diff --git "a/Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb" "b/Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb"
--- "a/Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb"
+++ "b/Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb"
@@ -111,7 +111,7 @@
{
"cell_type": "code",
"source": [
- "# @title ⚄ Define parameters for visalizing the reference in a 16x16 grid\n",
+ "# @title ⚄ Define parameters for visalizing the reference in a 16x16 grid
(the visualization settings has no effect on output)\n",
"from PIL import Image, ImageDraw\n",
"SCALE = 0.0002 # @param {type:\"slider\", min:0.0001, max:0.001, step:0.00001}\n",
"ZERO_POINT = 100 # @param {type:\"slider\", min:0, max:300, step:1}\n",
@@ -141,13 +141,41 @@
" y1 = (15-col)*cellsize + 2*tick + border_offset\n",
" shape = [(x0, y0), (x1, y1)]\n",
" draw.rectangle(shape, fill=fillColor, outline=(0,0,0))\n",
- " return (image)"
+ " return (image)\n",
+ "\n",
+ "num_plots = 1\n",
+ "try:\n",
+ " %cd /content/\n",
+ " _ref = load_file('reference.safetensors' )\n",
+ " num_plots = num_plots+1\n",
+ "except: _ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+ "\n",
+ "image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+ "show_encoding = True # @param {type:\"boolean\"}\n",
+ "#------#\n",
+ "if show_encoding:\n",
+ " # create figure\n",
+ " fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
+ " fig.patch.set_facecolor((56/255,56/255,56/255))\n",
+ " rows = 1\n",
+ " columns = num_plots\n",
+ " fig.add_subplot(rows, columns, 1)\n",
+ " plt.imshow( visualize(ref))\n",
+ " plt.axis('off')\n",
+ " plt.title( \"Encoding (local variable)\", color='white', fontsize=round(20*image_size))\n",
+ " if num_plots>1:\n",
+ " fig.add_subplot(rows, columns, 2)\n",
+ " plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
+ " plt.axis('off')\n",
+ " plt.title(\"Encoding (saved file)\", color='white', fontsize=round(20*image_size))\n",
+ " #------#\n",
+ "\n",
+ "print(f'Using settings SCALE = {SCALE} and ZERO_POINT = {ZERO_POINT} for visualizing the text_encoding')"
],
"metadata": {
- "cellView": "form",
"id": "YDu8XlehhWID"
},
- "execution_count": 146,
+ "execution_count": null,
"outputs": []
},
{
@@ -163,10 +191,19 @@
{
"cell_type": "code",
"source": [
- "try: ref\n",
+ "\n",
+ "loaded_ref = False\n",
+ "try:\n",
+ " ref\n",
+ " loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+ "if loaded_ref : prev_ref = ref.clone().detach()\n",
+ "\n",
+ "try:prompt\n",
+ "except: prompt = ''\n",
+ "\n",
"# @markdown 🖼️+📝 Choose a pre-encoded reference (optional)\n",
- "index = 182 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
+ "index = 596 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
"PROMPT_INDEX = index\n",
"prompt = target_prompts[f'{PROMPT_INDEX}']\n",
"url = target_urls[f'{PROMPT_INDEX}']\n",
@@ -178,9 +215,9 @@
"# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding
\n",
"C = 0.3 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
- "method = 'Refresh' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
- "image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
- "show_encoding = False # @param {type:\"boolean\"}\n",
+ "method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
+ "image_size = 0.57 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+ "show_encoding = True # @param {type:\"boolean\"}\n",
"\n",
"if(not method == 'Do nothing'):\n",
" if method == 'Refresh': ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
@@ -200,67 +237,55 @@
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
" columns = 1\n",
- " if show_encoding: columns = 2\n",
+ " if show_encoding: columns = columns+1\n",
+ " if show_encoding and loaded_ref : columns = columns+1\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow(image)\n",
" plt.axis('off')\n",
- " plt.title(\"Reference Image\", color='white')\n",
+ " plt.title(f\"Reference image at index={index}\" , color='white' , fontsize=round(20*image_size))\n",
" #-----#\n",
+ " if show_encoding and loaded_ref:\n",
+ " fig.add_subplot(rows, columns, columns-1)\n",
+ " plt.imshow( visualize(prev_ref))\n",
+ " plt.axis('off')\n",
+ " plt.title(\"Encoding (before)\" , color='white' , fontsize=round(20*image_size))\n",
+ " print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
+ "\n",
" if show_encoding:\n",
- " fig.add_subplot(rows, columns, 2)\n",
+ " fig.add_subplot(rows, columns, columns)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
- " plt.title(\"Encoding\" , color='white')\n",
- " print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
+ " plt.title(\"Encoding (now)\" , color='white' , fontsize=round(20*image_size))\n",
" #------#\n"
],
"metadata": {
- "id": "BwrEs5zVB0Sb",
- "outputId": "0f153aaf-be27-4729-d17d-70451a89e0b8",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 427
- }
+ "id": "BwrEs5zVB0Sb"
},
- "execution_count": 149,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "/content/fusion-t2i-generator-data/reference\n",
- "Prompt for this image : \n",
- "\n",
- " \"a silver and black cute kitten kittenthe kitten is snuggled up aganst it's siblings \" \n",
- "\n",
- "\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- ""
- ],
- "image/png": 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/bnVbvj/6ef316GeTUM5JCOEFomNk37tvMw2Grl/tvVOJ+jWZdOyTvn44BOPU7+Ce3hvHmWGGGe5bTK1ctrc7lSKQUhEEAVmWIYRgYWGepaVFNjc3AYcoXEuTVvoexRgB1gq0g9w4tHEYB9b5D72wtrtYB9Qsl7qCGQjjupKpfzaKSUJqt8/Hvj+FZTAJ90RgCiHuSyPpm0epeHcfwKTT/mbu3QwzzHDqMLVbTEiJNpYkSen1enS7XfLcEAYR83NzHNi/nzAMkYgqxoIQOAHGq4UxCkb4/12AcApwWOfIHWjjsMaCs3iXkvCuMwHW+f35x3JN6xCu8NsxEKoncrWMYpzbaLdxJ3KtDS4aUChEH2cRg7k5N/CQncBTNu2cdp3nLnP/ZlxkUx9zwtjSXcjoJan+xszFTTeHakxtqHUOxz1v4DbDDDNMj6ktl0Yco4Og6lgohECgWFoUnH7aaZx7zlncceedpGmKLN1iorA8cBjnkLWFtINCsPj9SMChccJhrA/gO2sRVpZh3Er42CKmUj56HWWRxTuFOqpcUuPcXuU5nEhgja6Cx62MT7xaLqM/1R79X3n8oZH/MXBPLQMninsjxFBs7l6Fw8fxSoUkJ1lKM8www72NqZVLGIYEQTC0ShdCoVTIgQMHOOfcczjnzsOsra+R53mhXASuaDBVX40OxT8YxGgc4KzDGEduLNo6jLM4W1hBuwSNRfW8NuHSgBmJn4xTLNMIuN3iMyf6vBRuO6yE8sPyGkwRtzjR2BN+XiQ4jPt80n5PNJcHsvvJf9eK50L4+zAzXGaY4ZRi+mwxbXDG4qzFGetNEmtRUtCaa7K8vMgZZ5zO3NzcINUXoKZURl0vQoz61v0L4xy5FYOYSzXRXbKgqu1dZbEMsrTGY7c5TRo3+v6J9l+3R4YSG07CtXXPceqONVUMiVOjmAYWrCDXjnanf58fc4YZZhhgasul2+3uyKASQuKExFpDGIbMzbdYXJxnY2PTKxgpEVb42o5dLBcoMsag8hhZ69AOtK3L4RPEHor4xUDZuB3JRd9MMH83N9jo9pOsmqHtT/jpAxe7ndfJB9FLJ+d9p2BKZ6RxjqSfsL6xxbFjK/fZ8WaYYYadmD5bbGsLpaQPrDuLtQ4kSBWQ5wZrNVIKWq0WSqqqaLKyX2pWxrBAKqO6VOOMxbvErA/aD4K0g/TeIWvCOZyrinD8e2PcP6MusbEKxg+sxpXbTas86uN3/bx25v51Ob/x43dzZU38vEwXHp7cQMEVGl0Uc6jfq9F931uZWPWYmxh3rJEQlXMjsanRRQJlPMXVtnFkuabd7XPk6AqHDx/h6PHj93juM8www/SYWrnk/S4uCAGB0RatLRqLUBLhBMIYwlDRWpyj0WiQlYF9RKUUTBmSF14k+AQqiRQWRSlcFbZW62KKPykEVkis9YWazgmcHYT4Zc1qsWIgvkdjLvU0WOvcEGtAMWhIKO+mWEaF4ug454YV466xl3sJowlnYuRx9Ji29o6orAmHHNni3lIqAGXCXFmBP2Jc1lAbUBZDnWhONW1tjKGX5KxtdLj7rqMcvvsoGxvb5NrcK+cwwwwzTIfp+7mUpfeIIvvGWwnCOZSQzDUb7N+zxMF9ezi+d4lOp11YMBZhbGHt2KoAkyLQOrR7vKgzzmGsxViLtgLnZCE8fTryIIDvhla2ZfGdcw4nBllZEy2JXVxluwu/KbGb9jgFsfDKMhhzuNFplVaLqz2/L+ZTzWUqZTXNGIfDFN8LyDJDu9NhZXWLu+8+zvHja3R6CT6nRH3Tc59hhhlOHlMrF+MAaxEUq14J0oGSgiBUNMOY/fuWOfv0A6weX+HYsaOkaY4Ufi1sncNah7V2YEmMiDkhiviMc94tZoptoOCWGqw+izALo0HzIdcYw26dcsNRa2bXOEulBE8cixlntUxyZZ0MTiZbzNXGOTdQGtMdqHy474oRS7fjySmaYtsRt6anDTIYB71ezuZWl6PHVllZWWdjo02aZT6t3XkX6wwzzHDqML1ywbscytoEEQpCFRNFEWEjQgUBrYUWQSDo9nvcefhuut0MIWwhQAZ1BydK5/WPYOygQt+44bQ2V/7nRtKPmeBqGqnWH/5o4McvLaydqcXjFdbu8ZjxqdPjMEkB7VCSo3Nn+NwrRQf3mqU0yQKcOp35mzjuuPestaS5odPts7KyydHjG6yubdLvpeS5JtcGYx3aaOw9VPAzzDDDyWFq5RI1WkgpiOOQMAoIw4AoionimLAZoQIFJmfP3gUIFEePrXPs6AZpaorFqV+rlpbLOK6vEs45jCndYrJKBBgZNCKw66nJOzPLROkmu7fiB6OB5THn4JMfJlesT3u8cceZattd3r8vbJNTkmbsHHme0+31Wdvqc/z4OseOr7Hd7pJmGqMtxli0Md5xZjyjwwwzzHDqMLVyOfdBD0IIRxgqwihAKYkKAlQQIgMFCITTxFHMQ63h+Lc/gmuvuY5ut4+s6NL9X+m9EkLgRBnYF1Ww3YJPRTaeb8wzwAy7vxyl4AacrBSYp4ChcvDvzBST1Rz868Gc6hlsldutnsFUc5cNXDPDGVnDq/vJlDMnixNZXENzLMcKEDuoT8aH+0UZpJl2HlUOee19BllcYw7hrx0Di6q6Dzvcd5Xfs7ie/joa6+gnGdvbHVZXNzi62mZtfZN+P/FxOmP9d8Z55gfwbR9mNfozzHBqMbVy2X/wNMAghEAqn70lpEAIhRQBXiAbaCr2LHd48IVn8PCHn8vhO4+RZQopJHYQEqmESinAncAnCtjCBecExgpP82+90qgLcCfqqcjSJ6E5ixA+vFtWaY9milFTbMNKZfg5eEbmQVygrmQG16U+pzpOdgV/IrfXicaX2HWrXfa36/zEdOH8IXdbldzlxiuVMTv0awFRfejXAoV2E5VWqfanjfXpxe0eq2vbrKxusL6xRbuXkmtTfY+EFEghQcoqg7CM480wwwynDlMrl7gRgosQKL/6F2XsAyzSrzyFQgWCZmuB/fv38bCHXsC//Ms36CYJGAFmckV8CWOd95kb5ZXS6IBatpgPXDvq9S+nFDW33cnSqHzzhxwf5J9k2dSpaRgXuC+D7ffQvXXCeJSraWkxvI0fVlIGCYyFLNf0uikbGx1WVtdZWd1gu90jzTXa2uqel+5WKSRC+uyw0g1r7Yz/ZYYZTiWmVi7+R68QIgIhAetdUIJCIPkfrxQBcaPF8vIiF5x/Jueec5CVtY1CyAz3ZCnTknfUfzhRBGJ93MU452lg6m4uBtlhlXtsyA1WGCp1weaoJQKMZB4xXhDvjHX4Aw3FXMas9suYyzjcV8pv3F53SzTw2KmU6oWU92b8ZNr9ObzhYoE8N3T7KRub26yvtVlZ2WRjc4tekmG0ryMyzqe5V4pFSq9chL1X5z/DDDOcHKavc8HHKyqrxWkQBmEBTOHTloBEqIi5+TnOPGMfj3j4hXzjxlvp5xpyn0pcCvfdVpMOV7jFypRki3Vq4DcvVt3DwfvB8+r9cniVOeVO6PI5kQAcKBN/rOkE1zjF+cB1z7hC+Z46oTxQ1FAoc+t7+SRpxuZWj5X1TY4eX2VjvU2/nxQLDs+yba3FEzPUm5dZpLBI6RvblZgpmhlmOLU4CeVSC1LbHOe64AzCCkAXfnIFBDjhCAPF4kKL888/m+XlJbbanjjQWedjKzVXxrhjOUdlvVjnV6lqxLNSd7EN/mo0MEwWKrul9paf7Uw7Hp5DFQTf4d45+eNOg1MmJOvnMxQRGZzsbnU943dXs5KqPQwSKxxgtCZJM7qdhLX1LY6urLOyusV2p+fTiQuOOm+xOKxxYOSIchFY4VCKoazEmXKZYYZTi+ndYra0WDKwXTBbuLznWZJtKWUFyAAbRNhcowhYXpzjzNP3cNfdxzAyIDcWrCwC+IOVvZSy8o87LAZL5hS5A2PxBZxCIpx3mznh7RBrHT4FQHhXiLVI5cbXfdR8/CcKwI+rZxnmQvNz8G634X2Kcjs3sJNGg8qn1noZBM0nRtkBKwZJYKPZWwOamF03P8EsfNKFE0VsTPpgvXN+AdFPcjqdHpvbbY6vbbK6usHWVocsMxgD1lmfMFJNx1WU2WVNkn97cI3L71PVvG6GGWY4ZTgJy6WkKLZgU0zSJW2vo1NdxEMkCIlUAUQNsqRHv93DZpr9S0s0w5BM1mMuFDxhZWryqPvIf26MX6VWbq7S21W3XKq2YZU4p+5yqfYtiv7sO+Iou2P8qnynsK6smPJ19c/9C7FDE4jaZyPnNTL/KU77JGIzdqDwhVcW1jrSNKfTTVnf9KnFK6vrbHY6JEmKNQ5rJc6KIZdnOQkp/Wf1gtdSqVhrMcYgpSQIgpnlMsMQLr30Ul73utfxYz/2Yxw9ehSAd7zjHQD84i/+4v05tW8ZnAS3mMY6AU4jTI5NevTWt+h3M6wTiED59E8VQNDHaI1OLCEh+xeXWV5o0e4kCOHrEKwtVv5i3GpeYHFoW/jXrfPHruDF1FDmmRuXUka178qaOAnFshvGu4PGuIt2qXP5ZiyX3VKVh6hqylmIYaVXXpgTJS/AzmvyzQT1xyVECCjIRIs6JutIs5xOr8/mZpvjq5usrm6xsdEhyTKMLS2UQbtsa306sjW2ptyEt4hqxzLGVN1SS8VSvp7hvkEpqHfDz/3cz3HNNdecwhnN8EDA9MrFFetOAViDyHJckmNTU/R0ARRorRE5gCBQMUuLy5x71pk86NyzOba2RZrmxe68whhlJfZKQBTMyF7B2B3U+yBkaaMMKPbLkL6zDlfWbVIXmjvpXCYK7aFLMD69tp6ZNrzB+LjQN4NJyQbF4cZ9yjgf1r2dDbYbBkrMuzBzY+l2+2xut1ld2+L4yjpr61v0+5kvmq1ZsVJ67jqH56Zz1sfhcD7F2Hs5BUqp6jha67HuxwdyIsW3Ct75zndy991373j/8OHD98NsTh7//b//9/t7Ct9SOAnlMmAkFlaijES5ACnBlk3DhMIJT2jphECGMa2FRfbv38/ZZ5/JtTffRrebYI2rdJUXzCMrZrwwKQP6pQXj1OBzhgT2QIjjBrxj7BAyrlICuwWjxwmm6eMx9y3GHWuH0HyAuX/K+5PntsgAa7OyuuFdYGtttjs9cq0x1gx4BIyPE0kFVvkkDaOLrEE7rKSlHNSyGGMq5VIG8621fsHzALsu34r453/+Z66//vr7exrfNLTW9/cUvqUwvXKxnoCyCqq6AKzAGYMRPlXYFIH93FosAlsE0MNGyPLyIgf272fl+CZamyKFVCDcwKoo/8pn1kFuHdqAMw7C4SJA5xj0RndlyY1FFB/4oHtReV8OG2OtnEho78YfVhdg9ddD+5/64k6P3dx6bjBgZIvxSQyycBNWeVu1rLfxWXK7sz5TsSFQxMQG52+NIclytrYT1je2Ob6yzvGVNba2u6Sp9a4ubGHJWp9M4BSl29MW/GDWDBYSg0OLHfeg+mTkXs0sl/sXp59+Oh/4wAe4/PLL6Xa7PO95z+PgwYMcOnSIt771rVx33XVD48877zxe/OIX8/jHP55Wq8XRo0f59Kc/zdVXX12NeehDH8rLXvYyHv3oRyOE4Nprr+Wqq67a4YI7//zzefWrX81FF13E1tYWH/jAB1hdXd0xx9GYy+Me9ziuuOIKfvVXf5Wzzz6bZz3rWSwtLfH1r3+dN7/5zTsssmc/+9n85E/+JPv37+fmm2/m7W9/Oy95yUuG9vmfCdOzIqc9hHJIDOg+Ls9wucFmDqPAKIdxBqMdmdbe0pCS3GissURhyOL8PGEUkeap1xzW+b4rJbWK8z50gUHgKWDyolmYM1Bw71cWjxPCB3vtQEEJ53vMeOFkca5gD8CnQQ2JxF2EKCNj6hhvxYzfX92iuqeZYuNiGfXXYnjwYNyYbf2YQpWPhIxq+mbH8UcVzGB/5fWGcoFgHWRZTq/XL9KKtzh2fI3NrTa9fupdYGbUVVi6wMwO5V1CyjFu1NrzMiuszBCbWSynDvPz8ywtLQ2955xje3u7ev2UpzyFVqvFBz/4QQB++qd/mt/4jd/g2c9+dhUbu/DCC7nqqqvQWvOhD32II0eOcNZZZ/HEJz6xUi7nn38+V155Jd1ul/e85z1orXnmM5/JO97xDn7hF36Ba6+9FoC9e/fyu7/7uwRBwB//8R+TJAnPeMYzSNN06vP6mZ/5Gay1/Omf/ilzc3M873nP4w1veAM/93M/V435sR/7MX7pl36JL3/5y/z5n/85Z5xxBm9+85tpt9sc/0/aBXVq5bJ+/DhxrIgDh0r7mO4mJk1BS4STGGPIrKGfpiRJQq41yAgDZIlGCEVzvsXcfItuP8UKV7m3/ENN6DrPNVa6xsqYi+8cORCuO2IZdZdYDZUgHBH4u8VbYGdcZce+hrYV471RdXfdvYjdVui7jOak84annENl/AzVsDhyndPvp2xt9VhZ2+TY8VVW17fo9hLy3JAb49cWu3gh6mnbpcKopxSX7q5Rq0UpVSmX+mczJXNqcPnll+94L01TnvzkJ1evTzvtNH7iJ36CdrsNwO23385b3vIWLrnkEj7/+c8D8JrXvAYhBD/7sz/LsWPHqm2vuOKK6vlLX/pSgiDgZS97WRXn+djHPsb73vc+Xv7yl/MLv/ALgFcMe/fu5UUvelGlcD760Y/yF3/xF1OfVxRFPP/5z6/cZu12m1e/+tVccMEF3HLLLQRBwEte8hKuvfZaXvGKV1RK8uabb+Z1r3vdTLlMwhe/doj5VoOFRkBLWOh3MZ0EhYRAgjAIZ3B5Qj9N6fZTjJNYlKfrcNBsNZlfnGd1YwudG68s3E7hXYgVLM43e7Ku4BcrMo7qgVpG4iSieNf57Oi6c6ouZsevwIfncSKBtNNdNGbbkffuqwDzifZV0njuOJfqOg+PdmJQf1LHOKvF0654RmznvMBPspztTpe1tS2OH9/g+MqGp8LP84JpwXcZtW7QzKycm63xhI1aLuXz8q9efT/6ef261JXTDPct3vKWt3DnnXcOvTeaqffJT36yUiwAX/3qVwE488wzAVheXuZxj3sc73vf+4YUSx1SSr7zO7+Tz33uc0MJBGtra3z84x/nR3/0R2m1WvR6Pb77u7+br3/965ViAdjc3OTjH/84z372s6c6r4985CND8Zj6nG+55RYe/vCHs7y8zJVXXjl0vn//93//nzpJYGrlctdqHyUTIgmRcAQ4IpszFwiacYgMHE44cgc5kl5u2O620Q6MteQ2J4pDWvMt4kZMlmSU7pQdK2shqgJJY21VpT8qQ70QGhZIXtlQWDDj49u7WSQnqjY/sRIaJCYMbb+LK+zeUCz3SFi6wVUftuDYEXOpNhl5z7ny3jmssWhtfX+VTR9XObayxsZmm36Sk+fGKxRrccIXxdqC3aFULqPuw3HnO7oYGH1dt1xKRVUqlplyue9x7bXXTgzolzUlJUpFs7CwAAyUzM0337zrPpaXl2k2m9xxxx07PrvttttQSnHaaadx6623cvrppw8plhK33377iU/mBHMu3XyLi4uAjyfBzqw4YwxHjhyZ+jjfajiJgL7/IedGkFPSmis2DcR9CKVFBQrjInrG0DeObp6T5hlWODKrEVLQaDZoNpt0212s1lV8ZAecD7EYS5GSXCgS1JAwKrOHKPdTuseGVsAndg2NxkNOxk022Ief9PDKfrzQvCfKZVLwendU6qS+tyrctWP0LvsfvO8ftdYk/Zzt7S4rq+scXdnk+Nom7U6nYLX2iwNjPPMCYhBXEU4i3LCSKI+xW0X9JEVdd4XNgvgPPOzGJ/hAVv4zRu1vDidRROmzv0r3lACEExgHWgiUUwgDuJDcCVxgCWLQ9LE6RRR1L3Nxi4VWi3YQ0c8dTnqBJ0phUPR0ccjCepEYKz2RpQPpRZSv9xbedWYL60U4hyw+FcWcobQqKJTOTgE9bVrx6Eq5Pl6U9CgnclFNWJ3vdszRfZwo6WCHIhDF3JzwStaVmVw+rjWc1+bnvzMTzZVPBi4s40jznG43YW1tm2PH1ji+us5Gu0M3SdDaeMVvHdaCtUUsRBZWBkG139LKKK/pbtbGuEVA/XrU6V5mGWL/MVG6uS688MJdx2xubtLv9zn33HN3fHbeeedhjKlcakePHuXss88eO+7eQmnZnH322XzpS1+q3ldKccYZZ3Do0KF77Vj/kXAShEtFVpfwqb1ecHi6cygyt/AxFqWaNJt7WFo6jb17T2fP8mksLe5nz/J+Tj/tDM4+6xzOOONMFhcXCYIQUVulClEWVno/fqlUKgulmEoZaxl1iXnNZKEW2B+MGZzNyQqf+vidz4djP9U2Y+Z4shi3zbhkhnFzK96orLqBG7HmSiznSelKHLNPH/3C4lsgZHlOu9Pj+PENbrvtbm666TZuue0ujh/foN3tkeU5Wmt0mTVYm7sorN7ybzRWUg/aj+MMG70OJdXLwJL1dS1lpf6sUdh/LGxubvLlL3+Zpz3taZx22mljx1hr+eIXv8j3fu/3Vi4pgD179vCUpzyFr33ta/R6PcDX3lx00UU84hGPqMYtLy/zlKc85V6b83XXXcfm5ibPeMYzhmKBT33qUyvX2X9GnEQ/l8EqsloZ4nBVN8GanhIhKgiQKiSKW7RallxnzM0lzM8ts7S4j9MOnsnq6hqbmxtsbW3Tbm/T7fbIsrQQ1hqHb3dsnE9L9hQwpcDZKQirbDCGBbCoqvhdlcFVntOk+Ev9/Hdzl1XCe/RajXGFncg9trvL7cTCcZzltdvcBzU4brw/bOfRKQdaZ8kyTbvdZXV1k2PHtji+ssb2tu9dr43DiKJ3feFJ8ApCVVbkqMIoFcJoNlidL6w+7/pno1llwJCyGU0CmOG+xXd/93ePtQi+/vWvn5Rr6f/+3//LVVddxR/+4R/yoQ99iLvvvpszzjiDJzzhCbzgBS8A4Oqrr+Y7vuM7uPrqq/nrv/7rKhU5DEN+93d/t9rXe97zHn74h3+Y3/md3+F973tflYp89OjRKs5zT6G15vd///d5zWtew+WXX86nPvUpzjjjDC699FIOHz78n3ZxM7VyGfWBlwFd/5stuVYKWnbhrY6yW7qQgjiOiKMWrdYie/Yc4KyzUtrtDltbW2xubrKxvsHGxgYbmxtsbW7Q6WyTpgkGg3YCY2UhsGo1LRTunUqpDOIBQ8K8dP+4na6UwbkMMCm+siMrqZrR6P5GiDN3Od6J3t9tnuPe3y37bdz5lT3rdxtfD/I7O+hdv7HZ5tjKGkePrLK21qbX66ML15e2FuN0IUgEZf8fKeSw9VJTFPWU4vJPSllRutQVxbjaldG6lhJ1hTVTLqcGZcHgKH7t135tyF00CYcOHeLFL34xL3nJS/ixH/sx4jjm6NGjfPKTn6zG3Hrrrfz8z/88P//zP8/P/MzPIKXkmmuu4Y1vfONQAH9tbY2Xv/zlvPrVr+b5z3/+UBHlr/zKr3zzJzuC97///QD81E/9FC9/+cs5dOgQr33ta3nVq15FlmX32nH+I0FccsklU6nVn3vRywYb1X6o41aH9eziUvAXjq5iMT9wU+V5TpqmdLtd2u02W1tbbKyvsrGxytrGOkl7gwXX50Bo2dcyNCJvdmrthVigBI1Y0ogbqDDCyAgbNMjCBk6FhYBThQtGgtzpegGGhFBdgI2e29jXUCQNjKywlaA112JpaYm5uTkajUa1Uj/hTZlCEE6KDZ3ofVG4MN0uY8s5OueJIPPM0OulrG+1ObaywbGVNdY3tsgz7WNe1tckaWOKFggOIRRKBSgVIBjEQOrXuX6c0esfBEE1l/LzugKpX0chxBDzcV1hln9KKW64/T8Gx9UM3xoQQvCxj32Mz3zmM/zmb/7m/T2dU47p3WLlqrDw01eCQPg4zNBqUpTjy5V0oVQKn5V3yVgkglgpGo0Gi4uLHDztNPI8p9/r0O1ss7G5QXtzFb29hupvE+ebCJtidQbGerYAB87Kwf7LGpcyMFMkSLnCdKm38S0fx1kqu7nGxn5eJAt4oV0NKMIdrhYTGMQGTuyT2k1xlMWagyZb5fnWN/Nzq29TuIzKgHyh/b2X0Bb09z6eorUlyzVZlpPlOUmakSY57U6ftfVN1jfbtAs+MJ9IQZVQYawFJz1TsZAF28JAGZTXoh5wr3r4jFiUJV1+/bqP9sSpWzTjFjz1bf+zuiZmODWIomiHhfIjP/IjLC0tnZTV9q2Ek8oWqx6d849CwJgCNTHGFeEL50rBOhBwUgyq26VzRHHE3Nwc+/Yf5MwsIe13SLpt+p1tXGeddHuN3uZxku3jmP42whqc9daMw/OKSVdSwAxQFmAyIsTGPd9N4ZRj/GXYhUespmRcacI5KAPpVej8hLJu3Ic+28tvN6CaL8e6kc0GfeVltU1BKIz19MJYbcmNJs0z0jQjSXN6/ZxektJPUtJMkxSWZa/Xp99PyNKcPNfFWZSxr/I8Jc55S9FbK4M0zroyqMdDRoshR6/1kEU8Yq2Mjq2u1IiVOcMM9zUe+chH8spXvpJPfepTbG1t8bCHPYynPe1p3HzzzXzqU5+6v6d3v+CbCuiXr8e6xEYCqUPjR7YDH60pBUO5qkVIhFREjQbN+QXYewCdp+h+l357k/7mcXobR+msHaHfXoOsi3YGXytuEGQI4uni1SOYFMQf916x5dh9OVs2Q6tbS5PmsFMgep1eRXVw1LlTRO2xfF5ac/66amPITc0q0ZY0zeknKb1+Sqeb0OsndLsJ/X6fXJtBFpnN0TovMvZKIe9Twne4osaQSY6Lc9Wv5eh3ZbeK+lGlUXdvjn7XRl1oM8xwX+LIkSMcO3aM5zznOSwuLrK9vc3f/d3fccUVV/ynZVs+KeUyzpddfz3us/o2siYUKlQeIoeSXj0gfYGmEAIhHU4ZwrCBiBdQ83uZ23uQrH0m/e01ehvHSTbuIu1uYHUC6GKu5c4p6nIK/xg757Ujk2pMkPtE79c/H7Z8KFbvA4sDGDA5n/CCM6Bh8VkS5Y2g8PtRRbKcqAl9T0uvdfno+9InaUo/yegnie9Tn+b0+im9XkYvyej3M7JMo7Xvt6OUIpAKpSTO5d7y8i1HvWKpSEcH51Vem3HKZfQajiqbSdZGtRipxV5Glcgor9ioC22GGe4rHD16lNe+9rX39zQeUDgp5bJb8HdU6YxuN/r5YEVJsSQfOVYlPCTgis6VFiccFoGI5mjsbdBYOsjCwXNJt8+ht75Cb/M4vfYaed7HCokUomjBXAq1XQgma+cyzgobfV5/PbrfYQVDJYit8f1IhuMlJ0BR4eitj8FzIbxL0gLOCowBnWtyrellmbdKMo3OLXluSNKMXr9Pt9+n10/oJ2kRT9FkmSHTpqig98WoCggCRVCwD1tjqlhRec98K4NBl8jyvHEOKVWV6VW/VvX7X7437vruZhGXjyXFy6hyGc0WG3VvzjDDDKcWJ6VclFJeKIAvoITKGhkfUC188jDkCxoEYgWIYIewlUKgpEPIQpAVK3iJxticXBtAEARNwsV5wvm9tJbOItk8TmfjCFubx+n3tzBGV/GPOgb2y2A+o3Mb99mJMRhnjMVYg0DhkEXWlPL2kwA7qetuYbX4mtWSf6sQsMITUWZa0+9npImm10vp9vpspwm9JCFJcoy2aO3Ispwky8jynDTLyY2nZJHGx2VMEYi31gCuUiwCh7D4GFURr/HXw0/QXyNZJUiU1tSuLtGR98YtOMrHUjmNWpLlZ3UOsbqSqsdw6sro5O7jDDPMcG9geuWiAu+qwtX6sFAF9RGiUiUWquproFzu+qfCk7OUn8tCB5VKS4BvYSylTzsrhZmTIEOstGQmR+c5SuU0Gw0ajQbR8n7i1jzx0n6irXXam0dpbx0nTdpYnRViUGKRyCIeIcATmMnyVMYH9usYF9Av06qNdaSZppfmpKmvDg+DPnPzGYuLOa1W5vfnarGIcr9Qy/YqLquQCFGm4ZYuIW/F9fsJ2+0+nW5Ku53Q7aV0sj5plhXdGH3AXThvkVhjqlRhay2ZqTEsFIolDBRSqGIanhkB5+9XmYzniqZx0gdzqkVEqfzKuqPRa4Vw+LRwvHtNOARqSJkA1fN6EeVoHGw31+to/KVURLu5MWeYYYb7DlMrF2s9AaQXFFTKRIwIB+dcxftFuSotPrPWYmpuFhEE1Wq3lF6uepReE1VSt6zN8AJMG0OeZ1ij0cbSbDSI4xbNMCZsLdJcXKK5tYetjWP02xvkSR+s9erFDQgUnaitvacI3u+mbKy1tLsJq5td1rZ7dBPfJC0IAprNZlXnUr9WO1yIQ/ssBbZvkKWkRCpv7TlrSdOMbrdPr5+SpJo014VVUl5fvy9P3eaG6FB8gN8rF2t9q4QoDPwxiliRd+GVt8UNHkuFOhIjEbV7bdzwtZTSJyWDV5SlSnXOYu1wrKRMTR5NUd6tvqUcU363Rt2wD0S6/X/5l38pngUM29C7QYPYPatuCE6f0PU7DEEYhlPsE3SeTSUsHJALBXKK+TqLQCOnmK91U8YqCwTBgBXiRDDGoJSb6jaYfHIyzvAkprwRGmC6+yuEZhdO12E4MNPu1gGmDENMGFrQa11yySUTx07fibL44dZXh6P0G4Mf9k6BXP8rBYExxiuncttyJV0aOmWHSSdr36yBEsvzHK0NqQFtLLoREwUhQWOOVtwgmFsknFuiu7lCZ3OdpLOByLsDwVfELmQt+D+KSRZMiSxL2djqcOfd6xzZ7NJJNSCIg5Ao7hHHXeIoIgiCarVeF3xSFAK4iNdXVlxZtyMolMsgScCYoojRCWyloP2l8vemuITOx3s8maQprn9Jk2IJVaG8RqyFgXU1qOYfz9Qw7AothX2lGJQkEMNuLP9CAIMiynHZXfVA/mhtS/24pcu2rnhKRVovsHzgICA+4y8Rcn7iSL31BvZf8nyY4sff++qv8PjHT25OZS007Q/w53/w/oljj68e5zcvfiT/dwoKl2uAS5/8Syz9wP+cOLZ/48e5eOPFPOZxrYljv/KlHh//yPbEcSX+77t/jfMuOGfiuCve8ns8+02fZXH/5H2+6bnwtc9MoYyB8EDAOa9/zOSB1nHbL6fY7uun2GuHxz3sxfzkD03+Lq9sOn6LFF46xW6/BKe971nsuej7Jg7duuELHPn0e6bY6UkoF1esfus/0nrvjKHVKwNRPbyClUP7K/dZWjimEmignKMsgSiDyFXg3LmBm0enCK1xRuOcRUeWKIwIwoB4bpkwatJqLdOYW6G9foxk8xhpmuCcLupibLHCmU74jPr4S2ht6HT6rK63ObbWJsNXjWehI8wdYWaJIk0YBARhSKCU518rrAVvNRSKe+iAsoh7WMht4XSEIipTXI/itfC9571QZRCIdwNlXpE6WouzliAoYhg1upX6uUKlsyrslvRAMR/v1qspZTcgmByy2orEAG9tuarR0qhlIqWs5i9HlGD5ejQWA94tVq7MH3jKRSDkPEJOwW8lAkTYKu7v7nDOUy1NY4xYA6ELWV5enjg2SRNiYHmK38gCDhE2kM3J+xXRHGEoaDQnK80wPLn712o1mV+Ym2K/Aa1FmF+evE9VScsp5iJBNie7ZJ1xeDr5yYsMcAQSWo3Jx29ExS92Gvq0JoggQsWTlbwM4yl26HESbrGBlVL3ZQ/HHUqLgKEf/9DkRi2fmudLKInF4YwbOh7UYhSuxn6b5+g8Q+QpTmdgNbbRxDWaOBsioogwaBIuxUStOeLWPP25BbY2V+l3NjB5F4EZ+10ZXT3vloZcuWXwq8E80+RpisGBCRDaYIMMpyMwGhdGOGMgDPwGSiKUwjqJE0UsSsrBlFwRyfKmHI7yuniLRolC+Vgfy6jHO4wpGJCtqSkXgzEaY20R1/EULaM09XXlUSqXqajwR67lwG3ohhTLIOFjsHCp39/6d6VUNnVFUl73UuGU29UD+uX7o4uiGWaY4b7H1MrFB4ld9eOOChePkDvjB2V22Dj3hYdAKYkUDl2sxH12lEBgi/bGZTV7KcCrsExVZa6NJs9SnDXoLMMZ4wPOziFsCNbgIocKQsK4xdK+M2g25gibi3Q2jtHbPkaWbIPd3U89FMAvzmvgIxaV4FVBQKMZ04hDlHBkeY6wDhM4nFFgjJ+P0TgT40wIkYMgQDggcMW1dEhnq1iQqFKXvXKhcjv6eXnKlcIV5ii62XiuL1tYeNZ4N5jR3trzFoJv7iaVrM7dWFvP/aqeOFHGgHavXynfM9ZUCQvWlf5N36OnHkPCUSjRYTdc3WoZ3f+oRVN/vx5vqX9ezyCbYYYZTh1OynIpA8JSSrTWxI0GYRT6H68UVQBtVE4PxVxQVFT90qEq100R+JeBd+M4W6y8B8JiUIw4UGbGehoTazWQFILWYG1EZHMsmtDFhEQoGdCc30MYNmk2WnSaLbY3j5J013F5zkBoDxp/iSpWgM+SUhKpFFIF/jOp/LigwfIy7DvQYbXdJdvqeqZgY31MRwh/DClxwuCEhPJROqT1cRcnBNb6ayml8MkHFerB8NJlVWSDOeuVi/Npw8Y5jBtQ7hhr0FajjcY6QyAgkIFPOxYFNYyrLRJczeVVxHxGqVfqFstAmO/sByOQPtuvMFZKpSOdHYqzlNbLuEywEnX32WjW2KhlNJoMMMN9j5OJd8/wrY2TdouVVAbGGIqOLsRxvOOHPoqBcil/+MWPX7jKnVOuMn3Kqh0cpxQ81qfUusJ3H0hBoKSPH2CxRpPrDJE6QOOsxlnjU2atxYVeEUaNBmF4wCvHRpPuepPe9jpZ1gOXUybflvxYAEhJGDYIG/NEjTniRhMVRohCudhMEzQ65HIRoxaI7z5Gr99H5zlG69IGQ2sDZEDRfllYhHAEIqosM1m4xawFMfprLQP3UChpan9uULRZ/RVpyHrgGhPCoYKAIAiGqO1hvDAv75+UcqxlMeoWHXVnSulZyOqth+uU+OV9rru/6llgpbVc/x6W25dxljKmU9++XBCNO68ZZpjhvsVJKRdgKLiaZTmyFkitrzp3z64ajKk3rKoHypXyaXHle2VXQ5tnZHnqXWHGpzAGSiJsIYiFBZtjDRjtyIUrEpgB5wgxOBfgZIiSIfH8MvNKEaqQKGrRaa+T9rewJsGTTCqEUDgZEEQN4tY80dxeGnPLNJotokYDKZVXmMbQWk5oLp3G0v4zOX99g263R7+f0Ov16Ha79Pt90jQlN1l1PQZZcwZZpkI461kKhHdxDUxBUSmbUhF591fhErPle4WysUVBp9ZYaypFrQI1pFjq1343jBs3armMWizldtY6pBgohPL7NLqv8jtWZn6V48rv1mg6cv31aFZfqYzKc57h1GAW2ZqhxEnFXEY5nJyzleAvV5H1YP+oe8KjVC74vu61r+Ng3HBPj9JqybKUNOmRpX10nmJ0hrMGgUUKC65IZzYOnRkfS3HeasEYnPEdMgPVwKgAqRRBFCOX9qHCGNVo0e/Mkfa20VmCMxYnA1QYEzTnCVrzRHPLRK1FwmaTIIqQMvQeQWcRcYugOc/80l7OSpKKtj7LMrrdLt1ujyTp00sS+r0+3Z5XOHmWo60pgt4+QQzrfHpxPfWu5nRwRZDf1v68heepZnyqsg/q12tc6vdpt17z45TMbgJ6NHtuXK8a54x3hTFslYweu17bMjpuNCOxfuz6dvX4St2amcVdTg1manyGElMrl36/X9ULVKtIMYjFVIF7KaGWnbNTwZRfPx9TsM4i3HDNjJSiCPwWKaZSYp0lzzPSpE/S76HzBGNyrMlxJh9y0SilCJVXTlZrXGx8HMZEhCrEhQ4ZhMhQ+BBPEBLMLRFLiVAhyBj6HXSWgFCIuIWI5hHRPAQRDkeuc4y1SKWLc9W+liTXOBxzrTnm532Kb3mN8jz31fG5qSyaTqfD5uYmq+trbG1vkaYp9bTrcTJ9OAA++HNF7Yq3VqzPCtOD96AgpCxcYnVlMBq3KI8z+ryehVWfS/0+lxhSInZgiYzb/7ht6q9H05iH05/HV+3XM8jKhcoMM8xwajD1L67b3iYIA5RUCOlTZoMwwDlbxPIFTkhcbggDWVS+qkrwSKkwVpSZtdU2AlVlEXkhUqzKRZG5JApmAGeweU7a79HvbZFnfaw2lcvHOVswBvjqXK0kYRhidY4zBqs1JjKYyBDgCNAoEaKcAmF8VhVghCQXklwotIoQMiCMmqg4RoYBDklmLNLmCJEV8REH1pBnOXmegxPEUYQKFdZKjAUpQ+YXF4njFtpAe3uT+aTH/v37yXPD+sY6x44d4+jRo2xvb2FNhnDWO/XqlkTh8rK2ZFsurRSLMz7N2BqNMbmvabH+tXMGpSSBkkRKFZX4u1stQ+8XjMwO72pzrujkUmWxwaDopm5oCeoN0kaVT/35qItrdMyo0hkqQB0pvqzHj8YpvRnuO8zcYjOUmF65dNsEQThUWR5GIXHBwigKFmMReqbeOkqGY1GseG0REKj4xWpBXS9AvDXjyoLAghsrzzLStE+S9MiSHib3RYElc6+3dkAFiihQRGHoFYpxVRqudoYIhxWOAF/86JxDV1ZRlyTto432DL9hhCoSAQQ+cGysQ0lfLW+0n5vJUrLMx1J8Fb4lN5Brg7YwN7fMwnJEGMeknS6bm+usrx+n1Wyxd88+zjvnLPbt28PePXu5667DHDt+F2naYyC/Xfl/IeAZUixl4N4UCqZ89NdHIwAlFWEQoOSJhe1OpSOq7DnnfB3SKPwCwe7Y76gVMk55lRbUaDxmlLpl1MqqswDU3XL1Y5TWrxn9Ut7v0GRr/xvEFBWP+TfIv/zrw4uMXZCsr3Pd5yfv0gE6/CLPeN4PThybJhk32IBnMLkocRsD1/4BbnuKBlmb6/zLiuCaL08eut0GaDGdyEp58+sup9FsTBx51x13c+gn9qPCyRmFt399C+IG06hQ00449ofHJk/VQWDWWVr+tYlDrdMcOmp51/872cWbphK2QrgumjyHLcPaxifYXv/XiUP1xvQsCVMrlyRJCAI9pFzyPCssh0F9RATIMCiEzcAPLpVEyGEXWV0o1Kv9S6UCVO6kLEvJ8oQ07ZMmfbI0RWcanZeWi6ssoCBQGCUxkUFrW8QeNJH1dCklTNEQK88z8jQjS/rkWYoxmV+IB6qopg+QRa8Za3yaNMpznxmdo7OUNPFJAEoFWGtJ09TX4eQaGcZIKQmD0M/NZLQ7Gxw7dhglJEmvzVlnn82B/XtYnF9geXmJufkWdxy+jXa7U7nGBq6oksp/kB5ujPEWS1mBX/vDOoIw8NX4gRpaye+WeDEk/KFKgS4/283CGMUk5TK6j7o1Ms7FNm77usUy7lj1rLEHDgKivb8KU9C/NLZ/kd++5CtTcXD91sfhWeuTx2ngM9+n+fm3b04cu7Ga86efeBovt/9r4thbuJHvfeGzef3r75g49mMfgxf86G+Rr/3kxLGW9wIbwGMmjoX3c9N1fz3FOADFproT5IHJc8h/HH74mb5cYgKiO36fR3764xPHOa1p/pen8f7/8T8mjt3sdHjeB36V57x9Mq/X8UPbXPdzK3DB5MUDx24l/y9/TP5fVyaP/VtgGqYaTkK55HmG1vlQMFhA4XJxI7o8Hlp9AjgkSqihQO0oKgUzRGxofL+SPCfP++R532eM6cxXw+cGZ2zhPhvUY1gpMcYVysWgdYY2OWW9uXMOhPRFhXlGlqXkWebTlqEoaPRMyj41WuEcaKsLfi+Hzi06S72i03kxf1dZDr6mRNCIZUWxIgtFC/jEhCxF6IxAeYtnec9B5haWaMzNETUa3HzzIba3tyuuMBgEquuKpXpufaxFm4GSCVTR90SpqmhxN4wqfsDnQ9eOXR877vmowhhVWKPxktFxo4pk3ONuWWSjxb5CiAduhX7xvZpiIFL49cyJ4JxfCExT1SPxvxU1BbmiUt5yVVOwIEo8g/c0IS5/2yRiKjEki7/prtfJQSHElHMoa9ummcOUjNxCCIIpEk6U9AXPMph8h6UqisqmqfES8j65tFMrFykFeZ5jTM16wWGtrqyGgeKg4nSq+ml4fpfqvd0yhqr9FvGXUnhq7WtYtMmx1scTdBmwNr7OZaBcJFbIqn7GWt9hUescZ0vqGA2yUC46w2ifFFCU1+CsRCg3EOpFcaLPeQZnwGpvUWmd+x+qEgjhKmvLWEsYxgRh6GsxjK9eVypCqQBnQacZfddhfXWFZnOOZnOBpeUDnHfe+YSxT3W+4YYb2N7eKlxhdlC/UlMs3nKpWSxFXYv/jhepx0USRhkrmfTFr+6LK5ucjY/RjCqZcQpj3H7HBd9HUVc05efjaldKhTOaaODcoDhzhhlmOHWYWrlEcYwuUoKN0TgncdaQ52klxKRU3n1UEwRlfphyoqDX9lXoQhYxmDL4StHvA6ikd5FlVK7EK+FR+3N468YUPUmEAGMVgfBpyRZXBNVtkQ7rV7JZlPnMNsqgsxkUjziBkw5ZNNYyxqCs9W49KJSWLViZfVGpUkG1Si7fF0oRRBFhGOKEI9ea0BoCFRCFMUoFGOvTuXvdNpsbq8wvLNNsLTK/tIdzzj0PKXytynXXX0en0x4olkp42qq41OgiBqWLBAVjUIEkkJJAKR/jcoxUZpbKwBXXvbj+lMLbFfVIO+tXBmOG4yHj4i7jrJVxymVcceY0SqicQ2m11FmWK7eYnI7RdoYZZrjnmFq5hHEDlefoNPExDit8KnBR62KtKZxIeGFfvBdZRxBFEAiUyXFS+DqO0r4qG4K5Soz5mIIBa3x6bWHCQFmx7kBgqz8nDNaVFd4CtMUEAm0NIQ4TBAQOb2UVfeXDPPf8ZoXQra/MRWECmcoyyNE2RYnAB7YtlTVlnS0KKcOKIDLXvpAzjAJUFBJGIUIKjDPetSgFcRwThjEWR2Y8q0C3u0Wvu0mStplziywuLKLOfRBaG3r9PjcduoEk6RUKZuAeK5kInPFUOGWSgRSOSApCpQilRBTK1TmBkJ67rWwlDYWicGXtS3mPxls34+Ifo893c2/tlqE2uv96cL58b1z2WJ30cnR/5X78d23sqcwwwwz3Aaa3XKKIOG6AsRid4fuC+FW1dmlRAKhwzhLnOXEjI85zmk1Dw/kqeQOek8tYhLAVzTvsFFDOuYpwsUo1LtOXq7/itRzso6znsNZX+jsGnGSu6JdiHGjrUFIVRZWylj9bsj4P5lIyMFvrkHiLSOvc1/cI6dOzBZUVYrRBqZAwiAmUL7QUQmG0Js8zcJI4img0Gkgh/LXUhqTXp9Npk/R75HlKszXH0tIS5513bpEl1+e2225B66SalzVljxZvvflKfJ96HCrpq/GV2mFRlLSYzo0IdjdgpB6tJ7knGN3X2NjOmO8BsCNON24/o5lkJSoF7NxJfNtnmGGGe4rp2xzjs51cFKMFhQArBZFf4adpHyEcWpeuobxKJcZZiGIQCicEgVQ4ucuq1g2EurFF75Fi1T1ULFfLNPO0NH417gWKwxivWEwQYAvW5TLvyQFGOqR1GFn2RhlUr5dzKS0UJyUKUTENa50DXukqFRRCueTukoSht0yCwBNmCnxMIMtSbx1QxqMEWmuUhDzL6HW79LodX8djM8Kwxd69e7ngggvIsgxrHbfffjtd3fHXyJXpyEXMxRqs1UjpUEoQjGSHVQFwN6RPKT7cVYDXH8dlmI1TRpMSB060z9HtR5VL/R6V96le4Dka05vFXGaY4dTiJDpReoHtBbREIJEyJAjKAKutAuclZ0kVP8Gv+L1bX4H0PUzKTpPjBYu3WErKeF/hPWY1Lfw/XrAIX4TpPCOwo7BQjEEXgjh2gbdmnE+kkEU3ZVlLSKhoQ4wB4RMYRKEIcIXLz9giMcHHmazxyQ7WeqsljhpEYRMlI5wrWhYYi3MZRjv6vV4tw8tgjcTkmrTfp9PeotPeptFsIpUkCmMOHjyIEJIgiGg2m9x66y2sr62Q52ml1ErFgvDp3yoYzs4bTs8tClcZXz4xLuFiNCZSvjdOUYy+d6Ln9WOWGA3OD383hudVL5octWTGuddmmGGG+x5TKxffUMq37vWxdOED31ZU1osXVbZaSZcdDxEUlf2eql4GhbCu1TQMBXzlcKFcWccCVKvTyoIpaFKELN93gME5PM280wjt4yd+niFeIwWUStAIh8Uh3YCZ2ViDM96t5pVLgELgbI7Oc5yjSk8ukw6MGQT3gyAkCEKkVGhtsZjCbZaT9HO2tzbo9XsVN5sWpWWT0e102N7eIG42EErhmo4obHDw4EHCMGZ+fp7l5SVuufkm7rzzDjbX14rVe5HNJyEIJGHR6bKOgVAWxTmU2WPF7RAMpStPomsZ9/5oxtaJiidH73/9Po9ateXn9cyw3Vxk4+Iws5DLDDOcOkwfc2nEXljnvp7DVUVp3m/vLQZTKJ68yJpKyYqiRM/0K3y6riyyxYTvG+8YxE9wlTEC0tPF+LA91fEGac8KsIgyv18IlBTYgoLdOYEpeLcyrbF5SeooaACREMiCi0ZQCFnrENbhtEU5jXIOoxRBkFdMAWWsxTlTnJv0HGfWImQAMsBKCUpghMPkuae815o0y+h1e7S3Nun1umS64CSTRQviXHuKm+02vcY8SoQ+SNSCIIxYXl4iCh/C/Nwiy0t7WJhf4NChGzhy5C6yxJN4+j4tgXfHCUHVqasM3FPalSMtlf3dHCYTFY7axd+B3QL4u7nKRi2Iupuzvs0opctu1lB9HuOslvocHojK5WTcddMOPRkH4DTHL4e4k9jzyXghT2a/J3d2J7HXk5rwyVyH6cae3PfgvpnrvX1pp1YujdZcEazW2IJypSSoLIWR9YUgOJcP9QmpLA9RumJ8v3evWgShELg6GWKRUixVaY3IShhKWcZYFFKayhUmBb5NsBzEYDxzgKhiQtZYtPXpxWXkJQxDZMFvhnNgrU+5ZrBqt5VSNVXdi5OuSgHG2qLHjEIGgWdLDgOsAGc0WZaRJil5mpEkCb1eh3Zni36/560g62rWniZLU9J+n6wg6QTQuSaMG0Rxg0Yj5qwzz2CuFbOwMMfc/DyNZou7br+ZbqdNoLzlIqVPVS6uAoVZMoShH3b5VJRCvLAUGMSkgB3KYpzLqa4gqt2Pia/sFhsZ534b97y+fflZGYOpj3/gNQzTZGtvgCmK9zJ9O790zYXj/Zcj2DS3c8VyPnGcA9pfNLzwib2JY40xyKXP8L/PeMHEsd20z52X7+Ov3rt/4th2t8v83t9h/9wfTRwrOutsiIPQmExRwvbNBJ1F5BRVgZoe561+F2oKUXg3R+l97dBU9yHbuINrn/19E8c56xD/fhNP+W9vmTxXo7mxext3Pufo5LE9y9y6YvHGuyeOTbf7mGth7uOTz6u3BptTaqHplUuz5QPmjqqdcMlpVXdplHGE8gddp3oXoqw98BlWVdV6rRWycw4nBsJp0NSqrJQvmJIDbwUZJZG2bD7m3XU+llLGYAoaeyzWOLQrXG1O45xlbk6gisyzElVjsoL7rGwp4M9x0G+kjM1Y8BaPVIRhgyBsAMpX8BtD0k9Iej2SJKHf69LttWlvb5H0upg890WjRcGmd5PlJGmPJOkSNZpAoVzyjCzLaDRiwjBg775lojgqrJi93LJ3L7fdeojt7XWEy3HCx6oERc8Z4V2Zo7+N4ZVQmUVWI4lkvJtq1IU1uqKqU/qfTOxl3DbjEgVGFUuJUdr/USqZBwYCon1vQMiFiSPT/i9z7JfeDJOquJ0j+K1nIi65c/LhDaR/8Qjuvu23ppjrOs/82d/gVy6/dOLIm75xjFd/d4tw/VUTxyo+xa+85K28+AfPmTj26v/X8bLw/8Ajf2TydP/29Zz+sa8TsWfi0HX+kXeZm9g7ea/8QqPBv/zsM6eiH1AffR/7/uppE2N9ThvMoz7LJRe+b+I+k3yDO4NHcOBX9eSxd1ge+cXzedr/unji2Jv+4Qgrb1vhyY+ffF5f+Ibmj2+fvHiBk1AuzdZc5VoQzvl4i3OFVSARdiBgRqlfqjTRokeLFApVFFyKmnKphBEUVo4kKBpbeX4vv62UCqUkYagKq0gW/U2Mj8ELr1yUKpWO8A3FsFiLz7pyGiGcHycFYRBUAsgY337YiUG22MB9M1CEpWVWKU4VIoMIUOTaYmxGnmvSpE+326Hf69Lvden1tul122RJ39ejSFHQ5Zer7pw07dPrbRNETZpQkXN6N4/FmAClAhpxg7PPPoeFhUUO7NvLwQOnceutN3L06GH6vW085UB5fcpFV+EiqwnxgYKox0yKm1/Pn5iQ1VUft5tSGefiGrWAdmM2rsdZdssCqwfx61llM8www6nD1MolbjSGYiLeCimFla+UL3m8xmXzuKIXvC9Y9Bw9QiqQvhd9FEUDASC9I8YrCEUQBoRRRBjGZCosLJjyc4m13jVmK7eYd7hZV8Zn/J8tcg6M0RjrihiR71Y512oNzaEs6KxSkQtlGQQB1sqhlFfvhgt9wzEV4RBkuW8tnGUZ/V7fpxj32nS7HdJ+mzTp+/bHzoItUtZc6cLTZHlCr99FhW2EEMSNlrfytCTPFMY4hNAIoYijBgcPHmBxfo49e/awd+8+brrxem699Wa2t9ZwLq9cl6XyFm5cNpa/djswRrDXtxtntZxImO+2Hxi2MuqPo83CxrnRRtOVxyURzDDDDKcG0wf0w8hHSKwDU7pbXKVcyr4rg4yxojMkoqJLEUC/CsQXq+CCzFIKgQrDGkWJDzcrGRAGEVEUk0chSRAUFfGyWJlKnA181bpxA+XiSqHv4xn150L4epU0zbClPpMKISVRGA4sMOfH6yLoPuhmWASIi4w3IRUyiFBhjJBhEc2xhXJJSdOEJE3o930MpWRf9vU/DFkGpWsxzzKSfp8w7Pq5CV+zIqXEuqKJWfWeQCpBa36Oc849j1ZrnmZrgWZrkUOHrmd9/Qg6zylD+TiHcIUlU0vvlrLkWCiGlr7VkXjGKOrKoi7QhxRItTd/wt6AGv5cCp/sIZGeqscWFKalNVsbg6OKVYG3psskEcp9l3OBimZohhlmODWYPhVZCO/zbTS80BW+OMRhi5qS0hLQRSTYZ2uJokK+DLpnWepdS8L3XZEqRBXKJQKfviukz5DCC30VhARRg7DRIEqb5HniGYzRCCdQIiAsWT2tw0mJsa6wrhxK+SlJCcp5l5fXkQ6baqzreaZT4RMFfJKyVxpe2NtCcQJFkSaFcpFBiFQBBBFCRjgR+rocp7FOY/KMPE3JM/+ndeap/q2jorwRYIV/dA6ccZjMkPVT+kEbpQRhFBDmkbcidI7Fp3c7Fxf9YyTGaYSULO/fy0MbMc35eVoL8xy66VqOHb2bfq+DEEW8rMiukJSJFkXQ3tXcUdUZ77RKximU+uflo5QDl1zJdi2EL3EqDOFq77bajauui1cQNSXhu8z556VZCthivmUTtGICPh42s1xmmOGUY/oK/Zr/utHwTXhUKZwG+WJe8FifklwVwokiC0u7iooFUcRfVFQ8SiyOOHYEBFUjMfAB9DCMiOIGUdwkS/vkWeIpWaRFFC6Tkmbd07H7uhUnBdaWK3NwVhRtkx1O+PqXLM3ptLsE0lsGrWZMIBViJH5UZqA5QOS5Vy6hJXSFawjflCzPc9I8JUsTb7lkCVmaeJJPnWPNzlV03W1VBvazLEX0fE+ZuNEkj2JAlB40UCEuDDE6J3MOm3hCyyiMWViY58EXXsjC3BxLCwvceON13HnHzWxvr+OEAefpu0WVSUZBbzOsHMbFIye5vEbjLF6BnJghuX7ug2OPD4ZWiQYFBfk40sxx8ZkZZpjh1OEkKPflDkFQ1pWUloh/H6zJq+A0eHeZw4E1WOfQuSCTkr6SqCAiDJVPnVWi6FkRIWVQpTNLKQnDAB3FhYJpeSsgLwS1zIeYcKVSKGexTmALl5G0vuDTC2bnj1O4TqxxZElGW3SKfQgacVzUsoxWgktwBiE0QdEorczwLYspszwjSfok3R79bte7wfIUk+fYIs6ym7CrKxd/4QVhLyQKYwIVeleWLJqvqcBfU63ReU6WZ1hrycMYJQRzrTnOPe885ubnmV+YZ26uwc0338DGxip5aoZSj8sFATUlUAb4YfC6Ps/6eycK3JfKZdy5lqgXzdaD+KP7ryv68vtWJlbUsVuK8wwzzHBqcFKWS/lY/riDMEIicY6CwqSsJ8mqH3NZtS6EA+v9/UZDRpHVpRRK+diJlL7gL7KWIGxUggS8eywMveUSx2lhuWRVZ8hSKCulsM4RUNLSF9lS1rvqyjiQAjCF6wWBM45+P0XITuG7V8hIoGqrX2tLJmEvzHSZZk1RjyOlL8C0Jb9a5q2XNEVnGVbnQ2wD9etaoh68NsaQpyl91UUVsSZjLEEU+QQLSoswQ+eatFAuzlp6XYWSkkZrjtNOP424EdJsxjSbLQ4duomVY0dJ0xSK+TivI7HU3GHVPMe7xUZRF+Tl98RbrsP1NaPbTiqQHMVumXu7VevXLaIZZpjh1OCklEu9AtsLDiAIieIGTT1XuYSszqqsMR+r8FaEY2DBGCPIs5R+r4tSZafGgv5dCByqUhYlPYhSgSeDjLySybMUozXCegU25BIRrujfAqLsAIavvi89/c45AmsxQmAFntq+1wNZNNgSLWQtg8wX5hXUM/VmXdYUGVhQBvuhTGPO0XmKzlNP9nkCIVcXiCWMcWRJSl92Efi6oShuIqQgDEOMVNjckWV50SVT4lxBM5P0cVLQaDbZu3cvURQx11pgcXEft958PXfccTvt7c0iRlRcH1FYGUMyvvZixCIQFZX/eBeXzzB0vqB0jFKpL1omuc1GLRnnTJU5GIxQ3ZTHL/8eeEWUM8zwrY2TIiHf+UMHpEWFirgRo3UDnTdAJxid4YwmdwakwQiLEdIbD85n91gcuczo9/uec0z5tGQhFTiFDQJcGBEEYUEZExCoiDhqYKMmNs5x2tC32ltB2OJPorQiUM5Tl1gfX7ESTCBx2hWRc28pIQ0O35xM54Zuu08gIgIZgowIlHedeZ6Zop2yc56WprBSslwjlCQzltRkvi10nvrkgzyrMs6cG786H5dia61FaIlBk6aJb66Gb2wWBpJcKTCW3Pi0Z4tXOM45jNUkaR9tckye02i2mJ9b4PwLH8LcwjJ79iyxtLyHm268kZXjR7Ek3sXmZ0PJwePnWtbFlM+KKI3wWWelvVAWW+74zowqpFKhOF+PVCYTWFcuDHa64HxYv3DDOrxr0TqQrlYHVbbULvjFiv0j5H1EGnIP4XKcy6YYZ0FnYCdXnDvncGbisGKMw/sQJsG7n7N0cvFenhkcGks6eQ7kaONI8skT1saBzCFPJk/XGv87YfJ+HY6c6a6CBU9mOwWcdZCaid87T2ZrMWZwvcpf3CiMzXAWbDb52+xyv2+dTp6vyX39X64n73dMuHhXTK1c6n0xBoF6EBIUkjAKaDRiTN7EZAlZnnoFY7X/UShfC0NRyAi+yZXOHVlS1KxISaAkgQoQThCGke8a5qyntcciFQSBIopjrG5gTIpxmbeQSjeYKylLvCCqBLfPbaOk2JRFy2BKg0n6TK08y2m3O8jAZ4M14wgpHGgA51mQC/2kjSXLchApTlpSbUj7CVnig/hpeS2MwZ4g1lJHpVjwgtwYi8g1mUoRSiCFIJBemOZhjimYoIUKsQV7gFdmmjSV5JlGa0NrfoFGo8lZ55zF4kKTpaU9NJuLXHvN1zh69A7yvF8o3dpkyrTsMuW3FPxSMKgTGo7PjFoh/jzGVNxXXjef5OHK+JWQQ4q2HF8qGIpHUcT76lxog1qsAWWNqMVoHjjQZBu/hRCTu2Pa7Gvwf546XuKMID6acsFtp00c54DN/CsInjXFWMdHP5/yqV+8ZuJYs+k4G8HZ/BFQLUt2QADH0Pzvv0x4+9/eMHG/mz1LmL4ZJf9w4litryNvBCCak8dmW/zP5kMJxGTFfUt+N9/+D59C7pJoUsc1166y8ah/mzgOHNu3/Ru/d8v3TzHWEDX20H7hIyeOzPIud4R38Llrbp44dnVji3/72hl85POTvzfGrgK3TjHXk6LcHxRHlsrFubJWwqfFhmHoLZis6VNujcZYT0OPNZ6oXwVF4NsrGk9inGPzjDxNyPo9UhUgHWANAlf0pgeEbzKmQkFoAoyJiF0Th/V0MtKvYE2gyXONUgYpAwLrCyGl9MzOkhyNRmIK5QM++dh6AkpnSbM+W1tF47DlJcLAt2R2TqKU8wSVzgv+LMtxJDiRk+SGNOmTpglZVlosRY976ybKh6EEAucQtuBBy0EoCVL4BmUqwAmfuu1EQaVjfSquVhJrVC0RwaGCgEjHWOuz7g4cPJ1mc55G1EIg0dpw/NhhtMmomCpFOZ+dSrHKMSvmK0VhIYwkBfhXOwP6rlgAjAbc61lz1bHKOJ8YVhDjYi3AWNJLKeWJ+DfvBwREe39lOvqXjZ/HPvTLk5WLg9PXFvmt/hMn7jPD8BN8hJDuxLEGWP1B6LxzilX71+Cn/l7wBiZbOR/B8b8XLBfum3xnDq2BvusXafIDE8du8VtcfPrpLEeTaWX+deX3+NFzfpD5YPJ9eN/qH/Hrb34VYTR5QfALz3sHc2f81a4ZjyWc1XztjseT5d8+cZ+Qcv6i4ymPuHLiyI3OTRw487f5X5ddNnHsP3zlKzz9S7eR88wp5vAJYDIPGnwTvfl2CINyaSIgCAKiKMY0W74XS9Wb3osWay1RFPtVqfXr0CgMiGKfBTa/sEhrbp4win0nxzAkCIPCdULRkrigamk0aM7NsZjvwRhNnmVVRpbOc880rAfV9VprkiSh0+vR7Xbp9Xr0ez36SR+XpghhcapsNpZitCFNU9rb28RhwMJciyKVDZAI7U1eoy2pLRqAqQCjLUbnmDzz8ZYio6zMnNstxba+Uq/ztYliJY81kOeVchH4NOowjKpamzAApSRZ6oW0ddbXCIWht9mM74QppCAKQ+bmFzjnvAeRZRntzjZJ0mO7vekTJZwB5++hcMOpwc45XwBKWUzr1VHpfNoR8BeCsuHlUPCeQoGOs3RqLjRvtYy4yUZiNWWcqswwLFPn6zHCB5Zy+RbFA9L/OMP9gW8qoF9xhjnrfYDWlkS6BErRaDS9e0p4dt4kCjHGEAYxS0vLtFpzxHGTZrNJoxHj6WAEzdYcUdwA4f3vCF/IqI0XHmEp3J3zqbgFkaVQvr2yMwZrCsZmvJumLqjzPKPb69PttGl32qyvr3L8+DHWjh9ne3ODXq9bpBxY0tQH4Pv9PltbW8RRQERYxEwM1mVkmQZ8UBkHMoqxxuGKwL0rA8qmUC6OHUJxHOqJCdZZpPDUNhiD0NoXClrP8Bwbgwp1ZRGCI68RgTZaJVGlQ+ucftIjzTPfpVJFIAUHTz+Nh33bw3BOc/z4UTY21tne3qCfdPzx3cBCqJSg9VfKh2ZEpRBGrRbY3TXiylDOyHhX3LO6grFFpt+JCjbr7+1W4DnDfYzZ5Z6hwEm5xYao7KvVpPdxV90bEUglieMGSklarSZSHKARN2k25gjCkED5hl1xHNOaa+GKTK0wbqCCiDzPEc73kPGxFoE2BuX9ND6wXVhDSF8rU6Yxl4HeQSB6WDHu1RpjbUUOubW9xfqxo9x9+E7uuOM2jhw5TLu9gZCCJMlxzpCmCZ12h+WlJfCEKRgLaZJU9DHWaEJjME6gsxSt84rm31hfFS/FTtqTE6E8R+sc0lrPoWYMuojD2EJhKWtRyhT3wTd0sw7CMCSMG5gi6cC6FMiKLLoiJdr6NO0DB/YxN3cxW1vbrK4c56677uDOw7exvr5GnvpgoyhqgJx1xcKiZs1QVOCPxEn8Z0XhbHlegzvjXWaupqQqV1y5QY0MlYGCk8X4emabKQpch+I0YtBcboYZZjh1mFq5+JiFrAKrAMJ5Dq9SmAljccYicMzNtVBqkSAo3FtBSJ4bVKBotVrefYGg1Wz6nBVtUEHouziGMc4OKPuFgKCggdHa+Na/UVRkX3lBpIT0q/dCsGhjaklOA7eJVAEqEAjRoNVaYGFhLwcPnM3ZD3ooDzp+hNtuvZFDN17H3XffQRx1yZKMRhSS5zk697EjFXouM5VIfDdkg9E5YeZzTowxRepx0evFFClyFDEFOaauQxTX0Q7cRD4bD8804BzSOoRx3mUlfH2NEDnKggocAo3REuMEQoSowCce5AZEloP19TBaa3SeVG2ZhZA0GjELi4vsP3CQs885hzPPOot9B07j0E03ceTInSRJr0ri8O3bqAKblaIf/FMkUFCdsy3YGyrqfyEwNcVQCn8plU8vrywlHzOyRaKAktIrMKVQomwiV+ykSDxQUqJOUAMzwwwz3PeYPlssz6DwZ5erU4fFWVN0YfTpvA5DGIQsLS3TaDTIMk0jbhA3WyRp5lfTYUCe+1iICBRSSCIVeL6vwuKQgS8CDMKIMrEo15YkSz2JprKoYECnbk3RuEv45AKE8EKqzBIqsrv8KthbYUp6ahUhfb/5ZrPJgX0HOPOMc7nx+mu59eZr2NrYROBoNZukaUIQBb5o07qCIEv6wH2eg/DFoEBRA1MWkPqOmD5vYEDVP+wOctVfPf23St4SZYwCpFUIIXHWNxeTUmKNIM9KbRQQNwICFYNQ5HmO0T41Ok36ZGkfk/kCVOssQRCgGw2cyVF797FnzxLLS4ssL+9jaXEfN964xO233UKns4VzGocuUqKHXVF1VAuQoWyzInuiMHgLX9rQuPLaBUXPjLoVI6XElG7OGktyPTZTXcry6UgMa4b7GLOYywwFplcuOgU7cC94eheNMxrrLGEYIoIQCBFCFdXxntxRFPUr3irxPFlhaIq4hsNhvBJxfiUaBN5tpnVOFEY+3hIESOX3mSQJadpmfn4BIXwzr6Dox5JlGUGgAEGa+qY2ZYGdEJJcZ+RZ7scHJfuxKQokAxrNRc4992EsLe5n37493HDdNWysrRAF0hNv5hlZkhBFDVLdR4qAPMuwOkMIQZ4PqGgcPotKSonWBdX9LoFpwBd+VEv5UhALRFFb43vYBN4aDCLvYgwjojj2dUJS4ZyvB4riJlHURDjI0sQrln6HJOmQ5wloU6VpuzAEk4LTCGEIJCwvH+Ccc85hbm6RxYVlFhf2cujQdayuHSE3uW9nQCn8oe5s9+uPgTuqTKrYId+LTLRR6hfwgfmy0RwM6IfKgsiSwaCuWHbr3bIjwWCG+w4zHT5DgeljLnmKq9xioqielwglyHNPye/wldJIRRgVWUwI4kaTIIzIje/W6GMoljBu0Gw0vBvJWrIsI2o0CaMInVuCUBJGMXmWI4TE2IwgDFmIIra2tzhy9CjGeFdboAKarSbOOnqJQUlVBPapmn1JKdHGswhoa1A6xxTCK8914b7yJsLcwhIXPuSRNBpzHLrxWlaOHKbVDEmSBGO8YgyUV3g+DmGIGjFJ0qPTSbxCCxRhEFSuRFekFQ+C3x5VQSquSLeWBVlnQBSFxHFMo9EoEiAaRKEvJA0Kd6OKfKGpCgJA4YREiAAhA5yDJEvp2px+QdKpc4Ow2kePhMS6Ii5EocRkgBABS0v7OXjwII1mi7n5eRYW5rjhhms4fPh2+rrtFxeOqttn6QrzpNGiSnEuU7BFwTpdVc0X5zxaPAqeHLXdbg8plzLuVyojU7bZrlkwwJASKpXPTLnMMMOpxdTKxVmDcQaBKFxbklAFRGFIonIaccsrlzAibEZY6wijCKMd1hUxEFWkgyqJikKUVGhj0MYQxw2Qyrs9pALhCq4ySxAGRfBYkeUZQRCwsLBEr5dw5+G7SJKEhYV5wjCk0WgihCCOoiETXeucKIpRYUBecnAVbjIZKEyuMdq3HJbSp/o6EbBv/2nYPEXYnI3Vo5xx2mkcOXqUNM1QYQRAI46JooBms8Fcs8XRo0fZ3t72x1KKOI4855n2wfbh+ouSzdkXkEZxRLPZpNVqMTc3R6vVLK53WFlgSioCoby1EoTeYsEhJJ4ENIqLdssRUgQY58izvfS6B+hst+l0OiTJFmnaJ8s8D5xxviBUZ4a0l9JW2wgZsLgsWN6zRBQ/mLn5JotLiywsLHPoxuvY3loFPAN1acGY4tzCIPD3VuuiDmrQHrr6E74gtHQTlpZLo9Hw+xpJIimvGQwTXMLObLH6Y7n/GU4BZjp8hgJTK5c8T1FBQBzHxI2YsGToVZKFhRYqCEEoZFgUGDmDEoogkoAgUKogn4yw2te+xI0IpSS9Xp88zypSxizPyI0mDENQkOmcLMsQQiGUICtqR5qteRbmFzh212GO33WYqBFhgVarydLiUuVSi+LYB6CVQgWBJ4TUeeXv9zELS56nvvo9UkVGlMPmOSqMWd6zD+Fy5iPJ3oVzOb6yTr/v+9kLqYibTZyD+bn5ap8rq8eJ4gicpdGIff8R4SvLA6kIlCIKA+IoYn6uxcJ8i/n5eebm5mk0Gt7VKApBag1pPxsI1cL/EIZRZX2FcYMobtBqztFotIjiBmEUE8QxjVaTxdY8dt9BtDakukc/6dHvJ6RJSpKmWGNRApxUZHlKe3sTsMwvWVpz85xz3rksLC2yZ99+5hcXuPYbX2FtdcWTcZqC7RkIwhBtHVpbyuZjQTAobCytDx+mdyClj4+pACckC0t72N5u+8Zrwse1fMp3WajpU5MFYodSGTAxDCzEMklicg32qYTDmQ2Yiv4ln46fBK/c15lMkZL7FIspCFKKkto+cHSKwWvQAY5OoWU2ceQW+lO0ZM8tWLYwHJ841tIjNW36enPiWOMyOrozlWWb25yN9a0qHngiaJ2hs2MTxzln8AQ0/YljIcNYRy+dfA2SfJ1eknB8Y2Pi2I1Opzj+5LFMUXRbYmrlsri0iLWWRqOBtT4dN4xCdK5BeDfX3HwLoQLvnnEOqRRZWlKfOIIwRkmJsaCkr0Mx2hBGIdbikwHCEIcgNxbrIM1y8jwvYhmmiCv4LpJpmtFoNmk2Y2695RBJ2i/87o69e/extLSECgIacUyz2cQ6VzQo8xlJxhZZXGV7Ya2Rynd3dM6ircVpi+73SJIE5xxxHHD+OWfzbQ/7No6vbpBry/rGNgQRWeaV4vLyMlIIjNFsbW/ijCUM/aUOwpDFhSbzcy0acUQUeNqcVqtJFMWEYViRTyb9HrZwF5YkmTBw+yilCIKAPM89k0AQEoYRSbPlrTQZEDYaxK0WzcYcYdQgDGLCMCZszLGwsOBP33rLMtcabXLSJC3YBXI6nS4ayYKDVtMzLM/NL9Cca9FqNrnxxus5dvQo7e0tnNGEkfL7yQ0UQr0RR0hpq9hYCSllUdPiY1FRFLOwsMDC4jJr65s4IYsmaP4eKSEqF5x/dFC64EQ95jMI+EPhjnSO5mQ2kFMIi27/MTAF/YvbhDN/FsoW1IN8D4/a87uv/VtefvCOift0zrJ1VOGiyZXpOMs5H9I8+vN7Jg7dznLes3g6n1h8zOSxyWFuO/INvnJkafIU2OI8Xs785NmS4/jqEQimCABtC3jbhRdAPPnLIf9tnf/z/pcxQhQxfr9bTZ548EUTxznnuDVOaJy2OHGstSm3z/0df3nhCyaP3ezDTfDhT31q4tjs7rtoXPgPRGf9/cSx+bEe/clsPcBJKJdms0mW5SgZEgZBVSgZxTFzc/P0kxQVBiAUaZYTKIUKfMykGQSFqyvA4ugnGXEcEQpJlntBFoQhQeTjNta6YtUu6HQ65HleBOq98Oz3E0xRcd/t93ChQkYBx+44iklTJI5jdx9mz959LC4uVrEKqXyxZhhGSCXRedHDvigK1Eb7vjLFqtoYg9OOPE3od7fodzdZbh0kiBqcc+55PPhhjyRsLZLkjpX1TY4dvZvbb7mZzfVV5ucWOOesc5EyoNfrFtdOEgaKg/v2cNr+/TRbDd9wzZtV5EXLgjRNfbpwYaGZIk5Uxo1goGBKxaOUwqV9r2yyBIRACkXUiAnjBnHcIAxjGnGLZrNF0Ih910+pCAo2hEbcRASLWOdZlpMkoZ8k5FrTabcRSObm5pifn+fCCx7M8uIyDzr/wXztq1/hphtvpNtps7m9gdbFelgIwjBABRKdZxXD9cDC8PEyaxzNRoNma47HPvax3HDjoSpmUiYDhMGAzmbgWgNPJDqcJFE+DjMoP9D8NYpw+b9PR/+iX49949tBTfi5Okd07TU89PzvnrhPazTHNo6i95w+eapG8+igy6sfNZnT6ubtNr93+Il814P/fxPH3rbyCW5b/f/jeMjkOXAjj+E4500x8l/whl5rCuvpZqHov+ItsPfgxLHB/3oKT7r8MCqePIcv3LTE237q5ydmKWpj+Nxt72bfw6+euE+dr3PL2dez/+pnThyb3bCC+6OAc9/0poljtz79aVZ+71qWf/DMiWO3/+kY/Rs2J46Dkyqi9C4O34/evycLmnxjfUC+FfospiTvoMIIGYboNCPTXmg3WzHGGhaWfCvgbq+P0RlSKRyCNMuJ4oAky3yqrXOARMoArX3/ll438dliWUKv16PdadPp9z3FvjWsrx8nFAInBe3tTZaWllhaXCSKIsIoqtxScRx7wa0HxYda555AUymcM1hjsMZhco3O+jiXsbG1Rb/fY2NtldPPOo/5xSUOLh7gwY9q0eu0ufO22/jSv36RW266ibDR4vTTz6TTabO5uU6Wp+S5xmSFS85YjHNYLBZJZq1PGy4UTD2uUMZGoigaG3fwMQWNcxqT50W2liJJekWKdIBSAfPzC54hoekLVoXwRKFxo0ncaBG3FgmiiGYj9lZhpuknPfJck+c5vV6fKPLFr62585hfXKTRmmPvvoN89StfJc0zSJy3AoUjigLStE8gB/1d6jEUrCMMAk4//XQe9ciLCKOI7a0twjCk3/eugmq8G467eIulVtQ75rqMNrmbYYYZTg2mVi6BisidRqmwEFQhQoLFglKEceiD8bbIdgpCnBPk2tBoNIoCSEta1Lo0my1A0rcGofyqvVQwzkKWpySJp5nXee7JIa1ge3uTdqeNc5Zut8P6+hrr62tsbW75qnhr6PW7xHFEknTpd7dJesvMzc0RRiGixjDg63CyAR+Y0TgcQeBrSIRz5LklCkKczYkjhXSW3vYmx7M+SZKy0E444zzJ3kCyZ+9e9u8/yNnnXsBnPvUZrvv6l8lTzfLyXqSU9PsdrDFstbvEjS3m51oI57PNrHOgfM0MUFkuzrnKleTjToIoinbUyVhriQKJMKLgdPPFmlmW+xbS1ls11miytE+j3yCKfdtkJQPCMGJufpEs6RPEDaJGizBuEYUR4cICuTakSUa32/Vp6DiCOGR+aZEHXXABcdwkbjb5+ldDbr3lJvq9DmEoSfpdwKFUVFkjZbp2ICXNRsyFFz6Yiy66iIc97OF88EMfIooittttoM65ZkuqzOG4jRvfubLcrkxXnmGGGU4tToJbTBIFIVL5VOKymM04S4yo6liiKCLNvP9+fn6ebq+HsZY4CJAqxGUp2ho6hauo2Wx6oYNgc7uNlBlSKvJM0+n0CAJFkib0+wk6N7TbbdbWjpPnKb1uh83NTY4fPcL21ga9XgfrNBZD0u/TiGPSpM/Wpu9AEuYRKlBVPUqv20EpRdLrFUK9bAvg61OE89ZaJgPiKEDJgCgM0HnKVr/L+mab4Pgma1tdznrQg9i37wAHD57B6WeezVOf9qM0mw2+9IV/Ymtjjfn5BYJAFIWmik4vxSEROJT0WWOuqC9MswwpBHleVKpjUEGAkj7bTEnlLUglC44v8GnMAbawWAS+DkQgEbaoWLeQpxlYhzWaJPExKqWCSvGYzXUazTmCuEXUnKPZmidoNFEqpN/398xaQ5olNOYaBEFEa67Jgy44j8WlRVqtGKkkK8fuZm3tGOCIwhCKc03TFCkcYRCzZ3mJiy++mMc+9vE88pGP4h//8fPkmabd6QwpB/BEMVIMGI/Bxw1s2WKhsFLqKcij6c0zzDDDqcPUyqURx2hjCMOIZmvOu7KEKGhbGlinyZKcMGgQRU3CMMJZSSOeI4oistRgXA8hFM1miyzLSNOO5wQDgijEOsvW1haBishShywyw7rdHlvbW3S7PdIkYWtzk/bmBp32Ju32Nhtrq2xvb2BNRqAkqtUgT3JffBl60swk8bUnSvhYjghCer0eqbUoaXEmQWtPNWOMZzz2LMwQhxHGgQpihIA0zUhyzxis9DpKhUgRsnF8k8N3HuHMs8/mwoc8mB+49IeZW17iw3/5fgICVJjhyJDSsxFkuSUKA3JtKHR2ISBBhQFS+joUIRVCKIKwXJ2HKOUzpwwOnWtybSuFL/AxK20cSnqXozVFXZIFaSnaBGSVVRRFEdvb24AgjBoEYYPW/CJxo0XUagGCTq9PFDfod7eJ4gZJN2Zufh4pA6K4wdLyAg99+CMQSvHP//gZdNan67w7LdWeRRmbs2fvAg++4Hy+/TufwCMe9VjOP/9CDt91N2tbXY6vb1VpyPX0Yeds0al0EEPxVkrBZ0ZZR0RtG8doyvIMM8xwajB9KrI2CClwQhLGDdIsJ27EWKlpLSyQF+4sKQN67Tbz876/S57nRFFE3IhJM09B3+/3Mcb4XiRO0Ot1ia1P5c0zDaEiSRK0yQFHu7NNu73N+toavW6P7vYmW+trrK8dZ3NjA4fn8tI6IwoVrWYL2VIY7QWU1po0TYnjmCiKiKKoWLErNjY2sDr3wlj7okBfsGfRuSaOfGdHCQRK0mo20VqT5waDxeYW61bJbMhpZ5yJk3D9tdfS7XZ4yMMfzvc9+cnkvT4f+dAHUEHMdqeLC3xfm1gAQhJFIcYVVDFSEsWNKoXaGs9YIJVEa+861AXrsy8+dfSShH6SIiyEyivpZrNZxGi8sjHGd+sMo8ArpYJx0xqHNoZezzc3KwklVRgxN79EGMZEzSZplnn+OKnQxiJVwMH9B+ls+kSMRrOJtZ6AtBHAwf17WD12mCTNSJIuQljCIORRj3oEFz/+8Vxw/gVc+OBvY25+iY2NTY7cfYTjKysYa3x75hGXnxQDaph6YaQo6bhr1DHULJ56EeYMM8xw6jC95dJaQGuNCiO2Oz2CIEQ76CcZUZShtaHZbBFFEQtFZ8QkSXytCtDr9VBBSKvVIk1Ttra2KroOYxztdoftdhvn8LQw1rtt2u1ttre36XTaHD9yhK3NdfKkT9Jrs7m+wnZ7C2sNc60Wc80G1hqUDGg1W4RBiLXW93HpdEjTlFarBVApGSkFm+trgKHRaFWB80D59pRGaxpRRKACT7JpDbn1LYu1FVgU7c4mVkbML7SIohAVxdx5+x2kWcZFFz2aH/yh/4ftzS0++9lPoHNBN/NWkrOWuZZDK0XZoEsqH5MoiTvz3BAGOUJKOp0OzlqiOCKMfJFiv5/S7nbJc0MjauGD+o5uLycIA1otcFaT6wwhPBVOpi1x4BmOtdY460iz1Cc1ZCkCiOMmna1trIO40fCtlI0h1wapQvbu3Yvud3y3UKkwhXtuc3OLuw4f5uabb+LIkbtIs4QoEOzfM893f/cTuOhRj+Gccx9EHLVYW9vg6PF1lAo4evRoQfe/BuwsfByt4h+Or/gxVaZYbfwMM8xw/2Bq5RI2mgQOWnNzaGv96tfogkzRkWYZubYsLi5hjKHValUpxFCsII2h1+sVVktAkiRkae4VUZbS6/VxeH6wbrdLkvTpdrusrq1w7OgReu1NttbX6ba3cTojDhXLi3MkSUKe9WlE88zPL3ih4rwCKefRaDTY2tqi3+9XCiaOi7oSAZ32dhE8D0mSFHBIHCqIkQgacUygAhyOPE/JcotFIUNfWd/rbHLTDW0OnHYmp59xNkHUYGt9gxuuv57zz7+QH3n60zl6/BjXfeOrHDt6O8458jwvWHypYgZ5nqONVzx5QYYZqAAEdDvdwhIMaTabqDBku92m2+171eR8kahAkGYpDRrYnhewWeYz7MIgIMlzmmFAHPpz1Tony3O0zjzvmAXYKGpQpL8mWQLCu0JbrXk67e2qvCI3hiw39PsJGxubHDt2lF6vA87QjEMe8pDzeeITvosHP/ihzLWW6bT7HO9uoIIGUaOBsT1WVlZYXT1OWbCxMxA/3Pa47hYb1SGOnZbKTNHMMMOpxfT0LwiMc77y2jrPedVooJSi0WyhjWcJTtOMJEkq4sEo8hQpnuLEu6Hy3BNHli6O9naHJM9wOHp93yJ4e3uTdrtNu73N8ZVjHLn7ML2tDSIlacYhRvhOia1mTGsuRueatO8bfC0tLPpK8cAHqksqlTAM2d7eJkmSKqXXU8ksQGFpKeUFap7nSGcIpPTV8kHI0sICAp/y7POlJGmaYIwjjFtIJBtrq0gRsLRnP855xbvd6XPRRY/muf/1p/mTP0xYWbmLXpJ4On5niaLQW1xKVR0zbaF8cAWRp/DWX5IkxFFIs9X09UX9lHang8V3qGw0GgRKIRQ4YUmzFCcU3V5CkvQJAoUBUukVZpZl9Pt9rDEkWYo2RefOguk5kAptNBaHNQ4RBMSNdqEcvcWaZp7IcrvTJss0zhoCYVleaPEdj7uIiy9+POedeyG5tnTaPZLMIESA1YamDHDA6toq3V6HJO0PpRYPU7nstFrGklSOqXuZYYYZTi2mVi6tuQW2traIwghlfT+PPNMkSU4c+4K9IPIti/M8J9eGra0t5hfmcVnmBa0QuCLDrLPtLYVQBSRZnyzP6PdT+klCr99lZWWFtdUVttZXybOEWIBsRKT9HqkxzLdazM+1Creb7xypYx9bscYxPz9f+OoljaiBaiqiICJUIe32Nq1GExv6nidSykHdS0GG6Ls9aj9ffIA9UL6XiLEWnRucFEWQX5JnGhVKmkFImiZ0O9uEUUhnawuc4Mv//m88/rGP5dKnP4P1jVW+/rUv0e91MNa3JPDhl0E7ZvDpyMb4AklrLLn2hY1ah2hjETIgzTKyPEcg6CWpJ63EW2Uq8Fl+WAvW1+s4Y+nRx8UhxvjjbXc6WGuqgkyd52idk2c5UsiBcrHO9+HpdHy8J8vRFjLtyK0BfLV9qBznHDzAE77jYh732Mcwv7TMVjcjDGIarRZWZDgnCKIQFUjuvPMIdx89ghABUdTAKlOwMKSVonFVSwJfqC7K9O16awI3+LwkxMSJgiz0gRbQd9j8FoRoTR5p23DDN0BOJrCxSY/O9srkcdbirIZsMlUM1rDtcm7ebk8cerjTpZetsNq+ZuLY7f7teDqRaWhHemwDq1OM7OOLKKdZVmiAW6+FtaO1LUqbvPzO+Oe23WHt6yCjMTtyw5v1OjnXHL5t4vGNtWT5Fr3210Z2tHOnRm9hN3qkXzsy+Hh0usWjvm0Dd1zR/erXdjudavvk5lvQGxnpnZOpXfT6lDxEnIRyAV+IZ4wtquW9IomjBtZY0iTFhl4QioKAMsvzgqbFr4iDKPJB5jj2FkqWst5dL+hdMjrdLltb26ytrbK2usLG6gpbG+tEgWBpYZ6FpUXswhxZ4mtgup0ey8tL7FnaU1kl3a53HVnjqWrSfkozbtJsND2LsfCV+d1OhzCQKOOthVKwCuGJOa01ZNripECGIa6gvJfC4IxBZxkWg4oEQlmsM4RCYK3vnWKNdzMFQUhnaxOhAq677loe8pCH8ENPfTobm1vcftsNtLttsjQrrBefepwkCVJK76oqKvNLN1qWeyZnbRxxo1mcqyeIDOMWWeazs6QKCa0jChVaZ+B8VplSCi0NufSFornO0cb6VgJpWtT25IWC8W5PbS25HvSmoXBPaeOwpmxJbZEKWo2AB59/Lpc8/nE85IIHE8Ut0hzi5jzaWDbbXXC+k+Z85N19t956q4/ZtVogBJ3tLV/jVLdQCsbm0t1lxhBXDjFNWzcUi3ngJYtZbP/zIMZJqtGhR2j8v28b0L+cAIvt27h08xsTx2kHNyvYs7Q1cawx8PmbJf98ZH3iWIdj34PafCO+c+LYdrjK0/kaF/P1iWP/Dcfy8+DBD544lLs/DZ/93ORxADjDE/78aYTR5ISP6241rL3jBVXW4olwdO0DvJpXTDUFOX+Q73zM5Er6NM049PerdF/9lck77fXhxk2O/9Pk+0tvBW6/i42PHZk8lukJYKcvogyCipk3iiL6/T79Xp8kzXyRpPJU6mmaesZgpWg0GjQaDfr9Plme0+33CxoZr3CSJKGXdNnudMjTlHZ7i6NHjnLbrTfTa28RBQFzrQCJA6cJw5hWcwGjDf1eD5Nr8iyn3W5z4MABzjzzTNrtNpubm6RFa94w9CnHcRwjpSQMQ5aWFjl+/BjtTodms0GW+bmUHF2+N4zPMnNl0Z+Y83EjawkEhEqgrcPqDGcMTllMashMjrXruKN3s7C4l3POfRD79h9gaXGera1NDh26mYd92yP44Uufzof+usuddxyinyUgKNyFPhsLY8lzXdSV+OZZvb4XuNblCBkU1otPNc61rRiEAfp9X8MiHORZVlD4h1WKb5JmRIRY60hSzyOWZjlS+DqbMuZhjEGbgUVXCnHrwFifiSaxRBL2LM3zuMc+kkc+/OGcdtoZWKfo5Za5RuzZsiNJEHgl5Wuc/Hcjz3LCKGRze4tOp121SCiPJaWs7sloD5fROAxQ9MAZdqs98KAIFn9mKvoXpw/R+MXnINSJLRfnHKd/7rP8r8d0Ju4zMfBna3DBwybPNE3grpslIZMVocVy2vkX8Ogf+KGJY++64Xqe/rWv8mImC+ursZz7U44f+ZHJ8zUGPvsPTNVbRgDf/b3zzM1PtgpX1zP+n599PkE4mQ/u9sOf5jtfvzlxUWM1/Pu/HuT5L3vuxH22tzq8/+t3YH/w+yeOZWUV1m+HRzx78tjj18Dtn4epqV2noTs9KfoXU3FcKaWKdF5fo9Hv96vVdrPVIskyOh1foLi1teXjLEb7XvBSkiQJm5ubdLodOt0O/cTTqRw7cjcba6tkvQ7kCXkOoWjSWpin1WggHDjjmJ+bZ3lhkaTfp9vpkOuc1dVV9u3bx759+2g0GqysrNDv95mfn0drTbvdZmlpiSAIaDZbxHFMr9crlEhOv98r+LsgimJfoGcNudHQc+QLc14wS4vWBq0NuXaghM/wsjmGnNAJoqjp2zpnGasrKwghSJOURmsOZyxb210uuugxbKyt8ulP5hw+fKtXHPgVeRj6uWljyTLPL+YTKHylugoEQgWkufZuOudTmOvEkGmaIqX0LRGKGFOZMFA2VfMuReHPJTe+vsQ5jHEYCyCKdgkWH1D3qcuudDRJ/3VshAHnnLmfb3/cY3jIQy6kNb9IkhnCqElrfgmpomLuuvquGKNRgWRra5t2p8PW5ha9Xg9rbBWfKxWEtZZcZ1g7+FKPsiEPVemPNGR74CqYGWb41sVJUO77avoyqylNUxCeFj+OY9rttk89LtxSZW1JmS0WBAH9JGF1dbVKDd7Y2GBrc521teNsrq2xvbmO1RnSGaJiBS58VBwhJGHgSTOV8MHouWaTOIrY3NokyzJWV1eRUrK4uIiUktXVVbrdLq1Wy/dXKawpWXCLdXudgr/KkKb9yhWTpr7g0miDK3qQtNtt0r1LLDVbIEOEFKhQ4mSADCLmoxgVRQgZ+gwzqci1ZXtrk36/z569+9i7bx/gcHnGrbfdxuMf/x10trexxnHnnbcgJCRJHyF9wzVnbNVnxaY5TkjSLCUUqnBFSR+rSlKiwiopYxXlPer1+kXHz0HNT57nhI0GVniFVCZpIAR5mmELQW2swRhbMFaXghrKfpqBcCzMx1z4oLO45Dsv5qwzzsIaSFNB3NpD3FggCmOsMWS5Z5yem5sjikKyzC9GDh06xPb2Fst79rC0Z5ksy0h6XZIkqaxJX3/kzfF6j5Y66rQvwA6LZqZgZpjh1GJq5ZKmqQ9+C1mtjrXOSZLMC6swQkjJ9nYbbT3vl9Y+9TgIAtI8p58mZGlKr9djdfU4K8ePs3bsGJubawhnUc7SajR8lX1R5Fh2WwyU7zYZhiFhEBYuIMXS0jIyUGxubpCmKWtra1hrWFpaZt++fayurtIp6ERWV1fZv39/Ue3tTUBPpW9IkpQ0TREFzbuUnjwT4zPGpBDoPEcFAXPzC8wtBIigASoCoTBOoK0lzY3PmMIWyiz2LMzSxyranW2CRkznrm10mvLoxzyObqdNr9dhe3udKIoxxtAs+OG9O4gifVtVTMFZlhXBfV2xKLcq5oO0um/9ft/HcdIUWZA8drpd9sYNtDEFEalPYc7ShEznlWvNOoe2BgpK/EK++3oZCWce2MvDH/5gHvWob2NpcRlLgAwbzC3sAdVACIXJLaEKCOfiIk6TonNP3JmkCfv27ydq+Bjc2sY6nU4bW/bvkaJSKqOcYUAtYuujk36MqNwhswZhM8xw/2Fq5RI3oyqbqhE12dpqA45e0ic0mna7Qxw36PcSsizB4dA6p5d44dbp9ekmPdpbm2ysHWdzbZW1lWOkvZ7nhpICISWuyCjzBI1h1YUxjmMaUbMShGXfGG00cavJIrC+vkqepqyuHQchaDV9861+v1+t2Dc2NojjCJ3nOOfJII3R5LlGFxQllb/eWsIoIJcOIeY8fYqzmDyhm+YYEdGcWyZqzkEQEQcBkVBFu2FBGHiSz36/z9bmJltbmywsLCADRRTF3LB2nD3Le9h/cB8PfshDuPGG60mSPr1elyiKCjekQQgvbJUKcHjFoZQiTVPCMEQpRVa4IhcXfV+ILMuYn58v3GwRne02c3Nz5MaSG+9m01oTRRGNZov1jTXfiAtPe2OM8VpECJQEo33mnQCCUPx/7P15sK1dfteHfdZaz7jnM9zhnd/ulrolWq1uzQhkQBFiMkNBbMrlIjYGU8EJmNiVQCUhtuLEcXDZjhMDQaSwLVcMjo0ZBAKBZGEhCdESrZbUc/c73vmeaY/PvIb8sdbznHPflrTPG+iXLnJ+VbfOveeu8+y9n73P+q3f7/cd+MDLL/At3/CrePX9r6Kto+sU6eyAKMoQMh3aWkp5m2djDFVVonWHbmvyPMdIicayKXwVu9vtWK2WFLv1kDh/KfmWZ6sXecU22mvcETTHrq67kX+5iZt4b+NdzVy6riNNM3RnqMqKNEu4iofTAX7slKQMrY2iKGibhrZtODt/wr233uTJowfopkY4E4b/kjzLGY1HqChYAgc9sLZtSVNvoBDJiMkkI0nioS+vYoVr3OArs6xqtLUsLy6wc9+/z/OMk5NTpJRsNpswxK+Dza/nxrStr8AcDhmqF5xD13420Gntzcu0QUQxqUywIkZIRZKmxEmOVDEyipBSgZAUZcl6vcZoTVmVNE1LUWyZLxbUqkQpxZdeO/EEzUixWCw4P9fkeTYoG1w9fLdd66sP5zXTroIWmlARzoK9QI9+S1M1bKxShuQhFVVdk5ENLU0PWvDQZRUM2brOz3C0sR4tJyR5qvjAqy/y7d/6TTx/+w5VU5NPpuT5lEhFZGl2WXWqCBuG8JFUTEZjmq7GZQlCCu7fv8/rr7/Ow4cPh3Zp1zaI4FB5KfGyn7fy7ADfDYi2/v9u4iZu4r2Nd6WKbK1juy0Y5WOcE96l0BjOzy+I4wStK8qqQgvfRmubml2x4/TpU7brM9567fM8ffoE3bVIZ0mShCiKSZOYJI5Q0rsN5mlKmiTelbKH4LYtznr00ng8YjKZDvOfLMtpmxYlvSjmdruhtl6fKs+zAGGFotgRRYpO1zhn6bqWpqnpjJe377T2XRVhkVYikESJouk6ysobZ6nokDTLMWWNVAlplpHEMVII2rqiblvqxsN7hRC0XUfXtiAEzhqK3Y7NZsOdO3c8Wq4sqOuGNPVInDhO/LBbqUuOS3AArSpPMIziiLZtg5OkG5BcPUem11AbeB/Sm3z17cw4jgYiq5QytMd8AomjhDhOsRYEPrm0XYNSisP5hPe9+Bzf9s3fyOHBgtY4stGMyWRBHGcI6d/DWEVEoaKy1qKE9AZkncEay2Q25uT0hC996Uu8/fbbbDYbTxiFYCegvfrDFbmXX0obzMMK+PIEZC85MTeJ5SZu4p9OXDu5lKX3L8nSnO228O0k62cLXddRVTWbzQ4hJKvdmqapOT875eLshJMnjzl5/ICyWCOcI4s9cz5NE5I4HWDOaewTzfDvAB+21g7kvqLcUVYF292Ww8NDxuMpSvk5SF15Zru1jqbxwpfGep4IwtE0Fc75astYTde1nmVuvaWxcRbX+60rUMKLREaxhwcXZY11ltl0DgiEitFGU25WSJVi+p8VUDc1682GtvOJq2ffZ3mGkJL79972kN8g5JllWUBRmQAs8G6Q/Qyll5H37apLb5c8z5FSst1uMVYPXJ08zwOqLw0ulPHQYtJaM0pSsiylqi5FIq11jMcjlFJeqywbYYwhVorDgxnve+V5PvL1H+LgYEFZNmTjKePJAVImCKG862RIAlIIlJTe1jrMqzwSL0FFEQ8fP6asysFyoW/z+UrSPJNY/HN7dvYCDDI/X55A3CBkeRM3cRP/dOLayWU2XdDUp0RRwm63YrvdoSIZevWCzXrLbrvDOTi9eMrpyROePH7I6ZOH6Kaha5vLzUIFfxEZkSQxWZqioog4nKR7z3Ng4NBI6a1ym6Zlu92yXC4Dv+U20+khUSQZjyds1yu0NhTFjkTHQxWgdYc2HW3hqwRj9GAuZqz1BlgDGsoFWK6veIRUSKVo2hbdtTjT0tUFTetbZV3nqFpvPxDHCSr2rpe3jo9puy4IbxZBPUBjhcCYSzRd27ZsNmukVBijsdawXtdhVuGGWVc/xzDGDEkny7KhbYhgmC31umnAM5bR/fC/Bwpo7QmTvYqwV1t+1oDrYDrh+dtHfODVl0nShOW6YDye4oTCOolUKQgveS8jMUj/4CBJveinRBDFMeko5a37b3FyekrXaYqiYLfbDUmurzoGZv47OCxfNj95Bwy5/564UrncIMVu4ibe+7h2ctkG3SdwPH36hDxN2awLVps1RdVwenZOW9dUu4KTp09YrU4pdivKYkvX1CAkURwHAcgwpE+8+ZgFpPNQWCkFaZZcQUlZosg7V8rRhKZtiOLYk+5WS+7de5vj44aDxSFCCMbTKRerM4w1bDcNXa6JIgV4x8e27XDODAoCOlgNex7H5UbkH9fLh3idLUMUJWw2JVkUs12vKcoSKxTIGGsi2rbj4uyUsqqI4oTRdMZ0PifPUqQUrNeWbbELqs+eN1SW5VCZRVE/ZzEDp0OqmCRJUZHyKCjhwineUdelrxaUVzgWcRy0UaCoSg7SAzrdebl8a9EhKUnpUXhXmf/OBQ8Y3YXHt0NFMZ2OWCwWnJ5esNuW3Lp1ByEbJkmGCC0wIEjpREgkIszErLa0usV1mnScI0XEm2/e58nTMy4uLmgbP2eRocVZNHWQevG8GocI1cllpYIM6DUI4ppf3vry1SpY3Fdpa8xi6p9BiGzvSmfP6X785xDXsA3YbBp+6MH+R+8slDWcPt2/Vne+BWm8WMqv/FxxbM/PePiFz+9de/7wPp8CfugaQi2fBp7+7LMzyF8uXnuN62m/hHjjtYYs339vdxvN5//hxwc1918pitOKt//W/sd2FlanWz7+E5/Yu7Yqa9x6DV/44v4LrzewO4HHn7zG2nv4G3YddOX1b+y1k0tRlrRd5+csXcu2a1muLlgXBafLDVXd0FQF6/Mzil1BWVTstlvvIyK94RXBQdE4jRQCIyRWKJwSGOtFK+vGc2CydMR0Oh0EJv1MQJDn42EWU9UVdbPiwYO3qKuK+XwBQD4acXF+4d0rjSZN42EDds7SdgZtvWSKCW20Z0+4Xo/KWINSEXGkhvbV6YXngsSRoKo7jDCoSHomfOdlXOIk8b4nuqMpS1zueTqTyQQDdLqj7dpBIdoE+ZYevXXViyROfJLtk4uQYnistlXUdUWSJGjdEUV+btMGoqt1FtNZyrK8tBcOTH3vWaMHHpIxhjiKaJp6qPS8q6Ogs4aHjx8TxzEf/MDXBCTalDTJiSKFEw4lIlSUeokg7Vtpw0dRCFTshU6fnJ1ydn5B03R0XYfRGiUlUviWn5KSVl+SJUUAMDjrLjWRPKfzGfIkXGXof7l45VdlfjFnuGskF1l33Pkv7LUY56tHU/7cm9+xd53FMhU/xveu9ieMAvgihvQazGwLfOSFl/gd3/Gde9d+6jMTPtv9I+QHx3vXfvYLBdn3HfOI2d61X3SP+IY7TxhdY3d7aymY/7ffRU66d62Kf5KTn/5TXMcaaHU+4m/81J/fv9Babr32H/KJ/+D1vUtb1/INj97gd37xtb1rz4E/m/5WxO5r9z8Hbfnnfq/lo9+9P7l87h/Cj/7A/kvCu0guq+WKNEmo6ppWa3bbLecXS9a7gjYMlJu6pqpKymJLXRV+ntG1YL25FNZBFGGlGqRUpBAkqZ+v+AqDQeajh6fO5/MwX5kghFdkXiw84a7Y7ai6HfcfvEVd32E0GiHw3BhrrZeJMTFxHA2bke46dJB38S0g6A8j7+zpG+1RcmLkFZRrbfnivfvcOliQqIiyrMnzBKkUh0e3yMcToiSmqhrKsqAoSx4/fsRqvSFJMqIkHpIIeOBDURRDu2uw9R3QT3ZoC/ZK0sCQkHa7HVmWhZ/TQyssTdMw6Jes1+tAHpXkeT6gyaIoGtpRUsqQjLrhefgEZFivNxRScvv2bbbbHbPpHCkUwjms1ljZEqcKnMEYnwijWA3tuyiKGM2mxFnK49OnrHZr6q4eEl1VeWsFrTXCQ9aGWVs/6JdcIt7696b/+mVtL+ew7zhhffVVLxI1/m3Xkn+x5uPc/sDv9Ae0XyGcc+jP/DDf3Hxs7zU1mh0/zu9Q+5PLhYMfMLBfYhM64Js/9s38od//h/au/aG/80PI9q/wW777aO9a62D603f4JnF379pzW1LMn7DYn7dZloJfX34TcyZ7135SfZqPfcvGdzT2xMfvL9j9zn+dvacao5n+v3+Abz/6NXuvWeoS8fiv87uvUTy8Bfw/448hx/vfB9f8Pb7+O/9jfvMf2H/dKPkKJBeBd0Jsmo5tWXK+XlFWNc5BpLzfhycOQhxLIiVIYkUkEyQSFSekec4osOojFZEmCWnsja/69o4Niste3NCEecSGqio5ODhgsViQ5yOyLOP27TueS1PsKMstDx/d4/atOwOc2bPMbYDlmivmZAbj7CB+2CvqXq1elFQgbFBAvoQq5+MxaToCJIuDQ27fyRmNPWpruVrx+PEjvxmXFWVV0HYdcbCIPjs/I8tHjCeTYfPWWjObzWi7lmLnN9g+Qfj/v7Qt6Dk/fRvLM9ddIIK6YTPuE5WUEiEYkleSJANI4qqEvbUWIQVN0xBJGVpncpj1tDhGo5y6aSiqmjbYQK9XS9qmYTado6Ty8vxBTsd74/j31TrfntqVBXXjPWV021Hsdhjtk8doNBpeU902AzKuRwRad3lw/6XY+M9+WN2XVe9ffcnlJm7in+14F8lFcHZ27h0JrUWqGIT0MwDnvUaqqqRuKtqmQncNWINwIKX3GnFApzUCgUiEtyFWNqCpJGmaBO+UCGcZXCSbxm82q9WKtmsZj8aMRmPSNOf27efompYv7j7HZruk6xqOj26TBNRZ23miYds2wxyhR11dDvC/nM3tnEMpSaSiQY3XWksiFS/eucPRwRRnLU9PT3njzbdoq4K6aWhaL9PStR0Gh3WW1apARjF5nrEtdhRlyXw+H5j4ffVwcHDAZrMZNlTnwFjvmxI5X+3513JpwHZ1o71acbRtG0RCfbKpgmiotTag07ytdF+dxCryrSd5+fqvPkbTdGy2BeNxyZMnJzx5/ISXX3iOO3fuUtW+vadHHXk+JssyhPAk2n5g0jQVu6Kga2pGacrd42MW0zE6ABtOT0/9DKZtvSrAlffBhgq31wy7+pqv/hk+qwKceBYAcDPUv4mbeG/jXbXF2rZDxZFnQUuFVBHCOJqqpG1a2qalqSvKckddlRjTeh6CAqlsQPH4ZOPhvkE/LLgM6jDXMHFMlubkeU4a/GFGoxF1XVLsdujOe76PRmPSJOPw6BYvVju+9KWC9WaJc3C4WDAej2m7BhDDpuy1suwVZJKPvjX0DPTVCJSwpCP/XKIowrQtxjnefP1NdsWObVlR1g3jIM0SS+GBD1bQGUPnvKKxd+rUjEY5ZdVwdnbGbDajaRrG4/GAgJvP54OPia+mFG3boY2HTvukp5451WdZRpKkXnonIMHqumY89r3sPtn0rHevtzYeVK796zVhI5bDhjwkXCGGg8GTJ09YLpf8qg99CJxgebGkyiom4yld21AWBaN8FNSzM5I08SRTqTiYz3n/y68irGWSppytzjlfXrBaXlAW3lNGSJDI4fWHJxD0zsAa44UDEMPrv0wghNdL4CsREqi/xk3cxE28d3Ht5FJ1LZPZjLppiOIMKRtkkuCMQQpJqiJ0nNClGWY0QuAZ3kJ4qZIs9Ygpqw1lV1LLmiqOyZKE6WRCFEUoFZMmqScUOuE1upwLsiySJMkRIsY5S1GUbLc7RqMRWZJwsLjFC883vPHmG6zXG6SA+WzKdDqhKApPtGy1R2k5gbMBhRSwSfBLuxr2yKx+7vF06UUyfaVWAZDnGbu2Jk8Tn7AiRVV31J3GhJYTApquoTMdWZYDgs1mxWw2pygso/EEY+H8Ysnh4SHWOqqqJIqFx+lo/9zaVvvkjkVK5UUlrWSU5+x2xZB8erHKOEqGqqtPOiKg8vI8oywL4iQKKsXKI8kiEYb5HvFlnOfW1E2NNTEHBwest1uctty9c4fpZIqwGtM6YutnVGXT+Qo3SYjzHKdiDo6OWBwccrcq2JyfgK6JhOHu7UNuH/mkut3t2OxKiqoKSdUFAuqVKUp43wRgtQ4J0WcUb0ngFZul9Fpjzt7Iv9zETbzXce3kkiQJndbe9VAIoigeEEkEi10RDMXGoxFZ4F70HiNRpBC4QXSxN6TathXb7WqwI57P5xweHDIejVEqCg6JNduth/BGkRrsi7vOcHryFCUVo/GI6XTG7du3efToAev1CilgMvGDOs9sj6nb9ldskQwtFHFpzeysC0TRiqbVrNZrr78F1FWNsZbZKKUzllj5dlacxDTagxaMMVjc0IIqil0AHvTD9pym7Tg4OMQYw9nZOc8995wngl4hYHoejKHngcSxF+8EwXg8IVpeDK2/JEkCl/ASUdWFYX0vzzKZTDk9PSWOE7qu87I6Qfqmn+d4jTV/UEiimDzP/QwHwdHBIQDLs3MOZ1OmozFNVUCcYI23JhAOyqahMY57Dx6AFNw6nHP7zm3mhxO2xZZit6Msy+Ajc0TdalarNRerNdvdjrppabVn3vfwY6/SDOC8craw4AQCGbq1AmsvW2a/FMP/Jm7iJr5y8a7Mwtq28zBgREg2Sdi02oEMV+x2NOUOa0zwTvHopDxLg0KwR3GVZclut6WpS4zWAZXkvV+ePnnKweKAo6OjwDSXJEnEbrdhtSqHRKSUN//arNdst2vyUcZkMuLo6JDz01NWqxVxHDMajdjtdsh3DIC9qyJXapfLEIjADdGY4FbZBRdIB2y3W9I0Jc1SqroijiSTLKUzxkOujfUyK9r/DIAVAZVlHVVVMxlPEUJRNzWRSri4uGA+n9N1Haenp8wXc1ariwENdnWAPVQfWT6QJPvNVckYISCK/AxrtytI03RQUPbIsQilermcHVKo4V70rabL8MP5KIAJIqVYHCzYFQWb5YqX7t6FyQTbdJiu42K1wWhLi0OmKaebFa+9dR+VJnzv934vk2nO2ZM1kZIcHx1w9/axvz+De2lHUZZstjuWqzXrzdYDJOqGuqlpghNp27a01gzvn8MhnJ/nC/vsPOZm5nITN/HexruqXJqmZTQeY3aFRyYFVFGapuR5jm5znG2IA9Gvd63slXv7E2Se5xwcHPhZQF1T7Lacn58Hm+KSi4sly4sLHj9+xMHBAcfHx4zHY2azmUdlLZdstxsv0hgnRHHEer2iKLdMJiNGo5xuNmW73VIUxYAS61s9wFCdeHOpLzec6iXcXZC773++aVs64+1/i7IkTmKSNKNuNFnilZE77ROBNTqINzrfHuOScyEE7HZbJhOP22+aDuu8b8xiseDk9GTQ/7okdprhuXszM0eSJqTJpdAliAG23G+qeZ4PVUtfufhqxXBwcMTp6enQVurft55A6YfpMlzfEy1ns5kHHgDP37nLaDLGOMvDp4+JENhOU1QNo/mcoql46/49jHO8+vLLPPfC8zx9dA/rHM5ajDMY5bXTpuMpaeoJo85B22nKuvHw99aTQeuqYrcrWK1XXFxccLZcsdlsKYqSttPegye8jquJ5aYtdhM38d7G9WcuQf9qOCUDXBn8uiBx3p8i+zU9nLXrPGejJwt2XUeWZeT5mPnsgDt3XmC323F2dsbjxw9ZXpyyXq/Y7bYsV0uODo84PDxkMp3QdZ1PRtsN49EYG1pFVVXSNN59chSG2V7qvXpGq+qdFYCSCmPNO/7fb0oqilCRb0c1TeOth7sObY2H7wbGexqn1G2HEt7XxnYBChwQWc45bC+REmYgUqjgkLlACElVNxRl4e9LlrNZb5hMRwO8uG9V9YfwHqp9sDgMySAauC3+7z4BTSZ+7tR7u0ipyNJsIF3GcUxROOJI4sJ86SqnJooiBJAm6XCtKIo4ODhAxTHn6xWrtSOSksQKmqL086Ys5nSzYdfUJHHOrVu3WK6WGOeIk4S2M/hUILFIGm3RrkEpf8+lihlPU8bTORKHUsJXn0GrbFcUbLYly+WSk6enPHz4mJOTUzZFQRlkbq7qlN3ETdzEexfvQhU5Ih9NkJEiiuKB7ObVhBuKYkux83+cbsNcJhpgs95pyg2yJr5tEzHOx8xnC6bTKaPRhFdeeR93797h6ZMH3Lt3j4vlOcvlOVW1ZVd4NeHDowNUpFgtl14FOPBA/DyjoutaJsHxUGsx8C6uJo9+w3fhBK2C42SPUlJKEfRHiFTsnSUDo91qg9V+yB0pLy3TtDWRTGgC093PBJ5V9DVGD487JBgl2WxWHN+6hbGapm25WJ5xeHDA6VmBEAR9NPmMLAx4hJdXjPYVTpanVMG62eGtgcGRJDOSJGa9XtK3uKyzKCWp6y6YpHlogwdWqGFW1t+XSEmm4yllWZHGvlrSneHs/IK7x8eoJKFpO7Q2nJyeMp4tKJcrzldrQDKejjk5ecziYIbCUjctVQAdIKCqmkH+xol+Vuefh1ASJUAFB9E8z0nSlKOjY56/m+KcP/xstzsuzi94enHBk9NTHj9+wpMnT4Pq8n6y4HsbFlP8EIj9zHDXPuTpa3/9WtXXrjvjE+yX/LBYHmP4wWvYoRd4cmS5fykW+MQnP8H3/+ffv3ftpz/7aT77hQJ7jbz/6S8UZO4JZ27/s3jDrWhXcLbf6p514/gf+blrMfSfmhWf/Ef1tRj61amDv3YNhr6zbIqnfJyf2ru0tS33nOW/339VzgHXfRJb7H8f0F/gs//AEyT3xRc+fo0HD3Ht5DKeTD3iymivSxW8QbwMh0FJiCJJFqe46JKA11cqXtrDG4CBVy1u2pqnWhNHMVmWM5suuHXrFgeLBXfvPs90OuXevbd5/OQRdV1ycvKUqqo4PrrNdDrl+Pg2ZVFgrRnk+50rKYoSZw2j4OboBja+5p3tL+c8F6UfnPcnduccSRQH2KsMKKTAjwm8i57BL4RARl66vjMGEQbm3nveK/S+s//vvxp/A4Xj/OKMo6Njzs7OPIBhtyHPU9q2QalL0uMVbUasdQNLfzafkaYxWkd0ukEqXzVaYxmPDVIJqrrABXKqcy4AJTYBvuxbh3GwSwaGVmYkJOPRmKqsQpszCe1Cy3SSoWQMVpKlY85WjzFSUjQNu6rGhdnQo0cPiZRgvTomiRTFdkfTlAMEupbqUkVBXm6ifZLrX3scJ4xG+ZAEJ/lkaAlOp2Nu3TriQ0pR1i2r5ZqnJye8/eY9Hjx8eP3fivcohLoDIt+7ThvFmz/7Ga4Dp341O+ePfuTv7F3XOvi+zy64Yz78yz+/8HVLRxed0WbfvPe62DV/+4f/Nn/3h39k/1Is+o91/NhvXe2/7t+Cf+0f/Cu8n2/Yu/R1fpD/4V/6h3D3GtvbX1nzpZMfItkvF8bqCfztv7l/HUBi13z9n/rDe9c54DR5mYvs1b1rO1vzGi/wF/j6d/xP/05dZmlNCc0P45ofvtbzPRC/hZfEt+1ddyp+Efjr17rmtZNLlmV+IzZ24EcIwcBeb5qGpm7ouo6u8xviaDQaJOGDjiDO2TBzcLRtQxnQQrvdltVqydOTRyzmC+7cvs1iseCFF14kSRMeP340cEGs8Sfs8dgjwbq2AVK6rqWqvB1AUzcIGHxNjDHPtL7gKhlPIrCDz3wUReAIqKuYKPKouHcSLS9bgg7TGRjk+ntIb4QJ8xbHJaGvVw2OomhIasYYVqsVs9mM5XJJGYiWdV0PCC24JHMS7mXvdWO0tyzuZw19QjfGM/iN9V70SiZobXDOstmsefr0KVp3JKlXkO4lYVSQZum6jmw0GtqYvQdMHMeDIrMK1exuV7BrGoq2RTlHYzvW2623LYgU6/Wai/MLJuMRTVWhgwJ0/5zbNohzSvFMhTfYLltLFHWDaoEA6qJXjzakacpsNmMym5HlI15++SVeeeUVvvEj38jyYsV/99f+8nU/7u9BSGT2rdeSf9Hb/xbHSwwM1182HFMV8ZsP9z96beFPi5TvYL+cygU1Qja4+H37L6xPPKjkWluLhg8Dv/kaS9+CD/Jhvo3v2rv0M3wSPvzz8L79R3HxEwV3Ssiu8XQ/f9r/0P61EphfY6kDtnLCc6OP7r1ma3ZE/DAjXty/lvX+J3klXn7/i3zbr/2mveuW56trX/NdDfS11kgloes5IP40qaRXPPZWxBFJfEiWXfq0KKX8Sd9amrZG6zaIPKakScxivqDrWlarFWdnZzx8dJ/V6pzjo9vcunWL2XSODdLzReEtgKdTP/ztWzZaayaTCXVdYcyaSIqB4d8Ppq/23Z+tYECKyw1Maz+Iv2TJ9/MkHz2k+JkkJVyATbdelFNKQBJHHl3XNPUwu+l/5moiUCoajL7yPKcsS8qyHE7lfTK6qj/Wf//qEN7DtaPh/+IIuq5Bmy4QChVJkmGM5uLigqoqUZGvoqJABO39YHoiptV9ZRgNLcU4jhmPPRETJSlrb2NdGU0yHlMUBevtFqkEzlikjIdDRhQpWiHQXbiHQmCNw1nfInPyci52dfbjk6cbQBZKKrqmHd7bXs2hrCvSLCdNM8ajKbP5nIP5/Nq/FDdxEzfxjx/XTi4AUklicSk/AqE94xxSCLI8Q+GIQs++3wj8ADklz7KgGhwhpUB3LcV2y8VyyXazZj73m9rpySnb7Yb7D+6x2ay5ffsu0+mEO7fv8vTkCSBo25Zbt24xHk84efoEYwxZljGfz2jbhq6tB15Ir8Xl7LMD/avRt6sGUy5rhspH4E/GNsCBr2qUXcJcA4DBGHTXIaMYYy1Np4liD8mu2ib8nB101Pw9vETXbbdbjo6OBq8XYKgi+mTWD/b7n9VaM8xSgpZaD7zQRtN2DXVdYq0jimLms0UARZxhnQ5tN0ukvIhkT7bsk3c+yomjeEhwvmocEwc0oLUWGUfI2Ju2PXnyJChKS4zpiJS3TgY86qtphj99680nCM9R6S2O+4qkB1j0rdb+q5SSq92MYXiPo2kazzeKLzgsjjg82C+OeBM3cRP/5OLayaV3Q+xbFnC1xeVP/0ZrmrYZNuHe6AsuB7Rx8HSRSuLSlMl4xvGtu5TFjuXqgrOzU5wDpQSbzZb1eu3n6u4Oo/HYK/NuvLlUXddMxhMWiwVPnz6hLKtBDqVr66CyK7GhJWavVC5fJno4fL1Ed/WQX3FF3r13qhwEFftWWTiBI7xYprdM9szxPrmleYY1dpB36ROw7FWihb/WdrtlMpmwXC6H+54knujYS+f3ZlwIhzEaEMznM05OntKLRvokZAddNYCDg0Mmkwn3H7zpAQbqchOPk5i27p6RVInjmCROBl0zzzEaD9VL3XjttiRJsA7W640/UPTzJYIFi+r5PRWj4MYppBgg2v3zkwicCJ8t6bWQjdbh7RGBf6SxxvvzKMEzrTOfVBxN7RO5UhG2MxTb7bV/KW7iJm7iHz+unVz6E2Z3Ba0UKYlwBmdaimJLudsQSUmaZeRpRhIppLAYXdPUhrYtKUoVNqPUzzNUgopi8vGUOMtZHByzWBzx5NF9Hj9+xMXynNXqnKYteeGFFz0UdluyWa3YTWcsZjPy0YjZfM5qaehaDxxQUUTbtQjjveFNP4x3VwUO++n4lRfqHFi/OfqhuAZipAzDZkwwLLII4ZCSIMEyeB9igt+IFHjxzpCEiu2OJI4Zj6eDnTHODJVPP3/pq4bRaERVlui2YzIa09b+pC+9sQsWg8Ciu4amqjk+fj7wkZqhwooi7/XSdYY0zZjPp6w351R1gZAMPBopI5wVSBkhhCJNkzDoT6jqljRJiGMvzaNURByndNobnEVRTFnW1HUT9M+uioL6xBgnGTKKcTLCyQgZA7pDdw3OuJDclYeKmUuTMOMMxhmEvTpxEFhpUVLgwuscBC7Dve6Tf5I4qrqgbq6DdbqJm7iJf1Jx7eTSD3dxl7a7XqyyAxRZNkJY5/1bAG00zmjatvGbjbGDx8bVeUeSJKRJyngyYTwekaUZBwdzssRvcF3XsVydU9c1RekFEkfjMZv1mtVyye27d8jznPlsjjOa1bIbRA37E/kAMYaAKvhlXuQVmPJVOfoe6huFmYkTV22R/dn8l/MZcc4hwqbnnKNpW5rOqyH3IpVlWeII3igBFt23x7q2w4ZWEDBItzh3BbFmDLuiQAhPUO05OZ7ZbzHG0bWa+eKAJEl5+vSELMupaz+4722RpfKbe18ljcdjL6yZeQFRX4VeDtsvW1r+dXo+zqXnim/1ZWR5zmgyZTqd+plPmjG/PWe727Fer6nKAmdaD5pwBpzn2wg/tvJukvZZHQVrBVaEP+H+9n+uzmt6OPzVOdVN3MRNfOXj2smlnwk4c8l0F0IBkrbpaOoOayFNM5I0QncdbdfhrCGO/cwFKQZtsb7n3rUVRnsOQpwkTCdT7ty5zWI2Z7FY8L73vY/4Yczy4gJnQSjFwcGB53soaNuGNMsIB10vcnhlg39G3ffKbOSXkgTpv9dvnL03kw2ulbI/JYdrXx3qv/Pn++9fbbENUGT88NmrE484PDyk7Vo22+0zJl1FUXCwWHBxcUFd18O1+hlEFCm09nDm3W5HUfrr9dpt3gdGkKYZVemtoJumZTQaY0xHEqe4UHWliYfETiYjiqJgPB5TliXT6dSrDIR72Csu9BWPhzS3w/OGSzRcz5MZ2lZCoKKYxcExL7z4Am1ruDg/5/T0KeuV5zJZYwZOlLACKYN8viPYUV/eawO48P5efZyrEvw9oOOGRHkTN/HexrtqiwF0jWfXe12qjOlkgtUdsRLUxYayKCjKLUkUM5uMyfOM0ci3yVQcDSdvj3rStHVFXVWsVkvW6w3Liwsuzk85PDzg9q07HBwckOcjHj58gIoTDo+OyZKEtqvZbteU1Y58NBqu2zsXXoUJ9//2wxP/en6lzcYnDo1DksQRUXjeAodUfoh8NYFcZc1fFUi85LNckimlEBh3edouisInmYmfJxVFMdgSF0VBEnnIb9u2gz5YX1nl+Ziua6lrjTaaMsCVe/dHf1oXJHFKmmYoFdF1mlu3bnNxcUqSpAP6Lk1Tkjhmu9sO0Ode9FNr7ZGAQaFBhwory7IrSC7/NY4jmsZXCX2CiZQiTWJUD/KQivnhLfLRhDt3X+Do5AkP7r/F0yeP2O22aN0El1J/H8U7rBGu3lvzDuLrVRTeVVTfTXK5iZt4b+NdQZHLshykWzyaqaHtKuqmYLtdsV6e4axlMpkwnUwYZxmTyZg0jQfZDnNl2B/HEZgpRmuOj46pqprNZs3JyRNOTp+w3W557u7zLBYHfM3XfJB0NPbsfAnGtrRveQOxKMouNbhCRdEDzJ9JMFx2xK6ixp41mupbal6y3TtRdiSRTzLWgRCXulU9cqsvjvrkMhA0rxh69QnFmMvqpk+0u+2Ouq6Zz+fcunWL1WqFtZbNZsPB4QFN4wfUWushUWaZn8t07RJrndc6i31SGYWEa4Oic297vJgfMJ8tWK2WARhgGY08MbIX49ztdkMSiaKIPKhce0dPO7TI3plgez+YflO/em8H0VABMopBRqRZxng8ZjKbcnh0zKMHD3j06D7L5ROausIZjTUdRj/7GEMCD++3vVIV9knuapJ5Jwz9Jm7iJr7yce3k0umOsi4HnkLPLSnLit12R9P6IXQcKaRQKCGJopimbtluNuAsSRrY1UrStTXFrsUGJBX4jTPLMp5//gUmkzFPn55wcnICTnB0fOxPv0IhgChKQUjOL84AyXQy8fMd8LOD1isEDyz4d+wtVxPKO1tbPgn41lPPzRg20v6k3KObhB+uKyWe2WyFCEilfp1U2EDi7CHD/am656V0WnNxfkGeZxwf32K1Wnpi4m7HeDIeiKC9J6NCcLSYs9vsKMqGtvHuk0pGxFGCkAIh/PwlTXKkilgcHDIajbDWDa8vzzOqqiJNE5bLi8FOOU1TsjTzOl5dG6DJlzI+nXFEyis/OwFxGqO6CKdbrMBLBaXe3lrFsX9vpCRNYiIlkeH+5fmINBsxWxxy+/kXODl5yNnpE5bnJ+w2S6yrcFdEO4W8FNk0Nry/FoS8bD/2bUt7HW2Rfyph0JsfALGf6Of0PcCb3u2Lp23Dv/PWNR7dwWt2x1/gM3vXVhic2UD1k/svbEssho5271KHhR8E7u+/LD8HP8IP8ll+fu/Sn+dn4AfXsNhPu3dfbPjcGtQ1JF12Xk3pl5/ZXgkNPNi/DAds9Am/ePHf7V1rXEfDGRf83N61luYaj34ZH/+JT7C+2E+8fP1Lb1/7mtdOLttiS6f9CVgIiKPII5jmB1hjiJVgs/SD98V0QRIlrFcbmroiz1Lm8xkCx3az5uz0BOcsxnQBZnp5ivftJ58Ybh3fYrlccn5+BvgTahzQSUmcMRpNKcuSrq2pa5/stPGQ4x666uzVKsVLkbzzFPtLtk2unIKlFGhjBufMfvCvpLxMNsOMpb++CxvxcLmBEGmdGP7et5gA0iTBOktd1Tx9+oTbt2+DEGy3W+IkQSpFFMe0TYsSDuEs4yxDSYeULihV61A9eIWAzXaFVBJjLUdHxxweHaF1R5plWOvveS/pf3FxhlKSyWRCmmZorTk/v2A6HpEkCcZYpPSVUBQnKCWoeqh0qIySrKOzHgKOICSWaIBlJ0lCEkco2YuaWqTys5jZwQGj2YzD27fYbtacnzzmyaN7nDx5yHa9RrdtYPS78PkxaMPAqzIWpLHDgcCfH8wzVepXT0hk/usRYrR3ZR79LH/yf987av7K8QP/d5h8zf512sJL74M/8J/tX3tx7vjs9zzl19VP9649B/4/eGmX68T/7IPP8V2/4WDvup/YLfm1T/4Hfu3x/oTxZ15r+Rd+6D/mBV7eu/a/4s/wPbJhwv4k/5cmP8f3//QF8TU0y/7V3w1f//r+dRb4If0iZ6t/c+9ax5bmG2Ka3/V/3H/h87fgz/2nwG/dv5bX+dmf+nP87E/t16R7N/GuVJGTKwKRfuCt/LDXWHbFjqZtWMz9APjk9IRIKY6PD4mVZLvdAI4o9r/4Xaep65qubYZ2yzCfsA4lFXmee1vg0rFerxDKc1im07lvraUpkVI0TU2SxAF0oJ5JIFf5LF8u+/LlG06/xhp7OdAPRMU4upTu72Po818Z4F9WJZeosgFAIARCRsjAG0mS5BmeRl3XXqXZGE5OTrh16/ZgTezRVjIkLV91TCYTsjRlVzRDiy7LsuH5dV2HchEvvnCHu3ef586d53j8+CHW+IQ4HnvOSl3XWGs5PjrCGMv5+TlKekOxputweAkgFUVIFZGkCdoY4jh+BqmV52OiKKFuvBSQQOICiFhKSZ6NyPIRQZTOgzkEYBUSSNOEJFkwm0w4Pjzixedf5vTkhIcP3+bx4weslxd0TYnV3mzOiggrBE4KLA4TlBz69+XLQB1fNSGQ8avXkn9RLuellzOU+pUTpHOOPBW8dA0xgs7A1Ei+/iP7tc1OnnSMBNzZf9nrHOqfiaO7Me/7uv3P4bP/aMcrI8lH5vuTy+1U8AIv8z4+uHftjDkvUDEX2d61YxXx4Q9Dul/jklEGh+yvNS2gGJHwdXuvaVnB6ACe/8j+J6ASYAbXSLDwleGAvSuzsCRJaNtLxeM4jkGAsYY4ihkfHdN1NRcX557cOJ/inKGq/ObvIbI1dV15UUVrPOEwtMaG2YSxdLalLAvyPCfLUoyxbLdLkiQa3rAekls3FY6g8tvUaNMhA9mzn2n08c45wdV4p1uhr0i0V4AOMwYp/dd+sH71Z/rH6zcze4VwCQxJqJdwadt2qBz6jXA8HqO1HvzuT05PODw8ZLPZUFWVl2WpqkHBeDwes1gsOF9uhgTd67l1XUccJ1jnuHv3OT76jR8jjhMePnzgq4wgowNQliWHR4dst9sgO5OSJukwYxmNR37A7yAKlUgcK+IoGpKvc44oThmNp0yDqKaxxqPspMThZ21ZmiOFQhvT04qIhETZCGGl5ynFCUrGZOmE2fyY2889zwsnj3h0/22ePHrA8uIUs9tCSN5eNw3MFQj6VQLvTdzETby3ce3kkuf5oJjbS37MZjOcMVTlDmE7Vhdn7LZbpuMJk8mItmsQOIzuKKsOnEBIR5qmAYpco7t26PH3m7AOEGYvvthRVV52pG0qtuuIJErJ0hzhIIkStt0Kaz2iqes8ka9tu6Hl9AwM+B1Q4T7eOdTvzcK82KYLmmF4PkgvEROSlpTSt8i44hGjFEI4jHn28ZUQSKGeeR5N03jTsZC8oyhiMp1QlhV1U3N+fs7R0RGr1Sq0DyOc8dybyWTCnTt3efD4ZBCx7N+v8/NzpJSM8hEvvfQy3/CRb2S1WnLr1m3vDhre081mzfHxEQ8fPaCtm4HAORqNGOVj8vF40BbrqyalInCOtu1CK1N6x9DRmCwf07RN4EI1dAGqPB5PmE7nSKlo2w7Rae98qSIS63AIImuJlUKpCKG8oKhKYxbxAbPZmOfu3uX05FUePrjPw/v3uDh/Sl2WOCzmynzlqp1zL7Z5EzfxjxNfbY3Vr/Z4V6rI/em0b+XEUYTRXi23rCrWqxWTSc5kPKKqStq2RgBxJIkjr05s3aXQ4nw+xxoT1IyrYcM32pM1eyVhj7rywopd17LdrkO1Y4jjiDzPQiLo2yCErsslF+WdLbBn/+31qIT/D2945vz3RUgcPVmwn1P4ysDzc/qNS/btsV8G+jr4urjL53D1eVhjEcI/lu408/kcuZOUZclquQoJp0RFCm31IAtz69Yx4/GI1brAOYsMLcUsyxBScPv2Xb7u6z/MrePb4OD2rdvD/T8/P+X4+Ii3792jqRvSJGE2m5NlOdZalsslu7JkPl+wmM+J45Q4bNa685pmzomhYlMyQgpJEqcY6cEHkVJgfXLJshznPHihFxQVNki9BGsBIyRKGa9BpwRS+BlLHEXM5wdMpnNu3Xme5198hUf3XuPRgwdcnJ8Gy+zOz/edxZkA/1aS5DqN8pu4iV8hvlqhIV+tce3kInE4o5F4FnWsJHGe0lQJo9EYIb044yhN2KyXg6JuHEe0bUenKySQ5TlSigHO3NSeRGmdCVIqNrCzBV3T+A3XOZIkRkWCVnuvFuH8Cd/qFhtk/6VSOG3AWAQOhEPIPlFYP+R34plKAgjumW6YDQh8gpFCIFzP1xBI4ecvQimcVDgM2jiUcsRKIJxDhLmKkhLtGNBKPbmv57v0MOX+8dumxUaWLMuJ45Qs8wKhx0e3ObUnniS5LUnSFG1rrOckIoVjnGfcunXErt4RpxJFzCjLOV4cUFQ1L738AT78kW9iNJ2R77Z0bcvh4SEnJ49JEsVmu6SuS7JsxGKxIIoi1pstcRwFZFuH7mqsHRNHKdZouvZSKt9XBt6WwFkz8FratkW3vjoVErLxiNF0TJwmOCm8EGriIeRKOKIAUtBOYpzxc540IVIxVgmsMQHKrJgtDshHY44Ob3H3ucc8fXyf85NHrFdnNE1BmiXMplMWC29EN18seOO/+Wv/xH5xbuL//+Kmcnl38S6cKEEpOZDzhPBIrCxLWSwW3Lp1G9eWrJcn4Bg0roqiCJDWJAzEHbtdMcjJJ3FCB3S654P4NpL3hdF0bRuEDX3yiSJ/2u1dCXs14H5GAZcD98ui4Jfmufg1AUUmZJ+DUHEYBmPBOe9E6ftjPrEKSRxJrJbeStg56GVHeriyf5ArLbIriQUBYT7Tm1711RD41ttkMqWua5zWvPT8C7x17x5lXfnBtTUoIcE6YpUQRZL3v+8DPH56RhrnpPGIOEo4PDxEbQt+9Xd+J8+/8DzZKENGiq5tUdL7q4zGOU+fPmE2naJUwna3YZSPGE9yRvnIo85U5KsVIb01gezl7w1R7BOLVArTmne0DB1xHFHXjjhJmS/mzOZzD1nPUpI4QYorMzERIaMInEf8GWdoDcQywiGwCJzVCCwSSywNB9OUcXyb2wcZ5Uu3aeqCritJksv7WpYl4/ymcrmJm3gv4/o8l6DEq5RiNBp5GZfGVwtZljEKHiRN4/1MejXfyWTiCZNRhDVdWNN4q9okQbcdWlsilZCkkZ/D9M6VSiLTFN22NE2LtR1xHA39/35ge5VA9yuhgt5Z1l62rgQuyJYo4ZNQpBRR4udAsZJB5dcFiKvAGegkOOeJgzYM+6UM/i/GDkP3Zx/LX7tH3gFEQU4lC86ZSZKRZTmHB4fouqJpWp67c5v7jx7Q1AVSCmIgT1OvATadc/v4Lndvv4CSMfP5AdPplIODGbe04QNf+0HSLCVKFEma0HQd282WLE3pmoZbR8c0Tcd2uyPLU5I0BjzZM8sSpIiRKkKqGBXFKOHoTIVxDoVAqgghBSqOEWGe5N8bRdt6FNvt+R3u3LnLeDwefGOkihEohPP3z6JQSCQOnMUZ0AROkTM+sTiNMzWYFqsbTFVRVwV1VWFNi7Qa29YYp0ApojQljxXj+F25S9zETdzEP2a8K20xYBjoAzhr6Tp/+o6TBBVFOGsxOLIsv3QqDHDhstjhnBvY4z3JbT6fB4RPh1aGLLusfJqqxjlHHMdUVePhy133DBNbX+HKQGhBXdnMr8qz/FIhpAhtL0ukBGkcoRSoSIR5UeS5GUoG1r6jCrDkqtU4vDKyxg6zGd8de1ZXrD9Je/6N9bBkIWjqmtJairIEPKkwSTLGkwmL556jLHaMVznWdjx89JAsjphmGdNJxm63Jstz0ijhhbt3aTrLiy++wuHhLW7dPmK2OODll18hyRMslizPkFJR7AryNEOqhChS7HYlWZYTxTFaGwSKNM1ZLG4xnx3QaU2axoClbsqAeAMC1NhDm327U4XBvzGapmlJkoTJdIIxhu1mQ5JmxFmG0Q6l4istQ4PuHLGUqJBwMC2dqdC6oG12NNWOri58YulquroMsypB13ZDtdrUDqkUZVmRxDFGf7VBkW/iJv7Zjnd1nOuJf23bBmZ5jJQeFZTEMXmWsQnEwqvs857P0qvU9kkgz0dEY59k1uuVr0rihLLraJoWE4a+UgjiKEIIS11XVx4/GtSae1mUvpqJRW9WdkX/q0cTXRmiSymJEkWWJaSxYpwlzMY5ozzzysxxQhwrlPTIsc5YyrZjvS2xSLQraVvPnNdaBwKnBwdc5dj0/Jeu68izEYv5AoCTkxOatkEohdQ6DLwd6/Wa9XrNy6+8zPN373DrzhEH8wnzUcZ0nDKfZAgl0KZms7lgNJozGaUskjGHB0e8731fw+27d3n+xReZTGfeq6VtmMymvPq+D3B8dMRsljOd5Z6k6s7ptKWsWoSIsU6QJBMmowPu3H2ROI4oig1tV1NWJWXdIOjFKKPgH9NhzKVCQdt6oMZ8PgtaaTuquiaKE6I4Jo68t0+W575SjKKgjGAQVmN1TVvuaMqCutxQV7vwGB3WaLI0oa5Luk6TJBldpz3PJo5ZrTakWTokrv5zcRM3cRPvTbwrnkuP7oFgB2y0Z8V3HdZ0aKMpypJICNLZFKM7dtvNINWfJAnWOLI8I0szojiiriq22+0gnljXFUniZym1A6Tzvh3OBhiw5690bUvXtn5OEZKZ1hoZkpd1dmivXUqA+JkPeK8VIRxSWJQQRNIyyTNuHy64c3zIbDIiiQNSjF4ZuaNpPWrNGkvddDSdwZiatvPqAM5ary8ghJfCYaCKD26dd2/fYbFY8PjJE5yzzOcztIW66WjrmrbZsatzomTK2c9/mnvzN/nOb/0or774PHeOj8gTRSw1T8+ecnp2Qdf6SmM8HpGOZjz34oscHN/i+NZdbt+5Q5RGSKHASfLRhI99yzeTxIK2KXjzzS9xcnKOsZKz8yWbqgOREcc5VnYIcUE+mvDKqy+zWi05O7ugKHbUVR28W2zwePEK2VHk79VuV7LdrION9Yy6LCm2W4QUntviLFEkybI4JHI/kxM4pDBgO5zuaJuSqtghHbRNPagrW2fpmoSm9aZsTmjaRmNcRZo6OmPIpK9c4iQmlV9tycXQLf9TEPtnQca8xV/4fnX1TPRLh4O3nnT8+f3KK1gHb9maf/tf36+9UleWtyz8zWuMtEsuDxf7wjn40b98wec/Wexde/+1mi8+bPmrj/XetZ+4MIzUn2bKfjbpp+0n2ApLwn6o+hfKLX/kD8N1PkqvPYTlNe6XA0peQ/NvX2Nti3jrU8i/9If2P4FqjeHngf33Fi6u9X4Nz+OasLlrJ5de+LCu6wEhVDSl9y2vS4rtmt12gzWGKEsogrqutwkOXhxOMJlOB/vc3XaH7lrm89lAKjTGUtctUkjSJKfrGopy5wULncFa7eX7++rIBnl20avgehdIETztPc8lzEYI7oUSYiVI05g8T5mOc2bTMYvZlIPphNkkJ4nVIFEyINjw0xklII0jJnlOWXXUdTtULAhBHMXESnkfF+FbfA5QUjKdTjk6mJOPR7RtTT7ySsNVWXigghCoOMVFU7LFB6k6x8nmCX/3x36a7/nnvoX3v/8VZpMc1zXUbcPZ2ZLNakWsMha3pozGHhk1Ozwgn47IJykqkRhtKYuW7HDEcy+/wHw64md++qf5whfe4nNffJu204goZ3L7BW698LVMprdJlaTennL/ySkf+ODX8sGv+3p2nyx4cvKU7a5AoIMxGWTZyAMQcORZDs7StjXjPKfYbdnuljR1jdWew4SzqESRZhGTPGWcpaSx8ok+OCO0XYexlqIsPJTdWrI088hCIdm1NVVjiOKE9WaNUhJVt0hZolREVdU0TYtzkMT75T3e25BEs38NIcd7V+bbB/xbk5dR19gB/kD6w/yPL1xjQ7Hw6sef585f+F17l5aUfDM/wr/Bfmb4W2z4Tz7w93n1A/ufwvkZvPUzO57+zG7v2gr4XX/2fXzsu/YrGiz/zFP+8r/8o1yD9I78X8Of/PAHmU33b4V/9k9Lvu+/lFwHGvLjjPnstaRXLMejH+Sfe/Uv7V3ZGRgf3uX/8a++uXft609KftMnfiMz/tjetQ0/y0e/94/xTd+6X4roM5+q+eG/sV+DDN7lQL9vMfTe501d09QVXdNQFDvvHS8ly4sLRqNRIDV6kl2W5uT5CCnl4OeiteZgMR+8S3p4bpamVFU56EdJ6SsOd4XNnyTJgErqfdWttXTaoI3xMF0pkdITGYUAJQWJ8iKTaRoxHefMZ1MWsxFHixmL+YxRlnhYMQ5jL3XDLvXD/DxFBQHG6XhE22karXHWG3hFSpHEsT8LuUuvEaVUQIJNBrXlOI6wzqKkZTTPUSJjua2J0wM+/I3fzfTlD/HW536Gtz75I/zUx3+e+SThYPIKUZYwn01RQtB1LZPxmEgIRpkHO2RpwjjMtrI0oTItdVWi5AFJnnF2r+BHf+TH+NwX3iaf3iIXgm1Vs7jzMr/6N/w2nnvp6zh5ekJXbnjj5/4Ou6rhV33kI0zmcxaHhzx8cJ+62AUzONjtNuwKbz8thWA8ypnPZmjdcn6+o239+4k1SOGruERHxGRoDLVpsZGf3QgrcUEiSEhBZwxatzhj6TqLihR5PqJpWxCXignOKeLYq3dnWebtrsPnr6rq637U36MQCDW/lvyLFBEHaToQdX+5cM5X+deQyfKoSCEZs39DcTgSFAfs1z25IEEpSK4hkRJFHjxzHXqrAMYzxeJo/9ae5hIOgNvXuHAGs2nEYrZ/K0yU4BhIr1GReB2RlOsIwEghyK6xE0sgiyW3Z/tv7nLXIchRHF/jujOSVDCe7C/J0vT6Jc61k0v/C+zdBsUguV/stiyX56xXK7brNRfnZ0TSI7eqqmIymXj4cphJdF03zF7m8zm68zObnv0vhGC33Q5ViAcD+O9LcQmFvgrdfWau0n91vpXm5x1iQH0lCrIsYZynLBYzjg4XHE5zpuOMPI+JJOCs3/yNHZJX33brGffgxTtHaUKdJRS1N0gT4RdcSRGgyHLwNUmShEgprHN0QWRTOFBxzCgfM8qnrDYFTbejLh/x4N7P8fWzEXPZcTQd8+jNL/KzP/8pDg7mPH/7iMkoJ89TFvMZYLG6IRIOKVwAH0QkcRyeD+RpQpoojNb81E/8JJ/7/Bsc3X4ZJxUnJw/ZbpdEZ484ffAGs8kxdJpISKRQVHWDUjHHt27z8quvkiYpXV0hhEPrjqLYslpdsF4vKYsC3bXUVYESOaarsV0d3DYv71GejBFW05Qa2ylqPJHUWhmUj73CdWc0cZzgrKMIFtCdsex2BRaftHWnieKIpu1ou5am7ajbjrqqGI91XzvfxE3cxHsU104u/ebaJ5mmaairiqooWF5ccHbylPXqAqM74tSbUPWyG03TkKZeGK4Xa+z5B0qJZ6ohYwxZng+Cls55n3Zr9TPcFGNMgPn27PsgsW56VJAAJweEl5SCURozTiOmkzGL+YTFYsZiNmU6SoiD4rE2NqgIONyV5NKLUXadQWv//xJII8l0lNFaT6C02hBH/jGt9kmqJxtqrYniGKM1SZC0r9uWNIpI4owkHmPtDmsbbNuwfvoZ1rlkuzqn3T3FOsMbb93n0eOnPH/7kCSJWcxm4LyQo0gMWA/ZVVISh3acFI4kiljMpyghuVgv+fGf/AlknBPnc5q2Zrtd0uxOOW+3/L0f2nL+5JTZ7IhEQrk5Zzp6kSSWNHWDcJYkVkSkWGcZjUbkec54PCVNErbpBVp3RFIgMFjTYTtN09QI50jThDSOEc6TRwVgjNcoM9qgROQReHjEV9O2xIkOls2GTDtk1WCM9aCQOAYhB8HONCgUtF2LkAoh1WCRcBM3cRPvTVw7uex2vi/qnKOuKoqypKoqLi7OOXn6hNXygq5twFmapibLvHhiWZYDHLknDRpjhiG+kj7J9ICBnh/jIc8xXeeGwXE0KN67Z9p0IAbZlF4PTOA9P3CeaZ+lCbPZhKPZiMV8xnw2YZT7IbKx1lc5eAZ+n1hs1wbWuRu+L5UiwhMsBRYXKbIkJs8sXZvR1A3YS5kbY8xQTdVNw3q95mCxCPprU1YPHzIaT4aTdRxlKBeTohiJhO3ugrLZ0ugGhEO3LW++8Trf8MFXGU/GHCwWaC2oq46urmjrynul9D401iGcQClJnqVIAffvPeDB4yeobEGSjxCRYDLJaApN3ewQaclmdYqyhlLXdNWOW4cLTNuwOj/l8YN7FMWW/k6PRxOUiskzxWI+R7clu13rDyPGJxW0JZIxUkKsYiSKpulQsUJKRWdNSIQKZy/nVzb48UgZYawmSROQgqbzhw1TVANisa7bS+5TUJY21gXNsRt+9U3cxHsZ104uTedVh03XecfI1ZonTx9zdn7KbrNktzn39rQCYulFG1erJUdHR17jCjEISfbOiEIIdkVJmqSoSLHZbD3yB1+ddJ0ZVJiFUGjTYY2fefQooTiOB/iClBKrvc2xdY5ISKJYkWcpB/M5R4dz5rOUUZ57V0kDnTWURmNM8IK5MtfhCpQZemix9HaKCJzzjMtISUaJwmQxpvNwXCdVIAZ6tn6ttR9WG0PRGI6SnFfe9zWcnK0oiobFPMc5w+FixihPETLydgP1lrLyZmydc3QW3nr4mEePn/LBr/0a5tM563VFWbVgvfyKC9WLwGK0xboYERtEBAbHG288xBhNsTzj+PA5RpMDXnzlI6h4RFVsWRy+TOQkQldcnN3n9vGCg6MjNtsNpxcn1E1BWW6RLvLVgjNEURxQa4JYJSRRQlnuaKoyVJ2eha+cpDGasq2JlCI2EUJ0gBiqRCV9W0ygPEHVOrT2CKc4wasEwLMHCiEG1ejeQbPnQ1lrB/WGm7iJm3hv4l21xeq6QmApih3rzZqy2LHZrFguz3zVYg0ikAvrqmKxOEAI4dtb8SWpsJ+XbLfbQfK9rpuBzV6VxaAI4L9nqevas7adP4UqpfzAVwTNr2Ah3LfWpPSCmZOAAptNJ0zGI5SSaO2Cfa5Bdx1VXVMF+Rhre46OvULpd+HavrqKlEL1CgHOYR1EymurGQtF2dDpcA3hCZPWGYSLMNZxcbFiNlvw/PMv8KEP7fjMZz7DZrNhsTggiiR5lgGCtq2p2oqmqTG6QxpDJCXVruLx0xPe/773hfmVb/s46ZA4mqrGj3wMVVmSpN6cS0pFWxseP3rMZDzm4cM3WRyccXR8l/lkTvTCB9GtJYoVSQabzX0EJd/0Tb+ONEt4440v8sYbr7NcntI2DaNkRBxHdLpF77zysZCOPE+oG+kHu0mE1haHN3Oruw6pNVEUY5xDGF+Z9ETTTmusUgjr5zJCeKmZttND8hmsk8NcppcliqIYr8Rz+Tnr7Zi/+szCbuIm/tmOayeXtq1xzlKXJev1mt1uy3qz5PzshN1m7cUJhUA4MdgVt11LWZYkSUqaXBIKhfDuilmWoZQaPNullEP7DQjEuyLIrggvOskVQy9rwyBXDddWYWMRArI8ZTLJyfKYOJZYZ2gai7UNbdMN0iRGg9aWtmsHEy1jg0hiSIgAjhoR9LLi6IoMTaRwFj9AT1LartfG8hDmocVjLVVVgha89dZbtG3L+973PpIk4XOf/wJnZ+fB7yTz3B5AtzVNuYW2Yp6l5HFM3RQ8evyEXVEQK0UceaFIZwzCOWIpsF2HUpLOaOqqIZUJSRLRlltiKUjjhMV8zoN7b+CsZb5YkEYJiRQYt+Xs6WOM7vj13/UbePmlF3jz9S/w6U//AmdnTwHLZDxmNM5BOKqquPSsQSMVpGlE20iauqVpalod2o6BXKtUqORib1ttglK2s6Ctr3AjlXgHTG0R6tKorYedO+fo2o40SzHGBZJtgVKXwqDW2uCieenpcxM3cRNf+bh+5WI1u92Ouiwpy4L1esnZ6Qnr1QVNU3nuAmANQ4vCGM/iN8bQaR3+7dsV4/EYIQRlWQ66YP3sRWuL0QZjA0Nf9g6NDCfXnnWtjee0wGXS8cZmcTDhkoMsSdt4HTPdabQ1tF3rZfNtr03maLs2PLb1zoY9BNl5pWYlBEkSE8cJqtFEURB29PjaQapGW4O1AmkFNlx7aLfF3pzr9ddf5/T0lK/7uq/ju/8nz3Pv7fucnZzgrEE640EOVjOJFIfPP8ckSyl3G85WDWXdsFyumY4yrNEoKTDOEzczFdHVDc4a4iRltyuRwVr47OkpxwcLvuPbv4ODwzd5/bW3ePLwbR4/vEeapWjbIGi4e/sW3/Frfz0feN+v4tO/+Ek+/4VPsd2ukVIwmY7D7KyX3A9+NwElp4TnlSRJSiUVDgb+UR8Ob8AmbNBqQ4JQIO0wM7P42YkIMyQZElhP5lVKYaQD5+drSZziLMHtVAxmbn1FfBM3cRPvXVz7t64oCo8Qqys22xUnJ085Ozuh2G38nMO5YPUrcMJvxFcVi611jPIxdV0Pplj9qbLfdL1+WIVzxnuWNBprDXXd4JxFoIb2V3+C7dsefYjQGsMJ2kYTx5auNeimo2072kZTBSMrO5A7PYZchmv5xNOhncU6z7gXUiCsQCJpygYpPaAgjj1hMsa3Z4SSIAWRklglcVYie7nlAEsuyoJRPho01T7/+c8zWyz44Ic+xDd99Bs9z8ZaurrGdS2ma+naGmc67r31JlVXs1pvePDwIS89dwfhHFma0LReFkUJgXAeHRXHEViBcxLTWR7ef4BuGr71W76V3/Dd38PnP/dFvviFL/Lo0ROqpiYdxbx49y6vvPgiq82Kn/74j/DGm1+gaSrAkeeZb8FZe6VC8L464EU5FQ6ZBmUDramqBm287XHvdxPH8VBdyNC+6gfvUomgygDaOrQxmFIjhRuSRd8ytcYNUG9rrDdbMx6td7Ul9kv569zETdzEVy6uT6IMsODNdsv52Rnr5RnldkXXVM+ISDoA4QZNsO1mw2w2Z5SPaNsqaGdZqrIiTlLatkGF06mHOltMry1mNB4JJvy8oGsDN8RL4UupgqfJZc9eCt968WKUCmscRVHS1DVVVdF1Xv/LOm8Q5oSvRkR4HBFOuw7nORf9Ot1LxnjZeYUILHI/C+icJ0M5AOkTVRzFZHniJXK0xjiHFAqBou06Vps149HYuzputvzcJz/JOMv56Ec+wt1bxxzM5oiupW1Kmrrg9OkjZvMp6uKUXVlydnbBYjphMV8wkRHnFxfkmbeFTpIE3XWM4wSpIEkUbVFycXbOxcUpyTjlA1/7AY6PjvnoRz/G2dk5m90GY1vOnp7wi5/6BZ6cPGBXrb1LZjr1rpajEZHyMOdOm4Cks6SJf8xIKozufHtRxKTZlNG4oWg6bNsFa2OHdRpjPZLPQ9YvuUvK9rQ6gXOdFwGVAuuCYoLWwT8G71YZKppWtx6o4QyR7A8iapijfXWFpr349xHX+BXU3Wv8b358GdB5v3KcrTvEG/tZ/zh47M74i9HfvMYz1Wj7mP9ztJ9Jv3WaJw8km+V+Ql7bWBplMNdgUWoL/8WfesRf+fNP96599FYDHwcm+6/rPgd/9hfXJIn0M9ZnPQSf+fenH2p+T5Z6Sac9ca9tSOU/2v8EgKLW/Pwb+9cZB8XjDb/t3//C/ms2HTZ7C53/4v7rdis+8QnHa2/vh+tvl9c/pF2fRGksZVFSFCVtU9NWBa5twAR5Fb8KIS1KSJw1Xv5jNEVJSdtUxHFCXZXeIyTyHFbvENhzVDSRklRlh5SCtrUDEbLXCUMKdG8vDEFQ8hIx1PfvpRRYo2lqO+iCyUiSxtkw+B8Y/oG3IoRPGE4IJBGJvBTZDGMTHP5EbazFBDdIqyRCcimo5EB3BmvcYN/snMNqTWdbhIzJshwEVE3D4eGE2XxOWdWUdcMnf+EXefnll/naD3wNrzx3lzjLiGLFdrNCAKM0RUY+mTx39zlefvmQUZ5jjCVNM+I0Y7Y4wAMfJEmuSCLYXdQ8fvSUsiz4/Gc+xauvvMqduy8xGueMmpwvvfEZPvPpT/L48SN2u12Q+UnpOk0cK5wT1HXLdDohzydEUXxFBdq3qWLp7QRindDpFCcEedOQFzt02yHc5dytN/zsf94jw8zg9tnfTinFUJHaoCZtCfMX7KXunbOgwHUeUeg15ewACf/qiojk4E+A3L8DupPvQz75fgT7N2z1tf8K87/yR/Y/fKup/4X/jOJD/8bepbbZ8Nsf/Jv8mW+v9q791Ap+3Q99Ha7cr73ieMr7P/LTvPDy/qf78G34/KfK/QtDpPeyQEfY8xyU4S/+6D/k1q1be9f+nt/3LzL5lm9DXkME9eC//Bv89g/+1b3rrNO8+eMf46/vlnvXroCPFr+RR8v/aO/ahjf5X/7x7+Xf+/fu7V374z8O/9Kf+b/R/pr9nwX9c/813P+De9fBuxro++F8URRUQfa+JxfCpaeKC4lCCBEEKP3PZlnq0VNI0jRBCBXY63rgw/Rrm6YOiKm+n9/RdS2IS9HMPuEoKYjCxnO1/dF/TdOUKFLEsTe0su5SrbdXUs7SbPjAOOdojUYIcwU55mVfbPB57zssV2VhrPNtot690asw26EN2Oup9Rtp2/acDOclVHBM51NiFTEZT0AJduWOXVkwGaVEcYKMIkbTCXfu3OZstWSz9lpuk8mELM954eVXQGbkkwkqjlksDlBSEMcghPWziyjjxVe+htde+wJvv3UfZxQ/8VM/yac//Qt0pqGsdkPLqbcL6FtYfYL33jr+9V6+90Gi5wqAQamYLBuRZv5gIYT0kjrh86HCLKVvdV597/rPVf///eP0KDBrLXEcIQU0TRMcM+0AIe/fJGstWZbRNA2orzLDMBEhriFc6Q878d4qx+FASERyjWuCt3yQ17gn0nchUrX/1JpI7+R6nUToD4WwR9XGr72ORsyXX33vKofwv5/X0KvxHkQeKbr30YVEingvSlFY6cnY13iusWeCIa+h7yPwtiHpNWR44hiQChFdY/G7EIB9V9pibdtirG9p+TnJ5S+99/Wwz3BOnIO2a0mSdJB9GY3ycNr16sLgWK1WQx9da02e5wOqyM9r/GbX6TaccJ+1C+43vX5w2w9z4ygehvu+EmrZlWVQBvAn7NFohDN+IN20Hk7rwmDeWoZTby/fHzQChscZZPVDO844R+y8EkGcxqgAVgDIsgwpO5pg66y1Dhu3paxKZnGClJKjo0Neeuklbh0dMYpj7yFvWxZHBxTlMVZYGt0gsKRZghOg4ohJNqaoLVZIVBRhsahIoPCukOPplA99w4eZT0ccHB3y1ptvU1Yt69UF1rborh4qrX4Y3qsh9Il4Op0ymUzC/ay97TM9+dSbpPnEEYWKIyJJc5JshBNrtLEeRAHeI0faL5uN9I99FaBx1Ufo0iSOAYbeA0WEEAG9Zwc7hv697szN3OUmbuK9indVuehOs91sKKsytBlE4Br4oW6fbKToXSFblPK/4JAPki8exaUATdNUg2yHUirMJzq6oJDs1Zj9PMZzVC61vWxQHe43oz6GFpkUw7q2bdkVBcZ6s7I4jtFas91u0Z32umXBEbI1mrZtaVsznJTjuJeKd5eJxl2SLG14XOkcCItUDmUdSgnicF1fzSXIpqbrNMZ21LUgimKaqqKNExaTMYmSZJEiiyPGsymxEnSZIk0kSjqENMQxaHMLpSSPHj/m2FgWR3eJ84zlZsOsrknbllSnHiAgBVEc8eFv/AhxAsd3D5kezPni57/I4ugAw0tstis2m21I6HbY8OM4ZjweMx6PyfM8gDQq6sa7ZPpN3Z8VHRBFMXGUEMexZ+7nExaHh+zKkqpp6YwdYMJSXFYuwJd9HVSp3eXcpE9AOIcLXJi+ktZaBzvky89J23q1gK+6yuUmbuKf4XhXyaUI5EallNfIUgobRaDBOYvse5xXNMD8fMMrIXddR5Kk1HWNlBF57i2T+1Nq27bkeU5VOkQCSRJTBil68JtXn4CGk204MfenXt+R8aKV/cbUdnbwjVeRHORnesOx8XiMQNB2HXVdU7UtQkiyLA1KAR5p1vvC9OffX6rkvcoGt1pglCKKI4y1JHFCnCTMszQkL+2RahKm4xFH8zmHsxnz8ZhEKhLl4b5KKkbjMaNUIVxHWa6ZTXPKqqEoG1arFTJKMCIlHS1wasfp2Tn5bMZ4MsbZGOFtUJjOR8Qp5BOFTBTj+ZS333yT/HHO40cPiZOMOPZy9V3njUHiKCYf5cRRRNt2g2FbWVbUdYUxlihSwWIhJVIRIAODXoKQRComCZXZcJ8cOGuGyqVP5F+G7Ar+Ly5I+bgrlY660grtBVGTKKZtmiutSHtTudzETbzHcX35l6ZBty3S2eAvn6KzHBPkTSTgjD9NIm3opUqSRNHpBqmcl3mRkMSK0Wjs+/BBFkYKSRbaZ146P7DvVUQklN9kVYS1mkjFYWDrvVyc0UNS8VWUrzBMuE6vkqyNQ9ce3ZamaaigBE3TUjc1xjo6bRnlE1QU0XUNVVWHuYu/D1LJYZANlydruJwJ9BIyXec3y8Q48lxhMRi8dXKepoyy7JK/YWrQJUp0zGc500lKnHi5kyxR5ElCtWtI04Tbt46oigjnViBgvWl4/PiEqnG8+oFDomhEmk1oraW1HYkVxC7B4ogS3zZNVMrtu7dJs5zxdMbdF1/lzuNHrNYr6qqkrSpsaAdGSqJ1x2q5ZFtsWF1csN6sQmWqQivNV49Z5u+rMZa21bRtRXDBAaE8Ws55JQEhJU54FJh0Fme1V9YJmnD+b150NCJCXWnV9TYMUkrPSQozLhEOHU44Gt0SqYjWdChnQdzwXW7iJt6reFeVixCCLEsxOgdnEFiUFNjYa2o5402gwHpeSKhcokgNZk/OmQHdE8cRAjkAA3rIbpIkHlJ6pSKpG+tVh61vT/WnVSkkXJHfB4ZTcD+M7+cGNvTh09TDZntbgLpuhvbeYr5AW8d2u6XtmoAigyjynBYVxVjraJrmmetfVWzu+Te96oC1lu2uGPxcRrn35piOc6ajjFGeMc4yZpMJWeJoqhXF1pFEGmyOsjnKpcSJJBnFxHVEUTiarqGqW9abLU0LceaJqeNxPmz2Qki0E9gOus6SJAqhBEI4YiU4WEyZjMbcuX2bF194ju1uw2q1pqobqtIrX68vLjg7ecJ2u+Ps9ITV6oKy9MoJ/X3sZ2yeLGp8RSYUSeLRgNYaxuMJcRIH1J2HgwsHCIcVEisd0jmkkMMwVoh+MBt0kofqtL/v3i3UBehZHEWePBpHoY1m/WxHKbrun8jvzE38CnFTG95EH9dOLv2GmSQJLnAdxnmG7oK7oO4wXYs1Gmu70JbqJTgMumtRKiKOk2FD8rwWX+Fc9YrhClzVo5MaoJf999BnY3zVYgUDYfBSJbln8jPI/iulSJMkbHYMIIBe5NA5x2w2p207ttstTeMRXEpJkiQeWixV3dC23bPEzaH9pwbUmpRyqML6AbRvhXlQQp6mXrfZGpTVSDqsqehqy25lUa5CmJLZfIG0M5xOQTpa22AFOOFJh6fn52wKzXbXcHz3Baq2RipBlkXEKsJoi1YCZwzbzY7DgylJpLxXvQjOnIkijRWTUcLRwYz67vNcbApOTs44efKUTp/y6PET7t27x/n5E9qmQOsuQJNroijy98s5pIgh9+8zOC80yqUenJIqVKDBJTTwhZz05xInrdebDuRVxCXkWAr1TPL2TqQeni6EREiB0R1RJBHOEknhD0EiXPxatlQ38Y8TNwpuN9HHtZOLR3qNyPMEZyYY3WK0xrQtVjfopqJrK+/D0pR0nb5EmGHDphIPMwyl/AC/x6L3VYBzDm00NsB1e2MwjwrrKwMZOBE2yM5cQoKvtqu0ds9sRiJI+QPPDIijSJFlOVXdsF5vqFs/a0jTyyrHGMNuV6C1GVSbe7Z4P6jv22P96+6rqf7vvZeNkBFV0yCcwerYb3zCoSIPZS52G5xuaMotZbHl6Pg2aTai7hpkIpB4FYG67bj/6DGbbYtUOY+ePGJ+dJvx/ACZJhCpkATWPHlyxsOHj/mN3/vdxJlCeX8BT4K8wp2K4hispTWw3ZU8ffqY+/fvsVyeU1YFbddQBUBHHKdBrkdT1759WBY1BweHTCbTICR5Of8i3DeEJ5kihLcE6Fth9O8vg8+PB2aEakYQEIZASPy9X0//fgDESoUkE3nV7HDdQt+cq7/ScXOHb6KPayeX4+Njv3ljcLZDd17i3RqD7WqaakddbimKDU0ReCvW4JREojCWYTP3cvu+ldXU7ZUTfUBfOQZfl0FDTGuSOLkCDbYDOu0q2gj6jb9HrNmBd+PCvKgHA1zOXnLqumaz2fj2H4I0TZhMx2RZRl3XX5ZYesOzq7pV1trB9KyvxnpL5p4r4quliqYuqdOEVo/QKDQSiwIiZuMcIWOMcVR1Td3UZNkY4QTnT8+ZjEek+Zh8MiVOMx5+6R5JMqFsOnZVy5v3HzI7uM1scQzOURUFu6rhe3/rb0FEktZZ6k2JbrtQkUGSxggETatZbmoePTnh3r23+NLnPsNbb3yRs7OntHXtzbxUAughYVx1KS3LktXqgtFozHy+YDzxBmJebSEM3ZXCGj3MVa6+b/6A0L+3gt4IV0qfcBC9+rVPRpH0SLje4leK4AIaODRXZfzfxcf9Jv5/jJvK5Sb6uPZv2+1bR97Nz/kWhTHae5fohq4uaVJFFQuUtJQikA57iXwByoGKEpQUtE1DFCehleKH5NZZmrrx/Iwrp9yeX9Enl16TzCcYE3rvEm184gCPLPLgYG8n7Gn7AoXvz/dJJ01T4jgJUiyb4MkOaZJwcHhAkiTUTcN2uxu4GQOcONgWX2V+t207JJE2VD+j0WhIZP1MqYfStm1HWbe0xuGERKoYqToQEhXFzCYjrIPz8yXbXcN8Pmc2PUBGilZ3zOZH3LnzAumX7rMtC1pt2O12vPnWPabzA6aLY6ajCRjHR7712/joxz6KUP2MC2SUgPQ8m6o1tJ1hs6350utv84l/9Anuvf5Z1mePWK1OaNuStmsCEsybnxttMMa3r5RUTKZTosgfBHZFQX36hGyz9iiyOKZuatquodOdh65/2acsIP4QwwwlihSeq+krnL7dKYQKBwwQoafWc16sNT7B4D8TRhtvm/xVRdLXtOffdy2QgbCf4v7Xfu+1rto+fEz1z/+7+xc6i3v0Jie7P75/rTX8SGn5PT+yf+m6g1HyBrfSh3vX7nTH8gsp7s39EPFV2zGluQZ9EEqglgnX2t5swXf/5l93LXHTN+/fQ/3Mx6+VQZv1lr9f/aa96xyOt5M13/Dq/g+nsVCd/H2KO79379q2rflv/l9zfu4HF3vXXhQ16fl/wOSnv3/vWl2t9q7p49rJZT4doaREt5q2abAqRsQJ2kR0CmJpSSKIpEMJhzWatm2wNkYq0J3GmQ4nFXEkvfpw6Mdb6ygCQ1+3Dik94bCfi/QnY7+xXSrjSgnGBF4DglYbj1waZi8BuWUMSIlwkk6bSz0rFdG2LevtjrJuPEcjjpjNp+R5Rt107HYFTdsNFUs/P8mybODP9C2xns/ibQIEeZ4DDOixq0mzb5P59WVI1Nr7xEQKsS3RxjGfjpnMRnTGcLpcoaKYLPdE1Mn4gFdeej8vvfSIX/zc5zFNSxxHKFqcKSk2p2wvzviOb/k1/Kbf9JsZz+ZYI3CdQeK9Zapa02mDcXCxXPPmm/f4/Gc/y/mjh5w8esD64glaVxhrPIzberXqNE1RmZ9V9U6jOIiTlMXhIQutWa/XdJ1GW42yIhwGvJyLxuKC6Ro9GTW0zMKoBaW8NnIcKW9CFob0XgVBYa0gVsrD4GVEmiaBrKuGeV109fPQfTU1bSKSo+8DsV/+JeV/wR//7CevRY7+kx885PS177rG41s4/BL6ww/2L23he9+A7//a/Ut/sYC/2jX8ux9t9q79Ww/hx3/sm/ntzat71/4N3uQJn+Rr9j8F/h7w45PfDfJ4/+Ly73D6pU9dq+JqALm73gnl1ljyn3zb/se3zvEvdoLP/PA1LrqE3/ZvPcf3/en9B417r53z33/zt/EHz/5Xe9f+PD9Dwh/gj2z367b9Vzh+/zWeKryL5JKnqWdJ5xlpHGMHlrnCJRFdmlDuJM50fh7TtWjdYq3GGYOVCqkkBMKbVPEgIVIHtWUhJOPxJPBLLtskvYSKQPiTqACvR2ZDEglzltDbj1UUhA09Os0TMBVW+JOuUgrp/POoqoqyqrDWD7fzPB+Y3UVReJMynmXjj0YjwNsA9FwZYwxZlg38mTRNLz1HQmK5iiK75M/4aicPG+PZ+TlGz3DMyEYjam3R64I0y4iSBBV5fS9rLVniWBwc8NGPfoxdVfPGvfusiy3Wag/vVS3f9LFv5zu/67uYTOdUZUsTWXTbBqa8IYoTjHWUdcXnP/sZPvULP8vq/IJyuyUWjlGaUwvLdrel1/9K02xAc43H4wHMkGUZKvbD/MlkRpaN2G43lEWJ1jrM0PrZmG91Cee5Kr1cf6QUKvYeNZGSXjvOBeCBEF4QNJi05WlKHEd+rmS9lw9RRJKmmFDxeuVmSxTFQHvdj/t7FtcyMXP92j3Lhtz5LppT72bpddeKd7EWriXTcuXS7+LCX5nrvqvne9037V29D+/mdf2TXvsVEK4UOP8LLxUijrA68krGXUrXVigczoxpm5ouqeiylKaJaWuJaQXaQdO2OCRxAknqkVUWzXa7QeuOKIrJ8yy0my7RY++cqcAlFNn7tdgBstoDAOIkQQY2vdYeZQaWWCpAoI3Ftt533ScpSJKEPFQF2+2OsrzUH+vf0DzPcc5Dkb1mWjaw2LuuGxLZaDQa1vRtsH6gXwfXyziOB/a7dY7VZoMEirLCCc8LsS4iSxVFvSVOEsZj42dWDirXYIzj1vEtvuPbvp10NOLB/fvoquS5l1/l/R/4Oj7yjd9CmqUsLy4oNQgV0WlNFCXer75o2O42vP7663z+s7/A5uIhp6cnbFcrL0BqvFVynl+SXfv3Ik+yQIy9vG9RnJAkWWDme/fOJE4pdmu6tg0AA982FRCSCl4fTlz6wURKInFE0g/sPXxdDk6gxhqChY7nv0QeSi6l1wnwPjsexKG7jiSO+GpMLv+sxc3M5Sb6uHZymUzGSCGIVYTVhrqugzy93xxi6dnWdtJB19BUW5T0La5eEt85T3Kz1mKN58JUdcl2u6ZtNYvFnDT1Io9a9z31y0zZu1vStWh9qWHmpWQuBS098EASxz6RONvgrMNZgsqWJ152XUfd+hmIkH6W0hudVVX5zJzFOTdsoF3XDfDovlcbRdFQtVxFjvXJqa+IyrIcGOOeN+RFFbuuReClaaxQvHnvAcXt22irWMxnREpgnaZtN8RSEScR2XhMawxKKg4Xh3zXt30HxYe+jtPHjzk8Oub5F57HmY7t+sKLJK4LktFksKK2WnN+dsqD+/c4PTlheX7Cbrem2K7Zbpe0TYdwktF0DHj14959M89ydGgJDskmz3FCorWht7TW2nvjVFVFUZbPkFKV8KKjcSRJgx9MEkeoyJue+cOMQEmvleacTzpxHJHKBJwjjRVW+YrGw9h7zTmLEN7OOs8SvI7yTXyl46up8XgT/3Tj2sllNpnSBLips5YkjhFKefOnKMJEkkgJsiQiwtC1BWW5oowUpWfX+UG8blHKEcUpGMdmu6Gu60BQ9PwLzz1RV1BhfsPI83zYODzPwasq93yafl0/8E/TlAThnScDp8Y6N4hidl1APIUZTC9+WFVV4LL41963vjwRrwtaaTwzN7kq7hjH8TMyMHEcMxqNKIrimZP/eDweEpVSklGeo6KIJ09PGY/HnC83xHFO22nGo5xRlpIlMQ6/YTfG0hpNGqWM0xyzqzC7immWIy1s1huQMWXdst3WjGZHJOMClGS323B2dsJbr79GWex4/UtfZDyZgpQUZU1nNGme0tQt292Ow8NDD5zQmjz3Ks2T0di/R4GT0nWafDzBIaiqmjbojjV1Qx2UtL0tg0A43/LyBNso8JE8CdJDj7l0ngwqBiJwjsB3PExI4D236OrXvoKU0oM4qtDevImvbNxULjfRx/UZ+k0znLit8Sq0QoIxHUY7HBHSRIgoJhp52fdZMacuKuqkoe0E1kmqpsVof6LsOkOx2+GcIY5yIuG93/M8RxBgpvSMf4eMBCkJDntpGYwI1YMXtew3d60NxjiUiomiJJAuxTD7GPxhAJAoFWOdwGjjB/hXjmA9Qsy3uBjmBlHk5wZSXs5Brioz9w6JPTihr3YAFovFIOTZt8cQkuVyPSS5sizYljt25Y5xljPKcw4XC6bjsd9kHYjO0DZb9HYDTUsWJ4ynC26/8Cqzo9teP0ZJus5wdvGU7vQR682as/NTHjx4i7OzE5qqwmrLo0f3ufvcc1iriWWM7jqyLAEE2+2a8XjKaDSi6zrSNEfGKSIiQH59BVhUTXCqdFRVTbHdUJZbynJFU+9wViPD3CuOFVGkSOIYGVphUSyJgg5aEsvh3kjhjeCU8qCCLI2BGEFISMKLZsoACsjTHsnnE1Uax8ANRf8mbuK9imsnl9Fo5BWEw+nc4SiLEhUJuqbxzH1rqJuaJMvJRhPGkynZekMUF0hZD2Q6FUW0TUPb6QGy6zkvDRJJGqTV57Mpp2ennnltvOthnMQe2ty3QQCwxHEycC28Q6WgCeKF/voq6FFdaZ31sxTpN6b+542xz5T3PULM81TsoGOm1CUq7Wo14zffdOBZKKUGBjsQJOtlEPCUA0Bgs9nQ+6d0XcfR0RHL5dKfBueO9WYTVH8di2hBnKRM04w0irBGk6QZ6WjE/Pguk4NjDm8/T1XWdG1HUexYbtaUpbcrPjs94eHDB5yfPQUc43xMXRecnDxmMZ8HVr2jriqSQCTV2gt7LhYL2qbFWMfx8bGv0KxvkdpwD70tQ0tVlazXK6pqjd/oPfnRD+t99SKlIAmeO0kUkcRyqDp6kqpSkjjy34uJBjJlJNXA1k9CxdgfgnrJIn+PEzxI9SZu4ibei7h2cqmqKggSXqKvptMpWZZyevIUGVBExhjSOKKrK6rthjQbBel1L/sRKUvbdYCfqdiAQALPYehbZFmWPQNxbduWuq69zW7YcHqSpXN9m6sbNuie/Hh1oO5Jj/GXDeAtbgAO9K2sq8ZUcRwP5Mi+8um7yz3YoJ+xXLL+oyGB9a2vfm4Tx/HA2p9MJsO8xhjDaDSirmvm8zlCCOqqJklSL1dfelSbQbCrGo4ODlnMZoynM+I4YjSfMp7NGU8XRGlO2TaUTc2jew/puo7ReEqS5hhj+Jqv/SAXF2ecnT3FGUPT1ESxoiy3TCb5gAorixKXEIbp/t6VZclkPENby8nJ09Ayg7IMqLCmoalr6roMlgopxnizOD+891ptkfJzlTSJiaQgiWOiSJLEakgu/Xs8uoIgvOr/kiVeYbpn419VdOjleK6+PzdxEzfx3sS1k0u/qeZ5jrV2qGTq2puBZVlKXRQ4C512pMmEyWTGfLFgvd2w3hY4msBr8STMpm1x1gWkD2jjuSz9xlHXPdtdBJHJ8kolIoO0hxsG+ovFYoD3jkYj0jSlLMthxtEnlb51NeiL2UvhzKveMP2spa90rm5Qz6oiPys701/7qjZaX5H0m2XTNIzHY9LUWxBUVcWdO3fY7XZEUUSapmw2G88FCmKQQirWuwKihEY7rIjQTqGJmc3mRGPFhBTTgTUtu2JF03bUrfc6GU0SxtMpVVUxbw94+eVXePLkPsvlGU3jSaVV1dA0NXXdcHBwxGQyZblZEsUxVbXj8PAY52BX7EizFGMsDx8+YDab0XUd2+3O++Rs1nRtA9aAcCRxBliiCJJIeCFQJX1CUX6gr6SXeOl5LD06zbdgvXX2Va5RHMdeP82YAd7dAwystQOy76oUz018ZeNmoH8TfVw7udy6dWsgM3olYT/cn06miKBSm+cjtNaMxhMudEec5CAkaZaTj0e0nabTFm16pnVgy+Ew2tA7o1aV9+rOQiKz1hEnEXWY+3g0mQ6tj2ZoOwFMp1POz88Hb5hehsXDgONge+xfdi9GiRB0uhvaWUNFE6qS3pK4Rzn13bhe7uWq3XP//UvJGTckrX7w37du+oTUI8jSNOXs7IzJZDJsiuPRnDiOKaoSB6R5TprnLI6OMdpSNJrtkzMOO4dIxyx3j7h16xajPKPtOra7goODQxAiuG1qtHFstwVFUflWWpp5s7K2RkhL19WUZcl4PEHJiCzNKIqC0WjMer1mPp/75Nle2h48efI4VLaaqiyQEhwesRXHEeM8RymBUpZYgZQOBcSRIksSlPQosCSKiJJoaFn2EjuR8ij8qwRUIQRWmytAj0vztqtOlr1qwlddOINz+joLsZrr7dwOuBYyzj7z5Tqrn+Gg+nHns18BE1ymu2tc1zqwOPQ1noQNjdrrUBgdeP6Iu86L6xvA1w93rdUC4xzCXa6+erv652mDyve1xoEGrAWtr96Fd17VX9nPwS0a/czjPbvKh8X69/car+vd1P/XTi51XYd2gyXLfA++qkqKsqAqS7Is9e2QroNOMxpP0V3FdH5IqzWtNhgLdatpGl9dOASR8kZa1vpdW0hJWVWoOMLWPsG41RLwLP9dkK4HQdt24ARlUWFs8JARXoSyaRuSNAmtLEeSZuT5mCT1p2KlPEqpZ/FrYzzZ0RiMNejO0BmN7vSQTKqq4vIA7IK8vDcRe6f9sgkQYRnUeqMoou06RqPRIMipA4u9qiqcczx48GA4fTdNi8DPY/JRTlGXWOc4ODjk5Zffz3x+QNNpqqrm6clTktGE0/WGt9++x0svPs+v+vqvJ00SOmvYFVsODw9xzlAUBavlikePHvDo8UOf2JxFKkHb+hmV0d4hUkqJ0ZrJbMrFcunRWU5wdnbGdDpDVx157gf8eZ7TNC2jUUaaRCGZyqD15SsUsKSJCtphhsnIgzg8+k+SpxngSNOkv8UDik8qDzPuK5m+ElE+iyHkJUrMAyt8O7VP9P7X5t67+NX4SoemXf5fENf4FWy6t/kTL42udVW7Kfjm23977zrnHJ/djUhf+4b9a43mv179In/9c/sfX2sw+hb/+dmtvWvrekvOQ36U3d6156y5/Y0ZJ4f779eDN1u+7t7fJWe/rMw9d87zgmusDAz9w/JaiLgnG8nv/fs/u3edA3aNJn7+Ghd18GO7J/zDv/339i41pmLxwus8emW/Zs9qXSPrr+Gnnn9x79q3nj6BL17jg8C7SC5lWRJFEVmWsNvtAueiI0tzotiT2tI886w2AJtQNyXpeMrCWowDbR1V01LVDeDo2hYpFD6vSLQx1G1Nqzsa3SHjaGij+VNsRF3VjPLRAA122IAEgzYgyJSMsK7yWlhJzGg8ZTQak2UpcSJIkjgwv62HuIak0Iscdl2HsRZjHW3XMZ2OaeqaNI7QofLobXWFFF5vLRD6/KnJhjaaCGq+vj0mlPcd6Yf/bdvShGoMvGLAdDqlbTVxnKBkwmg8JR/52ZMzliTJmUwW5PkYZMObb93n/HzJK6++j/XFkscPHrA6v+D48BYHBwfUdcvp9pQ4iUiSlLZrWa4uWK4v2BYFbasRIiJKBM4J8mxOXdccHtxiPJqEGUZCPpoSRQnz+RwQzGZTrHWDxE2vs5alPmHDZZXhNRSCZbXWJGniZ2pJjDGaOI6CaZpPaInyLbI+kcRxQtvWSCWJo8s2mDd0823ZJlgzy8C9MsZL1OggmCmlhJ/6uet+3N+DiEgO/w8IOd27sjn5I+zOtuwH+jo+ML/gz37PfvmX1hj+5Y8/4qXv+g/3r63O+cTHfwf1h65xbt3BdPnrcF/7P9271J18ko9dfIpv5mN7136CT/L7/q+v8T2/dbZ37Z/6dx7zkf/Th3k/871r/yM+wR+MCg6ukTH+BDD61XCdDus/+MmI9fj5a8kqxI+2HJ7tr8kscMrHaJv/3f4nwH3++d/3x/kD/9v9Sf7nf2pL9vYf4Y/+4T+6d+0P/MUf4Pf/z3//NR7/XaLFfDsptHSkCOrB6YDKiaKI3W5H29Te72U6Y14fESmFto62s1grUCpFnJywXK7RTemhwaYlIaKqSxyXw/V+/uLbRn6wXlUVeZ7Tac/gFzJIvziD7jpMAAk0jebwcM5sOiNNM9IsIYokaRqTRFEQO3QY40tH/5iGrmuHaqpPOnWaMBnlGGtpu5Y28F2sNQPMeeDlWC/aSUiaXoYGIhXRtd2gCOBbi0FvTPqNU0XxQAJ1wrFaralbzytpuoqm6VitVjRNw+OnJzx8+JDbt+8gpOTRgwcUux1NXfP40UOM7ijKktOzc6I4YzabstlsKaoGbSXzg1uMxlNPVFTezvhgcYDWXQBszPzQPPWn5l5PLUkTZtNZ0PSSQ5uq61qUEhjTDXIwVVURRzFxFAXE3iVQokeK9Z8dX/HZ8D05zNjyPGcS2hxShFlb+Ey4cPAYj+ZDW8w56xn8AcTRtu1lNXQTN3ET70lcO7l0nd/UwM8ZRuOMJPXw3/F4zNOnT8nz3G8E4zFCeFRQ2zREUUKcjogSb7+bj2ak2Yg0P2G5vGC72/nZhm7out7O+DK59OTF8UgSJwlFVSLUJQLIOo3WXZB66fDGVDH5aMTR8TGLxYI0zUjiCCk9X0Lg8J0WA1YjpRgIkkmsBjMrY3yVkkYjOq2DdbLn+lhnL8U1bSD1OReImn6Dsxa0tWhrA9hBB5+TfhbjB01KKdIs8wCJNGW922KNQ8iKrE58BQesNhvq118H5yjKiulsznPPPcd6taLrWu7evcN0OiGKJNZq0iTmueeeC5tsRxwn3L17l+OjY2xwDlXSu+pEsRokVrxWWoqUiixJUcJLudR15aV1hCBOoiDR43WJsuwIIezw3iVJwvHBYbAe9om2n39IKbHGXBIlI4UO0PRklAEwnvqEUVUVEkca+zanUBFCQFUXTKbT4VpVVTGZTP2Mqijouo7JdE7TNEOFdRM3cRPvTVw7uSyXS4wx3Lp1zOHhIVEsWS3XLJdLiqIYTolAQEKN/Pwg87LxVd1xcHDLt3Wmc+YHhxweH7HebDk9PePi4oKyLOl0jbGw2+0Yj8dDa8UYw67wm0nbNGy2G396NXZwKwQ/vJ1MJty+85yXqJ/PyLOUKI68DLv1lrvCebVdZw1O11jbkSbxkGCM7nChX5/FakCLCSl8yyy00Po/Pq9cDpTBJxUHOCcwTmCMH2I6J4L8vodQW+fHldaPFbDGMRrHKBkRJxlxEqEi6WHdSU4SpYzyjNFkyng6JU0yr6YsHHmWEcURSXJ5UlcqzCiECFbTLiQEF9wq9TB/kgEi3HWayXSCs5auaSGoEk8m2XBdKSVd5JOOEJ5L5KwhyT0qrgk+PYmUZHlG3dRo3RLFEUWxZTwaIfCtxyz3cv3jyYhWd7RNS921jEdj5rMpSey5UQhIEl/BJmlC2zWUZcnh4SHHt45ZLi9o2ojpdMp2t6Wsa6qqvGzX3sRN3MR7Etf3c7l9O6gAW5bLJSoS7LYFznlNqe12S1mWHqpsLE3jW0YqSsnGEUcqQQl4+PABo9GYxcEBt27doihq1pstVVVxcX7BxcUFm+2astgOsN0kwI+NtRRl6TdO57kXeT4iz8ZMJhMODg9YLBZMJ1Om8wXa6IBQ8ogKKSXS9qfmDuEc1nY4LVHCqy9LHLGSOBcHfxA38Cus9RYBxhi6MDi2oRUm5SUzv4fCikiFFp/AodAWQIG4dMTsw+IQkQcAKBUxGU+RShFFKVGiSNIk+L0kxCpGCUiyzHu/SE8sTSIx2AlESnl0jdZBAcFL9Xj3SM9W111HGqcYrXDWEifppXRNJMG0jLKcnW7DPTRMJhOatg1eLi2TiYd8F0XpSavBGK2ua69wnGXEccRqswxS+BLlBKNRSpIoiqIgz0cUxcZXGpNpaFM6JqOMtg2SQ3lGVVdMxmN2O2/qdnBw4Hk31Y66LhHS0bQ1QiiSNObw8IDtduttCNQNFPkmbuK9jGsnl9XF0qOKhKBuPJlxMp54tJExdG1LlOfUVYU2FqkiP7TNBEVRsGu3pHHEdLYYJDySZMud53y7qaoq74/SNOx2Oy6Wy0EypVcRbjs/u8jynDz//7b3Z7G6rWt+H/R7m9F+zZxzrb33OeVyoioZ44DsxDHYOkgWjZIoiRAkYJyAhBCKUG5QZIEvI5CCUCSuuOMiYGEQCESEiRAJkS3LiYUTJ1ElLrsKXKkqu06zz1ndbL5m9G/Dxdt8Y26XM+e2Ty0dzPdIS3vtucYcY3xjzPk+7/M8/6aOiaVit91kVJGIg2GAcRwCKa8MwICqLBhOHUoJxsEhvMN7jfEaKTxFWSJkNC9zDiWLzGEJmog+zGIIWlqpZWetRaJiwgutNxAs1uOlDiKdQkbfEomLFs2JDJqg0TJ6yxe6oChKpJJ4IYMnSUTSlUWFsxbhA8TXOotWwUumqoro+BhmOvM8IVy0ghYFhVIsw4iqaySOWim64yNlWVJXNUJC352om4ZNW/Pw+Ijwnt02KDz3XRdai3HOZRYD3jIOHThLoQRDd0K4MMOyJgp0esemroNQaT8wzDNKayQiV1XG2NzWnCMHSECY6wjozh1107IslrKscQ6WxYa5TFlxOBwYp4kvv/gC7wWPj0+M4xRsAFTBOL7sL3KNa1zjpxevTi7n7sh2u0MpyV17Gy2Bp2etoK47c3d3B4uhjAvd4/0jVVWx2bQ0VZXbV0opmrqlqEq6vs/cieTQaKJ+2fFwuAhLFhV9P1DVdTSTUlFN2GWI7zwHscSqLPF+F2cIgnN3QivFrBR1XVKVGrvMWDvjColZ5jAE9kGGBCFRKlgAB4UBCYgwMykKijQ3sSZComVWIgi8GI33IhM802dWWjEugQOT+Do+DvyDUrKMzPIwp7DOs2k3LHHRFc5QKhklbjw3+30kaVoEwbUxOXyGhOho6gC6cBFBNY1DbAtqBGCWhVkIlC4icdNS3t3RRH+aeQ7yPre3t3Rdx+l04ssvv+TLL77g/fv3Gc232Wwoi4LHh4csw388HGK1M+VZS5qJSBH8fazVbDYbuu6Msy4O4CvO5zPee96+fYvWBcfTmXGcsjLzPAdAxzwHu4a+6xk2I7vdnrdv33I+d3Rdh9YX4uw1rnGNzxOv/o2z80JdFnRdz9gPnM9nhqFnt91Q1BX77Z77+weGrqMfx6CcXBaURUHbNDze3+NXTGqtdWid6DAfSMiiJEEPgXRY6oLNdkNZlpxOHW3dgBDsdtsIj95TFIrHx8dgp1uW7HY7Dk9PSBm4GwG6GobQ+/0ej6XUzQZiHgAAaGpJREFUNUN/ZhyW3H6TUlAUFWVZhHbfZo9I8NjIPSmr4FmitQ6EUaUw1qKLQM4cp4naB/Sat46qKDMRsyxLjLWUNUilcNZRV0myRCOj3tY4TWil0FGA05jQbgq6XIFUuNlsuP/0iXEI866uP+Nsxc3NnnHosrWBUhIF2GWmLErsMoO1HM8H9vs9+/2e0+nEx0/vaZoNb9/eIaXk/v4jWhe0bQ0ejk+PuG2Ywey3G06HJ87HQ3SfjElpnrl7c4fbtDw9PVEUKr6DkfPhyHa3pTt13N3dcrPbMk4zCMH5dMr21Uop3rx5w3a75XQ64ZyLcjg1WknwFoFn6LtYlaisdhAUG+D9+/cIAXd3bzBGx4H+5nfoV+ga17jGbxevbkQXheJ4eMKZGa2Ch0tVarbbhsPTI8fDE0+P9xwPj2ybBuEshZQM5xOHhwesWdA6oMCenp4yCijpik3ThBCC4/FIXdU0ZYWdF5qqAuf5+O49ZpkpS41WkqaqUCJwTJLEi9aavu+z1lQyuGqaJiKogomXjGz1ZFO8LDPOWbQuqeuWoqgRskDpiqpukaqk3ewx1iNkQVHUlGVDUTTs93fU9YbFOBAKrUuqumG3u0EpnRNeWUakE6CVYrdpo9cN7LYbnFuQIgg7aiXwzlBqCc7g3YIUgYDozExdKg6P99RVwTKPWDNTKEFdFzi7MI09TVVQ6iCZcjodaJsa5y2fPn6gH87c3t4wDB0fP75HCB/k/80cE2zYADw+PnA+nwOaTMD79+8wyxwUjLXidDxwOD7F5xrkXc6nI1IJ9vstCDidDyzLzJs3t8GfRUu67sxm04J3dOcTeM9+t6UqC8ZhiPbIC7e3t2gdksPT0yPj0CPw7LcblBTUVcV+v6fvex4fH2nbls1mE+7tdOLDh/dYa6jriuPx8Dv0K3SNa1zjt4tXVy5SAN7StC19P/D0+BBk1l1UNvaet2/uOJ/P9EOHVopxGhingf3NjtKG3W3iM8i4Q/dRoiGJNSaCYZq1pDbKF198weF0Yhx6trtdGPoPHbvd7lnLY7MJVY6OgoUmDrTrumKaJmQB566jqYJtctM0zKPDLBNKFZSxAmnagsVCWdYM44j3grbdIJSmiqZhqihwwHa3w4sjUnh224au6yl0iVahYjHG0LYBPXc6ndgUDfM04swSGFneIYFlGql3O7SS3N9/oq5K7vb7oAxdBXMxLaA7HamqMGdo25amqfjJTx6QwuHrhu2moTufKMuS/W7Dx77n6emBN3dv0FrhrY16bWFmsd1uaJoad3R8/PiBzWbDmzd3GDMH9FXf8dWXXwIOvOP+/iOFLvjud7/i/v6Br3/0Q25vbvjiyy/58OE9n+4/UpYlb9684fb2hnmeOR4PbDcbbvc7fvT1jzDLxHe+83NM48g8DvjYspO7LV5Inp6eGIYhtr9mmqqin4KCdt+dWaaJHx9/xJff+Q5v377l48eP/OAHP+DLL79ku91SlAXWWH784x/Tti1ffPEKP/XPGh7vTrxKeMRbgvDJy4g36z3H+eX50mIdzi2Y6fjyOZdz1H95+VYx4O2MW15m3XszsrAwMLx8vxi6s+Xp4WW5nHF0dBiOr3AeXXCcAfWK12AELMvrgIc+yc+8dN44UnidUA2El3B6xdEd8+Q4Pb38vIbO4oaeh8eHF4/t+9cri38r+RetNc6eeHx6QiuJs+T21jxN3Oz3fOg/sJiFu7s31LG//3h/j/OecZy5uQnchWmaUFJyOp7YbDdhHjJNdOeOcRj56osvqaqST58+xbZZxTAEGXctJdZ7bnY7VKHpj0f2+z1CwDT2dOcjfT9E8cqSQit2+x1j39M2Dc7tcTExCpsMvwWFDtyYcRhBeHQRHCVPx2NAUXkPxiCFp66DLtn53CFFhVsWjJsolMQtM011i62DqrBzlqHvuLm5QSsZPoNqwHumKSDwikLTnTvmUtO2GzZtw/H4hJaS3XYDeIa+BwTn7swXX36Z/eGNWdhuNwxDj1JhlvP4eOJwWPj53/W72W23vHv/jrEe2DQ1y2L49OkjRVHw8z//8/R9z+Fw4M3bL+iHMasae+do6hpsqA7L4tK+fHh4oNm00S7AMy0zfd+hi4uQxrnrKIqC3XbLw/0DXd9jnYuq0AsfP34EwobAWkvfdUglmYyhqWu8t0zjQllWCOD2ds+79+8REsq6QijF8XSiaRrevn3LPM+cTifO5xPb7Yb9zZ5207DMUwQ6/CyFwxz+FRAvC49I/TW/74/vQp/hhcXqJ//2gf/Z9s+8eE5vwR1rvvwP/4cvHjtZC+928O47Lx4LE8Pv+xXmzcuLkDt/4s/u/yp/rn1ZzsT3nvd/wvN/3Ly8sv/Ne8/Xbz5y94rV7TeP8L8Y/wE06sVjfyC/zz/xm+dXJZe/Ngv225cP9AIsNX+Ity8eu+D4i9u/iP7q//XyeRfPv/OnLN//0x9fPPbkPB9v/0X+V//b/8mLx3bn16uLvTq5vHv3nl/8xV/g4dMnlnniu9/5Du8+fKA/93jreTg8hMW5bjifDxzw1HXLOAxY49ju9pRFMKAyUcNru2kZpxktFcs0hxaMECgRdtVBYsUwDB1aS4QC5y1SS6y3nE4nbm5uaDcNfX9inkbOpxN1VYH3GBNbOEpxejrQHY9oHM4snM+nOOjvKLVEqwIhVBDQBJy1lKVlGs84O7FMHdvtFuugLAo+ffyYh/LeOTZNy6dPn7i7uaVtWp4eHhEevFnQSjENA50QNFXF4fiEEsHK15oFZwz1ZoPcCp4en9CqwDsQXkSSosiyNEnQ0RhL22748P49dWz79f2Z8/nM6XQKdsFKcjw80ZQNwjke7z/xxRdf4IyhO5+DPpgJLp3n8zkM4es26Lt1HeMwMM8z3/3qO1Hx+MQ4jlRVxZs3b3h8esJay8///M/jnKPrOiAAE/Y3N7Rty/t370K11rboouDp8ETdNLRtS1s3vPvJTzhZEzlNnkIrxqnn8eHI3d1b6s2G0+nM+Xxiv9/z5u1bkEFbrmokeM/hcMg2CwFk8I6nx0fKouTu5o6hHzDzayQPP2coirs/+Sr5F+n/R/zjf/o/ndXD/3bhvefP/rH3/Pf+9Ms722WE/+s/fMP/9B/+Iy8eez8M/IUf/BrO/5dePBY+UfyjguZf+Gdfvod/65c4/y9/Gf+7XlE2fA3/xBfwD3335WP/1V+Ff2nr+QOvGLP9878m2L/7x9jw8sH/uv4/87/53pnq5TzEH/7zLbs/+I+tPKd++/DO0X341/gX5z/84jmPzPz7X33N5o++vFEyB/i9P4R/1L38vP4m8O/+nOXv/8WXf0d+/EO4fzlfAd9i5pKgrUophnHgdDoyDj1Ph0f6oWecBvqhw9ogxng+n5FSZOXhzaalKguE8IDjeHzCO8c8j9w/fOT+4SNVVdA0FdM8hNZLqWiaimWZ6LoTZalZlokPH94RRBCDttT5FCRP6qpCSUlVlXzxxVs2bUNVlozDwNCfmaaRp8eHaKEb/lhjmMYBD3Rdl7W+5nmm7wN3oyzLbKM8TRMfPnzI7b3A6C8iB8hzOp2COOTTE8Ys3N3dcj6f8N7z9PQUGhsC7u/vs++I956PHz/ma/d9n1FSXdfx4cMHhmFgvw8oKCUV8zjRJaKpD1BdSUB+nU/hel99+SXzPPPhw4fsiNl1HefzmdubW7z3fP2jrxn7gZvdnsPTgYf7B5q6ZttumIaRw+Mj79+/y1puDw/3PD4+sL/Zo6SgO5/59PFDcISMytPTNHE+nTg8BW5Lgpff3d5yd3dH33WcTye0DmTH8/nMu3fvANCFZrfb4j0MQ09d1+G5zyNff/2j+JlbpAzzuWStcDweub+/p+97druwYCePn2EcfjZVka9xjb+H49XJxZiFT58+gfB03ZmuP/H27Ztg5NQG34yHh4ewS41tDhAZaeW9o2lq7j99wi4BVqtVEDWchhG7GJw1oT02TizzjJnDcc5azDJzPh7ZbTY4Y3BmYZ5GHu4/BVlr56iriqooEZ48HLbGcDo+0fddSFzjiJIi6G51Z4wJM5GyKML8JcJgk2pxGsiP4xgkReYgjnl7e8s0TcFuGfIQ/Onpibqus1nZ4fCEUpLb2z3LMnE4PmXyX5LyH4Yh2BfsdpRlyfF4zP++LBcgxPl8jjpuYM2MAr588wbvDE/3n1jmmbZp2LQt3jrOxxNVtCWwztC2DcYuDEMfpO4LTXc68unjh2gFrDHzxMOnj8zjgFahujqdjpxOB8oysPiHoed0PITqSAqeHh94/+7HEdhxoNAB2n08PNE2NT/33a/ou57Hh0fqqgbveXwIxyZ4ctu2AeDBRWXBGMshPoumaZBK8P7DO86nI0IE586uC6ixzWZDXdecz+f482qy5l3btuz3LwseXuMa1/jpxbeYuYR5R1VqtA6igmkU9fj4QFEE3SnnLG/fvuF0OjOOI7vdjuPhxMcPHwL5znusMThr6c4nzBIW0KIoeLi/R0rJm7vAp+i7M313Zn9zwzlaLO92O3COD+/eU5YlVRHEFk+nJ5QIA/zz6UTfD8HvZZmCPa5QaCk53H/MLbGnpzPb7YZx6On6nqZus5wLkAfwyfArLGK7yLcYKcsyuEyOI8fjMUu+J/MvM090ztBuWhYz0bQ1x+OBIooofvjwITtTBhthk5Fup9OJx8dHbm5u2G63fPjwgfP5TFEUtE2Nd5aHh3sEDm8DaMG7kFC2my3GmNAyq2vevL3j48ePfPz0Ae8du/2WvjvjneP2JqCtPrz7CW/evGFzu+N8PjGPA9PQBz5QrBzGaWC3D+2Dh8f7wHyXQdJm0zY8PDywmIWb/Q11U/Fwf+J8CvyoTdPy9PgYNMC6AV3oZ4rQNzc3SCn5+OlTkICJicG7II3z8PCJsizQWgUQQqFx3vLw8MDbt2+jFlpNURQRJj8wDANlGdQDrLk6UV7jGp8zXl251FWQ4uiHLqrrHnl8eGCagnOhEEE1N/mjSCm4v7/n/fv3DMPA48Mj3gUZkvPpyDSNmGVhu9kwjgMfPnyg6wJ3ZllmwPPwcM80jZyOh7iodDw+PkQtLkfT1milOJ9OHA9HPnz4gFkW+r6LxETHbrvFe8f5dOL+/hN2mRnOZ7SQ2GVh7DvmaeJ0Cn3qgGw6Utd1HhBDMpyCrg+ziHEIhlpPT0+8f/8+t2CAzAFSSmKs4XQ68fT0xHa7yTvu5NtijMk77g/v3+dKJnGBrLWZ75F29EPfc3w6MPYDH96/Z54m6rLCLoZlmoMytDFB3PJ85nQ6YszC/f2niMAKM6DHhwe0lJQ6sO7HYWCZZkpdBJviYWAaJ9qm4fb2hq47Z5JlXVWMXc/QdbjFUBUFdVnijWUaBgqpkAjOpyMPnz4FhJqH4+MTzhgqXdDUNX3fB4RhlA7CB55KEtBM1smn04nT6cR2t2G33+WKcLu98FuOx4Ciu7u7Y7/f5wpSShFFV69xjWt8rnh1cvHWYWfL48cH7OK4ubkLqr0evnz7BRJBWzfMw0h/7lBC8nj/icdPn/BuwZiJrjtjrWGzaZmmkb7veHq8B2cR3oKzLOPI8fGRXdsivac/nXDGMMQB89PTA9u2oSoKxr5nmkY+fnjHMk8B4qwUddOgleD4eM/p8MTh6SmLa+73+4BumxekVAz9RNtsKXSJ9z60jswS2mWTQTjBpt5QFzXnw4m2rNlvNoFUuCxUStGUJXVRMI8DWIPEc7vfB4FMIbJz5+l0Yrfb0XcntBSBpzKNnI8HzDxFHocLvBUtkcLTdye8NfTnE84uvPvJ15xORxazsL/ZU7c10zJRNzV1W1PWJef+zOF0QKrAgH98fMxEwzTTESo8q/vHB8Zp5O7NG6xz3D/cM84jzaZBacViFkxMyIG3dOb49ATesdlvMc4ym4Xz0LFYgy5CCy/NlDbthrIqGeaBZtcyu4Vu6hnNxDj1FErQVJr+fODjh5/g7IwSEmdsnCGNOBM8W3RRscwGJSVKCLrzib4/45yhbWv2+33myKR5mDEzNzeBOHuNa1zj88Wrk4uSMnqcS47HE2YJkupmWei7jlOsZJRSHJ6eOB4OlEURJGKcQ8dd6DAMWSLlcDgEmfVppqkbBAEd9elTUEmuqioszn3PPE3c3d1RlVX2RH94eOB0PEbDqSICBzaM44QzhmkaOR4P7Hc7mshNmac5D+ursmJZLhbEh0Mg2g3DwOl04vYmiGAu8xK8WRD0XXDeHLqOuiwptObp8ZHz+URT16GFNo60TcMSZyq73Y5pmui6LkjhtCG5ztNEXVeh3ViVSCk4PD2F8xjDPE3BY14rwHM+nXj/7h3zPGHiQp4SxmKWqC9W5QQyjmMeiC/Lknfy9/f3oaUY4d39MGCsoSgLjDX0fR+qKxmUEE6nE+/fv+d4PGZLhdPxGAzUqip8hnkOSS+SH1NFYm2wIU62CU3T0LQNSkke7+95enqIxl4l59MR7yw3NzdBBft8CsRTAVVZ8vbNGzZty+P9PbvNhi/e3DGOPV9//UPu7z+x3Ya5y8PDfWjRLeE9n05Hbm9fNo66xjWu8dOLVyeXtTWvczZL5I/jyP39feApRJRTcmlMkvlpjpGUk621eYAbDLeCR0zSkiqKglOcsZRlyTwHfszT4xPLsvDw8JCH4EpJ6qrMA+TA+HZZSib5rRyPR37yk3fMSyBWpXZX2uGmAbz3nqqqooJvSHbH4zH/2+FwyMirZGLW930mFw3DkNtlzvuo+tvkz/vw8JBZ58YYlArSNcFCOaglpyqnbYOF8Lt37zJarSiKnFAeHgLpSSnF/f0953OAIgshsn7X09NTlthJXKV0zfSOiqLI1gkJPJCeT1jcRU686fullDzeP+CNoalq7GLwxmEWkz1+0rP7yY9/Es7x+MjpeAz+MFJxOB4wxrDf7/PPzOPjY4RaB7hlehb395/oujNVVXL/6SPv3v0YrQJwwBnD+XTk/U++Zts0fPHmTVA6kIJNU4f50s8cz+Ua1/h7O1490E/GWd479vuwE0+M+1Q1ABn5E1wWLW/fvuU+Dup9XGyBvMP+9OlTxoKHmQ3ZxnYYBjabDfM8hwV4Gpnj8N85x263C4Pz5RR37cG/XeDjIgtPT08ZrmpNIOSlIX1aXIdhZLfbopTKPjKHpwN4xTiOQDBLC4TBMOhPjphJESCxydOiPgwDxgTW+SlCg1PiSOfpui4nv3meefv2bT5vEm5M2lqppTdNE0rpXP09Pj7mhJCg1Gk2k76W0FIXtJlgHEemaWK73XJzc8P9/T3GGLTWWSkhzNOmrP6cZkSPj48BQu08Qz+waTdBDWFZUFrz8PCQxSxDa2rgq02LtwGCfXt7i6QOw/qowLDdbvnxj3+cddiSq+eXX34ZNiFm4XQM5F2lJIfHR54eAwz+7dsvqKuS8/nEj3/8I25vb7m7vcGYGSE8Zpn49On1zOJrXOMaf/fxrRj61oZdaR89VVLlUdd1tjh+eHiIMFLzbEieFrLUcmmahs1mE/kwF12wtm3zIp/aKBCGtfvbm8zwTpwSgWeZJ/AOZw3zNAS9rYgeSt7u2+2Wp8cHetwzjspms+F0OvL0ZGJLbQyLW6xIqqr6W6oKIC/+CUWW2kB1XXN7e4sQgvP5TNPUuT213+85Ho+8e/cum3kty8Jut8NGJ8WqqnI19/T09GxRB3IV0TR1hn8nBeLU7kumbQEC7pnnOfvRHw4HqqrKn+t4PGaJnfT9Usog/nk4MI5jYNnvdjkpnSJRtYls/VTxKBW02/pYvbVty263ywoOPjpPHh4f4faWuqroh4H3798HpNpmQ98PuXocxzFwg4SgqSv6rgPnQmWpZDYdc9ZQFmU0X7O8+8nXeIIPz3675e7mht/6rd/6qf3S/HTC482P8KJ98UjBwON/fESolxnf08nx7tdevrqdoZ8N3z++LP9ymCbwE/D48ok54Z/A/s2vXzzSvb+HCehecdoZHgb48SuUT84z/NYI5SuY9E8GDE8MjC8eO/qFXz9C8Yp+z2gsqnt6+UDvGL3hB6+QdOlYcEsgSL4U9gw98OnlQzkA8wTdy4o9TN/CueLVyWVeFoq4WCUux2azZZ4nDodD6OFHCfnscR9Z3dvtDikV4zRRxVbV4XDk7s1dXtSSt0la0NOOOcFxtdZx9hF2y1Vd0fc93/3OVygpni30bdtyOBwotGa72fD4+MjpdMzuiFVV0vcDKkrX7G9uAj8ieqaczucoBy+C4ZYLlsbDOOJ8sPGd5hlEqD6KqMR8Op8x5zNFTGiJTZ+9ZmIbbZ7nDJs9Ho9sNgHe++nTp+xTn/gaLi6m3ns+ffoUqzaLiTOWlPBSQq+qCillfidv377lcDjw/v37XGWlymC/3zMMQziv1rRtyzSGdl3XBX24VBUJQquqj8AIQTAm22424Wfi8RGlNd55mujro6KGnNaaoe+zFYJzjr7r2EVV5mka+fgxyNForfj48UOwZGganp4egwxNU6Ol4v7jJ27v7hBA2bZ8+eVXGGNYlpl5mvniiy+y7towjHy6v2e323H35u71vxWfJRy2+7Ovkn9xvONX/vfff7mJ7eHTDzz/j//dP/jyOY3jP/r6N/hn/8ZfesW9ev6+P/jI7/8nf+PFI48f4K/9qT/Czf/h5Qw38jX/0M/9Ln7v6eV38x+PD/xKY/i4ckL928UPygP/9V/5eeA11tY/4Lu/b0LplzPRp59o/vPf+acR8uXsMjW/hP+9X718ee/hrzzx3735sy8f6wD3e+iX/+LLx5oDxzd/hu9/+fKhn3r48eMf4KP7xRePXU4/BP6jl0/Kt0guQku6MQzjEcHbJKj+ytxCsdZGUUrN+dwHcUcVdKSEVMxmZDaxomgk47zQlKHFdTgcqOs62wwn2+S0827blsfHx+yZYqWkKSueHp/o+8ucIVVWSgj6LvBCzDIzCygLzencY71jWmZUoemGMSzuUnI4hQGyA25ub7EmsuUFyDgnads2+Nu7aIQlBFPfhXsVBGtfa4I0TF09a1mFGZHKemzp86WKB8ittvDsVP7e9NlSNXeKmlq73S5Xhmmukgb7xpi8EUhw30QmHIeRs4+V5TRjFxNMyqII6OlwzO2qwDnqQrJEsGkCD+UUJWPatsVDlJE5ZZHOZZ5ZpqAkkFp/ZVmy2WyYIvx7f7Pn7i7wcIxZuImJfuh6lilUjE1d462lrhu2ux3bzYbD4cjxeKLdbmk328BricoRd2++QKqKaRpYzMzH+0/B5vlnKhT65p97pfzL/5vv/c+fkC8sgN57nn7tyB/9l/9zL57TjJa/9q98jZ1esQBi+IU//Mh/419++cgf/TL86E/9UX6BP/nisff8ef7Lf98H/qnf85968dj/22/8OvK/MvAH/9CbF4/9v/yffotf++v/ILxCrwsx87t//3+fon75vJ35EfyJ/w6ifHlDMP+NR/z/+F+KuoX/CWEM/Pk/Db/wsngnC2D/Afhj/8LLx777Lf4z7/4M/+3f//Khv/oBfuX+D7H9hX/8xWP7H/1Fpg8/5eTivCfgpQSb7RYtFafziWUJC67zniUOqIuiynOJRJITSuZqJu3Gj4cDx8eZzabNnAYIi9TNzU1eYFMb7s2bN5kDkrSwxnEE4Z6ZjU3ThIrXT3L36fhlXoLhVFHk+UXf97EKcEghqcrA5J9nk9Ft5/M5e4ckkEIyyEqAhlShJH+YeZ4D4ivu1ud5zv4p8xyABakqGYYgtJkW3aQGDGHRcM7l1pcQXKyEtc7tsbZtcS6AGW5ubnK7LrleJg0x7z0mkj9TKyvNdtbOmKmdV1VVBiI0TUPXdflZpHlQmpWEZ6q4vbnJbdOu6/DeZ1h28u/phz5vGNJ7S+febre59XlzcxPajt05oNOaGmMNHz9+xANffvkV1f6GZZqZxpGf/PjHeARfffUFP//mu/zgB9/n/fv3r/1Rv8bfTbyiFXWN//+IVyeXYQiVidMeY4LdsTEWLyTGeYzzqKiaO80z1jt0VSJj/z/tutPAOu2KVWwtpdlIGlynwfMa3dQ0TUajJcFFqWReYFOLSEqJihL1CS11WTwVUgi8FzhLdltc5pGqril0wbIs2WUzAQ+AvHCmRXaeg9hmQoKlFlBd13z69OmiX2YDvPbh4YGHh4e8EKfqLM1zUgJLlU2CEScr5TDDafKcynuflQJSEk5IssfH4AC6Pj4lhDRjWf9XSkkfW1dpqD8Mw7N7Ssi4VD02TZOf+brKSRI64edmiEZvp2xyljYNTdMwRzTcsiz5cyROUl3XWX6n6/qYpAJKbr/fB4fLaeLp8YHb21uqsojPCx4eH8EbdtsNm7alLF7ebV7jpxBXCbdrxHi9n4vWSKURHhazIBCBx2ImTuczVVkhlQrVi9ax0gETUT94n1nYKUEsy4KEPDvYxP79MAy5HSalpKqqDCBIEiuJ2V4UmnEKc4Ngp7wJgpMRrZbaTykBLYvl5uaWoJ5v6LqBoihZFoOSmmEIHjPhukVGfe33+zxkTvOTJNeS+CPrqkZrzTyNNHX1bFifZkpJmywBGRI8OSXYNNjXWrMsS26hnU6nmIgDsiuh1BIqLaD6wjkSYs97jzHmWeuwKMtc/aQkpZTK102tuCR9k95Butf0POd5zok2VTnTNPH4+IhzLldN6Zj0Oe7v7xFSstvv8mYhJaSmaaIu2yFXwLvdNtstj+NA1wXpnrLc8OnTJ4a+C0ZsRcFuv2O/2yCV4NPHD4zjRHt1ovw8ca1crhHj1cllMWHRKpQCJNY6BjcBDq0LFmOwcYefOCJJGj79HYgWxuS2kcRTxt1zqjrSYpMW1sCcb/Ni17Ytx+ORaZqY56DZtW6rBb2vgXJlqQxEbsrM6XTO6KnANanioPyCUpJSxZZSSJJpx544Oek6qeWVFvFUISUeTPpMCcUlhAhJ4nwCT17E1wz69KwSHDtBehPKKyHM0mKdNMdS2ym10NbzmoQ4S1Dx8+mEFDIjyVLSSQP/NPTfbDZ5XrFuU6akkSqhBPRIFVK6/4Q+S8oIaVMxTRPTPOPxcZB/acUlWLtSAQr+/v17bm52BIyFp25K3r9/z7yM3Gxv2cTElUiq1oak3LYbtJSMXc+yXBn617jG54xXJ5fT4YAQkiXNFAClg1mVsx4dWzcCgUE8r469R0iBtQ4lJdY5PAKtC6xZOHV9TioySuanHXDaOSckWFpA08Kd/r8oS+ZpYRym2MoSSFVQN9usLjyMJ4QU4AMKy+Gpm5plXlBaBY00YFkMxgZxTa0LqqqJfioGrUucs3gvYgIag6y86VFShSqpKBmnGZHgPV6gpEYKiVCS09DjIyCgampwPs9K1ui5NJtKZMtEpLzAlDXWmnC/JrhtQuAapYU2o72illlIyMHi2TiDKjRSyTifGXKSS8i9VGkFhN8h30e619Rqa5qGu7u7TH5NVWSSw0/J1RiT32vbNJjFBD+cdgPeo6XKRNWmbZBNwzRO3H96CIrZwxTkfaTGLgEIst1un7UQu1MHAswcIMrf+eoL7u/vf2q/NNf4T4hrW+waMV6dXH79N38zDMeLIqCR6jr72Qug0AUy7my9sXngnXr6ZpyQUmTEVJhrzCgZ4LnOe1yUzp+mCQl5dyylzP3+1I5Ju/NQ2cDQXxj5QkiqqmYYJubZPDPZSpIlZZzJpEVyDYFOgpFVVYWd+jCgpEIXBcs8I6SgH3q00jjvWAwoFaosISWF1ljnKLWmKgr6vqOuAhJuHEfKqsrdA6kUHptNuNYVUkomqa2VwBCptbQsYdaRuEAJkZWSyjiOz/TE0mxHygCzTrBwYy3nKF3vzAXV1TRN1kVLyLhkytX3fa5k0n2ldl4S6kxft9ZyOByyUoBzLldfTdMEQIQJ7dPtdpsrltT6TIKgUsioSRfe9d3dXebmpOorbUCS4OX7d++isvS1LfZZ4toWu0aMVycXaxzd0jPIMBAH8MKhtURLRVWWaKkoyjL35NcKv0pJlBQZNZT+pCpBSYkntF6MdQgfWmzKO7wHKQXGOcx8mWv04xDbQRIhoBvCAjotM0pppui5HlRzw3WLssYB1nnmZUZJxWIcUhfMi0EXVdDZKkrmxcJsqKrAPfEIlC7ohyHMmKSkkiokRudo6hZjLdMY5hBmmTn3oSUzTB1FoZFaY6xDCYmUgqEfkQjKslrdK5kEmmYlxpi8gKZnmI5NnijLYp8lXiklx2jRnBQJUosstSFTVZSgw1rp3BZLs6U1Sz9VHanNllqDQogsdZPaaGkzAOTj04ZhPUtKx6aKS8qALEzosZSg1pVQSji3t7d8/fXXLMuSEWapQkvP7ePHj2y327+LX5NrvDqulcs1Yrw6udzc3eVBempTGWsiIir4bjhr0UoDoQ2T+u8BQVVRx2ohJR6tNUKKKIFiUCpIv0slEfjYfvKBsBcXLBvBAd57rHOc8wIWoNJpJoEXGc20jGMUlXR4DM4HyHFYfGZAIERYAMd5RiCwdkHlBGgRUrBMc1y8KsZpDgnPBH5IIhomT5jzuUdryTSHqgkpmY1FyfA1XGi54TxFRIxpLZ8NvZN+WkrEadefZjTrmVTg3RQURZ2RZWvSZtu2ec60VhxYz3gEgvNwzu8uJY70PU3TPGuzpaSUkF9932cocTKMS9ygdL4ED99ut9nS4JuovJRQE2w7Jdo0d9rtdnRdlwf+bds+I52m+VO67tPTU1aLuMY1rvF54tXJRekCL2TwL5cKpQust3hncDYs+Ms8M80zwrtngpVh1yvzJCYtSlprpJZ5MUstMBUX2tQCA54N5tNimv7uR59374kL4Z1gmme0LuJC2gM+c1DSEDpVNQDGTnEhDwKNxtowT7EWt7jIk5mxHqz3gKRqWpy1tE2LNYZxnHJFNk2Wsizy9SAAGsqixJowD3DWoZVGCsccbQNS1bDmzqR2WPrMqQWWIlU0aWCeWmppNw9hFpP0ytIOP81QEjJvfa10vlRJrBPRGimXRCfX7zy3Q6MMUFIeWCPcEoIsVTBJXWCtkVZVVW71pfZemrGcTqes1ZbkbJxzOamk+VF6jj9b4XDTX4FXyL94Tnz9bw2IV3i3Dx9nfvgXfvLicXZ2OLPwOu0Vx9OP4a//hZeP/PSbMPJDHnmZ+d/x/+H7xyO/9P7di8f+4HhEfH+iKF7uu338MAIf4BWSLvgzx49/FV28TGY1/SP8+78K+hUv4vEJ/r2/+PJxzsKwvE5ZxwDuHv76f/DysQ/v+dQHguRL8f0D2O4d0/2vvnwL5x+/fMIYr/6Nu717m3e6aTGyzmDtwjIvLPOEVgVNs8GamWkacd6j47zEmAWBQ0rFMo25ReKjuVhKDhAqISl5xgNZq/GqaG4F5GSRkk36HoFCCBkXrRIpVU4oScZ9DQMO5M+SVAFNY5q9XBbi4AETkHJFUSCkZBzDItidzwgpgyVz5N9UTY3H44HZhs8rtcJaQ1lWud2jlQrVDAIfW2TWOqTUlGWVh+FrkmPiqKRZR4Bvu1ydpOPSc01IujXU2RiTz1EURQRp6GDnHGHQad6T5k/ryir9W1IAEEJkSHayH07vJv1JMkFJHTtVZCnpbbdbiqJgWQKHqKpKhCAnjTTPSe99GHqEgLppmJeZcRpZlsSRUjw9PcaZzM+aE6XHLb+FENWLR06d51//4/8MrxlomO7/zr/23/zPvuL6FjN/n+L2v/rynfqeX/1z/y5//S/9Iy8faz/h6l/nof7LLx7rlr/JD37wu/lX3/2Bl4+dBPxojyx+4cVj7fjvoHd/P0K9zNC3/d/kb/zy9xHiZakY0/XoP3nkNWLyvjtR/Ik/9+Jx4DHdd9HD/+DlI/2EG/5t1P/6ZWkd7x75y9Mf4T84/dEXj3Xmh3h7z3x4WSXArTa0L8Wrk8t2dxd7/eCcZ54nxmmk648gJEVZgncs08Q4SpQu8SQdrpHCleA91hrs7NAq7LSlECzzknfFYcFawLi8cCbbYRcXypyE4t8TkGD9NSFkXoASiiv8KZAySNcURWrPFTlxpR13sEi2COEotGacZpzz0SFRshiL9wHaa6wNc5cl7P69FHG2YnHOxsQVlaO9D/OdSMgsq4auD66VzlowLt5DSHbWOLyTWO9wzlMUKieDBNtNM5i6bnLVEdBtC9Y6rL2IXn6Tb5LaR/M8Bx6RuCTr1FZMCLbUAksJOvntwGWmsq4SUutujXxLFVJKQmlTkJLcPE1I4aMg6UhdFVRlwdB3eUOSWoG60DRtjZBQFIrdbsPhcOB0OtI2DVJsQIA1c/75+NkJhd7+06+Sf7HTX0Ft/1uIF0oX7z12+iX07p958Zzez7jpV193rL1nMe9fdaybfx2G8lXH2uEv4e0n9PafevFYIwqE+i6q+S+8fL/uCdX8I8jy97x8v8vfQG/+awj1svyLm/4qavvHEK/Qg7PTL6G2fzz/fvxt79Vb3PTLr3sP7shifvC697B8H7x93XsYfwm//OarjjXdv4Ht/80Xj4NvkVy++uorpmnKKC/nHBWepvkCYxbGYcAsM9t2k1sm8zyxLDN1U+GsxVkfoMvOYm04D95RlVWuikLLR2PNkl9MbrtA3i2nVg9xgUyRFswAiFoDCFJSEjnpJChtWdaURUVRhEpBShEhyBVFUWZlYikFzoEx4hmzPlRELie2b84l0mwkLY7Ss0qkJrbxLFXZ5LaSc2DNiIqfTesCXaj47C7qzClhpOulIfYabZZQWqlFmY5PA/o0sJfx+1N7Ki3iiWWfZjDp+aU2WEpyabCfqp50D+mZjOOYk0NCdnVdl1FtAa03UmiVpXCGYYg/A4Y+6sClgT3xOSavoMTonyPoYBiGPF9KiLprXOManydenVyS10j6E8zBusCzMHOQTZknhPe5X77f75jniXkZMYvFGbDOMEYBTK1L8IHnEFpnJu5kPS62kTJ0FvL/p5lL2rUnAmAiCQJISR4UZ8KmTIvvEucJaeesEFwSQ9IjCxpYDVVVZrVlENm3BYitMxNbeeHaie0upYDoLZPuP1VQa5b/siwUWj2TvEkhSdWCDXBdb58t7qlVleYb6Rkksin4PDtZVxTpa2vCo4CsfpyrxVglpf9PsO50vVQlAc/sAhIJNik8p3OkyjBVT8lULOnJNU3NOTpaJm8cpRRCStRKSqeuaw7HA2LxmdjZ9z273Y6qqpmjGdvai+Ya17jG54tXJ5fExk5tESklmwjvHIce3zTgHFJA151ZlhnngmulVA2usMzTgveSslSh9YUHZ/NuNrVInLOYZc6LQ65qnM/JJ0EehRTIWEWUUdLk7du3bDZbzGKCV4qzSCEpCs3pfMpJKcFcg+pvzxQ5E1134nT2CGRuaSU/lu12l2VO1sRPJcUztnv4TCb3+tcDdC3kN8AOCmMtfgVSSFDbuqxi8jU4b4GLlEu6fkqeKTHCpU0VqjqfJV6AvPCvW4DjGAikZrm00NJsJ1eJ8ecAyMkqwZKnacocnQQjTgkkzXzWIIU0uE9D++Ra2bbNM5WDdL2qLAMPKr43pVTkvUzZfC0lqSLOitJ9JyDANa5xjc8Xr0eLKZVZ0Mn0a16WwDQvCuZpZBp7lBQ0TY1SEimJs4sxLJCtY57DwBVRBka803kXnBKJkoJpHFiMiSKTHmMtEpET2+3tLV9++SXWWr746kuqqspKymVZscyOpmljK6fALAGCbJ3BGoO1jnEcmOaZw+ERa4OHycPDA+fzmdPpxNCPsVIb6Yeep6dHqqphu92y3W6DH3zTUBQavZJ2SQkntNfUs0U6JJXl2bMNiSRUa+tWGoCzNidQhQxuB/AMuQXkpJKS1Zrlnzg+yXAsoal8rDKTRI2zlirybdK511XIGr23BkekpAWsqs+LZpxSKhuipdZYukYCK6S2mTEGJS//lsAWSivMNGc0XCboeplbfen5z9NlPpSUHH72Zi7XuMbf2/F6P5csphjaS9tNg3El1i5xcFuhZZinyELgpcdZy2bToGeBmSa8spSqQW7CALkQEqEEdjGUpcYuBmcD4bIuRCBWeo9Uiqqs2G23NM2GzWbDF19+RVEFTTBVaIgoq6IsQ9KzkqZpMwM/7GQd1tlcJUxREv/2zVusWXh6euJmf8cw9BwOh8CleHqKvfueZZ6Ypp5x7Lm/D+ZWTRsSTNs01E0TduKxQgHJNM0UkZ8CEc0mAHxe/JTWCO8pdJEXyJwUtAxgAWtBgHAC7x1aB2Y8Iph2WRd0t3xCoEXdtDCTMBm4EBK5xZhLQsgzmyJI2wgZTdWiOVxAby150L+uztIinv6kOU1CgqW5zrriTfpsax5UUpMOcOJogz1NzIuhQNCs0H6pJVmWJRWB9GkxSAQ4n8mnh8MhV2frBHiNa1zjdz5enVz2+x1SCJw1eGeZ50CiHGeJEgLnDJVSzPOE9Y66KjHzjMAj8cFgx5o4oPUUqqJQAuMWrPABwdXUeBeguSFRhHbUdrvlyy++QCmN99ButpR1jbGesirjMF0jlApAYlVQlkF9eVeWVHWVocLGWso0bygr6nZDOdRYY9jtbjgenui7LrpsLnxoPnA+HejPZ4ahZ1pm+qg2PC8T09PI4fCU20tN02TeRV01FEWFihDmUME4Fm+zIoFSCmdiBaguQ/CAjBMs1ucKQasEFJAZFu29R8UKQWuFgFwJpgQTPsuc/z/MMy7eMymstRATQlEU6CgoOUa5lXXFkRJXut/0tSSbk86XKqp1i2z9JyWdFCkZJXSZlDb/eyKPptmN1opCB6DBOIzPPku6VrJESG6f17jGNT5PvF4VeZ7CINzZ7FuvdUFTlPTLgpYKUSuUEIzTgEfgvAPvaOsKayaEkihZI6VgnhfKQoIv4k5co4SiKkra7Y43X34V+Bh1UDxOIodCKppmg7GWOknNH0/UTQtCInVB1/VstzuWeUEXRbTbDS2dru/ZbFqGYQQRNMnKsoqQ6AkQNE1LUZSBhFkUHJ5auvOJ4+GJ4/mMjsrBqUWT5id932ffdxUTZF2FZJPmNFprmrrExAojkRi1VDhzUe5NVQ3i0qJK7HbvXd69pwVeKYVZFoTgGZouzTa+2R5akyTTfOc5uk5ksqOLn289Y1ofv2bzp/lYXddZ9ysoFdtnFgprEmiq1NZJJ4EE2rZlHMesZQaXdtv5dEZK8v2vUXlCCLbbbb63a1vsGtf4vPF6Pxfh0Co4SpZFgS0DQU2rgrpULLPBLDN2GVEYPI62DpBiMy9IEYiUijAorwqBrhsKHUiRShfsdjc0zYab2ztsHHpv2g3TPOGERBWa7XbLYh0gaNod1jm2uxuULpiNwTrY39xRVlXgoiAYI1NfCLh5cxdY8WXYhbdbxTiMQVIGw83NG5Zlpigq7h/u2TqPAOq6QSpFUVX0Q59hrmmhWyPZspnZYujOHU9PKg7bK9q2oamDiVcV23pCCJwMoAOIOmrWxpnNZSHPA3xgsVNeiFNFocSFB5QWfiDPXpKXTqhgLiz+dUsrKRcDGX1WKJ2TWLpf4BlaL1Uo35T6Ty2wBFFO1088HCBXRakCSjDkhHxbw6LXHKd5nrFmpiiKDGWe5/lZCywlvOtA/xrX+Lzx+uQiYRonBJ6hO6KkRBCqGJzHmR6Jp9AWaxYWM+OWBa0EqnCYxaFLlRnZSbOqrBt0WaF1SVVv0GVNWTeM08zN7jbssJF5ZtC0G2zf09QtzWYX1ImFBCnZ1C3H44myagJDvg5zkC5Z+0ZLgLIsgypwVYUKqgzcCCU10zQghMJ52DsXDcxcXPBGmrbhdDrmBTlxelK1kCRO0sKbqprFzBi70A9nlJDZJTJxM5JXS0oUZVmCA+cEUopMZJQBY00RJSjS9VJb7Ju79NQ6S3yQpK6QEGTrCiYt+AlJlrk8i8nzkNRaW3NoEloNyJ4wKRmtE8V2u83XSfeWnlWajXRdlys6Y0yuWNaQ8/R9TdMwjSGBrhWjkwJA13X5/tI1f3bCYfs/B6J+8Uhvf4Lt/5+8ihlu3mO6f+Ply3uDsz953bHujDc/eNWx3r7HLb/+umPnX8f7E6Z7mZTopl9BqB/i3cs6KW75DRAlbnkFk938ANv/eZAvt02d/Qm2+zd5jQ6PN+/jO3vxrDj77pXvbMCbH77yPdzjlt983bHLb+Hth1cd66Zffvk+Y4jvfe97r9Ix/ef++T/BNAaJEWtm7GKYlwFnw7xgmUasWcA7nA9f886GQbQLlUZZ1RHWGwhtuijY3LwJ7SxVoHRF025AKMZpZBPFFsOCqdltd0zzzLnr2e72tO0mWOwqjdKafhhwHu7evFkZjgV/kM1my2LCAHyeJ7wLC+68LGyabUAyDT14xzSOHI8HhqFjHAdOT48Ys/D4cM80D0zTwOl0pu87zqdzaJGZwNdZExiTeZqzLsOhA8w6killIHeGRFPTbjZUZUVd1yitAgLNu8zzSLprIbGH+/eAdy7YSTtLoYMNAAQZnWRjQNRMCwtzgbMX6Re4oLzWqsyJOFlojbOOMkrGJKWBxHFJVU9KZOk8a8hzmvmkc6cWX4JMJxmZhCZb84DCDCcAHjw++ALFdqJSIrftBAJdXCyv072WZcl+v+eXf+O3Xv2L8TsZf/kvvyyNco1r/CzH9773vRePeXXlMhzv4wIUuCvWmTCw90Fewy8zRZTUdwBCgCrxEgpdgoCq3aCLMhhbNS2b/R6PRMgwn8ALqrJhHCc2zZaqrBmnkaIICy1CA5ab/V3uxS/zgtOeUlzcGs08MY4zUimECDMa74NtQFEUNNUFilsWBhD0/ZAhrcM0stnvcitLScnTwwNNs0Epj/czm6amUJJaa6Z5Zli5LKYKIbR9BE45jAmtJIvHK7FqLVn6/kw/nHk8PEbiZqxmyop61TpLsFy8p4rtniQVE4YzEutDLg/zGokuFAifCY/ee2azoBB0Xf+3tLsSyms9RxFCYJ3D2GCPEG98xUu6+O6s3TZTUsm2z1HZIX2WtPintl2ysU7vJtlHJ+7KRSXb413YdDivcqtMK4X04BH4KOWz1sP7WYnX/GJe4xr/vx6vTi5mnkKry1k8Qa9qWSawFilBKYIwpRYIpxFSsmm3OOfxHsqqptlssc5jY3VRVjXjGIbmQkjKokKpAsSCkAovBCIuNJvdjnmx9ONI27TMy8K579hut1TRiCslh6ZpWKyLApA2J52EopJSZi/6wOJvonqwzQQ97wK3xHlP3bS8/SLoYZ3OGi8cy2IphQg7eK3RRcE8T0xTWEx90sAitsaMxjoLvsDYAmsMiGBVYKzNEOlxHBnHgaenJ8qioCqrC/osgQLSAhyrI6VUBC2Az2oBgahq7JKlaULLKwAovCe3pJLApI8ItTQ7govt8BohJqVEyODjA885L2uRzXTe9EzLDNEm2ymnqiXxVFKiSu27lISCHBD5/TVNQ6MU1sfjoiKCFJJhGiHyYDabixzRNa5xjc8Xr04uzsxIH9oQ82zwZkF6i1BJTl8GPkWhqYs2wIYBLWRATTUbhNJgLNtmQ7sJLS6pNUIqrHWUVY0xFl2UYbZiHSBjq0bifIDV1nFo3G62JK2wNQQ2tcSIpMsErV2WJXu5b7fbPHj2Pqkq+6jCG/S0dpGUaY3hbB1FWbLf3wU9r3pD153pTifmaQrABOHRUuSKxRQyzyaWxcRF2+GcxhiVFZcLJ7E+JJ/1TtuYmSV6niQm+zrRJEZ+WrC1vKDMlEr2AhIpokglArOEyoTIR0qAgG9yYlKFkQbq3xymC8gIrwQzTokFLkP6JFKZ0GapMkqQ4rXT6DcrmDSvCoi1S1JJs6eyLJmWUA1561ClpqlrFmOYzUXxoa5rDofDT+UX5hrXuMbr4vXyL8Kz2ICA8tYgcTRNnXfguixDUmhahNT0w0hTN2hdopSmrGqGcUYXGpRiMhYvgnWwdS44RHqYFoOUAVOGEDgPVV0Hs60ltIGKsgrSKsZl47vU2kmJpCqrwGuJrZfUskouhgk5dXd3h3NJHDPongGUVcW56xFK00QkVFGWjGNHP/QUHlofDM3qpsRMJXVd4lxITMYsLEsQmgzzCJiXoHAcEiJx8QtaZ9Y5nBN4nYiI0cxLqjxD6LouOzaWdUVT19SxfSaVpC4vci5E4U8lFVrosAEQAilDe0mKS4WSCI2hRaWetdDWTPe1AoEgzDrScWvdsHXCSQCBlFBSO3BteZyqqjS3SWi1lGyKokBVwQ4h3cswDBhrcEmRwBimaaRtmmj6NmVb6TWa7RrXuMbniVcnl3kcYhUgghSHsXjrqMoGXRRU7YairDDWMRtDu9lRV3Wcp9Q450EairJku7uhaTc475mmEe98lIuHogw8Bqk1/flMWdeUVRWqHKWo6ppxCnwUXRaURYF3AQmUJEGmaWKap9wuW3MrkieIUior7s5z2BUXhWa73YQkM03owjBPM8NwptQarQvKsmaz3bPMI+BoXcMyT0hnwYcKoK5KrFYUOgzwF53UjEuMteBlJjMCWBvaU3Oc1yiZhDo91gfFZWsjWCD+PQk15mRaVTT1RXAzzVBKXaLFRS8sS+YLFyV6nvvCrH1cUgWTkG/pmHSONI9Zy9Ak5Fhqo62hyyn5p4S2Rq+lxNU0Tb7u2nAs2S+vuTrTGLhTWmu00pglqCyoQmdVhiQpsyZYXuMa1/idj9fb88lg4xuoGJKyapFSUVY1ddNSVDXGOhZjadpdJNEt1FVDVdWM88J238addQMorJmZpyg578NiJEUY7gcr4igB4xx2CUg0HUUix3HELAuV3mOcB6nwQkbyJrTtlmAWFhaWaZqZ5wUhJoRUCKmomzZAoougHDCMycTMIpViv99Fd8kKm60GPJt2x6wUTVUyji3zOLBMfVTiXZiiCOQ49iCIUGjHOI1oK5gXT1kGXxnvCarKUlKURRTntCyR7W6sx1pQgmAh7SOyy4vQRnOeoR8Zx5njwaNVaEPVTUNT1xTlTLlqnzkXqhNQAXQhFEtsz0kfXTGjRIxzMSlxsbL2AXkeWmkYlFbh50KsHEKFoNAaXRY46yJy0D3j3aQ2WapE0t/TnCW8sykLpi5LvAbgo96akiq4oLoL12YcBszscqstVV8/e06U17jG39vxem0xFQTZq6qJrRUXkFjtls12y2IcY98hVUg4PkOPS8ZpZpwMulQUhWJeDH5esjS/1gXTNOOso65blFR4GwQxt5tN1oaqyxIZzcrqsmSJi5jzgeQYJFkM4zRTFDBFAcO6DvyR/b7Ek2YyFmMs89wFwMBmw/l8ZrdrgxOjmZnmAQEUhaYuS7xztE1D351wdmGZLUKERKV0iVgMZRmJjt6jpAKRlIRD1WetpVg8UqpcuczzjPMOsaSBdZBxcUpSuMuibWywlBbRLdM6xWIsWoG1HrBY6zifO7outs/Kgrqpsg9Kbh1KFQbyMYEn50bvHcvig4toBA6sB+2ZL+NdaGnGGUqa0SQos4dn9giF0vnYNYdmTZBMXkHp5yI9G4jPIM59ijLK9+sCdPi+vu/DHGplv5C4SOn+rnGNa3y++BbJRVOVdWyZxEGv0kgVksW8mND/rxs8IQHsdhukUCyzRRdFlmMH8g61aZq4a3UURVj8bJRcT6z3NKCXMtn5+kzWS+zrZJqV+vhFUbIsJrdp0kK+GJNbQcGcKogqns/nZ/OBm/0Ns5nCMP90RgDTtFAUiqpuA3JpDqx/AVilAojBWXwgn4S2EcT2IAxDD/iQSJ1nkkR+RmhDlfrijZJmRdNi80xD2dDaKcsCZz3WOazzLDa0yvAqgwbS94yjYRj7jI5LxM1SBaHIoiiwaWBvJEqSn4F3ILyjkDJydC6GZGVRBfQblySS3sd6QA9BhNIuz9n2a0HLlEDSuVMbLc1JbEQkenye5QghWMyC8OTrZdMzefG7SYnsmlyucY3PG9/Cz6WgKMsLpwIZiHsytFUcgrZuQhUyB0kOvGQYx4wE22y3WeMqtT7S7EHroCYspcLFhTkNfdNONvXr01xlPUBOi8cwDHExuniNSCmzzPy56/J102KUEElt22bnwq7rEXGhvbm5Dcx+VWCdoetOGOsYlyVon+mg62WWmXkcqRtPWWj685GmrgOjX6vomzIEjsa8sG0bjAkeLSmZhnXWgwstM6+DwKUoNPMyB6toIXDS44HZmAAOUOEZhP9GLTARWmeOiylZkrGvipKmrqnqKgMgyqpECMU0z2HuExdnb20WxJSR81K4UFkkAEEiU8LFDTQRM60N87k0x0nVT6pqkjLBev4DlwQbNhaCqg7JJgEDnHO0dRPbqhdQwdoRMyW7aZp+Kr8w17jGNV4Xr04uaT5hTdh5a61p2i1VlGrBOkRss5RlyaYNMOHQZpcodZEJSazrtQKuUuF7AzHOMU1jhhanga9zIemkxSTtsNu2zfDd1G4JsOIqs8GbpskLePKMr6qK8/mcE0oi+TVNE6RihGDop4i6kjgHZdVQViV9V1H0BbiFeRrBhQrlabHs9hU4xzLNeAdFEcQmldQIIdltt5m93vd9hgKHBDo+k2px3mfgAV7hPRQ6OkJKiS40xgaOjDEWvMcYEZKM9zhfsMSFPC24zjmGaWQYR0SUlmnqhqapKWMSLGKl6VwQH1VSURYFJj734E1/cadM/00VZUoM6T3hL1L4awBBqobSJiLNZdYSMOE9X2YnWagztr+SlE4XNw7rKiolldRmu8Y1rvF54tXJpShLEJLFTiAlVbOhaTcIKXHDhBBhMcM56rIGIRj6Aec9VV3TtpswbI+LygWSWkSBxqBom7DFCQWVdq5pV7zeDYeWmcFEKZe1Z4rWFxmTJBDZ9z0IwfF4hNhiCZWQRork6hiugXfU1YayKDEZJhvgtCZ62FRVzfk0UVYN3nqEgLKukUCpFfM0UZWKytZM44AuNFVd4+2MNYZ5mWibKjLQ9wA0dZW1uKZpRCJp6hqtFfMc4NJBcj/YCywm+LyUZRGdL8ns+UDMdJQITHL6jIu3B2zktszTxDSOnE4KpSTtpqWpG+omKFKXSlPoMPMRQqKdRcyCIi7wqRWZ3ukyLxmQoZWO71mzpIQ6DJSrCiUhxuSqfbU2Ggtq0jLPrxKxs20arLE5KaU/yYr7Ap2eqaqXNbyucY1r/PTi1cmlVGWA7KJoNxt2+xuUVgzjiPOgC50lR4SWLNawOIMQirIqUTrMZpzzcbZSZjQPJJdGk/vsUpXPNKlS+6tt22fCh/0w4ubAWwlGUy728afc2y8KhTEzQgSSpLE2uGfOsW0iggwLOJQUDP0Z5xynaUKqID1TSIFsgox8U1VUusB5ixASs8zIDVizsL/ZxzafpFo2OBuUmbc3NfM0oguPcQ6lBZVUiDp8bmMMfdejozlYHc3H4DLoTp8LIXGeUCVWgjIOxEutWOagCuCciDIwIF1Qs3YCSl3n5+d0aDN6rYI2mvfM88IyHzmrLpA1m5qmKjNEuCxL7OwC4EAmcupFlyyQK20U2CQkMw+LDzMXYw0ImJY5tzl1VDgImmUBpAHk9z/PM9aFajTrtiWZGkSunJLiQnhmcx7oh891NQu7xjU+Z7wen+kTV6GkiG2TxdiQcGJbo6wbyrJCCCIxT6FUEb7GxWM+tS5S62QNR03tKSDLdqyFDlO7KGtYxVZZkhNZczxSK2QNDICIQErui3E2kBJYasVppVlWXIzUUkvtpWkeEYLgzCkEyxIEKdOQe5qChE1b1XTdGaSijLOFze0d4Om6M84Z5mWhKCqaNgyhtzKYXEmpmOeRoiix1tE0ZUR0QV0Fn3ohBSJKzFRlgY2WzvM842XULxMK6gprAunQOYddkUudg0IFGHdZqMzWH4eBcRg4a0VZFhkMUNd1QJKVAjNPsRoSGGdRQiAgZDVAConn4h659oJZ/39KeN57/OyeqRgrpZiXCaXkyihMZzuApACQkolzNl0+t8iu8i/XuMbnjVcnF1UUSOex3mOdZ5yWlRJtQVGUVFWN1kFjyzlQqqBpNmhdYp3NlrYX22H/bIi7hqeumeFA3jWnuUGaHQSyY5idJC/4NMdJvfzsRx/nLcMw5B5/6uGnhJfmBirKuqfZEATU0zAMQdJ90CgVWmxVWWK1pO876qbieDgExeahpyxKyrqiP5+pmgYpFVNEjUlVUNY1uqzAeTZbT3fughFa0yCFzInzHG0DtC6YxjHaG4d301RRTdg7Zu+RRUGhgy5aQxj6G2NxMrYepcQKHRFdCu/Cbt86j/MhWTkfuDihUrB03ZIJjEnjrKnryBOKhmxOhj2IB4izFiVQK6TZWjI/VSFrUEaQpgmtuzVyTCuN0heJ/jDHIW9SElAkvKcyVyzpZ6yua+D4d/nrco1rXOO18erkYh04BEVZUZQV1nuWqN+ldIGSGnxwmByGgBArdImSKvJPlqwNlTSm0m4zDfrTIpMWoJRAUqJJiLH0/6kSSskjJYM0v0kLV2JpryuXtNABz+CrSWZeFwXDucvw5HRPaSEL5wqtwCCuGRwspQyQ6KIMWmb9OOKdxTpHP0yURYlaLE2ctZSV5ng84qxhnkaKqqbRmmkaKcuSvjszRRHIpFKspESp8HyGvsc5i9ZBR6yuSpY4v0jIOSt9EBQVOrh/EpSTrb0YnAVnyGjH3DbPkFrWCcJMPvz/6XTKYpRJ5ywlHCkFOlYQFhBxXlbp4lnFmVtz8f3myiLygy6kTxc5Lg47m/z9xpggCmosTdNkxF+S2tdaPTMruzpRXuManzdeL/+yGJyHsqyCrIoxQZhSaYqiDJL5QjKPE8YEdFEVSZSpvRWgriK3udKgPrWtUkJJ8wW4cBjSLjS1ydbtlTTMz4thbGUlj5DEr0nuiOt23NqDJAz0RfQwiWRAf3F3XKOfrDEBXVWWwQ6gKDmfTygl8F7QnfvooSKomzAgVzJK1OgSaxd0KemHAV2UyLJkt9/T932ooKxjMY6qrKmrJifHZVloN5p5GgIQYbvJ84c0n+rwEZWl0VpRNaHVZ5YloMoWg5OgpWT2Hu8dhSqyrYF3jkmE5FOXJZNZImP/whXRWgdl6qh3piJnqa7LLKqpZHQZtQobE1f6k1pY6/PljYS4JPELZyW019KxqT0Zpj9BnDIlyWCjPeXKdG3lfI1rXOPzxLcgUUrqIohTpr690gVKlaiiQKiAXnI+cEzKqqJuGpTSLHGIuzZxSqzsIEbswAuEUHjnUCoILa4rEK01nrjjFRIhNdYvgIzyIEtetOq6pu/73EZLbbJktlUozaYJzpLeg5aKuqxwxkYHSI+SAiNAKYkxMx7HvBiUlghxcU40JizWOIfH07QbjsdDaGtJgXDwxRdfcHh6xNogMyMBYz11XdKogAbrIlFTigAU2G72QX7FOaQI5M8ySvILEWT+RWyNjUMwcXNRGqZpW2ycI01TmBVN8xRmY4VGK4mzxNaXXpFNF4x1SC0pdB1VDAwUGiUEMs48zLIEtrySeBEsFaZlwThHF597XVcRNh7Qc7XWYXAfq0zvfZaEscZmmLGUAoSL0G+LMTNKKoQMRmBArkRU1INblpllmWKbLdgGpOoYLmRMuFodX+Manytejxar6khEVMGzxFikLimqirKqEVIyT1PU5YqESxUIkWuDqL9FjsPDshiUinBgZ1FS5QUiVShVVSGkYh4D7FTFhUTEmU1qWYX2zpxbX2vjLrMYhPM4a4MOWFxUpYi+LFJSVxVLJBHq4pKottsNp9MJ5yzjNGKj2nHTNBFUUOCeLF5A1VQMQ8+m2aB1wel4vFRJeKqyoO86hABnHOe+wwOFLKkqATh0oTgej8HsCyiVZhgG2k1wzazbDdM4YJaZoqzo+zN12+Kdj4KZju58uvA+ipKqrPKcY5pC6yy1FgNku6SMjPcA5QUhPKUKKDTnHFVRsSjJYgxK6OC26UWobLwPDqXjyBznX6FdVuKiC2kRfwZ0oRE6VH3LskQYcnDr1OrCrk//joB5mXPFCYHKO0ddNu+TTE7Y3KQWbOJGJf7MNa5xjc8Tr04uYVgfUDfWBJ8VKS7toqRYa62lUM8H7+uWxNp+VinFPIU2WPL/SGzsMhIls66WMXj7vHWVoMbpnIkDA+TvXRMHVWy7AXnekr4PyAPh0JLRzzSz0hA6ETiFEFRVxRxdKJ0NiarrepQSuU0TkqnHmGBY1vVnhIe+H5BSsNm0eO/QUuOdQMwC60LLsW4a6qpimsIMa1/WUbTS0nVnlC6yYkFRFDgfuDZ9d842xFnKJcvBjPm5KaWgDAu+jUl9noP8f1EohChwrsAYS9nq/HyVKIIci5QYa/EOtAiEWRPbmKlSHIaBoe/pyyC3s9lsAu8oItzSz5CWAu88WoEx/tnsrayqXDWl95pRgxHUkOZlKZmk1unarOwa17jG54tXJxcpFdZYrAk6VloXmZyYkkvq+ZdlkRnUKRmsB7nPmfTkv+fFS13801NrY55ndFnl1td6CL9W203oszU0NbHCm7rGxB14cklMySZVP2mgD8/lR4BnumZJYDG13oIvTSIUGoahy0P3olDZUrgsCrQquL19gzFz9JVxKKGZZ0MlJd5byqpg6PsonAmbTUk/DEgvsctEUVbgHfv9DdYEWRhjF7rzOSsCtG2Tpe0TYCJVj20bZjS1DJ/XxhZYWQSTNylrTDQ9m+IzEx6cDO9OL4rFBrtklMTroBRQUmAi7Ns7B1IEG4ZlZpwnzn1HXdU0bUNTNxRaU+gCJyIs2TtUFOYUMvyZ5iknj/T+kzWBy3OW5xuHtQ0AcJ25XOManzlejxazjtkumMUipaKuW+q6RmqVFzAV/VbquonfExJDWrjhojuVEkOCJq+9RKqqyotD4q0YY6L8vM3/tmb7/3aaZSlBJR2yxRim2K5JiSIhsFKFlO6nKDSekITSefu+zxyLtDvebDYcvWe72TCOfUw8Q660iqKgrkvmOdxDWRV4A0IDePp+oG1bjuczha5ixebCjEpI7GJp210Q1pyD2oAuNHfNHdM4IPBMY/DFgfA826ZmHIc8d0kEx+SXYq0N/JSoQaZEUL0uizqMvxKENybSqgyJo6qKnPDLQqN00MAPgIxA4pxNmBEJH8zPQFGJMsKhY5IeB4ZxoNBBB6xtghpAgLOXFFG/TEqJy8rMydDNZlh6QDxfmPnJnAwuCg+JwxRi+Lv5XbnGNa7xLeJbkSi9CxVMWYVFUMSh6RIHvFpryqKMC4F5ZgKVqpJUxWTtLBu4G+H/bd5ZO2/zjjRdYzGBHLeGKUPwQ1nmBWctug4kTqXkMy5LXVUICMiookJrxTimyiOQIdP9juPINMFi5oxkS4tV0wTkVmqhPTzcsyyGaeyDo6S1EZIs2TQt52MwJtvvA3Pf2TDvCO01UCos3HXdsNsGC+ZxniPDXOO1YDaWom4o4txrXkasmzMZ0lhH226xJnjJd+dTJL2KrDQdVIQNPhJCE2E0LMxkGRgXXTmFEDRtE5xHxcqWOA7SpZQsZo6bBRntC2YQCusEpVZBKSCax0ldh/mbddnOeTEL8xIqwGoI+mBNU1PEdyeih02AN+s4i7vA1SPRJcLdg99QSiRm1ULzeKS4moVd4xqfM16dXJwQoAK8VJclUku8ECyLY14sxjhkqYM0ibu4FiZuStjxrpwJvcQsBhmElZlnA8JTlAVKS8y0XGYtq9YH3uPMAs5QVwV1FQUPlUCgKFRorXhrcGbGzCM4Cz54nXgcQnqsMwjpkSpWWALaTcNms8Fjo6imfNbeM8ZkocmgPGyo65KiUOA9QtRR2r6Kx46UVYBoC6Uw0xifpUdoSSEqQldJUlfBrbMfBwqtA4qtKNDOZs4HkKHY4zSiikCwbEVAhwUNNI/SFU0T5P8RgBAcnh6DtpeU1HWFbtqYID1jBEloXSAkVHWReUhSBMReXWqsCYixZZ5xgC7anPhTQi8KFc3EgpCmE6CkxhN4OOM84bxkEQKLDQnUe8ZhZBwmTlpT1gWbtqFtKkRh8ULgrcGIRKLUq9ZnmDVJBN56cOBwWByeqFVmuc5crnGNzxzfqi0GPGtpZedByMPj9EucSG1pJpF2/1qHZLPMYWcpRWJSp8G5yAPh9fkuGlFzroLWvJhn8u6rVkm6x7UvzBrenGDMCa56Pp8zwCCRBJNSsvc+D+qDN7tlWabA3I9S9t57uq4LTpzTxO3tLcsy03WnIPqodWz/VJnkeHOz53g44Gy8p3lms9nQti1mCXOs0+m0IggWNDLMK87HI95bpnHEI2g2GwqlkMLTnc8Yu7CYhaZpQ7sxckWGc5cT52azYZom6rpimsdn5NIiDvxTok8SO8ZavEjeLBKlAr9lMUlQ1GXHS601y2SZzUxTlUHuRpe54rEmzHOsdczTxDxPjP1AV5U0VU3bJiUAjXVBm22KvJmITkYicCK6UwqPI/xcpjbpFS12jWt83vhW3q9rW9rUTlmTDNPA1dqLBldKFGl3m6GwcxgiSwXe+MxTWSser0mRaRgNl2smAuZaij0tJKliSu2stS/8Gt6cWkAp0XxTWTeRPZ0LvvUX07I+qiSPaKXy/aZ5Udu2TMPI8XjG2jAH2G53FIWm7wfw0NQNizGMY9Bhw0PbtgBZgWDou5zAhRDc3t5izMw4Orpzx7IYwLHb7Qm2zpKx7wPCzHmk0lRaU2jNOPRMY+DEpMS/Bk1M0/gsoaeEYtxljrHdbsNcY56ZzcKmqUlS1uM4IqL4p6rK/D6naabQCiFKkIIi2gR458BZZCHRKiSaWhQ4Asdl6Cf6buBwLGiairZtotukDm0ypSIUOhRoOrpreuEpqiK3VNeKzde4xjU+T7y+LRZ/QdeqtDZCg1MiSf9uzJIHqWuYcOCuxIF85FOkZLBuoaXB7RqGnAh46TppEVxDilNls5YISQknxXoHmyRH0nXTNZJDZRr0p+omycAkvbHExaiqCmctwzBQFAVd1wEEMzUEb968RWvF4fAEXuIdqDIM2J1dqKsKrcIg/ng8YuO5nHN4YLvd5qrpeDxizMI4zpRlHfW9iijhHxjz0ziCd2y2O6ZppCg03fmEjwoLQkC11Rmimyo3Gdtafd9n2DJAoS9Ckem9FIWmqsogwRKPVVLQ1hUB6RUESL0ACo0UYbA/LTPWEdqbwlPoCmOCurQQMlgoGMc0W7RWWC+x3jHMM30EVpRlSduGSqws4gwoQpoXa5DqIiezhmNf4xrX+HzxLaDISZMqQJGFCF4h6yF92Kl6jLHPoMLAs0onHC+BMKg19mKBu65cUksDLgkg8RhSu23dQltXUN9Ef61beYmfAuQEtW7npUSVZh2pSkrPIR0jpcB7+0ynLN1rXdfUZUN37gDJ6XRmmuZ4rcAdKYoSrcOf4/GRJdoEpAqrqqpAkozOm09PT/H7JdvtlqosmKcREzXGxliFlVVFVRYs8xx8dYYpJpUwu3DOYqzJ72ibHEKnkXHsc9uwaZroKOqZpzm/u91uF9F4PlhMQwBMiGC5sMTNRKEVzgU+0DKZaH+tENF/xnvLvMw0ZY2zjmWxuDgL22zK0ALzBOUAE0RSjXVM547j6UxRaJq6YrtpKYsC72sKXWBN4PukjcRafPQa17jG54nXy79IHWbDUoGQ2Og7FVpPOgyDVxpfaVFe7xjTbMVH6Vyp5DNZmIQCCnL9Fz5DSl7rBX7tHLlui6XrJORXWijrus5VUuJMrFV6Eyky3X/6vlT9aK3jMN3Ev0+xDRiQSnXdhBZUWaL1hPM+IKHmES8c3jv2t3fM04gn2gAvC8YsnE5HYjFHFY3BfByKSxusmdN9bzab2OZTAZ5sgmEaSrGvK5wNibKPrH+lC8oiCEoGAU3J+XRCSSiqKsxZppFhGDBmwXtomk1g1cfKNMG327bNz0EIGIY+k19TBTROE1JAqTVFWWRBTF0Ez5i2rfHANE84pyh1gXNBCgbv2G6DygARcci8IItA9jTRDG3xPiDOjKXvO+YINW/bYO5WVyVlUSCkREpYrMFYC5Rc4xrX+DzxLWYuApmFHi+ViI4tk7CQX1jy36wEgJyEICB6lLrI668rl5RI1gTJlAgSjyEljkTchMs8J53zAlW2zwb+SfIk/VtKJsuyUNcB7iuEyD4hzwl4QWl3v98zzzNFEUQhpZSM00w/jLm6qeuam7ub/PdhGOjHwMwPzwzqqoyyLg3TPEWZk9CmctZjnWMbbZF3u13QSbOW0+mMMQuIwGBXSoJdGM3M+dxHp8wqCI0WwQbBeBv00QpNVWgEofKY54nFhKrnzd3b/KyWeWSeF4qyiJWBz3Mpa90z47bE/NexDZWebXCjDFI6FeXF154g3aN14BEFPbJUHYdZm1YKVUfEnrUYLZkXg9YST4XzDmPmYDUwz4xjIJc2VU3bNLGFpnK7j2tn7BrX+Gzx+spFiJxELjOUS7soLc4JFbauWtL3Jkb/ehbyTXVj4Nl5UxWUqog0+0gzl9Q+SmZUSbjy0rqSuRWWFsI0U7kAE3xupaVB+jc5OqnXnzzZ09dT4gw75zY4VUZXzBQJgRbABSVaS8qiQAnJ6XiMbo5LBi0k4uZms+GUE7NiHEeOxyMqclS0Vlm0cZ4n7DxiowzNzc1NOO88Mw4jyxKqjTArKbDzHNpOJjyTm5tbBGBmc0kAUvLmzRucNXjvMvE06HbVmJiQ1iZtqfpM7zi1MJ2HaZpxNsjM7Hc7ZmOC2sOmzc90WRZwPigZRE6RNSaWyZ6iaLDOsRiLR6KlxwVbTqZxYR4GlnHkdDxQ1zWbbRPvoUC1zbf89bjGNa7xdxrfKrms5enXToFwqQ5SCyslonTcuq21ltBfw4fXTo/rFtv6/EBOLusqJZ0PLovUutJZL3pJuiXNW9I111VKUlJO93YBBYSW283NTf560upaV0bTNDGOliZ6nQgh2G63WR4maIS5oExcFCA87abNFcAa0pzmH09PT1lrSyvFNI2rBC2CLbJ3bDabjNCb4jNMpESpBNM04KxnGMdY3YQZClFkNEvaRIQdOrTZnqPyREadJYRcerapckmVjfeesesQwKZt8vsSosDIQIy11rHMAVVW6pLgOumQwGgCp8mjmRcb5YEAIdF1FZ/1TFVqbCRpIoIczuEQnnVT1/zcL95929+Pa1zjGn+H8a0G+qndkxbjtMivW1trw67UplpzY9ZJ4psJJCWVdPwaoZYSxZqzkP6brpN2yym5pPtMx66Ph5CwkozLN+HMQK5E0mKZZGRSAkn3syxLRpela+33e5Z5Yr/fZUjzMAyxwpvRWiF98CHZtC1CQj/0mT2f4MwqKgacTqc815jnmW6eAU9VhbnP7e0NOgpkzvNM3/e5TRU8bTSn8xHrgnmWFJLddh/IjzrOumK7MCULrQOizNklbxISqi9VL9/kEKWqNT2n0F6baeuKJIcffgYshVIUUT5IeBfhyiILpOItUnh22xbrg4KDEOC8oKpKlljVKBWIoeE9Bnn+eZljwjLM08IyG37u7/z35BrXuMa3jNfbHCv1bKFew4FTpEUlsfDXlcW6almjy9aJJlUv6ySUkkVCkH3zHoL74UUOJrXUUnJakzFTgkxJ5MLLsXkxXH9fWjif/92uSJRFrl7Wbbr0uay1nM9nzudznuGEBTkoFbRVDUS5lQhCKMsq6JpFWZPD02NOtmv75nDti3nXsix0Q4+1BmNCJdU0bZDGmaaMDjPGsN/vUQQh0nmeOByOCOFjS7HB+TBgP51OsbKzVHWNj88ytcLgeULfbDb5HS5LAD9IGSo2wSUJCQFNU7NYl5GFSkqK2Jo0JsCQ8R6lk1OpQ0RNscU4FhsSzQWpJvP3LsZEUISPpN3ib6m0r3GNa/zOxreGIqcKYF1lpMV3/XV4PmBfVxff1AxLc5r1Yp6QYutzrhPHmhsDZLn/tm0piiLPTVKkBJUWxrXsf0pc6TOke1xDolOLqCwvrpbps6cKZj2LklIGgt+y5DZVahPtdju89xy7c57/KClRsojHWoZhZBp7dFFkU61QLTRoJfE+kBNDUg2DdpwLWmJRPVoKwbnrECooVbdti3cWgafre6xbGKYhLOCEttxiFsw0BQ4MwbK5aeuI8vMr/o2PVcPFfnoYBpy3QT5HS3b1NmuJucU9e19aa8Q8Y5eZ/TZosY3jGORwtERICZVmjvydpm0RUsWqa0ECbVVC3eRKNKAMQQpPW5UsKjD6s8ncNa5xjc8Wr04uaVH4ZqTFNS3c6wpk7a8Cz+cy64H4WoJ/fY51hbOuCtbzmfT3b6LM1vOWdNxzB0yXK5i08KxbZ6kqSoP/C7GzyDOVVP2kgXzykU9tpd1uyzgEJeXj8Rg874eBorgQRdPgXkmJ8BdZ/2EYaNoGZ4M1cQIMLMvCOA5oJVmWOd/rZrMBFwbvxgTHy7qu4/ykpCoLxr7HWUd3PmKdxXsX77dkibL2qX1XVRW7dhOeqZaMY2rpXdj6Sl6eUXoHoVVX5Q3FaMYwjBdEUdOLzbXWmv1+/2z4772niGKeSWp/t9tibHQtJSSmpO5sfXzXETChlKKuyuhJ44PuXRQGvcY1rvH54lsN9NeLL1wG52n+kHb3qUpI1UHq0a85KWsf9LUu2bq1lc61RpWlc68TxppLkyVLVsP/dN51yyrNhlJySedYc2tSNQM8q7TAZ9+XpDWWZiQpaYTriWfCm9+E8Ya2TVTxXRb6c5dRakVRxISjaPdNhgGvPWaKIsCiq6oCPMu85Ou0bZvv0Swzx+MRnKPvzrRNjbEWXShKrTmfg27ZITpmts2G3XaLloohSsmMU0h6yd2xKDS4i94YwG63Q8igPB2UpUNy0EVBUTfP+EPp5yhtKFI7dZ5nzLwgVUDEIYP8vjMGYyy7zSY8L2OQQmCnGUEwN6tlsdpw+EhUDdWLEFcS5TWu8TnjW8m/pISxbmOtW1TryiItuGs9spQkvgkOWEdKImv4caos1lVQusb6WumYdWWS0FYJLpvOmxb89W483XtKLmnxT7OZ8FlkNt5akzrTrj8N/SEwy3VMkmVZxoX5Yi2QKiBjDEuE6SayYtjJF+Atfd9n9JiUEjxUdZXvo+s6ikIzjxNVVbLdbrP/TNBE65ACtJTstrvA+ZCCw9MTkx84n0+B+FiWbDZBasaaYPAVZiRulVQSoCHwbVxsRabNQnDJHPLzL8symHqZy2bDe89ut8vPOUHHh2FAK40oQEiB80El2jpHWWi2cabjvQ/Cp9ZSaoWQFw+f8HNqqJKCA4J5MYzjpUV6jWtc43c+Xp1c0iKcWPFr+9xvIsfgeSKAS8WxRheth/TrpLCWWEmVybo6WSeS9O+pKlrviFOy++bsZj3bSQteOu83QQBrnbNxHGNlYzLKLC24ycAqJdHgfVLR990zteSiKEJbSwcNr5x8pWBTbzLseJ7nwJVZ8UvSIlzUQRbG+4sSQfq+IKE/ZkgzEGVSapQQeGsxZmaKMvvzNAUxSCWRUlFVNV3XYZfALdlutzgfpF7SeQGcdRRaI+JnD5+vZ1kuhm1JZufSMrtI46wr4VTNVNFzp9CabuizAnSlgyClNSYy8w11VeK8Q0h9qRQjWs85E5UfwFhHEA59/vN4jWtc43c2Xp1cko8I8LfMP9ZJBnhW4awX95Qsns1A5gUpFVK60OawFiGCD0lKHAmVtW5tAbmyWJM1U99+PW9Zo73+drDn9WdYJ6G0G076VEETS2dRzrIsc/JI1Uh6VkWhg7eJB+JQWciIRPOK/f6GZTE0bcMyTpRFgTFLbFMFaLDw0DRtHqh775nGkXkOEjnJN15KibOJAOnysyiLIiC1Iq9mGM7hWTgPQrLb75HRgG0cBs6nc/CTkY4mtvxMhD3Pi0Urjcdzc7vHRhJlkPa3OA/tZkvaB4R2WdADw0O72QHBXKzvO1T82SjL8iLXMwcIcXquwT7a4ozNnzPNdIQQjNOMwCNFgHWHnz+T52tuWSgk7G62PF1n+te4xmeLb9UWS4v6GnqbKoZvwo3Xg/L099+uDRZk4oOEeljcPRA0yKSUkaNR5ISVdsXBm77IM4915bJue6XhfuKmpP//Jv8l3de6mlpzc1KS07pgmsZn50qD+jRkTnOUum4QQuY2opGKpmmCblealYiRoiiZhzBIH8eBeQntrd1ug3cC7xzTNGJMauHZZ0oEF7XkUFFppdlsGuZpRArB2Hcsy0w/dAgBUkLVtNQA3mHmKRuGqfjM67rO6s5zFMTc7vb5OZhlwXvBMIyRdxI+l1mmPHNxNvj1lEWozurYWuu6jqKsELgMsU7PvoxKCmnTME0TPlo4rJNKSuBSQLNp8yYGgjeci9Iz28j+F0LwdHU5vsY1Plt8axLlN3f7qd2RFvZ1ElknHCEEUihUcZlhgEDJ4hnpMlQ2DudtlnYBnqHR1pXKehaU7jN97Zvos1T5rCuNhPRKsOSURNdGZ6niWrP2U9WWvjd9poQYS1Dluq45n8/BPCwimqwNi3Xf95d5St8hfJCm0UWAHC/LzDzNqIgMk1Ky2W4inJiMKuv7Pn+uzWZDoSVD32EWQ993ELk5ZVEiZBi89+McKp15xiVLA1VS6JK+74FL1bfb7fK7TIABawx2WWiaJv9bEL4M7bP0tVR1Ju+cZFkQ5iZjTiSJgJqukdqwqQJbv/e0yUiJ7psQ8tSmTTM2a21MlFfhymtc43PFtyJRruOb7aZ10klVwBoFZq3D67jg6Eg+FA6hL/OalJhAZH+ONfJsncjWM5P0fQk4sEagrZNWQiSl+18z6tcyMCm5rNtlkOY4Eq2rZ8PtpJ/VdV2+14SeK4oit+oS9HhZpmezpDDjKVBCsNm0zMv4DRRew3a7C74t1rIsI8fDESAnus1mk3fv0zjy9PQEzkVbaEe7aSlLTVB1LvA+oLmaumKOkORlmpjslOHlCdFWFApdaI7H4zMAxZs3b3ICGMcRY2wGNCRkWVrog8hnkZ9l3/doqVDl89mYc+6ZKnZ6D1rrnHDWyLI14TZtDtIzT7Og7DS61h+9xjWu8Tsa3yq5rFtOaVFfixz+dnOM/HVEhINKnAtaUs5Bylnr3aoPzh/AZeFPCwlcBvrrdlaCFieByvUMKKHZ0mK//vc1ZHrdskstlqSgXJZl5K9c7ikhlNJzWaPO0kKa2nGJuBmeyUXdoKqq0PpTYWg9TQPDOGBMWIy3ux1t0xAM2BaOp2Nwp4xJbbvd5vZYQIb1WBOqHCEFpS7DubWirAqenh45PB0hQX0h+sWMOGtZ5qC+nNw7tdZ0/TlzTjLSy1yskKdpCslCa2Rs160VEULiMTkJ930fEGZKME9TJr8mK+cATAjvuWmajMJLzysRZtfvNVUxKSEaYzL4IN3P113/7X47rnGNa/wdx98Rz2WN/vnmkDy1pdLX0oJqjMMsl/nMZViv0drmxdk5h4zIpXWbDXhm/JXaV98kXMJzFNl61rL+t3TfaVFO5073/k3ZmnTN4Jp4adOlBT3tlpN1cEooacfdNE1u75TlRXdtHMfcStJSMY4DCNhsNsEsbA7zluRAmXb1ZVGw3+/zvSepFiEEN/s94zhQao0UklKXfPr0gWlSaB1UqX18nt3piDULZRnuX3CxCzgejyFRliq3x9LMRCvF4XDIba0wS6qQokWIsBnpuo5lWfKspOu6nLiklFgzo6MKdUrgKbmkeUuS40+q0Km1mX7WEmE1QbrTcWsdulQVX+Ma1/h8Ib73ve/5lw+7xjWucY1rXOP1IV8+5BrXuMY1rnGNbxfX5HKNa1zjGtf4qcc1uVzjGte4xjV+6nFNLte4xjWucY2felyTyzWucY1rXOOnHtfkco1rXOMa1/ipxzW5XOMa17jGNX7qcU0u17jGNa5xjZ96XJPLNa5xjWtc46ce/1/YmcD3bbTVXAAAAABJRU5ErkJggg==\n"
- },
- "metadata": {}
- }
- ]
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "code",
"source": [
- "try: ref\n",
+ "\n",
+ "loaded_ref = False\n",
+ "try:\n",
+ " ref\n",
+ " loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+ "if loaded_ref : prev_ref = ref.clone().detach()\n",
+ "\n",
+ "try:prompt\n",
+ "except: prompt = ''\n",
+ "\n",
"# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
"URL = '' # @param {type:'string' ,placeholder:'paste an url here'}\n",
"if URL.strip() != '':\n",
" image = Image.open(requests.get(URL, stream=True).raw)\n",
" log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
- " method = 'Do nothing' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
- " image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
- " show_encoding = False # @param {type:\"boolean\"}\n",
+ " method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
+ " image_size = 0.79 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+ " show_encoding = True # @param {type:\"boolean\"}\n",
" #---------#\n",
" if(not method == 'Do nothing'):\n",
" # Get image features\n",
@@ -275,6 +300,7 @@
" else: ref = ref + math.pow(10,log_strength-1)*image_features\n",
" #-----#\n",
" ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
+ " ref = ref[0]\n",
" ref = ref.clone().detach()\n",
" #------#\n",
" # create figure\n",
@@ -283,17 +309,22 @@
" rows = 1\n",
" columns = 1\n",
" if show_encoding: columns = 2\n",
+ " if show_encoding and loaded_ref : columns = 3\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow(image)\n",
" plt.axis('off')\n",
- " plt.title(\"Reference Image\", color='white')\n",
+ " plt.title(\"Reference image from URL\" , color='white' , fontsize=round(20*image_size))\n",
" #-----#\n",
+ " if show_encoding and loaded_ref:\n",
+ " fig.add_subplot(rows, columns, columns-1)\n",
+ " plt.imshow( visualize(prev_ref))\n",
+ " plt.axis('off')\n",
+ " plt.title(\"Encoding (before)\" , color='white' , fontsize=round(20*image_size))\n",
" if show_encoding:\n",
- " fig.add_subplot(rows, columns, 2)\n",
+ " fig.add_subplot(rows, columns, columns)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
- " plt.title(\"Encoding\" , color='white')\n",
- " print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
+ " plt.title(\"Encoding (now)\" , color='white' , fontsize=round(20*image_size))\n",
" #------#"
],
"metadata": {
@@ -305,14 +336,23 @@
{
"cell_type": "code",
"source": [
- "try: ref\n",
+ "\n",
+ "loaded_ref = False\n",
+ "try:\n",
+ " ref\n",
+ " loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+ "if loaded_ref : prev_ref = ref.clone().detach()\n",
+ "\n",
+ "try:prompt\n",
+ "except: prompt = ''\n",
+ "\n",
"# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
"FILENAME = '' # @param {type:'string' ,placeholder:'IMG_123.png'}\n",
"log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
- "method = 'Do nothing' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
+ "method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
"image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
- "show_encoding = False # @param {type:\"boolean\"}\n",
+ "show_encoding = True # @param {type:\"boolean\"}\n",
"\n",
"if FILENAME.strip() != '':\n",
" %cd /content/\n",
@@ -333,6 +373,7 @@
" else: ref = ref + math.pow(10,log_strength-1)*image_features\n",
" #-----#\n",
" ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
+ " ref = ref[0]\n",
" ref = ref.clone().detach()\n",
" #------#\n",
" # create figure\n",
@@ -341,17 +382,22 @@
" rows = 1\n",
" columns = 1\n",
" if show_encoding: columns = 2\n",
+ " if show_encoding and loaded_ref : columns = 3\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow(image)\n",
" plt.axis('off')\n",
- " plt.title(\"Reference Image\", color='white')\n",
+ " plt.title(f\"Reference image from uploaded image {FILENAME}\" , color='white' , fontsize=round(20*image_size))\n",
" #-----#\n",
+ " if show_encoding and loaded_ref:\n",
+ " fig.add_subplot(rows, columns, columns-1)\n",
+ " plt.imshow( visualize(prev_ref))\n",
+ " plt.axis('off')\n",
+ " plt.title(\"Encoding (before)\" , color='white' , fontsize=round(20*image_size))\n",
" if show_encoding:\n",
- " fig.add_subplot(rows, columns, 2)\n",
+ " fig.add_subplot(rows, columns, columns)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
- " plt.title(\"Encoding\" , color='white')\n",
- " print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
+ " plt.title(\"Encoding (now)\" , color='white' , fontsize=round(20*image_size))\n",
" #------#"
],
"metadata": {
@@ -373,57 +419,136 @@
"cell_type": "code",
"source": [
"# @title ⚄ Save the reference\n",
- "try: ref\n",
+ "\n",
+ "loaded_ref = False\n",
+ "try:\n",
+ " ref\n",
+ " loaded_ref = True\n",
"except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+ "if loaded_ref : prev_ref = ref.clone().detach()\n",
+ "\n",
+ "try:prompt\n",
+ "except: prompt = ''\n",
+ "\n",
"reset_everything = False # @param {type:\"boolean\"}\n",
- "if (reset_everything) : ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"_ref = {}\n",
+ "ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
+ "if (reset_everything) : ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
"_ref['weights'] = ref.to(dot_dtype)\n",
"%cd /content/\n",
"save_file(_ref , 'reference.safetensors' )\n",
"image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"show_encoding = True # @param {type:\"boolean\"}\n",
"#------#\n",
+ "print(\"Saved local encoding to reference.safetensors\")\n",
"if show_encoding:\n",
" # create figure\n",
" fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
" fig.patch.set_facecolor((56/255,56/255,56/255))\n",
" rows = 1\n",
- " columns = 1\n",
+ " columns = num_plots\n",
" fig.add_subplot(rows, columns, 1)\n",
" plt.imshow( visualize(ref))\n",
" plt.axis('off')\n",
- " plt.title(\"Encoding\" , color='white')\n",
+ " plt.title( \"Encoding (local variable)\", color='white', fontsize=round(20*image_size))\n",
+ " if num_plots>1:\n",
+ " fig.add_subplot(rows, columns, 2)\n",
+ " plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
+ " plt.axis('off')\n",
+ " plt.title(\"Encoding (saved file)\", color='white', fontsize=round(20*image_size))\n",
" #------#"
],
"metadata": {
- "id": "lOQuTPfBMK82",
- "outputId": "2b5a8e60-166b-442d-db3c-b0af63c28b2b",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 461
- }
+ "id": "lOQuTPfBMK82"
},
- "execution_count": 150,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "/content\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- ""
- ],
- "image/png": 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\n"
- },
- "metadata": {}
- }
- ]
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# @title ⚄ Evaluate saved reference similarity to select items\n",
+ "EVAL = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+ "\n",
+ "# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
+ "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+ "NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+ "# @markdown -----\n",
+ "# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
+ "_POS = 0 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+ "_NEG = 0 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+ "\n",
+ "show_local_reference = False # @param {type:\"boolean\"}\n",
+ "show_encoding = False # @param {type:\"boolean\"}\n",
+ "\n",
+ "_ref = load_file('reference.safetensors' )\n",
+ "ref = _ref['weights'].to(dot_dtype)\n",
+ "\n",
+ "print(\"Saved Reference:\\n\")\n",
+ "for item in EVAL.split(','):\n",
+ " if item.strip()=='':continue\n",
+ " inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
+ " test = model.get_text_features(**inputs)[0]\n",
+ " test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
+ " ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
+ " eval = torch.dot(ref , test)\n",
+ " print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
+ "#-----#\n",
+ "\n",
+ "if(show_local_reference):\n",
+ " print(\"\\n---------\\nLocal Reference with enchancements added :\\n\")\n",
+ "\n",
+ " for _item in POS.split(','):\n",
+ " item = _item.strip()\n",
+ " if item == '':continue\n",
+ " inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
+ " ref = ref + math.pow(10,_POS-1) * model.get_text_features(**inputs)[0]\n",
+ " #-------#\n",
+ "\n",
+ " for _item in NEG.split(','):\n",
+ " item = _item.strip()\n",
+ " if item == '':continue\n",
+ " inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
+ " ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
+ " #-------#\n",
+ "\n",
+ " ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
+ " for item in EVAL.split(','):\n",
+ " if item.strip()=='':continue\n",
+ " inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
+ " test = model.get_text_features(**inputs)[0]\n",
+ " test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
+ " eval = torch.dot(ref , test)\n",
+ " print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
+ " #-----#\n",
+ "\n",
+ "if show_encoding:\n",
+ " # create figure\n",
+ " fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
+ " fig.patch.set_facecolor((56/255,56/255,56/255))\n",
+ " rows = 1\n",
+ " columns = 3\n",
+ " fig.add_subplot(rows, columns, 1)\n",
+ " plt.imshow( visualize(ref))\n",
+ " plt.axis('off')\n",
+ " plt.title( \"Encoding (local variable)\", color='white', fontsize=round(20*image_size))\n",
+ " if num_plots>1:\n",
+ " fig.add_subplot(rows, columns, 2)\n",
+ " plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
+ " plt.axis('off')\n",
+ " plt.title(\"Encoding (saved file)\", color='white', fontsize=round(20*image_size))\n",
+ "\n",
+ " fig.add_subplot(rows, columns, 3)\n",
+ " plt.imshow( visualize(ref - _ref['weights'].to(dot_dtype)))\n",
+ " plt.axis('off')\n",
+ " plt.title(\"Changes\", color='white', fontsize=round(20*image_size))\n",
+ " #------#\n"
+ ],
+ "metadata": {
+ "id": "Oxi6nOyrUTAe"
+ },
+ "execution_count": null,
+ "outputs": []
},
{
"cell_type": "code",
@@ -434,9 +559,14 @@
"START_AT = 0\n",
"if _START_AT.isnumeric(): START_AT = int(_START_AT)\n",
"\n",
+ "output_folder = home_directory + 'results/'\n",
+ "my_mkdirs(output_folder)\n",
+ "\n",
+ "\n",
+ "\n",
"# @markdown -----\n",
"# @markdown Select vocab\n",
- "general = False # @param {type:\"boolean\"}\n",
+ "general = True # @param {type:\"boolean\"}\n",
"civit9 = True # @param {type:\"boolean\"}\n",
"fanfic1 = False # @param {type:\"boolean\"}\n",
"fanfic2 = False # @param {type:\"boolean\"}\n",
@@ -501,7 +631,6 @@
"if (fanfic2): vocab_to_load = vocab_to_load + 'fanfic2 , '\n",
"vocab_to_load = (vocab_to_load +'}').replace(' , }' , '')\n",
"multi = vocab_to_load.find(',')>-1\n",
- "\n",
"#-----#\n",
"prompts_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text'\n",
"encodings_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text_encodings'\n",
@@ -537,13 +666,11 @@
" prompts[key] = value\n",
" num_items = int(prompts['num_items'])\n",
" total_items = total_items + num_items\n",
- "\n",
" #------#\n",
" try:vocab_loaded\n",
" except:\n",
" vocab_loaded = 'first'\n",
" #-----#\n",
- "\n",
" if vocab_loaded == 'first' or (vocab_loaded != vocab_to_load and not multi):\n",
" %cd {encodings_folder}\n",
" _text_encodings = load_file(f'{root_filename}.safetensors')['weights'].to(torch.uint8)\n",
@@ -553,8 +680,6 @@
" text_encodings[index] = torch.sub(_text_encodings[index][1:dim+1].to(dot_dtype) , tmp , alpha= _text_encodings[index][0].to(dot_dtype))\n",
" vocab_loaded = vocab_to_load\n",
" #------#\n",
- "\n",
- "\n",
" sims = torch.matmul(text_encodings*scale, ref.t())\n",
" sorted , indices = torch.sort(sims , dim=0 , descending = True)\n",
" #-----#\n",