From 9880eabe5a03fc31dd1b888de6a1a475e7bc796e Mon Sep 17 00:00:00 2001 From: LTluttmann Date: Thu, 13 Jun 2024 14:25:41 +0200 Subject: [PATCH 1/3] [Minor] updated scheduling notebook with taillard instances --- examples/other/2-scheduling.ipynb | 376 ++++++++++++++++++++++-------- 1 file changed, 283 insertions(+), 93 deletions(-) diff --git a/examples/other/2-scheduling.ipynb b/examples/other/2-scheduling.ipynb index 4c4c029e..2fc6856b 100644 --- a/examples/other/2-scheduling.ipynb +++ b/examples/other/2-scheduling.ipynb @@ -13,15 +13,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" + "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning_utilities/core/imports.py:14: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + " import pkg_resources\n", + "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/fabric/__init__.py:41: Deprecated call to `pkg_resources.declare_namespace('lightning.fabric')`.\n", + "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n", + "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/pkg_resources/__init__.py:2317: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('lightning')`.\n", + "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n", + " declare_namespace(parent)\n", + "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/lightning/pytorch/__init__.py:37: Deprecated call to `pkg_resources.declare_namespace('lightning.pytorch')`.\n", + "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n", + "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/pkg_resources/__init__.py:2317: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('lightning')`.\n", + "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n", + " declare_namespace(parent)\n", + "/home/laurin.luttmann/miniconda3/envs/cuda1203/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], @@ -59,7 +71,7 @@ " \"min_processing_time\": 1, # the minimum time required for a machine to process an operation\n", " \"max_processing_time\": 20, # the maximum time required for a machine to process an operation\n", " \"min_eligible_ma_per_op\": 1, # the minimum number of machines capable to process an operation\n", - " \"max_eligible_ma_per_op\": 3, # the maximum number of machines capable to process an operation\n", + " \"max_eligible_ma_per_op\": 2, # the maximum number of machines capable to process an operation\n", "}" ] }, @@ -91,11 +103,15 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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atQq7du3CxIkT8c0336CiogLx8fGjKutoZmYGf39//Pjjj/Dx8cHEiRMxY8aMEa31iIiIwPbt2/GnP/0JeXl5WL58OYyMjFBSUoLY2Fh88sknAyp9KcO3336L7777DpGRkZg7dy6MjY1RVFSEv/3tbzA1NcU777yj9D6J/qio6J0upUxvvAEsWaIdazbee+89HD9+HHl5eUO2YRgGgYGBo745REaGEg1CiNZ54YUXkJGRgfnz58PLy+up7SMiInDlyhX88MMPePDgAaytrTF//nx89913cO/z2/PgwYOQSCQ4cuQITExM8Oyzz+Kvf/3rgA+w33//PXbu3IlPP/0UHMdh+fLlSEpKGtUuzY8rHg22p4JQKISTkxM++eQTGBoa4rvvvoNIJMKiRYtw8eJFrFixol/7SZMm4ciRI/jTn/6ErVu3oqenB0KhcNBEw8zMDCzL4j//8z/xzTffoLW1Fb6+vvjqq6+wefPmEb+OoTg5OSEjIwP/8R//gYMHD0IkEmHWrFk4deoU1qxZM+rzHjt2DDt37sTbb7+N7u5u/PGPfxzxovIjR45g7ty5OHr0KN555x3w+Xy4ubnhl7/8JRYtWjTq2B57PArVd83I9u3bYW5ujkuXLuHEiRNobW2Fg4MDli9fjt///veYM2fOmPsl+mv79qevxRgpqbT3vOfPj+75mzdvxjfffIPt27fjyJEj/Y69/vrr+Oyzz/DrX/+636aoqpSQkKCTayE0FY/TxJV8hBBCiJZrbW2FtbU19uzZM+gULUIUJRKJUFFRAXd39yF3jy8sBAICVBdDYSEwxP6fw9q8eTMuX76M1tZW1NXVyddEiUQiODs7w8rKCgKBQCmJhiIjGppGkf9bbUZrNAghhBAVyMnJAYBRrUMhZKSOHOktYasKfD5w+PDonx8UFISpU6ciISFB/lhCQgKmTZvWbxTvp59+wuLFi2FjYwM7OzusXbsWZWVl/c5VU1OD559/HhMnToSFhQWCg4ORnZ3dr823334LNzc3WFtb47nnnkNbW5v8GMMweOutt+Rfu7m54X//93/lm5ROmzYNn3/+eb/zVVdX49lnn4WNjQ0mTpyIDRs2DLsxJ/kXSjQIIYQQJcrPz8e+ffvw61//GnZ2dmOaIkaIos6eVf60qcekUiApaWzn2LJlC7766iv513/729/w0ksv9WvT0dGB3bt34+rVq7h06RIMDAwQGRkpL+Pd3t6OiIgI3Lt3DydPnsSNGzfw7//+7/3KfJeVleH48eM4ffo0Tp8+jeTkZHz44YfDxvbRRx8hODgYubm5eO2117Bjxw55tTyJRIIVK1ZgwoQJSE1NRXp6OiwtLbFy5Up0d3eP7U3RA7RGgxBCCFGihIQEfPjhhwgODsb+/fsH7ItCiLK1tQHl5arto6wMaG8HLC1H9/xf/vKX+P3vfy8vGZ6eno4ffvgBLMvK22zatKnfc/72t7/BwcEBhYWFmDFjBr7//ns0NDQgJycHEydOBIAB6/RkMhm+/vpreUGLF198EZcuXcLevXuHjG316tV47bXXAAD/8R//gf3790MoFMLX1xc//vgjZDIZjh07Ji9F+9VXX8HGxgYsy2L58uWje0P0BCUahBBCiBK99957eO+999QdBtEjZWWAqlfcchxQWgr8XBl8xBwcHLBmzRp8/fXX4DgOa9asgb29fb82JSUl+MMf/oDs7Gw8fPhQPlJx9+5dzJgxA3l5eZgzZ448yRiMm5tbv6p5zs7OA6oHPmnWrFnyf/N4PEyaNEn+nBs3bqC0tHRAJT6RSDRgWhcZiBINQgghhBAt9vN2QBrfz5YtW/DGG28AAD799NMBx9etWwdXV1d88cUXmDx5MmQyGWbMmCGfoqTI5ppPVpTi8Xj9plaN9Dnt7e2YO3cuvvvuuwHPc3BweGo8+o4SDUIIIYQQLWZioh39PF7XwOPxBpTnbmxsxJ07d/DFF18gLCwMAJCWltavzaxZs3Ds2DE0NTUNO6qhTEFBQfjxxx/h6OhI0yBHgRaDE0IIIYRoMS8v4OflAyrD4/X2MxaGhoYoKipCYWFhv/1lAMDW1hZ2dnb4/PPPUVpaisuXL2P37t392jz//POYNGkSNm7ciPT0dJSXlyM+Ph6ZmZljC2wYL7zwAuzt7bFhwwakpqaioqICLMti165dqKmpUVm/uoISDUIIIYQQLWZpCXh4qLYPT8/RLwTvy8rKatCRAQMDA/zwww+4du0aZsyYgbfffht//etf+7UxNjbG+fPn4ejoiNWrV2PmzJn48MMPByQtymRubo6UlBRMmzYNUVFR8PPzw9atWyESiWiEQwG0YR8hhBBCiAZTZFO3Xbt697pQRYlbPh/YsQM4cED559Z3tGEfIYQQQgjRaK++qtp9NHbsUM25iW6jRIMQQgghRMv5+wPLlil/d3A+v/e8fn7KPS/RD5RoEEIIIYTogKNHVZNoHD2q3HMS/UGJBiGEEEKIDnB3Bw4eHL6NBdoxG3mYj2zMRh4s0D5s+0OHes9LyGjQPhqEEEIIITpi2zbgwQNgz55/PeaHQryKI1iNs/BAOQzwrzpAMvBQDg+cxWocwasogr/82N69wNat4xk90TU0okEIIYQQokPefRf44gvA17gC57EchQjADhyGF8r6JRkAYAAOXijDDhxGIQJwHsvha1yBY8eAd95R0wsgOoNGNAghhCimvR0oLQXE4t4tgr28lFNYnxCidNtwDFt4OyHjSQEOMMLwJakeHxfwhCjk+cOAOwhg2zhESnQZJRqEEEKGVlgIHDkCnD0LlJcDfbde4vF6dwlbvbq3tqa//9DnIYSMn717gT17YICRT13hc1JALAVefrl3Dta776oiQqInaOoUIYSQgSoqgOXLgYCA3l3Aysr6JxlA79dlZb3HAwJ621dUqCdeQkivY8f6L9AYiz17gC+/VM65iF6iRIMQQkh/x471jk4Ihb1fP20XsMfHhcLe5x07ptr4CCGDq6gAdu5U7jnfeENvbyBs3rwZGzduVHcYWo0SDUIIIf+yd2/vlAmRaOTbDEulvc97+eXe8xBCxtf27crfHlwq7T3vGFRXV2PLli2YPHkyjI2N4erqijfffBONjY1KCnJsKisrwePxkJeX1+/xTz75BF9//bVaYtIVlGgQQgjpRVMuCNFehYXAhQuqSTQuXACKikb19PLycgQHB6OkpAT/+Mc/UFpaiiNHjuDSpUtYuHAhmpqalBtvH93d3WN6vrW1NWxsbJQTjJ6iRIMQQghNuSBE2x05ovxtwR/j83vXYo3C66+/DmNjY5w/fx4RERGYNm0aVq1ahYsXL+LevXt49+fF5m5ubvif//kfPP/887CwsMCUKVPw6aef9jtXS0sLtm3bBgcHB1hZWWHJkiW4ceOG/Ph7772HwMBAHDt2DO7u7jA1NQUA/PTTT1i8eDFsbGxgZ2eHtWvXoqysTP489593JJwzZw54PB4YhgEwcOqUWCzGrl274OjoCFNTUyxevBg5OTny4yzLgsfj4dKlSwgODoa5uTlCQ0Nx586dUb13uoASDUIIIRo75YIQoqCzZ5X/M/yYVAokJY34aU1NTTh37hxee+01mJmZ9Ts2adIkvPDCC/jxxx/B/Vxo4q9//Stmz56N3Nxc/Od//ifefPNNXLhwQf6cmJgY1NfXIykpCdeuXUNQUBCeeeaZfqMipaWliI+PR0JCgnwqVEdHB3bv3o2rV6/i0qVLMDAwQGRkJGQyGQDgypUrAICLFy+irq4OCQkJg76ef//3f0d8fDy++eYbXL9+HV5eXlixYsWAUZl3330XH330Ea5evQo+n48tW7aM+L3TGRwhhBD9VlDAcb01pFTzp7BQ3a+QEK3W1dXFFRYWcl1dXYM3aG3lOB5PtT/HPB7HtbWNKO6srCwOAJeYmDjo8X379nEAuAcPHnCurq7cypUr+x3/xS9+wa1atYrjOI5LTU3lrKysOJFI1K+Np6cnd/ToUY7jOO6Pf/wjZ2RkxNXX1w8bV0NDAweAu3nzJsdxHFdRUcEB4HJzc/u1+/Wvf81t2LCB4ziOa29v54yMjLjvvvtOfry7u5ubPHky95e//IXjOI4TCoUcAO7ixYvyNmfOnOEADPl/99T/Wy1HIxqEEKLvNHTKBSFEQYOVn1Y2juvdsHNUT1UstoULFw74uujntSE3btxAe3s77OzsYGlpKf9TUVHRbxqUq6srHBwc+p2npKQEzz//PDw8PGBlZQU3NzcAwN27dxV+DWVlZZBIJFi0aJH8MSMjI8yfP18e42OzZs2S/9vZ2RkAUF9fr3BfuoQ27COEEH2ngVMuCCEjIBZrZD9eXl7g8XgoKipCZGTkgONFRUWwtbUdkBgMpr29Hc7OzmBZdsCxvgu2LSwsBhxft24dXF1d8cUXX2Dy5MmQyWSYMWPGmBeLD8XIyEj+bx6PBwDyaVr6hkY0CCFEn7W19e74rUplZUB7u2r7IESfmZhoZD92dnZYtmwZPvvsM3R1dfU7dv/+fXz33Xf4xS9+If8wnpWV1a9NVlYW/Pz8AABBQUG4f/8++Hw+vLy8+v2xt7cfMobGxkbcuXMHe/bswTPPPAM/Pz80Nzf3a2NsbAwA6OnpGfI8np6eMDY2Rnp6uvwxiUSCnJwc+Pv7K/Bu6CdKNAghRJ9p+JQLQogCvLyAnz+sqwyP19vPCB06dAhisRgrVqxASkoKqqur8dNPP2HZsmWYMmUK9vbZcyc9PR1/+ctfUFxcjE8//RSxsbF48803AQBLly7FwoULsXHjRpw/fx6VlZXIyMjAu+++i6tXrw7Zv62tLezs7PD555+jtLQUly9fxu7du/u1cXR0hJmZGX766Sc8ePAAjx49GnAeCwsL7NixA7/73e/w008/obCwEC+//DI6OzuxdevWEb8v+oISDUII0WcaOuWCEDIClpaAh4dq+/D07O1nhLy9vXH16lV4eHjg2WefhaenJ1555RUIBAJkZmZi4sSJ8ra/+c1vcPXqVcyZMwcffPAB9u3bhxUrVgDonYJ09uxZhIeH46WXXoKPjw+ee+45VFVVwcnJacj+DQwM8MMPP+DatWuYMWMG3n77bfz1r3/t14bP5+PAgQM4evQoJk+ejA0bNgx6rg8//BCbNm3Ciy++iKCgIJSWluLcuXOwtbUd8fuiL3icoit0CCGE6J68PGDOHNX3k5sLBAaqvh9CdJBIJEJFRUW/vSEG2LWrt/CCKtZb8fnAjh3AgQPKP/fP3Nzc8NZbb+Gtt95SWR+aSKH/Wy1GIxqEEKLPNHjKBSFkBF59VbVFHXbsUM25iU6jRIMQQvSZBk+5IISMgL8/sGyZ8ktV8/m95/15UTYhI0HlbQkhRN+tXq3aKRerVin/vISQgY4e7U04lPmzzOf3nlfFKisrVd4HGX80okEIIfqOplwQohvc3YGDB4dtIjUF2jyBVr/ev6VPWxZw6FDveQkZBRrRIIQQffd4yoVQqPw7oQIBTbkgZDxt2wY8eADs2SN/qMMVqF0PNC4ARM7of5tZBpjWAXbZwOSTgEVVn2N79wJUupWMAVWdIoQQAlRU9CYcIpHyzmlqChQW0t1QQsZoVJWJjh1D1wevo/iNbjQHA5Bi+NvLPx+3vQr4HDKG2X99RknGOKCqU4QQQnSfAlMuRoymXBCiNrWrgZxveGie83NVuafNYfn5ePMcHnK+4aF2Fd2HJmNHiQYhhJBe27YBH3ygnHPRlAtC1Kaqai+Ki1+GjBMDhiNMGAw5yDgxiotfRlXV3qe3J2QYlGgQQgj5l3ffBb74onfa00jLZPL5vc87dgx45x3VxEcIGVZt7TFUVOx5ekMFVFTsQV3dl0o5F9FPlGgQQgjpb9u23rUVAkHv109LOB4fFwh6n0cjGYSoRVdXBUpLdyr1nCUlb6Crq2LUz9+8eTM2btyoUFuWZcHj8dDS0jLq/ohmoapThBBCBnJ3B86f700cjhwBkpKAsjKgb/0QHq93M75Vq3pL2FJ1KULUqrh4O2Qy5ZaqlsmkKC7ejtmzz4/q+Z988gmo7pD+okSDEELI0Pz9gQMHev/d3g6UlgJiMWBiAnh50Y7fhGiIjo5CNDdfUMGZpWhuvoCOjiJYWIz8ZoK1tbUKYiLagqZOEUIIUYylJRAYCCxY0Ps3JRmEaIza2iNQ3f1jPmprD4/qmX2nTonFYuzatQuOjo4wNTXF4sWLkZOTM+A56enpmDVrFkxNTRESEoJbt27Jj1VVVWHdunWwtbWFhYUFAgICcPbs2VHFRlSPEg1CCCGEEC3X2HgWvZthqIIUjY1JYz7Lv//7vyM+Ph7ffPMNrl+/Di8vL6xYsQJNTU392v3ud7/DRx99hJycHDg4OGDdunWQSCQAgNdffx1isRgpKSm4efMm/vznP8OSbnpoLEo0CCGEEEK0mFTaBpGoXKV9iERlkErbR/38jo4OHD58GH/961+xatUq+Pv744svvoCZmRm+/LJ/Zas//vGPWLZsGWbOnIlvvvkGDx48QGJiIgDg7t27WLRoEWbOnAkPDw+sXbsW4eHhY3ptRHUo0SCEEEII0WJdXWUAVL3gmkNXV+mon11WVgaJRIJFixbJHzMyMsL8+fNRVFTUr+3ChQvl/544cSJ8fX3lbXbt2oUPPvgAixYtwh//+Efk5+ePOiaiepRoEEIIIYRoMY4T61Q/w9m2bRvKy8vx4osv4ubNmwgODsbBgwfVHRYZAiUahBBCCCFajMcz0fh+PD09YWxsjPT0dPljEokEOTk58Pf379c2KytL/u/m5mYUFxfDr0/57KlTp+LVV19FQkICfvOb3+CLL74YdVxEtai8LSGEEEKIFjMz8wLAg2qnT/F+7md0LCwssGPHDvzud7/DxIkTMW3aNPzlL39BZ2cntj6xyed///d/w87ODk5OTnj33Xdhb28vr1z11ltvYdWqVfDx8UFzczOEQmG/JIRoFko0CCGEEEK0GJ9vCVNTD4hEZSrrw9TUE3z+2Ko7ffjhh5DJZHjxxRfR1taG4OBgnDt3Dra2tgPavfnmmygpKUFgYCBOnToFY2NjAEBPTw9ef/111NTUwMrKCitXrsT+/fvHFBdRHR5H2zUSQgghhGgskUiEiooKuLu7w9TUdNA2JSW7cO/eYaimxC0fU6bsgLf3gRE/8/nnn4ehoSH+/ve/qyAu7afI/602ozUahBBCCCFabvLkV6HKfTQmT94xsmdIpSgsLERmZiYCAgJUFBfRdJRoEEIIIYRoOQsLf9jaLoPyZ8XzYWu7DBYWI1sHcevWLQQHByMgIACvvvqqkmMi2oLWaBBCCCGE6AAfn6PIyfGHTKa8kQ0DAz58fI6O+HmBgYHo7OxUWhxEO9GIBiGEEEKIDjAzc4eXl3L3lPD2PgQzM3elnpPoD0o0CCGEEEK0gCL1eyZP3gZ39w+U0p+7+144O299ekMyarpek4kSDUIIIYQQDWZkZAQACk9FcnV9Fz4+X8DAwBQjnyXPh4GBKXx9j8HV9Z0RPpeMVHd3NwDA0NBQzZGoBpW3JYQQQgjRcHV1dWhpaYGjoyPMzc3B4/Ge+hyRqBIVFW/g0aNL6E04hlu70Xvc2voZuLsfgqmpm3ICJ0OSyWSora2FkZERpk2bptD/qbahRIMQQgghRMNxHIf79++jpaVlxM+VSkvR2fkjurtT0dNTjf47iPNgaDgVxsZhMDd/Dny+p7JCJgowMDCAu7u7fENCXUOJBiGEEEKIlujp6YFEIhnD89shEpVBJhPDwMAEpqaeMDQc247fZPSMjY1hYKC7Kxko0SCEEKKY9nagtBQQiwETE8DLC7CkDyiEEEIGR/toEEIIGVphIXDkCHD2LFBeDvS9N8XjAR4ewOrVwKuvAv7+6ouTEEKIxqERDUIIIQNVVADbtwMXLgB8PiAdZhHp4+PLlgFHjwLuVHOfEEIIlbclhBDypGPHekcnhMLer4dLMvoeFwp7n3fsmGrjI4QQohUo0SCEEPIve/cCL78MiERPTzCeJJX2Pu/ll3vPQwghRK/R1ClCCCG9jh3rTRKUeb6ttKswIYToK0o0CCGE9K7J8PfvHZFQFlPT3sXktGaDEEL0Ek2dIoQQ0rvwe6RTpZ5GKu09LyGEEL1EIxqEEKLvCguBgADVnt/PT3XnJ4QQopFoRIMQQvTdkSO9JWpVgc8HDh9WzbkJIYRoNBrRIIQQfeflBZSVqfb8JSWqOz8hhBCNRIkGIYTos7Y2wNq6/47fysbjAa2tgKWl6voghBCicWjqFCGE6LOyMtUmGUDv+UtLVdsHIYQQjUOJBiGE6DOxWLf6IYQQojEo0SCEEH1mYqJb/RBCCNEYtEaDEEL0WXs7YGVFazQIIYQoHY1oEEKIPrO0BDw8VNuHpyclGYQQooco0SCEED13b/Zs9PB4qjk5nw+sWqWacxNCCNFolGgQQogeu3z5Mr4wNIShqqZOSaXoeeUV1ZybEEKIRqNEgxBC9NT58+fx448/os7WFgVTpih9VENmYIDCKVPw/j//iRs3boCWBBJCiH6hRIMQQvRQUlIS4uPj5V9/FxaGHgMDKDMVMDA2hs2PP8LW1hafffYZ9u3bh7t37yqxB0IIIZqMqk4RQoge4TgOp0+fxunTpwccW3T7Nn6VkqK8zo4dA7ZuBcdxuHXrFuLj41FXV4eQkBBs3LgRtra2yuuLEEKIxqFEgxBC9ATHcThx4gSSkpKGbLPq+nVsvHoVHIAxTaTauxd4551+D8lkMqSmpuLUqVMQiURYtmwZVqxYAVNT07H0RAghRENRokEIIXqA4zgkJCTg/PnzT2276PZtPJeeDj4Ag54exTvh83v/HDoEbN06ZDORSISffvoJFy5cgLm5OdavX49FixbBwIBm8xJCiC6hRIMQQnQcx3H45z//icuXLyvUnsfjYfvy5Zhz5Ahw4UJv8iCVDv2Ex8eXLQOOHgXc3RXqp6mpCcePH0d2djYmT56M6OhoBAQEKPRcQgghmo8SDUII0WEcx+Ef//gHkpOTFWpvYGCAl156CfPnz+99oLAQOHIESEoCysr67yDO4/VuxrdqFbBjB+DnN6oYKysrERcXh5KSEvj7+yM6OhpTpkwZ1bkIIYRoDko0CCFER3Ech2+//Rbp6ekKtTcwMMC2bdswd+7cwRu0twOlpYBYDJiYAF5eStvxm+M45OXlISEhAQ0NDVi0aBHWr18Pa2trpZyfEELI+KNEgxBCdJBMJsM333yDrKwshdobGhrilVdeQWBgoGoDewqpVIqUlBScPn0aUqkUy5cvx7Jly2BiYqLWuAghhIwcJRqEEKJjZDIZ/va3vyEnJ0eh9nw+H6+++ipmzpyp4sgU19nZibNnz+Ly5cuYMGECNmzYgJCQEFowTgghWoQSDUII0SE9PT04duwYrl+/rlB7IyMj7NixQ2MXYT98+BAJCQm4du0apk6diujoaEyfPl3dYRFCCFEAJRqEEKIjpFIpPv/8c9y4cUOh9sbGxnj99de14oN7WVkZ4uLiUF5ejlmzZiEqKgrOzs7qDosQQsgwKNEghBAdIJFIcOTIEdy6dUuh9iYmJti5cye8vb1VHJnycByHa9euITExEU1NTQgLC8O6deswYcIEdYdGCCFkEJRoEEKIluvu7sZnn32GoqIihdqbmppi165d8PT0VHFkqiGVSnH58mWcPXsWHMdh1apVeOaZZ2BkZKTu0AghhPRBiQYhhGgxsViMTz/9FHfu3FGovbm5Od588024ubmpNrBx0N7ejtOnTyM5ORk2NjaIjIzEvHnzwOPx1B0aIYQQUKJBCCFaSyQS4dChQygpKVGovYWFBd566y1MmzZNxZGNrwcPHiA+Ph43btyAm5sboqOjtWpKGCGE6CpKNAghRAt1dXXhwIEDKC8vV6i9paUl3n77bbi4uKg4MvUpLi5GXFwcqqqqMGfOHERFRcHR0VHdYRFCiN6iRIMQQrRMZ2cnPv74Y1RVVSnU3srKCm+//TYmT56s4sjUj+M4XLlyBYmJiXj06BEEAgHWrFkDCwsLdYdGCCF6hxINQgjRIu3t7fj4449RXV2tUHsbGxvs3r0bTk5OKo5Ms0gkEly8eBFJSUkwNDTEmjVrwDAM+Hy+ukMjhBC9QYkGIYRoiba2Nuzfvx/37t1TqL2trS12796t19OHWltbcerUKaSmpsLOzg5RUVEICgqiBeOEEDIOKNEghBAt8OjRI+zfvx91dXUKtbezs8Pu3bthb2+v4si0Q21tLeLj43Hr1i14enoiOjoaHh4e6g6LEEJ0GiUahBCi4VpaWrBv3z48ePBAofYODg7YvXs3Jk6cqOLItE9RURHi4uJQU1OD4OBgREZGUjJGCCEqQokGIYRosKamJuzbtw8NDQ0KtXdycsLu3bthY2Oj2sC0mEwmQ2ZmJk6cOIGOjg4sWbIEq1atgrm5ubpDI4QQnUKJBiGEaKiHDx9i3759aGxsVKi9s7Mzdu/eDSsrKxVHphvEYjHOnz+P8+fPw8jICOvWrUN4eDgMDQ3VHRohhOgESjQIIUQD1dfXY9++fWhublao/ZQpU/D2229jwoQJKo5M97S0tODEiRPIzMyEo6MjNm3ahFmzZtGCcUIIGSNKNAghRMPcv38f+/btw6NHjxRqP3XqVLz99tuq3yuivR0oLQXEYsDEBPDyAiwtVdvnOKqurkZcXBxu374NHx8fREdHw9XVVd1hEUKI1qJEgxBCNEhtbS3279+P1tZWhdq7ubnhzTffVN36gsJC4MgR4OxZoLwc6Psrg8cDPDyA1auBV18F/P1VE8M44jgOBQUFiIuLQ11dHUJCQrBx40bY2tqqOzRCCNE6lGgQQoiGqKmpwf79+9He3q5Qew8PD+zatQtmZmbKD6aiAti+HbhwAeDzAal06LaPjy9bBhw9Cri7Kz+ecSaTyZCWloaTJ09CJBJh6dKlWLlyJUxNTdUdGiGEaA1KNAghRAPcvXsXH3/8MTo6OhRq7+3tjTfeeEM1H3yPHQN27uxNHoZLMJ7E5/f+OXgQ2LZN+XGpgUgkwk8//YSLFy/C1NQU69evx+LFi2FgYKDu0AghRONRokEIIWpWWVmJTz75BJ2dnQq19/X1xeuvvw4TExPlB7N3L7Bnz9jP88EHwLvvjv08GqKpqQnHjx9HdnY2Jk+ejE2bNiEgIIAWjBNCyDAo0SCEEDUqKyvDgQMHIBKJFGrv7++PHTt2wNjYWPnBHDsGvPyycs+3davyzqcBqqqqEBsbi5KSEvj5+SE6OhouLi7qDosQQjQSJRqEEKImJSUlOHjwIMRisULtZ8yYgVdffRVGRkbKD6aioncxt4IJj0JMTXsXk+vAmo2+OI7DjRs3EB8fj4aGBoSGhmLDhg2wtrZWd2iEEKJRKNEghBA1uH37Ng4dOgSJRKJQ+9mzZ+OVV14Bn89XTUDLlwNC4cjWZDwNnw8IBMD588o7pwaRSqVISUnB6dOnIZVKsXz5cixbtkw1U9oIIUQLUaJBCCHjrKCgAIcPH1Y4yQgKCsK2bdtUt2N1YSEQEKCacz8+v5+f6s6vZp2dnUhKSsLly5dhaWmJDRs2ICQkhBaME0L0HiUahBAyjvLz83H06FFIFRw5mDdvHrZs2aLaD627dgGHDyt3NOMxPh/YsQM4cED559YwDx8+RGJiIq5evQoXFxfExMRg+vTp6g6LEELUhhINQggZJ3l5efj888/R09OjUPuQkBD8+te/Vv2dcS8voKxMtecvKVHd+TVMeXk5YmNjUV5ejpkzZ2LTpk1wdnZWd1iEEDLuKNEghJBxcO3aNRw7dgwymUyh9osWLcIvf/lL1ScZbW2AtXX/Hb+VjccDWlsBS0vV9aFhOI7DtWvXkJiYiKamJoSFhWHdunWYMGGCukMjhJBxQ4kGIYSo2JUrV/C3v/0Nil5uIyIi8Pzzz4/PHg15ecCcOarvJzcXCAxUfT8aRiqVQigU4syZM+A4DitXrsTSpUtVUzmMEEI0DCUahBCiQpmZmfjmm28UTjKeeeYZxMTEjN9GcNnZQEiI6vvJygIWLFB9Pxqqo6MDp0+fBsuysLGxwcaNGzF//nza8I8QotMo0SCEEBVJS0vD3//+d4WTjOXLlyMqKmp8P3zSiMa4evDgARISEpCXlwc3NzdER0fD29tb3WERQohKUKJBCCEqwLIs/vGPfyjcftWqVdiwYcP43+FubwesrGiNxjgrLi5GXFwcqqqqMGfOHERFRcHR0VHdYRFCiFJRokEIIUp26dIl/POf/1S4/bp167BmzRq1TaORurmBX1Wlug70rOqUojiOw5UrV5CYmIhHjx6BYRisXbsWFhYW6g6NEEKUQkVbzBJCiH46f/484uPjFW6/ceNGrFq1SoURDa21tRUnT57E5AkTEMHjwVAF9504Q0PIVqyAirYa1Go8Hg8LFixAUFAQLl68iJ9++glZWVlYvXo1BAKB6naBJ4SQcUIjGoQQoiRnz57FiRMnFG6/adMmLF++XIURDa67u1v+wVYsFsO5uRnvxcaqrL8Pf/Ur+GzYgIiICNjZ2amsH23X2tqKU6dOITU1FXZ2doiKikJQUBAtGCeEaC1KNAghZIw4jsPp06dx+vRphZ/zi1/8AkuWLFFhVANxHIfs7GwcP34czc3N/Y7tOnMG02trlTuqwedDvGgRTrz2GjIyMiASiTBz5kwwDAN/f3/6AD2Euro6xMXF4datW/Dw8EBMTAw8PDzUHRYhhIwYJRqEEDIGHMfhxIkTSEpKUvg5L7zwAsLDw1UY1UDFxcWIjY3F3bt3Bz1u19qK92JjYdTTA6V9/Dc1BQoLAXd3iMViXLlyBSzLoqamBo6OjoiIiEBoaCjMzc2V1aNOKSoqQlxcHGpqahAcHIzIyEjY29urOyxCCFEYJRqEEDJKHMchPj4eFy5cUKg9j8fDiy++iEWLFqk4sn958OAB4uPjcePGjWHjCgsLQ1RTE8x27VJe58eOAVu39nuI4ziUl5dDKBTi2rVrMDQ0REhICBiGgYuLi/L61hEymQxZWVk4fvw4Ojo6sGTJEqxatYqSM0KIVqBEgxBCRoHjOPz4448QCoUKtefxeNi8eTNCxmNzPADt7e04ffo0kpOTIZPJhmw3Y8YMREdHw9nZufeBvXuBPXvGHsDevcA77wzbpLW1FampqUhJSUFLSws8PT0hEAgwZ84cWgj9BLFYjAsXLuDcuXMwMjLCunXrEB4eDkNDWmZPCNFclGgQQsgIcRyH77//HikpKQq1NzAwwJYtWzBv3jwVRwZIJBIIhUKcPXsWXV1dQ7ZzcXFBdHQ0/Pz8Bh48dgzYuROQSnv/KIrP7/1z6NCAkYzh9PT04MaNG2BZFnfu3IGVlRUWL16M8PBw2NraKt6/HmhpacHJkyeRkZEBBwcHbNq0CbNnz6b1LoQQjUSJBiGEjIBMJsO3336LjIwMhdobGBjg5ZdfRlBQkErj4jgOV69eRWJiIhobG4dsZ21tjQ0bNmDhwoUwMDAY+oQVFcD27cCFC73Jw3AJx+Pjy5YBR48C7u6jfh21tbVITk5GZmYmJBIJAgMDwTAMfHx86MN0HzU1NYiLi0NRURF8fHwQHR0NV1dXdYdFCCH9UKJBCCEKkslk+Prrr5Gdna1Qe0NDQ2zfvh2zZ89WaVxlZWWIjY1FRUXFkG2MjY2xfPlyLF++HCYmJoqfvLAQOHIESEoCysr67yDO4wGensCqVcCOHcBgoyOjJBKJkJWVBZZlUVdXB2dnZzAMg5CQEJiamiqtH23GcRwKCgoQFxeHuro6LFiwAJGRkTQKRAjRGJRoEEKIAnp6evDVV18hJydHofZ8Ph87duzAjBkzVBZTQ0MDEhIScP369SHb8Hg8hIaGYv369bCxsRlbh+3tQGkpIBYDJia9O35bWo7tnE/BcRyKi4vBsizy8vJgZGSEhQsXgmGYf60r0XMymQxpaWk4efIkRCIRli5dipUrV1JCRghRO0o0CCHkKaRSKb788sthP9D3ZWRkhNdeew3+/v4qiaezsxNnzpyBUChET0/PkO38/PwQHR2tM9WcmpubkZKSgrS0NLS2tsLX1xcMwyAwMHD4aWB6QiQS4aeffsLFixdhamqK9evXY/HixfTeEELUhhINQggZhlQqxeeffz5sedi+jI2N8frrr2P69OkqiSU5ORmnT59GZ2fnkO2cnZ0RHR2NgIAAnVzXIJVKkZubC6FQiLKyMtjY2CA8PBxhYWGwsrJSd3hq19TUhOPHjyM7O1vnvxcIIZqNEg1CCBmCRCLBkSNHcOvWLYXam5iYYOfOnfD29lZqHBzHIS8vDwkJCaivrx+y3YQJE/TuLnZ1dTWSk5ORlZUFmUyGoKAgMAwDT09Pvf9gXVVVhdjYWJSUlOjc6BYhRDtQokEIIYPo7u7GZ599hqKiIoXam5qa4s0334SHh4dS46isrERcXBxKSkqGbGNkZKT38/I7OzuRkZGB5ORk1NfXw8XFBQzDYP78+SNb/K5jOI5Dfn4+4uPjUV9fr7z1OoQQogBKNAgh5AlisRiHDh1CcXGxQu3Nzc3x5ptvws3NTWkxNDY24vjx47hy5cqw7RYsWICNGzdi4sSJSutbm3Ech8LCQrAsi5s3b8LU1BShoaFgGAaOjo7qDk9tenp6kJKSglOnTkEikWDFihVYtmyZXidhhBDVo0SDEEL6EIlEOHDgAMrKyhRqb2FhgbfffhtTp05VSv9dXV1ISkrCpUuXIB1m7wpvb2/ExMTQ3gnDaGxsRHJyMtLS0tDR0QF/f38wDIOZM2fqzdSyJ3V2diIpKQmXL1+GpaUlNmzYgJCQEL19PwghqkWJBiGE/KyzsxMHDx5EeXm5Qu0nTJiAt956Synz3mUyGVJSUnD69Gm0tbUN2c7R0ZF2gx4hiUSCq1evgmVZVFZWws7ODhEREVi0aBEsVVyeV1M9fPgQiYmJuHr16vC7xBNCyBhQokEIIQA6OjrwySefoKqqSqH2VlZW2L1795j3cuA4Djdv3kR8fDzu378/ZDsLCwusXbsW4eHh4PP5Y+pTn1VWVoJlWfl+KMHBwRAIBEqd9qZNysvLERsbi/LycsyYMQPR0dG0PwkhRGko0SCE6L329nZ8/PHHqK6uVqi9jY0Ndu/eDScnpzH1W11djbi4ONy+fXvINnw+H0uWLMGqVatgbm4+pv7Iv7S3tyM9PR3JyclobGyEq6srBAIBgoODYWRkpO7wxhXHcbh+/ToSEhLQ1NSExYsXY/369ZgwYYK6QyOEaDlKNAgheq2trQ379u1DbW2tQu1tbW2xe/fuMS0sbmlpwfHjx5GVlYXhLsHBwcGIjIyEvb39qPsiw5PJZLh16xZYlkVBQQEsLCywePFiREREwM7OTt3hjSupVAqhUIizZ89CJpNh5cqVWLp0qd4lXoQQ5aFEgxCitx49eoT9+/ejrq5OofZ2dnb4zW9+M+oPoGKxGOfOncP58+chkUiGbOfh4YGYmBill8olw6uvr0dycjIyMjLQ1dWFmTNngmEY+Pv769V6mI6ODvnO89bW1oiMjMT8+fP16j0ghCgHJRqEEL3U3NyMffv2DbsBXl8ODg74zW9+A1tb2xH3JZPJkJGRgRMnTqC1tXXIdvb29oiMjMTcuXPpQ50aicVi5OTkQCgUoqamBo6OjoiIiEBoaKheTV+rr69HQkICcnNz4erqipiYGKVvRkkI0W2UaBBC9E5jYyP27duHhw8fKtTeyckJu3fvHtUmZ4WFhYiLi8O9e/eGbGNmZoY1a9ZAIBDQQm8NwnEcysvLIRQKcf36dRgYGGDBggVgGEZp5Yy1QUlJCWJjY1FVVYXAwEBERUWNeX0SIUQ/UKJBCNErDx8+xL59+9DY2KhQe2dnZ+zevRtWVlYj6qe2thZxcXEoKCgYso2BgQEYhsHatWthYWExovOT8dXa2oq0tDQkJyejpaUFnp6eYBgGQUFBepEcchyHK1euIDExEY8ePaLvW0KIQijRIITojfr6euzbtw/Nzc0KtXdxccFbb701ouo7ra2tOHnyJNLS0oZd6E13hrWTTCZDXl4eWJbFnTt3MGHCBISFhSE8PHxU0+q0jUQiwaVLl5CUlAQej4fVq1djyZIlepFsEUJGjhINQoheuH//Pvbt24dHjx4p1H7q1Kl4++23Fb5j293djYsXL+Knn36CWCwesp2rqyuio6Ph4+Oj0HmJ5qqrqwPLssjMzIREIsHs2bPBMAx8fX11fo1NW1sbTp06hdTUVEycOJHWFhFCBkWJBiFE59XW1mLfvn3D7rjdl5ubG958802FFv5yHIfs7GwkJiaipaVlyHa2trZUvUdHiUQiZGVlgWVZ1NXVwdnZGREREVi4cCFMTU3VHZ5K1dXVIT4+Hjdv3qRqaYSQASjRIITotJqaGuzfvx/t7e0Ktff09MTOnTthZmb21LbFxcWIjY3F3bt3h2xjampK+xHoCY7jUFxcDJZlkZeXByMjIyxcuBAMw+j8btu3b99GbGwsampqaP8XQogcJRqEEJ1VVVWFjz/+GJ2dnQq19/b2xhtvvPHUu9D3799HfHw88vPzh2zD4/EQFhamWzsst7cDpaWAWAyYmABeXoClpbqj0kjNzc1ITU1FamoqWltb4ePjA4FAgNmzZ8PQ0FDd4amETCZDVlYWjh8/jo6ODggEAqxevVqvSgITQvqjRIMQopMqKirwySefoKurS6H206dPx2uvvQYTE5Mh27S1teH06dNISUmBTCYbst2MGTMQHR2tG3exCwuBI0eAs2eB8nKg768MHg/w8ABWrwZefRXw91dfnBpKKpUiNzcXQqEQZWVlsLGxkS8eH2klM20hFotx4cIFnDt3DkZGRli7di0iIiJ0NsEihAyNEg1CiM4pKyvDgQMHIBKJFGrv7++P1157bcipTRKJBJcvX8bZs2eHPaeLiwuio6Ph5+c3qrg1SkUFsH07cOECwOcDUunQbR8fX7YMOHoUcHcfvzi1SE1NDViWRXZ2Nnp6ehAUFASGYeDp6amT63YePXqEEydOICMjAw4ODti0aRNmz56tk6+VEDI4SjQIITqluLgYhw4dGrbyU18zZ87E9u3bB00yOI7D1atXkZiYOOy+G9bW1ti4cSNCQkJgYGAw6tg1xrFjwM6dvcnDcAnGk/j83j8HDwLbtqkuPi3X2dmJzMxMsCyL+vp6uLi4gGEYzJ8/f9gRNW1VU1ODuLg4FBUVwdvbGzExMXB1dVV3WISQcUCJBiFEZ9y+fRuHDh2CRCJRqH1gYCBefvnlQfcAKCsrwz//+U9UVlYO+XxjY2OsWLECy5Yt050PiHv3Anv2jP08H3wAvPvu2M+jwziOQ1FREYRCIW7evAlTU1OEhoYiIiJC5/ZX4TgOBQUFiIuLQ11dHRYsWIDIyEi92HuEEH1GiQYhRCcUFBTg8OHDCicZc+fOxdatWwfMG29oaEBCQgKuX78+5HN5PB5CQ0Oxfv162NjYjCVszXLsGPDyy8o939atyjufDmtsbERycjLS0tLQ0dEBf39/MAyDmTNn6sYo2c9kMhnS0tJw8uRJiEQiLF26FCtXrtT5MsCE6CtKNAghWi8/Px9Hjx6FVMFpPvPnz8dLL73U7wNcR0cHzp49C6FQiJ6eniGf6+fnh+joaLi4uIw5bo1SUdG7mFvBdS0KMTXtXUxOazYUJpFIcO3aNQiFQlRWVsLOzg7h4eFYtGiR7lQvQ+/eI+fOncOFCxdgamqK9evXY/HixTqVVBFCKNEghGi53NxcfPHFF8MmB30tXLgQv/rVr+QfaKRSKZKTk3H69Olhy+A6OzsjOjoaAQEBurmYdflyQCgc2ZqMp+HzAYEAOH9eeefUI5WVlWBZFjk5OQCA4OBgCAQCuLm5qTcwJWpubsbx48eRlZWllT9jf/rTn5CQkIDbt2/DzMwMoaGh+POf/wxfX191h0aIRqBEgxCita5evYovv/xy2FKzfS1evBi//OUvwePxwHEccnNzkZCQgIaGhiGfM2HCBN2/21pYCAQEqPb8ulCJS03a29uRnp6O5ORkNDY2wtXVFQzDYN68eTqzCWRVVRXi4uJQXFysVaOGK1euxHPPPYd58+ZBKpXinXfewa1bt1BYWAgLCwt1h0eI2lGiQQjRStnZ2fjqq6+g6CWMYRg899xz4PF4qKysRGxsLEpLS4dsb2RkpD/zx3ftAg4fVu5oxmN8PrBjB3DggPLPrWdkMhlu3boFlmVRUFAACwsLLFq0CBERETqxCzfHccjPz0d8fDzq6+uxcOFCbNiwQavWQTU0NMDR0RHJyckIDw9XdziEqB0lGoQQrZORkYH/+7//UzjJeOaZZxATE4OmpiYkJibKp6IMRe8q4nh5AWVlqj1/SYnqzq+H6uvrkZycjIyMDHR1dWHmzJlgGAb+/v5aM+1oKD09PUhJScGpU6cgkUiwfPlyLF++XCsqu5WWlsLb2xs3b97EjBkz1B0OIWpHiQYhRKukpqbi73//u8Ltly9fjlWrVuGnn37CpUuXhl0wrpc1/tvaAGvr/jt+KxuPB7S2ApaWqutDT3V3d+PKlStgWRbV1dVwdHREREQEQkNDYW5uru7wxqSzsxNJSUm4fPkyLCwssGHDBixcuFBjpzDKZDKsX78eLS0tSEtLU3c4hGgESjQIIVqDZVn84x//ULj9ihUrYGtri9OnT6O9vX3Idk5OTti0aRNmzZql9XeDRywvD5gzR/X95OYCgYGq70dPcRyH8vJysCyLa9euwcDAAPPnz4dAIMDUqVPVHd6YPHz4EImJibh69SpcXFwQHR0NPw1c87Njxw4kJSUhLS1NK9aXEDIeKNEghGiFixcvIjY2VqG2HMdh5syZePjwIe7fvz9kOwsLC6xbtw7h4eED9tPQG9nZQEiI6vvJygIWLFB9PwStra1IS0tDSkoKmpub4enpCYZhEBQUNOjmlNqivLwccXFxKCsrw4wZM7Bp0yZMnjxZ3WEBAN544w2cOHECKSkpcKdyzoTIUaJBCNF4586dQ0JCgkJt29vbYW5uPuz0Cj6fjyVLlmDVqlVaP71kzGhEQ2fJZDLcuHEDLMvi9u3bmDBhAsLCwhAeHq61648eV4uLj49HY2MjwsLCsG7dOlhZWaktnp07dyIxMREsy8Lb21stcRCiqSjRIIRotLNnz+LEiRNPbScWi1FZWQlLS0tMmTJlyHbBwcGIjIzUiSo9StHeDlhZ0RoNHVdXV4fk5GRkZmaiu7sbs2fPBsMw8PX11crpglKpFCzL4syZM5DJZFi5ciWWLl067uV+X3vtNXz//fc4ceJEv70zrK2tYWZmNq6xEKKJKNEghGgkjuNw6tQpnDlzZth2PT09qK6uRk1NDTw8PIacSuHh4YGYmBh4eHioIlytJvP0hEF5ueo6oKpTGkMkEiErKwssy6Kurg7Ozs6IiIjAwoULtbKMc0dHB86cOQOhUAhra2tERkZi/vz545Y8DdXPV199hc2bN49LDIRoMko0CCEah+M4JCYm4ty5c8O2uX//PqqqqtDd3Q1vb284OzsPaGdvb4+oqCgEBQVp5Z1bVZLJZMjMzAS3axcW5ubCUAW/DmQGBmh+/nlM/PZbev81CMdxKCkpgVAoRF5eHoyMjBASEgKGYTRm3cNI1NfXIyEhAbm5uXB1dUV0dDR8fHzUHRYheo8SDUKIRuE4DnFxcbh48eKQbZqamlBRUYGOjg7weDx4e3tj0qRJ/dqYm5tj9erVEAgEWr0AVlWKiooQFxeHmpoaODc34z0FF9qPxh9jYsBNny6/c67362I0TEtLC1JTU5GSkoLW1lb4+PiAYRgEBgZqXZGEkpISxMXFobKyErNnz8amTZvg5OSk7rAI0VuUaBBCNAbHcfjhhx/Asuygxzs6OlBRUYGmpiYAvdMWfH194ejoKG9jYGAAgUCANWvWwMLCYjzC1iq1tbWIj4/HrVu3+j2+68wZTK+tVe6oBp8PTiBA6aefgmVZXL9+HXw+HwsWLADDMFQCVMNIpVLk5uaCZVmUlpbCxsYGYWFhCAsLg7W1tbrDUxjHccjJyUFiYiJaWloQERGBtWvXwpLWCBEy7ijRIIRoBI7j8N133yE1NXXAse7ublRVVeH+/fvy3cB5PB6mT58OBwcHebs5c+YgKiqqX+JBerW2tuLUqVNITU0ddEf1efb22PLRRzAQi5XXqakpUFgI/Fzu89GjR/I7548ePYKXlxcYhsGcOXNo1EnD1NTUgGVZZGdnQyqVIigoCAzDwMvLS2umwEkkEly6dAlJSUng8XhYvXo1lixZQt9rhIwjSjQIIWonk8nw7bffIiMjo9/jPT09uHfvHqqrq9HT0yN/nMfjwc/PT145ytXVFTExMVRachASiQQXL15EUlISxIMkEU5OToiOjsbMmTPB+/JL4OWXldf5sWPA1q0DHu7p6UFeXh5YlkVxcTGsrKwQHh6OsLAw2NjYKK9/MmadnZ3IzMwEy7Kor6+Hi4sLGIbB/PnzYWJiou7wFNLW1obTp08jJSUFtra2iIqKwty5c7UmYSJEm1GiQQhRK5lMhq+//hrZ2dnyxziOQ319PSorKwd8ODYwMICfnx/s7Oxga2s77lVmtAXHccjOzsbx48fR3Nw84LilpSXWrVuHsLCw/vPw9+4F9uwZewB79wLvvPPUZrW1tWBZFllZWZBIJAgMDIRAIIC3tzf9n2oQjuNQVFQElmWRn58PU1NThIaGIiIiQmvWQNTV1SEhIQH5+flUhY6QcUKJBiFEbXp6evC3v/0NV69elT/W0tKC8vJytLe3D2hvYGAAf39/TJ48GatWrcIzzzwz7nXztUFxcTHi4uJQVVU14Bifz8czzzyDVatWDV3n/9gxYOdOQCrt/aMoPr/3z6FDg45kDEckEsnvnN+/fx+TJ09GREQEQkJCtLLsqi5rbGxESkoK0tLS0N7eDj8/PwgEAsycOXPYjTI1xe3btxEXF4fq6mrMnTsXUVFRtK8OISpCiQYhRC2kUimOHTuG3NxcAL1TNCoqKtDY2DhoewMDA8ycORPr16/HunXrMGHChPEMVys8ePAA8fHxuHHjxqDH582bh8jISNjZ2T39ZBUVwPbtwIULvcnDcAnH4+PLlgFHj8rXZIwGx3G4c+cOWJZFXl4eTExMsHDhQkRERAxavpioj0QiwbVr1yAUClFZWYmJEyciIiICixYt0vifT5lMhqysLJw4cQLt7e0QCARYvXo1VUQjRMko0SCEjDupVIqjR48iPz8f3d3duHv3Lurq6gZdpAwAhoaGiIqKwo4dO+jD5iDa29tx+vRpJCcnQyaTDTju6emJmJgYuI8mASgsBI4cAZKSgLKy/juI83iApyewahWwYwfg5zeGVzFQc3MzUlJSkJqaira2NkyfPh0Mw2D27Nlacedcn1RVVYFlWVy5cgUAEBwcDIZh4ObmptFT4MRiMS5evIhz586Bz+djzZo1iIiIoAXjhCgJJRqEkHElkUhw+PBh3Lx5U77QWzrM3XJra2u8//77WLFixThGqR0kEgmEQiHOnj2Lrq6uAccdHBwQFRWFOXPmKOfDXns7UFoKiMWAiUnvjt/jUDJUKpXi+vXrEAqFKC8vh62tLcLDw7F48WJYWVmpvH+iuI6ODqSnpyM5ORkPHz6Eq6srGIbBvHnzNHqa46NHj3Dy5Emkp6fLf24CAwM1OkkiRBtQokEIGTfd3d04dOgQUlNTUVlZCZFINGRbExMT+Pj44H//93/h5eU1jlFqPo7jcO3aNSQkJAw61czc3Bxr1qwBwzA6d2e2uroaQqEQV65cgUwmw9y5c8EwDDw8POhDoQaRyWS4desWWJZFQUEBLCwssGjRIkRERGj0eoiamhrEx8ejsLAQ3t7eiImJgaurq7rDIkRrUaJBCBkXYrEY7733Hi5duoS2trYh2xkaGsLFxQXe3t747W9/S7/kn1BWVoa4uDiUl5cPOGZoaCifa67rmxV2dnYiIyMDLMuioaEBU6dOlZddNTY2Vnd4pI/6+nokJycjIyMDXV1dmDFjBhiGQUBAgMYmhwUFBYiLi0NtbS0WLFiAjRs3YuLEieoOixCtQ4kGIUTl7t69i7feegtFRUVDtuHxeHBycoKbmxtsbW3x9ttvY+rUqeMYpWZraGhAYmIirl27NujxoKAgREZG6t1mhRzHoaCgACzL4tatWzAzM5OXXdW390LTdXd3IycnB0KhENXV1XBwcEBERARCQ0M1MjGWyWRIT0/HiRMnIBKJsHTpUqxcuZKqoBEyApRoEEJUpqOjAwkJCfj000/x6NGjIdvZ2trC3d0dlpaWmDBhAt5++21MmTJlHCPVXJ2dnThz5gyEQmG/TQsfc3NzQ0xMDE0vA/Dw4UMkJycjPT0dHR0dCAgIAMMwmDFjBi0e1yAcx6G8vBwsy+LatWswMDDA/PnzIRAINPLmgkgkwrlz53DhwgWYmprK958xMDBASkoK/vrXv+LatWuoq6tDYmIiNm7cqO6QCdEYlGgQQpROKpWCZVkcP34cV65cGXKqlIWFBdzd3WFrawsejwcrKyvs3r2bKkuh9z1MTk7GmTNn0NHRMeC4nZ0dIiMjERwcrLHTT9RFIpEgJycHLMuiqqoK9vb28sXjmnjnXJ+1trYiLS0NKSkpaG5uhoeHBxiGwdy5czVufVFzczOOHz+OrKwsODs7Y9OmTaiurkZGRoZ8Pw5KNAjpjxINQojScByH3NxcJCQkoLa2Fjdv3hx04z1jY2O4urpi0qRJ8g/JNjY22L17t9bsMqwqHMchLy8PCQkJqK+vH3Dc1NQUq1evxpIlSzS6io8m4DgOlZWVYFlWvink/PnzwTAMrf3RMDKZDDdu3ADLsrh9+zYmTJiAsLAwhIeHw9bWVt3h9XP37l3ExsaiuLgY06dPR0xMDFxcXMDj8SjRIOQJlGgQQpSioqICsbGxKCsrQ3d3N27evDngTryBgQFcXFzg4uLS727lxIkTsXv3bjg4OIx32BqlsrIScXFxKCkpGXDMwMAA4eHhWLt2rcZvhqaJ2tra5GVXm5qa4ObmBoFAgLlz51LCpmHq6uqQnJyMzMxMiMViBAYGgmEY+Pr6aszoHcdxyM/PR3x8POrr67Fw4UJs3ryZEg1CnkCJBiFkTBobG5GYmIicnBwAvdWlbt68ic7Ozn7tHi/0NjEx6fe4vb09du/erdhu1TqqqakJiYmJ8s3OnjRr1ixs2rQJkyZNGufIdI9MJsPNmzfBsiwKCwthaWkpL7uqz9+DmkgkEiE7Oxssy6K2thaTJk0CwzBYuHChxizI7unpQUpKCk6dOoX9+/fjnXfewR/+8IcB1zlC9BUlGoSQUenq6kJSUhIuXbok33BPLBYjPz+/3+ZxNjY28PDwgOUgG7s5Ojpi9+7dGjc1YryIRCIkJSXh4sWLg25aOHXqVMTExMDX11cN0em+Bw8eyMuuikQizJo1CwzDwM/PT2PunJPe0YOSkhKwLIvc3FwYGRkhJCQEDMNg8uTJ6g4PQO/10NzcHCtXrsTMmTOxYcMGLFy4kIoQEL1HiQYhZER6enqQmpqKU6dO9Vt/IRKJkJ+fL9+Ez9zcHO7u7pg4ceKgH9qcnJywe/du2NjYjFfoGkMmk8nfw8EWytvY2GDjxo0ICQmhD7zjQCwW48qVK2BZFjU1NXB0dJTfOTc3N1d3eKSPlpYWpKamIiUlBa2trfDx8QHDMAgMDIShoaFaY+PxePjmm2/kZXxdXFwQHR0NPz8/tcZFiDpRokEIUQjHcbh58ybi4uLw4MGDfsf6JhlGRkbyhd5D3c2bPHky3n77bVhZWY1H6Brj8XsYHx+P+/fvDzhuYmKClStXYunSpbTpnBpwHIfS0lKwLIvr16+Dz+djwYIFYBgGLi4u6g6P9CGVSpGXlweWZVFSUgIbGxuEhYUhLCwM1tbWaomp72LwvmvWZsyYgU2bNmnM6Ash44kSDULIU929exdxcXG4c+fOgGNdXV3Iz8+HRCLB5MmTMW3atGHLUrq4uOCtt97SuwXN1dXViIuLw+3btwcc4/F4WLx4MdavX693yZemevToEVJTU5GamoqWlhZ4eXmBYRjMmTNH48qu6ruamhqwLIvs7GxIpVIEBQWBYRh4eXmpfESwvb0dpaWlAIA5c+Zg3759EAgEmDhxIqZOnYrc3FzEx8ejsbGRfsaJXqJEgxAypMd147OzszHYpaKzsxP5+fmwtraGu7v7UxdoTps2DW+99ZZe7WXQ0tKCEydOIDMzc9D3MCAgANHR0XS3U0P19PTgxo0bEAqFKC4uhpWVlbzsqj5O+9NknZ2dyMzMRHJyMh48eIApU6aAYRgsWLBAZYuzWZaFQCAY8Pivf/1rfP311wD+ta/QmTNn0NPTg5UrV2LZsmVU7YzoBUo0CCED9N0JVyKRDNqmo6MDd+/exZQpUxS6Q+fm5oY333xTb+a8i8VinD9/HufPn0d3d/eA45MnT0Z0dDQCAgLUEB0ZjdraWrAsi6ysLEgkEgQGBkIgEMDb25vW0mgQjuNw+/ZtCIVC5Ofnw8TEBKGhoWAYRq379HR0dODMmTNgWRZWVlbYuHEjFixYQN87RKdRokEIkZPJZEhPT8fJkyfR2to6ZDtjY2M0NTXBwsJCoV+Snp6e2LVrl8aUpFQlmUyGzMxMHD9+fND30MrKChs2bEBoaChVpNFSIpEImZmZYFkW9+/fx+TJkxEREYGQkBC9+B7XJo2NjUhJSUFaWhra29vh5+cHgUCAmTNnqu3nr76+HgkJCcjNzYWrqyuio6Ph4+OjllgIUTVKNAghAICCggLExcWhtrZ2yDbm5uYIDg7GlStX5NWlnsbb2xs7d+7Ui7ryRUVFiIuLQ01NzYBjRkZGWL58OVasWKEX74U+4DgOd+7cAcuyyMvLg4mJibzsqrOzs7rDI31IJBJcu3YNLMuioqICEydORHh4OBYvXqy29WIlJSWIi4tDZWUlZs+ejU2bNql1xIUQVaBEgxA9d+/ePcTFxaGwsHDINgYGBhAIBPD398cXX3yhcJIxffp0vPbaazr/wbq2thZxcXEoKCgYcIzH4yEkJAQbNmzQ2/1C9EFzczNSUlKQmpqKtrY2TJ8+HQzDYPbs2TRypWGqqqrAsixycnLAcRzmzp0LgUAANze3cZ/GxHEcrl69isTERDQ3NyMiIgJr164ddN8hQrQRJRqE6KnW1lacOHEC6enpgy5SfmzOnDmIiopCa2srDh48qHCSERAQgB07duj0gsfW1lacPHkSaWlpg76Hvr6+iImJwdSpU9UQHVEHqVSK69evg2VZlJWVwdbWVn7nnKoNaZaOjg6kp6cjOTkZDx8+hKurKxiGwbx588b9uiWRSHD58mWcPXsWPB4Pq1evhkAg0OnrJ9EPlGgQome6u7tx4cIFnDt3DmKxeMh2bm5uiI6Ohre3N4qLi3Ho0KFh2/c1a9YsvPLKKzr7S7K7uxsXL17ETz/9NOh7MmnSJGzatAkzZ87UrYWe7e1AaSkgFgMmJoCXF0B3XodUXV0NoVCIK1euQCaTISgoCAKBAB4eHrr1faHlZDIZCgoKwLIsCgoKYG5ujkWLFiEiIgL29vbjGktbWxtOnz6NlJQU2NraIioqCnPnzqXvF6K1KNEgRE9wHIfMzEycOHECLS0tQ7abOHEiIiMjMW/ePPB4PBQVFeHTTz8dsvrUk+bMmYNt27bp5F4DHMchOzsbx48fR3Nz84DjlpaWWLduHcLCwtS+S7HSFBYCR44AZ88C5eVA318ZPB7g4QGsXg28+irg76++ODVYZ2cnMjIywLIsGhoaMHXqVDAMg/nz59PGjBqmoaEBycnJSE9PR1dXFwICAiAQCBAQEDCuH/br6uqQkJCA/Px8eHh4IDo6Gp6enuPWPyHKQokGIXrg9u3biIuLQ3V19ZBtTE1NsWrVKjzzzDPykYiCggJ89tlnkEqlCvUzd+5cbN26VXc+ZPdRXFyM2NhY3L17d8AxPp+PZ555BqtWrYKZmZkaolOBigpg+3bgwgWAzweG+x54fHzZMuDoUcDdffzi1CIcx6GwsBAsy+LmzZswMzNDaGgoIiIi4OjoqO7wSB/d3d3IycmBUChEdXU1HBwcEBERgdDQ0HHdB6jvtXvu3LmIjIyEg4PDuPVPyFhRokGIDrt//z7i4+ORn58/ZBsDAwOEh4dj7dq1/aqv5Ofn4+jRowonGQsWLMDmzZt1buHrgwcPEB8fjxs3bgx6fN68eYiMjISdnd04R6ZCx44BO3f2Jg8K/v8D6E04+Hzg4EFg2zbVxacDHj58KL9z3tHRgYCAADAMgxkzZujcz5A24zgOFRUVYFkW165dA4/Hw/z588EwDKZNmzZuMWRlZeH48eNoa2uDQCDAmjVr9GZPIqLdKNEgRAf1necrk8mGbDdr1ixERUUNKMWZm5uLzz//fNjn9hUaGooXX3xRpz4gtbe34/Tp00hOTh70ffD09MSzzz4LNze38Q9OlfbuBfbsGft5PvgAePfdsZ9Hx0kkEuTk5IBlWVRVVcHOzg4RERFYvHjxuN45J0/X1taGtLQ0JCcno7m5GR4eHmAYBnPnzh2XqaJ919fx+XysWbMGEREROjlNlegOSjQI0SF9K5cMVx1q6tSpiI6OxvTp0wccu3r1Kr788kuFk4ywsDC88MILOrNYUSKRQCgU4uzZs+jq6hpw3MHBAZs2bUJgYKDOvGa5Y8eAl19W7vm2blXe+XRcZWUlhEIhrl69CqB3tEwgEMDV1VXNkZG+ZDIZbty4AZZlcfv2bUyYMAGLFy9GeHg4Jk6cqPL+Hz16hFOnTiEtLQ0ODg6IiorSzesR0QmUaBCiAziOQ05ODhITE9HU1DRkOxsbG2zYsAEhISGDjj5kZ2fjq6++GrbcbV8Mw+C5557TiV9wfevZNzY2Djhubm6OtWvX6u4dxIqK3sXcCpYvVoipae9iclqzMSJtbW1IT09HSkoKGhsb4ebmBoFAgLlz5+psJTdtdf/+fbAsi8zMTIjFYsyePRsCgQC+vr4qvy7eu3cP8fHxKCgogLe3N6Kjo3VvhJVoPUo0CNFypaWliI2NRWVl5ZBtTExMsGLFCixdunTIzfMyMjLwf//3fwonGc888wxiYmJ0IskoKytDbGwsKioqBhwzNDSEQCDA6tWrdXsqy/LlgFA4sjUZT8PnAwIBcP688s6pR2QyGW7evAmWZVFYWAhLS0t52VWdWhOkA0QiEbKzs8GyLGprazFp0iQwDIOQkBCVF4goKChAXFwcamtrMX/+fGzcuJG+P4jGoESDEC1VX1+PhIQE5ObmDtmGx+Nh0aJFWL9+PaytrYdsl5qair///e8K971ixQpERkZqfZLR0NCAhIQEXL9+fdDjQUFBiIqK0v0qL4WFQECAas/v56e68+uBBw8eIDk5GRkZGRCJRJg5cyYEAgH8/Py0/udQl3Ach5KSErAsi9zcXBgZGWHBggUQCASYPHmyyvqVyWRIT0/HyZMn0dnZiaVLl2LVqlUwNTVVWZ+EKIISDUK0TEdHB86cOQOWZdHT0zNkO39/f0RHR2PKlCnDnk8oFOKHH35QuP/Vq1dj/fr1Wv3hprOzE2fOnIFQKBz0PXR3d0dMTIz+1K3ftQs4fFi5oxmP8fnAjh3AgQPKP7ceEovFuHLlCliWRU1NDRwdHcEwDBYuXEhViDRMS0sLUlNTkZKSgtbWVnh7e4NhGMyZM0dlJcBFIhHOnTuHCxcuwNTUVL6vjy4V6iDahRINQrSEVCoFy7I4c+YMOjs7h2w3efJkREdHI0CBO9QXL15EbGyswjGsX78ea9asUbi9ppFKpUhOTsbp06cHfQ/t7OwQGRmJ4OBgrU6kRszLCygrU+35S0pUd349xHEcysrK5GVX+Xw+FixYAIZh4OLiou7wSB9SqRR5eXlgWRYlJSWwsbFBWFgYwsLChh1pHovm5mYcP34c2dnZcHJywqZNmzBz5kz9uq4RjUCJBiEajuM45ObmIj4+Hg8fPhyynZWVFdavX49FixYpdPfq3LlzSEhIUDiOqKgorFixQuH2moTjOOTl5SEhIQH19fUDjpuammL16tVYsmSJ/i22bWsDrK377/itbDwe0NoKWFqqrg891traKr9z3tLSAk9PTwgEAsyZM0c3CxdosXv37oFlWWRnZ0MikSAoKAgMw8DLy0slScDdu3cRFxeHO3fuYPr06YiOjsbUqVOV3g8hQ6FEgxANVlFRgdjYWJQNc7fZyMgIy5Ytw4oVKxSej3vmzBmcPHlS4ThiYmKwdOlShdtrksrKSsTFxaFkkDvqQ21WqFfy8oA5c1TfT24uEBio+n70WE9PD27cuAGhUIji4mJYWVkhLCwM4eHhsLGxUXd4pI+uri5kZmaCZVk8ePAAU6ZMAcMwWLBgwZAFO0aL4zjcvHkTcXFxqK+vx8KFC7Fhwwb6niDjghINQjRQY2MjEhIS5PX0hxISEoKNGzfC1tZWofNyHIeTJ0/i7NmzCsfy/PPPg2EYhdtrisbGRhw/fhxXrlwZ9Pjs2bOxadMmODk5jXNkmkEikaChoQEdly/D+8UXVd9hVhawYIHq+yEAgNraWiQnJyMzMxMSiQSBgYFgGAY+Pj40fUaDcByH27dvQygUIj8/HyYmJggNDQXDMEq/NvX09CA1NRWnTp1Cd3c3li9fjuXLlys9sSGkL0o0CNEgnZ2dSEpKwuXLlyEdZmGuj48PYmJiMG3aNIXPzXEcEhMTce7cOYWf88tf/hJhYWEKt9cEXV1dSEpKwqVLlwZ9D6dNm4bo6Gj4+vqqIbrxJRKJ0NDQgPr6+n5/NzQ0oLm5GQDg8vAh/msEU+hGjUY01EIkEiErKwssy6Kurg7Ozs7ysqtUkUizNDU1ISUlBWlpaWhra4Ofnx8YhsGsWbOUupi77zXS3NwcGzZsQGhoKC0YJypBiQYhGqCnpwcpKSk4deoUOjo6hmzn5OSE6OjoES/q4zgOsbGxuHTpkkLteTwefvWrXyE0NFThPtRNJpMhJSUFp0+fRltb24DjNjY2iIyMxIIFC3Tmji7Hcejs7ER9fX2/JOLxv/u+D2ZmZnB0dISDg0P/v83MYDV1Kngq/FXA8Xhora6G9VMqoBHV4TgOd+7cAcuyuHHjBoyNjRESEgKGYeDs7Kzu8EgfUqkU165dA8uyKC8vh62tLSIiIrB48WKlTvFsbGxEYmIicnJyMGXKFERHR8Pf319p5ycEoESDELXiOA75+fmIj4/HgwcPhmxnaWkpL1M40rKIHMfhhx9+AMuyCrXn8Xh46aWXsEBLprk8nn8cHx+P+/fvDzhuYmKClStXYunSpTA2NlZDhGPDcRxaW1sHTSQaGhr6Vc+aMGGCPIl4nEg8/trc3HzQBKurqwuclxfMa2tV9hrqrazwX889BxcXFwQEBMDf3x9eXl60UFlNmpubkZKSgtTUVLS1tcHX1xcMwyAwMJDuamuYqqoqsCyLnJwccByHuXPngmEYuLu7K+2GSd+1gAEBAYiOjlbpnh9Ev1CiQYia3L17F7GxsSguLh6yDZ/PxzPPPINVq1aNandZjuPw3XffITU1VaH2BgYG2Lp1K4KDg0fclzpUV1cjLi4Ot2/fHnCMx+Nh8eLFWL9+PaysrNQQneJkMhmam5sHJBGP/93d3S1va2trO2gi4eDgMKKpMH2r30QKhYgoLISBTKb8F8fno3vbNtzYsgW3bt1CYWEhWltbYWJiAl9fX3ni4ejoqPy+ybCkUilyc3MhFApRVlYGW1tbhIeHY/HixRr/M6NvOjo6kJ6ejuTkZDx8+BDTpk0DwzCYP3++UirlPa5umJCQgIcPH2rNtZNoPko0CBlnj+ubZ2VlDdtu3rx5iIyMhJ2d3aj6kclk+Pbbb5GRkaFQewMDA7zyyiuYMx4ViMaopaUFJ06cQGZmJga7hGniXbmenh40NjYOSCTq6+vR2NgoX0/C4/FgZ2c36DQne3v7MX2o6OnpQW5urryev7W1NcLCwhDh4ACrhQuV9VIH6rMzOMdxqKmpQWFhIW7duoWysjL09PTAwcFBnnT4+vrS+oFxVl1dLU88ZTIZgoKCIBAI4OHhoTNTDXUBx3EoKCiAUChEQUEBzM3NsWjRIkRERMDe3n7M5++7X1NPT49WjwYTzUCJBiHjpO+OrRKJZMh2np6eiImJgbu7+6j7kslk+Oqrr4asuPQkPp+P7du3Y9asWaPuczyIxWKcO3cO58+fH/Q9VPc848eVnJ5MJBoaGtDU1ATZzyMGfD4f9vb2A6Y5OTg4wM7OTum7Biu0Q/Hy5YBQqNzdwfl8QCAAzp8fsolIJEJxcTEKCgpw69YtPHz4EIaGhvD09MSMGTPg7+8PFxcX+rA7Tjo7O5GRkYHk5GTU19fDxcVFfuecqhNploaGBiQnJyM9PR1dXV0ICAiAQCBAQEDAmH9eOjo6cPbsWQiFQlhZWWHjxo06tb6NjB9KNAhRMZlMhvT0dJw8eRKtra1DtrO3t8emTZswZ86cMV3Me3p68OWXX+LatWsKtefz+XjttdcU2klcXWQyGTIyMnDixIlB30MrK6txq5wyVCWn+vp6tLS0yNsZGxsPmkg4ODjA1tZW5XFyHIeSkhKwLIvc3FwYGRnJd46eMtii7IoKwN8fEImUF4Spae9oxgiS5vr6ehQWFqKgoAB37tyBWCyGlZUV/P39ERAQAD8/P/3d82QccRyHwsJCsCyLmzdvwszMDKGhoYiIiKBpbhqmu7sbOTk5YFkWd+/ehb29PSIiIrBo0SJYWFiM6dz19fVITEzE9evXMW3aNMTExMDHx0dJkRN9QIkGISpUUFCAuLg41A6z0Nbc3Bxr1qwBwzBjXhwrlUrxxRdfIC8vT6H2RkZGeP311+H387QWTVRYWIi4uDjcu3dvwDEjIyOsWLFCqbXgR1PJabCEwsrKSi13/8RisbycaW1tLZycnMAwDBYuXPj0dT7HjgEvv6y8YI4dA7ZuHfXTpVIpysrKUFBQgIKCAtTU1IDH42HatGkICAhAQEAAPDw8aAGzij18+FBedrWjowMBAQFgGAYzZsyg916DcByHiooKsCyLa9eugcfjYf78+WAYZkSl0AdTWlqK2NhYVFZW6v0eRGRkKNEgRAVqamoQHx+PwsLCIdsYGhqCYRisWbNmzHedgN5pO0ePHsXNmzcVam9iYoI33nhDY+9O1dbWIi4uDgUFBQOO8Xi8Me1uO1wlp/r6enR1dcnbjqaSkzrcv38fLMsiMzMTYrEYs2fPBsMwmD59+shi3LsX2LNn7AHt3Qu8887Yz9PHo0eP5KMdhYWF6OjogJmZGaZPny5PPCZOnKjUPsm/SCQSXL16FSzLorKyEnZ2dvI755aWluoOj/TR1taGtLQ0JCcno7m5GR4eHmAYBnPnzh31DS2O43D16lUkJiaiubkZERERWLt2Lf3fk2FRokGIEj169AgnTpxARkbGoIuUH5szZw6ioqKUNgVBIpHg8OHDg34oH4ypqSl27doFT09PpfSvTK2trTh58iTS0tIGfQ+nT5+O6OhoTJ06ddjzqKOS03iTyWS4ceMGWJbF7du3MWHCBCxevBjh4eFj+8B97Biwc2fveo2RrNng83v/HDo0ppEMRchkMty9e1c+2lFeXg6O4zBp0iR50uHj46OUijxkoMrKSgiFQly9ehVAb/EKhmHg5uam3sBIPzKZDPn5+WBZFkVFRUq5RkgkEly+fBlnz54Fj8fD6tWrIRAI6GeNDIoSDUKUQCwW48KFCzh//jzEYvGQ7dzc3BAdHQ1vb2+l9v3ZZ58NWuJ1MGZmZti1axc8PDyUFoMydHd34+LFi/jpp58GfQ8nTZqETZs29dusUCqVoqmpSa2VnNRBFXcrB6ioALZvBy5c6E0ehks4Hh9ftgw4enREazKUpbOzE7dv35YnHs3NzTAyMoK3t7c88Zg0aZLGjEDpivb2dnnZ1cbGRri5uYFhGAQHB2vdz5WuU9qo58/a2tpw+vRppKSkwNbWFpGRkQgODqafMdIPJRqEjIFMJkNWVhZOnDjRbyHwk+zs7FRyERaJRDh06BBKSkoUam9ubo633noLrq6uSothrDiOQ3Z2No4fP47m5uYBx83NzbF48WK4u7ujsbGx36hEY2OjfNRjvCs5jTdVzr8eVmEhcOQIkJQElJUBfX9l8HiApyewahWwY4e8hK26cRyH+/fvy6dYFRcXQyKRwNbWVp50TJ8+Hebm5uoOVWfIZDLcunULLMuioKAAFhYWWLx4MSIiIkZdopuohlgsRnZ2NoRC4cjXcQ3i/v37iI+PR35+Ptzd3RETE6ORo+VEPSjRIGSUbt++jbi4OFRXVw/ZxtTUFKtXr8aSJUuUfndPJBLhwIEDKCsrU6i9hYUF3n777adOORpPxcXFiI2NRXl5OUQiEbq6utDV1QWRSITu7m44Ojpi4sSJ8rv0Q1VycnR0hI2NjU4uTH1cUUYoFKK6uhr29vZgGAahoaFKWdszIu3tQGkpIBYDJiaAlxegBfOzJRKJvIRuYWEh6urqYGBgAHd3d3niMW3aNJ38/lGHBw8eIDk5GRkZGRCJRJg5cyYYhoG/vz/d7dYgHMehtLQULMvi+vXrT69M9xR37txBbGwsqqurERQUhKioKDg4OKggcqJNKNEgZITq6uoQHx8/7KJrAwMDhIeHY+3atSopxdnZ2YlPPvkElZWVCrWfMGECdu/erbYN7J6s5FRcXIxz586hpKREnlQ8xufz4e3tjfDwcLi7u/dbNzFhwgS9+aCiyhr5+q6pqUk+xaqoqAgikQgWFhbw9/eXl9G1trZWd5haTywW48qVK2BZFjU1NXB0dERERARCQ0NpNEnDPN5rJzU1FY8ePRp8rx0FPB6hTkxMRFtbGwQCAdasWUP/33qMEg1CFNTW1oZTp04hNTVVvvHaYGbNmoWoqCg4OzurJI6Ojg58/PHHuHv3rkLtra2tsXv3bkyaNEkl8Tz2ZCWnJ9dNdHV1QSKRoKqqCo2NjTAxMYGZmRlMTU1hZmYGMzMzBAQE4N/+7d/GtFmhNpPJZCgoKJBPPzEzM5NPP1HGrr9kIJlMhoqKCty6dQuFhYWoqqoCx3FwcXGR71Tu5eWlvLUveojjOJSXl0MoFOL69eswMDBASEgIGIaBi4uLusMjffT09CA3Nxcsy6KkpATW1tYICwtDWFjYiCr89V1zZ2hoiLVr1yIiIoJ+jvQQJRqEPIVEIsGlS5eQlJQE0TCbmU2dOhXR0dGYPn26ymJpa2vDxx9/jJqaGoXa29raYvfu3UqrbjWaSk6Ojo6wtbVFZWUlrl+/Dh6PN+CXjaOjI6KiohAYGKiXd+s7OjrkC2ofPnyIadOmQSAQYN68ebSgdpy1t7ejqKhInni0trbCxMQEvr6+8tEO2rBu9FpbW+W71Le0tMDT0xMCgQBz5szRyg+hf/rTn5CQkIDbt2/LNzX885//DF9fX3WHNmb37t0Dy7LIzs6GRCLBnDlzIBAI4OXlpfB1um8VQQcHB72+zusrSjQIGQLHccjJyUFiYiKampqGbGdjY4ONGzciJCREpRfP1tZW7Nu3D3V1dQq1nzhxIn7zm9+M+E74k5Wc+v7dt5KTgYEBJk6cOOiGdY8rOfWtu97Y2DigL3Nzc72+01VVVQWWZZGTkwOO4zB37lwwDAN3d3f6RawBOI7DvXv35NOsSktL0dPTA3t7e8yYMQP+/v7w9fXV6BLImqqnp0demvnOnTuwsrKSl121tbVVd3gKW7lyJZ577jnMmzcPUqkU77zzjjxJHfc1VCrS1dWFzMxMsCyLBw8eYPLkyRAIBFiwYIHCG6X23RfJ29sb0dHRVApZT1CiQcggSkpKEBcXN+waCBMTE6xYsQLLli2DsbGxSuNpaWnBvn378ODBA4Xa29vbY/fu3UNWe5FIJAPKwT7+WlmVnMrKyvDPf/5z0PfQ0NBQb+fuSiQSXLt2DSzLoqKiAhMnTkR4eDgWL16skvU8RHnEYjHu3LkjTzwaGhpgaGgIT09P+aJyFxcXShJHqLa2FsnJycjMzIREIkFgYCAYhoGPj4/WvZcNDQ1wdHREcnIywsPD1R2OUnEch9u3b4NlWdy4cQMmJiZYuHAhGIZReGpuYWEhYmNjUVtbi/nz52Pjxo1UlUzHUaJBSB/19fVISEhAbm7ukG14PB4WL16MdevWjcuC0ebmZuzbtw/19fUKtXd0dMTu3bthZmY2aCJRX1/frxSvsis5NTQ0ICEhAdevXx/0uL5WI2lsbERKSgrS0tLQ3t4OPz8/CAQCzJw5k6odaan6+nr5TuV37tyBWCyGlZWVfIqVn58fJY8jIBKJkJWVBZZlUVdXB2dnZzAMg5CQEK0ZNSotLYW3tzdu3ryJGTNmqDsclWlqapJfz9ra2jB9+nQIBALMmjXrqdczmUyGjIwMnDhxAp2dnXjmmWewatWqUZXWJZqPEg1C0DtH/syZMxAKhcMu9A4ICMCmTZtGVfpvNBobG7Fv3z48fPhwwDGO4yCVSuXlYLu6umBsbIygoCC0t7ejra1N3tbc3LxfEqGKSk4dHR04e/YshEIhenp6BhzXx/rqj+8ACoVC5Ofnw8TEBKGhoWAYBk5OTuoOjyiRVCpFWVmZfLSjpqYGPB4P06ZNk492eHh4UFKpAI7jUFxcDJZlkZeXByMjI/mdc1UV2VAGmUyG9evXo6WlBWlpaeoOZ1xIpVL5CG15eTlsbW0RERGh0AitWCzGuXPncP78eZiammLt2rUIDw+nnxEdQ4kG0WtSqRQsy+LMmTPo7Owcst3kyZMRHR2NgICAcYutoaEBH330Ee7fvy9PJJ78W9pnt2ZbW1usXLkSU6dO7ZdIODg4qHSusFQqRXJyMk6fPj3oe2hnZ4eoqCjMnTtX66ZBjFZnZycyMzORnJyMBw8eYMqUKWAYZkRzmol2e/TokXy0o7CwEB0dHTA1NYWfn5+8mhVNGXm65uZm+Z3z1tZW+Pr6gmEYBAYGatwH0h07diApKQlpaWl6WU3r7t27YFkWV65cGdGas+bmZpw8eRKZmZlwcnLCpk2bMHPmTL35faHrKNEgeonjOFy/fh0JCQmDjhY8ZmVlhfXr12PRokUq+6U2WCWn0tJSnDp1Ci0tLf1GBwYrCWtqagpPT0/87ne/G9dpGhzHITc3FwkJCWhoaBhw3MzMDKtXr4ZAINCbykk1NTXyKi1SqRRBQUFgGGZEVVqI7pHJZLh79658tKOiogIymQyTJk2Sj3b4+Pjozc/JaEilUuTm5kIoFKKsrAw2NjYIDw9HWFgYrKys1B0e3njjDZw4cQIpKSl6W577sY6ODmRkZIBlWTx8+BBTp06VV9Ebbj1jdXU14uLicPv2bUyfPh3R0dEatcEsGR1KNIjeKS8vR1xc3LA7ahsZGWH58uVYvny5UuYGS6VSNDY2DroA++HDh/JkwsDAAMbGxrh58yZ4PN6AhGKwZMfV1RVvvvnmuFY4qaysRGxsLEpLSwccMzAwQEREBNauXQtLLdg1eqykUiny8vL61Z1//AGINn0jg+ns7MTt27dRWFiIW7duobm5GXw+Hz4+PvLEY9KkSZScDqG6uhrJycnIzs5GT0+PPKH39PQc9/eM4zjs3LkTiYmJYFkW3t7e49q/JuM4DgUFBRAKhfJ9gRYtWoSIiIgh1+hxHIebN28iPj4eDx48QEhICDZu3DiiPTyIZqFEg+iNhw8fIjExEVevXh2yDY/HQ0hICDZs2DDiEovd3d14+PDhmCo5iUQiHDhwoN/6iuG4u7tj165d41a5qbGxEYmJicjJyRn0+OzZs7Fp0ya9WH/weCfdlJQUtLa2wsfHRz6lYyQ76RL9xnEc7t+/L59iVVxcDIlEAltbW3nSMX36dL2rzqaIzs5OZGRkIDk5GfX19XBxcQHDMJg/f/64TVF87bXX8P333+PEiRP99s6wtramxc19PHz4EMnJyUhLS0NXVxcCAgLAMAxmzJgxaHLY09OD1NRUnDp1CmKxGMuXL8eKFSto6qkWokSD6LzOzk4kJSXh8uXL/dY0PMnX1xfR0dGYNm3akG1EItGg+0s0NDQMWcnpyYRiqEpO1dXV2L9/Pzo6OhR6XZ6enti1a9e4VGPp6upCUlISLl26NOh7OG3aNMTExMDHx0flsagTx3EoKSkBy7LIzc2FkZERFixYAIFAgMmTJ6s7PKIDJBIJiouL5es76urqYGBgAHd3d/j7+2PGjBmYNm2axq1PUCeO41BYWAiWZXHz5k2YmprKiy6oenPFoUZQvvrqK2zevFmlfWsjiUSCnJwcCIVC3L17F/b29oiIiMCiRYsGHZXv+7vH3NwcGzZsQGhoKH3/axFKNIjO6unpQUpKCk6dOjXsh3cnJydER0dj5syZAHrnlw6263VDQ4PKKjlVVlbik08+GXZBel8+Pj544403VH53p+9dpfb29gHHbW1tsXHjRixYsECnp3mIRCJkZ2eDZVnU1tbCyckJAoEAISEhdNeSqFRTU5N8tKOoqAhdXV2wsLCAv7+/vIwuTdH7l8bGRvmd846Ojn53zunDqebgOA4VFRVgWRbXrl0Dj8fDvHnzwDAMXF1dB7TvO5o+ZcoUREdHw9/fXw2Rk5GiRIPoHI7jkJ+fL5/jOdjx7u5uGBgYICgoCJMnT5avn6ivr0dXV5e87YQJEwZNJJRZyam8vByffPIJRCKRQu39/Pzw2muvqXSTwL7zZO/fvz/guImJCVauXImlS5eqfLNCdbp//z5YlkVmZibEYrF8IzFfX1+dTqyIZpLJZKioqJAvKq+qqgLHcXBxcZEnHV5eXuDz+eoOVe0kEgmuXr0KlmVRWVkJOzs7+Z1zfVg7pk3a2tqQlpaGlJQUNDU1wd3dHQzDIDg4eMD3ct/1gQEBAYiOjqbRZA1HiQbRKVVVVYiLi5NvnjVYSdju7m5MmjQJU6dOBZ/Ph62t7YCN6h4nFqqellRaWooDBw5ALBYr1D4gIAA7duxQaXWa6upqxMbG4s6dOwOOPd6scP369RpR6UUVZDIZbty4AZZlcfv2bUyYMAGLFy9GeHg4Jk6cqO7wCJFrb29HUVGRPPFobW2FiYkJfH195YmHqqcOaYPKykqwLCtfWxYcHAyBQAA3Nzf1Bkb6kclkuHnzJoRCIYqKioa89nIch7y8PHnFQ13/naTtKNEgWqtvJafy8nIkJSUhPz8fIpEIIpFIvvEej8eTl4UNCAjAqlWr4OnpCUdHR9jb26utpGRxcTEOHjyI7u5uhdrPmjUL27dvV9ndypaWFhw/fhxZWVkY7LIwY8YMbNq0SWfvHrW1tckXdzc3N8PDwwMMw2Du3Ll0h5hoPI7jcO/ePXnSUVpaip6eHtjb28sXlfv6+mrNDtuq0N7ejvT0dCQnJ6OxsRFubm7yO+dUWlizPHjwACzLIiMjA2KxGLNmzYJAIMD06dPlo8l993Dq6enRi1F2bUSJBtFojys5Pbnw+nElJ6lUiurqatTW1sLY2LjfHhOP/zYxMYG3tzdiYmI0pr55UVERPv30U0gkEoXaz5kzB9u2bVPJB96+u7MOFs+UKVMQExMDPz8/pfetbo/nCQuFQly7dg0GBgaYP38+GIYZtigAIZpOLBbjzp078sSjoaEBhoaG8PT0lCceLi4uejkFUCaT4datW2BZFgUFBbCwsMDixYsRERFBmyhqGLFYLF8fd+/ePTg5OSEiIgKhoaHy9XEdHR04e/YshEIhJkyYgMjISJ1fN6hNKNEYgfZ2oLQUEIsBExPAywugqZ5j19XVNSCJGKySk4mJiXxKk729Perq6nD9+nXIZDKYmJgMelFxcHDApk2bEBgYqDEXnVu3buHw4cPDVsDqKzg4GFu2bFF6yVSZTIb09HScPHkSra2tA45bWVlh48aNWLhwoc4touzu7pZXPqmuroa9vT0YhkFoaOi47keidegiqLXq6+vllaweTy21srKST7Hy8/Mb1w0/NUV9fT2Sk5ORkZGBrq4uzJw5EwzDwN/fX2N+Z5Dem0KlpaVgWRbXr1+XV/xjGAZTpkwBADQ0NCAhIQHXr19XSSVEqbQdXV2l4DgxeDwTmJl5gc+n69/TUKLxFIWFwJEjwNmzQHk50Pfd4vEADw9g9Wrg1VcBKoAwOI7jlFbJCQAKCgoQHx+P2traIfs0NzfH2rVrERERoVHTXm7cuIGjR4/22+17OAsWLMDmzZuV/kG/oKAAcXFxg76HRkZGWLFiBZYvX65zNcuf/FAxY8YMMAyDgIAA+lAxFLoI6hypVIqysjL5aEdNTQ14PB6mTZsmH+3w8PDQuRsMwxGLxfKbDzU1NXB0dJTfOac9TDTLo0eP5NNcHz16BG9vbzAMgzlz5sDQ0BBlZWWIjY1FRUUFZs2ahU2bNmHSpEmj6qujoxC1tUfQ2HgWIlE5gL4fmXkwNfWAnd1qTJ78Kiws6Po3GEo0hlBRAWzfDly4APD5wHA3nx8fX7YMOHoU0JDZOeOK4zg8evRoyJGJoSo5Pfn3cHeTa2pqEBcXh6KioiHbGBoaQiAQYPXq1Rp3Z/r69ev44osv5GtHniY0NBQvvviiUn/Z19bWIi4uDgUFBQOO8Xg8LFy4EBs2bNCpXVhlMhkKCgrAsixu3boFCwsL+e609vb26g5Pc9FFUG88evRIPtpRWFiIjo4OmJqaws/PDwEBAfD399ebKUUcx6G8vBxCoRDXr1+HgYGB/M751KlT1R0e6aOnpwd5eXlgWRbFxcWwsrJCeHg4wsLCYG1tjatXryIxMRHNzc0IDw/H2rVrFR616+qqQHHxdjQ3XwDABzDcDITe47a2y+DjcxRmZnT964sSjUEcOwbs3Nn7e1PB2S0Aen/X8vnAwYPAtm2qi09dZDIZmpubByQSj7/uO7+/byWnvqMTo6nk1NLSgpMnTyIjI2PQRcqPBQUFITIyUiOrrOTk5OBvf/ubwklGWFgYXnjhBaXdZW9tbcXJkyeRlpY26Hs4ffp0REdH69Qv0o6ODvnCz4cPH8LV1RUMw2DevHm08PNp6CKot2QyGe7evYvCwkLcunULFRUVkMlkmDRpkny0w8fHRy9+hlpbW5GWlobk5GS0tLTA09MTAoEAc+bM0aiRctJ7E00oFCI7OxsSiQRz5swBwzBwc3ODUCjE2bNnAQCrV6/GkiVLhv3+ra09htLSnZDJpBg+wXgSHwYGfHh5HcTkyXT9e4wSjSfs3Qvs2TP283zwAfDuu2M/z3jrW8npyQXYDx8+lE/5MTAwwMSJEwckEsqs5CQWi3HhwgWcO3du2MpMbm5uiImJgZeX15j7VIWsrCx8/fXXwyZJfQkEAvziF79QSpLR3d0tfw8HK6Hr7OyMTZs2YcaMGTozdaiqqkpeypLjOAQHB8t/4ejKa1Qpfb8Ikn46Oztx+/ZteeLR3NwMPp8PHx8f+WiHs7OzTv9syWQy+Z3zO3fuwMrKSl521dbWVt3hkT66urqQlZUFoVCIBw8eYPLkyfINGy9cuIDk5GTY2toiMjISwcHBA75vq6r2oqJi7Nc/d/cP4OpK1z+AEo1+jh0DXn5ZuefbulV551OWoSo51dfXo6mpSf6BmM/nw97efkAi4eDgADs7O6UvTn5MJpMhKysLJ06c6LcY/El2dnZDXiw0RXp6Or799luFk4ylS5ciOjp6zK+H4zhkZWXh+PHjg76HEyZMwLp16xAWFqYT87AlEgmuXbsGlmVRUVGBiRMnIjw8HIsXL9bLBa6jpi8XQTIqHMfh/v378mlWxcXFkEgksLW1hb+/P2bMmIHp06fr9JqGuro6+SaeEokEs2fPpk08NRDHcbhz5w5YlkVeXh5MTEywcOFCBAQEIDU1FTdu3ICbmxueffZZeHp6AugdySguVt71z9f3GJyd6fpHicbPKip61zEquDmzQkxNe9dRqmO68mgqOT2ZUNjY2Iz7h9Dbt28jNjYWNTU1Q7YxNTVVaPhT3VJSUvDdd98p3H7FihWIjIwc8y+rO3fuIDY2FtXV1QOO8fl8LF26FKtWrdKJevqNjY1ISUlBWloa2tvb4efnB4FAgJkzZ+pEAjWudO0iSFROIpGguLhYnnjU1dWBx+PBw8NDnnhMmzZNJ38WRSIRsrKywLIs6urq4OzsDIZhEBISohPXVl3S1NQk/z3R1taG6dOnw83NDQUFBaiurkZQUBDWrp2LsrIwyGTKu/4ZGJhi3rxCvV+zQYnGz5YvB4TCkU1Hfho+HxAIgPPnlXfOx4ar5FRfX4/29nZ526dVctKEuzB1dXWIj4/HzZs3h2xjYGAw4gVd6iIUCvHDDz8o3H7NmjVYt27dmP4v7t+/j/j4eOTn5w96fMGCBdiwYYPWL+rkOA5FRUVgWRb5+fkwNTVFaGgoIiIi4OTkpO7wtJe2XQSJxmlqapInHUVFRejq6oKFhUW/ReW6VGgC6L0eFRcXy++cGxkZYeHChWAYBs7OzuoOj/QhlUrlI9/l5eWwsbGBk5MTqqursWBBHJycasHjKbaOUjF82NoKMHu2fl//KNFA7w23gADVnn80e50NV8mpvr4eoj53Hq2srAaMTChSyUnd2tracOrUKaSmpg67UHqsJerG04ULFxAXF6dw+w0bNmD16tWj7q+trQ2nT59GSkrKoO+ht7c3oqOj4ebmNuo+NEFnZycyMzORnJyMBw8eYMqUKWAYBgsWLNC5MrzjTlMvgkRryWQyVFRUyEvoVlVVgeM4uLi4yPfu8PLy0vpF1SkpKfjrX/+Ka9euoa6uDr///e8hkUjQ2toKHx8fCAQCzJ49W2VTjcno3L17FyzL4sqVKzAzq8eaNbEq62vevEJYWOjv9Y8SDQC7dgGHDyv3Rt5jfD6wYwdw4MDgx9VVyUndJBIJLl68iJ9++qlfwvSkqVOnIiYmBr6+vuMY3eglJSXh+PHjCrePiorCihUrRtWXRCLB5cuXcfbs2UHfQ0dHR0RFRWnUZoWjUVNTA5ZlkZ2dDalUiqCgIDAMAy8vL61+XRpFnRdBohfa29tRVFQkTzxaW1thbGwMX19feTUrBwcHrfuZTkpKQnp6OubOnYuoqCgkJiZi7dq1yM3NhVAoRFlZGWxsbORlV62srNQdMumjo6MDWVkvgMc7CQMDVXwc5mPKlB3w9tbf6x8lGujd3LasTHXn9/TkkJ4+cOG1Oio5qRvHcbhy5QqOHz+OpqamIdvZ2NggMjISCxYs0IpfPBzH4cyZMzh16pTCz3n22WfxzDPPjKqvx/XBGxsbBxy3sLDAmjVrNG6zwpGQSqXIzc0Fy7IoLS2FjY0NwsLC5PXRiZKp+iLo5QWUlKju/ESrcByHe/fuyZOO0tJS9PT0wN7eXp50+Pr6at0NNB6Ph8TERGzcuFH+WN8bJT09PfIbJZ6enlrxu00fZGV5QSRS3fXP1NQLISH6e/3T+0SjrQ2wtu6/2a3ycXjppTdhZCQeUMmpb0KhykpOmqCkpASxsbGoqqoaso2JiQlWrlyJpUuXwtjYeByjGz2O43Dy5El5nW5FPP/882AYZsR9lZaWIjY2FpWVlQOOGRoaYsmSJVi9erXWVn1paWmR7/j6eOoBwzAIDAzU6Z8NtRqPiyCPB7S2ApaWquuDaC2xWIz/396fR7V1p3n++PtKAiQWgdg3g9lXx/tug5SOndhxvOLq6u6aSVel0q5UV6o6OTM951TXTNf5dmXq16fPuCrLVJIad1dNTU9XTZt4y+IkToLAeMW7jdgRGBBGIMQmJCFx7+8PolsSCCGBJCT0vM7hCPt+7r0fiavn83k+n+d5Py0tLXzBQK1WC6FQiLy8PN7xyMzMDPiJuTNHw4Yt9FOpVEKr1SIzMxNyuRxbtmyh0M9lxGodR319LBwrfnsbBrt2jUEkCk37F/KOxr17wPr1vr/PmTNqVFbGLouS03Kj1Wpx5swZ3L17d942DMNg165dOHjwYFBtLXMchzNnzuBzN5NdGYbBX/zFX2D37t0e3Wehz9C2bR+Mla45jkNbWxtqamr4ZMpt27ZBLpcjPT19ubu38vGXEbx7F1i3zvf3IYKewcFBfrejpaUFZrMZUqmUz+0oKSkJSEEQV46GDY7joFKpoFQq8fDhQ17MQi6XB2Sx2ZXO+Pg93L7te/u3ceNdxMSs8/l9ApHgjKvwIk5qmPmE9PQcxMf7516BgsFgwEcffQSlUuky0busrAxVVVVBN6nkOA6nT5/Gl19+6VZ7hmHwH//jf8SOHTvcvofBYMDHH38MpVLJh9jZk5ubi6qqKl4HPJiwyUPW1tZCo9EgNTUV3/jGN7B9+/agC5kIavxlBP11HyLoSUpKglwuh1wuh9VqRUdHB7/bcf36dTAMg6ysLF7JKjc3N2h2PBmG4XdpdDodamtrUV9fjy+//BKlpaWQy+Ukz+1HOM4/dslf9wlEQt7R8NeOZSjtjFqtVtTU1OCTTz7B5OTkvO0yMjJQVVWF0tJSP/bOO3Ach9///veora11qz3DMPj2t7+NrVu3utXearVCqVTi448/dvoZJiQk4OjRo9i4cWPAhxPMpr+/H7W1tbh27RrMZjPWrVuHP/3TP6WCVz7GYrFgaGhojoqd4MED/MAfHQglI0h4DZFIhKKiIhQVFeHo0aMYGxvjq5TX1tbik08+gVgsdpDQDRYJb5sdf+GFF3D79m3U1NTgV7/6FRISElBZWYmdO3cimsINfQrD+Mcu+es+gUjIh05NTABSqa/DkzmMjTErPjyZ4zjcuXMHZ86cwdDQ0LztpFIpDh06hB07dgTlqg3HcfjXf/1X1NfXu9VeIBDgpZdewqZNm9y69t27d3HmzBkMDg7OOS6RSLB//34oFIqgEgdgWRb379+HUqlEc3MzYmJisHv3blRUVEAmky1391YMZrN5XjnskZERhwr1HMdhcnISxsFBVH/+OXzq4lGOBuEDWJbF48eP+dodnZ2dYFkWqampvNNRWFjot3w/d0KnFqKrqwtKpRINDQ0AgE2bNkGhUAS9PHmgYrVOoL5eCsrR8B0h72gAvhdciY0dRHX1fcjl8qBVAVqIzs5OnD59Gp2dnfO2CQsLw969e/Hss88GbfIby7L43//7f+P69etutRcKhXj55Zex3o0YeLVajdOnT6PDycMoEAhQWVmJAwcOBNUK19jYGOrr61FXVwe9Xo/c3FwoFAps2LBhxX4XfM3k5OQc9Trb69jYmNNzhEIhEhMTIZVKMTU1xdfnsVgsEIvFeOPf/x3J85zrDUaSknDv3/+dlzAlCF8wOTnJJ5U3NjZieHgYIpEIBQUFKC8vR2lpKdLS0ry6czoxMYH29nYAwPr163Hy5EkoFArEx8cjKytrSde9cuUKamtrodPpkJ2dDblcjs2bNwfVIlMwQKpTvoUcDfhWQp5hplFaWoudO/8fEhMTcezYMaxfv37FhIgMDQ3h7NmzuHXr1rxtGIbB9u3bcfDgwaBevWZZFv/yL//CrzQthEgkwokTJ/DUU0+5bKfT6XD27Nl5r7t27VocO3YsaKpecxyHzs5OKJVK3L59GwKBAFu2bIFCocCqVauWu3sBD8dxGB8fn1NTx/bv+cIRw8LC5hTsTEhIgNFoRF9fH5qbm6FWqzHb5IvFYnz3wQOUX74Mxkke0FJhBQLc274d/2vNGrAsi+TkZD6pt6ioKGgXHYjAhuM4PHnyhN/taG1thcVigUwmc0gqX6pCn1KphEKhmPP/L774In77298u6drAzLjz6NEjKJVKNDY2IioqCrt27UJFRUVQin8EIm1tP0Rf37sAfDAJpDoa5GgAvi+Ke/z430Mme8L/Oy8vD8ePH0dOTo7vbupjJicncfHiRXz11VewuvDQiouLUVVVFfQTzOnpaZw6dQp37txxq31YWBheeeUVlLl4sIxGIy5evIgvv/zS6WeYlZWF48ePo7CwcNH99idTU1O4efMmlEolenp6+ITOHTt2BK3crq/gOA4jIyNOHYnBwUGY50mcFovFTuvsJCUlITY2FgzDYGRkhF/RbWpqmuOYMAyD7OxsfrKVk5MDYUuLzyuDm3Jy0NzczPdNp9NBKBSioKCA70tGRsaKWYQhAguLxYK2tjb++evv7wfDMMjJyeGTs7OzswM6nFer1aK2thZXr16F0WjEmjVrIJfLUVpaSt+bJWAwqNDQ4Dv7R5XBydEAAOzdC9TUeHdXg2GmkZ7ejOefd+7Jbtq0CUeOHAmqVYnp6WnU1dXhww8/hMFgmLddamoqjh07hjVr1gS9AbRarfj1r3+N+/fvu9U+LCwMP/jBD1BcXOz0+PT0NC5fvowPP/wQExMTc47LZDIcOXIEW7ZsCYrPbvbgV15eDoVCEfKD3/T0NIaHh+eEN9l+5nPQo6OjnToSSUlJiI6OnvOZWiwWtLe38xMojUYz55o2adDy8nKUlJQ4D7/zhREUiQCFApgl/8xxHLRaLZ/U29LSAovFgtjYWN7pKC0tRVRUlPf6QhB26PV6B4fcaDQiKirKIak8Li5uubvplNmLOsnJyaisrKRFnSVw//5e6PU18O6uhggymQJr17onf79SIUfja9RqoLQUMJm8dUUOQqEFx4//FFLp3OrNNkQiEZ5++mns27cvoA0Ex3G4f/8+PvjgA2i12nnbRUdH44UXXsDu3buDRm7QFRaLBe+//z4ePnzoVvuIiAj84Ac/cLoLwXEcHj58iOrqagwMDDg9d9++fXjmmWcCPgbX2Xb+zp07UVlZGVSO81KZT8lJq9VCp9PNK+scFxc3x5GwvUokEpf3tE3S7esMWCwWhzZCoRD5+fn8hMmtYmfeN4KAWDyzZbzA7q3NWbI5HhqNht95sa025+TkBPRqMxG8sCwLtVrNf6e6u7vBcRwyMjL45y8/Pz/g8spsYao1NTW4c+cOBAIBtm7dCrlcHvRRBP7GaFSjoaEULOs9+ycQiLF5swoSSfBGr3gDcjTsOHUKePll712vouJ3KC6+4lbbqKgovPDCC6ioqAi4CXp3dzeqq6vR2to6bxuRSIRnnnkGzz333IITpWDBYrHgV7/6FVQqlVvtxWIxfvjDHzqtafH48WNUV1ejpaVlzjGGYbB792688MILAV+s0GAw8MndQ0NDIZGgOFvJyf51tpKTDYZhEB8fP2dnIjk5GYmJiR6r4JhMpjlhR7NJSkriJ0WFhYWLq0XibSN46hTw0ksenzYyMsLH1qtUKkxOTiIyMhLFxcX8ewzmfC8isJmYmEBTUxP/fRsbG0N4eDiKior45y8pKSmgdmxnC2/k5eVBLpeT8IYHaDSn0NrqPftXVHQKaWme27+VBjkas3jjDeAnP1n6dZ55pga5uX/w+LyUlBQcO3YMTz311LIbMb1ej7Nnz+LGjRsu223ZsgWHDx8OGu1ydzCbzfjVr36F5uZmt9pLJBL86Ec/mpN3o9frce7cOdy4ccPphLS8vBzHjh0L+GKF3d3dUCqVuHnzJoCZsD+5XI7Vq1cv+3PqDQwGwxw5WNu/F1Jymu1IJCUlISEhYUmDO8dxDrKdHR0dc3ZHIiIi+IlPaWmp96oKe8sIvvEG8OMfL/kyLMuiq6uL/yxsCe1paWn8pK+goGDFOrrE8sJxHPr6+nint62tDdPT00hMTOSfv6KiooApMkpS4kuju/sNqNVLt385OW8gO3vp9m8lQI6GE06dAl59dSZU2ZNwZZFo5uedd4DvfIfDjRs3cPbsWYyMjHjch8LCQlRVVSE7O9vjc5eKyWTCp59+ii+++GJOSIY9+fn5OH78+IrT9zaZTHjnnXfQ1uaeHF1kZCT+5m/+xuFvZTKZ8Nlnn+HSpUtOP8PMzExUVVWhpCRwE8QsFgtfRKqrqwvx8fF8EamYmJjl7p5HeFPJyfYqk8m8GsozPj7usIo/Pj4+p01mZiY/ucnLy/PdSqU3jOAidjLcwWAwOOzujIyMICwsDIWFhbzTlZqauiIcYCLwMJvNvISuSqWCVquFQCDgQxXLysrcC1X0A/39/VAqlbh+/Tqmpqawdu1ayOVyKo66ABrNKbS3vwqWtcKznA0RBAIRCgreoZ0MO8jRmAe1GjhxArh0aWbcdDXW2o7v2QO8/75jOPLU1BS++OILfPrpp/Mqybhi27ZtOHz4sF9WIliWRX19PS5cuOB0kmMjOTkZR48exbp161acsTIajXjrrbdc1gOxJzo6Gq+99hoyMzMBzHyGV65cwYULF5yuhMfGxuLQoUPYvn17wMab63Q61NbW4sqVK5iYmEBpaSnkcjnWrFkTsH0GZpwJvV7vNF9iqUpOvmB6ehodHR28c/H48eM5baKiohySo2NjY33SF6d4ywj6EI7j0N/fzzsdbW1tsFqtiI+P5yd9xcXFKyackwg8BgcHeaejubkZZrOZF1+w/Sz3wozJZML169ehVCrR39+PtLQ0VFZWYvv27QGzExNoGI1qtLaegF5/CYAIrh2OmeMy2R4UFr4f8jkZsyFHYwFUKuC994CLF2eK+tl/WgwD5OUB+/YBr7wCuFqcHhsbw4ULF1BfX+80hMYVYWFh2LNnD5599lmfGAWO49DY2Ijq6mr09/fP2y4yMhIHDhxAZWXlioz5nJycxJtvvomuri632kulUrz22mt82JPtM3Sm+hMeHo69e/di7969AVk3gOM4NDU1QalU4sGDBxCLxdixYwcqKysDqn7H9PQ0dDrdHCfC20pOvmJoaMhBack0K/GaYRjk5ubyk+SsrKzld+68ZQT9wNTUFFpbW3nHY2BgAAKBgP9MS0tLkZ2dveIWSIjAwGq1orOzE48ePYJKpUJPTw8YhkFWVhb//OXm5i5bHibHcWhra0NNTQ3u3buHsLAwbN++HZWVlQEfvrtcGAwqaDTvQae7+HVRP/v5GwOxOA8JCfuQnv5KSEvYuoIcDQ+YmADa2wGzGYiImKko7mmRZo1Gg+rqajQ2Nnp8f6lUioMHD2Lnzp1em3z09vaiuroaTU1N87YRCoVQKBR4/vnnA1oZaykYDAb84he/QE9Pj1vtY2Nj8frrryM1NRV9fX2orq52mjRuK1Z46NChgJRKnJycxLVr16BUKqHVapGZmQm5XI4tW7Ysm0O0VCUnZw7Fcq1om81mtLW18RMPZ2pjMpnMYfU9oL9j3jCCfkSn0/FOR3NzM0wmE6Kjo1FSUsJXig50AQYieBkbG3MIh5yYmIBYLEZJSQm/U7lcuY16vR6XL1/G5cuXMTY2hsLCQigUCqxduzbgBGkCBat1AkZjOzjODIaJgESSD5EocO1foECOxjKhUqlQXV2Nvr4+j89NT0/HsWPHUFZWtuiVuZGREVy4cAFXr151ucOyYcMGHD16FElJSYu6TzAwPj6OX/ziF27/LWQyGV5//XWIxWKcP38eV65ccfoZBnKxwt7eXiiVSty4cQNWqxUbN26EXC5HXl6eX1Z7TSaTw07Ecig5+QKO46DRaPjJbXt7+5xdFls+gW2iQfkE/mF6ehqdnZ38pK+7uxsAsGrVKr7GSG5u7orcrSWWH5vAg802dHZ2gmVZpKSk8E5vYWGh3+2Y1WrF3bt3oVQq0d7ejri4OD55nJxwwhuQo7GMsCyLa9eu4dy5c/Mq27iipKQEVVVVfH6AO5jNZly6dAmfffYZpqam5m2Xk5OD48ePO5VqXUmMjY3h5MmTLkPG7ElISMAPfvAD3L17F5999pnTuP+0tDQcO3YM5eXlATWBnG9A2b17t09i/wNNyclXGAwGXgpTpVI5FX9IS0vjHYvCwkJSSAoAnCXf2yt52SRMCcIXTE5O8knljY2NGB4ehkgkQkFBAe94pKWl+XUMWe4FKGJlQo5GAGA2m/H555/js88+c6ny5AyGYbBjxw4cOnTI5WSRZVlcv34d58+fd6mClZCQgCNHjmDTpk0r3rCMjIzg5MmTTsNZnJGQkIDdu3dDqVQ6/QxjYmJw8OBB7Nq1a/nj6u0YGRlBXV2d17fIbUpOzipfB4qSky+wya3aJghdXV1zdmAkEolDeER8fPwy9ZZwB47j0NPTw/9NbXLCycnJ/N+wqKgoIPOriOCH4zgMDAzwz19rayssFgtkMhn//JWUlPgtrDIQQ2qJ4IUcjQBiZGQE58+fx7Vr1zxOGI+IiMDevXuxZ8+eOYagqakJ1dXV6O3tnfd8sViM/fv34+mnnw6J1dbh4WGcPHkSg4ODbrUXCASIj4/H0NDQnGNhYWF8scJAUfBwlvS3bds2yOVyj5L+bEpOzlScAlHJyVeMjIzwk4CmpqY5TpStirUtBIeqWAc3JpMJLS0tePToEV8g0b7aellZGTIyMoLuOSaCA4vFgra2Nt7m9Pf3g2EY5OTk8M9fdna2z21MsIiEEIENORoBSE9PD6qrq90uFmdPXFwcDh06hG3btmFgYADV1dV49OjRvO0FAgEqKytx4MABRAdwUqc3GRoawsmTJ51WV57N5OQkdDodUlJSnMbObt26FYcPHw6YFWubjGFtbS00Gg1SU1Mhl8tdyhh6W8kpOTkZUVFRQT0Js1gsaG9v5wd6Z0piUqmUH/RLSkpC5vsTanAcB61Wy6uFtba2YmpqCrGxsQ7Sw1FRUcvdVWKFotfrHRY6jEYjoqKiUFJSwj9/vhYb0el0/M64wWAIGtlzYvkhRyNA4TgOjx49wgcffOB2/oANi8WC8fFxCIVCl+FUa9euxbFjx0JqZUKr1eLkyZPQ6/Uu201NTeHx48cYGxtDeXn5HCejoKAAVVVVAVOs0L4wk9lsxrp16xwKM9mUnJztTASbkpMvsE0mbYN5S0vLnDBGWtEmgJlcJ3sntK+vj9/Rsj0btKNF+AqWZaFWq/nnr7u7GxzHISMjg3/+8vPzfZbPNruQa0JCAioqKoKykCvhH8jRCHBYlsXly5fx4YcfuiyiZ2vb19eHnp4efhU6Pj4eubm5DrGdWVlZOH78OAoLC33a90BjYGAAJ0+edJmjYv8ZisVirFmzxiGULDk5GceOHcPatWuXfZLJsizu378PpVKJ5uZmSCQSlJeXIz8/H2az2SEBO5iVnHyFyWRCU1MTnxDsbIcrKSmJH7wpRp9wxsjIiENS+eTkJJ+jY3t2/FFwlQhNJiYmeDv26NEjjI2NITw8fI6ogS/Gq+7ubiiVSty8eRMAsGnTJigUioBZgCMCA3I0ggSj0YhPP/0UX3zxxZxQFo7jMDg4CLVa7TRmnmEYpKWlYc2aNfjmN7+JrVu3Lvsk2d/09/fj5MmT86od2T7Drq4umEwmxMTEoLy8nHcyoqKicODAAVRUVCyr8pHBYEBnZye+/PJLXLlyBYODg4iMjER8fDwkEonTVVR7JafZCdiBquTkC2zykrZJoS3h1x571aHS0lIkJycvU2+JYIRlWXR3d/OrzWq1GhzHIS0tjX+mSHWM8BUcx6Gvr493etva2jA9PY3ExESHBRNv5xJOTEzgypUrqK2thU6nQ3Z2NuRyOTZv3kzPOkGORrCh0+lw/vx53LhxAwAwOjqKzs5Ol7sdQqEQq1atQl5eHp5//nk888wzIfXl7+3txS9/+ct5P6PZn6FUKkV5eTlEIhFEIhEUCgX279/vF8WP+ZScBgYG0N7ejs7OTj4hPSUlBWlpaYiOjnZQcpq9OxEMSk6+wlYwy/bj7BnIzMzkB+G8vLyQcbwI32MwGNDc3Mw7HiMjI3wdFZvjQXVUCF9hNpvR0tLCL65otVoIBALk5+fzwhWZmZlee/5YlsWjR4+gVCrR2NiIqKgo7Ny5E5WVlUhMTPTKPYjggxyNIKWhoQE///nPXVb0ZhgGqampyM7OdgiBiY+Px+HDh7Fly5YVP8A9fvwYv/zlL2EwGOYcMxqNUKvVDkpSsbGxKC8vh1AoxKZNm3DkyBGvG0hPlJymp6cxODgIjUYDk8mEhIQEbNiwAVu3bkVmZmZQKzn5AqvVis7OTj6MwFml96ioKIckXl/UECGI2XAch/7+ft7paGtrg9VqRXx8PP8sBnxleCKoGRwc5Hc7mpubYTabERMTwz9/paWlXsuz0Gq1qK2txdWrV2E0GlFeXg6FQoHS0lIaq0IMcjSCDIPBgI8++ghKpZJXC1Kr1TAajQ7t4uPjkZOT41IJZfXq1aiqqkJBQYGvu70sdHV14c0335wjRWqxWPD48WP09/c7hM7ExcWhrKwMBQUFOH78OHJzcxd97/mUnLRaLYaGhhZUcoqIiEB/fz+6u7shEAiwadMm7N27d0nV4FcqQ0ND/OTNNnjaIxAIkJOTwxfBysrKCtkdHiJwmJqaQmtrK//sDgwMzHlWs7Oz6ftO+ATboozt+evp6QHDMMjKyuIXYnJzc5dUawmYec5v3rwJpVKJnp4eJCcno7KyEjt27CCnOkQgRyNIsFqt+Oqrr/DJJ5/McSpYlkV/fz8eP36M8PBw5ObmepR8uH79ehw9enRFxaN3dnbizTffhMlk4v+PZVloNBo8fvx4zkRfJpOhoqICVVVV2Lhxo1uD+3xKTlqtFsPDwx4rOSUkJKCjo4O2nRfAbDY7TNC0Wu2cNjKZjJ+s0SoxEQzodDp+tbmpqQkmk8lh962srAxSqXS5u0msUGxhprZncGJiAmKx2KHwaEJCwqKvz3EcOjs7oVQqcfv2bQgEAmzZsgUKhQKrVq3y4jshAg1yNAIcjuNw+/ZtnD171mmxOBtSqRTPPfccdDodv9vhCQKBAAqFAs8//3zQ68G3tbXh7bff5le2OY6DTqdDZ2eng+NhIyUlBX/7t3+LvXv3zonPN5lMDjUlfKHkZDAYUF9fT4l088BxHDQaDe9YtLe3z3EUKe6dWElMT0/zq80qlQrd3d0AKJ+I8A824Qybze3s7ATLskhJSeGfv8LCwkWrEo6NjaG+vh51dXXQ6/XIzc2FQqHAhg0b6JlegZCjEcB0dnbi9OnT6OzsnLdNWFgYnn32Wezdu5eX3hwaGsKZM2dw+/Ztj+8ZGRmJ/fv3Q6FQBOUXvrm5Gf/zf/5PTE1NAZgxaJ2dnU7VphiGwfbt2/H//X//HywWy5wEbK1WO28CuTeUnLq7u1FTU4OGhgYAJA1oj8FgQFNTEz/RciZJTEo+RKgwPj7OCxo0NjZifHzcQSHNJmFKEL7AaDQ6iBoMDw9DJBKhoKCAf/7S0tI8XtyZLdEeExOD3bt3o6KigiShVxDkaAQgQ0NDOHv2LG7dujVvG9sk+dChQ/NWBO3o6EB1dbVLR2U+EhMTcfToUWzYsCFoVoZVKhV+9atfwWKxwGQyoaurC1qtFhzHgWVZTE9Pw2q1Ynp6GhKJBPn5+SgsLHS6ywHAJ0pOVOzIOSzLoqurix/Iurq65uwW2dcmKC0tDZhq7AThTziOQ29vLx49egSVSoX29nawLEs1Xwi/wHEcBgYGeFvd2toKi8UCmUzGh1iVlJR4HK7a39+P2tpaXLt2DVNTU1i7dq1D0VkieCFHI4CYnJzEJ598gpqamnmThQGguLgYVVVVbsU12kKvzpw547Qg2ULk5eWhqqpqSYnR/uDBgwd48803MTIygu7ubgwMDMBisWB6ehrT09P8pDUsLAwxMTHIzMxEUVERJBKJU0fC20pOOp0OtbW1uHLlCiYmJlBaWgq5XI41a9aEbGKyXq/nV2ibmprmJO1TtWWCWBiTyYSWlhZ+4jc0NERV7Am/YbFY0NbWxj9//f39YBgGOTk5/POXnZ3ttu02mUy4ceMGlEolNBoN0tLSUFlZie3bt3u9/gfhH8jRCACmp6dRW1uLjz76yKkMq420tDQcO3YM5eXlHg8aFosFNTU1TpPJ3cFXUq+eYK/kZJ+A/eDBA9TX12NiYgITExNzkrAZhoFYLEZqaiqSkpKwefNm/OVf/iVSU1MRFRXlswGY4zg0NTWhpqYGDx8+hFgsxo4dO1BZWYmUlBSf3DOQsVgsaG9v51diNRrNnDZSqZQfnEpKShAdHb0MPSWI4MRWeNQ26WtpacHU1BR9rwi/odfrHVQAJycnERUV5bAbPV8Uhj0cx6GtrQ01NTW4d+8ewsLCsG3bNsjlcqSnp/v+jRBegxyNZYTjONy/fx8ffPCBU+UcGzExMXjhhRewe/fuJa/oTkxM4KOPPkJtbe28qkjzIRKJ8PTTT2Pfvn0+U/HxVMlpcHAQ9+7dw+TkJFiWhUgkglAohFAohEgkQkREBLKyspCZmQmBQICdO3fiW9/6lk9XxicnJ3Ht2jUolUpotVpkZmZCLpdjy5YtIRXOwHEctFot71i0tLTAYrE4tKGVV4LwHVarFe3t7fzEr6+vj3YKCb/BsizUajVf16i7uxscxyEjI4N//vLz8xfMaRwZGcHly5dRV1eHsbExFBYWQi6XY926dUuW3yV8DzkaHjAxAbS3A2YzEBEB5OcDi10Y6u7uxunTp9HW1jZvG5FIhGeeeQbPPfccJBLJInvtnIGBAXzwwQe4f/++x+dGRUXhhRdeQEVFxaK+5PZKTrMTsN1VckpOToZKpcL/+T//BxMTE3Mmp86KFVZUVODP//zPfTaR7e3thVKpxI0bN2C1WrFx40bI5XLk5eWFzOTZZDKhqamJD4lyFq6XnJzMx/JSLHmQ4U0jSPidkZERBwnTyclJPvfJ9p2k3CfCVxgMBgdRg9HRUYSHh/OiBqWlpUhOTp53vLRarbh37x6USiXa2toQFxeH3bt3Y/fu3X4pvGq1TsBobAfHmcEwEZBI8iESkf1bCHI0FkClAt57D/jkE6CzE7D/tBgGyM0F9u8Hvvc9oLR04esNDw/j3LlzuHHjhst2W7duxaFDh5akW+0Ora2tOH36NB4/fuzxuSkpKTh27BieeuqpOYbBYDDMkYP1lpKTXq/H//gf/wNnzpxx6pTEx8cjNzfXYddFoVDgT//0T70+4bdarbh79y6USiXa29sRFxeHiooK7N69OyQ07+1lEFUqFTo6OubsOpE6TpDjbSNIBAQsy6K7u5vf7VCr1eA4jtTcCL/AcRz6+vp4p8MmW56YmIjS0lKUl5ejqKho3ryM2Qt7GzZsgFwuR35+vlfHeYNBBY3mPeh0n8Bk6gRgP+dgIBbnIiFhP9LTv4eoKLJ/ziBHYx7UauDECeDSJUAkAlzkZvPH9+wB3n8fyMmZ28ZkMuHTTz/FF198MSd8xJ6CggJUVVX5VeKU4zjcuHED586dg16vd/sci8UCo9GIpKQk3tmwORWzE3ttLEXJyWQy4bPPPsPvfvc7NDU1zXEyoqOjkZOTM0cW75lnnkFVVZVXjc/IyAjq6upw+fJlfitXoVBg7dq1K34r11bYyfbjzHFctWoVv0JKev9BireNIBHQTE5O8pLSjY2NGBkZ4evT2L7LVJ+G8BVmsxktLS2846HVaiEQCJCfn887HpmZmXOeP1uocm1tLQYGBpCZmYnKykps3bp1SbvlRqMara0noNdfAiAC4ML+fX1cJtuDwsL3IZGQ/bOHHA0nnDoFvPrqzLjpamydjUg08/P228B3vzvzfyzLor6+HhcuXJh3JR+YCSc5evQo1q1bt2yGfGpqCl988QU+/fRTmM1mcByHqakpGI1GGI1GmEwmh9fZRQGTk5ORk5OD2NhYryo5sSyLK1eu4MKFC2hpaZkTbhYeHo7Vq1cjJSVlzrWfe+45HD582CufKcdxaG1thVKp5JPTtm/fjsrKyhWdnGa1WvniYY2Njejp6ZnTxlbB2FaNOxR2c1Y03jSCRNDBcRz6+/v52Pq2tjZYrVbIZDL+O15cXOyzXD2CGBoackgqN5vNiImJ4Z3e0tJSB0l4juPQ3NyMmpoaPHjwYEniKxrNKbS3vwqWtcK1gzEbEQQCEfLz30Z6Otk/G+RozOKNN4Cf/GTp1/mHf+Bw6NAjfPDBB+jv75+3XVRUFJ5//nlUVlYuy6qvMyWnx48f4+rVq2hpaZk3YTwsLAwSiQQSiQRisZh/tVUo37dvn1ek6BobG1FdXQ2NRoO+vj50dHTwx4RCITIzM5GZmel0F+HAgQM4cODAkp0Mk8mE69evQ6lUor+/H2lpaZDL5di2bduKldtzZuTtEQgEyM3N5Q1+VlYWJZSuFLxlBH/2M+Dv/m7p1yGWnampKbS2tvKOx8DAAAQCAXJycnjHIzs7m3Y7CJ8w32JXdnY273jk5uby8wCdToe6ujpejbKkpAQKhcItOfnu7jegVi/d/uXk/AzZ2WT/AHI0HDh1Cnj5Ze9dr6LidyguvuL0mFAoxNNPP439+/f7fFXIUyUn+z5qNBqMjo7OcSgWcopiYmJw8OBB7Nq1a1ET0N7eXnzwwQdQqVT8v22FBxmGQUpKCrKzs+fdGj106BD279/v8X3t6e/vh1KpxPXr11d8ASGz2YzW1lbekDtTQYuPj3coCEarmSsQbxvBU6eAl17y3vWIgECn0znUwDGZTPyupm3xwR/JuURoYgvfteUGTkxMQCwWo7i4mB+jEhIS+AK5SqUSarUa8fHxqKysnLdArkZzCq2t3rN/RUWnkJZG9o8cja9Rq2fyGOcpEr0IOAiFFhw//lNIpY7KOxs3bsTRo0e9WpPClZLTfHkXzpScbK+JiYm8WlNTUxNOnz6Nvr4+j/uVlpaGqqoqlJWVuTU5Hx0dxfnz53H16lU+B6OnpwdqtRoAIJPJkJOT41IH/tixY9i7d6/HfQVmwrRsqhYtLS2IiYnB7t27UVFRMSf3I5jhOA4ajYZ3LGyJePbY4rNtEweKz17heN8IAmLxTDI55WysWKanp9HZ2clP/Lq7uwEAmZmZ/KSP8rQIX2EvSNLY2IjOzk6wLIuUlBT++SssLOQXDm/evAlgpjaYXC7H6tWrwTAMjEY1GhpKwbLes38CgRibN6tCPmeDHI2v2bsXqKnxLBx5IRhmGunpzXj++bcAALm5uaiqqkJeXp7H1+I4DpOTk0tWcrLPm7BXcloIlmVx7do1nDt3DmNjYx73v6SkBFVVVcjMzHR63Gw249KlS/j888/5MB2bAenu7kZkZCRyc3Mhk8lcTna/8Y1v4E/+5E887t/Y2Bjq6+tRV1cHvV6PvLw8KBQKrF+/fsUMkAaDgU/2VKlUGBkZmdPGpjhTVlaGgoICUpwJJXxhBEUiQKEAPv/ce9ckAprx8XGHpPLx8fEVrTz385//HGfOnEFzczMkEgl27NiBf/zHf0RRUdFydy0kMRqNaG5u5sc5nU4HkUiEgoICvm5MR0cH6urqMDQ0hOzsbMjlckgkf4/RUSU8y8lYCBFkMgXWrg1t+0eOBmYW3MrKfHf9l1/+Bf7qr3Zj48aNLifJHMdhbGxsTuVrXyo5eYrZbMbnn3+Ozz77zKV6ljMYhsGOHTtw8OBBvjIoy7K4fv06zp8/7zDx5TgO3d3dePLkCbKzs91aTf+zP/szyOVyt/vDcRw6OztRU1ODO3fuQCAQYOvWrZDL5Vi1apVH7y0QYVkWXV1d/IDf1dU1R6nLpqFvmwCspF0bwgN8bQRVKqCkxHfXJwISjuPQ29vrsHPKsiySkpIcwjCDuZbOc889h29+85vYvHkzrFYrfvzjH/NFSqOiopa7eyENx3EYGBjgn7/W1lZYLBbExcWhpKQEEokEvb29ePLkBp5//rTP+rF5swpRUaFr/8jRAPDDHwLvvuvdhTwbAgGLV17h8M47M0lKLMtCr9fPcSJsP7OTbm2IxWKvKjktlZGREZw/fx7Xrl1zWsvCFeHh4Xj22WeRlZWFCxcuzFEx4jgOPT09YFkWmZmZC+4oMAyDb33rW9i1a5db9zebzbh58yZqa2vR09OD5ORkVFZWYseOHUGfd6DX6/mEzebm5jnOKVUFJpziSyMoEgGvvAK89Zb3r00EFSaTCS0tLfzEb2hoCEKhEPn5+bxNysjICOoQzcHBQSQnJ6O2thYVFRXL3R3CDovFgra2Nn63Q6PRgGEY7Nx5BxkZtyAQ+GI6LEJGxisoKAhd+0eOBmaK29qJGXmd1NRx/OQnv4NWq8XQ0NCcWHgbMTExc5wI2+9RUVEBaXx7enpQXV2N5uZmt8+ZnJyEWq3GxMQEsrOzHWRpOY6DQCCA0Wh0S9GJYRi8+OKL2L59+4JttVotamtrcfXqVRiNRqxZswZyuRylpaUB+dm6g8ViQXt7O7+CptFo5rSRSqW8MkxJSYnL/BYiRPG1EczPB2bJUhOhDcdxGBwc5J2OlpYWTE1NQSqV8k5HMNqr9vZ2FBQU4OHDhygvL1/u7hAusC3MGQxHEB4+6LP7iMX52LYtdO1fyDsa4+NAbKxjsVvvw+Hb3/4RwsLMiIuLc+pIJCUlQSKR+LITPoPjODx6tLCUr8ViQXd3N/r7+x12QWyF9jZv3ozw8HBeaWohGIbBd77zHWzZsmXeNizL4tGjR1AqlWhsbERUVBR27dqFiooKrybj+wv7rWCVSoWWlpY5IWxCoRAFBQW8AkywrxASvsEmba3r6kLx1q3w6RPCMMDYGBBkk0bCf1itVrS3t/O2rbe3l9+BtdXoCfQdWJZlcfDgQYyMjKC+vn65u0O4gdU6jvr6WDhW/PY2DHbtGoNIFJr2L+QdjXv3gPXrfX+fP/3T/x82bhQiLS3NwclISkoK6vhUe+YrTsiyLPr6+tDT0+N0N0cikSAnJ4cPW3InfEkgEOC73/0uNm7c6PT4xMQErly5gtraWuh0OqxevRpyuRybNm0KugRnZ8lts0lOTubVoYI95pnwHu5IW2cODeG/njnj+87cvQusW+f7+xArgpGREQcJXYPBwOeU2RZR4uPjl7ubDrzyyiu4ePEi6uvr5xU+IQKL8fF7uH3b95PAjRvvIiZmnc/vE4isDDmdJTBPSoQP7sOhvb0d7e3tc47ZKmk7C5sKppwBgUCAiooKbNmyBZ9++ik+//xz9Pf3o6urCyYnkplhYWF8ondbWxtUKhUYhkFqaiqys7N5ed3ZCIVC/NVf/RXWOZm0dHV1QalUoqGhAcCMhN1f/dVfYfXq1d58qz7FXq5PpVKho6NjTq2TiIgIFBcX8wPuSlJxITxjMdLW9ojmqaPjdfxlbIkVQVxcHHbs2IEdO3aAZVk8fvyYDxH9v//3/4LjOKSlpfE2sLCwcFkXkX7wgx/go48+Ql1dHTkZQQTH+ccu+es+gUjIOxr+WvgVCOZXaBodHcXo6CjanMQwR0VFOa1zkZSUhOjo6IAMiRGLxSgrK8Pt27fR2Ng4x8kQCATIyMjAqlWrIBQK0dLSwheI4zgO/f39GBwc5Kt+22+Vi0QifO9738OaNWv4/7NYLLh16xaUSiW6urqQkJCAgwcPYufOnUET32tfgKipqcmpXPGqVav42OXc3NwVI7tLLIzBYPBY2tpdrH4KRfngo48g7O93WFBZDiELIvgQCARYvXo1Vq9ejQMHDmBychLNzc149OgR7ty5gy+//JKv+2NzPPxV94fjOLz66qs4e/YslEolcqhmTFDBMP6ZBPrrPoFIyIdOTUwAUqmvczRYlJXtQGKiGPHx8YiLi/PKyotYLHYYtO13QuLi4pZlANdqtThz5gzu3r3L/9/4+Dg6OzsxOjqKpKQk5OTkQCwWg+M4NDc3Y3Bw/iQssViM1atXIykpCeHh4XjllVdQ9rUMp06nQ21tLerr62EwGFBWVga5XI7y8vKAjuMFZuKROzs7+UTI2cpbwEzuin2lXalUugw9JfwBx3EYHx936ki4krb2BtOjo/hf/+//+TRHgwPwP3/+c/SNjkKv1/M5WjZpbmcLKfHx8QH/PSaWH47j8OTJEwcJU6vVCplMxi/MFBcX+yw64Pvf/z7+7d/+DefPn3eonREbGxu0eZehhNU6gfp6KShHw3eEvKMB+F5wJSysG3l5z/H/ZhgG0dHRiI+Ph0wmQ0xMjNedAvsBfLYj4osB3GAw4OOPP0ZNTc2cMB8AyMnJQVlZGW7evAmtVguWZdHU1OQ038AZcXFx+Pu//3s8++yzUKlUUCqVePjwIcRiMXbs2IHKykqkpKR49T15m6GhIX4wbG5uniNlLBAIkJubyw+OWVlZtNq7guA4Dnq9fk5400LS1r7qi06ng0ajwcjICD7t6EC2h3VxPMJOdcpisUCn0zn9HIaGhnj7IRQKkZCQMCesNDk52aNio0RoMTU1xUuYNjY24smTJxAIBPwYVFZWhuzsbK/Z1vmu85vf/AZ/+Zd/6ZV7EL7l+vV8mEy+mwSS6hQ5Gj6VkJ+pDn4eq1b9E0wmE8xm85y6EyKRCDKZjP/xdSKvQCBwqBZu/5qYmOjRAG61WqFUKvHxxx87XXVNTEzE0aNHsWHDBjAMA6vViq+++go///nP8eTJE7fuIRQKUVxcDJPJBJZlkZCQgMLCQsjlcmzZsiVgE5/NZjNaW1v5Ac8WHmZPfHy8w6obrYAFNzYlJ2dFN11JW/sLi8WC/v5+9Pf3w2w2QyQSQSKR4G/7+nC4vx8iXwwHHtTRYFkWw8PD8+7s2D4/hmEQHx/vVMEvMTExYG0C4X90Op1DWKrJZEJUVJTDbnFsbOxyd5NYRtrafoi+vnfh3argNqiOBjka8H1R3K++eoKpqfv8tq7BYIDRaITJZJrzynEcIiMj+d2O2NhYv4YP2A/gswdxe4UsjuNw584dnDlzBkNDQ3OuI5FI8Pzzz0OhUDg4LlNTU/jVr36Fhw8f4vHjx9BoNE53QGywLIv4+HiMjY0BmHFcMjMz8cILL+CFF14IqMqrHMehr6+PH9Ta29vnTCxtccQ258K+hggRHLij5BRIREdHQyQSQavVYmBgABERERCLxZBIJBCJRGAYBml6PX562neVcb1RGZzjOIyMjMybq2K/I7RSBDYI7zI9PQ21Ws0v/jx+/BgcxyEzM5O3yXl5ebRbFmIYDCo0NPhuEkiVwcnRAADs3QvU1Hh3V0MkAhQK4PPP//h/ZrPZoTKqfX4Cx3Ewm80wGo284zE1NYXIyEhEREQgJiYGEolkWSemsbGxEAqF6OzsxNjYGCQSicOkRSAQQC6X48CBA3OcALPZjHfeeQetra38/5lMJqjV6jl5GjbnKzo6GtHR0UhLS0NqaqqDElVkZCT2798/x5nxJwaDAU1NTbxC1MjIyJw2aWlp/CBWUFAQdPK6ochSlZz8TVxc3JyJdVxcHLq7u3H16lWnOUD2REVF4b989RWSHz0CMz3tvY45M4I+gOM4TExMzPlb2V4NBgPf1iaw4cwRCVSBDcI3jI+PO9jvsbExREREoKioiN/tSE5OXu5uEn7g/v290Otr4N1dDRFkMgXWrvWt/Qt0yNH4GrUaKC0FnKiwLhqxeGYhz5UIxezKqPPFaXMch6mpKURERCAlJQVSqRQREREYHR3F4OCgU/lYbzOfU2AjIyMD27ZtQ05OzpxBXCQS4Z133nEq7wvMqC61trZCq9XyIVjZ2dnIyclBQkKCy8E/MTERR44cwcaNG30+SWBZFmq1mt+16OrqmhMKFxkZieLiYr4at0wm82mfCM/hOA6Tk5M+U3LyNgzDICEhwWm4Y1JSkoPzqtVqUVtbi6tXry6YRL569WooFAps3LgRYb29y2ME/YD933r2LpRttxT4o8CGM6U/Usha2XAch97eXn487ujowPT0NJKSkviFIqpRtHIxGtVoaCgFy3rP/gkEYmzerIJEEtpKZORo2HHqFPDyy9693ksvud/earWio6ODN3S9vb0u29snuGVnZ0MsFmNoaGjOip79at5isFqt6OnpQV9fn9OwkJiYGOTm5s4b52q1WtHc3IypqSl+98O2EyIWi2E0GtHf34+hoSFMTU2BYRg89dRTHteGyM3NxfHjx5Gbm7uo9zkfer0eKpUKjx49QnNz85zJm616rc2xCPTqtaECx3EYGxtzOsH0tZLTYhCJRHzy82xHPT4+3uWuHcuyaGxshFKpxKNHjxa8z5YtWyCXy5Gdne14cLmN4DJgNpvnPBu230khK3QxmUxoaWnhbf/Q0BCEQiHy8/N5xyMjI4OczxWERnMKra3es39FRaeQlhbY9s8fkKMxizfeAH7yE+9c58c/Xto1bLUVbEWKFnIY7BPcysrKeDlU22qes4F0dHR03uvZalp0d3fD4kSRJiIiAjk5OUhKSprX2FqtVjx8+HDOCjHHcTAajZicnITVaoVEIkFCQgKysrLw4osvYmxsDA0NDeA4zmNDvmnTJhw5cgSJiYkenWfDYrGgvb2d/9w1Gs2cNrGxsfzWemlpaUDlioQSLMtCr9c7fb79reTkDuHh4U4TmJOSkiCTyTyerBoMBly5cgW1tbVOc6XsSUhIQGVl5cL1ZQLJCC4zVqvVaT7ObIUsm8AGKWStXLRaLR9iZVs4k0qlKC0tRXl5OUpKSoKmbhMxP93db0CtXrr9y8l5A9nZwW3/vAU5Gk44dQp49dWZfA1PcjZEopmfd97x/iKerTKqzfFQq9ULJp26k+Bmv5pnH07Q2NiIe/fuOV31FYlEWLVqFTIyMlxOjCwWCx4+fIiJiQn+/6xWKyYnJ2E0GsFxHMRiMSIjIxEeHo6IiAg89dRTvPKSxWJBT08PhoeH+Z0Q+1exWDzv/UUiEZ5++mns27dvweRPjuMwMDDgoMM+27ESiUT8SlZpaSmtZPmRQFdymo1EInEa3pScnAypVOqV56a7uxtKpRINDQ1OFwHsWVR9mUA0ggGGK4WsoaEh/u9CClkrD6vVivb2dt7x6O3t5Xe2bY4H7WwHLxrNKbS3vwqWtcKznA0RBAIRCgreoZ0MO8jRmAe1GjhxArh0aWbcdDXW2o7v2QO8/75/wpFtlVFtuQLDw8Mu29snuJWVlc0bltTd3Y3q6mq0traCZVmYTCY+MdtsNiM9PR0pKSkYHx936ehYLBY8ePCA34UxmUyYnJzE1NQUBAIBIiMjERkZyRvi2U7G7PeqVqvn1NxgGMZBQWe2IyIUChEVFYUDBw6gsrISQqGQP9doNKK5uZkfKJzV80hOTuYdC4rN9S0Wi2WOw2t7DUQlp5iYGKcTx+TkZERGRvrECbVYLLh9+zaUSiXUarXLtpGRkXx9mUUnswa6EQxgZitkzX4lhayVxcjICFQqFf9jMBggkUhQXFzMj7nx8fHL3U3CA4xGNVpbT0CvvwRABNcOx8xxmWwPCgvfD/mcjNmQo7EAKhXw3nvAxYszRf3sPy2GAfLygH37ZmTil6jeuGhslVFtToezFfnZzE5wm5ycxLlz53D9+nWn7desWYNjx44hLS0NwB9X85wl0/b19eH27duYmJjA5OQkJicnwbIswsLCEBUVBbFY7HBtsViMp556as7/z2ZkZARqtdrtRN3w8HDeAUlJScH27dshlUqh0WjQ29s7Z/IaERHhMDAsNvSKcM5sJSf7ZyYQlZxkMtm8MfkLPaveRKfToa6uDvX19Q67g85YtWoVX1/GXqFtSQSDEQwiZitkzf4+kEJWcGOLPrCF3nZ2doLjOKSlpfGhzYWFhaQ+GCQYDCpoNO9Bp7v4dVE/+ykzA7E4DwkJ+5Ce/kpIS9i6ghwND5iYANrbAbMZiIiYKXYbiCGZFosFra2tvOPR398/b9vp6Wn09fVhfHwcUqkUMpkMUVFR/ACWmZmJqqoqlLg5gdDr9fjJT36Cu3fv8hXAo6KiEBkZye+Q2Ie6iMVirF271u3dAo7jMDg4CLVavWD8PcuyMJvNMJvNmJqa4p0dqVSKyMhISCQSpKeno6SkBOvWrcPatWuRnp5OA/gisSk5Oav4HAxKTrNrxiznRIDjODQ1NUGpVOLBgwdzlM3sEQqF2LhxI+RyOXJzc3377AaLEQxilqqQlZSUhLi4OLJhAYIt+sAWmqvX6xEWFoaCggJ+USs1NZX+XkGA1ToBo7EdHGcGw0RAIsmHSET2byHI0QgB9Ho9b+Sampr4/IgnT56gu7sbU1NTDu3Dw8ORkZGBY8eO4fjx43xSuSvMZjMuXbqEf/qnf4JWq4VEIuFrX9jnhnAcB4vFApPJBIlEgj179sBoNPIDqbsKWSzLore3Fz09PZi20/2fmprinQtnsfsCgQDh4eFITU1FSUkJYmJi5rSxDeDOJqChPoDblJycJccGqpJTYmKi0zCnhZScloPJyUlcu3YNtbW1GBgYcNlWJpOhoqICu3btcus7SgQ/znLqFlLImm3HSCFr+bCNu/b5gFarFTKZjHc6iouLKWSOWFGQoxFiTE9P4/PPP8fvfvc7dHR0YGJiwmG1VCgUIjMzE5mZmRAKhXyCm80Izk5w02q1UCqV+PLLL9HQ0IDIyEikpaVBJpO5nJCnpaXhtddemyOJ66lC1tjYGFpaWqDRaDA1NeV05Tc8PJxPNrdfpRYIBPx7dXfCGQoDuCslJ61WO8cxXW5mKznZOxSLUXJaDnp7e6FUKnHjxo0FP9/i4mLI5XKsXbs2KN4b4R+WqpCVlJSExMTEgHO+VzJTU1Noa2vjHY8nT544yNaXlZUhKyuLvudEUEOORgjR29uL6upqNDU18f9nsVig1+sxMjLCq+W4CmOSSCQoKiqCRCJBf38/urq6wDAMBgYGIJVK3Ypdz8jIwGuvveZ0N8EVZrMZfX19aGhowP3796FSqTAwMACTyYSJiQmMjY1hamoKQqEQERERvHOx0A5EeHg4srOzl7x9bRvAnU16A20Atyk5zTcpCUUlJ39jtVpx9+5dKJXKeQtZ2hCLxdi+fTsqKyv5PCmCcBd7hazZOyHzKWTN/q4lJSWRIIaP0el0fMhzU1MTTCaTg2x9aWnpvPWqCCJQIUcjBBgZGcGFCxdw9epVpyv+JSUlqKqqQkZGBvr6+vjVlfb2doewJIvFgidPnqC/vx8mkwkxMTHIz8/H+Pg4xGIx4uLiFlx5WbVqFV577TW3605wHIe+vj7e+La3t8+ZBIeFhSE/Px+ZmZmYmJiAUqlEb28vjEYjjEYjTCaTyxh3G5GRkcjNzV1wN2YxLMcA7kzJyTbBCAYlJ/tXXyk5LQcjIyO4fPky6urqHGLunZGeng65XI6tW7f6NQGdCB3sFbKcCTWY7CrFk0LWXOrq6vBP//RPuH37Nvr7+3H27FkcPnx4ydednp6GWq3mx+PHjx+D4zhetr60tBT5+fkBtYBFEM4gR2MFY8ub+Oyzz5yGY6SlpaGqqgplZWVOJ3FmsxktLS1QKpW4dOkSOjs7AcwoVqWnp0MgEODhw4f8tQUCAeLi4hAXF4f4+HhIJBKH665evRo/+tGPFhyQDAYDLxPY2NjoNGQqPT2d1yvPz893CIliWRbXrl3D+fPnMTo6Co7jYDabeafD3gExGo1zJtwymQw5OTl+Lb4UGxs7b/iPq88r2JWc7N/nSp5IcxyHtrY21NTU4N69ey6dPIFAgHXr1kGhUKCgoGDFOFhE8LEYhSxnu44rWWDj4sWLuHLlCjZu3IijR496zdGYzfj4OJqamnhJ9rGxMQfZ+tLS0sVLWROEDyFHYwXCsiyuX7+O8+fPY2RkZM7xmJgYHDx4ELt27Zp3B8JiseDWrVtQKpXo6upCQkIC1q5dC6lUis7OTty+fRu3bt1yKaMbERGB+Ph4yGQyrFu3Dv/pP/0np3UyWJaFWq3mixF2d3fP2YGIjIxESUkJb1BlMtmCn4PZbMbnn3+Ozz//fN64d47jMDU1NccBMZlMiIuLQ3p6+rKGC3Acx9cKiYiIgEAgAMuyfJ9NJlNADeCBrOS0HJhMJly/fh21tbVOK8zbI5VKUVFRgd27dyMuLs4/HSSIJeAsp872ar9AFCoCGwzD+MzRsIfjOPT29vJjZkdHB6anp3nZelvtp5W8eEMED+RorDCam5tx+vRp9Pb2zjkWFhaGZ555Bs8999y8Bkin06G2thb19fUwGAxOqwo/fvwYJ0+eRH9/P/R6PfR6vUt9/9jYWDz11FN8Ze2ysjJIpVJ+x6K5uXmOWhHDMFi9ejXffvXq1YtOiBsZGcH58+dx7do1t0KobHAcB4ZhsHnzZhQWFmJ0dNRhRc9dhSx37mOxWObdcXGVLyESifgq6bOLFoaHh/tkAHem5GSfEE9b+UB/fz9qa2tx7do1h9ATZxQUFEAul2PdunX02RErhvkUsmyhmytRIctfjsZsTCYTWlpaeMdjaGgIQqEQeXl5KC8vR2lpKTIzM1eMQ0cEF+RorBD6+/vxwQcf4OHDh06Pb9u2DYcPH3a6E8BxHFQqFZRKJR4+fAiJRDJvVeGuri68+eabcxyDqakp3unQ6/X8TkdcXBwfmjU6Ogq9Xo/h4WFYLBbIZDL+Jzw8HLGxsbxjUVJS4nYeh7v09PSguroazc3NHp8bGxuLQ4cOYfv27fzA54lCli18y+Y8zHYo7HNhvIVAIJhTLd3mjCyUJB8eHj5vLHawKDn5G5Zlcf/+fSiVygWfsfDwcGzduhVyuRyZmZl+6iFBBAY2hSxnOyHOFLKCQWADWD5HYzZarZZfyGtpaYHZbIZUKuXDjUtKSvwaGkyENuRoBDnj4+P48MMPcfnyZadx34WFhaiqqkJ2dvacY5OTk7h69Spqa2uh1WqRmZkJhUKBzZs3Ow0X6ujowFtvvbXgCi3HcRgfH4dQKIRYLEZzczP0ev2c/gkEAkilUsTHx6OkpATbt29HeXk58vLyfDaAcByHxsZGVFdXuyxkOB+uChjaKzn19fVBrVZDrVajt7cXWq0WExMTMJlMMJvNHu2s+AKGYfjk67S0NKxatQqrV69GQUEBCgsLER8fT6tfbjI2Nob6+nrU1dUtmBeTkpKCyspKbN++PWSTZwnCFTaFLGd5Z4ODgwGtkBUojoY9VqsV7e3tvOPR29sLhmGQlZXFL+zl5ubS4hHhM8jRCFIsFgu++OILfPrpp04n/ikpKTh27BieeuqpORPGnp4eXrOfZVls2LABCoXCZVXhtrY2vP322y6rcVutVoyMjECv10MoFPLhTvb/bzabIZFIIJPJEBsbC6FQOOc69gluZWVlSEpK8vDTWRiWZVFfX48LFy54VLGaZVkYjUZkZmZi/fr1mJ6e9ljJyVYh3VmYlLsKWe4SHh7udEdDLBZDJBI5/XsH4gAeaHAch87OTiiVSty+fdvljhTDMHjqqaegUChQXFxMDhxBLBJPFbIWI7CxFALR0ZjN6Ogo73SoVCoYDAZIJBIUFxfzY258fPxyd5NYQZCjEWRwHIebN2/i3LlzGB4ennM8KioKL7zwAioqKhwm8TbN/pqaGnR0dCAuLg6VlZVuVRVubm7GO++8Myfx26ZIYguXGhsbA8dxSEhIQElJCQQCASIiIhwMWEJCAp48ecIbutbWVpcJ5QD4BLeysjIUFRV5daJrMpnw6aef4osvvuD7MT097dQBMBqNDo4WwzBITU1FdnY2wsPDvdIfe4UsZ/d35shEREQ4dSQkEolTR26pLMcAHihMTU3h5s2bUCqV6Onpcdk2Ojoau3btQkVFBRISEvzUQ4IITWzjkbNwrNk5dVFRUU4T05eqkBUMjoY9LMvi8ePHvIRuZ2cnOI5DamoqP+YWFhaGnIgH4V3I0Qgi2tracPr0aXR3d885JhKJ8PTTT2Pfvn0Okz29Xo+6ujrU19djbGwMRUVFUCgUblcVbmxsxLvvvstPwufLxbCRlJSEZ555BmvWrOG3ZF2FQVksFofKqAuFMwmFQoek8oyMDI8HBY7jMDk56bAaplarUVtbi7a2No8rX4tEIr7CuC+2n+2VnKKionijz7IszGYzdDrdnNW85cJXA/hyo9VqUVtbi6tXr87JT5pNTk4O5HI5Nm7cSAM0QQQI7ubUeaqQNTExwRfcXL9+PU6ePAmFQoH4+HhkZWX59T0ulcnJSTQ3N/PjsV6vR1hYGAoKCvgxd6mFbYnQgxyNIECr1eLMmTO4e/eu0+ObNm3CkSNHkJiYCGBmIm2rf3H//n2EhYVh+/btkMvlHlUVfvDgAd59910MDw+7VJcKCwuDTCbDjh078F/+y39ZkjSnXq/njVxTUxOMRqPL9lKp1CGB3JbgxnEcxsbGnFa+1mq18153fHwcnZ2dTmt3LERERARycnKQlJTksSG2KTk5S8B2R8nJ2Wqe/e+uVMH8RbBJXLIsi0ePHkGpVKKxsdFlW5FIhC1btkAulzvNhyIIInDxRCHL3k4nJydDrVbj29/+9pxrvvjii/jtb3/r53fiPTiOw5MnT/jxuLW1FVarFTKZjB9zi4uLV/wuNrF0yNEIYAwGAz766CMolUqnITO5ubk4fvw4cnNzAfxRs1+pVKK/vx9paWmQy+XYtm2bR3raQ0NDOHfuHH73u99heHh4Tvw5wzCQSqW8YlR0dDS2b9+OF1980asr+rb6GjZDN7u+hjMlp8jISERGRvKyr4uZvHIcB51OB7VavaCj44yYmBjk5uYiNjbW4f+XU8nJfjVv9kDqrNaKv3E2gNte/S1xaTAY+OTuoaEhl20TExNRWVmJnTt3el0ljSCI5ceZQpbt94UUsmy/B6JClqdYLBa0trbyuR39/f0QCATIycnhHY+srCxKKifmQI5GAGK1WlFTU4NPPvnEaZhGYmIijh49ig0bNoBhGPT390OpVOLatWuwWCxYt24d5HI5CgsL3Zpo2yqA2/ImbLUt7B8NsVjMOxZxcXEORnPnzp341re+5RMDY7VaMTw8DK1Wi8ePH+PevXtoampCR0cHRkdHXSZfi0QixMXF8f32tHgRy7Lo7+/H48ePF8wjsb+nLU+ipKQEBw4cQHFxMZKSkiCVSgNu1R6Y+fsPDQ053QmxX81bLvw1gHd3d0OpVOLmzZsua5cAQHl5OeRyOcrKymhgJYgQxROFLJlM5jSvLVgFNoaHhx2iD0wmE6KiolBaWorS0lKUlZXNWWwjQhNyNAIIjuNw584dnDlzxulKamRkJPbv3w+FQgGBQIB79+6hpqYGra2tkEql2L17N3bv3r1g1WyO49DX18evTLS3t/MTK61Wi5aWFjAM4zBJl0gkTifJFRUV+PM///MlTaAtFsu81WV1Op3TiS7HcTAYDA6J6AspPkVGRjo4S+5OEK1WKx4/fgyNRgOWZT1SchIIBKisrMSBAweCUrfcarXykr2zV/QGBwfdUtnyJUtVyLJYLLh9+zZqamrQ1dXlsm1kZOS89WUIgiDscaaQZf9qn1MnlUqd5rUFi8CGLfrg0aNHUKlUfPRBZmYmX6k8Pz8/6Hd1iMVBjkaA0NnZidOnT6Ozs3POMYFAAIVCgeeffx7T09O4fPky6urqMDIygry8PCgUCqxfv97ll9hgMEClUvG7Fs5yEKanp/H48WPExcUhNjZ2wYn4008/jW984xtuORkmk8npqo9Wq/VK6M709DQvoavX6xcMeRIIBIiLi0NcXBzi4+PnOFIymWxOCI9QKMSVK1dw//59j/snkUiwf/9+PP300yvG2Nqv5jn727q7C+RLZitk2X4XCARoaGjAlStXFsxfWbVqFV9fxlvqYgRBhC6ucur8pZDlSyYmJtDU1MQ7HmNjY7xsvW23gxZrQgdyNJaZoaEhnD17Frdu3XJ6fP369Thy5AjGx8ehVCpx584dCIXCBasK21YYVCoVHj16NCe/AZhZoS0pKUFZWRlGR0dx4cIFt8Nk9u7di6NHj/JGzpmSk70R9aRWhTcwGo280zEyMuI0z8QmCyuRSJCUlIQ1a9Zg06ZN2LZtm8stX1dO4UIkJibiyJEj2LhxY0AOEN6C4ziMjo46HUSXSyHLtsKo0WgwPDzM5/HMlgQWi8UQi8XYtGkTFAoFcnJyVvTfiiCIwGKxCln2r4EisGEfQdHY2Ij29nZMT08jMTER5eXlKC0tRVFRkcehzUTwQI6GB0xMAO3tgNkMREQA+fnAYqNhJicncfHiRXz11VdO48Gzs7Nx6NAhDA8PQ6lUore3F8nJyZDL5fNWFbZXbGpubp6T38EwDFavXs0nbtkK6imVSvz+979fsM8cx8FisWDz5s1Yu3btnOTixSRO+xKbklNCQgJYlsXo6Ch0Oh1GRkZcJoq7k+DGcRxu376Ns2fPLpgw7IzZifyhhP1qnrOQAm8rZFmtVgwMDECj0Sz4jEZERCAtLQ2rV69GRkZG0Chk+Q1vGkGCIDzG3Zy62QIb9rbM3wIbs/vf0tLCz1UGBwchFAqRl5fHOx6ZmZkBaWOt1gkYje3gODMYJgISST5EIrJ/C0GOxgKoVMB77wGffAJ0dgL2nxbDALm5wP79wPe+B5SWLny96elp1NXV4cMPP3TYHrUhk8lQWVmJsbExXLt2DSaTCWvWrIFCoUBJSYnDl8+dGhSxsbEO8q+zlXG+/PJL/Pu//zv/79lKTrOLxmVmZiIrKytgjIC9ktNsgzpfHsbY2JhDZdSFJra2BDdbrKn9boctcf/jjz9elKO1ceNGHD16lJcmJrynkGUwGKDRaKDVal1W7gaAuLg4pKenIyEhYcFnO5AUsvyCt40gQRA+wT6nLlgUsrRaLT8et7S0wGw2QyqV8mNuSUkJYmJi/Naf2RgMKmg070Gn+wQmUycA+ykzA7E4FwkJ+5Ge/j1ERZH9cwY5GvOgVgMnTgCXLgEiEeBKhMZ2fM8e4P33gZycuW04jsP9+/fxwQcfQKvVzjluq6BtMpnQ0tKCqKgo7Nq1C5WVlXxVYY7jMDAw4KBrPTsOXiQSORS0S09PnzNxsik5nTt3Dh9++KGDQ2EymeZN8M3JycGqVasW+OS8T2RkpFODmJycjJiYmCU5PRzHzamMulCCs7MEt4WkiF0hEomgUCiwf//+oEj8W05sq3nOdkJsUsw6nQ4ajWbBWigikQgpKSlIS0vz2uceSAP4kvG2ESQIYtmYrZA1exFnPoWs2bbMlwpZVqsVHR0d/Hjc29sLhmGQlZXFz2lyc3P9sphjNKrR2noCev0lACIArpQIZ47LZHtQWPg+JBKyf/aQo+GEU6eAV1+dGTcXULl0QCSa+Xn7beC73/3j/3d3d6O6uhqtra1zzrHFKlosFkxMTGD16tVQKBR8VWGj0ehQqXN4eHjONZKTk/kvYWFhISIiIuYoOdkbFJ1Oh+7u7gVVduzJzc2dNx/EG8TExDhdIbZVw/YX7nze9tgS3GyOB8dxOHPmDO7du+fxvaOionDgwAFUVFQE14Q0ABgZGUFNTQ0uXbqEwcHBOc6zvQMdFRWF9PR0PsHfXwSVxKW3jSBBEAGLJzl1/lTIGh0ddYg+MBgMEIvFfG5paWkpvxDrTTSaU2hvfxUsa4VrB2M2IggEIuTnv430dLJ/NsjRmMUbbwA/+cnSr/OznwHf/74eZ8+exY0bN+YcHx8fh0AggEgkQlRUFDZv3sxXFV5ohd22+1FYWIikpCRYrVa3lZxsK/jd3d1uv5e8vDxkZGR49P6d4UzJyWaoAjERzJ0dpNkkJSWhrKwMUVFRuHfvHvr6+jy+b3JyMo4ePYp169YFTIhaIMJxHNra2lBTU4N79+653EliGAaFhYUoKSlBZGTkvKt5y8l8Cll+l7j0phH8u79b+nUIglg2FquQNft1qQpZLMs6zI3UajVYlkVqaqrDQmtYWNiS3m939xtQq5du/3JyfobsbLJ/ADkaDpw6Bbz8sveup1D8XxQU1PH/ZlmWT3ZNSEhAXl4eKisrsWbNGv4LZJ8zwHEcrFYrTCYTYmJikJiYyK/uDw8Pe6zkxHEcurq60NPT4/Y5BQUFSEtLc6utQCBAfHy809WOxMTEJRuA5cadnBh7BAIBwsPD8eTJE96h9MTQFhQU4Pjx48jOzl5q11cUJpMJ169fR21tLTQajcu2UqkUFRUV2L17N+Li4py2sa3mOQsp0Gq1y6KQNRu/SVx62wieOgW89JL3rkcQREAxX07dbIWsiIgIp+HPixXYmJycRHNzM6+sqdfrERYWhoKCAt7xSE1N9ei6Gs0ptLZ6z/4VFZ1CWhrZP3I0vkatnslj9N6cgoNQaMHx4z9FeHgf+vv7odfrkZ6ejoqKChQUFGBqaoqvcm0L77CFfHAcB7FYjOjoaMTFxS1Zv5/jOKjVavT29rrVnmEYFBQUIDU11eH/bUpOzlZdExIS/BqKstzYq3w1NTXNmwzOsix6e3uh1WoRExOD+Ph4xMXFue14bd26FYcPH0Z8fLw3ux909Pf3Q6lU4vr16wtO/gsKCiCXy7Fu3bolhaHNp5Bl+93bClmLwWsSl943goBYPJNMTjkbBBFy+Eshi+M4PHnyhF+stUUfyGQy3ukoLi52uTNsNKrR0FAKlvWe/RMIxNi8WRXyORvkaHzN3r1ATY1n4cgLwTDTkEpvIzPzO0hJSUFhYSFYlkVPTw/Gx8f5GPLp6WkwDAOpVMpXrvbmKiXHcejs7HQ7jEckEmH79u3YsGGD20pOoY47dUumpqbQ3d2NJ0+eAACio6P5v7dUKnX59w4LC8MzzzyD5557LiDDzHwFy7K4f/8+lEolmpubXbYNDw/Htm3bUFlZ6dN8InuMRuO8yZXeKES5VDxSyPKFERSJAIUC+Pxz712TIIigZzEKWbNt2HwCGxaLBa2trXx+R39/Py9bX1paivLy8jmy9ffv74VeXwPPcjIWQgSZTIG1a0Pb/pGjgZkFt7Iy310/K+s5SCRdcyaSYrGYn2jGxcX5JAGY4zi0t7fPCfMRiUQOhcpsxcqioqJw4sQJbNmyxet9CSXsK7E/evQIY2Nj/LHJyUl0dnY6JJqLRCLExcVBJpMhPj5+3sTgmJgYHDx4ELt27VrRDt/Y2Bjq6+tRV1cHvV7vsm1KSgoqKyvnrS+zXExNTfGrebMHUvvVvOXCfgDPn5rC/v/8n313M5UKKCnx3fUJglgxeFsha3h4mN/tsEUf2GTrS0tLkZsrRHPzNp+9n82bVYiKCl37R44GgB/+EHj3Xe8u5P0RK2Sy3yMl5b9DIBDwk0mZTAaJROLTZN+YmBj09PRgYGBgTuVjZ2E7AoEAL7/8MjZs2OCzPoUitsqottWV9vZ2WK1W6PV6qNVqp+E3kZGRvNMRGxs7x6lIS0vDsWPHUF5evmISxm07bzU1Nbhz547L2hcMw+Cpp56CQqFAcXFx0H0GttU8Z6pwQ0NDC9b98DZ/euUKKlUqCH0xHIhEwCuvAG+95f1rEwQRUixVISsxMREGgwGdnZ1obGxEd3c31q+vR2FhExjGM2l69xAhI+MVFBSErv0jRwMzxW07Onx3fYmkDwcOvOZ0wrhU7JWc7L9MCQkJ+MMf/uBU8coZQqEQJ06cwNq1a73aP2Iutsqott2Ohw8foqurC1NTU07bCwQCxMbGIj4+fo6DWlxcjOPHj/stVMgXTE1N4ebNm1AqlQsKFURHR2P37t3YvXu3T2QNAwGWZaHX650mpvtKIesf/vAHJNvtunmd/Hygrc131ycIIuRxpZBlE+KxYRPYkEqlyMv7e4jFOp/1SyzOx7ZtoWv/Qt7RGB8HYmMdi916Hw7f/vaPEBZm9vhMgUCAhISEeQuAOduZmJ6exm9+8xs0NDS4dQ+RSIRXXnkF5eXlHvePWDqDg4O4e/cuzp49i6tXr87rcNiIiIjgnQ5bUvn27dtx6NCheZWVAhGtVova2lpcvXoVk5OTLtvm5ORALpfz9WVCFWcKWfYreu4oZE1PTzsITzATE/jw8mX4NBCPYYCxMSA62pd3IQiCmBdnClk6XQ/Wr/+v8O2mOINdu8YgEoWm/Qt5R+PePWD9et/f5+jRf0BionPFJ5FI5FC4a/bOhCdKTlarFf/8z/+MO3fuuNU+LCwM3//+91FaWur2PQjfMTQ0hH/5l3/Bl19+ieHh4QVVjRiG4ZWsbPU3nnvuucAq/mYHy7J49OgRlEolGhsbXbYNCwtzqC9DuIbjOBgMBmi1WvT09KCtrQ3d3d3o7e3lVe9MJtMcR7bYZMJZD4p3Lpq7d4F163x/H4IgCDcZH7+H27d9PwncuPEuYmLW+fw+gUjIlx82e77JsCiEwkhkZmY63ZnwlpKT1WrF+++/jwcPHrjVPjw8HH/913+N4uLiJd+b8A6JiYn427/9W/z5n/85qqurcf/+fej1ev5ndtgMx3EYGxvD2NgYurq6cPfuXbz77rs4cuQIvvGNb0Amky3TO3HEYDCgvr4etbW10Olcb1EnJiaisrISO3fu9GtV+GCC4ziMj487jVEeHBx0KKIVHh6O7OxsZGRkOOxk2H6P8k1y2hxYo9G3uyYEQRAewnH+mQT66z6BCO1o3PPPjsadOxzWr/fd3pzFYsG777674CqxjYiICLz66qsoKCjwWZ+IpcFxHBobG1FdXY3+/n4+/tTmdIyNjblULoqOjsb27dshl8tRWlqK/Px8nyibuaK7uxs1NTVoaGiA1cWElmEYlJWVQS6Xo6ysbEUrarkLx3EYGRmZN1fD7KVVksyhIfzXM2e8ci1X/Pfjx2EqLp43p8zfzyZBEATtaPiekHc0JiYAqdS3ORq+Dk+emprCr371KzQ1NbnVXiwW44c//CHy8vJ80yHCq7Asi/r6ely4cMGhGrzVasXIyAjveMwXnx8fH4+cnBzEx8ejqKgIZWVlKC0tRXJysk/6a7FYcPv2bdTU1KBrgZCcyMhI7Ny5E5WVlUhKSvJJfwIZlmV5LXlnRQFdOWfeIsJiwZu/+Q18GaLMMQzqP/4Y/Xa7MPbvj2EYPhdttiOSmJi45IKlBEEQzrBaJ1BfLwXgy6kw5WiEtKMB+F51ypeCK2azGe+88w5aW1vdah8ZGYkf/ehHWL16tW86RPgMk8mETz/9FF988YXTECqj0cg7HSMjI3zBI2BmIpeamors7Gx+0paUlMQ7HUVFRUsuBKjT6VBbW4srV64smFuyatUqKBQKbN68ecVPIi0WC4aGhuY4EjOJiDqHv9NysRyqUyzLYmRkZF6tfPsdm7i4OKeVz5OTk0OqgCVBEN7n+vV8mEy+mwSS6hQ5Gj6to+FLCXmTyYS33noLHW56SVFRUXjttdewatUq73eG8BvDw8M4d+6cS+lilmUxOjrKOx62mH2hUIhVq1YhMzPTITxJKBQiLy8P5eXlKC0tRWZmplu1KTiOQ1NTE2pqavDw4UOXoVwikQgbN26EXC5HTk5O0NW+cIXZbJ5XCWpkZGTZi/PNxjZxt03WN/zv/43k6mowvqjfsQgjaJ+D4qzgob1KWUxMjFMhjeTkZERFRa2o54wgCO/T1vZD9PW9C+9WBbdBdTTI0YDvK4P7oiju5OQk3n77bXR2drrVPiYmBn/zN38T1PUWCEe6u7tx+vRptLmxXWY2mx2SyoVCIVavXo3k5GSnEzGpVIrS0lKUlZWhpKQEMTExDscnJydx7do1KJVKaLVal/eWyWSorKzErl275lwnmJicnJy3SNSYL3cDFgHDMLwS2exJeGJi4lxVsiAzggaDYd5EePu/hUQicboTkpSUhNjYWHJCCIKAwaBCQ4Pv7B9VBidHAwCwdy9QU+PdXQ2RCFAogM8/9941gZlB9s0330R3d7db7aVSKV5//XWkpaV5tyPEssNxHB48eIDq6uoFJ/z254yPj0Ov14PjOMhkMsTGxs7bnmEYZGVloaysDPHx8VCr1WhoaFiw3kdxcTHkcjnWrl0bFMnds1fRZ78uVOvD3wiFQiQmJjrNa1hUcnUwGUEXmEwmDA0NOf0b6vV6vl14ePi8OyEymSwonlmCILzD/ft7odfXwLu7GiLIZAqsXes/+xeIkKPxNWo1UFoKuFHvym3E4pmFvJwc711zYmICv/zlLxesoGwjLi4Or7/+OlJSUrzXCSLgmJ6eRl1dHT788EMHaVN3sFgsSEpKQkZGBnp7ezE6Oupw3JawrNFoMDo6CpFIhLi4OMhkMshkMocYebFYzCtdpaameuW9eRN7JSdnCdjeUnLyFmFhYfOuyMfHx3t3MhwsRnAJ2OfLzP772+fL2DtxpJBFECsfo1GNhoZSsKz37J9AIMbmzSpIJIFh/5YLcjTsOHUKePll717vpZe8d72xsTH84he/gEajcau9TCbD66+/7jN1ISLwmJycxMWLF/HVV195rFgkEAhQUVGBDRs2oLu7G7du3UJ9fT36+vpc7l5ERkYiNzcXBw4cwLFjxxC9zNWfA0HJyRPEYvGcyaztd7+H9wS6EfQh09PT0Ol0Tp+ZhRSybK+kkEUQwYtGcwqtrd6zf0VFp5CWFhz2z5eQozGLN94AfvIT71znxz9e+nVsjI6O4uTJk3jy5Ilb7RMSEvD6668jMTHRe50ggoahoSGcPXsWt27d8ug8juNgNpsRFxcHk8mE6elpjI6OYnh4GHq9HkajkW/LMAwSExORnp4OqVQKhmEQFhaGgoIClJWVoaysDKmpqT6ZKLu7Mh0oREdHO3UkkpKSEB0dHVi5AoFqBJcRZwpZ8+2EzVbIsv9dIpEs47sgCGIhurvfgFq9dPuXk/MGsrNXhv1bKuRoOOHUKeDVV2dClT1Z/BSJZn7eece7i3h6vR4nT550OwY/KSkJr7/+OuLj473XCSIo6ezsxOnTpxcUDZiensbAwAA0Gg2fiyAWi5GTk4PExER+ImwymWA2myGTydyazMtkMt7pKC4uRmRkpNt9D1YlpxUxwQw0IxjAkEIWQawsNJpTaG9/FSxrhWc5GyIIBCIUFLxDOxl2kKMxD2o1cOIEcOnSzLjpaqy1Hd+zB3j/fe+GI+t0Opw8eRJDQ0NutU9JScHrr7+OuLg473WCCGo4jsOdO3dw5syZOc/R5OQkNBoNtFrtvCFFUqkUubm5vDTtunXrIBKJYLVa0dnZicbGRjQ2Ni6YNyQQCJCTk8M7HllZWTAajU7rSwSTktOKDZkJFCMY5MxWyJrvGZ+tkLWsIXQEEeIYjWq0tp6AXn8JgAiuHY6Z4zLZHhQWvh/yORmzIUdjAVQq4L33gIsXZ4r62X9aDAPk5QH79s3IxHtbwnZoaAgnT56ETqdzq31aWhpef/11SKVS73aEWBFYrVbU1NTg448/Rk9PDzQaDUZGRlyeIxQKkZycjPT0dFRUVODIkSPzVvAeGxuDSqVCY2MjVCoVJiYmwHEcLBYLTCYTjEYjjEYj/7vVakVMTAzi4uIQHx8fEBN1rys5rQSW0wiucGYrZNnvitgrZNmLApBCFkH4D4NBBY3mPeh0F78u6mc/ZWYgFuchIWEf0tNfCWkJW1eQo+EBExNAeztgNgMRETPFbn2V96rVanHy5EmHwcYVmZmZ+Ju/+ZugrlNA+JaxsTHU19fjiy++wIMHD6DRaOYNP4qMjERaWhpSUlIcJtdCoRAKhQLPP/88HwblTMlJq9Wira0NbW1t0Gq1GBsbWzDUKTo6mleykkqlPps8zafkRJM2N/CnEQxxFquQZf8ass4xQfgAq3UCRmM7OM4MhomARJIPkYjs30KQoxGAPHnyBCdPnpwjMzofq1atwmuvvYaoqCgf94wINjiOQ2dnJ2pqanDnzh1M21V+npycRFdXFx9OZQsNSk9PR1xcnEOoBsdxMJlM/G4Ex3HIzc1FXFwcdDrdgkpOVqsVIyMjfMFA0wISqkKh0EFC19Mch4BSciIILzM9PY3h4eE5oVjOFLJCKtyPIIiAgxyNAEOj0eDkyZMYHx93q/3q1avxox/9yKMkW2LlYzabcfPmTdTW1i6YO2G1WmE2myEQCMBxnEN4k/2rM1MhkUiQk5ODhIQEtyfvtnvYnI6RkZEFE8vFYjHi4+P54oIikWheJSdKrCVCGU9qxawoAQOCIAIScjQCiJ6eHvziF79wu+Babm4ufvjDH9KAQPBotVrU1tbi6tWrTitZT09P845DXFwcVq1ahaioKAwNDaG1tRVqtXpRRetiY2ORm5u7qNA9lmUxOjrKOx72z39ERATEYjEkEgkkEgnEYjGioqJQVlaGdevWoaysDJmZmeRUEIQbeKKQRY68e/z85z/HmTNn0NzcDIlEgh07duAf//EfUVRUtNxdI4iAgByNAKG7uxu//OUvnU4OnVFQUIAf/OAHDlWZidCEZVk8evQISqUSjY2NDsnXs5OwrVYrkpOTkZaW5tQpYFkWvb296O3tXVRhu+TkZKxevdrt59JZaIdYLIZer0dvby/a29sX/E5IpVKUlpairKwMJSUllKdEEIvEXYUsCk38I8899xy++c1vYvPmzbBarfjxj3+MR48eQaVSUTgzQYAcjYBArVbjzTffdCiG5oqioiL89V//NSIiInzcMyIQsa1KdnV14csvv8SVK1cwODjIOxTOHASxWIz09HSkpKQgLCxswXtMTU3h8ePH6O/v97hehUAgQEZGBlatWgWRSLSkZFWWZaFWq3k1q66uLpf9YRgGWVlZvIRubm4uJXcThBdwpZBlX9cm1BWyBgcHkZycjNraWlRUVCx3dwhi2SFHY5np6OjAW2+9tWByrI3S0lK88sorlMS3wuE4Dnq93unKYkdHB7q6ujA4OOgyt4FhGMhkMqSnp0Mmky1qlXFychKdnZ0YHh522U4gEPDhTbYQp4SEBBw5cgT79+/3mvKNwWBAU1MTX7tjIcEEsViMkpISlJWVobS0FAkJCV7pB0EQf4QUsv5Ie3s7CgoK8PDhQ5SXly93dwhi2SFHYxlpbW3FO++843ZM/Jo1a3DixAm3VqSJwGd6eho6nW5OqMJs5RhgZmV/cHAQGo1mQaEAkUiE1NRUpKWleS1/Z2RkBI8fP4bVanVwJmyv4eHh8zoyaWlpOHbsGMrLy70aUsFxHPr6+vjdjvb29gXDvVJTU/ndjsLCQvouEYSPma2QNdvWrSSFLJZlcfDgQYyMjKC+vn65u0MQAQE5GstEc3Mz3nnnHVgsFrfar1u3Di+//PKKWPEJJZyt9Nle7Vf65sNkMqG/vx9PnjxZ8FmJjo5Geno6kpKSIBQKF9XfmJiYOTHXtt8lEglu3LiBc+fOuS29bE9xcTGqqqqwatWqRfVtIcxmM1pbW/ndDq1W67K9SCRCYWEh73ikpqaGVGw5QSw3K00h65VXXsHFixdRX1+PzMzM5e4OQQQE5GgsA42NjXj33XfddjI2btyIl156adGTR8K3mEwmh50I+0HTPnbZXWyDr0ajwfDwsMvzBQIBkpKS+ORudybKSx2wzWYzLl26hM8++wxTU1MevTeGYbB9+3YcOnQIcXFxHp3rKUNDQ7zT0dzcvODOoUwm452O4uJikowmiGVktkLW7Fd3FLKSkpIQHR3tlwWEH/zgBzh//jzq6uqQk5Pj8/sRRLBAjoafefDgAd5//323FX22bNmCb3/72ys+gS7Qma3GYr/6Zq/GshSsVisGBgag0WgWFAaIiIhAeno6UlNT54T/zA5BsH/1ZgjC6Ogozp8/j6tXr3rsTIWHh2Pv3r3Yu3evX0QNrFYrOjs7ecdjodoiDMMgNzcXpaWlKC8vR1ZWFn0HCSKAsLfJs+2yPxWyOI7Dq6++irNnz0KpVKKgoGCpb40gVhTkaPiRu3fv4te//vWC4TI2tm3bhhdffJEmOH7AtnrmbOVscHDQbdnhxWAwGKDRaDAwMLDgs2FL7rYNmM7UXfydVNnb24vq6mo0NTV5fG5sbCwOHTqE7du3+/U5Hxsbg0ql4vM7JiYmXLaPiopCaWkpL6MbGxvrp54SBOEpZrPZaWK6K4Ws2Tsh8fHxbtmk73//+/i3f/s3nD9/3qF2RmxsbMCEdBHEckKOhp+4desW/vmf/9ltJ2PXrl341re+RTHjXsSm5DRfUuJiCtUtFpZlodPp0NfXN++OiE3JKSYmBuvWrUNFRQWKi4sDUiaS4zg0Njaiuroa/f39Hp+fmZmJqqoqlJSU+KB3ruE4Do8fP0ZjYyNUKhU6OjoW/J5mZmbyTkd+fj7lThFEkGCfNzd7UWk+hazZCzr2iznzjdG/+c1v8Jd/+Zf+elsEEbCQo+EHbty4gd/85jduh5dUVlbiz/7sz8jJWATzKTlptVoMDQ0tqgidNzGbzXxy99TUFEQi0ZzK17bX1atX4+mnn8bWrVuDpmYKy7Kor6/HhQsXFlTHckZZWRmqqqqQnp7ug965h9FoRHNzM7/bodPpXLaPiIhAUVER73gkJyf7qacEQXiTUFLIIgh/QY6Gj7l69Sp+97vfue1k/Mmf/AmOHz9OToYLbCtSzgYDd5Sc/I0tGdHm7ERERPAOhUgkcvhbCwQCbNiwAXK5HPn5+UH7HJhMJnz22We4dOmS26IHNhiGwa5du3Dw4EFIpVIf9dA9OI6DVqvlK/22tLQs+H4SExP5pPKioiK3q6QTBBG42CtkOQuvdaaQ5Swsi8KpiFCDHA0fcvnyZfzrv/6r2+337t2Lo0ePBu3k0pt4W8nJ18xWckpOTkZMTAzUajWuXbu2YDhRbGwsdu/ejd27d/tcjcmf6PV6nDt3DtevX/f43IiICDz33HN45plnAmaF0GKxoL29nU8q12g0LtsLhULk5eXxjkdmZiZ9vwlihWGvkOVMMMRgMPBtA0EhiyD8CTkaPkKpVOL3v/+92+3379+PgwcPhpSRsamGzF4Z8qaSk7dgGAYJCQlO43Vnb5X39/dDqVTi2rVrC+Z9FBQUQC6XY/369Stavri7uxvV1dVobW31+FyZTIbDhw9j69atAff90Ov1fIhVU1PTgqIBUqmUD7EqKSlBTEyMn3pKEMRy4alClrOdEG8oZBHEckCOhg/44osvcPr0abfbv/DCCzhw4IAPe7Q82Cs5OQtz8qWS02KwT/7zVMmJZVncu3cPSqUSLS0tLu8TERGBrVu3Qi6XIyMjw9tvI2DhOA4PHjzABx98gIGBAY/Pz8rKwvHjx1FYWOiD3i0dlmXR1dXF73Z0dXW53HljGAZZWVn8bkdubm5AJfgTBOF7bApZzhbd9Hr9HIUsZ+OTuwpZBLEckKPhARMTQHs7YDYDERFAfj4QHe3Y5rPPPsOZM2fcvubhw4exb98+L/fUfwSSkpM7hIWFzbtitBglp7GxMVy+fBl1dXUYGRlx2TYlJQVyuRzbt28P6Tjd6elp1NXV4cMPP3QIKXCXtWvX4tixY0hJSfFB77yHwWBAU1MT73gsVE1dLBajpKQEZWVlKC0tRUJCgp966gHuGEGCILzCfApZg4ODGBoa8lghi1gaVusEjMZ2cJwZDBMBiSQfIhHZv4UgR2MBVCrgvfeATz4BOjsB+0+LYYDcXGD/fuB73wPU6o9x4cIFt69dVVWFPXv2+KDX3sVeyWm2QxEISk6zEYvFSElJcRoD660CTR0dHVAqlbhz5w6mp6fnbcswDNauXQu5XI7i4mLa+rZjcnISFy9exFdffeXxMyQQCFBZWYkDBw4gOggmuhzHQaPR8E5He3v7gu85NTWV3+0oLCycU5jRb3hiBEtLl6ePBBFi2BSy5gs/nq2QNV+9kEDJfwtUDAYVNJr3oNN9ApOpE4D9lJmBWJyLhIT9SE//HqKiyP45gxyNeVCrgRMngEuXAJEIcDUnEIk4WK0MMjIasXv3/4VU6loOEwC++c1vQqFQeLHHS2M+JSetVovh4eGAU3KKiYmZV9UjKirKJxN6s9mMmzdvQqlUore3d8H+7dq1CxUVFYiPj/d6X1YSQ0NDOHv2LG7duuXxuRKJBPv374dCoVi+ifgiMJvNaG1t5R0PrVbrsr1IJEJhYSG/25GWluZ7p9UzIzhzfM8e4P33gZwc3/aNIIh5sSlkOUtM12q1Cypk2X4P5Z13o1GN1tYT0OsvARABcLUwNHNcJtuDwsL3IZGQ/bOHHA0nnDoFvPrqzLjpyUIrw0xDIJjGzp1/QHHxlXnb/cVf/AUqKiq80FPPsFdymr0CEqhKTvOpc/jTAGq1WiiVSly9ehVGo9Fl29zcXMjlcmzcuJG2qz2ks7MTp0+fRmdnp8fnJiQk4MiRI9i0aVNQ7hoNDQ3xTkdzc/OCIYcymQylpaUoLy9HcXExIiMjvduhxRpBkWjm5+23ge9+17t9IghiyXiqkDXfTshKVsjSaE6hvf1VsKwVrh2M2YggEIiQn/820tPJ/tkgR2MWb7wB/OQnS7kCB4DBpk3nsGHDRYcjDMPgP/yH/4CdO3cu5QYusVdymr2KsZgCar7EEyUnf8OyLB49egSlUonGxkaXbcPCwrBlyxbI5XJkZWX5qYcrE47jcOfOHZw5cwZDQ0Men5+Tk4Pjx48jLy/PB73zD1arFZ2dnVCpVHj06BF6enpctmcYBrm5ubyaVXZ29tISQ5duBGf42c+Av/u7pV+HIAi/MTk56XQxUqvVzlHIcpaYnpycHNQKWd3db0CtXrr9y8n5GbKzyf4B5Gg4cOoU8PLL3rteRcXv+J0NhmHw7W9/G1u3bl3SNYNVycnZikggJqlNTEzgypUrqK2tXbAidGJiIuRyOXbs2IGoqCg/9TA0sFqtUCqV+Pjjjxf1TG/YsAFHjx5FUlKSD3rnX8bGxqBSqfifhRYMoqKiHJLKParL4m0jeOoU8NJL3rseQRDLhjOFLNvvK0EhS6M5hdZW79m/oqJTSEsj+0eOxteo1TN5jCaTt67IQSi04PjxnyIuTo/vfOc72Lx5s3tnzlJysv9CB7KSk7Mwp8UoOS0HXV1dqKmpwa1bt1wm6TIMg7KyMigUCpSVlQXtqk2wYDAY8PHHH6OmpsbjPCGhUAiFQoHnn3/e+6FFywTHcXj8+DFfu6Ojo2PBzyUzM5Pf7cjPz5/fufe+EQTE4plkcsrZIIgVjScKWQkJCU7VH5dz8dFoVKOhoRQs6z37JxCIsXmzKuRzNsjR+Jq9e4GaGs/CkReCYaaRkdGC8+dN2LBhg8OxYFNykkgk8+ZLBOs2qcViwa1bt6BUKtHV1eWybWRkJJ/cvRJWyYMNrVaLM2fO4O7dux6fGxUVheeffx6VlZUBt4O2VIxGI5qbm3nHY6FduPDwcBQVFfFqVklJSX/87vrCCIpEgEIBfP65965JEERQ4Uwhy/53VwpZ9r/7Mpz6/v290Otr4FlOxkKIIJMpsHZtaNs/cjQws+BWVua7658504yYmN6gUXJy9gX3lZLTcqDT6VBbW4v6+voF6zhkZWVBoVBg8+bNQaVqtFJpa2vD6dOn0d3d7fG5ycnJOHr0KNatW7dinmV7OI6DVqvFo0ePoFKp0NLSAovF4vKcxMRElJWVYYNYjOJjx3zXOZUKKCnx3fUJYpmoq6vDP/3TP+H27dvo7+/H2bNncfjw4eXuVtAQCApZBoMKDQ2+mwRu3qxCVFTo2j9yNAD88IfAu+96dyHPBsNMo7S0Fjt3/j/vX3wROFNysv1bLBYvd/d8BsdxUKlUUCqVePjwoUuFLZFIhI0bN0IulyMnJ2dFTkqDGY7j0NDQgLNnz2J4eNjj8wsKClBVVYXVq1d7v3MBhMViQXt7O69mpdFo5m37p1euoFKlgtAXw4FIBLzyCvDWW96/NkEsMxcvXsSVK1ewceNGHD16lBwNL+JMIcv+1ZVClv3vCylktbX9EH1978K7uxk2RMjIeAUFBaFr/8jRwExx244O311fKtXim9/8r767gR32Sk6zYyCXW8lpOZicnMTVq1dRW1u7YK2C+Ph4VFRUYNeuXYiJifFTD4nFYrFY8OWXX+LixYswLSKvYMuWLTh8+HBgVuD2AXq9ng+xampqckiy/4c//AHJdooyXic/H2hr8931CSIAYBiGHA0/Mlshy35XxBOFrBs3CmAy+W4SKBbnY9u20LV/Ie9ojI8DsbGOxW69D4dvf/tHCAvzThK3vZLT7C9NfHz8iotDXww9PT1QKpW4cePGguEjJSUlUCgUWLNmTVAkrhOOjI+P48MPP8Tly5c9DkcUiUR45pln8Nxzz4VUcSqWZdHV1YXGxka03bmD1376U/h0345hgLExIAiquBPEYiFHI3BwVyFLIuFw6ND/gm8DFxjs2jUGkSg07V/IOxr37gHr1/v+PkeP/gMSE11Xk7ZnJSg5+Rur1Yq7d++ipqYGHQtsUYnFYuzYsQNyuRwpKSl+6iHhS/r7+3HmzBk8ePDA43NjYmJw4MABVFRUhN53y09GsP+TT2DxZTIcQSwz2dnZ+PWvf41nn312ubtCuMBisUCv10On02F09DaSk31f72LjxruIiVnn8/sEIiG/9O0vpViWnftRz6fklJycDKlUSrkBbqLX61FXV4f6+nqH7VJnZGRkQC6XY+vWrYiIiPBTDwl/kJaWhr/+679Gc3MzTp8+jd5e9x378fFx/P73v0dNTQ2OHTuGNWvWhM73z09G8Le//jW6kpP9ci+CWC6qq6tx69at5e4G4SYJCVrs3ev7+3BcYJUl8Ce0o3HPPzsaP/3peWzcKHSIE4yMjAydyYyX4TgOLS0tUCqVuH//vsuQGYFAgA0bNkAulyM/P58+8xCAZVlcv34d58+fx8jIiMfnFxcXo6qqCqtWrfJ+5wIN2tEgCK9AOxrBh9nciL6+/T6/TyjvaIS8ozExAUilvs3RoPBk72EymXD9+nUolUr09/e7bBsbG8snd3tUHZlYMZjNZly6dAmff/65x4UuGYbB9u3bcejQoZX9/JARJAivQDkawYfVOoH6eikAX06FQztHI+RDp6Kjgdxc36pO5eXR+LpU+vv7oVQqce3atQUnjIWFhZDL5Vi3bh2EQqGfekgEIhEREThw4AB2796NCxcu4MqVKy6lje3hOA5Xr15FQ0MD9u7di2effXZlhtuRESSIRTMxMYH29nb+32q1Gvfu3UN8fDyysrKWsWeEO4hE0RCLc32sOpUXsk4GQDsaAHxbR4Mk5BcPy7K4d+8eampqBPbRLAAAE1JJREFU0Nra6rJtREQEtm7dCoVCgfT0dD/1kAg2ent7UV1djaamJo/PlUqlOHToEHbs2LHyEsbJCBLEolAqlVAoFHP+/8UXX8Rvf/tb/3eI8Biqo+FbyNGA7yuDU1FczxgbG8Ply5dRV1e3YHx9SkoKFAoFtm3bFlLypMTisRVvrK6udlnEbj4yMjJQVVWF0tJSH/RumSAjSBBEiEKVwX0LORpfs3cvUFPj3QU9kQhQKIDPP/feNVcqHMeho6MDSqUSd+7cwfT09LxtGYbB2rVroVAoUFRURMndxKJgWRZXrlzB+fPnMT4+7vH5ZWVlqKqqWjk7aGQECYIIUe7f3wu9vgbe3dUQQSZTYO3a0LZ/5Gh8jVoNlJYCiygwPC9i8cxCXk6O96650jCbzbh58yaUSuWCcqQxMTHYtWsXKioqEB8f76ceEisdk8mEzz77DJcuXVqwuONsGIbBrl27cPDgQUilUh/10E+QESQIIkQxGtVoaCgFy3rP/gkEYmzerIJEEtr2jxwNO06dAl5+2bvXe+kl711vJaHVaqFUKnH16lUYjUaXbXNzcyGXy7Fx40aqek74DL1ej3PnzuH69esenxsREYHnnnsOzzzzDMLDw33QOz9BRpAgiBBFozmF1lbv2b+iolNISyP7R47GLN54A/jJT7xznR//eOnXWUmwLIuHDx9CqVRCpVK5bBsWFoYtW7ZALpeTcgfhVx4/fozTp08vKEDgDJlMhsOHD2Pr1q3BG9JHRpAgiBClu/sNqNVLt385OW8gO5vsH0COhlNOnQJefXUmVNmTcGWRaObnnXdoEc+eiYkJXLlyBbW1tdDpdC7bJiYmQi6XY8eOHYiKivJTDwnCEY7j8ODBA3zwwQcYGBjw+PysrCwcP34chYWFPuidHyAjSBBEiKLRnEJ7+6tgWSs8y9kQQSAQoaDgHdrJsIMcjXlQq4ETJ4BLl2bGTVdjre34nj3A++9TOLKNrq4u1NTU4NatW7C6+AAZhkF5eTnkcjnKysqCdyWYWHFMT0+jrq4OH374IQwGg8fnP/XUU6iqqkJKSooPeudjyAgSBBGiGI1qtLaegF5/CTMl51w5HDPHZbI9KCx8P+RzMmZDjsYCqFTAe+8BFy/O1LOy/7QYZqYO1b59MzLxpN4IWCwWNDQ0QKlUoru722XbqKgo7Ny5E5WVlUhMTPRTDwnCcyYnJ3Hx4kV89dVXLp1mZwgEAlRUVODAgQOIiYnxUQ99CBlBgiBCFINBBY3mPeh0F78u6mc/ZWYgFuchIWEf0tNfCWkJW1eQo+EBExNAeztgNgMREUB+PhW7tTE0NIS6ujrU19cvuPKbnZ0NuVyOzZs3IywszE89JIilo9PpcPbsWTQ0NHh8rlgsxv79+/H0008H73NPRpAgiBDFap2A0dgOjjODYSIgkeSHdMVvdyFHg1g0tsJnSqUSDx8+hKtHSSQSYdOmTZDL5Vi9ejWFRxFBjVqtxunTp9HR0eHxuQkJCThy5Ag2bdpE3wOCIAhiRUOOBuExk5OTuHr1Kmpra6HVal22jY+PR0VFBXbt2hWcYSMEMQ8cx+Hu3bv44IMPMDQ05PH5q1evxje+8Q3k5eX5oHcEQRAEsfyQo0G4TU9PD5RKJW7cuLFgYbOSkhIoFAqsWbMGAoHATz0kCP9jtVqhVCrx8ccfY3Jy0uPzN2zYgKNHjyIpKckHvSMIgiCI5YMcDcIlVqsVd+7cgVKpXDBMRCwW88ndQamyQxBLwGAw4OOPP4ZSqcT09LRH5wqFQigUCuzfv59knQmCIIgVAzkahFP0ej3q6upw+fJljI+Pu2ybkZEBuVyOrVu3IiIiwk89JIjARKvV4syZM7h7967H50ZGRuLAgQOorKyESCTyQe8IgiAIwn+Qo0HwcByHlpYWKJVK3L9/HyzLzttWIBBgw4YNkMvlyM/Pp6RWgphFW1sbqqur0dXV5fG5ycnJOHr0KNatW0ffLYIgCCJoIUeDgMlkwrVr11BbW4v+/n6XbWNjY1FRUYHdu3cjNjbWTz0kiOCE4zg0NDTg7NmzGB4e9vj8/Px8HD9+HKtXr/Z+5wiCIAjCx5CjEcJoNBrU1tbi2rVrMJvNLtsWFhZCLpdj3bp1EAqFfuohQawMLBYLvvzyS1y8eBEmk8nj8zdv3owjR44gISHBB70jCIIgCN9AjkaIMT09jfv376Ompgatra0u20ZERGDbtm2Qy+VIT0/3Uw8JYuUyPj6Ojz76CHV1dS5DE50hEonwJ3/yJ9i3bx8kEomPekgQBEEQ3oMcjRBhbGyMT+4eGRlx2TY1NRVyuRzbtm2jCQ1B+ID+/n6cOXMGDx488Pjc6OhovPDCC9i9ezftLhIEQRABDTkaKxiO49DR0QGlUonbt2+7XEFlGAbr1q2DXC5HUVERJaAShB9obm5GdXU1enp6PD43NTUVx44dw5o1a+j7ShAEQQQk5GisQMxmM27evAmlUone3l6XbWNiYrB7925UVFRAJpP5qYcEQdhgWRbXr1/H+fPnF9xtdEZRURGOHz+OVatWeb9zBEEQBLEEyNFYQQwMDKC2thZXr16F0Wh02TY3NxdyuRwbN24kvX6CCADMZjO++OILfPbZZwuKM8yGYRhs27YNhw8fRlxcnG86SBAEQRAeQo5GkMOyLB4+fAilUgmVSuWybVhYGLZs2QKFQkGrnwQRoIyOjuLChQu4cuUKPDXPYWFh2Lt3L5599lkqnkkQBEEsO+RoBCnj4+O4cuUK6urqoNPpXLZNSkqCXC7Hjh07EBkZ6aceEgSxFPr6+lBdXb3gAoIzpFIpDh48iJ07d0IgEPigdwRBEASxMORoBBldXV2oqanBrVu3YLVa523HMAzKy8shl8tRVlZGyaIEEaQ0NjaiuroaGo3G43PT09NRVVWFsrIyH/SMIAiCIFxDjkYQYLFY0NDQAKVSie7ubpdto6KisHPnTlRWViIxMdFPPSQIwpewLIsrV67gwoULGBsb8/j8srIyVFVVUT0cgiAIwq+QoxHADA0Noba2FleuXIHBYHDZNjs7G3K5HJs3b0ZYWJifekgQhD8xmUz47LPPcOnSJVgsFo/OZRgGu3btwsGDByGVSn3UQ4IgCIL4I+RoeMDEBNDeDpjNQEQEkJ8PREd79x4cx0GlUqGmpgaPHj1ymQwqEomwadMmyOVyrF69msKjCCJE0Ov1OHfuHK5fv+7xuREREXjuuefwzDPPIDw83LOT/WEECYIgAhCrdQJGYzs4zgyGiYBEkg+RiOzfQpCjsQAqFfDee8AnnwCdnYD9p8UwQG4usH8/8L3vAaWli7/P5OQkrl69itraWmi1Wpdt4+PjUVlZiZ07dyImJmbxNyUIIqh5/PgxTp8+jdbWVo/PjYuLw+HDh7Ft2zbXixT+MoIEQRABhsGggkbzHnS6T2AydQKwnzIzEItzkZCwH+np30NUFNk/Z5CjMQ9qNXDiBHDpEiASAS7yrvnje/YA778P5OS4f5+enh7U1NTg5s2bC4ZClJSUQKFQYM2aNaQkQxAEgJld0IcPH6K6uhoDAwMen79q1SocP34cRUVFjgf8ZQQJgiACDKNRjdbWE9DrLwEQAXBh/74+LpPtQWHh+5BIyP7ZQ46GE06dAl59dWbcdDW2zkYkmvl5+23gu9+dv53VasWdO3dQU1ODzs5Ol9eUSCTYsWMHKisrkZKS4n5nCIIIKaanp3H58mV8+OGHmJiY8Pj8p556CseOHUNqaqrvjSBBEESAotGcQnv7q2BZK1w7GLMRQSAQIT//baSnk/2zQY7GLN54A/jJT5Z+nZ/9DPi7v3P8P71ej7q6Oly+fBnj4+Muz8/MzIRcLseWLVuo8BZBEG5jNBpx8eJFfPnlly4lsJ0hEAjwveFhrP33f196R5wZQYIgiACmu/sNqNVLnwTm5PwM2dlk/wByNBw4dQp4+WXvXu873+HQ0tICpVKJ+/fvg2XZedsLBAJs2LABcrkc+fn5lNxNEMSi0el0OHv2LBoaGtw+Z2dzM/5jXZ33OnHqFPDSS967HkEQhI/QaE6htdV7k8CiolNISyP7R47G16jVM3mMJpO3rsghPJzF9773NkymJpct4+LisHv3buzevRuxsbHe6gBBEATUajVOnz6Njo4Ol+0Sxsbw09OnETY9Da8tcYjFM8nklLNBEEQAYzSq0dBQCpb12iQQAoEYmzerQj5ngzKKv+bECc9CkReGgcUCfPDBnnlbFBYW4sSJE/jv//2/48CBA+RkEAThdXJycvCf//N/xokTJ1wW8fyLy5chZFnvORnAjFE9ccKbVySIgKKurg4vvPAC0tPTwTAMzp07t9xdIhZBa+uJr3MyvAfLWtHaSvZPtNwdCARUqhlhFW/DcUL09ZVBr0+FTPYEwIyG/bZt2yCXy6lKL0EQfoFhGGzYsAFPPfUUlEolPv74Y0xOTvLH0/R6lPX1ef/GVuuMcW1qAkpKvH99glhmDAYD1q5di+985zs4evTocneHWAQGg+prdSlvY4VefwkGQxOiokLX/pGjgRmJ+IXUGxcLw0xDparEsWO1kMvl2L59O8RisfdvRBAEsQAikQjPPPMMtm/fjo8//hhKpRLT09OoUKkwzTAQ+iKSViQC3n0XeOst71+bIJaZffv2Yd++fcvdDWIJaDTvYWEJ28UigkbzLgoKQtf+kaOBmTpUvnAygJldjfHxXfjpTxWU3E0QREAQFRWFb3zjG5DL5Thz5gzK//AH3zgZwIxxvXjRN9cmCIJYIjrdJ/CNkwEAVuh0F1FQ4KPLBwEhn6MxPj5T7NaX9PSEw2AgJ4MgiMAiOTkZ3/uLv0DSAnLbS6ajA1hEbQ+CIAhfYrWOf13x23eYTB2wWkPX/oX8jkZHB+Br3S2OA9rbgXXrfHsfgiAIj+noAOMHI9h/+TIsZWW+vQ9BLDODg4N4/PjxcneDcBOzuRGAr8VXORiN7YiJWefj+wQmIe9omM0r6z4EQRAe4Sfj9Ntf/xpdycl+uRdBLBfV1dW4devWcneDcJOEBC327vX9fTgudCeBIe9o+KvoNhX3JggiIPGTcfrLv/or2tEgVjS//vWvUVVVhWeffXa5u0K4idnciL6+cz6/D8OE7iQw5B2N/HyAYXwbPsUwM/chCIIIOPxkBNN27waio313D4JYBiYmJtDe3u7w7+HhYcTHxyMrK2sZe0a4g9Uaj74+Br4Nn2IgkYTuJDDkk8Gjo4HcXN/eIy+PxleCIAIUMoIEsWhu3bqF9evXY/369QCA119/HevXr8d/+2//bZl7RriDSBQNsdi39k8szoNIFLr2L+QdDQDYv39G6t0XiEQASWwTBBHQkBEkiEUhl8vBcdycn9/+9rfL3TXCTRIS9sN3AT4iJCSEtv1jOM7XciOBj0oF+DJ0WKWiorgEQQQwZAQJgghRDAYVGhp8Z/82b1aFdGVw2tEAUFoK7Nnj/QU9kWjmujS+EgQR0JARJAgiRImKKoVMtgfe39UQQSbbE9JOBkA7Gjxq9cxYazJ575pi8cxCXk6O965JEAThE8gIEgQRohiNajQ0lIJlvWf/BAIxNm9WQSIJbftHOxpfk5MDvP22d6/5zjs0vhIEESSQESQIIkSRSHKQn+9d+1dQ8E7IOxkAORoOfPe7wM9+5p1rvfEG8NJL3rkWQRCEXyAjSBBEiJKe/l3k5HjH/uXkvIG0NLJ/AIVOOeXUKeDVVwGrdebHXUSimZ933qHxlSCIIIaMIEEQIYpGcwrt7a+CZa0APLB/EEEgEKGg4B1yMuwgR2Me1GrgxAng0qWZcdPVWGs7vmcP8P77FClAEMQKgIwgQRAhitGoRmvrCej1lzCTJO7K4Zg5LpPtQWHh+xQuNQtyNBZApQLeew+4eBHo6HAsnsswM3Wo9u0DXnmFhFUIgliBkBEkCCJEMRhU0Gjeg053ESZTBxwriDMQi/OQkLAP6emvhLy61HyQo+EBExNAeztgNgMREUB+PhW7JQgihCAjSBBEiGK1TsBobAfHmcEwEZBI8kO64re7kKNBEARBEARBEITXIdUpgiAIgiAIgiC8DjkaBEEQBEEQBEF4HXI0CIIgCIIgCILwOuRoEARBEARBEAThdcjRIAiCIAiCIAjC65CjQRAEQRAEQRCE1yFHgyAIgiAIgiAIr0OOBkEQBEEQBEEQXoccDYIgCIIgCIIgvA45GgRBEARBEARBeB1yNAiCIAiCIAiC8DrkaBAEQRAEQRAE4XXI0SAIgiAIgiAIwuuQo0EQBEEQBEEQhNchR4MgCIIgCIIgCK9DjgZBEARBEARBEF6HHA2CIAiCIAiCILwOORoEQRAEQRAEQXgdcjQIgiAIgiAIgvA65GgQBEEQBEEQBOF1yNEgCIIgCIIgCMLrkKNBEARBEARBEITXIUeDIAiCIAiCIAivQ44GQRAEQRAEQRBehxwNgiAIgiAIgiC8DjkaBEEQBEEQBEF4HXI0CIIgCIIgCILwOuRoEARBEARBEAThdcjRIAiCIAiCIAjC65CjQRAEQRAEQRCE1yFHgyAIgiAIgiAIr0OOBkEQBEEQBEEQXoccDYIgCIIgCIIgvA45GgRBEARBEARBeJ3/P+ybORx8l/VGAAAAAElFTkSuQmCC", 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" ] @@ -171,7 +187,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Build a Model to Solve the FJSP\n", + "## Build a Model to Solve the FJSP\n", "\n", "In the FJSP we typically encode Operations and Machines separately, since they pose different node types in a k-partite Graph. Therefore, the encoder for the FJSP returns two hidden representations, the first containing machine embeddings and the second containing operation embeddings:" ] @@ -208,8 +224,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([1, 5, 32])\n", - "torch.Size([1, 60, 32])\n" + "torch.Size([1, 60, 32])\n", + "torch.Size([1, 5, 32])\n" ] } ], @@ -235,7 +251,7 @@ { "data": { "text/plain": [ - "tensor([[ 0, 5, 10, 16, 20, 24, 29, 34, 40, 44]])" + "tensor([[ 0, 4, 9, 15, 21, 27, 31, 37, 41, 45]])" ] }, "execution_count": 8, @@ -250,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -270,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -286,7 +302,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Visualize solution construction\n", + "## Visualize solution construction\n", "\n", "Starting at $t=0$, the decoder uses the machine-operation embeddings of the encoder to decide which machine-**job**-combination to schedule next. Note, that due to the precedence relationship, the operations to be scheduled next are fixed per job. Therefore, it is sufficient to determine the next job to be scheduled, which significantly reduces the action space. \n", "\n", @@ -299,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -313,7 +329,7 @@ }, { "data": { - "image/png": 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+/fp66youLmbr1q18+umnLFmyhBMnThjVFRgYyKOPPkpGRgZLly5lyZIl/Pbbb+j1DR+xdu3a8cwzz5Cbe8SwzN7eD3t7X9LT5/DBB32ZM2cOXXy8KNbWGMo4KpWsjW6HHhixaTu9e/fG1taGIc/dDopL9bdq786hHWdY9+4ffPvBfpQ2Cv4yMRpb1aW3mq1KSebhfP744dS1HN7rVlOsofiHE2R/uI/sf+1Hk1GI12OR2Po6AqA9W0r+16lc+Ocf5C6v/dD3HhdltF/WysHWgdSCVN7e83addWpbNRFeEXx64FP+tulvvPDTC4S6hvLhPR9aINK6Good4FTxKd7Z8w4Pfvsgj/3wGGdLz/LpgE/xsPcwc6S3oD4vQLdx8P1L8FF32DoTek+CHs9YOjJxC7jmS7FBQUGsWbOGiopLiUFlZSWrV68mODjYpME1haenJy4uLmZrb9y4cXTu3PmG63G0UfJRZAhTjp2m6LIE4qKOzg6MD/Jh8tHMG26rqW1W1OjJqao2/JTW6K6p/h/zitmWX8Lx4lLS0tJYvXo1VVVVBAYGGspotVpKS0spLCwkKyuLysrKeutKTU0lLS2N/Px88vLy2L59u1FdgwYNYs+ePezatYucnBzy8vI4fPgwNTV19+silUrF5MmTeeqpp6iqutT7WVaWysFDz5Gbt53S0hxeeOEF3t9/GGdbJcr/T266uTkRpFYxKSWTlPwiDh06xNJ3v8I32IXA8EsflJs+PMDRXy+Qf76MvLOlbEtIwcVLjU+wq6HMn9vPsHfzKS6cME0PbGMqU/KpPFZAdV4l1bkVFG85hb6qBlVw7fum7LcLVJ0opqZAg/ZcGcVbTmLrrsbGQ22W+Cxp19ldfLjvQ7Znbq+zrlRbytM/Ps3mU5s5WXySP3P/5J0979DRuyP+Tv4WiNZYQ7EDfH/ie3af382Z0jNkFGYw//f5uKhcaO/R3syR3oKCusPR7yFtCxRmwpFvIOMnaN3V0pGJW8A1J3ZdunQhKCjIqKdl/fr1BAcHc8cddxiV/eGHH+jTpw/u7u54eXkxdOhQMjIyjMqcOXOG0aNH4+npiZOTEzExMezZs8eozKpVqwgNDcXNzY2HH36YkpISw7rY2FgmT55seB0aGso777xDXFwcLi4uBAcHs2TJEqP6Tp8+zahRo3B3d8fT05Phw4dz8uTJRvd98eLFFBYW8tJLLzVatjFzbwtka14xOwtK66xzUCpYHBnC9LQz5FSZ7hJdQ20CPOjnweHeUSR2C+eVtgE4KK+/y0apVNK7d2/s7Ow4c+bS5dxOnToxdepUFi5cyDvvvIOdnV2jdSkUCqKiogx1OTk5ERgYSFlZGePGjeOll15i7NixjX6xuP/++/njjz/Ytm3bVcv85S9z+e9//8uZ0nJq9KD7/w5AlVKBXg9Vuks9gtqqavR6PQFh7letz96hdrSDplx71TJmpQCHzj4oVDZUZZbUXW2nxDHGn+q8CmqKNBYIsGVzUbmg0+soqap77FoyW6Utf23/V4qrijlWcMzS4Vi/079B27vBq13ta78oCO4JaT9aNi5xS7iuMXZxcXGsWLGCRx55BIDly5fzxBNPkJiYaFSurKyMF198kc6dO1NaWsrrr7/OyJEj2b9/P0qlktLSUvr27Uvr1q359ttv8ff3Z+/eveh0l3qKMjIy2LhxI5s2baKgoIBRo0Yxd+5c3n67/ssPAAsWLODNN9/klVde4euvv2bChAn07duX8PBwtFotAwcOpFevXuzcuRNbW1veeustBg0axJ9//olKpaq3ziNHjjB79mz27NnD8ePHr+ewGQz3daeTiwOD/kitd/0bYa1JLipjc27xDbVzLW2uzyrgjKaKCxotkc4OvNo2gHaO9ow7dPKa2ungpGZT3F9Rj3sIjUbD2rVrycmpHY908OBBCgsLKSkpoaqqijFjxpCTk8M333xTb12+vr48+eST2NraUlVVZajrYq9dbGwsW7Zs4cKFC9x+++089thjfPzxx+Tn59epKyoqioCAAObOnXvV2P18h+Lt3Yn33nuR3UdTyaq6lIztLS6jXKfj1XatmJ1xDEdHRx4efz9KGyVOrvX/zaCAPg/dxrn0QvLPlTX1EDYLWz9HfJ+NRmGrRF9VQ96qI1RnXxr359QzALfBbVDa26DNLifns0NQY+qBADc3lVLFC11f4H8n/keZ1rLns6nuDryb+XfPR22rJqcih6e3PE2hptDSYVm/Xf8Eexd4/nfQ1YDSBra9CQe/snRk4hZwXXfFPvroo+zatYtTp05x6tQpkpKSePTRR+uUe/DBB3nggQcICwsjOjqa5cuXc/DgQY4cqR3ftHr1anJycti4cSN9+vQhLCyMUaNG0atXL0MdOp2OlStXEhUVxV133cWYMWMa7HGB2p6ZZ599lrCwMKZNm4a3t7dhgP7atWvR6XQsW7aMTp06ERERwYoVK8jMzKyTmF6k0WgYPXo08+fPb/LlZo1GQ3FxsdEPQGtnJ966rTXPHjmFRlf3g/M+L1f6eLjwWj03UlyvVvZ2DbYJ8Pn5PBLzSzhaVsn6rAL+kZLJEB93QtRXSVquIqNcQ+zXP9CjRw82b97MiBEjDOPi/vjjDzIyMsjOzmbnzp089thj3HHHHXh41D/mJy8vj08++YSlS5eSnJxsqEuhUBjq279/PxcuXGDz5s3k5eXV6TUGcHV1ZdCgQaxfvx6ttv6eM3v7ANq3f40NG15i/fr1HCso4nRl1aVYtDU8degk93m7khn3EEVFRTg6q8k+VXzVcX19H26PZ2sntiw7fE3HsDlU51aQtWgv2R/vp3T3eTweCjeMsQMo35dN9qK9ZH96gOrcCrz+3gFsb4FBdk1kq7Dlvdj3AHhzt/nG9N6o5AvJ/PW7vzLm+zEknU3ivb7v4an2tHRY1q/jA9DpIVj3JHx6N2wYD3f+A24fbenIxC3gunrsfHx8GDJkCCtXrkSv1zNkyBC8vb3rlEtLS+P1119nz5495ObmGnriMjMziYqKYv/+/dxxxx14el79H01oaKjRGLqAgACys7MbjO/yMXAKhQJ/f3/DNgcOHCA9Pb3OuLzKyso6l4kvmj59OhEREfUmr1czZ84c3njjjTrLb/fzxkdlx48x4YZltkoFPd2diGvtTcK5XEIdVKT26WS03WdRoewpLGPw1iaHYNDZxbHBNoN3HODK0XT7imt7c9o42nPqsgSnMVq9nhPFpeTv3csXX3xBdHQ0PXr0YNOmTXXKXrzk7unpSUFBQZ31NTU1ht638+fP07p1a3r06MGuXbsADD2BF+Xk5ODm5lannlatWuHs7MwzzzzD008/jV6vx9bWFr2+B4Gtx/BTYgQuLlGoVN6MH/9f9Ho9OsBWqaSH26VjtKOghJ67U3DJzCBn0jhGdHmWjzfMJD237jjBux5uT0gnbzYs2EtZYQu4pFmjpyavkhpqb5ZQBTrj3LsVhRtq7zLWa2qo1tRAXiV5mSm0mtkLh47eVByw/N2flnYxqWvl1IpxW8bdNL11ABXVFZwuOc3pktP8mfsnm0ZuYmTYSD479JmlQ7NuA2bDroVwaF3t6+wj4B4Ed70IB/5j2diE1bvu6U7i4uJ4/vnnAfjoo4/qLTNs2DBCQkJYunQprVq1QqfTERUVRVVVbaLg4ODQaDtXjsFSKBRGl2qvdZvS0lK6du3KF198UWe7y+/evNz27ds5ePAgX3/9NYChh8bb25sZM2bUm8BNnz6dF1980fC6uLiYoKAgfs48R+xvR43Kvt8hmLTySj7KzCZPW82qc3lG6xO7d+D1tLP8mHd9l2Z3FpQ02GZ9R7OjS+25ydLc2NgwhUKBrW39f2bR0dFA7Tm5lroKCwspLi7Gy8vLaL2Xl5fRdCgXHT9+nI8//hiovSlj69atbN/+OTW6E5w6tQTQUVx8gJKSIxQWahg58insp87mo0Gx9R6j/MoqioqKiLijLQ4uKk78mWvU3l0Pt6dttA8b/7mXkrz6bw6xOKUChW3DHfYK6bEzJHXBLsGM2zyOIo15bnhpLkqFEpXNtfXCi+tg5whX9uTrdLXToAjRzK47sRs0aBBVVVUoFAoGDhxYZ31eXh7Hjh1j6dKl3HXXXQCGnpaLOnfuzLJly8jPz2+w186UunTpwtq1a/H19cXV1bXxDYB169YZ3QWcnJxMXFwcO3fupF27dvVuY29vj729fZ3lpVotR8uMP+zLa3QUaGsMy+u7YeKsRkvmNfScXa6sRtdgmyFqFQ/4ebAtr5iC6hoinNTMvq01vxaWklLW9MTklbYBbM8r5qSzE62ionjkkUcIDQ1l1apVeHh40KlTJ9LS0qioqCAmJoZRo0aRmppKVlZWnbr69+9Peno6RUVFqFQqOnXqZKgL4JdffiE2NpasrCzDGDtvb2++/PLLOnVVVVUZemxPnz7N4cOHqa6uoLqmkLKyVGxsnOnc6WNQKFm79hnOnDmDR6UGrV5P4WXn5WF/T1LLK8lydWbwI4/w3Ot/58C200Zz1N09uj3tu/nx/eKDaCtrcPz/8XeaimpqtLXpoaOrCkdXFW4+tcmzV2sntJU1lORXoik3/Xx2rgNDqUzNp6ZQg0Jlg2O0L/Zt3MhdfggbTzWOnb2pTCtEV6rFxk2FS2wQeq2OyqN1e1GtjYOtA8Eul4ZXtHZpTbhHOEVVReSW5/LP2H8S4RXBc9ueQ6lQ4qWu/TJRVFVEta755h5sioZiL9IU8VSnp0g8nUhORQ4e9h483OFhfB192XJqi+WCvlWk/g/ungJFpyHnKPh3hl7Pwb7PLR2ZuAVcd2JnY2NDSkqK4fcreXh44OXlxZIlSwgICCAzM5OXX37ZqMzo0aN55513GDFiBHPmzCEgIIB9+/bRqlUro3F2pvTII48wf/58hg8fzuzZswkMDOTUqVOsX7+eqVOnGk3NcdGVyVtubm0PTUREhGEC3ZuZVq/nbk8XngrywVGp5JxGy39zCll4sm7C1RBvO1s+jAjB9/a2FA3oaZgw+Pjx47i6utK2bVt69uyJSqUiJyeHVatWcfbs2XrH2Dk5OTFy5EicnZ3RaDRkZWUZ6gLYvXs3tra2DBw4EAcHB8P6+i7pNsbFpSNubrVj81566Tcuv+k5vfxSYtvO0Z5X2gbgHt2Ok13D+e6Ln8j/07jXuVPf2r+fkVO6GC3flnCEo79eAKDj3a3pPrSNYd0DL3WtU8aUbJzt8BwVjo2LCl1lNdrzZeQuP4QmvRCliwpVqBvOvVujdLClplRL1YkichYfQFfWQu7kbUYdvTqyYtAKw+up3aYC8E36N3y8/2P6BfcDYN1f1hlt98QPT/B71u/mC7QeDcU++9fZtHFrw1/C/oKHvQeFmkIO5x7m8f89TkZh/UNOhAl9PxXumQFDFoCTT+0ExX+sgB3vWjoycQu4oSdPNNTjpVQqWbNmDRMnTiQqKorw8HAWLVpEbGysoYxKpWLLli1MmTKF+++/n+rqaiIjI696adcUHB0d+fnnn5k2bRoPPPAAJSUltG7dmv79+ze5B8/UHthf9/Lh5fx/2t+sbZ7TaBm5r+EYmuLFY6cB0KamkD/+7zzwwAN06lQ7VrC4uJiVK1cayh48eJD169fz9NNP11vXt99+22h7u3btqtML3FTffvsQt7Wv7VEtLNzDtu21yfu2rSXMmZOD5yersWsfYbTN28fP8/bx84b9e7zfdLq1v9eozEfj659T7HLJm06QvOnEdcV9PQrWpV11na6kiryVlr+5w1J+z/qdTgmdrrq+oXWW1ljsLyS+YMZohJGqUvhheu2PEGZ2TYnd5R/M9dm4caPR63vvvddwB+xFV95BGBISYhi7dqVZs2Yxa9Yso2WTJ082mrfuyjtZ65uP7spHkvn7+5OQkFBvm00RGxvb6BMOhBBCCCHMTUZyCiGEEEJYCUnshBBCCCGshCR2QgghhBBWQhI7IYQQQggrIYmdEEIIIYSVkMROCCGEEMJKSGInhBBCCGElJLETQgghhLASktgJIYQQQlgJSeyEEEIIIayEJHZCCCGEEFZCEjshhBBCCCshiZ0QQgghhJWQxE4IIYQQwkpIYieEEEIIYSVsLR3Arab6zCkUDo7XvX3NhbO19WSeMFVIJncxtoKCAs6fP19vmYKCAgBycnLMFtfl7WZmVtW7/sIFLdDw8b24Lq/0AqdzUk0cobG80gsApOedatZ2moOpY9ac05i0vpaoKrf277Il7GtLiKE5peToLB1Cs7DW/RJNp9Dr9XpLB3ErKC4uxs3NzTSVKZWga+Fv3ibEqFAosMSfX6OhNeX4mvEcKBVKdPoWfr6vwtHBkZSjKQQHB193HZmZmYR3CKeyotKEkbVgCqCF/FdWO6g5dvTYDZ2/liYzM5OIDuGUW/Hfk6ODmpRmPm8XP9OKiopwdXVttnbEtZMeOzPbsWMHzs7ON1SHRqPB3t7eRBE1j6bEaKn9aKzdlhb7zXC+r8bb2/uGP1yCg4M5dvQYubm5JoqqZWtJ59sU56+lCQ4OJsXK/56s8byJppMeOzORbzdCCCGshXymtVxy84QQQgghhJWQxE4IIYQQwkpIYieEEEIIYSUksRNCCCGEsBKS2AkhhBBCWAlJ7IQQQgghrIQkdkIIIYQQVkISOyGEEEIIKyGJnRBCCCGElZDETgghhBDCSkhiJ4QQQghhJSSxE0IIIYSwEpLYCSGEEEJYCUnshBBCCCGshCR2QgghhBBWQhI7IYQQQggrIYmdEEIIIYSVkMROCCGEEMJKSGInhBBCCGElbC0dgBBCCCGsU01NDVqt1tJh3NTs7OywsbFpcnlJ7IQQQghhUnq9ngsXLlBYWGjpUKyCu7s7/v7+KBSKRstKYieEEEIIk7qY1Pn6+uLo6NikhETUpdfrKS8vJzs7G4CAgIBGt5HETgghhBAmU1NTY0jqvLy8LB3OTc/BwQGA7OxsfH19G70sKzdPCCGEEMJkLo6pc3R0tHAk1uPisWzKeEVJ7IQQQghhcnL51XSu5VhKYieEEEIIcY0SExNRKBQt7gYRGWMnhBBCCLM4W1hBQVmV2drzcFLR2t2hyeXHjh1LYWEhGzdubLaYKisrmTJlCmvWrEGj0TBw4EA+/vhj/Pz8TFK/JHZmtn//fpydnc3apkajwd7e3qxtNgdz7UdLPl4tOTZTa6n72lLjuhHe3t4EBwdbOoxbRmZmJrm5uU0uby3n52xhBfe8l4imWme2Nu1tlWx/Kfaakrvm9sILL/Df//6Xr776Cjc3N55//nkeeOABkpKSTFK/JHZm1rdvX7O3qVQo0enN90ZqLubaD4VCgV6vb/Z2rkdLjs3UWuq+KhRK9Fbwfrqco1pNyrFjVpE8tHSZmZmEdwinsqKyyduoHdQcO3rzn5+CsiqzJnUAmmodBWVV15XYaTQa4uPjWbNmDcXFxcTExLBw4UK6detmVC4pKYnp06eTmppKdHQ0y5YtIyoqqt46i4qK+Oyzz1i9ejX33HMPACtWrCAiIoLdu3fTs2fPa9/JK0hiZ2aj736RYO/bzNbe4dO/sSl5BYuGvkqYV4jZ2jW19LxTTNz0Fm53PYpD25hma6fi+O8U7fyckSNH4uPj02ztXI+0tDR++umnFhmbqeXk5LBhwwaGdnuCjkHdLR2OwYXCTBK2z2Gitzd3O5m35725ZFRpmHb+PLm5uTd94nAzyM3NpbKiksCnA7Fv1XjPr+achjNLzsj5sYCpU6eybt06EhISCAkJYd68eQwcOJD09HQ8PT0N5eLj4/nggw/w9/fnlVdeYdiwYaSmpmJnZ1enzj/++AOtVsu9995rWNahQweCg4P59ddfJbG7Gfm5BRLk095s7V0oyAQgzCuETv7hZmu3udi6+WHvH9Zs9WvzTgPg4+PTpIkgzenipZuWGFtz8XL2N+v7pakCbe2IVKstHYa4idm3sschtOVcHhTGysrKWLx4MStXrmTw4MEALF26lB9//JHPPvuM+Ph4Q9mZM2cyYMAAABISEggMDGTDhg2MGjWqTr0XLlxApVLh7u5utNzPz48LFy6YJHa5K1YIIYQQ4jIZGRlotVp69+5tWGZnZ0f37t1JSUkxKturVy/D756enoSHh9cpY06S2AkhhBBCmIG/vz9VVVV1pkjJysrC39/fJG1IYieEEEIIcZl27dqhUqmM7lTVarUkJycTGRlpVHb37t2G3wsKCkhNTSUiIqLeert27YqdnR3btm0zLDt27BiZmZlGPX83QsbYCSGEEEJcxsnJiQkTJhAfH4+npyfBwcHMmzeP8vJyxo0bZ1R29uzZeHl54efnx4wZM/D29mbEiBH11uvm5sa4ceN48cUX8fT0xNXVlX/84x/06tXLJDdOgCR2QgghhBAA6HQ6bG1rU6O5c+ei0+kYM2YMJSUlxMTEsHnzZjw8PIy2mTt3LpMmTSItLY3o6Gi+++47VCrVVdtYuHAhSqWSBx980GiCYlORxE4IIYQQzc7DSYW9rdLsExR7OF09ybpSdnY2YWG1My+o1WoWLVrEokWL6i0bGxtrmGtz6NChTW5DrVbz0Ucf8dFHHzV5m2shiZ0QQgghml1rdwe2vxTbIh8pVlBQQFJSEomJiYwfP94MkTUfSeyEEEIIYRat3R1a1OO9LoqLiyM5OZkpU6YwfPhwS4dzQySxE0IIIcQtbcOGDZYOwWRkuhMhhBBCCCshiZ0QQgghhJWQxE4IIYQQwkpIYieEEEIIYSUksRNCCCGEsBIt+q7YWbNmsXHjRvbv33/VMrGxsURHR/P++++bLa7m0GVgCG3v8MHD35HqKh0Xjhfx64YMCrPKDWVsbJX0/msYt8X4YWOrIPNIPjv+c4yKEm2zx+fUIwCnngHYetgDoM0qp2RbJpWpBbUFbBW4D2mLQ2cfFLZKKtMKKNyYjq60+WO7mjE9Q3imb1t8nO1JOV/MzG8Pc+BMkcnbiYmJoVu3bri7uwO1E1zu2LGD9PR03N3dmTx5cr3bffnllxw5csTk8VyPhvYBwNnZmQEDBhien5iXl8fPP/9MSkqKBaO+pCnvn9i/hxMY4YmTmwqtpoYLx4v4Zb1xGUtwiInBa1wc6o4dsfP15fRzz1N62XMkFY6O+E55EZf+/bFxd0d75gz5qz6ncO1aC0YtrpezszPffvst3WO74+fix6Ttk9h+erth/Vu932J4mPF0Gz/0+MHcYYqb2DX12I0dOxaFQlHv5H3PPfccCoWCsWPHmiq2Jlm/fj1vvvlms7aRl5fHoEGDaNWqFfb29gQFBfH8889TXFxssjZatXfn0I4zrHv3D779YD9KGwV/mRiNrerSKerzUBihnb35YekhNvxzH07u9gwe38lkMTSkplhD8Q8nyP5wH9n/2o8moxCvxyKx9XUEwH1oO9QRnuSvTiFnyZ/YuKjwerT+hyCbw9DOAbw6NIIPtqYx5MNdHDlfwr/H9cDrGmYgb6ri4mK2bt3Kp59+ypIlSzhx4gSjR4/Gx8eHoqIi3nvvPaOfn376CY1GY0iaWoKG9gFg5MiReHt785///IfFixeTkpLCQw89hL+/v4Ujr9WU9092ZgnbElJY/cYevl20H4C/TIpGobBQ0P9P6eCA5ugxsmbX/3/M7+VpOPfpw7mpUzk+ZAj5//43/q+9inO/fmaOVJiCUqnkwIEDzPp+1lXL7Dqzi9i1scSujaXnez0ZPXq0+QIUTZaYmIhCoaCwsNDSoRi55h67oKAg1qxZw8KFC3FwqJ1ksLKyktWrVxMcHGzyABvj6enZ7G0olUqGDx/OW2+9hY+PD+np6Tz33HPk5+ezevVqk7Sx6cMDRq+3JaQw7r278Al25Xx6ISq1DRG9W/Hj8sOcPVZgKPPIGz3xa+NK1gnTJZn1qUzJN3pdvOUUzj0DUAW7UFOkwSnGj/w1x9Bk1PaIFXydiv+UGFRBLlSdLmnW2OrzZJ82rPntNF/9cQaAGRsPck8HX0bFBLF4R4ZJ20pNTTV6vX37drp160ZgYCA5OTmUlpYare/QoQOHDx+mqsp8s683prF9CAoKYtOmTZw9exaAn3/+mZ49e9KqVSsuXLhgiZCNNPb+ATiy65xhfUke7Pn2OA+/1gMXLweKcyvMGa6Rsp07Kdu586rrHaLvoGjjN5T/lgxA4Zdf4f63v+HQuTOlP/1krjCFiRQXF/Paa6/Rbla7q5ap0lWRV5kHQEVZRYtLHG5I4WkozzNfe45e4B7U5OJjx46lsLCQjRs3NltIS5YsYfXq1ezdu5eSkhIKCgoMV0tM4ZoTuy5dupCRkcH69et55JFHgNpes+DgYNq0aWNU9ocffuCtt97i0KFD2NjY0KtXLz744APatbv0B33mzBni4+PZvHkzGo2GiIgIPvroI3r06GEos2rVKl577TUKCgoYPHgwS5cuxcXFBah7KTY0NJSnn36a9PR0vvrqKzw8PHj11Vd5+umnDfWdPn2aKVOmsGXLFpRKJXfddRcffPABoaGh9e6zh4cHEyZMMLwOCQnh2WefZf78+dd6+JrM3qH21GjKay9l+oS4YmOr5HRKgaFMYVY5JXmV+Ld1a/bEzogCHDr5oFDZUJVZgirQufbya/ql2KpzKqguqEQVYv7Ezs5GQVRrNz5OvJTA6fWQlJ5LlxD3Zm1boVDQsWNH7OzsOHPmTJ31AQEBBAQE8P333zdrHDeivn04ffo0UVFRpKWlUVlZSceOHbG1teXkyZOWDfYqrnz/XMlWpaTDnQEU5VRQWlBpztCuWcX+fTjf04/Cdeuozs7GsUd3VKGhZM2Za+nQRDOJ8Y8hcVQixVXF/JL2CxMXTbR0SKZReBr+1RWqNeZr09Yenv/jmpK75lZeXs6gQYMYNGgQ06dPN3n91zXGLi4ujhUrVhgSu+XLl/PEE0+QmJhoVK6srIwXX3yRzp07U1payuuvv87IkSPZv38/SqWS0tJS+vbtS+vWrfn222/x9/dn79696HSXHhCckZHBxo0b2bRpEwUFBYwaNYq5c+fy9ttvXzW+BQsW8Oabb/LKK6/w9ddfM2HCBPr27Ut4eDharZaBAwfSq1cvdu7cia2tLW+99RaDBg3izz//RKVq/FLduXPnWL9+PX379r2ew9c4BfR56DbOpReSf64MAEdXFTVaHVUV1UZFy0uqcHQ1/eXF+tj6OeL7bDQKWyX6qhryVh2hOrscuwAf9NU69JU1RuV1pVpsnM0T2+U8HFXY2ijJLTX+55FTqqGdj1OztOnr68uTTz6Jra0tVVVVrF27lpycnDrlunTpQk5ODqdPn26WOG5EQ/vw1Vdf8de//pVp06ZRU1ODVqtl7dq15OfnN1KrBdTz/rkoqm9r7hzZDju1LQUXyvj2g/3oavQWCrRpst58C/83Z3PbzzvQa7Xo9XouvPY6Fb//bunQRDPYdXYXWzO3crbkLEEuQfyj0z/43//+Z+mwTKM8z7xJHdS2V553XYmdRqMhPj6eNWvWUFxcTExMDAsXLqRbt25G5ZKSkpg+fTqpqalER0ezbNkyoqKirlrvxXHXV+ZMpnJdid2jjz7K9OnTOXXqFFC7U2vWrKkT5IMPPmj0evny5fj4+HDkyBGioqJYvXo1OTk5JCcnGy6phoWFGW2j0+lYuXKloYduzJgxbNu2rcHE7v777+fZZ58FYNq0aSxcuJCffvqJ8PBw1q5di06nY9myZSj+f3DNihUrcHd3JzExkfvuu++q9Y4ePZpvvvmGiooKhg0bxrJly65aVqPRoNFc+gO+lvF4fR9uj2drJ9bP39vkbcyhOreCrEV7UaptcYjyxuOhcHKW/GnpsFqEvLw8PvnkE+zt7YmMjGTEiBGsXLnSKLmztbWlU6dO7Nixw4KRXl1D+9CvXz/UajUJCQmUl5fToUMHHnroIZYvX052dralQzfS0Psndc8FTqfk4+hqzx0Dghj4VEfWz99LTbWunppaBo8xj+Jw++2cnjAB7dlzOHaLwe/119BmZ1P+66+WDk+Y2A8nL90okVaYxp8H/+SnST+RlpZmwahuTVOnTmXdunUkJCQQEhLCvHnzGDhwIOnp6UbDwOLj4/nggw/w9/fnlVdeYdiwYaSmpmJnZ2eRuK9ruhMfHx+GDBnCypUrWbFiBUOGDMHb27tOubS0NEaPHk3btm1xdXU1XOrMzMwEYP/+/dxxxx0NjpMLDQ01JHVQeymrsQ+Szp07G35XKBT4+/sbtjlw4ADp6em4uLjg7OyMs7Mznp6eVFZWkpHR8NirhQsXsnfvXr755hsyMjJ48cUXr1p2zpw5uLm5GX6Cgpr2beGuh9sT0smbjf/cR1nhpcSwvLgKGzslKgfjXNzRRUV5sZnGatXoqcmrRHu2lOLNJ9GeL8W5dyt0pVUobJUo1DZGxZXOdtSUmn8cWUF5FdU1Oryd7Y2W+zjbk1PaPN8Wa2pqyM/P5/z582zbto2srCyj4QQAkZGR2NnZceDAgavUYllX2wcPDw969OjBN998w4kTJ8jKymLHjh2cO3eO7t27WzpsI1d7/1xUVVlDUXYF59ML+WHJITz8nWgb7WOBSJtGYW+P7+TJZM99l9KfEtGkplLwxWpKvv8fXnFPWDo8YQanC0+Tk5ODvb1944WFyZSVlbF48WLmz5/P4MGDiYyMZOnSpTg4OPDZZ58ZlZ05cyYDBgygU6dOJCQkkJWVZdFnz173dCdxcXE8//zzAHz00Uf1lhk2bBghISEsXbqUVq1aodPpiIqKMgwav3jzRUOuzHgVCoXRpdpr3aa0tJSuXbvyxRdf1Nnu4h2AV+Pv74+/vz8dOnTA09OTu+66i9dee42AgIA6ZadPn26U+BUXFzea3N31cHvaRvuw8Z97KckzHveTc6qYmmodgR08OL6vthfI3c8RFy81F46bfgqPJlEqUNgqqTpTir5ahzrMnYpDtYNibb0dsPVQU3XK/DdOaGv0HDpbxJ1h3mw5kgWAQgF3hnnx719OmSUGhUKBra3x26tLly4cO3aM8nLLTq/RVBf34eL7Sa83vmSp0+kMvd4tQUPvn3opan9s7FrOPlxJYWuLQqVCf8X/PL2uBpQyDemtwN/FHy8vL06cOGHpUG4pGRkZaLVaevfubVhmZ2dH9+7d60zz1KtXL8Pvnp6ehIeHW3QqqOtO7AYNGkRVVRUKhYKBAwfWWZ+Xl8exY8dYunQpd911FwC7du0yKtO5c2eWLVtGfn6+We5uhdoP17Vr1+Lr64urq+t113MxUbz8cuvl7O3tr+kb1t2j29O+mx/fLz6ItrLGMG5OU1FdO7ausoaUpHP0+ettaMq0VFXWcNff2nM+o8gsN064DgylMjWfmkINCpUNjtG+2LdxI3f5IfSaGsp+z8JtSFt05dXoNDW4/6UdmlPFFrkjFmDZrhMseOh2Dp4pZP/pIsb1CcVRZctXf5h+bFv//v1JT0+nqKgIlUpFp06dCA0NZdWqVYYynp6ehISE1PuFoiVoaB9yc3PJy8tj2LBhbNmyxXAptl27dia7K/xGNfb+cfVWE9bVj9Mp+VSUVOHsYU+XgSHUVOk4dciMd+jVQ+HoiOqyGQVUgYHYd+hATVER1efPU/bbb/jGx5Olqay9FNu9G27Dh5M1910LRi2ul1Kp5PbbbyfIr/aLfmuX1oR7hFNUVUSRpogJt09g66mt5FbkEuQSxOQBk0lPT6esrKyRmoWodd2JnY2NjSEjtbGxqbPew8MDLy8vlixZQkBAAJmZmbz88stGZUaPHs0777zDiBEjmDNnDgEBAezbt49WrVoZZcCm9MgjjzB//nyGDx/O7NmzCQwM5NSpU6xfv56pU6cSGBhYZ5vvv/+erKwsunXrhrOzM4cPHyY+Pp7evXtf9U7aa9Wpb227I6d0MVq+LeEIR3+tnU5i11fp6PUw6JlO2NgqyTySx8//Sa1TV3OwcbbDc1Q4Ni4qdJXVaM+Xkbv8EJr/n0qicFMG7vq2tXPX2SrRpBZQsNFy87Rt+vM8nk4qXhjQHh8Xe1LOFfP48t/IbYZLw05OTowcORJnZ2c0Gg1ZWVmsWrWK48ePG8rccccdFBcXN3q531Ia24cvvviCe++9l9GjR6NSqcjPz2fDhg0tZtxPY++faq2OVre5cXv/IOwdbSkvruJ8eiHr5v9hlgm+G+IQ1ZGQf//b8Npveu3/ycINGzg//RXOvjgF3xdfoNX8+di4uaE9d46c99+ncM0aS4UsboCjo6PRpPtTu00F4Jv0b3hz95u092jPX9r9BVeVK9kV2ew8tpNJ90+ynhsobhIXJ2NPSkoiJCQEAK1WS3Jycp1J53fv3m2Y7q2goIDU1FQiIiw3j+sNPXmioR4vpVLJmjVrmDhxIlFRUYSHh7No0SJiY2MNZVQqFVu2bGHKlCncf//9VFdXExkZedVLu6bg6OjIzz//zLRp03jggQcoKSmhdevW9O/f/6r74+DgwNKlS3nhhRfQaDQEBQXxwAMP1ElUb8RH47c3WqamWsfPa1L5eY15krnLFaxr5AO8Wk/hNxkUftNyEpd//3qKf//a/Jdev/3220bLbNu2jW2XPU2gpWlsH/Lz8/nyyy/NFM21a+z9U15UxaZ/tcwbfcp/Syalw9U/BGpyczn/ygwzRiSaU2lpKQqFgnaz2uEQWnc40vitxg8AqDhZ0eJuULoVODk5MWHCBOLj4/H09CQ4OJh58+ZRXl7OuHHjjMrOnj0bLy8v/Pz8mDFjBt7e3owYMeKqdV+4cIELFy4YJqk/ePAgLi4uBAcHm+Tq5TUlditXrmxw/ZUT+t177711Hpl05TidkJAQvv7663rrmzVrFrNmzTJaNnnyZKNs+co7ceubV+vKR5L5+/uTkJBQb5v16devH7/88kuTywshhBDi5qPT6Qzjo+fOnYtOp2PMmDGUlJQQExPD5s2b8fDwMNpm7ty5TJo0ibS0NKKjo/nuu+8anDrtk08+4Y033jC8vvvuu4HaGTpM8fSuFv2sWCGEEEJYCUev2gmDzT1BsaNXk4tnZ2cbpl1Tq9UsWrSIRYsW1Vs2NjbW0Fk1dOjQJrdRX6eVKUliJ4QQQojm5x5U+xSIFvhIsYKCApKSkkhMTGT8+PGNlm/JJLETQgghhHm4B7Wox3tdFBcXR3JyMlOmTGH48OGWDueGSGInhBBCiFuaJScUNjWZ4VIIIYQQwkpIYieEEEIIYSUksRNCCCGEsBKS2AkhhBBCWAlJ7IQQQgghrIQkdkIIIYQQVkKmOxFCCCGEWZwvPU+BpsBs7XnYexDgHNAsdScmJtKvXz8KCgpwd3dvljauhyR2QgghhGh250vPM3TjUKpqqszWpspGxaYRm5qc3I0dO5bCwkI2btzYLPHk5+czc+ZMtmzZQmZmJj4+PowYMYI333wTNzc3k7QhiZ0QQgghml2BpsCsSR1AVU0VBZqCZuu1u1bnzp3j3LlzvPfee0RGRnLq1CnGjx/PuXPn+Prrr03ShoyxE0IIIYS4gkajYeLEifj6+qJWq+nTpw/Jycl1yiUlJdG5c2fUajU9e/bk0KFDV60zKiqKdevWMWzYMNq1a8c999zD22+/zXfffUd1dbVJ4pbETgghhBDiClOnTmXdunUkJCSwd+9ewsLCGDhwIPn5+Ubl4uPjWbBgAcnJyfj4+DBs2DC0Wm2T2ykqKsLV1RVbW9NcRJVLsWaWVXQGezsHs7WXV3oBgPS8U2ZrszlcjL+6KAvNhfRma6e6KAuAnJycZmvjehUU1A44bomxmdrFfcwrvcDpnFQLR3PJhcJMAM5UazlSWWnhaEwjo0pj6RBuSZpzTTvuTS0nTKusrIzFixezcuVKBg8eDMDSpUv58ccf+eyzz4iPjzeUnTlzJgMGDAAgISGBwMBANmzYwKhRoxptJzc3lzfffJOnn37aZLFLYmdm//n5n2ZvU6lQMnHTW2Zv19SUCiVFOz+naOfnzdqOQqFosQ+EbsmxmZpCoWBT8go2Ja+wdChGFAoli3JzWZSba+lQTMZRrcbb29vSYdwSvL29UTuoObPkTJO3UTvI+TG3jIwMtFotvXv3Niyzs7Oje/fupKSkGJXt1auX4XdPT0/Cw8PrlKlPcXExQ4YMITIyklmzZpksdknszGzHjh04OzubtU2NRoO9vb1Z22wO5tqPlny8WnJsptZS97WlxnUjvL29CQ4OtnQYt4Tg4GCOHT1G7jV8MZDzY31KSkoYNGgQLi4ubNiwATs7O5PVLYmdmUVHR+Pq6mrpMIQQQlhIcHCwJGotXLt27VCpVCQlJRESEgKAVqslOTmZyZMnG5XdvXu34XwWFBSQmppKRETEVesuLi5m4MCB2Nvb8+2336JWq00auyR2QgghhBCXcXJyYsKECcTHx+Pp6UlwcDDz5s2jvLyccePGGZWdPXs2Xl5e+Pn5MWPGDLy9vRkxYkS99RYXF3PfffdRXl7O559/TnFxMcXFxQD4+PhgY2Nzw7FLYieEEEIIAeh0OsPdqXPnzkWn0zFmzBhKSkqIiYlh8+bNeHh4GG0zd+5cJk2aRFpaGtHR0Xz33XeoVKp669+7dy979uwBICwszGjdiRMnCA0NveF9kMROCCGEEM3Ow94DlY3K7E+e8LD3aLzg/8vOzjYkXGq1mkWLFrFo0aJ6y8bGxqLX6wEYOnRok+q/fJvmIomdEEIIIZpdgHMAm0ZsapHPii0oKCApKYnExETGjx9vhsiajyR2QgghhDCLAOeAFvN4r8vFxcWRnJzMlClTGD58uKXDuSGS2AkhhBDilmZN84PKI8WEEEIIIayEJHZCCCGEEFZCEjshhBBCCCshiZ0QQgghhJWQxE4IIYQQwkpIYieEEEIIYSVkuhMhhBBCmIX23DmqC8w3QbGthwd2rVo1S92JiYn069ePgoIC3N3dm6WN6yGJnRBCCCGanfbcOTIGDUZfZb5HiilUKtr98L8mJ3djx46lsLCQjRs3NltMzzzzDFu3buXcuXM4Oztz55138u6779KhQweT1C+XYoUQQgjR7KoLCsya1AHoq6rM2kPYFF27dmXFihWkpKSwefNm9Ho99913HzU1NSapXxI7IYQQQograDQaJk6ciK+vL2q1mj59+pCcnFynXFJSEp07d0atVtOzZ08OHTrUYL1PP/00d999N6GhoXTp0oW33nqL06dPc/LkSZPELYmdEEIIIcQVpk6dyrp160hISGDv3r2EhYUxcOBA8vPzjcrFx8ezYMECkpOT8fHxYdiwYWi12ia1UVZWxooVK2jTpg1BQUEmiVsSOyGEEEKIy5SVlbF48WLmz5/P4MGDiYyMZOnSpTg4OPDZZ58ZlZ05cyYDBgygU6dOJCQkkJWV1eizZz/++GOcnZ1xdnbmf//7Hz/++CMqlcoksUtiJ4QQQghxmYyMDLRaLb179zYss7Ozo3v37qSkpBiV7dWrl+F3T09PwsPD65S50iOPPMK+ffvYsWMH7du3Z9SoUVRWVpokdrkrVgghhBDCjNzc3HBzc+O2226jZ8+eeHh4sGHDBkaPHn3DdUuPnRBCCCHEZdq1a4dKpSIpKcmwTKvVkpycTGRkpFHZ3bt3G34vKCggNTWViIiIJrel1+vR6/VoNJobDxzpsRNCCCGEMOLk5MSECROIj4/H09OT4OBg5s2bR3l5OePGjTMqO3v2bLy8vPDz82PGjBl4e3szYsSIeus9fvw4a9eu5b777sPHx4czZ84wd+5cHBwcuP/++00SuyR2QgghhBCATqfD1rY2NZo7dy46nY4xY8ZQUlJCTEwMmzdvxsPDw2ibuXPnMmnSJNLS0oiOjua777676o0QarWanTt38v7771NQUICfnx933303v/zyC76+vibZB0nshBBCCNHsbD08UKhUZn/yhO0ViVhDsrOzCQsLA2qTsEWLFrFo0aJ6y8bGxqLX6wEYOnRok+pv1aoV33//fZPjuR6S2AkhhBCi2dm1akW7H/7XIp8VW1BQQFJSEomJiYwfP94MkTUfSeyEEEIIYRZ2rVo1+bmt5hQXF0dycjJTpkxh+PDhlg7nhkhiJ4QQQohbWmMTCt9MJLEzs/379+Ps7GzpMExGo9Fgb29v6TDq1ZJjq48545Vj03JZYl+9vb0JDg42a5ui+WRmZpKbm1tnuZznW4MkdmbWt29fS4dgUkol6HSWjqJ+LTm2+igVSnR68wSsBG6iQ4NCoTAMUrZ2lthXRwc1KUePyYe+FcjMzCSiQzjlFXWfYiDn+dYgiZ2ZvfiiF7fdprZ0GCbx229lrFhRyPTpPgQHm+YZd6aSmVnFnDk5xN81jn5te1o6nEal551i4qa3mOjtzd1Ozdujm1GlYdr58/g+4ItLZ5dmbcsUSv4sIXt9NiNHjsTHx8fS4TSrnJwcNmzYwJv97Ln/NvP8e07J0fHohgpyc3PlA98K5ObmUl5RyecjHYjwufQMAjnPtw5J7MwsMFDFbe2t45JSZmbtLevBwS13n4LcAujkH27pMJos0NaOSLV5En+VtwqHUAeztHUjNOdqZ2P38fEhICDAwtGYRxsPBV0CbCwdhriJRfgo5W/oFiWPFBNCCCGEsBKS2AkhhBBCWAm5FCuEEEIIsyjJr6SyVGu29tTOdrh4Ns/wlsTERPr160dBQQHu7u7N0sb1kMROCCGEEM2uJL+SL17fTU21+e7Jt7FV8sjsnk1O7saOHUthYSEbN25s3sAAvV7P/fffzw8//MCGDRsYMWKESeqVS7FCCCGEaHaVpVqzJnUANdU6s/YQXov3338fhUJh8nolsRNCCCGEuIJGo2HixIn4+vqiVqvp06cPycnJdcolJSXRuXNn1Go1PXv25NChQ43WvX//fhYsWMDy5ctNHrckdkIIIYQQV5g6dSrr1q0jISGBvXv3EhYWxsCBA8nPzzcqFx8fz4IFC0hOTsbHx4dhw4ah1V69l7C8vJy///3vfPTRR/j7+5s8bknshBBCCCEuU1ZWxuLFi5k/fz6DBw8mMjKSpUuX4uDgwGeffWZUdubMmQwYMIBOnTqRkJBAVlZWg8+efeGFF7jzzjsZPnx4s8QuN08IIYQQQlwmIyMDrVZL7969Dcvs7Ozo3r07KSkpRmV79epl+N3T05Pw8PA6ZS769ttv2b59O/v27WuewJEeOyGEEEIIs9i+fTsZGRm4u7tja2uLrW1t/9qDDz5IbGysSdqQxE4IIYQQ4jLt2rVDpVKRlJRkWKbVaklOTiYyMtKo7O7duw2/FxQUkJqaSkRERL31vvzyy/z555/s37/f8AOwcOFCVqxYYZLY5VKsEEIIIcRlnJycmDBhAvHx8Xh6ehIcHMy8efMoLy9n3LhxRmVnz56Nl5cXfn5+zJgxA29v76vOSefv71/vDRPBwcG0adPGJLFLYieEEEIIAeh0OsPl0blz56LT6RgzZgwlJSXExMSwefNmPDw8jLaZO3cukyZNIi0tjejoaL777jtUKpUlwgcksRNCCCGEGaid7bCxVZr9yRNqZ7sml8/OziYsLAwAtVrNokWLWLRoUb1lY2Nj0ev1AAwdOvS6Y7xYh6m06MRu1qxZbNy40XANuj6xsbFER0fz/vvvmy2uligk5BnC2k0l8/QK0tLeAkCpVHFb2Cv4+Q1FoVCRn7+TY8dep0qbZ+Foa9UXc6tWD+PvNwwXl47Y2rqw4+doqqtLzBqXU48AnHoGYOthD4A2q5ySbZlUphbUru/uj2O0D3atnFGqbTk76xf0lTXNFo9DTAxe4+JQd+yIna8vp597ntJt2wzrI47Wf/dV1rz55Jt48suufl0Z23EskV6R+Dr6Mmn7JLaf3m5Y3z+4P6PCRxHpGYm72p2/fvtXjhUcM2kMphQTE0O3bt0Mz3nMzs5mx44dpKenA+Dh4cF9991HcHAwtra2pKen8/3331NWVmbBqK/gEgAD3oCwAWDnAPnH4Zvn4Fzz3XUnWjZnZ2cCR82FrsPAyQcu/Injqnhgp0XjcvFU88jsni3yWbEFBQUkJSWRmJjI+PHjzRBZ87mmxG7s2LEkJCTwzDPP8Mknnxite+655/j44495/PHHWblypSljbND69euxs2t6Nn49Dhw4wNy5c9m1axe5ubmEhoYyfvx4Jk2a1KztNpWLSydatxpNSYnxB/xtYa/i7d2Pg4f+QXV1CeHtZ9Kp02L+2DvKQpFecrWYbZRq8vJ/Ji//Z8LaTbVIbDXFGop/OEF1bgUoFDh28cXrsUiyFu2jOrschZ2SymMFVB4rwG2wacZENETp4IDm6DGK1q0n8F8f1lmf2ucuo9fOd99FwFtvUbJli8ljcbB1ILUglQ3pG/ig3wf1rt+XtY/NJzfzxp1vmLx9UysuLmbr1q3k5eWhUCi4/fbbGT16NJ988gmFhYWMGTOGrKwsEhISALjnnnv4+9//zrJly0z+Lfu6qN1h3GY4sRO+eBDK8sCrHVQUWjoyYUHLli3DJaIzbHgGSi5A51Hc9sJ3tPoosvGNm5mLp7rJz201p7i4OJKTk5kyZUqzzS9nLtfcYxcUFMSaNWtYuHAhDg4OAFRWVrJ69WqCg4NNHmBjPD09m72NP/74A19fXz7//HOCgoL45ZdfePrpp7GxseH5559v9vYbYmPjSFTHhaQcfYU2oc9dttyZVq0e4vDhFygo+BWAIynT6NXzR1xdoyku3m+hiK8eM8DpMysBcHfvYYHIalWmGM8qXrzlFM49A1AFu1CdXU5p0jkA7Nu6mSWesp07Kdt59W/aNbm5Rq9d7rmH8j170J45Y/JYdp3dxa6zu666ftPxTQC0cmpl8rabQ2pqqtHr7du3061bNwIDA3F1dcXd3Z1PP/0UjUYDwIYNG3j55Zdp06YNx48ft0TIxvpMhqKztT10FxWeslg4wvIUCgUPPvggJxf/jbC8X2oXJs5F02YQEyZMsGxwLVhDEwrfbK55upMuXboQFBTE+vXrDcvWr19PcHAwd9xxh1HZH374gT59+uDu7o6XlxdDhw4lIyPDqMyZM2cYPXo0np6eODk5ERMTw549e4zKrFq1itDQUNzc3Hj44YcpKbl0aS42NpbJkycbXoeGhvLOO+8QFxeHi4sLwcHBLFmyxKi+06dPM2rUKNzd3fH09GT48OGcPHnyqvscFxfHBx98QN++fWnbti2PPvooTzzxhNExsJTw9m+Qm/sTBQW/GC13de2EUqkiv+DSrdrl5cepqDyLm9sdV1ZjVleLuUVSgENnHxQqG6oyzXtJ+HrYeHnh3LcvhevWWTqUm45CoSAqKgo7OzvOnDmDjY0NANXV1YYy1dXV6PV6i3yJrVf44NpLrg8lQHw6PLMTujxu6aiEBSkUCmxtbdFXa4yW67QV9OnTx0JRCXO6rnns4uLijOZbWb58OU888USdcmVlZbz44ov8/vvvbNu2DaVSyciRI9HpagdOlpaW0rdvX86ePcu3337LgQMHmDp1qmE91M7+vHHjRjZt2sSmTZvYsWMHc+fObTC+BQsWEBMTw759+3j22WeZMGECx47VjvPRarUMHDgQFxcXdu7cSVJSEs7OzgwaNIiqqqomH4OioiKz9BY2xM93KC4uHck4Pr/OOpXKG51OU2d8WlVVLiqVj7lCrKOhmFsSWz9HWr1xJ63f6oPHyDDyVh2hOrvc0mE1ym3ECHRlZZRs+dHSodw0fH19eeWVV3jttdcYOnQoa9euJScnhzNnzlBVVcWAAQOws7PDzs6O++67D6VSibOzs6XDruURCt3GQX4GrHoAfv8MBr8Lt4+2dGTCQnQ6Hb/88gv+908DF39QKKHzKJza9iAgIMDS4QkzuK6bJx599FGmT5/OqVO1Xf5JSUmsWbOGxMREo3IPPvig0evly5fj4+PDkSNHiIqKYvXq1eTk5JCcnGxIki7ejXKRTqdj5cqVuLi4ADBmzBi2bdvG22+/fdX47r//fp599lkApk2bxsKFC/npp58IDw9n7dq16HQ6li1bhkKhAGDFihW4u7uTmJjIfffd1+j+//LLL6xdu5b//ve/Vy2j0WgMl2+gdiyPKdnbB9C+/Wvs2/cYOl3TE1JLuplirs6tIGvRXpRqWxyivPF4KJycJX+2+OTO/cEHKNq0Cf01fEm51eXl5fHJJ59gb29PZGQkI0aMYOXKleTk5PDVV18xZMgQevTogV6v5+DBg5w7d65ljK+D2g/tc/tg2+za1xf+BN8IiImDA/+xbGzCYsaMGcP+/yXAlGOgq4bzByhI/gqd6+2WDk2YwXUldj4+PgwZMoSVK1ei1+sZMmQI3t7edcqlpaXx+uuvs2fPHnJzcw09cZmZmURFRbF//37uuOOOBnu+QkNDDUkdQEBAANnZ2Q3G17lzZ8PvCoUCf39/wzYHDhwgPT3dqE6oHSd45WXi+hw6dIjhw4czc+bMBpPAOXPm8MYbzTd43MUlCpXKm27dvjUsUyptcXfvTmDrMew/8ARKpT22ti5GvXYqlTdVVTnNFldDGov5p8QIwHy3wTeoRk9NXiU1gPZsKapAZ5x7t6JwQ7qlI7sqh65dsW/blrMvvGjpUG4qNTU15OfXjqs8f/48rVu3pkePHmzatImMjAwWLVqEo6MjOp2OyspKXnrpJQ4dOmThqP9fyQXIueKu45xUiPiLZeIRLcLx48dJWzCILsEuYO8CpVkohi7n+PHj0mt3C7ju6U7i4uIMNw589NFH9ZYZNmwYISEhLF26lFatWqHT6YiKijJc8rx480VDrrzjVaFQGF2qvdZtSktL6dq1K1988UWd7Xx8Gr5EeeTIEfr378/TTz/Nq6++2mDZ6dOn8+KLlz5gi4uLCQoKanCba1FQ8Au79ww2WhYZ8S5l5RmcOrWEyspz6HRVeHjcSU7OZgAcHdvgoG5NUZFlpkFoLOYWk9TVR6lAYduyn8Dn/tcHqTh0CM2xlju9yM3g4hily5WX1/bUtmnTBicnJ8PQDos7vQe8jK9y4NUOik5bJh7RsmjLa3/U7rhE9uebJVNv+qk8ROOuO7G7OCZNoVAwcODAOuvz8vI4duwYS5cu5a67aqdj2LXL+G66zp07s2zZMvLz8802Xq1Lly6sXbsWX19fXF1dm7zd4cOHueeee3j88ccbvAx8kb29Pfb29jcSaoNqasooK0u9Ylk5Wm2hYfm5c19x220zqNYWUV1TSvv2Myks2muxO2KbErNK5Y1K5YOjQwgAzk7hVNeUUVl5jurqIrPE6TowlMrUfGoKNShUNjhG+2Lfxo3c5bW9NEpnO2xcVNh41d6yb+fvhF5TQ3WhBn1FdUNVXxeFoyOqywbrqwIDse/QgZqiIqrPn6+NyckJ14EDyXp3nsnbv5yDrQPBLpdiae3SmnCPcIqqirhQdgFXlSsBTgH4OvoCEOoWCkBuRS55lS1j/sTL9e/fn/T0dIqKilCpVHTq1InQ0FBWrVoFQHR0NLm5uZSVlREUFMSgQYP49ddfyctrIfvy68cwbgvcNQUOb4DWXaDrWPiuZUzFJCzjvvvuw7WjA+iOg2dbuG82mguprFixQhK7W8B1J3Y2NjakpKQYfr+Sh4cHXl5eLFmyhICAADIzM3n55ZeNyowePZp33nmHESNGMGfOHAICAti3bx+tWrWiV69e1xtagx555BHmz5/P8OHDmT17NoGBgZw6dYr169czdepUAgMD62xz6NAh7rnnHgYOHMiLL77IhQsXDPvdWC+fJaWlvwXo6NTpI5RKFXl5OzmW+rqlw2pQ69Z/p22bSx9KXbuuBeDIkamcv2CeOz1tnO3wHBWOjYsKXWU12vNl5C4/hCa9EADnngG43htiKO87vnbcSv5Xxyj/o+FhAtfDIaojIf/+t+G13/Ta91Hhhg2cn/4KAK5D7geFguIGxn2aQkevjqwYdOnGqandauca/Cb9G15NepV+Qf14q89bhvXv9X0PgI/3f8ziA4ubNbbr4eTkxMiRI3F2dkaj0ZCVlcWqVasMU5l4e3tz77334uDgQGFhITt37uTXX3+1cNSXObcX1j4C/WdC36lQcAp+mA4Hv7J0ZMKC3NzcCBo9FzxaQ0UBpHxL2hezjO7wtpTi3GwqTDzmvCEOrq64evs2S92JiYn069ePgoICwyTnLcENPXmioR4vpVLJmjVrmDhxIlFRUYSHh7No0SJiY2MNZVQqFVu2bGHKlCncf//9VFdXExkZedVLu6bg6OjIzz//zLRp03jggQcoKSmhdevW9O/f/6r78/XXX5OTk8Pnn3/O559/blgeEhLS4DQp5rZ33yNGr3W6Ko6lzuJY6izLBNQEV8Z84sQiTpyo//Et5lKwLq3B9cVbMynemmmmaKD8t2RSOkQ0WKbwy68o/LL5P8x/z/qdTgmdrrr+m4xv+Cbjm2aPw1S+/fbbBtdv3bqVrVu3mima65S6ufZHiP/31Vdf8bLH93QJuNTpomvGp+M0VXFuNssnP0ON1nxPnrCxsyPu/U+bnNyNHTuWwsJCNm7c2GwxxcbGsmPHDqNl9T344XpdU2LX2BMlrjwQ9957L0eOHDFaduXdZCEhIXz99df11jdr1ixmzZpltGzy5MlG89ZdeSdufYnWlY8k8/f3N8wk3xT1xSGEEEKIpqsoLjZrUgdQo9VSUVzcbL121+upp55i9uzZhteOjo4mq7tljwYXQgghhLAAjUbDxIkT8fX1Ra1W06dPH5KTk+uUS0pKonPnzqjVanr27Nmku+YdHR3x9/c3/FzLmP/GSGInhBBCCHGFqVOnsm7dOhISEti7dy9hYWEMHDjQMD3SRfHx8SxYsIDk5GR8fHwYNmwY2kZ6Jr/44gu8vb2Jiopi+vTphjvvTUESOyGEEEKIy5SVlbF48WLmz5/P4MGDiYyMZOnSpTg4OPDZZ58ZlZ05cyYDBgygU6dOJCQkkJWV1eCzZ//+97/z+eef89NPPzF9+nRWrVrFo48+arLYb+jmCSGEEEIIa5ORkYFWq6V3796GZXZ2dnTv3t0wI8hFl8/i4enpSXh4eJ0yl3v66acNv3fq1ImAgAD69+9PRkYG7dq1u+HYpcdOCCGEEMJCevToAUB6ummebCSJnRBCCCHEZdq1a4dKpSIpKcmwTKvVkpycTGRkpFHZ3bt3G34vKCggNTWViIiGp6i63MWZO0z1uDe5FCuEEEIIcRknJycmTJhAfHw8np6eBAcHM2/ePMrLyxk3bpxR2dmzZ+Pl5YWfnx8zZszA29ubESNG1FtvRkYGq1ev5v7778fLy4s///yTF154gbvvvtvoOfc3QhI7IYQQQghAp9MZnhU9d+5cdDodY8aMoaSkhJiYGDZv3oyHh4fRNnPnzmXSpEmkpaURHR3Nd999h0qlqrd+lUrF1q1bef/99w2PKnzwwQcbff78tZDETgghhBDNzsHVFRs7O7M/ecLhGuaIy87OJiwsDAC1Ws2iRYtYtKj+pyHFxsYaHrowdOjQJtUfFBRU56kTpiaJnRBCCCGanau3L3Hvf9oinxVbUFBAUlISiYmJjB8/3gyRNR9J7IQQQghhFq7evi3u8V4AcXFxJCcnM2XKFIYPH27pcG6IJHZCCCGEuKU1NKHwzUamOxFCCCGEsBKS2AkhhBBCWAlJ7IQQQgghrISMsTOzM2eqcHCwjnz6woXaW9YzM6ssHEldF2M6XXSegxeOWTiaxqXnnQLgTLWWI5WVzdpWRpUGgKrcKipOVjRrW6ZQlVt7LnNyciwcSfO7uI8nCvTsPV9jljZTcnRmaUeY15XnVc7zrUOhvzgJi2hWxcXFuLm5WToMk1MqQddC/1+05Njqo1Qo0enNE7ASuIkODQqFglvlX5Ul9tXRQU3K0WMEBwebtV1hepmZmUR0CKe8ou4XRFOe54ufaUVFRbheMU9cZWUlJ06coE2bNqjV6htuS1zbMZUeOzPbsWMHzs7Olg7DZDQaDfb29pYOo14tObb6mDNeOTYtlyX21dvbW5I6KxEcHEzK0WPk5ubWWSfn+dYgiZ2ZRUdH1/l2I4QQQphKcHBwi03gqgsr0ZVVm609pZMttu7N02uYmJhIv379KCgowN3dvVnauB6S2AkhhBCi2VUXVnLhvd+h2oxDDWwV+L8U0+TkbuzYsRQWFrJx48ZmDevXX39lxowZ7NmzBxsbG6Kjo9m8eTMODg43XLd1jOIXQgghRIumK6s2b1IHUK03aw9hU/z6668MGjSI++67j99++43k5GSef/55lErTpGSS2AkhhBBCXEGj0TBx4kR8fX1Rq9X06dOH5OTkOuWSkpLo3LkzarWanj17cujQoQbrfeGFF5g4cSIvv/wyHTt2JDw8nFGjRplsbK0kdkIIIYQQV5g6dSrr1q0jISGBvXv3EhYWxsCBA8nPzzcqFx8fz4IFC0hOTsbHx4dhw4ah1WrrrTM7O5s9e/bg6+vLnXfeiZ+fH3379mXXrl0mi1sSOyGEEEKIy5SVlbF48WLmz5/P4MGDiYyMZOnSpTg4OPDZZ58ZlZ05cyYDBgygU6dOJCQkkJWVddVnzx4/fhyAWbNm8dRTT/HDDz/QpUsX+vfvT1pamklil8ROCCGEEOIyGRkZaLVaevfubVhmZ2dH9+7dSUlJMSrbq1cvw++enp6Eh4fXKXOR7v8nV33mmWd44oknuOOOO1i4cCHh4eEsX77cJLFLYieEEEIIYQYBAQEAREZGGi2PiIggMzPTJG1IYieEEEIIcZl27dqhUqlISkoyLNNqtSQnJ9dJynbv3m34vaCggNTUVCIiIuqtNzQ0lFatWnHsmPGjLlNTUwkJCTFJ7DKPnRBCCCHEZZycnJgwYQLx8fF4enoSHBzMvHnzKC8vZ9y4cUZlZ8+ejZeXF35+fsyYMQNvb29GjBhRb70KhYL4+HhmzpzJ7bffTnR0NAkJCRw9epSvv/7aJLFLYieEEEIIQe0YOFvb2tRo7ty56HQ6xowZQ0lJCTExMWzevBkPDw+jbebOncukSZNIS0sjOjqa7777DpVKddU2Jk+eTGVlJS+88AL5+fncfvvt/Pjjj7Rr184k+6DQ3ypP1rawhh6YLIQQQtxMGvpMu9oD62+GJ08MGjSIsLAw/vWvfzVzYNfmase0PtJjJ4QQQohmZ+uuxv+lmBb5rNiCggKSkpJITExk/PjxZois+UhiJ4QQQgizsHVXg7ulo6grLi6O5ORkpkyZwvDhwy0dzg2RxE4IIYQQt7SrTSh8M5LpToQQQgghrIQkdkIIIYQQVkISOyGEEEIIKyGJnRBCCCGElZDETgghhBDCSkhiJ4QQQghhJWS6EyGEEEKYRWFhIeXl5WZrz9HREXd392apOzExkX79+lFQUNBsbVwPSeyEEEII0ewKCwv517/+RXW1+Z48YWtry/PPP9/kxGvs2LEUFhaycePGZonn5MmTtGnTpt51X375JQ899NANtyGXYoUQQgjR7MrLy82a1AFUV1ebtYewMUFBQZw/f97o54033sDZ2ZnBgwebpA1J7IQQQgghrqDRaJg4cSK+vr6o1Wr69OlDcnJynXJJSUl07twZtVpNz549OXTo0FXrtLGxwd/f3+hnw4YNjBo1CmdnZ5PELYmdEEIIIcQVpk6dyrp160hISGDv3r2EhYUxcOBA8vPzjcrFx8ezYMECkpOT8fHxYdiwYWi12ia18ccff7B//37GjRtnsrglsRNCCCGEuExZWRmLFy9m/vz5DB48mMjISJYuXYqDgwOfffaZUdmZM2cyYMAAOnXqREJCAllZWU1+9uxnn31GREQEd955p8lil8ROCCGEEOIyGRkZaLVaevfubVhmZ2dH9+7dSUlJMSrbq1cvw++enp6Eh4fXKVOfiooKVq9ebdLeOpC7Ys1u//79JruObioajQZ7e3tLh2FweTzXGpsp9qUpdbS0Y3Y5S8TWko/H5by9vQkODm5S2czMTHJzc5s5opbhZjl/N6Il7eO1/B0K6/X1119TXl7OY489ZtJ6JbEzs759+1o6hDoUCgV6vd7SYVyiVIJOV/f3a93WFO03YzPNRgGY+3QqlKBvqQfkErWDI8eOpjT6oZqZmUl4hwgqK1rO3XTNSQm0/LN3Y1rSPqod1Bw7ekySuxasXbt2qFQqkpKSCAkJAUCr1ZKcnMzkyZONyu7evdtwLgsKCkhNTSUiIqLRNj777DP+8pe/4OPjY9LYJbEzs4e6dqK1p5ulwzDILi5l9Z79TPT25m4ny/ck/lxWyqLcXFxfeZuaC2cpW/4xrq+8jW1w/fP+XE7z265rKl+f6swTFL8zgyeecKd7d6d6y2RmVjFnTg6+D/ji0tnlutppLppzGs4sOYPbXY/i0DbGLG1WHP+dop2f4zV0CnZeQWZp83po806Tt2kBubm5jX6g5ubmUllR3uL3yRQunr93AwJop2oZPVqmllGlYdr58y3iPXvxPdqUv0NhOU5OTkyYMIH4+Hg8PT0JDg5m3rx5lJeX17l0Onv2bLy8vPDz82PGjBl4e3szYsSIButPT0/n559/5vvvvzd57JLYmZm3qxOBHi0nsbso0NaOSLXa0mFwXKMBMErMbIPbYNe+8W8/1Zknrql8Q/z97bitfcMfcipvFQ6hDjfUTnOxdfPD3j/MLG1p804DYOcVZLY2zcUa9+lKF89fO5V9i/gf0Jxa8ntWtAw6nQ5b29rUaO7cueh0OsaMGUNJSQkxMTFs3rwZDw8Po23mzp3LpEmTSEtLIzo6mu+++w6VStVgO8uXLycwMJD77rvP5PsgiZ0QQgghmp2joyO2trZmf/KEo6Njk8tnZ2cTFlb7ZU6tVrNo0SIWLVpUb9nY2FjDMKahQ4deU1zvvPMO77zzzjVt01SS2AkhhBCi2bm7u/P888+3yGfFFhQUkJSURGJiIuPHj2/+wJqRJHZCCCGEMAt3d/cmP7fVnOLi4khOTmbKlCkMHz7c0uHcEEnshBBCCHFLa+qEwjcDmaBYCCGEEMJKSGInhBBCCGElJLETQgghhLASktgJIYQQQlgJSeyEEEIIIayEJHZCCCGEEFZCpjsRQgghhFlUVp6jSptvtvZUdp6o1a2ape7ExET69etHQUFBi5qbTxI7IYQQQjS7yspz/Lr7XnQ6jdnaVCrt6dVza5OTu7Fjx1JYWMjGjRubLaYLFy4QHx/Pjz/+SElJCeHh4cyYMYMHH3zQJPXLpVghhBBCNLsqbb5ZkzoAnU5j1h7Cpnjsscc4duwY3377LQcPHuSBBx5g1KhR7Nu3zyT1S2LXgnUf8RCPvPNP/rHySyYs+ZzhL83AI6D1Vcs/8PIspqzdRFhMz2aLySEmhsDFHxP28w4ijqbg3L9/nTKqtm0J/Pgj2if/RvjePwj96ktsAwIarNfrqSeJOJpCv9lvGC3v2bMnG4feQ2bfzlzoF82+OyNRKxUEqVVc6Bdt9FP09ivo9Xr+0jbIsP3zwb5c6BfN7LBLx81HZcuHEcH8eWdHjt/diS0x7Rni42bU7t13/4P+92Rw222vGpZ1CH+LXr228+ST6WRnZ/PZhM9o49qmScetq19XPrznQ7Y9tI2Djx/knqB76pR5Lvo5tj+0neRHklk6YCnBLsFNqvtGdW/jybLHY9jzSn9Ozh3CfZF+Zmm3Ps/GtuOb53pz6I2B/P7qvSwZ05W23k4Wi8cSJvRtx8m5Q3h9aKSlQ7khjf2vsPHyImDOO4T9vIPwfXsJWroEu5AQC0V7SVPeqxe91vM1Dj5+kEcjHjVjhMJcNBoNEydOxNfXF7VaTZ8+fUhOTq5TLikpic6dO6NWq+nZsyeHDh1qsN5ffvmFf/zjH3Tv3p22bdvy6quv4u7uzh9//GGSuFt0Yjdr1iyio6MbLBMbG8vkyZPNEo+5BUZEsX/zf1n96kt8/fZrKG1s+euMN7G1t69Ttsv9w9GbISalgwOao8fImv1mvevtgoIIWf0FVcdPcOqxxzk+fAS5Hy9Gr7n6tzR1VBTuf/sblUePGi3vFtSaH374gWMFheRVVZNWVsnhkkp0ejhbWUWnpENGP29v/ZmSkhK2ZZ4HINrFgcdaeXG4tMKo3g8jgglztOfxgyeI/e0Y3+cUsaRjKFHODgDExMTQvftjlJSkGG1XXHKIlJRprF0by8CBA1EoFHw64FOUisbfRg62DqQWpPL2nrfrXR8XFcffI/7Om7vf5JHvH6GiuoJPB3yKSqlqtO4b5WhnQ8r5Yl7/puF/RubQo40nq3afYuRHSYz5bA+2Nkr+Pa47DnY2lg7NLDoHuvH3HsGknC+2dCg3rLH/FYEf/QtVYBBnnn2OEw88gPbcOUKWL0fh4GDmSI019l696J7ge+js05ms8iwzRSbMberUqaxbt46EhAT27t1LWFgYAwcOJD/fuAcwPj6eBQsWkJycjI+PD8OGDUOr1V613jvvvJO1a9eSn5+PTqdjzZo1VFZWEhsba5K4rymxGzt2LAqFgvHjx9dZ99xzz6FQKBg7dqxJAmuq9evX8+ab9f/jMKWJEyfStWtX7O3tG002TWX9nJkc3rGNvDOZ5Jw6wQ8fL8TVxxe/tmFG5XxC2hAzdCSbF7/f7DGV7dxJzgcfULJ1a73rfSZPpmzHz2S/9x6alBS0p09T+tNP1OTX3xWucHSk1XvzOf/a69QUG3+Yzbn/XhYvXkxsYAATj2aSU1XN8QoNVXo9OiCnqtroZ1hke7788kvKqqtxtFHyUWQIU46dpkhbY1RvN1cnPjuTy76ScjIrq3j/VBZF1TV0dnHAydaWL774gg0bplBdXWS03blzaygsTKak5Az79u1j3jfzCHAOoJVz42M3dp3dxYf7PmR75vZ61z8a8ShL/lzCT6d/IrUglVd2vYKPow/3BF+9t8BUElNzWLAllc2HLf8B9fiKZL7+4wxp2aWknC/hpa8OEOjhSKdAt8Y3vsk5qmx4/2/RvLz+T4oqrv6hcLNo6H+FKjQUx+hozr/xBpWHDlF14iQXZr2BQm2P25AhFoj2ksbeqwC+jr680v0VXt75MtW6ajNGJ8ylrKyMxYsXM3/+fAYPHkxkZCRLly7FwcGBzz77zKjszJkzGTBgAJ06dSIhIYGsrKwGnz375ZdfotVq8fLywt7enmeeeYYNGzYQFhZ21W2uxTX32AUFBbFmzRoqKi71glRWVrJ69WqCg81z6ehynp6euLi4mKWtuLg4/va3v5mlrfrYO9ZekqosLTUss1XZM2RiPNuWL6a8qNBCkf0/hQLn2L5UnTxJ0LKl3Ja0i9C1a+q9XHuR/+uvUZq4g/JffzVa7q22p1twa/r164eznS0fR4bQ0cUBf/v67/fp7OxA51b+hjfc3NsC2ZpXzM6C0jplk4vLGO7rjrutDQpguK87aqWCXwpLmdcnhv/+979kZPzc4K46Ojrytzv/xpmSM1wou9DIgWlYoHMgPo4+7D6327CsVFvKwZyD3O5z+w3VfbNzUdee78LyKgtH0vzeHB7FT8eySUrPs3QozU6hsgMw7snX69FXVeHQtYuFomoaBQre6fMOKw6vIKMww9LhiGaSkZGBVquld+/ehmV2dnZ0796dlBTjqzm9evUy/O7p6Ul4eHidMpd77bXXKCwsZOvWrfz++++8+OKLjBo1ioMHD5ok9mtO7Lp06UJQUBDr1683LFu/fj3BwcHccccdRmV/+OEH+vTpg7u7O15eXgwdOpSMDOM3wpkzZxg9ejSenp44OTkRExPDnj17jMqsWrWK0NBQ3NzcePjhhykpKTGsu/JSbGhoKO+88w5xcXG4uLgQHBzMkiVLjOo7ffo0o0aNwt3dHU9PT4YPH87Jkycb3O9Fixbx3HPP0bZt26YcJtNTKIh9/CnOHj1M3ulThsWxjz/JudQUMn7f08DG5mHj5YWNkxNeTz1J2c5dZI57kpKtWwn8cBGO3brVKe96//2oIyPJ+ec/66wLdXUGoGvXrrz7x0FGHzhOWXUN9/u408ah7uXJv7fy4mh2Lr/++isj2wXTycWBd46frzfOpw+fwlap4Ohdncjsezvzw4N44uBJbndxpLO3B9OnT7/qPrZu/Qjjxh2jrKyM2KhYnvrxqRv+xu7l4AVAXqXxB3peZR7eDt43VPfNTKGA14dGknwyn9Ssugm6NRnWOYCOrV2Z98MxS4diFprjJ9CePYfviy+gdHUFOzu8nnwSu4AAbH18LB1eg+Ki4qjR1/BFyheWDkXchDIyMvjXv/7F8uXL6d+/P7fffjszZ84kJiaGjz76yCRtXNcYu7i4OFasWGF4vXz5cp544ok65crKynjxxRf5/fff2bZtG0qlkpEjR6LT6QAoLS2lb9++nD17lm+//ZYDBw4wdepUw3qoPQgbN25k06ZNbNq0iR07djB37twG41uwYAExMTHs27ePZ599lgkTJnDsWO0/TK1Wy8CBA3FxcWHnzp0kJSXh7OzMoEGDqKoyXa+ARqOhuLjY6OdG9I+bgHdQCJs+mGdY1q5rd4I73s5PK5feaLgmoVAqACjZvp38hAQ0R4+St3QZpYmJuD9s3NNp6++P3yvTOfdSPPp6jruPoxqAFStW8O+U4xwqreBkRRVF2hpGB3gZlVUrFYz09WDV7/sJDAzknTu78uyRU2h09Y86nNbGHzdbG/66P52Bvx/j09PZLIsKZU771jyz/Vc0DYwHvHDhG77+ehB33303J7JOsKDvArOMg7sVvTk8inB/F/6x2jR3irVUAW5qXh/Wkclr9qOp1jW+gTWorubMxH+gCg0l/Lc9dNi3F8ce3Snd8TPoWu4xiPSM5NHIR3l116uNFxY3tXbt2qFSqUhKSjIs02q1JCcnExlpfGPT7t2XrrYUFBSQmppKREREvfWWl5cDoFQap182NjZGuc+NuK557B599FGmT5/OqVO1PUdJSUmsWbOGxMREo3JXzsmyfPlyfHx8OHLkCFFRUaxevZqcnBySk5Px9PQEqHONWafTsXLlSsPl1jFjxrBt2zbefvvqA1vvv/9+nn32WQCmTZvGwoUL+emnnwgPD2ft2rXodDqWLVuGQlGbiKxYsQJ3d3cSExO57777rueQ1DFnzhzeeOONxgs2wT1PjKddl26smfUypfmXenWCom7H3c+f51esNSo/bMp0zqYc4cvZV+95ag7VBYXotVo06ca9spqM4zhecXlF3bEjtt7etFm/zrBMYWtL15gYtE88wbRfaj/M4+LieEKhABTYKhXo9XqeD/Zl7vHzXHwLDPVxx8FGwX/2HaJr1674Oqr5MSbcUK+tUkFPdyfiWnvTe08K4wJ96LvnKMfKKwE4UlbJ/T7udHR24KcHB4JWi0IBNja2uLt3J7D1GH5KjAB01NSUUlSUx86dZ3nm02c49MEh+of0538n/nfdxy2vovaceqm9yK3INSz3UntxNP/o1Tazam/8pSP3dPBl1Ke/cqG40tLhNKtOrd3wcbFn0z/6GJbZ2ijpHurJY71CaP/q/7jKd5SbWuXhI5wY+QBKZ2cUdnbUFBQQunYNFYcOWzq0q+ri1wVPtSdb/rrFsMxWactLMS/xaOSjDFo3yILRCVNycnJiwoQJxMfH4+npSXBwMPPmzaO8vJxx48YZlZ09ezZeXl74+fkxY8YMvL29GTFiRL31dujQgbCwMJ555hnee+89vLy82LhxIz/++CObNm0ySezXldj5+PgwZMgQVq5ciV6vZ8iQIXh7171klJaWxuuvv86ePXvIzc01ZKOZmZlERUWxf/9+7rjjDkNSV5/Q0FCjMXQBAQFkZ2c3GF/nzp0NvysUCvz9/Q3bHDhwgPT09Drj8iorK+tcJr4R06dP58UXXzS8Li4uJigoqIEt6nfPE+MJ696LL9+YTnGO8eD23zZ+xcHtW4yWjX3vIxITlpHxx2/XF/iN0GqpOHQI+zbGU4DYh4aiPXfOaFn57l85PuwvRssC3nmb48eO8bdZsyh5cSbTukSy/vNVrPIMxi60Le93CCbEQcV/cwq5/HvN3wO82JJbTF55Odu2baP3l99jF3rpkvn7HYJJK6/ko8xsHGxqvyXprriHOK+qmu+yC5mb+AtFs6fy1FOePPHEvygrz+DUqSVA3W9SCoUChUJxwz12Z0rPkFOeQ4+AHhwrqO1ZdrJzopNPJ9YeW9vI1tbnjb90ZGBHfx5e8itnCioa3+Aml5Sey30Ldxgtm//X28nIKeWTHRlWmdRdTvf/Y4btQkJQR0WRs2iRhSO6uu+Of8fu87uNln0y4BM2ZWxiY/pGywQlTEqn02FrW5sazZ07F51Ox5gxYygpKSEmJobNmzfj4eFhtM3cuXOZNGkSaWlpREdH891336FS1f+5YGdnx/fff8/LL7/MsGHDKC0tJSwsjISEBO6//36T7MN1P3kiLi6O559/HuCq14WHDRtGSEgIS5cupVWrVuh0OqKiogyXPB2acFu7nZ2d0WuFQtFod2VD25SWltK1a1e++KLu+AgfE47tsLe3x76eaUmuRf9xE+jQuy/fzH+LqopyHN3cAagqL6daW0V5UWG9N0yU5ObUSQJNReHoiOqym2RUgYHYd+hATVER1efPk//Zclr/cwHlv/9O2Z49ON/VB+d+sZx67HGjenRl5WjS0oyXVVRQUVDA4cOH8dRW888dvzB99Gh27znIUZ0OL5UtLjY2fHjqUmIf6qCip7sTj/x5HKg9v0cLirDzudTLU16jo0Bbw9GySmwVcLxcw7zwIGannyNfW81gHzf6eDgz5s/jHC0oIv/wYbKyfKipKUerLaSsLBW1Ogg/vyHk5+/C2fk8vXoF88bTb6Cp1rDz7M5Gj5uDrYPRvHStXVoT7hFOUVURF8ou8HnK5zzT+RkySzI5W3KW5+94npzynAbvzDMVR5UNoV6X5ooL8nQkMsCVwvIqzhWZt7fszeFRDI9uxVP//p0yTQ0+zrXvoeJKrdVepiyrqqkzhrBCW0NhufamHlvY2P8Kl4EDqSnIR3vuPPbt2+M34xVKtm2jLOkXC0bd+Hu1SGN8t3y1rprcilxOFp80c6Q3H5WdJ0qlvdmfPKGyu3rn0ZWys7MNVw7VajWLFi1i0VW+bMTGxqLX137zGjp0aJPbuO2221i3bl3jBa/TdSd2F8ekKRQKBg4cWGd9Xl4ex44dY+nSpdx1110A7Nq1y6hM586dWbZsGfn5+Q322plSly5dWLt2Lb6+vri6upqlzesVfV/tbf9/m2U8pvCHjxdyeMc2S4SEQ1RHQv79b8Nrv+kvA1C4YQPnp79CydatnJ/1Bt5PP43fjFeoOnGCMxMnUbF37zW3tfiXZNi5jbdffQ0PBzXVevg+t5BTlZfG5I0O8OKcRktifkkDNV1SrYdH/sxgRttW/LtzG5xslJyoqGJiSibbGqhDp9Pg7taN4KAn6NrFlb59s/j93O+M+d8Y8isbn9W8o1dHVgy6NC51arepAHyT/g2vJr3K8kPLcbB1YGavmbioXNiXtY/xW8dTpWv+u0E7B7qx5ulLd3W99v8T4379x2le+urPZm//cmN61U5Qu/aZXkbLX/rqAF//ccassYgb09j/CltfH/xenoatlxfVObkUffMNOYsXWypcg8beq+L6qdWt6NVza4t8VmxBQQFJSUkkJibWO6XbzeS6EzsbGxvD7bw2NnUnD/Xw8MDLy4slS5YQEBBAZmYmL7/8slGZ0aNH88477zBixAjmzJlDQEAA+/bto1WrVka3D5vSI488wvz58xk+fDizZ88mMDCQU6dOsX79eqZOnUpgYGC926Wnp1NaWsqFCxeoqKhg//79AERGRl61y/VGLfhb078B3Mg216L8t2RSOtQ/KPSiovXrKbrsrummynzscX4qMv42/O6777K0ze3Yta+/zTnHzzPnKne/XvTA/nSj1ycqqnjy8MlG49m77xHD71VV2Rz4s3ZcRVqqhgkTzhL4dCDud7o3Wg/A71m/0ymhU4NlPtr/ER/tN81dUddi9/F8Ql/+r9nbrU9LicPSHl6yu/FCLVxj/ysKVn1OwarPzRhR0zTlvXo5GVd3bdTqVk1+bqs5xcXFkZyczJQpUxg+fLilw7kh153YAQ32eCmVStasWcPEiROJiooiPDycRYsWGc2srFKp2LJlC1OmTOH++++nurqayMhIk93yWx9HR0d+/vlnpk2bxgMPPEBJSQmtW7emf//+De7Pk08+yY4dl8bBXJza5cSJE4SGhjZbvEIIIYRoXg1NKHyzuabEbuXKlQ2u37hxo9Hre++9lyNHjhgtu3g9+qKQkBC+/vrreuubNWsWs2bNMlo2efJko3nrrrwTt7756C72rl3k7+9PQkJCvW1ezZXtCCGEEEK0NC36WbFCCCGEEKLpJLETQgghhLASktgJIYQQQlgJSeyEEEIIIayEJHZCCCGEEFbihqY7EUIIIYRoqjOVVeRrq83WnqedLYHq5plrNjExkX79+lFQUIC7u3uztHE9JLETQgghRLM7U1lF7z0paMz4AGR7pYKkHhFNTu7Gjh1LYWFhnenbTCkjI4OXXnqJXbt2odFoGDRoEB9++CF+fn4mqV8uxQohhBCi2eVrq82a1AFodHqz9hA2pqysjPvuuw+FQsH27dtJSkqiqqqKYcOGGZ5pf6MksRNCCCGEuIJGo2HixIn4+vqiVqvp06cPycnJdcolJSXRuXNn1Go1PXv25NChQ1etMykpiZMnT7Jy5Uo6depEp06dSEhI4Pfff2f79u0miVsSOyGEEEKIK0ydOpV169aRkJDA3r17CQsLY+DAgeTn5xuVi4+PZ8GCBSQnJ+Pj48OwYcPQarX11qnRaFAoFNjb2xuWqdVqlEolu3btMkncktgJIYQQQlymrKyMxYsXM3/+fAYPHkxkZCRLly7FwcGBzz77zKjszJkzGTBggKH3LSsr66rPnu3ZsydOTk5MmzaN8vJyysrKeOmll6ipqeH8+fMmiV0SOyGEEEKIy2RkZKDVaundu7dhmZ2dHd27dyclJcWobK9evQy/e3p6Eh4eXqfMRT4+Pnz11Vd89913ODs74+bmRmFhIV26dEGpNE1KJnfFCiGEEEKYyX333UdGRga5ubnY2tri7u6Ov78/bdu2NUn90mMnhBBCCHGZdu3aoVKpSEpKMizTarUkJycTGRlpVHb37t2G3wsKCkhNTSUiIqLRNry9vXF3d2f79u1kZ2fzl7/8xSSxS4+dEEIIIcRlnJycmDBhAvHx8Xh6ehIcHMy8efMoLy9n3LhxRmVnz56Nl5cXfn5+zJgxA29vb0aMGHHVulesWEFERAQ+Pj78+uuvTJo0iRdeeIHw8HCTxC6JnZnlFpdhb9tyDnt2cSkAZ6q1HKmstHA0tXEAVGeeoObCWcPvTXGt5etzcdsLF7SkpWrqLZOZWQVAVW4VFScrrrut5qA5VxtzdVEWmgvpZmmzuigLAG3eabO0d72uJ76Wvk+mcPH8ZVTV//duDS7uW0t4z158j4qWSafTYfv/n9Fz585Fp9MxZswYSkpKiImJYfPmzXh4eBhtM3fuXCZNmkRaWhrR0dF89913qFRXnxD52LFjTJ8+nfz8fEJDQ5kxYwYvvPCCyfZBodfrzTtb4C2quLgYNzc3S4dRL4VCQYv6M1Aq4eJEjZf/fq3bmqL9Zmym2SgAc59OhRL0LfWAXKJ2cOTY0RSCg4MbLJeZmUl4hwgqK8rNFJllKYGWf/ZuTEvaR7WDmmNHjzX6d9iSXfxMKyoqwtXV1WhdZWUlJ06coE2bNqjVasPym+HJE4MGDSIsLIx//etfzRzZtbnaMa1Py+k6ukXs2LEDZ2dnS4dhRKPRGM2pY2mXx3OtsZliX5pSR0s7ZpezRGwt+Xhcztvbu0kfpsHBwRw7mkJubq4ZorK8m+X83YiWtI9N/Tu0NoFqFUk9Ilrks2ILCgpISkoiMTGR8ePHmyGy5iOJnZlFR0fX+XYjhGh5goODb8kPXyGaU6Ba1eTeM3OKi4sjOTmZKVOmMHz4cEuHc0MksRNCCCHELe1qEwrfjGS6EyGEEEIIKyGJnRBCCCGElZDETgghhBAmp2uxUwfcfK7lWMoYOyGEEEKYjEqlQqlUcu7cOXx8fFCpVCgUCkuHdVPS6/VUVVWRk5ODUqlscH68iySxE0IIIYTJKJVK2rRpw/nz5zl37pylw7EKjo6OBAcHo1Q2fqFVEjshhBBCmJRKpSI4OJjq6mpqamosHc5NzcbGBltb2yb3ekpiJ4QQQgiTUygU2NnZYWdnZ+lQbily84QQQgghhJWQxE4IIYQQwkpIYieEEEIIYSUksRNCCCGEsBKS2AkhhBBCWAlJ7IQQQgghrIQkdkIIIYQQVkISOyGEEEIIKyGJnRBCCCGElZDETgghhBDCSkhiJ4QQQghhJSSxE0IIIYSwEpLYCSGEEEJYCUnshBBCCCGshCR2QgghhBBWQhI7IYQQQggrIYmdEEIIIYSVkMROCCGEEMJKSGInhBBCCGElbC0dwK1m//79ODs7m7VNjUaDvb39da9vapnmYKl2LeHyfW2O/TZ1ndZ0burbF29vb4KDgy0UkXllZmaSm5tr6TDM5lY6t+LWI4mdmfXt29fsbSoUCvR6/XWvb2qZ5mCpdi1BqVCi0+vq/G4qpj6WCoUSvYljtBSlAnRXHBq1gyPHjqZYfQKQmZlJRIdwyisqLR2K2Tg6qEk5eszqz624NUliZ2bvDoynk397s7X30/HdzN/5GSNHjsTHx6fO+pycHDZs2MCgqPZ0CPCtt46j57P54VAqf+8Rja+r+XobLdWuJVzc10VDXwVg4qa3Gjwn1yq7uJTVe/YztNsTdAzqfsP1XSjMJGH7HN7sZ8/9t93c/0ZScnQ8uqECt7sexaFtDADavNPkbVpAbm6u1X/45+bmUl5RyecjHYjwsf7RORfP961wbsWt6eb+j3wTaucZRCf/cLO1l553CgAfHx8CAgKuWs7TyZFAD7d612UXlwLg6+p81TLNwVLtWsLFfQ3zCjEsa+icXC8vZ3+CfEz3xaKNh4IuATYmq8+SbN38sPcPs3QYFhPho7SacynErcz6v54JIYQQQtwiJLETQgghhLASktgJIYQQQlgJSeyEEEIIIayEJHZCCCGEEFZCEjshhBBCCCshiZ0QQgghhJWQxE4IIYQQwkpIYieEEEIIYSUksRNCCCGEsBKS2AkhhBBCWAlJ7IQQQgghrIQkdkIIIYQQVsLW0gGIWi6xgTh09MbW1wG9VkfVqWKK/neS6twKQxkbTzXuQ9qgCnFDYaugMrWAwm8z0JVqjepy6hGAU88AbD3seUbXjZgD95OcnEx+fr6hTJ8+fYiIiMDf359PP/2U9EMH+XnZvyg4f9ZQZtTrcwjq2AmAL/5/2YEf/8fWZR8Ztdd9xEPc1r0Xnq0Cqa6q4lxqCj9/sdKoLoCA2zrQ5+ExBISFo9PpyDl1nHVvv061tsoUh/CaNBazq48vT/1reb3bfrdwDqm7k6677dsHDOb2Affj6uMHQN6ZTGoW/4vVe/Zj52RP2MguHJ1+lDYhIehqqqnRarGzt7/qsXV0c6fvo3GEdL4DldqB/PNn2LP+S9J++6Xe9rsMDKHtHT54+DtSXaXjwvEift2QQWFWOQD2jrZ0H9aGoAhPXDzVVJRqObE/hz3fHqeqsua697vJYl+G2OnGy3JT4V/dmqU577uf5MBzcbQJaw9KG9KySlnwtZbVzdKaYPKf4B5Sd/lvS+H7l8wfjxBWpkUndrNmzWLjxo3s37//qmViY2OJjo7m/fffN1tczcG+jRulu89RdboUhY0C14GheI+LIuuff6DX6lDYKfEZF4X2fBk5S/8EwO2+ELwf70j2x/tBf6mummINxT+coDq3gh/Tk8gMLmPatGl8+umn5OTkABAaGkp+fj7l5eXcdtttKG1s+OuMN1kxZQLVGo2hrj+3/sAn/3yP9XsP8XTfHvg4qOrEHhgRxf7N/+VCRhpKGxv6PPxYnboCbuvAg6+8wW8bv2L7ik/R1dTgE9IGvV7XfAe1AY3FXJKby+KnHzXapvO9g+g27AFO7Pvjhtouyctj5+oECi6cQ6GAyLv78+K8hSRs+Ql7d0fU7o7846WJhKlg6rsLcPX24VzqUX7+YkW9x3bwcy9i7+TMxnlvUlFSRIc+sQx9YRpfTH+B7JPH67Tfqr07h3acIftkCQqlgp4j2vKXidGsfmM31VU6nNztcXKz55d16eSfL8fFS03s38NxdLdn85JDN7TvTZZ9BP49/NJrXXWzNaUtPMvLL79MbuDdOLXvyYNdAlkxaRj7l0c2W5u3tCX9QGlz6bVvJDz2DRzZaLGQhLAm13QpduzYsSgUCsaPH19n3XPPPYdCoWDs2LGmiq1J1q9fz5tvvtns7WRmZjJkyBAcHR3x9fUlPj6e6mrTfdjkrjhM+R/ZVGeXoz1fRsFXqdh6qLELdAZAFeqKjYea/K9Sqc4qpzqrnPwvU7Fr7Yx9O3ejuipT8qk8VkB1XiWF5/J49dVX0Wg0BAYGGsps3bqVkJAQlixZAsDmL/+Dq48vfm3DjOrSVmkoys8jK+v/2rvzsCjL/X/g74GBYZ8RkE0ZQMMlwcQ1XI5WJrkvnUxSMu3kUakkO7mccslSNH9mWaapX9NSD7aoJaWJiJSmuKCiomiEgKwCIpusc//+ICZHBkUYmHh8v65rrou573ue+/OZB2Y+PM9zz2Sh8GYeym/fxt12hS7CxehI5F5PwY3kJOz/bHWtbQ2a/C/E7tuLE99/i9zrKbiZkYYrx4+gyoDP4YO4X8xCaFByK1/n5t3LHwnHjqCirLRRc/8RewJJZ08hPzMdNzPScXTnVygtKcHjjz+OorSbOPNpJMLDw5Gddh073p6NQ1+sR9tHfZGTmqz3uXXr2Bln9u9FZuIV3MrOQsyunSgrLq61L2uEf3IOl49lIi+jGLlpRYjcegm2DhZorbYDAOSlF2P/hgu4dj4XBTm3kZZwE8e/T4SXryNkJrJG5V5vmkqgKPuvW0ne/R/TQLfi9mHfvn34IyMHSTnF+H8HElBcWoHHH3+8yeZ8qJXk6u7bDgFA3h/AtSPGjoxIEh74Gjt3d3eEhYXh9h1v8KWlpdixYwfUarVBg6sPe3t72NraNukcVVVVGD58OMrLy/Hbb79h69at2LJlCxYuXNhkc8osqv+j1ZRUFz4yuQkgAFH51xEuUakBBKDwtKt7OyYyPP/88zA3N8f169cBAGZmZnj22Wfx448/oqCgAACgsLAAAJQWFek8vnP/QVi//xDOnz+P4VP/Dbm54r6xK6ysdbZlaaeEm3cn3C7IR+CSlZj++VcYvygUbTr+fY6I3B3z3Zy82sPJqz0uRB0w6LwymQk69v0HFJaWOHbsWJ2xld8ugdBo9MaZnnAJHf0HwMLaBpDJ0LHvPyA3M0fqxfP1ikFhWX3gvqykos4x5pZylJdWQmhEnWMMyr498OZlYNY5YNxGQNn2/o8xABMZMLKrK6wU8jr3BxmQqRnQ9XngzDZjR0IkGQ9c2HXv3h3u7u7YtWuXtm3Xrl1Qq9Xw8/PTGbt//370798fKpUKDg4OGDFiBBITE3XGXL9+HYGBgbC3t4e1tTV69uyJmJgYnTFfffUVPD09oVQqMWHCBBQWFmr7Bg0ahJCQEO19T09PLFu2DFOnToWtrS3UarX2qFSN1NRUjB8/HiqVCvb29hg9ejSuXbtWZ84HDhxAfHw8tm3bhm7dumHo0KF47733sHbtWpSXN8H1YTJANaIdyq7dQuWf1z2VpxRCVFRBOdQLMjMTyMxMoBreDjJTGUxsa58elTtbwe3dvpi5axHWr1+P9evXa0/DBgQEIDU1FQkJCdrxg0aOQdrli8hNTda2XTp6GD99ugpLg6chNDQUPZ8agmGvvXmf2GUYNPkVnW2pnF0AAP7/fAFxh37GrtBFyE5KxD8XLIXKxa1RT5VB6In5br5PDkHu9RSkX7lskCkd3T3w2tZvELJ9Nwb/ayZWz30Tly5dqjXO0tYOj4+bgLiD++uMM/yjFTCVyxG8OQwh23bj6VeC8f2qpcjPyrh/IDKg/3PeSP89H3npxXqHWFibodcwL1w8kt7gfB/I9VPAnpnAtmeB8NlAKw9gyj7A3KbJpvTx8cG1r5fiyvtDsXSsL6Z+8qPe/UEG1mkEYKEEzm6//1giqpcGrYqdOnUqvvjiC+39zZs3Y8qUKbXGFRcXY/bs2Th16hQiIyNhYmKCsWPHQqOpPupUVFSEgQMHIi0tDT/88APOnTuHOXPmaPsBIDExEXv27EF4eDjCw8MRHR2N5cuX3zO+VatWoWfPnjhz5gxmzpyJGTNmaIuYiooKBAQEwNbWFr/++iuOHj0KGxsbPPPMM3UWaceOHYOvry+cnZ21bQEBASgoKMDFixf1PqasrAwFBQU6t/pSjX4EZi7WyNvxVxGhKa5A7vZLsOxsD7d3+8JtcV/ILExRfr1Q5/q6GpU5t5G1JhZfv7kB69atw0svvYTWrVujY8eO8PLywv79+3XGO7i4IvzjD3Tazkf+jORzsUhN/B07duzA9pVL4d27L5R/Fmr6PDV1BhzdPXS2JZNVn76LO7gfFw8fRPa1P3D4y024mX4dPk88Xe/npanoi/lOcjNzdOo3EOejIgw2Z156Gr6a8zq2vz0b5yL2YfrCJejcubPOGEsra4yduwi511Nw7NsddcbZ7/lJUFhZ45v33sb2/76B0z/uwYiQuXB013OB+l0GTugA+zbWOLBJ/++xmYUpRrzaFXkZxTi5N6nhCT+I3w9WX2+VdRFIjAS2P1f95t9lbJNNmZCQgCdmfYgxnx3FtuPJWPOvp2vtD2oCfkHA1QigMNPYkRBJRoMWT0yaNAnz589HcnL1UYOjR48iLCwMhw8f1hn37LPP6tzfvHkzWrdujfj4ePj4+GDHjh24ceMGTp48CXt7ewDAI4/oXhek0WiwZcsW7enWoKAgREZGYunSpXXGN2zYMMycORMAMHfuXKxevRpRUVHo2LEjdu7cCY1Gg02bNmkLji+++AIqlQqHDx/GkCFDam0vMzNTp6gDoL2fman/BSk0NBTvvvtunTHWRTWqPSw62ePG5+dQVaBbaJZdzUfmylMwsZJDaAREaRVc3+6DyrgbtTdUJVCVW4obien478fvY8yYMejTpw8qKythb2+PefPm6Qy3VSox7NX/4Osl82tv608pl+OrY3Rxw62s2nk/OWU62nfvhbDF81CUl6ttL7p5E0D16s875aWlws6x9b2fkCZWV8x38n68H8wUCsRHRxpsXk1VpfaIWnZSIqzbqDFr1izgz7N/NjY2mPPRWpSX3sb3q5Zi0Iuv6I1T6ewCv2dGYsubM7XP743kJLTp1AXdAkbUWsF8pwETOsDD1xG7V8WiOL+sVr+ZwhQjX+uG8tIq7Ft/HprmOg17t9JbQG4iYN+uyaaoqKhAUkYubtgX4EJaAXyczKv3BzUdpTvQbhCwc9J9hxJR/TWosGvdujWGDx+OLVu2QAiB4cOHw9HRsda4q1evYuHChYiJiUFOTo72SFxKSgp8fHxw9uxZ+Pn5aYs6fTw9PXWuoXN1dUV2dvY94+vatav2Z5lMBhcXF+1jzp07h99//73WdXmlpaW1ThM3xvz58zF79mzt/YKCAri7u9/zMapR7WHZxQE3NsSh6mbtN9oaNdfdKdorYWJthtL4+19YLpPJIJfLcfjwYcTGxgIABgwYALVaDaVSiW0fr0LZHwn33Eabdt4AgOKbted7csp0PNLbH1+/Ox8FN7J0+gpuZKEwLxet3HSvk2rl2gZJZxu3wrQx7hXznXyfGILEUydwu7D+R10flEwmg0KhAFAJuYUZDhw4gMrKCuxbHYqBk16uM06zP695vHt1sdBotP+46DNgQge069Yaez6MRWFu7cUgZhamGPV6N1RVavDTZ3GoqjTO6mUAgLk1YO8FxIU125Qm2v1BTcZvIlB8A7j6s7EjIZKUBn9A8dSpU7FlyxZs3boVU6dO1Ttm5MiRyMvLw8aNGxETE6O9dq7mlKelpeV95zEzM9O5L5PJdE7VPuhjioqK0KNHD5w9e1bnduXKFbzwwgt6t+fi4oKsLN031Jr7Li76T0sqFArY2dnp3O5FNbo9rPyckBuWAE1ZFUxszGBiYwbI/9pFVj2cYe5uC1N7C1h1aw37Fzqj6GiazmfdAYBdgCfMvexg2koBBw9nLFu2DB06dEBcXByKioqQnZ2NXr16wdvbGxs3bgQA3C4uQmV5GeRm1dfrKZ1d8Pi4CXDyag9HV1eMHDkSL8x5G6nx55GTck1nvqdenoHOAwbhpzUrUX67BFZKFayUKu22AODU3u/QfehIePfpB5WzK/qOn4RWbdrivIEXI9RXfWIGAJWzK9p27oLzhwz35tM/cDLadO4Cu9ZOcHT3QP/AyejcvSe2b98OuYUZer01FNbW1ti49N3qOP/xBA5tXoeK0tu14sxLv46bGel4+pVX4dK+A5TOLugxYiw8fLvh95PH9c7/j8AO6NjbGRH/dxEVpVWwsjOHlZ05TM2qf9dqijq5uSkOfXkZ5pZy7Zh71IqGM+R9wKMfoFID7r2B57cDmirg/LdNMp3bmMUYMGAA3J1aoaOzLeYEdETfTm2xfTuv+2oyMhnQbSJw7n/V+5aIDKbBn2NXc02aTCZDQEBArf7c3FwkJCRg48aNGDBgAADgyBHd5exdu3bFpk2bkJeXd8+jdobUvXt37Ny5E05OTvcttmr4+/tj6dKlyM7OhpOTEwAgIiICdnZ2ePRRw6zstPGvXkTg9O+uOu153ySg5HT10UZ5a0son/GEiaUclTdLURiViqIjabW2ZWpjBvvxHWFqa44xRY/it1PHsWbNGuTn52vH9OpV/WGv//lP9QeC/nvBEgDA/s9W42J0JDSVlVD7Pobuw0bheXMFklNSEHckGhd/2l1rvm5DhgMAnl+se+1jzbYAIPanHyA3M8cTL/4LFja2uJGchO/eX6D3lG5zqE/MAODzxNMozMvBtbgzBpvbyk6JoTNnw7qVPcpLinEj5RpWhMzEwYMH8f5TIVC1d4IKTlj93V7tY0b/5x29cWqqqrBr+WIMeGEyxsxZAHMLS9zMysC+z1Yj6ewpvfP7Dqw+cjr2ze467ZFb43H5WCZaq23h0k4JAAh6319nzJdv/6b3CJ9B2bkB//w/wNIeKMkBUo4DmwZXf0xGE5DbtsaXX34J1zZtUVhWhcsZhQhctQcHDx5skvkIQLsnqgv3M18ZOxIiyWlwYWdqaqpdNWZqalqrv1WrVnBwcMCGDRvg6uqKlJSUWtd1BQYGYtmyZRgzZgxCQ0Ph6uqKM2fOwM3NDf7+/rW2aQgTJ07EypUrMXr0aCxZsgRt27ZFcnIydu3ahTlz5uh81luNIUOG4NFHH0VQUBA++OADZGZm4p133kFwcLDBTtdcn/frfccU7L+Ggv3X7jvu5ndXtT/vvngAr4e/j2nTpsHV1VXbvnjxYgBARkYGNmzYgBf6dEN3jzba/sLcHHz9bvX1drHJadgRcxYhT/dH21bKWvOten7EfWMCgBPff4sT3zfNUZcHVd+Yj4R9iSNhXxp07gOfr6nVdiG5ukDPu5yBfS9twrCtr9TaJ3XJz0zH3g9D6z3/2umH7tmffiX/vmOa1Lf6zwA0lZSvgtFjQzEcRrwJmy5PAADKMlPu8yhqlMRDwOLaryVE1HiN+q7Ye51iNDExQVhYGE6fPg0fHx+88cYbWLlypc4Yc3NzHDhwAE5OThg2bBh8fX2xfPlyvYWioVhZWeGXX36BWq3GuHHj0LlzZ7z88ssoLS2tMxdTU1OEh4fD1NQU/v7+mDRpEl588UUsWbKkyeIkIiIielAPdMRuy5Yt9+zfs2ePzv3BgwcjPj5ep00I3ZV1Hh4e+PZb/UdxFi9erD2yVCMkJETnc+vuXomr7/Po7v5KMhcXF2zdulXvnHXx8PDATz/99ECPISIiImpOjTpiR0RERER/HyzsiIiIiCSChR0RERGRRLCwIyIiIpIIFnZEREREEsHCjoiIiEgiWNgRERERSQQLOyIiIiKJYGFHREREJBEs7IiIiIgkgoUdERERkUSwsCMiIiKSCBZ2RERERBLBwo6IiIhIIljYEREREUmE3NgBPGwS81JhZW7ZbPOl3soAANy4cUNvf017XnEJrt+8pXdMXnEJACC7oKgJIqybseY1hppcf89N1mmra588qJrnMLcoE6k3rjR6e5n5KQCApJsCsRlVjd6eMV26oQEAVN7KQlnm7wCAitxUY4ZkFDXPg9Q9LHnSw0smhBDGDuJhUFBQAKVSaZS5ZTIZ7rWb79df3zFNwVjzGoOJzAQaoan1s6EY+rmUyUwgDByjsZjIAM1dT42FpRUSLl+CWq02TlDNJCUlBZ07dUTJ7VJjh9JsrCwtcOlyguT3bVOqeU+7desW7OzsjB0O3YFH7JpZdHQ0bGxsmnXOsrIyKBSKBvfXd0xTMNa8xnBnrk2Rt6G3KaV9oy8XR0fHh+KNX61W49LlBOTk5Bg7lGbzsOxbejjxiF0z4X83REQkFXxP+/vi4gkiIiIiiWBhR0RERCQRLOyIiIiIJIKFHREREZFEsLAjIiIikggWdkREREQSwcKOiIiISCJY2BERERFJBAs7IiIiIolgYUdEREQkESzsiIiIiCSChR0RERGRRLCwIyIiIpIIFnZEREREEiE3dgAPCyEEAKCgoMDIkRARETVOzXtZzXsb/X2wsGsmubm5AAB3d3cjR0JERGQYhYWFUCqVxg6D7sDCrpnY29sDAFJSUiT/R1BQUAB3d3ekpqbCzs7O2OE0KeYqTcxVuh6mfJsyVyEECgsL4ebmZtDtUuOxsGsmJibVlzMqlUrJv5jUsLOzY64SxFyl6WHKFXi48m2qXKV+kKKl4uIJIiIiIolgYUdEREQkESzsmolCocCiRYugUCiMHUqTY67SxFyl6WHKFXi48n2YcqW/yATXKhMRERFJAo/YEREREUkECzsiIiIiiWBhR0RERCQRLOyaydq1a+Hp6QkLCwv06dMHJ06cMHZIjRIaGopevXrB1tYWTk5OGDNmDBISEnTGlJaWIjg4GA4ODrCxscGzzz6LrKwsI0VsOMuXL4dMJkNISIi2TWq5pqWlYdKkSXBwcIClpSV8fX1x6tQpbb8QAgsXLoSrqyssLS0xePBgXL161YgRN0xVVRUWLFgALy8vWFpaon379njvvfd0viappeb6yy+/YOTIkXBzc4NMJsOePXt0+uuTV15eHiZOnAg7OzuoVCq8/PLLKCoqasYs6udeuVZUVGDu3Lnw9fWFtbU13Nzc8OKLLyI9PV1nG1LI9W7Tp0+HTCbDRx99pNPeUnKlhmFh1wx27tyJ2bNnY9GiRYiNjcVjjz2GgIAAZGdnGzu0BouOjkZwcDCOHz+OiIgIVFRUYMiQISguLtaOeeONN7B371588803iI6ORnp6OsaNG2fEqBvv5MmT+Pzzz9G1a1eddinlevPmTfTr1w9mZmbYt28f4uPjsWrVKrRq1Uo75oMPPsCaNWuwfv16xMTEwNraGgEBASgtLTVi5A9uxYoVWLduHT799FNcunQJK1aswAcffIBPPvlEO6al5lpcXIzHHnsMa9eu1dtfn7wmTpyIixcvIiIiAuHh4fjll18wbdq05kqh3u6Va0lJCWJjY7FgwQLExsZi165dSEhIwKhRo3TGSSHXO+3evRvHjx/X+80QLSVXaiBBTa53794iODhYe7+qqkq4ubmJ0NBQI0ZlWNnZ2QKAiI6OFkIIkZ+fL8zMzMQ333yjHXPp0iUBQBw7dsxYYTZKYWGh8Pb2FhEREWLgwIFi1qxZQgjp5Tp37lzRv3//Ovs1Go1wcXERK1eu1Lbl5+cLhUIh/ve//zVHiAYzfPhwMXXqVJ22cePGiYkTJwohpJMrALF7927t/frkFR8fLwCIkydPasfs27dPyGQykZaW1myxP6i7c9XnxIkTAoBITk4WQkgv1+vXr4s2bdqICxcuCA8PD7F69WptX0vNleqPR+yaWHl5OU6fPo3Bgwdr20xMTDB48GAcO3bMiJEZ1q1btwD89Z24p0+fRkVFhU7enTp1glqtbrF5BwcHY/jw4To5AdLL9YcffkDPnj3x3HPPwcnJCX5+fti4caO2PykpCZmZmTr5KpVK9OnTp8Xl27dvX0RGRuLKlSsAgHPnzuHIkSMYOnQoAGnleqf65HXs2DGoVCr07NlTO2bw4MEwMTFBTExMs8dsSLdu3YJMJoNKpQIgrVw1Gg2CgoLw1ltvoUuXLrX6pZQr6cfvim1iOTk5qKqqgrOzs067s7MzLl++bKSoDEuj0SAkJAT9+vWDj48PACAzMxPm5ubaF84azs7OyMzMNEKUjRMWFobY2FicPHmyVp/Ucv3jjz+wbt06zJ49G//9739x8uRJvP766zA3N8fkyZO1Oen7nW5p+c6bNw8FBQXo1KkTTE1NUVVVhaVLl2LixIkAIKlc71SfvDIzM+Hk5KTTL5fLYW9v36JzLy0txdy5cxEYGKj9/lQp5bpixQrI5XK8/vrrevullCvpx8KOGi04OBgXLlzAkSNHjB1Kk0hNTcWsWbMQEREBCwsLY4fT5DQaDXr27Illy5YBAPz8/HDhwgWsX78ekydPNnJ0hvX1119j+/bt2LFjB7p06YKzZ88iJCQEbm5uksuVqhdSjB8/HkIIrFu3ztjhGNzp06fx8ccfIzY2FjKZzNjhkJHwVGwTc3R0hKmpaa0VkllZWXBxcTFSVIbz6quvIjw8HFFRUWjbtq223cXFBeXl5cjPz9cZ3xLzPn36NLKzs9G9e3fI5XLI5XJER0djzZo1kMvlcHZ2lkyuAODq6opHH31Up61z585ISUkBAG1OUvidfuuttzBv3jxMmDABvr6+CAoKwhtvvIHQ0FAA0sr1TvXJy8XFpdYCr8rKSuTl5bXI3GuKuuTkZERERGiP1gHSyfXXX39FdnY21Gq19rUqOTkZb775Jjw9PQFIJ1eqGwu7JmZubo4ePXogMjJS26bRaBAZGQl/f38jRtY4Qgi8+uqr2L17Nw4dOgQvLy+d/h49esDMzEwn74SEBKSkpLS4vJ966imcP38eZ8+e1d569uyJiRMnan+WSq4A0K9fv1ofXXPlyhV4eHgAALy8vODi4qKTb0FBAWJiYlpcviUlJTAx0X0ZNDU1hUajASCtXO9Un7z8/f2Rn5+P06dPa8ccOnQIGo0Gffr0afaYG6OmqLt69SoOHjwIBwcHnX6p5BoUFIS4uDid1yo3Nze89dZb+PnnnwFIJ1e6B2Ov3ngYhIWFCYVCIbZs2SLi4+PFtGnThEqlEpmZmcYOrcFmzJghlEqlOHz4sMjIyNDeSkpKtGOmT58u1Gq1OHTokDh16pTw9/cX/v7+RozacO5cFSuEtHI9ceKEkMvlYunSpeLq1ati+/btwsrKSmzbtk07Zvny5UKlUonvv/9exMXFidGjRwsvLy9x+/ZtI0b+4CZPnizatGkjwsPDRVJSkti1a5dwdHQUc+bM0Y5pqbkWFhaKM2fOiDNnzggA4sMPPxRnzpzRrgStT17PPPOM8PPzEzExMeLIkSPC29tbBAYGGiulOt0r1/LycjFq1CjRtm1bcfbsWZ3Xq7KyMu02pJCrPnevihWi5eRKDcPCrpl88sknQq1WC3Nzc9G7d29x/PhxY4fUKAD03r744gvtmNu3b4uZM2eKVq1aCSsrKzF27FiRkZFhvKAN6O7CTmq57t27V/j4+AiFQiE6deokNmzYoNOv0WjEggULhLOzs1AoFOKpp54SCQkJRoq24QoKCsSsWbOEWq0WFhYWol27duLtt9/WecNvqblGRUXp/RudPHmyEKJ+eeXm5orAwEBhY2Mj7OzsxJQpU0RhYaERsrm3e+WalJRU5+tVVFSUdhtSyFUffYVdS8mVGkYmxB0fsU5ERERELRavsSMiIiKSCBZ2RERERBLBwo6IiIhIIljYEREREUkECzsiIiIiiWBhR0RERCQRLOyIiIiIJIKFHREREZFEsLAjohbnpZdewpgxY4wdBhHR347c2AEQEd1JJpPds3/RokX4+OOPwS/NISKqjYUdEf2tZGRkaH/euXMnFi5ciISEBG2bjY0NbGxsjBEaEdHfHk/FEtHfiouLi/amVCohk8l02mxsbGqdih00aBBee+01hISEoFWrVnB2dsbGjRtRXFyMKVOmwNbWFo888gj27dunM9eFCxcwdOhQ2NjYwNnZGUFBQcjJyWnmjImIDIeFHRFJwtatW+Ho6IgTJ07gtddew4wZM/Dcc8+hb9++iI2NxZAhQxAUFISSkhIAQH5+Pp588kn4+fnh1KlT2L9/P7KysjB+/HgjZ0JE1HAs7IhIEh577DG888478Pb2xvz582FhYQFHR0e88sor8Pb2xsKFC5Gbm4u4uDgAwKeffgo/Pz8sW7YMnTp1gp+fHzZv3oyoqChcuXLFyNkQETUMr7EjIkno2rWr9mdTU1M4ODjA19dX2+bs7AwAyM7OBgCcO3cOUVFReq/XS0xMRIcOHZo4YiIiw2NhR0SSYGZmpnNfJpPptNWsttVoNACAoqIijBw5EitWrKi1LVdX1yaMlIio6bCwI6KHUvfu3fHdd9/B09MTcjlfColIGniNHRE9lIKDg5GXl4fAwECcPHkSiYmJ+PnnnzFlyhRUVVUZOzwiogZhYUdEDyU3NzccPXoUVVVVGDJkCHx9fRESEgKVSgUTE740ElHLJBP8+HYiIiIiSeC/pUREREQSwcKOiIiISCJY2BERERFJBAs7IiIiIolgYUdEREQkESzsiIiIiCSChR0RERGRRLCwIyIiIpIIFnZEREREEsHCjoiIiEgiWNgRERERSQQLOyIiIiKJ+P9S5P+pr85j2QAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -358,97 +374,75 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "if torch.cuda.is_available():\n", + " accelerator = \"gpu\"\n", + " batch_size = 256\n", + " train_data_size = 2_000\n", + " embed_dim = 128\n", + " num_encoder_layers = 4\n", + "else:\n", + " accelerator = \"cpu\"\n", + " batch_size = 32\n", + " train_data_size = 1_000\n", + " embed_dim = 64\n", + " num_encoder_layers = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'env' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['env'])`.\n", - "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'policy' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['policy'])`.\n", - "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/accelerator_connector.py:551: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.\n", - "Using bfloat16 Automatic Mixed Precision (AMP)\n", - "GPU available: False, used: False\n", + "Using 16bit Automatic Mixed Precision (AMP)\n", + "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", - "/Users/luttmann/opt/miniconda3/envs/rl4co/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", - "Missing logger folder: /Users/luttmann/Documents/Diss/Repos/nco/ai4co/rl4co/examples/other/lightning_logs\n", "val_file not set. Generating dataset instead\n", - "test_file not set. Generating dataset instead\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------------\n", - "0 | env | FJSPEnv | 0 \n", - "1 | policy | L2DPolicy | 15.9 K\n", - "2 | baseline | WarmupBaseline | 15.9 K\n", - "--------------------------------------------\n", - "31.9 K Trainable params\n", - "0 Non-trainable params\n", - "31.9 K Total params\n", - "0.127 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c543880423f84865a05170d16a5aa6fd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00 20\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/utils/trainer.py:146\u001b[0m, in \u001b[0;36mRL4COTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 141\u001b[0m log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m 142\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOverriding gradient_clip_val to None for \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautomatic_optimization=False\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m models\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 143\u001b[0m )\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_clip_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m 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\u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/rl/reinforce/reinforce.py:119\u001b[0m, in \u001b[0;36mREINFORCE.post_setup_hook\u001b[0;34m(self, stage)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost_setup_hook\u001b[39m(\u001b[38;5;28mself\u001b[39m, stage\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 118\u001b[0m \u001b[38;5;66;03m# Make baseline taking model itself and train_dataloader from model as input\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbaseline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 120\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolicy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 121\u001b[0m \u001b[43m 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1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/models/common/constructive/base.py:231\u001b[0m, in \u001b[0;36mConstructivePolicy.forward\u001b[0;34m(self, td, env, phase, calc_reward, return_actions, return_entropy, return_hidden, return_init_embeds, return_sum_log_likelihood, actions, max_steps, **decoding_kwargs)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m td[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m 230\u001b[0m logits, mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecoder(td, hidden, num_starts)\n\u001b[0;32m--> 231\u001b[0m td \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 232\u001b[0m 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logprobs \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_logits\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[43m \u001b[49m\u001b[43mlogits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 345\u001b[0m \u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 346\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 347\u001b[0m \u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[43m \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mtanh_clipping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtanh_clipping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 350\u001b[0m \u001b[43m \u001b[49m\u001b[43mmask_logits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmask_logits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 352\u001b[0m logprobs, selected_action, td \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_step(\n\u001b[1;32m 353\u001b[0m logprobs, mask, td, action\u001b[38;5;241m=\u001b[39maction, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 354\u001b[0m )\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# directly return for improvement methods, since the action for improvement methods is finalized in its own policy\u001b[39;00m\n", + "File \u001b[0;32m~/repos/ai4co/rl4co/rl4co/utils/decoding.py:177\u001b[0m, in \u001b[0;36mprocess_logits\u001b[0;34m(logits, mask, temperature, top_p, top_k, tanh_clipping, mask_logits)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask_logits:\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmask must be provided if mask_logits is True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 177\u001b[0m \u001b[43mlogits\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-inf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 179\u001b[0m logits \u001b[38;5;241m=\u001b[39m logits \u001b[38;5;241m/\u001b[39m temperature \u001b[38;5;66;03m# temperature scaling\u001b[39;00m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m top_k \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "\u001b[0;31mIndexError\u001b[0m: The shape of the mask [256, 11] at index 1 does not match the shape of the indexed tensor [256, 101] at index 1" ] } ], "source": [ - "if torch.cuda.is_available():\n", - " accelerator = \"gpu\"\n", - " batch_size = 512\n", - " train_data_size = 100_000\n", - " embed_dim = 128\n", - " num_encoder_layers = 4\n", - "else:\n", - " accelerator = \"cpu\"\n", - " batch_size = 32\n", - " train_data_size = 1_000\n", - " embed_dim = 64\n", - " num_encoder_layers = 2\n", - "\n", "# Policy: neural network, in this case with encoder-decoder architecture\n", "policy = L2DPolicy(embed_dim=embed_dim, num_encoder_layers=num_encoder_layers, env_name=\"fjsp\")\n", "\n", @@ -470,11 +464,207 @@ "\n", "trainer.fit(model)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solving the Job-Shop Scheduling Problem (JSSP)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "from rl4co.envs import JSSPEnv\n", + "from rl4co.models.zoo.l2d.model import L2DPPOModel\n", + "from rl4co.models.zoo.l2d.policy import L2DPolicy4PPO\n", + "from torch.utils.data import DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Lets generate a more complex instance\n", + "\n", + "generator_params = {\n", + " \"num_jobs\": 15, # the total number of jobs\n", + " \"num_machines\": 15, # the total number of machines that can process operations\n", + " \"min_processing_time\": 1, # the minimum time required for a machine to process an operation\n", + " \"max_processing_time\": 99, # the maximum time required for a machine to process an operation\n", + "}\n", + "\n", + "env = JSSPEnv(\n", + " generator_params=generator_params, \n", + " _torchrl_mode=True, \n", + " stepwise_reward=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train on synthetic data and test on Taillard benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using 16bit Automatic Mixed Precision (AMP)\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "Overriding gradient_clip_val to None for 'automatic_optimization=False' models\n", + "val_file not set. Generating dataset instead\n", + "Provided file name data/../../data/jssp/taillard/15j_15m not found. Make sure to provide a file in the right path first or unset test_file to generate data automatically instead\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4]\n", + "\n", + " | Name | Type | Params\n", + "---------------------------------------------\n", + "0 | env | JSSPEnv | 0 \n", + "1 | policy | L2DPolicy4PPO | 133 K \n", + "2 | policy_old | L2DPolicy4PPO | 133 K \n", + "---------------------------------------------\n", + "266 K Trainable params\n", + "0 Non-trainable params\n", + "266 K Total params\n", + "1.066 Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|█| 8/8 [03:40<00:00, 0.04it/s, v_num=9, train/loss=1.45e+3, train\n", + "Validation: | | 0/? [00:00 Date: Thu, 13 Jun 2024 14:28:00 +0200 Subject: [PATCH 2/3] [Config] updated configs to match latest experiments --- configs/experiment/scheduling/am-pomo.yaml | 1 + configs/experiment/scheduling/am-ppo.yaml | 8 +------- configs/experiment/scheduling/base.yaml | 8 +++++--- configs/experiment/scheduling/gnn-ppo.yaml | 14 ++++++-------- configs/experiment/scheduling/hgnn-pomo.yaml | 1 + configs/experiment/scheduling/hgnn-ppo.yaml | 16 ++++------------ configs/experiment/scheduling/matnet-ppo.yaml | 8 +------- 7 files changed, 19 insertions(+), 37 deletions(-) diff --git a/configs/experiment/scheduling/am-pomo.yaml b/configs/experiment/scheduling/am-pomo.yaml index a3d2cde7..eb49e2da 100644 --- a/configs/experiment/scheduling/am-pomo.yaml +++ b/configs/experiment/scheduling/am-pomo.yaml @@ -14,6 +14,7 @@ model: _target_: rl4co.models.L2DAttnPolicy env_name: ${env.name} scaling_factor: ${scaling_factor} + normalization: "batch" batch_size: 64 num_starts: 10 num_augment: 0 diff --git a/configs/experiment/scheduling/am-ppo.yaml b/configs/experiment/scheduling/am-ppo.yaml index c5d38eb1..f9e5d354 100644 --- a/configs/experiment/scheduling/am-ppo.yaml +++ b/configs/experiment/scheduling/am-ppo.yaml @@ -43,14 +43,8 @@ model: batch_size: 128 val_batch_size: 512 test_batch_size: 64 - # Song et al use 1000 iterations over batches of 20 = 20_000 - # We train 10 epochs on a set of 2000 instance = 20_000 train_data_size: 2000 mini_batch_size: 512 - reward_scale: scale - optimizer_kwargs: - lr: 1e-4 env: - stepwise_reward: True - _torchrl_mode: True \ No newline at end of file + stepwise_reward: True \ No newline at end of file diff --git a/configs/experiment/scheduling/base.yaml b/configs/experiment/scheduling/base.yaml index e84f95fd..c15a6c45 100644 --- a/configs/experiment/scheduling/base.yaml +++ b/configs/experiment/scheduling/base.yaml @@ -22,17 +22,19 @@ trainer: seed: 12345678 -scaling_factor: 20 +scaling_factor: ${env.generator_params.max_processing_time} model: _target_: ??? batch_size: ??? train_data_size: 2_000 val_data_size: 1_000 - test_data_size: 1_000 + test_data_size: 100 optimizer_kwargs: - lr: 1e-4 + lr: 2e-4 weight_decay: 1e-6 lr_scheduler: "ExponentialLR" lr_scheduler_kwargs: gamma: 0.95 + reward_scale: scale + max_grad_norm: 1 diff --git a/configs/experiment/scheduling/gnn-ppo.yaml b/configs/experiment/scheduling/gnn-ppo.yaml index d9c04856..d2139eea 100644 --- a/configs/experiment/scheduling/gnn-ppo.yaml +++ b/configs/experiment/scheduling/gnn-ppo.yaml @@ -12,24 +12,22 @@ logger: model: _target_: rl4co.models.L2DPPOModel policy_kwargs: - embed_dim: 128 + embed_dim: 256 num_encoder_layers: 3 scaling_factor: ${scaling_factor} - max_grad_norm: 1 - ppo_epochs: 3 + ppo_epochs: 2 het_emb: False + normalization: instance + test_decode_type: greedy batch_size: 128 val_batch_size: 512 test_batch_size: 64 mini_batch_size: 512 - reward_scale: scale - optimizer_kwargs: - lr: 1e-4 + trainer: max_epochs: 10 env: - stepwise_reward: True - _torchrl_mode: True \ No newline at end of file + stepwise_reward: True \ No newline at end of file diff --git a/configs/experiment/scheduling/hgnn-pomo.yaml b/configs/experiment/scheduling/hgnn-pomo.yaml index eb688c03..a964143f 100644 --- a/configs/experiment/scheduling/hgnn-pomo.yaml +++ b/configs/experiment/scheduling/hgnn-pomo.yaml @@ -18,6 +18,7 @@ model: stepwise_encoding: False scaling_factor: ${scaling_factor} het_emb: True + normalization: instance num_starts: 10 batch_size: 64 num_augment: 0 diff --git a/configs/experiment/scheduling/hgnn-ppo.yaml b/configs/experiment/scheduling/hgnn-ppo.yaml index 8e3a62d8..7d46f7d7 100644 --- a/configs/experiment/scheduling/hgnn-ppo.yaml +++ b/configs/experiment/scheduling/hgnn-ppo.yaml @@ -12,24 +12,16 @@ logger: model: _target_: rl4co.models.L2DPPOModel policy_kwargs: - embed_dim: 128 + embed_dim: 256 num_encoder_layers: 3 scaling_factor: ${scaling_factor} - max_grad_norm: 1 - ppo_epochs: 3 + ppo_epochs: 2 het_emb: True + normalization: instance batch_size: 128 val_batch_size: 512 test_batch_size: 64 mini_batch_size: 512 - reward_scale: scale - optimizer_kwargs: - lr: 1e-4 - -trainer: - max_epochs: 10 - env: - stepwise_reward: True - _torchrl_mode: True \ No newline at end of file + stepwise_reward: True \ No newline at end of file diff --git a/configs/experiment/scheduling/matnet-ppo.yaml b/configs/experiment/scheduling/matnet-ppo.yaml index f0e30e3b..c88d2c64 100644 --- a/configs/experiment/scheduling/matnet-ppo.yaml +++ b/configs/experiment/scheduling/matnet-ppo.yaml @@ -36,13 +36,7 @@ model: batch_size: 128 val_batch_size: 512 test_batch_size: 64 - # Song et al use 1000 iterations over batches of 20 = 20_000 - # We train 10 epochs on a set of 2000 instance = 20_000 mini_batch_size: 512 - reward_scale: scale - optimizer_kwargs: - lr: 1e-4 env: - stepwise_reward: True - _torchrl_mode: True \ No newline at end of file + stepwise_reward: True \ No newline at end of file From 0c3e3596a7613ca9c90bc5e33b73ef101f12c493 Mon Sep 17 00:00:00 2001 From: LTluttmann Date: Thu, 13 Jun 2024 14:28:37 +0200 Subject: [PATCH 3/3] [Minor] performance improvements in stepwise PPO + some minor model adjustments --- rl4co/envs/scheduling/fjsp/env.py | 37 +++++++++++++++++++------ rl4co/envs/scheduling/fjsp/generator.py | 3 +- rl4co/models/nn/env_embeddings/init.py | 25 ++--------------- rl4co/models/rl/common/utils.py | 2 ++ rl4co/models/rl/ppo/stepwise_ppo.py | 21 ++++++++------ rl4co/models/zoo/l2d/decoder.py | 1 - rl4co/models/zoo/l2d/policy.py | 5 +++- 7 files changed, 51 insertions(+), 43 deletions(-) diff --git a/rl4co/envs/scheduling/fjsp/env.py b/rl4co/envs/scheduling/fjsp/env.py index dac1c8b6..dcf62608 100644 --- a/rl4co/envs/scheduling/fjsp/env.py +++ b/rl4co/envs/scheduling/fjsp/env.py @@ -79,14 +79,32 @@ def __init__( else: generator = FJSPGenerator(**generator_params) self.generator = generator - self.num_mas = generator.num_mas - self.num_jobs = generator.num_jobs - self.n_ops_max = generator.max_ops_per_job * self.num_jobs + self._num_mas = generator.num_mas + self._num_jobs = generator.num_jobs + self._n_ops_max = generator.max_ops_per_job * self.num_jobs + self.mask_no_ops = mask_no_ops self.check_mask = check_mask self.stepwise_reward = stepwise_reward self._make_spec(self.generator) + @property + def num_mas(self): + return self._num_mas + + @property + def num_jobs(self): + return self._num_jobs + + @property + def n_ops_max(self): + return self._n_ops_max + + def set_instance_params(self, td): + self._num_jobs = td["start_op_per_job"].size(1) + self._num_mas = td["proc_times"].size(1) + self._n_ops_max = td["proc_times"].size(2) + def _decode_graph_structure(self, td: TensorDict): batch_size = td.batch_size start_op_per_job = td["start_op_per_job"] @@ -142,6 +160,8 @@ def _decode_graph_structure(self, td: TensorDict): return td, n_ops_max def _reset(self, td: TensorDict = None, batch_size=None) -> TensorDict: + self.set_instance_params(td) + td_reset = td.clone() td_reset, n_ops_max = self._decode_graph_structure(td_reset) @@ -333,10 +353,10 @@ def _make_step(self, td: TensorDict) -> TensorDict: td["ops_sequence_order"] - gather_by_index(td["job_ops_adj"], selected_job, 1) ).clip(0) # some checks - assert torch.allclose( - td["proc_times"].sum(1).gt(0).sum(1), # num ops with eligible machine - (~(td["op_scheduled"] + td["pad_mask"])).sum(1), # num unscheduled ops - ) + # assert torch.allclose( + # td["proc_times"].sum(1).gt(0).sum(1), # num ops with eligible machine + # (~(td["op_scheduled"] + td["pad_mask"])).sum(1), # num unscheduled ops + # ) return td @@ -483,7 +503,6 @@ def get_num_starts(self, td): # NOTE in the paper they use N_s = 100 return 100 - @staticmethod - def load_data(fpath, batch_size=[]): + def load_data(self, fpath, batch_size=[]): g = FJSPFileGenerator(fpath) return g(batch_size=batch_size) diff --git a/rl4co/envs/scheduling/fjsp/generator.py b/rl4co/envs/scheduling/fjsp/generator.py index 17d3f99f..8d2f427f 100644 --- a/rl4co/envs/scheduling/fjsp/generator.py +++ b/rl4co/envs/scheduling/fjsp/generator.py @@ -15,7 +15,6 @@ class FJSPGenerator(Generator): - """Data generator for the Flexible Job-Shop Scheduling Problem (FJSP). Args: @@ -209,6 +208,8 @@ def __init__(self, file_path: str, n_ops_max: int = None, **unused_kwargs): self.num_mas = num_machines self.num_jobs = num_jobs self.max_ops_per_job = max_ops_per_job + self.n_ops_max = max_ops_per_job * num_jobs + self.start_idx = 0 def _generate(self, batch_size: List[int]) -> TensorDict: diff --git a/rl4co/models/nn/env_embeddings/init.py b/rl4co/models/nn/env_embeddings/init.py index fa3b6fb6..06391cb2 100644 --- a/rl4co/models/nn/env_embeddings/init.py +++ b/rl4co/models/nn/env_embeddings/init.py @@ -407,6 +407,7 @@ def _op_features(self, td): mean_durations = proc_times.sum(1) / (proc_times.gt(0).sum(1) + 1e-9) feats = [ mean_durations / self.scaling_factor, + # td["lbs"] / self.scaling_factor, td["is_ready"], td["num_eligible"], td["ops_job_map"], @@ -430,20 +431,10 @@ def forward(self, td): class FJSPInitEmbedding(JSSPInitEmbedding): def __init__(self, embed_dim, linear_bias=False, scaling_factor: int = 100): - super().__init__(embed_dim, linear_bias, scaling_factor, num_op_feats=5) + super().__init__(embed_dim, linear_bias, scaling_factor) self.init_ma_embed = nn.Linear(1, self.embed_dim, bias=linear_bias) self.edge_embed = nn.Linear(1, embed_dim, bias=linear_bias) - def _op_features(self, td): - feats = [ - td["lbs"] / self.scaling_factor, - td["is_ready"], - td["num_eligible"], - td["op_scheduled"], - td["ops_job_map"], - ] - return torch.stack(feats, dim=-1) - def forward(self, td: TensorDict): ops_emb = self._init_ops_embed(td) ma_emb = self._init_machine_embed(td) @@ -471,19 +462,9 @@ def __init__( linear_bias: bool = False, scaling_factor: int = 1000, ): - super().__init__(embed_dim, linear_bias, scaling_factor, num_op_feats=5) + super().__init__(embed_dim, linear_bias, scaling_factor) self.init_ma_embed = nn.Linear(1, self.embed_dim, bias=linear_bias) - def _op_features(self, td): - feats = [ - td["lbs"] / self.scaling_factor, - td["is_ready"], - td["op_scheduled"], - td["num_eligible"], - td["ops_job_map"], - ] - return torch.stack(feats, dim=-1) - def _init_machine_embed(self, td: TensorDict): busy_for = (td["busy_until"] - td["time"].unsqueeze(1)) / self.scaling_factor ma_embeddings = self.init_ma_embed(busy_for.unsqueeze(2)) diff --git a/rl4co/models/rl/common/utils.py b/rl4co/models/rl/common/utils.py index b23149f7..6c16976a 100644 --- a/rl4co/models/rl/common/utils.py +++ b/rl4co/models/rl/common/utils.py @@ -20,6 +20,8 @@ def __init__(self, scale: str = None): def __call__(self, scores: torch.Tensor): if self.scale is None: return scores + elif isinstance(self.scale, int): + return scores / self.scale # Score scaling self.update(scores) tensor_to_kwargs = dict(dtype=scores.dtype, device=scores.device) diff --git a/rl4co/models/rl/ppo/stepwise_ppo.py b/rl4co/models/rl/ppo/stepwise_ppo.py index 98186ea1..49d087d0 100644 --- a/rl4co/models/rl/ppo/stepwise_ppo.py +++ b/rl4co/models/rl/ppo/stepwise_ppo.py @@ -1,13 +1,13 @@ import copy -from typing import Any +from typing import Any, Union import torch import torch.nn as nn import torch.nn.functional as F from torchrl.data.replay_buffers import ( - LazyTensorStorage, + LazyMemmapStorage, ListStorage, SamplerWithoutReplacement, TensorDictReplayBuffer, @@ -23,13 +23,17 @@ def make_replay_buffer(buffer_size, batch_size, device="cpu"): if device == "cpu": - storage = LazyTensorStorage(buffer_size, device="cpu") + storage = LazyMemmapStorage(buffer_size, device="cpu") + prefetch = 3 else: storage = ListStorage(buffer_size) + prefetch = None return TensorDictReplayBuffer( storage=storage, batch_size=batch_size, sampler=SamplerWithoutReplacement(drop_last=True), + pin_memory=False, + prefetch=prefetch, ) @@ -51,7 +55,7 @@ def __init__( metrics: dict = { "train": ["loss", "surrogate_loss", "value_loss", "entropy"], }, - reward_scale: str = None, + reward_scale: Union[str, int] = None, **kwargs, ): super().__init__(env, policy, metrics=metrics, batch_size=batch_size, **kwargs) @@ -143,13 +147,12 @@ def shared_step( while not next_td["done"].all(): with torch.no_grad(): td = self.policy_old.act(next_td, self.env, phase="train") - - assert self.env._torchrl_mode, "Use torchrl mode in stepwise PPO" - td = self.env.step(td) - next_td = td.pop("next") + # get next state + next_td = self.env.step(td)["next"] + # get reward of action reward = self.env.get_reward(next_td, None) reward = self.scaler(reward) - + # add reward to prior state td.set("reward", reward) # add tensordict with action, logprobs and reward information to buffer self.rb.extend(td) diff --git a/rl4co/models/zoo/l2d/decoder.py b/rl4co/models/zoo/l2d/decoder.py index b0ab3041..833e9c6e 100644 --- a/rl4co/models/zoo/l2d/decoder.py +++ b/rl4co/models/zoo/l2d/decoder.py @@ -178,7 +178,6 @@ def __init__( actor_hidden_dim: int = 128, actor_hidden_layers: int = 2, num_encoder_layers: int = 3, - num_heads: int = 8, normalization: str = "batch", het_emb: bool = False, stepwise: bool = False, diff --git a/rl4co/models/zoo/l2d/policy.py b/rl4co/models/zoo/l2d/policy.py index b4b9b11c..0cfac356 100644 --- a/rl4co/models/zoo/l2d/policy.py +++ b/rl4co/models/zoo/l2d/policy.py @@ -35,6 +35,7 @@ def __init__( env_name: str = "fjsp", het_emb: bool = True, scaling_factor: int = 1000, + normalization: str = "batch", init_embedding: Optional[nn.Module] = None, stepwise_encoding: bool = False, tanh_clipping: float = 10, @@ -77,6 +78,7 @@ def __init__( het_emb=het_emb, stepwise=stepwise_encoding, scaling_factor=scaling_factor, + normalization=normalization, ) # Pass to constructive policy @@ -101,6 +103,7 @@ def __init__( num_heads: int = 8, num_encoder_layers: int = 4, scaling_factor: int = 1000, + normalization: str = "batch", env_name: str = "fjsp", init_embedding: Optional[nn.Module] = None, tanh_clipping: float = 10, @@ -122,7 +125,7 @@ def __init__( embed_dim=embed_dim, num_heads=num_heads, num_layers=num_encoder_layers, - normalization="batch", + normalization=normalization, feedforward_hidden=embed_dim * 2, init_embedding=init_embedding, )