TWI Class Generator.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
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          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_eeb322ac57694486ab8905a0bbea94b4",
              "IPY_MODEL_c920e4d0a0554025a70ad5b8c3f12953"
            ],
            "layout": "IPY_MODEL_80bb566448764cd78ef5bc612bd1c594"
          }
        },
        "eeb322ac57694486ab8905a0bbea94b4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatSliderModel",
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            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
            "continuous_update": true,
            "description": "x0",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_500874e2f3a34ca9a9e774b7a7816090",
            "max": 30,
            "min": -30,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 0.5,
            "style": "IPY_MODEL_437c009fcb4046b68a9427911c44235c",
            "value": 0
          }
        },
        "c920e4d0a0554025a70ad5b8c3f12953": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_c082c4f6bc1d4cbcae035062a6c2872a",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 720x360 with 1 Axes>",
                  "image/png": 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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Algon-33/TWI-Generation/blob/main/TWI%20Class%20Generator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "moY8IOKlrm9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "b9857a20-3906-4335-8534-78b559ffeaf7"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-d4f8a49f09a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m \u001b[0;31m# for making figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mTransformers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'Transformers'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt # for making figures\n",
        "import random\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# download the names.txt file from github\n",
        "#!wget https://raw.githubusercontent.com/karpathy/makemore/master/names.txt\n",
        "#!wget https://raw.githubusercontent.com/Algon-33/TWI-Generation/main/classes_cleaned.txt\n",
        "#!wget https://raw.githubusercontent.com/Algon-33/TWI-Generation/main/skills.txt\n",
        "#!wget https://raw.githubusercontent.com/Algon-33/TWI-Generation/main/spells.txt\n",
        "!wget https://raw.githubusercontent.com/Algon-33/TWI-Generation/main/dataset.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmmM8POvrtco",
        "outputId": "4fff9824-4254-4e67-ab92-473fc48078cd"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-10-26 22:13:48--  https://raw.githubusercontent.com/Algon-33/TWI-Generation/main/dataset.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 104606 (102K) [text/plain]\n",
            "Saving to: ‘dataset.txt’\n",
            "\n",
            "dataset.txt         100%[===================>] 102.15K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2022-10-26 22:13:49 (48.3 MB/s) - ‘dataset.txt’ saved [104606/104606]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "PS8m5wsR7eCN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from re import S\n",
        "# read in all the words\n",
        "random.seed(925)\n",
        "words = open('dataset.txt', 'r').read().splitlines()\n",
        "words = list(map( lambda x: x.strip(\"]\").strip(\"[\"), words))\n",
        "random.shuffle(words)\n",
        "words[0:8]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KZE_EtNru_P",
        "outputId": "207d08f0-26ba-4a5e-8fb0-facc26a939e5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['cafe manager',\n",
              " 'ambush commander',\n",
              " 'reckless charge',\n",
              " 'riders',\n",
              " 'renowned tincturist',\n",
              " 'heavyweight punch',\n",
              " 'arbiters',\n",
              " 'fashionistas']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# build the vocabulary of characters and mappings to/from integers\n",
        "chars = sorted(list(set(''.join(words))))\n",
        "stoi = {s:i+1 for i,s in enumerate(chars)}\n",
        "stoi['@'] = 0\n",
        "itos = {i:s for s,i in stoi.items()}\n",
        "vocab_size = len(itos)\n",
        "print(itos)\n",
        "print(vocab_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXRALF1xr2od",
        "outputId": "26d35b46-bc84-4625-b82c-5bc1b62506fd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1: ' ', 2: '!', 3: \"'\", 4: '(', 5: ')', 6: ',', 7: '.', 8: '1', 9: '4', 10: '5', 11: ':', 12: '?', 13: '[', 14: ']', 15: '_', 16: 'a', 17: 'b', 18: 'c', 19: 'd', 20: 'e', 21: 'f', 22: 'g', 23: 'h', 24: 'i', 25: 'j', 26: 'k', 27: 'l', 28: 'm', 29: 'n', 30: 'o', 31: 'p', 32: 'q', 33: 'r', 34: 's', 35: 't', 36: 'u', 37: 'v', 38: 'w', 39: 'x', 40: 'y', 41: 'z', 42: '’', 43: '…', 0: '@'}\n",
            "44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# build the dataset\n",
        "block_size = 3 # context length: how many characters do we take to predict the next one?\n",
        "\n",
        "def build_dataset(words):  \n",
        "  X, Y = [], []\n",
        "  \n",
        "  for w in words:\n",
        "    context = [0] * block_size\n",
        "    for ch in w + '@':\n",
        "      ix = stoi[ch]\n",
        "      X.append(context)\n",
        "      Y.append(ix)\n",
        "      context = context[1:] + [ix] # crop and append\n",
        "\n",
        "  X = torch.tensor(X)\n",
        "  Y = torch.tensor(Y)\n",
        "  print(X.shape, Y.shape)\n",
        "  return X, Y\n",
        "\n",
        "import random\n",
        "random.seed(42)\n",
        "random.shuffle(words)\n",
        "n1 = int(0.8*len(words))\n",
        "n2 = int(0.9*len(words))\n",
        "\n",
        "Xtr,  Ytr  = build_dataset(words[:n1])     # 80%\n",
        "Xdev, Ydev = build_dataset(words[n1:n2])   # 10%\n",
        "Xte,  Yte  = build_dataset(words[n2:])     # 10%\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lY9RYt-or3pu",
        "outputId": "7e86ffba-09da-426d-f64e-addd004884df"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([73798, 3]) torch.Size([73798])\n",
            "torch.Size([9272, 3]) torch.Size([9272])\n",
            "torch.Size([9110, 3]) torch.Size([9110])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MLP revisited\n",
        "n_embd = 10 # the dimensionality of the character embedding vectors\n",
        "n_hidden = 200 # the number of neurons in the hidden layer of the MLP\n",
        "\n",
        "g = torch.Generator().manual_seed(2147483647) # for reproducibility\n",
        "C  = torch.randn((vocab_size, n_embd),            generator=g)\n",
        "W1 = torch.randn((n_embd * block_size, n_hidden), generator=g) * (5/3)/((n_embd * block_size)**0.5) #* 0.2\n",
        "#b1 = torch.randn(n_hidden,                        generator=g) * 0.01\n",
        "W2 = torch.randn((n_hidden, vocab_size),          generator=g) * 0.01\n",
        "b2 = torch.randn(vocab_size,                      generator=g) * 0\n",
        "\n",
        "# BatchNorm parameters\n",
        "bngain = torch.ones((1, n_hidden))\n",
        "bnbias = torch.zeros((1, n_hidden))\n",
        "bnmean_running = torch.zeros((1, n_hidden))\n",
        "bnstd_running = torch.ones((1, n_hidden))\n",
        "\n",
        "parameters = [C, W1, W2, b2, bngain, bnbias]\n",
        "print(sum(p.nelement() for p in parameters)) # number of parameters in total\n",
        "for p in parameters:\n",
        "  p.requires_grad = True"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57RCzt38r436",
        "outputId": "efb7469b-7553-4b3b-eeb0-9038a60c0890"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15684\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Xdev"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z6GNOK3dAuBw",
        "outputId": "0cfb59ef-697f-4f8f-f61e-2372dfcc2ea8"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0,  0,  0],\n",
              "        [ 0,  0, 24],\n",
              "        [ 0, 24, 18],\n",
              "        ...,\n",
              "        [20, 28, 31],\n",
              "        [28, 31, 35],\n",
              "        [31, 35, 34]])"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ", X\n",
        "# same optimization as last time\n",
        "max_steps = 20000\n",
        "batch_size = 32\n",
        "lossi = []\n",
        "loss_dev_old = 100\n",
        "for i in range(max_steps):\n",
        "  \n",
        "  # minibatch construct\n",
        "  ix = torch.randint(0, Xtr.shape[0], (batch_size,), generator=g)\n",
        "  Xb, Yb = Xtr[ix], Ytr[ix] # batch X,Y\n",
        "  \n",
        "  # forward pass\n",
        "  emb = C[Xb] # embed the characters into vectors\n",
        "  embcat = emb.view(emb.shape[0], -1) # concatenate the vectors\n",
        "  # Linear layer\n",
        "  hpreact = embcat @ W1 #+ b1 # hidden layer pre-activation\n",
        "  # BatchNorm layer\n",
        "  # -------------------------------------------------------------\n",
        "  bnmeani = hpreact.mean(0, keepdim=True)\n",
        "  bnstdi = hpreact.std(0, keepdim=True)\n",
        "  hpreact = bngain * (hpreact - bnmeani) / bnstdi + bnbias\n",
        "  with torch.no_grad():\n",
        "    bnmean_running = 0.999 * bnmean_running + 0.001 * bnmeani\n",
        "    bnstd_running = 0.999 * bnstd_running + 0.001 * bnstdi\n",
        "  # -------------------------------------------------------------\n",
        "  # Non-linearity\n",
        "  h = torch.tanh(hpreact) # hidden layer\n",
        "  logits = h @ W2 + b2 # output layer\n",
        "  loss = F.cross_entropy(logits, Yb) # loss function\n",
        "  # backward pass\n",
        "  for p in parameters:\n",
        "    p.grad = None\n",
        "  loss.backward()\n",
        "  \n",
        "  # update\n",
        "  lr = 0.1 if i < 100000 else 0.01 # step learning rate decay\n",
        "  for p in parameters:\n",
        "    p.data += -lr * p.grad\n",
        "\n",
        "  # track stats\n",
        "  if i % 1000 == 0: # print every once in a while\n",
        "    with torch.no_grad():\n",
        "      emb_dev = C[Xdev]\n",
        "      embcat_dev = emb_dev.view(emb_dev.shape[0], -1) # concatenate the vectors\n",
        "      # Linear layer\n",
        "      hpreact_dev = embcat_dev @ W1 #+ b1 # hidden layer pre-activation\n",
        "      # BatchNorm layer\n",
        "      # -------------------------------------------------------------\n",
        "      bnmeani_dev = hpreact_dev.mean(0, keepdim=True)\n",
        "      bnstdi_dev = hpreact_dev.std(0, keepdim=True)\n",
        "      hpreact_dev = bngain * (hpreact_dev - bnmeani_dev) / bnstdi_dev + bnbias\n",
        "      # -------------------------------------------------------------\n",
        "      # Non-linearity\n",
        "      h_dev = torch.tanh(hpreact_dev) # hidden layer\n",
        "      logits_dev = h_dev @ W2 + b2 # output layer\n",
        "      loss_dev_new = F.cross_entropy(logits_dev, Ydev) # loss function\n",
        "      if ((loss_dev_new-loss_dev_old)/loss_dev_new)**2<0.0000001:\n",
        "        break\n",
        "      else:\n",
        "        loss_dev_old = loss_dev_new\n",
        "\n",
        "    print(f'{i:7d}/{max_steps:7d}: {loss.item():.4f}')\n",
        "  lossi.append(loss.log10().item())\n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4LMs4kwr6OW",
        "outputId": "34e98ad3-345c-4837-a3aa-77ed5978b416"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0/  20000: 1.8569\n",
            "   1000/  20000: 1.9727\n",
            "   2000/  20000: 1.9473\n",
            "   3000/  20000: 1.3993\n",
            "   4000/  20000: 1.9784\n",
            "   5000/  20000: 1.7915\n",
            "   6000/  20000: 2.0031\n",
            "   7000/  20000: 1.7514\n",
            "   8000/  20000: 1.9104\n",
            "   9000/  20000: 1.5560\n",
            "  10000/  20000: 1.6429\n",
            "  11000/  20000: 2.0409\n",
            "  12000/  20000: 2.0253\n",
            "  13000/  20000: 2.1243\n",
            "  14000/  20000: 2.2252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(lossi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "35HnjffFr7vm",
        "outputId": "0527f7a9-7338-47e4-94aa-0c958e1f2112"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb0abedff10>]"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calibrate the batch norm at the end of training\n",
        "\n",
        "with torch.no_grad():\n",
        "  # pass the training set through\n",
        "  emb = C[Xtr]\n",
        "  embcat = emb.view(emb.shape[0], -1)\n",
        "  hpreact = embcat @ W1 # + b1\n",
        "  # measure the mean/std over the entire training set\n",
        "  bnmean = hpreact.mean(0, keepdim=True)\n",
        "  bnstd = hpreact.std(0, keepdim=True)\n"
      ],
      "metadata": {
        "id": "Xe1XghzFr9hx"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad() # this decorator disables gradient tracking\n",
        "def split_loss(split):\n",
        "  x,y = {\n",
        "    'train': (Xtr, Ytr),\n",
        "    'val': (Xdev, Ydev),\n",
        "    'test': (Xte, Yte),\n",
        "  }[split]\n",
        "  emb = C[x] # (N, block_size, n_embd)\n",
        "  embcat = emb.view(emb.shape[0], -1) # concat into (N, block_size * n_embd)\n",
        "  hpreact = embcat @ W1 # + b1\n",
        "  #hpreact = bngain * (hpreact - hpreact.mean(0, keepdim=True)) / hpreact.std(0, keepdim=True) + bnbias\n",
        "  hpreact = bngain * (hpreact - bnmean_running) / bnstd_running + bnbias\n",
        "  h = torch.tanh(hpreact) # (N, n_hidden)\n",
        "  logits = h @ W2 + b2 # (N, vocab_size)\n",
        "  loss = F.cross_entropy(logits, y)\n",
        "  print(split, loss.item())\n",
        "\n",
        "split_loss('train')\n",
        "split_loss('val')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vsr5wWYCr_JA",
        "outputId": "1f9e3ec9-4c35-45eb-c4fa-50d4ed408819"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train 1.7613108158111572\n",
            "val 1.9592565298080444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## loss log\n",
        "\n",
        "### original:\n",
        "train 2.1245384216308594\n",
        "val   2.168196439743042\n",
        "\n",
        "### fix softmax confidently wrong:\n",
        "train 2.07\n",
        "val   2.13\n",
        "\n",
        "### fix tanh layer too saturated at init:\n",
        "train 2.0355966091156006\n",
        "val   2.1026785373687744\n",
        "\n",
        "### use semi-principled \"kaiming init\" instead of hacky init:\n",
        "train 2.0376641750335693\n",
        "val   2.106989622116089\n",
        "\n",
        "### add batch norm layer\n",
        "train 2.0668270587921143\n",
        "val 2.104844808578491\n"
      ],
      "metadata": {
        "id": "8FR_irzwsIaI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# SUMMARY + PYTORCHIFYING -----------"
      ],
      "metadata": {
        "id": "qXyAMq5esAho"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xtr[0:10]"
      ],
      "metadata": {
        "id": "WxXVhF-3E9Zh",
        "outputId": "3f9fce4c-a3df-4583-c018-97348133692d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0,  0,  0],\n",
              "        [ 0,  0, 22],\n",
              "        [ 0, 22, 30],\n",
              "        [22, 30, 33],\n",
              "        [30, 33, 22],\n",
              "        [33, 22, 20],\n",
              "        [22, 20, 27],\n",
              "        [20, 27, 20],\n",
              "        [27, 20, 16],\n",
              "        [20, 16, 31]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's train a deeper network\n",
        "# The classes we create here are the same API as nn.Module in PyTorch\n",
        "\n",
        "class Linear:\n",
        "  \n",
        "  def __init__(self, fan_in, fan_out, bias=True):\n",
        "    self.weight = torch.randn((fan_in, fan_out), generator=g) / fan_in**0.5\n",
        "    self.bias = torch.zeros(fan_out) if bias else None\n",
        "  \n",
        "  def __call__(self, x):\n",
        "    self.out = x @ self.weight\n",
        "    if self.bias is not None:\n",
        "      self.out += self.bias\n",
        "    return self.out\n",
        "  \n",
        "  def parameters(self):\n",
        "    return [self.weight] + ([] if self.bias is None else [self.bias])\n",
        "\n",
        "\n",
        "class BatchNorm1d:\n",
        "  \n",
        "  def __init__(self, dim, eps=1e-5, momentum=0.1):\n",
        "    self.eps = eps\n",
        "    self.momentum = momentum\n",
        "    self.training = True\n",
        "    # parameters (trained with backprop)\n",
        "    self.gamma = torch.ones(dim)\n",
        "    self.beta = torch.zeros(dim)\n",
        "    # buffers (trained with a running 'momentum update')\n",
        "    self.running_mean = torch.zeros(dim)\n",
        "    self.running_var = torch.ones(dim)\n",
        "  \n",
        "  def __call__(self, x):\n",
        "    # calculate the forward pass\n",
        "    if self.training:\n",
        "      xmean = x.mean(0, keepdim=True) # batch mean\n",
        "      xvar = x.var(0, keepdim=True) # batch variance\n",
        "    else:\n",
        "      xmean = self.running_mean\n",
        "      xvar = self.running_var\n",
        "    xhat = (x - xmean) / torch.sqrt(xvar + self.eps) # normalize to unit variance\n",
        "    self.out = self.gamma * xhat + self.beta\n",
        "    # update the buffers\n",
        "    if self.training:\n",
        "      with torch.no_grad():\n",
        "        self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * xmean\n",
        "        self.running_var = (1 - self.momentum) * self.running_var + self.momentum * xvar\n",
        "    return self.out\n",
        "  \n",
        "  def parameters(self):\n",
        "    return [self.gamma, self.beta]\n",
        "\n",
        "class Tanh:\n",
        "  def __call__(self, x):\n",
        "    self.out = torch.tanh(x)\n",
        "    return self.out\n",
        "  def parameters(self):\n",
        "    return []\n",
        "\n",
        "n_embd = 10 # the dimensionality of the character embedding vectors\n",
        "n_hidden = 100 # the number of neurons in the hidden layer of the MLP\n",
        "g = torch.Generator().manual_seed(2147483647) # for reproducibility\n",
        "\n",
        "C = torch.randn((vocab_size, n_embd),            generator=g)\n",
        "layers = [\n",
        "  Linear(n_embd * block_size, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n",
        "  Linear(           n_hidden, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n",
        "  Linear(           n_hidden, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n",
        "  Linear(           n_hidden, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n",
        "  Linear(           n_hidden, n_hidden, bias=False), BatchNorm1d(n_hidden), Tanh(),\n",
        "  Linear(           n_hidden, vocab_size, bias=False), BatchNorm1d(vocab_size),\n",
        "]\n",
        "# layers = [\n",
        "#   Linear(n_embd * block_size, n_hidden), Tanh(),\n",
        "#   Linear(           n_hidden, n_hidden), Tanh(),\n",
        "#   Linear(           n_hidden, n_hidden), Tanh(),\n",
        "#   Linear(           n_hidden, n_hidden), Tanh(),\n",
        "#   Linear(           n_hidden, n_hidden), Tanh(),\n",
        "#   Linear(           n_hidden, vocab_size),\n",
        "# ]\n",
        "\n",
        "with torch.no_grad():\n",
        "  # last layer: make less confident\n",
        "  layers[-1].gamma *= 0.1\n",
        "  #layers[-1].weight *= 0.1\n",
        "  # all other layers: apply gain\n",
        "  for layer in layers[:-1]:\n",
        "    if isinstance(layer, Linear):\n",
        "      layer.weight *= 1.0 #5/3\n",
        "\n",
        "parameters = [C] + [p for layer in layers for p in layer.parameters()]\n",
        "print(sum(p.nelement() for p in parameters)) # number of parameters in total\n",
        "for p in parameters:\n",
        "  p.requires_grad = True"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgNBcKs8sLX6",
        "outputId": "516eaf3a-4af6-49ca-b49a-9f0b6f986fe2"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18328\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# same optimization as last time\n",
        "max_steps = 30000\n",
        "batch_size = 32\n",
        "lossi = []\n",
        "ud = []\n",
        "\n",
        "for i in range(max_steps):\n",
        "  \n",
        "  # minibatch construct\n",
        "  ix = torch.randint(0, Xtr.shape[0], (batch_size,), generator=g)\n",
        "  Xb, Yb = Xtr[ix], Ytr[ix] # batch X,Y\n",
        "  \n",
        "  # forward pass\n",
        "  emb = C[Xb] # embed the characters into vectors\n",
        "  x = emb.view(emb.shape[0], -1) # concatenate the vectors\n",
        "  for layer in layers:\n",
        "    x = layer(x)\n",
        "  loss = F.cross_entropy(x, Yb) # loss function\n",
        "  \n",
        "  # backward pass\n",
        "  for layer in layers:\n",
        "    layer.out.retain_grad() # AFTER_DEBUG: would take out retain_graph\n",
        "  for p in parameters:\n",
        "    p.grad = None\n",
        "  loss.backward()\n",
        "  \n",
        "  # update\n",
        "  lr = 0.1 if i < 15000 else 0.01 # step learning rate decay\n",
        "  for p in parameters:\n",
        "    p.data += -lr * p.grad\n",
        "\n",
        "  # track stats\n",
        "  if i % 1000 == 0: # print every once in a while\n",
        "    print(f'{i:7d}/{max_steps:7d}: {loss.item():.4f}')\n",
        "  lossi.append(loss.log10().item())\n",
        "  with torch.no_grad():\n",
        "    ud.append([((lr*p.grad).std() / p.data.std()).log10().item() for p in parameters])\n",
        "\n",
        "#  if i >= 10000:\n",
        "#    break # AFTER_DEBUG: would take out obviously to run full optimization"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c21ja-rfsM-a",
        "outputId": "7813c18b-85de-4de7-f7e1-5eee7b576e35"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0/  30000: 2.9632\n",
            "   1000/  30000: 3.1584\n",
            "   2000/  30000: 2.8699\n",
            "   3000/  30000: 2.9406\n",
            "   4000/  30000: 2.8915\n",
            "   5000/  30000: 2.9553\n",
            "   6000/  30000: 3.0190\n",
            "   7000/  30000: 2.9024\n",
            "   8000/  30000: 2.8799\n",
            "   9000/  30000: 2.5801\n",
            "  10000/  30000: 2.6708\n",
            "  11000/  30000: 2.8914\n",
            "  12000/  30000: 2.9607\n",
            "  13000/  30000: 2.5502\n",
            "  14000/  30000: 2.6869\n",
            "  15000/  30000: 2.7691\n",
            "  16000/  30000: 2.6536\n",
            "  17000/  30000: 2.5645\n",
            "  18000/  30000: 2.6208\n",
            "  19000/  30000: 2.6021\n",
            "  20000/  30000: 2.6630\n",
            "  21000/  30000: 2.5645\n",
            "  22000/  30000: 2.5743\n",
            "  23000/  30000: 2.5944\n",
            "  24000/  30000: 2.5731\n",
            "  25000/  30000: 2.6563\n",
            "  26000/  30000: 2.9957\n",
            "  27000/  30000: 2.6382\n",
            "  28000/  30000: 2.8022\n",
            "  29000/  30000: 2.5003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize histograms\n",
        "plt.figure(figsize=(20, 4)) # width and height of the plot\n",
        "legends = []\n",
        "for i, layer in enumerate(layers[:-1]): # note: exclude the output layer\n",
        "  if isinstance(layer, Tanh):\n",
        "    t = layer.out\n",
        "    print('layer %d (%10s): mean %+.2f, std %.2f, saturated: %.2f%%' % (i, layer.__class__.__name__, t.mean(), t.std(), (t.abs() > 0.97).float().mean()*100))\n",
        "    hy, hx = torch.histogram(t, density=True)\n",
        "    plt.plot(hx[:-1].detach(), hy.detach())\n",
        "    legends.append(f'layer {i} ({layer.__class__.__name__}')\n",
        "plt.legend(legends);\n",
        "plt.title('activation distribution')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Pp3u0ZznsOK4",
        "outputId": "def01c38-a07e-4b48-a01e-529849fafed2"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "layer 2 (      Tanh): mean -0.00, std 0.10, saturated: 0.00%\n",
            "layer 5 (      Tanh): mean -0.00, std 0.10, saturated: 0.00%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'activation distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, layer in enumerate(layers[:-1]):\n",
        "  if isinstance(layer, Tanh):\n",
        "    t = layer.out.grad\n",
        "    print(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uG8OF1E6F3Q3",
        "outputId": "3b9f0379-3475-4636-bcbf-1bd85a6279c4"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 1.3824e-03,  1.5281e-03, -3.1783e-03,  ...,  1.2181e-03,\n",
            "         -6.1466e-04,  3.1799e-03],\n",
            "        [-1.6698e-03, -5.6713e-03,  5.5955e-03,  ...,  4.8794e-05,\n",
            "         -7.4165e-03,  4.4688e-03],\n",
            "        [ 2.7959e-03,  7.6794e-04, -6.8459e-03,  ...,  2.8487e-03,\n",
            "         -9.0766e-04,  3.5977e-03],\n",
            "        ...,\n",
            "        [-2.3035e-03,  7.5156e-04, -1.6972e-03,  ..., -4.8013e-03,\n",
            "          7.2532e-04,  2.5901e-03],\n",
            "        [-1.9922e-03, -2.7865e-03,  4.3913e-03,  ...,  4.6869e-04,\n",
            "          8.5170e-03,  7.6892e-04],\n",
            "        [ 1.0685e-02,  1.3851e-03, -6.7890e-04,  ..., -2.7758e-03,\n",
            "          1.8474e-03, -7.1317e-03]])\n",
            "tensor([[ 3.7321e-03,  1.5615e-03, -6.2705e-03,  ..., -2.2858e-03,\n",
            "          2.2138e-03,  2.7299e-03],\n",
            "        [ 3.6080e-03, -2.4029e-03,  4.7367e-03,  ..., -2.0009e-03,\n",
            "          6.6021e-04, -8.0293e-04],\n",
            "        [-3.3224e-03, -1.4623e-03,  6.0080e-03,  ...,  7.8795e-04,\n",
            "         -4.0002e-03, -4.0775e-03],\n",
            "        ...,\n",
            "        [-1.3778e-03,  1.0832e-03, -6.8829e-03,  ..., -1.7166e-03,\n",
            "         -3.3376e-03, -9.0412e-05],\n",
            "        [ 4.0062e-03, -2.4290e-03, -1.7207e-03,  ...,  2.9292e-03,\n",
            "          8.2800e-03, -7.8374e-03],\n",
            "        [-4.1921e-03,  5.2263e-03, -4.1020e-03,  ..., -2.0032e-03,\n",
            "         -3.6945e-03,  4.6683e-03]])\n",
            "tensor([[ 0.0063, -0.0071,  0.0020,  ...,  0.0012,  0.0057,  0.0084],\n",
            "        [-0.0096,  0.0027, -0.0007,  ...,  0.0019, -0.0032, -0.0036],\n",
            "        [-0.0017, -0.0039, -0.0012,  ...,  0.0088, -0.0018, -0.0029],\n",
            "        ...,\n",
            "        [ 0.0061, -0.0057,  0.0041,  ...,  0.0017,  0.0046,  0.0064],\n",
            "        [-0.0001, -0.0007, -0.0072,  ..., -0.0088, -0.0088,  0.0026],\n",
            "        [ 0.0017,  0.0039,  0.0090,  ...,  0.0083,  0.0086, -0.0049]])\n",
            "tensor([[ 0.0032,  0.0045,  0.0012,  ...,  0.0012, -0.0012,  0.0010],\n",
            "        [ 0.0025, -0.0022,  0.0030,  ..., -0.0062,  0.0014, -0.0073],\n",
            "        [ 0.0074, -0.0062,  0.0067,  ...,  0.0067,  0.0008, -0.0010],\n",
            "        ...,\n",
            "        [ 0.0022,  0.0063,  0.0019,  ...,  0.0005, -0.0010, -0.0032],\n",
            "        [ 0.0035,  0.0003, -0.0004,  ...,  0.0006,  0.0035,  0.0001],\n",
            "        [-0.0014,  0.0072, -0.0009,  ...,  0.0058, -0.0175,  0.0022]])\n",
            "tensor([[-0.0056,  0.0048, -0.0038,  ...,  0.0104, -0.0014,  0.0037],\n",
            "        [ 0.0034, -0.0028,  0.0023,  ..., -0.0030,  0.0028,  0.0013],\n",
            "        [-0.0004, -0.0048,  0.0009,  ..., -0.0037, -0.0052,  0.0008],\n",
            "        ...,\n",
            "        [-0.0027,  0.0028, -0.0023,  ...,  0.0058, -0.0012,  0.0018],\n",
            "        [-0.0026, -0.0001, -0.0054,  ..., -0.0008,  0.0070,  0.0061],\n",
            "        [ 0.0099,  0.0040,  0.0025,  ..., -0.0021, -0.0050,  0.0003]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize histograms\n",
        "plt.figure(figsize=(20, 4)) # width and height of the plot\n",
        "legends = []\n",
        "for i, layer in enumerate(layers[:-1]): # note: exclude the output layer\n",
        "  if isinstance(layer, Tanh):\n",
        "    t = layer.out.grad\n",
        "    print('layer %d (%10s): mean %+f, std %e' % (i, layer.__class__.__name__, t.mean(), t.std()))\n",
        "    hy, hx = torch.histogram(t, density=True)\n",
        "    plt.plot(hx[:-1].detach(), hy.detach())\n",
        "    legends.append(f'layer {i} ({layer.__class__.__name__}')\n",
        "plt.legend(legends);\n",
        "plt.title('gradient distribution')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "x2VNbUNosT9J",
        "outputId": "55f31312-bb28-4e76-d760-84bb83eb0379"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "layer 2 (      Tanh): mean -0.000000, std 4.249650e-03\n",
            "layer 5 (      Tanh): mean -0.000000, std 4.380565e-03\n",
            "layer 8 (      Tanh): mean -0.000000, std 4.500784e-03\n",
            "layer 11 (      Tanh): mean -0.000000, std 4.449452e-03\n",
            "layer 14 (      Tanh): mean +0.000000, std 4.146504e-03\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'gradient distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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9pWzPW600AyGEEEKI5iMJISGEEELckqLTo7HQWRDoGFjvuc0vn/Jadi7ZFaVsXPAoJ+4dTuHWrTjOmEGXzV/j9Kc/ce7IUYoPJUPgH2H/h1Cc00qzEEIIIYRoHpIQEkIIIUSrOZVbQlZhWbP0HZ0RTUj7EPQ6/e8PK8pg3wq6GXvzzofW9Fp/hEJ/D7p89SVOM5/CxMIC2/vvx9TZmdz33oP+M6GyFH5+p1liFEIIIYRoLZIQEkIIIUSrmbwimllr46+4ndF46VtSc0pzOJ5/nFCX0HrPK3atJHVrFac+/I22OmvWTenOk8NOk9nu94OlTczMsJ80iZK9eynNKAefERD9LpwruuI4hRBCCCFuVJdNCCmlliulspRSh+o8W6iUSlBKHVBKbVBKta1T9pxS6rhS6lel1NDmClwIIYQQN7e84nJ+yy5mz4ncK1ollHI0j3dn7mT/t8lojSSGYjNiAejj0gcAY3k5OW+/w29PL6Eo3RKnmTPp8tWXPP7E25hgwuxds6kwVtS2b/vHcZjY2JC77D3oPwvK8mH/ymuYrRBCCCGulrW1dYuO9+ijj9K5c2cMBgMGg4H4+Ib/eBUXF8djjz3GBx98UFvXzMyMgIAADAYDs2fPvuJx169ffz2m0CRNWSG0Ahh2wbOtgL+maYHAMeA5AKWUH/Ag0KOmzX+VUrrrFq0QQgghbhm/pOQDoGnw7aGMJrc7FpNJVaWRPRt+4+v/HqCsqOKiOtEZ0bTRt8HXwZeiXbs4cf/9ZC9ejLVLCV1ffwzHGdMxMTfH1dqVef3mcSDnAA9vfphNxzdxruocOmtr2o0fT+HWrZyrdASP/rDnP1BZ3uQ48zdspODrr5tcXwghhBAtT9M0jEbjRc8XLlxIfHw88fHxGAyGBtu+8sorzJw5k8mTJ9fW7dChA9u3byc+Pp7XXnutucO/JpdNCGmatgvIu+BZlKZplTVf9wJuNZ8fANZqmnZO07STwHGg/tUeQgghhBBA3KkzmCjwcLDiqwPpDdZZm7CWhTELa79rRo3kgzl06+XMnQ92JyUhj09ejib9t4J67aIzohmk60H6U7NImToNZaKj02R/3O6uRD94Wr26wzyHsaDfAkorS3n+x+cZvG4w/9r3L86NHozS68lb/gEM+DOcTYNDv//VruhMGb/+nMH3Hx5l47/2U3L292RRcXQ06XPmkD73eSpzc6/H6xJCCCFue0VFRYSHhxMSEkJAQACbNm0CYN68eSxevLi23ty5c1myZAlQndgJDQ0lMDCQ+fPnA5CUlIS3tzcTJ07E39+flJSUK46lsLCQAwcOEBQU1GidkSNH0rNnT3r06MG7775b+9za2pq5c+cSFBREWFgYmZmZtWW7du2iX79+dOnSpdlXC5lehz4igU9qPnekOkF0XmrNs4sopaYCUwHc3d2vQxhCCCGEuJnEpeTTvb0NQ3u4sPT7RLLOluFsa1Gvzhe/fcHBnIOM8x6Hh60HWacKKS2swCPAEe8+LrTvbMu3yw6xcdF+wkZ1xTC4Exl5p+j99QnGRCdTbGqG01+ewWHscNS/g6DXZDC3uSiWUd1GMdJrJNEZ0axNWMuqw6tYoa3g772d6bFxAw5PzqDcvi9pX+3g9MFgUo/lcza7FAAzCx3lZVUkHcjBb0AHqgoLOT17NqYuLlRmZpL3wQc4//WvLfJOhRBCiGb1zWzIOHh9+3QJgHubtpLGwsKCDRs2YGtrS05ODmFhYURERBAZGcno0aOZNWsWRqORtWvXEh0dTVRUFImJiURHR6NpGhEREezatQt3d3cSExNZuXIlYWFhDY41d+5cFixYQHh4OK+99hrm5ub1ymNjY/H3979kvMuXL8fe3p7S0lJCQ0MZM2YMDg4OFBcXExYWxssvv8yzzz7LsmXLeP755wFIT09n9+7dJCQkEBERwdixY5v0bq7GNR0qrZSaC1QCq6+0raZp72qa1kvTtF5OTk7XEoYQQgghbjJGo8YvKfkEu7flvkBXNA2+uWDbmKZpnCw4CVSvFAJIOpiDUuDewx4AZw9bxs3tjWeQIz99dpwvFnzP6bGR/GG3hskdYXTd/DWOjz+OOvAxGCug99RGY1JK0ce1D28OepMtY7bweODjfB6o49fOY1k2bxcrjzzLttQH+S32NA4d2jDgD90YNzeUx/51J23szEhJqF5QnfnSS1RmZuH25r+wHT6cvI/XUHnmTHO8RiGEEOK2omkac+bMITAwkMGDB5OWlkZmZiaenp44ODgQFxdHVFQUwcHBODg4EBUVVfs9JCSEhIQEEhMTAfDw8Gg0GfTqq6+SkJBATEwMeXl5vP766xfVSU9P53K5jKVLl9auAkpJSakd28zMjBEjRgDQs2dPkpKSatuMHDkSExMT/Pz86q0cag5XvUJIKfUoMAII1zTt/ImOaUCnOtXcap4JIYQQQtQ6mVvM2bJKgju1o3t7G7q3t+brg+lM6udZWyenNIeiiiIsdBZsOr6Jp4KfIvlgLu0722FpbVZbz9zSlLvvtcNy3xaOpAWR1WUGB+5aw5L572CiTKCqAmLfh653g2O3JsXn0saFx7pOw35TL9JdC2l35ihRAbsobnucRy2LuTdyJ8rk97+rufnak3wol4JvtlCw6QscZ8zA0mDA0dqas19/Td4HK3B+5s/X7f0JIYQQraKJK3may+rVq8nOzmbfvn3o9Xo8PT0pK6u+mGLKlCmsWLGCjIwMIiMjgeoE0nPPPce0afW3iyclJdGmTZtGx3F1dQXA3NycyZMn889//vOiOpaWlrVjN2THjh1s27aNPXv2YGVlxcCBA2vr6/V6lKq+4VSn01FZWVnbru5KpN9TLc3jqlYIKaWGAc8CEZqmldQp+gJ4UCllrpTqDHQDoq89TCGEEELcSuJOVR8obXCvvqh0eIArMUl5ZJ79/YfV+dVBkQGRFFYUsunA12SfKsQz0KFeX8byclIem4Lrvk8YHJBDvo3CO+sJDn6fVv1DKuErKEy/5OqgC5WcLWfjm3EU5Z1j2Ggngg+8zdNW7VDO5czW5zNhwwj2Z+6vrd/Jpx1lRRUkvv4OFgEBOE5/AgBzLy9shg3lzEcfUZWff3UvSwghhBAAFBQU4OzsjF6vZ/v27SQnJ9eWjRo1ii1bthATE8PQodUXng8dOpTly5dTVFQEQFpaGllZWZcdJz29+mxDTdPYuHFjg1vDfH19OX78+CVjbdeuHVZWViQkJLB3795G67aWplw7vwbYA3grpVKVUo8BbwE2wFalVLxS6m0ATdMOA58CR4AtwJ80TatqtuiFEEIIcVOKTzmDtbkpXZ2qr5G9L6Bm29jB3w+XPp8QGuU1Cl97X3bvqb7y1cPfsV5fZz5aTUVKCh2XLMHy0TtYG/AaZp7l7F6XyJZ3DnHux5XQ1gO6DWlSbCVny9m0OI787BLWWZ7jz8dL0Hr2ps2GHXw8YiMvlejIPJvKpC2T+PP2P5N8NpmO3asTW7mWnnR443WUXl/bn+P06RhLSshbterqX5gQQgghmDBhArGxsQQEBLBq1Sp8fHxqy8zMzBg0aBDjxo1Dp6u+7HzIkCGMHz+evn37EhAQwNixYyksLGzSOAEBAQQEBJCTk1N7vk9dPj4+FBQUNNrfsGHDqKysxNfXl9mzZze6Pa01qeZegtQUvXr10mJjY1s7DCGEEEK0kBH//gFbCz0fP/77j6Ohb+7CzlLPp0/0BeC16Nf4PPFzfh7/MxuPb+TH5cl4VwUx5fWBtcusK8+c4behw7A0BOH+7rt8duwz/rHnH2yM2EjhPj17Pj+Otcpg6NASnCOmXDau0sLqlUFnskr41PIcZh0sySsuxy3pMK/ufod2f5+HS7AVpesmsar3g7yft5+KqgqeOxVIya9DsHWzZ/Sr917Ub+rMpyn+6Se8vtuGzs7uOr1FIYQQovkdPXoUX1/f1g7jsoxGIyEhIaxbt45u3Zq2Rfxavfnmm9jY2DBlyuV/Y7SEhv5bKaX2aZrWq6H613SotBBCCCHElSqrqCIhvZDgmu1i5w0PcCUmOY+MguptYycLTuJp64lSiiFuQ3Er8Cbb+WRtMggg57//w1hURPv/+z+g+rp5R0tHurTtgiG8E6N8VmNUej77tisHtqdeci/++WRQXmYJay3KcPayY/30fnz3zED8ht/NsbZuJCz+H1vKQ7DoGMq0w9vZPGI9j1gOxHttNFXaMTKKzKmqMF7Ut+OM6RiLisj78KPr8QqFEEIIUceRI0fw8vIiPDy8xZJBANOnT7/o9rGbiSSEhBBCCNGiDqUVUGnUMHRqV+/5fYEuNbeNVW8bO1lwks52nQHIPVmK3mjOHtOtZBRX30Z27uRJzqxZQ9s//AHzbt3QNI3ojGhCXUKrk0YntuOSt54/jivA3c+eHz45xrfLDnOutJILlRaVs3FxHLmZJXxiUUY3f0dWRfbG1kKPnZWeV0YH4vHkE7QvzOajRR/yauUEKMrAIXY1I9ckY2Zty/7QXLQKSD9x8VlBFj4+WA8OJ2/VKqqasFRdCCGEEE3n5+fHiRMnWLRoUYuOa2FhwSOPPNKiY15PkhASQgghRIuqPVC6U/0VQl7ONvi42LD5YDolFSWkF6fXJoSSD+ZiYqpIsz3GumPrAMj65yJMzMxweupJoDqBlFOaQ2+X3qBp8N2LYNcJi36PMHx6IH1Hd+VEfDafvhJD9qnfkzKlReVsejOO3PRiPrUoo2dvV95+pCcWel29+IIeGonew4OZWXv4KK09UVpvMv77LueOHMXt5VcIHzwYI1XE7jva4Lwdp0/HePYsZz6SVUJCCCGEaH2SEBJCCCFEi4pPyadjW0ucbC5eYn1fgCsxSWfYfzoRgM52ndE0jaSDOXTytae/Zz/WH1tPwd6fKPruOxymTsXUsfqQ6eiM6otN+7j0gV+/gdP74a5nwdQcZaIIGeLBqGeCqaowsv6NWA7tTK1NBmWnF7PO8hx33+XOoj8Eoddd/BNJ6XQ4PBaJTfJxvrnDkqNqOPlHzCnwsiehazD3eg8l1zaNE0cyG5y3ZY8eWA8aRO6KlVQVFV+v1ymEEEIIcVUkISSEEEKIFhWfkl973fyFhge6AvDFkTigOiGUn1nC2ZwyPP0deMjnIc6U5nLixXmYurpi/+ik2rbRGdG4tnHFrU0H2P4y2HeBoIfq9e/q1ZY/Ph+Km3c7dq45xkfz9pJ9upj1lucYfa8X8+/3w8RE0Ri7Bx5A5+SItvI97vvha5SdJSFBR/nbO5+xcX82Np1N0OfYknUmt8H2jjOmYywo4MzHH1/ROxNCCCGEuN4kISSEEEKIFpN1toy0/FKCOzWcEOrqZI2Piw3Rab+iUHjYepB0oDq54hHgSJhrGKN+c8DitzScn/kzJhYWABg1IzEZMdXnBx3ZAJmHYOAc0OkvGsPS2owRfwoiZIQnhRVVfGZ1jsgxvjw9uFu9A6sbYmJujv3EiZTu20dFejru//oX+jYWvG63gRe+PEx3Xx9MMOGr3d832N4yIIA2d95B3vLlGItllZAQQgghWo8khIQQQgjRYuJSqs8PuvCGsbpGBLqSUXIKF6sOmOvMST6Ug0PHNtjYW0DZOUZ/X8pxF0jp41HbJvFMIvnn8unTvhfseBWc/cB/TKNj5JWU82rSaf5jU8qshwOZ1M+zyXNo9+CD6D3ccXr6aaz6DUL1/zOhZT8SpjvG+wfLqdJV8OvBlEZvNHOaMYOq/HzOrF3b5DGFEEKI25m1tXWLjvfdd98REhKCwWBgwIABHD9+vMF6GzduZMGCBbz88ssYDAYMBgM6na7289KlS69o3IEDBxIbG3s9ptAkkhASQgghRIuJT8nH1ETRo4Ndo3WGB7hiYp6NOa6cK6kg/XgBHv7V5wTlrViBWW4hnw61Ys2xT2rb/Jz+MwC989Ih9zgMmgMmDf/MOZ1fyrh39vBrRiHLJvZkZHDHK5qDzsaGrlu24DhtavWDvn8CG1fetP+M+LR8SuzLscl2IS4rrsH2lgYDbfr3J/f95RhLS69obCGEEEJcX5qmYTQa6z2bPn06q1evJj4+nvHjx/PSSy812PaNN95gxowZzJ07l/j4eOLj47G0tKz9PHPmzJaYwlWThJAQQgghWkz8qZMWYToAACAASURBVHx8XW0vusGrLk9HK3TmOeQXtOXUkTyMRg3PAAcqs7PJWfYeNvcMxnvQKLac3EJeWR4AMRkxuNt0wuWn/4GrAXxGNNj3b9lFjP3fT2SdPceqyN7c7dP+quZRb2uZmRUMmkO7vF+Y1yWRfcU62pW68Hn8l422d/zTDKry8jiz9pNG6wghhBCivqKiIsLDwwkJCSEgIIBNmzYBMG/ePBYvXlxbb+7cuSxZsgSAhQsXEhoaSmBgIPPnzwcgKSkJb29vJk6ciL+/PykpKfXGUUpx9uxZAAoKCujQocNFsRw7dgxzc3Mcay63aGqsSUlJ+Pr68vjjj9OjRw+GDBlCaZ0/EK1bt47evXvTvXt3fvjhh6t9VU1i2qy9CyGEEELUqDJqHEjNZ0xPt0vWSy9OB1VBRo4tR/dnYd7GlPZd7MicPx+tvBznv/yFh9pW8cmvn/B54uc82uNRYjNjGdbGEwp+hBFvQgNnAR1KK2DS8miUgjVTw/Dv2PgqpStmmAB7/sukkpV8bvs6lEDioTQKBhZgZ37xOFYhIVj1DSP3/fdp99CDtWchCSGEEDey16NfJyEv4br26WPvw996/61JdS0sLNiwYQO2trbk5OQQFhZGREQEkZGRjB49mlmzZmE0Glm7di3R0dFERUWRmJhIdHQ0mqYRERHBrl27cHd3JzExkZUrVxIWFnbROO+99x7Dhw/H0tISW1tb9u7de1GdH3/8kZCQkCuOFSAxMZE1a9awbNkyxo0bx2effcbDDz8MQGVlJdHR0WzevJkXXniBbdu2NendXA1ZISSEEEKIFnE8q4ji8ioMjRwofd7JgpMAaGVOpBzOxaOHA+XHE8n/7DPsJ4zHzNOTrm270selD5/++imHcg5RVFFE71Px4N4XvMIv6nPviVwefHcvFnod657od32TQQAmOrhnAbr8kyzpeYhik0pcznTlqxNfNdrEacYMqnJyyP903fWNRQghhLhFaZrGnDlzCAwMZPDgwaSlpZGZmYmnpycODg7ExcURFRVFcHAwDg4OREVF1X4PCQkhISGBxMREADw8PBpMBgG8+eabbN68mdTUVCZPnswzzzxzUZ309HScnJyuOFaAzp07YzAYAOjZsydJSUm17UaPHt3g8+YgK4SEEEII0SLiTp0BaHJCqJdFVzhnxCPAgaw3/o6JjQ2O06fX1nvI5yFm7ZjFothFAISeyYD737toddC2I5n86eP9dLK34sPHeuNqZ3k9p/W7bvdA5zvpevQ/mLX/gI7Zvqw8sIzxPuMbvL3MKjQUq9BQct97j3YPPYjSX3wjmhBCCHEjaepKnuayevVqsrOz2bdvH3q9Hk9PT8rKygCYMmUKK1asICMjg8jISKA6KfPcc88xbdq0ev0kJSXRpk2bBsfIzs7ml19+oU+fPgD88Y9/ZNiwYRfVs7S0pKCg4KpiNTc3r62n0+nqbRk7X6bT6aisrLzsO7kWskJICCGEEC0iPiUfO0s9nR0b/gF23smCk9iZ2zHA0gkjGur0IYp378Zx+hPo2v6eTLqr0124tnElPjser4oqHD3vAs/+9fr6fH8q0z7ah4+LDZ9O69t8ySCoTkTdswBKchnsepQ2ldaU5ZWxO6Xx20LsJ0+mMiuLomY+I0AIIYS4FRQUFODs7Ixer2f79u0kJyfXlo0aNYotW7YQExPD0KFDARg6dCjLly+nqKgIgLS0NLKysi45Rrt27SgoKODYsWMAbN26FV9f34vq+fr6Nnr72OVivVFIQkgIIYQQLSI+JZ+gTm0bXC1T18mCk3S27YxdfiVpOiMFS/+J3t0d+/Hj69UzNTFlnPc4AEJLSuDuv9crX777JM98+gthXexZ/XgY9m3Mru+EGtIhGALG4XG6+prZjmf8eGHn+41Wt77zDnROjuR/9nnzxyaEEELc5CZMmEBsbCwBAQGsWrUKHx+f2jIzMzMGDRrEuHHj0OmqL68YMmQI48ePp2/fvgQEBDB27FgKCwsvOYapqSnLli1jzJgxBAUF8eGHH7Jw4cKL6t15553ExcWhadoVx3qjkC1jQgghhGh2RecqOZZZyNAeLpete7LgJAPb3cPZ9BLMLM/SJi0Zx1dfRZldnNAZ43Y3W/cuYrhDELj1BKqXh7+5LZGl3yUyrIcLSx4yYG7a+K1m193dz2NzpBd2VoX4loRxsPIN1sYm8GCvi38IKlNT2j7wALkrVlKZk4NpIzeVCCGEELez8yt8HB0d2bNnT4N1jEYje/fuZd26+mfzPf300zz99NMX1T906FCj440aNYpRo0ZdMiYrKysGDx7Md999x+DBg68o1rpj//Wvf639vGPHjtrPjo6OzX6GkKwQEkIIIUSzO5Caj1EDg/ulzw8qOFdAblkuHfO6A9C75ADnTEwpDO3fYP12+z7kk7R0DOEvA2A0avzji8Ms/S6Rcb3ceGt8cMsmgwDaeUDIJNzUHpzPtkeHkZd3rSa9oLTB6najR0NlJQVfNH5NvRBCCCEad+TIEby8vAgPD6dbt24tNu6cOXMoKSlpsfGuN0kICSGEEKLZxafkA2Bwu3RCKOlsEgDmaQ7Y2JvT5Zet7HXtwZYTDSzvLsqCn98G/9GU2vuy9UgmU1bFsnJPMlPv7MLrYwIx1bXST53eU+mkj6OqHIK5E816L/+3/heMxouXlZt36YKlwUD+5581uuxcCCGEEI3z8/PjxIkTLFq0qEXHbd++fe1V8jcjSQgJIYQQotnFn8rH08GKdpc5x+dkwUl0VXpKkhQdHCtQ+Wc4GdiPrw6m16+Yn8K5TbMwVpTxfP79BL8YxeOrYok5mcec4T48d6/PZc8qalZO3enYrS1gZKAagjLP4KfU/azak9Rgdbsxoyk//htlBw+2ZJRCCCGEuI3JGUJCCCGEaFaaphGfkk9/r8ufj3Oy4CSdirypqtCwT92HiY0NnYcP4ZPvfiMltxjT1L2U7v4PntnbMdU0/l01ku+ybRnXqz33+LWnT2cHzExvjL93WQx4FOe431DJHlh5WeHgcYBXv+lMWFcHfFxs69W1vfdeMl9+hfzPPscyMLCVIhZCCCHE7eTG+MUkhBBCiFtWekEZWYXnMHS69HYxqE4I+RX1xtTMBIud67AZcg/3BToxTred0n/3xXXDaNpl/cx689Es77WJ8OlL+Gn23Sx4wJ87ujndMMkgALoNwc02iewMHcM7jaDINBZbq0oeeT+aE9lF9aoarcwpv6sXOV9s4Ptj37RSwEIIIYS4ncgKISGEEEI0q7hTNecHNSUhlH+S8JwhuNhXoorPYtchjzYrevKG/gynTDuzu9s8PAdOYlz7m+A2LhMdbiFe7N+mY1BJCOurPuXxe3JZuaUj45f9zNJHPEgq2c/utN1EZ0TjY2vFGNf72P7+AgL/EYqj5U0wRyGEEELctC77ZzSl1HKlVJZS6lCdZ/ZKqa1KqcSaf7erea6UUkuVUseVUgeUUiHNGbwQQgghblz5ZfkUlhcSn3IGM1MTfF1tL1m/wlhBUVYF+hIrHI59gc6iCqusteA5ACZ9hfvcOAb88S+43QzJoBqu9zyAjnJMfknD196XramfcfeAnyh2fonHvh/Fi3tf5Ne8XxmpTaR3/lxOuQ8m6Lcg3v7l7dYOXQghhLhhWFtbt+h4b731Fl5eXiilyMnJqX2ekJBA3759MTc355///Gej7TVN4+677yY5ORmDwYDBYMDFxYWOHTvWfi8vL29yPDt27GDEiBHXNKeGNGWF0ArgLWBVnWezge80TXtNKTW75vvfgHuBbjX/9AH+V/NvIYQQQtxmpm2bRjvzduSmTMS/g+1lt3OlFKbgnREGVGFz5Gdsw3xQf94Gbd1bJuBmYGrniKtDPqmpZvxxxP38I/YNMoozMLgYiD/WH3ujPzOr/EmKzsLFy46ilCxM9D3ZEf0W433H08WuS2tPQQghhLilaZqGpmmYmPz+O6V///6MGDGCgQMH1qtrb2/P0qVL2bhx4yX73Lx5M0FBQXh4eBAfHw/AP/7xD6ytrfnrX/963edwtS67QkjTtF1A3gWPHwBW1nxeCYys83yVVm0v0FYp5Xq9ghVCCCHEzSGtKI0juUfYk76Hg+lpGDq1u2ybxNQk/DL74Vq1C7Oys9hNf/GmTgad1ym4M7kV7gxNL2LtiLXsfmg3H45YxjuD/sTdSU4kRWfhM6gjI/8cjE9/NwrsujL4kD1v7nuztUMXQgghbihFRUWEh4cTEhJCQEAAmzZtAmDevHksXry4tt7cuXNZsmQJAAsXLiQ0NJTAwEDmz58PQFJSEt7e3kycOBF/f39SUlLqjRMcHIynp+dF4zs7OxMaGoper79knKtXr+aBBx5otHzZsmWEhoYSFBTEmDFjKCkpAeDRRx9l5syZ9OvXjy5durB+/fp6cx87diw+Pj5MmDABTdMuGUNTXO0ZQu01TTt//2sG0L7mc0eg7ptMrXl2wV2xoJSaCkwFcHe/+X/sCSGEEOJ3O1N2AmDUjFRZHsLg3v+ybU7uKACs6Z68Db1bRyxukdu23EJ9YVssp3+KpsfdU8FER2JsJoc+PIaL3pTPrcrYdDqD4LLOeA/yIub7bPyzevHxqW+IzYill0uv1p6CEEIIAUDGK69w7mjCde3T3NcHlzlzmlTXwsKCDRs2YGtrS05ODmFhYURERBAZGcno0aOZNWsWRqORtWvXEh0dTVRUFImJiURHR6NpGhEREezatQt3d3cSExNZuXIlYWFh13U+AD/++CPvvPNOo+WjR4/m8ccfB+D555/n/fff56mnngIgPT2d3bt3k5CQQEREBGPHjgUgLi6Ow4cP06FDB/r378+PP/7IgAEDrinOa76KQ6tOS11xakrTtHc1TeulaVovJyenaw1DCCGEEDeQnak78bT1pK3eBVObQwRf5kDp4vxzVB62Jt1uL7qUAmxHjEAp1ULRNi/HTjaYmxtJOdOByqNb2fnxr0S9dxiHjtaM/3sf5k/tSXJuCQ+/9zNaGx2O7YzktunBgIy2LIpdhFEztvYUhBBCiBuCpmnMmTOHwMBABg8eTFpaGpmZmXh6euLg4EBcXBxRUVEEBwfj4OBAVFRU7feQkBASEhJITEwEwMPDo1mSQQB5eXnY2Ng0Wn7o0CHuuOMOAgICWL16NYcPH64tGzlyJCYmJvj5+ZGZmVn7vHfv3ri5uWFiYoLBYCApKema47zaFUKZSilXTdPSa7aEZdU8TwM61annVvNMCCGEELey3N/ArhOYmlFcUUxMRgwTfCew49ds8tt8g61VxSWb7/82GU2DjvnfgqZh1wwHJ7YWExOFm58zpw704rPl+eQUpxF8jzt9RnZBpzOhn70F707sxeMrY5m0PJo5AzyJ/dKEP57050nXH9lycgvDuwxv7WkIIYQQTV7J01xWr15NdnY2+/btQ6/X4+npSVlZGQBTpkxhxYoVZGRkEBkZCVQnkJ577jmmTZtWr5+kpCTatGnTbHGamppiNBrrnUtU16OPPsrGjRsJCgpixYoV7Nixo7bM3Ny89nPdbWF1n+t0OiorK685zqtdIfQFMKnm8yRgU53nE2tuGwsDCupsLRNCCCHErehsOvw3DL6oXuq85/QeKowV3OV2FwU5PqCM7Ejd0Wjz4oJzHP4hlRMO0RhOlGDu7Y25l1cLBd8y3HwdKKmyo7DEkuEPO9JvjBc63e8/w+7q7sR/J4Rw+PRZFv+WCWgUp9oRZOHF0rillFc1/SYSIYQQ4lZVUFCAs7Mzer2e7du3k5ycXFs2atQotmzZQkxMDEOHDgVg6NChLF++nKKiIgDS0tLIyspqsO/rydvbmxMnTjRaXlhYiKurKxUVFaxevbrZ42lMU66dXwPsAbyVUqlKqceA14B7lFKJwOCa7wCbgRPAcWAZMKNZohZCCCHEjeOXj6GqHA6shV+/YUfKDmzNbOls04NT6Y5Y6xzZlryt0eZx357CWGXkuF0UDqdKsR1xXwsG3zK6h7anZ7gT45xn07mw4R9+g/3a8++HgonOKKDErIJMhyBm5YWSVpTGmoQ1LRyxEEIIceOZMGECsbGxBAQEsGrVKnx8fGrLzMzMGDRoEOPGjUOn0wEwZMgQxo8fT9++fQkICGDs2LEUFhZedpylS5fi5uZGamoqgYGBTJkyBYCMjAzc3Nz417/+xUsvvYSbmxtnz569qP19991Xb9XPhV588UX69OlD//79682hpanrcTL1terVq5cWGxvb2mEIIYQQ4koZjfDvELBuD+VFGItzGOTmTFiHftzn8lcmLo/mgbtj+SFzE7v+uAtrM+t6zYsLzvHh3B9pZ7GDQ4WfMX6nEa/vtqHv2LGVJtTMPp8KCZvhL0fBvOGzBTbFp/H+ygMMLTWnf/463p6icSD7AN+M/gY7c7sWDlgIIcTt7ujRo/j6+rZ2GJdlNBoJCQlh3bp1dOvWrVVjSU9PZ+LEiWzdurVFx23ov5VSap+maQ3eUHHNh0oLIYQQ4jaW/COcOQm9IuGB/3CwMp+8c/nc5XYX8Sn5KAV/8B1OhbGCXam7LmoeF1W9OsjovIUBR4zogvxv3WQQQO+pUF4Iv6xttMoDho5YdbFF04yknG3LM23HUlxRzDsHGr+tRAghhLidHTlyBC8vL8LDw1s9GQTg6urK448/3uDqoRuJJISEEEIIcfX2rwJzO/CLgA4GdnoPRKdp9C8tJT4lHy8na/q59cTJ0omtyfX/SlZccI7Du1LxttjJGSsH3LPB4f4HWmkiLcStF3QIgehlcIlV2uP6e3BKV0mmc09st+5jpNdI1iSsIeVsSgsGK4QQQtwc/Pz8OHHiBIsWLWrtUGqNGzcOW1vb1g7jkiQhJIQQQoirU3oGjn4BAWNBbwnATpMKgo2m2H4zh99OpWLo1BYTZUK4ezi703ZTUlFS2zxu6ymqKqvoafcF1gkVVJmA3b33ttZsWk7vqZDzK5zc2WiV4QGunLDVcc7CnlPfxTOjxzT0JnqWxC1pwUCFEEIIcSuThJAQQgghrs7B9VBZBiETAUgvSudYfiIDfcZBcTYzK97H4N4WgHs87qGsqozdabsBKDlbzuGdqXS32IVd2H102ZdBuq8Tpg4OrTadFtNjFFg5ws/vNlrFQq/DEOZKlWbktGV3rGKOMKnHJL5N+pZfsn9pwWCFEEIIcauShJAQQgghrs7+VeASAB0MAOxMrV7xcmePhzjW7XHG6H5ggDEGgJD2Idhb2NfeNha39RRVFUZ62W4k3+oOHM5UUXiXoXXm0dL0FtBzEhz7Bs4kN1rtof6eHNcbyXLuSd76DUzuMRkHCwcWxS7iRrgURAghhBA3N0kICSGEEOLKnY6HjAMQMqn20Y7UHXjYetDZrjOfWj3Er5o77j/OhdIzmJqYMqjTIHam7iQ/v5BDO1LobrmLtn2GkRH1HeWm0Cb87lacUAvrFQkoiH2/0SpdnawxdrKiQm9N6sEM9GeKmGGYQVxWHLGZcjurEEIIIa6NJISEEEIIceXiPgSdefX5QUBJRQnR6dHc6XYnZRVVRB3L4wOn/0MVZ8OW5wAY4jGEksoStmyMqV4dZLMBrc+TVG3byT4vhafrjX+l7XVj5wY+91WvsqoobbTaPYM9KcdIplMIeStXcm/ne1Eo9mfub8FghRBCiNZlbW3douO99dZbeHl5oZQiJyfnovKYmBhMTU1Zv359g+1LS0u56667+OWXXzAYDBgMBuzt7encuTMGg4HBgwdfUTwrVqzgySefvKq5XIokhIQQQghxZSpK4cA68HsALNsBsCd9DxXGCga6DWTxtkRS8kqJGDYc7ngGflkDv24h1DUUJ1zIjqmkm+UPtO0dTnFCCrr8In7yM8HD1qOVJ9bC+kyrPpg79oNGq9wb1IFkS8hw7knumk+xLKqgi10XDuQcuKKhCkoqrjVaIYQQ4pakaRpGo7Hes/79+7Nt2zY8PC7+bVJVVcXf/vY3hgwZ0mify5cvZ/To0QQFBREfH098fDwREREsXLiQ+Ph4tm3bdt3ncTUkISSEEEKIK3PkCzhXACGP1D7alboLG70N+oquLPvhBA+GdqKflyPc+Sw494Avn0Z/roihBeOhygSDzWcw4M+c/epryi1MyQjsiIWpRStOqhV49Ieu4RD1PBz9qsEqZqYmdAxyRDMxI8eqC3kfrCDAKYCD2QebfI7Q1wfSMbwYRUxS3vWMXgghhGhxRUVFhIeHExISQkBAAJs2bQJg3rx5LF68uLbe3LlzWbKk+mbOhQsXEhoaSmBgIPPnzwcgKSkJb29vJk6ciL+/PykpKfXGCQ4OxtPTs8EY/v3vfzNmzBicnZ0bjXP16tU88MADjZYvWLCA0NBQ/P39mTp1au3/pw8cOJC//e1v9O7dm+7du/PDDz/Utjl9+jTDhg2jW7duPPvss5d4S01nel16EUIIIcTtI+5DaNcZPAYAYNSM7EzZSd8O/Ziz4QgObcx4bnjN9i9TMxj5H1gWTukX87FNfIBfHfeREBSAg2V7Crdu5ZC/NZ0cu7TihFqJUjBuFXw4EtZPhgnroMvAi6r9YbgX637+mWTvYbRf/RbBd81k47mNpBam0sm20yWHOFNczrxNh9A0WBebQqinffPMRQghxG3hh0+PkZNSdF37dOxkzR3jujeproWFBRs2bMDW1pacnBzCwsKIiIggMjKS0aNHM2vWLIxGI2vXriU6OpqoqCgSExOJjo5G0zQiIiLYtWsX7u7uJCYmsnLlSsLCwpoca1paGhs2bGD79u3ExMQ0WKe8vJwTJ040mlACePLJJ5k3bx4AjzzyCF999RX3338/AJWVlURHR7N582ZeeOGF2tVE8fHxxMXFYW5ujre3N0899RSdOl36d8DlyAohIYQQQjRd7m+Q9AMEPwwm1T8jjuQeIbcsl8oiX46mn+Wlkf7YWep/b9MhGO54hmOxuRgrFAmu37LN3oXCqCiMRUVs7V5GZ7vOrTShVmZuDeM/BQcvWDMeUi8+LNrT2ZpCJz1n9J2oKKvEd9tvAPySc/nr51/86ggFpRWEerbjm4MZlFVUXfcpCCGEEC1F0zTmzJlDYGAggwcPJi0tjczMTDw9PXFwcCAuLo6oqCiCg4NxcHAgKiqq9ntISAgJCQkkJiYC4OHhcUXJIIBZs2bx+uuvY2LSeColJyeHtm3bXrKf7du306dPHwICAvj+++85fPhwbdno0aMB6NmzJ0lJSbXPw8PDsbOzw8LCAj8/P5KTG7+ptKlkhZAQQgghmi7uI1AmYJhQ+2hHyg5MMOGbaDvuC3BlSA+Xi9vd+X8kb15BO10qhnYa32X8zMS1v2HSxYP97qncd7smhACs7OGRDbB8KHw0BiZ/A+396lUJ7t+R0xuSOd13HB7rN+LwhAUHsg8wosuIRrvd8WsWn8elMfNuL/p0cWDCez/z3dEs7gt0be4ZCSGEuEU1dSVPc1m9ejXZ2dns27cPvV6Pp6cnZWVlAEyZMoUVK1aQkZFBZGQkUJ1Aeu6555g2bVq9fpKSkmjTps0Vjx8bG8uDDz4IVCd+Nm/+f/buOzqqanvg+PfOZNJ77yEhhIQUQu+9C9KLoFgQ9aeoT32WV+zl2TtWVEBEpCmKKL1Ih0AgIYFACum992Rm7u+PYHh5hCIlAbI/a7li7jn73LMXujLsnPIbJiYmTJw4sbGPhYVF45yaU1NTw0MPPURUVBQ+Pj68+OKLTfqbmZkBoNVq0ev15zxvru1yyQohIYQQQlwagx6OfA8dRoLt2aLC9vQdmBkCsNDa8uL40GZD640mZFV3wNcllxGd5+J/vIS6hJOUTx2Gqij427bhghCAjTvc+TOYmMOSSVCU0qR57NB2VGpVjtp1w1hRwR3H7InNjz3vcBW1ev790zECXa2ZNzSQ3gFOuNma8VN05rXORAghhLhmSktLcXV1RafTsW3btiarZCZNmsT69es5ePAgo0aNAmDUqFF88803VFQ0bHPLzMwkLy/vst+fkpLC6dOnOX36NFOnTuXTTz9tUgwCcHBwwGAwnLco9OdzZ2dnKioqzntTWUuQgpAQQgghLk3iJqjIga53Nj7KqcwhofgEJQUdeH5cJ1xszJoNzUwoxmAAv+n30q/jZCbvU6hytORkdzeAtrtl7L85tIM714ChtuFcofKcxiZTnRYzfxvMqnSog0bS8488UrOOU6Nv/sPm2+tPkFVazZtTIjAz0aLVKEyI9GJ7Qh5FlXUtlJAQQghxdd1+++1ERUURHh7Ot99+S3BwcGObqakpQ4YMYfr06Wi1WgBGjhzJrFmz6NOnD+Hh4UydOpXy8vKLvuejjz7C29ubjIwMIiIimDt37l+a58iRI9m1a1ezbfb29tx3332EhYUxatQoevTo8ZfGvpqUS72h4lrq3r27GhV17p55IYQQQlxHls1sOOPmiXjQNpwR9GX0d3wc8ybB+pdZMWciiqI0G/rHDyc5vieLe98dQH1cLKdvm8mK0dYwYxwbTm9g9227zxvb5mQcgsW3goMf3L2uYUsZcCw2nx2fxKJtV8+gRY+xfICGia8tJdI1skl41Okipn2xl7v6tGuyYis+q4xbPtrJKxPDmN373Gt0hRBCiOYcP36ckJCQ1p7GRRmNRrp27crKlSvp0KFDq87l8OHDvP/++yxZsqRF39vcn5WiKIdUVe3eXH9ZISSEEEKIiyvPgZMbIHJWYzFIVVW+Pfo7ar0j704cecGCTmpcIV4dHTDRaSn46isM1pasDa1mw+kN+Nv5SzHov3l3g5nfQ2EiLJ0GtQ3L3EPDnKkxU0jPNsFkYD/GHjRyLLXpDSc19QaeWR2Dp50FT43q2KQtxMOGjm42rJFtY0IIIW4y8fHxBAYGMmzYsFYvBgF07dqVIUOGYDBc35c5SEFICCGEEBd35HtQDdBlduOjFYeTKFHj6ebcD1+n8x/MWJJbRVl+NX6hTtQmJVGxeQuOo+cqfgAAIABJREFUt88CC3PK68rl/KDmBAyGqQshKxp+mAX6WhRFwauzE+61kDzsDqxrwLh6XZOw+VsTScqv5PXJ4ViZNb07RFEUJnbx4lBqMWmFVS2XixBCCHGNderUieTkZN59993WnkqjOXPmNG5du15JQUgIIYQQF6aqEL0E/PqBcyAABRW1/GfrWhSNnvt73HrB8LT4QgB8Q50o/PobFHNzXO+6h36e/QA5P+i8QsbBhPmQsgNWzQGDnuGjA9CgsDVBS1q4KyEbT2GsrAQatoR9viOJKV29GRjk0uyQEyI9AVhzRFYJCSGEuHTXw1Ez4sIu589ICkJCCCGEuLDU3VCU3GR10Etr46k3PYaliRU93Zvdln42/FgR9m6WWBpKKV27FvspUzBxdGREuxEABNgFXNPp39AiZ8HoN+DEr/D9dJwdjahu5jhl15E5cQTWVUbSF3+F3mDkmdUx2FvqeG7c+c958LS3oHeAI2uiM+XDvRBCiEtibm5OYWGh/Ny4jqmqSmFhIebm5n8pzuTiXYQQQgjRph1eAma20GkCAJvic1l7NAO30FP09+qH7syZQs3R1xnIPFlMaH9PihYtBqMRx3vuAWBUu1HUG+rp792/RdK4YfV+EHSWsO4J+GYUg4d+yY5lNaSWdMHC/3vCF3/HwsDhxGaW8smsrthbml5wuMldvHl6dQwxGaV09rFvoSSEEELcqP68bSs/P7+1pyIuwNzcHG9v778UIwUhIYQQQpxfdQnEr2lYqWJqSVlNPc+uiSXAq5R8YzGDfAZdMDzzVAmGeiPe/uaUvLkC21tuwdTbCwCdRsekDpNaIosbX7e7wN4XVtxJ6IEpbDX/GO0JM34aoCPy2wpOffUtI8dN45Zw94sONTrcnWd/PsZP0ZlSEBJCCHFROp0Of3/Z3n0zuqItY4qiPK4oSpyiKMcURVmmKIq5oij+iqLsVxQlUVGU5YqiXPjXVEIIIYS4rpTX1JOYV8GexAKif/sK9DV8Wd6PB787xKRPdpNfXsuQLsUAjecAnU9aXCFanQaLqN8xVlXhNPfelkjh5tR+CNy7EcXEjKHmC3Gq16BzGsQxPysmndzGy6PbX9JtbbbmOkaEuLH2aBb1BmMLTFwIIYQQ16PLLggpiuIFPAp0V1U1DNACtwFvAu+rqhoIFAPyyU8IIYS4zi0/mMbQd7YT+vx6wl/cyPD3djDrq/1oji7luNGXjxOsOZVXgYedBW9MjiCtKoYODh1wsnC64LhpcUV4BdpS9t23WA0cgHnHjhfsLy7CNQTu20LHdkWYK6UEnQpjeT899jXlmK5fe8nDTOziRWFlHbsSC67hZIUQQghxPbvSLWMmgIWiKPWAJZANDAVmnWlfDLwIfHaF7xFCCCHENVJaVc/La+PxcbTktp6+uNma4WZrjp+aSeefk6kb/iqx/Uc39q8z1PHWsmimBU278Lj51ZTkVhFgnYOhqAinuXOvdSptg7Urujlr8Hr1I2ryepIT6AhdnShc8BX2M2agMTO76BCDglywt9Tx0+FMhnR0bYFJCyGEEOJ6c9krhFRVzQTeAdJoKASVAoeAElVV9We6ZQBezcUrinK/oihRiqJEyeFUQgghROv5bn8qlXUG3pseyXPjOnH/wPZMiPQisngjKBpMOzct/MTkx1BrqKWne88LjpsW13DdvOXW77Ho3BnLHj2uWQ5tjqklkQ/OA8VAePYgkkOy0efnU7xs2aWFm2gYF+HBxvgcKmr1Fw8QQgghxE3nSraMOQATAH/AE7ACRl8w6L+oqvqlqqrdVVXt7uLicrnTEEIIIcQVqKk3sHD3aQYGudDJ0/Zsg6pCzArwHwQ2TQ8qPpBzAI2ioZt7twuOnRpXiLWlEdOUWJzum3tJ59uIS+fuYUNwby+C83ux37oUK18TCj7+GH3BpW0Dm9TFi5p6IxuO5VzjmQohhBDienQlh0oPB1JUVc1XVbUe+BHoB9grivLnVjRvIPMK5yiEEEKIa+THw5kUVNTyfwMDmjakH4CSVIiYcU7M/uz9hDiGYGtqe07bn/T1BjITinHKj8U0IADroUOv9tQF0HmYDyZGU0r1E3DrWoqxuoq8N167pNiuvg74OFqw5oh8VBNCCCHaoispCKUBvRVFsVQafuU3DIgHtgFTz/S5C/j5yqYohBBCiGvBYFRZsDOZCG87+rT/n8OhY5aDiQWEjGvyuFpfTUxBDD09LrxdLPtUKfo6I3ZJu3G6914UzRVdbCrOw9nbBsWrCs/0rlQ/sAynkCpKf11P1aFDF41VFIVJkV7sTiwgt6ymBWYrhBBCiOvJlZwhtB9YBRwGYs+M9SXwDPCEoiiJgBPw9VWYpxBCCCGusk3xOaQUVPLAwP+5rlxfB3E/QvBYMLNpEhOdF03nhDoGbiuk9Nd1VB0+TH1ODqrB0KRfalwhGtWAi2kJdrc2LSqJqytggD3WdfbsSjDi/LenMbHUk/PPx1D1Fz8baEIXL4wqrD2a1QIzFUIIIcT15IpuGVNV9QXghf95nAxc+NeGQgghhGhVqqry2Y5k/JwsGRXq1rQxcTNUF5+zXcxQXk71c2/wzG4j8CNZ/Hi20cQEnZsbOk9PdJ4eJFcOxL44BZc7b0cxNb32CbVhfXtHcGjNb7DbFuWlB3Eb8wuZq9Mo/vJ9HB966oKx7V2s6extx0/RmcwdEHDBvkIIIYS4ucj6bSGEEKIN2p9SxNH0Eu72dWXRU7s4vDEVo8HY0BizHCydoP2Qxv5Vh6NJmTgJ9z2n2DnKk6CDBwj4dS0+C77E/cUXcbr3Xiy6dkVVjRREn6Rcb4FzbSr206e3UoZth625LdkBcZBvTnZyGTb/WIKVp5H8z79Bn51x0fiJXbyIyyrjZG55C8xWCCGEENcLKQgJIYQQbdDnO5JwsjLFMqkKfZ2RvT8mseL1KHITsiDhdwibAlodql5P/vxPSJ09G6Oq8uJsHbV3T0RrY4NZYCDWAwbgcNsMXB9/DK+336Ldd9+heXY+AF3n/xuttVUrZ9o2OEZoqTWp4ujmdBQbV9yefx6jXiXv6XsuGjsuwhOtRmFN9NnDpVWjStzOTFb85yAleVXXcupCCCGEaCVSEBJCCCHamOPZZWxPyOfuYA8K0srpOyWQMQ+EU1NRz6r3j/NH8R3UdZxOXUYmqXfeRcH8+djecgu5nz7NCS+VXu69Ljh+6rFCbJzMcfS2a6GMRIRnGHGuu0k+mk9pfjVmg2/HaUhHSg9mULXm0wvGutiYMaCDMz8fycJoVCnKquSndw+zfWkC+WnlnI65tGvshRBCCHFjkYKQEEII0cYs+CMZS1Mt7UtBZ6YluLc7AV1cmPVCL8LdjxBbdQtLPy5l/93/ojYhAc+338Lr7bfYVx6DqcaUzq6dzzu2od5IRkIxfqFOTQ+qFtdUhEsEce47QVGJ2ZYOgPPrizGx1pDz1oeoJRe+Wn5SFy9yiqtZsySO5a8doCinkqF3BmPjaE5OcmlLpCCEEEKIFiYFISGEEKINySyp5pejWczs7E3akQI69nbH1KLhjgnTulz61b/KoJplaAqyiAm8i1O3f4Km3wgADuQcINI1EjOt2XnHz0oqQV9rwDfM6bx9xNXX3q49qlU9de0KOL47m9pqPRpbe9ye/ju1RRqKn58FRuN540NNTJlTYU723jw6dHfj9hd7E9LXE/cAW3JTylowEyGEEEK0FCkICSGEEG3I1ztTAOivNcegNxI2yKuxzRi9nNObnNAe2MfonmX0mehPZko1y17az74NJzlZeJKe7he+SDQtrgiNiYJ3R4drmodoSqvREuYcRpznTuprDRzf3XCNvM20e7AK8yN/Ww76jW+fE1ddUcfmRfFsmB+LlamWtQ56+t/REQubhpvh3ALsqCiupaK4pkXzEUIIIcS1d0XXzgshhBDixlFSVccPB9MYH+FB+oE8vDra4+Rp3dhe8fP31JXp8PrgXWxHj8YNCOzuzo5lCRz6KYOJVo/TqUvoBd+RFleIZ6A9OjPtNc5G/K8IlwgW5S5idPu7idmagYuvDQDGh9+l8J//pPaLLTjZDQOn9gCU5Faxb00yddV6uo32Q9/Rhg8WH2RlVDqzgwyQuBl354EA5CSXEdjNvLVSE0IIIcQ1IAUhIYQQoo1YsjeVqjoDk9ydiNmeSL+pgWcbc+MoPlSEztkFm5EjGx/bOlsw7uHOvPPDl9jscSf2i3I0Q0/R89aAc4o+5UU1FGVVEtzHo6VSEv8l3DkcvarHoaeB3GU1rHkv+mxj58cavi4uA84+dw+wY/DtHXHytEJ/ci+fVy6i3Rtx5CrluHYuwzlkElrd3eSklBLYzbVlExJCCCHENSUFISGEEKINqKk3sGjPaYZ0dKE4pggrezP8Ozs3ttdt/pqqPDNc5s1A0TTdUa4oCrstfsdzjA/TiudxZHM6iYfzGDSzI+3Cz46RFlcIgF+onB/UGiJcIgBIdzrB1GcmUV9naGxTa6rJeupJNMZSPGb2hP6PQnEB1id+o/r550mKT6GuWMUPMAJFWGM7uD8Wp37D1ftBcuVgaSGEEOKmIwUhIYQQog1YdSiDwso67gr3Ju6rE/S81R+N9kzhx2ik+OffQQP2t80+J7aguoCk0iTGdxvPkFuC6djLne1LE1j3SQztu7oyYEYHrOzMSIsrwtrRDAcPyxbOTgA4WzjjZe1FbEEMd4fd9T+tDtg9PpvMx5/AsPg4dV/+iL60jnJAozNi4WOD3fCuWI6cxrPxJsz+6DFyk21p51iLm00OsfF2GOqNaHVy/KQQQghxs5CCkBBCCHGTMxhVFuxMprOPPdqkCjRahU79PRvbjae2U5pgwKZHOCYuLufEH8w5CEAv914AeAbaM+PfPYjemEbUb6dJP15E7wkBpJ8oIqiHm1w334oinCOIzo9uts1m9Gis166lev8OLJ0qsOwTgOXgWzAbPhvF+uxKr3nBFfy2OoJBOw5ivDMA9+rtHNHfSn5GOe7+di2VihBCCCGuMSkICSGEEDe59cdySC2s4pnhQZz4Non2XV2xsjt7dXzF8s8w1Gqxv/vBZuP3Z+/HRmdDsGNw4zOtiYbut7QjsJsr279P4I8fTgLgK9vFWlW4Szi/n/6dvKo8XC2bnvmjKAo+n36KWl9LblU2NjZe6DS6c8Zo72KNcfQ4dF9GkVPZB/f6NcCt5CaXSUFICCGEuIlIQUgIIYS4iamqyuc7kvB3tsKnHFKq9YT/11Xz1NdQvPUIOgdrrAYNbXaMAzkH6ObeDa3m3JvD7N0smfBYJAn7c0iLK8Knk+O1SkVcgj/PEYrNj2WY37DG5+V15ezP3s+uzF3sydpDdmU21jprenn0oq9nX/p59cPL+ux/F7fdO564pfPJ25XHoL5FWFvWkZNSSmd8WjwnIYQQQlwbUhASQgghbmK7EwuJzSzltYlhxG3KxMnbGvf2Z1d51G3/jqocE1zuHHHOYdIA2RXZpJenMyt41nnfoSgKwb09CO4tt4u1thDHEHQaHdF50bhZuTUWgGLyYzCoBqx0VvT26M3sTrNJKkliT9YetqRtAaCdbTv6efWjr2dferj3oGzwKDquW0rpkM646xLISbZp5eyEEEIIcTVJQUgIIYS4yaiqyt7kQr7bl8rGuFzcbM3oZ2/DuswkhtwR3OSMn+JlS0BRsbv3iWbHOpBzAIAe7j1aZO7iyphqTQl2DGZx/GIWxy8GoJNTJ+aEzaGfVz8iXCKabBNTVZWU0hR2Z+1md9ZuVp1cxdLjSzHVmDJp4BA6rFM4cMwJd88DJJaGU1lSi5W92fleL4QQQogbiBSEhBBCiJtEaXU9qw+ls+DoIgqryzHHjQm9Qrm3dygJv2RhZmlCh55ujf2NJTmUHsrBJtwbnZt7s2MeyDmAg5kDHRw6tFQa4grNCZvDjowd9PboTR/PPjian38bn6IoBNgHEGAfwOxOs6nR13A49zCrT61meeoGBnQKwTw+AxffhiJiTkop7bu4nnc8IYQQQtw4pCAkhBBC3OCOZZayZG8qvxzNokYtxLrDT5hZgwpsLIFdv9hyx+EXyWoXx7/2rsXHxocw5zC6/fI7hloN9rPOvWoeGlaPHMg5QA/3HmgUuW78RjHcbzjD/YZfVqy5iTl9vfoS6BDIptRN5A73JfyjeDJKXdEo9eQkSUFICCGEuFlIQUgIIYS4AdXpjfxyNIsl+1I5ml6ChU7LhEhP2vtX8VEsLB69GDszO9LK0kjcUkKdqqWiQzqJBfFsSt2EQTXw0VoVbxsFq1ubLwill6eTU5nD3LC5LZydaG2ulq5EukayqiqFTlY2ZJ80xSUkmdzjOkBWiwkhhBA3AykICSGEEDeg59YcY3lUOu1drHjh1k5M7uqNnYWOtw6ux0xrRrhLODqNjnY2/qSe2IN7qDXzJr8LQL2xnkU//xv31LVsHWqLS3059lr7c97x5/lBPT16tmhu4vowwm8Ebx18C92YMfj/9DsFoUmczA7EoDeiNZEVY0IIIcSNTn6aCyGEEDeCuir4rD8sm0nukQ2sOpzO7N5+bH5iEPf088fOouGg4Jj8GEKdQhsPDk45UkBlaR3hg7wbxjEa0Z3awoSftqMqKivC6pj12yySS5LPeeWB7AO4WrjSzrZdS2UpriPDfRu2nR3uaYfOoKe0qAKDUUvB6cJWnpkQQgghrgYpCAkhhBA3goTfIDcWTu/Cbc101uue5imn3Sj1VY1d6g31HC88TrhzeOOz2O0Z2DiZ4xtoAns/hY+7on43g5KYMmy6duC9aYuoqq/i9t9uZ1fmrsa4xvODPHo0uZVMtB0e1h6EO4ezRhODWXBHHDIyAcjYu7+VZyaEEEKIq+GKCkKKotgrirJKUZQTiqIcVxSlj6IojoqibFIU5dSZrw5Xa7JCCCFEmxWzAmy9yb7/KE/r/w9LSytstzwN74XAhn9D8WkSihOoM9YR4RIBQGFWBVmnSghzjUbzfifY8E+wdqPc53EMNQoO//c0nV06s2zsMrysvZi3ZR5Ljy9FVVWSS5MprCmkl3uvVk5ctKYRfiOIL4yHscNwy07EnCJOx6a19rSEEEIIcRVc6QqhD4H1qqoGA52B48A/gC2qqnYAtpz5XgghhBCXq7IAEjdD+FS+2J3Nj8ZBqPdtgzkbof0w2PcZfBjJ0fVPABDhFAYJ64lbsAAtdYQUvwudJsD92+HeDRTvTUXn6YlV375Aw0qQb8d8yyDvQbxx4A1e2fcKuzN3A9DDvUcrJS2uB3/eVrY3zAR0OizqcqmocCA/L6uVZyaEEEKIK3XZh0orimIHDATuBlBVtQ6oUxRlAjD4TLfFwHbgmSuZpBBCCNGmHfsRVAOFgRNZ9nUak7t64e1oBY69wLcXlGZC1DfEnvoeVx24f9yD+to6EvIX0t6vHItH9oO1CwB1qalU7d2Hy98eRdFqG19hqbPkgyEf8NHhj/j62NeYKCZ4WXvhbePdWlmL64CPjQ8hjiH8VryLQUOH4pSUTKJ3CDtXf8vkB+V3fkIIIcSN7EpWCPkD+cBCRVGiFUX5SlEUK8BNVdXsM31yALfmghVFuV9RlChFUaLy8/OvYBpCCCHETS5mObiF8+Vxc+oNRh4aHNi03c4Lhj1HjEs7IpxCoeMYksK+oc5oSejU4Y3FIICSVatAq8Vu8pRzXqNRNDzW7TFe6/8aiqLQ36v/tc5M3ACG+w0nJj8GdewQ7PKOA2B9+gTpRVUXiRRCCCHE9exKCkImQFfgM1VVuwCV/M/2MFVVVUBtLlhV1S9VVe2uqmp3FxeX5roIIYQQojAJMqOoCp7Ckn2pjO/sSTtnq3O6FdUUkV6RQUTgWJjyFXEpnti7WeIRePY6ebWujpIff8J6yGB0bq7nfeX49uPZMGUDf+/+92uSkrixjPAbAcAfnmXYW9SgqAas9das3bG3lWcmhBBCiCtxJQWhDCBDVdU/r5pYRUOBKFdRFA+AM1/zrmyKQgghRBsWuxJQWFLRnao6A/OGBDbb7VjBMQDCncMpzKwgJ7mU0AGeTW4IK9+6FUNhIQ7Tp1/0tS6WLliYWFyVFMSNzd/On0D7QDZlbMFxwq3YlKeTU9MRY8wKauoNF40vzKxg4dO7yE4qbYHZCiGEEOJSXXZBSFXVHCBdUZSOZx4NA+KBX4C7zjy7C/j5imYohBBCtFWqCjHL0fsNYH5UFWPC3OngZtNs16P5R9EqWjo5dSJ+VxYaE4WOvd2b9ClZsQITTw+s+vVridmLm8gIvxEczj2M8ZbB2JalkKfvwEj9Ln45knnBOFVV2bXyFFVldcSs2AJGYwvNWAghhBAXc6W3jD0CLFUUJQaIBP4DvAGMUBTlFDD8zPdCCCGE+KsyD0FRMjvMBlNeq+fhoc2vDgKIyY8hyCEIU9WMhP05tI90wcLatLG9NjmFyj17sZ86tclh0kJciuF+w1FR+UM5hYsTGBRT7A2m7N65hYYTApp3+lAaGSeKsdXmkJxqRdXWT1tw1kIIIYS4kCsqCKmqeuTMOUARqqpOVFW1WFXVQlVVh6mq2kFV1eGqqhZdrckKIYQQbUrMclQTc148FcCwYFdCPe2a7WZUjRwrOEa4czhJ0fnUVunpNMCrsV1VVXJefhmNlRUO06a11OzFTaSDfQfa2bZjY+pGfEd0BSCtJIyIog0cTituNsYQt47di/fgoM3glkGnMaLjxMZDkBXdklMXQgghxHlc6QohIYQQQlwLhno4tppkxwGkV5sy7wKrg1JKU6ioryDCJYK4nZnYuVjgFXT2MOnSH3+kat8+XJ96EhO5yEFcBkVRGO43nKicKMzH9MC0rozMmq5MMNnDkt1JTTtX5MGKu4hdtIzSejf6TQvCafq/8AiwIr56FOrKe6G2onUSEUIIIUQjKQgJIYQQ16OkbVBVyPyCbvQPdKarr8N5u8bkxwAQYAwmO7GUTv91mHR9Xh65b7yJZffu2F/CYdJCnM8IvxEYVAN/FB3AyaqGQqMnjvoySuK2kFdW03DmVfR3ML8H1fE7iaq5E98Qe/wG9wYgdJAfpfWuZOZYwvp/XORtQgghhLjWpCAkhBBCXI9illOjs+fXqk48coHVQQAxBTHYmtpSclRBo1UI7u3R2Jb7yquotbW4v/IyikZ+7IvLF+IYgpe1F5tSN+HVxZdqcxeyM/24VbOTdX/shSUT4ed54BrCQb9l1Bl09J0W1BjfvosLZpYmxFs/AtFLIG5NK2YjhBBCCPlkKIQQQlxvastRT6xjnaEXXdq50ivA6YLdY/JjiHDoTMK+HAIiXbC0bThMumzjRso3bcL54Ycx8/dviZmLm5iiKIzwG8G+7H049vYFIC2/M+O0B7gtagZqxiEY+y5Fo1Zy7GAVoQM8cfK0bow3MdXSsZc7SdmuVLsOgLWPQmlGa6UjhBBCtHlSEBJCCCGuN8d/RdFXs7S6Dw8P8EWtqztv16r6KhJLEgkr70ttpZ5O/T0BMJSWkvPKK5iFhOB0z90tNHFxsxvuNxy9UU+8JhpFUSk0eKDPh12GULYM/QV6zGX3j0nozLT0vPXcImSn/p4Y9SonvF4BowF+vL/hqxBCCCFanBSEhBBCiOuMMWY5WYobLqbueDxyJ8m3jqc2OaXZvnGFcRhVI1aJXtg6m+PdseGsody338ZQVIzHq6+g6HQtOX1xEwt3DsfN0o3NWRtx9rKmzDGQYpPbeMX6Ob48UkvqsULS4oroMbYdFtam58Q7eVnjHmBH/OFa1DHvQOpu2PV+K2QihBBCCCkICSGEENeT8hyU5B1sTw3h0Z/fAr0eQ3k5p2fMoGL37nO6H80/il21K5WpKp36e6JoFCr37qV01Wqc7rkbi9DQVkhC3Kw0iobhfsPZk7kHpwBrymzbUbp1J3M62RKVUsTWHxKwc7EgfLD3eccIHeBJSW4V2VajIWwKbPsPZES1YBZCCCGEACkICSGEENcVQ8xKio5bErnnJJaBgbRbuZJ2K1ag8/Ag/f4HKFq6tEn/mPwYepeMQqNRCO7jgbG6muznX0Dn54vzww+3UhbiZjbCbwR1xjqK7DIwqFoqzVwYlryXbgYTqgpq6DslEK3J+T9itu/miqmFCXG7smDse2DrBavvhZqyFsxCCCGEEFIQEkIIIa4Txro60t5cQGqCP4n978djwTfo3Fwx9fbC7/vvsR44kNxXXiX7pZdQ6+tRVZW4vHh8syPw7+yMlZ0Z+R99TH16Oh4vv4LG3Ly1UxI3oUiXSJwtnDmo7gSgpsswytesY0CNKek6I44d7C4Yr/vzcOnD+dQYrGDKAihJg9+fbonpCyGEEOIMKQgJIYQQ1wF9YSEJ06ZSlmLOwZ5/J92kMz+8GUNydD4AWmsrvOd/jOO9cyhZ9gNp999PZlYCNpmeaGtN6TTAk+rYWIoWL8Z++nSsevVs5YzEzUqr0TLMdxjbSzZibqOjMqAniZbd0eqNbDGvY9Xhi98cFjrAE4PeSML+HPDtDQOfhqPLIHZVC2QghBBCCJCCkBBCCNHqahJOkjhlGvXJacT3uQ/V1J4hs4Mxt9bx+xex/PZZDOVFNShaLW5PPYXHf/5DVdQhiu68n8iMvpg7aPFub0P2s89h4uyM61NPtnZK4iY3wm8E1YZqyuxzSc1XyfAehE/9Sdq1t+fbvakYjeoF4528rHHztyVuZyaqqsLAp8CnF/z6OBSntlAWQgghRNsmBSEhhBCiFZVv3cbpmTMpLqshof89FGn8GT43hE79PJn2z+70mdye9Pgilr20n6Nb0jEaVewnT8Jv0UIqa81wqQ4i2E+laOE31CYk4P7C82htbFo7LXGT6+bWjWDHYA4ruzFUKRiUOvwOLMRY/ST5Nu9y3+9P8vnRz9mTtee8Y4QO8KQ4p4rspFLQmsDkBQ0N655ooSyEEEKItk0KQkIIIUQrKVy0iIx580i3dmFT34fJU8PZEbCS5TVfA6DVaug60o/npbYrAAAgAElEQVSZL/TCI9COXStPseqNKPLTyrHs1o2No0aDasDq83+R/8mn2Iwejc2wYa2clWgLTDQmrLx1JS9MegoA194qJlRxZ4IDploTovMP8smRT3hg0wNsSt3U7BiB3dwwNdcSvzOr4YGDH/R+CBK3QFlWS6UihBBCtFlSEBJCCCFaQXVMDHlvvMmpoG4sjnwUJ707WW7rOe62hx8SfuBQ7qHGvrbOFox7uDMj54ZSUVLLytcP8seKE1jldcDoU4pT3y5o7e1wf/bfrZiRaIvaB3sw8YkuTLtzLA5jb6XjgRzu83iBohPPsHLUNsKcwnhxz4vkVOacE6sz0xLUy53EQ3nUVNY3PAyfCqgQt6ZlExFCCCHaICkICSGEEK0g7/0PqLGyZaHfdPrUmeNss4df/H/nb13/hpe1Fy/seYEafU1jf0VR6NDdjVkv9CKkvyexW7OwqLfGp68dPp9+QoetWzFxdm7FjERbpCgKXkEOaDQKDrfPQq2q4tacI5hoFFZG5fHmwDepN9bzr13/wmA0nBPf5HBpAOcO4B4Bx+RwaSGEEOJak4KQEEII0cIq9+2jau9eVgZNYVi9Fd4+Bra3W4KjzprbQ27nxb4vklqWyqdHPj0n1txKx5Dbg7GeVsA+37X07hEOgKLTtXQaQjRhER6OeUQE+p9WMSbMnRVR6Tibe/LvXv/mYM5BFsYtPCfG2dsG13a2xO/KajhcGiBsCmQegqKUFs5ACCGEaFukICSEEEJcJekniti16hQnD+ZQWVrbbB9VVUl49S3SnILxcOiOs5c1/j4L2GVtyuywu7EwsaC3R2+mdJjC4vjFHCs41uw48bpDZHWIwd3G/VqmJMRf4jBrJnXJydxjUUB5jZ410VmMbz+eMe3GMD96PjH5MefEhA7wpCirktyUsoYHYZMbvsb92NinrKCa43uy2Ln8JBXFNeeMIYQQQoi/zqS1JyCEEELc6KrL69i9KpGE/TkoCvy50MHezRKvIHu8ghzwDLLHys6MQ8t+QZORy8nez2FjZ8at00x4bsshbKztmBEyq3HMv3f/OzszdvL8nudZPnY5Om3TFUAx+TF0dunckmkKcVG2Y8aQ9+ZbuGz+hU5BM1i85zRTunnxbJ9nOZp/lGf+eIZV41dhpbMCoDYlBdOPnkNrP5tjW1NxD4gAe1/KXEeQtSOZzKR4Mk+WUF50tgiUn1bOxCe6oNHK7zWFEEKIKyEFISGEEOIyqarKib3Z7F6dSH2Nge63tKPrKD+KcyrJPFlC5sliTh7MJe7MLUqWzuaYHD9JbeSjmFlYMP6RzuRumsJmSwvmhszExvTsdfE2pjY81+c5Htn6CF/FfsWDkQ82thXVFJFRkcGMjjNaPGchLkRjZob91KkUfv018ybNYd7mbG75cCevTgzn9QGvc8+Ge/jP/v/war9XKVm1itz/vI5iaoqb+wFOHewJqpGs01WUFz4EgHlRLl4dnYkc4YtXR3sK0ivYvDCeg7+dptetAa2crRBCCHFjk4KQEEIIcRmKcyrZ8X0CmSdL8Ai0Y/CsYBw9G1Y9uPrZ4upnS5cRvhgNRgoyKjh+NI/962KwdeiEamrOhIcicKzcy7vlJzG3teeO8HvPecdgn8GM8R/Dl7FfMtxvOB0cOgAQmx8LQLhLeMslLMQlcrhtBoVff02P2D9YeM8snv/5GDMX7GNyVy9mB9/LqsNfMmlhIla7Y7Ds3RvPN9/AfE8869brST6YiVeYG5H97fHaPRPHIdNQhj3bOLaTpzXpx4s49NtpfIId8exg34qZCiGEEDc2WWsrhBBC/AWGeiMH16Xww6sHKMioYPDtHZn0RNfGYtD/0mg15GiN/Csmme6H3qd/9pfc/VZ/PNvbkrn5WdZZWzOl43QczR2bjf9Hz39go7Ph+d3PozfqATiafxStoqWTU6drlqcQl0vn5YX1kCGUrFzJIH97Nj42iHlD2rP2aBZRixTe/0bBbG8MZo/eh+83X6Nzc8Nv0hBum2XNgKgX6Lju34R0tsepgx9K3OqzezDPGHhbELbOFmz6Ju7sdfVCCCGE+MuuuCCkKIpWUZRoRVF+PfO9v6Io+xVFSVQUZbmiKKZXPk0hhBCi9WWdKmb5awc4sDaF9pEuzHyhF6EDvFA0ynljfjmaxbTP9zIwaS8eFQX4PfU4FtZmcOR7FupzUTRa7gq757zxjuaO/LPXPzlWeIzv4r8DILYgliCHICxMLK56jkJcDQ6zZmIoKqJ8/XosTLU8ObQ9a81jeG7rZ1QYbXnuTgue9orCgLExxnFQH/y+XoChqJjTd9xBnfNQKEqGrOgmY5uamzBybihVZXVs/+7E2dvJhBBCCPGXXI0VQn8Djv/X928C76uqGggUA+eugRdCCCFuMKcO5vLTu9Ho642Me6QzI+eGYWVndt7+RqPKOxsSeHRZNF3dLbg7cQsWXbpgPWgQ1FWRv/01frKxYULgRNytLnxT2Oh2oxnsM5j5R+aTUppCbEEsES4RVztFIa4aqz59MPX3p3jp99SdPs3pmbNQly7GYdpUyj5cRIrJdBJKYpm9+jWq6vSNcZZduuC3eBFqdQ2nX19FbZk5HFt9zviufrb0mhBAUnQ+8buyWjI1IYQQ4qZxRQUhRVG8gbHAV2e+V4ChwKozXRYDE6/kHUIIIcT1IGZbOg7ulsx8vhd+oU7ntNcb61l/ej1V9VVU1ur5v+8OMX9bIjO6+/CheTLG/HxcHn8MRVFg3yd8q61GryjMCZtz0XcrisJzvZ/DVGPKI1sfobK+UgpC4rqmaDQ4zJxJ9dGjJE+cRF16Ol4ffYjnKy8zpV8Htj/4BF4m/YmtXM2Q+d+wMiqdbSfy2HYij70aZ3Je/oA6A6RsdaF08yq2Hc9h24k84rPKGt/RZbgvPiEO7FpxiqKsylbMVgghhLgxXemh0h8ATwN/XoviBJSoqvrnr3oyAK/mAhVFuR+4H8DX1/cKpyGEEEJcO6X5VeQkl9FnUnt0Ztpm+3x//HveiXoHD0tv6nNmkJrlwgu3dmJ2hDPJIx7Aqn9/rHr2hIp8Snd/xHJPR0b7j8bX9tJ+BrpauvJkjyd5Yc8LAEQ4S0FIXN/sJk2k4IsvMAsMxPPNN9C5n10J52BlyqppbzNxzTTy+IJ/bihGXx7WJN6j+318sPsjlPX1rC19jx8dB2GiUdj25GB8HC1RNArD7u7E8lcPsPHrOKb+oxsmuub//0wpTaGdbTsURSHrVAkn9mXTfUw7bJ1l26UQQoi267JXCCmKMg7IU1X10OXEq6r6paqq3VVV7e7i4nK50xBCCCEuqCS3itjtGVc0RsL+XFAgqKdbs+1V9VV8Hfs1ftZBZJdVUWDzHlOHH+OOPt4UL1qMoaQEl8cea+i8402WWmqoxsjc8Ll/aR6TAifRx6MPzhbO+Nn6XVFOQlxrWhsbArdtxXfRwibFoD9Zm1rzw61LCHXpiIX3d8wZm8RPD/Vlzbx+rJnXj8+enoDFZ1+hNVe5b+86lnfXolEUPtuR1DiGlZ0ZQ+8MoTCzgj0/Jp3zDoAfTvzA+DXj+W3vdn7+IJqf3j3M8d3ZHNmUds1yF0IIIW4EV7JlrB8wXlGU08APNGwV+xCwVxTlz5VH3kDmFc1QCCGEuALbv0/gjx9Oknu67OKdm6GqKgn7c/AKcsDawbzZPt+f+J7i2mIS4kbgVPoPhnrfwu8Z33H/ytsoWPgNNiNHYhEWCgWJVB5exFIHR4b4DGm8Rv5SKYrCh0M/ZNnYZQ1bz4S4zmlMTS/436qzhTOLRi9kbMBYViYvYHnqm4R4WhDpY0+kjz0RvbrQ7sHumFnrsXvtX9zeyZ5VURlkl1Y3jtEu3JmIod7EbsvgdExBk/EP5x5m4ebljIt/iNPfqhRmVtBvaiABXVw4eTAXfb3hmuUuhBBCXO8uuyCkquo/VVX1VlW1HXAbsFVV1duBbcDUM93uAn6+4lkKIYQQlyHrVAmZCcUAHLvMVUK5KWWU5VfTsVfzBz8XVZfyafRX6MuD6evdlZ8fHM6Hw9/g/cHvE7k+BWN1NQfGBWBUjbDlJVbY2lKm6rkv/L7Lmo+FicVFD6EW4kZipjXj9f6v80iXR1iXvI45G+ZQUH22sGPSZyaePQtQq6u5ozweg6ry5R/JTcboOykQJ29rtnx7nMrSWgDi41L46YNDjD/2CG7VvpwK3s3s1/oSOdyXsAFe1FbpSTnatIAkhBBCtCVX45ax//UM8ISiKIk0nCn09TV4hxBCCHFRB9elYGGjI7i3O6ei8qiuqPvLYyTsz0Gr09C+S9Ptzdml1by3MYFhX71CvVrJLd53883dPbCz0AEwyCyMkYcMnOjpxgvZX/Hg2lmkn/qNxY5O9PboTbhL+FXJUYibgaIo3B9xP+8Nfo+TRSeZuW4mCUUJDY2BIzB3t8Tc2wZl3RomRXqy7EAa+eW1jfFanYZRc0PR1xnYsOAYP394mG0fp2BX4UbQGHvM78xkq8NKqtQKALyCHbB2MOPE3uzWSFcIIYS4LlyVgpCqqttVVR135t+TVVXtqapqoKqq01RVrb1YvBBCCHG1ZSeWkHGimC4j/Ygc6YtBb+T47r/2lz+D3khiVB4BnZ0xtTDBaFT542Q+938bRb83tvLxjhhUmz+IdBzAuxPHotWc3RpT8OlnoKqMe+07nuv1LNFF8Yz39qDQWHPZq4OEuNmN8BvB4jGLMapGZv8+m21p20BnDsHjcPAtoPZUIv/nVEmd3shXu5quEnJwt2LA9CCyE0tJS85jn+/PhD9qwYgJXenh0w0VlajcKAA0GoXgPh6kxxdRUVzTGqkKIYQQre5arBASQgghWt2fq4PCBnrh5GmNV5A9x3ZkYjSqlzxGWlwhNZX1eHZ2ZsEfyQx9dzt3fnOAqNRiHhjUnjm3pGJUani+/+MA6AsLKdu4kdzXX6dk9Wocpk/HzNub6UYLVmRkEm7lRX+v/vRw73Gt0hbihtfJqRPLxi4jwC6Av237G1/Hfo0xbBK2HkVoLMyw3LiWcRGefLc3leLKpqv+Qvp5YD+jlG86/5vuowMY1WEEAOHO4ZhpzYjKiWrsG9zHHVWFE/tyWjQ/IYQQ4npxpdfOCyGEENed7KRS0o8X03dyYOM18WGDvNmw4Bipxwrxj3C+pHH2b0vHoFOYtiaaaoORHu0ceHxEEKPD3CmvK+bOb35gXmUo1u99S1LUIepSUgBQzMyw6tMH54ceBEM9bH6Rdg6BfDt5HWjlR68QF+Nq6crC0Qt5bvdzfHD4AzY6hvCkszPeYXaUrl/PQ3Mf4ZejWSzcc5onRgQ1xh3NP8o7GS/Ty7cXD0c+3PjcVGtKZ5fOHMo9ezmunYslnh3sObEnm26j/eSgdiGEEG2OfCoVQghx02lcHTTIq/GZf6QzVvZmxG7PuKSC0Ie/JcCJYuItjEzp4c0dvf0IsjWhfMtWCpZsJ3fPNt4trgKOUGabjGWXLthNnoRlt+6Yh4WiMTVtGOjAAihKgpnLpRgkxF9gYWLB2wPfZrDPYD48/CFzHC2Y2iGb6Qe1uO3bxqjQYBbtTmHuAH9szXXkVeXx+PbHcbd0580Bb6LVaJuM1929O58d+YzS2lLszOwACO7jwdZvj5OdVIpnoH1rpCmEEEK0GtkyJoQQ4qaSk1xKenwRkSN8G1cHAWi1GkIHeJIeX0RJbtUFx1hxMJ3Nm1IwQeEf90fyD6dSbN57lZP9B5D11FOU79zMUbdqDgw24j/LhqBHvPC5xQTnwAIs1aNoUndAzjEoSYftb0C7ARA06lqnLsRNR1EUxgWMY+3EtfzNfyK/++hI9FA4uWg+d/Z1oqxGz5K9qdQb6nli+xNU1lfy4dAPsdOaw9bX4HVf+P42SNpGd9fuqKhE50U3jt++qwsmZlpO7JHDpYUQQrQ98qtKIYQQN5WDv6Zgbq0jfJD3OW2d+nsS9dtpju3IpP/0Ds3Gb0/I458/xvCQUYu1STV1900mvaAAja0tdiOHYGd5iPftU1hlZ8MvDoMwryyFskzIPQYVeUAzZxSNeAlkO4oQl83cxJy5/V9iUtQqfu9iQ+Bvpby2ajrBHcexYCfkmS7laP5R3h70NkHlRbD0dig4CYHDITMKlvxOhHMQprZaojL3MNhnMACm5iYEdnMl8VAeA2YENSkiCyGEEDc7KQgJIYS4aeQkl5IWX0SfSe2b/YudlZ0Z7bu4cHxvNr0mBJzT59iRU2x95TM+z0viZOjfcE1dh1WXLtiOvxVrbwOatfPINtaw2t6FiR0m4dPnhaYvMNRDeQ6UZ0NZVsM/1q7g1e1api1E26DR4BQ6mVnFX5Bg4cPkeGtecF+F0Ws9PyZWcE/w7YyO2wwHvwI7b7h9NXQYDvpaiPsJs32fEVGVxcHYJVBaCT3mgqM/IX08OLEnm6TDeQT38WjtLIUQQogWI1vGhBBC3DQOrkvB3Krp2UH/K3ywN3XVek4eaHqzUOrxZMrvvoOZcb9jDOwPQO9Pn8H7g/ew1exDs/w2sHbli57TQdHwQMQD5w6u1YG9D/j0hNCJ0OchCJ96VXMUok0Lm4xGW4993xA6HSlmQZ8PsNI441Llw6O7FjcUg3o9AA/taygGAZiYQefb4P7tdA+ZxglTE8oPfAEfdYHvb8NDOYydiwXHZduYEEKINkYKQkIIIW4KOSmlpMUVETnCB1Pzpgtga/Q1qGrDVi739nY4eVsTuz2z8VlRQQkJcx/AVF+L9qslFPoNwCPQDgcnA3w7Hna+A13uIH3GYn7O2M7UoKm4W7m3eI5CtHmeXcHBHwe/ItTaWoJ2JbJOsWNr7m7KVQu4dxOMeRPMrM+NVRS6B01AVVWipy+AgU9CxkGU7yYRrPuNrFMllOZXt3xOQgghRCuRgpAQQoibwsFfT2NupSN8cNOzg6rqqxi/ZjyPbH2EemM9iqIQPsiLwswKspNKqa6tZ9tdD+FZmAnPv4a9TxDFOVV09C+Bz/tDVjRM/BwmzOfz44vRarTcF35fK2UpRBunKBA2BfPq/Zj7u1Hy5Xs4pa5nudUdTKj7D3UeF96eGVLtwOcfGzCf9zYFp1ypn7EJRr1Ox/qlgMqJvbJKSAghRNshBSEhhBA3vNyUMtLiCptdHbT61GqyK7PZkbGDl/a8hKqqBPV0x9TChNhtGax68F90Soqm+O6H6DZ9LAn7stFojLQ/eidYucD92yFyJsmlyfya/Cu3dbwNF0uXVslTCAGETQHViL1zArUlGmoGfIXb+BdIKzPwU3TGecMM5eXkP/IE5qqWYpM68t9/n8RRYzk9fw/6Aju8rZI4sS8b1djMwfBn6IuLKd+8mdw33iRl2nRSpkzFWFt7LbIUQgghrjkpCAkhhLjhHfwtBTMrk3NWB9UZ6lgUt4jubt15sPOD/Jz0Mx9Hf4zOTEtIXw8SD+USHvUHOQPH0P+ZeRjLCji16yT+un2Yd5sI920Fl47UGep45+A7mGnNmBM+p5WyFEIA4NYJ+j2G7dxn0VhaUrLpAIOCXIjwtuPT7UnoDcZzQlS9nszHHqcuNZW4J8byzCw9nr//jMtjf8NQXEzOtv9n777joyjzB45/ZnvJ7qb3TiAJoZNgqKIgYkURVFTsFft5ds/jzu5559lFwV7xB0gRpYgUaRJKKGmEQHpPNrvZvjvz+yNclMN2NizP+/Xa1yY7M08ZdGfyned5vl5sRevo7vBR8fbq3iBPoL6eriVLaLz/rxw47XT2jxxF3Q030vnOOyDLePftw7Fs2S99BgRBEAThJyGyjAmCIAi/ac2HHFTvaee4KZlHjQ5acmAJLe4WHhj9ACMTRtLibuHlPS8TY4pBabUAJirypjL9uT8htR+gZs59eAJX0+/kXJhyHwAd3g5u/exWdrTs4K4RdxFpiDwGvRQE4Qgn/Q01YD2jla7Fi4m7+y5uOCGLq9/cztLdDZw99MjgcPMjj+LauJGEBx8ga1QSoVXL2aNrYcy11xJ1zTV4dxVhe+AiyoMz2LNwHzx9L6qwMIKNPVPIVGFhGIcNxXbmmZjyh2MYOBBJq+XgWWfT8fob2KZORZKkY3AiBEEQBOGHEwEhQRAE4Tet6KOD6E0aBv3X6KCgHOSVva+QF5XHyISRSJLEfYX30e5tZ94nD/Pgq2oqBszClTIKuWYL6g8uorzjWvRGSDvjbAAqOyu5Yc0NtLpbeXzc45ySccqx6KIgCN8g/Nzp2N9/n64lS5l4wQXkxFt4dk0lUwYnoVL1BGg63nmHzrffJvKyywifNo3BATcaSUNRUxFjksYgSRLGoQUYrzqb7JUbKNNNQt/PhcbvxHT55Zjyh6Pv1w9JrT6q/siLL6bx3ntxb96MedSoX7r7giAIgvCjiCljgiAIwm9WS7WDQ3vaGdK3Dp3kPmLbikMrqHXWctWgq3qf3GtUGi6Jv4U75kuoJR+xZyXhdgQ4OPcx/MZkDvoLySpIQq1RsaFuAxd9fBG+kI/XJr8mgkGC8CtkzMvDkJeHff58JAmuPyGLA60uPtnXBED3xo00P/QwYePHE/vn2wAwaU0MiB7AtuZtRxZWcCW5ptWEZInu064h+ckniZx5EYbc3K8NBgFYTz8NdVQU7a+//rP2UxAEQRB+DiIgJAiCIPwmeZx+Vjy3BYPKwcDG2+CpwbD5eQj6kBWZuXvmkhWexQkpJ/QeIwcC1N16FwmdId66MJEH3PdjULewJzidqiGvEwwo9BsRx5slb3LDmhtItaTy7mnvMjBm4DHsqSAI3yb83HPxVVTgLS7m1IEJZMaYeWJFOZVFe6m/5Vb0ffqQ+MQTRwR18uPzKWkrwR34SiA5OovYnHQidfWUbWr4XnWr9HoiZszAtW49vqqqn7prgiAIgvCzEgEhQRAE4Tcn4Amw7JGPcTlCnJb3MfrL5kPCIFhxNzyTz9r1D1Bpr+SKgVegkr681G25/a9kV++l5ZLruDM9CUPQzbbkbTQ4U9i+pg1LtIGXmv/N49se58SUE3lt8mvEm+OPYU8FQfgu1tNOQ2Uy0Tn/A9Qqib+dmYe3vYOaa67Fg4qYZ55FHWY+4pj8uHyCSpBdrbuO+Fw67mpy9StpPuSko9H1veqPmHE+kk7H2oef4dk1+3+yfgmCIAjCz00EhARBEITfFNnrZuVD79LSYWbSsJ3EX/ssZIyFixfDzEUopnDmlr1NsiwxOaACRUEJBml+9XUiPlnEloFjODFmGYn7FvNC2lTKE4sJqYLYm92UR3/BgsoFXDXwKv45/p+YtKZj3V1BEL6DOsyM9fTTcSxfTsjhYEyajderFxLjdXDX0JmcMb+SDftbjzhmaOxQ1JKaoqaiIwvLOol+8VWopBBlmxq/V/21ioGtfQqI3fwpc5btZHt150/VNUEQBEH4WYmAkCAIgvCboXS3sv6h5znUlsy4gkYyr7ob1Novd+hzIltOf4wytY4bKhXs911FzSnDqCgooOOxR9kbl8H5I7YgNeyAaa/Qb+JDPH7yIxyI3gnAJtPHPDL2EW4adtMRI4sEQfh1Cz/3XBSvl64lS2n8+98J7thO6qMP8Zc7zkOSJGbO+4Kb3t1Ji9ML9KwjlBeVR1HzfwWEVCpMI2eQpiuifHMt8teksP8PRVH4oKiW057ewP9ljMEQCjC9sYgHlpUgy8rP2V1BEARB+EmILGOCIAjCb0NrBdufnsO+1tMYNszHwCtm9m4KORx4du7EXVSEa/X7vFYTQhuCVqzoIzxYk9xURycyOW0PRkULlyyF1OMAKIgvoGOGg0WblvPkGY8xJHbIseqhIAg/kHFAHob+/Wn917+Q3W6irrsW2xlnMBr4+OaxvLjuAM9/doDPylu4Y3IOF4xIZXj8cN4seRNP0INRY/yysKEXkrN8JgfbjqNkYyMDxiUdVV+XJ8B9H+5laXEDhZmRPHne8QTs6zmrdBNvpIxmcXE9Zw9NPuo4QRAEQfg1EQEhQRAE4dfv4HrK5j3P1rar6TdAS+GVXy4U7Vi5kvrb/gyBAIpajSsuRNvpx1Fw8iWYhg1FbdKy/OX7GdvyNobIRJj5fxCZeUTxJ+dO4OTcCb90rwRB+AmFn3suTbNnYzn5ZGJuvLH3c4NWzS0T+3Hm4ET+sngvf/lwL/+3vY7pY/oTlIMUtxZTmFD4ZUHGCNILskhes4d174JWryb7uC/XEtte3cFN7+6iyeHl9pOzufb4PqhVEp0zL8R13Q1M927i4dVtJMUNw6A9MjuZSlKRE5mDRiVuwQVBEIRjT1yNBEEQhF+3nW9T+8FcPuu4h6Q+Bk68thBJ1ZNG3rN3Hw133Imhfy6xt/6JeztfY3vXXlac83zv+j+7au3Mqj2Rm8deyK2T+oPWcCx7IwjCzyT8nKmoLGFYTjwRSXX0lM/MmDDeuuI4lhQ38MCyEu6f78LcT+KNnWuIkPLIig1Dffi7RVV4FafuGM9H0jw+fa0EgKyCOJ5dU8nTa/aTGG7g/64dydDUCACaXE3c7HiRqyJh9O4lLBum5opV0te2szChkOcnPI/2q9NdBUEQBOEYkBTlh81xliQpBXgDiAMU4CVFUZ6SJCkSeB9IBw4B5yqK8q2r6+Xn5ytFRUXftosgCILwRyPLsPZhWj/9gEX2R7HG2jj7jhHojT3PMgLNzRyafi5o1GTMn88BVTvTlk7jhiE3cM3ga4CeNT7OeWETNR0e1t4+njC9eA4iCAJ0uQM8sbKcRc23Ewpp8dRcg0mnZkCijYHJNgYl25i07UrUzmaWM5f6/XZKU7Us63Jw1pBEHjhrABZDT0Bnb9teblpzE+6gm4fbjifh+SW8dvEMlkgWHp06iEizrrfeSnsl/9r+L07NOJVHxj4i1ioTBEEQfnaSJG1XFCX/67b9mDvjIHCboig7JEmyANslSVoFXAp8qijKo5Ik3QXcBdz5I+oRBEEQfiv8LlBpQaP77n2/TcALi2fhKF7Psu6n0FssnH7TsN5gkL4SiWIAACAASURBVOzxUDfreuTubtLefQdNdDRz1z2GWWvm/Jzze4tZUtzAjho7j58zSASDBEHoZTNpeeCsAdi2TeDt0neYPS2H0gYvu+vsvLWlGl9QZrJqBC/q/k1VxAbatHlkV/sZeWIGF57bv7ecTw59wn2f30e0MZo5J82hjyGZyrfXcWV1E4tiClizM5Knzh/au//Y5LGElBBP7XiKGGMMfy7487HoviAIgiAAPyLLmKIojYqi7Dj8sxMoBZKAKcDrh3d7HTjrxzZSEARB+A2QQ/DyBHiuABp2fufujhUr6VqyhKNGqrra4I0z8e7+hCW+p3ArOkKn1tBEHYqioMgyDXfehbekhMQnnsCQnc2hrkOsOLSC87PPx6a3AeDxh3js4zLyEq1MGy4WdxUE4WgF8QUElQCZyR3MPjOPhbNGs/dvJ/PRTWM44cxLsGtjOcG5kLq8MKIzrHR91sT+bc0oisILu17g9nW3kxeVxzunvUPfiL6ojEbCzzuP4Pq13JxrYvGuhqPS0F8x4AouyLmA10te5/V9r39DywRBEATh5/eTPC6VJCkdGApsBeIURWk8vKmJnillX3fM1cDVAKmpqT9FMwRBEIRjqeITaC1F0VoIvnw62lNmQ8GVIB29jkaguZmGO+5A8flwrFhJwoMPoImIgLb98PY0cDaxImwuna06luU+T8PBSp48CHGmOK7ZamXAylKsf7oRy4k9i0u/svcVdGodF/W/qLeOlzdU0dDl5cnzhqBSff1aHoIg/LENjRuKSlKxrWkbBfEFAGjVKvISbeQl2sB/DUPXPMAbUyMIWLNY9mwxq17Zx1ulb7FQeY0pfaZw/8j70am/HBUZceEFtL/yClOqPud1y3E8sKyEhdeN6v0ekiSJOwruoM3TxhNFTxBjjOHUzFOPSf8FQRCEP7YfPXFZkqQwYAFwi6Iojq9uU3oe+37tIkWKorykKEq+oij5MTExP7YZgiAIwrG2+TlkaxorIj7kjdaX6F76MHxwCXi7jtq17fkXUGSZqGuvwbV+PQfPnIJr4RyYOxF83awd9jx1VWb2pH7GX865jVXTVjF75GzOropiwLJSPh0scar+RS5cfiFP73iapQeWck7fc4g2RgPQ1OXlhbUHOHVgPMdlRv3SZ0IQhN8Iq85KdkQ2Rc3fsJbl8EtBrYMvXkarV3Pc5QnYIxqJ3jSIm6z38cDoB44IBgFo4+KwnnIKrkULuWtcCrtq7SwpbjhiH7VKzcNjHiI/Lp/7199D0YIXCXZ0/Ey9FARBEISv96MCQpIkaekJBr2tKMrCwx83S5KUcHh7AtDy45ooCIIg/Oo17EQ5tJHPpfs5sNuBTzayVvckSskymDMOGnb17uqvrsa+YAER06cTe8stpM9/H5VWoeaef9NcbGPp+HvZtDKIy9jJ7VdezpikMcSb4znFlcmEt8swFuQz5l9vcPXga1AUhbl75iJJEpfmXdpbx+MrygjJCnefknsMToYgCL8l+fH57G7djS/kO3qjORoGnAPF71LSsI2Zqy9iWc4LmJPVBFbFcmBH69eWGXnJJchuN+MqNjIwycajH5fh9gd7NipKT4Dp0X489IXCU3NkzPc+xf5rr0AJhb6zvSE5dPRUW0EQBEH4AX7wlDFJkiRgHlCqKMq/vrJpCXAJ8Ojh98U/qoWCIAjCr9/m59npO4895ZEMmZhCWISBzz/YT8UZS8guvxrmnQQnPwwFV9L67HNIGg1R114DioKhcSEZo/bQdCCbjp0OHI/txRZzOidel0XfmJ4pxYH6eupuuBFNfDzJTz9NekQEQ5LyuX7I9di9dpwBJwlhCUBPmvmFO+q5bnwfUiJNx/KsCILwG1AQV8CbJW+yp3UP+fFfJmEJySFK2kvYGB3HpigTu1ddQaw5nldPn0eGqQ/Lnilm5bx9FH9aiyXKgDXK0PMebcQSm4Euv4DOt9/i/pffY/rL25izropbx8YR+uB67B+tob3CRsi9lZgUCysG6Jm4oYyq5/5Jn5vuOKJ9iqJQ7ahmY8NGNtZvpKi5iEhDJOdln8fZWWcTbgj/pU+ZIAiC8DvxY9LOjwE2AHsA+fDH99CzjtB8IBWopift/LeOgRVp5wVBEH47ZJ8PlV7/5Qdd9VQ8MotV9pvJyo9l0uV5KMD8x7fSXN9J0+R1jOzYwahD2wgLn8jB5/cRdeUVxN50PSy5AfZ8gHvIDO616vGu3MXAjnuI6dzLxOkphE+fjuxyU33BBQQaG0l//z30mZnf2DaRZl4QhP9Vl6+Lse+NZdaQWUztO5VNDZvYVL+JzY2bsfvsSEjkyWpGBWQuuHA1UeaepQ783iBbF1fR3tCNo81Ld6cPRf7qfbWC3mfHlmhjV6SZ6qbN/LNhHvY9QWS/CvOokUSNMGNqfIOqjEI+X9rI8NIAMW/NwzxgEFubtrKpfhMbGzZS310PQKollZGJI6m0V7K9eTt6tZ7J6ZOZkTODvOi8nlr9fiTdj8z0KAiCIPxu/Cxp5xVF+Rz4plU6J/zQcgVBEIRfJ3dREW1zXsK1eTPJzzyN5YSeBZ3rls3nU/ssEjMMTLykP5JKQgKK8z4mvmYU0qY07u37EaQm87f5e8jSSRzK12B7/Qz0dV9QPfpWbveWUF5bznXWR1B59AwKHKDp/rm4NmxA8QfwHThAyktzvjUYBLB0d6NIMy8Iwv/EprfRL6Ifc/fM5bldzwEQbYxmXPI4RieOZmTiSCIqVsHCq6BxN2T13ObqDBrGntevtxw5JNNt9+Fs9+Js9+JodVP/3lKczSES21VkelPY01BIvwIfMbfciXHgwJ4DdxbSZ+nNOMek0VUTpOWmq7n7Mg1uTQiTxsSIhBFclncZo5JGkWJJ6a2vorOC98veZ2nVUhYfWMzAyAHctEpL+MYSkv7xj95F9wVBEAThm/zgEUI/JTFCSBAE4ddJURRcGzbQNuclPNu3o46KQhVmJmTvImPBApwBLQsf/wKzycfUv5+JwawF4IvGL7hi5RVcx70om2PpPz2cZvsyBt/7Lu+Pk1gwWo1RljG6E+gwetBrg/w17jFqFsqMmd6XQSck0fHqa7T8+98QCBB3/1+IvOCCb21rlzvAKU+tJ8KsY8kNY1CLzGKCIHxPi/Yv4uODH3NcwnGMSRpDv4h+SF/NkBj0wZN5YAiHEVdD7ulgTfzOcjvmvUTTP56kIzKXgzln4tClorfpGHVGJtkj41GrDy/neXA9vH8Rm9sNhC9VUz0pj6h77mRIzBC0au231uH0O1lSuRjXP59l7MYu7GEqbC4Zw41XkXHdrUf2QxAEQfjD+bYRQiIgJAiCIBxFCYVwrlpF25yX8JWWoklIIOqKKwifdg7BlhYOnjONUEZ/vkieAV4H58xKwTKgEICAHGD6kul4Q14WnrGI5U/uw97iZmz7u0gVu/G9+AyLVs5mldaAHN2Bx6fCXTWDa7rSiY42cuF9I1Ad/iPJW1aGb38ltjNO/8a2unxBXtt0iJfWV+H0Bnj3qkKRWUwQhJ9e6TL49O/QVt7ze3IB5J4JuWdAZMbR+zfsQn7vEuw72gibdi2+k+9g5kMbGOFSY/MoWKMNDD8lnezCw4Gh1gp4exrNa510lBlIefllwsaO+V5Na33+edqefgbf1Im8NU4h78U1FJYplBYmEPfX+xmZPg6V9KOTCwuCIAi/QSIgJAiCIHwvit9P19JltL38MoFDh9BlZBB11VXYTj/tiDUp2pavYtk7TfhMUZyT9ybRN7/bu+31fa/zRNETPHPiM4xPGU9nk4v3/r6VyOZdJBfouNqdhVmv4aWL8xmSEk5IVpj3zA78pXbetwWYPqkP1xyfiV6j/ta2egMh3tpSzQtrD9Du8jMhJ5ZbT+rHgCTbz3Z+BEEQaC2H0iVQuhQai3s+ixsI/Q8Hh2JyYNtcWHEPmGNg2iuQ2hMw/6Colts/2M3dQ9OwHXDTXtvdGxiKSgwDrx15+b00LmtEJozEfz+L2moFQKWRiE4OO2rET8dbb9P84IPYpkwh4ZGHkVQqGp0NbH/kDvos3E5pMrx/STqnDZvBlKwpaDwGjBZtb+BdEARB+H0TASFBEIQ/MMXvp/2VV/Ds3Xvk54qCN+jFE/TgDrrxBD2EVTVj6fRxKE7iw9EazrjsQU7ve+YRx4UCMkuf3UVDeQeDi59l4KVDsF4zG4AWdwtnLDqD/Ph8npvwXG89ay5+mDLzSD42ulH1CeelmfnE2wwAtNU5mf9wERkFsXyk97FsdyOZMWYePGsAo/pEH9UfXzDE/G21PLOmkhanjzFZ0fxpUj+GpUb8DGdPEAThW3Qe6hk5VLoUarcCCoTFQXcz9J0EZ70I5i9HLMqywtQXNrGr1g4KjDKZGO5UYej+7nTzANmF8Uy4JLc3KNS1ZAkNd9xJ2IQJJD/1byTNkWuntS9dTNO99+EIU/HA1BBa7RBOKruM8BQdU64twBJp+KnOhCAIgvArJQJCgiAIf1D+Q4eo//PtePfuRZfVB0Wlwul3YvfZ8YV8KHx5DVChwhdupOKkbKTCoRS37WZP6x6em/Aco5JGAaDICqteLWH/tmYmZCzHsPwjfE4T6f/3AfrMTO5cfyerq1fz4ZQPSbH2LH7a/slKmm+5hWVjHkZvsHHx3wqJiDL2lrfwie10tXq4YHYhBrOWteUt3L94HzUdbqYOTeKe03KJDtMTDMks3FHPU5/up97uoSA9gtsmZVMopocJgvBr4GyCsmVQ+SmkjYbCWaA6ehSOxx9i68F29tR1UVzXxe5aOxp7AL0CakkiKcJIZpSRE4rfQSquIzI/DPO4CdR3Z7Bri5/hp6RROKUPzjVrqLvxJkwFBaTMefHI7I+HuXxBVi1YQ/wT9+PTRVE85CYchg7MARuKpOA+zsVZEyaSGx8h1l0TBEH4nRIBIUEQhD8YRVHoWriQpoceRtJq0d1zMwsS61lUuQiH30FWeBYnpJxAqjWVFEsKqZZUoo3RSJKELCu47D6amzt4cu0z+DthcvSZqF16ulo9dHf4KDzRyPCSyQTy7+LgQ0vRREfR/vSdXL7+Oq4dfC3XD7kegMYOF2Wnn0nQH6DinudRr2kjfVAUk6/uya6zb0M9a98uZ8IlueSMTOhtvzcQ4rnPKnlx3QFMOg0XFaby0e5GDrW7GZxs47ZJ2YztGy0WSxUE4XehqcvL7jo7u+u6KK6zs6e+C4fLx9yNj5Jobydzcgtac4i1zuspcU9kZMJWTP/3DvqsTFLffBt1WNgR5VU0O3lrSzULd9TT7Qsy3igzsk5C43djiD7A/AwVww6kEO2JZ0f8OrbaHPQ1TWRYUgaDkm1MyI370ZkaG7obeKH4BU5IOYETU0/8UWUJgiAIP5wICAmCIPyBhLq6aLz/rzhXrMA/JJu3zolghXs7aknNiakncn7O+eTH5SNJEoqi0Nnopr6ik/qKTlprnHR3+JDlI68NHp2TxIQYomNtJPYNJ6/1b0jlH8GfSujevpfaK69ix1ALb04N58OzFmPQGNhVa+e12c9zzcY3ab/tr4y56nx2rKhm86IDnHzVAJL6hfP2X7cQlRTGWX8a+rXBncoWJ/cu2svWgx3kxFu4bVI2E3NjRSBIEITfNUVRqOlws2XTXvrdex11liiWjyvkOH0NxrZC2gJ9GXbgOQoKPkMTHgFJwwlFZFDd4aGs0UGzw4tKksiINpMeGcX6XePw+bWMbpuLsnMftuMHEnHtTN77LIS7wkqj5QCr+76JM5CEt72QjLDBvHVFYe/U3v+17cuqlvHw1ofpDnQDcHbW2dw54k7MWvNPfaoEQRCE7yACQoIgCL8j+9r3Uees+9pt+t2VRD3+FqrOLj4ab+CtfD/R5lim9ZvGtH7TiDHG0NnkpqGik7pyOw37O/E4AwCEReiJy7BhizFiiTJgjTZgjTLSqm7gstWXYtPZmJ3/PCa3h/7vj6a1/yU0FN4PwMFnbiFnyVYqrzqHlHP/TEmDgwcXF/PiyseIiY8iZ8kiJJUKOSSz4PHtONq9JPUN52BxG+feV9CzmOo3+M8fRikRJlRiSoMgCH8wXYsX03DnXbSdfwWrInOY/MrDlA64FldYApbM3RwftRdLWzF6TxOKoqCSJHQaFVq1ipCiY3HTnbQFUjgr7jHidAdo2y7Tts9CWKKXpNEdVPrHssYxi6A6yLqstyiLKCXCr0fjPp5nzr2TvITYb2xbsLOTYGsr+qwsJJUKu9fO37f8nVXVqxgWO4zZo2az9MBS5u2dR4I5gYfHPMywuGG/4NkTBEEQREBIEAThd0BRFObtncdTO546aps6pDD9c5mzNik0RcDTU9REhPs43+VlzJBZOJKvYs+GJuor7HgcfgDM4XqSssNJ6hdBUr8IrNGGo0be+IMym6vaeX/3ejY4HyLki+Oa+ghuUC3neP+T1CmxSBoHlvQnuPt9if71Pm4ddyNV4Ulc7yjm9DVvkvLSHMLGjests72+m/mPbEMOKgydlMqoqVk/74kTBEH4DVMUhfpb/4Rz9WrUEeGgQMNfnqL4EwdBX4g3zT66NAonZsdy0cg0ju8bg0olIYdkPp6zl0N72jjlmoFkDonpKTAUoPP1uTT94xlMAzJJmTWRrg43n2zKocNlwxq7iuVJi9lr0KCR1YxPOY1Zwy+lb0TfI9rl2rKFuptvQe7qQmW14s1NZ7n1IDsTfZx88iwuHXIlalVPtsidLTu5Z8M91HfXc/mAy7l+yPVo1dpf+lQKgiD8IYmAkCAIwm9UqNsFoSC+kJ/HvniUFYdWcFLaSczsfzGSLEPTbtj9KaG3i1BaJKS+PtRn90eXPYmPGzJJLn6aeG8KW7tnoGgUNMlW0vpHMmx4ArGJ5q+detXlCbC2vIWVJc2sK2+l2xfEqFUzoG8d5cozFHp9zFYPpHxMTxaxNyofYWfHOv6S/iTZd/8FRavD+fizRN92NbrUVNLeevOoevasrWN/UTNn3DgErf7b08sLgiD80QU7Ozl45hRkn4+0N9/AkJ2NvdnNgse3I+lVjJ81kMxka+/+iqKw/t0K9q6vZ9z5/Rg4PvmoMrsWL6bh7nswDh5MyktzkPUmNrxfQenGRhIzjNj0r7BGtZblFgMBFQyPG875OeczIXUC3fMX0PTAg+gy0rHMvJCiNe+g3VtJcntP2ZJej3HQIIz5wzENz8c0dAgeHfxj2z9YsH8BOZE5PDLmEbIixAMBQRCEn5sICAmCIPyGBDs6cHz8MY6ly3DsLcenCyfM3fitx6iMahKum4Zl5i2srPIye8k+7J1ertRaMbQHSDNuZYLlad6Sx/Ov4DT8KiP94iwMSrIxKMVGTryFvfUOVpU0s6WqnaCsEB2mY2JuHCf1j2N0VjQGrZoFK25mdtMazowfyYOT5rCjZQeXfnIpVw+6mhuH3oh7xw6qZ16MOjKCUGsbaW+9iSn/a68/giAIwv8g0NQEsow2MbH3s6aqLj58cifRyWFMuXUoWl1PgH37J4fY8mHVd47CdKxYSf2f/4yhb19S5s1FExFB2ZZG1r1TTtAvo1YrhKtLsVvK+CyhlQpjOVevVzN+qxvtmEJ8f5nFXTv+ziHHIWb2n8kN6TMJ7tqLp2g77u3b8ZaUgCwjGY1EnDudyMsu43N/KbM3z6bb380tw2/hwtwLUUlHZ2MTBEEQfhoiICQIwh9aaXspLxa/yPiU8UzOmIxRYzzWTTqK7PHgXLMGx5KldG/cCMEggdwRbEs6D2/QgFfax2DtRoZ1boWgDzQGiM2FuAEQl4Pl5FNo1luZvWQfq0tbGGUN44ROFSFPiDHn9iWvwIy0ejZsfxW3OZllqXexzJXN7jo7dnegtx2ZMWZO6h/HpP5xDEn5rzTEsgzP5vOiWctzGjeX5V3GxoaNdPu7+fCsD3vPa/srr9Ly+OOYx44l9eWXfuEzKQiC8MdStauVT+bsIW1gNKdcM4D925pZ/VopfQviOOmy/kjfsfZa97p11N10M7rUFFLmzUMbG4vPHaBhv536CjvV+1qwN3kBCUn2E951kGbzflYOOUiLtZYIczgPjnmQwoTCo8oOdbvw7NqFY+lSupYtA5WK8LOmoLroHB6om8u6unUMix3G5QMuZ0zSmN4pZoIgCMJPRwSEBEH4w9rbtperV12NJ+ghKAex6qycnXU252afS6o19Zi2TQmFcG3ZgmPJUpyrViG73Wji4rCdcTruAaP5eLkDl+KlJmozeS0jCcoW0mMbKZgUR2zhONDoAAiEZOZ9fpCnVu8HBW5OjkPe3Yk1ysjJVw0gJtXyZaWHNsLSm6C9EoZciDLpQWo9BkqbHGTFhtEn5psXd6b8Y3j3fJRz5vFgdynzK+YD8O/x/2ZC2oQv+6UodC1chHnMaLRxcT/LuRMEQRC+tGdtHevfqyA1L4q60g4S+to444YhqLXfb+SNa8tWamfNQhMTTdorr6BNSjpie1NxBcW3P4pXl0h3Qh/s6jQAFHWIhD7hpOXEkNQvnNh0K2rNkXV2+4J4AyFCDfW433wD7+JFEAyim3gS2ydl8LzrQ9q9bSSYkzgvezrn9D2HcEP4T3NiBEEQBBEQEgThj2lXyy6uW30dNr2NeSfPo7G7kffK3+PT6k8JKkFGJ41mRvaMX/yppOL30/HGG3S8/gbB1lZUYWFYJp+MbdJ4TIYaKj8vYeX+k+jUd7C/3wv8I3Uo4X2nsbsyiV2f1uFzB0kfFM2I0zOolgPcu2gv5c1OJveNYaJDQ3O5nT7DYjlxZg46o+boBgS8sP5x2PgUGCNh0gOQMgIsiaD9lhTDr50OnYfgpl2EJInZm2cjKzIPjn5QpIEXBEE4xjYvOsCOFdVEJZk5+8/D0X/d9/+38OzaRc1VV6MKCyPt1VfQpacDPcGiuptvBkni/069gkT/Ss5XbaJBM5aGmIupb7bQ3tSTrECjU2FKNOOyqamSQmxzODnY4Tmingivg7MPrOe0g5sxBX1sjcvlg6HpVPWrRGM+iAotI6MncmHs+RhbI6ivsNN8yEHG4GiOn5GNztDTr5AcotndTI2zBrvXTrw5nlRrKhH6CHFNEgRB+AoREBIE4Q+nqKmI6z+9nhhTDHMnzSXeHN+7rcXdwoKKBXxQ8QEd3S2McMRwtiub9C4d0oghMDofSa8/qkyNSkOaNQ2N6n+7yf6q7o0baX7wIfwHD2IeM4bw6dMJy4lCtetVlN0fsN0xia3Oy2i0VOEfs52/nfIERp2593ifJ8juNbXsWl2D3xNivybE/hgVV4/OwP5pI26nnzHT+jLg+KTvviFu2gOLb4DGXV9+ZowEa2LPy5IA1iSwJgASLLkBTnoARt/0g/svCIIg/DwUWWH/9maSsyMxWXU/qAxvSQk1V1wJajWpr8zDs2MnTQ8+iC49jZQXXkCVmMQdC3ZzaOdnzLG9Roz3EAAe2cJ+fwFVvsG0+rPxhw6PDlWF0JpDaMPUaExKz8vc867DRfjmjdjWrUfy+GjJGcmOrAI6ZQPRnli0ck8fZKsLW4yCs8pMKMzFoWGr2W84SH13PQE5cFQfwrRhpFhSSLGkkGpN7f05OzIbq8561P7feC5KS+mcPx9dSiqm/OEYcnORtCIzmiAIvz0iICQIwh/K1sat3LjmRuLN8cydNJdYU2zvNtntxlNcjLtoO66ibbh27ULl63my6dGB0Q9uPWzNllifJ1GSKqF8Zf0Fi9ZCYWIhoxJHMTpxNAlhCd+rTYGGBpofeRTnqlVo01KJv+tOwuKcsPUlqP6ckNrEe6q7sdcO4EDkTvpMNTFr+LVHBHXsbj9rylpYVdLM5rJW8lwSIwM6pKCCpJKwROo5+aoBxKZ9/xteQkGo2QT2WnA2gKMBHI3gqAdnI7hav9xXZ4Fb94JRDOUXBEH4vfIdOEDNpZcRcjpRvF7M48aS9M9/orb0TD+WZYUHPirh7Y37GaEqI0FqJ0PXRa65mzRtFzFKGyq3hyZHAg3+POzBBJyhWJyhWIIc+bDFIDkwqzqwB+MJST0jVG3+GuIte6iN3M+y2HoqTD3X6MSuLCbuvxh90Eh78nzCMxpIST6O1MyJhJvjaOxupK7jIPLqDXS621mT7afa20hQCQKgltQMjB7I6KTRjE4cTf+o/qgDblBkMNh62xTq6qL1qafpfO89JI0Gxd9Tv2QyYRw8CFN+Pqbh+RgHD0Jl/K81Cd0dYIwAMUJJEIRfEREQEgThVyXQ1IR761bM48ahiYj42n1kRWZj/UbeK3+PkvYSJqVN4ryc88i0ZX5r2RvrN3LzZzeTYknh5UkvE6WPxF1URPdna7/MeBIMgiShz8nBNHw4pvzhtPSNptLVgfvTdgIVTtQtnUihEIpeSzA+mlBCFAGznnZvO83uFrplB059O8YILX1T0ynoO4TjMoZj1B55cyj7fHS8+iptL84BIPqKi4kcEEK181Vw1IEtleK8qSzZlEZkUzrVaTs5Y+YIxiaPBaC2w83KkmZWlTSx7VAnIVkh1qJnYv84Lh6ZRobVSPGaOtxdPkae3Qe96Sd+ehn0gbOpJ1BkioKYfj9t+YIgCMKvjr+mhrpbbsFcOJLY2/6EpD5yWrWiKKwqaSYoKwxMspEcYTx6VGrA2/NgwdEAPgeKAh43OBwSzsMvh0Oi2wlWm0JCjA/9ppW4Pl6D2mwg5sJTCZtQwPKGSvY3O6hrALddR1Z3P8zBSDKNG5hoeQ5Zo+JgWCHO+jiMm/eh7mgHQB0dTcSll+A5fSy1oTa2NxWxqWYNJY4qFCBcgZEuF6N8QUannUT0cbPo2nKQln/+i1BXFxEzZhBz043IXh+eHdtxH86c5isvB0UBrRZj//4Yhw3DkmXA6FiBVP05RPeDEVfD4Bmg/5Z1+QRBEH4hIiAkCMKvgr+6mva5c7F/uBgCgSPS0Grje6Z0dfm6+LDyQ94re5+67lrUshWfOwm1uQJJChFyZRG0jyTUnQsceYMaE3sAT8QrZNoyDqe7cwAAG+dJREFUmdPnbli5jq5lHxFsbETSajEMGoRp2DBMg3IxDh6I2hJGKChzqKSLsq3tVJc5UGQIC9ciASGfD9ntAZ8XAFmtIajTE1RpkBUJjXxk/QGVn5DZgzlSR1JCDNEeF6GVi9DWlRM9LIOUkSF0dUsg6IX0sTQMOY9nWsrQrMwkrjudsONdXHjuKZQ2ulhV0syqkmbKmpwA9IsL46T+cZzUP55BSTZU35E1RhAEQRB+i7wVFTQ/8CDubdsw9O9P3F/uwzR0KIqiUN7sZG1pC/btbYRVupG1HrLbXiVt314Uv4Qhxk9dTizbyCGzrIHs1hoko5aooSYikmvQqFx0qFRstkWzKSKWjZKfdtlLZqPCrE+CpDZJ1KTqWT41leZk01FtizXFMsY6jIJWC+bdZbg3rMZb1YQigzYMrKNysSW0oPftAb0Vhs6EEVdC5Lc/zBIEQfg5iYCQIAjHlLe8nPaXXsbx8cdIGg3h087BcvJkuhYu7E1Dq5xyPMsKtbzvWo9f9oE3HU97IWmGQibmJhJUHOz3fEq5eyVuuR2zKpp+ppPoa5yAUR1OtXcLe6r/xcjdZsbu0ZPR1YSiUhM2dgy2007FkmVAdXAllC0DZyOtgQzKPCdS4RmHV7FiUnWQY1xLjnENEZp6AEKKRBs2Gr0RuGoNaKsDWNrdvf0Kqg10RabSYUuk3WjFbQ7Da7AgqyMwBSJBZT7iPGgkL1azH2N8BDW6Zra7tpDdfBwWfxT+Aiv7JInddXbauv2oJMhPj2RS/zhO6h9HWtSRZQmCIAjC75WiKDiWL6flsccJtrRgmzqV2Nv+hCYqCoBgWxulLyxgy6F4giodg6RdZE2ORS3vw1z1MUZ3AwCedi3NJVa6WqLwWmLQjBhB2oxTiB4xAiQJf0c7Bx79G8rSVXjMaj4fq1De1wdaPdhSwJYM6p61jBQUDnb1rF0EkBIIMdrjZow+ndzgWLw7m3Bt2gyhENo+6Xgz05C0Lbj0Cu0xWXTE9KVdq0NCRZ/wPofXJFQTm24lIv77XeNltxt/TQ36vn17R23JIZnGyi7CIg3YYozfUYIgCH9EIiAkCL8TiqJQ0VlBRWcFg2IGkWpJ/fqFgwNeqPoMSpf2pApX6yA5H5KG97wnDgW95ejjvkPI4UAJBFBHRh5VrywrrNvfylubqylrcpKbYGVsoJHB6z9E/8UmVCYTERfMIPKSS9DExADQ5mljy/YldLzyKoO3tqGR4fM+0XzQ91SyC07mouPSKMw8sq6gHGRd7TreK3+PrQ2biXJrmN6eSeznZfSvUZAUaEvtx7Lo/jgT1EyJKuN4ZRuGgJ1uKZED5pmUtg+mvdOISiWTnuLEGt1Me7Ce/a0OXL4QahWkR5nIsoSIUdoJD7Zi8rWgczcR6uzG79QQcKsJuNQE3GqCrsO/u9UooZ62hjQK644LY9ngaDShCKy+KNLIwuZLxO8IYPaGow8ZcUsyH5oDNGhlsmLCGJQcTmFmJBNy44g0/7BFQQVBEATh9yDU7aL9xRdof+11VEYj0bNmEaitxb5gAYrfj27yFIqjTqOxzk/OqATS8qJwtHlw1tbhbLbT6TJhtyuo5CPLjdJ20Sc1hHnh00iODiIvuojoG65HbTbB/lXwxRw4sKbn/mnAtJ5RPo4GlK0vUlO3hY1hFjbGprNN7sYT8qFRaRgaO5RC73C0G2T8XakE1d//Pksdq2bQ6DSGj006Yup3sLMTz44dvdPV/jPtXZeWimbSBOoDYZTXxuMOhSNJCtkjYsk/vQ+2mKNHN/0oPifU74D6IqjbDvXbAQWGXwr5l4Ml/lsPV4JBgu3tqCMiUOnEvY0g/NJEQEj4XQvIAarsVUQZo4gyRB3zVKNKKHTUXPsfVI4sgyTR5etic+NmPq//nM0Nm2n1fLnIb1JYEqMTRzM6aTTHRfbHfGhTTxBo/0rwd4PehtzvZLwBGVXDdgyOgyhAh0rNXlMKB9WplMtJVJFCbGo2Bek28qwBNL52cLVhbGwgorSeUFkd7oOd+Np6FmaU1KC1adFGmpGiwmkxWNkZDGOfFEVLVAYDzQZyPvuQ7MYKHFoTH/YZy47hE8nMjCUmugGPppQD3dvZby9HE9Ji9CaQ2JjCOWUOsquqUQWCmLNsRAwyoFE6CbR1QlADfh0Br45At0Sgy0+g0wWBEADtMQYyp84gelAUus5NKBWf4PZoqfIPp8g3BnugH7pQz5OzyJQw/ClGtgS8rD3UjicQwqLXMD4nlpP6xzE+Owar4RvW4vF1f7kmgqMBupt7Flm2JKJYEgiFTLjbutkdNLGy3sfKkgZa/fvRhFWgNlegMtQTcmcQ6TuH4VH9GZAczuD0CAYk2TDrf3j2MkEQBEH4vfJVVdH84IM9I3C0WmxTziTqiivQZ2Qgh2S2fXSIoo8PweE/a/QmDZYoA9YoI5YoA0GDitU17ewqqWRSWwNh+iQ8pjjUSoCM/jbyJmeT1Dcc6avTsVvL4YuXYNe7EHD1fGZLgYIrYdjFYIrEH/KzrXoH2z4vx7NPh7UrDlkK0RJzEJ+lmrQDdfQraSbScfh4FWiMQdxmaLDpqbJocIblECYXYg4mEpKCBNRNpLjLyKzeirm+Z6STotWg5GQSSIukORBNS2c2DlMmkhIiUXOQ/vFFtHSa2OuZjIyWnOFWhk8ZgCXKQFVbN7vruthd10VxnZ3aDjeK5EdRdyAFY5FQHXGu1UqIPtQwTF3FKMMhsn3lhLsO0ntmIjIgaRgBj52DteuJliXCs04hlDGVgBxJoL6BQGMjgYYGAo0NBBoaCDa3QCiEpNNhGDQQ0/B8TPnDMQ4dijrs511nKSSHaHG30OBqYHjc8J+1rp+C7Pej+Hy9i7gLwk/hmASEJEmaDDxFzyIfcxVFefSb9v29BIQURflFghGugAujxohKUn33zr8BsiJ/777IIRmVWkV9dz0b6zeysX4jW5u24jp8oTZqjD1pRi2ppFhTvvzZkkKcKQ616usDNQ5vgD2HL5a76+yUNzkx6zXEWfXEWg0kmyFNZydR1Ums0kFEqBWjpxkcDfjranFXtuBqVuNuVhOyB5Gjw3AmxVMfn0pxTD9KNYmEx6YwICWKQUkWBkcGiAy1HZHNSW6vw1t+kO7KZloa/CjNEn4VlCRLlKZK1KbpScjtz8jk0eQmFLCrq5K1hz5jR8s2PEoAjaIw2OujwCdhU/IoVvJZFwynM9SKpG1HpWtHq21FpWtHVh+dphVFIaEDcmsVcmsVcuoU4uw9m7w6aEhS40wzEWY2E93hw9zmQtvpQ+qWkb1Hn1e1ScaWF6Srn4pNujhKiKVRiUXvj8TqjSTWG0GYLwop9P0zYmkC3Rj9HZhDbZjlViyqFqyaZmzWRmIiGpAlM/W+XOrlfBrkYdhdPRdTtV6Nw6Jiu8dDpSpIp0ZBViDBZji8Lk8cx2VEodP89P9PKYrCvgYHn5a2oFZB/0Qzw1KjCTeJJ2SCIAiC8H0pioK3uBhNXBzahKMzfHY2uQgFZSxRRvTGr3/AUtni5MnV+9m8pYzBIT/D03LRNXoJeEKYI/TEDopC18dCh0qmxeGl2eGjq7ONnLZV1PrNfC7lE5LUSAokeCGzG5LdoEaiQ6twwBKgVAvNvp6HVVq1RG68hfFyK4NcjST5Oglr2k/o0H4CbV0EXT1DlxTAGZZCU3whTXH5BLVhSMFOnOovKInfRrstgqz2QjI6BqJRdHQYG/BpdjC2fC+pFbVI4eFEnTUexdpIeWUk+9yTkFHTYrCzSGuiS61gCmsjIb4a2VBGR6gcmQBawkhU9WVA0MJoj5u87kpimysJNIO7VYer1UDI/b/fGylqFUpMJLrEREzJ6WgTE9DGxeGvqcVdVNQz0ikUApUKfU52b+Y20/BhaKKjj/p3D8hfc9/6FSGlJ+hT46ihxllDnbOOGmcNtc5a6px1vcdvGz8HQ+PunpFO9TtADoA1CSwJYE388mX5z3s8qH/iZB3/3fbubjw7dx4eBVaEd/ceoq68gpibbvpZ6/21+8/ffMJP4xcPCEmSpAYqgJOAOmAbMENRlJKv2//3EhA6dN75BFtb0SQmoE1MRJuYyJoOFRudGtQJCegTE4mOsRFnNfS+4q0GosN0aP6H/+DvWHcHn9Z8SrIlmVRLas+7NbU38JEQloBWdeSXl+zz9UTo/xOxb/hP5L7nPWQ//Je/zwlIPekyJdVX3lVIGk3PRTgpBW1CAtqkxJ73xEQ0CYloYqKRVF/fjy5fFzWOni/m/3xB/+f3Dm8HsabY3j78p1+p1p7+mLVm3AE3Rc1F7Hq+i4BLwa5txanvQLH4SIiPJjMlBZ+xmwapmhp39VEXAABrQENeMJY+nnAiO7WYWmX0LV7MbV2Ed3UhqcBr0qGyaDHbJHRGH0a9k3BjF5FmJyqNgqJAt0NDU7sRe5sRdbMG4+GHPm49lCVLNEdCdpNMSiNogz0BQtkSwhDjRxclERHTjc7ip0HR0tRhwNOmQ9usJbxFhbrn/oGaGChPlogOSmTXhDB19ZQjqWWMUQFMMX6kGAVLlAeVNsRqfQwfGJLZbVLwGTpQh7RY/BFYvJFYfFFYfVFEBRKJ9EZj8lpRKxIGtQej4sTgasZor8fY3oDJ0YLB24FXJ1EWE83e+FhacqPw9FWhGDpodtfT5m074t9WLWuIcEWQaI8k1hFJhDsSNVH49VGE+SMxBf4r4COFMOnd6LTd6HUu4qPVhEdbe1K+Gmw9o210ZhS/H+++EpAk1DYbQYMFd0CLszOAs92Ds91LMPBfY8AP0xnVJPaNIKlfOEn9IohKDkOlkuhw+Vm4o45uX5AJOXEMSLIe8xFlgiAIgiD8skoaHDy5uoJVJc1YtGrSvRLZHhXpQRUSEoHDQ40kQJKkntExX7ldkJSeV0gr4YrX052gx2/pCUJpNSpy4y0MSg4nJ8GCXvPNo8Zll4Pg3vUESjYR2L+HQGsVtdYuGoxD6XCNx+kZDIdH8CgqL3XmJjYYvDQbW/j/9u4mRpL7rOP496mq7urp2e55zb551+sN3hA2iXDiOAHMwUr8ekgAkYMRAiMR5YIPyOLgKIJEAURACBACDpFBijhgkkgojmIRmTgciBJie+OAFjtZO7azu17PemZ2p2enX6vq4VA1M92zs97dmZ03z+8jlaqr6l9V/+qeZ/pfT/27Ktpzimj4Jd59boFf+27GB152FkoRTx/7WcaP1riJfbzSvIvMQ17Zd4LvHnyS+cosP1Pdzy+VJrh1YY4TjVc5MwMHXg/4udPO8TPOcAuSMKa39wi943fwem2ENxZ6tHoRRp2ICdxTkmyeJGthwTwWTtOpnOHN2mlOTlxgaiTFi95W5aDM4Vp+kfZwPb9Qe3O0lwOvzlM9+SrtEz+g9cMf4u38AR698T1cGi0xU884N9zjlT1dztYzpkeM6Tq04qu320JiqtkoY92II70u703muK1zjjtaC5SAztBe2vs+QDw0TNycwuZfzy/SJq2B7ThGszRKhzJZ5qSZYwahGUFghIuDGUEYYbV9RKM3YfWboH5gRXLpAERlkulpms+doPncs7SefY72iy9ClkEYUjl+nOrtt1O7526qt1/em6mTpJxvdJgqkpVvNNqcb7R5o9FmqtFm+lKXPXHE/nqFffWYfSMV9tUq7B+pLF3krsXRdbd9W92Uqb79LO5/8fXsQpexarlvfzH76hX29u27Wh5M0qa9jPnZNo2iTd+YaTM/3crHM22GR2MeeOS2gf3kx9sZeP21h+9kck98XcezpNeG+deXe/0vDkvzzsFvfgX2v3dt299GtiIh9IvA59z9vmL60wDu/merlX+7JIRmHnuM9o9/TLKYbJmayrPffebKVebiPTiDgbj4D+VaOG3cerglQFqM+z9Hwzxg8Zur1soYaQ6eOGcGF/aETNfzYX4oxA0q3sZwArJivLzdMIX6PIzOG6MNqHYG65UEMFeD3oqLMmmxv4HjdSg5REDgkBokQGL565VlM8uP8MLER0nj/aSlCbrlcTrlcdz6vmg9I+41iNJW8V7lw+IX9+D7CGkASZiPAyBMnTCDIDNWfhqLxxAU20kD6JSgUzKSUgSlMlEYYTiJJyRpF+slxD2Ie1Du5ccC+TEGnn9CDnQj6EQB3VJIJyyRegn3MnjeCIg8pZp2GMq6lJKEIE2X1iUK84QdgBm9IKYbDna/NU+otGeptGYYauePY21VJuhUJ2nFY7gNfmhDtRKUAy51UxY6Cd0k//sxy6/UlEtOtQxRCjRXfOCBw54Eq/ewekJlJORdR45y9NBN1CeqDI+UB7tkr5G705rvLX+RTLcIo4CDx0aZPFzTU7hERETkLT1/+iJfefY0w3HE3lrMZBASnGlRTpxqHBFdoS1hBntvqXPL+yYJb3Tv4oWZ/B49Z59l4RK8nNxF9fA7OfrzewlLAZc6yfIJ8lyT/5k+yQsXnyH66ff4yHde4xdeyEgimBo1kqjOhXfcy9zYnWAB1fbUcvvWDM9YOoXIooBuKaAbDBEy2I5Mgi7pcJvh8ZiDByahV+H8Gws0L3SwZjrQZnacMGhQDht0LaBDQGIsDYNnLEU7n7xNGfeglECYQVS0yVe2yN3yYbWGvbGct7Pl2TiGY8XZjV1Wh8HSFKVYKrnaX8HlZ9DXuE7/SVEEhGDFeNWV+te/wmm7GctJS18+/1mlkkvlr9XVtnPZfq+UWrDlw+tlQ3Sy0RWLU4bCCwyFMwyFswyH57i5+vRlmwkCI1ocwoDJPfEV4/TKB5XBwjS0Zi9fFteXE3j1g/DLj8Dkrde3/W1oKxJCnwDud/dPFtO/BXzY3R/uK/Mp4FMAN9988+2vvfbaDa/HVvM0JTl/Pu+FU/yetnv2LK3pGTq9jHYvpZ2ktHsZnV5KL1vrZ+FkJKS0ybxDSpuUDovR26xEzNbLzI7EXKjHzIyUuVgrk11DryTzjJL3KHmHsncoZR1K3qHkXeJORn0+o9bIGJl3ao18OhzMgRFhVNyIPVgav9WeU6BtGR3zpXHoMJKF1DwcWNcxOmGddjROOxpbGqe2IlMcGkSGRwFhOSCKQzyO6JQimhbQMqflGZlZfoMcIOqlRN2MqJsSdlOibkqA4fU9hGNjDNVGqUZVSm/RlTRzp520aCYtmr0mvfk5gsYC1VZKWKkSjY8TjUzQSiLmWj0udRLKYUBcCqmUAipRSFwKqJTCgX92niSkFy+Szl4gazYH9hnRo2rNYmhRtSZDYY/Sgf2X9ewK6nVwaDa6RUa+RWM6z9Z3W8nSNvPGR4d2L+Xg6BBjxQ0Py5WI+mSFWvEb/fpEhepIrGSMiIiIyCab68zx3PefYOHxr1K7lDI5NMlIuU672ebl3hFa0Vh+o+ywnF9MLJcJx8fymz3Heds5qhoXS2/yqp8irGfc8a7buOOW9xNHq/fCyNKMhbkujekWc9Mtps4tMHO+SXvuIuXGT7GkSZblbWKAXl/7vlOMS26UKBESYwzhViUJKnStTJBkRL0upV6HsNehlHQJkx6WN+0JyQgtIyAj8AzzIkG1eJxBtJQBcc9vGZC65z1++sYGBJZfoO8fB8YVe9S4F9twlnoQLW4TnMDT/CK7F/Ujw8rglRAvh1dNAPUbrFueGAktn74Sx0mL9z7NBo/5Ws86V74fYUDxvlx5v5l7sc++fRfjfJtd4lKDStQgLjWIoznK0QJmi8k0I44Gz4cqpfyc6LqTP6syGJ4c/Klg7WDeq2sND93ZCbZlQqjf26WHkIiIiIiIiIjIdvFWCaGNulPTWeBw3/ShYp6IiIiIiIiIiGyxjUoIPQMcM7OjZlYGHgSe2KB9iYiIiIiIiIjIdVj9mYzr5O6JmT0MfJP8Fln/5O4nN2JfIiIiIiIiIiJyfTYkIQTg7k8CT27U9kVEREREREREZG026idjIiIiIiIiIiKyTSkhJCIiIiIiIiKyyyghJCIiIiIiIiKyyyghJCIiIiIiIiKyy5i7b3UdMLM3gde2uh5yQ0wC01tdCZG3OcWZyMZSjIlsPMWZyMZTnAnAEXd/x2oLtkVCSN4+zOxZd//gVtdD5O1McSaysRRjIhtPcSay8RRncjX6yZiIiIiIiIiIyC6jhJCIiIiIiIiIyC6jhJDcaF/c6gqI7AKKM5GNpRgT2XiKM5GNpziTt6R7CImIiIiIiIiI7DLqISQiIiIiIiIisssoISQiIiIiIiIisssoISTXzczGzewpMztVjMeuUO6hoswpM3uomFc1s2+Y2YtmdtLMvrC5tRfZGdYTZ8X8PzWz02Z2afNqLbL9mdn9ZvYjM3vJzB5dZXlsZv9aLP9vM7ulb9mni/k/MrP7NrPeIjvJWuPMzCbM7NtmdsnM/m6z6y2yU6wjxu4xs+fM7H+L8Uc2u+6yvSghJGvxKPAtdz8GfKuYHmBm48BngQ8DHwI+23dC+5fu/m7g/cCdZvbA5lRbZEdZb5x9vZgnIgUzC4G/Bx4AjgO/YWbHVxT7XeCCu98K/DXw58W6x4EHgfcA9wP/UGxPRPqsJ86ANvCHwB9sUnVFdpx1xtg08DF3fx/wEPDPm1Nr2a6UEJK1+BXgS8XrLwG/ukqZ+4Cn3H3W3S8ATwH3u3vT3b8N4O5d4ARwaBPqLLLTrDnOANz9e+5+blNqKrJzfAh4yd1/UnwHPU4ea/36Y++rwEfNzIr5j7t7x91fAV5CSVeR1aw5ztx9wd3/izwxJCKrW0+M/cDdXy/mnwSGzCzelFrLtqSEkKzFvr4TzTeAfauUuQk43Td9ppi3xMxGgY+R934QkUE3JM5EZMC1xMxSGXdPgDlg4hrXFZH1xZmIXN2NirFfB064e2eD6ik7QLTVFZDtycz+A9i/yqLP9E+4u5uZr2H7EfAvwN+6+0/WVkuRnW2j40xEREREZCUzew/5z8ju3eq6yNZSQkhW5e53X2mZmU2Z2QF3P2dmB4DzqxQ7C9zVN30I+M++6S8Cp9z9b25AdUV2pE2IMxEZdBY43Dd9qJi3WpkzxcWLEWDmGtcVkfXFmYhc3bpizMwOAf8G/La7v7zx1ZXtTD8Zk7V4gvwmZBTjr61S5pvAvWY2Vtzk9t5iHmb2J+T/lH5/E+oqslOtK85EZFXPAMfM7KiZlclvEv3EijL9sfcJ4Gl392L+g8WTW44Cx4Dvb1K9RXaS9cSZiFzdmmOsuGXHN4BH3f07m1Zj2baUEJK1+AJwj5mdAu4upjGzD5rZYwDuPgv8Mfk/rGeAz7v7bJGR/gz5HfFPmNnzZvbJrTgIkW1uzXFWlPsLMzsDVM3sjJl9bguOQWRbKe6j8DB54vQF4MvuftLMPm9mHy+K/SMwYWYvAY9QPOHP3U8CXwb+D/h34PfcPd3sYxDZ7tYTZwBm9irwV8DvFN9fK5+eJLKrrTPGHgZuBf6oOA973sz2bvIhyDZiSsaLiIiIiIiIiOwu6iEkIiIiIiIiIrLLKCEkIiIiIiIiIrLLKCEkIiIiIiIiIrLLKCEkIiIiIiIiIrLLKCEkIiIiIiIiIrLLKCEkIiIiIiIiIrLLKCEkIiIiIiIiIrLL/D9ACKDMu8PxKwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize histograms\n",
        "plt.figure(figsize=(20, 4)) # width and height of the plot\n",
        "legends = []\n",
        "for i,p in enumerate(parameters):\n",
        "  t = p.grad\n",
        "  if p.ndim == 2:\n",
        "    print('weight %10s | mean %+f | std %e | grad:data ratio %e' % (tuple(p.shape), t.mean(), t.std(), t.std() / p.std()))\n",
        "    hy, hx = torch.histogram(t, density=True)\n",
        "    plt.plot(hx[:-1].detach(), hy.detach())\n",
        "    legends.append(f'{i} {tuple(p.shape)}')\n",
        "plt.legend(legends)\n",
        "plt.title('weights gradient distribution');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "z0PCSZI1sVBu",
        "outputId": "832bb74e-8f3e-4916-e0f6-d8078005ea40"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "weight   (44, 10) | mean +0.000000 | std 1.244301e-02 | grad:data ratio 1.248448e-02\n",
            "weight  (30, 100) | mean -0.000435 | std 1.315273e-02 | grad:data ratio 4.820806e-02\n",
            "weight (100, 100) | mean -0.000047 | std 9.099237e-03 | grad:data ratio 5.487243e-02\n",
            "weight (100, 100) | mean -0.000140 | std 9.556308e-03 | grad:data ratio 5.891238e-02\n",
            "weight (100, 100) | mean -0.000076 | std 9.605983e-03 | grad:data ratio 6.076972e-02\n",
            "weight (100, 100) | mean -0.000056 | std 8.926910e-03 | grad:data ratio 5.953401e-02\n",
            "weight  (100, 44) | mean +0.000067 | std 1.139160e-02 | grad:data ratio 6.186265e-02\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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117Nv3z4GDBjQpq62tjbmPa0DSBs2bGDhwoUx261YsSJm+aBBg3jxxRfble/evZuDBw8ybty4Tud8tCkgJCIiIgmpPhjGucjnuqD2EBIREZHuueuuu9ixY0e7gFA8rrrqql6bx44dO7jrrrt6rb/eooCQiIiIJKTWR80rQ0hERES665xzzunrKQCRfY8SkfYQEhERkYRU1+pkMQWERERERHqXAkIiIiKSkFoHgbRkTERERKR3KSAkIiIiCalNQEgZQiIiIiK9SgEhERERSUgtewglmY6dFxEREeltCgiJiIhIQmrZQ+jE7DTqtWRMREREPFdccQW5ubmMHTu203b33HMPy5cvB+CGG27g9NNP54wzzuBb3/oWu3btAsA5x6JFiwgEApx++um8+eabXY7/q1/9iuHDh5Odnd2mvLGxkYsvvphAIMA555zD9u3bo3W33XYbgUCAUaNG8dxzzwEQDAaZPHkyoVDf/OFLASERERFJSC3LxIbkpGvJmIiIiETNmzePsrKyTtuEQiH++Mc/8v3vfx+AX/ziF7zzzju8/fbbnH/++fz6178GYM2aNZSXl1NeXs7999/PggULuhz/29/+Nhs3bmxX/uCDD3LCCSdQUVHBz3/+c6677joAtmzZwsqVK3nvvfcoKyvjJz/5CeFwmNTUVKZOncqjjz7a3VfQK3TsvIiIiCSklmViuf3S2Lr7QB/PRkRERNpZsxg+/u/e7XPoOJhZ3GmTyZMnt8m+iWXdunWceeaZ+HyRsEdOTk60rq6uDjMDoLS0lLlz52JmTJw4kX379lFdXc2wYcM67HvixIkxy0tLS7npppsA+O53v8tVV12Fc47S0lLmzJlDWloaJ598MoFAgI0bNzJp0iQuvPBCrr/+ei699NJOn+dIUIaQiIiIJKSWZWK5OVoyJiIiIt3z6quvctZZZ7Upa1nq9cgjj0QzhKqqqhg+fHi0jd/vp6qqqkdjtu7L5/PRv39/ampqOh1j7NixvPHGGz0a73ApQ0hEREQSUl1jiDRfEjnpKdpUWkREJBF1kcnTl6qrqykoKGhTduutt3Lrrbdy2223sXTpUm6++eY+mt0XkpOTSU1N5cCBA/Tr1++ojq0MIREREUlItY0hstN8ZKX5CIaaaQo39/WURERE5EsiIyODhoaGmHWXXnopTzzxBAB5eXns3LkzWldZWUleXl6PxmzdVygU4vPPP2fQoEFdjtHY2Eh6enqPxjwcCgiJiIhIQqoPhslMSyYrLZLQXN+oZWMiIiISn4KCAioqKqLX5eXl0c+lpaWcdtppABQWFrJ8+XKcc2zYsIH+/ftH9w9qaROvwsJCSkpKAHj88cf55je/iZlRWFjIypUraWxsZNu2bZSXl3P22WcDUFNTw4knnkhKSsphPW9PKCAkIiIiCam2MURWqo+s1OTIdVDLxkRERAQuueQSJk2axNatW/H7/Tz44IPt2sycOZOXX345er148WLGjh3L6aefzvPPP8+9994LwKxZsxg5ciSBQIAf/ehHLFu2DIDPPvsM51zM8X/5y1/i9/upr6/H7/dHN5K+8sorqampIRAIsGTJEoqLI0vqxowZQ1FREaNHj2bGjBn8/ve/Jzk58v1m/fr1zJ49u9feTXdYRw94NI0fP95t2rSpr6chIiIiCeTSf99AQ1Mz8/7HCH664i2e//lkTh1ydNfWi4iISFvvv/9+u715EtVFF13EHXfcQX5+frfvXb16NR999BGLFi06AjP7wne+8x2Ki4s59dRTD7uvWL8bM9vsnBsfq702lRYREZGEVNsYpn9GCtnekrE6bSwtIiIi3VBcXEx1dXWPAkLnn3/+EZhRW8FgkAsvvLBXgkE9oYCQiIiIJKS6xhB5A9LJ9JaM1WkPIREREemGUaNGMWrUqL6eRodSU1OZO3dun42vPYREREQkIdU3hshM9UU3ldbR8yIiIiK9RwEhERERSUgtx863LBmr16bSIiIiIr1GASERERFJOM456oJhstKSyUxrWTKmgJCIiIhIb1FASERERBJOY6iZcLMjM/WLDKFa7SEkIiIi0msUEBIREZGE05INlJ3mIyMlGTMtGRMREZEvhMNhvv71r3d6GtjPfvYzXn75ZQCWLl1KIBDAzPjss8+ibZxzLFq0iEAgwOmnn86bb74ZrSspKSE/P5/8/HxKSkq6nNNjjz3GmDFjSEpKYtOmTW3qbrvtNgKBAKNGjeK5556LlpeVlTFq1CgCgQDFxcXR8jlz5lBeXt71izgMcQeEzCzZzN4ys9Xe9clm9rqZVZjZo2aW6pWnedcVXv2IIzN1EREROVbVByPZQJmpyZgZWak+bSotIiIiUffeey8FBQUd1tfU1LBhwwYmT54MwD/8wz/wwgsvcNJJJ7Vpt2bNGsrLyykvL+f+++9nwYIFAOzZs4ebb76Z119/nY0bN3LzzTezd+/eTuc0duxYnnzyyeiYLbZs2cLKlSt57733KCsr4yc/+QnhcJhwOMzChQtZs2YNW7ZsYcWKFWzZsgWABQsWcMcdd3T7vXRHd46d/9/A+0COd307cLdzbqWZ/R/gSuAP3r97nXMBM5vjtbu4F+csIiIix7jaVhlCAFlpydpDSEREJMHcvvF2/rbnb73a52kDT+O6s6/rtE1lZSXPPvssv/rVr1iyZEnMNk888QQzZsyIXn/961+P2a60tJS5c+diZkycOJF9+/ZRXV3NSy+9xLRp0xg4cCAA06ZNo6ysjEsuuaTDeXUUoCotLWXOnDmkpaVx8sknEwgE2LhxIwCBQICRI0cCkayg0tJSRo8ezTe+8Q3mzZtHKBTC5+tO6CZ+cWUImZkfmA38u3dtwDeBx70mJcCF3ucLvGu8+qleexEREZG4tAR/sqIBIR91Qe0hJCIiIpGlYHfccQdJSR2HNF599VXOOuusLvuqqqpi+PDh0Wu/309VVVWH5T3RkzGSkpIIBAL89a9/7dGY8Yg3zHQP8Eugn3c9CNjnnGv5U10lkOd9zgN2AjjnQmb2udf+i0V6gJn9GPgxwFe/+tWezl9ERESOQS3BnyzvhLGsVJ8yhERERBJMV5k8R8Lq1avJzc3lrLPO4qWXXuqwXXV1NYMHDz56EzsCcnNz2bVrV1yBrZ7oMkPIzM4HPnHObe7NgZ1z9zvnxjvnxn/Zf0kiIiLSu9pnCGnJmIiIiEQyf55++mlGjBjBnDlzWLduHT/4wQ/atcvIyKChoaHL/vLy8ti5c2f0urKykry8vA7Le6KnYzQ0NJCRkdGjMeMRz5KxfwAKzWw7sJLIUrF7gQFm1pJh5AdacqeqgOEAXn1/oKYX5ywiIiLHuGhAKDXyVSM7zUedjp0XERE57t12221UVlayfft2Vq5cyTe/+U0efvjhdu0KCgqoqKjosr/CwkKWL1+Oc44NGzbQv39/hg0bxvTp03n++efZu3cve/fu5fnnn2f69OkAzJ07N7oHUDwKCwtZuXIljY2NbNu2jfLycs4++2wmTJhAeXk527ZtIxgMsnLlSgoLC6P3ffDBB4wdOzbucbqry4CQc+5655zfOTcCmAOsc85dCqwHvus1uxwo9T4/7V3j1a9zzrlenbWIiIgc0w7NEMpM9VGnY+dFREQkTrNnz26zpOy+++7D7/dTWVnJ6aefzvz58wGYNWsWI0eOJBAI8KMf/Yhly5YBMHDgQG644QYmTJjAhAkTuPHGG6MbTL/zzjt85StfaTfmqlWr8Pv9vPbaa8yePTsaQBozZgxFRUWMHj2aGTNm8Pvf/57k5GR8Ph9Lly5l+vTpFBQUUFRUxJgxYwDYvXs3GRkZDB069Ii9I+tOrMbMpgDXOufON7ORRDKGBgJvAT9wzjWaWTrwn8DXgT3AHOfcR531O378eLdp06YePoKIiIgca36/voLfPbeVrb+ZQZovmeuf/G/WbvmYTf8yra+nJiIiclx7//33Oz3uPZGce+65rF69mgEDBvRan/v37+fKK6/kscce67U+Y7n77rvJycnhyiuvjPueWL8bM9vsnBsfq323zi5zzr0EvOR9/gg4O0abBuB73elXREREpLW6xhC+JCM1OYnmxjAnJJuWjImIiEi33HXXXezYsaNXA0I5OTlHPBgEMGDAAC677LIjOsaROcxeRERE5DDUNYbISvNhZux5spxv7qxjWVOYcLMjOcn6enoiIiLyJXDOOef09RR67Ic//OERHyOeTaVFREREjqraxjDZ3v5BoZqD9GuIZAdpHyERERGR3qGAkIiIiCSc+mCIzNRkAJoPhkhtiux5WK9lYyIiIiK9QgEhERERSTi13pIxgOb6EL6mZswrFxEREZHDp4CQiIiIJJz6YJistGRcs8M1hDAgmy+OoxcRERGRw6OAkIiIiCScusYQWak+mg+GILJajBxMewiJiIgc57Zu3coZZ5wR/cnJyeGee+6J2faee+5h+fLlADz22GOMGTOGpKQkNm3a1KbdbbfdRiAQYNSoUTz33HPR8rKyMkaNGkUgEKC4uLjLub388suceeaZ+Hw+Hn/88TZ1JSUl5Ofnk5+fT0lJSbR88+bNjBs3jkAgwKJFi3Au8sXn2muvZd26dfG9lB5SQEhEREQSTm1jiOw0H831TdGyHHT0vIiIyPFu1KhRvP3227z99tts3ryZzMxMLrroonbtQqEQf/zjH/n+978PwNixY3nyySeZPHlym3Zbtmxh5cqVvPfee5SVlfGTn/yEcDhMOBxm4cKFrFmzhi1btrBixQq2bNnS6dy++tWv8tBDD0XHbLFnzx5uvvlmXn/9dTZu3MjNN9/M3r17AViwYAEPPPAA5eXllJeXU1ZWBsBPf/rTuIJQh0PHzouIiEjCqQ+GyUxLjmQIeSIBIWUIiYiIJIqPf/tbGt//W6/2mVZwGkP/+Z/javviiy9yyimncNJJJ7WrW7duXTRbB6CgoCBmH6WlpcyZM4e0tDROPvlkAoEAGzduBCAQCDBy5EgA5syZQ2lpKaNHj+5wPiNGjAAgKalt7s1zzz3HtGnTGDhwIADTpk2jrKyMKVOmsH//fiZOnAjA3Llzeeqpp5g5cyYnnXQSNTU1fPzxxwwdOjSu99FdyhASERGRhNOyqXRz/SEBIS0ZExEREc/KlSu55JJLYta9+uqrnHXWWV32UVVVxfDhw6PXfr+fqqqqDst7orMx/H5/h2OceeaZvPrqqz0aMx7KEBIREZGE0hRuJhhqjuwh1GrJWD9lCImIiCSUeDN5joRgMMjTTz/NbbfdFrO+urq6w6ygL4vc3Fx27dp1xPpXhpCIiIgklHpvn6BDM4T6Y9RqDyEREREB1qxZw5lnnsmQIUNi1mdkZNDQ0NBlP3l5eezcuTN6XVlZSV5eXoflPdHZGJWVlR2O0dDQQEZGRo/GjIcCQiIiIpJQar1lYdlpyZEMIQNLS2ZgUjL1yhASERERYMWKFR0uF4PInkEVFRVd9lNYWMjKlStpbGxk27ZtlJeXc/bZZzNhwgTKy8vZtm0bwWCQlStXUlhYCMD111/PqlWr4p7r9OnTef7559m7dy979+7l+eefZ/r06QwbNoycnBw2bNiAc47ly5dzwQUXRO/74IMPGDt2bNzjdJcCQiIiIpJQWoI+mamRDKGkDB9JWSmckJSkPYRERESEuro61q5dy3e+850O28ycOZOXX345er1q1Sr8fj+vvfYas2fPZvr06QCMGTOGoqIiRo8ezYwZM/j9739PcnIyPp+PpUuXMn36dAoKCigqKmLMmDEA/Pd//3fMjZ7feOMN/H4/jz32GP/0T/8UbT9w4EBuuOEGJkyYwIQJE7jxxhujG0wvW7aM+fPnEwgEOOWUU5g5cyYATU1NVFRUMH78+N55aTFYyxn3fWn8+PFu06ZNfT0NERERSQBv7djLRcv+H/8xbwKnv7WHpqpaLC2ZNz7Zz3Ojc/i3S77e11MUERE5br3//vtfmr15LrroIu644w7y8/N7td/p06fz3HPP9Wqfh1q1ahVvvvkmt9xyS9z3xPrdmNlm51zMqJIyhERERCSh1LXZQ6gpkiGU6SOHJG0qLSIiInErLi6murq61/s90sEggFAoxDXXXHNEx9ApYyIiIpJQWpaFZaYm01wfIjk7BUtLJtbAjdwAACAASURBVBsUEBIREZG4jRo1ilGjRvX1NHrke9/73hEfQxlCIiIiklBagj7ZaT6aD4ZIykwhKTOFrGa0h5CIiIhIL1GGkIiIiCSUloBQZloyTXWRJWOWnkxGGOobdOy8iIiISG9QhpCIiIgklLqgt4eQLwnXGCYp00dSRgpJQLOWjImIiIj0CmUIiYiISEKpawxhBmmhyEmoSZmRPYQAkhuVISQiIiLSG5QhJCIiIgmlrjFMVqoPdzAS/IlkCEX+huVraqa52fXl9ERERKSPXXHFFeTm5jJ27Ng25Xv27GHatGnk5+czbdo09u7dG/P+t956iyuvvBKAv/3tb0yaNIm0tDTuvPPONu3KysoYNWoUgUCA4uLiaPm2bds455xzCAQCXHzxxQSDwU7nW1NTw3nnnUd2djZXXXVVm7rNmzczbtw4AoEAixYtwjnX6bOsXr2aG2+8MY631DUFhERERCSh1DWGyEpLprm+CcDbVDoSEMrBONikLCEREZHj2bx58ygrK2tXXlxczNSpUykvL2fq1Kltgjit/fa3v2XRokUADBw4kPvuu49rr722TZtwOMzChQtZs2YNW7ZsYcWKFWzZsgWA6667jp///OdUVFRwwgkn8OCDD3Y63/T0dG655ZZ2ASeABQsW8MADD1BeXk55eXn0uTp6ltmzZ/PMM89QX1/fxVvqmpaMiYiISEKpDYbISvXRXB/ZLygp04elRpaM9ce8gJG+woiIiPS1V/78AZ/trO3VPk8cns03ik7ttM3kyZPZvn17u/LS0lJeeuklAC6//HKmTJnC7bff3qbNgQMHeOedd/ja174GQG5uLrm5uTz77LNt2m3cuJFAIMDIkSMBmDNnDqWlpRQUFLBu3Tr+9Kc/Rce56aabWLBgQYfzzcrK4txzz6WioqJNeXV1Nfv372fixIkAzJ07l6eeeoqZM2d2+CxmxpQpU1i9ejVFRUWdvqeuKENIREREEkq9F/CJBoQyfG0yhGq1sbSIiIjEsHv3boYNGwbA0KFD2b17d7s2mzZtarfULJaqqiqGDx8evfb7/VRVVVFTU8OAAQPw+XxtynuiqqoKv9/fboyunmX8+PG88sorPRqzNf15TURERBJKXWM4smTsoLdkLCsFS4n8DasfRn1QS8ZEREQSQVeZPH3JzDCzduXV1dUMHjy4D2bUc4c+S25uLrt27TrsfpUhJCIiIgmltrHVkrEksLRkLDmJ5pQkZQiJiIhIh4YMGUJ1dTUQCfzk5ua2a5ORkUFDQ0OXfeXl5bFz587odWVlJXl5eQwaNIh9+/YRCoXalPdEXl4elZWV7cbo6lkaGhrIyMjo0ZitKSAkIiIiCaU+2LJkrImkjJToX8RcejI53h5CIiIiIocqLCykpKQEgJKSEi644IJ2bQoKCtrt5RPLhAkTKC8vZ9u2bQSDQVauXElhYSFmxnnnncfjjz/ebpxVq1Zx/fXXxz3fYcOGkZOTw4YNG3DOsXz58mhfnT3LBx98ENeyt64oICQiIiIJpbYxHAkIHQxF9w4CsAxfJCCkJWMiIiLHtUsuuYRJkyaxdetW/H5/9JSvxYsXs3btWvLz83nhhRdYvHhxu3tPO+00Pv/8cw4cOADAxx9/jN/vZ8mSJfzmN7/B7/ezf/9+fD4fS5cuZfr06RQUFFBUVMSYMWMAuP3221myZAmBQICamproEfYffvghOTk5Mec8YsQIrr76ah566CH8fn/0xLJly5Yxf/58AoEAp5xyCjNnzuzyWdavX8/s2bMP+z1qDyERERFJKPXBEFmpyTTvCZGU8cVXlaQMH/0wPleGkIiIyHFtxYoVMcsHDRrEiy++2OX9V1xxBY8++ijz589n6NChbZZttTZr1ixmzZrVrnzkyJFs3LixXfnbb7/N3XffHbOvWKeiQWSD6HfffbddeUfPsnv3bg4ePMi4ceNi9tcdyhASERGRhNHc7KgPhr9YMpaZEq3zZadEj50XERER6akFCxaQlpbW6/0+/PDDR3zD6h07dnDXXXf1Sl/KEBIREZGEUReMBHuy0pJprg+RMjQrWpeSlertIaQlYyIiItJz6enpXHbZZX09jR6ZMGFCr/XVZYaQmaWb2UYz+6uZvWdmN3vlJ5vZ62ZWYWaPmlmqV57mXVd49SN6bbYiIiJyTGs5Uj6SIRRqmyGUleIFhJr6anoiIiIix4x4low1At90zn0NOAOYYWYTgduBu51zAWAvcKXX/kpgr1d+t9dOREREpEstR8r38yXjguF2ewglYzTWa8mYiIiIyOHqMiDkImq9yxTvxwHfBB73ykuAC73PF3jXePVTreW8WBEREZFO1HvLwbKJfHVIymoVEPJOHHP1yhASEREROVxxbSptZslm9jbwCbAW+BDY55xr+RNdJZDnfc4DdgJ49Z8Dg3pz0iIiInJsimYINUeukzK+WDLW8tkd1B5CIiIiIocrroCQcy7snDsD8ANnA6cd7sBm9mMz22Rmmz799NPD7U5ERESOAS0niGW2BIQyW2UIedlC1qAlYyIiIsezK664gtzcXMaOHdum/IYbbuD000/njDPO4Fvf+ha7du2Kef9bb73FlVdGdr3529/+xqRJk0hLS+POO+9s066srIxRo0YRCAQoLi6Olm/bto1zzjmHQCDAxRdfTDAY7HS+NTU1nHfeeWRnZ3PVVVe1qdu8eTPjxo0jEAiwaNEinHMA7Nmzh2nTppGfn8+0adPYu3cvAKtXr+bGG2+M4y11rVvHzjvn9gHrgUnAADNr+ZbmB6q8z1XAcACvvj9QE6Ov+51z451z44/0sWwiIiLy5dByylhGOPJlqPWm0i37CSUHlSEkIiJyPJs3bx5lZWXtyn/xi1/wzjvv8Pbbb3P++efz61//Oub9v/3tb1m0aBEAAwcO5L777uPaa69t0yYcDrNw4ULWrFnDli1bWLFiBVu2bAHguuuu4+c//zkVFRWccMIJPPjgg53ONz09nVtuuaVdwAlgwYIFPPDAA5SXl1NeXh59ruLiYqZOnUp5eTlTp06NBqRmz57NM888Q319fRdvqWtdHjtvZoOBJufcPjPLAKYR2Sh6PfBdYCVwOVDq3fK0d/2aV7/OtYS4RERERDrRcqR8esjRzCEZQl5wyBds7oupiYiIyCHWP3Q/n/z9o17tM/ekkZw378edtpk8eTLbt29vV56TkxP9XFdXR6ztjA8cOMA777zD1772tch4ubnk5uby7LPPtmm3ceNGAoEAI0eOBGDOnDmUlpZSUFDAunXr+NOf/gTA5Zdfzk033cSCBQs6nG9WVhbnnnsuFRUVbcqrq6vZv38/EydOBGDu3Lk89dRTzJw5k9LSUl566aXoGFOmTOH222/HzJgyZQqrV6+mqKio0/fUlXgyhIYB683sHeANYK1zbjVwHXC1mVUQ2SOoJST2IDDIK78aWHxYMxQREZHjRsuSsZSmSNCnTUDIyxBqqRMRERE51K9+9SuGDx/OI488EjNDaNOmTe2WmsVSVVXF8OHDo9d+v5+qqipqamoYMGAAPp+vTXlPVFVV4ff7240BsHv3boYNGwbA0KFD2b17d7Td+PHjeeWVV3o0ZmtdZgg5594Bvh6j/CMi+wkdWt4AfO+wZyYiIiLHnZYlY75gM43JhqUmR+vMl0RTEqSFlHgsIiKSCLrK5OkLt956K7feeiu33XYbS5cu5eabb25TX11dzZdt2xoza5PtlJub2+H+SN3RrT2ERERERI6kusYQGSnJcDBEUoavXap3MCWJjLBDq9FFRESkM5deeilPPPFEu/KMjAwaGhq6vD8vL4+dO3dGrysrK8nLy2PQoEHs27ePUCjUprwn8vLyqKysbDcGwJAhQ6iurgYiQazc3Nxou4aGBjIyMno0ZmsKCImIiEjCqAuGyUpLprm+qc1ysRah1CRyMBq0bExEREQOUV5eHv1cWlrKaae1PyC9oKCg3V4+sUyYMIHy8nK2bdtGMBhk5cqVFBYWYmacd955PP744wCUlJRwwQUXALBq1Squv/76uOc7bNgwcnJy2LBhA845li9fHu2rsLCQkpKSdmMAfPDBB3Ete+uKAkIiIiKSMOoaQ2Sl+WiuD7U5YaxFOC2ZHCy6tExERESOP5dccgmTJk1i69at+P3+6ClfixcvZuzYsZx++uk8//zz3Hvvve3uPe200/j88885cOAAAB9//DF+v58lS5bwm9/8Br/fz/79+/H5fCxdupTp06dTUFBAUVERY8aMAeD2229nyZIlBAIBampqokfYf/jhh202tm5txIgRXH311Tz00EP4/f7oiWXLli1j/vz5BAIBTjnlFGbOnBl9lrVr15Kfn88LL7zA4sVfbM+8fv16Zs+efdjvscs9hERERESOlrrGEJmpkYBQ8glp7epdejL9MOoaQ5yY3b5eREREjn0rVqyIWR5riVgsV1xxBY8++ijz589n6NChbZZttTZr1ixmzZrVrnzkyJFs3LixXfnbb7/N3XffHbOvWKeiQWSD6Hfffbdd+aBBg3jxxRfble/evZuDBw8ybty4mP11hzKEREREJGHUNYbJTkum+WBTzAwhS/eRg1HbqAwhERER6ZkFCxaQltb7f1h6+OGHj/iG1Tt27OCuu+7qlb6UISQiIiIJoy4YYmBWaod7CCVl+sjC+EwBIREREemh9PR0Lrvssr6eRo9MmDCh1/pShpCIiIgkjNrGEP19ybim5pgZQsmZKaRg1NU19cHsRERERI4dCgiJiIhIwqhvDDMwOfL1JFaGUGq/VAAaDzQe1XmJiIiIHGsUEBIREZGEUdcYYoB1HBBKy44EhEK1yhASERERORwKCImIiEhCcM5RFwxxQktAKKP9krGMnEhAqElLxkREREQOiwJCIiIikhAampppdtAPA2JnCGXkRE4ECddrU2kREZHj1RVXXEFubi5jx45tV/dv//ZvnHbaaYwZM4Zf/vKXMe+vrq7m/PPPB6CmpobzzjuP7OxsrrrqqjbtNm/ezLhx4wgEAixatAjnHAB79uxh2rRp5OfnM23aNPbu3RvXvPfv34/f7283DkBhYWGb57n22mtZt25dXP32lAJCIiIikhBajpLP9q5jbSqd5u0hxEEFhERERI5X8+bNo6ysrF35+vXrKS0t5a9//Svvvfce1157bcz7lyxZwo9+9CMgcuLYLbfcwp133tmu3YIFC3jggQcoLy+nvLw8OmZxcTFTp06lvLycqVOnUlxcHNe8b7jhBiZPntyu/MknnyQ7O7tN2U9/+tO4++0pHTsvIiIiCaE+GAnyZIUj1zGPnW9ZRtaggJCIiEhf2/fMhwR31fVqn6lfyWLAt0/ptM3kyZPZvn17u/I//OEPLF68mLS0SEZxbm5uzPufeOIJfvOb3wCQlZXFueeeS0VFRZs21dXV7N+/n4kTJwIwd+5cnnrqKWbOnElpaSkvvfQSAJdffjlTpkzh9ttv73TOmzdvZvfu3cyYMYNNmzZFy2tra1myZAn3338/RUVF0fKTTjqJmpoaPv74Y4YOHdpp3z2lDCERERFJCC0ZQhlhwGdYSvuvKZaSRAOO5MbwUZ6diIiIJLoPPviAV155hXPOOYd//Md/5I033mjXZtu2bZxwwgnRoFFHqqqq8Pv90Wu/309VVRUAu3fvZtiwYQAMHTqU3bt3d9pXc3Mz11xzTcwspBtuuIFrrrmGzMzMdnVnnnkmr776aqd9Hw5lCImIiEhCqPOCPOmhZpIyUjCzmO3qk8AXbD6aUxMREZEYusrkOdpCoRB79uxhw4YNvPHGGxQVFfHRRx+1+U5RXV3N4MGDe21MM+vwO0uLZcuWMWvWrDYBJoC3336bDz/8kLvvvjtmxlNubi67du3qtbkeSgEhERERSQh13pKxlCYXc7lYi4PJRkqTAkIiIiLSlt/v5zvf+Q5mxtlnn01SUhKfffZZmwBQRkYGDQ0NXfaVl5dHZWVl9LqyspK8vDwAhgwZQnV1NcOGDaO6urrDpWktXnvtNV555RWWLVtGbW0twWCQ7OxsTjrpJDZt2sSIESMIhUJ88sknTJkyJbocraGhgYyMjB68ifhoyZiIiIgkhDpvyVhKMNxpQKjBZ6SF3NGaloiIiHxJXHjhhaxfvx6ILB8LBoOceOKJbdqceuqpMbNxDjVs2DBycnLYsGEDzjmWL1/OBRdcAEROBCspKQGgpKQkWr5x40bmzp3brq9HHnmEHTt2sH37du68807mzp1LcXExCxYsYNeuXWzfvp3/+q//4tRTT40Gg1qeIdZJar1FASERERFJCPXekrHkxnDME8ZaBH1JpCsgJCIicty65JJLmDRpElu3bsXv9/Pggw8CkePoP/roI8aOHcucOXMoKSlpt5wrKyuLU045pc0m0iNGjODqq6/moYcewu/3s2XLFiCy1Gv+/PkEAgFOOeUUZs6cCcDixYtZu3Yt+fn5vPDCCyxevBiAHTt29FpGT1NTExUVFYwfP75X+otFS8ZEREQkIbRsKm0NYZIyOv6KEkpNIrP2aM1KREREEs2KFStilqempvLwww93ef9VV13FQw89FD1prKOMofHjx/Puu++2Kx80aBAvvvhiu/LXX3+dhQsXdjr2vHnzmDdvXrvyESNGtBlr9erVfPe738XnO3JhGwWEREREJCG0LBlzDSGSsjrOEAqnJdHPgXOuy00cRURERA510UUXUVNT0+v9/u53v+u1vkKhENdcc02v9ReLAkIiIiKSEOqCYXKSkiDkOs0Qcuk+UjBcUzOWmnwUZygiIiJwbPxRZv78+X09hU5973vf61Z757q/nF57CImIiEhCqGsMkZsaCQQld7KHEF6wqGF/49GYloiIiLSSnp5OTU1NjwIQcmQ456ipqSE9Pb1b9ylDSERERBJCXWOIXF8k46ezU8ZasofqP28k88TMozI3ERERifD7/VRWVvLpp5/29VSklfT0dPx+f7fuUUBIREREEkJdMMTglMhXk84CQsle3cH9waMyLxEREflCSkoKJ598cl9PQ3qBloyJiIhIQqhrDDMwOfLVpLNj51OzUwFoqNWSMREREZGeUkBIREREEkJdMMTApJaAUMcZQinZkWBRU60yhERERER6SgEhERERSQh1jSH6e19NOjtlLCMnDYCm2tBRmZeIiIjIsUgBIREREUkIdY1hcgBLScJSOj5OPisrlUYczfVNR29yIiIiIscYBYREREQkIdQFQ/Rz1ulyMYCsVB/7cTQfVIaQiIiISE8pICQiIiIJoa4xRGYzJGV0vKE0QFZaMvtxWEP4KM1MRERE5NijgJCIiIj0uWComaawIyPsus4QSotkCCU1KiAkIiIi0lMKCImIiEifq2uMLP9KD3UdEErzJXEAR3JQASERERGRnuoyIGRmw81svZltMbP3zOx/e+UDzWytmZV7/57glZuZ3WdmFWb2jpmdeaQfQkRERL7car2AUGpTM0mZnS8ZMzMOJhupTc1HY2oiIiIix6R4MoRCwDXOudHARGChmY0GFgMvOufygRe9a4CZQL7382PgD70+axERETmm1HvZPinB5i4zhAAako20JnekpyUiIiJyzOoyIOScq3bOvel9PgC8D+QBFwAlXrMS4ELv8wXAchexARhgZsN6feYiIiJyzKhtDJEBmKPLDCGAYIrhc+CatGxMREREpCe6tYeQmY0Avg68DgxxzlV7VR8DQ7zPecDOVrdVemWH9vVjM9tkZps+/fTTbk5bREREjiV1jSFyMACSMrrOEGpKTQaguV5Hz4uIiIj0RNwBITPLBp4Afuac29+6zjnngG7lbTvn7nfOjXfOjR88eHB3bhUREZFjTH0wRP+WgFAcGULNaZGvMM0HFRASERER6Ym4AkJmlkIkGPSIc+5Jr3h3y1Iw799PvPIqYHir2/1emYiIiEhMtY3hLzKE4thDKJzWkiHUdETnJSIiInKsiueUMQMeBN53zi1pVfU0cLn3+XKgtFX5XO+0sYnA562WlomIiIi0Ux8MdSsgZOmRNloyJiIiItIzXX/jgn8ALgP+28ze9sr+GSgG/mxmVwJ/B4q8uv8LzAIqgHrgh706YxERETnm1LbeQyiOJWOWqYCQiIiIyOHoMiDknPsv8L6htTc1RnsHLDzMeYmIiMhxpK4xxIBubCrt84JGzQe1ZExERESkJ+LJEBIRERE5ouoawwxMTsaSkzFf11scpmX4COJoqlNASERERKQnunXsvIiIiMiRUNcYYmBSUlz7BwFkpaewH0dTrQJCIiIiIj2hgJCIiIj0ubpgiP7WjYBQanIkIKQMIREREZEeUUBIRERE+lxdY5j+WFwbSgNkpfnYjyOsgJCIiIhIjyggJCIiIn2urjFEtovvyHmAbC8g5A7qlDERERGRnlBASERERPpcXTBMVnN8J4xBJEPoAA4aFBASERER6QkFhERERKTP1TeEyAgT95KxTG8PoaSG8BGemYiIiMixSQEhERER6XPNjSGS6P6SsaSwwzU1H9nJiYiIiByDFBASERGRPucLRjJ9urupNECz9hESERER6TYFhERERKRPhcLNpIYiwZ349xBK/iIgVK+TxkRERES6SwEhERER6VN1wciR8xD/krGMlOTIptJAc70yhERERES6SwEhERER6VP1wRA50YBQfEvGzIxQSuRrTPNBZQiJiIiIdJcCQiIiItKn6hpbB4TiyxACCKV5ASFlCImIiIh0mwJCIiIi0qfqGsNfBIQy4ssQAmhOiwSPtKm0iIiISPcpICQiIiJ9qq4xRH+M5pQkLNnivs+XnkwIZQiJiIiI9IQCQiIiItKnahtD9MNwcZ4w1iIzzUd9kk4ZExEREekJBYRERESkT9V7p4xZRnK37stO81GbpCVjIiIiIj2hgJCIiIj0qVpvU+nkOE8Ya5GV5uMAThlCIiIiIj2ggJCIiIj0qZZTxnxZ3QsIZab6+Nw1aw8hERERkR5QQEhERET6VF0wcspYSnZqt+7LTktmX3OzloyJiIiI9IACQiIiItKn6hqa6IeRnNm9TaWz0nzsUYaQiIiISI8oICQiIiJ9KnywiSSMpO7uIZTqYz8OFwzjQs1HaHYiIiIixyYFhERERKRPhevDACT1IENoPw7QSWMiIiIi3aWAkIiIiPQp84I53c4QSkv+IiCkk8ZEREREukUBIREREelT1tASEOpmhlCqMoREREREekoBIREREelTycHI/j9JGd1fMnYgmiGkgJCIiIhIdyggJCIiIn0qpSUg1M0lY9lpPj5XQEhERESkRxQQEhERkT6V2tSMo/sZQpnaQ0hERESkxxQQEhERkT6VHnIEfYYlWbfuy07zUQ840x5CIiIiIt2lgJCIiIj0meZmR2YzBFO6/5UkKy2SURRMSVKGkIiIiEg3KSAkIiIifeZgU5gcjFBq97+SZKYkA9DoM2UIiYiIiHSTAkIiIiLSZ+oaQ/TDaE5L7va9SUlGZmoyDcmmTaVFREREuqnLgJCZ/dHMPjGzd1uVDTSztWZW7v17glduZnafmVWY2TtmduaRnLyIiIh8udUFw/THcOndDwhBZNlYXbL2EBIRERHprngyhB4CZhxSthh40TmXD7zoXQPMBPK9nx8Df+idaYqIiMixqK4xRA6GdfOEsRZZqcnUmk4ZExEREemuLgNCzrmXgT2HFF8AlHifS4ALW5UvdxEbgAFmNqy3JisiIiLHlrqDTfTDSM5K6dH9WWk+anFaMiYiIiLSTT3dQ2iIc67a+/wxMMT7nAfsbNWu0itrx8x+bGabzGzTp59+2sNpiIiIyJfZwdogAMmZPQ8I7XMO1xjGhZt7c2oiIiIix7TD3lTaOecA14P77nfOjXfOjR88ePDhTkNERES+hBoPRAJCadk9DAilJrPPRQJB2kdIREREJH49DQjtblkK5v37iVdeBQxv1c7vlYmIiIi0E6qN7P2TlpPWo/uz0nzsbfYCQlo2JiIiIhK3ngaEngYu9z5fDpS2Kp/rnTY2Efi81dIyERERkTZCdZEMoYx+PQsIZaf5+DQUCQQpQ0hEREQkfl0e6WFmK4ApwIlmVgn8K1AM/NnMrgT+zv/f3p1HWZJfhZ3/3lhevCVf7lmZWfvaXb2ouyVaai0tJBBIwhgkG+yBY7HYw/iYAzMeMNgwzNgMeAZmsAX28djnALOA7TNm8YCEEUigFdSo1WqppV6qq6u6qrv2qtzz7bH87vwR8XLpzqqurqytK++nTlTEi/ci3u/F++WLiBv39wv428XLPwH8NeA40Ab+7g0oszHGGGPuEK6dAVAZKl3T8tVSwPNJVqzL7jRmjDHGGHO1XjMgpKrff5mn3rfBaxX4sc0WyhhjjDFbg3TzrJ5o4NoCQgORz6V+hpA1GTPGGGOMuWqb7lTaGGOMMeaadVMyFCm/5jWqDdWigOXi3hYWEDLGGGOMuXoWEDLGGGPMLRPEjraAeHJNy9eigBaAgOtYkzFjjDHGmKtlASFjjDHG3DJB7Ght4mikFvkooGXfMoSMMcYYY14HCwgZY4wx5pYpJY6Of23ZQQC1Ut7UzEW+3WXMGGOMMeZ1sICQMcYYY26Zcqr0gmsPCA1EeUAoLfl2lzFjjDHGmNfBAkLGGGOMuWUqmRKXrv1wpFoEhJKSZ03GjDHGGGNeBwsIGWOMMeaWqbk8u+daDUT5sr1ArMmYMcYYY8zrYAEhY4wxxtwSmjmqCC669sORWpEh1PXFmowZY4wxxrwOFhAyxhhjzC2RtfIAjpaDa15HtehUuu2BdjM00+tSNmOMMcaYO50FhIwxxhhzS3QbMQBSufaAUK1obtYq+qV2XWs2ZowxxhhzNSwgZIwxxphbor3UA8DbREAo8D3KoUfDyzODrNmYMcYYY8zVsYCQMcYYY26JfoZQMBBuaj21UsCS9gNCliFkjDHGGHM1LCBkjDHGmFuiVwSEwoHSptZTiwIWnQOwO40ZqEwAnQAAIABJREFUY4wxxlyla8/RNsYYY4zZhKQVUwGi+vqAkKry56f+nOXeModGDnFw+CDVsHrZ9dSigPl+QGiDJmMz7RnKQZl6qX5dy2+MMcYY80ZmASFjjDHG3HSdZoO0lZCi1IomY6rKyaWT/OKXfpGvXPzK6osV9oU7uUt3sDMZZXJgit1TB9k9eYDB4XHqfsZsmgF5kzFV5ejCUT57+rN89tRnOTJ/hHqpzk8//NN8+OCHEZErli1zGY+de4ySX2Lf0D4mKhOvuYwxxhhjzBuNqN7627M+/PDD+pWvfOW1X2iMMcaYW0ZV6bVbNOZmaczOsHhpBggY23UPceZzauYscTsj1IiBgQpDgwPU61XKtRLlWkDSW+b4l/+CZz73aebPnuKbxj/Ajupd/On8H+LJAElSIfND2pUWk3WfusQsL52nvbSMS90VyyYE/K29P8mRpSd4bumv6PkprXICY1Umdu7m5d4ZTrVOs7+0i3eMPEwQQ7fZoNts0Gk2cGnG+J69NEeFTydP8Gx4il4pf8+BcIB9Q/tWhrdOvZUHJx68CVvcGGOMMWZzRORJVX14o+csQ8gYY4wxKzqNBsefPMbxJ16gOXeBbmOGuLNIGi+TpU1UN76Ll/jb8II9eOEevGA7IvkhhmqCi4+Txc/h0lOAIv4UQflRomA7ictwrRjnXsbRAVGqLWjMQkNq+N4QlaRGvdNmsNfDDe2nUZ2mEQyQiKKuDa6FpDPELib0R3AySpj1GGrG0OgRv3SUaWCaUaDFcfkS4pURqSBeGS8Yxas7jrzwNaJmwgMID7CLaGwYmR6iMQrn4mWenP8SH3cfB+A9O9/DP3zLP+TQyKGb88UYY4wxxlxnliFkjDHGbEHOZcydPsXZ55/j3AtHmD93hvmz50h67XWvE8r4VAi0RKAlQucTOJ9AlVonwcuatIImjSihESkqguccw+0MXz3maj7Og0ADIuqMtkcYaXcZSBtMPfi9dFyL/9L4ZZKJYb7t4Hewd/QQnSxDSiMstge4OCdcvKQsL6T9AjE6XWPb7jrb9g4ysafORx8/yedPnuZ3ZZTKriHq330XS6dmWHj2JZZOXmT5/BKdhQXc0gKSKp6CE6VT8QjGRjkXeEh2F75GhNEyO0fnKMlZlhYuMLswS7Ozuk2q9UGSsYhnglNcHGjz0H3v4gce+CFGSyNkSUKWJKRpgktTsiShPjbO2K491uTMGGOMMbeEZQgZY4wxW1wS97h4/Bhnjz7Hmeef5ezR50g6HQB8LwSZAP8gFd9n29Ippoc6pHdP8szAHE92jjATdunVQg7vfZi3738v903cz8Ghg1Scj2s2yRoNunPznD36LKeOPc+Z0y/R7LbZNzjKHr/MaKuLxj16D5ZYGj3I6MI9kAX8u6k/Zfvbf5ifeMuPv6rj6D1rpltLPRpzXUa31yiV1x++VJ8OafdKlCdqSA8q9RKV+3Ywdd+Oda9T50jOnKH73BGe/9InOPXk59n+RI/7W5D6ETMTb+b81COc7N0FuoeRxRc4dOFxSp0FOmFKO4TWQpvmJZ/D5TJ3SwWeep6P/cefveK2rw4Ns+u+B9h9/4Psvv9Bhienrvl7NMYYY4y5XiwgZIwxxtwhXJaxPDvDwvmzXDr7EhfOvsT8+TM0L80Qzy+By7OClwZS0pJjV/cgWn03LtxG2DvOTPQZvrbrOV7crmS+AEcYK4/xnl3fyUd2voe3T799w7t9eeUywfg40b59DD38Tdx7ufK1ExY//iK1p2YIt9f4g+0l/uSrH+bY2/7aa2bQ1IYiakPRq59IY6aZ5WD8PJ5GZHNlNMmQ0H/VS8XzKO3eTWn3bt72wQ9wOF7m2MIx9rEDd+o0B/wAiUq0Oj7HjnY59kzEkZHDG5anTIKkM0hyjrAzR7m3TNRrEPWWoNzF21alvHcv3vg0zW7Kmeee5uhjXwBgYHSCbfvuZd9DD7LrvnsYnd6BeN4VP78xxhhjzPVmTcaMMcaYNwCXZXz98c/wtW98gfNzp8haMX7Tw+s6cD0kSfBjxVvT93LiOxrVlOVawlIthaERHlh8M6MXD9EpbUc0Y8doizd/5Juo7a/STtq00hatpEU7aTNSHuHesXvx5DLBirgNCy/BwkmYPwHzJ/NpdRDVIRrMx6UBOos7WHh6H67nM/hgQv2tJX7z8bP8xQsX+e0ffjhfRjNwWTHtiukM0h40L0LjQj5uXoTGRWhegM7CSnEa6YdYSv8bQv80I4e+TOmug7DrEZh+AIINgkmvQZ1y8eVl2osxSZyR9DLSYpz0MtJeRns5ZuFim8WLTVy2GtTysi6V7jyZF5IGFRK/guoiLjmFS0/h0jOgPQCCMGJy/wGmDt7F5IFDTO0/yPDktAWJjDHGGLNpV2oyZgEhY4wx5jbl1PHlZz/Hlz71h3Sfeomw51AAKeUdIkuESBkkAi8kCxOyaougNstg/SQT4UWG/EPEjfczu3CYxd4IAEOds+y/b5D7f+hbGJwcvHIhukt5oGf+xPrAz/xJaJxb/9ryMIzuAy+AXhN6DVw3ZbH1fbSzbyOQlxgNf5WS9+K1bRA/gvokDBRDfQoGJnnsUsBvfq3FR3/wm4mOnGXhyXFcWqLu/w6Dwe8igQ/bH4Kdb80DRLveli97HalTmrMNFk+dZ/HMHBfPLTBzqUnSnidrLuA3l6kuNRla7lBKO3hZlwtje7k0PE0ii5Ccw2WzqOTHZVEQcteBu7n/Xe9l4oEHCSYnLUBkjDHGmNfNAkLGGGPMbaoZNzndOM35uYtcuDTH3MwyndOLeMfOwsJ5JFkCBC/cj1+6n1IwwbbsBOPuRSbkGPVyi6Xam5gNDzOT7ma2OUjm8sBBmLVJ/LyJV335JbYvP8E9O59m+/vuQQ68G/Y+CiP7oD23JtBzYn3wpz23vsADkzC6P19udH8eABrdlz+ujq57affYAgu/f4xsuUf90UkG3zmAZC3oNaDX4N/82bOcWYr55e99CMQHzwfximlvdZ4fwcBEHnDaoGnZf37yDP/o977O53/6vewZq600TWs/NUM4kjJy8HFKi5+Bc1+DLM4X8gIIa1CqQlgtxmsf1/Kx50PShbQ/9NZMr3nca0JnfoNvWKA2vhLEyvwqF099nXOLs8wkAadcjUvpm6nFjxDqYXBLlDpHkPYRGmELBIa6HlOdgOnhCWq7tlPbM83gXXspH9hHuHs3XvT6s5+MMcYYszVYp9LGGGPMbeTlC2f5/JNf5sUjp/FPBwy1h5GshWazuGwGPzkJJHhSp8bdjLYjRpfPMrT8H6h0LrE2JNIAPD7PNj7PNsCJR6u2g8bkPbS238vwaIUDbxphfN80JX0QOdWEk5+BZ383X4EfQdZbs0aBoV15kOee71oT/CmCPtHAa34+18tY+sQJWo9fIJioMPGjDxLtfnUm0uNBmeVaCvvftYmtCbUo7y+o2cvvROZVQ0a/7zCVN42z8AfHufTkowx+6/dT/8g25NIzcOYJaM1A0oa4VYzbkLSgu5w3TevPdxmEFQjKxRDl46gOtYnVx6VaEfTZBgNTq5lMtQnww5Wy+sB2YPvyeTj9JXj5r8hOPcbXFz/D58JtnOx8M9vm3slo55uJXIOs9wxL8g2Wyi2OyjLB7B78xhTeNzKq3S9RbX+McjZPFDSpDGSMjJcZ27mdyvZdVN/6Vkr79todzowxxhizIQsIGWOMMZugqqTnz9N78UXiF1+ApIVfUvwgRUpKXJngfHeI42eanDt5kbihqFM0m2c4m0WzGVLSlfWVxWM66PCmygl2Dy3i7X4Y7v4A3P13kPpkkTkjaJyg3Q6u20U7HVyng+t0wRPKd91FMD29cSDgHf8AVGHmKLz8lzB3AoZ3Fdk++2F49zX1t+PijN4LC3SenaNzZB7tpQy8ewdD79+zYQfPAK1eykC08XOvRy3KD2facbZufuW+cUp7h1j8oxdZ/vNTdJ6dY+Rv3UPpHW/d9Htu2uA03Pc34L6/gQ+8pbvMW848Aae+xIsv/zaPz79M2wUkroJkFdLFKm6uQdp8jLT7GAAdhLnQh5IPeNDzkHMB3vk2kbtA9fe+RlkdOq54h+oMP3IPew4/wM7hHdTC2i39+MYYY4y59azJmDHGmNtDlsKxT0HjPOx9N4wf2rB50E2lCr3llQ6M03Mn6T79NN3jL9M7M0N8YYnuXI+uejTLJZpRicWBQZYrNbqhkHoZkG246kBhNHCMD0dMTg8yyVHG4xeIAoU974J7PwT3fHeeaXKbcp2UzvPzdJ+ZpfvCApo4pBJQuWeU2iPTRHuu3D/RB3/tC+warfIbP7hhFvNV++qpBf7mv32Mh/eM8PDeUQ5P1bl7qs6BiQFKQd58rvPsLAt/cBzXThn81l3Uv2UX4t/GffK4LK97vcZKf0z0GsyfP8PxZ46SdNpkSQ8X94jjDsudjGZPaMc+vU6Kizur65I6XjCJF0whUifx5omDc3iDswxtK7Nv6m4OTt/H9MR+Kvfcg5RKt+5zG2OMMea6sj6EjDHG3L6Wz8FXfxue/K31nRQP7oD974X93wL735M3xbneshQWX86zZWaPwtLZ/M5VzUuk8xeYPRVyYXEnl+K9LOouWuE4KU1SnSNjjlQXydwyrMnwgRLij4A/SBKkOL/NQNBjMupyaPsQuybL1MIW5WQWac/lTZe6S3lnx/d9OA8C3YjPep1kjZjOc3N0npml92J+K3uvXqJy3xiV+8eI9g1dMdDinPL4yXk+/vWz/Ocnz/LXH5zmo3/7oU2VqZtk/NOPPcM3zizx4kyTJMuPbQJP2D9R4+6pQQ5P1blnqMq9zy2SPjOHPxJRPjRCtH+IaP8Q/uCd1Q9P0m5z8djTXHj+aU4fOcr5ky/R6XY2eKWHrz6lNKPabRO5BG/7OCMPP8y+d34r27bvYWB0DD8IN1j28lQVVBE0b34XlCGwQJMxxhhzs1lAyBhjzO3FOTjxGfjK/w1H/yS/tfiB98HDfw+23QMnvwAnPgsnPg/dxXyZyftXA0R73pH32XK1ki7MHUMvPU96/Cm6zz5D78QpeueWiJuCS4VGMM3cwGEWa/tolCdpVQZw9HBuAU0v4NKzOLeQn+ACzgtIojppOEAa1shKNRpVx4WRC4ztrfLgoXt5x463c9/YfQTeG7OFtmsnJBdaxOdbJMUQn20igBsqMfCmCWoPjFPaWUe8y2dzqSrPnlvmY0+d5Y++fp4Ly12qJZ/33zvJj33LQQ5N1q9bmePUcXK2xfMXljl6ocHRCw2ev9Dg7GIeDBksB/z8PTt4VwvSl5fRXp7BFYxXVoJD0f5h/ME7L3jRaTZozM7Qunieuee/wYXnX2Z2pstSL8BpjLom6hqgGwSO/DJeqUpU9agPOEaGeoxWHcN+ylCjhT/XRmY7JPMxyWJKvKSog3DYwViNgaEGg3uHie65F2/H/fnf+cQ9xV3pNt9ssG+m0eOZs0u848AY5cs0VTTGGGO2EgsIGWOMuT20ZuFr/x6e/H9g4SWojsObPwLf9MP5ieEruQzOfx1OfC4PEJ36Un6XKL+UB4i8ANTlASWX5U28NEPTlM5yyqmljHNLGUuLCssh9WUPL/NJPY92eZjG8DY61RG6UiLTbn5CrE3Q3rpiSBRS3rmN4d07mTxwkF0H72NkcuM+emphjUpQuRFb74ZRp6SzHZILq4Gf5HyLbGl1O0g14GLZ4xOLDT7jEk7gXpWBc/dk3lRrx3AFzxNOzrb4+FPn+NjXz3JipkXoC++5axsfemg777tnG9XSzQuUNboJz5xd5jf+4gSfef4SE/WI//ZbDvA9O8ZwLy/TO7FE7+TSaoBooggQ7R0i3DFAMF65YtDrjczFMRe/8iRnnzpOd+YCS4uzzDWFtKUQp6ReQiIxiZeQaQu0uW55USinQpiVIBjARaNoNA3BLlIZR4qATxg3qHRnqaSXqIQXqQ6eoz5+ibHJMsNT26luu5fSxGGkPpXfma02cdWB38wp//Hxl/mVTx6l0U0Zq5X4O4/s5iPv2MO2evm6bzNjjDHmjcICQsYYY26+LIHmpbwJ1vI5ePYP4bmPgUtgz6Pw8N/N72J1FR0Ya5bhmk3cwizdo1/ghSOfYvnSy8TtkE57hE48TC+tkriIWIVUElSbONdE2aiZzJp145H5AXEZvJEyo9NT7NtxNzun9jMwNs7QtkmGLxP8eSNQVVw7JVvskS3lQ7rYW3282CNbjsEVxwMeBBNVwuka4VSN0nSNp3o9/odPHuXEXIvvfGCaH3l0H6cXOhwtsnCev9DgzMLqdq6VfCYHy5yYbSECj+wb5UMP7eA77p9iuHrrM2+eeGmeX/nTo3z5pXl2jVb4iW+7iw89tAMPSM418+DQKwJEUvIIpgdYqAc851K+2Gxz1lPc1dYLVSKFkoNUVgcVoRL6HNo2wN1TdQ5PDXJocuC2yW7JlpZY/tSniE+cJPVhLk2Y7VSY63gst5RONyVNmqhbRLMF1C0CbnV5zyMOBZGAwEUEroLPAPiDiJRBIkQiVEK6YUKr3KVdbpGWGwz5s+zy57mr3ObBAWGoPpYHkWvjUB3Lm6H5IWeWU37nqxc4MR9zYHqEd989zV+cWOLLpxo4L+SBw3fz4fc8wv27Rm/dhjTGGGNuEQsIGWOMyfupac5A3FjpoDbvrLbouDZe7biWpFPcTjtac6vtyisel/NMnc58fpvufvCn6ICZ9hwAsSvTdiO0gl20d3wnrYlHaWXDtBZ7NBsdeq5LlvYI2svQuETWmsd1WrheB9WMzDkc4ERwoqQS4+iR0UW1zas7bRbwyjivSuwPkgQjSGkbPReBlBBCmkFIeyTA2w0jO1IOjU/w6NSb6DUzluY7NBs9ZtKMs1nKuU7CXCtmttljvhVT8j3G6xFjtRLjAxFjAxHjAyUm6hFjtYjxWshA1yGzHZjronNdWOhCpnkn2UIxyEpxV+cLeP1xPgShRxh4iO/lGSoe+XNO0cShqUNTRVMHafE4WR27VoImbv0m8gV/KMIfKhEMl/GHIoKJCuFUjXCyihQdMV9qdPlf/vgIH3vqHHvGqvzih+7nm++a2LB6NboJL1xsFs20ljmz0OHt+8f46w9OMz10+2VMqSqfe2GGX/nTozx3fpm7Jgf4qfffzbffO7kS/MsSx7EjM5x85hLNU8sMLiXsV48K+fOxwLkSnI2E+UAoO6XqoOKgmkGl/zjL520U4kmBRKCrjhiIURLA8z0qoUfZ9yh7HiVPECUP3KnmMZd+EG8ghHoIAyWoh0i9BPXS6rxagIigTqGdQLMYWgk0ErS1Zl4nhchHKwFJyaPtQ0NgCWUuy5hJM87HCad7KSfaPdSDHVGJqSBkYLjE8EiJUVrUugu45dN0F06TtBfRXoz2ErQb44qxJslrfEs+4g3gSZ0oGCEKapS9EM918eIlKu1Z6r0W1bhLtdSgWm5QrTSo1ppEtZiwlq20RovVZzaYIpw4wNjuw3ijB/KsxE3cWc8YY4x5I7CAkDHmjuGccvRig/NLV876ACj5Pge21ZgaLN/y7A5VZabZ48VLLUZrJfZP1Ag36ng3S6CzAO35vO8c8YoAzJogTFheDcb0P1eW5kGYpTOweBqWTufTa4fe0pULKT6UB6FUh7CMpilp2ydphqTtkLQDWVwiy0I6XkhMSH6KG+BkgCQYJ/VGSLwhEm+A1KuRSRWVECFbHTQDeiTpMnG2SC9dppctEbsWsevmQ9bFFYEeD49yMEDFr1MNhqiGI9RKI9RKQ1TDOhW/QqAhqUDbKQ2n9ARK5YDhwYixkQph5KOBRyPJaCx16TZiXCfFjx0Vp9QRfDauIz2U5UBolTx6FZ/lQLjoMs5mGWe6CVEnZad67MFjLx478QjXrGsGxykcPRQPQShiPmvG/el8EPxiy/aHACGUfAiEvKweEHj54AtxUdaeUzrqaGVKK824mGWcyTIu4XC1kH0HRnjL4QneeWj8sk1p1ja/6SWOH33vAX70vQdum6yV68k55RPPnOejn3qBE7MtHto1zHc/uJ2vnlrgr16cY64VA7B/osajB8d5574x3jZUJZrrkpxtEp9tkpxroXEGAlIO8KoBXuUVQzXEqwRIyXtFAC+fdklGoxmz3IpptRLanYTlXkojyciAFKV/zzpXjDMUH2EMYQJhHI8xhOAVdTlFaaAMXqaeL+KYQ5ktXlcFhhAGEYbxqF/mb0OBVigs+DAjyjmXcSpJOecyLuK4hDKP4gM1hOrKOB9qqgxqxqDL2J602Je0GReh7JUI/Ajfq+D7FQL/8lllvaxNN2vRzXp0XUzPpfRcRi+LybIYl3Ug60DWxKXLlHSOYS4yGCwR+Rmh7/ADD782QlAfxY+qBNUq/uAAfjnEC0p4YYhXKuGF+eAHgud6SNaFtD/08kB62lt9nMUQlMmCCktZSLlap1obhFIVwloxrubN4taNN3h+7e+9McYY8zrc9ICQiHwQ+Ffkx7G/qaq/fKXXW0DImI11k4y5Vsxcs8dss8dsM2ahFVOLAsYH8uyEPEOhxEAUXDbooao0eymzzdV1zTRj2r2UkVqJiWId/XVFwe110nd6vs0Xj8/yl8dn152gXa3BcsDhqUHunhzg3m0lDo/CwWGol8sQ5lkv6kegAVkGi72E+XbCbKvHbDthttHDqTJRFabCLhN+g1FpUddl/O5CngnTns9PBsQjUWGhkzLfzphtp8y28qGVKJl6eOIYlRY7y12mwzajfotBt0wpXsKLezgqqFbzMVWcVlEqZGmVLKuilFDJs11UqqhXBuejWsJphGqEqg84VARE8rHnkeHh8MhUiB10Y0fmPHxCAgkIJCD0AgIvIPBf312FrsRphlOH4ujvd0IvumKgLpaEniTUXfVVz/VQmqEQVwLOpRkvtrv4CDtqEfuGy0xXI0Ilz5SJszxbJnZo5vDKAbLmRD0recy7jPnMEdZCaoNlBgdL1FXwWgnZUky2nDerypaK5lXZK/adAm4oojcU0qgFLFZ9OvWQTj0kC6/91uaq0OylzDR7zDXzLKW14zhbn/kjAqPV0pq/5zyTaaIeEQUeT768wF+dmGOxnWdm3DU5wDsPjPPowXEe2T9KvRzy9Jklfu4Pn+YbZ5Z49OA4v/Ch+9g/MXDNn+GNIs0cv//kGf7Vp49xfqnL5GDEuw6M886D47zr4NgVs5zUKdrLkMi/7v0MteOUYxebxd3T3GsvoErYzYg6GaVORqmbUeqkhLEjiXzi8pqh4pNEPuq/usyCMFQNGR+ImKiWGPE8ypni2gmulZD1/zbWNj1c6r0qG03hMuGkV+v4sOAcC+pYRMkin8HhMpOjJXaNBAyM17iIx+//5XO0Zy+xP1R2BSHlxCNKQypaoSIVQu/KzRKdOlJNSF2PxMU4zVDcym9UPi66JCu6kVcgUSVRSFRI1CPBI8ajJ0JPHIn0SLweQdggDJcphQ0ilqi5BQZlkQFS6kDNCSUVPC3hUUE0wqOMrgyV/HedCEcFcHjSRbwYz0+RIMULFQkcXkmQyMMreUgUIFEJKZeQUoiEgvjkfTh5fn6BQbxi2kfxiv3JIE7qOAbyfY8r49IAELxaiD8Q4tWKoRJcvo6r5kEwBPxwJYClqpApmrn8DoSeXP3fictW+427QufjzimLnaQ4VuqtHO9kTleObfrHTCPVkOAKd0K8E2ROOTXfXsnW3DNW4/Ca/t2MMVvLTQ0IiYgPvAB8O3AGeAL4flV97nLLbOWA0JmFNr/0iecZHygVzQ7W7rTycbXk3/LsBujfQpa8mYLTNf1NFDt2L2/yICLFUZQrOnnNVqe9ID9I8IINr3SpKsudND8Rb/RWmmnMFidA3ThjpBqyreYzURXGawFjtYjRWonRgZDIv9zBgq7rcPZV5Vo79gLwg+LgI3zFdPjqcqtClpClPRYabeaXmyw22yw1Wywut1nq9JhrOTqZj5OATAJUfTIJcAQg+fVaUSVJHUvtmIVWwmIrph0n/fwLBEd++Nafzh9LMa/sw0glYLTiM1IOCJzS6CY0O47lniPLIM+18JBi7OHhI4RAlZQKGRUyBgNlKIABH6q+o+xnlD1Hyc8o+UIYeIS+EAZC4OcZD3lbBpBinKc85MEIKcaIh6qPOh/NvHycCpqCSyX/SjIhjfNgWDdN6cQxaZqCZgSiRKFHFAphkH+vunKo7gDFE4fnEnAxuBhNu2gW4znwNATKOCr5QbbkAReRCkIZkVd3cKvqivcoghloEdR45fvnVaH4j/5Pq/ZzP6Q/7ueErMo3lRCKjydXd5CaaUbm0ny8Zkg1IytOZjzx8Yt1+pJ/8754+WM8PPERgTjrrmbmuC5x1iFxPXquQ5x1SVyXxPXIyHCa5Xk+ktdh9QQ8hSBPYPIiQUqQKHTFp6khSy5kphdysRfQlSo9LyKNBpjasYP7tw1zz0iVQ4MVdtdK1BG0k+I6Ka6doonLT0YGSywHwslewvPNLs/MNXn+YpMXLzXZN17jQ2/eznc9sJ1do68OHl1vqpqfEC/HuGaSN7saq6w0s7pZVJXlbspcs0ecOcZqV3eikznluXPLfPHFWb54fJYnXpqnmzh8Tzg8VefI+WXGBiL+x++8h+9+cPttsf+5mbpJxkyjx86Rypb77Ju10l/VmgBR1oiRwEMiHy8K8nHZLx77eUZV5COlPJimqhy/1OQvj8/yxeNzPH5ijkYvBeDwVJ0Tsy0i3+OnPnA3H3n7HvwNTm6TbkxjfpGlxgKtZoN2s0l3qU28kOCWMqSpSMvhZ0qogufyfbCnxe+xgq+s7C0FwROPwCsRetFrBpw2I1VH5vLf8lRTMucQkSJY7xOIT/A678qWaUqmCU5TlAQBAi/CJ9pwv3clqg7VDk5bqLbzvtq0B4T4EuBJCU9KiJRAyiilvJ+oVzSYXM13y/PfIENI82lNERL6ITeRBCFFJQMv3wc7yffIKUKqHqkKGR4pPik+mfrFtEfo9v9zAAAPl0lEQVSCTw/oAB2EDoIGPn7kEZQ8gigg84RMlFQ8Mk9wCJl4OMmbKyvgRFEBLVr4oqt7c9E81zPwfQarEUPliKFqicEwYjAqMVgKGSiV8MkzBCkyAzVVNFOk1A/q+XiRt/p3Ug7xohAphXkWr+cV38PqOmYXO7x4vslLF5c5danFmbkWJ+dbtNOUFEeCw+FIyQjCkL1TgxyaHOLQVD4cnh5itFbK/34Tx2IrZn65y/xyzEKjy1IzZr6dMN+JaSRZ3nybfDu4/gArx8eewEi19Kpzm/7Fx9d7M4GV84/VKO2aeboSdU5Vme8meTCwHefj1uo5RBR4ebPuNeXqn2sNVcI7IlDWP4/KLyL1VrdBo8dsK6YbZ3z0v3roda8zD+gWzZU9KQK72D7yDeRmB4TeAfy8qn6gePyzAKr6S5db5k4JCJ3/n/4Lmr5yrqwbrT5Y+WUjUwea7+DWzge38eKvsrbBgZ9Pq7/msbdmmle/x7rHqye3/WVkZfmr/QHPd/Cybrx2vavl7s/R4hPqus8iReOK/sDK/HxwxRLrP0OsZVApTtSLd+rvvNf9r8Xya9d/feRdgbzxrz45za/0CnJDfvSduuJANSVzSX7wqwmZJnlAAw+R4oBcikNz8Yry5AEtirL1v8fVssrKP0RwmpG4HqmLSTS/Kty/OpzPy8dOXbG+/nutL8NqWda/D3DZcmg/SNQPIOmaaTQ/CCMjcfGaMsYkLiVzGYlmxJqSaj6dbx/Hyt+vrv4tr/0d6a9fND+ITX2fJPBJggAXlfBrVSpDdcbHRhgdquMHxcGm3++/xgPfY2Jkml3bDlCqjnCmrbww01npSPjohQZLndV+QCbqEcudhF6a1x1PYO9Yjbun6kWHuXXunhpk92h1wxO510tV7YBkk3ppxldfXuSxF2d5/OQ8904P8hPffhdDleuXIWbMtUozxzfOLvHY8Vkee3GO7cMV/vEH7mbb4I2/c5hzytnF/Pfu6MXiN+/8EhcuzjPZa7IzaTHtumwjpZJBVQKGgohK6FHy8vBE4lKSzNHLEpK0R5J1SbM2mYuLLKWEVGNS19/3Xd1xeSB5cCoPUpUIJSLw8qCMLyGBl4/zIVh5HEiIoiSuS+x6JK5XTMcrwf/+fICyXyHyakR+hbKfjyOvSuRXKftVIr+GL8G6z5FqrxgnpC5eme/U4Ul+McITD48AT/pDMR9ZfV58PNZO9y9k9Je/usCYiBTbJbjqiy63m0xTUpfiieBJgC/XnsntigtITrOVYGe+zTebHd4//rg2a5dce7SeH/tfy/rSokz9IW9wu3ohb/15UH5Bsz+sb8i9mfMqWff4lZ/x1Z/61VvwcvO1uBi75rhPNyjDmkMkgY27K0CKzPL8orXig/or8zaWb1ORfIAUEYdIf7tfzxjD+vNB1fWN7pUiUoswNv5bRNFLV17d2qQF51h/t9r1d63lv/saDGy7jp/l1rjZAaHvBT6oqj9SPP4B4BFV/fFXvO7vA38fYPfu3d/08ssvX9dy3AqLv/ExtNMoKlDxR1gEe3JuzXPF0/3KrYKqkKniHMVQPC7mXfkgIf9RWDlJFLfuhFGlP9235kRW1/6RrQ2OuPxnU/J151cDdPUqCcXPnHig/YBRMV1kgvSDSVr8mOdrd4i6PNNF84BO3ohFCT0lEIfvC57n4fsefuDj+37Roaq30uwlyYQkgzhT0lRJMkeaKa2lkSLMIyu/Ra/8KVaELFnGpc38tFnXP8u6xxuFi/RVz+TnpLKaQJSkqzsdXT39X/kmdf2aMvLvWot6opKXq5+V8lq/q8XbF7ujvFmOCsXVvDwdXl1WPJehmuE0zfNeNMM5zb9XVVTz7z7/PEWQUbxiu+Z5SrLySdzqrkr726H/DazJGitelZFnsDjSPPhS7KtEpagP+cYUPw9MqCe4leCEFMGZ/PWe5Fd1ZeWzrtnCKxk8BS8/iMQLEM9HvADPCxAJ8HzB8xQ8h3gOlRQlv5La/zsKxMcXj8Dz8IsrtL6XN8tykm+7rOSTlH3SyCOLArS05uAqc0g3JoodA1nIIBFDUqY6NEo0PEI0OkI0OkZ5bIxwcIggDNdlgKxt7te/2rXYvrqThx3DlZXbgh/cdn3vXqSqXFzu8Xxxt6ljl5oMV8Lb8m5JxhhzPfTSjBMzrZXA+Hyrx8N7RnnnwTF2jlxdlmLcadNcWKCzvETXwZGLTZ4622IpdozWq4zWK4wOVhgpCfUgY9DLiEhwvR5xt0PcbpNkeV9hjThludOlnSrNRGnEjmbPEXnCcMmn5mdUsy5R0iWIY/w4RrsxaTcmc/nFGVf0UrUy+IrzlSwQMsmHFCF1oFme2bty3pQqLsuP43xZ7ftMUDwVfPWK48HiRK6f5UF+Eqb9k9Z+p/oA4q1k3cja81vXz8DVlSyRtZm6V1YcxUi+H+8PAUExLyiywmTlYpDo6kWn/sUiEW/lfftHHasXIvNx/2P284pVHWlQ9AWmaRGUybN8FVeUI2+6HUpA4IUErGnGLflzqo6s+I76QR0lWznudzhccaHEwy+CYHmwx8dfH2TDw6muZBirrq4jCCtUaqP5cVZxnSj//DAx2GBooLty7tJPm1qZLjhVUqekzpFm+ZBljtRlZFn//EiL84LVS8OQN3Pvh1Igz0JSKOYX37Yoog5BCT0pBo9AhED633Oe44d6qHqoCk7J673rj5VMJT/Xyldc1ME151LXfF7l0c/lzz/X6rnf6njtGtYHktZulzWffOVC5Nr3EvHwRYqs8H6gz8Pz8iz017p0Jl5ej/KgTn+6GHvFNKDqrWzPfOzn027NtN6IwGs/AMb64J0U262YN7DtGcLya/SZiaw0nd2oOe3KWDx47z+B8tAN+Dw3120ZEFrrTskQMsYYY4wxxhhjjLldXCkgdCPCd2eBXWse7yzmGWOMMcYYY4wxxpjbwI0ICD0BHBKRfSJSAr4P+PgNeB9jjDHGGGOMMcYYcw1eXzfvV0FVUxH5ceCT5E2I/y9VffZ6v48xxhhjjDHGGGOMuTbXPSAEoKqfAD5xI9ZtjDHGGGOMMcYYYzbnjXnvRWOMMcYYY4wxxhhzzSwgZIwxxhhjjDHGGLPFWEDIGGOMMcYYY4wxZouxgJAxxhhjjDHGGGPMFiOqeqvLgIjMAC/forcfB2Zv0XsbsxGrk+Z2ZPXS3G6sTprbjdVJc7uxOmluR1Yvb749qjqx0RO3RUDoVhKRr6jqw7e6HMb0WZ00tyOrl+Z2Y3XS3G6sTprbjdVJczuyenl7sSZjxhhjjDHGGGOMMVuMBYSMMcYYY4wxxhhjthgLCMGv3+oCGPMKVifN7cjqpbndWJ00txurk+Z2Y3XS3I6sXt5GtnwfQsYYY4wxxhhjjDFbjWUIGWOMMcYYY4wxxmwxFhAyxhhjjDHGGGOM2WK2REBIREZF5M9E5FgxHrnM636oeM0xEfmhNfNLIvLrIvKCiDwvIt9z80pv7kSbrZNrnv+4iDxz40ts7nSbqZMiUhWRPy5+H58VkV++uaU3dxIR+aCIHBWR4yLyMxs8H4nI7xTPPy4ie9c897PF/KMi8oGbWW5zZ7vWeiki3y4iT4rI08X4W2922c2daTO/lcXzu0WkKSI/dbPKbO5sm9x/PyAif1UcRz4tIuWbWfatbEsEhICfAT6tqoeATxeP1xGRUeCfAY8AbwP+2ZoTop8DLqnqXcC9wOdvSqnNnWyzdRIR+ZtA8+YU12wBm62T/0JVDwNvBt4lIt9xc4pt7iQi4gP/B/Ad5Pvb7xeRe1/xsv8aWFDVg8CvAv9bsey9wPcB9wEfBP5tsT5jNmUz9RKYBb5LVd8E/BDw729Oqc2dbJN1su+jwJ/c6LKarWGT++8A+A/AP1DV+4D3AslNKvqWt1UCQh8CfquY/i3gwxu85gPAn6nqvKouAH9GfkAJ8PeAXwJQVaeqsze4vObOt6k6KSIDwE8C//wmlNVsDddcJ1W1raqfBVDVGPgqsPMmlNnced4GHFfVE0Vd+k/kdXOttXX194H3iYgU8/+TqvZU9SRwvFifMZt1zfVSVb+mqueK+c8CFRGJbkqpzZ1sM7+ViMiHgZPkddKY62EzdfL9wDdU9esAqjqnqtlNKveWt1UCQpOqer6YvgBMbvCaHcDpNY/PADtEZLh4/Isi8lUR+T0R2Wh5Y16Pa66TxfQvAv8SaN+wEpqtZrN1EoDiN/O7yLOMjHm9XrOOrX2NqqbAEjB2lcsacy02Uy/X+h7gq6rau0HlNFvHNdfJ4qLiPwH+55tQTrN1bOZ38i5AReSTxfn2P74J5TWF4FYX4HoRkT8HpjZ46ufWPlBVFRF9HasOyK90P6aqPykiPwn8C+AHrrmwZku4UXVSRB4CDqjqT7yyPbgxV3IDfyf76w+A/xf416p64tpKaYwxdx4RuY+8ecT7b3VZzJb388CvqmqzSBgy5lYLgEeBt5Jf7P60iDypqnZx8Sa4YwJCqvptl3tORC6KyLSqnheRaeDSBi87S95esW8n8Dlgjrxi/n/F/N8jb/9ozBXdwDr5DuBhEXmJ/G94m4h8TlXfizFXcAPrZN+vA8dU9deuQ3HN1nQW2LXm8c5i3kavOVMEIYfI99VXs6wx12Iz9RIR2Qn8AfCDqvrijS+u2QI2UycfAb5XRP53YBhwItJV1X9z44tt7mCbqZNngC/0u2URkU8Ab8GyzW+KrdJk7OPkHflRjD+2wWs+CbxfREaKTlLfD3xSVRX4I1ZPgt4HPHdji2u2gM3UyX+nqttVdS95NP0FCwaZ6+Ca6ySAiPxz8h37f38TymruXE8Ah0Rkn4iUyDuJ/vgrXrO2rn4v8JliX/1x4PuKu5jsAw4BX75J5TZ3tmuul0Uz2j8GfkZVv3jTSmzudNdcJ1X13aq6tziO/DXgf7VgkLkONrP//iTwJsnvWhsA78HOt2+arRIQ+mXg20XkGPBtxWNE5GER+U0AVZ0n75fliWL4hWIe5O1sf15EvkHeVOwf3eTymzvPZuukMdfbNdfJ4ur3z5HfVeKrIvKUiPzIrfgQ5o2t6FPgx8kPDo8Av6uqz4rIL4jIdxcv+z/J+8E4Tt65/s8Uyz4L/C75QeSfAj9mnVKa62Ez9bJY7iDwT4vfxqdEZNtN/gjmDrPJOmnMdbfJ/fcC+V3vngCeIu9r7Y9v9mfYqiQPyhljjDHGGGOMMcaYrWKrZAgZY4wxxhhjjDHGmIIFhIwxxhhjjDHGGGO2GAsIGWOMMcYYY4wxxmwxFhAyxhhjjDHGGGOM2WIsIGSMMcYYY4wxxhizxVhAyBhjjDHGGGOMMWaLsYCQMcYYY4wxxhhjzBbz/wMYbgcDz1zmqAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 4))\n",
        "legends = []\n",
        "for i,p in enumerate(parameters):\n",
        "  if p.ndim == 2:\n",
        "    plt.plot([ud[j][i] for j in range(len(ud))])\n",
        "    legends.append('param %d' % i)\n",
        "plt.plot([0, len(ud)], [-3, -3], 'k') # these ratios should be ~1e-3, indicate on plot\n",
        "plt.legend(legends);\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "wnF7TZLBsWVj",
        "outputId": "791b74ae-db40-4af7-dc39-9624852ab572"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad() # this decorator disables gradient tracking\n",
        "def split_loss(split):\n",
        "  x,y = {\n",
        "    'train': (Xtr, Ytr),\n",
        "    'val': (Xdev, Ydev),\n",
        "    'test': (Xte, Yte),\n",
        "  }[split]\n",
        "  emb = C[x] # (N, block_size, n_embd)\n",
        "  x = emb.view(emb.shape[0], -1) # concat into (N, block_size * n_embd)\n",
        "  for layer in layers:\n",
        "    x = layer(x)\n",
        "  loss = F.cross_entropy(x, y)\n",
        "  print(split, loss.item())\n",
        "\n",
        "# put layers into eval mode\n",
        "for layer in layers:\n",
        "  layer.training = False\n",
        "split_loss('train')\n",
        "split_loss('val')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qDFBVoBsXck",
        "outputId": "d0606404-466f-4e32-95d5-cdc3fd7a4b7c"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train 2.635573148727417\n",
            "val 2.652531623840332\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sample from the model\n",
        "g = torch.Generator().manual_seed(2147483647 + 10)\n",
        "\n",
        "for _ in range(200):\n",
        "    \n",
        "    out = []\n",
        "    context = [0] * block_size # initialize with all ...\n",
        "    while True:\n",
        "      # forward pass the neural net\n",
        "      emb = C[torch.tensor([context])] # (1,block_size,n_embd)\n",
        "      x = emb.view(emb.shape[0], -1) # concatenate the vectors\n",
        "      for layer in layers:\n",
        "        x = layer(x)\n",
        "      logits = x\n",
        "      probs = F.softmax(logits, dim=1)\n",
        "      # sample from the distribution\n",
        "      ix = torch.multinomial(probs, num_samples=1, generator=g).item()\n",
        "      # shift the context window and track the samples\n",
        "      context = context[1:] + [ix]\n",
        "      out.append(ix)\n",
        "      # if we sample the special '.' token, break\n",
        "      if ix == 0:\n",
        "        break\n",
        "    \n",
        "    print(''.join(itos[i] for i in out)) # decode and print the generated word"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3E7c5ufsYam",
        "outputId": "c33c76a0-407f-474c-ac70-85922a80cc5a"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "porzois@\n",
            "'hiner s@\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DONE; BONUS content below, not covered in video"
      ],
      "metadata": {
        "id": "Dlpcrq5IsZn5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# BatchNorm forward pass as a widget\n",
        "\n",
        "from ipywidgets import interact, interactive, fixed, interact_manual\n",
        "import ipywidgets as widgets\n",
        "import scipy.stats as stats\n",
        "import numpy as np\n",
        "\n",
        "def normshow(x0):\n",
        "  \n",
        "  g = torch.Generator().manual_seed(2147483647+1)\n",
        "  x = torch.randn(5, generator=g) * 5\n",
        "  x[0] = x0 # override the 0th example with the slider\n",
        "  mu = x.mean()\n",
        "  sig = x.std()\n",
        "  y = (x - mu)/sig\n",
        "\n",
        "  plt.figure(figsize=(10, 5))\n",
        "  # plot 0\n",
        "  plt.plot([-6,6], [0,0], 'k')\n",
        "  # plot the mean and std\n",
        "  xx = np.linspace(-6, 6, 100)\n",
        "  plt.plot(xx, stats.norm.pdf(xx, mu, sig), 'b')\n",
        "  xx = np.linspace(-6, 6, 100)\n",
        "  plt.plot(xx, stats.norm.pdf(xx, 0, 1), 'r')\n",
        "  # plot little lines connecting input and output\n",
        "  for i in range(len(x)):\n",
        "    plt.plot([x[i],y[i]], [1, 0], 'k', alpha=0.2)\n",
        "  # plot the input and output values\n",
        "  plt.scatter(x.data, torch.ones_like(x).data, c='b', s=100)\n",
        "  plt.scatter(y.data, torch.zeros_like(y).data, c='r', s=100)\n",
        "  plt.xlim(-6, 6)\n",
        "  # title\n",
        "  plt.title('input mu %.2f std %.2f' % (mu, sig))\n",
        "\n",
        "interact(normshow, x0=(-30,30,0.5));\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368,
          "referenced_widgets": [
            "01b16f72c8e24bf4970afbec903735ae",
            "eeb322ac57694486ab8905a0bbea94b4",
            "c920e4d0a0554025a70ad5b8c3f12953",
            "80bb566448764cd78ef5bc612bd1c594",
            "500874e2f3a34ca9a9e774b7a7816090",
            "437c009fcb4046b68a9427911c44235c",
            "c082c4f6bc1d4cbcae035062a6c2872a"
          ]
        },
        "id": "Ak-nkUivsa6O",
        "outputId": "e1f2de11-fa3e-4c2c-baa2-366ab54e61b5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='x0', max=30.0, min=-30.0, step=0.5), Output()), _dom…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01b16f72c8e24bf4970afbec903735ae"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Linear: activation statistics of forward and backward pass\n",
        "\n",
        "g = torch.Generator().manual_seed(2147483647)\n",
        "\n",
        "a = torch.randn((1000,1), requires_grad=True, generator=g)          # a.grad = b.T @ c.grad\n",
        "b = torch.randn((1000,1000), requires_grad=True, generator=g)       # b.grad = c.grad @ a.T\n",
        "c = b @ a\n",
        "loss = torch.randn(1000, generator=g) @ c\n",
        "a.retain_grad()\n",
        "b.retain_grad()\n",
        "c.retain_grad()\n",
        "loss.backward()\n",
        "print('a std:', a.std().item())\n",
        "print('b std:', b.std().item())\n",
        "print('c std:', c.std().item())\n",
        "print('-----')\n",
        "print('c grad std:', c.grad.std().item())\n",
        "print('a grad std:', a.grad.std().item())\n",
        "print('b grad std:', b.grad.std().item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LzwPjLUscUo",
        "outputId": "93180817-9256-4cdb-bd92-a6de6f66c9c6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a std: 0.9875972270965576\n",
            "b std: 1.0006722211837769\n",
            "c std: 31.01241683959961\n",
            "-----\n",
            "c grad std: 0.9782556295394897\n",
            "a grad std: 30.8818302154541\n",
            "b grad std: 0.9666601419448853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Linear + BatchNorm: activation statistics of forward and backward pass\n",
        "\n",
        "g = torch.Generator().manual_seed(2147483647)\n",
        "\n",
        "n = 1000\n",
        "# linear layer ---\n",
        "inp = torch.randn(n, requires_grad=True, generator=g)\n",
        "w = torch.randn((n, n), requires_grad=True, generator=g) # / n**0.5\n",
        "x = w @ inp\n",
        "# bn layer ---\n",
        "xmean = x.mean()\n",
        "xvar = x.var()\n",
        "out = (x - xmean) / torch.sqrt(xvar + 1e-5)\n",
        "# ----\n",
        "loss = out @ torch.randn(n, generator=g)\n",
        "inp.retain_grad()\n",
        "x.retain_grad()\n",
        "w.retain_grad()\n",
        "out.retain_grad()\n",
        "loss.backward()\n",
        "\n",
        "print('inp std: ', inp.std().item())\n",
        "print('w std: ', w.std().item())\n",
        "print('x std: ', x.std().item())\n",
        "print('out std: ', out.std().item())\n",
        "print('------')\n",
        "print('out grad std: ', out.grad.std().item())\n",
        "print('x grad std: ', x.grad.std().item())\n",
        "print('w grad std: ', w.grad.std().item())\n",
        "print('inp grad std: ', inp.grad.std().item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUTxd4WIsd8i",
        "outputId": "ab66ec77-fbc0-4580-bc37-c48443757948"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inp std:  0.9875972270965576\n",
            "w std:  1.0006722211837769\n",
            "x std:  31.01241683959961\n",
            "out std:  1.0\n",
            "------\n",
            "out grad std:  0.9782556295394897\n",
            "x grad std:  0.031543977558612823\n",
            "w grad std:  0.031169468536973\n",
            "inp grad std:  0.9953053593635559\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hr5de3OBsfjy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}