XORtexte.ipynb

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    "id": "-5J6QzrNdB-m"
   },
   "source": [
    "## I.CODAGE/DECODAGE D'UN SEUL MOT\n",
    "On veut coder le texte avec la clé "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7b/74/54c5f9bb9bd4dae27a61ec1b39076a39d359b3fb7ba15da79ef23858a9d8/numpy-1.16.0-cp36-cp36m-manylinux1_x86_64.whl (17.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 17.3MB 869kB/s eta 0:00:01   15% |█████▏                          | 2.8MB 38.7MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: numpy\n",
      "Successfully installed numpy-1.16.0\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1nCbkqgcW6-u"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "texte=\"NEWYORK\"\n",
    "cle=\"CARACAS\"\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xq3c7U9ObAXz"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1208,
     "status": "ok",
     "timestamp": 1548343501935,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "-ABiekolbBUG",
    "outputId": "15f6215b-d5b5-4946-d968-45f7031acb48"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[78, 69, 87, 89, 79, 82, 75]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=[]\n",
    "for l in texte:\n",
    "    t.append(ord(l))\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Us53jwS4bBqL"
   },
   "source": [
    "à quoi correspondent ces nombres?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 35
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1189,
     "status": "ok",
     "timestamp": 1548343501940,
     "user": {
      "displayName": "mathieu chretien",
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    },
    "id": "RpCupLSobB2E",
    "outputId": "fbe7219d-9b50-4f37-d66b-48a1f5941205"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[67, 65, 82, 65, 67, 65, 83]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=[]\n",
    "for l in cle:\n",
    "    c.append(ord(l))\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G1mRwAAbbCI0"
   },
   "source": [
    "et ceux ci?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1179,
     "status": "ok",
     "timestamp": 1548343501944,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
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     "user_tz": -60
    },
    "id": "t_FLJqt_bk0B",
    "outputId": "8865e5b0-47c2-4116-97ef-8dece19d89e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 4, 5, 24, 12, 19, 24]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\r\\x04\\x05\\x18\\x0c\\x13\\x18'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=[]\n",
    "for i in range(len(c)):\n",
    "    m.append(np.bitwise_xor(t[i], c[i]))\n",
    "print(m)\n",
    "messagecode=''\n",
    "for nbre in m:\n",
    "  messagecode=messagecode+chr(nbre)\n",
    "messagecode \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u2wa0X1xc_ON"
   },
   "source": [
    "que représentent les variables m et messagecode ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1180,
     "status": "ok",
     "timestamp": 1548343501957,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "De_DZu7SbvkM",
    "outputId": "da8da5cb-e846-4e9b-eade-607d692f9509"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "message decode NEWYORK\n"
     ]
    }
   ],
   "source": [
    "d=[]\n",
    "D=\"\"\n",
    "for i in range(len(c)):\n",
    "    d.append(np.bitwise_xor(m[i], c[i]))\n",
    "    D=D+chr(d[i])\n",
    "\n",
    "print(\"message decode\",D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2Y7ocIL_d6JP"
   },
   "source": [
    "# II.Codage/Decodage d'un texte\n",
    "On va créer une fonction qui va faire en plus condensé ce qui précède"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 824,
     "status": "ok",
     "timestamp": 1548344554511,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "y78-lyEYeNjb",
    "outputId": "47995a0a-cece-4798-90f5-5b08542df8d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\r\\x04\\x05\\x18\\x0c\\x13\\x18'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def codage(texte,cle):\n",
    "  t=[]\n",
    "  for l in texte:\n",
    "    t.append(ord(l))\n",
    "  c=[]\n",
    "  for l in cle:\n",
    "    c.append(ord(l))\n",
    "  m=[]\n",
    "  for i in range(len(c)):\n",
    "    m.append(np.bitwise_xor(t[i], c[i]))\n",
    "  messagecode=''\n",
    "  for nbre in m:\n",
    "    messagecode=messagecode+chr(nbre) \n",
    "  return(messagecode)\n",
    "\n",
    "\n",
    "codage('NEWYORK','CARACAS')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 731,
     "status": "ok",
     "timestamp": 1548344540211,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "Lz_eAuNEfPI2",
    "outputId": "98693d02-e50e-4e6c-cc7a-f9fc0d839c69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NEWYORK'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def DEcodage(messagecode,cle):\n",
    "  t=[]\n",
    "  for l in messagecode:\n",
    "    t.append(ord(l))\n",
    "  \n",
    "  c=[]\n",
    "  for l in cle:\n",
    "    c.append(ord(l))\n",
    "  m=[]\n",
    "  for i in range(len(c)):\n",
    "    m.append(np.bitwise_xor(t[i], c[i]))\n",
    "  messagecode=''\n",
    "  for nbre in m:\n",
    "    messagecode=messagecode+chr(nbre) \n",
    "  return(messagecode)\n",
    "\n",
    "\n",
    "DEcodage(codage('NEWYORK','CARACAS'),'CARACAS')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-OIiXPFhynw"
   },
   "source": [
    "ça marche, modifions un peu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 792,
     "status": "ok",
     "timestamp": 1548344635116,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "2OHnwyshh6l7",
    "outputId": "07180111-09ed-45b5-f8eb-d96597285be3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NEWYOR'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codage('NEWYORK','CARACA')\n",
    "DEcodage(codage('NEWYORK','CARACA'),'CARACA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cma0lr5ZiF0P"
   },
   "source": [
    "quel est le problème?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 634,
     "status": "ok",
     "timestamp": 1548344791722,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "fJEJUCG9iIHc",
    "outputId": "d798ec1f-748e-4b6d-960f-325ad3489bd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NEWYORK IS MAGIC'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cle=2*'CARACASS'\n",
    "texte='NEWYORK IS MAGIC'\n",
    "codage(texte,cle)\n",
    "DEcodage(codage(texte,cle),cle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HMi24ehsirdH"
   },
   "source": [
    "qu'a-t-on fait?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xl1igT_qix_6"
   },
   "source": [
    "On veut coder le texte suivant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1081,
     "status": "ok",
     "timestamp": 1548345122142,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "VlCQk8mCi8YD",
    "outputId": "ba0df2ef-8aa7-448f-a57e-071555e1c6ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "790"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texte='Une demande de la physique amene parfois la naissance de theories caillouteuses  le praticien est presse le mathematicien fignole ensuite son travail et la route raboteuse devient une belle autostrade sur laquelle le praticien roule sans difficultes  Le physicien taille son chemin a la serpe  a la pelle  a la pioche  Les mathematiciens trouvent le sentier interessant  lamenagent  Si le physicien a besoin que son chemin soit plus large ou aille plus loin  le mathematicien arrive avec son bulldozer  Puis il perfectionne  et ainsi de suite  Parfois même il trace une route qui traverse une region ou le physicien ne sent pas le besoin daller ; puis vient un temps ou le physicien eprouve le besoin de voir un peu de ce côte-la et il se rejouit de trouver la route prête   Raymond Queneau'\n",
    "len(texte)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2HyBay18kEV5"
   },
   "source": [
    "faites les modifications nécessaires sur la cle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 835,
     "status": "ok",
     "timestamp": 1548345242027,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "RhTtBhiIkPYu",
    "outputId": "3f9cde74-ce2d-479d-c6a6-7b5131ef13eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Une demande de l'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cle=2*'CARACASS' #a modifier\n",
    "codage(texte,cle)\n",
    "DEcodage(codage(texte,cle),cle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5NhfemJtkOB1"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B6bCiVRhkmwp"
   },
   "source": [
    "# III.IMAGE XOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /srv/conda/lib/python3.6/site-packages (4.0.0.21)\n",
      "Requirement already satisfied: numpy>=1.11.3 in /srv/conda/lib/python3.6/site-packages (from opencv-python) (1.16.0)\n",
      "Collecting matplotlib\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/07/16d781df15be30df4acfd536c479268f1208b2dfbc91e9ca5d92c9caf673/matplotlib-3.0.2-cp36-cp36m-manylinux1_x86_64.whl (12.9MB)\n",
      "\u001b[K    100% |████████████████████████████████| 12.9MB 1.0MB/s eta 0:00:01   54% |█████████████████▌              | 7.0MB 50.9MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 (from matplotlib)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/0a/001be530836743d8be6c2d85069f46fecf84ac6c18c7f5fb8125ee11d854/pyparsing-2.3.1-py2.py3-none-any.whl (61kB)\n",
      "\u001b[K    100% |████████████████████████████████| 71kB 24.3MB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: python-dateutil>=2.1 in /srv/conda/lib/python3.6/site-packages (from matplotlib) (2.7.5)\n",
      "Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/a7/88719d132b18300b4369fbffa741841cfd36d1e637e1990f27929945b538/kiwisolver-1.0.1-cp36-cp36m-manylinux1_x86_64.whl (949kB)\n",
      "\u001b[K    100% |████████████████████████████████| 952kB 11.8MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting cycler>=0.10 (from matplotlib)\n",
      "  Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: numpy>=1.10.0 in /srv/conda/lib/python3.6/site-packages (from matplotlib) (1.16.0)\n",
      "Requirement already satisfied: six>=1.5 in /srv/conda/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /srv/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.6.3)\n",
      "Installing collected packages: pyparsing, kiwisolver, cycler, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.0.1 matplotlib-3.0.2 pyparsing-2.3.1\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python \n",
    "!pip install matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 993
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1012,
     "status": "error",
     "timestamp": 1548362932664,
     "user": {
      "displayName": "mathieu chretien",
      "photoUrl": "",
      "userId": "14410747210325459459"
     },
     "user_tz": -60
    },
    "id": "GusB2T-0ksAo",
    "outputId": "d3c3c38e-2714-4014-9f92-21988c2dde68"
   },
   "outputs": [
    {
     "data": {
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k19XYSM0ZzNRnhBa6eNMmTm/ZxMR/PA8Y89tayE0OjiwXuvniH75jzH1/Vv8nWccnh4eY1Wn6tP/68lIO5q2l9PutAFjLfkSqdqhfPi1BTo8Ixg2LYO33wjNWPHMtFM84KjxE9cZbC63MMT6mke17xfjqTb5zHbKHjGXKvOeITBGlEhrq3Lw4eA6Dsa1SbFpQfhhEEEEE8T+EX4RHrqVW/CX1aOcrw1oonLkSEdZTJXplSqDUihEN4w/+0vDPhQM3gt5L9+WhS7V1LaJT8j/9jwcHHmcxEZHSGYCLn3vBg3O1HSum8Js8olxckykijMpTwjMuO7Qfa3Ehx/O3qcvbK8o8+PT0MWOJjhcCNkt6BvGdRPZhTEIbQuNj1c/2kIhwwkPMqrfb5GhCdthVT7Gq/CSnNm0B4MSO7Rxd8dXPSr0o59Rp1Gj6Tr9UDA+90PCLY9vbQqq4cr5Ie7/rG5EQZ8no5pPyaA5hQNHGVQAsf+gh6k4fJXnYeABmvfEuK++9k4xRowGRceuvaJak8bI3P/8PThUc4aKFjwNwfEc+vYaMojYq0vAYtDA6/oqCgyyaKhRBgSaKKbhsUjzxruf789VVqhTRF5R7eFDfaA4dD6PkxzN+l9eud/EEcd856uBoSR35+33zIIraSeuNK2gttfLvO4Zy8sCviFpp3z1XvvYF1ydhXeDUSnOVxrSfMb7oFl/USkuqGYJnZqd+PV/VDLXz/UHPifurlwIt4MG1qK/gg3vmcvAHYQj0nHj2kLGMf/ZZAOI6dMJ2rJidH70PwPZFb2CvcMu+LAlJxMSLoGasq8RoQqww1I0NDkLDzDTWuHnOKqeEs0qM19ZXUV/r1qVHRMVhsYjzCGuTSmqv7mTmCv12+rhRhKek+KQJnPZqNj73DwC+e/nZwK/FeURKZjbpY8aSfclUADoOGIS1qICPLhPjChV00RP/BGDwVXMMA43QvFHXctt1J4/x5Z/uVXXzXXOGcrzwiPo/9Zh+KRctfFylcsxNDtY893cA+kyf6SG5lJscbH7+H9TbhScz6k8LPPbrtFerJRiKf/gOa7E7mBsaE0OPsRPUjMvwxORzMuRxFhMTR7hK8cbB6g01OEPFvWU7W+51344bIh6uhHiJT1bUBLSPnB4RtEsJZ/9BcWzNUTTZQ8YyfcEiAEyWtl7zFQcT/FMrevv1q+PIjYKdCvwVyNIvExtnrAdXYORVB+JlGxl1f8bZnyceaGBTWTcmTihRlGHwNuB6j9yXgTeEixP/4J65/JC33GOWEswcMGUGfX93O6UnSgAoXLGKHR/+28N4dx03iZmzRRnbjj3SaBspvLVEczjxsTEe2210+PaiKq0VVJx1e02Fp05TUSICgyVFhZw5UUhhYREAVquVpKxepLnqqXSZOEVVjuiTWE7s28WOj96nZF3L9N3nG6k5g4kixKMQGEDOrCsBuOTZl3wm5ujR3Hy5ycGaR0U9nC1vvkxi2xSSskTxskOb19I1ZygTXnwFEC9nJUC57aXXGP/03z22U3niBKsevg+AGf96A5MlmpJt3wGw7rGFxKWLr6geYyfQJruHum7lqRMUfL2a44eFiuOCm28na/RY3hw7AqBVNWsUL3tgn3CyOoURZRYMcY2jiQqbi6u3mwizhFDhenZ88dqZ7YUTkJVuJjFe3C0VNpktu6qbVdtovXBTnPDCFUPtzwMPi/BMWlQQTNEPIogggvgfxi/WI/enBfdXCKulkkRfdIo/asWebCfkR4sqP7RWeBbGUrfdisxOZb5W/631yPXUyrmoVmrPFLHovtsAvFQEig4Y4HihqLYnO8UJ1lbasSQkMXTYSAAmXHoVFwzohyUuwWsf/rxvXwg1m9R1xXCIa9zzE7e+0kZhwWE2rF8DwIZN3yGZxYXtffXlZIwc45XZqPC+Zw/sZ+Ozz7J/xeIWH9/5hKKFn/m88Iy7uXTOrYG5ycGmd94AoGLvEYgKJSEjAxDFy2SHnVmfLQUg/5032Pjs31W9/1Vvfah696sXPkDuddeT4kr5V2SPH0wTcss5X67hxP495D//HAAjFjziETPRfymYmxyUFhUAsGbeXfSZez0VRcITz3/56XPSmScnmrjqIu+yDsfLmth92L9McNwQC1mdFC/cwZ7D4t7wt46Cdt0HcN0zXwAQldDWZ6wNPG2ZnloB39776zf8yqkVLfzVH9dLe6B5ekVBIMFPBYHUG1eMOQTW5SfQBCC9UQd8Ui3gXU/FJ+orePf3V5P/nTCCgSTXKN1nho+fztVXzibdZSRAa3Td474Qajbj1MzWy9mMlhfbbF5OtvX7PAC+XPIZO3Zup+eMmQB0mzrDoxYJiKDbtnfeAkQAz0iX/XNgzLwH1FrfWjro0Hfr6dg/V12uoqiAmHbtCHPdaPrYQNm+Xbw2eaQ6ntg2ReXglWD1jLdE5cTkjCw2vfOGGmyd+fwrarLMj9s3c3DdWkbOe9Bj2+9dKa6lEv+4+DkhbUwZMMSLx1fkpvpAb315KVWnTtGmu6Bfdn7wPltcL4RAaJbkRJNXgFOhR6B5Pvt8ocekGUy5R1T/9CfA0ENvrLX8+blQK784Q27Urs2XR67no4yWP1/BTH9QkoMC8cgD5ci1nrbecCswUqIE6qX/5+6rferEwdOwZ3Xtxvhr7mH2JePEvPiEgD3tULOZRodnEpA/w603Tr6UFL735/bmd+/dyevPPwPAnp1bScvJpc80YYy6TLzYw8iYmxycPLCfHa4AbqDJIueKHpNmcOVz/+ehADm45EMANr0tvOuoBGG44ztlkpTdg8FXzQG866WcLjjIy6OFZ5zYNoVrP/2CYxuFfvvrpxd4qIWmvfQGnYZewKczLwGgsqJcTUo6svIrNr39Bte8/xkgmoH8uH0zb06bpB5j2sDR3PjZEvU4yvYJDnzHR+9zfEe+ulwUIcT370//2aIOTZvuPZCdJiJcvHYDIjAL8P4VMw1jF9oytiMGWigs8c97/xy47C/v0uVCoUxy2s+wd634sus1doZhwFMPo4THqFA7NY0W1X61xCMPcuRBBBFEEL9y/OI8cvDNkfuKAAcCI514a71yI5WKEbXSWig1U8DNiyvToWUeuh7LFogKhqs/fs3vcoltU5h2m6jTcdXM2cTHxqH0qFe8ca33q/WWQ80mL49duyzglbLvy9PWz2tuXA9lv9t35PP0X+ZTekDozM3x6WRPnU7fK64ARAlYrQa61l7N7iWfApD/yr/Ou8pFoamuXvwl4YnJ6r5/eH8RXz9wFyD+g5ThI5j23EvqevovFi0fLTc5eGvMcAAqzx5jzhfrVDpJaZygfGUktk1h9MOPUhstHoCvfzuXMfMEzZI6YiSfXnMlv131DQBtO3SiZNNGvrp1GgAj/vA89pBG9cvgwNrl5D0yXxxfeBQxacnUVAg1lLPCSpW1XI2v5Pz2DobfegchrgdPez6nt2/mg7lXe6ihtIizmBjUN5rencV/eui4k627qj2oFqWKZGy4qIyopOxv3ydztNhOeKT7mbGEu/3YsMhwol1/QkKcREFJk2HmKMCgy29kzJwF7PxUSEY3fPa2esw3vfoNSdmDDddToFDC4L9WVEuolV9E0SxJ913gr8uPdl5Lgpq+qJVA+nAaFckCEfQEoNQi1vejFVdgVGsFPOWGynK+oDXgWiqlITQcf4nXBz5+q1kDrhiYhc++wsC+/VxTm2h0OFSjqBhqrbE2mZpodNU20k5XHlSF39YacGVcX39FP0+7LaNl9dvUHqOCAf0H8oc//ol7fvdbAGwnCil7+VkOfCqolDZ9+5Nz7fV0ctUYN1mi6e8yVH2mz2L3kk9bxOU2h5xbfg+4U/IPrBXSz/VPPqbWA5n40APEZ3Tzmf4OnkFFKcRM+hghidvy5susmXcXU54TxiZlwBAuevUt1j22EBAGdvmCh5j8yF8BEdze/JYo3DU5I5O6GpvaR7UBOLZ1O50mXA+IFnMgXnYAx3ZsZ+pLYl2F+1bQVFdPdflZjqwW6f4r5z9AWIikxgS055AyYAjDpl7qs667ze5k9SYbuw650/BLy52q8a6rsREbLo5p8vBYUtrKFLlavh8rrWX8iCjifTcJUrF9n2xoxJNSRXnhjKy+vDNvqmENGWtpCR36DPbSjUeFigdcoU6MePGWlBjRI0itBBFEEEH8ytFqakWSpI7AO0AKou7sq7Is/1OSpETgQyADKAJmy7Jc4Ws7YBzs1MLfG6q5N5ivxhG+PHB/CUJGiT6+5p8LtaJVqhjJDX1RK/4QVraPB6+ZSVWJb28yKTWTF19/B4CMrr3U6b7oD0VN4nR6e8Z6pYnea/ZFywBEhrn9C4csoZUfSiFmIhqFD1cbInmtq92v1itXllv5uWiY+8+n/2IYzFT6MPa96nJ6TxWBUcVrVopVbX/nTXb++61zCobO2yGompjEZM4cK+bti0UgOS0nlxn/EkHOkIhww6xVffEwBfXlpex+XwRKVz0lEoGU4Obwm+4i93e3qs2H7XVVrFn4MIWrRCZvx6FD1SzQlMxsThce4urFywBIz72AT6ZPZvDD89Vx7XHoYXTMkTWi1O5zI/tjjo4zrPYYhlDq/OeKaeq0YQMikSXBeew/0HySjpIslNSuLXHhVuz14jOxT1cLoZEObJpiiNlp4WppWhCeOLQuiKp8FYz87WPkzrq5xeuDN7XSkhT9c6FWGoF7ZVnOlyQpBtgmSdIqYC6wRpblJyRJegB4ALg/0I02Z5h98UtaaI2zL8Psq+OPnn4BY1rFSF54PtLwHQ7ZK5tTb7hbU2N86aL3/BpxgJtuv5tOnQW14mGMm4wfWq0c0JMjN6Pw6YoO3FwvjG9daBhRERLV9gZ1vlbVIjc5MIW6b0sTqMsCmExgcv1Jigmoc7iNt3JMihFXOsLUu140E6fNBqBf/1zm/f56Cg4f9Dgn5XP51PztbH1BpPV3n3UVvabPUGmDoXc/wKCbb1c59KOLP+fYoV0+uV09UjKzCXXdELX2alY9fB9NDeK4L7zlDtW4+UrN18cXCly0zLL5DxJZLbJvE9umUFtfpR7TqqceJimnLxkXjAIgxhLNJU88x9JakY2r1dMr8YCYhDaAkGnW0ERaD5E167RXs3f5l/SdeYXXufkqgWuPFP9pWJtUKk//iL2uSj0OBQ1AXLt2Hts7WtzAqEHi/+7TJYYNW/1rwxVDbys45THdaJ2CLAd9uor/IT7u3FQwSm3y1PSOhjbEn9JOC6VdJYDcZLyMEVptyGVZPgmcdA1XSZK0H0gDpgGjXYstAvIIwJAHkpavGHk9/6QvkmWkIzfyyLXTDT1w1zQlkKkYc73U0Egj7stw+5qnGOsIizDm2mXOpTlE7ZkiAHas/cJwvpKGP/HiGUyddYWHN+XbmzZ7GV+tR67Xe4eaQzC5gmpRrmnRFmGmFCMdFaGco2fZpZo62UOfHqGTVtY5nJgl5drpSjZp1jMThrOxEVOoWCa6Rw9u/P08/nT3LYbXBdx1UL5zcekpOcIT7X/9XDr2z1U59H5XXIW1qICDXwhjeDBvrV9NepW1nEbXTbN/+dccXrNC5cXDk9tS9N16ACpPncRaVEhYiDjnNtk96TVlqkebsvryUjXQWFVSSJ9bRdGzobfeRsWhQ1QeEX1W5SY77bv38Oq7edGTT4pzPbTXI6CbkplNaIq4NzY//QSZE6d4eM+p3Xsanpuvl06D6+GxnThIaHwKYZjQIwxosHnWDy8td7J1l6iRkpVuZsRA94MRSOKOL3TOakefrnXU1It7Z/3q6lZvC2DiNSJAnT5AvCi1Nqqu3rOxhL96UPpkoUBxXjhySZIygAHAZiDFZeQBTiGoF6N1bpYkaaskSVtrbYF5MkEEEUQQQXjjnFUrkiRFA58Cd8uyXClJbo9JlmVZkiRDEl6W5VeBV0Fw5L5K1RpVMFTbwtk8my03h+boFSOlijJsuD1d2n1zRbT0w762ZdRAAtzywkCrG9acLWPxg78DfBeJGj1hCgAP3L+QWrwTcMCbk1W8cb2HbjK5vgVNWg/a2/PSIrSZ+SZTk8pzR+s97gC277GtUM/bfcLUWZS4UsUXvfm830p85WdOU+6iH/avWEy77gMYMHcuAD0mX0RKVjcSXEqDtvoMAAAgAElEQVSMkXc/wGlXAaqjy1ZStPortXTAoc1rsVeUsdmV7HPwo4+wJCSR2k74O29fPFH9TAehJnHWCc8zIrm9ShGB8GDLTp2i8qxIqElsm8LQW0XJhfDEZNoNTaadS4WjQN8JSHbd5OOfeY6vbxaqlM5jL6Hj8EFYJHG9Stat5erFX3psR9+R3l+FxoNLPmTzOyILsrbSzvDfzibGFXfQN322Fhd5qFDAna1ZeNJBWnK46pWXW53NlrFVEGcx0bmTmdw+4pmJj67j9BmJ9VsExRPodrRQefHr/kDu5X8UExvtXh64Hlr62F+BLb2azx/OSUcuSZIZ+BJYIcvyM65pB4HRsiyflCSpPZAny3I3f9sxStE/H9pxLXyl5Ptc3uJJpWh14i2traJM0y6jhb5ioa9qhkbLGkGhUxbdd5vfTizjJk7ivseEPC02PkE1yOBfr60s5yuwGGo2NWtga+rEObgpFW9U2xsINZtU6kRviM8VdRoJ5aKXnuXl555o1XaSUjPJnDyJ3OuEIdSXApCbHNSWiFojH999Byfyf1CDcil9RlFWsFelcdqnp9JhvChx22nwBXSZfBGVJ0QHoLgOnbzP4eQxXpk8GoCIUBM9bhCGfNKcW1ReGvCo424EucnBtv8Tsr+u06cSk9KRki2ia5G1qIh+V1xlqGFXoK+tolIqwPrHH6FwmQjCZs28luG33uEV5ASoKi/lvRm/8XI6FPqv/MxpwzK2vtLyM9ubaddOXIPeXcOJj4bTZ8T9dvhYHfsONrS6zktSaiZj5ggaq8e4a3CEtl7dYFSOG36mFH1JuN6LgHJZlu/WTH8aOKsJdibKsnyfv23pVSvgXWgG3Hx4czXGFQTSh1M7X8+D+zLkvjhxf305tfCV8ONrWQWBJPzUnC3j33+4FvAuhAXuh+IPDz3KmIumq8ZYaMU9b2pPFYqnLlsb0BRwuw96Hru1qHM4W+RxtxTOxkZqG8Q5RIaF8PnHH/DPp/8C0CpFiqIQufCePzD0uhsAt/ZbW073vStnegVGlXUnP/JXteYJNF+D3GmvZt1T4pi3vOnWX7dPT6XJFE1sltA+J/fpT4f+A+g2WqhjQmsbcUZFegUmAaxFBax/7mkqDojywdNfftnj5WRUPlfrkf+4Zwdt24jzCU9Jobq2hqYK8dAavYwU1JeX8s6USTisImCrGNkpo4UBOHW63qO5Q06PCHL7RJC/27j122WT4tWEIGs1FJaI8rTabbcG2UPGMv6eF4hr11md5k8dp43jNWfDtI7qGzf9PKqV4cC1wG5Jkna4pv0JeAL4SJKkG4BiYPY57COIIIIIIohm8ItJ0b/xDeGRG1URC1S6o8BXdqbRMlr44sj1VIqvzM2W0CkKlGqFWk/bqAuQdnmj5UB47P930ySvpgVazLxKfP7/cYE3jeCrYJU+o9Jk8vS66xwy0HTevOc6l+f/U3rjChSKR+Hi130tCkH9/a/zz0kn3sPV6q3H2Akk9+2vloSttVeTd+uNHk084iwmsi8R/8uUp//ebGMJPaWhVBo8svIrdn8uCl1RYfO4D+IsJlJHTOWSf4qKhbuXfOpBl2g9+x+/28wFv7uVLhMvBnzr2bXHoy/gVeXS3G94/hkumHs9lgy3Ry/reH4tli18wOPLAiC9oyhCdfkEB/l7G1iz2f3wZbY3M7Cv0EJ9vMJT8QLuglut4cC1iE7uxKjr7gVg8KybDeNrYJyfoofW6zY3ikJZ8F+iVs4njKgVBc31u2suGcifvNBfGr4RB+6x7WZK0/qqeKgk++h14r6CnYFUMlSWbbQW849LR/o0QFfMvZVbfy/qp0TEJ/lMjddDCjFr45fnjTr5b0N5AYF3/ZhvVy/n0YfEg3uuVRDbdR/AVR8IjnjvC6+y4o2/ey2jUCsD5txAz8nCgGq7HUHzNIv2/9v8/D9Y98wTasmF9MEjGfPIQg9uWun8A7D6nnuI6yWWHXTjbcSmpqrL+eLGAxH/2Y4Vs+X1l+h2qdCcd+zd3ye3HgaUbNrIW7N/o07TVuFUqBJ94k5uj1gAyq21562M7aCJl0JCvDo+Zs4CohLEC6U5kURz8FW6Vu+o/lzUynmFrz6cWkMdSK2V5hpF+OLMlVopeu24fhjcChVftcUVY60YWA8duMVzWpVNNtSJhzWKA20gvFljrsw/a63waXQS26YQHx9PuIusU2qj+ApoaoflJgeYzP/fGHAj6OvHDB0zhX+8KP70e2//7TkZ81MHtvPeDGGcqqzlHvPiLCZMUUm06dsfgCFzb1DrjYO3V+6Pn66tq2fx7wU3n+CUuPztDzxqx+hhLSrgi3kiIajf7KtVXXxzXwJ677vBz7y2HToxbv5jasu5yJAQknr2Vcvenjiwj4GuxKIGIGXwELWpyeH8TR5c9votVWSkxTCgp3IfxrF6k03lxscPjaPcdu4ceLvuAxg7ay6hPdzFr8LMseqw3uv218TdCHpWQa9g8deQ2ReCtVaCCCKIIH7l+MV45OZG4dY2YDGsCKYMN8ePG5Wq1Q4bacmV+dpxD048HC9deO3psxQdKgIgPCKGsJgElMbwhRsWc6yokIZKMcHeaKNjW9Gc1tyuM+2zutChi6gsGNshCam2jsoGcQCxYfXIkREqZ15nkzG7It4+VSuuBsqLH7nbe54Lg4aPZs5t93howY3khkb1Un6pnnhsZCiVtY0BjevngXeJAXBqasA46N5/IAD3P/IU9/9uzjkdqy8df/Kw8Vz5xrse2ZpaaNvTSS4Jod77PeHybpfefRcd+4mOQoMfvJeYxPY+j8dpr2bpH+6n93RRT6bfFVd57NMI9a7jKN2Zz7Gd26kpF/fdwJkzwcWBG9Eu4SFmBt0oZJGfXn0Zs977mOgE0WVo+ztvqlmiST37IoWYaTt4GIBXrKe03Mna75sYO0z4nwN6ShwrDVMzPLfsqqZHd/HlsSm/9en2pw5s58lb3PROdHIn4tIz6dVdPLM9L7mBhPRsdX5zX/56+LJheqbhZ0nRP99QdZiuZ03fzg3c0h2fCTq6CxhID049FaOVGGqNuti/+ITb+N4rbH/3TbWcaWxMPKER4dQ1is85o5ob+3XjSknMxI6Z9Bs7nT5jJovzTUkHTRBTS7v4kh7+4OrTaBTkVOSGv7/7D2IbGipFn2av5cKdaOWF/oOORkYy1qVh1hpS7XhL4Gvdlowb7VcfH9Br4ZXhkeMmM2n65axwde45V8TGxNNl4kUAjLrvIaQQM0avSoXiCNdosrW/IDrzfHHbjQDYnQ5GPCiUvlp6RrstBUdWfoUlLpwcl0xSqzM3olZOb9/MBle3peP52zzu8SOfL2b0gkcByBo7Wd2GggYgOlHUbYlNSGT9i88y9QlRErj39JlsfPl5AGY//zoNgKOiymPf44cKA7BxN2zbbyMrXXDXGWkwYqCFcqv4n0rLnew/cG6p9kaoLi2murSY41vzAMhf+wXXPPs5kSnCmLeUWgHfjqqWOm5JQlCQWgkiiCCC+JXjF+GRSyHeBL/2LaUvKuPr08RX42TtNH/Ui5ZCsSfbYY8IcH309JMc2/cdNTVi4erSYo/9VlZZwdOJaBaKN192opBDm9fy1ctCtZCWk8v4qXNoN9jV6LaZglk1Z8tY/+m/fM6ffbWQtbVtn+4xXaFS9MFNpTmEv+xMvQdu5O025y23BM15+62BIjv0pdQ5emA/CW2ESiEuMZmb7/wj234QhbDOtbFE7k23MlTXWMGIkjCiOLT0l77rz8RHn1BL7oKnZ63sQ5EFbnr7DYbOvcEjA9PXfgvWLmfVH+8iZfgIQCQtJfftz9FlKwHY9taLfHGnaNjRYdAI+kybqUoXlSCr8ps5fTrfPPk3yq4Ty/effQ2FS4Tk88Da5TjrGtj95cfqvuMsJjLTlWdAZssuEys3iIdt9m9iiI+GaeNFpudrH1nPKcgZKKpt5Rw/vIsuKW56xV8RPiMPXc8yaIcVe/ero1bkJm9ZYSAn6otmMVKm+JIh6i+4035G7GvtGj5/URjIkye3+D1+S0ISlsgYElz1MhJi45HDoomLE5+3TTVnqWp0f/xYy45RcUo8fIpRUD5VD69ZweE1K9TI/agb59PTlY1nhL2fvc3JkhOG87K6dmPObfeo49ru9Z4lZwGEikXhiM3GJXKAczOgCgIxxkaUjb91fC3vCxGhThyyZ7VH5Xrs3JHPay+Kz/8//nkhI8dN5r3FosvNWy89xwdvG3exCQQJiW28DLeW1lC46PAQM1XlpXzx1OMApHXvTtbIMep2Tu4/QOexlzCwVxcALrxsDlaXoY5JTKYBkS4PbhppzxdCZy6XHaPLxIt9SgHBfY/kPTKfNn37c4mLDlGM8sDbBS/ee9YMlv9tIQCFq1Z5VHO85J8vEKlRzPSeOpP8l19h3/KvABg570Eyp08HYOk9t9PU4PCoNWOzO7G6Rgf0lJDlcFVHvm13HeOGRahdf3J7xPpsz+YLijwzLrkDxw6JWIOvcsQKHfqbe54h88KL1OlGCpZAlCvg31FtCbXyizDkeo/cqFStdrq/gjRatERHLledJe+tv1O4PQ/AsI2TgqTUTBI7ZpKeLlJ0O2VmYYpyc5IVpafYsOZznIkioJPRK5eMtiJXOMxVaKfBdbPmLVlEwb589FD47rMLrodH3gJQDboia6wPq2DdkkU+j/NGl2Yc8Ahseo67ddSB1EgxQms48kAMrtEy/oy1v8CngjrXtYtobMAUEa6y/0oSknKNRo+bxCefiEJPTy64j7Olp7jMxSdff9vd/PDtOq9a5oFi/9bNdLvuJo9pWsOucOK19mq+vv9++l8/F4CMC0ZRX17K1/eLqtB9r7ycPo/MouzgXgCWPv0IHfuLxhjdXGn+da4HIMT1zOxZIgx5dNc+mCzRXtJGLQ7krQEgNiuTwbffZShhBIho30HtK1qYt4ZVf7xLrW/e4c0cBt5+h7pseGIybQfnUvjNOkAY8v6zrwFg9+vPc7LE24geLRHGISMtgj5dIqioFPds/v46OqdHqGn4OX1C2KYPRjUDW6lwqPpNvpYZfxKOW8Hubexc+qZq2JsaHPQaPYPxN4vrHhKX6VN+qIwr8BX8NJIf6odb4pEHOfIggggiiF85fhEeeXPUiq83lh7NNVDWLwsQ0ngWgH8/fK3fSoHZQ8bSu6+QoyV1yCIkNILwMOHP1Tc4KS3aR6UrLbT8eAGnDmxH6VGS/90aopNFsaDsLl3oPGAEbVIzABg9XcjajLxyEFmFS58WssLMXkuJbJuhKlp+WPyZoazt0stFgoW2KJa+9Kwyru+oQwAe+fnmyJujRLTevJHn3xL6RYHS6EKB0xlCqNmd4Rkbn8CLL70NwIO3z+GphferJW/vevARFj79InffJDzJliYLFa/PY/VCwZHnXnc9CVndPDxjhVpZ+sDdDJ5zFamurj4Am15+iVEPinVTsrpRVV5K3h/uBKDTJTPIdrWnA1GEatWzQmly0YN/xmGzYi0UVRjT+l9KVflJ6k8JKjG1Z18vaqV832EA+kybqbZ3A0G5lO7MJ7lfDiBoG4W6yRo7mdkff6EmGm1760V6z5pBRPsO6vo9xk5g+YKH1G0pnn6fG+/k5Hx37EDB0WJxPXp3iSSlbRMjc9xp0Ou3VBEf56qGGN3ylHyFxln50sOcLXEVCXvgr/QedylylbANtbWVhMRlqusEUg5bu2ygZbb1rd7+v6JWWpKSr+8Q1By1Unv6EJ8+I4yk3ohHRMXRsZto7TVy/DRSeuRgdnWXkRsdVFdWYDsuHoriwgL2btzhl0tXAqT5pcXs27mV1E6Cluk/fLxqzMHYoCvGeslfH+LK596j5qz4/Nz+1vOG+4qPF/KsULNZbbHmDA3z0okrkjsFRvW+jdCckWwpV90cJeJvWy3l6/WaeM+Wc1oduZNYl4TvX+9+wT//tkDlxQ/t3c7Cp19UqaunFgbcyRAQHKxST+To0sVM+9cbat3wqvKTrFkosiCzRo8n9cIJar/LvZvX065nd7USYa29mqX33kGXa0VAe+h1N6jVFisKDrLtnbcY/7vbAVEvpfZUjcr/msMiabRWqtLF9DFj1TK8KVndqG9ysG+toFayRw71OH4pxMyxndvZ8MgCAEY//pRHOYGErG5c/LgoQ/DvWZewd8WX5M69VV0/smNHdbjyxAm1ImKnwRcQERVHuOzOzsztEcuRYtEhaMPWaqaNjVH7bOb2icBe38TuI4IuG9FfIjE+MEMeZzExOjeKXUfEtS086WDbV28D0CYhjuG/fxIpRkgmQ8La+DXezVEremhT9P31W/jVBzvBuGhWoHXIfXnk2gt+ZMWrrFv0rJcCIaun8DIGT5pFJ1cCgClMPBx2xK8lFH48tB+bTXjgCclpXHJrLmXHRCBqx9rtEH6W0BhhUBurrKq3XnaikLoam2qwC/blk5SaSXpGFuBZW0KPLSs/IW5BAs6O4gbzxdE2mdweiyNc/MWyziPXt2VrrsFDIDy4gua85kB57tagpYoWfZNoIx05CC+8wZWm/dlrT3HLddO4Y54wZF1zhvotVOYP5WdOs+z+eUx96XUAlv15PpY4V4/K6bMAwX0DVB8/zbRXXlWLZC296w7stnpVC+4IMVPhamjxxbw7GDr3Bg9P2KmxKo6GWuI6diWxnzDAW958mWRXmYCErG5IIWZSOomEos1vvU7f6TM8Cl/lXHcDP+ZvBeC9K2dy+StvA6gvJMWw95w8hR+/3exhyJVeoAAOTW+zxspK6mps6DPUB/UVHvvqTTb2HHWqKfrJqSYG94ri8zUiwNmhbQSWcE83NrO960sh3exRbMtmd1Ja7WDYYFFwq2ZDlWr8v1n8NsQkMfw60SzCKFlQi5Z65Eat3xQEdeRBBBFEEP+j+EV45EY68kB5cSP4olbq6sFavA2Apc8/7CFzAlEsZ/zlN6vjFadEC62yYwUcO7yLikpRJnPcrFvpOnAYTlfnc1OYmbpKKyGhItV4/HXCuw4JdR+4wqefPlFC/ndrPKicshOFAWuTV3/8mtpdxhdGu7IGnU6Qm4SXoe/4o5cfNpeG74+HrnPIJMeaA/ayA+W5A/Hkm+Pr/XnodQ5Z90Xi9muUrkdi2ElkWAh//IOohrjj29UU7MtXGzcrvGxrcbrwkEpxAOTcLrIkpRAzZft2qTTMzOdfQQoxs+Mjkcm7f8Virvzgcw+Vy+LrRVMRU0K8B18OkJCRpVZZtO09RIg5hInzhNe5f8knHFoqlCZ9ps9CCjHT7TIRazm8ZRPLHpnPjH+J/SplbS9aKGSR719xOR/eMheA6xZ/RYqG88+acBErHl+gfkUYKV8UQi80Npak1EyPZ+FIcQ3XXCY48GOlYWzZVU1muosTl+pJSYae3cQWVm2oI1xnK5RqiONHRJEYH6Zq0G12J99vr6V3V+FKjxoU455XY2PjB/+k3wXiCzup70CvgnpwbqoVBQ11EBUqvhRqGi3/f+nIwX/PTl/wF+xUsHWZkPIpRlzpu9ez30A6j5pG2THBe3+3YZUqQYyIimPKVbeQkyEMdVySeBgUyiUUiWprBXnLPgKg0laBxWkHs/hsS8jsS/9e4rPUEhnNrGnTWJkkEnT25i32eqE0B39JD2OnzCQ1PUMcn6vCIbgTSRQDHm0JU/nhQOHLKCovAVXa18LaLK19AfhbVzHwzVEsvups69P1axvc1zynT7ZHLONca12DOw4yZt4DdHeluTcA2157STW+nYYMx2mvZudbbwLC8ci+YJRqNL99+QV1O5fe/47XuZks0cRnCifj2KFdOO3VRKaL8dScwRxes0Kcz858UgYModNQEeDskJPL/iWfsOXVFwEYcue9yE0ONflo1AP3s/QewcX/8MJzTHr2JXWf5vg4nFVVNLoy7UyWaKoqziI7xXhtU5N6/Ek9+zL+8hv54NmH1PXDI0DxW0YMtLB6Qw17DgurOKRnJOaIBtUY7zvY4PO/yN8tarQobeI+XmElzmKiSRbPQ0aaWZ23fksVpeVlfP7ifABmPbmMpo52LKWCsjTKTfHHizdXd8WBqx556K9cRw7Grd3AW7WiD3hq33b+gp0AZ/at9cgaA0iIFQkBgy++lkPffsF360WAR2tc45JTCIuOIzHVdxGirSs+Vr3sOIuJU1pjuy+fvXnipFI7daZP7ghyB4sAUqfUZPKWLDrnmtcg2nv99dmXVE/S6QwBPD1y5eGutjcghfguTesv6GjkgZdWOkiONfscDtRTbkmg9Hxw7UptGffLSNGTu/8/bRAUoE/ucD758IOAtt9SHNu5XTVscpOD4/nb6D52AiDaph36wq1UmvjoEzQg1CkA2xe9oSa4ZI0ea1gCVymStXL+AxTkraXrFNEfdMIf/sjSu4QxXnb/A/xu8TKcUZEAXLTwcWwlJWx+S/D47QYPI+OCUapTkDV2Mmk5oljXgbWrGHXyGLEubj4k3ExMfKLHOTbaKtVG17E6wrn9FdNI+lDsp+xEIaXlTrX++ICeEuNHRKkJQubwJpBD1YSgLp2ifCYEHSmuISs9RtWcDxsQyffba1m6RhieaePN6rwKWzSrN9nU5/nIxk/oPe5Sr20G2rSmJfXKgzryIIIIIoj/UZyzRy5JkgnYChyXZfk3kiRlAh8AbYBtwLWyLPv9hpdCPN9EmkC2B7USFSq6OWjfbkYRYiNqxV5ayNKn7/bwtJNSM5lyjfBQtq3/ki0rP/M4LsW7mTZ3HknpmejhCBXUSWijnSGX38DASZcBUFHXAJXlOBuE11G0dxv53wlPv2BfPgX78lV1TFavXEZPn0OeK0PzXDzzyPAYKq0VGi/H6VXdUM+TV7sC+dGWMOocsuqZ+vNojagUvWevrB9hllpEiTSnJy+tdAR0jNp1fC2rP2bBmSt0imdzaZu1ghMlRQBMnDabb1Z/yZqVK5rdf0txPH+byhmfLiqgylpO10umAeKrav9Xy91Uy2BBfRzME56jvaKM4TfdBaBmbWozRhuALuNF/GTrC8+y8+P3VI889cIJjLlHUAnLnvgjHz72ILMefxoQlRQvf+lN9nwqOPTk1Hbq8SjoO05s5/CaFRRv/lZtIH1m9y76XnatRw2Ygm/y1HMI0UlercVF2Gs9CxcpDZN7d44jPrqJ+BjxXxYek+mQFK7KEbPSPTM7E9umkBwjCO0DBQ2s31JFWpL4Mh7SM5KjxW6zpKT7g3d53J1L36T3uEsDKvMBzZcN0UOvyvtv6cjvQlRpVVpoPAk8K8vyB5IkvQzcALzka2UQnxB6HbkCLbWicEm+LorR9JoKkfDwzv1XeSTPJLZNYcyMORS7EiR2f7vKY72snjmqvjvSEkVdpbsfYHSsy1A2Cn1rI5AQ24awaPEJGV1tY9eR3RTsFYHVE8VHvY5LKz9MbJtCiNlPY88AcarsLBVnz6iG3Mhwe5etFQ9BTZ2MyeSb5zYKKuqXKa0U+0qONXsMV9Y2qtv1RbNop/laVtmn3jj723bL5Ixuw+1ZzsDE0YP7efKxhQC88v6n3HT3n9m+XfQcPx+0mBZKYSsFCRnCiQgDyg/tVamKBJdktfT7reqyHYeLrjZyk8OwNK2i2e537fVsfut1TmwU933qhRPIvuZqsW50KMsXPMTXEeLBG3Pfn4lo34ELXKn2So105V6qLy+lcNtG9Rgqigo1w0VqyV4FJ3YdUIej45PUl03RxlUsvet2r1onSkzoeFmToD9k8f9LwDf5dnL7iOPMSPNMCIoLLWfEQOFslVudlJY72X1EGJo+XSIYOchtaApKnFhdFXDjowUfrxjyQ5vXcmz3D3ToI66tPoDZUh25Hvr+nT+7jlySpA7AxcBfgXmSJEnAWECpUr8IWEgzhlzvkWsRSEKQUbIPiAJY78wTnoK+dkrO6Kk4G+xsdAUoFU9d8ZRHT5+j1kWptddQfGAnndJFcaKI2HgcoVFENrp1qfUNDXz/Qx4A277+tEUV8s6XIZg67VI6dc7W8LlNrrribi5YKZqlr3YITpyYiYow5swDSQJSislrjbzWM/e1HV/B00C9du9G0K1rhqHlxfW1aTKysqmpFU/6Q3ffxmNPPsfMmaIw1OuvtL6Alh72ijJKXX00U7O6EJvSEdspkSMc7zLcJlcvyUiX120t+1Fdv6ZCeKCSxogDagEtJWFo0M23cywvj+UPicDiFYvSVZ14n+mXY46NY9n8BwGwFhcy7uFH1UQkZfsKwhOT6X/dXADyP/0PYbZGdd9D7ryL6Pgkddmq8pNUlhYTmyKSgj694RrO7grshbhyQ5Va8RCE4d66y8lHX7qrIWoTgrT9O0cNiuHjFVa27haWNaVdGJlp7nPQDoMw5ooGvfCkgx1r3lENuVatouB8ZHY6Qi2EaSzyz6kjfw64D7cr0wawyrKsPGnHgDSjFSVJulmSpK2SJG21W42rjQURRBBBBNE8Wu2RS5L0G6BUluVtkiSNbun6siy/CrwK0C47V9arVowkh83VIddje97bXp644nHHxiew7P1XPDjzrK7dVDpF8cYVpKV0xBTvbsBqbqwBV1+X4/u3sW7x262uhne+MHyQyKbTepaKakXxvJU0fEW1osBXXW4jNKda0dMkRlRLIFA48eYUMAqM6B4tDeNP5aJcD20tGo/txCfw4IOCQ15w9xyu/M0oBg0fDQh5akslpP4QGSL8K5Mlmgt+dytHFi8FoNPQCxh+z71selvouRUKprLA/fW3Z+VyANplZWK312N2ScCiE9uAJdrNmVui+c1Lr/PhbaIu+GvTLmbU/aI0QL8rriJr7GR+EyFUK3n33cZHl02l89hLxHGMGYkpIoxMVzXOcFfKvoKQru4UfH27OXtxCY3W05wqEcd8ys2yGGY1p3dsS8mPgh612Z18vrqKqy4S52QOb6JzJwuFm8S1X72hBlujopAR3v2GreKr+ZLRFsYPFc2aAfJ320keFq3y62pGvm4AACAASURBVEYY2FfQMoUnrRRuWIN8g6i9oqTuK/AneTZStPjTlLc0Z0aBJMu+6077XVGS/gZci/iejkBw5IuBSUA7WZYbJUkaBiyUZXmSv221754r3/CaO8VZe0JGBl5/sr6olUXXXELBUR333VV8Hp44ccrj4YuNiWfSdXcT46ohXnJ4D51cmtvP332VkRdfRlIHMZ7QTkir1n76NiBS55uDolePjkvEFG7GWS8MRbWt/LwZgXETJ/HEy/9R6RNwUyogjJNSKqDi7BkyuvbyMOBaw65d71x6duoDlIBHUNWXsVaGjfYdKI/fEtTUyR7Xwrvsr1tX/tGiV3jpn/84r8ZbC6WWd+XZCuQmOxVH9gAQ26YDVdZyzK4ereb4dEzhZupOixiM1ggqL5fIWIu6bmxWJhm5IoU+LqsTSd27ERkpjNWXf7pX1ZF3zRnKuGf+qVIpFQUHWfPYfHXbCU4JOSuDIXeKwGoYJt6bIXpcOqptzP74Cw8aRiuD3Pb2y6w0KIylILO9WaVEYmPiSW1rJ8qV5XOkuAab3cm4IeKcsjqFEWUO4fO1glrRUil6XHdJAiltZT75ukZd9rJJ8arkUAtrNSp9A7A0z86Bggbu/lCUtVX6dfoKfjYnwPAHre17/YahnDq0LaCHr9XUiizLD8qy3EGW5QzgCmCtLMtXA+sARXQ5B/i8tfsIIoggggiiefwUCUH3Ax9IkvQXYDvwRnMraFUrytvIVyMIo08P/VtO6fJzqty7OYQv+iOt72i6pCWw8luxTqfUZD5/91XAu7VXxaljrH73WZ/bioiKo2e/gSSmCQ8+vWtvdV5MQiJVFeXquPXMcX4sLgjIq28OhYVFHl73K6+/SbxZeKhFRUUUHd6rHnPXnKGMGHoBVoe4BbqktWHcRdOIc8nEnE5PrxxaFkhUlgVPpYnicStUizJfGxRtTvGipXAClUw2hzCgQfNFou0W1Ohw0OhwSzlnzxGp+c8+Pt9rO+cDSlMGPWorPUsWJ0bZqbbVoTwqKZnZREW6XElHDTWNKeqXn630NKcLD6let0JjKF+KseHupsWH8zfBY/MZ/UcRCE3q2ZcZ/3qDENdDKYWYPdrGLb33DmSb+CroPP0mUjTeuCJ7VOA8WuT33MMjJaaNEBTm5xusVFbBZZOEB54QJxJ1lOJXW3fXM6hvNONHiK+KbbvryN+vL7klsOdILYmxFg+65GhJHRlp3gZl2+46V8lccd/16SoULPvyhE+qL6alhS9qJdCGzFpa+WfP7JRlOQ/Icw0XAINbsr5WtaJ8Wui15BA4rbJrldBs+2rZZITe/ftzWtOAu2jvNtWADxh0KR2z+3D6RAkAK9993ivCrhhvgBEz5hIRm4CjTnzG2atrcdhOArB73XqKiopUSeL5/Dy3Wq38828LWLde6IqNapUrOJy/yati3+v/eob5f/0HAMPHT3ZlhkIdLaPftAoWZVwvTfTFZRtRK96qFPe2jV4S2uFAqy6aIyTqNSULtLXaFShSzcYmYcxtViFJfX/RGz8ZzaJHVs8cMjIyAPFyLt+XT7SrBVl6176MnTVXHKurfIQim622VlB2rIDd274B3B2olON23aoApA0cTb/Lria8nehZqtQMV+qlVBTtp2jtera/K0oF2GurmPzI/wHQZeLFXnXNtdRKRbX/61RudZI5TPyfQ3Pi2JRvY/0WQZ1MGx/DeOJUXbnN7mT1JhvdSwXzP2KghQqroJf0NEv+/jqOnXao/T27Z4WRv9+tHdeirDqGPUeqSGkr5sW77E/F0Z0ey/mr6aSgJaqV5noS+8MvIkXfyCPXnoxyIQLxxgH2bQ2czVE8kpi4BHZv3UBcqHhY1363hlhXGdqcMbmcKDrKusXGSTvZQ8Yyesps2rs8EamhHjksnDMuY71xzeeU7BGacn2Nl/OJ8jOnz6mXZPmZ0zz6kCgM9VxyCtm9RNq1wrkrHrqRQXR74KA1tOC7ZZt2nq+EnTqHTPkZ4fkt27aZ9qmptM8QMtD42Dgvb95fIlIgdVcUnlyrk1bGtUFkKcTMTXeJOuRx8fE/mXeuh/XMcdbqatYreQNxcQlEhorrqhjOiNh49Tcuqzd9RgpN9+mSYn746t8c3ZEHePLrcpOdnR+/x7ePCy5bSuqAs8KdR6E4CKk5wl+79JkPVE5cMdraRKT6JgdHlot+p0Xf+G7eAkI2uGm/OJahPUw01EaoXraStKMUzVq9oYbCkw5V7+0gkREDxUu/fE2lV+C0tNzJtt1iW8NzzZw85aTouJin5cqToqs4WuxQG1hEmcUXzNnC3R7b8xXgbKmOXItgin4QQQQRxP8ofhEeuRG1oiAQSY72zei0n+HM7h0B71vxkHdv3cDZYwUUKjUqgW7DxgNgikpg25evenDliW1TVKli14EjCQ8LQ2oQr98Tx4rY/u1qjm7fABgnOvxcn+IthXKseSu/Vj1yBb64aIWnbkk6vF6iqJ2v0DCbvlnFjn0FOE6I5sKffLUce0UZiW1TANEJKaNrL7KzhYogIbm92h3JYolg9LiJLTl1TCa3TFOfFauFvgDZuXLmloQkmlwlkZu7L7T3Us6gISRm9CWrt/if2qVn0eCHBjM31iC7JLNnpBpGzJhL5wEjAFi3eJF6f5/I/8FzxZITRETFEeKia8bMe4DUESNJ6yHkrtrStEa1OEp35vPVfaKxhFIsyx825Ytr0KdLPLl9IlS6JH9/HRJhjMgR99m0sTF8k29XPfajBaeIcylcEuNCDKuEKttKjImgcycn5VZxxBlpnqUCbHYnNQ5xM0SZhb9rdVFpctVZ6jVdg1orN9RDb+t+ddUP9Z8Q2lZIgfBF2gtVcSzw2t5abFn5CVldu6nrxsbE07mX6Eh+ZPNKj7KlSamZTLvmZtJ6iAeo0fXwNLn0vwV7ttGxUxZ9BoqHpMpWQUWp+IYrP17Avp1b6dmzu9iYOVqtw3K+kdg2hQxXoDU7uyvtk+KIT3Z3jAlxugNDVfVNOKrL+fzzJQAUH9lLRKi46WsbmtTAnxba4GedQyY20j3dSFOujCvLA+Rv3kjOkAvVqoOr8/Io3iVSznt1TcdWvJNlK1cD7piHYszKz5ym4PBB1i4zPv+xU2Zy/wJRM7t75zQqaxtpHy+eupNW76dL6WMKGMoy9bSLttvSb29zd4pvqTQxJj7RbzxDi6yeOeSOEdnKMXEJVNkqKHDRdmXHClSJLEB0fAKmRMEZmBtrCEUipEk8bLbtO9i4dT0jp4gk7GnX3Mw6V2VQo3aDHYcO5ZJ/vADgUTcFvGkowKM0wI533g7IgOvx0ZdVTBwRowYzP19dxbb9lcS7JJU5vcIYmWMh3JWN/P32Wp/BTgUKd26ths7pEaohN0KBqxZLTq8wbHYnlnDX81BbiSWmTUAtJfXTfbV+00LfmyEQ/CIMubaxREsIf6PAQsnmL1p9HFoVSlJH9wOhlLZNcgWVZt5yPyGhEdQ3iD/aFGZWjTnAgKlzMDfWqI0n2gK2sgwArB0yKCkqIH/L5lYfpz8ktk1RA5ZDRk0gMky8XJoaTR7eksmkNJ7wNFgXThGFv44c2Etdo8k13USjw0Edng+r1ov2xU/rvW0Q/PjGbcJYN9bXYrOeZcsOEUiKDnH3yvzL358jKlTyClorHjl4eqiWhCSPZdcu+4xN34vgXu9+A8np358hw8TLdeAF7mbC2vPXt8IDfE6LbBL/eV2I0KFffr0oAxsfn8CC+9yGvTk0Z8SV883o2pu2yUlsWyfu8RPFRz1eGNo+szERUQy4cDwpLo788NbvKd67iSyXB547fhrpXXuz7LO3AeiY3tlv79jDa1aw+/0PARh4u+e5hYeYOe1qMVdRVEinoReqXnpVeSkH1nrmcgQKm93JxyusjB8q4knTxsewbXedmmaflhxOStsmLhwgLGF9ndysIdciIw2OlogXm1Y7nhgXDpqGc0oNlohQ8TzIuOJcLUjR9+eVa4UdYRGt69kZ5MiDCCKIIH7l+EV45EYdgpQ3nD/v3OizpXCP92dhaxAXlaUWlq+rsREbE8+YGcJj0bZwUxCKpHrlkY126hs8+VVrmaBWVn/4equoH39QPLbRE6Yw+5rf0qlztjqvtkF5rTd5eJSKtE5PG6SmifTqtI5ZHt76uq+XMOai6QCGNIsvaZ+vioTD+gnK59HHnya7Rz/Gjx6tzu+UIa7PhaPH0tTgYPe2bwGIb59Op4ws4uLi1WXzVi3HHCb4zSOHDvLm/z3jcU0Uj/2HvOX8kLecj94THaIeefp5vxy6/ktFbnJ4TXO4LoOsu5btOnbyud3WQDkHu73OL2Vz4ZTZ9L1QxHUUtYqCrgOH0TG7B3u2fAdA/nfP0LvvQC6/YR4AH77xDM5qER8a85vLqLRWeN2nq54SKfzWsuOMuHOeSrGc2LeL964U5aDtFWX0mDSDi558EoDjO/JbJAM2gpJWn2uLZWSOhe7Zrnta57GOGxZBWKQwBgrP7gsFxQ3k9AojIVb4svW1EkSL59diFhRKRaXYQWSYuH9DneI8GuosRGm2dS4p+nqv/lfdIUhPrWh15GERzae3KtNDQio5UehdMrY1qHYe4/iRI+p415HTaZOaAUBDjY3tqzYwYIL4TG3XtbsHtdKITHhYmDqtrKSQle8+D5z/kqePPPUCE6fNNpwXajapWnDT/2PvzOOjqO///9zskWQTspuQgyshh4ncQgBREOVGQRCteNUD76qtV2sP22rV1tbWVq32a9WvFc+qIILWA+UUReUIyKlEcnAEQkLuLJvdZPf3x2c+szOzs5tNQKu/b16PBw92jp2dmcy85z2vz+v9eltF8PZ5RAcVr9K6LF7h9eNTXFitkJAggmKrN6jrWWl1JNByTPCcyThVzxYJbeGOlkqJ1CtT+tT86lc/p93foZP2ZfQVbfCkzW7B4MHq94w0zQWXXq5+rqutZe7ZkwBIzR7Mi//8q+pMmJaRRV1NtXr+77rlBubNv5Rf3vuAOD82/a2gLdE3kyJqnSPlsj2KbfFD99wVdswnAp3x7isWPUPJGkG72JNdpKSmYVHskV2uNFyuVNIH5AIwbOyNrFv/IR89IgZoR06Yxr594t5Z/Z9FTJg6l+UK7WL83T2vPsO+1auYcIfSw/SfT+mC9e7lb1K3Z+fxHawJNu9uoq7BznlThPzQzCdl4khxfWRnuPlgXXPEtogNTeLvneYW13FVTYCsDP215WnTb7/dKlwc4xMMFGMXdORmbeBk7DMWBHUF34lAbszI5QFB9IMynqT6qhraWxsif6EL2P/VbvUCTunl5pRTRuFTptcs+h9a24OMSxL+Eh0+v1qAIdFOkM2K1/PaV5+JeBOmZWR1mmlFQnq/PMbPOFedloGvXdOubFuJyMBWL3+XPTu3UFFRoa7v8XhxOpWih4z+5ObmkjdsLABnjR9DoaJKADhz6tnqZ5mRGysutf4pkTTb4jsBXT9MLbSeJrIAx6szAQtvdCzVMqSn43SdoRy7n+nX3c7kM8XDts5v4967blUDube1kVcX/lNVudxwy891AdtY2Wm1BrC3iWM6FmcJC+zJTgfP/8/DQOTq4W8DaqJQU011lBc/WcDmaxZZ+Mfvvq4WtH15aB989r46IG8cz2n0dNBYvoclt94YcfuxDt4CKge+bU9Lp/1Pyw/5WVci/g5TTo+csub2h7EjktVs3oj91a3421wkOsQ1fKCtDZQa2VblLbbtmFgWF9QnLclpKQSI3LPTLCM36/MJIr7JWOdt02fkPRx5D3rQgx78H8J3IiPXUisQTq1EgvHptu9wxQnTZ+tawmXn40hy8fVWwdWWlX7FtPnXq8srv/yCDp+f3NMEP9lRd5CP33yODWveN912er88Rk4Q6+YUDmPpMw92a79zcvPJiE/Eq+G5O7BTuVf0u3r6iUdY9a5oX5eVV0T/vtmMGjVSrGtLwtLeSnl5hTgmpQUdyvovJ7mYMEE0iJ5z4RWMO2s6lXtFllUwaCgQWVduRrPoKz9DkI2N2yM0PQZ91t7i8amZstx2yLcloFJJNrudnJRUGCPOcz7w1yf+xaFDBwDwK4qjN154XN32VTfdoePBtS6SHR1xtMdZ1G1rpYo2u5UWj4/6+hPzNngi0Jm1rre1USd71VYae+prKauvVSubv2kElU72svmDhMsp/u5GekQ2WM7PCXcv9LeFctO8nADoXShUtCnxpqpGpL27vvKp6hfJjccnir93wKJozRVrBG9QeM3EUsEZSbWirVbXLvve68iNATtWvwEtveJrqY++cjeRP3Q0vtZGtn4i9Mzp/fLIKRym+qe8tfARzr7wGo7uFhzp0mcejMiFyxZyvVKFb/L6D5d1mzcvKirEH+/QDbZ9seljlaM9dcJknvq34EwHDRqMw5kScVtNDfV8tvZD3nn3HUAMDsqelJ988hlnTQ9RK7//W9SGTwA6zxMB/XuidpBVa0ilN+oKUFdTy57dQppYtucrDlVV0X5M3Ox1zT7sDjv9+4m72eV200dpZTZy9DhS3Kk6//VRp57KGFtIdrj+43XYEoXUceEzT+Lz+bj1LtEVx9tu1ckOjbSLzrs8AG1NjTra6r+BlF5upl1yAwDpA/JpajgK9aJ7UG1rByVr3op4rXlbG2lQiuFGjb2QLRsX09T87TyYpAnW/JkOZk1y8e4a8QBq9HTgclrJyRaeL9KXXEL24JRceUMLvP4f8d3pExMYkB6va/2mRaOngz0H29i2J5RByoeA5Mad8UohUKNybeYPB8BhD7+PIllpm+nLjUFdOz7YXU/yHmqlBz3oQQ++5/hOZOTaV4iuluhrn26BuBNb9p6VJ2R8J42cwMb1a1Q51pRZF+BIcvHBy8JEK8FmJbtoOO/++x+AuTJl7Axh0T7lBwtorK2lqkIoBL76dEW39y8nN08ZlJPqEj+fr1vDjXfcLX7r7Nnquu3+DvY11ROsEVlNam+R5cjimxR3KrMuvFRVwGz6dA1LXxHudp988hnLl76mkfQdoV+/UFGOGUKZuIT8I8uBTr0BlTa73VYiBtdee3Ehn336kaqIcKam4+rlIDFeqBasvVLpaK5n8wZRUh7we9Rzn5DkYsiQQYwcOwGAi6+6AdypJGuu+BHF4/jHS+KNpXLvHl5/6V98uVu0rMktHKquZ6zk1M6Tn7fv3NLtNyupqOkqJF0mkZGZzg6lKnbSgHyyi89SOllBti2J/GGjWfzEvYD5NSqLgKbNH83Y1AtPiLVyV7C91MOcSU4OV4sbvmS3l0ZPBwUDhfuhMz/U2R5Elr2upF0d9CzfF1RpmM3bfAyYEiFNVuDz2kiKD12XsiT/0GF99r5hpziHroCgWg7v2UyvjByszgx1nUjKulgKgrT2JFrVyrfWfPlEQsuRd7VEX57EuMCJdRQcPuYssW+tjVRuWavO7503gs1r/6Ne+EXjpmB1ODi631wmkJVXpNqLAsQ7rGp1nnx9lVWjKe5U0xJpM1gdCTqZnCXOzg13/IZGxSd6yasvs2GNqF8vL6+goaEBb7u4SBNsVuzJLgYOFBWss2fNZsZ5F6kPhdPOnM6Y0ycB8PnaD3l78Ysq1VJ/tCYskEdWqgSAuDBL2BDvrV/26ztuYrPi+9zo6eDUSWcze5Z4IJ08bCSpvTOwKt9NShKvuNquR2V7dgFQ8ulqFr/2qqq4WLroZWbMPp+rb7odQPVdlxhYUMRd9/5JnTYGajMJYuhY7GzZ0L1K3aJxU2hvbogayCV/7crMIqdwBP2yBJXUOzufbI2yyIOdcUpD8DafD2t7yJvW3t5Kek6eWr255Jk/R/y9uoNljJw0l+2fCNXVt+ULJN0IRw8XN/3eSj+Nng72VopzfdG5vfD4XTqK5evKViYWi/NT1xiyAahrFIE5FKjD1TDBoJ9WhUZJio+jQTlM+TAwVomuWPSM+n96vzzm/UYkbtmjpkQt0Y/Vb8XYAe17z5FHaiBhBu2Jc2fmnND9SkoQGtwtH66jtqpc7fdpddjVixxg2IgxLH3qobCbUWawsy+9hXhHSMJUumd7mHfL3AW3q9PG0utIqD5QqZuuKN3J0tdfjsnK1gOKRE0MYG5Y8z6P/eX3XHX9LQCcd/4lxCu6qNMmz2LcWdMpP19YoErJohbaAU494oCAqkF3K9sMBW/xpvCry84DhDHRacrby9U3/JhBgwfhD1qU78jBTxFQpcWA9q2iYJDQnE8+Zx7X3/k7XnteNAdZukiclw/eEU0bfnHfn5k1Zy4tnsheG2ZFQVqo/uT+DtZ9tj7idsxQPHYcAM1+T6cPbikLbGioZ++WdWxUrrO0jCzi7E6s8eJ8JCUmU1hUCMDQyRdgdbixKXr9doJ4mxrIzB0CwLT51/Pxu6+bXmcl61cyctJckl1iHOfbCuSNng7WbmzmqnnijWvGRDH4KQPrjtI2pp/mZ/HR0CBoo6eD7V+LgGvBgSytb/R00HbMQqpbrGvWBm57aZuGO++grrE55n2trSpnyb1XA3DryyUkKH08YykAihTUu+tFDj0ceQ960IMefO/xncjIzThyoxzRDMaS1iSX+4R2NF/3/qtAiE+U8qMOnx9va6OaoTcc/JqjFV+EfV9ahGYMLFBd5461t/PJiqW69UaMHIVDeX0OtHtxZWbhLe/8GBoa6tn75U6ee/oJsb9rV8ZcDj11xkxSU1N1zoJ1NdWqFesr/36JO5VWX7IYyK1kvkerj5BXcFLYNiNLDePolWTMxAV8nib+etftuDOEK+Mv7v8LxePGK+t24G230tYiupd/VV7Ooa+/4otSocTITIojxZ1KtvJ36ZdTgEtp55KUlEKKO5Ub7vgNAOMmTuKhe+5Si3UeuvfnuFNTGVEsMmOtBNI4bczG5bR2/Ui0WiQ0tIrtxUKjNdQIyWRTq4+6mmqVhpMNwdvbRIrnOdZC7YEKQHQEMpbp2xOSCCg01EkjJ5DiTo1Isewr3aG+6ZxoS4loOFLXQXWNuI5y+wcZPThFlRvu+srHsMJenDdVvN0uU5pHbC8Vxz+8UL+tY76gWoIf6be0iFQFGgn2ZHGtedoDasl+tEbMZtNaGOPct8aRWywWN/C/wDAgCFwDfAW8BuQCFcBFwWAwqi7Q2CFIW9kZrQO1kYJJzCoi7eSTw/2UuwkjVdI7T/CRFaWiq/lJxSJQl6x6y/QiGD9d0AXxDofqEx13dC+NR6pV3nPIKWPIHXmW7ntq38VOUFG6i5uvujCmgbLRo4fg7p3Nri9Fl5MXFz5HWu90Zs86E4CVK/UPgOryPfz1N9cA4G15VAyC+oUNnDVeX+km+XEzakUGbpUTNzQCbbHYuPbGm9WAKiBuvqaGWl57/mneee8/6j51hvxC0ammcOhIpsyYpT6EiseN58mXlvHjmxYAotXZU48/xh8fETynDFpaWwKJYMBPfJxddY+U021KcJe2B11BrOMgEF4pKgPrmqXPM+PyW8kfMUZdpv0MqA6dlV9+gTMxWU0YQFTzyoeCMViXrHmLOKW8/9vGjq+PARCfmEDx8Di+rgxRKZu3e9XOPQUD7ZTs7lADsiy7lwjix2IJ9wU6EcgfUsyUH/8eQDfoCdGplEC2B+cRp26eMfZJGHvmRsPxZuSPAe8Hg8ELLRaLA3ACdwMrg8HgnywWyy+BXyIaMkeEtrEEhPfs7AzaQH/6OTfzxgkK5Eb0combvXzHZhKSXLhc0TOW/XtE0HT0ySPOIwY1y5pFtnHGLKEOGXDqLGyeUCBub2tTB+86Q2dWuGkZWdz3O6FSmHvxFXy8/C1uvv5KAJ59/mWS7C2sXBmhYoJQxvLQH++joaFeNdbP0iglQMuPixSioyMubDDQmIlLuFN66YK4zW7lw7eEL/bDfxBvB/MvmAzAsBG3k+Z2sfoj0bBjyZI3wx5iMuiVlX7F8qWvqW9NDz78GLmFQ3niyYUA3HT1ZZSsX8lTjwlzp1/9/i86zxejv3abgR/XTjucKSI7O8E+Os7UdJyJvVQOvKPNT8DvoV2JAnU11axe+jz9lNZ3CSlu7DbxkPW3+7BhwaaMzezduYXtn3zI4CHTATjp1GGkZvYhLds8kJ9oT6CuQDvIOHp4AmNHiMRmxWeNlOwWjZHlMu26dQ3HdNupbwhysEY/r7tIy8iiYMocAMbMuhr3wFDTFaNGPFqJflOpE68mYKdo9BnGbL6jCy8I3ebILRaLCzgTeBYgGAz6gsFgA3Ae8Lyy2vPAvO7+Rg960IMe9KBzHE9GngfUAM9ZLJZTgM3AbUBWMBg8pKxzGIguOEbPBUmOvDuVnQnxkDvxEorGvQCg2tCeCDhT02luFJlybVU5xeOnUtZJJZ+sBC0cc6ZqqpXdJ4sFdz9GZr9+6npHqvQH29JYd0L2+fFH/8YPLpyvTp99zkxOGiyoh9/+KnaHPk99LY88eI9KWyQ4Ir3zSafFgM4ZUGtAFY6Ajo9+9u8P88Lz/wvArBnTuOfXv6bo5JN139Ae05IlbxKfKPbn0L6qsK1LCuPu266juHgU2QVCtXHr7Xdy/69/ylbF1rWpvglncpJphyAJrVrGuM7QwcO6ZBQVCVl5RUyYJnIfd0Z/bPH6NK29rU2lRxpqD7Ln81UsfUK8ufTOG87E2eLcJKS49c1OJkxj75Z1bNkotOH2pBZ697uEYQoVcyLvlROFkt1e6hs6mDhGZOSZaVaO1HWw82vxB5hYbGOQRltefsivK+tPczvUJhTHi6kX3cSwy39pukybhUdSqWhL8o0Zu9b9UEuvfFvyQxtQDPwkGAx+brFYHkPQKCqCwWDQYrGYmmxYLJYbgBsAUrJydDxRV2C0iExxwZlX/Qw4sRdnwOfnaPk2ddrtdlGhmTaDlIJpkZySij0hSb3JHIEgrvR0GmtDHPXxDtbKgDt58mTd/KSkFJ57TnR5WfCj26O2mLvuxh9x7uxZAFxz7fXU1VRTXCxa3+Vm9wnruSkQkuOFCgZ58QAAIABJREFUB0T5tI7TWdG2eITXyur3xADwPx/9E3f8TDBxf/zDA1GPs/zrrby9bAm9koWefMSI4RHXLSv9St8Bql8ebrdbnbdi+dtccMkVapBODAQ5pniraEvztdDy6WNOHaf62hwPqsv36AYg0/vlqfRHbuFw8gePUpOAtH59yS4+C99hQYvs3PARLz8qPMMnTJtH4Zgz8XuFljzOlsCkeVep29628VMGFI7AnSE06d0tSvqmUX7ID5vE2MyIIuFmKAc/h56UyoDMRF2RkHasatO21i4PYEbCon/8ji82iW5TZ137O9KLTo3Ys7MzD5ZY/ci7guMJ5AeAA8FgUBK1ixGBvNpisfQNBoOHLBZLX+CI2ZeDweDTwNMAfYpGB43Nl7sS2I0nLnvUFEDcBCdqxN3b2siuMrGthCQXcUm9qaqMHAjzC08m2HeYOi3bvvmdbhIJ9fn0xVl0GnPo/KYyNk0wot0bOaOX2e1/Fr/I6tWrWf7eewAcPFJD/8wMZp4jtOI/uHA+mzdv1H33jKnCMje88CcUwEEqPrQceSiDN/qJ2+xWDu0r57HHRHu6O372i04DeN1R8dDbsq2cXskpNLeIG7srwai2qhzt8O5rz/0Pk6bOVAuFjsVZNG8RAd3gp1G1YrPbKVBarJ0IyIFwb2uj2E/lGt7z+SrVqweED36cpwFXurgeRk87T23EvHrZQj56ZxEBv9Dvp+cPZdKsi9QK440fLGbN0ueZd/3d6vJvIpCn98uj+Ec3kpqTC0DJi89RunJ5l7YhNeBp7iAup1UNzht2tjKsMPJgppl2/HggE8N9OzZz1iXXc8oPbgMgKTUjZj9yM39yY0Yu49m3YmMbDAYPA/stFot8750K7ALeAmTzv6uAZd39jR70oAc96EHnsASD5vaiMX3ZYhmJkB86gDLgasTD4XUgB6hEyA+jkr59B40OXvG4UE8Yy1S76gamfeKVvPgblj/7cFcOKWbkDynuVEJ2w32iwrKjtR5Xf1EKb9T2+m1J2NtbcSiNfA8fPsyqt16ISgs9/o//AcBjSeLwgUoqK0TGtnXrF9TuL1PL/qfOmMmLC0Vrs7Te6V06vurmNhb+ayEAj/1FyKze/OBjsa10sS19w4cQhDWtno4wWs9KyMpKaZE7aPAgBvWLbrUgM/J+/foxdcZMtmzZKuYfZ0Y5bf71PPB7caxm2nEJOV9LrZTu3saV86Yf1++DyGBlS8GUpHgqy8vUsZbaqnKy8opUeerkc+eTNbgYv02omJ348XpCZfmLHv6pjk6aNv96cgrFW6J06Bw1VmToeSPzo5btdxXSp+iS51/EnRsa4wgG/Hz2wrOse/h+AI41eUy/HyumneaK2Dzim0bROPHmP+/e5019V6BrJfoSMkN/8SencejLzZboawscl/wwGAxuBcaYLJrape1oSvRlEJcnw+cN76YRiWMC/bLBM65k83tvACe+qCEWHXBTgyhkSS4chV0p55Yl0xIdngZsDgfH2kVwcCYnUjx+KoeV7Tt6pYbte9EYcQGlZWTqTLMS7Fb27t7NCwuFJ8TMOedTWi1u7HGdBPLq5jaajrXrStbzikSHmLqaai68+BJcbv1DKEH5XWOZuxzglJ+DAX9YAJeQA52yz6g/aKHFJ6ibZIf55SkfSsVjx6n+L50hv/BkpkyZrLZ+C9tmRha33nKDOt1Zeb5WUmmz29m9a1dM+xEJki6rrSpn0T9+Bwg72VHTJ3LSSGH85WttpLmxnsFjlIIpgvhtSaq0NehwqDRdO0Emzb+ZhmceBMTf8ON3X2fOAsGJTzz7Epa9+BiVFULKWTx5tOo/3lX72qy8IvoXC0qn5I1/k5aRxexHhc97Yk4+uqsjzs7oBT/ipGmCwnvjh/OPa5D4vxXEIUS1LL3vKn7wkPA0MgvasQbxrgo9tOgp0e9BD3rQg+85vjMl+sbqJu20VrpjHNU1M6lRkVXE5KvuAGDRH2/9ZnY+ClLcwkgn1RpQM6U2n0/X31POl/9bHXay+uUw80phovXFpo90Gfl1N/6ItAwxICczQjno1uLvoE/eIH5+nxg41GaRWyrqSQwE8SoFI2byOi0scXbqjoSoiunzLiO1lzjxZi6H2qzV6BwYWXoooB0Mbfd3UFolzIuSnQ4KM5MifY3HH/sbN91wNdt2RM7opDnV44/9jWdfeiPqfuzYWsJkpfGzsZIT9PSKdjC33e+n9ItPo25bIi0ji4JREzl6oAwIvdlJO4dJA/PZvlmoI7ZsXExV7TZmXyqMzNxZfRhQGBpUtWHB1u7h8OED6rz0HKFw6fD5cSS51IHRFa8+TVNzA6vfFCUecxfcrhMD1NfXk5gurquuZuST7r2fjJGnAGBt9jP67rtIzRd0igMwsyVzKQ1ABg7MPyGyzf8m9ny+ivpPhJ1H6oRLgNi6BxlhpJW/dza2RvfDaH7kZq8d0eQ/J80Ur8szGhr54MnfnqA97hyFxadRf+QwIDjy/oPFq2e8w0Gbz8eWdcI9cczUWdiwqCoWGxYSUlJJzewDiO7n8rW73dvGtPN+GLFKEsI7vscrnzus4Ceg6yakDeaJgSD+eIe67aQEC28tFXK64rHjGHfa6REbKhs76Mj9iA3CHVGveAlx79sPNpGXIUqyjVTL6NFjef+DNbzxppAubvx0PR6/2Mf+GfGcevoMzj5nJgDrPl7Pirdf5ILLhGPdmg/fpa6mmmtuvhOAESOLGXdWiOM2VnJa4uwktIuQ5I+3qTp5EPy+9KzpDHU11bBlnRpgR06Yxvp1H6pumvtKs1Qd+cCC4Wz9ZAVb1wjL4+lX3aHThnf4/AScIbpr9bKF5A0RD67BimOi1SHOXXp2Pk27StTAvXXNW2T3H61O768sw5Up/G5iDaySB8+bNJU9b4lrZeSdt+LOzUfqsHyYB3N5fRxoOjE1E/9tbN4mxo+mKYG8K5y4hDFJ/d7Z2Jr17DT73NnAp5FD1wb0CVfehcvt4u2/i2D+TVtzDiwYTv2Rg2Iisz+uJlFMlJDiZvW/nyDoEANWkjOX/7f5fAScbpLdgtsepfT2BNi7ZR32hMSw3zKWk2szSDUgBfTrhgbsxPQxvx+LZuyyqqqarRtFj9Jf3PdnUnslRAzkxt/VbhfCBzi1SLBb8PpRW7J5NRp0yb3LDB3EeimJocs2q3c61193HQDXX3cd1c3ij99YVUFZRTm/f1gMDDcfreLO3z6itoIbmJtHZUU5B6vE3+jKq38Udn6MDyb1bcbwIH3u6SdiNisDEczlwOKosRcye+5FVJaLDH3rJyv46B1hUXDe5TeQU3g7B3eJoqXNK5YxYe5l6nZsWGhXfMYBkq0D+OClx8TnxJ+TmTtETQhshv6bJetXqgkCwM6Na+jTV28DLQN1zuQpZI8cxTGPuEmbDldxZPtWJt4qHoLOY+1UV4kawKK5F+ivO6CNUEIBcLTuEA7EW9jg4cUnzBtJC5fTypCTHXy65cSU6JtBKxP1toSuyUie5PIz6Ev0jb0XtLEwVvRw5D3oQQ968D3HdyIjN1IrWvdDrXmWUbEiIefJ5WZuiQnxgmaZkyTM8t/+4y3faFae4k5VX2t7uVLxtIjMoKWhnh1fbOLsC4WzoMzA67YL9UBleRmjp51HkrTIbAllFHF2J8lJ4c6IxibB2vnGDNy4rtaYR7v+Z2s/pLdSUThz8kzTbLzVq5euaiWHWkWH1x/sJCu36j+ryZtDJ3Fs93fg9XeomXpiIEh9TSskiO8vW/Qq+/fuBCBgdTIwN4+zJok3mqyBBXy08n3uvk1k79iTePDhx1S1jDwf2vOnbRyhPXc2u52ODvhik3idXr70tYjH5kxNp0/fHJ3KqWjcFGrLxH5KHlzSKXMX3M47SsvA1f9ZxLwbf8GAwmsBOFJVhdfTqlOmeJsaVEnr2HOn8dXXawD46J1FXHjLvcTZxI018azpYZJWrVzTU19LmeatIn9IMec9J6wuEvoKykXSJW2G1nceYMKC69Tz6AB8mgxcS6201R3hq1cW02uA4OMH33Eb+7YL+WhpSWQDt66iYKCdYYXx31hGPnr2AkbNFsZ39iS9XLazak4IxTujg6uWdfjeceRGasXsdQMil68a5YnG+drpYVOFbtadmcObv7vqG/Na/uidReQpVXajzzpXnb/lkxV46mvVTi3xDgctdQd587lHASieNFfwnj5RluxKT1cHxlKS9BWgRhjpADkPwnlsYx9K40PgoxX/4fJLfgiANdm896G02ZRd5fWa8dBVGC2Idwa71uFB4c/lA0P8WgC7VzxkZp7/Q5ISxG+1eoO64/nLfb/kozVr1HN4x89/wcCCojD/FLMBTSk11Jbq2+x2Xn/uH+q6kapKPfW1uAeforowlu0qoW5/OTMuF4Pvez5fRcn6lbz7ylMAnHn+AnVw862Fj/Lyo79lssKnDxx0ClaHnWM2kSDsL1nLWwsf4fLbhfY9tc8ABg0Wg44l61fy9dZP1ErPZMXvPVa4B5+sBnAQ14vPQDVpeXCrM5RgtAX8HP1ytzrde9BglVr5aOGznHrpFcRnCVonPs7OnH+JB8bOx5/ms38/ckLK6kcPT6CsMnL3p+NF9c5K0m4RlhDGyk4jOuPL7Up7vtZ2py5Z/d5x5MaMPFLAjhTEtaqWaCdNe5IHDD+Vqx96kRf/8BMADn+5pRt7Hhm1VeXk5Oar04F28aTaV7qNtIwsnMmC6z5mc7Jz9bNqEJA3Xn2d6EuYkJKq2trm9M9UfbMjIdIgozEjB0ho94WpWNSs0+nmvDnzdduQ3LTMzrWDrtqHhH4wtgMv9m4Hc6stdIn6/UFsdsLK/5evFlpyb0sT+2rFA/CM0SNI7JXCpzsrALhywfXceFvITVmeR+22IhUBySAuM/QO7NTVHOGTT0IZ5ElDR7Fhzfumx1CyfiVTZl2gTpftKmHz2rcBmHj2pWRkprP2Q/Hdj95cyJwFQmk1d8HtLH78XpY+Lfj0M89fwGlTZ9Oh6Mb3fL4KT30ta959HYBJsy5S1S+7vtjE9k1rVQ16c70YVOzM3kHCNXCgbtr40Af5IAUMbzINFWW8dZPI0KvL9zB43oVM/rkYm9r98kIccRZOu13YMvkAh/K36D93Ota3XgRP9wu7Rg8WvjvuZDhYo3+LnD/TTWub2M9317SGfbcrOFCxmtWPir/Tufe91C2VioRfeTA7bN1XrfRw5D3oQQ968D3HdyIjN+rIQU+XSBh1lmD+9DN7vTGDLXsMV/xaVKA9dfO5XdbPdga30nYs3mGluVVRUxyp1jUvtre3svWLLapCINmdiu9wOa/+jzCOuuTm34ZsbTUZvkSg2UNcr+51cjkWZwFDBl+6Wzg6njVpCqkZQsMtM3AtT97i8YWpOoyl6wJxYdm47CjUVUiFi7bc3+sPMnK0kNzVH61BFoMPLCgi0RHHoLzC0LFGQaR2btrjk1xSMODnWGNd2BiLlj4xQjojzpx3MfhbdXzw1B/8iGCyUJd8tux5Nq8WcsNL7voj0+acz5JXhM3CpuWvkls4GFe+KLM/dfYVfLn7C5X7Hlw0nN7Z4hrpN7CAqsq9+BsVR2lb166RvLMmq8fvAPxxdvXvbQ/4Kdu1k4JhIwE9H1695XPeuf0nnHK1GANKyezHJw/+ksObxPH2HjGST596ktRcMf5y8ryL1e1mjRrHOU8/x/JbBLXUHdqzeLjITRta0LkiZqZZye0PWMR1ULbPoVveHWz8YLH6+dz7XgI6bywRLVuXY4My5n2vqRVJ9ms5cm1Q105HCuJmbZSMg6cpLmU7Jwm97cwf38/bf//tCR0A7dVbdNJp83VwYJ/gub2tjSS70uhQWnCVbdtEdfkeiscLV4NeaZns+mwtKaliULa5PhQwcnNzdC3GgG4H8UjY8rHQQ190/Z3UN4uTp6U3APzeoC7Q2exW2gOE+auIZeFXozGIdyWwJ9j1XugdHaiOha60TJUjb/H4OOYLYFPa0tkUrbqxlVukMQMj7aQdAwB4/OH7dPtR39RA/lBBi0Wzb1j74fvMuuxGsIvzXFryGcnWAYw9VwzK5v7sT6x47WkA6qoOMWj6JRRX7AMERfPWv5/iqjvEQz6zXz+Gjp2kBpRdOz9n6iBhNZw/dDRlu0rYtnUzAMPHCMolFj+awqkzyR41Th2g9AX8oLS3A9j/RQlHvthCthLItbTaV6tXkZKfR/GVYoD26Je7ObSvit2rhE5+6m/vZ9mV5/P+vaIfbO+iwfQeJIqcLHF2ckaPJ2+iuBdqX/vfTvdVi0H5DtwKVb/4PUGdSIlgdpYy1hIU1/LEMbbjDuQSGz9YTIui3b34p0/gpbe6zOhHbgZtEvu9Lggyy8i1gde4zDgQqg3sRqWKMTtPieDHNGzuDbjzRrJ18d8B/dO2u7Bqfry9br/62aYxztq7U3DzuUoQ8GDH11TPwAIxkNJQc1Bd1+1OpS3gJ9AsBkdkEE9UDLeOxVkiZuidZe6BZg81dYfUaanrjgQjJx4+2Bk7upOdA3S0tJGQYA170Ejoj8GKl1AAT3Y6dAOi8gEpj8kbpzfH0gbxX99xE6sMHi/1h6uZePalgFCqRNKVe1sb+eidRaoxFgjlSuaIXABGnzGd+bfdry7z25KYeP4CQPSKPfzlFjatFz1Mx4w/l9zCYeq1uv+r3fiUh740x5ID5SiBPBaMv/Y6fID3kKgYPXasldT8k9UE4v37fsuoK68JO3cAR7ZvJb6mnIByI9pTXDhT06lcuwYA96NPMuNPj/H8ZWL85Z27f8Yl/xLZbHxaJmWr3mfvqrdj3lcthhc62bJL3AvSwlYmQXUNdiBUJexORteU4nixe/mbgGhWPO9X/44pA5eIVDfTlYy8hyPvQQ960IPvOb4TGbmRWtHCqFTRaszl0y6a30pnTolGJUtalnht3Ve67bg9IKSOHKBC0xbOqeFrZcaUPkBwm3GeBrZvXsepM38AhDJ2ENWI2sxaftbyv8as25i9a+fH9XKqywHWrlnBuCnnEgl+RTdu1BFDeIYe4snDc4VoVEosyzraQ8uN2bjUmBvfKPzeIPb2dlWi3tHSRjDOotIFZsekhc1uZdELzwKYdgJqaazDc0woZvr0zdFpso2orSpXu81MmncV+FtVl85Bg04hISWkTIrzNODuI2SAY2ZewgcvPcaWd0X2l5s1kN79ctXxleryPewr3QEI9VN6vzyqKvcCcHR/WcT9AVEJmXm6oHcGjp+EBSjbLbbVf2QxwYCf8jWikUpVyQbO+vmvI1bB7ti2h1OUsZb+g0fQy52m3kvVGz6n3xnTGXuNqKTd+K9/suYB0SA8MTuHLc8/26UqWS3qG4MR3RDrGgM0tKBSLwDDCu0nLCOX2L38TfKHP82Ic2/ofOUIkDHwe0etQDh9YtSVQ4g/6ux1RbtcajStSU7dMjPaxdsGzkwxCDPnrkd55dei43x3LywJX2tjGEUi5WBVlXtxpqaT2iek2W2oOYivRVyQjZr+nQlKSzNtcNYGYuO8uF7OsKCvXc+IxoYGdXCwM97arIRdL0+LfBVG2652WUd7uy5Q19foJWNS397REvqDSwMDvzeIPcGiLjN79UwMBDmGXiuu5fm1fHpTQz3/etrcAhfEK/ya9wTFMSQ/T+XJ84cU09RQj69ZSEjlgLocoMzJKWDej+/nlSf/BEB11T5OVq6FoGJtLP8fP/kcast2qC36tm9YS/H0Sxg6WNAo1eV7qNy7HYCTRk4gJzefkvViwHD75nUR9x1Ei7QLbhRSXL/yQItT7CAc7lQscXb2fyaagSWmOHH16aN+16+5DnJHn0HpyuVUrF0rjm/0eE65+ho+uEfIDT9+6nHOHzGSc+4RCVPjzj2UvPHvqPsWK6JZ2vbtY9UFcYBUV9cowFix+tn7GTrlfACszoyY/ciNMbCHWulBD3rQg/9D+M5k5EbVirFACMzlh0Zon34J8eBFZJ7+CJVXkTL07FFTOOMS0ZOvu66J0jTL11QfphaQr+He1kayMovUJrlGNB4JWZTG2/SKDWOWbUajGDN247TE4iUvMjA3L0xyKOH3BtUBLbNO88bsXLoDJjv1+2yEWeYvKZy49g5ItoUy7gQreENVfx0tbQRsDtNsJK7dh99rvkwLlVoxDNha4uwa+4IOnnvy0U7lcLKobEh+nqqWqKrcS7+BBdQqGXnx+Kl8vX2zmpmXrHmLoaeeqSpRALXLT8DpJsnXQiBOHEWrI5ncoaPVjHzbxk8ZNv4cMk8WA+W8u4T6w+I687U20tIRqortrBFK0bgpZJ06Tjcve+Ro9VwA1B/er1tuZu+bPeFUAMo/Wg3AtDt/xciLLqd8rZguXbmcN2+5luHniQKpYPI3kxVLuJTrb0BmIqs+DRBUtDijhyfgThayRIAjdSemSTOIN/jyEvFGctIZF3aajWuVddp1v3fUipkfuVmrN6MvgRmMXiuRNOVyWTTN+YQr7wLg6L69bH5nYWwHo0HZVsGDNrXqeThrcipffRzivv0tjVRVfA3Aitf+l7qaapVf97dEl0NGC9SReHHt9J69orPN88/8g3+9/GZE2qPDGsBmlVRDuMWrWQedWGD8vY6WNl3w1fLh2iAuEdceOrcBm0M3rf0cCXKdROAYfpVb13Y9qty7h1cXRqZVjDhwtIF+AwsAEUDd7lQaasSF1lBzgJOGh4JxXU017y1+jstu+DkggrW9XXmoexrwOeygWNfGeRqwOpxqcGpsbWT7pnWqtDAtI0t92FjaPSQF0mLe5+QBWWpA7vC08MnC/+XMm29XlwcDftqbhQulbM+mDeByRCLzlGL6FZ9KR714UDXXHSE+LZM5fxX1Gu/94hdUrXuLJV1swNxdyHJ/I+1yoNrPedN6dSmAyzqBwtOnUL6jhH07hLTTTLIcZ+sVdVtGSbX2s4yF35qO3GKx3AFch7jStiN6dvYFXgV6A5uBK4LBYNQ7SvvkkYHbGNjl52gwajajdbCWn2MpHppz2x+oL98WU3s3LTxWETRT3Em6bK6XLcDWik90625Yrm9JJ5tPeDzd8LRUYJZ9y3n+oJ/DNdU88BthRTpy7ARyCwtNtyNkevoLXtvqzKjBlqZSgMYAK4SoWbhxZW+HaoplFsi1MAZuY2DvDPFxdjWAa4/nz/f+TLeeMas2orZsJ8WT5gIikO+rKGPi2cKnevmShWBPIr2fGIuprSrn8K4S9u0VvTVzCk7Gpwxe1x8+QO2BMgrHnAlAY20tvfvlMna2KLZZsegZyndsVgO5PdkFyptfU2sbFnt9zMfeXF5Oh0e8JTYdPsiRjZ8SDNyiLrfE2bH10gcnbUbu06w3/oYfs/hHYnzp1WsuZ+7fHlcbTZz31NMcWn8ZG55/BYDqkvXH3Wv1eNCVjPxIrWjdOHfGlYy/7ve0fL0JgI/ffYPStcIT39csGnQMHCGCfiR+3MxLypjEfisl+haLpT9wKzAmGAwOA6zAJcBDwCPBYPAkoB64tru/0YMe9KAHPegcx0ut2IBEi8XiB5zAIWAKIN3vnwd+BzwZbSNWDY1qRq0YqzIjZeZmTU+jdQ8yZuiRYOnVm5m/eJznbz8PiF3F0reXeL1OzQjosvlGS4puPUfvfrQqnLlE7YEKAOKDLcicXNsiTsJMuRIL2gIB/nzvz2hoEFnlrbf/LGJhTaRWZ0Ze3OgOGAlhdEp7u+DDIyFCJm5NjsfvDUbMuuPafWHKloBSrWn2nbh2n6p6IeDjtTeFPW3Jxs916zXUHIhq56DNMCXdIYvDxp81lVXvLmHsDOHCWVtVTlNzA2uWihZsl9z1R/w2MU6R6Ezi/cX/4kCpkPING38OjiSXWuwjvy+lha7MAarMr7GxXseRd4bSks/Y+PQTAIyYdz4HSzarcsP8KWcTDPhJyy1Q1z+ybauaZWsRDPg56exzKP6BKI4qeePfrPrFrzhXaXodn5ZJvzOmM3/8JADeX3BFRLOxE42EJJdKgyTFx4WpWDpDy5FKAHatWcboi+/Cli2qws++dQwTrxBvtvUHyrGn9MbqzAAiq1WM1rVy3rfeISgYDB60WCwPA/uAY8AHCCqlIRgMyjv1ANC/s21pPbGNfgPacv1IfuRGRNORGwN3tACeoNDJXg/kjhzN7B/dA8Te/9OeJIJzRcUR3XxXsElHmWTnFIRVkn6sONp5NZaeHb5wjXN3gjjAU489RMnGz7njZ8IRMLewMIzykH7jZl7dZry4setQrLDabHQQOZCbyQzltPFaN65r/I4M4Nbk+LBlWlTsL+OpRx40XVZW+lXE76nr7BT8acGoidR9sJjyrSLYzrjuGioqKjhaLmSCxeOnUrJ+pfqgX/zPhzj3shvFPjocJNisbNso+oEOKBxBZpKL5qDeN2b3HrEtLSfeXrefuv1d8ypZ/TchgfzixeeEq+J94nrvk5+HM/dk3Io/CsD2ZUsomisGLLUPdPl52h/+AkBzQx17Vi7n7WsE1TLpQeHkuPV1Qa1s/JaCOOi57IKB4uZOipdXUIeO7oqGHZ+/rY6fgYgpMnCnF2V0eb/MamXgWxrstFgsqcB5QB7QACwCzu7C928AbgBIydK3mNL6ipsNBkSDGS9uXC7RWcbu9eg/D5srRP5frFoaZtJvhtojR0znr1+7kvigolqJ8F2zAZSqqoNQHJpOSkqktVVvnG82T4uNJYKbX/H2m6T0cjNirOBfrbbIuvHOvMsj+XXHCmNA7SwYG5HpElf8kca4iOsa+XJV8WLIzOW5e/zh+yJyt6dOOrvTLFIW4oycMI29GVlUVkgd9wJOnfkDVig+IjMWXAGgDn7u+XwVC5WmEzMuv5X0/KHqtVa+tYzsouH0UjzapQe6DNg5Ewpgo/iV9WtXdts3SB63zO4X3f4T5j3xDCdPmgLA6tR0SlcuV/t09jvzTHql9VW/Hwz4SVBupDl/fZy3f/oTSpXBzf0/mIPTmfBf5cUBUl0n7VGUAAAgAElEQVTiYRifGHooykbt6958LqqtddMBse+xvM3HqliBcFXet6UjnwaUB4PBmmAw6AeWABMAt8VikQ+IAcBBsy8Hg8Gng8HgmGAwOMbpTj+O3ehBD3rQg//bOB6OfB9wmsVicSKolanAJmA1cCFCuXIVsKyzDWlfISIZY8nPnTVgjmQjKRFJmuhtA3cqKD0cTD/Lng5nXvUz9u3Y3GnGI1+Xnan6B5W3tVGXiX/1aWwd2GWDCQmzzDtaNg7w978IvbKlo5lrbr+XU4pPj7hugk3QHcd84vMxX2i+Pxjqdi+pMbU1WiB6VyAjhWOkOaJl4GaUyJFGkY+YZdihbN2cEzfiqUdFzcBKgymWFu4BJwPvq39XszETeW2Uby1j6kU38c4rgn+uqdzLwEGn8MM7hDFWQkoqReOmqBk5hDLi1UufZ8K0eWpGXlmxjub6cPMrSQXUHQyV4Z9IF8+qkg288cP5TLj2egD6F4+mdOVy3v2VkEymnXyyanzVKy0TNCqWYJuPprIQVeFtbTxh+5aZZu2y/ltKN90u8LfFUdcgvu9MTaf/yEkATPI2s3R/WcT9DPhbaa2vISlV0ChaBiHWphJm2nGj/PBboVaCweDnFotlMVACtANbgKeBd4BXLRbL75V5z8ayPWNpvlmpvnEwIBI681fRztfJET2a+ZrPdckenB6nSrVkj5rC8AnTY3ZI7GxwNFYf9NJdWzqlTqJhR+l2ld+94LKruf3OO6KWy8vBz2TlKknWXC3HNH7kRptX2acTzAN607F2XbehlEQb9SbB2yxoa6c7040HbA5dAA/YHPRJEheUDP5a/POpR3nhpZfC5huRnCgujASlQKtP4ckRefPKinVMvnw+Q8dOAmDH+veYPPDHqp+K1WEnq1+Oaeee6vI9VOzcrONuG2oOhnx57Po6Ae3D4ESjunwPS35zl26eDHRVJRv44pG/AXDKHXcSbPNR+bmg8D555K/H7VlkhJQMnjW2F4uWd62HQLwSP9zJULLTqz4I+gzKJjVHeNYkZv2UOQm9Io6FBXx+fM31Ki8e69idhL3dow7AReu38K3pyIPB4L3AvYbZZcCpXd1WpABu9COP1M/TDLEMbsaiI3ceCfclOWXeNWz/RHgsf5NNnLUoP1BDQ0sTdouef05KEloLGeCN03Le5+vWqNMXXX5Nt+1jQZhSmWmuQWToMjsXqtRwaH87YhGSErRlVn24NaHTwB1puVxmFsABKko38Mqz5kU/CUkukl1pGo2/uB5k0J160U1UVT1ieh3U1VSz7p1FTL9UZLOlm8TAZcAprIzj2z0kpWcKDTioOnCJkvUrdW90lVVH1ED+XcLK58W5+/Qtkdwcrz+RFlqtt8tp5bxpQs++eXvXaywKBgrVkqcDtpeGou/Ey3+qfk6IF+NhFWVCLbTRxBfdF7SqCqfOWAAj/JomH8YKdm1862n11oMe9KAH/4fwnSnRlzDLzLV8kvbVoyt8eaSnpJEzjzYSnaBJzLNHTWHwkOmAaAzwbaB2fxmHa6rJztR3RDdSLZG484aG6JV+3W3BZuaEiDU21Yr8zUj6bi09Eqcwr3LdPkleXYYdrYoz0rKGliYA7n/gDxEpLkechbxho9WMvMnboipGQNAj0ai2jR8sZtQEYRFbOOZ0Onx+OuqEBqAtIYmA083wMWcBmNIQ2uw2WL+fONukiMf534bcV9VGwNM1DtuI0YNT+Loy5EM0fWICSUrXqQPV5pbD0ZDqEtdOeWWQI3Udatn90FMnh607+1rRBaqxokynUnNn55M1sEC3brSxODOYVasbOwR1Bd+JQG4c7NQGb+Orh/HAO7OIjEUiJJfLAU/QD25KbtxrkGyPvGsBALuv/vBboVeamhs4fKAyLJB3B4cPVDJ0+DDdvO5SLdpyfeP0K6++yk9/cj2HGsz/SJ39ZqTCHRA8d6Qy/ExXIIyK0UJ+b9lrYgjHWPSjhS0hnuyB+VLZR0dTo46f7vD5KRg6KirVtnrZQgAuuPE3BJxuGhW72fIdGxk582IG5sVGlzQ01KtdgL7LkFz06IEiEHcnoI8enML+6tB3XU4rqS47B2rFtdQdoyu3Ijt850PhGzP19kcBUfSnhbcNEpR5s+58FNez97N/n5CUnn2t0NeHtY0kco8EI6L1XzDOiwU91EoPetCDHnzP8Z3IyLUw0iXaabPy/VgR6QlpzMAl+5AQr5cfGrNxgNxs8bqcd8YUtWffN43P1n/MpInid7taDHRS0SD181tLl3Dm5FnYE8xlgju371DNojL653LKyGLOUApCMvrmkOx06JowaAc8tdn5xi07Yjqu3qliW0c17E9X3AtBP9gZLRvXfm/l+53/3TweLw45GImQ+aVl54WoloO7GTThXHKGCdtXs2Kx0hLRRb5000cMGz+Nj1YsU+fb0rLJLRQNiLPyiqKqPLQSVM+x5k73/b+B4sEJFOSIm3TtxmbTbDyaWZVctr+6VbdcNodYtqJ7RnIF+X34dIMwvmr0dDDj8tsYMNxcl6F9g0/NKeLc+16iozXUpMbbZi457GoTCaPipTO6OBK+c4Hc7NXCaPlo5MwjaThj8VIxBm7tepGCvDGwz735QWq2b+20tPdEYM/OLSqva1SvdCZLHD9+oipzW/XuEl4cOZKLrhLl4MbWaK+/9K8Q3bDxc5YvfY0/K8vSMrIYNWokuYru9spLL8ThTNF11GltFfvojmuOSKtoISkWe4JFdULsKrpqYVtRuoFtOzqXxnlbGzlWe0SVATY01NNboxwpP1TDyJRUpswVZejV29dGpBKWPPNn0gfkq821S0s+44tP3iN/8CgA+vfNjhrIpQQRTqwy5HiR11dci2NGJFHfGGTtRvGQMQvUg/IdDC8U1JRRPjgo36Fqu43fHV7o5ECdv9ve4XvLDqufi8ZNYeKNP8cTI/VqRKTYEmsQN/pHSfxXbGy/CdjbPfhw6rJuo9zQWLIf6eRp55uto/Ujl4OdnkylOcN+Z3SNuTMUzJ2ZeUy++QEW/ebybhxx11Cy8XMalBs4Q1MWbQZjhp6R1pd7/vBXQBQGPfLgPexX/MgvvOoWcguHqln1rXf8iosuF3apX+3YyhdbS9i69QtAFDqt/GA5KEUzdl8j196qt3rd+9VuAApPiVxwBOYDrPItwe/VP1y6YkkL0XXjAZuD9SW7Y9pOWkYWvbPzscaLYFW7v4zRk+eqg5u1Skm9K13IBIvOOD9qjcGSpx7izNmii3xCkovq8j18vVXortP65wPR9eAfvbMopv3+piEz5xFFIfepTdta1Q72ZpBBXAZ6I4w9NBOSXPRNETdabn9Y+enxDZ7Kh/HUmx8gYOtNQgwRUMYGs3aR2uloMCajsUioe+SHPehBD3rwfwjfmYxcPplavc4wesX4GiKnu8KRm706aeWJ8n+1+MdYOOQML9lXv+uEYVMvxHpU2Fy++sivY9+xbmDtGlHSf+EFV0Tlxc3mS37d5Urlgft+y+LXXgXgg3ffZ9qc89UsvGDQYAYqJzq3cChzzv+h2uqtdPc2lr7+Ch+tWQPAwmeeJNEW5KLr71R/Z8vn68XvzZgd9ViiqVYkzSJ/N97WtQzdWACk5dDj2n1sXh/Z+CzfUK1pi4+nf99sAErK99Dh8+gqLiu//ILsYiEhnDh7PkcPiHJ5s2YktVXl7NgmmhLkDBvNns9XsX2TaA02Ydq8qMckv//fgJQU9u1jJT8nkf6Z4s1pxbroWXhmmlXN2jP6tPPZ560x0yPe1kbGnCGKpyoOQsnu8JtettVzZWZ1WkV61o/Fm2OfIjGeISXFWpWavLejVYFrp3X7EmMjCTMbW4nvNUeuPRgjh2Q8CcbpWDoIdXbSo3FknkwP3v1OnRzRzCVx8EWiOmyGN8DHrz72jXGYa9eIm/7CC66gtfWYaSVnJEh+vahgCH979AleWPgMAGs+fJclrzzH+++9DcCsGdO4/s7fAZDiTkV7+goHj+C+h8ZQVSV01Iv+9ShPPvlPMhTeePrc+Zx/6QIA+vXLOq5j1cJHqKUYhPTkkWAM9NrpfdX72bJla8TvaoO4x+Olva2N9HxFrrl+JfsryxipaMNXLHqGPZ+vomjEWADaU9zMu1HYA7/w0C9NA+9hJcDblAtJBqCKrWujHtO3BZfTSppLPART3Vbyc6xq1/mySh+btrWwSVnXLCjLoD92RDJ5ORbV+3vlpx1Rg74RxYMTyFWMsN9eE644SMvI4oL7nhOfc4ez+lHhYGhGbZ1+6R2MmCz63Bi9lbTiBrMq8FhqUjpbJrdpLMs3ojvyw+9MIDfC7GAj8UqxPME6y8ijFQQ5jzghXj/AqeXItb7lAFN/dBc5w0fz9l9Ez8MT7TVRUSqUIDV1h8hI6xsxgJtl63KA1B/0k+hK48bbRMCZc96FvPj8s2zdKLjaxa+9yr5qMRD12FPP67Yhy/NTlCfbtXfex4Qpc+inNh4InLAAbk+wYCfUR9MXxU5Xi/g4O16bQw38MojL4L9jawkdrfoHrbbIRwtvayPNjfW4ssXxpfRys690G5fcLAy2Nry/iJL1K5l4/gIAXOlZWNNE9Dlz9nyWPPPnsG2qxUcGqjianv2bgBykTHVbGZAuwkFGH8Umt5dYVtfsp2JfkM3bRGFOLNl3Xo7I1mUA97eJh0J9Q+wcd1pGFmcW+6k4KMhiyZ/Lh4TdncP5v3teVZ4kOGHubx8Tx1NwCh+98LBqF10052pm3fwzJO0cNu7VFp6RS0SKDZFaSpohUt9h42CnFt+WjW0PetCDHvTgO4DvZEauNY6B6CO8seouY6FWOmvGLNc1cuRGjbnXI0r4b3pqNQDvPHxbzE6JsUBmjds3f8KU6RdGXC8a1dIWCJBsjaelQxxUzkmD+cW9D9KmaJN3bC3hb38WHWM+Wvk+I0ePIz5OPvf174M2u5XBxaer2XGCPdwoS+t2GMu0GbRGXRBuDaA/PrEv8s9u0WTnACs+WB4mEeydnaeeW9m5R6KidAezL75OfD59mu7vefLp0zh6oIzqqn2AyMjjPCLjLhxzJvmfrOhy4+4TDZfTSt8+VoYVKhm4y65rdVZ+0E99g8hXD+wI4GkLcOhw6B1fe64y06ykua04le46nraAKid0uwhrodbQAii5cGtb51IMmXFPKW7BHm9n07ZW3fLeuacAMP+PL5Om6VoE4OkQ1ZgTrryLkXMWYPGLjNyZmUcA/duzdtwrQfPGbYwFxumummRpoZVOR1renebL38lAbnzdiEStyIPurq7crIenhFlQN1rcGgdKtFSL1wOJWeKimvOzBynfIVp/nciBqjfffj9qINfCq3SVaVcohvi4ODWIy/k2mwMShbPc5HPmMWSI0DrX14urvS0grqy2hnqVVgGhG7fZISEhMmdtDNCdTUdCstOh6szbDO3ntJC0izbQ+4CORnERbdsa3gGmqb5ODSJGX5ryHZtpUiSD46efR/mOzezc8BEAk664XQ3cAO0ESXCKvpvV+yo5deYPVO33t9kZR6vXdrvg4BE/9Q3i3B080kZ9YweHDosA3ejpiFikk9fXzpCTHeQOFIF7QG8LBKOHjgalBe3m7V4OVPtpU+7RRo9wMIxWsj92hHgS5PW3sGVXUEflFI2bwmUPvAiIsnpjEqUVIECG8i8EY9cvub4n06NzOTXGCrPYESuMRY0QnVL5/4ojN9ORg7mNbXd15dH+IJ0FdTkACuEFQkadOb1yuPohcfEte+KemNrExYKvt29m74GvKRhwkm6+DNA2m0MN3EYYM3IgbF1pDJQ1MLypBaArADLLwr8pqNWo3lADA9Bn6Mb2dBI7SoXHidkDNSluINUeMZ5RoHlQyQbK2z4WaqEJcy9j2sXXkeIWD2oZxOMdoQdZ9b5K9bM7vT/Fk+YCYmD0m4TkvE8b52BAmp3yg+L4l63whgVol9NKwUCx/qhhCcQp4yfVh9pp9QXI6iOOp7/ehgQM9VoNLVDfKH6nodHGgSPHdA8II1y902j01Jjuf/HgBEYNsajbXfFZ6ObPyivi/Lv/oSZIRsj7Tn7uCpxHnGEceDS1SlcGQqNZb2uXGU0Bu4IejrwHPehBD77n+M5m5MbOQBLaJ5aU8iTZDPKRGNAVTtzsySyVLGCuXjFmBLbsMQD88NF3Wf+/v2H5sw/HvK+R0NTcwPo1K+l/hcicjRm1SpdoPst1bDYHLe2h9EEu01Im2ow7Gmx2K15/sNP2bhAbhRLruvYEC3iDqiLFa3OoGXh8nF3lybXQNtfQIr1fHr0LXJQJgztyh45mX4XQgttS3BQXDqNkzVsAjDhjmio1BDhmc5LY7qHNJ/Zj1RsL2btFNFueNO8q0gfkM/RU0eS6sbH+hI6XaHH6qETOGCUuyoYWWPyeXt9dkN+H4kJxYwXxMyA9HnuCcs1oqBJ3oQ0s7Wrm7W+Lo9Uf4OARsa36xgCHD7erfHdXS+b37TfPxgflO5h6eoKqcFm2Qs8/nH/d3aTl5kXMtnVvwYZp+Tlaxh4p445FpRKNajFab4N5ib78/L2lVqLJbIzFQdppyT35iT2AS8RCrXRGr4RtM8JFI+dJTLn19+rn4w3oH3zwgarZlsFYG7xl4E62xusCtzagy+m2Y81UN9YBglaJFMBlsJfLbXZ7WBA3BuNIQdmsRL8rdrqCZhF/mKBmILTNZCA0GPCzY9cu8/1IcpDSN9SJJ31AvnqcTQ31jLn0RnZ9IZTT695ZxDmX3kC7EumSfIIQ9nvFoFxadi57PhZmXEue+TOFxaep3irDT8phX2l0Y6yu4vRRoo5g3JBEtuwSf5ON21po9HRQPFhEkIKcBPqn+7AnKOc2aAeLTw3gB49C9WEjDSemD9a0h5XOR4OWA592mohYWorECMnNn316Mg0tAd5Yrcge60L9NAF6T5wFRM7XjEHctMhHS4FGuKfNBjflfO33YuXJjfSJt00fwLVBPsnmobW96/GsU2rFYrH8y2KxHLFYLDs089IsFsuHFoulVPk/VZlvsVgsf7dYLF9bLJZtFouluMt71IMe9KAHPegSYsnIFwJPAC9o5v0SWBkMBv9ksVh+qUz/AjgHKFT+jQOeVP6PCqPMxizrljBzDtNWdnYmR9Q+VaNl2dEULabb1bzGRWpEocX460RWXr6j5LgGP8t2lVBVIfiAooIhtKCnWGTGLbNxbQYOUFMt1BRPP/EIm9atwhcQWWa/fn247hZRcj/5nHmkuFNJVJYdA2oO7WPHViGpszoSmDVnrm6/omXV2iy8K9l3Zx2M4jUZuJmiJdDsobKyzPg1AKy9UhnYf7DqDpnoTFIrN5sa6umbfzLDJ4iOUHu3rKNx+nkkpWcC4IuzUN90lI4GUTWblz+M2slXALD5nYWUlnym2tgC5A8pPmEddDLTrPTPVKiIVc06KmXaaS514FA5A6oR2YHaNuobgmza3nxC9kOLRk+HmomPGmIxrcjU7r/swdnqD7BsRTN1dSEFkMtppWiGsC1w2FOA6PeVTl6oycClZLizoh91OyYFQNoBy1izcdkkJ1JVulxHwm9zilb2XUSngTwYDH5ksVhyDbPPAyYpn58H1iAC+XnAC8FgMAh8ZrFY3BaLpW8wGDzUlZ0yBmsjZ6Q9GcZ1Oxvx7cwvobPyXO08bcm+16PXmRv5uEivg+ff/Q+evWnWcckS160Veud+uQVhPLgZby4RHxfH0088AghbWy3KShu5+3ZhcXvHkWqOeTxs2CB65OyrKMNzrFlnQfDB0pn8/AFRVddZVWd3OxHFxJlLeO06eiUY8NPS0RaR0uiVkETGwALS84cCkJCSyqDxUwHRAcjf7mOiIj/cuXENLz9yD7YU4QGS0yeLlIxstctPu83JGZPEdwekufhk5Vv4moXqp6m5ocuackk9tHnNA+6H68QN0ujp0NnJ9k+Pw69ckwdq2yjb16G2RzOTGBYMFBdpMOin1W9n95ct6na7gkH5DvUBUl1jiUrLnDW2l/p52Ypm3X4lJLk47ZLr1aRHnR/hvtKOTZlx4katuG6bJrpwY4yIJGuOhkhV6JG8pLrDj0P3OfIsTXA+DMg7tz+wX7PeAWVelwI5mOssjTrxrlhCGhHJS9hsQCNSEDeaaOm8WAwDL5GyiLTcPCZfdQeL/nhr7DtvgLSXvfLqONo0gdrIlydb41mx6j8ArF2zmiuuulYtyY+GRx68p9N1Vn6wnC92nAPAvQ/+iUlTZ+iWfxODndFgT7Dg8+rL+Y8p/L8p/C0kOJM4/5o7wxZZHXZlm0IbPvGCBWJso0osP/yl+F+aNyW70sjJFUHdmpxK3rDRHC0XskdZmi/XjdYicFC+g+LhTlX+9/YaD41l+qCqDXynFbs4bbAI+q3+ANu/9qqd4s0GJPP62hkzQhxT//Q47PHi1bihxcGKdd1rz+ZyWpk4xkl1jQjky1Y2ma43dVxI375shd67XJ6b2b9+kBGTryXFIY7BZ4s3Dd4S0d6KI92z6ne7aZIVCdH8n4zNcaJpymPFccsPley7y50ALBbLDRaLZZPFYtl0rPG7Y5Dfgx70oAffN3Q3I6+WlInFYukLHFHmHwSyNesNUOaFIRgMPg08DZBVODqmB4F8ckUqCJI0i9mIcCQY2zRp55tl4nI9Lcdm1iYuErVillGMueQGvli1FDBvE9YZpIlWQ30tia40kq1ix1o62iLKDVe9u4SK0p0ntNJQ0kM/WXAxP7r9lwBce+vPSbBbwrJrbdZt5L67onCJBgfoXBs7orgl7jt4BK+nVc2+Afw2ka3a21v5cv1KtaHDmbPnh9ncQii79rY2RqXKEpJc9OvXBxAUlhbaLFm6/lUod5AZReFyWpkxUdATbhd8VCJS0L2VfvNiHIWbHzM8nvyBDo2pFWzZJW5DqXjpCuR2Z0zsRUMjfLBOZOLG7cjy/vyB4m+xYp3e0ja9Xx7zfvMPAAoniNaCPkWVZPama8zCjfJC2SgmrcUZZqvRGb0SKTPXzjNDQnwoIze6HYJ+DDCanW1X0N1A/hZwFfAn5f9lmvk/tlgsryIGORtj4cctkeXHOkhpTiT7WkmzaHXmsb4OGdc1m440GGKkVuqSRbmvGbVivBAlzvvx/QA8t7+8y3y5DMZffV3KyNHjTCs7Jc3SJ3sgAFNmXRATrdJd/PNR4dOyYcNG7vndAxQMHqwuu+3Ga3G7xevzfQ/9LebgbPYwiPZdo448OSk5wpriIVR/+ACpfQYAgk6xtws5od3moKmhXv27mLkZdgXe1kY1gBfk9+HknGOqt7c7GRwpSql8VYewjN1ufhFnplmZNrEXR6pFAP5gXfQAnNfXzrSJServNLSEHhJrNzZ2SQ/uclqJV+6zgoEOhhWKm6Os0sfKzyOPRqa5rZw6NIl1m8KdFIvGTeH8u/+BMzPkn2IWtI31GtHqN2TZvRfze9ZMOy4/G/sfxBpLtDHIbNyus77D3anstAhmJMoKFsu/EQOb6UA1cC+wFHgdyAEqgYuCwWCdxWKxIBQuZwMe4OpgMLjJbLta9B00Ojj/L8K+MyEhfH8ieauYnaCuZuRadFYcFGnEu9PtmgTvSIOf5f95lYUP3haVO42EKbMu4IH7/6oru5eQQV0aX7UFArS0tlC1ewMAz738hs4k6kTjwosvASC7YIiOc3/q328x/oyJpt/pagauRas3SFKCRTXZssTZ8TbUcuF5M4HQ24NUqdTVVFM8fipTb7oPgMT20B+snSC1+8p5+t4fdWtfomH+TLeaeUuU7BT7vGl7W9TAPH+mm737vKbNFoyQxUKy2Eby55GaH6e5rQzIFPr0JHsQu7MDp2JBEJ8YJMke4tQBPt4irrmkeIcuo5eacjkIO/hkJ7u/8ugC+FjFjGzuj+/DkdIbu1KT0NwYDHt7NSvy0aKrJfqxjokdD8wEGXbl+jJLTCVeuOU0Du/ZHFOaG4tq5dIIi6aarBsEbonlh3vQgx70oAcnBt+Jys5gIJSJe70WUtzBsI5B2s+RnmBGakW7jqyo0qIzRzPjq5aOA+9Chm58PTTO0yLv3EuYE7Sx8jHRbagrHPaqd5dw8RULyM0vAoS8UDoWymxcTgP0SelN8hjBQ95TOJLrFpSRZMkBoLpha6dvBWOKbwCgtbWGhqYD+C1i0NoeDFVItrXvw+12U1Ii3AZLtu9hxLAiWgKC5ji8vxIwz8iPR7UC6CxvAeJTXGq1pszIR46dAAg3xJL1KxkzU0gME3PyaWkSf3BPi7ADzh8i6ttOhCWtzFDdLkFxNCineu3GkARP8s5GSMVHZ42Otfx5//Q4Kg4GWLtR/JDxNwYPSqaov8jWszL0b8X+Niut/jgSHeK3jvlsNPva8Cjuslu2+dXsfduecHrntOKQZ+snG8TylF5Cujnzx/czbK64juSV2dwoft+sQtqMB49Grajfi9AcIlopvhax0rRmrIFWWSfX8Wmq0Y+HG5f4bgTy/9femcdHUZ9//D3ZXCSEbAIEIRBycN+EAKIiNyiKgOJR20ptq7a1Veuv3rWe1XrUq1qvelu1giJqsSCnFEWOgICAQA4iCDlMdnORazO/P777nZ2ZnZndBBSo+3m98srszHdmZ2Znnnnm8zzP59FdN/HxquOBOVEr5uV6zV8rrspKDdFqeVBQJMGfqmiTk6qNsXgFtLr49BeqpwryZs0ja6hQNPz43ofZsjF8bY4Xnv079zzwGAC+qGhoDQQ7G/0piCACoXoKpvTbCmLULkyZdAsAK1bfT3J2DwDKK/ZR4ytjSKaQzN1RvJBTh/4KV5TY8eG5Y1CPeFm9/q8ATBp/A6vWPgTAw3dfR89BY0h2uw376Ypu+6UXDtUiJW7N6ofyf1ZPIWta6K/ULy4uBmDUmDEsfa+IratE0HnSj37LWl23+lETzmXMjAsA8JQfPOogsTTAb39Yo3HNYEwTHNQ/lqaGGDbvCqTwDciOpb5JNWzDDtPHJ5HeJUClrPi8Xir+2mUAACAASURBVEvtS0utDerk46kV2y0+CAUlDVo3n7rGVk2GFtD2V7+vdrni3nofu3bX0qiIB3dDvY9uWf04/7o7AciceL5GpTQ3Bwy4hNV9ZKX/L5fr0wv14+w4cTujLcdo3xEmvWLXk9OO4j0WRhxOEEOuD3ZaeeR6D9zOU5fjrCo/9dtwgp1HLpeZG0mY9cjt8srNOebmbZgvOIDEtBEATP/jPaS+KzzcLz//nMO7g3W09diw+j/s8muCjBg5RisSk8FOK6EsgNgoF40tJWzbJQqD6tQSTh8iGtWu++RhTh9yPSUlqwEYkjmP2poKOiYFPO/PNj3LoGFzAdj/9WZGjBVBw8GnTQ/LaFvljoebzaKHXuLWXBAE0HfQSEDkvQOaTninSefRKcnNvi/F+R2yv0ATvqosL8VXW6V56xPnzGf1e68ck4wfb71PRJP8SE5wcfoYcbEOzo5h4UeBpgppqYK33ritNuR2r7hIPDhlFovk0Uf2E7/3kL5JWsATYMVnDRTsbw7skxPaKBHbqHTE3Uvk1Y+fezlZ4+fSOT2gEy4NuF3rRKfPQd667h51emt2Es1rT/64uSeCXVyvLYi0eosggggi+AHhhPDIVVV44iColWqPYshesfK8rSCffu2p9DQjnHREcwduc46qoXWUjQdulRsrxwHEZ2Yx/XpBlUwHKrZtYvWHLwNQsPIDS6/w/feEVz1weF5QTrmVMiIInZYRo09n5ZLnAEjveSbVDaJI1xUfi6+1Hk/1AQBG5v6c9RueYPAwkTK5v2g1ycnZdIoXJQQ7v/oHT9/3hlg3TArFytsOtwrUalxUtA+aCNJbmTj9HCCQHinPX8GWtXTokqaV8Jfs3YG7a7o2Jv/TFRw8JM7HmTMvZcpFv2bzsjcBgvLJw4G+sjOrewyjhonfpWeXOA5UiAttxWcNBvqksQHyC+Lx1tvHLubNSNSm126q1yiPtFQXE0YnaRkyzY1RrPisPqyMl6PFpdfeTe9ZQu7BKlvLzvOW85w+23HisuOP+S3ZqSOYeX6ojkB6DRZ9uqG5kYRVXM8xzucRtvCka/WmKIFgpzhANXBiTEYd7F9TzNSK+eQ7LQPjD+bUBUR+DpLItMkrd7rwwr0w5dio0wZxbp8nAaibfweVxdv54r0XATSda9nG7Ii3EpJTtfXN2ivR0bG4WoURrPU1MnX6DIPmyub1LwGQ3n0kW3c9x6gh1wFQWLCa7MzJ2rgvd75L1y59WL/hCQBuv+8euqam2R9IG2A21OHSLa7oaKJ1wZeWZkGzdO8uCnG69Mgy5Ovv/GITPfwdkQAKt37CiImzxfTOfJITXJqR/+DlR+nRO4d6V9vlRrXjiBOcxqwz3LiToahE7OvmbTW23LegYUo1adoqj49D1QlaUHrqqcmkJAsjsHytCIRKnZbZUwWV0t688aNBTUJnR+lZM1XiFNx0Mup6yPzxIOcqRBm+fp7dZwmzsYbg9GdZHGQW9rPLH29qCNjCEJnhBkSolQgiiCCCkxwnhEeup1YatCeVP4JtUSAkYVXpaU71kd2D5Dgn2iUUlRL02ZS1oi/Z11Mv5kCoE7Xi9OrZUA8J9QkB2iWhK4kpk+k9VHQV//ZAIYU78zVPc83q5cw7X8ipSmolqHxflq23NjFq3ARyRwvV4fyNn2jfX1UtPNG9hUJwq6JsI+k9JrHrq4+05eMmDeGa34vS6s6nGBveOiFUJsrRNJ2IjzEWBKmtzaR2EQHaqdNn8NbLz2hjG+q8hrTCwr1f4e4qGmZ36ZFFrbcS8FqODQdS1hVEil6vboICCZVCaEZaqovUZPEb5u/yAl4GZIvfMK2bwtLPRHC3xO+NS4lYd0dRgu/U4OFoINMpozp0pqDwsGHZFx+8SN6sQJNwvZdtl6FiNc9MvThRMlYI1SxC+06bxhJm6CvIJczZcXbp0GabBcHsQ7gV73CCGHIztWLXEcgMaZyd9Mhl96DY6MCYcBAOtWKXyuSkfdxQH9yxOxT1Yp6ObQmowYl5Qh5vxlV/4OlrL9XGb9u0jrNmXwQEaBV9yqGVzO11twnJ0Mce/ItW6Slf3QsLPtbGNR1YSa/+ouz+hj89xdgJ0+iYYK9lYoejyRVvS+Wny9VKS6uo+ASYc9GPeW/Bm4Zc+dSu3bS88pVL3jVUuo6ZeBa7NwtNcalg2BZU+XOjvy4V2iJlldaqgE5IThA895qNNYZ54/PEhbFjbyMlXwf2bdzgRE1LZe1WlfX5x8aIy8pPfcqhlMB1J9dQYJJ8ryj8koptIpOqY58829RcSas4xZPM0KccQtv0xuV8+VlPq+r/28GqZ0I49ss8T9Nl0dXSQNuyVk4IQw7BHnkntzioao+iTVs9zcyBBCvDrl/HCU4FQk6fzYEUs0duTnHSG3GwvnjtvIyGeiDBWkQoc+L5jJl4FhtW/weAzzZu1qRbO/i5cnOwU++hA3TrLgKW9z/6FFs3C9mEz/+7Gm/FN6h+EanMzEyGjchl4HDRh1TfdMIK7S21by8/rodMP2xpbiY6JoaWZuH99s7px6Xzf8GLf39EG1tZXhqkyy6xe/N6uvhT6KrbURCkzwV3Qlqqy5a7Hj1MWGX98tHDOpIYI+74gv0BwzogO5a07lFa6fz6LUcM25p6ajJVXjXs/dIjp3csZ4yM04qAlq/3UrBfXITj8zoCdYbxleWlbN2+SnzvMHHNOKUQmqfNY8154+a+ABDsVYeqEWlPGb6dp22VQy6XJ0bXU9eQYNhGcKq1rIUIf18iHHkEEUQQwUmOE8Yjl5DFQDIFp5NbNUybI75O1IqTt25Vsm9X6WnVFs5cIOTkoTvB7vXRijO349P1Y0Zdfjs7/AVB9VUVfL5JdPWZOEWIRUkqJS4qCl9UtG2BEMDY0eO0/zFKQN61QVEN3ny1p4pqoFu8oHjMKYehPOfu7jgOeYJdoqPhxyX0jSWkVw7CQ7/i2pu0phyhBMOqazyaJx6fmMxpE6Zo6pFWKaCSMzbz38kJLlKTo4jqIM6VmU9utKARZTk7qCxYGqBO0lJdjByksOIzcUGUVfq0kvvxeQls36cGeeJyufSgrTBqYCeNArJCwf4mzhgZx5Acsa0DZbEBmmWTdbHStgWvAXDG3BsA47VrJWEB1qm5QfElXawqnEwy7XvaKVErYaZww2EFILiVW1D3M0+AGG9L1soJYcj1OywJf8kX6QMAVly5lQ6LFZ1iNvINjaH7e4aiVszjnLSO9QinZN+83Ly+9t0mGiZzxCh69hsGCG3zFctEQHLGtLPxRUVrhlpOS6rF1dpCo+5zvKpodEtUXBx6c5TQooArjqgkWdraSCd3Stg542aKRG/E20rDhDtecuSSWpF45PGnAbj7jpttaRUzGuq8bNu6hcy+Q4CAIddrcvtVeqnyiu9LSQ48CEsPtbB937eW27aqqhzcp9G/rRjD/GH9Oopy+v2BY5LaKoX7m9i1O9giye1bfY9MVcwdGsXmXfapiWWVPooPQma6ePcfn5dApb+c3y5wK1M3a/dtosuwPFv9IbvyezlOf71b9QEw05lmulPb7lFUb1rRJxKhKjtD8eX6qvaTOtjZ0KAYkuL1UVyr8n09rE6ihDz5dj9GKDh55E5Pd/14sM8xt8pkMU+bx4G1hz581s8BYchlyblsOiEhjXi8Ks5zVKdkzLvdI8GNE3wtwoB27NFNmw4HbclS0cPKaIcy4pIj9/kAfEEl+/FukcVy3pzz+XTNirDlgyu+CdaNl8ZxwVKP5pFnZ8SgKDHs2Ct+pEqPL8jTlVKvobBvv+CepcHNylBYvLxGW3fK2ARU/yM3lASuFRLjBNPqCeMUFJQ0kJkubiB3R7TsmOffdg4Ef/HpKk73Bzwlws0Nl2X32lhTIV5QEkEYjliooh897BrYmLNU5Bi5zKk8X7+soUGhIZJHHkEEEUTww8QJ4ZGbnzzmJ5i+fN/p6eb0NJQdP8wC704wP7WdnvBW461Es+S0mdezU3hz4hCt5jXUw7AJkwD4JKuf9kr7+aaNzJh2tiZjGx0dS0LHRMN22po+qKdSQtEq7WmoHCpLJRxaJdEvolXXoBIXFaN1DZKe+dJF/wTg8YfubVczDztIisGKakhLddGzm/j+nIx4f3MIe+9551eCDpMetlxXtkmTeeQpboWP1zYYxuoxIDuW7AyRadLUQFBOudzXusaaoHXNyN/VQE6GuIEy0yErU/y+U09NdsxV37dmEadfdoNlDMgqY8spJmSmMM3eeKj0YWhf1kpsfLAtsKtPcargNEOyDVr64clGrYDRWOv/A4b0Q0mvyM9m6E9STEu9pvsrDbe5rNbJsDsZajvlNP04K+5OUiutvcTVKVvCmXk/K8rF6bVTP0bmlWeMOVMz5BtWL2HGtLM1DvxojXgomA13ewKVodYJZ5t1OllbX4xLE71WW5tZ9ObLPHjnTW3eLyskJ7jofkpAP7zxSOD6jeug4HYLA9qrq6Lx52DUQ7GD2SgXV4gNVJaXkpzgIjtDGPZPNjYEjZUUT96wRDrEKnxTLk7Atj32CoqhSvcvnOFmzcYalq0VBv+ic5OQEbyRgxS27bFPofxmf4HGk4M1pegU3AwlPS0RJD0dgloJB4YUQl0hollrPJzt6O2dnAbVYN9OumCnmSPXH5wU0ZLTobxyfQCzriEhKChhDoDaidtYIVwNBqvGE9o60osw5ZJbCWxBcOAzVBWoflvDJ5zPxn/9A4AtW7ZyuLyUU/ytzQDiomICsq/HGKGMrJOHbrfMzgN32taeL0V1ZvfMPnQkgQ6xgk185fmnDS3njga5A+MZNTReK75xgqc2dF9LJ2R1j6GusUL73P0UF5u2iRvCynh26x5eE4q2wJ0ME0YnaRk0y9fWMXO6cAwSXMZlZjTUeSnema8ZcrAPdprzxM1OjhPPbVexqe1HG/PG9bahmQRio631VcxxOrmuhIz/6Z1Rc/W6tHdt8chDcuSKoryoKEqZoig7dPMeUhRlt6Io2xRFWaQoilu37BZFUfYpivKVoigzwt+VCCKIIIII2oNwPPKXEQ2VX9XN+xi4RVXVFkVRHgBuAW5SFGUQcAkwGOgBLFcUpZ+qqo7vanqtFQmzh24Fu3JY2di0iQTbnHP55NSrIR7LRs1WWiyO23Oq5NSNceIQzdvqNXIyXXqIjuQV3xSRv+FT5v7oZ8B3643rYedFW3nbcr5+WW19E9vyRYWpXZNmJ+9fVqp27CBOyivPi3TDY+GNy5ZruYONtJRscAxQ09SItxp27BXe8KHDvjZnk0gOPL1rNHXNMYZS+1DbO1Zl+XoUlahMPjOGukZx8yxZ7WXFWnERzpqYQGZ6YJ+taKPizZ9o7d0gOB5kRzNa0ZJgvL/C4cXluLby4nbzzDK2YGyurIf0wKUd0ts3s607ptSKqqqfKIqSaZq3TPdxPSAVcWYDb6mq2ggUKYqyDxgDfBbqe/Ql+foDMhtxo8G3lreVJ88pMV+efPljhqJVzHC6CJxe6+x0WfSf7Ur0Q6UqBu1HAow6W7QnW/rCwyxftozzf3wFIDrpHE2X+lBokF1fmpuJjwn9wLDbj2pPFcNyx1puG7Dd9uoVy8gbN5Fkv5yu2trM8ndfPWZ0ytRTkxk5KPDdzY1RWjeeKo+PSq/gottqtPWQPTelhviWncF6KU7bj09MPqYBXImC/fVMJpkzxgj+vbS0k6HUf9bEBIb2FRellSEv2rHZuJ92Ylhx1imHhAhq6sfafW4rNx5KQ8VsZ8wG3A5WTurxCnb+HPiXfzodYdglDvjnOUJRjBVNepifUk5qiGZYFQtp24kjqBVcuMbcKWMFrPPJzfPsioekYbZSSwzXI9f2KQF6jRK64fFvPU/+pys4XLQbgJyBA6kqr+NIlDjvRxvsbGhW/bnagX6Z2jKcfzMnQ9+jRzfD59r6Jqr9Jye1a5rBqFeWl/HAXbcCkJaWRt64iVq++KqP3uPP994b/gE5YEB2rMGIe2ph+Vp7HfG2QhYWTRsfT3qXKK1yM39XA7kD44nzv0l9ZqraNOO7MOIQnIlz1owYKj3i9y4u7cDKz1oZnytMi5V2TMU3RdSXiRz8hLQs2zfKcN5owxW2k5/bAqe3d7OtsCv4MUNy5FZ2zDzve8sjVxTlNkS4+p/tWPdKRVE2KYqy6Yi3IvQKEUQQQQQRWKLdHrmiKD8DzgWmqKr27DgI9NIN6+mfFwRVVZ8DngPo1neU9uzRa6uY4cSXW8GJWrHyvu2qtYL2wyIyHm7jZrtt1KcJ9yPq6+DOJto67agC7TVSeOQ9eudQuDOf118QXXx+d/M9NDTpPaUUlKgYLe86HMjUPgm9rom+xZoPY2m5Hi5/xp7UDS/dX8DI3KFAMN1S16CyLf9zPFXCI58wbRZqa7PGoT/80H2ce9ZMAC6afxXRMTEcKikQy/78p2PmoUraQHLhx9obnzZeXHwHy1r5ZGNgn2Ubt2WfyvNp75FndY/RpGUlNm6rPSqqR4+VnzQz+UyxH4mxClPHi317+8MaNu/ykZ0h8h96dUu0lOz17hC/WcLkLMP8+rR6Gvz3gFm73wwzTWl1z7VH1VC+rZs5cKs3eDPsqsr1HrsdXSyz9eTy75xaURTlLOBGYIKqqvpT/T7whqIojyCCnX2BDaG3h0FbBYLzytsCq5NpJZpl/mHCKRLSwy61yWmZvMDM87RiBnP+qwXNYjbWdnm3+nF9JsylcGc+C//1FgDT5lxK/z4DtOWSrmiIEsYprlOyI91S16BqRtisX2KmVvSfXS6CxKveeOstSg/sB2DORT8KMuC+WnEyXXGx5I49w0DhLHrzZRa+LZi92++4hwEjAmltBbu/5NY/XAtYC1u1FbI0XnLWkhNvqxGXet4QzCFPHJVIYYmYl7+rgazuMcw7O5Dz/8p7NVTXGGVirfbx0os7Ee/qyOEKcZHnbzOW7Gd1jzmqh8/y9V5SkoWxHjk8mj4DxO853StSD2WOuZTeNaPwgOh/mmWiBhPKErR7QPLjdhrjVhK14cpQW8FJjE//X461E+ez2q6dsbaCHHtMg52KorwJTAS6KIpyALgDkaUSB3ysiMfGelVVf6Wq6peKorwN7ERQLleHyliJIIIIIojg6BBO1sqPLGa/4DD+z8Cf27IT+vRDSa1YpeSYO2jIp5u50tOsRgbC27bKWtGjLd64GeEEP/Xz9Z5DUBVago3kpgW1kpSsUOPvPuPUlGLkWfPYtPBJzTN94dm/c88Djxm+Ny4qSivhb/RUUV8baEKhl7iVYltqUsDl1zdt0E+Lz67AZ78br18+fEQuvS+4BAgOun65fQderzgZo/POoLG1WXt7eOT+O6jyeHn6pTcASE5N0yid9995i6WL3sBTbsnstQsTRidp055awupAbxbEku3Xlq81etWyoXJsQpRhu3nDAt74is8aQlZdujqK4PAb/yqn0uuxpVKmjk9k8fIa2+0NyI4lvaswD3bFS7LoJ7pDIqMHif0fOTyawpJAFoudXG5VgZAPDoc6MSsaOgld2VVshkOxOBUPhtvlx2qbZjrFXPBo9s6/N2rlWMOKWrGD+bXESq9cDz19YubAgztzWM8LF6GMt36ZE2euz2QxK7qZIY04BGex6KdTM7PImTyLSn+l54bV/2H1iqXMmHY2AI2trfiioqFVGOuOrjhNqzwuKorGliZcMdIIN1EL4JefdcW4SEzsRHRMwEibVQblMk91jZbTLZHZd7A2rq5BxVtZBsC/XnmO1R//m/lX/BYA39gz2LRuNU888SgAU86cyB9+dpW2PW9lGY/cfwcAiQkJzJh7Kfffeq3tuWsrtNJ6pYXFy50zRlL9FbRTchtZsNRj6Ga/Y2+jgdaYMjaBVLd4gOnbuM2cmEy/gS72+HVY9HK1djhU8o02PSA7Fvxy59KgB7TNhWaLnSFPSXYxYIDYp8KSZkcaZtHH0XSMFdvJyozmrBkx7Nsvjtduva/9sQsn2FEn5lRePcxGvr2l+GZb4aSh4qRwqM8XByzTqvUp1Xojf9KV6FsVBOlhPgnm8n0nvXI9zJoITk1RzdsLx6g7eQehLj67i9UsBGQVALIrcTZPT770/yhaKxooVHxTxLOP3ofbLW7ssaPH0ahrKqHv7dnY2iqMebO4Wc0NKHz++S1yuYN3XlS4l6zsvrg7JWnrqK3N1B4RO7tyyfv86/VXAOjbry9Pv76Y+FhhFJ594Fbyt+/l1ptuB2DI6HG0NDez6bPVADz64APM/9nlAEyffRF//dOxM+JpqS6tBL/ogBrSM87NERfPmo1C00R686WHWgxpg1NPTSYlWTGUtMtGzYOzFb4uaNX45lCByrRUF1P9euTpnUUw9o2P/Be9/1oI9Bt1k9LJPmktIVbBnwXJqGGxjoa8oc7L82+L6SsucpOVGe3XX7GXtW36VjxwQjkqhu8x3SdOvHdb88XNwld2ctgSThrjltt3kCAx2zWJY1qiH0EEEUQQwYmNE8Ij18OOB7caY56W6zmpI0rYUSvmMUcDOzEfM+0SKlVRP9aqZN9OZCspWUE5Ig6yuimO1MwsJs2dD8CCp+6ksryUB+64EYA/3HY3Y8dPM/Dg+ml99yA5X9+4ub62TlNUbGkO8NibP1vDpLPnaPs3dPBwomNitMyT2iM1rFzyPgsXvin2OT6Ra667HoCxE6axLf9z/vqY4PKH5PTm/kef0qo1PdVe3nn5WfK3bgXgvocfp3dOP3G8nipWrlwVdA47dBLe35HqYHI2VCZHbCfhou5Y7Sx4lZzg0rjhskqf5mEDvL9WeOiyvH/kIIWFHwX48lEDO2nFRp5a+M+6akdPPKt7DOPGiPM+pH/gdj5Y2sKCj7y2bw6tajMpbnuXL7t3IFaRlR7jeG5OzU3WKk4XL69h9tQkTdY2d2C8ZSyhvl7MU7+qgxR7jzxUA2Wngp+2ZKrYtW4zT8uxemXVtiAUtaLHSUetQPCrh9MyO6pFGHB7dUQ7iVv5WV9Wa/XjtVeLpS3USjjdhrT1TCqIZqMu+PM4w/ghP7kZgOK9O9i4bKEW/Lz1uqs4/9LLmXPRpYDQKOnkz/uqr60jKi6OeH9WYJQ7hWpPFQ2KOP9HvJV8sHwNRTtEf9Di4mLciYIjnzDtHGqP1GtUCkThqfaycsn7ACxd9AbEdOTqXwkefOyEadpD4PnHH2DrxnX84vLfADD5rFmASCsEePCOPzBi9Ok89OjfAEhITtHonWcffyAo5XBAdiwJcSKYm29KbU5OcDFuTCJFi+073NRUiB/l0GFnisNbH9A/yR0YT1aGYghuDsiO1fRZtuxUKTrUrHHo43Oj8dSKfXQKRgKMG9mBWdMScflz/30NKnVN4jdZssy+5yaAtxp6dokjLdVaObFwf5NBQyandwJFh6zz8Ht1VUidKB4mS1bXsXh5DfPniN/77BmJHChttt2XmvISElO6Wi47WkPd1gCn/C5JqzjZkboGZyNuR8uEola+1zzyYw1FMT6VzE+oUPy5ub+n3rDbRZb1eixgLXkrYdUmLhTsouZ2Oix2JfxW2zXLetrlmdtlsACcd/vjeL2V7Pl8pTbv3TdeYsli0bdy0KABuLuKhGm3P8pXXFwCQE1DHcP7ZlJZIzzzfdvXUvJ1uVZafuFPr+AX198FBDjyynJRvbvozZdZ9u9FZGVlAnD51deTN26itg/LFr/N62+JQuHxp57G355+i9hUYRS8nm95862FbFn1HgA33vWw5oED7Nq2hZefE6JYsjGyHolx8VR6rIOUcfGQ6PDQLKv08U25uM7CKaqROuBn5ibwSX695s1m9OrKrInNePxy4Bu31TIgO5bxeeKHqqxWWbzCnhOX+isAeWNj8DWo+PyFWXVNKh8tFQ+MUDniVR6V5E6NtgZ2xef1VFW3aseQldFqFN/QoaCkgSnjxE3R1CAaS3zgf2u54LwkZk9NCuLKZXFWdZWHU+Q8Cw5cztd/xmb80YhiNTUEFw/qM970wc9wNcfBOsjpJJSlT96ItHqLIIIIIvgB4YTwyK2ePHZPLKenmZmSacuTM6ibdXLgiS5plaPlzMHeuwhX7MfcNVyfqmhXwg9WOeadufSe13jj9p8CaJ659JTyN37ueBx78wPuWZceWVzyswuYc9GPAQxecktzM688/SjLlgnBzL79+nLT3Q8ZFA337tqmedK19fXc/qd7xNiBwwDhaQO88veH6doji/ufFFktkvr5xwsvAnBgz1au/O3vAXjkwXo2rP6P9h3ybcHOU01yd8XdsUHzpK3GKQ5SA3okJ7i0kvWDFa2GtMGxg5ppboxi+dpAmuH4vATKDvnplLXB5exy35M7p9IvvUmrLN2/v57ywzEcKBMXyOHDLWFXa9Y3qUQpzkJpktuOi1cYO6gDGb0EBVLydXnQuFFD/XnkgxSqvIE88hWftHDe1BhbWdum2kDBhFXKoD6GFIpaaau6oZ4ujY03Uiv6/+Zp2SFIDzsqxSrmZ6ZW9IyDPv36pKNW9HAKdoYq15eGPdyyfnMKkf6zVf65VUluuHAq4dcvt9KP0K/r2DHFor+n2bgbxtKZubc+BcDKp25n47KFbTsoPzIysxk4aLD2eX/BHj7/rwg0Ll7wTzL7Dubme/8CiGCnEhXDfo/IFV/01GNs3raVuefNBWDuj36my0f38crTj7Jhg+DeL7/ySvLGTdSWF35bwbN33MrwESMAuPOBJ2iqFwbkcIFRKjWndwzduimwy/oYkuM8QLzWUcfMB6elukjvEqVNO/HPcfGQGOPXYdkmutzLgp/0LlEGqiWre0xILjxQ5t7Etj21LF9/9MXShw+30DPNnkvS9978bMsREuNicbXY9/JcuymgRz55XBR1jcJwr8/3ktop2VbW9uCBzfRpFCrY8lq3ywUPRa2EI7Ghd9zMdKlZZ8lMpVrJZMv5Tn2G9f/lMitnVCZqyG2ddMFO/ZMnHA/cTrcgFP9khvnk2+WWK1qoLwAAIABJREFUmzkyux/aCU5t4fTL7YKecp650s3QRq4RoqKqqfpGeEyxio+UjIB3bNU2jjQhWnTp/S8wYt41FO9bA4Bn/35tvdbaFqKaa/F6KwE4vDMfxVejcbj5n64g/9MVlsd332PPMu28C7QgZLWnSvDkfg999pw5PH/19cR1CmR2fPz+OwAs/OeLjDl9Eo8+9Yw4noROhuUrlrzLT39xDQNzx4n9KtrN3XeKHHOz15jSKYr0NHsX50BpMxBPaifr5Y26ayXV7WzIyyp9rM0XkWFpsGWj4h0FPkMWRzgetL6ZcVqqS3sopCbH0aNrFB+uF0bTHNwdN7IDBfubtH3So9Lbytfl9vfGkByXpqWyYKmH5eu9WkDWCtJA90zrwMhBSoDz9/hYt6HWVnOl8euqoHl2To3VOL3xDseI21VzO+mlhCr2kdK0EL7hDscjbwsiHHkEEUQQwUmOE8IjV1UjpeKUpeIEJ87cKZ0IBO/VHB3cGk7CqtLrWHDmEMwL6mHmyt0pwV56U7OgE1a9cT973n+PWr/nDJCcJkrFs089hzNmXkDHPgF1QD2qm+LoOXQMPYeOCVpmpmzqy4rY8p+FbFr4JOCsLLjwny+yZvVKUvx80IZ1qxgx+jQeeUys2613DiDoGIDnnnxUW/d3f/gTQ0YHuPTK8gqeffwBYmPFSbnxnsfp5E7hi03/BYSM7WUXio5InvIDFO79yrAv7o7YcuBllT48tWheu1kjJS4e6poFj13pCU1tSI5Ydump8ksp6L3rcJCW6mJYP+HNZmUoWn42gCteoWR/M746az3/nmlxHD4slSSN++yt97Frd63leskJLmLiWjUufsrYBFZ8Xh+ymhVEFk5KcqCrUa9uiWzeVc1/t1uPr6ko0aataEWnuoq2qpU6VXJbSV7rs1ZA2Aiwb98G9inS4cAs4X1ScuR2xtuJSglHnzywXedxdS0JJBLILXdq7XS0OeaG/XPQVNaPkcv0Up4NjVCxZwMfPCJK0Q/v3hK8bpG4CkuL9rDl/ReZcIlo9ZY3/0YaqjppfLs5PXHdqw+RPmkkAJm9phrEuRLSsjj9shtI9qclLrj/Gtvjy9/4OegCp7fc9ziX/vQyrbNPwe4vWfjKUxT6qRDJg4NIXfR6vmXZ+yIlcvmyZYbl1Z4qwaGvE3z8Nddcz+mnTwPgpdfetNyfUcMEDWFFadQ1itJ2EN3pvYUBw9XYAB7/DR2OQZOQweNQBnzUQMHpVHqO4IsWlMaQnCYDHVTXCDu+atH2EWDBBzW26ZAqzZxyirjFrY7Xbr0407WcOziWg+Utlm3brLa5ZmMN7mSRIpk7NIpKT4wWczC3n6v8ughfvfjtE1O6WgY77agVGaBsj+a4XNcsZQ3WcTKwb99mJeznJJJltmESZnomkn4YQQQRRPADwgnjkdshHJolXI89VPm+/onrFBgxL29Lv0/L/W+0npaw81DKd67krTt/TW3ZfsN4ma7mrfcZphvqvCx94WEAinbkM/fWp2iIM3ZoiWr5FoD8xS+y7GnRVzH3tCmMOeenJA8RNEdCWhbxCTBspqAxVr3yKBXfFBm206WH2O6cWTP4YNlKSosEdfKvl/5O0Z5dmljX2/98iV9efT2/u/l8IBDQBFiy8J98uHwN506dAMBTfrnaT1b8R9tWv8EjtXTEqm/L+fjjDwCC5GurqltBaaFnF3Ey01KDJWH1BUFD+yYYPFBvvc8xONheJCe46NM7kcnjhE918NtE8rcLV3DTNh9LHLz/1K7dqKy0r0Q9WNZKetqx8dXG5yVoFa2hCqLKKn0sXi4yXGZPTTJUhZq7NFV8U0TVAXHtuBK6BnnYobxtp+XhNHxwkqoNF+F43OEEOyW10h5q+YQ15NLgtjeKC9aR41Dl+04pRVbj5bSTlnGb9jkEtaL/X7J9M6dd8Eu2rRLUw+HdW+jSI4te6aMA2LJxoe1Nt+fzlbx213x+cq/IAOmc3pWGemiNFu/tWeOnUOGXvJVZKVKaddTlv+WMuTeAf2zu5PNY9vrjhu23Nguu5vTJsxg9cSZNlQcAePSxx3nr5We0calduzHl7NmaAV/10Xu8994isd0RI7j3nnu1fPH9BXt46bknKT9YDMBV19/GsNyxLHrzZQD+8dQj2nbNvP2B0mZQ44nxl+hPHZ/EG6Zy/Fa1Gfy54pnpBOU+mzvYHwvExcOcmSmsWSf2pS0ceqiuR1VeH317ygvRvquQGTJe4E7y8+tqNO6OMHCA4OqtzsOpuYJmKyyupazSpz0kNe2V/fb5+ZX7CwHoOXSMZbwoXKrFDHNHMKv1rO5xc/ewUHCKzdmlUztRKxInJUduxtEYcD3MTzv9A0Kfs2n+bzbM+h/X3FXbKj2xvdosbfHIT7/sBhoaoWv/XAAqtq9i4PTL6NBNpBxO997DlgXCaOavfJ9ab6XBI/omfwOL/nghAOff8RIJaQHvfOK5PzNI3kLAcKx96mHOnPVzzehnnDYDTIZcjr384nP4xY33M23iGdqyG+98QNNTeeaxv/Drn8zWlk2a+zODdkrB7l3865XnANjw3w+ZdcnVnHeB6Du6/csvuO33v2blEvEgmzzzfP50l8hXv+e237Fi2VJtu40NQoRKStGmdw4E8STWbW6mZ5q4JQr219umBiYnuFBdSTpJ2PZj/pwk1qzzWBrw1K7dcLvdjBwmfhcl2o3a4mHVJyIWEsqQHzrsIy5P1fa5Lf06D5apuDsazcPQPuJe2rU7eFuJMeJcXTgridcXBHj7skofy9fWacJelR8Hc/olGz8EYNi5lzg6MuZpJ5iL+Mz6KVb3qB03blUABMG2BZzrYOxgJ2Mb4cgjiCCCCH5AOGE9cieE4sStUn+0p63uyelEnTh1GjJ31bZq3NxWasWp6MGp60l8HPQbOwWAjBFTjB5NWhajfvkAAKdddiPFX2xg++rFAHy5ehENdV6t1P7l62YzNG+C1lbt65ICQxqjYV/rvGxb8g5DzrsSgJ59htMpyY3iCxZ7mjzzfH564QVc/xshBTDvxz+nR490Fv5TlNXPu/gSevXqzRc79wLwu8svZ3eRmH7+pt+yZctWOvcSHuk9f3mB3jn9eNzfBWjDhg3MnjmNrRsF5ZOWlqYVFhUVFRv22Vvvw+MN0AXNDbHUNxldnt2FTWFnZkD7vfG0VBdDR44G4JReVVR94QLExTRsSD8mTRQFTsNGDSSpo9ETLCgoYe2qjw3zsvv2B6Bw71daTKT7KS52FzZR6C8IiotHay4RDtZtqAXE68vgbHHPyLeZnN4x5O8yetUHy8V5nXlWLy46F40jL6v0UXSoGV++oFZSk6NoVDoa3gyLdogq3LqqclwJASXE9ghf2RUHWVGn+v9OsCrHN7/dh1sApB9jNR2OB2+FcJovvwicC5SpqjrEtOz/gIeBrqqqViiiE/PjwEzEZfMzVVXzQ31HuK8Q7cnN1CM+Xg28avl/CKc2cXqYebOYloBwidUr2tFw5mbNCKfyfXMJs52CXHVDZw7tXkfRho/EtkxBp9KiPVpAMhwUb/5EM+RKUmcS0rMs0x/nnjOVe277naakmNN/ILdccwVzLhS6LNPPncuD997JdTcKed2lq5by7KP3AcLo1zQpmmaK11vF848/wLJ/Cw79zQ/XUFy4B155GYBhI0by/jtvAeBKShHBQD/9kJzgYtnaGlKTxUtoXeORNqURHitk9+3PFb+YRXZPwb8rcaeQd/hTJk85HYDRo4c6rv/8Cx8Y9jujV1c6xgXuB3l8Mli7fa+4WBrDMFh6eOt9LFktrpHDpfGauiHAqKHBGuMyt77SW0FWZjSzpxo7BMlK25zsU+jUWE6DjrLXqLvi7XQdNFmb3x69FD0nbk4ttEohbk+A08opbKttsqNT2ltDE45H/jLwJPCqfqaiKL2A6UCJbvbZQF//31jgaf9/R4Qi9Z0OOtwcTfm5Qf7Qbus2cXY/sNnjrmtIgJbAOlbeQHsLh+xEs+w8cjtdloZGOFIqjPOSR64zSNaC6N8YmyQCieask1Ao2rFZy3Bpje5Mes5wS0N+x513UV/fwENPCZ77tv+7ll9efT1nTBQ37J233sxVv7uWyjJhcJ998nFuuvshAGKjXDx4501MnikyWjxVVbz490fI9RcJVX1bzi3XXKEZ63889Qi5uSL3/dU3F/P84w/w4t9FAFS+JbSFJ5ZITnARFy+KWwCyM6JYs9FZH8WMKVNOBeCqX10avGzqabbrrVj+qTa9b1+xocgpd/RYRudm8fHKwHmvaxTB3LRTnDVhRg0MCFuFQv6uBqo8Pk0ITBZWWcUQmqp9JHaJps8A4YFfOtvNv3W8eMXhckYP68iBMiEnrH8D+mLNu0zVGfJQsOLT5b0m74lQuikSdjy41bbN8TUInZViZ5MMx9NObxzC4MhVVf0EsHrHfhS4EWOlzWzgVVVgPeBWFKV7u/YsgggiiCCCsNAujlxRlNnAQVVVv1CM7nQ68LXu8wH/vEMW27gSuBKgY5cMfP5KP1dM8BMpFL/UlvJYfRMKq2izU6NV8zKz4I75tS5Uc2cnWL1WhpLolB56XZV4jV372iN8+eFLAJYZFtIbt0KnJDfd+g4AIGvsOXxdmE/5dtFSreKbIppqqti//QsAeo2cTN9+fdn87+DtVJaXcv4VN/KSPzVw+JB+TDl7ttbtfvacOTQ1HuH+++8G4KFHn6JHRiYAV/7kYrL79mfuRRcDcN+fhETt1HOEh/7gHX+gsrxUS4u85obbGTtBVHbu3rqJHTt32h6fhOSUQfDIkoIY1D+WxDhBfxwoO0LjEVVrSlHpCb+6MznBxSU/vdDR6wbBfQPk5GRo85595g1WrAju5qA18LhgPDk5GSxasEBblhgnfLMEF/TsFmO7n7lDo/i61N5jT0t1keoW37O7sImiQ80GhcMUt8vgkcvzJhpmtJDSRZiWIf2jKSvtqGXleOt9bNxWS5dTJBd+WNvG9g8XcPr512hCb3bXublZjJ2XbZWlYletHcobByOVYs6qayvda+V5t9cbh3YYckVREoBbEbRKu6Gq6nPAcwDd+o5SrQy4HcJ5TTEvt9IB1v8YMoBh9YPYXSB2qUxWGhDHKs/cDCspzw/uEn05zVQKwCkDBPUwfspsEjOH8+Gjoj+mTE/rN1a82k74xZ0k+tXvzDfW8vt+wsZlC3nv3qsBmPPHp1C7DTEUH0n86rqb6eROYfV7xQDc+dCHLHrzZRITxI0zJG8cN1z1Y2655U8ADOs/lL88cCcAhTvzmXfxJdpD4FDJN2T37a+lLkrNdMm/N7b4uPMmIRewfpl9Dj0IQzVhdBJ+lQGKSlRUtZm4DuKaKCxpZufh2qDjCRcyAHnFL2YZjLOE2igM2NuL8sn/dIVmFCUFA7BixXoDzy/hSuwCCKO/ceN2gzGW1AoIxUUzly0VDGM7OKs4NjZA7lDxGw3tm8CCpR6NBnG7OwRtW56jxiMKdFSpqhC8Y0qXaCafGUOlV1zw+bsaRCu8wsOY0VDnZd27T3Dm5UKHx+oeseqtGdNi6nlogfi48OtErHSZYuPRaFkITjlsSwLG0RhsO7THI88BsgDpjfcE8hVFGQMcBHrpxvb0zwsJJ4+8vbBqzGzXVskMJ5EtO75Nf0GZLxo77709QVBzzrn876sv54O75lsacIk5PxW6LCmnX0JMSz2dOvQBoNDPkR/eKWLTnrKSIAEt+b2Zo85k47KFGq/+4RO3MfeWJ2lUpFRpIMrUvUc6rzz/JH+4TXjcHo+HDetWcedD4ma9984/Mv2cuZw+9SwAlrz/DovfFeGYGXMuZvXazw38/cRp57Dw7X8Z9qvQv883/Wa+7XFLnrtnN8Hdjhoaj8cLO/a2vSmDHeSbwYXnTwrJey94V+jDmI10eWklM2aKStYL5p3D+nVf8errrxvGTDpzpDadv3m74QGq94zTu0RphttssBNc0Ewqeo9YD2+9j6IScW7GDurAhTPcLPM3w1if76V5oLXe756DrXTrGrinqipa6H6Ki3mzxLVR5fE5nuevlixkxJTLgECBkB5WDlFzdHDSgfn+ak+bNj30Tp4wxMe+yteM7zSPXFXV7aqqpqmqmqmqaiaCPslVVfUw8D5wmSJwKuBVVTWIVokggggiiODYIZz0wzeBiUAXRVEOAHeoqvqCzfAliNTDfYj0w8vD2QlVDXjivmY1pFdux5k7RYit5ulflcyNm52euFbUCug6a7fYp0KFK4JvBTuOXKrHfXzH79mz0d4bB/hWFT95Cn5PJqrEsFxy6Z+8cDeDx0wCRHohwKGVIrXvoxf+aljn8O4tbPrno1jhleefxO12M2qc8DJv+e18rrr+NhYvWQ5AUqzK/F//noLdonXPw3/+E927iZe60047g6XvBbzv7EG5HPzmIPVHhGc4ZfoMQ/WmFaRsbd6wRKq8KpVecfLe/tBeNTBc6D3wzKyeJCcLnqZLl2TDuIoKL+8sFAGE7Vs2WtIZ0nP+0U/O06iYgoIS/rPEqOJ47W/nc/qEUdp2AVRXkn+px3BMMXGtGmcuZWzlZ0+tyCCx2ofEuCiKDjXz2RYRE+iZFkdmOlrT52Vra2wzXnbtrmVonyQt5xxEhankzGdOTzRUfpoza6prPKx54U4AZt21xJGStPK+nbJTwslM0cMpVdnsoYdLpYRDq0h24piW6Kuq+qMQyzN10ypwdfhfH8DRUCtt4aPsghJmjlwvsGVXzh8qyGLFmVtRK3Ya6OFixVOiK86WjaFbtbnThJGIjxO64mbNbonSoj18uUG8/g+ZMo8D2zfwz4f/AEB9VbD+tV2buMK9X/H7W+/W9FBGjD6dpsYjrFokPj/9+mKqPVXceu0vAUE1XHvDHwFYvMBIKeQO7cfHHyzglPQ+IY8ThJFI8QfsFiwNXcDTljL2jF5dufhH5wHOud8FBSU89NfXQpbTS2O2ecsB7YHw0F9fo9Jk9BuaA09xr9cLrmTLQHZdI6T1cJHT27p1HQRz/9LIyziBxKZtdbiTEzWN8enjk2zPZ6PSkbWb6jlrnLDkUttGIr1btGH9skof8YnieGVtg6QGdy9/jgFTrwzKEzfrhIfT5QecA5pWVKpdcLMt8iFWollOCMeRtUKkRD+CCCKI4CTHCVGiryhto1bMaMuri723bmzarC/htyvnDzcd0Ums52gzWArXL2Xzv18Oa2y/sZNJ6RkQxtqx9mOH0VD8yXsA9DljHvu3rLH0xENh6oVXkN2vP088IaiXvz7yN/7v+t9x8eW/AaCTO4WH7rpZezOYMn0GqWlpQCArRVIYK1euEhkP/rHmt4m0VBfVjcITbKjzUlbp0z6DdZRLUi/ZGTHQcRifbtwGwJFq+3r2UaMGMXnK6WRlBWejmPGP514P6Y3rUbRvG898KYK35vXSUl0MHxFocl1cdAB3kvX1XlTSSHrnOFKSrZe7k1qCaA2Z8ZLqTgQChTpFh5rJ396qSe2md4ni1NxkSxXEhjovuwshIU6cvzNzE4iJazVksQwbksz2vWL57sKmoCpjiVUv3E2PfiPolCGC7rI60+yJSxxN5abhDdoie83cbSyUV272wMPNVNHbvpOu+bKqBqgVME63NS0xVNcgpx8oVHfrUOX8TvmtdjosR1sF2jmzT9CrqRVGT5/HpOseJTEloGWx2Z9jboevSwq06arGsJKPgpCZ6uKl557j1psE/fPGyy8wvG8m511wCQCl+wvYvnuXZqyvuO6PPHjvnYZttMUQms+B/rM+Z7xP70QG93GR2ilwfS1euZkj1fYZFVddJfZ5+IjBxMXHBOmg6HHzrU8BUGiRZqdHatduZGV0xu0WWSBbthVprdtkKqLMJe+ZkxfEv3s8RopD8tyixVtw6aM+PdGcfqhlvDQmAEYO/OvSOjy1giN3d2zl1IEuSg/ZS9PK1ERZESo587omlcTYOmZNFOfu0GGfLZ0V01jFB49cy2WPvA9AU0NXg6KhXc2H3bJQ0DtuZmpV7/jJ2JoThdveFEO97fufkLFtj4felqdfqOIh82ercn4zzNydHQ+uh5OcplzuhORTcrS+nLKtm0SXHllMuFwU3gyeJPhcGSj17N/M4d1b6JTk1sYrPusAoK++HOpaguaHg388+ww33vkAtUdETvaGdat4+vXFWo/O38yfR2V5Kb+/VaQnlpd9Q/6nK9r1XU5FOskJLnJ6xzBqqDihwrCoFB8Uv6lTyX1q124hUwr1KCgoQfHZP1RzR4/lwgvGA1BZ6eXdhR+wb5d4u+gzsD+/+OVvARE0LSgo0d4SRucam4BUVHgMBUMXXHAWRSWBHHtPLVqevBnNDbHovW4IcOZxnvKg8VKOFkTgOL0LjM8T1rnogyrDWH2f0eXrvbz9YY0WKM1UGknsHM8pvcQ1e/oYVdN0McNb78O7e4sWA5r4u2csC3rC7SEg15FjzdDbDXNqslMeOQT3HA6XE9dD2jppzCMythFEEEEEPyCckB65/ql0tAVCbRGzsfPQpWpik+6VyopeMQvSWz39rbwIc9OKtvDm8XGgxCVqn7v0yKL3SJHqN27uNST1GQSA+eW38lAF/cZOZuhUUXwR1SGaVX+/nXhFyOq4e2Uz63rRLEJPx7QV2X37kzvmNG7/4w2AKKN3xbh45ME/i/0oL2XMxLO4aP5VAFz9k/Pa/V1mSC8c0Dxx2UB5x97GkEVAkoqZdOZIMrN6amX0yz/+L0UlVaR0EstvuvlXhvVycjK4/4FbAVH8s29fMcVFxQCcP28WWVkZWjrips82ktClG1f+9udAcAZMTk4GPboI2kXPj4OgVfQ8d97oIWTniAPM3/g5B8tUEuOM948cW9PUSOMRa5evrNJnKbglz1XRIQ8ZvboyMlO83pnHNjagcfOSS5fFRBNHJdKvbwspXYTg2hljYti0zbpoSeLL1ULtss/4uWSfOiOg6NmGIh8Jp7F6W2AeZxbI0nvj4TSBDwfS1rXH5p2QhvxY8OUSR1MOa845t9I3d4JVHrmVDrI+PdGqvF+/PfOyhkaIbhD0yOiLfxmkVWHXNmvQhBkMmjDD8ABJ7ztM2647pTOtUR20zykxgYdFWzB58iReeu5JJoweAcDpU8/ij9dfrUnTpnbtxvU33sayxW8DwgBJvnzy5Eks/Ndb7fre3IHxjBoaT1GJ+M0WL2+bWiFA3jihGZ43egg5ORn86jeBh48eBQUllmX4IJQN9ZRMQUEJf7nvCWJihPGaff6POHf2GMt1QeSKqy7r/PSikirKKn3a+crJySAnRyzL7tufXV8VaumHZlRXx1DnoG1rPldmtcOSr8sp+dq8loC33seBMnHBjR8ZR/ORgNLi4rXVjKvtwOQJ4vg7JxAkeWuGjHOseOI6Omd+SJNbHKTMC9c7RYnRIogaTr64OWhpDmhKKsXK6XOqCpdjwoXexrUXJ6Qht0N7cywhfA0Eu8bNVtuyyzMHIy9u9tSt2sY55ZXbBUKlYb/4mY8My51aY9mVPDc0Ck0VX524EZTEBFy6dVKGTEZIzbcNK1euIrPvYC656v8AKNi9y1Dkk5mZSUJCPH97LNBvc/4VohRh26Z1bfouqZ8CoNJ8VEU/F1xwFhdfMlP7XFBQYht0raz0agbUCgUFJSz/+L+AkLH99dXzbQ2/GYcOl5KVYRQ3k4VAtd/uAyAro3PQeh6Ph8ryZuoaay23W+VV26RRPrB/AilucfGY9VusIIuJhvSNY/K4KFQCWis7v2rSmkLnDu9AVqYwQwOyYx0be1R8U8Tnrz3ExN89AwSMtf5+aUvBj7luRLsXTFy7leE2r3s0DqPeprXXqEc48ggiiCCCkxwnhUeuz2AJxyvXe8oS7eHIQ411ahvnRL1YtY3TI5RSotlDP5o8dD310tAI8YkJ2mf9mKr929q1fY/Hw3U33kKyW3iV/37jH4blKSlu3n/zBU0Y65dX/Yphw0UJ+qP3/Sms78gdKE7AqKHxmgiW9AjbiinTZwAYvHEQXrcdUlNtUkP8yMnJIDn5HMPncLFzxz769Mk0zPti65cAtLi6ANZvCVkZnaksLzVQJAOyY7VOPhu31Qa9rWT0ErEQV4snKHZwuLTR0CWoYH9zWG87y9fWccVPkzXRrAOlzZRV+ti8TXje/QfG06mT8CfH5yWEbLX35epF5M4W8YQu/cY43mdO2WJmL9t8P5vvd31rt6YGe+qkreqGx4JWgZPEkJsPNlQg1I67stIxb0/7ODnWnIOu/6HDhd3FZmWk25NrHmodO+pF33lo24dv8cmrD4f/pTq43W6ycvrw4ccipXDbNqFpLmVeZ837KTf//mqtC9Avrr+Ly340O6xtJye4yBsaR3qaeBotXl59VO3bzHSKHoUFNoRwmDDz2+HC4/GQN9rQYZF9+4qBAFdfVPJt0Hp5ecPYvNmox94zrQM9Ra2Vpg9u2MeOIiA5pE/HoJTCA6XNeGrFRTRlnIg/yHREp4Bx0aFmVn7SzOQzRdD5wllJvL6gRltnzy4fI4eLezArM5rcgcHSu3o01Hn57yuChpt5u3P8xCnFUBrucPRSpME325Wj0RTX27AfHEduRlszW5wEbNoaebb64duibyxh1XIKdAJcpu2Y13WqGrVaxw7mRrcNjQExrnXvPO5YbOSImER8tY0selV44htWC5Ert1sEaN9443V69DiFm+4QfTrff+ctrSG0HWQ2ybTx8TTXu3jVZHTag9nnTrU14gDFxSVB8/RBRjusWP4pnZKTtM/VXmEsKyoCgT0ZTLXC1GlnBC3LHSUyW1asWE9O9im4U1IB2L5jD0OHiED3mFNH8OyzRkNX5VXJHepnUy1O8YFSvyb6uHimnpqsGfv4xGTKKr1axo+7o/jLGyYC4EWHnHVslq/3kuLXts8bG8NF5yZpgc01G2vITBfnp0vPGMt+oGbsWicqjsdu/4jOQ892HCshA5vmuJaTpriEufnM0WrHkIpDAAALUklEQVSM61mFY+WRRzjyCCKIIIKTHCe1Rw5tS0m0UkIMJ2vF7K2bP1s1sLBSSwwHUiS/kz/bz0x9OOWk6+fJaf16Tt65VVutL1eK/F3ZVFl6wgMHCL6zsFhkRDjSGc11vPLac1q6oYTUUQG468G/adPPPnqf/bb8+yCrBLfvrWd3YZ3j+FCQnPiP55/jOM6KvtA3eDDjlpvEcRSEKNEHeOed/zBqlMj3t8pJN0Ny8skJLs6fN0ub/+l/N2keeVLHBHKyTzF8f6XnCO6O4sKyyhOXn5sbo8jKaCVrvyzBF674mo3ibUJ60FINMZxGzkv9PaQz05vJyoxm6qniGJav97LiM3HBzpsVTVZmtKZ/E6rRx+bVS5kepkcuaRQzJx7O/S7h9DbfFhwrOkWPk9KQm4Of5vnhoq0/QFtSFduj9QBQs28ni565HqVCfNegWXMZcfZcAHxRXS2NsblAQi8VYBcQNVMpcp/lunVV5Xz+hlFjPM6/jaF9FNJ6uDhvqghgHixtoWi/dal14d6veObJv9geb3bf/kyYNIO777gZCK2rkpocpRmUo+HDwZkT18Mu9dDMXdfUClrsvnseC8uA67Fzb3HYY4uLDojvHzfaUEC0coUxXXPIoCGG/ahrbEXGJ536ee4o8DFykMLU8cLoyzROOf7LQpXB2YF7Z3xuNHWNor+pXbBSSu2+8p6LC2claZz5tj0ujUrJyYgnb2wMA/uLbYUy5HtXv8n4n/6ODu7g3E9z7MtOh8kpueG7wLE24BIRaiWCCCKI4CTHSemRmyO+RyOBayc3GYp2MW/D/LTXV43ZqSVaYfOK1zi4abX2+cDfVrHzA0FxzHvgcZroZ/gOvaynq7UcX1RXQ3qj2Vs3V5DqaZjY+ICXvvWDlw29MmXAC4SHBsI7BlEsMjhbobDE2is7Ul2vBQbNnm3fwSP4dN0qVi55N5zTE7KkXnbLsWq2oEeo4KYeVqmHF1xwVhDtseTD1UB4dIoZM6ed2eZ10rsbG2y43Z3YuHE7IEr9T0nvYlheVumj9HDovqTL13vJynBrioWjh3U0ZLls2lZLelqgC1BMXCvj86zVDFO7dsNXV6HNK6v0seCDGubPEb/T1PFJvLE4EPgcOTyFM8aIiuIlqwVtZke1NNR52bduBUPPCXjkTo0cnOjRcKjUtjaJkPiuvHA9TkpDLiFPkJ1hDwftfZ0KpZwYjlqiFb7+bGtQp5oDxaJTz5JHrmPWXUu0+XK7xWtFdsK/HxONlTOGiDzsOXe8QrVX5Abb5aof8RT4v2MfVfu3UeMRWit717xnGD84QyV3qMg88HhF1xh5Yx2qhiWrPZqEqhmpXbsxf/7PAHjh2WeprvHQLUs8kM6/+Mc8+8ifQ50WS8jva2gWRiEupobGhhrHdaQ07I/nh9Z0kRWU7y78IOh7rR4CW7ds1aZzsk8BYOToieTnb7HtxNShUwId4pKCaBonyPRDmb0icdoZeXz6302AMOSZWT2D1i0sEddVbIc47DTaAdZuqtekZkcOUthzsCslX4ssJqmEKKkXd0ehbw7BRj8tqYqeOcZ5ZZU+3lwuxs8/P44B2QEHIP+LI4weK75X6rTILk+p7g5BXPy+JR8w9Jwrtc/BHb6MBlk/zu6znSxtexQN4dhUbobCSW3IJcweeXsEt46WE7PKKbfKZw+V4tjQVGxbaLHn85VsX/wQoy6+QZvnqy9n3TuidF6mCJbs2AxAUf4a+pwxTxurD3geKd3D2lcfYdenHwLW7dvMyOglCPSszDpGDk/WmgUUH4Q1G4ODZxJzLvwxO/3Gp7rGQ2rXblx7rSjZ3719qyHwGQ5koKxH1ygqalo5XCoMUn2jy7GgZPa5U8My4BIvPPEQAAWFRuMx/5c/t96vaRMByNxXDH59lKqKYrIyUkjpJIKZnqpKqmpUrax+3oUz21QgpIe5EGnokH4s+VC0SSsoKKH+SLChLtgvHr6D+jtf77sLm0jwt36bMi6eEZlHDNoqRYeatTxyoTcuTEmPrsbtHjrsY3yewiivEP6K6aCwPt/LgRIRKF+5xkfu0ATtO3fsbSZ3uLhHhvZRWJ8PxRXiOOdOrGXffqOTU1jwMdUlGwDolDEmKE9cj7Z65GbD3h4b8X144xDhyCOIIIIITnr8T3jkR0Ot6GHsAtI+ekbCXNKrHxsqtckJ+YtfZOjsgEd+YFu+lh6obcvvmR/4cjMZeQGPPDYeSraKCsulD/7WwIGHwuZd1ZpaXkqyy/9fvBInxinMnprE2k2B9l0S2X37M37CFK795Y+1eZXlpTz85z9p06Fg5tetqhJDYcqUU8PyxqVU7fMvfEDhXqMnnjt6LGDfbFmqHIbbgKK9uOpXl9ouy8sTCpabNu6gSxd30HLpzapKLOAsYyCzSVI6RTG0T7yWfiq3Iam1xctrNLGy9C6KgRr01vsoPdRC7lBhajZvbzCIY3225Yh2HaWlijcq+aaXlRkt0iT9v3tzQwo5vX3k7zK++a1f/AYAZ14xNqirjx7hUitybHupFDj2lZuhoKhtaUPxXe2EopQDdUDbm0L+76ILkfOhR+R8GBE5H0b8L56P3qqqhtUM4IQw5ACKomxSVTXveO/HiYLI+TAicj6MiJwPI37o5yPCkUcQQQQRnOSIGPIIIogggpMcJ5Ihf+5478AJhsj5MCJyPoyInA8jftDn44ThyCOIIIIIImgfTiSPPIIIIogggnbguBtyRVHOUhTlK0VR9imKcvPx3p/jAUVRihVF2a4oylZFUTb556UqivKxoih7/f9TQm3nZIWiKC8qilKmKMoO3TzL41cEnvBfL9sURck9fnv+3cDmfNypKMpB/zWyVVGUmbplt/jPx1eKosw4Pnv93UFRlF6KoqxSFGWnoihfKopyrX/+D/YaMeO4GnJFUVzAU8DZwCDgR4qiDDqe+3QcMUlV1RG6FKqbgRWqqvYFVvg//6/iZeAs0zy74z8b6Ov/uxJ4+nvax+8TLxN8PgAe9V8jI1RVXQLgv18uAQb71/m7/776X0IL8H+qqg4CTgWu9h/3D/kaMeB4e+RjgH2qqhaqqtoEvAWE17Dxfx+zgVf8068Ac47jvnynUFX1E6DSNNvu+GcDr6oC6wG3oijdv589/X5gcz7sMBt4S1XVRlVVi4B9iPvqfwaqqh5SVTXfP10D7ALS+QFfI2Ycb0OeDui72h7wz/uhQQWWKYqyWVEUKeXWTVXVQ/7pw0C347Nrxw12x/9DvmZ+66cKXtRRbT+o86EoSiYwEvicyDWi4Xgb8ggEzlBVNRfxSni1oigGcWpVpBb9YNOLfujH78fTQA4wAjgE/PX47s73D0VROgLvANepqmoQwvmhXyPH25AfBHrpPvf0z/tBQVXVg/7/ZcAixKtxqXwd9P8vO357eFxgd/w/yGtGVdVSVVV9qqq2As8ToE9+EOdDUZQYhBH/p6qqsgtJ5Brx43gb8o1AX0VRshRFiUUEbd4/zvv0vUJRlERFUZLkNDAd2IE4D/P9w+YDi4/PHh432B3/+8Bl/syEUwGv7vX6fxYmjncu4hoBcT4uURQlTlGULESAb8P3vX/fJRRFUYAXgF2qqj6iWxS5RiRUVT2uf8BMYA9QANx2vPfnOBx/NvCF/+9LeQ6AzohI/F5gOZB6vPf1OzwHbyLogmYEn/kLu+MHFESmUwGwHcg73vv/PZ2P1/zHuw1hqLrrxt/mPx9fAWcf7/3/Ds7HGQjaZBuw1f8384d8jZj/IpWdEUQQQQQnOY43tRJBBBFEEMFRImLII4gggghOckQMeQQRRBDBSY6IIY8ggggiOMkRMeQRRBBBBCc5IoY8gggiiOAkR8SQRxBBBBGc5IgY8ggiiCCCkxz/Dw2e8YblYXXIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import cv2 as cv\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "img1 = cv.imread('crypted.png')\n",
    "img2 = cv.imread('key.png')\n",
    "img1 = cv.bitwise_xor(img1,img2)\n",
    "\n",
    "cv.imwrite(\"res.jpg\",img1)\n",
    "plt.imshow(img1)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
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