{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fdba9947-3526-4d76-90bb-6da553743290",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "This notebook provides a gentle introduction to the concept of anchor boxes used in Object Detection. You are expected to have attended the lecture (or watched its recording) before you go through this notebook.\n",
    "\n",
    "The notebook is designed to help you understand how anchor boxes work and how one can use the anchor box generator in PyTorch to generate anchor boxes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be71bf3d-c293-439a-bd31-bc184094055e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCESS\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import seaborn as sns\n",
    "from seaborn import color_palette"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1348ca-2180-41f3-bdfa-05eda924f2bc",
   "metadata": {},
   "source": [
    "## Anchor boxes\n",
    "Object detection methods give us bounding boxes around objects. It is hard to get a model to output a bounding box directly. What most object detection methods do instead is that they adjust some pre-defined bounding boxes. So, instead of trying to find a box from scratch, they are provided with a bounding box, and their job is to modify the bounding box. These pre-defined bounding boxes are called *anchor boxes* or *default boxes*. In this notebook, we will see how to get these anchor boxes for a given image and a feature map.\n",
    "\n",
    "### Anchor boxes on a feature map\n",
    "After an image has been processed through a model (say ResNet50), we get a feature map of a certain height and width (the number of channels in the feature map is irrelevant to our discussion here). If the feature map's width = height = 8, then the feature map has 8x8 = 64 elements (ignoring the \\#channels). Each of these 64 elements has a *receptive field*. Please refer to the earlier lecture notes if you don't remember what a receptive field is. But to recap briefly, a receptive field refers to all the pixels in the image that the element in the feature map is connected to or think of it as all the pixels that the element has information about or, simply speaking, the part of the image that the element has \"seen\".\n",
    "\n",
    "Each element of the feature map generates bounding boxes for its respective receptive field. A feature map early in the network has a smaller receptive field than the one later. This is why object detection models typically downsample a feature map multiple times to get more feature maps with different receptive field sizes to detect objects of varying sizes. But for simplicity, we'll focus on just one feature map in this notebook.\n",
    "\n",
    "To get the portion of the image for which an element (of a feature map) will generate bounding boxes, we simply divide the image into equal portions such that each element gets assigned the same number of pixels. So, if the image's dimensions are (256 x 256) and that of the feature map are 8 x 8, then each element of the feature map will generate anchor boxes for a ((256/8) x (256/8)) = (32 x 32) portion of the image."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3526df5e-d78f-4cc7-96a9-5e7026f2396f",
   "metadata": {},
   "source": [
    "Let's visualise what we just discussed. In the image below, the blue rectangle represents our input image of size 256 x 256. For a feature map of size 8 x 8, we draw 64 red rectangles on the image representing the part of the image that those 64 elements will generate bounding boxes for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f6aa257-de79-48d6-b0d9-ec1dbf4c9a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's first start by defining a function that we can later re-use.\n",
    "def plot_image_with_rectangles(\n",
    "    fig=None, ax=None,\n",
    "    image_size=256,\n",
    "    feature_map_size=8,\n",
    "    extra_grid=32,\n",
    "    rectangles=None,\n",
    "    draw_image_on_top=False,\n",
    "    rect_colour=None,\n",
    "    title='' \n",
    "):\n",
    "    \"\"\"\n",
    "    Plots an image with rectangles and returns the plot for further customization.\n",
    "    Can be called multiple times to overlay new rectangles.\n",
    "    \n",
    "    Parameters:\n",
    "        fig, ax (optional): Matplotlib figure and axis objects to update an existing plot.\n",
    "        image_size (int): Size of the square image.\n",
    "        feature_map_size (int): Determines the stride and grid size.\n",
    "        extra_grid (int): Extra space around the image for better visibility.\n",
    "        rectangles (list of tuples): Rectangles to draw in the format [(x1, y1, x2, y2), ...].\n",
    "        draw_image_on_top (bool): Whether to draw the image (blue rectangle) over the rectangles.\n",
    "        rect_colour: Colour to be used for drawing rectangles.\n",
    "        title (str): Title of the plot.\n",
    "        \n",
    "    Returns:\n",
    "        tuple: (fig, ax) Matplotlib figure and axis objects for further customization.\n",
    "    \"\"\"\n",
    "    # Default colours\n",
    "    palette = color_palette(\"muted\", n_colors=10)\n",
    "    image_colour = palette[0]  # Blue-like\n",
    "    if rect_colour is None:\n",
    "        rect_colour = palette[1]  # Red-like\n",
    "\n",
    "    # If no existing figure/axis is provided, create a new one\n",
    "    if fig is None or ax is None:\n",
    "        sns.set_theme(style=\"whitegrid\")\n",
    "        fig, ax = plt.subplots(figsize=(5, 5))\n",
    "        ax.set_xlim(-extra_grid, image_size + extra_grid)\n",
    "        ax.set_ylim(-extra_grid, image_size + extra_grid)\n",
    "        ax.invert_yaxis()  # Invert y-axis for top-left origin\n",
    "        \n",
    "        # Draw gridlines\n",
    "        ax.set_xticks(range(-extra_grid, image_size + extra_grid, 32))\n",
    "        ax.set_yticks(range(-extra_grid, image_size + extra_grid, 32))\n",
    "        ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "        ax.xaxis.tick_top()\n",
    "        ax.grid(which='both', color='lightgrey', linestyle='--', linewidth=0.5)\n",
    "    \n",
    "    # Draw the image boundary (blue rectangle)\n",
    "    image_patch = patches.Rectangle(\n",
    "        (0, 0), image_size, image_size,\n",
    "        edgecolor=image_colour, facecolor='none', lw=2\n",
    "    )\n",
    "    if not draw_image_on_top:\n",
    "        ax.add_patch(image_patch)  # Draw below the rectangles\n",
    "\n",
    "    # Draw rectangles\n",
    "    if rectangles:\n",
    "        for rect in rectangles:\n",
    "            x1, y1, x2, y2 = rect\n",
    "            width, height = x2 - x1, y2 - y1\n",
    "            ax.add_patch(patches.Rectangle(\n",
    "                (x1, y1), width, height,\n",
    "                edgecolor=rect_colour, facecolor='none', lw=2\n",
    "            ))\n",
    "\n",
    "    # Optionally draw the image rectangle on top\n",
    "    if draw_image_on_top:\n",
    "        ax.add_patch(image_patch)\n",
    "\n",
    "    # Add labels and title if this is the first plot\n",
    "    if fig is not None and ax is not None and ax.title.get_text() == \"\":\n",
    "        if title:\n",
    "            ax.set_title(title, fontsize=16, pad=20)\n",
    "        ax.set_xlabel(\"X-axis\", fontsize=14)\n",
    "        ax.set_ylabel(\"Y-axis\", fontsize=14)\n",
    "        ax.xaxis.set_label_position('top')\n",
    "    \n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f28fb3b8-3def-41e5-85f3-64a73dbbce95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's draw an image (blue colour) of size (256, 256) and divide it into an equal number of parts (orange colour)\n",
    "# where Number of parts = Number of elements in the feature map. So, each orange box corresponds to an area of the image\n",
    "# to which an element in the feature map is assigned.\n",
    "image_size = 256\n",
    "feature_map_size = 8\n",
    "stride = image_size // feature_map_size\n",
    "rectangles = [(i * stride, j * stride, (i+1) * stride, (j+1) * stride) \n",
    "              for i in range(feature_map_size) \n",
    "              for j in range(feature_map_size)]\n",
    "\n",
    "# Call the function for the first time\n",
    "fig, ax = plot_image_with_rectangles(\n",
    "    image_size=image_size, \n",
    "    feature_map_size=feature_map_size, \n",
    "    rectangles=rectangles,\n",
    "    draw_image_on_top=True,\n",
    "    title='Blue image divided evenly for a feature map'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e50215e-6477-429d-af7b-e5417a2cfa62",
   "metadata": {},
   "source": [
    "The blue rectangle above is the image and orange rectangles in the image represent different parts of the image. Each of these parts will have its own set of anchor boxes centred around the top-left corner of the part. There are 8 x 8 = 64 parts in this image. If each part is used to define 2 anchor boxes then we'll have 64 x 2 = 128 anchors in total."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122eef96-f208-4adb-afe5-5d88d8a581c0",
   "metadata": {},
   "source": [
    "### Given the dimensions of the image, feature map, and anchor, generate anchor boxes\n",
    "Now, let's write some code to generate these anchor boxes. The function below takes the dimensions as input and returns a list of anchor boxes on the image. The total number of anchors returned is equal to the number of elements in the feature map. If we wanna generate multiple anchors (of varying sizes) for each part of the image, then we can call this function multiple times with different arguments. Each anchor returned is a tuple of 4 values representing the top-left corner and the bottom-right corner of the anchor box. These values correspond to the pixel values in the input image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eab2b78d-4144-42ca-b9de-34c46309cf31",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_anchors(feature_map_size, image_size, anchor_size):\n",
    "    anchor_boxes = []\n",
    "    # Stride = Size of the portion of the image that an element in the feature map will generate anchor boxes for\n",
    "    stride = (image_size[0] // feature_map_size[0], image_size[1] // feature_map_size[1])\n",
    "    for i in range(feature_map_size[0]):\n",
    "        for j in range(feature_map_size[1]):\n",
    "            top_left = (i*stride[0], j*stride[1]) # Top Left corner of the receptive field\n",
    "            # Get the top left and bottom right coordinates of the anchor box\n",
    "            left = top_left[0] - (anchor_size[0] // 2)\n",
    "            top = top_left[1] - (anchor_size[1] // 2)\n",
    "            right = top_left[0] + (anchor_size[0] // 2)\n",
    "            bottom = top_left[1] + (anchor_size[1] // 2)\n",
    "\n",
    "            anchor_boxes.append((left, top, right, bottom))\n",
    "    return anchor_boxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bed6cec-05d1-47d6-abb1-804871eae937",
   "metadata": {},
   "source": [
    "Now let's call this function to get some anchor boxes and then plot them to see what they look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61075b16-04d1-4020-a079-71b2fc031e27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_size = 256\n",
    "feature_map_size = 8\n",
    "anchor_box_size = 16\n",
    "anchor_boxes = get_anchors((feature_map_size, feature_map_size), (image_size, image_size), (anchor_box_size, anchor_box_size))\n",
    "\n",
    "palette = color_palette(\"muted\", n_colors=10)\n",
    "rect_colour = palette[2]\n",
    "\n",
    "fig, ax = plot_image_with_rectangles(\n",
    "    image_size=image_size, \n",
    "    feature_map_size=feature_map_size, \n",
    "    rectangles=anchor_boxes,\n",
    "    draw_image_on_top=False,\n",
    "    rect_colour=rect_colour,\n",
    "    title='Blue image with green anchor boxes'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72c9648-1a24-46e9-8337-bf8d93236d9a",
   "metadata": {},
   "source": [
    "Green rectangles are the anchor boxes. Note how they are centred around the top-left corner of their respective \"feature map areas\" we drew earlier. This is how PyTorch's AnchorGenerator (which we'll use later) generates anchor boxes. If the object is elsewhere in the image, the model will move the anchor box accordingly and also adjust its size to cover it.\n",
    "\n",
    "Now, let's get some more anchor boxes of a different size and draw only some of them to avoid overcrowding the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4a111f1-9a94-4160-81be-a4196c15964a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anchor_box_size = 64\n",
    "anchor_boxes_size64 = get_anchors((feature_map_size, feature_map_size), (image_size, image_size), (anchor_box_size, anchor_box_size))\n",
    "indices_to_keep = [0, 7, 20, 50, 63]\n",
    "\n",
    "# First, plot the previous anchor boxes\n",
    "palette = color_palette(\"muted\", n_colors=10)\n",
    "rect_colour = palette[2]\n",
    "fig, ax = plot_image_with_rectangles(\n",
    "    image_size=image_size,\n",
    "    feature_map_size=feature_map_size,\n",
    "    rectangles=anchor_boxes,\n",
    "    draw_image_on_top=False,\n",
    "    rect_colour=rect_colour\n",
    ")\n",
    "\n",
    "# Now, we'll plot some of the new ones\n",
    "rect_colour = palette[3]\n",
    "fig, ax = plot_image_with_rectangles(\n",
    "    fig=fig, ax=ax,\n",
    "    image_size=image_size,\n",
    "    feature_map_size=feature_map_size,\n",
    "    rectangles=[anchor_boxes_size64[i] for i in indices_to_keep],\n",
    "    draw_image_on_top=False,\n",
    "    rect_colour=rect_colour,\n",
    "    title='Blue image with green and red anchor boxes'\n",
    ")\n",
    "\n",
    "# # Explicitly display the updated figure\n",
    "# from IPython.display import display\n",
    "# display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4197719-7854-4a38-af07-c6122715d77a",
   "metadata": {},
   "source": [
    "The new rectangles you see are some of the new anchor boxes of size (64, 64). They don't have to be square in shape. Feel free to play around with various dimensions and see what the anchor boxes look like and what area do they cover on the image.\n",
    "\n",
    "These larger anchor boxes can be used to detect larger objects. Typically, you'd want to detect smaller objects on the feature maps with smaller receptive fields and larger objects using the feature maps with large receptive fields. We're using only 1 feature map here for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e722fff-9e8d-4846-be32-7c849bba2117",
   "metadata": {},
   "source": [
    "## Torchvision's anchor generator\n",
    "Now, we'll see how we can use Torchvision's AnchorGenerator class that it uses to generate anchor boxes.\n",
    "\n",
    "While our implementation of the function for generating anchor boxes was simple and easy to understand, it was not efficient. Torchvision's implementation is more efficient and gives a (sort of) similar output to ours, but it defines anchors' sizes in a very different way which I'll explain next. You can check out the AnchorGenerator's code here: https://github.com/pytorch/vision/blob/release/2.0/torchvision/models/detection/anchor_utils.py#L10-L133\n",
    "\n",
    "### Defining anchor box's size using \"scale\" and \"aspect ratio\"\n",
    "Torchvision (and other code bases/libraries in general like Tensorflow) uses 2 parameters to define the anchor size: scale(size) and aspect ratio. Aspect ratio is simply the ratio of height and width: $Aspect\\ Ratio = \\frac{height}{width}$. Scale is used with aspect ratio to define height and width as: $height = scale\\times\\sqrt{aspect\\ ratio}$ and $width = \\frac{scale}{\\sqrt{aspect\\ ratio}}$. If you divide height by width, you'll get aspect ratio, which is exactly what we want. $\\frac{height}{width}=\\frac{scale\\times\\sqrt{aspect\\ ratio}}{\\frac{scale}{\\sqrt{aspect\\ ratio}}}=aspect\\ ratio$\n",
    "\n",
    "Check the function `generate_anchors` in Torchvision's code link above. It does exactly what I just explained."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2200e983-8c2d-4d5e-9e77-f83e5c724405",
   "metadata": {},
   "source": [
    "### Finally, let's use the AnchorGenerator class and plot the anchors\n",
    "Now that you understand what the parameters in the Torchvision's `AnchorGenerator` class mean, let's go ahead and use the class to generate some anchor boxes and then visualise them like we did. Let's see if we can manage to generate the same anchors as we did above but this time using `AnchorGenerator`. Note that `AnchorGenerator` refers to \"scales\" as \"sizes\" in its init method but if you look at the `__init__` function, you'll see that it simply calls the `generate_anchors` method using \"size\" as \"scale\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "569cb0bc-d20f-438d-a4f5-9c20cea28eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHICAYAAACWKznOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACI1UlEQVR4nO29d3xUVf64/0xJMunJpJJCsYCiVJVVV1kVFbuLDVbRZUWFdbF/LIhlbV8bFmRdlQVXUUEsiA1dfpYtIGtZEbKKuqImoaVn0pMp9/dHmMtMMpPM3Ewyh8z7eb18Se49c+a555573nPvPfd9TZqmaQiCIAiCMKCYoy0gCIIgCLGIBGBBEARBiAISgAVBEAQhCkgAFgRBEIQoIAFYEARBEKKABGBBEARBiAISgAVBEAQhCkgAFgRBEIQoIAFYEARBEKKABGBBEJTg008/ZdSoUSxevDjaKoIwIEgAFoRBzgsvvMCoUaO46aabAq53OBwce+yxjBs3jp9++mmA7QQhdpEALAiDnJkzZzJp0iTefPNNPvjgg27r7777biorK7nhhhsYMWJEFAw7GTt2LGvXruWiiy6KmoMgDCQmeRmDIAx+tm/fzplnnkliYiLvvPMOdrsdgPfff59rrrmGX/ziFzz//POYTKYomwpC7CBnwIIQAxQVFXHLLbdQU1PDH//4RwCqq6v54x//SHJyMvfff39Iwfe///0vd999N2eccQaHHXYYY8eO5cwzz2TJkiU4nU6/su+++y6jRo3i8ssvp+vv/EDrgt0D/vnnn5k/fz4nnHAChx56KJMmTeKss87ivvvu61avIOxLSAAWhBhh+vTpHHPMMfztb3/jnXfe4fbbb6euro5bb72VwsLCkOp45ZVX+P/+v/+PkSNHMn36dM477zw0TeORRx7h+uuv9yt7+umnM23aNP75z3/y/PPP68u3b9/OnXfeSXZ2Ng888ECPgb+iooLzzz+ft99+m4MPPphZs2Zx5plnkpOTw8qVK3G73cYaQxBUQBMEIWbYvXu3dvjhh2uHHnqoNnLkSG3OnDlhfX7Hjh2ay+XyW+bxeLT58+drI0eO1L744gu/dU1NTdpJJ52kHXroodo333yjuVwubfr06dqoUaO0f/7zn35l//3vf2sjR47UnnjiCX3Z8uXLtZEjR2rPPfdcN5e6urqw3AVBNeQMWBBiiLy8PC666CI6OjqIi4vj7rvvDuvzBQUFWCwWv2Umk0mfOLVx40a/dcnJyTzyyCNomsb111/PwoUL2bRpE7/97W859thjQ/5em83WbVlGRkZY7oKgGtZoCwiCMHDU1NSwatUqAJxOJ3//+9+54IIL9PUffPABW7du9fvMpEmT+MUvfgFAR0cHL730Eu+++y4//vgjLS0tfvdhKysru33nmDFjuPrqq3nkkUf48ccfOfjgg7nhhhtC8j3++ON59NFHufvuu9m4cSPHHnsskyZNori4OOxtFwTVkAAsCDHEnXfeSW1tLTfccAPPPvssDzzwAMcccwwFBQVAZwB+4403/D4zb948PQBfffXVfPzxxwwfPpzTTjuNrKwsrFYrDQ0NLF++nI6OjoDfO2XKFB577DE8Hg8XXHAB8fHxIfkWFRWxatUq/vSnP/GPf/yD9957D4D99tuPq6++mlNPPdVoUwhC9In2NXBBEAaGN954w+++77vvvquNHDlSmzVrVkif37x5szZy5Ejt0ksv7XYfeNOmTdrIkSO1m2++udvnOjo6tGnTpmkHHXSQdvjhh2uTJk3Sdu3a1a1coHvAXevZtGmTtmjRIu3www/XRo0a1e2esyDsS8g9YEGIASoqKrjvvvvIyMjQ7/uedtppTJ06lU8++YSXX3651zrKy8sBOO6447rdB/7iiy+Cfu7RRx/l66+/Zs6cOTz00EPU19dz44034vF4wtqGuLg4xo8fz9VXX82CBQvQNI2///3vYdUhCCohAVgQYoBbb72VhoYGbr/9dnJzc/Xlf/zjH7Hb7Tz00EPs2LGjxzq8l6n/85//+C3/3//+x5IlSwJ+ZsOGDfz1r39l/PjxzJs3j+OPP56LLrqIzz77LOhnfPnvf/9LU1NTt+U1NTUAJCQk9FqHIKiK3AMWhEHOyy+/zPr165k6dSpnnHGG3zq73c4f//hHrr76am699Vaee+65oM/ljh07lrFjx/Lee+9RVVXFuHHj2LVrFx999BG/+tWv+Nvf/uZXvra2lptvvpnk5GQWLlyI1do53Nx88818/vnnLF68mKOOOopx48YFdX/zzTdZtWoVRxxxBMXFxaSkpPDDDz/wz3/+k4yMDM4555w+to4gRA85AxaEQUx5eTkPPvggWVlZegasrngD87///W9WrFgRtC6LxcIzzzzDueeeS1lZGS+++CI//PADN910EzfeeGO38rfeeitVVVXceeedfrOWExISeOSRR7BYLNxwww0Bz3C9nHHGGfz617+msrKSd999lxdeeIGffvqJ3/zmN7zxxhv6Wbkg7ItILmhBEARBiAJyBiwIgiAIUUACsCAIgiBEAQnAgiAIghAFJAALgiAIQhSQACwIgiAIUUACsCAIgiBEAUnE0Uc+//xzli1bxtatW6mtrSUtLY2DDjqIK6+8ksMOO0wv19rayurVq/nwww/5/vvvaW5uZtiwYVxwwQVMnz69W2q/SNDR0cGiRYt48803aWhoYNSoUVx77bX88pe/jPh3BeJ///sfixcv5uuvv6a6uhqbzcYBBxzA7NmzOeGEEwDweDysWbOGdevWsXXrVhwOB0VFRZx22mnMnj273zMdff311yxevJgvv/yS9vZ2iouLueCCC7jkkku6lW1oaGDq1KnU1tayaNEiTjnllH5x+u9//8tjjz3Gpk2b0DSNCRMmcOONN3LwwQd3K9vR0cGzzz7LmjVr2LFjB6mpqRx66KHcfffd5Ofn98mjubmZZcuWsXnzZkpKSnA4HNx///1+yS/C3X+NjY089dRTfPDBB+zevZusrCyOOuoo5s2bF/IzvaF4+fq9/PLLrFq1ip9++onExERGjRrFrbfeykEHHeRXbtmyZaxcuZKqqiqGDx/OnDlzuiUuiaTbiy++yEsvvUR5eTmZmZmcdtppXHPNNSQlJelltm3bxuuvv86GDRsoKysjOTmZ0aNHc9VVVzFmzJiQvbZs2cKaNWv49NNP2bFjBxkZGYwbN45rr72WESNG6G1g9Fj84osv/F5JabfbI+oGcMstt3R7UQjAiBEjeP/997stLysrY9GiRXzyySc0NzeTn5/PqaeeynXXXReyW38jAbiP/Pzzz5jNZmbMmEF2djYNDQ289dZbzJw5k2eeeYbJkycDnQkR7rnnHo466ihmzZpFSkoK69ev56677mLz5s08+OCDEXe75ZZb+Nvf/sYll1zC8OHDeeONN7jiiit4/vnnOfzwwyP+fV3ZuXMnzc3NTJs2jdzcXFpbW1m3bh2///3vufvuu5k+fTqtra3Mnz+f8ePHM2PGDLKysti0aROLFy9m48aNLF++PGhmpr6yfv165s6dy+jRo7nyyitJSkqirKyM3bt3Byz/xBNP0NbW1i8uXr7++msuvPBChgwZwrx58/B4PKxYsYKZM2fy6quvst9+++llnU4nc+bMYdOmTZx//vmMGjWKhoYGNm/eTGNjY58DcF1dHU8++SQFBQWMGjWKzz77rFuZcPafx+Phd7/7Hdu2beM3v/kNI0aMoLS0lBUrVrB+/XrWrl1LSkpKRLy83Hrrrbz99tucffbZzJw5k5aWFrZu3aqnsvTy2GOPsWTJEi644ALGjBnDhx9+yA033IDJZOL000+PaJsBPPzwwyxdupSpU6dyySWXsG3bNj2xybJly/Ryr732Gq+99honn3wyF154IY2NjaxatYrp06ezdOlSjj766JC8li5dypdffskpp5zCqFGjqKqq4qWXXuKcc85h1apVjBw50vCx6PF4uPfee0lKSqKlpSXktgrHzUt8fDz33nuv3+dTU1O71bl161Yuvvhi8vLy+N3vfkdmZiY7d+4MemxHjei+C2Jw0tLSoh199NHapZdeqi+rqanRvv/++25lb7nlFm3kyJHazz//HFEH75trli5dqi9ra2vTTjzxRG369OkR/a5wcLlc2llnnaVNnTpV0zRNa29v1/7zn/90K7d48WJt5MiR2oYNG/rFo7GxUTv66KO1P/zhD5rb7e61/HfffaeNHj1a+9Of/qSNHDlSe++99/rF6/LLL9eOOOIIrba2Vl9WUVGhjR8/Xps3b55f2SVLlmiHHHKItnnz5n5xaW9v1yorKzVN07QtW7ZoI0eO1F5//fVuZULdf//5z3+0kSNHai+++KJf2ddee00bOXKktm7duoh5adretz31Vu/u3bu1Qw45RLvrrrv0ZR6PR7vwwgu1yZMnd3vzU1/dKioqtNGjR2s33nij3/IXXnhBGzlypPbhhx/qy0pKSrSmpia/crW1tdqRRx6pzZgxI2Sv//znP1p7e7vfsp9++kk79NBDtRtuuEF3N3IsrlixQps0aZJ27733aiNHjtRqampC9grVTdM07eabb9bGjx/fa31ut1s744wztPPPP19rbW0Ny2WgkXvA/UBiYiJ2u53GxkZ9md1u58ADD+xW9qSTTgI6LzVFkvfffx+LxcL06dP1ZQkJCZx33nls2rSJXbt2RfT7QsVisTBkyBC9beLj45k4cWK3cv3VLl7efvttqqurue666zCbzbS0tPT4dp777ruPE088sd+vHHzxxRccddRRZGZm6styc3OZNGkSH3/8Mc3NzUDnWcfy5cs58cQTGTt2LC6Xi9bW1oi6xMfHk5OT02uZUPefN+VkVlaWX1nvd4R6uyEUL4DnnnuOsWPHctJJJ+HxeIKenX3wwQc4nU4uvPBCfZnJZOI3v/kNu3fvZtOmTSF5her21Vdf4XK5up1Zn3baaQC8++67+rJDDz2U5ORkv3KZmZkcfvjh/PjjjyF7TZw4sds7mIcPH86BBx6o12PkWKyvr+fxxx/n6quvJi0tLWSfcN18cbvdPaYvXb9+Pd9//z1/+MMfsNlstLa24na7Dbn1NxKAI0RTUxO1tbVs27aNRx99lO+//56jjjqq189VV1cD+A24kWDr1q0MHz682yW9sWPH6usHipaWFmpraykrK+O5557jn//8J0ceeWSPn+mvdvGyceNGUlJSqKioYOrUqUyYMIHDDjuMO++8k/b2dr+y7733Hps2bQqY7zjSdHR0YLPZui232Ww4nU7+97//AfDDDz9QWVnJqFGjuP322xk/fjzjx4/nzDPP5N///ne/e/ZGoP136KGHkpSUxKJFi9i4cSMVFRV89tlnPPzww4wZMybky6mh0NTUxJYtWxgzZgyPPvoohx12GBMmTGDKlCmsXbvWr+zWrVtJSkpi//3391veX8dKR0cH0P0HR2JiItB5G6I3qqqqyMjI6JOHpmlUV1f3eoz1dCwuWrSInJwcZsyY0SeXUN1aW1s57LDDOOyww5g0aRJ33XWX/qPUy8aNG4HOHxTnnHMO48ePZ9y4cVx33XXU19dH1LOvyD3gCHHNNdewfv16oPO9pdOnT+fKK6/s8TMdHR08//zzFBUVhTWhIhSqqqoC/hL3LqusrIzo9/XEAw88wKpVqwAwm82cdNJJ3HHHHT1+ZunSpaSkpOj30CPNzz//jNvt5sorr+S8887jhhtu4LPPPuOFF16gsbGRRx99FIC2tjYeeughZs2aRVFRUa+v7OsrI0aM4KuvvsLtdusT8zo6OtiyZQvQ+V5fgNLSUqDzLM/3Hb/PPPMMl112Ga+99prfJKOBJtD+s9vtPPbYY9x2223MmjVLX37MMcfwxBNP6G9LigRlZWVomsa7776L1WrlxhtvJDU1leXLl3P99df7uVVVVZGVldXt/mZ/HSveiUVffvml3w9R7zuVvfs4GF988QVfffUVv//97/vk8dZbb1FRUcHVV1/dY7lgx+K3337LqlWrWLJkScQnkQZyy8nJ4bLLLmP06NFomsa//vUvVqxYwbfffssLL7yg95+ff/4ZgGuvvZZjjz2WOXPm8O2337JkyRJ27drFypUr+21eSbhIAPbB4/HgdDpDKhsfH++3E//v//6PSy+9lF27drFmzRqcTicul6vHy2r33HMPP/zwA0uWLIno4AOdgaPrZR3Y+6u7vycT+fLb3/6WU045hcrKSt57771e2/npp5/mk08+4c477zR8Was3WlpaaG1tZcaMGdx2220AnHzyyXR0dLBq1Squvvpqhg8fzpIlS/TJTgPBhRdeyB//+EcWLFjAZZddhsfj4amnnqKqqgrYu9+8v/qbm5tZs2YNQ4YMAeDII4/k5JNPZunSpSxcuHBAnLvS0/6z2+2MHj2aiRMncsABB/Dtt9+ydOlS5s+fzxNPPBExB+/l5vr6el555RX9lYcnnHACU6ZM4amnntIDykAfK4cccgjjxo3jL3/5C3l5efziF79g27Zt3HXXXcTFxXW7AuNLTU0NN9xwA0VFRVx22WWGHbZt28bdd9/NhAkTmDZtWtByPe3L++67j8mTJ3PMMccY9gjH7YYbbvArd/rppzN8+HAee+wx/va3v+mX9L37fsyYMfoxMHXqVBITE3nkkUfYuHFjRK+29AW5BO3D559/rr/ztLf/ut6bOPjgg/nlL3/Jeeedx7PPPktJSQnz588P+l1Lly7llVde4ZprruFXv/pVxLfFZrPpl7p88R7cgS5z9hf7778/Rx99NL/+9a955plnaGlpYe7cuWgBXsS1du1aHn/8cc477zy/e3KRxrv9XR8zOfPMM4HO+3Tbt29n2bJlXHfddd3uw/UXv/nNb5g7dy7vvPMOp59+OmeeeSZlZWXMnj0bQPfw+k+cOFEPvgAFBQVMnDgxrPuWkaSn/VdeXs4ll1zCueeey9y5cznxxBOZN28ed955J3/729/4xz/+ETEPb/AsKirye99wcnIyxx9/PCUlJbhcLiA6x8rixYs56KCDuPXWW5kyZQq///3vOeWUUzj44IP9HkPypaWlhTlz5tDc3Myf//xnw32yqqqKOXPmkJqayqJFi4Kevfa0L9euXcumTZu4+eabDTn01c3LrFmzMJvNfPLJJ/qyYMe29+8vv/wyos59Qc6Afdhvv/24//77Qyqbm5sbdF18fDwnnHACS5Ysoa2trdsBvHr1ahYuXMiMGTN6vUxtlJycnICXsrxnUj359zdTp07ljjvu4KeffvJ7rGbDhg3cdNNNHHfccdx111396pCbm8v//ve/bhOCvM8vOhwOnnjiCfLy8pg0aRLbt28H9t4Pq62tZfv27RQUFGA2R/Z37HXXXcell17K//73P1JTUxk1apR+SXz48OG6P0B2dna3z2dlZQ3oPX4vve2/1atX097ezvHHH++33PtM+JdffhmxH6O9tY/T6aS1tZXU1FRycnL49NNP0TTN76pWfx4reXl5rFy5kp9//pnq6mqGDRtGTk4OxxxzjL6Pfeno6OCqq67iu+++Y9myZX6P5oRDY2Mjl19+OY2Njbz00kvk5eUFLNfbvnzooYeYOnUqcXFx+rHR0NAAwO7du3E6nUHr7qubLzabjYyMDBwOh77Mu7+6Htvev72eKiAB2IecnJyAD8wboa2tDU3TaG5u9gvAH3zwAbfddhsnn3wyd955Z0S+KxAHHXQQn376KU1NTX4TsTZv3gwQMKnDQOG9pOc7k3Hz5s3MmzePQw89lMcffzzil+S7csghh7BhwwYqKir8fgR47/fZ7XZ27dpFaWkpJ554YrfPewelzz//vF8uk6enp/vNuP7kk0/Iz8/XXUeOHElcXFzAH1mVlZVhJUKIBKHsv5qaGjRN6zYj1XsmGsmZqnl5eUF/hFZWVpKQkKCfQR588MG8+uqrbNu2jQMOOMBvm7zr+4vhw4frAfeHH36gqqqq2xjk8Xi4+eab2bhxI48//jiTJk0y9F3t7e3MnTuXn3/+mb/+9a9+2+pLKPty165dvPPOO7zzzjvd1k2bNo2DDjqIN998M+JuXWlqaqKurs6vvx9yyCFA93vpvse2Ksgl6D7S9YF+6PyFtW7dOoYMGeL3K+zzzz/n+uuv5/DDD2fhwoURP3Py5ZRTTsHtduuTn6DzV/Tq1asZN26c32XL/iJQ2zidTt58801sNps+63Tbtm1cccUVFBYW8swzzwzI5fFTTz0V6Ex04Mtrr72G1Wpl0qRJXHPNNTz55JN+/11zzTUAXHbZZTz55JP6zNX+ZO3atZSUlPDb3/5W7zPeSTGbNm3yezxk27ZtbNq0aUDvcYW6/4YPH46mabz33nt+y72D+OjRoyPqdeqpp7Jr1y42bNigL6utreXDDz/kyCOP1NtyypQpxMXFsWLFCr2cpmm8/PLL5OXlMWHChIh6BcLj8fDwww+TmJjYbUbxPffcw9q1a7nzzjs5+eSTDdXvdru59tpr+eqrr1i0aFHQbQp1X3Y9Lp588kn9MaoHH3ywx9tvRtza29sDPnr05z//GU3TOPbYY/VlU6ZMIT4+ntWrV/s9Wvjqq68CKHP/F+QMuM9cfvnl5OXlMW7cOLKysti5cyerV6+msrKSxx57TC+3Y8cOfv/732MymZg6dWq3QWjUqFERnbU6btw4TjnlFB599FFqamoYNmwYb7zxBjt27OC+++6L2Pf0xB133EFTUxNHHHEEeXl5VFVV8fbbb/Pjjz9yyy23kJycTFNTE7Nnz6ahoYHZs2fz97//3a+OoUOH9ssAOHr0aM4991xef/113G43RxxxBJ999hnvv/8+c+bMIS8vL+AlMG/WnTFjxgQ8M+4rn3/+OU8++SS//OUvycjIYPPmzaxevZpjjz22W3rM66+/no0bN/Lb3/5WX7d8+XLS09OZO3duRHxefPFFGhoa9LOHjz/+WM8mdPHFF2MymULef9OmTePZZ5/ljjvu4JtvvuHAAw/k66+/5rXXXuPAAw8Mqz1780pNTWXOnDm89957XHXVVfzud78jNTWVlStX4nK5uP766/W68vPzueSSS1i2bBkul4sxY8bwwQcf8MUXX7Bw4cKwZ/iG4nbvvffS0dHBQQcdhMvl4p133mHLli088MADfik5n3vuOVasWMGECROw2WzdzipPOumkoPeMfXnggQf46KOPOP7446mvr+9Wz9lnnx3WsRhoX3lve0yePDmss8xQ3Kqqqpg2bRqnn366fhVo/fr1/OMf/+DYY49lypQpevmcnBzmzp3LE088wWWXXcaUKVP47rvveOWVVzjjjDP0x8tUwKQFmgkjhMxLL73Eu+++y48//khjYyNpaWmMGzeOyy67zO8S4qeffhowv7CXefPmcdVVV0XUrb29nccff5y3334bh8PBqFGjuOaaa/x+LfYn7777Lq+99hrff/899fX1JCcnc8ghhzBz5kz9gNm+fbvfwdOVadOm8cADD/SLn9Pp5JlnntF/MBUUFHDhhRf6PSLTFe9+7K9c0GVlZdx11118/fXXNDc3U1RUxLRp05g1a1bAmbpff/01Cxcu5KuvvsJkMnHkkUdy0003BbyPaIQTTjgh6KNXH374IUBY+6+iooJFixbx6aefUlFRQUZGBscffzzXXXddWIN2b15FRUVA58SvBx98kI0bN+JyuRg/fjw33HBDt0HY4/Hwl7/8hVWrVlFZWcnw4cO54oorOOuss0J2Csdt9erVPP/885SVlWEymRg7dixz587t9nx8sPzHgba1Jy6++OIeU3Z+9913fT4WFy9ezJ/+9Kewc0GH4tbQ0MA999zD5s2bqaysxO12M2zYMM4880wuvfRS4uLi/D6jaRovvfQSL7zwAjt27CA7O5tf//rX/OEPf+hWNppIABYEQRCEKCD3gAVBEAQhCkgAFgRBEIQoIAFYEARBEKKABGBBEARBiAISgAVBEAQhCkgAFgRBEIQoIAFYEARBEKKABOAIMWXKlB4fYo8mqrqp6gXiZhRV3VT1AnEzispuoSIBWBAEQRCigARgQRAEQYgCg+JlDJ9//jnLli1j69at1NbWkpaWxkEHHcSVV17JYYcdppdrbW1l9erVfPjhh3z//fc0NzczbNgwLrjgAqZPnx520nVBEARBMMqgCMA///wzZrOZGTNmkJ2dTUNDA2+99RYzZ87kmWeeYfLkyUBnYvZ77rmHo446ilmzZpGSksL69eu566672Lx5Mw8++GCUt0QQBEGIFQZFAD7//PM5//zz/ZZdeOGFnHjiiTz//PN6AM7Ozubtt9/mwAMP1MvNmDGD+fPns3r1aq688kqGDRs2oO6CIAhCbDJo7wEnJiZit9tpbGzUl9ntdr/g6+Wkk04C8HuxuSAIgiD0J4PqdYRNTU10dHRQV1fHm2++yTPPPMPcuXO57rrrevzcK6+8wu23387LL79s+OXv69evx2q1kpycbOjz/cmuXbsAGDJkSJRN/FHVC8TNKKq6qeoF4mYUVd2cTicmkymkWDKoAvDs2bNZv349AHFxcZxzzjksWLCAhISEoJ/p6Ohg2rRptLW18be//Q2r1dhV+S+//BJN0zCbOy8qWK1W3G43mqZhMpmwWCy4XC4AvYzH4wmprMlkwu12h13WYrHg8Xjw7mKr1Rqxspqm+fmHU9bXH9C/s7eyZrM55Hoj0d6apmG1WvV2Cae9Qynblzb0lg23XYy2YThlTSYTJpPJUP8Otw3DKev9d1/7bH+0dyAnI322P9rb5XLp66IxRvTUhmazGU3TojZGBGpDt9uNyWRi4sSJ9IZy94A9Hg9OpzOksvHx8fogDvB///d/XHrppezatYs1a9bgdDpxuVw9BuB77rmHH374gSVLlhgOvtAZ8F0ul+Ez6P6mtLRUyfvbqnqBuBlFVTdVvUDcjKKiW0lJSchllQvAn3/+OZdccklIZdeuXcv++++v/33wwQfr/z7rrLM455xzmD9/Pk888UTAzy9dupRXXnmFa665hl/96ld9Ewe/HwOqYbPZoq0QEFW9QNyMoqqbql4gbkZR2S0UlAvA++23H/fff39IZXNzc4Oui4+P54QTTmDJkiW0tbV121GrV69m4cKFzJgxgyuvvLJPzl68lzFUJDMzM9oKAVHVC8TNKKq6qeoF4mYUld1CQbkAnJOTwznnnBORutra2tA0jebmZr8A/MEHH3Dbbbdx8sknc+edd0bkuwC/e0yqsWvXLuUu1YC6XiBuRlHVTVUvEDejqOwWCuqesoVBTU1Nt2UNDQ2sW7eOIUOGkJWVpS///PPPuf766zn88MNZuHCh0metgiAIwuBFuTNgI1x++eXk5eUxbtw4srKy2LlzJ6tXr6ayspLHHntML7djxw5+//vfYzKZmDp1Ku+9955fPaNGjeKggw4y7KFyKku73R5thYCo6gXiZhRV3VT1AnEzispuoTAoAvC5557Lu+++y3PPPUdjYyNpaWmMGzeORx55hMMPP1wvt337dj0xx913392tnnnz5vUpAKv8RJd3er1qqOoF4mYUVd1U9QJxM4rKbqEwKALwRRddxEUXXdRruV/84hd89913/eahcmeor68nPT092hrdUNULxM0oqrqp6gXiZhSV3UJBboAKgiAIQhQYVJmwoon34esxY8ZE2SQwbrdbyXvUqnqBuBlFVTdVvUDcjKKiWzixQM6AI4jKjyFVVlZGWyEgqnqBuBlFVTdVvUDcjKKyWyhIAI4gKl9M6OjoiLZCQFT1AnEziqpuqnqBuBlFZbdQkAAcQVRORdlTPuxooqoXiJtRVHVT1QvEzSgqu4WCBOAIotq9CF+ys7OjrRAQVb1A3IyiqpuqXiBuRlHZLRQkAEcQ7+uqVGTHjh3RVgiIql4gbkZR1U1VLxA3o6jsFgoSgAVBEAQhCkgAjiAq55VW9a0hqnqBuBlFVTdVvUDcjKKyWyioGzEEQRAEYRAjATiCqJyKsq6uLtoKAVHVC8TNKKq6qeoF4mYUld1CQQKwIAiCIEQBCcARxGpV990WhYWF0VYIiKpeIG5GUdVNVS8QN6Oo7BYKEoAjiMqpKKurq6OtEBBVvUDcjKKqm6peIG5GUdktFCQARxCVU1G2t7dHWyEgqnqBuBlFVTdVvUDcjKKyWyhIAI4gKqeijI+Pj7ZCQFT1AnEziqpuqnqBuBlFZbdQkAAcQVRORZmbmxtthYCo6gXiZhRV3VT1AnEzispuoSABOIKonIpy+/bt0VYIiKpeIG5GUdVNVS8QN6Oo7BYKEoAFQRAEIQqo+9zMPshAp6J8cOu9NDgdIZX1eDTMjt7vUafFpXPzwbf1VS1kt1C9IDJu/dFmIG6+SF8LzwvEzSgZGRkD9l39gQTgCDLQk7AanA7qnfWhf2AAn5IKy01VLxC3PQwaN1W9QNwMoHL+/VCQABxBovUcsAkT6XHpPZZxud1Ye5gk5nA60Ij8Y1S9ufXmBf3jFok2A3ELhPS18LxA3IxSW1tLamrqgH9vpJAAPAhIj0vnvrEP91imtLSUYcOGBV2/YMuN4f0iDpHe3Hrzgv5xi0SbgbgFQvpaeF4gbrHKvn3+rhgqP4Y0ZMiQaCsERFUvEDejqOqmqheIm1FUdguFmAzAHR0dPPzwwxxzzDGMHTuW888/nw0bNvS5XnkbUvio6gXiZhRV3VT1AnEzispuoRCTAfiWW27hueee48wzz2TBggVYLBauuOIKvvjiiz7Vq3Iqyra2tmgrBERVLxA3o6jqpqoXiJtRVHYLhZgLwFu2bOHdd9/l+uuv5+abb2b69Ok8//zzFBQUsHDhwj7VrXIqyri4uGgrBERVLxA3o6jqpqoXiJtRVHYLhZgLwO+//z4Wi4Xp06fryxISEjjvvPPYtGkTu3btMly3yveA8/Pzo60QEFW9QNyMoqqbql4gbkZR2S0UYi4Ab926leHDh5OSkuK3fOzYsfp6o6icirK8vDzaCgFR1QvEzSiquqnqBeJmFJXdQiHmHkOqqqoiJyen23LvssrKyoFW6neu/tP3VNW3YbF8E7RM6mQXZhvUNrqYeX/wcqESan1ut6tHr0i7hVOXuPWPm/Q1cYsUbreLnIzveWLeyAH7zkgScwG4ra0t4CusEhIS9PVGMZlMlJaWAlBUVERFRQVOp5OEhASysrLYuXMnAJmZmWiaRn19PQCFhYVUV1fT3t5OfHw8OTk57NixA+hMtWY2m6mtrQWgoKCA2tpa2tracLs7Z1273G5KS0tJT0/HarVSU1MDdF6ecTgcVNW3Ud+sAc6g7skeDTOdaedqGoKXC5Xw6ut5fSTdwq9L3CLtJn1N3CKJiXZKS0ux2WxkZmbqtxHtdjsej0cfZ4uKiqisrKSjo4OEhASys7P1cTYzMxPYO6u665icm5urv/ih65g8ZMgQ6urqaGtrC/uedMwFYJvNRkdHR7fl3hc722w2w3WbzWa/B9YLCgr81nd9mD09fW8Gmq73MrqW9c32kpeXB4DFYQYPWC0Wv/K+l9dzc3OxWKoBJ2YTZKYG7iBms0n/f1Za3yc2hFqfpnkwmXq+ExJJt3DqErf+cZO+Jm6RoK7RiUcDc5fxr6dxtutzw13LpqWl6f82MiYD+glQKMRcAM7JyaGioqLb8qqqKqBv75eMVirKUMlMjePF+aMDrluwxUq9E+ypVp4KUiYcQq0vtCw7kXMLpy5x6x836WviFglm3v/NgJ9tR5qYm4R10EEH8fPPP9PU1OS3fPPmzQAcfPDB0dASBEEQYoyYC8CnnHIKbrebVatW6cs6OjpYvXo148aN61NqM5UfQ1IVlR8jEDdjqOqmqheIW6wSc5egx40bxymnnMKjjz5KTU0Nw4YN44033mDHjh3cd999fapb5VSUqtLQ0BBwVroKiJsxVHVT1QvELVaJuQAM8NBDD/H444/z1ltv4XA4GDVqFE8//TRHHHFEn+pVORWlqrS0tERbISjiZgxV3VT1AnGLVWIyACckJHDzzTdz8803R7RelVNRqorKl+3FzRiquqnqBeIWq8RkAO4votVRHU4HC7bcGHR96mRX53N6ZhMLtgTe5Q6nIypuANT2XkekCckLxC1AnX11k74WBHGLOSQAR5BopaLU0Hp8GbbZtne2Xf0Az9rvzS1aqOoF4mYUVd1U9QK13WIBCcD7MGlx6b0XojM9nGfPGbA9teddHmqdvRFqPS63G2uIVw4i4RZOHeJmrI5Q3aSv7UXcYhOTJjOHIkJJSQkej4dx48ZFW6Ub3gfWs9KCJ+KIFrW1tdjt9mhrBETcjKGqm6peIG5GUHVcKykpAWDMmDG9lo2554D7E5mEFT59Sf3Z34ibMVR1U9ULxC1WkQAcQVRPRaki3hSgKiJuxlDVTVUvELdYRQKwIAiCIEQBCcARRJ6XCx/ft4iohrgZQ1U3Vb1A3GIVCcARROazhU/Xl2KohLgZQ1U3Vb1A3GIVCcARRHJBh09zc3O0FYIibsZQ1U1VLxC3WEUCsBBVzGZ1u6C4GUNVN1W9QNxiFWnZCGK1Sl6TcCkuLo62QlDEzRiquqnqBeIWq0gAjiDRSkW5L1NWVhZthaCImzFUdVPVC8QtVpEALEQVlSeuiZsxVHVT1QvELVaRABxB5F5J+KSkpERbISjiZgxV3VT1AnGLVSRiRBBJRRk+SUlJ0VYIirgZQ1U3Vb1A3GIVCcARRFJRhk9lZWW0FYIibsZQ1U1VLxC3WEUCsCAIgiBEAQnAEURSUYZPTk5OtBWCIm7GUNVNVS8Qt1hFHlyNIAM9W/DBrffS4HT0Wi51sotkj4bZbGLBlp53eVpcOjcffNuAuXk8npAnr0XCLVQvEDdf+sNN+tpexC02kQAcQQY6FWWD00G9s77Xcmbb3ksd9c5+VdIJ1Q2AAbx1HpYXiNseBo2bql4gbjGIBOBBgAkT6XHpQdfXNrrw7DkDtqcG3uUOpwONyJ/B9+bmcrux9nLpvj/cevMCcQtEJNykr3VH3GITCcARJFqpKNPj0rlv7MNB18+8/xtqGpxkpcXx1PzRAcss2HJjeL+II+QWCv3hFgkvEDcjSF8zRqy5xQIyCSuCSCrK8CkvL4+2QlDEzRiquqnqBeIWq0gAFqKKyq9wFDdjqOqmqheIW6wyaC5B/+9//2Px4sV8/fXXVFdXY7PZOOCAA5g9ezYnnHAC0NmR1qxZw7p169i6dSsOh4OioiJOO+00Zs+eTUJCQp8cJBNW+KicZUfcjKGqm6peIG6xyqAJwDt37qS5uZlp06aRm5tLa2sr69at4/e//z13330306dPp7W1lfnz5zN+/HhmzJhBVlYWmzZtYvHixWzcuJHly5f3KYhKLujwSU1NjbZCUMTNGKq6qeoF4harDJoA/Ktf/Ypf/epXfstmzpzJOeecw1//+lemT59OXFwcK1euZOLEiXqZCy64gMLCQj0IH3300YYdJBVl+FRUVDBs2LBoawRE3IyhqpuqXiBuscqgPmWzWCwMGTKExsZGAOLj4/2Cr5eTTjoJgG3btg2onyAIghC7DJozYC8tLS20tbXR1NTERx99xD//+U9OPfXUHj9TXV0NQGZmZp++W1JRhk92dna0FYIibsZQ1U1VLxC3WGXQBeAHHniAVatWAZ33ZE866STuuOOOHj+zdOlSUlJSmDx5cp++2+PxUFpaCkBRUREVFRU4nU4SEhLIyspi586dQGeg1zSN+vp6AAoLC6murqa9vZ34+HhycnLYsWMHABkZGZjNZmprawEoKCigtraWtrY23O7O2Ykut5vS0lLS09OxWq3U1NQAkJ+fj8PhwO3e+3iU1y81NZWEhAT9x4c3i6bL7aa8vJzi4mLKysrQNI2UlBQSExOpqqoCIDc3l5aWFpqamjCZTAwdOpTy8nI8Hg/JycmkpKTg2nM5XtM0amtr9asQw4YNY/v27bjdbpKSktA0TXfIysrC6XTS0NAAQHFxMbt37/apa6+/3W7H4/HobVhUVERlZSUdHR0kJCSQnZ2tt6H3h1VdXZ1el9vdua/i4+PJzc1l+/bt3dq7o6ODYcOGUVdXR1tbG3FxceTn5+uPZaSlpeHZ03Aut5v29nYaGhpoaWnBYrFQVFTk1942m01vw7y8PJqammhubsZsNlNcXKy7eTweWltb9bfQ5OTk0Nra6tfeu3fvxmq1kpSURGpqKhUVFUDnYNne3k5jY6PPtnb2j8TERNLT09m9e7fe3i6XC4fD4dfeTqcTm82G3W7X+6zvdpaWllJYWEhVVVXA9rZarbjdburq6vQ+W1NTQ3t7O3FxcXq6BpfbTX19PRaLRe/fQ4YMob6+ntbWVqxWKwUFBZSVlentHR8fr/eX/Px8Ghoa9O3s2r992zs3N5eamhqqq6v1NvT27+TkZJKTk6msrPTbB966uvbZtLQ0vQ2zs7Pp6OjQ++zQoUPZuXMnLpcLt8f/+LTb7bjdbr29fccITdOIj4/vcYzwHu9utweXyxXSGNG1z3rHCN9jqrKyMmB7e8eInTt3Eh8fT15eHo2NjbS0tOh91tuGni7bGsoY4e2zOTk5tLW1BR0jfNvbd4zwjmuePd9ps9nIzMxk165dgPExwtvevmNysDHC22d9x4hwGHQB+Le//S2nnHIKlZWVvPfee3g8HpzO4PkXn376aT755BPuvPNO0tLS+vTdmqb53SspKCjwW9/1Pkp6+t4MNPn5+T2W9Z0IkZeXB4DFYQYPWC0Wv/K+L9DOzc3FYqkGnAHrTU5OBsC0Jy2s1WKhuLgY6BxMgjklJiaSlZWl/+39jBerxQKezpnhdrsdu92urysqKtL/XVpa2s3J90pEQUEB1mpvXT234ZAhQ4L6QucAbnV01mWxmP3WB2pvb4D2tnegsuZyk769CQkJ3RLXd63X92+bzeZ3duFtM7PZTGJiol/ZpKQkv/a2Wq1B/ZOTk7Hb7T7baulxWzMyMvR/B+uzZsfe7fQuC9bepaWlpKWl+R1Pvv3bVL13e73f7du/c3NzA9bru31ecnJysO7q3M5AZX3/7np8Burf3jYzm/37h2+f7VpvcnKyX58tLCwE9hyf7u7HZ6D2Li0tJS4ursf+bbGY9b7bdf9D4DEikC/4Hp+9t3d8fLy+zGaz+a3ztqE5wLb2NkZ07d/BxohATpmZmVgs3wBOzL3073DHCC9GxmRAPwEKhUF3D3j//ffn6KOP5te//jXPPPMMLS0tzJ07N+CLEtauXcvjjz/Oeeedx4UXXhgFW0EQBCFWGXQBuCtTp06lpKSEn376yW/5hg0buOmmmzjuuOO46667IvJd0UpFuS/T9SxEJcTNGKq6qeoF4harDPoA3NbWBkBTU5O+bPPmzcybN49DDz2Uxx9/PGKBUx5DCh/vPS8VETdjqOqmqheIW6wyaAJwoOvuTqeTN998E5vNxv777w90Pmp0xRVXUFhYyDPPPNPtnkZfGOj3AQ8GVM6fLW7GUNVNVS8Qt1hl0FwzveOOO2hqauKII44gLy+Pqqoq3n77bX788UduueUWkpOTaWpqYvbs2TQ0NDB79mz+/ve/+9UxdOhQJkyYYNhBUlGGT2JiYrQVgiJuxlDVTVUvELdYZdAE4NNOO43XXnuNlStXUl9fT3JyMocccgj/93//x5QpUwCor6/Xp6g/8sgj3eqYNm1anwKwpKIMH98ZiqohbsZQ1U1VLxC3WGXQBODTTz+d008/vccyRUVFfPfdd/3mIPeAw2f37t3KprkTN2Oo6qaqF4hbrDJoAnAs43A6WLDlxqDrUye7SPZomM0mFmwJvMsdTkdU3Fxud+ezl73UEWl68wJxC1ZnX92kr3VH3GITCcARJFqpKDU06p31QdebbXtn29UHz0nSL/TmBuhJFAaSkLxA3Lqwz7up6gXiFoNIAI4gAz0LOi0utHsztY0uPHvOgO2pPe/yUOvsjVDr8XpFss5I1SFuxuoI1U362l7ELTYxafLsTEQoKSnB5XL1aRJXfzHz/m+oaXCSlRbHi/NHR1vHj0CpKFVB3IyhqpuqXiBuRlB1XCspKQFgzJgxvZaVabuCIAiCEAUkAEcQSUUZPl0TtKuEuBlDVTdVvUDcYhUJwBFEHkMKH+9rxlRE3IyhqpuqXiBusYoE4Agit9PDp6dXRUYbcTOGqm6qeoG4xSoSgCOIpKIMn0jm4o404mYMVd1U9QJxi1UkAEeQaD0HvC/j+xJu1RA3Y6jqpqoXiFusIgE4gshbQ8JH5VediZsxVHVT1QvELVaRACwIgiAIUUACcASRS9Dho/LlLXEzhqpuqnqBuMUqEoAjiMyCDh+PR90ks+JmDFXdVPUCcYtVJABHEOmo4VNfXx9thaCImzFUdVPVC8QtVpEALAiCIAhRQAJwBJFUlOFTWFgYbYWgiJsxVHVT1QvELVaRABxBJBVl+FRVVUVbISjiZgxV3VT1AnGLVSQARxCZhBU+HR0d0VYIirgZQ1U3Vb1A3GIVuWYaQQY6FeWDW++lwenotVzqZBfJe16qvWBLz7s8LS6dmw++bcDc3G4PFkdovwMj4RaqF4ibL/3hJn1tL+IWm0gAjiAD/Rxwg9NBvbO+13Jm295LHfUDlFc9VDcABnDyeFheIG57GDRuqnqBuMUgEoAjSLRSUZowkR6XHnR9baMLz54zYHtq4F3ucDrQiPwl9N7cXG431l5+uPSHW29eIG6BiISb9LXuiFtsIgF4EJAel859Yx8Oun7m/d9Q0+AkKy2Op+aPDlhmwZYbw/tFHCG30tJShg0b1mMd/eHWmxeIWyAi4SZ9rTviFpvIJKwIYjZLc4ZLRkZGtBWCIm7GUNVNVS8Qt1hl0EWMr7/+mrlz5zJp0iTGjRvHGWecwfLlywOWbWho4KijjmLUqFG8//77A2wqgNrvUBY3Y6jqpqoXiFusMqguQa9fv565c+cyevRorrzySpKSkigrK2P37t0Byz/xxBO0tbVF7PslFWX41NXVkZaWFm2NgIibMVR1U9ULxC1WGTQBuKmpiZtvvpnjjjuOJ554otfLwd9//z0rV67kyiuv5IknnhggS0EQBEHoZNBcgn777beprq7muuuuw2w209LS0uMZ6X333ceJJ57I4YcfHjEHSUUZPgUFBdFWCIq4GUNVN1W9QNxilUETgDdu3EhKSgoVFRVMnTqVCRMmcNhhh3HnnXfS3t7uV/a9995j06ZN3HjjjRF1kFSU4VNTUxNthaCImzFUdVPVC8QtVhk0Afjnn3/G7XZz5ZVXcuyxx7J48WLOPfdcXn75ZebPn6+Xa2tr46GHHmLWrFkUFRVF1EFSUYZP1x9HKiFuxlDVTVUvELdYZdBcM21paaG1tZUZM2Zw222dqdBOPvlkOjo6WLVqFVdffTXDhw9nyZIlOJ1O5syZ0y8epaWlABQVFVFRUYHT6SQhIYGsrCx27twJQGZmJpqm6e/ZLCwspLq6mvb2duLj48nJyWHHjh1A5yMAZrOZ2tpaoPNyUG1tLW1tbbjdnZfYXW43paWlpKenY7Va9V+s+fn5OBwO3G5XN7/U1FQSEhKorq4GwPvbweV2U15eTnFxMWVlZWiaRkpKComJiXpS9tzcXFpaWmhqasJkMjF06FDKy8vxeDwkJyeTkpKCa8/VAE3TqK2tpbGxEYBhw4axfft23G43SUlJmM1m3SkrKwun00lDQwMAxcXF7N6926euvf52ux2Px6O3YVFREZWVlXR0dJCQkEB2drbehpmZmUDnZBJvXW63h9LSUuLj48nNzWX79u3d2rutrY2Ojg7q6upoa2sjLi6O/Px8ysvLAUhLS8Ozp+Fcbjft7e00NDTQ0tKCxWKhqKjIr71tNpvehnl5eTQ1NdHc3IzZbKa4uFh383g8tLa2UllZCUBOTg6tra1+7d3e3k5paSlJSUmkpqZSUVEBQHZ2Nu3t7TQ2Nvpsa2f/SExMJD09XZ+UmJWVhcvlwuFw+LW30+nEZrNht9v1Puu7naWlpRQWFlJVVRWwvTVNo6Ghgbq6Or3P1tTU0N7eTlxcnJ6uweV2U19fj8Vi0fv3kCFDqK+vp7W1FavVSkFBAWVlZXp7x8fH6302Pz+fhoYGfTu79m/f9s7NzdXbwduG3v6dnJxMcnIylZWVfvvAW1fXPpuWlqa3YXZ2Nh0dHXqfHTp0KDt37sTlcuH2+B+fdrsdt9utt7fvGOH9r6cxwnu8u90eXC5XSGNE1z7rHSN8j6nKysqA7e0dI9ra2igtLSUvL4/GxkZaWlr0PuttQ0+XbQ1ljPD22ZycHNra2oKOEb7t7TtGeMc1z57vtNlsZGZmsmvXLsD4GOFtb98xOdgY4e2zvmNEOAyaAGyz2QA444wz/JafeeaZrFq1iq+++gqr1cqyZcu44447SE5OjriD1Wr1e2C9672Trg+zp6fvzUCTn5/fY9nU1FT933l5eQCd+Vk9YLVY/MqnpKTo/87NzcViqQacAev1toNpT1pYq8VCcXEx0DmYBHNKTEwkKytL/9v7GS9WiwU8nY8w2O127Ha7vs73yoPb7e6WwtN7MEBnG1qrvXX13IZDhgwJ6gudA7jV0VmXxWL2Wx+ovb1u3vYOVNZcbtK3NyEhgZycnB4dfP+22WxkZ2frf3vbzGw2k5iY6Fc2KSnJr733339/v3bzLZucnIzdbvfZVkuP2+r7nGewPmt27N1O77Jg7e1tN9+Zs77921S9d3u93+3bv3NzcwPW67t9XnJycrDusuipEntq72HDhvm1WaD+7W0zs9m/f3S9Wta1vX37rPf1fRaHGdzdj89A7e1ts576t8Vi1vtu17EGAo8RgXzB9/jsvb0POOAAvd2846wXbxuaA2xrb2NE1/4dbIwI5JSZmYnF8g3gxNxL/w53jPBiZEyG8C7ZD5pL0N5O5LvDAX2nOhwOnnjiCfLy8pg0aRLbt29n+/bt+q/p2tpatm/f3qdHiaKVinJfxvurUkXEzRiquqnqBeIWqwyaM+BDDjmEDRs2UFFRwX777acv917Gs9vt7Nq1i9LSUk488cRun7/rrrsA+Pzzz+WZN0EQBKHfGTQB+NRTT2XJkiW89tprHHXUUfry1157DavVyqRJk/T7S758//33LFq0iMsuu4wJEyaQmJho2EFSUYaP7+Uh1RA3Y6jqpqoXiFusMmgC8OjRozn33HN5/fXXcbvdHHHEEXz22We8//77zJkzh7y8vG73RWDvdfwxY8YEPDMOB0nZFj4D/QrHcBA3Y6jqpqoXiFusMmgCMHReRi4oKGD16tV88MEHFBQUMH/+fGbNmjUg3y/PAYdPbW2t32QGlRA3Y6jqpqoXiFusMqgCcFxcHPPmzWPevHkhf+YXv/gF3333XT9aCYIgCEJ3BlUAjjbRulTjcDpYsCV4Vq/UyS6SPRpms4kFWwLvcofTERU3Tdv7CFRPdUSa3rxA3ILV2Vc36WvdEbfYRAJwBInW25A0tB5fhm227X3erN45IEo6vblFC1W9QNyMoqqbql6gtlssIAE4ggx0Ksq0uNBmJ9Y2uvDsOQO2p/a8y0OtszdCrcfldncmBYhgnZGqQ9yM1RGqm/S1vYhbbGLSJIFxRCgpKcHtdjN+/Phoq3Rj5v3fUNPgJCstjhfnj462jh87duzQMwephrgZQ1U3Vb1A3Iyg6rhWUlICdD5Z0xvy4GoEken64aPyq87EzRiquqnqBeIWq0gAjiCSijJ8vInfVUTcjKGqm6peIG6xigRgQRAEQYgCEoAjiKSiDB+V826LmzFUdVPVC8QtVpGIEUEkFWX4xMfHR1shKOJmDFXdVPUCcYtVJABHEElFGT7e10GqiLgZQ1U3Vb1A3GIVCcCCIAiCEAUkAEcQeQwpfPLz86OtEBRxM4aqbqp6gbjFKhKAI0i0UlHuyzQ0NERbISjiZgxV3VT1AnGLVSQARxBJKhY+LS0t0VYIirgZQ1U3Vb1A3GIVCcARRGZBh4/Kl+3FzRiquqnqBeIWq0gAjiDSUcOnqKgo2gpBETdjqOqmqheIW6wiATiCSCrK8CktLY22QlDEzRiquqnqBeIWq0gAFgRBEIQoIAE4gkgqyvBJTU2NtkJQxM0Yqrqp6gXiFqv0/HZ2ISwGehLWg1vvpcHp6LVc6mQXyR4Ns9nEgi097/K0uHRuPvi2AXPTNA3T9tDaLRJuoXqBuPnSH27S1/YibrGJBOAIMtCpKBucDuqd9b2WM9v2Xuqod/arkk6obgONql4gbkZR1U1VL1DbLZaQADwIMGEiPS496PraRheePWfA9tTAu9zhdKAR+eeYe3Nzud1Ye5k93h9uvXmBuAUiEm7S17ojbrGJBOAIEq3HkNLj0rlv7MNB18+8/xtqGpxkpcXx1PzRAcss2HJjv/wi7s2ttbWVxMTEHuvoD7fevEDcAhEJN+lr3RG32ERmDUUQyYQVPs3NzdFWCIq4GUNVN1W9QNxiFQnAEURyQYePyge3uBlDVTdVvUDcYpVBFYD/+9//Mnv2bCZOnMiECRO49NJL2bp1a8CyHR0dPP3005xyyimMGTOGo48+miuuuILdu3cPsHVso3L6TnEzhqpuqnqBuMUqg+Ye8Ndff82FF17IkCFDmDdvHh6PhxUrVjBz5kxeffVV9ttvP72s0+lkzpw5bNq0ifPPP59Ro0bR0NDA5s2baWxsNPz6Lat10DTngDF06NBoKwRF3IyhqpuqXiBuscqgiRiLFi3CZrPx8ssvk5mZCcBZZ53F1KlTeeyxx1i8eLFe9rnnnuPzzz9nxYoVjB07NmIOkooyfMrKypQ9wMXNGKq6qeoF4harDJpL0F988QVHHXWUHnwBcnNzmTRpEh9//LF+H8Pj8bB8+XJOPPFExo4di8vlorW1NVraMY/KE9fEzRiquqnqBeIWqwyaANzR0YHNZuu23Gaz4XQ6+d///gfADz/8QGVlJaNGjeL2229n/PjxjB8/njPPPJN///vffXKQVJThk5ycHG2FoIibMVR1U9ULxC1WGTSXoEeMGMFXX32F2+3Wn8ft6Ohgy5YtAFRUVAB73+zx3HPPkZGRwd133w3AM888w2WXXcZrr73GQQcdZMhB0zS9/qKiIioqKnA6nSQkJJCVlcXOnTsByMzMRNM06uvrASgsLKS6upr29nbi4+PJyclhx44dAGRkZGA2m6mtrQWgoKCA2tpa2tracLs7Z1273G5KS0tJT0/HarVSU1MDQH5+Pg6HA7d776Vxr19qaioJCQlUV1fvcUevq7y8nOLiYsrKytA0jZSUFBITE6mqqgI6ryy0tLTQ1NSEyWRi6NChlJeX4/F4SE5OJiUlBdeerGCaplFbW0tjYyMAw4YNY/v27bjdbpKSkoiPj9edsrKycDqdNDQ0AFBcXMzu3bt96trrb7fb8Xg8ehsWFRVRWVlJR0cHCQkJZGdn623ovSpSV1en1+V2eygtLSU+Pp7c3Fy2b9/erb3dbjdpaWnU1dXR1tZGXFwc+fn5lJeXA5CWloZnT8O53G7a29tpaGigpaUFi8VCUVGRX3vbbDa9DfPy8mhqaqK5uRmz2UxxcbHu5vF4aG1tpbKyEoCcnBxaW1v92tv72aSkJFJTU/X+nZ2dTXt7O42NjT7b2tk/EhMTSU9P1ycaZmVl4XK5cDgcfu3tdDqx2WzY7Xa9z/puZ2lpKYWFhVRVVQVs76SkJBoaGqirq9P7bE1NDe3t7cTFxenpGlxuN/X19VgsFr1/DxkyhPr6elpbW7FarRQUFFBWVqa3d3x8vN5n8/PzaWho0Leza//2be/c3FycTielpaV6G3r7d3JyMsnJyVRWVvrtA29dXftsWlqa3obZ2dl0dHTofXbo0KHs3LkTl8uF2+N/fNrtdtxut97evmOExWLB6XT2OEZ4j3e324PL5QppjOjaZ71jhO8xVVlZGbC9vWNEQ0MDzc3N5OXl0djYSEtLi95nvW3o6bKtoYwR3j6bk5NDW1tb0DHCt719xwjvuObZ8502m43MzEx27doFGB8jvO3tOyYHGyO8fdZ3jAiHQROAL7zwQv74xz+yYMECLrvsMjweD0899ZR+ALa1tQF7p9Q3NzezZs0ahgwZAsCRRx7JySefzNKlS1m4cKEhB03TGDZsmP53QUGB33rfddB5MHjpOvGra1nfhOh5eXkAWBxm8IDVYvErn5KSov87NzcXi6UacAas1/vr1rQnLazVYqG4uBjoPvnC97OJiYlkZWXpf3s/48VqsYCncwal3W7Hbrfr63zfL1paWtrNyfc2QkFBAdZqb109t6F3Xwbyhc4B3OrorMtiMfutD9Te3gDtbe9AZc3lJn17ExISyMnJ6dHB92+bzUZ2drb+t7fNzGYziYmJfmWTkpL82ttkMgX1T05Oxm63+2yrpcdtzcjI0P8drM+aHXu307ssWHuXlpaSk5NDWlqavs63f5uq926v97t9+3dubm7Aen23z0tOTg7WXZ3bGais79+VlZV+fwfq3942M5v9+0fXd+J2bW/fPltYWAjsOT7d3Y/PQO1dWlpKXFxcj/3bYjHrfddqtYY0RgTyBd/js/f29u0/Xa8yetvQHGBbexsjuvbvYGNEIKfMzEwslm8AJ+Ze+ne4Y4QXI2MyoJ8AhcKgCcC/+c1v2L17N8uWLeONN94A4NBDD2X27Nk8/fTT+kHr7UATJ0702xkFBQVMnDiRTZs2Dby8IAiCEHMMqpuW1113HRs2bOCll17irbfe4vXXX9cnEAwfPhzY+2vP98zDS1ZWln4pyQjRSkW5L9P1jFElxM0Yqrqp6gXiFqsMqgAMnZcbDj/8cEaNGgXAJ598Qn5+vv4c8MiRI4mLi9PvP/hSWVnpdxkkXGS2YPh4bw2oiLgZQ1U3Vb1A3GKVQReAfVm7di0lJSX89re/1Wcop6SkMHnyZDZt2sS2bdv0stu2bWPTpk0cffTRhr9PUlGGj3fihYqImzFUdVPVC8QtVhk094A///xznnzySX75y1+SkZHB5s2bWb16NcceeyyXXHKJX9nrr7+ejRs38tvf/lZft3z5ctLT05k7d2409AVBEIQYY9AE4Ly8PCwWC8uWLaO5uZmioiKuvfZaZs2a1S1F5AEHHMCLL77IwoULeeqppzCZTBx55JHcdNNN3WYPhoOkogyfrjMLVULcjKGqm6peIG6xyqCJGEOHDmXZsmUhlz/kkEP461//GlEHt8/ziEJobN++vdsjB6ogbsZQ1U1VLxC3WGXQBGAViNYkLIfTwYItNwZdnzrZRbJHw2w2sWBL4F3ucDqi4uZyu7HW9jx7vD/cevMCcQtWZ1/dpK91R9xiEwnAESRar+3S0Kh31gddb7btnW1X7xwQJZ3e3AA9icJAEpIXiFsX9nk3Vb1A3GIQCcARZKBzQafFpfdeCKhtdOHZcwZsT+15l4daZ2+EWo+mdWbjiWSdkapD3IzVEaqb9LW9iFtsYtLk4dWIUFJSgsvlYsKECdFW6cbM+7+hpsFJVlocL84fHW0dPwKlolQFcTOGqm6qeoG4GUHVca2kpASAMWPG9FrW0BlwU1MTdXV15Ofn+yWfXrt2LR9++CEJCQlcdNFFHHLIIUaqFwRBEIRBj6EA/PDDD/PWW2/xySef6AF4xYoV3HPPPfpEpLVr1/L666+z//77R85WcSQVZfgESgmqCuJmDFXdVPUCcYtVDN20/Pzzzzn66KNJTEzUl/3lL38hLy+PF198kccffxxN08J6LGgwIFfzw6ejoyPaCkERN2Oo6qaqF4hbrGIoAFdVVfk9F7Zt2zZ27drFxRdfzOGHH84pp5zCCSecwBdffBEx0X0BSUUZPn15+UV/I27GUNVNVS8Qt1jFUADu6Ojwu/f72WefYTKZ+OUvf6kvKy4uDvjCA0EQBEEQDAbg/Px8vvvuO/3vv//976Snp3PQQQfpy+rr60lKSuq74T6EpKIMn64vRVcJcTOGqm6qeoG4xSqGAvCxxx7Lhg0bePDBB3nsscf417/+xfHHH+9X5qeffvJ74X0sIKkow2fnzp3RVgiKuBlDVTdVvUDcYhVDp2xz5szh448/1nMp5+TkcM011+jra2pq2LRpExdddFFkLPcRZBJW+LhcrmgrBEXcjKGqm6peIG6xiqEAnJOTw7vvvsvGjRsBOOKII0hJSdHX19XVceONN3LMMcdExnIfIVqpKPdlfGfSq4a4GUNVN1W9QNxiFcM3LW02W7fLzl4OOOAADjjgAMNS+yoDnYpyMJCRkRFthaCImzFUdVPVC8QtVpGIEUHkHnD47Nq1K9oKQRE3Y6jqpqoXiFusEtIZ8Pz58zGZTFx//fVkZ2czf/78kCo3mUz8v//3//okKAiCIAiDkZAC8BtvvIHJZOLyyy8nOzubN954I6TKYy0ASyrK8LHb7dFWCIq4GUNVN1W9QNxilZAC8IcffghAXl6e39+CPzILOnxUvmwvbsZQ1U1VLxC3WCWkAFxYWNjj30InkooyfBwOh7KTPMTNGKq6qeoF4harGJoF7XQ6/VJRBqOiokI/axYiz4Nb76XB6ei1XOpkF8keDbPZxIItPe/ytLh0bj74tgFzc7ndWB2hXbqPhFuoXiBuvvSHm/S1vYhbbGIoAF9wwQU8+uijjBgxImiZdevWcfvtt/Ppp58altvXGOhUlA1OB/XO+l7LmW17p7vXO/tVSSdUNwAG8MJBWF4gbnsYNG6qeoG4xSCGIsZ3333HOeecw6233sr555/vt669vZ377ruPV199lczMzIhI7itE616JCRPpcelB19c2uvDsOQO2pwbe5Q6nA43I38Puzc3t9mCx9Pw0XH+49eYF4haISLhJX+uOuMUmhgLwCy+8wI033sgdd9zB+vXrueeee0hLS+Pbb7/lhhtuYNu2bfzyl7/kgQceiLSv0kRrElZ6XDr3jX046PqZ939DTYOTrLQ4npo/OmCZBVtuDO8XcYTcSktLGTZsWI919Idbb14gboGIhJv0te6IW2xiKAAfdthhvPnmm9x+++28//77bNmyhTPOOIPnn38eTdO46aabuPTSSyPtqjySijJ8EhISoq0QFHEzhqpuqnqBuMUqhm9apqam8vjjj7NkyRIeffRRli5dSkZGBs8++ywHH3xwxASbm5tZtmwZmzdvpqSkBIfDwf33388555yjl/F4PKxZs4Z169axdetWHA4HRUVFnHbaacyePbtbB2psbOSpp57igw8+YPfu3WRlZXHUUUcxb948CgoKDLvKc8Dhk5WVFW2FoIibMVR1U9ULxC1W6VMqyn/9618sX74cgOTkZOrr63n++edpaWmJiBx0vtjhySef5Mcff2TUqFEBy7S2tjJ//nzq6uqYMWMGt956K2PGjGHx4sVcdtllfpeGPR4Pv/vd71i5ciUnnngit99+O6effjrvv/8+M2bMoKmpybCrvDUkfFR+1Zm4GUNVN1W9QNxiFcOPIS1cuJAXXniBlJQUHn30UY466ijmz5/PmjVr2LRpEwsXLmTMmDF9FszNzWX9+vXk5ORQUlLCeeed161MXFwcK1euZOLEifqyCy64gMLCQhYvXszGjRs5+uijAfjqq68oKSnhjjvu8Htd4ogRI7j11lvZuHEjJ510Up+9BUEQBKEnDJ0BX3DBBTz//POMHz+eNWvWcNppp5GZmcnTTz/N7bffzu7du/nNb37DkiVL+iwYHx9PTk5Or2V8g68XbyDdtm2bvsx7htv1sor3O/pyv0PehhQ+Ks+UFzdjqOqmqheIW6xiKGJ8//33zJs3jxdffLHbPdOLLrqIV199lREjRvDYY49FRNIo1dXVgH8HOvTQQ0lKSmLRokVs3LiRiooKPvvsMx5++GHGjBmjnykLA4PK6TvFzRiquqnqBeIWqxgKwC+88ALz5s0LesY3cuRIXn/9dS688MI+yfWVpUuXkpKSwuTJk/Vldrudxx57jMbGRmbNmsXkyZO5+OKLyc3N5fnnn+9TMg1JRRk+9fX10VYIirgZQ1U3Vb1A3GIVQ9Em0OXersTHx3P77bcbqT4iPP3003zyySfceeedpKWl+a2z2+2MHj2aiRMncsABB/Dtt9+ydOlS5s+fzxNPPGH4OzVNo7S0FICioiIqKipwOp0kJCSQlZWlT2bIzMxE0zS9YxcWFlJdXU17e7t+yX3Hjh1A58uwzWYztbW1ABQUFFBbW0tbWxtud2fAd7ndlJaWkp6ejtVqpaamBoD8/HwcDgdu997JYV6/1NRUEhIS9KsE3h+5Lreb8vJyiouLKSsrQ9M0UlJSSExMpKqqCui8L9/S0kJTUxMmk4mhQ4dSXl6Ox+MhOTmZlJQUXHuSkmiaRm1tLY2NjQAMGzaM7du343a7SUpKwuPx6E5ZWVk4nU4aGhoAKC4uZvfu3T517fW32+14PB69DYuKiqisrKSjo4OEhASys7P1NvReAamrq9Prcrs7vzc+Pp7c3Fy2b9/erb1bWlro6Oigrq6OtrY24uLiyM/Pp7y8HIC0tDQ8exrO5XbT3t5OQ0MDLS0tWCwWioqK/NrbZrPpbZiXl0dTUxPNzc2YzWaKi4t1N4/HQ2trK5WVlUDn7ZHW1la/9m5tbaW0tJSkpCRSU1OpqKgAIDs7m/b2dhobG322tbN/JCYmkp6ezu7du/X2drlcOBwOv/Z2Op3YbDbsdrveZ323s7S0lMLCQqqqqgK2t3cf1tXV6X22pqaG9vZ24uLi9HQNLreb+vp6LBaL3r+HDBlCfX09ra2tWK1WCgoKKCsr09s7Pj5e77P5+fk0NDTo29m1f/u2d25uLh0dHZSWlupt6O3fycnJJCcnU1lZ6bcPvHV17bNpaWl6G2ZnZ9PR0aH32aFDh7Jz505cLhduj//xabfbcbvdenv7jhHt7e04nc4exwjv8e52e3C5XCGNEV37rHeM8D2mKisrA7a3d4xoaWmhtLSUvLw8GhsbaWlp0fustw09XbY1lDHC22dzcnJoa2sLOkb4trfvGOEd1zx7vtNms5GZmam/v9joGOFtb98xOdgY4e2zvmNEOAxs7sQBYu3atTz++OOcd9553c7Cy8vLueSSS3jwwQeZOnUqACeeeCKFhYXccsst/OMf/+BXv/qVoe+Ni4vze2C96+X5rg+zp6fvzUCTn5/fY9nU1FT939782haHGTxgtVj8yqekpOj/zs3NxWKpBpwB601OTgbAtCctrNViobi4GOgcTII5JSYm+t1H937Gi9ViAU/ns9F2u93vlWZFRUX6vzMzM7tddfC9ZVBQUIC12ltXz204ZMiQoL7QOYBbHZ11WSxmv/WB2tvlcmG1WrvlM/ctay436dubkJDQbb5C13p9/7bZbGRnZ+t/e9vMbDaTmJjoVzYpKcmvvQ844AC/dvMtm5ycjN1u99lWS4/b6ptoP1ifNTv2bqd3WbD29rab7w9f3/5tqt67vd7v9u3fubm5Aev13T4vOTk5WHdZ9FSJPbX3iBEj/NosUP/2tpnZ7N8/fPts13qTk5P9+qz3ZTUWhxnc3Y/PQO3tbbOe+rfFYtb7bqCygcaIQL7ge3z23t4HHnig3m42m81vnbcNzQG2tbcxomv/DjZGBHLKzMzEYvkGcGLupX+HO0Z4MTImA/oJUCgYDsBut5v33nuPTz75RP9V0RWTycTzzz9v9CsMsWHDBm666SaOO+447rrrrm7rV69eTXt7O8cff7zf8hNOOAGAL7/80nAAltd2hU91dXW3jq4K4mYMVd1U9QJxi1UMBeCWlhYuvfRSNm/ejKZpmEwmvxv13r8HOjPU5s2bmTdvHoceeiiPP/54wPu5NTU1aJrWLVh6n+HtSxCVyQrh097eHm2FoIibMVR1U9ULxC1WMTQJ66mnnuKrr77iqquu4t///jeapjFv3jzWr1/PY489RnFxMaeccgolJSWR9g3Ktm3buOKKKygsLOSZZ57pdqnEy/Dhw9E0jffee89v+TvvvAPA6NGBcyWHgqSiDJ/4+PhoKwRF3IyhqpuqXiBusYqhM+B169Yxfvx4rrzySr/l2dnZnHrqqUyYMIGzzz6bZcuWMWfOnD5LvvjiizQ0NOiTUj7++GP9pvzFF1+MyWRi9uzZNDQ0MHv2bP7+97/7fX7o0KFMmDABgGnTpvHss89yxx138M0333DggQfy9ddf89prr3HggQdy4oknGvaUVJTh09sz3tFE3IyhqpuqXiBusYqhALxr1y6OO+44/W+z2YzTufdFs/n5+fzqV7/ijTfeiEgAfvbZZ/XZatD5A2DdunUAnHXWWboTwCOPPNLt89OmTdMDcGZmJq+//jqLFi3i448/5uWXXyYjI4Nzzz2X6667rk+/9iQVZfjs2LGj1zetRAtxM4aqbqp6gbjFKoYCcGJiot8zwKmpqfrZqZfs7Gw9KPaVjz76qNcy3333Xcj15eXl8f/+3//ri5IgCIIg9AlDAbiwsNAvQfeBBx7Ip59+SkdHB/Hx8Wiaxr///e+Yu3QRrVSUDqeDBVtuDLo+dbKLZI+G2WxiwZbAu9zhdETFzePR9Edceqoj0vTmBeIWrM6+uklf6464xSaGAvCRRx7J6tWr9WfXfv3rX3Pbbbcxffp0jjzySDZt2sTWrVv53e9+F2lfpYnWJCwNrceXYZtte2fb1TuDFusXenMDIApPb4XkBeLWhX3eTVUvELcYxFAAvuCCC8jIyKC2tpbc3FzOO+88tm7dyooVK9i6dSsAJ598MldddVVEZVVnoJ8DTotL770QUNvo6vwVazZhT+15l4daZ2+EWo/L7e5MChDBOiNVh7gZqyNUN+lrexG32MSkRfDh1draWsrLyykoKIi5y88lJSW4XC59spdKzLz/G2oanGSlxfHifOOPWfUHpaWlyk7wEDdjqOqmqheImxFUHde8j9+G8jreiKai7JpyMNboy4scYpWuqQ9VQtyMoaqbql4gbrFKRGYNffDBB8yfPz8SVe3TSCrK8PEmNFcRcTOGqm6qeoG4xSoRCcDffvsta9asiURV+zSSijJ82traoq0QFHEzhqpuqnqBuMUq0XluZpAiqSjDJ9zXdw0k4mYMVd1U9QJxi1VCDsD3339/wDceCXuRVJTho/JbVsTNGKq6qeoF4harhByAn3/+ec455xy++eab/vTZp5FUlOHjfVG4ioibMVR1U9ULxC1WCTkAz5s3j59//pkLLriAJ598Eo/Ho6+bNGkSf/jDH/pFUBAEQRAGI2EF4FdeeYURI0bwpz/9ienTp/PTTz8BnQF43rx5/Sa5rxCtVJT7Munp6j64L27GUNVNVS8Qt1glrIgxevRoVq9ezeWXX84333zDtGnTWL58eX+57XPIJKzwUfnZaXEzhqpuqnqBuMUqYbdsXFwc119/PVOmTOGWW27h/vvv5y9/+UvA1/iZTCY++OCDiIjuC8hzwOFTU1NDSkpKtDUCIm7GUNVNVS8Qt1jF8E8bu91Oeno6mqZRVVUVSSdBEARBGPQYCsArV67k4YcfprW1lZkzZ3LjjTeSkJAQabd9DnkMKXxUfsRB3IyhqpuqXiBusUpY94ArKiqYPXs2d999N+np6Tz77LPcdtttEnz34DszXAgNh0Pd94iKmzFUdVPVC8QtVgk5AK9Zs4YzzzyTDRs2cPbZZ/P2229z1FFH9afbPoekogyf1tbWaCsERdyMoaqbql4gbrFKyJegb7nlFux2O3/605848cQT+9Npn0VmQYePyjMsxc0Yqrqp6gXiFquE3LJTpkzhnnvuienXDfbGQN8DfnDrvTQ4e788lDrZRbJHw2w2sWBLz7s8LS6dmw++bcDcAKgJrVgk3MLyAnHbQ3+4SV/rgrjFHCEH4CeffLI/PQYFA52KssHpoN5Z32s5s23vvYZ6Z78q6YTqNtCo6gXiZhRV3VT1ArXdYgm5tjAIMGEiPS54tpraRheePWfA9tTAu9zhdKAR+XvYvbm53G6svVw56A+33rxA3AIRCTfpa90Rt9hEAnAEiVYqyvS4dO4b+3DQ9TPv/4aaBidZaXE8NX90wDILttzYL7+Ie3Orra3t9bZGf7j15gXiFohIuElf6464xSaSvDiCyCSs8FH5ETZxM4aqbqp6gbjFKhKAI4ikogyf6urqaCsERdyMoaqbql4gbrGK8gG4ubmZJ554gtmzZzNp0iRGjRrF6tWrA5b1eDysWLGCs88+m7Fjx/KLX/yCSy65hG+//bZbub/85S+ccMIJjBkzhjPPPJN33nlnIDZHEARBEIB94B5wXV0dTz75JAUFBYwaNYrPPvssaNlbb72Vt99+m7PPPpuZM2fS0tLC1q1bqanxn0P/2GOPsWTJEi644ALGjBnDhx9+yA033IDJZOL000837CqpKMMnLy8v2gpBETdjqOqmqheIW6yifADOzc1l/fr15OTkUFJSwnnnnRew3Nq1a3njjTf405/+xEknnRS0voqKCv76179y0UUXcccddwBw/vnnM3PmTB566CFOOeUUw4FUUlGGT2NjIzabLdoaARE3Y6jqpqoXiFusovwl6Pj4eHJycnot99xzzzF27FhOOukkPB4PLS0tAct98MEHOJ1OLrzwQn2ZyWTiN7/5Dbt372bTpk2GXSUVZfgE208qIG7GUNVNVS8Qt1hF+QAcCk1NTWzZsoUxY8bw6KOPcthhhzFhwgSmTJnC2rVr/cpu3bqVpKQk9t9/f7/lY8eO1dcLA0e0Ht0KBXEzhqpuqnqBuMUqyl+CDoWysjI0TePdd9/FarVy4403kpqayvLly7n++utJSUlh8uTJAFRVVZGVldXtkSHvWXZlZaVhD7PZTGlpKQBFRUVUVFTgdDpJSEggKyuLnTt3ApCZmYmmadTX1wNQWFhIdXU17e3t+hn/jh07AMjIyMBsNlNbWwtAQUEBtbW1tLW14XZ3XvJ2ud2UlpaSnp6O1WrV73nn5+fjcDhwu/dm6PL6paamkpCQoM9w9J68u9xuysvLKS4u1ts1JSWFxMRE/b3Pubm5tLS00NTUhMlkYujQoZSXl+PxeEhOTiYlJQXXnhnhmqZRW1tLY2MjAMOGDWP79u243W6SkpLIzc3VnbKysnA6nTQ0NABQXFzM7t27fera62+32/F4PHobFhUVUVlZSUdHBwkJCWRnZ+ttmJmZCXTOJ/DW5XZ7KC0tJT4+ntzcXLZv3x6wvTs6Oqirq6OtrY24uDjy8/MpLy8HIC0tDc+ehnO53bS3t9PQ0EBLSwsWi4WioiK/9rbZbHob5uXl0dTURHNzM2azmeLiYt3N4/HQ2tqq98WcnBxaW1v92tu7L5OSkkhNTaWiogKA7Oxs2tvbaWxs9NnWzv6RmJhIeno6u3fv1tvb5XLpb7vxtrfT6cRms2G32/U+67udpaWlFBYWUlVVFbC9MzIyaGhooK6uTu+zNTU1tLe3ExcXp6drcLnd1NfXY7FY9PYeMmQI9fX1tLa2YrVaKSgooKysTG/v+Ph4vc/m5+fT0NCgb2fX/u3b3rm5uSQmJlJaWqq3obd/Jycnk5ycTGVlpd8+8NbVtc+mpaXpbZidnU1HR4feZ4cOHcrOnTtxuVy4Pf7Hp91ux+126+3ddYxwOp09jhHe493t9uByuUIaI7r2We8Y4XtMVVZWBmxv7xjhbYu8vDwaGxtpaWnR+6y3DT1dtjWUMcLbZ3Nycmhraws6Rvi2t+8Y4R3XPHu+02azkZmZya5duwDjY4S3vX3H5J7GiCFDhviNEeEwKAKw9xJJfX09r7zyCuPGjQPghBNOYMqUKTz11FN6AG5rayM+Pr5bHd5n3dra2gx7eDwehg0bpv9dUFDgt953HXQeDF66vnOza9nU1FT9395JERaHGTxgtVj8yqekpOj/zs3NxWKpBpwB601OTgbAtCctrNViobi4GEAf6AM5JSYmkpWVpf/t/YwXq8UCns7L+3a73e9B/qKiIv3fZWVl3Zy8BwN0tqG12ltXz204ZMiQoL7QOYBbHZ11WSxmv/WB2rusrIz4+Phuk1B8y5rLTfr2JiQkdLtd0rVe379tNhvZ2dn63942M5vNJCYm+pVNSkrya29N04L6JycnY7fbfbbV0uO2ZmRk6P8O1mfNjr3b6V0WrL3LysoYOnQoaWlp+jrf/m2q3ru93u/27d+5ubkB6/XdPi85OTlYd3VuZ6Cyvn9XVVX5/R2of3vbzGz27x++fbZrvcnJyX59trCwENhzfLq7H5+B2rusrIy4uLge+7fFYtb7rtVqDWmMCOQLvsdn7+1dU1Ojt1XXe8He5eYA29rbGNG1fwcbIwI5ZWZmYrF8Azgx99K/wx0jvBgZk4Fuk357YlBcW/AGz6KiIj34QueBcfzxx1NSUqLnabbZbHR0dHSro729XV8vDBwq3zcXN2Oo6qaqF4hbrDIoArD3F5zv2YQX7yUL7zstc3JyqK6u7tapfC9VGUXulYSP79m6aoibMVR1U9ULxC1WGRQRIy8vj5ycHP2egi+VlZUkJCTol60OPvhgWltb2bZtm1+5zZs36+uNIqkowycxMTHaCkERN2Oo6qaqF4hbrDIoAjDAqaeeyq5du9iwYYO+rLa2lg8//JAjjzxSPzudMmUKcXFxrFixQi+naRovv/wyeXl5TJgwwbCDpKIMH++VBxURN2Oo6qaqF4hbrLJPTMJ68cUXaWho0GeFfvzxx/qsuIsvvpjU1FTmzJnDe++9x1VXXcXvfvc7UlNTWblyJS6Xi+uvv16vKz8/n0suuYRly5bhcrkYM2YMH3zwAV988QULFy6UbFaCIAjCgLBPBOBnn31Wny4OsG7dOtatWwfAWWedRWpqKtnZ2axcuZIHH3yQ5557DpfLxfjx43n44Yc56KCD/Or7v//7P9LT01m1ahWrV69m+PDhPPzww5x55pl98pTgHT59uefe34ibMVR1U9ULxC1W2ScC8EcffRRSueLiYv70pz/1Ws5sNjNnzhzmzJnTVzU/ZLZg+LS0tCh7j0ncjKGqm6peIG6xyj4RgPcVopUL2uF0sGDLjUHXp052kezRMJtNLNgSeJc7nI6ouLncbqw7er5y0B9uvXmBuAWrs69u0te6I26xiQTgQYCGRr2zPuh6s23vbLt654Ao6fTmBuhJFAaSkLxA3Lqwz7up6gXiFoNIAI4gVuvANmdaXHrvhYDaRheePWfA9tSeHUOtszciVU+k6+wPr0jVG2tu0teiV6/KbrGESZMblxHBm22rL48x9Rcz7/+GmgYnWWlxvDh/dLR1/PDmnVYRcTOGqm6qeoG4GUHVca2kpASAMWPG9Fp20DwHLOybqPwOZXEzhqpuqnqBuMUqEoAjiKSiDB/fxPqqIW7GUNVNVS8Qt1hFIkYEkVSU4aNynllxM4aqbqp6gbjFKhKAI4ikogyfQPm7VUHcjKGqm6peIG6xigRgQRAEQYgCEoAjiKSiDJ+uL7BXCXEzhqpuqnqBuMUqEoAjiDzRFT5tbW3RVgiKuBlDVTdVvUDcYhUJwBFEpuuHT2NjY7QVgiJuxlDVTVUvELdYRQKwIAiCIEQBCcARZKBTUQ4Ghg0bFm2FoIibMVR1U9ULxC1WkQAcQeQxpPDZvn17tBWCIm7GUNVNVS8Qt1hFAnAEkUlY4aPyjxZxM4aqbqp6gbjFKhKAI4hkwgqfpKSkaCsERdyMoaqbql4gbrGKBOAIIrmgwyctLS3aCkERN2Oo6qaqF4hbrCIRI4LIpZrw2b17d7QVgiJuxlDVTVUvELdYRabt7sM8uPVeGpyOXsulTnaR7NEwm00s2NLzLk+LS+fmg28bMDeX243VEVoGsUi4heoF4uZLf7hJX9uLuMUmEoAjyECnomxwOqh31vdazmzbe6mj3tmvSjqhugEwgPlLwvICcdvDoHFT1QvELQaRABxBojUL2oSJ9Lj0oOtrG1149pwB21MD73KH04FG5P17c/N69UR/uPXmBeIWiEi4SV/rjrjFJhKAI0i0UlGmx6Vz39iHg66fef831DQ4yUqL46n5owOWWbDlxvB+EUfIrbS0tNcH/fvDrTcvELdARMJN+lp3xC02kUlYgiAIghAFlD8Dbm5uZtmyZWzevJmSkhIcDgf3338/55xzTreyL774Ii+99BLl5eVkZmZy2mmncc011/g9x7Zt2zZef/11NmzYQFlZGcnJyYwePZqrrrqKMWPG9MlVUlGGT3FxcbQVgiJuxlDVTVUvELdYRfkz4Lq6Op588kl+/PFHRo0aFbTcww8/zD333MOBBx7IggULmDp1Ki+++CJXXXWVX7nXXnuNV199lUMPPZRbbrmFWbNm8dNPPzF9+nQ++eSTPrnKY0jho/IjDuJmDFXdVPUCcYtVlD9ly83NZf369eTk5FBSUsJ5553XrUxlZSXPPfccZ599Ng899JC+fPjw4dxzzz189NFHnHDCCQCcfvrpzJs3j+TkZL3cueeey2mnncbixYs5+uijDbtKKsrwcToHaFq2AcTNGKq6qeoF4harKH8GHB8fT05OTo9lvvrqK1wuF6effrrf8tNOOw2Ad999V1926KGH+gVfgMzMTA4//HB+/PHHPrlKKsrwsdls0VYIirgZQ1U3Vb1A3GIV5QNwKHR0dACQkJDgtzwxMRGAr7/+utc6qqqqyMjI6JOHpKIMn8zMzGgrBEXcjKGqm6peIG6xyqCIGCNGjADgyy+/9Fv+xRdfAFBRUdHj57/44gu++uor/YzZKHIPOHx27doVbYWgiJsxVHVT1QvELVZR/h5wKBxyyCGMGzeOv/zlL+Tl5fGLX/yCbdu2cddddxEXF0d7e3vQz9bU1HDDDTdQVFTEZZdd1icPTdMoLS0FoKioiIqKCpxOJwkJCWRlZbFz506g8xelpmnU19cDUFhYSHV1Ne3t7fol9x07dgCQkZGB2WymtrYWgIKCAmpra2lra8Pt7nzu2OV2U1paSnp6OlarlZqaGgDy8/NxOBy43S7d0euXmppKQkIC1dXVe9zR6yovL6e4uJiysjI0TSMlJYXExESqqqqAzvvyLS0tNDU1YTKZGDp0KOXl5Xg8HpKTk0lJScG158eIpmnU1tbS2NgIdL7ce/v27bjdbpKSkvB4PLpTVlYWTqeThoYGoHP25e7du33q2utvt9vxeDx6GxYVFVFZWUlHRwcJCQlkZ2frbej9BV9XV6fX5XZ3fm98fDy5ubn6O09927ulpYWOjg7q6upoa2sjLi6O/Px8ysvLgc4k9Z49Dedyu2lvb6ehoYGWlhYsFgtFRUV+7W2z2fQ2zMvLo6mpiebmZsxmM8XFxbqbx+OhtbWVyspKAHJycmhtbfVr79bWVkpLS0lKSiI1NVX/kZmdnU17ezuNjY0+29rZPxITE0lPT9cn1WRlZeFyuXA4HH7t7XQ6sdls2O12vc/6bmdpaSmFhYVUVVUFbG/vPqyrq9P7bE1NDe3t7cTFxenpGlxuN/X19VgsFr1/DxkyhPr6elpbW7FarRQUFFBWVqa3d3x8vN5n8/PzaWho0Leza//2be/c3Fw6OjooLS3V29Dbv5OTk0lOTqaystJvH3jr6tpn09LS9DbMzs6mo6ND77NDhw5l586duFwu3B7/49Nut+N2u/X29h0j2tvbcTqdPY4R3uPd7fbgcrlCGiO69lnvGOF7TFVWVgZsb+8Y0dLSQmlpKXl5eTQ2NtLS0qL3WW8berpsayhjhLfP5uTk0NbWFnSM8G1v3zHCO6559nynzWYjMzNT/8FgdIzwtrfvmBxsjPD2Wd8xIhwGRQAGWLx4Mddeey233nor0JkWctasWXz++ef89NNPAT/T0tLCnDlzaG5uZsWKFd3uDYeL1Wr1e2C9oKDAb33Xh9nT0/dmoMnPz++xbGpqqv7vvLw8ACwOM3jAarH4lU9JSdH/nZubi8VSDTgD1uvdZtOetLBWi0V/7GDo0KFBnRITE8nKytL/7vqogtViAU/nfXG73Y7dbtfXFRUV6f+22Wx+2wb+l7wKCgqwVnvr6rkNhwwZEtQXOgdwq6OzLovF7Lc+UHs3NjYSHx+vt3egsuZyk769CQkJ3eYrdK3X92+bzUZ2drb+t7fNzGYziYmJfmWTkpL82ruwsNCv3XzLJicnY7fbfbbV0uO2+t56CdZnzY692+ldFqy9GxsbSU1N9XuLjm//NlXv3V7vd/tuS25ubsB6fbfPS05ODtZdFj1VYk/tnZ+f7/c9gfq3t83MZv/+4dtnu9abnJzs12cLCwuBPcenu/vxGai9GxsbiYuL67F/Wyxmve92HWsg8BgRyBd8j8/e27uoqEivu+v9YG8bmgNsa29jRNf+HWyMCOSUmZmJxfIN4MTcS/8Od4zwYmRMBvQToFAYNAE4Ly+PlStX8vPPP1NdXc2wYcPIycnhmGOOYfjw4d3Kd3R0cNVVV/Hdd9+xbNkyRo4c2WcHmQUdPtHKHhYK4mYMVd1U9QJxi1UGxT1gX4YPH87hhx9OTk4OP/zwA1VVVd0eLfJ4PNx8881s3LiRRx55hEmTJkXku6Wjho/38pCKiJsxVHVT1QvELVYZNGfAXfF4PDz88MMkJiYyY8YMv3X33HMPa9eu5e677+bkk0+OkqEgCIIQy+wTAfjFF1+koaFBn5Ty8ccf6zflL774YlJTU7n33nvp6OjgoIMOwuVy8c4777BlyxYeeOABv/tazz33HCtWrGDChAnYbDbefPNNv+866aST/FJXhoOkogyfrvd6VELcjKGqm6peIG6xyj4RMZ599ll9thrAunXrWLduHQBnnXUWqampjB49mueff563334bk8nE2LFjee655zjyyCP96vr2228B2LRpE5s2ber2XR9++KHhACyPIYVPZWVlt4kRqiBuxlDVTVUvELdYZZ8IwB999FGvZc4555yAL2joygMPPMADDzwQCa1uyCSs8PEmUVERcTOGqm6qeoG4xSr7RADeV4hWKkqH08GCLTcGXZ862UXynpdqL9gSeJc7nI6ouLndns7HNXqpI9L05gXiFqzOvrpJX+uOuMUmEoAjiMViicr3amg9vgzbbNs73b1+gPOq9+YG6M9wDiQheYG4dWGfd1PVC8QtBpEAHEFcLlfvhSJIWlx674WA2kYXnj1nwPbUnnd5qHX2Rqj1uNzuzqQAEawzUnWIm7E6QnWTvrYXcYtNTJrcuIwIJSUluFwuJkyYEG2Vbsy8/xtqGpxkpcXx4vzR0dbxo7S0tFuGGVUQN2Oo6qaqF4ibEVQd10pKSgAYM2ZMr2UHXSKOaCJvQwofld+0Im7GUNVNVS8Qt1hFIoYgCIIgRAEJwBFEUlGGj/ftIyoibsZQ1U1VLxC3WEUCsCAIgiBEAQnAEURSUYaP99VtKiJuxlDVTVUvELdYRQJwBJFUlOHjfbm6ioibMVR1U9ULxC1WkQAcQeSJrvBpb2+PtkJQxM0Yqrqp6gXiFqtIAI4g0UpFuS8THx8fbYWgiJsxVHVT1QvELVaRABxBopWKcl8mNzc32gpBETdjqOqmqheIW6wiATiCDHQqysHA9u3bo60QFHEzhqpuqnqBuMUqEoAFQRAEIQpIAI4gkooyfDIyMqKtEBRxM4aqbqp6gbjFKhIxIohMwgoflX+0iJsxVHVT1QvELVaRlo0g8hxw+NTW1kZbISjiZgxV3VT1AnGLVSQAC4IgCEIUkNyJEWSgH0N6cOu9NDgdvZZLnewi2aNhNptYsKXnXZ4Wl87NB982YG6aBqbeiwGRcQvVC8TNl/5wk762F3GLTSQAR5CBfhtSg9NBvbO+13Jm295LHfXOflXSCdVtoFHVC8TNKKq6qeoFarvFEhKAI0i0UlGaMJEelx50fW2jC8+eM2B7auBd7nA60Ii8f29uLrcbay9XDvrDrTcvELdARMJN+lp3xC02kQAcQaI1Czo9Lp37xj4cdP3M+7+hpsFJVlocT80fHbDMgi039ssv4t7cdu7cSUFBQY919Idbb14gboGIhJv0te6IW2wik7AiiKSiDJ/8/PxoKwRF3IyhqpuqXiBusYoE4AgiqSjDp7y8PNoKQRE3Y6jqpqoXiFusovQl6C1btrBmzRo+/fRTduzYQUZGBuPGjePaa69lxIgRQOfEpzVr1rBu3Tq2bt2Kw+GgqKiI0047jdmzZ5OQkBC0/i+++IKLLroIgI0bN2K32wdkuwRBEARB6QC8dOlSvvzyS0455RRGjRpFVVUVL730Eueccw6rVq1i5MiRtLa2Mn/+fMaPH8+MGTPIyspi06ZNLF68mI0bN7J8+fKA92Y9Hg/33nsvSUlJtLS0RMRXMsaET1paWrQVgiJuxlDVTVUvELdYRekAPGvWLBYuXOj3PsrTTjuNM888kyVLlrBw4ULi4uJYuXIlEydO1MtccMEFFBYW6kH46KOP7lb3qlWr2LVrF+eddx7Lly+PiK+kogyfuLi4aCsERdyMoaqbql4gbrGK0qdsEydO7PYy6OHDh3PggQfy448/Ap0vi/YNvl5OOukkALZt29ZtXX19PY8//jhXX311RH/dSSrK8KmpqYm2QlDEzRiquqnqBeIWqygdgAOhaRrV1dVkZmb2WK66uhogYLlFixaRk5PDjBkz+sVREARBEHpD6UvQgXjrrbeoqKjg6quv7rHc0qVLSUlJYfLkyX7Lv/32W1atWsWSJUsi/tiQ2WymtLQUgKKiIioqKnA6nSQkJJCVlcXOnTuBzh8FmqZRX18PQGFhIdXV1bS3txMfH09OTg47duwAOl8FZjab9YToBQUF1NbW0tbWhtvdmXnL5XZTWlpKeno6VqtV/8Wan5+Pw+HA7d47O9vrl5qaSkJCgv5DxZtDxOV2U15eTnFxMWVlZWiaRkpKComJiVRVVQGQm5tLS0sLTU1NmEwmhg4dSnl5OR6Ph+TkZFJSUnDtuRqgaRq1tbU0NjYCMGzYMLZv347b7SYpKQm73a47ZWVl4XQ6aWhoAKC4uJjdu3f71LXX32634/F49DYsKiqisrKSjo4OEhISyM7O1tvQ+yOsrq5Or8vt9lBaWkp8fDy5ubn6S8d929vj8dDR0UFdXR1tbW3ExcWRn5+vzwpNS0vDs6fhXG437e3tNDQ00NLSgsVioaioyK+9bTab3oZ5eXk0NTXR3NyM2WymuLhYd/N4PLS2tlJZWQlATk4Ora2tfu3t3ZdJSUmkpqZSUVEBQHZ2Nu3t7TQ2Nvpsa2f/SExMJD09nd27d+vt7XK5cDgcfu3tdDqx2WzY7Xa9z/puZ2lpKYWFhVRVVQVs75SUFBoaGqirq9P7bE1NDe3t7cTFxenpGlxuN/X19VgsFr1/DxkyhPr6elpbW7FarRQUFFBWVqa3d3x8vN5n8/PzaWho0Leza//2be/c3FwSEhIoLS3V29Dbv5OTk0lOTqaystJvH3jr6tpn09LS9DbMzs6mo6ND77NDhw5l586duFwu3B7/49Nut+N2u/X29h0jrFYrTqezxzHCe7y73R5cLldIY0TXPusdI3yPqcrKyoDt7R0jvG2Rl5dHY2MjLS0tep/1tqGny7aGMkZ4+2xOTg5tbW1Bxwjf9vYdI7zjmmfPd9psNjIzM9m1axdgfIzwtrfvmBxsjPD2Wd8xIhz2qQC8bds27r77biZMmMC0adOClnv66af55JNPuPPOO7tdYr7vvvuYPHkyxxxzTMT9NE1j2LBh+t9dH173XQedB4OXrs/adS2bmpqq/zsvLw8Ai8MMHrBaLH7lU1JS9H/n5uZisVQDzoD1JicnA3tzvVotFoqLiwH0gT6QU2JiIllZWfrf3s94sVos4Om8L2632/1mmBcVFen/rqqq6ubke9WioKAAa7W3rp7bcMiQIUF9oXMAtzo667JYzH7rA7V3VVUV8fHxensHKmsuN+nbm5CQQE5OTo8Ovn/bbDays7P1v71tZjabSUxM9CublJTk195JSUl+3+VbNjk5Gbvd7rOtlh631fd9r8H6rNmxdzu9y4K1d1VVFWlpaX7Hnm//NlXv3V7vd/v279zc3ID1+m6fl5ycHKy7OrczUFnfv5uamvz+DtS/vW1mNvv3D98+27Xe5ORkvz5bWFgI7Dk+3d2Pz0DtXVVVRVxcXI/922Ix633XarWGNEYE8gXf47P39k5JSdH7ms1m81vnbUNzgG3tbYzo2r+DjRGBnDIzM7FYvgGcmHvp3+GOEV6MjMkQ3iX7fSYAV1VVMWfOHFJTU1m0aFHQs9e1a9fy+OOPc95553HhhRd2W7dp0ybefvvtfnGMVirKfZlIzUDvD8TNGKq6qeoF4har7BMBuLGxkcsvv5zGxkZeeumlbr/uvGzYsIGbbrqJ4447jrvuuqvb+oceeoipU6cSFxenX07wXjryXnoLVncoyCzo8FE5e5i4GUNVN1W9QNxiFeUDcHt7O3PnzuXnn3/mr3/9KwcccEDAcps3b2bevHkceuihPP7441it3Tdt165dvPPOO7zzzjvd1k2bNo2DDjqIN99807CrdNTw6XqpSSXEzRiquqnqBeIWqygdgN1uN9deey1fffUVf/7zn5kwYULActu2beOKK66gsLCQZ555ptt9Ci9PPvlkt2Xvvvsua9eu5cEHH+xzzlNJRRk+paWl3e6tqIK4GUNVN1W9QNxiFaUD8AMPPMBHH33E8ccfT319fbez07PPPpumpiZmz55NQ0MDs2fP5u9//7tfmaFDh+qB+8QTT+z2HVu3bgVg8uTJkopSEARBGDCUDsDffvstAB9//DEff/xxt/Vnn3029fX1+rTzRx55pFuZadOmBT1zjjSSijJ8fGcSqoa4GUNVN1W9QNxiFaUD8AsvvNBrmaKiIr777jvD33HVVVdx1VVXGf68LzIJK3yC3S5QAXEzhqpuqnqBuMUqSgfgfY1opaJ0OB0s2HJj0PWpk10kezTMZhMLtgTe5Q6nIypuLre785nEXuqINL15gbgFq7OvbtLXuiNusYkE4EGAhka9sz7oerNtb87ReueAKOn05gboSRQGkpC8QNy6sM+7qeoF4haDSACOIAP9GFJaXHrvhYDaRheePWfA9tSed3modfZGqPVoWmc2nkjWGak6xM1YHaG6SV/bi7jFJiZN0jdFhJKSEjweD+PGjYu2Sjdm3v8NNQ1OstLieHH+6Gjr+FFdXe2XjlElxM0Yqrqp6gXiZgRVx7WSkhIAxowZ02tZmbYbQbwJyYXQaW5ujrZCUMTNGKq6qeoF4harSAAWoorKj26JmzFUdVPVC8QtVpGWjSCB0l8KPdP1DSkqIW7GUNVNVS8Qt1hFAnAEkVSU4eN996iKiJsxVHVT1QvELVaRACxEFZXnAIqbMVR1U9ULxC1WkQAcQeReSfikpKREWyEo4mYMVd1U9QJxi1UkYkQQSUUZPklJSdFWCIq4GUNVN1W9QNxiFQnAESRaqSj3ZSorK6OtEBRxM4aqbqp6gbjFKhKABUEQBCEKSACOIAOdinIwkJOTE22FoIibMVR1U9ULxC1WkQAcQWS2YPi0trZGWyEo4mYMVd1U9QJxi1UkAEcQSUUZPk1NTdFWCIq4GUNVN1W9QNxiFQnAQlRReea4uBlDVTdVvUDcYhUJwBFEUlGGz9ChQ6OtEBRxM4aqbqp6gbjFKhKAI4ikogyf8vLyaCsERdyMoaqbql4gbrGKBGAhqqh831zcjKGqm6peIG6xigTgCCL3SsJH5Sw74mYMVd1U9QJxi1UkAEcQyQUdPqmpqdFWCIq4GUNVN1W9QNxiFYkYEURSUYZPRUVFtBWCIm7GUNVNVS8Qt1hFArAgCIIgRAGln5vZsmULa9as4dNPP2XHjh1kZGQwbtw4rr32WkaMGKGXu+WWW3jjjTe6fX7EiBG8//773ZaXlZWxaNEiPvnkE5qbm8nPz+fUU0/luuuu65OvpKIMn+zs7GgrBEXcjKGqm6peIG6xitIBeOnSpXz55ZeccsopjBo1iqqqKl566SXOOeccVq1axciRI/Wy8fHx3HvvvX6fD3TvYuvWrVx88cXk5eXxu9/9jszMTHbu3Mnu3bv77CupKMOnvb2d5OTkaGsERNyMoaqbql4gbrGK0gF41qxZLFy4kPj4eH3ZaaedxplnnsmSJUtYuHChvtxqtXL22Wf3WJ/H4+Gmm25iv/32Y/ny5dhstoj6ynT98GlsbMRut0dbIyDiZgxV3VT1AnGLVZS+Bzxx4kS/4AswfPhwDjzwQH788cdu5d1ud495S9evX8/333/PH/7wB2w2G62trTJxShAEQYgKSgfgQGiaRnV1NZmZmX7LW1tbOeywwzjssMOYNGkSd911F83NzX5lNm7cCHRerj7nnHMYP34848aN47rrrqO+vr7PbpKKMnxUTnMnbsZQ1U1VLxC3WGWfixhvvfUWFRUVXH311fqynJwcLrvsMkaPHo2mafzrX/9ixYoVfPvtt7zwwgt6YPz5558BuPbaazn22GOZM2cO3377LUuWLGHXrl2sXLmyT8k0VD+brmt0MvP+b6Kt4YfH7cas6OQ1cTOGqm6qeoG4GaGu0RlthT6zTwXgbdu2cffddzNhwgSmTZumL7/hhhv8yp1++ukMHz6cxx57jL/97W+cfvrpALS0tAAwZswY/f7x1KlTSUxM5JFHHmHjxo0cffTRhv08Hg+lpaUAFBUVUVFRgdPpJCEhgaysLHbu3AlAZmYmmqbpZ92FhYVUV1fT3t5OfHw8OTk57NixA4CMjAzMZjO1tbUAFBQUUFtbS1tbG3FxceTn5+u5WtPT07FardTU1ACQn5+Pw+HA7e7MUe3RoKZBxU6r8r1zcTOGqm6qeoG4GcPjdlNaWorNZiMzM5Ndu3YBYLfb8Xg8+jhbVFREZWUlHR0dJCQkkJ2drY+z3iuqdXV1QPcxOTc3l+3btwPdx+QhQ4ZQV1enj8nhsM8E4KqqKubMmUNqaiqLFi3q9ZGfWbNm6Y8aeQOwd9LVGWec4Vf2jDPO4JFHHuHLL7/sUwA2m80MGzZM/7ugoMBvve866AyYXvLz83ss6zujOy8vr8eyKSkp+r9zc3PJyajHZGrHbFbvV6zH41bSC8TNKKq6qeoF4mYUj8dNdnqC3xjY0zg7ZMgQv3Vdy6alpen/Njome0+AQmGfCMCNjY1cfvnlNDY28tJLL3ULQIGw2WxkZGTgcDj0Zbm5uQBkZWX5lfX+3dDQ0CdPVVNRPjFvJO3t7SQkJERbpRuqeoG4GUVVN1W9QNyMorJbKKgZMXxob29n7ty5/Pzzzzz99NMccMABIX2uqamJuro6v+nzhxxyCNA9tVplZSVAn6faq3wPOBLPOfcHqnqBuBlFVTdVvUDcjKKyWygoHYDdbjfXXnstX331FYsWLWLChAndyrS3twd89OjPf/4zmqZx7LHH6sumTJlCfHw8q1ev9ntm99VXXwXo0+VnQRAEQQgHpS9BP/DAA3z00Uccf/zx1NfX8+abb/qtP/vss6mqqmLatGmcfvrp7LfffkDn877/+Mc/OPbYY5kyZYpePicnh7lz5/LEE09w2WWXMWXKFL777jteeeUVzjjjDMaOHdsnX5VTUXa97K4KqnqBuBlFVTdVvUDcjKKyWygoHYC//fZbAD7++GM+/vjjbuvPPvts0tLSOO644/jkk09Ys2YNbrebYcOGcf3113PppZd2uy975ZVXkp6ezgsvvMD9999PdnY2c+fO5Q9/+EOffVVORelyuaKtEBBVvUDcjKKqm6peIG5GUdktFJQOwC+88EKvZdLS0nj44YdDrtNkMjFz5kxmzpzZF7WAqJyK0uFwkJGREW2NbqjqBeJmFFXdVPUCcTOKym6hoPQ9YEEQBEEYrJg0la+b7kOUlJQAnUk+VMTj8Sj5mJSqXiBuRlHVTVUvEDejqOgWTixQy3wfRx5DCh9VvUDcjKKqm6peIG5GUdktFCQARxCVLyY4nSqmoFTXC8TNKKq6qeoF4mYUld1CQQJwBOnLixz6m0i/+zhSqOoF4mYUVd1U9QJxM4rKbqEgATiCqPwcsKov1FbVC8TNKKq6qeoF4mYUld1CQQJwBFH5mTTvm5hUQ1UvEDejqOqmqheIm1FUdgsFCcCCIAiCEAUkAEcQuQQdPqp6gbgZRVU3Vb1A3IyislsoSACOICrPglY1S5eqXiBuRlHVTVUvEDejqOwWChKAI4jKnaG+vj7aCgFR1QvEzSiquqnqBeJmFJXdQkECsCAIgiBEAQnAEcRqVffdFoWFhdFWCIiqXiBuRlHVTVUvEDejqOwWChKAI4jKqSirqqqirRAQVb1A3IyiqpuqXiBuRlHZLRQkAEcQlSdhdXR0RFshIKp6gbgZRVU3Vb1A3IyislsoSACOICqnokxISIi2QkBU9QJxM4qqbqp6gbgZRWW3UJAAHEFUfg44Ozs72goBUdULxM0oqrqp6gXiZhSV3UJBAnAEUTkV5Y4dO6KtEBBVvUDcjKKqm6peIG5GUdktFCQAC4IgCEIUkAAcQcxmdZszIyMj2goBUdULxM0oqrqp6gXiZhSV3UJB3YghRBRVJ4ip6gXiZhRV3VT1AnEzispuoSABOIKonIqyrq4u2goBUdULxM0oqrqp6gXiZhSV3UJBArAgCIIgRAEJwBFE5VSUBQUF0VYIiKpeIG5GUdVNVS8QN6Oo7BYKEoAjiMqpKGtqaqKtEBBVvUDcjKKqm6peIG5GUdktFCQARxCVU1G2t7dHWyEgqnqBuBlFVTdVvUDcjKKyWyhIAI4gKs/Ii4uLi7ZCQFT1AnEziqpuqnqBuBlFZbdQkAAcQVRORZmXlxdthYCo6gXiZhRV3VT1AnEzispuoSABOIKonIpy+/bt0VYIiKpeIG5GUdVNVS8QN6Oo7BYKJk3lG5f7EF9++SWaphEfHx9tlYC4XC4lZ2mr6gXiZhRV3VT1AnEziopuHR0dmEwmJk6c2GtZtcz3YVS+/wvqPiKlqheIm1FUdVPVC8TNKCq6mUymkOOBnAELgiAIQhSQe8CCIAiCEAUkAAuCIAhCFJAALAiCIAhRQAKwIAiCIEQBCcCCIAiCEAUkAAuCIAhCFJAALAiCIAhRQAKwIAiCIESB/x9gEjES7S/p2wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchvision.models.detection.anchor_utils import AnchorGenerator\n",
    "\n",
    "p_anchor_generator = AnchorGenerator(((16,)), ((1.,)))\n",
    "# Instead of calling the forward function, which requires images as input, we'll directly call the \n",
    "# grid_anchors method that the forward function calls internally to generate anchors:\n",
    "# https://github.com/pytorch/vision/blob/release/2.0/torchvision/models/detection/anchor_utils.py#L127\n",
    "p_anchors = p_anchor_generator.grid_anchors([[feature_map_size, feature_map_size]], [[torch.tensor(stride), torch.tensor(stride)]])\n",
    "p_anchors = [tuple(i) for i in p_anchors[0].numpy()]\n",
    "\n",
    "rect_colour = palette[2]\n",
    "fig, ax = plot_image_with_rectangles(\n",
    "    image_size=image_size,\n",
    "    feature_map_size=feature_map_size,\n",
    "    rectangles=p_anchors,\n",
    "    draw_image_on_top=False,\n",
    "    rect_colour=rect_colour\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aaa6d07-bf71-4339-b747-bf51c48ec751",
   "metadata": {},
   "source": [
    "So, we got the same anchor boxes that we plotted above using our simplified function. Notice that we passed the aspect ratio as 1.0 in the constructor because we want to draw square boxes. You can instantiate `AnchorGenerator` with not just 1 but multiple scales and aspect ratios and `AnchorGenerator` will use all combinations of scales and aspect ratios for anchor boxes (check the constructor `__init__` in the code link above).\n",
    "\n",
    "You can try and plot the bigger anchor boxes that we plotted earlier.\n",
    "\n",
    "Let's now make the anchor boxes slightly bigger but change the aspect ratio to 2.0. Aspect ratio 2.0 should result in boxes whose height is twice their width. These boxes could be used for detecting pedestrians for example. You can also try to change the aspect ratio to 0.5 to draw anchor boxes that could be used for detecting other things like cars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ac5902f-98f2-4fd4-b9b3-9c7ad9d4b92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_anchor_generator = AnchorGenerator(((20,)), ((2.,)))\n",
    "p_anchors = p_anchor_generator.grid_anchors([[feature_map_size, feature_map_size]], [[torch.tensor(stride), torch.tensor(stride)]])\n",
    "p_anchors = [tuple(i) for i in p_anchors[0].numpy()]\n",
    "\n",
    "rect_colour = palette[2]\n",
    "fig, ax = plot_image_with_rectangles(\n",
    "    image_size=image_size,\n",
    "    feature_map_size=feature_map_size,\n",
    "    rectangles=p_anchors,\n",
    "    draw_image_on_top=False,\n",
    "    rect_colour=rect_colour\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c3e9869-1225-4fca-a26a-aab6a98e6c8b",
   "metadata": {},
   "source": [
    "## Wrapping up\n",
    "I hope this notebook helped you better understand anchor boxes AND see how you can practically use them using Torvision's `AnchorGenerator`.\n",
    "\n",
    "Anchors are only one of the many components in an object detection pipeline, although probably the most challenging component to understand. If you're interested in learning how to use the whole object detection pipeline in PyTorch (using Torchvision), then please go to the Torchvision's tutorial here: https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html\n",
    "\n",
    "If you find any bugs or mistakes in this notebook, then please send me an email (or a message on Mattermost) to help me improve it."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}