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/xn0JbG+to55sHTWZflhUZ3CX5Dkltx/i4x0rsf2kv+QJ4f/wCwiv8A6AaofGHUIPDvxS8F+JdTDR6fAGV5QpYLtf5uBycBgcDn0FXxFQlTWKwtWvN06VShJOUndKT968t7LddnqgrK3NFt2TRveP8Axp48+HHw+tLnVJra9127uTAbiNP3KIQzghSFBbAwMjHU84rY+H//AAtxtTtrnxTqdhq+iXdu0omtgmVY42KpRY85znOGGAec4qXxV8QPhRrnhVpdcuF1LR7i6W0cpG52TbPM3DhXG1edy59PUV4r8PL6y8PfF218O/D3VptV8O38btNG27ZGdjN3AGUIU7sDOduSa9fHZnGhmuHccU6lKXJFRjV1Tel5R19pGXV7o0lO1Re9dadf6ufZkokMbiIgOQdpPQHtmvibwVp3xSm+IviqDRNVsoNXib/TZZUJikO/+AeWcc+wr7cr5M8P+KtF8C/Gjxj/AMJVP9gjvjuikdWKnJDr90HqpyD04x1xXtcf0KTxGAqV6rhBVGm1LlteEra9HdWT7NrqaYtK8G3bU7fx54/8XReKNO+HHgdIW1u4iWS4uZVzHFlSTgEYAAG4kg8EAAk1lWHjj4i+B/Gml+FviRLb6lZ62wjgu4ECbZCQo6KgOGKhgV4BBB7Hm9X8Qab4X+NVh4/vXLaB4hsk8m6CsVAaIJnGM8FVJGMgNn2pvjvxFpvxQ+I/hDQfCEn26PTJzPPOgIRVLxs+CQPurH16EkAc18pjc8q89fERxUvbxrRjCmpaSg3FL3PtKUW5c33MwlVd2+bW+i8j63ooor91PUCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA57Sf3Oq6xadvOjnUf7Msag/+PIxr80/2kfCH/CK/E+/mgj2WmtKL6LA43SEiUfXzAx+hFfpY37jxUh6C9syPxt5Bj9JjXzv+1l4Q/tnwLbeJ7dN1xoM2XIHP2e4IR/ycIfYZrwuIsJ7XDNreOv+f4Hn5nR56TfbU/MlhhiPSkqWcYlaoq/IZKzsfJhRRRSAKKKKACiiigBkjbEZvQVV0uTytRgf/bA/764/rTrtsIF9T/KqCMUdXHVSD+Vefjo88XDyJb1PVqKRWDKGHQjNLX5G0dQUUUUAAGTgV6TGnlxqg/hAH5V5/Zp5l3Cnq6/lmvQ6xqlRCiiisSj/0/38ooooAKKKKACiiq17eW2nWc+oXj+Xb20bSyNgnaiDcxwMngDtUykopyk9EBWn0XR7nUYdXubGCW/txtiuHiRpo154VyNwHJ6Hua8e+LEfxG8Qyt4I8LaVE+l6jCn2i+lYAJlzuUZIxgKCcBjzwM16z4f8RaN4p01NY0G5F1ZyMyrIFZclTgjDhTwfatqvEzDLKOPwrp0qjjCpq3C3vJrvZ6NdVrbqZTgpxsnv2Ob8IeHLfwj4a0/w7bNvWyiCF8Y3ueXbHbcxJ/GvD/hV8NIPN8Rv438PxSNNfF7drqFHJTLElScnByPrX0pRWWJ4YwtWph5Sj7tFNRjo1ZpR1v2S0E6EW15FSxsLHTLZLLTbeO0t4/uxxII0XPoqgAVBYaNpGlSXEumWMFm922+ZoYljMj8nc5UDceTyfWtKivdVCC5bRWm2m3p2NbI5e/8ABHg3Vbw6hqWh2V1csctJJbxszH/aJHzfjWndaDol9po0e80+3msBgCB4lMQx0whGBjtgcVq0VjHL6Ccmqa97fRa+vf5i5F2OYTwV4Pj099KTRLJbORgzRC3j2My8BiMckZ4PWtHUtA0PWbNNO1awgvLaPG2OWNXRcDA2gjjA9K1qKI5fQUXBU1Zq1rLbt6ByLsRwwxW8KW8CCOKJQqqowFVRgADsAKx9U8MeGtcmS41rSbTUJY12K9xBHKyrnOAXUkDJ6VuUVrVw9OpHknFNdmrobSejOVg8C+CLWaO5tvD2nRTRMHR0tIVZWU5BBC5BB5BFaml6DoehrImiadbaesxBcW8KRByOhbYBnGe9a1VL6/stMs5dQ1GdLa2gUtJJIwVVUdyTXPDA4aj78YRjbrZK3cSjFa2ON8ZeFNLutA8Q3emaTbtrN7YXUSzJCguJHkhZAvmY3HPA5NcP8K/hrosHgjS18WeHbY6tEZvMNzbxtLzM5XcSDn5cYz0FbI+OvwtN19l/toZzjf5E2zP+9sx+PSuwg8c+FLnxEPCcOoK2qsgkEOxxuQoJAQxXYfkOeG/rXycYZJicbHFwrU5NJwsnBpyk09v5ny6dXqc/7qUua67HUxxxwxrFEoREAVVUYAA6AAdBXLv4D8EyXjahJoFg1wxyXNtGSSe5+Xr79a19Z1nTPD2mT6zrM4trO2AMkhBIUEhRwoJOSQOBRous6b4h0yDWdHm+0WdyCY5NrLuAJU8MAeoPUV9VXjhatRUKvK5JXUXZtLa9t7X0ubvlbsxdQ0TRtWhit9VsLe8igYNGk0SSKjAYBUMCAQO4rToorsjSim5JasqxiweGvDttZXGm22lWkVpdktNCkEaxyk9S6AYY8dxVldI0ldN/sZbKAafsMf2cRr5Ow9V8vG3HtjFaNFZxwlKOkYJaW2W3b08g5UZ0ekaTDpp0aKygTTyjR/Z1jUQ7GzuXywNuDk5GMGpLTTdO0+yXTbC1itrNAVWGJFSMBiSQEUAYJJzx3q7XJaR410LW/EOp+GLB3a+0n/XhkIUc44bvzWNSph6M4RlZOXux210vZfJXt5CbSsOtvAngqzuvttroNjFPnIdbaMEEdx8vB+lbN7o2kanPbXWo2MF1NZNvgeWJZGibIO5CwJU5AORjoPSr8siQxvNIdqICzH0A5NYXhvxToPi+wfVPDt2Ly1SQxFwrph1AJGHCnow7VmqODptYdRinLXl0V7dbdbaa9BWitC9qWjaRrKRxavYwXyQtvRZ4llCsP4gGBwfcUmq6NpGu2v2HWrKG+t87tkyLIoI6EBs4PvVfxB4i0bwtpkms69cfZbOIqrSbWfBY4HCBjyfauGsfjb8L9QuUtYNdRXc4BliliX8XkRVH4kVzY/M8to1HRxNWEZSS0k4ptbLR6tdiZTgnaTR18fgzwjDp76TFolktlI29oRbx+WzAYDFcYJx3PNWNH8L+G/DxdtC0u2sGkGGaGJUZh6EgZIrcBBAIOQaWu+GXYeMozjTimtnZaehaguwVz+r+E/DGvzR3GuaVa38sQwrzQpIwHpkjOPbpXQUVtXw9OrHkqxUl2auhtJ7mRceH9CutNTRrnTraWwjACW7QoYlA6bUI2jHbApmj+HPD/h9XTQtNt7ASY3eREsZbHTcVAJ/Gq6+LPDzeI28IreqdXSPzTBtbITAOd2NvQ5xnOK3RNCZjbiRTKoDFMjcAehx1xXLRjhZz9pTUXKPu3VrpreN+luxK5XqiWiuSvfGuhWHi2y8E3DuNT1CIzRKEJQoA55boD+7autrooYulVclTknyuzt0ejs/OzRSknsFFFFdAwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA57Wf3N/pF52W5MTf7s0bAf+Phata/o1p4j0O/0C/GbfUIJIJPULIpUke4zke9VvFPyaHPdDrZmO5/8B3WU/otdBSlFNWYmr6M/DvxHpN3oOtXmjX67LmxlkglH+3ExU49sisOvq39rfwf/AGF4/h8R26bbbX4RISOnnwgJIP8AvnY31Jr5Sr8TzTCujiJ030Z8ViKXJNw7BRRRXAYhRRRQAUUUhIAJPagDNum3S47LxValYlmLHvSV5s5XdzM9L02TzbCB/wDYA/LirtYnh+TfpwX+4zL/AF/rW3X5fjqfJWnHzZ1x2CiiiuUZqaMm/UIz2XJ/Su4rkvD6ZuJJP7q4/M//AFq62ueruWgooorMZ//U/fyiiigAooooAK5fxx/yJXiD/sH3f/opq6iuX8cf8iV4g/7B93/6KauHNP8Adqv+F/kyZ/CzwDwB4pu/Bf7P8niWxhSeeznbakmdh8y4WM5xg8Bs1LefGD4pJoaeM7bwnBF4fCozPK5MrK2BvADKQpPQ7CMEHkc1ydl/ya3f/wDXdf8A0sjr1y+AP7PSg8/8SGE/lCtfi2XV8ZPDUqNDESpxhhYVEo8usve3unporpWuebBycUk7Wimbut/FnQtF8BWXjmSJpF1FE+z2wIDtKw5QnsFwdzY7cA5ArirPx78cJkttUk8FwPp1yU2xrJtnCueC2XJUe5j46nivHvEkM0Xwl+HetyRNNYafcTfaFAyDulyufqEYfjX1x/wsTwMNNh1ZtctBbT7dhMq7iW6DbncD6jHHevZy7OMRmNdrEYt0FCnSkkuVcznHmlJ8yd0npZaGkKjm9ZW0R5p47+LniDwv45g8H6Ro6am91bo0ShmEjTSbgBnptBGTx0zyOtWpviX4q8IeFb7XviVpUFnd+eILK0tn+aYlc5Lb5Bt9x0weMkCsDUlVv2l9KJGdunsR7HypRVP9pqxuJNL0DUyJPsVpcyLOY+q+YF2nPY/KwBPGTWGOzXMKWHzHMYV5N0pyjGNlype6rvS75ea61sra3FKpNKc09mS3fxV+Lfh6yj8SeKPCMMWiOU3GNyJkV+BuG9ip5/iQc8HBNd54o8d+LDp+i3fw50L+2k1qIyrNISEiAAIEgyoUnPVnAyCOTXmV14K+Hd7oq3l/8R9Qm0+7CjbLfIwbcRhWjKk5B6gjjvjFVPGpvtF1/wAL/BvStck0XRfsweW8ZwkkgZpDguNox8u1QMDLc5AFcrzbMcNRqe2rycJKCi1OnKfPKVtGkoxjJX1kna2hPtJpO7007Xud94O+KXiefxmngP4gaNHpWpXEZkgaFiUbapbH3nBBCthg55GMZrCm+LfxC1/xJq+lfD7w/b31rosjRzNO+JG2sy5H7yMfMVOAAx/lXnlvpHhnw18cPDNhoGsS6qi8TSTzLMUlcSAIHVVHOQcc8muh8f6D4Wsr7WvHXw/8Ux6RrmnvKbu2WYL5kqn51CnDbmYdMMrNxxzXHHPM0lg5qVd/uqslK0oe0cFFPSTjyycW3fRXWhPtZ8r12flc9Y8SfFGXwl4L03XNc0xo9a1VQsWnBju8w8kEkZAUEZGMgkL15rh7z4r/ABS8Jpb6x458KRQaNO6qXgf97GG6bhvfB9mC5PHBryjx5rupeLvDvgLxrr7SwW4lmgup7YbWR0mUGSPsHZEJHbcpxwK9Hv8AwB8Pb6wgTUfiJf3VrftGkaSX8cqyMxG0bCD3x1HHfFXiOI8yxlap9SrNKEabheVON+aKlzTUk73btaLSXqN1pyb5Xtbt+J9PWV5bajZwahZv5kFzGssbD+JHG5T+INfMvxrkvPFnj3wz8MopjDaXW24n29TuZhk+6ojEe55r6P0PSYNB0ax0S1dnhsII4EZyCxWNQoJwAM8dhXzZ8aGufCHxH8L/ABI8kyWUIW3m29QVZyw+rRudv+7X2viDKf8AZMXX0i5U/aW/l5lzfL9Dpxd/Z6+Vz2uP4YfD6PTk0oeH7NoEAGWhUyHHcyY3k+pzmvFvjnaS+E/Ffhn4maemBayrb3AXjIQllH/AkLqfYCvY7r4rfD210b+3G1y2kgKhlRJA0zZ/hEQO/d6ggY74qLx1pFl8RPh1ewaYwuVvbcXFo6/xOo8yPHpuI2n6mjiDLsDi8BVoZa4e1ilOPJy3vF3ht0vdL1YVYRlBqG55d8f9bfWNM8PeDNDcSzeIriOQbejR8CPPszMD/wABr6F0XSrbQ9IstGsxiCyhSFPcIoGT7nqa+NPgTa6l4z8bWusawfNg8K2KQQ5HCn5liU/QFz+Ar7erLgHEPMZ186nG3tLRiu0YLX75OX3Cwsuduq+p5n8QfE3jnQ5bCy8FaANXmvd+6V2IjiKY4cAqBnPBLgcY5Ncn4O+KXiefxmngP4gaNHpWpXEZkgaFiUbapbH3nBBCthg55GMZrn/i54g1m+8faL8OoNXPh/TL2ETXF0reWzbi/wAu/I4+TaBkAs3ORivP7fSPDPhr44eGbDQNYl1VF4mknmWYpK4kAQOqqOcg455NfP51xLiqeZ8+Gqy5I1YU5JuCjrbmSjbme9+a6t0TSMalaSndPS6XQ9V8Q/FjxfaeP9U8BeGdBj1W6t1iMDbyuN0aSO0pJC7RuwOV7c81oeAvid4k1TxfdeBPHOlRaZqsUZlQwklGAAOMFnzlTkENjgjrWB4c/wCTlvFH/YOT/wBF2tR6sjt+0jYLC2x30x8N6HypQD+FaU8zx8aqxbxEmvrMqXJaPLyczXa910dxqc781/tWLuofFzxfr/iS90D4W6HFqyaads9zO2IywJB2neigEg7SWy2CQMCuc+CV5qmofE3xhea3aiyv5UBnhHRHEmCBnPHpzTP2dfEGiaFp2s+GdbuI9P1WK8aRlnYRllCqhUFiMlGVsjtmtT4U3lpqHxg8cXljMlxBLykiEMrDzMZBHBHvXm5ZjauMr5bj6+IcpTqSvDTlhaM9Erc11s7vW5EJOThJvd7H0Zqv/ILvP+uMn/oJrwX9mX/kn95/2EZf/RUVe9ar/wAgu8/64yf+gmvmj9nfxR4a0XwPdWmsataWM7X8riOe4jicqY4gCFYg4yDz7V9zndeFPPcFKpJJclXd2/kOqq0qsb9n+h3P7Qn/ACTC+/67W/8A6MFfPnizxX8MdV+G2l+HdGsYn8RCG0jM6wLB5UqBBKXlYLuDYIPJHOT0zXt/xw1zRdc+FupSaLqFvfpFPbBzbypKFJkGAShOKwtf1P4aH4MW9vqU1hNqH9kwrCiGNrlbryQFIC5cEP8AePpnPevh+NKf1jH4r2VWmougruSTuuafwu+j87P0OXErmnKzW3+Z7x4LsLvS/COjabfSrNcW1pBG7qdykqgHDdx6Hv1rh/iZ8RNU+H+paFJ9lil0jUZvKuZX3b4sFclcED7pJGQehqP4DDUR8MtL/tHd96Xyd+c+TvO3r2649sY4rQ+Mvhn/AISj4fanbRpuuLNftcPc74ckge5Tco+tfeYjE4irw/HEYG8J+zjKK3eiT5X3ulb5nU23SvHexX8f/FOy8D+INA0WVUkXU5M3Lk/6mAkIH/76JP0U98UePfiFqPhzxN4d8J6DbRXd9rMuJBLuxHEWChvlI/2j9FNfNvhLwbq/xg0jXvE2sMZLmysobLTzkgNPbop/ULhu2ZCewrqvgYuq+N/G8/jLX/3jaFZQ2cRI/wCWhTywee+0OW92r4jDcZZljK8KVNOMMTJOk+sYQf7z5tRuv8RzRxM5Oy+1t6dTsPCXiXVtQ+Ld/pniHw3ZWGtw2bt58bbpWUBfLBYOy4ZSM98ce1ea+C9X+J8PxF8U3Wj6JbXWrzH/AEyCSYKkOG4CsZBnnjqa9L03/k5fVv8AsHL/AOi4azPAusaXonxq8bw6xdxWRuSTGZnEYbDBsAsQM4OcenNcUqU6lWhGriJR5cTVjzLlT+F2bfLbm0te2t35WizbV39pmV8TtW1rSPjb4a1PS9O/tHU00xQlqpwGkkNwh5GeF3ZPsOo612/h74q+MLXxtaeCviLosOmT6ku63kt2LDJztDfPIDkgrkEYPUYrJ8Q3EF3+0X4QurWRZoZtMLo6EMrKy3RBUjggjoRUvxZUD4r/AA8YDk3GCe+BKldiqYnDTxePw9dpRxEVy6cslL2abel7tPSz0tpuXeS5pp9f8j6Tooor9xPTCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCveWyXlpPaSfdnRkP0YYNZ/h65e70KwuJf9Y0Ee/wBnC4YfmDWxXPeHf3cF5Zf8+t3Ov0Ejeao/BXFAHiv7UPhD/hJ/hZd38CbrrQnW9THXy1+WUfTYSx/3RX5VV+6mo2cGo6fc6fcoJIbmJ4nU9GV1IIP1Br8TvF/h6fwp4n1Pw7cZLWE7xqT1ZAco3/AlIP41+ccaYXlqwrLqrfcfO5zRtJTXU5yiiivijxQooooAKr3TbYiP73FWKz7xsuF9BWdWVosTKdFFFeeQdd4Ykyk8XoVb8+P6V1VcP4ck23zR/wB9D+Y5ruK+Az6ny4mT72Z003oFFFFeMWdX4eTEM0n95gPyH/166GsnRE2aeh/vkn9cf0rWrlm9S0FFFFSM/9X9/KKKKACiiigAqrfWVtqVlcadep5lvdRvFIuSNyOCrDIwRkHsc1aoqZwUk4yV0wOMT4feEI/C0ngtNPxo0rbmg82Xk7xJnfv3/eAP3v0rXfw3osnh7/hFXts6WLcWvk73/wBSq7Qu7O7oOuc+9blFcdPK8NFWjSivd5dl8P8ALt8Ou2xKhHsc9a+FPDtn4eXwpDYo2kohQW8mZV2sSxBLlieTnJPFcLb/AAL+F1tdi7TRQxByEeaZ4wf91nII9jkV63RXNieH8BW5PbUIS5FaN4p2XZaaLyJlSi90cy3g/wAON4jh8WmzH9q28XkRzB3AWPBG3YG2dGP8Oa2tQ06x1ayl07U7dLq1nG145FDKw9wauUV3U8FRipRjBJSbb0Wre7fdvrcpRXY8ltvgb8LrW6+1poiuwOQsk0roP+As5BHsc11Hir4f+EvGkMEPiGwWf7LxEys0boD2DIQce3T2rsqK86lw3l8KUqMMPBQlulFWfqra26EqjBKySPMYvg58NrdLRbfRY4mspRNHIryCTeCCNz7tzDI6MSB2FQal8E/hrq2oyapeaRmeZzJJsmlRXZjkkqrgDJ9MV6rRWc+FsslHkeFhbtyR6bdBewh/KjBm8L+HrjQx4am0+F9LVAgtyo2BR0wOxB5yOc85zXD6f8Evhjpt2t7BoqvIjBlEsssqAj/Ydip/EGvVqK6cTkOBrSjOtQhJx2binb0utBypRe6CqGp6Xp2tWM2marbpdWs42vHIMqR/iOx6jtV+ivSqU4zi4yV090aNHkdv8CvhdbXn2xdGDkEMEeaV4wR/sl8EexyPavWIYYreJIIEWOOMBVVQAqgdAAOABUlFcGX5NhMJf6rRjC+/LFK/3IiFOMfhVjmvDvhHQPCpvjodt9n/ALRna4m+Ytl29Mk4A7AcCuloorrw+Gp0YKnSioxXRKyKjFJWRx/izwF4U8bxwp4ksRctb58twzRuueoDIQcH0PFYMXwc+G1ulotvoscTWUomjkV5BJvBBG5925hkdGJA7CvTqK8/EcP4CrUdarQhKT3bim3bztch0oN3aObtvCPh608S3XjC3tNmr3sYhln3udyAKANhbYOEXkKDx9aJPCPh6XxLF4wktM6vDH5KT734QgjGzds6Medua6Siur+zcNa3s4/FzbL4r35v8V9b73K5F2POPEXwl+H/AIp1BtV1jSle7fG+SOSSIvj+8I2UE++M+9anhn4feD/B11PeeG9PFlNcoEkIkkcFVOQMOzAfhXZ0VzU8gwMK/wBZjQgql783Kua/e9r3JVKN+a2oyWJJonhlG5JAVYeoPBryT/hQ/wAKP+gH/wCTNz/8dr16itMwybB4uzxVGM7bc0VK1+10xzpxl8SuedW/wm+H9pot34et9K26ffvHJNF5853tFyh3GTcMexHvVC0+CXwusZluIdBjZkOQJJZpV49VkdlP4ivVKK5Xwxlj5b4Wn7ui9yOivey001bfqyfYQ/lQyOOOGNYYVCIgCqqjAAHAAA6AU4gMCrDIPBBpaK9tLoamJ4e8O6P4V0uPRtDgFvaRM7Bcljl2LEknk8n8uKTQ/DejeHBeLo9uLcX9w91Ngk7pZMbiM9BxwBwO1blFc1PA0YKChBLkVo6LRbWXbTsSopdDm4vCPh6HxLL4wjtMavPGIXn3vygAGNm7Z0Uchc1g+J/hZ4F8YagNV17TRNdgBWkSR4i4XgBtjDOOmTzjjOK9CormxGS4OrTdKrRi4t8zTimnLu1bfz3E6UWrNHGWvw+8IWWp6ZrNtYbLzRrcWtpJ5sp8uEBgFwXIbh25YE89elX9W8I+Htc1XTtb1S08+90lt9tJvdfLbIOdqsFbkD7wNdJRVxynCqDpqlHlbTa5Va6tZ2turKz6WXYPZxtawUUUV6BYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/Y/uPEOqW/QTx29wPckNE35CNa6Cueu/3HiTT5v4biCeA+7ApIv6K9AHQ1+av7WXhL+zvE9n4qt0xHfq1tMR082HlCfdkOB/u1+lVfOn7QXhL/hKfBWr2cUe+5t0W8t+MnzIRkge7LuX8a+b4owvtMN5o4swo89Jo/Kqiiivyc+QCiiigArGkbfIzeprUnbZExrIrlxMtkTIKKKK5STS0iTy9Sgb1bb/30MV6NXlcT+VKkg/gYH8jXqgORkV8dxNTtOE+6/L/AIc3pMKKKcql2CjqTivmDU7+wTy7KFf9gfrzVukUBVCjoOKWuRmgUUUUgP/W/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK57xB+6Gn33/PteQ/lNmA/+jK6GsTxJC8+gX6RDMiwu6f76Dcv6gUAbdcr4iiHmRSEZDKVP4f8A666WCZLmCO4j5SVQw+jDIrI1+PdaK4/gYfka4swhzUZEy2Px5+J/hY+DvHWraEibLeOYyQenky/OgH0B2n3Brga+z/2q/Cu6LSfGVunKE2U5A7HLxE/Q7xn3FfGFfjePoezqyj0PjsXS5KjiFFFFchzFK8bhU/GqFTXDb5W9uKhrz6sryZDCiiisxBXptjJ5tlBJ6oufrjmvMq7/AEGTzNNQf3Cy/rn+tfOcS070Yy7M1pPU2auWCeZewL/tg/lzVOtfRE336t/cUn+n9a+Jk9DoR2tFFFchYUUUUAf/1/38ooooAKKKKACiivNPjDdXVj8N9bu7KZ7eeJI2SSNijqfNTkMOQa4czxqw2Gq4lq6hFyt3srkzlyxbPS6K+K/AP7R+o6f5em+OIjfW4wou4gBMo/214Dj3GD/vGvr3Q9e0fxJp0eq6FdpeWsnAdDnBHUMOqkdwQCK8LhjjTL82hzYWfvdYvSS+XX1V15mVDEwqL3WcR4x+NPwm+HusQ6B458WadoWo3ESzpBeXCwuYnZlV/mIwpZWGTxwa7Tw/4k8O+LNLi1vwtqlrrGnTZEdzZzJcQsVOCFkjLKcHg88V+cvxv8VfCHwf+21Y6r8bPsR8PN4HWJft1ob2I3DX0xTEQjlO7aGwdvqM88+HaF8VdW+EUXxU+Nv7P2gCy+HmtazotnpNpd20kdvdS7HW6e1iypQMwOMDA3qu35di/Vm5+0tFfFnjT4xfGX4C/DDXviX8aTouo3d49nb6LpWlrLDHDd3G/fHcTzE7lQAMWGMhG6ZGPLdO/ap+IngvxZ4Qi8e+K/A/jXR/Fd/Bp1zb+GLsSXulzXOAkmPOk8yBTkMxHtkErkA/SWivz28IfE79rr4rQ+ONZ+Hs3haCy8I+INS0m3try3ufPvfsZUrGzLJsX5WUBsjcxOdoGai8V/tg+M7n4B/Dz4seANDt31vxN4ig0S90y43uDLicSRROCCpkeNdjENtDcqTQB+h1FfD3jT4q/tD/AAr0zRvDHiM6B4h8f/EDVksNBhs4riDTrOJY1a4luC7ebII2cDAIO35snla2PDPxd+L3w/8AjFo/wj+PLaRqFv4rsbu70rVtJjlt1WWxQyzwTxys3SMEqy4/hHJY7QD7Jor4P8FfFL9qX476RdfEz4SQ+G/D/hFrmeLSbbWI7ma61CO3do2kmkhYLErMpACjKkEHIG5pvi7+0j4s0n4oW3wT8Ja34Z8K6rYadFfazrPiG48qyiklClbe0jd4y8h3B/mJ+Q9MgmgD7qor8vL79uHx7ofgXxnaTWug674q8Jajp9p/a2mSyXOhS2eo7tt43lOz4j2hHUP991HUFa9e8R/Gr43fDT4A+J/ivrU/hrx01p9jbSLzQxObWSKeQRzS3Q8w5WMsMeWV/wBojnAB9y0V88fAHxL8VfFul3Gu+ONf8M+JdEuoYZNPvfDwmBMr7jNFMJGKjyxtAwA3J3AEYruvi9qnjrRfA15qfw8udHstVgeItca7I8VhBblgJZZGQqRsU7hk4478CgD02ivgz4A/tJ+MvFHxku/g1441rw54tEmmtqNjrHhqRmgJjfY8EwZiN+MsMYwAPvBgR5/o3x8/a08Y/CDWfjT4dg8L2ukeE31Brm3njuWuL+LT3d5mUK+2ILEAoXduZlZsgECgD9M65KLx34Sm8bzfDiLUUbxJb2I1KSz2vvW0aQRCXdt2Y3kDG7PtXyx4u/aR8Za9YfDHwt8GdKtJPGfxO02PVVOos7WemWflCSWSby8M5B3KuD1U8ElVPB/CGL4l2/7b2v23xWuNMvNZi8Doqz6VHLFBLB9uhKsySlism7cGAO3gEYzQB94+KPFXhvwVolx4l8W6lBpGlWmwS3Ny4jiTzHCLuZuBuZgB7mvLrP8AaX/Z+1C7gsLH4g6LPcXLrFFGl5GWd3O1VAzySTgV5R+33/yan4y/39N/9OFvWb8Hrr4f+MNb03R7j9ny58KtFbiddU1HQ7KG3EkKgqRIoLB2PKnrmgD7Zor80Lj9rP4geP8AUvEusfDnxb4G8I6FoF3NaWFn4kvAmoaq1uPmlK+dH5UUhIEZxnOQTwTXqlr+0B8QPib8AvDXxY+Hl54c8HPfzywaxc+I5nW1sTAXRjCwZQxd1GwNnhhkZyQAfbdNV1cEoQwBI455HUV8K/AX9oL4jfFo/EL4c3Go+Hr7xX4Xgil07WtLMk+k3aXKkq7Lu3ny2wH2kckrgFcni/2FZ/ixY/AK08RXmpaZP4RitdXmtLfyJjqIvEupGLzTF9jIWDnAUHBHPBoA/SCivjbw38evG+rfsZzfHy6isx4lTTL68CLEwtfMt7iWJP3e8tjagyN/XvXG+Jf2gfjhc+MPhJ4H+H1jpE+o/ETw1FqlxJfRzCC2uGi82SX92+7ykUNhOrHA3eoB990V+f8A8Qf2gfin4C17wz8Eb/xD4WsfHF7Zzalq+v6o5s9HtLYyusEcMbujPO6qOCevOCpJTt/2df2htf8AH3j/AMTfCPxpfaHruq6BbRX9trHhyfztPvbSQqrZG99ksbOoYA4ySMDALAH1F4u8aeEvAWkHX/Gmr22iaaHWI3F3IsUe987V3NgZOOBXBaH+0N8DPEur2mgeH/HWkahqN/IIoLeG7jeSWRuiqoOST6V8+/8ABQhlT4CW7vCblV13TCYgAxkAZvlAPB3dMV23wbm8CeLvEkrx/AmfwBdaXGLmC+1HRbO0zIGChYZIwWEgznjHAPNAH1hRX5maN8fP2tPGPwg1n40+HYPC9rpHhN9Qa5t547lri/i093eZlCvtiCxAKF3bmZWbIBAr03xR+0X8RvGOqfC3wN8GbTTtN1z4jaKNemvNWEktvZW3leY0aJHgySAqw7jgcYJZQD7bv76y0uxuNT1KdLW0tI3mmmlYJHHHGCzuzHgKoBJJ4AqnoOv6J4p0e08Q+HL6HUtMvkEkFzbuJIpEPGVZcg88fXivz78P/F79oHxR4V+OeleJbrw5Nr3w7EcL2ZsZZNNkto4ZnuDkyb5PtMcbDY4G09cAlRYg+OXxI8PfDb4B6d8MdC0K3vfiHBPbtZvC9tYW8iwq0bRiJiUjR3LuoDFwCoIJzQB+idFfFngX41/FXwf8WPEnwp/aAfSbgad4ek8UWupaRHLGhs4ZTHKjxykncMMRjptOS2RjwI/tifFfWfCN98X9F8ReA9M0q3M01v4Uvb4HWp7SByCHImUi4dVJRAnOR8pJAIB+qFFfn746/bBvNWvfA/hX4Z32ieGL7xbosWu3ep+J7gRWWnW0wISEfOgluC4YYzjgHBBLJV8I/tX+MptK+K3hbW73w/r/AIl8B+H7nXdO1bQpftGl30UUO4CRRIxV45GQOoYZyRxgMwB+hEssUETzTOI44wWZmOAqjkkk9AKxfDPijw54z0W38R+EtTt9X0u73eVc2siyxPsYo21lJB2sCD6EYr4x8AeMP2s/iX8Mrf4nyz+F9G07UtHa4trCa1uZpJJFCfvZjuwEmUSOqKflUxqd3zMPPfDXx98deF/2cPgvr3w98OaFa6n4416TR/7Oigaz09TPc3UaeWsbHysyKrO3zZyxxk0AfpdRXxf4M+LHxv8ACHxytvhF8cf7G1KDX9JuNU0680WKePy3tcmWF0lJLDarYwCfu8nJC/PfiD9pz9qDVPghrH7SPhhfDmleDXuHtbSykiln1K3ha4+yJcFi4iaVZSPkYbSOdmOCAfqpRWVoVzLeaJp95NOt1JPbwyNMkZhWRmQEuIyzFA3UKWJHTJ61q0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFIyhlKsMg8EUtFAGB4WY/2DaQMctaq1ufXNuxiP8A6DWhqcfmWEy+i5/755rO0P8Ac3WrWX/PK6Lr/uzIsmf++mat51DoyHowI/OoqQ5ouPcGeAfEXwwvjHwVq3h/aGluISYc9po/nj/8eAB9s1+UTKyMUcFWU4IPBBFfsmQVJU9RX5kfHLwt/wAIr8R9ShiTZbagRew+m2YksB7CQMB7CvyrPaGiqfI+ezalopnkNNdtiFvQU6qt22Iwv94/yr5mcrJs8RmbRRRXmmYUUUUAFdj4ZkzDPF/dYN+Y/wDrVx1dH4ak23ckf99M/iDXlZ1T5sNMum9Tta6Lw8mZZpP7qgfmf/rVztdb4fTFtJJ/efH5D/69fnNR6HUjfooormLCiiigD//Q/fyiiigAooooAK87+LGmahrPw91jTNLge5urhI1jjQZZj5qHj8OT6V6JRXFmWCjicPUw0nZTi4v5qxM43TR8leAf2bYovL1Lx7L5j8EWULfKPaSQdfov/fR6V9U2Gn2GlWkdhplvHa20IwkcShFUewHFXKK8jh3hPAZVT9ng6dn1b1k/V/pt5GdHDwpq0UfKWo/CzxNqP7YVr8T7vR47nwnF4Q/sw3UjwuBfC8aUJ5LN5udhzv2be27PFXv2sPh14v8AiV8NNM8O+BtPGoX1trem3bxCWKELb28hMjbpXRflHYHJ7A19P0V9IbHzp+1H8G9S+N3wsk8NaBNBBremXlvqmn/ahut3ubbcBHKMH5XR2XoRkjPGa82+G+n+PtW8UaLaeK/2eNA8I2lsxbUNU+06dOUkjQlGtIoI2kyZApBZvlGRuyM19qUUAflX8BvHXx38Oaf8VtG+E3w3TxXHeeNtcMOpSapbWkVtdt5ass0EpDuqKEcFWAbJXIIrvG/Zf8feFPg/8HfA2kQprmr+HfG+n+Iddlimjjjjj3yvcSIZmjLrGGVQFBdsZC9h9ueAfht4T+GltrFp4St3to9e1O51e7DytLuvLraJWBcnaDsGFHArvKAPlv8AaT+GHjjxZd+BviT8MoIL/wAUfDzUmvYLC5lEEd7bzqq3EAlb5Udgi7SxCgZyc4rz/SvAPxa+N3xu0H4m/Frwmngfw54N0++trHTXvob+6u7nUojBNIzQZRI1Q8ZwcqpAIY7fuSigD89/hjY/tRfs6+E5Pg3oHw5t/HulaXPc/wBi6zHq1vZR+RcStMBdQzfvNyM5LbcZ+6pIAarPxF+EXxF0L4zN8ddC+H2l/ESDxPpdra63oVzNbLNaXduiKJrWa6Xy2UKgTpubnjBBX7/ooA+PrNPijpXw31XWNK+BOhWOs398lvJ4fhvLJTc6Ts+czzoiwNJuLAIdy4PQ8g+R/B/4c/G/4aWfxQ8aeGvhta6VZ+J57FtL8ETanBLAFT5byTzs+TF5iFtqcDJCkbEUH9HaKAPhD9mP4V/EDwz8XPGnxDv/AAXH8MfCmvWkMUPh6K9iu1e9RlLXSrB+7jAAcBQF+/wMDNeg/tefDTxt8TPAOh2fgzT018aLrllqd/oslwLVNVs7fdvtjIxCjJIOGOOMjLBRX1dRQB+evgj4efFn/hovwh8VpfhZYeB/C1tpt1oz2NjeWbz2iMpkS4nSHZGVd2CKsW9lAJYDjPT/AAq+D3xG8N/sk+OfhjrWkfZ/EusW/iNLS0+0QP5jX8cq2481JGiXeWH3mGM/NivuOigD85ZPgt8bPAFn8GPir4F0KDWfFHgfw9HoWtaDNdwwNNA0fzCK43GLdG7PzuIztKhhkH0D4UeG/jPr37Tur/Gf4h+CP+EO0i88LDSYIzqNtfP5yXcUoD+S24Myhm+7tAABbdxX21RQB81ftefD/wAXfFD9n7xL4I8C2H9p63qDWJgt/Nih3iG8hlf55nRBhEY8sM4wOcCvobSoZbbTLO3mG2SKGNGHXDKoBHFX6KAPzO8JfB34l/Am613wTpnwZ0f4qaFdX093o+qSXNhazwRTnIt7oXSFz5eOq8dQCcgL2fxr+EHj7W0+EninTPh5puv6d4Va5n1vwba3EFtZPdXcSgSxmQJDKIZNxwwOemCrOa+/qKAPhX4GfD34r6D+0D4o+IPizwJZ+E9A8X6PBFDBp93bzJp8lmyokE6x7MySIC5aJWjGQN2c4q/sy+Evjh8LtA/4Z58ZeBw/hi2l1JI/E8Go2xi+z3PmSxkWn+uYtI23sV3fMABz96UUAflXY/D/APa00L9nXV/2YLD4eWV5Fbw3ltDrzatbpDc2s87z7YrckOJX3FUMjIFyC4Hf3vwv8IviHp3xe+DHii80ry9M8J+C/wCydTm8+A/Z73yAnlbRIWf5uNyBl96+2aKAPh345/BzxnbfGrTPjz4G8Iad8Q4ZNLOkatoN+8ETtGsnmR3NvJchow68Kc84GADvJX2X4I6brpGra14m+F2lfDWd2SK0ispbW4upbcgM/nyWqKg+cAhQx9xlcn32igD5T/bG+Hnjr4l/COHQPh5pX9s6vb6tY3gtvPht90duzMx8yd0Qdu+fQGrXg74o/tM6x4n07TPF3wSTw9o1zKEutQHiOxujbR45fyYxvfHoOa+oqKAPhz4VfB74jeG/2SfHPwx1rSPs/iXWLfxGlpafaIH8xr+OVbceakjRLvLD7zDGfmxXivxR+Cfxbn8DfAfQPD+g/wDFTeFdKaK5On63baTrdvLFBGjpb3UjSRtCpJMpjSQ9MFQxLfqbXj3xU+Anwr+NH2GX4haL9vutM3C1uY55raeHdyQskLocEjODkZ5xQB8dfAuG2Xwf8Yf2fLPwdc6J8Q4dMuJtQkutVi1aXVLnUbZ1hklvQI0DneuVIVV3EnB310uj/BH4n2umfs0W8+i7ZPh+851wfabc/ZA8KovST95kgj91vr6v+FvwX+GnwX0y50r4c6KmlR3ziS5kMkk007rnBkllZ3bGTgZwMnAGTXqVAHyH43+DXiTxp+0zdeJ72xI8G6p4AuvDlxerLFuW5urqUtGIi3mE+U+7ds2ds54rwnwF8OvjP8LfDEXwvu/gP4d8c3OlM8Nn4je6sIILi3LkpJcRzI1xuQHBH3iB7bj+mVFAHwf8X/gd4p0T4s6H8avhz4F0nxpZR6QNF1Pw1cG3gVYkfzI5rRp08kMnC9Adq4UHedvTR+DPGvi74PfE2wl+E2lfD/W9b0e90/SrSyuLOS6uRPbMoWeaFY4kzLtwC+B/FjbuP2VRQB418MPCmv8Ah34BeGfBOsWv2fWtP8O2thPBvR9lzHarGyb0YocMMZDFe4OK+P8Aw78Avi1Y/Bb9nvwldaFs1XwP4ug1TWIftVsfstml7cStJvEpSTCOp2xlm5xjORX6S0UAfMXjP4eeMNW/am+HnxH0/T/N8O6FpOp215debEvlTXCsI18tnEjbieqqQO5Ffm/qPwm8a+BrDXvGvjzwHPrPw60vVbjU7rTtL8W20Ph5wkxAMWn7JJSUOFKNKGJBXavCj9vSARg18ozfsR/szT6ydZfwgBul89rUXd0LNpc53G383y8f7IG3HG3FAH0V4N8R6f4w8I6H4t0mJ4bHW7G2voI5FCukVzEsqKyjgEKwBArpKht7e3tLeK0tIlhghVUjjRQqIijAVQOAAOAB0qagAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5+L9x4onTteWkbj/egdlY/lItdBXPap+51rR7v++81sT7SR+Z/OIV0NAHn+oR+VezJ/tE/nzXyd+1J4W/tDwxY+KoEzLpUvlSkf88Z8AE/RwoH+8a+vtdj2X27++oP9P6VxPifQrfxP4e1Hw/dcR38DxZ67Sw+VvqpwR9K+GzTDc3PT/ryOLFUueDifkRWZdNulx/drcvrS4028uLC8Ty57WR4pFPVXQlWH4EVzbMWYse9fmmJdlY+PkJRRRXGQFFFFABWrosnl6lCezEr+YrKqa2k8q4il/uMp/I1jiafPTlHumNbnqVdzo6bNPj9Wyf1rhq9EtE8u1hT+6i/yr8oq7HbEsUUUVgUFFFFAH//0f38ooooAKKKKACiivPvipq+o6D4B1fV9InNteWqRtHIACVPmoOhyDwcciuPMcbHDYepiJq6gnJ23slcmcrJs9Bor5a8A/tH6dqHl6b45iFjcHCi7iBMLH/bXkofcZH+6K+nrW6tr23ju7OVJ4JQGSSNgysD3BHBFeXw/wAUYHNKXtcHUUu62a9Vv+nZkUq8Zq8WU73WtG06VYNQv7e1lcAqksqIxBOBgMQTzWkrBgGU5B5BHevzo+J/wx8C/Fn9umy8LfEPSl1jS08CC4ELySRASpfyqrbomRsgO3fvVz4d6dffs4/tM3XwN8GT3Oo+CvFGgTa1pel3E7SfYb2B5A0MMkhO1JPLbJbruXcSVJP0BqfoZRXww/7UnxY8G+JvDEHxm+GUXhfw/wCLL6DT4LmDVobu5tJrrPlC4hVQcHBLfdwAerDacP4x/EL46aN+1t4R8M+CNKTUtPOk3k1pp8mqG0tr8+WfNmnUIyo8LZVAwbIGQRmgD9A6K+a/iV8R/j94cn0+18CfDW01tW06O8vru51eG0t4LliQ9onmBS7Ajh8hSCM4rqPgD8Z7L46/D9fGMOmyaNeW11PYX9lI4lNvd2+N6CQBdw2srA7R1xjIoA9sJAGTUUE8F1BHc20iywzKHR0IZWVhkEEcEEcgivm79rLx/qHgb4N6hZeHSW8SeLpotA0lFOHa71E+VlT1BSPewP8AeAryn9mr4jat4M/Z/wDGnhTxGq33iL4JHU7CeIsUFxBYpJNasGwSsbopjQ4PypnBoA+7KK+XNf8A2ibvRf2Wov2i10NJbiTT7O9/s43BCA3U8cJXzthPy7852c4xWH4u/aO8dT/ECH4W/BvwOnizX7XSoNX1Q3N8llbWsVwqtHCruvzyOHXHQDIOCA20A+v6K+ANO/bO8Rj4Mn4t6v4OiaWTxYPDw0m1u3a6hQjlZGeIKbgEcKMKwK8rzV+7/ag+O+hePLH4X+I/g+kfiXxLbNc6LDBrMLwOsfMouZvL2p5SBmYrkkgALhg1AH3hRXyJ8PP2pRfaL8RT8X9APg7WvhcI31a2inW7jeKdGeFoHAXc0m3AXJ+8vzfNgcTdftYfFTw34a074teO/hYdJ+G2pvbn7bHqST6ha2t0wWC4mtgg+V9ynYCCNwGc4yAfeNFfHvif9ov4jS/FbxJ8IvhV4Aj8U6jotnY30d3JqKWlr5F1D5jNKXXhtzIsaqTuyzEqF5808fftHeOvHf7KPjfxn4U0aTwz4q8OXtxo2swi92TaZJb7fOmgmRQXYbkUAbTkthvlyQD9DaK+YPhZ44+NB+Ch8R+I/BUd1qlhpVnLpdtb6ms82rDyAS0kjxjypG4ODuJJPU1ynhL9pL4i2fxX0D4V/GrwHB4VuPFiz/2bc2epxX8fnW6eY8MyoMq2CBnPUjAIJIAPsqolngaZ7dZFMsYDMgI3KGzgkdQDg4+lfnn8O/iP+0HfftU/FHw+uhRanpljcaLBPaz6uVg0qyeMlZ7ZChV3njJmkRQpDfKSeDXVeBPiHoOlfHv4yeIPHXhGHwtrPhfSba41DU4tQkvPtWnRKXhbycLGjGFUJC/Nn5TznIB9z0V8Et+1x8TdO8I2fxn8Q/C17L4X3ssWL1dRSTUobOeTy4rt7UIBsclcKG7g7iCCe/8AG37RXjWz+LJ+EPwu8Ep4v1G50O31q2uTfpaW6wSyMjPMXQ4QDZt2klmYDA60AfW9FfPvwA+Nmo/F6x8Raf4o8Pnwv4p8Iag2nanYecLlEkAyrxyqAGVsHpnpkEggnqfi14s+JnhXTNPPwv8AByeL9Qvp2ikE17HYw2iBC/mys4OV4xgY5x3IBAPWaK+Qvhz+05q/ifS/iNpvjHwqvh/xh8N7Jr26sY7xLq2uYjA88TRTxjADBQDw2AwOScgeZTftkfFCw8CaL8adX+FRtPhzefZlu77+043u4xOyxNMluE3GISkrHuwXG0nZuFAH6FVmWetaNqF9e6ZYX9vc3mmlFuoYpUeWAyDKCVFJKFgCRuAyOlfOnxR+P2vaH4/074Q/CLwuPGnjG8s/7RuEe6W0s7GyJ2rLPKQ3LHGE4OCDnJUHyT9krVPEetfHP4/al4u0YeH9YlvNDFzYrcLdLC6W9wvyzKFDqwAYHA4IzzQB933d9ZWEYlvriO3RjtDSOEBPXGSRzxVa31vRruZbe1v7eaV/uokqMxxzwAc9K+E/+Chn9gjwB8PT4pjM2i/8Jnp325FDsXtfIuPOUCP5ySmRhefTmtD9n3wX+yF4i8SXPi34D+GHtvEXhdBJHPdLqVusb3SSRLj7SxVgwDBsKxAOcZxQB930V8Kn9tAW3h690G/8KunxZtdZGhx+FEuNzzXDtmOdJ9g/0Yx/N5uzGePukMeY+KPxF/aF0r9qTwP4c8P6LBeK+iXE6aWurvBY3sxh/wBIkkJjABgfcsRdCWADDbkigD9EaK+YPiR8evFHhjxJ4b+FfgnwkviX4i69Yf2hPYfbFgs9Pt1G15ZrllOV8wMqAAFsdiVDefzftca9oHh74iWPjvwYNE8c/D2yh1CTTReCe1vbWd0RZoblE4A3ruBUkZAyTuCgH2/RXxpo/wC0T8Wr/wCHOtfGPVvh3Bo/hC08Pzazp7Takr3d3Iiq8aPEiHyklQlwTuIUDqWwOXb9rf4l6VZeEfH/AIu+GB0f4deLZ7O3j1E6lFLdwi+AMU726LkRtyVGclcEkMQpAPvOszVta0bQbZbzXL+3063d1iWS5lSFDI5wqBnIG5j0HU18+fFr4m/Hfwnr9xZfDr4a2/iLR7K2juJNQutWhsxM77swQxuM+Yu3uTnI45Gfmn4+/FzSfjj+yl4S+I+j2r2Cah4l0xJbaRg7wTQ3DxyIWAAYZGVbAyCCQDwAD9LKy5tc0W3uxp9xqFvHdEgCJpUWTJ5A2k55FalfmdpHwR+F/wAaf2u/jpZfEzRF1mLTo9BNtmaaFojNYqHKtC6HJ2L1z0+tAH6Y0V+eHwW8QeNvgz44+LnwE0SO78c2fgm0tdW8OWdxcBbkwXMasbMTuCAq71CDHUMQPmwPRfCX7SXxFs/ivoHwr+NXgODwrceLFn/s25s9Tiv4/Ot08x4ZlQZVsEDOepGAQSQAfZVFfnd8O/iP+0HfftU/FHw+uhRanpljcaLBPaz6uVg0qyeMlZ7ZChV3njJmkRQpDfKSeDXsXxe+Mfx58C6xrE3hH4YW+s+GdCjSZ9QuNXhtXvFMYkkW3hZd26PledxYg7QTgUAfV9FfGHij9r22i+H3w28U+AvDh1jVvijM1rp1pd3SWcFvNERHMJ7gqw+SU7AABv5II6HrfEn7QXiT4a/CxvGXxW8FvpPiWfUl0nTtFsr1L1tRuZQPJMUyooVZPmJBBKqp4LEKQD6ior5A0T9of4leHviB4Y8D/HTwDH4Tj8ayPb6XfWWoJfxC6UAi2nCqCrHIAYEgk8DG4r5b+z98SP2g9d+NvxJ0vWdCiv8ATLfW7a3vhNq5ddGhCPhLSMxkSKR8xC7MnrzzQB+iNFfGPhL9pb4l/Efxtren/Dv4cxap4Y8N62dDvrubVore8R1lCPc/ZWjJESAltuSWAODkMB9nUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHPeJv3empeDraTwTf8BSRd/8A45uroaz9Xs/7Q0q8sP8An4hkj/77Uj+tGk3n9oaVZ3//AD8Qxyf99qD/AFoAyvEMfEMv1U/zFcxXaa5HvsC39xgf6f1ri6+YzSFqzfcxnufnF+0v4Y/4R7x5LqcCbbbXIxcKR080fLKPrkBj/vV82V+hv7UWhf234SjvYF3T6I3n8dfLlwsg/DCsfpX55V+VZzFLESS2PkswpctV+YUUUV5ZxBRRRQAUUUUAep6eftMNuR1lCfma9R6cCvK/CB+0JZr3Rj/46Sa9Ur8ozCHJVlDs2dsNgoooriLCiiigD//S/fyiiigAooooAK8x+MsMtx8M9cggRpJHjiCqoJYkypgADkmvTqK4c0wP1nDVcM3bni437XTVyZx5otHw34B/Z11vWvL1Hxi7aVZnBEC4+0uPfPEf45P+yK+yPDvhzRvCmlx6NoNsLW1jJIUEklj1Zickk9ya3KK+e4W4Hy/KIf7NC83vJ6yf+S8lZGNDCwp/Cfn18WNZ8XfDn9sWx+Jll4C8ReLtHfwcumb9F0+S6Vbl72WQK8nyxrhVBYFsgMDjBqlpPgT4+fETxX4+/aM1bRm8I+IP+EautE8H6TLKj3cJId1mmONqSGQnYG7udw2qpb9EqK+vOg/DjUfh3BdfDfwxdeFfgx4uk8faJqFhqXifWdRs52mleBwLlbcyuzz+ZIwYJEmVQbm6E19vfHufxR4L+Pvwy+Oun+FtV8TeHLHT7/Tr6PSbRrm9tzdITE5g4YAlhnOMYIPzFQfumigD8ufigsvjj412PjP4yfDbxf4q8AXuiW83h/SLWxnc2d+cGZL+1ikUJPncPnYjaRnKjK+x/sN+Htf8FeGvH3hHxF4UvfCMy+JrjUre0uYz5K2d9DEIEhnGY5fLERVijHHGcE4r7jooA+BPjL4F+Kfxt/aZ0DRPDtxeeD9A+HFgdTt9bm0z7VaTarcMmFhE+2CZkjK4OW2Mr9DXAXXwd+M3gT41+JtNvtRu/HFj8ZPDF/p1/q8OliztrTUobdo7VrlbUNFGNoCK7bdxkYkZBNfp1RQB+PviPxT8VNZ/Y+k/Z3tfhH4qPibSrW2sb2Y6ews1itLqN1kgcFnuGcKo2RoeCz52Lk9h8c9D+Lnhb4y6F4v+FWj+J9GvL3wvbWN9qmiaTHrKXTggCCa3neKOB4gg+dnLH5cKMEn9Uq+efid+z3b/ABD8VJ410zxx4m8H6t9mWzkOjagYIZYEYsFaJlYAgsSCpHJyQTzQB8KRaF4Z8Qfs6aH4N+Dmk6/qOq+H/HunzeIbfULJ/wC1I73LNczXMMO8IijC5BwoXDHcCT9c/ETw14ivv2vPhL4lstLurjSNN0zWo7q9jgd7a3eWBhGssoBRC54UMQSelewfCH4N+EfgtoF1ofhd7q8m1K5e9v7+/l+0Xt7cyfelnlwu5j7ADqcZJJ9YoA/N3xr8FPGvxJ+IH7T2gWun3NjF4rsPDY0m8uIpIbW8nsbZJDHHOwCMPMjEblSdpPzeleefD7wD8FtQtvD/AIO8Vfs1+JIvFW+2stTlNrcLpsTZCS3Qu2uAhi4LjA56Anhj+stFAHyD8OPCOt6N+1v8S9aOj3VpoNzomjW9ldNBItrKYIlVo4pSNjlMYIBJHevGtG+Ffj3XPhH+014Wi0S6tdR8S+KtcutLjuYXt/tsTMkkTwGQKHSXbhHB2k96/SOigD85dR+IXxY+IX7Ker+CPh54N8T+F/Gvh/RdNtZmvLCSz88xMkV3HYSk7nk8tHIAAfaw2/OePFdI8E+G7D4j/CTxf8JPg14p8PaR4f1VF1vUtSspvtcsl2oRWdGeSWRI2DNJKFEaE4GMgV+wlFAH5+3PiDxT8Df2qviN4r1XwP4g8SaB8QbXSGsbrQrBr/ZLYW4gdJQrAJzuPJzgA4wc0eIvhJ4s8dfGf9oTSlsLmxsvFvhnTrLTtQmhkS0muVtgNqTEbX2uAHCkkDORX6BUUAfln4g8U/Fbxt+znpv7Llj8LfEFl4zaysNCurq6s/L0eCGyaNGuhe5MbK6RcY4yTtLYG73jwN4A8QeFv2ukuf7Pu5NB074dWmkpqTQSC1kuLe8iHliYgp5hRd2zcWxz05r7UooA+R/2ffDXiLRfjL8dNU1jSrqwstX1qzlsp54Hiiuo1hcM8LsAsignBKkgGuO/bH0XxFrOtfD1dW0PWvE3wxiurs+JNN0FZJLqZzGv2QypCyyNCr5JwQOvIYoa+6aKAPyj+Gvgq/0fx18XpPCvwv1rwX4a8X+DbhNEt57OTMklvE0TJIqmTyp5nJZYXbzCOcfMM97408DeNbv/AIJyW/ge28P6hN4jXTNLjOmJaTNeh49Rgd1+zhfMyqgsRtyACTwK/R+igD8+NeHjb4EftFXPxek8H6v4s8KeMtBsbC5bRrY3d5p93ZqiAPACG2EICTwCW4JZcHqf2W18T6v8X/jX4+1vwrq/haw8T3WjTWEesWj2ksscME6MQGypI+UsFY7dwDc19vUUAfFn7bWmeJbzwv8ADzVvDXh/UvEj6B4x03U7m10q1ku7j7Paxzs7BIwcdlBOFyQCRmuy8H/tNyeK9fh0S5+FPjvQYpI7iVr3UdDeO2jFvC8xDFHdiz7NkaqpLOyqBk19Q0UAflNN4R/aA1DxnP8AttWnhmWLWLO++zW/hGazKajL4fRTbuxBXzRdsCWA27tvIym2M+wfGTWPEuhfGr4UftEaZ4O1zXPDcWl3tlf2tnYSPqdm14hMfm2jbXQ7mAbdgDawzkqD980UAfmB8bPANr4j+LPhv9oXxZ8MNZ8Z+CNf0JLG/wBIW2lXWNJuUkMkc0lnHIr/AHflYBiq/OSc7N0msfDPwHrHwF+KeqfCD4N634O1a709NPtVvbWdb7UoXlilcRWpklkwGQfw8gAg9QP07ooA+ZPiH4f1y7/ZD1XwxY6bcT6s/hEWqWUULtcGcWYTyhEo3l93G0DOeMV4l8ZvBXjHVf2Mfh14W0zQb+81qxh8LiexhtZZLqE28UQmEkKqXXyyCHyBtxziv0IooA/MX4seHGvP2j/FOo/HD4deJPiPoBtbIeELfSreW502LEWLlZVjdESRperSEgDLEbdmPO9L+GHxRX9jfU/BFv4L1G28S+EPFo1FtKeFt9xBHMJf9FcArcKFbAaMtu2nbnjP6/UUAcz4N8Sjxj4W0zxQNNvNI/tKBZvseoReRdwFuqSx5O1h3Ga+Bl8XeOPhB+1N8X/EcPwy8T+K7LxUmjJYXGmae72jtaWaK+64fbGF3Ntyu7BBBGRX6P0UAfl9qXwp/aNl+GHxi+ND2kml/Ev4h/Y1ttLsJRJdWWk2rIphR0xmdoMqQvzYUYxI2F880jwT4bsPiP8ACTxf8JPg14p8PaR4f1VF1vUtSspvtcsl2oRWdGeSWRI2DNJKFEaE4GMgV+wlFAH5+3PiDxT8Df2qviN4r1XwP4g8SaB8QbXSGsbrQrBr/ZLYW4gdJQrAJzuPJzgA4wc15T458JT6j8b/AIhzfGv4ZeJPiPf3Uix+DzaQyzaRDZuhVUMgkSGBgxUyu+dpBbAP3v1YooA/K/wNY6ToP7J/g/4X/Gr4ReI/E0On6hqtpfw2enySXWnzLcSTxXMe0o+x1nCrLG204cZYcHi5Pgx8afHv7PlvPq+haprdl4O8XHU9F0HXmaLVrzw7Guw2s3IIcqflGd2N4j4MYP7CUUAfnf8ABvwV8AvEPxA0Wbwx8A/EPhe700m9/tTWLSezgsrq2IeNQZZ28x9442qcHBIxkrd8F6/4p+CP7RXxN03xD4H8Qaxpfj/VLC90zUtIsGvLVVZTHL58gIEYj3gt1ICsSMFd36B0UAflD8XYtX1z4qxeIPgf8MfFvg/4pQa55M2ppaPBouo2KyFWnupQ3lMkqgMSVGckMzYFfq9RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc94Z/d6a1metpPPDj/ZSRtn/jm2uhrn9L/c61rFp/feG5H0kjEf84jQBq30fm2cydypx9RzXn1elkAjB715Tq8hsrW4PRkyo+pOK+fzy0Uqj8zKp3PM9eit9bF7a3a+Zb3avEw9Y2G3H5V+Xeu6TcaDrN7ot1/rbKZ4iemdpwCPYjkV+odfF37RPhz+z/E1r4ihTEWqRbZCB/y2hwDn6oV/I1+PYtuV5s+fzKnePP2PniiiiuA8QKKKKACiiigD034eHzJJUP8Ayyy3/fQAr1evMfhrbkxajdHoDEg/HcT/AEr06vzTP4Wxc/l+R2UvhQUUUV4xoFIzBRk0EgDJqszFj7UmwP/T/fyiiigAooooAKKK4n4jeIL/AMK+C9T8QaYENzZKjIJBuQ5kVSCARwQT3rlx2Mhh6E8RU+GKbfoldkylZNs7aivCfAPx68LeLPLsNYI0fU2wNsrfuZG/2JDgAn+62D2BNe7Vx5PnmEzCiq+DqKcfLp5Nbp+pNOrGavFhRX59/tQ/HbUfhf8AtF/B7TLe7lg0iIzz6zGjMIWtNRkSySSZRwVhYO6k9GxjmpPiX8c9R0v9t/4d/Dizu5U0O0tZLTUY0ZhE17q8UjW6SAfKWXyomTdzhmx3r1jQ/QCivmPxv+07o/hzxvqXw88HeEtb8eazoUSTaqujQJJHYrINypI7uoMrLyEXOeRnIIFib9q74RQ/B63+NK3VzJpV1P8AYobJYc6g+oZI+xiDOPO4PG7bj5t20g0AfSlFfm6fixrPxE/a9+DcOoeFNd8Fvb2Wus9rq8QhFwktnIY3TY7B9uz5gwBQ4GO9e2eJP2uNB0/Xde0vwZ4M8Q+N7HwpI8Or6jpFqklpbSxAmSNWd1MrxgfOF4HXJFAH1tRXwJ4s+I+hfEn4/wD7NPjHwTqD3Oh64niWRSCybtlmqlJE7NG4KkHoQa++6ACivzZ8Y/tBeI7D9sLTLi1lcfDrQbqDwbqEoc+Q2q6sjT7iOmYpIo0cn7oRv71fUnxn/aL8K/BHX/C3h3xBpWpapdeLvta2aabCs8jTWwj2wiMsrM8zyoiY4yfmKjmgD6Cor5a1j9qXSfC/hnRtR8V+D9d07xL4ju57TTPDawJNq115DBWlEavtWPnO5mGRyu4V5N8fPi3rHj79mL4ni88H694H1DR7ez3DVIVhWXzrmPBt5Y3YSYCndjGMgd6APv6ivij4d/tZ6AbrwN4M8TeEfEPh608TQ2tjpWsalaCKzvbnYqqFO8sBKceWSMtuBIAOa0NH+I2n6h+034xs1sPFcWu+HPDVwyaVLLENKvre3uVCXFpBvyZpmIWN22grnOM0AfY9FfC37J/7Q/xA+J1tqVt478P6pKv9p6l/xOXgt4dOtIYAClrIUZSHQDaTtPJ5Y9a2bv8AbW8JJb3nijSPBfiTV/AunztBP4ktbNWsPkbY8qBnDvCp4Z8DGOlAH2hRXzv8QP2l/AfguPw1a6LbX3jLWfGNut3pOm6LCLi4uLVl3C4O4qqRY/iY54OAQrY8v+Kf7SXjSx+AXi7xz4e8Ea/4V8QaPItmBqVnD/ozyJv+1De7JLAgwpYA/MwG080AfbFFfI/h/wDaUutH+Al38WPiD4R1vTo/D9lpzzNcxwJJqTXXlxme2Cybdhdwx3beDwO1erfEb40aB8NfDXhjxPq1ldXVv4p1Kw0yBIAm+OXUFZkaTcyjaoX5sEn0BoA9ior5N8WftZ6JoHxG8TfCfQvBmv8AinxP4bW3ke30y3SZZYZoFnaUNv8AkSMOiHcNzO6hVbki/wCK/wBqDTtE8Tv4F8L+DNc8ZeJNPtYbrVbPSYY5hpgnUOsU8pcJ5uDwi5zjrQB9R0V5f8I/i54R+NPhIeLvCDTRxRzyWt1a3UflXVpdRY3wTx5O11BB4JGCMGvUKACivjjx1rmtW/7avwz0G31C4i0y70HVJJrVZXWCV0WXazxg7WYYGCRkV9G/Er4j+FPhN4M1Hx541uja6VpqguVXfI7uQqRxp/E7sQAOnckAEgA7qivljwf+1Ro2t+L9E8G+MvB2veBbnxSGOjzazbpFBesoDeUGR22SkEYRh3AzllBh0/8Aaw0PX/GN74c8JeCfEniDS9M1X+xLvWLKzjeyivvMWMgkyq3lqWBdyBtBBxggkA+raK+U9W/av0ODxvrng7wv4K8SeLIvC90tlqt/pVpHNb21wx27AGlVm2NkOcDbgnkc19WUAFFFfHOsftleGLe41q/8LeDPEXivwv4bmkg1HXdNtUksYmh/1xjZpFMqxjl2GAB8wJUgkA+xqK+APir+1N4jsfil8I4vhlpmo6/4S8U297fyLYwQO+sJ9nzHDbmYh0e2Y75lJQjIBzgivffHv7RPhv4c6D4Yutd0TVZvEni5V/s/w7awLNqryFVaRGjD7F8rcA5349M0AfQVFeQ/Cv4qah8SP7Ut9W8Fa54NvNKMQePV7dYo5hMGKmCRHYSAbfmxjaSPWvXqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK56X9x4ogfteWkiH/egdWUflI35V0Nc9rn7m60m97RXQRv8AdmRo8f8AfTLQB0NeNeOpPKu/so/jbzD+XH6k17LXhXjab7Rrkkg+6oEY/wCAdf1r5bi6py4TzbMK7905CvKvjN4c/wCEi8B3wiXdcafi7i9f3QO8fihb8cV6rTXRJEaOQBlYEEHoQeor8rkrqx59SClFxZ+VNFdT418Pv4W8ValoRBCW0x8vPeJvmjP/AHyRXLV5rVtD5eUWnZhRRRSEFFFFAHvXw5tNnhO4uCOZbgsPooVf8a6qmeC7TyPBtlDjmSNn/wC+2LD9DT6/O+Il+/5u/wCh3xVooKKKgkfPyjpXzzYxrvuOB0plFFQB/9T9/KKKKACiiigAry341/8AJLte/wCuUf8A6NSvUqhuLa3u4jBdRJNE2CUdQykg5GQeOCMivPzfBPE4Sthk7c8ZRv2umiKkeaLR+eXgH4I+LPGvl3twn9laW2D58ync6/8ATOPgt9ThfevvDwp4atfCWh2+hWdxPdRW44e4kMj/AEGeFX0UAAV0dFfLcHcAYLJouVG8qjVnJ/otkvx82YYfCRp7bn5teKPh7D+0J+0B8etDlUSpovhXT/Dtm7/dS7ucahGwz/zzuIufofWvnyPw54ttf2XJv2nfGKPL4uXxhpGvySMpWb7NpEo02FGB5GGZ257EGv2opqIkahEUKo7AYFfcnUfm38P/AIr+CP2b/i38XrX4v3UujweMNVHiDR9Qa3mmh1CzuFZxHE8SNlotwXacckjtXzzY+HPFvhPwn4U/aL1/w7eR+FV+IupeKLjThCWmtdMvhAsF00OMjYYWKnoQUOcMDX7UvFHKAJUD7SGGRnBHQj3p9AH5tXPxp+G/xm/bF+DF98NtVOsWum2WvLPMIJoUV5bJ2CAzImWAGWAzjIzya+fvhnbfDf4Pp4n+Hfx48f8AjLwB4k0/VLqZYNNvr23stTt5SPLubdLaJ1dpAMEnk8e4X9o44o4UEcSBFHQKMAfgKGiidkd0DMhypIyQTxx6UAfll4m0T4ffAHVf2dPiJ4ej1OL4caRLrU1xdXkUstxaf23bx+UZ0WMMo3MeNueDjJ6/oT8XfiHYfCj4Z+I/iHqO1o9Es5JkRjgSTn5II/8AtpKyp+NeiSRxyoY5VDowwQRkEe4p9AH466b8AP2lPEH7Lepaa6+GpbPxQx8XSyS/bhrr3kgW6Uhl/cCZ1URgbcAMQSDk16Vp3xEtfjR8TP2TfHF1tnuby38RC7UgELfWlpGkpx2/fRl19sGv1AooA+Cvj9qafCb9pvwF8evF9pPL4Jg0i60a6vYYXnXTbmV5GSaRUBYLIJAmRkkBsAnAOd+0N8d/hn8Z/wBmf4qQfDfUpNWh0W1sfPuPs00MDNNdRkLG8qJvK7fmAHGR61+gpAYFWGQeopscccKLFCoRF4CqMAD2AoA+BP2mlX/hVvwElwN6eMfCwDdxm3lzg/gK6zRv+T/Nf/7EKH/0vjr7SooA/Lb4CeOPDWmeDfiT+y7r+oPoPxE1vWPENpYWk8Mw8030DeTKsojMarwSCzDIAIyGXPjXw3uvgXofwuT4a/GLxz478L+JbBJtO1LwtBeXohlMjsCkFrHC8TRTK2SpODk54IJ/aryovN8/YvmY27sDdj0z1xSmKIyCYoDIBgNjkA9s+lAH5qapZ+GP2Uf2hfC3jfxDDeD4bz+EI/DlnqskUlz/AGfcQziVVn2JuXei8fKCd5wMKwHrfxU+JvhP9or9nD4o2vwgluNe+wWTQ70tZo1ndQJiIPMVTJgKwwBncMAcgn7RdEkQxyKGVhggjIIPqKERI1CRqFVRgADAAoA/NHxL8UPB3x9/Yt1/wL8Mrp9W8S6H4c0p7/TkgmE0DWrwtKg3IqyEeS+PLLZwCOozwXxj/aM+G/xb8BfCDwz4Hmur6/tPEvh6a/8A9FmSGxeJWjMMs0iKhkZmOwIW3BWbOMZ/WmOKKLd5SKm4ljtAGSepOO9KkccYIjULuJJwMZJ6n60AfFfwVt4G/bB/aDumjBmjTw2ivj5gr2RLAH0JVSfoK+Xb/TfBvwq+P3xQg+OPjLxT4Ag8UakdV0jUNHvLq1stQtpCz+W5to33SQb9oyOOR6bv15pkkUUoCyoHAIIDDOCOh57igD47+EV18H/gz8EPE3xj8DSeINc8P6lPc6xeSXqSTajcyRt5UkqpMsTEPt3l3IyDuZgM4+nvBHi/SfiB4P0bxvoIlGna5axXkAmTZKI5lDAOuSAwzg4JHoSOap/EPwe/j/wdqPg3+1LjR4NWQQXE9qE842zsBPEhcMFMse5N4GV3ZHIFdDoei6Z4b0Ww8O6Jbra6dpdvFa20K52xwwoEjQZycKoA5oA/P79ob4keDvhP+2B8NPGvjy+OnaPa6DqMckwikmKtKZEQbIldzliBwOO9Yf7Qvxl+H37R3wuYfBW8n8WXXgTVtN1/UrCKzuYpJbGB3jkCrNEgfG/cQMkBSegr9MaKAPzX+JvxZ8GftP8Aj/4Q+EPgrNNrlxoniOz8Q6ndpbTRJptnZ8usryooV3yQByNygZywz5Z8QPiR4D+H/wAVLnX/ANl/xFqcHjnUvEZtNZ8HNazvY6nJ5rLNOqOmyMv94Ork4bKhOa/XhIo4yxjQLvO5sDGT6n3pvkw+b5/lr5uNu7A3Y9M9cUAfkr8efiB8O/h/8SfEfi/9n7xRqWh/FpNVgtNQ8NraTy2Ouy+YqOzQlDGTtZmEgfJIO0B3Dn9Cdb+Nnh7wv8Q/Bnwt8R6ffW+ueNLeWW2eOISWcUkKF5IpJgw+YYI+VWA4LbQQa9hMMJlE5jUyKMBsDcAewPWvO9R+Gun6z8TdK+JesXs11L4etJYNLsiFW3tZrrK3NyMDc8kke2MbjhFDYGWyADudWtJtQ0q8sLeUwS3MMkaSDqjOpUMPoTmvxc+FQ+EXw58BXvwv+OXjnxt4H8V6LNd213odje3sVpeJLI5R7SGGJ43SZGAJJw5y33WBr9s6Y0UbOsrIC6Z2sRyM9cH3oA/Mv4oaX4M/Z51z9m3xZYJfQ/DzwpJrMc13dRySTWq6xCjxGdQgcHLOduwEBSuMjFbHxI+JPhXS/jn8NP2rY3m1f4YXej3ujtqtvbTOlhcedMvnyRlBIquWMeduSA2AeA36OSRRzIYpkDo3BVhkH6g0u1duzA24xjtigDyH4XfHf4Z/Ge61eD4calJq0Oi+T59x9mmhgZptxCxvKibyu35gBxketewUyOOOFFihUIi8BVGAB7AU+gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsDxQp/sG7nUZa1C3A9c27CUf8AoNb9RTwpcwSW8nKSqVP0YYNAEdzcpb2kl3nKohYe/HH514HrIZ1WZuTuOT9ea9Cn1Bz4Q06KQ/vpY0jk/wB6IYf/AMeFcJqKb7R/9nBr4Xiyrzv2a6I5cQ+hzNFFFfnpynyT+0j4c8u603xVAvyzA2sx/wBpcvGfqRuH4Cvl2v0e+JPhz/hKfBep6Ui7p/L82H182L5lA/3sbfoa/OGuKvGzueDmFLlnfuFFFFYHCFFFaOkW32zVrK0xnzpo0/76YCk3bUEfWml232PTLSzxjyIY0/75UCsGRdkjJ/dJH5V1tctqJ8u5kA6nn86+Bz2N4Rn5npNFOR8fKKgoor5dskKQkKCT2parTPk7R2rKpPlVwP/V/fyio4ZUniSaM5SRQyn1BGRUlJO+qAKKKKYBRRXE/EbxBf8AhXwXqfiDTAhubJUZBINyHMiqQQCOCCe9cuOxkMPQniKnwxTb9ErsmUrJtnbUV4T4B+PXhbxZ5dhrBGj6m2Btlb9zI3+xIcAE/wB1sHsCa92rjyfPMJmFFV8HUU4+XTya3T9SadWM1eLCivz4/a5+C0OheAfHXxq0bxt4psdYt40uo7WDVnisEdpI4yqwqoIXBJwG61j2UPgH9lnwt4C+OHjDxR4x18+IxZ2UlvPqRvbSKXULVpXla3YKzLGFYqFLNnGATXrGh+j9FfMDftH6vB4SuPFd78KfFsDG/hsLKw+xxtfXTTRGUS+Ush8uJQMMxJAb5eoOLXw1/aW0f4iTeKNAfwtrOg+LfCdqby40K/gWO8ni2kobcbsPvO1Rnbyy9iDQB9K0V8FfDL9oDwN4G+DHjb4vX3/CT3em2/iue3vbbWZYri9s7i4eBHhgVW2rbwmQYTcWBDdcivSfC/7V+g658QdF8Ba94P8AEHhQeKRJ/Y19q1oLe3vjGu7C/OWRmXBUEZ5XcFLDIB9WUV8W+DPitoEvxk+LuvXlv4osdQ8KaTHLeaXqMsX9niC1V2SaygDEq0yJvBYgHcT/ABHFFP26PBf9maP4uufBfiW18Gao0MT6/NZKljBNLgFWbflljclGdeCykJv4yAfcFFfNnxN/aV0TwD42i+G2geGdY8b+Jvsn9oXNnosCzNa2mcCSVmZQC2RtUZzlQSNy56f4EfGvT/jz4SvPGmj6PdaPp8N/PZwC7eIyyrCFy7JGzeWckgo3IxkEgg0Ae2UV+cf7Smq2urftK+FPhz8V/GGoeDfhxe6K9zbSWV01jDeaqJmQx3FwBhQqbT8xwvy4ILmvqLTZvAv7M/weuNU8QeKNQ1Pw7pZe5F9qVyb65dLh/wB1DGwHzj5lWNVGO57mgD3qivlbwz+0RqHjycaPN8M/FXh6y1mzuZbPUr+zRLXYkLSAzMsjGEsB8gOdxIx1r5O/Zs/avsPhh+zr4Tj8U+EPEmoaLpclzBqPiCK1ElhA9xfSsn7xn3SBRIgYgcMQgy2BQB+rVFeB/E/9obwp8OrrQdD07T77xf4i8TxG403S9HiE9xNbqNxnYllVIsfxE84OAQrEVvh3+0l4I8cQ+J7fWbS98G6x4Li+0axp2sxCCe1t9hfz/lZg8RUZ3A9wSMMpIB9C0V+UP7Uf7TkfxN+A+ojwt4P8S6ZomoX1mthr89sILK4ENyrMQwk3orhSEJX5jwdp4r9CbP4taJe/GS/+CsdncLqun6QmsvcEJ9naGSVYQgO7fvy2eVxjvQB6rRX51ftd/GPTfF/wQ+M/g3Qobux1HwHd6JbXM7FUSVru8hdTCUYtgBSDuA/Gvq74o/Gjwr8G/Dmj3uvw3ep6lrUsdnpumafF599fXDKPkijyM4yNzEgDIH3ioIB7PRXzr8M/2jNF8eeM5vhr4g8Oat4J8Wx2v22LT9YhWM3NtnBkgkRmVwpzkcHg4ztbH0VQAUV84/tWfFjWfg78GdT8TeF1B168lg07TiwVglzdNtD7W4JRQzAEEEgAjGa+fvFf7LPjTwR8Or74h+G/in4nl+I+i2T6jLcz6g01leT26GWSBrdwR5TYKorEgZG4MMigD9D6K+ItG/bK05fBPww1C98Larr/AIg+IdhcyxWujRRzH7VYfu7hQryIQpkDEHkIoJY4BrvPBv7UGj+MG8TeH28Ja3pHjTwxZm9k8PXkCJfXMPGHtvn2SAllGSV6jsc0AfUNFfCn7J/7RHxE+I/wwXVPGvhfWNXv4LbUr06vFb28dnfNBO/l2lsqMp83biNQUAJU5buavwy/aA8DeBfgv42+L99/wlF3ptv4rnt7221mWK4vbO4neBHhgVX2rbwmQYTduBDdeKAPvWivie6/ba8O6TrdpoXiD4eeLdNuNbjMmiJJp6mXVeVCpDH5gYO24HDdBjcVJAPd+Av2nNL8dw+MdNj8Ia5pnizwXCs91oFzAi38ySLujMA37W3jGMkdVIyCCQD6dor4Q+D3xqtPDnwV0zxN4M8NeN/iBa6tqupKy3HlX+o2rJJ8wlYOFWLPCAEnOc9a9A+Gv7V2jfEPxtrXgi78Ha34Xn8NWjXuq3GrJBBDYxBQ6+eRKSpdTlRjOAT0BIAPq+ivitv22/CCWQ8XyeCvEyeAGnEA8TGxAsTmTyhNt3+b5JfgNtzn5du7iu0+IP7VHhPwJ8QbD4aWmg6t4n1rWNJi1XTk0iKO4F2k0joqId64wkbys7YQIpOScAgH1BRXM+DdevvFHhbTPEOpaRc6Bc6hCsz2F4ALi33dElC5AbHJHUdDzkVQ+IfxB8K/C3wfqPjrxpeCy0nTEDSPgszMxCoiKOWd2IVR6nsMmgDtaK+TfDX7Weiah4n0Hw5418FeIfA0fiuQQ6ReaxapFbXUr42RMyOxjkfI2qw78kV514H/AGkfiTqf7SPxA8B6h4R1vUdD0650qztLeG3th/ZSyqRLdXT7wxjnJ81DuciMDhTxQB970V8z+O/2mtE8M+OLv4beDvC+s+PPEemRJNqFvo0CyR2SOAVE8sjKodlOVUZ9Dg8V7R4C8Xf8J34UsfFP9j6hoP20Pmy1SD7NeQlHKESRbm25K5HPIwe9AHYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU12VEZ3OFUEk+woA8avZit9e6Zn5bK5lwP+u58/+UgFUpk8yF0/vAiqbTtL4i1CR/8Al6SOcfXLK35ALWhX5nja3takp9zhk7u5xdFSzJsmdP7rEVFXyDVnYxCvzp+Kfhz/AIRfxxqVhGu23mf7RD6eXN82B7Kcr+FfotXzL+0h4c+0aZp/imBMvaObeYj/AJ5ycoT7BgR/wKsK8bxOHMKXNTv2PkGiiiuE8EK7DwDbfafFlgpGQjNIf+AKSP1xXH16d8K4Q2vXF03SC3bH+8zKB+maxxErQbLpr3kfQUkgjXPftXL6kCZg5/iFbTsXbcay9SX5Eb0JH518Xmq5qL8jscrsyaKKK+RGNdtqk1RPPJqWV9zYHQVFXnYipd2A/9b9z/Ad9/aXgvRLsnLNaRKx9WRQrfqDXW15D8Dr77Z8PrWHOTZzTQn/AL68wfo9evV4vDmK9vl9Cr3jH77K/wCJlRleCYUUUV7RqFeW/Gv/AJJdr3/XKP8A9GpXqVQ3Ftb3cRguokmibBKOoZSQcjIPHBGRXn5vgnicJWwydueMo37XTRFSPNFo/PLwD8EfFnjXy724T+ytLbB8+ZTudf8ApnHwW+pwvvX3h4U8NWvhLQ7fQrO4nuorccPcSGR/oM8KvooAAro6K+W4O4AwWTRcqN5VGrOT/RbJfj5sww+EjT23PmH9s7/k2Dx//wBeUf8A6URV4T+0Bbw3fwl/ZutLqMSwzeKvCaOjDKsrWzggjuCOK/RSivuTqPi39s3xz4o8H6L4Ns7PW73wn4T1vVltfEOuacjNdWNoQNojdVZo/MJb51GQVAGclT8+/s5ah8PbH9sS4l8C61rGt6DrvhOe1sdU1p7iV9Svbe6hln+zzXCqZEREbOAFBVsDGM/qjJGkqNHKodGGCCMgj3FBjjJRioyn3Tjpxjj04oA/F/xN/wAmbfGz/spF3/6V2lfaH7VAH/CzfgE4+8PF0Yz3wUGRX2nRQB+c+rf8l8/ac/7Ey0/9N7VxvjxQ3/BLy1yM40rSCPb/AImdvX6lUUAfkx8dfiDq/wAEP2jbPxd4D1G10vVvE3ha1iv/AO27K8uLC4KEJCbY2CSzNMgjG4FVQADltxA+rf2LNP8AAum/CGZfBniuPxhcX2p3N9q15HE9sF1C5CGSPyJQskYVQgG4Ddy2BnA7T4nfDX416/4qTxP8Lvik/hSFrZbaTTbjTYL+0JVifNTeVKyfNySCTgDIUYrS+BPwTi+DWka19u1qbxJ4h8T38mp6tqU0awfaLmXqUhQlY1HJABPJPOMAAHCftDfGT9mvw7eL8L/2gYllgv7L7dEtzYTXUDAs8YEckKOyTDacEbSoIO4ZFfB+keB/H+u/sj6neeHtE1C/8LaB44XXdC026Dvd3Ph6E48tUOSUy5fAHJDsMjGf2Yntra5CrcxJKFIYB1DYI6EZ71PQB8p6B+1v8DvibJB4S8F6xPf6zrFpct9mFlcI1t5UDyMLhmQImMbfvEFsAEg5r5h8Fokn/BLu4V1DAaVqhwRnkanOQfwPIr9RkhijZ3jRVaQ5YgAFj6n1qSgD8gPG2h6f4S+I/wAM/ij8SdZ1/wAOeCdZ8DaZpaa3oU81u9jexIj+VcSQK7iKRTkDHzM2eiNjsrj4XfDP4o/C74s+KfgJ4i8SeNvEt3pKaW95q81xcC7jjkiujDbvcRI0jEQ+WACcE4wAwJ/Ut0SRDHIoZWGCCMgg9iKVVVFCqAABgAdAKAPx0+OP7SHwm8c/sj6d8NdBuZbTxTZx6Ra3GkPaTxvZvYyRLKHYoIwilcKd3OQMZyB9B/EH4geHPgX+2dJ49+Jckul+GfEvhFNPttQEEs0P2uG6EjRHykdt21M8DjcueDmv0GWKNXaRUAd8biBycdMnvQ8ccoAkUOAQRkZwRyD9RQB+Lev3V38X/Af7Vus+DtNvJBqN/wCHby3t5oHjuXtreXzDJ5JG8BokMigjO0jjNetftC6voXxCk+Cv7R1he60nw5sVvrbUb/RjNbX+mNMPJE+VUyIqyxskjAEEJtUneuf1NpiRxxrsjUKvJwBgcnJ/M0AfA/wH0P8AZ78cfFuDxr8P/HHijx3r3hSzkKXerXV3c2kUV2GhaISXMKfNhy2wH35wcfUHw3+M3hf4oa/4x8NaHa31nfeCL/8As++W9gEIdzuCyRYZt0bbGIJ2tjBKgEZ9YjijhQRQoEReiqMAfgK85+HPw10/4ex6zdi9m1bWfEd6+oalf3AUS3EzAIgCoAqRxRqqRoowAO5JJAPJf2xPhp4i+J/wP1LTPB8Judd0e5ttVsoVGWmltGyyL/tGNm2ju2B3rwvxj+3D4C8Y/Cm98MeDbLUb34l+ILKTTo/D6WNx59tfXEZibexQIUjZiRtYsQAMA5x+i9RCGFZWnWNRI4AZgBuIHQE9eKAPycvbXRf2X/G/7MNp8SLxdPtfDuk6+NRmRHlWG4vYCXGIg7MqzTbSwGMfMcDNew/D3xPp/wAff2u2+LXw4imn8G+FvDT6RLqrwyQQ3t5NM0gii8xVZggkycgEbc9CufpXxn8IbnxX8bPh98WE1GOC38FQ6pFJaNEWa4/tG3MIIfIC7M5OQc9K9wREiRY41CIowABgAegFAH51/sKfFfwXoHgPSP2dvEN2+m/EDSbzU4ZNLmgmWQ7ZZblm37PLACE8FgeOnIz85eJv+TNvjZ/2Ui7/APSu0r9nxDEsjTKiiRgAWAGSB0BNSUAfFv7Q3/JyX7Ov/YQ1z/0lhpvg5FX9u34gFVAL+FNOLY7nzYxk/gMV9qUUAfkz8Lf2gNO/Z7/Yw/4SFdk2v6lrGr2uk278h7gzsTI4H/LOIfM3qcLkFga1/hf4g+DHi74G+Lfgd8MvGf8AwlHxU8d6ZqN5qFy9peQPqGpSQtJKpmuII0EY5jXLDglsbmNfqfRQB+MfgK5/Z21b4a6N8NfiP488e6b4gEVvpWo+FPtV+yrcoVjdEtBA0Yh3DeqZ+VcAjcMV9VaD4Y0rw3+3JoXhuwRpLXQPhlDbWjTHfIqQX3kKxbHLbMgnvk+tfd/lR+Z52weYRt3Y5x1xn0p9AHknw3+M3hf4oa/4x8NaHa31nfeCL/8As++W9gEIdzuCyRYZt0bbGIJ2tjBKgEZ8R/bq8DeI/HHwN2+G7S51B9D1Sz1O5tbMkXM9rAHSVYsAncocOMDI25GcYr6A+HPw10/4ex6zdi9m1bWfEd6+oalf3AUS3EzAIgCoAqRxRqqRoowAO5JJ9JoA/LzwbB+y58Y/FvhLQbD4leN/GGqreR6lbWGoXt7cxWdzaK0oe4E0PlxkYKbg3JOAcHNdXB8VvBfwG/a/+LN18Vrt9CsPGlpoc+l3TwTSxXAs7UQyKpiR+Q5IyeMqRnpX6KpFFGWaNApc5YgYyfU+ppHhikZHkRWaM5UkAlT6j0oA/OPwR8S/Cf7Mnxv+Lek/GuSXQYPGer/21pOqvbTS215bOGPkiSJGO6HftwRwdwznG77r+Hnj/wAO/FDwlZeN/CbTSaVqBk8hp4XgdljkaPdskAYK23KkjlSDXYywxTLsmRZFBBwwBGR0PNSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYfiK6+zaVLg4aXEY/4F1/TNblcF4wud00Fop4QFz9TwP5V52a1/Z0JP5feRUdkea3X7rWLGbtKksJ9yQHH/oBrXrI1j5Ira5/54XETfQO3lk/kxrXr88OI5nUU23b/AO1g1RrX1dMSRv6gj8v/ANdZFfPYqNqjRDCud8W6DF4n8NajoMuP9MhZUJ6LIOUb8GANdFRWDJkrqzPyrmhlt5XgmUpJGxVlPUMDgg/So69e+N3hz/hH/Hl1NEu231QC6TA43PxIPrvBP0IryAkKCzHAHJNeZJWdj5epDlk4sZLKkMbSynaqjJNepfBhnvF1fUnGELRRRj0ChmP4nIzXz3qupG8k8uM4hQ8e59f8K+nfgxafZ/Bonx/x9XEsmfYYT/2WvMx9W8LIxo1Oadkes1Tvl3W5P90g1cqKdd0Lr6g18/iIc1OUfI70c5UcjbV9zUlUpH3tntXwdepyo0GUUUV5oH//1/1t/Zzvt+k6zppP+pnjmA/66qVP/oFfR9fH37PN95Pim/sCcC5tCw92jdcfoxr7Br4HwyxXtcmpLrG6/F/o0cmBlekgooor746woork/HPiWTwh4Vv/ABJFALlrFUbyy20MGdVIzg44Poa58XioUKU69V2jFNv0SuxSkkrs6yivNfA3xW8JePI1i064+z3+MtaTYWUY67ezj3X8QK9KrHLsyw+LpKvhpqUX1Tv/AF6ChNSV4sKK+DfitqHxe8X/ALV9l8IvAvxBuvBOmDwousEwWkF2rXC3ckJ3JLjO5Sufmx8vTnNWPhb+0F4i8CeIviZ8Pf2jvEljej4cCynGvxQC2FzBfJvRJIYhtEw3IFSNck7l+YgMe4o+6qK8F+G37S3wh+Kuvt4V8LarLHrPk/aI7O+tZ7KaeD/nrCJ0USLgZ+UkgckAVm6b+1f8DNY8bx+ANK12W71Ga9GmxyxWV09o97u2+QtysRiLd87tmOd2KAPoyiviPwp+2Joeq/Hnxh8MNZhlh0rTrnT9P0iSDTrx55bqbKXBuSFZY0EpCxsyopUbssDmvRPGf7XHw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Gold - 14th place with teammates: Bojan, Shai, Yasin, Chris, 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Initialize Enviroment","metadata":{}},{"cell_type":"code","source":"#!pip install efficientnet >> /dev/null\n!pip install ../input/tf-efficientnet-b4/efficientnet-1.0.0-py3-none-any.whl >> /dev/null","metadata":{"_kg_hide-input":false,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-05-19T16:30:39.127625Z","iopub.execute_input":"2022-05-19T16:30:39.127956Z","iopub.status.idle":"2022-05-19T16:30:48.322572Z","shell.execute_reply.started":"2022-05-19T16:30:39.127923Z","shell.execute_reply":"2022-05-19T16:30:48.32153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# LOAD LIBRARIES\nimport time\nstartNB = time.time()\nimport tensorflow as tf, os\nimport tensorflow.keras.backend as K\nimport efficientnet.tfkeras as efn\nimport numpy as np, pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import recall_score, accuracy_score\nimport albumentations as albu, cv2, gc\nprint('TensorFlow version =',tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2022-05-19T16:32:11.081507Z","iopub.execute_input":"2022-05-19T16:32:11.081901Z","iopub.status.idle":"2022-05-19T16:32:18.240322Z","shell.execute_reply.started":"2022-05-19T16:32:11.081866Z","shell.execute_reply":"2022-05-19T16:32:18.238996Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# CONFIGURE GPUs\n#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\ngpus = tf.config.list_physical_devices('GPU'); print(gpus)\nif len(gpus)==1: strategy = tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\nelse: strategy = tf.distribute.MirroredStrategy()","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:21:54.729595Z","iopub.execute_input":"2022-05-19T14:21:54.730144Z","iopub.status.idle":"2022-05-19T14:21:55.162349Z","shell.execute_reply.started":"2022-05-19T14:21:54.730101Z","shell.execute_reply":"2022-05-19T14:21:55.161291Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# ENABLE MIXED PRECISION for speed\n#tf.config.optimizer.set_jit(True)\ntf.config.optimizer.set_experimental_options({\"auto_mixed_precision\": True})\nprint('Mixed precision enabled')","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:21:57.4588Z","iopub.execute_input":"2022-05-19T14:21:57.459143Z","iopub.status.idle":"2022-05-19T14:21:57.466589Z","shell.execute_reply.started":"2022-05-19T14:21:57.459113Z","shell.execute_reply":"2022-05-19T14:21:57.465376Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# VERSION MAJOR and MINOR for logging\nmm = 1; rr = 0\n\n# BEGIN LOG FILE\nf = open(f'log-{mm}-{rr}.txt','a')\nprint('Logging to \"log-%i-%i.txt\"'%(mm,rr))\nf.write('#############################\\n')\nf.write(f'Trial mm={mm}, rr={rr}\\n')\nf.write('efNetB4, batch_size=512, seed=42, 64x64, fold=0, LR 1e-3 with 0.75 decay\\n')\nf.write('#############################\\n')\nf.close()\n\nBATCH_SIZE = 512\nDIM = 64","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:22:01.959001Z","iopub.execute_input":"2022-05-19T14:22:01.959336Z","iopub.status.idle":"2022-05-19T14:22:01.968776Z","shell.execute_reply.started":"2022-05-19T14:22:01.959287Z","shell.execute_reply":"2022-05-19T14:22:01.967599Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![white6.jpg](attachment:white6.jpg)","metadata":{},"attachments":{"white6.jpg":{"image/jpeg":"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STEP 1 : Preprocess\nLoad image data and resize. For optimal GPU speed, we do this one time before the data loader instead of every time each epoch in the data loader. In this tutorial, we only load 25% of data. ","metadata":{}},{"cell_type":"code","source":"%%time\ntrain = []\n#for x in [0,1,2,3]:\nfor x in [0]:\n    f = 'train_image_data_%i.parquet'%x\n    print (f,end='')\n    img = pd.read_parquet('../input/bengaliai-cv19/'+f) # Pandas dataframe\n    img = img.iloc[:,1:].values.reshape((-1,137,236,1)) # Numpy Array\n    img2 = np.zeros((img.shape[0],DIM,DIM,1),dtype='float32')\n    for j in range(img.shape[0]):\n        img2[j,:,:,0] = cv2.resize(img[j,],(DIM,DIM),interpolation = cv2.INTER_AREA)\n        if j%1000==0: print(j,', ',end='')\n    print()\n    img2 = (255 - img2)/255.\n    train.append(img2)","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:37:10.65024Z","iopub.execute_input":"2022-05-19T14:37:10.650629Z","iopub.status.idle":"2022-05-19T14:38:06.037013Z","shell.execute_reply.started":"2022-05-19T14:37:10.650594Z","shell.execute_reply":"2022-05-19T14:38:06.036047Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = np.concatenate(train) ##Gắn 2 cái hình lại với nhau\nprint('Train Shape',X_train.shape) ","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:37:07.512816Z","iopub.execute_input":"2022-05-19T14:37:07.513234Z","iopub.status.idle":"2022-05-19T14:37:07.834286Z","shell.execute_reply.started":"2022-05-19T14:37:07.513191Z","shell.execute_reply":"2022-05-19T14:37:07.830769Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del img, img2, train\n_ = gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:37:03.374015Z","iopub.execute_input":"2022-05-19T14:37:03.374366Z","iopub.status.idle":"2022-05-19T14:37:03.400077Z","shell.execute_reply.started":"2022-05-19T14:37:03.374326Z","shell.execute_reply":"2022-05-19T14:37:03.398674Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Preprocess - EDA","metadata":{}},{"cell_type":"code","source":"row=3; col=4;\nplt.figure(figsize=(20,(row/col)*12))\nfor x in range(row*col):\n    plt.subplot(row,col,x+1)\n    plt.imshow(X_train[x,:,:,0])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:31:02.299393Z","iopub.execute_input":"2022-05-19T14:31:02.299752Z","iopub.status.idle":"2022-05-19T14:31:04.065208Z","shell.execute_reply.started":"2022-05-19T14:31:02.299707Z","shell.execute_reply":"2022-05-19T14:31:04.063108Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('../input/bengaliai-cv19/train.csv')\ny_train = train.iloc[:,1:4].values[:len(X_train)]\nprint('Labels\\nGrapheme Root, Vowel Diacritic, Consonant Diacritic')\ny_train","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:31:19.237479Z","iopub.execute_input":"2022-05-19T14:31:19.237859Z","iopub.status.idle":"2022-05-19T14:31:19.408052Z","shell.execute_reply.started":"2022-05-19T14:31:19.237819Z","shell.execute_reply":"2022-05-19T14:31:19.407069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![white6.jpg](attachment:white6.jpg)","metadata":{},"attachments":{"white6.jpg":{"image/jpeg":"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STEP 2: Data Augmentation\n# IMPORTANT: Optimize data loader for GPU Speed!\nBuild data loader. We want the time to process one batch on CPU less than the time to train one batch on GPU. Also we want to maximize batch size to fully utilize GPU compute.","metadata":{}},{"cell_type":"code","source":"class DataGenerator(tf.keras.utils.Sequence):\n    'Generates data for Keras'\n    def __init__(self, X, y, list_IDs, batch_size=BATCH_SIZE, shuffle=False, augment=False, \n            labels=True, cutmix=False, yellow=False): \n\n        self.X = X\n        self.y = y\n        self.augment = augment\n        self.list_IDs = list_IDs\n        self.shuffle = shuffle\n        self.batch_size = batch_size\n        self.labels = labels\n        self.cutmix = cutmix\n        self.yellow = yellow\n        self.on_epoch_end()\n        \n    def __len__(self):\n        'Denotes the number of batches per epoch'\n        ct = len(self.list_IDs) // self.batch_size\n        ct += int((len(self.list_IDs) % self.batch_size)!=0)\n        return ct\n\n    def __getitem__(self, index):\n        'Generate one batch of data'\n        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n        X, y = self.__data_generation(indexes)\n        if self.augment: X = self.__augment_batch(X)\n        if self.labels: return X, [y[:,0:168],y[:,168:179],y[:,179:186]]\n        else: return X\n\n    def on_epoch_end(self):\n        'Updates indexes after each epoch'\n        self.indexes = np.arange( len(self.list_IDs) )\n        if self.shuffle: np.random.shuffle(self.indexes)\n\n    def __data_generation(self, indexes):\n        'Generates data containing batch_size samples'        \n        X = self.X[ self.list_IDs[indexes], ]\n        if self.yellow: X = np.ones((len(indexes),DIM,DIM,1))\n        y = np.zeros((len(indexes),186))\n        for j in range(len(indexes)):\n            y[j, int(self.y[ self.list_IDs[indexes[j]],0]) ] = 1\n            y[j, 168 + int(self.y[ self.list_IDs[indexes[j]],1]) ] = 1\n            y[j, 179 + int(self.y[ self.list_IDs[indexes[j]],2]) ] = 1\n            \n        if self.cutmix:\n            for j in range(len(indexes)):\n                \n                # CHOOSE RANDOM CENTER\n                yy = np.random.randint(0,DIM)\n                xx = np.random.randint(0,DIM)\n                z = np.random.choice(self.list_IDs)\n                               \n                # CHOOSE RANDOM WIDTH AND HEIGHT\n                h = np.random.randint(DIM//2-DIM//16,DIM//2+DIM//16)\n                w = np.random.randint(DIM//2-DIM//16,DIM//2+DIM//16)\n                        \n                # CUT AND MIX IMAGES\n                ya = max(0,yy-h//2); yb = min(DIM,yy+h//2)\n                xa = max(0,xx-w//2); xb = min(DIM,xx+w//2)\n                X[j,ya:yb,xa:xb,0] = self.X[z,ya:yb,xa:xb,0]\n                \n                # CUT AND MIX LABELS\n                r = (yb-ya)*(xb-xa)/DIM/DIM\n                y2 = np.zeros((1,186))\n                y2[0, int(self.y[ z,0]) ] = 1\n                y2[0, 168 + int(self.y[ z,1]) ] = 1\n                y2[0, 179 + int(self.y[ z,2]) ] = 1                \n                y[j,] = (1-r)*y[j,] + r*y2[0,]\n                    \n        return X, y\n \n    def __random_transform(self, img):\n        composition = albu.Compose([\n            albu.OneOf([\n                albu.ShiftScaleRotate(rotate_limit=8,scale_limit=0.16,shift_limit=0,border_mode=0,value=0,p=0.5),\n                albu.CoarseDropout(max_holes=16,max_height=DIM//10,max_width=DIM//10,fill_value=0,p=0.5)\n            ], p=0.5),\n            albu.ShiftScaleRotate(rotate_limit=0, scale_limit=0., shift_limit=0.12, border_mode=0, value=0, p=0.5)\n        ])\n        return composition(image=img)['image']\n    \n    def __augment_batch(self, img_batch):\n        for i in range(img_batch.shape[0]):\n            img_batch[i, ] = self.__random_transform(img_batch[i, ])\n        return img_batch","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:31:24.342021Z","iopub.execute_input":"2022-05-19T14:31:24.342362Z","iopub.status.idle":"2022-05-19T14:31:24.387991Z","shell.execute_reply.started":"2022-05-19T14:31:24.342328Z","shell.execute_reply":"2022-05-19T14:31:24.386658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Augmentation - EDA\nCutmix augmentation mixes two images together. In the examples below, the first of the two images has been displayed as all yellow to help us visualize the augmentation better. (During training, the first image will not be converted to yellow). We also observe rotation, scaling, shift and coarse dropout augmentation below.","metadata":{}},{"cell_type":"code","source":"print('Cutmix Augmentation with first image displayed as all yellow')\ngen = DataGenerator(X_train,y_train,np.arange(len(X_train)),shuffle=True,augment=True,\n            batch_size=BATCH_SIZE,cutmix=True,yellow=True)\n\nrow=3; col=4;\nplt.figure(figsize=(20,(row/col)*12))\nfor batch in gen:\n    for j in range(row*col):\n        plt.subplot(row,col,j+1)\n        plt.imshow(batch[0][j,:,:,0])\n    plt.show()\n    break","metadata":{"execution":{"iopub.status.busy":"2022-05-19T14:31:32.365548Z","iopub.execute_input":"2022-05-19T14:31:32.365915Z","iopub.status.idle":"2022-05-19T14:31:33.979182Z","shell.execute_reply.started":"2022-05-19T14:31:32.365883Z","shell.execute_reply":"2022-05-19T14:31:33.978155Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![white6.jpg](attachment:white6.jpg)","metadata":{},"attachments":{"white6.jpg":{"image/jpeg":"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STEP 3: Build Model\nWe will use transfer learning from a pretrained ImageNet 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build_model():\n    \n    inp = tf.keras.Input(shape=(DIM,DIM,1))\n    inp2 = tf.keras.layers.Concatenate()([inp, inp, inp])\n    #base_model = efn.EfficientNetB4(weights='imagenet',include_top=False, input_shape=(DIM,DIM,3))\n    base_model = efn.EfficientNetB4(weights=None,include_top=False, input_shape=(DIM,DIM,3)) \n    base_model.load_weights('../input/tf-efficientnet-b4/efnB4.h5')\n\n    x = base_model(inp2)\n    x = tf.keras.layers.GlobalAveragePooling2D()(x)    \n    x1 = tf.keras.layers.Dense(168, activation='softmax',name='x1',dtype='float32')(x)\n    x2 = tf.keras.layers.Dense(11, activation='softmax',name='x2',dtype='float32')(x)\n    x3 = tf.keras.layers.Dense(7, activation='softmax',name='x3',dtype='float32')(x)\n    \n    model = tf.keras.Model(inputs=inp, outputs=[x1,x2,x3])\n    opt = tf.keras.optimizers.Adam(lr=0.00001)\n    wgt = {'x1': 1.5, 'x2': 1.0, 'x3':1.0}\n    model.compile(loss='categorical_crossentropy', optimizer = opt,\\\n              metrics=['categorical_accuracy'], loss_weights=wgt)\n        \n    return model","metadata":{"execution":{"iopub.status.busy":"2022-05-19T13:30:49.041811Z","iopub.execute_input":"2022-05-19T13:30:49.042312Z","iopub.status.idle":"2022-05-19T13:30:49.055368Z","shell.execute_reply.started":"2022-05-19T13:30:49.042273Z","shell.execute_reply":"2022-05-19T13:30:49.054594Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Validation Metric and Logging\nCompetition metric is macro recall","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nclass CustomCallback(tf.keras.callbacks.Callback):\n    def __init__(self, valid_data, target, fold, mm=0, rr=0, patience=10):\n        self.valid_inputs = valid_data\n        self.valid_outputs = target\n        self.fold = fold\n        self.patience = patience\n        self.mm = mm\n        self.rr = rr\n        \n    def on_train_begin(self, logs={}):\n        self.valid_f1 = [0]\n        \n    def on_epoch_end(self, epoch, logs={}):\n        \n        preds = self.model.predict(self.valid_inputs)\n        preds0 = np.argmax(preds[0],axis=1)\n        preds1 = np.argmax(preds[1],axis=1)\n        preds2 = np.argmax(preds[2],axis=1)\n        \n        r1 = recall_score(self.valid_outputs[0],preds0,average='macro')\n        r2 = recall_score(self.valid_outputs[1],preds1,average='macro')\n        r3 = recall_score(self.valid_outputs[2],preds2,average='macro')\n                \n        a1 = accuracy_score(self.valid_outputs[0],preds0)\n        a2 = accuracy_score(self.valid_outputs[1],preds1)\n        a3 = accuracy_score(self.valid_outputs[2],preds2)\n        \n        f1 = 0.5*r1+0.25*r2+0.25*r3\n                \n        # LOG TO FILE\n        f = open('log-%i-%i.txt'%(self.mm,self.rr),'a')\n        f.write('#'*25); f.write('\\n')\n        f.write('#### FOLD %i EPOCH %i\\n'%(self.fold+1,epoch+1))\n        f.write('#### ACCURACY: a1=%.5f, a2=%.5f, a3=%.5f\\n' % (a1,a2,a3) )\n        f.write('#### MACRO RECALL: r1=%.5f, r2=%.5f, r3=%.5f\\n' % (r1,r2,r3) )\n        f.write('#### CV/LB: %.5f\\n' % f1 ) \n       \n        print('\\n'); print('#'*25)\n        print('#### FOLD %i EPOCH %i'%(self.fold+1,epoch+1))\n        print('#### ACCURACY: a1=%.5f, a2=%.5f, a3=%.5f' % (a1,a2,a3) )\n        print('#### MACRO RECALL: r1=%.5f, r2=%.5f, r3=%.5f' % (r1,r2,r3) )\n        print('#### CV/LB: %.5f' % f1 )\n        print('#'*25)\n\n        self.valid_f1.append(f1)\n        x = np.asarray(self.valid_f1)\n        if np.argsort(-x)[0]==(len(x)-self.patience-1):\n            print('#### CV/LB no increase for %i epochs: EARLY STOPPING' % self.patience)\n            f.write('#### CV/LB no increase for %i epochs: EARLY STOPPING\\n' % self.patience)\n            self.model.stop_training = True\n            \n        if (f1>0.000)&(f1>np.max(self.valid_f1[:-1])):\n            print('#### Saving new best...')\n            f.write('#### Saving new best...\\n')\n            self.model.save_weights('fold%i-m%i-%i.h5' % (self.fold,self.mm,self.rr))\n            \n        f.close()","metadata":{"execution":{"iopub.status.busy":"2022-05-19T13:30:49.056993Z","iopub.execute_input":"2022-05-19T13:30:49.057375Z","iopub.status.idle":"2022-05-19T13:30:49.080932Z","shell.execute_reply.started":"2022-05-19T13:30:49.057336Z","shell.execute_reply":"2022-05-19T13:30:49.079883Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![white6.jpg](attachment:white6.jpg)","metadata":{},"attachments":{"white6.jpg":{"image/jpeg":"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STEP 4: Training Schedule","metadata":{}},{"cell_type":"code","source":"# CUSTOM LEARNING SCHEUDLE\nLR_START = 1e-5\nLR_MAX = 1e-3\nLR_RAMPUP_EPOCHS = 5\nLR_SUSTAIN_EPOCHS = 0\nLR_STEP_DECAY = 0.75\n\ndef lrfn(epoch):\n    if epoch < LR_RAMPUP_EPOCHS:\n        lr = (LR_MAX - LR_START) / LR_RAMPUP_EPOCHS * epoch + LR_START\n    elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:\n        lr = LR_MAX\n    else:\n        lr = LR_MAX * LR_STEP_DECAY**((epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS)//10)\n    return lr\n    \nlr2 = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose = True)\n\nrng = [i for i in range(100)]\ny = [lrfn(x) for x in rng]\nplt.plot(rng, y); \nplt.xlabel('epoch',size=14); plt.ylabel('learning rate',size=14)\nplt.title('Training Schedule',size=16); plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-05-19T13:30:49.082815Z","iopub.execute_input":"2022-05-19T13:30:49.083428Z","iopub.status.idle":"2022-05-19T13:30:49.223698Z","shell.execute_reply.started":"2022-05-19T13:30:49.083388Z","shell.execute_reply":"2022-05-19T13:30:49.222791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Train Model\nWhen validation score doesn't increase for 15 epochs, we stop training to save time. In this tutorial, we only train for 20 epochs. If you increase training to 150 epochs, then this model can score CV/LB 0.98. (Also change code to load 100% train images instead of 25%). Using larger image size and larger EfficientNet can increase CV/LB to 0.99","metadata":{}},{"cell_type":"code","source":"# TRAIN MODEL\n\noof1 = np.zeros((X_train.shape[0],168))\noof2 = np.zeros((X_train.shape[0],11))\noof3 = np.zeros((X_train.shape[0],7))\n\n\nskf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\nfor fold,(idxT,idxV) in enumerate(skf.split(X_train,y_train[:,0])):\n         \n    print('#'*25)\n    print('### FOLD %i' % (fold+1))\n    print('### train on %i images. validate on %i images'%(len(idxT),len(idxV)))\n    print('#'*25)\n    \n    K.clear_session()\n    with strategy.scope():\n        model = build_model()\n    \n    train_gen = DataGenerator(X_train,y_train,idxT,shuffle=True,augment=True,\n            batch_size=BATCH_SIZE,cutmix=True)\n    val_x = DataGenerator(X_train,y_train,idxV,shuffle=False,augment=False,cutmix=False,\n            labels=False,batch_size=BATCH_SIZE*4)\n    val_y = [y_train[idxV,0],y_train[idxV,1],y_train[idxV,2]]\n        \n\n    cc = CustomCallback(valid_data=val_x, target=val_y, fold=fold, mm=mm, rr=rr, patience=15)\n    h = model.fit(train_gen, epochs = 20, verbose=1, callbacks=[cc,lr2])\n\n    print('#### Loading best weights...')\n    model.load_weights('fold%i-m%i-%i.h5' % (fold,mm,rr))\n    \n    val_x = DataGenerator(X_train,y_train,idxV,shuffle=False,augment=False,cutmix=False,\n            labels=False,batch_size=BATCH_SIZE*4)\n    oo = model.predict(val_x)\n    oof1[idxV,] = oo[0]\n    oof2[idxV,] = oo[1]\n    oof3[idxV,] = oo[2]\n\n    # SAVE OOF and IDXV\n    np.save('oof1-%i-%i'%(mm,rr),oof1)\n    np.save('oof2-%i-%i'%(mm,rr),oof2)\n    np.save('oof3-%i-%i'%(mm,rr),oof3)\n    np.save('idxV-%i-%i'%(mm,rr),idxV)\n    np.save('y_train-%i-%i'%(mm,rr),y_train)\n    break","metadata":{"execution":{"iopub.status.busy":"2022-05-19T13:30:49.225495Z","iopub.execute_input":"2022-05-19T13:30:49.225973Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![white6.jpg](attachment:white6.jpg)","metadata":{},"attachments":{"white6.jpg":{"image/jpeg":"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STEP 5: Post Process\nCompetition metric is macro recall. Training loss did not optimize this so we will below.","metadata":{}},{"cell_type":"code","source":"oo = model.predict(val_x,verbose=1)\noof1[idxV,] = oo[0]\noof2[idxV,] = oo[1]\noof3[idxV,] = oo[2]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof1b = np.argmax(oof1[idxV,],axis=1)\noof2b = np.argmax(oof2[idxV,],axis=1)\noof3b = np.argmax(oof3[idxV,],axis=1)\n\nr1 = recall_score(y_train[idxV,0],oof1b,average='macro')\nr2 = recall_score(y_train[idxV,1],oof2b,average='macro')\nr3 = recall_score(y_train[idxV,2],oof3b,average='macro')\nr = 0.5*r1+0.25*r2+0.25*r3\nprint('CV/LB without Post Process %.5f'%r)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"EXP = -0.9\n\ns = pd.Series(oof1b)\nvc = s.value_counts().sort_index()\nmat1 = np.diag(vc.astype('float32')**(EXP))\n\ns = pd.Series(oof2b)\nvc = s.value_counts().sort_index()\nmat2 = np.diag(vc.astype('float32')**(EXP))\n\ns = pd.Series(oof3b)\nvc = s.value_counts().sort_index()\nmat3 = np.diag(vc.astype('float32')**(EXP))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof1b = np.argmax(oof1[idxV,].dot(mat1),axis=1)\noof2b = np.argmax(oof2[idxV,].dot(mat2),axis=1)\noof3b = np.argmax(oof3[idxV,].dot(mat3),axis=1)\n\nr1 = recall_score(y_train[idxV,0],oof1b,average='macro')\nr2 = recall_score(y_train[idxV,1],oof2b,average='macro')\nr3 = recall_score(y_train[idxV,2],oof3b,average='macro')\nr = 0.5*r1+0.25*r2+0.25*r3\nprint('CV/LB with Post Process %.5f'%r)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"# Let's Review","metadata":{}},{"cell_type":"markdown","source":"![slide3.jpg](attachment:slide3.jpg)\n![slide2.jpg](attachment:slide2.jpg)","metadata":{},"attachments":{"slide3.jpg":{"image/jpeg":"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3rja4ruwOSc2FqTxND9+nLkUm7z0966+1yLVbXenSx5+PzzlxdKGFr/uGo+0lFLlhr7tn9lTej3stdL3PXU+NP7SXjf4m+PvAPwt0Xw69t4NvEhN1qBuEIRy4RGCy/O8mw4KqqqFOeSCOr+Gf7UE19oPjmL4waQvhzxJ8OlMmp29uS8UsZyFaAEtyzAKBvYHcrBsNx4H8P/jl8PPgv+0B8bz4/u5rKPV9TtzbvHbyTqzW5uNyHywxVj5g25GDg8jFZGg+AvF37Q9p8cPilo+lz6XZ+NLW3tdEiuR5T3YsnikBx935xbopOSu52XcQCa78RkeGs44miqdHlpNVNU25cnMrt2ejk3ppa55+Gz7E3jLDV3UrOVVSp6NJR5+V2SvHVRS11vY9TT47ftU6j4Ok+MWleDtBj8ILE17Hp808x1OSwX5zKGDqmSgLD5ASOQhyM6vxH/a08R6boPwp8S/DLQINYj+IrXMbWVyX88TwvDCIIpEZVDebIyFmVgcAgCvlXwXefsX6b4LtNM+J3gbUrfx5psS219py/wBo/aLm6iG1nQCZI1MhG4qxTaSQBgA19BfFbwz4V8J+P/2Z9D8Eac+l6HHqd5NbWsvm+ZEtxJazkOJi0gbc5LBjkHI7V0YjKsDDFQpzwrVnUtePLGUY05O1+eTlqotS0vqc+GzbHzws6kMUndU72lzSjKVSKvb2cVHRyTi720Oo+Jvij9pvQfg/4n1/4h+H/COoWsDp51iyXM0LaeyHzc/vhvcSGMKOMYY85Uivr3x98c+B/DPwU0b4d+FNLu5/H2kgrp6b7aGCfyIDCsDbyEiR5SWD7iUXG5T81e4/tU/8m9eOf+vA/wDoa18lXYP9v/sh/wDYOX/0ltK8nJFQxWHjUq0Y25qmiuk+Wk5LS/db7nsZ7KvhMRKnRrSu409XZtc1ZRetuz22Osm+P/7VGg/EaH4Na54Q0C98Va1bi602a3mnis0hBcvJLudmdVEbjAKMCP4sgH0r4N/Hvx7qPj/xb8KfjdplhpOu+F7L+02ubAv9me0URl2IdnJwsqMCD0yCoK84fjYH/hu74ent/wAI3d/+3lczqOgy+Kf2xPiR4Yik8l9Y8BTWayEkBTcfZow2RzwWzxVVMPg61LllQjDmoqo2r3Uufl01atZbWFTxGMoVeaNeU+Ws6ai7WcXDm10Tvd7mhpPx1/aU+Kllf+OPg54W0SPwlbyyx2S6tLKL3UFhJVmjCSRouSMANgBvl3Ng03Xv2xdXb4M+Fvih4T8PwzahqGvpomo6ZcM5KSLG8kkcMilcO4CFGZWC7uVJFfKPw3079lvwRoVz4M/aT8H3uneOdGmmjlJOoH7ajOWiaMW8qxj5SFBwqsAGDHccew/Gfw98OtE+BvwxvfhfoNx4d0bXPGFhqH2S7877QsskMkbGQTPIwOI1AwxUgArkHJ9nEZJl8cVCg8O+XntGXLaMo2f2ueXPfRppJry2Xh4fPMxlhaldYlc3JeUea8oyuvs+zjyW1TTbXra79df45/tBfD34geD9K+NHhzRbbQPG14ljC2mSSvPZzyMqIsrvIyMQXBbC4IyVbjFeb/F26+NK/toeGU8KQaNJqUelXH9ipeNcfZ2tGjn85rrYwYS7hJt2YGAme9eo/tkDOpfCDj/mbrL/ANCWua/aF8S23wo/ah+HXxd8VW86+F4tMutPmuoYzKI5mE4wQvf96px1K7ioOCK8zJvZz9nWo0YqdSlVXKk7Nq9rK+7Wj7nq517SHtaFavJwp1aL5m1dJ2vd22T1XY+5PDLeIn8P6c/i5LaPWzAhvFsy5thPj5xFv+bZnpu5xW5XHeAfHnhv4meFLLxp4Sna50q/Mohd0aNj5MjRNlGww+ZDjPbBrsa/LcTTnGpKM48rTd1tZ9reR+rYWpCdKMqcuaLSs97rvfzCiiisDcKKKKACvxX/AGt9Zn1f48eII5GJj09ba1iB/hVIEZh+Lsx/Gv2or8Zf2w/D1xofxz1a8kUrDrMNteRHsR5Qhfn/AH42rxc9T9ird/8AM/CvpBRqPJabhsqkb/8AgMv1/Q+XaKKK+RP40CiiigC7puoXOk6ja6rZNsuLOVJo2HZ42DKfwIr+iazuVvLSC8QYWdFkA9mGa/nl8O6Jd+JNf03w9YKWudTuYraMAZ+eZwg/U1/Q9bwR20EdtEMJEoRR7KMCvpeH0/f+X6n9R/RwjU5ca38Puff71/wtf5EtFFFfRn9OhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFIyhlKsMg8EUtFAGB4WY/2DaQMctaq1ufXNuxiP8A6DWhqcfmWEy+i5/755rO0P8Ac3WrWX/PK6Lr/uzIsmf++mat51DoyHowI/OoqQ5ouPcGeAfEXwwvjHwVq3h/aGluISYc9po/nj/8eAB9s1+UTKyMUcFWU4IPBBFfsmQVJU9RX5kfHLwt/wAIr8R9ShiTZbagRew+m2YksB7CQMB7CvyrPaGiqfI+ezalopnkNNdtiFvQU6qt22Iwv94/yr5mcrJs8RmbRRRXmmYUUUUAFdj4ZkzDPF/dYN+Y/wDrVx1dH4ak23ckf99M/iDXlZ1T5sNMum9Tta6Lw8mZZpP7qgfmf/rVztdb4fTFtJJ/efH5D/69fnNR6HUjfooormLCiiigD//Q/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD50+FHwc1vwP8TPih4y12ezu7Dxve2l1ZxRF3kjW3M5PnB0VQf3oxtLdDyO/0XRRXZj8fUxNT2tV62S+5KK/BI4sBgKeGp+ypLS7fzk3J/i2R+TCZROUXzQNu7A3Y9M9cVJRRXHc7bBRRRQAUUUhIAyeAKAGPDDI6SOis8edrEAlc9cHtUlctJ458ExXP2OXxBp6XGceWbuEPn027s100ckcqLLEwdHGQwOQQe4NaTpTjbmTRnCrCV+Vpj6jlhinjMU6LIh6qwBB/A1JRWZo0IAFAAGAO1LRRQAUUUUAeJ+OfHvg3xDeav8FNL8VJpXjHUrOWGAKJN0UkkRZf3gGwPtOdocPjkDOKxv2ePhl4n+EHhK48O+M9ci1K7vbt5oI4pHeKJdoBWMyhWJbG5gFAH5mud8Z+Hv2dfA3xgsviP4z1WPS/FF5iWKGWZvJLhfKW4aNVOzgYDMwTIJ+8CaX4u/s+X/xV+Ivhnx/p3iZtOtdKSENGilyFjlMokt2VgAz5wSfQHnpXmvm5nOyck7JJ9H38z8vrPFPFVMd7KNXEUpOEYwqWtTk1rNN2UrXe35afU1fKX7V/wWufil4Ni1nw9D5viDw/vkhjUfNcQNgyQj1bgMnuCo+9X1bRXbXoxqQcJbM+4z/I6GZYOpgsSrxmrenZrzT1R/OK6PG7RyKVZSQQRggjqCKbX7FfGz9k7wj8UbmbxHoMw8P+IZctJKibre5b1ljBGGPd157sGNfBPiT9kn45+Hp3SLQ11aBTgTWM0cit9EYpJ+aCvjcTldWm9rryP4j4o8Js4y2q1Gi6sOkoJvTzSu15307NnzXRXvelfswfHXV5xDD4TuLfJ5e4eKBQPUmR1/TJr66+E/7D9lpl1DrXxVvo9ReIhl060LeQSOR50p2s49VUAf7RHFRQy6tUdlG3qefkPhnnWYVVCnh5RXWU04xX37/K7OM/Yv8AgneX2rr8XPEVuY7GyDppiOMedMQVaYA9UQZCnuxyPu1+nVQW1tbWVtFZ2cSQQQKqRxxqFREUYCqo4AA4AFT19hg8LGjBQR/a/BXCNDJcBHBUXd7yf80nu/0S7IKKKK6j60KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5+L9x4onTteWkbj/egdlY/lItdBXPap+51rR7v++81sT7SR+Z/OIV0NAHn+oR+VezJ/tE/nzXyd+1J4W/tDwxY+KoEzLpUvlSkf88Z8AE/RwoH+8a+vtdj2X27++oP9P6VxPifQrfxP4e1Hw/dcR38DxZ67Sw+VvqpwR9K+GzTDc3PT/ryOLFUueDifkRWZdNulx/drcvrS4028uLC8Ty57WR4pFPVXQlWH4EVzbMWYse9fmmJdlY+PkJRRRXGQFFFFABWrosnl6lCezEr+YrKqa2k8q4il/uMp/I1jiafPTlHumNbnqVdzo6bNPj9Wyf1rhq9EtE8u1hT+6i/yr8oq7HbEsUUUVgUFFFFAH//0f38oorxn4s/Hn4efBXUPB+l+O7ieCfxzq0Oi6YIYGmD3k7KqByv3FywyxoA9mooooAKKKKACiiuavvGXhHTfEuneDNQ1qzt9f1hJJLPT5J41u7iOFS0jxQk72VApLMBgY5NAHS0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSMyopdyFVRkk8AAUAeC/H/4/+FvgL4XXVdVX7dq99uSw09G2vO69WY87I0yNzYPUAAk1+JPxT/aE+K3xevJpPFmtSrYSE7NPtmaGzjXsPKU/Pj+85Zveovj98U774wfFLWvF88rPYmVrfT0J4jsoSREAOxYfO3+0xrxmv6p4I4Hw+X0IVasE6zV2308l2t1e79D+SuO+PMRmOInRozaop2SX2vN979Fsl5hXqPw4+NHxM+FF9HeeCNeuLKJW3PasxktJfUPA2UOemcBh2IPNeXUV95icLTrQdOtFSi+jV0fAYXF1aE1UoycZLqnZn78fs1ftOaB8e9JksbmJNK8VadGHu7INlJEzjz4CeTHkgMpyUJAJIIY/UlfzNfDjx5rXwy8b6R440Byt3pU6ybc4EsfSSJv9mRCVPsfWv6UNC1mx8R6Jp/iHS38yz1S3iuoG/vRTIHQ/iCK/mHxG4QhlmIjUw/8ADney7Nbr07f8A/qnw14ynmmGlTxH8Wna77p7P16P/gmrRRRX5wfpYVg+JPFHh7wfpb634o1GHTLGNlUzTuEXc3AUZ6k+g5rerifiF8P/AA38TfC914T8UQGa0uMMrKdrxSrnZIh7MufoehBBIqJ83K+Xc48wlXVCbwqTqWfKntfpe2tj8+Pj94a+E/xb8dw+MdD+J2kWAnhiguorlnbYIsgPFtHOV/hO3kZ3c8fXnhn41/Arwx4b0rw1a+N7GaHSbSC0R3l+d1gjEYZuOpC5NfH/AOyj8Jvh/wCMrvxt4b8d6HDq1z4du4UinZ5UOHMsbrhHUYzECO/J5pnxG+DXw/vv2mvCfwl8PaUujaRc2ouLvyXkLzbVmmcbnZiMpEFGOhJNeBSqVIr28Yq8nbrvc/nLJ8zzLDUf7ewuHoqeKmoNXqX5nNxtZvlXvJt2Z+m+m6jYaxYW+q6VcJd2d3GssM0TBkkRxlWUjggirmRnGefSvyp+L/7Ufi7w3rGofC/4WwQeGdE8OSyabHJEge4YWpMTbWfKouVOMDd3LZrt/wBhzWtb8SeLfGeseINQuNSu2trRWmuZWmkO55D95yT2rup5pCVVUorU+/y7xbweKzWllFCDlNtqUtopxTb5b6tXVtUu59UfE39o74Z/CjVv7B8TzXUmp+Ws32e3t2c+W+dpDvsjOcHo31rwi9/b28BRuRp3hvU517GVoYj+Ss/86xf28vA/2nSPD/xDtY8vZSNp9ywHPly5khJ9ArBx9XFfmZXm5hmVenVcFsfmXiT4o59lea1cFSlGMFZxfLdtNXW9/R+aP1Et/wBvnwczAXXha/jXuUmic/kdv869G8Mftm/BzxHeW+ny/wBo6ZcXLrGi3FrvBdzgAfZ2lJyTgcV+OtfTf7JHgb/hM/jLpt1cR77Lw+ralLkcb4iBCM+vmsrY9FNYYbNcROahdO/keBwt4v8AEeLx9HCKcZOcktYrq9X7ttlqftASBjJxngUtfLf7YxkT4GancQsUeC7snVlOCD5ygEEdDzX55fD/APam+MHgF4oRqx1zT0wDbajmcbR2WUnzVwOgDbR6GvaxWZwo1OSaP3LivxWwmTZlHAYynLlcVLmWtrtrVabW3TfoftjRXy/8I/2rfh38TpIdIv2/4R3XZcKLa6cGKVz2hmwFYnsrBWJ6A19QV3Ua8Ki5oO6Pvslz7B5jRWIwVVTj5dPJrdPyeoUUUVqesFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBz3ib93pqXg62k8E3/AUkXf8A+Obq6Gs/V7P+0NKvLD/n4hkj/wC+1I/rRpN5/aGlWd//AM/EMcn/AH2oP9aAMrxDHxDL9VP8xXMV2muR77At/cYH+n9a4uvmM0has33MZ7n5xftL+GP+Ee8eS6nAm221yMXCkdPNHyyj65AY/wC9XzZX6G/tRaF/bfhKO9gXdPojefx18uXCyD8MKx+lfnlX5VnMUsRJLY+SzCly1X5hRRRXlnEFFFFABRRRQB6np5+0w25HWUJ+Zr1HpwK8r8IH7QlmvdGP/jpJr1SvyjMIclWUOzZ2w2CiiiuIsKKKKAP/0v38r8sf+Ckv/I7fsw/9lG0z/wBHQ1+p1fm3/wAFAPAvjfxn4v8A2dbnwf4e1HXYdE8fafeX72FpNcrZ2qSxFppzErCKNQCS74UY5NAHNfEf4tftY+Mv21PFP7MXwT8VaT4a0i28OWWsG/1DT47yXTkDRrNJbpgedJK8yLsmJQLkjBFdb+yb8afjx/wu74rfsv8A7QmtWfizxB4Bt7PUbHW7S0jsjdWl3Gr4khiVYwQssRGFyGLqWYAGpfBvgfxpa/8ABT3x54/udA1CHwveeAbezg1V7WVbCW6W5smMCXJXymkARiUDFsKTjg1B8NfA/jTTv+Cjnxv8dahoGoW3hrVfC+lQWeqS2sqWNzNFDaB44bhlEcjqVYMqsSMHI4NAHzR+zd4s/wCCjv7UPwNT4xeFfjDomjTwXF3b2mnS6FaMdQe3JBFzP5JWHLHavlIeAC3Oa0Ln9vr4ueMv2fvhH4+0MweGfFd/8SLTwZ4pgjt45oZNqF5liWcSGMSI0TZB3K25Q2Bk+K/sE/tb+K/g7+ytD4H8M/BTxn461g32oSaXeaTpr3GkXMsrbtkt0mWjMbcOFjcgc8Z419d/Y5/aC8CfsT+HNbttBbW/iZp3xBj+Iep6LbATThTEYfs8axlvNkULHIyJk/M6ruIGQD9EP24vjZ8RvgtafCSX4d6imnN4q8caXouoF4Ip/NsbkSGSMearbCdo+ZcMOxr4v/aK8F/HjV/+CnXgay8E/Ea20DVNU8N3k+h3cmkQ3S6VZRxXCz2zxOQLgyusrCRsFfMAH3RWd+0j8Vfjb+1dqnwYTwl8BfGfh7w14c8Z6TqGpXuq6dIk6To+0hII97/Zo1MpkuGAQEKDsJwfeP2vrT4j/Cj9r34SftVeH/A2r+PfC2h6TfaJqlroVu13fW5nE+yTyl7Hz8qThSUKsyllJAP0r8F6f4m0nwlo+l+M9WTXtetbSGK+1COBbVLu5VAJJlgQlYw7ZIUHAzgV01edfCf4gS/FLwBpfjufw3q3hF9U84/2XrlsbPUbcRTPEvnQknaXCCReSCrKc816LQAUUV5j8WfjN8L/AIF+FG8bfFnxDb+HNGEiwrNPvdpJWBIjiijV5JHwCdqKxwCcYBNAHp1FfAH/AA9G/YT/AOimf+UbWP8A5Co/4ejfsJ/9FM/8o2sf/IVAH3/RXwB/w9G/YT/6KZ/5RtY/+QqP+Ho37Cf/AEUz/wAo2sf/ACFQB9/0V8Af8PRv2E/+imf+UbWP/kKj/h6N+wn/ANFM/wDKNrH/AMhUAff9FfAH/D0b9hP/AKKZ/wCUbWP/AJCo/wCHo37Cf/RTP/KNrH/yFQB9/wBFfAH/AA9G/YT/AOimf+UbWP8A5Co/4ejfsJ/9FM/8o2sf/IVAH1/4q+Kvw48DeJfD/hDxl4jstE1fxV9o/suC8lEH2xrUxiVInfCFwZkwm7c2flBwcegV/L//AMFWv2ovgV+0o/wvPwU8Tf8ACRjw6Na+3/6FeWfk/a/sXk/8fcMO7d5L/czjHOMjPgn7N3/BST9o79nj7Lojap/wmnhODav9law7ymKMfw21zzLDgcKuXjH/ADzoA/r7or4C/Zu/4KQ/s4/tE/ZdEXVf+EN8WT7V/snWHSIyyHjbbXORFNk8KuVkP/PMV9+0AFFZms61o/h3TJ9a1++g02wtV3S3FxIsUUa9Ms7kAc8cmvHR+058ACcf8J3pfH/Tb/61dmGy7EVk5UacpJdk3+RxYnMsPRajWqRi33aX5nutFeHr+0r8A26ePNJ/G5UfzqZf2jvgM3Tx7o/43kY/ma3eSY3/AJ8y/wDAX/kYLPMF/wA/4f8AgS/zPaq4v4kS3MPw78UzWeRcR6VfNHjrvEDlcfjW3oHiPw/4q02PWfDGpW2rWEpIWe0mSaIkdQHQkZHcdq07iCG6gktbhBJFMpR1PRlYYIP1FcdNulVTmtnqvQ7KiVWk1B7rR+p/LTRXovxa+H2o/Cz4ia54G1JWB0y4dYXYf623b5oZB/vxkH2OR1FedV/b+HxEKtONWm7xkk16M/hLE4edGpKlUVpRbTXmgooorYwCv37+Dni//hC/2S9B8a6tE1wmh+H2ujGDhpEtkYogPbcqgA9q/CPwt4a1fxl4k03wroMJuNQ1W4jtoEHd5GwCfRR1Y9gCTX9JOk+BNC0/4e2nw1uIRdaPb6YmlSRsMCW3WEQsGx/eXOfrX4z4v42jGnhqFTX3uZrryrR/ffT0P23wZwNeVTE16enu8qfTmeq+62vqfipc/tyftDzeIn1yDW4ILZnJXTxaQtaqnZPmUykf7XmbvevrD4Y/8FFNB1B4dO+LGhtpUjEA32nbprfnu8DEyoB/stIfavMPiX/wTv8AGelzXOofC7V4Nbs8s0dneH7NdqvZBJzFIR/eYx/Svgvxf4H8X+AdVbRPGej3Oj3q5IjuYym4D+JCfldf9pSR717FDJ+G84pKGGjG6X2fdkvlu/mmjxsRnXE+TVXPEylZv7XvRfo9Uvk0z+kzwn4z8KeO9Ij17wdqtvq9hJwJbeQOA3dWA5Vh3VgCO4rpq/mZ8B/Ebxt8MtbTxB4G1efSrxcbjE37uVRztljOUkX/AGWBH41+u3wA/be8IfESOPw78STD4a8QqvExbbY3RAydjMSY3P8AcYkH+FiTtr8v4q8MsVgU6+GftKa/8CXquq819yP1ThPxTwmPao4r93U837r9H09H97PgrUfHnjPwR448VN4Q1q60c3moXHnfZpWj8zy5pNu7HXG44+pr1b9mvxT4i8X/ALSvhTV/FGoz6pe7L2PzrhzI+xbK4wuT2GTXzd4n1C31bxLq2q2mfIvbueaPcMHZJIzLkeuDXrn7Mmuad4d+OvhPUtVk8q3aea33HoHuoJII8+g3yLk9hzX87Yeq/bRTel1+Z/JnDmbVP7ZwsJVX7JVoO13yr307228zifi5/wAlW8af9hrUf/SmSvQfgN8eZvgbLrlxb6KusSawkCDfcGAReQXPQRvu3b/UYx3zxz37QHhvUvC3xk8W2OpLhrnUJ72Jh0eG7czRkfg+D7gjtXkdtKkFxFPJEs6RurNG+QrgHJVtpBwehwQfQ1EqkqdZyjo02cFfMcXlmcVa9CXJVhOavZO1209Gmtm+h+uGhap4+/aZ+Dnif/hJtDtNG0vVLZ00lUMjzT3MJ3xylnIAjEihR8uW55AHzfkW6PG7RyKVZSQQRggjqCK/db4IfFTwh8U/BdrfeF40sJNPjjguNOXANmyrhUAGMxkD92wGCBjgggflP+094H/4QX4y65aQx+XZ6o41G24wNl0SzgD0WUOo9hXq5rRvShVTv0ufr/i/kiq5Xgc3p1vbO3LKenvXvJOytZJ8yt0uk9T5/r9MP2XPCvjLwd8ENa+I3g3T7fUNf1mfzILa5DAT2dnuTYjKQVdnMhXOQcAEcgj84NI0u91zVrLRdNTzbvUJo7eFP70krBFH4kiv3tsx4Y+EPw6tYNSuksdG8N2UcTzPwNsShc46l3bsMlmOBkmssloc0pTbtZHleBvD0a+LxGPqy5I0oNKWi5ZSTV7vTSKe+mqufmP8Wf2tNU+J/gLUvAGr+F49OmupISZ0uWPltBKrkGJo887cfeGP0r45r034w+NtI+InxE1jxdoelrpNnfSbliH3nwMGWQAlRJJ95gvGfU5Y+ZV52KrSnO8pXt1PzHi7PMTj8bKeJr+15fdjKyV4puzskt73111Cv0K/ZQ/aS11tesfhb47u2vrS+/c6ddyndLDLj5IXY8sj/dQnJVsD7p+X89a6DwndTWXirRr22YpNb3tvIjDqGSRSD+BFGExMqU1KJpwbxNicqx9PE4aTWqUl0kr6p/p2eqP6HKKKK+/P9FgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACue8M/u9NazPW0nnhx/spI2z/xzbXQ1z+l/uda1i0/vvDcj6SRiP+cRoA1b6PzbOZO5U4+o5rz6vSyARg968p1eQ2VrcHoyZUfUnFfP55aKVR+ZlU7nmevRW+ti9tbtfMt7tXiYesbDbj8q/LvXdJuNB1m90W6/1tlM8RPTO04BHsRyK/UOvi79onw5/Z/ia18RQpiLVItshA/5bQ4Bz9UK/ka/HsW3K82fP5lTvHn7HzxRRRXAeIFFFFABRRRQB6b8PD5kkqH/AJZZb/voAV6vXmPw1tyYtRuj0BiQfjuJ/pXp1fmmfwti5/L8jspfCgooorxjQKRmCjJoJAGTVZmLH2pNgf/T/fyqlzf2NlJbQ3lzHBJeyeTAsjhTLLsaTYgJ+ZtiM2Bk7VJ6A1br43/aD0zU9b+L/gzR5ftA0268KeM0tDaSSw3A1horJYPKkiKus32ZrnyjGQ+PMxxmgD6k8LeMfCfjjTG1rwZrFprmnrLJAbmynS4h82I4dQ8ZKkqeuDXQyp5kbx5xuBGfrX5g+Dfhf8ZNF8KJZeEbjxRp114R+EOhSeH9Pub/AFCKwbxVMuqmWOdJHCyywHyENtKSkSGJWjCpEFqeDNG+KPjL4j6B4Y0m/wDiLafCy5nhjubnV5dX0/VBeDSL+S98y4uBHdJb/aRaBDuEXn70hOzC0AfWf7JP7Ocf7LHwbs/hHFr58SraXd1dfbDa/Yy32l9+3yhLNjb0zv59BXuFp458E382nW9j4g0+4l1ia7t7JI7uF2uprBmW6jhAYmR7dkcSquTGVYNjBr49/ZQ0/wCL+k6joM3xBufEd0viPwJo2q6sNde6lS38RNLKLqKNbj5bWTYy+ZbRhAoVTsByT8v+DfB/xu+E1r4q8b6z4T1jxSvim28ZaZ4ft9Ot7yz1Lw6w1u+vQC8KvLHHqiP9oW6SPexit4k3b4gQD9i6yNe17RfC2i3viLxHfQ6bpenRNPc3Nw4jihiQZZ3Y8BQOpNfij4y8S/tG6R4GuvDl9eeM5dKtr/xFBZX8cXiTTp7e2n03T59O1O5nEd5qU1pYzteK0U5O/gSgFVjqHxtdfE7x/qvjXwd/a/iDVo9R0nxHDoDSTav/AGZ4pifSkm0+2061eP7AZoIBNIX89pZiNxDNgxAH7jyyxQRPPM4jjjBZmY4VVHJJJ6ACmWt1a31rDfWMyXFtcIskUsbB0dHGVZWGQQQcgjgivzl8IXfx28TftISatrWt6/oemT639p02ym0XWDpdx4aayWSOKZwyadbSsS2/7TGt5HcDYfkwtfRv7In20fALQFuQBbLdauunAfdGlLqd0NO29tn2QRbMcbcY4oA+lK/L7/gp7+yN8UP2pPAvhG7+EzxXms+D7m8dtMmnS2W8ivViUskkhWMSxGEbQ7KCrv8AMCAG/UGigD+QL/h1z+3Z/wBEz/8AKzo//wAm0f8ADrn9uz/omf8A5WdH/wDk2v6/aKAP5Av+HXP7dn/RM/8Ays6P/wDJtH/Drn9uz/omf/lZ0f8A+Ta/r9ooA/kC/wCHXP7dn/RM/wDys6P/APJtH/Drn9uz/omf/lZ0f/5Nr+v2igD+QL/h1z+3Z/0TP/ys6P8A/JtH/Drn9uz/AKJn/wCVnR//AJNr+v2igD+QL/h1z+3Z/wBEz/8AKzo//wAm0f8ADrn9uz/omf8A5WdH/wDk2v6/aKAP4gvjn+y78df2azog+Nfhn/hHD4i+0/YP9Ns7zzvsnl+d/wAek023b5yffxnPGcHHgFf1ef8ABRD9jD4j/theJvhVp3g2/stG0jw4NZOqaheMW8hbs2XlCKBPnldhC5AyqjHzOuRn0P8AZu/4Jvfs4/s7fZdbXSv+Ey8WQbW/tbWESUxSDndbW2DFDg8q2GkH/PQ0AfgD+zd/wTb/AGjv2h/suttpf/CF+E59rf2rrCPEZYz/ABW1txLNkcq2EjP/AD0r+n39m/4Ef8M8fDi0+H3/AAmGs+M/s+D9p1ifzTHgY8u2TnyYR/DHubH94179RQB+bv8AwUfg8UyeC/CU2niQ6BFeXH2/ZnYLhkQWpfHbHmgE8ZPqRX5C1/UhqGnafq9lNpuq2sV7Z3ClJYZkWSORT1DIwII9iK+cfiX4D/Zo+Fng7UfHHirwNoUVnYpkIun2++aVuEijGzl3PA7DqcAE1+x8EeIcMFhYZe6DlK7ty21u+3fW3pY/FuO/DeeOxc8xWIUY2V+a+llbftpf1ufz+0V1XjXxLF4v8T3/AIhttKs9Egu5CYrKxhWG3gjHCoqoFBIHViMsck9a5Wv6FpSk4pyVn27H84VoxjNqLuu/c/RX/gnHr+rRfEvxJ4XSdv7MutIa8eHPy+fBcQxo4HY7ZWBPfj0FfsPX4yf8E5l/4vLrzemgTj87q2r9m6/l7xVilnE7LpH8j+rfCWbeTQu9pS/M+TP2o/2ZNO+O+ixaro8kdh4u0uMra3DjEdxFkt5ExAJC5JKNg7STwQTX4ieNPAfjD4d61L4e8a6TPpN9ET8ky4Dgcbo3GVdfRlJB9a/purD1/wAM+HPFdgdL8UaVa6vZtyYbuFJ48+u2QEZ96rhDxHxGWwWHqx56fRXs16Pt5fiiOMvDPD5nUeJpS9nV6u10/Vd/Nfcz+X+tzw54Z8Q+L9Xg0Hwvp0+q6hcHCQW8bSOffC9AO5PA6k4r9/pP2Vf2eZLn7W3gawD5zgCRU/74Dhf0r1vwx4K8H+CrQ2HhDRLPRbdsbks4I4AxHdtgG4+5ya+5xnjNh1D/AGehJy82kvwvf8D4LBeCWIdT/aa8VH+6m3+NrfifIP7Jv7Jy/B5P+E58ciO58X3UZSKJCHj0+Jx8yqw4aVhw7jgDKqSCS33LRRX4jnOc4jH4iWJxMryf3Jdl5f1ufu2S5Lh8vw8cNho2ivvb7vzf9aBXL+LvBXhPx7o0vh/xlpVvq+ny9YrhA2D/AHkb7yMOzKQR2NdRRXn0qsoSU4OzXVHo1aUZxcJq6e6ex+QHx/8A2D9Y8MR3Piv4ONLrGmJl5dLf57yFep8lv+Wyj+7jzBxjea/OR0eJ2jkUo6EggjBBHUEV/U1X4HftRahofjz4v+Itf8K20NtBHN5CtCoUXTQfI8zEYBaRgSG7jGfWv2bhLxaVKUMLm0tG7Kfb/F5f3unXuv5m8WuFcuyuNPF0JcnPK3J02u2uyXVbaq1jwHRtdKlbS+bK9Fc9vY/412qO8brJGxVlIIIOCCOhBrx5lKsVYYI4INdn4f1YyAWFy2WH3GPcen+FeL4ueF8Iwlm+WRst5xW1v5l/7cvn3P5+zTLrfvafz/zP0M+OCJ8Y/gh4V+OlmofVtHA0rWto5yDgO3oPMIYD0mHpXw/X2D+yX4l02+1XXvgv4nbOjeN7SSJAT9y6RDgrngMyZweu5UxXy/4t8Nal4N8Tan4V1ddt5pVxJbyejFDgMP8AZYYIPcEV/OuL9+Ma3fR+q/zPd4w/23D4fOo71FyVP+vkEld/442l63Nz4b/EfxN8LfFNt4q8Lz+XPF8ssTZMVxESN0Uijqpx9QcEYIBr6/8A2t9V0X4i/DT4ffFzT7d7SbUTNbmN8FgjDcVZh12OjbTxkMTgdK+A6+0fix/yaZ8K/wDr5n/nNV4Wo3SqQe1r/ijq4UzKtPKMxwE5XpKCmk+klOCuu2+vc479j7QLHXfjjpUl8N66XBcXka9jLGu1Cf8AdL7h7gVB+0h8eta+LXiOXR7XfZeG9KmZbe2JwZZEJUzTY4LHnaOQo4HJJPR/sS/8lsX/ALBt1/NK+WNd/wCQ3qH/AF8S/wDoZolUccMorq3f8Ar5lWw/C9HD0ZcsatWpz2+1yqFk/LXYyqKKK84/NgrY8Pf8h/TP+vqH/wBDFY9bHh7/AJD+mf8AX1D/AOhiqjub4X+LH1R/RJRRRX6Mf6chRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc9L+48UQP2vLSRD/vQOrKPykb8q6Gue1z9zdaTe9oroI3+7MjR4/76ZaAOhrxrx1J5V39lH8beYfy4/UmvZa8K8bTfaNckkH3VAjH/AOv618txdU5cJ5tmFd+6chXlXxm8Of8JF4DvhEu640/F3F6/ugd4/FC344r1WmuiSI0cgDKwIIPQg9RX5XJXVjz6kFKLiz8qaK6nxr4ffwt4q1LQiCEtpj5ee8TfNGf++SK5avNatofLyi07MKKKKQgooooA96+HNps8J3FwRzLcFh9FCr/AI11VM8F2nkeDbKHHMkbP/32xYfoafX53xEv3/N3/Q74q0UFFFQSPn5R0r55sY133HA6UyiioA//1P38qndafY3slrNeW8c8llL50DOoZopdjR70J+62x2XI52sR0Jq5RQAUUUUAFFFFAHA+OfhV8MvietgvxH8KaX4oGlu8lqNTs4bvyGkAD+X5qttDhQGA4YAZzgVW0f4PfCfw94uuPH2g+DdH07xLdgrLqVvYQRXbhlCtmZUD/MoAbn5gADnAr0eigCOaKK4ieCZQ8cilWU8gqRgg/Wo7W1tbC1hsbGFLe2t0WOKKNQiIiDCqqjAAAGABwBViigA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Predict Test and Submit to Kaggle\nAfter experimenting many times, we use our best model to predict Kaggle test set.","metadata":{}},{"cell_type":"markdown","source":"## Preprocess Test","metadata":{}},{"cell_type":"code","source":"# PREPROCESS TEST\ndef getTest(x):\n    f = 'test_image_data_%i.parquet'%x\n    img = pd.read_parquet('../input/bengaliai-cv19/'+f) # Pandas dataframe\n    img = img.iloc[:,1:].values.reshape((-1,137,236,1)) # Numpy Array\n    img2 = np.zeros((img.shape[0],DIM,DIM,1),dtype='float32')\n    for j in range(img.shape[0]):\n        img2[j,:,:,0] = cv2.resize(img[j,],(DIM,DIM),interpolation = cv2.INTER_AREA)\n    return (255 - img2)/255.","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Predict and Postprocess Test","metadata":{}},{"cell_type":"code","source":"EXP = -0.9\ndel X_train; gc.collect()\n\n# LOAD BEST MODEL\nmodel.load_weights('fold%i-m%i-%i.h5' % (fold,mm,rr))\n\n# PREDICT TEST\npreds1 = []; preds2 = []; preds3 = []\nfor x in [0,1,2,3]:\n    preds = model.predict(getTest(x),verbose=1)\n    preds1.append(preds[0])\n    preds2.append(preds[1])\n    preds3.append(preds[2])\n\npred1 = np.argmax(np.vstack(preds1),axis=1)\npred2 = np.argmax(np.vstack(preds2),axis=1)\npred3 = np.argmax(np.vstack(preds3),axis=1)\n\n# APPLY POST PROCESS\ns = pd.Series(pred1)\nvc = s.value_counts().sort_index()\ndf = pd.DataFrame({'a':np.arange(168),'b':np.ones(168)})\ndf.b = df.a.map(vc); df.fillna(df.b.min(),inplace=True)\nmat1 = np.diag(df.b.astype('float32')**(EXP))\n\ns = pd.Series(pred2)\nvc = s.value_counts().sort_index()\ndf = pd.DataFrame({'a':np.arange(11),'b':np.ones(11)})\ndf.b = df.a.map(vc); df.fillna(df.b.min(),inplace=True)\nmat2 = np.diag(df.b.astype('float32')**(EXP))\n\ns = pd.Series(pred3)\nvc = s.value_counts().sort_index()\ndf = pd.DataFrame({'a':np.arange(7),'b':np.ones(7)})\ndf.b = df.a.map(vc); df.fillna(df.b.min(),inplace=True)\nmat3 = np.diag(df.b.astype('float32')**(EXP))\n\n# APPLY POST PROCESS\npred1 = np.argmax(np.vstack(preds1).dot(mat1),axis=1)\npred2 = np.argmax(np.vstack(preds2).dot(mat2),axis=1)\npred3 = np.argmax(np.vstack(preds3).dot(mat3),axis=1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Write Submission CSV","metadata":{}},{"cell_type":"code","source":"row_id = []; target = []\nfor i in range(len(pred1)):\n    row_id += [f'Test_{i}_grapheme_root', f'Test_{i}_vowel_diacritic', f'Test_{i}_consonant_diacritic']\n    target += [pred1[i], pred2[i], pred3[i]]\nsub = pd.DataFrame({'row_id': row_id, 'target': target})\nsub.to_csv('submission.csv', index=False)\nsub.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Notebook elapsed time = %.1f minutes'%((time.time()-startNB)/60))","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}