From 04d57a04067e3daba447bd0879de1d9f9a6f1ca6 Mon Sep 17 00:00:00 2001
From: Sucio <esteban.cosserat@gmail.com>
Date: Tue, 31 Oct 2023 18:18:36 +0100
Subject: [PATCH] correction code

---
 Rapport.ipynb | 222 +++++++++++++++++++++++++++++++-------------------
 chatgpt.py    |  98 ++++++++++++++++++++++
 mlp.py        | 128 ++++++++++++++++-------------
 test.py       |  25 ++++--
 4 files changed, 321 insertions(+), 152 deletions(-)
 create mode 100644 chatgpt.py

diff --git a/Rapport.ipynb b/Rapport.ipynb
index af22073..b2f43f2 100644
--- a/Rapport.ipynb
+++ b/Rapport.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 33,
    "metadata": {
     "dotnet_interactive": {
      "language": "csharp"
@@ -28,7 +28,8 @@
    "source": [
     "import numpy as np\n",
     "import pickle\n",
-    "import os"
+    "import os\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -40,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -106,8 +107,8 @@
      "output_type": "stream",
      "text": [
       "[6 9 9 4 1 1 2 7 8 3]\n",
-      "[9 7 4 3 1 6 9 8 2] [1]\n",
-      "[1] [1]\n"
+      "[4 3 7 8 6 9 1 2 1] [9]\n",
+      "[8 9 3 7 6 9 1 4 1] [2]\n"
      ]
     }
    ],
@@ -118,8 +119,8 @@
     "    print(l[0:10])\n",
     "    d_1, l_1, d_1, l_2 = split_dataset(d[:10,:], l[:10], 0.9)\n",
     "    print(l_1,l_2)\n",
-    "    d_1, l_1, d_2, l_1 = split_dataset(d[:10,:], l[:10], 0.9)\n",
-    "    print(l_1,l_1)"
+    "    d_1, l_1, d_2, l_2 = split_dataset(d[:10,:], l[:10], 0.9)\n",
+    "    print(l_1,l_2)"
    ]
   },
   {
@@ -131,12 +132,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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aBbv3IPhC7NwYbd++Hf5taGhIOjs7m2x1c896oeeICDzrheahtpn7e3R2dkpHR4fceuut6r+n0+ldqo/M7PnVLC82NwKBgLS1tRl2rV/mz58vP/zhD0VEZO3atXL11VfLRRddJLVaTb73ve9N+/1vfvObpj81Jv5jJs+JZvcoO331U5/6lJx22mlqXUuWLJkuu88++8gXv/hFtdzOP1ruZCaJuL4h/5y0c4C8EtD/+7//+1o05wU5/vjjpVKp/N0fJ9bexXVd+f73vw9lX6m/nhL/M1N8fifXXHON8YlRPp+XG264QQ4//HDja2bNcuSRR0o+n5frr7/esF955ZUvua2keWaKH1qWJaFQyPC1crksP/vZz6Bsf3+/PPHEE4btzjvvlEKhYNi8nyrt5JBDDpF4PC4///nPDfu2bdumv+b7cnH00UdPb+b+lp/+9KeSSCTgpyeuuuoqQ1158+bNcu+998qqVat26bknnXSSjI+Pi+M4csABB8B/Ozdb5KUxU+ZXsyxZskRmz54tV155peGHxWJRfvvb304rEO8Kixcvlv/6r/+S5cuXyyOPPCIiIscdd5yEQiFZv3696p8HHHDAy/pe5NVjpsyJZvcoS5YskT322EMef/zxF/TVnX8MPOmkk+Spp56SgYEBtZz38DqTeEMeXg899FBpa2uTf/mXf5Frr71WbrzxRnn3u9+9y1+F2l3OP/98CYVCL/p9+Xe/+91y5JFHyr/8y7/If/7nf8qtt94qN998s1x44YXyy1/+UkREjj32WIlEIvLud79bbrnlFrn22mvluOOOk8nJSahv+fLlMjIyIt/97nflgQcekIceeugVeT/iP2aKz+8kGAzKscceK9dee6389re/laOPPlpyuZxcfPHFu/X8s88+WxYvXixnn322fOc735E//OEP8pGPfER+//vf71Z9ZPeYKX544oknSqFQkLPOOktuu+02+eUvfymHH364+pt373vf++SWW26Rz33uc3LHHXfIt771LfnABz4AX23fe++9RUTk//v//j+555575KGHHpLx8XHJZDLy2c9+Vq6//no5++yz5ZZbbpGf//zncuSRR0osFpMLL7zwZXv/Cy+8UMLhsBx55JHyi1/8Qm655RZ573vfKzfddJNcdNFF0OaRkRF5xzveITfddJNceeWVcswxx0gsFpNPfepTu/Tcd73rXXL88cfLCSecIJdcconceuutcscdd8hPfvITOffcc+Xaa6992d7xjcxMmV/NEggE5NJLL5XHHntMTjrpJLn++uvl17/+tRx55JGSzWanfyLq7/HEE0/IEUccId/61rfk1ltvlTvvvFP+67/+S5544gk59thjReSvf4C65JJL5DOf+Yz8y7/8i1x33XVy9913y9VXXy0f+9jHXtY5SF5dZsqc2JU9yv/+7//KHXfcIccdd5xcddVV8sc//lGuu+46+fKXvzz9k2wiIpdccomEw2E59NBD5bvf/a7ceeedcvPNN8vll18uJ510kmzbtu1lf99Xizfk4bWjo0NuuukmSSQS8t73vlfOO+88SaVS8qtf/epVeb7jOOI4DvxeoJdQKCQ333yzfOpTn5Jrr71WTj31VDn77LPlnnvumf5qy9KlS+W3v/2tTE5OTv+Uw4oVK+Sb3/wm1PfhD39YzjjjDPn0pz8tb37zm18wv4O8/pgpPr+Tf/3Xf5Vjjz1W/v3f/13OOusssW1bbrrpJjnssMN26/mJRGJagOeTn/yknHHGGbJt27bpPwKRV4eZ4odHHXWUXHHFFfLkk0/KySefLJ/5zGfkjDPOkE9+8pNQ9uMf/7h8/OMflx//+Mdy8skny29/+1u5+uqrJZPJGOUWLFggX//61+Xxxx+XVatWyYEHHig33HCDiPz1K2A/+MEP5PHHH5e3v/3t8q//+q+y1157yb333it77LHHy/b+S5YskXvvvVeWLFkiH/rQh+Ttb3+7PPXUU/KjH/1IPv7xj0P5L33pSzJ//nx5//vfL+edd5709vbK6tWrp38/s1mCwaBcf/318ulPf1quueYaecc73iFvf/vb5Stf+YrEYjFZvnz5y/WKb2hmyvzaFc466yy57rrrZHx8XM4880x5//vfLy0tLbJ69Wp5y1ve8qL39/T0yMDAgFx++eVyxhlnyKmnnio33HCD/Pd//7dccskl0+U+9alPyW9+8xtZu3atnHPOOXLcccfJJz7xCdm8ebMcccQRL9v7kFeXmTIndmWPcuSRR8oDDzwgmUxGPvKRj8gxxxwjH/jAB+T222+XY445Zrpcb2+vPPTQQ/LWt75Vvva1r8nb3vY2ed/73idXXHGFrFixAr5yP5Ow3JczyhBCCCGEEEIIIa8Ab8hPXgkhhBBCCCGEzCx4eCWEEEIIIYQQ4nt4eCWEEEIIIYQQ4nt4eCWEEEIIIYQQ4nt4eCWEEEIIIYQQ4nt4eCWEEEIIIYQQ4nt4eCWEEEIIIYQQ4ntCzRb83OfwB8ynhreDrVKsmA+IJrGyAJ6ZBxbhj54v1H4I3fOztIPbtkKRZx58EGybNmwAm6Mc3QNh7JJoPGFcZ9ItUKaltbUpW1s7/ihwa2u7cZ1IYZl0GuuKpxJgiyUUW9wcg2AkDmUaYik2xG32zx2OOU6NBtYWCGJlB+67rMkHvLr84tF/ANuf79wBtnRsqXGdTKCvhC30sVQyDLbO1j6wtSXmGNcZxce2j20B24bRx8HWMrsAto7ZRbCFoyXjulzMQplYLAK2oJUBW8OxweY4eeO6rWUOlIlG0a9DkgfbVK4KtvEdZn9XCthnpWoKbK7gT2BPTmDMK5XwmbnClKcufO/JCez/n3/uXrD5hR9f8M9gKxdrYAuGzHltze2FMtkExqB9WtGHtjzxKNhuuO8xs65qHdugxBbLwhgXjsbA1t7VCbaWuFnfHvO6oMyqww4Cm13Hto1N4biH02bMf3bdZihzx133gU1C+J7RMNpaw2Z8iYQcKFNT2mrXsc/ExVgeDUbBVnJN35is4HwK4CPlhj/fj0YfMHfRUrAFXIzbwUTQvG8J+r/iirJp/RDYGg1cK9Ktac81+nAqEgRbb28P2LIFjKHj2UmwtXeYc6I2WYYyhR3jYGtLp8HWM3823mub+8apcayrkMe1KahsYetV9O2pnBmP420Yf+oOOmNdmRNOA+t3FVvEs5eMx3CcajWMn4//+TGw+YW5fbifj8exL72xNhRAfwwo5wBb6UdtsmSncsZ1LIBrRzKAvpGvot8GEhi74lGlvqT57q2tGSgzOTkBtloR9wcYCUXqNY+vKTEiGMJ+jGjxPom+1ttlrjGDO3D/Wqxh/7e04HnEruMbFItTYJsz29z/hpXzVSiEtqtveAxsXvjJKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ39O0YFNbF4rHdHXMAtu8OfPN+9pR/KJmociBFcIEadfFpOBKxUy4XtLTD2UGlu4Dtg1r14JtSkmuzk6gbcvmjcb11i0boUxISa6OR/A9nVoJbGFPEnYshgnSIUVYJJZWkufTKDyT6TDFRTLtOJatGXxmqhXFhtKKLZ5CUYagR2AnqCRlh4KYfO5XFD0SSXai8MoTD5uCO3N79ocy6SQKHFRq2BflPPp/OWM6mm2hP7X1YV/vMRdt5Rgm7OcbWbA1cubcjDrod24U21p3sG2hIPpne4sZIxIRpa4i+liuiEIo+fEc2LasNcVvglFFiiyMwhzbBofBlk5hnCrkUeTAtr3l8J0UDTNfMzmoxD0HXyIc8ojquShY8XwZ+3ufZQvB1qjhvbM6TX+JK3Vp/a0JNpWqWP/UBArWFCxzjKsVFP7Yd/+DwVYvVcA2No71z4qZMaFRQz+OK3OsocjqdStrwN4LFxnXoyODUKZcRvGeQgFjnARwXYuGUJCsr8cURqtHuqHMumc2Yf0+xVVESjTxnrJH+Gd4O453dyfG0JgivhWwcK0IN8y1ojqprAFdKHA3Z1YH2JJxXBdKOdwDSdX0g2XLUHSp51AUtErFceGMptBWbZjCRdUqivblsuifmvjh6NAo2DZuNudJpB33McEYrsGOhYJK8RZlL6YI/KRj5hiHlT1Qo6FJ9/iXsLJnc2yMvw3PumBFsH+qNsYMTZBIE2zKpE3/bknifKopAl+NMo5nIoxzrFURFEzEzXFPKfv7sTKuJw1l/YvFcA50eYQCJycxbsTi6Ht9vRhXg8r6191tCsOGlbo2bkXRuEhY6f8M9ndK0ebt8AiKWooKVbGE49QM/OSVEEIIIYQQQojv4eGVEEIIIYQQQojv4eGVEEIIIYQQQojv4eGVEEIIIYQQQojvaVqwafGSZWB7fs3zYBubMpPqE+lWKBONY6JzpYLCEBElybtRM4UyilUUK+jqRiGXQ2b3g21wyyawlaayeO9hbzGut+9AsYtIGBOwM4qQ0VNPPAi2u++42bh2RjZAmUAAE51dJZE9qAgHePsx2MD7wkpfh6L4TglFbKhVEe5Kt5uCC21t7VCmowMFJN60N4o++IHBkXGw9S1Akatg0Bzz9hSK0IigwMHgRhzzjYPbwTa7z/T3oos+1hbCRH+75TmwBVL4TtU6zs181hRWaA+hGEhEEVlqaUXhmHQchTiqdbM/ajaK1YiNwjRTO7rANrkBQ9rahx4zrpNzUShi9iIUPYglsS9yeWxbtYL1iUeUbmwcRURqdRTz8TMbKxgPSuUpsEUsz3s5uAYELIw3Y5tRQOzhoW1ge27E9G+3iv2viTPFYihQUbdRcEcC+DfdmEd4JltGf3zgSVwPezvw3au2ou7nEdiIKitzWBHOUPSaZMnAANj655lCil7BExGR4e2bsHrFR1NtuL46iuhJImqu6X2dGA+2BrEdfiUawUFxHRwTx/HEQhtFaLrbUMiyMoF7mXIBfTsWNPs6kcA+XLZkEdj2WNwPtqmCIoIUUz7TCJjvtOdyrGtBPwpB1qooxuIG8J0Cni4KhTH2Nmo4V+tFFOCpFXvA9uaKuX+1whgLAglFjCiCa3VAcdmAMjcjnjUgoMQkTZTUz0QUUTHLQltbp7m3K5YVoVIH+9tWRJwspY96e8z1uqcL95Ib160HW2cI43FPH/pLwNbE08zxa1EEjzpacT/mBpU9cyu2I5H0iJwq86RrFsaNmCIclc/humy7pi+3ZrANs23s66CyFoXCWC6qqJp652xLGoXS3PruKVfyk1dCCCGEEEIIIb6Hh1dCCCGEEEIIIb6Hh1dCCCGEEEIIIb6n6ZzXtjR+l3vhoj3Atm3rZuN6YgLzmFq0PNgYfi88EsTvVScj5nm7XMGcBy0PRfkqvbS2Ys5irYo/Pm875jPmKjlF8VgGbKkE2jrnLgBbyfOd/j9c+ysoE1S+ix4J4nfdww3sD+8PMwcczOOoKDm1DSVHY1RJsnLXYa6XBL3f38f8hqiSU/v+f/0A1uUD1q7F3KD+hZhzuWDJPON6w/ProEyxhPndSSUHLa/kEz615knjOtWHc7AjjT5gB3Dctm3AnFdxsR1tETOXyRUlDyuCfdHeirnQhSnMdXzuWbO+tiTmoKRb8O9s9Q70qeIg3ju8I2NcL5iD9yVSWL/dwL6oKbn5oQjeOzlh+kupiLmDlvJ77H6mHMR4MBHAPDTLMX+UvSOEy0yqBWNvpYj+ns3jD7znKmb8cpU2OA7aghWMeyHt77d1jLXFmtmOlJKH9cDjT4Bt8SLMPVw6MA9soYjpa/39uMYUGxjvd2zHXOpcHtcwiZm/IH/AEftAkccevBtsZWXhzNdxXowXcTzby6bPzw5iDK0UtPxff5LMoB+HGug/acfMhYtHMTfOwhAtiRCWq1Qwx75UGDOu3QS2YWQI63rUwbzDSg3nV0c35v/3zjHjam8f5t7FM/hMjPYiiiyHxCJmMHS9ecMiUi9iWyWOlVWVeOxWzfUv4Chb3yj6Yrwb96p2HNtWVQbUtcxyjQauwQ139/L9XitaW/AcEFNyP7s9PjQyjnuNmLL/m5rMgm1WJ+4tolHTX+KKjs7subgXSCaTYKvXMMZFFM+NRsz2lsoYZ+f24dxxwzjGEWUS1GqmD3UqegkhZR9XVfLK0y0Yo8ues01+CrVRqlVcNzs6cczjSSUWWnhvqGa+Z6WonK+quC43Az95JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie5oWbHr2ycfB1tKByclxz48YT46PQJlyGZPbu3tm40MVIY66a9ZfU4SMrAbaAootHMbXb2vDH9H9859XG9fpOCaa77nXQWCrKj/ArvzOtrR0mYnl9RCKV01OYnJ1IoTJ2wlFxCnqEUyxQth+7aeylS4TV9HXcDXRgVreUwYry5dmzg90b92CA+cKJp/nOrYa17UAitA4IUxQz7S1g22PJSjutWPErK9YRyGgJ55GcQRbmUuZThR7Elf50fqo+Yy2dmxrKoECHvkcOsvYDhTdaNRM/4wpohC5GgrCPFlZCLZqO/5YeaDbFJFLxLB/JrMTYNs+hH1hV1HcoV7FMSgUTaEV7cfXYxGch34mamEf9SbwvTJixqD2NoxnGxU/S8YxjkQtjBEJy/SXehL7sW4ronRV9D1H+fttPIFxOxI136lnbi+U6ZszF2xjBfSN4RzGjYMPNtePiR3DUOa00w8D2803/h5s9917P9jm7b2/cX3UPm+CMusHN4Bt458fBNtUDednwcaxW3ag+cxyHdewzk4Ue/Er/XuhAF20gu9t502fHRzMQpk1T2AMCri4H6nmUGTJsk3/CVSxDRsfwnVnSwTrt5W1u3MW7usmPYJNyQYKfnW3LANbTy+K5iSiOKe987ymiI4VFGGdWg73koVNiojZiOl7tTzOy7JgzOhcjHM6oMSzWHcKbFbGFBWyFFHMsCJk6Wc6O3F91YSoahWzf2f1oE8lFJHWaBD7o7cLBZvqdXNejI/hOSOt7CNCYYz3jRq2PxzCsQoETB8tl1BMTZT9cSCG71StoX9XPeJpmqBpIaesmylcrzTBwvEJcw5EwyhepWi0Sk0RdcsXULgyoLx8LWe2o1bDOZZSRLSagZ+8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPU0LNk1kMQn+qcf+ArawR7ihZ8F8KFNTxB0SKUzaTSRQFMP1nLeVqqRUxqRmLS++riQiP/f4w2B75K4/GNdJJcG4twvbOmsuJqRHFJGo5Xvua1yH3vdBKDO4dTPYprJjYMvnUFSlkMsa18ViEcqUy5hAXq9jcrWrSDtZFv4NJOIRnYqEUUgqoQij+BW7iu3PjqBYRL3kSYpPYn+19aDgkRtFMYruRSgCkWuYifIFRfwsLlj/+DgKVKQjrWDrm5MBW11MMYSpBtZVnEBfjAWx/gK6maRbTGECO4LCLiNFFHy4+Vp894Y7BLaBiHlv0MVgMDaE4gu1Co5dUBFyqGjzxKN8kEpjX1ia+pmPiSQxdi1M47gs8AjPtEYUUZ6pbWBKZFCgohhBwZpG2BSBOGDF/lBmVje2a8O6dWDbumUQbAFF9M61TZ+PKQJohxyM7RjF5ssDd98FtjVr5hnXTlm5MYmiZdkirmGFOsbjddtNgaBiA+dA0cb7RrJYfzWGcWmP+SielpnVZ1yPjqNI0VFH7QU2v/K2tx8OtuImFIq57xZTMCtYxfW2lEP/cRxFPExZb1sTpn8mw1hXhyIWmUlgDJKQtjFCW2DQjI+P3fhnKLP5sWfAtuqth4Jt76X9YEuGzWdGpnA9tMYUEZotuN+pPLcdbMVhc/9aqeJCNOTZJ4mIbH5+K9hCHdiPiXk4N/c8drlxHU4ownKOsoH1MQFRxJkUwULHs7e2A3hftYIxLhTEOZBTxBQtMX3BVQSKBrejH7SmUMQpEYrgM6soeOYVHY3EcD3UhAK1c4YVUISjbPMdGkF8p2gE1yZNbbVUxmdGomZMiITRHxMx3JNEo9g/U9msYsM+S8XMuWIpglyJFiUuNQE/eSWEEEIIIYQQ4nt4eCWEEEIIIYQQ4nt4eCWEEEIIIYQQ4nt4eCWEEEIIIYQQ4nuaFmxqacWk2o2lAtjGhncY1+UGJjCnO1FMw7IwUTgeQ6GPji5TBCIUwgTmqiJ2EY9j0vHza58F2333/AlsAU8yeHYMxWmGtmFifzTdAbZIAsUuMq1msv/hq47CNgSwf8oVRQiihGJVxbyZSL1jG4o/bdq4EWzPKwInmljVnDlzwdbRMcu4jsdRvKq9HYWF/ErUQj+rl1GooK2nx7ge3LEDyuQqKBLjBtaCbd+9F4PtkOPM+pMRFCCol9C2di0KVOQmUYQtHsckfidi+v+23BYo05HGed7XhnMu3a6ImHn+hla0UYFgveKzG+5BgYBafj3YrLlmudIIijP1zkeBk3gG2y8BHPNAEMslPKIqNUVYKxyYOYJlIiKFGs6B1iDGg/qYKbi1NYv+/pZ9l4KtXMN4NlvRM4klTP94cwbbsGdXJ9hKDfSrsSj6e2kKBcMcz/CFahhn52/BGBrPovBMe1cGbPWnHjWuNdGo+57B9WrNEAqUVWwU6xjcYgpkjYzj3D9ovzeDbX4GY/s3r7wObLXyMNgeftBcJ3fswLm5/9HoB35l7xWzwbZOEUaZmjT3Hx0JjMe2IvI2lkdhml4lBi3KmPWFBIVdwhZu7dpacD8ViePccZTPNGIxM24nk7gfmRrB9q+5cTXYMsP7gK27rcW4tiuKGF8Nnxku45yOKvO85BW3VOKKM4X7xuwYzvPEKMapehbLVfczRcyC/TgmDrqBr7EUdaBIBN/LK25kKy9areCepE3xx7Cy9w0FzPhYqaEQUCSK/l6rol/VcjiekZSyT4mYc9EK4zMdJfbGY1hXvaaci1oyxnVMOf9YFs71fAHPYfUalrM8Ak1a/aLEpWoJ38mpaSKteLZp8ezx63VcD3NFRZywCfjJKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ39O0YJOEUNgi04aCOzs2bDKuY4p4Um4bCr7sUIRtHn7kEbDtuee+xnUi2QJlalVFVAVzvuWJRx4A21QuCzbbNpOfGw5m+yvVQ9K6iJ6oXXDNhPGEouMSDWPSd1x599Y2FMOKRczk9kgAxUByiljBUUcNgG3WrFlgS6WxHaGY+RKNBvaZmjDuU/KTmBTf0onjO57bblzHUugZhSImrddtTLB/7hkUgNk+aM6ddBr7cNYsFFnp7kfhj9JmFCrYOoqiKvG0OXYdXTjebS3KnAtsA1soooiGBEwxOLuGYjuNujLDGiiss2w5ijgtXWDa0gkUIGjrQv8slVA8olbDfsyPY+xyamZ98YgyqR30Hz/TFcSxmy0oWtHSYgrKPDaJfjBZxXGa39MLtjNGFoAt7BHY6Hge64+u3w42RxEP7FfcKuygMRAy391RBNyqD+B61aoIeDQ6FZEc2+N/OYwHLUEUxKgWcQ6345BIwjXFUXLDKIA2exkKxKWTOOYHDaBw0cgUCqEMF8w1pVRCQZ8Nzz+PjfUpra045mNj42ALB8zxTSnzZrKBYjXiYgyNuOiL89Jm/fEoDriipyLVGj4zr6z7kTgKTLlhsx0JC9+puxPjdiSkiCdtRXGv7SOmgJjtVUgTkUAA90Di4ruHothnXqHAag7nZUIR+JkoYJwq7UA/bk1j21KWuWd2Arju12bWEiCBADqWqwhkxZNmf1QsXF8jivinU8RxEUV8rMezD7XHlY600YeSETzHVPO4t2vtwbNNqfTiwkKds7qw/gK2I6isH2GvoFIUfapSxrZGI1guEMG1YsrTt/U6rjFBB320UlFUxRo47zSB3ZBH5KpSx74YHUPxwGbgJ6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnxP0zmvFW9OjohEYpjHFQyZVdrKd5zdEH5fenhoBGzrN24F23333W9caz/mHgria3W1Z8AmdcwxCSnH+XzO/AHqjjR+nzwSxVw4S8kPcBr4PfOG5weFw2GsqzXThvcpubeVCr7T2jXmj9v/+a47ocymTRvA1teHuU1jk5jj4yoZv6GYmc8QCuM4aT/UfvRxx4DND1gNLQ9OyWctZ43rWbMwBzkorWAbGsK+yLmYQ5CbNOdTKIb5AuNFtLWm0X9iyg9xt3TMAVs8as6nWW2Ym6jlXYngO2l5FvW66VNuGOdNbhJzSVow9VZWHdsBtqiYsaW3R5u/2P61T+L8mpjEvJdKDnPJXE8Oc2snPtNR8pz9zNI0xvvk+BjYggGz3xbPQZ/K71DyXJT8vtkW5jIlIma5oJJLaSl5WLgSiVSVGC1KXlTYo18QUtbDcEDx9zT6lav86LtdNet3lJg6K4BvcFRcycu2cP1w+swcsdimTVCmhLeJtGD+415LF4Gtt4Rt6/X8IP3igT4os0iZF34lrviFpczh/GTWuA4oOa8hC33FtdEXbRv7p14319JkQvHFINaVz2N+dCSGa0A6he0NR0w/LhYx904c3He1Z9A/K1X0f8fTjfWqEmeLOM/zeSyXSKIjt6XMfhzJob/GlP2s28iDraLolmzdgnm8C7aaMa67H+Og01ByPH3M4CjmAGvaLsmq6ZOpVsUPajh3tPzw2b24d4kmPGsAyl9IWwL9IJPA+tM9mKtdDeA7rR0eMuvK4AakWsSGVEqYRxpW3rOeM8tp86Rh4XoSDKOtUEC/tT3blJqiudGVwTnQ3oL9/3wezwsdbVjO29yWJMabRh3XmGbgJ6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnxP04JNmU4Untnx/LNgCwXNDN1KWflh3wg+NqyI33iFYkRECh6xC030pxHCRO1cFoVFnAoKGLRmMmCrecQ/tETqQgEFDDThqEIF721Jm4nfjToKMIwN7wBbsYhJ2WvW4pg89OBfjOsNG9ZgXUr7N25eD7ZwGN+poQitBILmGASDmFRu25jIfvElF4HNDxTy2NfBIv7tJ+3pn7ryw9YBQVs8in4RUH4IPt2WMa6dIPZhuYZiOKUdKFCxYPZeYGuNozCS1E3/r0+hiEhbEhP9JYzPLClzTkLmOzSUebNhHQp+tc1CAZX934SCTXHZw7iuO+jrlSL6sF3HOVcrox9Eg9iOeNK0Ke4vVgDnuZ+ZGEKRhqqN/VYOmu9VakV/iZcwbleexXjjBFHUw06a/hEIop9FFUElS3A+2Urschp4r+sRnEOpC90W6l4ItnQW40bF07TafBS/aLPRb5MVfHc7izGhMGIKrZSG/gxltj/0ONha9loMtvFhjC+1RDu2wyMQUhpHMZOcEiN8S10RXlE018KezwQyrShIkmigL27NYWysKiJI+YpX4BHXk1AUY5ImnjlnLooItXbgWI6Nm6J6daUuW9lN1mvK3FQEKStlc/1zlH1jKYdrZG4iBzbXRlGYVJc5n+rKWBaKGJNKVUVg0MaZXhnDdWHjWlNwtPMQFCwLKWI7fqaqxNWJCRTSSpRM4dB2xV/CyvEjllKEnUo4xgWvCBKGcQkq+8tqHn2oSxFgXfP8RrClPIJeqTj6WbWK4o1tvTifLEcRMPWcbWLKfPLOfRGRaBRjyfCOIbBJw2xvqjUDRbTzmnbGisfQb9OKUNpE3lyzKlUUlE2ndk+0j5+8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPU0LNs2d2w+2tQ/eC7bxKVMYojyJCdJz+ueBLWApoj8BPFt7i7kuJpA3XEzUtmuY6JyMY6JzThHmyRfNd4gr7Xr4kUfAtskjkiEikm5FIY5kwkxSj1iYzL127XNgm8yicMamTc8r5UyxBcfFvnAbityIkgTvONq9WM5tmDe7Ltavja9fCUaxreUKJrIXNpv+Ux3DBP7uPuyLZBwFNqbKWbClQ6Yvts/CxPnRUawr6GBSvFPFeysFTNiPWqZ/BoIZKDMxpoiGJNFXxhXBhLJXLCyE9W8dxFDVOwfnVyyF4g4hj6hNuYziUm4VnzlnNopMtCrCVMObUWglmTLLuQGsS5nmvma8kAXb1iIKMNgNM/5GrB4ok2jrxPoVMaweTQyrYs5FJ4fzsFpDm3TiM5OLF4GtoggjFcZMv4o2MDgGFSG/6ii+k0RxDbAy5vwMWRgjGjns6/heKAglEZzriREzDhUHB6FM9rl1+MwtKFqWbkcBookMLgLjw2Y/bh/ZBmUWRHrB5ldyiuBUUbG1Jcz+iUXQh2tVTWgS42XJwvVjsmr6f7oFA0lY2U+1JFFgJtOK8SydQuGVqazZtvEcxt6goN91Kb6iUfEKWdbQ/2s19LFCAedEoYjzNxo138kJYP+MKXu/SUVgs6IIalbqWG5o0BQJ1cdck3nzL93KeNoV7O90yvR518b1LxjCPVU8jr6nbB2lVDbrq9lYV1RRPFq2BOP9sCKGWq3iQzu7TDFL21HGU3AuJhQRqloJfSgYN30yGMB4UJzAeTdVQltrSwvYCiXznZwGtj8axvbXFeGr2fPmgq2hHBgmc6ZvNBQxxEy7IhLaBDPn9EAIIYQQQggh5A0LD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnxP04JNiSCKG/UqIk51j/CMrSSpV5Vk/KwiRlF3MQE47BFZshxMAHaUJHs7gInIriIGEopiuVDVTJyuunjmf+p5FEoaf/gxsCXiKGoQCZnD4CrvXS6jIE5DE15S1JOCQe87oVCPBHBMVJGloOIyQUXZyXOvVpeqCOVTLEUEzK1g/3e1mKIwwbIiHpZHH2tEsV9rFRSQGBszxYHcMPZhMowCAV3dfWDr7kABm65MN9ikbvpLOIiiCvUgijbkiigotm3HRrANbzMFEyZQP0Hs6j5gS2ew/uGxZ8DWapmiJInInlCmu28x2PpmoziFZWMczC9DIZSaR/THsXD+lqooxuJnJisYo4dLOO71nOmjnbNQkMGdi34WbcP+juZw/oSGzHGvKSJjBVHWhRSOU3g+igeGLEXcL2M+o752C5SpKyJRlQDa0keg/5WypriLrEGBPlFESWT7GJiqjSzYwj3m/O9Z+WYoE43jujCxdj3YMiUs1zof19ItHiGUeBDXgHAYY4lfaSjjW8+j77WnTD+eyqKI3GgZRVY656OQV1sS14rhbcPGdUsFRa+iIbyvoz0DtlQC41koiHOnpcUsN7QFY0GxiGuRJtBSUOZrpWTaGqjvI5PKHjGbx4INF22hYXOeRNK4RhYaGGumFLGaqrI/qyoCbpWGOU9sRRTTqSsv6mNSUZz7ywYwhsYT5pqr7RuHt24Hm23j3j2ZwrUi6xHqCloYRyxlf5mfwj3V6AjG0Lqi9yceMaaCV2hSRBou3lgqoaBjQfHlFo/QW02wLtdCfwwqwqctaVxL4wlzDEIhHMt0GuNBMIDltHm9cctWsFkhc1wiQawrX8K+aAZ+8koIIYQQQgghxPfw8EoIIYQQQgghxPfw8EoIIYQQQgghxPfw8EoIIYQQQgghxPc0LdhUUYQJZvfNBVsq025cl3egKMnEJIoVFEuKyJKSLC8BMwm74WBSc8PB+2qCyfKTORRSiERQ6MDyPLNcxST7QhWTjqt17Z1QDCTo+RuCogcglpKUHbCaE0jw6gQELE08CXEUMSydF69PE2xSmu9f6ji+EUUUIxUxhUvCDk4xu4Y+YEWx/kQMRVDGR0x/d5Rc92ULcV7O7lgAtlAIRQ4qRXynsJhCN5Yi0FVQRNjWbERRm+1ZtAXqZn80stiGdhfjyOI2nBO2kvxfC5kiBME6CjRo8ysSx7pmde4Bts4WFKzIFSeNay0WJEMdYPMzc+fOAVtg4yDY4p6hchTfiFo4xpNFjMf3bt0Gtj6PkNlSQd+oKuI65UFsa+0RFPgqK/HMmj3buK4s7oEyJTsBtn0GUJypGEDRvvLQJuM6MoW+Z7fgfK1tUYSjdqBASLh7xGzrLBRBCbe3gq3t6P3BllWEVjKdKMSxf2q+cX3bPZNQJppBMS+/ElL+1h+2FKG9sjnXc3kUdikrwi5vOfZQsO21J4ox3fOLm43rsUH0/97WFrC1ptHvajVl36Lsu7z7rKqyBxJlvzA+MYHlGhgL3Ya5BhQLWFdWmROOhWtkQFmXh8fN2NKbwf6RBAq65Rso8FNtKOuOhf4fTJj97Wj7uib3Yn4hFcH3TCZQ/Crs2Ue3es4FIiJxpT8mx8fB9vSza8Fme8YgGkHfbk+iANqQsgaMj+F+oKIIM+a8Yk8W+oGilyrZLMY9Taer5plTiQT2dXsHxmhLaUdVOWe4noNAuYJxw5XmzmHVKpZzGvjMuOIbXkK7KdrHT14JIYQQQgghhPgeHl4JIYQQQgghhPgeHl4JIYQQQgghhPgeHl4JIYQQQgghhPiepgWbqkpybyiIt7e1mEnStnKfpu9TKmO5SAgTlssVM2m/UUfhg5AiKKOJAwUC2JBKBYWpAt6EaKWyWk3JwFbQhIsarpno7GqNVYSYMD26uWc2lAEIBLQ+231FJXhP5b1nklRBSyuKscSSKPDghsw+S2ZQSMB2tKR4FFkpTKEvBgtmr0VD2AYpo2CFlDvBZIVQLMWxsb3RsGmrKyJpU6hJIG5uGdjidUW4wTXbGw3OhjLD2YfA1h9C0Zk5sb3BVg+Y7S2XUEBlqoYiNI0JFJazGigqlEmirREwhUTyOZytEUVQws/09M0CW34QxS4SbZ64oYiqhJV4s30MxTp+8PjTYFvSYfrjv8dQFCKh/FnWLeK4TzyJgk0TXSiKsaFqzk9NALBvcR/Y5rVhXbXtO8CW8oggWQ1lPcljn0UDOP9zZYwbzoYNxrU7NAxlJtM4TsklKNLVt2AAbJVhfKcuj1jHfnsvgjJzF2D9fiXq4hrQ04V98bBj9sWk4Hj07YWx69BVKO61dBn6VEfC3HfdetUdUCaXRV8vFXGeTIxh7Kop4nJuyJxQ+aom2ofrQlsZ64oqOxfHIwqTVQRCazbOuXAEhXUqyp5wsmLun8KKiFw5qAipCa7LNcG9WMnG/g565lMiiW11lH2Rn5nTg36rCfW0Zcy1LagIWoU7cf3r6UIRwztW3w22RsOsL5NGfxzerggutuEYZFpx3LMjeB4ZGzFjZqYNRb+SSRQfalXKpZO4D0q3mmtFMoX7OFs5J21YtxlsQUWMs+QRhNLOLLWqIigbxMXUUuZAXBEYdTzCjHVlbtYVsdtm4CevhBBCCCGEEEJ8Dw+vhBBCCCGEEEJ8Dw+vhBBCCCGEEEJ8T9M5r6USJrVt3vQ82OIx87vWmZY0lKkq33sOZPGZXR34vXDv97TLJSU3Qqm/puRjhJScWu373fW6mY9hKz8A7Cg5qXqep5Lz6r1V++FqJf9Uy5/V8lRdzwMsJd/s5cbbNjWzYwblewSr2FbHwh9vrnt+fL6k5XcXMI8mHMGCLRbmWEUDps9GbCXvIjgfbMEq5mY1ypjDGA9nwCaOOScsB/2/N43P7Mm8GWxlB3/0vThh5nFsHMEcjrYQ5j62Kjlo87rxPZ8dXm9cByzMtQlbSsxQ8j8qZbSVU38BmxMxcxFzFcy1yWcxz1aWn4g2nzDl4BoQcjEvOBwyl5VaEH07a2PuzkQZy9kuLlG5sNm3g2H0g4yLc7MWQJvrYk7eVAPXlG0j5pxtCeB4Tirp59cPXg+2JbMxp3ug3ayvI9oDZYqbBsHmlDGWuA6+5+TkqKcM9nVNyVmqT2FOc+0JXPcTSoSvxsx8p/l77oX1D+Fc9yulnLJviWL8rXr8oG/+XCjztjMxNi5agroEkTj2615vMXNjbWUXd8/3bwDbY+s3gM2q4s2OrexlIua6M6HksrYr+YShOObelXO4BuSnzJzRopLyHVQ0Vqo2FpyqYA5dybNuPjs4CmW2jGFdeQf7oqHsW6qCe6qWTjOHMZXEODWh7AX8jOtif0QjmJvp3UfXi/ieUUWbxg2jzWngnjwQMJ+pfgrXwPk6f/4CsHV2ofbHnO2YwxyNms9sacUc8qDyTiMjGLcPPfggsPX0mfnttot+nBtHv50cw3V5PIv9HfKsw12dqMfQaCjnE2W/15rCPOHJKZzXruesUSvjOzl1XK+agZ+8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPTy8EkIIIYQQQgjxPU0LNj3wIP5Q8OCWjWALh8yE32Ihiw+NobJFSkkAntPbC7apCbO+SSWZOB5H4YnJLLYjoBzdbSVBv+wRxQgKihC8FPEh0FhSRJeaFWzS2N2WqeJPmgjVbr777t73WtAYURLZ4+grtYCZkB5RBCsiYfwh7kAN63IVMYqGR52ju28FlAk7S8A2OoRzziusIyJixzF53qmZ4hzlMrYrFkexjoASXVozOKcjLR4xkC7si4gidpGroFDBjvJTYEv1mBM95qBgU7WC8Sfo9IHNVYQ5hiceBVs0bArVtbfvA2UCdXymn4koYh0hRRSj0yOmUQuiT4Xq6EOlCtY/WxPTWGAK4AwWUPxJi8eRGAqLWIraTa2BYjS9HaaYTkjRmMiNDoPNnUDxp6FxFNOYSphxYl5VEQcaQ+EPKWNDAjYubGXbfGbJwf53FRGqRBn9ffvgNiynrBVF22xbpopt7dxnMdj8yrZxHN97n7wXbF0DphDKO//5NCizcE8UZ7JC6MfVKvpKrWbuefZ+0zIos/mR9WC7/Vd3gi1SQ9GZuiJU1/AIoLXGcLzn9qIQmSY+Wajh/Jr0zP1sFfdw2ict4TDWnw9j/eGMuX5s3TYOZYbzeF/nvG6wDW1D0Ry7juKfAcuc07lJFLSp2PhMP7NlK879VBJ9KJ83/TYTxX1QTTDGOSGM0Yk0ir7WPHGvuwvX9GgA59PAQvTRqNK2QBj3SxGPYFM8jm0NKGKobhnHvZpDQah6q9nejl4UVAooQofz584BWzSWA1uumDWuIxFc+0KWsidUBHCDititU8U1JRgzfcNV/D2VRGHeZuAnr4QQQgghhBBCfA8Pr4QQQgghhBBCfA8Pr4QQQgghhBBCfA8Pr4QQQgghhBBCfE/Tgk3r16AQysTYGNgWLpxvXEcVIZdKDYUbarUK2MIhPFtbYooJBBWhiHwJk5rdACYYRxXhKLuIydWuRxSq1sD2N1TtIUV4ScF7qyaU1KzttWB3hZcCmmKWT9lzzpvA5iRQVMIJm0n8vRkU5oi1toDNauBYjo5uAdtE0fS9YGwRlKlUMmAr13F+xeJTYNPmYblois4Uiygi4ijCaY6D86RFEV+Ip8x5ODg6AWUqQRRs2l5E4YzUOPpisM2sv57bBGUSARRtaIv3gy0UwXGyq3hvMmqKcs3p2QPKhEUROPEx8TKOwZCNohLdHtGytnIWyoRGtoPNzqMA17I9F4Bt3hKzLyceXwNlei2M96KIu4RdjEHxAvp3yBOlEwlcO9au3wS2ziLWv7AfBSq2RUxRjB3rsH/ieZwXlo3vZDn47hWPaFZNib21Is7XCQfXw0QC41deEeEpVs22TQzugDKheT1g8ys9AyiMYqdQpGTFAfsa14v2xXd0XEWwxcHYW3NQLEWCZgyKpHAbN285xpvCtavBFqqj/+SKOJYRz15sxdKFUKZ/AdqmiviexRHcnw2XPP5fQvG2YBDXmGAI/TPVg/5/2AmHmvXf8ACUGaoPge3U9xwDtj/eeR/Y7r97M9gGPcJO9eo8KGNpccrHlMroGw1ln1uzzbFq78KY11D20ZUK+vvcuXPB9sxTZswPh7ANvT0o9telCDsFLfS1MGoxSSRqzrNEAs82waCyJy/j/C/nUFBpYnTEuHYDGA/iilCa1o6WtDKvS+b64SqxJa6ciawQ7m/qiuBiSxz3B45nXFoSWFd4N6fAzDk9EEIIIYQQQgh5w8LDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ39O0YNPYtkGwNRwlOblhVhlPZKDIyOg2sKXiKbDlCyjgEfYIplQqisAM5hJLXBGZmJrC+l0bk5gT8aRxnSujcEBDEc4IqIJKaHM9YiDqXS9BnKkZQaWAImil3be74kx+Fpxqhn32XQW2QCuKDwVSpq9kYpjEHoyi0FNQUCHg6TUPgW18iyl6snG4BGXCISXRP4XjG6kr4mR1TKgvTpkCG7arCHpEsP2lAta/YdN6sKVi5jOdBoalgiIQMJofB9tAvR9sE4PmnN6y6VkoE65h/2RSKDDT148CRVM2Cuk0Mua4t4cVcako+o+fmSpibLxrCkU3bFOrSg5r4NjFR4bBFqujL+/3pqPA1jfXFCm74YEnsa1VnANOCNtfVwRT4i7Gpco2s73BdhQgWdiG4mwVB0XRQkmcY/u85SDjegKnmEw8PAK2qqIU2AhhfCl73imZ7IAy4lnnRETKEeyfRgeKnlQEyw17hNemsijwOPnc82A7CVvmCzK9OOb/+B/ngi0SNz8TqAcwDgYE9xABZTsWj2OMcF3zXruBztI3H0ViFi9DEadtT2Jcch2sLxg2hVxqIRSJeWw9ihaNZNH/h0dRxGl0yowROWVeBoIo9JSK4Zw++MjDwXbQ8Qcb1/c9vhHKlNZtBVsyg3P15NOOANvap68F22MPmSKnq07G/u/px7nkZwJBXOerFYzvUY/IT1URdIvG8LOzQB3Fk5wajnt+MmtclwoogLRg3gDY4lGM7akEzrHWNhQuqnvOBo6D7x0M4jt1dmL9I4po2XZPvHz4qSegzKJFKPo1MorvPrQd57Ut5hhkWrBdYcH+j0ZxrtshnJ9V5Szm1SFNtGegTK6A8aAZ+MkrIYQQQgghhBDfw8MrIYQQQgghhBDfw8MrIYQQQgghhBDfw8MrIYQQQgghhBDf07RgU66MybiJMCby5rJZ8wFxLJNQbGGlJdUKJnmnEqaoRKWCic9uVRHmcDG52rUVm6JH5HiMtoNJzZrMkmXh3wZ2V/Bod+9rtq5gANvaUMo5DgpN7C6NhtaP/mTRPgeCzVX83ysKEwoWoUzQwfusOCbAl57Cvh7caooUTVRQtCidQvEze1gRIotiue72brB1tJgiRYUSvlOthvGhrgg5FLIoLlBpmKI/AUXgp1BBMY1CA8WCcg0UR7ECph+HrVlQ5pl1KCTV2ol1TYYUkYMk9m3BI4Y1PomiBAtmHQC2N816L9j8Qi03BLZ14yhqVfaIfmXmoJDRvmHss3QIx3PB3Llga0mZwjlVRTijWkJbJIzzqaKsC5EAirREambbyhMo0hUI4SLWCGIM3TGOYlWTzz5jXCdiGA/yMUXUMI6CcNUU+mixaM7ZRCeKD00oczhvK8JCdUVsZBj9OxDzCB0qomvJHAr6+JViFeNBsh1jeUNM3/YKLImIWIqwi13F9dB1tc8XTJ+q1XHcMrPQB04+/Xiw/XL4erCVstq6bPrjeADHsrMbxewKNo5vtY6+HUqafhwPYizo7sK4ffAhe4Ltzce8CWxWxuzHvgXo/40GihGtW4fCTiefeBDYlizpBdvDj6wxrrdt2g5l5i/qA5uf6elEIbBoGH00ETVjaDyB+2NbidthRYCuJYa+MDDb9IVMAgWW+rozYEtF0fdakjiHKwGsL9Iw3ymniBXGknhfOIF+pYmWbZ0wBQvXrMO1dXgE53puCuuq19G25zLTR1MxbJdTUpQCG82JucYU0U7Hs35YQVwjbQf7sRn4ySshhBBCCCGEEN/DwyshhBBCCCGEEN/DwyshhBBCCCGEEN/TdM5ruYY5SkHB7ypPjJl5UV2z8Dvys/swry4WxTyjiXH8UfOxUTPHr+EouXwBtEUC+L3t7j5s2/AY5mhM5szvjzef84o2DW857b5XOufVUfJPA0oerNY2LQ9Wu7eZuvxKohXzeewGvqPjfaUwzpGGWwJbLIX+WS/iD03veN7MjXNTSSjT1bMX2NatwXzFsoX5GVYRcx5Cs01/sQT9Z/uWTWArljC/tVTCXIygx38sF3NqJZYFkxvGHIutw5gb29Zq9tHceXOgTLWKfVGuYVtrVbSl27EdFU/+Wk3J7YsK5tnK3mjyC2+dj742OoF5mA9uNP37tk347vGFWFciFQVbOog5nfW8mffjWBh/ilXMDYop+TaOknsoilZBwxPPJoroB24F53qkqOSCZxWthfVbjOuE8nflWqIFbE/aOF83jY2ALeYJ75EG5q2GY9g/Vh1jdCWL+b5FF3MsQylzXjhhrGt+WwZsfsVWNDKUJUDEk+MaUnI8bWUNdpXtmOuirW6bPuUGcO22w+gXc/fpB1u8B31q6tlBsFkhcyznHrwAypzyzreCbfsOzPMcGcmCLV8092y2hXNpdi/mzs+bh3vJWgj3f5Nlc984Zz7mvIYCGJM2rMW+SP4D9vcB+y8C26OPPG9cl4voP0595uh+iIi4yr4upuTdh0NmuXAU76vk0UfrdYzlrWn00RUrTF+Ih3E+hcN4pgiF0KbtfSWAcTsaMediKoXrfiSq7N0bOIfDSj8+85yZI10soR+Lg3ujqqLxEwli2wIBc311lf13I4D9nyvjWpEvYf+Egti3NY9WhK2sy7WqkmfbBPzklRBCCCGEEEKI7+HhlRBCCCGEEEKI7+HhlRBCCCGEEEKI7+HhlRBCCCGEEEKI72lasMkuo+hGQzv7OqbNcjHxPhTCBOmeXhRP6u7EH6W+Zf3NxnVfL/7IcxxzlaVUwWT5opIcbis/kux9z0BAEVtoUk9JEylqRriooSSVa8JLel3u37l64fqbEV16oXJem9bWl1OE6pVG0fsSV/lx5Xrd9DPbwQT1RgQT1Bt5TLq3CuNgswvmD1e3daFwRnUUf9y6OIJCRnYDfaVeQJGlcU99QeWHvsvlvGLDuvIlfKdgwBOGgthncxZgqOruRSGHBGr+gJ8V68NQZkH/PLCFnNlgK9WeBlsgtA1sNccUgEqmUCSqoegx+JnFfTgG5yWw3+ZGTZGTO9eguNEdm/DlV8zHWF5YvxFsWU88DiqxK1tDUbSuBIoKOa4ilKYMzKhrPmMsgUJVlRDGg7SFfZZsxXY0PMIWMo5zJxpFQZltFRTTGHcwrvZ4xM0SSWx/Oon1u2Wci2M1fGYoiP0dnDBte7so6JFS4p5fsRRRRruO7Q+FTJ/S9GBKJVwDNHEmEbzZsc1nhmO44akpS3c8g76e6suAbbiIsby11Yy13QNtWKYffSrWNx9siyy01cvmulmoKGukst4GFIEZy8U+iwbNhaGzqwPKpFtiYIuEFWG5NIo37nvQHmBru/Zu41qL9/Fo01twX1Cr4xjkizj3A2lTxKmcRZ+q24rYahxjYzCAcSM7bp5Hqopg01QB41TdQb91q/hO4RDO9bBnE1hyFKEhdEeplbFcQhn34WFT3Kzqoj9Wg4o4kyJCFYzhXC+VzMbZNTwTRSNY15SyxgyPT4LNFW2T7BWjxQ7a3TnAT14JIYQQQgghhPgeHl4JIYQQQgghhPgeHl4JIYQQQgghhPgeHl4JIYQQQgghhPiepjNl53UmwNbRjrZMmymyFE6gqErFwUTh0bERsM2fPQC2ubNNgZCuzgyUsR1Mah56+lmwjSlJ5DVFXMHyiA9ZliY0tPviQ80IF+lCTJr4k3q350p5SbWuFxeSEtEFm4JBM3nbtjEpfiZRrqFwSa2MyecVj5iJ42Kyu21PoE1wTpSm0D8DUXNMQkmcwtkxFHsZ266ICrn4TraD4gupTK9ZpoKJ+Q1FIKdUHgVbxcF5bkVMwZGQIr7QOacXbIsWo1jV8DiKVUU8IcgKYJlaEcekp2052CSAokJuCvt7zXOmoEFvF4rPJaMYP/1MVRnj9hjGiEMWdxrXY0WMNw8PogDgsztQBGIPRSyiFjF93m1g/Mkrgi9uFcUowjGcP64i2iceWzyKYhp5ZT7l5uG4d+y1FGxBTxc9+fu7ocxc5Z3mtHWBTaoYS2IekcSpOvZrcRzHt0cRpurrRLGbiFd0TUTCE+YYz8+jcNfcTAZsfqVcQ78IBtH3IiGzL2xlb1BSxqhcUeK9Kppo1pcM4hg5liaiiP6Z6UUBGzuIAlCBsCl41N6O99UVQaWa4F4sYKMfW95yihBTrY59ZrkYf1ylvyNBc+6nWtCH2zrxvXtnY7x3Aiji1DEPnzlvwHyG62BbQ03usfzC2GQWbH3d2JdeESe7gb7X3tGO9+UwBtk22qoesSEtZD+3DsX+AhauRRFlDs/rx3EPpMw5UCmijzqKCJKtCNxFlWdmJ814uXZwM5RZ0IX7oHZFQCzUjueuYtGcY5M2rsGhCMbxvCLaN6nYGi6+k+U5YoYtjBFFRbyuGfjJKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ39O0YNPA3E6wJdIoFBBOZozrzUNjUGY8jwInpaIi4jRPEVGZbSYsj44OQ5kNm7aCbXAYxWPEQuEZV7N5ssGbFTLaXTQBp0CgOWECaWBCOjRXaX/DxeRzV0nA1oWplP5opotmkFaB09D6DMvFImnjul4tQpladjvYJupZsCU6MmBb+dbDjeuhEorcbJ0YBFvXQBRsDUXUw6mjOEJNTKGVZAuKGYxsxXeq1HDO7bECRRokbnbk+NQ4FMl0x/E+CwU2ygUcp/YuU2DDdrHPOmeh6EFXlyZ6gnEwW0bhpa6MeW80iGVGhlDIwc9YQVwuLEV8pTdjihkdugD7NqcIoG3KKqJfQRzP7rlzjetgBPu2YuPkrORRECdUx7gXCaOved/A3oG+3aII1lQVAZKJOsboTJspgJNR5ma4gnXNTqJ4TET5m7SVNOe/Fcb7AgVcg2eFsG8VjS4JVLEfS57+bg1i+wfmofCVX6mg9pAElPW27hHfq9cVgSJF9DESRUExx8Z+bXgWnooi/lRRlCfrym4v3Yp7uGAE90DhmDknomGMg9USPtMO4Ls3qugHoYb5zAa+trjKhsGu45wrlRWBn4DZtxMTuC6XFUG6RBJjwdgECt3YShxJeoR0iorAT6mkOJWP2To0BLZwGP3FK1I0d24PlNGEenIFTbBJEUoLmM8s2TgHnl23AWyhALZ1SNm7dCqCZK2tGeP6+efXQRltT37KiYeALeqioFJbxtw7xnPoG+PZLNgaylzXxiRXMGN5UdmblhRxqUAE944VZQ3T9gcNT3ycLODc6Uwre7sm4CevhBBCCCGEEEJ8Dw+vhBBCCCGEEEJ8Dw+vhBBCCCGEEEJ8Dw+vhBBCCCGEEEJ8T9OCTclWReAhmgFbyTHPw40gno9DFgoTxKOYYJwvYnJv0SMos2HTRigzMYGCULamrqMIAFiKDQWU8J00kSXN1pTYkyLm4Cq3hRQRp4aSMO56kqYb2nsrAiF1RYDEcTFRW2mGBDyupbVLF3/yJzUlKd5Spo/V8PSjg2XCMUyAj3mS9UVEUkW05TeYYmQH7NUFZQb2wrkkgVlgqpVxzB/8I4qdjY2ZwkjxNLarVC6ArbUdBZX2OXA+2DaOrDENaXSovnko+NDW1gu2VBLFpMr2DuM6rwhFNFxs67axp8DWntGESlCQqDVuCj7UyyjWUa1gO/yMqwQhV1FWiTTM99qzHefAaC8KxRSr2B92GYWdOjtMn4+lsP+zSryv11AAw1Zs1SA+M+AR8mtR/uyrSQ/VcriGSQXrd4dHjOs5SowOBzEep8tYf3cQBTAmPWJY0TQKkjTq+FJ2KQu2nCK4o+g1ScMjCNK7ZzeUWTAP45dfKdaw/+06CsWEwmY/5vNZKJNOord0dXSAzQ2/+L6iXME2lEsovOIEcQ1zGvhOgQj6XrZg7qk2b0TRu7ZeXBeCcVwXXAfnXKNuzq98BdtfqWF80PZY9boyzz39uEUR6ZlShEQDYZwTuQK+U8DFPW25Yj7z+XUopDiliPL4GVvp7/EpjEEtCdO/NSGmYEgR+BHcuxQVAa6AZ1jcBvpLOo51jUxgXY89uRlsyTgK8lVBsQ3nUySGz3z2eax/VgL3EemkuQfp6cEy45tRoNYK4XwdGcX2z5ljxhdNhLSqiGOViih0aCv3OtoYtJjrfE1Zl4vK3roZ+MkrIYQQQgghhBDfw8MrIYQQQgghhBDfw8MrIYQQQgghhBDf03TOa2sn5pxt2Y7fhd683fyutaPkeNbKmGdRKWPeRraIuUFW2GxyVflxaC29NaR9v95RckaVHx0Hk9Xcd7Sbz4M1r0NKnnBDyTV1tZzLMOZTuo55b1AZk4aD/Wg7WvuVfFkX22tZIc+10meWkijlU5wattVRctdCIbPPrJCWB4A5aU45C7bBLc+C7fmnzB/GTseWQplKO+ZFlJXcrI74PLAFGvhOXW2LjetoHPPfq8qPVrd2ZsBWV35MPJ8fM65nz8E8OMvBdt1951/AFk5gO7rnmWMXCeIcGR7CHJGaMw62iQLm1LbHZoOtNWX+CLkdUvIJlVjjZxpKXryj5CiJbeYGtSo5OfvNVfJ58hNgq+3A3LR60cyljCRxPlW0HH4lTgUamHPmKGuK5VkrbKX+WljTM8C1zrKVWBL05MwpQgKOjXW5SgyKOZi/7Xrm/3AsC2XqUczba+BUkXAS6y+VcF5HPGtWl5K3HgvhM/1KXsl1jISx/dGQ2T+RCHZiwFLWbsVWq+H4lkpm3l5d8VdNTkJTmKi76FPBGPp2NmvmuN508+1QpqXjBLD1L8TcdkeUnFSPvkapjPmtWv/bypwIR9A/Aw3Ttn0HxvaaMi9DUWVMtPmr5ON64/vQliEoMz6O7+Rn2jowbre04H4g5tmnT+TwrBCPJ8BWV/ZZ6rh4cpEjSuyqKbnVIxPYjoqN/t6ezoBtzkLz3et19L2ckt++aRvuLSJdio965mIqge9kdaNWQUu8BWyFLOZvb9q8ybgeWIz7v5qyv68pey8l3VfNjZ3XbrYtHsN3qipnv2bgJ6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnxP04JNVcxNlm1DI2gbNpOTtR+llYYiXqL8AHgiiYngIdvMFHbqiqiQ8kztx6YVDSRVsMlbm6Wc+QPeX01+ARpK27z6SZaqtoA2RxFZCgZQQMXytC2itN8NYqK2Ji6lClo5is0jYBBQOjsQ1CQk/Ek4jMn/deWHt0MRs/8rzhiUGdrxBNiee+hJsKWDKHaRrJs//v3sXY9BmWg/juW4IuySGMiArX8Oiihs22GOpaPM1VAEE/FnzdPE1FCgolEy700EUOBk45rnwXbvX7aBbc6eijBb2vT3sN0BZewctr+9C+vatHE92J6bQqGhtx55uHHdMwdFhYo2iob4mYgi1BWMob/UsuYYawJIfRm8b/kU+uiz2R1gGx7aYlznyihOUVDiVEWJ0WElHtsutjfgmr5QVETvSorYRUiJtY2qEi+r5rtbimCTpkRYCSlzTBGxKXrurURRYEYCWFdMEQBsOCiwkWxgfYtmpY3rtgi2vzSeBVsaLP4grojCxBQBkohnrxFra4UyUUWoqlxG/5/KTinlzHUnlULBFreBY+kVehIR9eOLZCvOzf0O3N+43rQV4/H3v/MzsK084iCwLd1nLthaZ5l+5rq4jwkFY2CzRBGaVNan0amscb1u/SYoo/WFowhaOQ2cm+Uazol4yrPu5HE9Ke6mWM1rRV7xoYYietc3q9u4jijiTKWqEkcS6MuWEuMsz94xHMGYailCTKUy1hWJo1+lOnDvVQ+YvmCH0DdiyrrWCKE4U17ZO+6xcL5Z/7AiUFZEAdCpAu4/9li0B9i2eeZsXRHCspQjYSGnjLkyWVIJfHev6FSxiHUFE7sX8fnJKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ38PDKyGEEEIIIYQQ39O0YFNZSbSt1zFRO2CZ52GnriWkY3J1KIjn6KAiGBTymCKCyfONKIpM1JTkZFHuRXkmNClaHRJQBDYUzRAV772W0j9BRZggoLQ14KDoQ9BTfzyEwx4KKUJPFtpsZcxtRbBJxFtOeSdFJMqvTNa3gq1WxeR57zTZkUUhpqHJu8E2NpwFW094L7B1eMYkV8b7wsMoehApo7jANmct2JYcNR9s4w3zGZND6D9dveif+xyIczqWRHGEsbF5xvXoKAoQJFOY1L9s2RywtczBOOU65jg5dWz/8GARbMUJLFer4vzKFlBUZXBZp3GdTHdDme1jKNzlazQxOAvFKEIebapKAGNGWBHvmdeLgg8bt+H6UauaY+U0sEzWRtuYheOZDirvpKw7lifoTykhb7imxGhLW9dePO5pf1UOC7Z1hyKWMqWsFQVPe2cr61VGEdYKTuTBNiuEc/hNc3vANjDXdIREGQVIqor4k18Fm8LaGqy0PxY039tV1mlXFT7EctEo9nXEI44XV4TU8nnsa8fB2BhLYP224FoxsMRcFxYvnwVlbvoVrmvXXvlnsL21uD/YDjjarL8RwLlqK/5pKfPLVebXyIgpjpcvYByfO38e2PIF9P/hkVGwhZT2tnaYtkAY14BCEdcdP5NIYox2lFhb9ewTQ2GMXeEwipYFlXisRcOAZ9kJhZvbbFeVeGkpe99EK7Ytnzd9IR5HEUZt7xIKYURri+M7JTLmvi0Vw/3lrC4UfxtzJ7GuBK7L3d2mUGU+h0KHyhImmnZgS2sGbOkW7I+cRyhtbAwFTN0AimM1Az95JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie5oWbKooiet2GROKLY94T1ARK3AcFATQxIHcehVsIW/2sJJM7CoiB7aLddVsbIerijiZOJrYgiLyoZhUvAIDDaUN2l8ZEiFsRyKM97YkTAGrhCLSEFAS5UOKsFMgoAkkYDu8olaaIFc4MnP+djJZ2A62Ym4YbE7ZFGDIFtZDmUYF501rAp2lNLUObMl2c5wCKRRnCscwAb6ljon+gVkovtDWhb7R0moO5pY1WShjKWIyEztwfKs2JuzP6jGFl7YOorDI+BgKW7hhFIroxuZLNOoRRFMU16pV9OHta1HQIBnGByxesQBsBY+I09gkjm84qonI+ZiGMp5lHCuvIJGlKD64NRTOSCVReKazBcd4YnTEuM4Pj0CZKSXe3KuIdbQpMbpFEaFKenymHsAbczbaKorIj7bCBD1xNaLE44R+J1hClrIueNrbqOPaV3Ow/rjS/tYU3it1nCuFSbMduRbsV8vGMekEiz+wayjyY9cUUUnPkCQSKGSiitUooj8RpZzr2VhUK7i3aWjiYQ72v13FcnVl3zUxaQoeHXLEMihz8FsOANv9dz8Nto2bt4GtZ6u5R4mmcA1rbW0HW00RBM3lcK3IF8w4tceeA1Amk0HRsZY2nF/ZKfT1oCJmN2+P2cZ1pYQxqVSbWYJNsTj6Y8BCW7lm+lC0gf0Tj+J9liIWFlHEnsQj9tmi+EYlh0KKtRD6SyiK8bKszPVg0GyvMk2kVsZ4sL2Ce5722bPBVt9urmNxC+uKpbEvulpRCGxsfAs+s9WzV/SqXolIwcaXWtLbB7aGi+0olTCWl4qmrV0RelKWoqaYOacHQgghhBBCCCFvWHh4JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie5oWbGrYmMDcrggwhDwiQlUlGddtYKJ2OIh1RUKKzZMY7zSwzJQixBQL46vaMRSoqNUweduue8QusIgq4uQVVhARsRTRjWDQLBcJKSIZSRSKmdWOIjytcXzPWMQj8hPCv1loIjbBINYVUsZEu9fyCIQEFQGSoCKq4lfKeRRnsoKjYAunzXnSmlDEgTagUFK6C5Pd650T+MywKUzQ1743lNk2iG2deh7FC/acvSfYUin02blzTJGD8SFs14Zn8L5yThnzBAr8ROKmgNWsPhRfGN6GogfVhiJ2oc45c262ZKJQZsFAG9hG120Fm13HeZibQJGD4e2mqEfVyUKZjs4M2PyM01BE6RSb5ZnXkZAiOlNGf1e0/aQ7ifc+8uRTxvX4EM5D28LYNarE3pyNAh4JRxE88twaVWKXG1HETBSBOy1eeuOqo4jg5RzsM9vGtUIT0ANtPEUlo6G8UyCEg9IQbEe2kAVb0DWfEQ2koYzVaHoL8ppTVARJ6orgVN02+7FWw/FOxDUhS0XAzX3xddlRxJnqyvwqFXDMdwyOg21WF0pmtXmEVkqKWs385V1gm6ygLaLsPwoeDaR6QBHuiaPNUUTSQlFcX2fNNkUB+xfiGlCrKUKiyhalVsd1bUoRB0qmTKGueExpawL3U34mosSIRAL72+vLQUX4LRhE33bUGIcx2vW0I5/HMSnnFGEtpR2xGMagmhIf62XTVprCORAJoThbuj0DNomg/9VL5j4oGEF/iSgiV65ytkm3YDuiHiW5TDvOTTen7DkD2GeVPO69yiWlbz2+oa19TSvbepg5pwdCCCGEEEIIIW9YeHglhBBCCCGEEOJ7eHglhBBCCCGEEOJ7eHglhBBCCCGEEOJ7mlZLsBSRhq52TB7u6jAT0BsNTOINCCYrBwPNNaXhEUbyXouItJQwwTscTWI7AoqYTgXbW/PkZTcrzqTZAgFMLI+Ezb8hxCPY16kE9lkijonymghS0CMaElAS5bX+DwQ0MQFFqERTWoFiyn2K2ItfKU88B7ZgFBP2q5bpG5E0Cvz07tUHtnod/c6OYp81plqM69wICiAVsmgrby+D7ckH14Kto0Xxg3DKuH7zKvS7/gWzwNbehf3T0o1+HO8w+ygQ6IEyY4MLwDYysQ5sjegWsEnd48eKYFwkgTYLmyrpFM79RiMPtoJHHMVWBEhiMRRV8DOBMMaDsDKFLY/NUoTfRBGncYoFsPWm0dc6wua94Qr6dksDY1xFUV8JKDY7hGNc9MT8sha6FLGRoCIoo4n2BTyiJOp6YmG7FPkLCVu4xoQ9YxBX3jul/Ck7aeE4hRVdIVGEUKplU9RDGV5JBHB8/Up2Cv1Mw3HMsSyVsW+sBsaDquLHmmhiNGbGy4gi/lIoocBmXfHFdDuKaB2y8k1gm9ffa1wHwtj+dDvusVYciKKAiQiOeUuLua5VRekLZY9iKeJPUWWP5d2iVGpK/9Rx/sbiigBPGvssElX2tBGzvbWqIvCj3OdnkoqvhbR45rmOxXAfVChgQNCEPbU+iicTL15GiWflqSzYZnXPA1tFiWcZj2hquEsRT1LEXOuC4247OH/iKXP+hJU9iRbw64oIUmdXCmwRjzheUBFfjUZxnFwX259IYP1xrb2e8SyXcV5rtmbgJ6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnxP878QruTghJR8A68tHMbvUIeD2vf88XvbWt6P98ePtR+W1nIj0i2YZ9FwMTfWEiVfwmPTfrTX8iZ6/f9rw3JKfoDHpv1FQfttX60uS8ll8pYLBvG77kElT0TLebUsLTdWaYfH5mpvpfwAu1/pieN7l6LY/pCY/u4qcyTSht/xr01iHk1pBNsx+az5o/KRAuYetFQ7wGaHsR1Vxf8bDs6TyR1mflC+jvctXIA/bF9Vfuh7Yus42AIF80VjSvLdggX7gm3WbMxHmqxgbBkdNXNSGzWMScEIjuW+B/djOWcSbA1Rcoxtc4wtwWdagZmT8y0iEgjhHAi62rz2JP6oOa8YW0JKHElZ6GtHeHLGpxSNg0e3jIFtrIr+WFHy7qtK3G543qGhxDNHqSugrAtaLA804QtBJbaHlNviyvqX8MTydAgbkQ5gwlaHMnQJ5QXCgn0b8byTq+R5VZQ8T7/SEMzpCit5Y+Lp60IR39HxCmmISLFQBFtQWT/aMkFPGYwtouQAxhLY1p4IDnCyE3MR42mzHU5D8cWGkg/Zhs9MKvojYU9sqZexfwIO+p2taEXk8lNgq3r6W8uVDSl9oeUwRmPKeyp6AMWS+cxAQMlNzmPurZ8JazouyryOeOKlmucfUDQ9FD2ZiNK3tm0+s6HkkMeU+lvTuF9Slh2JKXnZjZrpa4kUlqlXcS2qlHF/ULXRbxMe/wsr+cXFEtYVS7eArayci8qetoXdJs8BQYwvjrLsl8o4dtmsuV/yjpuISCSi5Mo2AT95JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie5oWbLKU5Gf1B4UjZhJwLKYIcyiCQVpCt5a87RVscpUyiTAKuYSDSrK/owgvKaIVQc+r6wJFilCS9ovCmkaRJwdeyYl/AZEPJblaVXbyvoAmzqTV1WQ51Tc87VCEXawZ9LeTTrsNbNVeTJQf2Zb1XO+AMnYCxShCtVawBQbRP2MTnoR3RQRCbGxXchGKC3QMoKMFlXbISNa4HN6A7+RMopBA9wLlnRroP/Fqr3E9MYXCJWFnC9g6Zs0CW0/7nti2yqBxvXUQ2x9XxBfaurBv7QqKF4TCypwbM/u2OoVjWa+geIGviSjCMIpQj+UNYIrQk23XwdZQliNN5KfXM1Qn7TsbyswKYxxftyMHth1FbMekjeNZ8fhtVYnRtoVtdRWRpYCybnrXUm2ZCCuCUCFFUCapCGRFPe2IWnhjSxB9tE0Rdkp6F0QRiYXxmSHPa9br2NclC5/pV2p17H9beady2bQVixgbo2EUKQmGUMhI0zpzPeuyJv5SdXB86zWMq67gvdEWZb5aprBQrYL3OVV8ZrWIa10tiKI2XuGrsQlUK2xvy4CtoWyWxraPgq1SM5/Z2dsDZRxl7zSRQ4E+2LCJSEAZqO1D5r0NZf46jZnj/yIi8Qju3b17chER1/NemkhoSwvuU7Q9vyZM6hUCchXBptY4ngNSqigXxuNyVTkbeMavUUdRwHQSBaG0/bw26kWPqFi4jn1WVoTM7AAKwo1N5cFWGDfXv0wGRTbHi+jvsTjGe9fFfpycwDiX9whMxZUx0WzNMHNOD4QQQgghhBBC3rDw8EoIIYQQQgghxPfw8EoIIYQQQgghxPfw8EoIIYQQQgghxPdYrqulExNCCCGEEEIIIf6Bn7wSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9vj28WpbV1H933XXXS35WqVSSiy666GWpq1m+9KUvyXXXXfeqPU+jv79fTjrppNe0DWTXeL3Pi4mJCXnXu94l3d3dYlmWvP3tb3/Vnk38xevd17kGkF3l9T4nXoy77rpLLMuS3/zmNy9a9txzz5X+/v5XvlFvQN7ofrirDA0NyUUXXSSPPfbYa92UF2XTpk1iWZZcdtllL1r2xz/+sViWJZs2bZq2vVrzLvSKP2E3ue+++4zrz3/+87J69Wq58847Dfuee+75kp9VKpXk4osvFhGRVatWveT6muFLX/qSnHHGGdyck13i9T4vPv/5z8u1114rV1xxhQwMDEh7e/ur8lziP17vvs41gOwqr/c58XLy2c9+Vj784Q+/1s14XUI/3DWGhobk4osvlv7+flmxYsVr3ZyXjRNPPFHuu+8+6e3tfdWf7dvD65vf/GbjuqurSwKBANjfCJTLZYnFYmJZ1mvdFPIa83qfF0899ZQMDAzIe97znr9bznEcsW1botHoq9Qy8mrzevf1XYFrABHhnNgVBgYGXusmvG6hHxKRv457V1fXa/Js335tuBlqtZp84QtfkKVLl0o0GpWuri55//vfL6Ojo0a5O++8U1atWiUdHR0Sj8dl3rx5cvrpp0upVJJNmzZNd/7FF188/XWHc8899xVrt2VZUiwW5Sc/+cn083b+RWnnx/B/+MMf5LzzzpOuri5JJBJSrVZf8OP4iy66CDY1jUZDvvWtb8mKFSskHo9LJpORN7/5zXL99df/3bZdfvnlEgqF5MILL3y5Xpe8yszEebHzqyq33367PPvss8bXjnb+26WXXipf+MIXZMGCBRKNRmX16tUiInL99dfLIYccIolEQtLptBx77LHwl2ERkd/97neyzz77SDQalYULF8o3vvENde6QmcNM9HURrgHklWOmzgkRkV//+tdy8MEHS2trqyQSCVm4cKGcd955UK5er8tnPvMZ6evrk5aWFjnmmGNkzZo1RhltrliWJf/6r/8q//u//yuLFy+WaDQqe+65p/zyl798JV/rDclM9sPvfOc7csQRR0h3d7ckk0lZvny5XHrppVKv141y/f39altWrVo1Hc/vuusuOfDAA0VE5P3vf//0O1x00UXT5ZvZw+yM8U888YT8wz/8g7S2tkp7e7t89KMfFdu2Zc2aNfK2t71N0um09Pf3y6WXXgrt2rJli7z3ve+V7u5uiUajsmzZMvnv//5vaTQaULbRaMgXv/hFmTdvnsRiMTnggAPkjjvuMMpoXxvWcF1XLr/88um1qK2tTc444wzZsGHD373v7+HbT15fjEajIaeeeqr86U9/kk984hNy6KGHyubNm+XCCy+UVatWyUMPPSTxeFw2bdokJ554ohx++OFyxRVXSCaTkcHBQbn11lulVqtJb2+v3HrrrfK2t71Nzj//fPnHf/xHEZEX/WvCqlWr5O677xbXdXe57ffdd58cddRRcuSRR8pnP/tZERFpaWkxypx33nly4oknys9+9jMpFosSDod36Rnnnnuu/PznP5fzzz9fLrnkEolEIvLII4+8oJO5risf//jH5Zvf/Kb84Ac/eMWDA3llmKnzore3V+677z754Ac/KFNTU/KLX/xCRP76taOJiQkREfnmN78pixcvlssuu0xaWlpkjz32kCuvvFLe8573yFvf+la56qqrpFqtyqWXXiqrVq2SO+64Q97ylreIiMitt94qp512mhxxxBHyq1/9Smzblssuu0x27Nixq11MfMJM9XURrgHklWGmz4kzzzxTzjzzTLnoooskFovJ5s2b4auoIiKf/vSn5bDDDpMf/OAHksvl5D//8z/l5JNPlmeffVaCweDffc71118vq1evlksuuUSSyaRcfvnl8u53v1tCoZCcccYZu9xugsxkPxQRWb9+vZx11lmyYMECiUQi8vjjj8sXv/hFee655+SKK67Ypbr2339/+dGPfiTvf//75b/+67/kxBNPFBGROXPmiIg0vYfZyTvf+U5573vfKxdccIHcdttt04fq22+/XT74wQ/Kxz72MbnyyivlP//zP2XRokVy2mmniYjI6OioHHrooVKr1eTzn/+89Pf3y4033igf+9jHZP369XL55Zcbz/n2t78t8+fPl69//evSaDTk0ksvleOPP17uvvtuOeSQQ3apDy644AL58Y9/LP/+7/8uX/3qV2ViYkIuueQSOfTQQ+Xxxx+XWbNm7VJ9IiLizhDOOeccN5lMTl9fddVVroi4v/3tb41yDz74oCsi7uWXX+66ruv+5je/cUXEfeyxx16w7tHRUVdE3AsvvLDp9hx11FFuMBjctZf4G5LJpHvOOeeA/Uc/+pErIu7ZZ58N/3bOOee48+fPB/uFF17o/u1Q/vGPf3RFxP3MZz7zd9swf/5898QTT3RLpZJ7+umnu62tre7tt9++y+9CXjteb/Ni5cqV7l577WXYNm7c6IqIOzAw4NZqtWm74zhuX1+fu3z5ctdxnGl7Pp93u7u73UMPPXTaduCBB7pz5851q9WqUa6jo8OdQWHwDc3rzde5BpCXyutpTlx22WWuiLjZbPYFy6xevdoVEfeEE04w7FdffbUrIu599903bdPmioi48XjcHR4enrbZtu0uXbrUXbRo0W61m7y+/NCL4zhuvV53f/rTn7rBYNCdmJiY/rf58+erMXzlypXuypUrp693vvePfvQjqLvZPczOGP/f//3fRh0rVqxwRcS95pprpm31et3t6upyTzvttGnbJz/5SVdE3L/85S/G/R/4wAdcy7LcNWvWuK77//ZbfX19brlcni6Xy+Xc9vZ295hjjpm27VyrNm7cOG3zzrv77rtPbffWrVvdeDzufuITn/B2X1PM2K8N33jjjZLJZOTkk08W27an/1uxYoX09PRMK5OtWLFCIpGI/PM//7P85Cc/eUkfU/8td9xxh9i2/bLUpXH66afv9r233HKLiIh86EMfetGy4+PjctRRR8kDDzwg99xzjxx99NG7/Vzy2vN6nhennHKK8enTmjVrZGhoSN73vvdJIPD/QlkqlZLTTz9d7r//fimVSlIsFuWhhx6St7/97RKJRIxyJ5988ivSVvLK83r2dRGuAWTXmclzYudXK9/5znfK1VdfLYODgy9Y9pRTTjGu99lnHxER2bx584s+5+ijjzY+6QkGg3LmmWfKunXrZNu2bbvTdOJhJvuhiMijjz4qp5xyinR0dEgwGJRwOCxnn322OI4ja9eufVnaKNL8HuZv8arDL1u2TCzLkuOPP37aFgqFZNGiRcZ8uPPOO2XPPfeUgw46yLj/3HPPFdd14RsOp512msRisenrdDotJ598svzxj38Ux3Gafscbb7xRLMuS9773vYYv9PT0yL777rvbKtIz9vC6Y8cOyWazEolEJBwOG/8NDw/L2NiYiPw1af/222+X7u5u+dCHPiQDAwMyMDAg3/jGN17jN/j7vBT1rtHRUQkGg9LT0/OiZdeuXSt/+ctf5Pjjj5e99957t59J/MHreV5458T4+LhqFxHp6+uTRqMhk5OTMjk5Ka7rql9N2a2vqxBf8Hr2dRGuAWTXmclz4ogjjpDrrrtObNuWs88+W+bMmSN77723XHXVVVC2o6PDuN4p3Fcul1/0Odqc2GnbuaaQl8ZM9sMtW7bI4YcfLoODg/KNb3xD/vSnP8mDDz4o3/nOd0SkOR9rlmb3MH+L9xcYIpGIJBIJ46C5016pVIxnvdBz/rYtO3mheVKr1aRQKPy91zLYsWPH9P7L6wv333//tC/sKjM257Wzs1M6Ojrk1ltvVf89nU5P///hhx8uhx9+uDiOIw899JB861vfko985CMya9Ysede73vVqNXmX0ERkYrGYVKtVsHsHv6urSxzHkeHh4RfdAB1yyCHyD//wD3L++eeLiMh3v/td4y9AZGbxep4X3jmxcwOzfft2KDs0NCSBQEDa2trEdV2xLEvNbx0eHn5lGktecV7Pvi7CNYDsOjN9Tpx66qly6qmnSrValfvvv1++/OUvy1lnnSX9/f27nGf3Qmgxf6fNeygmu8dM9sPrrrtOisWiXHPNNTJ//vxpu/YbrX8vHnd2dr7os5rdw7wcdHR0vOBzRATa+0LzJBKJSCqVavq5nZ2dYlmW/OlPf1J/HWJ3fzFixq5QJ510koyPj4vjOHLAAQfAf0uWLIF7gsGgHHzwwdN/QXnkkUdEZNf+avdyEY1Gd/l5/f39MjIyYmzCa7Wa/P73vzfK7fz6wHe/+92m6j3nnHPkl7/8pfzoRz+a/moEmZnM9HmxKyxZskRmz54tV155pSHMUCwW5be//e20el8ymZQDDjhArrvuOqnVatPlCoWC3Hjjja9F08nLwEz3da4B5OVmps+JnUSjUVm5cqV89atfFZG/fo3z5eKOO+4w5o/jOPKrX/1KBgYGpkV0yEtjJvvhzj8a/u2hynVd+f73vw9l+/v75YknnjBsa9euBeXrF3qHZvcwLwdHH320PPPMM9P9upOf/vSnYlmWHHnkkYb9mmuuMT65zefzcsMNN8jhhx/+oqJof8tJJ50kruvK4OCg6gvLly/frfeZsZ+8vutd75Jf/OIXcsIJJ8iHP/xhOeiggyQcDsu2bdtk9erVcuqpp8o73vEO+d73vid33nmnnHjiiTJv3jypVCrTamHHHHOMiPz1r0Dz58+X3/3ud3L00UdLe3u7dHZ2qj9JsJOjjz5a7r777t3+Xv3y5cvlrrvukhtuuEF6e3slnU6rE/pvOfPMM+Vzn/ucvOtd75KPf/zjUqlU5Jvf/CZsNA4//HB53/veJ1/4whdkx44dctJJJ0k0GpVHH31UEomE/Nu//RvUfcYZZ0gikZAzzjhDyuWyXHXVVUZ+IJkZzPR5sSsEAgG59NJL5T3veY+cdNJJcsEFF0i1WpWvfe1rks1m5Stf+cp02UsuuUROPPFEOe644+TDH/6wOI4jX/va1ySVSk2rGZOZxUz3da4B5OVmJs+Jz33uc7Jt2zY5+uijZc6cOZLNZuUb3/iGhMNhWbly5W71h0ZnZ6ccddRR8tnPfnZabfi5557jz+W8jMxkPzz22GMlEonIu9/9bvnEJz4hlUpFvvvd78LXd0VE3ve+98l73/te+eAHPyinn366bN68WS699FJQQx4YGJB4PC6/+MUvZNmyZZJKpaSvr0/6+vqa3sO8VP7jP/5DfvrTn8qJJ54ol1xyicyfP19uuukmufzyy+UDH/iALF682CgfDAbl2GOPlY9+9KPSaDTkq1/9quRyObn44ot36bmHHXaY/PM//7O8//3vl4ceekiOOOIISSaTsn37drnnnntk+fLl8oEPfGDXX2i3ZJ5eA7xqZq77V0Wtyy67zN13333dWCzmplIpd+nSpe4FF1zgPv/8867r/lXp6h3veIc7f/58NxqNuh0dHe7KlSvd66+/3qjr9ttvd/fbbz83Go26IqIqiP0tK1eufEkqpY899ph72GGHuYlEwhWRaWWynepdDz74oHrfzTff7K5YscKNx+PuwoUL3W9/+9ugNOm6f1Ux+7//9/+6e++9txuJRNzW1lb3kEMOcW+44YbpMjuVJv+W1atXu6lUyn3b297mlkql3X4/8urwepsXf09t+Gtf+5p6z3XXXecefPDBbiwWc5PJpHv00Ue7f/7zn6Hctdde6y5fvtyNRCLuvHnz3K985Svuv//7v7ttbW273V7y6vF683WuAeSl8nqaEzfeeKN7/PHHu7Nnz3YjkYjb3d3tnnDCCe6f/vSn6TI71YZ//etfG/fuXCP+Vs31hdSGP/ShD7mXX365OzAw4IbDYXfp0qXuL37xi91qM/krryc/dF3XveGGG6bbPXv2bPfjH/+4e8stt7gi4q5evXq6XKPRcC+99FJ34cKFbiwWcw844AD3zjvvBLVh1/2rAvPSpUvdcDgM6snN7GF2xvjR0VHDrvX9zj7w7qU2b97snnXWWW5HR4cbDofdJUuWuF/72tcMpeOdc+mrX/2qe/HFF7tz5sxxI5GIu99++7m///3vjfqaURveyRVXXOEefPDBbjKZdOPxuDswMOCeffbZ7kMPPQRlm8Fy3d38ISRCCJnB1Ot1WbFihcyePVv+8Ic/vNbNIYQQ8gpiWZZ86EMfkm9/+9uvdVMIIS+BGfu1YUII2RXOP/98OfbYY6W3t1eGh4fle9/7njz77LO+V50lhBBCCCF/hYdXQsgbgnw+Lx/72MdkdHRUwuGw7L///nLzzTdP59YQQgghhBB/w68NE0IIIYQQQgjxPTP2p3IIIYQQQgghhLxx4OGVEEIIIYQQQojv4eGVEEIIIYQQQojv4eGVEEIIIYQQQojveUlqw41G4+Vqx8xDkbmyLMu4LhdLUGZ8Ygxs7e1tYHNqFbDFEwmwBSNRbJpl/k2iIRaUCYLFPwQC/vubyltWrgJbNjsBtmgA50R7xHSWeR04jl3tSbB1ZlJgiwTDYAtF42CToDm1JyazUKRmoxO3ZVrBFnDqYKtWq2CrVEyfjcVjUMYRB2ylcgFsrZkWsImL99aqNbAFxeyjYBC9PZ3Cvk0mcQzCYXyHsvJM75yTAIZWra22i3PzQ5//Htj8wPevvx1s2557GGyjG581rh0H+2LWvKVgmzewDGxtPfPAFoub9a19+l4os3ndE2Cr59HPgkrbWtpwDoRi5pw96LAjoMyixfhOlSmMEU8/9SjYGg3TN2p1jP/PPP0k2HJZXE+qNZyb9Zo5BybGcW0qlPCZtoN1dXW1g62tHeeT4+bNujCMSKWMMei6a36PBX3Al2/bBDangTHJUfZF3qgdUdY4KxgBW62B8SFfK4MtqC2ZFXOMWxK4V2hJYXyzbawqX8cYGrCwbXVPfG8o8c1SbK80mi6pK8r+VSnXUDVNm3iHJqVQvftGEZELj+9v7uZXmV/fdB/YtHNAPGr6WiSGftYIoj/aLjpySNmtBj3TLqwdRbQxD2H9dUvzDSTgeKwu7sXsOt7pBDBGNOU+Wvs1X1TqajSUdngKau+o1a+Nr+Mo76TV57m21XfC+s87Za8Xrdt/pwRCCCGEEEIIIcQDD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnzPS8p59WNuop+olqbANrFtA9i2PovlpnJFsB121NFga1HyCr1/k7CUL8Vz5HaNp595GmzZMSV/WRkOq8M0djppLBPvBluxgflyBW/ehYi4FuZKlSpmDl2prOTBOZhrMBZEX4mF8Jm2jfcGPXme0SjmtJQq6Nd2A3NBrUoH2AJKonZdyb2Nh8z+Lii5phMOJnYlEpjzagUwr8VS8o7FEwtLFUzws+toC4awj/xKbhL9sSOD+Y9u1yzzOoT5y73zFoLNaWD/BBqYm9komWNXmRzHNpQxf3N2J86xeXMXgW3uovlg65s9x7ju7p4FZcJhJYcrg/ntc+f0YDnb9NFKBfMas5OYszs2hmMSimhByJw8bR3Y1lgSnzmVmwRbNIbbhoaL8yns8e3cVBbK1KpNJgb6AFeZ95qehLa4lqsen3XwvoiSp2YFsFxIyae3GkqiqqchWv5psYLzJKisJ1oc1PZ/AW9/KLmI2n7k5cbbk9p+J6j0bUDRZKjXFVsTci9Np/YqOa9+RUnBllAUfaPmyQUvTuWhTDipaLGEFf0OpSO9885W8lYdZQ2uTGGMi8QwFjqK4xY82hwBC+9LJVEvQcutbig5o97c52ZzUpVXV3NevX2mpc9q+a3aM7WcVy13u+F5Cy1/fHe1k3iGIYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie3h4JYQQQgghhBDie16SYJP6g7lvELR3D3gyp4e3boQyT9z3R7DVyyhKEk61ga2cQ2GnlnYUTIHEbAv/RuHnkdMSv19r4iGlTYrWzvwOFEvpn2Um8Xd34ZjFNbEgpR/KVRTYqNRRtMj13BuJK0IItiL+1MC6WttRdEb7Me6IR2xB+x3rYAQ7rVrDd6rb+O4J5d5QEt8r5ilnWygSFVB+GNtWhEQU/SpJJbE/CkVzDtdtRXxIqSuvzGnfoghO1apoK5VM8aH+xbOhTKGIY1Krox+0d6IARihsxrM99lgMZQ598wFgmz1rDthaW7vAVg+h4yY8oh6KhplYNormlIsoslRV+jERN32qLYPiUgML9wTbs8+uURqC9Verpn+2tuD6EkadHpnK7QCbKyiApgmETE6aY1wuKXHKzwuRh7oiUucqAnra6hXwqM1pdTU0wTJtpQ4qnzko4nsRbxwMKgJ6dfTZeFgRYgop7669qUd8Rd8jaj2k2Jr1DU0oxtMObS0NaPsipb2u0pBm/LbZ/fFM2kfnlHhWV+LZ2KgpordtcATKBGO450mlMS5FA+i3Xg2nmrLeNhTfLuWx/XFFaE8C6O/5mik6VauhTy1csAfYFg2gAGA8hvtEr8+qQkbqNFH8X1Nx8phUX38JvqjOMU/bGpqC227CT14JIYQQQgghhPgeHl4JIYQQQgghhPgeHl4JIYQQQgghhPgeHl4JIYQQQgghhPielyTY5EdhnVcLV0k8rntEMYa2boYyLQkUmElk0mAbmcyDbXz7INhmzZ2HjfOIQ6iSCZp6DHlBYhYm/6fTOH0Wz0bBgY64OR7hBgrTFCZQBMVp4N+WyiVsR0ARWmnJpIzrkCJ2lJ1CHwspEaE9jQJF+ZwiuFPxCLRUUERBExdIJVG4oV4rgy3gYOPCUXwvxzGfG1JUl6qK0FBEUawJNLC/q4VJsIlHuCUaxCK2IsAwVUQRG79iV3BMLBvFjaIRM8ZNjY1BmY4eFE+at9cisHXP7QNb2DtOilhH3cY59tz2cbCVNozivQGci2uefNy4PnAZiicdcdCBYNMEMHKKSNeWzUPGdSSMgh6RSAvYOrtQDGvL1ufx3pg5hwtlnL+5HI5TKIxzp6UF40FZER10PFPHVkSKolElePmUl1PgxLIUASStriAGEq2ctherV835GlGEtiIh9LMwtkKlruyBvC1reouoblJevj2KJn5T1/pRu9fVPuN5ceGZZvfHM0euSeTe++8DW0ERcQp4vKhcxbesOBiPwxG0BZV9kOPp2oqL67SjiBYlI+jvcQv3FTFlAXc860KxiOvOQ088CraRsSGwLVywAGydnZ1muxIYZ11FGM9RlDEbiiCl5e3Hl1kozFXmmFc4VItdqjBVE/CTV0IIIYQQQgghvoeHV0IIIYQQQgghvoeHV0IIIYQQQgghvucl5by+UdC+px1Qvk8/OmF+X3/Tpi1QpjqB3+lPxzDvp1TIge25x/H79D39A2DL9HjyoNRcHTC9oXOYX4y2KE6VuJJv2ZrEnOauFjP/w2lgjgJaRIIhJXEygH9vqio/bh/yJK+GlBwIp4o5jG4Q6x8ZyeK9dWxxvmTmvZUczLFKxTFvT6pYV1DJKdLmXDCKOSzlopnvmAjjM0PKBKhUsL1l5YfOG0qWUrZgPjNbwjEpKPnKlfrM+fthtYR5kqk49n9Le5dxvf++K6DM3IX4Y+55G/tnzYatYMt5/KyQzUKZ8SzG2e3DmKvc0toFNglgHvKNv/qtcR1+J47bykPeArZwGP2gpwfzeMU1802ziubBI48+AbZQGGNQMo3+bntysmuFLJRRpr50dbWDzVHm9fgE5ssGxMzZ8sYkEZFMphUf6lPqyry3msyb9NoCWo6qEmuCSs6rpawBjrKCeMczoeQvK8uV2CXMX64GMP+uKsr65EHrC1dZi6SJul5u1BzmJsu9vMycfVe2oOwZXGy/5enJUAQzqRNKrmkwgLaI4P644vF3W/kcLq+sV+Ui2qIW+l7Kxbga9DQtHMXJUymg1sL6rahVs3n7MNgyLWYsnDsHdSG6OjvwvjbUWQkF8J2CnnnXrF87SrGG4rO6JoD5zIaa87p782vm7JwIIYQQQgghhLxh4eGVEEIIIYQQQojv4eGVEEIIIYQQQojv4eGVEEIIIYQQQojvoWBTU2iJyCiQMLhtm3G9ccs2KLN13QawdaZTYJvTmQTb9i2bwfbkQw+C7YBVGeM60aKIYswcjQBf0JVBYZp0GJPiYzG0BYKm/8TjmOhft5UfmlaT4lEspWYrP1xdM4ViGi4Kx7iK8IobQnGEfA1FDhwH37PkmMn5toPCHHnlh70HJ7D+cADvbSkoIifDKBRTnjIFR+Z1LoIy3d0ohmClp8BWnUThn0IB2zuVN4UaxqZQ2GLTVqzf8apA+JhoFEU36sE02MpxM55tzGFfPHbPA2CbGMcfux8c2gG2cND0A81Xqjb6tibI1duF/T8yjHG2JWrOi3wWBfXWbtyI9fd2gi0cxmf2zu0xrvs81yIiW4ZRvGrNk2jr7kURqk1bPPOkjn3WqCmibiGMS7EIiplEQ+gb5Yp5b0uLIpwWwrr8iqJLIwHFFtTitscUsPBzA61+V9l7hBT/CSjPDHrWnbqjCMYVUBisMLQdbJ2L9wZbXfnsw/a4kCbGor2n1dDWOqUcmprayjQrxNS0ONNuazgpN77iglAvH2UlRmjxzDsqrqPsPwRtVhDjjaLTKLW6ud7WlSakE7ivzudQjCxXw/Wp2sD3jETMNSAdUQQkg7h/KtooABhsKMKbY+b+IJvF9TCZwr1jby8KAA4sWAi2lCduRyPY1nodx0RZKsRVBNYaihCbdz5prq4JQjUDP3klhBBCCCGEEOJ7eHglhBBCCCGEEOJ7eHglhBBCCCGEEOJ7eHglhBBCCCGEEOJ7XqJaiJLJ21T6/EtQC9Ly3VWjp20WPtNq+uyO9zYaKH5Qt81k53ypAmW27ZgA2w7F5jjdYJvTje197kEUPunu6TWuFx94EJTRhj6gKCloyfJat3lvtZTk7aZRxCxea/q6UECrJYI+kEpgErwFYknYqVp/VcsoLqAJc3SkUZArmTQFpnJTKGzUqgio5CuYsL95EO8tVDFhP+J5hdkJ9LFQWBEyGs+Crepi/WHFGVtbUDDo0D0PMK5z21EEwi0pdXWi6Ey1hO9QKKB/RsPmvXN7sF3d3bPAtiOHMcKvJBLY/pEszoF1W00RoWeefgrKBBSRD6eKvlfOozhW0CPQVK6ieFI2j7Z8EQUwNm17FmzJOI7dkoElpkERhPrzn+4C2/wFC8C2eMlisHV0mHM4GsP+aW1BcaOAjSJgxSr6Z7lkioaUsyjU4zjoi7E4zolCDu9tSWMsiXrE62o1HN9SCWOcXxncuAVsQUsRsAlh7LIiZj9aQS2G4NoRaGDsCivj2wihv8Q8wmaiiALaLj4z2tMPtskSis4UlXU65BGscZWYrQm7aHuxQEDZBygCULrgkUcwSBPRUu5qVjvG0pS6vM9wFVEu5QkNC+eFXylXMUZU6/ielme/HYuh4KXa/0q3NjQf8tiKSmyPxbGyqCKy6dSxXKWK+xTbM9c1344EsH79mKEIsXnihlZ/voTvOfU8rmFj47hnS8fMNWbObBStbGtrA1skiiJR6pnIxr2AV8DNVjrDUcRvm8F/pwRCCCGEEEIIIcQDD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnzPSxRsaja93XtXk4JNakY3Gl3NJmbysCrOpIo4aTYNtM7r7zeuE4qIRa6IieCaQNFTW0fAFg+hYEeogsIhT997t3HdMRuFVtrmLMRm2JqQkCZ0oCRrB8x7A7vnGn995kvQ83qlaE9j0nqolgVbVBGiSUQTxnW1jAINdUUALJPB5HnN12sO+k+9bgorJFIpKDM0iiIc6zejAMxoHttWQpPMj5uCA28/fAWUmdOL7fjNwxvAdt+6YbDZDfT1kOJo+eyocV0q4Hum0yhEIw46XiyG5SIxFGVIWGY528EOmje3D9sxgeI3fiXT3gm2dVvXgm37po3GdSKM/T9VnARbIYcxz2qguEs2b4pWZMsoIhKK4rh1zkIRvLgidja7f1+wzfWM+cbH74MyQQv9s+6gGMXo2DjYli9fZlwv2gPj89zeLrCl3rwf2J54DoWFqhVTMKUaxn5tCK5XDRf9eHh4CGyRKK5NrW3e/kbxrXJZWQ99yiNbtqNRERvRhIbCnjU+pKyhoRD6rCZSp2jOSEVZM7tbzfHsb8fx7VGEwVIJFCcsV3COWQ1syGTOXD/KNbzPUYRdgopYVSSCPqUJHgUVsapqxYw52r4uoGw0qjWcw1p7Q2Ecq3jM3CMELGyXti2yZ9BHSDVNbMtRYoknbjdUgSuFqLLfVMTNGgFzTBQXkHoNY0skhMJRqTj6XknxW9tzpqgqg1lV9tDRADYuKDh3XM8ZRdsT2tJcvBmewLV0qGquO+s24zrR1YVrfF/fXLClUihqGIsqolweAau6ImLmKGtkM8ygaUMIIYQQQggh5I0KD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnwPD6+EEEIIIYQQQnzPSxRs2r2zr6JBoKKJ00gDbQ1FNKFum4n3kQgmZVtqQzSBIq0YJly3tZnJzm85YhWUefKx58C2aeNmsDk2vtO6IIrYxPpRBMZZ87z5zLv/DGUOPhnFP+IJFNNRNGxUQSWvyW5SzEsTUniJTvmK0N3eAbbyBCb1ayINhZIp0FSuKQIQij+V6kpyvtK2ch1FJjJtpjhHzcHx2LANhVcmcvhMN4RzJ6iIKLTEzHu7QyhGFJtA8Z49WnrAtr0d69+RRRGCagnf/dG1pohQwEZBiXoSxUukFYXNRBFbaG1NgC3tiUuVGopyubUc2Pq7UBzFr6xf/wDYnlu/DmxD29cb104ehXrSrfjeS/boB9vey/YG2/ZRU4hj8yjW39WDYzl/YAG2owNFnHZMYn3umClCtUURuxjNohDTsj3BJMcuXga2YsF8p4aiYeEqYjJP34/CUXssWQG2WbMzxvX9D/wRygzvQP+s1zFWVcrYjslJnOvxlPnMhiL2UixhX/sVK5lBoyoWiXjFXbAHRRztTkUwK6HsgeoOxptkyVyf3BQKIGXaMb71pnFNDmZwbzA2hWO3fqRkXK8bxzJWUFGckhKWU/Zn0aAiahXA+mpV892b2bOI6IJN9Tr2rSaSEwPBJkWUR5kDEa07ZC/N+JpjK+3XcDxiQ5UCxoeQorKk7TdDARwTr45oOKztI5WdpCIAqB1IUhH0M6+wVkPZjNWV+m0H2x9QRFpdzz7FUcSZnKASI7S1QilmeUUl69jW3BAKKW7evgls0QiKMyUSuC+KxcxyUeUcFlbEz0T2UWwm/OSVEEIIIYQQQojv4eGVEEIIIYQQQojv4eGVEEIIIYQQQojv4eGVEEIIIYQQQojveWnaON6saRE9Cx7u00QO0KZVZSsCBs+vex5s5bIpFLB0GYpkRKOYKR/QMvsVGi7e2/B056GHHQ5ltmwcBNsPvvcDsNmKKMaW0SzYogkUYdjDI3az5k8PQZmuOQvBtvSwg8BWEuzvsJKpHvH020RpCspUayjWowlTLZiFwiqvNW2dKHDVloqDLRDA5PNszkyCrxcLeJ+D/dAQTKh3wzhlUylMnq+LaXt2w1ooU6yimEYshv4Ui+Az40lMzm8Lmr7y8LodUMauYV3VVhRs6mrDd7IERZbqNopmlWqm+E2xhLGlZqNfW4rwlRaEwgFF1M0jGhJWxCjsKvq/qwhp+ZX7/3gb2EKzloBtYNly4zpeQz9etuceYFuyeA7YnIoiehLwjK+MYbvC6D/BYAZsdRv9vZifAFurR2TNVsZtywiKXcRSGO9bW9rAtnCg37h2lb8rl7MoavPcXx4Dm1vG/t77uLcZ18v3wfhffggFm9av2wS2hCLs15pBQTuvkkguh/1TreI7+RVXm7+KeJKl7CEasL9pUglRWQNsC20xRbQy4BHNGZ4qQ5lGA+PgJsXPqg2ch9kiChlNlcz6Sso8ySkiYAHF37W+DQW0eKkIKnnqsxShIU3URlwUlGk0MJarcduzl3GVMdEe2uSW0xdUlTVS9XfP2Gniq3YV/bGsxIOwIp4U9AgeRUNYxlXmiaXt2xWRJVdRzPO6Y8lBP64p8zWgCYopfRb2nKfcgCI0GVAENRVXDGiiaJa5V1I0x1SxuYay36+VcQ+bKyr+7hWrquJ9mv+IvE+xmfCTV0IIIYQQQgghvoeHV0IIIYQQQgghvoeHV0IIIYQQQgghvucl5bw2tO/va7+z7SnnKt8VV36zV00G2DqIPw5/w803gi2XM3MuDx0bgTJHrjwKbNEo5kBp76n9VLPtmNZUOg1lTjr1JLCtW4P5iLffgvllWq7Ic4PDYGuzzFzMWAU79/5b/wC2UAfmMgVmZcBWzGI+a9iTI7A9tw3KTOXxvkoFcxYXnPDPYHvNUXJZLfXHlZFozCyXkCSUCSl/R9J+CL2ueF403gq2sWHzR8FLY5hvtrAd8wKrOBwSU/JblwzMBlvAc7Ot/KC8lvcWCqJfpCPYRx1tA2Ab2GMe2DZuedC4fm4t5h1GQkr+mov5GLaNITIQUn5o25OXo+XRNJQ8N0sNfP5kZCvmlu6374lgi0bN/PB2Jf2mtw/zlyey+EP2W9dh/mmtYcbogIW5NsEQ9r/j4piLMr6OkovlemN7ayeUGS9gDnlA8WNtPYFsI2WBScWwz/r75oItpvyQfUBM316+N+oKZDIZsF1fxnVieDvO4dndfWBzPDlWYSVfP5fDPFu/4ih58lqWmKXkxEM80PZOSry3lJhhK5ustJILF/NUN1bAfMJKHWN0IIvtKNXwmbGg8p6ePVtSaVetjjbHwX1XWMuDFSUXUWuHJ8dVy39U0mBVHRdtuupz2IO2GVb8RW2HTykp+7WQljzpzRNW1sNyETUxIhHsn/ZZqIUQ97hBQDlTBOO4TrsBJU97chzbVsC4NH+Bqe+Qr2Nsn5zEvUw0ivunupY77PFt1ceUEKSV01KyI57c8EAQK7Pr6P+OkvOqHdhcRUOlkd1qXI8PbsC63N3bA82cnRMhhBBCCCGEkDcsPLwSQgghhBBCCPE9PLwSQgghhBBCCPE9PLwSQgghhBBCCPE9L0mwyfsj5CKiJvJOehKipyZRhMNSku6HR1Fk6b6HHgDbw08/DrbcRNa41n5cea/le4OtuwuFOIJBRWgij+IH2az5zP45mGjeN6cbbOf+03vBtnVwPdj+8vgTYKsWUQ3l+W2miFOiB8uMP/UU2ErXgEkGDtsfbJMFFFYplcwE96qVhTK1OgqmeH/M2q+UK5job9VR2EXLqC8Wzb6p1XGO2AEUTyqUsJ9zim32XOVH1G2z3PxOnF8DfSjWUapgudmL9wVbxEXhhskps4/imQ4oI+Poi3N7esGWLWLy/8Kle4CtpQ3FEFralpntGsU+m5xSRMcUcZ2Ai0Iide0HzD16FI4irqbouKg/3u5XEql2sIWV5mezZtyOtmegTMlGAQ9FC0TibSh6F214OrKi/HC7srJV6hizY3FFkMvCtaIRMMulOlCgKOLiuhaMt2HbIjgHGpbZNstRfFFZh8JJFCWJp9BmV805MD6IYikdyS6wnXrCcWB76PFNYCuUsc8q1VHjulrGeJlJZ8DmVwLKBLYUUUlNaNJbTpv3el2aCdcPRxE9iQbMOVYIxaFMro7zMBnHh4YUIZ2oIsA1VTbXgGQYfT0Vwfs2TaL/lJT3DCv7RK0/YBuqxVmlbxU9JbWc9qkPCJMq68RMRxUtU/qsLWr6Wosi+lhOKEFaib3hAsaNmG2OQHc37qsrcdxT1Wzcx8Vj2LZgAudKosUUzMskcd/S06ntcZW1TvHHkqfc8CjG6HoxC7awi+8UsnExDTbMvq3XcV8UCmJfNAT70bseiohIWdmvDm0yrquT+E6FgiKk2AT85JUQQgghhBBCiO/h4ZUQQgghhBBCiO/h4ZUQQgghhBBCiO/h4ZUQQgghhBBCiO/ZBcEmLRFZE2xC01RuzLj+0733QJnNQ9vANpbLgm2yiEnBAUW0IlY1BS9GxsegzJ/u/RPY+vvngi0aRdGWwW2jYKvXzITocikLZQp5tCm6B7LswIVge2zdk2Cr5THxe1vWFAhKRLD9c1oxCXvjQ4+ALRjFv28E+lC4Zco2BUdQpkFEXBynanX3krVfbRxLEYVxULxAE+KIx8zk/1Qak+KHRlGUYKPiYyFFISeyYwhslR3mvXt0ozjT0atQAGn9IIrOpGejkEtnRw/YRjwCA5mMIjrTwHZEAugtI6ODYAvFsmAbzW4H2+D2gnEdDmN/Z1pQRKFcxr51Q5oYCAY5bywMaKItAU1oBUy+pXfeArBp71SpmPFnRw4DXCSDwnh1G+ODFUZ/KRfM8a0rYjWhEMY8O4g2rwiHiEh3RxZs7oQ5P2uKIJfVwHbE4yj8obi7NFyzPsfBeBNQxG/cID6zoKyRlkcMJKqMW04RCIknMNYfccg+YFuzfjPYnnrGFA4s5FCELRLGdci/4Jx2XYwjGs0Is6llFEFDR4ktFQfbYRfMPY9rtUKZcDQFtlktigiY4mfzO3EOL+g2Y20yhvcFlS7707phsN31PO7ZJmr47kFFMcgrfmXbSmzXNJw00SyloOtqak8mzWpRao/0LTYKKrUmUFQv4xFjGty+BcqUlX1pVdlTWcMYWxZ0mAJN3XNnQ5nnhnBf5HrF/kQkUcS9V2sS49KTW01h2FQPxrNUFNerjWufAZuTRCG/zB5mXE31LYIyxc3Pgi1YyIGtxS2ArVTImtd5FMSNhDEe5Cq47sQzuCfsUITeCuIRk9IE6JS1qBn4ySshhBBCCCGEEN/DwyshhBBCCCGEEN/DwyshhBBCCCGEEN/DwyshhBBCCCGEEN/TtGDT088+DrZQCJOTvaJFIiKT2axxnS1MQZkt21GgpbW7A2ztrSiA0dGJycOj600hl2efQrGj226/DZ/ZgvUHQ5iwXK1hNn6tWjGub/19BcqElT8X9M3pBluiE/t23xVLwfboPWvAVhJTEWHtuCLE4aCYTpuNiffr7n8YbNkuTGafCJjPDNewjK2InJRKJbDJB9D0WpPJYCK7HcL3KRRwzN26Kb4ylUf/37wFx6hQwKT7uCKAsX0jJuzPipmiG7Nnz4cymT4U4AnnFTWNGPrinH0PwmLD5hyO2yg45Qj2T7GItt4EzumaIkpiJXFc5iT7jOt0BsWl8uMoEDKyYxxsdQvfvVJTRMYCZjxIRtH/a2Ucz3AE6/crroVxsK7N6bwpGBRVRIvyORQGq1WwX0s5FB8Ke0Qf0kkU/uhqQ6GhlnaMeV0ZbJsTQmGbctR8z4n5fVCm6qB4mNQxvjmK6EnDIyTiBBRfVwSbMu0o/NFwlGd6xqlVWUcjFq5pWUVg0K2jH69YhnMskzbH5cYb/wBlRnegKI9fqTt1sGl//Q9YaG1GsElV7tHEgpSHOspOLizmOB2QwXmy75sOAFt3C1bWUB6qCe3N7TLjWUAR9bRtvC+0ZBbYcmW89/frs2BzXSxnedaKkBK7XEUoxlXHQFkTFWEhxyvah3eJq4hLSRPiT34hoMyBnhSuwTsmTTGgehrfMZTG/WZAGSe7Pgm2+fvvZVxPCo5RrQ2FGoMW+nagBdfqrLLu5CumsFNDEWStVtAvWpX6typ7u+Kouf+Yn8lAmb4lKJaXfUbZUw2iyNXkDtOWK+J+x7HRa6fKOHbxNtyfpeeizS6Ze9NKGdf4gKZg2AT85JUQQgghhBBCiO/h4ZUQQgghhBBCiO/h4ZUQQgghhBBCiO/h4ZUQQgghhBBCiO9pWrDp3gfuBVs5VwRbMoaiGCeddKpxbbsoHPDwk8+BrTWNYhTlBiYn93Vjsn99h5lcPVVEEYvS8yh21BbF83yyFd8ppSQsx5Jm0nhrBhORW1tawNbSggnv8RQmm6866mCwTY2h+M9TT20wrp06JlxvySpiUmEUjwkNYwJ6fhJtdtoUAAnEO6HM4FYUNMkpPuRH8llMbg/VNDEZ5e9BHjcIBdEvSoqIWVsa/S6TxOT/8iQKNnX3mWJns/dZCWWe2obCMWvXoe3QXhS/yWax3KyBfY3rgOCcq1VRxCmjCGLkRrC/4zUUi+htV9rmmPElvI8SR7Loi3+++XqwbduK7Q2qIkvmHCsruhx15W+FgTq+k29RhIZCDbS1elx0bivGn6ULM2BLxRSxPGU+FXNZ47pSwrkTT2K/LtkDfWXu/DlgC4RR3KzgER2c29uL9W8cAVtLO87X9jZcA0IhU2Ctoem6KLoWsSSuE7YiGuLRE5OwIlZTERTT6OjEtamgiOwVsyiANrvLXCPffvJbocx1N90ONr/iKoJxmsCP6+1srS5NiKmhiHSJUr8iThMMoZ8F0/1mXQkc82oR585ECNeddALrf34U150Hn8sa18XxISiT6EGhwICD71kv4RxOKUJmlYbSRx5hHpR0EhEX63eUMdBEsxo23tvw3BsKYrs0aSbXbXob/prT3oIiS50ptGUnTAHKdkX0MepV3hNd1LN7YAnYFvbONa6f3rIBymSiEbDZdVyvunsyYAsoca8YMudPII31T45iHJzfjWtMKaII2zrmXnhiEvcegd55YJuz55vBNrgNz1OVshm3w4p/ug76erCBvl7N4lo3Krgftj1rRSCIMchRJ+eLw09eCSGEEEIIIYT4Hh5eCSGEEEIIIYT4Hh5eCSGEEEIIIYT4Hh5eCSGEEEIIIYT4nqYzxTdswoToqZFJsO2xYA+wxeOmAMDQECb7bt64BWypJAp4VOsoFmHlymArZz2J3wFMTl40sBBsA12tYEsrAhsjI4rATrv5t4DeuSh8kM9h+yOKRkCsgeocLUrbjn3bkWCb8Aj47NiG/T1WxYcmphThH0VgKmRhUvfstCmGkpzVA2UGN20CW62ESd5+RMltF6dcAJurSDIExPRFx8KxnVR0e3I5RdSjion+vYqg2IFHmn4xZwkm9V/zoyvA1pNEoYJgDefX4Ib1eO/CPY3rWMciKJN0cbxLE+if8QaKLNXKOHfG8mjLdJmCIB09/VCmXEC/DqBJnAgKm1lKLKl7hCAsG1UILBdttj1zxDpWHvImsC3cc1+wDQ0OGtez+1AoafEeA2Dr6eoGW9DFvs7ns8a1uiYoY5RKKsJ7KUXoJoLrTtgjTFUuopjG/nuj0FP/4n6w1RUBDNfzd2S7gcIlrhKEgmH0n3pFEZjxCKEEQvh3ayumBDmlXFURGQsFUZDFqWWN6y5FBOUthx+Iz/QpQU08SRPzUUR/vOVUwSbFZimCUJqAkNXA/t9aMm3PTeG4PTO+FWyt7SjA01CEXLJTuC7Utz1jXIcmN0GZt78HBZtGB1HYaUBZ1wIxbNu9m3EfGvQ0tzWC8yQdxXU4GkERHksRWKwq4oHlkkcktILxfrQ6c+K9xvwejOWnHX8U2DZv6Deu8xXcK1UruJexqxj3+vtQpMj1KNq5nbjfnFLEmYolbMecTlx3bEVEslA09wJuDIVnUy7uW4IN9INZrbjGFEfMNaUwiOtaXdm3J2ehIFTfXoeDrVE3zywjQ7iHKxWU/bjS/pYkzomQYDzwapHVS1iXtmduBn7ySgghhBBCCCHE9/DwSgghhBBCCCHE9/DwSgghhBBCCCHE9zT9BfziFOZ4lir4Heeo8mPWU3nz3s1bN0GZTCsmnDlFJd+sgj+kvn14HdqGxsz7AnjfO08/DWyNwgTY7rznLrBtfmIQbB2tZr7E8PP4Xe7Zyvf3p+o7wCZhzANs75gFtuVL9gZb7e3msF7xw59BmXIe+3Yoi/kAEsIckGpNyQcYGzeu+5TxjMQxL6ezO4PP9CFKmq84Su6XFcC/B3nTxtyycp+S99zekQBbTwJzQvY/YDHYlh1q5rhOjuDYRm2c0wvnYP5EQ2lcT3cX2OyK2bZSFnNOaja2v17GMOQI5setH9wGtiefeghsh77ZfG5HTweUyeVxfoWxu6WzH/Ou/n/tndmPZOd53s9Wp7aupfdtlp5dw+HMkByKlChKNE05sgLFkeI4C4L4KrkKkItcJvkb4gA2kgAxYCBRgACJIVmWLFiyRJEUh1o4HHI0G2ef6Z7p7umlqrv2s+bCV995HkAF3+QM9PzuzouvzvJt5/u6+v1VQto4DsxcjojkJu9ttSE26pCL5pQL5z4DsTMvYs7r4Hkzn7VK5gLS3a2U5Pc5JJdyqmrmN6XkT7Dsr7IsFzEKsT9aZFyPRua77thxnMfLPvaVQQ/HWOqQ165txlIy4SQk1zEmdZYkWC4YmPcfJySf0GP5+liTnR3MxXp4H3Mnv/D6i8ZxP8R8qgrLs80pLstvtTDmu9i+2Ry6EZkHWc6rRc7POrxtYS7ZKDHrdmeI/d8nedS1YQ9iMRkmE8NtiA1T05sRktzBqLUOsY3VT7Fcihf9/Ju/C7GZMq455ybMeePgNObKlgtYt6Uirnc8j7yf2FwyMteY9zfaUOZPf/oAYuskNzav1F1cN37+JZwLXzmzbBx3+rj+Dkk/DiNsk6hPnDaZfcCRYBnK9EdYr90enqtAvAGtffS/lI6YfWMwwmdKmzMQe7yB/f02cfw8N2nm3j7awr2IRVw4MckDnzj8EsS+eGzFON5dxZzXTz+6BLGnGzg2qzbmmVsjnDeGsXm/Nhk3XgGfaRz0zasQQgghhBBCiNyjzasQQgghhBBCiNyjzasQQgghhBBCiNyjzasQQgghhBBCiNwztrApGGGic58k6N65j/Kkb337z43jn77zDpSxyY/Rb+6jZGbrIYohCsT+EWZ+WNdfaECZ9999D2KjfZQQXL99C2K9TZQJtLfMazanUSSwtYGf29/Depxs4o8YBzHex09+8hHEynVTUDNJfoR5O9yBWJ/8QPRjInZKi9hWlcwzuFsoxGlOYxu4RG6RR5IIk/8H5Aej/SqKhjzPlEe4Dsp8ji/gj1uXyvi3pZXDByF2/vU3IbZ46pxx/PEHfwZlDh3Eay6cOQsxf/YYxLwKtmU/80Pkg30UtGw+wfHb2kQRUxyiFKZcw/E0M4NCn9Unl43j+UWUOUTkx8rTAQoY7B6KCeKU/Bh3RrBTLuJ9+QsY2ydjKa+Uqyj5mSA/1F6tZMa0h0IG4hSybCZsYkKijAQmCXEcMrkRk6lFRB3lkCZJbfOzE80pPFeM54qJYMNK8AJpRrjjsJuIMRZ72KdSJvmJzDnHJj88XyT3WoixzqpDLJdu4pjYumeKCA+cQhnctkMkgTnFJ2IX28F6bJRxTPQzIho2N7JvEqjDid2bi59OLbO/eESedKiO9/rcfBNiu602xPY6OEdn111PyRruJ2T99/zLn4dYsYj1PTmBgruD8ygPnM0Im5oVfE6HiAgrJRQ2OaRugwClbu2uWR+frj6BMnFIJKRsjsgp3V18H67dvwqxA8tHjOPlRZSNehUUDSU2tvn+Nq7J223zPqanUMrYI2LM/gDXXr0urr87XVzfnDp21PxcjwiKBjgPzpL5oDDCe7vw6mvG8W4fyzzYQAFg4OC6KB5gP7MmzXGydO4IFJk99zsQi1oolN298XOI3b/6S4ht3zX3LI6PdeZ4TN/469E3r0IIIYQQQgghco82r0IIIYQQQgghco82r0IIIYQQQgghco82r0IIIYQQQgghcs/YtpzGFCYwh2Tru9/dh9j1jz82jjfv34cyDrmVCpFR+A4m1KcBJmE7GVnBASJtmaqhsKbVx4TroyunIPYwxsT19q4pQYqLTSiz2cNE6n4fpQ/tXUyStl1M7B/a5D76d41jx0f5U+KSevTx/H0iNIkjjFUz15hoYN26RHyQpPjseaRAxFItIqyIhyhVKVfMunEdtHDMTaOIYnW9DbFjL/0uxA6cxZhlmfUfdjBRvlHDMT178gWI9TyU01y7jMn5o4F5jf39NpTZfvwIYm6M47dUwvpePoJj+NzJ4xCLXFMsVHCbUKbgowzBG5Kx+fAxxJi8K8p07S4Zq5VpFB7NL6FoIq/UGtgPUhfn6P7IbM90hCKs0Wg8cUYQYrlRRnYRkfkoDLF9Q3Kufh/HcL+HMp0oMa9RI+/DWqMJsWZtBmIlH+feOMncm43yPMfCWI1IzHae4nMOB6Y4J0lwfrYtvK8kxrar11BAcvgQClkGfbM90wTvv1HDMZFXqlWco10X5/LdPXwn9wOzXBwTExMRijGJmUXESw4RcMWZ+n7pQBPKfOkEjumEiBv3yEoxjsh46phCmYk6jpPzF16G2Mufex1iE0SyFJB5g7nNrKwAlJTxi3h+Nm+sPUCh4LsffgKxD9fNeeNGG9tkL8D+7njPjrSvWcb77+xsQGw9M1/OLOAzNsiaqlpr4kUbKHZybbOdarjEtRoT+LmU7B8i8l64cf0mxGZnTeFRpXIIyvTJO+z8Cq5b3nj5JYgNMlK3Pg5D68RB7FObO7hnebKxC7GN+6Ys8xGZg4ZEolVuomiv+TyuOV84hdK15ftXjOMrF/8Kymxt4H5wHPTNqxBCCCGEEEKI3KPNqxBCCCGEEEKI3KPNqxBCCCGEEEKI3KPNqxBCCCGEEEKI3DO2sGmCCCo8IlsIdjBhefuWmSh8cALPZZNE6s4ABSpDB7OY7TJKK4q2KUzZ2sQE5ks/x6T7+RomLO+02hDbG2CSdDfjURhso7yKmQM8Ik8qF0gyNRFTbbXx3mLHfPaKh9nsNpFDOCWUzFhE2GSlKDXo9cz62N/H+pmcbpLTPxuyghHpi5UiDh+b1GEh02fTGPtweQI/93v/9Pcg9tpX34JYfQZlKZv3bhjHLhk37Yxcw7Isa+vBpxB70kFJwE++/W2ITZRNec9w1IUyC/M49utkHrm/tgqxgDzD1NIKxE6evWAGYhRz7LZRwtEnsq3WgMw3Kbb7cGCOk26K4zftYh863YRQbvn2d74PsbjwHsRaLVM2193bhjLEWUYlTpubKK6LE/PDU7NzUGZyBkVYRSII6e22IXbr9g2I7XfNvnzwyGEo4xZQXlWv4X0cOYKijwMHF8wyR4lgsIj9s1bCayaNOsSsjEAsJHOQ6+E7wSXXnF8hEqo6kd9kZHzkNWdNTZF7zSn7+/g+j0N8PwbkHZ9m3rf+mCuv1MKBwr5xcG0sd3zenFf/xRtnoMweEUi29toQmyTvusddfH+ce/454/jV138bzzWFsrAykXMWyTpjso5rvRKpTD/zrtjZ3oIy127iu+69D34Gsfffex9iLa8JsanXvmYc9yMyNm0iqCQis7yySPYBdoDttLv51Dj+5ModKHP5Ktb//PJBiH3xjS9BbHnWvI9hC8V7Lln3WmSf4XnYfw4tkT6amWuLPo7Euo9SN6uG1wxjPH9nYNbjIMZ55MbtBxBrjbBvv3R0FmLdOfM576+jaOvGQxRVfXIP265DZLQzdXz25+bN99jLX/odKHP5gx9CbBz0zasQQgghhBBCiNyjzasQQgghhBBCiNyjzasQQgghhBBCiNyjzasQQgghhBBCiNwztrApIcnJKUko9l0sVwjNJPVD9SkoEzkorOkQKZJbn4CY42MS/2DTlAmM2pjQ3dnpQGw7wftvj/CzKy+dg9jG1o75uRYKDSYmUE4z7KPkKizgMw1HmNg/IMIIxzHbpUTqJ7UxyT4mciaXJLM7EcohksT87NOtNpSJiKvA858NYVOSokzGSvCB7AjrMMqIJ2wi1ygVUVzywoULECsSKcz1jy9DrPXkrnE8GqGYo9NCidnqnesQ66YoPijEeL4JzxzD9RL29dlJFD6sb6I4IAqxf/Y7KIBavf8IYpZ1zTjqdnGclzxsg6iI4p+dCNulTARxlZpZR2UPBTadPgpfomdI1vHDty9CrHngFMTS2GynyxffhjKHDxyA2Mw0yo0er5G+kRl3lakmlAkcHIebRAL21iufh9gL51Bs08+MH6eA8+L9Rw8hduv2XYj96iqO12bDfK/9/j/+BpT5wpmTEPNTfF8dWETpSZARNtkOzrsJkYyFFs5xjoexYhPHRDkjKUpcnENxNssvQYzPnabYzzwP69Z2zVhK3oUR+S7Bt4n8ibxI5ydQCvONV44axweaWKa/j3PqfBOllZNFXJ/NVHHsnD512jiuN3CtFwQjiBVd0s+IsGn36TrEHj7AMfaLDz8yjn/5Eco579y9B7FOl0i5LHz2yVe/DrFBbI4BOyL9nayPLTKG88qVy7+EWLqD815j2hQGXbqGIqCbRD70hTdRSPnN//U/IfYP3nrdOJ4skTVVGfuxV0Cp0GCI6/vZaVwLJEVzPdMaYT9m2KTNQzLW7cya/85DlEr+0X/6I4htP8V13Kufex1iX/uDf2kczy2g1Kka4Z5rKcI56Fob572ECDWfZt6JJw6hXPToqecgNg7PzqgRQgghhBBCCPEbizavQgghhBBCCCFyjzavQgghhBBCCCFyjzavQgghhBBCCCFyz9jCpnYbpSejPiakVwNMbp9dWDKOdx4+hTJ3HmDS91aIUpipKRQAOCUUyvSSlnEch5h0HPUx4Xo4QnFARAQ7WxvbeM2umfidhvi5ShETxoMBPqddROFLNMT79asoxUljM5l6OMJ2Shy8t4AIBooFlDz4Jby3iYopHClXUKwVkvpwnGfl7yckQZ3UFxMCxBnBRmBhYvt8YxJif/2d70Jsav4axOaYoKVvysIKBdJmVZQReUScViWSqIU5lOsMOuaYK7t4zZ0tHDdhgGOuRsZ00EW5yO3LH0Js/eYt43hEJARWAZ8zZs9+AMeXVcV2d4rmGC4REdOkhc90+swRPH9O+YN//ocQK86dgFi/Y0qWbv8KZSmLC9hn2VxQLmEfDRKzPU8+j/cwuYjCjf4MjrGvffXLEMvKtyzLsnoZYVNCPHMRkfcMI5zbnxLBxsP7T8x7qOBzb6ztQOzBtdsQc4Z4zXsb5jv3lb/3MpQ5vLIEsTDGfuyU8J1gFYi8LjsGbCzj21hnecW28P1lkbncTnFZ5TtmrFHBOhxZZI0S4fndEOvxwASOnVOLZn8fDHHesmNcU1SJaO/wkcMQc44uQ6zom3N+HODc29lGCdulO3cgdu0avusuf4Jzyd17RLzUMcVLManHhAi4XNLEpWmUzNRm8dnTzDUS8g5IifyJrS3yyhYRn94sbEHMfWrOVY/WUbT1pbd+C2L//j/+B4j98Z/8F4h97y+/Yxx/ZhnXIwWfvM9rOK/GpB9MEdHY7JTZDzwiM/V9HNeOjeW6ZF4NPHMM/9f/9mdQ5vrNX0GMrdG/9Z3/A7EDp84ax2dPoACwXETxXj3Fe13C5b0VeTgH9TJS35TI2g4vH8KTjcGzsnMQQgghhBBCCPEbjDavQgghhBBCCCFyjzavQgghhBBCCCFyjzavQgghhBBCCCFyz9jCJmuA0hYLc2+tyMbk4V4mb3rdxkTq9QiT1rsBSWTf2YOQW8Ak8n5ifjYlho0BSeJPUyKVIAnRj4l4JsqIkmwiYNhqtSBm2VguJUnkhTKKROokQTwrCEpTtBC4JLm6bGEbOy6WK5D6sDP3kSbYdjY5F0tmzyMJ6T++h/245JE+62SS1l0UYiRBCLFtIrbobmGsHO5DLMmIIaYmUWjQXJqFWEQEHo+f4DVTIi9xMlKSgMlGbOxj1RJKrsh0YLksSGRqcWDOEQ5pu/0+jsOgiHKR2hLWR6/chlgnMWUowx729en6UYjNEPFVXin6+Ey3bl6F2P6e2V/Y/BMGKI/pdnsQs8ncWCqafSjso0xwbwuvufloFWLf/+vvQ6zVIefrmn2qVkfxR2MSJR/VOkrL1taeQGxuxhTAlOoonHrve3ivu7evQCwmc8mdjU3zHnr4jCdOo/iqUcex2ZhsQKxcQdFHo2q2U6GE82WlgvWTV4ouWQMR/87JJWy7Y4vmXHt4CuurTfr/Hon5RAJWC8l8NjTXAaMRzse1GrYvk0oyr1a1is/QaplisLfffg/KXLz4c4jduHkXYts75JkinI9jstaw4uz4J2sgF9cero/PXphGoYxNyjmZd4BNzp8SqVtKhDh5ZXnlOMRiC+eSMCNb9ato+Fk8SKRX5H1+cOkAxP7mL/7cOO5soIyvUsa5pUjW0BZZpxc9HOtZKWmljH2A7RVKPl4zJdLTrYFZj9duXIcyX/7yWxA7/8J5iP33P0XZ0wfvmu+PowtNKONXcELb3sD13ye3b0GsUMXnnK+b14gHuK8pk3XFOOibVyGEEEIIIYQQuUebVyGEEEIIIYQQuUebVyGEEEIIIYQQuWfshEOP5KqFJJepO8CchN19Mydvl/xQbVQg+QER/v/1cID5HvYI86fCTG6B45AfLG5g3pLrYjmX/BhxSrb92dwuei4Scxz8n3uHnD8hQYfer/nscYL/Z56ya9J7w2uyPDTLNssl5JokBZL+CHsecWzMUSgVSS4D+dH6aiY3olqbgTL9EPv1dA3zJzxy/mBvE2KJY362X8Bcm/n5I/g5kot46hzmnFx8+0d4H6mZe14g/WTQxfz0OvnhcJ+MOZckXnWHWG/3181cqXYb62xkYy7Z7Ens68tNbOMgxXZpbZvP5Q9Jbi/5IfVBH8dJXunsYO7Lj//iexBb3Vgzjp0Qc4mvXME8bZb7T+eHTD/44Xd/DEX8Ao7XF158CWKBX4PY/gj76L1HZi7fzs4NPNcQ++eTjQcQu/8AP/vyixeM43/7b/4dlPnFzz6AWLS3A7H9Eb5fB5mcv3sfYv7ve5fWIVb1MH+24JN3ThHru5bJeT1weAXK/MPf/2cQuwCRfPDGOcwJblZwDXRsFuezasZh0fCwX4fEoTCo4jwS9XDuGvXJgiH77ib5hBWSb1ZwyLpuG/O0u09wDP/o55eN42/+X5wftp9uQYylrSbku5WE+FKcFPtoamX8I2Q+8Elur+9jfXtzmJtpeZjva2XWPAmRwtC1E/Gs5JXIwnuNE+wv2bqt4pCw9slaYJP0je1dzH1e2zDnvTTCPsDWZ2FI1sJ4a1aR7EeqGdeCS8ZruYT9okScHomL/eDRVmYdl2KZr3/jGxB77bXXILa6ugaxb33nL43jy58chjLxENd/rU30DAU7jyHmxfgu7Udd4/heC987lSKup8ZB37wKIYQQQgghhMg92rwKIYQQQgghhMg92rwKIYQQQgghhMg92rwKIYQQQgghhMg9Ywubup0uxPb3URzQ66Kco9czpSosZ73exIzuIvmRYYZNpEJlz0wCLvh4LiZPKpBEbSZsYj+MnRU2sVRw4riyXGZnInKFOGYSJBQ/ZO8jJGVi9qPdJAHdY7Iq8hClTKI6S3hPicSpSEQfecT3sI36RIzilqoQS1zzGftEYOMWsE6L5MetCwU8v19pQKxRN8ttZGUAlmX1l1HENHcQf4T88dNtiJ357Bcg1t0ypR73bl2DMr1uG2Kei/XRIDI128Ixt/4YRSKPHpqCAaeIdVafR4nC7BS5JhFC2bt4vsmW2d+X56agzIEm1ved6yhBehOdDLlgcX4RYidWUPqVlaV4DrabS14CjotjLGUykOwYK6AkY2kJJSu/9ZWvQKxWwX7QKOEP3l+/+olxfOvOXSizsLwCsSEx+7nkx+2v3rppXu8W/gh8ZeU0xJ48wXudbGJszjffh5UJnFt2Nx5CbOfxHYhtbeNcMoyxncLEbOP1Nr4TXnuLLAZyyj/5LPZ1v4jP/XAdpTMX33nPOD4zh/VvF1BcEpB1wN1Pr0Ls+ImTEHMycr/2Y+yzvRbKWDbWn0Ls9l387Oo2ysKiyoJxPLVM5gcX3/lxgGuUiCyLRiEKZaJ+B2LlgtmvHCJFGvZx/RqXUKZYnpyDWBqjICjKrG9SIjdiwqY4fjaklZZlWdttbPMwwnekl1nTphHWxeUr2I/Pnkdd2+Urv8JrZr53CzwiVgxxPbu+jmuZ4QjvnwkjC5nTsZmrQIRfbE8Rp0w+aa6DpmbmoczMNEofO/soTltYXIDYbsucl37wg7+CMsMujomdHdz79WwcnB7Zr7kZ6dTk/CyUmZvHex0HffMqhBBCCCGEECL3aPMqhBBCCCGEECL3aPMqhBBCCCGEECL3aPMqhBBCCCGEECL3jC1s2t4hidoBSYIfYkJ9EJixQokkNZdQVjAYoMiFST0cBxOzrUwsTTG9OiKJ8g4R85QrmIjMJFFZGxOTOjFYEr9N08GRfr8PsazYyWPyJIdckzwTuzcmbIL0dVKkVMKk+mdF2DQ/i3UTkjExiLHNe5kc+NTBccPEWPU6Juf7BRw7gx4m7JezbR7g+T+8eBFiR0+hjGVtDaVCDuk/laJ5by4Rc5TLKDtikjc29qMI55YJIgl47UVTXlKqoYgpconELMSxNFhFmYPTQUHQXKVmHL948gyWaaKA4dL6fYjlld2tXYh97tXXIPbaG28Yx8UiEcHReRxjCRFbuJZ5PvYeGgTYljtrWNe7QxSv7G7jc97LCJqePMUxMTG3BDGriH3F9lHYFESm/O2H7/wUyhw+dhZiB6dQTFVycKxXCuY4GQ1RcnNvHwVrE2TsxCmOnY0WSj1mZlaM436Ibfnjd34BsX/1r/8QYnlgkGK97vZwfri5jnX7/tXrxvFaBetimki0GgWs63qtBrFyDaV9axk5ze2H+L669PFHELu9hhK8zpCsZTyce3/7xeeM479/+iiUKZGlU4kINR8/RXHUGpEH7pP3x61rpgzo00v4rkuIANNfPIHlmGCqj3OEZZvzkkMEXFzYhPeRV2Ib+4Ht4nN2M+vSQRfnh40t7I//+Y//BGIP76BIrpuZ8+88Rkkak/2xug7Jms2OiYwz810fW6PbAzx/auMYpqv7zLq6XMV72CFrzqKP9b+/h2vC0ci8jwcP1vC+iFiLTNtWWsJ3GNsV+JkxUC1OQJl+7+/W//XNqxBCCCGEEEKI3KPNqxBCCCGEEEKI3KPNqxBCCCGEEEKI3KPNqxBCCCGEEEKI3DO2sCkMUZZipbj39TwUymSdPMUyiglYBrNN7s51Uf5B8rKtOCNoYonaLhE9uT7GnAI+p0+eMysyYtfksiMkITnMTGjSbDYhFoamhGQUYNvFNt7HuHKmKMIE9CjKiE9iFKGwlO5nRVZw6CAmxTdslLHcWUVRzOaW+dxBjAKIiQns7L3+HsTiBMUHWZGAZVnWbkaG0Olimw1DPL+bYqw2MQmxzQ0UVqxl5CUJkaTNz6KEyk6wr7TaLYgVq1hvzQbKS/yMDGhEhD4WGb+9EdZj0MVy1QTLHT+4YBwvLeBzrq6hDGtnC/tLXqkScd3OPgprLl+5ZBzPzWH/mZ+bgVh23rIsy2q12ngjQ/OaHuk/y0dQnnRwEvvK41vrEOt1UZQxN2+2b2W6CWXcEsqN+gOsn8XFQxDbeGLKM7Z3cBwuLvUgZpP5uTsic29GrhOSF0yRyNSK5J0Q7KAcxXJwnMwvr5ifG+F7aMzXYS742ROck0ZD7CvrmyhsqmT8Jrt9LHN/AwVFSzUUnPyjr38RYs+dPQ8xv2z29+nFg1Bm7jOnIPZmgO+KuSkUQjXL+M5qlM0HLZbwHVklsQJZ23RHWLe7fexD620cY+/OmvPLgCwSnxD5Tepiuf4uCqxisl4tV8y2Ssn6cnwBZj6Zmp4iUXzOQdecq0ZV7MeOjW3eJvP99OwcxBpTs8ZxRNo3SbGvRCH2qZisZ8MQ58ck/PXr+xGZ4xLWvkRE6GTWce19lC69f/F9iL355psQu3b9BsSytxuQOsvKEC3LshLSTkxyFbP3TmBeY/XhKl6ziO/lcdA3r0IIIYQQQgghco82r0IIIYQQQgghco82r0IIIYQQQgghco82r0IIIYQQQgghcs/YwqbpaRSQOBZKGuIYk4DDyEzuZbKg4XAAMdvF5HabJA8nCSYPB5mEYjfBRGQGF0JhYnb2mSzLsmxmncqWIUUSkjgdRSRhnNSt6+H9ZoVKIUtITzDmkGcfVzCQrTdnTDkTa7s8Up/Evj4gsp3JOdLPqqbEYnsTpQFDItXyfBTAkGJWQuQCYWxeY2+AspFqGQU8wz7KLwbDbbwPcs04E0tTrIvuPtZZvY4Ct3odBSGDAX52ewefa2LCFM/YRAZiR9g/fQ/vo4huEcsnUreV4yvG8aCP53/33esQu3ILJS15pVjAsToatiF28eKPjOM0xD5Vr2BdhyGRig3wveBl/uZ6eAVFNM9/7jmIHTuEEqf26hrENlrY3/3MWDk2vQBltrZQpnb21PMQO3MWJTn/+5v/wzj2LBTEhT2sxyDAWEreHVbJrFs3a1G0LGvlyFGIPV39FM9FRDRlIlM7ffqkcTzsY/0cXEQZS15p7eJcQ16tlk1khb5ttmfgYH0tTOGcceD4CxA7ev6zEKs1UXqSFTzWJ4hAbxr7ok/WKA4RzNjkHZ9dA8VMVhPj+y8g6ynHxn5W8YkYrIHL2Fdfftk4Lk40ocx3f/wjiD168hBicYJzUFTAF4PjmvfGxrAzpsQpr8QWthNbw3lF89mLxQqW8bDdJidR5GextXBmzczWrlGA64UkJvJSIh9iz5TtyhF5X3V7OMeNiHiMCXDjzHOyz333e9+D2NXruK748NJHELMzUr2Y7FciMl5jMvZTMl6TmIhcM8es/5dSJnf99eibVyGEEEIIIYQQuUebVyGEEEIIIYQQuUebVyGEEEIIIYQQuUebVyGEEEIIIYQQuWdsYVO9jvKYJCaJ5inuh0eBmZC7T8QNXgETeV0SY9Ifi4QKGVlBRBKwEyYQInImi0ii7JSZl4icAIpgmYQkjKfk7woJSZwOBpj4HYZmfSdErGA5RMSEpXjiOilZKZkCA5+IpBwiJmBJ+3nEK+F9luooZJiawHbzBmbifaGMdbrfIvUQ47nKJRScxESkE4/axrFfwfMXPLx/10Wxwoj1OyIcSDNjgnjZrJQIZmIMWQUPxRyWj5KTdgslKoPMfNNo4tzlEYmTQ+qjD8oBy9rc7kCs1TXLdXp7UOZvfnITz4VOidzSJ8Isi9TjV776NeM4CXpQxiWyCzoPEhGHm2mnUhX77EYbJSud9i2I7Q7wPuwSylg+/fiecbzzwRaUOXoE5TefPX4CYsEAO3w507fTECUWffI5x8VxnZBX0yAzj3tErnH4AAqbht0diD1Xr0LsF5cuQ+zJQ1P2NOhhP0j7OH7zymIDnzska4jQbkKsWDVjj9DFYvkNlNV88UsXIDZVm8BrMoFKZi3TJW5E38PxW8NpkOKRNZDjmudzyTqDraesBOsxJR2ZySLZwqVZNwVWp44dgTLXP12E2OPHKGyKyL25RDyTff+x+0rpeurZwSYSrUKBrI+zslWyVygUyDue1RlZNxaz7wVSxidLKtvCuZ2Jl2ImEk1/vSRqemYKYkxEmJI1VVYclZB+1+vhO3hjcxNiKyvY3zs9853SJzJE1gBjS5xInWXrKCuR+9vY301Ypm9ehRBCCCGEEELkHm1ehRBCCCGEEELkHm1ehRBCCCGEEELknrETDm2yz7VJUlsQYjLHcGT+bzX7gV72/+MsLy0leVEB+aXwUeYHf23yf9U2+/9r8r/z7P+0kwifPRth/8lN/pOe/k8/+5/71Cb/U+6RXAKX5BLAuUiM/W97THJ0WZJG5n/gHZbXQv5PPgpJjnEO6XZJnbqYezRRxby0QtmssGoR8y4aDayb7j7mJHT3Mb+h2yd5V0MzVvOnoUyJ5JxE5IexPZIX5ZPmLRTNMWyTPlCZwCnHIbNQRHLy/DIWrDcx33F318xJ7ZB+V5/C+uhHOC/dfoA5fzd/tQqx+Skzr3b+AN6X5eB9zDRqWC6nVCcwGa5B5oLa7EnjmP3Yeom8T3wbz5+WyxArVsxyyRAdCp3OPsTcCuY+zx1rQuxYZRtit+/fNQMs96uCOdmP1x9BbHpm8tfGggHmh45GmEfd6+F8MyJOiXBk5kp5Jeyf80uzEHu4jvPN5qO7EBt28d7uXvvYOJ6exvOnk5gjlleOzmD/iROcM9oejvN+o2kcn5jEPnDswnmILS8fglhA8qHdbI6hRbLXyFhNyMs8TclazMXx6rI1Iayz2PnHy1tlMAcHe4ZixrlRr+A79/ghrNu79+5BbG0X55LUw/M5tvk+telakrTTGK6UvMD6BstNtjOrX1IVtC1pHizxp2Trlq3b2edcspYvkPrPemMsi/h22BqanMu1yTqLrG+yW6Cst8eyLKtca0Js+RC+N9mYGATm/bNcXNYmNhn7bAyzz7qZh2LOIrY+GAd98yqEEEIIIYQQIvdo8yqEEEIIIYQQIvdo8yqEEEIIIYQQIvdo8yqEEEIIIYQQIveMLWxiybijEcoKmIwpCEypREA+F7DkYSJaySaCWxYmBVuWZZWKpjzDIcnbMRE9jZuIbJMfqc7eGxM9+eReGcMhijgicr8sAT1bH+yZWJJ0v4+CICYdKJVQVpC9jyjA8zOJU6mEkpM8soa/XW6N2lgPtVlso1LZTP5voOfJmprCodglP0jdbmOstYMJ+62MZ8hNsN8lVNBFBFrkx7LZX72ysg7Xw2caxCT5H6vMKiQoTIj6uxCLB1gfsWcKEtpdLBOQx9wlgqwHd1DY1N5BmU7QM0+40FiAMqcPL0OMXDK39Du3MJgQAYZtdvDNTZT53L7+AGIlD+VMfkZ0Y1mWNTNnym6WZhpQhsn+phso6SL+P2s4aEFsbs6U9SwvoWhofWMDYrdu3YDYSoA/IJ+djzsdrLN+H+VJ+3sok2HCpjgwO5pbrEKZa1dnIMbe1XNz8xBbPvc8lps1y83M4pgokfvIKzM17J9hQObtPk5olecvGMcHifzp1FEUWvlkpnUKeM0CkccUMlM+WQLR9ZRHRJzEM0QlPNk1z7iCotTCCZm9F0ISTMk1XMt82GoZ1xnnzp6G2IiYo37w0w8h9nQP12dZaZDLpJWkvtkaK68EQ2wndv9Zxw+TD1HBD1kz2KTjppl2Ski7MWFkVqplWZZVKGMsdXH9USTiIoT0d7LOYmv5MDDnWrb/YZ/rB1iOreOGkflMtN8x8Rs5V0razvdxHeqR9sxSqRC55Rjom1chhBBCCCGEELlHm1chhBBCCCGEELlHm1chhBBCCCGEELlHm1chhBBCCCGEELlnbGFTGGICM5MzsYRiK5OwTJN4qQAJYXImJkbKJvGH5L7YfbBEZ5skg7suJnk7mWdgCdEseXvc5Gf2nOOInQoFvNdx65HVB2tjPyNeqhQxCZu157MiK4gLKDMJ/ZchNkqIqCraNo5LDXzm5izKnyYdrOepPvaV9i6KRNrbZvsOeqSvR9jHrJSIFSK85nCA/S7bZ10iWugM8VyDLp6rkOLcUnNqeG8OCmvC0HzWYhXHXKmAAo+mj9c8ajUhdvY8SmZOnTtvHK8cPw5lXvkciqPWnqBcJ68kAZGUkL9/eqHZ7vUCtvmln70DsY3NbYjZpJ1eecWU37z+eRyHe3soPLry0c8h1iPz561HqxC79+CBcTzoY1umKZHb1VHCs7/fgVinZT57bx+lUWym9Ihgo1HDuXfpiCmJmpxehDJzSyhUWnrxLMSm6tj/mYgQ3jE2MQaR+SavpBHO7UMiPiwX8JnOHD9kHC9N4nxfdogYj7SvS4RKZIliOZm1BvtYVjL0t+XIGoV5/BxSLvPZiAj62JoijPFcPSKi6Q6xvgcjIqxJzXfAIMJrxmQNt3jgMMSmJx9AbGcf54hsu9hMOErmCD6y8wmb49j9x9n6trH+i0Wc29k+I44xVvDNtmPyJ8/C9o2JGDZiw4mJWzODjMnI2HrWJuvqQpHMlwVz/cTOxcYOe/YwwjpzEvPZE7a2JzGXtHkypuyWxeC+SP2Mw7Pz5hBCCCGEEEII8RuLNq9CCCGEEEIIIXKPNq9CCCGEEEIIIXKPNq9CCCGEEEIIIXKPnY6TUSuEEEIIIYQQQvx/RN+8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPdq8CiGEEEIIIYTIPf8PdlvlJmKXLx0AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1200x500 with 10 Axes>"
       ]
@@ -146,8 +147,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "def affichage(d_train, l_train, d_test, l_test):\n",
     "    long, large = 5,2\n",
     "\n",
@@ -245,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,15 +309,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Couldn't find program: 'false'\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -350,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -388,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -418,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -451,15 +453,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Couldn't find program: 'false'\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -607,16 +612,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
     "def learning_methode(k,dk,learning_rate):\n",
-    "    k=k-learning_rate*dk\n",
-    "    return(k)\n",
+    "    k2=k-learning_rate*dk\n",
+    "    return(k2)\n",
     "\n",
     "def learn_once_mse(w1,b1,w2,b2,data,targets,learning_rate):\n",
-    "    # Forward pass\n",
     "    a0 = data # the data are the input of the first layer\n",
     "    z1 = np.matmul(a0, w1) + b1  # input of the hidden layer\n",
     "    a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)\n",
@@ -624,14 +628,17 @@
     "    a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)\n",
     "    predictions = a2  # the predicted values are the outputs of the output layer\n",
     "\n",
-    "    dc_da2=(2/data.shape[0])*(a2-targets)\n",
+    "    # Compute loss (MSE)\n",
+    "    loss = np.mean(np.square(predictions - targets))\n",
+    "\n",
+    "    dc_da2=(2/data.shape[0])*(predictions-targets)\n",
     "    dc_dz2=dc_da2*(a2*(1-a2))\n",
-    "    dc_dw2=np.matmul(np.transpose(a1), dc_dz2)\n",
-    "    dc_db2=np.matmul(np.ones((1,dc_dz2.shape[0])),dc_dz2)\n",
-    "    dc_da1=np.matmul(dc_dz2,np.transpose(w2))\n",
+    "    dc_dw2=np.dot(np.transpose(a1), dc_dz2)\n",
+    "    dc_db2=np.dot(np.ones((1,dc_dz2.shape[0])),dc_dz2)\n",
+    "    dc_da1=np.dot(dc_dz2,np.transpose(w2))\n",
     "    dc_dz1=dc_da1*(a1*(1-a1))\n",
-    "    dc_dw1=np.matmul(np.transpose(a0), dc_dz1)\n",
-    "    dc_db1=np.matmul(np.ones((1,dc_dz1.shape[0])),dc_dz1)\n",
+    "    dc_dw1=np.dot(np.transpose(a0), dc_dz1)\n",
+    "    dc_db1=np.dot(np.ones((1,dc_dz1.shape[0])),dc_dz1)\n",
     "\n",
     "    w1=learning_methode(w1,dc_dw1,learning_rate)\n",
     "    b1=learning_methode(b1,dc_db1,learning_rate)\n",
@@ -639,9 +646,7 @@
     "    b2=learning_methode(b2,dc_db2,learning_rate)\n",
     "\n",
     "    # Compute loss (MSE)\n",
-    "    loss = np.mean(np.square(predictions - targets))\n",
-    "    # binary cross-entropy loss\n",
-    "    # loss = np.mean(targets*np.log(predictions)-(1-targets)*np.log(1-predictions))\n",
+    "    \n",
     "    return(w1,b1,w2,b2,loss)"
    ]
   },
@@ -654,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -675,13 +680,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
+    "def softmax(y):\n",
+    "    y=np.exp(y)\n",
+    "    v=np.sum(y, axis=1, keepdims=True)\n",
+    "    return(y / v)\n",
+    "\n",
     "def learn_once_cross_entropy(w1,b1,w2,b2,data,labels_train,learning_rate):\n",
-    "    Y=one_hot(labels_train)\n",
-    "    w1,b1,w2,b2,loss=learn_once_mse(w1,b1,w2,b2,data,Y,learning_rate)\n",
+    "    targets = one_hot(labels_train)\n",
+    "    a0 = data\n",
+    "    z1 = np.dot(a0, w1) + b1\n",
+    "    a1 = 1 / (1 + np.exp(-z1))\n",
+    "    z2 = np.dot(a1, w2) + b2\n",
+    "    softa2=softmax(z2)\n",
+    "    predictions=softa2\n",
+    "\n",
+    "    loss=np.mean(np.sum(-targets*np.log(predictions),axis=1))\n",
+    "    \n",
+    "    dc_dz2=(predictions-targets)/data.shape[0]\n",
+    "    dc_dw2=np.dot(np.transpose(a1), dc_dz2)\n",
+    "    dc_db2=np.sum(dc_dz2, axis=0, keepdims=True)\n",
+    "\n",
+    "    dc_da1=np.dot(dc_dz2,np.transpose(w2))\n",
+    "    dc_dz1=dc_da1*(1-a1)*a1\n",
+    "    dc_dw1=np.dot(np.transpose(a0), dc_dz1)\n",
+    "    dc_db1=np.sum(dc_dz1, axis=0, keepdims=True)\n",
+    "\n",
+    "    w1=learning_methode(w1,dc_dw1,learning_rate)\n",
+    "    b1=learning_methode(b1,dc_db1,learning_rate)\n",
+    "    w2=learning_methode(w2,dc_dw2,learning_rate)\n",
+    "    b2=learning_methode(b2,dc_db2,learning_rate)\n",
+    "    \n",
     "    return(w1,b1,w2,b2,loss)"
    ]
   },
@@ -694,19 +726,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def train_mlp(w1,b1,w2,b2,d_train,labels_train,learning_rate,num_epoch):\n",
-    "    train_accuracies=[]\n",
-    "    pas=len(labels_train)//num_epoch\n",
+    "def accuracy(w1,b1,w2,b2,data,labels):\n",
+    "    a0 = data\n",
+    "    z1 = np.dot(a0, w1) + b1\n",
+    "    a1 = 1 / (1 + np.exp(-z1))\n",
+    "    z2 = np.dot(a1, w2) + b2\n",
+    "    softa2=softmax(z2)\n",
+    "    predictions = softa2\n",
+    "    prediction_2 = np.empty(predictions.shape[0], dtype=int)\n",
+    "    for i, ligne in enumerate(predictions):\n",
+    "        prediction_2[i] = np.argmax(ligne)+1\n",
+    "    indices_egalite = np.where(prediction_2 == labels)[0]\n",
+    "    nombre_indices = len(indices_egalite)\n",
+    "    return(nombre_indices/len(labels))\n",
+    "\n",
+    "def train_mlp_cross_entropy(w1,b1,w2,b2,d_train,labels_train,learning_rate,num_epoch):\n",
+    "    train_accuracies,loss_evo=[],[]\n",
     "    for k in range(num_epoch):\n",
-    "        partial_data=d_train[k*pas:(k+1)*pas,:]\n",
-    "        patial_label=l_train[k*pas:(k+1)*pas]\n",
-    "        w1,b1,w2,b2,loss=learn_once_cross_entropy(w1,b1,w2,b2,partial_data,patial_label,learning_rate)\n",
-    "        train_accuracies.append(loss)\n",
-    "    return (w1,b1,w2,b2,train_accuracies)"
+    "        w1,b1,w2,b2,loss=learn_once_cross_entropy(w1,b1,w2,b2,d_train,labels_train,learning_rate)\n",
+    "        t=accuracy(w1,b1,w2,b2,d_train,labels_train)\n",
+    "        train_accuracies.append(t)\n",
+    "        loss_evo.append(loss)\n",
+    "    return (w1,b1,w2,b2,train_accuracies,loss_evo)"
    ]
   },
   {
@@ -718,12 +763,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
     "def test_mlp(w1,b1,w2,b2,d_test,labels_test):\n",
-    "    w1,b1,w2,b2,test_accuracy=learn_once_cross_entropy(w1,b1,w2,b2,d_test,labels_test,0)\n",
+    "    test_accuracy=accuracy(w1,b1,w2,b2,d_test,labels_test)\n",
     "    return(test_accuracy)"
    ]
   },
@@ -736,22 +781,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def run_mlp_training(data_train, labels_train, data_test, labels_test,d_h,learning_rate,num_epoch):\n",
+    "def run_mlp_cross_entropy_training(data_train, labels_train, data_test, labels_test,d_h,learning_rate,num_epoch):\n",
     "    d_in = data_train.shape[1]  # input dimension\n",
     "    d_out = max(labels_train)  # output dimension (number of neurons of the output layer)\n",
     "\n",
-    "    w1 = 2 * np.random.rand(d_in, d_h) - 1  # first layer weights\n",
+    "    w1 = np.random.randn(d_in, d_h)  # first layer weights\n",
     "    b1 = np.zeros((1, d_h))  # first layer biaises\n",
-    "    w2 = 2 * np.random.rand(d_h, d_out) - 1  # second layer weights\n",
+    "    w2 = np.random.randn(d_h, d_out)  # second layer weights\n",
     "    b2 = np.zeros((1, d_out))  # second layer biaises\n",
     "\n",
-    "    w1,b1,w2,b2,loss=train_mlp(w1,b1,w2,b2,data_train, labels_train,learning_rate,num_epoch)\n",
+    "    w1,b1,w2,b2,accuratie_train,loss=train_mlp_cross_entropy(w1,b1,w2,b2,data_train, labels_train,learning_rate,num_epoch)\n",
     "    test_accuracy=test_mlp(w1,b1,w2,b2,data_test, labels_test)\n",
-    "    return(loss,test_accuracy)"
+    "    return(accuratie_train,loss,test_accuracy)"
    ]
   },
   {
@@ -763,29 +808,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_10836\\543686923.py:9: RuntimeWarning: overflow encountered in exp\n",
-      "  a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.10551200057736793\n"
+      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_26032\\2793120310.py:10: RuntimeWarning: overflow encountered in exp\n",
+      "  a1 = 1 / (1 + np.exp(-z1))\n",
+      "C:\\Users\\Utilisateur\\AppData\\Local\\Temp\\ipykernel_26032\\1996586622.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  a1 = 1 / (1 + np.exp(-z1))\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -796,15 +836,27 @@
     "if __name__ == \"__main__\":\n",
     "    d, l = read_cifar_batch(\"data/cifar-10-batches-py/data_batch_1\")\n",
     "    num_epoch=100\n",
+    "    dh=64\n",
+    "    train_param=0.1\n",
     "    d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)\n",
     "\n",
-    "    loss,test_accuracy=run_mlp_training(d_train, l_train, d_test, l_test,64,0.1,num_epoch)\n",
-    "    print(test_accuracy)\n",
-    "    plt.plot(range(num_epoch), loss, label='evolution de la fonction loss par epoque')\n",
-    "    plt.xlabel('epoque')\n",
-    "    plt.ylabel('loss')\n",
-    "    plt.legend()\n",
-    "    plt.show()"
+    "    train_accuracy,loss,test_accuracy=run_mlp_cross_entropy_training(d_train, l_train, d_test, l_test,dh,train_param,num_epoch)\n",
+    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
+    "\n",
+    "    ax1.plot(range(num_epoch),loss,label=\"cross-entropy\")\n",
+    "    ax1.set_xlabel('epoque')\n",
+    "    ax1.set_ylabel('loss')\n",
+    "    ax1.set_title('evolution de la fonction loss par epoque')\n",
+    "    ax1.legend()\n",
+    "\n",
+    "    ax2.plot(range(num_epoch),train_accuracy,label=\"cross-entropy\")\n",
+    "    ax2.set_xlabel('epoque')\n",
+    "    ax2.set_ylabel('accuracy')\n",
+    "    ax2.set_title('evolution de la accuracy')\n",
+    "    ax2.legend()\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n"
    ]
   }
  ],
diff --git a/chatgpt.py b/chatgpt.py
new file mode 100644
index 0000000..ffcbb98
--- /dev/null
+++ b/chatgpt.py
@@ -0,0 +1,98 @@
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.metrics import accuracy_score
+from read_cifar import read_cifar_batch, split_dataset
+import matplotlib.pyplot as plt
+
+# Charger CIFAR-10 depuis votre source de données
+X,y = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")
+
+# Diviser les données en ensembles d'entraînement et de test
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)
+
+# Prétraitement des données
+# Vous devrez redimensionner les images, les normaliser, etc.
+
+# Définir l'architecture du réseau de neurones
+input_size = 32 * 32 * 3  # 32x32 pixels et 3 canaux (RGB)
+hidden_size = 64  # Nombre d'unités dans la couche cachée
+output_size = 10  # 10 classes dans CIFAR-10
+
+# Initialiser les poids et les biais
+np.random.seed(0)
+weights_input_hidden = np.random.randn(input_size, hidden_size)
+bias_input_hidden = np.zeros((1, hidden_size))
+weights_hidden_output = np.random.randn(hidden_size, output_size)
+bias_hidden_output = np.zeros((1, output_size))
+
+# Hyperparamètres
+learning_rate = 0.1
+num_epochs = 100
+y_print,x_print,y2_print=[],[],[]
+# Entraînement du modèle
+for epoch in range(num_epochs):
+    # Forward pass
+    hidden_input = np.dot(X_train, weights_input_hidden) + bias_input_hidden
+    hidden_output = 1 / (1 + np.exp(-hidden_input))  # Fonction d'activation (sigmoid)
+    output_layer = np.dot(hidden_output, weights_hidden_output) + bias_hidden_output
+
+    # Calcul softmax
+    exp_scores = np.exp(output_layer)
+    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
+
+    # Calcul de la perte (cross-entropy)
+    num_examples = len(X_train)
+    corect_logprobs = -np.log(probs[range(num_examples), y_train])
+    data_loss = np.sum(corect_logprobs) / num_examples
+
+    # Calcul du gradient
+    dprobs = probs
+    dprobs[range(num_examples), y_train] -= 1
+    dprobs /= num_examples
+
+    dweights_hidden_output = np.dot(hidden_output.T, dprobs)
+    dbias_hidden_output = np.sum(dprobs, axis=0, keepdims=True)
+
+    dhidden = np.dot(dprobs, weights_hidden_output.T)
+    dhidden_hidden = dhidden * (1 - hidden_output) * hidden_output
+    dweights_input_hidden = np.dot(X_train.T, dhidden_hidden)
+    dbias_input_hidden = np.sum(dhidden_hidden, axis=0)
+
+    # Mise à jour des poids et des biais
+    weights_input_hidden -= learning_rate * dweights_input_hidden
+    bias_input_hidden -= learning_rate * dbias_input_hidden
+    weights_hidden_output -= learning_rate * dweights_hidden_output
+    bias_hidden_output -= learning_rate * dbias_hidden_output
+
+    x_print.append(epoch)
+    y_print.append(data_loss)
+    predicted_class = np.argmax(output_layer, axis=1)
+    y2_print.append(accuracy_score(y_train, predicted_class))
+    # Affichage du loss à chaque époque (pour le suivi)
+    if (epoch + 1) % 100 == 0:
+        print(f'Époque {epoch + 1}: Loss = {data_loss:.4f}')
+
+# Évaluation du modèle
+hidden_input = np.dot(X_test, weights_input_hidden) + bias_input_hidden
+hidden_output = 1 / (1 + np.exp(-hidden_input))
+output_layer = np.dot(hidden_output, weights_hidden_output) + bias_hidden_output
+predicted_class = np.argmax(output_layer, axis=1)
+accuracy = accuracy_score(y_test, predicted_class)
+print(f'Précision sur l\'ensemble de test: {accuracy:.4f}')
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+
+ax1.plot(x_print,y_print)
+ax1.set_xlabel('epoque')
+ax1.set_ylabel('loss')
+ax1.set_title('evolution de la fonction loss par epoque')
+ax1.legend()
+
+ax2.plot(x_print,y2_print)
+ax2.set_xlabel('epoque')
+ax2.set_ylabel('accuracy')
+ax2.set_title('evolution de la accuracy')
+ax2.legend()
+plt.tight_layout()
+plt.show()
\ No newline at end of file
diff --git a/mlp.py b/mlp.py
index 16c2d71..6ba4db0 100644
--- a/mlp.py
+++ b/mlp.py
@@ -5,38 +5,18 @@ import matplotlib.pyplot as plt
 
 def learning_methode(k,dk,learning_rate):
     k=k-learning_rate*dk
-    #normalisation de k entre [-1,1]
-    # max_k=np.max(k)
-    # min_k=np.min(k)
-    # k=(k*2)/(max_k-min_k)-min_k-1
-    print(np.max(dk))
     return(k)
 
-def softmax(y):
-    y=np.exp(y)
-    v=np.sum(y,axis=1)
-    return(y / v[:, np.newaxis])
-
-# def reugalisation(W)
-
 def learn_once_mse(w1,b1,w2,b2,data,targets,learning_rate):
-    # Forward pass
-    a0 = data # the data are the input of the first layer
-    z1 = np.matmul(a0, w1) + b1  # input of the hidden layer
-    a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)
-    z2 = np.matmul(a1, w2) + b2  # input of the output layer
-    a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)
-    # s=np.sum(a2,axis=1)
-    # a2=a2/s[:, np.newaxis]
-    # print(np.max(a2,axis=1))
-    #a2=softmax(a2)
-
-    predictions = a2  # the predicted values are the outputs of the output layer
-    dc_da2=(2/data.shape[0])*(a2-targets)
-    # dc_da2=(1/data.shape[0])*((-targets/a2)-(1-targets)/(1-a2))
-    # dc_da2=((np.ones(targets.shape)-2*targets)/(data.shape[0]*a2))
-    # dc_da2=(-targets)/(data.shape[0]*a2)
+    a0 = data
+    z1 = np.matmul(a0, w1) + b1
+    a1 = 1 / (1 + np.exp(-z1))
+    z2 = np.matmul(a1, w2) + b2
+    a2 = 1 / (1 + np.exp(-z2))
+    predictions = a2
 
+    dc_da2=(2/data.shape[0])*(a2-targets)
+    
     dc_dz2=dc_da2*(a2*(1-a2))
     dc_dw2=np.matmul(np.transpose(a1), dc_dz2)
     dc_db2=np.matmul(np.ones((1,dc_dz2.shape[0])),dc_dz2)
@@ -50,18 +30,8 @@ def learn_once_mse(w1,b1,w2,b2,data,targets,learning_rate):
     w2=learning_methode(w2,dc_dw2,learning_rate)
     b2=learning_methode(b2,dc_db2,learning_rate)
 
-    # prediction_2 = np.zeros(predictions.shape, dtype=int)
-    # for i, ligne in enumerate(predictions):
-    #     prediction_2[i][np.argmin(ligne)] = 1
-    # indices_egalite = np.where(prediction_2 == targets)[0]
-    # nombre_indices = len(indices_egalite)
-
     # Compute loss (MSE)
-    # loss = np.mean(np.square(predictions - targets))
-    # binary cross-entropy loss
-    # loss = np.mean(targets*np.log(predictions)-(1-targets)*np.log(1-predictions))
-    # loss=np.mean(-np.log(np.max(targets*predictions,axis=1)))
-    # loss=np.mean((np.ones(targets.shape)-2*targets)*np.log(predictions))
+    loss = np.mean(np.square(predictions - targets))
     return(w1,b1,w2,b2,loss)
 
 def one_hot(label):
@@ -71,28 +41,74 @@ def one_hot(label):
         mat[label_indexe,label_im-1]=1
     return(mat)
 
+def softmax(y):
+    y=np.exp(y)
+    v=np.sum(y,axis=1)
+    return(y / v[:, np.newaxis])
+
 def learn_once_cross_entropy(w1,b1,w2,b2,data,labels_train,learning_rate):
-    Y=one_hot(labels_train)
-    w1,b1,w2,b2,loss=learn_once_mse(w1,b1,w2,b2,data,Y,learning_rate)
+    targets = one_hot(labels_train)
+    targets=targets+1e-15
+    a0 = data
+    z1 = np.matmul(a0, w1) + b1
+    a1 = 1 / (1 + np.exp(-z1))
+    z2 = np.matmul(a1, w2) + b2
+    a2 = 1 / (1 + np.exp(-z2))
+    softa2=softmax(a2)
+    # predictions = softa2
+    predictions=softa2
+
+    # dc_softmax=-(targets/softa2)+((1-targets)/(1-softa2))
+    # dc_a2=dc_softmax*(softa2*(1-softa2))
+    
+    # dc_dz2=dc_a2*(a2*(1-a2))
+    dc_dz2=predictions-targets
+    dc_dw2=np.matmul(np.transpose(a1), dc_dz2)
+    dc_db2=np.matmul(np.ones((1,dc_dz2.shape[0])),dc_dz2)
+    dc_da1=np.matmul(dc_dz2,np.transpose(w2))
+    dc_dz1=dc_da1*(a1*(1-a1))
+    dc_dw1=np.matmul(np.transpose(a0), dc_dz1)
+    dc_db1=np.matmul(np.ones((1,dc_dz1.shape[0])),dc_dz1)
+
+    w1=learning_methode(w1,dc_dw1,learning_rate)
+    b1=learning_methode(b1,dc_db1,learning_rate)
+    w2=learning_methode(w2,dc_dw2,learning_rate)
+    b2=learning_methode(b2,dc_db2,learning_rate)
+
+    # binary cross-entropy loss
+    loss = np.mean(targets*np.log(predictions)-(1-targets)*np.log(1-predictions))
+    
     return(w1,b1,w2,b2,loss)
 
+def accuracy(w1,b1,w2,b2,data,labels):
+    a0 = data
+    z1 = np.matmul(a0, w1) + b1
+    a1 = 1 / (1 + np.exp(-z1))
+    z2 = np.matmul(a1, w2) + b2
+    a2 = 1 / (1 + np.exp(-z2))
+    softa2=softmax(a2)
+    predictions = softa2
+    prediction_2 = np.empty(predictions.shape[0], dtype=int)
+    for i, ligne in enumerate(predictions):
+        prediction_2[i] = np.argmax(ligne)+1
+    indices_egalite = np.where(prediction_2 == labels)[0]
+    nombre_indices = len(indices_egalite)
+    return(nombre_indices/len(labels))
+
 def train_mlp(w1,b1,w2,b2,d_train,labels_train,learning_rate,num_epoch):
     train_accuracies=[]
-    pas=len(labels_train)//num_epoch
     for k in range(num_epoch):
-        partial_data=d_train[k*pas:(k+1)*pas,:]
-        patial_label=l_train[k*pas:(k+1)*pas]
-        w1,b1,w2,b2,loss=learn_once_cross_entropy(w1,b1,w2,b2,partial_data,patial_label,learning_rate)
-        train_accuracies.append(loss)
+        w1,b1,w2,b2,loss=learn_once_mse(w1,b1,w2,b2,d_train,labels_train,learning_rate)
+        train_accuracies.append(accuracy(w1,b1,w2,b2,d_train,labels_train))
     return (w1,b1,w2,b2,train_accuracies)
 
 def test_mlp(w1,b1,w2,b2,d_test,labels_test):
-    a0 = d_test # the data are the input of the first layer
-    z1 = np.matmul(a0, w1) + b1  # input of the hidden layer
-    a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)
-    z2 = np.matmul(a1, w2) + b2  # input of the output layer
-    a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)
-    predictions = a2  # the predicted values are the outputs of the output layer
+    a0 = d_test
+    z1 = np.matmul(a0, w1) + b1
+    a1 = 1 / (1 + np.exp(-z1))
+    z2 = np.matmul(a1, w2) + b2
+    a2 = 1 / (1 + np.exp(-z2))
+    predictions = a2
     prediction_2 = np.empty(predictions.shape[0], dtype=int)
     for i, ligne in enumerate(predictions):
         prediction_2[i] = np.argmax(ligne)+1
@@ -101,13 +117,9 @@ def test_mlp(w1,b1,w2,b2,d_test,labels_test):
     return(nombre_indices/len(labels_test))
 
 def run_mlp_training(data_train, labels_train, data_test, labels_test,d_h,learning_rate,num_epoch):
-    d_in = data_train.shape[1]  # input dimension
-    d_out = max(labels_train)  # output dimension (number of neurons of the output layer)
+    d_in = data_train.shape[1] 
+    d_out = max(labels_train)
 
-    # w1 = 2 * np.random.rand(d_in, d_h) - 1  # first layer weights
-    # b1 = np.zeros((1, d_h))  # first layer biaises
-    # w2 = 2 * np.random.rand(d_h, d_out) - 1  # second layer weights
-    # b2 = np.zeros((1, d_out))  # second layer biaises
     w1 = (2*np.random.rand(d_in, d_h)-1)  # first layer weights
     b1 = 2*np.random.rand(1, d_h)-1  # first layer biaises
     w2 = 2*np.random.rand(d_h, d_out)-1  # second layer weights
diff --git a/test.py b/test.py
index acf2c61..35b173d 100644
--- a/test.py
+++ b/test.py
@@ -19,14 +19,21 @@ import numpy as np
 #     if len(dico) > 1:
 #         filtered_dict = sorted(dico, key=lambda item: item[1][1])
 #     print(dico[0][0])
+def one_hot(label):
+    nbr_classe=9
+    mat=np.zeros((len(label),nbr_classe))
+    for label_indexe,label_im, in enumerate(label):
+        mat[label_indexe,label_im-1]=1
+    return(mat)
 
-mat=np.array([[1,2,3,4],[6,6,4,4],[3,2,4,85]])
-mat_exp=np.exp(mat)
-v=np.sum(mat_exp,axis=1)
-print(v)
-mat_exp_norm=mat_exp/v[:, np.newaxis]
+mat=np.array([1,8,6,4,7,8,5,2,4,6,4])
+print(one_hot(mat))
+# mat_exp=np.exp(mat)
+# v=np.sum(mat_exp,axis=1)
+# print(v)
+# mat_exp_norm=mat_exp/v[:, np.newaxis]
 
-vrai=np.array([[0,0,0,1],[1,0,0,0],[0,0,1,0]])
-print(-np.log(np.max(mat_exp_norm*vrai,axis=1)))
-L=np.mean(-np.log(np.max(vrai*mat_exp_norm,axis=1)))
-print(L)
\ No newline at end of file
+# vrai=np.array([[0,0,0,1],[1,0,0,0],[0,0,1,0]])
+# print(-np.log(np.max(mat_exp_norm*vrai,axis=1)))
+# L=np.mean(-np.log(np.max(vrai*mat_exp_norm,axis=1)))
+# print(L)
\ No newline at end of file
-- 
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