automatic_mixed_precision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic mixed precision training and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows how to apply the automatic mixed precision (AMP) feature of PyTorch to training and validation programs.  \n",
    "It's modified from the Spleen 3D segmentation tutorial notebook, and compares the training speed and memory usage with/without AMP.\n",
    "\n",
    "The Spleen dataset can be downloaded from http://medicaldecathlon.com/.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/Tutorials/blob/master/automatic_mixed_precision.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU \"monai[gdown, nibabel]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temporarily need this, FIXME: remove when MONAI v0.3 released\n",
    "%pip install -qU git+https://github.com/Project-MONAI/MONAI#egg=MONAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from monai.config import get_torch_version_tuple\n",
    "from monai.data import Dataset, CacheDataset, list_data_collate\n",
    "from monai.transforms import \\\n",
    "    Compose, LoadNiftid, AddChanneld, ScaleIntensityRanged, CropForegroundd, FgBgToIndicesd, \\\n",
    "    RandCropByPosNegLabeld, DeleteItemsd, Spacingd, Orientationd, ToTensord\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.networks.layers import Norm\n",
    "from monai.networks.nets import UNet\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import compute_meandice\n",
    "from monai.utils import set_determinism\n",
    "from monai.config import print_config\n",
    "\n",
    "print_config()\n",
    "\n",
    "if get_torch_version_tuple() < (1, 6):\n",
    "    raise RuntimeError('AMP feature only exists in PyTorch version greater than v1.6.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root dir is: /workspace/data/medical\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(f\"root dir is: {root_dir}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "Downloads and extracts the Decathlon Spleen dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "resource = \"https://drive.google.com/uc?id=1jzeNU1EKnK81PyTsrx0ujfNl-t0Jo8uE\"\n",
    "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
    "data_root = os.path.join(root_dir, \"Task09_Spleen\")\n",
    "if not os.path.exists(data_root):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = sorted(glob.glob(os.path.join(data_root, 'imagesTr', '*.nii.gz')))\n",
    "train_labels = sorted(glob.glob(os.path.join(data_root, 'labelsTr', '*.nii.gz')))\n",
    "data_dicts = [{'image': image_name, 'label': label_name}\n",
    "              for image_name, label_name in zip(train_images, train_labels)]\n",
    "train_files, val_files = data_dicts[:-9], data_dicts[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformations():\n",
    "    train_transforms = Compose([\n",
    "        LoadNiftid(keys=['image', 'label']),\n",
    "        AddChanneld(keys=['image', 'label']),\n",
    "        Spacingd(keys=['image', 'label'], pixdim=(1.5, 1.5, 2.), mode=('bilinear', 'nearest')),\n",
    "        Orientationd(keys=['image', 'label'], axcodes='RAS'),\n",
    "        ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
    "        CropForegroundd(keys=['image', 'label'], source_key='image'),\n",
    "        # pre-compute foreground and background indexes and cache them to accelerate training\n",
    "        FgBgToIndicesd(keys=\"label\", fg_postfix=\"_fg\", bg_postfix=\"_bg\",image_key=\"image\"),\n",
    "        # randomly crop out patch samples from big image based on pos / neg ratio\n",
    "        # the image centers of negative samples must be in valid image area\n",
    "        RandCropByPosNegLabeld(keys=['image', 'label'], label_key='label', spatial_size=(128, 128, 96), pos=1,\n",
    "                               neg=1, num_samples=4, fg_indices_key=\"label_fg\", bg_indices_key=\"label_bg\"),\n",
    "        DeleteItemsd(keys=[\"label_fg\", \"label_bg\"]),\n",
    "        ToTensord(keys=['image', 'label'])\n",
    "    ])\n",
    "    val_transforms = Compose([\n",
    "        LoadNiftid(keys=['image', 'label']),\n",
    "        AddChanneld(keys=['image', 'label']),\n",
    "        Spacingd(keys=['image', 'label'], pixdim=(1.5, 1.5, 2.), mode=('bilinear', 'nearest')),\n",
    "        Orientationd(keys=['image', 'label'], axcodes='RAS'),\n",
    "        ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
    "        CropForegroundd(keys=['image', 'label'], source_key='image'),\n",
    "        ToTensord(keys=['image', 'label'])\n",
    "    ])\n",
    "    return train_transforms, val_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a typical PyTorch training process\n",
    "Usage of PyTorch AMP module refers to: https://pytorch.org/docs/stable/notes/amp_examples.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_process(amp=False):\n",
    "    train_trans, val_trans = transformations()\n",
    "    train_ds = CacheDataset(data=train_files, transform=train_trans, cache_rate=1.0, num_workers=8)\n",
    "    val_ds = CacheDataset(data=val_files, transform=val_trans, cache_rate=1.0, num_workers=5)\n",
    "\n",
    "    # use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "    # to generate 2 x 4 images for network training\n",
    "    train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=1, collate_fn=list_data_collate)\n",
    "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=1)\n",
    "    device = torch.device('cuda:0')\n",
    "    model = UNet(dimensions=3, in_channels=1, out_channels=2, channels=(16, 32, 64, 128, 256),\n",
    "                 strides=(2, 2, 2, 2), num_res_units=2, norm=Norm.BATCH).to(device)\n",
    "    loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "    scaler = torch.cuda.amp.GradScaler() if amp else None\n",
    "\n",
    "    epoch_num = 300\n",
    "    val_interval = 1  # do validation for every epoch\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    best_metrics_epochs_and_time = [[], [], []]\n",
    "    epoch_loss_values = list()\n",
    "    metric_values = list()\n",
    "    epoch_times = list()\n",
    "    total_start = time.time()\n",
    "    for epoch in range(epoch_num):\n",
    "        epoch_start = time.time()\n",
    "        print('-' * 10)\n",
    "        print(f\"epoch {epoch + 1}/{epoch_num}\")\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        step = 0\n",
    "        for batch_data in train_loader:\n",
    "            step_start = time.time()\n",
    "            step += 1\n",
    "            inputs, labels = batch_data['image'].to(device), batch_data['label'].to(device)\n",
    "            optimizer.zero_grad()\n",
    "            if amp and scaler is not None:\n",
    "                with torch.cuda.amp.autocast():\n",
    "                    outputs = model(inputs)\n",
    "                    loss = loss_function(outputs, labels)\n",
    "                scaler.scale(loss).backward()\n",
    "                scaler.step(optimizer)\n",
    "                scaler.update()\n",
    "            else:\n",
    "                outputs = model(inputs)\n",
    "                loss = loss_function(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            print(f\"{step}/{len(train_ds) // train_loader.batch_size}, train_loss: {loss.item():.4f}\"\n",
    "                  f\" step time: {(time.time() - step_start):.4f}\")\n",
    "        epoch_loss /= step\n",
    "        epoch_loss_values.append(epoch_loss)\n",
    "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                metric_sum = 0.\n",
    "                metric_count = 0\n",
    "                for val_data in val_loader:\n",
    "                    val_inputs, val_labels = val_data['image'].to(device), val_data['label'].to(device)\n",
    "                    roi_size = (160, 160, 128)\n",
    "                    sw_batch_size = 4\n",
    "                    if amp:\n",
    "                        with torch.cuda.amp.autocast():\n",
    "                            val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "                    else:\n",
    "                        val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "                    value = compute_meandice(y_pred=val_outputs, y=val_labels, include_background=False,\n",
    "                                             to_onehot_y=True, mutually_exclusive=True)\n",
    "                    metric_count += len(value)\n",
    "                    metric_sum += value.sum().item()\n",
    "                metric = metric_sum / metric_count\n",
    "                metric_values.append(metric)\n",
    "                if metric > best_metric:\n",
    "                    best_metric = metric\n",
    "                    best_metric_epoch = epoch + 1\n",
    "                    best_metrics_epochs_and_time[0].append(best_metric)\n",
    "                    best_metrics_epochs_and_time[1].append(best_metric_epoch)\n",
    "                    best_metrics_epochs_and_time[2].append(time.time() - total_start)\n",
    "                    torch.save(model.state_dict(), 'best_metric_model.pth')\n",
    "                    print('saved new best metric model')\n",
    "                print(f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "                      f\" best mean dice: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
    "        print(f\"time consuming of epoch {epoch + 1} is: {(time.time() - epoch_start):.4f}\")\n",
    "        epoch_times.append(time.time() - epoch_start)\n",
    "    print(f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\"\n",
    "          f\" total time: {(time.time() - total_start):.4f}\")\n",
    "    return epoch_num, epoch_loss_values, metric_values, epoch_times, best_metrics_epochs_and_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic and train with AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "amp_start = time.time()\n",
    "epoch_num, amp_epoch_loss_values, amp_metric_values, amp_epoch_times, amp_best = train_process(amp=True)\n",
    "amp_total_time = time.time() - amp_start\n",
    "print(f\"total training time of {epoch_num} epochs with AMP: {amp_total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the memory usage during training with AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tesla V100-PCIE-32GB\n",
      "|===========================================================================|\n",
      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
      "|---------------------------------------------------------------------------|\n",
      "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
      "|===========================================================================|\n",
      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocated memory      |   37839 KB |    1876 MB |   45927 GB |   45927 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active memory         |   37839 KB |    1876 MB |   45927 GB |   45927 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved memory   |    2590 MB |    2590 MB |    2590 MB |       0 B  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable memory |   23600 KB |    1012 MB |   32888 GB |   32888 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocations           |     245    |     609    |    4695 K  |    4694 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active allocs         |     245    |     609    |    4695 K  |    4694 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved segments |      50    |      50    |      50    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable allocs |      47    |      60    |    3179 K  |    3178 K  |\n",
      "|===========================================================================|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.get_device_name(0))\n",
    "print(torch.cuda.memory_summary(0, abbreviated=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic and train without AMP\n",
    "In order to correctly measure the memory usage, please restart the notebook and skip above AMP training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "start = time.time()\n",
    "epoch_num, epoch_loss_values, metric_values, epoch_times, best = train_process(amp=False)\n",
    "total_time = time.time() - start\n",
    "print(f\"total training time of {epoch_num} epochs without AMP: {total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the memory usage during training without AMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tesla V100-PCIE-32GB\n",
      "|===========================================================================|\n",
      "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
      "|---------------------------------------------------------------------------|\n",
      "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
      "|===========================================================================|\n",
      "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocated memory      |   38319 KB |    2788 MB |   61299 GB |   61299 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active memory         |   38319 KB |    2788 MB |   61299 GB |   61299 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved memory   |    4118 MB |    4118 MB |    4118 MB |       0 B  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable memory |  107088 KB |     771 MB |   30136 GB |   30136 GB |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Allocations           |     245    |     542    |    3755 K  |    3754 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Active allocs         |     245    |     542    |    3755 K  |    3754 K  |\n",
      "|---------------------------------------------------------------------------|\n",
      "| GPU reserved segments |      57    |      57    |      57    |       0    |\n",
      "|---------------------------------------------------------------------------|\n",
      "| Non-releasable allocs |      31    |      48    |    2422 K  |    2422 K  |\n",
      "|===========================================================================|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(torch.cuda.get_device_name(0))\n",
    "print(torch.cuda.memory_summary(0, abbreviated=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training loss and validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure('train', (12, 12))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title('Regular Epoch Average Loss')\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='red')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title('Regular Val Mean Dice')\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='red')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title('AMP Epoch Average Loss')\n",
    "x = [i + 1 for i in range(len(amp_epoch_loss_values))]\n",
    "y = amp_epoch_loss_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='green')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.title('AMP Val Mean Dice')\n",
    "x = [i + 1 for i in range(len(amp_metric_values))]\n",
    "y = amp_metric_values\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.plot(x, y, color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time and every epoch time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure('train', (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Total Train Time(300 epochs)')\n",
    "plt.bar('regular', total_time, 1, label='Regular training', color='red')\n",
    "plt.bar('AMP', amp_total_time, 1, label='AMP training', color='green')\n",
    "plt.ylabel('secs')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('Epoch Time')\n",
    "x = [i + 1 for i in range(len(epoch_times))]\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('secs')\n",
    "plt.plot(x, epoch_times, label='Regular training', color='red')\n",
    "plt.plot(x, amp_epoch_times, label='AMP training', color='green')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time to achieve metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NwJ9i2Yk50FYONPlocgHzKYf5+NHmEzVRzlQUN9qOjfn4MZ/SaHIB84GEjgFRDdlsVt0Br4SkeoO5h8LcwzDV1TpmjgW+VBgHogG40jn3PRG5EbhCRN4H/BW4pLD+JcBXCoNM9pGf+QLn3G4RuRK4EcgCr3LO5eYrk8lkuOuuu0ilUgvaqcnJSRoa9LSPa/KJ06W1tZU1a9bQ3Nxcch1t56z5+DGf2JmaqWhERJqBa0Tkh8Dryc9UdIWIXEx+hqLPMGumIhE5m/xMRc+Z75tGURdrqvegfn0qqYdB37mkyUeTC5gP1FEDRH6g9+SRVG8w91CYexhCuDvn/gacOsfy28iPB3Ho8hTwrBLbej/w/oX43HXXXSxZsoT169cvKI9cLkdjo55ZQDX5xOXinKO3t5e77rqLDRs2lFxP2zlrPn7MJ16imqnIzfN66SjqYk31HtSnT6X1MOg7lzT5aHIB84E6ugRDU6vpfEiqN5h7KMw9DEl2j4pUKkVXV9eCP8zsw7k0cbmICF1dXWV/QdVW7s3Hj/nEj4g0ish1wAHgp8xjpiJgaqaieRFFXayp3oP69Km0HgZ955ImH00uYD5QRw0QUyM/J42keoO5h8Lcw5Bk9yiJ4kuZtsGRNfnE6VLJsdRW7s3Hj/nETy1mKio3I9Hk5CSQ71rtnJvOefa9c276+Vwux+TkJLlcrujvycnJ6XWmLjM8dFuzn5/9uii2Mdtn9jpHyj5Nzdrj2ydg+v18s8JMzUKjZUai3t5eNTMSpVIpNTNHDQwMkE6n1Ryn/v5+JiYmanKcfNTNLBiZTKbs9VMaSao3mHsozD0MmUyGRYsW1fXI6zfddBNbt25d8LYXcm1tY2MjO3bsIJvNsmHDBr7yla+wbNmySH3OPPNMPvKRj7BzZ3WH+qqrrmLz5s2cdNJJ83rd1Vdfze7du3nb295Wcp177rmH17zmNXzzm9+syu1Qyh1Tbees+fipFx+ts2BUO1OR7xKMWtXFCx3jIOq6eC6feqmLKzme9XJuV4MmF6gfH5sFAxgdHQ2tUBVJ9QZzD4W5hyHJ7jVDpKpbQ2Pj3M9VwOLFi7nuuuu44YYbWLFiBZ/61KcWvBtTvyZWSy5XPJ7nVVddxY033jjnur7BTJ/ylKfwpje9yfteq1atiqzxoRK0lXvz8WM+8RLhTEULFYmuHg5UFy+0Hgari+NEk48mFzAfqKMGCE3zrc6HpHqDuYfC3MOQZPcjlQc/+MHcfffdAPz973/nrLPO4rTTTuNhD3sYN9988/TyBz3oQezYsYN3vOMddHR0APCrX/2KJz3pSUD+l7zzzjuPyy677LD3eMUrXsHOnTvZtm0b7373u6eXr1+/nre85S3c//735xvf+Mb08t/97ndcffXVvOlNb+KUU07h73//O2eeeSavfe1r2blzJ5/4xCf47ne/y+mnn86pp57KYx7zGO677z4ALrvsMs4//3wAzj33XF7zmtfwkIc8hOOPP376i+6+ffvYvn379Pr//M//zFlnncWmTZt485vfPO1xySWXsHnzZh74wAfykpe8hPPOO6+qjLWVe/PxYz6xcyzwSxH5G/Bn4KfOue+R7wHx+sKMRF0Uz1TUVVj+euCtAZwjJ4q6eGrAR6uL50bbuaTJR5MLmA/U0SwY/f39dHXNexyf4CTVG8w9FOYehv7+/tAKxixyuRw///nPefGLXwzAS1/6Ui6++GI2bdrEH//4R175ylfyi1/8gvPPP5/zzz+fc845h4svvrjktkrx/ve/nxUrVpDL5Xj0ox/N3/72N+53v/sB0NXVxV/+8pei9R/ykIfwlKc8hSc96Uk885nPnF4+MTHBVDfq/v5+/vCHPyAifOELX+BDH/oQH/3oR4HiMSD279/PNddcw80338xTnvKUou1Ncd111/HXv/6VlpYWtmzZwqtf/WoaGxt573vfy1/+8heWLFnCox71KE4++eRKYj0Mbees+fgxn3iJcqaipBJVXZzL5WhqKv1vi9XFus4lTT6aXMB8oI4aIDQd6PmQVG8w91CYewTcey/09cE8rgtV417njI+Pc8opp3D33XezdetW/umf/omRkRF+97vf8axnzXyvT6fTAPz+97/nqquuAuC5z30ub3zjGw/bpu9L75VXXsnnPvc5stks+/fv58Ybb5z+0vuc5zynYu/Z695111085znPYf/+/UxMTBRNvzb7GuinPe1pNDQ0cNJJJ03/Mncoj370o+ns7ATgpJNO4o477qCnp4dHPOIRrFixAoBnPetZ7N27t2LX2Wgr9+bjx3yMuIi6LvbVw2B1sbZzSZOPJhcwH6ijBoiDBw/S3d0dWmPeJNUbzH0uJt0kuckc2cksOVe4j/hx/2A/be1tcz6fyqa4rf82Ni7fyJvO8F+/GIKgZeaWW+Cqq/K33/8eHv1o+OlPK355JaP+GrVn6rrjsbExHve4x/GpT32Kc889l2XLlnHddddVvJ2mpqbpa44zmcyc06DdfvvtfOQjH+HPf/4zy5cv59xzzy1ar729veL3m73uq1/9al7/+tfzlKc8hV/96ldccMEF08/Nvg66paVl+u9Sl4nPXqexsdF7XXM1aKvnzceP+RhxEXVdPDVQntXFc6PtXNLko8kFzAfqqAFC04GeD0n1htq43z10N7sP7iY3mcv/M+9y5CZz876fyE0wMjFCdjLLpJs87JZzuYqX5yZzjGfHGc+MM5YZm/47lU0xns3fZyezZCej/bCpluWty3n2tmdX9+LxcTh4EHp68vdz/T04CJOT4Fz+BjN/z77Nsbx7ruXpNExMzP3aQ++rfS6Xg8L0Qdz//nDhhfC0p80rmiSfq0cibW1tXHTRRTztaU/jla98JRs2bOAb3/gGz3rWs3DO8be//Y2TTz6ZBz3oQXzrW9/iOc95DldcccX064877jhuvPFG0uk04+Pj/PznP+ehD31o0XsMDQ3R3t5OZ2cn9913Hz/84Q8588wzy7otWbKE4eHhks8PDg6yevVqAL70pS8VPRfFfN0PeMADeO1rX0t/fz9LlizhW9/6Fjt27KhqW9rKfV37VDA4YEU2Mc6Opu14GdETVV08Na2g1cVzo+1c0uSjyQXi9ZELFz41OoB7d7SfC3XTAKGttalSkuoN0btfccMV/NvV/8ZoJprRWpsbmmlqaKJBGmhsaKRBGqZvOGhubC5aNnU7dN0GaWBx02Lamts4puMYFjcvZnHTYlqbWqdvTQ1NNDU00SiN+fuGxsgfT/09ODDIUSuPmnPd5sZmlixagojkGwkGB0s3JBz6d08PlBopt7ERVq7M35Ytg4aGw0fMLjWS9qzH6Uxm5leCqeUtLbBoUX6bs5dP/X3ofbXPnXhivtHhuOOqKk/WA0Ifp556Kve73/24/PLL+drXvsYrXvEK3ve+95HJZDj77LM5+eST+a//+i+e//zn8/73v5+zzjpruovs2rVrefazn8327dtZv349p5562GXcnHzyyZx66qmceOKJrF27ljPOOKMir7PPPpuXvOQlXHTRRXOOkn7BBRfwrGc9i+XLl/OoRz2K22+/ffq5KEaCX716NW9/+9t54AMfyIoVKzjxxBOn93u+aPuMMh8/B3fsoPv660NrTKMtH6M2RFUXb9iwweriEmg7lzT5aHIBfT47OnZw/Ui8nwsSxew+cTPXnMfGkYdzjsH0IAdGD3DRHy/iU3/+FGesPYP3Pep9tDa10iiN0/94z/e+ubGZRY2LQu9ibcnl4OabYdcu+Otf4Z57Dm9QKDW4Xnt7vjGhuzt/m/p7rmXd3dDZOdNAUMdonXu+FtRq7vm4GRsbY/HixYgIV1xxBZdffjnf+c53yr8w4YyMjNDR0UE2m+XpT386L3rRi3j6059+2HpJPKZ1S4XTI5Ylgd8LD8Xq4uSdt1YXl66Lk3g8DR2E7AHhq4frpgdEX1/f9CAvSSKp3rAw9xd/58V85W9fITOZmV72xge/kf949H/Q3NgclWJJEpf75GR+DINdu0hdcw2tN9yQb3SY6rHQ3g5r1+YbDTZtgoc8xN/AsHhxkN1IXO6z6OvrC61wxJDNZssOOBYV1157Leeddx7OOZYtW8all14a1KccUblccMEF/OxnPyOVSvHYxz6Wp83zkqMptJ2z5uOnb8sWVuzZE1pjGm35GDPEXe+Vq4s11cNgdXE5NPlocgF9PlvatrBnLN7PBT1nco2ptktTaJLqDdW7//nuP3PpdZfyz1v/mTPWnsFR7UexdeVWTlt1WsSGpUlE7pkMfPCD8MtfwrXXwtAQAC2LF8Opp8KLXww7d+ZvmzfnL5NQTiJyL0GS3bXRGGNZfdjDHsb//d//edeJ06ccUbl85CMfiWQ72sq9+fjpvO220ApFaMvHmCHueq9cXaypHgari8uhyUeTC+jzuW08/s+FummAGBkZUXfAKyGp3lCd+0Ruglf+4JUsb13OF5/6RZa2LK2RnZ9E5P61r8G73gWnnQbPf/50Y8PQqlV0Kpvip1ISkXsJRkZGQiscMZSb7z1uNPlocgF956z5+BlZvZrOfftCa0yjLR9jBm11jfn40XYuafLR5AL6fFa3rGZfal+s76nnzKkxiwN1KV8oSfWG6tzf+Yt3suueXXzzWd8M1vgACcn94oth61b485+Lrv1dPDERUGphJCL3EiTZXRtRjC4eJZp8NLmAvnJvPn4W9/SEVihCWz7GDNrqGvPxo+1c0uSjyQX0+fRk4v9c0HX21JCJhP5TllRvmL/7z277GR/63Yd46f1fyjNOekaNrCpDfe5//Sv88Y/w8pcfNvCYencP5m5A6XnUQ6HJR5ML6Cv35uNnYmm4hv250JaPMYO2usZ8/Gg7lzT5aHIBfT5Lm+L/XKibBght145VSlK9YX7uB0cP8oJvv4CtK7fy8bM+XkOrylCf+2c+kx8o8l/+5bCn1Lt7MHcDyE8TqwhNPppcQF+5Nx8/jel0aIUitOVjzKCtrjEfP9rOJU0+mlxAn096Mv7PhbppgDB08+ofvpr+8X4uf8bltDW3hdbRzeBgfvyHc86BZctC2xiGOq666ipEhJtvvnl62b59+xAR3vGOd0wv6+npobm5mfPOOw/Ij0a+evVqTjnlFLZv387VV1992LZ/9atf8bvf/W7eTrt27eI1r3lN2fUe8pCHzHvbhmEYGrG62DCMuaibMSByuVxohapIqjfMz/1Pd/+Jf976z5x8zMk1NKoc1bl/9aswNgaveMWcT6t2L4O5H1lENf/0FJXOQ3355Zfz0Ic+lMsvv5wLL7xwevmGDRv4/ve/z/ve9z4AvvGNb7Bt27ai177uda/jjW98IzfddBMPe9jDOHDgQFFX21/96ld0dHTM+eXUNy3bzp072blzzumwiyj3hVpbt19t5d58/ORaWkIrFKEtnyOVI6Eu3r9/f9G4C1YXF6PtXNLko8kF9Pm0NMT/uVA3PSAWLVoUWqEqkuoN83Mfy4wFHXTyUNTm7lz+8ovTTsvPejEHat0rwNyNhTIyMsI111zDJZdcwhVXXFH0XFtbG1u3bmXXrl0AfP3rX+fZz372nNvZunUrTU1N9PT0THe13bdvHxdffDEf//jHOeWUU/jtb3/Lueeey8tf/nJOP/103vzmN/OnP/2JBz/4wZx66qk85CEPYc+e/Nzav/rVr3jSk54E5H/de9GLXsSZZ57J8ccfz0UXXTT9vh0dHdPrn3nmmTzzmc/kxBNP5HnPex7OOUSEH/zgB5x44omcdtppvOY1r5nebgi0lXvz8bOoMF2zFrTlY0RH1HVxb2/v9DKriw9H27mkyUeTC+jzGcrG/7lQNz0gxsfH1R3wSkiqN8zPfTw7zuImPaPCqs39mmtg9274whdKrqLWvQKS7m6E5zvf+Q5nnXUWmzdvpquri2uvvZbTTjtt+vmzzz6bK664gqOPPprGxkZWrVrFPffcc9h2/vjHP9LQ0EB3dze5XI6GhgbWr1/Py1/+cjo6OnjjG98IwCWXXMJdd93F7373OxobGxkaGuK3v/0tTU1N/OxnP+Ptb3873/rWtw7b/s0338wvf/lLhoeH2bJlC694xStobm4uWuevf/0ru3fvZtWqVZxxxhn87//+L6eccgove9nL+M1vfsOGDRs455xzIk5wfmg7Z83Hz/jKlSxSNGWwtnyM6Ii6Lu6aNb241cWHo+1c0uSjyQX0+axsXslILt7PhbppgJhqyUwaSfWG+bmPZcZY3KynAUJt7hdfDJ2dcPbZJVdR614B5m4slMsvv5zzzz8fyH/Bvfzyy4u+9J511lm8853v5Oijj+Y5z3nOYa//+Mc/zle/+lWWLFnC17/+dUSk7IBRz3rWs6bXGRwc5IUvfCG33HILIkImk5nzNU984hNpaWmhpaWFo446ivvuu481a9YUrfPABz5wetkpp5zCvn37aG9v5/jjj2fDhg0AnHPOOXzuc5+rMJ3o0VbuzcdPx913h1YoQls+RnREXReXuqRiNlYX60GTjyYX0Odzdzr+z4W6uQRjcHAwtEJVJNUbKnfP5DJkJ7OqBp9UmfvBg/DNb+ZnvmhvL7maSvcKMXdjIfT19fGLX/yCf/u3f2P9+vV8+MMf5sorryy6VnfRokWcdtppfPSjH+WZz3zmYdt43etex3XXXcdvf/tbHvawhwHlr9dsn3U+vvOd7+SRj3wkN9xwA9/97ndJpVJzvqZl1rX4jY2NZLPZitbRdu2otnJvPn4Gjz8+tEIR2vIxoqEWdXEldZ/VxXrQ5KPJBfT5HL84/s+FummAWLFiRWiFqkiqN1TuPp7Nd13XdAmGytwvvRQmJuDlL/euptK9QszdWAjf/OY3ecELXsAdd9zBvn37uPPOO9mwYQO//e1vi9Z7wxvewH/+539WfMxm//K2ZMkShoeHS647ODjI6tWrAbjsssvmvxNl2LZtG7fddhv79u0D8tdOh0RbuTcfPysK18FrQVs+RjTUoi4+tAeE1cXFaDuXNPlocgF9PnvG4v9cqJsGiIMHD4ZWqIqkekPl7uOZfAOEph4Q6nKfnITPfhYe8Qg46STvqurc54G5Gwvh8ssv5+lPf3rRsmc84xlcfvnlRcu2bdvGC1/4woq3O7vr7pOf/GS+/e1vTw98dihvfvObedvb3sapp5465y9pC6WpqYlPf/rTnHXWWZx22mksWbKEzs7OyN+nUrSVe/Pxc3DHjtAKRWjLx4iGWtTFh15CYXVxMdrOJU0+mlxAn8+Ojvg/F0TbNDKVsHPnTjc1cq6RfG7vv53jLzqeLz71i5x7yrmhdXTyox/B4x8PV1wBc1wraehARK51zpWf3+sIYK56+KabbmLr1q2BjOqHkZEROjo6cM7xqle9ik2bNvG6172uJu9lxzRBSERTLSbwe+GhWF1s520cxFUX2/E0qiWqKXgrnXq36L099bD1gFBOUr1hHj0gstYDoiyf+QwcdRQc8ovCXKhznwfmbsDhv3SFRpNPJpPh85//PKeccgrbtm1jcHCQl73sZcF8tJV78/FjPSCMStFU74FOH6uLS6PJR5ML6PMJ0QOibmbB6O7uDq1QFUn1hsrdxzJjgK4xIFTl/o9/wPe+B295C1QwbY8q93li7gZw2BRoodHk09zczOte97qa9XiYL9rKvfn46b7++tAKRWjLx5hBU70HOn2sLi6NJh9NLqDP5/qR+D8X6qYHRG9vb2iFqkiqN1TurnEMCFW5f/7z+S6xL31pRaurcp8n5m4ANbledyFo8tHkAvrKvfn46VXWjVtbPsYM2uoa8/Gj7VzS5KPJBfT5bG2P/3Ohbhogli9fHlqhKpLqDZW7T8+C0aynB4Sa3DMZ+MIX8uM/rF9f0UvUuFeBuSefKMYVmprHXQuafOJ0qeRYaiv35uNn+d69oRWK0JbPkcRC62JN9R7Ur0+lx1HbuaTJR5ML6PPZOxr/50LdNEAMDQ2FVqiKpHpD5e4aL8FQk/u3vgX33guveEXFL1HjXgXmnmxaW1vp7e1d8BdfbfOra/KJy8U5R29vL62trd71tJV78/EzVGFDdlxoy+dIIYq6WFO9B/XpU2k9DPrOJU0+mlxAn8/6xetjf8+6GQOivb09tEJVJNUbKnfXeAmGitxzOXjPe/LTbj7+8RW/TIV7lZh7slmzZg133XXXggdYcs4hUY3oHwGafOJ0aW1tZc2aNd51tJV78/HTvn9/aIUitOVzpBBFXayp3oP69amkHgZ955ImH00uoM9nfzr+z4W6aYBIpVLqBrCphKR6Q+Xu0z0gFF2CoSL3r38dbroJrrwS5tHVT4V7lSTdvd5pbm5mw4YNC97O8PAwS5YsicAoGjT5aHIBfees+fhJrVhB89hYaI1ptOVzpBBFXaytrjEfP9rOJU0+mlxAn8+K5hWMpeP9XKibSzA0Hej5kFRvqNxd4zScwXPPZuHCC2HHDnjGM+b10uDuC8DcDdCXpSYfTS5gPuVQ5zM6GlqhCG35GDNoOzbm48d8SqPJBfT5jObi/1yomx4Qk5OToRWqIqneULm7xjEgguf+//4f7N0L//M/0DC/dsLg7gvA3A3Ql6UmH00uYD7lUOfTpOtrn7Z8jBm0HRvz8WM+pdHkAvp8miT+z4W66QERxcjsIUiqN1Tm3jPWw5/v+TOg6xKMoLlns/mxH049FZ72tHm//EgvM1pJsrs2tGWpyUeTC5hPOdT5KJtJQFs+xgzajo35+DGf0mhyAX0+jRL/54KupvAa0qSs1b9SkuoNpd2dc1zzj2v47LWf5Rs3foOJ3ARP3vxkGkRPe1jQ3L/yFfj73+Hqq6GKAY6OxDKTBJLsrg1tWWry0eQC5lMOdT7j46EVitCWjzGDtmNjPn7MpzSaXECfz/hk/J8Lev7jqzHpdDq0QlUk1RsOdx9IDfDff/xvtn9mOw+/7OF8b+/3eNlpL+OGV9zA1edcHchyboLlnsnkez/s3AlPelJVmziSykySSLK7NrRlqclHkwuYTznU+XR2hlYoQls+xgzajo35+DGf0sTqIlL2lt68ufx6MdLZFP/ngq4mmBrS1qZngMP5kFRvyLs75/jzPX/m4l0Xc8UNVzCeHeeBqx/IJU+5hOdsew7ti3RNRTNFkNwnJ+GNb4R9++BTn6q6Akp6mUkqSXbXhrYsNflocgHzKYc6nwMHQisUoS0fYwZtx8Z8/JhPaTS5gL56+MBE/D510wNieHg4tEJVJNY7Pcx//+6/uf/n7s/pXzidK3dfyQvu9wKufem1/PHf/siLTn2R2sYHCJB7KgXnnAMXXQSvfjU8/vFVbyqpZQbM3cijLUtNPppcwHzKoc5n7drQCkVoy8eYQduxMR8/5lMaTS6grx5e2xq/T930gFi2bFlohapImvd1917Hxbsu5mvXf42RiRFOPvpkPvPEz/DcHc9lacvS0HoVE2vu/f35wSZ/8xv48IfhDW9YUPerpJWZ2Zi7Afqy1OSjyQXMpxzqfG69NbRCEdryMWbQdmzMx4/5lEaTC+irh28di9+nbnpA9Pb2hlaoiiR4j2XG+OJfv8iDvvAgTv3sqXzp/77EM096Jj98xg/568v+yst3vjxRjQ8QY+533AFnnAF/+ANcfnn+EowFXvuVhDJTCnM3QF+Wmnw0uYD5lEOdz7ZtoRWK0JaPMYO2Y2M+fsynNJpcQF89vK0jfp+66QGxcuXK0ApVodn7xoM38tldn+VL//clBtODbF25lf963H/xLyf/C8sXLw+ttyBiyf266+AJT4CxMfjxj+HMMyPZrOYyUw5zN0Bflpp8NLmA+ZRDnc8NN4RWKEJbPsYM2o6N+fgxn9JocgF99fANI/H71E0PiIMHD4ZWqApt3qlsiq/97Ws8/IsPZ9unt3HxtRfzxM1P5Nfn/prdr9zN+Q86f7rxQZv7fKi5+09/Cg9/ODQ2wjXXRNb4AJZ7KJLsrg1tWWry0eQC5lMOdT7bt4dWKEJbPsYM2o6N+fgxn9JocgF99fD2jvh9xDkX+5sulJ07d7pdu3aF1qgrbum9hc9d+zm+eN0X6R3vZePyjbzstJdx7inn0t3eHVovWXz5y/DiF8NJJ8EPfgCrV4c2MiJCRK51zu0M7REHVg8bhjKimrotgd8LD8XqYsMwgqCsHpYLo/Fx756/j68erpseED09PaEVqiKkdyaX4Zs3fpPHfPkxbP7kZj7+h49z5voz+ekLfsreV+/lTWe8ydv4kNTMoUbuzsH73w8vfCE84hH5QSdr0PhguYchhLuIrBWRX4rIjSKyW0TOLyy/QETuFpHrCrcnzHrN20TkVhHZIyKPm7X8rMKyW0XkrbHvzCy0lQNNPppcwHzKoc5H2S9v2vIxZtB2bMzHj/mURpML6KuHQ/SAqJsxILq6ukIrVEUI730D+/j8tZ/n0usu5d6Re1nXuY73PfJ9vOjUF3HskmMr3k5SM4cauGez8KpXwec+B89/PlxyCSxaFO17FLDcwxDIPQu8wTn3FxFZAlwrIj8tPPdx59xHZq8sIicBZwPbgFXAz0Rkc+HpTwH/BNwF/FlErnbO3RjLXhyCtnKgyUeTC5hPOdT57N4dWqEIbfkYM2g7Nubjx3xKo8kF9NXDu0fi96mbHhADAwOhFaoiLu/sZJar91zNE772BI7/xPF88H8/yANWPYDvnfM9bnvNbfz7w/99Xo0PkNzMIWL30dH8NJuf+xy87W35SzBq1PgAlnsoQrg75/Y75/5S+HsYuAnwdat5KnCFcy7tnLsduBV4YOF2q3PuNufcBHBFYd0gaCsHmnw0uYD5lEOdzwknhFYoQls+xgzajo35+DGf0mhyAX318Alt8fvUTQ+IJUuWhFaoilp73z10N1/4yxf4wl+/wF1Dd3Fsx7G88+Hv5N/u/2+s7Vy7oG0nNXOI0P2+++BJT4K//AU+/Wl4xSui2a4Hyz0Mod1FZD1wKvBH4AzgPBH5F2AX+V4S/eQbJ/4w62V3MdNgcechy0+vtXMpQmd5KJp8NLmA+ZRDnc+dd5ZfKUa05WPMoO3YmI8f8ymNJhfQVw/fmYrfp256QIyNjYVWqIpaeTvneN7/PI/j/us4Lvj1BWzr3sb/PPt/uOO1d3DhIy9ccOMDJDdziMh97154yENg92749rdjaXwAyz0UId1FpAP4FvBa59wQ8BlgI3AKsB/4aETv81IR2SUiuw4cOEAqlWJsbIzR0VHS6TRDQ0Nks1n6+/txzk1fdzk1AnVPTw/OOfr7+8lmswwNDZFOpxkdHWVsbIxUKsXw8DBDQ0MMDAwwOTk5PX/31Dam7vv6+sjlcgwODjIxMcHIyAjj4+OMj48zMjLCxMQEg4OD5HI5+vr65txGb28vk5OTDAwMkMlkGB4ennOfhoaGFrxPmUwmkn06cOBAJPsUxXHKZDLcd999ao5TNpvl3nvvVXGcpvZp//798R2ndetId3YyeswxjHV3k1q+nOE1a8i0tTGwcSOTjY3c86AH5bexY0fRfd+WLeSamxlcvz6W4zS1T3fffXfNjpOxMLR9HpuPH/MpjSYXgLGjjgqtUMRRi+L3qfksGCJyFvAJoBH4gnPug4c8vw74ErCssM5bnXM/8G2zmhF/0+k0LS0t83qNBmrl3TvWy8oPr+TpJz6dD//Th9m4YmPk75HUzCEC99//Hp785PxouN/7Hpwe34/JdZ17QNLpNK2trbGPvC4izcD3gB875z42x/Prge8557aLyNsAnHMfKDz3Y+CCwqoXOOceV1hetN5c1HLkdW3lQJOPJhcwn3LE6lPB6Ovpzk5aBgf9K8U4C0at8tEyC4aIrAW+DBwNOOBzzrlPiMgFwEuAqdaSt0999y3Uvy8GcsBrnHM/9r1Hreriuj6XKsB8/Gjyqed6uJJZMDqbOhnM+n0SNQuGiDSSH9js8cBJwDmFQdBm8w7gSufcqeQHR/t0LVyy2WwtNltzauWdyqYAePwJj69J4wMkN3NYoPtVV8GjHgXLluUbImJsfIA6zj0wIdxFRIBLgJtmNz6IyOwBW54O3FD4+2rgbBFpEZENwCbgT8CfgU0iskFEFpGvi6+OYx/mQls50OSjyQXMpxzqfBYvDq1QhLZ8asDUQMEnAQ8CXjXre/DHnXOnFG5TjQ+zBwo+C/h04bt0/OLKjo35+DGf0mhyAX318OKG+H1qPQbE9MBmACIyNbDZ7JHVHbC08HcncE8tRCSqeVljplbeUw0QLU21axFMauawAPevfx2e+1zYuTPf86G79DSltaIuc1dAIPczgBcA14vIdYVlbyff2HsK+fp1H/AyAOfcbhG5knwdnAVe5ZzLAYjIecCPyfdEu9Q5F2yYZm3lQJOPJhcwn3Ko88nlQisUoS2fqHHO7Sd/GRzOuWERqXigYOB2EZkaKPj3NZc9BG3Hxnz8mE9pNLmAvno45+L3qXUDxGrKD2x2AfATEXk10A48phYiDQ3JHO6iVt7pXBqA1qbWmmwfkps5LMD90kth40b45S+hrS1aqQqpy9wVEMLdOXcNMNcna8nL2Jxz7wfeP8fyH/heFyfayoEmH00uYD7lUOej7JdAbfnUkggGCo4VbcfGfPyYT2k0uYC+ejjr4vfRcETOAS5zzq0BngB8RUQO81ro4GcHDhwIMgDVQgdrGh8fr8ngZ/sP5gfGSo+ma7ZPAwMDNR38rJbH6b777pvXcZrepzvuIL1lC7S1BdunqXW0DFI3n30aGxuLbfCzqPcpk8lgRIO2LDX5aHIB8ymHOp/29tAKRWjLp1ZEPVBwHAMCz/4up+E78ZSrlu+P2r5rTb1f6OM0tU+9vb0qjtPg4CCpVCq+49TaWnZA4PSSJfRu3ZrfRqkBgSM6Tm0NbaxpWcPypuV0N3dzzKJj6GzqZF3rOlobWtnUtomOxg62d2wHKLoXhE1tm2htaK3qOPmo6SCUIvJgygxsJiK7gbOcc3cWHt8GPMg5d6DUdqsZcCeTydDc3Dz/nQhMrbx/d+fvOOPSM/jR837E4054XOTbh+RmDlW6O5fv9fDKV8JHI5l0oCrqLnclZDIZFi1apGLgszio5SCU2sqBJh9NLmA+5YjVp4Juxpm2NprLjQgf4yCUtcpHyyCUEM1Awc65kpdg1KourutzqQLMx48mn3quhysZhLKtoY2xSb9PogahpLKBzf4BPBpARLYCrcyMChwZo6OjUW8yFmrlPTUGRC0vwUhq5lCl+4EDkErBccdFLzQP6i53JSTZXRvastTko8kFzKcc6nyOPbb8SjGiLZ+oiXCg4NjRdmzMx4/5lEaTC+irh49tid+npmNAOOeycw1sJiLvAXY5564G3gB8XkReR37AtHNdDbplLF26tPxKCqmVdzpb+zEgkpo5VOl+xx35+/XrI3WZL3WXuxKS7K4NbVlq8tHkAuZTDnU++/aFVihCWz41ILKBguNG27ExHz/mUxpNLqCvHt43vi/296z5GBDOuR845zY75zYWBj/DOfeuQuMDzrkbnXNnOOdOLkxF9JNaePT399diszWnVt5x9IBIauZQpftUA0TgHhB1l7sSkuyuDW1ZavLR5ALmUw51Pps3h1YoQls+UeOcu8Y5J865+82ectM59wLn3I7C8qcUZsuYes37C9+ZtzjnfhjKXduxMR8/5lMaTS6grx7e3B6/j4ZBKGOhq6srtEJV1Mo7jmk4k5o5VOmupAGi7nJXQpLdtaEtS00+mlzAfMqhzuemm0IrFKEtH2MGbcfGfPyYT2k0uYC+evim0fh96qYBopIROTVSK+84puFMauZQpfsdd8DSpbBsWeQ+86HucldCkt21oS1LTT6aXMB8yqHOpzDSuha05WPMoO3YmI8f8ymNJhfQVw/v6Ijfp24aILq7u0MrVEWtvOO4BCOpmUOV7nfcEbz3A9Rh7kpIsrs2tGWpyUeTC5hPOdT5XH99aIUitOVjzKDt2JiPH/MpjSYX0FcPXz8Sv0/dNEBoa/2qlFp5x9EAkdTMoUr3fftUNEDUXe5KSLK7NrRlqclHkwuYTznU+Sj75U1bPsYM2o6N+fgxn9JocgF99bD1gKgh2lq/KqXWPSBaGms3BkRSM4cF9IAIPAMG1GHuSkiyuza0ZanJR5MLmE851Pko++VNWz7GDNqOjfn4MZ/SaHIBffWw9YCoIX19faEVqqJW3lPTcNZyEMqkZg5VuA8MwNCQih4QdZW7IpLsrg1tWWry0eQC5lMOdT5btoRWKEJbPsYM2o6N+fgxn9JocgF99fCWtvh96qYBorOzM7RCVdTKO5VNsahxEQ1SuyKQ1MyhCnclM2BAneWuiCS7a0Nblpp8NLmA+ZRDnc9tt4VWKEJbPsYM2o6N+fgxn9JocgF99fBt4/H71E0DxMjISGiFqqiVdyqbqunlF5DczKEKd0UNEHWVuyKS7K4NbVlq8tHkAuZTDnU+q1eHVihCWz7GDNqOjfn4MZ/SaHIBffXw6pb4feqmAWLx4sWhFaqiVt6pbKqmA1BCcjOHKtwVNUDUVe6KSLK7NrRlqclHkwuYTznU+fT0hFYoQls+xgzajo35+DGf0mhyAX31cE8mfp+6aYCYmJgIrVAVtfJO59I1b4BIauZQhfsdd0BrKxx1VG2E5kFd5a6IJLtrQ1uWmnw0uYD5lEOdz9KloRWK0JaPMYO2Y2M+fsynNJpcQF89vLQpfp+6aYBobGwMrVAVtfKOowdEUjOHKtynpuAUqYnPfKir3BWRZHdtaMtSk48mFzCfcqjzSadDKxShLR9jBm3Hxnz8mE9pNLmAvno4PRm/T900QBjFpLKpms6AUXfccYeKyy8MwzAMwzAMwzC0UjcNELlcLrRCVdTKO45LMJKaOVThrqgBoq5yV0SS3bWhLUtNPppcwHzKoc6nRdcPD9ryMWbQdmzMx4/5lEaTC+irh1sa4vepmwaIRYsWhVaoilp5x3EJRlIzh3m6j43BwYNqGiDqJndlJNldG9qy1OSjyQXMpxzqfIaGQisUoS0fYwZtx8Z8/JhPaTS5gL56eCgbv0/dNECMj4+HVqiKWnnH0QCR1Mxhnu7/+Ef+XkkDRN3krowku2tDW5aafDS5gPmUQ53PypWhFYrQlo8xg7ZjYz5+zKc0mlxAXz28sjl+n7ppgOjo6AitUBW18k5lU7Q01rbLTVIzh3m6K5qCE+ood2Uk2V0b2rLU5KPJBcynHOp87r47tEIR2vIxZtB2bMzHj/mURpML6KuH707H71M3DRCDg4OhFaqiVt7pbO3HgEhq5jBPd2UNEHWTuzKS7K4NbVlq8tHkAuZTDnU+xx8fWqEIbfkYM2g7Nubjx3xKo8kF9NXDxy+O36duGiBWrFgRWqEqauUdxyUYSc0c5um+bx80NcGqVTXzmQ91k7sykuyuDW1ZavLR5ALmUw51Pnv2hFYoQls+xgzajo35+DGf0mhyAX318J6x+H3qpgHi4MGDoRWqolbecVyCkdTMYZ7ud9wBa9bkGyEUUDe5KyPJ7trQlqUmH00uYD7lUOezY0dohSK05WPMoO3YmI8f8ymNJhfQVw/v6Ijfp24aILq7u0MrVEWtvOPoAZHUzGGe7oqm4IQ6yl0ZSXbXhrYsNflocgHzKYc6n+uvD61QhLZ8jBm0HRvz8WM+pdHkAvrq4etH4vepmwYIba1flVIr73Su9mNAJDVzqKIHhKIGiLrJXRlJdteGtiw1+WhyAfMphzofZb+8acvHmEHbsTEfP+ZTGk0uoK8eth4QNURb61elWA+IMFTsnsnAPfeoaoCoi9wVkmR3bWjLUpOPJhcwn3Ko81H2y5u2fIwZtB0b8/FjPqXR5AL66mHrAVFDent7QytURS28c5M5spNZWppqOwZEUjOHebjfdRdMTqpqgKiL3BWSZHdtaMtSk48mFzCfcqjz2bo1tEIR2vIxZtB2bMzHj/mURpML6KuHt7bH71M3DRDLly8PrVAVtfBO59IANe8BkdTMYR7uU1Nwrl9fM5f5Uhe5KyTJ7trQlqUmH00uYD7lUOezd29ohSK05WPMoO3YmI8f8ymNJhfQVw/vHY3fp24aIIaGhkIrVEUtvFPZFFD7BoikZg7zcN+3L3+vqAdEXeSukCS7a0Nblpp8NLmA+ZRDnY+ixnLQl48xg7ZjYz5+zKc0mlxAXz28fvH62N+zbhog2tvbQytURS28pxogaj0NZ1Izh3m4T/WAWLu2djLzpC5yV0iS3bWhLUtNPppcwHzKoc5n//7QCkVoy8eYQduxMR8/5lMaTS6grx7en47fp24aIFKpVGiFqqiFd1w9IJKaOczD/Y474NhjoaW2jTnzoS5yV0iS3bWhLUtNPppcwHzKoc5nxYrQCkVoy8eYQduxMR8/5lMaTS6grx5e0Ry/T900QDQ3N4dWqIpaeKez8YwBkdTMYR7uyqbghDrJXSFJdteGtiw1+WhyAfMphzqf0dHQCkVoy8eYQduxMR8/5lMaTS6grx4ezcXvUzcNEJOTk6EVqqIW3nH1gEhq5jAPd4UNEHWRu0KS7K4NbVlq8tHkAuZTDnU+TU2hFYrQlo8xg7ZjYz5+zKc0mlxAXz3cJPH71E0DhHMutEJV1MJ7egyIGk/DmdTMoUL3yUm4805VM2BAHeSulCS7a0Nblpp8NLmA+ZRDnU9jY2iFIrTlY8yg7diYjx/zKY0mF9BXDzdK/D510wDRpKy1qVJq4R3XNJxJzRwqdL/3XpiYUNcD4ojPXSlJdteGtiw1+WhyAfMphzqf8fHQCkVoy8eYQduxMR8/5lMaTS6grx4en4zfp24aINLpdGiFqqiFd1yXYCQ1c6jQXeEUnFAHuSslye7a0JalJh9NLmA+5VDn09kZWqEIbfkYM2g7Nubjx3xKo8kF9NXDnU3x+9RNA0RbW1tohaqohXdcDRBJzRwqdJ+aglNZA8QRn7tSkuyuDW1ZavLR5ALmUw51PgcOhFYoQls+xgzajo35+DGf0mhyAX318IGJ+H3qpgFieHg4tEJV1MJ7egyIxtqOAZHUzKFCd6UNEEd87kpJsrs2tGWpyUeTC5hPOdT5rF0bWqEIbfkYM2g7Nubjx3xKo8kF9NXDa1vj96mbBohly5aFVqiKWnjHNQ1nUjOHCt3vuANWrICOjpr7zIcjPnelJNldG9qy1OSjyQXMpxzqfG69NbRCEdryMWbQdmzMx4/5lEaTC+irh28di99H16gcNaS3t5eVK1eG1pg3tfCO6xKMpGYOFbrfcYe6GTCgDnJXSm9vb2iFIwZt5UCTjyYXMJ9yqPPZto2VN9wQy3vJhVJ2ne0d27lhpLSPe7eu0evrCXVl13y8mE9pNLlAvPVwJWzr2Oath2tB3fSA0FTw5kMtvOOahjOpmUOF7nfcoe7yC6iD3JWSZHdtaMtSk48mFzCfcqjzUfSlF4j9S69ROerKrvl4MZ/SaHIBq4ehjhogDh48GFqhKmrhHVcPiKRmDhW4O5efBUNhA8QRnbtikuyuDW1ZavLR5ALmUw51Ptu3h1YoYnuHLh9jBnVl13y8mE9pNLmA1cNQRw0Q3d3doRWqohbe6Vx+DIhaD0KZ1MyhAvfeXhgbU9kAcUTnrpgku2tDW5aafDS5gPmUQ52P/fJmVIi6sms+XsynNJpcwOphqKMGiJ6entAKVVEL71Q2RXNDM40NjZFvezZJzRwqcFc6AwYc4bkrJsnu2tCWpSYfTS5gPuVQ52O/vBkVoq7smo8X8ymNJhewehjqqAGiq6srtEJV1MI7lU3VfPwHSG7mUIG74gaIIzp3xYRwF5G1IvJLEblRRHaLyPmF5StE5KcickvhfnlhuYjIRSJyq4j8TUTuP2tbLyysf4uIvDD2nZmFtnKgyUeTC5hPOdT57N4dWqGI3SO6fIwZ1JVd8/FiPqXR5AJWD0MdNUAMDAyEVqiKWnins+maj/8Ayc0cKnCfaoBQOAvGEZ27YgK5Z4E3OOdOAh4EvEpETgLeCvzcObcJ+HnhMcDjgU2F20uBz0C+wQJ4N3A68EDg3VONFiHQVg40+WhyAfMphzqfE04IrVDECW26fIwZ1JVd8/FiPqXR5AJWD0MdNUAsWbIktEJV1MI7lU3F0gCR1MyhAvc77oCODlge7H+0khzRuSsmhLtzbr9z7i+Fv4eBm4DVwFOBLxVW+xLwtMLfTwW+7PL8AVgmIscCjwN+6pzrc871Az8FzopvT4rRVg40+WhyAfMphzqfO+8MrVDEnSldPsYM6squ+Xgxn9JocgGrh6GOGiDGxsZCK1RFLbxTuVTNB6CE5GYOFbhPTcEp5ec5j5sjOnfFhHYXkfXAqcAfgaOdc/sLT90LHF34ezUw+5PmrsKyUsuDEDrLQ9Hko8kFzKcc6nyOOiq0QhFHLdLlY8ygruyajxfzKY0mF7B6GOqoAaKlpfb/cNeCWnjH1QMiqZlDGXfn4G9/g+OPj09oHhyxuSsnpLuIdADfAl7rnBua/ZxzzgEuovd5qYjsEpFdBw4cIJVKMTY2xujoKOl0mqGhIbLZLP39/Tjnpgd+mpoCq6enB+cc/f39ZLNZhoaGSKfTjI6OMjY2RiqVYnh4mIaGBgYGBpicnKS3t7doG1P3fX195HI5BgcHmZiYYGRkhPHxccbHxxkZGWFiYoLBwUFyuRx9fX1zbqO3t5fJyUkGBgbIZDIMDw/PuU+NjY0L3qdMJhPJPqXT6Uj2KYrjlMlkSKVSao5TNptlfHxcxXGa2qepL76xHKd160h3djJ6zDGMdXeTWr6c4TVryLS1MbBxI5ONjdNffA/u2FF037dlC7nmZgbXr4/sOK1pWcPypuV0N3dzzKJj6GzqZF3rOlobWtnUtglBOLblWGBmELTtHdsRhE1tm2htaF3QcdJAlOP0xI22z2Pz8WM+pdHkAtAyOBhaoYjBbPw+kv9umix27tzpdu3aNa/XjI6O0t7eXiOj2lEL7yf9vydx78i97Hrp/DKcL0nNHMq4/+Y38IhHwGWXwQuDjtU3J0ds7soZHR2lo6PjWufczjjfV0Sage8BP3bOfaywbA9wpnNuf+ESi18557aIyGcLf18+e72pm3PuZYXlRevNRTX1cKVoKweafDS5gPmUI1afCnrkjR5zDO333utfKaLvhXJheZ9jFh3DvROlfdy7q3MRkdjr4hIexwLHOuf+IiJLgGvJXxJ3LtDnnPugiLwVWO6ce4uIPAF4NfAE8mPyfMI5d7rvPWpVF9f1uVQB5uNHk4/Vw37K1cNQXV3sq4frpgeEKOwqXwm18I6rB0RSM4cy7pdcAkuWwDOfGZ/QPDhic1dOCHfJv+klwE1TjQ8FrgamWsdeCHxn1vJ/KfzK9iBgsHCpxo+Bx4rI8sIvcY8tLAuCtnKgyUeTC5hPOdT55HKhFYrIOV0+URPhOD2xo67smo8X8ymNJhewehigKfZ3DERDQzLbWmrhHdc0nEnNHDzug4PwjW/AC14ASlp2D+WIzD0BBHI/A3gBcL2IXFdY9nbgg8CVIvJi4A7g2YXnfkD+l7VbgTHgXwGcc30i8l7gz4X13uOc64tlD+ZAWznQ5KPJBcynHOp8stnQCkVknS6fWrLAcXr2EzPqyq75eDGf0mhyAauHoY56QGQymdAKVVEL77h6QCQ1c/C4X345jI/Dv/1bvELz4IjMPQGEcHfOXeOcE+fc/ZxzpxRuP3DO9TrnHu2c2+Sce8xUY0LhV7VXOec2Oud2OOd2zdrWpc65Ewq3L8a+M7PQVg40+WhyAfMphzofZQ3n7Y26fGpF1OP0xDEez9RYHlrG45ly1TIez9Q6WsbjmXq/0Mdpap96e3tVHKfBwUFSqVR8x6m1tex4POklS+jdujW/jVLj8UR0nNoa2sqOx9PR2FE0Ds/U/ULH4/FRN2NAZDIZmpuba2RUO2rhveMzO9jctZlvPftbkW73UJKaOXjcH/AASKfh//5P5QwYcITmngAymQyLFi1Scd1xHNRyDAht5UCTjyYXiNmngjo309ZGc7kRz2P83lPP+VRy7XFbQxtjk6V9kj4GBEQzTs+s3hKHUau6uK7rmgowHz+afKwe9lOuHgYbA6JqRkdHQytURS284+oBkdTMoYT7//0f7NoFL36x2sYHOAJzTwhJdteGtiw1+WhyAYU+xwa5XL4klo+fqVkwjlQiHKcndtSVXfPxYj6l0eQCVg9DHY0BsXTp0tAKVVEL71Q2RUtj7ceASGrmUML9kktg0SJ4/vPjF5oHR1zuCSHJ7trQlqUmH00uoNBn377QCkVYPn72je8LrVBrIhmnJwTqyq75eDGf0mhyAauHoY56QPT394dWqIpaeKez6Vh6QCQ1c5jDPZWCr34Vnv506OoKI1UhR1TuCSLJ7trQlqUmH00uoNBn8+bQCkVYPn42t+vyiZoox+mJG3Vl13y8mE9pNLmA1cNQRw0QXcr/aSxFLbzjugQjqZnDHO5XXQX9/fnLL5RzROWeIJLsrg1tWWry0eQCCn1uuim0QhGWj5+bRnX5GDOoK7vm48V8SqPJBawehjpqgKhkRE6N1MI7rkswkpo5zOF+ySVw3HHw6EeHEZoHR1TuCSLJ7trQlqUmH00uoNCnMJK4FiwfPzs6dPkYM6gru+bjxXxKo8kFrB6GGBogROQsEdkjIreKyFtLrPNsEblRRHaLyP+rhUd3d3ctNltzovaedJNkJjOx9IBIauZwiPvtt8PPfgYvehEom0t4Lo6Y3BNGkt21oS1LTT6aXEChz/XXh1YowvLxc/2ILh9jBnVl13y8mE9pNLmA1cNQ4wYIEWkEPgU8HjgJOEdETjpknU3A24AznHPbgNfWwkVb61elRO2dzqYBYmmASGrmcIj7F7+Yn/Xi3HOD+cyHIyb3hJFkd21oy1KTjyYXUOij7Jcly8eP9YDQi7qyaz5ezKc0mlzA6mGo/SwYDwRudc7dBiAiVwBPBW6ctc5LgE855/oBnHMHaiGirfWrUqL2TmVTQDwNEEnNHGa553L5BojHPhbWrQsrVSFHRO4JJMnu2tCWZaw+Zab4rcgkovnDK0HdsVL2y5Ll48d6QOhFXdk1Hy/mUxpNLmD1MNT+EozVwJ2zHt9VWDabzcBmEflfEfmDiJxVC5G+vr5abLbmRO091QDR0lT7MSCSmjnMcv/pT+GuuxIx+OQUR0TuCSTJ7trQlqUmn74tW0IrFKEpG7B8yqEtny1tunyMGdSVXfPxYj6l0eQCVg/DPBogRGSxiNTCsAnYBJwJnAN8XkSWzfH+LxWRXSKy68CBA6RSKcbGxhgdHSWdTjM0NEQ2m6W/vx/nHD09PcBMt5tMJoNzjv7+frLZLENDQ6TTaUZHRxkbGyOVSjE8PEwmk2FgYIDJyUl6e3uLtjF139fXRy6XY3BwkImJCUZGRhgfH2d8fJyRkREmJiYYHBwkl8tNF/pDt9Hb28vk5CQDAwNkMhmGh4fn3Kf29vaS+9TT0zPvfbq35958HuOZmu9TQ0PDvI9TNftUi+OUyeTzSX/607iVKxl4+MO9x0nTPolIJGUvxD4tXry4pudTLfeps7MTIxq0ZanJp/O220IrFKEpG7B8yqEtn9vGdfkYM6gru+bjxXxKo8kFrB4GEFdBV00ReTLwEWCRc26DiJwCvMc595Qyr3swcIFz7nGFx28DcM59YNY6FwN/dM59sfD458BbnXN/LrXdnTt3ul275jc18uDgoLoCWAlRe9/cczNbP7WVy59xOWdvPzuy7c5FUjOHgvvEBKxeDeedBx/7WGilikl87gl2X7Zs2bXOuZ2hXeKgmnq4UrSVg1h9ylyCMbh+PZ379vm3EeMlGJqyAcunHHHmIxeW91nfup59qdI+7t3VuYiI1cULpK7r4QowHz+afKwe9lOuHobq6mJfPVxpD4gLyI/nMADgnLsO2FDB6/4MbBKRDSKyCDgbuPqQda4i3/sBEVlJ/pKMyJtiFi9eHPUmYyFq7+lLMGKYhjOpmUPB/StfgUwmUZdfwBGQe0JJsrs2tGWpyWdxoeeOFjRlA5ZPObTl05PR5WPMoK7smo8X8ymNJhewehgqb4DIOOcGD1lWtinEOZcFzgN+DNwEXOmc2y0i7xGRqd4TPwZ6ReRG4JfAm5xzvRV6VczExETUm4yFqL3jHIQyqZkDTKTTcMklcPrpsG1baJ15kejczd1AX5aafCaWLg2tUISmbMDyKYe2fJY26fIxZlBXds3Hi/mURpMLWD0Mlc+CsVtEngs0FqbNfA3wu0pe6Jz7AfCDQ5a9a9bfDnh94VYzGhsba7n5mhG1d5zTcCY1c4Dma6+FG2+Ez38+tMq8SXLu5m6Aviw1+TSm06EVitCUDVg+5dCWT3pSl48xg7qyaz5ezKc0mlzA6mGovAfEq4FtQBr4f8Ag8NoaORk1JM4eEEmm8bLLoL0dnvOc0CqGYRiGYRiGYRhHBBX1gHDOjQH/XrglklwuF1qhKqL2jnMazqRmjnM0fve78PSnw5IloW3mTWJzx9yNPNqy1OSTa6l93T0fNGUDlk85tOXT0qDLx5hBXdk1Hy/mUxpNLmD1MFTYA0JEfjp7akwRWS4iP66ZVQ1YtGhRaIWqiNo7zh4QSc2cnh6krw9OOy20SVUkNnfM3cijLUtNPouGhkIrFKEpG7B8yqEtn6GsLh9jBnVl13y8mE9pNLmA1cNQ+SUYK51zA1MPnHP9wFE1MaoR4+PjoRWqImrvdC6+MSCSmjl79uTvN28O61Elic0dczfyaMtSk8/4ypWhFYrQlA1YPuXQls/KZl0+xgzqyq75eDGf0mhyAauHofJBKCdFZJ1z7h8AInIcFcyCoYmOjo7QClURtXecPSCSmjl79+bvt2wJ61Elic0dczfyaMtSk0/H3XeHVihCUzZg+ZRDWz53p3X5GDOoK7vm4yVWH5Gyq3Q0N+ensvfh4vlXUt2xsnq44h4Q/w5cIyJfEZGvAr8B3lY7regZHDx0FtFkELX39BgQjbW/3iepmbNnD665GdavD21SFYnNHXM38mjLUpPP4PHHh1YoQlM2YPmUQ1s+xy/W5WPMoK7smo8XdT6K6hrLxk+IerjSQSh/JCL3Bx5UWPRa51xP7bSiZ8WKFaEVqiJq7zin4Uxq5uzZg5xwAiibtqdSEps75m7k0ZalJp8VU5eIKUFTNmD5lENbPnvGdPkYM6gru+bjRZ2PorrGsvEToh729oAQkRML9/cH1gH3FG7rCssSw8GDB0MrVEXU3nHOgpHUzNm7l3RCez9AgnPH3I082rLU5HNwx47QCkVoygYsn3Joy2dHhy4fYwZ1Zdd8vKjzUVTXWDZ+QtTD5XpAvAF4CfDROZ5zwKMiN6oR3d3doRWqImrvVDZFozTS1FDp8B/Vk8jMs1m49VZanvKU0CZVk8jcC5i7Afqy1OTTff31oRWK0JQNWD7l0JbP9SO6fIwZ1JVd8/GizkdRXWPZ+AlRD3t7QDjnXlK4f+Qct8Q0PoC+1q9KqUUPiDguv4CEZr5vH2QyDB97bGiTqklk7gXM3QB9WWry0fbLiaZswPIph7Z8rAeEXtSVXfPxos5HUV1j2fhR1wNCRP7Z97xz7n+i1akd2lq/KiVq73QuHVsDRCIzL1yXtWTnzsAi1ZPI3AuYuwH6stTko+2XE03ZgOVTDm35WA8Ivagru+bjRZ2PorrGsvGjrgcE8OTC7cXAJcDzCrcvAC+qrVq09Pb2hlaoiqi94+wBkcjMC1Nw9imrrOZDInMvYO4G6MtSk0/v1q2hFYrQlA1YPuXQls/Wdl0+xgzqym6cPiJlb70nnVR+vRhRd7wU1TWWjZ8Q9bC3B4Rz7l8BROQnwEnOuf2Fx8cCl9XcLkKWL18eWqEqovZOZVOxDEAJCc18zx5YvpxlGzeGNqmaROZewNwN0JelJp/lhUZSLWjKBiyfcmjLZ++oLh9jBnVlV5uPsnPJ8imNZeMnRD1crgfEFGunGh8K3Ed+VozEMDQ0FFqhKqL2jvMSjERmvmcPbNnC0PBwaJOqSWTuBczdAH1ZavIZUjZDj6ZswPIph7Z81i9eH1rBKIG6sqvNR9m5ZPmUxrLxE6IernQqhJ+LyI+BywuPnwP8rDZKtaG9vT20QlVE7R3nJRiJzHzvXvinf0qmewFzD0OS3bWhLUtNPu3795dfKUY0ZQOWTzm05bM/rcvHmEFd2dXmo+xcsnxKY9n4CVEPV9QDwjl3HnAxcHLh9jnn3KtrKRY1qVQqtEJVRO2dyqZoaYznEozEZT48DPfcA1u2JM99FuYehiS7a0Nblpp8UitWhFYoQlM2YPmUQ1s+K5p1+RgzqCu72nyUnUuWT2ksGz8h6uFKe0DgnPs28O25nhOR3zvnHhyZVQ1obm4OrVAVUXvH2QMicZlPXZO1eXPy3Gdh7mFIsrs2tGWpyad5dDS0QhGasgHLpxza8hnN6fIxZlBXdrX5KDuXLJ/SWDZ+QtTDlY4BUY54/qNdAJOTk6EVqiJq73Q2vjEgEpf5VAPEli3Jc5+FuYchye6xUsHo4pPr1qkaXVzTsZ1sqvh3g1jQlA1YPuXQlk+T6PIxZlBXdrX5KDuXLJ/SWDZ+QtTDUTVAuIi2UzOcU684J1F7x9kDInGZ79mT/8fmhBOS5z4Lcw9Dkt214RobQysUoenYWjZ+LB8/2vJpFF0+pRCRD4nIUhFpFpGfi8hBEXl+aK9aoq7savNRdi5ZPqWxbPyEqIejaoBQT5Oy1qZKido7zmk4E5f5nj1w3HHQ2po891mYexiS7K6NpvHx0ApFaDq2lo0fy8ePtnzGJ3X5eHisc24IeBKwDzgBeFNQoxqjruxq81F2Llk+pbFs/ISoh6NqgIi3P2wVpNPp0ApVEbV3nD0gEpf53r2wZQuQQPdZmHsYkuyujXRnZ2iFIjQdW8vGj+XjR1s+nU26fDxM/QfzROAbzrnBkDJxoK7savNRdi5ZPqWxbPyEqIejaoB4QUTbqRltbW2hFaoiau90Lk1rYzwNEInK3LmiBohEuR+CuYchye7aaDtwILRCEZqOrWXjx/Lxoy2fAxO6fDx8T0RuBk4jPzV9N6BraP2IUVd2tfkoO5csn9JYNn5C1MMVNUCIyD+LyC0iMigiQyIyLCJDU887526onWI0DA8Ph1aoiqi94+wBkajM77kHRkZg82YgYe6HYO5hSLK7NobXrg2tUISmY2vZ+LF8/GjLZ22rLp9SOOfeCjwE2OmcywCjwFPDWtUWdWVXm4+yc8nyKY1l4ydEPVzpRTEfAp7snLupljK1ZNmyZaEVqiJq7zjHgEhU5rNmwICEuR+CuYchye7aWHbrraEVitB0bC0bP5aPH2353Dqmy6cMJwLrRYqGjP9yKJlao67savNRdi5ZPqWxbPyEqIcrvQTjviQ3PgD09vaGVqiKKL2dc0zkJmLrAZGozPfsyd8XGiAS5X4I5h6GhbqLyBki0l74+/ki8jEROS4SuYTRu21baIUiNJVLy8aP5eNHWz7bOnT5lEJEvgJ8BHgo8IDCbWdQqRqjruxq81F2Llk+pbFs/ISohyvtAbFLRL4OXAVMj+ThnPufWkjVgpUrV4ZWqIoovdO5/KGLqwEiUZnv2QOLF8Pq1UDC3A/B3MMQgftngJNF5GTgDcAXyP+69oiFbjhprLxB11V9msqlZePH8vGjLZ8bRnT5eNgJnOS0zedXQ9SVXW0+ys4ly6c0lo2fEPVwpT0glgJjwGOBJxduT6qVVC04ePBgaIWqiNI7lc2Pl9TSGM8lGInKfO/e/PgPDflTIlHuh2DuYYjAPVv4cvtU4JPOuU8BSxYslkAObt8eWqEITeXSsvFj+fjRls/2Dl0+Hm4AjgktESfqyq42H2XnkuVTGsvGT4h6uKIeEM65f621SK3p7u4OrVAVUXpPNUDE1QMiUZnv2QOnnTb9MFHuh2DuYYjAfVhE3gY8H3i4iDQAzQsWSyDdyn4d0FQuLRs/lo8fbflo7wEhIt8FHPnG4BtF5E8U9wR+Sii3WqOu7GrzUXYuWT6lsWz8hKiHK2qAEJFW4MXANmD6v1fn3Itq5BU5PT096rrgVEKU3ulsvJdgJCbzdBpuvx3OOWd6UWLc58Dcw9DT07PQTTwHeC7wYufcvSKyDvjwgsUSSM/27aq6KGoql5aNH8vHj7Z8tnds194I8ZHQAqFQV3a1+Sg7l+o1H7lQyq5TST3j3h3f1VXayk6IerjSMSC+AtwMPA54D/A8IFGDUnZ1dYVWqIoovePuAZGYzP/+d5icnB6AEhLkPgfmHoYI3BcDn3HOjRceHwR+s9CNJpGu3btDKxShqVxaNn4sHz/a8tk9osvnUJxzvwYQkQ3AfudcqvB4MXB0SLdao67savOJ8Vyq5J9sQXD4/4mO859sTXWNtnpGUzYQJp9Kx4A4wTn3TmDUOfcl4InA6bXTip6BgYHQClURpff0GBAxTcOZmMwPmYITEuQ+B+YehgjcvwHkZj3OFZaVREQuFZEDInLDrGUXiMjdInJd4faEWc+9TURuFZE9IvK4WcvPKiy7VUTeutAdWSgDJ5wQWqEITeXSsvFj+fjRls8Jbbp8PHwDmJz1uGz9nHTUlV1tPnYuedGUj2XjJ0Q+lTZAZAr3AyKyHegEjqqNUm1YsiSZY7lF6R13D4jEZD41BefmzdOLEuM+B+Yehgjcm5xzE1MPCn8vKvOay4Cz5lj+cefcKYXbDwBE5CTgbPKX0p0FfFpEGkWkEfgU8HjgJOCcwrrBWHLnnSHf/jA0lUvLxo/l40dbPnemdPl4qKZ+TjTqyq42HzuXvGjKx7LxEyKfShsgPiciy4F3AlcDNwIfqplVDRgbGwutUBVResc9DWdiMt+zB44+Gjo7pxclxn0OzD0MEbgfFJHpAc1E5KmAd2AJ59xvgL4Kt/9U4ArnXNo5dztwK/DAwu1W59xthS/VVxTWDcbYUbratzWVS8vGj+XjR1s+Ry3S5eNh3vVz0lFXdrX52LnkRVM+lo2fEPlU1ADhnPuCc67fOfdr59zxzrmjnHMX11ouSlpa4rnsIGqi9I67B0RiMt+7t+jyC0iQ+xyYexgicH858HYRuVNE/gG8BXhZlds6T0T+VrhEY3lh2WpgdjP3XYVlpZYHo2VwMOTbH4amcmnZ+LF8/GjLZzCry8fD7Pr5TvL180vLvSiqy+QiR6TsrWXduvLrxYidS360nUua8rFs/ITIp6IGCBE5WkQuEZEfFh6fJCIvrq1atGSz2dAKVRGl9/QYEI3xVOKJyXzPnqLLLyBB7nNg7mFYqLtz7u/OuQcBW4GTnHMPcc7dWsWmPgNsBE4B9gMfXZDYLETkpSKyS0R2HThwgFQqxdjYGKOjo6TTaYaGhshms/T39+Ocm54ZZGoO7p6eHpwI/Zs2kW1tZWjdOtKdnYwecwxj3d2kli9neM0aUsuXM7BxI5ONjfRu3Zrfxo4dRfd9fX3kcjkGBweZmJhgZGSE8fFxxsfHGRkZYWJigsHBQXK5HH19fUUeU/e9vb1MTk4yMDBAJpNheHh4zn1KpVL+fXKO/v5+stksQ0NDpNNpRkdHGRsbI5VKMTw8TCaTYWBggMnJSXp7e+f06evrI9fczOD69Ux0dDCyahXjXV2Md3UxsmoVEx0d9J9wArnmZvoKjaaH5tK7dWtF+1T2OFW4T319feX3KarjtHEjmba26TIy1t3N6DHHkO7sZGjdOrKtrfRu3YoToacwz/rUfOs927fPlL0ojlOF+3RotvMte/M6TiXOp0xb2/T5NJ3LoefTli0zZS+i82lNyxqWNy2nu7mbYxYdQ2dTJ+ta19Ha0Mqmtk0IwskdJwMz89Bv79iOIGxq20RrQ+uCjlOUHFI/by3Uz3+v4KWXscDL5KLZg/mTXbw41FvPibbvB9ryWdygy0dTPpaNnxD5iHPlR0QtNDx8Efh359zJItIE/NU5t6PWgnOxc+dOt2vXrnm9ZmxsjLa2thoZ1Y4ovb+x+xs8+5vP5oZX3MC2o7ZFsk0fici8rw+6uuDDH4Y3vnF6cSLcS2DuYRgbG6O9vf1a59zOal4vIkcD/wGscs49vvBl9MHOuUvKvG498D3n3HbfcyLyNgDn3AcKz/0YuKCw6gXOuccVlhetV4pq6uGCVNlVxrq7aSv3T0QFn11REWu5LJOPZePH8vETZz6VjNzf3dzNwUxpn2pH7ReRquviEtvrBN4NPLyw6NfAe5xzZX86PLSOFpELgBHn3EcOWW/OOto593vf9quqi5WVlUqwc8lPuXMJIpwFQ1E+lk0ZnYD5+OrhSseAWOmcu5LCCMDOuSzFo7Wrp6Gh0l3VRZTecV+CkYjM55gBAxLiXgJzD0ME7pcBPwZWFR7vBV47342IyLGzHj4dmOr6ezVwtoi0FKaU2wT8CfgzsElENojIIvK/wF1dzQ5ERYOyX7o0lUvLxo/l40dbPlmny8fDpcAw8OzCbYj8D3PVMp/L5IKgrazYueRH27mkKR/Lxk+IfCo9m0dFpAvyE8yKyIMAXRewlCGTyZRfSSFResc9DWciMp+aAeOQBohEuJfA3MMQgfu8G3pF5HLg98AWEbmrcGnch0TkehH5G/BI4HWF7e0GriQ/iPCPgFc553KF9zmPfOPHTcCVhXWDkWlvD/n2h6GpXFo2fiwfP9ryaW/U5eNho3Pu3YXBem9zzl0IHF/lthZ8mdyCL4ebfUlSicvhBo4/vujyndCXw01dahTL5XCzL0kqcTlc3+bNsV0ON3VJ0rrWdXQ2dXLMomPobu5medNy1rSsoa2hjRPbT6SRRra254/Tjo4dRfdb2rbEezncSSfFcjlcszSzvnU9HY0drGpZRVdzF13NXaxqWUVHYwfrW9fT2dTJlrYtc+aytX0rjTRGdzmc5/LSqfMpvWRJ6fNpquxFdD61NbSVvRyuo7Gj6DK4qfuFXg7no9JLMO4P/Dewnfyvad3AM51zfyv74hpQTXezTCZDc3NzjYxqR5Te//3H/+Y1P3oNB990kJVtKyPZpo9EZP72t+cvvxgbg1muiXAvgbmHIZPJsGjRooVcgvEr4BnAT51z9y809P6nc+4RUXpGRS0vwci0tdFcbsTzGLv+xlouy+Rj2fixfPzEmU8lXX/bGtoYmyzto+gSjN8Db3LOXVN4fAbwEefcgyt47XoWcJlcqEsw7Fzyk7RzCeK9zCCufCybMjoB81nwJRjOub8AjwAeQn5U9m2hGh+qZXR0NLRCVUTpHfc0nInIfO9eOP74osYHSIh7Ccw9DBG4v578pQ8bReR/gS8Dr17oRpPI6LHHll8pRjSVS8vGj+XjR1s+x7bo8vHwCuBTIrJPRO4APkmVsxRVcZlcELSVFTuX/Gg7lzTlY9n4CZFPUyUrFUbhfQKwvvCax4oIzrmP1dAtUpYuXRpaoSqi9I57DIhEZL5nz2GXX0BC3Etg7mGIwH0j8HhgLfmeEKdTYR19pLF0377QCkVoKpeWjR/Lx4+2fPaN7wutUBHOueuAk0VkaeHxUCWvK1wmdyawUkTuIj+Q5Zkicgr5y5r3UWjIcM7tFpGpy+SyFC6Ti3RH5oG2smLnkh9t55KmfCwbPyHyqXQMiO8C5wJdwJJZt8TQ398fWqEqovROZVM0SANNDfH8T6M+81wObrnlsCk4IQHuHsw9DBG4v7PwpXY5+bEbPk3+WuG6o3+OczIkmsqlZePH8vGjLZ/N7bp8SiEiXSJyEfAr4Jci8onC2GhenHPnOOeOdc41O+fWOOcucc69wDm3wzl3P+fcU5xz+2et/37n3Ebn3Bbn3A9ruEtl0VZW7Fzyo+1c0pSPZeMnRD6V/ie6xjl3v5qa1JiurrKfEyqJ0judTcfW+wESkPmdd0I6PWcPCPXuHsw9DBG4T/3S9UTg886574vI+xa60STSddNNoRWK0FQuLRs/lo8fbfncNKrLx8MVwG/I904DeB7wdeAxwYxqjLayYueSH23nkqZ8LBs/IfKptAfED0XksTU1qTGVjMipkSi9U9lUrA0Q6jMvMQMGJMDdg7mHIQL3u0Xks8BzgB+ISAuV19FHFFOjQWtBU7m0bPxYPn605TM1En0CONY5917n3O2F2/uAo0NL1RJtZcXOJT/aziVN+Vg2fkLkU+mX2z8A3xaRcREZEpFhEano+jctdHd3h1aoiii9U9kULY3xTMEJCch8qgFijq5Q6t09mHsYInB/NvmpMB/nnBsAVgBvWuhGk0j39deHVihCU7m0bPxYPn605XP9iC4fDz8RkbNFpKFwm6qvj1i0lRU7l/xoO5c05WPZ+AmRT6UNEB8DHgy0OeeWOueWOOd0jQZTBm0tp5USaQ+InPWAKGLvXli6FI4+/EcM9e4ezD0MC3V3zo055/7HOXdL4fF+59xPIpFLGNp+HdBULi0bP5aPH235aPtl0sNLgK8B6cLtCuBlSfxBrlK0lRU7l/xoO5c05WPZ+NHcA+JO4AbnYpzwN2K0tZxWSpTecY8BoT7zqRkw5pizV727B3MPQ5LdtaHt1wFNx9ay8WP5+NGWj7ZfJj10kh+M/b3OuWbys8I9Jok/yFWKtrJi55IfbeeSpnwsGz+ae0DcBvxKRN4mIq+futVSLGr6+vpCK1RFlN5xjwGhPvM9e+a8/AIS4O7B3MOQZHdt9M0xLktINB1by8aP5eNHWz5b2nT5ePgU8CDgnMLjYeCT4XRqj7ayYueSH23nkqZ8LBs/IfKpdBaM2wu3RYVb4ujs7AytUBVReqeyKVqa4hsDQnXmo6P5WTBKVAKq3ctg7mFIsrs2Om+7LbRCEZqOrWXjx/Lxoy2f28Z1+Xg43Tl3fxH5K4Bzrl9EEvl9uFK0lRU7l/xoO5c05WPZ+AmRT0U9IJxzF851q7VclIyMjIRWqIoovePuAaE681tvzd+XaIBQ7V4Gcw9Dkt21MbJ6dWiFIjQdW8vGj+XjR1s+q1t0+XjIiEgj4ABEpBuYDKtUW7SVFTuX/Gg7lzTlY9n4CZFP3Uzxtnjx4tAKVRGldzoX7xgQqjP3zIAByt3LYO5hSLK7Nhb39IRWKELTsbVs/Fg+frTl05PR5ePhIuDbwFEi8n7gGuA/wirVFm1lxc4lP9rOJU35WDZ+QuRTNw0QExMToRWqIkrvuHtAqM58qgFi06Y5n1btXgZzD0OS3bUxsVTXmG6ajq1l48fy8aMtn6VNunxK4Zz7GvBm4APAfuBpzrlvhLWqLdrKip1LfrSdS5rysWz8hMinojEgROQM59z/llummcbGxtAKVRGldyqboqUxvjEgVGe+dy+sXQvt7XM+rdq9DOYehiS7a6MxnQ6tUISmY2vZ+LF8/GjLJz2py8eHc+5m4ObQHnGhrazYueRH27mkKR/Lxk+IfCrtAfHfFS4zFBP3NJyq8cyAYRiGYRiGYRiGYUSPtweEiDwYeAjQfci0m0sBXU2RZcjlcqEVqiJK77gvwVCbuXP5BojnP7/kKmrdK8Dcw5Bkd23kWuLrqVUJmo6tZePH8vGjLZ+WBl0+xgzayoqdS360nUua8rFs/ITIp1wPiEVAB/mGiiWzbkPAMyt5AxE5S0T2iMitIvJWz3rPEBEnIjsrU58fixYlc7akKL3jvgRDbeYHDsDQUMkZMECxewWYexiS7K6NRUNDoRWK0HRsLRs/lo8fbfkMZXX5GDNoKyt2LvnRdi5pysey8RMiH28DhHPu14XpNh80a+rN9wJfcM7dUm7jhSmLPgU8HjgJOEdETppjvSXA+cAfq9iHihgfH6/VpmtKlN5x94BQm/nUAJSeBgi17hVg7mFIsrs2xleuDK1QhKZja9n4sXz8aMtnZbMuH2MGbWXFziU/2s4lTflYNn5C5FPpGBAfEJGlItIO3ADcKCJvquB1DwRudc7d5pybAK4AnjrHeu8F/hNIVegzbzo6Omq16ZoSlbdzLvZpONVmXmYKTlDsXgHmHoYku2uj4+67QysUoenYWjZ+LB8/2vK5O63Lx5hBW1mxc8mPtnNJUz6WjZ8Q+VTaAHGSc24IeBrwQ2AD8IIKXrcauHPW47sKy6YRkfsDa51z36/QpSoGBwdrufmaEZX3RC4/fVGcDRBqM9+7F1paYN26kquoda8Acw9Dkt21MXj88aEVitB0bC0bP5aPH235HL9Yl48xg7ayYueSH23nkqZ8LBs/IfKptAGiWUSayTdAXO2cywBuoW8uIg3Ax4A3VLDuS0Vkl4jsOnDgAKlUirGxMUZHR0mn0wwNDZHNZunv78c5R09PDwAHDx4EYHJyEucc/f39ZLNZhoaGSKfTjI6OMjY2RiqVYnh4mEwmw8DAAJOTk/T29hZtY+q+r6+PXC7H4OAgExMTjIyMMD4+zvj4OCMjI0xMTDA4OEgul6Ovr2/ObfT29jI5OcnAwACZTIbh4eE592np0qUl96mnp6fifbq3914AsulsbPvU3Nw87+M0n32q9jjlbryR3MaNTBSem2ufpgY7qvQ4hd6n2cepsbExkrIXYp86Ojpqej7Vcp9WrFiBEQ0rpnopKUHTsbVs/Fg+frTls2dMl48xg7ayYueSH23nkqZ8LBs/IfIR58q3I4jIa4C3AP8HPBFYB3zVOfewMq97MHCBc+5xhcdvA3DOfaDwuBP4OzBSeMkxQB/wFOfcrlLb3blzp9u1q+TTc3Lw4EG6u7vn9RoNROV938h9HPPRY/jUEz7FKx/wygjMyqM28y1bYPt2+Na3Sq6i1r0CzD0MBw8e5KijjrrWOVeTgXS1UU09DIBI2VUO7thB9/XX+1eq4LMrKmItl2XysWz8WD5+4sxHLizvs6NjB9ePlPZx767ORUSsLvahrKxUgp1LfsqdS1D9+XS4kJ58LJsyOgHz8dXDFfWAcM5d5Jxb7Zx7gstzB/DICl76Z2CTiGwQkUXA2cDVs7Y76Jxb6Zxb75xbD/yBMo0P1ZLUf2qi8k7n0kC8l2CozDyTgdtu8w5ACUrdK8Tcw5Bkd22U/WCOGU3H1rLxY/n40ZZPuS+9Rji0lRU7l/xoO5c05WPZ+AmRT0UNECJytIhcIiI/LDw+CXhhudc557LAecCPgZuAK51zu0XkPSLylAV4z5upbtZJIyrvVDY/vmec03CqzPz22yGbLdsAodK9Qsw9DEl218bBHTtCKxSh6dhaNn4sHz/a8tnRocvHmEFbWbFzyY+2c0lTPpaNnxD5NFW43mXAF4F/LzzeC3wduKTcC51zPwB+cMiyd5VY98wKfeaNtpbTSonKe6oBou57QFQwAwYoda8Qcw9Dkt21oe3XAU3H1rLxY/n40ZaPtl8mjRm0lRU7l/xoO5c05WPZ+FHbAwJY6Zy7EpiE6Z4NuZpZ1YCpweKSRlTe6Wz8l2CozHzv3vx9mR4QKt0rxNzDkGR3bfRu3RpaoQhNx9ay8WP5+NGWz9Z2XT7GDNrKip1LfrSdS5rysWz8hMin0gaIURHpojDzhYg8CNA1H04Zli9fHlqhKqLyDtEDQmXme/bAypVQZjRlle4VYu5hSLK7NpZPNRQqQdOxtWz8WD5+tOWzd1SXjzGDtrJi55IfbeeSpnwsGz8h8qm0AeL15AeP3Cgi/wt8GXh1zaxqwNDQUGiFqojKe3oMiKb4xoBQmfmePWUvvwCl7hVi7mFIsrs2htavD61QhKZja9n4sXz8aMtn/eL1oRWMEmgrK3Yu+dF2LmnKx7LxEyKfisaAcM79RUQeAWwBBNjjnMvU1Cxi2tvbQytURVTeIXpAqMx8zx54whPKrqbSvULMPQxJdtdG+/79oRWK0HRsLRs/lo8fbfnsT+vyMWbQVlbsXPKj7VzSlI9l4ydEPpXOgtEKvAZ4L3Ah8KrCssSQSqVCK1RFVN4hpuFUl/ngINx3X9nxH0Ch+zww9zAk2V0bqTKXSMWNpmNr2fixfPxoy2dFsy4fYwZtZcXOJT/aziVN+Vg2fkLkU+ksGF8GhoH/Ljx+LvAV4Fm1kKoFzc3NoRWqIirvED0g1GU+dc1VBZdgqHOfB+YehiS7a6N5dDS0QhGajq1l48fy8aMtn9GcLh9jBm1lxc4lP9rOJU35WDZ+QuRTaQPEdufcSbMe/1JEbqyFUK2YnJwMrVAVUXlPjwHRGN8YEOoyn5qCs4IeEOrc54G5hyHJ7tqYbKr0oykeNB1by8aP5eNHWz5NosvHmEFbWbFzyY+2c0lTPpaNnxD5VDoI5V8KM18AICKnA7tqo1QbnHOhFaoiKu8Q03Cqy3zvXmhogI0by66qzn0emHsYkuyuDdfYGFqhCE3H1rLxY/n40ZZPo+jyMWbQVlbsXPKj7VzSlI9l4ydEPt4mDxG5nvzUm83A70TkH4XHxwE3114vOpqUtTZVSlTeIS7BUJf5nj2wfj20lO8Fos59Hph7GJLsro2m8fHQCkVoOraWjR/Lx4+2fMYndfkYM2grK3Yu+dF2LmnKx7LxEyKfcj0gngQ8GTgL2AA8Ajiz8Pfja2oWMel0OrRCVUTlHWIaTnWZ79lT0eUXoNB9Hph7GJLsro10Z2dohSI0HVvLxo/l40dbPp1NunyMGbSVFTuX/Gg7lzTlY9n4CZGPtznROXdHXCK1pq2tLbRCVUTlncqmEITmhvgG8VGV+eQk3HILPPKRFa2uyn2emHsYkuyujbYDB0IrFKHp2Fo2fuLMRy6Usuu0NrSSmvSP3u/eHV/Xcm3l58CELh9jBm1lpZ7rmkrQdi5pysey8RMiH139mWrI8PAwy5cvD60xb6LyTufStDa1IlL+C1NUqMr87rthbKziHhCq3OeJuYdheHg4tMIRw/DatSy/5ZbQGtNoKpdxZlPJP9ib2jZxy5jfJ85/sLWVnbWta8vmEyeWj1Ep2sqKpnoY9OWj7VzSlI9l4ydEPpUOQpl4li1bFlqhKqLyTmVTsY7/AMoyn5oBo4IpOEGZ+zwx9zAk2V0by269NbRCEZqOrbZsbh3T5WP5+LF8jErRVlY01cOgLx9t55KmfCwbPyHyqZsGiN7e3tAKVRGVdyqbinX8B1CW+d69+fsKe0Cocp8n5h6GJLtro3fbttAKRWg6ttqy2dahy8fy8WP5GJWiraxoqodBXz7aziVN+Vg2fkLkUzcNECtXrgytUBVReYfoAaEq8z17oL0dVq2qaHVV7vPE3MOQZHdtrLzhhtAKRWg6ttqyuWFEl4/l48fyMSpFW1nRVA+Dvny0nUua8rFs/ITIp24aIA4ePBhaoSqi8p4aAyJOVGW+Z0/+8osKx8BQ5T5PzD0MSXbXxsHt20MrFKHp2GrLZnuHLh/Lx4/lY1SKtrKiqR4GffloO5c05WPZ+AmRT900QHR3d4dWqIqovFPZFC2N8V6CoSrzvXsrvvwClLnPE3MPQ5LdtdGt7NcBTcdWWzbaflmyfPxYPkalaCsrmuph0JePtnNJUz6WjR/rAVFDenp6QitURVTeIS7BUJN5KgX79s2rAUKNexWYexiS7K6NHmW/Dmg6ttqy0fbLkuXjx/IxKkVbWdFUD4O+fLSdS5rysWz8WA+IGtLV1RVaoSqi8k5n478EQ03mt9wCzlU8AwYocq8Ccw9Dkt210bV7d2iFIjQdW23Z7B7R5WP5+LF8jErRVlY01cOgLx9t55KmfCwbPyHyqZsGiIGBgdAKVRGVd4geEGoy/+QnoakJzjij4peoca8Ccw9Dkt21MXDCCaEVitB0bLVlc0KbLh/Lx4/lY1SKtrKiqR4GffloO5c05WPZ+AmRT900QCxZsiS0QlVE5R1iGk4Vmd90E3zhC/CKV8Bxx1X8MhXuVWLuYQjhLiKXisgBEblh1rIVIvJTEbmlcL+8sFxE5CIRuVVE/iYi95/1mhcW1r9FRF4Y+44cwpI77wytUISmcqktmztTunwsHz+Wj1Ep2sqKpnoY9OWj7VzSlI9l4ydEPk2xv2MgxsbGWLp0aWiNeROVd4geECoyf8tboKMD3vWueb1MhXuVmHsYxsbGQrztZcAngS/PWvZW4OfOuQ+KyFsLj98CPB7YVLidDnwGOF1EVgDvBnYCDrhWRK52zvXHtheHMHbUUSz9xz9Cvf1haCqX2rI5atFR/COlx8fy8WP5GJUSZ1mRC8vPULaudV3ZsuLe7aJSKoudS3405WPZ+AmRT930gGhpiffX/6iIyjvENJzBM//1r+G734W3vQ3mOX90cPcFYO5hCOHunPsN0HfI4qcCXyr8/SXgabOWf9nl+QOwTESOBR4H/NQ511dodPgpcFbN5T20DA6GfPvD0FQutWUzmNXlY/n4sXziJapeaiGwsuLH8vGjKR/Lxk+IfOqmASKbzYZWqIqovFPZFK2N8TZABM18chLe+EZYswbOP3/eL09qeQFzD4Ui96Odc/sLf98LHF34ezUwu5/dXYVlpZYHI7t4cci3PwxFx1ZdNosbdPlYPn4sn9i5jMMbdKd6qW0Cfl54DMW91F5KvpdaMKys+LF8/GjKx7LxEyKfummAECnfvUsjUXmHGAMiaOZXXgm7dsH73gdVnOhJLS9g7qHQ6O6cc+Qvq4gEEXmpiOwSkV0HDhwglUoxNjbG6Ogo6XSaoaEhstks/f39OOemp007ePAgkJ9GzYnQv2kT2dZWhtatI93ZyegxxzDW3U1q+XKG16whu2gRAxs3MtnYSO/Wrflt7NhRdN/X10cul2NwcJCJiQlGRkYYHx9nfHyckZERJiYmGBwcJJfL0dfXV+Qxdd/b28vk5CQDAwNkMhmGh4fn3KdcLuffJ+fo7+8nm80yNDREOp1mdHSUsbExUqkUw8PDZDIZBgYGmJycpLe3d06fvr4+cs3NDK5fz0RHByOrVjHe1cV4Vxcjq1bllx17LLnmZvoK0wofmkvv1q0V7VPZ4+Qcm9o20drQyrrWdXQ2dXLMomPobu5medNy1rSsoa2hjdUtq2mkka3t+eO0o2NH0f2Wti3RHaeNG8m0tTG8Zg2p5csZ6+5m9JhjSHd2MrRuXb5MrV2LE5me5uxg4b5n+/aZshfFccrlWN+6no7GDla1rKKruYuu5i5Wtayio7GD9a3raZZmjms9bs5ctrZvpZFGNi7euODjNF32SpxPmba26fNpYMOGuc+nLVtmyl5E59OaljUsb1pOd3M3xyw6hs6mTta1rqO1oZVNbZsQhI2LNwIz08Bt79iOINNlbyHHSQMR9VILguRyod56TnJOl4/l40dTPpaNnxD5SP77abLYuXOn27Vr17xek0qlaG2NtwdAFETl3fb+Ns574Hl86J8+FIFVZQTLPJ2GE0+Ezk649lpobJz3JpJaXsDcQ5FKpVi8ePG1zrmdcb6viKwHvuec2154vAc40zm3v/Dl9VfOuS0i8tnC35fPXm/q5px7WWF50XqlqKYeLgiXXSW1fDmt/WWGoIjxsyvWclkmnzizqeS67OVNy+nP+n0iuy5bWdmxfMroRJBPtdmISOx1cSnmqKMHnHPLCn8L0O+cWyYi3wM+6Jy7pvDcz4G3OOe8FW1VdfERWFbAzqV6zMeyKaMTMB9fPVw3PSAymUxohaqIwts5F2QQymCZf/rTsG8ffPjDVTU+QHLLC5h7KBS5Xw1MzWTxQuA7s5b/S+E64wcBg4VLNX4MPFZElheuRX5sYVkwMu3tId/+MBQdW3XZtDfq8rF8/Fg+uqi2l9qCe6PN7hFUojfawPHHF/WeqWVvtNk9gtoa2ubsPXNC2wlFvWdm95qZuo+sN9rsHkEleqP1bd6sqjfaie0nquqN1nvSSbH0RmuW5rK90TqbOtnStmXOXKbKXhTHqb+/39u7c+p8Si9ZUvp8mip7EfXuLHU+ze6N1tHYMef5tNDeaD7qpgdEJpOhubm5Rka1IwrvidwELe9r4X2PfB///vB/j8isPEEy7++HjRvhAQ+AH1f/P1RSywuYeygymQyLFi2K9Vc3EbmcfA+GlcB95GezuAq4ElgH3AE82znXV/il7ZPkr0ceA/516pc1EXkR8PbCZt/vnPtiufeuZQ+ITFsbzeVmFYnxsyvWclkmnzizqeSXk7aGNsYm/T5x/rJk+fhJWj5HaA+IefVSmzWmz5zUqgdE0soK2LlUj/lYNmV0AuZjPSCA0dHR0ApVEYV3KpsCiL0HRJDM/+M/YGAg3/thASS1vIC5hyKEu3PuHOfcsc65ZufcGufcJc65Xufco51zm5xzj3HO9RXWdc65VznnNjrndszu1uucu9Q5d0LhVrbxodaMHhvssuc50VQutWVzbIsuH8vHj+Wjgvn2UguClRU/lo8fTflYNn5C5NMU+zsGQssc7vMlCu90Ng3E3wARe+b79sFFF8ELXwj3u9+CNpXU8gLmHooku2tj6b59oRWK0HRstWWzb3xfaIUiLB8/lk+8zO6lJiJ3ke+l9kHgShF5MYVeaoXVfwA8AbiVQi+12IVnYWXFj+XjR1M+lo2fEPnUTQ+I/nKDfSglCu9QPSBiz/wd74CGBnjvexe8qaSWFzD3UCTZXRv9mzeHVihC07HVls3mdl0+lo8fyydeouqlFgIrK34sHz+a8rFs/ITIp24aILq6ukIrVEUU3lMNEHFPwxlr5tdeC1/7GrzudbBmzYI3l9TyAuYeiiS7a6PrpptCKxSh6dhqy+amUV0+lo8fy8eoFCsrfiwfP5rysWz8hMinbhogNM0LPR+i8A7VAyK2zJ2DN70JVq6Et7wlkk0mtbyAuYciye7amBoNWguajq22bKZGEteC5ePH8jEqxcqKH8vHj6Z8LBs/IfKpmzEguru7QytURRTe6VyYMSBiy/yHP4Rf/jI//kNnZySbTGp5AXMPRZLdtdF9/fWhFYrQdGy1ZXP9iC4fy8eP5WNUipUVP5aPH035WDZ+QuRjPSCUE2UPiJbGeC/BiCXzbBbe/GY44QR42csi22xSywuYeyiS7K4Nbb8OaDq22rKxX5b8WD5+tOVjzGBlxY/l40dTPpaNH+sBUUM0/YI1H6LwDnUJRiyZX3YZ7N4N3/wmLFoU2WaTWl7A3EORZHdtaPt1QNOx1ZaN/bLkx/Lxoy0fYwYrK34sHz+a8rFs/FgPiBrS19cXWqEqovAONQ1nzTMfHYV3vQse/GD453+OdNNJLS9g7qFIsrs2+rZsCa1QhKZjqy2bLW26fCwfP5aPUSlWVvxYPn405WPZ+AmRT930gOiMaGyAuInCO1QPiJpn/rGPwf79+d4PIpFuOqnlBcw9FEl210bnbbeFVihC07HVls1t47p8LB8/lo9RKVZW/Fg+fjTlY9n4CZFP3fSAGBkZCa1QFVF4h5qGs6aZ33cffOhD+Z4PD3lI5JtPankBcw9Fkt21MbJ6dWiFIjQdW23ZrG7R5WP5+LF8jEqxsuLH8vGjKR/Lxk+IfOqmAWLx4sWhFaoiCu9QPSBqmvmFF0IqBR/4QE02n9TyAuYeiiS7a2NxT09ohSI0HVtt2fRkdPlYPn4sH6NSrKz4sXz8aMrHsvETIp+6aYCYmJgIrVAVUXiHmoazZpnffDN87nP5WS82b67JWyS1vIC5hyLJ7tqYWLo0tEIRmo6ttmyWNunysXz8WD5GpVhZ8WP5+NGUj2XjJ0Q+ddMA0djYGFqhKqLwDtUDomaZv/Wt0NYG7353bbZPcssLmHsokuyujcZ0OrRCEZqOrbZs0pO6fCwfP5aPUSlWVvxYPn405WPZ+AmRT900QNQz02NANMY7BkRN+O1v4TvfyTdCKJoazzAMwzAMwzAMw/BTNw0QuVwutEJVROE91QCxqHHRgrc1HyLP3Dl405tg9Wp47Wuj3fYhJLW8gLmHIsnu2si16Gos1XRstWXT0qDLx/LxY/kYlWJlxY/l40dTPpaNnxD51M00nIsWxfvPd1RE4Z3OpmltakUinqqyHJFn/o1vwB//CJdemr8Eo4YktbyAuYciye7aWDQ0FFqhCE3HVls2Q1ldPpaPH8vHqBQrK34sHz+a8rFs/ITIp256QIyPj4dWqIoovFPZVJDLLyLNfGIC3vY22LED/uVfottuCZJaXsDcQ5Fkd22Mr1wZWqEITcdWWzYrm3X5WD5+LB+jUqys+LF8/GjKx7LxEyKfuukB0dHREVqhKqLwTmVTsQ9ACRFn/pnPwG23wY9+BDEMCJfU8gLmHooku2uj4+67QysUoenYasvm7rQuH8vHj+VjVIqVFT+Wjx9N+Vg2fkLkUzc9IAYHB0MrVEUU3ulcOkgDRGSZDwzAe94Dj3kMPPax0WyzDEktL2DuoUiyuzYGjz8+tEIRmo6ttmyOX6zLx/LxY/kYlWJlxY/l40dTPpaNnxD51E0DxIoVK0IrVEUU3qF6QESW+Qc+AP398OEPQ0zjWCS1vIC5hyLJ7tpYsWdPaIUiNB1bbdnsGdPlY/n4sXyMSrGy4sfy8aMpH8vGT4h86qYB4uDBg6EVqiIK71Q2RUtT/GNARJL5P/4Bn/gEvOAFcMopC99ehSS1vIC5hyLJ7to4uGNHaIUiNB1bbdns6NDlY/n4sXyMSrGy4sfy8aMpH8vGT4h86qYBoru7O7RCVUThHaoHRCSZf/SjMDkJ733vwrc1D5JaXsDcQ5Fkd210X399aIUiNB1bbdlcP6LLx/LxY/kYlWJlxY/l40dTPpaNnxD51E0DhKZfsOZDFN6hxoBYsPvgYH7KzbPPhnXropGqkKSWFzD3UCTZXRtx/jogF0rZ2/0+er+y68SF/XLix/LxY/kYlWJlxY/l40dTPpaNH+sBUUM0/YI1H+q6B8Sll8LICJx/fjRC8yCp5QXMPRRJdteG/TpQGsvGj+Xjx/IxKsXKih/Lx4+mfCwbP9YDoob09vaGVqiKKLxT2RQtjfGPAbEg91wOLroIHvYwOO206KQqJKnlBcw9FEl210bv1q2hFYrY2q7Hx7LxY/n4sXyMSrGy4sfy8aMpH8vGT4h86qYBYvny5aEVqiIK73Q2zCUYC3K/+mrYtw9e+9qodOZFUssLmHsokuyujeV794ZWKGLvqB4fy8aP5ePH8jEqxcqKH8vHj6Z8LBs/IfKpeQOEiJwlIntE5FYReescz79eRG4Ukb+JyM9F5LhaeAwNDdViszUnCu9Ql2AsyP0Tn4D16+GpT43MZz4ktbyAuYciye7aGFq/PrRCEesXrw+tMI1l48fy8WP5GJViZcWP5eNHUz6WjZ8Q+dS0AUJEGoFPAY8HTgLOEZGTDlntr8BO59z9gG8CH6qFS3t7ey02W3Oi8A51CUbV7n/9K/z613DeedDYGK1UhSS1vIC5hyLJ7tpo378/tEIR+9N6fCwbP5aPH8vHqBQrK34sHz+a8rFs/ITIp9Y9IB4I3Oqcu805NwFcART9pO2c+6Vzbqzw8A/AmlqIpFKpWmy25kThHaoHRNXun/gEtLfDi18crdA8SGp5AXMPRZLdtZFasSK0QhErmvX4WDZ+LB8/lo9RKVZW/Fg+fjTlY9n4CZFPrRsgVgN3znp8V2FZKV4M/HCuJ0TkpSKyS0R2HThwgFQqxdjYGKOjo6TTaYaGhshms/T39+Oco6enB5iZGm94eBjnHP39/WSzWYaGhkin04yOjjI2NkYqlWJ4eJhMJsPAwACTk5PTg8pNbWPqvq+vj1wux+DgIBMTE4yMjDA+Ps74+DgjIyNMTEwwODhILpejr69vzm309vYyOTnJwMAAmUyG4eHhOfdJREruU09PT0X7lM6lIUfs+5ROp+d9nHp378Zdfjmp5z6XbEdHsOM01Z2+0uPk26dKj1NU+zQ+Ph5J2QuxT865mp5Ptdyn5uZmjGhoHh0NrVDEaE6Pj2Xjx/LxY/kYlWJlxY/l40dTPpaNnxD5NMX+jiUQkecDO4FHzPW8c+5zwOcAdu7c6Vpbi3/Rb2nJX2IwNRDcypUrgZmp8ZYtW4aITD+/dOnSotcBTG1z2bJlAHR1dRVtY+p+RaHlqrOzE4BFixYd5ju1bGrdQ7cxte2p95rrn5eWlhbGxsZK7tPUY98+tbS0kMqmWNa+jIaGhlj3qa2tjfkep65vfAMmJmh905ugqSnYcZryq/Q4+fapkuMU5T51dHTQ2Ni44LIXYp/GxsZoa2s7zCeq86mW+zQ2NoYRDZNNaj6aAGgSPT6WjR/Lx4/lY1SKlRU/lo8fTflYNn5C5FPrd7wbWDvr8ZrCsiJE5DHAvwOPcM6layHinKvFZmvOQr2zk1km3SQtTfGPATFv93QaPvMZeNKTYNOm2khVSFLLC5h7KJLsrg0XaOyXUjSKHh/Lxo/l48fyMSrFyoofy8ePpnwsGz8h8qn1JRh/BjaJyAYRWQScDVw9ewURORX4LPAU59yBWok0KWttqpSFeqey+evSQ4wBMW/3K66AAweCTb05m6SWFzD3UCTZXRtN4+OhFYoYn9TjY9n4sXz8WD5GpVhZ8WP5+NGUj2XjJ0Q+NW2AcM5lgfOAHwM3AVc653aLyHtE5CmF1T4MdADfEJHrROTqEptbEOl0TTpW1JyFeqdz+deHaICYl7tz8F//Bdu3w6MeVTOnSklqeQFzD0WS3bWRLlxipIXOJj0+lo0fy8eP5WNUipUVP5aPH035WDZ+QuRT85/snHM/AH5wyLJ3zfr7MbV2AOa8rjwJLNR7qgdEiGk45+X+m9/AddfB5z8PIjVzqpSklhcw91Ak2V0bbQdq1hmuKg5M6PGxbPxYPn4sH6NSrKz4sXz8aMrHsvETIp9aX4KhhuHh4dAKVbFQ75CXYMzL/ROfgK4ueN7zaic0D5JaXsDcQ5Fkd20Mr11bfqUYWduqx8ey8WP5+LF8jEqxsuLH8vGjKR/Lxk+IfOqmAWJq1PqksVDvdDbcJRgVu992G1x1Fbz85bB4cS2VKiap5QXMPRRJdtfGsltvDa1QxK1jenwsGz+Wjx/Lx6gUKyt+LB8/mvKxbPyEyKduGiB6e3tDK1TFQr1D9oCo2P2Tn4TGRnjlK2srNA+SWl7A3EORZHdt9G7bFlqhiG0denwsGz+Wjx/Lx6gUKyt+LB8/mvKxbPyEyKduGiBWrlwZWqEqFuo9PQZEgGk4K3IfGoIvfAGe/WxYtar2UhWS1PIC5h6KJLtrY+UNN4RWKOKGET0+lo0fy8eP5WNUipUVP5aPH035WDZ+QuRTNw0QBw8eDK1QFQv1DtkDoiL3yy6D4WE4//ya+8yHpJYXMPdQJNldGwe3bw+tUMT2Dj0+lo0fy8eP5WNUipUVP5aPH035WDZ+QuRTNw0Q3d3doRWqYqHeIafhLOs+OQkXXQQPeQg88IHxSFVIUssLmHsokuyujW77daAklo0fy8eP5WNUipUVP5aPH035WDZ+rAdEDenp6QmtUBUL9Q7ZA2La3TkYGIBbb4U//AG+/3340pfgjW+Ev/8dXvva2N3KkdTyAuYeiiS7a6PHfh0oiWXjx/LxY/kYlWJlxY/l40dTPpaNnxD5NMX+joHo6uoKrVAVC/WeHgOiMaIxIJzLj9vQ0zNz6+2d83HX7Me53Nzbe+AD4elPj8YtQpJaXsDcQ5Fkd2107d4dWqGI3SN6fCwbP5aPH8vHqBQrK34sHz+a8rFs/ITIp24aIAYGBli+fHlojXmzUG9vDwjn8uMvVNCYUPQ4m537zZqaoKsLVq6ElSvJbNzIooc+dPoxK1cWPc/KlbBkCYhUvX+1IqnlBcw9FAMDA6EVjhgGTjiB5bfcElpjmhPaTuCWMR0+lo0fy8eP5WNUipUVP5aPH035WDZ+QuRTNw0QS5YsCa1QFRV7OwcjI4c1FqTv/iEArW9/JxwcP7wxIZOZe3uNjcUNBlu2wBlnHN6AMPvx0qVFjQkN2Wy+USKBJLW8gLmHIsnu2lhy552hFYq4M6XHx7LxY/n4sXyMSrGy4sfy8aMpH8vGT4h8kvnfYRWMjY2xdOnS0BqV4RyMjkJPD6l9++hIpQ7viTBX74Q5GhNSDwLOgpbv/hCWdOcbCjZtggc/+PDGhNkNCp2dC+6ZkKjMD8Hcw5B0d02IyD5gGMgBWefcThFZAXwdWA/sA57tnOsXEQE+ATwBGAPOdc79JYQ3wNhRR7H0H/8I9faHcdSio/hHSoePZePH8vFj+RiVYmXFj+XjR1M+lo2fEPnUTQNES0tEYyBExe9+Bz/6UenGhIkJADoOfV1Dw0wjQVcXbNwIp58+9+UNXV2kbv8K/P69tN5+J8Q8EKW6zOeBuYfB3CPnkc652aNjvhX4uXPugyLy1sLjtwCPBzYVbqcDnyncB6FlcDDUW8/JYFaPj2Xjx/LxY/noYT6NxCH8rKz4sXz8aMrHsvETIp+6aYDIZrM6/kH4xz/gLW+BK67I9zCY3WiwYQM84AHFDQgdHbSuWTPTwLBsWb4RokLSd+fXjWwQynmgJvMqMPcwJN09ATwVOLPw95eAX5FvgHgq8GXnnAP+ICLLRORY59z+EJLZxYtVfUAvbljMIDp8LBs/lo8fy0cdlTYSx46VFT+Wjx9N+Vg2fkLkUzcNEBJ6oMOxMfjwh+E//zN/icW73gVvfjO0t3tfNjk2Bm1tVb9tKpuipbElyP4Hz3wBmHsYzD1SHPATEXHAZ51znwOOntWocC9wdOHv1cDsiwDvKiwL0gAhpWbNCUTO6fGxbPxYPn4sH/WUaiSOHSsrfiwfP5rysWz8hMin8p/SE07DPHoNRIpz8PWvw4knwgUXwJOfDDffDBdeWLbxARbuncqmaGkK84tysMwjwNzDYO6R8lDn3P3JX17xKhF5+OwnC70d3Hw2KCIvFZFdIrLrwIEDpFIpxsbGGB0dJZ1OMzQ0RDabpb+/H+ccPT35H/YOHjwIQE9PD06E/k2byLa2MrRuHenOTkaPOYax7m5Sy5czvGYNueZmBjZuZLKxkd6tW/Pb2LGj6L6vr49cLsfg4CATExOMjIwwPj7O+Pg4IyMjTExMMDg4SC6Xo6+vr8hj6r63t5dGGtm4eCNtDW2saVnD8qbldDd3c8yiY+hs6mRd6zoapZFNbZsQZHq+7Nn3gtDf3082m2VoaIh0Os3o6ChjY2OkUimGh4fJZDIMDAwwOTlJb2/vnD59fX3kmpsZXL+eiY4ORlatYryri/GuLkZWrcovO/ZYcs3N9G3ZMmcuvVu3Mjk5ycDAAJlMhuHh4eqOk3NsattEa0Mr61rX0dnUyTGLjqG7uZvlTctZ07KGtoY2VresppFGtrbnj9OOjh1F91vatkRynCYnJxnYuJFMWxvDa9aQWr6cse5uRo85hnRnJ0Pr1uXL1Nq1OJHpedYPFu57tm+fKXtRHKdcjvWt6+lo7GBVyyq6mrvoau5iVcsqOho7WN+6nmZp5rjW4+bMZWv71umyt9DjNF32SpxPmba26fNpcMOGuc+nLVtmyt5Cj1Oh7JU6n1obWqfPp41tG+c8n6bK3kKOUwKYaiS+VkReWlhWqpE4dhqU9erLOl0+lo8fTflYNn5C5CP576DJYufOnW7Xrl3zes3w8HD8o9T/9a9w/vnw29/CKafAJz4BD3942ZfNZqHeL//ey/n2zd/mvjfeV/U2qiVI5hFh7mFIuvvSpUuvdc7tDO1yKCJyATACvAQ40zm3X0SOBX7lnNsiIp8t/H15Yf09U+uV2mY19XBBpuwqw2vWsOSuu/wrRfTZJReW91nTsoa70n4f9+6IPkvL5GPZ+LF8/CQtn2qzERGVdfFsRGS1c+5uETkK+CnwauBq59yyWev0O+cOm5u60GDxUoC1a9eetnfvXiYnJ3HO0dTURDqdpq2tjeHhYZYtW0Zvby8rV67k4MGDdHd307NjB127dzNwwgksufNOxo46ipbBQbKLFyO5HA3ZLAPHH0/XTTcxeuyxLN23j/7Nm+m66SYO7thB9/XX5+//9jf6+vro7OxkZGSExYsXMzExQWNjIwC5XI5FixYxPj5OR0cHg4ODrFixYtpj6v6kj5zE3tG9rF+8nv3p/axoXsFobpQmaaJRGhmfHGdz22auH7meta1ruXXsVrZ1bOOGkRvY3rF9+v5vr/8bAwMDLFmyhLGxMVpaWshms4gIDQ0NZDIZWltbGR0dZenSpfT399PV1XWYT9+JJ9J5222MrF7N4p4eJpYupTGdzu9TSwuLhobo2baNo//yFwaPP54Ve/YU53L99fRu3cryG25gaGiI9vZ2UqkUzc3N8ztOPT10dXWx5SNbuDN1J0ctOorB7CCLGxaTczmyLkt7Yzt9mT5OXnIyfxr8E5vbN3PT6E3s6NjB9SPXT99vadvC7tfvXtBx6u3tZfny5Qxt3kz7/v2kVqygeXSUyaYmXGMjTePjpDs7aTtwgPtOO41Vv/sdvdu2sfKGGzi4fTvdN9xAz/btM2XvxhsXdpz6+jjmk8ewumU1PZkeljYtJT2ZP04tDS0MZYdY2bxy+vGesT2H5bK1fSt7R/fSc37Pgo/TwMAAS1atmvN8yrS309rXx+ixxyKZDNn29sPPp+uvp2/LlnzZO3hw4cdpaIjV/716zvOps6mTAxMHWNu6llQuRWdz52Hn0+6R3ZzQdgJ3pu7kvvPvm/dx8tXDddMAkclkaG5urpHRIRw4AO94B3zhC/mxG97/fnjRi/JTW86ThXr/63f+lV/c/gvueO0dVW+jWmLNPGLMPQxJd1+0aJGKL70i0g40OOeGC3//FHgP8Gigd9b1xSucc28WkScC55GfBeN04CLn3AN971HLBohMWxvN5WYVifGfpLaGNsYm/T5x/RNp2fixfPwkLZ8juQFiNuUaiX2vraouPgLLCti5VI/5WDZldALm46uH1fUZrhWjo6O1f5OJCfjYx/LTXH7xi/C618HevfCSl1TV+AAL954aAyIEsWReI8w9DOYeGUcD14jI/wF/Ar7vnPsR8EHgn0TkFuAxhccAPwBuA24FPg+8Mn7lGUaPPTbk2x/GsS16fCwbP5aPH8tHByLSLiJLpv4GHgvcAFwNvLCw2guB74QxtLJSDsvHj6Z8LBs/IfKpm0Eoly5dWts3+MEPZhocnvCEfEPEFm+jdUUs1DuVTdEa8/SbU9Q88xpi7mEw92hwzt0GnDzH8l7yvSAOXe6AV8WgVhFL9+0LrVDEvvF9oRWmsWz8WD5+LB81HA18uzB4cRPw/5xzPxKRPwNXisiLgTuAZ4cStLLix/Lxoykfy8ZPiHzqpgdEf3+NplG++eZ8g8MTn5h//P3v528RND5AZd6ZXIbb+m/jF7f/gkv+cgnv/MU7ef7/PJ8zLj2Dn/z9JyxuXhyJy3ypWeYxYO5hMHcDoH/z5tAKRWxu1+Nj2fixfPxYPjpwzt3mnDu5cNvmnHt/YXmvc+7RzrlNzrnHOOf6QjlaWfFj+fjRlI9l4ydEPnXTA6KrqyvaDQ4MwHveA//93/lpMj/2MXjVq2DRokjfpquri9xkjnuG72HfwD5uH7id2/tvZ9/gvvz9wD7uHLqTSTc5/ZpGaWRt51rWL1vPc7Y9h2dvC9OAHnnmMWLuYTB3A6DrpptCKxRx06geH8vGj+Xjx/IxKsXKih/Lx4+mfCwbPyHyqZsGiKkRORdMLgeXXAL//u/Q25sf3+G974Wjjqp6k8457hu9L9/A0H87tw/cPt3YcGvPrdw9cjeZycz0+oKwaskq1i9bz8OOexgblm1g/bL10/drlq6huTH8QH6RZR4Acw9D0t2NaJgaDVoLU6Nla8Cy8WP5+LF8jEqxsuLH8vGjKR/Lxk+IfOqmASKSf2p+85v8tJrXXQcPe1h+Ws1TTy37MuccfeN9Mw0L/TMNDFPLUtlU0WuOaj+KDcs2cPra02caGJZvYMOyDazrXEdLU5iBJedDUv+RBHMPhbkbgKoPZkDVFxfLxo/l48fyMSrFyoofy8ePpnwsGz8h8qmbBogF/bJ6xx3w5jfDlVfCunXw9a/Ds55VNNXKUHqouGHhkMskhieGiza5vHU5G5Zv4KTuk3jipicWNTIc13kc7YvaF+4dGHMPg7mHwXpARIf9OlAay8aP5ePH8jEqxcqKH8vHj6Z8LBs/1gOihlT9T81FF8Fb3sJYM+y74FXse8ajuH1sP7f/9E1FjQ39qeIB6DoWdUw3Kjxy/SPZsLz4MonO1s7aeivA3MNg7mFIsrs2NH0wg65fTywbP5aPH8vHqBQrK34sHz+a8rFs/FgPiBrS19fHihUryq73+zt/z/UHrs/3XLjvZm6//ipuf/0iDiyaAD4F3/oUAK1NrdMNCqevPvwyiRWLVyCzekjU2lsj5h4Gcw9DX1+wwcqPOPq2bGHFnj2hNabZ0raFPWM6fCwbP5aPH8vHqBQrK34sHz+a8rFs/ITIp24aIDo7y/c4uOrmq3j6158OQHNDM+voZEManrLlyWzYcP+iRoaj24+OpIGhHJV4a8Xcw2DuYUiyuzY6b7sttEIRt43r8bFs/Fg+fiwfo1KsrPixfPxoysey8RMin4bY3zEQIyMjJZ9LZVP85o7f8IFrPsDaJWu4Y8eljP/jBdz60Qw/vfE0Pv8v3+TtD3s75+w4hwevfTDHdBwTS+NDOW/tmHsYzD0MSXbXxsjq1aEViljdosfHsvFj+fixfIxKsbLix/Lxoykfy8ZPiHzqpgfE4sWL51z+6T9/mtf9+HVM5CYA+MxPW1j3vy+CJUvgiU+Et789Ts3DKOWdBMw9DOYehiS7a2NxT09ohSJ6Mnp8LBs/lo8fy8eoFCsrfiwfP5rysWz8hMinbnpATExMzLn8J3//Cd1t3Vx99tX0/u9Defk9q+CHP4SDB+Hyy2HHjphNiynlnQTMPQzmHoYku2tjYunS0ApFLG3S42PZ+LF8/Fg+RqVYWfFj+fjRlI9l4ydEPnXTA6KxsXHO5feO3Mu2o7bx5C1Phj3nwcMfDmedFbNdaUp5JwFzLyaTyXDXXXeRSqUi3/ZsnHOxXSIUNUlxb21tZc2aNTQ3N08vS3J510ZjOh1aoYj0pB4fy8aP5ePH8jEqxcqKH8vHj6Z8LBs/IfKpmwaIUuwf2c+WlVsgnYY774Tjjw+tZByh3HXXXSxZsoT169fX9J/syclJGhqS2bkpCe7OOXp7e7nrrrvYsGFDaB3DMAzDMAzDSAy6v+lHSC6XO2yZc457R+7lmPZjYN8+cA42boxfzsNc3knB3ItJpVJ0dXXV/Bd+51xNt19LkuAuInR1dR3WkyXJ5V0buZaW0ApFtDTo8bFs/Fg+fiwfo1KsrPixfPxoysey8RMin7ppgFi0aNFhywZSA0zkJjim4xj4+9/zC5U1QMzlnRTM/XDiuLwgCZcwlCIp7nN5Jrm8a2PR0FBohSKGsnp8LBs/lo8fy8eoFCsrfiwfP5rysWz8hMinbhogxsfHD1u2f2Q/AMcuOVZtA8Rc3knB3MMwOTlZ8rnGxkZOOeUUtm/fzpOf/GQGBgYif/8zzzyTXbt2VfXayclJrrrqKm688cZ5v/bqq6/mgx/84P9v796joyrv/Y+/v7lwDULCxQtQE46I3BNIUUAt1SppRZQKivb0J1XrBRGFqlVrj7Q9Xeco2iKn9rCs+MNaDyhYL/3V1qMiBYuCIAHkZgFjASmXAAkhEHJ5fn/MnnQmzDyZSWZm7818X2tlZbJnZ89nPnnyJNnZe491nS+//JKJEye2KFtz/DxmvOZ4t25uRwjTLds7ebQbO+3HTvtRsdKxYqf92HmpH+3Gzo1+0mYHRE5OzinLth3cBsBZ7bvD8uXQsSOceWaqo1lFyu0Xmt0dtoshtm/fntLSUj799FPy8vJ45plnUpgsstBTFzIzM607IOrq6qJuZ/z48Tz00EPWxzrnnHNYsmRJy4I2w89jxmty9uxxO0KYPTXeyaPd2Gk/dtqPipWOFTvtx85L/Wg3dm70kzY7ICoqKsI+rm+oZ+pbUzm387kMe/FdWLIEbr8dPHYIeNPcfqLZ3RHrtQhGjhzJHmcS3LFjByUlJQwfPpxLLrmErVu3Ni6/6KKLGDx4MI8++mjjH9nLli1j3LhxjduaNm0aCxYsOOUx7rrrLoqLixk4cCCPPfZY4/L8/Hx++MMfMmzYMBYvXty4fMWKFbz55ps88MADFBYWsmPHDsaMGcN9991HcXExTz/9NH/4wx+48MILKSoq4hvf+Ab79u0DYMGCBUybNg2AKVOmMH36dEaNGkWfPn0adzqUlZUxaNCgxvW//e1vU1JSQt++fXnwwQcbc8yfP5/zzz+fESNG8P3vf79xuzZ+HjNeU+GxiwH3ae+dPNqNnfZjp/2oWOlYsdN+7LzUj3Zj50Y/afMqGHl5eWEfbzm4hX9U/YMF1/xfzrjuZ3DllfDUUy6li65pbj/R7Bb33QelpYndZmEhzJlDVlbz39b19fW899573HrrrQDcfvvtzJs3j759+7Jq1SqmTp3K0qVLuffee7n33nu58cYbmTdvXtyRfv7zn5OXl0d9fT2XX345GzZsYMiQIQB07dqVTz75JGz9Sy+9lPHjxzNu3LiwUyVOnjzZeFrH4cOH+eijjxARnnvuOZ544gmeivC9u3fvXj744AO2bt3K+PHjI556UVpayrp162jbti39+vXjnnvuITMzk5/97Gd88skndOrUicsuu4yhQ4c2+1z9PN69Jm/bNrcjhNlW7Z082o2d9mOn/ahY6Vix037svNSPdmPnRj9pcwTEgQMHwj7+aPdHAIw8lgc7d8LEiZ47+gFOze0nmt0dtbW1Ue87fvw4hYWFnHXWWezbt48rrriCqqoqVq5cyaRJkygsLOSOO+5g797A9VE+/PBDJk2aBMBNN90Ud5ZXXnmFYcOGUVRUxKZNm8JOrbjhhhtizh667u7duxk7diyDBw9m9uzZbNq0KeLnXHvttWRkZDBgwIDGoySauvzyy+ncuTPt2rVjwIABfPHFF6xevZqvfe1r5OXlkZ2d3fj8m+PnMeM1BwYPdjtCmME53smj3dhpP3baj4qVjhU77cfOS/1oN3Zu9JM2R0B079497OPlXywnt10ufV99P7DjYfx4l5LZNc3tJ5rdYs6cpG06Ozs76n3Ba0BUV1czduxYnnnmGaZMmUKXLl0ojeOIjKysrLCLXTZ9SUqAzz//nCeffJKPP/6Y3NxcpkyZErZex44dY84euu4999zDzJkzGT9+PMuWLWPWrFkRP6dtyMscRXt5z9B1MjMzrdeYaI6fx7vXdN+40e0IYTZWeSePdmOn/dhpPypWOlbstB87L/Wj3di50U9aHgGxr2ofr2x6hevPuhyZ+1/wve957uKTQX7+r6pmd4ftCIigDh06MHfuXJ566ik6dOhAQUFB47UYjDGsX78egIsuuohXX30VgEWLFjV+/rnnnsvmzZupqanhyJEjvPfee6c8RmVlJR07dqRz587s27ePP/3pTzFl79SpE0ePHo26TkVFBT179gTghRdeaHab8frqV7/KX/7yFw4fPkxdXV3j82+On8eM1+h/B6LTbuy0HzvtR8VKx4qd9mPnpX60Gzs3+kmbHRCh/518ccOL1NTXMOP3eyE3F2bPdjGZnZ//q6rZ3WE7AiJUUVERQ4YMYeHChbz00kvMnz+foUOHMnDgQN544w0A5syZwy9+8QuGDBnC9u3b6dy5MwC9e/fm+uuvZ9CgQVx//fUUFRWdsv2hQ4dSVFTEBRdcwE033cTo0aNjyj558mRmz55NUVERO4Ivjxti1qxZTJo0ieHDh9MtCS9l1LNnTx555BFGjBjB6NGjyc/Pb3zeNn4eM16j/x2ITrux037stB8VKx0rdtqPnZf60W7s3OhHoh2a7GXFxcUmeEG6WJWXl5OXl8cbW17jtt9PoU9tDqt/uheeeAIeeCBJSVuvvLycrl27uh2jRTR7uC1bttC/f/+EbjOSurq6mC5EGYvq6mrat2+PiLBo0SIWLlzYuHMiGRKZvTWqqqrIycmhrq6OCRMmcMsttzBhwoSwdZp+PcvLy+nWrdtaY0xxqvO6oSXzMBDTtXbK+/en65Yt9pUS9LNLftJ8nv4d+7PlmD2PeSxBP0ub6Ue7sdN+7PzWT0u7ERGdi21Ow7EC+r2Ujv1oN83EcbEf2zzs/m/6KZKbm8uLG17k5tdvpk0dPPzqURg9GqZOdTuaVW5urtsRWkyzuyMzMzNh21q7di3Tpk3DGEOXLl14/vnnE7btSBKZvTVmzZrFu+++y4kTJ7jyyiu59tprm/0cP48Zr8n97DO3I4T57Jh38mg3dtqPnfajYqVjxU77sfNSP9qNnRv9pM0pGJWVlSzc8BJnVmdQ9eEYJnxcBR98ABEuhOcllZWVbkdoMc3ujvr6+oRt65JLLmH9+vVs2LCB5cuXc9555yVs25EkMntrPPnkk5SWlrJ161bmzp2LxLBH289jxmsq8/PdjhAmv32+2xEaaTd22o+d9qNipWPFTvux81I/2o2dG/2kzQ4IsuG9z5fyr6UNZM98wPM7HoIivVKAX2h2d2Rk+Pfb2s/Z/TxmvKaj8zKwXrG3xjt5tBs77cdO+1Gx0rFip/3Yeakf7cbOjX78+9t+nFZ+sZJaU8dl5WfA2LFux4lZpJc39AvN7g4/XtclyM/Z/TxmvOZEXp7bEcLkZXsnj3Zjp/3YaT8qVjpW7LQfOy/1o93YudFP2uyAWLPnIwAuGjYePHKeeSxifUUDL9Ls7ojldAGv8nN2P48Zr8k+dsztCGGO1Xsnj3Zjp/3YaT8qVjpW7LQfOy/1o93YudFP+uyA2PIu5x+EvKuvdztKXBoaGtyO0GKa3R1+PorAz9n9PGa8psEDr4QSKku8k0e7sdN+7LQfFSsdK3baj52X+tFu7NzoJ212QGw/vJ0BB4BRo9yOEhc//0Gm2b3p9ddfR0TYunVr47KysjJEhEcffbRx2cGDB8nOzmbatGlA4JUhevbsSWFhIYMGDeLNN988ZdvLli1j5cqVcWdas2YN06dPb3a9UR7+/j2dx0yqGY8dpZYp3smj3dhpP3baj4qVjhU77cfOS/1oN3Zu9JMWOyDqG+rZ0VBO3/rO0LWr23HikuWxvWTx0OzuaO40hoULF3LxxRezcOHCsOUFBQX88Y9/bPx48eLFDBw4MGydGTNmUFpayuLFi7nllltO+a+/bQdEXV1d1EzFxcUxvdpES3ZupIqfx4zXZB0/7naEMMcbvJNHu7HTfuy0HxUrHSt22o+dl/rRbuzc6CctdkDsqtzFyYwG+uYl9yUEk6GmpsbtCC2m2d1h+098VVUVH3zwAfPnz2fRokVh93Xo0IH+/fuzZs0aAF5++WWuvz7yKUv9+/cnKyuLgwcPNi4rKytj3rx5/PKXv6SwsJAVK1YwZcoU7rzzTi688EIefPBBVq9ezciRIykqKmLUqFFs27YNCOy4GDduHMYYZs2axS233MKYMWPo06cPc+fObXyMnJycxvXHjBnDxIkTueCCC/jOd77T+LzfeustLrjgAoYPH8706dMZN25cC1qMn5/HjNfUdO7sdoQwnbO8k0e7sdN+7LQfFSsdK3baj52X+tFu7NzoJy3+Zfe3nR8D0LfPV11OEr8OHTq4HaHFNHt09/35Pkr/UZrQbRaeVcickjnWl7J84403KCkp4fzzz6dr166sXbuW4cOHN94/efJkFi1axJlnnklmZibnnHMOX3755SnbWbVqFRkZGXTv3r1xWX5+PnfeeSc5OTncf//9AMyfP5/du3ezcuVKMjMzqaysZMWKFWRlZfHuu+/yyCOP8OqrrzZuI5h969atvP/++xw9epR+/fpx1113nXKRx3Xr1rFp0ybOOeccRo8ezV//+leKi4u54447WL58OQUFBdx4440tK7MF/DzevabD/v1uRwiz/6R38mg3dtqPnfajYqVjxU77sfNSP9qNnRv9pMURELXlB+hf2Zbzh13hdpS4HT161O0ILabZ3VFfXx/1voULFzJ58mQgsLOh6WkYJSUlvPPOOyxatIgbbrjhlM8PHt1w//338/LLL8f0qhWTJk0i0znfraKigkmTJjFo0CBmzJjBpk2bIma/6qqraNu2Ld26daNHjx7s27fvlO2OGDGCXr16kZGRQWFhIWVlZWzdupU+ffpQUFAAkNIdEH4eM15ztHdvtyOE6d3OO3m0Gzvtx077UbHSsWKn/dh5qR/txs6NftLiCIhvXTGVb37jLl++xF+XLl3cjtBimj26OSVzkrbtzCgXtzl06BBLly5l48aNiAj19fWICLNnz25cp02bNgwfPpynnnqKzZs3n3KhyRkzZjQe3RCrjh07Nt7+8Y9/zNe//nVee+01ysrKGDNmTMTsbdu2DVsW6foRsayTSn4e717TZft2tyOE2V7tnTzajZ32Y6f9qFjpWLHTfuy81I92Y+dGP0k/AkJESkRkm4hsF5GHItzfVkRedu5fJSL5ychRXl6ejM0mnV9zg2Z3S7Q/xJcsWcJ3v/tdvvjiC8rKyti1axcFBQWsWLEibL0f/OAHPP744+Tl5cX92J06dbIeCVBRUUHPnj0BWLBgQczZY9WvXz927txJWVkZELiORar4ecx4TXmTi5+6bWCOd/JoN3baj532o2KlY8VO+7HzUj/ajZ0b/SR1B4SIZALPAN8EBgA3isiAJqvdChw2xpwH/BJ4PBlZunXrlozNJp1fc4Nmd0vTayUELVy4kAkTJoQtu+666045DWPgwIHcfPPNLXrsq6++mtdee63xIpRNPfjggzz88MMUFRVF3NkQLXus2rdvz69//WtKSkoYPnw4nTp1onOKLvbj5zHjNd0+/dTtCGE+rfJOHu3GTvux035UrHSs2Gk/dl7qR7uxc6MfSeZr14vISGCWMWas8/HDAMaY/whZ521nnQ9FJAv4B9DdWIIVFxeb4JX6Y3XgwIGwC+b5hV9zg2ZvasuWLfTv3z+h24yktra21X/IuyUR2auqqsjJycEYw913303fvn2ZMWNGghL+U9Ov54EDB+jRo8daY0xxwh/Mg1oyDwMQw6lwBwYNontzP6AT9LNLftJ8nkE5g5r9AW0eS9DP0mb60W7stB87v/XT0m5EROdim9NwrIB+L6VjP9pNM3Fc7Mc2Dyf7FIyewK6Qj3c7yyKuY4ypAyqArk03JCK3i8gaEVmzf/9+Tpw4QXV1NceOHaOmpobKykrq6uo4fPgwxpjGlwc8cOBA8PMxxnD48GHq6uqorKykpqaGY8eOUV1dzYkTJzh69Ci1tbUcOXKEhoaGxkOqg9sIvj906BD19fVUVFRw8uRJqqqqOH78OMePH6eqqoqTJ09SUVFBfX09hw4diriN8vJyGhoaOHLkCLW1tRw9ejTic8rNzY36nA4ePOjp59SuXbu4v05eeU5BsX6dYnlODQ0NGGOor6+noaGB+vr6sNsNDQ3U1dVhjGk8OqC2tjbsfej9oZ8Xejs41uPdRtM8wXWMMadso7a2Nuz+RD2nzMzMiM8pnm08++yzFBYWMnDgQI4cOcJtt92WtOcUOvb8urPNi5r9wZxiXvrviXZjp/3YaT8qVjpW7LQfOy/1o93YudGPby5CaYx5FngWAnt727VrF3Z/8IJ0ubm5wD8Phw79o0BEGu8/44wzwj4PILjN4MXkunbtGraN4PvgufHBQ7vbtGlzSt7gsuC6TbcR3HbwsSL917dt27YcPHiw8bk0fU7Bj736nCoqKujUqdMpzyk0s1efU/CCpbF+nWJ5ThkZGYhI1ItEBtcByMrKCnu84Pvg8uD7SIIXl2zNNoKibaPpthL1nKIdARHPNmbOnMnMmTOT/pxEJGzsBXc8qdY7OGiQpw5RjOW/A6mi3dhpP3baj4qVjhU77cfOS/1oN3Zu9JPsIyD2AKGv7dHLWRZxHecUjM5Awq/mFvxDwW/8mhs0u1ti2bHgVX7O7ucx4zVdm7w8q9s2VXknj3Zjp/3YaT8qVjpW7LQfOy/1o93YudFPsndAfAz0FZECEWkDTAbebLLOm0DwincTgaW26z+01JEjRxK9yZTwa27Q7JEk85orQfX19Ul/jGTxS/ZIX0c/j3evOXLeeW5HCHNeB+/k0W7stB877UfFSseKnfZj56V+tBs7N/pJ6g4I55oO04C3gS3AK8aYTSLyUxEZ76w2H+gqItuBmcApL9WZCE1PBfALv+YGzd5Uu3btKC8vT/pOCNupA17nh+zBaz80PQ3Mz+Pdazrt2tX8Sim064R38mg3dtqPnfajYqVjxU77sfNSP9qNnRv9JP14Z2PMW8BbTZb9W8jtE8CkZOeorq5uvJ6An/g1N2j2pnr16sXu3btPudBlojU0NDReM8Fv/JK9Xbt29OrVK2xZdXW1S2lOP9U9enDG3//udoxGPdr04O8nvJFHu7HTfuy0HxUrHSt22o+dl/rRbuzc6Me/J1zHKfQihn7i19yg2ZvKzs6moKAg4dttqqamxrfd+zm7X3OHEpES4GkgE3jOGPOfbuRoW1HhxsNGVVHnnTzajZ32Y6f9eJ/Ow5F5baxoP3Ze6ke7sXOjH+//qzFBgi/f5zd+zQ2a3S2a3R1+zg4gIpnAM8A3gQHAjSIywI0sde3bu/GwUbXP8E4e7cZO+7HTfrxN5+HovDZWtB87L/Wj3di50U/a7IAIvqyi3/g1N2h2t2h2d/g5u2MEsN0Ys9MYcxJYBFzjRhDx2MVI64138mg3dtqPnfbjeToPR+G1saL92HmpH+3Gzo1+0mYHhB/OK4/Er7lBs7tFs7vDz9kdPYHQKxHtdpalXIbHjiapM97Jo93YaT922o/n6TwchdfGivZj56V+tBs7N/qRVLwsYKKJyAHgizg/rRtwMAlxks2vuUGzu0Wzu6Mb0NEY093tIC0hIhOBEmPMbc7H3wUuNMZMC1nnduB258N+wLYkxfHaOPBSHi9lAc3THM1jl6w85/pxLo5lHnaWp2IuTpex0lKax85LebyUBdInT9R52JcXoWzJDxURWWOMKU5GnmTya27Q7G7R7O5wsue7naMV9gC9Qz7u5SxrZIx5Fng22UG8Ng68lMdLWUDzNEfz2Hktjwc0Ow9DauZir31tNI+d5onOS1lA80AanYKhlFLK6mOgr4gUiEgbYDLwpsuZlFIqneg8rJQ67fnyCAillFKJZYypE5FpwNsEXv7teWPMJpdjKaVU2tB5WCmVDtJpB0TSDxtOEr/mBs3uFs3uDj9nB8AY8xbwlts58F6XXsrjpSygeZqjeey8lsd1Og9HpXnsNE90XsoCmsefF6FUSimllFJKKaWUv+g1IJRSSimllFJKKZV0p/0OCBEpEZFtIrJdRB5yO08kIlImIhtFpFRE1jjL8kTkHRH5m/M+11kuIjLXeT4bRGRYirM+LyL7ReTTkGVxZxWRm531/yYiN7uYfZaI7HG6LxWRb4Xc97CTfZuIjA1ZntIxJSK9ReR9EdksIptE5F5nued7t2T3Q+/tRGS1iKx3sv/EWV4gIqucHC87FwpDRNo6H2937s9v7jmlm+a+hiLyFWe8rHPGbrPjIhk5UpklnlzRMolIvogcD/l+mpfMHKnMEk8uETlXRN5z8iwTkV4h9yVk7ouxm6TniDeXLZNz/xkisltEfpXMHKnMoqLTubh1uVIx/+k83LocqcwST65UzH++mIeNMaftG4EL+OwA+gBtgPXAALdzRchZBnRrsuwJ4CHn9kPA487tbwF/AgS4CFiV4qyXAsOAT1uaFcgDdjrvc53buS5lnwXcH2HdAc54aQsUOOMo040xBZwNDHNudwI+c/J5vndLdj/0LkCOczsbWOX0+Qow2Vk+D7jLuT0VmOfcngy8bHtOyR7vXnuL5WtI4DzEYJ8DgLJEdxjrWEpFlgT2k0/IvJbsHKnK0oJ+FgM3O7cvA150bidk7oujm6TmSGQ3Ifc/DfwP8KsUjJ2kZ9G3Vo8XnYtdnIsT0E1CcrSwm6TPf62daxKZJVH9hNzfqvmvtd0kKkdzb6f7ERAjgO3GmJ3GmJPAIuAalzPF6hrgBef2C8C1Ict/awI+ArqIyNmpCmWMWQ4carI43qxjgXeMMYeMMYeBd4ASl7JHcw2wyBhTY4z5HNhOYDylfEwZY/YaYz5xbh8FtgA98UHvluzReKl3Y4ypcj7Mdt4MgYl6ibO8ae/Br8cS4HIREctzSjexfA0NcIZzuzPwpXM7kR3GOpZSkSXeXNEyJVJr+0mWWHINAJY6t98PuT9Rc1+s3SQ7R0tyRcuEiAwHzgT+NwU5UpVFRadzcetzJXv+03m49TlSlSXeXMme/3wxD5/uOyB6ArtCPt6N/Y8ftxjgf0VkrYjc7iw70xiz17n9DwIDAbz5nOLN6rXnMM05BOl5cU5jwKPZJXBYfxGB/8b7qvcm2cEHvYtIpoiUAvsJ/IDaARwxxtRFyNGY0bm/AujqVnYPiqWHWcC/ishuAleBvyeOz01kjlRliTdXtEwABc5huH8RkUuSnCNVWeLNtR74tnN7AtBJRBL5PRjrdpKdoyW5ImYSkQzgKeD+FOVIVRYVnc7Frc8VLRMkZv7Tebj1OVKVJd5cyZ7/fDEPn+47IPziYmPMMOCbwN0icmnonSZwLIxxJVmc/JTV8d/AvwCFwF4C33SeJCI5wKvAfcaYytD7vN57hOy+6N0YU2+MKQR6EdirfIG7iU57NwILjDG9CJxK9KLzwzDdszSXaS/wFWNMETAT+B8ROcOyndMtS9D9wNdEZB3wNWAPUJ+ix/ZijlDRMk0F3jLG7E7TLCoyL81/XsrSXKZUz39eyRHKS/Ofl7IEeWX+czVHVjI37gF7gN4hH/dylnmKMWaP836/iLxG4A+dfSJytjFmr3P4/H5ndS8+p3iz7gHGNFm+LAU5T2GM2Re8LSK/Af6f86Gt55T3LyLZBP6Af8kY83tnsS96j5TdL70HGWOOiMj7wEgCp7RkOUc5hOYIZt8tIlkEDocsx5vfs26IpYdbcQ6DNMZ8KCLtgG4xfm4ic6QqS7y5ImYyxuwHapzla0VkB3A+sCZJOVKVJa5cxpgvcf6j4+z0vM753k3U3BdTNynIEXcuS6aRwCUiMhXIAdqISJUxpiUX+m1tP4nMoqLTubj1uZI9/+k83MocKcwSV64UzH/+mIdNEi4s4ZU3AjtYdhK4OE3wQhwD3c7VJGNHoFPI7ZUEJpPZhF9g8Ann9lWEX2BwtQuZ8wm/kGNcWQlc9OVzAhd+yXVu57mU/eyQ2zMInE8IMJDwixvtJHBhl5SPKae/3wJzmiz3fO+W7H7ovTvQxbndHlgBjCNw4Z7Qi1BOdW7fTfhFKF+xPadUjHcvvcXyNXTG7RTndn8C57NKIjuMdSylIksC++kezEDgwlN7Wvq9nYB+EpalBf10AzKc2z8HfurcTsjcF0c3Sc2RyG6arDOF1l2EslX9JDKLvrV6vOhc7OJcnIBuTtt5OM5+0m4ubm03icrRbM5kbNRLbwQOSfqMwLnbP3I7T4R8fZzBsR7YFMxI4Nzx94C/Ae8GvzGcieUZ5/lsBIpTnHchgUO7agmcV3RrS7ICtxC4YNB24HsuZn/RybYBeJPwP4x/5GTfBnzTrTEFXEzg9IoNQKnz9i0/9G7J7ofehwDrnIyfAv/mLO8DrHY6XAy0dZa3cz7e7tzfp7nnlG5vkb6GwE+B8c7tAcBfCcyHpcCVyegw2lhyI0si+gGuI/DzoxT4BLjarX4SnSXOfiYSmA8/A54Lfm869yVk7ouxm6TnSGQ3IduYQit/2WxtP4nMom+tGi86F7s8F7emm0TmaEE3KZn/WjvXJDJLovoJ2cYUWrcz2PPzsDgPoJRSSimllFJKKZU0bl/ERSmllFJKKaWUUmlAd0AopZRSSimllFIq6XQHhFJKKaWUUkoppZJOd0AopZRSSimllFIq6XQHhFJKKaWUUkoppZJOd0AopZRSSimllFIq6XQHhFJKKZUGRCRfRD5NwHbGiMioRGRKBRFZICITndvPicgAtzMppdKTzsM6DyvIcjuAUskgIh2BV4BeQCbwM2A78AsgBzgITDHG7BWR84B5QHegHpgEVAMvA2cQ+D65yxizItXPQymlPGgMUAWsdDlH3Iwxt7mdQSmlEmAMOg8rn9IjINTpqgT40hgz1BgzCPgz8F/ARGPMcOB54OfOui8BzxhjhgKjgL3ATcDbxphCYChQmtr4SimVFFki8pKIbBGRJSLSAUBEhovIX0RkrYi8LSJnO8uni8hmEdkgIotEJB+4E5ghIqUicknoxkVkloi8ICIrROQLEfm2iDwhIhtF5M8ikt3M431fRD4WkfUi8mpIvgUiMldEVorIzuB/0iKRgF+JyDYReRfoEXLfMhEpdm6XiMgnzmO95yzrKCLPi8hqEVknItckrnqllAJ0HtZ5OM3pDgh1utoIXCEijzsTc29gEPCOiJQCjwK9RKQT0NMY8xqAMeaEMaYa+Bj4nojMAgYbY4668SSUUirB+gG/Nsb0ByqBqc4vo9F20D4EFBljhgB3GmPKCBwx9ktjTGGUI8P+BbgMGA/8DnjfGDMYOA5c1czj/d4Y81Vnh/AW4NaQ7Z4NXAyMA/7T8hwnOM9zAPB/COxYDiMi3YHfANc5jzXJuetHwFJjzAjg68BsCRxRp5RSiaLzMDoPpzM9BUOdlowxn4nIMOBbwL8DS4FNxpiRoes5OyAiff5yEbkUuApYICK/MMb8Ntm5lVIqyXYZY/7q3P4dMJ3AEWLBHbQQOG1tr7POBuAlEXkdeD3Gx/iTMaZWRDY62/qzs3wjkE/gl9JojzdIRP4d6ELgdLm3Q7b7ujGmAdgsImdaHv9SYKExph74UkSWRljnImC5MeZzAGPMIWf5lcB4Ebnf+bgd8BUCv4QrpVQi6DwcoPNwmtIdEOq0JCLnAIeMMb8TkSPAVKC7iIw0xnzo7Pk93xizSUR2i8i1xpjXRaQtgUm4O7DbGPMbZ9kwQHdAKKX8zkT4WIiwg9ZxFYFfJK8GfiQig2N4jBoAY0yDiNQaY4KP2UDg9w7b4y0ArjXGrBeRKQTOcw7brkNiyNESQuC/cduStH2llNJ52E7n4dOcnoKhTleDgdXO6RaPAf8GTAQeF5H1BK7pEDwc7LvAdBHZQOBiPmcRmGzXi8g64Abg6VSGV0qpJPmKiAR/4bwJ+ADYhrODFkBEskVkoIhkAL2NMe8DPwQ6E/hv2FEg4tFjMYr4eM59nYC9zk7i77Rw+8uBG0Qk0zmn+esR1vkIuFRECpwMec7yt4F7xPmXoIgUtTCDUkpFo/NwgM7DaUqPgFCnJWPM24QfMhZ0aYR1/0bgPLlQO4EXkhBNKaXctA24W0SeBzYD/22MOelcTGyuiHQm8LvBHOAz4HfOMgHmGmOOiMgfgCXOhcHuifcVgiyPtwn4MbAKOOC8b8kv2K8RmNM3A38HPoyQ4YCI3A783vkFfz9wBYFXTJoDbHCWf07gXGellEoUnYfReTidyT+PyFFKKaWUUkoppZRKDj0FQymllFJKKaWUUkmnp2AopZRSynecC7G92GRxjTHmQjfyKKVUutF5WLWEnoKhlFJKKaWUUkqppNNTMJRSSimllFJKKZV0ugNCKaWUUkoppZRSSac7IJRSSimllFJKKZV0ugNCKaWUUkoppZRSSac7IJRSSimllFJKKZV0/x/ymlzkdnc1aQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_best_metric_time(threshold, best_values):\n",
    "    for i, v in enumerate(best_values[0]):\n",
    "        if v > threshold:\n",
    "            return best_values[2][i]\n",
    "    return -1\n",
    "\n",
    "def get_best_metric_epochs(threshold, best_values):\n",
    "    for i, v in enumerate(best_values[0]):\n",
    "        if v > threshold:\n",
    "            return best_values[1][i]\n",
    "    return -1\n",
    "\n",
    "plt.figure('train', (18, 6))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title('Metrics Time')\n",
    "plt.xlabel('secs')\n",
    "plt.ylabel('best mean_dice')\n",
    "plt.plot(best[2], best[0], label='Regular training', color='red')\n",
    "plt.plot(amp_best[2], amp_best[0], label='AMP training', color='green')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title('Typical Metrics Time')\n",
    "plt.xlabel('best mean_dice')\n",
    "plt.ylabel('secs')\n",
    "plt.bar(\"0.80\", get_best_metric_time(0.8, best), 0.5, label='Regular training', color='red')\n",
    "plt.bar(\"0.80 \", get_best_metric_time(0.8, amp_best), 0.5, label='AMP training', color='green')\n",
    "plt.bar(\"0.85\", get_best_metric_time(0.85, best), 0.5, color='red')\n",
    "plt.bar(\"0.85 \", get_best_metric_time(0.85, amp_best), 0.5, color='green')\n",
    "plt.bar(\"0.90\", get_best_metric_time(0.9, best), 0.5, color='red')\n",
    "plt.bar(\"0.90 \", get_best_metric_time(0.9, amp_best), 0.5, color='green')\n",
    "plt.bar(\"0.94\", get_best_metric_time(0.94, best), 0.5, color='red')\n",
    "plt.bar(\"0.94 \", get_best_metric_time(0.94, amp_best), 0.5, color='green')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title('Typical Metrics Epochs')\n",
    "plt.xlabel('best mean_dice')\n",
    "plt.ylabel('epochs')\n",
    "plt.bar(\"0.80\", get_best_metric_epochs(0.8, best), 0.5, label='Regular training', color='red')\n",
    "plt.bar(\"0.80 \", get_best_metric_epochs(0.8, amp_best), 0.5, label='AMP training', color='green')\n",
    "plt.bar(\"0.85\", get_best_metric_epochs(0.85, best), 0.5, color='red')\n",
    "plt.bar(\"0.85 \", get_best_metric_epochs(0.85, amp_best), 0.5, color='green')\n",
    "plt.bar(\"0.90\", get_best_metric_epochs(0.9, best), 0.5, color='red')\n",
    "plt.bar(\"0.90 \", get_best_metric_epochs(0.9, amp_best), 0.5, color='green')\n",
    "plt.bar(\"0.94\", get_best_metric_epochs(0.94, best), 0.5, color='red')\n",
    "plt.bar(\"0.94 \", get_best_metric_epochs(0.94, amp_best), 0.5, color='green')\n",
    "plt.grid(alpha=0.4, linestyle=':')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}