{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fcee07fb-62f2-44da-bf3d-3df8f483b477",
   "metadata": {},
   "source": [
    "## Ukesoppgaver IN 3370: Kompresjon og Koding I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d842cba6-86e9-4aca-9122-1c73b74067d7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import heapq\n",
    "\n",
    "from PIL import Image\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "# Setter opp noen bedre standardargumenter for plotting med bilder\n",
    "plt.rcParams['figure.figsize'] = [10.24, 7.68]\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "905640f6-9d26-47f4-8c17-3d440b59a91b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Last ned testbilde vi skal bruke\n",
    "url = '/studier/emner/matnat/ifi/IN3370/h25/undervisningsmateriale/bilder/lena.png'\n",
    "if not os.path.isfile('lena.png'):\n",
    "    urlretrieve(url, 'lena.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ce7a1c0-5983-4e51-8191-a09083398e70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Laster inn bilde som array med PIL og normaliserer til flyttall\n",
    "image = np.array(Image.open('lena.png')) / 255\n",
    "\n",
    "# Eksempel på en funksjon som henter ut leksikografisk skannerekkefølge\n",
    "def raster_scan(height, width):\n",
    "    idx = np.arange(height*width)\n",
    "    xidx = idx % width\n",
    "    yidx = idx // width\n",
    "    return yidx, xidx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab12e05b-00a2-442b-be80-c655464ce223",
   "metadata": {},
   "source": [
    "### Oppgave 1:\n",
    "\n",
    "La $f$ være et gråtonebilde med høyde $H$ og bredde $W$. \n",
    "\n",
    "#### Oppgave 1a:\n",
    "Skriv en funksjon som returnerer to sekvenser med indekser $(y_i)_{i=1}^{HW}, (x_i)_{i=1}^{HW}$ slik at $\\big(f(x_i,y_i)\\big)_{i=1}^{HW}$ returnerer gråtoneverdiene i kontinuerlig diagonal skannerekkefølge. \n",
    "\n",
    "Eksempelvis, for et $3 \\times 3$ bilde ønsker vi en rekkefølge gitt av\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "1& 3& 4 \\\\ \n",
    "2& 5& 8 \\\\\n",
    "6& 7& 9 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "som gir indeksene (med start i én) $x = (1,1,2,3,2,1,2,3,3)$ og $y = (1,2,1,1,2,3,3,2,3)$.\n",
    "\n",
    "#### Oppgave 1b:\n",
    "Skriv en funksjon som returnerer indekser for en kontinuerlig spiralrekkefølge. \n",
    "\n",
    "Eksempelvis, for et $3 \\times 3$ bilde ønsker vi denne gangen en rekkefølge gitt av\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "1& 2& 3 \\\\ \n",
    "8& 9& 4 \\\\\n",
    "7& 6& 5 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "### Tips:\n",
    "\n",
    "- Det er lett å hente ut den $n$te positive diagonalen fra en matrise `A` med bruk av `A.diagonal(n)`. \n",
    "- Dersom man ønsker en negativ diagonal kan man flippe matrisen med bruk av `np.fliplr` eller `np.flipud`.\n",
    "- For en kontinuerlig skannerekkefølge, kan det også være nyttig å huske at vi kan invertere rekkefølgen på et array ved hjelp av `arr[::-1]`.\n",
    "- For å slå sammen to eller flere arrays langs siste dimensjon kan man bruke `np.concatenate([arr1, arr2,...], axis=-1)`.\n",
    "- Det kan også være nyttig å se på eksempelfunksjonen `raster_scan` som implementerer den standardiserte skannerekkefølgen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98e84259-33ea-44d2-b2aa-7fb69b122618",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fasit\n",
    "def continuous_diagonal_scan(height, width):\n",
    "    grid = np.arange(height*width).reshape(height, width)\n",
    "    diagonals = np.arange(width-1, -height, -1)\n",
    "    idx = np.concatenate([\n",
    "        np.fliplr(grid).diagonal(d)[::2*(1-i%2)-1] \n",
    "        for i, d in enumerate(diagonals)\n",
    "    ], 0)\n",
    "    xidx = idx % width\n",
    "    yidx = idx // width\n",
    "    return yidx, xidx\n",
    "\n",
    "\n",
    "def continuous_spiral_scan(height, width):\n",
    "    # Treg, kan sikkert forbedres med litt triksing...\n",
    "    grid = np.arange(height * width).reshape(height, width)\n",
    "    indices = np.zeros((2, height * width), dtype=int)\n",
    "    top, bottom, left, right = 0, height-1, 0, width-1\n",
    "    idx = 0\n",
    "    \n",
    "    while top <= bottom and left <= right:\n",
    "        indices[:, idx:idx+right-left+1] = np.array([np.full(right-left+1, top), np.arange(left, right+1)])\n",
    "        idx += right-left+1\n",
    "        top += 1\n",
    "        \n",
    "        if top <= bottom:\n",
    "            indices[:, idx:idx+bottom-top+1] = np.array([np.arange(top, bottom+1), np.full(bottom-top+1, right)])\n",
    "            idx += bottom-top+1\n",
    "            right -= 1\n",
    "        \n",
    "        if left <= right:\n",
    "            indices[:, idx:idx+right-left+1] = np.array([np.full(right-left+1, bottom), np.arange(right, left-1, -1)])\n",
    "            idx += right-left+1\n",
    "            bottom -= 1\n",
    "        \n",
    "        if top <= bottom:\n",
    "            indices[:, idx:idx+bottom-top+1] = np.array([np.arange(bottom, top-1, -1), np.full(bottom-top+1, left)])\n",
    "            idx += bottom-top+1\n",
    "            left += 1\n",
    "    \n",
    "    return indices\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162159c1-9899-4b0a-9795-a332871fb678",
   "metadata": {},
   "source": [
    "### Oppgave 2:\n",
    "\n",
    "I cellen under har vi en funksjon `scan_2d_to_1d` som tar ett bilde og en skannerekkefølgefunksjon som parametre, og returnerer bildet som en sekvens mhp. skannerekkefølgen. Skriv en funksjon `scan_1d_to_2d` som rekonstruerer originalbildet gitt de éndimensjonale sekvensen, bildedimensjonene, og skannerekkefølgefunksjonen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04d92df6-993c-4095-a9fb-a98c09dd4a77",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def scan_2d_to_1d(image, scanfn):\n",
    "    height, width = image.shape\n",
    "    return image[scanfn(height, width)]\n",
    "\n",
    "def scan_1d_to_2d(seq, height, width, scanfn):\n",
    "    # Skriv resten av funksjonen\n",
    "    return ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36787e74-4dc2-4f06-b1ab-cdc19d32ea56",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fasit\n",
    "def scan_2d_to_1d(image, scanfn):\n",
    "    height, width = image.shape\n",
    "    return image[scanfn(height, width)]\n",
    "\n",
    "def scan_1d_to_2d(seq, height, width, scanfn):\n",
    "    img = np.zeros_like(seq).reshape(height, width)\n",
    "    img[scanfn(height, width)] = seq\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e9e225-6992-4f26-a24c-7ebca66a6379",
   "metadata": {},
   "source": [
    "### Oppgave 3:\n",
    "\n",
    "#### Oppgave 3a:\n",
    "Skriv en funksjon som regner differansetransformen over den siste dimensjonen til en sekvens eller bilde.\n",
    "\n",
    "#### Oppgave 3b:\n",
    "Skriv en funksjon som regner invers differansetransformen over den siste dimensjonen til et signal eller bilde.\n",
    "\n",
    "#### Tips:\n",
    "- Husk at indeksering i Python aksepterer det som kalles `Ellipsis` objektet `...`. Dette gjør at vi kan \"hoppe over\" indekser vi ikke ønsker å endre.\n",
    "- Det kan være nyttig å benytte indekseringstriks som `arr[...,a:b:s]` der `a` er en startindeks, `b` er stoppindeks, og `s` er steglengde.\n",
    "- Ett annet tips som kanskje er nyttig er at å bruke indeksering `arr[...,None]` utvider dimensjonen med én i siste dimensjon.\n",
    "- For a gjøre kumulativ summering for inverstransformen kan man med hell benytte `np.cumsum`.\n",
    "- Vi har også funksjonen `np.diff` i numpy som kan gjøre differansen for oss. Les litt i [dokumentasjonen](https://numpy.org/doc/stable/reference/generated/numpy.diff.html) hvis du vil vite mer.\n",
    "- For å sikre at du har gjort det riktig, kall gjerne `plt.matshow` for å se at differanse og inverstransform er implementert korrekt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34a4e356-388a-4c8a-80b5-eae0d926cc58",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fasit\n",
    "def deltatransform(arr):\n",
    "    return np.diff(arr, axis=-1, prepend=np.zeros_like(arr[..., :1]))\n",
    "\n",
    "def inverse_deltatransform(arr):\n",
    "    return np.cumsum(arr, axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f910093-6b19-47fd-8fa3-80067c706f65",
   "metadata": {},
   "source": [
    "### Oppgave 4:\n",
    "\n",
    "#### Oppgave 4a:\n",
    "Skriv en funksjon som kvantiserer et bilde eller sekvens fra flyttallsrepresentasjon i $[0,1]$ til heltall med bitlengde $b$. \n",
    "- Du trenger ikke representere tallet med korrekt antall biter i datatypen, siden ikke alle bitlengder har en egen datatype. \n",
    "- I stedet bruker vi metoden `seq.astype(np.int64)` som midlertidig representerer tallet med 64 biters presisjon.\n",
    "\n",
    "#### Oppgave 4b:\n",
    "Skriv en funksjon som kvantiserer samt regner ut entropien til et array (en sekvens eller bilde).\n",
    "\n",
    "#### Oppgave 4c:\n",
    "Regn ut entropien av `image` kvantisert til 4, 6, og 8 bit dybde (også kalt bitlengde), og sammenlikn dette med entropien til bilder med differansetransformen (kvantisert til  samme bitdybde)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1da1633-0493-41f8-bd6d-5c5feec0152f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "H8: 7.44776 \t Hd8: 5.07434\n",
      "H6: 5.43760 \t Hd6: 3.11490\n",
      "H4: 3.41475 \t Hd4: 1.46953\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fasit\n",
    "\n",
    "def quantize(seq, b):\n",
    "    q = 2**b-1\n",
    "    return (seq * q).astype(np.int64)\n",
    "\n",
    "def entropy(seq, b=None):\n",
    "    if b is not None:\n",
    "        seq = quantize(seq, b)\n",
    "    m = seq.size\n",
    "    symbols, freq = np.unique(seq, return_counts=True)\n",
    "    n = symbols.size\n",
    "    p = freq/m\n",
    "    return (-p * np.log2(p)).sum()\n",
    "\n",
    "im8 = quantize(image, 8)\n",
    "im6 = quantize(image, 6)\n",
    "im4 = quantize(image, 4)\n",
    "\n",
    "dt8 = deltatransform(im8)\n",
    "dt6 = deltatransform(im6)\n",
    "dt4 = deltatransform(im4)\n",
    "\n",
    "print(f'''\n",
    "H8: {entropy(im8):7.5f} \\t Hd8: {entropy(dt8):7.5f}\n",
    "H6: {entropy(im6):7.5f} \\t Hd6: {entropy(dt6):7.5f}\n",
    "H4: {entropy(im4):7.5f} \\t Hd4: {entropy(dt4):7.5f}\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd72c6e-53fc-44ef-ac50-b8e1177a067d",
   "metadata": {},
   "source": [
    "### Oppgave 5:\n",
    "\n",
    "Regn ut entropien til differansetransformen av bildet med skannerekkefølgene fra oppgave 1 og sammenlign resultatene for bitdybder 4, 6, 8. Hva observerer du, og hvilken metode vil du foretrekke?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6fe1e3c6-68ce-4b3d-a90f-f5f38927e3ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hc8: 4.86099 \t Hs8: 4.84055\n",
      "Hc6: 2.44710 \t Hs6: 2.42517\n",
      "Hc4: 0.60941 \t Hs4: 0.61556\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fasit\n",
    "result_string = ''\n",
    "for b in [8, 6, 4]:\n",
    "    cd = entropy(deltatransform(scan_2d_to_1d(image, continuous_diagonal_scan)), b)\n",
    "    sd = entropy(deltatransform(scan_2d_to_1d(image, continuous_spiral_scan)), b)\n",
    "    result_string += f'Hc{b}: {cd:7.5f} \\t Hs{b}: {sd:7.5f}\\n'\n",
    "print(result_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff86e6f-5dde-442a-a0c9-e593169883ca",
   "metadata": {},
   "source": [
    "### Oppgave 6:\n",
    "\n",
    "#### Oppgave 6a:\n",
    "Skriv en funksjon som dekomponerer et kvantisert bilde i bitplan, og plott planene for bitdybde 8.\n",
    "\n",
    "#### Oppgave 6b:\n",
    "Skriv en funksjon som konverterer til gråkode. Bruk dekomponeringsfunksjonen til å visualisere bitplanene til gråkoden og sammenlikn med resultatet i oppgave 6a.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ae9f761-9c47-4a03-a053-565859ea9308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqUAAAFhCAYAAABJSdMNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACwtUlEQVR4nO29T8stTXQVXufHqyQYA0Yk+QJmEoMvzsSRIDgKgsNMHDgVHIqzDAL6BRwKAUGdqQMn+gFEcBIkCHGkAxEnEkzUBOT5De7tN/uuu9bau7r7nNPVz17w8JzTXbVrV9Xae6+uvn8eHx8fo9FoNBqNRqPReCf+v3c70Gg0Go1Go9FotChtNBqNRqPRaLwdLUobjUaj0Wg0Gm9Hi9JGo9FoNBqNxtvRorTRaDQajUaj8Xa0KG00Go1Go9FovB0tShuNRqPRaDQab8elROnj8fhzj8fj3z4ej//5eDz+8bv9aTRm0RxurI7mcGN1NIfXxaVE6Rjj748x/vPHx8ef+fj4+NtHDD0ej7/zeDz+w+Px+MPH4/Fb5P4vPB6Pf/F4PP7g8Xj8l8fj8euVe0fvP9N24xI4k8MZFyTHm8ONA3glhx3Pdufwgu3m8L1xFQ7vzuEn2F6Swz+82wHAXxtj/N2TbP23McZvjjH++hjjZ8n9fzTG+KMxxi+OMX4cY/zrx+Px2x8fH7+T3Mv6vtN24/04k8PZXjuON4cbe/FKDrv7R3J4dr85fG9chcNHcvhR22ty+OPj4+0/Y4w/Ocb4vTHGxxjj98cY/9G0/Xtf2/zVou3fHGP8Flz7U+PLZvxyuPZPxhj/0N3L+r7T9rv38LP/nM3hmb1GjjeH+2fPz6s5XOUC8vuo7ebwfX+uxOEZnsxy/M4cvsTr+4+Pjz8aY/zlMcb/+Pj4+LmPj49fNc3/5hjj/4wx/saBIX95jPH/Pj4+fjdc++0xxq8k97K+77TdeCOewOEje90cbkzjDRx+F8ebwzfFxTh8lCefksOXEKVf8eP4sigSj8fjl8YYf2l8ecL5tQNj/dz48jQV8XtjjD+d3Mv6vtN24/34cZzH4SN73Rxu7MWP43UcfhfHm8P3xo/jGhw+ypNPyeGlROn4Qp5/P8b4p2OMX3w8Hn9h51i/P8b4ebj282OM/5Xcy/q+03bj/fhxnMfhI3vdHG7sxY/jdRx+F8ebw/fGj+MaHD7Kk0/J4SuJ0r84akT6Vx8fH/93jPFvxv7T0t8dY/zweDz+PIz/O8m9rO87bTfejzM5fGSvm8ONvXglh9/F8ebwvXEVDh/lyefk8Lv/UGv4Q7b/fYzxq+b+z44x/vf4+gdzxxh/a4zx70z7H8YYPzPG+Afjyx/g/Zkxxg/h/j8fY/yz8eUP/P6V8eXo+leye0fvP9N2/9yOwxkXJMebw/2zCIcdz3bn8Obw5/25GId35/DPyuG3E+jr4vzSGOMPxxh/wrT5tTHGfwrf/+z4+s8ZiPa/Mb78Dbz48xvh/i+MMf7lGOMPxhj/dYzx65V7R+8/03b/3I7DGRckx5vD/bMIhx3PJL+bw/2zCId35/DPyuHHV+cajUaj0Wg0Go234Up/prTRaDQajUaj8UnRorTRaDQajUaj8Xa0KG00Go1Go9FovB0tShuNRqPRaDQab0eL0kaj0Wg0Go3G29GitNFoNBqNRqPxdrQobTQajUaj0Wi8HS1KG41Go9FoNBpvR4vSRqPRaDQajcbb0aK00Wg0Go1Go/F2tChtNBqNRqPRaLwdLUobjUaj0Wg0Gm9Hi9JGo9FoNBqNxtvRorTRaDQajUaj8Xa0KG00Go1Go9FovB0tShuNRqPRaDQab0eL0kaj0Wg0Go3G29GitNFoNBqNRqPxdrQobTQajUaj0Wi8HS1KG41Go9FoNBpvR4vSRqPRaDQajcbb8UNy/+OI8cfjsbvvxwcf+vF4yHuvwuYD/t7uKbzC72z8yp5gu+D3/g19Ix6Px6GFP7Jvar2re/FMMA5vPrk5n+337PpWcwC2C3NbkccflTyD6xL3c2/ubA77sd19N/4eDodry3H48Xh8VPYH1xT7VGuYs8lsvwuzHGa6Yy8wp7B8mfkxMwZ8pwv/tJPSoxv9eDxeRpbZcdTGvZvcGaqFbKbv3XE06D8+Pl72ELVH3LG+737oy1AtZjN9V0Dlwdf1OzLunTm8d27POnTJxlyZw5UHBoYjvNj634HDih97uZj56nLOXg5nDwLLvb4/48kgfq6ecmKf6E/Vp1cIO+dLlUTuialxDo6uKSav6gkR9on+VH06O7mzcZ0v1YcrlvxW5fK2P0ffxJw5/7tw+KxT0xns5fDKgnTbr6Mn2GyN9mJVDs+8FZgBcu3sPFyZ23KiFJEVNPbExD6z79VxIzGukDSOPMFcwf/PCLbujquO4zOJlo3xyrcUG9wrO0TGUeb/qmLU4WqxujKHWS2YrQdsfmdy+I44Y45nrtMKHJ45XJr1YeaBYZbD1T92sLwonSWRw8xTNo4RnwSuViwcNl/VfBqvwezDlcPMyT1LGpuNlTjAnsTxNOFuyB6GX/1Hi1bnsFrHI2/C9tQfNZ87YkaAveOPZCCH3s3h7NRRreOM33vfBMe+as0qJ6ZLitKjT5HqdUglicUgYsfn2ZH6qwrkkQC+axG/Go6eTKtXkJVCFjka27OHq6PFtoqZ09Jn9F8Vqgixgn72vt2Fw3tPNLP2s7XqM/CVQT0MzAqavWPvfbu49Wftn8Fhl+OOzAFtVq4puBPVba2XPilVG7FHSEVb7OmkagvJpoJlhQSzos8rQnF29pUI2mKn9lVb7Cmc9XvlSc2e9WDt7nq6VC0Yz5j/Z+Ew5vZZUbl37e/KWUT1JO8Ztegoh5nAYnafxeE9p/ZHHxyjveqhXfw+e9L8NFF6RoDh6SP7ceNViIZCNY6X+RU/X1HMVYvDhivO4d04Y03wqZf9uPEqD2CYION4mV/x8xULY/WBc0PMF3cDezNT7YOf94x7Zw6rA4vZon50jfeOuwrYaWCGrNa79njtCIezWHoFhx0vlEieyYdsX/aK4T356tInpS4xVAK2IriyzUXCYOLas3GvxB4yNc6FSw6VZFHhmLLBkhx7wr/Sw4l7RZVhpu0dkfHpjFO8O3M4CgglQCoFtnIa5/pesZa8Ctn89+7tsznsbJzNYfeGk4lB/OweCLb+6gR3z/qruGK4tCiNYJvgiMXub8g2Xm0EIxHzaZWEcuVT3ruCvd5wD1zs/oYsSTKe4ykii5FtvFV4UX3DsTLYCUyWZ7LcdsQX5tsdOcwKc+WEr3JanLW/G3DP4m+FjBOu38zD2RkcVvefweEZjYGaJH6f1SpOM6AQVuuS5q2kwW51dVaQOXIpccnEYpYQ3fjKlvs8Y/MVcE9EVXws+L+IjDHG48D/6HTWXrmHFcXd6olhxUdny32esXk2ZuJ48jR1RR5PE5EViSN8/iwcPuthfcanqr+xyyHn3oA9ebh6cuj6gQ/LczjmO9VuB5+on0zMsjlU7ke7X6/TDV3mpDRCbVJU5PHJA5MM3sueYLMTVyVOnc13486nSqtAJSjFT+Sz4jtD9Ul/5tTx1Q9XZwjSuyHyBU+f9j6Iz44fcQcOZ35UEW1kp6pZHboz4r7iCd5Z4m+m7REOZ2OfwWHsq8ToET5VBS7TVnH8PT5c+i86jaEXhG0WLsz2eftBEkWbGBgzCX2FosiEfCPHWeukkix7iMHEvH3efvDhSvE9e5hCHD1JOxvM9+qpxWeAKlYs1qsCyeGZHI73Z3xBXI3DOP8x9L59Rqi6pDimOJ7ZxetncRi5jO3VfJyPezh8Zh7EXIHz2Ss24ZRUtnuaKH11MVcTPqLYVV8kmiKns/sOfObktxevfrhSnHYiUwFtZSImJlrmq5rTK4Hr9VkF6QzOOoU7i8PMv2xMlVuvzmFV4Ju3f4xKXpsVXartGRzGaywnsX13h1r4O+MwG+9IjGN9wXXCk1uXgyt+uL3cJUor5DhTAOEJp3uqeaYP7ImhKkQb18PR4JkFng650/4zfMgSzHZtpoi/Gk7EfOaYU8VVta2eUlTHPcJhxsdqwWbtr85hxNX9exVULWdwh1Cz2MthvBf7oTZxekQdEBzh8LPeGGWCt5JXWK469aT01YUA1bt7QkAV/wxfzwqMxnvx6sKAr57YaT6evD+jyLIkvGKRrMbeinOrYu/c9p40Vzgc7c5weCavPvsA4hWY8ffOdWbvCWhV4LDxKnlYtan4hrlV8VUdGlTHYTaz9czeOLh+e3mI4j3D5V/fR6ikh0fPW9tnJarVEmBjP56x1+yUJ/LZnQrtAUvgK3F45hQj63sHzM5Jvf478spPcXj7XuEw21flExPSZ8TGClgpVjOoA6RqP7zmRFhm2+Vh1lblIaU7XN5ifSvr8Yw3d9tnrA1YjyqHGXgNT5UrOWdKlM4+VZ+B7AkaFTgS4lmJ65mi91n4DAm8gnecUrhgZomM8foMvlXEwRXxzLcfK2LmhOkVHI7X9nDYvfZU48S+K/DirHqxwlwr2HMKiZ9nUX1DVOUwfsaHM+WzEm/bZyVaK8DcwMR35gMToEy4OsT2tzoprRxFx8+s/TPF4x7b70gqd0lkr8YzHq4q7bLToj3jq6fU2Vdosd8rcfRU5Q7YI87V3GfXxLWvnsYfFRR7uarwSl4cPeGffc17ReAe7onp7KCKjcn6unEyDqsHMBwT9yzj8IyQddedf+5hoBrj7M10BPNdvdVGlEXp1UTNjFJ/tu+zp1hV38/CGbZWToQRV5tHZW+coKz2ZWPG3+qVk0PV91eDJcSr5a8jOGNNX70vFQ67As/eLuBvdcLkcFUOsyK/J0avijNi8shDKrNVEaxOU6hX4Mq+4rB766t8z66701M11uxBytYvvvXAMd96UvpMVAk9c9R8Bs56vXrmq8o7JLC7ohqkZ56OoU1se+bJ8KvjT/lxhxOmMa75Rxhmxd9M3s7aZidmR9fpbA7vtYF5YmUeYzy+4i1mxoMoNNUDfPzs3sbGNtmJYOyneJbNwdmP64tiWj30HDkAQZ/R/6puK4nSKyXBmSesCinOxtEgOzNIz5r7ykkw4krzmH0SnfXdJbHNXuXUai+u8IoVk+OV8thefCYOu1eMruirkxpsk6EqLCo4IkiZT6tiz9vLMx4uKv4wjmbfnY+K807cRtEYTx0ZKjHFYsH9duM5MY73s1NZ53cqSq8WALP+rF6M3u37lYrgEVxtHntE5pE5qKSrcNZ6nXFKuScG7iZIzy7m7xBaFQ5nD1NVURYL7BEhd9bp0R64U7sVsefk2eWsvVxCG9V1VjzAeVXmFnnl3lo5wex8VW3YKSbzHf1CAav8QfF7+9f3e8n56qJ0dvLa4/s7n+4bHntPb/YUxlkx+gwcFaZHfV+9mO/FzAnL7Bq9ksNV2wjM+0dOPmfFlPPj1f2vgj1r73jGTuDwe/ZghrmpKvJmThPZaWx8aMKTfyfk1FuuStyhL1m/yriZn84XBStKrxYIR17FvLooP/PVaIYWpN/ianPZ+3A1xr7C/Cw8e11nCoXC1XLYXuwRJjP58sxX63v9wv5H9+7suvBqMbv1y8TXKtjzcDzDM3bi6cbDvWEiUUE9aDPBh5yOfrGTUvSHtcMxKwdz6jp7jc98VrbQz8yHLJctc1K69wl1jHoyObuAHUnciJVPeht/jCOn3jOvvGba78WeV8nPFgFn9r8q3h2fz+QwFr8rPcC/Kq4i7iJIx/B/zrDar9q3yjMUoEw0sj5oi4k2JiiZXSUA2at2Jk5nOYynpfEUlAlSNc/tGhP1bFxlA7GEKJ1JTmxRZov5mXh1ErlrIb4DZl5dnpF8ZvtcCSsX32dgb0F/hh/P5jC+xrwSF/pwYD+YyJrtN9N39lUzvqJWAtLFohJxLh9Hf9ypJ56iHnk4zPzIxq9Cnfw6LCFKZzDzRL53Y2dxVnKp/rmRI7hbIlwVVVGZ/VkffD3zztPHamwePSF7xQnbO/GsGH3Wm6IZDjMuV4v2rF97+1aK6xl+3umUFPGs+ETOqXFY/Y/XmJh0eSXLwxmHo33ml5rnEahX91Wgj+zUefudrV/E5UXp7ClpRDUZvgLqSHwWzyy2d0p6V8PsCVNE9QFLfY92ztjjIydYlb4tSD325sPMRoVnz+Qwtmd9jj5YRWEwMx+0sbdvFXfn8F4eZTaYUMJ72UlkfJU96y+zxXxE2yxvOa7HV+V7eRiFYuUhi11z/feejF9elFaBR+74eYxvn4RWFWDuSewZdhvnYDYJuydlljhd8jtzb88Wtdkp2czYn0GQOlxl7rMc3r67B6kz53bWG6U9sVV525UJ9Kvs8zNw1gngBneyiW3UKR+zhfzNThodh7MHrUzwuXEd2EmpOq1VY7EYwLXFE1Tmf8SlRekMQdkCOkK9A8987XT0lVTjeZg53Zl54sxe8ajxq/2eBUzkEdk8nM3PXqxnhQsrxkd9UO0qpyZZflM2ZuvEGVypnuwijp5er8zxGU6OMcd5JhC369mbJLTBhJa6pnx19lmfTNjF30xMziLOE9fO+en8Zj6xWMv29dKidAZxUdnmbd9XFGDP9HvF9VgNex6uWKKIbc4qqnv2/+gDkBu7eg2xcrGuYDax7y3o1fErbTMOH4V7uKn4x2xV+7MTMndyerYIXhGzArt6Urf9zk5FswcCZped+sXPeBI4mxsVh52wU6eT1Qd69XZCvZVwp8dsLmpO1bXZLUqvEiQVMbqHLM/CXtI2noOrrG+lkM+e8DCxwZ6EXxHL7hQiQ+bfVfbwVWDF5FWoFL4Kh6s+M7HBTr+OrkH1FC8rzGpuex/+KmOuCMaLs6HypRuXnR6ydihIcbzsEGGWwygY2cnvnrzK/FT5hc1Z9UXRXnkw2PDD1CwCrl4IshOZq+KVxWaldXkWrp7o2anMTF9m66hQqIAl9Gc9HF59D5+BVz9UsHGrYCdM7F5lPFUYVbE8gkx4VPytYu+p0srYe3A080CjhBz6kOVYZgvzpuO5Er8VDlcegNTcWKwp4bxdUxrEXWd298biZV/f732yfOcpQhWZT+yEq7Em9gQme/I9q9hmr17OHuvIqy30a8/9u4Cd3uzdp9n8suekDvc8+z4D1WdP3q+ekLLaUrVZOV2OuCuntz0/oz6z08cIdVrtTrGR54yj6iFJnYQyn138HYnpapvI5eh7RUQzH9mhxpEasvuk9CrAJ434ecXgfpXPK67NqqgEqEoIZ5/+oE2WfLbf1USXCd0jXMsKz2c8Jd3gCoeDaqeuV08+Mg6fuV+VNwhHx1O1ZRbZnriTsLujIuYYVLvsIbhyWhptsbbq9BX5GPOfOp10Qs/5nPnO/FHXHJSIrvbfk/sve1J6FFVCNhrPxFHx/4yTFPUEn53wYJFWvqnTgz2+KfRD1fc4+vpT5cyjeRMLODv1zfoimIBgdp3QyHzY88Zq5jCEtVXzujP2nLBVhDyeTDoBl9lkp4E4Bxcr7DS2utcVDrtcHePC5e3ss4rf7A3ObA5ZQpTOHE279ncO7plNv/M6XBmzr/6efYKCycwVbHylFa+zpLe3mM706YfMfVBFonIic5TD7LWheyCqnDKqgoh2VRFV4mCPIN8jYt31Mx4Kro4986sKOHYvGw+5iSeeLmfiNRc3lbdSMxyObZkIrsR31tbVDNaW+ZphCVF6RHDdPaBn0YL0fTgiuJ6xb5WT0GpRxKfoPeOra4gzXwOvhjNFihKMeI39Pss+O8maGadyilTlZCzke0/szzjpqwryVVE5oXZwPGECj/GQ8RqvRVu4J0oQRh6zE9a9J6FqTuqBLvqbcVKNrx721IMl+ozXq3iaKD0zoCq2FBnvGtiN1+BM8bNXcF1VhLHkXcVeQfrZsecEWr2umz1pUraYjT0cnuH52cJ8T4ztWT/Vjr0SjdfvhMqpXUXs7znxj/dRQKLAYqeg7mQ+2sMHLRSsbPwKHF+ZqJ45UHCHFIqD7BQZ7c0+SC9xUjpGHuyfWZDufapvvB7VhLnhioKUPclvqCSg6qsiNu4V1+NVmC1gFVt72uH+4b1ZDmeng+o0ZhaqSOL3ihDce7Kr3j6geHFtV0b1xN3NefbUHk8yo/3sQQA540412Xh4jQnReD079VS8Qz5XOYxzzk5Z2Tpg7DJxPhOvTxOlzwikTK0/c+zG58RRIT/zJK+efK+EPa+D9o7T+B57cps6lavYjsWKFWt1aqTGdlBzw1egrm0VR0+EK4cebj74XZ3a3RF7YpvV+CrfInfZwwh76MHrSmM4QcZ8cLmfPeCr+VbqSvWUFEWksov+scOIM074l3h9Xx0ne+L+zOj12IdXFQd1UnOV4oSvr6oPiFVUE+pV1uNdmH01t7XF75WCfuRkcpbDTJjF3+wUp2ob26lTKda2Yled3LlTtGy82dPAlaBEzobKA1P1wcrxsHJSuP1mp5ns5JPxAXMnm2+07eKzyv/KSTN+dm1dvGR7MZuzL/n6fvZYPus/uygrFb5qwm+8HjMFs5pgZ3DmvrNkqE4ZKnYiqn6uFJdng3FkZn9d39l1VadWeznMRCITAigw3OkT8lOd8qIw2bMGRx4Q1PW9Bf3KUIK8Wq+dmHd7l4lg5UvkF/OdidYsrtjhQyb2lHjGNtk6oK9MAKv8XDn5VDlmtg5dUpQ6uKeQ+H2DSpbbPVZcr3ZKtcE9RTWuCRWY7kQofo92ZpPX0bcG+ETNnvhnEo9KpLO+fEbg2s+ux96c4R481PdZDsc5oQjNOMxOsCpzwRMu5m+G2b2YPWm6G3DtZ9d9b13GvWbCkfnCBKraJxR5LA8jcD0ynYKfZznMcnbGO3eIgGL2LF2y7P/olJ24zCQzZ5OR9Mhpz15U/WhcD44b2elSttcsoanTKLSfnVCodnuegJktl4gbHO9aL5cnz+YwClMHJVyr3J7JoUzc7O0fv3+2PD7zwM2w94FM7V31lBW/swMs9uAe/a6cum79M3G7h8NxfDWHPXbOzEuXFKVsQzMCuWKnvu95Mn4lzhC/XfTfBycOs6fnrW3le/WUUr1dmPGf2auMy+J5xo/PVrifjZhPnah0BfRZHGZjK1EQ76EPGW+qvFLzVadqDJkIUe0aGijg1P3tsxLDmItmOOv4yexVTk7ZuNkDnQObJ4pd9kDnRLfz7ajmuKQoncVM8awkq9nEcKbwy5JopV1jTcwU0IogrZyeZkn1KPYKhsbzUX2omMEZHMYTRFbU8dQLxz6DX+wkU4nnqi1ExwJHdU2OnFSzU8+ZB5lsbPcQw/xBX44A7TgNpB7wqjXhLJ83LCVK3cnTGHzx3InpWT6dZbd6MloNxD4lvSZUcdrAiuxswVWcVE+/mMDUk33GdzfujP/sNKLxOsychrJrezicnbi40x02Lvscv7vTJzfHas5X47ATvGy9PxuyQ6OZB6pMTKkxMw47f93JqPMHbezlsBqHnYoy393JcZwfa3NUdyzzF50qCQA/zyRUNZYjEHsK2QM8Wkf72LaxHqpP14x7M0k1cil+x+vMN/Y9a4vXlU8VG43XYU9uqQqnCoedIIy5j/EKiyaenCof1HccO44R/d+b87O2aszZcVbGnvWsHuLEtvE0NHtYwM/uIWmmffSJcT3zx80z2sY5shrEHiSdkK7m/4qQV7isKK1MhG2UerKYsXtkQWegAiMLrAo+SzK7A5hoc0/a8bpKsFub7XdMxngfP+8BS7gzRQNRFfGNc1HZs0perXAYbVW5keX4PajE0YYqJ1khr4qJ2bFWwRm17cwxMI9ie8VhFJ6Mi0wIo92KjzNg+gHHY+tTEdBVH48+WF1WlM6AbbZ6kt6Ls05FN8z6c7fk9NngHjZiG2yLiQ2DXZ0MqROmDSxJVR8EMbYqmBkD+zT2Y+YhIHs7U9nvCoeZbfzsbDs/KvPFOqEKqDrhn+HlLN9nT8RWA1uPbI1mcox7sMD77mEp9ok+KF2hThlju6pYq4jqbGymVxS3nYhF27MPVThWBbtF6btEUtxctnjY9sxx4/hn2KpcP0NQNzRekfTdEzomq5nTRVXQs8TPTk2rHGNJlp32V/x3uFsxfiaeuVYqr2IOrnLYcaV6woVjzR4Y4AOeerBzPlSwp1YcLehXhluLZwl9bM8e7rc2Sli6sR2vGS9V3sw4jFzCeWR8jaLSxQwT38yew9H8v1uUvquYs+tHjoorcJu/154bY8PehNQFvY5XJH3FzyOnRs4+Ps27B7bstECNx57K45ix7d4HqzsV5GfCrdOeHIqYuec47E5nsB2eZKrT2uoJjhuD+Y3fWX6ujhN9reDMWnMl7OHpGFrkzNRL5A/blyjamE0lMhk3HU+Z/YzDKofjmCwe2LX4PcsRew8vZtpHXP71feXJJBbfI4vhfHh2gTz7CbkL+nWhBF0swCoBVuyqk6ojDz6sL/Pt7EJ6t8J8Nmb2b29fZgeFY7zPOKxOYNAXJwDVvSpHWGywYs382oMj4nKv2F4RM/u3ty9rz05QY5vs9BTbIzfxgKA6phODTosoX5kYZocjGF9qLlWwcStY8vX9hmefkEbsfVJ29hrvxzv2wRXCmadWljwrY+85GWWfK7aOCJ+Gx8wJ3tGCjsUVx62cOroHrYovrrBmcKdfe06pZsZTNtyeVE/QVodb/8rDR+VBHdsqEVnJVUz4ofhyQhT7urnEQ4bKCW4mVitzRPGb9XV74gS9w6Vf3yPUU+ye4hyR9XnVXM8Q+p/pCfsMvOPhSiXbyGd8NYOoJLXs/lGOqFPXM7i39yn7s+HoQ8ZZUBx2onlGFKi8dsYbJSYozkC0zfYp3kPgKdud83lVpMXrM/vuTtfZQxPLw/gduc1OJvG6ewOg5hn9UkABGX1j4jV7AMP+SsTjWMqvaLeCy7++d2AJ7awixo7VV8GdE9gd4RIHC+Y9xZkluJmTgSr2iCRnq6Ex8/YG9/3svOZOMBlfmSBTc3HFee8psbNzxlsxtb5R1CixyUTsXWNhJl+wE+7t80xfvJY9HLBr7tSe5WwVf6p99h19UaIPxXcWY5V7cUw3LsZRNaaWe32POOM0RT3B7lnQd2IFH6+GV65ZFvgzr0pYQlYJ9BXF7cxYuUpuuToqRXmP0HO2XHv16k/xuiKMn3E66sbCz3u5XBHbW7uqcLgjKqd3TpC5+1lbdfKH/qBoVQLXCTTse8bDu3rQQx/daaazVz3lxAeEo5psiZPSs47xK2CC9Fn4TMmnofcbE4Y7WYpgiSB7As982dNmTwy6tWich2cIOHd6xE7442/G6+3erEBTp5CujbKzZ53OPGXuBzH9IK44k4H1Q3GKOZQJM5VXmaB2D+dMHKpTSBwrA/Oz2o/5OKuBzni7sOFSonSGbGcuQsS7n1T3Jqcu5tdB9VWIek1XKehYvNkJwBmFbm+xzvjoHjQbHtmpzVljzPjCeMp+q9NIVoz3FGQlmGdx5KGNnZLO1KvPEAP4ABN/x/tH1iI73YvtKg8GTFDiyWrkosuD7BACT2RV+2xOGG+ub/XNx/a7uh9H9Mil/qKTO9VRm38mniV0G9fFq06StrHwSZ09vSub7KmajVGJjWqxn31a3nvNXW98C3UiefYYDEwQs5P6ygm9O/13wnu26FbWx4mg6rUMUXx89lqD+7vnhHt2LHYAMAb/c5mqLeoQtM/u4bXtejWGq7xkD4jsN5tTNSdH4ZzVmWotQizzZ0rPPP1R9vccdzfWxqsLQyVJIZz4ZPePnPIof4+2Zachjf2oFBSHvfsQRacqWAqVE6AoNGfE297ToJm2R0+nzzrFvQsqp8gq98XfVfvsNBKFKI6pBKvysSogWT50Inn2VB7Hiqe3qp8bR81DXYv2UMhmuNTrewSb0DOEaeWJe4zvn+KvIlA/c2JbAeokwBX4rP8Y3woEvMY+s9c71aJ/FtfdXK4STyugGvNn5gZVtPH+dr16ss++qxNTJiBmRQoD46Ur8tmDYvTtqnXj3VDrENfX7cus/agh4jUljjG/qn7ZnDDXM04wIcpszsRQhOKwOw2Ovu+pGS5PuBi4rCitPBmcNU71tIFtyJ4kU0lOs8WkE9014RIva4uJQiUBderCCjf6ovhXiTn3pJ0lzEoy6wesc6H4N3sCjjlSiQZ2uuPyHSv07AGK+Y4HFch/NY47oasU6SymnbBBO523vwfLS3tOmbMHlj0PSSiYt98zhwHog+ItxlGFw5WHpfgd49XNUwnbsx+QLylKlfMzC1BF9ZQqSyLV5NJJ6PNAJVcUjlkxU0/faFs9UVeecl3iRPsuZl71MNmoYXafENmDzWYLxWFs63inBC32R8GqinFW5J0wYD5lp1QZmvfzYEKpkgPdfXWg5D4zbqqHeTxRZA9JTPChf5hjWUxErqv4RNvs8577r8DlROns08TZY0WcccqwF9UxMjHReA9UEnUnThncqY5qlwmBI1xmD2vulKPyUNdF/DqonBpt31mxr/BVncYokYAcyuJnhk8ql6oTJDVWFCSNOtwDgBKTe8dx9TI7Qce27kEN+yuhyh7gVBwxn5C7sxxm/mTrtAcVez+cOuJBvPqUxS36nmSWjTWDTmrrIhOLjHdZElRPzntPvLa+lQc9FLkxYWUCt5LYmus52AlktZ+7znjnTmI2qFNRB+yDY1bEQnavIk7R75mHOIXKidVnR0VojVHbk3hdnVwq2+qae7DG69kJLsuZeI/FWtV3FyvVE9TMxpmYiYfLnZSedYqTofLEW+2bFXZF8sY9gfxQJ4Xs5MclZPb0ymywcdGv+Bt/3Lzwib+5/Bqc/aYoOw1yYtS9zYq+qv5o33GJnaCyk9PKSebWZ4+4rzxYzbT/jIgP1ni90neDe/OSnRhu11AQss/KPyWu3ZjONjs5VXGC4lXFbCUWYz/m+1m5ZtbOpUSpeop22Bv8M09S1bHPSnJVO69IfP3EPwf2tO0SCzv9rCSRaCsiEwyVGMtOi6qJq3Lakdk4C3cQCZUHB9anci1rixzCwogPTFURxwoy8juOw+LF2VfxgQ9XykaV6684Ka2u7ZXhHqJdn8q1aH8M/godTy3RViXPMZu4N8pn9I/9jnaZLYxJjEcc0z1IMv+O8OwMvXYZUTqTACL2Bn+l8GJRzTa38qS/5yn9nVg5Ab4K6ol1j3g4+0GHFQBVFFiSzvw6yo9X8WuVeLsC8MGlegp49ARSnUBhbDnuMcHBTp8qD0yu2LMxM5zB9Tvn48rcXBv2kK+E6YzNaFuhImyjHcYtFIRuDtkJMfrs8n3lIY/NjYE9qOI99BNxCVGKCWim4J1xcrCNy+whiZi97LRIza9xHyAP2PcN6gl9j9BTfZhQxqdmPKXKijybj3sSd7hzcX02zs4flZOoShGsiji0yYREtIdghXbrrw4NVB/0fQaz8XrkAOVu8ZIdCu3FbI1meYydUip9gGMyTro8z+wqDrMcjnNgtQXts3ZVfmXtXLyiHwpv+4tOlRMhRg7VZiYR7jn1qd5TiXFrd1ZBuVuSWhXV4K4E60xhdFzae6rjRDW2qxbKbD1e8YB2ZtzdEepUxp3cbFAF/Oip1yyHGZ8qHI6fs7ZKmD+TW+p09y6YzXe4DmxfkIe410xEKjHq9lg9CGEurxyEsevsHou92VhxByRncwzzR8X+20Rp9Yl65ok7gyPwq/CKRNao4YxEz5KZOyV1PuzlxkzA7wXOySUxldgzm2fisxTwM+fpTmoq+XpmP5GvGX9ZIa0cbLA+lUJcLdLP5O+e09urw+WLPcB9irmwsnbsxNHZz3ypHhbgQVrGRWzPvme+szqFfh0BeyDcY/utr+9ZQZ7FTP8s8d0tATReAyXYtntZ39inmkjVE35lzGfjSnGkTrca34IVyA1MQGK/yskQjof2q3FTPaWvjH2kzUy7GbjTsTGuFV9n4cjhFJ6CoiaonnKz/lH8OX4ihzOORnt7DyEq7di46t4eYOzH664mOf9fLkrjRqtENwM8qnfjqr7vwJmJsfF6xGBkHFRJyYnXGTFbbfNsVE4FXn3ig8nwDkX8zJPwyFe3N6xwKl5jTs/sss/vgvJh5iQX2++FEktXiPWjcA/ve2xVoNZRiSglZJHb6vfMAxOOx66rebh5RnuqrrBrs2860G8cfy+HXy5KqwlxjHOLrnvaPxMzwXL0dLixH0f3nz1Nq6TrnoizJ3A27iuKetXuEdH3DN8rgmlVnHXq606S2AlH9ppPFWtXsJw/Z+FMDivfzuSwW+dKvVwB2Slw9VoUgPGE053ox2vZKWb8rA4e0M7MCWa1XzXms7GZQKwe6Knx3L0jHH7L6/tqEZ4NQLW41aPjM5LiK4p54xpgCZCJn9mie6RIRl+O4FkPV88SIM+yeUewE/pqjjya36qnJ+wkaxbP4jB7yDwrn9+9JlSESUW4oBhF2+wQin3Ha04EsntVIRhtuUMFXJ/q6XwUmhWf0D5bswxqvY5y+GWi1D19n1VMVMGrjHHGYt49odwJeznHnqI3e/gWYOYUIDv9jKcC2YnK0Xja0/9ZD5pVvOItyNVwhnBjAis7hcp4h7EQ4QpndnKzRwCwe5VDCdfnGQ9WmAPu+IDlTi7xuqrHSthVDwIcr9j+qwc2zPHot8rT1fHxe5UfGKfYZ2ZdFKpxyMaojPOWP1Oqijm223MC45ITux+vn5UInlkU75isXo3Kk3jFBnvKVE+g22dETBKV1zjIcxb4Z/Hv7KIb0TFyDLP5kSGKTSyA+Nmd3OApDROx7hTqGactai5H8rwTqmfz+Y4PVkzAZSfq7HfkIhNaKteoE1T8jHZUnDD/4lwz8VbdY3bCqx4mZ7H3QYjN1+V490CLeJkoVU8eWRGetV/9/ExUNveOSWcVHCnm+ETMEqMr6lXRiWM6n58lwjKfnzn2DK7gw6txJH+wU514PYpIdwqF/jCeKrHL+lfABG7l5MadwFUERLRxNs4U4StAPaQg3MleZk+dbGeHYCq/R5uK5zMi0z0IsvlhG3UKmZ08uxPgGagcoNqqXMLw9j9TitizUJkKr9rc+6SBZD0zwXzGgvtsPPskkSVJJSzZGwOVfKpP13tjiJ38VuxWnrb3PpFn+KzxoR5e9trBPWcizuVXLDyqOM76Ndsn43ClRswelBzl4GcSpBHu4Sbrh6eY0Z7KXTOnk+r0f2avKpxiYzIOzx6usVN9Fgt7NAu2r/ZXNQ5xif9mNEI9xezpw55yVkHllKqxD2cKGRX8M0+S7ikfBS5LAK/iyExC31A56WjMY++eK8EYixMrOqovsxP7uNyM12bnoewrzJwO4YOhiumj+MxxURX/7NRya8c4i6JOiVHkg8qzGBsKszqFneLP9Md72RsA9H8v92brD57OZuNeSpQqJV855n8X2JPN9vks++xz4xjOWkslPtmTZGXMaoBnPsyC+XyW7c0++9zYjz0PJ6yPKmjV3FMVqjN+zbRn+Rd9d3l5pqiyPs3nGirCsPIwz4QWO2VU9lRuwxP/OEb004ncGbCTVORw9HVPTnZvClgMZWDCstJ35qHuUqJ0zyJhvz3397aNfapPAXvHaJyPs0V+5EDlJEcF9hknM3v6Or+VGM5EMhM7jfOwJ2eqdngIoH4re6yQx89Z/9nTU+aXex2pTmgzIeQKe+M4Yt50HIl11olO1nf7jfyO9pwYrHCRHT5k4g25H8fLTj/RX8XTGX8yVER/Zj+Ln92i9NnFxSXCykLsPbE8WszZ98Z1cbTAuCdm9nSrBJt70p8t1sqvSh/2JP3MU+Ur2VsVR08ZM365QsbeCKDdjPvMLmvDgP6rUy5lc8/aHS3sR8ZfGWqtlBh1td8JHifG8J7iHZ6Mok8VTqu+zmc1DptXReS6cav3s/ZZ/8oBTcRb/kmoyjUHltDwKcg9dc341rgnniFGZ8CebuOJEktG2P9ZvlVtfaaCekUcffhVRdSJOtZfFSklELL+6prywZ2uZWtUrUdnP6CdbWsFzLxtmbGhTim3e+xkMf5mttxpLBN87qRS+RPtZSf4lfhRAjjO92wez9SH7IFiw0tF6cwTEfarTqhq8+x+z7L/jGTYOLaeMfmpRKSefvF1EZ6QVvx0hdj1m8GeYu7uzz4tN3JUeOD6xj2JAi5yWxV5LMaqCDpeZ6c5RzgcY8y1YVBi2Z2cNfaBCclKH/zsBBAKVifm0K/Id2Yjy2vqoafCm0xsOiiusuvZ+mPMZ+NibZyNkZeK0qNCMdvMMwTclcTf2QmvE+gXnHHK6e6xk3s1vntCzk6B0JYa4wiywl15lXl2TF0pRt+FoznOCSxWiNnDE/tcEYM4nuLyWRzO7FSKfnP4fFTyqALmUBSf6nQ0fq4+ODORheOx00hmK17DU1x1kIHzcO3QLluDWe6pfOHWr1IXFN7y34zugXqSZk8uzxRfe04n3BNZBZ3AjgNPd47YYd9VIsLgZImB+bXnhGcGlaTPTsWcn9Vk3NgHVRxmgTkMT5K2a7E965fdd7nyjIcpx2F3+rX1jXayz3v8a3yPKocVd9hJfjW3KwHIeKQEnDtxxfsMahwcQ9WQKILVQyKuj5vDUaiTYHc/ww+HPCoiCsWKaGRtZo+89wBPBZzt2eS5F2cK0s8qbs8Qo5sd9xBUCX6WbNgTJvIdn/6zxIcnBniffc7gHq6ymD2zmH9GYeBOcfasB3IkEwqKj1EYRD8ZHL8z/9h99LfC5UwIqRg+m3NnCoNVoDiW5Shsq04pMyG3XcfcjfmXiUyWf/dwGPcd5+DmoWxm9nH9KlB+KgF+pvB9iSidEaSZjc3O2fbRFlvUvYkQ+7wDnzEJMhwRqEw8ZgmM2UChxgLbJZOK7674szWo8lPN7VX8eoZAWA2Re7MPx5jfcN9c0XYPZBuygsraOX+zcTKbMzlbPVidjebw97lUPfCofhFMdCJHVc5VNmJb7KcetB3/slhh4yi4QwW3ljMPWCp2q3aOxM3u1/d7JnYGkDBnCtK9vqyAlXx9FmaeyLN+sb9LntmpIkuOGY+xzVkPYme3fQaOPFTcAaxYVDms9o7dUxxThTr6UvEJ2xzdTycmj3D2GYVXiZrPAvYQpE42WT8nEJkgi9/dySpyXvEz+hztxQOJSg5Xh2v4HU9Tmch2do8+HMa2bL33xIEb96n/TmlGEGV39un/GYI0e1KJ5KsQ8N24gg9XxOzDFUugKkmqBKj8qAZ4VYhk48Yxq0KiOt6zRONnFaMOR04+8Ls6OcVTpL1+xLZOJFftKg4fEX7ZA2XFr4r9xh+jog3YgxLyUu2POgVnIhJzOvNLic/shDZ+jr+Rw0psM5GNvsXv7jSW9ctQyQMMUStlcbP79f2sQp4NYHUM/SpxVSmuLgDY9cb1sOchaEOlqMZkw06WWPsM6lTIPSVnhR3bs+t7xE82duMYtj2ffajBvnhP7SMrzMxm1YdoG20yfxWqHK745dalcT6yk1MHdkiANpFbyFsVDxm3GYezuHE5NeMbE7xOwOP4Zxze7ek/e1Dxsv/RKTuqRjCyKUJcoehVT5veiSus09UwuybqSVVdYwlQJYxZVE9hZ6CeZtUY7+D7lWPsldhbaLKHIHbC44p/5MyeE5TNjvJxL6o2stOkZ6A5/AUzJ2jYj31Gu2PwP/KHYlSdREaoWIvcV/GSzSGOUT2AyzTQ0cOPDFUOz9a3l/2TUGcF+RmvZ16Fq/g2+6TS0MiK52xirb4qVP1nru+F82MmcZ7lx1Xi6grYsxbqNH2z5+6zsbPT1Rlf4vVZoYL23KlWxYcNZ/LtM3NY7eeMuKlcr77NYg9S7HApe2jJRJd7cIzjZeLRaR+MwbMf8pQfex5Cs7h+6l90Ogo26bPVfsWH1dFi9FtUi9VesJNQ5BE7UUV/Zp+U1atHbDuLq8TAVfy4AmJB3bO3FW6pV41OhKLtGcGH/MUinBX+FfLcZ+cwO02cFTdMEzhxqXJvvI9tVXxF7itxqE5Ns3kysabexDG+Y92J42Ux66AOXqq2Zg8QX/b6fi9YInx18tnzxH4lvPJV2Ao4+mDDXg0pqFcoyKnK6yPmR9XPzd7e2KmeUjwTR4X16nEcMfPwovqroo3tkOvIYVdIj56ko4+Kw9lplvPlHaf9e2we6XsFzJxouv2MYpHZRF4yDm922AHC1i7L2ZX9QEGLQrea77P8xUQ5+o7jVfOG27cqqg+0Y7z4vxmtQiWdswrsXry7qL1j7HfP+SpwyYMlmO17dkJ61voqn1Ty2/PaxV0/ctIxi5n+n4m/swJvjO+LlSqiWOSfkXuVMFEnQNlJESJ7sKqI1j18z2xmYGJjRVQE0ewDQnaizk5RnU2VL9RpauY/xg/GH4st9KsSd2iH2WKnppX8qNavwmEVL67v0//2fRbwM0DyVJ70zxzznVCBUD1NO0uE3A0z/FFJz/Ew8l8JxDPWmj3xzyQCh4qPs/lg1je2jpk/W787c5kVPdd2DH1ig/2VYK2e1syCCWIVN5kPR04iq+2OcLg6TmVfV4MSYky0IRgfXAxkwgtPUdGmG1/ZY7bZ9zgH5/MsL5mPbt2YP+hvNQeoeWXrGvHUk9KqEo9Qm8Q26qynVoWjBW3z+6ynahUk8ecM3LGIH9kDVsDRLkuG2/UYkM7WUd9VuyNFjT1dz4zN+irxfla8RJu4VysX96NxqU7clIB3pzbOHsPsgx8bt7p3mAsxPzpBm9k8g8MzHERbZ+b5V0PV7Op+4Fo4DrsDgNkH1kqOV35HfzL+uLlmdT4Tu/gg6ta/micr80d/K/M/JEqrajk6M2tbPSXjBj+j2GRJbAZHC2ImCjYcFamrJjyH7GQjKyAqeTF+YHuXSN09Nk4Gdxrg7FcR7ey1VY2pvSI15oYj+eeqYAXkjPxXET6M05HvrsgffbA6i8NHhGnVxoa9HFY5Z/XcjBypPvBufZXYqfTHcVFDxHZK0GbczOpJ5udM7Wbt3IOT4hJbg0z8ZvzH/IDX4nUGK0qfdWKRARdWPT1jn2fhzIK2p6CrwDm70K6e9BRmn9RY30rBrRRylyhiuz2oPLDswVm8YInxGSc/7lRgVcT5VB6mlI3s3sxDFLOJp1GzmDnocGA5c6+t2P8oh2cER1zLu+Vmd+iEYLxjJ33qgZT1YfbiWmd5w+mSLMdn19RDiRPQM37iWNV5V2JIPSzMcDg9KT1aJI+eqKiFiAR4dtF5VuF8tp3ZE6Y7A4Nlz+lbBmYTH64wMbKn9zhudbzKidmeeKwUCyfYZ8X/GTZekRPeASZWZvpWi03lhIQ9rLH4mnlQcoIi+nr0oR7HZ7FZ7Y+29txj480e5KwCJ2JU+4qwU8I1ruVMbnVCDsdzHFY1YZZrmSBU8cZOKt3DuquRMUbUCajyz80NYUUpm/Qsjgi66hPPs4qQe7I62/6e+7HdEf/umPwi2FP1bP8s+HAMlgyRzy5JPuMULGt3BC7BnXmK5PpWhdCKYPw6C0zostwXc6074dp7uuf2b+/D2gxcrTmTwxXhURFdq4Htr3uQj58zsadOSXE/UC+oHHXkIE5dq3B4NmdWxlEPsUrfZQc4Kk7ZoUv8XM0JU3+m9NVBUTl9OYtMbpxnzvtM21mAn2HvDjhLmLkTpqzAs8+ZeJ45BWUJ58i8K33ZidlZY1VsKrF2x4euPcKU9anmT1e0YhtXzNh4eE8VyjM4XFkvLLh7uaw47E5A1X28doe8HEVKJhC39qxfJoxi36pfKKDYoYOCE14VDs8eVGVr5cbDNUW4GjLL4ex7RCpKV0noWcDvxSsSwLPHyAr5nnur4YyTZBZY7J5KlJWn4DNO/p8lzConQM88lc0SYUVQrQo8CZ6dk+oTr2dFCttsfrH7R5EVyb3I/HOnPWfArSuL/cqerAImQNVcVQ6L/fABKK5hHCPaR6HphG0mRCsnsqydguKae1hT7XF9KuNXMSu6Zx+opv/2/VUS/NknQQyvnOuzT0w/M44WuFmxk532uIShBGkmNK/2cPEKEfyZUDl9cJhdS1bIKnyLBbHir3tbkPVVOIs3Z+TRqmhXD7ho5w65PeORmqvipFrjqAsyUa9O/PdwmNlRfSv+sO/oU1ZzKv1cu8w/HF/VPWUv4qn/TunRE58KjhzV3xVV8n+mou+K6N7+1YBmwaiCmr3WmvHz2aiclp6Famx/hph3ubQy/yMnbe4ED31EX2dPSc7i+Z4TKdf3yNrFz9UT6FjY7yJIZ/1nglCtkzoVRWEa+8Qx9pxmPjsfsQMLNUYmPit9svm4h8bYlj0wzBwclkTpkURxlWK6Avq09Lk4sibVws8+Z6cgESw5zo59J2Rr8Nnyy7M5rIozE1fqBMUV/2zs2VOVFZCd1LHXys7W6shOJZED2C97SHL5FvdBjbX1ZQcD6k2Weut1Vv6ejX0l1lmb6kMr46db44oPiKf/j07PxrOCdLUif5en6StiNqgcKk+jrK1KhBneUcTOWJ/I5+b08T8XVn31XylO7uTF9d1zAnnkhPddhylMUFfXxb1BWR3q5CyLcbUmmz382eyphyP2UBWvq4cp9TCmbKGP2dowW0fjHn2v3tt7khzv733AfKoofQbc05NqswerPZHGgLmqWFkZ7slvux7buX1giYr1w/tKWDjB+oxiVuHO3vFjUagkvzsU6wqccNljS50M4WllVshY0XSnVCp/M1vPFGTVHLn3AaDKYRRQma9HRPq7gUINrymok1Q1huKhOtFTuZedfmYcjt9nNUllfnvyQOVhkQn/yGEm8J1veE2Jdoan/5nSV9hEwXDGuO8qds84hWtwzAS1KzL4umLGJvOHFejshFVhxQK2os+vgnqYGaNWrJ0gnT3RqIyL4zNUT1724F1cqo7LTuLOsHtlMLHHPrN+rI3LlZlgrDwUxHYV/5gvM/vmTktdHwf3EKr6s5qmDkbi99mTb8Syr+/ZU05cxBlxwPDu4N9zwrQnETZyuMDdU2zxaR75WnkbMDPWM/AsrlVj9zOdkiL25CxXLPAkpDoWCl328FY98cxOZZ+Bo2La9anErHswHaP+Ry5WhBJdeJpaFayxPxOg7JQWx6jmV1UPVA3eo0f26BdXnxSX3GFHVVCzhwz1AFyZ13Kv7ze4orRHoO1ZvCthhviN87CncFROR2ZPw1jSrfqzB88Uu3cqvs9A5QTD9UNUT+VjexQS7PQKT6GyvcU3BXtPbhF7T2iPjLdnP1jc3jGnqxNEdqqpBD6r1xVR6frNiLf4UBbvI4fdgYbzE7HnZDlDdqBSPRVVfu6N32VF6Rj7/nwFA27OFRLB7EbeOYldGdlpB6JyMqr6Za91MEm8W9zteVA6S4jcDeqE093H/jMP8dn644mTOh3NfMgK3Rk8OJoTj3B4j//ZQ8eqOV5xYvudzZGdvsWTQMdhZs8JY/Yd3wjgOChI3bwznH1KruK/csjhxnci2tUrN58lRSl7cjoiyq5WAI8U84bGGYGN32eSIXvF4drv9e8VXDj65F9t3yen3wL5NCPiFVezB6vs1H6Lg/h7hoNnnK44VE6fXN+jHK72qbw12Wv/SlB5cIZzeK168qoEIz5QVexFrmO7s/OWepjLHvLi9fiTHYq4Bwhln+UB1dfZWlKUIik2uONohSuLOSTSGSRfPaFdCZVikRXEGMRqn6vF86qIpwt7HrgQ7LVRw0OtWaVYuBNWdpKk2lZ8rLadxVkPpGdxOHugmCniq6GS32Itr4jY2E9xkp3k477Gayg6lbh1IjY7iZzBXg44cRhFKuOcmkMm9JkWUw8aiOX+9r0b44xj8qsEf/WJ58piZAVU9rtazLOTSlW043WWBGaK1jMLO6IyhkvKZwrVKu4YL9mcssISv2f8UWIh42zG+9jmGaelCtX4R0ETC3mFw2p9sLjHtiq/X6VGHUH2QI9CD3NkvB7bq2sbog12aojjZQcNGc/V/lViNkMlDuN1JiDVuCgyt99sfk6cq3p2+knpO4PilQXsnZiZYzV537EgH8ERUY+JbragZ4lyr09Xi42ZuVROFa44x3eish7ZaX20M/NgxTjvTrGqWGWP2QFB9rAYr2cHKpgX7oqKUGK8ZA/irK07zWN+4BjqJBR9m5kv2qi022MbUfU18zE+iGWnptvv6tjLvb53CvwoVkiECn1q+h7MFAwVoLP7tleEPANHkyAr6jOnBDP4zPHh5j5TMNxJCl7P/HGnSs/Yq4o4z/oxfrKT0ozHas3jg6l6cL0bGJeqJ/vRRpaL2UGC6+NsqX3KTlnRhhpHHXBUH9wdos+Kg9GOOklWfsQ8Xt3biLIo3ZsknhlEZzyV3wlZEvzMYEGyXa9ABaIryO4761fZo8oJ1kpwr84Q1WR/V6i1qRQ/9+rOFbRMCLAxKvNQfj6bx0e4M3sKyuqTE5cVgbAyGF8qwix+zk4qGQfdSSceFOC+qXGjzT17dITjR99C4Hecp5o/43Fsl9XIan54+knps554HdHugrOS813Xp4oz1rF6ejdzOsJef+zB1QXpGa+UMHnune+V18lBzXvmBK3apnJCpK7NnrhGvIrHe/OhewCtPlCqujVTy1bO5+wBSD1sZsLdnRoq8RnX2YksFwfMxuzbAnfayNo6H2bsuIMRF38qxtn4FS67e2VResVkfmZwXnF+Z2DlBPYMPGM9qq9OMNGdVcjd91fgDNGZnRJVitHdUT1V2oNqUWV7MTN2Fh9ZmzNQPaWcRXZKpNq6E9e7QZ08jlHPo0rEOoGmhCae1uJpKbMz+8bBCb0KKnl/xp7DNjd2Sow8VT7EQ8M9ufvpr++fiTsG7Zm44p69A7NPsVVUiig72aq8usrsxMC/SxyouShxOoOrnyZncIn9jNd5mRDF7+x1XXZqUxFoV4cTNdt9JU6VmKqOuafvFcHifM+pGubPKKaqQi6zzfxEwaYEWDYG+q2wZ8/dCeZsLcH5Ig9dLWPrks3nh2xyjXWgXic0rgMsMFVBcfVTlDNFn3pVdMT+nlfMV8MR/8/eH7RZEVyqwL96P9xJ15G8qebB3o5gHxffePq0Kmb8Z/N34kgJPCUYszdM7DRw5g0Cm0/1dLPCQ/bWTZ0Gz0Cd4LtTYPYd13zmYGi5v33/DFxZyM0moey04rPirEDNrrMiq4TV7N7iCavy54qFa3b973QCfBZcUc3WaqYoqtMOdh/zTeZHPGF1Y1957896dVoRpGq8FaHE4navOk+VX92JdRT2KDjdQ0MECuI4LovNmdNBN5eKjT21hNl3p55bP7aP2OfIIcpuUfrqpDG7SRV7uJhXToSN56DCoQovVCCrAj5TyDMhGu0efVI+gjOT5pm424NadspT6eOgTm/wHhZmNw6+ImQC4pUcVvafxVMVwzNi5S71KdbamdM9ljMZ/1R8MBGJ66q4r9Yex53hMDtlVPbZfXd6ya5HP9k4bCxVv5QQj/fYyXblAPDSJ6V7njSqYCRq3BsscPb0rd5nrzFcMDNUBetVTvvfPT7iyBP7FRF55E4cI47kuDgOXttsZwUYCxIb49UcdmtRWaeZBx0mJqon12x/rxZjs8BTy70PTKwvE6vIYfYZ90Q9bKl428Ph7IQWa4frz/qyduqhMrPBbKr6dvRNwqVF6TNPL48+ud4JqxfqWRydLyY8doq0fVdFu5I0KnBPvp8NWZK+05oczVNYlNUrzZiDVZsxjp1uvoPDZ+T5PbG6/XYiBNvH73etT5W1ZIIPBW7WX52WsjZxrMqD3xEOKzF79OFJ9WMxrNqyE1T2YOUeqNhDgMLTROls8Ox5GjiKVYp5RsyZp5/Pjr1P5tnpZuW+etLEPih2qz5GvKt4OZ+z0zT1qmgP7l7AZ2JccQyRnQYpP/CVXia+nI/Mn2dB2a++vchE41EOsn1eOb/H9cjmwcQZW0/kKjvBV583u6wv3qvgKIf3cEWdHLN1Yv2UaHQ2Z/YRfVT+fHNvVYI3Go1Go9FoNO6DS7++bzQajUaj0Wh8DrQobTQajUaj0Wi8HS1KG41Go9FoNBpvR4vSRqPRaDQajcbb0aK00Wg0Go1Go/F2tChtNBqNRqPRaLwdLUobjUaj0Wg0Gm9Hi9JGo9FoNBqNxtvRorTRaDQajUaj8XZcSpQ+Ho8/93g8/u3j8fifj8fjH7/bn0ZjFs3hxupoDjfugObxmriUKB1j/P0xxn/++Pj4Mx8fH3/7iKHH4/ELj8fjXzwejz94PB7/5fF4/Hr1/pG+X+//ncfj8R8ej8cfPh6P3zrLr8r9xttxFw4725LfZ4zdeDs+A4d35/BK/8Yl0Dw+yPN34Id3OwD4a2OMv3uSrX80xvijMcYvjjF+HGP868fj8dsfHx+/U7h/pO8YY/y3McZvjjH++hjjZ0/0q3K/8V7chcPuvuP3UduN9+MzcPhIDq+M3Xg/msfHef56fHx8vP1njPEnxxi/N8b4GGP8/hjjP5q2f+9rm79q2vyp8WUjfjlc+ydjjH+Y3T/Sl/jxm2OM3zrDr9mx+6c5/GyeIb/PtN0/zeFncXiGg3s43j/N4yvw+CjP3/Vzidf3Hx8ffzTG+MtjjP/x8fHxcx8fH79qmv/NMcb/GWP8DdPml8cY/+/j4+N3w7XfHmP8SuH+kb4Zjto+MnbjibgZh9/J8cab8Ik4fJSDzeELo3m8dq69hCj9ih/HlwWTeDwevzTG+Evjy9PNr5mmPze+PClF/N4Y408X7h/pm+Go7SNjN56PH8c9OPxOjjfeix/H/Tl8lIPN4evjx9E8XpKnS4nS8YU4/36M8U/HGL/4eDz+gmj3+2OMn4drPz/G+F+F+0f6Zjhq+8jYjefjx3EPDr+T44334sdxfw4f5WBz+Pr4cTSPl+TplUTpXxw1Ev2rj4+P/zvG+DdDP9387hjjh8fj8efB/u8U7h/pm+Go7SNjN56Pu3D4nRxvvBefgcNHOdgcvj6ax6vy9N1/qDX8Qdv/Psb4VXP/Z8cY/3t8/UO7Y4y/Ncb4d6b9Px9j/LPx5Q/7/pXx5dj6Vyr3j/T9ev+HMcbPjDH+wfjyB4t/Zozxw0m27f3+aQ4/m2eO383htX8+EYd35/Dm8PV/msdl25bnb9m7d5Pn68L80hjjD8cYf8K0+bUxxn8K3//s+PpPHYj2vzDG+JdjjD8YY/zXMcavV+8f6fv1/m+ML3/zL/78xkm27f3+aQ4/m2eO383hdX8+GYd35/Dm8LV/msdTti3P3/Hz+OpYo9FoNBqNRqPxNlzpz5Q2Go1Go9FoND4pWpQ2Go1Go9FoNN6OFqWNRqPRaDQajbejRWmj0Wg0Go1G4+1oUdpoNBqNRqPReDtalDYajUaj0Wg03o4WpY1Go9FoNBqNt6NFaaPRaDQajUbj7WhR2mg0Go1Go9F4O1qUNhqNRqPRaDTejhaljUaj0Wg0Go23o0Vpo9FoNBqNRuPtaFHaaDQajUaj0Xg7WpQ2Go1Go9FoNN6OFqWNRqPRaDQajbejRWmj0Wg0Go1G4+1oUdpoNBqNRqPReDtalDYajUaj0Wg03o4WpY1Go9FoNBqNt6NFaaPRaDQajUbj7WhR2mg0Go1Go9F4O35wNx+Px8fHx8d4PB5jjDE+Pj5Uu2/ube23Pnh/1gbawXusHbsex3E2MpvoC1ujrL2at7rG7rEx1b1Ke+V7HJ6v0LXheKzWRPEvflb75vpW7SkOsD7Z3ql9Rp+dT1kOcGPjvCvjYntm3/GZzTv0XZHHH8UY/ea+2weVu1h71o+Nh+3wGvNP5catnYuzLE9mv5k/bGzFPezD5hbbZH6ouZPxluNwzMMzNXO7p/JTJXcYn2Q+jMh4mXHYzUl9zmpA9CurbZW1dT7jWG7/1D5UOZyelFaKEUOWtPDaNpE4XiVJOpt4XW3y7NwU8dlnJEC2gXgtCw7WL0u4WcHCceK+rAi2xm4PcX3UfWUnclf9xs/O54wfrqix79t+KhsqAUd+4RrENtgv2lR5IBNbbJ44Hps3+jYb61eB4gLeU3kuWyvVPlsvtr4YB8yOqw9sHshVnDNC5V+0wfq7fMdiHPcGizfWNjd/FpMstlaFWqsNcb22dijSWD7f2ka+qZiZrWWqFiJPs5qtOMtEnuMkctsJ0mgfY1PFHKtvSsC6uuH2ysGelLogQKJkdlRQZgWRjet8rW4w9lFtcUyViCp94r1szkzEsP5K9DI/2F6pAja7L1cG7gVy14m+rT9bi4xXLEmwpKySABN0Kqk4/9kc8Lor7MwGW9OsaDjb6LfiNUvErIDjvUqeujKUqFJ51HE7fs76MQ668WIbNQ8UWWyvmf8qdioxjGA5IBM7keNqTjh/xmGXj+J1/Dxb3K+GLA6z64w7CmyNcQy17ozb6IcaD8Up4wr2Yzx042NfnK/LzThnN3cWZ0pnbe2q6+Z4MP1nStUC4X2Gzelsc2eCzwWsIw/rM1NIVdJQ5GfzYt8zUazEsBI28ZoSAtn4FZFxZUTeqWBkYk+JLLf2LHE4VMZQ+8lsqfaxDfMXE47iGfZDMYrjs2TL1p7ZcbxTIomJ1mhrVR7HHDJbkLffjP9qXTJRhHax8GOuxz7VOTN+shrChB/zDfvjeOh/XKOMX2qeKufEz0pgbN9d/lkJKs+pfKPWO/ZXXGOCUAlG5E30Je57NUfGe6xftp/KT5YD0L6r+ypOcUxcl7gPzjc1H+c/YlqUonPsO04InVbOqn4OjERICDYHBpb04nVlF5Mnu1dNOPG68rfyXRUZJaiyIJnZkyuCJTXFZbVfrEixcVTQop1oK35X+1u1yxKnWgMmUlQSxqSOtl18o/CMY7J54p6gv2y+zGZFjKwELCpj6D2MfVh/tV9Z0WQ2styr1l1xCOfg9lXlYuUH89XxQuVD5jvujZu3EljoG467OqoiCO+pPnh/pg/mfWYj7injZ+XBxPXBfFsRlmruTsPEnMrqTmZbja9qEdZRrK0Kj6SBvYlFQ31WE1BtXTKK910RVMj6IjmcHdUOC3kEI1y0x+xgX+dftgfZg4K7/7HmXxAZ4yuPmcjaoAq926sKx2M/1WYmZirt2TyU35kNtU7MnhKwrj1ryxKysu+St/J5jDX/ksj2eXZtv/a3a6P4Evuqda/Yy+pEFmc4huIyzsGNwfpWxkGb1ZhAn3Ac/Iy+QN9lOexqYlbjsn2v5JStLd5zecUhE4mKY5UYYP5hXzW28lPZZu2d726N3JqGz3RhS6I0E1fZhJij6lomGqSjBaGaJeVsTtFOFhDRvlvjigip9HXFIrteJfiqovQBf+uT3Jd9FS9coWT9qmNmgk2NrfytjBVtZsIjE5WZTeVHbJfNzc2V9a0mw4uDTnKGG9huhstuLGYnK4qu0Eb/XD1QwmPWz0rRn5ljRRShjaw23oXDTsh/03Cyjn030GROcTnMPWTM6BU1Brun5lzRC9W1VH64sZSvbEzGWxhrvyiNTs4WPez7nQOFjd5T3Dc7lQ1SPmVCmNl0G1EhdFUoop2Z4qzmWLi/YiIcQ/B4+1w2kggrl+xmeVwVDRk/KgUAx419mX8uUVeEKIuDiuhgNrBtsXgsx+MHnDK5vKYeGLCty+ezQm5P8cfrrF9so75n86rEe6VwuzXGa8ov5iezyXyC68txeIRDrm8uinwgjSR13XFR5dKZa5WcxsZCZLFTqf3VsbOYVmNWfNyzhtslOrczRGm8P0ZevGNbdi1LbnitgixxZgUYx87mn13LNswJdGXP2agkusyHsWYiHKMoSmeCOxOnijepo6bAKc5VRYHqy67HuVTmlYnZ6hpUxJXjs/If5rAcjx9fT/vD97Io2qDawzhW9GXFkvgteVwt7vG68jvey6CEqrM5K4yZXTWmE2nm83IcHkSUuj1wNTDbf1XPcdzYhuWarW12/cgcsjjYk0/R5yzOnc6oxPDMGkQ3mO/2Lzo9Ht//DbP4O97HxcBr0eltknidTURNCPvjOOw+2sY+8Rq23eaF82c+oe/4OUNcn/hd+avgkiFr5wJ9xv+rgfF4g1rPSlKrCD0WzMhNlTCwPc4nzgnji/mF/dnnOCbykI3lwGLazV3xjyVPjA+cixNnq4IVBSdIK/lv+86K0IxfLNcpX9heoj3cZ/Qxjov32A/acvNwc1G1gnFxu+4Kt1sjVnOYD6sg1v4xeIxW1iOuC+6V4ovKo7Ef+uSEGvrO8qvKf5ivVIyyXMvGdvWb6TWlJ1Q9YZxHf9BHbM/8USj97Xu2AHifFfs4AbShCqcrVix5qYXADcDFcEJLFWPmFyvw6K8Te+y721TXXyXGeB/XIttPFsyrwRUDJdbiNYa414yrLCEpe9jW8Zz1ZzEWfcsEK0uwrChmopHFVuU3E7kqIat5siRZySErAfmEhZmtAfaL3xnP3NgsX7h9Yvsax0Wh4XxRxY+JQiZY3PycHZbv0WcmZNR81Fqotmz8OyBbS7aHLtaxr+MDjqV4imPHvor30RdsF8dj+676s1rCbDp/43qptsqPrAayvuq6iveIqX8SChdVBTPrF39HW1ufmWLBJsyIp0iDm8OKabStSMREDhM8qtjitWqRyIo+zlO1YzZn92IFqAISvzMBx/bEiRvGJ9aG+YUikCVRVqSUH2wcJVRUAmOcVvNV9l2OqCTIaFv5g21cXlm1qCM3Kvu5fUa+YxtnKxNN7B6uv8pBrAa4nKd8cr4rPjH7LLYU55RowLjNBIvrizF0x9y8gQlOvOf6bnDcwLbqM+YrpwFUXXFjM4447lf4Ev3EMTEG0Q/GMxfjDGz/0L+qrbIoxcXHwqkKPVv82B7bqY2tJBcmGByhmH3mHwuUzBfXnvXJBAz6otrNCiO87pLnqmD8UmvGgl4FmCuAKCSZAGN2Yl8nQKtJ2olJ189ddzHKBDFrr8SR8hFtO86jr3co5Cw/oRhXPFO5ycUEFitsi2O7seI9JvbiGLGNErNx/ngf47eS91m7qi0Wl1mdUAJIzZv5vDLY3jmt4PIV4wTrh9xT+6l46cbM8rTab3UNOYG2MfYxPiv5juVoxTfXX9U0VYNYDCCmTkpxMVy7rPBVCnHWXiXDrOCyJKbsIxGYryqInH9ZMkeiKPKjL4pYbHyV9J2IWBFVPqhEodo6wYX7pYJwhscoktEfV9QYdxWPcS5uTlnSZv0jtzMeV9aS7YFb71WRFaisYG7XqmurchhyjO2x4qASJNEf9NflOCVusFhjf5b7WY7H+apxVRHGeTixxfKJu74iVA3LxBTmCPwc+yt+xWvMn+274r3bA+V/JhaZFmHXcT4u3lXdwLhwNUPVQvRV5ZuKXYUf0hajVhgQKlEy55VtTHyYeJxNtIP9ox22WW4c5rciN+uL/uI99JclPgUmGuJndd/1UW1Xwyx/t98oqKK9LOmoNi5JsYKKttC3eJ/FAc7B8VfNU8UFS2DoI9pxhTu2ZYnazRn9YPlnRR67/JcVkA1qzZQtHCuzpfbB5Um0G685waH8zebqfGaxOsMdxn20y+5jO1fAKzXg6nA50K3vDOcqY0e7maaI/bG921u19863+Bn9dbGK11VbtXauPjJOqmuu1lR0RHpSypyYCYwtsURB5JJq7INCCkUVs8nuY2FiyWD7zgqgmocDmx9LWtkmub6zqO4fW6u7gM2HiTd2fbuWCZ1ohz18VB4WnL+ZuNjGYBxmPrqx0C76zIq4KqosMTHxG7/H+eCcmf+4BrNxe1UovqmiEvtgDmP7qPqoNi4fZaIK+cnaZiIh+sT2nIGJCeSYW1fkNyu4lYIfr6naF31i/VaE0xKqTjsBy3IryyGKm2ov0V+Vx2M71RZzVUWUqXyLa6h8Q7+Q87g2LiZdjlX+svko/2l71+hh/luwOCks5IoszAbarxR7ZxfvuQRZ8YMlxxmB6OYW21TnrdZeBSOz7e6r/ft6b1WF+qH2k63jGJxrsQ+7pvYJ7WWJifVV47D2ypb7nPlTEZOKa2wc5nOVp64QFfdhRR7/NGnHV5ZHsvWL11yhd2O6gh79QN9wHPbdCbOqv1kNqvpdzefKHzY3t2/M5maaDn5hPMJ/lbtBrQPja5a3K/kl2xvlS7zHfEe/1GenSSp23PrhNbzu+qqYRjvVupXl4K/fqXP2pDQayRIKfnYBpRafwS127Iu/1aa6uTifPz7032CL92I/t4E4R+yH9tE/tO+SeuZX9B3HqJDx6kB+sr3C79taxL2O+6zGqHCZ2U4K0HdjY/us4LpkH/1hfRU/2JiKZyxHKOD+uASt8geLc+bzSmBzYrloDJ3TcK+dEHQFit1THFXxxnI38iQrtipXqlrBuB1/HFQ9VLk7fo/jOyGGPlf8WgWqTm6oiLV4X+XvLEawDeMVq7vIL1YbMFZi2/hbxROukbPJwGoLa8PWiYHliko/pyUylP6ikyp6bhB0Sok4Vzgqbdx1tvnqu0qoLqHFtqowOh8jCTHJYrJ1YyuoBIz3cLw45gwRr4ossVcTpBK3sR3jU+S8E5NOFDpBwniCCRDHUsWczdkldLVWiIxH2Zyd79s1NQc3zkpgc1SFngmjSoFhHIrtKuvKiqqaixKS7Luak6o1VX8xfnCue2uVa89ESlbEs5q7CpzQcrWWrZMTmazObm2dLRVTTjtk8YF1XeXeyMFM97B6wj7Ha5neUX2YRolQ91S+cbWi/I/ns2KqNo/1ZSIrSxiuDbaLv1mbzUdmm4kxnBObKyvmKslmyQr9Y+OzebHAiP4q0qtxs3FWhEpYVcGFCU3tMeM4K5pon+0H+oT7iXx0fK6uBfrN+inhgBxSBZfFUJbgcR1ZQVfzRRsrQ4kU5JgTeaywVnIHrn1sq/JnbBPHi3NRHMa+7LvKcRjbzEeWW1ldY/VA5chog42rxldjsHhePQ+rnMt44WqRq/WMR2yNXW5ma672SeX8eB/nrOIL99+tFQPmRteO2avWx3hf5WHkcJW76X8zypzEHzdYZWHwu0pEuPkV0cbmkSU+tMHI6PpjAVUixvnJAgjHrIgeRhQ2T5cUnP0VEIMUg1EllyNQAYmJLrbLChAr9m5ObE83m07ExXEV59A3VzzYerg2yh8Vv2xdon1X5FYFcmcMPl/WB69tfVysq/wW7zPhx37juC5vMd6y3OrGwaLpxArawX4uztzcYx+2tuibW5/V8zD+xjzFeKu4WRFxzA6LEbZ/ESyPuFyouM3yM46Z1WI2Ntp0Ggp57DicaTFsk61jJQenf9FJEcc5hcXLTb46WbQf21bbVXzF9mwO1XtsjkqYsOuqnRIBOFa8x/avKghC2yUzIuPx1+tyz8fwaxi/k/GsP44rSrg53+N3F3esr5ubmmfGTcUtxV+0iWuhfKrGL5nDijz+EoBiztu97Zrbu8peZX2/c874pPZKFWYlUlTuc3kx44Tio0PGabYPcUy0xeBqw9Z1yukLYMvD4ftPn6vXM77gd8Y5RFbfcXwnKJU/ezUC+uDWwY3N5opwebTar6r/nJ6Y+sfzHR4P/1otOrh9rthEuH5be9UvLnZsu92L17EPtmVjbve2+8xX5X/sw8aJaxd/x/VEG6wvrk8kmtuvLJiuDFwX3GdsE/kQ7zvuOt6wz+r3ZkvFEAY7+lQtzGw/cUwVL3iP+cdiCe8xbrH1Z/vh/Ik22BxWheIYK0ospymByNqg3Qi2vji+8ysbx3EcuZOJgxhL6L/iI/qiciDLKzi2y8vMr2xOqyOuteLQBoxdV49wrRmfo103JvOLxY7SC4z3bGyV39X1TDup2KrWdTZfBZfflY9VXqd/+14FOjqYDcaSiyugcRJsAdEWu85s4lxUYkfi4TUkryv8jqxKvChkRMHfrkiofXOFZkXgPm7APWNrpZIRi4M4FrOvfHNtWBHLBJYrbsyeKq6Z2InflR9sjbNi5ESI8ofZYGOuClas2LowoRmvKzjhxPq7Iqn4nxXV6AvjXoxfJQSVb2r+iv+YL3BsnGv8zu6zmodxxPqwMVcH26cNuOaxT5aX3PWMo4z3TLNE+4oPzKarF7g2jHtuDaIPWK8yH5RfbJ7oC84X55XtjUL6Z0rRCLtWEZIukBWBmJBAm1myxT4Z8eI9tahoT4kENY+K32qOLDmy5ObWEEnLfHb7tRrUPFFgqsBha84KlgpYFEVsrzCBxHaufWzrRAj2YfvLCqjziyVJF6tsnXFeWYJEqNhjPlTaroDIW8btjC9KDGUFnNlmwqESI3EeEcgpVUhVe9YmroGbExuD5X1VM9h3NU9lm9XPOwnSMThnWI1UUPmU2cf87nIs8gpzFn5He+67igUWa6p+MJ7HtVIaR2k1NgdXF5ifFXG62WX7zFB+fe+SkSqA+Dn2x4moxMecV/ZZ4nH9WVJnZFHJMUvQ+BkTGiJLXMyOIhTrw+bHAsYl3RWhkhQGu0pyFduuwGzf47q6JLBddwJyBmzcaFfxhhUP9I2NlfmQ8Zj54vIJKzQqUd+Bz2N4MVblDrbBYqoEVyb+XNFUeVv5mM1V+VLNkew6+pXlBVcX2HxdnKM/lXq7Clw9j7/VGmxQsV7ZNybSMpHEckxVjGWcYVxQ9cjFjItJFMdMUOJvFw9s/HhdiWPmG4P9i05jDHpTJY54vyLqYr8zAo0JLuUf9nGf3TiuDY5XJTYjpUr2LOmqa+gXWws1/tfvS2bDB/wB+6/XpGCPn12b7Xu8j9fZmI7rGQ+yPjgO+422MiGBbZztjE/ZnFzssgKAc6rE/oo8foT/DYflBccbYuu7+1ludvtVzVdo141bmYNahxmbWYwo0ZLlW/QR+7h5sLniWGOs9xedxvjyP+vN7pXKyRGZHbzmch6z5/KmG4f5xMbIai+Ot93Lar9DJT+o9Va+4Wd2P4xNnfzBOa0CRiWoSoC5cSriK45bERoMTjBuv1VRUz45W8oG2ldt43ccd2YsNY8INaeVwYoO45maa1aw2ffYV+0Zfsb26Hu1mClxmBVxx2UWW+q380HZZ3Nl68L645hxrdD3lZGJxKyATAgem9tVf/QzK45OFLg+yI8svqJ9ltvV3DKRErnF4jGLAbRd2YOV4fKOurZ9jv2jvXgf77F9dePE+y6+1L6wnMP4gXaVDTYn5pfiC7Ol1jRexzhxdcJdj36rdWAo/TejapAILEzMWZfUsA9r74JUCQc1JpsD80eNyxaXjedIpPx2/lSFkwpI156Jizshzi8mn8hPtV9KkG33I1zSw77Yf6Y4YrBX+6h5YBuXfBT3XTFW+QD7VGIzu87i5g68RvEVoQoE9o9tVY7ENcO9wrjBnMpyJBZttJdxmAkEnBuOq3I94xyuEY6jfrM5qxwT/cH7THTgOq8OlTeZOMR+yDNX31j+U7Gj+FcVgYrXlVybaaJo1/nM4pT5x3x12gn7MsGKNlSdxfkolF/fKyNKKLmNq4pP104V48xHRTK0p64h2D21HkgsZVe1q/jK7Lg9YPtk9nPJqv4grz7Dval9Veulfsdx1fcqr9WeYRucQ3Y/myObJ/ZjNl0iZ/4wO8wW86eyNmG8FXn8ofi3fWefHRwfq/lE8VXtS+Y7u8+uZ74zKF+c8MDr6Dfzk82JjeXssLbQbjkOqzycxXFlT9V310bxxn3efKrax/YVXrE1ie1d3mU+VWqYuoe+VfUW+ibyETWW/u376Hi2GBU1HBeabRAjpGobbcb7sY9LGNsPEkAlZZwXtsdNjr+VvUrxiG2VMHBrH9cz+h5/x75ob8bHqwLXgAVkFnBOLLJ2jJfKpttPTAjKB1XYVSxk+4r83eyyBIXr54QGS1qqr1oP1oaNdRfEuFd5J96L3+Pn+OPWGPugHxsyEeBiiuUeJeaif6wuuBqAc1Q1S+VStKm4zNaK+c/mxdaM2VqZ01hzVT2q1kmWB1jexfGV7Up+xPEUH9h1nLeyw8bGmN+uxd/YT+VfvF+NccV7jDWmkSr1dYP9M6VqYSoqmgmn2CbeUwWsMiaOjYkLbbhAVxuqxAhCzVmtAYLNgY3B7Cs/GJkr46p1XxXIN7yG7VT/DYxPTDyyxOW4H/tFm6wt2y81V+yTrY8bj81fxR22x99uLZQvTMyotVXrsBrYGihuqUKt7G5QuTmOh+2wGGX7jD6p+FGCQF1zxTirMcym4roSNqyWVPJ5JsKyvVgJKGoqe6DWplqPoy30If6O95CbLD8rn7K2jJtsXkp8Kp5nfqqcXMmTlbXOahpbZ8VhK0px0Kw4MMwkGpbMqv6wZO1ssu/Kf5VkGUFUsY5jZOvnCj27j20qCbgiiHCMO8CtkxNBlaK+tY22MmHG+rDChmOxGGGJnHGtGldZW7SPcefWwc1XiR/Ga5X87shjlhPVGjh+VPKQ41687/I688MVPiYU4n3sz2LUFeJK3lRwMcHEExvT5ZUZQbQyl5UIc2B77ziPfTKBpPIPjh/7Yk5z/qoczHzF+5XYdD4w/108xO8sntS8VR1VseU4fPjPlKIjbFAX+CroVMJRCa4i2nBMlZwdwdUYlcBBuABQhIhw5MxEKdoo+r3qcVP5nyKJqPKD3cN2bvysmLI2zI+qDTeei1uVNCtF1MV1dTy2vngf14fZHgvy+EH+3/DKWo+RC0y3dpkIcjkpE1SOi9F2JR4qQsLNwXGb+TOzZjNCc4LHS3J4jHr+3FAVb47z7NqMfoB5TPHU9cH1qOojvMbmWbVb4RzaZvOr2s04XPozpZUAR2WsbEUbm4M4BvZx4gvbMx8dEasiUvkafXQ+MF/Yd9U3ri/6HeeCfjF7jDiV9V0VbM3jOo7x/Zrgnrr1cWKVjY/jbd9VYVJtHe9YrKEtFq/Im/id3UO7KkYUsrXd2ri9wmuqf2Wsq4JxI661iuOs8CJfFQ+Zfbb/0YZaa7UPKqe5a9UiqNaNIdpXOTbjYxwjqwWudmBcVWvhFaHWLN5XGiGC7SfLvZjj3V5s99EXN1b8PpPP2X2mS5h/av+dLoi/8TPLrVkdwjbRBosrt+cM9qT0AU82cRAnANU1JW7ZphBfZDHKirMiZSYg3NycEGE2lF9qXRFuPpXx3TyytY9d3c0L4wP366cbZt0qYlIVFmYn7lXGRZWAKpxRc1TtqjHM5sPsMvsZvyrrhv2zJK3afCz4j+ePwOFyByN2FCqc39plRTe2y3It9p3lm4ut2IfZUDyt1IesX1ZPMltbO7KXy3H4IU77XW1k61mtwY47ldxUyTkqNhCZ0MN7jluur2vD/GTjsPGr61TJC6EN5XBJlKJBhizhZQk129xKQssKVlbAlXhVY1aCQY2B48yu6x6ROytyCDGXS4RfIQu6S3bxfrye7fuMGKjYyXyqFLLMZrRt9l+KhT2iMRMAah7ss7NPxliRx18cF2uq9mq2SEVbaD+2Ydcq9UHxHedW8aEab9WiinNQc95TrNlcsU9FMN2Fw2PMHZhkXHM5JNqO42brXeHLdxNMchLeq9TsOC7jH947Ol5WC6t1kI1RycP29T1CbVJFkH58+H+OYbMdf8c+apOZXbZp8Xu8xvzARY3zVhvA2jNf8V7si765dc3EBRsb+ymb2D4L9hUQueJ4g+3jdcWXigiI+4x7rrC1c23ZvmIs4T1mU83NrYvyHeNB+YzXVMxVCgXzP/rrBPIKcDklfmdFYrsXv2f2lGDIfKzkL2ZH+Y22qkKhIlhi7Ga+Kh4xv7N6oPrsEWMrAecZr8ffG5RYjPdZXXX5R+WGOL7KJQoqhzK9wMZmuSzez/KpiztWi1jeUHvCfHG+s3VxnxnSfxJKqWacwIxzSCBVkFjCYMGL/dgmu6Lkip1LdmxMlliY7zhWRjwHtj/V/WB+uPsrghVml9zZvYzj6j57uFKJsFr0lUhj8aoS/fZZjc38rQi7LF7cGLG9ElusHSsK2X6tBicWx/AnkGgD+233FL9wz7M4QB9YMcvEoLKp7EW/XJ1in+N3JQ5VzMV7Kj6yfMvu7ckPK0AJOFXXXV1VdVvV3ay2s7yMbdU4KkYUJ9AnltcUFyv1Cdsy/9g1Nk/Fd6cP99TKn+4nnT/QWQblXEYCZUNNTtmN92fJgfNTCcMJjmzuivwqoVZtZUWG2ausgbG/alb8UNxw67V9j/exDbOn1tIVT7VHjkezY6q5ZoXRcR/tVLnt5qTmyHxEG3fl8ePrn8fL1stxR4k2td8z3GZ2Kz6inwgWjyqOXSFlv11bNn/mk5tHxndX89z8vn5elsPhe1o/1V5tbdx9RKW9izHWn/mM12byYFa3q/0y4cewh4vKV/QP+zoOp6/vmRPbZ3Ytft4cysRknEwca/u+/bBEgGNEH2J/vK4WldmIG8P8ieNEH+O8lS0H9HO7hnNn9zEo0N9oGwOHjbsyXDFi61vhLa4X9sc9j/dxPGUbP6u9UUU++qGAc2RciJyPdllsqcSJc1Lx6tYF54bXMh6zcVeC41Vsg59V0WexwIpLVgMw10Vgn0peQb6h31m/OJYrioqH7LPyAffCrbnKP9naZ36tCtyfuA6Yb1i8R8Qal+VY3Hu21uzzkXmyeSgfnaZhtUnlYrSlxne8ZuMrDYO2mWBm80WU/vH8PZuiApq1Y5NRQagSjeurkgJb5OgrFjn0pyIqEW6jKgVTJTsclwWdm9ddEp0DzlUFKK4PW/P4mRVAvK/so202HvOFCU/H/UzMuuLN+MPWkfXB+bNrbl6ZcMB+LO8wn1aGyxMqH1a4qcZRBYjlERUriq/YR81D2YxiJJtbNg6LRbUmjFPZGmc1LbZD3qr1XgkuNrf7bn5qfWN7to5qL9V+KE6z/XbfMa+pXKrmrLjlclmFFy4nqnyBa8psuvlU4nND6S86scKmChr2iU4poqmExgo4K1abbbbA2330WfnIiJX5zZJJbIdJjxXbLBhVWyzKOA7r78Zh6+KK4ErI9jsTVUoAMS7Gz1VBgMXHJT3HB7QZ28Wf2AZ9VYVQxY8TGCx+8b7iaiW2lXhiMbo62Npsn+N9NW+Ww/fGOxOYjoOKs8oPtMdynRIbLrYreVflVsbDyGtWOxz/XY1yfF4Vai6MgyxXIZRecKJN7YvL00ysYZ5HP9jcGP+yto5TeB1tYGyq/mzeaDdrh/Op1ofv7CYNvvn3HXEgdIg57DbUJSFVIN042/esPUtYOBdHQmXD2WP92DyVv+y7mztejziQ2FZVp9/xeIzaaYi695NhwxF2H9sxPyrcU2O4BMK4x3zIbDF7Vb+rXJyJJ/cZbX61sSKPP9iejVHLj98YEnvk7Crez3CpwotKvkRfq7yq5n7Wj/nGxnM5PqsZk7l7SQ6PUauDlb2qcHNDlqdSx0Xcqe8zea+qqbBtpnkqMYX+Kn/imJluUXWJjEkHsq/vs6KXberWdkYEVYnKxsFrarHj7+jj4+H/Vmi8x2wwn7JAUv2xr/OjIq4qhFVwPq4EDEKX9DawJJAlNJWUsJ/6nPEBOZolczcG66v8dOsUge0Y113RQBvxM4tZ1p7Nf7YQXQ2VnFIpngjFPTYO+oN7m+WsSlFk7XGu6K/jurIb+23cQlTiAa+rtchyTRyzstarQdUjtffxXlbjNijtwPbcxQyOzcZS8aH6OU2RXVe5GeeDbVl/NQfH66g10D9cB5aL1fwZSn+mtOJ41nc2iFzAYxuVVHEDVVJ0YoMlJLbh6Edsy4pzFS4RZ2vryLFiUtuLihAco3YSEu8prjlRFu9XhCsbQyVY/J2JDZXk0B9MSNhO9WVrw9aj6h/jPUu8ao9X5zzLG1nOQVR4ptq7fLvxIyvwbBzlb1VcqALuRKCafyb4mLDCOMnygrKJbZi9lVGpYYoLbh834N5EqLhw/MI2Lv/Gdo4PbM44L+yLQpBxtTKOW1cGpntYG1XvWExWMPU/OuFAM866gs9sZ4vsJqmI6xK2IiObEyM/I67ypZKk3FpkZKusNWtfIPaqx0xfnDeCyfHaFe/MpuKMK66OX5kN5r+bk+JZVhgq4oblACcwK/ccnEAFP1bksX19H8H2Z6Z4YbtMbM7arAhExynsq+bl+rgc6uaa2UVfKnvh1tPMazkORz2h8tnXdnLNtvtbP9Y+XoPxbd7Fdtg39qmMm3Es42zGZ7c27H6W3yOUjnExsGNO1InSSaky7khQTRDZOJstXBRVsON39G2m4Du/tzbxGhK+Ms9qsVD2sjEqwiiC7Znax5WA+4/7xPiTJaxoM/ZTe6JiQxVp5Rvzwd1nNtX64H28phIOW894Xa2988sJGIdqwl4Rbu0zcVjNR2wszB1xz5j9uP/Mj0xMqL7MF5cjHdcqYhZtMf/YurP8UPFV+TkbA1dFlasuj2Y1bGvDcrbaC9VO1c3tc/Qn0xIqFymxx8Zi7bNanmkyVdtUrnbXVRu3Vwz2b987I9miqgTn7G+LsUcwMcKhLZYUVNCzxKvmhkU3+pIFA1s/JBRbC7SLNnF/lICuFqvVC/oGXGe2VyzgVIJQyW37rZIXS85OYFahuBl9j9fc9Tg+ctzFP5sDS2ZuXbM1QI4z0ZTdWwls3ZTo2tqoPJSJHJUb4nW1pionubkwQef4pWpBNk9Vq9DO1raaG/GzE1zsPqtZbG7YdkWw2FQ5sCq4VB5VWgL9UGIL22e5GNuwHL9d39rHvOpiS82FjefyLPNXrQ3zI4tDnDPzJUN6UrrHqHO0ch3HZ76oTWY+M3ss6THBgGNlY7M26ns2jvI9jq1ElJov8xvHd4ngDlDr7sSQ23NmD/mF7Vhixb5qzV0sqbG3fiypqP12AtrNk60n8t0latdXjVFJsJXCclU43riCg2uMog/7ILLc7wql4wsbQwkSFQ9q7zEOXLyoObjYxrVQOdjZx3bZ/VV5G8H2Ue351l7xN/ZH+5mtiu5gY2LMKT6ib9l8WB9sU81j1biObdn6Z7ldrbvzsRITYxT+ndKqYFQbUkGl+KokgD6469hPFWm2OUpkKFQIj+Nu48Qft8ZuHBesrC1bh7vAJTLGCXavEgdqbeNv5A/7Ye1ZgVdxEMdkPKgmYzUX1zYTCKoIs2voL8YhyznRzp04zdZY5SVXLLMCE9uweMD1ZbnSja/2h/mOoqyaF7O8zNZA8UrFEhvP2VK5O+Mly0GrchnXR+2hu8/2WnEu8kblAhR8DjP5mQFjRQljFevqPo6h/GP+K1/QTxwrrquLfeZThqmTUpYkcOA4oe1zxXY2ZoRT3JkAU/3VeHg/m4fyVRVuFzAYUGrObO0rQRL9VfuH9+4AFpgRjLu4phlHXZJkNlgf1xeFAIsDxhvGd2VTxZ7yna2BEgAqP7CxFZ9VwcbYyorNaqjkY2zL2qtr2Vj4XRXoLA8pziquM94oTim+KD+xncvbqkgzG/FzNRaYXddmFWSiRQkv1ja2YZx041XGYeOx72iDcV5xVcWb4iPjuPOR5cFMTzlOO5+UjsP222fHX3tSikYqBZQ55gSOSwg4EeaLS1I4F5WE1OIrIZH5j/PAdWB+4P2qr4yk+NutS2VOdyjqrnBvcIlQJZoMFQEROR1/0I4LfsYd5IRK1iq5MaHHOIyxwfxURRo5zkQmjovrGceI61EpKCtBzZW1Uesa90HlEiUSsC9yFvff5SPmS6W4xbaqrrCcGL8jP1SBZ3k/ywGZ0GAxyOos80nVhFWA683EoVpvNWe31xFsTTF3sRyqbDGfMx+w7fbd2Vd5knHYrZGKwTgm689yuhO1sQ/Ohc2fofQXndQGowPMqdgeN4AV5+03jpERTt1Tc3GLEje8shFs/Owaa8OCwQkDVpCQHJX5Oh/ZOKvCFRYWlIozrkiyoqLsKx8z8YjIuIztWOHNhMp2rxKTuKbMPq5VXD+VB9S4uGZqTap7cEWo9XZ5mM2XiTI1nmuH/mzf428mLFzOx7mpPtiG7TnjBM7FiYHIT1UHMxHg5uRsM6EUr2e5/KrA+uY46/IS5iCWf7FthBNN231Vb9k+ZbzIYg3bVOvQ9tnxCHmm8mwW4+w38zPWPlev3Hqkf6Z0G0x9dwHCHKsWBScMVbLBxXdEdgRjycnNmZFpJpFVhKCauxI/LvAV2LrcBVmQzNpS+zPG94GvEkY1aDMRwWwxfjAOMzHBCiryCe1jgUC/XOJjsZcV7UrhuROPWWxWRHYm3ip9ncBnuXe7jxxhYoLdY9xz4zDfUKygT9tYcUwW09FXZsPVFSZqIlxMsfmtzudsD1ndUiIKofZW2VdriXkM9561R9voV+Yfy50s9yHHkBvsO8ubzCflJ84V1zDL6bFdVZOURKkrFtgOHXFt3DjxOyYltxkRuHgqYbBAUL5honEJSrVj93GOOIesqKukiLbcnjAxcSe4vVaFznEC94YV2c2eKnhxPOULSxzZProiy8C4w76z+av5MN9wHqqAK/5h8mb2o38sf6yKOG8UWiy/sIIebblCqOByBBOC7LcTD2iT5Xyco5o384nlR8ZBJyBwvVDEsKKMYDHA+qMvq/N3DH4A5PjM8l/8vcFxzvGI+aPaMD8ZB50N9Be/M5sRat4MWc2v2Nju436p2qhitYrpk1IsVqpdJqZY+4yssZ1aCCYMlF8KMwVSXa/aV+KkmoiYaFDJMbZTRN3jw9WhkhjeU4kwricWImUX9zRry8bHfXIJVBVdFYeM21i8sS3zA+egULmv1oH5wHx1hZ4VkFWBecPtWZWjuObIIWaPFVG1z0zkMWGJbeL8KsKUjcUKKhsH546C0wkb9InZRt+cPyoOVoYTNmPo2qTy73Yvtom21Fo7DRPHjGC8x3vxvqolWD/QB2ZLrU9FSKJtzBUqrljdcDGA9rMc4GD/9r1KGHsDRAkeBkZM5pdLVjgm+o591SZi22xsHKd6vSoa2L6o9VE+u/7u2opg+1TdB2yL66f4hAHK9sPFl+IL3lP2MgHmCisDs8sSMPJQzdn5qoSCKmJOBKDvq8LNG6+zdmzP2PrNcGhrw75ne4pjZXWmymUsmkroVMZQtcPlB9XfCaR4PeNzpXZeFU7I4HcnRDewHKrysdt/lqejDTaW2juX4yr2Mj9nuRHbO+6oHI22mTB1uinmGbYODOXX99G5zCgWo9hXbYJLbvGH3Y82ceOyZOREJxvHLe5MUVRJGufM7ETfVIJk68WSYUbWO4EVWxU827XYJtrJ1hNtoA/KP9w/xeE9CUGNGftiQcd7Lk7iujhRUhU+bg0qSZrli1XBioAqoNgPRWhsy3Kw4o2qA0pgZqIMv7Ncz3I65jbMy9t37KtqSTVemT1sg2vjrsX5IMdVTlmdx2wulbrL8okShQhWSxU3sT3zw+kLtT84byfU2PhML+HasPql6hmLW5yHq3ts7kxLVfoh7Elp1lltaCaeWILKEk/FN9bekQQ/q8Vkn11BrCQORqh4HdvMzNH5syeprZ4I2fzVPsd2bP8Zb1RyZYkn3mdJ1vXNkmBmj7WJYEkRweaaFUxXiONcMQZUXDBbKjGrua+GLKfidRezWfHCttv4uNZsTJUXVR6LtpwoQbhijDZiH5yTGketo4oxJ2xVjDK/3J5m+3p1qHzDruG+Ov4jD5yYwnEc99EftOf4zcDqg5oTs6m47nJ9lgeycXHtKjES2ykNhbYiSielm4FM7GQbjH0y1YwLhUkk2qwUW7SDtuIis6LL7Lp7Di74XDJlY8bf0f+4J/gT146tKfu+Otha4HXF49mHARa8WQyxRFndg7jfaIONx8ScE64qOTsfcR4uCSvhzeJB8VW1yWJxBaiiyvbOrWWEElMsd6ANlTvZOAouHti4MVeynO3GcPfZ/HEd2W/0WxVclWtwXOaD83c1sByF6+a+K44xXaDiAf1hORfburqYaY+sRjgNoeoArh+CxbnK+9Gm0kEVzrl5xGuVHJH+4/lqAdxizwCJVk1sKmkwMcCElxqLJQ3mqxNyLEjUnFTAZWRQwcr2BUWq8kWt3epwgrDCYxX4LDFiklMCl3ES+6kCphJhJX4qSThbC5b0ss/RP5dIVc6ZFQlMCKwOXDvkAovtylqj2MuEKtrCtUafWA50QoL57fYe4wTbHslhOA7+MD6jT4zHWX1g3F09F7O9V99Zv6p9lz+y/M72NMsjav8ZmG/KX8YVVcdif6Yl4jxwPmpfcE5xPNZOrY8TyQzlk9JoXIm2zTHVjyULTE5OKDDy4LhYzF3xRVIgEVSSRJKwJKU2MRPALNmpQs3WRxGWXWNFnZH6DmB7WuFxLLos6W1tsHgoYYB2t8/YryLg3DxxXCdc1JyUz4pnme9KoLjvyhbGhxNsd+Lw9pvlNcWjSqFB+9Gm4izLS8oP9EUJOuWPir04D8VtzOtKLDI/K2vmCrGzU+17l5ysOMvqXWyD17bvikuYh3CtGQ/VvThehNoLxinFS/Sd1RiVv2b8yXxTPGR22D6wNavoJ4UpUaqM4WSZ4wyOHLFNvI/XsB1LQMovJibiPWZHjcl8ZveUL6p/XBdcH5XkMcgcsTPxFG2sCpUkWDvkMYpZ1tcVXxyXrXc1MURb22fGATU3HJtxhok77DNrW8UMG4dxjSVsV5BUvCr/VwCLabXu8bMSYayAs7HiNdc/gu2FqwFqXlnujmNlyISCs8/mw3KxigE2N1bP1PrgHFbl8Bh+3St1VGkLVdtdvmJ7FsdSeRvBeODym5oTi1c2pziOmhvzGzmNvrK4U3Nlv1n8qtrp1rP8F51cUmEDsvau4LHJuGSkyKRIi/exvxNeiqRu01nSQkJVxESWkFniVvNR39n4KCxY/9VQEW2Ox6rYZlzM9gNtscTNimkl5mIfxTcnLPC+414WP+gr/lbzdAUMRUP0nxWm1Tk8hn4wGkOv7Uzsu+tZcVbcwnvIRbXfyn/GG9bGzVfNj3FbxTmC1R8W08pXVgfvAhbH23W1buy724uZvMhsqbFYfXC6g81bjanyvOOsErkub1fruKtp0Zay6TRLBemfKWVGcWNwAV2ii78rRTreY8RVJGG/cYyqmI1t8Xf0l8EJDYcskFQBx/lWkLW/QyEf49sCoxKjSoQRrGht17drrA/2VYLJcYQVKyeOXf/oE64LFnmXlHHOSiCw2IlzdvmgklBZ7tns3oHDSrhVxB72w7ZMCKlcrvjC9je2R5vMR2yPthGOz9t9bK/GYnXDiaFMbKCtrM6wnBL73kGkRp7Fa669yq9uj3G9FEdc3UaeRtuMF5kGQptO2Kl1wVh17RCsPauFrB+unxpPxVI1h4+RiFJVxJn4YcHmgoz1YeOrAhfvYx8ckyVt1j8jKtrHa/G7IoXaHLdhbp7xc+Z7FUx4rF7U3R6pQs1sYIC6wopjs8CtFkmXnLENxg3zW3FKxSaLK5XoWc7Avtgev7v4Yn6p75gDVi3qWX5QYHytFMFMcGW5feuDdQDtYSwoMcqEYlZTGN/wN/MXfVd8xzmx+TifnB9sXqtyN0LtG8tX1b6sj1orJi6zPK7yt6q3WI8x5yKf3N7P1hfVB2MR80D0jdmL4yi49cC5Oy7b1/doGI25xMQmHZ1H22rRHSGUbUyC6AtbGBzHba5bk3gd14jZVWuo2qIv8Z4iDSMLC4aqEFgNbI3Ymqj1dtx3RdStqQtgl4CcTeS2SyhufsymKwhuPpWx1JpleUKNqdZg9aLOODWGPgBgXNm7pmxM9R05w3I2CgE1L0Rl/qqvmosrwhgTLB4cB9V9tIX9WH1gtleEylMoXLa2WztWh10+UevkcpnLYVn/CleYnziG23eXR5UOc+uNtnGd0A6zjXPI8niG9PU9EztOAMU20fnNnhJw7HeWHFUSY4ugbGUJJSukah5b+/jDxmc+R7/U2s9sfiZ+KsVh5USoEr+aF0sWWQFyQYr92HrjGG7/M16pRK/m4K4rnrNrKEBUgY1zdMka41PFpUqeLk5XhONtlvNcfsLr2z21R1t7xU02XtY+jqn8iX3QJ8UjNi6bD5ubyq2sOLO1xLhw8Vq5d4c8vCGru0roYTvF4ay2O14wxPau5johp+bl4hlrMsujzM8ZrigtUcn/yE2cP1vvzK/0b98r55xhNhF0rlocHOncwjOCu0S19WEJKfod26lx2FwrpGJFgCXgCFYIXPFi66Nsq4K1Gphownt4DQum4x4r3JkAzK4xH13RR1/ZPBk/cK5KwGIfti5sbJZUVbJSNrCNi73q3FeF4yt+d1xT++vW0flQuRd9YKKV+RfbRy4p0Zv55nis5uzyB5uL4qXyTdUJNb9V87ESlts9vJ/lYezPco1qVwHuN9ZiJQBVLnX1hPG5UrsZH7PYZvdUXohaqCJUWW5nses4nL6+Z4vjRBICgz+zjWPgorCFZgrdtY9+qblg/3idzcsFDvOHjclEsRK9yo/o+4xvbE2U/dWQ7a3iz/Y9tsGiyHilCiq2i+Ohrzhu1Z84R9YO1yCOqebCEpibPwNyEG27OMYxlX0VsywprwaWK1lBHIPnJlaQHFgMOHvoR0UcOl7O7D+77mrU0byP/rj6w/xT86jwdOV8zAQPXkcBlMWyyisRVR6zazPaRc3L2cnqt9MbWIeidmB+KU2A/iltxfxmcc/qhqoXdE0Sgn93Uy2OIla8Fp1hzjnhqH5/46wgMLZhi8jaqfniPNl4KqlWC7pKatXE7xK6sqHWOZqVA14Yj8fjY4w8WVQCVV3PAli1qQSs44XinEvoWVsVy1lidXyvzjuLbTcHtkbYbhueTuTCeDweH9n6qPw4s2/bdWbbwe2BynvRtyz2ZuK0EoOqb2WOVXET7VdFRsWXr+2W5PAYfs9dTcQ2le8Mjh/RRlZflZ9qj9U4rI+Lbza+Q8ZZtV4ubtkcWJ/EPr1R+m9GN6NxwbKg2dpmRVQ5zr67IC4ugv0dP+O8GQlYW0dCRg62JoyU2xjbfRxXzZXN4bOBralLJri+cd2jHRewrO9mm8UVxogTZSyuGNB+9IP1UwmTzdMlKJWUcE3U2me5JUvgcfxKkVoBSuCz/Yy/GT9xD9Reo238UW1wvNiOcQx5rApdlsMYH5wYZLGGa8fmyeIqfmdrzdYqiyXWR7VfBZW8xWqkauPyWmX/2Bgsf6k6rngeoTjG6rmaZzZnNS/0gc0F7WfxrdaKrRneV3seUXp9H51XhcC1VcWKtWHCgBFKjalEHRsTv6uiHMdxfqCwYNdcAWXzyvxTQZAJlTgG+sjmtzqy+SixlyUgVdSVGNj64bhMFMQ+rLgqX1g7Ni+EKwYudtVcmH/MvuIy66NiwP1mc1sNir8ZP9haxHbxO66dsxfHd4KP7cGM6MiuVfIpm4/yha1nZQ2ULVUT0Q6uYUWw3gGVmpPFsMtxau8cbzIRif6jb0rTqHmzOao4U7oCxaNbF3ZP7YOrN7hWmU9VTVH+J6FmkAWNC8xKMmWbhWRzi6+KlUvUbH7VgucKKbOLa4Q2nOiMY1Tmovx3xWc1sPVWYtMlgjFyUYh9nRhUNpxtlvAYD1lcsM+4Tmzd0DabCxsfr1U4q2JAJV62Nuw+28sVgXNV3I6fs5yTFVG19m682C4Tk0qYoZ8z7dl4rJa4+SlB6GyhPRWb2VhKpFbne0Vka4DXVV91j627yilMgFXy/HaP9We+ZGLMaRtmk83TxYbK84pfaiwcA6+rnIO+YFuG8j+eH43iZ3Q2W9ho1zmIE3OCygU/W5QqYdhmRNt4HftFv13wZeuAa49+uCSvxmF+szncCU4UjqF55topjuJ1t6ZZkWPXGSdUrDqhgb4xQYdjKpGnfN3usRhSY7ixXNLEsap7emVgPlFiPLZXvItryNY0flbX1NhqndUeYxvmX6U/zoPVFsbx7bNaQ+QR3ld5wM2T+e7aKr9XA+Ynt3YYu0y8qbzD9sqtMbaL+87yTfRD9ce22IfVDMxVOD8nAuN9pzM2u3F8FjtO92T51NWlSv/yfzPKBmTt3KJh+7iImHRZQVd+IHmVj4z4FcyITicWlf/MJ7WZygYSm80hmyO7phLy6mCJgBU+Jt62fixRKN66oobfVVFTwiv6iYVUcSj2wfvYT11T99BXFbssrnHtEU4kqbhmAmU14FwdL53IdOuqcpnKv67QuHytxmO+YtsqlzA/s/rgcrTjS5ZfMeZwzlk+VRx3468OxaeMt7F9dm27HsdUQg5zaRZrjMfMD5ab1HeWl3EMZVvFSRbTrL+KHZZXmSDNcgMi/XdKMaCZUSzMyg46zQon2sDCm21q7MN8cAvCCjiSSs2TtWWkVSKlWpxZklPts31SiR7b3yEJKj5s91zwKDGGbdg6sf1QBUvxL9rBdswf3DcUoGo/sZBXwJIXu6fsuryg/MW5uWSq5rgaXCzOJn6WU9T6Zz5hHxzbiVmcA353dnCfWa7FtiyeVRypfK7sqL3BuHT8ZKIDv6vatgKw/igBFq+p2ovxz9a3KhrZWGxM9K+SS5gwY9xV33EubM2q/GD1Dm0xuDhEP1h9quaAn+wlC/vTTWYYHassMFP87DsjmrrGbDC/WD/VDu/hvLOkHe+zz2rT0Afmv5q/2mxm043NPm/TkhO+MB5f/zmdr59tcWDB6oIoKzTgx3f9HNfQvuNT5j+OkYnH2K4S0xXfFYfVffRvds1MPKzI448x8lzCrn9nyIihSh5hdlxeU/xw9iucYnNK8lfKrco6sDGzGGNQbYocX47Dj6//JNSGvXxCG7EPQ5ZDcbysXjrdwtqhb7P8Uv45e1mtxzGUxsG2br/cHERup51Kr+/jxLYfVZji782B+KMmw8gSf7sFqG5svMbmFu2o+TE8Hvovp8TNYIRimxzbxzbYNvqUJcDsfrSvEvrqYHuUiUpcC1xzVUAUJ9gY6B/zge21S1AsJhgf1ThZ8kOf0AbjOa4Xa8tiW60vrofrc1cex/2I99Q6x364jmrP3Voip1hBY/WDfa+KaFVHnMBEf7Kx1DqoOoT5OovxOBbzJ9q6Sy5mcYrfXS2t7DsbL8aIqsPoT3Zf6QYnHLE9qyv4W/EKc6fSAmwNGJersYj243riPZwju89Q+tv3GNhqMViCQ+Bmqk2NE3CTxj4sMaqFcskD7TPf1Max61liV2SoJiIMPHW/4if6s3oyjFBiafvs2mL7eA3bK3HHxt+uO0HI7ChbOE6WHJnvGJ9ZslXryPKEW0ssHI57zn+XQ1YEWw+XHx3HXN7Fe644ucKs7rv4Y36oPWM21b5jP/edxSO7jjWErdtM3XFrhGuyOljtczVnhos4BsvhLKcxe4yX7Bqbl/OPiTr0WdnMcjC2ycRiNo9sjbCNi8PKuo9R+DOl0bk4QFwAR5bovHIS26rEgWPi2CwZskSBc2HjxkSg5ufIzIjHCoULOLU+yiflpxIM2Xyq/VaBCna2nm5vFffi3rOAZYmC8RB9cNyMvkWbLNEx312irfIb+7H2uH7oN9pSYHuo1lv5sCKU2My+K2G1/UZequLLCpfyCf2ezT9OiLJrKmZcUY5tts9ORKJt/O5iWkHlHLVv6vsqYFx1/FB8jcB67gSaE1fMNtpB/3C8LA5w7qhHMn2Q1SjkHuZHHBfnr9ZD5Vwcu1LbXG7fkP6TUBFM1LF20cl4HzdCkTRec/4w8aeuqfmxRWZFOZLCJSc2TzW2mhP7niWiSiF3YsMV+1WTIMIJue13hTtZwKoCh6JM7QfbL1Zk0QeXcNG2Gsf5w+I2zi2Oh8mWCYSKqGHfXUyocVbnMeOPKmjYj+U23LOIWGBw/9hYyEUnALY20bdZEYZ7qYSBsqHaKW6pa9maM79U3Kqa4WrIimB1n3EG+ZYJMLSH15G7LgdFW7Ev+uM0C9s3NRelgxgHWb8sr1XqmKtZbBysTfgZ26v9Ypj+x/PdokcHYvus2DEnmQ1mC6/hAuFv5pcbm/mCPrNkhYmlUhCVSMlEjPJLjT3jyx3A9oIlQCfgYt/tMwpMxcMIljTRhkrCLAkqfjO4++6eEw4qxpiAYmsQ76mYdGus9qu6JitBJf1MuCguznBH5ZGK8GRtXYygb6roM9tqHlV7aIsJTJcXKt9ZjsY1Yz6uDoxZVc/Z9w1OC8Q9jNdxLLzmRKK67oRwxlfmQ4Tig5qf0gdKOEabTlyqvI6o8tvli4iyKFULGgdV1zOiZcWMBXm0M7vZak4qYbHkrzbaCUCVSPGemkd23wlQR0ZmC9flDolRrVMmqty6MrA9c0k2+uBEgosTHMMJGNeHzQXXAjmj5pStA4ttnGu2bgx35LErGpnIGUM/TLuYYP0wZ7F7au3RF8U39IlxT/nuYjb2c8UY2zEh5ObHrqOtuA5sjXF+8f6qUOKNzXWDE1ixr+MuWz/cS5cfMVc5brH5OL4r3+I9Nt5MfmRzddqj6r/TX2puaj4RVpS6wswmoYp7xZFsUpiA2XhsY5yoVWOhfbYucU4sYTJiO0HifFPjsXaOwPhZ4cieXRFZQUWhpQJRFQ2VHCqJwRVOd80lJReraHNrP7PXLmmymGV+qT44Xxd/sV3m5+o8VtxDZMXK7Q+7j9f2rilyzc1LiQLHcddWIYuxbD5KxMZ4ygRXNqc7AdcnXo9ga6DEKmuv9kAJ0WjvjJzkasuMgGMxU+G3q00VfeF4qXif+cLql0LpvxmtGlM21HVVDFWidMks2sR+bOEym+gn8x3HyOaNfRRZGcGrCWuG9C7QY9vVEyUmqA2V4o7tcM9i0Lk9zMREbIv2cR+w2FULmyvkKs5xDBYjaCf6H+M8QtlhbSrXKkVuZR4jh+KeVfKey38ux7B9dbkNOYpjKVvoK4sZlYcdR7GIqnyP19n6xD5sHVUbdz0TaOinarMCMGep/Bb3VcUygrVjgozxMrPF6rbaM6Vn2NxwPoyPuF5xLviZ1Y74XcW3q0+VtYjjsVxbqXsRpf/RSQ1QDRKVMF2Bi+OphazawH5xHhUxyZ4O8J7zNUvo8ToTIMqGKrYsoNU6qaRwJ7AExZKBKzhZYGWJS4lXlniVYFB9KvscfYj9WAFnfGNJSMHdU8UG/cG2rFApjt+Rx+zBId4bw5+2RBsq5lmeU4I/239VKFlBZvle+Y25X4lfhtiPFVO3Bug7W19cI4wlZVflBrW2K8Ptl+M25gW2j/F+hbPsO+4Rci3OIavlLNc6LrPvca1YrnPjq3vINcZPVZPY/rl8y7RKxuH0H8/HBKMWRg2mBJkiCgqB+NltFBIJ/a8uCrvvggYFixIjlQKpxkH7WdFlxGGfGRm3/neEKoKV+WMgOzGJtrM22BYThhKEldhR80UR6ubu1gY5lQlYXEM1V2yLdlTcY3snglcCK1gRqsCywqFyAtpxhYmtK/qF7XEMl7tx3uhPFlfxuiq+ivtM4ChxomLW+aZEmWubCaEV4Hxnsc44i/WW2XfiU+Urx/t4HfdccSmOzWq2quPZHrMci7zL6jfjp8u1+B3XS11j9yu5OP2LTmqjZ9rhRmIfbLvdU8KJJUeV6OJiOJGgkjojIUu2rBiyOTFRoNqwdXE+VzArOu4AtdbYxqGyLpnIZ4Fd5Wd1Pi4WnB2VbFm7OC8VC5U1x/mpeFfzxjky32L7lQt6hOMEQgmn7TuzyfbUCafYVj0U4JisIDOovMi4hf6qMZRozPI5Ey+qJuGasLiuxBx+rq7bFeHqHfu8tXM1K65bli+2Ntl6zgiuSl1xbTBGnNhUfii+ZfkA/cF1VBxTsa7u45zSmE8afLgNUgvABlebrzbLjeMIxGxkRTz6MltQK8WWzVUlSpXwmb9sjmzuan7VuYQ2SyrWx+PxkQVrFoDse6VAqCDMCqPjqLLj4s3x2nGIjVWJvyz2Ecxf9I1dZ+MWxluRxz9Nxu2burb1y3JzdY+rORWvoR/MFqI6t8qasPlUYqrCNZXjqzUi9lPzDG2W5XCWY6o8dGvObLrr1byS1VVmdyYms/zP1s1pK6YJ2DzVGro6hevCfEy0CeVw+vp+G+zjQx+7uoSi2m82Eds4cRKqGEc70U/1e+uD99jmKOGG4zHfo384ZiYsYh8cV43FfN2+41pnif+OUAGI+xjXLO5bFry4X9EW2mM+xHa4P5G3zDbjsoovFVPIFRw3rgX6s12LP2wNGdi6RD9UDMZk52JZjbcqsvVU+SDOmxUdXFMcMxNPCsiJrV/8zWyxWHFjIReY36yYog30nV3P/GGxivkmq6OsRjnfVwHbV1WnWF+3dmwMlzddzsN9ynhVGY/lLJb32P4rLaDGU9zfPis+ZrmRaSg1VzZvtKWQ/kUnVXzVxqhrKrGxjUFisMKM4zhRENsqkerGiO1ZknBj4tyriVzZUKTPbKnAqWLVRLgBCwn7zBJHBAotlTxccsiEmPpcKWRsriouWcLF78p3Jj7jZ8VfJY7c3OJ9lX9YW5W8V+UxrrtK9jP2lBDb7rPPsZ0qNqpQsj1StlyeRR+YfVwfvMfmgbEdx1frrMSLE5VxndS64JzcWqyCbH/VHkZgvUb7ag+QCyrv4b5UBRvOCX2On1lew/k5YRr7YCwhfxmX8Sf6j3ka94nlXdwPl1cy/TPG5N++ZxuHTmQOsD5MhCpfVEKJ4+Hisk1gi8k2hRVPLM57kpIKKJXQ1fqijwgMLrW+LEiz/VgJKjFkYlCtfYTbfyYmXEKM1+KPCmTGfZZQFFcYv12yVOOrMZzvuMYYv9gW/a4mzjvwN0IVTLUmjF+ZXSyM2G6zh4UNBV6E4hWzpX67eVVEAZtr9FvFINpXgsWtF5tPnDu2cXVtRTChxPYQa6/TDGi7AuS2Wnvnp7LJ7KNd7Oeu43f0L5ujimeWX1nMqrGcSEadpHxTSEUpOs0mxxZfOYO/FTHcIlUTb0V8sSSqijPbCJd8cfFV8mHf2bqqIHRzdgTJRFa2D6vA7Y/iMV7HAI9tEKqgKxGGttj6M/usDV5H39l6YPJn84ntGBjPXbxjwc/GcmvOBIWKszvw2BU7JrJwL1ROcoUxiw+Va9l4jAcstuI1ZouJlvij8i1bmxgnLNeqHMLGYbYU113dOiK8rgg3R1cnXR7A/Y9Q+cpdjzbRt6xeKm2x3WNcxf4qDyvRx3xBvrq8zuan6g+ra24d8X7Wb0Pp9b267gyz9q5Qx8/KeVZ8na9qAZkfbqMRmChZX0bwKrkrArJSgJX9+Fn5vnoCjMA5qiK2IStCan1UUUMb+Dn6hJ/V+KyQY1snFPCaS5aqH5srKxIsZvEamx+C+abs3BGqWI7xfXF3XHOFne0fFrpqoVd9GB8ch1Q79LECJ1YzH2Nflh+YEEUbbB6YU3CPVZzfASpX4dqpuce1yXIG0w9ME7A9j9dZ/WD30Q+cm/ITr7naxPjMoHxT82YPTNGGErRuXFc7v+mTEP0jGo6FEJMQTmhPkVC2sQ2OwXxT15GESnzgnKrzcsXBtWPjOBGd2WLzZHYzwLirKtXveLzB8Xhrm/FRFXW13hm3sS/7TScpEqzyj/XDa+h3xadsvi4RVtbf+V8ULcvx+PH1X5AI38cYfK2zGHdrmvHGcVrxl9mL7dj4ZP5TfrPPbNzZdgwu5p3vbo5oi8xrOQ6P8eVf88nqj6td2/0st7g6PKMvXO6cqc9qfuw7W4+KzzO2M15nugk/Oz9MGzrh9N8pjQ5WgwvhAhbvx2tsYtjX+bZdjzayJMr8rc4x+hb9Qn8ROO/YR42P193eKLGh4BLrqmBF0SUuDOZMLKm1YUmhIhrQJhvLFWScH+Mmxk68p9aGxUklebF5MU5l/ir/96zXSnCxXeGLK4wfH9//hSO2n/F3bIscUvlTFcqqKMR54xhsLDcmQ0VoqLqUzdUJAbbmbH5xr1ZDph/Y+ig+KK6pfBLHr9hV13DsuG9uPpl/aEf5zXzHsVwsqpzJ1gX7uLmq3Mr8znRV6X90YoNXxZZa5Ky/E5CqaLIkWUkE6EO2QXiPFWdmL9tQtQ7YVm1otjfMZ4W4ji5Brwa1Py7ZMY7gXrtYwfHj/c0+4yrjMdsTJRwdn7EftmexVYl55pviHxMKLM5dUlaFA++rpLkKmMhS6x+xt0i7XKn4gjZZv2if5TYFV1+cIGSFVHEg8o35xfxXfSox5+pTJc+vDLWmGO+VGr5dZ7md5Wm0y+6zXIRtGViujveQL8gjZssJ1Ez0OSHJ1hBtsnHYnjE/0a/43eVhK0qVIziAu8aSolsE5bASa3ExVCHNEoCbD+uXbbQiWbznCkomIrPCiuPPiMps3VaEWkdcI9XXiUvGA7ZuiqfMBxSi6B+LS7XfLrlG/xVnWKJna6HaRPvILeYLrpMaV8V6JmJXLuhKYLH4VkLMrasrKC6fqAKb5T+2V4rbaL8SG2ydFFjMqb6VvIr2MN5YXLKcwea2aj52NRzvK+6ofWS8UnzJ6nMca/PFaSGW35iNCk/ZNYzZCrechmL+sz6Yp5U9dc3VBIfS63tchKpAUqJzu+aEAEsQaqOcvayoInGRxIrY0Z5KWpU2bE2Vr+w+rqcSG24sHK8y95XgkksmuDJ7iEz0ZPvBfK2IDuWfasf2Nivcyhf0ic3LCVPHVTcOjhHvKUGyKo9Z/lAFd7sf4XItG0uNj2vvcnRsw3xEnjIeqjGRT+gj84XlzPidXWccdX3U+qm1ZGuHdcjVjRWh4jfLHfGz0yCKq9k9528E0wqMj2jbiTiVr1hedDx3sZKJTBc7bpyKH24sREmUKsMIRZZK32zsmLTUoscxnd8skbG2GbEzZMIxFk9WrNFflZxUkVfroOyjjysnvoismFeTnivGWztlC/fJ+YO2kH9uL93c41yqQhQTjyrSKnErP1RbxnVmQyVChPJvNbgkHu+r4qiEUDZWla9qv1UOjH7h2NiO5cyMf8zfrZ1bA1cbVFy7a8ymG9eJ0MzGCmC5OH52tUfxRonAjINKn6i4UeOoayx/q9rseBH9cHNUOkDlbLY+qrawtWe5ms1P1TeF8r9TioNU7rNEwArv1raSeCsEYgsV26pCjd+Vz/Ez/ih7GESuYLjk465hklbzQmAhU2RcFWpvWAHP5s6uMX6pZKBEF+OuEhK4t/G72y9MPtHP+OPEgFobjF8nPthn9EfNk9lGH2J79HdVPrP8odZbFU/kWCbUFPeYmGD+ZnvuvivuVFHNp2wNXZ5E/9g15rdbj2gHRZkSVKuC1X8n2th+sD5sHMyLLkdiDYjXMH/jGGpuTEdkMVvhGBuXtakISWxTnROuHfrB+rK6x2D/SajH13+GBDfeBZ9TzSoQmVDAe9Vii9fVGM5vZTdLVtiHJXDVBn1Wa6fmqJJVNcG6vQptV82GH2PoBDazD/He9nmzEe/Hayy5xbGxDd6LNpnveJ+JBhU/zDcXg65wK7GyN37QTzUOzoP1BzvL8fgB/yRUuC7XBr9nnE3WjNrL+J/FD/NHxULGSTe+aq9qRBw3Wwe1Fi6mmD22FsbH5Tg8TB6u5JosPyGqecqNm+kRVzfQHl0QwfFKXcExM9vsnosDNUc2bua36E8XZZcojROdKTSqT7WYZeLB+eQWNRMgDpnwqAiNTDCwzzh2HCsr6DNjwDgrJsIxCsnwm8YJF911tIFwwq4aT2g/44WyUxGAqt8EZ2yCV/FRTW7KF2djLFzQES4nftO5sDffDVhYb7SnxsD7rB1eQx/dvGdqSlUksLGcwFRzmV0DtebQd3kOZ/XS5VNX62a4VxVTij8zgtDVEucn2mHjqLqivlfqmVqnytoz22xK7GL5b99vzm2Dqo3ESTlBFK+pzVftt7Y4drzGfI2/nWBUYyIyYZnZZRu7/cRrjmybHeyT+aHWb7se7bpgWQUuUcQ2al/YZ3aPrWvVv4xrcS/Y/mCyyXgZ2+P6YH9XNBVnFG+dCGFrzRJexkmMi9U5jOvuciTLI8yGE7DIjbifjquVIu24yXI0y1PMV7yPY2LbzI7zDz9Hn3FsbIu1ie1jvI7xtSKU/y6vsL4qD6g1ZLa3e2pMtsdqz9RnBqWBMKfjXBVnVR1zPMEagdewdrDxZvIqxlTG3/J/M+oKjVoQJQLYxNB5FoxqTFawWGJiwjTec0SrCFO3Bow8zB9HWkUILBJVUcTssDmtnAjH8AmJBVcWbCxRsGsMzDbjrrPn2rOxUCxu91DMZjxRvmTJD9so8eoScWVdlRhTfqwEJ0izeW+fMQfhHsR7StAqHqg8rfx3c2R1AP1lYoKtUSb6XHHN8j7zWc2frXn8rYQWjrMycC/V3LENXse8tV2r1FzMBVg7I5RgY/fiOK4fa+f4omKJCUrFIXZdcd/FsPKLxSTLI26+38zPNXiE1/dxMPUdA031YRNVxCQ+yXHRZtZnu6cIztqjTTWfzLZrh4RxgoPNUV13xUT5A1g1M36o/XJ7jetQ4XnsV7WHvqC9CMYVNmZmz9lxc0G7jlcKVR+xfWU9nB/Bzoo8/mDr4GJa7VO2jmjX2WECK52IyXXok5oPmy/advdZGxxTrYvKz9XayObnrrF1WZ3DP10o5KTt+3fGJuuk2+9oj42RjV21FeeW8V757/I261Pha6V2MWTaKdkbarT0t+8VcZRjysb2w/rF+48H/1v2sR0uWvwdbbAgdwIz2q4ITzav2FcRmiUsJAQjtUtWbOw4Fq6BWxO0Vyk2V0acb/yMe6+SkEqYzFZcb7XPRvh/15b5otqr2HRFd/uNc0QesTHxN1sPZkutJZsn+pSJCeR2JnpXgMuHG/Azy5GM3+o75g7GecxXeM+1dwVZ5fcIF19oEzmXCZTYD8dEf1gb5a+rC+inarcy2PwUH1gNx/hmeUTZwvbxe1bPsU3cF/SP+RPn4/Kd68fmo2yyNa3oIeYb46W7z3IMywUK6Z8pRTGYJYn42QWX2li0pRZXiQomMnAeSohUEjwDI7zaxExwsrVWa6REA5KDjckKfCbGVkflYYM9GLD1zQRjVvyRcyrJsqKPn6NNN2fHfzYu4yH6rdYAx1IcyvJAvMZ8xblnRXzVIs9yKeYLtuaKyzH+UaxVCgf64e6zazg+fmb5kuVGlUezIog2ZnnDOJvVF5xfJcczUbFyPlb7hfdVXtzaqPVH25Ux0S7WzIw/LveoHO34FO2r2lOJg3id8a3iA95nuUblGFW7MtjX92N8OW7PjLkFcRNg7b9zwCwiSwCxbZYsldhkG6psuD5OhKiCqojgbDFfq6Ig8xn8WLOij29fG7n9ZGu73c+ED+Mf2ye3L5V+SoCoz+izS2TuWoXTqk21D5sj8xXn4eYZsTCPv5uM4ukYuYh1fVTuzHJMHEP58M2EhD/MdhYzrI2KITYXBpVjmX3F6VnRWVmT7at0/Lqwf4xqw5Gchqhw2eVUhYxbLm9GnytjunxYyZNxPOxbXQ+l3ypxqXxQedielCoRqQZ0E0THtmtsE/Cem9xmi43xeOiTwNie+YNziD9s/NnEzqDmy77HtoqU23UkH84Fx2HruTJwXtt3twasL95nHEabOJ7yT4nBeE8VRRZnyl+1ry6e2Gc3L9XfJS3GU4yrzCcXc5VCc2WwmGU5jnEReb/d235n9+NvVYTY/uAYzD81HtpVhdS1ZzHExsB8wOyyOSJcsce9wHbKN5ynqx8rAOce9yvLrYynrmYiB2I7VguYn+7+TCzGvIocYHkLcxabC9pQYzubqB9iW/zOfHWcR1+qubf0+t4NpPptfeLksyBkm8XGUonJFUcscsyW+8wSkgqIiv0IR3jWh5GQCVPWDm2qcVwSXw1s35SIY31dcVWccLxzxYkJMZbAI1QhqxRo9JXZYXZZPKtEzcZw+YAVDDdnlqTZnFbmMc7JCU5X8FlOx/zMcgHyGYH7ysZgfrJ8j+OxPYxzd/66GGdjMuEQx439XG1jMZ7FLhtPrffKcCJL5ZUxvq/1aj1dblJ5kPHb7YHKJUxzRL9c3Y52KzXL+cDiQ8VLNi9XG1k/VgO335Uc/EPaQgzOCMH6KSXNgp4VZFcE2eaqIMc2ToypBIltcBxVJBBOqDgbaszMd0U0tpd793o1ZEmGJQC3tsgBVcixWLsx4zWVKNU4zDe0kfEci27Go/hdjRmvqflnRYrZcPGf5ZHVoLjLuKD4if2dYGNQ/HV+snsql7J71T10fGF5TtUEFWM4X5bL2RyyGoXjYa5Ynbdsfdl9xiOWE9xv7MfsbGOxdXW+uvhRc1Lr4fjgxCDGPItfNQ/FaSUyWV2p5FSn2RyXS6I0DqCS10ySyCYfC6Eqsk4wqSIdx8Nx2FxVEa62ZQmJJUPWj80TbcY2lYSl+rqidJdCvgHn5ESV41i0tX1m/bOi45KZSoiMc5l4Y76wzwxsLi4e0W5cZ7VesS+2UfeZ724/VgdbD8WNSp6JtuL3bJ9VYXG5VN1Xc3M2Mt9dTo3zyPKoygUMKo5VLYrruF1DsTHrwwqo5IrqWjOoml3NAUpERTsqtygtoniJXKv4z+Lbjck45dqr+249EGp/WR1gKP2TUHEAlpTwXlbkWBsnGvE6ts82CftHG67wx3EUFIkZ4dTc470ZYYF91ZoiqVVhqe7fnYBJBaGKQkUwqoTA1hwTCCYWNV5lXpX9rCaeyBFXNJh4wJhwtmN75L9K3Gwsl2BXBBOcbC3HyLmCHFGcUbnWCUQFzOkqrlweZNdY/MTr6LcTCW4d2WfkLKsJTNDg3rFclOWnleA4ioLF6Q0m6KMNlSOwPeYw3AemE1x/5g/OG/dbrQW2xfFcrWdzwfWJNpDHmZ5SMZuhqqnSv+iETuGisjaxP1uASrFkxGA+VUixfWYFMI5VsY191ZhHhR3zx9llQZO1i0DyOmGyKlAwjpE/JWacUMLTjRf7uqBWiVnxWiVwJTSUwMD43stpVVzUfFRRUblFjYm+uwS7IrAY4lyzgpyJelcsq3kex1P5hfE12nTCVY3B4knFBVvH+JnFqyr2Drg/WD+dCGV7siLYmrN4VWsT+7i8wrQG2mC5iNnG9shFNr9M16Atth6K89hXjYl22Txiuyz3qnXFdVJzjj5nfdK/6MScY/edKHIFhC12pZAgKV0y3T7HjVTzZPaV365YKhKx69ifBZIq0k7wMN8UeZl4yhLtSsA9YyJs+xzvY8CiTcftCBXc+B3tYtJQ/rOCn82/ykHGC3adzSFbZ7zH7ruEycQM+nOXgu6us33H/Mp4xfZQFXK8pvIO7gu7j/2Y0Iv3XD1QBVgBxSFeZ7GU8RXngXPC/amMwcbLRM+qYPG+QdVZlqOqAouNjfnY1fnYXs2nyi0Xw3Ecl9NYvODcVV1gfFV6zMUs8p35h/mBwf6ZUrVYzCiKPtxQVVCdLQa22awQqiLJiliWfJRPykeViHDuFbCiwvzNxJGbR+zvyL4y3LwVV1g7VViYPVVI3HU1lkuMTGzEPkwYbNcxiThBqqDEBBuP9amsWbSlhE4WG7Pi5WpwsazWTM05E3nYv5J3WB5X9+J1tKn4UpkX+qv8iWNj26wmOP/Vuqp1xOvOnhNIK4DF4wa2BwglFlUcqNrIxmG8Vvol2qnkdTU+41zGWUTGTfadcc3lXKah2LrEeGLzVDkfkf5FJzcxvK4EIJs0Lgiz5YpjZXLKjrPBEpFKcFkQYVsXDDhm7JcJTpwvfsekp3xeuWA7qD1Xax/XTAWSKs6q8MR7mcB0xRn9cMKRJUcVQ7gGyjdXXGNb5QMbm80lS45qHBeHK/Obrf0YvMCooqh4lOUZtRe4b6wYub2vXFNiIfqlCrnKhds9th74uRKDqp2qiVntUHHFRMtqyMRetmdqzZX2qKx3bMfGiNcd59DPSl5lfdX8cc7xd8wBVV2CNtk1tR7oH67RrJaJSEWpEmlZHyyscVLxM0uMapOibVWI0W7Whn3O5hAJoTasCkxAFWGgvqv2bj3ZvmbruyJUEZ8RZgpK+DE+4DWVcFg7lcizgs2SLUtmah6Z0FOJKbNTGc8VGRULWKRmCsRV4USR4gleY2up+sS+2xjOBrbFMdl9bKt8zubL1kcVVdY2m7Obn7OrxJaaA/rrfF+Vx8g5NZ9Mb7jaxrgX+zhu4fesRrC8hH7ivFwNZu1U3mf9la8xfp2PbB/wvsqnmV5SNhGlP1OqFlIVtOhMdi/bpDgO3mO+IblwY5mNqtjDQofX4m8UAoxgyh+FzKdIOla03VjKdpbAV4IT2hhs8briBwY62nDik+0NcsYFOUsu6nuVgyrBYCLPBAxCxT/2wbWMdh0PcX/Qxh04zIrpGJxjWaFgeRlzrCpmyCGV59A/le9wTmwcF0eu2CKqwoeJpYyDao4Vn3COaj1d/loFShNkPGL11rVnfXGtUYOoesn4p3iexSNy1eknZkPl5Tgf9JPNhcUN66PGYn7GvVVCthIX6T8JxYJSOecWH4PL2WRiUhEJicmEY7SPJGACQQk0Nl+3uNkGuGKN/qgioUgdycH2gNlUY94JLGiUEMV7lSLmCodKZGpsbOdEJtu/jAMusbIEg765605UqoSs5q7suX1UXF8ZOCeV5Nl+qyLD7rHxMF8yAVbhPosbFm9MJLuao3Ig+8wEDhOszC9VlNXeYOzGz7Edju9ywx24rB4Q4nqwtVGaAWsh0xOu/rF4Ynunck4Wh5mwczmUrZWKWfyM46p54bhKV7D2WX2KdrM4GqP475TioMqwK6ZZUUQCVpKtE8Hxmit6yk/0b6awuWRfST4uWTIbat4YBEpUoJ2MuHeHSvwx4an7CMYF1QYTR5a8VDKK47HizkSJ43r2Wa2XKv6VxOQEmEvYjN934PLsHFjMq9+q6DJRqNZX7RfL54zbKveq+SqRx/zOxlWx5mLCiRHmJ1tzFwfKb1XDVgPjC/sc4fKj0iiMgzj+9p3lYbyvxKXzS8UMzsfpDzXPOAaOr+IS7VUErluzSmy5tUWU//Y9Dsa+KxHoHGAFh42dBacqirjpzG/lM9sUJUYUmdiasOsKag+UXRwD1wLnqNq7sVcD8kDdj5+z/WIBuIHxB/vGYFZia4Ybbn6OK6q/G5vFXqWos8+srRI42RgM2dxXAeNIvOfiWcU3W1O3t1mOdUCRwHI+tnX53fnkBAoW8IrtSmFla4DXsuLNfGb2VuWwilUXw0y0Me5kXM40wKx4Unlb+aLqhYspNhabc4U/1Zh2ObZSC4/m6zGKf6YUF8hN2Ak3Zt9tvAt6DNC4QPEHx842oeIPWwPlN4Mas5IcXfBVAwzXBcfZfIlJYHVUiicmFlXEnABQAYvJNd5Dm+iPa49JIO4b8wdjqJqE2FzQBvrLBHYlTpX/yEmW2JVgw8+rQuVWJ4hwj1x+UzlT8UbtJ647xpMan8Ug228Wn24sF0txXjhHzM0q/7o1y+aL/GQiR/m5EtTe4n0393jtjHVQWkbVRpZHXeyx3yr+nHDN5optMF6dr2p85ovyz/mreK1Q+jOlKsAj2OTYYjDiqeSBCcwtOktKOA/2XV1XG7QnEFiSyUSSChb0042l1h8TtxqPJeUVoQrldg/bxs+4ThG4pk5Yxu+VAHWCUfkUbat54DXsG+eD/jO+qHhjYkEV7UwoqTVQuYF9VvG+ElSORS6rHODsVXI65gPce7bXjI84RvTVzVHdzwqw415EVXCyOSr+O4HJcpHKyyvzdoNap/gZeYNtXR104lCNF9thHnc1IqvPmz9MnCk7yPFM/2DsKE5inLK2cf1YTlfzUPx0dUHV05/aJIXxgxXbimHmaNVOHFMlHrbh2Thmnt+My3zHttje+ajIwMbM1iJLwC55ZuJKzTWMveQj+uPx+G6CuF64R8KO3IcK39RYjCOsvbq/tcmKNLZTa6HWg8WkuoZ+zPR366jWB9dSJfCFeUxJyTj1U4ck7yiovD67XypelO94zY2F46l1YPGFc8psMjuV9jgndt3ViMTnJTk8w8HZHIo2VV6Ndr5zcLIuuBxUmZPy311n4yn/nc1439lQ8anm5+ohjEM3oSRK2eAKTnThZKpFWtlXBW7WR2Yr+hP9VcIOfcgKfzYP7If+OBFQES7Mr0JArpgIv+GxE0bQ56fPmcjZrsW+mTB041R9UrYrxTPjXCUpqu+VROlsMH9n+hTyz4o8/uK4Sfy0UyHfqvvqWuW6nESBd+gj++7mgf44Pro4UXNS9Wo2FrMa5dqqgn5loJ5wAme7P7PvmW5w19W48fvsfleEoeO8gqv5zm42lywunC+Kz0pfuDycilKclHO0WvizTcgKqrLpEp6yzcZ3c2ZjoT8ZqdC/zCdVhDKRWfEhA5B2uUQ4xrc8zoJvux7bY99qoXH7xsZx3MliKF5jfTKfsySWrQ+Oye5nCT0bw4kxVQhE3+V4jAX967WfPs8K1Ni2Igqc3c2225tMMMyK3Cw/q3GzNciKu1pz5q9qr3KFGh+v34HDlfoU4fJUpRZmAiwTidn4btzYRuVAl6sresr5zVDhsZsH+o12sphxeiL9H52YI5txHBA/V4ssbghOolqI2SZUFo21dxuK/aPPLuGgv8ymGlPNIyOLWrvoL/OxkoBXAgv2ioBSfcfQceG4EK+zNVWJxtna+jGOsBid4SNDxgVXIFROUPvA+mdjb/YY/7O4XgU4N+QBa8NsqGssb7gCy/yqFrKKiHS8Z3mK8c0JCjVWNg6CxSyuDeOyqp0qnlflsKvRThwpYbj1R7C9dMIyy0usdsT7VVE5yyH0A+25a6o/1h7lN7Pr1jpDtZ0VpU7QuALvvrM+rKDOJiSVGHEu2bXMdtZf2Yh9nEhm64FzysSmmqsTUWquqyfBMY6dvrBi4ARf7BPjhAU+E5RY1NAe+lUp4q4Au33NxmDtVJJ2SdPlFWYL+6FvLG5cwl4NjB+qaLvYZYUSx9jaMVtuDORYJtRwTMY5V9BdLVJzdnkhK85qvXHOLg5Zv4pAWBmZCNygcg7jn1tHtg+u1ik/lT7JNIqyxfpUanrGD7SntIjTaorbWb94nY3t4uubeaxO8kaj0Wg0Go3G+ij9j06NRqPRaDQajcYz0aK00Wg0Go1Go/F2tChtNBqNRqPRaLwdLUobjUaj0Wg0Gm9Hi9JGo9FoNBqNxtvRorTRaDQajUaj8Xb8/+ee6pMczbKgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 4, figsize=(10, 5))\n",
    "\n",
    "for i, a in enumerate(ax.flatten()):\n",
    "    a.matshow((quantize(image,8) >> 7-i) & 1)\n",
    "    a.set_title(f'$f$ ∧ {int(bin(1 << 7 - i)[2:]):08d}')\n",
    "    a.axis('off')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a946bff-636e-4eeb-b633-4daae9b3ad7d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_gray_code(image):\n",
    "    shifted_image = image >> 1\n",
    "    gray_image = image ^ shifted_image    \n",
    "    return gray_image\n",
    "\n",
    "fig, ax = plt.subplots(2, 4, figsize=(10, 5))\n",
    "\n",
    "for i, a in enumerate(ax.flatten()):\n",
    "    a.matshow((to_gray_code(quantize(image,8)) >> 7-i) & 1)\n",
    "    a.set_title(f'$G(f)$ ∧ {int(bin(1 << 7 - i)[2:]):08d}')\n",
    "    a.axis('off')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc1da43-3d82-431c-89b7-233c4b03b0d3",
   "metadata": {},
   "source": [
    "### Oppgave 7:\n",
    "\n",
    "Til slutt skal vi implementere Huffman koding. Fra forelesningen husker vi algoritmen:\n",
    "1. Konstruer enkeltnode Huffman-trær med hvert av de $n$ symbolene $s_i$ med tilhørende vekt $f(s_i)$.\n",
    "2. Repeter følgende til vi ender opp med ett enkelt tre:\n",
    "    - Velg to trær $T_0$ og $T_1$ med minimal vekt. \n",
    "    - Erstatt $T_0$ og $T_1$ med et nytt tre som har $T_0$ som venstre subtre og $T_1$ som høyre subtre.\n",
    "    \n",
    "Resultatet skal være en Python dictionary.\n",
    "\n",
    "#### Tips:\n",
    "- Bruk av `np.unique(arr, return_counts=True)` er veldig lurt å bruke for å hente symboler og frekvenser.\n",
    "- `heapq` modulen i Python kan med hell benyttes. Se [dokumentasjonen](https://docs.python.org/3/library/heapq.html) for mer info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1041aa07-ccea-4613-8333-8526b29ca958",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_huffman_dict(arr):\n",
    "    huffman_tree = [\n",
    "        [weight, [symbol, \"\"]] for symbol, weight in np.stack(np.unique(arr, return_counts=True),-1)\n",
    "    ]\n",
    "    heapq.heapify(huffman_tree)\n",
    "\n",
    "    while len(huffman_tree) > 1:\n",
    "        lo = heapq.heappop(huffman_tree)\n",
    "        hi = heapq.heappop(huffman_tree)\n",
    "        for pair in lo[1:]:\n",
    "            pair[1] = '0' + pair[1]\n",
    "        for pair in hi[1:]:\n",
    "            pair[1] = '1' + pair[1]\n",
    "        heapq.heappush(huffman_tree, [lo[0] + hi[0]] + lo[1:] + hi[1:])\n",
    "\n",
    "    # Remove the frequency count to get the true Huffman tree\n",
    "    huffman_tree = sorted(heapq.heappop(huffman_tree)[1:], key=lambda p: (len(p[-1]), p))\n",
    "\n",
    "    # Generate and return Huffman dictionary\n",
    "    return {symbol: code for symbol, code in huffman_tree}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6832bf9-b836-496f-a2c8-9092d2d4b886",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b242e9dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58ce6378",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
