Text2Image_GAN_MS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Text2Image-GAN-MS.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBCYFO1r5qT4",
        "colab_type": "code",
        "outputId": "b90d45d9-158d-4963-e65e-ab1aa221b3af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKmcaTq1vLu_",
        "colab_type": "code",
        "outputId": "d181a3b5-4489-428a-add6-977c715713a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQLnyKmcvOgL",
        "colab_type": "code",
        "outputId": "d45d7f72-cb8c-424f-a899-cfe8193196d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.1.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "km5s1d3KrRCl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "import gensim\n",
        "data = pickle.load(open(\"/content/drive/My Drive/bird/image_vectors.p\", \"rb\"))\n",
        "word_vector = pickle.load(open(\"/content/drive/My Drive/bird/word_vector_min_bird.p\", \"rb\"))\n",
        "model = gensim.models.KeyedVectors.load_word2vec_format('/content/drive/My Drive/word2vec/GoogleNews-vectors-negative300.bin', binary=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8qIb1O4zAUR",
        "colab_type": "code",
        "outputId": "1387a910-6d8f-4708-e979-238c8ce4a3f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as pyplot\n",
        "from keras.preprocessing.image import array_to_img\n",
        "from numpy import expand_dims\n",
        "from random import randint, choice\n",
        "\n",
        "def random_flip(image):\n",
        "  image = tf.image.flip_left_right(image)\n",
        "  return image.numpy()\n",
        "\n",
        "def random_jitter(image):\n",
        "\n",
        "  image = expand_dims(image, 0) #add additional dimension necessary for zooming\n",
        "  image = image_augmentation_generator.flow(image, batch_size=1)\n",
        "  result = image[0].reshape(image[0].shape[1:]) #remove additional dimension (1, 64, 64, 3) to (64, 64, 3)\n",
        "  return result\n",
        "\n",
        "image_augmentation_generator = tf.keras.preprocessing.image.ImageDataGenerator(zoom_range=[0.8, 1.0]) # random zoom proves to be helpful in capturing more details https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/\n",
        "\n",
        "n = 227\n",
        "image_embeddings = []\n",
        "captions = []\n",
        "labels = []\n",
        "for i, k in enumerate(data.keys()):\n",
        "  image_embeddings.append(data[k])\n",
        "  captions.append(word_vector[k])\n",
        "  labels.append(k)\n",
        "  if i % n == 0:\n",
        "   image_embeddings.append(random_jitter(data[k]))\n",
        "   captions.append(word_vector[k])\n",
        "   labels.append(k)\n",
        "\n",
        "print(len(captions))\n",
        "print(len(image_embeddings))\n",
        "\n",
        "pyplot.axis('off')\n",
        "slcie = image_embeddings[40:50]\n",
        "for i in range(9):\n",
        "  pyplot.subplot(3, 3, i+1)\n",
        "  pyplot.imshow(array_to_img(slcie[i]))\n",
        "  print(labels[i])\n",
        "  pyplot.axis('off')\n",
        "\n",
        "pyplot.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "11840\n",
            "11840\n",
            "Crested_Auklet_0001_794941.jpg\n",
            "Crested_Auklet_0001_794941.jpg\n",
            "Crested_Auklet_0029_1824.jpg\n",
            "Crested_Auklet_0074_794949.jpg\n",
            "Crested_Auklet_0045_794940.jpg\n",
            "Crested_Auklet_0077_785257.jpg\n",
            "Crested_Auklet_0018_1817.jpg\n",
            "Crested_Auklet_0041_794910.jpg\n",
            "Crested_Auklet_0066_785251.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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KECgxOsU68N72AKd3fji00HgBUvTAKncx0S0keIcUCiU0QSh86RBSoo2O1VcXCDLG70ut\nFkoJtJAMDw0jpMDo6AVIYvVGSInqASL6OKkUQDpIhaLwDq0EWsRQOXiPsCVl6eO5utBLw2uM6g0Q\nawe8stVoJ8uzfX+2sgdljMEXFqcNd3/l8/zjDR+nudjBGEWnU6K15t5776XRnAXg3G0X8e7zH6U2\n+3keO+tCHnnCcfF5Z9A4dJBWCBxdHGFxVFAUCyiTDcBsau3aE0CtP/n2dvQikR8M0HvGti10ckeB\npdlcot1poI0kURVCEPhgEdKBlCRKMTIywtTUFNXKEK1Gk0d372bfE3vp5jnNTj7wrrJMUpSWoggg\nEzKVgAFd0SiT0u4WJAmURQPTylk7VSfzFXAgSBhNh8hEjWPdg5R2Hryl4x1SS3wv4rN5b2wLSap0\nJKILSykkWgpk8ISeFxlUwGiJc4EQBJL69x+6Dq3PGZqaYnO6RKudo1SCURWs82jt8F6jBLjQ8+Z8\nAAFGKAoPLgi0MjjvcXhkAGcEwseUSS8UjwWLEAi655JKQX24jvKBTrfFv3zzm1x5xRX4vED3igkk\nCgT40kVAFQEpNbaXF0x63obWCmsBKfEOpFEI70nQvYfe98Jj1aOdgJIJ3nf/9U/dD7i1Wi0Sqfmr\n972X6YceBO2o1+tYa3li3wGEELzjHb9Glg6TZRnCGP7+8wu84lkp6+QTrJ2qohtLtBo5t+2Hobf+\nFk1VR0kJxPv8dAopP0g2tkZx4Zr1ZOk0m9esZdPwGvYdm2b7lm20XCDxmqLoUrqSTZu28uCDt5Ni\nUWs2RJpWljA8PgpLSyy2WjTabazXZKWktDF8zLsemRqkLdEEvLU4YXAESgSL3TbeH6NaGyHNRqgn\ndQQxzzeUlOQu4VjnKN5ZfGlxpteR4UCiIqgZMEoiVYpERoI5YsDUEAIsDi89WhoQjiK4U16XFYHu\n2PQ4ZXGQPC+oVKp4G8FABoiTpsfR/2KBkgboVURDBKFSSBAgkJE64k+s1gKRMyfj6/3Z2HVLHnrk\nYZaWlli7bpJbb/8Ol1z0XJyMnppw8TtNlkb6itCkTtA1CcKHSDIWEucsXkpwvVxfsGitCB6UVhRF\nHDDORve5tCVKi5OHd6vc/ntB42TXSC7zhJVM+eLffpiDD95FkiSUpWNuYZGyLPmjP/5T3vGOd/CJ\nv/s0551/cayE5y0+cLjCf3qsRegcI5NHCEXJj/+H3+cV//fP03KQhUAwLnrmAljW1WH7nMgyeov9\nc4zdM/6EcHa12jPOvwpDi4e/eCvn7ryWD3/svfzyz72PPXt2c/jI41x48QvROiHLKizO70fIhO8+\ndB/GPEwtq5FlQ9TrNXQQNFtt8rxLu92l09aoAT9WkQSBqFRQOt5zUzHUkyE8CiVzhAw0movUhkdJ\nQwbCkegEJRM63TreSFrlLIXqkNsST8C6gBE68mNFINEKJQRexvGYmiSOeTTWWgIWGwL4EoNAhuyU\n12VFoKslm2g0FhkZGWN6ukmaFUiRgOgFj4HIvRI6VjMlKCmwjgGHbnlFrp/HU0oie2R1pRWWWL31\nzsf9PmCUJuAoyzbPfc5zuO32W0HHf9ZINfhc7x1SKpLE4JxHeo/vFUiAyL3zJcJo8jxHQmwpkvF4\nYzR5Hr/f2hyjDYXtEkLxr33mfmDsVJNCXVk+/6nrGR8dodlsYq2lkxdccsmlvOtd7yJJEoaHhwfH\naykwUyndvEmz24kPv/W8/Gd+kW5ZIIQZfPZKcNUvdAghBoTwHxSbPniEYb2Gqy5/PUE00Cbhwf0P\nsO+RexnZuJ1b/+kLPO/qlzFTdFFimMnJcUrfAcC6gnZ7kURXkFIylGRQH2a2MQchsh1CiBQPYwxK\nCtJMkyQJSsDE+imMSLDWo3VCpVIdjPvx2lq0lnTKJg2RIkSvdcw30B5EUARZooRBSANBkDsLSIw0\nJCYhE1nkwuJIkgTnFMJ4KB0qKKQ/dbppxUTUwsI0d9/1IGftOgsnl0AdJwpIATqN3JbIbVMEKQkB\nkiQBLQfEvwSJRmCERCmJtS5WNVUkE0aL7mglS6kO1fjmt7/BoUOHkEpy2+23s2vXMwCBK8qeB6gG\nNBRjNLgAPpCmSUxUCoHqcfC0MQQfqFQqPY9DoETM7ZWlJUmSyPMLOnp00gDmpNdktVm/chYEeAJB\nPPW1J/e/rrQNOk+E4Nfe/hYmhmoUPY5iJ+8yMTXBHXfdjnMlaWpinleACD560jKglOGM9Vt40TUv\n5W//+U5AYlQSmfS9rU967ntsQgjksk1JieCpvbtyFedeATau2UiadhgfXYvoHOUNP/SzXHHWBmr1\nCuduXY8eVkyNrKVoPMjOrSNM1MZZP34Wu3ZeyJm7nsXOnWeTaUlRthmpZ0xN1BmtGLJUk2qDkYpM\nG6TQpEoxUkkZr1eZnFzDULWGyRRJlpJVM9JKZEfEaA2ETDCyRj1by1h1E+PJZjI7TC0MoUMGPj6H\nBE/wjtJCM/cUziNRBCki3ggIAoTRZColTStoaRDJ/Cmvy4oe3Y3fWsvZZ1e4/ZZbuOu2O3jRZT9E\nl5xEx84EX3qMVJS2RCIxIdI0hI+VEikVqY4g1Ac0LSTC9Fu+Akpn5HNHuXBzSl5dw7duvZdWY4kd\nWzay2Gpw4bMvodlqxge2dIhU9RLOkYZiJVTSSAsRveprmibkec8j0xLlAipNcd73ZiJPUZZIFYnO\nhXNobQjK40JBCA5bnqYqLLeVlEhOtk8Iwf133MGmtaMgEsqyJMsyxsbGOHToELWsgnOx2Tu46CmY\nJE5QlUqFBSX50be9i7LVjjP8Kb/nqfuWh6dPBurVbvtmDrF5dBzfabBm3SbmGoEv/dlHGDpzCy0K\nRmpTfONb/41d511NvX4GMr2XZ45s4f47/4W77rgVlRi2rT8DU6kzc/AAkyPDjNcVh+ZaLC116HRy\nkII0gSTVVCqGNJFIA2XewSMpSii6HaSvI7MKEoe3Di9iVVRJQ0VWyPUoY2mXhlUooShtF+9KYmen\nwRax2EkhKZRHpLEDI/Q4syZIlKkAMUAL+fdJGH50z+OcuWMLY5MjXF1/Ls2lLrqiKcoIIlrpGG6q\nXreDsCidxKIEAdUjFQsRk4jexaIEgPWeitJ0O13eti3jjPp9nLEt4eKbFONrquTtJts3bWL60EG6\n3S6TU+vIu44QHELGNjJtNDIEchvPR5QR5GLjcSw2eOuQvbBYK0VHOUwXpDHoWAkh+DImQ3tdF2Xp\ne+oOq9OW5+IGoHASLOh7ZnEbHH3Sz3vyZydJwlA9JS8c1aoiSRKKouCh+x+gmmYIIXo9xZKlcgEA\n7wqGK6Nc/OIX81NvfcdxhnyIjfmyRzQIMPA8pezLMfVFAPzgfJb/3e9zXu3tfWdt2YHzJcX0Pmpr\n1rHhjM2c+abXMtOSrB1fwxjw6P7dfPfmGznv3GcyPnwGmyZrTF54KWfvOo99u2+iWYLydV736vfx\n6Q+9i6RWZZOEmYpmqaHpNAsqNU2WGrLUIKWkUxS0Wku4xOCdx3YKlubmyGoJ7XaL0fEG1WodLaII\ngFICWzh8KanoEazLISQE79FSRqaEjBGYUgbhA4UtAY+SEh0ELpXoXgTRwSOT5imvy4qjuXRNbr/r\nVnbsPJeRiqQyMcHi4iIQB0FRFvGhCqBQOCVRePAejyUITbAFIEhMQhk8Oih8WWKkwtoYrrhbbkCs\nqXDnoxlnbrmMornE8GjJ7Y8+wWhVQzDsvv8+6iMTjI6NI7RABIgNFQJhA5kxeOVwhUWafhuZQMk4\nSLWKhYeKLBFJhrIO5z3CSERhoxKLksS6a5dSrO4B0bfl7XWnej2+5+m1ewkhsNayb9++gd6Y1jF5\nXK1W0VrH5LWJXlpRFDiX9IpQmtrIGt7w5rcPcjt9dQs4zpd8MmAdP6fjoeyTCw8/CIUIgHQipWim\nfOHrf8MvvvkPmNl7M+NjWzjjGRs4dMudPDY/zcbRSS666iq++fUbePZFKbfecQ/zfowtY5ILn/XD\n/Mut3+Ll117D9OweKmNbWGzux1JFi5JUG3QVsqpHG4HWUUHIC0e33aE108RUM0bHx5Cml78L0Gks\n0W22yCo1arU6RgsSWSGko3T9AomokTGElQHvLZ0ix+DIsgwVHEokmF6OVoiAkAKNjrQUW2CDHGjd\nncxWBLoN67fyrAsu5KbvfJFzz93EXd85SEijgGWRH/eaJJLSlYggKcuSYAMqUQRfxlYuIenmnSik\nWUI/NRiCRzjJX2z9Ifbccx+JP8Tfn30zjzZaPCNtc8GeIa6/6Zu88Y0/hV9StLoFHsfExARCK5RL\n4kwtoFuWSKNIEL28W+yqcD7gvCM4hzYG6xICoI0htzlGSrSOF9B7jxcSYzKkWr2E4YAfAFtYxjLv\nm+ztC4MG+BNDwcHv/f3LycG2xGQJd3/7FspOG5lV0FJRG6rS7XYZro/EfsgegIVq5L1lWYYvOrzz\n/X+BMobgl7WQ9XJwfc8t9ufGn94fz7sNChBePAXYBmIPqzyM/fx/+ySve+WbeNsv/QnHGkf50g3/\nzMT2s6necScvuvQazn/Gs/nyV77IBSNHePmrX8lN3/4Oe/beRTq1ky9+8ktcceW1uAI+8qE/4Ede\n8+M8//nP55OfvR5bCgJVQPXc6SLmsoOi27EE60iFwlUSaiNDsVhA5OApqWm1mhiTIpVBqS6lMQzV\nhjBB0pqbp7QBfAaiBDoE7/B4iqIgEWEgJxZCYLm+b/9+xs6P77Mz4vE9DzJ9ZC9v/uVXoutzPHSL\no2X3UIQYpzvbC0kpcZRoKoNQtd9yYr0nBId3Ad9rlFepprQe4aM0U6IVZ1xwAXfd4fjh/bEKp9sp\nW8+0vOHHfpTP3fAlXvKyF1Gv1dh/8ADXvuglBC9QRlGWPboI4LoWKwRKaWxZ4oMEHwnIQkSPzchk\noJ4ylFbIrUUpCWicsyTaYG0OxQ9OMWL5T4Dj+PU9wtQT3w5Eagda8cCDD5JlGVprpqamaDab1Go1\nrPVUq1WklLRaLWq1Gp1Oh5GRUYq2pF6v0xXiBA9zeSj6vXpt+xyr5UWRlWSnVps5N8RnPvdnXHzh\nq3jw7tv4mbe8lbWbz6VYnOUfPvtBdDbCK176etziArsfnWFqaifNZpvzt1/N+msTtp3/UvY/eAuH\nZxe46ZvfIO8UvPp1v8Q/fPQjtAOEILGkFG2JCIKApSwc7dwiRULSE+1IEwWqAr1G/T7lRylNXjqk\nCzgXsK5D2Y35/Jqu0qFLlwIjQYqAUooSifGxg6Yv4BlwJE7TT1k4b/D+1EyJFYFu246NbNoAn/nk\n33DNNddw1QvqfPHrCi8FOIEjR/noPmpt6HRbPSUTjQxR8lxKhcOjBvVaAXmJMAovY5O995EEPDQ6\nRLebk6YpnW6brKopnOdF117F0PAwpbUklQxbOpQUFEURNeW8G9BJAKwtCUoTQonSGhsCwoPqgbAP\ngSQxUYYdSNKUsihASQye4AUyXb2DQi6TPh+AxJPec2Je7uTJ/5PhRlpJEEgevf8ORkZGqFRqLC4u\nDjy4eL8s0A9hE7KsSmIM40NjfP0Ln+eKH/4RnOxRl+IZD871qaAWlp2H6IW1y2TYYyblhIrwarZd\n285juPpsbv3O19k8sZ4jh5/gH7/4d9x5z1d5z+98kaI4yhNP3MOubWdwybbncf9ju0nTCW6//VOc\nceEL0GqRDeecT8MvUJaa/Ycf5BPXf4ilrkeKKH8mhKdTSjo2Sqt6LyitQAiL0+CWWigExpSEAN2O\npdVts7TUotlsMzIyig8CoyXWF3RsDpnD0yJoRyYlXglCiCRjKcFJTyJ1fA6FB2IHlumJbyjpEXb8\nlNdl5RxdI+exB7pMTWxix4YGN998H1aMIUoxaLjte2l53kGpBIdD9GJlKSUlBZYoupkGTbfIkYnG\nd0qkBqUkRVGgtSZRGidLfBnFAvCKapZi6jXOf+YzeeCBhzh27ChCeYRQUV3WelJtCMt4en0F29i+\nUg48TCOTSCwUYuB5htKipML6QJakqACC4qSDeDXaqby2JwPdyd5/KtAQQjAyMkI+u0iSJKRJhVar\nRZZlZFkkdRqTMDQ03OtBLmNPNJaZx58gSzTtwi37vJN/x5MpLcfP6cRCS+yj7IPg6s69nrljO9f/\n/QfYsmUr5152Ee97z7v5yZf/GFe+5E0cO3o3wWesX7sJzzBLeYPNm9azdutauovPo3SSX/j5H+c3\nfuPtfO5TnyHXFfJWm6kNm/+yNlAAACAASURBVOjmAt1r1cJ58L1rKXysnkuBEQKjBJ1WTnCCLg7l\nAiQan5eEEDjWbXPkyDRCxU4ltEOnATUSSEYtQliCKkmExkuFFIJUKbQCpcRgHZkQAiL4AfvCB00i\nT52WWJFU9H+9fRfV2ig//BrF+FrBy16xEckxrLNR960sKWxJYS3OQ1EWFGWBdbHLoFvGNqpEKXzX\n0uq2YgHAB3QQSCfwuafwjk6nQ7vdjmAlo9fVaDYHvYtf+cpXCMFx8aXPJbgo4RJDcomzsY2r37Ts\n/XEV2/5A6Ht+eZEPFuTJXYlKEoKKIp8qgC2jQOeqrkWI8NQtqvWABKkET1Yz6udITviYPo9t2aaR\nIBybtj9jUJiw1oLwJGmsoNVqI2RZhZmZGUySMDExHrmXXtBoLfD+X38niwcP4r1FOxAyblKJwaa0\nRGmJFJGkrpVEibgZJVECJKFX4VO9QRK31Wzt3PLa1/0yU+vGWWx4rr7ycu6bP8AXPv0RxlSdu+7+\nJxrzlu9++2vMLjU4fGwOXwoy2WbPA1/jF37xbXzwg9ej1BB7Hz9ASyhs6cnLeZbaLeYbS8y3myx1\n2zTyDoudFoud6Kk1G232HzrMgcNHeXz/YSazcWrVKkePHqXZbLK4uMh8Y4mldoulpSUWmw06eZfc\n53hKOjbHijLigT2eIy+KHqui97xlQpMJfcIk1y9cncpW9Ohu+7Yk1eMcPDTPuuE6f/T+Nj/9xp/k\nL//qBpQBV4bY0eCPV8WklLjC4XX0rvIiHyi/Ou+iaF7ZayWRoI2gLEvyRgshNCbRPQIyeF/QbrdI\nTMLY2BgLCwsRhIzGFZaYmJagNL6IYXL/c2M1xtHvxOifnwDKoqDUUVoKwBWWgMcEidEGoSVW/GAx\n6r8fO5nwpveetFLh4osu4pFbbhx401mWRYpJXlIUC3S7XcbGxrHWMjs7i1KakdERQulIS8fH/vwv\n+Q9/8Ls4GwaTznJaTN8zCycplGgp8ULghaAMJ3p2q70Y8Y2bPsszzzmb0lZ48IF7ed5zXkuDFi94\n3hV84A/fzZFDc7TPW0SN7mLrxrUopbj7/oc4++xzeP66XSwutviFN7+NN7zmxbz8FS9k+mjBwvxh\nXGFpdXIg6kMWPQekcB4pFLPHFlk3MUStXueCs7azYe1WZmdn8MGwY8vzuPeBx9ixYztf/ubXqFQq\n7Nm7lyArLM41MR4mJuHMK7ajqiWKqExkQxT4laGg3e2QmgytA65HFbIEtIvcy1RVMO7UcLaiR1cZ\nHuHxg98gycb4+Gdv5tD8Hj74gc+gdVTihf4DJwi9vEhZlHj61S8fuxRsGISViTKUtsR5GyVGgFqa\nkVYyRkdHAEhMQq1eZ6hep1arU6vXCMFz2RVXUq8MIW2IzfkDteL42bq3epdwIbaD9eSetI5Nv1hP\nYhJkEpc5VChMEKTGoJU+LjrkVrM796SOhz7N3IMIAhFODgT9mXP5djIybgiBPM+56667GBubQErJ\nyHCdkaFhbBGXsRsaGmL79u2xYu4Dw0PDGGMo8gKUwAxVGK0KPvLe38MeOkgq+x6aGHhtqdRx02aw\nGSVJdFTTkTJOcFqK2FqmNFqqntDk6rUrXvAyHrr7FjatWc+PvOQltBYeYc3mnfzkT1zD+o0buOYl\nP8+Xv/QFxtdM8Nd/+HscfGiWI3se5S//82/zn37rVzj8+B4ev+cO/uLDn6EoA77bJkkUlaqkUtUk\nqUAlip27zmGpWbLvicMszS8xMVpBKEneKrjtvsf42rdv496HH+WWex5m144N7Nw4wezCMS6+5Pm0\n59tsnlzHwX0znP+sbbTrHS659nymKltYbLXRfhQcqOCR3mG9ILdQWk9RxmdIBDAkSJMhdIrsFR1P\nZSve9WdfVvLTP/FzHNo7zvMufzHPvXKKV73+JSwtNAbrpwYfZ1wRINiYCzGJiXF7GVfvEkIgA+g0\niX1tQuBVVHuVAUaHR3oKwZIzd+6MwNYLP11PcHPu2By5sxS9BmDf069SSlNaG79PRDe3n5cbkIa9\nw1tNUAJblgNg7IfX1lmcCIRUI0z8bMSpta1WpYlAiFf2hLVT+3ayfSezRBiktdx28+fACxKdQtDk\nXYctYcuWrdRqNWZmZqhkGdpoijJW5ooiH9y/drdL3m1y/Uc+iBYFrihRhMEW6SVx5bnjW0/Itbdm\nqJQxjI2bINGSRK9uoHvwrts4+8IXsX3bNo4cnmPv7DSpc1y08zzu/cYN1EcKzj/zDMqFxxnbdg5/\n+OG30C4OsufAXq685oWkwwnDm6f47k03sue+xym1Y3SswuhYlXo1ITWKam2cW269g+kjR0gSTXWo\njvXQ7RQEKUiShMbCItt3bOZtb38jcnyUdt7g2TsnWac7vOl1L8J32rz6jZcyuhOufs0FzKcH+e5N\nDyGNZ65xiHa3oFtail7XDEHhEQQh8e746m8u2F7vfYpfIQhT11133Slf/Okff9N1m7dMIXWDz39i\njn/329v48ufuwRYVnA8oLRFSEVwEDhscCtVTKwGRRJ254CK9RIYYaEipopS6CDE312qRpilaa0Lw\nTE6tiSHNsdlebk3wvMsvR0sJIXZFGK0J1uMJaKVQSuJEdKPTAIW3eB/QSLTSaEGkmfRmdaNVDFG9\nj2vFImIrko89uQLFS158zXv+Rz+I/zvY3rnudYM/+tj1NCK6p9NOZfFMDg9zw8f+GiU1Q0ND1KpD\nbN68GWsdRVmipKJWr2Gto93u9O5x5Mh572m1WkipsDau8bH7od0885KLEL2q26m2PmF4+bk++W+A\n7VPVVXlfASjEdSKzLMws8ehj97M4v0ApA6NrtvGMcy/jwL7DbNh6FkrVCUOaiWSEjTufxea12yia\nh7nz1hspyjGGhi2H79rPQqcZCwZ1TyggMMy99zzIkelDjI6O0Gi0mJycwjlLrVJlcmyMLEkJEoQ0\nfPWm73Dgocf4uYtG6axbw1RtPba7RGd2jsPVBUQ9ICtthJKYjYGqGKFruyAEOqjY/yqjh+5RFKUj\n4OiWlq4rMYAKEidy8IoXX/azJ723K3t0z7mKc3bVeOgex9Bay3/9wAwPPdwgd12cjxWYqB0lCTJS\nNqSSUT9KSYT3SAdKJ7FtQ0T9OoVAqag3dfaus2LlLc+ZWDNBkqZ4D7PHjjG5Zg2VSgWtBPgueenx\nLlZZpYvsaOGjlHJcmSDmC63uNfxrBVrQzruUMsSWNe9iiF1aXGHJen27ANZZSlvSLVe3cskJ4aek\n544vC2N5aqh6smNPtjkLb//Z15MpQ31ohPUbNjE8OoxJE6r1KpVKRpJGone1WqFaqVAUeVygXEcg\n0zquyFYUOd47KgieuOduVKowWpIqOfDYlq9oJmWfSCxOKJ4sB73V3tT/lTv+gaFylE1bq2zbvpnH\n9+4mn53ltrs/jmOBzVvWMVyboDn9ELvWnkHeabJ3716+9NVPsHe2ixo6m8MH7uaMnZfxslddzHuv\neys//IrXMpTv4NA+x/T0IapJyrZt29i2bRu7du3inHPORgjB4uIiRxfnabtYYZ2fn6cuMr5++4Nc\n2vkkP/bl9/Pa+q38/HnTfOgXDKrqCCbHSYuTlsqIohStyJ0rSxrdNs28Q7PTptVpx+JnUdAsurSK\nLs28w9HWEsfyFtbaQdHiZLaylHrnKEuNa3Hlpxma8Nxzz2HStI71OUnaS/z38mKDh0oJlNTgbVQY\nkAKJRSZxxlXBUbiCN/7UL9FeavCtb9/Mjh072X9gP+12m2q1Sqo0Z595Fo/ueYRu3qUsPU5lhBJK\nAt2i6LUQiSiNbm2UX0fgeksixhxhwHpPZjS212e7fIaPyzL6AbdOCIlWEuf9ql5R6ukm5E8Wqn6v\nY5cOPsb0wQO87vU/wZEj0xhj6Ha75HlOvV6n1WojhOgtIB4nnfHxMSCSjbvdvOfZB7rdDo2Goij2\n4GzBpjPPIR2qYZVYUZJnuXu6HOCebvj9f7Jd+4zL+MbtN3P2eWfyrK3DvPbH3oDT67lYBrbsPIPd\nDz1MV7QRazbTKpbYvutCLnrBFayvjrBuwxYO5p69D/8Tf/ex91PkVW7dcxv33vMQs3Pz6Czl7jvu\n47xzdpGmKZMTayiGS7qtFvV6nWP5MY5OT9NptajUq8gcJicnufKSC3jO9Rmdxb3cc3ULbruJfPN5\n/H8/8V7ecsM76ZZ5bOJ3sY/deggqkBcFPvdIHXuVc+cj6VjE9k8tBFrGxayOuRy5QjPTikD38MNL\nVOT1HJnvoptDUa5cOBKT4JzF9sKNvshh35yP3BqjDUoCTvLhD/05B/YepJN3mZ6eZt/ePTy0+2HK\nbsn6ySmqZ53FsdlZmo1FpjZtiqGsERw6sp8tW3bSLUpEUCTK4PGUvsQQG4j7wgKFFD3vDghx9fm+\nLJNzLi4n2ePm9E32QK6vemLLuL6EX8VAd8JgD3ECerrHPZmwC9EzL4Cqlfz6W36RN77xjczMzLCw\nsEQIgeHhIRqNZlzwvCxptVoMDw8RfFzxaWmpQZokSAWdTnPgjWktaTYXKQvN7kcW+Nu/+EPe9Gu/\nS8DiZX/93hM7O/pV3t4ZI3Rc+9O5qJe4ynGO6fY8OzaO8eG/ei+XXPlCPvPJGzjvjM1cc+3r2X33\nfo4sLvL8XVsZG9nE/tklhAoc2ruPoQ1bqE5U2P2Fz3P2ORex55FHeGLvY0wfmWU8qfLgzBG2bt3K\neec8Y5Bjdc6xZniYVtGlmmbI8QkOHT5AmiZ0OrF5YHrmEGmaMrRxnImdU7ziHz3BPJfm/DSNm3+J\noc0pru0hBFQ/9eADpbNYGVBAGcA6gSo9yARrRa9n2kQ5OAfBWkKen/K6rAh0u7Zv5ZnPbvPgwwGP\nIJHpCaRLrTSut7qXCxYtzcCbktrgsdSSlKuuvIJPfuJTZFmN2dkjrFmzhgOHDiKkYGxsjNRUyJtz\nrJ9cg5iaZGrNJAcO7efMHWfgyoJ2q4UyFYIPWOnorynrRUAFhcnSGLcsqwYaAWUQaB2XUqtUMigd\ncpnybN+Mjq1kRVFGMVHx9Af/D4qt2HqFpq49/+YNr+eVr3wljz/++GCtVYC5ufmeUKKj3W5SrdZo\nNlvkeT4gdksp8YVldGSUVruNc5ZmM6pR5N02APPz89iyExcml8eFG55yPuKpHt1qp5X0bf3as/j4\np65n/YZn0D10mJdfey0f/dCHuPaHXsvHPv5e3vnrf8LRfYt0Ri0zjx9m957dOAndZsm+x0uET/nC\nZz/HzbfdytGZGTZu3s7dzQUuPOd8ZudmkNbG1r1KlaWlJlIItm/cSG24zv27H2bL9s1x2dCQDDxo\n5wJCgXcB59oEXZJYzcj4MMVSGy0FQsTFsSQCFBghCEWJC/17aOg6R9HNMUYRQlwEXRIZA90A6Qoi\nqysCnWx7vnzjKN7N4oXD9ry0GAYQE4a9diIjk6g5r2MzvY98E8ZHR7j9zjsxKqVSqRBwPPTIwxht\nqA8NMTW5hrxTMD4+zvj4OJVKylC9wvj4CGvXreOaF13Fde/5PUbXbgTnCcQ2IhUiW1pKhSts5OoZ\nhQuu1xYS0ESdKqUM2H4uL+ajpJZxMZ4Q4hqRvff2+yn1KqYhLCdaDsb/KWglp7IQAgZJqQPl7EHe\nc91vsXaizuOPPRKvn9b40E9vRA+/Wq1SqVRjo3YSRRmLIirgNBpLVCsZwXu2bN7E9JGjPLFvD5OT\nE7TbvXwwgZkjB9i84UyKAbnuuOcdxHFZ/ydz5vr3dbUD3mz7MP/4pU/xsvOvQkxN8vDBeV736lfy\nV3/2X/jzT3+Br93wX/mT/+f3+dM//iBrt52FT2oc2L0XaST37d7LvgPTVLOEXbt2MTwyQX14mFom\nkcoTQuQlTo6OMbfUwKQVZhYWGRsbQ6UBJ0u0iPighaT0kQrinEO4KLobnEM4i+3kBFNghIvCqyKy\nMAZRQ/AkwpGXARckNniETEBqdE3igqNjHTITKBQ1mRHUqa/LiqP5hS+7gqXGEvSWKtRKobRCquNJ\nXqmOz5haq97ShIokjXm6x/c+QbPRZG5ujunpI+zbv49Op8P04aMcm5nj8MFpsiwmpwHm5hZwVjIy\nOoxzjoW5Ja77j7/B7PQhmkUDXNlb0NqjtMS5sicaECWVTYhKuRZQQvVydccJzdpolDZR81iKgTxT\n0pMPOm1P34QWVITgT373NxmtVFCqMuhnzfO8R9ZWlGVcy/Po0SORHJ53aTSWesUGj7U28uiKnMWl\nRaanj9ButxgeHqbb7dJutwciEc1et8yK57VCZXa1A53tVvjb6z/GBc+/gFe96o3UbZdb776D697z\nn/n4X/0xf/fRj/Ke3/kdyiC48cbP8tCD36Xb7XLfffdy11130Wg0mFtYotXuMjkZ14XNamMcPrpA\nrVZj8+bNtNtt5uejmm+r1WJmZgYlJaG0A1muvvU7UyJ9qCDP8946sl209yjnUMGigkWUOdIWiDKH\noksoHCEPhBxCHon9IgR8KRBen0BbKvK4vOqpbMWRfettj3Dl5RdETTkXFQacjaAiZVygOq7w7lEm\nAoZWOtI0rKfVbLK4tMjM7Cydbou5+Rm8jdy74eFh5htLNLod5ubm8N4zPT2Nc44Dhw6y5/H9zM7M\nMHP0KPPHFvn5n/nJQVO+xVLkxWBhmxhO2+iZCYUrLbG826/Gxbyc1FHsL3gXXWV64Kc0QTAQED2V\nSsZqsZOFeP89x/a3TqfD7/ybt/D43j0cm58hzQxSGTrdAiE1zWaD2dmZXlFhEe8dCwvHyPMO7XaT\nRmOxl8uBhYU55uYXUTolSbNB6JvnOWWZ02wuIYxm3fgayiSu6P7kPj0ZYkXfCwhSEKRYxrU7sUK7\nWu2y5zyHb331bkbWbuC69/4m7/itX+GMzRtYyI/ymhf9NBecfTH33LmPhx96DOtLHttzmIOHZpmZ\nnSfPHVs3b2JoaAgpNXOzM+StJs12i2a7xczMMQ4fOcrswiIjIyPU63V27NiFTFIe2XuQ83acQ805\n8qUlbIh8TF/2VvpyJS4UOGdjKFs6XDfHdwOu7Qi5I5QWXzh8DmWhcYVEWQVti20V+I6AroC2wDcD\noSGgk2CbkqKhKI98n6Frljqas7M89yLL+PhVfPFL3wAp8Bxvp/LOEzyUPdAhRLqJdx4hHGliIrVE\nazqdDgEHQdHotOl0OswcnUErzdz8IuMToyw2llg7ORV1rDRY69i3fz8/+tqX8pY338gLrr4Gkw1h\nTIb3cVFeqRTEhdegKJCJjtLKy5q7pZKDRn4ho5usjab0AYeNYW3QOJ/jvT3t3a1gfc/o/33f+5g9\nvJ8nnniCiy+6lNnZud7i5jGPq5RC6yh/Za3tdcfE/tNOp0MIgSzLmJuLx23cuJk1aybYv38/WdZX\nij5O/p0+cJC/+chHeMs7f/Mpvbj984ITs6urecI6md2/+wGed/lzOLIwy/honV9966/xwN7HaBQf\n5ZZHvsvm8Q2ce+b/z957h8l11/f+r9On7mzvq65Vt4otybLlbmQbG2JjDElswJgLIYaEkJDkQoCY\nJHBTSQj1JoRmTDXVBmzZuGBZsi2r97Zabd/ZnZ0+c+r3+/vjzKxkYouE370kj7jf5zmP9pld7c6c\nc76f8ynvorPvwH4yxTJL589neHiE5cuXkynD8dODJGrmQoIA3dQolm0akknS6TSqbYfagUJQKBRo\nbmlEcX2uXb+KmaDK0osuQvVdnj9wGNt2Zs3hhRaA9NCkhxLYeI4f4l5rhlroKrKWkQVICEAnJB5o\ngQqVAE86aBEFN64hDTBUA2mF3uuqo+EHvyQz4vDecdYu7SCaWksul+Hq6/t5290385GP3oYQXij3\nIxV0M3yDYZYXkuoNM0IiEa+VHkWy2SyBCMjnSmRy2bARLSSxeIz0+AS+cElPpWlpaiadTiOEx5mh\nYVzPxXarfONL9/Pn9/4O//qpv8fxXGy3gu/5BEEISwibnrUMzw8I3BBzI6UImRH4CHl2k/oywA98\nIEDXasKbMrRpM0xj1trtQlwvq892DsH/fGWfFDaBLzi47WmO7XuWmWKW237jdtLpNKap1zTHQul0\nTVPJZmdmjXBA4PlVcvlp8oUMufw02dwUnu+QbIgjZcDp06fwPIdsNku1WqVcLoecStfFEz5Lli59\nSUZaZ0JomvYS45zZDE6r2WyqytmvL+CVzU8xPT3BwInDLF62nOuuu4Y7bryJ4wNDvOm2t3DrjbfT\nlGqhr72ZhoZGSlWb3r5OTENl7aI2UtFwoJgrFIjHUyQSjXR1dFDM5zF1lahlkIjFMDSFeNSiXLJp\naIgxmJnGrXgkTJNK1ecP3/Veyk6xlv0HBMIh8MMkIggCVCGRNiiuigxUFE9D9XQUV0NxTFTPQriC\nwAFcHWGrUFHwCwF+ziXIB8iiROYEsiBRHAfh/JIuYJetS/DDR3/IVRcZNDYfYmp8Ek0vcnjfMO97\nz73ImvJnnfeq6WEPT8oQnJsrFmty6ypC+PieTzRuEo1GiegmnluBwMMwVRRFpVKucOzEcbLFAhMT\nE5RKRQ4fPszw8BBHRkbZNTjE773n91FrGYIvXPzARQTg1bTlbNvG88K/pdWUTQI/QPhhlukGbtjP\nE6GbkFJrgFLD0xm6ETZFL/AS55dduhGnMZHkgX/5VGhQbZpMTE5g2zaZTIZCoUCpVCKbzTI4ODir\nXpLNZikWi+RyWYrFAjMzM7OZX19fH6ZpMpOdJpefoVwpEolE6OjoQFFC3UHXdTEtixtvfjVu8B+n\n5/269eicyTSLFvTwz//01wzuP4FbtWlqauX2126hN9nI8WPH8KsuQ5MZVBFQKpWYyuYZHxrFEyrt\nqQTrly1hdf8ihs8Mkc7NMDQyUhPQrSMqdExDIZmIENN8WlJJ0uMz2NUKxw8eprmjlenB/dz1hnvw\nHRfbtkPFIkWDQKArOvgavm3iOzqqF0e4USzPwnRNdNdAs03UgoVeMlEdHUsYWCKC5hqovoXqW+ie\ngebrYCtUKx5UfsnStam7hcZCko997nE6mpuJKpJtO/bhOhG+vPcb/MbrruNHPz5GJJlEkQq+CK0C\nFUXB9VwM08CIhIDRszaEKoVsDtPSiEZj5PI5Ug0pDD0U8JyamiIej5NTBJ7t1EQUA6TvEgSCYjHP\nFVddycEjp2tlUVD7myGIWFfrsugBnu/NTvcQEs3UkUKCFpbcipCoBrg13q6mqPj4CBnMDkcuxHVu\nRnS+jf+y35MKg8f3kc+liUbiXHXVVRw+fBi3BuL2g2qoMgxIxadiVwikixCCSqVCpZILS9ZoBCkF\nzc3NZLNZCoUCnmcTiUSJRBKgiFm8VrVaIRqNsvrSKyh40JDQQjFNXvr+1Vk+W+gHiqy9Is9+71y1\nkwtxfemxh3lsxxOsuWQTY9NDdM2Jks8VkVqZnS/s5eJLLiVldlHxyvxs+04aGxupVKtk41HihTyx\nWIyJTIaTw8P0L5tPb1sHJ06PUqlUUDQoFovYtg0oZCdm6GkNFcFjDRZNbS3Mu3QJB/ceoDXag2uH\nWnGaqhEEbq1iUBDSQ0EPlYL80LhaURSqUkHTTEQQoKo6QSCRIvRqNomg6BJF0VA8DV2qBKpEdV1U\nVUfXVBCvvGfPj6NbcoSD+ztQjSSmZbN8fYXxw3Mxm5Nsfu+rqFRX8/Z7ljE4VOLpHTvCwOPXqFmK\nQrmSRwkMPOmBD5YVxdB19IbQItGsoebrk7Tw/1QIhCCZiIW/R0K5WGUyPUIsFicWizF6/DCq1YTn\neWj1EgU97MEZKl7FxYhYITBYhNmlEzhYtc/lquG0z9DCPtCsW5QWDiQ0NYqUrww+/HVehq7x3W9/\ni2jEYtmydRw+fBQpQ8/cSqXyEj3/QqEwy4IIISRFFEVgWRapVAoRhBm471dq9B0xO7GtVCqkUqnQ\nkV2GrmKXX31D2Cj3SwjFesX3eBYHeVaj+NelV7dl/RU0NiXo6VnMZz7zcbY+8QJLV6yiOunilGyK\n2SmUFoldKYf2oEBbUzNT2Rn0WAzF9ZjMDLJyUT8jU+N4jkM8amC7GvliEUUD33WxLItELIbneczk\nCyxcuJB8bppvfPVrLF2yhJmST+C4eG4F1VJn74tQMUcL20Vu3dHtrOSW0ELMnFAUDHQCGSCkj64p\nqHWFYcKBJlIQBDpooAsDjF9y6trXcRHHzszQ3lQgk7P5/gMK2XIDnu4SuCvY+vDXGUsP4QdjXHv5\nQmTgYxkGlmGiKTpze5eBHk7DFKGgKyq2XSEatXCc8Cm/YMEC2tu62LRpE+lMhlgiStWtMDU9RbFc\nxogYmPEYuhkBRaNUKoIex3U9fCnwa/0bIcNS1rareMLH9z1cz6XqVPGEhxe42E4Vx3EoVyphGVvj\nxoUmOgGuF5a1nltB+BfuxviPlnEvx3UVUmVw/34S8RSqqhKLxfB9gW3b2E6FfKFAsVSiUCyiauD5\nYW/GrpaIxyyam9pIxFMYeoQgEDiOg++7BIFHMtlAtVoll5vB911su0JHRxsicFi96SrWX3stgfBw\nFBM0FakqgJg9zlJ1a7e1IlAJj1+X0nXjqosQSo7vfu/z3P6Gm+lsTXHdFZu58c1vZt2WTRwaGmbX\n7t1MZ2eY29WFI3xGpqY4fmqAzMwUqbjFqsVLGBwaYsWCxViGQaFQQLdUGmNRlsxZiGnqKJpGMhGh\nr6cPISEWjbOsfwmrly5h+aKl7Do+zPL+Pv7ln79IqjFKLGIRiURoiERIWhEsU8eKG1hm6PInhY4i\nDFRpoioRLDWGrhpoio5lRIhaMeLRGNFoFMuIYBlRLD1OxEpgmXEsIzTXfqV13ozu4Z+k8UppDuxq\n5K133cTBU09i6CfZsHEJjz30FDdcvZlHn3ye1rYmyuUcF69exIaNV/PlLz+I0ECiYhoRAsdFiHBg\nkC+EAMNIJFKzuguw3TK7d++mp7OTYqFMMho2mIPAo5grIqBmKO2iSki1tDAxOIomo/iKj1prRqs1\ndoQiJZ7n1jw/NcDBC5M/kwAAIABJREFU1AykDgKBpVg40kFVtNmNrGnn4ANVCIILN9CdO1F+qQT5\nS9e5QaHuyxpTfCJRjex0jkw2g+3YmJaK43ihVFbg4bluqBmmhCKYQSBoa2uv/V21Nk21iETC9+L7\nLpFIhGq1im3bsywKCHFaiqbzvvv+Fk/WnbxCeUE416kMlFkvjHNhJ6HEjzr7Of9/nrz/5qsqHG64\n+kY625cxc+IYDV0rGDo1Qed8wZmxMVpTrTQ0JIgpASdOnsFSYCo7w/LlS3EDwZmpDDNFh4rnsefQ\nAebOnUNDQwNGIKg2RxkYH6KtoQFTNcmWy4yMp0mlUgxnMhw7Oc2i+XM4PXKKeUmD8YkRfK2DWMTA\nDXxUTYC0kFIHRaAIAaYOUuL7AaoMVYMURUMEYf/cqOFb625/SImm1yibilGDEClomPjnYW2eN6PL\n2ev4gz98Ezff3Mfay11WLG9hy43dHN/jsnLVGk4PT9HX08lll17M4oXz2PPibobPvMjdb92EoTg0\nxhoxdDBqfTLbrpCwouQLNW9YVWFqeopkIjnbKyiVC8Tj8Vkli2jUIhGPACIcMOgKU5kJLDOKZiio\nphHarolaT0bKcCorfGwvCD0fPQ/Hd3A8BxEIyuUS1UoV2wkPX3g4rkPVCeXcbdvG9P6fwvC5Kywx\nfR7/wUNI38PHIxKJEI/Hcd2w4fzz+MMgCJHxuq7jOGfJ+vWfqZtK16EmdQZFmOX55PN58vk8mpZE\nEPxSaiovd1zI6+FHHmbbjx6hIaEyMzrDjie2sm7zFTyx7ces6LuIgYEBcrkhTh0/ScF36OrowFQ1\nAselXLKZnspxeniImUKeZEsnew6dIBGPMndBM/Pa21gxbx5RI85kUGD1nLmoUYuy75LUFXLlgL37\nDjE6nmbp4j46mrqQfoASCCKRCIZhEDVUYpY+6+9bxzaGbBZlViQkvE7hg3FWmEGoqELF1HSipgW6\nNvvQrsOaXmmdN6MbGT/MTdffRFP3M2x7+gy339HPMz9pImr6eG6FBQu7iUbBjKZ5zS2vZmn/Alpa\neti971kuv2Ixuq4Ti6/j2R3b8KRA1RRc4RHYkGpoolotIxTBwMBJmpqa6O3pIhKJIKVPtVqa1SML\nncIEPgK34qObUeKGCcrZD6preo3/pkJMCT0lHQcUgevbSC/M2nTVDE+apeLbAk/x0Y3wNFimhS1t\nTEsnKy9cX1f496DhlzONOTej0zUDSxU88IWP09zczOT4JPFIimQyie/7RKNRfN+v3bg2ui4IgnBA\nEYvF0TQt7OGpKqZpzvbtqtVqaFKsaTiOjRABqqoQBD6OE7IiPvKP/4Ir6mXqS9+/ds4tfPY+P1vC\nhJtGcIF74syu17/mLTz75Nc5cPIMX37wAdatXMfH//L9+JEIT//4kyS62mjJm1x59fU89/w2erta\nyGbGMAyTaqlIY3sXhq4RBAH5YgGAhkSUoycmaU410t2ToLejkZFtQ2Sayizu6yA9nuXk6DSqdOls\n6wBTp6e3i3yuzNDIKJHGKK4jME0LvHAvm1LgUPNSJiT0+7Weuu9pKKhQM88KS1sVRQ1CRAQgZNiz\n82t+MXgCj1fes+cNdMuWruLBH28lakXJTI/xk4fHOHi4xFTGxVQVTK2N3rlz2Hz5xcykZ8jOlNl/\n4BHODI5yw5YrOXl6gDm9GnviCYJ8vR8WoCtm6AoVD8uV7rl9ZCYmGRkdQVM1EokEJbVCe3sb5UqF\ncqkQ9mNqN2u+VGH+vB68QKLX6nK35hmhqmGKG7genqojRDj5cdwqMgDHLmOYEaqOj1Zz/vZFzSkM\nD0Mzccuh5eKv0/pFvSuBYN8Lz1GXSm9vb8dxnBo/OTqLZTxrIi1qUlpny17HcUgmkwRBQLlcRdd1\notEopVK4oerBz7ZtgiDAsixM02TTldeSK+dRzejZN1QLauIXZGj17ODXJdA9t/1xmjt62H3gCe76\nzd8mPTjCve//IDt3Pc8/nRni9JEBAreH09/6Kkvnz2F8fAI9nmDh3C7mtjcRaWphKFMiN5khagrm\nLltIteLgezZlp0DZiTI2Ms78vvnMndPNgSNDFL0KPQkLPRqhvcHi0g3ryRWrzF+6lGl2kZqOYDse\nwgsQqkoQSFxHQUfBFwoIgajdflKEgrw+AYGnogkFQ3dDH4mwdq1leKBpIjQkExBISeWXVS/5/kMP\no6llFi3spbGlmRf2FbGLPt3tc/E0nUVzFjM1PYQR0Rk+M8ahg6e46eZruOZqk0xmnMWLFyPUDB1t\nzVTLRQLCcrVcKtLW0hZuiEDg2w6GYTKVnqKhIUl7RztLW9tndcxUzUAhHFFH9AjLl12EqhkYujFL\nEPdrtmdSSoQvEdFwwOD7Hr4Q6JYJvsDzrNDpC426zHoQgB8ERJUInuOHAp0XsG+EpilIWS/7wtd+\nIRNEBHzzq58nX5hBVXXm9i1FyFDJwjDrPdVgFixsGAbVahmAajUMavWHR8iaAE0Dz7PRNBXHCUtf\n1w2HVKpm4okKnS1zKfoeqhl9SdZZb839oqukyPCzBr/wJy+MtfHydTz2nYdYOq8fx4/jK6f56UP3\n8+yBIa6+fAOqYfLM87uRUmE6X2bRgjkknDgRzaB79QK2P7ONlYsWU0rFUNSQ15DLl+hsbEYYCsKT\nGLrO4WMnGJ4YZOOKRbgiwarFc2jt6mZiZJx9R/bT2dIELGX7ySdoiCbAEPieIHAUqIR9Ns9WQ81I\n6oy+UGlIinprI+Sp+4GOBBSlxmySKlIKfO1su0PKAM4D8j9voOvoaKWjZQmGDj3Jfcxf28vkVJR8\nxWDjmgh2xWT/3gmOHT/EnL5FNLeZnBo4ie8XKRZgIj1JW1eCpjaFM8MaChLX8zB1jWKpgF8SGKbJ\n0MgIABHLIh5PkJnO4KdCUGhzcyMtLU1MpdMYispdd70ZVTHwEOg1T4jAD8KvvQBfSFRNCRWE/SBU\nQpaCcjmPrhnYtofnezVoijYbIE0p8b2AkI8czAoBXIjr3F5afejyi3pXlqYyNHiaqzbfQiYzTWNT\nilwulF+q2sWa14M/m9HVM8SQq+rhOA7RaHT2xtR1PbRBhFlQcV3ANRKJIKSP4kf4568/iF2jHb3c\nOp98FIAq1V+L3lx9DQ0N8cZ3vI23v/MtvPn1/4MX9xa44vbX8me33s13Hvoa89VmvjP6CG+4525w\nXfbt3cXS7l4effpZNqxezYqLVrP92R287e472bP3ALn0CE1dfczpa8Uvu1RzU6SaTFZsuZKn9x4m\nV7LRLR27PMWewzar50YwksuZN6eR0bFJEskIjuegmgqar+OpPihaqPnoewSBxAxqUk41mNdZyFfN\no1f4CD8MZIqioNd8XT3XCQdgIpy6e+dR4DlvoAtcl1ijhe9I3viWT7Jt+7+QaLTJZCcYOt3B4v4x\nNl++jmy+yo5n97Jh/eUcPLyHaiVgyw2Xs3XrMySjCeb0mex6YRBPBpimieP7+Ci4rk2jFSVmmVx7\n7bU4PuzavY14JI7jhFi3SCTC8uVLWLBgEY/8+GF2Pv4AN7zhXWiagRA1DmsgUKQ6S+IPXB/b95GB\njytCHFY8Fv5OKxKyJlzPRUoPEUTxg3CTJRMm1WoJTQ9Nci/0da5s0bml68v16zTfRHo5onELvahR\nKBaoOuVZ5sLZAKbieQ7Vapmg5u9rGKEKbLVawbIiqDXp/CAISKVSFAr52SlrXUY78H2uuPG1VJV4\nqBDtC5RzCK719zjLbHnJZzr7c35N7076F+6D69zV2dbPDx76OpdtvJJEZwORWJSRIychA/HAomvh\nSq685BIG9h8gXRjlkrWXs/XxR1g+byHD42lGJia5bMNGntr2MzatXM1PJie59OKNTJ95Ed9RyNjQ\n2dyDXakAKgdPj5MbH2RvWxsXXXQxcxuaODL0AoV0J2uvvxoxWEbTVXwXfFWi6yq6ZeAFHno0iqqI\nkBbm+iiBMqt4E6rahD03v26wpYdqRHb9etd69+Hh459ntnreQGcYEWYmCyyYt4Tfe/d7mDNnPRs3\nXc5DD/0vArGXsYkNvPNdy6kUOvnmt7/HqcGDXHF1N1+7fxfHTx5GKFUqtlPz9UwQ2AUCP6gZXCu0\nNrbiBz6pVJzd+3cDhKq0DQpWLEpLsonF/YsZGx7FMFX+8v1/ztLl8/jpsy/OQgnC7CFMZRVFxfcC\nfCMgEgRhEzoIhxGe4+KbOo4bgPTxXYEnAgJ8vAAkPrrUiFtGqFj6a5IBvCzv9ee+ByH4V0rJ1HSa\nmWwGKSW+78724+q6Y/XXAAzDwHFCZy/Lsmr9N2d22loPkvUneD3I6bpOc3MzH/ybT1BxbbyXYXz9\nfHA79+uX4P7qE7xfk+uZbEqSnpwhKxyiMY1bbr6VyzavZdv253hh5wF+8rOtrFp5MWPHjrF+3WYW\nLu7gM5+Zpq2xlfa2Noq5LI8//TgdHR1896FH+K23vZ1tj29F1VSmCw5rFjYxNHyGR57bw7LFC7AU\njda+OZxIZ3n9nGb2DUyzrDdF86LVPL7/6+hRjcBXQLooCBQUhKaFpaojMAyFhmgE3/BD8LCUs9zm\niq3geQFKUPdwlQRqyJMNlaTPOtZJYeD9slNXz3Mp5svs3jOFE0QZGN7LwNAhNmzYxMJlZZySzaOP\n7Cc9dIA165YTicT5whd+xKbLFtLSFCWbjWLqCVYvauFbX9uBqhoI3ScVT2LbNkNjIyTiCUrVCqZh\n0pBqoLm5mcxkhtB1VeH57c/R2dlJe3sPZemz7fmDNMRTBCJAUdTQ2aumSCICgbAkvvBr798nCHx0\nR0XUcHuRSIDvB4hoMLsxRaDNmuYoagThg+DCzgDOHen/fJCr9+vOfT3V2Eh/fz+FQo5yOY8kIJvN\nYhgGEdOaDXQAuq7XenSVWcJ9vTyNRCKUSiWi0WhNcbhMXf5cCQSRqEkkFuX3P/C/yFcqmIpJSOkC\neU7fVKtxHkQgXqJWovBzkBOl/nkvfIkmgKrnctutb2TnzhfY+pPvMTY+wYHDP+PGW+7CaE1y55rr\nODx0it+/915+74//Jx/4wJ9w5dUX4TgOExNjoCosmr8Iz/MYy8/wb5//HPlchg0Xr6elQXJ8cBqn\nnOedb7qTR7Y9TVAsISIGmaExVGHS1dpCtKmDY4f3MJY6hG4l0BSJFCFYRKCG6AhNwzNCqS1L1zGE\nhoqCj0T4EBUJjIpNuVTBBRRPEkgPUZuyCk2B4KzauSp15HkmTuc3sI4ZWJEGdH2KTRuiuEHAu97x\ndg4eGEaz2+ld5JNPO6xY5zEyupcfP/Ig0ikwNljkxNHTJJtUfKtIoWTxrt99HYGnYyhqGFRqU7Vi\nqVizwXOZSk8xdGYITdcolUqMjo4ipeTEiVC1dv/+/cQbYqgaRCImuh4KZ2p6uJkMU0NV6soZOqZp\nYNX6ftFodJZCFokYRKNRotEoiUQDDak48YRFsiFOPB4nnowSj8f+T95//63WL2IJvBwRPhq3OHry\nxKz8UrVaDafZjsPU1BSVSoV4PE4kEsGyLDzPq10HbZaUL6WcZaPMDprOBS/rPlXXo3fOUlZuugp0\nDSVw/sOYuJf7vi4VFF/MukSFPM0LdymuQ8YtcMsdb6I91UwiGiOmGnRFkoxlx/jKt/+FkckxPvnp\nr3LblpvZuesFJtNZNq5bz3gmQywWY/XF62hobqazq5fW1l40PcrRoWFKJY/mVALH9ji68xkmjh+h\nrEiqwqO3p4vxiTSO4zI6NY3nKlQVm1Jg40gPoYRtDD8Q+CKUYtI1DV1RQy9oQ0fRFFQkUtEQikAz\nFRqa4ySTJpYZ2qoGLvi+RLphkJNCCUVFPBFmjq+wzpvRTU9lCYTNwnlr2LgaPv/JJ/lmy3fp6nQ5\nemiQ7AtDdHf2s3D+WjrbSgyedmhZoNOQjHPy9AhySPKaW65hx47n2HDZCl59ay9bHz0ROm5FdZRS\nlVQsSiSqkcmGAoyWFQ2FMXWNimMzMDqEcFymZqZ5x9vezoH9e4iaMRACQ9PCaQtidqam6WGWotTE\nFwO08OQFoJk6QuhYRtiDC5vhIVI/Go3OllN+Tbfu12OFBkeqqp2XJeF4UCyFgov5fPYlA42enp5Q\nJklRUGpsE103cZzKLAA7EomgKAqVSiX8vmkgFXA8d3YoYagaDfFW7vvk5/H8MDBWZc3CEELhsfqq\n9xZrL0kpmRo+QblUYGroFCdPHicatdCkJKIbJJIRCoUclmVw/UUXrq2roSnkB6fYl36Ihs7FvGXF\nqzha2sdzj/+UN918J//7K39HPJni9NAerrzhdTzx6HG6587wzPbtLJ23nt/8H7fyk4d+SOD5LF22\nkFOnztDR3sP4+Di7J7IsXLiQguuxbsMafNMikD6GGoeGJub2trNg8XJOD51iojSAV5SYkQBHguYb\nSF9CoKIEAhmoqKqJogWzBkdShhJv4b8CXQn3JoqCZoSSTigqil/3KgmhKCgKwglC98FXWOf3dV27\nltNDw0zlJhCc4oN/9zau37KEuNFJoWzw1ne8kYPHRvniA4/y6OOPErcUxtOTnDxzlCuvX0iqxWRk\nPM+CZR0MjeWYt6CfO990PePpNNUCtHS0oZk6XV1ddHa1oWoqfuCSLeYZn5qkUCgiAkFPVzdr16zl\nxptexb99+bOoioDAR3hu2GQWEoJQoVT6PtJ38QMfzQhBhoaqErUsIqZJ1DKwTBND00nG4kRMg0Qs\nSjwaI2JYxCJREtEYVg0D9v9WuIaGByiXi0xPT8+ajddL1LoySZ3Cpes6kUhkFv1eVzapK8KEg4nq\nbCO5TtyPWgpv/f0/QtW0l2Rs58ve6uyLQ4cOcebgixx74RlOHj+Erkoy02Ok89NkilkcW2AaCeb0\nLfqVn7tf5To9uJf5c+ezct3lbLhoE+lGh0Z3Pv1XbSbZ1Mg//f0XSSUTXH3lFXzh3+5nzSU9ZIYG\nGRkfJFc6yb7djzM2MozwXabSaYrFIp4iiTelaoOjAlPTOR7Z+hzj09O09c2lkM+wbF4HvoCDhw9x\n8NCh2Z4stgGOjmtreI6GsA1EVUM6Bp4jEMHZyqJ+TWf7brXDVcKjvurtkPoBoJg6/nkGiOfN6M4M\njtPQFGP5ijT5XDv79g3R3pZm7eYm7v+3HfzT341z191rmDjhcuLwGPHuBnIDk1iJKMeOTJDNONz/\n7Le4fstlDA4NsW79m3hx2/Ms7t/I0MAeAqGRaLAoVR2qVRtFUSgVSzUogYrUQoydpqtU8zmiVoTh\nE+O0NadIj2eQqAgFlNomEDVfAQVQpCBwBCLwkcq5RsZg1Dh0Uko0JZSEl0IidTXcZIGCE1zYzIj6\nUhQtzH4VcY6LmvbvgsvgyQOofoSWljZGR4eRQiERb6BUKuH7AsOwsCwLx6lSKhVmVUiklARSkM3n\niMViBNInkD4E6qyvRB3onWjr59pbX0+pVAp7cD8/WDgXNlLrzGmGyv/8g7swKi7XbL6G9s4OKkFI\nKUummqgUiriOw+jkIG1tLQhR+dWd3P+CZcZ7GB86TCE/SUDAEz99mv6ePhpGm3lh7zM8unWEZKyX\nJQtX8s67380H7nsf9/zWXXT3zCcSi/MXH/soi5bMxTAMMvkc1XKJLZddw/5d+3DMsDc65Pjk3QLZ\nfJ6dB44yp7eHaxA0LVnC8w//iJGyYN3KfuzMC4QsFQ3N0ZA2OJ4AD6TrY9s+hgKmpp/juxuCgqWU\nuH6AY3v4jkS44bU3a1Jv9WDoEzKeFBlg6K+sU3jeQHf8xAF6ehdw5KCKU8mQyxkMn9Y4cbTIkpU9\n5PNZ/uFvHmHtml7iiRSVjMd0XmH+wmXsevEgy1euguOj7NjpMT0NEzNPceLoXlSlSiwVJZet1D5g\nkcAP0HSNWDyGqqoEvqRYKGI2NHLixHHafvtOPvaRv0BDZWJ0CgCBH37oGtlbKrX0V4YNTb/WnJQy\nPAH1fpDg7GYONfIUFE0BVDRVRdEVLOW8p+bXbj3/wgukUikqlQqWZZHPZ2enp+VymWQyCTCLj6tW\nq6GCTA16omlh31WpKRiraggw9jyPRCJJIpHg45+7n1IlwFDADwTnK6XrrwglQX54iGi8m8cfe4QA\nlzteezuLFy8mkUjgqAq5XI6WpjiVcpHerq5f1Sn7L1mGatG9ZCVbv/sD7n7v+0hoJgdPHMG1iiye\nt4Yxo5nVCxew/eTPGPzyERb0LaB13jy2P/8zprJZ7n7Tb3Hk2CHi0RjdnT1MNaXZ/vwL6LrJ6YlJ\nNDPM5lOJBrQgYP685eTzef7ui9/gDbfewGPbD7Ds4sUc2HcMvSOB54aJSOAG+LZE2ArCF6iBhqhC\n0XWxIhLD0FBqXr2e8PE9gesGeK4kcCW+E+pOqioEwsdXQuF1VYaqNYqhoMlXZjOddzdHYxqu53PV\npR3s2H4Eokt5759dwvvf97/palnJ8ouS6EaZQ4fTXHxRN3OXRCHay9ann+INv/Fuvvmd73Dtbe/h\n5J7jtLf2sm/vgxiqQTKZCksXDWKxOHa5jG+GE1S9pkePqbJ4SR9eoZE7Xv8qhCtREThqgP6SOVtt\nAlOTLhABoblxDSjq1/FZaljvB0g0VScQQehcJAVB3e5Q0/BrUAf1Au7RiZcBVgazslSh2VH4FbOl\nxKkdj9HV002hECoDNzSELm1BENDY0hI+nGqE/HK5XBs8hGBhw9CoVEJj6jrNS1H8WYViGQg+8g//\nimqa4FVwpeTlWBD1S6JpGrnpDI7jcHTf92lp6ePKjZfQ09nNkv7FDO7dRXZ0hNj8ucRNk1Url1Fy\nKpjd3bNesxfqSrW10NPZxs2v/00q0xNM5Yo09y2go7ebjjbJsiW9EIvQPznKYL7CyhV9+OUst97y\nal547FGe2bGNy668jsZonImJEeKRKF/46kO0RmH16mUUi3maWloxVY1iQ5x0NsfI8DCL+5fyte8/\nztyebgwZZaDssiBIMmiXwQOvKvDdAGGrSKGgyQDXV0DoVMoeUrpo2tny1fd9PC/Ac8UskNyXAgIR\n3gdCImpS+kIIFAyC82Alzxvo0pkivY0TdPZGWbP+dh768QN855semhKnWHAwI81Uq2VicZ1jZ46h\nxl7HydOnqJYbeeDBb7N23at54LOfINmQYN7cpZhGG6lGHde2oWYq3diQQCbjoSqtphP4HqqmUyxW\n+MwnH2DbU4+jCknguyH4UwGhqKgos6Be6Z7dFD4SDQ3fC+Wb6iPngLOlraeGJ0QGKvJc2IHvEzEs\nAt+7oMElv4jAXy8LzKCKZliUPI+73vM+/vy999LU2IqmacTjcaQCufwMuhFi6SYnJ0kkkgghQviI\n4+MFLjIAQzeZzkzT3NwYDiiccCqr6zoNPQvp6V+BFzihlaWUVG2bRDIWYuwkqKrO1u9+laeffILp\nqUmkU6W5qRPDjDMxkWZ4bpZMuchQZoKGRg0Ch0e//zMCGaNYKNDV1cHo6CgjIyPsev6FX+Xp/pUu\nK6KSbGhGaBY/+MrnWXf91WSyJRp1nWcPPcOJ3UOYHc3c8cbXoTy1DcVqJKZIjh8/ycXXXs/iXMDe\nk7sZGCzhBEW6Oxfw7ne+no7ePoaOn6BYLLG0v5+tj/0Md7qIl9DZsHoVR04M0traRMV2yaQnULwK\nPYvWcXBgG0aqBemqSL82iBACT9VQhCCQksCvB7cQAKy4AVUCAhn6Q+uKgu95BErNH0QJubFSCf1j\nBQqK7RGIV05OlPMBY1ddvEhqMk4ypbB06VrSU8cJPAOpFMllKzSkYgS+Q36mmfHMOFEr7OF87GP/\nyD9+/B8ZPH2UQHiYEdDVCF5QDjXKNAPbLWFXPUxTwao1tG0vDESaYrJ0yZVce+0mnn32mxRyJa68\n9EpaGtpQpEIgX0ozklKiGFqIf6tF//oKy9NzaUAqmiEJvLCE0nQ9zPZqm187RzbmznvuuSDTum2D\n5X930c8NfrNNYKGiE3Dq6EH++J7b8ewqne3d6HrIdPBFOK0uFguz1K9isUQqlaqJLITqJMlknHQ6\nHWLragomViQ2G1A/8tef4NCJ0+x8ciuqqlItltCNCPPmLkYEOjPVAitWrKG9tZXs9BTtbS08v/s5\npmdG8ErTbN58GYlEDCENSqUCyWQjp0+fxvd9Mrk0qqrS0NAwq7Tyg+9894K8rgAPPvglGTNNejvn\n8437/5mrX7WFFfMW86UHPsfQ6WHWrN+AZWk0tTbR3tyLbXt0zpmDUCIMHT3MYHGAvqa5FMtp0pMV\njpzYx9pVa5G6gRqE1gXHB/bDTJWx6QJ79h9h4+UbOTY8QotpIgJYMX8OqZTO4MgYq9Ys4Zlde/AF\nyNr+VJQQEuLNgszDYZQkDHQOAs0XuDXmgwwEmqIgaggJDQ2hKuhKAEHIotAkVKXK8YHxl7225w10\nb7v3Gjl4vMRlV6TYf1BlcmyQO377Gp575ijNqQipngoPf2uS6cwUgYSe7nkYZgvl6hQ9Xd2cOXOS\nQrGApkF3TyeGAZ4n0PWwUW3bHpYZxTQVTEvHNJJcu+Ut/Otn/4Y//aMPs/mKy/jQn93L1des5Btf\nf5SVK/pZ3L+YtnhHrccT8lTPnbYIPyxBzy09hZSoihL+q4rQRs0PUIyzkIp6cNRq5rsCwT3veOcF\nuSGePlWQZwHDL88VlVKSMlV+69WXYTsFrrzmt/jWVz5Pc6NFQ0MjTU1NoVyOkAwNnWFmZia8YZWA\nG199F0uWXMzBQ3t541vexIoNV5JOp5mZmeGBT/0Vxw4+z+jYGK1tHTQ2NmPoIY/VroT6c52dnSRb\n57Jk8SqOH32RaFSjXC7zwo5tANzz1rfR3dXK+OQE2fzMbI9Qt0Ldu7pP8Lz587CrFQqFAuVylWQy\nzDYfeegnF+R1BXjoR1+SmfE8olGl0YvxzJ7vc/XVt9MSaeDksaO0JlooIyjmijz1+I/4nbffQzZX\noaG9hXikia998dOs3LyWJQv7scs+ne1dfPzvPszr7ribA0f3ogSStq4OulI97Nj2FFEczqSL9Hal\ncKoq2arPiqXN1ArIAAAgAElEQVQtTI5mUXWThGbx1PF9YZXlB/hebdIuQ8+WkDoYsmxETVJdCDH7\nIK0HunprhBp27twlpUQogkDCwOn8fz7QLV40R971totZ1DXA/Q/2snhxMx1zJ4nKPr52/y62vGYR\nfqAwMjJCfiagWHRx7LAsETKCFQ2F9CoVG0PX0A0dQ9cwdI3GxhSu61Gq2KB0cutr7+Sr93+CQinH\n637jLg4c2MnoaJq2Fpf1G9axcEkTg6dylIoVkpEYczoWgzRQlQBfBR0FNwgDmq7r+IGDrteMcs6p\nQ3WUs0GvltG5IkBRz7Is6qibt7/73gtyQzx5IifPzYjh5UvXQy/sIGEZtC+9hIl9T3Jy19N4Qmd0\n7AzzF8xheGyKTCbLyPBYODEvlUIJp445xJONBIHFDbe8hi985iOcGRqgoSGJjEeZ09PL4X0HsKwo\nsWicOXPmUCqVaG9pZ926dezcuZN8fpJMOs/y5XPYcs116LrO+NQU7e3txGIxmpvbeXbHDsyYzu7d\nu+nt7WUmX6BcLmPbNq2trTQ1Nc2Keh4+fJj+/n4uu+wyPvinf3ZBXleAL37ir+XQxABKOWDTxo30\nrl/F0WeewmzqoOpK1q1ZzcjwSR5/5FG6lvTS1NSFXjEQrkM5yNPVOY99u7eyftMWijMFhDrNxz/2\nN8xdeRGv2XILE9PD7H7uIJFG6EstIRYDVzFojCRJD51my2tfRy6dJxKLknNsTh87wp4X91C2ai59\nNcqfECqKX5PQUkLRh4CQL+0HYrb/e65PsFLD19WLD/UcDKUUOoGic3po8mWvrXbfffe94kk7cvzB\n+57fOkFTi8GZYZVqRaUh0UVXTzuKXiZfnGLXc6NEjFYmJsbJzXj09nVRyFaJRDwS0QQL5s6hubkR\np+pQqpSZP6eXfC6P57i0d7Ry7NgIwg84uP95FEVgRVLcccdb2bNnO5qqcNebfofdu5/nhR376ezs\n5MzgMJWKYOOlG8hkskgZoIgaIBgFTxCSyQUgFTSp1ASrQotDRVVmT56UEq8OUhQS3/dqJ1cgpMqG\nTRsuSGTpwHT1PqiX9WeFN+vnRJEKSOjs7SLe0seZfU+QHznM7gP7GB8boaOjk+PHhrBiFrlcHlU3\n6OntorO7GU9IpqfGSE8Osf7y29i69QHufc/fky869C9aT3tzF1GjkY62dt74ureweuVS1q1aSFtT\nkisuX08+N01vbxeWFmXd2n6WLJqLLyWXbd6MCFQWzu+mr7cJ37Xp62mjp72dcr7A6osu4vqrLmPV\nsn5kUEF6Lgkrwm2vu42nfvoUqWQzXe1dPPqjx3j3u++9IK8rwIL53fe1t3ahNEhiVjs7D++iGpj0\nz51Pb28nqi6ZM28p+TMDrFq+ic6mFL4piRgJqu4Ma7q7EJEOsiPDVB3JskVLGc9lWDa3jx8+8xTG\nRJJspYDMeAxOnGb3M0/SFm3jqSN7uGnLFoZyE9zwmltI6Cbf37udNUvXIRsEQ5kpihWXlGVhqjqm\nLrFME00HXVPQdRVNk6iKxPM9kApSBiHsq35IJVQ0qdH+AuQ5AzMFqaj8wR/+8cte2/NmdHe9/bWy\nMVJg9ZIyj29dTF4cxrKiLF8xn5/8aBuxSCc9fS0UC3mqVY/uriYGBgaJxVOoeuin2toRIfAUZqZt\nDNMk2ZBEUTVUYdDeGWX7tgOMTUzS0tYL6Hz2U1/iQ3/xISZHz5BIJvj6Vx/gT99/J4poJxK3ufa6\nyxgazHDy5GlWLF+DiTabsdWR0VI5R19NBGgoiNprplSpSAdF6uiGgV+bAoKGrBkRSBFKPP/R+993\nQT75HzuSkfUMTn0ZDS+lxn9VNAW7VOXzf/m7dLQ1Y0WjCN9lYGAQVTGoej7Nza2sXbuJFStW8eQT\nP6Ort4sjBw+x68UXue2O2zl8bJqdO7/PXW/+U1JNPXzun/4My3K54vK1zEzlue66jRw8cLTW9xOU\ny1WsWIKpyXF+53fewpnTx1m6fBVHjhyjXMmhKyrtrW0MnpkI7S11led37uJtb38HDY1NpNNp7EIO\nRfWZM7eTasknEpMcO3WcT33i61x8yQo++dn7L8jrCrD9xSfk/NZOfvLtr5MXgv6VazDUgFzF5fo1\nl+BbMDY0RnN7K5PDWRpSUVTFgUiSfUdfxBmaoaWrDSueoFTMoCdbEJVJ/KhFYWqKicPDLOifw0+/\n+VX6FvYzmWhgZTRC1yWXMnrkGKKhAbeQ4bqbbyU3Pk2ivY1EKkZ0wOXez/05UeGhoqApPn5go6jg\nCS+EhCkhdrVgB1TKVXwf7KqPV3FmMzkhRLifa2KdGuG9GvgqQjU4MzL1stf2vMwIhUl0y+PoSBSl\n9RAbr2qnXFJ4fvsJNCOGE+Tx/AwTExNcvLGVlg6D/uWtxBsg1djK5is3ErgWXR2LSE9NkWqM0N7W\niPBLeH6ZwTPDLFu+iDVrFtCQUEhPpvnLj30AuxzqnWmayh//8Xu56tpL+c07X4sINL7w+QfJZHIo\naCSSCWzh4QQejuthuw62W6VacahWq1TLFWzXpxoElB2PsusxXc7j+WEKXak6uL6gUnWpeA4V26NY\ncciXy+TLFy6wVPcqaG4Z3augelU030YPnHOOau0IaLAcvEKRaqVCb3c3tu3Q1NgMQF9fH5ddeh37\n9v6Mb3zj8wwOHWH+3F4sO8vSJpMdj36PzZvXYpcFX/m3f+DbX/1bervbWbW8n9VrLubHjz5MseQy\nNj7N6NgUi5csxPcVysUsC+b3svWxn7L1qe3886e/AIrGqZMTDA5l+OnTL7Bz3yHmL1rC409uY9Hi\n5Rw9corADUglUjR2tFAoKWzfsY+R4X1MnXqKBdYYc9sU5vfO/y8++/93V4OWZNvz27ntrjtYtPwi\nEokEO4/uod2M8NPnnmZ4fILe3m7SMzNMjA8wNTOJasUxVIWDL/6MTHYER/h4JZsffO/rPPiVz9E8\nZxl+xqanrYO2uR30zF3K6+/5I7a84S0MHXqOEYpc1NrCpqsvJyJ8UqkmpqenkZpOaSbH5MlBCq0R\nvvCp+/nXT92P4uYxNEk0pmDFFEwrIBL3iMYDrGhAY4OgsTmCFYFIFKykgaKF/hH1kKUoCppQZ9k1\nv4iyed7S9aGtf3vfE1sFL+4eYHSkSKGgMzQwxbz+ONlMiWuvvZTTZ06iqQlOnRxF+gqtrT0YOkyO\nT5DJ5MnMTLN8+WKampsYHBxGylBJItkYoak5gmFWMPRGKuUKDQ0WDSmN+XMaSMQj+F4Jxy1i6ib5\n7Ax989rZ8+JRzgyNEo2beE6AlOB5Etvz8YTE9T2qTii95AQ+jufjC0HVtimVK6DqtZ+xmcnlKJZL\nIa+2WqGttZWZbAHHreJ5Pjfc+KoLssQZGM/cF6LPBUrtkIGPFOERKrkEjJ/ezeGnH2dsZpTjx4cx\nzSilQglN0zH0KFddfR2lcp5ioczY+AiXX7GBRDzG8/v2MpYvMTY5xNTUEO2dXaiqxPNsBgYOE49b\noaNYLMqC+fN4+OEfk0gkiEQMpqdyrFq1nL7eeViRGPlCkY6ODk4eP8rAwAhvfvPdDA+P0dHVjeu5\n+J7L3LnzGB4eIZ2eZPv27Ty17Qm2P7uTTGaCXfun8I1uIm39SL+ZR5/dxt1veesFeV0Bvvvdb97X\nM7cHwzDoX7SQ7OQ0MTVK3inS2jyP8YFT6M0WqUQCWwHHcSnky/zkia9SzBe5/c53MXT6NK0dKVRh\n0NGR4NDR3Sy/aD0NTS1MDZ4iWyrQNr+PbKnM0sXLqOYF2Yk0Uimxe9d++heuoampFU2CXUzjIsjN\nZJnMp5GKzr5DO0HaKKqHonlYcUkkomHENKy4jm6omDEVDRkaU0vwvADfDaXXFFRAIRCipr4V+lwK\nVN77R3/yny9dF6xskviNuE5oLrxk2SJOHB/kVVv6ObB7FNC57TdXcfyAx/GTR+mbl6BaCvAcn3JF\nYFk62WzIM5QERKw48+YvJB43SE9NMDNdAa3E9a+6MgT8VSpM5bNoJNm75wDXXHcpuhZjZCTLxg3z\naEi2MToxwuSYw5NPPk2qKcEl6y+lXKjJKddQ97puEHgehmHWQMMON930apYuXcwnPvFJpFD48Ic/\nzF/91UcpVwr87u/ey8UXX8xX7r+f66+/ng996MNICV/+0r9eqCXOfytxtroPRR22oqrqK3p21MHI\niURiVrvu3Kd5LperucdFmZ6e5vTp06xZs4Z0Os2cOXNwHIdYLHahXlcGj+yXzz71CJ/54ed535vv\nRfgWmeIYsr2X37j+Ru657hbufPc7UE2HmNXHyPBhbthyKz/85ucYy07whpvfxGe/8hVu2XITppWm\np201D3zrAQq5En/yoQ9x5PB+hk8OsG7Desx4nPSpEbq625iemaZklxGVEu1zFxFNJMkVC8yrcYtH\nZvIUMxlWreznQx94O1ZMw7AEuiVRdTuUTUfBkwJfguv5VEoSpyiwKwH5rMP0VAnfq7VcAhnKrtc5\nsUJFaiZDo9P/+anrqrXzZC7voRkK1YpDPJ7iupsW0tkeZ/tTp+noDqWOkokkJ49PhE1BL0Z7VxOu\nP0G1pDA1PcOqlctRFJOxsRFc16apqYlyyQuDoAvJpMW6df109TQxOZ5GVRLM6e1garqM7XooEvyg\niuu6LJi/jH17j2BZGgv7uxkenGJmxsAPXNDUUJ9MhpQSFRVV12oerxpShj6j1AYSnqzLMDOLqvaD\ncDokAsH3vv31C3VD/LcKdB/96EfZuXMn3/rWtzh16hSf/vSn+eQnPwnA448/zqZNm/jsZz/LypUr\n2bx5M4lEgptuuol8Po9hGDzxxBPous4HP/jB/4+9N4+27DzLO3/fuPc+wx2qVKrSPNuykLHBNjY2\nmNiOscF2M9kmMdCJCXQnkE5DaEK8VgidsALpJIus7s5aDZ0AvYA0gzE2EJvBAyiAwcaWItmWJ8my\nVFKVarrTOWcP39h/fOecKjWWvJpuFs1d99W6SyXdW/eeu/fZz/e87/u8z8tb3/pWptMpdV3zlre8\nhcViwSte8QqklHzwgx/kl3/5l5lOp4f1vvLBD34gP/mp/8Loli1+4d/9At/z97+XCxcucPetL+Wz\nFz/Chmo4eWqb973jHn7pvb/Kd333d+MPDAf7+3z4nt/njq88yZve+F18+E//hK1Tp3jg9z5GnvT8\n9a/6Dm6+s+GB+/+If/xDP8Q3fPPfZKPZpEkjTm4f57Yvv4P5YgfhNZvbW/zRn36I7e1tvuJFL+Px\nxx+n73uOHz/BxtYxfuLH/geS6KkqiakCYpQQMiNVaQbOgyD4TPCS2UGH2xs42Mvs7HYMC4hpuGIl\n4jKypBvgyYuL/+dAdxRHcRRHcRjii6x+OoqjOIqj+KsfR0B3FEdxFIc+joDuKI7iKA59HAHdURzF\nURz6OAK6oziKozj0cQR0R3EUR3Ho4wjojuIojuLQxxHQHcVRHMWhjyOgO4qjOIpDH0dAdxRHcRSH\nPo6A7iiO4igOfRwB3VEcxVEc+njGdYfv+OBOTrnHWouxEqEV1lrS0pVW67Jdy6hM0hJksc0ha5RS\n641cQuRiv2M0Wku8j1Qm43yHtg3AelGLEGLtKLKy30kpoYVEa03nhrLLMV424Csr8cr2Lu/jeuvX\n6vsEn0mp2AH5FGEISKnXy5VjjCgl6DoHTqGUIoTAW15+zaF0ufjgvfdnLSR10xTn5ZhZxB6ZBFlk\nlNbIALuLA4Lv8UJy0C3IoUfqmq7vCSEiEQzDDJk1nS/Gp4qEkQolYcgRpGKsR7R5YNpMGBkDMdF7\nh1UWU1WElGiqirZfEJMjJMlYGwbRMdJb9NnxIz/8T1k88jg9mVortEogJHmIOGmJbsAnT60VTdPg\ngqfSksFFhJKorAjJM1aS0xd2DuV9BfiOf3QqX7n4u3yUz6VIef8jIS1dejPELPAxLffnSlIKl5+n\nFNFZIJRE5sCmVUynFePmOKPxVVx17DpOnLiV6cb11M1VjEfbKKXQFowCIXpqw9IiPdD7zGLe0/cd\nLpbXsGhbHv3Ug5w89Tw+/Mcf5OabruOqq0+SIswXB5jllsDFYs5oNF6abUbScnuYc46uG5hMruYf\n/6O3fcF7+4xAl1LCVnb9Zy3KliVhNHVdk3O8/DltSVkQfEJrCCEgpUQp6HuHUmq97GK1dHZld55z\nJi53rq7AbQV4q6/NRpFl2QQf4+VtN6t1hlqosuthuRV+vRPCe6Qw669VSqGsZBj80rtOr3dIKKXI\nq21DhzgWBzPMqCKSqEYjtJRYp2iTo5EKFSGIjNGKSMUJWyNT4kAIQgg0xuJ0h0oGk0fsti26khiv\nqIUtOzcBowyT2pCy5kS1gZQOI03Z6xoiepwJgNaKlDL1eIJMkd3FAdlqRvo4Qghk62lPXwBhqGxG\nRMGorpkNA3VTc7B/QKMVAYtA40lMxpZZ59FWQJYQFlR2gtOHO4mJOZWdx2vXXYEQLJ8vhRKamBMx\nZYTMhJCJaWmfjyTFiEIut+sJQgRURsaMQhBiJvmAGWsmoy1sfQJljmPscepqE1tZRnX5aJoR2hiQ\nlpASIQRGYc50K5BCxMWAcwHn4MSJ2/j8J+/n2c/7ch5/9HOMNwcqY5iMN5ZLxxPj8YSEBCkxSpKU\nRgqN1D3aWob+4GmvyxcFumG5aBgEfd9T1zVpCSBQLqY25XMroIrL3Qtaa4ah+LtJKUl5xfAEKXlY\n3gBgzQCvZHErVqa1xueEcxEj5NqHagVqMUZ8yktgvQyoSimMMZAVLE36EIIQ/BpkVz9/ZcmsZGGj\nzrk/3zvtr0D00aGCJlsgZXoCQ3RkMvPk0SkiBIx0WTCdpaSyNVtSkqQk+kD0BkXkYoooCdEFmkqX\ne6gNRikqDW4IYDU+LFC2RidAZKa1IaKwQiCNJrmAjAJjNE3VMDKaLkS0KBvatW3IfmDoO4LMDEES\nBsc+A41W+BSRQpNNKEtU+kyjFDIl2jAQKovqO3Q2f9mX/y80RMwILZ9CJmIsz5WUEGIEMkppUgIj\nMyJFkhSEkBBSE3NZEKURWClRSCCSpELKsshGVmOM3UCqihA9OcyRucaqCqMEKQuGmMhSYI2isc3y\nmTtBzAHvB5xzHMxbhGjxLnLiultp9vap1B1oMlldJkFtN2Nje5ta1+Ww7A4QFNxQaoxPEVuHp70u\nzwh0OQWUNKQkSVGjpIeYkVotGVBxd/UxIYQkW0Vc0kohBCFAFUFagfM9RjdEXwAoCwVZkWUu6wnD\n8kUub4pQ5aIOw4AxEhX18pRxKKXJSS2NPoEsyVBulLh8mqVUbJcH1wMaYwyhdyAlIZS0NmdQSkOW\nCJPIBNzwZzfZH6YYbWwwqWqUNiil6NsFffJYFFKAjw5TWZKSJFeWDNfGonR5ePoQiAqyqKhVIlYj\npK5I2SNSQGSF0gKdBc1U40IgDIGelrrZJJtUFiQpiUyJHCIhd1R5Sk4CYyri8tCRIrHz8OMoESAE\nOhVBGIYYkNoifCApRUg9I+nJVUNcREIFPgmMhBAlovUoI1FfYBnQYQpjBSlCyhG4XA6C5aJ3mVFZ\nk8lIJQihZDIyl71akIkpo6QCCjuEjNYCkRNKK4RpUGaLEA3jZotxfRzUtADbbJ9EYGv7JJPpFk21\nSZ0aBEvQJRGTxNUDggWjCoJLJNEjqyk7Fx/k1LV30rcdfliQpWQ0GjEajZBS4lOk7VtAomQiJo81\nNSIKhH76Q+wZgS7GiNZ6za6UlHhf3jBwmVHBkg3l8u1Ezigl2Z/toyuLVGYNTMZoYkxLAM2kJRsD\nVW5IppxAS/pcV81y+88ytZUGNwRyjhhjiO5yOryKFbMMIRCWdtshUNLuJfMTIi9reRT2llkvyLXW\nHur0dWs8RSYQSjGRGnSN856FHxjVFbWqEVqSU146M2eEAOGhUpZcZepcIRFMmzFdNyJpWRYU9y3z\nwaGzYOEGRJNodEUQI3COSmuE2KQbWgY3J+saOSiMsSChSxG5LIW41POD3/538SKSVKS3ChlqtPAI\nD1JB09TMh0BtG9q+J+93TGqFi6BUpp23jGzFftYk55H6cBvNWg1RRHSShAxkSRK5rAlEYJZO2yFl\nyLk8Z3iE1EgtyDGhJAgRqKxaftclYGKISSPSJolNlDmGCzXt4EC2SDmiGY1RxmJsg1Y1KhmQCcll\nkAsyITAIoVHKIkRh3oMfEM2Y6PeRpiEMCnyHVnZdd/dty3g85mB/F+dKFulkj61GSGOf9ro8I9Ct\n6ldaGUIIiCX7cs6tff1DCGvQSKGwNaIiJcHmZMoH3v2f+Gtf/5o1+PR9aSbEJaOKooBlYWDlZpSC\nKEDGuQJiigKsMYlyYeRT63rrepwstD2EUE6q5QVaPbCQ14wvhFB2uublTReCTAHEVSPjMIaRkiEk\ncJ797Kirmm21wRADKpXGUhgcRmqEFGhACk0fQUlBU2+wwDHGMhAYjUbgI7vtAb1tGGXBfjcgK4sY\nAos4Z9xIjJRkJZkqw8L15C7jrKepNK3vabJk1BSQFSHzo3/n+3F2IEjJlBrpBpJI+FwaDqFfMEue\nGBZsbGyQXaT1YKRBhcReN6eppxyEHiUzxmrccHgPMKAwMAVJgEmiHOQxouWyFg2EHNBSl/e9EYRQ\nYKAyGk8hBlJAzhEpNTEJpKgBhc+a2TAw6veYqQ2MWYCUKKOpsGVJTb5cPvIyIKghlVcXZCIiGcJA\n6zxt1+HcwHw+Zza7yKX5DsPiAldddTtVVSGTJqVQQFtkjDFl2XVOSK3puhYpSwrr4p8zdfWuvCnW\nTQJT410GLVBSE/vVchIgUVLDEMgikXJmiLt8xQtehooZbweCsSQnmBgDCZ4cOmrrkW6DLF2p72Wx\n7KoWoNIykXMkrNhjjKhlqptzhmQoOJXLXtecSSGhZE2KCZ88FkkKgrwE0lXtL6VUagFZrf8bDEL4\nwuIPabgccXmgTobz7T5bKWKVRqjyUBhfGPzCl72bjtJxQ2S01GgEY9vQDi0xQy0lqrFM0hhrPYPR\nVFYS0HS+Iy8WuJS5+tjVyEohUMiFQo50KVgnqOSIjCQQsVnzM7/4c7RVhznQCJtwciBrS0wLUgj0\n7QBoOiGwwrI3c4hssDqyN+u49poTHGuhjxpbCdyQaJoGHw/vAQYgJIi4rImTEFlQy5KVaSnIWSCj\nWB76ApES2kqWSIQ1GoVa/RchgRYWKQQhZYy2RGG4eHCRjQhKBer6JkLUDL1maEY0WTC4RD6YU9WR\nug5oaUCYsrktBEJwuHbB3u4Fdi7tcH73DOcvPMKjj/0+5568wJtf/b0045MIU3Ew20UlyDIhTYYY\n2Nw+jhAKcbBL0zRoqcjRP+11+aLNiFWTAKDve6qqIoVIloqYQgHAJStaMaxVGvnJD3+O5z7nOfz3\n3/4W/vXPv52pGPimr305afcCk2On+Kl3/TatajBpKG3uJbgBSCWX0o8l08qSFItMhAylpCfIy9ew\n6pquU84lw1PGEF14CkNbAV1hoR6Q61rGcIibEKuwQjNqDFIYnAjEEGlDIIdIGzusrJBaIaQk+0gU\nCXIuHfQIXkXoM1ZARJJjJKZMrQwKQdXUxKYmhMRoUPh6ggyJ0WhEZSxt33Py2HG0kOU+hUAS0Lme\nWlqUELzxjW/kIx/4AKnKxCFixhaTHX0PWlUEP5BEpBIBbSwx9Ghr8NIi+sD5gwMaaalMJiZDpUs5\nIj/9oX84IisUmZgzlZL4mMlopAQpLTEkjCzLn1eKBYCYgWiwShGQKExhclqWrjUaKRNZCPSyYYcU\nJBmZd7uYqqa2I2I09H1CSoccVWQHvWtLDVwIYupo2132di6waFt2DnaZH1zgzBP38tAjDzB3M4KA\n3/qDX+XrX/XtLHYjmUjTNDTGgo/EnBjajuNXHcd1FSoLcsho/edMXXPORSaiNFJKjDF47zEKQjeA\nETjn0EYtNWx+Le0AuOvuu6hHG1TdBe75jV/j7f/uR5nKSLVRo8XA973lDfwvv/pe+hRAaIS4DKqD\n8xhjEEi88+tuLVkQfEQIuZawrLRwJc0uGrjoh6Kx6RKVWG0Cu9yNWm//8h6yWa/a01rTdR0K9bTX\n5a965JyIy0L/8dGUfd+TekcnIypo9uczAgKtBLU2GKVxnUM3BiECIUWUkMyjY9M2GAFRSLwPaGPY\nrGqEtJyb7ZKFpJKAUfSuI6RIJrM5bsii3AsbIsZYdjpJLRQDiTETkgAvBIMypHaBNhO8zKiYqY0m\ny5JttIMnoxmEJwWopGGvn5FioLOGSgEi4qNFDMNf9uX/C456mYxIfBBIVVLQKCUJSRaRLB0khRSC\nvFQjaKlJQMym5L1CoKUFIUloYgKZUyEZMaN1xMeBRbeP1hV17xiNNN4JnI4IFfFxgRAtxlRAwg8z\n9vbOM5sfMO9agt/n3LlP88jnP0Y/zEDBtKkZV4EULnLfve/m2mtfyK233sVsNscNjt2dc6VUIjIX\nLpxD5EzK5Vn16ekbTV+kGZHWFFfKTM6l+KeELejsXNm/GQQhRqralFawD0XWkSJ+Nme+GPi9X/kJ\n+n6PuU/U9YR25yyTyYS3/Y1v5J+/8z2kWUuWArImC0GKGRcALQCLiEWbFZY1NiMVKiuiKyzS9wGt\nFUEKvAdrNSEGaqUYhoFycCW894QQ0KopDFVoUhI4FwBB7IvMQarDrLdSRJdACbSAWln6WqD7DCkR\ncmnIeO+Yec8QSroxDSM2J1NSiHhRunrRR7JVdO2CUd2UeyjKe8ZKRS8cUlsGX7oHQgi0VDi3qqEq\njFForbl6vIlRAh8zvew5mA9sNiO2x5n9oUakhDWJkCJBASlTL7WVpExGEY1gGBxTqckkehHwLjGZ\n1rg+0TSHW16S8lVkUco6QgnCKpNJPZmASCCzIaAAUZoREdIS9JSoEFpAVgSKNCvnjEQu9yZnfHAI\nBykdkOJA1x/QLhYs5rsc27yZqhnT1FOqpkGJjBsWdMOcPvhSSx8O2Nn5POcvPoQbekwDzWRcdH7L\nrCz4RCYUQxkAACAASURBVEx7DPPPsb9zDFOPCSlz/OR1zGa7GK1wfeBgvkfXzahtw3TrxNNely+a\nuq6Y1Kp4Xx6A5QSEkHRdWTyccyb3ffmmxpJCQOREzJHJdMTp05/FO48xFVVlmM8Tfd9z/szDiN1d\ngqywUtAvWeHq5+ZlWhxWrwXQSyaQ4mUZidblVyn6vssNhVXjxPu4FlCCxA8DSghSioRwOUVefb98\niHOcPnRYUxGjI2mN945FO0cpjU8JhWBrMqVtF8y6dr1YeB4HDvYGtpoRlVFEF6AxpJipTIXIYGNk\nr91HZIU0gkaMaPuOmBP4TC8FFjBC0roBYyySjJVQKUNV1VQJtLb83r0f4g0veTlz55ikEUF2tCEg\ntCXkSG0UOSUiDqVrNAIZIemIlooYJU0KNEKw2wa2R5YhPH0d5zDEnbe9igcefB+2EiwVqcgciUkQ\nSgUCpEbnAMmSI2QllymvRqARCIQwKKFICYoYQpITxOABTTs4ch9RiwGjBHOzw87Ow5zW91PXm1T1\nBlY3RCnRyiBEpu0P6PvzeNchRERqxWRrhDEF4FL0KJHQyaKUQCmDELucOfcH3HjTK5kt5lTGorVl\ncD1KFyY3Gk2Ybmyj/7zyEkLE1oYry7ertC/nDFIsEbiAhBuK5CMMDmMMMmWChH5+AGHAB8cwDLzs\npV/NPffcw3Of+1w+/sC9/PO/91b+x597F6Gdo1RhhTIAQhSGAGSWNcBcmJ2LAYVYj4Ct0mUp5Vrs\nuzodcs6keBnEvI9YuarnpeV42CpVX359OrzdCCV0qcE5cNHTu76ctLmwudFohKkM22abyWTKQTsn\nOc9+2xJjWArJE5Wt+dtv/S5+6T/+PHvtnBwjI1vTNA2LoSN1mSxLk6pCoFRJh1KKOCOonUDpwtxz\nTKXbCvQs7+fegtFIcNALBuY0wmDVCOUHhFUsCKggqXSNVZKFF6TgGTc1fedQQmDrmtb1THRivj9n\nc2P7L/vy/4XGnbe9miSnfPKzv0MOHVKWDqfMmpwhkZEpk7OBLMgoQixKBjIF1cjE7IloyBEjDTlq\ngtO4XlPVmZwlPh7gc3me2t4h5mVxvJIWrWrqaoI2I3JyIEsKjUhYrRhNR9SVYmQUdTWiMRapJePx\n1lJVIdFCIlUZTZvPHufEVbdy8cmLVKMKKSW2qhhvHCOmwGiyydB1T3tdnhHoJIKh60tnVWvQihwK\nCyIV4bDzheWlnEDKQj+DIifBhh24JMfYXNTzV02nCGV47/t+i2Hw/MmHPoi1lsdPf4JrNTzUSNx+\nma0NzhV5Sy4sbWSrQmmXwmJl9LImKMh9AbaUElmXzmxVVXjvSK60oY0QxBCRSaBCIonyGpMo7WtF\nGW8LodT//n+2zP7/05Axk10AIfDLQ8HqomOrm/Gym52xVc1UjGh0xV47o/MeWVWklGiHHmXH9Bcv\nYZqK/UvnqJVEB80w32M5HYi0FnJG6nLQSKsw2VCRCTbic2IkICwbRH3viEQUgpgSehgzpIFNY0ld\nS5YSUVWYnBmHUr9ToWeuDKnr0FVpOigtiAJ6XwrzhhpjWtq2/0u88n/xYeopt1zzQp48/1l2Ln6K\nnMukSMgBkTJKBFIE1k3GjKAi+gwSZJakOOFgeCGPnn6CYCfEoWND7ZLsCcLBHtZ/nkkdEAhiv8tk\nWjM+NWEWz5KiRogWoRSLbg9ja5S0KAHaKEZ1RTPSNDZyfGvERnOMphlR1xOMbjCmomnGWF3KHKZS\n1FUBtgcf3KOeTnHOUdcjfAxMp5tIqQkpFkXI08QXTV3h8nD9quCflyduSJG6rpdaOkFcte6jwFrL\n+ay5XT7BC6+d8psPSmptOX7sGs5d7GiaAlQ5Z+bzOd/0+tfyIz/z81Rqqc1jOS9rzbpLuhrrCiEQ\nl/IS7xNyKW1RqgwtK2XoumH5/8Rl/VAISGHJWZBzWjczhBCkZT1yNSQ8HOKidWJ1PQM2CrxUZXrB\nxSIoFYX1ueTRqjRwjNVsb2yipeFgvk+lFBuNwQpLu7PPtq2oxxMO2gVDjMTBlfpb8IjKoHI5oXPX\nM65H1FWF1QYjwPUDSRc5hM9hWRxXGAFdmjPOgiEOSGnLMFKfyCnSktioIj0CGxNBlwJ7S6YuA56g\nQFBRC0srelpxuLvqcXDsX9rltlMvpbEn+Nyj70frTA6ZnAMiJYTKkBMCgXeAgMwW+xc3eOzSSYI6\nSd9fpLt4Djv2+K7nUnLc/pxjaB1J3bUssiC6A7brAZE6+nMXmYy2iFsDXejQRKRYNitVJCsFwhCj\nplKK49NjTJop29tXUddTKltjTMN0usV4NGU82kRZRWXHVEqQSdxw7cCvvPMTCGXo2wO0FLgwYK1C\nSYOs/l9ORqy7m0sJhxDlzY8sM66rSQKxlIQoUcSJzgbufcd/4G++4Bq69klOy5P86UfvJ0mznnNd\n/YzYnmF7tEU7dKX4uawJDl1Y1tj8U+Qrqz97l6hQeLd0Klmp+NEMfTnNlNIEHxDCEGMG5FNB8wrZ\nwQrcjTm8ReuF61DLe5gEEHIZsk6eED3CGCZC43vHpdCihEQrRRd75qGnNoab77iZb37V11FtJH7s\ne9/ED//UO8lVxanKEnxmf3GAi325tg6srRASYkrs9XNOVbYcLClTNQ0VEHOECFoqtDGkWcfGeMRe\nO0dFQ5QDiaL+dw4mtsLFgVpNcASyFviUGJtSPK+qCt97NIILwx4igJaHuckE3aJl6BbMe8cLn/ta\nbr7xS/ndD/xbKt2DdKSQSA5yEjzxuS1e/OJv4L7/8gCLS+eQao8Tox0evP9dtL4mRdBW4Wf7uOx4\nwg5sX/cCxPhWZDiLaa7DzwOVuojIDtdJkjNsnbDM8wExO2QW+JxJuRAmsxwQyLIYauSkGTVTptNt\nppMtppvHmdRTbG7QQpXxFyQpFbHuN3y94D/99qcReUzft2hRRtNSCjzTrf2ijG4NRDGibKmfCVnq\ndLau1k0KrXWpnTlHtZyJrLtdHvj453nHn9zDMKn42JMfxVjJEHtC0E/5u25xiUoaDsIMYwx915WU\neDmA771fM8siSmYJVpYc0nraQWmDc24NhDmBknJZh0vkZQt6pdNbGwmIkiKvfudVze8wxtB1DCJh\nhcZIiVcwtjVbaoIbWnwYyixrnziYH5QOXC5FfKU01bjmda9+HbHbRfmaWyrJSFccDB1eGLRVVKMK\nBktOgarkLYWZExE+LZtGElkZlJSMZU3nO4IGtMEmwZvf+l100ROEYFpl5i6hcwXZY23ADR6RNM7O\nGfyyU5wG/CDYMBWX+hYYIUTHZmVJJtN2h7sZMfQzdncv8ay7vgQStHszjL4VeJjsZ8RcattZCK67\n6QyPPv7TnDw2YfNUsVfKqeJjD1Q0V9/F5qk76bqBqmrwMVLVY7avvYOundPPN6CpYPtbGLIlukvo\n/jOMFo8gz59DjUd4m8hGIHImpzJloaXEaI3ShoRGmyJzqq1mPKoY1ZoqK5RQICtAETGIJYptNTfy\n7DvP8/mHZjjnaOczRJPQtl7aTH3heMbjbZWSrlLH7AMiJmSUyCjxLiHQl8EhAFGQWdZI8tW85K0/\nwMHV1/LohTlB1iRpl7OraQ1I3ntaP/DD//WbyC6ifabSBrk8BdYD/1BGx7LAJk2NRThHSoHSVAgM\n7QItSq1BJAEp4Zbd4DLkn8gpEH3AD44UYmEzzhNCXINfYX6HM0aTCRPbMATPuX7G0A0IFyEMWKVx\nPrG3u8uldh8lFPvtLkM3Y2+xwHvHpcU+8/MX2XWeF1W7/MbjHbu+Y9b1zIaO3UVL8omUPUZJtLao\nDFYqrLCM6hEAM9cVCVPKODzCaipZ00hDRuCGPWZ7C1zr6B1IVYERpJCJVCAETVWRUBhryTlSe4Uy\nioUUbI5HSN9C1oisyEhGdfOXfPX/YuOxs79P3+5gtObJM2eZ7c95yxu/j62rXwpqgtQKVJn1TkqQ\nKkdo9jnjz+EnX8Ifn/1SRs9+E1ff/kKuu/l6jl9zE9JuUI+2MVZy7vQnme+fRc4eQZ+9h+niXmx4\niKvyI2zrA3TdkJrrCO0pzGybGBJp+SxpIkoactIEl0nJIfDk5XOeEShqlLJEqQhJEmMFUZNRkAww\n5gV3fBk+Oza3TzLduAphFTG5pcvSF45nBLq1F1y+PPmwAqaUEiKWDyl0KeyntDbJ6/uevu8Zpjfx\nPf/Tu/i2t/3PjEYjrLVFe0f55VYCY60sZ89/ik2lGPxADEUBr7NGZ40UhhhYM60QwlNY3moiYtVZ\nvVK4HELAudXoyaprOCwHlRVhOfK0mspYgfthjrquGVc1m9IipeBCu8/eYsbFgz1SjnR9z95sTtu3\nhJiZ9Q4JCCFRLnLjjTdxqm54f5gwHmnO7F6g7TpmwwI/DJdZvihvsTIqKIgpYK0hkRFKgQQhy2yr\nFopKK7zzeOdYHHjqsWUyrUEkjJDszg8IGaySSDzz2GNzOfC0T0SZiFSoGBiGFr1dY3WC5MlSEPzT\nd+YOQ5w+8yGuv9OwvT3F9QvOnz0PIfG8W17GV77kHyLlMVLWJFFG/nQq3VBddezO3s/Lb7nIV9+2\nTxp26XZ2ON50XH/dBDkqmjqpNRUzrFwwno4Yhg53cJ7dLnLJb7HrRuyHY1wYNnhir6Y9f4phiJAi\nnUv0eIYo6b0kOInzGSUrUpZIDEpqUhSoZBHSopREKAlJImUkEZAa/qvXPA8tO+qRRQpDyiwJzxeO\nL1qw+ELD7WvkTJngPM55QKyBYjVa0lgL2VM1Y579gq9ja2trrXcDqKoKrTVbW1uMmuOE4Pgn//D7\nEaLU0bpuILpStxFClRY4rOt1V6aaq8mMy4wsrru0a53fipkuX18B8svzr6vGxGF2LgFIIYLPSEqd\npEmG2lT4pbkBWeByxMfA+fkOve9otKRSCp0yQ0qcmZ1jMSS8EATv6fwBIpYDZAieeSyHSkiOMkcp\nWAw9WUm6oQjPJ7ZGCUki43QmklgMjiEFsoRPnn6YRSp1Po/De08TI7bRBO8JTAHoco/MkJUlCUnI\nrjji2go3X5T3gm1QRoA4vLVXAGkk9z/4O/zG7/wzNqYNwXm6rsP1nmlzHSeufjVCj8hqhMpjsmlI\nsiJicSR2w0OcmMy5RT/IyD+E6B8jnvkwkyfey/bOO2lP/y47lx7mwtxz5vwu/c6nifsPcu6RDyHm\nj+EX57jw5FlEhqraxEVNuHiC7lxNdBHXFtLh3KIYaoTyvJlqhFSmAJqQIAMqOaBHpgWKOSntQ7pE\nYAdheu64UxbBsaxRyqDE009GPCPQrYS4VxpdSimL3orLtueXLdAzMRUQSikRkidnUcSiON72r36N\n57/kDYwri60qjLXr0SspYdRMEe4sImpCDkTiOi3NwUMMqOU/VzYlVmyzzO1pnCuMcsXshmFAZFBC\nrtlajJ4Q3Prvi2WHaMX04M8C/GEJYwzVuEYrS0iRjoEsYVo1TEYjgnewZL4ESYyCRefZn8945InT\nHHRzfuZnfo4eGPlEzIZn3XAbbfTIXDSOOpZ7UsmKISbmfUs/zJn3C/a6jugDWpa5W9/3eB8ZeocR\nEi0VxMSnP/Zx3vmu3+J3f+PdWBqcyIynxxmWIlcR5uQQqaWmlyWTiERGCAYXyT6wUY8Li8wBN0Sa\n0eEGuno8LnO9bp8/fOB/5XXf/GpyEsz3D5iOxtz9rFch8vNJHKPPDVnUJGFBaDKSVi64EM4zveHr\nmMjIqeE+rjKPc7D5LPZPvAa2XkA9uZnNq+8iNDdyzl/N2UXNxsbVnDt/ji5EmjpyadFyKSlmg2F3\nIdhvp5z7/IjFRUd0M0QEkR1C9YS4oO8u4sMlhniGjnN4zhI4jU+n6TlNJ0/Ty3N0co82zPEhcsO1\n13PttaW0Zazh4s7O016XZwS6FYCs/rz+d4hoxDp1XDUrVgzqylQ3LS2UAfz0Gr7jH/wITO8gxIEQ\nYHvrBFcdP4XRI1LU4AJ5dukpg/ermdvV91p9vyut2Fe1vNV0w5W1xZUxgXNu3WmVwq53W1z5+175\ncVgjkQl9j4vD8jBJRO9JBFyMdMHTiyKa9jFAbIvjSQxoawn9Hg88+imOjccIq6krxX/7nW8lpkzI\niYltULIHEr0fCNERomMQCqMtTVNxbLqFtSPS8v1jskAliDljlUJoTVKCsamY5czJa69hFCOdcmgP\nfujJ0mJUzdAv0CqTVMAqTVQKbzSLocdHj0yCtguI4Jn5w3tfAch2qZszaNXwO/f8e3p/lsXBTvH8\n8567n/VKrLwVa4/h8jZZbZNVjVDFY9DIA66avYuT6U/Yuv42XvTqN/Mdr7uF7/9bX8Nd10OV5ix2\nPkOcnWVrZCBrBrNFs3EdlZ9h80DuLhL2nyQMAy0WL7eQ9hoefnTKJ/64xYWW3vd0vcP5Be1ixnx+\nQO8dLvUM9PTS0aeeLnoOFh1tO6fvWpwLdIuetnXcccc12Kpj6DtOXnvqaS/LFx3qB9asZyW5EKno\ncPJaj5XXNS6pnkoflVJ4X+zYN3XP4AT/5qd/nf/uTbejRENVNeQMN1x/a7lg7SX+7Y/9KH//x3+i\nMIr81CU5V6aXV9YPLy/VuczQViAdQlh3ZBaLBdZaunY1uhbX6fmqxui9X4+SHcaYH8yIInFh7xLH\nJtuInPGDp8tFPqqVQjhPNnY57J3IMRGzx7uIUVO2c8YrGKmaPg5cW0/QCuqNDXKMaFP84TyRPgTq\nCEJJGhQqS5Iu98sqCcoWH9sIxipUgrBcyqIoLrg/+VP/ga977asYBUErIlKWca4kEsqOyDqRF4lJ\n3dBljxUgzaj4HRoNUbLwGZ0P730FCDHhogYFWVXMWsk9H34nL3v+a1FKMp/tkoaer33ld/GfP/Qf\n8e3jhDAnk4oqAfAhsn3VJmH8HOb2OL/+O/dzzfHIohe8+mu+is88/Dh3f9nzefz0k5w8PuHskzP2\ndx/jE596iM89dJ5pzkzGY1rn6PqBUyeuYmd+gN6e4onses29v3+Jv/76ujRFUmJwC/qhpmsX6/0W\nIEkhMzi/lrgZVSMoO0ZS9sQcmGw6kthgMX967eszT0ZIvdShibXEo3i0FwsftQQ3LQQhhuLxnuRy\nikQWL7Mliyp1taU4NwxMmmMMLtO1jhNXXYPSFuccd7/gJdx3330Mi4gdZ7JfNgeWW4qiL4xRmRoh\n0tK9IBF8wJi6mHu6wjRXXy94Kkh67zFmWRyPl0XCqzR6tR3ssEY79MWHzlYMwRN9WVIyzPdJCKRa\ngZDh+OYWzg+0w8DMOZrpBNf2ZCFoZMX+4jzKWHYvPcm/+P4f4l/+zE8xsTVeCHStyGFgEiQdEZ0j\nTgqMFPh+YKIqlCmW+V3wKCvwMeJyRkiQFhgi1lpcjrznve/lic8/xt95698qQmbdEFVAuJ4YLSIl\nOu8YkmNrNGZQFMPW4BhyYHujIanDzegGJ4hpTMqSLAzGaIbgOLP3BMeP387jjz7Gq17zdVzc2eO4\nvYGcYWd2moQANeBJCF2MVgc/IP0jfOqjn0F/+Vdx76+8jzx4dK348B/8Lnc++xb2piM2jx/n1uu3\nedEL/gaqkrz73e/lyXMXyLsXQFyi7+ZMdWBvP7HZSEJ3wJ6c8Jtvf5hve3Nmc2MKlPUGg3NlIQ8Q\nQ9FFplAUElqNqaygruuCO2qMFYY777qBD//RnPPnLj3tdfkiQHeZPeWci65tqTsrYPHUqYVIYjwe\nk3MpShur1n9/xbjqusZ7z8mTt3Hp4hlyzmW4//x5jh8/zsWLl7jhhhsZm0imIsrlNjChCMGjdbX+\nmUII+qHHGLM2G1j9LCjgVlUVwzCsWeXlFHyloStNCWstwzCsv+YwM7qdgz20UfQhgVsQQsSR0Fkx\naw8QArQSaFHxhO9RSmLQKCzJL73hlGYIEWnKdfRxxGTTYrJAW4UMmewSUQj6lDFSUtc1I2uxUlPb\nCqckRudSrxECEUTxmYsZLxNGVQTlilNNSjS65tTNN/GL7/l13vjqrydVHtVHcmWpdcLFTOsHlNZ0\nAWRIRFOmZYIf6CuBcIdcMNwrlB2jdEVwkj4Gsoh85rFP8/CjD3Hr9t0opZjt7LHYn/GKl7+RX/ut\nn0LVgRBL4ygTOHXbzVz4vd/m9i99Gd/9rVfzs7/yhzizxU233MiFS0/iFrA3+yQXz56hnowRdkRO\nCS2g957bbrodlTQbVYOpNDkEpmHOrN1gMr6G3XaPSXOc97znc/y9O+5AyRohFf3gqaxeuqQEQsho\nERFEcu6QUpRtY03ZQpiywmrDdTfucP7Jp78uX7QZsRbeXpEO5pzXuyRWzGclHXHOrUFwFcaYda1t\n1d3shiIebJpmLTNJKXHx4gVGo4Z73vceSKIIfLMkJ4EUmhiKJGTVPDDG0Pf9n+mUrtxLrrRFvxIE\nV69n9fusaoCrJsZh7rwKVazsvfeEFOmjw0qJtAIpEqF3yKwYREBkj0+J+WKO1BKfA2RF7zKL2Qxl\ntpAiIivNfNbzh7/5LoZQdofOdCo1Oa3Z2tji1PETTMcb1HVDZYrPXfa51P9E4qDr+Rf/5Af46Z/8\ncf7V2/4uf/rOn+XRT30cIwQjYckoNqsJUzvmtz7wQaSE6XgDmSHFYiAhs2RqDRLISpBiJObAxuYY\nFSXhkO+MSHILP9T0nSQAPiaUmZCp6ELmoUv3oWTk9OmHGY1GTMdj7rzhhSgmy+F+gZIVjz75GCev\nv4kYB2677Va+52+/gtd/9U3sPvE5XvnVL+RLnvtsrr3xGu56wYtwznH9iS02tjaxTUblwKc+8UGq\nceSRR87y6KOP4uuTDONnU9UjJtc+F3vs2dSn7uDh+fX8b//6V4g5FqfvXOMi+CiXq3p8mYapLNNa\nM2kadFUjpaS2DU1lqUaKL3vhbVx3y59zqB9WUpECWqspiSs/twIO7z1WSKIUoBIQkKtVhE5QVRVR\n+DVr2jpxPRfceZzrGY8bvOvoWo2Qkt1Luzz65H/mtW/4JnbigKICkUi55OkhuqU1tFqnnMCa5aWU\n1sxsDcyyuDOs6opKS6RUxBiKs7GQSFNYnZIS0uF9INzgkErghabRGp2LLZXxhhMbV3NR7tIHj0wS\nLSU5hKJglxJbGRqp+T9/8RcwVcBqh1NjRFSIJHj3e/6Ab/2eH2R28SwiDKAz02rE1niDqW2IORXH\nixCQ1lIpQ9t7fvyffh+7e3voWvGh93+cqBPvkzW7738/19iGkzdew5lHT/O/v+Me5p844I3f+jrq\nJGiHOVEpxloyFmMGBL0fMBIyAVxA+EiKHofEHl6iDsDe3jmMrrHNdrFckpZF21He/IHWdfz7t/8Y\nX/P8b+T84xeIIdOYES+6/TXs9z2fOf12klxwIV7g1MYJXvpV30jbV9zxgjdw02cf4NhV9/HgZ86z\nP29J/SVuf/YNvOzbXs+9n93h7Gc/yetf+WIe+PinefQxxSc++gAvffFdPPDQeR775H08667nsBMy\nZx++n6Hv6Q822DnzeeJVFZ9879u5+zXfhp1MSV6RaRHBEdNARFDbKblqivtx6rCqIckBYkYpi0Tx\n9a97+dNel2dkdMaY9azrihWtZkOBp3QsVwBTwMYghMG7XAz41kt0xNoE4NSpU8XrXdfkXDqhXdfh\nvef8+XPceuIk7/w/fpZajslJrgXAwzBc3tfK5X0WwzA85TVc+XpX9TkAUkaJy1ZOK5C8spN7pXj4\nMEZlKyQSFQLz2R5WGSb1BGcSZmQZj0frJUi1tDTjCmsqtCoLqvtG8MC9H+XY8ZNYtVHcL4TH5wXz\nfo/vfNNrmfuBSmpuv/5mbr/7bqSR9N4RYkAuM4KcM0MK/MSP/yD33/cRTm5vcqoe85y776Suxzz4\n8MN88mMf4+zsIk/unOPCwT4ve/71/Dff+a1IUQwlEAadFcEtp2OyJ4VM73syAi8EdjIiSkVjylKd\nwxxDUgxZsuhnzNtdFt0+Pji873GxZRCePs55/30/z/EbDIvFjMH1XHv9zWw2G3zVl30Lp04+DxjY\nE/v89od+kxvuejFRTNi66nZe/JKv4bWv/DKu2crMvOQjH3uCd/zuRzj75DlOHL+G+x/4PHfceANv\nesPX8A1vfj2fOuN54Zdcw7e86Wu5sAjcfN2Ipqo42Jtz4dP34Xaf5PyTC+65P5Jdy+LgDIv5eYau\no4/DZfOOEOiHlpA8MTr6YYYLAx5fxgqlAFE/7XV5RqBbSUZWceVI2JXykdXnYAl4MZOXHm9t2623\nc8UYmc/nQGFf29vbRcvlL4t4V99nNp9hcs9s9yxZXZaUGGO+YMd19bpWP6f4zl1OoVevV2UgXJ5l\n/b9vEltJZQ6zvKR1HX30xBjwyTNfLNjtWqywhL540+mUaZqaRdfRtW2pi+ZMIvMvv/ttpCEyOxho\nvaPCoZLAiAYXBi5emmFMxb/5gbfxTV/7Sl7xghfyPW9+y7q2yoppe88/+wffzXx3j7tvup5TJ46x\nv7fDY4+dJWfFRmV5/t1fyg3XXMcnHzyDqDS33Hkn5+czut7Rh0gQnpwcUanSofNLb70hsZiXmm30\noQyXI9l7Bs+ywxBZCiJF5pNzJOdAih2ZgZQHZG4RsmU8Mdxz72/i0zmMVFx94gTjZsSnP3GGfqe4\nB8MlIg/zex/4ebJIjDav5qD3bB6/mhd/5Vfw0ufdzDUntrj1xls5tn2MUbPJ+X3PRz7+OB/608/y\nuY99hluP13hv+OiHP8E4XuK+D38cNX+Cv/biG6k2j4OwDIs5n/ncLu/6pZ/EDTsM/Q7/F3dvHuRp\nftf3vb7Xc/2OPqane2Z2jt2dvbW7uiUQGBsEBlnCcUIMpATEgI8iEDDBRxWXicFJAYkFScBVDobi\nKmIIJJaEkQEJJCFpde6udlfSnrNzz/T0+Tue63vlj6d/3b8ZNFqVq1yu9Leqq7t+3b/j+T7P8+nP\n8T5cO6GTVulaLVHMqjWPdRVNO6FtxhACPkRqV2Hd9Lb78iUD3YF9IKg92Mh8mTj7PrMWnFcGhg6g\ncGRfwQAAIABJREFU23EzOhMUiSLRKSJKyrJEYPYzp35/gHMtwVtWlo9x/toNWt/wmfe/h9hY2uC7\npuZeAJoNHhJtCK4DC4fGdr6UogM1S0Q3dY3s09VCcJ0LFd0kV9uIigfld6gaxBx+8DCupqxI9s5X\nagzRNVg7xfsOQK2FRChIVTekyVUOEpxt+Le//Vvs1pvkqQLX0FORJgp8nHReBEhEDPzED/1Drmxc\nZLecsLNxlXNbV/n2d7wDFwK+stz9wFn+55/4QUw/sD7Zplg+ynB5kVholEkpihSVJSRpxtqZhzh7\n5iSjqedTT5/v+kgEQuP3rPkEVWlxOBK6a9ULCdley8THTprLBuJhtzuUIKJF4kA4orIE3SJVS6pb\nEBO02KUsXwTxEh9+4l3Y7CLeB65cusTpU/fwwNm3srLwxj1FooaXr78PKQJCBIYLRwmiE9UcDPs8\ncs8J7jyWkSnP0lK/o/ylGTemllpkoHKaynL8+CqnTj+Ady1msMyf/8UzjHfWWV5bAN3157dHdzEa\nXaNpdmjbEVL4fRXhDsAfcL7FtiW2qbCto2kqqnKHcrJNVY5uvy9fatO6i8Tvl36zbAkOguBszUrE\nGVRjng4G7HNNs6xLL+fdw51zjCc7tLZiPB5x/sILpEry3OeeYePaVa6//Cwav0+0n/nAzjiss6An\npdxXFVYRZAR8Jw0ON0svzQ9GbpVjn0k4HdZlejnBWazrWCCJyfBBEbzCKQFKQqIQJqHQiiQxpFKQ\nasPn/+Ixji/3IFqcrxm7wKTcpq08vpUkwlJe3mQ8usa1cYmtPF/xVW+inVpqP8XWDfr4kNedOs1C\nL+XxzzxHPS1Zv7HF//sHf8TFGyOca7CuYZDmfN8//VGe+sxjXNtd54VzVyHUNG2gbgKtERghEVKj\nlUURSXRKoRNUDonzJEqS6wIRIlPvSfu3J34fhmVkQOx5O3jhELFFYfcpVImaImVJohuMbLB2i+tb\nv48PGxRFyqtf8zoWl5bZvnicjYt/hasvPMBLT0V+/Xd/hCvXr5BnfZaPrLF8ZJFeL2d5ZYnlQcHp\ntSHCbkCoUDJh3FSMK8fWuMKJhK2NEe1oi7e87hEuXlvn9Q/ewcrqGlubJVkmsa7iyqVLNE1gPJlg\n2xLXVmipydKUNEk6zJ0tcc2Ypp5Q19tMJteZjDeoyhE7Oxu33Zcvq3SdyZPPel/OdeY3bdvuMyMO\nsHIHOm/A/u+g48hWVXWTWMBswlnXJUmiSVKJtQ3r168Qg+X8y88z2bhAM9nB7pWUs/eZDR/mYSNN\n0wluiggxdETwWak6e86MJzs7vlm/bl6C/TDj6BbyBfK0QBd9EpUTBQzzAiEjKoBCghedw7pRpGmG\n8xHbNogsYzyq2K074LiPLWmi8UZik8A0WH7nfe9l2jQkUmAJfPTjT2ASwf2PvIHLLz7P//Dd38Ub\n3vggO23FQw+d4Z7jJwiiZbgwgMZiBiWMS77/H/xD/vx9f0iaJjx3bqsrd0OH+8u1QDcREcG6svun\njMLRUuMxMUElBtdGGlWBDCzlKb453MKbMTqEaBCiIqEmURWpqglxhGIXIyuUdBDt/jVeW8m/+9O/\nz5k7l4g+srmxgc5yvvFvfhuXLjQ898R5nvu04hf+15+mbBqQKVlvpVMFNpLeMKfIU46tHePVr3kd\np+69l9PHTrK0uMjmZEplFUH2GNeRsm553X0nafWQ7Y1r3HPfcXwDF66OGW3vEsc1MUicFSizjI+G\nqvGU9YjJ9AZNs42zJU09pa4jbZNT1ymjsWNjY/e2+/Ilp64idHJJnUFGp+km9zwcZtAOYB+1LLXq\njDX2silFBwD1UqBNQjutwCiCgPsfOMuH3v8Fsixja3sdH2pGoy7wFEWB0inTcsxwsMQzn/wECwur\nLN41xGmDiWF/UGKbLtiaeAAb2feJCAE7K0H9LLuMBN9R2Do3KzrbtxBJlaZqux6OOMRGx62dgE7p\na4kc9LF1BUF07loScJaFxUWgU4SwruTIoGD9havU5Yg6RrQSBGOYjssOH6kNUSVkQrGyssya6XEh\nbhNtSxCGOkQ+9fEP8+QTn+LV95/h5etjlpeHoCKjzauceegenvjM5zl65CjRg1i1/B+//C6muyUx\nRO5YLjh/o6ZqO3OdMrQgu7IbVeBjILQNUoFrLVnSMSfSPXXjcurpJTXJlzBQORRLWJSICGE7w2rR\nopgiTdNle/hOSl0IlAa9px1Zt4FPf+79vOVNxzl37hzPf/6znHv6gxzJA2//of+S6+MnEWqBX/jZ\nn+Xr3/YN3HXXGfLFVarJCCn3tCljxpLRTH1C+9DXE2LOm88+y3jrBi+98Cx3HF9j/cZ1+uPAytEF\n3vj61/LJT3+eE3esEasRUid8/mOf5HXf9A20rmZr+wpJVqClIoQxUjSkJkFqjSAhthGlHc4GJpMR\nm18io/uyDKzhIMvpmvZ/+ffzXNhZY7+1FpUl+L2MKs8y2uhBCOpyihKRre11nG/IshS5pyxRVRVF\n0VFAJpMJTX2Bjz32Qd524iRGLXfTurrZf+8QQmfeJsSe0bLal0effaZ5KMyBaglzdLIDEPJ8iX4Y\nl0oLjIhEJI2z5Crp+p9K4rxHSQXRMSgGVNYiBKz1l/iJ//2fUgfNNNacUBm1ByNtJ5EtAknbkKUp\ndblOub1FqjKClDjRIoPHK8Eg16xf32LsIshdVteOcPKuezn/4kusHF3h2cvrMLWcPDkgJn0uTzdQ\nTrG2MCRPDK0PSAnGZ0yqkn7Wp7ETpMyJomtSDPoFbVnh91RpdJ7Siw4RJUEcbs8ILRqMUcToMdIj\nsAhaYmwhOiLsM1+MSSB4tBQ4Ap95esSo+h5ee/+P4ienec1XvJPPPv5ZaE+h2x7p4qd469+OfOgP\nP8jFy6/ia7/2q/n0pY8z3r7C2btPYcyQ0kke/8J1WtNjUk9ohg/BSoK9MCa4khN3nOH69Ss89+x5\n1o72edNrzvL4E5+nyCXXg+X6ugQqYtRY56CxtKJFCIuSBqUHxDYihCfEKU2zhXWB0XhKa29/br9k\n6ToDBcPNE9c0TfcnlbMSdEbmB/Yfmz0/eo/eK39naiIXzr1AsA1CRGL0+2XkAYBYIET3GsYYiiTy\n2Afeg/IVIYp9VsYsMAH7nq3zZe2sdJ71GOexdQf82G7Njm/mZXFY11LRI1MJUir0nmhlkILGO4zW\n9NKUpcUjGGM4OlhkbeEIRhm2qm1q37KYZgiZEW2L1oYYPYk0OAS/8Vt/gGfIu977bkQ/Qed7opuJ\nJk1zRCvYnk5oyhFTJzn/wjnWr12grRwyBJrplBOnBkzqlpcvXWU567F2dBFSwV1nTnPPPcdpqhYb\nPLnMGdclRiRIHxGJBKlonGVUtWRGk+QFvopY12EFY0z/c2//f9KVJx4tSoxqkHKKlCOkblCqM5IS\nMaBkl4FDACmIArQ0nD37cdCWZ17+BZbv8uT5kKXlZe659wGOH3mAsyffxsryg7ztW49z7I6K9777\ndzBxh0w6rly4zpOf+givunOJd/6tr2GxaFlavRPXlNQ7G6w88HbK49/KFfNq7nngQe5/8DWcv3qd\n69cuceJYjyPLfU4cX2FiM5rtTVKtSFNDpCEGS6Zz+sUdpPka2izSWk812WQ62WZ3Z5PJaJvJzs5t\n9+UVPSNm/bRZ0Jrh2WZQkCRJboJnzOAfQnTmub7dw9yZhFZ2vbthWGDz5ZexicG6mizLujLT1fsB\ncjTe7ia5ckKWHOGpp55kbfUMF7/weU4+8gbaWCFrTTAdjCRLUpzrhhDReWIIqH2oSEdYds4jReca\nPsPezY5vnjUBB3CZw7hujHYO+qY6wTpHohRGqG5CLQSLptf9zZxIgwwRg+rwd6EmKzR1WaPSguga\nUi2ReUrmAh/+g9/lLY+8ik888VlGoVPSSJTDxxYVJSvFgNA2bE4r/HZCO7lB4yInjw7Z3dylDYaF\nXHHs2CKJzvBaM9raYLUYcDHJcCpQ2pI0T5GxE/LMbU2JZGQDRZ5TS48cj4iJgTQHWzGtbw9BOAwr\nM47ga5ABgSOGgBSBKDrBhGgkMQaEj53pTIh4YtdmQhK8ogwT/PTPeUh/DQvDgqWlRS68fJ6P/umz\nPPz6VVRxkezOkpVTBW1Tcqc+RRaOceH8EZ577iXO3Kv59q9/FKcK/tXvnWfj+g36CwsY3aOZlDzd\nrOKqDNt7iOUTga2rL7O9vU05nXDh5ZJzLz/F//gLP4hOJAOTEUnRepEk6fQHiVN6JsORAQ1SOKSo\nKeXktvvyZRH/ZpnPLHOab9rPpp/zE89Zdjd7zuxxLUALwfOf/wvygWZr6wLGGKqqoq7r7sYSkdZ2\nKgRaa/K8x8lTJ1heXOD61fN88M/eg7aBYDUx6cyokyTD+4jWyd4hSZTqeovOBaTUdIY4c1JTe59/\nlunNDyBuAhgfyiXItCFLDUJC2bbU1tLEtoObxNipuiad7HaU4CX0hitkUiGTAUTLpGzQJkW6zjpx\n5BuCdUzalus76wwWj7C0sMLiQo+sr1k9skIIHm1SNuuK1loaJ7i2sU1Mj7BTTUikxKuCtSNDjh9d\nZjKuub61y4ULl2jbwJUb2yAFSWKQKsWFDmNZlhNq3U3m+jIj6RmKqAlKkcYAVU0UkPeK/9yb/590\npSmYFJRyCDzagE4kJjN0Fp6xK/1TiTaSNDdkaZfvSMIelEwhfMVHHv8p7n/4bjY3NhHeMugvQ3Mf\nq4NTLC8uMyx6LC0OqbnBpfrPcYMn6a9ayukFLj33UYam5Xvf/gDKXqEdn2cyfpne0hJpkuFDjcyG\nPHtN89KldZaPLBOiQicJaf80WoX9+zTTAxKTIGhIlGPQP8Ly8sOsHX8La8ffwurqmzh69BFOHH/o\ntvvyinfzPNTiVhVfOChpZ9Lms6B3a4YUY0RpkK7it3/lR9BZTioDo9Fov6TtyteGti33n2Ot57nn\nn2H96iXqap31S8/zSz/7j/jIv/stXHPgX+HdnnIKmhhkZ1iNRsmEpnYELzA62//c86XpPNRl9v0w\nl67GGDwRKz2ZkiwNB/SKgmHSJxhFlhiCoKPGIXCu62tuja+SDXooLVlIDYXqU4WA0gKjEn7t//xN\nvuFt7+Dn/8kPIxPF8dXj9PsZi2aBQdJjY2MDERUXNzbZnZZc3LhOVVVED5tbu/SLAS4KlgoFITCd\nRja2dtjdGiEbT/CCza0RqtDsli2JilglSVJNkiRI3xkztdF2dotK0TcJVYwo0xKiRLjDO2QCUFpS\nJAlpkpDmiiRNSEyfRBUkMidPUvIkB2nwUWJ9xLs99W2xh48NDhFAJ54PfuJnWFyOWNvw4IP3k6SS\nx95/g0V9DzE0BDdCiBFGWqSuuLL1IZ69+v9wtf4T3vt7v8GJZcnP/KN3ElyN3bnI7sWPM9l5iV6v\n1zEKpGJ4+lX8T//iLaSZ59iC49GHE6Q0GNGhKZzfQfiSXBtWFu9kbe1RlpbO0hscYXHhKEdXj3Hy\n5L2sHr/ntvvyisKbSils3XSGwnulrDFmTuJonnbVSZ7fKtY5g56MWs8f/96vcHV8gxubl1BKkWUJ\n1k0RovtPM18ud/20hn4/o6p3qOsS2wZefO4xnvrE+xgUhug7rbREKjTiJg+JWeCdx8fNMztmWdu+\n5WE8UCo+zIEu7slt9XWvg/RIhUgkWWLQPjKpG6xtadoGGxzBt/zq//ZLOKdZLAoCllEQbE22EK2j\nbWBSViTDBb7/+95Jm3m2d6b4nqZXFNx75yl2d3dJFo8jtCGREenAl57Gy31l55evbEN0LPR6VE3L\n7miTcWlJEok2hvVr6wyKAWkEV43RwpC4QOUbBIHWB6rad6DUPfjTyDlAYIPCEAiHWJUGoJ8pjFF7\n3rlDkrSPNjlSZCTZEJMsocwATQLCzN0PoIXEKMhzg8k1QqZ4OeUDH/sZ7j57iqWVNWxTInWOVg8g\n7QL4McJNMTT0EkkvU2jpsJPAxc3P8d4/+T4WzA4nFlOyfor3lma8we7V51hYOUJiUqjg0pVN/tWv\nfCf//Bf/Lt/2Pf8V/cEiWZ6RJClZ1idNU/JsQJINurYZHbOH6JDCoU1gYXB7CtgrCm/OxCi7PtwB\nlarrzx2Af+e9GGI8yJDmy948yfnEYx9GRrVHEROkaU7dlPu4thgjZVmysrKKlJKmqRES6j1qEnFE\nkiRcuXIRFVqUMvtZ5OyzADf1CmeT1Xke662Z3ezxWW/uMAe6dJiTkdBPMtoQ6BfFnq+rp/aWPEkI\nHQYB27Z4H/ijf/9nGO/ZrabYEBEhIqMhIdCGBivgO//mt/Dg2WUyk2JUoL62w5HFIb1iGfTT0Gzj\n2oZIN1EzeYpDYEQngy5jYGcaaCc32GlLhNCsLgw6VWlhoZcyGPbBJaSbO1gRKbSmrFqi0hgp8C6y\nMMgIbcRohSOgtUG6QFCK/iH26wXI0h5ORVrvyMQeAkJGvOkk6qO3xOgRqUZ6h7UtKoNURCSOTGt8\nBBEjLiZ7fdqWpy+/i4fu/kacW+GOk6fJiz7T3RM89Kq/wsX1P2U6fZm6LvEhkCtNrb6SBx9+D0mu\n+OhT/4z/5lu+it989xEuj18iKXo4qxmPt5CqBaX56Mef5r5HX7dnZJ12nh8xoomI2MUObXJC7OKQ\nMQlKBbT3IBOE7Cq6261XzOhmXzdJolu7T8qe79HNgtq8Bt0sqMQYcbUnuDG+sVgbUTLBWs/CcIkY\nI1VV7SuPzIJe25aMx9v7mnJ1s0ue9WnsiJ/+yR/eH17MPte89t28EvH8Y7cGs1kwnDEtbuX4HrY1\nUDkLad4plCAIruvN5CZhdbDIsOgTlcCLQOsco3HF3//mv0F5bRcdHbJpqdqK/iBFye7iWxn2efSB\nY+ikR39xwNJwCZdpnFGYHI6dOMHudESxuEjtG5TJCM6TG8nIWabeUjWeS1euc257Qi9J6Wc5O9sT\nJtOGrUnFoou0okWJliQpsF5ghex08bRAaY0YaMI0YGPLtGmILlCPK1rZeYuGQ166FllOsoc0EMp0\nXyQkOiEGSZQpDkWIBqLp/l5AoiVpWhBNH1QfL/tEZZCqhyYFkXLh8ic5djJjYWHA+vpV6omHuEQm\nvoJXP/x3WF5eZnFhAaOPsrP7OEeOHGFlcZWlhSNIrjNc2qLXy6h3NlGxxI2vY5TEHD3JBz+5SILC\nKIHEoULnChacJ1UanS7hgqC1FUJ08m79QZ+F4SoL/TWW+iv0iuFt9+XL0qMD9kvAWYZ3EIjaW0pN\nvx9cZnpw+8MJD1ubVxn0hiwuLBMC1FWzN0jQ+1aJw+HwpteeacRVVYV1FZPJhBBadjeuAOx7V8ze\nc37AMHvN+XJ0ftK6vxF7GeFMXPQwL6kVymhSkyCUwEZPFN1QJ0szJAJCxKiuNfDoa17F1//d7+JD\nTz3B8tIqSNH55jrNuz/2AR586H5WlvrkSYLSEWLDxz/0x7jRLsN0SDmZ8MBdpxjkfbxrKbwmihqH\noK07BoUQitY7jO5Uj89vT8iLHv2jfVbWFugXGXecOU07qugJjckURgRs02KSgtbRBdAAQgZ8E1ju\nD1EoEtPJs9fB483tnaIOwxrmC/TSgn6SUwiDigIdAOsRMaIiKKEQdH67UhjSZICRPZyVCK/ROsXI\nlEz2EEKhZEqeZWSFIIqPsHbqHADVtDtHwadMxpbF5fs4dvwszz+xyJu+co211VWWlhZZObLCYLjI\nt7yjj2gqeqlGtyVHFvvUO5exl5/ANo7Na+ukaQ+p+9iowCmUzDukRIj7xklKSEw0JD5FR4mJERkC\nib49ve8VS9eZmoeUkug6PJyIYn94kCcptT0wj+2CiUApvS+/3gUhSMQGJjM8dN+raR2E2LC41KMs\nS6SUpGk6x1sVVFVNN/z2+NCZYgshyIuEja3I0NXIJEfGGmvb/efPMje4WaPuVjMd6PqHsynybM0y\nvMO6DIqIIMsT+uRELZFGEayn2aP6RdWJJPTzHhuXr6GNJgj4+V/8Rb7ru76Dre0pRxcT3vYVX8fd\n9x5ldXWFzY0NalcTXZ8YNFVomWxtkWQF7e6ERElOn76bz24/wW4bMarCBdX1pH1LIgQ1nlQKQht4\n6dJ16sbz2ofWSEOPlkBvkDFpLXa/WtAY4VHaULcWjWASLFJ5qqYrZRsRSYwkjZp4iBkvAL3eElIb\nejZ2rBZv8a6lsQ2t2zM4EhKTpCA8UphOQSg6pOoGiz7KTiVaCFKpSdOUJDUUWUqaCoyecuxYzflB\nQV1V7O7u8sE//yzbN9ZZv/oM/f46w4W3EoJG4MAHjIBFY9H9HrtXLtKMN4k+sHLHSRqhUNHx7t//\nC37wx95A2dguKCeSVKXkeZ+lxWWE6BFEglEFSmQIJBIJsqMGmjC5ber2JQPdPGbu1t7WDIw7+/0M\nYzc/pZ1lf7O/n1x/DhEdiIwbNy52Uuh1vV/2zmeNsyDpg93XoJu9/3Q6RSlDVW9h612UzDo10lum\npvPB6taydRbM5ie+s8dvFRg9bEtI8ET6SQ8hOvNog2Qamv3zOTAZPpFdj0TJzsbQ1og84eve8XY+\n9O/fgyg0qXMQPLtb6+ho2C4tstnkrjvO8pE/eR+Pvu6NBGvxecEjr30Ddd3y2tc/xJOffYHt8YQY\nZHeDxc6ERwdJKUAET2ha7jt6FK1ytt0OOzducHT1KCpOUdFStg0+7EkTjWqyHJwNmCSjqUtS7dAi\n0DSeVGpE22I5vGINAFlqSJNlrOv+eVvb7FmQOgKRphnhvadsaiISRCQK12V+dKBvSQfCN0jSLCHL\nUtIsI5OGPC9IVIIWT/K137DGufNjfu+3f4vVZcPr3vAon2rG/NA/fjvO7nYS9q7F4yEKooXveOfd\n/Oav7LDrWvJeys7ly+TLR4m64FMvppy/fI3aCrTqoXSCkoHFoWSnaUmNIE09g1zQSwOJyPEoCHRq\nNujblqiveDfPm8rME/Fn5SsA8qD/NQt6wD6gePa7D73/PfimZHdUoZSil/SYTEY3GVInSbIf0KSU\nTKYd93Q2KLDW0tSewWDQObHLAO5AKn0+g5sF3fngPAves2OZBb/Z386C6mH2jCiygl6aI6Vm0k4g\nQGs7elCiDXGPjJ8KSdzTNmvLCpFqlNQ88bEPUmQdKPfEwjK9Xo6bjhgsFhRXPPLIEk8/d44QPU2M\n5EZjg8NWFqMV2mTYWKKFBuXRQmB1gChweHILpRJYW7G+u0MdWu46e5pxPWU6nZApTZYUNHXAGKid\nJzER6xTOtfRS2Q0fgiW6QGY0rdb41tKThzvQDXpdue6JONfifI21nZ1AbTt1b2cnJEYybWpciHgX\nEDFFqID24L2FqPFG4IUgolFRYoxEC1AyYLTAiJb7z7Y8+qoTnD5zF6fufTVH77iXqE9R8GEIDT7N\nKeuGxgbqIFjpWyZWEfBsb2wRIxRFxmTaMmoV07ZksLDG9qiiLsfIENmZlBAuU2Q9kiRhaagZFop+\nf0C/t0A/STtHMNcg05NfdF9ekes6C3KzTEcpdRM1DOgSyNtAMrz33YUWBJ994jGqEPYkVkqcc6SZ\nYTwe72dQMUbG4zFZnjCejPczyZnKiNaaKBukzAlB0AaPjn95Uup9REpNCBGlZmKdEefCfikthALv\nEOFgOizEAaf3sC6lTSd2sGc1V6Q50oEzEeM7p/TgA1Nf4VpHEJAoxYnjJ/j2t301qVQkaY/Uj0mz\nArRly+7itjy91QFZ0OjYInoLfOYTn+JNb3ozSoOTEl3WKKN44J77eOpzz9FPFbujKf1BQTVtqVwk\n0ZE0eFSaMLaWUDfsjjaRe0IOedpjPLlBkqW0bU0qE2ImEK1CGcl0WpMnGdFBUClaCex4ghCCsTu8\nQyaAhf4i0F3LLgRa291n1nWqQU3bovQAIcBHR123ECMhVFjnCTZig0coTS4KhPYIXyOCRhHxriEz\nEiU8Rrf80YcfoxYZJ07dj6tbrl7a4L77X0tIXw/+BabVLrUX1G1JcJBmnvseWuSJD13uwOM6cO3i\neXrDIbZs+eF//F7+7e98N86PqdodtqZTdq9NkVF2feR0QC/NWOrnLBYDFvpHKYoljIAkRh64/4vv\ny5cMdDM5JjgADs+T9mdBcBaIpNL7mdJszTIxZTICU4IXTKvN7j+L94xGHSVH7rlENU3ntzqZTPaz\nsVt5tPO8VaUUvvXdydnLKLvvN0+E99Vt4aZyO86CWow3ZYOHeSAhYyC0nqZxZEUPIzU67cpUKzxR\nRKq2JoRI3FN3OX3nnfy9b/sb9AdL1M0EnWhkLOgtrLBx4SV6vaO8/OwXaJEME83aiSOMyxbncz7+\nsQ/xwMOPdB6+CFTVncuz993JdDKhKh1GKioCi31Dlhmqqe2k1xEco5sOZyYhRMdwsWNA9IYFzfoU\nnWiiKBCxIkhJ3mYEFcmdZxRrgknJspyqrkmTQ57RDQZA7KAWOqJYQoqIby3BNzRNTVmXBF8zme4y\nLXcYjcbUTUNtA7UL5EYjk4QsT8kzQ5olJIlAyIBJDFKCNgohIt/y14+hv/FuSAs+8mHH6TN3QYxc\nvBg4eeYtXL/+fzHZfRlCRZoXrBan+ZHvfZjv+eR5CA2urRHe4isBDFhZXuBf/PQv8+P//O8gVItQ\nDa217O7WWC+J05JdVbCxaxhmu6TqColKSJRHi4YH7v+vv+i+fFl2h7fi1L5YEFBK4fcen1HD9gPj\n3t9YN+kAxaGirh1KyZuGAU3T7E9ulVLUdb3/nvPg3hjjviT7aDSipw/GygdCoTcf2jzbYR5UPKvp\nD5gZfo/Z8aV25v/fq+tLaiIKX9Y0PYHXmkxqbNsSlMS3EalAusjUVfy9b/8vCFrgm5adEEjsmCIr\nqEebTKrA0axkdWmVq+MbnDh2nCdfvsBa0cc2HlGkPPn4Mzz0yMMkicZ5xVClVLbFjia0vuJotoB1\nASUjN7ZbpLQkQVJoSW9hgal1SBd46fw1Ji9dIzeG3asTVGoIrQRVkWlN3jdsb9Uo62hjRBqsrhQm\nAAAgAElEQVRJplMInkwmxOrwtiQAhsMBMXbq3lqbPW/mA+sD57pgZ71joa1pJjuMptuUdUtpWxpr\nO264cIg90r8UkojDBkeMneG4khptuoRDJoLIlNe8MePDH6xZX79KsA7fCtYW/xavfWSJzzz+cwS2\nSTJPVrS8+73/Ld/0Nb9IsC2DQtIbOEpbMh0rnn26RtjI2tpxsqzgSJGzM54yLS1laRlPS+pGsF41\nJAp0qDCqIob/SBeweexZ9/Oe7pw8yNxmdC8pJXLvOSKyL5dktCEGSzQF0feJcRttLErknflKlnUg\n4WpyE1d2PquaBcz5NTt5RX8R6Tq7u0x0vYnudwf9unmc38EKe0wMAULgbLs/7e16j4eX6xqISAGJ\nUN1F7QLIyNQ3CKOQtSUIT6IzHJbHP/Cn6FTSVi1lOWHYSxlkSwzSAc14l7UFxc54gtKK40eW2Blv\ncXrQo5rWDHuaqsmQhWL7wkWOnlrl/MVrnDpzmu3tbXaalsGg4PKNG5goKJ3DaNBI8gJOLg25PK7Q\nVctzL26gTUB4hR8IdBZwXoGynSeo9/jrJaHoo3WAKLFVTU0JrcPIFJ8c3vMKMBysAg6tOttCGcGL\nA9ZP22qyPME5i/cFY2MQJsOUU3Rb4tuGxte01iJl2OtfV8To0Mbg0YTg8AScUiBgWjmcq7HtFvc9\nbLlybokbNzY5eec9bG9sUFY3qH2Pfq+mN1hCGYFiyh9/+PvZujblX/8vP8+P/ssfZ3FpyKvPfh9L\ny0v8kx/4Kf7N7/4ywtUY10Nh0NJzZEFT1hO2tjeZVC1GapYGC/TSJQLlbffly9Kjm9dxm5WB8xnW\nPMUK2Fc0mUFTBFA3Y1798FfzHz7w65RlS6IbhsMhu7u7+1la03Rk/rqubyqPZ58FmAu6BzJMfg8S\nwdz09GDyy/50ePb8+TJ2nhY2/z6HmRmhtKGf5DgVcU2Ljp3opkTgy5qQGKSXNK7lZ3/yRynrTepx\nxcriAkUqCR6SCNe2p4Ckly9yenGNKxdfRguHyntkK4pzT77I5IbljrWjCCW496vewCc+/DiJlHz+\nmXMESlzjsCGQCoOPjixVJIni7jNrCA8mLzi+MGXj0hiVRhKdoKToFJCNpHU1SdTUtsLoBL04RDQt\nQSWIKDhSCJq6pVIKGS1CHN5pOsDAZDCroWSHh5QIApEoPToxSN8pEbetRUpIM0MkQUhPTcAgcabr\n6ykVsb7jHjvfUSpD4miCw9eeEGuEaDCJgQRcucWJO65x/VpLXZaEaHn+C5d5/Ru/g2iewZgJJisA\nBaHlyFHDj/38T5IqiasbPv3sL/Hd3/zPuO/0IqGaoIIGNDF4lgYLZMqgFoesDFLaEFCiY8QkRiPF\n7e/ZLyvQzeAWIfi9vpi5qWF/K3d09njbtuR5jrMNCMvZux7FGEWaFNjWsbGxcVNaPdOCy/Ocqqpu\ncuT6YnARY7pBRi6TzpbROhDsv073nANa1/xz54+vO4YugM8msId5GKGVpm5qkjTBZCmN9xgB3nqQ\ngqqtkbXl5D13QjNFODh2ZImNyZhCZ1TVLtPiDurJNsePrOBEQxSR5VPHeP75F2nLkt3dMV/55tdz\n7aVruEyxePJ+/uzPPsFKElleOcr21gsYqVAalO8k2YXRPHDnMU6sHeHqTsPmeIOeNMgosS6Q6pTp\nZISQCUUmEG2Cby2tChR5D+8cuztlp7umHUFoXNtJdgklqdrO7/VQL7nH9wwC8CBBBokUHi9s97jQ\nIBKU9iSpQqoMrXtoU5Mm3ZTW+oraGaJt8cFgbYUSEYnEOYWrLVqVpFlBPhiCMExHVymba5T18xw7\n00PIRwm2ZWXlGMEnhOYIyaqnl/XJkxRl9N491+K9Y3s0gbbiF/71DyBFzmjH0jRThFCsLK9hTEKR\naGL09BdygjRdpea7MjszC7fdllfs0d0cbARCHFgSKiWIMQBqP9OaLxVNlnYXlzFo53j06/4a6a8n\nCHKk7LI2RKCsJrdMTA9S7Xl8W/eeB1mbkYal/hLl1KGip/EWMRfVu8zNotQBc6Pr97E/fY3xwENi\nBknpNv/w9nLWtzZYHA4ITpAGg9EdGDwEj5BA7fj9d/8en/3UX9DLFItZwfruJqvDHuPphGGRs3ll\nnaACl268SGIKstVVekpz+sQaxxbXaGvFpFonGWq2Q5+joSbzY4TocfHiRaSEQiumztFbPoJZMJwZ\nDMllyjMvbbI52mE6qTBimyJPiT5215JOkULQtJbFJKcpsk7YYVzSJIL+IO9gJ7oL3LqnCXUgOIgi\nQ7aH2zOCIOiaq3TGWTKCDPgAyJQQBT4EnI84Dz60OG+pncf7hCAC2oAPkAhPkBHX1AQLyI46FkMn\n89RfWqDoLxOkwra7tNUGu1vn2R3tUFeOweAetm/0WD1xirKqKEeB/tKjnDhRYfb+49impKpKJlvX\naKsxRiwSAwhjSTLBYHCEXlFgjNrHvCqTk7oeLjoQAd92A7PE3F6C6xV7dPOerF9Mo20G7M3zg57b\nLGO6tbwd7XpM2ifNMqrtMcPhkKatSJKEtm2p9jw3Z4KYs+W933cPmwU5GcGRsr6+znCwQohub8Lb\n3vTc2XR1Nr2dn8zOl7Dz5fmtDmeHbaVpigkSnWmkF7TWIWIgikjjLCGRXDj3GKsrQ6Y7O4yjY7i4\nwOa1DdZOrDGtDWtHp8gsp5zssnJ8EVU51lZWCEgW+hm/+v73cd99r6PSi/RsyVNfeIFCZmjVQ4aK\nhTxlabDEWl/jZEue9tjYrnjx8vOYKAhRo3RERkVZNaAC0Qsa3yCFQGjD2JekUVALTaP32Dttp8EW\nowAJ3geEFMToUELj4uE9r0AX5IDOGAJC8NR0Tnmts1TTLrDUe9TM0Doibk7Bx+NDi5BtB62WEiU0\nWNVZSGpFajJWjq6xtnqcNOsxLbfYnO5QT64zmVZUISemks9dfD9x8CD3PvD1fPbJp3np+WdZO3GW\nj/zpy7z+qxax022qako5riinjugTpHAUwz7DYUGRpyjV6eYJEcl7xV5CFLC+wbmmI/Pj8RGsv73w\n5isqDM/6YTPO6CxAdFndzE/C7PNS7Z4E0Dz4duYWVvTXCCLjyvo6g0wjhGA6ne7LKXUnJuwH2Fnv\nb/a6M/n0TjATZL7EcDjcK1kPguutLmOzwDbLTuf5uLPjvFVf7zCT+odZgZSKULY0RiL8ngl4CIgo\n+NV3/RTTcaDRJcFJWrtDkSyyfHQRKxNsVbK1c4Wvfsvr+fTntkjaSLFcsF1PKGvPxctXWRgOGe9O\n2N29hq1KHnj4Ea5tJpSbmwyOLIOwpL2UQS+hajRf+MJ5gu0ohk3r0FqgpSCJinGM5Mrg9zJuLxUx\nOgam13Gi65pBYfBNoFV+L1uXyBBxApIokFIjJXsl3eFdwTuk6DwgApEmNDRtRVVVlFXNaDylnE4o\nm5LQWkJwKNlBjoSMGJ0hCMgoIKQEqxFCopKuN5slBXecPMPJk2cY9JaQAVJtGO9cI7hACBpFwOIQ\nsUaEP2N9/DBpMsS3lp3tHWIY8MJTfYhPEr3r+ogqQZqcPDEkJuJ9i7Oxk5JyCVIFQtvFCOcbqnZC\nCF0VZtIUHxVVM77tvrwi13UW4GaR1PsOVNsNKQ5YBkIIPB6BoNOo615jBjORUlI2DX/17X+bd//G\nr3HkziExZDjnmEwm+1JQs+A2C3zzUu5wAF0Rsc/pu++j8YEk07QtmNjJt88AwZ3S8J5L2d4kWIQO\n7hIjSCFpvbsJbnIAMzm8wwihFMSA1x3h2xvR0Wgc/PJP/RCXtneQWcqSSrEm0jMFG6OW00sDru1W\nHBn0kNkJplNP7RQfeOxxfEzIQqDNFJPRhNfffT9OTunli9xx55u58PwF1q+8yGte9QiTckSS9rh4\n4RoWaKuWqFMinau8TNJuQFVHZObRIWBtJAiPUinWViQCynFNspCxIBTWR3SiGZUlRZrRzwumZY1z\nLdZEDIJJ3dJLD7dMU0nTndvYZWlNW1PXNaPplHJaMh6PmU4nTOsJTdOQiUiRJqQmJVEaISxad3YD\nPnYakXlakCUGaztPl15SkOoeCo2VlnJcM5m2TOqOReNERLqK4KZIKXnys7/E2tG3c/quM6RJwuUL\nWwwXVplOH+bY8RHV5AKJEQg8UnSKRlF6xJ5YejQe4eNeMuPxoSbEhta3OBvYHm9TVRXj6YjXn/ni\n+/IlA90sSwP2e1vza76clVLS7gkezuPs5oNH467z1rf+A/7DH/0bYowsDFfY2rlImqb7/bMZ+2Ee\nr3crDzZJEjAN/90P/BjN3rBhJiZwK0NjH9gc4k1Z2hfzbp1Rv6y1CHl4MzqlFQUJjkBdtkCksS3/\n/fd+K+mC5uSRFabThkJ1+mVFb5FUTmlDJI2Wly6d49y1azyuEqSNyFwjdWR31DLZDXz3N38NVzc3\n+OgnNzmyusyVi5dYPJryhsVXM663mE5rnrs6YqAzcIJEScrQEJUkth2QPN0bEnUZtiREgRKSED1O\neaIwLC/k1HWNTxUygC2brkFtNNPxiDLu9ZmcxgeBJoI/3FPXrZ1NYpyxXiyTuqKqptRVyXhasrO7\nRdlUlNMK4x29YQFohOiGtEmSdgO6KEgTSSTdl00L0ZLqSAwtm9uXMUlG27aMd2+weW2d0aTtrhcp\nIEmxKqe1EKQn8ALLZ0bsjJdZv7HO6bMPMpl4Xj4nuOPEKo4JIVrapiIN4GMkxgyEQweNNgofa4yS\nWN9SVROm013G0102t6+ztbNNXW7DW7/4vrziMGKepH9Amj/At80HCzMnanjQ+D+ApAyLs0Q/xqsT\nCAJ1u3GTEMA83WsWQL33+8FznruaFyD0CiLU+68vAgc/i5tBwvPKxfMlKxz40s7KVmMMh5jqirSe\nMnrKpgKh8M6yfHyFe84eZzT1bG9ugEwp2wJUCrZicWmZdmS5NFpnoV+gpCGXHpel2Kam3rSoPGXB\nSM6dv8zCwhJ/5c0PcuHSZQaLmlC3XLqxwWS7JBpJkqRE20GPfKooWk/VOqIKpEIhZde+iFISfEQJ\nh3MCZVJym3ZMGCAxmumkxqQJOtXkrqMK5klC7j228WS9hLZ1KJ2QmMObqQNM63HXoA+OpqlobUNd\nNzRtSd1M8aIl+An9tGWYZuQmQSnoFxKjM5RRSJ2SJwnSJCSJ3r+XCC3TdkIda+y4IkpBYx3b25u0\nvkJ5z6lj95Nni0xbx7SaUtdTlIoMFxboJRqVv8D29QFFlqGF5JOPPcORd3wTgWcIfhdFg8CghEEp\nunIYi5cKER02Rpq2pKkmbG2vs37jEteuX2F7q+FLYV9fEV4CB6rCSZIQI3Sy5/IvQUz8Xq9H6wM5\npFngiTES3DZpmvJz//L/5td+7p1YW+w3/mdTz1lWNf/681i3EAz9LLK48tcJ3Mx0EEIghd7rubmb\nJsY6zfZuHEFE4P3ez+4guM4fyyy4H8a120y7yZYQeGcZtRN+/Du/F+lrTDpg6egaVy5tUBQQbM1w\nsY9vLGPfcvfxNT7z7DVK5/HOIEWNrRqSfsKiSlk+MeTNX/3XePqZZ3G7Nzhxx3GefPpZtkdTApAK\nDR4q74kCvNLEqSeY7jJNhMDLvSw+aDIZsSZAK5BKYr0jM4LYgnSeygXyIiFE8EF0N6pzjH2kn0gy\nNMEF8kTT1g7H4Q50Noy7ffUeFxtcaLF+hHUNbSyJoSRXloV8QJLkpCrvZMrzHipJSdMhSsgO9qMT\nhJBEIbqAaeO+krMxBkHEpAGpBGkCi70B66OWSenxUaKEwSQG7xom0y3q6QTXXufoqRXq9rVMp1Me\nfeQRnDMIfwZhalr/MtIrZEg7odQQiRhc24CCpnFU5YRqMiKRkcWlZZI8ZWlpC9v+Rw4j5hWGtdY3\ngYNnP8++d5nXQck5rwc3b67T7oF76zolMLmJ3jVvHH1rWbkPVBYRnQ/48Z99Fzt+imw6CMxsCOK9\n29e2+2JZ3PxrzzLE+WHI7NgO8zBCBkB2ZtWFNvzcT/wgOjpsTLGTXSaTMSYRrAwlVZmTpAneJEyv\nr7O6epR+6hmqlLxvEJWgVobV4SI6kdhpwx++74MsZoanXjxHtBZpJLhIkWW03hECrMiEMRaDRvYV\nsfE4nRBxiBgJoSUzEJEYIWho0aQE5/HCEGJka1oyHPS6oFg1OOHoyZSoUmRnYEadRrxtSdKCbNhn\n1BxuA2shLbHTnCHSEmmRIiBEQ65ahn1DYU6Q530SWWB0QpFmZGmfPB90UDBj0DrBO4F3Nc63TIPE\nCU3VOAKe0EzIs4JcF/T7BUumYKJHSLvO9Z1tnI0411BNJ/x/7L13uGRVlcb92/vEynVz7NxNdxOa\nnEFRCWLCMI44CojomEYR1JkxMwoojiiYA5hFMSsKAk2WTANN03TTOdwcK9eJe39/1L23L6FbZz6/\n53Oa+z7PfW6FU6eqTp16a639rvWumADHsgmlIlIRKq5gO3dTq2Y48tgTGBwcY3iwj+UHHYeTbCKq\nPzm1Th4RxCGRp6bIO8KrlxE6bDy3zNNsN0ZdVmtFCpXiXo/L37RgMVu9nG2RPk2A0/+nCWVaWJhW\nZ6dFhtn7MGyX4nD/jEXT7LW8Z3clzI4KDaFJNnVR8kOYun3a02525Of7/gyJzlZVn+2vN91uNtte\nanq7/RZSkJAOwjX47fU/wLVNJit1mrMpfD/CNJJIF4ZGh2nJdTA2No5lZZnX28HOXaOIRJL2pA/E\n1EyT7nyOhGUQ6xglTEYnS4xGNRxDIMwE1Xow9WMYYQqBMgVVJYgDheXGBH5AKDS2UphSUBcalIFl\nQhjEoARCukS+j3QcAq/hxOEpsEONZYMrTWo4FEKfSPvkMy71WoQKPMyk3XBACQKU3r9V11JVIbUm\nRqKDBJaRI520SeZsXCnJJhuDZizDxhQSbZgIw0KaNkpaNDoWpmpWLY3EB79CUlVoFj6haaJ1w/PP\nTiQxEiliKTEihZ+pkzT6SZr9DI4MMjQRIEIXExtLJTEtB2EJ4iCmNDxAS3sTQpWZHBtibKzIQWaa\n0F/IeHk7BjWSKR+rCtK0KHuThH5laonKIpPNkUynQWWJ/Ap+uUxx+H/ZAhbEEVJLHHOP8jpNHtNe\nc3EcN4wPo3BGRJgxAdAm0pAILHwvQspGL6pSinQ6ycToHoFgep97CCieWRuYJj/btjFkCLoJdIyh\nJJhgxCEVmcaQGhEWZ97WdETXcCdpvDZNMEXIjQJoaPTuep6HZVmNSWNT0eH+CluY1MI6rmXzs59f\nQ1NLjqZsivHxYZJWkpbeZnxVR9guwxMlhGMRlEcJghw+gsNWzufBhyZoa2pjrDxBue5j2S79A8Pk\nk2kMS1D3IuJY4IdlZGhw8mmncMftt2FaNsI0CGp1bNtqOAULAykNIhGh4oiknUTpAN/XCEMBgqxl\nUzYkQhg0xvRq8jQijkQyRaDBQpNSBnWl8eoRqUyCyLOJdSO9spOSxP47lxyAXZvHEUpjmRaGTOA4\ngpxjkEglSOazpN0MZjrRMKk0TJSiUWoUGxAbU21UmpgYg8aPTCwtDDeL4SrsKAuWgTbMxmCqOEaE\nirAeUyn51EtVypMlSuNF6sVJgkghhU3ZqCGlh2U3emXDSJFKxzwyeB35thcRh3VMw6C/f4xs6zFs\n3nQjOpwAS+GmLIJINWptK0UMCclUEiFAyBitDMKaT30fteB/dY3u2Y3901HQ9LqaaZpo9ggT0+Uf\nUkoEBlEcNmTjqS4L0OgoZLbgAMyKvBplLNMdWLO3sW0b05S88rX/PPN8QgiiRIZEWEZoA9/JYgUe\nltlQcsOo0T87o+JOza6cXbc3Xb4yEzU+jyK7P0HECoXikovfRVdPK7JaJWO3Y7cKDE9RKxRAauJA\n0tXaxqNbNrKkt5fJYpH5nS2UqkWCWkDcFJJ2E/hejcG+EXp6e5gslFCBRhgGVuQhzQQVN+buu+/G\nSbiEQYRRs9Dsaf9JYGApk6r2EMImoS3KIgAjRiOwpEHB93ClSawiYqGxpImKYjItzXjVCr5QpISJ\nrwWxEtimRaVUwzUSCKth558UDlXt//99+P8/xbr7tmIaje+QIxJkUibZrEFPZ56wrQ0ddpOPWnBc\nh9gyGpX3SgARIIl8H+1rfD+mVKujfEUcBQjBTForpcQwBVHg44c+db9KoThJqVSkf2SQyXKZQqVM\nxQsbRGp5EINlGLhKknAM8q0JHDcgGWocZyPLFi5iYnKQYqlEvq2bAw95E3++4TNYTmN5LIgC4kgR\neh4iCImVj1CN4EloF8MykanEXo/LX01dZ9eYWZY1k4rOtktXYg9RTXcxNNLdPeMGp7cNAh/HNalW\nq88ZrNMgSjlV8AlC7CHbaYI13RTHn3w6lWhPv6qu76RcqZHKd2H6VUJtgzKnhJMpE4B4iuymUteG\nshrOvMfZg3We7am3v6GqQt73zjfj1wr0LszR3NzVsMhSJlUdkhAxuVQas9li7fodKN9gdKRCR0eS\niZKPJQRC2lTGSjhNeZZ259g1VGDL9j5aUi5WBBUVYycsCuNVlJAoU+MFiqRQGEa9MbdAGMgYMAWe\niqYcKhRVKiAFprQafel+hKEdakKTELrhNhMHIA2qxQKZhEM10MS2IPRDLNsm9CMSloUX1RERSMsk\nDqpg7H2Ayv4A0+hrEJeKiEyXcmiQTCwgyAp0ZwK/KaScLBMYVRo/IwqlYmIVEsYBKorxgoBiqUq1\nEDI8XKNSVpjEGCJCWBrTlMRa4gUefhhQD32qvk8YKwItqccRJFJETsNKXasI25D4gOkmsTJZhGti\npzKYWmK7Kdq7A+IYqoUahpBMjpY55oT3kcla9O1Yzc5dm/D9ANPXYMVkklmchENzcwv5fB6hFUXv\nf1kwPNOcHzcEhyACKS1iHRBHUx0Q0kBggTawTAchFHHUUGV8GWNizXQ6CBXhJl2CqV9Vy7KQQSMy\nnO6eiOPZ62QghYFAzESKdradwEgSxjXcSJEyJ/ncZ9+P5TTI1TETnHXuh5BtS3AMk7jeKGKOZNQo\nOtQNJwTNnjavKGoMDNFSUEcjbYPA23u+/38dtXqdtBMiTRvbzBOIqUJNEaIjwZKF7VRjyT3rNmKE\nippXo+aXUKKdE486mrsfvYcVhyxgpG+UpOVgmQbN+QSm0YKOoG5WSEUuyvNJuzbatggDn6Trog2F\nNAyMMCSONGYMnlKYBliRIOG6mLZLVYZUSzVqnkJojWWEKF+BZeBpgaUNDKkhNqhEMRnDQHkRiWSj\ntksYmvpUxmGaAttwUIZFVN2/xYglhx2EnWiUhbiJBLbVEBsMx8KPNb4yMY1Gk78hBYIIJUKwFQSK\nUEVUQp/JeoFKPSAwNGRM6jpCopFmY/6EJsQXitiMwZU4SQsjBEPFqHpjjoMOYqQhiWNJLKY88uzG\nen0+10rabcYyEyRTOdLpZhAGHb15hPYhDli75nHOfPUbkE4PvfMmMbQmYTpTBeGNkYe5TAdWIkuk\n6gyPb9/rcfmrEd1sJ+Hphf7Z1k2GYTRS1KjRtjO9tBVFEbFQuI6NihsChSkM6p6HmWh0VtTr9Rmh\noKGcPv8CSmM+RUgymeS1r3sXtUqZlqzFFy95L6o8ihcGGJbdECGCErf++Td0dC/mxDPehJolOJhC\n7HGwmRW52baNX9tj2vdst5P9DV+69INoE9KOS70yQT1wCWKJ0gEDE0Um61UqVY8oiEllHALAQjI0\nOsmu3VvJJ2w2b9hESzZDPmNj5VPkOhJkEwvZNTBAYSTB4PgwFanRCYOkY9KUssinUxhOkmzS5ukt\nuwnRhJZPp+HiC4WVSFKpVYkqZZQSEGkMrUCDFBrHlighyBmSIBQgNF4ckcEGBGbKQYUKQ0iqKsQ1\nTaykBTGUqnUcx9rPi0ugKW832t1EjIjqeF6MVwPXMUkkLEoFv5FyJjWprINNg4DqUcNWvVqqo8KY\nbCKHYwhaWkDHIdiNdXitY5jyetS6sR5frtcIQh8JVDwfP7Dw6gE1T+KHIZ4XYVuCbMKgJWXSnkvQ\nnMqTSLVimSkUNpbhECrNqiO6GRuGQqHAkccei1/3aM4tJ06niOpPk0slsM0EjmWSy+VJuSnsZJJa\nLSadzuz1uIj9OUWbwxzmMAfYn2105zCHOcxhCnNEN4c5zGG/xxzRzWEOc9jvMUd0c5jDHPZ7zBHd\nHOYwh/0ec0Q3hznMYb/HHNHNYQ5z2O8xR3RzmMMc9nvMEd0c5jCH/R5zRDeHOcxhv8cc0c1hDnPY\n7zFHdHOYwxz2e+zTveSH3/qyNu0OBDXq1QlM0yThpgCoe1XSqSy27TZs1WlYLVUqFUzTxHGchiNo\nHODYWfL5HrSqE9Qq1OoVwtAnCDzQmpofMDxewLQdlvZ2UfVHCMgzWhwk0nVSVo6JyX7Cap2K5VCN\nRrGMLC35ZrrTbeTdLP2VcXaN9pFOd5AxJW7kMjbSh6/qtDd1US+MYVk2LS0tJGwXJaCluQPDEFhW\nAkSAFDZKRTh2Gk3Aa99y/n7pu/2ja3+o65XxxpWpwd9MT7AXauayEBKt98zslc/eRvwVc9K9bC8w\nnvHY2cYSUphTBql75o48Yz9TEBho4r1ef77XogyNcPO864K375efK8A3Pnam3jpY5jOXXcFf+k7n\ntGO+yxlnv4VVB7Xzp2+V+dJXTuZTH7qdBT0ujw14nPIOwTUX/4CLvvQ2Hnk05PKLLubEkxZwwWcv\n5qp/vZ6gPMxn734f3ek8GzfUGdjkM7BFEKU1By13eewBzeSYT+cicLLwxA2jHHVqByteYnLlv/yY\nA1/5JppzsHIF7HoA5h8NW7eA2gUveVuSzaOS17Wfzoteu5I7/nw/N/XdRTNZJsuTbN5tsuZHf+YD\nV57B6EiSXHOeXC5m9V8miI2Yi15rMrDmYG6493EsEXLahRmu+EDpeT/bfRKd5bgkXUkcmnhT07iC\n0JsxroziACO20DpG6Qjf9xt2TYYLumHtVPbrjAtJUkWoWBEq8FWAFBbS8IkVEBtYSN1txWYAACAA\nSURBVIhj6rWQiXIZ7TYcaP0wxqv3MerVmJzcQRQK0pk8vhhBh3VSkUFrbzdhrZ+U0BiizkR5Alu2\nU/bKdGSbsFEox8V1Gw6kWoJtuiQSDdJuuCWDYRnYtkQTUKvtv75lZ11wHl+9qW+GYKbtt2aIZRrx\n1IyPWeZG8n9idtNwPp26qGee59nXZ7tI73no/9xV59nv4fmuX/jqvUw43k/w3g9/HF13qdTH+N0f\nTJbO72PHgw6HHhbz+n+v89lP30o10oyoiNPPUwxXFH+68+NsHg15ycs6uPy7XyLzG4nlLOean/6S\nD537KvwCVGOT4xcu5+dD61h2UivdPTW2P+VxzIubWPuA5sVLjuLOP97HS0/poWOFwk2HPFb6CU4E\nX/u393DU4cdhuzGZxV2ceO6r2LZe8pcbTcqZEqOdHfz47m+xfmicc19/Dt+/7ifIGF5/Qjuxexei\nx6E0XuaGq65m8WnvwAmzJGWBem4Z8ohNfPQVC/nlT4rs3jqy1+Oyz9Q17aYxZTjjLuw6SSzTacxq\nFTbCdFAK4niPg68QIA2FkJpUdj7tHQcihGSiUib0YvwwwhIObsIkl82TTmYQZkQmkSbpJAjRDFcm\n6S/voCZCmvLtzGtbQgKJwCXhujRn28gaKeoTFQZ376B/cAuW7zMv38miXA9SK8I4oLWtg87OTjra\nOlm8bCXz5x9ANtNEa0sHmWxqZqiPaZpIw0GpAM/zmJycpFbb++i0/QnTxPUckgMwpoYI8dz7tBbP\nmJv7vHi+fbLH9Xmvz/s82+xtu2c/ZjaeTaB/yz7+r+PaH/4cEVf4yn3/xG9urJEyU2QX+6Q7FKEE\nT6b4zCc/zIvf1Ma1X1Oossm1Nw/i1+Hex4aRKRgaVDyyZgPvfeM78Uppvn3ubbzvzf/EiBly+CGL\nOf74Lh76S40tT0g2PDHOR972Lu4dvI8LPnoQwzm4Zw1s2Kj52nf/xOpf/ZD7dtyNLQWmn2DdzQ/w\nyy9+m57lCtOrsOJgMNvuZNvOCa7+yEGo4v20dDiM7rIZ8Eqcf+nn6GlzyGXBiyt87OI0okXRHKe4\n/eGneO9Fr+N7vx1gzB3h2KOa9npc/srMiAiNRkpw3DSGMIl1gNAarTSIBFo3BtoIGuSXTBhksiks\nO4npZnCloOZN4tUGEbaFZRqkkznS6TSREkyOj4ASOGmLWlihUB8nYWQYq+zETylSiWaScUxzOsWy\neSeScDM051qYGB9h/ZP3s2vXDixp0NyUxxAufjBMynRJuVlcy6Yp30E6lUBIEy8OcR2bRMKaMvyM\nAQvDtHClpFYtEtQDPM9Eyv13ZsQ0GkOD9h05admwn5+GElOPi6fmiTT8L58TsU1PWnvGc8Eztvmb\nX+P/Ai9Un8ULLngHUbiRynidU1/r8L3fXk6mF9Y/FfLQNXD1lZ/j7ju/z5nvfD2PvfFqWlua2fRk\nBbsli7I8lh9+IPXRrZzQ3ETN2MDnr30/Op1j59NF3nLOR/naH79M1xknEk0KOrqSFItVfvn7n1Gr\nw3V3bmLJ8ojeHodNG3zaj1P88bF/ZWR0CdVyRKqpm43rV7Pj6StZcUgra24dofJ4grWjE+w0DC76\n8HoOODhHvRxyxtmHMdkXMzK6m8cfKPPb7/yIz3/h7YzGDoeu8Nlxv40oJfnit3/BHbf6/PGeTv7w\ni9G9Hpd9El3dq5JIJBojBUMfywTHchGyMXxDWgkkASgFWiNkY42k4UAcY8sYEYa4GjQaVwlc28Yx\nBTpWREGMbZjkMs2EUUTCTTNSGUYbBmacZXKynzjSFA2Dha0HsKhnAbYJjmWTc1PEPojQprd3KaEh\nGS4PkDXytLjzyGfT2EaChGuSSqQJw5BSUCbvZBoRqbQJQ39qfdEEFPV6nbpXJYwUtmn9Pc+/fzho\nAUY8lUYyRQxSNEhLgIw1ymhcNxAgFGoqAZAaYqnRGGihEEI3xgiqZ5Lc3lLJ5yOv502dZ9337BT3\nrxHZCyF6ez4URvsRNNOWSbJmd4Xlp76R+9f9iHq5wq2PfZ8vvec/uPOJEQ496Xj+9NmbWfbWV2DH\ncM3XL+ad517Kv/3rRzn3ovPJdw6yPnUnawYsujvrHHRIC1/5wte46w6PYPIJNj+YYMWxHgd0Jnjo\n0THyC0BJi3v+pHj1G30OWyV4cmOKS99e4UtfP5Qrv/IFop5t/OGPAVuf3ARemeOWHsNRS5aybNXB\nHHz0idxy241sHVmNF8a8uPdE/vP+r3Nk2yoGJzdxy4Mf5pDT38COTb9lyIfcKh8dSW66LuSan8MN\nfyiTSew9ONln6jpZLePVA+r1KhMTYxQKBbQGx06ScDOkXAvTsLFNG8O0GnboKqBUrVGrVvG9Ccrl\nYYJ6AUMJksnkzLQtz69QD+tYjoltm6SSSdxkmmQiTyxMKpUSUbFhgS7sLOVqhV3DQ2zevZvtfdsZ\nLxXwvBqtrT10dy3ATSeZqA5SDmJ8kcVOuKRSWVQk0NppDMUhxnEcTNPEsiy0FhRLZVTsE4UeSilc\nJ00um8ay9m+ik7EmFhBP8YEQDVKDBgHGshGpCdUgmVgLFI0fuFgCSITQDWJUAjk1iEgJ9prSPpv0\n9nbfvh63r/29UKO4Z0BmMfK3EyUFK1barH7gFnwfRA5ec8z5GCeNUAslP73+B5x+0ZksXqh42ek5\nEnnFp654C0sPO5Gly3tIiizbx4fo2xzSPxTw+IYiw0GNl7+mg1J5nGxbFXyfx7aX6VhusWuXyzFH\n1jjhJTGFgk3eXkln2MwbPgn9wb2cdL5LV8epHHgi7H7gRt7/xgso2Hk279zKw/c+Qt+6zfzxzj8j\nUgFmbHPPA79lUUuGdU89xokvPoPVd5e59nO/ozQacP0lv8BxJWEcUy1JvGIn999XZcvg3ulsn0T3\n9Mhmdo7sZGhsiDgOsUywTAMpBZZpookxDdAEIGIEiopXZahUZqQwwdjYGMMjBSYrJYRtooREysYo\nRMswMaUmk2kh39yEZVlMVusMFEcp6yraiLDtBMQmlckq67ev496Hb2XT+sdZu24t27eP8tTWYcZG\nR+kb28pQaYC00UoikWDHyEYKgU9omdQ9n127t7FzVz8jk0XCMGSyMEqhOILSMFEcIQyqRLFHPp+n\ntbW1oSY7f9Ns7/+ziCWNCG5aLJjFJbEhkFPrclruuUPoxglj6FnDwFFoOZW2SoHRYMfGfoWaIaB9\nEdvfQlD7Ehlmxmu+QKO42fjCz0/hWzdewVi5zODWgGJtiGJJcPG73sLxb8zjZkwyPTBRqBIphyA2\n2dk3wYcv+BJL5/fitvaybEkHBy07kH95zZW8/XXNzGs1qVUiWloVm/vGaD1AsfLwLPnOJIcc4lCr\nhpxwckhrUwepFjjzJacyUvYIOwI6/KP5+RW7OLj37bzrvJOQpR565x3KlT+7kNecOc4f7xgnnbIY\nHBji0nd/ng23Qbkc8ac/jHDmCWfyvne+kp/89Ga2b6rTc0QLnt/JyvlvYfU3Q5YtypPvUNx42xC2\nA5u37v3z3+e3Wcc1dhe24eoUq5YeQy6fgRjCyCeKPRA2KopQU2JFoAImCyNU/ZCk7RBUatTQaNmY\n4RpphfQVPgGu6zBWGSHhGth2gnHpsbl/FwP1rVhmgs6mXtLJFPWwyvDOp/EqErQkECHl0SG8iRpe\nUKVn3nzwITI1SAcMk5QMSZqSvuJWnIKkVK9S9T2ctKSa72R0coh6fZye9gPpaZ9Pa2sTSkVY0iJG\nMzA4juu6f/eT8B8JEsG0mGpoGgO9p0htOpKLZWM7JcAQGqFMtAgAiSHixl60gVKCWCjQCo2cIj+B\n0NMLeM9NOQU0xAqhEM/ze7uvVHZvt88eeLRXJXk/x9aqwhtrfBXu/b3F564+i7FWl19e9zO8hIl0\nMrSkSzT3JBkOakR1iCdTJHMhbzzrCk4+spXVd/+FrgPSOJlxPnLWr1Eq4KyPnoFha3SUxUCw7NgF\njPQP4kiFZY3x/csu4Z0f/RSbNptsvPM2Ei0+mZSgvzyE0wSbSjdx9idupUULRsb6ecWp7+Th2+/j\n/uu+yMcv+QjHnnA+b/vnMh84+33IFgPv1C1c8+OfEdYl933vV5DK8F/ffiuD48Nk2ww+9u6XcsXn\nb8dZDJ25HF0tzewc3LnX47LPiG5F73F0ZJdipnKkc1mSiQxeVEGImDBWjI2NUqpUiCKPMIrwvZg4\nVLhukkrNY3KiynC1iNYWVWUwVK4yrhyqyiIIQkqex+jwCMXCGNVqiVKljzi08euNKUMZ6bCyZSFH\nLD2e9uY8OSdB4Pl0dS7ATdscfuChLFm8gnQ2B0JQjiqMVAfJ5/JEUYQmJhARHT1LOWjpcWBAOfBx\ns+2054/CdlLU/TrFconJySKbtm7iyafWsnt3/9Sw7RcGtNbPiNymIZlSVgXEWqBlY8jx9L2g0EKh\nZdyI5ABQTE3cbexbPVNdFVI31niniEir5xcnnk1QfwvpTQ8d/2uK7v4MWzYEo8tedyPbV9/Bead8\ngaVtFQb6Y/woBKkxPIe+rSkO6unkHef8C75UOM0hBx2SpmNVTHIRrDx8IXftvBHDMHjHf5xBxRIc\ntjTDaLHEQP8Ef/ndY9z8nSFE7GK7Dudd8RkeewyCcYNXvtVHaTBti81Pw+kvPZlbfzbJZRe9m6rQ\ntHS18fDqu5BRwOoN93PHveeTW26xffBx7lxzK2v/8gSmv5RvfuiDdBwo+df//ie2b3mEz5/7Q5LF\nJqoViy/+9+285s1wwLIMfSNFqvVB3H3Ude7z25xIdpALLVRYp+r7CBlTLpdpzregvZhKfZhsKo3S\nUKmXGR2ZIJtfgGNItpa3YtmaQBsIKwEkCUSICjRJI4mjJyjUSnjVSbxqgVBoIr9E6IFXHcPK57CV\nR0t6KUsWLSaZTLJ7xw5SmTQdi4+iMrydbCaBlXCwowx+sJ7WVDsT3ghGtouJahEPn7hUo729HTuV\nIR6BoeIAzek8UUJh6xbWrruXdCKDVjG+7zM+OUy2NUnLaPLvfQ7+w0LNIrnpIEwJQIBAIKdEhkgI\nTBrkJKRGK6NBXFqjhIEhFLFupK9axwgslNToPTIGCjCEaJTYTde2aPkc1XZfqerzYfY2z7f9CyW6\niwKo1A2cfJr+8oN85t0fxWuDTEbim6C9ApuGoWdegFtfwEXvvo4vfe5CvvC7q8nJOm5TjYzI8YHj\nrqZp6UIojNO2NEWweQkfOPV9XPe7d1OvCsb7NYkk7N4+TMtig8FNimyTJq7ErH3QxFURy1oPo581\nPDp2D7XNcMddD3DICRZhLcLSk0QWfPKy73HpN+fxxD2bOe60hbR3GGx6eBMmPoctP5uPvv5TXPrb\nSzn7A5/gTau6GBtRTIx4HHt6DtIebSKkLQFr13mk9vGV3WdEV4p9KsUSKoSxyQG2bt3M8PAoOjJQ\nUQxRYwE7iiJUEODXJ9GRploaxEdhdCwk4aSZ9AtgCIgsKsUJnhx+mF1lxUCpwEgQUqmEjJaq1IOI\nuF4j4aQ5pPdAOruWUNAeozog13UwiaYcbe0dJGWdSm2UciTYMTBEya8T+aBiA+kJSl4FQ5pEnqIU\njVMol3hq9xoGS31MBONIV2ErRb0ySEu2jeamJtLpDJ0dPaw6+EROPPQCIhJ/51PwHxt6SkQwVCNt\nlbohSjxjrq9uRHZCanQcIYSBmFJdJRGg9nRP0EiJQWKiZ4QKY3ZkJwSKPVGe1hppTJWgED8v6T0f\nZiu5zybM6dtm/9+fYYQW3a1J7tz+aT552YdpOWAp3fkE3/vCj0hnFKkkXPXrLJ//zgLkvIBDD83T\nV7ma1AT8+4djutsVZ5+XJuVYMF5DByb//eFb+elXfkzPikUcdrDL9gfhire9HG3CZR/7BiNP1whr\nMX5dcvTJEQ8/HDGyC/7880cpRDHKccgcCC879hQ+8oZfYyYTmFYSy10B0aO4hsvlHz2TWig48+VN\nXPLVl7O9vAnDSLHlye2c5JzJ0Ss6uH54BN9P4Zom6x4sMm9hG5VBF8PJ0dSUJLEPottnRFculymN\n92Hku4i1T7E0QalSpuZXqNcq2I5JENaoFcqkUikcO0lQGaNSnSTpuFjJHE5YZ8KrUZUh43ENvzpO\nuV6h3xql4NUwTEk+14K0Syy1lzE8vJPAEjS1zyNO2PRNrmN0fCstTTGR45FKJNm96xEq1So62cTW\n8cfJyxZ8P6TqDyHtCN+rUwtj6lENLRMM18aJ44CUSqN9RZezjKoeBNelqTNFd2sLlXoZHfno2CYU\n/UT+/l8wPJ2WmloQT9XHaSEg1rPSvz2KpgGNVFWDMCQQoGJjpqyosX2DdGpBhIXEdW1i1SC/6fQ2\nniIcQwiMKbKTQoDUKARaqCkxZJbgIDUq3vMcz47gng0h9YzC8kKI5Kax88mQpNPLdVueopzVbLp/\nKw9v8Bn33sqRy5bz/es2MzhY4uyzbNY9VuITn4VHN0PaMnGTgnPe0sy2jePYh7agAx9NgBE3oYwW\nHMsn45jEA4+h1RAXX3Yrhy5cSXEX5A8AQ8Epp72CX33vVnJtzaSWFWlNgEVAmDTRB93Nj9Y8yYWd\n19GVb+Nd57+S7oOfZncFLjzvMg5e9TBf++63uOHH/8Wp15U4522v5mc/9TjlSLjg7Hdy4FA/P/jZ\nn2ltNRgoSga297HiiGZ+96cJRNVg5Ul7Py77jOiclIkX11H1EoFfw5QG+WSGerXC8OgQQoPv1wmC\ngDAMsR2TWNXJZlpob+4iEDHSsilWSwwVykShwEg3kWnqZawwBqFBLa5hdOXoWnQALV3ddLQtpC2f\nZ/PIGnYM3EXgbWPXprt57NHVKK0xbYtMqhsjkWdwpB9ZsRkdGqA8UKQ2MkROZrHqilvu+CVbtjxF\ntTxGpVIkDGP8qAJhRNkfp+JXiYXETrtM1CtIPcX5IkYFEaXy3qd+/1+HYKojQjbU1dmlJVrrBumw\nh7SUmFJfhWj0lAoBSPSMVCsbf8oAEbFuYJJaPeTXZZ+CNiEuEynVEArUnuhrmvCmT0Ih9tTeTUd6\nCIUS0+t+e25DqJk1uWe8NyFAx88QQF5IWJBdyOP3b+ecNzVxxkkZhgKfngMcFixezK7d49QqBhvW\nC+69qZVDVwWs2R6SS8IprzEYH1UUSmXu+ItH3+gtFIqbUMY4sTnAca9rJ6yZ/Oaqx5DSwHBbkEZM\nJuty07XfYEGmk3RW8skP30h7U8TTTz+GafjYSZfAgKvedxU6Vtgpza033gpGkg997HI+/p6byNuv\n4LBl83nV6Wdwy2/u5Kntl3Hk8jN4/yfyXHOdzRvefyRPrH+Mux64g7e89TTGxiIydpbvfun73Hbv\nBF0dSRatElRLez8u+4zo5mXmM5LcQT5pE4dl2po7kAZ4XkAQxThS05RfSuhvIfAVjpPCNJI4CZtS\nZFAsVkAKorjO+MhOktkcra29yKKDCkaJ/AArnyZyIQ4q+CWLKJKYvkNUE/TMX0G9VmKk9iij/naG\nlEWbM0rGTlHxRjC8ACcMcI0Utm2Sa2shk8phWynmtc/DtdN4k3VsNUZFB6QTLWiRYHBklMCKiEYK\nLFh8EE/vfAS3amKZ0N3djedVqHh9f+9z8B8KWja0To1uqOFTxbha65k1O6n3REuNdbep26f2ITGm\ngqbpdbsYjcfNxEzcdCudv/kC3znp1TibH+Hz/3E+m9vPIuVYKB2ghDG1YjcbEkM8l2yN6bsNmI7y\nhBDEQiOmVF2l1FQUJxsFz8yIyghmFUXv52hr7WJHfQe55kVseGATOdegUIs5rulMrt/4dQ491qBv\np+bBJ7djmS7a8Og9JsX3v+PzX5dkOf+M35GZvJSr7/gmK/PzWb16NeN9sPSURg0sUR1lakToYACx\n0vz0hsvYsHWIBx68is9f+RXuuzlADozjCE29FtH3iM2yf1/GOWe9nwOs8+jqOZrFyw5h87r7eShx\nG//x7ncS+gKj9WRU5VEWLjib9dtu4J/fOM5HPhaytfgYn/vCLVz6+euYHH+YAw93efjJAu/5j/Np\naoWwWmPDGpDh3o/LPomu4I+DLck4Nm77PLKZZrxqiVqthm27lLwJnEQ7hhkRK0EikyBQGjeX5amd\nA+wc2oFpJehpOooJbwt+fQhhNONmHVKTgqrjYzrNeLWQsFonGbUQVn3C8gALO+cT+ZJaFBFZFkf2\nnkFfdQtjw7sp4eKoiEzLAmJ/HKU0hmFgphM4iRYKo8O05lvZ0b+Nml9AxsuYUIOIZososGi2K7jZ\nDMPbt1Iz64TVCuWxIpZp0trayfjEAM3Z1r/zKfiPhWnRAaZTO0WjCHhPmhdJEFo0TBsQexJJ3egk\nQWp0TGOdTkrQJp+88R507PGSQ1YwGLwZ3fc4Rm0Xp736LN7RtZjLb91FEALPIbk9+FtTzZnIEA1S\nI2ZcV8RMZ5ueZSzwQkhhTzr6NHyjwLrBe7ntesWFn7C47qshn//2d+hYAcmWmGzBYHyXxcrlOQLV\nz003Bxx9RsRPfjDJnTeewQGnepQL8JXfbaK5NcGSE3q56frNqIskUpkI5RLGRS79UZpTzn4pBy9T\nbHywyMWXd7OtP6QCiJ4Khy2TDIWaB759H9LVHN3yDaL4/YzuXM2O3VtZdObZdLa+HdtKE7UXkVvW\nEzWXaeo5jFq0muvufAu/+dEQ8xZlSLZ187GLziHd+x5OO/ZUli0SbNlZwzFtmlok818WU5jI7vW4\n7DO+V8UqLhaJfIZ0sgXHspHSBkxMTNLCYrDyOJlcO5lkE1oJbNumOZEmm3EpjW7myYd/R//koyi/\nSqEyTqE4iReXybVm6O5YhhcUiYIYJ9FJsb6LXNqAKGay5rK172kCP8JI5xj3J1jWdDQjo9sIVYwI\nLcaDZrq75rFg/hK6uhaSTeeYSJZJJA0q5RKRVydrpVGeTybKMz7UT92vopwsiXwegUckJZl8B83N\n7cybvxSlob11Pvv6Iu4PmE1yYioV3fs2xp7bpkhRiCkjh0YODICvY8xEhmWHLkOaFqtOvwD3tPfz\n6csv48JPXsUlf7iPIIzgeZ/tr2FPkfKe19fQcw2hMYTYI2pME5pQSOOFEclN45AlB/PZj/0nX/1U\niTedcySxn+O40206FofMX9CEF4MdxSw4ULNl0zAP3AKV4YhTT++gfT4sfqmHabeimE8cwKYtdW68\neTNPr1uPUCaxKhOJndz96FV8+UL44idvZKJQ5gOXHsKIBwes7CaSAatefTyHLM5SDXxkoorvhayY\nB+e+4uUceeIbOPHkD/LL669m8/bd+HKMlQuO481XrCIuOtx285kcecjpKBnx/v88jrFqlViN4kY+\nwa4RHl1fYnyoRjItkKUYV0PfrpCeFdW9Hpd9nm9S+nTkMmSSOSxTUK9VGBkZolqbxDAE6XQrppFC\nO2ki16XoB5TqdYaKY0TFEWQY0N7RSnFiB5XCcMNPrjzG8OA2BibWUpjcQb1QwbHbaU71kJAR6XyO\nps4llL1+6mbIgBpFqix927dSK02SS/XS1tyKiWZosABI0uk02ZyL07GQMKhgZzI053MsnLeEpYtX\nsnLpctpbO+huW0pbRzNx2sM0WpjXs4AgKlMyJklkUwSmRc0vEEQBpfLE3/sc/D8A9az/z4QUYsa1\nRABKyJm1MK0E16xdj8pnOfGAw9kwPs7u/i0ku+bzoW/9nFWv/SCY3Uyfcn+NfOJn3K9mHifELKIT\n4lnpb+OyhBnvuuk1wRdCNAfgJmIeuuchmpYmicQ6rESJ01/RRroTHl5dQXlpTn5FO7mM5AOf7uSp\n7YIXn9rCrb8fRWYkVs0mHCzx9MO7aOk2MJWBLro4o3XOu2gFRhbWPHonX7vyV0i3xjeu+2dkGzyx\ns58dmzxuuXUH+a42mhIWV19b4/ijlnHIyS+iu+d4Hr73L/zg2p/w+N23MDzyAB/63A+55YYb+P53\nv8fFV2boOBy+9sBb+P3dJZ54ag033DjJG894AK+cZ+GKFyMdibCynPfO15OwTBxPUtzehIhCjjkJ\n/vC9YK/HZd/lJfUC0oqIdMBkscDQ0AC+75NwcySTzdSjAL9cJgzHqYaTFMpjFCZGeXrsaTYOrKU1\nkWfVgS+js2se8xYcSmvzYtpSCxnr20Y8XKUwNIIlugjHJul7cjVp1UbW7KCjuYN8Ok1rqoMOo53u\nZJZFbfMpFsv0dM4j19RCc2eGBR11PK9GFGqSySwlbwSlLTyjzuIlS1l+wIHMX7CQtu4OrFwrnQce\nRfO8FUzWJtg2shaVW8iKzoNJZ+ZjpBI8Ongvma6VDAVFUvn2v/tJ+I+H566RPfP/7O0kERolIrTU\nKOJGd4RoCAwIRS3hED9yPz941yuxhvq5/ze/5Mmn7uCKr96M5VrP62ayNxjPuF/u5fKzsee+aXKb\nifKI/6pTy/6A8953Dn/41e38/hsP87JVb2N8IGDjU/20tUpyy2wqk4LIG+HAhYey/uFJznl3iic2\nTrBwpc3JxzlkCgfxjU8FdNs5XnxMlnktMU8/sAkzuZCNns0xbziGt53/aXRnBktodu0qkzZcRGBg\npCRdLUniapkPLl7Eqw8ROAMhr31dgqG+NSQMB6El2lbE1Sre7icYtH7Cr399M0emr+Gqdw3T3TnO\nzXcVuObHZzAvmeEXq4/g4KURqiCpj8VYHQYnH6PxShETOySF4QIHLTsAVWzltS8/aK/HZZ9EN66L\n+EENFWq8Wp3JqocwFJlMikQqiUTRmWsnb+eJvYiJ8jjj5Umqk0WyHd2EZkygxshk07T3LiTV1IJf\nq+KNjiACQU6lGd++jdEta9nx5KNs2/EgIwMPoYNBMo5Fu5NkQbab7lw7C3oXUwlD8s2tWLZBd0cv\nBy3uIZFIMD4xwmSsMI0UZhCiTIlp2Li5JE3tvYSOiU7E1OIB/OpAQ0X0mxmv9NPRvYRavUzNTZDM\nzKdmxDy++wEc94VQMPx8H/++1s40QhiY032w06rpVO1b6fdf5+g3nMorr/wVD/NotwAAIABJREFU\np778NSx509sor32U/kKROIj/xxZN+3pd+jkR374hpspX9nfccfdqdu7eRmlrP1seMqngsm1LksqE\nQkea++8tEwYGO0f/wsLlCe64q8LTTyjWrPW463GFl7NY9FLJfXdqfn9TjZX5eZz11sN5y7+sorIx\nwM618tQj6/jptbdQK0t2Pl7iuxfeyh0/3EVP1qZS8fj0i0/jjvF2HhjtoTSZ5NH76xBWAEWsFDpQ\naOlw2ntPYvdWSXNW88T6x4mijfzppjKf/fqLiLojXLWM05Zdz41/KLBspeK9//UB0Is44RXLyfa0\n8crzFmJ2RNx05wZuvWWSirF3AXHf5SWZlZSKVWzHJJPJsKB7Pt1dizBsFyFjQuniilY2bnuS8b6d\nVGtlKqaJZ4Lt5Eh2LCLSCivdzJjfx8DwGnZtvB9vrEbGyhD7Bcq7nibvmBx/1CkcuGgpSSuBaTrk\nMnm6OjtxXEEmY9HbMY9Dls0jnZ9Huqkdx0mRTDfR0tJBc0uGnK0pihKxkkRRRBT7RAjiOGKgXqOm\nfAb7N2GbbSTdJnraWuhu7qTg1dndt4F0+gBy+XmU/JiuxDy2VXf9vc/Bfxg8M5ZSPB9RTDuVNCCZ\nndZGUwXAyD2Jo5QSccIrWbtL0yRK3L5hK6fM7+AHH/syUbwnpfjfp5B7UmQ9tTanZm4z9vXABrRk\nln673+I9l52KSvlcetkHWLP2MSpDkqHJGtm8w4temuG9Fzls3W6w9QnBL788idUPZ73RIRHAuj/7\nrL7rUWJLMlitYVo+bz73s3Rkchzx6hPZuP6rlMujXH7NIThegt7WBMUxhyBWROVhfnDJfXzwDZfw\ni+jfuE+9gifESTxV6mJe54nIpINSEdKxGsKRGCUEnuy3+c4Pfso9j13Lw3/eyJWf/hGPPr6Gd598\nI/etXcvCRQfSO6+V08+3+MmvN3PxJd28+R1X0LGyzEPrttE6r5lCBRJZyYMPTe71uOxTdXX9CsqW\nuHYCy7CJlMCyLOIoYrJeoh4UmSiW6evfSj7XSXd+PkXLAwMi5WMlI1xloHyFYUQ0NWUQlRiVbyZh\npeic38zy3gPJt7TQ3NSKEBZR5BFFEZlMBr9WJp/KIKwklmWxoHchWlUZUx5NRoJcOo2Tckg6BzNe\nKXFwqpO1FR/PmyDfvpiUnWTL6AjCtBgZ2EU+2cGGx2/n0MNOYHzoKYYqoyT68jRlFvH0tgdoa9OM\nFfvZObKTg3r3X8vtZ8Y1ezzm1AwHqamG/Od//HRaqbWeuqxQscHiwjbemN7N5vUHsExaOLkMQ34I\nxlQ9nlL/L9fKptRdBIYWUxQtkXLPfmc6I6bS6T3vOX5BrNNtHo5Y0PovbNx4HV+45rVc87saxWFQ\nSZM/3jtMcVxg2Joly5OccdKr+PH1v+CAxSbDi22qT5apDkUUDehdajCyA158ynEc0HM5r/rQOYxt\nu5W87XD4S3x+/6N38+iWCu2tLjKdRgYxSpX59viF3H75MdjjA+Rf+2+MSJP5TZO86ZzX8fPv/AJt\nCsIoyfX3v5wLLjmIdx5xCVofhLtsjFsevpybvrKT5DJ47Ogf077cRGBht43x2+sF3/nFCYz02bR2\nr6FW8/CrMSPjE3R2mSSTDn3j0V6Pyz4juoVZl0XdSxtebkbDctwwLNARXlTDMVyCsMwRR7yY5StW\nkc83kXeyJFUrWRyaRIYolkT1Mtqr0tJ1JAuPOJLDTzyVXHMr+VwbPb3LaMq3EQQ2mwYVbiJPU76d\nZLITJSVWIk0ymSSSMESZomWgNKTcBIZhYBtN1KMCxeJu7lr/K7T20cplNBijFNUYHh9jdGQX7Wae\n8b4tHHPEiXT3HkIYdPLIrbcTBwmkH9CS7mXz9g0MD21k6bxV1Av7f2fEbDRIbray+dwor3H7M9fB\nAAwlacl2cFjvclTn4RwpRrnq6FGWOgaxtp+z/Z597RvPFhJULPeYAAiF1MyIEdO3z3RNzEpTtRIg\nzRmDgf0ZHzj3w3zz8x/nykt/wv2bb6RnEVz1sR/T1iVISPjIxZcyL9tMV3OaP9/0OBde5XLfbVWO\nPrpK+3GgqvCi5S/lm784kd1PCVrcFbQlF6K8kKVHtnLDlat54ob5nHXuF1jWK4m1ByqDzBf55SOf\n4pXVz7JyZS/qjA8RR6P0T9ZJZrt51evezLev/TIyCImlx9tffx9P3z/Mi875J5xUJ0MTJqP5Acx2\niOuw6uAv85cHI457TcBvr4ZE2uCmX+9gZ//jtCWqvORF5xB40JIUJFIwMMAznLCfjX0SnSEk2VwO\nbTgUqhFBJKnUIYwiQikxLYf21k5aOxbSlOvEMCDtpMASNNutGLZCGgkqQQ1p5TCkg5Fy2U2RfEc3\nG8fGufeJDWzYPMiDT25k3ZPrmJioIk0Dpas05ToJDYlhOiRMmzF/mPVDjzCqAqTrUHPz1L0yW4eG\n2VDcwukLXolheUi/hBHY3LPuNsrlbeQiSVd7F2e+9G0EYYb+bY9w8x8/wbEHHIpd2kSYgkSLpCN/\nIM2JNkSmie396/7uJ+E/OvYUBz+73GSK9IQFImI2IWqtCSX87tdfx1Y7ObvtUV7duQajtI03LByG\nWU7Ns73p/qYZELPJaroPdqpHNtZiZmjPMwhsaijT7FRVSI0kfGFEdE98kdgdZ/GSlWx7XNDZBV//\nydvZsDGitzvLxz/6cT72kQt5+PERjj4px5qHPLoXJ/FqipcdD+lD+X/Ie8s4Ke603f9b1e7d0+Ou\nMMDgboEQJ06cuGzcXTcb25z4ZuPCJsQTCJ4QgrszDDMw7i497d5d9X9BIJAEzvM8J/9zstnrTfen\nq6uq+9d335/bftfFdQ+GKduxj9nXZSAoRNQ5MezZBsYOvxC9WsPDt3+HEDagN4n0ekGpkXn6qxl4\nKkeSn1RIoLudeMRPQoKeYrODQVkq9te1UO3U8I83/44qquGzeRez4odevD644f5chg9LJi1LZMRo\nBYPyU3A176Zr3QcQKebFeYPILszhhJNzadkfZPnnEql2D/X7QaM2AqBRRDBpj02We1xHF5dEQtEg\nIjF6+2pobq2ip7cajUaDQWXHLcbQGi1U9++ksruMaDhONBjAqLURM6gR9WbURjMavQGd0YRBKeL0\nOBEVEp2+Pvbu2sbePZtYsXo+ykQ93lAlqzd9R3NvD3JcR3O0h9ZgH/6IF6VShd2Qgs6kwunvxSsr\naQ46QKmitseBTp9KQ6AOGUjR24iKAWZOPAu9MZn2jiaa6uoJx6Oo/c3kWWw8e++7OBwOpk47G0ER\npKFlF529B9Cl6JGjSmym1N/VAP9dcOROCBGQ5fjh1xVEfqqH/TyYKwgCRWlJ6NVxrMYsjNFu8O0l\nGGpH6e5igKrp52v/t8Y8DkokSvFDOyWURxF4HhnNwRFO8VC6KhxMY6Q4B0dg5D83Y/Qh3DNzLdpI\nIkq1gnsuW8TIvAFcOvNCHrn0bcaMlvjim2eZs/BJtq4G1B40cRV9gSCiCK98CM2lKuZ+tJmK/W7s\ndjcloyVmX3MHg7P9TBz8AnsdzxBRuxDCHtJGi1zz90R2V35BR4sKRdIyNlTMpaG3i3i0kYRgD/dM\nz+aDVR7KXelsaQjSr0jhkcdOYc3eKkYOh9QcJc3ODpIMSZRvVzLadjcnnziUi+85na92fst5Z52F\nL9DEmbO0vP1uOSlDLDS3KQhK++hqMqM1iAR8ceJiFE3sf5i6otYQi0SRYjFSrZnoFBZSEnLRGpJw\n+zvp7ThAr9tBb28b7rYQzY01hMNBoiJENTpiwTBxMYY30EVnczUuTx/9fb2oRA3uiBs5EmbcjBMJ\n+HrQ2fUMmzKVuvpKyhtL2VG5Fk8sSDTsQW/PRKG1YNVkIMt2jKKFXV17cHrcdDga8Ee7iUbd6OMa\nItEQfZILq8aIRW/E7+1GGVcT9gfpai6lIdCOIEmEAkG0ahP1TY1o5AAKoqh1ZjqbynF6q5H4z0pd\nD9XblEKEuD+AGD2yBice8XiE9CGArOKdv9/Jgr4A60trcZpHo848D7vOgLd1PWmhUuDISbiDONrh\n/VbXVOQobVchdtixHuTP+1l/QlQccf6hPa4/RXkH9UsOsqEI/wFd1yeee4jK1q0IBiUJ1hSum1zD\nw3d8ydsvvkpMipOiSMeWpOLyizVccvY9fP2lwNoV0LrvRL7/aB4b1p3O3542U1ygpGx3mJGXgnFg\nKUExnVPPU/L5NweYeuVULvj7GOwjDKxd2MdjH71ATzhCRNVKSXEUs8rFyXkSTwzfwDP7JlCfMJH2\noJLJJTbkUCsd0hAKMkpQyWpOHDqYUETmn8+VUl8VZebJtxGIN9Lf62LL6lq27F5FVrqN+V81kjcs\nTn1NP+ZMgb+c3cHAMT7M2gjhgIAk6Qgr/4epqxwzHWT1jYYOjpOofcTEIA5/Fz5vgBRdCa3NDbgb\n3bQ2VtDf04tFYUERjdHjqCOsFvH6+nH1OjAazMRiAXw+B32OJmLRMOglevvqSLEmUb11PQ5vIyXT\nTqSzowuXYSBeKZm85BHUupup9jTR5K4nHPLhCXfS1dtALORmb0sFKaIKtVPAqFASc0oU2caSoLfT\nG5ZIHDAVe24OxSPHY7Bn4Ap4qKrez4JFX2LQ66ndX0Z9ZQt+pxetKBGRBNQxBcnSsaXT/gw4tB//\nUFSkVESRZZkYRka3PgAGDbIgIHFI6CbOoQ5tXD64p1SUteQmqjjpqrvIrPqBAwVDSU0wotOF2NVj\nJSU9j96KpT/d5+DZh2jPj8bRDMRHkWkednbiz/U6WfnzGbKILCl+6sTyM307SuJiDEmO/cRirPiP\nqNE9+7d3yUopQIyo6O1zkl84gmAvlNVV8uVrcOc1N1O7PIHXnq7EJKdiU0rcfvn1dMY2MXrAbNZt\nWMvzT8ZQqKPM/9dyNJhx+w+q4qnTUummnodvvAeVQonP46VoVBKiP0RukgpvOMwb79ex4oMHuTB9\nB7g8bG2uxGyUueCUk8mwZ1K+dRPRcC9t5UmEuiU27NyHFJTJHW+koEhkyNRCHpxdjyTD7nWVlK2s\nZ93yLtZ8FOBAaYxpMxV88vUIxo6GiEOmtT7IyKJkPIEA2uPovBzX0e1r2Y1Gr0arMyArtKQm5aJV\n64hHJZKt6dQ17AB3gCSDmfTEBAwGEbVajUEWCPh76Og9gEVvw2ZKpal9F35fCKshCR1mHG3t5BaN\nomrLLoJRH+0dVUjBCE5XMxc9uZBAxkzc5jPY1xUjFI/S62uhv6UFOeQm1V5MTvYoOto3MjRtBDpd\nChPGnodJZyHY08v28uWsLd9BfXsTiQm5WHPyUZtTUWQOJikpn2A4ilaTiNGeRHF+MTOGTifRYkeS\ng5h0WgR1GE/kOFQIfwII8hFpnxwnLoiIokg4puLpRUtRRdVH1NPkn7aBHWQpUfxEga5UhHnigWtZ\nuXAOGz5bwOS8ifS3VREXcxh34kUEzPkMHTfm5/sc0Rn9pZD1sajPRX7duBAVsZ8jN+En2vZfpsRC\nhGjIj6TL5BC/039CjU4WZVQKM9GQB7Nex/sfvcN3P+zFIUiYhipoaowQ9kCktwJXX5yRqcm8/8Wn\nmHPh0rslFn7tIyapWP2DhuHjTyY7TcGrj59K/gAde8paOWXsVVz3l9c47dRiHn/8AdwNId5/czP1\nFVH2lWsYO8DGhBkPMSYlAZUYhN46bk5YzfVDKnjw9lNY+t1bTBw8BIWgAvcoVE47A0dFybFb6fOH\naG/ZwYKvv0Cyi5xxzgyc1So8TTIteyspSImwemWELWt20dTk48dP3qejFbJSRhN2g8147CGS4zq6\nUfnD8MoCvqBEva8HUaFCpdJhSCwgZcBIxJgPe3ISeemDycjMpSh3NKJaRE5IJTVpEEadHWVIgUYZ\nR6/NJxzqx+FsQqPRkG5NRC+H0WiVJGdlM7R4HBG/BzFqYd76Juqa2mhr6mRVbZzOvt0ISj2DiweS\noU5DDouIKgWTBl2MpE1GVmqpatvLDz8swe/x4fB1UFu3h05HFYLsQhZAm2NCI1pxd7kI6ES0Ghmb\n3sIPy5fR3FBDYlyDIGrIEqx4OvrRRf7cac6hH/5QlBWLg7LjO9pqnbS2QMaGhJ/44uTfMBKJODGM\nSRqWvvUY508azuWvzOV+6S1c3j7KWrsRAn34XQ5Ey1AQDtJKCEc0In6L7vxIgZvDOxt+Qap58PHo\n0RFBjh71nfRKNU/9JY+NHw2h6LtJR43D/NkhyGqUmiAiMlqFClFUIhNlx6ZmUvJ8lNbH0VpV3Hv/\njVQ3l/Hy581ccdYpbP44yhXnn8cD901l3XI3p08UqKu5g/r6MHdes56V8xv56Msb0OtTcTW9x57t\nzTTsLmPTkm6e/8tV7NukxOmK8MHnjSDsxdnehTYY5xzzHp5d3MxZ1/6D4aPPIX/E9WxriTN/yQ4y\n0mLs3dGPvysFjSMBuRpOHHQCn336GCek5RBqUKAzqOk4oOLlF+4gJEEEBTsqQSkIXHH9g1hHatm/\ncyd6O3T3Bo+5Lv8bYQQnXQHo727AnJpOV08jOoMFpd5Ke89OpoyfQVSWURDH1xegz9dLd0iHIPfi\ndXdw9in3s3rDu/gDEmajjKzS4o9oUensmJMt+BSdXHT2ZahT09Fo9XS01RA46x7Wb6lgSJKJ1rZ2\n9DSzeM1AZp60DZP+JHZ27CA1WoRMAMFaSMP2cgaOPJXdO1fh8bjRGfQkqizoUq0k6LJx9HaTU5BP\nR1sNpZu+oVCXgBhXkZCeS1dnIxkZWcSI4+zuw6XtJypGEW0yU4ed8jub4B8HhwZuRaTD7B4aQYHo\n+4Apjf9g+mKZeFcXcixymPr8SEgCqGMCWcoAp137EJPHnoI63oNClY7BmozGmE5PTI9J7KXP0YMs\nKQ/q4PziOv99MWsZWVIe7vweIhuQBcVBmk5BRpYhEo4xtEBNnBupUr+J5vBM4J9/YDgaDyAqAAXI\nsYN1TY1KRbjXi6XfxJk3KkjPhPr1At+vWMSO+i+5+6Z5HNgg8NYri3nlhRd46ikPJ4+/Ab16ChHV\nu5RVi8zIU3DfhZ+RPVTLay/6UHYK7F6ykf5H7qTe2MN7703ijbd243Fu4oRxo3j02izMHg23jXKy\n7PlW7n7jHQYMHMS3n37OpBknMmXSZIw2M7ONm1AnS6yumsfsa2ex4wcN876chy+gwqJvxGbV0u3Q\n0GPdzHk3mgl7TOzc3MmUCTa62n0YVCKVtW5sA8ErHPv3PX4zQjBgN1qx2BPR68wkJaajN1tZW7EU\ntaaQeEI6ocQcHHobOZkFTBgxlURbjAxrIoXZw9hQ8RVqlQGEOAaNCYIxrFoDohAmEI0ztHg8eksS\namMiEQJMvuB2Prt2ItUvX8jiv56NyaKmt1eNzVmGNZ6Hp7WN68ddxZD0Ynq6K+isLyc5IZGNy9+m\nu7UGW2IqVpMdLWpMog9tcgpuXxR/sB9HUyP5+jS0BgtWoxmHuw+VWs2oiZPITS0iO20A6YZCmjqr\nsdqTcQb/vKnrkcSXhyMohYIVy9y4+nN4/HKBt1847SAX3GHn9HMEJUpqFr54OWcOK8Gck0Htmo8p\nESuwpBWgSipCUJlRaJSo7ENoDWUeN2WUj3HstxzgwUjuUFPiZ6MWjzguCAJoZOSQRGDzHBa+ARzW\nxIj/8pJ/QsSIRaXDJQdRkojHgsSlAN1OL9+9c4D2CiVrNlfR2tLD7u4mZt83hmlD87js9iLu/esD\nLP8giBxP5fmXbiMUlHnlyZn88+N1PHjbTej6EzG4FLS2RWlq91LZsZcl/+oiS382LQ1hNHqBmpjM\nK2tb8IcMaKQoc+4w8PFLt5BEOQ/ecjLjcqNMH5dAIhVMGJRCqMWJWhVm0Vc1fLToK+5+4lQefD2N\nqbOMPPjXy7jq4US06cWsWRRiy9Z21FqR71c66PZq0USiXPf4SIL9oLf9D5sRTa52dlR8S4ffg8cf\no9RVj1/Q4OrtISpFWNW8Eo+7B1M4QFCtIKzT0K8J0NlTg6xRQlRCkmKYzJn0OjqRwyGUBhsdnfWI\nGgu+cBSXGKU34kCty+atv7/C0++/j3nAZOwTr6anrYNEo5JQ0Qy27/Vjl1RU15bS3FKLVZ+Jq70H\nb8xNfn4J5558Fda0BMaMGU1Koh0pHKKjeyVCJMjKtV/RUFuHJ9hLbfc+Spt3kmS3k5s7hFBSEgFF\nFLVVg9zfS9GQE7CozNS2V//uJvhHgUI62IQ45MTMeh3P3JxOab2Lr5vXAjpOvm0BcKTD+Zl1REEE\nhSkXe7Id75bl7K6qZ8KUi3D7vezfX4/TF6O5tQdP2ECleuxxo7ZfGuChcZbfGi4+zHj80+u/7NUe\nOkeUoLo3RqoU5JIFHYjxn+fp/uyIxGOgEInHo8SlEIYMI88tugmiIm8+u4MX/nENZ195Gf3Nu1iw\nqotuh4hHiDF66jBU2hBKk0TalAb+cvtlnDTuchSdEuuWLWD4mKls7nudxGwLCanpxBTwxDvn8uhN\nc1A7DjAocxplS7dw0yVjEIIQM2ho7etHpRM4e3wJq16ZzN/uvJeHn3+T5559kzMuvZXXvthOU0MX\nJ515KX17k9i3sYJrHjKyrnYNq5d4+eKDpSyp+YjPFzTR0laBJEZwBQX6fHFuvTePsSV2OrpjdLY3\n01wqYE7+HwpYtzrqOWH89WhMCprb96ATrOzqaiTgc6LVyKQY8wkFIgiROI3BAPt8DaRa8oirNLgC\n/XT312GyJ6NSRgjG3YwcMBZ0arR2E2Z9KuWOMuKmMKF4gO0N33HqFYN4+p5nSBswBZvZRtDloMvd\njVZnIu4zICekkpM3hBFJuYghEY3Jis5goSCviL5QHwk6G2JuKs2ik4zcQgSXiNHRxpBBIxh/2gmE\nEtyk2nKYPOo0rBYzCQlWhFCYwrwBNEW9ZI2djs1koaW7npDv2NxW/+5o3rPxKEdy98WpmFUCn78z\nhBfmBXn47Ta0eg3w6waCKMooFVC5dR0ZJ81g7NixfPD202QkZxAKK7Gk5eN2+fH5neyrqUOL97/V\nBJB+wySPPD8u/7pmGD9iJ4cgg9mSRWqaku2pBuRoBH/pSUfV9P7siEYPdtARVSAa+HpRHVEi6KPQ\n493Bm+99yjN/vZeYs56NH+xEI8DHC79kRsk7DJgGNeUiWaNifPH9bTx110ZKpqdwzTMGsvPglZfP\nYd+ObuI+kQ8fWYIh6ESKx5FjEshB0ovv5tpLh7DsmVXUdoNb0hI0JGK3prDguVGMVDawas1O+mIa\njLYUXvuxjpbS+bz4xq2cdfkZPH7va9x8xg1smrOcT99/GEkQef+xp1nwapSkLCXpqUaay9VELY1s\nK20hIqmxJ/lZ9cNjWALHFrQ6rqPLTBnC8rIvaO/rID9/FBFJhxTopqhwPN3OWsorN6PQWzjQ305t\nUwXBQBy37MMd9NLeXo1Oo8ZkSOPEyVeSkjWCNimIIhrErk/DqjehU1gxONyk6GzYjQlU1ewha8J0\nvGEFbk8Et9eH3NmBJUGHKXcgbTW19Hb3sH3XZkyikUGFAzBKUbw9PWgCcXxymMbyMtz9fQwaMpFW\ndwvJk8YTD4i4Q63k2ceQkJ8Beg3uhEJ2B6qo6SynNehCrzKwt3kN7X3VqN0i1RVlv7sB/lEgmlKP\n0lvIylKQnKFhlCWfzWtLcfsDvzrnUDQVk1SsP+Bgy5plDNaK7CmvID1vAA63j3AkRrfDSWdnO1k5\nhTTUVBPn2C3/+G9Eer+sBx73exx1zs+mrNGAxajAYFPwxOx83vu06r98zX93HBLxVokKBEGBq6Gd\n/EIFxvQxqGwKzh9zH++98jJ94Qghp4PL7hpGp09Baftatm74kA0fKBAtEpmpenKT7Kz4/iWuv2AL\n27al8NmLGtavinDqVQIhrURSTiqKiBGECIgiO7as4Km7pnLtnePRFmXy5HsLsaUMRzIXE9LkYEzM\n4aaLc9n01UVcMP0UNm0vxdXRyScb3exYs5sElcwpQ27kxWff5JU593Pd7cuxGCTeX/wMtz9roSsg\nIqsDJBaGkf16socrMWkilJZ76PCtp2VX6NjrcrxFa3M3E+zuRvbFcYUlXHEBtTKRDkcdHq+DZHsK\nZn2Mps4aetrr6Ggso6GljPzMXNQaHQa9DbffRUtXA0VpI4n5ghQXDSMWU9LVVE2JNZOkgpGYJT2Z\nISPpifnMmCoSM5rIm3gCuqQ04ukTUXl66OlsoLJfR2VTN/EkO+PGnkh+zihUKjU+tRfZrMNgTURM\nMGFMTeH7XfPIycknEmjDiJY0+xCMKUn41BAK+3EGKmhu7WZQ8QzWbFtBdc8B0hLGITcGsZuSGT58\n3O9uhH8UOPt7Do6SxCNoQyIPvu4iQRvhlKc/RZuYihIZpKNHPQ53QKUgXWtf4uRLLkDniTD74klE\nw3FEpQKTyUpt2XbsiWYc/d1UNO8jLv9cF/tlCnu0U5OO+b4jcTyDFYSDqmYWs4RJp0H0CMxdE+K+\nN5p/dY8/KyRJQqVSEQ6HDwpWKcOclXk5JdNVzH3jCaaccz/DBs9gZ1kArdnEZy8txi6DJd3EFzvm\nM23oyWSkmultDxC2yqzu3szdD5+E2aslMT3CKw99TO2yAKOmGxl+gglJE0OQ9QiaKK9ueJm737yA\nbxb1I7XUMPTsS/m2tJJ6nw6HqgiHkEMwpsMquTijOIDW3crwaWNo63WyprqXc2ZO48orTiMYh7+9\nugm11o5JCwk2EdHgZvVrL6BwC9jsWqrrlWxaEqK+HA5Uw2fzyv+LTIW/gWg4iLO3HZNRR3v3RjQ6\nH05nLYmGdJQKFckp2Wwq+wqXz0GS0UK8z0OoxcX+qnIGFU2ns78brcpMX8BPWdNGBg4oYVhSIRNT\n8zlt2DhS7Tb2N+0i7O4kPSWLroZ60kgi2aoiJS8fc3IaJquKUGcZGn8HXV1OOg3F+HvjfLP4HX5c\nMhezKRFBFOnoa8Yp+6hq3YAv5GTMuFOw2XJIyhrMztofqOuqoLxlGyot8HWOAAAgAElEQVTRhCIz\nhYAjhN1qZOXyL8jPG4HNmkdXdzMFgyaiT7GRnVX4O5vgHweDJ0xClmW2fDaEgH8/be4+evon8P49\nCcSlCMvezj783iOdThQlj99+Hxqdjfc/W8tHCz6lpSuOLGuQZA2LVnxHduFgfD4HW3YeYODZzwOK\nw0RQR9GwSz/TQ8WPQRbw8zF+cew3CAcEgVA0DHEldTUdmNIHUb9nCU6ng7h0aE7vzz9HJwgCaoUS\nhUJxkIBDoefqa/6Cqw5mXvMgNz+ShVmhY+HSD5DiYWJBO0I0jssRpGMfbNiwEqEyH51aRU2Ph8rS\nHuZva+Kc006nYZ+MW27nmc9vpr0Who5poXDiuQiygvMvnow5S6KsJ8aMMbO58vErOLBwGy/8dQFl\nFeVEoiLdsUzSBw8nNVnDoKR+PvvbVPLS0+mNmkgyJTDlsseIBUIoRCsnXSrx8EN5rP8C3L0ydquR\nE065F7E7H+c+EUdlnHPOSsIC2NUgqlykpBmPuS7HdXQFhhEMKziTvKxJFGRMJzXRhtvTjcGoJu7q\nQ6uEnJTB5GfkYVbbkCIi5uR0PM5Ovlr4OLGAyJ7yHwkGgxRmT6e6uZxdLRVEZZluRxtB1NiURuTU\nBJJychiWORanp49Zs2fiDvRg1ujp3bOHxtoePH0dSN5K6veuoyNghFAGl198AwaNkTgalJnpTMga\nQTyawIih49lZsYY+l4/dezZx7Tm3YxBFVBjZtXM9B3aso7+zGosumTPHTqOxagcRj4aiAUU0uatw\nxZx4NcfeN/fvDuVPjqSpIUp3xcW4dl2Of9suxox8jY1fF7ClgsOdyiNn20RC6LwbOXl4ATdeMJUf\nln2CTmej39uPw9GLu99FVXUFXX1+1q78Bt1PQ70/iSH+hJ92Wmgih19V/Ebj4RB+O5X9tdnKxHnp\n6gEo1UpmtNxIpLeJf829E280gdrl2f8RM3SHEA6HiUrxw+UJpRCifsdmJp03nb8/uAaEGF+te46Q\nzokmxcqStzfQUB3ns6UnMeuuTKoqO1G3TOGVG97gituGc84Zg/lixT/4ce5K8kYM5+1//YuIz0eo\n+zy215zNTf+czMIVy9i/NYZFq+eBh67g009qkBQBohqJu+94hNqWGpq62ulwqtlf00RdcxXdjn4M\ngSrybnqV2pG3M/WC2/nnnLWoLGE6+5Q8/fRb6DVasgwCloiASlIQ0NTQ0BigsTpAyGci/zQTCqNI\nexVIwrG76sd1dF5XD32BXhbPf5qv3r+PeZ+/RqJRTcjXQWtPDX3dTcQUKgRJQ3HmDAyiioS0HExG\nI1FPDJs2GZ0uAY02yoHKzeTaC/Ar22lrrESWVAiySHr+cHY3LmNndzlmQUNCRipJ4W7O7Z/PRDYz\nYZgKgxq0tmx0pmSS5Q7cHQc40NDLovUbKRlcxFmTLqa98QBvLf+AgUXjaK1txxI14/d3MqBwFIs3\nzWPw4JMwarPRiSrMmiSUkQJyMtNwu/oozBtMPFZJR18HJq2eiM5Af/jPO14S56C8YYpJYt48Hw7f\neC4dLfD0+0/ik2/lmZ9SPUlQo1JqfnoukZWWS2HxIJ589H5cjj4aaptQqNQkJCRRWVdDb0cfA4qL\n8QZkzn1sOdLhcY6f2U6+eSQbPhlB42tjD7OLHBmfHakJ8d9JNEVZJKJU8+VryXy3ZT0PXJzNre/1\nUfWdie7goWv/+SM6SZIO1+lkWUav16NSKFFqNbTuUKCLGAh6eti6t4GL7juZ9qrPeWXhTE4bV8K3\nH27DTQtTr1eybMNG3n/2C9Jdo1EanFx1/WhaejvwBpr49tswN9yWSFn115w65ht6+yKcfc9ZvHTH\nHLSilRPOSmDRstms3PMRcX8MyRumsqKGYNBPh1dFRZ8Vha2Adm8AdbCbW/VL6PryUerdIcZd9wg7\nN0SQHBIv/Gs2piIZb5KRlMyBaGKZaDQ2sgeo6Y+AKrEBV3cMS4IRdZ6WYUOPXW46rqNr6CsjPXsw\nNnsKQwumY1QlMjJtJI4uN2nGAYwdfjbupkYycgbT4tlNyuhBhAPNmGwpTJ16KcoEHemZ6cSdAZI0\naXiCHuJhHebiIah1ZvySn0gkiOA2oFFpaIh244l60Fcuxt1RSqRlPZs37EFtTyeoSkQxYAaoTRgN\nKvJTVZRWNbNi7VbkUAtT84aSYlXS11tLNO4i6HHgqO6ir7+X/KLRLPn2XzR3bSTZnkRuYg4nTilh\nQsEEzDoTnvo2ktIyaK7bimSI43X5SUvK/b1t8A+Eg1FV7rkVmFJtlEy8kTbb6ahHruX0ix8C8WBq\nKSvMnFL7JLIiBGh4bGYK5Zu3gV7LA/dcSWnZPpRo2b59C13tbbijHsr27cbrrD1I3PkTDnVSVVIc\nvQoSYy4y8xLY8f0bP32aoz/br5/913D/G7uYMNHASuNjvHyHi5ceuINJQ29k6gWt/xFODn4awhYP\n7SiR8XrdB+chY10EI3Us/PYR1ux5D6tVRqWL8/G657nlpnepaj1Ah1+LIBSwYmczF581idWlW1m9\nbC1ZvmJyctPZuPczHr72Le65uIhVP/YhW43c93gu06fYufWM07j5wdvp6u4gt9DOh0tW8voz83nz\njZf5aO4cdCj4ct4mPpw7B7Qmmrp95OQWMnhwGt0VS/nnbafx1h0XU7ZjHX956XV2bZIZO+g6NDaB\nqi1Oavam4PA20dHqxGoXufuvY1GkpBP2h9i+0UcsGuLFf75+zHU5ri2lCYloNTYuu+glivKLyB1e\nQtxk4oLpszAmJrJ192L0MZlW1158chCvs5mKAzvJKyjEoFVgUxtRCHEqVy/DL/hwtbTgbmvCHWhG\nlt044hHskoZ0dRZxSWJ/205CYRl/JIRKpyWkS6F42FCkYAeKuJu+fZvx6zKwqSMoTTYU/ga21jTz\n5Jx11Ozbg06dTIGhgPTcArrLm0jPSMfh7qKqppGExFQadzZh1cVp8DXSHeqlZMh4tAWnkltSTEnS\nACw6Ay319RjiKrbt+vF3N8I/GgoKdaxdNpfvn3uPYQ+8jsQRm/1FkS/vzOaqd94hv3kEQlzmhHMu\n5MYrZpNq0VFQMpq03EySUy14PT0sXfo16QlRAt4enKIdheKXA70SUu0KOvtNqMbdyPLKNsbPvOWo\nz/N/0ioQBAGzyciHz4QYEGzknssKufPmR6muXX7U3to/OwRBIBaOoPmJB1D4SXhckZzL2CvzaPc4\neXXOd9itFrRKNTdd8hFm5yiskp2WHRqeunYBX97xPdubN6AtsrBmaxO79law9X0FS75YSaJBzT8W\n1hEzq+lsk1i2ah8lOa8TT9FTWKQmFlRyx537MRkSMdsKaVI4qe3v4qvvl/DlB68zMDuf+pYufJhp\n6Iizv76MzCIbXy56i50/3sT7j17A5rWLuf+ltzhxwj18/8YuLFqRTxf9QFk7hLSQN0wkFkumbHcH\ntkSZcy5Rk5ltYtpZ/0NxHNOQkXiDfRzYv56Ay0VBWhEdPi/KUJDinELyzblYMooQIl7aK/diT01m\nQOFwmmrL8btbQRRoK9/PqPHTGDGwAKfPyVO33IJWrWWnI0xjXzUNznYsRbl4A36KMiaxb/cK3JKM\nHJM56YQSJhYrkOyj8HW1IgTb6G2px4sdmyqIS5PGts17QfSzpkZg86JSqvo34dpbR2t7C3G9gLe/\nl8a6MlxNXVx41QXUdbezfcd6Vq5fziffvMmWDa8jmKN8tf1z8q35ZCQXMHjUFJy93t/VAP9oEAQB\njaDiy52jmWCZj9MfRThip4SkhHh3L5debKXMNJ/1K3cwJtXCB599ytTJI2lr6WDzlu3E427Kd2/m\n2ovPJj3Dzup1uxg58zGi8V8rjF1d0s4Vr5Xy7vwPSEmNElcofvGOo/HfdXyCqOLZ5W0UXvIED+3a\nTGaWzJLvrf9RcocqQUatEpHkGKJCRqXSARIdzu+xRYyociSSjCo62n3IcT1llTvxK/q4/4nr+Oec\nm3jgpeFU1q8gbtRx3lVaxkzTc9asW/lk2XKmjhxJJNTNnuXvMnDgAPT6OH09GhZuuZJ35yzio/f+\nF1uWwC0PmDmwpYtm1y7WL9vOnsZ1dARkzFodzzz8PNZcBUqlks079mNL95FoK2J0USHX3XA3L39y\nP8UJtawr+5xr/rGEnKIShs4wkJSjpnAslFfBuvUBepyryU3SI4hKtq+PsnmZF7Ut5ZjrclxHF9OF\nkFwONDoT3XKYToeL1OQMaiN+yhvLURv1ZCi0+KpcZKYnUb6/Ajmqx+N0EVfG6anZhVbScKCuCr87\nyPmnXsTixYup725D49yJ2+mnwdtBTA7S0VyD2pSEr8+DzpKBymygr7cLo+Bnan4MdFbMpkzeff1x\nZp0znS/nbSTkD6Dy9OLr7aZuxxqcXU78bWosSblcctFlONt6CQf8pNvTyR1RQkQlM6lwCuddch9N\ndT1sKt1If8hDWfVeZuaeiGS1EG1rZ/WPH2FI/N9sA/43hizLRGXISjDij0UoU/fw4k1Fh48rhQj5\nvk+o9UCmeQoVHQk0Lruae598noT8HOyp6ezduxu/qwdffzMJqUbU5iBz3l3PCVMKEeTf1obQmEeg\nUSu5bHYyF1yzl9+iCzgy8jqkR3Ek08mxvg8cTJF//PAZOts9uNweFAoFz3+1/f9kqf7tIEkSSlGB\nKIrI0kE9DVEhowwXcuU1KtpbXaxd5yKmDJNTPJybb/+QB586i09XziXR8CQnjrqMfsMiBowGt9uN\n3F+ATqcjSJCK+i6WrFhMhjqLk0vGEnbB2JkFxGU9zm4tp0y9A+ugGKsWeCgo0HPBdUOZ+/kLtDoj\nnHIxoI4TEXup6C8le4yDrVt7efSRFfzzmw9Z0VHGmWfcS8AHG0ubMPdtY/WnDzHyrPPxRX34o1H2\nfw9XXqsnxSLi6orQKwUpr4vhdsqk5kBHV/cx1+X4NE3lZfSG6unqaaSgeCz+/jY2r/+Y1o5eUmQL\ndAR55dUXGD/qVHRZWYgEMCKhV6kJR9R0erycPG4sF8yYhaQ10uGX6JJNtNdX0dfThTcqEvJ10edy\nkmQfgiISZ3jKMPq1atRGI3prJgGfjFYT4Yk7zuG266ex+Ju5vPvBZyglH8MHF2IvzmHK9LEMGHcS\nQ4YPZcuPZVR27UPOzWOgNQONrMTR0UVj+QG2rtrOi2+8zKq5H3DZpbOYOGwUSUmZzCg6i8suuoY9\na9aTNX4igZ4Azlr3722Dfygo4lGunJTAjucL2b0+SFZhDuJPajhRUc9jf/8rL33fQmpUBRsuYmDW\nMBILC5gxfQLRgJdIPEhmzgDqGltJ1mlZvbqKgrxERl21GEHxMxvwz85J4qmPr2L1x/MIDK3gwVvG\nI8aOJ614yFEeZLL7JY/dr1hPZFAQpXzvBkp6/86UU5REYgIN7R0H7y79+WfoABQiIIrIsgRRGQz7\nkWMWkjPTGZCezkWTHseaDIq4gu++Wc+gCRFqugN0N7WDHszZIT5a3Mm+A0Fq90hUVlUwb9PDLF/x\nCXtbO1mzYz2fffsetrCdM4pm8enjjSxf46byew1JaenYtGpum306e/dFMOTUMO/jL1DooHj0cOb8\neD1bqnaRbZVp7qwhNUVk7fcvozCLmExqMgo1dFULGE0iIZWSvi4vqz5fys4NuehdMXqbPsUbCXD+\nVbn0Nxpo3K5h+OgE7FY9rdVqhuQNO+a6HNfRdXdWYEkoYXh+GlvXfE1vfx8xMZ2d21exZNU83nrr\nGRBUvPDcI3S3tXHa1KvIKhmEOwBFRUPQ2hU88MFrzNuzgo6aHVR3drFh42JGjjiT9mAqugCk6tNR\nhVUY8ROQRDR5ubgCQdDYcLn9qDRavl2wkjff/JA35s5n3roGSrfsJKo0MDRTj81oZ9V3WzGZbViK\nhnHKrAs4f+JFuFy1NPZ2gMNHdlYqB6p2E3R6ueCKq8geP5R9rft55tVn0Sh1+Hvaee/j9xHVNtyO\nAPYsGydOHP87m+AfB2pBALWSZZ/fyQVnn860czQUFZ58uD6nCfuwanUEnTE+3rGQlUtaWFpewROP\nPsN1l85mxjmXU1ZaxYJvl9PeUk+Dsw2PM8Bf3t1DTA4fpmISRfGwcxIRWLAzgC/0AJpomJMmhpEV\nvx7h+SUlU09fCPP8PGyW9F9FdEdROKGko8fHqh/f4+3lXyJ1WuhrFv9jUtafIRGNRhAEGY3ZSbd3\nKcqkGJGefXy3uo5xk3N54Ymb8PkErBlg0at44L4ROKOw4qvrmDtvGWGngKcPXn+slJa2g7qwj798\nIzMvVVIyaCgdze0sXPIpucl2XrzrGq4ePo6NOx/lrucsJCTq6K6rpXBojO/mKyhv249VpeDMERfy\n1XfVKJX97G9Q8dlbYEuJ8X3pala+L/H89V9z811XoNHB+MkGGluDhDp24pdi1JbWs/WHJsaeeyXr\nPhNZ97mfiNrDrBtDoHLzxMM3ImsiVJQeW6JUOF6R9tybz5fzMrKpaymnv6MRiyaXSSecyvo18+nr\n60Nv0ZGWOgBftxdLmonMgiRcIR+BThl1Esi4GDX0ArztNbQ3NdMX8uPuczJ85CTU0SBfL/uCmedf\nTzDQjUJpo2bDBjJOnEZhfjZJzg6kmB6lUsDp7udvT72DiEha0Whuv/tSLGo1H81Zyt5920jPzmXQ\n0PF0d3bi7mlBpVATFxQk5YNKFNizaRvxGAwcVkh+3iBMgpZ5SxZgS9Bz2R2PojfFGJM7hMqactbO\n/4Fugtx0+c1cf91Nf8p/SY+nRv7XFiXTDXN59qm/kz99Nimjnj6qgfDufVnM0iqpn/I6062dvLJ8\nJ4PNXcz+y13Eogr++frj9JDO+aMTueLGKdx443wue3oRSvFgqqlEIHaE0LUoirzz4AAsVgl7UoTT\nr25FUh0/yhJkePb2EZyUF2bC/bsBPaIgYdVn0hdoQ6tNJxLsQhlXIqkkXnkgF2IygwtGc8ub22is\n7iQuiSiIHVY2u+30HMzHFHL890ekabWsUKmR5BCxcBxtwb3cc0clLzy6AY0yxLffPsnlD27F076N\ni+4/CdEUQJAVNO2NMXikFkkVwtkNA4fruWHmLbzy3iu0uCAUEhmab+KqWaewv2k3rz/YhCypSdCG\nGTdsNNOmT0KXNoo9pd8Slfdxy3XPU3za5Sx+6RP6OrbQ3h3F0V5Jh8OFUmfC73JSVxdHNLpQymFM\noprkodnsK6/EaFCSqbNhS8/k083bGJqvwNVpo0foJ8moor8uTPoQmDItiwULWnH3g84CxRnw49Lf\nngo/rqPLG5oiz7jwFMw+AwZtBvGYH3efB0GMs610LWPGT6Fsz1YmTj8TWWVCqw5T27UbGQmVqMMX\ndmKLpZGuM7J5xzYyUzIwJifh8Lqor9tO4eBJZBcW4Ikn0Fu7Db2oormrhuLRIynS2kmwpqFURECM\n43T5WbGtnYaqSlxeD5lpmewv28KJMy/A43LidjpISU5jUEEO4YiXUFCipqEdpUagYucGxk/Ko6PH\nTU5SLl6XC2tOEdk2Az6LEUdXLVGVzGN/fZJbT7+aAQX5FJ0witf+9taf8g/R0VMtf7FdQcvqASz9\nl5YbFzYjxMJHvScmSsyS7uW0SxeSeeqFJFoTyVD30tPTQ3JaKgqVgnUrdnHnbTXsq9by7cc67v6i\n4iBXnCQgKUVEST6cMsbjcd5/qBiNTuLOV3vx+QII4s/CO7/FVQew+NEhFOR5GHZTK5IEoqxivGIR\n3z58K4qZJpInlxM78CnKwbN59NJ8zp6lYN6rm/hXRSb9PgklMhLCYa3X287I+FM7umjzGlmp1iDF\nwoRjDjrD73Pltau4bPaVnDE9lYXb1/LpnF1cfu8IPn6nirHTYgS9MXQaLfXlIRxeKBoFUkTBpMlJ\n7NjcTX+/zPiJA6kqD+J3tRNWCKQo9ZRYSvhg7la0gsyM6bkU5RejUQQ58+wr8Xq9VHVvIu7vwtWq\npaqiC6UhRH1TJ/bEBKKSgnhMiccVJRBvBcFMYZoCRaaCRfN7yUwXibtl5ESZ3m4TvV1eBk5VQkBD\nXqKBel8f3moJBQKTL8hlT2kjg/PTWPRlx2/+tsdNXYdPnUr17h3MOuMCPFEH3XEvUTlMZXMVw4qH\n0u/wcN3V99PWFWDPmiUcOLALb4eD/dvKMZvSqdi9l3NnXsmQkpGMGjgUhRjDJEtoA1GK00ZwxuTT\nGTNgKmJ3KzalCm2imSEF47HEElEaiwmHw1gTs5HiUZLsdv5654WcNm0Q191wOXkFyUycOQtV3Mnw\nHD3qBDtdCiNlyoFEik7jxwY1pBeRNOF8Iqp8hky9jZFFQ8jLzeXH9ZvItuhJyCkg0NDJtMkzmTHp\nPD55/V/cc+cdnHraGZTYB/z/Y4l/AFiTC2jYPIiZD8e4aUH1r5wcgEpWcPpdi2lzSzz60P8i2rEJ\noyjSULeN9+d8jaOvn4Kp97HwxxlknlFNOCWPcDiMxV3Jgg9HIMSlw/tlBUEAKU6XW8XeJ18l7Pch\nKmLEJeUxNCQASUZGz7kD9XS5TGyYm4OAglDlHYSb7uW9DokBw15DRs0odSWbXy9kT7eEIhLj6V2D\nCEW1LH05mzcezEeKyYe1YP/siMVDSFKMSDyELFkpa17LE/+cTUZOO8+/PR9rkgWFQoEJF5OmSrQ1\ny3jdoFfLNNcoEDQK2lvBZJa4fNp8lrwpE+yC7to6dMZOEEREIYZO1rDkxy1IRgFNoZKV25vZWLaD\nXbtb8FiX8/mnz7OzbznGLA3r9q5md3sDy3bU0+4NsXpXG3sbmlm4tp41pS1srZCpaAiy6UA/O7b0\nEZOM1LVJpA/MJCd9MBmFXu56eiwxFWDx0+joIxpUojOAN6ZDYQ6RkCywYnHPMdfluI6uKGMY9rRE\n/vrhM7T11FO1Zy2pdhljLAaBAG09TVxy9omMSXFz90Nv0NfaQcAnM2n0yXgcfTx21+v09rUyuMBM\nOBxm8qQZ2BMtPPLII7z5j2fYs3k9K+bPYVTeINIMSUgtDlRRiepdpfh62+h1yVQ19SArbcTiIaqq\n9jK8ZAApahczJ5cwJkdPkkmLMaMYuzWBcSVFXDwhmYFCG7efP5gzRxdx0nAb4ydO40BXDOWg6xl1\n6bPceuMD7N5VSs2+cnKzM9m7eRftbgc5o4bh9LiZPH4cyyrX/c4m+MdBn6OUwWf3cmOm5iC9zpGI\nygiyhFIOMn7mZGpqmnjjsbMJhQK88u7XXP6Ck7eXehh46SeccN5lbF2wBq0pi0su1RFf8TbLtp7N\nzQ+3HNxz2badmAjISgwGAxl2kR/mLiemlFHLaso/PxE5fnSd7lB0JyEjKRxIV27l4hc8zHoohM5o\nJ9z7LY9/FmV4pprnHr8OQSVyySuf0eqFl6cJLFoksPT56whHfKgVGtqrRSoqd/zfW9z/x9BlOZFR\n0dZUzr33XsOsKV/QtrUb1JXcdvXtPH/7aoaMVPPZ/A60RiXDBxkIe5QoFTpOP8/KqWcZMGktVJQJ\nbN76Di9+kUh6agLDR0/k4cvPxRWK4agTiMTgsvunY8mRUGvijDxLJG9iP3u6m3nzkyWUtzlo2xbg\n2zkbcBrUtPfI5EyBv714O7oCBQ4F9Nf/wIgx4K5fRGfdZ1z2lIHBZ8icdL2PkskGUooSGDvdgS8K\nCxeVolfGsKUo0ScJxEMxTrp4KKIhgKh0EwpBUU7CMdfluI5OVJjRmxKZMeQU9v6wkWHpJfgdQUwJ\nuYyfehLrVu7A59yLt6+avas+RRsQad23n52rfsSoT6GsvYH5y77CaSjm+muu5rkXX6Gzw0NeyQQc\nnWVcculsbr3lDgwWGDl6NJMmTmdwcREnnzQNIe4mN8WIWanAFVASiurxewOEAiGkeBCPr5f0ZCMl\nQ/IxK0OcO2MykwYkE2s4gE2MY5Q8rP1hA3Ofe49TRun45LFZzCrp4quXHqOqJ0RyxlCMeiPfL1vK\nqSefiM1mo7J8HxX7yklKTEN9HDHcf3ekWwbh9fsZkWZHEBRHFfkFJMKRdAQxlRsyDFxz62TMJgU3\nPr2bEUOVzHm2CLH/cb5bvJEqt49hpxko/TSNres3c8W9s0mM5eL0xWlqVdDTfgOlS7NRBZzE4gp6\nO4Ms8W6niPVsmDeMHaXVRPw/8/4dGd0pRBnlxgFEYlo2fTOJ125Wo+qcx7wfZLwBgRzLWG75RzW1\ny4rJTNGSpIvzTRWccsZtFBtWEY9F2FoW4aZnX2D+Y1f/xzQlbv/bVcjKKLvrtnDng1dy4YOXcMNN\nDzHr5BXccPV9nDhJias/xt5tERJMAv3dHrSCiCDG6PM7ECMaGqvd9HdIvPX61zxzt5M9dW4+/WAT\n28p3gkZmxshcJMlNWNFBagFEg9DRKdNQD4MmJ7B1UxR1SpxsawGGQUpEYwS/NYo1B84/+0aCEYGd\n37/Hd+s/ZNV3S5HEMP5AJ+cPfZADa2D3BkhMtbJyVxkbypwoVGCzx5AkGDhQwmhWEJVFVm0pZ8Tk\nbCorA6TlCtQ0OI+5Lsd1dM7a7QyUM5n79j+wpmXj8XnYsms3AwaX0Fh3gIkjBzD78ispb1STkZJG\nSnoqEyadzlnnzCYhPY+Aow3ZZEQ0mvn+x2VMnnwCI0dPwNvXwBeLdlK6vZxYJE51RSXVTa3kFf5/\n7Z1nlFTVuq6fVTl0V3d1V+dIJ+hA7CbnHEUMqBvFBIqKioiKICooAooBAya2W0RBEUEECQIiGRpo\nuqEDnXPO1ZXDqnV/uM8944xxdIxzxrnj7oM8P6v+rfnVu745653fm0iIKZKoiFjCo9NQKP0IM4WQ\nEBGL2SqCM4wh6unIfVHI5Go8LiteUcRoCOSL7R/gVriIiomk8Goe0SFRTH5wHH+7ZwQBwdG4u4vp\nm9LF6MwEVNSTPj6Nxc8+wxvvfc6V2irCQiAzIYP0O9M5XHEGi+UGHrxZU0FwkIz5P+Qg7yz5vyLg\nFgX2/fgLio5RFH87htF3zmfW8hr2HrtGp9mNTJAI6L+KNa/uoEnJVtoAACAASURBVHHbXE6/lcSZ\nEzba3XIEYzTnKoJY0X8c4kupfDqyFKvXx8iUqfy4IR0FEp9tHIVNZ6Gu9DUunzGzedkyGj4fjSD9\nx/yI34dGenjtc2j6ewQDJu1i1Mhk3M3f4pAk3Co3kYoyKo5m0OG8gwA/OZX1wwhP0RMaIDJDMxTX\n3mU88ICe6mv1rN1X8pcRutzTBua/Pp7skoPs+i6bgaq7mD3lDo6fvI11b6whOkGOQaZi6gx/Cqrs\nNLRBcakPtVJD+SXIu9RBtEnBosejyZwq0HLxKx6b9RB4oVfMMKaNCGHKo1HMnhtIWVkpJfkKYtI1\nqAUf+CBtZDteB8TFK7nl9rt5ZukTaDSwfkMmPc1q8ksOEhfgJTFpEcoAGw11RSCTGDp+CUNHrCY/\nV6D/KC2S2ICfAhoqPCgUAVi71Cy8PZP2Brh2ysfgvlp6xWrRy1swd+nQSj7iov94jeWrV6/+wy9P\n5J5YHRpkIjTYRHRIJL2i43lq8SNMGDmUlLRBGCJMjJowm6wRw/G43JiMQQzJGo7Fa6Wm6BqJ/UYg\n00JbRzsmZRApA4aw6+ezxJqCKC9vZsSIkeRey0enMzFgxAQqCy9jNtsJj+iFTOmlvLQcmUKH6HYi\nOR2ExkZyufMkOVcKcYoCKoUfKpU/HlGOQqvi18O7CTP24qFnV/DFlg+I0JtQKtSkJPSipLSDZosJ\nm9NGwtBQ5HY5JZ11XDx4iCpzA+NSJ1Fj76attZhWswu5YOb2SfPW/D+oxf/vNDS1rz63eC4hnMWW\n/hxuj4jCJyEpXXz/7mT8rxsoqS3g+6+OktK7LxcvfIu9/BN0wS6iAwqZ/1IFp/ZvwWSQiB0g4bDa\nGKnxMTs9nIU/reNwjZOjpTmk9V3OZys/QDAJpPYdwdisIFLCh7Hi3aOYe0RmT5yOVumBxFmI/LsV\nRCGo2bsujoVvVmIa9QIJl+4nz9ZOc72eqqZG0hMURA4dQEN1J3dkBvHrV4U0680kB1mZufQA9fs3\nIB8RQE/nNDLvXIbE7z46QRAYkhSAGm7IdQVYMGv26ozZOax6sJbGGh92VwGFuRZGzwpl88afqasV\nqbW7SDUOxK6pp6kKUjKCmDMxnfXP/8aD9w1m+3dHuXbeydTpE3DZ2/nqsy8Z86CeA0dyObbbze5d\n1cQm+KhtlZg4zZ/WRjndZhdxyUoMKn/0LgPdzQK5TSdZMPtpjp/4gZyyJlpqvBw5dpQNj66g2JHH\n5aNFPPnoPdic7RRygrZuH2tWjmba6Mf4cvNR0jPlyFQ+EntHUFvWQVl5Kyf3SQzpG0p+hYc9375M\nt7UZu7WVXqnDeHPNGkJD+v+na/unHZ3Q0024MYo+KWkMGTqM4tICJk69jdrr53E5zVRUlNFoaaW9\n3Ubq+BkEBQX9biXwwJFfjnPkm200FBRwbs8ebp+3iIuNucSH6dj53XaaGsrpMncSHRVOWlof+vTu\ni07tj8ft4OLF07Q1N9M3PQm1wgMqP/JLr1OSfxVbj4sTBw8T3SuJnEt5aOR6urvd2Bu6mTv9AeY9\nMJcvfviGYYMnsWL1SxSU1IHCh9tnRe5xE9knmSRNLyrbr1NaXcRDK1YwYcAMBqbH09LaiEv0Eucz\nEqmO/bNH87+a1PRUbJ1mIlP8GT5URXTtU3jkbtTokAlKRj0Vxd9ensHw+xPpMiWx+N1rlF2x8tt5\nBWfPViNaehg0xo4h0kpUVDDtNg8XG+LImL2U2jw5rRYP2eevExbSweL1SbzwgES6dxc1eW+gupJL\nXISNEcPlvL1zFceKjuH1/Mc3sdbfSKMooJLJWD5vEruLz+LqcZF46xfMnnwrvioXEZnfMeT+64T3\nNqLqpWbCWAvFl2Hfl2PY4exi9zdHSZnxHPj+2SX+Ra6Bie46Pnq5nOr6MwSPa2brlk/JK/qIkQnP\n49WFMG2BApUe6hXZtDco8cqVBCraUcls3DMvjYcWL6ClwczUmUp+3XOcZWu/JmqsD4dVotMs0DfF\nxKRJGj54w0lKfAYum4AGG8F6OZZuD0d2wLkLbWSfceIL8vDc+kWovQLzJzzJxZ8P47bAwifWEy2l\n0FCvQlD0Ys2mZ2ksNpDWR8lr75+mtvlN0sapcXn1tDVp0OjdmIwaappFjhycR01FM88+GcKkmS+y\n78g1PB43H7x9lvCQP5azP7WXfPzOBkkpl9HdbUWr0iBXKZCLTlq6uhk8ey7NFfnUSmb6GRK5eOok\n4YMzqay8xrC0TJQuEbOfAW9dJXb82ffNdnpP7Evu3oNMmz2bgb37g1zGa6+9xqMvv0rT9SKamwpI\nSxlNzsXzmK1dTJw6nebGGuw2N5I3gJi0FI7+eoSsrBh+++lX7pp3PzJRoKu7jZ6ebhLiInFd2kC1\nOISHV3/Azm1vcOFsJSnpfbD4q+gfZMQW6s83m7YyYV4qMsmAvzEEhTeWUHc7jXI7krkbZVwcpUVl\n7Hnvuxvyl3ExL0/af+IuCn5p4I67/XhqdSv9+mhImX2BqhOZSPkKbnm4H/P+dhcZg1+k9uhUjlRM\n5c7blhA30o/aMg9j+nlZ8HgJi59NIjUVXnzmFwo9aXy/eQb9Rpi5e3QgL75xnTtnTiY4bg4bX32M\n73+cxuqp2dgSbEyYPp7PPj3CM5vKEGUq4N9NwmplIOsXR/DJN13My9LiVnt56+dyPKIWpetBFJYX\nEMPT2LQkARBBkvjsnXk8u+p7lmx2su9hPRM3FSEhR5L+Y6TiE1Ojb2h7yaZ3BOneh+/l8eXb2fH5\nRSxVlwiOSWDLj+u5N+s57tk0B69HIjOmF+fz65ELIh+uXc/KN1/AZoN5i1LRtA/ki327sHV5CDcp\nCImTsNX4ceyomTHDI9GGNRKmH4YzsJ7iimZKc0V6JyiITVGRe9SG2q5hxpxEatWd9NjaeeGOdbz1\n3iZEUxMZ/UOYOXomsYpbMFsq8GkFnnt5A6XNFgINTooLlYwfFYrk10ljrY/Lp70sfm4AB77PIX1o\nOIWnm1mx8E4iR4t0Vgex//RRmhpqcQlaWq87qCj4z310f9rR9U5JQKlUM2nSJCZPH0dKn0T8TLEM\nHTuOy+dz0BgNBHuU5He2EJGSwNnsXYgqiUpLO4pgP5xWJ/4pvZCC9Ex/7Dbm3b6INRs3YAgIQG0M\nYOcvP7PmtddpbuskLjyY5EHDCTD4kTZ5Gi6tjz7xkSxdfBulNdXoQ0KQ9H5MmDOd6P6JjBwzFbO5\nC0FwIJdEXFYrTU31NI98jrC+I1n9xqu0u5RoggM5U3SeDIORus5uLLZmHn1mAdW/tXHXmHvIO1/O\n0cPf0um1YnZ2UllbT1tbM/4Bxv83lfgvwMBoLXfGz6W1M5Er+a30ipIjC1Dy1bIB/LpHxOIWKanx\nMv+Rzby9ZRYrHzxGvTUHD+Do8iC0eFl20MX57K0kRQk0NIzEmDEQn9zLoytPMHvICszheVjsEobu\nCna89RhPLkrnsTG/0KLtoKHFic9nxRiswPlvJuV/JnXJkPCI3chEJ2+/ZeTwd/fwxbaXkHl0KLwu\ndmysoaPqXfZ/EEtLvQt/mUBAqB8rnv6ahxY9yhD3PoQBPlY/1If3X+kFvr9CxOG/c++8HQQGvMEP\nn18m++rb4BVx2lrYsvcUzaoLeL0K5AoNr634lLLrbloLRXIuNaKxRDK0dzoVed3s2beDSKOXznpI\nGhRAmFHH8T0upoxR0dTZSFujitjBAscPW4gI9CcqQoYhwEt6v3BCgoMJT3UyqO8gOu1NnD7p4YeT\nL7Fx5XIqSpSMTXmYte/8A2t3Pdu+3o/SCf7hHsaOSaXNCnqdh67OLk4fdaBSCCx7MYttX+aw8LFZ\nXDrgIv/4djZ+cIDxA9excO4XXLtUj86hYWCaRGjsH/+B+KdCV1NTjQcVguSip6eHU78dxtxRw+C0\nFB6/dw7Zh4/TZGkiIyoChcfBsJiBhAcFcuS3v3O1tp7mjgam9B1AVnwcZZW5PPnUXaA3UtxiwWw2\no9SoIEbPi48v5LfCXCpK63jvp68xWxt4cM7dHP55H26PjXvumouoFyjOL8StbMLoCcRtFAkI9Mcp\nV6MPNNF/9CgUyckofT6yy4t4afnzrFl2B1EpcTz93DoCwkMw9gpHkhy0OXvIHDKaq9mXGBQXTp+B\nCeiSeuHrgVBdPKFBRiyePw7a+N9Oh1Pi6XWbGNovn/e2yVDqBTyihaRoI6/vrSd29gB+PpxLbnkd\nD8z+BWe/aN5avo3W/KXI5XJ+fm8S84aoKIi5Ff9Aibqis5wrEMEHXTY7jdZyjr1rAruK9dtqSUky\ncf5CPn7pIk/M6UtcLHTbqrl7sAGptQWLpRtR6vnn1lKGIMgZ3zsesc1BgHsIb6x5C4vPSYpvJUsx\nUXumkLMHRbxmWDg4C1Eu8MCSadDrZYZOnUOn2UlaphKtBX5Pc/7rMPfuefz040C27XuAT788xE/5\nW1H61CSlqNiydx3I3CA6yM8rxtIo4/udJxk/YAJij5233irkQnYT1Z2gVsLdjw0gp8iGTYBQk5PG\nboEQfwG3A3Z9nU3WCAmLxYPLq6SpQomtdBBff/Es1woFIiPiCNKbEJDRJz2LRRufRhMop496AC4H\nSEofTzx8Oz6XmexrTkrzmrD1yFG61dhUdnqccro6VXz7QzYmrQDqZqL8HBw+egWfyoNUV0/1ta0E\nhusZe08sRq2G1ETVHz6XP59e4lWw7PlnySuoIDe/GqvLgF9AGBfyyvjhzDlWr3mFRHUsnfV1hCcl\n0OW0YbXbmDlxJlcuH0Gr8GP6xMlMHLeAqLAk3nr9bVqsDdw6YiC9Y8OZPXIcuz/6hofunoPRGMDM\n4YMZFGhC1WLm212HqZYlkZ/fRaNPgTbEi9roxeOy09Rtxd/kT0NDLX4aNcMmjeBsXjaLHrgP//AA\nBk8dzYETh9n0zXlcLisGj5LHn3gOwaVF7JYhdjiJCQ0jI8aHTtmFzCejvDCPqutNqJJN2HraCVb9\ncXTa/3Y6zR4sOjfbTkJcjJKpgx4h2BjLw5+UUvPOGM6duIy5TUNbvci+t9M5VNhIcAp8ueMANYuW\ncmBvKwafig3j3qW8KZStJ5sRPAJyuRJBEFiy4GNOlugQBDsjgzWUdbViShlNS6eMrcWl+Hp0yDQ6\nOtzJHNo5haqTU1Cq/MEn4XFLeHxusmalcvuT17lQeoIP1y2l+UoCqf79CFrYjwOVTUjIue1vaRw6\ndJHMtG5Shz2Bw9FF6hAter2erkYPj7xV+peZQ/dvZMyEnFo7F2sL2fDsSs7mFNLc0IQpwE1tjRp/\nPxUvLXifo9/uQ232UZ5/jDtuvwcpzcb6N29BGxRMY7UcwWvk8uUCvG6Riiobc6ePob1d4sF7H6K1\n002PVcaJ4z1Ye1xILicagx/HT+/C2uHBp5A4kLuT61fbkStkfPL5ZdxaBWPH2WlW5jB0wEhcHht+\n8QLLvnyT2+bGIVN1o1SKZAz3kdY7krETQ1D5i6i9Kkrz8mi7GoQQFMB98zfRf7QMj6sVjVaHTNLw\n1us1vHjfahpd/81R6g67neeXPE13ZxseuZflT/SjvKuCw7m/YtQr+GHfESLSkzAGBzItcygGj0RH\naS1XLuYxLjmL47/s5cuPl+B2FKBQyejobOPYwZ2cOHiADbu/QhRFBmb0ZfDgwUwdNBS720O/voMY\nM2Yck8ZkMSlJw5lLlYQH+GGxtCCqPfgEDXWFl0DrJLx3MvsO/MSPl07R2lXCyxuXUVVWyoLptxMR\nHotZsOJSu6lvLaa1uRiztYqGyga6uwRS4wtwlhwi71o7BmMgnh4zmZMy8LpdBAT74dX+V+fb/u/B\nJSpwuNRoFXr8TSLKMcupVC1GJiipCaxCg5rf8nYwdIycmQ+8zoSR/Qk3xpE19HkeObqFr8/l0mHz\nMmTIQfYeb6He4kSUi0iShNInZ/j0ZXQUN5A2IJJcjYsJc/pz8WguAf4ynE6YcPd9BEe8wuWmK+Bx\nEBU6CZ/PiyDzoay8j0ApkOae6yj0akJf3M2FmkiKs/1595WVNGe8g1wnZ8t5N1NnBjNiYSIrFx2m\nMG8zP69Nx1NkZ8rCSh55vY6ert/vUcr464jdRy/sYfWMbaTFJfLGl6sxOzyEpgSQfTiSKbPDUNaJ\n/PTj2+w+c4VOGRSbz1FdZUfl9lHVuR97Uxdjx4rkXOrGaPAi9XhorVQTHGKi1e5h83f/IDoDREFk\n5DRIz4hi1NTeBEZ3sXP7KpxWC+lZci6dK6OsQklssI8ho+Ro1V4aGmH+ireYNjGKWvN51Cojcp2b\n4utlqEwine2gClCTm9NJVV0zXo/A/CmzefbFJ8i9VkTe+U4qr39M7RkdoiQDuZbDG19H7eeittyO\nU/xvbl2Te2dQWVFJZFgoC/92B1/ktoJSICIyGoukJDoihJN7f+LWabdxvfgqfkEmPv9wGzMn3EN5\nUTVLHn+cdms4p3/ej1PysWvXLhQGHa5+MTwz/V5sNgeaCH/sHoEruVeRIcdkCmXrV19x7NgxNAY/\njpy4xJCsoajCI6i6dpFOSyvHz57H1+2hvOYaQ6ePoau5lh67i9cevIOZoyZyrrKY3WcOE2IIp/xa\nIWW2yzzywmLUGgjVhZAYFc2hS0rK428nOigGSZCR2D+WiopiPF4lDtGF8gY+2vH3F3G7PMT2tTNh\nVDyrFwxm7dw8DiwN4r73fPjcSuTNv5JoEJGbZjDrVgPKajPtPz3N4h/rmfiIP30S9fTpb2VMpoBM\nUONDRBLAIxP56uVpSCHQ1tyDuUXFmsVXaWy2YwiA9S8cximW4+zcQXC4EblGhiZpKSADQUFFSzVP\n359ERX4dFdU72TA1GVO/xSRnxbPk3g/pvryY5hwHW99UYmrTEzazhNc23ctYp4bCSi9iD7QfmAty\nKxq/UGz5o9HqAv+DT+9GRqyV45F8pGtSGJLRn6ULs/jmyALGTVZw6oQFyU+iXVVHxMge3tyeyPm8\nIh5/aQJF1SIeQY4+XE5jO3j1EmYbtHuh+aSD8uuNzJsyiMheatQ6PUkDJfxUKgrLG3G2d6CQy6kr\naeeljz/k7C/Q0CSjKd/DZyufp/KqD7mkossKIeEqbn/0Bxav3c/pU1dobRCIizBy5ZzE6FE6PB4r\n6gAJP7eacRnDWL/hB3748iyITsJjJGbOX4QvzMFj7/yNibfci9PtxtUjZ9FHK+ms/ONhuX8qdEd+\nOcmYSbPoCvDyyCur0aj9aSyvo7K6Fr1SxdWCIlqFTgquXmPLD9sZNnIc1vbLFDYXUdNQyeXmXI4e\nu0SVsxtdk4u+U4ciCHJaWq/jVkokpCTwy/FjmKJNTBw/iXPnLxJmCmbWjFnMmDGL9AFZHDz0KRVl\nOYheCIuMoKq4hGG3TURy6zBGh2Ohk8cn3sXYmVO5uHM753NzEdwicRFhlDcVkjq4H2opiMTh/UGu\nw9zTzdA+YWSlDMbR5kCdFkFtaSUN1a1EJcah07kQXW4idTfuGZ3oAo/VzdoMBZLcSHy0g3f/vpc2\nn4+nxgn0GRLB3Ke+ZPdlGZKvhCERIzlZ1M3GcgcDBS0dVYOoKDPTZRaZf+8YJEQEQSA0KJAQUwQp\nmaOI0kv4JBG9zklmRhinvlhGdZWK59ZPZe2zx9HHvofDbsZqlxhgWUdrXS3u7m7O5dQwf66TluMu\nXh8ZQ05uOUvuVlF0uZSUOZHs372VPvd+hKPFx5VNlzn2kZYhdxcxaeNPHC2RmPv6Vv6x+xTFFz9D\nkNs4eLgFrfL3Iar/6Z3aGwy3EpQqiZFpKxg//CIffXiN9NQsokLlDAsaSEOZmpZ6JekJOo592oKl\nQcLqclJ6RsbJwyJOp49pM3uz8lUJpQLmjA3E4weTZ9zDz2dyuJgtUFUgEu0fRWupHENDGAqPPyFB\nAs9//imrFt/F+Bkiw0coiY5X8lPed4wdG8rS2c/T0KQAn5vAYBlZoyEmOZrGqzL8/IMx6L1M6DeE\nL9e/x7iUCbRdd/Hd+8cYOy4Il0tOyTUzqmAZxZfh4OZ/8MXzezi192PUahP7P/+WYzu/B/1/s6NT\nhcXxj69/Rt3mIDUmlJxfT2EQNJRcy2HGxPEoEgJZOH8Rm/d+xNVTeax68XmyzxVTfCqHyXdPxXGp\nlus95dQ1VhOYFIbL1o7F7CQ+IZ4vT+zkTF4uQoCP6F69KGusZczEcXz24wlMUeEcPH6JztLdvP3V\nN5QXN2JpbCUqLYnevfux9JF7WDR1AF63jWu/XiEh1sUtA0aybncRdV1lbPlgE7bOVjpa66nKq8Hm\n7cJVaSX71+tkTM2kd3oSQ4clMrGPkrSwCO6ccRchxiBEi4i1vhav242k1f+PF+G/Cna3ku8+e4tx\nC9ZRWdlJt8OGrcpH0iAjcgP0jQ0gRh+MQu6j7/h+TB23hlU7ymmzw9acNkbd9zIvrv2BZWvPER4c\nBvgQkLPpk2+RX7yLyZMVhBsErJ0y/Ez+ZA1X8PGhjTzz6oNo8VB+bQVb30jjyVHTMYYPpVp0cvSz\nETjtlTy7XmL21MUs/66M4QseIDBCzitLM9AHuFCG38GpYyo8Zc+z8e293PnJPJ56sDend4Tx2kuZ\n5OTVkjjlPpRKOVkTV1HXrGL6HV/R1t3yl+jmAHSxc5CptEhyD0bnRtKSfCQHPs3Wd6o4V3ecr1dv\nQulRsfcbN7pgQNfKu29eZMxoH1YrJCSFsGNHGW+ukdFYD78d6eZvC+LplRRBbYfAvLsCycrSk320\ngaN73LicIvYeFY0tMsov6Phg3zeEJYQgM7joM0Li+8MNfPNdK2s2byBS8/vuTafx0laiYMmiVRz4\n7CXGxg4k2dCHrR+e4LYxyzD3lBGbKUfSQ3F+Jz09IqYEL7ogN0Nngs5Pi0/mwSXKcHrdXL7yDI+/\nPp8Baeo/fC5/bi8x2QhStNPWbub1F9cybOZktMlRLJr3EPt3/Ehlfg2f/+NzVj/6Mo8++jSpqem8\nuXUHYyfN4tLVbJpcVuxdTvb9fIJjvx0hwRhBfWUNBdnFYLZT72rAI0JFYTF2tUSRuwtTrIz9hedI\nnJzFwTodLrn795wKnQa1aKCz1czGdzez/dhOIuVGwoMj2bhlD90tbQwdPZCCffkEBanR+RtJSY5C\nrwKjn4lbJ4xh2eLJKFraaHRaaGxrxtJdRG+hFo/oxiu3YwwzoDUFosKfysq6/+ka/JdBq/Yw/cmV\nxM56lThsJERJ9HgkFr30MF0lkDZsCFZTA9uXP8aESf14cNFtbFyVhM+ixGTQ8eG6x7FHzOHA9hGo\n+24En4CEB13to7zy+V58ggaVpKS7y4ZWbkHyNnDvwz8iKUwMG9WX42e+Z1imko/PHCQ1qZXz+VdJ\nT4/A41LQ2lSPI3UDjQ1vovNz0ysliUl/u4SlG84duZsnv/gJJ27e2Pw+D2/YzPECJacvBWC3G1g+\nM4WG6loSUgR6ip8jIdSEKXkMgmj4S5iFAeY8rUB0unl2zRS++uk8Ly/bzvatn+FUBXDpNy0xI430\ndNvpExlAYICBU4d8jH9UQBUr4/YH1bQ0dBASKsdi9WE0yEmIUvDz8QbqaquwlsqZk/kcqxas5u+v\nv4FKkLhW1EjD9Vb6Giay9MloTMGBTM0cxeH9StrqJeIVAj9tXkeAwsfGlQswqCWcPuibMRCFSsnl\n4myig/px+XQxPXZY++4LnC6sxivTYoqWEZUFMoWGa4WgUyk59tNlHA77P6eoyFEqJA7mNuGvkGht\n/+Nd2J8KXUGdk/mLHiMjvT9//2YrTfWlzJoyhzVrX2df8S+0dl4jrn8/fjl0mJbmJuISkxiSNYz+\nMXGIPhez73kQnejCU1VGYnI4ly/lMXhSf6yNHYg+H7V1xXhFF0cO/EJJdSn5+edxWdoxaf1xO9tR\nhxmoKKlCGayiva0OBxbmDkui29xFu8PEr+cvoepxUFhSh6/rLAd3ZzN4fBaZwwchyry4zXKSB2Ry\n4tMiCvLP4u2+zJTRvXA6fNglL83CcISQCcQbItB745AkLz65hNylQOvV/I8X4b8KguiPUvKh1ztY\ntORlSgr9KMtxM6PPJh59SsX2LX9HIfpYd2gHvXuP4q2v9xLqB2veW8CZeglbcxk75isov6xA8ihA\nJiAIcoo6w1CJWkYOH4rHLaOq0M4rTyymqSeZbTuWMSN9HhHRel7Z2IImtA9zVJMoyqnDJAbQO1JB\nSHwSbqeF6op6XnzuH0huOfffEk+CPhu0Cg4daCUmoheC2UfNpbOsfn4550630Vllprijg4qdS/BT\nq/APVtMsfxJBbaZH0CAJVuCPMyduJEQ8aLR+fPjBfoqqW5l963x2n8yhudXOrBGTeeixuwiIUVN5\nReTzT2p4ZekYKq96uJItUdcoIOGPTudh0jwNdz0wgA4PeBwC4cndnC5IJdBPi8whUVGWj+QScNoF\nbps3Bbfdg9g1mGXzVvL+V/soLPZQdl0kdUIEoSaR89USL2z+hm4nGALhVM5V3v9oC9fKe/jwnZ00\nW2Wc2vMWQTIZAUESQ0a6CE0IosWmwS/Gg6SQKC7yMHbCbAwhdiS3F4WgQOnWEmVI48Q5N+XX/zh0\n/k+FrrqlAG2QHofdg6PVit4h8OrytQyaOYReNjWaTj9Of78PtVpJcmoy/XonE632p6LgOiGGQE7s\n/JrBt97CkPsnkhyeSqfDTEeNHWcLGIIiyEwcxrDETGSaOMrP19JaV0l9XSs1pfU4RJGGqhLiUsL4\nZdv3JCYk0t1Qgc5Ti1pn5PHbxrPq6cWsWDGBNWtfIqx3FpHBRvTqYE4ezaHhQiMvP/YMVlc36z56\nmfK2aCq6lBzdV4bVKuNsYQXxcU4amlp4ZdVHeLRaHJ2tOH0emix1mD3u//Ei/Fehd1oYd9w3hoUP\nz+CBp15goNLKheINvPCOD6e5N2aPH8Fd0FTupLGphiljfBRUpCLoX2Xj0jn4vIHUOOXMXxIJ/D53\nzif4cDW3YzApcQTX0d7oZe+hx8gp+4X9+8u4kt3My++nQcdmkAAABmFJREFU8sXq8/SJsTI4YSx+\noxrJvWBFJjZTcr2TusZ6zh1exWt3xGBuFWhuEalubSV1+HTUJaCesIv4cD8UAWpmRiTzw9fruHKt\nlnd+rubcnmK+b9nN+8tjMV61EWf0wyfJUXh7EPQmZAjEXJvMje6q+/iJz7E47Rw+9Buff3GGH386\nRI9XJDJCw8I7byd+cDSJvZ0Ifmb6pWl559NTjB0VS0+nRFe3k2mzxpKcaqIkz0ltSw7DJuoZOU3B\n0eINKKRUXIILFQoyxw1hwjwtY6f1ojQ3m8NnjtLVaeG7vUeor1XSeyAkD9cyLWYWZnkbUyYLFOc2\n01IDlhJ4aOZcBqXew9tvnsNihclzdXRbvNgtDtzdMq6f8pCQ3kFTkwvJKNAnSU1CmhK/1GYO5ryI\n7J+3abyCl0+WrcRqlRMe98dy9qdCN3HCHDpr6sHoj9dn5tKlk1jljbz5/CcEhKYRFBzP7HG38OmH\nn7Hlky1s374TlVpNWXM3XtEPdWQwkUYN5k4ZtVWNhBoiGTV4As+88jR6l5LCwkLun3cLLzx5Oyte\nX06gLxa1SY3MZUbmg4bqMrq7GsicNICigsuYnbUcKO/AZ+4gL/s3zB4lxtaTuB0+mut0fLh2Livv\nH807L67k26/eQ6GwoVH5cF35lHitg/JzPUQmRTO8Vx0CcrYfKkKlVjDqlkFY2zqReYNQi1okB0Qb\nbtybEdfLmzi6/wSXio7TWOvFEiyjsSmXTz5WUdSQT215N/GpQTRWutm/9xBDpi8jJLQat0digKEQ\nld6Cxl/D6V1tyBAQ5CD3yUhIUSFXmMn+rhNZqIzdew+x7bsCDu55nXsfiWFq0Bw2Lh+KMj6Qb499\nxrEiB88vG8stE0dxqc7GR6vGM67XHj450cNL08bQWuJmz1fX+a3AjFgAJdmL6KjahOT2cffjc2hR\nB7P8sQUMTIjBGKlnbIYfJz79gP5zJyMPiOGW+Cq2vZaMQetFkINQVXnDG02CZGp8HgUj+w1g8lRQ\nCBMYNSqEoVk+kvqIVBa1IFSNY+AsD1afQL/JOkoLm3hhtUBrbSj5JftRydrJ6APN7eB1isSn2rHJ\nZSxb/j0z5q9E5+fH29+8SWGxl0pLJf0G3UVBERS1nuSTncewmp1Ex2gJMTr5+sJ+lrz2CW0tRpRy\nsDvlWKo0bF6/ncAIFXo/I08/MZmWFit3P/8Sk554j/omD78dUvDqokdZvcKAQealtlnBiJEqEiOV\nzOz7CZJg+f04Qq5DFAOo/PU0XS1/Erb0Z+38grvvlYaPHEZTVTmy1H4UXt1LjCKM8qIahk2+BX9/\nf5orCknPSOHEyTOkpPSmo72dlLQUdp3cR2ygifT0fpw4c5y4xCHEhwdz5UouFy78hstlo9+kfqy6\nfQiSbiT+QaGcOF9Iq7mT2o5Suq025DotWqsVi9SGWq1kfGIGZ+su0+OxEmmX8eAjb0D5Pmo9sXT5\n/BmQ2As/+xVchkz0gSbKDr/BwXYNs0ffiqunEJ27g/Pl7cQEgUsdgseUiaKpi+vtHUQoLDTr/HHY\nymiusJDQL4MNi9+8IQ92vt6+TfKJCgRJwONzoFSI+CQVSL9/JopeFJIbrxrkbhmSJKFWq+nsaMbP\nGPx7DoPMh9UBMncX0j8n9+o0wch1Ai09IpEKFR6NSHObmV5hgTS2eND4B+JzdxKoD8TmtOHFh88j\noPdTIZe78YoCnWYwhvhQiwZEmR273Y7k9UdydGIIDcDSI6A2iKi8anxaJ6ILBLkaj01CLvnwKH0I\nXgmlRkCjUtPRUo1cpgbBi84YDD41D9x/3w25rgDO2p3SFxfmU1GqZMFdj7HolU3YbBJhGn92vfss\nS99fTd5hSB8fQEKMgs++svHAU05CwhT0TvNnyz8Epo6ycDbHg92pwE/tZXBSMt2yGo5876a9HmZP\nCeKHk2Y8DongeBktZV78g7TMuTWGsLBAPv4sl+gUDz4JTn3xNY8sX8ZHS96nraubR55cQnmFQMxo\nH6LeQ2xwCF1lDq6UQnScA5lgQPR1cf0seNUwYqw/8iYLVd0BROrtRITBx+v/js/agyhX4KcPxC/R\nwx2PLyK30EXlefG/nhlxk5vc5CY3Aje+segmN7nJX56bQneTm9zkhuem0N3kJje54bkpdDe5yU1u\neG4K3U1ucpMbnptCd5Ob3OSG5/8AMRV8EezQ5IwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVfxaAa5QGwe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def get_random_word_vectors_from_dataset(n_samples):\n",
        "  ix = np.random.randint(0, len(captions), n_samples)\n",
        "  return np.asarray(captions)[ix]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8wykrY8qtfA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def generate_random_vectors(n_samples):  \n",
        "  vectorized_random_captions = []\n",
        "\n",
        "  for n in range(n_samples):\n",
        "    rnd = randint(8, 25)\n",
        "    result_array = np.empty((0, 300))\n",
        "    for i in range(rnd):\n",
        "      result_array = np.append(result_array, [model[choice(model.index2entity)]], axis=0)\n",
        "    vectorized_random_captions.append(np.mean(result_array, axis=0).astype('float32'))\n",
        "\n",
        "  return np.array(vectorized_random_captions)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3g-rNcqr4lX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import array_to_img\n",
        "\n",
        "from numpy import expand_dims\n",
        "from numpy import zeros\n",
        "from numpy import ones\n",
        "from numpy import vstack\n",
        "from numpy import asarray\n",
        "import numpy as np\n",
        "\n",
        "import os\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.initializers import RandomNormal\n",
        "from numpy.random import random\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "from numpy.random import randn\n",
        "from numpy.random import randint\n",
        "import time\n",
        "from keras.layers.advanced_activations import PReLU\n",
        "from keras.utils import plot_model\n",
        "\n",
        "# Discriminator model\n",
        "def define_discriminator():\n",
        "    word_vector_dim = 300\n",
        "    dropout_prob = 0.4\n",
        "\n",
        "    in_label = layers.Input(shape=(300,))\n",
        "\n",
        "    n_nodes = 3 * 64 * 64\n",
        "    li = layers.Dense(n_nodes)(in_label)\n",
        "    li = layers.Reshape((64, 64, 3))(li)\n",
        "\n",
        "    dis_input = layers.Input(shape=(64, 64, 3))\n",
        "\n",
        "    merge = layers.Concatenate()([dis_input, li])\n",
        "\n",
        "    discriminator = layers.Conv2D(filters=64, kernel_size=(3, 3), padding=\"same\")(merge)\n",
        "    discriminator = layers.LeakyReLU(0.2)(discriminator)\n",
        "    discriminator = layers.GaussianNoise(0.2)(discriminator)\n",
        "\n",
        "    discriminator = layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(2, 2), padding=\"same\")(discriminator)\n",
        "    discriminator = layers.BatchNormalization(momentum=0.5)(discriminator)\n",
        "    discriminator = layers.LeakyReLU()(discriminator)\n",
        "\n",
        "    discriminator = layers.Conv2D(filters=128, kernel_size=(3, 3), padding=\"same\")(discriminator)\n",
        "    discriminator = layers.BatchNormalization(momentum=0.5)(discriminator)\n",
        "    discriminator = layers.LeakyReLU(0.2)(discriminator)\n",
        "\n",
        "    discriminator = layers.Conv2D(filters=128, kernel_size=(3, 3), strides=(2, 2), padding=\"same\")(discriminator)\n",
        "    discriminator = layers.BatchNormalization(momentum=0.5)(discriminator)\n",
        "    discriminator = layers.LeakyReLU(0.2)(discriminator)\n",
        "\n",
        "    discriminator = layers.Conv2D(filters=256, kernel_size=(3, 3), padding=\"same\")(discriminator)\n",
        "    discriminator = layers.BatchNormalization(momentum=0.5)(discriminator)\n",
        "    discriminator = layers.LeakyReLU(0.2)(discriminator)\n",
        "\n",
        "    discriminator = layers.Conv2D(filters=256, kernel_size=(3, 3), strides=(2, 2), padding=\"same\")(discriminator)\n",
        "    discriminator = layers.BatchNormalization(momentum=0.5)(discriminator)\n",
        "    discriminator = layers.LeakyReLU(0.2)(discriminator)\n",
        "\n",
        "    discriminator = layers.Conv2D(filters=512, kernel_size=(3, 3), padding=\"same\")(discriminator)\n",
        "    discriminator = layers.BatchNormalization(momentum=0.5)(discriminator)\n",
        "    discriminator = layers.LeakyReLU(0.2)(discriminator)\n",
        "\n",
        "    discriminator = layers.Flatten()(discriminator)\n",
        "\n",
        "    discriminator = layers.Dense(1024)(discriminator)\n",
        "\n",
        "    discriminator = layers.LeakyReLU(0.2)(discriminator)\n",
        "\n",
        "    discriminator = layers.Dense(1)(discriminator)\n",
        "\n",
        "    discriminator_model = Model(inputs=[dis_input, in_label], outputs=discriminator)\n",
        "\n",
        "    discriminator_model.summary()\n",
        "\n",
        "    return discriminator_model\n",
        "\n",
        "\n",
        "def resnet_block(model, kernel_size, filters, strides):\n",
        "    gen = model\n",
        "    model = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding=\"same\")(model)\n",
        "    model = layers.BatchNormalization(momentum=0.5)(model)\n",
        "    model = tf.keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1, 2])(model)\n",
        "    model = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding=\"same\")(model)\n",
        "    model = layers.BatchNormalization(momentum=0.5)(model)\n",
        "    model = layers.Add()([gen, model])\n",
        "    return model\n",
        "\n",
        "\n",
        "# Generator model\n",
        "def define_generator():\n",
        "    kernel_init = tf.random_normal_initializer(stddev=0.02)\n",
        "    batch_init = tf.random_normal_initializer(1., 0.02)\n",
        "\n",
        "    random_input = layers.Input(shape=(100,))\n",
        "    text_input1 = layers.Input(shape=(300,))\n",
        "    text_layer1 = layers.Dense(8192)(text_input1)\n",
        "    text_layer1 = layers.Reshape((8, 8, 128))(text_layer1)\n",
        "\n",
        "    n_nodes = 128 * 8 * 8\n",
        "    gen_input_dense = layers.Dense(n_nodes)(random_input)\n",
        "    generator = layers.Reshape((8, 8, 128))(gen_input_dense)\n",
        "\n",
        "    merge = layers.Concatenate()([generator, text_layer1])\n",
        "\n",
        "    model = layers.Conv2D(filters=64, kernel_size=9, strides=1, padding=\"same\")(merge)\n",
        "    model = tf.keras.layers.PReLU(alpha_initializer='zeros', alpha_regularizer=None, alpha_constraint=None, shared_axes=[1, 2])(model)\n",
        "\n",
        "    gen_model = model\n",
        "\n",
        "    for _ in range(4):\n",
        "      model = resnet_block(model, 3, 64, 1)\n",
        "\n",
        "    model = layers.Conv2D(filters=64, kernel_size=3, strides=1, padding=\"same\")(model)\n",
        "    model = layers.BatchNormalization(momentum=0.5)(model)\n",
        "    model = layers.Add()([gen_model, model])\n",
        "\n",
        "    model = layers.Conv2DTranspose(filters=512, kernel_size=(3, 3), strides=(2, 2), padding=\"same\", kernel_initializer=kernel_init)(model)\n",
        "    model = layers.LeakyReLU(0.2)(model)\n",
        "\n",
        "    model = layers.Conv2DTranspose(filters=256, kernel_size=(3, 3), strides=(2, 2), padding=\"same\", kernel_initializer=kernel_init)(model)\n",
        "    model = layers.LeakyReLU(0.2)(model)\n",
        "\n",
        "    model = layers.Conv2DTranspose(filters=128, kernel_size=(3, 3), strides=(2, 2), padding=\"same\", kernel_initializer=kernel_init)(model)\n",
        "    model = layers.LeakyReLU(0.2)(model)\n",
        "\n",
        "    model = layers.Conv2DTranspose(filters=64, kernel_size=(3, 3), strides=(1, 1), padding=\"same\", kernel_initializer=kernel_init)(model)\n",
        "    model = layers.LeakyReLU(0.2)(model)\n",
        "\n",
        "    model = layers.Conv2D(3, (3, 3), padding='same', activation='tanh')(model)\n",
        "\n",
        "    generator_model = Model(inputs=[random_input, text_input1], outputs=model)\n",
        "\n",
        "    generator_model.summary()\n",
        "\n",
        "    return generator_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZW0v8CJHzEYv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import clear_output\n",
        "\n",
        "\n",
        "def generate_latent_points(latent_dim, n_samples):\n",
        "    x_input  = tf.random.normal([n_samples, latent_dim])\n",
        "    text_captions = get_random_word_vectors_from_dataset(n_samples)\n",
        "    return [x_input, text_captions]\n",
        "\n",
        "# Randomly flip some labels. Credits to https://machinelearningmastery.com/how-to-code-generative-adversarial-network-hacks/\n",
        "def noisy_labels(y, p_flip):\n",
        "    n_select = int(p_flip * int(y.shape[0]))\n",
        "    flip_ix = np.random.choice([i for i in range(int(y.shape[0]))], size=n_select)\n",
        "\n",
        "    op_list = []\n",
        "    for i in range(int(y.shape[0])):\n",
        "        if i in flip_ix:\n",
        "            op_list.append(tf.subtract(1.0, y[i]))\n",
        "        else:\n",
        "            op_list.append(y[i])\n",
        "\n",
        "    outputs = tf.stack(op_list)\n",
        "    return outputs\n",
        "\n",
        "def load_data():\n",
        "    return asarray(image_embeddings), asarray(captions).astype('float32')\n",
        "\n",
        "def smooth_positive_labels(y):\n",
        "\treturn y - 0.3 + (np.random.random(y.shape) * 0.5)\n",
        " \n",
        "def smooth_negative_labels(y):\n",
        "\treturn y + np.random.random(y.shape) * 0.3\n",
        "\n",
        "def generate_and_save_images(model, epoch, test_input):\n",
        "  predictions = model(test_input, training=False)\n",
        "\n",
        "  print(predictions.shape)\n",
        "  pyplot.figure(figsize=[7, 7])\n",
        "\n",
        "  for i in range(predictions.shape[0]):\n",
        "      pyplot.subplot(5, 5, i+1)\n",
        "      pyplot.imshow(array_to_img(predictions.numpy()[i]))\n",
        "      pyplot.axis('off')\n",
        "\n",
        "  pyplot.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
        "  pyplot.show()\n",
        "\n",
        "\n",
        "def discriminator_loss(r_real_output_real_text, f_fake_output_real_text_1, f_real_output_fake_text):\n",
        "   alpha = 0.5\n",
        "   real_output_noise = smooth_positive_labels(noisy_labels(tf.ones_like(r_real_output_real_text), 0.10))\n",
        "   fake_output_real_text_noise_1 = smooth_negative_labels(tf.zeros_like(f_fake_output_real_text_1))\n",
        "   real_output_fake_text_noise = smooth_negative_labels(tf.zeros_like(f_real_output_fake_text))\n",
        "\n",
        "   real_loss = tf.reduce_mean(binary_cross_entropy(real_output_noise, r_real_output_real_text))\n",
        "   fake_loss_ms_1 = tf.reduce_mean(binary_cross_entropy(fake_output_real_text_noise_1, f_fake_output_real_text_1))\n",
        "   fake_loss_2 = tf.reduce_mean(binary_cross_entropy(real_output_fake_text_noise, f_real_output_fake_text))\n",
        "\n",
        "   total_loss = real_loss + alpha * fake_loss_2 + (1-alpha) * fake_loss_ms_1 \n",
        "   return total_loss\n",
        "\n",
        "def generator_loss(f_fake_output_real_text):\n",
        "   return tf.reduce_mean(binary_cross_entropy(tf.ones_like(f_fake_output_real_text), f_fake_output_real_text))\n",
        "\n",
        "@tf.function\n",
        "def train_step(images, epoch):\n",
        "\n",
        "  #define half_batch\n",
        "  latent_dim = 100\n",
        "  n_batch = 64\n",
        "\n",
        "  noise_1 = tf.random.normal([32, latent_dim])\n",
        "  noise_2 = tf.random.normal([32, latent_dim])\n",
        "  real_captions = images[1]\n",
        "  real_images = images[0]\n",
        "\n",
        "  random_captions = generate_random_vectors(n_batch)\n",
        "  random_captions_1, random_captions_2  = tf.split(random_captions, 2, 0)\n",
        "  real_captions_1, real_captions_2  = tf.split(real_captions, 2 ,0)\n",
        "  real_images_1, real_images_2 = tf.split(real_images, 2, 0)\n",
        "\n",
        "  with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
        "    \n",
        "    noise = tf.concat([noise_1, noise_2], 0)\n",
        "\n",
        "    generated_images = generator([noise, real_captions], training=True)\n",
        "\n",
        "    fake_1, fake_2 = tf.split(generated_images, 2, 0)\n",
        "\n",
        "    f_fake_output_real_text_1 = discriminator([fake_1, real_captions_1], training=True)\n",
        "    f_fake_output_real_text_2 = discriminator([fake_2, real_captions_2], training=True)\n",
        "\n",
        "    r_real_output_real_text_1 = discriminator([real_images_1, real_captions_1], training=True)\n",
        "    r_real_output_real_text_2 = discriminator([real_images_2, real_captions_2], training=True)\n",
        "\n",
        "    f_real_output_fake_text_1 = discriminator([real_images_1, random_captions_1], training=True)\n",
        "    f_real_output_fake_text_2 = discriminator([real_images_2, random_captions_2], training=True)\n",
        "\n",
        "    #### Calculating losses ####\n",
        "\n",
        "    gen_loss = generator_loss(f_fake_output_real_text_1) + generator_loss(f_fake_output_real_text_2) \n",
        "    # mode seeking loss\n",
        "    lz = tf.math.reduce_mean(tf.math.abs(fake_2-fake_1)) / tf.math.reduce_mean(tf.math.abs(noise_2-noise_1))\n",
        "    eps = 1 * 1e-5\n",
        "    loss_lz = 1 / (eps+lz) * ms_loss_weight\n",
        "    total_gen_loss = gen_loss + loss_lz\n",
        "\n",
        "    tf.print('G_loss', [total_gen_loss])\n",
        "\n",
        "    disc_loss_1 = discriminator_loss(r_real_output_real_text_1, f_fake_output_real_text_1, f_real_output_fake_text_1)\n",
        "    disc_loss_2 = discriminator_loss(r_real_output_real_text_2, f_fake_output_real_text_2, f_real_output_fake_text_2)\n",
        "    \n",
        "    total_disc_loss = disc_loss_1 + disc_loss_2\n",
        "\n",
        "    tf.print('D_loss', [total_disc_loss])\n",
        "\n",
        "    #### Done calculating losses ####\n",
        "\n",
        "  gradients_of_discriminator = disc_tape.gradient(total_disc_loss, discriminator.trainable_variables)  \n",
        "\n",
        "  gradients_of_generator = gen_tape.gradient(total_gen_loss, generator.trainable_variables)    \n",
        "\n",
        "  generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
        "\n",
        "  discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
        "\n",
        "\n",
        "\n",
        "def train(dataset, epochs = 2000):\n",
        "\n",
        "  checkpoint_dir = '/content/drive/My Drive/checkpoints_2'\n",
        "  checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
        "  checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
        "                                discriminator_optimizer=discriminator_optimizer,\n",
        "                                  generator=generator,\n",
        "                                  discriminator=discriminator)\n",
        "  \n",
        "  ckpt_manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=3)\n",
        "  if ckpt_manager.latest_checkpoint:\n",
        "    checkpoint.restore(ckpt_manager.latest_checkpoint)  #ckpt_manager.checkpoints[3]\n",
        "    print ('Latest checkpoint restored!!')\n",
        "\n",
        "  for epoch in range(epochs):\n",
        "    start = time.time()\n",
        "\n",
        "    for image_batch in dataset:\n",
        "      train_step(image_batch, epoch) \n",
        "\n",
        "    if (epoch +1) % 10 == 0:\n",
        "      [z_input, labels_input] = generate_latent_points(100, 25)\n",
        "      generate_and_save_images(generator,\n",
        "                            epoch + 1,\n",
        "                             [z_input, labels_input])\n",
        "\n",
        "    if (epoch + 1) % 40 == 0:\n",
        "      ckpt_save_path = ckpt_manager.save()\n",
        "      print ('Saving checkpoint for epoch {} at {}'.format(epoch+1,ckpt_save_path))\n",
        "\n",
        "    if (epoch +1) % 60 == 0:\n",
        "    \n",
        "      clear_output(wait=True)\n",
        "      generator.save('/content/drive/My Drive/46stage_new_gan_animal_model_%03d.h5' % (epoch + 1))     \n",
        "\n",
        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
        "\n",
        "ms_loss_weight = 1.0\n",
        "\n",
        "binary_cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "\n",
        "generator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.000035, beta_1 = 0.5)\n",
        "discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.000035, beta_1 = 0.5)\n",
        "\n",
        "discriminator = define_discriminator()\n",
        "generator = define_generator()\n",
        "\n",
        "images, lbs = load_data()\n",
        "BUFFER_SIZE = images.shape[0]\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((images,lbs)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "\n",
        "train(train_dataset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGsK4VaCwyGF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#\n",
        "from keras.preprocessing.image import array_to_img\n",
        "\n",
        "from numpy import expand_dims\n",
        "from numpy import zeros\n",
        "from numpy import ones\n",
        "from numpy import vstack\n",
        "from numpy import asarray\n",
        "import numpy as np\n",
        "\n",
        "import os\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.initializers import RandomNormal\n",
        "from numpy.random import random\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "from numpy.random import randn\n",
        "from numpy.random import randint\n",
        "import time\n",
        "import matplotlib.pyplot as pyplot\n",
        "from keras.preprocessing.image import array_to_img\n",
        "from numpy import linspace\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "from numpy.random import randint\n",
        "import numpy as np\n",
        "\n",
        "def create_sent_vector(sent):\n",
        "  result_array = np.empty((0, 300))\n",
        "\n",
        "  for word in word_tokenize(sent):\n",
        "    result_array = np.append(result_array, [model[word]], axis=0)\n",
        "  final = np.mean(result_array, axis=0).astype('float32')\n",
        "  return final\n",
        "\n",
        "def generate_random_word_vectors_from_dataset(n_samples, create_new_captions = False):\n",
        "\n",
        "  if create_new_captions:\n",
        "      e = create_sent_vector('This bird has  white breast with brown feathers')\n",
        "      f = create_sent_vector('This bird has  white breast with blue feathers')\n",
        "      v = []\n",
        "      flag = False\n",
        "      for i in range(n_samples):\n",
        "        \n",
        "        if not flag:\n",
        "          v.append(f)\n",
        "          flag = True\n",
        "        elif flag:\n",
        "          v.append(e)\n",
        "          flag = False\n",
        "      return np.asarray(v), np.asarray([])\n",
        "  else:\n",
        "    ix = randint(0,len(captions), n_samples)\n",
        "    print(ix)\n",
        "    return  np.asarray(captions)[ix], np.asarray(labels)[ix]\n",
        "\n",
        "def generate_images(model, test_input):\n",
        "\n",
        "  predictions = model(test_input, training=False)\n",
        "\n",
        "  print(predictions.shape)\n",
        "  pyplot.figure(figsize=[15, 15])\n",
        "  for i in range(predictions.shape[0]):\n",
        "      pyplot.subplot(1, 9, i+1)\n",
        "      pyplot.imshow(array_to_img(predictions.numpy()[i]))\n",
        "      pyplot.axis('off')\n",
        "\n",
        "  pyplot.show()\n",
        "\n",
        "# Credit to: https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/\n",
        "def interpolate_points(p1, p2, n_steps=9):\n",
        "\tratios = linspace(0, 1, num=n_steps)\n",
        "\tvectors = list()\n",
        "\tfor ratio in ratios:\n",
        "\t\tv = (1.0 - ratio) * p1 + ratio * p2\n",
        "\t\tvectors.append(v)\n",
        "\treturn np.asarray(vectors)\n",
        " \n",
        "def generate_latent_points(latent_dim, n_samples, interpolate = False):\n",
        "    x_input  = tf.random.normal([n_samples, latent_dim])\n",
        "    text_captions, labels = generate_random_word_vectors_from_dataset(n_samples, create_new_captions=False)\n",
        "    if interpolate:\n",
        "      text_captions = interpolate_points(text_captions[0], text_captions[1])\n",
        "      x_input = interpolate_points(x_input[0], x_input[1])\n",
        "    for index, s in enumerate(labels.flat):\n",
        "      print(index, s)\n",
        "\n",
        "    return [x_input, text_captions]\n",
        "\n",
        "gen_model = tf.keras.models.load_model('/content/drive/My Drive/146stage_new_gan_animal_model_120.h5')\n",
        "\n",
        "for i in range(40):\n",
        "  generate_images(gen_model, generate_latent_points(100, 9))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4-KsJKyePZe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_index_by_label(label):\n",
        "  for idx, l in enumerate(labels):\n",
        "    if l in label:\n",
        "      return idx\n",
        "\n",
        "\n",
        "print(get_index_by_label('Western_Wood_Pewee_0061_795060.jpg'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vckssmF19_o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Show image in image embeddings\n",
        "\n",
        "pyplot.figure(figsize=[8, 8])\n",
        "\n",
        "pyplot.subplot(1, 2, 0+1)\n",
        "pyplot.imshow(array_to_img(image_embeddings[5432]))\n",
        "pyplot.axis('off')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}