acceleration/fast_training_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast training with MONAI features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows a regular PyTorch training program and a MONAI optimized training program, and compared the performance.  \n",
    "Mainly includes `AMP (Auto mixed precision)`, `CacheDataset` and `Novograd` optimizer.\n",
    "\n",
    "With a V100 GPU, we are able to obtain approximately `12x` speedup compared with a Pytorch native implementation when the training converges at a validation mean dice of `0.93`.\n",
    "\n",
    "It's modified from the Spleen 3D segmentation tutorial notebook, 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/acceleration/fast_training_tutorial.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": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.4.0\n",
      "Numpy version: 1.19.1\n",
      "Pytorch version: 1.7.0a0+7036e91\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
      "MONAI rev id: 0563a4467fa602feca92d91c7f47261868d171a1\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.2\n",
      "Nibabel version: 3.2.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 8.0.1\n",
      "Tensorboard version: 2.2.0\n",
      "gdown version: 3.12.2\n",
      "TorchVision version: 0.8.0a0\n",
      "ITK version: 5.1.2\n",
      "tqdm version: 4.54.1\n",
      "lmdb version: 1.0.0\n",
      "psutil version: 5.7.2\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "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 glob\n",
    "import math\n",
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset, DataLoader, Dataset\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import compute_meandice\n",
    "from monai.networks.layers import Norm\n",
    "from monai.networks.nets import UNet\n",
    "from monai.optimizers import Novograd\n",
    "from monai.transforms import (\n",
    "    AddChanneld,\n",
    "    AsDiscrete,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    DeleteItemsd,\n",
    "    FgBgToIndicesd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    RandCropByPosNegLabeld,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    "    ToTensord,\n",
    ")\n",
    "from monai.utils import get_torch_version_tuple, set_determinism\n",
    "\n",
    "print_config()\n",
    "\n",
    "if get_torch_version_tuple() < (1, 6):\n",
    "    raise RuntimeError(\n",
    "        \"AMP feature only exists in PyTorch version greater than v1.6.\"\n",
    "    )"
   ]
  },
  {
   "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": [
    "\n",
    "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://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar\"\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(\n",
    "    glob.glob(os.path.join(data_root, \"imagesTr\", \"*.nii.gz\"))\n",
    ")\n",
    "train_labels = sorted(\n",
    "    glob.glob(os.path.join(data_root, \"labelsTr\", \"*.nii.gz\"))\n",
    ")\n",
    "data_dicts = [\n",
    "    {\"image\": image_name, \"label\": label_name}\n",
    "    for image_name, label_name in zip(train_images, train_labels)\n",
    "]\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",
    "        [\n",
    "            LoadImaged(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                pixdim=(1.5, 1.5, 2.0),\n",
    "                mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"],\n",
    "                a_min=-57,\n",
    "                a_max=164,\n",
    "                b_min=0.0,\n",
    "                b_max=1.0,\n",
    "                clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            # pre-compute foreground and background indexes\n",
    "            # and cache them to accelerate training\n",
    "            FgBgToIndicesd(\n",
    "                keys=\"label\",\n",
    "                fg_postfix=\"_fg\",\n",
    "                bg_postfix=\"_bg\",\n",
    "                image_key=\"image\",\n",
    "            ),\n",
    "            # randomly crop out patch samples from big\n",
    "            # image based on pos / neg ratio\n",
    "            # the image centers of negative samples\n",
    "            # must be in valid image area\n",
    "            RandCropByPosNegLabeld(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                label_key=\"label\",\n",
    "                spatial_size=(128, 128, 96),\n",
    "                pos=1,\n",
    "                neg=1,\n",
    "                num_samples=4,\n",
    "                fg_indices_key=\"label_fg\",\n",
    "                bg_indices_key=\"label_bg\",\n",
    "            ),\n",
    "            DeleteItemsd(keys=[\"label_fg\", \"label_bg\"]),\n",
    "            ToTensord(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "    val_transforms = Compose(\n",
    "        [\n",
    "            LoadImaged(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                pixdim=(1.5, 1.5, 2.0),\n",
    "                mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"],\n",
    "                a_min=-57,\n",
    "                a_max=164,\n",
    "                b_min=0.0,\n",
    "                b_max=1.0,\n",
    "                clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            ToTensord(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "    return train_transforms, val_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the training progress\n",
    "For a typical PyTorch regular training procedure, use regular `Dataset`, `Adam` optimizer, and train the model.\n",
    "\n",
    "For MONAI fast training progress, we mainly introduce the following features:\n",
    "1. `CacheDataset`: Dataset with the cache mechanism that can load data and cache deterministic transforms' result during training.\n",
    "2. `Novograd` optimizer: Novograd is based on the paper \"Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks\" `<https://arxiv.org/pdf/1905.11286.pdf>`.\n",
    "3. `AMP` (auto mixed precision): AMP is an important feature released in PyTorch v1.6, NVIDIA CUDA 11 added strong support for AMP and significantly improved training speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_process(fast=False):\n",
    "    max_epochs = 300\n",
    "    learning_rate = 1e-4\n",
    "    val_interval = 1  # do validation for every epoch\n",
    "\n",
    "    train_trans, val_trans = transformations()\n",
    "    # set CacheDataset for MONAI training\n",
    "    if fast:\n",
    "        train_ds = CacheDataset(\n",
    "            data=train_files,\n",
    "            transform=train_trans,\n",
    "            cache_rate=1.0,\n",
    "            num_workers=8,\n",
    "        )\n",
    "        val_ds = CacheDataset(\n",
    "            data=val_files, transform=val_trans, cache_rate=1.0, num_workers=5\n",
    "        )\n",
    "        # don't need many workers because already cached the data\n",
    "        loader_workers = 1\n",
    "    else:\n",
    "        train_ds = Dataset(data=train_files, transform=train_trans)\n",
    "        val_ds = Dataset(data=val_files, transform=val_trans)\n",
    "        loader_workers = 4\n",
    "\n",
    "    train_loader = DataLoader(\n",
    "        train_ds, batch_size=2, shuffle=True, num_workers=loader_workers\n",
    "    )\n",
    "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=loader_workers)\n",
    "\n",
    "    device = torch.device(\"cuda:0\")\n",
    "    model = UNet(\n",
    "        dimensions=3,\n",
    "        in_channels=1,\n",
    "        out_channels=2,\n",
    "        channels=(16, 32, 64, 128, 256),\n",
    "        strides=(2, 2, 2, 2),\n",
    "        num_res_units=2,\n",
    "        norm=Norm.BATCH,\n",
    "    ).to(device)\n",
    "    loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "\n",
    "    post_pred = AsDiscrete(argmax=True, to_onehot=True, n_classes=2)\n",
    "    post_label = AsDiscrete(to_onehot=True, n_classes=2)\n",
    "\n",
    "    # set Novograd optimizer for MONAI training\n",
    "    if fast:\n",
    "        # Novograd paper suggests to use a bigger LR than Adam,\n",
    "        # because Adam does normalization by element-wise second moments\n",
    "        optimizer = Novograd(model.parameters(), learning_rate * 10)\n",
    "        scaler = torch.cuda.amp.GradScaler()\n",
    "    else:\n",
    "        optimizer = torch.optim.Adam(model.parameters(), learning_rate)\n",
    "\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    best_metrics_epochs_and_time = [[], [], []]\n",
    "    epoch_loss_values = []\n",
    "    metric_values = []\n",
    "    epoch_times = []\n",
    "    total_start = time.time()\n",
    "    for epoch in range(max_epochs):\n",
    "        epoch_start = time.time()\n",
    "        print(\"-\" * 10)\n",
    "        print(f\"epoch {epoch + 1}/{max_epochs}\")\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 = (\n",
    "                batch_data[\"image\"].to(device),\n",
    "                batch_data[\"label\"].to(device),\n",
    "            )\n",
    "            optimizer.zero_grad()\n",
    "            # set AMP for MONAI training\n",
    "            if fast:\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",
    "            epoch_len = math.ceil(len(train_ds) / train_loader.batch_size)\n",
    "            print(\n",
    "                f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\"\n",
    "                f\" step time: {(time.time() - step_start):.4f}\"\n",
    "            )\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 = 0.0\n",
    "                metric_count = 0\n",
    "                for val_data in val_loader:\n",
    "                    val_inputs, val_labels = (\n",
    "                        val_data[\"image\"].to(device),\n",
    "                        val_data[\"label\"].to(device),\n",
    "                    )\n",
    "                    roi_size = (160, 160, 128)\n",
    "                    sw_batch_size = 4\n",
    "                    # set AMP for MONAI validation\n",
    "                    if fast:\n",
    "                        with torch.cuda.amp.autocast():\n",
    "                            val_outputs = sliding_window_inference(\n",
    "                                val_inputs, roi_size, sw_batch_size, model\n",
    "                            )\n",
    "                    else:\n",
    "                        val_outputs = sliding_window_inference(\n",
    "                            val_inputs, roi_size, sw_batch_size, model\n",
    "                        )\n",
    "                    val_outputs = post_pred(val_outputs)\n",
    "                    val_labels = post_label(val_labels)\n",
    "                    value = compute_meandice(\n",
    "                        y_pred=val_outputs,\n",
    "                        y=val_labels,\n",
    "                        include_background=False,\n",
    "                    )\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(\n",
    "                        time.time() - total_start\n",
    "                    )\n",
    "                    torch.save(model.state_dict(), \"best_metric_model.pth\")\n",
    "                    print(\"saved new best metric model\")\n",
    "                print(\n",
    "                    f\"current epoch: {epoch + 1} current\"\n",
    "                    f\" mean dice: {metric:.4f}\"\n",
    "                    f\" best mean dice: {best_metric:.4f}\"\n",
    "                    f\" at epoch: {best_metric_epoch}\"\n",
    "                )\n",
    "        print(\n",
    "            f\"time consuming of epoch {epoch + 1} is:\"\n",
    "            f\" {(time.time() - epoch_start):.4f}\"\n",
    "        )\n",
    "        epoch_times.append(time.time() - epoch_start)\n",
    "\n",
    "    print(\n",
    "        f\"train completed, best_metric: {best_metric:.4f}\"\n",
    "        f\" at epoch: {best_metric_epoch}\"\n",
    "        f\" total time: {(time.time() - total_start):.4f}\"\n",
    "    )\n",
    "    return (\n",
    "        max_epochs,\n",
    "        epoch_loss_values,\n",
    "        metric_values,\n",
    "        epoch_times,\n",
    "        best_metrics_epochs_and_time,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable determinism and execute regular PyTorch training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "regular_start = time.time()\n",
    "epoch_num, epoch_loss_values, metric_values, epoch_times, best = train_process(\n",
    "    fast=False\n",
    ")\n",
    "total_time = time.time() - regular_start\n",
    "print(\n",
    "    f\"total training time of {epoch_num} epochs with\"\n",
    "    f\" regular PyTorch: {total_time:.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable determinism and execute MONAI optimized training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "monai_start = time.time()\n",
    "(\n",
    "    epoch_num,\n",
    "    m_epoch_loss_values,\n",
    "    m_metric_values,\n",
    "    m_epoch_times,\n",
    "    m_best,\n",
    ") = train_process(fast=True)\n",
    "m_total_time = time.time() - monai_start\n",
    "print(\n",
    "    f\"total training time of {epoch_num} epochs with MONAI: {m_total_time:.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training loss and validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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(\"Fast Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(m_epoch_loss_values))]\n",
    "y = m_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(\"Fast Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(m_metric_values))]\n",
    "y = m_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(\n",
    "    \"regular PyTorch\", total_time, 1, label=\"Regular training\", color=\"red\"\n",
    ")\n",
    "plt.bar(\"Fast\", m_total_time, 1, label=\"Fast 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, m_epoch_times, label=\"Fast 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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqikfxvDw8JRen0wm0+77PvQmEokJ93PGGWfwyU9+0vte8+fP58Ybb8xdcpJoO5fMx4/5lCj5bIdjaoun2g6DtcXFRJOPJhcwHyihDohZs2bFrbAbGp1Ap5c5hWFOYWh0MvLPMcccw/PPPw/As88+y2mnncbLX/5yjj/+eJ566qnRx48++mhWrVrF5z73OWprawG45557eP3rXw+kvsm7+OKLufbaa3d7jw984AOsWbOGgw8+mMsuu2z08cWLF/PpT3+aww8/nJ/97Gejj//xj3/k1ltv5ZOf/CSrV6/m2Wef5cQTT+QjH/kIa9as4dvf/ja33XYbRx11FIcddhgnn3wy27dvB+Daa6/lkksuAeD888/nwx/+MMceeywHHHDA6AfdTZs2sXLlytHt3/zmN3PaaaexdOlSPvWpT416XH311Rx00EEceeSRXHDBBVx88cWTyljbuWQ+fszHiIN8tMUjCz5aW5wZbeeSJh9NLmA+UEIdEG1tbXEr7IZGJ9DpZU5hmFMYGp2M/JJMJrnrrrs444wzALjwwgv5j//4Dx566CG+/vWvc9FFFwFwySWXcMkll7BhwwYWLlw44b4m4ktf+hLr16/n0Ucf5d577+XRRx8dfa6hoYGHH36YtWvXjj527LHHcsYZZ/C1r32NRx55hCVLlgAwODjI+vXr+fjHP85xxx3HAw88wF/+8hfWrl2bNtR3/Lzjbdu2cd9993H77bdz6aWXZvR75JFHuP7669mwYQPXX389W7Zs4YUXXuCKK67ggQce4A9/+MPoPwCTQdu5ZD5+zMcoNvlqi33tMFhbrO1c0uSjyQXMB0roMpwNDQ1xK+yGRifQ6WVOYZhTGGlOiQT09UF//9hPXx9Mnw5Ll8YnaUyKvr4+Vq9ezfPPP8/y5cs55ZRT6O7u5o9//CNnn3326HYDAwMA3H///dx8880AvPWtb+UTn/jEbvusqJj4T+UNN9zA97//fRKJBNu2beOJJ57gkEMOAeDcc88N9h6/7datWzn33HPZtm0bg4ODaZdfG79a9ZlnnklZWRkrVqwY/WZuV0466STq6uoAWLFiBZs3b6a5uZlXvvKVzJkzB4Czzz6bZ555Jth1PNrOb/PxYz7FRUSuAV4P7HDOrYweux5YFm0yG2h3zq0WkcXAk8DT0XMPOOfeX1zj/JHvttjXDoO1xdrOJU0+mlzAfKCEOiB27txJY2Nj3BppjHdq62tjxrQZTCufFrOV/qy0YE5hTNmprQ02bUp1FCQSkEyO3Y7/PdNjI78PDMCLL8ILL8C2bSS2bKFi505oaUltk4nXvhbuuGPy3kYsjMw77u3t5dRTT+Wqq67i/PPPZ/bs2TzyyCPB+6moqBidczw0NJTxMmh///vf+frXv86f//xn6uvrOf/889O2mzFjRvD7jd/2Qx/6EB/72Mc444wzuOeee7j88stHnxs/D7qqqmr094lWZB+/TXl5uXde82TQ1uaYjx/zKTrXAv8J/GjkAefc6H+4IvINoGPc9s8651YXS66Q5LstHhoaorKy0triCdB2Lmny0eQC5gMl1AExmWA3t2+mpa+FxHCCoeQQieHEbj9DwxM8nmH7ibbd2rmVO5+9k6a3NPHm5W8uwNHnhqaTYgRzysDgYOqf89bW1O3QEI0jf/h8tyHbTPY1yWT6SIL+fhrH3x8Y2O35jD/jt+vszE9e5eWwzz6w775U7L8/HHsszJ2bGulQXb377YIF+XlfIxZqamr4zne+w5lnnslFF13E/vvvz89+9jPOPvtsnHM8+uijHHrooRx99NHcdNNNnHvuuaxbt2709S996Ut54oknGBgYoK+vj7vuuovjjjsu7T06OzuZMWMGdXV1bN++nV/+8peceOKJWd1mzpxJV1fXhM93dHSwIKp/1113Xdpz+bhe9xFHHMFHPvIR2tramDlzJjfddBOrVq2a1L5ibwd3oaR9AhYHDLIp4uUOtZVXvnHO/S4a2bAbkrqswznAq4sqVWTy1RYPDw/T3t5ubfEEaDuXNPlYO+wnjrIqmQ6IXHt3vv/Q93nf7e/Lu0dlWSUVZRVUlFVQLuVMq5jGzGkz+eARH+TgxoPz/n6TQVvPHJSAU2srbN6cuh3pUMj0+/j7ylbR9SKS+qfe9zN3buq2qir98cZGWL4cKiuhoiLVkTByO/5332PTpkFDQ+p3dNYnI/8cdthhHHLIITQ1NfHTn/6UD3zgA1x55ZUMDQ2xdu1aDj30UL71rW/x9re/nS996Uucdtppo0Nk99tvP8455xxWrlzJ4sWLOeyww3bb/6GHHsphhx3Gy172Mvbbbz9e8YpXBHmtXbuWCy64gO985zsZV0m//PLLOfvss6mvr+fVr341f//730efy8dK8AsWLOCzn/0sRx55JHPmzOFlL3vZ6HHnirZzyXz87Fy1isYNG+LWGEVbPkXmeGC7c+5v4x7bX0T+AnQCn3PO/T4etfySr7Z4//33t7Z4ArSdS5p8NLmAtcMAMtFQIc2sWbPGTeW6v9m4d9O9nPSjk3jV/q/i4iMuprI81WkwvvNg5Gfkud0ez7BteVl5wZyNPZjrr4fzz099278rVVUwZ07qp75+7Pdd79fXp/7JhrHeV99tyDaTeU1Z2dgIgvE/FRXBl+7akxGRh5xza+L2KAaZ2uEnn3yS5cuXx2Q0OXp7e5k+fToiwrp162hqauKWW26JW6vgdHd3U1tbSyKR4E1vehPvfve7edOb3rTbdntimZYs+Wpj98DPhbuiqS2ORkDcPrIGxLjH/wvY6Jz7RnS/Cqh1zrWIyMuBm4GDnXO7DQMUkQuBCwH222+/lz/zzDMMDw/jnKOiooKNGzeyYsUKksnk6HD/ysrK0WkMQ0NDVFRUjD4/PDyMiIxOHxj5XUQYHh6mvLycZDJJRUVF2j4qKytJJBKjz5eVlY2+DshpHwMDA0ybNo3y8nLWrVvHunXr+PnPf57mU1ZWtkcdU6Z97Ooz8jfIOceZZ57Je9/7Xl7/+tenHdMTTzzBsmXL6O3tpaqqikQigYhQVlbG0NAQ1dXV9PT0MGvWLNra2mhoaBj9p3LktrW1lbq6Orq7u5k+fTqDg4OjVxZJJpNMmzaNvr4+amtr6ejoYM6cObvto6Wlhfr6+tERJ/39/VRWVqbVvYGBAWpqaujq6mL27Nm0tLQwd+7c0X00NzfT0NBAe3s7M2fO3PuOaf58el/yEqo6OkhMn44kk5QlEgzNmEF1ays9++7LrE2baDvoIBqefHK0M2LktnXZMuqee47unTv1HNMky8nXDpdMB0Rra+voIi8TsWH7Bi675zJue+Y2ltQv4cH3Pkhd9eR6IvPlFAcavfZKJ+fgiivgssvguOPgYx9LfUs/vmNh+vTiOhWAUnLS9KG30BSyAyKRSGRdcCxf/P73v+fiiy/GOcfs2bO55pprOPDAA2PzyUa+XD7xiU/wf//3f/T39/Oa17yGb3/726MfsMeTrUy1nd8l7RPQAdG6bBlznn7av1ERPxeWQlucqQNCRCqA54GXO+e2TvC6e4BPOOe8H3gL1RYXu93L1hZraoehuG1xSHmWdNuXBWuH/cTRDus5kwtMyJCmi395Mb/b/Ds+dOSH+Mxxnylo50OoUxxo9NrrnPr74b3vhZ/+FN7xDvjBD1KjHeJ0KhDmZOTKSG9/MTj++OP561//6t2mmD7ZyJfL17/+9bzsR9u5ZD5+6p57Lm6FNLTlU0ROBp4a3/kgIo1Aq3MuKSIHAEuB2Aqs2O1etrZYUzsM1hZnQ5OPJhewdhhg6iuo7CF0d3dP+Jxzjit/dyW/2/w7vnDiF/jOa7/DvjP3jdUpTjR67VVOO3fCySenOh+uvBKuuy4vnQ9Tciog5mTkSrbrvRcbTT6aXEDfuWQ+frqVLa6rLZ98IyJNwP3AMhHZKiLviZ5aCzTtsvkJwKMi8ghwI/B+51xr0WR3QVtbYz5+tJ1Lmnw0uYC1w1BCIyCmTzCUfTA5yLtveTc/3fBTzl5xNh89+qOxO8WNRq+9xumJJ+D1r4dt21JrP5xzTvxOBcacjFzJx+ri+USTjyYX0HcumY+f6c3NcSukoS2ffOOcO2+Cx8/P8NhNwE2FdgpFW1tjPn60nUuafDS5gLXDUEIjIAYHBzM+/sV7v8hPN/yUK191JdefdT0zq2bG7hQ3Gr32Cqff/CZ16cfeXrjnnrx3PkzKqQiYk5Er2tYm0uSjyQX0nUvm42dw1qy4FdLQlo8xhra2xnz8aDuXNPlocgFrh6GEOiAyzdV6bMdjfOUPX+Gdh76Tfz7hnzMuwFVsJw1o9Nrjnb73PXjta2HRInjwQTjqqPidioQ5GblS7LY4G5p8NLmAvnPJfPyUDwzErZCGtnyMMbS1NebjR9u5pMlHkwtYOwwl1AGxK8NumAtuu4C6qjq+8ZpvxK1j7K0kk/DRj8IHPgCnngr33QcvfWncVoax11JeXs7q1atHfzZt2pTT67/1rW/R29ub83M+/uVf/oX/+7//825z66238uUvfznnfRuGYWjE2mLDMCaiZNaA2HWxmNufuZ0Htj7AdWdex9yauSqctKDRa4906uqCt74Vbr8dLrkEvvENKHAv4x6ZUwxodNobkS/k9xsid1n2Ia/Tp0/nkUcemfR7fOtb3+Ltb387NTU1Y+8bDbXN9NwII9eez8QXv/jFrO97xhlncMYZZ2TdTtuwX23nkvn4SeZpweN8oS2fvZW9oS0e3/ZZW7w72s4lTT6aXMDaYSihERDTpk1Lu3/fP+5jWvk01q5cG5PR7k5a0Oi1xzlt2QLHHQe//CV897vwrW8VvPMhq1NMmJMRJ93d3Zx00kkcfvjhrFq1iltuuQWAnp4eTj/9dA499FBWrlzJ9ddfz3e+8x1eeOEFXvWqV/GqV71qdB8ikvG52tpaPv7xj3PooYdy//3388UvfpEjjjiClStXcuGFF45+QD3//PO58cYbAVi8eDGXXXbZqM9TTz0FwLXXXsvFF188uv2HP/xhjj32WA444IDR1w4PD3PxxRfzspe9jFNOOYXXve51o8/FhbZzyXz8TOvsjFshDW35GIVjqm3xyJQHa4szo+1c0uSjyQWsHYYS6oDo6+tLu/+n5//EYfMOY1p5fJVyVyctaPTaI5yGhuAHP4BPfAKOPBI2bYJf/CI1/SIuJwWYk1FM+vr6Rof8vulNb6K6upqf//znPPzww9x99918/OMfxznHr371K+bPn89f//pXHnvsMU477TQ+/OEPM3/+fO6++27uvvvu0X0ODw9nfK6np4ejjjqKv/71rxx33HFcfPHF/PnPf+axxx6jr6+P22+/PaPj3Llzefjhh/nABz4w4TXgt23bxn333cftt9/OpZdeCsD//u//smnTJp544gl+/OMfc//99+c5vdzRdi6Zj5++ufGM+JwIbfkY+SPfbfHw8DCAtcUToO1c0uSjyQWsHYYS6oCora0d/b1nsIcHtj7AsfsdG6NRupMmNHrtEU633QYXXghXXQX77Qd//GNq3Yc4nRRgTkYxGRn2+8gjj/Dzn/8c5xyf/exnOeSQQzj55JN5/vnn2b59O6tWreI3v/kNn/70p/n9739PXV3dhPucaDhveXk5b3nLW0bv33333Rx11FGsWrWK3/72tzz++OMZX/fmN78ZgJe//OUTzos+88wzKSsrY8WKFWzfvh2A++67j7PPPpuysjLmzZuXNkojLrSdS+bjp/b55+NWSENbPkb+yHdb7Fsoz9pifeeSJh9NLmDtMJRQB0RHR8fo73f9/S4GkgOcvvT0GI3SnTSh0WuPcPrDH6C6Gjo64E9/goMPjt9JAeZkxMlPf/pTdu7cyUMPPcQjjzzCPvvsQ39/PwcddBAPP/wwq1at4nOf+5x3bvBE8yOrq6tHPxT39/dz0UUXceONN7JhwwYuuOAC+vv7M76uKpr/WV5eTiKR8G4D6XONR74F1IK2c8l8/HQccEDcCmloy8coHFNti33z1K0t1ncuafLR5ALWDkMJdUDMmTNn9Pfbnr6NWVWzOP6lx8dolO6kCY1ee4TT/ffDy18OMc412yNyUoBGJ6MwdHR08JKXvITKykruvvtuNm/eDMALL7xATU0Nb3/72/nkJz/Jww8/DMDMmTPp6upK20dFRcWEz40w8gF37ty5dHd3F2Q+8Cte8QpuvvlmhoeH2b59O/fcc0/e3yNXtJ1L5uNnztNPx62QhrZ8jMIx1bZ4pB3O9Nx4rC3WgSYfTS5g7TCUUAfEzp07gdTlN3/xt19w6pJTY13/YbyTNjR6qXcaGICHHoJjjolPiD0gJyVodDIKw9ve9jbWr1/PqlWr+NGPfsTLXvYyADZs2MCRRx7J6tWr+cIXvsDnPvc5AC688EJOO+20tCG1Q0NDEz43wuzZs7ngggtYuXIlp556KkcccUTej+Utb3kL8+fPZ8WKFbz97W/n8MMP904dKQbaziXz8bNz1aq4FdLQlo9ROKbaFo+0w5meG4+1xTrQ5KPJBawdBhBtl5EJYc2aNW79+vWTeu1DLzzEmh+s4bozr+Odh74zz2ZGyXLNNfCe98BNN0E0p9AoPUTkIefcmrg9ikGmdvjJJ59k+fLlMRmVBt3d3dTW1tLS0sKRRx7JH/7wB+bNm1ew97My3YOQPF1qcQ/8XLgr1hbbeVtoitkWW3nuQVg7PIqvHS65ERC3PXMbgvC6pa+L2Uhfj9wIGr1UO113Hbz3vXDiifC6eOuV6pwUodHJGGP8N10a0ORz+umns3r1ao4//ng+//nPF7TzIQRt55L5+LFv3oxQNLV7oM/H2mI/mnw0uYC1wwAV2TfZO2hsbATg9mdu55j9jmFuTfyXQBlx0oZGL7VO3/8+vP/9cPLJcPPNqUUo43ZShjkZuVJZWRm3QhqafO699964FdLQdi6Zj5/GDRviVkhDWz7GGJraPdDnY22xH00+mlzA2mEooREQLS0tvND1Ag9te4jXL3193DpAykkjGr00OvV85SvwvvelRj3ceivU1MStpDInczJyZaLVyONCk48mF9B3LpmPnxZlw7i15WOMoa2tMR8/2s4lTT6aXMDaYSihDoj6+nru3HgnAKcfFO/lN0eor6+PWyEjGr1UOQ0Pw1e+woxLL4U3vQn+939jH/kwgqqcIsyptMjHukK+673HgSafYrqElKW2c8l8/NQ/80zcCmloy2dvYqptsaZ2D0rXJ7QctZ1Lmnw0uYC1w1BCHRCdnZ3s7E3NcTlwzoEx26To7OyMWyEjGr3UOD3yCBx/PFx6KYNvehNcf32sl93cFTU5jcOcSofq6mpaWlqm/MHXd733ONDkUywX5xwtLS1UZ+lc1XYumY+fzsWL41ZIQ1s+ewv5aIs1tXtQmj6h7TDoO5c0+WhyAWuHoYTWgJgxYwbJ4VRjUSY6+l1mzJgRt0JGNHrF7tTeDv/yL3DVVdDQANdei6xdC8rmJMaeUwbMqXRYuHAhW7dunfKCRs45JF8rSecBTT7FdKmurmbhwoXebbSdS+bjZ8a2bXErpKEtn72FfLTFmto9KF2fkHYY9J1Lmnw0uYC1w1BCHRD9/f0Mu2FATwdEf3+/ukV1QKdXbE7OwY9/DJ/8JDQ3w0UXwRVXwOzZ9Hd1UVlVVXwnD1Z2YWh02huorKxk//33n/J+urq6mDlzZh6M8oMmH00uoO9cMh8//XPmUNnbG7fGKNry2VvIR1usra0xHz/aziVNPppcwNphKKEOiMrKytEOiHLRMY9M08kwHo1esTj99a9w8cVw331wzDHwq1/BYYfF65QFcwpDo5Mxhrby0eSjyQXMJxvqfHp64lZIQ1s+xhjaysZ8/JjPxGhyAWuHoYTWgBgeHlY3AmJ4eDhuhYxo9CqqU0cHXHIJHH44PPUUXHNNqhNiXOdD0Z0CMacwNDoZY2grH00+mlzAfLKhzqdC1/dO2vIxxtBWNubjx3wmRpMLWDsMJTQCwjlH0qXWgNAyhywfq8UXAo1eRXFyDn7yk9R0i5074f3vhyuvhAlWhy3ZnHLEnIxc0VY+mnw0uUCRfQL+drt58+DFF7NsVDxndeWl7EoC2vIxxtBWNubjx9pi39soKytrh0unA6KiooJhN6xm9AOknDSi0avgThs2wAc/CL//PRx1FNxxR2oERJxOk8CcwtDoZIyhrXw0+WhyAYU+fX1xK6Rh+fjRlo8xhrayMR8/6nwUtTWWjZ848tHz33iBGRgYYNgNq1n/AVJOGtHoVTCnjg746EdT0yueeAJ++EP44x+zdj4U1GkKmFMYGp2MMbSVjyYfTS6g0KeuLm6FNCwfP9ryMcbQVjbm40edj6K2xrLxE0c+urqECkhNTQ3J4aSqERA1NTVxK2REo1fenZyD//f/4BOfgO3bx6ZbzJkTn1MeMKcwNDoZY2grH00+mlxAoc+OHXErpGH5+NGWjzGGtrIxHz/qfBS1NZaNnzjy0fPfeIHp6upSNwWjq6srboWMaPTKq9Njj8GJJ8Lb3w6LFsGf/gTf/W5OnQ95d8oT5hSGRidjDG3lo8lHkwso9Nlvv7gV0rB8/GjLxxhDW9mYjx91PoraGsvGTxz56PlvvMDMnj07NQWjTM8UjNmzZ8etkBGNXnlx6uyEj38cVq+Gxx+H738f7r8f1qyJzynPmFMYGp2MMbSVjyYfTS6g0GfjxrgV0rB8/GjLxxhDW9mYjx91PoraGsvGTxz5lEwHREtLi7oREC0tLXErZESj15ScRqZbvOxl8M1vwnvfC08/DRdcAGWTrw97XU4FwpwKi4h8VEQeF5HHRKRJRKpFZH8ReVBENorI9SIyLdq2Krq/MXp+8bj9fCZ6/GkROTW2A0Jf+Wjy0eQCCn0OPjhuhTQsHz/a8jHG0FY25uNHnY+itsay8RNHPnr+Gy8wc+fOJel0rQExd+7cuBUyotFr0k6PPw6vehW87W2wYAE8+CB873vQ0BCfUwExpzA0Ok0GEVkAfBhY45xbCZQDa4GvAN90zh0ItAHviV7yHqAtevyb0XaIyIrodQcDpwHfFYlvxV5t5aPJR5MLKPR57LG4FdKwfPxoy8cYQ1vZmI8fdT6K2hrLxk8c+ej5b7zA7Ny5U91VMHbu3Bm3QkY0euXs1NWVWmBy9erUJTb/+7/hgQfgiCPicyoC5hSGRqcpUAFMF5EKoAbYBrwauDF6/jrgzOj3N0b3iZ4/SUQkenydc27AOfd3YCNwZHH0d0db+Wjy0eQCCn1WroxbIQ3Lx4+2fIwxtJWN+fhR56OorbFs/MSRT8l0QDQ2NjKUHFI1AqKxsTFuhYxo9Ap2cg7WrUtNt/j3f4d/+qfUdIsLL4Ty/HY+7dE5FRFzKhzOueeBrwP/INXx0AE8BLQ75xLRZluBBdHvC4At0WsT0fYN4x/P8Jqio618NPlocgGFPsq+WbJ8/GjLxxhDW9mYjx91PoraGsvGTxz56PlvvMA0Nzfzpxf+xOLZi+NWGaW5uTluhYxo9ApyeuIJOOkkOO882Hff1AKT3/8+FGho0R6bU5Exp8IhIvWkRi/sD8wHZpCaQlGo97tQRNaLyPodO3bQ399Pb28vPT09DAwM0NnZSSKRoK2tDefcaM4jvevNzc0452hrayORSNDZ2cnAwAA9PT309vbS399PV1cXL774Iu3t7QwPD4/OTRzZx8hta2sryWSSjo4OBgcH6e7upq+vj76+Prq7uxkcHKSjo4NkMklra2vGfbS0tDA8PEx7eztDQ0N0dXVlPKbt27dP+ZiGhobCjmnaNDr235/BmTPpXrCAvrlz6Zs7l+4FCxicOZPNp5xCcto0Wl/2MhBh5yGHpN22rFgRdEz5KKehoSE2bdpUvHJasoShmhq6Fi6kv76e3sZGeubNY6Cujs5Fi0hUV/P3U0/FidAcfcM08k1T88qVOBHali7NTzkFHtNzzz03pbqXUzktWsRAXR098+bR29hIf309XQsXMlRTQ/uSJQyXl/Pc6aen9rFqVdpt67JlJCsr6Vi8uODn0/hjevbZZwt2PhlTQ9vfyaL6iGT9aV61Kvt2RURdeSn6lt+y8RNHPuKcK/qbTpU1a9a49evX5/Qa5xwHfOcAjl90PD96048KZJYbzjmkyA1UCBq9vE5dXXDFFakFJmfOhH/919QCk3ke8ZCTU0yYUxiFchKRh5xzk7usyuTe72zgNOfce6L77wSOAc4G5jnnEiJyDHC5c+5UEbkz+v3+aMrGi0AjcCmAc+7fov2MbjfRe0+mHQ5FW50pqk+W93EiSLa/20X8u64pG7B8slEq+RS7LY6TQrXF1g77KZVzKSN7WD6WTba3Kn47XDIjINrb20kMJ6gsq4xbZZT29va4FTKi0Sujk3Nw/fWwfDl87Wvwrnelplu8//0F73yY0ClmzCkMjU6T5B/A0SJSE63lcBLwBHA3cFa0zbuAW6Lfb43uEz3/W5fqhb4VWBtdJWN/YCnwpyIdw25oKx9NPu0HHhi3QhqasgHLJxuWjxGKtrJR52PnkhdN+Vg2fuLIp6Lo7xgTM2fOJDGcoKJMzyHPnDkzboWMaPTazempp+Dii+Guu+Cww+DGG+Hoo+N1UoA5haHRaTI45x4UkRuBh4EE8Bfg+8AvgHUicmX02NXRS64GfiwiG4FWUle+wDn3uIjcQKrzIgF80DmXLOrBjENb+WjymbllS/aNioimbMDyyYblY4SirWzU+di55EVTPpaNnzjyKZkREL29veo6IHp7e+NWyIhGr1Gn7m649FI45BB46CG46ir485+L3vmQ5qQIcwpDo9Nkcc5d5px7mXNupXPuHdGVLJ5zzh3pnDvQOXe2c24g2rY/un9g9Pxz4/bzJefcEufcMufcL+M7In3lo8mn9yUviVshDU3ZgOWTDcvHCEVb2ajzsXPJi6Z8LBs/ceRT8A4IETlNRJ4WkY0icmmG5xeJyN0i8hcReVREXlcIj6qqKoaSQ6o6IKqqquJWyIhGr6qqKti6FVasgK98Bd7+9tR0i4suKsp0iwmdlGFOYWh0MsbQVj6afKo6OuJWSENTNmD5ZMPyMULRVjbqfOxc8qIpH8vGTxz5FLQDQkTKgauA1wIrgPNEZMUum30OuME5dxip4cDfLYRLIpFIrQFRrmcNiEQikX2jGNDolUgk4JprUp0Q996b+j3mHkS1OSnDnIxc0VY+mnwS06fHrZCGpmzA8smG5VNcROQaEdkhIo+Ne+xyEXleRB6Jfl437rnPRF/YPS0ip8ZjnUJb2ajzsXPJi6Z8LBs/ceRT6BEQRwIbo+HAg8A6UpeMG48DZkW/1wEvFEJERNRNwdC0uvB4NHoJQFMTnHBC6kcBKnMypyA0OhljaCsfTT6SjG1pjoxoygYsn2xYPkXnWjJfGvmbzrnV0c8dANEXdGuBg6PXfDf6Ii8WtJWNOh87l7xoysey8RNHPoXugFgAjF9pY2v02HguB94uIluBO4APFUKkrKxMXQdEWZnOJTg0epU//nhq4cnzzotbZRSNOZlTGBqdjDG0lY8mnzJl3+RoygYsn2xYPsXFOfc7Ugv+hvBGYF20js/fgY2kvsiLBW1lo87HziUvmvKxbPzEkY+GEjkPuNY5txB4HakV2nfzEpELRWS9iKzfsWMH/f399Pb20tPTw8DAAJ2dnSQSCdra2nDO0dzcDMDOnTsB2L59O0mXZGhgiEQiQWdnJwMDA/T09NDb20t/fz9dXV0MDQ3R3t7O8PAwLS0tafsYuW1tbSWZTNLR0cHg4CDd3d309fXR19dHd3c3g4ODdHR0kEwmaW1tzbiPlpYWBgYGaG9vZ2hoiK6urpyPqbm5GeccbW1teT2m3t7eSR/T8PBwQY4p8eMf4yoq6D/99KKX00TH1NHREWs5ZTqmEa+4yinTMY28X1zllOmYBgcHC1JORn4YGhqKWyENTT5DM2bErZCGpmzA8smG5aOGi6N1z64RkfrosZAv7YCpfyYO+Xs38vc07s9aI8c04lqUz1rLlpGsrKRj8WIGa2vpnj+fvoYG+hoa6J4/n8HaWloPOohkZSWty5alXrtqVdpty/Lle+9nrSVLGKqpoWvhQvrr6+ltbKRn3jwG6uroXLSIRHU1LStW4ERoXrkytY/otnnlSpwIbUuXFu3/sf7+/uJ9Jq6upnPRIgbq6uiZN4/exkb66+vpWriQoZoa2pcsYWDmTFqWL89Yb0br3l7+mVhSl4AvDCJyDHC5c+7U6P5nAJxz/zZum8eB05xzW6L7zwFHO+d2TLTfNWvWuPXr1+fk0tvfy4yvzOCKV13B5074XO4HUwCGhoaorNSzJsUI6rycw+2/P7JiBdxxR9w2o6jLCXMKpVBOIvKQc25N3neskMm0w6FoqzNF9ckyFHKopobKbCtWF/Dv+q5oygYsn2yUSj6a2mIRWQzc7pxbGd3fB2gmNQX5CmBf59y7ReQ/gQeccz+Jtrsa+KVz7kbf/gvVFls77KdUzqWM7GH5WDZ+4miHCz0C4s/AUhHZX0SmkZrbdusu2/wDOAlARJYD1UDev07s6E6tOFpZpqcx7enpiVshI+q8HngA2bwZ1q6N2yQNdTlhTqFodDLG0FY+mnx69t03boU0NGUDlk82LJ/4cc5td84lnXPDwA8Ym2bxPLDfuE0XRo/FgrayUedj55IXTflYNn7iyKegHRDOuQRwMXAn8CSpq108LiJfFJEzos0+DlwgIn8FmoDzXQGGZdTMqAFQtQbErFmzsm8UA+q8mppw1dVw5plxm6ShLifMKRSNTsYY2spHk8+sTZviVkhDUzZg+WTD8okfERn/38ebgJErZNwKrBWRKhHZH1gK/KnYfiNoKxt1PnYuedGUj2XjJ458Cr4GhHPuDufcQc65Jc65L0WP/Ytz7tbo9yecc69wzh0arQb860J47GxNDarQ1AHR1tYWt0JGVHklk3DDDQyecgooa0BU5RRhTmFodDLG0FY+mnzaDjooboU0NGUDlk82LJ/iIiJNwP3AMhHZKiLvAb4qIhtE5FHgVcBHAZxzjwM3AE8AvwI+6JyLbbl8bWWjzsfOJS+a8rFs/MSRj57/xgvMrNmpf141dUA0NDTErZARVV733APbt1P1znfGbbIbqnKKMKcwNDoZY2grH00+DU8+GbdCGpqyAcsnG5ZPcXHOZbp019We7b8EfKlwRuFoKxt1PnYuedGUj2XjJ458NFwFoyhs37kdgMpyPWtAaF05X5VXUxPU1rLzyNiuRDUhqnKKMKcwNDoZY2grH00+Iytla0FTNmD5ZMPyMULRVjbqfOxc8qIpH8vGTxz5lEwHRF19HaBrBERjY2PcChlR4zU4CDfdBGeeSeOiRXHb7IaanMZhTmFodDLG0FY+mnwaN2yIWyENTdmA5ZMNy8cIRVvZqPOxc8mLpnwsGz9x5FMyHRAjIyA0dUBo65EbQY3XnXdCezucd54ep3GYUxjmZOSKtvLR5GPfnPixfPxYPkYo2spGnY+dS1405WPZ+IkjHz3/jRcYjWtAaOuRG0GN17p1MGcOnHIKjYquRT2CmpzGYU5haHQyxtBWPpp87JsTP5aPH8vHCEVb2ajzsXPJi6Z8LBs/NgKigLS0tQBQWabnH9nW1ta4FTKiwqu3F265Bc46CyordTjtgjmFYU5GrmgrH00+rcuWxa2QhqZswPLJhuVjhKKtbNT52LnkRVM+lo2fOPIpmQ6I6hnVgK4REHV1dXErZESF1223QU8PnJdaQFqF0y6YUxjmZOSKtvLR5FP33HNxK6ShKRuwfLJh+RihaCsbdT52LnnRlI9l4yeOfEqmA6KruwvQ1QHR3d0dt0JGVHitWwfz58PxxwNKnHbBnMIwJyNXtJWPJp/uBQviVkhDUzZg+WTD8jFC0VY26nzsXPKiKR/Lxk8c+ZRMB0RZZepQNXVATJ8+PW6FjMTu1d4Od9wB55wD5eU6nDJgTmGYk5Er2spHk8/05ua4FdLQlA1YPtmwfIxQtJWNOh87l7xoysey8RNHPiXTAdE30AdAZbmeNSAGBwfjVshI7F4335y6BGc0/QIUOGXAnMIwJyNXtJWPJp/BWbPiVkhDUzZg+WTD8jFC0VY26nzsXPKiKR/Lxk8c+ZRMB4QTB+gaAVEefbuvjdi9mprggAPgiCNGH4rdKQPmFIY5GbmirXw0+ZQPDMStkIambMDyyYblY4SirWzU+di55EVTPpaNnzjyKZkOiMRwAoBy0VUJjV3YsQPuugvWrgWRuG0MwzAMwzAMwzCMPFEyHRCDidTwEk0jIJLJZNwKGYnV68YbIZlMm34BOrMypzDMycgVbeWjySdZVRW3QhqasgHLJxuWjxGKtrJR52PnkhdN+Vg2fuLIp2Q6IKQ89W26pg6IadOmxa2QkVi9mprg4INh5cq0hzVmZU5hmJORK9rKR5PPtM7OuBXS0JQNWD7ZsHyMULSVjTofO5e8aMrHsvETRz4l0wHR298LQHmZnikYfX19cStkJDavf/wD7rtvt9EPoDMrcwrDnIxc0VY+mnz65s6NWyENTdmA5ZMNy8cIRVvZqPOxc8mLpnwsGz9x5FMyHRDTqlK9O5pGQNTW1satkJHYvG64IXV77rm7PaUxK3MKw5yMXNFWPpp8ap9/Pm6FNDRlA5ZPNiwfIxRtZaPOx84lL5rysWz8xJFPyXRAdHanhrto6oDo6OiIWyEjsXk1NaWufHHggbs9pTErcwrDnIxc0VY+mnw6DjggboU0NGUDlk82LB8jFG1lo87HziUvmvKxbPzEkU/JdEBU11QDuq6CMWfOnLgVMhKL1zPPwMMPZ5x+ATqzMqcwzMnIFW3lo8lnztNPx62QhqZswPLJhuVjhKKtbNT52LnkRVM+lo2fOPIpmQ6Ito42QNcIiJ07d8atkJFYvNatS11285xzMj6tMStzCsOcjFzRVj6afHauWhW3QhqasgHLJxuWjxGKtrJR52PnkhdN+Vg2fuLIp2Q6IGpm1AC6FqFsbGyMWyEjRfdyLjX94oQTYMECHU4BmFMY5mTkirby0eTTuGFD3AppaMoGLJ9sWD5GKNrKRp2PnUteNOVj2fiJI5+S6YBo72wHbARECEX3evRReOopWLt2wk00ZmVOYZiTkSvaykeTj31z4sfy8WP5GKFoKxt1PnYuedGUj2Xjx0ZAFJDpM6YDutaA0NYjN0LRvZqaoKICzjprwk00ZmVOYZiTkSvaykeTj31z4sfy8WP5GKFoKxt1PnYuedGUj2Xjx0ZAFJDOLn1XwWhpaYlbISNF9XIutf7DKaeA57q4GrMypzDMycgVbeWjyadl+fK4FdLQlA1YPtmwfIxQtJWNOh87l7xoysey8RNHPiXTATGtehqgqwOivr4+boWMFNXrgQdg82bv9AvQmZU5hWFORq5oKx9NPvXPPBO3QhqasgHLJxuWjxGKtrJR52PnkhdN+Vg2fuLIp2Q6ILr7ugFdi1B2dnbGrZCRono1NUF1NZx5pnczjVmZUxjmZOSKtvLR5NO5eHHcCmloygYsn2xYPkYo2spGnY+dS1405WPZ+Ikjn5LpgCivSHU8aBoBMWPGjLgVMlI0r2QSbrgBTj8dZs3S4ZQD5hSGORm5oq18NPnM2LYtboU0NGUDlk82LB8jFG1lo87HziUvmvKxbPzEkU/JdED0D/YDuhah7O/vj1shI0Xzuuce2L496/QL0JmVOYVhTkauaCsfTT79c+bErZCGpmzA8smG5WOEoq1s1PnYueRFUz6WjZ848imZDggkdaNpBERlZWXcChkpmldTE9TWpkZAZEFjVuYUhjkZuaKtfDT5VPb0xK2QhqZswPLJhuVjhKKtbNT52LnkRVM+lo2fOPIpmQ6IoeQQAGWi55CHh4fjVshIUbwGB+Gmm1JrP0yfrsMpR8wpDHMyckVb+WjyGa7Q04kOurIByycblo8RirayUedj55IXTflYNn7iyEfPf+MFJuESlEs5IhK3yijOubgVMlIUrzvvhPZ2OO+8oM01ZmVOYZiTkSvaykeTjyvXM40QdGUDlk82LB8jFG1lo87HziUvmvKxbPzEkU/JdEA4nKorYABUKOsBG6EoXuvWwZw5cMopQZtrzMqcwjAnI1e0lY8mn4q+vrgV0tCUDVg+2bB8jFC0lY06HzuXvGjKx7LxE0c+JdMBMZQYQtAz+gFgYGAgboWMFNyrtxduuQXOOgsC5x1pzMqcwjAnI1e0lY8mn4G6urgV0tCUDVg+2bB8jFC0lY06HzuXvGjKx7LxE0c+JdMBUVlZqWr6BUBNTU3cChkpuNdtt0FPT/D0C9CZlTmFYU5GrmgrH00+NTt2xK2QhqZswPLJhuVjhKKtbNT52LnkRVM+lo2fOPIpmQ4Ibb1fAF1dXXErZKTgXuvWwb77wvHHB79EY1bmFIY5GbmirXw0+XTtt1/cCmloygYsn2xYPkYo2spGnY+dS1405WPZ+Ikjn5LpgKiqqlI3BWP27NlxK2SkoF7t7XDHHXDuuZDDIiwaszKnMMzJyBVt5aPJZ/bGjXErpKEpG7B8smH5GKFoKxt1PnYuedGUj2XjJ458SqYDok/Zgh8ALS0tcStkpKBeN9+cugRnDtMvQGdW5hSGORm5oq18NPm0HHxw3AppaMoGLJ9sWD5GKNrKRp2PnUteNOVj2fiJI5+S6YConl6tbg2IuXPnxq2QkYJ6NTXBAQfAEUfk9DKNWZlTGOZk5Iq28tHkM/exx+JWSENTNmD5ZMPyKS4ico2I7BCRx8Y99jUReUpEHhWRn4vI7OjxxSLSJyKPRD/fi00cfWWjzsfOJS+a8rFs/MSRT8l0QPT29qqbgrFz5864FTJSMK8dO+Cuu2DtWsixM0hjVuYUhjkZuaKtfDT57Fy5Mm6FNDRlA5ZPNiyfonMtcNouj/0GWOmcOwR4BvjMuOeedc6tjn7eXyTHjGgrG3U+di550ZSPZeMnjnxKpgNi+vTpcSvsRmNjY9wKGSmY1403QjKZ6oDIEY1ZmVMY5mTkirby0eTTqOybE03ZgOWTDcunuDjnfge07vLYr51ziejuA8DCoosFoK1s1PnYueRFUz6WjZ848imZDojevl51UzCam5vjVshIwbyamuDgg2HVqpxfqjErcwrDnIxc0VY+mnyalX1zoikbsHyyYfmo493AL8fd319E/iIi94rIhJcKE5ELRWS9iKzfsWMH/f399Pb20tPTw8DAAJ2dnSQSCdra2nDOjeY88k1nc3Mzzjna2tpIJBJ0dnYyMDBAT08Pvb299Pf3849//IOhoSHa29sZHh4enSc+so+R29bWVpLJJB0dHQwODtLd3U1fXx99fX10d3czODhIR0cHyWSS1tbWjPtoaWlheHiY9vZ2hoaG6Orq2u2YNm/ePOVj6urqCjumZctIVlbSsXgxg7W1dM+fT19DA30NDXTPn89gbS2bTz6ZZGUlrcuWpV4bfbYduW1ZvjzrMeWjnEaOadOmTcUrpyVLGKqpoWvhQvrr6+ltbKRn3jwG6uroXLSIRHU1fz/1VJzIaJsz8q1/88qVOBHali6dejkFHtOOHTumVPdyKqfqajoXLWKgro6eefPobWykv76eroULGaqpoX3JEnYccggty5dnrDejda/A59P4Y9q5c2dBzicf4pzLupE21qxZ49avX5/Taz76q49y9V+upvMznQWyyh3nnLpOESiQ15YtsGgRXHkl/PM/63CaIuYURik5ichDzrk1ed+xQibTDoeirc4U1SfL+zgRJNvf7SL+XdeUDVg+2SiVfDS1xSKyGLjdObdyl8f/GVgDvNk550SkCqh1zrWIyMuBm4GDnXPeD66FaoutHfZTKudSRvawfCybbG9V/Ha4ZEZA9A/0x62wG+3t7XErZKQgXtdfn7o999xJvVxjVuYUhjkZuaKtfDT5tB94YNwKaWjKBiyfbFg+OhCR84HXA29z0TeBzrkB51xL9PtDwLPAQXE5aisbdT52LnnRlI9l4yeOfEqmA6KyslJVTy7AzJkz41bISEG8mppSV76Y5EmnMStzCsOcjFzRVj6afGZu2RK3QhqasgHLJxuWT/yIyGnAp4AznHO94x5vFJHy6PcDgKXAc/FY6isbdT52LnnRlI9l4yeOfEqmAyKRSKi7CkZvb2/2jWIg717PPAMPPwznnTfpXWjMypzCMCcjV7SVjyaf3pe8JG6FNDRlA5ZPNiyf4iIiTcD9wDIR2Soi7wH+E5gJ/GaXy22eADwqIo8ANwLvd861ZtpvMdBWNup87Fzyoikfy8ZPHPlUFP0dY6KsTF9fS1VVVdwKGcm717p1qTlR55wz6V1ozMqcwjAnI1e0lY8mn6qOjrgV0tCUDVg+2bB8iotzLtM3L1dPsO1NwE2FNQpHW9mo87FzyYumfCwbP3Hko++/8gKRHE6qm4KRSCSybxQDefVyLjX94oQTYMECHU55wpzCMCcjV7SVjyafhLJLSmvKBiyfbFg+Rijaykadj51LXjTlY9n4iSOfkumAEBF1UzC0dYiMkFevRx+Fp56CtWuntBuNWZlTGOZk5Iq28tHkI8lk3AppaMoGLJ9sWD5GKNrKRp2PnUteNOVj2fiJI5+S6YBw6LvcqMZpIZBnr6YmqKiAs86a0m40ZmVOYZiTkSvaykeTT5myb3I0ZQOWTzYsHyMUbWWjzsfOJS+a8rFs/MSRj64SKSDJpL4pGENDQ3ErZCRvXs6l1n845RSYO1eHUx4xpzDMycgVbeWjyWdoxoy4FdLQlA1YPtmwfIxQtJWNOh87l7xoysey8RNHPiXTAVFRUaFuCkZ1dXXcChnJm9cDD8DmzVOefgE6szKnMMzJSEMk60/1vHnZtysimupLdWtsi+JnRFM2YPlkw/IxQtFWNup87Fzyoikfy8ZPHPmUTAfE4NBg3Aq70dPTE7dCRvLm1dQEVVVw5plT3pXGrMwpDHMycqVn333jVkhDU32xbPxYPn4sHwMI6gjuWb5cVUewtrpi55IfTflYNn7iyKdkOiAqKyvVTcGYNWtW3AoZyYtXMgk33ACnnw552J/GrMwpDHMqLCIyW0RuFJGnRORJETlGROaIyG9E5G/RbX20rYjId0Rko4g8KiKHj9vPu6Lt/yYi74rviGDWpk1xvv1uaKovlo0fy8eP5WOEYnXFj+XjR1M+lo2fOPIpmQ6IgYEBdVMw2tra4lbISF687rkHtm+H8zJdAjt3NGZlTmGYU8H5NvAr59zLgEOBJ4FLgbucc0uBu6L7AK8FlkY/FwL/BSAic4DLgKOAI4HLRjot4qDtoIPieuuMaKovlo0fy8eP5WOEYnXFj+XjR1M+lo2fOPIpmQ6IqqqquBV2o6GhIW6FjOTFa906qK1NjYDIAxqzMqcwzKlwiEgdcAJwNYBzbtA51w68Ebgu2uw64Mzo9zcCP3IpHgBmi8i+wKnAb5xzrc65NuA3wGlFO5BdaHjyybjeOiOa6otl48fy8WP5GKFYXfFj+fjRlI9l4yeOfEqmA6K3r1fdFIydO3fGrZCRKXsNDsJNN6XWfpg+XYdTATCnMMypoOwP7AT+R0T+IiI/FJEZwD7OuW3RNi8C+0S/LwC2jHv91uixiR5PQ0QuFJH1IrJ+x44d9Pf309vbS09PDwMDA3R2dpJIJGhra8M5R3NzMzCWd3NzM06EtqVLSVRX07loEQN1dfTMm0dvYyP99fV0LVzItjVraF+yhOHyclqWL0/tY9WqtNvW1laSySQdHR0MDg7S3d1NX18ffX19dHd3Mzg4SEdHB8lkktZowacRj5HblpYWhoeHaW9vZ2hoiK6urozH9OKLL/qPyTna2tpIJBJ0dnYyMDBAT08Pvb299Pf309XVxdDQEO3t7QwPD9PS0pLRp7W1lWRlJR2LFzNYW0v3/Pn0NTTQ19BA9/z5DNbWsumUU0hWVtK6bFnGXFqWLw86pqzlFHhMf//737MfU77KackShmpq6Fq4kP76enobG+mZN4+Bujo6Fy0iUV3Nc6edhhOheeXK1D6i2+aVK8fqXj7KKfCYnnvuuSnVvZzKaYLzaaimZvR8ejb6UmC382nZsrG6V+Dzafwxbdy4sWDnkzE1RuqGFrSVq+XjR1M+lo2fOPIR51xh30DkNFJDhMuBHzrnvpxhm3OAywEH/NU591bfPtesWePWr1+fk8f7b38/Nz91My9+4sWcXmdMgttugzPOgF/8Al73urhtDKNoiMhDzrk1RXy/NcADwCuccw+KyLeBTuBDzrnZ47Zrc87Vi8jtwJedc/dFj98FfBo4Eah2zl0ZPf55oM859/WJ3nsy7XAkk/trMlHgv12xkY98LBs/lo+fvSCfYrfFcTKpttjqih/Lx4/lMzGWzSi+drigIyBEpBy4itS84xXAeSKyYpdtlgKfIfUB+mDgI4Vw6e3rLcRup4S2HrkRpuy1bh3MmQOnnJIfIXRmZU5hmFNB2Qpsdc49GN2/ETgc2B5NrSC63RE9/zyw37jXL4wem+jxWLBvBybGsvFj+fixfIxQrK74sXz8aMrHsvETRz6FnoJxJLDROfecc24QWEdqDvJ4LgCuiuYd45zbQQGYXj1d3RSMxsbGuBUyMiWv3l645RY46yyorNThVCDMKQxzKhzOuReBLSKyLHroJOAJ4FZg5EoW7wJuiX6/FXhndDWMo4GOaKrGncBrRKQ+WnzyNdFjsdC4YUNcb50RTfXFsvFj+fixfIxQrK74sXz8aMrHsvETRz6F7oAImVd8EHCQiPxBRB6Ipmzknf6B/kLsdkqMzKPUxpS8brsNenrydvWLETRmZU5hmFPB+RDwUxF5FFgN/CvwZeAUEfkbcHJ0H+AO4DlgI/AD4CIA51wrcAXw5+jni9FjsTCyvoEWNNUXy8aP5ePH8jFCsbrix/Lxoykfy8ZPHPkEd0CIyPRx37LlkwpSl4Q7ETgP+IGIzM7w/lNa/CyZTCJI/hYKy8OiWjNnzizaQmG5HNP06dMnfUyuqYnhefMYOvrovB5TRUVFbOU00aJaFRUVsZZTpmOaPn16QRYKm8oxOediLadMxzRr1qyClFMcOOcecc6tcc4d4pw70znX5pxrcc6d5Jxb6pw7eaQzIbr6xQedc0ucc6ucc+vH7eca59yB0c//xHZAQF20cJ8W6urq4lYYxbLxY/n4sXyMUKyu+LF8/GjKx7LxE0c+QYtQisgbgK8D05xz+4vIalLfkJ2R5XXHAJc7506N7n8GwDn3b+O2+R7w4MgH3mhRtEudc3+eaL+TWXDnXTe9i//b/H88/7HYpjXvRkdHh7qTAqbg1d4O++wDF10E3/ymDqcCYk5hlJKTLXwWQMBUuI7Fi6nbtMm/UREXaCpqHc6Sj2Xjx/LxUyr5WFucBasrfiwfP3tYPpaNnzja4dAREJeTWs+hHVLfuJG6/Fs2/gwsFZH9RWQasJbUHOTx3Exq9AMiMpfUlIy8dw2Vl5fne5dTZnqeLlGZbybtdfPNqUtw5nn6BejMypzCMCcjV6ZHo1O0oKm+WDZ+LB8/lo8RitUVP5aPH035WDZ+4sgntANiyDnXsctjWbtmnHMJ4GJSi5k9CdzgnHtcRL4oIiOjJ+4EWkTkCeBu4JPOuZZAr2ASyQSCrkUoBwcH41bIyKS9mprggAPgiCPyK4TOrMwpDHMycmVw1qy4FdLQVF8sGz+Wjx/LxwjF6oofy8ePpnwsGz9x5FMRuN3jIvJWoDy6bOaHgT+GvNA5dwephc/GP/Yv4353wMein4IhIuqugqFxVAZM0mvHDrjrLvj0p/N3DdypOhUYcwrDnIxcKR8YiFshDU31xbLxY/n4sXyMUKyu+LF8/GjKx7LxE0c+oSMgPgQcDAwA/w/oAD5SIKeC4LIP2DCmwo03QjIJa9fGbWIYhmEYhmEYhmEoJGgEhHOuF/jn6GePZHh4WN0UjGQyGbdCRibl1dQEBx8Mq1blXwidWZlTGOZk5EqyqipuhTQ01RfLxo/l48fyMUKxuuLH8vGjKR/Lxk8c+QSNgBCR34y/NKaI1IvInQWzKgDlZeXqpmBMmzYtboWM5Oy1ZQvcd19BFp8cQWNW5hSGORm5Mq2zM26FNDTVF8vGj+Xjx/IxQrG64sfy8aMpH8vGTxz5hE7BmOucax+545xrA15SEKMCMZQYilthN/r6+uJWyEjOXtdfn7o999z8y0RozMqcwjAnI1f65s6NWyENTfXFsvFj+fixfIxQrK74sXz8aMrHsvETRz6hHRDDIrJo5I6IvJSAq2BooqKiQt0UjNra2rgVMpKzV1NT6soXBx5YGCF0ZmVOYZiTkSu1zz8ft0IamuqLZePH8vFj+RihWF3xY/n40ZSPZeMnjnxCOyD+GbhPRH4sIj8Bfgd8pnBa+WdwaFDdFIyOjl2vbKqDnLyeeQYefrjgi09qzMqcwjAnI1c6DjggboU0NNUXy8aP5ePH8jFCsbrix/Lxoykfy8ZPHPmELkL5KxE5HDg6eugjzrnmwmnlH23zfwDmzJkTt0JGcvJaty512c0CTr8AnVmZUxjmZOTKnKefjlshDU31xbLxY/n4sXyMUKyu+LF8/GjKx7LxE0c+3hEQIvKy6PZwYBHwQvSzKHpsj6Gvv0/dFIydO3fGrZCRYC/nUtMvTjgBFizQ4VREzCkMczJyZWeBrqYzWTTVF8vGj+Xjx/IxQrG64sfy8aMpH8vGTxz5ZBsB8XHgAuAbGZ5zwKvzblQgqqur1U3BaGxsjFshI8Fejz4KTz0Fl1xSWCF0ZmVOYZiTkSuNGzbErZCGpvpi2fixfPxYPkYoVlf8WD5+NOVj2fiJIx/vCAjn3AXR7asy/OwxnQ+gbwVU0NcjN0KwV1MTVFTAWWcVVgidWZlTGOZk5Ip9OzAxlo0fy8eP5WOEYnXFj+XjR1M+lo2fOPIR5ya+mIWIvNn3Yufc/+bdKIA1a9a49evX5/Sat/3v23hw64Ns/PDGAlmVGM7B/vvDihVwxx1x2xiGCkTkIefcmrg9isFk2mEgtWZMPvD87dqjyUc+lo0fy8fPXpCPtcVZsLrix/LxY/lMjGUziq8dznYVjDdEP+8BrgbeFv38EHh3PiULzcDAgLopGC0tLXErZCTI64EHYPPmgl/9YgSNWZlTGOZk5ErL8uVxK6Shqb5YNn4sHz+WjxGK1RU/lo8fTflYNn7iyMe7BoRz7p8AROTXwArn3Lbo/r7AtQW3yyOVlZVxK+xGfX193AoZCfJqaoKqKjjzzIL7gM6szCkMczJypf6ZZ+JWSENTfbFs/Fg+fiwfIxSrK34sHz+a8rFs/MSRT7YRECPsN9L5ELGd1FUx9hgGhwbVXQWjs7MzboWMZPVKJuGGG+D002HWLB1OMWBOYZiTkSudixfHrZCGpvpi2fixfPxYPkYoVlf8WD5+NOVj2fiJI59sV8EY4S4RuRNoiu6fC/xfYZQKQ0VFhbopGDNmzIhbISNZve65B7Zvh/POK4oP6MzKnMIwJyNXZmzbln2jIqKpvlg2fiwfP5aPEYrVFT+Wjx9N+Vg2fuLIJ2gEhHPuYuB7wKHRz/edcx8qpFi+SSQTcSvsRn9/f9wKGcnqtW4d1NamRkAUCY1ZmVMY5mTkSv+cOXErpKGpvlg2fiwfP5aPEYrVFT+Wjx9N+Vg2fuLIJ3QKBs65nzvnPhr9/Hz8cyJyf/7V8kuZlKmbgqFxXQrI4jU4CDfdlFr7Yfp0HU4xYU5hmJORK5U9PXErpKGpvlg2fiwfP5ZPcRGRa0Rkh4g8Nu6xOSLyGxH5W3RbHz0uIvIdEdkoIo+KyOHxmVtdyYbl40dTPpaNnzjyCe6AyEJ1nvZTMHyXG42L4eHhuBUy4vW6805oayvq9AvQmZU5hWFORq4MV4TODiwOmuqLZePH8vFj+RSda4HTdnnsUuAu59xS4K7oPsBrgaXRz4XAfxXJMSNWV/xYPn405WPZ+Ikjn3x1QOj7734Xht2wujUgNHaKQBavdetgzhw4+eTiCaEzK3MKw5yMXHHl5XErpKGpvlg2fiwfP5bP5BCRr4rILBGpFJG7RGSniLw92+ucc78DWnd5+I3AddHv1wFnjnv8Ry7FA8Ds6KpzsWB1xY/l40dTPpaNnzjyyVcHhHrKyvRNwahQ1gM2woRevb1wyy1w1lkwbZoOpxgxpzDMyciVir6+uBXS0FRfLBs/lo8fy2fSvMY51wm8HtgEHAh8cpL72mfcleVeBPaJfl8AbBm33dbosd0QkQtFZL2IrN+xYwf9/f309vbS09PDwMAAnZ2dJBIJ2tracM7R3NwMwM6dOwFoXrkSJ0Lb0qUkqqvpXLSIgbo6eubNo7exkf76evpnz2aopob2JUsYLi+nZfny1D5WrUq7bW1tJZlM0tHRweDgIN3d3fT19dHX10d3dzeDg4N0dHSQTCZpbW1N8xi5bWlpYXh4mPb2doaGhujq6trtmPr6+vzH1NyMc462tjYSiQSdnZ0MDAzQ09NDb28v/f39dHV1MTQ0RHt7O8PDw7S0tGT0aV22jGRlJR2LFzNYW0v3/Pn0NTTQ19BA9/z5DNbW0jt3LsnKSlqXLcuYS8vy5VmPKWs55XBMPT09/mPKZzktWcJQTQ1dCxfSX19Pb2MjPfPmMVBXR+eiRSSqq+ned1+cCM0rV6b2Ed2m1b2pllPgMYnIlOpeTuU0wfnUtXDh6PlUNjg48fk0UvcKfD6NP6by8vKCnE8+JB+9HiLyF+fcYVPeUSBr1qxx69evz+k1b/zpG9nYsZHHL3q8QFa509nZyawiXcYyFyb0uuEGOPdcuPtuOPFEHU4xYk5hlJKTiDzknFuT9x0rZDLtMAABI9E6Fy1i1j/+4d+oiD32Ra3DWfKxbPxYPn5KJZ98t8Ui8phzbqWI/BC40Tn3KxH5q3Pu0IDXLgZud86tjO63O+dmj3u+zTlXLyK3A192zt0XPX4X8GnnnLehnVRbbHXFj+XjZw/Lx7LxE0c7nK8REO/I034KRll5GeWia8hLTU1N3AoZmdCrqQn23ReOP764QujMypzCMCcjV2p27IhbIQ1N9cWy8WP5+LF8Js3tIvIU8HJSl6ZvBCa7dPz2kakV0e1IoTwP7Dduu4XRY7FgdcWP5eNHUz6WjZ848gnqgBCRN0er9XaISKeIdIlI58jzzrnHfK/XwODQIGWia8ZJV1dX3AoZyejV3g533JEaARHD3CWNWZlTGOZk5ErXfvtl36iIaKovlo0fy8eP5TM5nHOXAscCa5xzQ0APqTUbJsOtwLui398F3DLu8XdGV8M4GugYN1Wj6Fhd8WP5+NGUj2XjJ458QifffRV4g3PuyULKFJKy8jJ1HRCzZ8+OWyEjGb1uvjl1Cc61a4utA+jMypzCMCcjV2Zv3Bi3Qhqa6otl48fy8WP5TImXAYtFZPxn5x/5XiAiTcCJwFwR2QpcBnwZuEFE3gNsBs6JNr8DeB2wEegF/imv9jlidcWP5eNHUz6WjZ848gn9j3z7ntz5ANA/2E95ma4pGCOLdWgjo1dTExxwABx5ZPGF0JmVOYVhTmGIyCtEZEb0+9tF5N9F5KVxe8VBy8EHx62Qhqb6Ytn4sXz8WD6TQ0R+DHwdOA44IvrJusaEc+4859y+zrlK59xC59zVzrkW59xJzrmlzrmTnXOt0bbOOfdB59wS59yqbGs/FBqrK34sHz+a8rFs/MSRT+gIiPUicj1wMzAw8qBz7n8LIVUIKiorKEvoGgExd+7cuBUyspvXjh1w113w6U8HLa5SFCcFmFMY5hTMfwGHisihwMeBH5L6du2VsVrFwNzHdM3q01RfLBs/lo8fy2fSrAFWOG3X8ysgVlf8WD5+NOVj2fiJI5/Q/8hnkRoO9hrgDdHP6wslVQj6+vvUTcEIuUxJHOzmdeONkEzGNv0CdGZlTmGYUzCJ6MPtG4H/dM5dBcyM2SkWRi7XpQVN9cWy8WP5+LF8Js1jwLy4JYqJ1RU/lo8fTflYNn7iyCcvl+EsNpO55NDJPzqZ/kQ/9737vgJZ7cUcfzy0tYGyHjvD0MZULv0mIvcCvyI17/cEUiuj/9U5tyqPinmjkJfhDGIP/NsVRD7ysWz8WD5+9oJ88nUZThG5DXCkOoNXA38ifSTwGVN9j6lSqMtwBrEX1JWMWD5+LJ+JsWxGmfJlOEWkWkQ+KCLfFZFrRn7yq1lYBgYH1I2AaG5ujlshI2leW7bAfffBeefFJ4TOrMwpDHMK5lxSH2zf45x7kdQl2L4Wr1I8NCv7dkBTfbFs/Fg+fiyfnPk68A3gcuBM4F+j+yM/ey1WV/xYPn405WPZ+Ikjn9A1IH4MPAWcCnwReBuwRy1KWVZehsS0fsFENDQ0xK2QkTSv669P3Z57bjwyERqzMqcwzCmY6cB/Oef6ovs7gd/F6BMbDY8/HrdCGprqi2Xjx/LxY/nkhnPuXgAR2R/Y5pzrj+5PB/aJ063QWF3xY/n40ZSPZeMnjnxChwQc6Jz7PNDjnLsOOB04qnBa+WcwMajuKhjt7e1xK2QkzaupCY44Ag48MDYf0JmVOYVhTsH8DEiOu5+MHis52mNub3ZFU32xbPxYPn4sn0nzM2B43P29vn22uuLH8vGjKR/Lxk8c+YR2QAxFt+0ishKoA15SGKUCIaibgjFzps715Ua9nnkGHn441sUnR9CYlTmFYU7BVDjnBkfuRL9Pi9EnNmZu2RK3Qhqa6otl48fy8WP5TJqSa5+trvixfPxoysey8RNHPqH/kX9fROqBzwO3Ak8AXy2YVQFIJBOUi64REL29vXErZGTUa9261GIqMU+/AJ1ZmVMY5hTMThEZXdBMRN4I6Jq4WCR6X6Krf1tTfbFs/Fg+fiyfSVNy7bPVFT+Wjx9N+Vg2fuLIJ2gNCOfcD6Nf7wUOKJxOAVE4AqKqqipuhYxUVVWlVl9taoITToAFC+JWUpmVOYVhTsG8H/ipiFxFatX1rcA741WKh6qOjrgV0tBUXywbP5aPH8tn0oxvnwG2AO+I0afgWF3xY/n40ZSPZeMnjnxCr4Kxj4hcLSK/jO6vEJH3FFYtvySGE+o6IBKJRNwKGUkkEvDoo/DUUyqmX4DOrMwpDHMKwzn3rHPuaGA5sMI5d6xzbmPcXnGQmD49boU0NNUXy8aP5ePH8pkcu7TPy6P2+dm4vQqJ1RU/lo8fTflYNn7iyCf0P/JrgTuB+dH9Z4CPFMCnYDjn1HVAaLsqxwgikhr9UF4OZ50Vtw6gMytzCsOcwhjp6AV+5pzr3hM7evOFJJPZNyoimuqLZePH8vFj+UwOEakTkX8H7gHuEZFviEhdzFoFxeqKH8vHj6Z8LBs/ceQT+h/5XOfcDUQrADvnEqSv1m5MgrIyXR0iI5SJpNZ/OOUUmDs3bh1AZ1bmFIY5BXMte3hHb74oU/Zthab6Ytn4sXz8WD6T5hqgCzgn+ukE/idWowJjdcWP5eNHUz6WjZ848gl9xx4RaSA1LxkRORrQNYElC845dT1gQ0ND2TeKgeQf/gCbN8N558WtMorGrMwpDHMKxjp6I4ZmzIhbIQ1N9cWy8WP5+LF8Js0S59xlzrnnop8vsKeuiRaI1RU/lo8fTflYNn7iyCdoEUrgY6SufrFERP4ANAI6xuaHoqvvAYDq6uq4FTJSffPNUFUFZ54Zt8ooGrMypzDMKZg9vqM3X1S3tsatkIam+mLZ+LF8/Fg+k6ZPRI5zzt0HICKvAPpidiooVlf8WD5+NOVj2fiJI5+gERDOuYeBVwLHAu8DDnbOPVpIsXwzPDyMKOuF6OnpiVthd5JJ5Gc/g9NPh1mz4rYZRWNW5hSGOQWza0fvj4APxasUDz377hu3Qhqa6otl48fy8WP5TJoPAFeJyCYR2Qz8J6nPw3stVlf8WD5+NOVj2fiJI5+gERAiUg68DlgcveY1IoJz7t8L6JZXtM3/AZil6B/8Ue65h7IdO1RNvwCdWZlTGOYUzBLgtcB+wFuAowgfpbZXMWvTprgV0tBUXywbP5aPH8tncjjnHgEOFZFZ0f3OeI0Kj9UVP5aPH035WDZ+4sgn9L/y24DzgQZg5rifPYZEMqFuDYi2tra4FXZn3TrcjBmpERCK0JiVOYVhTsF8PvpQWw+8Cvgu8F/xKsVD20EHxa2Qhqb6Ytn4sXz8WD6TQ0QaROQ7pK6CcbeIfDuaMrfXYnXFj+XjR1M+lo2fOPIJ/XZtoXPukIKaFJjy8nJ1UzAaGpT97RochJtuQt70JlB2jVp1WWFOoZhTMCMLTp4O/MA59wsRuTJOobhoePLJuBXS0FRfLBs/lo8fy2fSrAN+R2p0GsDbgOuBk2MzKjBWV/xYPn405VPK2cgX8vO/r7vM5WU/I4SOgPiliLwmr+9cZBLKLnkCsHPnzrgV0rnzTmhro+N1r4vbZDfUZYU5hWJOwTwvIv8NnAvcISJVhLfRexU7V62KWyENTfXFsvFj+fixfCbNvs65K5xzf49+rgT2iVuqkFhd8WP5+NGUj2XjZ1Vt8X1CP9w+APxcRPpEpFNEukRkj5r/Vl5Rrm4KRmNjY9wK6axbB3PmUPeWt2TftsioywpzCsWcgjkHuBM41TnXDswBPhmrUUw0btgQt0IamuqLZePH8vFj+UyaX4vIWhEpi35G2uu9FqsrfiwfP5rysWz8bOguvk9oB8S/A8cANc65Wc65mc45XSt6ZMFGQGShtxduuQXOOoudHfqu/KcqqwhzCsOcwnDO9Trn/tc597fo/jbn3K/j9ooDbd8OaKovlo0fy8eP5TNpLgB+CgxEP+uA9+2JX8iFYnXFj+XjR1M+lo0fzSMgtgCPOefyOwGkiJSVl6lbA0JVj9ztt0NPD5x3ni6vCHMKw5zC0OhkjKHt2wFN9cWy8WP5+LF8Jk0dqcXYr3DOVZK6KtzJe+IXcqFYXfFj+fjRlI9l40fzCIjngHtE5DMi8rGRn0KK5ZtkMqluCkZra2vcCmM0NcG++8Lxx+vyijCnMMwpDI1Oxhity5bFrZCGpvpi2fixfPxYPpPmKuBoYOQa5V3Af8anU3isrvixfPxoysey8bOspvg+oVfB+Hv0My362eMoK9O3lltdXV3cCina2+GOO+Cii6C8XI/XOMwpDHMKQ6OTMUbdc8/FrZCGpvpi2fixfPxYPpPmKOfc4SLyFwDnXJuI7JGfh0OxuuLH8vGjKR/Lxs9zfcX3Cfqv3Dn3hUw/hZbLJ8PDw+qmYHR3d8etkOLmm1OX4Fy7FlDkNQ5zCsOcwtDoZIzRvWBB3AppaKovlo0fy8eP5TNphkSkHHAAItIIDMerVFisrvixfPxoysey8bOgqvg++oYFFApdfQ8ATJ8+PW6FFE1NcMABcOSRgCKvcZhTGOYUhkYnY4zpzc1xK6Shqb5YNn4sHz+Wz6T5DvBz4CUi8iXgPuBf41UqLFZX/Fg+fjTlY9n4aR4qvk/JdEA459StATE4OBi3AuzYAXfdlRr9EOWjwmsXzCkMcwpDo5MxxuAsXWu6aaovlo0fy8eP5TM5nHM/BT4F/BuwDTjTOfezeK0Ki9UVP5aPH035WDZ+ZlUU3ydoDQgReYVz7g/ZHjNyo7y8PG4FuPFGSCZHp1+AEq9dMKcwzCkMjU7GGOUDA3ErpKGpvlg2fiwfP5bP5HHOPQU8FbdHsbC64sfy8aMpH8vGz8Bw8X1CR0D8R+BjanHOqVsDQgVNTXDwwaDsmrSGYRiGYRiGYRjG3oV3BISIHAMcCzTuctnNWYCu7qQsOPRNwUgmk/EKbNkC990HV1yR9nDsXhkwpzDMKQyNTsYYyaqquBXS0FRfLBs/lo8fy8cIxeqKH8vHj6Z8LBs/VWXF98k2AmIaUEuqo2LmuJ9O4KyQNxCR00TkaRHZKCKXerZ7i4g4EVkTpr7nM21azFdwuv761O246RegwCsD5hSGOYWh0WkqiEi5iPxFRG6P7u8vIg9G7e71I5eLE5Gq6P7G6PnF4/bxmejxp0Xk1JgOBYBpnZ1xvv1uaKovlo0fy8eP5WOEYnXFj+XjR1M+lo2fzkTxfbwdEM65e6PLbR497tKbVwA/dM79LdvOo0sWXQW8FlgBnCciKzJsNxO4BHhwEscQhMYpGH19ffEKNDXBEUfAgQemPRy7VwbMKQxzCkOj0xS5BHhy3P2vAN90zh0ItAHviR5/D9AWPf7NaDuidnktcDBwGvDdqP2Ohb65c+N664xoqi+WjR/Lx4/lY4RidcWP5eNHUz6WjZ+5lcX3CV0D4t9EZJaIzAAeA54QkU8GvO5IYKNz7jnn3CCwDnhjhu2uIPVBuD/QJ2e0Tb8AqK2tje/Nn3kGHn54t9EPELPXBJhTGOYUhkanySIiC4HTgR9G9wV4NXBjtMl1wJnR72+M7hM9f1K0/RuBdc65Aefc34GNpNrvWKh9/vm43jojmuqLZePH8vFj+RihWF3xY/n40ZSPZePn+YHi+4R2QKxwznWS+hD7S2B/4B0Br1sAbBl3f2v02Cgicjiwn3PuF4EukyI5nFTXCdHR0RHfm69bl7rs5rnn7vZUrF4TYE5hmFMYGp2mwLdIXR5uOLrfALQ75xLR/fHt7mibHD3fEW2fta0uJh0HHBDXW2dEU32xbPxYPn4sHyMUqyt+LB8/mvKxbPwcML34PqEdEJUiUkmqA+JW59wQ4Kb65iJSBvw78PGAbS8UkfUisn7Hjh309/fT29tLT08PAwMDdHZ2kkgkaGtrwzlHc3MzADt37gTGpmC0tbWRSCTo7OxkYGCAnp4eent76e/vp6uri6GhIdrb2xkeHqalpSVtHyO3ra2tJJNJOjo6GBwcpLu7m76+Pvr6+uju7mZwcJCOjg6SySStra0Z99HS0sLs2bNpb29naGiIrq6unI+pubkZ51zux+QciZ/8BE44gZ3RvKjxx1RbWzvpYxoeHi7IMVVVVcVWThMdU1VVVWHLaRLHVFtbG2s5ZTqmsrKyWMsp0zHV19cXpJyKjYi8HtjhnHuoSO83pXa4ubkZJ0Lb0qUkqqvpXLSIgbo6eubNo7exkf76eroWLmTmli20L1nCcHk5LcuXp/YRXaln5LaY9WbWrFnFO78rK+lYvJjB2lq658+nr6GBvoYGuufPZ7C2lvKBAZKVlbQuW5Yxl5bly/fe83vJEoZqauhauJD++np6GxvpmTePgbo6OhctIlFdjQwP40RoXrkytY/otnnlyrG6V8TPACMUpR2e4HwaqqkZPZ9cWVnm82nZsrG6V6R22DnH8PBwwc4nY2rMefrpuBXSmDNnTtwKaVg+fjTlY9n4ebq3+D7iXPZ+BBH5MPBp4K+khvouAn7inDs+y+uOAS53zp0a3f8MgHPu36L7dcCzQHf0knlAK3CGc279RPtds2aNW79+wqczsvibiznupcfxkzf/JKfXFZKdO3fS2NhY/Df+619h9Wr4r/+C979fj5cHcwrDnMIolJOIPOScK9pCuiLyb6RGoyWAalJXKPo5cCowzzmXGN8Oi8id0e/3i0gF8CLQCFwKaW3z6HYTvfdk2uFIOusmO1etonHDBv9GAX+78kVR63CWfCwbP5aPn1LJp9htca6IyDLg+nEPHQD8CzAbuAAY6UX5rHPuDt++JtUWW13xY/n42cPyKeVs5AvZfVbVrmJDt9/HXZa7j68dDhoB4Zz7jnNugXPudS7FZuBVAS/9M7A0WpF9GqlFzm4dt98O59xc59xi59xi4AGydD5MlvLycnVTMGL7p6ypCcrL4azMFzLR9s8imFMo5hSGRqfJ4Jz7jHNuYdR+rgV+65x7G3A3Y1cqehdwS/T7rdF9oud/61K90LcCa6OrZOwPLAX+VKTD2I2sf5iLjKb6Ytn4sXz8WD46cM497Zxb7ZxbDbwc6CXVeQypBYRXRz/ezodCYnXFj+XjR1M+lo2fbJ0PhSCoA0JE9hGRq0Xkl9H9FYx9iJ2QaI7xxcCdpFZov8E597iIfFFEzpiCd84kkonsGxWZWIYJOpda/+GUU2CCVVg1Dl80pzDMKQyNTnnm08DHRGQjqTUero4evxpoiB7/GGMjHx4HbgCeAH4FfNA5F9uFs0eGhWtBU32xbPxYPn4sH5WcBDwbfbmnBqsrfiwfP5rysWz8rKotvk/oFIxfAv8D/LNz7tBo6O5fnHOxJDiZ4WYHfPsAjlt0HD96048KZLWHcP/9cOyxcN118M53xm1jGHsV2of95pNCTsEIoohDW4tKPvKxbPxYPn72gnz2pLZYRK4BHnbO/aeIXA6cD3QC64GPO+fafK8v1BSMIPaCupIRy8eP5TMxyrIJmYIRQixTMIC5zrkbiFZZj0Y2xPbt2GQYvxiUFkYWTioq69ZBVRWceeaEm8TilQVzCsOcwtDoZIwxsvCkFjTVF8vGj+Xjx/LRRTQ9+QzgZ9FD/wUsAVYD24BvTPC6qS0IPH5R1gkWBN5ywglpC5jGvSDwP/7xj+It+D1+UdYJFgTe/KpXqVoQePPmzaoWBN50yilqFgTeuXNn8RZm9yywPXI+7Tz44InPp5G6l6fzqaashoVVC6mvqKexspF50+ZRV1HHoupFVJdVs7RmKStmrGBlbap8xt8KwtKapVSXVed9YfbQERD3AG8BfuOcO1xEjga+4px7ZdYXF4BJLUL5rcW8cvErue7M6wpklTvDw8OUlYX2AeWBZBIWLkyNgLjpJj1eAZhTGOYURqGc9qRv3aZKIUdADJeXU5at07iI35wUtQ5nycey8WP5+CmVfPaUtlhE3khqyttrMjy3GLjdObfSt49CjYAolbqSEcvHzx6WTylnEzICopxyklnGFcQ1AuJjpBYpWyIifwB+BHwoZ5MYGR4eRtC1CGVnZ2dx3/Cee+DFF+G887ybFd0rAHMKw5zC0OhkjNG5eHHcCmloqi+WjR/Lx4/lo47zgKaROyKy77jn3gQ8VnSjCKsrfiwfP5rysWz8LJ6+uOjvWRGykXPuYRF5JbAMEOBp59xQQc3yjLYrYADMmDGjuG+4bh3U1sLpp3s3K7pXAOYUhjmFodHJGGPGtm1xK6Shqb5YNn4sHz+Wjx5EZAZwCvC+cQ9/VURWAw7YtMtzRcXqih/Lx4+mfCwbP9sGiu8TehWMauDDwBXAF4APRo/tMTjn1HVC9Pf3F+/NBgdT0y7OPBOmT/duWlSvQMwpDHMKQ6OTMUb/nDlxK6Shqb5YNn4sHz+Wjx6ccz3OuQbnXMe4x97hnFvlnDvEOXeGcy62/1SsrvixfPxoysey8TOnsvg+QSMgSE256AL+I7r/VuDHwNmFkCoEDn0rrVZWVhbvze68E9rask6/gCJ7BWJOYZhTGBqdjDEqe3riVkhDU32xbPxYPn4sHyMUqyt+LB8/mvKxbPz0JIvvE9oBsdI5t2Lc/btF5IlCCBUKh1O3BsTw8HDx3mzdOpgzB04+OeumRfUKxJzCMKcwNDoZYwxXhP5pKg6a6otl48fy8WP5GKFYXfFj+fjRlI9l46dCiu8Tugjlw9GVLwAQkaNIXZ94j0JbB0TIFUjyQm8v3HILnHUWTJuWdfOieeWAOYVhTmFodDLGcOXlcSukoam+WDZ+LB8/lo8RitUVP5aPH035WDZ+yqX4Pt4uDxHZQGohnErgjyLyj+j+S4GnCq+3d1NRrB6w22+Hnh5YuzZo86J55YA5hWFOYWh0Msao6OuLWyENTfXFsvFj+fixfIxQrK74sXz8aMrHsvHTN1x8n2wjIF4PvAE4DdgfeCVwYvT7awtqlmc0LkI5MDBQnDdqaoJ994UTTgjavGheOWBOYZhTGBqdjDEG6uriVkhDU32xbPxYPn4sHyMUqyt+LB8/mvKxbPzUVRTfx9sl5JzbXCyRUqSmpqbwb9LeDnfcARddBIFDforilSPmFIY5haHRyRijZseOuBXS0FRfLBs/lo8fy8cIxeqKH8vHj6Z8LBs/OwaL7xO6BsQej3P6FqHs6uoq/JvcfHPqEpyB0y+gSF45Yk5hmFMYGp2MMbr22y9uhTQ01RfLxo/l48fyMUKxuuLH8vGjKR/Lxs9+1cX3KZkOCGV9DwDMnj278G/S1AQHHABHHhn8kqJ45Yg5hWFOYWh0MsaYvXFj3AppaKovlo0fy8eP5WOEYnXFj+XjR1M+lo2fjb3F9ymZDojkcFLdGhAtLS2FfYMdO+Cuu1KjH3I49oJ7TQJzCsOcwtDoZIzRcvDBcSukoam+WDZ+LB8/lo8RitUVP5aPH035WDZ+Dq4tvk/JdECUlZWpm4Ixd+7cwr7BjTdCMpnT9AsogtckMKcwzCkMjU7GGHMfeyxuhTQ01RfLxo/l48fyMUKxuuLH8vGjKR/Lxs9j3cX3KZkOiGQyGbfCbuzcubOwb9DUBAcfDKtW5fSygntNAnMKw5zC0OhkjLFz5cq4FdLQVF8sGz+Wjx/LxwjF6oofy8ePpnwsGz8ra4vvUzIdEGVlZeqmYDQ2NhZu51u2wH335Tz6AQrsNUnMKQxzCkOjkzFGo7JvBzTVF8vGj+Xjx/IxQrG64sfy8aMpH8vGj42AKCDDw8NxK+xGc3Nz4XZ+/fWp20l0QBTUa5KYUxjmFIZGJ2OMZmXfDmiqL5aNH8vHj+VjhGJ1xY/l40dTPpaNHxsBUUgEdWtANDQ0FG7n69bBEUfAgQfm/NKCek0ScwrDnMLQ6GSM0fD443ErpKGpvlg2fiwfP5aPEYrVFT+Wjx9N+Vg2fh7vLr5PyXRAOOfUTcFob28vzI7/9jd46KFJjX6AAnpNAXMKw5zC0OhkjNE+iY7TQqKpvlg2fiwfP5aPEYrVFT+Wjx9N+Vg2fg6sKb5PyXRAKBv8AMDMmTMLs+OmptRlN889d1IvL5jXFDCnMMwpDI1Oxhgzt2yJWyENTfXFsvFj+fixfIxQrK74sXz8aMrHsvGzpb/4PiXTAeGcUzcFo7e3N/87dS7VAXH88bBgwaR2URCvKWJOYZhTGBqdjDF6X/KSuBXS0FRfLBs/lo8fy8cIxeqKH8vHj6Z8LBs/L5lWfJ+S6YDQSFVVVf53+uij8NRTcN55k95FQbymiDmFYU5haHQyxqjq6IhbIQ1N9cWy8WP5+LF8jFCsrvixfPxoysey8dORKL5PyXRAOPStAZFIJPK/06YmKC+Hs86a9C4K4jVFzCkMcwpDo5MxRmL69LgV0tBUXywbP5aPH8vHCMXqih/Lx4+mfCwbP9PLiu9TMh0QuLgFdifvHSLOpa5+ccopMHfupHejraMGzCkUcwpDo5MxhiSTcSukoam+WDZ+LB8/lo8RitUVP5aPH035WDZ+kq74PiXTAeHQtwZEWVme43/gAdi8eUrTL6AAXnnAnMIwpzA0OhljlCn7tkJTfbFs/Fg+fiwfIxSrK34sHz+a8rFs/CRc8X10lUgB0XgZzqGhofzucN06qKqCM8+c0m7y7pUHzCkMcwpDo5MxxtCMGXErpKGpvlg2fiwfP5aPEYrVFT+Wjx9N+Vg2fmaUF9+nZDoglA1+AKC6ujp/O0sm4YYb4PTTYdasKe0qr155wpzCMKcwNDoZY1S3tsatkIam+mLZ+LF8/Fg+RihWV/xYPn405WPZ+GkdKr5PyXRAaLwMZ09PT/52ds898OKLU55+AXn2yhPmFIY5haHRyRijZ99941ZIQ1N9sWz8WD5+LB8jFKsrfiwfP5rysWz87FtVfJ+S6YDQyKwpjlRIY906qK1NjYCYInn1yhPmFIY5haHRyRhj1qZNcSukoam+WDZ+LB8/lo8RitUVP5aPH035WDZ+NvVtKvp7lkwHxLAbVrcGRFtbW352NDgIN92UWvshD5d2yZtXHjGnMMwpDI1OxhhtBx0Ut0IamuqLZePH8vFj+RihWF3xY/n40ZSPZePnoBnF9ymZDghA3RSMhoaG/Ozo17+Gtra8TL+APHrlEXMKw5zC0OhkjNHw5JNxK6Shqb5YNn4sHz+WjxGK1RU/lo8fTflYNn6e7Cm+T8l0QGi8CsbOnTvzs6OmJpgzB04+OS+7y5tXHjGnMMwpDI1Oxhg7V62KWyENTfXFsvFj+fixfIxQrK74sXz8aMrHsvGzqrb4PiXTAaFs8AMAjY2NU99Jby/ccgucdRZMmzb1/ZEnrzxjTmGYUxganYwxGjdsiFshDU31xbLxY/n4sXyMUKyu+LF8/GjKx7Lxs6G7+D4l0wExPDysbgpGXnrkbr8denpg7dqp7ytCW08hmFMo5hSGRidjDG3fDmiqL5aNH8vHj+VjhGJ1xY/l40dTPpaNHxsBUUgEdVMw8tIj19QE++4LJ5ww9X1FaOspBHMKxZzC0OhkjKHt2wFN9cWy8WP5+LF8jFCsrvixfPxoysey8WMjIAqIc07dCIjW1tap7aC9He64A849F8rL8+IEefAqAOYUhjmFodHJGKN12bK4FdLQVF8sGz+Wjx/LxwjF6oofy8ePpnwsGz/LaorvUzodEOhbhLKurm5qO7j55tQlOPM4/QLy4FUAzCkMcwpDo5MxRt1zz8WtkIam+mLZ+LF8/Fg+RihWV/xYPn405WPZ+Hmur/g+pdMBoXAERHd399R20NQE++8PRx6ZH6GIKXsVAHMKw5zC0OhkjNG9YEHcCmloqi+WjR/Lx4/lY4RidcWP5eNHUz6WjZ8FVcX3KZ0OCIUjIKZPnz75F+/YAXfdlRr9kOfjmpJXgTCnMMwpDI1OxhjTm5vjVkhDU32xbPxYPn4sHyMUqyt+LB8/mvKxbPw0DxXfp3Q6IBSOgBgcHJz8i2+8EZJJOO+8/AlFTMmrQJhTGOYUhkYnY4zBWbPiVkhDU32xbPxYPn4sHz2IyCYR2SAij4jI+uixOSLyGxH5W3RbH5ef1RU/lo8fTflYNn5mVRTfp3Q6IBSOgCifysKRTU1w8MFQgEu5TMmrQJhTGOYUhkYnY4zygYG4FdLQVF8sGz+Wjx/LRx2vcs6tds6tie5fCtzlnFsK3BXdjwWrK34sHz+a8rFs/AwMF9+ndDogFI6AmDRbtsB99+V98UnDMAzDMAwjNt4IXBf9fh1wZnwqhmEYhaF0OiAUjoBIJpOTe+H116duC9QBMWmvAmJOYZhTGBqdjDGSVVVxK6Shqb5YNn4sHz+Wjyoc8GsReUhELowe28c5ty36/UVgn3jUrK5kw/Lxoykfy8ZPVVnxfUqnA0LhCIhp06bl/qIXX4Svfx2OOQYOPDD/UkzSq8CYUxjmFIZGJ2OMaZ2dcSukoam+WDZ+LB8/lo8qjnPOHQ68FvigiJww/knnnCPVSbEbInKhiKwXkfU7duygv7+f3t5eenp6GBgYoLOzk0QiQVtbG845mqNF73bu3AlA88qVOBHali4lUV1N56JFDNTV0TNvHr2NjfTX1zNYW8tQTQ3tS5YwXF5Oy/LlqX1EU39HbltbW0kmk3R0dDA4OEh3dzd9fX309fXR3d3N4OAgHR0dJJNJWltb0zxGbltaWhgeHqa9vZ2hoSG6urp2O6aBgQH/MTU345yjra2NRCJBZ2cnAwMD9PT00NvbS39/P11dXQwNDdHe3s7w8DAtLS0ZfVqXLSNZWUnH4sUM1tbSPX8+fQ0N9DU00D1/PoO1tfTPnk2yspLWZcsy5tKyfHnWY8paTjkcU19fn/+Y8llOS5YwVFND18KF9NfX09vYSM+8eQzU1dG5aBGJ6mp6587FidC8cmVqH9FtWt2bajkFHlN5efmU6l5O5TTB+dS1cOHo+VTR0zPx+TRS9/J0PtWU1bCwaiH1FfU0VjYyb9o86irqWFS9iOqyapbWLKUr0cXK2lT5jL8VhKU1S6kuq55UOfmQVPu2Z7FmzRq3fv36nF4jXxA+f8Ln+eKrvlggq9zp6OjI7dq0ySSccgo88AD86U8QncyxexUBcwrDnMIolJOIPDRuLu9ezWTaYSDoqj0dixdTt2mTf6Mi/u0qah3Oko9l48fy8VMq+expbbGIXA50AxcAJzrntonIvsA9zrllvtdOqi22uuLH8vGzh+VTytnIF7L7LK5ezKZ+v4+7LHcfXztcMiMgAHUjIGpra3N7wRe+AHffDd/9bsE6H2ASXkXAnMIwpzA0Ohlj1D7/fNwKaWiqL5aNH8vHj+WjAxGZISIzR34HXgM8BtwKvCva7F3ALfEYWl3JhuXjR1M+lo2f5weK71MSHRAjozy0rQHR0dERvvGvfw1XXgnnn5/6KSA5eRUJcwrDnMLQ6GSM0XHAAXErpKGpvlg2fiwfP5aPGvYB7hORvwJ/An7hnPsV8GXgFBH5G3BydD8WrK74sXz8aMrHsvFzwPTi+1QU/R1jwEVT6LSNgJgzZ07Yhs8/D29/O6xYAVddVVgpcvAqIuYUhjmFodHJGGPO00/HrZCGpvpi2fixfPxYPjpwzj0HHJrh8RbgpOIb7Y7VFT+Wjx9N+Vg2fp7uLb6PjYCIkZBFOkgk4LzzoLcXfvYzqKnR4VVkzCkMcwpDo5MxxshiTFrQVF8sGz+Wjx/LxwjF6oofy8ePpnwsGz+raovvYyMgYqSxsTH7Rp//PPz+9/CTn0C0YmqhCfIqMuYUhjmFodHJGKNxw4a4FdLQVF8sGz+Wjx/LxwjF6oofy8ePpnwsGz8buovvUxIjIEbY40ZA3HEHfPnLcMEF8La3FUcKfT2FYE6hmFMYGp2MMbR9O6Cpvlg2fiwfP5aPEYrVFT+Wjx9N+Vg2fuIYAVESHRBaLzXq7ZHbsgXe8Q449FD49reLJ4W+nkIwp1DMKQyNTsYY2r4d0FRfLBs/lo8fy8cIxeqKH8vHj6Z8LBs/NgKiQGidgtHS0pL5iaEhOPdcGByEG26A6dN1eMWIOYVhTmFodDLGaCnSdLNQNNUXy8aP5ePH8jFCsbrix/Lxoykfy8bP8hnF9ymNNSCULkJZX1+f+YnPfhbuvx/WrYODDiquFB6vGDGnMMwpDI1Oxhj1zzwTt0IamuqLZePH8vFj+RihWF3xY/n40ZSPZePnmZ7i+xR8BISInCYiT4vIRhG5NMPzHxORJ0TkURG5S0Remm8HrSMgOjs7d3/wttvg61+HD3wgNQoiBjJ6xYw5hWFOYWh0MsboXLw4boU0NNUXy8aP5ePH8jFCsbrix/Lxoykfy8bP4umLi/6eBe2AEJFy4CrgtcAK4DwRWbHLZn8B1jjnDgFuBL6abw+tIyBmzJiR/sDmzfCud8Fhh8G//3s8UmTwUoA5hWFOYWh0MsaYsW1b3AppaKovlo0fy8eP5WOEYnXFj+XjR1M+lo2fbQPF9yn0CIgjgY3Oueecc4PAOuCN4zdwzt3tnOuN7j4ALMy3hNYREP39/WN3BgfhnHMgmYSf/Qyqq3V4KcGcwjCnMDQ6GWP0z5kTt0IamuqLZePH8vFj+RihWF3xY/n40ZSPZeNnTmXxfQrdAbEA2DLu/tbosYl4D/DLfEtoHQFRWVk5dudTn4I//QmuuQaWLIlPil28lGBOYZhTGBqdjDEqe3riVkhDU32xbPxYPn4sHyMUqyt+LB8/mvKxbPz0JIvvo+YqGCLydmAN8LUJnr9QRNaLyPodO3bQ399Pb28vPT09DAwM0NnZSSKRoK2tDecczc3NQOraryMjIADa2tpIJBJ0dnYyMDBAT08Pvb299Pf309XVxdDQEO3t7QwPD4+umjpy/diR29bWVpLJJB0dHQwODtLd3U1fXx99fX10d3czODhIR0cHyWSS1tbWjPtoaWkhkUjQ3t5O4oYb4NvfJnHRRfS+9rVBxwTQ3NyMcy7vxzQwMDDpYxoeHqa9vZ2hoSG6urpyKiffMXV3d8dWThMdU3d3d6zllOmYBgYGYi2nOM+nXI4pmUwW5JiM/DBcoWt95OHh4bgVRrFs/Fg+fiwfIxSrK34sHz+a8rFs/FRI8X1kZHRAQXYucgxwuXPu1Oj+ZwCcc/+2y3YnA/8BvNI5tyPbftesWePWr18f7NE10MWsL8/ia6d8jU8c+4lcDqGg9PT0MGP7djj88NTVLu67D6ZNi1sr5aVsvpQ5hWFOYRTKSUQecs6tyfuOJ36//YAfAfsADvi+c+7bIjIHuB5YDGwCznHOtUlqGNi3gdcBvcD5zrmHo329C/hctOsrnXPX+d4713Z4nHTWTXrmzWPGiy/6Nyrg365dKWodzpKPZePH8vFTKvkUuy2Ok0m1xVZX/Fg+fvawfEo5G/lCdp950+bx4qDfx12Wu4+vHS70CIg/A0tFZH8RmQasBW7dRe4w4L+BM0I6HyaD1jUgKpLJ1LoPInDDDSo6HwAqlPXMgTmFYk5haHSaJAng4865FcDRwAejhX4vBe5yzi0F7oruQ2pB4KXRz4XAfwFEHRaXAUeRWrvnMhGJ7bpVFX19cb11RjTVF8vGj+Xjx/IxQrG64sfy8aMpH8vGT99w8X0K2gHhnEsAFwN3Ak8CNzjnHheRL4rIGdFmXwNqgZ+JyCMicusEu5uKB6BvDQj5xCfgoYfg2mtB0SVZBgYG4lbYDXMKw5zC0Og0GZxz20ZGMDjnuki1swtILfY7MoLhOuDM6Pc3Aj9yKR4AZovIvsCpwG+cc63OuTbgN8BpxTuSdAbq6uJ664xoqi+WjR/Lx4/lY4RidcWP5eNHUz6WjZ+6iuL7FHwNCOfcHc65g5xzS5xzX4oe+xfn3K3R7yc75/Zxzq2Ofs7w73ESDhpHQNxwA9N+8AP42MfgjW/Mvn0RqampiVthN8wpDHMKQ6PTVBGRxcBhwIPAPs65kesqvUhqigZMvDBw0ILBU1mLB6J1NkRoW7qURHU1nYsWMVBXR8+8efQ2NtJfX0/XwoVUdnXRvmQJw+XltCxfntrHqlVpt8VcO2TatGnFWw+lspKOxYsZrK2le/58+hoa6GtooHv+fAZra0lUV5OsrKR12bKMubQsX17UNV6GhoaKt8bLkiUM1dTQtXAh/fX19DY20jNvHgN1dXQuWkSiuprBmTNxIjSvXJnaR3TbvHLlWN0r4ro1Ix98i7IWzwTn01BNzej51F9fn/l8WrZsrO4VaS0e5xx90TeBhTifjKlRs6Mgg5Injba/25aPH035WDZ+dgwW36ega0AUilznu7X1tTHnq3P45qnf5CNHf6RwYqH87W/w8peTWLaMij/+EZStztrW1kZ9fWyjrzNiTmGYUxiFcopr3rGI1AL3Al9yzv2viLQ752aPe77NOVcvIrcDX3bO3Rc9fhfwaeBEoNo5d2X0+OeBPufc1yd6z0KuAdG2dCn1f/ubf6Mi/u0qah3Oko9l48fy8VMq+dgaEFmwuuLH8vGzh+VTytmErAGxtGYpf+v1++xpa0CoQNUIiP7+1LoPlZWU33ijus4HgNmzZ8etsBvmFIY5haHRabKISCVwE/BT59z/Rg9vj6ZWEN2OdG8/D+w37uULo8cmejwWZm/cGNdbZ0RTfbFs/Fg+fiwfIxSrK34sHz+a8rFs/GzsLb5PaXRAaFoD4iMfgUcegR//mBZlVwYYYWQ4oybMKQxzCkOj02SIrmpxNfCkc+7fxz11K/Cu6Pd3AbeMe/ydkuJooCOaqnEn8BoRqY8Wn3xN9FgstBx8cFxvnRFN9cWy8WP5+LF8jFCsrvixfPxoysey8XNwbfF9dC0LWiDUjID4f/8P/vu/4dOfhte9jrnx2kzI3Ln6zMwpDHMKQ6PTJHkF8A5gg4g8Ej32WeDLwA0i8h5gM3BO9NwdpC7BuZHUZTj/CcA51yoiV5C6chHAF51zrUU5ggzMfeyxuN46I5rqi2Xjx/LxY/kYoVhd8WP5+NGUj2Xj57Hu4vvYCIhi8dRTcOGFcNxxcOWVgN6FkjR6mVMY5hSGRqfJ4Jy7zzknzrlDxi3ke4dzrsU5d5Jzbmm00G9rtL1zzn0wWhR4lXNu/bh9XeOcOzD6+Z/4jmps4UAtaKovlo0fy8eP5WOEYnXFj+XjR1M+lo2flbXF9ymJDogRYhsB0dsLZ58N06fDunUQXY+2sbExHp8saPQypzDMKQyNTsYYjcq+HdBUXywbP5aPH8vHCMXqih/Lx4+mfCwbP3GMgCipKRix8aEPweOPwy9/CQvGrmzX3NysblgQ6PQypzDMKQyNTsYYzStXqhqiqKm+WDZ+LB8/lo8RitUVP5aPn2LlE3KVh5W1K7P+kz2ZqzxMFm11JySffFMSIyBinYLxox/BNdfAZz8Lp56a9lRDQ0PxfQLQ6GVOYZhTGBqdjDEaHn88boU0NNUXy8aP5ePH8jFCsbrix/Lxoymfx7v1uICubCCefEqjAyKuRSifeAI+8AF45Svh8st3e7q9vb24PoFo9DKnMMwpDI1OxhjtBx4Yt0IamuqLZePH8vFj+RihWF3xY/n40ZTPgTV6XEBXNhBPPqXRARHHCIientS6D7W10NQ0uu7DeGbOnFk8nxzQ6GVOYZhTGBqdjDFmbtkSt0IamuqLZePH8vFj+RihWF3xY/n40ZTPln49LqArG4gnn9LogCj2CAjn4KKL4MknU5fe3HffjJv19vYWxydHNHqZUxjmFIZGJ2OM3pe8JG6FNDTVF8vGj+Xjx/IxQrG64sfy8aMpn5dM0+MCurKBePIpjQ6IYo+A+J//Sa39cNllcNJJE25WVVVVHJ8c0ehlTmGYUxganYwxqjo64lZIQ1N9sWz8WD5+LB8jFKsrfiwfP5ry6UjocQFd2UA8+ZRGB0QxR0A8+ih88INw8snwuc95N00kEoX3mQQavcwpDHMKQ6OTMUZi+vS4FdLQVF8sGz+Wjx/LxwjF6oofy8ePpnyml+lxAV3ZQDz5lEYHRLFGQHR1pdZ9mD0bfvITKC/3bh7LVTkC0OhlTmGYUxganYwxJJmMWyENTfXFsvFj+fixfIxQrK74sXz8aMon6fS4gK5sIJ58dl8ZcS+kKCMgnIP3vQ82boTf/hb22SfrS8rKdPb/aPQypzDMKQyNTsYYZcq+ydFUXywbP5aPH8vHCMXqih/Lx4+mfBJOjwvoygbiyUdXbS0QRRkB8f3vp6528cUvpi67GcDQ0FDhfKaARi9zCsOcwtDoZIwxNGNG3AppaKovlo0fy8eP5WOEYnXFj+XjR1M+M8r1uICubCCefEqjA6LQIyD+8he45BI49VT4zGeCX1ZdXV0Ynymi0cucwjCnMDQ6GWNUt7bGrZCGpvpi2fixfPxYPkYoVlf8WD5+NOXTOqTHBXRlA/HkUxodEIUcAdHZCeecA3PnptZ9yGEIVE9PT/598oBGL3MKw5zC0OhkjNEzwaWL40JTfbFs/Fg+fiwfIxSrK34sHz+a8tm3So8L6MoG4snH1oCY0o4dvPe98Pe/w733pjohcmDWrFn59ckTGr3MKQxzCkOjkzHGrE2b4lZIQ1N9sWz8WD5+LB8jFKsrfiwfP5ry2dS3KW6FNDRlA/HkYyMgpsJ3vws/+xn867/CK16R88vb2try65MnNHqZUxjmFIZGJ2OMtoMOilshDU31xbLxY/n4sXyMUKyu+LF8/GjK56AZelxAVzYQTz6l0QFRiBEQ69fDxz4Gp58On/jEpHbR0NCQP588otHLnMIwpzA0OhljNDz5ZNwKaWiqL5aNH8vHj+WjAxHZT0TuFpEnRORxEbkkevxyEXleRB6Jfl4Xl6PVFT+Wjx9N+TzZo8cFdGUD8eRTGh0Q+R4B0d6eWvdhn33guutyWvdhPDt37syPT57R6GVOYZhTGBqdjDF2rloVt0IamuqLZePH8vFj+aghAXzcObcCOBr4oIisiJ77pnNudfRzR1yCVlf8WD5+NOWzqlaPC+jKBuLJx9aAyHlnDt79btiyBX7/e5hCj2NjY+PUfQqARi9zCsOcwtDoZIzRuGFD3AppaKovlo0fy8eP5aMD59w2YFv0e5eIPAksiNcqHasrfiwfP5ry2dCtxwV0ZQPx5GMjIHLl29+Gn/8cvvIVOProKe1KW2/lCBq9zCkMcwpDo5MxhrZvBzTVF8vGj+Xjx/LRh4gsBg4DHoweulhEHhWRa0SkPi4vqyt+LB8/mvKxERB+4sinJDogRpjyCIgHH4RPfhLe+Eb46Een7KOtt3IEjV7mFIY5haHRyRhD27cDmuqLZePH8vFj+ehCRGqBm4CPOOc6gf8ClgCrSY2Q+MYEr7tQRNaLyPodO3bQ399Pb28vPT09DAwM0NnZSSKRoK2tDecczc3NwNg/qc0rV+JEaFu6lER1NZ2LFjFQV0fPvHn0NjbSX19PdVsbQzU1tC9ZwnB5OS3Ll6f2Ef3zNHLb2tpKMpmko6ODwcFBuru76evro6+vj+7ubgYHB+no6CCZTNLa2prmMXLb0tLC8PAw7e3tDA0N0dXVtdsxVVVV+Y+puRnnHG1tbSQSCTo7OxkYGKCnp4fe3l76+/vp6upiaGiI9vZ2hoeHaWlpyejTumwZycpKOhYvZrC2lu758+lraKCvoYHu+fMZrK1lWlcXycpKWpcty5hLy/LlWY8paznlcEyVlZX+Y8pnOS1ZwlBNDV0LF9JfX09vYyM98+YxUFdH56JFJKqrqejvx4nQvHJlah/RbVrdm2o5tbZSKZUsrl5MbXkt86vm01DZQENlA/Or5lNbXsvi6sU81fMUy2pS5TTyz/bI7fIZyymnPH/lNMH51LVw4ej51PDEExOfTyN1L0/nU01ZDQurFlJfUU9jZSPzps2jrqKORdWLqC6rZmnNUh7rfoyVtanyGX8rCEtrllJdVj2pcvIhI6MD9iTWrFnj1q9fH7z9U81Psfyq5fy/N/8/zlt13uTetLUVDjsstd7Dww9D/dQ7pVtbW5kzZ86U95NvNHqZUxjmFEahnETkIefcmrzvWCG5tsOjBIxEa122jDlPP+3fqIh/u4pah7PkY9n4sXz8lEo+e0JbLCKVwO3Anc65f8/w/GLgdufcSt9+JtUWW13xY/n4UZSPfCG7y7KaZTzd63dxl+WprBRlA/Hm42uHS2MNiKlOwXAOzj8ftm2DP/whL50PAHV1dXnZT77R6GVOYZhTGBqdjDHqnnsuboU0NNUXy8aP5ePH8tGBpD6QXg08Ob7zQUT2jdaHAHgT8FgcfmB1JRuWjx9N+TzXp8cFdGUD8eRTElMwprwI5Te+Abfdlro94oi8eXV3d+dtX/lEo5c5hWFOYWh0MsboXqBqLTZV9cWy8WP5+LF81PAK4B3Aq3e55OZXRWSDiDwKvAqY+nzfSWJ1xY/l40dTPguq9LiArmwgnnxsBEQ2/vAHuPRSOOssuPjivHpNnz49r/vLFxq9zCkMcwpDo5MxxvRofqUWNNUXy8aP5ePH8tGBc+4+yPitWGyX3dwVqyt+LB8/mvJpHtLjArqygXjysREQGV/gUgtOfupTcOaZsHgx/PCHQfN6cmFwcDCv+8sXGr3MKQxzCkOjkzHG4KxZcSukoam+WDZ+LB8/lo8RitUVP5aPH035zKrQ4wK6soF48rERELsyPAwnnJAa+VBRASedlJp6UYC5VeXl5XnfZz7Q6LU3OA0NDbF161b6+/sLZJSq63m53Gwe2RudqqurWbhwIZWVlXm0MkYoHxiIWyENTe2PZePH8vFj+RihWF3xY/n40ZTPwLAeF9CVDcSTT2l0QOQyAuKOO1KdD5dfDh/+cN4WnDSMrVu3MnPmTBYvXlywf8iHh4cpK9M1sGlvc3LO0dLSwtatW9l///3zbGYYhmEYhmEYey+6/isoEMEjIJyDr38dFiyAz3624J0PyWSyoPufLBq99gan/v5+GhoaCjoaQONldfc2JxGhoaGhoCNZSp1kVVXcCmloan8sGz+Wjx/LxwjF6oofy8ePpnyqyvS4gK5sIJ58bATEeH70I7j3XvjP/4QiDK2eNm1awd9jMmj02lucCj0VQdtUB9g7nTQe097EtM7OuBXS0NT+WDZ+LB8/lo8RitUVP5aPH035dCb0uICubCCefGwExAgDA/CZz8Axx8AHPlAUr76+vqK8T65o9DKnMIaHh73Pl5eXs3r1alauXMkb3vAG2tvb8+5w4oknsn79+mCnXbn55pt54okncn7fW2+9lS9/+cvebV544QXOOuusnJ2M4tI3d27cCmloOtctGz+Wjx/LxwjF6oofy8ePpnzmVupxAV3ZQDz5lEYHRMgIiKYm2LYttfZDkear19bWFuV9ckWjlzmFkW0RounTp/PII4/w2GOPMWfOHK666qrYnXYdNujrgEgkEhPu54wzzuDSSy/1vtf8+fO58cYb1S3WZKRT+/zzcSukoelct2z8WD5+LB8jFKsrfiwfP5ryeX5Ajwvoygbiyac0OiCyjYAYWfvhkEPglFOK5tXR0VG098oFjV7mFEYucwCPOeYYno8awWeffZbTTjuNl7/85Rx//PE89dRTo48fffTRrFq1is997nOjf+DuueceXv/614/u6+KLL+baa6/d7T0+8IEPsGbNGg4++GAuu+yy0ccXL17Mpz/9aQ4//HB+9rOfjT7+xz/+kVtvvZVPfvKTrF69mmeffZYTTzyRj3zkI6xZs4Zvf/vb3HbbbRx11FEcdthhnHzyyWzfvh2Aa6+9losvvhiA888/nw9/+MMce+yxHHDAAdx4440AbNq0iZUrV5JMJrn22mt585vfzGmnncbSpUv51Kc+Nepx9dVXc9BBB3HkkUdywQUXjO7XKA4dBxwQt0Iams51y8aP5ePH8jFCsbrix/LxoymfA6brcQFd2UA8+dgaEAC/+hU8/nhqDYgizu2eM2dO0d4rFzR67XVOH/kIPPJIvlRSrF5Nxbe+FbRpMpnkrrvu4j3veQ8AF154Id/73vdYunQpDz74IBdddBG//e1vueSSS7jkkks477zz+N73vpez0pe+9CXmzJlDMpnkpJNO4tFHH+WQQw4BoKGhgYcffjht+2OPPZYzzjiD17/+9Zx11lmjjw8ODo5O62hra+OBBx5ARPjhD3/IV7/6Vb7xjW/s9t7btm3jvvvu46mnnuKMM85I219FRarpe+SRR/jLX/5CVVUVy5Yt40Mf+hDl5eVcccUVPPzww8ycOZNXv/rVHHrooTkfuzF55jz9dNwKaWhqfywbP5aPH8vHCMXqih/Lx4+mfJ7u1eMCurKBePKxERDJJFx2WerKF+eeW1SvnTt3FvX9QtHoZU5hDA0NeZ/v6+tj9erVzJs3j+3bt3PKKafQ3d3NH//4R84++2xWr17N+973PrZt2wbA/fffz9lnnw3AW9/61px9brjhBg477DAOO+wwHn/88bSpFefmcL6N33br1q2ceuqprFq1iq997Ws8/vjjGV9z5plnUlZWxooVK0ZHSYwwktNJJ51EXV0d1dXVrFixgs2bN/OnP/2JV77ylcyZM4fKysrR4zeKx85Vq+JWSEPTuW7Z+LF8/Fg+RihWV/xYPn405bOqVo8L6MoG4smnJEZADLvUgnPlkmHe93e/C3/+c2oNiCKvINvY2FjU9wtFo9de5xQ4UiFXsl27ZWQNiN7eXk499VSuuuoq/n975x4mRXnn+8+PmREYLsNVUZgIrpsIDnIRL9HVjXeORLxhwol4wprIJq56smY9yUqOEpM8JybEEM4m5tl1PZrIqomX6DFejgpe1xiFcJGLhghGkHATBgaYYS7v+aNqhh7orunuqep6mfl+nqefqa6qrvrUt996e/rtt96aOXMmAwYMYGkBPTLKy8vbDeSY7ZaU69atY+7cubz11lsMHDiQmTNntluvT58+ee8vc90bb7yRm2++malTp/LSSy8xZ86crK/pmXGbo4Nvu1kR3uUmc52ysrLIMSZE6Ri6YkXaCu3wqf5RNtEon2iUj8gXlZVolE80PuWzos4fF/ArG0gnn27RA6KpJfhSUdbjoAaIDRtg9my46KKS934A/1orW/HRS0750VEPiFYqKyuZP38+P/rRj6isrGTUqFFtYzE451i2bBkAp59+Oo8++igADz30UNvrjz32WFatWkVDQwM7d+7kxRdfPGQfu3btok+fPlRWVrJ582aeeeaZvNz69evH7t27cy6vra1l+PDhANx///15bfNgonI65ZRTePnll9mxYwdNTU1txy9Kh2+/Dvh0riubaJRPNMpH5IvKSjTKJxqf8lEPiGjUAyIhWhsgyntkHG59PcyYAU1NcPfdJR37oRXfWitb8dFLTvnR+st+PkyYMIGTTjqJBx98kAULFvDVr36V7373uzQ2NjJ9+nTGjRvHvHnzmDFjBt/73veYPHkyVVVVAFRXV/O5z32OmpoaRo0axYQJEw7Z/rhx45gwYQJjx46lurqaM888My+v6dOnc9111zF//vy2wSMzmTNnDldddRUDBw7k3HPPZd26dXkfcytROQ0fPpxbb72VU089lUGDBnHCCSe0HbcoDb79OuDTua5solE+0SgfkS8qK9GUMh/7djzfUdztruOVYsKn8qMeENGkkY8d3DX5cGDSpEmudUC6fFi4biHn/eI8Xp75MmcfezZs2QLTp8OiRfDAA3D11Qna5mb79u0MHjw4lX1H4aNXV3BavXo1o0ePTtAouE1l6wCLcbB371569+6NmfHQQw/x4IMP8sQTT6TqFAcdOdXV1dG3b1+ampq4/PLLufbaa7n88svbrZPt/TSzxc65SYlIe0ah9XAbeTT2bh89msGrV0evVMLPrpLWPx3ko2yiUT7RdJd8VBd3gMpKNJ7lk08DxOg+o1m9J9ontgYIj/JRNh3opJhPVD3s17eChGi7BKN1DIiZM+GNN4K7XqTU+AAwcODA1PYdhY9ecsqPsrIs45x0gsWLF3PDDTfgnGPAgAHce++9qTvFQUdOc+bM4YUXXqC+vp4LL7yQyy67rDRiAoCB772XtkI7fDrXlU00yica5SPyRWUlGt/yeW+PXz4+5aNsokkjn24xBkRzSzMQXoKxcCE88wx85ztwzTWpeu3atSvV/efCRy855Udzc3Os2zvrrLNYtmwZy5cv55VXXuH4449P3SkOOnKaO3cuS5cuZc2aNcyfPz/7HXREYuwaOTJthXb4dK4rm2iUTzTKR+SLyko0vuUzsvfItBXa4VM+yiaaNPLpFg0Q7caAmD8fjj4abrghZavC7gJQSnz0klN+9Ojh3yktJ1EofcLbwPqCT+e6solG+USjfES+qKxE41s+mxr88vEpH2UTTRr5dK9LMPbVw7PPwt//PfTqlbJVcOvCQgYNLBU+eskpP3wc00VOolDqBw2iYu/etDXa8OlcVzbRKJ9oSplPPtcej+g5gg0NG3IuL+WgeaI9Opei8S2fQRWD2Nvgj49P+SibaNLIp1s0QDS78BKM19+AhgaYNi1lowCfKtJMfPSSU374eKmAnEShVOzZk7ZCO3w615VNNMonGt/y2dPsl484QCnLSj6NVQPLB7KjaUfkOqVssNK5FI1P+SibaNLIp1v0Q267BOO112HIEDjjjJSNAlpaWtJWyIqPXnLKDx9/2ZeTKJQWz+6a4tO5rmyiUT7R+JZPufnlIw6gshKN8onGp3yUTTRp5NMtGiDOGXkOT33uKap/twZOPx08GZXf1y9BPnrJKR7KysoYP35822P9+vUFvX7evHnszdFtLGpZFLfddhsvvPBC5DpPPvkk3//+9wvetjg8cZ7U0a34dK4rm2iUTzS+5dN2dzLhHSor0SifaHzKR9lEk0Y+fjXBJMRRfY/i/CGn0vOdd+GqL6St00a5Zy1grfjoJaf86OjSgt69e7N06dKitz9v3jxmzJhBZWVl3svMjObm5py3vrzjjjs63O/UqVOZOnVqcdJZ0CUYflO+b1/aCu3w6VxXNtEon2h8y2dfi18+4gAqK9Eon2h8ykfZRJNGPt2iBwRA05tvgnNwyilpq7TR0NCQtkJWfPSSU34U+mtbXV0d5513HhMnTmTs2LE88cQTAOzZs4cpU6Ywbtw4ampqePjhh5k/fz4fffQR55xzDuecc0677WRb1rdvX77+9a8zYcIE3njjDe644w5OOeUUampqmDVrVpvrzJkzeeSRRwAYOXIkt99+e5vPmjVrALjvvvu4IbxzzcyZM7nppps444wzOO6449pe29LSwvXXX88JJ5zABRdcwMUXX9y2rLM5idLSUFWVtkI7fDrXlU00yica3/KpKvfLRxxAZSUa5RONT/kom2jSyMevpvkE6bViRTDhUQNEtl+RfcBHr67m9LVnv8bSvyyNTwYYP2w8P77ox5Hr7Nu3j/HjxwMwatQofv3rX/P444/Tv39/tm3bxumnn87UqVN59tlnOeaYY/jtb38LQG1tLVVVVdx1110sWrSIIUOGtNvuTTfddMiyPXv2cNpppzF37lzMjDFjxnDbbbcBcM011/DUU09xySWXHOI4ZMgQlixZws9+9jPmzp3LPffcc8g6mzZt4rXXXmPNmjVMnTqVadOm8dhjj7F+/XpWrVrFli1bGD16NNdee23WHHQbTr+p3LIlbYV2+FT/KJtolE80vuWzZb9fPuIAKivRKJ9ofMpH2USTRj7d5r/whoYGOPNMGDw4bZU2du/enbZCVnz0klN+NDc3Ry5vvQRj6dKlPP744zjnuPXWWznppJM4//zz2bhxI5s3b2bs2LE8//zzfOMb3+DVV1+lqojW2rKyMq688so2p0WLFnHaaacxduxYFi5cyMqVK7O+7oorrgDg5JNPzjlGxWWXXUaPHj0YM2YMmzdvBuC1117jqquuokePHgwbNuyQXhqZdJSTSJfd1dVpK7TDp3Nd2USjfKLxLZ/qXn75iAOorESjfKLxKR9lE00a+XSbHhC9b78d5sxJW6MdAwYMSFshKz56dTWneZPnxeaRSaGXFixYsICtW7eyePFiKioqGDlyJPX19Xzyk59kyZIlPP3003zrW9/ivPPOa+u9kC+9evWirKwM5xz19fVcf/31vP3221RXVzNnzhzq6+uzvq5nz55A0IDR1NQUuQ4UdzlFrvEohB8MWLs2bYV2+FT/KJtolE80vuWzdq9fPuIAKivRKJ9ofMpH2USTRj6J94Aws8lm9q6ZrTWzb2ZZ3tPMHg6Xv2lmI5Pw2L59exKb7RQ+OoGfXnLKj1xf2HNRW1vLkUceSUVFBYsWLeKDDz4A4KOPPqKyspIZM2Zwyy23sGTJEgD69euX8xe9XMuampraGhuGDBlCXV1dzrEZOsOZZ57Jo48+SktLC5s3b+all17KuW6hOYnSsv3EE9NWaIdP57qyiUb5RONbPif29ctHHEBlJRrlE41P+SibaNLIJ9EeEGZWBvwUuADYALxlZk8651ZlrPYlYIdz7ngzmw7cCXw+bpeDr1n3AR+dwE8vOeVHRUVFQetfffXVXHLJJYwdO5ZJkyZxwgknALBixQpuueUWevToQUVFBXfffTcAs2bNYvLkyRxzzDEsWrSo3bZyLauoqGDAgAFcd9111NTUMGzYME5JYCyWK6+8khdffJExY8ZQXV3NxIkTc146UmhOorQMeeedtBXa4dO5rmyiUT7R+JbPO3V++YgDqKxEo3yi8SkfZRNNGvlYkqPBm9mngTnOuYvC5/8M4Jz7XxnrPBeu84aZlQN/AYa6CLFJkya5t99+uyCXrVu3MnTo0CKOIjl8dAI/vbqC0+rVqxk9enSCRtDY2Ojdl+tSOtXV1dG3b1+2b9/Oqaeeyuuvv86wYcMSccr2fprZYufcpE5t+DChmHoYgDxugbq1poahHX1Al/BOJiWtfzrIR9lEo3yiKWU+9u2OfWr61kT+8+tuL85FdXEHdMGyAsWXl0OFlE+0kD/5KJsOdFLMJ6oeTvoSjOHAhxnPN4Tzsq7jnGsCaoFDRoo0s1lm9raZvb1lyxbq6+vZu3cve/bsoaGhgV27dtHU1MSOHTtwzrFt2zYg+PAPX49zjh07dtDU1MSuXbtoaGhgz5497N27l/r6enbv3k1jYyM7d+6kpaWlretk6zZa/3788cc0NzdTW1vL/v37qaurY9++fezbt4+6ujr2799PbW0tzc3NfPzxx1m3sX37dgYPHszOnTtpbGxk9+7dBR/Ttm3bEjmmqqqqoo+ppaUlkWOqrKxM7X3KdUyVlZUFHVNLSwvOOZqbm2lpaaG5ubnddEtLC01NTTjn2i4RaGxsbPc3c3nm61qnW8dNKGYbB/u0ruOcO2QbjY2N7ZZHHVPruVfsMRWSy5QpUxg/fjxnnXUWs2fPZujQoVmPqby8vFPH1LrOwWVPxEOHH8wlxqfGT2UTjfKJxrd8fPtlUhxAZSUa5RONT/kom2i6Yg+IacBk59yXw+fXAKc5527IWOedcJ0N4fM/hetsy7XdYlp7t23b5l1XSB+dwE+vruCkHhD+oB4QnSfJHhDbamo67qJYwl+xS1r/dJCPsommlPkcjr+8HW75qAdExyTVA+JwKyugc6k75qNsOtDppj0gNgKZ9/YYEc7Luk54CUYVEPuoTYM9uv1mKz46gZ9ecsqP8nL/bmwjJ1Eog3PcojUtfDrXlU00vuWzss4vH+Uj8kVlJRrlE41P+SibaNLIJ+kGiLeAvzazUWZ2BDAdePKgdZ4EvhhOTwMWRo3/UCw7d+6Me5Odxkcn8NOrqzgl2eMIoLm5OdHtF0NXdEr6fezu7Dz++LQV2uFT/aNsovEtn+Mr/fJRPiJfVFaiUT7R+JSPsokmjXwSbYAIx3S4AXgOWA38yjm30szuMLOp4Wr/Dgw2s7XAzcAht+qMg379+iWx2U7hoxP46dUVnHr16sX27dsT/fJaVlaW2LaLpas5tY790KtXrxiNRCb9Pvyw45VKiE/1j7KJxrd8Pqz3y0f5iHxRWYlG+UTjUz7KJpo08km8H7Jz7mng6YPm3ZYxXQ9clbTH3r176d+/f9K7KQgfncBPr67gNGLECDZs2JDogIUtLS306JF0x6bC6IpOvXr1YsSIETEaiUz2Hnkk/f/857Q12vCp/lE20fiWz5FHHMmf6/3xUT4iX1RWolE+0fiUj7KJJo18us2F0D179kxb4RB8dAI/vbqCU0VFBaNGjUrIJqChocG7rOR0+GBmk4GfAGXAPc6576fh0bO2No3d5sSnslLKbPIZvKqqvIrapmin2Ab3ygPfyk5H2ZQa5eM/qoez41tZUT7R+JSPsokmjXy6TQNEU1OTV/9Egp9O4KeXnPJDTvnho1PamFkZ8FPgAoJbJr9lZk8651aV2qWpd++SfUDn8yV72BHD+Mv+v0SuU6ov2aXMJh969+hNLf74KJ9olI/fdNd6OB98KyvKJxqf8lE20aSRj1/9ohPE8rgtSqnx0Qn89JJTfsgpP3x08oBTgbXOufedc/uBh4BL0xAxzwYubXb++CibaJRPNMrHe1QP58C3sqJ8ovEpH2UTTRr5dJsGCN+uQQc/ncBPLznlh5zyw0cnDxgOZI5EtCGcV3J6NDWlsducNDl/fJRNNMonGuXjPaqHc+BbWVE+0fiUj7KJJo187HC8nZyZbQU+KPBlQ4BtCeh0Bh+dwE8vOeWHnPIjKadjnXNDE9hu4pjZNGCyc+7L4fNrgNOcczdkrDMLmBU+/RTwbkI6vpUZn3x8cgH5dIR8olFdnEE+9XA4vxR1cXcpK8Uin2h88vHJBbqPT856+LAcA6KYDxUze9s5NykJn2Lx0Qn89JJTfsgpP3x08oCNQHXG8xHhvDacc/8K/GvSIr69Pz75+OQC8ukI+UTjm48HdFgPQ2nqYt/eG/lEI5/c+OQC8oFudAmGEEKISN4C/trMRpnZEcB04MmUnYQQojuhelgI0eU5LHtACCGEiBfnXJOZ3QA8R3D7t3udcytT1hJCiG6D6mEhRHegOzVAJN5tuAh8dAI/veSUH3LKDx+dUsc59zTwdNoe+Pf++OTjkwvIpyPkE41vPqmjejgn8olGPrnxyQXkc3gOQimEEEIIIYQQQojDC40BIYQQQgghhBBCiMTpFg0QZjbZzN41s7Vm9s2E91VtZovMbJWZrTSz/x7OH2Rmz5vZH8O/A8P5ZmbzQ7flZjYxY1tfDNf/o5l9sZNeZWb2BzN7Knw+yszeDPf7cDjYEWbWM3y+Nlw+MmMb/xzOf9fMLuqMT7i9AWb2iJmtMbPVZvZpD3L6x/B9e8fMHjSzXqXOyszuNbMtZvZOxrzYcjGzk81sRfia+WZmRTr9MHzvlpvZ42Y2oKPjz3Uu5sq4GK+MZV83M2dmQ0qZlchNrvc/Y/knLKg//xC+RxdnLIut/unIo5QuhXjlcjKzkWa2z8yWho+fJ+lRSpdCvMzsWDN7MfR5ycxGZCyL5XMiz2wS9yjUK5dTOH9J+F6tNLOvJOlRSheRG9XFnfMqRf2nerhzHqV0KcSrFPXfYVEPO+e69INgEJ8/AccBRwDLgDEJ7u9oYGI43Q94DxgD/AD4Zjj/m8Cd4fTFwDOAAacDb4bzBwHvh38HhtMDO+F1M/AfwFPh818B08PpnwNfDaevB34eTk8HHg6nx4TZ9QRGhZmWdTKr+4Evh9NHAAPSzAkYDqwDemdkNLPUWQFnAxOBdzLmxZYL8PtwXQtf+1+KdLoQKA+n78xwynr8RJyLuTIuxiucX00wiNcHwJBSZqVHzveqw7qY4DrE1vNrDLA+jnOqUI9SucSYz8iDz4EkPUrlUkQ+vwa+GE6fC/wynI7lc6KAbBL1iDmbI4Ce4XRfYD1wTMJlJ3EXPTpdXlQXp1gXx5BNLB5FZpN4/RdDXdNl6+IYsilJPdwdekCcCqx1zr3vnNsPPARcmtTOnHObnHNLwundwGqCL7aXEnzhJvx7WTh9KfALF/A7YICZHQ1cBDzvnPvYObcDeB6YXIxT2Ko1BbgnfG4Ehe2RHD6tno8A54XrXwo85JxrcM6tA9YSZFsUZlZF8OXx3wGcc/udcztJMaeQcqC3mZUDlcAmSpyVc+4V4OODZseSS7isv3Pudy6oXX6Rsa2CnJxz/8851xQ+/R3B/cpbnbIdf9ZzsYPyWLBXyI+B/wG4jHklyUrkJJ+62AH9w+kq4KNwOs76J9/PhFK4FOqVyylOOptPUuTjNQZYGE4vylge1+dEvtkk7VGMV1an8LO3IZzfk871jO1UPjG7iNyoLu68V9L1n+rhznuUyqVQr6Trv8OiHu4Olftw4MOM5xvCeYljQZf8CcCbwFHOuU3hor8AR3XgF6f3PIIvYy3h88HAzowvj5nbbttvuLw2XD/uHEcBW4H/E3Ydu8fM+pBiTs65jcBc4M8EDQ+1wGLSzwriy2V4OB2nG8C1BD0EinGKKo8FY2aXAhudc8sOWuRLVt2VfM6LOcAMM9tAMAr8jQW8Nk6PUrkU6pXLCWBUWJe+bGZnJexRKpdCvZYBV4TTlwP9zCzOOjnf7STtUYxXLqfWS0eXh9u40zlX7JeYzuYTp4vIjeriznvlcoJ46j/Vw533KJVLoV5J13+HRT3cHRogUsHM+gKPAl9zzu3KXBb+muqyvjB+j88CW5xzi0uxvwIoJ+g6f7dzbgKwh+DSgjZKmROABeMqXErQOHIM0Id4WkRjpdS5dISZzQaagAUeuFQCtwK3pe0iiuK/Avc550YQXDLzSzNL63PKJ5eOnDYBnwjr0puB/zCz/hHb6WourfwT8Ldm9gfgb4GNQHOJ9u2jRyY5nZxzHzrnTgKOB75oZkfl3kyXcxHZ8an+88mlI6dS13++eGTiU/3nk0srvtR/qXqkfQKXgo0E14O3MiKclxhmVkHQ+LDAOfdYOHtz2KWb8O+WDvzi8j4TmGpm6wm64ZwL/ISg+3l5lm237TdcXgVsj9GnlQ3ABufcm+HzRwgaJNLKCeB8YJ1zbqtzrhF4jCC/tLOC+HLZyIFLJTrtZmYzgc8CV4cNI8U4bSd3xoXyVwQNSMvCMj8CWGJmw4rwijUrkdd58SWC8UBwzr0B9AKG5PnaOD1K5VKoV1ansAvy9nD+YoLrPz+ZoEepXArycs595Jy7Ivyne3Y4b2cBx9RphxJ5FOwV4dRuHeAdoNhfSzubT5wuIjeqizvvlXT9p3q4kx4ldCnIqwT13+FRD7uYB5Xw7UHwS/v7BF9KWgfjODHB/RnBteLzDpr/Q9oPIviDcHoK7QfG+304fxDBgIgDw8c6YFAn3T7DgUEof037Qf+uD6f/gfYDK/4qnD6R9oP9vE/nB6F8FfhUOD0nzCi1nIDTgJUEYz8YwVgEN6aRFQcNIBRnLhw6sOLFRTpNBlYBQw9aL+vxE3Eu5sq4GK+Dlq3nwCCUJctKj6zvRYd1cZjxzHB6NMH1rBbHOVWIR6lcYsxnaKsDwcBTGym+HuxsPrG5FJHPEKBHOP094I5wOpbPiQKySdQj5mxGcGDg5YEEA2ePTbjsJO6iR6fLi+riFOviGLLpsvVwgfl0u7o4hmxKUg/HujFfHwTdkt4jaAGcnfC+/oage/xyYGn4uJjgOvcXgT8CL3DgC44BPw3dVgCTMrZ1LcGgOmuBv4vB7TMcaIA4juDL1VqCL3+tI572Cp+vDZcfl/H62aHnu8RwNwBgPPB2mNVvwoKeak7At4E1BC1+vyT4YCtpVsCDBN3nGgl6inwpzlyASeHx/Qn4F8CKdFpLcH1Yazn/eUfHT45zMVfGxXgdtHw9BxogSpKVHpHv1yHvP3AHMDWcHgO8TvCBuRS4sKMyFZdHWi5x5ANcSdB4uhRYAlySVj5xuxSYzzSCOvI9gkGXe2a8NpbPiTyzSdwjrmyACwg+g5eFf2eVoOyUxEWPTpUX1cUp18WdySZOjyKyKUn915m6Jm6XOPIhxvqvM9nE6RH1sHBnQgghhBBCCCGEEInRHcaAEEIIIYQQQgghRMqoAUIIIYQQQgghhBCJowYIIYQQQgghhBBCJI4aIIQQQgghhBBCCJE4aoAQQgghhBBCCCFE4qgBQgghhBBCCCGEEImjBgghhBCiG2BmI83snRi28xkzOyMOp1JgZveZ2bRw+h4zG5O2kxCie6J6WPWwgPK0BYRIAjPrA/wKGAGUAd8B1gJ3AX2BbcBM59wmMzse+DkwFGgGrgL2Ag8D/QnOk686514t9XEIIYSHfAaoA/4zZY+Ccc59OW0HIYSIgc+gelgcpqgHhOiqTAY+cs6Nc87VAM8C/xuY5pw7GbgX+F647gLgp865ccAZwCbgC8BzzrnxwDhgaWn1hRAiEcrNbIGZrTazR8ysEsDMTjazl81ssZk9Z2ZHh/NvMrNVZrbczB4ys5HAV4B/NLOlZnZW5sbNbI6Z3W9mr5rZB2Z2hZn9wMxWmNmzZlbRwf6uM7O3zGyZmT2a4Xefmc03s/80s/dbf0nLhgX8i5m9a2YvAEdmLHvJzCaF05PNbEm4rxfDeX3M7F4z+72Z/cHMLo0veiGEAFQPqx7u5qgBQnRVVgAXmNmdYcVcDdQAz5vZUuBbwAgz6wcMd849DuCcq3fO7QXeAv7OzOYAY51zu9M4CCGEiJlPAT9zzo0GdgHXh/+M5mqg/SYwwTl3EvAV59x6gh5jP3bOjc/RM+yvgHOBqcADwCLn3FhgHzClg/095pw7JWwQXg18KWO7RwN/A3wW+H7EMV4eHucY4L8RNCy3w8yGAv8GXBnu66pw0WxgoXPuVOAc4IcW9KgTQoi4UD2M6uHujC7BEF0S59x7ZjYRuBj4LrAQWOmc+3TmemEDRLbXv2JmZwNTgPvM7C7n3C+S9hZCiIT50Dn3ejj9AHATQQ+x1gZaCC5b2xSusxxYYGa/AX6T5z6ecc41mtmKcFvPhvNXACMJ/inNtb8aM/suMIDgcrnnMrb7G+dcC7DKzI6K2P/ZwIPOuWbgIzNbmGWd04FXnHPrAJxzH4fzLwSmmtk/hc97AZ8g+CdcCCHiQPVwgOrhbooaIESXxMyOAT52zj1gZjuB64GhZvZp59wbYcvvJ51zK81sg5ld5pz7jZn1JKiEhwIbnHP/Fs6bCKgBQghxuOOyPDeyNNCGTCH4R/ISYLaZjc1jHw0AzrkWM2t0zrXus4Xg/46o/d0HXOacW2ZmMwmuc2633RDLw6MYjODXuHcT2r4QQqgejkb1cBdHl2CIrspY4Pfh5Ra3A7cB04A7zWwZwZgOrd3BrgFuMrPlBIP5DCOobJeZ2R+AzwM/KaW8EEIkxCfMrPUfzi8ArwHvEjbQAphZhZmdaGY9gGrn3CLgG0AVwa9hu4GsvcfyJOv+wmX9gE1hI/HVRW7/FeDzZlYWXtN8TpZ1fgecbWajQodB4fzngBst/EnQzCYU6SCEELlQPRygeriboh4QokvinHuO9l3GWjk7y7p/JLhOLpP3gfsTUBNCiDR5F/gHM7sXWAXc7ZzbHw4mNt/Mqgj+N5gHvAc8EM4zYL5zbqeZ/V/gkXBgsBsLvUNQxP5WAv8TeBPYGv4t5h/sxwnq9FXAn4E3sjhsNbNZwGPhP/hbgAsI7pg0D1gezl9HcK2zEELEhephVA93Z+xAjxwhhBBCCCGEEEKIZNAlGEIIIYQQQgghhEgcXYIhhBBCiMOOcCC2Xx40u8E5d1oaPkII0d1QPSyKQZdgCCGEEEIIIYQQInF0CYYQQgghhBBCCCESRw0QQgghhBBCCCGESBw1QAghhBBCCCGEECJx1AAhhBBCCCGEEEKIxFEDhBBCCCGEEEIIIRLn/wNZ7JaDNp3+IQAAAABJRU5ErkJggg==\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",
    "\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",
    "\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(m_best[2], m_best[0], label=\"Fast 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(\n",
    "    \"0.80\",\n",
    "    get_best_metric_time(0.8, best),\n",
    "    0.5,\n",
    "    label=\"Regular training\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.bar(\n",
    "    \"0.80 \",\n",
    "    get_best_metric_time(0.8, m_best),\n",
    "    0.5,\n",
    "    label=\"Fast training\",\n",
    "    color=\"green\",\n",
    ")\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, m_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, m_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.93\", get_best_metric_time(0.93, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.93 \", get_best_metric_time(0.93, m_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(\n",
    "    \"0.80\",\n",
    "    get_best_metric_epochs(0.8, best),\n",
    "    0.5,\n",
    "    label=\"Regular training\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.bar(\n",
    "    \"0.80 \",\n",
    "    get_best_metric_epochs(0.8, m_best),\n",
    "    0.5,\n",
    "    label=\"Fast training\",\n",
    "    color=\"green\",\n",
    ")\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, m_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, m_best), 0.5, color=\"green\")\n",
    "plt.bar(\"0.93\", get_best_metric_epochs(0.93, best), 0.5, color=\"red\")\n",
    "plt.bar(\"0.93 \", get_best_metric_epochs(0.93, m_best), 0.5, color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}