SSD300_debug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from tensorflow.python.keras.applications import imagenet_utils\n",
    "from keras.models import Model\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "#from scipy.misc import imread\n",
    "from imageio import imread\n",
    "import os\n",
    "import h5py\n",
    "\n",
    "from ssd_model import SSD300\n",
    "from ssd_utils import PriorUtil\n",
    "from ssd_data import InputGenerator\n",
    "from ssd_data import preprocess\n",
    "from utils.model import load_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_shape = (300, 300, 3)\n",
    "batch_size = 32\n",
    "\n",
    "voc_classes = ['Background', 'Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',\n",
    "               'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',\n",
    "               'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',\n",
    "               'Sheep', 'Sofa', 'Train', 'Tvmonitor']\n",
    "\n",
    "\n",
    "image_size = input_shape[:2]\n",
    "from data_voc import GTUtility\n",
    "gt_util = GTUtility('data/VOC2007/')\n",
    "gt_util_train, gt_util_val = gt_util.split(0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = SSD300(input_shape, num_classes=gt_util.num_classes)\n",
    "prior_util = PriorUtil(model)\n",
    "priors = prior_util.priors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1_1\n",
      "conv1_1/kernel:0     (3, 3, 3, 64)        [3, 3, 3, 64]       \n",
      "conv1_1/bias:0       (64,)                [64]                \n",
      "conv1_2\n",
      "conv1_2/kernel:0     (3, 3, 64, 64)       [3, 3, 64, 64]      \n",
      "conv1_2/bias:0       (64,)                [64]                \n",
      "conv2_1\n",
      "conv2_1/kernel:0     (3, 3, 64, 128)      [3, 3, 64, 128]     \n",
      "conv2_1/bias:0       (128,)               [128]               \n",
      "conv2_2\n",
      "conv2_2/kernel:0     (3, 3, 128, 128)     [3, 3, 128, 128]    \n",
      "conv2_2/bias:0       (128,)               [128]               \n",
      "conv3_1\n",
      "conv3_1/kernel:0     (3, 3, 128, 256)     [3, 3, 128, 256]    \n",
      "conv3_1/bias:0       (256,)               [256]               \n",
      "conv3_2\n",
      "conv3_2/kernel:0     (3, 3, 256, 256)     [3, 3, 256, 256]    \n",
      "conv3_2/bias:0       (256,)               [256]               \n",
      "conv3_3\n",
      "conv3_3/kernel:0     (3, 3, 256, 256)     [3, 3, 256, 256]    \n",
      "conv3_3/bias:0       (256,)               [256]               \n",
      "conv4_1\n",
      "conv4_1/kernel:0     (3, 3, 256, 512)     [3, 3, 256, 512]    \n",
      "conv4_1/bias:0       (512,)               [512]               \n",
      "conv4_2\n",
      "conv4_2/kernel:0     (3, 3, 512, 512)     [3, 3, 512, 512]    \n",
      "conv4_2/bias:0       (512,)               [512]               \n",
      "conv4_3\n",
      "conv4_3/kernel:0     (3, 3, 512, 512)     [3, 3, 512, 512]    \n",
      "conv4_3/bias:0       (512,)               [512]               \n",
      "conv5_1\n",
      "conv5_1/kernel:0     (3, 3, 512, 512)     [3, 3, 512, 512]    \n",
      "conv5_1/bias:0       (512,)               [512]               \n",
      "conv5_2\n",
      "conv5_2/kernel:0     (3, 3, 512, 512)     [3, 3, 512, 512]    \n",
      "conv5_2/bias:0       (512,)               [512]               \n",
      "conv5_3\n",
      "conv5_3/kernel:0     (3, 3, 512, 512)     [3, 3, 512, 512]    \n",
      "conv5_3/bias:0       (512,)               [512]               \n",
      "fc6\n",
      "fc6/kernel:0         (25088, 4096)        [3, 3, 512, 1024]   \n",
      "fc6/bias:0           (4096,)              [1024]              \n",
      "fc7\n",
      "fc7/kernel:0         (4096, 4096)         [1, 1, 1024, 1024]  \n",
      "fc7/bias:0           (4096,)              [1024]              \n"
     ]
    }
   ],
   "source": [
    "weights_path = '~/.keras/models/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "layer_list = [('block1_conv1', 'conv1_1'),\n",
    "              ('block1_conv2', 'conv1_2'),\n",
    "              ('block2_conv1', 'conv2_1'),\n",
    "              ('block2_conv2', 'conv2_2'),\n",
    "              ('block3_conv1', 'conv3_1'),\n",
    "              ('block3_conv2', 'conv3_2'),\n",
    "              ('block3_conv3', 'conv3_3'),\n",
    "              ('block4_conv1', 'conv4_1'),\n",
    "              ('block4_conv2', 'conv4_2'),\n",
    "              ('block4_conv3', 'conv4_3'),\n",
    "              ('block5_conv1', 'conv5_1'),\n",
    "              ('block5_conv2', 'conv5_2'),\n",
    "              ('block5_conv3', 'conv5_3')]\n",
    "#load_weights(model, weights_path, layer_list)\n",
    "\n",
    "#model.load_weights('./checkpoints/201708011145_ssd_voc/weights.174.h5', by_name=True)\n",
    "#model.load_weights('./checkpoints/voc/weights.25-3.60.hdf5', by_name=True)\n",
    "#model.load_weights('ssd300_voc_weights_fixed.hdf5', by_name=True)\n",
    "\n",
    "weights_path = '~/.keras/models/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\n",
    "layer_list = [('block1_conv1', 'conv1_1'),\n",
    "              ('block1_conv2', 'conv1_2'),\n",
    "              ('block2_conv1', 'conv2_1'),\n",
    "              ('block2_conv2', 'conv2_2'),\n",
    "              ('block3_conv1', 'conv3_1'),\n",
    "              ('block3_conv2', 'conv3_2'),\n",
    "              ('block3_conv3', 'conv3_3'),\n",
    "              ('block4_conv1', 'conv4_1'),\n",
    "              ('block4_conv2', 'conv4_2'),\n",
    "              ('block4_conv3', 'conv4_3'),\n",
    "              ('block5_conv1', 'conv5_1'),\n",
    "              ('block5_conv2', 'conv5_2'),\n",
    "              ('block5_conv3', 'conv5_3'),\n",
    "              ('fc1', 'fc6'),\n",
    "              ('fc2', 'fc7')]\n",
    "\n",
    "weights_path = os.path.expanduser(weights_path)\n",
    "f = h5py.File(weights_path, 'r')\n",
    "\n",
    "for name in layer_list:\n",
    "    layer_name = name[1]\n",
    "    name = name[0]\n",
    "    layer = model.get_layer(layer_name)\n",
    "    g = f[name]\n",
    "    weights = [np.array(g[wn]) for wn in g.attrs['weight_names']]\n",
    "    print(layer.name)\n",
    "    for i in range(len(layer.weights)):\n",
    "        w = layer.weights[i]\n",
    "        wd = weights[i]\n",
    "        print('%-20s %-20s %-20s' % (w.name, weights[i].shape, w.shape.as_list()))\n",
    "    #layer.set_weights(weights)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    }
   ],
   "source": [
    "w_fc = np.load('vgg_reduced_fc.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'conv4_3/kernel:0'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = model.weights[4]\n",
    "w.shape.as_list()\n",
    "w.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 3, 64, 128]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = model.layers[4]\n",
    "w = l.weights[0]\n",
    "w.shape.as_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "freeze = ['conv1_1', 'conv1_2',\n",
    "          'conv2_1', 'conv2_2',\n",
    "          'conv3_1', 'conv3_2', 'conv3_3',\n",
    "          #'conv4_1', 'conv4_2', 'conv4_3',\n",
    "          #'conv5_1', 'conv5_2', 'conv5_3',\n",
    "         ]\n",
    "\n",
    "for layer in model.layers:\n",
    "    layer.trainable = not layer.name in freeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model.load_weights('./models/ssd300_voc_weights_fixed.hdf5', by_name=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1_1                        [64, 300, 300]       [300, 300, 64]       \n",
      "conv1_2                        [64, 300, 300]       [300, 300, 64]       \n",
      "pool1                          [64, 150, 150]       [150, 150, 64]       \n",
      "conv2_1                        [128, 150, 150]      [150, 150, 128]      \n",
      "conv2_2                        [128, 150, 150]      [150, 150, 128]      \n",
      "pool2                          [128, 75, 75]        [75, 75, 128]        \n",
      "conv3_1                        [256, 75, 75]        [75, 75, 256]        \n",
      "conv3_2                        [256, 75, 75]        [75, 75, 256]        \n",
      "conv3_3                        [256, 75, 75]        [75, 75, 256]        \n",
      "pool3                          [256, 38, 38]        [38, 38, 256]        \n",
      "conv4_1                        [512, 38, 38]        [38, 38, 512]        \n",
      "conv4_2                        [512, 38, 38]        [38, 38, 512]        \n",
      "conv4_3                        [512, 38, 38]        [38, 38, 512]        \n",
      "pool4                          [512, 19, 19]        [19, 19, 512]        \n",
      "conv5_1                        [512, 19, 19]        [19, 19, 512]        \n",
      "conv5_2                        [512, 19, 19]        [19, 19, 512]        \n",
      "conv5_3                        [512, 19, 19]        [19, 19, 512]        \n",
      "pool5                          [512, 19, 19]        [19, 19, 512]        \n",
      "fc6                            [1024, 19, 19]       [19, 19, 1024]       \n",
      "fc7                            [1024, 19, 19]       [19, 19, 1024]       \n",
      "conv6_1                        [256, 19, 19]        [19, 19, 256]        \n",
      "conv6_2                        [512, 10, 10]        [10, 10, 512]        \n",
      "conv7_1                        [128, 10, 10]        [10, 10, 128]        \n",
      "conv7_2                        [256, 5, 5]          [5, 5, 256]          \n",
      "conv8_1                        [128, 5, 5]          [5, 5, 128]          \n",
      "conv8_2                        [256, 3, 3]          [3, 3, 256]          \n",
      "conv9_1                        [128, 3, 3]          [3, 3, 128]          \n",
      "conv9_2                        [256, 1, 1]          [1, 1, 256]          \n",
      "mbox_conf                      [183372]             [183372]             \n",
      "mbox_loc                       [34928]              [34928]              \n"
     ]
    }
   ],
   "source": [
    "from utils.caffe2keras import compare_output_shape\n",
    "compare_output_shape(model, './models/ssd300_voc_shape.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils.caffe2keras import compare_weights_shape\n",
    "#compare_weights_shape(model, 'ssd300_voc_shape.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-22.69574     -17.98938     -10.883713    -21.097084    -11.40815    ]\n",
      "  [ -5.202896     -7.5436935     7.904457     -7.61792     -10.565018   ]\n",
      "  [ -0.017959595   3.827835     21.810905     14.148605     -4.133919   ]\n",
      "  [ 29.64         34.182953     33.38861      38.32965      20.931015   ]\n",
      "  [ 40.08278      42.427795     34.917053     44.693527     46.11183    ]]\n",
      "\n",
      " [[-30.69574     -25.98938     -18.845383    -29.097084    -19.40815    ]\n",
      "  [-13.398209    -15.658699      0.7329178   -15.61792     -18.565018   ]\n",
      "  [ -9.716293     -5.172165     13.51091       6.3402634   -11.805206   ]\n",
      "  [ 20.129059     25.425797     26.02887      31.627945     15.1826935  ]\n",
      "  [ 31.53186      34.710587     28.262054     39.456665     41.678482   ]]\n",
      "\n",
      " [[-38.580734    -33.874382    -26.807053    -37.058746    -26.68222    ]\n",
      "  [-17.69728     -20.1987       -5.6940002   -22.183601    -25.33503    ]\n",
      "  [ -7.4712906    -5.022171     12.26091       3.8835983   -16.312622   ]\n",
      "  [ 28.540237     31.575592     29.085526     32.94127      13.097488   ]\n",
      "  [ 44.358704     45.277252     35.62538      42.616867     41.47017    ]]]\n"
     ]
    }
   ],
   "source": [
    "from ssd_data import preprocess_image\n",
    "\n",
    "np.set_printoptions(precision=10, linewidth=130, suppress=True)\n",
    "\n",
    "img_path = './data/images/fish-bike.jpg'\n",
    "img = preprocess_image(img_path, size=image_size, lib='skimage')\n",
    "\n",
    "print(img.transpose((2, 0, 1))[:,:5,:5])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "caffe preprocessing\n",
    "(3, 300, 300)\n",
    "[[[-22.6957397461 -17.9893798828 -10.8837127686 -21.0970840454 -11.4081497192]\n",
    "  [ -5.2028961182  -7.5436935425   7.9044570923  -7.6179199219 -10.5650177002]\n",
    "  [ -0.0179595947   3.827835083   21.8109054565  14.1486053467  -4.1339187622]\n",
    "  [ 29.6399993896  34.1829528809  33.3886108398  38.3296508789  20.9310150146]\n",
    "  [ 40.0827789307  42.4277954102  34.9170532227  44.6935272217  46.111831665 ]]\n",
    "\n",
    " [[-30.6957397461 -25.9893798828 -18.8453826904 -29.0970840454 -19.4081497192]\n",
    "  [-13.3982086182 -15.6586990356   0.7329177856 -15.6179199219 -18.5650177002]\n",
    "  [ -9.716293335   -5.172164917   13.5109100342   6.3402633667 -11.8052062988]\n",
    "  [ 20.1290588379  25.4257965088  26.0288696289  31.6279449463  15.1826934814]\n",
    "  [ 31.5318603516  34.7105865479  28.2620544434  39.4566650391  41.6784820557]]\n",
    "\n",
    " [[-38.5807342529 -33.874382019  -26.8070526123 -37.0587463379 -26.682220459 ]\n",
    "  [-17.6972808838 -20.1986999512  -5.6940002441 -22.1836013794 -25.3350296021]\n",
    "  [ -7.4712905884  -5.0221710205  12.2609100342   3.8835983276 -16.3126220703]\n",
    "  [ 28.5402374268  31.575592041   29.0855255127  32.9412689209  13.0974884033]\n",
    "  [ 44.3587036133  45.2772521973  35.6253814697  42.6168670654  41.4701690674]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs = []\n",
    "images = []\n",
    "for img_path in ['./data/images/fish-bike.jpg',\n",
    "                 './data/images/cat.jpg',\n",
    "                 './data/images/boys.jpg',\n",
    "                 './data/images/car_cat.jpg',\n",
    "                 './data/images/car_cat2.jpg',\n",
    "                ]:\n",
    "    images.append(imread(img_path))\n",
    "    input_image = preprocess_image(img_path, size=image_size, lib='skimage')\n",
    "    inputs.append(input_image)\n",
    "\n",
    "inputs = np.array(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 12ms/step\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(inputs, batch_size=1, verbose=1)\n",
    "\n",
    "results = [prior_util.decode(p, confidence_threshold=0.1) for p in preds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O1/2xM+ahM3BZmhEEAUTOsKVpijAaBCgpEUGAFAJrCiSCUAXOwXjDpCvkDkp5pkkI7EjU\nMETSukKlxhiMhjAI/N9FFSEmhUYqqKnIf650YqAVGM3Q2RiLN+VYY7HaGWsZCHLjQBtSEKnArTdB\n1X9d5CilvMPErwHnmNLCfVZYB0QCAbnOneOwLioq085WDp2P66+oDDbweZHok2lfLuP9aWC/EGI3\nzmh/F3xhaZwUgmee/DAAb9oHP/muf0Vb+OGt8CPFYZiDXzoKh8bhm2fg06vwLZ+F5QI+e36Jv4vg\nYS8qTHbBL7znswD81l74rsGDcB5+ah5etn2BbcN5xFvPw4sn4aUr9/MPYzcRBAHP3jrGW+4/w6sP\nbee/PGcvb7zvDP3CoI3lpQem2TkZuyBJgJBgrOHVt+/m//rEcd7+8HkmahE//ezdLrSyhplWzP/2\nns/RzzU/+ezdhNJgMX5SZ3znDbP8zkeP8ecPnOb2bZNYQJsCozUWgzFFFeIaq5mqR/zvz7uWX/7w\nw+T+4b/y0B52T7TQxqFsKYSfgKJCOFprdJ75SeKcQ60WY7Bok5GnhqRwQas00qEMbRBiiExU5Cax\nkAFhGBJFETIMqNVq1ULNk5Q8z8lzN7H7/R5ZtoJSijiOacQxxoCRVMhQa00Uhh6BgRTWDa5v+3jb\nE86bTfxo9Vog2M0fPuFxM/wgM/zguvcK1qixn/gJUip7+bMnPE+N3VzD+77k8bv4b0/4+R28lh28\ndt17YzyfMZ4/8tk/eMLP/s9sxlisNeTe6EopyYsUmydIKalFikjEJEmCNoYoijDGUBQ+KsZi0iEa\nFyOGyxg/l6REiiFSrai7wgnDhFAE0lm/PB8VizkjGgah/5ynbrxRt0K6z1mBMXgU7IxtGcmgTUWF\n5EZXhjnPM6SwHrW794NQkec51hQIKcEKlKeJosA7KONstcYS+CijXDOl49IMjb+1FmNt5SDESJRZ\nObcn0b4snDeAEOIbgNfhpIJ/Yq39jSuPKTnv37/U+uWf2tT9vHP8e7W1An7oMLzj5i98zPunn1lN\nIOXDSPfahVYl4pChdMAYS1Fob3zcBBZCEIUxhS7Qhea1Hz/CHduneM72ySHHKIdMnxDC8YXOZLr3\n/aRyCODzNYuKwNFsrgv+RCOe3FqklPz+wyvc9uL/5O/Dh2XGIqQlCH0OIHITpTAam2uUEAgkgVIE\nBEgZVH3QiIriSDO3SNMir8asVqshPQqXUlKP3M8k7RMGMUopsixjdXWVJHGLMQgiVBS6BezP9Y63\nfZxnTP00AIc/+GwAbnjxpzz/6DlKH55WY+W5yvKnQ0ZuoQZR6BcM1WIX4gq+fYTrFzg+WXjDkud5\ntfDLlqZp9ffyJ1DNk1GOtKioBmDkPM4QeK60/Ckkxho/N9xUEbio8lPH/w+uuXGSLVs2MzMzQ3ti\nvBrrNE1pt8fp9fq+r8oboNJoFSilKLSbB1Y6SgVtsFZgChct/cVPvAKAb/+9twBDOgBYR3ForQlk\nSJqmgHGRlBgxTMZg/GeCIGCQZgTSOf4oitbRaKGnJSrjm68N+y3E0MAJUVEn1lpSj561dZFjebx7\nlhIj8EbVYj1Slt5ISjuSrzEG7aO+wmhCZEWbWGux0jmB9TmhkuP2/blimWpsdQ5d+DGRqrpeOUaj\naLv8rzCWP/rtn/2MtfYQX6J92Thva+17gfc+qWNHJskjN7zETXLhQhwhBIFUdLtdZ0ilD12UIgxD\njLGEYcggTfzvhn5/QJZlZN4gZHnOq2YlHxKCOIoqVBiEIc899wkAGrUaRVH4UMZitKMJcuuTdEI4\n71powihCqZBGM6DIC4wZek2lJPVGy3GK4SmarTHGJiYqnj33YVhR5Ag/iaUUFIUGa4niiKIokEpR\naO3RgiEMQ7Q2FCZHCEkYKoyxKCUxxlLogiAIHHqwlkwXyBB3HW2rhQ6OzsjznN5al7GxMfpJQiBB\nhBFWG3c+aSmyAUqFCJQ7t9EEMiKIHbqQJkd6R2dMAUi63ZQsyxgbG6vGU6l+xV1qrQlrIUL4RWk0\nWEPhcxkIh76NHYaURmuE9Fk3a1EqXMftSv9s3NoSGKOrGeUMzPrm7Ob6BBSAkAoEFDrHWlBKok3J\nMZdcrKBWr7sciKd5SmM/Mvc914wfFz/LR+aSsAIhZLWIrTVok/sEYUGaJiSDAVma0O916QxW6fUN\nRx6/xLGjkulNWzDWUVKLi4tMTW3kuuuuA6EYG5ukyDMCVaPb7RDGdXSSYnBrJ4giZ7C0G+NAhevG\nJ67XPN0m/LPVQ3QsJXEYMshzglo4gnihyDKX8lQKWSaa0Y6PFmCFIcvWb7nQSqG8kRYIRODpN1sa\ncEXu6Qkw1bOK49iPqsZYi7FFtQaL3IChikytcRSmFcJdSzqqNcsycl2Ql44kDMFSAbGS2y758NJR\nF0XhaVHXRyWGwCvPcwIpQTkHoj2Y0EJ5xzkEHUVR+PH14gEpCILo8+brF2pPu+3xK6urANTimgsh\njKEQEqv1MPwSjtsyRiOFcOGaMR6tCFqtJkK0QLrQf5AkFEVBnmUkaYr2xlHneXVdXeQUeY7FnUMA\ngZKEUQTWkuc59VqNTLvEqQAGaebDRcf/BSp0Ri7LGAwG/OSzdrp7Wl6l3qxT5Lkz1kKitUeDCPJC\nMz4+VSHDIAwRCLI08chMkmXOESmlkEqgy+QPLlloHDxAe8dWouQgCFheXqaWZdTrdZLc8ZfaGpqN\nFoM04+KlS9TqIVs2zxLGIWk/RRqLUBKhXFKwoEAXlqzIwTh0XyYmnTMqKrSR5zmdXpeiKKjXIvfs\n/MQPgsAZ8FCNIGdBJN2kLc8nR2iTMBpGANZaRrdEWevGsXRMMOpsbfV6tGldVNdalxzz71dJJFx4\nb73xKlGTu4Y3OC7h4t8b9qG8dhiG61Ge1pQhU5ZlxHFEmmbU6zUCYTl9+hS9Xtc5XQthqBgkfdJ0\nwFqnQFpDv9+n2Z5gbGyM+flLCCE4euxR1tZW2LlzN71ejy1btjHoDygNnhUuP2SKgqIwWKsr46TQ\nIxIjqsjIUW5iiKaHJDJWlc/POVrjo5xq/MoI0xgCJdB54cDYyHyx1qLzHOMNpZsjwkdJLp+kR5yj\niyjcs8l89If0z0OWz9Bd31yJlP1/1bNxC51ABCCdE02ShHpUH/atHB+lPJgwCBEgpaMkjZXVMy0T\nxIHn5x2oc45ZComxTrtT3Ytw+R7pr11eM4yfvEl+WhjvbMSIFsYQKEV/kDDo92m32hR57kLLXp9a\no+YWLKACRbPZIM8KLJYsd4vP6HJA3TIJfEKlFsdMTEy48C0IHQK/5K47u2kzxhgGg4EbxDCsQkWt\nNUVRcPnyZazwKNa66xca1zdjsKagyDRKBW5SGkMUxVgMSwsLPrxtg7UMBi6pOj09TRSFXJy7TKE1\njYaTBo61WtTrdbIs8xPakGc5DiJblHTovVxPDr0XSBX4sQnIioR+v8/45DhSSjqdLnEcE9bqDHpd\nHn/0CLVmA6Uiup0ep9KzjLfHiEPl+x5BLil3+0X1hnOCaYpEUK/XyT2yVV5xEtdqBJFCKNwYmxwh\nlEeXmmKQE4Yh/cQldnTuQvrQUzlaG3JdMGpvHUJxrx2VFVZG3HhusZK3WbA+eWWFczKjhsmdA8+3\niuGiHrlg6Qi0drxnnufrnEMZyl8Z+kq53okASFF6GkGgXPJNCEGaptRrIUVR0KjHLC7Oc+TYY8RR\nhBSSwaDvqR9HSxijSZKcrbObkAoefuhBarUa7bEW9XpMHCkW5ueIYsXS0gJp0mfvvmvo9w2F1iRp\nQhy76NI9Kz9u1pIXQ9mfa7YypKVxkVJiSsVXlqGjIbVYjqH00ZQVfN54xbVomMQsNDJQ6wyk1h6F\nUipTAK0RwlQRlh9R10Ndzju3RkVQRoCebvSRjbUWoTwd5qmz0gmXNFC5zg2WohihvZRASkWSpn4M\n/HMOlOO0tKzmtbW2ivKklISRAlQVyYf+tfW2QhvjaUqLlAIVOlOc509+r93TwniPTpyVlVV380FA\nMkgBF87s3rWL8+cvsNJZAwRKScIoJNcao41XNLjQPfdhTS0KneFVQy9YSv7AGfWyGV1QaI3WBc1m\nkziOq4fY7/ep1WL27N5Fmud0Oh201jSbTaKoDDmd5VhYWERrQ7vVKBVJRLU6k+PjDAZ9kiTF6ALl\nw6Vep0MaBHS7HeIoolmvY/xE0LogDEKKvKAex7QaDTKdO0mYHlWhCBCCzqobu8Jaer2c3N9LkvQx\nBsbGxjDW0hukdNZ6dAcJK90emzdvAQStRpOJiQkCCUl/wKDvHFm93iT3EUdpyIwQFFo5RGWdQ9O6\nIMshy1OUdWNrpUAIjdIuBLZuXWG88bDCgAyGErFSgjs6P/z/S8Y4SZPKWEspUdIhI2G9/rgkry3o\nohieZMQBVP8IgQwcOkpzF91gS15SU+ROcYBSHjUNkdx63t1WtNCoURtFf6O8aRy7hF8Zol+8eJFA\nSpLBAGsNeERb8u6DQcLKSp/x8XGWVzsut+HzHFEUsbq0Sr3epLO2wqpdY2VlhSwr2LJ1K3EUI2SM\nChRCGLQ2TqnlwhJk4BQT1XjbYXIRqCKMyEdNcRyTKSrJXRl1CacZ8nTY8FxSQjZIKhRbJuWEcAn1\nUWSsrR2JUJzkzzkv72y8dNWURtxYMNrRb7hIx3WrTFgOaVlR0mPGJ4qsRFvjojwpiCKFzQzGcZU+\n2nBryBhDVuQYU5D0+2itadTLig0SR4vpYT+986qow2DI15f3Xv69iqilrEDMk2lPC+PtOux1kBbC\nIEIbQ1oUjNfrzoDmOdv37Obo8eMURU4/S5GFQYjMh86OC1VKkSYZURTR7XaRUhHHUcWNaq0JvALD\njsTfgzLZYyz9bpfO6lo1wFIKeoUmCBRCKSQQ1+uO0slymq2WS2wh2DK72d2H5+pXVlZI05Ss36UW\nhrSnJsjyHClc6OjQjWLHtq0opej3up5acQu83+sQBAH9pM/q2hrCo5cNU1PEtZg8cwi/30tot9pE\nUUy33wNbEMch/b4z3Gtraxw/cZJ6q82evfuptdts2bqDKI7p9AdEWlELIrL+gBxDIGCsUSdNUzZO\njLPa7ZBnmloYUkiIAhdSiyhA5wVZlnqUpjEYgkB6qqlJURT0BgMkgkajQT9NEMYSRRHWQpINCEPn\nLBtj8MjCf0UIwTmc/jpceoRSCgYOnbvm0GGZNBaiBNnl5iMQdphRKTnxMAi8kXHvS+808pHsovT8\ntvGGdDQxaYyjCaw3rmEQIqRgMEjWJTYtoEr64AqUClQhdp7nHF06ShCoSqtduiwhJQKBilKSVLMw\nv0KWGdqNNs1WncGgy/x8Qr0Rs7w8jzGTJGnOjh27uHDhDGfPnuS2W55NvdEg6/VACpr1RtUXU2iy\nIkOMektPtWjtnMMovejkexbCyhcSywChBKFSVZKwHFytNVZBrVZ3yNvTL6MyORCgDSAQgcKa9eME\nUBSlCsSN5UCXa9PTEEoihCXNvFTVSqdqwoLwlF7hDHzJUddqNYQQVW5MBgqMp+2Uyyc4rpwqKVn2\nQUpZSR2NHerqXW7KReoIUxnlUvIbeMdV5RC8Hh0sVhcgn7za5Ck13is5Zw59kl0dcro9996n2zkq\ncKqMRmOc/uVltDFsuhSgzWVW1zp+IbiQUgoX6riFldHr9ljrdBgfGycKvYdn4Lg54/21X4i6KDjU\ncde9//JZojAEhEMoHtm5aw0Rl7YOtZS7Dh2SWPFhX+mBHRI1VUioSZKEMAgdh45zWEmSVAguajQR\nomBpcY00S2k1GvT7y0xOTpDnGRcvXMBaQ5LnhEFIo5FgtEZr57XTPGdycoowisizgnB8ika7hQwD\n+p0+cRwzM7uZWq3BpUvzzC8uO4OqFJMbpwlVRJqmFFlGLQrIMDTqdbSPYkxeUKvVHerCEIchhXcc\neZp5HjusUFOe5yRJUrnHLHObcKIiQghbTfDyZ6vlqhi88Btu90lOxd9+5mcA+Jbv+q8IOcJZmqGa\nY4jI1svOytfBiDktF3qJ5Eb1xwBRrTXcwAXrEGKJjqy1qGiIjrTW9Ho98jxnbGys+kwZ6ieDXmWs\nRo13nucURUEURTz22GOsJpqVlXk2z044x3XuPPV6HancuVpxTKMxjgpDdJpWCDYIAqI4cBSW1ly+\nPMfuPQcYDAZ0O0sArK4u02zWqTdijIYsSSqqAKAWDZPZ5TiNcrnCj2cYhgifpCMY8uAOybLOAShv\nhJQIyGU+jEpw68g5bp90NhbGmmZ9AAAgAElEQVRKiZyUUDFdQ5579NkCjPmNYlprCpMPN/+EIdb3\np9ytGYSx45V93miYiHVzJ0kdx19T9ap/VoPxgDLAOftyzCKfqE0HLs8RBLJyamX+SfgcQzkXVHBF\nhKH1ML8zouK5guH7ou0pNd7l13Tt2rnt5b+yz+3heeM3/iC7d+/l/IU5mu1xjp88yeHDhzl013OJ\n45hWqIkiZ2iiyEnNwjDmsUePUBSW9tgEu3btYTAYkKYLLizxi7FMcgwGAzdQ2vBTH3EKxtc+5zuG\nk6EYJq5guOjLxVLSLXmuPUoOqgcWx7XqWF0MqoUqEdTjmCxJ3YJT7jyBl9DF4202btzI6ZMnWV5c\n4q47buf++z6DEpIDe/fx7n/8B9I0ZXr7Zs6dO8fS4jxbt25lcnyKwWDAuXPnEATcfvsdPH7kGMvL\nq5y45x601kxOTHPp9Dm6BSx2unzPD7+SR4sjHD57ngMHDtCdX2Yu7XD9M2/g8RNHacV1yA1BLSFo\nNDh85FGydEDbGm6+7jq0kcgkJbSWftbHRJKwHiGNJTSSuNZisci5sNZhPEup12vU6zWwGp0nSATN\nRuySu1pibcBiZ8D0WIhNzqIJsI091TzJ0guk9SaSiCgV5Aps5jjSyCeUbSjJvH5eagtWoyzoqIHO\ncyQWaQ1yxGgLoSgMKBWCEBw5e4G11JDHKYN0hUnboInmlr1TKBmx3G9QBJYiDSueVkoIPe1iTUZW\neHRmHFzTEkSgCEWAZKhMaGxokGUZf/CG/8a5C+dpNpsQRVxeWMQUmiiIsRpUGCMVaGPodvpknQSE\n4ejScaYmJhhvNuh310g6IYOkTyQky5cv0en2UXGNbrdLq9mkKDJmt27zkZAmDCNKjbRUZl1ks2Gs\n7RBpnrtrV6oLl7Rzi8OjyRHHpIWt9kBoj0aFFEgrKqdWJjO1zj8PXQshEDqvzg9gKbBWMMSn3lEU\nfQBC6XZW2niYAC+KYpjQtxZVSBSglGVUhljqwJWqV9cnNFXfysg7S1PWVc/2XdTWiQ+UUEgFVloM\n2omjPQVU9iGQI+omrAfYBm3FMJL0dOKTbU8L2kSOhAr9fp9er8djjz3GC17wAjCGelzD2IL9B64j\nXV1w4XWjAcLS7Q8IgoCJyTH++C1vZdOmzTznrudy1113cWl+QBRF5B6pjKIeY0wllQLYvHlzZbhz\nz33mXh2SJEmFJnWROkQkSoUFPrkZEKgAJS0CSRQEZCJwzkO4xUegiBouCRkot7BdYkpw/tRJLi/M\nM3/pMsvLy2w5vZm5y5fIktSFdYGEXHDkyBHSNKXVHqfXG7CydNLz0AGDJOH4yRNcvDRHURiywm2g\nmbt8CWM13/e/fA/vet8HeOThB5lfWKLZrJPnObNbNiPZiBzk3LRjP9Obprl4YQ4bxqysddk7vY2L\n586yY3YjykgMhrzIydOClUGXeHIMbSFLncQuy3pcXF6k0+vTbLcwBiQBgVNQ0WjUXUhqBHkywBoY\nkw1ibWjVW/QKSX8wfDaiW9CQDs1FSU4ngsijfKUteVE4ntpoMBYlnBJG5JrMOuQbqDKaErRaTlGg\nrYSioN6ISZKM1bUl+llAljuHnBcF1AwTEzVmpmf53JEuKhAERoJxER/guE6tGfT7VcheauRDT9kp\nIZxKRzij0B90EUJw+vRpGo1GpZNXWkNkCYSksE5aZ3DOSFgQgUN9e3btJkkGFeAYG9vAli1b6HQ6\nnDh1GhXG5IOUKIrdvegCa0Qlr02SBCn9Bql4vepmZWWpoiLK3bHuPoe8fbnL0OUXhhtlhuhymGS0\neoh2SxLLK8GrRPGQAy6joSuMmDfmwlNiupQBln0yAj2SjxD43Z9X0F3uJGrdZ335oipyLkrKyic8\nS7NtjMGaYd7CCOnyKqVQYl1idYQ+svYLfnPClVFOEH2FSQXlFQW05+YuoKThnrs/yKVL8/T7fR68\n71+591/u4YUv/Brm5uZoNhs0Gg0GvR5nzpzhI/d8lEOHnsXdd9/Nz/zUT3D61BGOn36MKIrYMDHp\nVR2RD9ncwpIjIfXs7KZ1yaUqRL8iyRCSkabDMNAYKp5SF7Yy/MZkJMLQT/tVsrQQmiRPiGsxpy6e\nQynFxo0b6aysYALJqQvnaDZq7Dmwh/nVZVbTAXv27EYAFxYus23bNtYuX/ROxdJoNFAqoFlvkOc5\nygrOXrzAs+68i0sL8zz0mY+yYXIKk6XMzMzwvvf+PWONJiuXz7Bv7wEuXF7g8qXzrK4sMb1hivTy\nPP2lVcIbD4KyTE5vYHpyE7Vcc+34ZpYGS5w/P8e+/dsh1ehQEI9PsJQnFIUFo2gpRa3e5JrxFoM8\noxnXEQKMzkEXGFswSPuYQZmwFIhAkGQdglxS6JSerlPbOKzLs5ZItsxuoBaEdBZPMzaxkWa9Tr/b\no9fpIoQrnlQuzlqzReSTmJnICQKLUhadF+TGsHZpyZdcaLhnGwmWO8usdRaQ0UZEHjFIEzZtmGBy\nXKDiASpOyHWfNBVEoVcnWDcPgiAkDINKrlkaDCEEVpYcryVwELrq66WFeeq1iDBSDPpdmmGA1G53\namZBBgFh5JaoG19L7pHpo48+zjX79yFtQXNsktMnz3DttddyzYFr2b17P587/Ahjk5Pc8ey7mN25\nm1tuvo0Pf/jDLCwsVAqqshaJTdWIHn24Hq3W5CM0kq1oxCHvO9oqaZyUSDlU5DQC52ix1r0vhI9u\nTbW+Cl+jJQ6eOGE3uh7BEsQjssSSKhuhtqr3hBgaa2+MjTfyUgXVuq7WubDIcL2Sxuj19Nmwjs96\npyBEMPxMuUnH11Aqo/7R+4hrjSrKLzd45WY9lffF2tPCeI/SPPv37qEoCjbPTIN1YXFpEAEuLy5Q\nq8Xs2LGDPM956IEHOHnyJPVGjWuuOcD73/9P1Osh7XaTdrvtuMA8Z2lpiZmZmXWecd3kE3akOtqQ\n4xwtpmOtxRpLGCqiKCAIooqfNYZKvF+K9Sel8glDg1Buq/C5c+cwWI6dOI5SipnNm6i1m+Ras2Pn\nNhYXFxlkKfUoJqrFRLWI1eUV2pMTXJy/TBAENBoN9uzZgxCCpaUl2u02y0srhFZg8pxUF8S1Gt/7\n/a+gt9bh7g99kB3btnP/A58lqMVQa3H61DHOXFykyA3N1gQGjVrtMDMxxaatW6hvmKJjLTYzrJ2Z\nY3ysBUqR6gKdJtTDOoO8IG7ETLSajns30MwLsjQhCBUqsBTW6fHzLEUXmTcSTkOulKLeahIEAVma\n07UFJgpZGljy3qB6NEmjhWlPQhhg2pdY7XfJihQpBFMzU65GizB+tx20ay0CpTBpztLCBZQqN4lY\nhBLUGjVCbchyTZJnzF26wMraKmEYUhj37MIgIkkSVmzKhk03kKQZ9XpMkWrqda/lLRxyK2Wli4uL\nSK/Dd/puiylkxfEqJSkKXc2RiYkJms2mywdIgfXSRACtc4R0So3R5J4KQ4SQxIWXpnmEO7lhyvG9\n1tJqjnHrzbdy32c+yyOfO8w3fst3MDnpKkyWlKOUkkJn69QxZcuyrJrXMDQ4JXK2gAyGtTncz/W7\nBUuHpbWGbFgfZHgM1Tmds/O2wIxag3JtGicOscMqi7pYf0iJtkvOukS8xlqEHDWaLgISgBDGG/IR\npU2Re1WaQIrAbaSzZZkAlwRXwtdaKe9/dIzwwiZPnYb+nqPa0KmU/S2LV42Ow+AJNpV9ofa0MN6j\nE+eb3vyKf9Nnv7V80QI+8CCvvhb41X/7t5lV3vSKLatAFToqpTD5SNjkEuROWqQcNRD6SarCkHpc\nc/K8kRCvtq9GlmW86EUvckglCKg3m2RJQhAEPPLII0xv2Eg66LNp8wyXL18GYHZ2lrNnz9LatJmx\nsTGarRaX5uaw1pJmGbNbt7Da6XFh7hLdfo+4Uee+Bx5kdmYTt9xyCwdvOIg2BZ3BgE/e/wC3bbuD\nyX5Bf+CihsRqIgVhu8Gxc6fZiObi6hpZkrNveguddMCLXvINfORfPsZkTRNYRRhA0Gyx2lkjjuvI\nokAZS6QCFxEjHXdrLTpQWDTWuoJXJY/b73fduLfGSPopjakN9NOMM4uL1bNZyCFY6zFeizCxoshS\niiQnkIrc+l2xvmiS9XyrRKCTglqtRlG45GCpww1jt10/DiNqooEMFK2xJjNFjZU1y/HzZ6jFIb1B\nn8DkXF5a5chjx0nzzYT1FnHgdvc5hOlRf55TbzQq9JZpN65hMFKwyyuchIQgUDTaTa6//iBnz56l\n2+1CkqCiABkoitwZxTRNPdrzRsuH+rV6nXMXLyCx1KKQrbOb2L13D2EYc99nPsvGjdNE9RovfPHX\n0+33GaQpjUbD5Yo8stTa+voydh2QKY16GDlVSlkGQozQEuUGWKficnSD2yU5krD1ycEsSyolGEqu\noxekUBU945KjV/ILprp3p9/3UcEVRr60ICXCFXZoV0pV2Xr0LqvjrN+daYWTLYtSUqxzf0/+Zk1Z\nyMLx+pFPQmqGiXNrTCXhLcdHCEHqFTvrooKRpPETyZe/VHtaGO/1sqGnqhnPeX++FvNKtL4+bHST\nVwiHuEpNbCgEAUM5kaMIoB47Tmt6wwZ/VX8t7cKs3du3+w0RugoBjx89Vm2CGQwSbjl0GysrK5w6\ne4bde3b77duC1Gq27NzKrv27mZ9fZHxyMxfnl1ibu8iZM+doxhFRLWTnzp08/NDDPOcFL+LkmbP0\nBzkTG8c51V/jX48/zI31G7CLEZ3VHjYIWLID1gbL/O7v/g6d5RWesXsDk61xrGzSN7D/mc/k7Jnz\nbNu8ibHCsHj5IptnZ1ChhL4iVJJB0kPrnCxNKKIGzWadwmgMLs/R0QITT3J5IWft0gAbjVdjng4U\nJ08tMz5WY2MtZPtMm9MnT1KLIgotWV1bI2zVnXxTSnTu6rMIKcj7hd9xGhHXIr85pk2/3yeKnEY8\nT3ICoTh6ao7uwDA+pkjSFWQhSVTIo8dWWVgRjE/E2MiSGV9fo0psO2VNJReLIyyCLMuR1rjEdhCQ\nps5IrXTW2DA1zfLyMocOHWJycpJjx44RyYDMaFcszO+ADIUiDkN6PgE50DlGSNqe+rvxhuvZMDXB\nxz/6EQ4fOYIxhunZWbS23PW855MZzdzlBQ5ecy29Xo8wUm5/iclQgaQWhvT6ybrEYb1ZlknIsVYS\ne4oiTzOiMPQ5FuEVE8rJQ43j56UMhpRM7jToQcs5jcIap9wQwzWmrcZqS/k9UAGeDvGFobQuqjVl\nTFGpM4xdL79UUvn16PIRFfctRFUkamhnJH7jun9+Xq1kqQpLlUbYFKaiNoRQ6ygx4yOoKFAIMczb\nldTKqL0wI7rv8v00TasyAtXGIfEFyPEnaE8L4y2Av/2BP3KvvUcNlNfv+mRApfAYMaRqhLN2Trms\nP4AfMJfcUYh13q5KupTzVT7xNwaOGm3wmk5k9eUDWpcTZLh5QGpNiQMCoyvvGoYhQkoCub6Ij/bn\njXy9jigos+ZOJ26M4dprr8Vay969e9HGo6UzZ3jBC15Av9/n9NkzjI2NMTExQXeQcObMGVrjY2yf\n2o4pNP1wCYGk1R5nfGqSO7766+gmCf/wvg/Rbk3wohd+HWtL85w4cRQZBmydnSUWiksrHerjbR78\n3AM0GzXIUg5s28rZ04/RaU7wQ6/6cd729+/k0/d+ilqjzt7t2/nYh99H2ltjdnGWTq/LzvZ2JifH\nmZqaIAhiEl0wMbGR5lgTpRSDNGFpcYW4kzE5OcncA4/Su7QMk8MvQDp07S08ePw02SBn+zP2YBZO\n0QoiTp88zaFbb6YRhRgV0M8ycp1Sa9VJ1lLyJEMpxWRrnA0bNlSla0+ePImUAefOXWBirE3myxl0\n1zQTG6bp5ucxRY8D+25hdWGJXs9gbR0ZxKRpHysc/YAUfjNVjpCCNEtZ67qEtlKKJEmoNZsYnSMj\npy4aZC55/sgjj7Br1y4mJyfJsox2u03az5xSQzsaJQpCgtzQXVwhjGN0ljLIUiZnZgitMypJ0ieu\nbSaq15ibn0PKgFqjjZQBt9x2K51OjzCMwdNUQgiCUCAylzQsiowoWm8GtHY7mt38LirkWaoijDG+\nnsxwt6K1hnrdqTbS1JVIyLKMvEgJ46Ciiqy88osNnCy4lPfl1iFUtznJ0TuDQZ/Qy35luaeD0fIK\nloJhrkH52ipC+l2iIqwM6jqFi/j8KFtbv0NTuOqKxhjiem3YV4YVEsPSfFqc4smWBbRslU+TfjNY\nWYGwolwAO5KcLPuQ6q9AzrsKpfyN5UVZL8DXj/CGVNky470+iSAsSFWi4mENClvyXv7BjnJ8pWxL\nF+t5v2FGfejZyxaE0bpjrmyj74W557Z8yF5O1CuvgRSk1nNdduhcZODusd1uA676n/Z1Ra674TqK\noiCQgru+6k63bbnQICWPHz3qKvlZiTY57UbE0sI8hZQsdjqkjz/OdTfdwFfddSdjY2MsXjqL6STU\nLezYMkuQJKzOL3HL3r2sJQOmJltMbxjnkUsX0YuL7N6+g9WFFf7H3/w17bFxrCqwaZ/TJx4HUbBt\n5zY+e/gBJmemmNmwDWEzbNZxxrPVYEWnXDi/TFRzG3MKJdi/fz+Li4t87v4H6YomyHo1jrfffCPv\nvvteVCOgflzzrftmObq4xsE7n0ejUWNqwzTnlxZI8gKrAh4/eoo0yel3EubWlrn99i3IsM2lxQWa\n9QZbtuzg1Inj3PXM2+l3O5w4doRaXKOxrYE2sG12Bqk20l/psXv7Tj7xiQ+RDDI2bk6otwTjYxsJ\nQ0WzVaPRrpFmBSoQREbR0g2SJCEI6jSDGF1oxsfH6fZ6iEAR12rEwJ4D+zDG8LrXv54LFy4QhiGT\nE9MkmatHIooMsowJEdGuN1grMtbSlHjzRh595EGaOCrv3JnjfOreFmDYtGkWay1LK0vs3r2HDRs2\nsHPnTs5cWOb06dP0Bl2wOc2a/wIMnWOlIq411im+3LIQBMKhzRLJ1jw1VatFDLKBqwVkbYVItS8J\na01BFIaEgd8SHypISy1zXuUAjHGaE8cTDxPOjl7BSzFDVOh2QrrksK8VrofGWAiBFQLjvxRC6GII\n1kYAXkVf+s1aFVVTrnPhQFxlR4xFWEnht9y7c2p0MVS0XKkuuVKPbqw/t+9rFIZY6eTBkRoqYbSX\nLUbBV8gmnbIJRjZEVOnLUucp/RFOHSKFD0dKvSeu4pv1h1kvQbJCU2pXS+ZLCOlRufvd4d6hVPGJ\naBL396GHLsoSoSPh0/qbGZkM0ie0UEQq9Dyjrs5VOhZrh8k2cDv6JAH9ZOAWmXWRR6fXpdFqYYTb\nGZemA4xPUoVxRJEMsEIxNTFJp9clCgMmJ9vs2DzDwsIC4+NjTG7cQNxuIpRkcnKSSAXs27WdvKu5\n/7EHuX7vAaYnJwg3bcJoiQlg43iDQMDa6iK1IKQVtGk2mxRZwuzkTiQF9UYLneW0Wg2CULFn3162\n7tzBaidDyIKsvwZdj9DikKIwxHm9KrmKiEgHCTYKKEzBxJhiyY/I3/+PtzOzaRONjW2CdsLq8gpb\nN89y+eIlnv/VLyAtcvbtPcDZixe5977P0k00mzdt4eD1W1nNeoyPj1NoTS1uIGXA5PgEqxMTmDQn\n6w8Yq9WYnZ3lY/d+moPX3chaeok9O3ZyKu+QDXoYndGox3RW1rh4fpGkOEq9Xmdysk57rMbC4nmE\ndYmubVu2Uq+3OXjNTpaXOyxdWkIpRa1WQ4Uxq2trGCxRVOPRRx9leWWF/QcOoIuCXneAUgIpFGSG\nsUaDF9/+HGxecOL8WQ6fOEZnZZHJsRb0cpSS1OKILOnTbDZZW1kiiGqsLM/TrDfprnV8cauQWqNO\nq92g2YjorS6S5U4nHQSKPE+d1t23drvpkHPiK2Bmhefyh7xxFNWqhL0r1DSsAeNqgGivyMoRJkTn\nZWG10KF2rdEetftsMphh9cssc7s+C5/s1F7aa4VhmJr0xehKhO1ldy5X6Y26Eths1GgPqYxq7o2u\nY1MCuSGIKnfCugSmQHnNttHDnadlElZ6x2cq6sZXQrTG1T+yAcJv5w+CwG33k4qqYuKTB95PE+Mt\nxbrQrfTko1y40WClJCj1oxXyHhbD8Z8GDFYElUMYrd9bVYKDKtSxVwzDUDJYnq8MES2hFJXxFyOb\nFtYlQ3ykUChJuXVfpw6VlPRPifxDub6Od3nfwhfOyrVGBpBaTWOs7dQvRUGeuQTnYNBjsd9ncmIC\ni6vlMTU1yZYtsxR5l127ttBZ6TK7bTMqjplfXERSMDU+RRZ22Dg1RdbtcaFhec1rfoW1hQWa7Sad\nIsWGAUIb6PZIuh12HtyNsZYDu/dz7JEj7Nmxm/HN09ywfRsWWF1ZQW7d4pxMNkF7wyRx3X1dVpb0\nMYX7xhyJRGLRSU7S7xPXGxy5eIJWo82uZ+yk3k2Y2FSrjHeU9yCXXDx3noObtzC/PM+xo0eZ2bCR\ne+79BBs2baKxcZq/+8AHODc3TyFi7r7vAdrtCZr1gPHxNmurq8xs3EhvrcOtN1zDbTfdyGQjRprt\n6GzARz96Dwf3jLOhbolMjU984G42TO2hOdZisHaBZnuKUDTpLSX0bcrM9AbazRAlcjZN15mcmGbT\nphkunD7H8vwi177kGwnDGnFtipWVFTZt3cb8/Dzv/qf3Uhj4p/d/iDNnziDDiPPnL9JqtSiKhDhU\nSGvYPLOB6UaLqNfBZDm7J8eYuPl6Pnn6CGnRpy7qTIyPEcQOgW/dvpWHH3mUhYuXmNm8jQtnzvKO\nt/013//9r6CfWuJMIYRlfv4yClcbp9FqEoaKflags6HVKHXeGEc5GKOR1lXvtNaSDgxho+ZLEOdk\nWZlU1dRqsU9sGqQUHmELgmhITFprybPClU6VZQITt8VdZ+jcuI09XrGRZQVxGDr5pwadakQ4AqiK\nwtfQEdUuX2P9V5lZqNVCz8lnGAMD399areYckhly4lX9d/8lKEIIBnlGSZesA21ZVtGiTxSJj9I6\ngS8RK6zbdCSFpcjTod3wYDX4N6T/nhbGG4bGryzyUhlbWX55sAuJlC0RuvQUhPt2CzuyfdoIiSy/\nTFWIKikwGs6sR9lPQGWM/D7aP1clrqRuSmE/VUEei9OzCgTaF3Evebgrm/+yM3ctU1TfgWesQZmC\nMIoI/OSM43pVhzyOQ5qNGoNBj7jpiknlWUahNXEcURQZ3W5BoHKS/oB6s4lA0RsM2LxllqXOMksr\nizRCxcXz52nVYrSqs9btOClZkUIoiNp11uaX2DkzQ78W8vIf+kEWVhYIE8GOzdto1RuErSZr0tLt\n99i0cZpQO7XHms0ZZCmNqYZLWvnFIbSm3++T+i31CwtLLtRsBBRZzvTMBkxtgB7RHe/buY25kwnN\nDePUgwgtgUD9v9S9d5wlx3Xf+62ON0+e3dmdzQlpEQiQIEiIJAiJQQmSKEuWLQeJsk3LtJJtWX5O\nT3KQLPMj+/n5vadH5SeJtvSsSIqSmEDCjCBBAkRaLLAJu7OTw50bO5b/qK576/b03QXtj/0B6/OZ\nuam7urq66tQ5vzrnd+hnHDf9lWXSnR0eeNODXLu+xlY7xLJ9nv7qc3TaW8RxiADK5TIvvXCej65c\npV4u8ab77+NXf+kXOXvmFE986XHe+97vZ+nlXV5aXoFIMj87R7nqUy45yCSgF3SJ+jGNuRr1ep1e\nb5sk7VIpRyDKtJqbTEw2kLFkZ3uTamUCx51ASqVRer5PvT5Bu9vl8uXLWJZFt9tlbnqGMAyJoxDf\nK1GrlDl17BilKMEXFoklqDRq+LZAXD6HYws81ybsB1SrZdySx9bWFvvnVaxC2O8zOT3L888/T7fb\nJqFEt6s0bVsoWgjbYoAjCzEMeoGhx0YUxDi2jeeWMsUgI4VKQoJ2PIib0JappvwdwIQkCEtCbA/9\n3rPNQL/kYglnKHz1pqOtuOYREIXxMCo11e5/DOAKbcmqhUO1LY7jEWhS+U6reaMipAWeXRrRvIWd\nKWqWbcRvDCNHfb88suE5+D6b0yYdsqYANr13ZCYPhv7gIqvXH+wZDNobfJ1h3qSSONArXTaIBnzB\n2QaGEJCmyAx+0KJeymTgXKkGiD2izapjhlpxkRA1MW0lmMnOV5/195YFIttYlKD4xY0z1TIw5G82\nY6Xyi4bpMqR+UIRXIs0SAKeCNFGDy0GQhgG65WkYERJhYyMlBEGEbTuDMGS1oQO2XcYuyYxlTWGV\ncb/HpF8ZXNcp+UTARBIRpymRlGpViSDu9HCB1abSxNprLcBHujblA1WCOCZxHMq2TcWvDO5LpCmz\nVgU380zI36tdcWlkA3j/kQVATQDXdYmjjKTe9vnJf63u99RdR5k/uk21WqXb7bKZTnHPffcpbaof\nqMkWBMiNdQ6XHWZFRJL0mDwzTX+nzh13nlXh2o7Nyf0znDx5km63yx996M+wy5M889IyJ29/gMef\nuUrJrxHJCp24RbPb4sLVy8TJFLVqnX7UJ/USPFfS2t5k/8wk7W1JtJ1y4cUVRJpQ8spIAa3THSaO\nzPCJLz/Bn3/04/z9f/RPaUwvcHGlw9L1K1hll83VFQ5O7qO31aHuT7JbE7gyZl+5xEwS4siE8xdf\nwilXqJQOYFdL1OpVWt0OliPYPzfJqX3zeJZgfXeLrfYub77/Tj70qc+xu9rFtqv8y/f9W97w1jcw\n3ahTtgX1CZ/NHYH0XVb73SyqMyUNDW77NCSJEizLodIoEYiIdr+PV3IJ2yEVr0YiO3hlh27mqRKG\nIZ7j0+6o1GiO42EJlzBKCJ0AIomV2jhCuSYmcUJKhllLCzd1II6JvATLVtimSBNSW8EdnYy90nFd\nhCNod9dx7BJVp4Fj+QgLEhmT2gn9pKuuH3vY0lFcRELFG0gZg4yxhcQWSvaksVoY7FQRrbm2IBUC\ny3VBWsgMQ7eMDUmZSpoyGMIzqbIydIJ0kQ4jUQHcICVDRzI5IEmkZksUeqsvH1d6w/LqEN5iyH0w\n8MLINha1a9DQXWi4ekxgFpsAACAASURBVMFwk0CXEYEII6ugufk4Dt/On2u+N4VQUV36vakV6N+s\n3IKSL0O+jWG7zPpNjun8/Zv3bLbf9KfNm3X5dti2PXJNs658n+r71xrHXvfJUcKovEVTtFegj7Ud\nAbY3soHm+y6zs7OKpbDsc+zYMUquwlv7GW1tmiREmQYnheKvqU9OkM6lTM9M0A8DekFAKiMuXlKY\ndWOiil9yCDq9bFNxk24nxHIVHPDcc89QqVWplv0M7kmplHxECD4e/d2AtZVNPEviuw79bkgv3KHR\nqHHx5RdJrYhuErO+u0F9ahLb8fjMpx5nYrJKsCuZrEwTd/tYaUIadHDKKWXfp16uqGjMWFKv17m8\ndJ27bznDanMHK1EBTPWFfTRq1axdUPFctpKU1aVlHnz9a7l09TrLq1t4tiTudEjLZXY6fRop1Go1\nlWmJjClT2GBkcFHEXSrhdafTAVsi4kTFJFR80kgFo21tbQ20zSSJBp5gEjnwFLFdB0umKq+lVNlt\nbNsi0ULOUhueWHamLaks9YqbJFYJHiwoC5dUMNgQbDQagEXciyDD3VMS4iy4KYkCLKGYL5MoJs0o\nW21HUQALYeEIS7lECpWd3haCRAw3HJVWb0SeCmdk3JY0rzyAlfHXy1FXQdsxnR7EQKu3bUEaa+3e\nxtbUuKP0jjcsrw7hzVC4aD7bvDDXf0Vmic78oQW9WZ82dwbYk+PsEWimIDTfa4GW/z0veLQbVP6a\necE1MOvYu3iYDHa65MOPhRADU9UU7HpRM81X83UAJ6Wj+HzRQmP+6Yky8EHN1WsKfF2n7gv93ty3\nKLruoE7XApHtfwhnRHiroBIHx5ZUygqDdSwLsHFsdX7Q61OylDksBUxMNtgnBIQxfrlEt99j2p7g\n8LEDCtt0HLbXN1SY+tIypVKJg0fn2Vjb5OMf+ySdVptqrUwcCXY2l6nWGzz8lrcQxylPPPUkqYi5\n+vISp44e5/577uX3f//3abf6nDl7K4dPHGHqyALPry7Rix3e+k1v5EMf+hAXLi4xMXGIrc0t7r3/\nm7i2dJFuZ4XN1Wv4dkR5Fw6e3k/d9dTkTxLuuvc+Dp1qsdHtUvVL1J0S/uQch+YbTFZcrCTEsTwm\n/QpMz4Dvs7G5zmzZ5R//25/h4sXL9MKIKJEIrwFuhY21JWpVD5EGxAhwJrDcoXdPIqHdD5idnsEW\nkrC1S0mmyDSkG8XUJqcphcnAZTYIgiyPZeYAkNGzJsSEUT/bxFPwZqIylKk9DxKsRP0WixikBSId\nzD2EpNfdVeyLWdqzMPM4afXbOJaLi4+QFkGUkKQRdsnGcWziMCJK2sSyi2UrHDtOI6zYGhBLiVTn\nrLWyoB4VROQ4Dp5rI6XOcqMVxnQwV1KZ4mcogB7nUa87lD+D+A01HyzbIUmiwfwH1MIUK08LPYfj\nZDRN3I3Kq0J4C9gjdE1aTq2BCyHo9Xoj5+qVH0a1PlOQwqgAK9IStSAxhYr5va7DFK768x6XIzmk\nfDSPBQYhzHmBmhfU+aLPyQvkIs0+f883szJGYaNhMT1jdBkn6PO/DXf+R7X3vNWi718l3lChx5Zl\nYYnh0OzsNpW3hqWIgFIx5NBAkyt5Dl5JRU5GWex0EEc4loftKGHS7uwqKCeOqZTLNBo1NX7sg4rj\n2rWZmpphZnqesB9gO3D1yiWqNRUlGfW7rG9scuTEfra2djh64Cynj53h+WdfQGLjeQ1a3YSt3YjW\n0gbLG1scOnSIilfh8S88SXM3pt2NKFcbBBHMzh9geblJt92jdKBBtS0oO16WGNjC9hxWNtbxKnXi\nJKHbD5is1on9mFrJxbElTmrhILBtj6mqTSQk+w4c5JkXXyBoNZFRj4X5BbabXZaXdvD6EccPHyPs\n7SASQbXW4PpOQhgbEZap0kDXt3dIul0mPBffVjlVUynpJxFxpzPIDF8qlbAcm06ng7RUSoYRjpFY\nbUimQqhwdESWl1LPxYREWAiheOSTNEFm7ruK896i22kNlJcojPF8V2XuiRJUZq0EKRK63QA3sZAJ\nWEmCkIJyRUE1QmhXPzXmXFul2Bn4fyehGjMZc+gQ97YHiqW5Qem5agM1ReA46rdBEM4gF+fQf3w4\n1xl41MhUDOpUwXlfZ6yCQgwzU6vPYkSbNQWWzjyii16li8x9XZcpXExzvgiHNuvIC2tgMJhgSMSj\nOVAGm5rWMFdjXriOE9JFArTonvIc1PlNFPPe9KKWr7sIujEzeOSFen433bwnUyib0Jb+M+l0zevv\ngbYMv33S0d9LZV8lvsjM9cRKiTK80RbWgOS/1+up87MNK9t1IPMPrtVqTExPGUmRbcqez+7uLpVS\nWfGrxwnlqkutWqfb7uD7gnrV5fY7ThJFCbXqBHGcUp+UpAgiWUI4VeZuvYO3vXuW//v/+SWeOn+J\nry6/QL/bI4liEvtLOI7H7nafg/sPs768w1sfeoi161cpl0psdQRvf+PbubZ0kWMnJ7BI6QZ90kgp\nJVXHpb18jX6kkwXbWBJcCVaUIGKLOJFUqyWsNGZhZpq11jZ/+/v/Ou0o4NjiAr3IodeLqEzMgbAp\n12Y4fuQgrbVrrG3t8Nzzl0ciWuf2HyLa2EAmMdNzC8QbG5QcF1lysdMUWa5xeGaf2nDe2qQfBBAm\npNjYwgYJ7ZaCsyqVCo6QJGm2H+VlPN+Wj5Aqn2eKEVCXRiMJgBOpuLBLWXCPSr6h6H9JJUkaZtGg\nZFi7ncV82Ig0wbNtoljxl9sZ77bIfK/7YTgIzrMybn6npCJGEwEJCQgLIbJN0WioNUsJ/U7bmNfO\nCKQ7kBHZdK3U6riunVE0pPi+i51aSCnQfs5CCMrl6p65Oa68KoS39rgwvTLKWQYdXUzhlDfh84JA\na8KmdqfNf32MWfKQRV77LxLi+trmcfpcKeVYTnBT2JnCr4jToEh4F2245o/VJS/o8yW/COaF9s14\nFkwYxnwO+r3Z1hG3z5z1I6VEGnzHKq+fGW6cZNaZyi4Ui2gQiYtU2pSye8lwU5Hdf4RIsmcUJYTx\nMAgkiSX9NMT3y4o+Vii+dSHBcjzKJR+ZhlRKLpVKiWazCdKl2+0TtZv0g4jAg8S1uLDe5v0//wuk\nuFRqE6xdX8EPU8quT9ovKR76SoXe2gZlJJ/+2J9y9913kiYBCwuHuHzlClEsaXXa2BUf17YQjkUv\nCum3WtiOS4Rye6tUKkiZkoYx5UYVz3awpIVne8zP7ePa8nVuvfNWSl6Zdq9PyS2x0+1z7vxLbIQ+\nludxYLaGk/RolMqsr13AdksEhsJ38dJVrq6tcPzIUQ4eOkZSrnPh/AtM1xdobqxz7vx5Ft0yR44d\nw7I9arUyiZQ8+eSTzMzMMDs7y+FDC3Q6HYJej05rV1HZusrC8nyHklDBPLad4cy2crFN4oQki4zt\n97uKPbLbpV6vZ9pxohJpd9pKAErwKormthf0sRwHy3IQqSSOVOJxXKVM9UMV/apcBdPBQpKEEWQR\nnb24j5MFF1mWRbvdHkTMBv1wkH3H933SMMIaeKioPLqlUkmhA4mF7/vIzGuqk1kOSRIN3BY1ORtY\nJLGST97XGyUs7NVIx2mieXgF2COUTV4B/VoEHej32mVICyst6McJynwZB0eYxdSQzSAB3T4TC9Ml\nvxGYx9vzi1b++3HCN4+763blF69xnDNmu8ZBL3l4xyxmPw+sH01IpLzBEIaroBBSaUDaMUfY2HbW\nLwypDgZpqTK3zQTlnZBvo8yEeMTQCgMI02CwcaUDwvpBh27QZ2Jign4fJkp1YvcUfhzz2x/4HS5e\nvsbK6gay2ca2HJY2LuH7Lr2oz9TUAY4vvo5rS1fo0aRa99jpbTI5PcVXr3yVYydOE0mP+RNncF2X\n1WcfxfGmKQmlifXiCEdYKhhEWthCJbtwLIFre/R6AZVaDddyiZOEerXBmx88RZeEoBuyvtlkbbuF\nXZ/k1ttvw63vp93t4zkWCZLdTp8LV66y03MRXn3Q3y9fvkon6HO+9yLbK2vceewIh06c5JOf/gwR\nAttx6HVDhHCYm53nIx/7KKvrmxw7doxadYp6eZrHPv5ZgiDAtR22Nlawy2Ue+IY3sbxyma2tDaZ8\nj4X9cxw6eJAgibmwdJ2pmVkaVZ9Os8lEvUyS9JDCpdao0O506Pf7+L5PL+riWi5BN8B1BEG/Tz8I\nFIeN69Pc7dJtd3jnQ2+mUvZ5/MkniaKQSqWEbTtUHJvWbmeQf1b4AkdY4EEJf2T+T9ZrBEFAmiaU\nfY8wUPkDgjQmlkPI07IdhSB4DnbqKmoAIsJUCWvfdpBpFrSDcm0s+WW6nXY2BnV+gK8zzVtQ7OBe\n9DkvYIA9G4s3OsecrPrVxKzN44ow67wWXgS56M9FEMm4haSoFNU57r0ZfHQjwQmjgUK6mLDPOAtl\nXLvGHTOuD0apQfWfvtZ4zE/zSZvnppoLx7IHgVoDal8xdNky7yd/n7q4tqs2sfRCIlKqtTpxHLOz\n2yGJLRJps+2WufjiSzz9zEWsIGbpy+dY3LfANzx4P1dWXsYqOVxvrVNuVOj7ZY4fupf/+vifE0Yt\nrApsbS0R91JKqzVqTp3bbrmdJ77wOLbrE6cKRggjxRWfphLhWKQyAbLoRRLSjAsER+HH1UqDdrdF\ncDWkJ1JO33UnYazmlkxSdrY2WXrhMp7ncfZbvpFLL75AEveYmppio7lJyRtuWB4/doROt8vStWvs\nrK/zxe0Ngl6XRAhOnjrDV547x3SjwdTUFOfOnUNKwcTEBOvrm5BA1E9obu+yuHCAycYkrkhpRwmr\n65ts7WzT6wXUbYtzzz7PLSdP8fyTT/LS0nVeMzlFr5uwb36BZrPJ9Mw8Kxlz5sTEBFHcY2Z2klim\nyG6foBdiVxQ+7fs+MRCnkl6vz+bmFp///OME/TZHTp1geXmZIAxJEkmpUhuOJ5mNRyEQqSRJFTRn\nCWeQ2q9UKmUKnj0g3VIp3QwFRygu9zDqE4Q9wiDOclo6Wbq/OJMdeuzFxHGYPVdwHRfL8r6WFJav\nDuGN2Ctoi7iEi4RmYXWGUNZ15DFs81V7cJhueOOw6ZEITUYXhHHaZ75tee8NU+vNl7yAudF96+sX\nadZFJX8f+cXwZkLaFIamFWRCVEVFbxCZxc6GohxEyOasGUthgxKFzwsdfpyRJQmERktGfO9VGq1s\n4U1HoSdhjbbBBSzHRUftplIBetL2SBLJP/0XP8vM7H5q+0/zlS8+zvGJGU6dPMZrp+fZWlnhsG1z\n6613sNXe5VzYp9+J2HdojrXNFb71oTfzqf/6cQLZp1ybJHVtgp0ure013n7/G/ji9gZv+6538IVP\nf4ogjKlUPJI0UiHjlqOSOlsCx1bZ7IWtAlpSO8L1XIKkw1eePY/r+IhqlWYa87p3fBOpJcCGY0eP\nIKQKwGltrTAzWaYfuhw9eoTFhUVIBb///6t+qIuEiu9y54NvYG5+lv/0u79NvV5Xnjaf/yzHF48z\nOT9Js7nNyup1+v0uUZTQbnXxhEsSJtRLVfZNz7Gz1UQIwdraGqJUAsui0agxWa8xWfI5ceQwH/7w\nh9k3M82zT3+Vo4cOMzkxw8EDR4nShHkcOt0+rW6X6blDbLU6LCws8C3f8iArS9dYWbvC8vJ1elFI\nozFJnFj8yYc/wcH9C9xz72vZWF1SEFSvx+TUDHGc0m63EbZFpVaj2+0QBSHCklSrVYVrS4njCba2\ndqhUKtRqNdIkwXEElZLKwhQFIeVqaTD2B3PHErjVGn27j5UJaikk0koUWZZQ4xsBqQzxfZULIIx6\ngIWw/hdtWAohLgMtVER+LKW8TwgxDfwOcBS4DHyPlHL7FdY3VuDcTBDBqCtf3uzX3xXh23lsuEhT\nM78b532SPz7/YM0FxDzvRp4m4yCZokXI/D3/vqifivYUzPbcCDbKWyqmtZIX5uPOH4F9BgRCArAx\ncypalqM40wdcziKT0Gbf7IWNpATbGhL/69ck46TIu066jkMqhy6XwnIQqDR8zVaXn/s3/5bJqTke\nefgRXMdh35GjbK1fJ223+f4f+EusLS1Tshwe+bZvo7Pb5ZmvPsNzj19msVxm5YUlvvfsG/kvn/xT\nOlGENzHD5s4O+/bv49f+y2+xcGiOra1t4jim7Ppq40qn6UtSyiVPBYnJYaJn4QjCJKTbbTPh16jV\nSpw6dYaPfvoL3HbfvfSjkF4YICxIw5Cqa1HzPCbqM3SjCMvzCfoRZdvFShN+P+u/xf1zdFpd4l6H\nq5d2aLd3cTyb2YkZ5m69hfZmi93dHbAsDh1apH3uBeJYMjc/QzmLGqyVK+zuNDl/7gWqMx6TUw1c\nz0YIlzQMFByBcvGcm5ni0voalcYEq6vrbG3tcNvZOwCLe17zGtIUfvO3fpuvPPk0u502Bw8e4oFb\nb6PfD9k/v8Da2iqdnS4z+w5y/sWLxHFKq9Wh0+nwwvnzvPNb30mr06bdUZDHRKmC5Thsbyux5Gf9\n2e/3kVE0IM3yPA/P82g2mwMvMdfxB9p4EHbQ6e70mA+DQMkIBCVP9UUSxdiZIqkzFjmO4i5Srs/2\nwOnC+l8lvLPykJRyw/j8U8DHpZQ/J4T4qezzP3wlFekOMIXZHo1bZt8xKtTyQlWm6cDrQH9nXkOf\nY/5WBLuYgsk8FvYG3uSFc5FGncemixaIonaOg2fyxTw2r4HfaNPTxMf1cRqTH3cNs2/M8/NafN56\nMvtyD1QzbNHgnWVlCaeyH9M0zjYmgYwWITSspsFCLQRRZLo1WiOWVt5K6HgtrNChFDWwRebxIBI6\n3T77Dy7y5offjhQuP/Dw9zI7P0d1ssalpaucvPMs2/NzPHP9Gu984I18+fo1wnMvEG5t8113TdHY\nN8uV6zUOTOzjzqTMH774FH8cXcdyq8xLFzv18Q8e5I8/+hh3nzqGiPo4aULJSgmSkH4/xK/O4jgW\nYTeiUvKolmpI26ITCRaOnuLaxgZ/+cd+BMv3ufVd34ltu5TL5czjaeippO+3YimGzbSSDsLTdZmf\nm4S5ycF3f/voX88oGvwhNXMS0+mo/KAnD82pYJ7sOaZpSrfbpd1uc/SWOteXdzl2+DCnT5/mE48+\nilcucfHqFaamp/n4E1/EmZ7itcePMT09rTZj0xQch337FnjmuWcJggjHciEVzNfm2bm+xb9+3z+j\nudvmG9/+DgQ2l69t0g1tPvvZz+K7kqDf5NLVC1glj2ov5sz0Ap+98jS9JOaJFy7STxOm5xcoez61\nUpm5mVnuOnsHbXGZwwdPsHR5ifPPXMSLPfbNzPOmNz/IoSOLpGnKz/7cz1MuV3FFG1D0wLVag3ar\nR8mvsrD/MK43icAjCiw8r0Ro7dLr9dhtrhGGAZ4rScINHNSmresL+v0uHdsvnHNF5X8GbPII8Jbs\n/W8An+QmwltrxEVac1745QWmKbTzwmHI1Hfja+c19CLvkvz3Rbh1/n1RkM84D5Yiq2McHFNUbrQx\nmMfAx20G59/nrZSi8wcah85gcpP+yS9qppDPH69L3qPIXHz0AlCEo+cDpW4GB3n4SNvKIJlI4emO\nRckr8/M//z4qfp0kkdQnaqytrTDFNEGnTdl1uHj+PIv7Fzh29ChNx6HZbLKTJFxqbVBxEqqzk3Qt\nwam77uBQf4PGhTWa7RZbqYXwHM599RkmJyfxfZ9yySWOO1lwioftOvT7fUIJriWI4xTP90mQtLpd\nZmdnefsjj9AJAkhTUqm5uPV4Swf3r/2JtS9zUZmfnx9JxK1dMNNU8XTHcYxX8qnX61iWNdBKNe2x\nPi4IAhYXF3mdqNHqdpicnOD0mVP0+31WNzfwXIco6LNy/TovX77M9PQ0x44fYXpqlt12m82NNWxL\nEPZ7yDTi+OFD1KsN1tY26HeXqZQsttd3kcJm6do6a6s7WKJE0O/S7Oyysd7EtiwuXLzI9Owszz73\nNP1EUmlMknYDeu0Wx247wsLcLKdOnOTo4kHCcg0RO1xNU86/8AJ33HoHL104z113n6W8oXi9T5w4\nRRiGbDf7KpXhwYNMT0/zlaeepVQusRv2aW9epdMOmJtd5NkvPsu9r7+bxIIDh45Rb1RZX1uiNjmF\n50qWl65RrpcoN6ahuVT4TIqKeCWCYezJQlwCtlFG7P8rpXy/EGJHSjmZ/S6Abf05d+7fBP4mwPzc\n7L3/6f/7vwoF4R7hzV7hkMfHB1gyxfdmTmjz+LxGWKR5Fwke0+WvCKox70EP9Hz4eJHmXVTGfV8k\n9PLBNOMsjHHf6e/zIe9FbTH7wPxtDzySa8u4dggheM/9Kp3d+7/4iVdkwRRp9EXvNcWn+QeQJkKl\nKBMxUihyYdsv8fDD38HdZ+/h4W94J1vrTay0R9QPePHc8xw9dJggkdxy6+14lSoXXnoJTwjecs+d\nHD+wn2TGU4mPe308x2f/xCyOtPjm7/pu4pLHlm8RSaj7VXbaW9x16hjTZYey7EEcEYsEaVv0AsXo\nV/M80jhhampKbdB6Ht/6HY8wd2BRMevZDlGs+DKEUJuVinhp1NIwYxD069976yMA/PtHPzjy3LXf\n9Yg1JciiD4fp5XS9+jstxKOeyqmqsw3ZrkO5ViWOY8I4GiwqUko2NzeIU8nOzg7Cdlhf2yRJJNPT\nM3TbHaQUzM/Mctttt1GtT/D441/iysvX+OznP0+aqLb5noMQ4DqwuLjI3/i+R+j2enz+y18hEQ6t\nfohfrfH4l56kXK3Q2t3hllOnuXz5IjMHbTbXm9x+5g52N7oc2H8QpOSFF56nVCpx8vQpzr1wAdd1\ned2bX0+v1+PK1ZdV4o1ShUajQb02QT9SWX+OHD5Gt9vFdqrMz8/zC7/wC1y5coX3vOc9fPbTn2F+\nfp6ZmRnuvvtutra2cMOUH/rB73lCSnlf4QQxyv+o5v2glHJJCDEPfFQIcc78UUophSgO1pdSvh94\nP8CZ0ydkkZZqlsFAk+O11Dw0Mk4g5UteKOlS5L5oBrPk26sxX/P4fBtM09Wso4jvIy/kiuozj82X\nASXuTbxPxp2f/z7/bPTncf1n9of5fF5JyePxpnWV51Mpsp70n4ZIzHYOsrrkFhqPKoiI1G4h05AY\ni6Df5wMf+B0e+9hn2F1tEu126MQbbKyt0aj4dHY2mZ7ZR9RuceuZWzh96gTPPPVVRLXCV146T7jh\ncuzQIg++4QGeef45rm+vUe3B+374x3n3v/wHWLecRiAJ2x3a3S7HTxwlajfZubqK7zkkJARBCpaD\n63oIy8ZxHZrtFgcOLfLen/gJVlbX6UehCrdOkgweQhHv2gLLEXvmh/ma35dIyWDHLMKk0+uOaOmm\nBi+EGPg+5xdFfY1Os4WUkn5fYclhHNFv72YJRBQHeK1Ww3EcDu6bJ5Yps5OTWI7N4v59hGFIpVwj\n6PexhEO/26XX22Zz6zpPfuUzJFLQqCmlKAxDgqBFuVwiCiPWVi/R7vf4yEc/wm6nR2NyGqdS5cD8\nHPffeze33XYbG1vrOI7D9FSVxNrkxKHjiMTh4vplnnziyzz0pjcj44StrS22trZYXFTwya+9/9eZ\nnJzk+77v+wYRk5OTk3Q6HbaaWwhL0mmuUiqV6PR3+Y//52/ywOtfy7d/2zvxvSqLi4sIYSFTi+Xr\nG9QadR7/4vOveI78DwlvKeVS9romhPgD4HXAqhBiQUq5LIRYANZuXtEoPmuu+vq7gSlv2SOTFsZ7\nptwMNinSCE2BbJrd+hhT6BbBGkXadl5oabPVPFfjy+Z3Jg+LLuM2EYs2BvMLwjghmy/jLBKTHGuc\nNp+HtYqEat6qEWI0IMp81e9NTXEYtly8X2Cel993KLJ09KubOmCnxC5IYWPbVeKew3/+wH+m4Tbw\newG9jTVie4cJB6quhSVTdtev02nvsHD4AL/5e7/HqdO38PS5p0nDgOaVa1Rdl8duP8Pf+Yc/zma4\nBKvrHE8dnv2tP+G2n3o3ScnDb9SpEvJnH/kYpw4dYNLzmNs/RzvoIXt9giQhFTadIMJCMLe4H+mV\nOH/5Mo3JKZJ+D0XIqaIW1T2qe06idI9ANd1F8/CJaR2CIrISQsUlmNq7nrMmc6TZ1zrozvUElmXT\nmJim3+8PQ8EzjhnNV5SmKcIaen51u12aVhOrXsu0/7py2Zuts91aBhnxze94gFqtRrPZot8PqFar\nNJs77OxsIVEWSifuceqOMySxJAljnn/+Bf74C5/n7tfcw3/92IfZaG5y9uztHFiYZX5+kS9/8as4\nToljhxZ56I3fwOrqKrVqianpBmdOnWJtfYvLl1/mzuOn8TyP3/mV38R1fI4ePcqTT32ZAwf2c/e9\ndxCGfR54x8O8+OJ5Dk5O8Z7v/w5aux1sG+5//d28+OTnqDWm6bTWeX75miJTq75yBee/W3gLIaqA\nJaVsZe/fBvwM8MfAXwN+Lnv9o5vVJRkOlCLujvxENoVm0cQ0Pxe0e6Qu85r5a+QXA/OccXWax+TD\n9vP3ZZ5XFPZfZH18LZp3Ubv/ezTs/LPJ99e4/jPrKoJvzOOLMGuzPUX9YR477r5MTyLzuRQVy4HU\nksQpRMIikQ5TUwvcfvu9yG7Ec5/5FL3mOqVKgmvZ2DIljRNSBIuHDjAxM4HjezhllztP3s3ZW2/j\nD/71f+CB197H5574DP/uX/ws3/wt38TR+gRPP/dpXrt/BqvVpVGvgWczPT2NHXbwfZ/GhMvmTpP3\n/Yd/T3N3l6eefZ6V68s8/9TTdNttpG1Rmaizb+GA4mH3hxHJUmYLVxIBFrZrjfS7lHLIZV2gbOg+\n10I9CIKBIpBXCPLPRX/WNARCCBAW7U6XcpZk20YgM/6ZlGSYHV6qcHffVxw1nudx5MgRhBB0ui3C\nMKRc8en3+xzYf5B2u00YxMSh4jARErY2NhFCsHjgUBYSL0ilpFavE4cJ3oTDw/vfwpsejGh12ji+\nRz/ss761iSVSJmt13vjAA3zliSe5vrXNFQlnzpzBdiQ7zRbN7W2SOObYsSNUfY8DBw5w+9k7mNu3\nwNbWDmfuuIVmRVhsYgAAIABJREFUc5utnQ2eP/cMX3rqi+zfv4/NpWXiOGZmbp44TvnTD36IqZkZ\nlq/ucO3adR75ju+iVCpxZe1S4dgsKv/dmLcQ4jjwB9lHB/iAlPJfCSFmgN8FDgNXUK6CW2OqAeDM\nqRPyF//jz40dRGZxrFGtQIi9mPegHkuOTHzz2JHjKBbKRRs6RVSn+TJuUdDnF2nm5oZdkUDS74uw\nynHtz8NH+YXP1MSL7tXEus3rFQnXV1rGHVu0UP2N+x4C4Je+9OhIm4vquZFfeZ7ZcJzwjqOAfthj\ncnaGnVbAr/7aH7O4eAt26LK5dJVg9QWam9dYqM3Q63Wo1Ets7TY5fd+9bHba9LA5ftudXLpwiftu\nO8uV8y/xjdOH2dndZv7oAsfuOM1P//xP873v+gucrs3zkQ/9GfHsFL/+5x/kednl1kNnWLt6gQMz\nk5w9fZj3/K13E5ISI0izCCQPCxtBTyi4IcnIooY8NnstwRv1jVn+7oPvBOD/eOxP9vSfaQ2Pe4bm\nxvKIlefYI95EI9p+MmoVhHE2TxEg1PsgCLCzhOQqgXGCk3qD+mxrSApVr9eHC7alxkszaisa2yhC\nxgn9bpBF5Cr3wFgkKnIz6OPY4AoPx/GII3XPJ04co9qo0WzustNs8cQTT1Gt1Ljeukocp0QhRGHC\nLWdupxdGyo9cSPySy/Hjx9je3uZASSXUdnyPnd0m281dLly6yE6zycbGBivra4DFlZ2Ipace/5+L\neUspLwJ3FXy/CTz8NVUmRsO/89hxrv4RoZSHV8zXIpM5jzXrOs3zbtrcMZr2jTQZXYo8Y24m0Irg\niXGv4843225OsiKhmT8/f3y+zTe6h1dabnQfZn8WPXMTdik6N69956kI9L0lacj8/BzLq1tMTx/g\nyIHj1Mp1Ll6+QNhtUauUiHoldlsdyhWf3XaHaq2O63usvbzBzIGDSqDKlE5bETN96olPU67X+Mr1\ncxxZvcStd9/Fhz75cd7+xof4Kz/xI/yTf/zPOLl4hKWNl1lZXSfqR0gE7373u7FsF9IwS/ohsCSk\nmReJTlhg2S5CSnSariILJd/HNyv5uZefJ+OuUQQfAqQYmr9QZKlROJrLdTDWHEcJVlULSAWtpEJ5\n0aRxon5LI2NBTxQfNpKd5hb6NnXEbX2iqqC2JFGcJ1UFUWpu7V7QRbOTljybMEhwnBLIECEEa+ur\neM1N+mFMGEbMzc3gOA61maNYtotluXTaPWq1KuVegJum9MIAKxGIWBJ2+iw1I3Z2djh89ChBBAk2\nzXafRDjUZua45/gJ2u028fNP80r9TV4dEZZGMVdu00wbCD05HFh6d1tjw3s35kY1zzwLnp7EeW1c\nv+Y5UvKCz6zX/Jx/b5b8MebEKFp8xkEUeUjjZgE1Rd9prDEv4Ewt32yn2R/m4vNKNLui+zNLPmDG\nPMa0djRWmreAbkTYpTFcDRWUSqU9xwD41Rrryxv8ye/+GQcPHKUaumwsv0hr5WWEjHEnS8ztO8SV\nqyv0Y9g/f4DDhxZ48cULTJYbbF1f5f43vJVWs8Xk7DSO53Jp7QJXti9x4OAh1ppbHDtynG9+5Lt4\n7MtPcFF2+cG//xO41TJvete3U144ilWuEYQp/SilOlGGSGbKja3IuESKTCWWrTHrYd5Tk4Y1/xzH\nLdaFfWaNLpZouMl87vHexVI/Ez23Bgu9PWS31PUWBcvB0L3X0t8J7YqYMftlNHaWVJ4rlhCkaQRp\nOMi2Zbtq4zTK5vfOxpAje6i9Sxxh4XouE6UpokCRYQkbxKSLTAWum212i4Q0jbFci5JnMVFbpN1u\n0/AnabfbSCmYqlaxLYh8h2PTi6wsrzEzM8dL5y6wb3aWpF7BL5e5dn2JOE0oVSr4vs++6RlimdLr\n9zm0OMPB6Tkef/RzN31G8GoR3rKYT6NIu0wznmxTwy2iLJVS4tjOYGDrgWTSt95oIGvt3zzOhBz0\nZxiP048TmuY1ihYAE5u9kZZf9P24eyn6XATh6N9N68dkCcz3R9F9jSs3skq+FqunyGoad//5328E\nIzR32pw5dRtrS7/OdHmWXqvP+tIS0/UqrVafJEpJE4k1McH1a1c5e+etrK5tUS/ViKUgtHyqXomF\nuXl22i1AsrW1SblWZWNlmc1Q0hAVehst/spf+wF+6Vd/DRHEvPVNDzFbrhMKZwB7/MZv/AY/9Ld+\nkMSWpJZNImOVCT6VCFLSxBSCo0rF8H7TQfYZGGXmvFlU74jmnJ034iZb0N9mnaNzZ++1kkywJjnN\nW/GMpIAYEJFprxcp0oE2nUjF7e7aluIYIcXSGd+xsSyB8FykEHi2DiwKlfCXkiRNidOUNI4QqSAK\nMmpZN8X3qsRxQqmUXZcQISSOZeH4Dq5TxrIFVdujVKqofk5TgiAgSlI8XzA90wChkoFLmeKJEOEk\n7J+pE6eSar1O2RHMzM3il0psbm6TCthYLw6MKyqvCuFtbljmJ2ZeYKaxHPHWGMdZna9rcC1jEhcJ\nXfM1f46JlxZBGvnPN4Iyitp6o+/ywsps6ziBlG/buO/y3+dhoBu1TwuCGwnfm2ndMLq5W9Rm8/6L\nXB9v1K8DgvyCAC/zdboxywf/8E/wrBJ1r8Ta1jW2164zUV1kouYRBQllv4LjeRw4foJWr08QBEzW\nqvTDGB8LESVsb2zS7PcJ44hua5uttVWm6lPErYAnVj/LtctL/PDpk3z3X/7LfOADH+DC8jX6Oy3m\nT52lKQOCoMOLFy6yePAwF5YvKbxbCjKVEwUnZO1OZQYkS+JMyA2smIy7xbWG7q35/hz3LIr6x1R6\nrOLuHhxv1mHyYA+OcTIKBjkaH6E8i5U4zmBvld1dgBm2IayS8njRBoBMkalW1GwQluLDATy0guQo\nywWVz9KWLrawcG2bkudjIejEPSxhYVkyCzyTxEkf2xYEaQIILBGq67gSSYrr2liejbAdXClBWMwc\nmCcKY6b376fdbuPSJ4oifL9Mp9ej2WpzcKZBEHRBwHS1hBQWU6VXzkz1qhDe6qFYgwk86l6m/gZa\nruuobB6hCgzQGx+2sLAQpGmsKD0ti1j6WR16RRZKc8pSZ+mAn4RiDRQEQqBWcVP7z7ncFZ1rat/5\nxWjk1iWD34zOyL5LDe1b7BGqZt35qEMoxvzN19F2qHstKsN2K/ezgR4l1TSTUiqTtahYe9tdtNAo\nVkF9T3lhbPrOq4m35zLj9khSJxMEEkEMpMPgLan8leNUKQHv/7lfZuPqCgePHOVac512f5n5aojT\nXkFMTbPcE9hti3u++W1MlDye/NgHqXiCuLVB0OriSItnP/IRKtUJ2hsbHJqZ4uXUZbZWY7ZSZeLE\nEXaXNwm3lvnpn/hR/sG/+hl6L12gLVO+uPY00+Xj3H7HSSZnJ/jFX/x3vLx8lZLwMirRbMESKalM\nULcrVdaZdIyykHVTnBYTleWtwMH3qV5ERwnUHKFS1d2smNCkgh32/p634oqsTLOdebxdShX2HyfG\nwiyUT7tS2RPIyMv6BQu7Ws5SkClROqzDy6x11x76rFdKjRHYTrcnDBUc0w/DEQgXEnq9XtZmxZ/S\nTzJZQEpqwczc9KA+7VIphBjw7ryS8uoQ3mIU1zQfal5bMtNs6RU+CAJklo1HSIm0LFIUAZEpQE3/\nUoA4zpKBuqOYudmGIvPc/F1bAbqYk6BII81ju0XFFHb5RMyDlE05WKaIR2UcR0yx5r1X+I2DW4oW\no7GarxERaz6HfInjdGSSjrYtf197tZMbwybqT6KS6iLUou86PmEYI5OEaqPBiy++yF23nqXZ7jM1\n2yAoV7FkQhxF2GGECC2wYX+1zvLVK+xcX0aUbNK4TxynxKlFLFMSWxK5ksiziJHsNJs0HIekH/LW\nBx+kubzNk+vLfPKDf8rWy9c5e+Q4q0+f5/Tp0xw5tAgiZGFhgeWVa9SrNdXPwowaLt7sHiuMC4Si\nCZkVWTr6NT/ub1R3UUDWiBA3zimyJMcv7HstrHwAmvn7OCu1yLLM30ceKjXnnNknelwJIUbcfDXW\nn+/XOFZCWdMLFClyAK77daZ56wltdtjYLC6DjUuloYeh8guVUtGDJhKiUHWU5ymDSaD+pTLByzbp\npJTEAOneza785p0+3twUMk3DPBSTf5B5DbyomGZmHiLQQjyOYxxnfMq3/GQuGiDjhOeN2rSnjoLg\np3HCU29AjRusumSK0x73RP3b6LXGtzVfhMpqjOIJzzKUpxKZQBCFWJZDo1rnV3/lN3jom97B0uoa\njYbP848/yYQTkMqAasWnu7HJd77je6nuO8gn//zD7O5sUI8C0n4Pt+zjOzb4PrvdbRbPHOelzSUm\nKjb+ZJ3piUk6q5t4tsXnH32MB267h299zQOUDy8wFQv2eWX+8J//AvPTdV5z9x187vOPsbK6pFj4\nLIUZWyILSZcSpMCxh3TH4/p3nAAdp5QMnpnhjgrDMTluXujPRQlF9G/5klfObjQ+ihYLvfjo+agF\nrLnw5MdSUcSxntdac9Z7YubipuegCRupa6k6dIBdUeCYvla16g3q0KyFxff4dZbDUm+4DD6J0Swz\n+g8YmOeS4SqouRUGne2ohxRFQbYQeAMTanenieO5g8gux3FGMp6PtKpA4OqBYgrV/GDIawfjYJX8\nqx5Iplavr+M4zmBgmVXp9yYLwXBC7bVkdPvzxbYLNpUMbxPztah/xk+8YZvM+/payo3M/ZsVy9Zg\nqU6woP5JKSlXaqyurPG5577E5voWd7z+dXSBc597gt7OLjPTZVLHRtgWnoClSxc4VKsj7QgpYrph\nB0/GOE6ZOEmxZUrY7RC0WgStDt3dFhNTk8y7NVo7HZavX2euNsFjn/0M5YUFDt53J/e/7SEWq1Nc\nPvci1595imeefoqlay+zvbmFaztq8ZOAJUaYNIsW5bxGeaN+zo/5IsGfF0RaqI1L2fe1PBfTgoah\n5f1Kvaby8kIrf3o+mu0xN1zNtpqKWN6LzGxHfjEbtmVvf2uKgDxjpTn/tPVvzp9xAXA3Kq8S4b03\nwlK7c5mdYJojGkbQgynRg88YDL6rOzIZwaSsLANGkiRIIYjCcCAgizRTUxjHcTzAp7RmbUarmXUE\nGbevWcbxjOh7cxxngIFpZjbzQY/bTMpr/0ZX7LlW0QDRUW5mGeeVYQ8WjGKT3Sx5AT9ugsZxmLVj\ndMFQ70fbVtTW8YM+HX2VFq7jYVkOQTfi1Ilb+J3f+j2majO8cOUKi8eOcL3xIsdf8xqi5horm9fZ\naa4zPz3HlZcvs9Jrc+DEfupzJV7qrIC02e13qVbr9HsBTslje3WdiXKdtatrzJ46wublJfbP7+ed\n3/mdTO+bo275tIGNqs0fvfQUJ6b24U1P8+/f97P8nff+Td76trfg+UoQhHFCKgWx9hTJ+iIvfIsE\nsX4dR6Y2DjYxQ+fNY4qSmxRdL1/y1AfAwE3VbP/NrDOzjDsuT6Il5dBV1NS09bw1LQAtT0xcW4/X\nonmm3RfNPTCtSOatbTvD0PVv+Q163UdxgQvmuPIqEd6qjGJEw02EEddAVEoo3bFJorKKSKlY4JAq\njZREICPVkTrDuOd5WacJUqQS+LlOLhpQ5mfLUolFYZj70nwImhpVt92s0zTv8gNeD7r8YpVPWTbe\n3BpONi0c03TUjB03WfP3rcu4yWpZe03uccIzSZOR/i26B1XnKNb4SiCmm7V/eK3R6yVJQqfTY2Zy\nH2vLa1jY7J9fwJ+e58K588RxzLWNDWbLNnNz8/RXAq6urrFwYoIDc9PE3T52ItndaVH3XSp+mSBK\n8CtVrm5uEvsVvNl9VOuC2aNHsYKU6OUVnnjqae6+725Cp0KYCoKkzNvf+Q4WvBpf/tIV/uk/+d/4\nC9/zLn70R9/L6uZStohnzdcCQgAiRe9RFAnwImFqKj43OzYv4DWniVZMoijaw80z7rnkx4a5oJia\naZF1WlSXLvm5pevJQxd6DuUXBiHEIKmFCcEUzf8iigXzvXl/1WoVKYf0APpcUznVC0TRWC/aexpX\nXiXCW4ysjmbJd1YSx1lIcDKABaoll63tJvWpKaI4pR+EWI6NUyoRByFJKim7DmkqiSKlZWMJZG7A\n6E7XA0m7IeY1EFNA63P0e3N112xrWujfbIDmV/F8NKbW9vMavp5QZhvM8/XnJAtF1gvFuOOGx4+G\n4uu6hdDDRg8+9QyLSl7Q683G/IKgikDDG8Oclur9qDZ084mui2MJpLCR0tD+pAVS4Ds+P/uv/g3v\n+vbv5qWXXqIjNti+epWKY9Fr+IQCpsoN9rsObVJuff3rOffUs3idDvfccw8XarOE3R5LzR3m5uZw\n6j7Hzt5OfX6B6dO38OyFiyT1SeyFA3Sub5JECR957NNM+jX+1l/8AUS9SnMzwq0mbF5YpeS5/PiP\n/hhXr12mMuHjeR5xEOJY1gD1kWR8JfbevY+80NLFtkc1T/WnahuOyb2LqwkpmALQXNThxoJ3nOVZ\nhDsXHW/CHWbJLwKjGvFou/K/573a9DWH3iJDRSsf/DRqseyNAwmCobeIEDaOs3ffSAtuk3jORBZe\naXlVCO98c7VwKiqlUklpr6RYllrt2+025XKZIAr50If/jHd971+i1WqBnZJaFiXPBSEolTw6rbbC\nD4VAGAJBa/gmFpfnxCiCVGBvFpqhVZCMfAbwfb+wHlNrNs21/AStVCrAMLpUm2lmG3S7tWWgNQqt\nGWle5bzJqNuhB6i2MPJCIY335qAcp3nrsZjXPHS9w74Zr3HnBY8OBTePH3t9UF4mQuObipmyUqry\n6McfpbXT5NKFi3RbPXq9NnYc4rougQW7vS4yjOmTMHv8MPuPHiHoRlx77mmeOvcctbl5mlvbzE1O\nUKuWqVSqRHHCxuoqjcMnkKGG5qAd9BTHh2PRlgFPPf8sR0+f5sQDd7K9uUXTTvjJn/xJtra2aDQa\ndII2vV6PUlVxYti6XzLYxGYogG62CI9bWG/Wd/m6zD9TmOe1+PxzNDcBNdRY5DU2royztPKCMz/X\ndNs0PJJfZMYlLtd9mt8EzfdLfnHIwzVFVk1RHeZny/o6E96pTAfabJEpYt5gmOHTyJRo4IHhkAiI\nEsnHH/0k19Y3eemlC/z4j7yXu++8k9Vr1/Bsm06vj+24aMtECoGwJLZwBguG53kDM7Fo0Ogdaf3Q\n8scVrbD6HrTmbB6X38E3N3DiOB4IVr1KB/2h1q8EuMZyRxMvwHBw5id1fj9Bazd5yCaPHxoPbOS+\n9KJXVPTmsXmP5qQ1+1W3WbdNF9cdpoYyowT1PemJVlTiZJgOT1oSS1rEUUi92uCDf/hH3HX2Tvqd\nPiJJWXr2SWqOgzU5wVytxnqaENkeE/P7mNp3kCuXV1jd2uU13/52rl+7xvGDx/jS575AuLaBCFXA\nTt2x2W3vcsDz6VfqTHkVdqt1+vUqk47FNj06luSLF55idXeDyl1HKS/M0njH3fS6fQ4cWOT6ylVc\nxyfBRgjVH85gn8FGkBIno4KzCA4bCufRZzBO4A77290jiPW40PMkH3KfHyPm+DAXa23NFuHwpiDM\nP998Gbd46+vlFSazLlNrN+vKL4ZD3+u9mD2Mjlk9nk2Pm3FC2+xX83rjrIxx5VUhvIWwKJfLI/h2\n3oNEPyTHVQxiaRIjZYJ0LCrVKstr60zN72dyepq3PPwwt9x+O//kn//vPPzWt/JDf/WvEnX7pCkI\nG/RQliTIFKyb+DiPG5j5AZR/fzPNfdzg1xPEFHB60EVhiGUpvgfztShjfGq49OWhF90+U2PI9/W4\n8Hkh97Z53KDL0IqRvjInw+B7TV6UYeRm/6fJEDcVWIya+8WCa3B9JKnI/PrTFIkcbDxPT0/TqE1Q\nssssLS3hRiGpjGnvpHR3W5SnZqBSZf7QYeoTUzzzzDMkUUSno5Lbzu/bR6VSY6t9GRH0KNmCkmNh\nCYnnWMzMTuIKC9+Gbq+Fk6TsJG06dkra3mD1q+c5evYW9t15mrOvOcuLn3uKXq9HkkjckoslVWJa\npAAhldItEsBCpHvhinHPwLLsPYIrr/GNE/zq/CE8UGSV3ugZ6HO0YDSTkWiN27T8zDFnjsO991Qc\nLWt6f+miLVDdnvx9Fd2H+VekUBQpEPn6zc9F2nu+H280jovKq0R4q04Ps0ilG5lRllAmWBIr4e56\nJcIwpNFoYLsOk9NTTExM0Nxt822PfBe1Son3/t0f5ZaTp/hHf+/v0drdQcgUYYHQGlkydN/R1y/y\nxQQGWoc+rugB5CeVOcD0wMprmkmSDHy5taZtuiRqzTsxglnMQaMXO/N6SRoPtB1TOyhyfRqn6RSZ\ngqbw1s9q3KBL01Hc3OzbkUhVWdxnqo50pH2Wvbft40x/y7YygQ9kmbknqw1+9O/+GPffdT9XL13j\nxOGTvHzpMuVeD7tRJu518HG5stWkdvQwx6dnaO10aLg15k8ucv6zn2J5ZY3+tW3ecP8b2H7xJcVc\nF/bx6h6u63J+5SI7Ycz200/geDZLVy4SWTbV2TphFHBQTLLPc2h97HHOzi9wSbb5zu/7i1w6/zy1\nWo2dzo66p2zhUWHwKkpUQQHFHiTm5yLhlx+fRQLDdKEb9GMmYPWxN4JNiqCL/DWKFJP8mNbtKw7q\nGt1n0u/1ODd/y19L11vkCpuHc8y5lhfYeQ568xm8EiFsJjUx++qVlleF8JZSEsehccMCxxnlLNE3\nmSAJSbCz7CCx1afhVUl3u3TLHmceuo9LTz3JcavOA296M5euXOHAX/srvHDhIn/0qU/w2tfdi2+X\nsOOYsmWRRjFO3MG2XNIU0lRi+5UsYjMbCJmroXouo76Z2q1PaxOmNmsOdn2s3lg08/aZJmTer10v\nFBq3Mzf8zFKpVAbH6p1umVqKflPGJNbwGo5rcocnpKnCkQcLRDao8/sOA6gnTrH0BLcsknR8pB8y\nGdGmpFQJbfM7+1Ecjl20hTWkKFD9sJf+tAi6klLiOJAmUWatCTy3yicffZIkbrB6vYkdw5OPfZhp\nz0NWqxDHJHToAjExItzhwlNfpN0JePM3PEjZdbjWbDMjJS8//yTf8o0PUp3wIUxwvDK76x0cx8F3\nrjNVLeMEFo502azPk8QJ/XaEk0quiA12HB+/nPDC2jLXZsvctbxLVLLpypiJ1MGvV7gkd3FTmIps\nEgtCy8NKwXH2CoehMGLk1XwuecG+53kVfJdXYvLRvHnBZZ6TH/9mUmwTpigSfvp3sx5dPM8b3LPp\n3pffZ5JSFgrpfHt1MTcsdduKNPSi+kBp+WY0tL7voqhKDblquER59HzdeZswgAlAdVy/r3gDTC8D\ngJSY1IaK72NZFgEq3NT3fXb7fe69624+/iu/y/1v/EYuXbmGV6kx7bq8/v4FltdXefQTj/HVr3yF\nn/oHP0kkJUESMFGp0O33qVbquI5Nu61y9gnbxkJiGe56YRiOaMs63FU/JNN/NQ9JwF480fRugeFG\npB48+v5139jWqL+oLuaegRaCtq21h9GdeV3UYBKk6VDrN7UM/Qx0+wYeLtr6yQanbdtj3ak0ob7u\nL71HYfrYjjxfw6Ixi6lFhWG4p29N09jss2azydzsNOvr6/h+nYlGlY9/8pN827c9wlc/9wXm5udZ\nv3yexBdU/RL9oMPM1D6anTYnFxcoT81y7uJlbNvlC49+gvXl6/idFl65xMTEBL/yy7/Mwf0LbKyt\nYyNobWzhlUvEtuDIyeMsRTvMTs1TWZwj7XY5Pj3N/oVZmsvLtK6usXHhCidP3cq++WmYmmLlY49x\n5PQxYsci6nWpeSqyMvFsbAmObZMIgZWNG1PbNvOr5p9zvtwIWzWVD92f5rjQWnfReUXPdJw2aY77\nPJyYH+N5bVzPGy308vTNZv3jIoX3zgU5ELTmODSFf36Byt9bPtenLuZipYtegMzfv/64TeToSp2m\n6YibHowm7k0FhFLl8guJCIM+dpzSTmxmjx2kHwSktsCv1qg3JomRbGxtUilVEVMWvlfhh3/47/Db\nv/mb9II+3SjF8SusbSkSmVqlRKfTwvd9IoPkPgX8DIIw8TpzcPR6PaIootfrMTk5uQcyMEN09WAY\nZ0Ka0VpRFKlBNMZNzjTzTC1E/6b7EJQm6ziOIrlPFZVlo1EdQDfaOsi3Rxct5E1LIx9NakI3JhyV\n91vX7/Mat3m93d3dkcVtcnJyj8luehSY5/436t47TrKruvf9nlynQofqHCZpskbSKKIACGEwUSb6\nYmxfssFgeDYXHEi2ccT2BWNjGxuMscHXxsB7gBEmiSAJRjlrpJE0o4mdY+U6p07Y94/Tu2rX6ZqR\n7HufP/L+fPrT3VUn7LDW2mv9VtjZbJZ6o4GGQasVsrZaYnJyEtOyuOSyS3n8oQcxLQe/FZJ3bXRd\nZ71UxY9Czt+5k1K9RcHNMj09TW1lCQKfMGgRRgHF/nFs28I0DWIR4VoOtm1hGwZes0EUBvQX+8lk\nHQzHYH2hwkOzs9QqU4SVKmO5fmjCAw88wKuufz7N0yf48mc+x5ve+VaM4Sz5fA6z1SIwwBMCK0pK\nqAaGhtMDXkib4epanEtwpVs6VFAKNQkRSmUl3XrlBaRpJ93OZgmoQlu+V90MVEVPVXh6QQ+9fCxq\nf8/VL3mfnJc0zJEemwonqZteLyiqF5Rzrj6l29NCeGt6d5y3uttJbU8OyrGzRLpABEl9Y9dy8TQv\ncQwN5llbLTG3vMiDp45hOiOsrtYYGhlmdHAMJ+ty9OhjXHLRJVxwwQW8892/Qjab5Q//8A9p1mrk\nhzJYJoTNOlkjgy4J13ASLdw0iVNRMdBthmUyGSzLwrIsXNdtE1WiCXdrqGd1CCqQi7RIcrkcmtaJ\nNlGTEuQ9aUJWrZaujcNINBd5GKxt2zSbzS4Bq2ka9Xq9PS4VyrGs7rBEWRysV9MNrWs8Mlo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puhv28nWt3gioufyb2HH6RZLTHtGoS1KhYxrm6zKmKyU1uISitUVlewBwucPLzC3bffQTHfx/Dg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3Pp0iVWlpZFaXoJouQzfuw//yamabJvzw6Ov/Qi4+MT9D2ffKHI00efwXHSEMWM\njo5SzKfZtmMHcRwzOzvLlaUl/vRP/5TnnnuOixcvqga2tIolFCZh9Yag1JmWzrYdc2zxptVzvrpy\nlaktU3i1Lrfffhvf/OYDZHOOGgJQKpVYX98QECG6oCwaGTKpDGEc0XN9mq0uExMTYo33uuSyecUE\n6XTEmgsj6PseS0tLAjrwA7o9l9FRwYo6d+6csnh96tlj5HI5ZmdnKRUEG6mU+NivrKxgW0bShBMe\n73Igh0gwBo1LwaeOCeLEpjgY4MAAmiFC0AAmiYnimMAPhDtgCKah4fsBISGGBroBGxtraj8bhkkU\nxQozl1xr6aW9fOEca2tr7N+/n8nJac6fP0+xWMR3BRzTbLaVdiKfz6rxZb1ej263KyrwBKqRDBiJ\nm8uekXSilPi8rJQkBCWx/GF22zBklMvlFOnhlV6viuAdhZGy+JTBsdlsqqYloBglw6ZRMmOQi0Z6\nDks8VzYW1fskp6fEn+WNlw3B5eVlZUDV7XaV2klmv6ZpbsqyJfY8bPEqJfue52ElfyeZA1EUMT4+\nqaTMw8ZRXr83aIomFEbZ8JSvLw6mtJqKA4MTPp/PK0yu1+uIJlPaUgcNgKM5xLqB54fYppj7aJom\npu2gRaKC6fV6TE7N8Ja3vIVqZV1Q+pLp21okphZpjoljm6Qnc2i6hdfvsFxb4fojh2g1q7TbDR74\n5rfo9XoUMhqvuW4f73//+2m323z84x8VG1ePcGydsfFJFhcXGCvmedtb38KRaw9RXa+wd+9uctlr\nqVQqzG3bxvLqCv2+x8TYKP947BnufNMPcvXqVfKlIqalUygU+Pa3v822bds4ffYsqVSKPXv2sL4m\nsMilK1eZmprC1MWQXs/zSFk2LvCzd72He++/j0gDY8jKoNPpsWXLVqIQUslkGcdJk9XgypUr3Hjj\njWre5YkTJ9i6dSuVSoVer8fGxgalUol8yiSfzyvbXDncuVQo4jgOy8vL/NEf/RH3338/hZLgTn/0\nox/l/ge+oTa+xJYb9RaZTA7HSQufb99n6eJ5bNtmdnaWam1DJSDtTovyqKgyriyvcGV5icuXL9Np\nNZiZmeHKlStcc801pLMZWq02ti2w9F6vR73ZwE6L2bGNTptmt6NYH9l0JhkdKCqfxcVFxsfHyeUy\n4nkk31UmRqNjZbX2oygijEOIxVT5YY+ROEpEOCSiOF3DiC1Mw6Df7+HYwsLX912i0CedGRAVBEXW\nTRr6pSSJE1qLMAy59dbXqfjSbDYZHR3l4uJ5xQCT03Ky2SyuK+x8Jc5dLBaVfkRSf2UGLVWUMkaN\njo4qZs6wuG5YbCSxbV3XVbwC1GvImPRKrldF8IaBMkt2YKVng3wAckOtra2RzWbV6dXtCt8FaQgv\nMSjf93GT005e0gNcNi1ABO50SsxuHB8fV9RDSfWRpkHD9KVhD4l0Or2Jw7lJBBRFig0z8IloK3oi\noEaXpRxLzaE0TVOZ4cjgL90Fpcxe/pyum+oQkZt9bEx0znVtIISQTRpdj8hkchiGKOUz6SSrSKhX\n6XSa1dVVYW1r2aCLctDzQ44dO8auXbs4duI0pl0nCAR8c+78eSxb53vfuY+1lUtkUmmmJyY5sH8/\nr7/lem688UbRpKut02s16PVd7HSKdqfD0soSN998M167zcmTJ7n77ruxTZ2bbrwxwRXP8ba3vY3b\nX/8DtFpt0HXe9a538Y377mNiYoqpLTOiWkFnfX2dlZVlBbcZlokWxdhpm063RafTwu32yGQypFIp\nwfsfGyWXy1HM5alWawC4vkcmm+f0yZO8770f4Ny5c0yOT7Fnzx6OHTvG088+hW3bvOENb+Dhhx/m\ns5/9rOIEj40JzYJ8trYm6HWyKrAsAQVtbGzgecIGd+/evTz44IPc/vo34fa77Ny1XdFWQRyo2UyR\nQkEDdKanpzl+/DhRFHGNdA/sdTAMjUajxujoKM1mk+PPPcvp06fZtW+elZUVyuUyO3bsIJdJMTc3\nx8LCgmJzeJ7H7MwMnU6HfhSg6Tpu6GOGot9UyOUpF4r0+i7FYhHdgKWVZVKZNLVGXSRUcUDPdYni\ngQLSDwLQhRmUDMq6boiBCDFIZSQJJaHvdhAOEBq6ZmKaNul0FrfXwuu7aJFPOmVjWobikBuGgW0X\nSaVSNJutBHYVwTaVSnHp0iWmp6ep1+tiPN56hcnJyaGBCLqqhHUdNbFHsmQkRVI2QWXWLUV/MuP3\nPE8dCFLlLJu1w1m3DOzDDcuX89dfycPSagMAACAASURBVPWqCN4SypDMCJlpAuoEl1+0VCop3FmW\nLS9XTQqedzfxLhhk3l6/h6FDyrGwchnBWAl84thRrz9gABhK+iqzX8mHVgvT91UwjuOYarUqBrQ6\nDp1OJ2m0xHgJa8ayLIh1HNvGMkWwHimWkkDvK87oMNVIZgzygJDYmGXK6dSiget2ZbMzptcRfYIw\nGmCBckGbpgmey2qrRRwK6XOnJzyhMymbxQsXeP3tt/OdRx7lB37gB7h08Tw7d+5E00Pe9MY7aLVa\nvOUH34hupvA9qDXqjI6OEMUBi4tnyedSHD9+nJMnTvK+9/00ceDTqYuMsFVd549///e4cOkib/rB\nt9DqtPn9P/5jNE0jN55JsGULDJPx6S2MjZX58Xe/G9tK8bW/v48LFy7Q7fU5f+a0mDIzNsbC+Qui\ncbdnH+fOnRP3DUHnWltbw2010HWdvXv3UqlUBDRHzHe++wgPPPAAU1tmVHnbC8S4s/W1DTRNY/fO\n3Tz00EN85EO/QhCE7Ny5k71793H85PM4jsNHPvQh5ufn+ZVf+RXCMOSxxx7jySefpFwaYef2Hcqi\nIQxDfukXP7gJg33qqafUlJrCbIHbf+AOiGMCz6e2UaWYLwhaqKGTsfMsr6wkWGkWK/DZd2AfjUaD\nteoqpiasjq9evUK/32fxvMGuHTvZt3+e2173WsoTgrsvR8MBpJNkIZPJqIHGaSdFpbrBJ/7i09z2\nA7dTLo9Qr9f5yt99lX179rJ1egZbNxgrj9ILeoyOjnLmzBnq9TpXrlzh1KkzaJoYKvCTP/mTzMzM\n0O8ERHqM46ST/lAat9fBsdN4nhgfGIYhhUKJRqNB1tSJg0jw+hMCgmTQZNIprl46jx9YmJGoTC3T\nIYpjNE0wosYmxtE1U0GYbt9nZGSUft/H0i0a1QZxDN1uIvrz+xh6kkXrBkEo5l2GYaASHpACIvOf\neKnIK5MRQh/JCZfq62EfJAm5yORx+Pfl6w7TB/+l61URvKMhfGwYcxbTxfOKKJ/L5dSJJuEKQHW1\nZaCVmWo2m9mkWJLZr/QwkaefpCINl0MSghiGRIaVmLJBIcsi2TyUAVc2KWRGJk/mbCavJOySMhRF\nEeXRkjoQlPcCAyxQHiRy+ITMCtQoJ1vHtlKqegnDEN2wieMBLu95YmF0W22mZmbFmKxx4ZiWL+RV\nheG6LgsLC8SxEOU4jsPq6iWWl33y+TxrK1fxg5DyyBQWAd+8/+vE+LzmhhtAC9DjgP175wm8Hp12\nN6mghNvjSDlmdmarGH5cLJLLZEll0pw/t8iW2RlWV1dJWSZf+vI9ONZgCIWum8LpCJ1yucxIucyJ\nEycoj44l31kYmLmuy+TMNF7fZ6NWxTE0HMfm4uJ5vvCFL9Dr9VT1VSgU8N0+83v3sH//fvYc2M1G\npcY/PvMMN910E9vmdvDJT35SQBqFIl//+tfpdrtkUikqlQqzs7NcvXqV3bt3c/HiRX7+Z3+OX//V\nX+PKlSv82Z/9GcVikdp6W1Vcw/zlHTt2sG/fPt74xjcSxhGaoVOt1JDmSQcOHGBpaUn1QOTYNcMQ\n7KVqdQMAw9BYWV3m7OlThKFQ9V577SF27dxJFIq1/Nhjj5FKpbjuuutEMOx7+J7gL7daLZrNJjMz\nMywvL2MYBtu3bxcWqLk84+Pj7N41L8yobrmV1eUVXK/PiVMv8q53vYsnn36KD3zgA5xfvMjExAQv\nvfSSWrvPPvccExOTdH2XdrstKLmR2K+hHyX6irGkWs5y8eJFiinB2kpnCsQxpJwMY2NjaERMTIwz\nPjWJocX4GAqK0XUz4WYLURlxkNxrgBgzEtREfWjmpWyaWoYphDvJnk5n0iqGDOs8pHBOwpcyfgwn\nesMcefm7snLe2NhQsUXGrmGIRI4EfHkj85+7XhV+3vv27Io/8ae/ozJwuVhlEJI3a7i0GO4Gy2xm\n0MAQZUwQCCbGrc+9C4Anr/+q+t3hZqOmDUYSKXZGcpPjOKbVaqmm47BxlPw8ctL08NAFyece5nDH\ncUyr2dn0meWi0I3B5Hj5+lIFOSxe8Dw3oR8a6lCRMI+oJAZMmhBx6pu6ganLBpOFYTusb9Q4f/ky\nn/v8F8gXSjimoe57Nu0wPz/Pm++8k0xKqE9NQ1PsnsBt4zhpojAxzrI1RkdHWFlZYm52hupGnWJx\nhHq9ieEIWGl0dJS/+qu/YmpyhjvuuAN0g4uXL4Ou8Zd/+d+TnkWDT33qU/h+n/X1dbZv20aj0QCg\nXhODnwuFImdPvcDWuTlBz4zFVJRPffrTLC8vo2kGViph2/T7jCRsjw9/+MOKvZPLFTBNk8XFRVXx\nVatVMvkMx44d46mnnuLuu3+BF198kdOnT5NyMnzta1+jmM/zute9jsJIjve85z2cPXtWHarV9Qrj\n4+O0Wi31fD3Pw9BFT6PREDMsFeUvGf03rMp1O76gTZomk5Pj3HPPPezZt1eMO8uXEuxYrLcXTp5i\nefkqly8tMlIuUSoVcLttdu7cyetuvV1k/bGOaVq4/TDx7hmYKFUqFZaXlti2bavoBZgmX7jnS7zh\nDW/AdV1+4zd+g/ff/QFGR0c5e/Ysly9fZsu04HYfPHgQP/R5/PHHOXLkCOPj4+i6werqqmoY79y5\nU7lbmo6t/tuyLHwvxEkotL1uX8GdL7zwAufPPEu91qTRagr6XrMNhs5/++P/yqOPPsrZ02fYunUr\nUzMz6LpOqVRS611k856i7cpmfqvVSaAVW1WhMLCfDcIBHBLFgylCErYa1lrI9xIJoq5IDcNiIdhs\nrCU1LNLWQvbjxsfH2fdtMUnn0o8cVajBwevf+G9nko685CknA1i73RZKwQT7FkFygFmLhl4gOtRR\ngGFYCNewADFrztk0mUIGVhlIZVCUuNuw+goGktVh21hZ1shSe5iBIv2TgyCgUqkok3vZKInjmGwm\nr05mmdkHQaBOfxnAZZNELoJB1j8w75H/loeRCCQaGqKh61gCD9fRiAKh/hS+GEUsy2J+fp5KpUI2\nV8D3IyUuqFarCq6pJuPIBFe1z8zMDN/8h/u49Zbb6PX6FEolWq0G3/jGN5ibm0XXdUZGRmm2e7Q6\nXZobVdK2A0mPIIx8vv/973P4yLUU82KwwN0feC/3fOkrvPvHf5x2S8wL1Yh45NsPcfPNN7OyIpS0\nzXqdtaVlSqUSDz74IHfeeWeyRkpMTExw6dIlMhlHfY9mq4Vv6lx//fWUSiX6/T7VapX19Y1Nm0zK\nmOsbVQ5dc4ibbhCeLrfc9FpuOCKy1Z/8iXcTxInvdRSytrxCLp3BcRxqtRqTk6IRHfg+XtJMlgIz\n07QpFkfU+gbhfhfHMenUwPMjn88n69zk8uWrvP71r+db336IUqlENpMXZlPVOrVajWPPHyeXyxAh\nqrVOpwNhTK/bx9AtHDtFu90jCDzyhRK2bdPpiIrT9zxaiSL3xImXeP755zlz5gzv+w93o2kajzzy\nCB/60Id4/Hvf48477+SaPXt57JHvcujAQQqFgjJxumbfNdx+2+0qkcpnc/zgm96M6wrLha1bt9Ju\nt+n1XTY2Nqi5NcFuiuIB9BkN1vFLL73E1GgeN+XR6rTRNDAtKJQKhIQcvPYwtVqd9Y0Ndu/Zn8CQ\n4PuBksNLjUUYxpgmiqk2SM5AOhlahqk+u6z2bdmc1wYeQ5JDLqvsgXZjMGxZJoPyteR3kuusXq8r\n8oDsewzHpvX1dTH96/9NS9j/P64YwZYANlFqhN9DQ92YSqXC6OiIyoIBlb3KBoJUSknKznAZIuk+\nw0MLxMbNbPqZ4Qx4uJRpNBrqvyUXHVAZssTgZYNVZlryNXRdx0xmAlqWpRqSAJ7vqoUkvVEk/DLs\nXAZsGqEGAx6p5J1HUaKQSzyTfc9Hi2GkUCTSoOcGoOnYqRQf+tCHuP/Bb+G124ot43oBjz/+OFcv\nX+YXf/EXuXLpAucWznDrza9FiyOuv/G1eCGkc0W+9fB3ed3rbqXnBcxu20Wz2eTsM8+zf99hKtUO\n6bKDk0nx5NEnefuPvYNsOs25c+dw+2JMmd/yyabTvOsdb2N6epovf/5z/MRP/AS2FjO/cxv1jQqV\n1SX27d4nhFMpB9s2eec738lLL73EQ995mLW1Naq1BplMhlwuQxCLwFksFoncNj/6Y+9gdW2ZwI+S\nbLeHbacUDdNxHCzDJGNnSFsOKVtMEAo8Hy8QRkzr6+vEuiZ6Lt2eoIFGEZ12m1w2SxgE6JpGIZdX\nmz2bzRJG0G51N1WP6cS9L4oidEvoB7KZLGaoYxk2mgHpdEy1WuHtb387URRx4cIFyqMlbrrlZkzT\n5KkP/Uc0zcQ0UkSxjmk4lEZLmKbDxkadTDon7AQiCP2A5fUK2WxWrZVSqcToSJmJsXGOHTvGrTff\nhuuJvfFj73wnL75wgqDj8txTz+A4Du/+0R8nP1Lk03/5l9Qade569//BxMQEjzz4MLfddhu+7zM3\nNculhQvCzyOE5QtXaTQaZHJZ0rpDsZQnZ4q1m8vliINQ6RRM00TzQy5cOEW33SSOvGRIcMBIOc/6\n+jqNRotD195AqVimUavh+zGplI3jSAWlpzB0XY/VaEDLctQ+EcHXQWdIdKMPhjb0E/qu/POwOnRY\nyyHogIM9Cmz62eE5lbou5mNKlpz8mWFaoGmaijX3Sq9XRfDWGGDNwplMBCl3SKqaSqUolUp0u20V\nlIcpOVI9lsmIRqTkZA7btMqNOpwBBUFAq9XaRKmTD0E+XIUrW5Y62aWCTtM05cEhYRJN05TrmKwQ\n1IP3epuwNJndh5GvmhlhGLK+vr5JJCS/pze0uATuhsoOJCNFZgixlhjIo6HFSckXRSLD6wlR0vz8\nPN2vfR1r6HuOjY2xsnSFmZkZPvOZz/Dv3vKDihYXBAHp4ghxpPGd7zzCnXe8gdNnXuItP/TviAnp\n9X12zO+m5/lMTm/Fjev0A5/bbn8dtY0qQWJilLJt7EQB6vs+G2urlPI5Xnvja3j++HMcPHhQlNhu\nnx3b5jj50glmt8yRSlvUajXqjRYTExPceuutPPDAA8p/Q9AQBQYZJ4wjwzBotVpMjE+p3km/7yfK\nOIv11TVSKSECW1tbE9mybZHLZ/A88TwmJyfFgOxkHTSbTSzLUlXc+vo65cQbZrhvE4exGkorn51s\nfPf7fUxdU/42midsHianJ/B9n+npLbTaTTKZDKOjoxiGQbVaQdOSBr0mDmzxOT263S7lUonyyBia\nZuDYKTwvUP4cEqqL4oiU7fD444+zf/9+brvtNnK5HFcqoqq5ePEi5XKZP/iDP+BjH/0o1xw4QDab\nJZvNMjExweT0FOPlcdyOy0033MipF18in0xJKhdLRBFotugtbZmeppoYroV+gKVbZLOC7quZg4HD\naBFRKEaDSRaHZmgYZiJJ93rohkGr3UbXTOFR3m4ThjGGMRC/SA2HTOZUv2QoBohRawMNRxQHqpKV\nE4Lk/pQHy8uFOzBoMkpjuV6vp6jEw2sAhKeR3L+ywh42n5My/H/N9aoI3qARBhqaKTZdEAsMyraE\nqVAQBGiIwcTEJnEUsrZWEbzrjINpiAGorbYL6Hieju+HpDOWwqUB0UmOY1Iph7QtYADNsskmp7X0\nZ2i1WuiJND+KIiEaSAKuKUumOKbXG/hTyMxbiw10YtxeG9AIAg9dF41Dvy8enG2ZOI6ZHAxJeZ00\nZmXlkcuJDS89TOJYS6ARhzjWiGMxIUQGhGEqkzzMTN3AD31MTceybfRkjFmlUhHYa+RTzOapr19l\n996D1GoNRkojNFtdsoVxHvnuk9z5hju4Zt8BOq0mKV3HcWwefewRDh06xPXX7SeIOuzauR0vCZJu\nu8Ps5BYhnzZCjMji2LFjHD58mEJGBICmbuD7A3/zlJMiUxhhcsscmcIIi4uLBIHO2bMLzM/PMzIy\nwnZDNIuOHjvGnl07iUPQdZtvPvgdCiNTlFoBpm3T6fXIpQy8vk+tWuFXPvRLdNs9pidnRBaUwHGZ\ntEMcBbg9j0JRwF26rjMyVkowypSCxfwECpHZmIaBZeromk6n3UvgjywxOrqhkzIFrhxGEBsaQWJZ\nLD1wpH2DruuYmo5m2ergKSBgwkw2j9v3MHSHbscjnRrw+DU0LEMTEvKEduo4JkHo02o1cfstioUC\nthFgOhH9CBbPL9Dv9xkZGWFu63a8fsBrbroZgBiT1bUqjmViojE6Oko2m+VPPvHnfPijv87Zs2cF\no+TkEq99zWu499576eNzyx23EYYh11x3kEuXLnHi9ItYlqWmB6VSKRYvnwMENp3VsoICnLJVtaxr\nEd12h+npaZ778lcw0mlC06Pb65DJOGSyJqXcGDknT8NtMpIXGHejUVNajW7XU0lSvV5XyYvMdG1r\nsH+l14oS4EWG+vkwDGm2NpRFs1ReC2hWAwZMNBm05ahCyWWXqmlptSGRAckukWtIZu/ykgfNcLL5\nL12vjuCtgWmK4Z+NRk01MHRdx3ZMUmk7+VKxkpY6KYsw9Gl12hQKFlEcECVZrGWbmFYKXY82lSGy\nVJElTTqdJpXJEvni4fR9ccNjTZyu/ZdREr0wIAjEQFVRKhnEcUQunydMcK8gFDh2RIwfROi6QYwI\nejioh9+VvGonRRxrCkqRlEC5ycXCEJ9D/q48zWWmLbODYWxOLiS5IIJ+JMZexZpieAS9mOrGBgf2\n7efC1SV03VRTPup1ARE99thjEPT5kbe9FS8x5Tl8+DBBELB161ZOnz7N6EgZEL2Bubk5ojjAsg1s\n22RjtcnsrMDCz5w5w969e4XycnmZbdu20W4LNsb0zCQXLi4yOTnJzBZh9Ts7O6sofwIGMrj99tt5\n4rHH2X/gIH3fZ3Zujm99+2FKxTKjYyPEhPTdPn1PTGXft28fy8vLiqU0zAyQGdZw81n+f8nlH6Zu\nKagsHoirJG93uMobZi5phng92eyVFZjMxqVVgtQMSKvTYXGWrAqlBbEcd+Y49lCZHuLYFqXRsng9\nL8DveTQaDVL5PDvU2DIEoyeV2sScyOVyOBkhHJEzXP/zf/kvXL5wgSeffJJSqaTuzRe+8AXuuece\nnnrqKSzLYs+ePYyNjbFt6xye5/HCCy9wzTXXUCqJg7DRaCkKneu6inlRq9WwLIt2u8358+eFV3g+\nq+6PMMnarHh2bOE7JEeYySAt75H8syQmSBuK4axXstNkH0RaHei6Tj6fV+8VBIF6LdlXkFRIXdeV\noZ2EcDOZjGIVyc8mqzvlzZS83nBSCSi5/785hWUcDehw0nNDNuA8T3wZiVVbdgovKTNNS6dcHhGw\ngKZhpAbG51EUCwnscMNSiymPFKnVaiJLTaxS84Uyum7Q9wIlYW61uxiJrWgcyWZijGFYBFGMFmvo\nBuimRRAkY5g0A03X0GKfIIyINB00nZ7n4yWzJ+M4Ro991URxkwXVajSHjHYMoiik3/eTgDHw81bl\nXYLpS1hJQj8wCFC9fkJZtMXPounYhkGvKwYmaHFMNpXmo7/263zlvn/g5MnTrK1V0AqmsBYwLTTD\noN7p8Dd/+yXe8kNvEkOFa2sEns9jj36PmZkZ2u022WyWY8eOcuTIEcHnzaQS5Vuf3bt3K+hCKtZc\n12VsbEyJnBzL5sUXjlPM50inHRbOnWHnzp3U6hucPHmSa6+9lkazRrW+wd4Dh/jkX/4FR66/nstX\nl8REdVvAEToxdsam3arzf3/mLzh58iSZTEapauWBKO0RJEtHbhrpmyPXm7zvMiDLYN7v92m26up5\nyAnuMmjLZ6Ub1j+tzoZKenngCqqY8NqWzw8GjTFNi7FtE8vKJge1p7K8drvN5OQ4ft/DNCy+//0n\nede73oXb7VE2xvDiUE12iqKITDZFyhlMjAkCnygK6Lgu58+fJwgCLl68yMzMDN1ul7vvvpv7779f\nsEx8nwceeICbbrpJBch2W7BcWo0mL7zwAnv37uUf/uEfuOWWW9i9eze53AADdhxHwY27d++mXhcN\n2NnZWZ46+jSGodHr9cgXcgSBgNjq9TpRFJPLFvB9Pwm6PXX/hu+pbD5L3NkwDNxeZ1NTX0KdcgCM\nbHbK9QAkDd5OMohaDGqW0Kz8edm8lAFbwiByTciDQjKK5AEi9+wwbCJh2n+NSOdf5/79/9Gl6brw\nj0Cn2WzT6QzGCdlWinQqi2Oncew0UQSW5QiVoG7R7YpALbMoiQ+nk0ECm95H04QvQ3LqlkolyuWy\n2tjS30RKXgeqxMHUj2G8OQgCQs9XoqDh0imfzwsvjGKRQqGgVH2SuSL5xtL2VmYNw+PNZFY4/I+U\nxsuMzXVdNQ9Tct3l5y0Ui9gJ3i5xNmkTqqNhaDr9nsvy1SXe/OY3s7CwIMQ0yZSebF7ACc8cexYn\nk2Vu207WKhuYpsnMzAyzs7Pkcjkh8Eiyxnq9ju97tFpNOp22Gta7vLys1IeaprF9+3bOnz/P+vq6\nYv1ks1nhi1xvsL6+iqaJpt9rX/ta0DRyecE7zhSKTG6Z5eixf+T4Cy8wNzeHZerYtkmr3cTQobK2\ngkbE6OioOihl0AA2cezlvR0ZGVGez54nstbhSS4yqMpSuFAoqO9fKpXUs5GbVQZ7uSblwZBOp8WM\nyWT26djYGBMTE2QyGdEPSNYIDKwZpGpU9oFk07rf7+N7IUEQ4QUhy8vLVOtCndnp9NAtcygR8tS6\nWa+sqgywXq9z8uRJUqkU73znO+l2u/zwD/8wV65cUVYPqVSKv/3bvxUDCxKdwZNPPqmC4cmTJ2k0\nGhw8eJCzZ8/y/ve/H8uy+P73v8+zzz6r6LNyNKBpmrRaLQqFAmNjYzx99KioYJP9JveDrMIl7CGn\nVMn7K7NumbhIHxJAVReFQkG9j9SN9Ho9pbWQh2elUlHJn2maQnmbsD+kXbPv+6ysrLCysqL2qQy6\nkroo7QGG2WIKJtIHJnXDvkvykpXXK7leFZn3yuoqv/9f/4xeTzSTJiYm2Lp1K/Pz80zPTNLzfLp9\nj2IqTbfRI0YauUM2M5JgkyFhJG6GZYpSVjM2y00txyYVDrxSZGMhDAWdqN1qKNWZpmlkk7I0n8+r\nMWNuz8NJpo0b5sBDReDdSUdb0yEM6LUHo5dk6Wwm5V0Uh+iaRiYrGrQpu7TJcrI0XVLzKuXGlZCI\nPGykJ/Dw3EtAZW9B6KEbIojruk6QLDQ/6OOkLLodF9sUTcNWvcFtt9zKQw89xPadu7h06RIT45MY\npkmhPMqzx18i+JsvcOn8BT78wffx9/fex6233kq73aVcLnH06FEOHTqE7wvnRtd1OXfuHLt2zaPr\nOqdOnWL//v3UajXV/Dp9+jQ33XQTjUYDQ9c4dPggnidsXd/0pjfxxBNPcvDQISobG8zNbeczn/kM\nHbeHnipRqayxuia41f1+n06njUaMbehcOr/IPV/6WzY21lXGJ7NECTHBgFcvs6bhbE0yM6IoUn8n\nN5sM4MP+NPKAlJsUpMBK2/Re8jMMe97Iw0WuJcm3H26Wye8g18Bdd93F5z//eVWtEesUCyO4boBl\nOjz73HGmp6aUvFtmpsvLy/i+n6hmY7q9NiPlIuuVVV544QVyuRwHDx5UGLKcBGNZFu9973sB2Lt3\nL3EcUy6X1eeemJiASNzL/fv38/DDD3PrrbeKzLTbod8XgbJcLtNutjh3fhFd15ndupWRkRFOnV2g\n3elimqAnDUjLcsjlCuixyFKbjXZSkUQqAxZNy3CTB5I8HOR97/dFwJdVl6YJ0Z9UQYZhSD6fp9Xa\nDO/IZ6AgsCRLlxJ4mc0PM9parZZaA8PrS/ZOpDDn5X7eUjg2nI3/S9erIniDBrqJncoRaxZLq2ss\nr63z2PefxHEc5ubmeOMb30gQ6WQyuWRTieDbTkY9xZGGZTqgJZssAMe2iIZESFEIuWxBufhZto2R\nyMcNQycMZWkqB7uKBee6yaR3NHRDJwg9PN9Vi1VkdIOxTmaSmWfSDg3PJfBFZm1bBm63R2gMXBPD\nZKJIzxtwg4cz+3TGURxyyf2W3FUJnchmpQzqqrEVkRwawgDHtlKYSfYYRzGptC1c23SdOJvnZ3/m\nLi5cuIDnBUxPT2OZInvQDQHrfPvhR3jzm9/MAw9+ixuOXEc+XxSikXSWTDavSkIJQXS7XUxTzPyb\nnZ3DdT1qtQaTk9MEQcDevfupVoXtqk5MGIhN2el10VyPicnJ5LNsod5qcvcv/BJ//ud/ThjHXLh0\nhS2z0+QzaTaq60ngFYfX7/zub7O2tkK72aJQGlFZ23AWPTwYelgdK7FTmZlLafZw9SX5/u12W7GO\n5O/JJpQSW2lyxJeGYQyxFqykUabFeImvvK5pGMlrhMFgYK/MLJ2hKeWHDx7iswlOHiZ9Flkp5EfK\nXLhwiYMHDuB5HsePH6fZbHLdddexa9cuYYSW2CPnknmZ8/PzTEyM8c1vfpODBw8S+gEdz8ftCgvj\nM6dO85obbhSsqsDHTjncfPPNfOUrX+Gtb32r+MyRuJfZbJZ9+/bxxBNPcOjQIcYnJ1hcOEc6jcpq\nd++ax0mn6Pb6PP30M4mvtvCel8mTZVlkMhl2zG1jdXWVSxevsG/fNeoeqqRJGyhYZeYeBMFgSHHS\ncJTUQXnICqMxRx2ggAqw8mBttVpiWAcDL36ZZEmTKVl1yrUk6cLDkK3EvMfGxoQQLpvdhAwMK7df\n6fWqCN4TExO8/+67ieOI06dPc/78eS5evIhh2kRxzJWrV/nil74EQNgXN85PKFAS1zZNk1RWQBPl\nsqBFGbbIVq5L2EirtQ6jpRHSOTEjUJgx6ej4oOmgWQRhSLPRVeW0rluEgUYcQTqdUovPNExMR9w+\nTQMjsbb0PA8vCahRp4dpJjBGFGNaFumRzKaS2EAspGwujVTciQZHknX7kfI9GaYBysAzTGeSplVy\n0kccDLjnILxOZCADoTDTohgP6LouxWKJX/vIB/n3P/0e9u67ho1mUzREDdEsK4+O8fTRZ9i/dydf\n/vp93PXvf4rZrVv57ne+w+HDYZm/0QAAIABJREFUh4kCj3xe4KqnTp1i3x5hmrRlyxba7TamaXLm\nzJlNga5QKFCtVllYWODQoUNUNhpYjo0Z6UzNzNHruXgR/O7v/iGHrzvC+ctXWau1GB0tUy6Wqawv\nU8wXaNer9DttpsYnsAxTSOuzWYVlysYiDDahzGgtyyKXyymTMFnuysAwPGxD4ua6LmT6suknn+dw\n1jVMGZPMh+GyeVhgJeGz4aAjIQMZDGQGHIYh6bzB/K4drFeqaJoY0CF5x+msEPvU6k1StsPu3bsB\ncaDI/XLixAluu+0WarUalcoaMzOHOXbsGDfddJMKcJomPGK+8Y1vMDs7+08a5s8//zzbtm2jUqng\neR5bpmeSZMdVTocvvvgi1xw8QL5YIJfJcmHxPNPTW4iiiLXKOtlcge27dvLoU99P1JK+gv7a7TaF\nfJFdu+YJgoBrrx1NPpehaLjy3soK4OXCPHWIJvdSPpPh/ZPP51VAHoZdoihSQdbzPJX0SWfTqamp\nTU1oYNNsWVlJ+b7PZDLOLggCRSkcboZLY7l/DV3wVRG84zimtiGUfHv3zLN/3x5SqRTdbpeFhQVW\nV1c5efJFut0uaVvcOCeVJmVksNMZ/KgDhkmj2aZaa7C6JrArjITPfYt4n//xPz+neM9xHFMoFCiX\nSkShWAjT09Ps2rWL+fl5fN8nl86ohx+GIe1OlyiK0XQNNF00LjUNzwuJIp84lPiphaaL7r/rukSx\nhmWKLLnvDRodhi4zZwgToxzbsRL8PYNlCbzMS7whNn2W5M/DHHRA4XJhUlHEQMAAjtGNQZAxdQM9\n8VKn22d1eYnR8XF+9Vc/wv/47F8xUh7DNG36fsDOnTuFVBlYWq1QKpX4n5/7G256zWvYOjMNaBiW\nKG3HxsbYv3efaEb2B1x92WyS5eH6+roqT//i0/+du37mPdh2iv3799NsC1vbr/zd/6LZaXPoyHUc\nfeYYum6yZ88e/L7L6uoyhh7RbNTptlrs2TPPr374I1y6eB6nmBiKxdGmrBkGYicJOfT7fTWsFwbj\nryRtU/6cvIfDkJsMcqZpqkNT4pxSCSwrJsuylK+NtIGAzRYPsi8yLLcOw1D52Eth1+rqKnfccQdf\n+/q9wgUyiinm82oK+ka1zszMDOvr65umRC0sLJDP57n++iO4rkuz2WT//v30+32uv/56FhYWKJVK\nykfIdV22bNnC7bffztWrVwWdsVAgiEKlvbj22ms5evQonU5Hjf+q1+vkcjl27NjBqVOnmJ2dJQwT\n3/hej3K5TKzBzJatfO3ev1ej2frdhgrE+XxewDGA2/PIZq0kMAuPHcng0bTBWEA52gwG1ZXvuerw\nlPtF7kHP89S9lc9rGAqT1a1pmgr/lu87bDolqyPpUSKzaqk9kTCnXCsvb07K5yzv3yu5XhXBWyNm\ntOAkWGIfUzOJ+yFm5LN72zT7ds7y5tffIkoKSxOeDXaajY0GD3/nUTbqNcw4QrdsDFv6A0cURsqb\nTrdccUJsErefbGybetsnIMLzAlZPX+DUxRXSjz0z2JjxQCxkmiadbkuVVTITkXJbkinVuVyOffv2\nkS9kVROrVtlQmU8URYyMjDAxNs78/LwodR1TNcksy6LTbSdBRGB4MQOFl+T2ykCh6IcJa0JCKlos\nAkCsJfQ1I1ks8dDQV93AQMO2PDTNxut22LdnN3/xqU/ywQ9/hH7fZ2R0nDNnzjAxMUWtVqM4OsZK\ntUnk9Xnu+It879HHGSnm+dF3vJNDB/byrW8+yLWHD1GrtiiN5jlx4gRHjhwRIpIEJxU+JBoXL15k\n//793HzL69ioNSmVTDK5In/9uc9z7Llnue76G3nq2POsb7TJZgXWuL6ySi6bwXP7zG2Z5PLlS3zq\n//o/RSndaVEujdDr9el2XbL5nGr6yYxMbrRut6uCs9zUsnklN6tkcwCqiQ1CHCXFIbquYZo6xWJ+\n6BmJA0sqh+XsQ9kIk2wTeeiKzS55yDq2nVKZoa5DJpNK4LC+MGwbGWPnzp00Gg0mJiZpd136fohb\nrRLHMbt37eKLX7yHt771h+h2u5w5c4Z9+/axfft2crkMKysrXLhwgQMHDuD7HufOLTAxMcnU1BT5\nfJ5KpaIqjW63q+h1krIYJetxdHSUhx56iD179tBtd1hcXGT79u3q3gVBwM7du1g8u8D8znkhKsqJ\nYb2eH/LZz/01ly5fpjg6xvLKGoWMRTabo1arMTMzw6233srq8jKGPpgGLxrCbQVZDvcS5F6QQdIw\nDGWNLPeKjAm2bTMyMqIw6W63i+f7KrhLweBwb2mw5wf2rvLwGOaQdzodBcXJwQ7SikFCb8NZtgzs\nYjjKK7teJcEbbF1Hj8UUDYOQ0PfE1PG0eDhuryuw6azIyM+dv8qFi5e4fGWNXC6LF0bohqHkxoTQ\nanc3EeFjdDpdF2JpkK4TBBF9DWLdIgih0/UIwuR09ERDKtZMNMvCsG0yiR9CGIZEvk8YxsohzcmI\nxpbrB5w6e45mo6ZKbJl9ra5XVSZqmiY8+BBhGDKeF+5phw4dYHZ2lqmpKZU9A2xU19VikYFAZfDJ\nYpIZm1Rumrqmgrau6xgITrjX9xSuamhJVhrFdPsudspBs2ziMOBH3vY2Pv+FL6qmzNramqCP+RFe\n36ObuDNOjY2SyuT48t99lUcfKeFYNocOHiSbzamJ2s1mUy1cmaHJzFbTNNK5PM2mMLz62Md/k4OH\nj3DdkRt4+umjTE7NUC6XWd+oUSwWyWVTtFoNLNPk5MmT5BN4pO+6NBo1SgXhIzI9PY3rDczChjNv\nmdnKgD3MBpA/J6Ee6Song4I8vGVpLP+RnHX5M5JVNMwjl+8xHGCAZH0MkgJJZ5RlvLx3mqaRzWaV\n14/wdimqQFsslXFdMQyi3W4LjrwWs23bNjUZZm1thTAM2bdPwFrnzi0wPT2N5/lcvnyZQqGgfGCO\nHDlCqVRSU2LkxJxqvUalUmHLli2KzmebFhMTEywsLKj3E03dmPn5eS5duMSObdsJw5Dp6Wla7S6L\ni4tMTU9zdXUNzdApZgd+QGEQqQakoVubKpEoGjh4ymAo94N8tpL1Y+gDphgMmse+76uDSUF5ScYv\n7Z41TVONTTlbQK4hGGhHJJwl15RkTsGApSShMrkGJJYuY4H0A3+l16sieEOM64uucSaTpeuKTnCr\n1SFjZcWpZZmknAye3yWlm2yfnmTv9jlyuRy6LkrSEydOcGV5iUa9Rb/f52qlsqkBYJpiyrhhCFlz\nr+8TR2CmkuDqmNi2CIA938d0bDTTxEsejhsBGFjpNHoco4chcTwQVHQkpmk7dMKYbK6UdLcjLDON\nF4rsIEbHTjmgizI6IuJKM2ClW+Hk0qPqtI7jUGGwnpdMI/FCBftoMYwU82re5cGDBymVCoxNTpBN\npdD9psoahGpMnO6R38MPfTKp7KBc1AOMRkg6k8EydTr1DQ7v2cWtf/on/Mz7foHJqS30PFhZb1BK\nmaQ1DSubw7EcqtUOaxstms02C5fXmZqe4eoX76Pb7dLrCgHE4qpYmJZl0e9eJo6FTebly5cJ732c\n8alxRkdH+e7R41xcq9E7/gKmpTO9bZbA86k31nFMDc+tEsYRxVyWhYUF/vqv/4pGrc5apZ4IMjLU\nu10sy6LV7aEhMiV5YMgGVT9RhOq6TpBUVbaRBHASRoAbEIQhWVuo6LxEFZoupIliyWLRhg4A0ezW\nNI1YQ0yRt4Rcup/whCOkACgmimLiWPrWG2hJdplOWBGmZtDt9UVDNGkeAzipmIwe0uu6/OiPvoPv\nPvY97JRFo9VEM3TGR8dYWquwf+9uvviVr/LBuz8gfjcO8Xpd2jWXPXv2CAFNu8PhQzcQhjHdXo1i\nQfhrPP300xw6dIijTz8lpichYEDb0okjn1KhiG1aZNNpWo0G11yzn06nQ6fT4drrruX7Tz/FkSNH\nRIWz1kQLQ/bs2MULp17k8OHDhGbExeXzuG4XyzQZLwprAZLm6/T0NLOzs1TW1kjZDkHoo2smjp0i\n7WSwLUEv1YwBk4M4xPNcFUStlKhYtVjDtiy8wFfNf9d10SScputEGrieRyGbU5bREnuXzUYZdGXg\nllm5FFNJ6EwSBmQDVRiW9VXTHDQa9SbtdpvtSWySWfi/OaogxJu4nJqmUa1W6fd9Verm83l6bgc5\n3blYEpCIEB8IHPzw4cNcd+MNRKG4GR0/YH19Ha5+FYDK6gqTM1vodlwCr4+OMMXSMHGS93cT10Kh\nfIwhEsFSR0M3dWJNIw4TOEIJbyI0DSzLxLadAS3NdcnkxEkaBAFGLAYBEOn4XkhfSyS6toUWSTw7\n2CSlldS1MIzRNJ18wkOOkkXe6fXpuh4bGxtcWV4SHgv9LmEYMpIRmeG2bdvI5/PMbtnKTGKlaRgG\no2OCefPUU0dJ51Ls37tbNG/8ProulJatdov777uX9/zse7Edhzjs02x20QzBczZNnbbnYtspyuUS\n/b5PpbLO+rpQRc7v2oGu66ytrSluLIBjikxk586ddLtdnnvhOVGKOjaTk+MUS4KPa+oGlcYqW6am\n6btdqpUNxkeKLC4s8LPveQ8rS2J0naHFmLYJxLiuR8YRzbtSAmVI9ZtUyQ2X0V6/T9918W0zKX1t\nLMNkpFwk8EX2Z6cFbBEHIf2kHFaluSkyO8mSGDQ3Y7KZtKKJ6rpOOpNLcHPBuZY+HnEcJcKUQGWC\ncgq7roupR7JpGUUR7U6DLVvn+JG3/zBf/urfURoZY2REDE8YKZaUWdro6ChOOkO33aJYyHHmzBkO\nH7hG2PyaJpcvX2br3HaazSaZtKjqKusbHDp4mJGREtu3b0/WoqFEap4XYFqimmm12kxOTtFqtVlc\nXOSaa/azvLzMoWsOsHD6DDt27BBYbj7P2toae/bs4fkXjnPg8CH+/uv3MjI0F3Nubo5mo06hUAJg\nbm4O07SwTTFoOQpRTCYNJ2HrDHyRpDJTQhexnlTQXdGLYOigfXkgBsHllvNsh3sbLxdrDePbkoLZ\n6wmzslKpNAjYCcxj2zaWuXkSV6/XI5sb+HkPv+4rvV4Vft57du+IP/3nvyuymmQqfLstaEByoozs\nJCvVVCLjFjBBoDDqnicyldHRUfV7Rx69E4BHr7tXdIMNQbk6fvwE5y6cZ3llbcBYSaVotdpKNahp\nGrkEaw2CgFAX5u8SGzVMMYosigbe0tIgx+0N1FSqCRJHaJquWAxycVgM0YS0aNPCiuNBNzudEsHI\n64tsIPR8JTCSbJJYUhWdgaw4lRKipVajSblcVt9ngBtC4PWJw4BmvYJlm4kviw6mxfyevaxWNjh+\n/DjFkfFBA1Q3KJfL6sAxDEtMGE8Wu2naqlmmaRp+XzRyJB4qsUTTNBMKpp+UrwGh72PqUK1WcUwh\nQjr90kk++As/zx133KGoYhMTE1Qr65s4tZCMltI1xQeWpasMbDCY1BQEAaZtqeAgud5hGKIjnku3\n2xUBVR80FA3DwLAGDS4ZYCGZpYqv7pW85wMhWVqxTMIwJGYwhFvSE+V3kdQ+2dfQCMkVCnRdj9mt\n2/i1//gx+n2fiBjP9ZmdncX3fcGD77SZnhjnDXe8nmw2jWlodLtdTp8+yZ49ewjDGN00iHwh9Z6Y\nmGB1bZmLFy8yOjpKvlhIRC1ChxFFERoWV69eZe/evURRxNGjR7nptTdy8uRJjhw5IgZ9jI6yuLjI\n/vk9tLodXK9Pp+syNjnBJz7xCWzHoed2hCPh7NaElSUM2t7xjndQrdaEqMlJhqrEA39sO2F6Sa9t\niX17SWAEFITSSmZFSpGWhLV6Q46A/097bx5k2XXf933Ouet7r1+/18vMYIAZYDCDlSQAUqYI0JRF\niItLEiUrUSmx5FSiVFRhpeyUGMtViVRO2U5VUi5RildScuQosexSiZFJKXExtmVx0eKyTYkmQYok\nOAQGwACYrZfpfvu768kf5/zOvQ1SJBiTmBnN+1VNTffr7vfuPfec3/r9fX9lWfrBw2JQXzn+TBxJ\n+RtJy7Tx4LLPBU0ym8183l3eS9I8tVG8+VPvAeDL7/xdD/N9+LG331p83m2Lo7U+Mn35lc0NeZ6T\ndjqe4L7fH9gJJ66yW1WW1Dww9ZH33ej3LIg+soWFR153P2985CF01NDLLhaZhz59+tOfZm9vj50r\nLwPW+486MXVVEIQBw/UeVVkzz5ZoHZKGIUoFlKZmOh2TxD1/AMu6KTZCTRBaJVcbiwhZFjmhEYia\noaorn3/NlkvPVjZduGad0imftIPWdiMFUUQYRhDI5poTxwmGgunSenPxWp9lUfmizWI6Jd/ZZ3vb\nMu6FgaLX36Dbi/mhH/pzzOYTPvPZz/Lc889wcDjm9N13cm3PtuJv9jepqoqrO1f9lJE4rlBF00k2\nm00oisL9PKSO3CSRsiQObJohyxcslrVD4+QWNgmOiS6z004wvPTCRT72279NqHJGB4dsDm31fzYZ\ne5a+oijopolDA2R019d9wVE6UQVlIGGt1CMWLj0WhiHKGPb29nxHoC1QaaJIs5w15PzWKwsBN9C6\ntiO5/L52hSzZ4218uextOfAYCLTl2aurirpq5pdiDGVR0Ot2GQ4GGGNz5Hk5Zj6bcXh4SK/Xp8gK\nTtxxhx3+ZzS7O/tsDte58MJFvr9rU5Cmsv0D586ds9S2xjAej9kcbvK7v//7vOlNb6LX6/HGx76D\n/f1935WYZRkGzXgyYWtj25/PoijY2NhgPlty37kH+PznvsCZM2cYH0w4tnmcj//OJ3nirW9jOOhx\nz9ltfvM3f5NO1xoBcZIOD6+ztb3Jen/A9evXmc8XXnl6Y2u0N3RBqPzzk3WV/LMgNoQHRupHwJEo\nSJ5Lu84h7JR1XbO/v+8dH/k9qW8A3rjL58t7i4RhyLFjxyyv/8wOdJE6gMBkRaR4/s1ABW8Kz/u+\ns/eYv/Nz/yPGWJylFMg6TkGLhzIejzlx4gRRFHFweOgLFFGUePiVCgPfJVnMJpRlyeOf/WEAPvHA\nPwEa4iA5yGmn7wH2DalNUwGWELvb7WICzdRN097ePsbLly/zoV//pywXObPFnDwviVzzg9IRFeIF\nRn6TtAtmNh2i0IYj0L+qaljHqqrytJNVYAuUaWw7sYTqtaoqKocuKR3SoTKO5L9rC3rLzHKIlGVJ\nVZQeNmiVVUx/vUu2mGHchO6yzMBUjKcT65FkGYPBgCpKiaOIixcvMp/POO5GkfnQT2l/j8la06lY\n17VncxSjKq9TG4+9tpjciCgI2N/d4fDggP/7Ix/m+v4+67014sR68FLMyovlkZCzzAuuX79OEChq\nbY2u8CkLrW8UhP7wSYMTbjpRGGmnCKxnli3nvgsvz3OKzP48UA0JkqHhaYZmFuo8s1j5TqeDqZX3\ntLWbbDSdTlkul6yvD713J+G/5FwFeiaFtDzPKYsld915irI2LLMCFSW87yf/Mkm3wzKr2NjYoN+3\nk4MG/XWMqQmU4fG3vJk3vO5BPvfZz3Dv2XtQxsHghuu8/MIVjh07Rq/fZ39/Hx3YrmQZbHH+/Hke\nfPBBG53Uyp+5qrKfFwWhV3YHBwdsb29z6dIl8rrki198mkceeyM//TN/lde//hG2jm9x7doV0jhg\nMOwxm44IQ82pk/fwPd/zPaRpyqVLl0jiDmtOiQOY2qY9ltn8iDEUpVq5IqZENlVVEbtiohhq6WJd\nc5G7GM+qqvzgFPGCBRkme1WMgCh7MWribU8mkyOt8RK9Vy5NJqkWrTXzRcZbP/MfAfCvH/uIL7q+\n+U9//7fG81ZKpcDvAYn7/Q8bY/66Uupe4EPAFvDvgf/cGJMrpRLgHwN/CtgH/rwx5oWvexGhpTyV\nMFSA7bPZzIdA/X6fzc1NxuOxx0NKPgkaD6oscs9/cMzBlUQEpiYPwLaK58wXQtoeEQSKPM880VC7\nQSIv5kwPZ2xubjLPcg6u77LW7fCX3/eTrihVcvnyZXZ3dzn/lWc5GE24fPkydV0zHA5dGiFqIGIK\nAl27wy80lcYVwUJfA2iuoSRxHaayYdthOBhUEBCagAqDrgKiGCpqiroijBOUsg1B2dKmXcIoccWb\nkv3RARpFrC1NaRSFlFnOXae22N3ZI0oixtOMOqwQtrZOp8szzz1HL+2QJInNg7dQMNLiL4qtrmvK\nrBki4cPNovTPypia3d1d5tMp9529l9OnTvFXfuqnbBU/7TDP5s5DtgowSRKGjj+92+3ynvd8H3lV\nE2nrRReVIYxTsvmCQEfELtKaL6wRCcKYoiwxpmmEwVQu9C08MqSqKuqyoLc2bEVR1ljUdcl8nnue\nFIGQySEWIyUGZLFYOm6PIdvbx32uVZSC5EblTJRlyf7+vk/JDQe2sLbIcpQO0UXFe37g+/j4xz5J\nHEa2J6LTJUlTRpMxWxubHBxc55O/83vs7+/y5Hf/GRazKadO38lLF1+g2+lx6tSd9lpNyRe/9Ec8\n+eSTzBZTsmyBoaI2Jd1eSq1qkiBmmc0th/tyRrqMqaPYGslJxFNPPcW73vUuumlKr5vw3U8+yQd+\n4Rd4+OHXM13Mmb+0xJiC4WDo9smEBx54wGPOjTGcOH7SoaOyBh3kUljC5y9rJZGkchFfXdd+5Fge\nNvzb4vC1G2vkPYR5Up6DGARJz8nzlkYc0U3idYvzFwSB5znyTTx1Q98rvC06aHLeopva0OZvJN/Q\n81Z2N/WMMVOlVAT8a+B9wE8Bv2GM+ZBS6h8AnzPG/KJS6i8Cjxpj/hul1I8C/7Ex5s9/vc948P5z\n5pc+8LNUtc0ptTuk5OD4rrTa5uu6az1/WLW2sK5ut8uyaCBVlaOTfNf5HwPg9x/9MACxy4UJ5Ksy\nTaOLfI7W+ggJjickoomJI4dMqVrDJJQOW91wAdDkLeW9kyTh6tVrfPnLX+app56iKAomy6b7Tlqy\nReGJRxCGIem64zO+fuDTER6upu0hL+vKjtlSEcbl1GVjGWM8n0mba6HSdtOUZWm7LpcFgdZEYYJ2\n76uwGzOvp9Rl06QSuHy9VWglmIYBcVllfj2qqqLMcl/syVqHMqhdp+NgnTi2sxYDZce3GVORhhaJ\nkUQxGco3RuhA+eKxGDLbvWmNy2x2YAmh4sTPIg3DkDiMePjhhy3Xhitknrnrbo4dO0ZtbOR3992n\nGI0P6CQpWbaw3Xtljmo1WcRxTOmY42zaKAZfEIMss6iDfr9PEFiERNXqfC2KyitodKMwZF3aU5jk\nmSVJQqhlbqLlCy/K2k4HihP+5s/9PHt71+n2+i71YFx9qKLX6bK7d4177z7Nj/3ofwJ1zbPPPG0j\nWpdue/7iC9x//30emRNFEUEU8vzzF3jggQfs8OjeOvP5nP39fTqdrh+fl6Zd35W4v7/PuXPn2DkY\n8/f//gfY3Nwk6fTY3d2lKDJe9/qHmU8P6aYJ/fUuylT84Ht+xHLdOEw0QOY6Nm1qyaI/UjfuTtAZ\n4rXOXG1Dno2tOzQiNablcsmacwAFw5610maiRIVBEPBpObDKfuE+u43xvnz5MhsbG0doLLIsQyvj\njQa4oelJh+/8gx8ArOct1/Zd7/jhb43nbazmmYq+cv8M8A7gL7jXfwX4G8AvAj/kvgb4MPABpZQy\nX8dKtL2MOI4h1NbCuwMvnW+ykeX3pao7GtkQbTqdsr4x5MUXX2R7e5swTo8QvVSVXcCyMhhTo703\n5AYjFDVVVWBoOBOku04Y2Qb9dW+VxZLjHq4BysJ6Db1ezyEKZr7AEQQhURRSZUuGa13e9vh38o7v\n/i6yLCMZbPr7McZw8eJFdq7tsbu7S5Zl7OzssJhNGU0mnkc6TiNCh/Gua4MOFSaKqNxjrWooTE0S\nKgI0wq8hebUo1CSx9SCiyHrBURChCYijiDKvqEr73mEQW0VRVkRRiAqd11DVR7oNoyjxKZDaNMWc\nIAjQLo0j3YR1XRO6EVTzid1iYRg6rH5OVpakcYyqA8oaWyhWQBBZit2qacgIghATWMVXVjVVZSir\ngiS1aZtFUZF2bcNMElpDfOG5i1x47iJzRwr0uc98HqVsS3iSRORFxh3HT/Dud7+T7e1toiRlkeVU\ntcMFu31plCJKElKX5rCeVg0oP9d0Np37xo4mclJHipLSyCHOgt9f4Bt8JDrZ3d31eVMdRpi6ZDab\nEBYFF545zz33nmM6szMse70+eZ75FFG/3+eLXz7Pl57+Cvedu5f1wRYbm9so14l7R3YH3W6Pw8MD\nf707V69RVYbDQ8vnYRW1PaODwYBOp8OLL77MiRMnLWlUHLO5dYxf/dVf5Y+ePm+9Vq25evUyx48f\nJ04irrz8Er1uyqwqiQJ497vfzd7e3ld1Sm5sbHjFqZQhSSOvE8R5EWN37Ngxr2ClYJm5ImE7RQL4\nrlopJIoOajsbWZZ5x9DOIw291x44J0+MahiGPPTQQ76pr42A0ero3FqwvOoiUlf4ljfpKKUCbGrk\nPuCDwAXg0BgjPv7LwF3u67uAlwCMMaVSaoRNrez9ce9fm9pXjeWGx+Ox3/hjx7EBYGJjCea7HZ/z\nkokXW1tbLIucs2fPujxm7B8U4BnDbFikqUuDMbCsS4/TVEpTVxpDjakN2WxhN4rWpN0u84XdQLbC\nbQ9f7EJkm5+0in42OkSHCuqKNHVt9sZw7eplmwvrdCmNYTa2kK3C847YyOOBe09z+o5t0uQ7gIa0\nv7tm+Zy7acd5ctbrWx8O+Lef+kMuX77My1cu22LffIauKpYLQxjG9Pp9los5qbaokCDQZPmMbhwQ\nayjLAtBoDSYKMBJuhrEP+8MwZT7LANfYEgZobQ1kXddky4KCErSyed208RqVUgTKdYymxoe7dV0z\n6IcWRgkQaM+059MIpWVhrKqKooQ4Toicp1pVFWVeAgqlbFFRRzgv28E2tcVV13VNsSwxBtCuRV6X\njKZz+q4IVxQFWV7TSfpceHmHl3/twz51AaCxB/Dhhx/mDW94A/efu48iz5lnEAX2jcu8wpiafOmm\nlSvcmrtIp6rJ84WPqIpgXCYyAAAgAElEQVTCzhkNlDWyoZt6VJY26qp14Dt4qQ3rww0fXVDbSeZl\nuSQJU/73f/gP+NLT5/nl//MfWdKkxQSlK6IwYW1tYBtt1of8m3/3aX79I7/BI697PU++/c/Q6Vq0\nVbc34MrVPU6cOMHTT3+R48eOcddddxPv7qIq7YqCfYtsyQzLRUmWz9jYPM6lq3vcddcpfv5v/i9W\n0ZWGM/ec5fKVl5nOJgw3emTFmCjssH1swNZgg8ViwTuefCdp2CWj8qlQoRaYz5Y2/+4iZWjqUZKm\n8KihFtOjQAI9Pt7xnsjfVE6ZCkx5bW3tiHKVCPzkyZMN5C+yY/gmkwmxcwzbnyEF2Mlk4ojZ3IAT\nNxmr36K97fb6cBH3nO3e+pY36RhjKuCNSqkh8JvAQ6/6E/4YUUq9F3gvwInjx1hfH1KWuc/7RGHi\n89ii0IuioL8W2vxQYA9llmWkacJgMODKlSscP3kHX/nKlzl79ixlmR+ZTJEkkQ3JIzkYrm06q6mD\nmiCViRxNVbmiIlS2gDkajej3B/56pAiZ50uf1w0Udi5hVaNd00WelT4ElOEFGE3fFczquub6eM8X\nWeIwIlSgTY2mpCgqkiig10lcE4JmfzKmKhyBfBhw9eUJD99/jsceeR0EmjwrKJYTh/KwULl/96k/\n5NlnL5CXhiAw1FVJuZwTdLrM9qcUbkaj0Q62qALK2ioPHYY271lrOg6mVddQVBVZtpBnSqeb0tUd\nZwiVn9oSue9NKw8oRdpAawIJcZU1AnlVYrD5f60UBBqFQgN9lzIR5e5znrWxsxDrhhdkmTUNFMYY\nUIIyUMwXGUVZ0+30iOOUoGOheOmaLWRNplOMUqggZpnnBIENp4ssI0kSzj/zHF/64nkfonfSlBPb\nWwyHQ+644w46nQ4njx9HazvWTuo5ZWlx0sCR0XVtXgvJmYpn+cqOWqPFa68xdU0YaAaDdcqyYnR9\nn8cefQPjwwPSOGK+tAU1kyqm8xlhZIvfk9mMXnedz33uj9jZ2eM73/IYURDy2GOPUVS2mJ6ma5g6\nYDJesN4bcO3aNRuFRInLK884e2+fTq9L2s15//vfzzKzDW5xmnLnqTu4+NzzFhjQ7aCDin5/wGI2\noRgv6SUpP/ieHyAgZj6f0xv0vQIUaKYyeHI2SU9Ic46NDGvvPLV7JCzM0+4d2RM+fVeW9By/jPQB\nFFmD35d95WtpZcn169d9LUNw+O3MgKDc2oM9BIAh0F55L2MMUZz65y364ZsBkHxTUEFjzKFS6pNY\nqqehUip03vcp4JL7tUvAaeBlpVQIDLCFy1e+1y8BvwRw37kzRiq8UWTTAJPJhNpYj1i4Oqqq4pln\nnmFjY4PKlEdCnuVy6THhg8HAetlhSBo3t3ji+LZ939KGPcY90PX+wHuWpSk9Lnl9fc29DmlqYYhj\nNydTYVo5OZuG0Ng8J0A3SSl0QJyGBD61UXM4mhBq++CNah7WtitY5HlOFAS89OILtjW6Lqkqi3Gv\nsK2+URTQiW2+tCxLlKnpdWOyfEmRLTAud1kuZuTzKWnaoZPEPPn2t/GmNz7KxRdfZrFYcnXH8lds\nbR/niTc86se/EVlltyhyDg+vc/nqFbJsyXMvPM9sNmMymqB8bSAkirUvJFmq3prxZElVGjaGQ5/q\nUko1rc8Ony5rGDlWxso4CGSQorT2xSutQheqKsrKeCJ9IdhCNdjqdiG0Lkp7+F2TU6A0Og4JnIe9\nXObkud1Ly8IiWGptUxdx0kGlXXRoycyiOCYCNoYDm7suSjq9LmWeM58vyPOS2XSB1pcp/vCzbh/Y\nPPdgMGBjY4OzZy3vxyOPPOINDVjY2c7Oji8+y74YjUbNSMA49u3c6xtDd5Aq6soZcaVYLOZEcURZ\n5vzkT/63fPCDH2S4tc3u7jXuOXOfTa0EziDMM+6+5xTHjh3jhQvP8bu/8/s2RVhUPP6Wt9Dv9nj6\nSx/jz77rnezs7LC1uckLL7zE5mCL3Nh6SZ5VPH3+K1y6dIWP/vN/zv0PPoSOlqRpBxVovvT0V9hc\nX0MHEetrPaazA+oyB0oeeuhh3vKmx216o9AM+0OyupmMI3WNIst9DciPE3MKU8d4/HxZlr6eBVAV\n1gFIU8tJE7TqVZK6E2WsjFWgRjWwZXEyZrOZN5oiAjeVPDXYnHi3aycU9Xo9giDwHDFJfHS4eRAE\n5EVTnBRnp13v+0byagqWx4DCKe4O8K+AnwV+HPhIq2D5eWPMLyil/hLwSKtg+cPGmP/0633G/fed\nMX/v5/+GX1TjsM/i/WrdUKUKZK7T6Xg0iih4yadKXk9wtN/33H8JwG/d9499s4qQzFRVxborJrWJ\ni9pID8mX2W66+AhGV3KC4im1q81aW9pQsd7y0OfzuZ8gLqkhG/qGrphT+KYDobKUXJsgbYQXRFqS\n1wdrPsfnNyVBgzN396O1pqbxYCzyYUHcsc0JodKePa10xTH5uzi0G7Z2Z0DWS3J+aZpyMB4xndpu\nu+l0yqUdi5UVngjxfuRa5f0XuUA3mxqDMQalQ0cPUPriajFvioW4GZFSg/BoIncQTNAw/8lhE4ih\nDJqVMJmyKU6LFyfe8hESMFrXFwYteGkLSuYMaIzyzUjt5yd/LyyQWmvyYu69v0BZY3fP6bt54Nx9\n/Okn3sr6+jqLuW36yJdTm2cN3PsLTLLKLU5Z1a4olvBT/8NfJ+11Gc3m9NfXSXp9gigkCqURyD5D\nXSnfpJKLt+7qN51OB4VlC9RaE6V2AERRVIxHM7rdNU6cvIPR6ICyzJnNx6RpTBQHbA47FEs7/Hhn\nZ4czd9/D5uYmDz/4IL1eD+PqJjalYI4UK72Bdvl+0QfQGGvJ5Sul6HbXfBFTvODh5sDv4TaktMir\nI89Ma818ZtFkeZ57kEK7U7Kua29Acte7IUVN4Sdp1+jSNLW6SzcTuIwxPqJ65PfeBcCnH/9/7XuF\nIW964nu/ZU06J4FfcXlvDfy6MeajSqkvAR9SSv3PwGeBX3a//8vAP1FKPQtcB370VXzGV8F2irI5\nrKKclXQmuXRJGIYegykHczAYeBSDdIOJCMeHKDMJf6RVWKanyAORPK181uHhIcbl4qIo8oxkgP9b\n8RT7/b4n8mlDiYTLd3t72w9jDcPQdx4OBgPPQiZrUZalvychFxIFY4xlKrx+sMfa2ppXJEop4shu\nonbxyxhDUZV+cocoytLYnF+Z5Vy+fNlik9PU5xbrumYxs2O4Nra3vEIUHgfB1G5tbXHy5EnLh6EU\ntYPrzWYzxuOxV+wXL17EGOMaMuYknXXqUmG8N62wNLP20FpyIYt51kFNkkRUVc58bItOQRSRZU31\nv6oqVyQqbCOUMRbvi2GwZomcssWcsmj4o/Oq9ORfYmRkz0jeHqDT6aGU49aoFRXNlB2BvJqqQmlN\nZancbUbH5a2LsvI8KZUr7gZBgKlLdKCJnVHVBi5dusLLL1/mYx/7GP3emjf4d548zl133cWbHnvE\nHZrKoS8CFi6dVqmSw8mU97///fzsz/2vZFXNYrFkWdZEUULasfs8CiJCHWDctSil6PXXXFt6wXg8\n9gRQApsLc2Pfw3UzG2NHDIahptNJQVWsrXVZZnML0QwCdnZ2OHHsOMYYvuftb7eORpZTGEuDcfXq\nVdYdmkoiNZH25wukVkbJtQEPeV56XSH0uVES+mKh/G5d14RB7DH43lA53Do0XcBt/L5gt7XWVAr/\nnuKcSe9AG+stkaacQfn89gQecc6+GW6Tm6JJ54H7z5pf+Yd/y6MtAGJ3YMSDFcvpc+LO2skCiAcl\nyl0UThAEvP0L1vH/xMO/9grctFPsppnULpSdwign3qHPqTnEilh8KVa8MtzJsozDw0NOnz7toWjS\ncSWeYvs6ZYSaeGfyHjLPUF4risKTx7dzfUWZHYFUAmRLe00SVnoYZBj46noQBBw/fpzR6MB3tEnk\nkLgoRza2YLYFuha7rkrpMhMvpXZ568ViQaQbLKvW1quXVnV5X601edm0DksucekMstaaw8NDRqMR\no8MJFYbz5897BEZd10xmU3+opAg1Ho8pK0uxsFgsPOG9rKd4v8IhklcNl3e7qUM8elkvVeGvW+5B\n1lnyZn6/5EuvhORZS4gvHqB4dUI+BvgaQRSEUNVEcUigtO8SlbShMtbbDpV2sL6ARZ5hTM0DDz3I\n1tYWFJYe+fEnnuADv/gLvPjSJYLIdgMrpegkHdsa3m0mmNd1TRrFR84c4A1+ENauKGsbZ6Qrejw5\nRClsV+90TLfbQVdLTp6005Pe+973snDsfPOpJaAbHRyytbXliabkc3y06/Zj6pyJpnge+hqCsHfG\nLo8skXBZlqCN37MyNu2VTIWyr0sXQco6SJQmz7uua69gA6dwJXoSz7zduSsc7jJoW/anNDM9/Ikn\nAXjqbb9l7zsIeOTN77p12uPLouDChQs2/7y+7oanznwoDPjDJ23YcqBE2QuZj4Q7gPdYRabTKWma\nHin+pGnK9BWVYfGORSl577QsqY06wochIZIcOlHSgvUUL3k4HKK19nzdYpgE76uMvUcZXir5s3bl\nXD5DFG/7Zx2He217IVEUH+GQHo/HHB4ecuepu/xmlaLiiRPHmM/nTCYT78GLtyKep+QgpVuxqiye\nXCIFyf+ZsvS5w7VOz197EARkC9vdGCjtJ/+EYchav0scuGinzlGmJgkUBNYLTYINNtfXSO/vUil4\n21vf4p+3j2pyC6m8dMmWX1588UUOD+fMp3Y2Yb/fJ3Mjr0xVEgXaK3ylFHl1dMiCdMyBNTx+rqSn\nH8goq9JHB0VRUElEF4bUZYl2P5NwOVQhQRwRSeHNKXZbhIswuDmZDm2iUagIW3zPl6QdW/gsXNqg\nLEoIbP1ksGU9Td2xnuyXn30OdeF5zNJ6gv/iX/4r1jcseViqAqoqQ+sAZRT5MkMvM+8x2mK9cSgZ\nDWVjsGxhbukUa4jBGqCXXr5IksR0Owmz6QStoNtJ6Hf6nDp1ire99a0cXr9uo4duj3pjkyLLCTYb\nyl5RpNJVaou9a945EhiepBWTJGkhyBTz+cKfDTkfysBiZutSgdYEoaYuK7LCpjUCLcXP0tMty7mX\n8yv7QZqsxBBrpSic07F0c259U1ptaZfLoqBocfk08NYmh+6jW6fPXo3cFMpbPFKttVeauVNA7Ydg\njOHw8NAforqumc1mvkVXID+iWEajUZNTBk88I56pbJaDgwOWyyWnTp3yuXTxJMSjlNxVknY9R7NY\n2nYaxo5yElbEjK2trSPGoK5r30EqGyHPc0Id+K7Sdl5MPGwhzRLvUZoJFouFKwjWR0I7gDiKvQES\nxIV4L+3Ke57nXL16lWPHjvlRTGVZkrp0jzwD+RvhKQYo3CELw5D9/X3W19e9R6u1pooaci5T6ybf\nXdWs9ToN214UkC0rqqIgCB3O2xmwSIcQgDaK8WjXv0cYR97AKKVIg4Bzp+/ggbOnmUwmPP6nHqXX\n3fDdrb746RSFTAQX5f/ilX3quubKlSvs7u7ywgsvUC5m1HVJoIx/hmVp85r9tQZ5VFUVKuh6xS97\na7pcoHVTIANjsf4VgO24tca4cgVjl+KSOouyqbikk1LFkV2bLGde1MRhCDoiUDCeTCicB1xUFXlV\nY3SC0Yo6yBkvM7a2NrlydYftbQuJtNxAS7vmQYAxJYfjwnuR6+vrRA5hI/UnSSWsdTvWsBlFJF5y\nAFoZiuWCfDljOBjwF9/7E6RhZOG9ccJyvmB/d49iLfMGry4ar96SuymUgjSVc17R7doIO477zuAp\nv4/b0Xi7o1r29mDQ9564nNnlcul1wytpMARSKGRV4u3LWRJ4p6lqyqKhsZAJQcvlkto1ZInClnMj\nkZrQfYh44/VNdFjeFMrb0JCRj0YjD/5XSnkvr110kocwnU4ZDAYewRDHMXt7DeROKtQiosREgYrn\n0+l0uPPOO32oLdPN256xhE+iSMXjlkUXSz0ajew9uby3KELgSGus5Fbbm0IMkmyWNE39Bhectyhg\naKZZWyjZwnsn0sTT5lGQ1t/hcEhZV94TFYzs+vpaU+AZDv10bfGAJH0k3CCiQKHBqIrRlJrBfD5n\nXNX+WYpBkveTqGhzc5Pnn3uBfr9Pt7/moy4pzBZFgVG2JXor3SKJYuZL6+UkUUywFniPeZnbwnav\n0+HatWsM+5tceOY8YRi63KxBRxFhFLGYTdjbueqNqCmWBFpz35nTPHz/Wf7sO96O1prLly9zeHjI\nxYsXOTw8ZG/PDtQYH+7bfRkG/nkppexQU6UwpvawSqUUGutRKgOJG3LdTSNCbSPMZVk7HHvZFPF6\nXQwl3W7fKxQdatZcYTdbLAnjmE43Ybmc+zRiqDRBYvetiWO2jx1jb2+Pk3fexf71PZt+UdrXI/I8\nJ+nbiEtGtI0ODv2zlAhBCrnZwhIpaRR1ZRE9tSk53N+jqgve+1//Vzz66BuYz6fUud2Xzz//PPfe\nc8anjWazGYO1PlXYABV6nYZvRqK/V9ag7J4OfMEX8FFcHKdel3Q6HXq9HkWRea8Z7IT3Np5bjO/h\n4aEHOrRTHpLHbkMGATaHGz6iFOdRUn/SQSwGQmCGci8euupEHLt2HvwbyU2hvMFwcHBwxMNrV4Dj\nOPZ53ulkQrfTochzup0Oy8WC5cI2O2AMW5ubvvgofMki7/zyX/jjLgBe/Hbf40puiFyA1/0HvsW5\n9jfr7t+tKA++hp91+VNw+Wu8/sxreA23kCzmtg40nd5iwxjahbfRaGSLE52Ozw/lec7BwQF1bRm+\nksSyCArkSsIYsV6C6pBQbyUrWclKbmYRpJrMPH01clMo7zaa5PTp0z50lJSC5LEkVM+yjGvXrnk4\nnqRQJLdclqVvlx+NRnz6LR/1hTnhEJCQTLDOAtNpk0JJ51UbFdIeUCp5TTEw3W7Xp2OCIGA5zzxs\nTThWJH0gOXR5jzCOEOIinwd2BcXcER91Oh2yxbJBjbjKdVEUJGnkIxeBJI1HzeBcaDpKa4wvQnq4\nYNZwSQiyonZhqWWo63ikw1rH5rxni7mvN8j7a61RWvu/KU19BPIl9QvhX18ulwwGA4p5SV62Q9mq\n2RdRSBw3udc0SPzeEAMv61HWlS84TSYTtrY2ODg48PhlSVW1GytkmESkG8ywPE874Pe4R9vEcUxR\nWZy/FDvRqlnbuuHu1tpOTJKaCeDrJSIycSXLMk6cOOFTI+AgnqGwGTb3Kr0OWmtGB4dEke3kywo3\n9EJrkm6H+dJOMldBgiktjHMysrQTQl0aJTHThW38eeYrz9rZlXuWU0dqGRLul3nh6wOyd2yNSTuO\nfbkuOzU9TVPOnbmX03eeZG1tzY4sDAOPr5fmOjnTUhwX+J+cHaltSaFS0EFSjBcorkAFJTUhjpwy\nzSAFqU/ZCL+ZIi/7N0pC/wx6bjaq3KvogXY/iZw3mdAknys1MEk5SRpI9r+kXaVPJXFnQ7jnX43c\nFMobwxHmtDAMmTklKDcpCnXuclunT5/2yg84cogFDy5TQYRnQIqHohTEM5cc6/r6uk+zSKW50+l4\nQvrxeIwBr7AFlaGUhaO1m4OkqCgFNT8tpiyPGApRwMvc5tbk+rMs84ZASG+kgCebSDaDbR76aipR\nac2V65BCowq0bz5ZLpe2DTkKqUztc7eSs5/NZgyHQ6us1vu+6GdzuA1MTg6BQCflMMp7eeysy7OW\ngRtVNptjysrfdx1IwSZCBZrFYkZd5LY9vjb019bIs4w4CSlLMFhDX9aFr2doLVQIG0wmE/r9vlfu\nsl7STCGGXqCmgkCiqtFRzNpgyPXR2O9Bl8xzuVWLsmlDHAuX31VxAAZ63a6dpuL2XF1VhE7ZBEHQ\njLWra8IgsNwcDjqYRDHT6dgf/FmW+/z0xpZFath8bMH+/i5Jxyo1FSjKMqeqCjqdhG5kW80XoxG6\nriHP2ej1qLE4+Fgrwjji8e98M0Vh51OKApSCun3eTX7Y1BClsS1aqtr3SlRVcUS57uzsMBlZhFNR\nFFzfOwBge3ubtbU1XzCWWs5oNKGqjNs/lS/KD4ebPqecZRmTyXVvBIWf2xbu8Z27AuPrdRqEmSDH\nrMI/CvcsioK8zDwZnShk36T2CoCCb+5ykqapd3DEWAvgQP6u3SwmekvQNXJ9r1ZuCuWtlJ0DNxwO\nPV933/EzV1Xlp1lPJhM2HcPYcrn0B7Pb7R5ha5PimizI5uamhxhKq7ZsSMAXFqXI0B5P1W6I2djY\nYO4spRgKUaxSZJTOxyAIqAq7efr9/hE8aRs3Kw9LKtGA3/yDgSURkrZ/pRTz6cx7X9B4uwIxk41l\nW3KbgyGfXZalG8WmPI2pKFuttWXtc++7v7/PYDDg0qVLVvEH1tCosDlUcRwzXzSFTlGGAr+iBFMb\nkijxGzfPc9a76yynS4ZrQ7JFhtaCte1icB2pWqG79hDUdY0KFOPRdZK0x2Q69c9Ka01HKEMd50Ve\nOQRDYtveI2w3pNKaeWapFK5evWoVuHEj7HSA0YFDagQs8oJnn32WRx55hKqq2NnZodvtsp50rXeW\n2bmKSdzxEcxat+/XsaoqZgdzqqrGGAh0yEZv03uUEuEYRzjVixMoKowOSdws1LXOGrqnSdPYoxSM\nMQSpNchaKQJtSb2uX79uo4XZ1EcEeVVSjXcB7FADBd0wICtyJtMJo9GI3mDonQxtYIrxhUHZF+oV\nOqWsBffsPFOl0aYmiGMf5XQ6HQaDAWtp4g2rwGyFv7ysGwTWWt2MkWuTSfnmMnfWwjCk6/aFOETS\nVWxnbOY+8rKF2WZYsJwRWw/LvfPldULQQCLbBl+ikPl8bpvZyoafWxyc8Xh8BJUj0bucQTECEnn4\nqNv9rN238qr05s2QE7aTdP6aRxiIhWp3RIVhaDHITtFKFVdry7BdVZUf/imeFa0GiiRJXIjfkM0I\nFA+aaq+EiJanuTiy6EVRUFSlb9WXsE08uDYqY2Njg6oqjoTTYRiyXFqDMhwOj3jf8/ncD5uIoogk\nSZhMRv5hijcg3oj3hrRwXkx9y7/c12QyO9JcIHjYyWRyBP5VFAUDN6gX8MpcFKPM4ZMNKYOhO52O\nT1H0+33/vmtra35ztps77CGsnHHueSgWQF01jQ3ivcuhlcMgKYksWxyBj4rH3E6zyaGWPSJQrTbt\nqhg1UbxlC54q0D4dNs1YspdUHR5J9cnBlt9pvwdB5fdzu8EjidKvCtkF3982ttBMcJH7l3AcGmIr\niXDaHYHiTMjP5G/F85Q9lSSJn1gFsL+/741xp9NhPD70vy/PUs6kKB1Jp8hntQcevDLSBcuw6Jvk\n3D3KNYuTIfcvRHHtfS9/I8gN8W5lz7WhufK57dqanKl257Hd5026RQzNbDH1vyP3qpRiYzD0nP/i\n+cu5kt4HOXPtkXeATxXJuRQlr7XmzW/7wVunSQea0WTyoOVwCeRMoD5Jmvq8tWzCspUnFCssRU/x\ntMWT7PX6/sBmWeY3tzyQNq9Jo6wy30YbVqWHlomSgIZBDNp5NeXDouahWe9pNBr5DSmHezwee5jR\n4eEhcRz6OXzT6dTlBDve8FRVQ5oj8L12yAhNyCdNQUVRcO3aNZRSnDlzht3dXU6cOAE0DQKiiNq4\nVvHC7AY0XgFKqmR/f99HBMvl0ueZ5f5Bwll770q3D3JAEveOeCwC1/INI62DOHADceU+5bmJgWl3\nR8qhFSxvWZa+mC17o73ntNb+54LlFTiYeGkSicnvy2ti1NtKaJE57nT3N2trdhJSVdh7OTg4cPc0\n8JGA8EaL4Ww7WHJORPG1FZcoY/mdNE2PDOuQ16EZltxuhhHFuL6+fiRC7PV6/jnL2RDF3F5/oW6Q\nbkg5t5KykzNjHbOjg7nbylf+F2Mo1yB7Rc6mnPW2Ry0evfRLyLW1100Mq+wz8dp7vR6LReYdSLlH\nmdol1ynn3lTNDExp7pPUqyhiabwRYyv3KcZalLq8T3v+wDfUmTeD562U2gVmfB3O79tMtlmthchq\nLRpZrcVR+ZO6HvcYY459o1+6KZQ3gFLq068mVLgdZLUWjazWopHVWhyV23099I2+gJWsZCUrWck3\nLyvlvZKVrGQlt6DcTMr7l270BdxEslqLRlZr0chqLY7Kbb0eN03OeyUrWclKVvLq5WbyvFeykpWs\nZCWvUm648lZKfa9S6rxS6lml1E/f6Ot5LUQp9X8opXaUUl9ovbaplPptpdQz7v8N97pSSv09tz6f\nV0p9x4278m+tKKVOK6U+qZT6klLqi0qp97nXb7u1AFBKpUqpP1BKfc6tx//kXr9XKfUpd9//l1Iq\ndq8n7vtn3c/P3Mjr/3aIUipQSn1WKfVR9/1tuxavlBuqvJWdi/lB4PuwzJ0/ppT6D2XwvBXkHwHf\n+4rXfhr4uDHmfuDj7nuwa3O/+/de4Bdfo2t8LaQE/oox5nXAE8Bfcs//dlwLgAx4hzHmMeCNwPcq\npZ7ADvz+28aY+4AD4Cfc7/8EcOBe/9vu9/6kyfuAp1vf385rcVSk++pG/APeCvxW6/ufAX7mRl7T\na3jvZ4AvtL4/D5x0X58Ezruv/zfgx77W7/1J+wf8P8C7V2thALrAZ4DHsY0ooXvdnxngt4C3uq9D\n93vqRl/7t3ANTmGN9zuAjwLqdl2Lr/XvRqdN7gJean3/snvtdpQTxpgr7uurwAn39W2xRi7MfRPw\nKW7jtXBpgqeAHeC3gQvAoTFG6Bbb9+zXw/18BGy9tlf8bZW/A/z3gJCgbHH7rsVXyY1W3iv5GmKs\n+3DbwICUUmvAR4D/zhgzbv/sdlsLY0xljHkj1ut8C/DQDb6kGyJKqR8Adowx//5GX8vNKjdaeV8C\nTre+P+Veux3lmlLqJID7f8e9/id6jZRSEVZx/6ox5jfcy7flWrTFGHMIfBKbGhgqpYRErn3Pfj3c\nzwfA/mt8qd8ueRvw55RSLwAfwqZO/i6351p8TbnRyvsPgftdBTkGfhT4Zzf4mm6U/DPgx93XP47N\n/8rr/4VDWjwBjCApORcAAAElSURBVFophVtalKVq+2XgaWPM32r96LZbCwCl1DGl1NB93cHm/5/G\nKvEfcb/2yvWQdfoR4BMuUrnlxRjzM8aYU8aYM1i98AljzH/GbbgWf6zc6KQ78P3AV7C5vb96o6/n\nNbrnXwOuAAU2b/cT2Pzcx7EjWj8GbLrfVVhEzgXgj4A33+jr/xauw3dhUyKfB55y/77/dlwLd3+P\nAp916/EF4K+5188CfwA8C/xTIHGvp+77Z93Pz97oe/g2rcuTwEdXa3H036rDciUrWclKbkG50WmT\nlaxkJStZyf8PWSnvlaxkJSu5BWWlvFeykpWs5BaUlfJeyUpWspJbUFbKeyUrWclKbkFZKe+VrGQl\nK7kFZaW8V7KSlazkFpSV8l7JSlaykltQ/j8/k8JO+tOzGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b51c2bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.4157962185  0.0107474527  0.6993477309  0.503131606   0.9999206066 15.          ]\n",
      " [ 0.0741041999  0.4223928914  0.9860058867  0.971092706   0.1758668125  2.          ]\n",
      " [ 0.0029863237  0.0160644011  0.0986781779  0.07140763    0.1019328907  1.          ]]\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(images[0])\n",
    "res = prior_util.decode(preds[0], confidence_threshold=0.1)\n",
    "prior_util.plot_results(res, classes=gt_util.classes)\n",
    "plt.show()\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "caffe model\n",
    "array([[ 15.     ,   0.99992,   0.4158 ,   0.01075,   0.69935,   0.50313],\n",
    "       [  2.     ,   0.17587,   0.0741 ,   0.42239,   0.98601,   0.97109],\n",
    "       [  1.     ,   0.10193,   0.00299,   0.01606,   0.09868,   0.07141]])\n",
    "old ssd keras model\n",
    "array([[ 15.     ,   0.99901,   0.41135,   0.00572,   0.70994,   0.52878],\n",
    "       [  2.     ,   0.89177,   0.04868,   0.33151,   0.96607,   0.94722],\n",
    "       [ 15.     ,   0.17636,   0.30408,   0.10984,   0.75223,   0.86614],\n",
    "       [ 14.     ,   0.13323,   0.06391,   0.22835,   0.89587,   0.9746 ],\n",
    "       [  7.     ,   0.11903,   0.01144,   0.01547,   0.0819 ,   0.07419]])\n",
    "old ssd keras preprocessing\n",
    "array([[ 15.     ,   0.99993,   0.38126,   0.00142,   0.65999,   0.48787],\n",
    "       [  2.     ,   0.16089,   0.13141,   0.40145,   0.7893 ,   1.     ],\n",
    "       [  1.     ,   0.12236,   0.00273,   0.02082,   0.10185,   0.07517]])\n",
    "ssd linspace\n",
    "array([[ 15.     ,   0.99992,   0.37913,   0.     ,   0.66268,   0.49133],\n",
    "       [  2.     ,   0.17587,   0.04077,   0.3258 ,   0.95267,   0.8745 ],\n",
    "       [  1.     ,   0.10193,   0.00211,   0.01927,   0.0978 ,   0.07462]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mbox_conf_softmax                    (1, 8732, 21)       \n",
      "mbox_conf_flatten                    (1, 183372)         \n",
      "mbox_conf_reshape                    (1, 8732, 21)       \n",
      "mbox_priorbox                        (1, 2, 34928)       \n",
      "mbox_loc                             (1, 34928)          \n",
      "mbox_conf                            (1, 183372)         \n",
      "\n",
      "mbox_loc_final/Reshape:0  (?, ?, 4)\n",
      "mbox_conf_final/truediv:0 (?, ?, 21)\n"
     ]
    }
   ],
   "source": [
    "# output shape\n",
    "\n",
    "import pickle\n",
    "shape_file = 'ssd300_voc_shape.pkl'\n",
    "with open(shape_file, 'rb') as f:\n",
    "    shape = pickle.load(f)\n",
    "    for k, v in shape['output_shape'].items():\n",
    "        if k.startswith('mbox_'):\n",
    "            print('%-36s %-20s' % (k,v))\n",
    "print()\n",
    "for i in model.get_layer(name='predictions').get_input_at(0):\n",
    "    print('%-25s %s' % (i.name, str(i.shape)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8732, 25)\n",
      "location [ 1.039652  1.959443 -4.791593 -4.198446]\n",
      "classes [0.967713 0.006406 0.000671 0.002122 0.001996 0.001133 0.001179 0.005525 0.000246 0.000999 0.000606\n",
      " 0.000482 0.000355 0.000442 0.000462 0.006809 0.000408 0.001059 0.000347 0.000469 0.00057 ]\n"
     ]
    }
   ],
   "source": [
    "# decompose network output\n",
    "\n",
    "np.set_printoptions(precision=6, linewidth=100, suppress=True)\n",
    "\n",
    "p = preds[0] # first image\n",
    "print(p.shape)\n",
    "print('location', p[0,:4])\n",
    "print('classes', p[0,4:25])\n",
    "#print(p[:,25:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### general model info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pydot\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from IPython.display import Image\n",
    "\n",
    "file_name = 'ssd300.dot'\n",
    "dot = model_to_dot(model, show_shapes=True, show_layer_names=True).create(prog='dot', format='dot')\n",
    "f = open(file_name, 'w+b')\n",
    "f.write(dot)\n",
    "f.close()\n",
    "\n",
    "graph = pydot.graph_from_dot_file(file_name)\n",
    "graph.write_png('somefile.png')\n",
    "#Image(graph.create_png(prog='dot')) # {‘dot’: ‘’, ‘twopi’: ‘’, ‘neato’: ‘’, ‘circo’: ‘’, ‘fdp’: ‘’}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### prior boxes"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "preds.shape\n",
    "(5, 8732, 33)\n",
    "number of images = 5 = len(results)\n",
    "number of priorboxes = 8732 = 5776 + 2166 + 600 + 150 + 36 + 4\n",
    "4 location + 21 class confidence + 8 ( = 1 xmin + 1 ymin + 1 xmax + 1 ymax + 4 varicances ) = 33\n",
    "\n",
    "results[0].shape\n",
    "(200, 6)\n",
    "number of top priorboxex = 200\n",
    "1 class + 1 confidence + 1 xmin + 1 ymin + 1 xmax + 1 ymax = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fstr = '%-28s %-20s %-20s %-12s %-9s %-9s %-28s %-22s %-8s %-8s %-8s'\n",
    "print(fstr % ('', 'input_shape', 'output_shape', 'img_size', 'min_size', 'max_size', 'aspect_ratios', 'variances', 'clip', 'flip', 'step'))\n",
    "for l in model.layers:\n",
    "    if l.__class__.__name__ == 'PriorBox':\n",
    "        aspect_ratios = [ round(r, 2) for r in l.aspect_ratios ]\n",
    "        print(fstr % (l.name, l.input_shape, l.output_shape, l.img_size, l.min_size, l.max_size, aspect_ratios, l.variances, l.clip, l.flip, l.step))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from utils.vis import plot_priorboxes\n",
    "from ssd_utils import calc_priorboxes\n",
    "        \n",
    "img_path = 'pics/fish-bike.jpg'\n",
    "image = imread(img_path)\n",
    "input_image = preprocess_image(img_path)\n",
    "\n",
    "for layer in model.layers:\n",
    "    if layer.__class__.__name__ == 'PriorBox':\n",
    "        prior_boxes = calc_priorboxes(model, layer.name)\n",
    "        nratios = len(layer.aspect_ratios)\n",
    "        nlocs = len(prior_boxes)/nratios\n",
    "        print('%-28s  locations: %-6i  aspect_ratios: %s' % (layer.name, nlocs, layer.aspect_ratios))\n",
    "        #plot_priorboxes(prior_boxes, image, nratios)\n",
    "\n",
    "#np.save('ssd300_prior_boxes.npy', calc_priorboxes(model))\n",
    "#pb = np.load('ssd300_prior_boxes.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compare the prior boxes\n",
    "\n",
    "mbox_priorbox = model.get_layer('mbox_priorbox')\n",
    "f = K.function(model.inputs, [mbox_priorbox.output])\n",
    "pb_keras = f([[input_image]])[0][0][:,:4]\n",
    "\n",
    "pb_caffe = np.load('ssd300_prior_boxes_caffe.npy')\n",
    "\n",
    "print(pb_keras[:10])\n",
    "print(np.array([np.min(pb_keras, 0), np.max(pb_keras, 0), np.mean(pb_keras, 0)]))\n",
    "print()\n",
    "print(pb_caffe[:10])\n",
    "print(np.array([np.min(pb_caffe, 0), np.max(pb_caffe, 0), np.mean(pb_caffe, 0)]))\n",
    "print()\n",
    "pb_diff = pb_keras - pb_caffe\n",
    "print(pb_diff[:10])\n",
    "print(np.mean(pb_diff, 0))\n",
    "print(np.nonzero(np.round(pb_diff, 6))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compare the locations\n",
    "\n",
    "pos_keras = np.array([pb_keras[:,0]+pb_keras[:,2], pb_keras[:,1]+pb_keras[:,3]]).T/2\n",
    "pos_caffe = np.array([pb_caffe[:,0]+pb_caffe[:,2], pb_caffe[:,1]+pb_caffe[:,3]]).T/2\n",
    "pos_diff = pos_keras - pos_caffe\n",
    "\n",
    "print(np.array([np.min(pos_keras, 0), np.max(pos_keras, 0), np.mean(pos_keras, 0)]))\n",
    "print()\n",
    "print(np.array([np.min(pos_caffe, 0), np.max(pos_caffe, 0), np.mean(pos_caffe, 0)]))\n",
    "print()\n",
    "print(pos_diff[:20])\n",
    "print(np.nonzero(np.round(pos_diff, 6))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# computation of prior box locations\n",
    "\n",
    "steps = [8, 16, 32, 64, 100, 300]\n",
    "img_width, img_height = 300, 300\n",
    "layer_sizes = (38, 19, 10, 5, 3, 1)\n",
    "aspect_ratios = ([1.0, 1.0, 2, 0.5], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5, 3, 0.3333333333333333], [1.0, 1.0, 2, 0.5], [1.0, 1.0, 2, 0.5])\n",
    "\n",
    "idx = 0\n",
    "for i in range(len(layer_sizes)):\n",
    "    nboxes = len(aspect_ratios[i]) * layer_sizes[i]**2\n",
    "    print(\"%i  %s  %i\" % (layer_sizes[i], aspect_ratios[i], nboxes))\n",
    "    print(np.max(pos_keras[idx:idx+nboxes,0]))\n",
    "    print(np.max(pos_caffe[idx:idx+nboxes,0]))\n",
    "    #print(np.min(pos_keras[idx:idx+nboxes,0]))\n",
    "    #print(np.min(pos_caffe[idx:idx+nboxes,0]))\n",
    "    \n",
    "    layer_width = layer_sizes[i]\n",
    "    \n",
    "    # keras way\n",
    "    step_x = img_width / layer_width\n",
    "    centers_x = np.linspace(step_x / 2., img_width - step_x / 2., layer_width) / img_width\n",
    "    #centers_x = np.array([(0.5 + i) for i in range(layer_width)]) * step_x / img_width\n",
    "    print('%-20s %-20s' % (np.max(centers_x), step_x))\n",
    "    \n",
    "    # caffe way\n",
    "    step_x = steps[i]\n",
    "    centers_x = np.array([(0.5 + i) for i in range(layer_width)]) * step_x / img_width\n",
    "    print('%-20s %-20s' % (np.max(centers_x), step_x))\n",
    "    \n",
    "    print()\n",
    "    idx += nboxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### layer activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "po = np.get_printoptions()\n",
    "#np.set_printoptions(formatter={'all': '{:+.6e}'.format})\n",
    "np.set_printoptions(formatter={'all': '{:+.8f}'.format})\n",
    "\n",
    "print('%-28s %14s %10s %10s  %4s' % ('', 'max activation', 'mean diff', 'max diff', 'ratio'))\n",
    "\n",
    "for layer in model.layers:\n",
    "    layer_name = layer.name\n",
    "    \n",
    "    file_name = os.path.join('./caffe_activation_dumps/ssd300_voc_activations_fish-bike', layer_name+'.npy')\n",
    "    try:\n",
    "        caffe_output = np.load(file_name)\n",
    "    except FileNotFoundError:\n",
    "        print(layer_name)\n",
    "        continue\n",
    "        \n",
    "    if len(caffe_output.shape) == 4:\n",
    "        caffe_output = caffe_output.transpose(0,2,3,1)\n",
    "\n",
    "    input_image = preprocess_image('./data/images/fish-bike.jpg', input_shape[:2])\n",
    "    f = K.function(model.inputs, [model.get_layer(layer_name).output])\n",
    "    keras_output = f([[input_image]])[0]\n",
    "    \n",
    "    #from tensorflow.python.keras.applications import imagenet_utils\n",
    "    #from keras.preprocessing import image\n",
    "    #input_image = image.load_img(img_path, target_size=image_size)\n",
    "    #input_image = image.img_to_array(input_image)\n",
    "    #input_image = input_image.transpose((1,0,2))\n",
    "    #input_image = input_image[:, :, (0,2,1)]\n",
    "    #input_image = input_image[:, :, (0,1,2)]\n",
    "    #input_image = input_image[:, :, (1,0,2)]\n",
    "    #input_image = input_image[:, :, (1,2,0)]\n",
    "    #input_image = input_image[:, :, (2,0,1)]\n",
    "    #input_image = input_image[:, :, (2,1,0)]\n",
    "    #input_images = imagenet_utils.preprocess_input(np.array([input_image]))\n",
    "    #f = K.function(model.inputs, [model.get_layer(layer_name).output])\n",
    "    #keras_output = f([input_images])[0]\n",
    "\n",
    "    try:\n",
    "        diff = np.round(caffe_output[0]-keras_output[0], 9)\n",
    "    except ValueError:\n",
    "        print(layer_name)\n",
    "        continue\n",
    "    \n",
    "    max_value = np.max(keras_output)\n",
    "    min_diff = np.abs(np.min(diff))\n",
    "    max_diff = np.abs(np.max(diff))\n",
    "    mean_diff = np.abs(np.mean(diff))\n",
    "    \n",
    "    diff_ratio = np.nonzero(np.round(diff,5))[0].shape[0] / np.prod(diff.shape)\n",
    "    \n",
    "    print('%-28s %14.6f %10.6f %10.6f  %3i%%' % (layer_name, max_value, mean_diff, max_diff, diff_ratio*100))\n",
    "    \n",
    "    n = 12; p = 5\n",
    "    #print(np.round( diff[:n,:n,p], 8))\n",
    "\n",
    "np.set_printoptions(po)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from utils.vis import plot_activation, mosaic\n",
    "\n",
    "for name in ['conv1_2', 'conv2_2', 'conv3_3', 'conv4_3', 'conv5_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2']:\n",
    "    #plot_activation(model, inputs[3], name)\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l = model.get_layer('conv8_2')\n",
    "W = l.get_weights()[0]\n",
    "W = W.transpose(2,3,0,1)\n",
    "\n",
    "print(W.shape)\n",
    "from matplotlib import cm\n",
    "plt.figure(figsize=(15, 15))\n",
    "#pl.title('conv1 weights')\n",
    "plt.imshow(mosaic(W[0], (6, 6)), cmap=cm.binary)\n",
    "plt.show()\n",
    "# may be wrong, dimensions..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for name in ['conv1_1', 'conv1_2', 'conv2_1', 'conv2_2']:\n",
    "    print(name)\n",
    "    layer = model.get_layer(name)\n",
    "    weights = layer.get_weights()\n",
    "    new_weights = []\n",
    "    for w in weights:\n",
    "        new_weights.append(np.random.randn(*w.shape))\n",
    "    layer.set_weights(new_weights)\n",
    "    \n",
    "    weights = layer.get_weights()\n",
    "    #print(type(weights))\n",
    "    #print(len(weights))\n",
    "    print(weights[0].shape)\n",
    "    #print(np.max(weights[0]))\n",
    "    print(weights[0][1][1][:,:])\n",
    "    #print(new_weights[0][1][1][:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer = model.get_layer('conv2_2')\n",
    "old_w = layer.get_weights()\n",
    "new_w = [np.random.randn(3,3,128,128),np.random.randn(128)]\n",
    "layer.set_weights(new_w)\n",
    "updated_w = layer.get_weights()\n",
    "#print([w.shape for w in old_w])\n",
    "#print(old_w[0][1][1][:,:])\n",
    "#print([w.shape for w in new_w])\n",
    "print(new_w[0][1][1][:,:])\n",
    "#print([w.shape for w in updated_w])\n",
    "print(updated_w[0][1][1][:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer.set_weights([np.random.randn(3,3,128,128),np.random.randn(128)])\n",
    "layer.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for name in ['conv1_1', 'conv1_2', 'conv2_1', 'conv2_2']:\n",
    "    layer = model.get_layer(name)\n",
    "    weights = layer.get_weights()\n",
    "    print(weights[0][1][1][10:20,10:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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