DrWIu3iMWhwXvB6JBPCHkmCG150tcjBIoSMiIrhxPHAyKG+2691YTpkoYrY9qr5Nnpc2VMhfmrJTcoPQeqNakChLnn/+ci5//GXp2zuvXb9hPE/ucOTs5sF6PrE9G2nhumFtkIaJ3IIFgibcYGlGEMQmnJ2KCa0GRqMg0ojqDZmveSy/HREKzztktGNpMG5TOsHErtKw0GN7xOGmLr+KdW+pC+EfD5f7Q8YOlMd82pg8zwn+86PLug9vN4Ag8EEt/a9Al2A/eWNWyitU/Gv2z1lmZLtaFHvGl3ciCuzNYIyNUXAVdHORQneEPxvOti2u7uEpAztVWB+3Fb+ycVb1cYllcUMZk3aySVFNDV9wNtQERhkhtE0MbGEPij/8P/wnf/PeR3/7Xv6fMl8Y6Wa1p84DohGh1ZfaERo7QNp98kOxuqUKrnigK5pYGQ7IEjDC+oFpUbJ+YkDD4b2leEo0GFRyWhYTqMaxUJVYhyUBLxgE91MNyzQTT8i21sZ8qc7XGyOOYFvnTSKBOB6YyUw6B9EDo4qbL8Pf73z2wIgY/bDGgyQB8Whu5zZQqZrzRDLq52x21Y8wsAd58Uh48JKlqyLgQXIO33YJ9AiLFwfdicXn3BlWOSaEfG3PeZYT/Rg3urm967xfeys7d8uAtrdBT1ibP2MHKUVza0f2dDq43t6+XAm65yIC1q3M+onYI4JIbZsnm4cf00gseV5TqyQ7VxeW147qB4uQANcpSipAcH9vze8TgNdxGLtEIxSjD00eEswtaG2z1iSZDguJutMXDwdkb0jAQgCghHieSYwhlM42IxZLGf3DEzxJTif/O4DGexdsd6mdewe1inMe6y2fd929+HsH4lqjpu2prXE2ZQy0UbaxCL+eYHm0Uuw41V2Q+UMNgUiXLF74diywuekfyCBb2NPOW7A7YbzOklN6agPs48VU4CrXH6naYxVV9uzziIc+yVrhJhr7/vV7t+1bO20/+TZnlu477Iauz3Prfn0vXElWU6AZq7lUvTksckWB9N2LAkCOevOlMCREX2dKGVoGQIEavfZoOqVFd9O20Ptrpnu7G2E3NxVPqqMsqHoMYdT5qaYZ0GUMjRSVFWYxTktBhN6shsq8jLQ5stSAE9jpS2okZXjYoXEL6ck6gLsYuAVqrpCSMY6DqYEV4dc8BH5wLg8UxuL0dg2Njmx4nmK6gH7vXoJVWOozeDEJw9klUqhZq827SqqSYCClCVfYls6vK17sDn82ZB7VwgjVgQiuruCKN0XWLCnna02JF0soRN9J9S7pyvNKNR3y1V5Dm3cxsYixVEQriIhIavOzk8jEJJQZhGKBNfpNVaAWflKIn8W3EiXQYZ5/skiWN+qr5liH/cHtPl7E/zCD/jZjvvTZ6xMAq6hKk1a9Nd2U9LgAHestx5VhWWgcquNSGS48a4XhhEmR3hoXSil14WMSujn4tHq3IkY2vyuGwJZfZWsb3/xZKlHVIbqUZiFwKItaXY0jR4yCDDKqa5un1m9ekVePRmQHf08ljNs9+zs1vfkdohRCsfJOS0cRqEVrEmv+kyDAoMUZaVKSsSNEmgNqqkcUbtDaZUUlEYrKFrlW7ZmKG3+ps3EtJiCQTDPNVSLv1iiWmmv9WowZmE62Wam6C+mQXISa4eHjK/+5/+895/k/+HVYPn3L17XeU3Su7MasLQjq1kKUpr29ukLRmWLtL6sJdwVku3eMJWI1SmhCTT1JFiXEg1GqqC1TL4XqWtRoEClXDDDeFXAWaiXvHwTm8YutvbWbwAWvL2GRw+ZpC7/tqFEE30R/LSvnDDOUPSQfd2vMPKK3ceRTBZkDtbtYRgUEHWwtLIqQDl/u8Y9fr7VlMuoX11xWs52UHJLQFtStLRk/cvetxRFswvrVVdvs9++nAlDPNXbmePOpupGojBYeAOWbQJEMMbhiCS1pqQF3iMkokEInDhuH0ocVF1WPKmmk+6yPBGhEJCIkQB0RMoDlgq28H9y6SIXTZEqwd4CKl7i4fx2I8oUM3PHZVW6v8tpjBupeg7Rh7RwJjEIo2hy0qKTXiuOLxs+dsTk8Jg5GmV5s1qmLoICk+dCqr0Tp8xxQXEP9yXZc7Gt2TbZS5GMxQgtWo8Zyzez7HwSEIRx5xHy91GXh9/PX0j9weBnZtlo7qPvlg5SQWSZt3j+8PBiHoH2pE2te1D93/R4AF+4XpwtHqTHhxVYImS/fltwAEvgL2i9ONtbPS+4Xug1EEqN7Jyw2u65hakqKfR2daNNDidT0otXB5c83NdsvucLBOYv1nN11iH2mFMTqv07meEgMhWp+VEJJlSlsjxDUxDkSx7GIaNwxnF5a8qLY6CYWoA2CdrKtmz1RDDCvQSs17QmqgEWry7mTmgobQzwNCirTixXgJSDJj19K8lIVdc9HFH+nRsiVB2kLEp5qYciAsjI+kBt6oYkYs45qT03PGMUGCYTWQ1o9oCqUoc54WQvSD81MkrAhxsInaDXQJHd34AFqtzLuZ5DzW0pShG6fHxUivZAeCp8GsTWcnThjwwC2Q3kZDuI2zVkprQPH96pJ/aM0aAy/x/Tu2+0sptd739ls28q5n7/rAD/bqg/w9x7r9iiVkXD6/C1iltSNZMkpclA6SGmjOtHpcX8eNTNu8GGetYcnytdqIlnkAx0f2wrLN/h6btiPbQJxlLxrsvrSGzpnrN6+4vHrFzfbKtHjEEyi1QYXQCrEdkFIISUgxsTpZE2SgSaQ52wQiaQg8fvLc4WKJGA3AX1v2YryBLMJ6IA6DyZV452vbtdKqcT1DCobi0e7uRwxlO9Mmc78kNGqebTIiMKhlKqE66sXiN0Jxpk0AormPWJ1TabQqXuapdKEvy2ZCVCs7Fa3sm6knXu1nogpJIloyZTpQG2QSM6BiE/H++sBuumKfKw+efsz5o0dIOCNospxAMBc0KORp4rsX3zLnSlP7/loz4s2Tmte5JeDjxKfyGKyWehuU4itmdQ/BKGS3PDdRXzmFyLhwTe0tXQAn79rekxA6msK/sXzQe7bvn7p+75ldEy8kixK0u5NOZ1L1ODJ6Lc7cjS7CtFwtjweMJaLulR0xtccJxY5XPOkUbrvIx12PN0lMu+jVixe8+O47PvrotYHZ1fmUObO/ueSw3TFtd1ycbRiHNcM4ogyoRKoESrM+JiFY7DmsRjunGIgJaJk63dAB2yFgK2v0jtOtO5E2wfTGmwrueorVd1o1F14biJGKQ3AGjydHqhRzSVUMRreUHKrxLsUmkSWkoK9itwrxSzAaIejieLamaEiUkJg1UInmlkdj+dgtjLdqiQagjyEwpkYrE206UIdEWyXn17Ko5ZVa2R22HGaDU8aYoGUnHejihgNYA19/mNSJ4QrBPAB33OwiOk55kRKmJ8I8a++EfPWQB7p+4o80zvvEh5ZReHvTD4s7f7jP3Z96r6Pr+QYk+o8sCBXUemAuSgJqdKZkwQ8FJS4oIT2WElwsWHuSR47f43yE40uehWtq+q32mrs+ixfgQISmvHr5ihfffsfrV6+ppVLEAAfzvGd3/Yrt5Zbd9YGz1ZoQBoZhw+wBjAJzs96YUWBIQhysHBCiEFJDdKYdrm3NCq7MlwaCq/0to05M9Bp3H6sKLeFSlgFyPtZjR3GpkmCJMR/oNSgjlsxIzdrcd3c+OEOoJ9noQ1CPWd4+IaqXYkJoHn9Go+mlRIsDWQJVoq2ccUCTYooypha/gDgCjONAbMHCiXygTZF2srKJCV2Mr7bKft4zZevsHUIELeaOvz27Ah5Pi6+gXdJU6jJ2+gLRnwUP+W1u95DL8xkdvGGGWbtNv3O73619p9F86PYjkj3v+Mi7pgnLwnoQL8lrczYkVuvNIgZWORDDhiiBVHBRKsuujePGyL5zJgbP6mH8Re3SISKuPWs3RDnGiikZhzCAx6Yu4iHirqiy3225ev2aq5cvuH71itelWL+Rlmn5gEjm5ATiuhJWbhjeXKcpzHMjUAhRaRtLRKUUGFaRgcLQ9gzTG4ahEpu53qLRkkeG5F/gekoiaEbVQN86RDR6v5G56wFVkoBSjedaG9rS8V7o2lfpTG2ziWWr1wtjRkMhEoABCYlAYcC+szZBQ7TBHoxgEPrq3QJxM5CGE2Q1ImkgDBvi+WPqWNHcSLkQtPSEATUEBtKyskcq9bDl8ts9J+cXDOMJ43ACzfIGhYkYITps0LQMXFS6ZVRWiCRSVapn76UIAwlVU2YgJtCKttwxZLaO++TfPTar7Qo1WH7B0E82Yfc8yLu2+7O17whWl8P9CNu7e/tDfOa3ok56HU+1ZxC7eyomze/UK4mRqkoHNS/htAZaK05TUjptDFVCxmVaennF3DEblMFLNmY8wWdXg8ceSzx9ElYah/0115eveP3ia6+JBYYxsBpXrnxuHbprbS73sojRMA7CPFusKBnLUPok9Pr3r3j1zUuubi6pszcJCub6EQxXW6mLLm2uhQRLsV0QS2xovbXC2m82lYdGq16S8BVjaZ/XNZkwUrHW3lvFXPBWZyNsa0PKrbpi15JV1ztsQgUa0SRKGuQ5IyESxpWtQHVGtBCbEA30S0cA2EQohGiSIVZympmur8nxwCHsgYbWmQ5Rsdt87OrtfSQ88WP80KYGUkiM3gm7uodkxHw0Ol/fwpjcLGkoTmM0LVzM68DKULIIXt8/8v+wUsp92wetePKON+47RXnnLup/LD6yHZr2PiS2YkpHb7jUx9KzxF0rARdL7uBkB46rwcwW1fAoPRNOa5b9FJ8hezmvq8YdT1CPWHsah8OWm5s3XL75jtV4ymq1YhhOGFcrwyX5RFBrs25fhAXTOgxiYmbeaWs9JDpU7Orrl9y8fM1hf0NoSgvm1sduRHQwvLnIWft1kWUC1r46kt662EfjVHM6e1E/+oXt7B7tEqbqLBTLlGsrxpBpxmYR5ZjR7DArz+I2hRYTDJGwOiGNo7WRT8nKWMHqyDG4ILSPAsUkZkIIpGEwJQpVQsnkaY+2CdXMepPQZj1lerSnWCJRe9uGEIxR09SExDE1ehVxBcC20MbAHNSOFhWU0jzLrv2nvY2SWgaz11vuW9/eQxn7fh+HOyxE3vH67f0/2BDftd0dzArNEhtqmc8en1k7c2i12IVTCC34bG9Z7ThYLNBUocweM4kB1P37ImvDn0YhDWkRpc5NTQ4Ed/tc+KzTi6KYcHDxrGlXDbrZXvL69Te8+Pb3PH38OUMaCXFktTkzCllTrm62hEMFmUibtCj8DcnA5HMpbOdMbpmgjaTC5d/9JfN3v2Fdt7AarOOJNjaiyDwRJLBJiSkWsggt2toWqIgWc7FqQ3OzcoT2UpMsbBStDQ3Wnk9LpfOAVAsxDLSGq9pnWghUAkkHkghBA6Vlp/N1SKUspagp76y9i0bK6WMe/fQXfPzZ5zz9t/8M1iOzNqZ8oJRqxf1hsKZBqoBNXEEiMUTSOC4wxRYSVzfX5JxN2iWOaMnEW1A9EQOya2ievOmk8oCQDVjhJRVaQQSGYbCmx6USakXGwSfK4gRzG7Nzmb3uHhjEcuB9AovBAA23ySXf396DEApvP7/LzPW+HfTdn7v3uB/mLxtQvfMPWdwrHwNdEWOpP1pQ73GRluU41f3/KIJKZ7UoRSvJuxFrx8aCIXac3mUuc/PaKiCB6A158jSzlIg0UkpjnivznKnV4GK5qkHnQnK3bKYh5NJb8AFipHENWKY2RkMdXW/ZXl2z/fLvmV+9oh4muxZezmkal8L6QQtIYtDAoJEYBkIYiEmZtgdz4wWGaOwQmlrM58yephNVZ+vHWxVJFuMFEWSwLmaiDZJYyVRAnCJnS6KYS9hXjWoiWaWaWkA4vSA+eMzJL/5XxKfPSY8eEtKISGRAOYkj89jQWmklu5diWeHQE2OuKk9KvppGBhVkLEiBw7Rld9hTymShjK/66gFIjxmPY9BDAxpNCjEcHdGkHjN764rWDAE0SMRL10ZXc6yxlkoSgw0GrAWiQReP4/D723tizg9+8Y7dPjQgvWO/d330e4uuJVkDbyVWfdbvRrHUnLQfotc22/L5o4trqSDpBtGsAN2hXIBr9cjxJ3bX0E+gQ//s0D3Day+U2phdDLu0Rm1WqG4es4QQjTnirnNrR7dn0c4VYYgRoVDmzO5yS9ntqdNsE0gHVTvGc1F8U2dZiBJbxUjmVoivnp1tQey9zi7pcbz/T9PFpZNmnEtCQJMnzZojXxb0kBOgmy7IoIaDyb05Uc6VPIysTh8wPP+EzWefE88vCJuNhyL2e2KIhHjrPngJRZClaZJEkwQlRiRE82BqQ0NCorJ984LtbmvE9y7a1Yebf4/f2aUc59AFGmUBuygdrCDWGe1WD8ojlvd2zdO8D6ILXC+5iE7Kv3u73zjfteT+AZ7pH1wf/cD9uwGqRFrJVNR0ckQtiRCCF5vCMU7y7J5QaTmbiNcwIj1midENpevBNFNQ6ytYsNrdMWLBkgitJ5JMCa5mRyrFeKQKlcI8ZXa7A5c3NzypmYLp2Rge1Gba9WbNXCqlKnOuxKH5ealTkoSTYaAWU5BvpaFxjaaRJgm0+BSj/pudCdMGUMtV7sgMabRO0DERV+qrutdtw4gMBkxQB+s3ErH6McWQQgGjzswjTtC27tJ9gFfLblmWtJhBGvLKZDzmCvsM+vRTNp//CWd//CesPv6EOA5IjGiwNu25KdnLYeZqq98zCLEhSZCQLIPq5SObJGAoJmFTWuGrF9/y5bdfcXnzmlImn6BdKkGO1rms8H2iBquJOpul0tCWDUkVgqlMVEtqtajUYBzcGKLFsxgQw8zJ3Ny5qsX3Pwgdj9v9MecPPid3PHrfdnec+v7P37+HxXvi4ajFHi0kGzQhEIMw6exomQ4m8CxslxPBsnNRXIc2JSTPnlyIFI2La68uhdmL8d04DSGUlzi3Niu3BLFIRXt7iFY57A9cX93w3avXfPFTY7uU2fjQo69CIVh3rMMhI0UZhoFhGEidliXOpFFI48jJ00c8/OWfsB0iu1qZL79CnFahUhweCDqb8pz0hJhYmaVVX/QqOJMYRtMqaYeMWo3EPIiY0BposyOcRAlV0RJo1RJmrc7ec0R8AFktOJeGFJ/EtCDjBs4fsH74Eelnv+T88y84+/RzZL0x7Kyr7kW1PiVBFXFtS4P2ik/C0buaOWLeGyKJimfDbZzkfeG7b7/hu2++ZX+z9wxyX796f1JXFPSEVaCaMDSBrJHq8TIBW0lVEU1GWkCNkC1eUnL0UvUsojGXbAw2QOrsCah3j/H3JITe/uSPSeXccdR/lKNo/3vbBfYEgb3jqWqPPRc8LJgRYPGAElw+P7yVpLBso2vZKCB1McgoZnQ9E2duDkdEEn2VxrVwFZVGLpnDdOBmu/XBYUmjuVXGVhkYFrfZVhibjbXJ4iKjuvTxaAhxNXL68U/gcKDe3DDvXqN6AMeELu5+O14r00w1kLqlrDhexxD8HKy00ptMIWbEgmWAm2BqBdoM8ugQnBAW9qmtGrUu5GXtagrDQDw9Jz37mNVnf8zqs885efKUdHqyeCd4KBEDpNhIpVmCzWvN5sraeXVxsaW50JKA6MQFk8/c3tyw2+4oc/Uf7RP7MiQt8+t+Gc7itXHUJ3ixiac7xNJwPDG38p/dfWVxHQXP3PobS3OnH+vW9uLqvdt7bO1D4Ow/eh/t7hTQWQ8SPCuIKdi5i5tLNgxkEOKYCMF+eq1qMh1+o5On5EMIRCJRQ+cE+81WkkSKtzZYBi54rCGOTFK09rygySfOZWa/33F9ebm4O7Nm9nUitEggMYRISIlhhGroAYPeeQG70thPmZLNNQwh8OCLLxhCgiZsX/w9TIrWA6bdAzRh0Y93rwANThimyzJYr8wxGQGbhoZKa9W89jDS1MTJJFXCOJr7XKGoWNduGsPqxEpTtaAlUx1xpDqaEQ0j8uCC+PwzTv/oz3j85/8hJ2en1ntEej3ZEEWxm4UE2jxbj1Fv24dTAbuwGB2Nw62cANBaI5eZ7e6Sm5trdrs9JYO0SKNRQ0O9P6lqQDSxNE32ZI0d3nMbKiSNpvGLZXerV5N6bfNYQ+1d7oDFVcYnHszzuGfMvwch9GHAd7sQPjPfaaxvvyg/2O9DVtPjz5BbD44/3l2U4GejtgiAzdihY2kRqLLMYpWuTwtCRVI0+chS2KwiJZv+aBoGinqrIm0uwdY8B+Iq501I6Si43I3Zfm5wdEnhMO1QzUbVKoV82DEHYY6RMZ0Qo6F2aA2VSqOY0pxiinrV5hN64T0Jp5//hPHRA6bLF+x//yvyqy9JOiGh0VOQKXhTouAp/J7NDscBlTVa6UBg0alV65YmcU0NQMiMuqI1qKURh4JKo9Bo15d2PSRYRrpUSs7kuudk85jx+ees/6P/hGc/+yXD6Tnh5NTwrZ41bVJuZWgcI+zAhYC7054A6rezNy7WblQuFFaraxRNMze7LYf5YCD30AjJDC6GQC7m4opn8tRFvCuFqSedmsWQi7pGGBaET/OJIXgs3vOErVVCGui9VUSs4XBR10rqtvCO7R+NbN0zku96961HP9jvfd8jdz/zsRO8vqn9j9dUbiurSbg1R71FcLXVJyiGEuotB9TizJ4lrYorvJkQZweLxxAsMeKfSTEaadeJ1/00e3yMmuapabwbfUu9CU9fZQAkytFVbopGw9USDQBvn3b3URSGRDo74fyznyLznn2e0JvvAEPAmEyLnYyv64vrZd3VbFDVai64qrh4sp27tkaRurh7sdWjZEtp7nkEtBQLikW921wjq7APa578/M84/eIXDJ/8lPHhE+I4QhdjFrsvzb2EJWPadOmD0lUOLAwRFiyDa/p0F0bdbbRVs5DnTJkmg0uqexM+TiyTf7w3vbCyIM9qHyXQ5c1awwTLPJ6WIMsiIc3jKMfq9vCmNsNEW3jVvD/L4l3fuX0AK+VdhiO3/t793ruP+QHbB6zAxyDKLhpdt1R9pVoutbqMhN0TrQ7qd7ciBZPHqLmho6cIRMllpmo04HwDLXYpszSnEwlDMFpaU9OLTTEYSKv54Ok3fgG0mHEKrhUkASq03CghU9fV+2sKS0erpiT1btAAEijFVqbqxGUFSIGLn/0cnfbodGC/v4Y6gWZjm/igq+pegt+PQKKJtctruRA0WD0VM8yuxVpr8WurlGpCZkEh5EqIycnghQ4BrCVTFXIY2Y8POf/3/mMe/uzn8PARMq4gmK5sbw4FUIsuKoWKPa6lUea6lK6MQXMElHfh67fCC1WKWo+SeZrIhz2tlCOaTPp+dsz+fdawraFO9QrVrlILx7yFtsog1tuztMYqJHr2gSamFdyjUv8O6/1kFYCmlaEDJe4xgfewUt5uGnCvndy5yZ0PP3zzleEHS/+t15tlDW3UtMW9XTwjwQq/IRy5c8FXA58g6Z2horJKa2/Ya/00qtesTuPI1LwHp2v5tKa0qXF6eoa0hpTCghfEsn69vrdAvqpS54xWc6eH1RrSgGI6Q4d5Jo3G6o+IuXStktRW0uDE517zq+ronWbdvtaPHnD46FMOh0y4fkncXyHzwdBeLpFi8ZM36R0MwWNNh+13azG2SByjUVLdC6nZWhhKEhoHkgSLv0qEWJBUWQ1rqmOdm5xw/id/xqPnn/DxR59w9os/Jl08QIdO6ra4S0Ky1b0Vttsb9tsD0zRRpgO0ShBYjSMybCyr3r0icbCDHBMwIp7kUmUulZvtlss3r3n53bdsr6+ZDgfTHnKjseig0wet5lzx+QWTdrEMeUCaIaM0QGsTgcAqBIJWijo2OM4kIqKRGipoNV5rNDFzQUkNiJavKD/WrX3b+/veUfStf+769Duffshnjl/3vmSRpdl7AHVUqVcWiFCfGfpCK/7EA/zmKHirs1tcKYLLidgxFWsES1+JnZEtsCRnaEKdLVJvFqotq068tYDm0jhMM7kWToeEJktCNBFKqwS1/lZB+kG83qFgDYAshg7+2zUEE2iWRh0Tq4cP2Bw+Yvf1U8q0R+vWyNwON6qeBJLA8h3aGq0Y8bt31q7NWYdOyzqSAxqtCA1Hw2igd7aOIZFRWkycfvozNl/8MauPPmH46Dnj6SkqME0TrR48Y+mUOzXx7J13Y2vFjtdbIEYxXqottboo+3XUcA8dLKuqlhaYC4ftgZvrG169esX1zRWH6eAerSzj7NaIeSttsuhJAQvssI8TL5mJ9Dr3EZLYM7nRP2f/NNDeGtHRUk2Qe5bOD1N8d0vRu9+9vcs9e+kd9nqH4ek73zlut2K5xdDMMr9nnMedtfVb6DdmsWdxbR37bG3F4xjQoq4GbzFX8KavzdkbveZV5roYMcVbygVBXC5E3Th7lm7KlZvdjsM0W6JnMFkQPK5tDsI3CRQraaDRgfB2ZbpCYBCMnCHWho+WWD08p2rjzaPn5NffURuMpRBWg7mb1WNEa3Pm2OB2q/YHBDwb3eU01WccscmiGqGwNUP8ZAq1FcbhlIMEWlrx9Gd/wubzn7N+9pzN00ekECi1cDjsmQ+ZWivaiq3qao+nPB9/YzDvLUp00ri469rZR3p0NeWoMqCOrsqHmcNuz/b6hjeXb7i+ueEwHdwDCY6r9oHQB7Djr3tMuzSxUqUzVoLHmkv2dVmBjc5ePQmY9GjwFefIquUMcrAxFP5/7b1pjyRJkqb3iKqZeRx5RGVWZ93dXdM9nNmD2OFisORywY8L8LcTWHIJYgdDDMnpnrq6qyrvONzNVFX4QUTULKIyI7OqZmabYFohKiPczd3N1VTuV15pr3cpbxXOpczXBOJWz/S1HuxWkq4/Kq847eYf8srn4yF39WJlSNd8+OTg4gaWXQ34lWd0TbYcIuYZt1oLkgwxFAJlCJMWe7m7VQ3LLqZd6tYnH002AKgaNtYUZaalDHWhlMKz85f8zd/9HeyO+cUvf01KMMrAJBOjOPOONjRlZ4eHhcrYG5jX9bOJyr5ps9hipDukceT9f/VXPKkLF8DVl/8nbTQUUsADa8M6L4q5YVkzadj3kGCdJWkzT+rRDlAGWWwGSBVKgSqVpTYD3D84gzt3GB4+4sF//z/B0YiMxrejWinzgcvzlyyXVkNFGnnwRA5qIBJXPDbD1DyFQ66MqOcKEoP3g5ryjJqtZVhKqxyWAy+ePef85QsuLy+YDwcu9lccloVhyORhpJTCoczUts4KxV1tX4LuXUlT0uDr3AyFFmyCTSyQCtSU7afGOGTq3vnWclB5Wp4gJZ84cBO/vjluFc6c3yZmfIUg/eCU2wPOW5NEt0in20TXWmY5qUGDgTOke+Up26IqahnBZBAws5KrliTatAh3NqysW0G3ZqZFA5rlbaM0Jo0JYsowCbXY54JzoKpSlpmvv/4H7p/d49nj33L26BNkZ5QgOuSeEApLIMkMVnDqGkNFr6ITmXJrwB76it7/4BHlk1/BMvPsyR9ZyiWpVnJODL4nRA3tElhee0+Lm1OeHAbXrK5LsQ6d3URb7Nry0Li6PFAQdHfC/b/6D0wPHzCd3SPtBtogRvehNoC31AWthlMdstWgU5a+ZhGN4HIZje7BKCBYg4JDiO0n6ot+HwyKWNjPexLKNGSOj0akFaMxoVFaMusnFsZYtpiehLOfyAhHA8LqzyVZR/l1SnE193XNhFsPp0YW2JkgLBlornn7qcD3tKkl3W43bxe+tz3vdeL3yiP2ka5/GGAlUuH9SXp2Du03vm8Gf77XyELIce1NvFXUK+19UwhuvL+/Va+DAWkQiwU316I0al149uwxz55+x8XLp5zeOWOQgTHvDOaVTCKNeeG6+lv/tutVdQyBK8CUU+/IPDm7x/7Rh5SrS1787iHl+UIq1eF1fUmI4koHlUemZOMqVGcRSGos8NbDasx8s0I6ucPxw0ec/uYvufP+e+zuHFsCztBvWJay2Lj2pu6yeikqpb6pif7buKGbcCXYEjL0GN2U18a9VMskz8vCsswMSTgaB3a7yQWkOvLJ3PGG0VdKZIEdANE9Wfp2YLs8yUtuSR0xFC52UGH6i2zXOKY47LtzJvfSwWuON8D3xu1ft526OfTaPzf/2ArTjQevx6frw7ecr/5/xYMonPQFxLrltW88784XYwko6pSF4hoNNhyvHn8G8FGjLhj11D6kvKNSko9bt0FEQvL5oNaMHPFRoWm1hup5ph1m9GrP17/7Pffuv8fDh4+4c3ZGlh2aRooIWiFXYTcYuqm5EjHP3K9vYzkS+MxMGPLA2aefkPPEy2+e8uLvKvXie0vGZL+0AsM42VxR70hJebKEl/fAWoJosvarqpQXM7MYb4EonD/4iF/99b/nN//+f0Tu3GUYEkohH08MEXKkTEsVKTCKUCZP9mjua654GamJ5/JqLymt2JvEIInW2ehNQSTP0pfWOBz2XF5eUA4X3Dk5YuAe3x2fkIfsljGZh7URnZQCrmmKKeJWsgtasqnlpjh8KJQokhrS7NqaiM/hNLtuw5XUt6aBR2zPQFtMQG/zGm9PCNXCdfnQ15z4quf0FSWQ137S25548xP8t7Bg2jVp4ILXUXviSllJWr2B3xcXICWGNBAzVVSESmNINnGq0GwYl4ZrXKAvr6vWFMB3j19L6y6tNa6Hhq+01MhT5v79O1xdJdpSeP7iBSrCSTvlSI/JabBR7CLk0yOGZCz0KZI5QCilbYLE9IZhZKe7dzitwtmf/QuWl885fD9QX35lMXTzTa0mHCkLWhvWybPOltSq4DA9A0UUFoHd+x9w9/Pfcvf9X/Pgs18ynJ6QdpORiiWnElHb5IcyM5BQzZRUSD4NTtiCNTKk5l9NkOqF3Z4JjfAlOc0J7upq927KXChzoS7GQj9OR4bUmQaP+RxNLB1oZ7SYql6LdB6NJCBeW/XZnjHxO1gExde6qSsRuzFWmlFDmdkeTNEg5ckutTY6e/K1+/tW4WzblrHXWML1oZvW7yceb2ug4/TwOeIITdu/dAiE9I17Tagdxhcunlx/ilD8W5eyedH6uoO5/m91hdc3DSb62ExVDby9201WggGW+cDh6oo8ZlJO7AZYltLDC9+X5sb5NUW9tru5bK5VIE8j451T7nz8Eed/+JhWDxyunqDtys4WWeu/vl01Ytgknc+WarM3qyqLKunkHsP7H3Ly6/+Gkw9+yfH9+6RpZBiNANuSSdpXQ1Tt8SgFiQ+FEleS4WjL1lKmjqH1hesuf8fU0pv3rIxcKrUY2N54eq23sxGhSVtd1e2+0dWYqF+THYaWirEJsdSdfiRQUhslLbpunnVi+nZPsvrMtxy3W05ttz19/fiRQvX6818XecoPTlm/sEQuYbOosPKj0jlzghGvhiurUI3TBGnNutVFnSbTwMpNhEEhG3yEpSWsICgdlK7aKLUyTZM3Ujej4gwG8pShGdSvodbHiTINE8dnO64W5WJfOVxegHh540jZ10YespVXPBxOzodkX7JZn2PXHNEg7HVMhPFk4v0//5j58C+Q4x1Xz57BxRckFmQQlmYa3rhwMg2b1J2mEaphhakzLTUW4FwyH/zZX3L/t3/B2Z//S07vPyAPRtadx/AQouxjZGJTmlgEWkocya4Lnk0687mW7jK6ZjBWg/B3JFOlWk1XBlQceL5J4FRVDnNhOSzoUjieJvN6lmrNAt5JdK3cxiZjr+7CNucOIuhaTOjVWQ0MQxuVAFOsWRODSu/tFYQko1nf7R4OTe6xaVu36w+OH9Uytj7x6j9eLW+vePTG63+sXG8VfS8mh3RGYO4NuZLXVjAzXMI262bZCht6m7DMpFPIWLtWWEjBSYWxATaMhvCoxkEblmbKI6pWw7MueYs/mhp6yYrqmbJfeP7kOb//+y/4y3/1L8m7jOTCcrWwHA4UtayyjDZyoTowwnCozoeEgIYwav86pr3xUo/akNgBzj7/mHw8MB+uuPg/LqhXT5nSHnGS5NrUhsliVp1sSJ6UrJeySYLdEbuz93n4r/8t9z/5hNOzM6bJLJQld8yzsLjYcwHJ1j1pIqWRNows3m5mxtCVZr83BserOTH4Zl6qMA6TPU52L8Qs/+oNNUqrLMtMXWbungzsL2cuXlzw7TffU0vrtCFhKQ29tcI8a1foBt43tkbHNfsyG2uhhRhGWt0wDKZaKSUlH3BVmUYjCNVlsRVRJ56TyAi/fpvf2hO2bur4iVHj25/1P175Q/+9/6fbH99XP+YnDqUv6rUnHZenG9rEVTDju63fb32ATjOiSi+p9E/QbZJK3D1zt0ZxOhPTiEHIba8RH9+AuaEi1FJ4+ew5f//3vwOFaZzY7XYMwwgKZV44zEY9ErikyCaGTo9rCEvVLY6I9U16sipOmY6POD47485nnzI+/AA9vsu8GFVIKdVBAUFTYj9deHKGaUe+e8a9X/6GO48+4uT+e4zTxDAkhmwDl5IkG/kg/rrkNJ0+7SslR/vEfnJBs72V0WQ0I4QQiaGCWldKyeCTfXtpt3omOE76RmPIA7VU9pd7njx9Rmut35e47dr3OWz3kNzYw9fm4kBX8PY9Yz/FLXG3vZNYeWjTw44VqH+bcL4d+97bZ3ZWd+v6o5v//+AFb/3er3w5bBbWNz9KNFZLkDRdc/c3PEMiiAZvkIPj/YZb7NB6B0p0GJiONgTLMAmpQNAq7ovNFMk529iFZqpfWulcPCpC2S989923/Kf//J/4j//xf+boeOL4WGkl2UyQZeGw3zOOA+IZX/tuiaYJkdbbgnsZiDAkoWE8zR+qNGV2d0558JvPWb77mpfaePHdd0ibGYIlvhnRs6aEpgMpA5JJ0wAndzn68BM++qu/5uyDj5hOdoxjYuygjdQ7XjQY2Te8O7jXYIx2IQT4fcgoubMJiMBQgZSpgk0gT0LgqNeNrtY43ixxpQ65zAnSMFKWxsXFBX/84x+opXgHk3lDIaDJCYdXXmI/JCybZ/GxcwKRFJLZ2lqGaQqDr4c0qx40MQ+mRSUhGbdu1Ua7pS3zDdnaGy+UH/xy+/HK0248uA2S3/i2188NmcuE4fPOClVfRJP9Vh2e160LnlSJjb0p+IPD1LRbdWtQMASIYP2Tzt1mcZqPKdDFES/J4q99qyZECcZpZF+Lz0lJkOH8cMHff/k7Docr7ugZQx7JxwOjX9dSKsu8MB9mlrlykSq7mjk6YpMUYo1jImbqGRJWC5sSebDZK0cMPPiL/5bx6BQBDr//L+S2MNpIEsJJPNTGOEgnQf7lv/k33Pv8zzn7s88ZjibymBhG86YCer4NhapIX7NQwjUnliFbBtgvW5JxDijGBi9uWCyJbI/ntk13Jc+6eqVZlaqVuRVaqRzlCY4SWSYKyqHOXF6+QHUBqbQqffgTqqaAqjdb+/v1UCT2h6xpM2taN14gNOoApjhJDm5Q65et1dVIxZgjXPEn96huIzS4vZ8z3ZSWW6TnLeT11ujyreT9hyf1UCBCBfMgNvsz/ogn/Xd/QQhxhDvX9bL28LR7J10prNZqZYQLV9Yttzbry0xiLmIHFZh1qs7Huq+X1HZgSJkRMWLlYaI2m+U5L5WXlwul7KlHyvHOgPIhoIFFNSsKN78yPWEknXh5unuP6ew9hrOH7L/aGcYs1d73KmLJjuZE0ofdDu7cJZ2eMuxGSwC5q7qBNNA5lzaKfF0e83drplt2uyXRKbk6leqhZ5aI07fW0sOOrqHdmlVLyomIj000K6qqHOqyefXmTdSYDJpn7c2QrTfdtksIql1r6mGSdiWu/neHAa4X2e+BhAvOCkxJNy5pe7xBOG8S92209c3jZ3inb2mHX/vaKHlEYLgFL5iL6lxAIX0Sr2moTyiT/gZ0zRmSGfFkT9prqBlb+aZR27RYsKLeq+v1TRFj+8MHtWqyGRzaqFq4qucs7YpRd0yaQDIy7pirJVcOS+X5y5nD0KhL5b07E3maTKmn1cpbEmizUVwZaY91ARHGLIwnpwx37pHuP6BNx9S5MHh22pJoiSR2fQVhPj1lPjqmTDsbIBaC6QRYoRt7gzt0xRdrFZPMmgMgzJWU7g5fm7AnZo+yxGhb1nKIyGa7JTQ523xVlrIw4gmtFGARE06P3O1qmnbaztJC8I1QrI+Kz2vuIGhJkmAzUbu3Etbb3eJNmHYtmBNZydZCONmwOL7ieAOH0M8Rm+uX+PqHb5PqzdfTHz4OXfmtFlE2CkTVQQBY2j5ZV4ZGu5cquRUfsyABwCVo4vtELI8nHH7gj0ciI1MpIAZFE5+toaiP3rPidgCzbfTfAdFim04HhqXBUljSQiFTHdS9201W+HeMpggcDpV/+P0ln/zqI3bHo+dLrHyhwkp27UvRLcy6KGiCmoV0esrJB59y9eHnlMdfsH/xB45ZkMhUAtqUcRr55Ne/4Ref/Yo7jz5g2B0ZeNyB5xqJEjFQQ1CQtmoRQhMHCIo1MYtvaOv4SN0IRFE/6pw9N4DhljXYJVQdEeQ7oVnoUpbC4eKS3Zj7uIo0VFIuqC7mYai1eS3VMV9i2VTr8Ww2KKpazD6EYsVYFaJ9X5IJv/p3jbavyP4G4skHtNJjV+3+mGFqNfyFVx9vIJV+xQt/IEvXH9DXCNEtH9L/ef3Zt7xP99/W2NE2igmOeKsOYpptTQ5JXyzx+qWm5mgQ6Z0u6s3Tmtwa4gN6fGlEApgNAQXsJFotRqMrSOpZXaLlzNEP3z1+yunpAx7s7mOf4BvSP0TU+wfFh/QAT54+R14kmggff/iA7BxDvfSjbH1LYwxMUKUZ24AIeXfEyYOHXH3wKVfliv3lM1q7sJYwYlaL1QGPjk84OT5ltzvuWfvOFKjmVXiHp691NC1HLBl4fiNdq16qQjfxvtjGFW8+kGAuxIckY2WP5Mm5+C/a7GprzPNMy5N9ZlEu9ldc7K8cdI5fa5TI6DpLvcxl11J9Y1UiwShs8cjuorioqSRXvGz24NYLa92F/4H3uc253DjeEoSg1/65ds6tgvOTxO3as68878aDkUlbv7SXbJJ04VAJN8+D/CBzUt2kvBvI0ImbRKKF0V8vrtVT3pjrbRLE9aLYWLuqKwXjqhWaG7ZVmXzz7XecnT3i4UN199sEMuZoiOCzbp3QKyVePL9g0cai8P6Du0zT+n0jw7gt5wYti6qBzEmJYbcjv3fGyaOPKS+fcvj+D7TZhFNVjKdVLO4bx4lpt2MardbYWeAjrsBi6sRCNAzYjh4BcWyzeldNpqR1ewQixxJDEobfBFfXe2fT3Nz1dctqZNsG+mitsSyFemS9r6Uo5xdXnF9eWszYImu95gvsY+J7OOJK3OfeusH+uf3r9gjZFG+UzHy39f0Q/bw9iPbdqQFLu8U5vZ19r23aWW4IhN7y1+2P/thDNv9/1bOh2TaZkF5jVAI6IzE2XrWXIWzNtFMPIYJ1qLv7k62onpIgeehClWSkZXOzFp8QLQTvDV7Py7Tq/ZOIecv4Z4jTcmBZxv/lf/3f2O3u8NvP/5KcLc4sKEtdM5JjdUKELOSk7MaR1AqUmS9//w33z+7y4OF9Uhp6nyFZevg8eikmN9iJMaNXgTrBnc8+pe0vqefnLF8/QbHx9cUtd2rVmAd9jaJGHVav1ZlBGjk1kti0MrRxJGZ/qw5QBzKJ1gmonWYF2GEWzfCmleYKTJvNhCFZt83i/LeDGIbZsLJGl1lqc5b8hbkNzCTmfeOrr7/jq6+/9/YsE8QkiZxTH0Cl7vEYu0GyUfFiszlD8Sa30EpD2tzBDJ25UYORo64xq++xTlETjzX1GePrXn3V8QbLeduzq3i8SnTeoBRe8z4//eyeZr/xZWO8fIAAIDCxYfVCg1mCo9WwrlbHjGyflqW7PtS0xp4Ooo6YYnDKROtIWImf1DscEBBNtoGxuPbLr77g8ePH1Gali4oNBB7ESgbSxCSzNd/UhrrJJKZhoC6VF8/Oubo8cCiVhw/PuH//rtGRuLttDRGm+hOQdok6J9o+sTu7x+mHv6Cdf8y3X/zvllV2RndzGSullcjh2Nu4W6dik9mQSko2OsJ4c5q3ltXuATYGf4+NK5t90jgm8DYKMJxC5yKuWA+o8w41qVYjjFEHvo7VhyO11tBqocDTJy949uT5hknQ7WG2MKO21id2R99lmAONWpm72imbARBtHpmYKvEIqcfLgeNu2tZsrEr3AGKgh3W9v36Pv2E+p9zy15vPf/NrXv/sLQqlHyu+NFZAbrj00pMLEFm4cFP8MYnzHP0kDTw32FE+qFEzYkVwaWujbaiFKLPnLqiNTnjdr9O/WOgGd7mfPXvC+cVLG1k4+ewshSELqOM+m6F3tHnOV7CyiAyUWjhczVycX/Hy8oppyJweT8iw64tRcQiahLeZSdXi2PHkiOneXcaz95irTaEecnx353B1mlATTst0JgRNtjaVQknNBvraPECaww4r7vJJjG2X9XZ5eGyr7hvbl1ZT665tVZtVYue7l6IBdrA1thqy15IttcvFxQXnL883MZ9nkIM9wnmK+vObjKufbvecsJL2fEDvDLkVdU6HgNJPY7Mzfa/pte1wmwX8GYzvP9dp3Zj5n3hENrJbwYj//OanwZEaIQYduwAAHNBJREFUiqE4/HUBK+sSElCxzg6Q6OO6sGVP0pwpwKHOW43XvAaWMoWCkTKnlZVBQZJbhNggEZ8qtLpQlgOHZc/p6RkTlp8tu4GZZvzVZTGYjArKaLGpW08ZhLnN1FrZX1zx7dd/ZLm85Nd/9ivIA5qERWy0OoBkYRrNzcySqYcDl3liHk54Uo4Y68xRLpxOE6iRaNZ56aWAohZLJRVvAzOMcW0LbSlktdF/y7CjJgPmi/c0kp3uo2XbxlGj9P9yYJoJN9FHZngGWWhINUuLWo/lkDNIoaRK1T2lHFNLY0qZq6uXvLx8YV1Avva1NPKUTIFqJaXJk3WWfxClK3LR1mGc5EgPNVJXwqZ0TONZIS1IvITsfFOAqFleDdZEWJkaX328Ab73Opv741zQt3uPn/aevRjPqpnELZTSfDLzFijgmGB3UWibwnGoPFtpY0fz+KO7O3HjIgmk0DwLLJ6ECQ6iPvYeT3aojTbQPkoPxCn7a2sc5gXRbDC6JJSdfe68NOZSKNoYXGt3ZLNvCh3Nhb5ztKPOhWePn/PV+AcaNvDo0WePNusCJOvfHHJiEWG8e497n37KZ3/111z8w9+yPPmKmOSVpaF6ALEpbgEOj+TIOO1oBeoC5/srBm0OYhc0ZaO+lIFgM8h5LZXY6niHBuYNRG26I3YcVqfO8CeqvTlBUEpttLnCvpF1svdMjSnvqO2SeX5OW/YMo1GQ1mIlFkPxVJJUT/iJNwG03iETiUECyulJqBXG5KVmd61jgDKmTjy3EJvTFX5SC1ECyP2a4y3Gzv9cQfzHEOTXv98m0bZxFekbZ31AHHcZf2/+dRelB0fb1934LPx9JV0/RV07GlTNH+tur/YaGzgQf/NVVJIjgaxQHh7AlDNlsBHxbbEETWlr/BqbXR35k3JmN40O9ys8ffyc2hrT8cTDR/eRYfRuk7URPSULrvNux9H9+9z/9FeU8yeUixfUem61v1Zt2Ky7tWH3IwZNMhgQICst22hCRJA0WbZWrMfTnJzmCJsIAK1MFZ4PkZjbLG6PATXi9L7qIFBroZZCK2bNrSwtDHmktUKtswm11xtbix5Nz6a2inh5e/uhHRAvXRW5ENo1/gChFGGLr632GNSzvjf0v2zOf9XxBuF8/ezAn378HFf2h66wdg0XC+jwKmJxokhuZErmjlWcVdTl0T2EgBIL5rp2RaD9RtimjHYos5Qp9pnn+pt3xBgHrt+ERUnZKDLTYLFYq6Ay0FJm0cZhuaDpQnJmgkkGrgYnoDoI+9rQpbAbCmMeDN+ZbNSgYGiko6MdCWF/WPju2yeUunB0PPHo0T1O7t5j2E2kNIYfYIwFOaO7CU2J408/5+jlSw77wuWX/5laFsZh5HBQlqZMrJ5Cj7QrIDt02jEc3WUQjx8lO1dYiJpt4BR4H7UatDou0ntgfGqXeh04NnZoXu33RLGIYpkX5jIz1wOwMCBMMpLT5CyezQAofq9rW0hyTNWFUgt6OJDVWAZtuG9yIvKV/SAnKy2hgfOKtr2thrad0mPRWGVHO1kLorqRkE4s97rjDcJ5U5B+jmC9nQW9frFv6fq6trWsaaNEY3XP+tG1JFhztUaHgGp/126BvatZJNq0trG3W9rO4i7hdfXP6ecE+B46mVXfhN4bqknJuXFYDnz/8iWftcIuT+RkU7xO0sCQKsvuCpYFbZVlFFIbqF42GbJPDxOFNlhzs8B0lfEZQXz91TecPTww7XbIMHLv7D45Dyac42BTl3Pi5BcPOH32CfPlJU9//1+QcoClonO1WDplKoKoDfXJFhb2VjvJa2yt1b2AG613weuqBGhDOsNBa2v3j3gPLGAFZ6+NOqOmLXODpVau5pmLw95YJMTChSFHaNaoUhgYkATjNFCWA60pOR/1Nj0U8jBS62JTwjEvQMQQVZVgiVAbQa+uYNLGOGh0n9jzAc8w7mPpkKlOnvfz3NqbUvAWZ90qYD/1uO19Vg22JohCUOIMXa/NC9Kr7yv9ont5JBat+7KsIHn19whXZ4UdrdfSH4qNGQ+Yex1Ca8ZBOb8455tv/sBffL4nJbNuiJLF2PSmYfC5KJZ8mXR1LVWMHWFKUIbBkhhNGAYTIhW4vNozvHhJHvc0FY6Pj2CyF0uydi8EptMjjt474+j998l336M8PxjXaqnhl3hXxtatoyOk0ub7+re9caf0uiVFezgR2Vmnke9hifiNjcigr18YX3DGvdIRVK0ph+XQ3dn4NEHWZI6I9alK6mCBHJ8B1MDc+kJH/dVKNx0i1uNS2xaOSNNQP/G90ytsndwUlmvHGxJC20132/v8fAF8rU3Wmzf3lnN0jU/sYhs9R+vZWIcEWCZQzUUN9jqVRNWF3s6sedX6rgXDUkpw0SYFGdYYRbfN3fj7xvub8Le6wtJiIz3+7jF/+zd/y3/3r/8dQ55srugogDGdHw8Dh2Z1taW2DgRovnkGSQwycjXAopZZzSOIDtRmIxCePXuONmU+FN57cJ+jk4YysDs+MsvbErvTI04fPaDWmdOPfsWL/UuW5ZJlXsyyiKCaGfw7amS5xdA/WdUTHRFaKUiQfvQvbGuHucipVDR7d0vDBqCoeRWBEBEpBhRQZ2xwGQjodK2NeV7YmfGi1srz8xc28g8Aa3sLBVMCPJDxeTgNaRbuqM9SaUofI7k0hRasCImyGUWYxRSKhU41EPqry45BHZWN4Oom0fSa4w3jGK6/8PUlmW34rrdI2psd4zeL+c2rgt6zgzp0bC1OByJypSFuNuXLyymu90ywW6TIpUPFHGPgGjcAzeKL3hCtliIX6bFRCGaiElOXq9cKu8ZN/plqscjTJ0/4v9rf8uLZE+6dnCAnxxiOxKzi8TSimAtXqg3iFVF2OiKi1AzLIKRyxNEgDAzk08TVMqNlYUCpkklj4v5ux/PvHvMiP6emgV989IjdZCwM4zRy+vABebfjs3/3H/h92XPx7Zdc7PfOLAijWG+iyaAJYyg9whVVzDuJWyRep/XBumV/ZUmcWilVzDXMmUHyhkrEW7loqBRnvQNEqN68XKLTozZ7PxZaa1zNC//P//07Xr54iaqS04C22WWjubU06U7JXWkBkcVCHTeXydkXqbUDWizPkKGCVOegirAqNR+n4U0AhIBW1MHuol5Lv277fnC8fcz5Bqm6XajWF9923o+xvzfPdae1B+ONmDXisUBIjFNEKGbF3DG19HzgD3pLGF3bi6yj8Mwzcxt9wx0WXRnjEenkM50yheufESWesixcXrzgMF/ZeAD/PslpYcZBGKpbjlJotRmEMGtvoQoazuywszpkahstDlqwnlLsmpZ5ASk0lO//2Lh/dsbZ2Xvgc0mm3TGnv/iI0w9/5crgyrOcrSuo1RRKd1Ubq1+ova6LdaT0RFmz2Z3FhNWLDlbykjVWi3xL4HhbL0uEglZUnW2vVFgqErDKMvPlV19zeXXlc24GF0zrEsqDcUfVVg0rzSbC8fsqHkxr/66b/aba40n3u7tVDOaK2DfhcfY2sS0QRW688eZ4Q1fKq1/4UwTsx73mTfb1+pk9kUekx12jb9gCetKBRmupJx0UerrVyh2pR0v0jeXC2VZQQVCaGDO7a0OtJEYMXC69ZGGp9phKZe1GrQXe1lvJWmGer5gPV5TqbHhu1aOTY8gGWSuqtFKpOdEmRQP4v2GNywLTmNFmHTYHMYysKLQ8UkpFqGhd+OOzJ6CVu3fvWP1ThGEYOX74iDuf/ZZFEy+/+Btjfdfm8VVirQ9vE291tZystcyW7LoiCRScRTRjjrClFFpqXagHTX0j15TtfG1IbV7bVrQVllIpS0WXQsJql3OpfPHll1w66D1KN+FV5mSTw2u1qeHN9wyClTCblYhci1tHUtRaEh6jGidSiqaK4F6S6u68zU7Z7ukA2QcTx08WzuG6s/qjj65Y3/B6fc3vb3vU1lMtDv502gw3U9t6Fe6elhpzTqQnY5ulGK0tieQbwFE1OKLFFz2sYGQjPZKlUkkxqlyFVlzLNsOdmkZuVF0i9DBia7Wfy8srDoeZUhtTEtoAmk2LTz4QaJ4PLLMNZhqnSk4D1EqrhcpirjZK2k2MUiApR+UU9rMNMFLIOpJkZBxH7g9HtP3Mt19+wb0HvyBNxzANjPePuPv5pyyT8Pi7xywtUxYLCcg2zbvnXdVTto6H7fcz3Hjn2WnVEEYWRyZSdihhzuZ6gidVDL1TSdbBVitNjOFOq9iYi2Yu9RXCoVWWWlhQpApSGo/PH7NUc1OXcmCUwScIKtM0skilFJ8I0Pt03WeRyLD6fJPkalSMeiVwsgaKsPfsvLjOmxSwRQM0uAHw/Sc+Df2WkPMNpNI/RVLguinc/P5T3+5NH7a2bIkFAQbTcQ3fjKpR1WIEfP0j25oMlULzrOsGaRBzOc2FSauSczcmwASxRfGET7cibfsCMfifmCsWULhuXdwNnPcHyqFQl0YJwnMMxBbsdSkn6mLzNFtRc23dCkRs3USsYz81m5+y20EVn3IllojJrhwGUztlruxfnFO5pEnm+P57nNy9i3z8Mcf/w7/l6M6JjZtImRpM+RpKLfClK9eOZWKtXhkW0PH7gI9AkAxDhmSUklosNm21dDyzuocSNSv7XE8aqVp3TitoXSC76++ueGkWq0oDhnBXlVIXwwtL4lomuMfMYedbv4PXYh0JgXPEUE/jrxDNrrzDODTPe4ii2JRwvUXI3m4+59sebx00vsWJPyYAZU3ra/RruvuoQXsf2rprOewciV5HaGKdB83pK0TWOC7ql7J+ZA8btC84BM2+iBpDY5/p4W5guIbdrV6tjSrsr/bMh5laKkUhawAe7GtFP6clV6w9K7hQY6CrureQ/d+UE8M4oZNAqUhZYWgq2DwQt/KH8wsOS6NqYnfnHrujY46miUcP77DbTe7Cr4rbcMv0zRnC2bdq8wxplJWbrEpfDEYpeYCUjRxblVYKrSy0OntMJ6Q80ik0sba6ABmlWpBazKXGXNDalKUeqGqA/dSRqBZalDJbR4rkTWnL6uTaoj94g32VLnpEAnH1Gvx+xr6NX8ODwiGmPUyKOii3CtmPJPj6ZzjiS/8IzaDNbqLNODFWdfFYoHkWdUoNBivaL/Ns5Q0BpYLaMqQkjOMRpSxUXZxeA68zu4AQ1le79ozaHao9Tgx0S/LYonmmExFyGqjV+ktbC6ySJT2++v4bPjz/mA+XDzjlhFxtaI7ktbg9DQNFFpIadrgWB1drctFIKBmb1WQbOg9COxJ0qYzMnTysVnW2AtugCweS2Nj78ydPOT49Jo8j+3ZCojK6cEpbvQzbl9YQoD6oKYoWzWuYGfVaqgcgXuPTlK0HFRsCNR8O1Ks9dTlQdG88t2lgGCzjirjbmPDxEFE3dhuXj2jZ5ppK2wyrEhuuhAh1SLQy29q05KUtE87m8zW1NQbPqFoyy2KQhineVqOBwoutvjk0gPJqdVHx8y1zvWZ7DRCfCErwVx1vnRD6p3FJ3+5402dLiuyqA9DF3Rj3YVWtiN6taNt0GjQs04crhfngVsjiT/sAE8GuFOO1YXlkFc5rxWARryaYlVpb0CCsuNsBu5RW+OIffsdHjz7m0fufcPfe+7RUSc0ysOYhZIPH5ZkmRl851EoW47w5tIpgzeTaAxonyEqCDIlRM20poM2gbWqtXyrNSK0Gs7ZZoC2FWUGHiaJmTESLKUCRjgxKCsnb2xQM70vyTo3A/gq5CmPOpCGbQg16lVqpS+FwcaDsD7Qyo0NlHH3Vm9UQk9OthJVFrcOj2WKS0sptdI32XBw2l5J3viSHV0a5K3IWMUhXUB1duDC2xBIue4qtQ1IbZxhJSQ3l5e6tevaZtrF1sqKmbtvd/yhu7dsL7s8Q8VuMuJf4f3he1D6J5IEvbnQM+yWpd/xrUmc9D5dTDaCQrscRfhfCwdlkLW8KJz1b2Z/rN9EeXqmtbPN9//23PH3yhPOX55sssNik7E5baaRYqmoJjWYufJLMWu63ZIpE1gmLt1ISGBKpRvznZFmqkH0H+XcektBaNQucR6o6hrc1Bmm9VqspaslYKal346WVDd3hgaJOuuzDhQJy2UqhzQvL/kA5HGh1MezwEJ6IrWHz+opldV1omwPyPX/QWqPVuvY24F6Sn587GNo8p5SyldK8HBb7VMnX8wweGqnnMlIXMIdi3vD41hJM6yFMNGPfKNa98rhdOP8reLU/9YiIp7sRYk20KQWooGJTrqClpd+cpkAKdEvqYm7IIe09nuGWihgaZwXvOWbS/d24BksU0IHT21i3tmrY3Hg8DHGD50+f8Pz5Uy7OX5ilbwnVREvRT+oeklg8l5aC6DHCYHZCC41iLc6ayC35gCJjfA/lkQer+9XZlEZD0VwRJisFIRwdDRyWYnQ1ZYZ0RE2JUpWTjPdb2khCcbd62MTQLYE6EZhkQWQk5eZNKhW8EWGZD9T9wrJfOFydsyx7VCt3xhNGMiNW0qgRQmCIoNbUuGp9QFHCQpPz8wuePn3CsljRP2axFCf9bkFEFJ7ImGilmBchoyXKPMmlTgpdswlarGOZZ7sSSbRoIseY96KeagFJxODeOB6gGAk/7CcK5xst3T+nr6s/+GV9qsfaG0yQRM3Tb6ioxZe4hfFeVZGYb2JIniSOSnFrFI7RNI3UxSBk6kTLFj/CFu0Rg28QrEHazxPSmnjYpOzDTY5idq02iOcwHyiLMcaTzEUTkzKSCOMwUKgsrTK3BU1Ga9KcmlM0w+LuumRUMslZ5QyPOyEpM6Tkzc22+dMCiYGUJzTDzocAL6UYvE0zpdnUMyvlCkMwEGqgYqzU0EmvI7wg2BSq9bY2d0VVWGqjLDP7cgAtCFCaeMtaYdBESgPdFNfmtKOWCKOtSZ/HTx/z1Tdfcbm/NE9IdG2ox7wjG5xkTfjNWR7stiUPZ2z6WW0rJ1CA1bXV7iBVFKnWb6aiJF1LSTEgKmqeq1vXrpHlvO54Q8x567Nv+bqfIcE/9aV9M4QQhA+jvlkcx+nnBhdNjxHANKtIjx+SJ5sCFar9Y3S1fP6GGv+/4eaugtkvMjwlP8Hc21orS7FJZdlbMFJPG9nPkE3YFzGUS2qR4Np02WgLX8LqkP55DSthRJa6yNorZ2Byi4nMtfUSkhNqmauWqOr0obgCc+ehf18JR/fmJnQfp2kHiceck1piM9MFqamVwtrGjetubtu+ZbiqjfPLc569eMphsWxtKEOrmpgbHQqjXwubexV7pitcufbddIPZbmJ8T6JhENY6J1hqbkURrVtrLbi9Xjx/HELo51jKf0orG03OHrutnUZBS9h6nCABSujn+gbBmm6b03/E+LcAabVSIDnZsmNzFeuGSEP2zKu3VYXpdIoNEErTnpdLQdis5n6vUED7LksrHJaZuS6kOjB6QmJwRylS8y1j8XCtVHEqj5YQqYhUS1SEdaFRPMGV2sDom7ElYfH4UTSxJBDvtRxyZhgHgv39UIoB6hmp1b7n4GsVBi2ynhDJubg/HqI6m2GUgaBR5kZZoC5i4IgspNzIqUIbaAJzwvtEQVTITi2rkjBaGfNaWpu5uHzOsxePOcx7ai1Oi6kMyc+roULtemuN8puXUnpZKMpiwuAkL+oZafPNcY/MunpEYOGSLDuz8moULREiKa7zQ5n0m/7q43bhbLc+e7tNvvXct3zh275/uHJeUEZqpwO2+qd1GgSgWlozhItrUY/WLUXerJAvPuYgKnetNu8sWIHy/ePdzcmSffEtO5iGgdgGVau7pe6VFd8AWtfOBbdw83Lgcr5gbgeGNiDNUv5DguBIJXKRahbH8Jw2zdk6VayZG3UFUYwo2uxOQ3PzGDkzHNwKpIYw+FCgGT3ccQFNlF0m7zKlKpf7SrVl9gHBhO9qWd+gpYhm4rBS3miQUnX4ouNXEapWFp1peBeJpWQpxeuUEyQZfb1tNL3pXyE1MSFuE0sRDoeFw9WeepjxrIAljpJdTJJMq074HRl3v0zjCnYfKEpUBGa7RlDg72tBj7aKpuZ/Z7P2dbEseCcGN+UXU9KsVo2xPb7muF0431r4/pGF7accfrOuoXyALniEojPXTnom14Swu2XXkB7hzrg19haV1U1T1n+kv+6auxouVVwErBncfpPjDSMmWpgPVyzLTBmOSUnJasAAey+7gnC3a1CXqNqoAFcq2rtC1hjXLIID6yW8BOv4b0Q/o2Wsl1LYaUaJjhFjJ5zGxn5fjWVAk5Wtkn8X6SBHW6sQWol74+6j2Dc3RrtiSlAqTZozJWxXWVaBxz2ghEMCYziUOgQyMtRrvkFRpLkzKtHxItc9Q13zAf1WtAhU6CGSuCJaoxkhmko1Xrhd/4hlNbLZ0j2JsM6vO94gnP+U0gT/eL5ux2EYKNldStnGG+AoDgMEJE+u2ASw+B2Hu4RllP57c39IVLyGZ9dvGyDRQ5ZoyLZPciLiSI74+TUGKFkSYaXvNPqOMu+5unzBfr9nHO9YfdBJnm1D43GNsRGU2IitIUNsFnVEXVv9TWxjFwpNRxLKmNTAGTSHuTm7bG3slz1TyzZPZMiW9aVxfNQ4nF/RqlDKgJya8lFRsuTV25C8+W6RtZRV7kToRFliddZ1u64dOfYzeJu3CzhixlXBBhZVMoWsySaPB/+axo/RZaYUbH3Gvifa1vviecFwP4NOJBoX1DggLCxZda2FTdfCmApqJZ4O37umHD3T3eleXn38vGztq15x7SX/lIHm9jO9kC546tsxnoKRFTuIgPjdZyhaucOK9uaCODxObMhracEjZIvbmSVUO+Mf2pC0FtJbCsiWDzFyITccqbjrjFv6BAwrQTMNTY3n5+d89cfv+P77bzne3WE4PumoE1pDkwPbkzBOR7TFBvZWhdJmA9m3RsGdahVUc7cEmsw17NZsCHe8QlbnCFJquaK0Y4oKkxZLDmVhkCPuPBAO+8r+qvJyv3B0lNhNgrTBY3UlBV4Zw/m2aplVGxhkCaailbnMLK36uliiKotPGEvZklernVqtXrM8gSk5AUcb5ZTJKdF8YoGQkZwRB/a2GPMnplCGnGlVqdLW90bpnUeE5fQ7qxYmNX/coil1fLPd4zUh6K9PzaMKF3bfkT8Z+P7qKdV/iodsfgsnB3cv7FBCS5m2jlcYBMtrfjVGK0h3Zbwz1N/HwfRa+3sogvg0sS0QQcO566pxE4t1Fxj3uj1x5cXqq6sLnj99zPOnT/jgF5/YOc4QoNkpRbBElbgCakBVg5x176oPmN264uJWaC2dp+gvVUFkQKQSiarmCSUcvJ68ZrmbBrQJZYGrpZjbO1jjt2V0nXZNzVLWLk/OzF6NFb+0wlJmllKc4QFvMPfr6dfuvaCb767RotW9AlPQKWUDI7gptEhndbeDkqSDBjbbvIcf8dTmvemvr52BAle42mGEK4hhTYit7x/74sbHvvL4kRxC/0zHj3Snxa1TdzYjfmyrhjKiJ5uHYJOYocd8kqi1WrG9ju5pmRsb+IKIpyRe1gXdhFMC6rcR0B4I4zd5A6frsukNyGE5BWF/dcmL5094/uwxy+HQXS51NzDhrHC+j6LjvqiCZrRv5xaOoMdiXud19zn2TXIhV5KlX7tLH2D9ijZHJznf7W7IMAplFJ5fXjJk4WiENIXnEp6BZStrUqjWrlaKWa9aKnO5Yl5mlmWhlGbYXXfdzWPxeJIod7kHFOumLcLdnimWaA5QpVOhxt3WELhN+UhdXCLr35NEgWeGnp1Fe9O3pS1W9JFBPr0fVa5zQvabtbnTds2v3+vy/x3r+O54d/z/63g9JP7d8e54d/xXPd4J57vj3fEnerwTznfHu+NP9HgnnO+Od8ef6PFOON8d744/0eOdcL473h1/osf/CyBlhpfBXNjAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3., 4., 5., 5., 6., 6., 7., 7.], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the class names; this defines the ordering of the classes\n",
    "class_names = [\"brad pitt\", \"johnny deep\", \"leonardo dicaprio\", \"robert de niro\",\n",
    "           \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
    "\n",
    "\n",
    "# Class ImageDataGenerator() returns an iterator holding one batch of images\n",
    "# the constructor takes arguments defining the different image transformations\n",
    "# for augmentation purposes (rotation, x-/y-shift, intensity scaling - here 1./255 \n",
    "# to scale range to [0, 1], shear, zoom, flip, ... )\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rotation_range=10,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,\n",
    "        fill_mode='nearest')\n",
    "\n",
    "\n",
    "dir_iter = train_datagen.flow_from_directory('./brandnew_images/', \n",
    "                                         target_size=(image_size, image_size),\n",
    "                                         classes=class_names,\n",
    "                                         batch_size=25, class_mode='sparse', shuffle=False)\n",
    "\n",
    "plot_img(dir_iter[0][0][0,...])\n",
    "dir_iter[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yw30IIEeGhI9",
    "outputId": "efd081d3-d8d9-4429-e6f9-573778f3391e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_img(dir_iter[0][0][0,...])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V2fYccc8GhJF"
   },
   "source": [
    "Before you continue, you need to split the downloaded images into a `train` folder and into a `validation` folder."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "colab_type": "raw",
    "id": "VamXG4FoGhJH"
   },
   "source": [
    "./\n",
    "├── train\n",
    "│   ├── brad pitt\n",
    "│   └── johnny deep\n",
    "|   ├── leonardo di caprio\n",
    "|   └── ...\n",
    "│       \n",
    "└── validation\n",
    "    ├── brad pitt\n",
    "    ├── johnny deep\n",
    "    ├── leonardo di caprio\n",
    "    └── ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9322su6vGhJJ"
   },
   "source": [
    "If you want to use the example of this jupyter notebook, then download `celebrity-faces-train-validation-dataset.zip` from Ilias and unzip it in the folder of your jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xPqJWgeAGhJL"
   },
   "source": [
    "## Define a ConvNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UuJV4JBKGhJO"
   },
   "outputs": [],
   "source": [
    "batch_size = 20\n",
    "num_train_images = 480\n",
    "num_valid_images = 80\n",
    "num_classes = 8\n",
    "\n",
    "model_scratch = Sequential()\n",
    "model_scratch.add(Conv2D(32, (3, 3), input_shape=(image_size, image_size, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model_scratch.add(Conv2D(32, (3, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model_scratch.add(Conv2D(64, (3, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# this converts our 3D feature maps to 1D feature vectors\n",
    "model_scratch.add(Flatten())  \n",
    "model_scratch.add(Dense(64))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(Dropout(0.5))\n",
    "model_scratch.add(Dense(num_classes))\n",
    "model_scratch.add(Activation('softmax'))\n",
    "\n",
    "model_scratch.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JFdkIokMGhJT",
    "outputId": "63e7d032-4083-4fe0-d970-c10bf0c39a94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 0 images belonging to 8 classes.\n",
      "Found 0 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "# This is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "# This is the augmentation configuration we will use for validation:\n",
    "# only rescaling\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# This is a generator that will read pictures found in\n",
    "# subfolers of './train', and indefinitely generate\n",
    "# batches of augmented image data\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        './train',  # this is the target directory\n",
    "        target_size=(image_size, image_size),  # all images will be resized to 150x150\n",
    "        classes=class_names,\n",
    "        batch_size=batch_size)  \n",
    "\n",
    "# This is a similar generator, for validation data\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        './validation',\n",
    "        target_size = (image_size, image_size),\n",
    "        classes = class_names,\n",
    "        batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cytHiQUTGhJb"
   },
   "outputs": [],
   "source": [
    "name = 'cnn_face_1'\n",
    "\n",
    "tensorboard = TensorBoard(\n",
    "        log_dir ='./tensorboard/' + name + '/', \n",
    "        write_graph=True,\n",
    "        histogram_freq=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C7dCbyXPGhJg",
    "outputId": "98b4085e-ed6d-43e2-831f-aec32161583f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 2.0994 - accuracy: 0.0500WARNING:tensorflow:From /Users/mirkobirbaumer/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n",
      " 9/24 [==========>...................] - ETA: 5s - loss: 2.2143 - accuracy: 0.1056"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mirkobirbaumer/.pyenv/versions/3.6.8/lib/python3.6/site-packages/PIL/Image.py:961: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
      "  \"Palette images with Transparency expressed in bytes should be \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 9s 385ms/step - loss: 2.1319 - accuracy: 0.1167 - val_loss: 2.0843 - val_accuracy: 0.2125\n",
      "Epoch 2/20\n",
      "24/24 [==============================] - 9s 370ms/step - loss: 2.0783 - accuracy: 0.1771 - val_loss: 2.0755 - val_accuracy: 0.1375\n",
      "Epoch 3/20\n",
      "24/24 [==============================] - 9s 375ms/step - loss: 2.0634 - accuracy: 0.1750 - val_loss: 2.0554 - val_accuracy: 0.2250\n",
      "Epoch 4/20\n",
      "24/24 [==============================] - 9s 364ms/step - loss: 2.0375 - accuracy: 0.1979 - val_loss: 2.0051 - val_accuracy: 0.1875\n",
      "Epoch 5/20\n",
      "24/24 [==============================] - 9s 375ms/step - loss: 1.9816 - accuracy: 0.2021 - val_loss: 1.9332 - val_accuracy: 0.2250\n",
      "Epoch 6/20\n",
      "24/24 [==============================] - 9s 371ms/step - loss: 1.9020 - accuracy: 0.2521 - val_loss: 1.8476 - val_accuracy: 0.2375\n",
      "Epoch 7/20\n",
      "24/24 [==============================] - 9s 370ms/step - loss: 1.8338 - accuracy: 0.2625 - val_loss: 1.8151 - val_accuracy: 0.2750\n",
      "Epoch 8/20\n",
      "24/24 [==============================] - 9s 382ms/step - loss: 1.7885 - accuracy: 0.2833 - val_loss: 1.7453 - val_accuracy: 0.3750\n",
      "Epoch 9/20\n",
      "24/24 [==============================] - 9s 357ms/step - loss: 1.6681 - accuracy: 0.3667 - val_loss: 1.7424 - val_accuracy: 0.3625\n",
      "Epoch 10/20\n",
      "24/24 [==============================] - 9s 354ms/step - loss: 1.6367 - accuracy: 0.3792 - val_loss: 1.6754 - val_accuracy: 0.4000\n",
      "Epoch 11/20\n",
      "24/24 [==============================] - 9s 357ms/step - loss: 1.5509 - accuracy: 0.3979 - val_loss: 1.6502 - val_accuracy: 0.3750\n",
      "Epoch 12/20\n",
      "24/24 [==============================] - 8s 353ms/step - loss: 1.4806 - accuracy: 0.4604 - val_loss: 1.6305 - val_accuracy: 0.4000\n",
      "Epoch 13/20\n",
      "24/24 [==============================] - 8s 352ms/step - loss: 1.4683 - accuracy: 0.4354 - val_loss: 1.6400 - val_accuracy: 0.3875\n",
      "Epoch 14/20\n",
      "24/24 [==============================] - 9s 367ms/step - loss: 1.4524 - accuracy: 0.4187 - val_loss: 1.7125 - val_accuracy: 0.3250\n",
      "Epoch 15/20\n",
      "24/24 [==============================] - 9s 383ms/step - loss: 1.4358 - accuracy: 0.4521 - val_loss: 1.7748 - val_accuracy: 0.3875\n",
      "Epoch 16/20\n",
      "24/24 [==============================] - 9s 357ms/step - loss: 1.3321 - accuracy: 0.4708 - val_loss: 1.8418 - val_accuracy: 0.3250\n",
      "Epoch 17/20\n",
      "24/24 [==============================] - 9s 355ms/step - loss: 1.3460 - accuracy: 0.4896 - val_loss: 1.6966 - val_accuracy: 0.3250\n",
      "Epoch 18/20\n",
      "24/24 [==============================] - 8s 351ms/step - loss: 1.3164 - accuracy: 0.4812 - val_loss: 1.6043 - val_accuracy: 0.3750\n",
      "Epoch 19/20\n",
      "24/24 [==============================] - 9s 356ms/step - loss: 1.2315 - accuracy: 0.5312 - val_loss: 1.6626 - val_accuracy: 0.3500\n",
      "Epoch 20/20\n",
      "24/24 [==============================] - 8s 353ms/step - loss: 1.1592 - accuracy: 0.5417 - val_loss: 1.6480 - val_accuracy: 0.4000\n"
     ]
    }
   ],
   "source": [
    "history = model_scratch.fit(\n",
    "          train_generator,\n",
    "          steps_per_epoch = num_train_images // batch_size,\n",
    "          epochs = 20,\n",
    "          validation_data = validation_generator,\n",
    "          validation_steps = num_valid_images // batch_size,\n",
    "          callbacks = [tensorboard])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wt_ONw5PGhJm",
    "outputId": "e75d8a73-da49-4dbe-ffcf-7cb316be39a2"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tensorboard --port=8061 --logdir=tensorboard/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y8oAT4oUGhJs"
   },
   "source": [
    "# Part II : Transfer Learning\n",
    "\n",
    "With transfer learning we reuse parts of an already trained model and change the final layer, or several layers, of the model, and then retrain those layers on our own dataset.\n",
    "\n",
    "We will continue using VGG16 model, which comes among others prepackaged with Keras. You can import it from the `tensorflow.keras.applications` module. Here's the list of image-classification models (all pretrained on the ImageNet dataset) that are available as part of `tensorflow.keras.applications`:\n",
    "\n",
    "- Xception\n",
    "- Inception V3 \n",
    "- ResNet50\n",
    "- VGG16\n",
    "- VGG19\n",
    "- MobileNet\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "TensorFlow Hub also distributes models without the last classification layer. These can be used to easily do transfer learning. Any [image feature vector URL from tfhub.dev](https://tfhub.dev/s?module-type=image-feature-vector&q=tf2) would work here.\n",
    "\n",
    "Note that we're calling the partial model (without the final classification layer) a `feature_extractor`. The reasoning for this term is that it will take the input all the way to a layer containing a number of features. So it has done the bulk of the work in identifying the content of an image, except for creating the final probability distribution. That is, it has extracted the features of the image.\n",
    "\n",
    "Let's instantiate the VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Luec7pbGhJv",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# General imports\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import tensorflow as tf\n",
    "\n",
    "# Shortcuts to keras if (however from tensorflow)\n",
    "from tensorflow.keras import applications\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard \n",
    "\n",
    "# Shortcut for displaying images\n",
    "def plot_img(img):\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "# The target image size can be fixed here (quadratic)\n",
    "# The ImageDataGenerator() automatically scales the images accordingly (aspect ratio is changed)\n",
    "image_size = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eRes_n9BGhJ0"
   },
   "outputs": [],
   "source": [
    "vgg16 = applications.VGG16(include_top=False, weights='imagenet',\n",
    "                           input_shape=(image_size,image_size,3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vEIWLeqSGhJ5"
   },
   "source": [
    "You pass three arguments to the constructor:\n",
    "\n",
    "- `weights` specifies the weight checkpoint from which to initialize the model.\n",
    "\n",
    "- `include_top` refers to including (or not) the densely connected classifier on top of the network. By default, this densely connected classifier corresponds to the 1000 classes from \n",
    "ImageNet. Because we intend to use our own densely connected classifier  you don't need to include it.\n",
    "\n",
    "- `input_shape` is the shape of the image tensors that we will feed to the network. This argument is purely optional: if we don't pass it, the network will be able to process inputs of any size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M7Bk7t1MGhJ6"
   },
   "outputs": [],
   "source": [
    "# predict_generator requires compilation\n",
    "vgg16.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "05hqhVtUGhKA"
   },
   "source": [
    "Here's the detail of the architecture of teh VGG16 convolutional base. It's similar to the simple ConvNets you are familiar with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B8bXc_qZGhKC",
    "outputId": "4b81be24-0527-44b5-ed98-b3e57f511350"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 150, 150, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg16.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DBSrhVORGhKH"
   },
   "source": [
    "The final feature map (output volume) has shape $(4, 4, 512)$. That's the feature on top of which we will stick a densely connected classifier.\n",
    "\n",
    "At this point, there are two ways how we could proceed:\n",
    "\n",
    "- __Approach 1__: Running the convolutional base over our dataset, recording its output to a Numpy array on disk, and then using this data as input to a standalone, densely connected classifier similar to those we saw earlier in this course. This solution is fast and cheap to run, because it only requires running the convolutional base once for every input image, and the convolutional base is by far the most expensive pipeline. But for the same reason, this technique won't allow us to use data augmentation.\n",
    "\n",
    "- __Approach 2__: Extending the model we have (`vgg16`) by adding `Dense` on top, and running the whole thing end to end on the input data. This will allow us to use data augmentation, because every input image goes through the convolutional base every time it's seen by the model. But for the same reason, this technique is far more expensive than the first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mlpIDmSCGhKI"
   },
   "source": [
    "## 1. Approach : Extracting Features Using the Pretrained Convolutional Base\n",
    "\n",
    "### Fast Feature Extraction without Data Augmentation\n",
    "\n",
    "We will start by running instances of the previously introduced `ImageDataGenerator` to extract images as Numpy arrays as well as their labels. We will extract features from these images by calling the `predict` method of the `vgg16`model.\n",
    "\n",
    "Let's run the training images through the convolutional base, and see the final shape. 480 is the number of images, and 512 is the number of activation maps in the last layer of the partial model from VGG16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pC-jzxh_GhKL",
    "outputId": "f2e6f646-c55a-451b-d46e-0a5984217bd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 images belonging to 8 classes.\n",
      "WARNING:tensorflow:From <ipython-input-15-5a7bfed0524c>:27: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.predict, which supports generators.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mirkobirbaumer/.pyenv/versions/3.6.8/lib/python3.6/site-packages/PIL/Image.py:961: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
      "  \"Palette images with Transparency expressed in bytes should be \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of last layer feature map of training dataset: (480, 4, 4, 512)\n",
      "Found 80 images belonging to 8 classes.\n",
      "Shape of last layer feature map of validation dataset: (80, 4, 4, 512)\n"
     ]
    }
   ],
   "source": [
    "# These are the class names; this defines the ordering of the classes\n",
    "class_names = [\"brad pitt\", \"johnny deep\", \"leonardo dicaprio\", \"robert de niro\",\n",
    "               \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
    "\n",
    "# No augmentation \n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "batch_size = 20\n",
    "num_train_images = 480\n",
    "num_valid_images = 80\n",
    "num_classes = 8\n",
    "\n",
    "generator = datagen.flow_from_directory(\n",
    "        './train',\n",
    "        target_size=(image_size, image_size),\n",
    "        batch_size=batch_size,\n",
    "        classes=class_names,\n",
    "        # this means our generator will only yield batches of \n",
    "        # data, no labels\n",
    "        class_mode=None,  \n",
    "        # our data will be in order\n",
    "        shuffle=False)  \n",
    "\n",
    "# the predict_generator method returns the CNN activation maps \n",
    "# of the last layer\n",
    "bottleneck_features_train = vgg16.predict_generator(generator, \n",
    "                                                    num_train_images // batch_size)\n",
    "\n",
    "print(\"Shape of last layer feature map of training dataset:\", bottleneck_features_train.shape)\n",
    "\n",
    "# save the output as a Numpy array\n",
    "np.save('./models/bottleneck_features_train.npy', \n",
    "        bottleneck_features_train)\n",
    "\n",
    "generator = datagen.flow_from_directory(\n",
    "        './validation',\n",
    "        target_size=(image_size, image_size),\n",
    "        batch_size=batch_size,\n",
    "        classes=class_names,\n",
    "        class_mode=None,\n",
    "        shuffle=False)\n",
    "\n",
    "bottleneck_features_validation = vgg16.predict_generator(generator, \n",
    "                                                         num_valid_images // batch_size)\n",
    "\n",
    "np.save('./models/bottleneck_features_validation.npy', bottleneck_features_validation)\n",
    "\n",
    "print(\"Shape of last layer feature map of validation dataset:\", bottleneck_features_validation.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nbw0M5JeGhKP"
   },
   "source": [
    "##### Load numpy array containing activation maps of training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EpwO5BOkGhKQ",
    "outputId": "ab179875-edb2-4940-f7ea-167119ed2a52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 1],\n",
       "       [0, 0, 0, ..., 0, 0, 1],\n",
       "       [0, 0, 0, ..., 0, 0, 1]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = np.load('./models/bottleneck_features_train.npy')\n",
    "\n",
    "# the features were saved in order, so recreating the labels is easy\n",
    "train_labels = np.zeros((num_train_images, num_classes), dtype=int)\n",
    "for ind in range(num_classes):\n",