{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc34c831",
   "metadata": {},
   "source": [
    "# Workflow 1: Train Use Chips and Masks Saved to Disk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78e433b",
   "metadata": {},
   "source": [
    "This first workflow demonstrates training a model using DTM chips and associated masks that have been pre-generated and stored on disk. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e1ef79",
   "metadata": {},
   "source": [
    "## Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b7a20f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\amaxwel6\\AppData\\Local\\miniconda3\\envs\\terrainseg311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "from terrainseg.models.customunet import defineCUNet\n",
    "from terrainseg.models.unet import defineUNet\n",
    "from terrainseg.models.convnextunet import defineCNXTUNet\n",
    "from terrainseg.utils.chips import (makeMasks, makeChips, makeChipsDF, makeChipsMulticlass)\n",
    "from terrainseg.utils.dynamicchips import (makeDynamicChipsGDF, checkDynamicChips, saveDynamicChips)\n",
    "from terrainseg.utils.spatialpredict import (terrainPredict)\n",
    "from terrainseg.utils.trainer import (terrainDataset, terrainDatasetDynamic, terrainTrainer, terrainSegModel, unifiedFocalLoss)\n",
    "from terrainseg.utils.checks import viewBatch\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import rasterio as rio\n",
    "import pandas as pd\n",
    "import albumentations as A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b135cc",
   "metadata": {},
   "source": [
    "# Model Architecture\n",
    "\n",
    "This package implements a model architecture for geomorphic semantic segmentation. The architecture has the following components.\n",
    "\n",
    "1. **GP Module**: The Gaussian Pyramids (GP) Module generates generalized digital terrain models (DTMs) using Gaussian pyramids (GPs) to capture terrain patterns and textures at multiple scales. This is optional. If GPs are calculated, 31 predictor variables are generated. Without GPs, 6 predictor variables are generated.\n",
    "2. **LSP Module**: The Land Surface Parameters (LSP) Module accepts an input DTM of elevation values and converts it into a set of 6 LSPs including a topographic position index (TPI) using a local, annulus-based moving window, topographic slope, a TPI using a larger, circular moving window, a multidirectional hillshade, profile curvature, and plan curvature. All predictor variables are rescaled to a 0 to 1 range. If GPs, are used, all LSPs are calculated at all scales except for the hillslope-scale TPI since it uses a large window and is already generalized. This module is conceptualized in the first figure below. This allows for fast computation of LSPs using tensor-based calculations on the GPU. It also avoids the requirement to generate LSPs and save them to disk prior to training the model or using it for inference. \n",
    "3. **Crop**: Outer rows and columns of the LSPs are cropped so that values calculated using an incomplete window are not passed to the trainable component of the model. \n",
    "4. **Trainable Model**: The cropped LSPs are provided to a trainable model architecture as conceptualized in the second figure. Several models are provided in the package, as described below. However, the user can use a custom model or one provided by another package. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5c20fb",
   "metadata": {},
   "source": [
    "![LSP Module](../lspModule.png)\n",
    "**Figure**: LSP module. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6df21bb",
   "metadata": {},
   "source": [
    "![Model Architecture](../architecture.png)\n",
    "**Figure 2**: Full architecture. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c91055",
   "metadata": {},
   "source": [
    "# Trainable Component\n",
    "\n",
    "The `terrainseg` package provides several semantic segmentation models as described below. You can also use your own custom models or those generated by other packages. The workflow generally assumes that a class logit is predicted for each class at each cell location with the shape `[mini-batch size, number of output classes, width, height]`.\n",
    "\n",
    "1. `defineUNet()`: Define a UNet with 4 encoder blocks, a bottleneck, and 4 decoder blocks. The user can specify the number of input channels (`inChn`), number of output classes (`ncls`), number of feature maps generated by each encoder block (`enChn`), number of feature maps generated by the bottleneck (`botChn`), and number of feature maps generated by each decoder block (`decoderChn`). The model returns a logit for each class at each cell location with the shape `[mini-batch size, number of output classes, width, height]`.\n",
    "2. `defineCustomUNet()`: Define a custom UNet with 4 encoder blocks, a bottleneck, and 4 decoder blocks. The user can specify the number of input channels (`in_chn`), number of output classes (`n_Cls`), number of feature maps generated by each encoder block (`en_chn`), number of feature maps generated by the bottleneck (`btn_chn`), and number of feature maps generated by each decoder block (`dc_chn`). The rectified linear unit (ReLU), leaky ReLU, or swish activation function (`act_func`) can be used throughout the architecture and the user can choose to include attention gates along the skip connections (`use_attn`), squeeze and excitation modules in the encoder (`use_se`), residual connections around all double-convolution blocks (`use_res`), and/or replace the default bottleneck with an atrous spatial pyramid pooling (ASPP) module (`use_aspp`). If leaky ReLU is used, the negative slope term can be specified (`negative_slope`). If squeeze and excitation modules are used, the reduction ratio can be defined (`se_ratio`). If an ASSP-based bottleneck is used, the user can specify the number of feature maps generated by each component (`dil_chn`) and the associated dilation rates (`dil_rates`). The model returns a logit for each class at each cell location with the shape `[mini-batch size, number of output classes, width, height]`.\n",
    " 3. `defineCNXTUNet()`: Define a modified UNet with a ConvNeXt-style encoder, a UNet-style decoder, and attention gates along the skip connections. The user can specify the number of input channels (`in_channels`), number of output classes (`num_classes`), number of output feature maps from each encoder block and the bottleneck (`features`), the depths of each ConvNeXt-style encoder block (`enc_depths`), the bottleneck depth (`bottleneck_depth`), the MLP reduction ratio, (`mlp_ratio`), the number of groups used in group normalization (`gn_groups`), and the upsampling method in the decoder (`upsample_mode`). If interpolation is used for upsampling as opposed to transpose convolution, the method can be defined (`interp_mode`). The model returns a logit for each class at each cell location with the shape `[mini-batch size, number of output classes, width, height]`.\n",
    "\n",
    "Below, we demonstrate instantiating models and using them to make predictions to randomly generated tensors with the correct shape. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55f3272f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 2, 256, 256])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unetModel = defineUNet(inChn=6,\n",
    "                       nCls=2,\n",
    "                       encoderChn=(16,32,64,128),\n",
    "                       decoderChn=(128,64,32,16),\n",
    "                       botChn=256).to(\"cuda\")\n",
    "\n",
    "t1 = torch.rand(10,6,256,256).to(\"cuda\")\n",
    "tPred = unetModel(t1)\n",
    "tPred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21140e2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 2, 256, 256])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unetModelC = defineCUNet(in_chn=6,\n",
    "                         n_cls = 2,\n",
    "                         act_func = \"lrelu\",\n",
    "                         use_attn = True,\n",
    "                         use_se = True,\n",
    "                         use_res = True,\n",
    "                         use_aspp = True,\n",
    "                         en_chn = (16, 32, 64, 128),\n",
    "                         dc_chn = (128, 64, 32, 16),\n",
    "                         btn_chn = 256,\n",
    "                         dil_rates = (6, 12, 18),\n",
    "                         dil_chn = (256, 256, 256, 256),\n",
    "                         negative_slope = 0.01,\n",
    "                         se_ratio = 8).to(\"cuda\")\n",
    "\n",
    "t1 = torch.rand(10,6,256,256).to(\"cuda\")\n",
    "tPred = unetModelC(t1)\n",
    "tPred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef8dd95b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 2, 256, 256])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnxtUNet = defineCNXTUNet(in_channels = 6,\n",
    "                               num_classes = 2,\n",
    "                               features = (64, 128, 256, 512, 1024),\n",
    "                               enc_depths = (2, 2, 2, 2),\n",
    "                               bottleneck_depth = 2,\n",
    "                               mlp_ratio = 4,\n",
    "                               gn_groups = 8,\n",
    "                               upsample_mode = \"transpose\", \n",
    "                               interp_mode = \"bilinear\").to(\"cuda\")\n",
    "\n",
    "t1 = torch.rand(10,6,256,256).to(\"cuda\")\n",
    "tPred = cnxtUNet(t1)\n",
    "tPred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03b086f",
   "metadata": {},
   "source": [
    "## Step 1: Make Masks\n",
    "\n",
    "The `makeMasks()` function is used to convert polygon vector data to categorical raster data. It requires providing an input DTM, which is used as a template raster, input polygon vector features, and an attribute column of numeric codes that differentiate each class. A value can also be specified for the `background` class, or areas occurring outside the reference polygons. \n",
    "\n",
    "The `crop` option specifies whether or not the mask should be cropped to a defined `extent`. The `mode` can be either `\"Both\"` or `\"Mask\"`. When using `\"Both\"`, a copy of the DTM is produced alongside the mask. This is helpful if you want to crop the DTM relative to an extent and so that the DTM and mask align perfectly and have the same number of rows and columns of cells.\n",
    "\n",
    "When assigning codes to classes, we recommend reserving 0 for the background class and ordering all other classes sequentially. For example, if you have 4 classes plus the background, you would use codes 0 through 4. For a binary classification, 0 should represent the background class and 1 should represent the positive case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ee0ce77",
   "metadata": {},
   "outputs": [],
   "source": [
    "makeMasks(\n",
    "    image= \"C:/sub/trainSub.tif\",\n",
    "    features= \"C:/sub/trainSub.shp\",\n",
    "    crop = True,\n",
    "    extent = \"C:/sub/trainSubExt.shp\",\n",
    "    field = \"code\",\n",
    "    background = 0,\n",
    "    out_image = \"C:/sub/dtm2.tif\",\n",
    "    out_mask = \"C:/sub/vfillR.tif\",\n",
    "    mode = \"Both\",\n",
    "    all_touched = False,\n",
    "    dtype = \"uint8\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5cf44df",
   "metadata": {},
   "source": [
    "## Step 2: Make Chips\n",
    "\n",
    "Once raster masks are generated that align with the DTM, the DTM and aligned mask can be broken into chips of a defined size using the `makeChips()` or `makeChipsMulticlass()` function. `makeChips()` should be used when there are only two codes, 0 and 1, while `makeChipsMulticlass()` should be used when there are more than two classes differentiated.\n",
    "\n",
    "The `makeChips()` function requires specifying the input DTM and mask raster grids. The generated chips are saved to a directory indicated by the `out_dir` parameter. Inside this directory, the function will generate “images” and “masks” sub-directories. The `size` argument specifies the size of each chip and mask (256x256 cells in the example). The `stride_x` and `stride_y` parameters specify how far the moving window moves or shifts in the x and y directions as the chips are generated. If strides smaller than the chip size are used, overlapping chips are generated.\n",
    "\n",
    "For a binary classification, three options are available for `mode`. `\"All\"` indicates that all chips are produced including those that contain no cells mapped to the positive case. In contrast, `\"Positive\"` indicates that only chips that have at least one cell mapped to the positive case are generated. Lastly, `\"Divided\"` indicates that all chips are generated; however, chips that contain only background cells are saved to a separate “Background” directory while those that contain at least one cell mapped to the positive case are saved to the “Positive” directory. \n",
    "\n",
    "You can also save chips to a folder that already exists and contains chips generated from other predictor variable/mask pairs using `use_existing_dir=True`. This is useful if you want to merge chips generated from multiple DTM and mask pairs representing different geographic extents into the same folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef255990",
   "metadata": {},
   "outputs": [],
   "source": [
    "makeChips(\n",
    "    image =\"C:/sub/dtm2.tif\",\n",
    "    mask =\"C:/sub/vfillR.tif\",\n",
    "    n_channels = 1,\n",
    "    size = 256,\n",
    "    stride_x = 256,\n",
    "    stride_y = 256,\n",
    "    out_dir = \"C:/sub/chips/\",\n",
    "    mode = \"All\",\n",
    "    use_existing_dir = False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90cb82a",
   "metadata": {},
   "source": [
    "## Step 3: Make Chips Data Frames\n",
    "\n",
    "Once chips are created and saved in a specified directory using `makeChips()` or `makeChipsMulticlass()`, they can be listed into a Pandas DataFrame using `makeChipsDF()`. As with` makeChips()` for a binary classification, the same three options are available for `mode`. For a multiclass classification, you should use`'mode = \"All\"`. For both the binary and multiclass problem types, the generated `“chpN”` column provides the name of the chip while the `“chpPth”` and `“mskPth”` columns list the paths to the images and masks, respectively. For a binary classification and when using `mode=\"Divided\"`, a `“Division”` column is added to indicate whether the chip is a background-only or positive case-containing sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c84e4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDF = makeChipsDF(\n",
    "    folder =\"C:/sub/chips/\",\n",
    "    out_csv = None,\n",
    "    extension = \".tif\",\n",
    "    mode = \"All\",\n",
    "    shuffle = False,\n",
    "    save_csv = False,\n",
    "    seed = None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b552fc2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   chpN                                  chpPth  \\\n",
      "0    dtm2_1025_1025.tif  C:\\sub\\chips\\images\\dtm2_1025_1025.tif   \n",
      "1    dtm2_1025_1281.tif  C:\\sub\\chips\\images\\dtm2_1025_1281.tif   \n",
      "2    dtm2_1025_1537.tif  C:\\sub\\chips\\images\\dtm2_1025_1537.tif   \n",
      "3    dtm2_1025_1793.tif  C:\\sub\\chips\\images\\dtm2_1025_1793.tif   \n",
      "4    dtm2_1025_2049.tif  C:\\sub\\chips\\images\\dtm2_1025_2049.tif   \n",
      "..                  ...                                     ...   \n",
      "395   dtm2_769_3585.tif   C:\\sub\\chips\\images\\dtm2_769_3585.tif   \n",
      "396   dtm2_769_3841.tif   C:\\sub\\chips\\images\\dtm2_769_3841.tif   \n",
      "397   dtm2_769_4097.tif   C:\\sub\\chips\\images\\dtm2_769_4097.tif   \n",
      "398    dtm2_769_513.tif    C:\\sub\\chips\\images\\dtm2_769_513.tif   \n",
      "399    dtm2_769_769.tif    C:\\sub\\chips\\images\\dtm2_769_769.tif   \n",
      "\n",
      "                                    mskPth  \n",
      "0    C:\\sub\\chips\\masks\\dtm2_1025_1025.tif  \n",
      "1    C:\\sub\\chips\\masks\\dtm2_1025_1281.tif  \n",
      "2    C:\\sub\\chips\\masks\\dtm2_1025_1537.tif  \n",
      "3    C:\\sub\\chips\\masks\\dtm2_1025_1793.tif  \n",
      "4    C:\\sub\\chips\\masks\\dtm2_1025_2049.tif  \n",
      "..                                     ...  \n",
      "395   C:\\sub\\chips\\masks\\dtm2_769_3585.tif  \n",
      "396   C:\\sub\\chips\\masks\\dtm2_769_3841.tif  \n",
      "397   C:\\sub\\chips\\masks\\dtm2_769_4097.tif  \n",
      "398    C:\\sub\\chips\\masks\\dtm2_769_513.tif  \n",
      "399    C:\\sub\\chips\\masks\\dtm2_769_769.tif  \n",
      "\n",
      "[400 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(trainDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7a88e4",
   "metadata": {},
   "source": [
    "## Step 4: Check a Batch\n",
    "\n",
    "A random subset of chips can be visualized using the `viewChips()` function. The function requires providing a Pandas DataFrame created with `makeChipsDF()`. The user must also specify the integer or code assigned to each class (`cCodes`), name of each class (`cNames`), and color to use to display each class (`cColors`). \n",
    "\n",
    "Before using this function, a mini-batch of pairs of chips and masks must be extracted by (1) instantiating a DataSet using `terrainDataset()`, (2) instantiating a PyTorch DataLoader, and (3) extracting out a mini-batch using `next(iter())`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f27b88d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7fUzOMMW2/dCpeNc83m9dpQBe6j3u3BKNwOxeIe6vBMe8lT/v5/jL/lFyDOAdExiYTvU3T5CFbsT4ilcTOl0N0M7AfUqKl1/za37NSS9CP2IyL75VEK8HxNHxosZevIrRjZsf+IEfOI2FeO0iDldP2IPJ+LoErfRBUrRBTcJt2rRp06abQRt4b9p0n1B88XnlPn49N/5PcwSssRob71jGu78RNMcq05GvCmeeT/u0T7t6q7d6K5kmV6oDFMdqU3wkKlZQI0+8xxwrugGeHcWEQPwrsVjhwhXFCHRjZTBW3eJd0jji3fIA8/GBsAkpYBdtitWrWCFUFIDjktT1W/ATQXq0T6XlnQD3E8WqeazwMuW9/ObAgzL+Lk2hGzEO4jsPinDyKMDhZAdB7JbAf/k25SMoPhTnJuHy+wdJRyZVsp54L5x3qrjJwevul5U+SIpJiviw3qZNmzZtupm0gfemTTeA4uNm8YXv+AhXFQjGF9HjY2u8MhMrTkj58a1YmVVfHVdAK1aq44iVoPiwVnw4rALesRoYFCv0HLwHSIjtrXFEABzlxke/YnIhAvAjq0rRpvgQVdceV3auLn/3d3/3ct2On+iHWCGsQH/0Wa7AxYftkh555JGT7N7yLd+yrUu1KYBDfHU6PkTHFF+Xjomb7sNtjmKyJj5WFn2HH1iLVxKizktPctxNyv4JOWL/5L18jv3IxH0QuvGv//W/Pn1srAOy8XGyyYRUfFiQv+KteIkP30UfJbCMSbHYQTOxA4rSlsS4cWXk2IpdHEfrUXTO6vNKHwTFVvgXvehF5f+d37Rp06ZNDzbtd7w3bboBFKs/vNIT7yjye5GxmhT38F/0xPbfz/3cz31MutgyHoHns5/97BNYZcp/2xMBeay4IcUqUaxodv8yKt4vzXdkkeK9TqQAbgnMs8x47z0nFqYUK/Pf/M3ffFpJZ4pyYvU9KECHKjuASHxtPP6NUvzbt3NX2eKL6dFv8W/LOH9cpxziXypF3fE1+wjsk+Jry9P2h7w4bdQd2/jj33vhv8eKr1jHl+7f4R3e4TTxcoTiX0JFObHrISnefQ2djMkU9f73/UrRP6Hz0T+o87GTIb7cHe965+RUTEgESMYxE+9fx2QY62qMrdi9whR6in15zjveMR5wi3u8WhD6EHoR+hFHvOMe73mrCSf8912OYhIuJpfiK+Osg6n3Ib/4kn5MrqmdEpN6pno/pZU+CPrP//k/n37jPw9s2rRp06abSXvFe9OmG0Dxr3o+6ZM+6fRvbyLwi/8/HFsk+R3g+Pc48e9w4l9txUpZgIFIlx+FyhWiALvx769ixTre8Y1y4/3KAO3xUaEAZPEvtGKbeLyDHkArVl5je3SsEn3rt36rDPSRgrd47zPSxxZ5/MBcfGwpVg+j7Hg3Nv7FVoCWXE2K8wAFsaU9QEwAuUjv3scM+piP+ZjThEPIKj5MFpML8RG3kFXsGAjwGbsDYnUr3pmNf0sUK7OxPTf4jCP+hVkA0gAT8XGnABSRLz6gtfr/e2Ni4y/9pb90+ndmUUZ85CxWF2MVO7btR/mx9TZWASNd9F208QM+4ANOaWL7/fQd72hryPkzPuMzTpMiwXd8JCzKDVAWbYpdBfGxuwA/ASDj3f2jFPoQ/4Iq9CZAZcg1vj8QQCYmGpCiLwKMRpvy42bRp9HnQfl/40Nv4+NcccS/jeooZBrtjC3SMUlxXRT9E3oYbY1V5Rhb+e/Eoj0f/dEffTtt/AuxAOIh79D50PNoZ4wxnOCKcqK/I33oVQDhqCdWqGPyIsoOGZ/7jnfodEzG4b8TC8I++it/5a+cxnzoS3yEMeoKvgOwh07FeUVhS+Jf9MWES4zbkFPYndhVEf9CLCfCnvOc55zkEv9mLuoJ3Q45xuTAj//4j19953d+53L7Qu+j7tDz2CkT9oF3JTha6YOgGEfx3vn+V2KbNm3adIPpbn9WfdOmTbN/Jxb/gokp/hVO/Escpvi3N+/xHu9x+zr+Bc+f+TN/5vRvf+JfkL3927/9rW/+5m8+5ed/p/NDP/RDp7yR7lVe5VVO+b7iK77ixNO3fMu3PCbtd3zHd9x63/d939O/wYp/txT1vv/7v/+tf/Nv/s3pefx7q4/5mI+59ZZv+Zanf0EVbYjzz/qszxp1+2d8xmfceumXfunH/PuhL//yL7/1bu/2bqd/0RT/nue1X/u1b334h3/4rZ/8yZ98TN7P/dzPPf3rovgXQvivgVg2SPGvy+LfQb3+67/+qexXfuVXPv27omc/+9mnfwuV9E3f9E233uZt3uaUhv+12Hd/93ef/t3Sy7/8y5/+9dIbvuEb3nrmM5/5uH8nFv+WTPU7/1uskP07vMM7nGQXR/xrt/j3bN/3fd/3mHQh0/i3TNEPb/u2b3vr67/+62X/Kop/QRf/Hiz6PHjAfy327d/+7ad/Gxf98PDDD996p3d6p1P7O6r+nVjQz//8z5/+FVXoTpQbfKLOJ73f+73fiS/8V1P578HUgf/yqfp3YvFv2eJe/Lu4jiId/0s89y/Kst3xL+2QvvRLv/TWW7/1W5/65xVf8RVvfdAHfdCtH//xH39cXdHfb/zGb3xK9yZv8ia3vvIrv/Jx/8oK/2Vc6GHIJ8ZX/Lutj/3Yjz39y7dzKdv8RV/0Rad/1xX8BP+qP3/6p3/6lPa1Xuu1bj35yU8+/Yu1+PdfwV8nl6Rv/MZvPP0bwrQTb/EWb3HrMz/zMx+TJv6t3Qd/8Aefyo96XvM1X/PWe77ne55sQmU/sX7k/6d+6qdOtiDqxH+pNvl3Yit9EP/KMWxv/IvETZs2bdp0c+kJ8edug/9Nmzbd2xTbQGNlLlaW8MvB102xshkrW7G6+mEf9mF3rN5N51O8LxzfFXjuc597eg82VqJjxXyVXu3VXu304bz4KN8q5Zb82CIduxCijNglEBSrt7ENO76qHXVseizF7paP/MiPPO0k2HQexRiIf8EXuhar+Zs2bdq06WbSfsd706ZNjyH+AnK84x3bi+NfC91J0J3/minAUQCmI19f33T3KbbIxzvoq1vtg2Krcejjx33cxx2euIm6A3Qzxfbo2EK9Qfem66Z41SBef9ige9OmTZtuNu0V702bNj2G4r3teBcx3rcM4PJFX/RFJwAU73rHqs2mTROK/5ecH5QKineA8d/C3QmKj1zhl7rjnXz+/86bNO0V702bNm3atOmytD+utmnTpsdQfEwpPpwWQDs+dhUfS/qSL/mS00e7Nm2a0iu8witc9F8/HaHY2n63edi0adOmTZs2bQraK96bNm3atGnTpk2bNm3atGnTNdJ+x3vTpk2bNm3atGnTpk2bNm26RtrAe9OmTZs2bdq0adOmTZs2bbpG2sB706ZNmzZt2rRp06ZNmzZtukbawHvTpk2bNm3atGnTpk2bNm26RtrAe9OmTZs2bdq0adOmTZs2bbpG2sB706ZNmzZt2rRp06ZNmzZtukbawHvTpk2bNm3atGnTpk2bNm26RtrAe9OmTZs2bdq0adOmTZs2bbpG2sB706ZNmzZt2rRp06ZNmzZtukbawHvTpk2bNm3atGnTpk2bNm26RnroEoU84xnPuHriE594+3jSk550+n3CE55weh6/ceDzTMOEedS9OCLvy7zMy1w9+clPvl1O/Mb1Qw899Bge8Ii8t27dOpWVv1kmnmPdnD5+85z55nxx4D0sh9tYUZbDPDPfio+XvOQlVrZZXqRR7WL+uGzXV3yd/Kv0eY19FL+YJ/v8Az7gA0o5vdd7vdfj5DYl7he+djJPuWX61O04QhfxOu/lgeMgDk6PdeF4qnhnmTr95v7O9MHDJQl1Fn/zGfKX/Z7nTqerevDc6TPbAR5fyJsarx/yIR9i+fie7/key4OSOZ/H8xyPeC/7h+XBYxn1Ecd+Xsfx4he/+HQ8+uijj7nGI5519zg/25GUX95/5JFHrl70ohed8sV5HHE/8uZ9bDfWlWXEL6bDMcQ+JWWWadI/POUpT7mt5+yPsJwcg0q3OnK2zt07Us7Hf/zH23yho2hP1XnHC/uvc9ui0iu/Gmmyn1PPQlfiNw+lk5g+zyPfC1/4wtvnWFa2j20y2uE8V7EEtgH1HHlivpmHzDORW+qk49f5myqNaiuPB9Yb1J3nPOc5Jd/pszl26XSTz1WeycF+sDpYP/k5l6PS4D3Wj+rgdGjv8JefV3mqctIXdPfVMz5Xulr1E+sWyl3FVTh+1BiP46u/+qvbMbRp071CF4my2TghOQOe6Tgwx4BXOfVwDk972tNK0O0mADjwRT6QB26bAr5scBTAUOmOEBsnZbDQeLPRx18ulyckuL4EQxnEc30KFCnQymXjNZYf9cU568NqgDeVu0qnAJtypnwvQYjKh/qe7Q39zHvpyFDmDmw5QIl9ooC7GguYR02EnUsqcHG6VulKpdOur7k9mS51TQX9qjw3tjqqQDc+R5k4PlJHcHyofKoevK+CNXWoNFV+V2/VliQVwLngDut2dqGyR1iG0imVvrP9XXtXyen6qu6hHQ1CW15N5E3arWyRk43yocwrlsl6vSpvzqNAEOYJm+3AFNpzzs98OvDCPGLaFT2p7BLbc5d3WgaXh/5/xR9jfRXAnj5zgA59Ht9n0MztcG1aBd55jb9YVidjFQfhLz93ZWB/qr7P8irdCH5xAhh9DuqCA+DsI6JvmCc1RlGebox0/nrTphsHvPHg1TtcQeDgRwUW6KzDUAS4fupTn3o6sFyc/e1ANxpRBQQcraZRBsS1j2VZla0cxMQQuXspW1VvyogDOBX0u3aoIMUFlnyO9ZwToKw+53a4QEOVwzsMUteUs0WgrRyimqmvAIMLMnD1Lu/xamg1Ds8hFTQhsV6pQNCRCyb5+eS6GjOrjt0F2+o5g5KuvdxvE36rYK9LwysfDlRMZMHgXfHKNhNlhMBG8Y3BoQKGlXzUfUdTn8FlTseYspPVfUUBGNN+s8zyHvNV8e7sjrNFTJM6Mt20P5Qusz7wTgwuG9Oh7jHgRhnwxDA+r1YR1diaUqcznU5U/aRsLvKI/ouB6IQ43qoAd3V/ko/rqla7kTeWkQKCqixMw/mw35wus91HWQcx6K5iCRzXeI9/sVzkUcUEaFc5tnGTp2zzOQaPI+IRXsxR8mRf4/zWpk03DnjzKrYyVgmElaNURoqNYADrANy52h3XWW5cu5VuHLDJE64GOie0Erhx/g5kOiepDDQaZnd+ruFB2SRVs41sKJnf6h7Lg+tlfbgErZYT6dXsNvOP1wxQ8lk6vDzYOWJ/Yv1VoFAFzQxwUd8rnV4JpFT78Zzv4RZg3unCTto9U9crVJXVlbsCBrryKz4mgbiyD9VzvIdggA9OP7UrDtCoul2deO0mIDAQTeKVfwYHlSwUf4pcOSvguyIMhBWvzt50xLaIA3ae5JsC6BVe2F8xf668Sp+q/Kw3VcCO9xB4JACf+gSlx27nCI6/FdDdkZNtB7bdgWAJ4zEGRpM2uBVrB7Y7AD4F4x3odr6V5ebS42KFy9f1WWUbcVK+WuXmscL6zr8MuPMX68u+deMQ+17FjKhj/EzZn6oPJu29hB3etOm+Bd5smHjljQczA25HAawDcD/88MO3QXaCcXynm98d70B3RZcK8l2AodruggNOqwLXLHdKmRb7CI1zXrtZSSzHgYEu4OpA5HXI3qVFHtQkkmoj38cgkwMwdWAdbhsbz8azTFl+VcCB6dG5cp2TgJ+DMA46sFycBHOBQtdn1+lcJyCqGqNISh/wfgfiOluIZU/4xjHdBSyrQUyVHnXA2asVXpwdyZXdTqe6tk0CZeZ9qhOuHg7eVdkuKO34zf7GLZ6VrJk3x3N1b5q3klf3ekGlR67+Sd8h6MYJHSyfbTbXzcBCgW9Mdy4pm440ta3Me/IfvwocqWtHFUCuXkN0AJxjyyMr3e5QMjwnryO2jWgj1aQ9Am6MzfCeWhWfAG7WC4xheKs5pknicjudyx0o2baJfVPja2LTN2164Lea52+18qwGZQUo4jpBd650c/l84Ixd8sQGezW4Pzq4sU3stJKPzlkiHQn0OD8bOjyyjzi4UDxyexR/VSCnAmt0HMrwrlAXZCieWE+69k4CKQxiOqfEPOSv44P1qwo4XLs5uMn+V86Q2985SryeOkoGIFwnl+vSr9KlykkgiFQFOhh8TQM3LFcFipMAm6+7MZHX6nnHYwf6unvMQ8g35JxpEjApuVY84bjo7C7aNAXgspyKcDwjD6zHPP678VbpIebBPuD6qvaqdlTXFVVyRD+EMlqxB5yG7Zazh7idFle90Scqm4p1K/CK291XXtGYtpdlNBmPnFdNtGdZXO6KDgZNVrEzlot77kN2ClC7FfTKD6o+rPq1SqvG5io5e8LgmVe9WcYYO+G52l6O5abOZ90Yq+Q1roZnPqYcI1WcgzYXX+2o5M5lr8YTmzbdi3TRTxijQUIj6IKpCkzEEUD76U9/+mmlO1e2s3xc7cYjiFfy2EieQysDHYMq9zz5rcqe1FnVUTkbxadyKsoJVHxWsq70wOWbyMDNylaBIjo6bDd++MyBBw5mWeeUs8ZrNYuvDgZKrh048eScGRJuQcedItP8LhhUY/7IuJmkW0nf1TEpp7Mh8aVV9x8bglxQUtVV9f804Fbl8r1pAKrSuGAMzzHQ5/uKLwfM8sDyukBsqh8reqd8Wpefx0O+S4yBrQMdXH9FCCSVnUJ7lfx05ToeFPiYAGaWF+dh+6HO1bOOd0zH5aS8cmKHwanyn1ye00u+PyUnXx6HEyDCAI3bl8Rgq+tvR90qNn//p/qaegW6q7TVke1wdo+f43Uni+wjNbbYdqut23jN/aV0yoFu7EuXlvlVOlqNMY4Pq7yss06PWLYuDlv1/Zs2PTDAm40fv0etBmJluKOM+LcvudKN5fO/kMn3vbkuNtL5/AgdCezxV5XnAupVmgbSyJMDBiwzDooxkOP6JyCA+x2vnfObBBSuHciP48vNqqNDwlUL5gsdDctYzeCr4EPt3FB6WwVfmGcaKCn9cJNUXaCMfCoeXZpzaAp0FHF73L1VfgJ8u9VG5Knqj0w31f0JcOI61XibBKmu3GpCLseJeu+20iG2M0qm/OqGkhmPoyp4dNdH9afyg9iu6qvMbkw7QjBVBeyoi2yLOxDrfpWNn5ICsMrmcDqVF+85+WHZDD4ZlCrwpXRGgSJ8t/tc+8fv53dAiHnJscq/mR7bqwCh89OKcDK48od8n/1ZtfLN186PO3s2sXFdHkU4ppR9Rv1B35t9kNfTd+k5Twe6lS2svougtohnWfnrxgXSxA+ijNjWYzlHYsNNmx6YreYMIDgIqgYZG6fIm6vdAb7RCLuvl7NDY8N9iTZOArEJXdJguGAM256GkR2H4ks5Ne4fFbRg/SyrCShgwMjpJ7JSsuicaRADXQyCMTjJ1SkX+KEzcv8TtQPcnId1WgERDNhd33Aelo0KOF3gsKK3Tu4Tx7z6rKp/leejYzPl5FYwVFrmFZ+v8oH9tUIOmBwhDJSSJ/euawVEsB3q1SEOyNxYUfWuypnHh6qH73XBfJbFk8kOXKwQbx92AfHENiP/zn64PBN9ZD+TfFUgXAFc189V/Qw6+Bm/+tS1WfF27jbzSoYO0OHYUPEGgxgEZuxLut+KFJieTEZ3K92T88mqN8oL5cek0k/lUIFv14cOKLsDQXHmx77krepcNvKXaXnXA97Dchl0T3ze0XHgfMimTTfy42oZOOCW8AnoRkoQkSvdbIAZzDA4YQB0JGBxfF0KKAddqpwk5QgU0FZBa/KD99PQcj9i2Z3BY0fmjG23UuuC5SlV4AYDAd6mjZNHvJqmXg3IcnPSKHUVf/HINHgwGHdBPRLyXI21Sv4VYLlEH1wCWE/qrsANP+tskgICVXpXX5dPBSITeXcBHD53/e7K6gJSbltFCizxuUo/AWooNwXsle5OdZnHANff5XMBPuevgPc5/gtBFPODckI9Z11V7VY8OUDBeTs97MCFOzKvKgOfcdtQj7MfEHRkX/BKuDtHOTI/vOp9lNBXq5VvNX4UOOO2cwzgPnzFsutIAe0OeKf/xXgEf/m+S7cCvJFU25wcOkI5d76GdWMyrlDODL5TBg68Zx14sE5lGkXOvnR+idtV2QXlPyc+YtOmBx54M3AJch+UQuIBGOAjAHeudOcWcgbdaKQVMZhTjnc1kHfpjwZGlyB2IrylKkk5oKo8dFxBamZcBTzOYSn5Z1nYj1WQP5WHqlsFlHngKwvJR56HzkXeXOnO/z2J5aWs0mEFoE49RQCO4BvBNYLw5CP1ngNjlDe2BwMnJxMuYxoQ3EnqwFbXRhcEV0DBAV8OzPPc2Zxp2yqZXmcgMQEOzjZM9IB5V4F/Bnb8rMo3rVMFixWoW2lLEOqI0jNVpwrwVcCfdogn/7CcqUySeIdABVyrscUymIKNFV1mO4ZAFQGrAwsrAB3ry3MExCwLBqAqf6e/WH6+srRKTp9V/dgWpWsI8PAa/RnrLPfXVBfYv64eKsap7mHs0gFvlAuTs4Mr+VjnKrvC/aPqcOOYd1ehr6pWzJNHvqdWtSs5Ov04J2ZmGSg+Ob7btOnGAW8EUKszUjGAAmwn6E7wola6eXUg68Oy0OgeJeXkFPH9Ow1Wsk422Gh8KyPJRj/liwYvn+Wv2karJjmQPwYv1aqOuzeRg+oPFfiq2Xfcss0fGwtSHyXBAIhXt3HFm1e9eddGt4vDyYGd0FROl0hzhKbjSPFR8eSClEnaIF4p4LTnyHZVlkdl7/h25brg2gXVE3vOcq8AkLOrHfDlclVAWslQpat8VhVsdzrp7A8fCnSfo0uroJT93RH7y+mxb47ym6CVvwyu3ptWHzPDe/z+ahXAo1ycDZqALObl0ttiozz8EGgny3zOfl6BrLzP+ujsw1HgzT6XYzj2224Mqbwu/sHzTj+nbXU+SPnmCpS7cd/5M4yv1NZyNeZ5fLA8+R3xSpZOL86JJSpbvgH3pvuVLgq82eEwsXHJIwD3S73US51At9qW1IFudJpsaDm4wvqnoHka0N0NqowdT0CgTJQjYOeHMlUOJP9HLBtdliv3Ee+OwDzKgGdbOlLBEpeHW9k4KFC6l2XifQceeOs4H+49brXqPqUu8D8n/6WpcpQrgT4HzHkP+1yN1Sq44fxcz1E5dfnOBTiujA7wuEDJAQsk1PkKPPNqpcqn+rJrp+vHCjw6YMXlufYeCSir4LQCE5OyO6o+rOaedWNulScFKCayZlDN5wpMqzTunnvXmv1H1+aJjXL6fg5Vesz9y7vVgtx7uW5lE9uqgGtFanKbgXEFxifjxT2r0h2hc8bkkbHA/qxLNwXcDnwHZawThNvOnc44ezVp3zmEur7f8d50Y9/xRsCAA8EFLHkEIEnQzca3A9287RcDs4mTmxg0FTBWbbsbhACZV28VaHUBbGVMURYVsFUy5b5QdSi5Iv8TQJpAWfGF8ol7FQDGOtMRqZlflr96f7ta6cbvIWA/dQDxCE0CyEsF/VNeuC0V0KvKqcrFeyxfNa67gHtV/lVZ5wZy10GTYK2yrwzwYqUSt9eifVD978qsbDzapq5tR6gKLqf9XB2uvnMIAZg6qv5U9VfgfGJbuGznIzig5mt3cJrUOdQ/Bdx53CvwPZH15N51UbYdJ4VxdZvlinaQfY/TT/bJeO+cFW/0tXwfwTMvIBwB3xXPChSv2O0qnunq68ZeRwy4GYhW453f+89zjLE7m+ViwQkdbXfnhzZtuhH/TswFU44S/Lz0S7/0acWbjawC4QjA1Kw1Gr18LxcB29SRYjludhzbeolAaWqwHQ8sJ3Y8XDbeUzLnAAQNO+ZXBhmdlgI5k3bnfZxE6CiALNfP/Y9tVSCYZZdOCNvIAD/rTMCdr0qo7eYO7CtQeA6A5mCju+7KvgQph419xeMTn3fBrQrwK2Do8mOQwuVNqArYuO3V9fSZSnepfnQTFHw+AVGcT30DxJWTbUJ7xPypdmNfVgE3B6ZH+qsK0hUw4Dwdn5dc8a6Oqv5L86h8bbdS7Z65regdWFf2JvlB/vAen99JcuCJxwR+j4T7l9/hdcBKgdZVG+NWtCvwjdeOR+QHx1O3yq3G3J3oy64OjLGc75v6CATSylY70M1xj6rvumKGS9uVTZseeODtBqkiNIz5ATW8jyuSatU76+P3MTmIR4fEM7x57vjDOlYN86oBQYO3CroYSCKg5DTMFxpzBdqxPuxfdnbsnJk/dm4YAKjAJ0mtBHeE/88dy8E2s2NnYMztR/7wg2uKZ1ztZlCvztUMvwKO3CY3SZHnVQB1JHjitBUYxrQMqlU7WJYcgFSkxrOrh+tjEKeCHjxfBeAsi+reNKCaPMc2TYFUdb8Cafnc/br0CLxd3QqEq+vVoHRiz50d5nE2qS+vHWhgvqr2dL6L28C7dC4FvqdyYN6V3czx7gB05OF73TXex8OVjzKt/PDdAtsroDv6OtqY/YR9r1Yv2f9wvzifsRLvqdVu/J4KxzCrwFsdrk0oS5ZtprkECHR6pHQI6+Qx4nycKgMJY17lyxTonq5yO9mo+yuTBytlYtvu9rjctOmu/R/vabo8AqA8/PDDt1cH2fiywc460qm4+lVQGJRlVIOUV0cvRZ3hTN4yEJgE2+x8cJICDXYFRDI/5+W0eU85QuXgVF0uCMc2Y5uw36fGNXSJZc39jrzyu2d5IL/ID/cdnruvlivAjTLHtqp+VpTBlErrAirM0+lDx0cVfGE/YvCHMsxnPLnFgUaWVfV/BQSqQKuyFaoNU+e+EpRM0zEvVT62YTih4AIWB8DUSrU7kLiMvMe8qICQn7ly8brSkaOBtOvvLgCtxkQVzGad4d860DBpC6548TZSLNv1o5IZyn1Fnm78IH9qNRt1UAFnPudt5eo531ftc6DpXiEG3NmXPL4YaGG/Y8zhfDj6JaWLEx2YrHJ35y7OqMaVGmccG3R6P43FprTiQyrfxH5c8cnpuJ24my/JTbSs9LdrS+VDp+Wqvtq06UZvNVekjEEcAUICdOeKN27zdTOkLrjqrnkliw2AGvQcMHa0ApYZZK46eGcYeeVZGU4VWKCj46CdDTXXhXlwcoRlk79VQK+cpQKYE+CNQQgHE1yH2m6uJniw3Xid5wy887kC39g+lGHV1onzwzqVTnCfrNCkDKwb+1TpBj5XQTCmn0wY4D1c5VHp+bcLLo4EHCt95urprrkOllkHZCdgurvflVfxogLKrsxqpZzLRxumglUO4io5TfvF+Tw8ZxvObUDbxWNnqou8koXga9LfR3V+Sll+tYW8Wq3u7rlztdrNdK8G9DwOuI94Z4OaeEGbi+BbxRUKlHP8chR4q/iOfSLzrGIcNbbcCnkn2ywnr6d2e0pVfNTxtRof8sRLPsPY1rVh4rc4neqHrrwj42zFTm/adCOBNxIaxXinOw4GIw54V0FZUHUfQR4CA5cPnf+5A7wCHswDO8uOGNjwzoDKYLMxxnIwTRICTDcz3QWTHDBg4ONktEoIvFGe7ATQMaHscqs6TrpgOiyD+eXVbpSpeu+eD9dXWR/yjrxlHl65Z2c31WUFcidgFOufAsiUS36PQdWNQYILhioA534zbVVu1+YprQZxK7zwuMNxprbTOkDdPXcyVXq1snKebVDPFEDi/uN61TWmq8ZAJQ+s0wWSbsy4wNT1M9YfY2MVRGTetLWqDFz9VrKfjLeVsaOoAszqSHkwkFZAO45HH320BO/3I6lVevRX7PsqgK18eBA+5zghz/G3os7vcYw0Ad1qPLhn1RjjA20o24yJ/eY8R+NHZXPzPqfj+lV97BM5PcuL703arNI6flQ5HVX+Z9Om+43uOPCOr5fHajeudFegDkFJFzRlOXzfBU/KsahA8Ug7lcNQ/DLvEyM/WbHm9lcGPMvBXzxnkKocaRcUMhDOgJCBqXM6E0J9wnqrgCHbgZM/3AeV00dZuA+0qbTuo2odZQCFwagKapTsV6hzvlge9+Gk7GwLlh+BMj53oA5lweVhPg4kJuNJPcd0lyRVngpeqvzMv+trB65XnquD63AAuwPTnA9tsLqPeTngvQQ5G6kACD7n+0p/0OexjcVfls2K/vHqdoJvXv2uVsEr0DIFJsr35n23cs32jdMq0M1H2BL3/H4P1tWkAesOA27nw1AXWL9xcobt39QeqhiBfWMFujkf+hgeRzw2FY9sVyZx5aSd5/gGZ1srW678YFdHdd3lqWwb0lQvVsYg26cg9QrVpk1XNx14M3CJI0B3/OuwfK+bDTJ/SI0D6ZU6mTBoY4OOeY+CPuTBTRqo9qggt2qbcizK2KGx6gjTKRlWK9wT0K34xTbje9Vd+1dWvDGAVYGDax9uzVNtYBlkGdXWOUyPOs76VgW0KDcMIvODb9zfU91y8sa2ugBabTE/WleUwavfXFcSP1/VHVW+q1MFHavkxldVZveMdXoKlCtw7J6rcrlOfK4CXAWmJ/y655U88Tnago5/1WYney6feaiAigIKyOfRsYRtYNCUoJVtMaZVssd0WD62kWWgeOfn/B63At0KgLv3ttXqNp8/KEE6r3LHREP6mryHfmz6jncQ+1C+n+cTYp/orpGn1RVvxWNl8/HXTWKgbNUzVVdnY7gsZ4OdDeR8ji+VXo1tx+fE/rEMVvhRvHX38r6alHtQxvSmm0PXArx5YAYwiH8bFsCb3+dOg8rbcRH4XGpg5SBFp4LOKp300foYbOH9LtjjtHnOz7iulfzoSLH9qhzkH9uDv5WTq9qXgUACbuS5A1wV4WQOy1gFuHzwDLrqR9RZvIfnnF/9H3oFupWs8ByD5wwmU2YMcBiAHKEq0EIdO1oXOljcop7tcrrsdIQDpgl44XYo3cCypzrZBSOrwYqyBcgz6pFb1bnEUdEk77QMLk+V39nqrv8TdGDd/Is+Q8k+r/GXz10eToOgCfVyKjsmtAV5qG3naFvYV7k2uYC54jPTK2CNvpeBNa50c/DdrX5z2Q8SYd+mXPLfiKEv4olk1DP1nG1h3sNnK3YQ/aHTPbdTzOVR97E+pgrkYvsc76qOyj9yvRU/ii+cWEF+8rfiW9Xd2dLJfSzL2Te+V9kCLJt9N7eZxz3KaNOm+42udat5go1Y5XagG8GJ+or5dThMDKYw0MAZcWVcHLHzUCulWSbz4cpz18oZTILiIFy95JnmoDRkDK7dlq6pfLC9mQeBJ7dVBZ+TulC/nHPgYILbV22d7oIHVSbrN/5bvGm/IW+pp7h6xQGpm0xZJVUOO3zUQeZ1hVJm6ExV2c7ZqrTIs+NJ6cBKQDdtW3WN96pn6v4KWO4A8ZHnXDbSlK9J2oqXarJByZXL4mcTqoJud610qbJRVeA65TPtAuozTmzxgeOLd/84W7BKHERXBwIRd7jt5gzAH0RCXcYJ52rVmFe5GaCjLlSAc2IPK5/pdoh159WRdaJ8lKyU/eK8rj2uvsq+cZ+p9FX+iQ/J+5VtXa3Xycz5e5TLxD5U8lY2G+3Bpk33K10ceCN4CSD01Kc+9XTwFnL+10rKsN6pwZUDWRnjabCNAAtpGqwog+kCNbU1rKoT73e7CtABccClHB3KrJLV1AinzLnMla3mCKK4DzmwZTlysM71VwEA8ozp8COCk5XuSoa8ypPlXXLrVfKsZK50jdu/Mm5Y3jiG2PF222EZbLl63M4NNza4nR2xvk3J6aFKx5M3HJR0QNUFUe55F7TlMwWAsWy+r/pz0g6uB1eIlG5gXdwmRZNx5PTB2WNOrwLUrs7VMY7jhut2ABzlifkYfFc8uDHIfVatZLtV7+5Q74LfBMJ2Yp9XK8k4ucK2j/0f20BeYFCEadVOx4pHZZO7+0jK9vD5ZCy7A9tXxXWOHz7vxvbElzjgXNntKh0+62jqHzvfxs8Vr53cN226McAbjWIAjVjlDtAd/zYMt9viCmCmZ2Oc5U2dZjfg1WDP63TSq2VmGrfSnc9XDBfzqRwOBkAKKHL+TMP52Xg5h8ZlM1hPmgAuB5gwKFbPJk4+V7xdoMtlcxrsq9DN+OXVcJaBWglSwQaudKccOH2lI7kbg4NMBi4MarpylVxYx5BWAv7J+GFCuSY/Cmy5iQan1/g8y+cArwr4OL+j6rnTE3XtgkD8VUCmC7BQLqoMpmnwqvhQz7jMKk2Xj+uMMZLjNp+rwNcFui4gVaSCb3ymrh0wWJX/yi4w9HEKdGNblC1kO6DAt2qvs29HVrQVMGdZqG3nzq/fBEJ9wkkUBNzVdu4K2ObziQ66RRV1dDa44ovttBvDnb1LcrFPV09VLo+Lzt5hPnXOVNl5x6PzFzimqjTOJ6ScFA9TH4plOHsbz9CWbdp0Y4A3D5T8iJr6H90JQhAkOdC9Glx0pIIEZyhXQINyTAgYOC0ap87IOnCg8uQ7XkEszyQGgWlgsXxsA5ZfGdNKxlgGB2ROPmxwJ/2BOsVyrHhiQJwyUXKoDnY6qs8wjeKn0kV8V1E5WV716f51nqNK3lgepjuiE4o4IMz8KiDgdze5n/g9Oexv9y/eeMxlem5/R26MqutpYIVtmwRDanKiC/S6tEGuXBe0cVncni64Y76Q19VXK1ybXDrFM95TwWDX96oNSu7qHNNXFGMj7KGqi+0WB66RjicwkZccK8ivAyNc7vRr5mpHw8pKN+vfTSbsJwbh1YoyLoxkHrSJkzHX+Ux3VCvi6n7Wle1kna3GuioXn63IeJKms8Gq7mkMpOrsbOqljlW+8nelXdivOM43bbqRK95pGOL/c8eH1PLL5WjEcyW8+7dLQdfhNJVBZaOxatzYWDMQO2KUshyemFDOzs0K45Z3zIOTHxi4VlvLpnI54hgm5U2dDn7VPOWQQWRXPhIGfUoOajU0+xv5xXRH9Rl1KQi3XaLzyTrwmndhdPVzMFal50D7En3P9bsJEWwjr9wxSOZgB/Vfveri+la1u5OLS3+kXCYFsPk+P6/skQO5fM5burlOLtOtmmSbO/s7DVZZlgq8TgLGqu2OnB1x/oZ5cfJxbZgC76hTfVMix44i3u2D9lP5Byy30mEevzyWJ0CaZTYpZ5Pvj/QlrKdpH9GPOB/YUQeuFfifrpBXNtqNj24cu5ij0+2ubOarsjETO1K1aRJ3diDaTYZWk2OdbXX8OllxjKH435Nrm270inc6+VjtDgCkQCO+58rGMQGgGmQuGJvypoICV9dqm92scWV4GKSp9rL81Du36CQZXKtzdlaYxq2QTwHBOcAL+wh/Vyn0awKeFM8MkDmYzCBU9XkS9ikHpthOR90zDkjxfvKOXy3Gdzg7Urq00qdd4F3Vm7/4ccXkA8cSOlr+OnPWj1uNky8m/N4E1sMTFUoObhLHtctd4/2pvFkeVeCDoATvpUw6kMf2Sz3n+1XeLhDDZ84+KtBa8Yqruyqv4qOS/ZRcoMw8MHCs+mXCP1L8e6msn3fcIPBWPkG9aqNkoL7FoGShAnYGzk6nHcB2es6/m3risaN2TOEEjlsEcP3vQPcUbFfPsA5uC7bJ2TKl/youuA5inrpxVIHuym44m9H5DjcOu7QT+3RETvx7Xf2yadN9AbxzBTtWumPFm1eVOJhWA4cDAnyORqkysFNyhrozfq4cBbRcINmVF4SgG+WHlGlSxop/5ZiCcCXAgXfXropvR0f6isuclJHA24FvLDtlpWSj+g/zuMDDOX2Wt0pbtU85OS4zgXfwhQAcJwwUCMj81eRLRRWgnOTFulnfFWCIX7U1Nmjyv8QTVLj/osCTgvmLPK3KY3JvKvMqCKoOBXKwTCw7z6sAi/NVB9aD91JnVRoeJ1wv8oWTEdymCvji4cZy1S+Zb+I3WMbYj6pvVLvxulvNZeDNH/3ELcfVRAEeOTawbrUazu2e6OMRgL1B9/VRyBh1CFfGcxFlFXi7Fe4JwO4OxX/nY7tyeCxUvg5twYR4fF8KRDo7yc+VbLqxitdT/6PafCR+YJ+zadPVTQfeAbgffvhhaVR5ZYmDGjx3AUDmWTFsSA5QXmIQK4OtjI7Kp4I7BMsIRri+lKeTq2pvVYcCHywrFXRfio44r27Fm4NC5Xh5O3MCVUzrJj06J5bnVXvd9aS8/OAaAm9c5cPtpigXDtpRByYrupXeYprKqXI/8ccX1ZZ3BFqqLkzv6s4+5v+yoMAbjgf+V3BT6mRQpXH8u6Co2h7oAJ0KlFzg5oKqLvCqgjFFCMZ5XLiJA2c7WBdWbJhLW7WH9Y9l0IFu14dY92Q1N57nqiXbAR5PVbu5bZw+x5PSZ6cTVZsV4K5Atyp7r3ZfllgfQp8ClE9sYQeqzwHbFehW5+ra8TsF3StlOzvrYkFO4+pT5bKdrGy0klc3ditbpeQwsfudzLBsZYs2bbpxwJsNaG4r5/+HjI46CA3wKnXGsJtNYyNRlccBlTPQXf4JvyiTPLJu3oKOoEEF8spJMfBmAIL1IO8oryq4WTGy7HRU0MptchRfzw9yW80d+K6cs+LHBZgqf6UfK5MMTt4ZYKcj4i3YuLLF/OQ1vu884WcFKKq8vFuDgT9PnGQ+137eFcI2Ju8xD2oHDl/z7h3+YNVRGbig0VEXOE1WIDpwp4JCF4gp/jqeOZ3TNbZf+cvgCtvBE2hV+Vh2lheEO4cmsuexlWlV8Ix8qzZMtp27iYeqfVl/7oZRPosnsyb9kpQ7bJR/qWSndLLbcl7JkI9N10vpdzqaAOfKX5/ja5DX6nrSBs7v+GLb06XjOqaA39lljBMmNv7o80l6bh/LBNOpmDXTuAm6yh9t2vTAA28MpNW5SsuGd5VWZiAV2MDzSYA2WSnGOl05+FwFmEnqXdeUa6Z3YBrLdI6MQTevePJqI56nQ+BtqdyWLoBTZat0Sj6KWOcQSGV78xwDTgb8LiitDL1zLC7NJDjgclT9HNRju+PAVXHFs1rJPTcwqe651W1elXcywH5U+TIdvyPOZaDd4ftBPL75X9UdkYsKJp09QFL9PQUzK8HT0cCqes7PWBZqQgn7x9lwlo/aIp/XHMh1csTnCVixDgadqEesu9wvDjTyPe4/5H0KLLMMnJhz/lbpEMod7T2eY3/xeOOxt6K7nTwqOa7ar03XRx3odukm5XWkbBSXhWVeily9ynY6P6l8H5eJ4yBoZTW62lnj7He1c6qaLOT2KnvvrivbqeratOlGAW9cFcJAHgcRf7iJg4BLGr+KYqAmvy64cfxUoDufrxqCChTjs2o7uCsT26GAhQLejn8Ogjmty1cBa2eEsa6pY+R/f5LlOLDDAbnr++nqPrdHOU6VptMX5wjdCkQG23kfx53a2cBb0FUfrOi0GhNsI9y2cpU/eWH9xRVrBDn4jPlHWWJeBkzMH4OKozJQ8p3SKnDh+5NtzFUZjqcJb1WblCyqSQtuD97nQ/Vbx5+SHVOOF17xVXU6YJ2TYvj1bhVkqrHf6U7yjB9YZP/igEfWq2wCAngF5rP93KcuYK/0DdNXExVdX226e8R6dwSEH4kLlV1R44brnbZpWj/fU76/At+uzIkPcBOwapJS5e124VS2v5JBR2hbXJlT/7Jp0wP/cTW1eoZO3jn+6yJXPg5WHuQVTxyQu7SrATrLiLfb4n3MwyAT26wcGZ7zaiP3WQc2WYb8nOXQge8KcKt2KlKTPYpnrovbikGkc5bTa+SH5TXREbeSwxMG6Dh5C2CkxVUv/pK34+XcoIT1OQ71CgrygYE+ghi8l+e8Op6/qdNO3g4YYLlsy6ZBfWUTuC5+XulDFWhd4lATkOpZFdAeDYRUXjWuKoCNfHLZbqxknuxzvObnyFPex4lH1FU+n2yNznHrVngre1vJlHfBsCzVDjT0iZleAXAso9s95OS/opuuLJTdqu5tul7qwDP75SqWcDqlysNnnV1SY0OlcaTKVnUp26nitUzjfIKyxSv2nfmt0jhbxXnwGbZN2SxuG9qLST8rOW7adGOBt1q1YoB6FHQrA94FaooyDQZYK6DCrXSvtAPJAWwMFjkgQl64PE7DvwyEsD3KICNVho75Vf2ykoYDu1XgXQEBvF4FU0oGTvc4baejjk8VfGLAq5yl4jHu45dqE5xy0HPUoSl9YxCLk3Rqe3sXpOEzNUGCIB7L4zSO1Ap8F7hN5VJRVfY5YMXpRxeEcWDntvZVcnHpsE8qGShbmYCXeUWAHMT8cqCHefA8xwkDUAf4GZirQJpXuVfAN7fBBeNK9mgH+P/dO5CsrjmYxjIYdFfluqN77vTWrextuneI9YLte2djnW7yOZaB525Ho+PFAeEJKZ3m505G6p6znytjZuIfuExlq/K+22Xi/IlrXycLLsvFdJX/2bTpRmw1r4wsg0tF7KyTOG+mQSOQ6VReZQBXB27H/5HBj0E+f7mcg8sKaOO5cmoqWFIfpMIAdeo0mDDoYqPZpeF2YJsnwFsFekcIA22kqjwFWhVgnwbODDAxwHW7E1TQgenQafHuCd4qqvqr4tWNed5anh8om9THOsGBCNfPuuT4VjLAslDn1FbfipwNq2xUV965QMStUHSBkwskp3LAMhShTqrJIs7rAm11X/HpwHPWz7qrwLdqA4NNBzgdwO7AN5a/Ai7ZN8bBH18MypV7xzvKLvspxwaWXY3pSo9XDqXbakfQpnubHIDGiTWVR/lRZa+cnVRlKr9V8YzkymXb6cpydbp8XVtX7PzUT6gjeVDjULWT7ZCy65XsO3lv2nRjP66mQJIybCvkQBfex6CoGpSYhoO2CR+Ol1VDwLJSW70Z6KtVXxUoclv5WdVXlzJqE/DNfCp+ect9Ryrgm/KqyuLnUx3r6q90TjkunKDJwASBoQKqDChipRu/2hzgl50lAk3mUTnQ/GUek5JP/PaD+xdelTOO+wkOJuMb+UQZOBnnPbZVd9vBT4Ipdd0Fnyoo7IJVF0h2Aea0fVW7mVQfYVrla1Ra1g0ul9Pgr+KFwTfrYAey+T7mPypr5B8nEfLIcYWTuhhY4xZ8R2yL8p7iZaK/bsW/0uW92n1vEo+PSbzl4jK8z7Yrf52eOP2tYsCVeFXZrMp+qd/VuvIc7/EKtZJDN7bUyjbfc2O2IhWnKLkomWKaIzhi06YHDnjzR8CcUakGHad1gFENQhcwZZ1cFgdFE56c48j802BIARVVNtbLq5NZF6/AO0PunJ8Cuy4QPkoOfLu24qHeAa7IBaarYFg5dZdWpVNlYcAwAfCYH/UlADMDAg56uW4M4pMXDvKnr0+wXuIkAAJv1nP8l2WqHgYuLB8+V+MW07gVyqxLgS63xZzLvhNUgYzqmKxiVGVj3VUaJRM3/lx6JLY91Vhmu5c6HJNLT37yk23wq8AwAmoeU06+CEbVajGPfw5cE2wj6FYAPMvlPuhkic9dm7JOHo9cD+7E6vpOvWqG1AX9lR6u6O+me4tUPMLnyk+qctR91nXWEZ6U4XgDfckklpqQs5OXBI1unDg/4IC0A9ucTpWD9ypi29vJwtn+Dbw3PQh0EeCtAJ0CGkdAdzXQcABj0M+GQZWDxtgZRgaBDvxdAnRzGuSF+cB2qZVyrpNlxP2Tv9N2VM4P6+2cp9IZBbhXgLejCtQxr26yoCIXMKiy1TNXX6ZP8MqgMAN2BowsFwzAE2izM1Ur0aqdDKhRn5U+4ip3t2uDd68o/XRBjZM/AgOUQ6Z3r18cJRVEugBzUk4FOlaBOeepVsLVr9OLqh+meZRcVH9mXzFgfeSRR073EXxj3Q4Qo94xAM8DwXk+V9utVT0qeA2+K+CdPKt7UzuNPOSYR56RV56oyvEyKRsnxZ3PrvQW5VTpJZbjdHbTvUUqJuxspSuj6mNn41jPWMe7mInzVHV2uqomHCqZdHXhfTyvQHcFuFfTTMcet31CWKbKq2S5adPVTQfe6tl0gGCArvJyAMLBcn40CgMMfpfZ1Vvxs8I/8spAzq1yY3v53F2rPKo8BbgnRr+7xno6ubLzY91QgHtVdypSskOe2NgrZ400DRJc+5XzdG1NeaROM2ioyuaAFsE3fu08jlxRdzJgwI2/2G+pXwi4lS7huFRydX2C412tbqsVS9Q7vIcr8vgvyo4CHUfIQxWMuGCuAtnqWbXq7VY4uM2XlkFQF4DzKrKy2QyKEwjjRBTLksvjMYFgm1eHcexxG5z/w/xVYMsgHMc4B87TvlBjKMtRoFjtnuL2V32pbDX7wQoYsC3s9P069HLT5anSiSnQmup7ZxMdwO74n6ZlXvg8yzk3lnHjgNte2ZyVe8oOKR4cr0rurk8yD/46ulRcuGnTff2Od9LE0DJxYIzlMGDE9AroBGUQjc8wuGFygDWDsOm73RxYV21RdfE9rIcDP+fUuE4+Z5nzToFL0QSMo3wrwH1dBrbicZI3SQEDdc89q4BY5kE9jPTuf/QiPwmsc7s5gggO/hlcc8CAq9rqvW22A2r1Gx01gyAGLEo2fL/rO6wLr/E+Tx4o0LJKbqx1/cx5qmBn5ahWEx3wmQZWLlA6R3Y8sYLyUPqR9xPE4r+Tc0Eey0CBTRcY8uSAAq3Jewe6+ZzzIk9H5Rp5Yis+72pJG5LtVbtqsG7VV+xHnNyqlTOn5yu6v+nepgp0O19aATO+51Zjlc2viPmZxiDKLiG5WLUql/l3tt2NnelKdtqCLo2zwY53/FUyVfLu8m7adL/TxVa88/ccoJQBDZbFW2ir7W8IHHhgo7HowBaDBw4kVF4FpityW26x/Xg/AyMERJWDcmCscmST4KUD05WzqSYceKKjmjC4NGHweMTpc1p0FhMnq9K4eniFWE3WYH8m6MY8DLbVgfJA0M1fKOeVYtzKyrwqYFYFKipw4rYh+KmCAUyHclt5lUHxOEl31BbmbxVcqWfTc1XmtN0uvUp3xI7kkfoWoDH5jXNcJVZ6VE20uEAWwTaDaSwbbTTquEo7CX55xTtI9c8Kuf7CfycYhN+HyB0DOPHGgJvHIAN5NwGh5FGtrq0cl54wvlPk4iZuDz4/Z/LlbpAbR0md3536W6cTPHaUb3M8T0nZn8o2Kjuh7infiL+TFeluvK2A7KmvQDkqGUxly3a6ssubNt3YreZ4jb9TJ8H5HHic5lMGqQv4qvZx0NblY0KDxW1TgFOVjYEQBzorbVI8TWkiQ36unOtkssDJYZU6XWRHPNURp4/OyTJxYN21F/tcvbKA/GN7kiee1caVbjWOGXSrfw/Gk0XMq5IXy9qlq+TnQJ7qQ24Xpp1Orl2aXB0ToBHkVq5XzzsAXsniqJy6ccjnKJf8kFqC8UyrbH9n35Rsp8HdxFZ3wW+CbrXanbtUFHCYkOrHkJmzvUEMuPkaZYur/mq1m8ehAwhdcN+NhyM+7G5RyER9jDafsf0OQjmjfmDb8f+038tUgTFuY5cW73UTN1meqquK+ypivVM6eyl/oOqt2r2y0s3n7h6en0Ps+ydtVvhi06YbDbyTqoDliBFCB7+ycoz5M6BRvLo2cFsSZGC558w8o0PFeqsJh3QSUa9aSXTBn3IsCmBwPZh2JRCv8k0N5qVB95QmTl/lUWkmjiUDbpb3NLiuJmtQR7G+BBYZ2Gf6/FgV5uF3t90WcxUsqnssDz5nIM6Bpwq6uBzVbypQw3GNkwdKZ88JNJQOV8Ee8qCARwW2+Z5a4Z4GVauB41EZcVCchLqAvOIrEziZmnrI7+hzO5SO8Vhxk1l5nvWq7ebYLuZTbeVUH1XjvkT5TP2OGyMOfE8BN37DAf0zykHJWslkstI9WQm/Hyh1NL6hgXbT+W48T5sdxO3GmADlea9QBZh47E+Arkqr7GSlH1zOpG72rdX4cj6ua1t1X7WHx4mzKeoZ26bKb3BalCHLh9viZKtsMttm5RO6uGrTphsHvBUQRuOK145cwL464NwKDudXgJdBt/pqa5a7CgpVwDNNi6TeqWXiQOkogHVBMdel8nXPJnWv8NnxNCnD5ZuAsE5WqTe40oX9pMpjUk5fBf+oX7ytXJWJOo26n3wo8K36mJ2wAwtusgefubHL9blASLUT2xGBsALd5wJubl9Fqp7pCoQLmqrgyq14uNXGc2kKkNzEC7cHx04Q7sBI2eEEJfKg9F8Ft7jyzRO3qT+8Oq70nmXswDeubldB9hRYVfqPq6P4BfiUGY7ZbCd+B4R9YD7v7EvmV3o5PbjMqW7dTUqwzZOVFSh1IMPpKgJvtgF3m6aAOs/xvitH2Qg3ZlQZHA+5+GjCu7KZU3vXTSDwfVV+BZTdf05gPzIF6EoulYy62JNjb5dfyWXTpvuVLvqOd/U8B4sLaDOw54HoDJALkjGfWunmOrFe9WGvOBBgVDNyExnhrwJbygBVEwP4XNGqkTpq1JQDwWcr5TpHfA5PR/I5PWXqAgQm/D++2VYEtEfqzYCa9QLHhSoLdcltKXfbzFN/cUVG8egcrxqzHZDHe5wer9F2YH7VFsf35N6EVPsrYKQACT+bgBYVkKk0KgBDHi8Z7Di76UA31o98Vfzgx9WwbVhXBWZSnyu9znLUaq8LgivgzatJqp38fIWUrcDJiiD+PkPyzh9aRJuVMkIZsF9SgbMK5qt2u+OoPK6TUAYh36c85SmP+7eLmLY7R8LxkQf+e0j0K24y7V6iiif3jMcZ60JXpvMZylfydTUe3XhV/E9jEzV2JnbfHRN/4HyDa0v+Vj6TbSSXgZPfE9+m6ti06cateLPTQCPGwVQFxJQx5IAL83Jg4GYRFb8MrrvZ6PxVAUM3o8d1uueOPwe+Vd6ubMX3JY1ZFVgrUkYVV1YuSVVA49J2spgA9WqlG8Hgiuxcnzonx/fVhJObhGI+VXu7+jhA54mxKsBJWWEeZ3MqmTDwvpeC95XAaiV4UgGZK0+VjcQAE+8fae9EDkwKpFcyQmCCQNPVi4C7A954rnhJPqr/2a1kjRMgzF+l651ssfz8ynmueqMvxevkg/1OytX5JyffKpg+OgbuJcqdNEEh2ziUTU1SQK/zl2k/0S7iTg/UO2UL7rTMlG+orvke+wa+VjrkiOMeVV4Fxqf6y+13MR3zdnR8OH/Qvc6iynE7U1R/oD1S7XbEclDj2vk6lNemTTcSeLOhwiCkAt+ZPgnBFs6AoaPhPEH4rmpHDsAi+Oa2YV42YKp8ZRhwa7gL2BxfKrCp2sT3VukSxmwSHHKdKsC7NPC+FL+uDFcegoBsJ68o4+pZVz6Wq4gdIY7Ljv9Kv/C3qpvrrca9AhOYPseaki/qTMcDjnE3yXFdVPVrPke+XSCp0qFddNsIpwGWCnAmQID7rgp+3bPJfbYNrt5siwLDLi/+4qq3I55IYj6UbN2BeRXoxjrxd0KuH8IWPfLII7d9npvY4i+fZ5kpn6n/mQT8nR65vHebcMIyQHcccc3vdE98G8ux0lnWL5xkynOc5EV7gX1d6fk5NLEdk2cchznb6HSC/Y+qi3UV01c6yHy4cqu+n+j45KhWud24UXnZb6DsVRuVvDv9RtmyP6rs57007jdtuife8VZUDRBlgJQBnA60zkg4HngLmCs7D/wolgoEMchLwq1mirdqUsAFXPgOt+PZPV8BckdoAmz4Oa663EngXdEKQMNg1TmPfF6tdE8AMgdkyvlxmbw7JIgdbPYBBx34i+9+n6Mz7HyZOEDg+y7AcvrODl+lPac9VcBR5cnf7sB0Lk+3Ij55pgKgKe/quctb9RfKk/sMX3Ng4KrkhauBCsQofjowUgXRVSCME3DIw2R1SV0rQkDMhG2MVe8XvehFp+3Qars5Tmzxe+1YHgLEFeDNk9iq/9z9SR/dCcoJPf52BG8vr3w697eTo5IJrngn0MaJI56kZPnFb+7+cfK/FE3tg5sYw+eqHJWH72Fepccd/0o3+Tnfn0yYKd3mX7bbkxXsql+dneL3wSvZqD6t4lv+9oYC+6rNCnxv2nQ/0sWBNxsPNTjYQauBy4HDyiDDQCDrQHIzj5yPy2Q+eHVA8aucLjpkLrMD33m4rzCr9irqyrjTxHLlQG5ClwCB00AA77N+YKCDjow/BpVBGpehgiznyBh4q0A0eWIdVcGN0310fmq7rgqMsl7sT9ceHl9V0LVCKFM1QTbNr/hSdB2gu8rLhwvSJmBclaXSd3V37XB65/pc5VeywEm7it8sw/keLI8p9RnLqMBlterNNg/vIW+rdhB5UbaE0wTwzpVanjRUANCBR+Vzp8B7Rc9YN+8WIajGV9Via7l6dY19N9tilp8Da5iOxziCbwThed/pOJaHz1SaVXJj19k35Sucb2RdOcqnmkBDPtHHOrvG9XaTUNi2Ss/ZtqoxM7XriteqXG6Lkk11X9kJrFPZxont3OB70/1MFwPeaJzcLDQGLPnrZhxXjacK5LEsZzgwjQIIWB5fK0Om6kxnW614d5MBnIZ5ck46+XQBU9XGS9AE5KDu4HXmX60vy7gufh1f7PhzV4T6iBrrAeuo09kqWHMgAs85f0VKp1VAjsED8l7JUfHcBVBdeS6wwPHnAP+kjilh+1f0dxI4rQZp1X0X+DggdPRQpCaBUP55rT5E5XTHBY+sm1W/ZDoE8TzBqlZ+1RjpAstMr0C34mtFl3BnixsXyG+CbzWxkOdowxzwxrSuzyrdU3o71ak7QcqX83cj8j8lsN1h2aF8sHz013nP6QR+RDCPrIfBt7OvKGde+a5swRFSY5YJJwkwD+uVGteVD1kdQ5hn4nuOyIXzTOx7N26mNrkbY117lIyVXecYgdvjVrSnq9530x5s2nRPbjWvDCADDwbmPKAqo8mDH+vhc5Uvjb2bNOC2oDGp+GLnW61yd0afZ61dO3mbc1LmdR8YmoLO6yDn4Kpg9LrIyaGalFEOLQ/Wg3z/L9vEgf1Eb3kCBlfUnfNTsuRyuC0u6MNynAPGtlUB0KrzdDpSEbfzOvWpG8/umaMqcFKBpwtqOGhzgZcLdFQgFKRWKqarEqyblVyUP6iAHfKbtp1fY3HjOX8xD/soNTYmsuQ2qXuVvFLmE1JjU9UTW85f8IIXXD396U9/3MRC+hQFBhlAKtvjdJT7SG1v7YDBnSLcnYQr2byd3IFu9dFKlhv+ovzcsyCUhdpinj5BrXZjftSpzKP0F+ud6iDmOaKbVbrqPpOS7TlU6aLzscpeqLI6W75itxWPVT5lk1Tb87fS2SQ3pl29UwB+p+3Apk335Ip3nudvnnPgz9f8frUrL9Mf5YtBBhqJ/HdMVVk4yJFvntVTfOP72NM2cH0sAy4LHSLXj++lVyvvlzRkjv+VfEeIZXIpYv3B8p2j46AM9WAVdHOAmwGr+lI6rxY6AI9tqAIJdbDz5/KS8BnzkjynDBWtBF0d71PqAr5LURd8dTxVQVsXwHHw1d1zAZv7crdrR5AKRiugxjJAEOoALf+rpSn4rvoFQambuKqCy6Tkb6JfaqKtImWf+JzvxYfWgqenPvWpt9/5Rl6DUnboOyrQXcnSyWi6inenCN/ZzjYy4FaHAt8cL7gxwL94ruILnhDKWIZjDiXDfJYy59c1HNg5B7iq+t0YV8+5HJV3dVypshVfToZsl1RdXD7mrXS+W/1VE6SOT6y/ArqOlLyzbSqOdz4n7VinZxNQvmnTjV3xVkEzAwweoDxoVEBUAZEJYT43SHmm2JWDRgcdMfKJwBefOaCr6sHnrt2qXehAnTOKA79kq+rl+lfIpb9uA+l47SZTjhI7RnYCGGzxO3/cPxPQjX2M4wrBt0qHvKh6K3KAW8muAvZ8j4Ouo+N6ci/bXv3f7rtByvadc2AZeN4B7eq+e47/K1gFUt2KhANprPfcFhU4ss3Le8gHb8nlyaOqb5Sck+cqnZL7EdDNfOFYr9KpX9fGvI6V7+wD3pmT7eWJCwUUK36cjJzedSD8Oont9hRo43XGCep97w585znbXbahOC65vPx3cdgXKLuMV4JPBXKyv3kS6yixjcLf5Fu1kZ/zfVUu07mTBR2p+E75NzceOn2vwDZPeir7jfVX9U3ar3SU06uJAm6LOsffIAe+N/DedD/Sxf+dWBI6CWWkMc8kSMAyj/KoyuBgKB1NR25VMZ+hAcMgkMF3BjIIjPJ+BboV/9gGZWiTF+QPgwokBofXHeB0NK1/daLgyMSC4geDEbUigqCPdUOVrcAtOresLwJl3GLIEzyTSSvnbPO+CxSR1ESZk5W6vq5AmgPeKd1Jfe/kz7JxsqrAyWQFsQJB6nme8zuk00AuSIEOlc8Fn1kf2060tzw2MX2nE64PGBw4/lVbVgNGHvcT4J184C+fO4qyX/jCFz5u7GB7uy3TFS94qG3NU/B93YR22wFmBNvqPoJutd0c5aZk6eKqykYn+MbxgXkrW6LGd57j2FkF35V947ZVeutiIs7Dft1NcFS8qtjA2SZV7gSYVra9s+EV0OY4UNnSbpypPslzjHWV7XMAuQLQeQ9XwbHuCqRv2nQjgXcQDkBlmKsZzEvRilHlezmQuzIY4KDzw/JVMOicBr/vjnnwvgL7LihVBkm968Xgmw2aA+WVfCp5O5oEwncTfCN/ro0MtHkFhPWLg3fubyWPdLaoHyrA40Bs0r7KUbPMsO18b5L+XICgymCqJgtUUHVJqmTObZ4GWVWgxuW74E0F1tNjmsfxxfJBUMe8sx1TwVjqv7KpyEeOO+QR7Xg1Ocz1dmBb9TO364iusYw7UuNKXQex/GLbedyLbecKIKLcK0DDPr/S80rXOG3K4zqDbgWcKsA93Xo+Bd/K7zqdq9Iqn8P58UBgneXkeT5zNpXJxTvVdfLfxQPKFlbE/bdKaixXfgfrdOVgWTwWunHCY4YB+Kpv4B04Li5ScmQ+K2BdjXF8ZU7Fw66MTZtuHPDGjzol8QBmBxwDBv+V0iVoWpYbqBigIWiu6uJAoErPRhDPOS8aGXZ87LwxPYMyNUOZzjUot2Dyiix/lMVNmkwp81QgdxUwV3TJsoJU8M99h1+25e2FWIbiUzlnF2RlfQnwUU84GOQ8K+MN+4x5QX3FejhYZdldV1+tBAl3k1zwxgc+Z/DJ+SflqaMCNxzQqXTVlkbkTQFrBCQqQOSyuv5DnrBObAPadeZvooNsTyf+gflzwXBXjgpip/nwt2obtynf+Y73vbtt0qrNiodOD7vAnH+vi1QbHeBmMO7SMAifAHCWK8sYbb/jW+1oczqJfZJjJvlEQJT3JuT8G/66PMoPuvSOWLZ436V35TubW9Vb2S6WA/dBNQ6mYBapG2vKj/O1iy8cP1y+myCofEjltyaTkJs2PbDAG0k5FhxEadTzXePrdKJTQkDD/Cj+pgEUGyjlCPMZnqPhTIeN+SfAG/njLebcDlzBz3qSfzVxgO1zslp1uJekS4NvRQp04xZDDj6RJwwsmE+UGzuYHDfZPwy8uV4HhKp+mwAEFcy4+wxaMK3TKdajo32JcjkSuK3WtVKWC3oVSOsAzPSoViG65+rgj6qp4LSTt9JXlUcFsA6kM8jmdrCPqvhUQaAav4p/1QcdqbHD8p5Q1w9VPjwc+Fb8cjsmeuv0rFrFu07bXoHm6n1vdd+9Iz4B3g6MpzxxBRrT4eH8DY4n7ofMy+MF/dBE/m7MqvzIp8tbgWJXVjexMaXpOOZx4XhesecKoObYcBOgKAdVVzVR6tqFEy7OL+R9NXYrQI56pXxfZS82bbqRwBuNOho4BIroBHLAuNnYVVoto3MIzqgi/12dWIcKpBgAZb38b1WC0IFnHtxCzEZNOYd0+PHRHKyPZ+EzbZ53bcU6KjDXETuJVXIA9joDNATB/CGeCa/qFwmdaxDXE4SBEb9KkPzx7LcKnjMf640LtBWAZj13gT8Gf5fqbzXJUQU+10VV4JJ1u6CCA5HKDq0czja4oIivq/cIGXArXauCVAXglAx5myuCA+xztG84NrjdFUhx8sZztItVgKsCySkxz7kVcxJsonymdWG+vJfvfMdHuirwvdKWTke74xy70RG3rwPbDmivAHUHENUvypP7VvGeaVVf4CJDxmM8NnK1W427S5CLk7o8/NvFD+cA7oqc3aj8gOL1iB1312p8KB/D16rdKiZMu9BNxCI/+C2Q+DYNT+A4n+jk0PnITZseaOAdg0gZ+7yfAwOdAB7OOVwnTUF3FYhN62BQy2XgLxsuVy4be2WMsNx0qNkvGSDkM+Qxv2abvGMd3IedXBwg6+heNqa8xY6DLgc+K7CNMkKHhKAmATf+axvlLDlwdkEy31fggINPTjOd2FDgCst3gcvKfS6fg9qpTk3G/yo58O8CIBdErgYnFdhWIF8FNiqQSp3kScIV0J3PK9vgAIkCzAkO8uvL2f8TefE4YJnzb9UmZxenuoTAhvsI5X8pPeY+wrGO/fqiF73ocZN+HZDBCQ+UQxdMK71lOVwnKUBcAewKoLv8Ko3Tc0Vs25nXDoCxr8k+x9cycJJH1XMpUn6Q7YKLJZyen8trN36cb2LeK36rstW44PHhJqSqfldpJjYa4xP0C/gqEvsKTJMfgmW5si4mVf6J5bNp041c8U4HwsYcjSU7eF6puFSQ21FlhHEwT4w1OwJVFzrxzIN8OKDFslJtqAIafM6rM3mg48+givnjOo+AmJW+vbQedPxOHCE7VJyY4AAqy1C6zbzg+EDHGoTOFPuHQTCmcbqIAATrVoExPmfginyirqBMsGyWBdff9bWbXFgp61IB4kQvu7oqIKGCLVcGPldlTYOzbvVEpXUrHZ2OK1mtBMcIXvBVmSC1rbGSN4MUtOOV3Hm84LiYBK8Tfc903Be42t2Vo8azs+tcFr4nz5Nt+e/GUv7TsVX1h9Nfp8/XHWwrsOz+S4Wy/92KdwXYHQDPX9Q/tPl8KF3l/khbHsTjBifqVRxwDvBWcRDrqQLZ+JyvlW472UxsNINlrEfJ1JU5iTW78aB+1dio+pqfqbpU27MNqZ+ZNm1Rtd0dbRY/Rz6Yd9ZxloXqj02bbuRXzXMwqpk+dBCcD9NeN/jujCAC4amBxl+uhwE35kOZOIPIeRBgqWdZJ16zjON+OlUXHDheud3OOTqHPZHl9P51kQMI+Yt6kbLiPlYBRQVGuG9xtSJlyX3FwXhc84o7lo8BNPOA+ZFfF6hkGv74TgVc8B5PamGQN+nvKRjnyZAj9qXqQ0cdwHHBhApMJnWp4KkL5jhYcwHeZJVFAUEVvLmx5WytuocTvDj5gyvArs24kpc6yFtrefKY+47tp9LDozZLge5sl9pZMCXlH7heJpRV8pVl8Ee2pjxMdZP1rQrcL00MeFEGCmhP/sc3P1Np2d7ijgLmCWXlgGb2T+dX+f/bZ5lcNpY/jY8yT/fMxTTYVrbDmWbqr5nfCtBP/UvVJh47XX5nx/HZZCKVeXX2SY055D+v1YICj1G1yo3PVduV71GxSR5YpvJ1mzbdOOAdxIOeB3AOQgR2OJBWQJqiKuhfLWdqKB3PGJR0Ew7Omaj2VE5UGWnHF/5GOt62jDx1/CNx2ZN+7QKDS5Hr064eFwSpFR8XBHAfZ1p2Ygwq8pf7gJ0cO07lJDkow7JwhYuDTeZZ3cP24L1Mp0C1041Knh1oUDKrgJ2iSh8mOtnpWLeSx0FUVVYHYjBNVY9aTeHnFRCfEvcf9xOn7fqMgzMnEwQKOFmEz3DV2/kA9GVuRVAFv1PZILDhoDWDz5UVbzUmuc7qGfoRZYO47VyeAkhOR9UEVLZZ2bvrouS5A9MIut3/6u5WwR3wrraes/xR1jy5oiZb8Tn2R+ozTlBx+ayjU5u6EtthfRxLMO+uLge8HS9KXysf5+yzA92Kb2evq+fOJrsxNS1vIsvKvnIdSg752oIqK885jsnnaPPYJm7adGOBt5p5xUFRGctqtu0cHpjcMwcq07nwCrBLq8pFIMV5OC0bEycDZSzZCXdlKGPKZXdGGQEigjsMXo8axrtlUFln2YHy7K/jVel/pkXHiQ5FfSyNHVk6oUyDwTiWy32Tkyvu/XPsMzcesH+xTViPA8YuIMf8qDMI2CsdZqfv0q0EfucSy0sFuSqAcmALy63GqQq8unsKUOM562E1OXBkvHMeXn3ONPiL+Vz7cmwxgMm8uLLHgFztOlH9i/6G65gGtiwLzo8TGy74dOTkg7Ks/JfL4wB4xwvzpHhU4NuNjesiBgsdAGdArfJPDs6n9JaBIYJtTDPVP+wHNR64XL6eToCgTVfyRl4Ub+qX003AdmVTVNlTUrJRNqGLb5Utr2x218eT/p+2WdWN7c1z3AWUH3VFe8vEY9q12dmOrHfTphv7cbUkPMd3QxSQxQGFgQ/+TkkZeMWXcmDIQz4L4kBwpV4HMNw1B91skJh3F0hNgjK+zr5hQ8dtcmVw/bxyq8pxTrTjt6LOuU3KrQIvBlJcN/YJy9AFkjxGEjhgsJ2BDoJzBBlZF28LQyeIwYBz2pWuO3DeyVuN427F8BLEtuVcmtojBXZcAOVWvSdtqspW9qMK5iZBH5dzRD6cZhIEu75TdbE8OTDENnOAzPfcKxA41jqfhtcTmuhLXnflqP7DOpStrGTN+diWdG1x504XnS28LlK2/giQ5rTTvFyfAnAsQ+wHnPxUNshRPM+JKpyojf/jzrQCuLlupW/unO/hr/PxSmbOvqh6VPzZkQLzarfYqu+Y2uDKxqj+X/WDlU9hW6naiGMYX21wskj9Ugty3C6sY9OmG/lxNTZUaHhwyxOny0GN27UyDQOIFafLwQGeKzB+aafOwR3ex7rROPL/33aOyBnYKV9I7KjV7GKmw/zOKE6M4Tn8HqVJ8FEFWRhkYnksF3Y+KnBm0B2E9/CDJbgSp0A1BqTO4eO2eDdBVE2GONnhWK4ceuZHgIKgWzlz9ewcmpZ1bp0IijCIUwBDfZBGlbcKmDnfFFhj2i5P1qHk555xmtXJnUm+4Bv/y4biS000xnPcYot2gMdzEq7iqHRTv4VtnvTDxFepyRwGFOyLsX1VP7KeKoDB44D1UsmI23gvrHZPgTO/652/vA1dgerJqreKmRhwK1uT6ZWdxbKCuJzkIwC4+ndiKyCu6jvHL46pyuZke5Rcsa0VTYC5y6PiS+dTK1/a2XFnSyqZMIBN2U76kGWOaVG2KWu1eq3qUGkwv5uMRh+pdHrTphu34s2DMwN+BN1MyjDhKgIOWg5OuTxn/PIeG2F2ZLi6waQclkqjnGOQ+uAVlserlpNgzfHHZXe8q+C8W3nrgNqKQ67oOo0ql80rDrg1mwOaafn8UaCULf5LDXayapUndUQBb3R2CARcsKjazGNj4uy7gB3vs+NX8lflV6CGy2NdV4ELlz8dNyuk6uLgtgowkAdVBgMxBjduLKt81bjvgr9J/01kOAnm2ZYzYEEAnTLmD/+p9igAyd874F1ASs9UOU5ebDcdsFKB5nRXRMqA0yL/ig8kVb4aX3zO5Sqdxed8n/WS7eV1UQWCJx9QW1nxRr+iymbgjfYZ+wsnL4+sdmNZuNqNgC1XKHNsrPp29FvKLqp7eI4TZKo9Cmyr+E+VPyVVN9sjxQumnfRDZ287O1zVo+xN9jnGOPzLbUziBQHWP2wH8+HsMdpa/Lgj+63cnTH5l4qbNj3QwDuJA5YkN2OdgxUNAIIENhQMUDFNEAN2PmfDrgJ8F4x0hAGNuq8MLDuVVccwMeiODwfiMMjDIGG1/rtFLih0xEEOr2BMgpiUDwNn9282Ut8RTGN+LCd5Q2CN7XOOPycP1Pvd2S7VvwxMMACajgmuCwNFlDu3IevG9iJ/buJN0Z1aKUNC2+TGl1vNU4CIy+Rxqq5dcLYa0GHZzNOEpgGvCpSre0FpN3O84tiI+xi48ZebeTxlWxl0404RN4HMY0WBH5QF215sF9te18eTvlBgtxq7ileXp7Kvii8HvjG98j+sf9dJHUhW28GnIBz1UJWhynMgkmXMQMnJWMUmmQYXO9DvZBqcsD0SE+GYc2nc2ML4EGMlLotthquv8p/T9iiq6nZ51Hie2mvVHlUP+6C8h32uysRr5/dR59Cm8phGP48Tpejnmb+8z3YD7T3q66ZNN/Ydb+csnOHnNPnLYIfLdqtDHKgxOYfPpPh0ZTpSjgTBRz7jLz06ni5B3A7lxDj4Y/DJaTFoVMCw4uU62jkpk50KBthupXtad8ggxkP+qv9ziQ6JZZbP1f9yT3mjI1QTTRi4PfTQQ4/5/99IObGivmqPARg6Tvx144SJ63XBAraXQRDndeOL7Qfzcd2BvAoUGEwpwM32LmkCxKYHy20S4Ln6UN4TqtLyykt1ZFlYLoMYlnHqsAo2MWhL+6z+TR7WjXJQ/gvJ6Sjzz3mqfp4Em2oyJ8t29lwR2kHm3bVZjW9euXbl3GnAHbQKqKsvlPP/6FZ61IF4BcqRePx1q91VjMM6xfdz0grzTXWH617J59rKvHCbKtCtaOrDXN7KTrk8qzHKSlk4VrEc9u9BajWZ7Xumx7alX2P9Q8CseEjby4tN2LcYc6LuM388cb1p0438qrlzLEFsMDkYwAGNafLZJJDnMlepMuhHyqnK4MCtcpBH60DCoFHlccaMHXmW4WS1CqZVWecEW13Qy3W5YIeBpquH9RXBdhwJwNWWc8zvgKhz6DzWHPDGf3ejvmieEw6ZT8mLV955XKugHkkFTBwcchplB1BO06CEy1B2ZkIrwaZameTAhkG4C4yd3FYOzuvOK35dmcyvoqr/UF9Yt/KagbELbtGe5nXoLK92Y6DoKJ6pf7HHANTJo/IlzvcFqQk67Atua0UY/FbjlMvBgBoD6464HPZr2R9ONzHdOT7gCKWuIWie/Psv9htuRbsC1N2qN4+LlBMDHGU/Vb/xmMv+jTpw56LzE0HdLjgkXu1G3XK2uALDzsdwHaocLGNKU1/j7NKRctV4cOOCfRPqQ97DtOhvUrdUHONiJ7aFbMvxGa52B2VdqT9uh0ueM0Cv0m/adGP/nZgyPiroVQYSAwFnmFXeJFemc/JMKkA5hzDA40ANeVo1GucEJcpAJ/FKEB+qP7prR1MZHwFJScpxZDvUKkXe5/yT+rOvEWzjareazMCycdxU27pUOpwo4DQIvHkyoRuvLDfm/dwxo+p0zj5J8dAR8o99jKtElyQ1sVIFFwqwYB53TNK49JwXZVWlc4Ffp0N8j0EZB80OmK4Gp+pete2Vx0QcCRYZbDhQqsYDt13Vp3ZD8ORM1xdOPqhT6rwqQ41N1x9YNufJcwe+7xZNVrjVijb6jClIr1a60167tBxHqLgiCM9xtdr1X9zHCS7cgZd6z/mzjKPA2409Pldpu3ovFb8FdfqpfCj/On+q6unss6pfjTu0KYpfTotxj/of7qhzQSreYECPepX5M77M9Lj7E3f6qQUKzK986aZNNw54uwCM76tgAMkFdpiH8yrDx6tyuMVtYrQvGRBUAf70nuKRr12+DuCk0cr/udgFso6PCe+Y7pz8k/LVeRVMVc5q2ifqS+Ood/ivNFgf+d99OQfJwJvPMS8HhOxAs9xuTKCzy+3pKzQdc2wjkirQV/UN8pyrORho8GqqA4crhMEE3puCJ5SDO1gm1Xjt8rmymCcno/xlnUVd477lvFwG66Sy8VhXNZGBvPDHeCb9i+CDJ7nyXqeDTv8ZPLHdOBd4s+13gTry42wnl415lM5jWj7ulUCZQS7uDmKQ7fxGlxbtc3WvWlFX46vbXo7nzm9PbEYeauLpHODN7ctnqJ8TPzXhQeVx/sSRGiv8q/ie0sT+Yt2TNqRtyvGWMucVbfSJec3gF3nBHa7ID9oB5QNwpVvFQ3mf685yecdSlo28b9p0o4A3EhtXBsIqsMLgDoOaHFS4YqcGPRucfNZtK0SemJd85hwXpqnKRsPX8cFUBUMsAzTe+QyNGZeLz9HgqW2wqn8U79yXk+DtUlQFjdWqBAYwK3wygGMZ8TarTKOcoQuykjhYcwEMzioz6EbeVF7lZF2gNg0yKpCA9aIOqsDyCGU5AbrjXXecOOBAtqNKl5EU37kbAvlSuor8cECiABnqUQfMODhfOVgOfI0H6wzWr+Sd9lyVpcYDypZXO7h9aOP4P29Mg2PVT8xvJXNXD95nm4uH+m8X0zGhxq/SY/bNK+ON/UOW54DEddj9o8R2sAPSKwB8CrpXwHdS9pPyz3g+tVeYDle4K7s9/aI0A2/0iWp88MQWnrMdUD7IxR7Kbp2ri6r+qV1hfqtzdV3xg9esC7yzkdMziMVXbrIctIMoA/xWjIsdXWyZ9g7jUHyGtqnS/U2bbgzw5oEU5ABCpmejhcF3EDoxTJfnyghzEJ9BXTUjj/nxfGUwu/RoJJRBngT+VaDFgWAQ94NqOzs8NMQuOEcjq8pYdTjXSdhWDmIwSEJ9XCUVvPPKct5j3jDQTofF4JvrwvI5KEN++F5eo16oAE85aaybx286cE7T6cckuKj4cHld2QxmOJBgOZ/rxBXwroCZIwSX6pzbx/kUSOPzKr2z6RhE5a8C31W7FCmQ7Z4rmfMEBMpGgW7lT9RKLMoDQQmvfisAXoFc5I8nPLlu1e8d4fhz55xWydaNKSS1zZTlscL7naJsM/sEBZ7dofK5/+mtwLSy6ZWND0pZdjpb+WQug/sJdzWxTq/sWEDgzRPAXC63s7IxLo/q4wmh3k7TKx65PKTOLvMYmYyXCb8o9yD03d3qN+oJ8pf50A5iXRj35jWutqMOKd+Mq+EMyjMNroRv2nRjgTd+PZaNJA7Ozkiyo0pSZXQGOfMd+R+UWJYzgiuBEJfbgQe+rs5dgM1lsZFVIEE5YqzTAUNMj33RBZ8VrYIhduQYBKl/rdUZ7Yp/BiLotHiFexV4o8Piw62QMxBAHnklXG2JdIE1y4L7WQWHSl58rmSjgJ4DCpMxiYECrtIoW9KVO6EqEGbeKr4xXxWsdWmrII/HeQXwWC5o17vgd9LWzO8OrlflUWUH6OaPCHL6zON0wvWLKo/HkZINylv9y8GqX1cJdRrb5OTBeqr0VtkzbDvKoHoN4G5T2l62qWhbuy+Y8+GAN9bB99QzZd+Rb/azSKiblY3EvuvsRpSFX/ufjvUufuGYQv3y+JrGfVO+8nqa1/HK51V9eL+yL04+fE+Vgc+UvcKyeIEG9Y93FqGtQADOPCJ4TtCdv2mT8R7Wh3zxxDmmYbu9adONAt5qFiyP/JdGPKiVIUFgoAypMzxVgIYzba4spqkBVQGco864d04Kzyer2t25KtMFXLxljNvDxl3NtGK6lSB9Sli/CpRSD9kZKVLODO85vVUfteG+ygmqlAMGclgOls1HF6App4lji8G+kiNfd4Ee8lzRJACv5I+r+Gpcq/rU+72q7R0PHanXSjrwovhVgLiaGOOx2z1XvFVBNwY7rGMu8Onkx3rFAAUnS9HOcDreHstAD1e8cbwcoSiHfZna+aHGCvLHgHR6sOw6XpGXKoh3dt8BA36m5Hlkpf5O0XSFujs4rZrQxDR8zvmdTXc2elUnMC3n7XQuaHXsTOIY1lEHpKvz1fFcgVUuX913/naFqr5T5aJ8MqbF+8wnX6cNZ71CMMu7V1AX1esFqCfKDrLdwXR5jv4Z/Qy2VfmCTLNXvDfd6BXvJJ4B5oE2BRYc0E0MMd5D4OeM4hEn4oxyPmODOqnDOU3nrI48q+ri9wyxT7PfnHNhw4plYh50rtcZiKmVCV7ZdfVXgI+JA8/UeQWCktKB5H0X5GWZytGrIHHq+FMeMQmBwHPaH7hSr2RX0aSOCqyhzCbEY4Dfsw7qwPdRUuNvBcirAHgK1Di/K2/yzAU72Q8uMKzGmLJHCmSoupWPCF1mHhJsZ9n47/BwhUWRaivzjsEmAyfV19l/7n1u9y8Hlex494ZLpwANBsPKZzj/MQEJ/NxNLtxtSlvN/cbbxle+XD4B8qh/3b0O1Cm7PZEtgzG87/qJx9z0/W6uD7c4s86hjjJvzrepuJHznuNrqnzV9aWosnssSxd3KtmybcfyMT+my7rc7pX0rype4TK47zPOTN1CHXQfRnbx1aZNN+rfiQXlwOIgRBlyPncgA6kyrCotl1cF7SvGk/nnYIWBpiJlCPEZ3++CI5WvcsousKp+O2dYtRdpBYCskAKxagJoypO7l4TBLD9TjoVBa55z4OYCL3a+HLRhnVkfOrwsH0G3A0F5znJAB4tb9jGAWpHxSh9wkLaiQ6zjuUrKclZBzAqdq9cYdBxZEcV8XB7LwB1JPN5Tf3hVRNnzPFdlV/LFoNIF1rjKzf8CKSiBqao38uGWWcXzZAXFyZ1XfTKtmthMoI2gm79sruqY7PLIelXfOF1AGVb3syxl75JWJhPuNCkfkbuhHMBGuzsB3Qjap79K54OU33X9kqTus23La9zBgXXw/7LPPCu2l31QjhHWTbyu4jzmRz2r8qr0R/RyyqsjV6eyp+z3UYbKX7t72F60c1wH2iEsL/UTd9G5uJL7U+mzis/Rv+B73ayPWN+9Ylc2bborK968FVEFzLwqMDGqLn1l/JzxUlQZTRWIOoDg0nN5yrhWxsMFRe559Uw5XkzrgvOKLwVosT7madruVcLXE1QQpfjowJ4KLrvgCNPm2ECwivVh4KWANzsnDgIxqAnCoJ3zJuBGwIxt4sAszxHQ8Pte6SyRB1X3Sj9zesWTkrX6ZeIZ++TTTcpM6j7Spooc6FJjs1pRxH7ryuHDjQHUYTemVXuV3eMJAlWGGgPVgTyynsYYjJXwXFlx/udoH/FKOo6frB/7DANcfs+bx1n1zPGnfEylA9gPrl+cfLgMblPeuxcobaYC192qtgPcmNd92RzTsP3mdCpGSErQVQEftu94D/s/+8R9BwGBWJ4rnhzhO7w42avsDdc9qWM6Xh2t2nPFm+K18lGcFstFPVHE70RXsZ4650WyajdD3MMVZ0yPOujayHGm87WsF6gvuCMIeVC2a9OmG/mO96nA//+2PwdYkHAAKidTOZ/KMLMB5MC5M9Yd0OyMXUWuPRUPzom64MnxGOQcZxdQVU4D7yv+VAA4pVXA5v6fasVrVR7zwKvnaoUr83BQnitAmZ4BNANv5Rw5gMO2MGBK58iB4kQWKpDO8rNcBHTpKN176jwOK5m7fsFVk+67Bq5N+JvlsGxZjneKHAjmg4F0tzI+LR9l041zZ2NdPpz4YllzuQxC+Fknu0zPq/MZwAX4zqCSeVbtUnLkiaY48tsNyhao/9HNW83VFnTerj0F3iyvSkeSR/zlspzMlexdG+4FUivYbFOPHN1WdK7PgW2+DlJ2k/VVpeF7LnZwq9BMCix2hL5BxQOr1OVn2z0B7iuxW2WDOG3y4+pyMazy9wrwsiww1lDjmfsBy4ijig3R36ItdxPZyBPypupx7XK+ISdP47mKZzZtulHA+ylPecrpwOCVjaAz8M748DO+V+VbCSaPtpnPsw4XwHT8O165PrVtWf26c+Xs1eowO04F9tBQZhoMbKtgjvXjHLDDX5hVBn1KGLDmtftwDuunCkA5Lcu/CtJUegYvyCe2mctXAQHLRW0NRd5xJhrbh2M7D1555EkQFzywLJHv1f7kdCkvfifNOfGjeqmCB1dOd9+BJgXI+D4HSNUxaS/3WXXOdgLvqzKdrUf9wlVllrcKUhX45nHDvKCt6/oE9R5546AXeVFfMldgtToqYrmzPrhx5PqS2867CZSesj7ebYp+4i3lyv5eAmizD3L2m5+xn1BjRgGUKlZKUv2Nvt7VN41dFDHYmx7KRihCGSRPbJ86mtj3Ll5VfDE/yg9hecp+qXJRpq48vE6Zpi9W45XbpPqYJ7uTx3Mm1bi/s77kNeNIrJv99L1gWzZtuuPAe2rwcYsrnldGzBn7Ki8GB9PAt6NzB7dyXFi2C44QAOLz3AKUgAEDLcWz459n3rOu5BeDQjb2yuBisK1I3VcAK9OqINYRfjQs26OCDVe/kjUGZlg2yo35zvow+MSPO2F6LB+3yrsVP64X+5y3oalVFZRrJ3N2hqhjeJ9X6xj4cH/gbxfAcB+qfpyOTa6ft1CirFywNCWWmWpPxb8DM5NDgWwuVz2vxuykn6prbjv2v7rngIgquwtgsY7sc7cCPyE1+ajK4LGJfYPbzjvg7Z5VVE3AKJlV5PwpPnP6eq+sdOfY5slZ/N7FEYA9PZxeoi6xjebfynYom+7GJOqtqgfTqrxpMycfWZvYK5de8eHqUL5tOp6zns7Wr9iISdlOF5J4wp4XETo/wn2KMZxb/VbtRPvGk/LJF9/jtnf9qXwAx5suxtqr3ptuNPDGWV52ILiSiudd0KMCLgVIsC4MLlSAh2ldnfx8EkAzj1iPayMGYMk7BywcoGEgxUAM61SGGPljQIZ1saFFI8grm87gsmGvaDWwV5SrGQqUZhmOF+yvlAFuW1db2FPnsWwO8hnc4co0O1UHvLnPEBzyinoQB31IKjBhGWEeBSTwyPQ4K43tSx1VPKngqAsmpjTpdwY0qNeOJiBlJTibBE9st845qjKyfcpO8hh37WT7h2WyLVR2WT139aweDBwTeKHNq+TMz3iccH5MywCa77Euclr+AFsHelSZE1K+Q+mOayunv1cIbXf6CTWRijb26MH/1UXZc6fbTv4upuC8bnzyOY+9TM96kukQbKt0jiZ6wP6kSqdkwD5t1QYnKX9YpevSdDEm+3+etGZKmVdxlbPdvOKN990KOvLK/a3AN9t4lkUnV/Ucxw7uoMz23Iu2ZtOmOwq8q4GL//KFjYpyIGoQ4687RweinI8KshzPzJNyVM5Y5H0GxGiEMqBSK1Q4ecCzjAqgp3w5SGJiHhx1wX62DctSgG3qBFUgt0rqK9uuTVknO/OUKwZOCOjVCohqH/ZvAlN+B5Qdb5afz1ywhrPW6j6Wh+2eBijKkbI+MEhQMsfx7gJaTOeCORXYuzTuuWoj9rcbDyyvS5LTT6WnKu+RFUh3H9vK51x+N7YrsMw6q3ifABSXLvUKbTwGsqyzmZaDOhX45nO26SmHDrgr0M3PHBjHrelx/sgjj1xVlP9OTemI4xP7QP2qsY1p7lVKuxp2PME2bjl3E6vq34t1q+HOPyhbvmq/qrGuQGMHvPOcAZnjk+30dKVRjYVOZ7BNlZ3ofNkRUuBepTkK7tn/pw1Cu6J8e/JUxXhKjzAPxgg84cy6k4eb5MNyMy1esyyrvmcdxjKx7CAE3GpxYdOmG/OOd+VU0rhXQRSX5xy9ciD4jAe+Ik7vniMvnEdtuXEAAsvDf3OTafF/zmIgVhkrvI9fzObnlQyUQ2PeOCjELdMo78zPBpjljXXxPdUnzsEoYqA5ITT2GdymLN2hAisXzKbjcuOCAQNOHqhVb7Waz86bHbUKULqAhYMbRTwOuCzUI2xjtQKOsqkAAdcxJdUeXvVmR79CLvCteO7G6aROllUXvDu7gnKvnuO4qWyyyqcOFfRx+zlPBoxp9zgYy3OWSf6G3c1/L3a0r7kefKZsuQLeDpCz3Q1+A3Cnv6jItUn1bxUQV22/HyhBNgNtBNP43x4UYJ6A6+pAfXUgsjpXYJBpAma4LPQTDMiYGIhNQeeqjk3iN9W+6yBle6o06lrlx4lB9NvVijcD5IneKN+Jdib1ke9hfcg7A/DMM5EJx8aVP2edVbGPih82bbpxK94ZALFzQRATpJwPBl6do8BfLoOfY6DHvDI4mQa/HBDhfZUH25VGjlc3cDUD8ygjpeSDKzbOOKu2sEzUNW4t4y9ycx1qyxKvaio++JzbOw3yVoJBdATYp0H8DiCvfqvgDMvEwBmdFjsIBSTQkfDqeN7DstNhJ9/TQMSBbwV+8ZonGlCHMy1PyqC+4MSGClBRfsyPu3+uDkxA5lQXcbxz251cHV+TsToJaNXYxjxYTzXhV+mMA9uqbai3zjZnGpy0UgeOBzzPVRH+3+MMbl/0ohedfnFHFupYdSBYYXngwZNTbqu5ypPHC1/4whPonrxXm/3I/aDqfJAJQTeudqtV7W71uzrUvw/jSVOe0HP2DX8dSL+kT+SJdDeO+dl1Al4m1e4V4N+lVc+PjA1n/zDezHGpZKlsI8YWaCeQR6VDeO3S4zME/zwR7uLI5NFNvCtZ4L9TRXvndBr9AdrZDbw3Xd104J2DAoMXJFyJ7WanqiBH1Yn5VnhlsKeAh+INt/2putnYKlDD5ajV8845MyHIQzlWbeFz1Y4sGwNgBPlutVL9qrLVNd6fAp4VQtDIq51qO6H6GA8DUAZYCiQox8TBmSq3khOCDyYFGp1+umdcntNL1GEO4pRsUt4sc+QD80zGwBFStuYcfVNyvQ5S47sLutTB9qYCZZXOVEG6e46ThAwoVRCq8qo6eRUv0+FrHGl7cfWY+a18FfollAUHog50KxCMQTC+z52g+xydZHD/oFP0Q7VjSW0jd9vJ3fbzboWb7foECDm6pP3D+pzPwh2KDizeSfCNfDPvHAdWfmwl9pgQ2zoVkyofzivLPJGOZSIg5vFb6RC2P+9zWQpo8wKKWlCpwLfTT2678kfdhKCKK+60Hm7adM+seHeBChtDJLfykQORAxumDIAwHz5TQFDx0fGtgiTFC+bhZ3iegRUH/S5PR1mmkoNL7+7xM1zxzrIDhAZVATZeu7R4XwUDR0kBHwTd/H+1OdhS7wG6XR0dAOkAtArYsA0MoDIPBkVdANeBbgZiE/3CnQJ5nv9nk1eBuE24MsnPeQVc0crY6EAwO/9z9K4K9M7lU9Wlgvkqfdowlm9n37pAl4PyDnjzwYAAgQuCbQbiXG6mS6DNO4fyXtYZNiBWvnGs4XuE6kB/w/aAdYltPPsSBdSDnwDbmB/7YKIj02D2QSO24U9+8pMfY88ZbLvV72pl262Ko3/oQHc1Zu8UmEBwlfxm/RzPHbFrzv+qZ0eeu1jOxXfu/hF5d3m6epT9ynPUmW6buYs5Wdc6/WMQrkA5p0/QjWUqe8tt5rga28GvybA/2MB70/1OFwHeOECZOADj2Txl0DkAzkAIgzAHGrA8DuRUHWywKtCMH0LriGcnMajCL5m7IGoaXGGbmG+XX4E0x0P+ooHNMrIdDJIcyFTgWznmql+OUuocr3IHcSDF28tVUJXE14oYVKq+YvCh0ipZJPjugJIaY1OQh7qrQFqCbdTt+OWv+mJQh+3lZ/yleGxzB8a75y496gTu0FmVVda9kl71cVU2pl0BYajzXKcaFx2v1dhWY30KvDEdrtDwyhsfaHPzHP0Syg5XcOI39fcpT3nKCailz3GyRf/BslAyVSAYn+UYwkP1bTfx674lclMo7XT0IW4xV8BbrXa71e8OdFf+IkiBJWez+fxO9WHabZ4wxedHy8XzrpwJ+OYxl2ndM1fPJCapeFwBgcpmqtVu9auAc/Wb5ygTFY9y/VW5bDcznuLYSpVTyZ19WRXTqD7YtOnGrnjHYJl89CWNO79Tp8BdPsMgSm2pVgaPg0wXIOVzvsc8qCDKyYGNCebBLY5sXNggqeBdAVUlD2X4kC9+xvc5SEU5ZZu6IJv5dGkuZUCVvLgv+H36aJtbCZmudHOAhI4S9Y7bydu3UOYdGOO+PCIb94wdIetypsEtsXyesub3Hx1QSn1KsMOTPF0bkNwYnuTDgOSIrq4GfytB5eTgtFwG18N9q8C54zN/K9DtdH4SsLK9x2vHB4IF1mmsO89xi3t+wAxlyB9u4wAUy+S+UqCbt2Tmdaxux5ZyXO3p+rOiavLkQSW05Qm03YfVMq0D0xMw3h0qJulsemdzVnRAAXdnQxlMpW6nTVax2VFybVQxzHXYW07L+To/4+wc35/Wg3k70K1sg/rNc6VrDIzRjjngz4tryLuKyXnVe9o/SmfV802b7me62FfNEdTgihECEDezivdVeTwDqwwYG19lcNSgroKaTK8CJkVcN6464HZctXKOjo+NcOUEFCBi8M88qoCd0ycP+K6XC5Q7o8rA6TqNp9KDIJ44ybbx6nYHuhk0KEInxvJU/CKf2IYkfpbtqfQW61XnE2IwpsYBp2Hd5vGL4Mgd3C6ld5fSIawH23BOPZOgje/hGMQypsGWs00M+LA+9ZzLwLG0EjjhOY/JCehW6VxeXoVRZTobxrJAIK7GIMqIJxEwLY8LvKe2lQfg57o7f7NJg+7cXs6r3bwynZP/bOfdvxqrVrsZcKO/COLdQko33Thimtpz9azTJbaHGZ+h/T6HKiDKfHD7HKCtfOzReMPJtgLdyk4q/vFa7eipQPfEF2C7Vf60PRhndqAbbW3yjhO0OClT+Zp8zkCd4yWkyXjZtOlGbjV3/8IkriOoSMengIMKXPJ5DmoHvFVgyAYaAyXMh3VlPjWoMUjqKOtSH06rgqkOKE/kj8SrrsgbTozgNiMGRBmYMGiakpJj/iqDzLQacLrgN/sD5aHe4a6ANzopPK/4Rmea/HU6ym1x97qAXJXvyDnm6lBp8p15lDsHpAp4Y3+4FU3Xz5ek1JMoP1frEZxNxiKmc4HgkXYo4OfuV/ZG6ZCalFL66Npe9ZHT97zH51xnFdhyXgbCbnW92sKJvgB5wTGN8sFg1PVD9ZugW/Wj8qU74Hy8nqHdRtCtJlYVAFdbxp0/6FbFUR+q8aT01xGPHdRNB/KUDq8SAjSMD1x9roxLkbJhLhas8k3J2bUqHuJ72QcqRsM4Au0R2ya2SRzT5W91n3lQoJ/5COKVbhWzcZp8jr6c9V/FgFVco/palbtp0437d2IYVOAgiSD2BS94wen9Od6GxaAXZ9jQYPKsoHI+KuDlQF8FWjgDh8E2tg3rVW1HGSQASTCijFUlR+Sd29rlQT4qZ8+GMQidK79Tv0rOIeZ5Jc9JG7u0uPLKQXL2c7W6rbaXo+6yo1PtT+DJwUvex3Yhnxi4KQDgZoY7cNTpCYOCCfhW+RlgZH/g6gmvDiWPvAsm+TxHFytSExTYX0fADgcVLoDjsa76Z1pf/rKtqUBcNynY1e9sYTXxoMA0Hlg36gPb8AqEVzwyKOcJypRLfiAw86gAVwWPQWqFm+/lB934a+5ubDt532RSdly9261WrSsgrVaxKz+hbFn6t3MAd5Kz9ZzGxT+ujJU6efFjCnimbcQ2OB4YxHF7j9hpLtu1AdvCoFvd4zLRvqnYh1e80VYgEK5+OU5g+U1WtlXfsj/Me+in8Tr7BOWh+FyJbRSt6vSmTQ8U8M4gIleslAFN8M3buVSQHcSzZXzPBbbqnFce8xxBd6ZHB4MAC/lQ/GI7+V1X5seVw4CO28ezps7woHHndCrg518GRpWMs3w2qMrodnw7mjr5IFx55S1SPFOdgZv6mJp6p1u1nUkFB1ivKgOvc8IDZcpldvVP9JTBMv9WK9spYwfcOC3rA0/q5M4KvMd2gcfipC9U291zHGPJi5rJn9Tl+uAocHIBaJ5Xfc7jMognpbgfMa8jBzwVEHUAXJ2rdKkPGCBiIKgCYVUGHgy4MQ/LInUU5YJ1s63lcaXGVvhL9g8rdu6mkwLDDLgVyOb/5e0AuMrLvsG9iuTihUpH3b0p8Xh0PqbzDe4ZjgU1OXoOrdrxqQ/uylJ2U9mlCnirZ5gny2d7k/fQdvDCkNqVo4Bs/io7zOVhvRXoRhuLMkOfyGk43kJ+lf92bej8zko/b9r0QL/jnR+HSXAdM888aNF4RPCXs9M4eFcD68pQ5jmCaw6A2HDGPbXqXbU9fxGQ4Hui3K6uLNdGFdR2ZbhrVY4Cp1i/qosDX+WQMRhwIMFR115Oix+tw5V7BnYJujGIUv+jGw90SFXgxO1lp4NBmnLeKFuWwxFix1aBLrWq7Va8+UNUKIMcA3gvz9Fh42sP2H6clHNBZTVeVuWFuszyWnHwKAe2Za4sBWIdKV1Z4ccFOXyOK3ZqXGdaXunAiUtny7tAlvPxuOXArguEncywnOQb/2Vi7lbifMrusUwU4M4JQd4FtcH2nNBuIOBm8D3Z0eRWv6sdULxwgHbK9SXriRoT6rqTA5Kyi8qeKODW2dNMw/ES2u6KulhF2dkKEKv7R2WJPKgdTpVNqeyLs/dob/CawbF65kArA22OIdC+4XkFuhFMc2yuVruVHBiQu3hmxRYqvVgZN5s2PVDAO4LwAN8BvHN1IJ0Tp8UBnu9/Y3BVGdIK8KhzvFYrO8pYphGuglwOUjMfryZVwbS6zwAG25JGcuLIHCF4zEkGbHum6UAHGmGUW5ap2qGcWsWnOq8I3+PGYIkDdjx3K97IIwJA5ke1AdvJulW1KdOmnp5L7LQZCKhf1N8J+Fb6yHWhTDAgwa1rCXpwRZNlqILFS5IKQjmI7ertdANpArRdPmXnHO9YF+uCsol8PdlayL9qtwD3fwWKsxy0exwk5hhRgDxtQPJagRAnQ/RReJ9BP/KLeXlc5aQgpj/S/zeVEuwxyEZbX4FuB7gnoBtXu/l+kOtHBdCqdHmuylQxQeVHqrqcr+/KS11Gv7hCDCKV7zjSniOk7JYC2avUgW+12o33ld9mmWHZCriyHB3o5rxuV1EQ3uOdbNNjJRZmefK5ut606UYB79w+l7PBQXEvHaEDKDjYOR0HaS7gceCGy1FAgQ0tB40MuhT/CphUYL0qL0mV4QI9Rd0z5XSyHVEHvnvIK1gc8CLlvQTfGAB3fGHbjwSkmcdtEWTgnXxycIXtwMBKBUQVGHMBFII5zIuA52hArvpT/SrglatyTqc5DYN0vOdAHTp9lHVe804RnMTBNlZyP0d2PKaz/qmDd2ndfQfcjrTFBWHKDjkdY73J/BiAMW8qiGPeHchVgZwaX+5Q49OVq2y94o3rzwmhfMY8YQCM8kN5btB9HqlVbt5S3v2HCgbP7p4D32oLe/S7AlBK5xQpvcRnKj1fK9Dsxj7Wg/lyvLsYSt3j7xNUVNmayh7xOJ3Y4U7mVZ6VctgWKb4rHiqQjYsjWF4Q23iMM/CeKi9IrX5XoBuvM/8EZFd2V9nfSv+q/lrp502bHriPq8WAjO3m6czyXmwpx3dXg3BA4tY7N4hwhhAHMgdBHX9uUGd+3HJYGVYFcHCl0m3NRL6rIL3iEa+Rhy4/PncgjJ2p2gGApCYy0kijU2fj2pEK5DvKet1qhdpCjmnYQaCjmATpE/5YnpPgoyPlkBXgZgDN91WabtWbwbcC3jgZlSAGA9cc2zjmsB+S0K5wwNHJZkWWyasC/ZP8Ssc72+T4rQKMSWCfzxgIKp7xXNlKrov55tUUtSruxlgFvoNwDHPA6MC58g14Pu1b1O285npQdupagbNNM2IgzF8sR2B87jby7sC6nP1HkNKNSwdQziFlf/CeKh9300382lE/pXydAoxIE1s6iQGn+c/pCxefuXIwRkJK28Y2bgJaOQ7A8lI3K9CNNhuvFeCuZMfPJvKc6F/lEzdtulHAOygN9/Of//zTF8x50OEHanAWDQe6CgwzcOTBqwY45+PgsAtyldN0W43zl0F3pnOBe8WD4peNq2pnt0qMzxhY4CojO0NeuU75cMCJM7QIsJDXFWfOYH7i6HPbfAW8GWArUM6Oxxl3pT9qskDxfy7YVrxwoO/AMt7P/sfzCTCvyuQv+jP4wKABQQ07b56BVwHbFNCukgvOJjqMPK7yVwWF0/HD9fM47XROlZPkbJoD3W48cNswIHQ+AIG3Ws2ugDjXpfjANrMtYv+DPkGBCDzHoHoHinNKH8XvVef3Yxhsu1XsI6vfqozsd1xEcHra9bUCK1PgqIiBGd5HXirQwiucmf8cO7qav+Pf8T4pqyun6we+h+VXfaeAMt+vVr/ZpuSvs/NuwYqvVX9jzMv+Vx28S83ZXSxnRZedHFV/bNp0I4F3UAbvz3ve804D7elPf/rpvtpGmoE6OjkMxiugndfOkDpD5IJKNBbKkGDZXK5a7U7jobZoOp6q4NoZfczLRrZrP67g8HN0BNk3CL65XgRQWQ4GPa4N2G4MmlXfdITfClCBlHqVgcF4xWf3vCoD07lg5EiAo4B2Bb7VO9xTQM15ELSn3uEHpHAFhXnmGX7mUW1zU4FIJZcpqbHOdU2dPE8KsB5P+KoCOKxnIgdsD696V2XwM7Uio/LwLwPwKtDlQ9mALhBUgSGmZ7lgXZUsUXbKpjvAzsEx981RUPOgB53RPgTW6n3uDkyr1W8FuF16XFFH4I+T0cyzGhecJn8zvQIqSJWOoG3ic1UW36t8lyrvKB212U7PK5vsnlVjpor3Op47QNi1i+OfDnhXMaKyQ8wPx4loJ9UKeGVflexwHPAOSqVbyDtPzis5Oj42bbqxwDsoBk/+j9L44NrTnva0EwB/6lOfKgdsfrwmB7t6vxhpYhjZGPJAxxXiahCr+wwYEZTwqlCCzopfNqzcBsUf5+FZdg6U2ZChjCNPXqfhDb4TCGV7VFvwF40mp1X9qOSp2j6lDMyyvmq1O0nd4xl/F3w4vXDtq/RxldDBOoCswHW1FTzTpy5w2g6EYx2ZF2WlAgg+UN8U8FMgaSU4nKZxoLTrMwTq0z6uAimVbhoEK1l1OuruoX1TvAVVq93Ovimg7IJHDgR5VUaV6+rBYyITnNDifnXtY9B9boB4U4LNBN144Cq32nqONh6vOxDebStPPUPQ7+yCG69TPZz2q4sR8LnzSy6WYpvlAL0qs+PVjQ9+znbc8VPJZGVcqH7hZyqt4pHzIN/cbhXnTHhVcacrA+Mxto98L/lB2xs0Bd24qNbZ3qqfWGbcHtU/D7ot3PRg0rUA7yD8390BwuPd7wDf6UQRJAXh6lie8/YUN0tWBU482F2QjquhLjhU5ecvrzTjNu4ExdWWdT5UnWnkUkZozFyQqoJu50hT5uqc5cEyxX5UcsJ3epm4Ddj+qYNXK94ZwLnJGxd8q3OX35ELztmBqzpVWVgGglT+dWB4ArzVNvPuK+cuPQIVNe6YN7UtGQMHt+K9oiOdjFPnKx2d1ME26wg5/XBBidNZJTMHGtwYUG3LPmNSqyaqbg6c3DZxZXvclnNlu5AXlmtl0xWlPVcBIrdPBdrKlkz1iicOH8RgE/1D9aVyXH1WK9j5SpsC3AjQGXyrfyfJ75E7Wz7tDwVgsL6OlO7lNf/ic+ZB2QAXF7AfU2VOiP1Y1T5u64qvnVCV3tkIde78kIppMF8XB7mYkH0f53WxMttBzI92E+1kdWAalb4qQ8lD6S2S83tOlps23UjgnYMoA5UA3vnV83j/++GHHz79ZvCGQS+CAA7C0lEl8fZJfMaDHAMXdpwu6GSwzOldgIq8KkeL4MIBCm4Hth3blM/weQXsGNjkPVw9Cvmr9zmV0WVHioA/fjOgUn3DMuV+UfyvbDVXAQ32iyIFAPL+1GGr4IKfrzgM1A/UGbftG591wJvvIQCfgO0K+Ls+5bzqcCvmrKsTB9wBqkreHPhM+oknE5DYLlXlKD7ZVqgyJ7xWwX41NrLfqi3nDLon8lA2ku1s3sd0brLQ2V0e16q9zr7nOHOyqQj7ZtI/mBZf16n67X6kaEtuJQ9C3zn92BkfqBvutaME2wy4eVt5XgfxZDb3hbPr6HPQ96j4IMthQhvk0vFzTONiGNbFauxPbNeU2IZNYg3F4yoA47wONJ5TX2U/OD/HPmrxQpFaGFmJUVS6bns5psv6lE11MlV2z+mSs5FV32zadGOAtwMmbktMgPBcAc/t5/mct6pm0IGDMB0mOo7KWFVODo2BCvadMWbHxU4zt94oI8ZGK+tEp85GmYPHfIb/UzPLxcBDfaUc607KwDh5xq3iLA+UJa9uVdvXURasN/yLfYLPVreaY95p0Kr6FoPBKl3Fa+WEK6oAd3cwIOcPnynQ3QF55sU9y1/WYWxX1om6xOAGx1H+OuBZyXvSJ7h6iu3gvu/6SoHjCU3B25F8jh8ViHZlpW7gCmLKh1e9VZDIdSpQzIEa9otaVedVbba5rvx8zvV1ASLqdtcPqtyuz9iu4/VEF+8H4m3kQawjDK7xWgFv1slupZufqdX1tD1Tm6LAwUT3ujIZ/OK9SvfcfQd2V9s3JTehoOxCxw8/c2VU+dkOqfIrP1P1m7O1Lg7J/kgbp8rjvlfPuDxlG91Kt7KRin8VUym7y7xMZOSenaN3mzY9sCvelQHCgDmD/ni3O1bBEVjnvTQIbosyzlCz4UbjVTleNhAd8Oa2YjDunAAbbczDoEXJkn/5Xy7lM2xrygfloXgLymACA9PJamUGI8wDBhLcxiBcuWF+zjWq/K6fm5XNdk9IOV12PJy2C6wnAU8+71aYqxVsTI+TWZzPnVcr3gy61cSAk4MDDViHcuSqXrQBlXyPkJoA6ADPucA7y6gCHzx3bVXyqmhFZikXtr1BOOZ41RvTunGlQDKnxVVuBjBZh1oZV6vwWIeSdyeTCejGZ9ieCmzEgX4P2/0gAO9sY+x6U1u8VWDP4FgBcge2FRjH2II/psblso+uYgPVTneoOATJAW2uo6OJL1LnVdmrto1jLWefON7g/GwDK3JgVdlRPq/4mfDWyYHLqwA+tofTK/3g/LiSrcpUsY3itzpU21Qa16Yj/vLcmHHTpgdqq3kSAxM0DgGyc0tzUnyMDQdfBr5s/DAIdkYTV634g03K0bttZM4QZ/28PQfbGoTlIijFdDgjqYyWCj6RlNHCAJUnFVR/YD9E3yh+OMjmADaOBOVxYMCSdeGqPdM5wQUHcJhXyUYF365e51DYySlAf9Q5YL8pAKwOB5wd8FYr3rgargC3At8TkId96yZnFFBjoMSrqJhP9XXWwX1SBaNBbjLsOoA3ykLxi2mqIL2SwYQHBjxYtiJlv4JcX3JfYd18cJ+i/VBAWgFqV94kiKyI5eF0jvtC8YZyQ5/kVrrvZ+CdoDZBbr5uhm0MQp+Bv5iu2nbuwDanSX/l3hdnH5Y8TPVE6eCq3ikwzICSn3Ne5MfZFtZXLkelWWk/l4XX3AbU7yP+swKoigfHt7rPadSOhc63uOskt+qtSNlS18eq3M4Ou3a4mGhqX6d+0rWF7eSmTfcLXSvwnjiSIF5V4mBEGZYgBqvKoGPQlk4VBzuWpUC3ags7PA7m8B6CFv5XZcpgIA/ogFhmbvYSjSrmR6PLhg6vcbsf//s3Duw5SIzfyJ/9mbIOEJ/lJpjj9nOfcx9OKIMnJtQP7LMuiJgGRO5atYfTqfYpwF0B72rF2l0jEEegrb5kzmC7At5ODq7tSobVweBbzeZPAqCOR9SNHAc8Hqq8LqBQdsrxo0BwV78rf4WyPvUFZ+Yp60NbrHb0dJN1XLdb9UbbiROL3P+qDD7yuZqgjGv0S1X/VPcntqULXNG23o/AO9uAgDvfrU5wm+1DfVK6hhOrFaCuVrgVaOePs+FOGtyFM20v/1YARPmYFZCrxocC4ys67OqY8OPyq3MG9C5f5YddmXnOz9X1pFy+3/FUleX6/AiPri7le6p6prbKle1s+SR/1t+BcFfupk1XNx14Tw0k38MgKe/js86QsHPoAAGmRXDMAxqBIvKfeTAQYgfg+HZGkPnkmXZ0KsqBY70YrFXt4royqEFQhmUG8QdsMlidtBHrVcaa++eok1ROtwLLeK5WzVVdlX5VTluBbyzHrWjzs24Vu3u/W62QM9DFOhX4drJWhOkVCEfgk8EuAztc6cTVbyXbI6SCWPdBrVXgrcgF3VX6alwcCWyUbanyp23hscXfdeD+xv7t2sXnWDeCr7TBUS5OFriVbz4YwKuV9ilV9gXbqc7xXrdN+n4A3shvvMetVpWrVXxe9Q7ivqve+UY5du+EK9Cddbv6FSnfrOIUV47yzQ7oJDFodQCZx5I7v4QNxTorm8L8KxtUyVK1mfl37VGyn8Qazo5U6Tkft1XxpFanJ7a744PL7fTS2WUng87WnqtjK7LftOmB/rjaysBnYiOp7vMzZ8wcYSAWhMEfGzcG0ZhHOYUq+FeOBAPFrn14rQwWglMMTJPX3MqPKwu48s2EYMeBpARhuIOAg6ZJcJkycx9BQ1oBMCx/bBOXx21N+Tg++P4kMGddDVLA1QFeda8C2d0KuFrt5noq4I+A1wWm3A/cl67dDJrUORIDPAZb5wSQOJ6m4Bv1aQpyXeDMeXhlP+9PiAOV6XjK8llfsgx8rSR5d32EdnJ1zLA9xSAu7U/aInyeeZC3LjCsdMjxPdUzF3xjexXIxiNB7L1GKEMHcLlt+J8osoyUi9udlvUwkFfbyPF+t9KN+ZIHNSFftR/5c6CE5aWe5TXbSlevApouZujyq3uTMTAh5dc5duH0SmaqLOYP+9HxoPq1ilcUb5y/A9QdKV64TavEPPG5Kl/ZP07rZDk9sG0rbXCy37TpxgDvI7OLilSaiRFzwB0HKQbmzsjkL24p5QCMjb6qQxk1/FXGjNvneFO/ShYc2Of/E89gpHLIWAaueLOh5PbwqmRS1ov3MljGFQZniCfgFknNEneBRRckueBrEtTkb05+KABXge6VD6p1vwi+Ffh1q+mKJ5ZttnWyFXYFfKfuYIDG6bLeo9TZJxcYunImq7tcPuqSC24wv+NlxQarcngM8C4I/LBaEK4u4jjH/qp2KaAMOn7xGU5eVgGZe1bd56OyQcpe4Tn7DjVmUo4Mtquvbt9tyj7AnQ/Ib9q86kNn7kOnQWwfgnhSNHnodghMgTfW7yannSyYL+dLmJwvXvF7qyCmKgd1k8+Plt/FdlO7ytdKX5QsXBw2qa+LzzpezyFlOyZ1TeId9jEsm4lNxPwreXHRq9INNeGjxtemTTcOeOO/A2I6d2A4g8vkAhzn0FR5KlDj8o/MKk6ci0vjynCOQdWRxg1Xwbm/WHaZD4PbBIwccOc97n/1f1kRsKEzz/wO1E9okk4BGuWMlTPhciaAm1f1VTCH99yWcgWAHUjuPqSG/azKSXDF+bFelIGTadV/DpCxU2XwjfLl/K4/zrVBKhCtyNmfKkjEe6q87sC0nV4elQfyzHrMQDB1COtNYsDMMlBBI8qUAzxepUZ94AkTrk9tu8x73cp85VfUNVMFRPk8+cL3oiuKNCvAEYn1iWXPPCF4Vu1R91V6p5uoE1gX+hXmxb33jdvJ3Up3UNo8lgHypHxyNT5VGXm/spETu+aAloqJVJ3KTqlYSrX/UqRsWSU/V7/irYoPK366a3XvyGRN8tOV48YHxzJVGjdB4WzkRL9XDmeveWFGtZdtv5PJpk03YsVbBUQrg+KSg2diXDsDtsLfEQNQ5VkpTxnI6hyB7WTGGdNnIIfb1uPDaXlP5cOPqgVhUMtpkw81y7mymukcH5frHCcSBt8cgE4cIwafvL1c8YSAtNv+rbaL81ZyPJK/LFMBdC7LgX+1syDbywFO1U8MuN095p+3LbtV1CkvU1oNqqpAN59PAzq2rapOtsEVCJgEjQrk4iQRtxH7H9O4cab4mI4vBEsrgZ9Lr1bQlVxcYIvyUsFsAuc8r1a1GbhmHgUSFcXXwtmGKHCWckxArfpK2X81QYDtYsDtzvke6wPzHc9QJni/WuHuVr6z7WhrUU+cXLD+id45vVH9gted3cGx70B4V1Zno86xoyt5K7/M8nTjrQLfK6T6frU/kbcqryun6/8uhnEywmdKf9T9FTs7OZzN5QlR1+5Juk2bHuit5pWBcQbsUkGxqovPL0VTR1xdT/Ic4ak6V9cq0M50aosvB2f8jmfmiXTqa/VozCsQytcTwDMFRUoW6pydEj9zZSVhAFq1F8EmplMAnEF49X+3VZ3ddnRe3a7eBVcySH3JYL7SaeaRQTeeY/lcX8o60yvwXdE0EDoyPpW+dOVUwTgHoOp5FVh3gZ8LenmMZz05xnMXDafF/uG+4/pwnKhJKsVrgi3cnaN4VW128qvyVIE1B4AcYPK/rZpukc4y8N8ldhORkRYnpBTvEx/FYz19AK8cYzu71W1Ox88YGCDfqlwVdzhw7d47z7q6le5zyYFibjM/V+dMWJYCURP/2Nmr1fKm7e9sFvJRxTcoS5Yrt+OI3a/qc/nxl8tx8YQqQ+XveMbzygay3XPHkTzqyDHH/t21QbWvatemTTfiq+Zs6NkBoHNzweMq3a0BVxnbLpiZGsyq7IkhPXLO1+kQsZ+yf/H/q2e6XN3G1e/se94SXAF+56w6Sj6r8lEPsb4ukAlyM6suL/PDoJsDWgbV1VZzBqoIwBG4JC/u34i5reqZX5WL8uOAHNtS9SG3uwLdFUBnQIcrsl0frdIkWFOBjgrWkK8OlLsAiNOjvlZ5XNkdyMVxwAA5nqWecH3YV2oyBctnvejIBYXqnHWlqsONV06TxKs2WV9OTKptzQqw4rlbCUd/6ig+wIY2oAI2LCvOkzYBV8YTeKuV7TzcJAOmV6vdXdvSv6BMsg0s6w54o33B7150sYkCH0fsB8dN3Bd4rfKzX67GMdoHVa9K69q9Alzx6OIfrKeKb1bjKHW9Yssdr0r+nIafqbqV7etsoKrL8TPRie7AiU2e5HTpldw4L14rPWbZsI1djRM3bXqg/p2YCsZzcPEgTXKBFz6r6lOOwJ2rvFMH0tV/SXK8Kad5Tv1VkKqIV5WS8h4G5LkakvlYB1y7sZ1VYOCoCkDYyTh5sNNaAdx8v3OgDlQ6oFmBcbdKqLaPu/fA8RrrZDCt2lUBk6rPsX0YCPN9fs6r3XgfnT73+4qNWSUOMFfL5yDI8Yo63NVRBUcrfPE1b8lVwRjm5dcBGICvBJ8V752uqTSqPv518lB2Jg4H/irAmb8KcPMxWfGu2jCRFebPccUr9/wuOrbHpXFgPAh3yzjAlOkReDMP1ao32nbU46yjk+9ElkovqueZRvn9rj5lKzBO6Oqp0ri6jwAeNf4m9mjF3mU9bFs4rwOcE76m8mJ/cAnCergfq1i3ilcm4FuNTXc4YO58hPJ5fM7tce3dtOlGfVxNDfLK8HDQlueOKqNy5HxSDj+vnql0ndG9FIiuiJ2Ju1edI78IcJJvnHlM45rpcHUc0zrwNnV0jlRQjEadgw02+ArssAOonJjjybVBgWx1ZFq14sjgONujtqW7d7sVCEceFdAPymAXAe8k4GBgnTwrsI2/SQpw5/3kpRqrq4R1OMLJgW4sOX5SFgwGVCCkfjkwqmiSBnnFc9SR+I3vPeAY52AJ8zGwUWCjA9+YDmWg+HWTR1196trxwJM93cq1A99TwNqteFf2tbOxPA6TmCcGs5Nt59gW/K8WDNwdb/lcrbirSQoG3WivcFdAtm8yZvBXUWVnuC2T+ATrc2nQh7n7VRrF37nEY6qKNSow5mTeyauym24cdPW6OEHZMFce5lHycjTxLcxDF49WMsexjPFedVSr4aqOrr3dsyOTZJs23ffA+2d+5meunva0p90eAPFhl3D8nRNTIChIDciVcrrzilQelXc1gFdG2QU/rs6V54qck1k5zzryF88z8OKVcQW01aEMaAZIE+oC9OSF24Xt40CzCuydfKq82H+cbuXgbaQcWPGKuFrR7s6zbAXOMQ0HvQ6cOH3lduH452fcRiTUMy57ZaxO+taRAitd0OjsiwuQVQCXv5O2HrUDVVtzKzK/680TMWwbsC1se91YzjwIpnFM8NhQO0GcDXMH5mEZKGKQOVkVVnmqNJOPq6k2K3k6WeDuF87Pbau+bq7ax++sYz7kmcdwpuNdBHxfbSvPMvB1GizzqJ1g+6vimgmpsbASCzEPzNcleMw8k3zOhuG5AmQ8ThSAU3UgGKvyq3KYR2W3la/FPK69auxXcnLlOF+BfFb94nyQ6gO03yvbyqu0FShXsnE2F+O5I/q7adN9D7xf+MIXnlY70rjE+cMPP3wC3/y1a0UrhqIqwwVwfD75reqe1DXhsyKXxhlOV+6Re5O6w3hm32L7eVVUkQt0+bnKU1HlvFBGeY6ywz7vHJhyyC6Nun+0rQqIugBIpXFgnP+FWMol08V4roA3vnOp+KjGRwV2VreYqyD9HDpSBvPKulKViXJXbVDX6mC9V0GNGu8qOK945bHrPrCWAWemwwm5qnysp6pf8bPyfKUcxyPaFxdkY/CJk1V8z4FRzneJd7xVW9QEEo9J5s2BYJ6Ui/uZFj/MmbrAgTcH4Vynkw3Wx2Wgf8L6L2EzXLyxSuzTOzCi8k3iIMyj2jKps2sH88/XHQib2E4umw/WLy7b8daNE2VvuSw1CTBpg4tfuG623yp+cXqRQNgBYpZZ+lyW6wRMrxwr+naJcbtp033978R426FybuzoeIBnmZwf0/JsNeaZnFeBLTvszgmodkzSYH1cD6fh8piqus8hV14aWgwAM6jJ1a8McFLerCN4sH5keattYYONeZM/Xhnl9O7+hFx/rZTLaSeAAccFr1ZXq98MpllWCbrzV/VbEK5sIs8TuU1A0SWOadDj+mVCWB/KpytvGmi4wLEKIJ2dm6Tt+HBjGl8/CmJgg2MRy5zIgX1AlsW/PAnI15x3CriR1HMFRB2YThs6XSm+xFZzxb8D3vmaUF6n/JPX7t+KMSDm98NZjytyK+zV9nKlc2wPeGIIaWq/KlL+TIGiad1cXpXPxUir4Fv5owlVNsrZMXdg/ZWfVWXxsxXqxsykzJV6VXzqfKmSqdOtrlyUF/vzpIztktQkKo6tcw4sjwnbdKlYd9Om+/LfieV5DMZHHnnkcQMTnWJnPLHc/EVjwHWyY+FzLHtyYGCA+Xhri3Msyjmqsjh/BcKxHJQBG0/VL1NyfaLScPDsgF61MokAJX4jMFO8rwZALj0GkHndBTITqvhTgQ4/n7avAgYoT/6f3O5wAAPLCNAd4xn/1U7m54kX/J2OSZWfV7t5m/nkOELnOnEFuHGVYJI/9cG1A+0Hji8VgHYBLaefAl+0kaEfDnAjMVhEWa3aL9R13vmh7EsHrhmUVWOM+WDZqNVf5Vf4+RRw4wfFjgJvdw8P9H9xnSDcrWCr963zugLdXV+nXJHcB+vUu+bct1heNyaV7WJbXtk5BxpYzzkfxwJOJityU/xXPLKuKN3pyNmZzj5V7cC2dPbN1cXluHqUTLDfnX2elMd5XP+odvO9ih/HW1D6DrTD1So4PkPi/Mknr5CvHNg2JmzzuT5706b7+t+JIeFsOa9q8Mw5n7tyETAhqTJ4AKMTw4BCBUZ8rsrDwMQZSdWGTK+CJ3a2WDa3zxkkdirKgU7IOQ8unwNnt9KE4BvBVAbqboX8iFFdzcNBaJVfOe9J8KYCdFW2c6oVGEB5OcDhQDeudGc5CLoTeMeB9TqQwqvhODY6sMCy6oCSkwfLjq+rQPZcUhMFWP50S6sDe5OARelHFdC64FXVz23B1UL8iGL+ht4g5URNBWarAJLtjNK7Cch2+lPpVWU/UcfV1tIJwHYfCkN/gau5k6+ac5sU36r9uKMs5Y3/LpJ5rFa81b8emwbWTDxxoVa7la9mwDTpU0zj7DL7Zz6vdNmVp3g7xw+uADOl82oMrAKeyvawT3UgLOvnsjp7qNp4JEZQspjGAZ3vUTrD+oRpXJluXCn9xbQOdDNwTh/OgFzZu0yHCzMT/4V8dHQ0Rty06YH5d2JBOAhw5Qefo8HgbV6Vc1DGgstRwQ7W4YKgLIef430MbJ2DVzxz+7FOvI8Gi50Rt1lds3GOXwSy5zhvPkceuOxqxRuvEaTkeW5lUqtjK7weSeOenWvYWaexP7oAIX8dIMiy+EBgzVvEXXAdhCuXudIdB9aJ5WBf8lbjIOe4VVvdgTqMzhjbjvJy5Xb9eAknrnjkcxzjLuDOIwMQFRzxOeuR0iunc5UeMikbi33OtivvJ6E97sCJsp+sG9XW8Q6ET1a5FR8q+J34oeqYpMGV3umKN/Ksxgv+4rjGfsuPZqIPnU4e8Mq0o87Gs89m/842htvK+q1sQ2WL1Xh1gGrir7BcvlYri4ovNXacv5nkRT6cbT1iKzifuqfSdHWo9Mq2VfkdqbGjZFGVo/qX8030fkpVPzsdRb/EoJt1UK12c5lVn1R9hdeT8XMJf71p030LvJWBVEEzAi9eEcs8lUHiezzw3GDOX54Rz8BA5XXgG+tSv8kX3uN2sDN1ZbDBrOpUfCnDhPVVgIXLV+VkXg6gXVDM24cxwMAgD+WnAqY7SRPHXz3r9LkjBww4TbXap8B3B7rxwHRYFubB/kbgmXk7Z716YNl47mSUz5QeVzZllRh053WCmbyveFD2gm2kCyzVGOkCUpWG+cMx7cpMkKZW+R2plf8OLKB81A4O1L0KkLtJKLzudIkp5ZAfFHUr3+iHGKxW7ywzkM1Xco6seKOtVv4Z76ddRuCtVp3zQ2n4jD+ipmzAFHQoYM/14Sobl610DX1RVTfnwbLVtfJfSvZuvGcbeIJN8eR45vGj+OK2TewsllfZdMcXXnNsVR2qvIltWyEXM3WguyPWFVeve8ZlVXw6mvKqYky0W5PV7iOHmkCe8LzB96YbDbx55Xpi1HGgJbngTQWF7loFPFwGOzMVGHF6RexYKwfAxrsyuIoqR+3SuzqwvV2AWckc8ydvGMCp/kaQrZxH9ZXsozQNWKp7So+O1DXpP5QpyzLzKfmqgwE4/yudrI9XrBFMxXPOizzgpAr++yF+pcI5Sqcr7ro7VJldf62MRUdovxiEJ/9q1YDHqRqPyn4pW8r2SLV3al/dPXyW9jL7HsviVXCkBFEKBFTUrXCr5wgoJ/rDE0gdb9EOBN3sSxSAViB1CrwnW82TXzUmsD14pE7xu/puxRvbqd63Rnms9HHKVJWv/n1Z6gWXrXRdgRcHZLp76jrr7drYgQa0E5XPqOxaxcOkD5QNmoIiTO/yumddHe75JFbreFTPMX/+Vu1xOpZp1H1VZ0dTn1XprEqHvwi42W+hLDL9yjHh+xJ+edOmBxJ4q/MJYdCY11iWOthwKKONDpmBeKZxzix5yrxdm51xxWeVE1ftwXbxfa5jEjBUhM5dBZuqzYpUEIKBLG5fzD7ivsd3QHkC5DpoFWhM5ZztwGBQAYxJcIZpXRkoZ16xdsAD86s8OEFSrZTncwRaWEfqeOpAFRhxW3CcMt8qjyqrk28VdK2SmzhEXXf9qMYOtpXLc6Bb2QkX2FY2VdXPvGd+DM7yP1ogX24beo5xrMvZS5QdTgChXiOPrO94rSazOG/XB/k821i9b9yBb7VqXKWbAm/kPX8RQE8mSONelIcyVgDYTRAo/9QR6ibWk23HCT1sT1fmqm9U5MqYAqsqXnBlMuhR43yF/0qvO0KbMUmLv1wG2yR8pvK5exWxvCcgMZ+rNnYgUtmyqQ5WMWKV3/Hp7Kgjtc0cd1241e4sN215JdvuqNqk6IgOb9p03wNvdghHiAMddrwcFOJ5ZQTwPgdCE0PPRlPV79q96kjUCjxeK0OueHBBoqu/agcbQ1cGBqG8CoGrJwisVcDH5WGZR6jSxxWnXgVGlXzivJrMwHLcPQV2ON1kxY9Bd7fanYT3nO4hH7x9E/u2Cxwq4OPSOFmoe+cE3pN81Y4d1nE1tpJfPke58UQZrjhXgUsX6DCvHPBxu1QAG5Tjne05UoBzfs0neXZ8p3wrkK3AuHrGeTm96gPuo7wf7cjVbpYJb4V2oDoA5WR1fLrijb6ksi08EYETpOh3EnxnPgbA/C63+3deK5S+H+WDdbh+qnx5Zd95PFTjfWJHHBhS+dW1WvWuxiu339lBrmPaL6jTR/KpuIbl0sl/xXajvFh2XF/FW+XzuvsKdK+0R8V2Ls5T91filiQFupWc8MDJ06q/J/ElP3M2LNtxyZ2Rmzbdd8BbbfGeDCzeIsYDOstVAEQFN1lftR2OjXDHJ59XxoTLVWWwk1STAqoulJMysCgnfJ7/wg3TqEAj06mAWZ1jHQ5w5ZHGmc+T0tBnWRh8TsAry36VjuRxepv3XXAzBZT8jPsXA+ZuW7kCLQy48ZefVwAE61U8pny6YE21X10r2XSBJt4/2tfnAG8mtpHMexf8pa3AnQQ8pt04x7wqD6Z1clTps7/za+aqjfyxtbQ7FWVeNYmk9MAB8Ooel6HazekSFOYKM/ojd7DfcivG6noVeCdPzD/6nSC2AZg+fQLKjCcCkNcEyZX+dn2d5eA73Viu8gfOV7k0HQ9TWrEnDjjx+FeyU/qoyqrS8/2p3ifxpF9HFWjjNqhY6YisWcbOFne/qi3crip2VDac03XjQuV3erOiTxgDVvVW7eNnHFdW8W/lHyexNdKKbdm06YH6qrkyRMroMrEBr8pxzjYds6s3n02/rqrqU0akMwxshLAsJDVxoCYSVPnKEfP2ZuWUK4PKcp4YScWXA3sIDFUgiPwjAO/onKDqnECNwUkV+Kh0Ko9Lh3WpQwFwF1DzCjf2SdbJ/cTEdaf+4bjkNE6nObCo2sl1VnLHtLwaeGlywSgHQsgH0zQYY3tXgZ0jKxWOD5Q36gnqlLPp2W5lQ3Kcc/9xm5R+OHCt7rs00xVvfJ7gk+Xv+mYKtrvt25fcAYR6EOe4Qynu8b+Ai1/kB1e/ERyzXZzyx23FSWM3udUBiSOgY4VnrqNK6wAY2zHFe1W28yeraVRa1m8e444U8DqHzu1fNz6dP+p44bwqBpjw7crn644nZzeP6DT7hZQp+we2HyoGd+1x11z+VO83bbpxXzXHIKMCwviLoMyVoQaXMngZAKGTyIP/zQnmQ364DjbglZHlvOpcyaxaEUE+si5noLg+Bj0MbFR5mB+frRhuDl7Vqne+B1rJLQNAfr/tHOqMf/esC9Y44HQBPPcLP8c0ec7lMniYbK1NHhXY4HKmq90IfjEoxkkU3OGA4Fu12bWT28wAUMkT0ztQNwnYp+Q+HOgCvCr464K2SV5nN51drupQ5OQZB25X5sAdCd//xuANy+y2meP1BGwrG+X0yLUX/Yril/2YWv2uPkjm0qv3phWfqi9Rzuo5bv/nZ5kH/+WjWol3Prsi1BEG3Qi8eVeV4vVOU2c/8LnywRxTqLGM48rVoXyNsq+T/Eyo4zmGeOdKlRd/p9TZP5cW5VXZW1d2p7edTZz2O5Y3uXeUJrwiKXvueMPnlV/h+KjjdYXnFTuzadMDteKtgPYR0K0ClSDnqNgwIB+YJstLcgG/qov5dmCKeVPE7WTArSYbVHDJvLs2VTy5gIGdlgoqHanAVQXM+J4n861W7i5hXK87QMu+dQF7/jpwWIFHLAcB7GSlT61OOCCCedXXz1W7HPhBsJur5hhAdmBuKhsHrrJPGHTzRI4L0hRfXRoEBjxucCIC7+czFyh2ASTKlANQZTcnwWcnh+65+ohiF6gnH/yudJbJutgB7Onhtp2rtqLu8le72X/hwWn4d7LizWWvEo4btq8p62wXl8+7kzKdA91OflN/eO7KfkVoh5APfn6kTHWfn7NfRn4c0Kl8vJO585sT/eb6cTdKNRnraGJrmO8V4OVkifUfBYOTdql2rPCtrrH8FVmv8Dotm31NF+urerkPqjyu3k2bHgS62DveVdDBAUhQGu4qndp6qEAg3uPARJXtBnDnPNV1ZTTU6nXec0GbqqMDPh2PyplOAiLlvCaUzhnlnYE3BpwIyJAw7zlOkdszuXduGRMHwX3GYFKlRwfM+Rg45zO3dZZXtBUIWdlOqHjiZ5MAD9uqymQ+I537xXQYMAYlEDwKvo8Ab1cWb0/udEkF7a6vWY4Tnph4/FV6imlQv1DfXGCJfafAn7IRTi/4ueJD5VWHazfba+W/1Hnn6/gDZfz8HODNfcR9gG3julUa1UbWAUfc9jzntjtelY/H5+fIBMddVZ4CTlUaNXb5HH9dnRw3Kbvl9Fe1t9L35Bf5xFeUOlJtdXxMSKVV8nK+2h2TepWMXV2VvJlHlnvHzyTGwLTV+HCxxSVoIlcVp2/a9KDTRYF3F2gk0KoCTeXsj/CjKMuu3sOcGNrVYAK3jDvgneVPAttVcsF7Vw+23wXMVZ3cnnTYqA9B6p3uI86xa8MK/8nPKhDjgEWBUDx34JLTc4ClAAfKUaVRIA3f8cZJkFX9w4CRP7CV/Ku+VEFE1U5uBwI2Bl98jyfxjoLvivjdZjxXq938i+cqaHey5jSY9qgt6QJl7g9MnzoW8uWv5DsZ8DZ9DvjVuFDjhvVb6X73/nfVXmebWF48qaL8pALUahcU570ksc1S/CvZJG9VeVMfmUBbbd3nvBPZV23FsYV9q0BiZw9UfpemK9/xhoS2zelD5d/VdZWex2D6i9UVby7TUWVTHI9Zpkuv9MSlU/YV610de25sTZ9zmimhPKY+t6r7qB9R8u5keF12btOmG7HVHO85Y5m/7mDwMSFlaFyQUFFlaLn8SeCUcsJAA9t4FPQkD9N8K2m5repaEQMgXPHmchSY4j47YoTvpOFmna2oApP5HEEBB4gKdOCqHq9a4/jh9AqQVIFY1yYXsHC6aXkqrzscAA9yW80zD+rZqr1B4v9XXZEKBicBIusJl3eU96ocLk/pm/rf2mkDlVycjcbXFFTdTv953Bw9sGxVL/LrDuTd+URut3rlCNPjRO1R0LPq85KHqDv7JF8Pmuiokhduo+d2cX4lOyVXrGfaRmyDkyenU+1jv+1AHdenzhWPfE+BKpSDiiNU+zqwlXyjreRjSufYo07+rg4eZ27scV5VVxULVjy5drCMXTvzuWuvKofLWhmnlyA1JvnealnXafM2bbpvP66Wv84BsFGptt+hU+4CdWVc2Ikrnlba1bWXt8ZxIMVbytmYVDxdwtgoo6WCHPVMldWlZWAQafIXv5rNOxt4xevcVZ7KWU4c6dE8lZNwgFGds6NWefIZl+8Cow7InkNcBuvY0Xo6sJ11oZ5VadVWc57wOUpYDlLyxve632p8qYmWHDcTUOGIg37XjzmZFjsb3KRPPA/7l5NuCMC5z/B5/uKESac/R1e5+Xk16cX94vqM/Znyb90WdJf+uonbieNFTVixf1X+RO30qtrUyZAPzKfaotro7HTe519Vpmuv4gXLQx6Yn64Nqn9UmybAmmM0JQu12j3das5lOdmoulXc6PIoWXe64/JW/DjelZ/OtMoWKx1z1NlvZ+e7NrqyWK7M85ScH7wUb5s23WjgjYYGwZYyVM5AKke8OsiwbF7dRud0pExVNhr15D3L5/fVMGhRzs7xNTU2LjBYaeMquXwcqAelk47rXDGp+lwFXkfIOct8dm4gWwUQrm8rIOkct3o2LUcFZJPga1XvXB+xHFSAUAVe6nDbyRPIKcrxmSACAR/yPg2IFGHdle7hfQfeXP1ZrgOKHHBinqM6rcAGBuIBvuNQ/5YOA3UGc3ntJj6wL5zuq0moyVhR733zhBWXW4E/7j8V8E/KUEe3e+w6SI1T9n28Cs1BO/LP/lDppmqzmrhwcu3aoWyQkicDo6ocB3gcyDhiV5gXJPX6lmt/da+7j/am+heTE1Lj2vmsiawwXadDvBulKlP1b5eW9UTFwivtOprG6WGl73jd2RqWXyfPipyNnPKyadONA94Y8PJqi3M+XXCBRtmRGqT8jM8rcny7YBafo4EP4n+HUhm9LIfB1iWpq3+a/wghUHKBsAvCgnDS4hweuFy8r9q32mbsfwdWVoCBagMDz26MdP3tgsojeqICVV5ZVvVNyuW2I4hO25O/SdW2c95izsDPBUwd8ZZqZZuSJ97ePgUTCnQquWb78IvyXE5HE1DL209xyzmD7unKN/LIY6oKcpVs1GsVbuXbTdoocMVb4bnv+J4KJqt7ld+5U5TtRt+cslMgOtuBeRToZlLtXQHeVdDf6a/6deMW86uyuC15jW1m+1LxrXwVlqHIPavuOTCWZbmxPSFuA/v6ie9zxDKajCfmSZV5tP7Jfax/NT+WwbYPadLWKn8nA9axyleqmJp5vJOx8KZND8w73miIlUNxxrDail0NOFW2O18NWNC5K6fPaVU7OHA4CmYqOlKek+vESVTpJzLGYDgnI5wDv06DW4EQFyS4e+wgqoCM82A6vo+OmQFWByAmdXd5VvSV82DfOeeuAgV+zgGqky9vN+f7Qd073pPgbKLjWV5FPBHRreApHeT2V0AC803b0RHrh+IpCEE3T5IowKRkzjuoXNtU+52+4PhxW/a57GrLqgvyGXy590yrwwHbO0XMb8qK24Syw2fuS+UsA/adbrVS+eVqrPAvn/PkEtsvtsV4rmyt6lvVzgm5MbviGzsfpO4hn2qcVJNUqly25ViP8xXK/zIpe+H0Q9mbimflo1zezu+q9Ir/fFbx2PlXNxZU/cqOq/OKVKw26bcJn5s2PYh00Xe8O4OiAgr3vttK4I9lY/2rq6Tu4y9dWzjA4HxTB3UpUoaQn0+M/NQgrjjH5CtX4XJHQMXXERA45aNzcJN7WF4lTwdsnfPDcVA5dZVGpe0AmeNnQhMg5OpTQSUHaigLPOd3ut2KNz7nbc5oK7j/VGBXEdfv2odgLn8zrQoQcSxwGV3fK7BTtcWBTybmlfs1ZYxbzNV1HrxlFsGdals3JvJXrXyrFXlnp93EYBXUV+fs61QavndpH7BSDvLAwBfBGbZP/R9y1q0qHnDXmHYKqJy+8BhEveB8q7YW+cExnrZHTeCzveM2qPapeqt+n4AslC+PEZTRRL+UDXdtPofUWKrGZ8Wvs3VVHndf9aMqr4obziG24VW/OX2/Ln42bbqpdLF/JxakAtm8z05YOVd20BPjXoGjI4Ocg4bKwal3uLntnHfqKM9xRiv5zjWEK+AUCUFSOh2UG7+yMJm86No9cW6XcIAV0KqAAgLLIHxP1pVTPcP2rKS/VNu5/zjAxQAv83L9rh1YJq+kch0d6M40vHVYtanTDQQgirAeLJMBieNFycUFw+rcBX55jWW4uhX4STuIK/5RjtpSztcMUlKGWR62odvlgW3onk3eWXW2uQPcygfhfQYgVdm8onwniflU4EVtPcdfJgWau7gA5dBtNVdgw53zwbaSxw0/q0Ao85TpOF7C9JX/cc94nE98XHWN8sR/NckTV6sLCp1dUbxM8nXA2j1zoJjPnc1aoaoN05jDjX+nexNi31PFASyfKp5Z5Ydl3PG6adPVTQfe7FzTWaZBUQFat5Wbt61PA0A2JJgWf5VhRUePW6E5bT7H97f534MldYZixZhOqXIol6DKiFZGU1E6crXipeR5LrGTmwQD57zLPsnjzhVA4GDviP4453oJnVPBK7dHAeNJsKj4xnKQjzyv3u1GHVN8dIGaIhWMToJ9PvC+k0dVppMVt6ezWV2wW4Eg5IOfRSCvttzyfzZIfrK/sM0OgLNcsl/ce954n2WO95N4sqgL7PGZkkd1zRPAXb+oPjyXOluS/o/9YqczHBdUk/FVPiw7z5n3VXvE95Uc1DWDGOXL8ujGjgNpLNOuj6q+c32Uz90W83g2/ao52zhV5xHi8pwsJ+U7nzjlsfLHXV9eIvZQdE65q3EXt30SWyhdnsQtmzbd73TRFe8g5xAR0PI73UjKeK0OSBeEY1qul7eLIy+ZhwMLfnetAkKTtpwDMl1bj4KrI6BjUk7Fa/5i/628LlC17bqcmyrbga8OJEzSr7zTPXWEzmkqUObaPwlyOmCo0mMdGMgmdaveLDuc2OMAvbIb7pqJg9EKDDLYY2Ayqc+Vjc862+fa6frelcdtiIP//RCmyWvMq+rBVfBum6uSAY+Z6t3uzFO1UQEmbr9r1wo4iHsIYI/6h3PAWedn2W860N1tGa9A97kr3lP7m3rGtjbljhM1nVyVP8MdPzz2sW0OqLk+QT7QTk77kfs08+LYVWNook8dL9xnq366G4NVuexPnE9SZXdtrtrSPZ+WfydiHZQPnyt5dDFw5/cnMfSmTfc7Xdv/8e6cKjvnLiBfAQDqvCqHA4cqzUMPPfS4/0WKgf7dIBdwKJ5UUMJluECiAiQVXy696x+ss9u6e4QmgYkKYBwwWK3bgaUsT4FrF/ioZ51T69IiPzgJxc8zjQqeXMDT1d2Nc8cvyybK4QkQt82ceVd8c7sq/vAVAeQ36s5/pYdtZZuJ97h+J48qKGaZTe1UFzhy+5QfyDT4gTUG4pyHt6snKMK63e6PyYF5q3e7VYDMoA95V3JY0R0HGlb6piufaVUXuM0MvJXOOvDsAPilgbcaIyreQLvvvh1RgTTWSeQhykp9x7pxzOMkMy9mXMLHrQA2HLduDE22mq/YUedrqzK6w+XHdqpfrrsaj52/QlvW8ePaPBmnq/EZpmU7XtWnnk99I/6659P7mzbdr3RtK94cTLrt4JmfAy9HaAgrB5zp1JZSBS5wSx+DIV6tT/DNTnTFSHHwXZFKz4ECBgxKXnyuHAOWpUgF80eCAsw3cbYrdSggkPdXrlfrrfhROjK9PwEQVb5KPi4AxV+nnxwoniMrHJ9O/1R/cTswWFYr3wp0d3qI15PgIrdQK/7ZviQIRzuJwbjjh3mrdEONWcWb6nd+xvXmijaWo3hl0I0r39wnHal0SoedTvM7qrzNnG0p20fWl0pXHP9YVtd2FbgfpRXgoPjkawbGPIY6wF39dqAbd9S53XPYNmcb+R7qJI8hfoa/mDeI8yr/jbt18FUKlmEVW7j2TuKKjjK/2zEyLZ/1Ymp/p/HAZAypfFOq6nY2R/FwtP7VfjxSD9s+vncJfar6h8vmMXlu3Zs2PZDveHNwwEEyBzT5HAPMo4ZLAfvkAa8RXGOZOFPvnB9vL2eH6EBBPjtiPCtZVI4PgxIu09WVv6pelhn+qvPO8K84hi6QRVLGm/tl5XrC15SfLgDEdAo8HTmSTwVAWHed/qo88RvgCb9rwMEwllmN6wl1zpdlxnYGg1wMJBSgymumLrhL4o+BVX2T5SIg52C746uqS30LoNJxrvOcgId54W3maus5PuP2ulVvBjhu94cCDhWA4DGU5IBeBSKmdlg9c/11JLie6FQHkhSAZsKdYx3g5lVvB7ircwXksL2VLvA9XNlOXeSx49LwuFYr4FyWsm1ZFvvxSZ9nWS42meZ3W8x5HE3KU/1TjRfOX5XrSMlgxb+r+qq63H2uV43BFf/CejTls/JByj8fsf2VHNxzvuf67ShPmzY9sMA7VoCR0NBwkKKMLBumzpAhsdNWThwHLga6zIMyPnGo97orA6AMxCWDWOSbZegcG8sNg1slU5Q19xn3l2vbiqNTerHqJBUfzEN37fJWwUFHLvhXDpCDNfURqCkgZx7cM8cz8s0gJX4ZOGWwuOL8u3HighfuwyqwxpWlrGM18JvqZMoFA2fFE7YDJwXUVvNpMOpeUUCgoIIgBVaUfFVbuQx3zwFt3G5eEYMUtZ33yDZzp2sK/PPErQqWq3Pn+1RbK38xCdAr4vHj/AS2jwExty+vedu5A97T55hmcq3a6mwDXytbjL5epcExjGOHt6az7eSV8iwLX0XhfnD+iPtRycDRiq1WvqgjHu/dwfkm5bv2In888crPJ2XzdaVXLj5yPuwcOlrOSizg8nUxxaT8Sd08VjZtup/pYsCbjR07c3aSmEYFe0guOHHn1Qo4pslr5UQ4COCPwk2M9hEDgU6YnXkFarh92G52OOmEmDgIcE4C03DZWJYqW5U3kclK2qruyfXRerE8PHfBPwdqFeiegAnnBNlRKr6q9G51MOWmdCnbNQmgWN+qdFku8sGyd6BbAYwKRPD5CuBB8JbjRKXh1e78nfCoyqsOlmNnn5xuuLwsH9ahoNyejm10/3KM5YQAROlxN1bUNnPXbm6PAnzcbqczLCOXbwV4uDHHNNVXBYDURDaWybqq/q2m2j6OspxsQVfxg3ue/LF+4LmzgegXq/xZB7+moL4tweOOJ8GcTnMa5/vRZhwh5QuVbLAdk5VuxaeKT1TfTWIc1xbmE/2T29KvyndpXHzRxRwVqXRH+1Tx39l5zqf8qyqjKteNmykfXd2bNt3PdLGt5jy4XJC7ErwGuUHHdTnnrla8MR86b3ynD8usPrymnBY+mxo9lB+CnOTPfbwG83PAxPwhn5XzdIaSAwPl9I8a/nOCB8VjVz62YYWc/Ct+HDDIvNU7dLwtVqVVv1Vw4PrWrSK6MpNw1RJfO8n2sdxdQHfU0VdgK/l3wV3XnytBoeIfAb/jL9O71W5lt7DtHLwrANoFlMy/Ai/K5nZ5M3/KIXXFrX5z+1ybO31WaXmcYF7uI8WHA3yOV6c36nyqV6q8I4Q64drl2ony4e3hDmx328u5vg5gd1vNWTdwPOY13nPpHYBI+bl3uZWs1LvjbAPiyHgqf3E3jOoLnoThsVuRiw9Uu3n8TLeau2vuM6fTE11ne5s6kvdwwUTxxHKq7C/W191zbVhpk7vveM7zia1fTVvxWvl0TIfpHT8u36ZNDwJd7KvmPHi7gBHvIVDkgK2rjx04vneK5WNa5gvzMPjmoKCTw6qx4yAQgwAG/bzVkYMnJXfkiet0wTgHA5hGARrVZg4GVL+eGzwqyjLde6LqXnXd8emeMQBSW/RS3xSw7s7za8yqbJa1kj07Ste/09VBVR6uIFUyVPrDeoX9grrpABnrqBonji8VHPJ51xbkNY9q1ZvtmatXjWuuj/VAgY0qyGUg4PpYtVu1D/sBt5znL4Mo/pdsCFpwRbJqi+M75eG2yrKtZPlW5PS4Ou+CVLTFeW/qZxzQyGd5323t5nSc14Funix25R8B3NV95lXZPvZpyr/xL59znKCAO9bJv2j3Vfl4jr4/83L70cZNfSrLguWiJno7G+CI+XL2rPKl7pnbvZf58Lzy7ZMxpXySk0PnVxxVba346+pb6av8zWOaV/FbjbUqz7l1b9p0o/6dWPWr8qEjUQDB5XEDVaXBgI35yoPBtwsCVg0BOzfkWwEl5gkP5VwnDq0KhJW8lYFkA6ref2Nw5vjksi9Jqo8UQFP8dIFL1R4shwOXaqu2S3fkUIEAB0rT59yOCSWoYnDk5O2C2op4rLhyUMerwG/Sx5Pg0PGH91iOGVjnr7JLqm6nu0lKFxzIULZJBadqTHF5SseR73yONhaBuLLtzAc/c+1iPlkvVD9VxH1ZpVm5X9nzlbJcugpopF9TALjj14FrdagPramy+JiAcHUkVfru/Jjya8q2qGf4m/LCSa+8Vj4A76l/4YXyRF/l5MOk9LYaX5gmD5zsZT/iqLK7Tkex3gnxpByOVacb0zE/5c3ZEzX+pmPYle/qW4mxXDzkYtFzdGsl1ss6lG5eV8y4adN9Dbzx2jlBF1xOjXjlXDm4UvkyL69gp1PDbbM424w8Vvy6Z9hudKbYDjTo/O/LMGjBclRgzm1m2avVHtU2F7ikE+bn/D9xOydzHYbUOWyWwQTkIU2ctgry1ZHkAHTKUa10K5DdgeSqLAVI1ITQhFIvUFcxkEXd53wsa9U/bvxhAN2N+7zPVN3D/J3OqLagzjF/3F6uS92bEOpCNZ4d36pNCnhgGnym7KBa7UadV3JDeYUtzGsFYJgP5AfzdavdfM3glMnZ3a58zrtCq6CHeUPA5trG/sVtIVdAu9uCXun6FJh3wEDpJ56jbcKVU5U2dVE9Q1mlnvF51sE6jfpZ+YzMq/6ThLIfq/YCeen814p/6PxsNU6qGGtyz+Xt6nTjRuWZjsPVfqj02NWh7qHf5fuYj+02p1E2ruLf8XsdMd+mTTf6HW8XbOI9BQinASCTAh0cxKWjmzpsfI9bBQpssBTPytBxu3mWHZ20+5J61olAhkkFMswHz+h38k2e0elyG1VQgXVW5Xf3jwQP3TU7GOdYlAxXdJFBbsronFVtd4/BRlICUk43LQf7v+uLSIMrmCgbzqt4RblPbQGOIwykqyC0a0elDxVhAK/KSt64rY7X1UBaBc+YRwX9Kuh0tk5Rxx8CD0yTelnZZywD0zEYwDZz+6tAtpMjt4/vVf2kdEiNgapeZx+7XShqh5cDtDwBjPy67eAd6FbbzHnLuZMf143nLm0n3wno5jSpc52O8TnLESd6uH9xhxCny8nt5C/LyvQT2U5shxsPqk1q3E2oS7viG/je5Oh4U3VN8lZtq2wGl++AsKrnSGykbEneY9vItn8S86r7Snf4fIVWbfimTQ808A5ywSYbBvc+zopx5gHtQDfny3Pn/JG/NEC8xaviC3mrDCLOUuM2KawzVrn5K7FcxpQXxTf3j0sT95M3B1SDUvYTPUBjrpz4UcPMQTY7EW6va0uS2unA9XHdvJKsVthYZztwjQFYtwKu+FNOng8sF2WV7cftjxzUs5N24EelwXRMFfDoQMk08OzG7iRw5fqr8ZZpcKw4PqeBM/PPv2olr+KVbZ3SKx5Tabd4ApHbr1a9MT9/4Zzzo06yrlVycGNRtc9Rp0edfjlyelzZr4542y37PbUtF68ZyDEAr1a4+WOkFThkHlkfnL92eZR8lI9BXVUAnK/5WdTDQJlXzavfIAb8ec/t1EDwjb9qVwHLD8nJyF2jf2B/cY5/dmlQFtgO5L/SBZem0o2KjuZzeStS8pnEnV2ZVRlsX5IH52OndXLdnf9R41Txq+rZtOlGAm80GDw48P7RoIcJgy8G3WxI1L+r4S1byGOu4KfRx1XwbuArQ6YcHwdEWF/8JvB2ASjL1fHkDF3VJxiMMRhDXvlX3av4VobWGeKpk1MgT8mm4hXLUAEslsM8KmDMwUQFoB0Qn24/V6R4dAAf+zn5zXw8vnKsKKDmAJHTVeSPdcUBE/UMgzVXp9NFlaYCW4owmGfiMhikujo7u1rJPQN0TINpOdBXY1ONQ3WPx5Di1ek8f2iNJ4AUoO94dLJZDYZdm7htfN3Vg3aT24aEE0mZrrOHalJHgRiUCYM1d69a6eZzlU8BIZaBqn8CrNSYQL/gQHXKzOk5nqtxVv2i/qpxwbsXMC2WoyYUcwJBrX4r/zzRTdde1/aOlA12dbJs+LrTZ9dGZ5vYXnRyqeIVR25cYxkuHnHxC+bjc8VvFRtMScnJ2Vr+rerpnnOdmG9iCzdteiCBtzIYlcE7MlBcwB5O59FHH5VBbdzjbefdgXVxAN/xxYGdcwyYPq9xdSC3l7t6OudZGUW+3/WFqqNyiF1edV/xw0HSCjkgoK7ZeOMkTdaPwJKD/iBe4UZwoYIntZLNz7prvDcBHWoVPolBe7YR+eSvqKNu87/Zy2AywVTn/Dv9rYIQdrzZX8kj6wHXU42VaaDKPGFg79qWxME0p3NBpAtmVWDmAhbWoZU2qvJUAKx27DDQztX/HHu88s3y4zGoQI0LklfaifV3QaTqJ+UPmBzPDLZXCWXuVrddP7lrvs8+q1rtVuU4f1GBbpRl5cNZpko+OE4rcOl8EoJeHFNqjKn+Vtd4X+1kQv3N8hl8p+7gpKha/e78sDsquap2oA9QfePsk6IqXqt2UzjeFF8r43Q1bpq2tdMZ5SO7/sC+6+pS/Y1pXF9y+irdUf06ahM3bbpX6Am3ptHwpk2bNm3atGnTpk2bNm3atGmZ/AvXmzZt2rRp06ZNmzZt2rRp06azaQPvTZs2bdq0adOmTZs2bdq06RppA+9NmzZt2rRp06ZNmzZt2rTpGmkD702bNm3atGnTpk2bNm3atOkaaQPvTZs2bdq0adOmTZs2bdq06RppA+9NmzZt2rRp06ZNmzZt2rTpGmkD702bNm3atGnTpk2bNm3atOka6aFLFPLMZz7z9PvEJz7x9M/t4zeOJz3pSbfP81kcQZhOHfkcfzNfEj7DspHi35TngenywLIz/Ute8pLbeZg4P95T5+oXz7mOvFZtUYR8Yll8D+UQ7ctDXb/4xS++fZ3nfA/vqzQqD9aH/Lg2cBu/4Au+oJTFc5/73MfJja+5LqwP+45lyQe2szpcnzGPPB7yOon7qpK16jfuLz6Q3xwrimdsv+qroGxDpHn00Udv18Gyi2ePPPLI6ch0+It58ZnSJ9bneM78Jw+sF2gH0mbFr7Mpv/iLv3jl6Au/8Asfp+Os94pv11/dwTJSY9PJlO2csh+ol09+8pOvHnroocfZdWfn1cGyr8bEKimb58rH+5UNCj5wHON5jhM1XpQdyfHMdoH9j/Nr7CM/7/M+z8riO77jO0rfqvoleVHtYVK+rKPKJyn7quws3mNfhffZ56fcnc1Mfrox6c5Rt7N9IevkYzKeWV9VmzCd81cq7uBz9jPVuOX+w7Kf9axnlX3+Az/wAzZemsRH1bm6rghlpfTExXuoD1Wcwvmwvmm+rjzubxdjVFTZwkk+pX/MI/sF9K0uvlfPMA6q/AbGSps23Qjg7Rw2O1M3SKdB2iRg42cOQCPPVXkdEHY8dfc7Qzw10EeAdwU+XfoqrcqngqSKj+siFxQ7AKbSqDLVPW5T5cy5nCqgULLjehV/Ux3gNjtdV8GfokrHK7135TtekV+8drw7Xl0AqOTQBXoc0KnAuZowWBkT03QqAF8lFUQmBd8pe+63zp6rfl1p21T3XZmsb52Ny/TIqwtAMS23vbtOMMeAbtVuVgDb1c/3K5uwQmxfsE3VhIYa3+q54h3r5uAc4xIsKwGz+8W8XA7ygLyquvk+g2oEHyyjIFemG2usr2pChtOyDKvnihA0HYnzJnHgNC6MdCxjVQb3JffRCthVfvwIoY6h3HByZ4Wno7yg7JwPVpM6k4W1aiLI8X4Ju7Rp030JvHMgoIFaMbIuDa+SV3W7Z84oqQF8J0ChC/4Uf9VzfFaBL07jgkzV7gqwTcpwz6r2n+MUJlTpktMJpEpe0/rY8atx0QW7rs8d8JjIVIEg1qMV0O30ZEJOtspeTMd3Ra6fVh27At4rK3jVcYSOBiYoP8VLgm1sdzV+EOBUPK7qaTc+8b6zPUr2+Bx9g5NVd67Ssx1QbVEgdaoLXf143tnF1bEx6Wuk3CHDPGU9Lkbg627MM0DI9KkDCGiyne43j0ibO2yq9qHscPcDgrvUR+SXwWvyqVb5JgCWAZLajaj6D2UztStY16T/FO+TdjFVcQaONxVfKj2K1VouS/lW1x5X9iplvaqec+OmijdsL/cb960C2KxnamUb07J+T/p806Ybt+KdxIasMraVMZ0MLg7m0HE6wMF5zjVaVQCCdbngxZXlAkpVBhtFFZgqEMTgoAr4XTmKj6p8xfOlaGqQue9VGdfBn6rDPat0ysmdn1d9h/lXaCV918/OJkxsBN6r8mC6DO6n4O6Ig88AnHW/WuXuQDjy1N1XNLWnFblxjUFp8u1WzxwQcLZa8dC1OctzcuL83D/M55Hxmc95cqLyB13/ZLn4ykZFq/09afuUOhk5yvHpdAcPlqEa+0o3MC0C7yAGvBj8p8x59ZvvY36WKYN5pMpmc/485zHkxhfLGIF8tdLd2ecJda83KPtQjY0pAHMxSpWP5cn6gK8fuQUMbo/SCb5/hFR93N6KVnjg9vHWb9SjySq3A+FOPzZtetDoIsC7C5yVoZ4YYWVUuBw2ZJUxcYEP3j8KuLhs9ZvtqsDPKjDCoFHJRpXvgmgVsK6kd8GsclAVZTvOdU4sh0tRJ6MpVQGF6kvVjxN+3HMuvxpTHIh0bXL18q+zFbwCthpcqHHmbImSBT6bBntBrPM8FhI4qS3mq6veXT/y9Uo7Mo2yjTzWMXjCe5if63a2SZHSWzWB4nSP9S0J+0DphgMA06BwAlDUc6WbKLN8X/gSpPxGdb/ie0IrMkMdUunYfuB9rIvHAK42Ixh2/cpAW4HvLA/zq/GG9XMdyIebfEPwp/SR2678SrX9G3lhIIVlTVe9HfDm+h3PTo58zuNE2S5VNlO2F/sBn6lvfzgb3dV7blyi/OhKuV1656czL+oH64p7V9s9U9dHbcymTTfqHe/qWC3LkQokK0PjHA/+qjznGEVl+KdGrrun0qgAXJWhnMLk4Pz4OwELFXi4G7TihDtg0OnepL1VIFLV6Ry9Ax2cR4Ef5AfLXQ30XR1Kd7BethcOAOHYqoKnTv5qjE6eOVLjQa1mu3tqBVb1Gcu5ayPKxtFU3zgYxWveUqvKVNcuHdfNMsWxzNt1nc3hulTwz+k5bSdHLrcqE2XAdNReun7s9JmD+EtSVy7WzcE+H9jvbC8VgEQe3PvRQbhl3L3jre5lGZVvqXQhJ6twIkjpP/Km2qXkouTv3qFF2STQxFXy5I1X+B3xRIEDb9wOxXtFqgwk7hc3BrKt2Db1bj3rD9ptvO74PjcewjpSlmr3ztRPqHjH9Zt7J1utdKceVMD7uuzOpk0P9Io3nt+JQbQSECtj4XicGkMOZJTTz7pd8FsBu4oPFZB2aSf1qUMFsXi+Ar5VvSoIRblNABSmn8ghr7tA0OWdlJ88TZ5Px4wLqFm2DrC4dFV9CuSovMh/1WcVby7PZJyz/PDdS3xeyRCPKnB1hB/fUV8j7oD3dBxV46kbLwxU+JmyVXEvvohe1YdywxVwLEfxMrVD+Iy3XCMQcavhq33KIA3HqFopVddVuSjLFaA2JdUv+Jt1qDZcon6kqizlQyuf6crgfK4M3NKO/Rlp8xn+5n38DVK/6mN6yI/6Dg6CbgRO7H+qcqbAFfUdxwyXjXJJ8N3Zf0WTbcSKZ9eOjpQt4TLdGMW2J+9YjrMfmZb1RU2eOjs0keWkzajL55bp+otjaLed3D1jHXP6sWnTg0oXBd7VYMp0+OuoM1Kc1pXJdasPN6w4kY5n5egrw68ACPOiAkjMg2lcXSp9B36mQCADjVWQ0AGDo/0x6U8n0wm5gKgDMFxPFVROnI+S55FDyQPrUO89Kj6UjJy+dKu8lb64ABvHOd9HHvndTCVnVWdnh5IQEKL8KuCtfnkMur5mmSe/5wIhRQm+U4b5bjf3F4KIFdvP5UQZ6p1m1ssgfp/aBfHTYF/5NH6m5MblqX6pbDjzxO08ahdVGUfBzVE9cjJLneFfHutTPjuAFeS2e2Me1IG4r973zmv1q9qNsQfrNPKsfErqgtqpN9H95BkBFNtKXqHmd8JdnY7SRlRjz/GL9zrbxs+VbDs9wvGeNiav1WQI18PjH30dpmc+8N51jOnq+xXK36IcuG2sF0cBt9pxoXRk06YHkS76cTUenEeCO6QVZ9vxxbO4KoDPOic8OAOKbWOjzA4g0ygwpHhx/CkeXVDugJcCSO662k7bHUoeHR1xSl0e5eyc03b90PGkyuNneN3pugvwnKxX+6RqH49p5gEDSscTBiLV/8TF/3mb+Zx8OWCfEI+/vJfXLkCblJ+rwhjs4OTUFHBPxhzLWfXbqjy6dNg2FZxiwKb8gdJz1ZbUBdf3VaCpeJ4E9/mrgkouU40F1T62z8rvqFcu8BzlMvWHEz/RlbdSX0VKzs4mdmOZ9YjTKZCmZKvsFQJRtyLO+bB8Hu/cdm4jvhPOq93Md+apYqtKr1l22OYg3lKu0nN7J6TKWB2TfJ7Xle92ulHZeOaVV47RDk8Iv4S+ku8ooT0OQn3CNqAcmCeeZFF2i+3jBIBzfqXLl7A1mzbdqBVv984QO3o104zUBbxucDqQoAwG8qvy57VyfHjOQUIVVCBvmK8DQ1x21W4lP+U0VPkTgLa6RbajibxWSDnj6tr1oyP1fNLerp1VYOrKd8EZ51F91/GsApaVAIzLx/oDmCLAjus8R8CtAoZVwvxVEIdpFdBZASC84o28OyBdTXJV48vV05ECdhP9d6vfeUTbOdAKcq/3ZL1q1wzqwEpApgJG1271i+1JMKKCelcWP0M9Ummc7LlfjowHfvdejYdKvkcCYQWQ3H3HS/6u1q/Gatc+jA0iLQLsvMaykLgv8DnuwOj0jreaK9uc/c8TW+qX5ar4yLJQxyuZT22/yjeRQ3Xu7rlxo/ir9NClwb5AOzUB0ahXSef4Mkcc3+Q9ZYPw2slX6QC2BePoCQBnu68O5J3PN216EOliwJuvVQBSBbEcoFTpVJ0Vb3w40F2BHOV4uD2VQXVlTAPpLgB1fHQBuzLKfExW5/B+JUtF7EBdgDMhLmvSNyvE7XM8qDqr/quCHidTpzN8XQHuTi5qxa/Lo9qtAgFVDgfCDFi4nk42VfluNZXrULbCEW+NVvx146dKpya+XFsnwSGPlc72qUCJV/fV9mwG5K6vnP1TvKvgrQvYK18Q9Sng7eTRycVdI59dW7Ov8XpiV9E/cT68r3wy98+5gXAF5pStZv4m9at2sE/kNAoYpQ4wX5g2y3BbzVX9SLwiiWnxWrVPjS1O5+pGuaZ+V2VhO5BvxZ8jLnsCtLox3N2vdNuNXywPbRSPhaQJ+D4ac6gxsZpPtaUiFbNXoDt1hz+a5sA32+iJrd606UGla/l3YnkvyQWFaNTYqLPzVfkdL3xeDfYJkFJ8u3vnBER8VPVU7cb07qhWqauA2JXj6pzI9ugzJQOWLz9T5TsH7fjoAAGW2+Vh3XSOUvWNeub4dH1dtYHHTwUSKgefz3DrHZMa66lfnTN2+svnKuBWk0V4vhoIJPB25eW124pejc3p82rcKupsLabrguQoJ2TAusPvfGN6/K3KZj44qKvy49jKfBk0ogxUcIljWslJ1VPxw2kq24I8Bbl3TZlQf50tUs9cWRVQUvereEC1syPny6s6FP8VsMBxkhNzee54x+eYt9ty7t4pZjvLY3PaZm43knqFYmpjnRwqUv5E1Xsp0MU+WPkAdU+VgdRNPHT+3vnHilZ9ELdLxb3KjjnbjjpagWsFwDGv4ukS7d206X6ka/t3YorYoWA+FyRw+o6maSuwggcbrEnb8HfSNlUvl4G/SZ3T7YJ23NaLK2iOLyc3F/Rjenft5DhJq6gLNCte3LXiQ7V1CmA4kFLbc7OsqSyrvnDlcHkclEzGcjfmsWz1mgneT8CGQagCPqpNFY+qTvzlslH2q/o7HUOT8TlJ16XviO1Tla4DlZ19mwK2ige34sdldGOsCwzVmFTjfVVG7rkb56wzvBo7JefTsA4FSq4rOFZ6h/cqkMnPEiRPwKPiI/Pzdnz+oBmXg/2h7BjzpfTc2Xm+djKp2sp1ZDvUe9x47uyHmzyryI1TF9NMyuh8rPNr+LyyOdzGqo+wbzAtpsEy4+BY6xxSdpttLk8qYZs73WR7iN8CmGwzV7JUhH1yCfuyadON+bjakUHEwa4yci6oy/xInLYy8JNAVZXl2ql+O/m4NjieO1JGuAru1bva7n1C5VCU/I6AABd4TAFEBSJcQMm6NgmIV/ip+Mxf96GRLhBzfcF8un6o9LQbM/xMBUQqLzpxXP3OABY/NlSND6fLTgaqLVWgdE4QwFtFu3HC7w2q7eM8VivQ7eQ00V0eN9yfLoBywZxKW/VDdZ+DWwzwqvZgegbTWA7n4a87s0wrfpX+V/7I9Rs/V3ruiPucARXaylUwxfXwWOEyXdmob87HYz9hu/B+AkL1+gi31fGB5Slbi8/wN9NgXbjirV6ZUXVzHyHhtw7Uqz+OmC+WJfcV9qOyLTxOVvxy9TvJW5XHbeW06l51zf3t0vE3FFQsiO/vcxnutQKmiodJPjU2JnZ35Z1u9253Vx/ze9QObdp044C3+3hCZ+SU08ZfzscB3qrhx3rZOE4DVGUYjgZGlVNR96ftVbysvE+qVsFVu1xwyPUzXxXPivcV4iCrcroq4OXr7nD1d/fZQeG4cW12AbnjlfOoZ45cUFPJswoqK+rk7trnjulHuTjYVPUw/9O2uPHQjUPXj2o8TvKpPuv6nfWUn3NapwNd3i4d9s9ktRr55me8dZx9lAo4OR3Xw21wfm8KOCrdyd8jPgB1GZ91vpnL6vIrqvpZ2WnUKT5X10pPqm83OPlV24hjBVvxz+lV2Y4XpTvOvmO7ETQ7frm/UV5Kt3G7e9ok3Gqv6ln1y5lnJY7hmI/HXWWnsC4Xs1W6yfezLJS/2znAY46/Xo/Erxd05GxPZeOxj6uV9nNBt7LLExuxadNNo2sD3hNjluQCDZe3cgDOIbHRnwStE5o426lhXTHAK/y5dikQsBLgZxlYHt/j+tS542mV2NFynyvnjXU6fiue0MGqepg/PEfHtuJInfwdb12/MW9HCYNi99zdq8BFla/LW40pDHhVOWqb6YRceTzWsA6e+FL5eLW7G6duMo3lpMA2jqMJqFSynZArC+vE9wfZz6iAN4i/SI7lYPmqPhU8Vu3p5OR+p+NgxT9ieQim+Oj6x/noKTlgrMYj88mgJoEgt42BQuo5nq/w68Y5loXppv2S+av+Zz2uvmjOslS6wrocpCZ4VXu4DmVnVvwF+8UJcFZlVPnYr+MEzMQXKx4cT2FHKjkof+v4SOKP7HXpXXuU71Svban8yv45YF2tcqt6JvpyiRhk06Yb+XE1vOY0QZUxynM2FLxdK8tTzgYDDny3hR2+ClKZv0mbVHDNVBnoqszqnqKKFxeMK8etgvUJGEceuF7F5zT9CqGesT5gmir4q+TpnGrFh3qm3oFCvlyfT+9VaSpZYFC2UnYHTjCtC1iVvnbtcDrIcnRjzQW8qDddnyj+qjGlxp4CyaqNDLzV82o7+io5gLpahrp2wS/afbdFXAFc7qsqH+d1aVTaqg0r6ZnUeJiMFVfWxDcxOR2fBs6dbLksxRemw23bHA8kuGLQjRNmed/5ggn4Rjkq3hm4VvkreaqxhXlTn/kZyyzTOjCFYwTLQdlmGfGbK/5sTyak7O70Hstmpb44EiB371OrcZplqUkJ9YtplE+KdOo1CCR8jvLoYpepTDi/aguDa7bD+I4365WaGHJ0ib7etOl+pYsC7zxXz9V5BQjSWOA7XHifjQ8Hx/irynaHa5+6VxlaR8p5Kxk5uag28z3HFwfnrv2VfFQw3zm1qo/dvSOkdKQKuFQ/MF8rutL1K6Zj54bPpgHcVKe5PRXfGKgd1UHX7o7H6Riq+KjyquCuaqv6nYxvpz9u0kuBZP5IW5duuvLtiO2Zes7AYDVAYiDGY0QFdHhkGscP887BoFvFVoGnAosu6K6ededT28hB+WSMKH2/jmAX61Fyq7aeurHPK8QKRKoPoqkxw+/qKv5RDm5FO/0m8uDkiL9qUp/TYz1Kn+IZ+wosQ40pV45qP5bN/tLJe2qn2Q9zW6+LWAbKH2JadZ6yceWr60qfq1caMt3EZ03aXvHZ2T4Fpt3z7tWcireVNm3a9KDQtax44/2K2PDwSnfnRFxw7wwt1tkF/BXvVbDn+GG+qvLPBR7XeXQrbkf4vRSxnLN81iW+5/hz4EUFT5N7eV8dWK/i41zqylDj62hZ7rnSlSoYcnwiv8peKHIBcxWc8tid9AOuDk3GkhpP1U4Ut8rtyuMt7dVY5TY6Pe1ApRoLLGt+7la21X3mrWpDUJWfy+pW1is/VPknN6aYXx4H+YtgcNUerKZXdrQqr5JTZeuwPfzhJ1Ue7nxjAIMfscpfBRSVrLmfJr5ZASjMh6+QMJ9u7Cn5VuOvegXGxUb8nO0ny4m32K9s3+e6J3q1+mySB2Wn7HBSNV4rH+NskBqr+NpE5SfdtbOxFS/quhqf1fZxB7TdaxAdf+7epk0PMl3L//FeHUjsDHnwVkFQnrsg2wX1Kqg9Ql0ZjkcFEM+tH/moAvXrPlZ572Swok9dWhU0qnorGVb1OieJejCZIe62pSmdPkpHx24ly4rfib5col2qTLYpfI5p88B0HV8MvLEtanv59FUOlV+VxeWy/k5pam/VPVyVRmLwiAAitzGqgG6yCq34wb51r3YgDyqQxGdODu5XyUDxmjKZnq+AnwqYHE3vnlfBuOq/CvTwFnNccc37/K4tbynPexM/pWyZ23bO4Cn11k0ecHtZ/yfyzrzOTndjm2XNZVQyUTKb6pTSJ7yn4iD37BzKsrBP0UYmqT6J59UkB5Lil/N2vpvlzf0/JWenedcQA2tM51a8+VrVy/7SpTuXLqknmzbdl1vNJ8QGRQW97EAdOHD14nteCMSxfnYsXZnMv5JDZcQvaWj4WjnJVdCttre68lwadf8onSsvFeBV/aR4reQ1rZOdngMCnEcF2I7HI8Rgwm03zL6v6ryEbncyrepyzp7TV0EC8zAJ0Jk4kMMx4baIK+CttqeqZwyuVTmKHyUnF9xXdlelxftBuNVXBZABXPjdQTdeuGzVh/kc61LjjYNM52ucPKq6J9SBJ3fdbZ3u6sH7DuS4PCqd6qduVQztC24rx7oRCPCOFezf+I0JL952nmVMfCG3F+MG5AEBdzwLvVV22t1Lu+58iNIJNf5UPh5b2C6Wb7flmfOFfDvb2BHy485X0ql2TXlAGVTxn9L1zFMR6jnrUeeflK7imOnI2Sbkyx1ToF3ZR6d3qr3n0Dmx5aZNDwTwxt8kZSiqe5cIJrCMNGBqZq6ra2LklPNRjuGooVH8uqAeeVEra1XQwWkn6d02c75mXlfbfa68VHkOfFQ66AI654hV2ejAFBBQ5VSkdOFo8K3AhZPhUYd3SUfp+tTVUQVo2Se5Ws3l4epap5sIivO3Gj/d6vTqmFVAHsthGTmdRz1wwafSGadPKRsFcBLA4Io3ludWqx2vmE4BNk7jVtWrtqjgVulVF/zz/er6HMDjZHbE9vI4mMitsi/ZNzxZjkA607AM1Ll6Hz7yq4kt5IXlynHDQw89dNtGxK/r8+pQ4FvpwxEfOMmD44B9Nfs09lmPPvqoXCXuiP0k+0zWBUynxhm3dVVWWJf7ABvXo3SQy3PkvhmQhNv5cdeGOpA/548q28R2r9LVbvJM6X9HR/R606YHjS4CvLsgQ6WZGG7l3Ktth6pe5dgmTkC1xwVGUyfEdXVUBfqTwHwVcHfPsT7Fj3s3tZKH0w8ltxXCfnTAorqveFNtZ31x7UndnTquSd/zeUWurS4wZsogmHePcHkVrTxXgSjeWxlzU1uD4BvrmepzkBsD1fjK1SQXaHVjVm1Zd69HuHaw3qrg29nd7siy+eNQaNOn4Bf7xvHA97B+BlPVCi3z7H67eyxnpds83jsftAJ8uJzVscugiPOxb1a7FNy2VGdjGIgjH2pc4DX2BYMqLF+ND9VeXh1mn4g7J9xrAKyPmZZ968TXVf3BdXLdbNc4nbN30caYeAhe0V5NiX1kp+vVeGaep/U7nnBHA/PAdUQe3nHBzxWpXT+KnM4qv+Bk0NlFfDadcFTjfCp7xeOmTTeZLrrizeeOKkDE5WBgpgzFpF40YJyu4sEBKr52DtPxhG3vnK1zGFVg7la8jwJxl796N5Xl3snayamTpZPrJcmVywHLNK0LhJIm8uv0QvXHCj+VLFS7VZouUFdpuiBG6RffV3W6sazkgAE86nqsjnTUvYddXVfjpxpzbpt5pQcqyHbBWdV33YHEwHu6lZHzqoDc8YcADu9lsO22VqryWGdYLtXzJKV7Ko/T5SO29Ny0PCbwPsuuC+SxPC6r8h15zr5TAZQcqzkeFOjJdKqdfC+IgXqmc++gcxk4YZntwPsOsCMPrq9Y//C++t6CsgWZlu0IlsfvtV8XqTHM+sNtUradSfkIJSvUTVVXPu9e+cB+dn6L25tyR13kRQ+no+qeG39Oxu55NZYre+d0d9Omm0zXvuKN5IJfJv74g3Pu03pVUDM1CC74r66VMerq6Jwr33OBugPH7przT4GBe0+tAxGuTRWtyFMFT4oU78ib4ndaNqer9HfKY8WXa8uET+dMuS0MXhz/CoSoPEo+eK3ud2Ox6p+pncDAWAX6Ha1MbqkxlfV0Y9Klm/KJ6ar+UsHYZJUEg1i3atwB3WlavGbfgX2Sz6uJ3KrdKCPWGyVHRdOg1On6xH8eoc4nqmerOoLlKBujxlpl71RavhdlKhCpVr1VHuRdrZJjHkyHbUNwjb958PbiI/GMuj+xXW7cpow4f/dueMdrxbPipYr5Jjad6+QDy+BXGar6WDdc21lGDNaVPigeO/te2aTOxk5tX5dW8XGdpOS7adONAt5T6gYKB05VoMSkgpKVoCjzsoGt+Fd14f0qAFNGtaLVIH5yjwMJ5H2lLlc2y2jVUKrAd5KHA7lVmTpeXZmse+zQnaOakNK5Cnx1bVHOsguIqzyKOqfu0lXkeFB9PR1XPN6xTE43KU+9R+q2n6+MlSqvek8c8yu+ud3cfndMPp6lPkCl7K+aYMXnfG8CkhXwxg9loU+Zlsnlsbwmz6f3sX/4HG3M0QB3xfdV9zuZVbLs+HHbchkQVXqev27FO8d4jhu8F4cCSKkvWJZ6Nx3bzWA7+Vdgi8fvSv8pu8T3VB+qc/UuPZanVvWPkNIZ3oHC8d+59QW513CYL9Q1pcNuXKjnqFu4M8Lp5ZG4RF1XY3IyXt09dV7xs2nTpmsA3p2RnwCuygirwGYS1Ljzjv8qjWorG85pYIW/HX8q4GajPN1mrsAy31P1ViDbraQ7+U4cSN47YsirPu0cW+eYu/KRbxVQsINWvOGzTqaV/DmATF2rxpZKPxkj3G68rtKrc9X+1SC+Ih5zGFjjs04fkCbjTLWreqbSVeO6ai/Liu9NDgVceUI0ylVBswsI8Z66z3UGVVvTmZfU3+mKt+OV5efa0Omneq7sAN+fjkU39lQdDiwoXqY64tpYtZ+JwS+PBwVeGGhHPbxyi/eSX0yjZMV2m+vH+pD/rk6uS6VlnqYxTUXcR9zW3Eru5Ndts+Z6HN9qDOLYxNdCVLmrxP2m+h15cz5nUr8aVywPtRuisvuVzKf2qbOZLi3LxsnhaN8coYkt3LTpgQTeneI7sOCCMA4WKurSVYbCGTJ2MhzsOCCDztcFMFxPJ5sq6F5d6VbB+vS90OoZy0yBCCVf11d4vaoPXL4Kpro2qbYpniujPw1OuQ7mDdvgAvFzHPU0gMDAgZ061uWen0vO+eNx3Q64k2eQGlvVhBTnU3V1k2lYD+bHXyS2VXi/OtQKVPzy18hRD3C1u+tD5I2fc70qYHdBYlB8EIpXvfnc8TP9rQLzIwGqGtso40sR64LTGTzvZF4F5oq4/7sxgfZGrSrjWOA0WAa+o411JoDHcvAdaJ6g477Jc5WObaniL89V/6g+OULcR8gbA3AFvlfe867GvZoE40m2c9vKfGQbsH8mfHe8uDGEebhO1uMqBlNlO77UmMVzNXa7a9f+S/XPpk03ga4NeFfgB6kyBhzMcJ7pvS4I6Ayaao8ysMrxurIqcIrBh8pzzrbyaqWsqkPJoONrhZy8Vg2647XiqZNDx7fSgSrAZ71Wfa146+Ts+kzplwMMrm2qnV0AUo1hJz+W4yqdC0yQZzXmjwJv902ELLvry2r8VqB7RQ6VvVWBsvrf20gIyJ2+YXl5rerOerLOIB5bmF+Vk/8CarraXQWdqj0qveLF3XN2QPXnUf2u8uJY76jy1xUAmFCOs8ynVpZT7/M53+dvDGD78jfTZX0JfLJMBEJIaks5txH5zzJ4nHN+tDHIC7a96vepnLt0rAc5jo98zZzrdbbE+cmVdq3ygueqb6p8VSyJvDvfhnqV9cY99XoSljORfWWDKns8sX+qnk2bNt0F4I3vSCWpoPVSg5bL687ZgLjglw0wBwDcLkedUeZ7XVs7YFjdd8E6A4MO3HXvqbprlsvUabMDXnH2jh+WqZKvCpCYL26b4q8L8JlX5kvxqfhV+VTZzL8KFjOPCkqqMtRzV76iKh3z4gIDle/cAPFIfgWIc7W1CloRDCgd4DKdHnA+bhcGlqzD1bkKlB3oxudVYIdpnY1W9U2AM/PP9pz5Wwk+VZ1uXDl+3NhT/VfZ2EuR0wvm2QXglUywjClhnbzCnPzmM44/EIg7cI5jPMvgLcipJ7jyjLqjVsu7OAd1EPnOa/xlfcg61OskR+WLZfMz9AXZxlXw3Y0ZZVOqMXJpwv7APnHtOFJ+Ndby6/spU+Sni6f4npKbsl352z2rfOu9RPcaP5s23TXgXQX+3cA+SsrgdCshaAR51QgN4LTeVT47Y9qBZ3Wvuj6y4j0B5qtOopMf6seRQNOBWBXMuOeYhvUAecPnGEw48MHlOV6ZByXfrh9YpsjjdY1BDNguSapvUP7nlOuClVW9y8A0SAFvJKwz0+U58+fGYD53eq2uuf7KTuO5C5C5LRWonabF5zyeVraYK73gd0ddGe66Olw7V8CEG+8IAFf1EqniYTJ2XbvcBOM5oAX5YZuJwJftIsYkbvt4niNw5neXFQhOwKTk5mIGp4fJHwI/zs92Lvl2cVcXX3SEMkd/kW1OOzUpezJmeCLNyeu6CNvabT2/BG/cP2qiqIupOpte2Z7ut+J306ZN9xDwrpxN5YQ52FIBZRfMu2cuiMo8mMYFOy6w7wKUih91z4EKB4BXwDZeV++g8tbYbnVtAvY6cjJR8j5Cji/XDq4L9Vr1ldN7tSI4CSoUX1xnBbY7OfFYcKBzhXhcORDF7ajoqC6p/jhCR8vBFSEeQ8qmcFrV566Plc1gqgCAs22cvgqSuZ8VMMaPI7FNVtvGld2egO4O6Ka8HfDm8wlPqn5VluNJ9ZMb6/iO8apuTmwOysjl6eR+aUKZoa3iFWoVMyCwZlCdcuSVa7ahDEJZDgq8B/H74dwGHPsKfKt6cUUc+UM5sdy4DZxvkgfrQB2c9Hs3Xq/rXe4jpGxFlaa6d6TOoJjccHFWUjX2nS+vYpDO/l83TeVc5b+berNp0117xxuNswtU8hp/uRx2SHyeedkJYv4qOJg4IG4Tpqsc2vSZS1sF2pNV6+pZBdpdne5YWX2rqAvsXF84cgGrap9KU/HpglNOV63MVTxP+Ji0SaWrdC/T8xhS/Cl+XLs6cNHlqWzIdZOqa6qDjzzySDs21NZCBug8mal+1XPHsxtr2IdToMW7ODCtA8cMvp1tdoF699qG+uVz5aNU+u7dbzWxMOFHyVjZks5mTYH3EZDAPlaNQe5n1+eT+io5MM/Od/N2cgbZbqs6Pst7WJaqI0ltNe/iFCcPtL0uDZaNMs7VZ0VVeRVV6XEcu23ZSOrjipfeGXFpQjlPY5lL1ZnnrBPs37uy1HUn3zshf46jqnQTfu5U/2zadM9+XE0FK1Uw4spKI6RASWUUp4alM6iVkavAzBFHpwysArqTreEMuqcr5G6lu1r9rsD3tN1sXFlfUOYTJ+/6VfUhry525XHZri4VpFfpFSmQpdrg+r9rE/665xPiNqn+VGWv6slRmtQzcfDTICBIrW4zaHr00Ucf8yzlqF51UcQ6VqXjciZBPtfT2fVMW61K884PB+jcMd1e3tXBbVN8qfId2O5WxVnGbvwpn6P059KAwPlQBniuD7pJin090YQAAK5BSURBVBXC9G4CIMmtVONKLJeHsmTAHWlxRZltcN7HMaq2m2fZPKarMZrtwTqVfmCb8XmC70lMMtWbKi/KvKPVHSr3CqHeTfzppepEQj2o7MVRXu62zDdo3nRT6SLAuwuWMg3+VqSCUjaCVTCsHFBVbxX08HNMh21iJ1UFIgrMKSDVgecKUFcgfFLu9JmaFJiQ6j/lkJGHc8i1QfHM/anOudwV3We9qtrGurlycJv4t5Npxd/UaVd9XI3fbsxyetUvK8T6mPXz/a5cDrQxPZajQDauJuH91QA10+C4xHfPMw2W3QWXXdAcR35xvAJkzi6eu8KM7VblqjTq+wuTw22n53I6HpiUXVD+QW1jdjqwSuzXWE44ucJfrXfjtmtvx7+yn8gP+iPkIdOxvNQ73WoruvtFf+D+pZZ6/5zbzO1B3thOKz+TZSldYL+Vvyt2n/uBeZ0Q/j/wlbFwL5Cy/3e6/iPPHgR60Nu36WbTxYH3ygy4cgz4jAPPSTA9cfoqKFYAx6XNcpM3dogVqXomK9OT7eXqfPquNwNp9+63K1eBvdV+Yvkxn0eokpXqf+5XvMfn3JYjQba73wHucwKIiZ4qPo/U28lEgSVVTlX+Ud1wZWJf4/VE9g4QcbkI1NzEBtar5OP6kwEaBuguqHbyr3QGnyHoVuNh9WA5TdIqXarSuOd8MPB096p6nQy5/6fgezUwVTrG/ov7lmWgXqPhc9aNilbawLqqeEbQmuMQ24D+K/lF+fL72aq8zI91sp2oxjWS8jlKbzItpkFQm1/GZsLxz5N6HbHNOeoHqg+mrerw3aBL+5ebREfldj/oxaZNdx14d6scajCx46wC70nAy0GNC3KwTPzlc5fGOcxqlhjTcpsqUNjdnwDp7lkF0l26XD2brHRPAzAHHrDeKalAVQFX7jMFqB3g7nRSBRku+D1CXdu43gqgdHysgH0MDjMoz/7jD/MoYII8KjsykcklSNV5Tl8lcVCfdan61bXqO07Lk1Wow2rlu2tbZacZfFVgla9THpW/qJ67j6PhPaf37nnHPwPNSmcdb4pWwfdqcOrSY7mqT7tD9WlV55ExNBkfSbgdO6+zTh576FuSf/QzaKcYuGK6fI6r32jnnN9Bn6BWupEYeKuYxMVU+Vz5sEov8rnq22k/stxW9fZeIOe7N3naoHvTpjsAvNPAumAECQ0+OiEVrKDD4HtJVYA+CaAdGMPrzpC4ermdXFf3PvYUNE9+HchW6d2KtwLnU5lU6dS70Fx3R64flSwV36hnDnCrOh0wVGDABdiKJ9cWlUbJQPHTjUdXr6uvIgVGuoPfY3RH1+YVPl0AikGuClxXCOtQ70eyrrjJEpU20+dvfhkX31lFUq9usM5zuU7+CoB273lPV7G5fOVXlIwmzx3vjt9upXtaZ6Uf3O/KhmX/TW0rl99RNS6dDLh/uvJXbcjqMwTRDJCDUI6ZHse4AtzKBuOqugLe+avGILej0hkVL2RdyTdvp8cy+LUWR8oPuPhsuoK+qqf3KqkxuenxVMUMzufhdZdm06YHgS4OvKsgigM7Bi2TIMWBbnWtynHBDD6bAstVYvCr6qoAtXtegeeqrAlg78qrQOyEHJBSYH9FzvjL9yrQoe7heaUb0yCbeaueKb65/k5XHYBz7ef2Ol74GY9rrNO9S+meY1Bb8c58njtmGWC7IPgoqaC10hsF5joeHOjGcnBMZZlqHDKPXI66r4Asg7YJaEV5OTvv6nP3Js8YVHYA3AEnJR/VV/iM+1bp90QHXH2TPEoG6lqB7glvU/6r9rDcsFwE1HmP7aUbKylfXgF3u1SyXBzXyFt++AwnFNm24bkbW4p/9MVqMg/LYxA9kbOrL9/tX92+/iDQpfzATSClP+q58kEs5yO2bNOmGwG8K+fBgTk+q4I6FYjhM5eWnyMp0HBuAO/AiEqzAqS7592KOYNYV1aX39XlnP0R4iCJQf+0D1D2jj8XwOI9fs79gfeVLld8cnnVM3ev6gPV5u555SArnjktjjtcTcIAEQ8O9DMdbxvF5xWIOUquH48CBcyP5LZIq6BO2TKle06HMK9a+WYeXd9zWZhnAmCnz5RcVBonG1XHSlrkS4FMBbwVz67vGUzxcxecYh9Mxrzzm10e1zfZdv6AHm+RzvPKrio5rJKTa8oIfUfWjZN6bGsyH95jsK34xFc3Mj/+C60gZ6+Unih9yrrZPnM7lEx4zLuYiNuY5Sp7H23Gvr+JdDTGeZDJ+aDqN0nZdTx3tiPL2v2x6Ua/462cCA4MNUhcMMTPMD3/uvxICuRUxuIo/f/aexeo+7ayrn8fOCkot8FFATMI8TYsuakB3hAoQUE0k4smmCJmYoOSMEPNSzpK0CQV81SKZWjgBYuLXCqJSyqBlgpGpmiWRiGpmAEHzn+s/R/zjOd8+T7PfNbee72/9/29n88Ye+y115przmfen++aa619g3neKhOxcRKtBPTaleosrbUr39lt51k5rRkAo3MXyyh+Yrpryj7ap0JEB3Hdr2Ey4Zk5tx3Hxn1X7c4dO1SEq41ZvmfpZXbFNJyjG535WM9Z+WX9eeZEnoJo48KsfCsnIIrXbMU0hh3pL8wE2tgX08juFFn2ab4GXYEQf2eiVr87gjcLMxO6a+Jzdmk6mejOLiK4PLvtyoF0devGhzX9PLMrxq3tT/OUPTagz/7qGOrsqOaM6txO/iJxBVjj1ZVpJ8o1jizdODfF8lnE6fgeaejjVK6dVGOg2hL78LBd56VRV/H/vt3Yr8RxxI35hzzuAJebyuepxh/9fax/DnBVCe/KQa5Et353nap4PMYxIxPccbvjYI80Z872+K5WsaNdWZgsDrf/kNvGZyvbawTIDNdeom1qYye9rBxPYW8mbrN2X7XFbAKq8joLc6xjfkj5dETEgorvmIeZaJr1/45ta/PkJvlTTPgj//oGcB3LXLlm6cc6j2HGt67a6bH4OysPJ8474/2s/mYiNoZxwjtLo3MbuHP0MsEdb63VNDRejdulVaFzSTVWnJqZw1v1we7cW/WjtWXjykl9jfjYinuERVe44xi1EC/4DoE78r4cu/baa3fXX3/9TcazhbjqHYVq7KdZO9E8xvJyq/OzvhjHlWhLt7w1HMIH1jDzrzOfStuvO7bVWAhwoYR3RiZO3QTemfCzid9NEjOntdo+hKosKkHbEded1euZ2NZtF0cm2jsDXVbXrpz0PCe+u7eau3rO7I1h1RGKbWYmiKs2ucZOpSOyXZgsXnX4nBNb9YFun3DOpHMS3S3o6kRmE67G3S33Q3ArRofEMb6H6K5Ep56nZHXj2sEQCDHsEOKLLcfkqyLmJ3tGuBrT1whpF6ZzC3u0ddiZrXJ3nu3W+NaUVSa6XZ/tOpuH9AlXH51V/rVpHDrXVn5EjC+KZye+x3F9/CW7MKhiXH2eRXxrGUaWfqbi25VHlbfRlzv+ULTDzesjT93ydm0UwQOHkIlvbV+ufcf22PUzAa76//Fec072ne3rOOMqtOP2OO6+x/YagVEJAbWpEtiVEM/CdG4vr7bX3o4+fs9wA6gru+i4jLiHcxL/rmz87giErM5jus6Jrc7RPMXwM2GQ5V0njyw9t9+115noPsYpPiT+qt4rh7E7hmR971Rkk/8asaP5yp4V7gq4TpvQ+tLnVKPojiIjuyV9oHflaH6r+tMyqASsxlOJaS1XLeOZMMnG7nFuXOVeK7yVWdt09VyJ7m5b1PKP33Eci/mv9lXl2cl3NiYr3bzNRPjYVqEdj6sA14tUo84X4mr5cmw866x9aYSJd7ZEW1z9VW1T7cnqSu0f4bIV7my/uwCktnXHari8OL/bhdG5xT26sRAf1VgzJwNcWuEdJ3w3yWSTicbfGfCzCfmUwkTz4PY7h1jF7WzfTGx3BXh35dttx99rmInv2CacvVF0r1nxXmNrHLzVsXWfeF4loDSPWZrjt5ZblnaWv7V1cyWcpln/PbR/xnrbgjUTfSX6jhGGirsAM7a1v+oKVxQK7llYTcd9nHDV7Vk5ZPnXspqVmx7Pws/KNabnBPfald9ZHVb9QOsklnunra9pSzFcNb91598sze6c7MbDLs7P0Lzo3TZj/zgWV8fduJ9diBrnqfgdjyvEcMe0l8w/cnmNx2PZrGlLzgZ9vh9gJrgrIZ6Nd7H96oWn7lgEcGmEd+eKqdtXTS7xipdLY+0E0r0CHyeszN7s3HHeTER1Vr2dENZwLmxXhDs7DhXdVbnHSdvZH0X3mlvNKzFaiaZ4rCu643YmCqq0Y7jYdt3EU7Ubte3YuqriXRu/TpZVWtk+V24unlOSCcuqDQ3GSmn2UQEX487GxFmao284Ae6e71bRPW6lzwRP7JtRqFfMhFvmOGVl5cR0JbizZ8MzYv93K+1dMX+IoHLhM8Edt0+Fa4tx2+VzFt+a8BVr53W1IbZzbc/urhC99TzOO3qRKor0Jb14V1asH11t1/lmlgdXHt22N/bHc7KVfZeOswexA65dZr+z+bwjvrXNd+YQgEsnvDPHoxIuLh49HieNGU4g67EsH247C+fyrQJqbGdiOltpzkR1dkt4R1DrJxPra4RnRdYWtD6q/Mbbz09BFIOZMHdtJDq9MR/diWDmLLtJKKuP7Pw1zrg6Vln6WbxuX7f/uHgyER7706wM1JY15VGVxZpJ3q2MxguG3VXTrpMbBYBzcKKzP9LPxLde3NIycRfhumU8G9udmM5WnGeC253j8qPlOMpB4+reZp4JlapMqrmr2l4zBmf5r4SVq6tjnNxKFA57Ih1huiadwWjnIy9x9XnUsd4O7i54xH3RJ9FxO6Y7Vr1nfabje8Ttqm5iOcZxYuSlmtfdHJHVF8AM7Tu6rWH0O7ZxdwEU4FK/XM05OJkY0e3KEdWOFuOunAjd7yabEUclNNQBmqU1zstEbCWuqpXn2S3nXeHdXeVe6xRV+7X+nG26r3urubM1syNzIpwQrISY+x33VWWjYTLx2fmsIbblylnr7td8dW2oJt+sHrJzDiUbO7RsupO8e2O5isqs/agNlY2RYWt09qO4iLeY6+8ovqv43RjgLoK6fqDHsr4zE72VEI+/3VvIZ+18jDmu3tQ2He+r/B7aV1z7i2JvRtWeOnTbZmZzNf9m6em8nM07M5s7+6J9KsZjWnH1W/dpH1P/IYrceEyFeeZXuDxoONdG4nc8nuVxNp/PfCGAbnvI/Mo14ntsd+9mArhqV7z1xQcLa52EbLLPBE4mHrJJP0tXnU89rxIqLq04iFS3hVe/3bFqpdrtq8RsFuewPxPdM6oy13rN8q23mK+91XyN+NbtcX4m+DLHfNZ2XTqZrZXA7tyRsIa1gteV4zETctXWOnnLzp+l2x0jYvjObdadZ5KrtjOz041/uoKnjONOgEcxl616a591jpDaP8tXDBM/7hnrmRCPgnu2Oh3Rtq6CpVtf2ZxQ5T3DiadTks1xmZDL5jdn37G2Vue7stD+PotDbdZ86F0gceXbjcOxT8Vb1+MFqXgrerytXceRqu/rvtmY4PZFW4ddbt7LfB/9vUXbhIuNjp+VP6Zjr9uuvsfjUfFCKMClXPGO4nuhIzLcRD9z5jLnQMPEYx0nfGy7STGzXSe8GE5XoLoiyonq7LzZqnfcr28Lz1a8s7qbTcoV0Rke5dFdhT/kVvOqrWWCW8+biT7nvHYcwCydbBLqiuI14ts5VB3bZmkcOglWeavKwtkc85f1565NOjbMHnVRMZiJSyfiOnZF0an5dHnP0tcwcV/8dmUy+mS0IR7L7O6M2ZXgHivZUWC7t49Xt/NrXqLd0f6s7DQ/WX+f/c7Kac14U5GFnQnT8Z3NwWuZzREdoazxZeeuScetci/f8cKT3hky2n08pnGo8I7njvY6zqvmIVf+rn912p3mMbOvW5bV+AAw2khsK7HtaNtz+zvfo78ivOFSr3hXE3Z3As7ES8eR0sllrfMRnc5od3We2uOcOf1d7Z8J9Gq1untbudu/VmSuLdfqObiZrR3Bp3Fr+lqPM/EdHbAYVvM6a5NrbO20mZnoXiu+1/yOaHvv5lXzrH2tm18NX9l4yol5Fld2m3Mmuivh7dqxG9uicHbnjX2V+HY26HhaOUKuj+hvl5YK7mx//P/zuF2tgGdziStjFVaZ8M7Kq5qP1tARPNHuU5PN32vHN7V1jUjOwq3J/yz+KKrjnR96a7k6+XE1XC/CjbBjDBj74i3ncVU9nnNM+5m1wVh38YJDzEvVhw+1Cy4H2hcrn9L138yPqXwbfSwI4FKveLtOkIlu3V85oeqM6bHKiVwzSTshpmGqY13hdKjojttOuGZCNh4bK9/j+1ROXOZgxY+zb2azCuEqfbc97FDBl4m4TOjF/BziLFZ2VxOR+7i2cgzOhopYn5Xg0/2VuKzqYFYvHZtPQcehzwSkE85OvDkHOHMyRnzZCnQmtJ0IH8czZ8k5+HpOVgcd58gJbl3xHt+60h3L9RjhXZW5267mLE1jVh6d87t93c2J4/x4zKVblZvrv6d0fNVWl6dTxb9sL+0oe5Qpjm9DLMcLMwvugnLsa3pbeWyza/ySEX/8noXPVu4HccyIK/Bu/FE7EDuXk2renc3Vztd3PofzKdw3whsuKid/uVo20Ueqjji2Z4J85pR2J4a1IkqPVcJhQZ+broRTd392+3g8NruFu7PS3b3YMMNdmHB50d9q+wyXn1hH2WCfTRRu4nDxd4S1267s79iTCVG1r2NbNtlV9mga6tBnOAe6mtCzvFbleirHcK2TGf9ObCbAnaB16VXCPG6f8hPTifG7O5tiWVXtw+Wn+rjV7CG63Yp3ZmNXFLv+ns0tVblp/GvmmGyujNvd8bAzHx4yZ67No2vnmQ26v2pL1ThX2eTscSvdGn9c8R62jb/h0+e39Q6R+KhUFN6dW2WrcVjnNs3/sFPTc6v143OKNnEe6PSRqyGfW+Pm6sxvyObomY8580Oy77XzM8BVf6v5ODZwk4TuqybpmUN6aAfsnpdNbh2h0v3EeFQcO5Gq3x0BfqzoXlOu6sjPbHN5y1YklCw/lWBRuyrna40TnZVrt+6rfdW5Lr01jp0rx6w8qvzP0pr9zmyJ4ZxtW0zGa+LLVl3dXUGdMc6RhXfCNTrgaz5xbItlOhPd8bce7+BEdHaLud5d0Hm2O/vtxvJ4vCorV6fHCO+s7EYZd1ZLs/zOwh0jutz8PrNnjX1Vnl05z8o+tvNZmFHuQ7iO+cI9660rzLFPjTi7dViNcxpG23LMwyCzV9v9qcfQs8KVy8zH0bo5pQ1VuueJmc1r/JO4z53fGfecX+PiuKjtFC43m6x4Z8wmxq5DmjlWazvgMYI9c9QqcZQJOzdguVVgJ5q7t5zri9WizWupyqua9LOymK3OdwRfTKOyOxN5WR25PK1llqamreG6cVTpd5zMLO64v4NLQy/AaJ6yPMb4Ovk+tE2fimwVVn8v6PcheXHjYia43f4srJa9S0ftWNtXNX4tGxXfKsSr1W5n+1oR5solq8/O/Jflfba/cnA7cWf9MTveyYMrq7XjzZqyOqRfd9KOt4+P/VrOKrQ1/KyNxLFdxbnOSTGvnflf+2mWz5gvJYrwmP6yX59XP4+48uvMW26smZXnGjvWhDsr8TjzE6rtbBya+SxZvK4OsnNn3wAXiTMV3pE4oS10BXjlpLk0Zk7KWmfJ2R7TittrntmuxFh2O/ZMvGZCfNg4y2NW9l30mf+sLE654p3ZGfPj6m32yfI+a1/ZxNSZvLJjLr+zMug4w53JtEvVP6o0XXrZ7+qcU1OVW/VytTXibE19ZfvU4c+EtYZ3InzYofFrOY+yd8c0jizfOv47ceveIZLNAdX8kAlGtX9WVpnY0jRm7aYqu2hXJZ46xDqt2qLG79qlbmfhs33HCI3Y3zPb1qDiNO6P5R1vJ4+3lbv+lfWtQVwVj+U5fmci212YjnnQMtBtHUedfTHfI59nJQzXkI1D1ZwZ0XEu1tehbelQtk5vNi7H8qqOzXwVF76yoeO/z+zr+IYAV+Wt5jOncoRb41Q6J0Z/q7OTde7MOZ/Z7OJxE1hMI0u7uk28CtMR3O6vwrJJuuu4ZYNhp46rZ8iqPFUXDo5BhUQl3tyAPztnpLHGGehceKkmO/db03Z5r8rIxXdo2Q8HcuYQaVrx4lBWDlnZbkEsl9lYEUWZW5XtENOpxqfY3sY57sVO6lTPBLiOr9F2177VPq2XteIssy+ztZp/qnypbVX9qojq2FHNW65/Vcd1juv25W7ZH8qszKrzsnBV3rLx+dA8zUS3pq0+ydhX1b/Ln4YZ+6r5cbbaHeOYjRujHas9ms9hg87nV4JqjsvmRDfHZHUXifU/a1unmnuObcsdO7Q8Dvle4490/YhZf1kbH8ClWvHuDFpxctDOo85RxxGK53VEhrPNTTruPGez5quaBCrx3bllvJqQK9HaHaRO4ZS5yWyWr3HxoLqlfq39WZ7dZB2Paf3EuPXcYVd8/k/j0ni1Dl3byMK7/S5vrhxc38gmzKx8TtF+XPnr6k1WB24SrsaQQzk0juy26CpOZ78bmzI7td2tudXcHRvxduu+al/ud5YvN75mzlgVT1cMuzgrW7ufWP+ZnVkZue/ZPDkrFzcWuzLrUs2RLu01aVRtJxvzok3RtpmdGj7u07FIbzPXsp21sUpMuPx1PjFe7btVvlVwxwt02W338Xb0U4yvMzpzd/atZaRl7XzFqj+vnQPV1rXl1e3rM7vcPB+3XfnMyjVuz367eByqFyofqhsnwKUQ3gtOCA/UKXOTngs/O16FyXCT5iyuzAGqqARDR3QdI8Sd6J4JKVcOa/KbTfxu5b1a8c/yuRbn7GTbs4lj5GP8/cxA3xbrnOvMcdJ8urZQrXZU9eocjdnke8hdEacS3y7vMazbF48da3OXWRpOeHfGsspJq8rTnafOtDqWI0y1r8qzc2RH3zhk3HDl4NrvIfGpGK7ic/NR3LdGcHfS03T0+5g2vqbM1M6OcFTb3djftcHlyeU3GwNGWhrPzMfo2Jj5K2t8iKxPV2PgbNx3eR1xdURjHCPcWKD5duVyFgI8puf6QdZvqj4T8+bm7FE2yqHtOaurQ6j6yux3NX/OxposXPU7S1vRvuPabvS5sjEA4FLeaj62szDjOxvU1zqdzkns2JpNNpFsEs9+Z1QDlE60OunqMTcpzwS33rY7s9U5EWsGt+q5bieyZ/nSfBxK5rzMPuPcwVj5UJsq0VJ9Zrdir7U12hNtUJuqNE5FFWdVztlFmczmeO6hF+FOle/q78RcuoO19utYNMRv/N0R19UYOPp+1U7ieBpvSV1bnp221xE07lhWDmtFt8blXp5Xvb1+ln/dPqaf6jybhclE9xpBqnFm57syzuLWPLqx2G1XYnuWdtYn4/FOu1a7qjFQw2R5d+dn7VXbXkxn7J9dmIvEZ9mztE/BbJ6K21l76IQb9mu+9cJDbFP6jP6srczyuab8srjdOJGd545Vc2sWf9Zm3fGZDZUG0H8GmOUF4NKueC90JryOU1DFd2xHUyeqm67aoJP8IRNs91bjNWI7xlXZ5fJcCbZZuWST1yxv3fx27F/rCHUmEj1fby93eXfpZXU/q8vuZ1Z3Wf7dvkMuas3Sc+0ghnF2Z3nN6seRXexTcebS7x6P4aoXqmVpVmNkNfbEsPFxh+gsZkJ8dut5vIXd1WM837XDY8fp2fluDFbcRYaxreE0TRfGlVP2HeOYjU2uXWqfGGNN9w6gTpk4G6s220mvCl/VWTUujN9u28WvaemjFGqzi1NFa7c9O7uzcS2b/2fxZKK7EtCxXPTW8ejDaT2MctBVx1PND25fNje4eUDjcu1IbZ7ZP/6fPQt/7NjWoWrjul3Nn9U5VZm5uGfhqz5b5cWNedk4eBZlD3DuhfeCm8iz36dMK5vA18Thfs/ii86DOp3dSSNOuG6Fu7rF3Am1TLRFe7tlugY32TtnwuVplq9jbjXXvFQTsjvPTSRRjCyo2OkKb5f/rE10Plk+Ora4c/X3rP3EcLP03V0Ys7y7MFkdzWw79XgUibdbd8R3ZU+3T8ZjLq01gjtz2kc6epeHjnsdp6tT/m4MPeX8tFZMxm0nurO32GscLn51PjPHU+ecU1DVfzaedebfznzqxpxqbOq2rZh+5pesbYMzO0eY7q3h1d1frh/pvqwO3GN/Lv04DsTVRX10KvZvfYv7oWTzrX5nY39WprM4NG134SGOi7Gcs7LP8jErHxdf1b6z9j7LY9V3svLNjrlzZ+Er22P+XZgxlmbjAcDust9qHr91f3WOczCqOLJBu4rDxXWos5D9zq7OxfCdTya4sn2ZAM/KclbG3fKL+9Rh0wF4ZvusPMb3jCx/brLu1IWGU+GS3dobbZnVY+fuhU4bcfXkJsIoerP6VofrGDKbZw5qVlbVOVn7zI45ZuFmx/Xun+y/uyt73XjmvrOx0QnoSmRX4jympenpRagqX1q3rjyqNunauR7v1F0miqs2FPe7corCu0rjWJwTWoXN8pW1j+z7VPZnxLEpGwvctyPaqndkxDBxfzw+mxNm47PaqXNXNi9oWbj8uH3al2J96e26MX4VnJp3tWXZHheZZ23PkfVpTTvu787LLs4qL1qGOpbpmB3Pj22l8k079ehsnsXjjs/6R9WP1m7HfVX4rI1U85aWV3x0qcofwKUW3rqdhdVJsHuuO/+UndINlrNBMIaLzk416DubxwTttqvJPxNrs7ydAudkav7V7ixvnfO6NmmdHXLrttoTGXFmzriGndVv9lb3bCXctQPNf1amY19Wdkps11nf6+Bs7pS7Xnip8n5eqATtOB6/9VxlVp+jXkZ5ROFctdOO6I7nOaqxPMuL/nYOrgubpR/HXbettjkbq7btROjsL+Nm4jsrt1inMR+uj3fQdCsBnrVXRW3MwnTqf+yL/Xrsc3fGuDjUHlfver6r92ru6cxn2V1czv4s3ll5dUV3/K35U0ETGTa795iMeHV8qMjqqvNdzQvV3BzDuri1fOLHvSAylms1xnTKoSPWnb1r8tmNz5Vtpwyr86o0s7x02rZLA+CicnLhvebYMellg1KnU2YdvIrr0I6fTRazySRO3u53JWDU3mNxjoxzRjU/HVtn+Y/ba+yt4ouCrVsvkei46G3mlfDO6nT5nv2VWkewVk7k2Le2DMd5h/TjqoyziwtOTGf1p3l3eThrZv/x7GyrxFFWjq4/xDY54qnEf8fGrO2MNpGNB5no0s+sjjLHTcfmTjyV6Ix5qMKPbSe8s3LWuMdvl7dMZEdnv5NfJw46dT8T3FUZ6T43Lrk43Xg/m9d0W/Pt2rFbyXS2zAR1NY5ndzANxgVWN69k82hV9uPb3VkTf0cbxsp19d/cIy/xUSot++58onWX1aWOc1oOWiYxT65dZG3HlWO8rTmWjfY/Vxea/try6O7L5sXsPFcW2bnZdhbHmvPXlouOH/ERh0PjBrgqn/GecWhHmQ1w3Qk5i6+KM0sjm/Az4RW3D/l0J3lNo4ubkDpCeyYy1WlZe3zBrRisyVc1gTvxkoVTJ8cJ7pnwdk6bis9KgGftYdiVtbVYx65+nSPhymXkr1sXs3btwmT7svhcftTms0SfsXRvt56JsoE6IFrfsQ2M3yP98XKgWVvtCLDY7pYVoWhf/KgIVZsHhzpklbA7hlgPGmcm1mei242XWbqax5mta8ZDJ/izCwPd8awzVri0XRg3NnbGCxef5iWOWaN/ZHmczU/uO5uHdZyOZHfpxLanx6vwKrrdM95d8bL81gvK8VwVpp2L4S69rC6reUB/Z/nI2kocJ8dvHZ81TGf+O2Qsy8bD7Hu2XYXP9mX1km1n32vCz9C6WdA2tmbsA7i0wnutQzGb1GO46HDGtA7tmGsHsMpGjTebYKoJv3ObWzWRHkNXaK/Jk3NYsvy4cB2bXXzZRQuXbvVRxzyWjRNYWjdOYLu8zj6aF60LTVtt0n1dMufDxTdrtwvVHRsah2sfsS5mNp8VMwGTiR6NQ/MwvrU9xnKJInvZvv7662/cjmW7Rnhp2jMRn+Uh5kPzUlG1DQ3nhLPmN8uzC1eF7370/Kw81PaqXGbHNa3YB7oCW4lxZP1O+5rWiYuzOybrHB/j0Dx3XiSoL9PqzlXdMTuO79HW2VxW9XVX1iq8XZ269LJV70zoxP635lbzGIfmrzrmyqH72+3X8l3I2nMsc1f2M1+1au+uTLJjme3uvDXHOvVRHa/iquyYkZVpVaedsRDg0q54Z8yctdk5s4GhE293sNKw8ZzZIB/DufM6n+yKepb+IXQcRi2PbDDuXjhw4bR81lxdn6Wn+3RVPYtjlE1cDYgCxTmwLk23qr1GeGu9xzpa0w5cu6+ctQ6VuIjl6xxp1wb0vEPauebtSq54d0WZOhRZm44vPHLiIv4tjhMimrZrw9q2qrGwykPMS7R5Fo9Lc42ozeKPjl5HdMdws0cKsnp2cast1XaXWXozW7uCXO1340hWNzqHufksm9+ysohp6V+IxTrTPlCN/WvG6eVYHN+jTcv+zGbX112eYplm7bC6hTyiF+qWePXcak7tkPkGbtuFq8Jn80I1RsW6z+ZP1yY0DueDdsrAxZnV/ey7sz0r/7X7OnF2+mkHrSON3x0HuDTCWwevtece4xDPBoU1Vye7A5E7f+Yg6LmziSZzPNQRcBPUIeWZOVvRKXBlUJXVqT8zOisTWqZVWbpjzlGPzk7cV8UXnTNnb3dFPmtPh9BpN6MMnGBaK36y9jL2ZXWTxVeldRaie6Sz5hNtq8YobUdu5UlXo+PtoPpXQGttnJVpJdKyuo+3r8/G2SyeLm4O6IhT971GdM/q1qWb9etKzGZxujFrzUfTdXkZYTIbXb11VpB1DsziUruqes7qJ8bj5odqfM5Wu9XGaKfbzsZ0tTG2Q73NvCMIY5h4a7mugg/bsjmoO6ZW43dn3zFhnC0zu7O60t9r4snmrszmLG8a/yy+Ks7MrplvMbOxM0evYVaGAJd6xTubKE7l/GYTedYxneieOXXZJDEbzNWJqAbazgC2Rmi7NNaSOc5qx5oJ4JSfNSvemcDOjun+yvEbZRXLTCfj2E5dHWYrJWtWv6t6OIRDL9a4Pl/F3xGXVVvX+qjE/pVAnXrnGKsg076nzvj4jv0gbmdxLgxnWkX5TCS68SAL4+pgjLvquM76icbRLfN4XschdnODxpd9u/xrnbvyit8zO6s5Qst0VibRllkb7IwD0XYtSz2mtlTz2+wiaTUHaz6Xz3iJmf5PtauzEefoU9VF2kp8u1vMXZkNW7My0jzpOdXf2FW+gEtT2+wYKzQfWhYdqnE829bz3H4XRsO5sq+OaRm7fubCuzxnecjqppNXl2aW3+73rPxmts/qcQ1Zuc/yAXBpV7xn4rZycNakE7fd72pQrZx0NxhVg6bGoxOSiu8s7tkANztP83EImeMct2N+Dpl4Zp9ZXjt5zIT2TNjGc7orzM6hVudH24WzJ77hdo0AP7TOM6d97FvjeLt24CZNbTczR3zWJs4zKsAq4d0RPLEvRfEcx5kR9wgbbxsd33HlWwW72q2/tcwrWzOnSNtI9YZpt29tvXfnFjcvxPMrgdoR3S4eta/Kr46ls7F3lo+sHcbwbj7IbNS+7ISdC5eNa9lFz07bytryODf2h9hnYji9sFVdTK/GbFd3Wi9Vfbo8jf0qunWlO8OlOc4fK93O9linMS9rLobHbVeXnTG/OifrD+545e/oRRh3630WT2V3lYeqrDr5rPpFt+w69rjjnXAzqjFN297auAGu6hXvmXOmk281UeikU4Vzk5qb0GZXgjW+avB0tnZXT6t01nxOSSYWs3xlF1nc/k6ZnirPrswzR65y7pwAz8SuOkb6tySzdlGllYXPymKNaM7i6LYtV++u/jMnp5r4Z/W+lQiryqWLOsNOeHf+fmoh5j+uQsXj8dzxTPeIJ6a73M49XrZWpT2zKVL16/it27G8K9EZxyRNL7NnNq90BKSzryO6s7C6L8Y/I+sX3TbdEd+aV2eb9udog9bXTHC78bdz909My21rGQ/RFP+TOl54isIq9q0Rl5tDNB8ub7EM14ynGkfmQ60R3bFO9DuiL2DM8jrKI+u7Wb7cBe14XNPM0nbHNT23XdkXv7VNrK3P7phY5aWaF7N8rUmnG3dWzq7dZ+dmVD5kNb4fMv4BXJqXq2nn6HYUN5l2wmeDQ1dkZA5ONkBpPM6ZcI7GbEVVP9mEFcmcpA7RSRrpOXGn+7uTUqfsY/rZdncS1TxUKyiZ8+fiieGzMlwYz6tWdVjZ59rKmpXutcJjTVvpxDcLr3VbrRB1+9+x6Dh1TBoqtLPflfiOREdDy9uVy0xEL+Hcqnd1nuura1grYF06h6SpNh/qrHVFd1eIx/2VzceMhU4UV/ly6Tq7XBtUoa1zxPh242ImumfCO/YLtWV8dEVW60P71iAKczdPjLuU9D0dY7+W4azO3PFKmLgxRMNpXFlZuX6hK+B6t0zVbpTK39G8Z/NAdsyVY5fKd3FtYuQlGxfj7yqvs+81ZePyU4XL4u2k19l2+7I2Uo0vihs/AC4yV+Tlap0rtGvirhy0bBB151UDZ9bh9RwnpKr9OrFrXB1bqoFojZBwTlAc9MZ+nXTV2dLymU1EGq6Tr8p+tbezuj0T3O7OBS3nYYPmJxPWWbuYrcpnaWu5Z2whvmfCceQh7lNbdALu9L9jyZzPQ5kJ7WzfsMWNf8O+Snxk5R+PxVWtSnxXZaXhZ+es7c9VmGPGhiyuWZltIbpd2bntbHxc0xdm6WqYTllpf4nHXV/K5shs7HPCO5sftBxiW1+IfUbf6O/+FiubgzvfsQxmdaLpdObw5Xu5sOue09dwsR5m267O3F0Csey6ZHWoPkC2r/OdpVsdqy5sxHyP47Nbzl19ujxleXbnunLJxsCqbKryy9phVsbVb1fmWT10xXZVLgAXlc1frtY5pxNeJ4zOJDyzczYgZZ9Db5/TY9k5lcMRbcjyvOYCiHOSxlV9/fsTdTBcPOoQZDZmk8kh+RhUFyq64naNcK9srtKubJidp2m5dpBNTpnIcGGPJZvU107cGtep6Y4/g1nYTFhXojsTRbMxJxN6emzsj2J77HP9WD+dMqqcOleGM/FetRfXrka8MxuzvOj8cowgn4XXONX2sa3CsFO2MY5svOjY43DtMJ4T5yWNrxqHs08M3833OB4vEKvoHv0wE8vVnF6Nz1r2MV4VEppW3HZzcrxYEL9dXWRt2bWHaGc1P8SyXEM2H0d7OnPXFrg0ouCObWi20l+1ofhbj0c7qnPWlls2z1ZpVftn5VYdy8bbrOz0t7tgD3CRufZKOrOxc2cT06HpD9wkpt/V4FNNHPEcd5ucfqrb6artTOydchBy9uqtczGf6pTrR/9ey5Wdln+2vzvgZs5RZ1U5K/fqZWgx3ZjXrDw17bjtztFjmeDuTsrZxRHnwK0ly7uu0urxipkDMPavvUBzDDOb44uPZs95Z7eM6lgYxbIrX61XJavrSjC67UPKQ+NZ67SN/bPPmjawJrzrK1k5x3PW2OLOi+LbnbO23Cv7q4sfGpfO1ZnA1zhmY3I1Rs/GgMzu5VtFlLvTYzanZ/NFNY45IaY2Oj8hE90Ly2q3Gy+qcugI8LWsOd/11VNxyIWAaFd2fhTZbqXb9VM3v8XtbF6ehXP7qzlez62OO0E762+HlHfWP7Lj1e9Tth+Aq+JW82OcNdfxNc5Op+sMFtkgo9tjYHK3D2t86lBk4s2FU8ejEu9VGWSD2Wz1w6Wh9sV441XhEZeu3I0JSuPPytHlce1k7Rw2zVO2nYnuLEx3gnIXTdzFlKqus/ir42pHDK8XRtxqRuWYdOqhqg/NbzcPzt7ZWHOWonwh3go6W/1WMT5z6MZvXX1x58b+58SXUl2MyeKLNrlvl0aHbFzQ3/rR8S+OPU6A6O+sPWX5ntVBlieXdjwv9lONb414quq9cwEhs3dWZm7scPPabG7UcbNTpnosCu7xW59VdudWc2JnTq7aaXZuVY7ZHTKdOsrKy/Xbajs7fgpimz9k7tkqrBtrqzqLaWRzXPU9myOrsbZzrJNeFsexVGI7C5+1WYCLzKYvV1uYOTV6TjWRRQeiM+lWE89s0KkmySweNymvFeEd8T1wTlssq6qM4sTtbB+/xwtkIsO2GM9IM3MMqjLqlHVnwJ2toMT0s9WMTv107kJw7cqVuWtHWVtz6czSrr714onW5Zp25PrarG5dXtbkd+w/RlyvceY7uNXumdjOxJvWU+xj6hC6vGT51HEiO6/jVKqtFdXFBD3WaSezePR3lZ9OG8rqqxLcMf7ZvJbVh6tftxI+w80fWfuZlUkc03TbifPuhc5qbFU69o1vFdr6V2LabnQMzuYJPdfFsWae0/wt+/Uljd0+H8siEzGuT3VtVr+gUw8Za4X2mrjdOW47lm12p4kr79nFm/jbpevaTXZM813lZ5bfBb0jrRp/M7rzbxwnquMjjBtjsvEF4FKveGedIrsKG8O4gafjDLgrkWqX/s4GmjggdSaceKwScOp8uHCZ8NP0O3l34VwZZnbGvEUnI5aZOjH68hoVmsd8Orjych8V39VdB9VKeLaS676j2FKbtY50v6tHrfNqEq7KaTh1GndMQye+aEt2geUU9Zw5lzOhcwinmrzHindnpbsScEoU3PEZ1ZkD7sZht1/R+ljrXK1pi7O4q7YSf1cOcDX/dJid5/q16z9VHteI/y7aV9ZeZJnZGX9nedZ5xM1tXbHdxYnhQRz7tF+4NpbZWYnZNeOcq/tolxuX4/fYrtqcmyuqvlTNl9kF+QxXB1uyNh1XXnqnxKwfZ3N3xz9w5Z8dy+LXtLMyr+yozuuUX3U8llEn7niOK4O1NgJc1W81dxNxDDPC6bnVZODOW2ub255NOJ3J001S1VX8Q1ZVO1STgpZhFOvu45wA91cpTnRVt8J1HJGus+Loiu5s1Tu726C6k2Fsx3J25aR1Nbtg4hyorvPfmTyjaFtQ8e22nfNxjPDuEtM9ldCOcZ+S7jPe2SdrQ4OZ0HZ0LmBUZTITGNl5mS2xDy0XKiqysaFKb9ZOsnGuw+xCiZv/nJ1V+nFs1ZW3bj/QccPZPbvgk6Wj49EsL9XdXJ07u46hGmOjcFThHY9XtlXzaDdPs4s2box15X3ohYAqnzoHLt9DdHf6T9VHzxNZv413SmiYSHbhJBuz9PgsbDUvdI4727rh15ZfFoeOG5lNSuxjh9oHcFW/1bxyOqrJ+ZCJt7qCFjt5NehV6R/yqUR0dUudc0Q0L5rnyqlw52ZlrmmO/Vpf7mVr+pcb6iw4URZfUOZsyH7P0HLMnIdqf3VBRPe5SdPVTTaxduus4+DEsnZ5ydKJdaGr39lEubYfdz+zfFWi8xCBc5bPeOtn9jZzl49sTKjQvpylmZWRu+Cy1vFxaY/2pm3WtQmX92rsrsZId1zDHnKRJ/aVLD3328UTj2crbx2yOs/6lBPhbu5xtrryG/vjdnVBMxuvsvJb2w51fHbtv7LbXQhfO551qES32uvyl9mV+RudsVnnwi6uL8/yfQyz82dpVO24GgNnwrva58ayLLymnYV3NmbzyixMlzVz0tp4j+1TAJfmreY6aWeTeHfyik5rPNd9a9xuW9NecAI0m6CqMNWK9yxMlo6WhZZz5vDoeYMsTV0JiJN/nHhdepmDp/UxrppXqyGVw+OoHDm1U+sjq5dZG3BtNTqzbpLM8tJ1UJRMGM/E0tinF1TUKc9syZy/qi1n/VHTqf5XNoo3N9Ycw0wArxXea0R31lZmn8zerO9pmDh+uPOyspm1Zxd2xOPEr+bD5a/Kr4ZbI3j1/E7Y2PaqvqLl687rpJXFX5GJbie+D0HnHjfmje81bTmzq3OxqGrjapsT3JndLp3ueJ6V0exCR7eO3Lh6CtHtLjh37MnsyvLqfmfxVWFPNRdkY8jMLmdn1qay7WzMy/qV+67sPdVc2aGqp44tcZ7XsQbgorL5y9UibhDrTMyVY1bF1Tm21jHoTlLZhJcJ8cwGtT1zRrJzq/LPPlo2ev6wefk9thfBMfZlDt6Ib3ZLd3Z3wIyuM9GpN1dnVdlmZKtEOom4so77ZpONCz9E3pqV7/iMcqzPGa781ghwF1/MR8yXe1Z+ZtssfOUAr0FvM++K7phmNR7E8ULLNjrp0cnvCHANqzZqfXT7Qjyu4bN6r9qKxuO2DxWSnXPX9MksrBPr2cW6wVLX8Rn/tXmsxuVTlpHmKxvnswvZw9ZjcE59JrCd/VW4rH6qOTeO+TPRrf1Whbf2x8xmN2botrM/G7+d6F4zRnb6vfs9q69hT9V+1tThzPYu2fjk9rmxrcp/59ihrBn/ZnTHxmx/Ns8cm0eAq054dztF5nBmn9mVx2wwmjlp3fR10qr2z25Pnq3ydgZedYBn4qoqr3iOuziQCTV3q7mKDlfXzhlwK+C6b0a2ij27o6BTx9mxNcR6G+UWV5udgxC3q+MujDprMQ33PWxZbBsCXJk53dnFi0671nScGBn74gWFDnp+dvxY3H/tqvAe9qtAydqcGy/c4xqz8axTJvFizUygadwdp03H8/hb92fnq/1je9Y2NX5NU/drmVZzUNZ+sv44vtcI4GzMdlQXBbrCuUpf43J1V82Juv9U/a8rwKN9Lo8uv07wrrF7dqEmhtM+meXH1Uln7K3GBj03m3877XbWT7r96JRkY0tVvkrnols11645lm0fWnbddpid20mrmm9m52Xj7Fm0DYALLbxnVx11/2xSGB1S3+Yb49G43GpyFjZLvyO2Z7ckZyu4lRh0tg3cikLXGcvKOPs4x2gM3PHYWPEen0x4j/QzQVHtm5GtnnfzWTkj0faqbKvwLm63gpHF3ZkwnUiN/Sab2Mfv+Lba5bf7j+ksn51+Ux1zeYkf17ZG2mvEy7ErahXZi9Q6F6Nm7bYaN0Y8Mc4s/9nvuD3ueMjKqxIwlT0x7No2pftc2u682cqihom/R7nHN2C7cJpf9zu7EOYuzrhzqzw7XL914l/p1k+03227/n7M3UMuH2pzFbYq02rfGpu07GIbm9mvbdGJ7xhe85T1EXee7svG6PFdtfMZa8ei7LtiTb25CyjddGa2Z9tZ3+3k+dD2eAoBXZ3frdcqnVlYvQi6pt8DXPXCu3JKqs7hHEfncGk6cYKrJo7KUasGxa5wqLa7q97qjFRlFifoNasFmaMf6a4KRxtVpC0CfDiqmWAbceiK9vI77jt0xds5DtlqS5XH6nhse85517qK+8a5uuKl8cW669RztsKjK5izdraEfde73rW79tprb/LMcpY/Lftu+bq+nZVP/Gh7cEKlIqZxajpvMo92zNpfNm64vhjjdHnuhNFyqUT3bGxwYVz643dVH1l70e0qniodzbuz16XhbHC/VZSpCB/txJ3n0lnT3l3b6+DsqMI5Z3w2P8ayndlXHe8Iw2x8rPqCO8/NAZpONkdkto7j8S6jzoUp3a7GV7Ut61Nx/j1WtGXzTLevdOJaS9Vnjwk/G59mZVGNFx07j7lodKq4Zn2pGybaUJUdwEXk5P/jXe2vHMKqg8WJa4i6WRxusnfh1bYqrhjfTHxXAruzip6V16Giu3KW4/6qDNwdBGPfEN1je9SZu3qvk3sU3FWZzchuv1Xh0q3jrKy0bDV/eiyWWXTEnMPt6iPWu8ad1bWGjXUyhHgsU21zy/e73/3ufRh9YZjLW9a+15R7lp9KkGTlc6Wo/r872ln1tbWr3Vk5zsp2Vl5V29a8DLviRRD3rdvZf9vPxqVxIUmPZ6K6s8IYbXUCRS9edcozphPLTYV3fH67SkfznHHIRaWs/LK8uTKNxIuy3fZajaNdZiLc1cesPJ29M4HdtTXG0ZlHuvFm56iN2Rgzq98uel6nrJ292l879lRpd+2aheuMBYeOwYPZBZ5q3yxetUPH/VPmr1v+Ov7G764vBHDpXq7myDpw5WjFcNUqw5g4FipnX8/JvtXJy5yG6ip+V4RXK1hZGXacr47odg5FJ92Yx7h/POM9nnV1zsQIO3uue419C7MV7TUi0NVpLFNXzjF/mdMSj0VBXJVxnHA6ZM8/D/HXXf1efl9//fU3im+9sKJhs3Jb84l2x/YTBcoIF/OZtbNTM6uDmfB25bXg2mp110Y2bri4q/6u2+5izMzR0bFNVypcHccwOj7NxvnZGKb7tG+Ofdm3y3u8qFC1AdcvtMxiW9U+GfdpXM4ZnaH9Z8ZsnMvCxvBr2mIMfwrRnZ1fleWh8Wob0X0xXZdHd76GyfKStVMXRqn6VXWR+xDB48r4EBFZ5S8r88wWl/+Z3ceeU503mOUna2enbttrOLb8Znbq3DaLG+AicGbCWwc9HUAqR0rjyOJX5063YzhnU2WHi3u2cj1WYJ0gr4SlbusEd8xKt6ZdlaHGFb+zMo/PeutqlrPFie/swsSMbr24tlF9tBy0TnQFazjprmxdHY3jWZlqPFkczp4RT7QvitjZXRajHY960LfXx3CdFdnOam10KvSvA2PaevFg6yvfnfiHze6/vGMeIrOxpuoTh5b3SDfaUNno+nE81+Ujy5vuH3G69Dtxqg3ZhUbXJ9y3y9tCvGAwbNYy1HMr53mMFdlFpcyWjmOdhcnqMcbt8qP7XV/Qssjsz8bCLemKkU6YTARW9eP6TAyXvcjStUs3B1X5jRePXHsd31F0Z33cXVyYlVUWjyuPjM74ntmY1ems73bDVvHOznEXC7Ix47yK74ibW2bHs3GqU+Zbz/kAF1J4zwahQwYHd05HbMVzO99VfJ19TmyvuU00bs+u5nZFd8eJjelm6Q1BMURZTCt7u/mwy5VJ526AGSqs4z5tC7P6dWkP2/V26ygCtey1PB1Vmxv7MwGeTTy6GuwcvDUr37F8lme/9aKKq08tx2yFtkJFtl440LJzbe0siW81d892a7/O+mKnHF2b6HyiHVn6ax0btT2+nK1rW0fQ61hzSHx6sUbz2xmHtc+48PrbCf14cWnUpwrwTODNcCI65sHVc1b3s3xqeGVms7t42A27Jp3uOWvn23ienlPVWWxP3eOuHcZt/c76/fjWl2o6W5zgPkT0ZPOitvEq/Spu1047F9hieLftfq+xY+YPzC7MbC2+Oxdz1O6qrGIesrDZ+a79VrYgvuGisekz3lVY5+xX8cycAHXO3PdsAsric58sjTUrUtmqdzUpzMpplJVOulnaWqbZADpzHLI8qiDVfIy7AtzKd2Zrhp7j9mVlX92B4ISrE9rOeeg4zdoHoj3LOfGikRPTWhdjWx36eMyJ2Lji4eyMx/Xvxqrn9rMLKFWbijZXoltvq7/SE7GudndEdzUuZBfsqotIMX5X9p3xbS0xrVF/2UWdyhYdI+Iqc3aOCzOLW8O7vuvGMdf3Om06phHbxMIQ2GN7EO8eGuetbd86Dmna0e7sQkU8vzqvk/e1x9aGrWxZI0Qibi6YiZ7K7nh+54JDNc5r+plo0e1sjHC2urw7u2b5dqxpz50y74jsKg+HtpFDWHsxYWvbOm3P4cY/N5e4etDf2va0zTqbz6q+AC78M95uEhjfXUcjczRnDmplQ7bPxZ+tQsVjs1Xc2WAUy6EzSMcJpbM6v5A55h30nHh7/RAhzraRrn4qAX7MinclwDVcLHNXtioCdV9sv5kzG8NpHUWHIdoTRfHMGVvQZ1LVLhXdan/WV8ZL15aVb21vWf2pcxdtdoJa+74T3y4Pa5y5tU7fof/jreXn+lo2ZmRjimszs7Eq6//ZWKT5ju3HlaWmHetTyyB+6/lO+Lo0Zh/nxLn6qJx4XWHPHPnq94gr63+xjWt5dYTEKcTP7NwqTNaPXB/u2DOzpRt2bTkpbozVOtA86nyVidhxrJOnmTh37aLbR+KYktVhtF375lrbY1xZ3mbtMZv7XJ3Ebzf3OtuyCwxr2lT34kQ2tsy2s7bYSXNGVTa6reOz+67OdWQ+U2bToeMJwFWx4t0dmA7phNq5dJWrcjrdx6WdHcvinn3r5DazLyOugrjwsYyqvGcCO1tdrspO09bw0Ra9NVvTjqJb9x0jvLOyrkR4FKCxTCvRN8I5B9M9u6dl4MpaRWrc1vaQ1ccQgvGc2Fed/W4FWdMaL11bvuOtsNlFpvjG+o7oHt/x72xcOG1r2UWDsyR7xCKra/2djRtrhPNsLMwEudqp43klrmb5nNnoBEr1MsC1n3hu5pBXaPuKdmq4qozU0Y59LrbruIpfOdldMpEXy96l0U1P6/BYgeXsPwVZ/jQdtX1Wr9qOKsHt8tPNXxZO7ZjNcXoxz41XLq/aB7s2z8aRmG48XrWTLF7XZ9Tu7PchPuws751z1d+otjX/a8poRhbWtXP91m1nvzuejTGddrY2fwBX3Yr3mgFr1mkjcVLQMGucu1kYtSuLJ3Ncs9XubNJz9oz0Yn47k9wS3q34qo0xrs7FgNlA7PbHyaB6E7ArKy1HrYuK2QWOTLzE4zFvKqT1md1MbI/86ou1XDnF7WjbqM+svNXmaEMkiv/YLnR/FADa31wfHeJb45ldKInpZ6K7yp+K89jesrI6BVkbVuLb/AfajqvxybXRWbt1cVSr5XpBxNVZNRZn+0Y6sV5VUMYw1bgc+1FWVrOPnufEd8xrNcdkDl7V1tw4EvMU24mWURZ3ZYurk47I0TLX486Wjoh3NnX60KlE9lmQtd1xLBLnxEPyn134ycaG2fyu72ep0or7NJ+dvJyyTrMLRWqn1kM19lbhKkEf6c47VR+J9mfbW4nNanzLxqWsPVbna/yuPWndVDZfpPECYLNbzWNn6YiO7NxxvrsC6q42V5PMGkfN2Ti21652q02VqHRlUR0fZaNxO9udM17Z6C4kuMHQOVNu4tIJuCoXtdHlvfM/3mtETEwjtjH3iWK6K7xdGWn9xHatgtMJGhU1MbwTg9pvsgsIKsJdnartHbQO9f/eo+h2wlpFuv7W+uva1J24O2Gz/zk/hVjujGGVwHbP3Ws/c4I05if7Hc+JF3Li78xZ1farx2Kf0HYR8x37hrb3qp9X46+WSdYuKmI8+u1eQleVgYqKY4RKJlDieVlaLmxVFjq+ZO2hOncrMjtm9rm+s+DKV8NoOp19Mc6sD6hd2XwXV7oj2uddX1nT/tbUXRZvt23FMG6f9jM3Nmv42XdnXDikfWXiW49nbaw7/6mt+jsbl/SYCzcrv7id+ZDVHASwu+y3ms8GR3ds9ltFQSd+/cRzsgkrOyc7vxKKzmnWia+zkjvOdajI0zSjzdlqcjV5O/vd5K3263acpCuB7mzMhH6FqwPdr2FmOAHt8qNO9YKueFeTyxAMMb8qJFx70DZVOWzxtnNNw612x+2u85Hldeyr+rgT4Fk7UgF2luK7QgXFrO+4C0xuPJmNU+68KLIzwa11o4J57HNllJVZ7GsxPmevy4srv7FvyYf2iUyMdC4cxbT0AoEK1m4bcMe1Pbo2HO2pxtZO2676obMxE9eZOHdjWSXiMpsOEQld1sTd8V8Gru/EczXdrD1kv6sLHdX4ke1zv+NjPGqHy5vm79TCuzMGH9oGszGgsx3jycJm9Z5dcMnK1x3TuLfoL1lb1XSqMsq+s/Bx2/1Dio5/zp6txw+Ac7/iPZtcssGq6jTZAD8bhGYDROakdQfljjidCVbNu25nolv/DiqzYxzPhKy7Ou7iqgSxqz9XtirmNJ8xnay8OhN4XPGe5StDxZ37ZE6t/nZiXcsq1kG8kBJFhW5HO7XdZraoHXFCV9Edf8ft2eTqbKjyn4VRcR3Lp1oVV4GX3Q2w5USdORDZeDPbl31G/K6v6lvl9dsJhyj44j7XX7L278awEUcmvl05jfAqQkd6Krar/hLTdnYoMZ347ca5jM744sK7cdWNf93xsLItE9Tj2Jo8VXZVbacSeWv3dW3s4nwMHUuiLRq2Y+/s4ojixorOhXGdv4forsRgNq+P42va36FttRtftEv3Z/VT9TXd58LE+GZlFc93dru8aT/NyuGQNu/szS4WuXHabbvzsng0f9XvQ/IDcKnfan6Mg5AJNTfYVFcaM4fVTWJZuOycjvCeCdZq0NY8RmdlxKcvJIs2xmOzibmy3b30bPzOysnlx02MWVm6spmR5SOmEeN14lT/Cmr5Hrdqj7/Q6joeMZ6sPY99bkKP385RjiJj7IurfDGtKFpdvcxEd8xDvCiU1bWLdzkW3z4e7XTPPY7wUTjF8ON4VnaZeMwcy1M4iNXY4/poNRZ1hfioExXemdjWdqF1qeWpY5c7X53QLO+zW8VjHep2FreLJ/YLrRMV4EpM15WTlldnvxtrYh6rdA5hFk/V3nVcyvJ+qEDv5tGV2Sy8pr9WnDgBou0vE6xVHrK+M7tQoW1+JrizC3rjM16KOcu3G8d0HuvOgZrvTjlV57g6rvbHYzquuGO6rwqjx9127CtubJnVh/MHXF3N2np2LCuLWX6rvFfxxHLQbT2nM46t6d8AV43w7l6lyiaVWbxuYDn0al9Me/apwjmxmAnwTPhWdmk5RPE1PupUZ8J5NjnPzss+VZ7cvsoRrsq/66hVZdERDiq4xyf+RZR7eVbMs9aZ1p+m7/I9E95DVMR4l8/SHqqX2enL1LRM1N6Ynu7LJl79HYWWxpPVgX6y572zZ73jhYFhQ2wHlQ0Vh4w3Vfs+ZPyJ8WqYWV+NeVVRpXFF293FC1duOj65VWd3XM/LbIn1p2OJtul4fGzHt4frRZxOPXb2uf06j2kYNx5oGO3T3bZYiea17f+UZGNBFb4b7yy+jh/h2mMlhjJ7dc6phGtW767fHCK+ZyvdDp0f9DNjbbvS8M7WLM6q7rO6i/Fm4051TOsqfs/O74wbh8w5Ls5qjq5sn43H1bn63c1LNgYuOLF+bBkBXFUr3tmkv2aidU7L7DydLDJHI3NmKycwW8meiV9dNRpxxjxWk0a01z2vqWk48ZxNzJWtmdiuxHdWfprHzKHQc7tUK/yxnGciT4X3ENzdl6Vp/PG32qHtK5ZFbLtu9dnZMeol5jMSV5DVZl3tHvG5NGcTatyXCWetl0xw698sxTiyvGgZxD7mbKjaWuUsZ2TjR9X/qr7i4p3F7dq/9oGZvUO4dsV3Zm8cM1z70Y86WNovxnEV2CrKs4sw4/zsro7u2JzlV88d/Un/fs+9zTyml5XHjKyeY/xnKbYPGc8PxdVhp39nc5dri2ttidtuLohzlmsDHZFdzetjbpzZqmHcHLZGeB9STp0w2idm8eiY7+pU20HWD6tjMT3dzsa6WX667S6bP9x8onHpMWdzdqyKuzO/al6rsFUeAC7livepJnUnUGLc8Vu3YxzZxFsdi3FmHzfBdVadO4OKm/giIz4V386Ozgpwdp4ey+LOhH818bhtd2EiawMZLr8aRxTXmeh2wltXu9XxcJNoR3i78lk++lxqR3RrWbjj8SVrWk7OzupT1bVuO4Hs0h1lP8JUL1pTYa5pRic2rnrHNB2urta0w2rsqJywaP8sju4ny8/Y1vpyn5n41jhHuccxZOwfL0fTdwfEcK5MqnCxb6jQdnapwI6iWOtc85qN39kcErfdRbtZG9H41rTDNYI77u+mMbMnGx+y9j7S1jhPIfAqHyCz1Qm9WXuoxhT9VtFdlVUcqyuBne13+ZmVi770Ssn6wloOqd+OEJ2lV7VfFeGZ4I7fA7c/6+c6XmXtzOVb24sjG09mNrt5KPudxV2lN6vztWMdwO6yC2/FDRCzAUMnqmMG2dGJNa7u4J0NmG7fTOhWaVTlEOOKv7O3FXdWu912dn4msl06MT/ZIN0pk7VoXmP5qbhWke0EePVytUo8x7rLRJxr11ombtV7CBfnwCtaBtGucSEhppv1mdln2Opsmd12rna6VfflePYGaBXmWo9xAtd9+pxvVV9VOWe4dp45YO4zi3uUexwHYp8cds/eM9BxstaI73gr97BxOR5vdY0vT4vtXEV1tq19I15o0nDRrpi+xqHtMhOo1T7XrzMx6URPlm61vxP2UCe2M7dXx6p2rduunLYU29U5Xb9Dw8f97nc2bmflk83P1b4FnY8PKYusj2fzSlY+aznWJ+jmMx6rxLjmO/vW8+PxOCbFuqr+QcLNA7O5IZtzMnu1zNx3Ni9UZZClswY3Np4iXoCrUnjrIDM4ZBJ04fXbOU/xfDfp6KBbOQTj201ysxXheG5WDm6/K9cRj769W8X37P+s4+ScHc9Wwd3qdueK/dp9Lv8zoh3q6Kho1t9ufybAY7xVHpxAzZxLN9HFdqrttTMROYGkZaJtyvWd2aeafGOeFqJQHjaO31EoxnMze7O8xPxrXkb8Woa67epr5vS4/HacGrft4sscIO2bEW3PGq/GX6UX6yNbWY5lGc+N4WPfysbqaIs6svECwDgns8mVZbxTYtZHKwHmxnEtq+zumo7I7Iq/ThzZ7zimrCEbV7JxPWtfMS4duyu7uo54JTizYzPfJPM34nFtP27bXbx35VFdzK/2ufk0q7eYbhbO5fW84fp7N2zWJkZYHaMyga3xZ+Pq2KcCPLMzmyOqsTuzIyuHam6q7HBxZGlUbSibY48ZAwGuauHtnPBqEnOORdch0QnSTYJx33D29CpwNRFndjuHIk56uvKbDXgaZ4WK5pjWENtDPLnVr2yF3k3g3dXzyrHK8p5NGDM64bReF7IVaydIKmHuhLqm7dp+5Wy7iTvGtXx0ZU7PG2FcGVX9rnp+epTFSLf70frM7FnCjtX2hbGKr85NtHOcr/bG+nPtKa6EuosmlYOs4ZywVTIHqOOEZcertFz/1vIb5e3qKbPR7Y9iwdV3RB3JWF/jJYCx7bhV7+V7sTur12jTwggb09S2oe0+tp+uKNZ+GPdFOkI7c/Q1zFpOJZB0nq3Qu15m7Wzs13O6FyTc76weuvPMDE3LjRvxFu0sL1Vfc8fWiu+sXLplcap22DlP7XF+YXXu2vo9pC2s7afRLv2Oc1Ksa7ff2Zy1keq3puXirr5ncVbbXdx8k4Xbom8DXDXPeHcmwrWDfNbR3MDnHGwV3M6mKl03IWa/O85zFkYHuEwcq1Be/jKkI7yzCd2taLtVbWd3Z8DvlnP2e4aK5ep57ep5785qVRQgMX+uvVUOuJtYoyMaBYaGcWU/8hCFUkd8a59Y+8ls0t+avq6Ial1EO7OXtalIj6Iqc4gzJz460SM+XTHOyPpHp990447xZHd6LOhLAfXcuB33Zf09tsPqQkQcT6PNKr7jxR0dl0ZasV7jvBLDjW29hb2z4h3b0Ex0Dxuq+cK1PRemE5dL+1AOPTebm7LjVdvSNqvhZ/Z26mbtfNEhE7HVmOXOcXPs7GJ2duHbbUe7ZuWg4XROy8J3+4mW0SnrrGp/nXOOCbPG/1XRvaDznj5eld2p5sbjzm+37fKrx2dxV3FVaDgd07W8NMza9AAu7TPeI1w1OURnVwd2Decmvxg2xqWDWyYY3G/nPLuJ0TkSpxwYZo6Mc5idKNeJPJvgx7eWZywTVz5aV87+LEy1v4MTziq247eueDvHaSa6B7GstC3NztXztE7WiAItw6w8o1AdToC2pbWiO9qnFwmcfUvY+Jy5HtP6iHaOb31uOGtnTnCr46zbbgzqipcszzNHx52bxZ/VV2x/eqeHO9/9ju0wiuARt4pcZ/OwQVfixh06VRty45s6Y9G++HukPfp1/LcDnQ/cXSyun2q9xxV3R9a2OnXaoRvnIbh2no0ps/bc+Zw6f5lvccjcoo+wOL/DzRfOjo7voOGc6K4Ed5bnLP+xb438Ottd/zhlG+zWz9o6XBP+GN8j4urBhYkr3KMN6YXIaFs2Zuvx6rfmtdN/s/j0vIjrJzM0fp2vZtsAF4XNhXfsQM550gnMOWDH2uMmjE5a2cAXf8ft7ErlocTVtiwPzjG49tprb7KiNMRJXKFSpzVbcdT8Z9vRqc7Kww3aM9ack91GHgW3iu9DRKVzOpwA6cTj8lhNctnxbCU4W/XWCT+eP9pLlYd4q67rR8OxqOpu7M+eb3NpunJyt5MrmZDKhJWray3DKk9qZxamsrVKQ/uv5j2Wmfvv+UoMjPP1YmIMH7dnt9+7izpjLFruztE6jqvYmW0jfc3/2BeFtdo/0o9ttZoT9DuWsbNJ26CbR7Q+TiliHJ34Y15mju4axz2bJ/X4KfLQjacSCzEtDad1PtpsHJ8yqovhrmwq0Z0JbrXb5TX2LTd+ZPNENo/Nyroq59m5x/hTp/TF1uLaTyxLnZ/j/BXHh9nc7/a54y4OPd6NM+uz3T7lcGO8tuksDYDdZf8fb+0k6rTqPj03EycaR5X2CBNXJWYOVta5nfOXfVyeqoFiNojEstJ8DnGpjrJ+x3RGPqOwig7qbECuBuHo0I6yj/XQvShxzMCqz7PGle740ZXw6DRlbTBrk2p7LJfolEX7Blk/iI5VrM8s3fFsa7TBtWFNQwXPEA2avhPA8da4mNfYZmMbdPmM+0b5q0MZ08ye2x3nabixmp45kjGNaIsTRaMtd283d8z61kyIxLDxIlpsZ5ovt9rdTT8bS9Y4YyNdt1o3Vr1HXlR0OwHuxv/Z7e4qKFx71rKp+tus7cR4ot16boyvI2JOSdYetHwrp9ztd8ddG3MX5TplfgqyOVrrUm3TcHHuqOzTedCJ79kt5Lqt9mZzibNjhNP8jW/NSzauHErmw1W+3bB5RuYzzcIf43e4cuzYEcs0Pm6V9YOs3Vb9rYqj04dn43tmWxamypeOlbN8A+wu+4p35XTFwa0amJzoyTpZ5fBomExEZQNcdPaUajCaTdhr0XjiZKfCIjqt41tXLpcw0dkdk7m7RdflqXLSx7beyhmv5i5Em5zzfwzu9nK30h1Xu/XbtZWxHb9nE/goC9feqjjG+fFCiApYPUdX9tThd8/IxjKL9kbRoXEM9Hm0KJrjtsYz0nF5iOUdxwaNzwlwZ5faEOOd1WU8pt+z1d0Zs/Fidl50wBe0bvUCUvXYjvse8cfy0osnakt0GrN2tvQ5vbtmCTvuzNHbLMdHL57GNhZxF3icQxvbQLxVftbv47bOT9ncEs+bjSXuHLd9KDrfajus5tusnbh49Li2CzdvxPyfKq9r9nX8lxgu2joT3eP82Lc64jv+1tVxtdltZ8c1P9VYkbXhU9aVltOViKcbfubLZeWsYRT3+IvOw5XtVb/M+uksXBavi+sQtA1WaWTjFsDusq94O0ckc8I0TDWgV467iz+bhKoBTCcaN+jowDBjFrYblzqKTgiNvI2JMz73OlBh7iZ7l3blMGUf58zqsWpiOJS40q23lLtV7xE2OhwqXGIZZ/UTj+uEkbVtF19Vpi7NMUnrBRlXH5n9aptehHGOcXWbeZbfamLVclRbndDK2ktsY3qLdVX2rlzcdnelJ8tftd2JL4ZXW7TMs7bs0o/fsY9qXerqdGwvUeA6snaq45HrKyPumF7Md4xrXGAcYl8FhsadtQ83BlT92fUld+GjOk/Lb02bnTFz4GfHsnG7mpvdGJbNN1le1+Q9m8dm+Zz5B9k4OLNttO3xTyP6jyPulvNjBHeVp2rs0PLK+odrrxVuXtySqh6PpRPvmvR1XnF9ZM38kPlUrq/quVUcx85dLi1tF862qu2cVXsCuDC3mmcTg1I57x2x17Fl2NCZNNZ2ZucgzOI4dLCKk72mOdLVW5WiAFeHVm8p7jpO1e+4ohVXOrK6jWm5OnV2VbhbzJfv5Zl3Fd/ZincmVqq60Xoa54/vaiUhiyOKCVcuyye7Qj7SjMd05W+g7SFuDwEz0JXtqv9mYmJ2W3DcrkSPiji9VX381lXNrRx+l49hm6MaF7Owkfh3bE4UxN/OxnGejrXVeBLjjf3biWWX1kKsp2i/3jofP+6Wc1c+ekFA78zQdlM9wjH2uUdQ9KKns1nDZ3Xi2rgLp/1hhp5bbc/mwswxP8TRz/pf9kjEmryvcdArJ9/NUepHzER3bNPjrz6zVW53O/nstvLMx+qOc5o39z1jjS/T8Y1OkVYVbs2Ye2ja3bjdHDf2q3+Wxel8KLd/zb7suPs9I+tjLlzWd7NxDOAic3LhrXQGIueoRyqxPZv8Rpi151Z2Opt10nNpHjvxOIdOV50y5284AHFQdxN85TxpPcTfQ+yo4+JuH1XHJqvbtWWW3Waut5jrR50prefKpsq+LN+ZQx0FhoaLjl6su1iXVX1UAiMKbc1XFGDDjhFntmqicTlBNKtXLYdop+sHMU1d5XTf3e1YFu5YZvsa57USbc4xGxeYKvs67Tdz4NXhz241X6hEt9qh52p7jRcK3fgViYJY+1C8yJQJ8KyMhy0xXb04F49n9rq43W/9zuYqV6ZrqRztaq6q5gI97r6rfMRyjeeszWPmuM/CVMe0LrS+XD5Hm4mfuNod59zs26VZ+TJr24yOzy5MNkepLWuo7JzVn557jAg81P6MTvln57l9OsfqY1suXR1fs/6rHHJORaePZeWl/nM3/WP8aoALK7x1YM6cPedE6nfVKdfSdWQye/RY5rCqc5l9nwodkNV+t8I6nNoofFTodRyrbFudhzXCw13VdU76jExYqwjX7ZnT7CaBjgOR1cc4phNMrFd1DoZDF21T0T0T4tpuNd0oKKrnzIbTFo+rXdnz1d3npZ1zqWk7ByUeH+fN2mIV5lAnoOsIrx0b9IVxVXrHOpbutu6q3ro2xDqKd8l0hGtszy7NWJ5RfGf2xf0x3tGmZjg713Aq59/F221XmUB1Ze3mhOyY2pPN9zHdQ8qj4y9kwmsmBqo0s/F1tOsovOM+DaPlnM0Nmeiq6DymU/k9Y7+rvw46Th3qEx3qPx3rd631HeP2oefqvKp3q1XtXY9XfTvbv7Y/raVT/+qnnCJdgEux4n2MANdJbZx7ChuqtON+N+FUH83bIRNM1/GPjqF7/jGmrTZFYVSVc9fJUufZhcnSqSaCtfWuwtsJ8eyt5qM8M6c/2uW2s3qu4lPHapRhFN/atjLBrWJZxW88J2vr7vbtKGLiZBjDq6MwE1DRNifisvauaeut9FqWTkQd0xe7Ducx41Un7cxR69iaOfQ6XsT9M2Kbc3UWw8R+MvbF8aPKfzxf76LI0McrqnYZx1Ft41n5j3xo3K58Ok5kNu4cgoqcWPa6rbZm4/FsrsjsyKjmKf3t4lsrEmb5r4hj4fjthPPY724rj2O29i/XLx1aXi58nM+i7VlbrMpAfaA1ZebyVuVp0BFlM2bhzpuIq/xN9QkG3fE5+67KfNans/Grc37WZrVPnrc6AjjXwjtzCF24+K2TWIwrHj/EnszhqpyybCDMjlVirJpM1+ZFxU7MRyyjsUIW9+nVU+dQjXOy78wJc07G7KOoc9KlEtruVvP4TLhOcrE83SSwRpCMOFybc4InHo/ho7hRe1256v4qfhWuLh1ty1pe8VxXFjGMxtspzyr/+siFxuduo5+hzsGhY0+Vn+74pO002rjGrqzdub4/ws5WfuN5UTxXY7+2GR1Duvmobh+PFyb1lnPNYyXC3DgQy8yNox1hU5XPrD5n82rsI5mQc+OatrsoMl0ap3aMnaB0YdbEt2Z7Fs9sLstWtON3jE/9JNcvO/7D2J9dPHY+VUxPx5R4XPu/hq+YtTE9tgWn8rsOSVfJ+pGel/mYVVozn8p9Z9uz8qqOZ/PIGtacdyXqFuBcCG83sMYBbzZo6LbrTIcOoHpe5TBkDpxz7mZ5OcWAUKWjIimbWOMxdciiuHXOt26rkzFuYc+cDXflXz+aJ7ca0MGtaHef8c4cFteWXfkoOoFVgjra7+4ciOc6oR1FZ/U8q2sn0Y41t5yrwI4OmusrepeFCqNZfasQVBtHmCwOXfGeObCuT3Tb5KydOFFd2bMcj29od+OZppOFiXHG365PaplX9rn+kqUXj2cCY6G6GyLao21xoH1B7xBy9VCN764MnfCK+dFwlRDuzJVdXNzVnOfaqNZ/FI0jrzN7O6LhlMzmL2db1m+yvu/qPNungrsS2hlV/cQ2E8dD7ZPVuFfNcTFuDb+G2TmdclhDLJdZ2cbwas+pqcbleLzyE5zv5PLYneOqsGvIxjM3tmfnz/znSh8AXOoVb+1ATkTosXE8w01YXVsO6aROgGUTUDZxHZr2Wjtj2iO9ONHHFVx1DvW8ajDuOBvZJ3NIIuMlNFU+K1RcZ9uzFe9MfFdkQkidI5cndXCX3/qs9whfrXiruHYr4dGBVptUwDqxGgWE2hDzEm1xK4wxfRWTlSMcy2Fs6yqSc2y1ves4ouWUOaqncsbc2BIZ6br6duFm44yG07LSfjzsivFmdzLENhHzt3ZuiPmM4XVMWPptHMdi/3YiPI6J1ZielW/lOGZjRcyPtjEtd2XteJMdXzP3ZGnGOSNePHPleQhuDj2UmdBYE4fWVfbJLibr/pnQ7orOSnA78a1xZ2m4dq3z1ikFtxsHsr7RKRc3pnfOdce76R4rAg8979A8ZD7KFjZ049V2UI21a+IFuBQvVxtUg/Ns4HaDfYy/MzlVDnTmjGSToAqCeFwnDffc9KF0B/TM2cuef3RiO66UZmk6p0LTc7fY6QtmnBDXdFweuxP++NuwSnhnjnpnxXvGzFnTest+622xzgFSka2OcfWp7NC4nYDW58817CC7xTe+vTqKcr0oMPbr75iuu314xD1zbquxQfvKqeg4uGPb3eKpdJ1S99uNkdq3nf1aZzouVPa6+KJNTnhHoad5GG1g+Yw+rW0kxquiRNPUOo/tQMPHsUPvnMnEv8ZVlcuaMWMLYhpxHIqs7Rs6hs3G1255rXX8qznWjeNdgV2N9248mY0vbs5e0Meh4m9tg+ofVOUR7XTHjm133fNjnmcifG3fWmvnlRR72Tg18zfGvvit27ovy+dW+Z/VWzb/nJV9ABfqreZu31l0mEMGjs4E7UTLOLY4e9kqrVuJW8Map18vEugzryq2qyvykczJcLeOO5HtBLdb7Xb71ohu9z/e1Wq3u908c9bVpqx+dOLLnuvLJhonnON50cmK9abi2DmHuhJe3VngVqqdQFYHMvu4W9KdeHe36TpUpMWPqw+tm8xBcWWROYDH4OzWY9VdAtG2DpnjFduprl7GdqvpujY4+lF1jo4zKryq/q51HNuks3f0aR3fMtu0vJwodPaqbWvyVNmS1flZCG6X7kDHgXERbXbe+N0V3BEd/9yxbF/lvLu5Tn+7Oc6Nr5mozfrpbH91vGpnoy9mbc/lsaoD7Qux/I7BjQnxWGd8zuw9lW2Hhj/UBuf/jG+tg7ViurJpNpesnfe2FMKIbLgaOOkz3m5wrgaxbAI8ZvKqnOvs2FrbnFjIBEcmcNSZyJyLbLDNbI4f9/x2dESdSMyEZBSP1S3l+nz3EN5j3/hrlVOL7kx4x9vP9Vi2WpWJosyurIxH3vT5SI3P1V9cXVYBEcOMcnX74l0Pem7WPzX9TOCo4+RuQx6ofbGsRpoqNLuOxUK8UOL6dtbn1cmdOb0dqvFibDtiGC1vDRePOdu7dqnwjv3Q9clhmz7GEoVwVm5OdGueXJ/TY8OGrL+6cs1+d+oiG9c1nAub2ensce1jlrezRO2KY8raeanjGzg6ffJY0d0R2O7YiNttH4OOc1m7jMddO52Noe63Huv0HWU2Hrn+MBtDZnV8ak5Vl2vS65Rv5d+OfdX3lchnNkfp/DDsAbgaOZnwPqaTzDp8d8LtfLJzOrZEh2NBbwfWyVAnikywZelmA1RVRur0qXBTp2GcG8OMfSqiD/3EuNReV/4Leutwh5mo1lvN437ntFTOsJa9CpZ4m38UqjG8E0t6TkxPhWoUvbFO4z53O3rmMI/w7tZx/R3zGsul+9EV05ivGPea+u/0e+2X3e8ulXMa48xE1fgXgpinY8nGEW0v119//U3a7Wgzel7so5ofJ76rcVCFRNyfiYzYP50YiX05y3+2z6Wb2ZfFqaLH2ZV9MjtmtivVHDaOd+PRutM8ubtf4rYrjy2YCTIdR+N2HL/H7+oT+4WWj9s+NC9V29C5ctamXDnoOKDlo+evHZMzjm0DpyjjreytbFq7P6Nqw2vjyPrKofEeips3HFuNHwAX8uVqHUeh6sTu2GwydftVZGbPH2dx6MCpDmWcqCpnOh7L0snKqXLsZuUQb7NcvlXEuAm342isFdq6ytsp92rldMYQ051byytRvtYR1rIcolnzWt39EOvLrTbHssyEyCj3rC2qGM+IYdVxdgzb9Bb1ihFuJt5H2PidxTe+NdwxYvoQp8O1lcopXogC0uXJpdGxoyK22+VOFP3rQR3zYpzxt66CaxrOjrh/1tdcO87ERVXGMV21KztX4+iEmYXVsaZaEXdxnYpqLqrm3azslWwsvRIOu5s73ZjRmQedSF0zpnTyMmvXbv9CdYEjG1f1sTEtM23/cV44htn5bg6NnJUgjOlVNs/syfrUMRdndHzufq/hVG072n1M2znregc4l8K76kRu8F8rvrN4q0mvO5E6Rz3arSuGceKprjRHstuMszJxZdYtG70oEPOgz/q6/KuDEQX14pzrM9s6acdz3W9XLlm+1w6wXbG9Rnh3HPnY1lR0a1uJeXb5U7Hrnr3VMHFfVr96d8byHV9ylqXfIZaB2lyJDxXBLs3sEYi1Y4N+a7guhzgMrn875/lQpywTfdk+jV/74dIudNU7iuvM7jVl486bxdcVprNws/Oy9hrHCr1LphtfnDPcnTZZPKcia0/d/Zkwc+17Nm4em69s/Fwbx8xXiHPhyJ8bQ3SMOUQgOOHsxopsDHF9KWtDejzmtQob8zvjmDFhzFOVr6Zj+laocM3G6+pc99sdWzPfHZrvQ+Y97eOZmO/UR+XvAlztnPxW8zWOWDXZV4NPnOA68WicOqFm+akEVEw3cwDiOSq4upNFVp7VucNRdpO3c7Q1L7F8nOCuBHgmxvXZbrVfV/wOdVrGbeROXOvt5epIRwc7bo/yix/Ni7a1rE5imKp/xLYWw8a03a2+lcMf26TG5dJfI0rcm8qH7ZmdMX/RockczGOdhUPQslrTZzMnWC80qMMe0XamY1/mUHeFjY5LUWjHOtP4Zs682jIrq7idiYoqjm648V0JXo0zGxs6F+lcnjqCO9qi9T3L56lExUwgOFz/PrT/ZnYecrwSOJVvsEZwH0psF67+szmoOubaSFVn8SWxzj9wfeSUaHnOwmldnaqNraGTVrdNVv5E3HeoHVvSaf+dus3OWXsewKVd8T51R6nEwuzq4GyS1Q4eB/c4KapAyFayo9hYiCvlawYY3V4zwLpnhONqpEtfBbf7zAS3K1snuuPvY24vjxyzyu22u86MtqP4nGws32X/cHCqiz4qUuNquTrjGi5zIt1dDyNOh6adMezQ243j+Voear/rW50LXIc4HLOxYqQfw0TbZmlWwlOdZM33GltnjnBn/NU4Y11Fh1bbfWZvFOsubMdOJ1RdH6z6aHZOJlq66bmV7nisSneNSM/KaAvnU8f+uM+Fy3BlORNQa+xbE05FWDV+nEpwr52fF7I7JvTbtcvZsSqfWfxxXoj2uP58igsN2f5MpGVlrBeVz0KIHiq6uwK18/tKC+5It9x1fsjCzPZ15iOAS/WM95oJco1DO8KPbzdAZwNAJrTjOSpqXBhn7xpBMLvtPMu75nPtwO8m2srh15Vtt8rtRHh8i3lW3pldx5KtZFfCOxPcM8c+5iUTZ3FFV8O7eBxZ+jGNmH68uKJCauxT0V45OpkNsZyGPcvvzm3m7ry4aq5pxjyprcc6gJmzFp3wEXbNBaKq7cR6iG3GjUPRHo1/ze+sD8Z6GH0gPrOvbSlru9UY1enbmfjQMpv1zaqtHXKuxuPGibgv2q1jiTum5TY7ruWSkZW9G4vc/nhudfHE2ZLZF/vZoQ54RhaXu7AQf2fCW+11Y0S13x2brWzHcFr/cf9A25XmL+alEt7aniv/KZbpGjpjQAxbjR3jeKyzaOdFoLIz85vWluOpqdr6GrTvZ3PeLA6Ai8pJhfdW6OSjE0BnQJg5otWA5hz0uIoXt4cDG7czobM27928VvmswsXJOuZztuqdOTFxUnSTtnMujkEd4UNXvZ2TvxAdeHVo9AVjY190/Ed4d2tc5di51d/YJqPDpO3PHY+fGI9LO9qg7VjFhfYRtXt2bhR9I13nhKoDuXYS1raoTmX8Peo//iVdxUy8jTgr58rZG/Oe2VDZpk75ElZfiObEnhuzXLlpP9a2sHaMj9vaJ3Xb2TsT2N2LbGNcGI+pxP3x0RUnWqJd43tmd9Vu1oyVrvxdu9d6jLZnAkzrKKvHK0UlEuN85D4d1rbrrH+Nfe7bnZvtq/Kj+Yrlko3VY//acsnyf+h5s7Y2iHfoDNRm1y8zNN1TibzuWH+quI5l7dw6GzMqENJwmdjk78ROMfl24lBnVJ37bjpOzDg7dBJS8T0cqxE2CorMtmzA6Tj4awdp50jrpBzF97hFXF+0pgK9WunWW5md8K7yv2ZAdg702lVvdcbdapmW17BRVwp1hXSEG2+Pzp77z9rhsEGFu7YvXamMIrty7F0dRDvciq+mu+BWrt3FgGirE+4abpSZtn3XF1y/dmQXhrQtXHvttTcKrYpKwI34NQ9rnQ5tgx1HMhsr1cmsBKHGGduV2nYqZ92Vo5ZpZbPrzy4uN17Ed0UsZGOIxtUtz8yGLJ6sjBxdwe3qyvUvF78TRrMxJbPRpbVmjlO7qjzOBHd37MjiX3BtM+ZvVq9VO9L0dd6NeXS2xXjjHK1jlGsDnXo9Fdn4FPfphdpIVm+VoHf95hAq4T87J2u/rr0eKnSzdt2ZW9ccO1ZQz+Y3BDtcRM7FinfVeapOpw6kGzScUxDjrYR2nIjGd2dAzcR2xyGZCbCY70PJnOhMOEfRvWbFW5/tjnRWD52tFZnAzpzrZXuI4JkAd/XnJkK9NVfTdRdonPOQOWvxEx0IXUXNVrjdBJ450c4OV5axPGI+3TnuYkQ85gShy/84N9rmxgDNn9un5agiagjv5dNts67NjHJZ2lwmXF25d6jEUVYmHbE3syOu8nbFSjcfascakbrmo/G68cP9RWF2vtoyvrOxpJNPzdeMSnzO6kbb5Mz51XqL53VZ294ds7GtEtvDhqoNV+26ansuf9n+GIduu/E7u3DYEU8q4HQcjMeO6dPHoO1w7Ivj/SDOO526O2V+oi1r0qnCVm01htUwa+vqlHV7yrhi/mZ9E+BSv1ytmkA7E/4sDSesZxODOivaibO0dNDPHPi43XX2znrgUDv1mA7iuqKtK92zF6u5l4dtnWd1XGer3erkHiq83aMG7jZzxyiT6mVrmjc9N2t3Kv6zlfDosLh0RzpRGLsVbL3YpI5SZuOsPLJ8jHAjjaxstb6ck1m1gYVFdHcuFlX1oGVYCaEsnTUCRcW9tpduenpM61XTG/mc2Zal59KOdRKPuX6pZd/t351Vbh1DsosBWV6yMG6/257Vl5ZvV2y787Vu9XdlSze9Ne35UNGt+0a6s+019md126k31xZceJ2LD63bmE4cHxbiuJ6lcaVEz7CxansjTxp2S5vXppFdBHHt1oWvxPiMQ9p6dc7a/jLOWct5aH8AF/blapFZB+rGt2YA0W3nRK4V3lGMzMT3mgsGMY1TOiguP+qkuE92m7m7FT0rKy3XU1I5wtlqt+7Lbj91Dv7Is3O04kpgZkcmHGeiJMYRX3jlVrmrlfC4nbXDmKdxzrDbtfkouNS2jEy86wQf0162lzsqRjh3C3qMP+Yzc5Y7AnzWdl15xFvp1Rat88zpdr+rcUH7oLuwoue7capKXy9EaJradjp2x3S0P2n/c0Jc85HVZ3YRzq1yZ6vds7HmmI8rc3c8I3vEpzv2unDZeL4VGr+2y2ye0mMax6EiW23o9B1Xl67f6fFjxV6Vt5j+COfEbDaWXmmGzdl4qPZfSZvX9JmsDbtjSjV/63zqttf0hVnbOgXnoZ0BXLi/E5txHgbFhcxZi/vcxD7Q1U4nRqpPnECcM35I2R46GTih7Va6M5Edz4vP+Y59scy3yFclorLbQytnulrN0rrRW8wXqpXvbAVX24PmT/Op7Wgm9roOqpZptMu19/FbV+RV8LqPnhPj0zKJ+VMH3IkNl3aMJ+7T+h52ZM52pw1m9eDqN6tr3Tcjxj/6qN7VoOWi9ldOdyw7Pa550/g7zmMlYtz47Larvu7GCR0DnNg+VHRneXD9oFrV17KatQE3jmw1p5yKmT/g2lI1tsXzZv2tapfuOx7P6tvlL453WX7i8TgOnarudEx18boyUXuvFHGsiGON+97C1k4/6qZZtWEXT6cdz9p7xTHnDrIxZ4ux50prCIAL8Yy3ToxuotTBtIpnDc6xdA6QOqPRuR+OtLvdtuNgVbZVv6s8dPM+7B3fmXB2YfS28uxWc/e/3bM8dW2vqAS2c27j79l5Wn+VoIzCMPut5zphlJWDOz+K4NE2nX3VCnj2Qh1dGY92REYcMa3uyncm4qNNrn9VDoqry2z/bGU7lkWF5jvWffVStarvd50WLYtRpqN/xrarDusap17FQHYhwV1sy4S3y288z21nx6v+rvvcsSi649vsu59YRq5cnY364r7ZeTPcyx2PZY2Q6LaltXPYbO7qiG2334XLBPKsDbo4s3xqftxxLU839h1CNn91WRN2S7TM43y4hSjLxjs9Ptuv867zwVx7n6H5d+VxyrLJ4uraewyIbriIXBHh7Zy1bDDTgcINQGscArWjcsT13HG+OtSZsIm/s4n70IFD01rLzHHR28mricHt17Si3YdM7p1zKuc6E9zV72iruxjjbjOP4aN4dIJsHNd9KpJcebiLPtGObLXb1b3aVtWDE9AdUa3h1OmrVtFjP4kXKDJncQiZIWZi/xvf40VpTjBldNpuzN/4jsdm45Zz6iu7sjhjfxy/Y1vMnPmqPLRvRNw4pOOTE5JuO6bVKZNsHF/zXa1uZ2NI9cnCallrmFl9aNlcJtwYNrYHOm7G/lHNt7FPZIJbf2t9Vn07Hs9ESpXvzJ6KWd5dW9V0L4Kwcfms6noNa+NaE67yn5z4ztJZY1/WDtbm07Vn51+ckovQFgHOnfCedWw3qc4mmmoyq8LHwSZzgmLaYyDMBIfGlTlHcbKeDXbROVZHWeOK51TxZS/xcgN9dNrj8WxyiCvfjrXOojo2p17xnoXXW/yiHVr3equ5E93ZW86jyNRbk7P2kbUzPTfaqELbCfCsHrLVZ3VUIzEtPdedM8rMle/Y584f5y1C+xa3uEUZRut0bKsdmn89J2McV9HZdWzUpsxGh3N8YjmOxz+iUJgJPk079hcVGpkQisdifToR48rB2eHKx43lmWDORHYlwjuiOxP3rq/EMK78tTyqNqto/1o79h7j8B6T1syGar6PbbLT19RWJz5nv9W+mf0xHu0b1blZX+nmU891bTOmdxFEt9a3jrOxjA9hrW+5Jt6Z6HafiMuXa//6PYujcyzLk8tbZ848RfoAF4lz83di2dWyTBC4wagj6iPOYdLf8VwVT05wa7yZQDq0nFweOmGjwzsE8gjnBn83CcR4ZxNHZkcso6q+1EHokJV35ox3HGgXr0728ZzYPuIxjSfmKd5BEVd0neh0ZRNFfjwv61c6KWo8s74S+0NGFNFxnzs3ikN37mhbsRxi+CG6x5vHYxm5OtDj0QYtaw07a4sjbLwgE/8+rHOus3M2drh6jhdxqjEqplmVWyxvLauZMMrq3uWtsknLJOujw8Zx10M2BqwR3p3xQseXTIBH22Obdnmvyqfi0PkmnnelRXc2l7hxWAXG2HZzjWtDHfsPLZtsvpvNg2vj64h9veATy0/n8vOOloOr81lZubl1C7Hd9bequw2jjVvZ2okzy0/c5/pS3H/MOHPseQCXRnh3OnMcaLJJsBLcawR55jSpGFm2s9tbK0e2SnPmGBxKNRjrQDe7XTybgCtRp7cqZ4NrJ79aJzOyla3MAe6sYEU7sjyN43F7ttqt+YsiqbrlW8smOlAqqHX1261yx4+u0mqe9fgo81gWUcBr+WSr5i6OiCuTYVsURlFkxTCHChcN3zkvhtW7GWYiIEurGlfc2Kd9U9t05pDP8uX6RkxXbarEd1dQujDV2OsuDoz9bizoCPBqfHDjibvAV8WTlXVW/llZHcKp4hlU83b3/AxtuzrXdebTrNzX2nyoWD70XHehvJveQNti5KIJ7qpsY/132oRyahGY+UnVXOxebLvW/kP94lm+dD529h8yx7p5Sfv7Wh8S4FIKbzfouYHICbkqzuxcF8ZROVD6e4gBt5o5izdzsrpOwozsfJ18hhCoyjHbl5WnmyxmNg4xUtmtdAfyjtM80piFdfZk2zNBELdHmlGU6Up3PE8diJieE7da5yOcE9fuU614jzIeNmveF+G73M6crVwPspetxfAxT3p3gJZpVddZfWX7HK4+q7BxzBh5mTkJbkyp2lvHsXNjnBPi0e6YTnSisv6h6bvvse3a9ExIVuWS2a9jeLwo41amtf04EV7F79Jz31Vcmh9XBlk9VeV26PGz4FTitZpDY5hZWzsV3b4e7VpTFoeOW5XgdvN7Zdd5aD8DnffcfLg1HdE9E9tVGBXhWVrqy7lvZ3MWJjte+eBV29iiDyLA4aKxufDOBr9ZB4/bWSfNBGPHpvGdOVIu3ZnDunZC1H2HDCDVYJqFz8qtuoLZiaObfgetg269dlaxnONbOcCuHTgHRW12ccYLONFmfWGfhsvEsBNUUUCr+HPCS+PRtMf5GjZeKNBV+7EvQ8W32lqdq6JcxdUhoqPrLMzijvmP5R1td+lVv6vxbxyv7szptPcq7/GW1HhO5oy5NqvtV/vM2rJw9jshXInoSnBXoru7yp2FzeabWM7dvB9C59xjnNmsXjt2rJkfqrBxHJyleUj6M9vWxN8pq3jBM+tX2j6q9uLm9yyuiyC+dUzpXkA4RX13hPAasZ29N6d6DDDz2SrfrPLLs7CV8K7KpzOfuXqa9XOAi8iZ3Gp+SOeZTV7ZwDIbiNygXDlWI0z17K7a7ZxbPWeLSSsbNNXx10F99gbzaoCNYn12e3S0ZeaAHFI+megeoqxz2+cawRPDxLBuRXukq9vuO57jHCYtsxGf9gUVsp3JOp7j6mS2Sh/DRJvVztEPRzq68u3Od7Y5wVTVXXWsEoFdsTPCOQfQCQGX7uy31k1MK7Yx166r39qWtS6r9ph963aWp9l+d1xFqxu/40p31v+rC3XV3ODEtru4NxPimpesjrI6WCt+Z8cOwbX1Q8ZwjWdt2GPnkGOYjdWVgO3kW9tBLONqLnP+kbMn803cnH3WZTtDx9xYBrGcsrxrXLMws7rqzLX6yQR3PNbx1TT9+HuWlypM5WePcpvNu7O23in7znGA88y5eLlaJBv01VFf6GzP0skmLDdwO8fUTXQurcyp18ki2t91Xiqnt5qA43bnZWrjW585ygb1sX+NMzVs1t/VCqiGc860c7KrOl9r6xCUUYiOb207mejWc6LQjBc1XP1mjsaIZ4gynehjWrG+xm8l1kP8ayqtJxXb8Rz9rWnqCn52McIJGb1oFsusGlviMS0/FyZDLyx0zsnGBvdb43T9S9uDlqfLU9bn4n5XTm7c7Y7Fa513tWVBV+Orvp71+0xAa3pudTs7L7PHpRnzU41Ja8eo2C+zY6eiEsCHxrEm/LF5XJu2G2fX2qSCZSb0XLuN57t20XnhaYyjsj3aUYW50rjxsFu/nTG7K7qrOwhn4nl23K2Su7Tivsp23V/5cxp/NT9F3Hxz7DiEAIeLyLkT3gudgV0HlWywySYZ/e46N4c60lXYysYKnUxn8cQBeyFeRdV4ssF+TdpZPlV8abjo1KxxNJ2TPXOE9biWmxM5rmzdeSoeo7h2oruzIp6VVxRYo4zjPie+dAVZ6zG7g0HrLornNRdKNM2xQhltqsjqUUXTCOu23Qq5lldMa9YOXTrd/l3Z3IkzhlF7OyJOy86Jz0HmzM0cvLXM+pjmYSaKswtu1TjghPVsZVzDZuOPpudWwbtzU8ZMDJ6StfFfZMfZzRHaR7Jz9Hi88DlLU8dWV+Zuzs7O0THAfZ/iwspZEP2M6EusYY0vM4sjW6RQ8VzdRh6PHXJ34iHCW4/p+WNf1Q46Ptwh9QNw0Tkz4d3tYG4yi1ROXiYgnB2ZMzMTYpXNan/l/HbKIMt/LMvOZL3grpC654dinNmAH21x9VGhYiYLM46vcTSr1e5s5XtWz7PJJeYlE9Jun567ZkXclWPMV2wjuk9XvYfIHHHGNqBOTOb8xbicYxjjc/1tQZ8X13Y1u1BT1Wv17ezVC0NrnMxouyu7WZydsFo3LvxMsLnf8fxslTzakDlzlXMX2+Wsb1XlkOUlW1XuiORsNbwjujsr2y7eqs7ifnf+Iawt8y6zOI91rk8tnvRixLD/lCLAjdfVRZClTpeXU7p4svE384cqv2B2l4WzrcrbecSV9akvCjq6IrgS3dkjgMfcbt7NuxPUma+5dl6s5qxDQLDDReXcrXhnA390Yl2HcwNLNtjMHFPnaM7sdOfoZOlsOGTw6IpddSiO+bg4Yvq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      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainDS = terrainDataset(df=trainDF, transform=None)\n",
    "\n",
    "trainDL = DataLoader(trainDS, batch_size=10)\n",
    "batch = next(iter(trainDL))\n",
    "\n",
    "viewBatch(dataloader=trainDL,\n",
    "        ncols = 5,\n",
    "        cCodes = (0,1),\n",
    "        cNames= (\"Background\", \"Valley Fills\"),\n",
    "        cColors= (\"#999999\", \"#DD0000\"),\n",
    "        padding = 10,\n",
    "        figsize=(10, 10),\n",
    "        mask_mode = \"auto\",\n",
    "        stretch = \"percentile\",          \n",
    "        p_low = 2.0,                  \n",
    "        p_high = 98.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d391ee",
   "metadata": {},
   "source": [
    "## Step 5: Train Model\n",
    "\n",
    "To train a model, the user must:\n",
    "\n",
    "1. Instantiate the trainable component of the model. If not using Gaussian pyramids, the number of input channels should be 6. If using Gaussian pyramids, the number of input channels should be 31. \n",
    "2. Define a loss function or criterion. Below, we are using the unified focal loss provided by `terrainseg`. However, other loss functions can be used. \n",
    "3. Train the model using `terrainTrainer()`. This function saves the model checkpoint, or state after a specific training epoch, to disk that yields the highest F1-score. It also saves a CSV log file of the training and validation loss and assessment metric values per epoch. \n",
    "\n",
    "As explained below, `terrainseg` provides functions for generating a UNet model (`defineUNet()`), a customizable UNet with added modules (`defineCustomUNet()`), and UNet with a ConvNeXt-style encoder and attention gates along the skip connections (`defineCNXTUNet()`). It is also possible to use models generated by other libraries, such as those from [Segmentation Models](https://github.com/qubvel-org/segmentation_models.pytorch). Users can also define their own custom models by subclassing `nn.module()`.\n",
    "\n",
    "Our models are designed to return logits for each predicted class with tensors of shape `[mini-batch size, number of classes, height, width]`. When using alternative loss functions, users should determine what output is expected: logits or rescaled logits generated using a softmax or sigmoid function. \n",
    "\n",
    "It is also possible to use a custom training loop as opposed to the one implemented by our `terrainTrainer()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c7cf62",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDF = makeChipsDF(\n",
    "    folder =\"C:/22318522/chips/train/\",\n",
    "    out_csv = None,\n",
    "    extension = \".tif\",\n",
    "    mode = \"All\",\n",
    "    shuffle = False,\n",
    "    save_csv = False,\n",
    "    seed = None,\n",
    ")\n",
    "\n",
    "valDF = makeChipsDF(\n",
    "    folder =\"C:/22318522/chips/val/\",\n",
    "    out_csv = None,\n",
    "    extension = \".tif\",\n",
    "    mode = \"All\",\n",
    "    shuffle = False,\n",
    "    save_csv = False,\n",
    "    seed = None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4532b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = defineUNet(encoderChn=(16,32,64,128), \n",
    "                   decoderChn=(128,64,32,16), \n",
    "                   inChn=31, \n",
    "                   botChn=256, \n",
    "                   nCls=2).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9df06adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = unifiedFocalLoss(nCls=2,\n",
    "                lambda_=0,\n",
    "                gamma=0.7,\n",
    "                delta=0.6,\n",
    "                smooth=1e-8,\n",
    "                zeroStart=True,\n",
    "                clsWghtsDist=1,\n",
    "                clsWghtsReg=[0.3, 0.7],\n",
    "                useLogCosH=False,\n",
    "                device=\"cuda\")\n",
    "criterion.__name__ = 'unified_focal_loss' #Requires a name parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb2454d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "terrainTrainer(saveFolder=\"/data/terrainSegTests/modelOut1/\",\n",
    "                   trainDF=trainDF, \n",
    "                   valDF=valDF,\n",
    "                   trainableModel=model,\n",
    "                   lossFnc= criterion,\n",
    "                   nCls=2,\n",
    "                   do_gp = True,\n",
    "                   cropFactor=64,\n",
    "                   epochs=25,\n",
    "                   batchSize=10,\n",
    "                   lr=0.0001,\n",
    "                   cell_size = 1,\n",
    "                   spat_dim = 640,\n",
    "                   inner_radius = 2.0,\n",
    "                   outer_radius = 10.0,\n",
    "                   hs_radius = 50.0,\n",
    "                   smooth_radius = 11.0,\n",
    "                   device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"),\n",
    "                   doMultiGPU=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a6bc21",
   "metadata": {},
   "source": [
    "## Step 6: Use Model\n",
    "\n",
    "Once a model has been trained, it can be used by:\n",
    "\n",
    "1. Re-instantiating the model with the same settings used during training.\n",
    "2. Providing this trainable model to the `terrainSegModel()` function and specifying the settings for the LSP module used during training. \n",
    "3. Loading the saved checkpoint. Note that the code below assumes that the model was trained on multiple GPUs using `nn.DataParallel()`. If the model was not trained using multiple GPUs, a checkpoint can be loaded using `load_state_dict()` without the prior modifications. \n",
    "4. Predicting the new DTM extent using the `terrainPredict()` function. We recommend including cropping (`crop`) and overlap with the `stride_x` and `stride_y` parameters set to values smaller than the `cell_size` so that only the center of processed chips are used in the final, merged surface. Note that edge predictions will be used on the edge of the raster extent being processed. These settings only apply to interior windows. We have found the setting used below to work well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bbc90b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = defineUNet(encoderChn=(16,32,64,128), \n",
    "                   decoderChn=(128,64,32,16), \n",
    "                   inChn=31, \n",
    "                   botChn=256, \n",
    "                   nCls=2).to(\"cuda\").to(\"cuda\")\n",
    "\n",
    "\n",
    "modelT = terrainSegModel(modelIn=model,\n",
    "            cell_size= 1.0,\n",
    "            spat_dim= 640,\n",
    "            t_crop= 0,\n",
    "            do_gp= True,\n",
    "            inner_radius = 2.0,\n",
    "            outer_radius= 10.0,\n",
    "            hs_radius= 50.0,\n",
    "            smooth_radius = 11.0,\n",
    "    ).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3013dad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint = torch.load(\"C:/22318522/models/model.pt\", map_location=\"cuda\")\n",
    "\n",
    "state_dict = checkpoint[\"state_dict\"] if \"state_dict\" in checkpoint else checkpoint\n",
    "\n",
    "# Remove 'module.' prefix\n",
    "new_state_dict = {\n",
    "    k.replace(\"module.\", \"\", 1): v\n",
    "    for k, v in state_dict.items()\n",
    "}\n",
    "\n",
    "modelT.load_state_dict(new_state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e3e7c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "terrainPredict(image_in=\"C:/22318522/preds/testDTM.tif\", \n",
    "               pred_out=\"C:/22318522/preds/testPreds.tif\", \n",
    "               model=modelT, \n",
    "               chip_size=640, \n",
    "               stride_x=256, \n",
    "               stride_y=256, \n",
    "               crop=64, \n",
    "               device=\"cuda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d655be18",
   "metadata": {},
   "source": [
    "# Workflow 2: Using Dynamic Chips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3b557f",
   "metadata": {},
   "source": [
    "## Step 1: Define Dynamic Chips GeoPandas Object\n",
    "\n",
    "Instead of using chips and masks saved to disk, models can be trained using chips and masks that are dynamically generated during the training loop. The `makeDynamicChipsGDF()` function generates a GeoPandas DataFrame used to guide the generation of chips during the training process. \n",
    "This function requires the user to provide the path to point or polygon features that define the center coordinates of the chips to generate (`center_featPth` and `center_featName`). If polygons are provided, the centroid of polygons define the center of the chips. The user must also provide the path to the polygon vector features that will be converted to raster masks (`mask_featPth` and `mask_featName`), a polygon defining the processing extent (`extentPth` and `extentName`), and an input DTM (`imgPth` and `imgName`). The rows in the generated GeoPandas DataFrame can be shuffled using `do_shuffle()`, and this shuffling can be made reproducible with `use_seed=True`. The seed value can be set with `seed`. Note that it is assumed that the mask polygon features contain a `\"code\"` field of integer values that differentiate classes. By default, the background class is assigned a value of 0. \n",
    "\n",
    "When using polygon centroids to define chip centers, the function can generate random points to serve as additional background chips using `do_background = True`. The user can specify the number of background chips to generate (`background_cnt`) and a minimal allowed distance to the nearest polygon centroid (`background_dist`). \n",
    "\n",
    "We recommend cropping outer margins using the `extent_crop` function to avoid generating incomplete chips. \n",
    "\n",
    "If you would like to merge multiple sets into a single GeoPandas DataFrame, such as when you have multiple training or validation extents, `makeDynamicChipsGDF()` can be ran multiple times and the results can be merged and subsequently shuffled if desired.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d9efa8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "valGDF = makeDynamicChipsGDF(\n",
    "    center_featPth = \"C:/22318522/vfillDL/vfillDL/vectors/\",\n",
    "    center_featName = \"testingKY1.shp\",\n",
    "    mask_featPth = \"C:/22318522/vfillDL/vfillDL/vectors/\",\n",
    "    mask_featName = \"testingKY1.shp\",\n",
    "    extentPth = \"C:/22318522/vfillDL/vfillDL/extents/\",\n",
    "    extentName = \"testingKY1.shp\",\n",
    "    extent_crop = 50.0,\n",
    "    imgPth = \"C:/22318522/vfillDL/vfillDL/dems/\",\n",
    "    imgName = \"ky1_dem.tif\",\n",
    "    do_background = False,\n",
    "    background_cnt  = 0,\n",
    "    background_dist  = 0.0,\n",
    "    use_seed = False,\n",
    "    seed = 42,\n",
    "    do_shuffle = False,\n",
    ")\n",
    "\n",
    "trainGDF = makeDynamicChipsGDF(\n",
    "    center_featPth = \"C:/22318522/vfillDL/vfillDL/vectors/\",\n",
    "    center_featName = \"training.shp\",\n",
    "    mask_featPth = \"C:/22318522/vfillDL/vfillDL/vectors/\",\n",
    "    mask_featName = \"training.shp\",\n",
    "    extentPth = \"C:/22318522/vfillDL/vfillDL/extents/\",\n",
    "    extentName = \"training.shp\",\n",
    "    extent_crop = 50.0,\n",
    "    imgPth = \"C:/22318522/vfillDL/vfillDL/dems/\",\n",
    "    imgName = \"train_dem.img\",\n",
    "    do_background = False,\n",
    "    background_cnt  = 0,\n",
    "    background_dist  = 0.0,\n",
    "    use_seed = False,\n",
    "    seed = 42,\n",
    "    do_shuffle = True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4eedf2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1105, 553)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trainGDF), len(valGDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4aa5f5bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      chipID                        geometry  \\\n",
      "0          1  POINT (452284.508 4134006.285)   \n",
      "1          2  POINT (440635.254 4188562.846)   \n",
      "2          3   POINT (399470.748 4188524.49)   \n",
      "3          4  POINT (479100.541 4224557.368)   \n",
      "4          5  POINT (394016.337 4178433.976)   \n",
      "...      ...                             ...   \n",
      "1100    1101   POINT (444503.37 4175020.002)   \n",
      "1101    1102  POINT (387552.897 4186047.607)   \n",
      "1102    1103  POINT (434482.232 4203836.016)   \n",
      "1103    1104   POINT (492533.076 4234807.07)   \n",
      "1104    1105  POINT (417323.773 4217216.019)   \n",
      "\n",
      "                                 imgPth        imgName  \\\n",
      "0     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "2     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "3     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "4     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "...                                 ...            ...   \n",
      "1100  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1101  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1102  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1103  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1104  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "\n",
      "                            center_featPth center_featName  \\\n",
      "0     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "2     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "3     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "4     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "...                                    ...             ...   \n",
      "1100  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1101  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1102  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1103  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1104  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "\n",
      "                              mask_featPth mask_featName  \\\n",
      "0     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "2     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "3     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "4     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "...                                    ...           ...   \n",
      "1100  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1101  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1102  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1103  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1104  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "\n",
      "                                 extentPth    extentName  \n",
      "0     C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "1     C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "2     C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "3     C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "4     C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "...                                    ...           ...  \n",
      "1100  C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "1101  C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "1102  C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "1103  C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "1104  C:/22318522/vfillDL/vfillDL/extents/  training.shp  \n",
      "\n",
      "[1105 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(trainGDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2754de",
   "metadata": {},
   "source": [
    "## Step 2: Check Dynamic Chips\n",
    "\n",
    "Chips and masks must meet the following criteria to be used in a model:\n",
    "\n",
    "1. Contain a full set of rows and columns for both the DTM and mask data\n",
    "2. Have no Null, NA, or NoData pixels in the DTM and mask data\n",
    "\n",
    "After creating a GeoPandas DataFrame with `makeDynamicChipsGDF()`, each chip should be evaluated with `checkDynamicChips()`. This adds additional columns to the data frame, which are then used to filter out chip and mask pairs that are incomplete. We recommend always running these checks and removing incomplete chip and mask pairs from the dynamic dataset to avoid errors. \n",
    "\n",
    "It is not necessary to save the dynamic chips to disk; however, if you want to save them, this can be accomplished using `saveDynamicChips()`. The saved chip and mask pairs could then be used within the workflow described above (Workflow 1). \n",
    "\n",
    "Lastly, it is not necessary that the training and validation data use the same method. For example, the training data could be generated using the dynamic chips workflow while the validation chips could be created using pre-generated chips.  \n",
    "\n",
    "As with the first workflow, a mini-batch of dynamically generated chips can be visualized using the `viewBatch()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea17bf8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "valGDF = checkDynamicChips(\n",
    "    chips_gdf = valGDF,\n",
    "    chip_size = 640,\n",
    "    cell_size = 2.0,\n",
    "    nodata_ok = False)\n",
    "\n",
    "trainGDF = checkDynamicChips(\n",
    "    chips_gdf = trainGDF,\n",
    "    chip_size = 640,\n",
    "    cell_size = 2.0,\n",
    "    nodata_ok = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "357aa8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1079, 532)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trainGDF), len(valGDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f2966968",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      chipID                        geometry  \\\n",
      "0          1  POINT (452284.508 4134006.285)   \n",
      "1          2  POINT (440635.254 4188562.846)   \n",
      "3          4  POINT (479100.541 4224557.368)   \n",
      "4          5  POINT (394016.337 4178433.976)   \n",
      "5          6    POINT (475827.52 4199371.51)   \n",
      "...      ...                             ...   \n",
      "1100    1101   POINT (444503.37 4175020.002)   \n",
      "1101    1102  POINT (387552.897 4186047.607)   \n",
      "1102    1103  POINT (434482.232 4203836.016)   \n",
      "1103    1104   POINT (492533.076 4234807.07)   \n",
      "1104    1105  POINT (417323.773 4217216.019)   \n",
      "\n",
      "                                 imgPth        imgName  \\\n",
      "0     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "3     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "4     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "5     C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "...                                 ...            ...   \n",
      "1100  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1101  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1102  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1103  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "1104  C:/22318522/vfillDL/vfillDL/dems/  train_dem.img   \n",
      "\n",
      "                            center_featPth center_featName  \\\n",
      "0     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "3     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "4     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "5     C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "...                                    ...             ...   \n",
      "1100  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1101  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1102  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1103  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "1104  C:/22318522/vfillDL/vfillDL/vectors/    training.shp   \n",
      "\n",
      "                              mask_featPth mask_featName  \\\n",
      "0     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "3     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "4     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "5     C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "...                                    ...           ...   \n",
      "1100  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1101  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1102  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1103  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "1104  C:/22318522/vfillDL/vfillDL/vectors/  training.shp   \n",
      "\n",
      "                                 extentPth    extentName NoData  cCntImg  \\\n",
      "0     C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "1     C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "3     C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "4     C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "5     C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "...                                    ...           ...    ...      ...   \n",
      "1100  C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "1101  C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "1102  C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "1103  C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "1104  C:/22318522/vfillDL/vfillDL/extents/  training.shp     No      640   \n",
      "\n",
      "      rCntImg  naCntImg  cCntMsk  rCntMsk  naCntMsk  \n",
      "0         640         0      640      640         0  \n",
      "1         640         0      640      640         0  \n",
      "3         640         0      640      640         0  \n",
      "4         640         0      640      640         0  \n",
      "5         640         0      640      640         0  \n",
      "...       ...       ...      ...      ...       ...  \n",
      "1100      640         0      640      640         0  \n",
      "1101      640         0      640      640         0  \n",
      "1102      640         0      640      640         0  \n",
      "1103      640         0      640      640         0  \n",
      "1104      640         0      640      640         0  \n",
      "\n",
      "[1079 rows x 17 columns]\n"
     ]
    }
   ],
   "source": [
    "print(trainGDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f813d2",
   "metadata": {},
   "source": [
    "As with the first workflow, a mini-batch of dynamically generated chips can be visualized using the `viewBatch()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3145a664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainDS = terrainDatasetDynamic(df=valGDF, chip_size=640, cell_size=2, background_value=0, transforms=None)\n",
    "\n",
    "trainDL = DataLoader(trainDS, batch_size=10, shuffle=True)\n",
    "batch = next(iter(trainDL))\n",
    "\n",
    "viewBatch(dataloader=trainDL,\n",
    "        ncols = 5,\n",
    "        cCodes = (0,1),\n",
    "        cNames= (\"Background\", \"Valley Fills\"),\n",
    "        cColors= (\"#999999\", \"#DD0000\"),\n",
    "        padding = 10,\n",
    "        figsize=(10, 10),\n",
    "        mask_mode = \"auto\",\n",
    "        stretch = \"percentile\",          \n",
    "        p_low = 2.0,                  \n",
    "        p_high = 98.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1e998f",
   "metadata": {},
   "source": [
    "## Step 3: Train Model\n",
    "\n",
    "The training loop when using dynamic chips is the same as when using pre-generated chips. As noted above, it is possible to mix chip generation methods. For example, dynamic chips could be used to train the model while pre-generated chips could be used for validation. \n",
    "\n",
    "The processes of loading the model and using it to make predictions to a new geographic extend and/or new DTM data are also identical to the first workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b8015d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = defineUNet(encoderChn=(16,32,64,128), \n",
    "                   decoderChn=(128,64,32,16), \n",
    "                   inChn=31, \n",
    "                   botChn=256, \n",
    "                   nCls=2).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14a7538e",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = unifiedFocalLoss(nCls=2,\n",
    "                lambda_=0,\n",
    "                gamma=0.7,\n",
    "                delta=0.6,\n",
    "                smooth=1e-8,\n",
    "                zeroStart=True,\n",
    "                clsWghtsDist=1,\n",
    "                clsWghtsReg=[0.3, 0.7],\n",
    "                useLogCosH=False,\n",
    "                device=\"cuda\")\n",
    "criterion.__name__ = 'unified_focal_loss' #Requires a name parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fb48d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "terrainTrainer(saveFolder=\"C:/22318522/models/dynamicModel/\",\n",
    "                   trainDF=trainGDF, \n",
    "                   valDF=valGDF,\n",
    "                   trainableModel=model,\n",
    "                   lossFnc= criterion,\n",
    "                   useDynamicTrain = True,\n",
    "                   useDynamicVal = True,\n",
    "                   nCls=2,\n",
    "                   do_gp = True,\n",
    "                   cropFactor=64,\n",
    "                   epochs=25,\n",
    "                   batchSize=8,\n",
    "                   lr=0.0001,\n",
    "                   cell_size = 2,\n",
    "                   spat_dim = 640,\n",
    "                   inner_radius = 2.0,\n",
    "                   outer_radius = 10.0,\n",
    "                   hs_radius = 50.0,\n",
    "                   smooth_radius = 11.0,\n",
    "                   device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"),\n",
    "                   doMultiGPU=False,\n",
    "                   doFlips=True,\n",
    "                   doRotate90=True,\n",
    "                   augProb=0.3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "terrainseg311",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}