docs/user/advanced_examples/PoissonRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b760a0f6",
   "metadata": {},
   "source": [
    "# Poisson Regression\n",
    "\n",
    "This tutorial shows how to train several Generalized Linear Models (GLM) with scikit-learn, quantize them and run them in FHE using Concrete-ML. We make use of strong quantization to ensure the accumulator of the linear part does not overflow when computing in FHE (7-bit accumulator). We show that conversion to FHE does not degrade performance with respect to the quantized model working on values in the clear.\n",
    "\n",
    "Contrarily to other models like LinearRegression, a GLM for FHE has currently not been integrated in Concrete-ML. So we have to define this here, explicitely, in this notebook. In a further version of the package, GLM will be natively available."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253288cf",
   "metadata": {},
   "source": [
    "### Import libraries\n",
    "\n",
    "We import scikit-learn libraries and Concrete-Numpy quantization tools:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6200ab62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import PoissonRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm\n",
    "\n",
    "from concrete.ml.quantization import QuantizedArray, QuantizedExp, QuantizedLinear, QuantizedModule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f43e2387",
   "metadata": {},
   "source": [
    "And, finally, we import some helpers for visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d104c8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e676b8",
   "metadata": {},
   "source": [
    "### Insurance claims dataset\n",
    "\n",
    "In this tutorial, we show how to build a regression model that predicts the frequency of incidents in an insurance setting.\n",
    "\n",
    "We download a dataset from OpenML that contains 670,000 examples giving the frequency of car accidents for drivers of various ages, past accident history, car type, car color, geographical region, etc. We take only the first 50,000 examples to speed up training.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d451e829",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, _ = fetch_openml(\n",
    "    data_id=41214, as_frame=True, cache=True, data_home=\"~/.cache/sklean\", return_X_y=True\n",
    ")\n",
    "df = df.head(50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a70df7",
   "metadata": {},
   "source": [
    "The target variable is the number of claims per year, which is computed by the following formula :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e163891",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Frequency\"] = df[\"ClaimNb\"] / df[\"Exposure\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f4fdb7",
   "metadata": {},
   "source": [
    "Let's visualize our dataset, showing that the target variable, \"Frequency\" has a Poisson distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a124a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ioff()\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 7))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax[0].set_title(\"Frequency of claims vs. Driver Age\")\n",
    "ax[0].set_xlabel(\"Driver Age\")\n",
    "ax[0].set_ylabel(\"Frequency of claims\")\n",
    "ax[0].scatter(df[\"DrivAge\"], df[\"Frequency\"], marker=\"o\", color=\"#ffb700\")\n",
    "ax[1].set_title(\"Histogram of Frequency of claims\")\n",
    "ax[1].set_xlabel(\"Frequency of claims\")\n",
    "ax[1].set_ylabel(\"Count\")\n",
    "df[\"Frequency\"].hist(bins=30, log=True, ax=ax[1], color=\"black\")\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8310ab",
   "metadata": {},
   "source": [
    "We split the data into a training and a test set, but we also keep a part of the data to be used for calibration. This calibration set is not used for training, nor for testing the model. Thus, we ensure better generalization of the quantized model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d81db277",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=0)\n",
    "df_calib, df_test = train_test_split(df_test, test_size=100, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4690cc15",
   "metadata": {},
   "source": [
    "## Simple single variable insurance incident frequency predictor\n",
    "\n",
    "Our initial example only uses a single predictor feature, so we can easily visualize results. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa5247c",
   "metadata": {},
   "source": [
    "We first train the scikit-learn PoissonRegressor model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "682fb2d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = PoissonRegressor(max_iter=300)\n",
    "reg.fit(df_train[\"DrivAge\"].values.reshape(-1, 1), df_train[\"Frequency\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084fb296",
   "metadata": {},
   "source": [
    "We can now test this predictor on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4953b03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = np.sort(df_test[\"DrivAge\"].values).reshape(-1, 1)\n",
    "predictions = reg.predict(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28155cf",
   "metadata": {},
   "source": [
    "Now, let's visualize our predictions to see how our model performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "111574ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax.plot(test_data, predictions, color=\"black\", label=\"Float clear trend line\")\n",
    "ax.scatter(df_test[\"DrivAge\"], df_test[\"Frequency\"], marker=\"o\", color=\"#ffb700\")\n",
    "ax.set_xlabel(\"Driver Age\")\n",
    "ax.set_ylim(0, 10)\n",
    "ax.set_title(\"Regression with sklearn\")\n",
    "ax.set_ylabel(\"Frequency of claims\")\n",
    "ax.legend(loc=\"upper right\")\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429d8cc8",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "The trend line obtained from the model suggests an increase of incidents with driver age, but the data shows that incidents peak around 30 to 40 years of age, with a decrease afterwards. This simple model does not seem to be a good one. We convert it to FHE to visually display some details of the conversion. In the second part of this example we will train a more powerful model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d959640",
   "metadata": {},
   "source": [
    "### FHE models need to be quantized, so let's define a **quantized Poisson regressor**\n",
    "\n",
    "We use the quantization primitives available in the Concrete library (QuantizedArray, QuantizedOp, and QuantizedLinear) to define a Poisson regressor which is a generalized linear model with exponential link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f5acbfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuantizedGLM(QuantizedModule):\n",
    "    def __init__(self, n_bits, sklearn_model, calibration_data) -> None:\n",
    "        super().__init__([\"module_input\"], [\"invlink\"], {})\n",
    "\n",
    "        # Create a QuantizedLinear layer\n",
    "        self.n_bits = n_bits\n",
    "\n",
    "        self.q_inputs = [QuantizedArray(n_bits, calibration_data)]\n",
    "\n",
    "        q_weights = QuantizedArray(2, np.expand_dims(sklearn_model.coef_, 1), is_signed=False)\n",
    "        q_bias = QuantizedArray(1, sklearn_model.intercept_)\n",
    "        q_layer = QuantizedLinear(6, q_weights, q_bias)\n",
    "        quant_layers_dict = {}\n",
    "        # Calibrate and get new calibration_data for next layer/activation\n",
    "        calibration_data = self._calibrate_and_store_layers_activation(\n",
    "            \"module_input\", \"linear\", q_layer, calibration_data, quant_layers_dict\n",
    "        )\n",
    "\n",
    "        # Create a new quantized layer (based on type(layer))\n",
    "        q_exp = QuantizedExp(n_bits=7)\n",
    "        calibration_data = self._calibrate_and_store_layers_activation(\n",
    "            \"linear\", \"invlink\", q_exp, calibration_data, quant_layers_dict\n",
    "        )\n",
    "\n",
    "        self.quant_layers_dict = quant_layers_dict\n",
    "\n",
    "    def _calibrate_and_store_layers_activation(\n",
    "        self, layer_input, name, q_function, calibration_data, quant_layers_dict\n",
    "    ):\n",
    "        # Calibrate the output of the layer\n",
    "        q_function.calibrate(calibration_data)\n",
    "        # Store the learned quantized layer\n",
    "        quant_layers_dict[name] = ((layer_input,), q_function)\n",
    "        # Create new calibration data (output of the previous layer)\n",
    "        q_calibration_data = QuantizedArray(self.n_bits, calibration_data)\n",
    "        # Dequantize to have the value in clear and ready for next calibration\n",
    "        return q_function(q_calibration_data).dequant()\n",
    "\n",
    "    def quantize_input(self, *values):\n",
    "        q_input_arr = deepcopy(self.q_inputs[0])\n",
    "        q_input_arr.update_values(values[0])\n",
    "        return q_input_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab82ae87",
   "metadata": {},
   "source": [
    "### We can now convert the scikit-learn model to our quantized version\n",
    "\n",
    "First, we get the calibration data, and we then run it through the non-quantized (float) model to determine all possible intermediate values. After each operation, these values are quantized and the quantized version of the operations are stored in the QuantizedGLM module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09d12194",
   "metadata": {},
   "outputs": [],
   "source": [
    "calib_data = np.expand_dims(df_calib[\"DrivAge\"].values, 1)\n",
    "n_bits = 5\n",
    "q_glm = QuantizedGLM(n_bits, reg, calib_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2528092",
   "metadata": {},
   "source": [
    "Once the model's parameters and input ranges are quantized, we can quantize our test data and perform quantized inference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0f0699a",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_test_data = q_glm.quantize_input(test_data)\n",
    "y_pred = q_glm.forward_and_dequant(q_test_data.qvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a50eb8",
   "metadata": {},
   "source": [
    "Let's visualize the results of the quantized model. We can measure the goodness of fit on the test data using the Poisson deviance. We then plot the two trend lines (float value model and quantized model) to check for differences. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04777aeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean Poisson deviance (float): 3.711521947502187\n",
      "mean Poisson deviance (quant): 3.711584939155082\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_poisson_deviance\n",
    "\n",
    "y_gt = df_test[\"Frequency\"]\n",
    "gt_weight = df_test[\"Exposure\"]\n",
    "\n",
    "dev_real = mean_poisson_deviance(y_gt, predictions, sample_weight=gt_weight)\n",
    "dev_q = mean_poisson_deviance(y_gt, y_pred, sample_weight=gt_weight)\n",
    "\n",
    "print(f\"mean Poisson deviance (float): {dev_real}\")\n",
    "print(f\"mean Poisson deviance (quant): {dev_q}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fb15eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "# ax.set_yscale(\"log\")\n",
    "ax.plot(test_data, predictions, color=\"black\", label=f\"Float clear trend line, d={dev_real:.3f}\")\n",
    "ax.scatter(df_test[\"DrivAge\"], df_test[\"Frequency\"], marker=\"o\", color=\"gray\", label=\"Test data\")\n",
    "ax.set_xlabel(\"Driver Age\")\n",
    "ax.set_ylim(0, 10)\n",
    "ax.set_title(\"Poisson Regression, float in clear vs. quantized \")\n",
    "ax.set_ylabel(\"Frequency of claims\")\n",
    "ax.plot(test_data, y_pred, color=\"red\", label=f\"Quantized trend line, d={dev_q:.3f}\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.grid()\n",
    "\n",
    "# inset axes....\n",
    "axins = ax.inset_axes([0.5, 0.5, 0.47, 0.47])\n",
    "axins.plot(test_data, predictions, color=\"black\", label=f\"Float clear trend line, d={dev_real:.3f}\")\n",
    "axins.plot(test_data, y_pred, color=\"red\", label=f\"Quantized trend line, d={dev_q:.3f}\")\n",
    "# sub region of the original image\n",
    "x1, x2, y1, y2 = 60, 65, 2.3, 2.7\n",
    "axins.set_xlim(x1, x2)\n",
    "axins.set_ylim(y1, y2)\n",
    "# axins.set_xticklabels([])\n",
    "# axins.set_yticklabels([])\n",
    "axins.grid()\n",
    "ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
    "\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8854b2",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "In the graph above, the trend line of the quantized model is more jaggy and has slightly higher deviance. The tradeoff between better fit and compatibility with FHE compilation needs to be made by the practitioner."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af6bc89e",
   "metadata": {},
   "source": [
    "### Now it's time to make the inference homomorphic. Compiling a model to FHE is done with a single line of code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe9935bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = q_glm.compile(q_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46753da7",
   "metadata": {},
   "source": [
    "Now we can test the model on the test set in FHE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca928b78",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [02:06<00:00,  1.27s/it]\n"
     ]
    }
   ],
   "source": [
    "y_pred_fhe = np.zeros((test_data.shape[0],), np.float32)\n",
    "for i, test_sample in enumerate(tqdm(q_test_data.qvalues)):\n",
    "    q_sample = np.expand_dims(test_sample, 1).transpose([1, 0]).astype(np.uint8)\n",
    "    q_pred_fhe = engine.encrypt_run_decrypt(q_sample)\n",
    "    y_pred_fhe[i] = q_glm.dequantize_output(q_pred_fhe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f67b3f",
   "metadata": {},
   "source": [
    "Finally, we check if there are any differences to the quantized model on non-encrypted clear data by plotting the trend lines. Sometimes, FHE noise can create minor artifacts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92c7f2f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAHwCAYAAABdQ1JvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACotUlEQVR4nOzdeVhV1frA8e9hRkZBHAAVUWSSQQaZHDADcx4wc8pMDafKssluN7XuzdtgZYNDlqWZpWV6HTBTSZxRUVEUBxxQRMUZmcf9+4Pr+UmAoMI5DO/neXiUvdfe6333OeLLOmuvrVIURUEIIYQQQghRIR1tByCEEEIIIURtJ0WzEEIIIYQQlZCiWQghhBBCiEpI0SyEEEIIIUQlpGgWQgghhBCiElI0CyGEEEIIUQkpmoWoInd3d2JiYrQdRp23fPlywsPDa7wfRVF4/vnnady4MZ06dSImJgZ7e/sa7xdg9uzZjB8/vlrPmZycjEqlorCwsFrP21D16tWLpUuXVus5Z82axahRo6r1nKJicr2FpknRLBocBwcHjI2NMTU1pVmzZowZM4bMzMxKjzt+/DihoaE1H2AVqFQqTExMMDU1xc7OjmnTplFUVKTtsKpk5MiRbN68ucb72bVrF1u2bOHSpUvs37+/2s5bleL1H//4B99991219SkeT3nF1R9//MFzzz2nsRhiYmLQ0dHB1NRU/dWvX78K44OSf+dnzpxRt9HX1y91vKWlpcbirwkODg5s3bpV22EIUWVSNIsGaf369WRmZnLo0CHi4uL497//re2QHtqRI0fIzMxk+/btrFy5ku+//77a+6jLo5oXLlzAwcEBExMTbYdSq9Xl17iusbW1JTMzU/21fv36hzr+mWeeKXX8nTt3aibQKqorv6gLUV2kaBYNmp2dHb169eLYsWMArFu3Dnd3dywtLQkNDeXEiRPqtvePiuzfvx8/Pz/Mzc1p1qwZ06ZNAyA3N5dRo0ZhbW2NpaUl/v7+pKWlAXD58mX69++PlZUV7dq149tvv1Wfe9asWQwdOpTRo0djZmaGu7s7cXFxVcqhXbt2hISEEB8fr962YcMGvL29sbS0JDg4mKNHj6r3HTp0iI4dO2JmZsbTTz/NM888wz//+U8A9RSGjz76iObNm/P8889TXFzMhx9+SNu2bbG2tmbo0KHcunWr0nyXLFmCo6MjZmZmtGnThuXLl6u3d+7cWR3Pnj178Pf3x8LCAn9/f/bs2aPeFxoayrvvvktISAhmZmaEh4dz48aNSq/J4sWLGT9+PHv37sXU1JSZM2eWaXPixAlCQ0OxtLTE3d2ddevWqfdFRUXRsWNHzM3NadmyJbNmzVLv69q1KwCWlpaYmpqyd+/eMue+f+Tw3sj00qVLadWqFU2aNOGDDz6oMPacnBxee+01WrdujYWFBZ07dyYnJ6dMu/T0dMaNG0eLFi2ws7Pjn//8p7qIOXv2LE888QTW1tY0adKEkSNHliqwHBwc+Oijj/D09MTExKRM4Txp0iRef/31UtsGDBjAZ599BsBHH32EnZ0dZmZmODs7Ex0dXWE+9zt8+DA+Pj6YmZnxzDPPMGzYMPV77+/vCyg90vqg1+RB13jTpk3Mnj2blStXYmpqipeXF1Dy3rr3aYCXl1epEVyVSqWeihUbG0twcDCWlpZ4eXmVmqJ1/vx5unXrhpmZGWFhYVV6b2rKg+Ku7N/Vrl271Me2bNmSJUuWADBmzBgmTZpE7969MTEx4bPPPqNZs2aliufVq1err/GsWbMYMmQIzzzzDGZmZvj4+HDkyBEAnn32WS5evEi/fv0wNTXl448/rjTu2ny9RQOhCNHAtG7dWtmyZYuiKIpy8eJFxc3NTfnnP/+pnDp1SmnUqJGyefNmJT8/X/noo4+Utm3bKnl5eWWOCwwMVH788UdFURQlIyND2bt3r6IoirJw4UKlb9++SlZWllJYWKjExcUp6enpiqIoSpcuXZRJkyYpOTk5yuHDh5UmTZoo0dHRiqIoysyZMxVDQ0MlKipKKSwsVKZPn64EBARUmAOgJCUlKYqiKCdOnFCaN2+ufPbZZ4qiKMqhQ4cUGxsbJTY2ViksLFSWLFmitG7dWsnNzVXy8vKUVq1aKXPnzlXy8/OV33//XdHX11feeecdRVEUZdu2bYqurq7y5ptvKrm5uUp2drYyd+5cJSAgQElJSVFyc3OVyMhIZdiwYQ/MNzMzUzEzM1NOnjypKIqiXL58WTl27JiiKIryww8/KCEhIYqiKMrNmzcVS0tL5ccff1QKCgqUn3/+WbG0tFRu3LihKIqidOvWTXF0dFROnTqlZGdnK926dVPeeuutKr3O9/dzLzc7OztFURQlPz9fadu2rfLBBx8oeXl5SnR0tGJqaqqOd9u2bcrRo0eVoqIi5ciRI0rTpk2VNWvWKIqiKOfPn1cApaCgoMK+Z86cqYwcObJU+/HjxyvZ2dlKfHy8YmBgoCQmJpZ77OTJk5Vu3boply5dUgoLC5Xdu3crubm5ZfodOHCgEhkZqWRmZippaWmKv7+/snDhQkVRFCUpKUnZvHmzkpubq1y7dk3p0qWLMnXqVHUfrVu3Vry8vJSLFy8q2dnZZWLYvn27Ym9vrxQXFyuKoii3bt1SjIyMlNTUVOXkyZOKvb29kpqaqs7vzJkzlb4e9957n332mZKfn6/89ttvip6envq99/fXS1FKv8+r8ppUdI3vfz3u6datm/Ltt9+WifObb75RnJ2dlfT0dOXSpUuKlZWVEhUVpRQVFSmbN29WrKyslGvXrimKUvJz4NVXX1Vyc3OV7du3K6ampmX6uef+99/flRff3/OvqE15Kov7Qf+ukpOTFVNTU+Xnn39W8vPzlRs3biiHDx9WFEVRnnvuOcXc3FzZtWuXUlRUpOTk5Ciurq7Kxo0b1X0PHDhQmTNnjjpmPT095bffflPy8/OVTz75RHFwcFDy8/MVRSn9M7UqcT/M9RaiJkjRLBqc1q1bKyYmJoqFhYXSqlUrZdKkSUp2drby/vvvK08//bS6XVFRkWJra6ts27ZNfdy9H/BdunRRZsyYoVy/fr3UuRcvXqwEBQUpR44cKbX94sWLio6OjnL37l31tunTpyvPPfecoigl/7n06NFDve/48eOKkZFRhTkAipmZmdKoUSMFUIYNG6bk5uYqiqIoEydOVP75z3+Wat++fXslJiZG2b59u2Jra6suhhRFUUJCQkoVzfr6+kpOTo56v4uLi7J161b195cvX1b09PSUgoKCCvPNzMxULCwslFWrVpUpyu4vjn788UfF39+/1P7AwEDlhx9+UBSl5D/3f/3rX+p98+bNU3r27Fnhdamon3u53StaduzYoTRr1kwpKipS7x82bJgyc+bMcs81depU5ZVXXlEU5dGL5pSUFPV+f39/5ZdffilzXFFRkWJkZKTEx8eX2Xd/v1evXlUMDAxKXduff/5ZCQ0NLTeeNWvWKN7e3urvW7durSxevLjC+IuLi5WWLVsq27dvVxRFURYtWqR0795dUZSSgtzGxkbZsmWLuvipiu3btystWrQo9d4LCgqqctH8d+W9JhVd46oWzTt37lRsbGyUU6dOKYqiKB9++KEyatSoUm3Cw8OVJUuWKBcuXFB0dXWVzMxM9b7hw4c/sGhWqVSKhYWF+mvlypXq+PT19Uvts7CwKFM0/71NRa/3g+K+l3tF/65mz56tDBw4sNzzPvfcc8qzzz5bpq8RI0YoilLyS7CxsbFy+fJldcz3//JfVFSkNG/eXNmxY4eiKGWL5uq83kLUBJmeIRqk//73v9y5c4cLFy4wf/58jI2NuXz5Mq1bt1a30dHRoWXLlqSmppY5fvHixZw+fRoXFxf8/f3ZsGEDUPKRY8+ePRk2bBi2tra8+eabFBQUcPnyZaysrDAzM1Ofo3Xr1qXO3bx5c/XfGzVqRG5u7gPnmx46dIjMzExWrlzJvn37yMrKAkrm8n766adYWlqqv1JSUrh8+TKXL1/Gzs4OlUqlPk/Lli1LndfGxgYjIyP19xcuXGDQoEHqc7m6uqKrq0taWlqF+ZqYmLBy5UoWLlxIixYt6NOnDydPniyTw9+veVWuS1Vu2qzM5cuXadmyJTo6//8j8P5+9+3bR/fu3bGxscHCwoKFCxc+9kfBVcnjxo0b5Obm0rZt2wee68KFCxQUFNCiRQv16zJhwgSuXbsGQFpaGsOGDcPOzg5zc3NGjRpVJv6/v+73U6lUDBs2jF9++QWAn3/+mZEjRwIl04Hmzp3LrFmzaNq0KcOGDePy5cuV5l/ee+/vr/2DVOU1eZz3SkpKCkOHDmXp0qW0b98eKLnOv/32W6l/S7t27eLKlStcvnyZxo0bl5ozX1k+tra23LlzR/01dOhQ9b6hQ4eW2lfefOW/t9m2bVu5/Two7nsqulYpKSkPfP/9/X0zatQo1q9fT1ZWFr/++itdunShRYsW5bbX0dHB3t6+wvdLdV9vIaqbFM1C/I+trS0XLlxQf68oCikpKdjZ2ZVp6+TkxC+//MK1a9d46623GDJkCFlZWejr6zNz5kwSExPZs2cPGzZs4Mcff8TW1pZbt26RkZGhPsfFixfLPffDUKlUDB06lKCgIN5//32g5D+pd955p9R/rtnZ2QwfPpwWLVqQmpqKoijqc6SkpJQ55/1atmzJH3/8Uep8ubm52NnZVZgvQM+ePdmyZQtXrlzBxcWFF154oUz8f7/m1XVdKmNra0tKSgrFxcXl9jtixAj69+9PSkoK6enpTJw4UX3N/n59qlOTJk0wMjLi7NmzD2zXsmVLDA0NuXHjhvo1uXv3LsePHwdKVu9QqVQkJCRw9+5dfvrpp1KveVXyGD58OKtWreLChQvs27ePiIgI9b4RI0awa9cuLly4gEql4q233qo0t/LeexcvXlT/3cTEhOzsbPX3V69eLXX8g16TylSWa05ODgMHDuSVV16hV69e6u0tW7bk2WefLfXez8rKYvr06bRo0YLbt2+rf1n9ez7a9KC4q3Lsg95/f7+WdnZ2BAUFsXr1apYtW8azzz5bav/9P1+Ki4u5dOkStra25Z6rrl5v0XBI0SzE/wwdOpSoqCiio6MpKCjg008/xdDQkODg4DJtf/rpJ65fv46Ojo562ScdHR22bdtGQkICRUVFmJubo6+vrx6xDg4O5u233yY3N5ejR4+yePHialtjdPr06Xz77bdcvXqVF154gYULF7Jv3z4URSErK4uoqCgyMjIICgpCV1eXr7/+msLCQtauXVvpcmwTJ07knXfeURe3169fZ+3atQAV5puWlsbatWvJysrC0NAQU1PTUqO69/Tu3ZvTp0/z888/U1hYyMqVK0lMTKRv375VytvBwUF9k9LDCAgIoFGjRnz88ccUFBQQExPD+vXrGTZsGAAZGRlYWVlhZGTE/v37+fnnn9XH2tjYoKOjw7lz5x6638ro6OgwduxYpk2bxuXLlykqKmLv3r3k5eWVateiRQvCw8N57bXXuHv3LsXFxZw9e5bt27er4zc1NcXCwoLU1FQ++eSTh46lY8eONGnShPHjx9OzZ0/1+/zUqVP89ddf5OXlYWRkhLGxcbmv7d8FBQWhp6fHl19+SUFBAatXry713vPy8uL48ePEx8eTm5tb6ka/ezlV9JpUplmzZiQnJ5f6Jel+Y8eOxcXFhTfffLPU9nujqH/++SdFRUXk5uYSExPDpUuXaN26NX5+fsycOZP8/Hx27dr10Kth1JQHxV2ZkSNHsnXrVn799VcKCwu5efNmqZuMyzN69Gg+/vhjEhISGDx4cKl9Bw8eZPXq1RQWFjJ37lwMDQ0JDAwESl6X+/8d1dXrLRoOKZqF+B9nZ2d++uknXnrpJZo0acL69etZv349BgYGZdpu2rQJd3d3TE1NmTp1KitWrMDY2JirV68yZMgQzM3NcXV1pVu3buqRl19++YXk5GRsbW0ZNGgQ7733Hk8++WS1xO7h4UHXrl355JNP8PPz49tvv+XFF1+kcePGtGvXTl1YGhgYsHr1ahYvXoylpSU//fQTffv2xdDQsMJzT506lf79+xMeHo6ZmRmBgYHs27cPoMJ8i4uL+eyzz7C1tcXKyort27ezYMGCMue2trZmw4YNfPrpp1hbW/Pxxx+zYcMGmjRpUmnO+fn53Lx5U/0f8MMwMDBg/fr1/PHHHzRp0oTJkyfz448/4uLiAsD8+fOZMWMGZmZmvP/++6U+Rm/UqBHvvPMOISEhWFpaEhsb+9D9P8icOXPw8PDA398fKysr3nrrrXKLvR9//JH8/Hzc3Nxo3LgxQ4YMUX/8PnPmTA4dOoSFhQV9+vQpU8hU1YgRI9i6dSsjRoxQb8vLy2P69Ok0adKE5s2bc+3aNf7zn/8AJQ+ucXd3L/dc9957S5YswcrKipUrV5aKq3379syYMYMnn3wSJyenMitpPOg1qczTTz8NlLzffHx8yuxfsWIFa9asKbWCxs6dO2nZsiVr165l9uzZ2NjY0LJlSz755BP16/Hzzz+zb98+rKyseO+99xg9enSVY3oU91YAuf/r3pSc+1UW94O0atWKjRs38umnn2JlZYW3t7d6xYuKDBo0SD2Nq1GjRqX2DRgwgJUrV9K4cWOWLVvG6tWr0dfXB+Dtt9/m3//+N5aWlsyZM6fWXW8h/k6lVPXzLSFEvRQQEMDEiRN5/vnntR3KQ9m1axfz5s1Tz7sVdc+YMWOwt7evk+uki9Latm3LN998U2ogYNasWZw5c4affvpJi5EJUX30tB2AEEKztm/fjrOzM02aNGH58uUcPXqUp556StthPbTOnTuXGY0UQmje77//jkql4oknntB2KELUqBqbnjF27FiaNm1Khw4d1Ntu3bpFWFgYTk5OhIWFcfv27ZrqXghRgVOnTuHl5YWlpSWffvopq1atKnW3uxBCVFVoaCiTJk1i3rx5VZrbLkRdVmPTM3bs2IGpqSmjR49WP23tzTffxMrKiunTp/Phhx9y+/ZtPvroo5roXgghhBBCiGpTo3Oak5OT6du3r7podnZ2JiYmhhYtWnDlyhVCQ0M5depUTXUvhBBCCCFEtdDoZylpaWnqj4GbN29OWlqaJrsXQgghhBDikWjtRkCVSvXABecXLVrEokWLAEhMTHzg06tqSnFxsczR0hK59toj11675Pprj1x77ZFrrz1y7Uu7efNmhU+A1WjR3KxZM65cuaKentG0adMK20ZGRhIZGQmAn58fcXFxmgpTLSYmhtDQUI33K+Taa5Nce+2S6689cu21R6699si1L83Pz6/CfRr91aJ///4sXboUgKVLlzJgwABNdi+EEEIIIcQjqbGiefjw4QQFBXHq1Cns7e1ZvHgx06dPZ8uWLTg5ObF161amT59eU90LIYQQQghRbWpsekZFT+mKjo6uqS6FEEIIIYSoEXX2iYAFBQVcunSJ3NzcGuvDwsKCEydO1Nj5RcXk2pdmZGSEvb09+vr62g5FCCGEaJDqbNF86dIlzMzMcHBweOAqHI8jIyMDMzOzGjm3eDC59v9PURRu3rzJpUuXaNOmjbbDEUIIIRqkOrvGSG5uLtbW1jVWMAtRW6hUKqytrWv0UxUhhBBCPFidLZoBKZhFgyHvdSGEEEK76nTRrG26urp4e3urv5KTk4mJiaFv376PdL74+Hg2btz4UMeEhobWyBrWMTEx7Nmzp9rPe8+YMWNYtWrVA7ePHz+exMTEGovhQXGUZ+3atXh6euLt7Y2fnx+7du0q0yYjI6PUe6JJkya88sorAOzYsQMfHx/09PRK9RkfH09QUBDu7u54enqycuXKaslN1C4pKSl0794dNzc33N3d+eKLL8q0+eSTTxg/fjze3t506NABXV1dbt26BcCmTZtwdnamXbt2fPjhh5oOXwghGrw6O6e5NjA2NiY+Pr7UtuTk5Ec+X3x8PHFxcfTu3fvxAnsEhYWF6On9/9shJiYGU1NTgoODK21bU7777rsa7+Nh9OjRg/79+6NSqTh69ChDhw7l5MmTpdqYmZmVek/4+voyePBgAFq1asWSJUuYM2dOqWMaNWrEjz/+iJOTE5cvX8bX15eePXtiaWlZ0ykJDdLT0+PTTz/Fx8eHjIwMfH19CQsLw83NTd3mjTfewN/fn9DQUNavX8/nn3+OlZUVRUVFTJkyhS1btmBvb4+/vz/9+/cvdawQQoiaJSPNNejWrVsMHDgQT09PAgMDOXr0KAD79+8nKCiIjh07EhwczKlTp8jPz2fGjBmsXLkSb2/vMqONRUVFvP7663To0AFPT0+++uqrMv1t3ryZoKAgfHx8ePrpp8nMzATg/fffx9/fnw4dOhAZGYmiKEDJKPUrr7yCn59fqVGv5ORkFi5cyOeff463tzc7d+5kzJgxTJw4kYCAAN58803Onj3LU089ha+vL126dFEXj2PGjOHll18mODgYR0dH9Yiqoii8+OKLODs78+STT3Lt2rVKr9/9o+impqa88847eHl5ERgYSFpaGgDXr18nIiICf39//P392b179wPP+Shx3GNqaqqeJpGVlVXplInTp09z7do1unTpAoCDgwOenp5lHlfavn17nJycALC1taVp06Zcv369ynGJuqFFixb4+PgAJb9cubq6kpqaWmH7X375heHDhwMlPzPatWuHo6MjBgYGDBs2jLVr12okbiGEECXqxUjzK6+8UmbE93F5e3vzr3/964FtcnJy8Pb2BqBNmzasWbOm1P6ZM2fSsWNH/vvf//LXX38xevRo4uPjcXFxYefOnejp6bF161b+8Y9/8Pvvv/P+++8TFxfH119/XaavRYsWkZycTHx8PHp6euqPbO+5ceMG//73v9m6dSsmJiZ89NFHfPbZZ8yYMYMXX3yRGTNmAPDss8+yYcMG+vXrB0B+fn6Z6R0ODg5MnDgRU1NTXn/9dQAWL17MpUuX2LNnD7q6uvTo0YOFCxfi5OTEvn37mDx5Mn/99RcAV65cYdeuXZw8eZL+/fszZMgQ1qxZw6lTp0hMTCQtLQ03NzfGjh1bxVejpEgNDAzkgw8+4M033+Tbb7/ln//8J1OnTuXVV1+lc+fOXLx4kZ49ez5wqboHxfHqq6+ybdu2MscMGzZM/SCeNWvW8Pbbb3Pt2jWioqIeGPOKFSt45plnHmo+8v79+8nPz6dt27ZVPkbUPcnJyRw+fJiAgIBy92dnZ7Np0yb1z4LU1FRatmyp3m9vb8++ffs0EqsQQogS9aJo1pbypmfcb9euXfz+++8APPHEE9y8eZO7d++Snp7Oc889R1JSEiqVioKCgkr72rp1KxMnTlRPi7Cysiq1PzY2lsTEREJCQoCSYjgoKAiAbdu28fHHH5Odnc2tW7dwd3dXF83PPPNMlfN9+umn0dXVJTMzkz179vD000+r9+Xl5an/PnDgQHR0dHBzc1OPCO/YsYPhw4ejq6uLra0tTzzxRJX7BTAwMFDPFff19WXLli3q63L/vOe7d++SmZmJqalpued5UByff/55pXEMGjSIQYMGsWPHDt599122bt1aYdsVK1awbNmyKuUHJb9sPPvssyxdurTMaLSoPzIzM4mIiGDu3LmYm5uX22b9+vWEhISU+XdeFYsWLWLRokUAHD9+nFatWj1WvHVVcXFxg/131FBzb6h5Q8PO/ebNm9y4cUMjfdWLonnu3Lk1ct6MjIwaOe+7775L9+7dWbNmDcnJyYSGhj72ORVFISwsrMyTGHNzc5k8eTJxcXG0bNmSWbNmlVq6zMTEpMp93GtbXFyMpaVlhb8wGBoaloqrOujr66tHbHV1dSksLFTHEhsbi5GR0WP3UZWR5nu6du3KuXPnuHHjBk2aNClzzJEjRygsLMTX17dKfd+9e5c+ffrwwQcfEBgY+GgJiFqvoKCAiIgIRo4cqZ7rXp4VK1aop2YA2NnZkZKSov7+0qVL2NnZlXtsZGQkkZGRADg7O3Pq1Klqir5uiYmJqZafrXVRQ829oeYNDTt3Pz8/jfXVMH8t0ZAuXbqwfPlyoOQN3aRJE8zNzUlPT1f/h7dkyRJ1ezMzswoL9bCwML755ht1sfj36RmBgYHs3r2bM2fOACXTGU6fPq0ukJs0aUJmZmaVV4p4UCzm5ua0adOG3377DSgpjI8cOfLA83Xt2pWVK1dSVFTElStXyi1OH0V4eHip+d33Cvn9+/czevToh4rj888/Jz4+vszXvYL5zJkz6l8CDh06RF5eHtbW1uXGdf981Mrk5+czaNAgRo8ezZAhQ6p0jKh7FEVh3LhxuLq6Mm3atArbZWZmsn37dgYMGKDe5u/vT1JSEufPnyc/P58VK1bQv39/TYQthBDif6RorkGzZs3i4MGDeHp6Mn36dJYuXQrAm2++ydtvv03Hjh3VRTBA9+7dSUxMLPdGwPHjx9OqVSs8PT3x8vLi559/LrXfxsaGJUuWMHz4cDw9PQkKCuLkyZNYWlrywgsv0KFDB3r27Im/v3+VYu/Xrx9r1qxR3wj4d8uXL2fx4sV4eXnh7u5e6U1JgwYNwsnJCTc3N0aPHq2eOvK4vvzyS+Li4vD09MTNzY2FCxcCcPHiRYyNjas1jt9//50OHTrg7e3NlClTWLlypXr0+97c9nt+/fXXMkXzgQMHsLe357fffmPChAm4u7ur2+7YsYMlS5aol6qr7jn6Qvt2797NsmXL+Ouvv9Sv88aNG1m4cKH6fQsl07rCw8NLfQqkp6fH119/Tc+ePXF1dWXo0KHq948QQgjNUCnV9fl5DfLz8ytzs9qJEydwdXWt0X7lUc7a87jX/o033uDZZ5/F09OzGqPSLk2856Fhf8xXG1Tn9ZfpGaHaDkMrGmruDTVvaNi5l1cj1tT56sWcZiH+7pNPPtF2CEIIIYSoR2R6hhBCCCGEEJWQolkIIYQQQohKSNEshBBCCCFEJaRoFkIIIYQQohJSNAshhBBCCFEJKZofg66urnq9VW9vb5KTk4mJiVE/7vmeMWPGqB8qEhoairOzs/oYbT3MIjk5udRaz3Fxcbz88suPfd77c73fkiVLuHz58mOfvyIODg7lPkbz/u3BwcE11r8QQggh6jdZcu4xGBsbl3kIRXJycqXHLV++XKOPfSzPvaJ5xIgRQMm6hDUZ05IlS+jQoQO2trZl9hUVFaGrq1tjfd+zZ8+eGu9DCCGEEPWTjDTXQjdv3iQ8PBx3d3fGjx9P69atuXHjBsnJyXTo0EHdbs6cOcyaNQuAb7/9Fn9/f7y8vIiIiCA7OxsoGfl9+eWXCQ4OxtHRUT0KPH36dHbu3Im3tzeff/55qRHy3r17q0fCLSwsWLp0KUVFRbzxxhv4+/vj6enJN998A5Q8GvjFF1/E2dmZJ598kmvXrpXJZ9WqVcTFxTFy5Ei8vb3JycnBwcGBt956Cx8fH3777Tc2b95MUFAQPj4+PP3002RmZgIlI8UzZ87Ex8cHDw8PTp48We41qsozekxNTYH/XwR+yJAhuLi4MHLkSPXxBw8epFu3bvj6+tKzZ0+uXLny0K+fEEIIIeqfejHS/MorUN1PHfb2hn/968FtcnJy1I9PbtOmDWvWrAFQF6P3XLx4sdSUjZEjR6of8RwWFlbmQRzvvfcenTt3ZsaMGURFRbF48eJK4x08eDAvvPACAP/85z9ZvHgxL730EgBXrlxh165dnDx5kv79+zNkyBA+/PBD5syZw4YNG4CSQvKejRs3AiUF5PPPP8/AgQNZvHgxFhYWHDhwgLy8PEJCQggPD+fw4cOcOnWKxMRE0tLScHNzY+zYsaViGzJkCF9//TVz5swpNZptbW3NoUOHuHHjBoMHD2br1q2YmJjw0Ucf8fXXX/PBBx8A0KRJEw4dOsT8+fOZM2cO33333SNdo/sdPnyY48ePY2trS0hICLt37yYgIICXXnqJtWvXYmNjw8qVK3nnnXf4/vvvH+rcQgghhKh/6kXRrC3lTc8A6NKli7oYhZLR3vtVNj1jx44drF69GoA+ffrQuHHjSmM5duwY//znP7lz5w6ZmZn07NlTvW/gwIHo6Ojg5uZGWlpapecCuHHjBs8++yy//vorFhYWbN68maNHj6pHqtPT00lKSmLHjh0MHz4cXV1dbG1teeKJJ6p0foBnnnkGgNjYWBITEwkJCQEgPz+/1PUZPHgwAL6+vurr8ijX6H6dOnXC3t4eQD0f3dLSkmPHjhEWFgaUTBtp0aLFQ51XCCGEEPVTvSia586tmfNmZNTMeR+Vnp4excXF6u9zc3PVfx8zZgz//e9/8fLyYsmSJaVGjg0NDdV/r8o0hqKiIoYNG8aMGTPU00EUReGrr74qVYzD/49KPwoTExP1ucPCwvjll1/U+zLuu/j34tfV1aWwsPCR+7vf/dfk3nkVRcHd3Z29e/dWSx9CCCGEqD9kTnMt1LVrV/XKFn/88Qe3b98GoFmzZly7do2bN2+Sl5dXajQ7IyODFi1aUFBQwPLlyyvtw8zMrFRher/p06fj6enJsGHD1Nt69uzJggULKCgoAOD06dNkZWXRtWtXVq5cSVFREVeuXGHbtm0P3V9gYCC7d+/mzJkzAGRlZZGUlPTA+Cu6Ro/D2dmZ69evq4vmgoICjh8//tjnFUIIIUTdVy9Gmuua++c0N2nShK1bt5baP3PmTIYPH467uzvBwcG0atUKAH19fWbMmEGnTp2ws7PDxcVFfcy//vUvAgICsLGxISAgoMIC9R5PT090dXXx8vJizJgxdOzYUb1vzpw5uLu7q+dlv//++4wfP57k5GR8fHxQFAUbGxv++9//MmjQIP766y/c3Nxo1aoVQUFB5fY3ZswYJk6ciLGxcZmRXBsbG5YsWcLw4cPJy8sD4J133sHHx6fC+Cu6Ro/DwMCAVatW8fLLL5Oenk5hYSGvvPIK7u7uj31uIYQQQtRtKqUqn9drmZ+fH3FxcaW2nThxAldX1xrtNyMjAzMzsxrtoyocHByIi4ujSZMm2g5FY2rLta9NNPGeh/9fXURoR3Vef2dnZ06dOlUt56prGvL7uKHm3lDzhoade3k1Yk2dT6ZnCCGEEEIIUQmZnlEHVOWBKUIIIYQQoubISLMQQmhASkoK3bt3x83NDXd3d7744oty28XHx+Pt7Y27uzvdunVTb3dwcMDDwwNvb2+tP1FUCCEaIhlpFkIIDdDT0+PTTz/Fx8eHjIwMfH19CQsLw83NTd3mzp07zJ07lx07dtCqVasyT9jctm1bg7q3QQghahMZaRZCCA1o0aKFekUYMzMzXF1dSU1NLdXm559/pkuXLurVYJo2barxOIUQQpRPimYhhNCw5ORkDh8+TEBAQKntp0+fJiMjg9DQUHx9ffnxxx/V+1QqFeHh4fj6+rJo0SJNhyyEEA2eTM94RDdv3qRHjx4AXL16FV1dXWxsbADYv38/BgYGDzw+JiYGAwMDgoODK+2rKkvOzZ49m3/84x8PkYEQQhsyMzOJiIhg7ty5mJubl9pXWFjI6dOnOXDgADk5OQQFBREYGEj79u3ZtWsXdnZ2XLt2jbCwMFxcXOjatWuZ8y9atEhdVN++fbvU00EbkszMTMm9gWmoeUPDzl2TpGh+RNbW1sTHxwMwa9YsTE1Nef3116t8fExMDKamplUqmqtCimYhar+CggIiIiIYOXIkgwcPLrPf3t4ef39/TExMMDExoWvXrhw5coT27dtjZ2cHlEzZGDRoEPv37y+3aI6MjCQyMhIoWae5oa7d2pDXrW2ouTfUvKFh565JDWZ6RkJCAnPnzuW9995j7ty5JCQkVHsfBw8epFu3bvj6+tKzZ0+uXLkCwJdffombm5v60dTJycksXLiQzz//HG9vb3bu3FnqPDdv3iQ8PBx3d3fGjx/P/c+fGThwIL6+vri7u6tHk6ZPn05OTg7e3t6MHDmywnZCCO1RFIVx48bh6urKtGnTym0zYMAAEhISKCwsJDs7m3379uHq6kpWVpb6KZ9ZWVls3ryZDh06aDJ8IYRo8BrESHNCQgLr16+noKAAgPT0dNavXw+Ah4dHtfShKAovvfQSa9euxcbGhpUrV/LOO+/w/fff8+GHH3L+/HkMDQ25c+cOlpaWTJw4scLR6ffee4/OnTszY8YMoqKiWLx4sXrf999/j5WVFTk5Ofj7+xMREcGHH37I119/rR75rqidtbV1teQqhHh4u3fvZtmyZepl46DkE6KLFy8CMHHiRFxdXenUqROenp7o6Ogwfvx4OnTowLlz5xg0aBBQMoVjxIgRPPXUU9pKRQghGqQGUTRHR0erC+Z7CgoKiI6OrraiOS8vj2PHjhEWFgZAUVERLVq0AMDT05ORI0cycOBABg4cWOm5duzYwerVqwHo06cPjRs3Vu/78ssvWbNmDVCy7mtSUlK5xXBV2wkhNKNz586lPjWqyLBhw1i4cGGpbY6Ojhw5cqSmQhNCCFEFDaJoTk9Pf6jtj0JRFNzd3dm7d2+ZfVFRUezYsYP169fzwQcfPPLUkJiYGLZu3crevXtp1KgRoaGh5ObmPnI7IYQQQghRNQ1iTrOFhcVDbX8UhoaGXL9+XV00FxQUcPz4cYqLi9VPAvvoo49IT08nMzMTMzMz9RzFv+vatSs///wzAH/88Qe3b98GSor8xo0b06hRI06ePElsbKz6GH19/VLTTypqJ4QQQgghHl6DKJp79OiBvr5+qW36+vrqJeOqg46ODqtWreKtt97Cy8sLb29v9uzZQ1FREaNGjcLDw4OOHTvy8ssvY2lpSb9+/VizZk25NwLOnDmTHTt24O7uzurVq9UPOnjqqacoLCzE1dWV6dOnExgYqD4mMjJSPQ3kQe2EEEIIIcTDaxDTM+7NW46OjiY9PR0LCwt69OhRbfOZZ82apf77jh07yuzftWtXmW3t27fn6NGj5Z7P2tqazZs3l7vvjz/+KHf7Rx99xEcffVRpOyGEEEII8fAaRNEMJYVzdRXJQgghhBCiYWkQ0zOEEEIIIYR4HFI0CyGEEEIIUQkpmoUQQgghhKiEFM1CCCGEEEJUQopmIYQQQgghKiFF82O4dOkSAwYMwMnJCUdHR1588UXy8vKqvZ+YmBj27Nmj/n7hwoX8+OOPj31eBwcHbty4UWb77NmzH/vcFUlOTqZDhw4P3B4XF8cbb7xRYzE8KI6KPPXUU3h5eeHu7s7EiRMpKioq0+aTTz7B29sbb29vOnTogK6uLrdu3QJg7NixNG3atEyfb7zxBi4uLnh6ejJo0CDu3LnzWHkJIYQQomZI0fyIFEVh8ODBDBw4kKSkJJKSksjJyeHNN9+s9r7+XjRPnDiR0aNHV3s/91RUNCuKQnFxcY31e4+fnx+ffPJJjffzMH799VeOHDnCsWPHuH79Or/99luZNm+88Qbx8fHEx8fzn//8h27dumFlZQXAmDFj2LRpU5ljwsLCOHbsGEePHqV9+/b85z//qfFchBBCCPHwpGh+RH/99RdGRkY8//zzAOjq6vL555/z448/kpmZyZIlS3jxxRfV7fv27UtMTAwAkyZNws/PD3d3d2bOnKlu4+DgwMyZM/Hx8cHDw4OTJ0+SnJzMwoUL+fzzz9VPD5w1axZz5szh8uXL6pFNb29vdHV1uXDhAtevXyciIgJ/f3/8/f3ZvXs3ADdv3iQ8PBx3d3fGjx+Poihl8po+fTo5OTl4e3szcuRIkpOTcXZ2ZvTo0XTo0IGUlBQ++eQT/P398fT0VMefnJyMq6srL7zwAu7u7oSHh5OTkwPAwYMH8fLywsvLi3nz5lV6bWNiYnj66aeBkgfHjB07ltDQUBwdHfnyyy/V7X766Sc6deqEt7c3EyZMKHf0934PG8f9zM3NASgsLCQ/Px+VSvXA9r/88gvDhw9Xf9+1a1d1AX2/8PBw9PRKlksPDAzk0qVLDxWXEEIIITSjfjzc5JVXID6+es/p7Q3/+leFu48fP46vr2+pbebm5jg4OHDmzJkHnvqDDz7AysqKoqIievTowdGjR/H09ASgSZMmHDp0iPnz5zNnzhy+++47Jk6ciKmpKa+//jpQ8mRDAFtbW+L/l/e8efPYvn07rVu3ZsSIEbz66qt07tyZixcv0rNnT06cOMF7771H586dmTFjBlFRUSxevLhMbB9++CFff/21+rzJyckkJSWxdOlSAgMD2bx5M0lJSezfvx9FUejfvz87duygVatWJCUl8csvv/Dtt98ydOhQfv/9d0aNGsXzzz/P119/TdeuXR9p2sXJkyfZtm0bGRkZODs7M2nSJM6cOcPKlSvZvXs3+vr6TJ48meXLlz9wBL6iOE6dOsUzzzxT7jExMTFYWloC0LNnT/bv30+vXr0YMmRIhf1kZ2ezadMmvv7664fK8/vvv68wDiGEEEJoV/0omuuYX3/9lUWLFlFYWMiVK1dITExUF82DBw8GwNfXl9WrV1fpfLt37+bbb79VP65769atJCYmqvffvXuXzMxMduzYoT5nnz59aNy4cZXO37p1awIDAwHYvHkzmzdvpmPHjgBkZmaSlJREq1ataNOmDd7e3ur4k5OTuXPnDnfu3KFr164APPvssw/9iO8+ffpgaGiIoaEhTZs2JS0tjejoaA4ePIi/vz8AOTk5NG3atMJzPCgOZ2dn9S8JD/Lnn3+Sm5vLyJEj+euvvwgLCyu33fr16wkJCSl3ZLkiH3zwAXp6eowcObLKxwghhBBCc+pH0Tx3bs2cNyOjwl1ubm6sWrWq1La7d+9y9epVnJ2dOXbsWKn5v7m5uQCcP3+eOXPmcODAARo3bsyYMWPU+wAMDQ2BkukehYWFlYZ45coVxo0bx7p16zA1NQWguLiY2NhYjIyMqp7rA5iYmKj/rigKb7/9NhMmTCjVJjk5WR37vfjvTc94XH8/b2FhIYqi8Nxzz1XLHOCqjjQDGBkZMWDAANauXVth0bxixYpSUzMqs2TJEjZs2EB0dHSl0z6EEEIIoR0yp/kR9ejRg+zsbPUqFkVFRbz22mu8+OKLGBsb4+DgQHx8PMXFxaSkpLB//36gpLA2MTHBwsKCtLS0Ko26mpmZkVFOAV9QUMDTTz/NRx99RPv27dXbw8PD+eqrr9Tf3xtF7dq1Kz///DMAf/zxB7dv3y63P319fQoKCsrd17NnT77//nsyMzMBSE1N5dq1axXGbmlpiaWlpXoUfPny5Q/ItOp69OjBqlWr1H3funWLCxcuADB69Gj19a5KHPdGmsv7srS0JDMzkytXrgAlc5qjoqJwcXEpN6709HS2b9/OgAEDqpTHpk2b+Pjjj1m3bh2NGjV6uIsg6pSUlBS6d++Om5sb7u7ufPHFF+W2i4+Px9vbG3d3d7p166bevmnTJpydnWnXrh0ffvihpsIWQgjxP1I0PyKVSsWaNWtYtWoVTk5OWFtbo6OjwzvvvANASEgIbdq0wc3NjZdffhkfHx8AvLy86NixIy4uLowYMYKQkJBK++rXrx9r1qxR3wh4z549e4iLi2PmzJnqmwEvX77Ml19+SVxcHJ6enri5ubFw4UIAZs6cyY4dO3B3d2f16tW0atWq3P4iIyPx9PQsd6pAeHg4I0aMICgoCA8PD4YMGVJuQX+/H374gSlTpuDt7V3uzYePws3NjX//+9+Eh4fj6elJWFiYurA9evQotra21RZHVlYW/fv3x9PTE29vb5o2bcrEiROBkuX/7l1fgDVr1hAeHl5qdB5g+PDhBAUFcerUKezt7dXzyV988UUyMjIICwvD29tbfV5R/+jp6fHpp5+SmJhIbGws8+bNKzWNCkqmEc2dO5d169Zx/Phx9SotRUVFTJkyhT/++IPExER++eWXMscKIYSoYUod4OvrW2ZbYmJijfd79+7dKrfdvXu30qpVK+XgwYM1GFHD8TDX/n7p6enKkCFDqjma2kET73lFUZRt27ZppJ+Grn///srmzZtLbZs3b54yatSoMm337NmjhIeHq7+fPXu2Mnv27Er7aN++/eMHWkc15PdxQ829oeatKA079/JqxJo6n4w0V5Pg4GAuXLigHlEW2mFubl7uGspC1CbJyckcPnyYgICAUttPnz5NRkYGoaGh+Pr6qqd/paam0rJlS3U7e3t7UlNTNRqzEEI0dPXjRkAhhKgjMjMziYiIYO7cuer1v+8pLCzk9OnTHDhwgJycHIKCgtQr11TVokWLWLRoEQC3b99Wrw/f0GRmZkruDUxDzRsadu6aJEWzEEJoSEFBAREREYwcOVK9vOT97O3t8ff3x8TEBBMTE7p27cqRI0ewt7cnJSVF3e7SpUvY2dmV20dkZCSRkZFAyU2uoaGhNZJLbRcTEyO5NzANNW9o2LlrUp2enqFU001lQtR28l6v+xRFYdy4cbi6ujJt2rRy2wwYMICEhAQKCwvJzs5m3759uLq64u/vT1JSEufPnyc/P58VK1bQv39/DWcghBANW50daTYyMuLmzZtYW1vL2raiXlMUhZs3b1bbuttCO3bv3s2yZcvw8PBQPwRo9uzZXLx4EYCJEyfi6upKp06d8PT0REdHh/Hjx9OhQwcAvv76a3r27ElRURFjx47F3d1dW6kIIUSDVGeLZnt7ey5dusT169drrI/c3FwpVLRErn1pRkZG2NvbazsM8Rg6d+5cpU8Mhg0bVmoZw3t69+5N7969ayI0IYQQVVBni2Z9fX3atGlTo33ExMSoHxctNEuuvRBCCCH+7tatW+zZs4fdu3ezZ88ejfZdZ4tmIYQQQghRfymKQlJSkrpI3r17NydOnABKHhil6WV+pWgWQgghhBBal5eXx8GDB9UF8p49e9TTcC0tLQkODmbUqFGEhITg7+9Po0aN8PPz01h8UjQLIYQQQgiNu379Onv37lUXyXFxceTl5QHQrl07evfuTUhICCEhIbi4uKCjo91F36RoFkIIIYQQNUpRFE6dOqUukHfv3s3p06eBkvvUfH19efHFFwkJCSE4OJhmzZppOeKypGgWQgghhBDVKjc3lwMHDpS6ae/mzZsAWFtbExwczNixYwkJCcHX1xdjY2MtR1w5KZqFEEIIIcRjuXbtWqlR5IMHD1JQUABA+/bt6d+/v3qqhbOzc518xoYUzUIIIYQQosqKi4s5ceJEqVUtzpw5A4ChoSF+fn68+uqr6qkWTZo00XLE1UOKZiGEEEIIUaHs7GwOHDigLpD37t3L7du3AbCxsSEkJITIyEj1VAtDQ0MtR1wzpGgWQgghhBBqV65cUc9D3r17N4cOHaKwsBAAV1dXIiIi1FMt2rVrp5WpFkqxwo6vjmi0TymahRBCCCEaqOLiYo4fP15qPvL58+cBMDIyolOnTrzxxhsEBwcTFBSEtbW1VuO9e+kuP71+mPn/teV4nje+vprrW4pmIYQQQogGIisri3379qlHkffu3Ut6ejoAzZo1IyQkRL30W8eOHTEwMNByxCUSfj/NgplXWXa8I5l0w69RIt8/v5N5RzUXgxTNQgghhBD1VGpqaqlR5Pj4eIqKilCpVLi7uzNs2DCCg4MJCQnB0dGxVq1qkZ+Zz+p/xDF/qQk773phREuGtYtj8j+t8X/ODYB5mnsgoBTNQgghhBD1QVFREQkJCaXmI1+4cAEAY2NjAgICmD59OiEhIQQFBWFpaandgCuQsu8yi14/zbd73EgrDqat3gXm9I1hzGeeWDt10VpcWimaP//8c7777jtUKhUeHh788MMPGBkZaSMUIYQQQog66e7du+zbt4+ff/6Z2bNnExsbS0ZGBgC2traEhITw6quvEhwcjLe3N/r6+lqOuGLFhcVEzznM/C8LWHfFH4Xm9G12gCkvXSTsLR909FprO0TNF82pqal8+eWXJCYmYmxszNChQ1mxYgVjxozRdChCiEeQkJBAdHQ06enpWFhY0KNHDzw8PLQdlhBC1GuKonD69Gn27t3L3r172bNnD8ePH0dRFPUg5KhRo9SrWrRu3bpWTbWoyO3zd1jyajwLNrYmqcAXG9V13graSeTH7XDoHKDt8ErRykhzYWEhOTk56Ovrk52dja2trTbCEEI8pISEBNavX69+ylN6ejrr168HkMJZCCGqUWZmJvv371cXybGxserHUFtaWhIYGMjTTz9NUFAQeXl59O3bV8sRP5xDy08w770b/JLkSw6hhJgdZdYLu4n4jx+G5qHaDq9cGi+a7ezseP3112nVqhXGxsaEh4cTHh6u6TCEEI8gOjpaXTDfU1BQQHR0tBTNlUhJSWH06NGkpaWhUqmIjIxk6tSppdrExMTQt29f2rVrB8DgwYOZMWMGAA4ODpiZmaGrq4uenh5xcXEaz0EIUTMUReHcuXPqEeS9e/dy9OhRiouLgZK1kQcOHEhQUBBBQUG4uLigo6OjPj4mJkZLkT+c3Du5/PpmHPN/tmRfVgdMyGS0axyTZjXDa6intsOrlMaL5tu3b7N27VrOnz+PpaUlTz/9ND/99BOjRo0q1W7RokUsWrQIgEuXLmnlDZGZmVln3oj1jVx77XnQtW/WrBnNmjUrd5+8Xg928+ZNhg8fTvv27cnOzmbChAlYWFjg4OCgbhMfH4+rqyuffPKJetu965qbm8sXX3yBhYVFqe1CiLonOzubuLg4dZEcGxvLtWvXADAzMyMgIIB33nmH4OBgAgICaNy4sZYjfjznYi6y8M1zfB/nwU2lMy4GZ/kyYjujP/PGolXXxzp3RkaGxn4earxo3rp1K23atMHGxgYoGUnZs2dPmaI5MjKSyMhIAPz8/AgNDdV0qMTExGilXyHXXpsedO3nzp2rXs/zfhYWFgwfPryGI6tffHx8sLOzK3OtV65cWe71NzIyIiQkhCZNmmgmQCFEtVAUhQsXLqinWezdu5f4+Hj1E/bat29Pr1691KPI7u7u6Orqajnqx1eUX8Qf/z7I/IUqNl33RQdbBtkdYPI0I0Jf8Ual07Za+jEzM9NYvaDxorlVq1bExsaSnZ2NsbEx0dHR+PlpcJE9IcQj69GjR6k5zQD6+vr06NFDi1HVPcnJyRw+fJiAgLI3uSQmJuLl5YWtrS1z5szB3d0dAJVKRXh4OCqVigkTJqgHFYQQtUtubi6HDh1ST7PYu3cvV65cAaBRo0Z06tSJN998k6CgIAIDA+vdL8LXT9zg+2nHWLi1LcmFnWihc5WZoTsY/4kzdn5B2g7vsWi8aA4ICGDIkCH4+Pigp6dHx44d5Ye/EHXEvXnLsnrGo8vMzCQiIoK5c+dibm5eap+Pjw8rVqygV69ebNy4kYEDB5KUlATArl27sLOz49q1a4SFheHi4kLXrmU/1rx/atvt27cb7DSOhjzFq6Hmrq28r1+/zvHjx9VfZ86cUQ8s2Nra4u7uztChQ3F3d8fR0bHUKPKxY8eqJQZtv+ZKscKFqJts+MWS9Ve6kE8oXUwP8HKvvXiOtUbXSJekzFMkxZzSWozVQSurZ7z33nu899572uhaCPGYPDw8pEh+RAUFBURERDBy5EgGDx5cZr+5uTnGxsYA9O7dm8mTJ3Pjxg2aNGmCnZ0dAE2bNmXQoEHs37+/3KL5/qltzs7ODXaaU0Oe4tVQc9dE3vn5+Rw+fLjUVIuUlBSgZAqVv78//fr1U0+1qOgekOqmrdc861oWP79+kPmrmhKf0x1z0pngGcukD+xx7esP+Gs8ppokTwQUQggNUBSFcePG4erqyrRp08ptc/XqVRRFAWD//v0UFxdjbW1NVlYWxcXFmJmZkZWVxebNm9Wragghas7Vq1dLrWhx8OBBcnNzgZLppveerBcUFISXlxcGBgZajlgzTv1xjvlvp7D0iDfpdMXT6BTfjNzBiDk+mDbvpu3waowUzUIIoQG7d+9m2bJleHh44O3tDcDs2bO5ePEiABMnTmTVqlXMmTMHCwsLjI2NWbFiBSqVirS0NAYNGgSUrHM/YsQInnrqKW2lIkS9VFBQwNGjR0sVycnJyQAYGBjg6+vL5MmTCQ4OJigoqME9Y6Iwt5B1M+KY/50B0bd90Meep1sfYPJbZgRP8ECl46ztEGucFM1CCKEBnTt3Vo8iV+TFF1+kQ4cOZT5mdXR05MiRIzUYnRANz/Xr10tNs9i/fz85OTlAyVzk4OBgXnrpJYKDg+nYsSOGhoZajlg7rsSn8e20Eyza7kxqcSCtdC8xOzyGcZ+509Q9RNvhaZQUzUIIIYSo14qKijh27FipFS3OnDkDgJ6eHj4+PkRGRqqnWrRs2bJOPIK6pijFCju+OsK8OTmsueRHIaE81eQACyam0PtdX3QN7LUdolZI0SyEEEKIeuXWrVvExsaqi+T9+/eTmZkJlDykKSgoSF0k+/r6qm/AbejuXrrLsmmHmb/OjsQ8bxqrbjPVdzcTP2pDux7166a+RyFFsxBCCCHqrOLiYhITE9mwYQM//vgje/bs4dSpkqXNdHV18fLy4rnnniMoKIjg4GAcHBwa9ChyeRJ+P838GVdZluhDFt3wNznOD6N28czHvhhbhWo7vFpDimYhhBBC1Bl37txh37596mkWsbGx3L17FwBra2uCg4PVRbK/vz8mJiZajrh2ys/M5/e345j/owm77nphREuGtYtj8j+t8X/OXdvh1UpSNAshhBCiViouLub06dOl5iInJiaiKAo6Ojp06NCB4cOHExwcjI6ODiNHjpRR5Epc3JvKojeS+HaPO9eUYNrqXWBO3xjGfOaJtVMXbYdXq0nRLIQQQohaISMjg/3796uXfYuNjeX27dsANG7cmMDAQJ555hmCg4Pp1KkTZmZm6mNjYmKkYK5AcWExWz85zPwvC1l/1Q9oTt/mcUx+8QJhb/mgo9da2yHWCVI0CyGEEELjFEXh7NmzpUaRExISKC4uBsDd3Z2IiAj1ihbOzs7o6OhoOeq65dbZ2yyZdoQFfzhwpsAXG9V1pgfvJPLjdrQOCdB2eHWOFM1CCCGEqHFZWVnExcWVKpJv3LgBlDxCPiAggHfffZegoCACAgKwtLTUbsB12MGfTjD//Rv8nORHLqGEmB3lvRd2E/EfPwzNQ7UdXp0lRbMQQgghqpWiKCQnJ6uL4z179nDkyBGKiooAcHZ2pm/fvuoVLVxdXdHV1dVy1HVbzq0cfn3rIPN/sWR/VgdMyOQ51wNMmtUMr6Ge2g6vXpCiWQghhKgH7lxI59d3jnDxwg2O2GzXaN+FRUXcuXOHW7ducuvmLW7eukVeXi4AeroGtGk8CN9247CyssLKyhpDAwNIh5xNEL3pJtHseuwYrl/XfN61xbFjmUScyeGW0hlXg7N8NWQ7z37qjUWrrtoOrV6RolkIIYSow/Iz81kwei/v/9eDW0otLJKKgBv/+xI1QpdCBtkdYPI0I0Jf8Ual01bbIdVLUjQLIYQQdZBSrPD7G7FM/9KWs4XdeNLqIP/5/CrX9S8SGBhYbf3k5eVx/PhxDh8+zMGDBzl8+DBX0q4CYGxohKenJz4+Pvj4+tDRuyNNmjSptr4fRmxsbLXmXZccOLKf8IHh2g6j3pOiWQghhKhj9i5K4LXXYG9mEB0Mk/hjZhw9/+GLSkdFTMw1GrexfORzX758udRc5EOHDpGXlweAg4MDQU8EqVe08PLyQl9fv5qyejzGF4weK++6zOCCgbZDaBCkaBZCCCHqiDPRF3j7ucusSg2ihc5VvntuJ2MWBaNr8Gg30RUUFBAfH68ukvfu3cuFCxcAMDQ0xNfXl5deekldJLdo0aI60xGiTpGiWQghhKjlbibd4l9DE5gfH4QB1rzXPYbXVvhj0vThnuB27do19Qjy3r17iYuLIycnBwB7e3uCgoKYOnUqwcHBeHt7Y2hoWBPpCFEnSdEshBBC1FK5d3L5amQsH2zsSAadGe+ym1m/ONPCO7TSYwsLC0lISChVJJ87dw4AfX19fHx8mDBhgnoUuWXLljWcjRB1mxTNQgihASkpKYwePZq0tDRUKhWRkZFMnTq1VJuYmBj69u1Lu3btABg8eDAzZswAYNOmTUydOpWioiLGjx/P9OnTNZ6D0JziwmJWTN3LP75pzYWiUHrbHODjbxvjPqDi1TFu3LhBbGwsK1as4P3332f//v1kZWUB0Lx5c4KCgpg0aRJBQUH4+vpiZGSkqXSEqBekaBZCCA3Q09Pj008/xcfHh4yMDHx9fQkLC8PNza1UOw8PD/bu3VtqW1FREVOmTGHLli3Y29vj7+9P//79yxwr6oeYufG8/o4BB7ND6Gh8gu//dZgnXvMv1aawsJCjR48SGxtLbGwse/fu5cyZMwDo6OjQsWNHnn/+eYKDgwkKCqJ169aoVCptpCNEvSFFsxBCaECLFi3UN1GZmZnh6upKampqlQrf/fv3065dOxwdHQEYNmwYa9eulaK5njmx4Sxvjb/B+rQAWuqm8uOEXYz8OhgdPR2uXr1aqkCOi4sjOzsbgGbNmhEUFMT48eMJDAwkOzubXr16aTkbIeofKZqFEELDkpOTOXz4MAEBAWX2JSYm4uXlha2tLXPmzMHd3Z3U1NRS803t7e3Zt2+fJkMWNSjt2HVmPXOCbxODMaEJH4T9RZc3DDiUeJCRz84jNjaW5ORkoGQucseOHRk/fjxBQUEEBgaWGUWOiYnRTiJC1HNSNAshhAZlZmYSERHB3LlzMTc3L7XPx8eHFStW0KtXLzZu3MjAgQNJSkp6qPMvWrSIRYsWAXD79u0GW0BlZmbW+tzz7+Tz57+zWHTwSXIJorfFz1xrsoBZMQcp2FIAgI2NDW5ubvTq1Qs3Nzfat2+PgcH/r8mbnJysLqjvqQu514SGmjc07Nw1SYpmIYTQkIKCAiIiIhg5ciSDBw8us9/c3BxjY2MAevfuzeTJk7lx4wZ2dnakpKSo2126dAk7O7ty+4iMjCQyMhIAZ2dnQkNDqz+ROiAmJqZW5p6bm8v+vftZO/04v+zvzxXsCGA1V5nO5tyL+DX34+WBLxMUFERAQAD29vYP3Udtzb2mNdS8oWHnrklSNAshhAYoisK4ceNwdXVl2rRp5ba5evUqiqIAJfOYi4uLsba2xtLSkqSkJM6fP4+dnR0rVqzg559/1mT44hEoikJycrJ6HnJsbCzKQSvuFn/EaSbRQbWfp3w/oePotgQGLsfLy6vUKLIQonaRolkIITRg9+7dLFu2DA8PD7y9vQGYPXs2Fy9eBGDixImsWrWKOXPmYGFhgbGxMStWrEClUqGnp8fXX39Nz549KSoqYuzYsbi7u2sxG1GerKws4uLi1AVybGwsaWlpAHQw8MO0+ENii3vgoHuBxc9v5flveqDS6aTlqIUQVSVFsxBCaEDnzp3Vo8gVefHFF+nQoUO5H7P27t2b3r1711B04mEpikJSUlKpFS0SEhIoKioCoH379vTs2RMfW38O/OzCzxefwFKVzqf9Y5iyLAhD89ZazkAI8bCkaBZCCCEqcffuXfbv319qFPnWrVtAyRKCAQEB/OMf/yAwMJCAgAAM8gz4ZNhB3v6xE0Xo8qrvDt5Z6YVV21DtJiKEeGRSNAshhBD3KS4u5uTJk+oCee/evSQmJqo/KXBzc2PQoEEEBgYSFBSEi4sLurq6ABTmFrJ43B5mrnAhrTiUYa32MHtZS9p0DdViRkKI6iBFsxBCiAbt1q1b7Nu3T10g79u3j7t37wLQuHFjAgMDGTp0KEFBQfj7+2NpaVnmHEqxQtSsA7z5kTUn8rvSxfwI6+Zep9PzwRrORghRU6RoFkII0WAUFhZy/PjxUitanDp1Cih5/LSHhwcjRowgMDCQwMBA2rdvX+njpw8tP8HrL+ay7U4n2uufZ830fQz4oBMqHXlstRD1iRTNQggh6q1r166p5yDHxsayf/9+srKygJIHhwQFBfHcc88RGBiIv78/pqamVT73xb2pvDPyPD+d70wT1Q2+fno7kUuC0W/UpqbSEUJokRTNQggh6oX8/HyOHDnCvn37WLt2LePGjePcuXMA6Onp4e3tzfPPP69+/HSbNm0qHUUuT/rFdP4z9DBz9wWiwoq3g2J4a0VHLFp1q+6UhBC1iBTNQggh6hxFUbh48SKxsbHq+ciHDh0iLy8PAGtra7p168akSZMIDAzE19dX/bTFR5Wfmc/C5/by/poO3FK68qzjHv79syMtA0KrISMhRG0nRbMQQohaLyMjgwMHDrBv3z51kXzvwSFGRkb4+vry4osvEhAQQEBAAGfPnqV79+7V0rdSrLD6rVimf9GCMwXdeKLxIebMu07H4Z2r5fxCiLpBimYhhBC1SlFRESdOnCg1inz8+HH1km/3HhwSEBBAYGAgHh4e6OvrlzrHvWkZjyv2u2O8Nq2YPRlBuBsmETXrAL3e9ZOb/IRogKRoFkIIoVVpaWnq4njfvn0cOHCAjIwMoGTJt4CAAIYMGUJAQACdOnXCysqqxmM6+9cF3n7uMr9dCqK5Thrfjt7JmG+C0DOS/zaFaKjkX78QQgiNyc3N5fDhw+oCed++fSQnJwMlN+t5eXnx7LPPqp+s5+Tk9Eg36z2qm0m3+PczCcw7HIQ+TZgVGsNrv/hh2ryLxmIQQtROUjQLIYSoEYqicPbs2VKjyPHx8RQUFADQqlUrAgICeOmllwgICMDHx+exb9Z7VLl3cvl6VCwfbPTmrtKZsc67eX+FMy28Q7USjxCi9pGiWQghRLW4c+cO+/fvL1Uk37x5EwATExP8/f2ZNm2aehS5RYsWWo4YiguLWflqLP9Y2JLkwlB62Rzg428s6TCoq7ZDE0LUMlI0CyGEeGiFhYUkJCSUKpBPnjwJgEqlwtXVlQEDBqhv1nNzc0NPr3b9l7PjqyO8/rYeB7KC8TY+yZYPDvHkm/7aDksIUUvVrp9gQgghaqXU1NRSq1kcPHiQ7OxsoOTJeoGBgYwaNYqAgAD8/f2xsLDQcsQVO/XHOd4ae521VwOw173M0hd2MWp+MDp6OtoOTQhRi0nRLIQQopTs7GwOHjxYqkhOTU0FwMDAgI4dO/LCCy+oR5EdHBw0erPeo7p2/DrvDUvkm2MhNKIJs8NjeOWXAIytZL1lIUTlpGgWQogGrLi4mNOnT5cqkBMSEigqKgLA0dGRrl27quche3t7Y2hoqOWoH072jWzmjtjPh1t8yCaEiR67mfGLG03dQ7UdmhCiDpGiWQghNCAlJYXRo0eTlpaGSqUiMjKSqVOnltv2wIEDBAUFsWLFCoYMGQKArq4uHh4eQMmqE+vWrXukOG7cuMH+/ftLLfmWnp4OgLm5OZ06deLtt99WP1nPxsbmkfqpDYryi/hpyl7e+b4tqcWhDGwRy4eLm+Lcq5u2QxNC1EFSNAshhAbo6enx6aef4uPjQ0ZGBr6+voSFheHm5laqXVFREW+99Rbh4eGlthsbGxMfH/9QfSqKon709L0i+cyZMwDo6Ojg4eHBM888ox5FdnFxQUenfszrPfXLdV7pdYYjuZ3xNznOLx9eo8uLgdoOSwhRh0nRLIQQGtCiRQv1EmtmZma4urqSmppapmhes2YNERERHDhw4LH7vJxUyCudTgGW6Or0wcFoMM6NDTEyNMTA0BCddB2yNkP0ZojmNrD3sfusDS7dNmF7+tM46KXwy0t7GPpZoNzkJ4R4bFI0CyGEhiUnJ3P48GECAgJKbU9NTWXnzp18+eWXZYrm3Nxc/Pz80NPTY/r06QwcOLDSfgow4apuyP9u0lNBPiVfGdWWSq1koFPI9KDfmbWpL4bmLbUdjhCinpCiWQghNCgzM5OIiAjmzp2Lubl5qX2vvPIKEyZMKHeKxIULF7Czs+PcuXM88cQTeHh40LZt2zLtFi1axKJFiwCwsElj8a8XaiaRWi4z05C9h+rHyPnDyszMJCYmRtthaFxDzRsadu6aJEWzeKCEhASio6NJT0/HwsKCHj16qG9GEkI8nIKCAiIiIhg5ciSDBw8usz8uLo6dO3fy8ccfc+PGDTZu3Iienh4DBw7Ezs4OKFnNIjQ0lMOHD5dbNEdGRhIZGQmAs7MzoaGhNZpTbRUTEyO5NzANNW9o2LlrkkzyEhVKSEhg/fr16jvr09PTWb9+PQkJCVqOTIi6R1EUxo0bh6urK9OmTSu3zfnz51mxYgXJyckMGTKE+fPnM3DgQG7fvk1eXh5QsvrF7t27y8yFFkIIUbNkpFlUKDo6moKCglLbCgoKiI6OltFmIR7S7t27WbZsGR4eHnh7ewMwe/ZsLl68CMDEiRMrPPbEiRPqaRvFxcVMnz5dimYhhNAwKZpFhe6NMFd1uxCiYp07d0ZRlCq3X7JkifrvwcHB8gmPEEJomUzPEBWysLB4qO1CCCGEEPWVFM2iQj169EBfX7/UNn19fXr06KGliIQQQgghtEOmZ4gK3Zu3LKtnCCGEEKKhk6JZPJCHh4cUyUIIIYRo8GR6hhBCCCGEEJWQolkIIYQQQohKSNEshBBCCCFEJaRoFkIIIYQQohJSNAshhBBCCFEJKZqFEEIIIYSohBTNQgghhBBCVEKKZiGEEEIIISohRbMQQgghhBCVkKJZCCGEEEKISkjRLIQQQgghRCWkaBZCCCGEEKISUjQLIYQQQghRCSmahRBCA1JSUujevTtubm64u7vzxRdfVNj2wIED6OnpsWrVKvW2pUuX4uTkhJOTE0uXLtVEyEIIIe6jp+0AhBCiIdDT0+PTTz/Fx8eHjIwMfH19CQsLw83NrVS7oqIi3nrrLcLDw9Xbbt26xXvvvUdcXBwqlQpfX1/69+9P48aNNZ2GEEI0WDLSLIQQGtCiRQt8fHwAMDMzw9XVldTU1DLt1qxZQ0REBE2bNlVv+/PPPwkLC8PKyorGjRsTFhbGpk2bNBa7EEIIGWkWQgiNS05O5vDhwwQEBJTanpqays6dO/nyyy85cOBAqe0tW7ZUf29vb19uwQ2waNEiFi1aBMDt27eJiYmp/gTqgMzMTMm9gWmoeUPDzl2TpGgWQggNyszMJCIigrlz52Jubl5q3yuvvMKECRPQ0Xn0DwEjIyOJjIwEwNnZmdDQ0McJt86KiYmR3BuYhpo3NOzcNUmKZiGE0JCCggIiIiIYOXIkgwcPLrM/Li6OnTt38vHHH3Pjxg02btyInp4ednZ2pUaRLl26JP9BCiGEhj3UcMbt27c5evRoTcUihBD1lqIojBs3DldXV6ZNm1Zum/Pnz7NixQqSk5MZMmQI8+fPZ+DAgfTs2ZPNmzdz+/Ztbt++zebNm+nZs6eGMxBCiIat0pHm0NBQ1q1bR2FhIb6+vjRt2pSQkBA+++yzR+70zp07jB8/nmPHjqFSqfj+++8JCgp65PMJIURtt3v3bpYtW4aHhwfe3t4AzJ49m4sXLwIwceLECo+1srLi3Xffxd/fH4AZM2ZgZWVV4zELIYT4f5UWzenp6Zibm/Pdd98xevRo3nvvPTw9PR+r06lTp/LUU0+xatUq8vPzyc7OfqzzCSFEbde5c2cURaly+yVLlpT6fuzYsYwdO7aaoxJCCFFVlU7PKCws5MqVK/z666/07dv3sTtMT09nx44djBs3DgADAwMsLS0f+7xCCCGEEELUlEqL5hkzZtCzZ0/atWuHv78/586dw8nJ6ZE7PH/+PDY2Njz//PN07NiR8ePHk5WV9cjnE0IIIYQQoqZVOj3j6aef5umnn1Z/7+joyO+///7IHRYWFnLo0CG++uorAgICmDp1Kh9++CH/+te/SrW7f63RS5cuaWX9QVn3UHvk2muPXHvtkusvhBBVl5GRobGfmZUWzefPn+err74iOTmZwsJC9fZ169Y9Uof29vbY29urF/UfMmQIH374YZl296816ufnp5XllWTdQ+2Ra689cu21S66/EEJUnZmZmcZ+ZlZaNA8cOJBx48bRr1+/x1pw/57mzZvTsmVLTp06hbOzM9HR0bi5uT32eYUQQgghhKgplRbNRkZGvPzyy9Xa6VdffcXIkSPJz8/H0dGRH374oVrPL4QQQgghRHWqtGieOnUq7733HuHh4RgaGqq3+/j4PHKn3t7exMXFPfLxQgghhBBCaFKlRXNCQgLLli3jr7/+Uk/PUKlU/PXXXzUenBBCCCGEELVBpUXzb7/9xrlz5zAwMNBEPEIIIYQQQtQ6ld7Z16FDB+7cuaOBUIQQQgghhKidKh1pvnPnDi4uLvj7+5ea0/yoS84JIYQQQghR11RaNL/33nuaiEMIIYQQQohaq9KiuVu3bpqIQwghhBBCiFqrwjnNnTt3BkqetGJubq7+uve9EEIIIYQQDUWFI827du0CSp7pLYQQQgghRENW6fSMe65du0Zubq76+1atWtVIQEIIIYQQQtQ2lS45t27dOpycnGjTpg3dunXDwcGBXr16aSI2IYSoN1JSUujevTtubm64u7vzxRdflGmzdu1axo0bh7e3N35+fupP/AB0dXXx9vbG29ub/v37azJ0IYQQVGGk+d133yU2NpYnn3ySw4cPs23bNn766SdNxCaEEPWGnp4en376KT4+PmRkZODr60tYWBhubm7qNj169OC7776je/fuHD16lKFDh3Ly5EkAjI2NiY+P11L0QgghKh1p1tfXx9ramuLiYoqLi+nevTtxcXGaiE0IIeqNFi1a4OPjA5TcYO3q6kpqamqpNqampqhUKgCysrLUfxdCCKF9lY40W1pakpmZSdeuXRk5ciRNmzbFxMREE7EJIUS9lJyczOHDhwkICCizb+fOnUycOJFr164RFRWl3p6bm4ufnx96enpMnz6dgQMHajBiIYQQlRbNa9euxcjIiM8//5zly5eTnp7OjBkzNBGbEELUO5mZmURERDB37txyl+/s0qUL7777Ljt27ODdd99l69atAFy4cAE7OzvOnTvHE088gYeHB23bti1z/KJFi1i0aBEAt2/fJiYmpkbzqa0yMzMl9wamoeYNDTt3Taq0aL5/VPm5556r0WCEEKI+KygoICIigpEjRzJ48OAHtu3atSvnzp3jxo0bNGnSBDs7OwAcHR0JDQ3l8OHD5RbNkZGRREZGAuDs7ExoaGi151EXxMTESO4NTEPNGxp27ppU4Zzmew8xkYebCCHE41MUhXHjxuHq6sq0adPKbXPmzBkURQHg0KFD5OXlYW1tze3bt8nLywPgxo0b7N69u9QNhEIIIWpehSPN8lATIYSoPrt372bZsmV4eHjg7e0NwOzZs7l48SIAEydO5Pfff2fBggVYWlpibGzMypUrUalUnDhxggkTJqCjo0NxcTHTp0+XolkIITSs0ukZsbGxuLu7Y2ZmBpQU04mJieXewCKEEKJ8nTt3Vo8iV+Stt94iICCgzMeswcHBJCQk1GB0QgghKlPpknOTJk3C1NRU/b2JiQmTJk2q0aCEEEIIIYSoTSotmhVFKbVWqI6ODoWFhTUalBBCCCGEELVJpUWzo6MjX375JQUFBRQUFPDFF1/g6OioidiEEEIIIYSoFSotmhcuXMiePXuws7PD3t6effv2qdcAFUIIIYQQoiGo9EbApk2bsmLFCk3EIoQQQgghRK1U6UizEEIIIYQQDZ0UzUIIIYQQQlSiwqL5iy++AEoW5BdCCCGEEKIhq7Bo/uGHHwB46aWXNBaMEEIIIYQQtVGFNwK6urri5OTE5cuX8fT0VG+/t27z0aNHNRKgEEIIIYQQ2lZh0fzLL79w9epVevbsybp16zQZkxBCCCGEELXKA5eca968OUeOHCE/P5/Tp08D4OzsjL6+vkaCE0IIIYQQojaodJ3m7du3M3r0aBwcHFAUhZSUFJYuXUrXrl01EZ8QQgghhBBaV2nRPG3aNDZv3oyzszMAp0+fZvjw4Rw8eLDGgxNCCCGEEKI2qHSd5oKCAnXBDNC+fXsKCgpqNCghhBBCCCFqk0pHmv38/Bg/fjyjRo0CYPny5fj5+dV4YEIIIYQQQtQWlRbNCxYsYN68eXz55ZcAdOnShcmTJ9d4YEIIIYQQQtQWlU7PMDQ0ZNq0aaxevZrVq1fz6quvYmhoqInYhBCi3khJSaF79+64ubnh7u6ufurq/dauXcu4cePw9vbGz8+PXbt2qfctXboUJycnnJycWLp0qSZDF0IIQRVGmoUQQjw+PT09Pv30U3x8fMjIyMDX15ewsDDc3NzUbXr06MF3331H9+7dOXr0KEOHDuXkyZPcunWL9957j7i4OFQqFb6+vvTv35/GjRtrMSMhhGhYKh1pFkII8fhatGiBj48PAGZmZri6upKamlqqjampKSqVCoCsrCz13//880/CwsKwsrKicePGhIWFsWnTpgf2JzdsCyEaAkVRNNZXpSPNCQkJeHh4aCIWIYRoEJKTkzl8+DABAQFl9u3cuZOJEydy7do1oqKiAEhNTaVly5bqNvb29mUK7nsWLVrEokWLuHnzJqmpqQ32xu3r169jY2PzUMdkZGRgZmZWQxFpzqPkrm3Vce3rYt7V5XFyr6vv+8LCQu7evcvly5c11melRfPkyZPJy8tjzJgxjBw5EgsLC03EJYQQ9VJmZiYRERHMnTsXc3PzMvu7dOnCu+++y44dO3j33XfZunXrQ50/MjKSyMhILly4QOfOndm1axdGRkbVFX6d4efnR1xc3EMdExMTQ2hoaM0EpEGPkru2Vce1r4t5V5fHyb2uve8LCwvZvXs3+/btIyQkhKlTp2qs70qnZ+zcuZPly5eTkpKCr68vI0aMYMuWLZqITQgh6pWCggIiIiIYOXIkgwcPfmDbrl27cu7cOW7cuIGdnR0pKSnqfZcuXcLOzu6Bx7du3RojIyP5eS2EqDfOnTvHggULuHLlChMmTCAkJESj/VfpRkAnJyf+/e9/4+fnx8svv8zhw4dRFIXZs2dX+oNfCCFEyby7cePG4erqyrRp08ptc+bMGfX8vEOHDpGXl4e1tTU9e/bkH//4B7dv3wZg8+bN/Oc//6m0T3Nzc86cOcP58+dp06ZN9SUjhBAalJmZyZ9//klKSgq9evUq9dA9Taq0aD569Cg//PADUVFRhIWFsX79enx8fLh8+TJBQUFSNAshRBXs3r2bZcuW4eHhgbe3NwCzZ8/m4sWLAEycOJHff/+dBQsWYGlpibGxMStXrkSlUmFlZcW7776Lv78/ADNmzMDKyqrSPidMmEBoaCjr169n0qRJ6Ovr11h+tU1kZKS2Q9Cahpp7Q80b6m/uxcXFxMXFERMTg4+PD/369cPAwKBUG03mrlIque2wW7dujB8/niFDhmBsbFxq37Jly3j22WdrNEDQ3jylujbPpz6Ra689cu21qyau/+rVqzE1NSU8PLxaz1vfyHtfe+Taa09tvfaXL19mw4YN6Ovr06dPH5o2baqRfh9Uc1Y60hwVFYWxsTG6urpASdWfm5tLo0aNNFIwCyGEeDxPPfUU8+fPx93dvdK50EIIoU25ubls27aN48eP8+STT+Ll5aVeflPbKr0R8MknnyQnJ0f9fXZ2Nk8++WSNBiWEEKL6NGrUiKeeeoq1a9dSVFSk7XCEEKIMRVE4duwY8+bNo6CggMmTJ+Pt7V1rCmaoQtGcm5uLqamp+ntTU1Oys7NrNChtS0hIYO7cuVy5coW5c+eSkJCg7ZCEEOKB7ty5w5AhQ3BxccHV1ZW9e/dy69YtwsLCcHJy4tVXX0VfX7/Uo7nri/Jy/+2333B3d0dHR6deL0NWXu5vvPEGLi4ueHp6MmjQIO7cuaPtMGtEebm/++67eHp64u3tTXh4uEbX8NWk8nK/59NPP0WlUnHjxg0tRvhwbt68ybJly9i5cydDhw6lf//+NGrUqNy25eU+a9Ys7Ozs8Pb2xtvbm40bN9ZInJUWzSYmJhw6dEj9/cGDB8vMba5PEhISWL9+Penp6QCkp6ezfv16KZyFELXa1KlTeeqppzh58iRHjhzB1dWVDz/8kB49epCUlESPHj04fvw4+/bt49q1a9oOt1qVl3uHDh1YvXo1Xbt21XZ4Naq83MPCwjh27BhHjx6lffv2VVpppS4qL/c33niDo0ePEh8fT9++fXn//fe1HWaNKC93gJSUFDZv3kyrVq20HGHVFBYWEhMTw+LFi2nXrh2RkZGlHuRUnopyf/XVV4mPjyc+Pp7evXvXSLyVzmmeO3cuTz/9NLa2tiiKwtWrV1m5cmWNBFMbREdHl3n8bEFBAdHR0fJkRCFErZSens6OHTtYsmQJAAYGBhgYGLB27VpiYmIAeO655wgNDWX58uWsW7eOsWPHoqNT6bhJrVdR7paWllqNSxMqyv3+Gz4DAwNZtWqVliKsORXlfr/7H0Vfnzwo91dffZWPP/6YAQMGaDHCqjl79ixRUVE0a9aMCRMmVOnheVV53WtSpT8x/f39OXnyJAsWLGDhwoWcOHECX19fTcSmFfdGmKu6XQghtO38+fPY2Njw/PPP07FjR8aPH09WVhZpaWm0aNECgObNm5OWloavry96enrs379fy1FXj4pybwiqkvv3339Pr169tBRhzXlQ7u+88w4tW7Zk+fLl9XKkuaLc165di52dHV5eXtoO8YEyMjJYtWoV69ev56mnnuKZZ56p8tOmH/S6f/3113h6ejJ27Fj1mvbVrUrDDAcOHODo0aMcOnSIX375hR9//LFGgqkNKnrh5PHhQojaqrCwkEOHDjFp0iQOHz6MiYkJH374Yak2KpVK/dWvXz927NhRY/+xaFJVcq+vKsv9gw8+QE9Pj5EjR2oxyprxoNw/+OADUlJSGDlyJF9//bWWI61+5eU+a9YsZs+eXat/SSguLmbfvn0sWLCAxo0bM2XKFNq3b/9Q56jodZ80aRJnz54lPj6eFi1a8Nprr9VIDpUWzc8++yyvv/46u3bt4sCBAxw4cKBe31TRo0ePMg8A0NfXp0ePHlqKSAghHsze3h57e3sCAgIAGDJkCIcOHaJZs2ZcuXIFgCtXrqjXObW2tiYkJIT169dTyVL9tV5FuTcED8p9yZIlbNiwgeXLl9fLKQpVed1HjhzJ77//ro3walRFuZ8/fx4vLy8cHBy4dOkSPj4+XL16VcvRlkhNTeXbb7/lxIkTPP/88+XWWlXxoJ91urq66Ojo8MILL9TYJ2mVzmmOi4sjMTGxXv6jK8+9ecvR0dFAyQhzjx49ZD6zEKLWat68OS1btuTUqVM4OzsTHR2Nm5sbbm5uLF26lOnTp7N06dJS8xyDgoI4fvw48fHxdOzYUYvRP56Kcm8IKsp906ZNfPzxx2zfvr3CFQjquopyT0pKwsnJCYC1a9fi4uKi5UirX3m5+/j4qOsWAAcHB+Li4mjSpIkWIy1ZgS06OpoTJ04QFhaGp6fnY9WTFb3uV65cUU9FW7NmDR06dKiuFEqptGju0KEDV69eVQfTEHh4eODh4UFMTAzDhw/XdjhCCFGpr776ipEjR5Kfn4+joyM//PADxcXFDB06lMWLF9O6dWt+/fVXdXsdHR369+/PsmXLaNeuHWZmZlqM/vGUl/uaNWt46aWXuH79On369MHb25s///xT26FWu/Jy9/f3Jy8vj7CwMKDkZsCFCxdqOdLqV17u48eP59SpU+jo6NC6det6mTeUn3ttoigKCQkJbNmyBWdnZ6ZMmVJtK6+Vl/vLL79MfHw8KpUKBwcHvvnmm2rp6+8qLZpv3LiBm5sbnTp1wtDQUL193bp1NRKQEEKIh+ft7V3u1Ln7R5/+rnnz5vj6+rJx40aeeeaZmgyvRpWX+6BBgxg0aJCWItKc8nI/c+aMlqLRrPJyr4/TMcpT0b/3e5KTkzUXzN/cuHGDqKgocnJyeOaZZ7C3t6/W85eX+7Jly6q1j4pUWjTPmjVLA2EIIYTQhq5du7Jw4UISExMbzLQGIUT1KygoUN//1rVrVzp16lQvlrW8X6VFc7du3bhw4QJJSUk8+eSTZGdny2NYhRCintDT02PAgAH8+uuvtGnTpl4/vEoIUTPOnDlDVFQULVq0YOLEiZibm2s7pBpR6a8A3377LUOGDGHChAlAyR2QAwcOrOm4hBBCaEjLli1xc3Orl3N+hRA15+7du/z6669ERUXRp08fhg4dWm8LZqhC0Txv3jx2796tvghOTk717hGsQgjR0PXo0YPk5GTOnj2r7VCEELVccXExsbGxLFy4kCZNmjB58mTatWun7bBqXKXTMwwNDUs9orCwsLDBLD8nhBANhYGBAX379mX9+vVMnjxZo4+mFULUHZcuXWLDhg0YGxszduxYrS9rp0lVmtM8e/ZscnJy2LJlC/Pnz6dfv36aiE0IIYQGtWvXDgcHB6Kjo+vlo5eFEI8uJyeH6OhoTp06RXh4OB06dGhwg6iVTs/48MMPsbGxwcPDg2+++YbevXvz73//WxOxCSGE0LCePXuSmJhISkqKtkMRQtQCiqJw5MgR5s2bh0qlYsqUKXh4eDS4ghmqMNJ875GEL7zwgibiEUIIoUXGxsb06tWLdevWMWHCBPT0Kv1vQghRT12/fp2oqCjy8vIYPnw4dnZ22g5Jqyr9adimTZtyf5s4d+5cjQQkhBBCu1xdXUlISGDnzp10795d2+EIITSsoKCAHTt2cPDgQbp164a/v3+9W3P5UVRaNN//1JXc3Fx+++03bt26VaNBCSGE0B6VSkXv3r1ZuHAhbm5uNGvWTNshCSE05PTp0/zxxx/Y2dkxadIkzMzMtB1SrVHprw3W1tbqLzs7O1555RWioqI0EZsQQggtMTMzo0ePHqxdu5bi4mJthyOEqGHp6emsXLmSTZs20bdvX4YMGSIF899UOtJ86NAh9d+Li4uJi4ujsLCwRoMSQgihfR07diQhIYHY2FiCg4O1HY4QogYUFRWxZ88edu3aRadOnYiIiJB7GSpQ6VV57bXX/r+xnh4ODg78+uuvNRqUEEII7VOpVPTv359vv/0WFxcXrKystB2SEKIapaSksGHDBjp27Mi4ceOwtrbWdki1WqVF87Zt2zQRhxBCiFqocePGdOnShXXr1vHcc881yGWmhKhvsrOz2bp1K0lJSXh6evLss8/Kv+0qqLRo/uyzzx64f9q0adUWjBBCiNonICCAY8eOcejQIXx9fbUdjhDiEd1bc3nr1q24ubkxZcoUYmNjpWCuoiqtnnHgwAH69+8PwPr16+nUqRNOTk41HpwQQgjt09HRYcCAASxZsgQnJyfMzc21HZIQ4iFdu3aNqKgoCgoKGDFiBLa2ttoOqc6ptGi+dOkShw4dUt9BOWvWLPr06cNPP/1U48EJIYSoHZo2bUqnTp2Iiopi2LBhMjIlRB2Rn5/Pjh07OHToEN27d8fX11fWXH5ElV61tLQ0DAwM1N8bGBiQlpZWo0EJIYSofbp06cLt27c5fvy4tkMRQlTBqVOnmD9/Punp6UyePFkeUvKYKh1pHj16NJ06dWLQoEEA/Pe//+W5556r8cCEEELULrq6ugwYMIBffvkFR0dHGjVqpO2QhBDlSE9P548//uD69ev0798fR0dHbYdUL1RaNL/zzjv06tWLnTt3AvDDDz/QsWPHGg9MCCFE7WNnZ4eHhwebNm1i8ODB2g5HCHGfoqIiYmNj2b17NwEBAQwZMkTWXK5GVRqjz87OxtzcnKlTp2Jvb8/58+drOi4hhBC1VPfu3UlJSSEpKUnboQgh/ufixYt88803nD9/nvHjx9OtWzcpmKtZpVfzvffeIy4ujlOnTvH8889TUFDAqFGj2L17tybiE0IIUcsYGBjQr18/1q5dy+TJkzE0NNR2SEI0WNnZ2WzZsoWzZ8/Ss2dP3Nzc5EbdGlLpSPOaNWtYt24dJiYmANja2pKRkVHjgQkhhKi9HB0dadu2LVu3btV2KEI0SIqicOjQIebNm4ehoSFTpkzB3d1dCuYaVGnRbGBggEqlUr8IWVlZ1dJxUVERHTt2pG/fvtVyPiGEEJoVHh7OyZMnuXDhgrZDEaJBSUtL44cffuDgwYOMGjWKp556Sj7x0YBKi+ahQ4cyYcIE7ty5w7fffsuTTz7JCy+88Ngdf/HFF7i6uj72eYQQQmiHkZERffr0Yd26dRQUFGg7HCHqvfz8fDZv3syPP/6Ip6cn48aNo0WLFtoOq8F4YNGsKArPPPMMQ4YMISIiglOnTvH+++/z0ksvPVanly5dIioqivHjxz/WeYQQQmiXi4sLzZs3Z/v27doORYh6S1EUTp48ybx588jKymLSpEn4+fnJmssa9sAbAVUqFb179yYhIYGwsLBq6/SVV17h448/lrnRQghRD/Tq1YsFCxbg7u4uo15CVLM7d+6wceNGbt26xcCBA2nTpo22Q2qwKl09w8fHhwMHDuDv718tHW7YsIGmTZvi6+tLTExMhe0WLVrEokWLgJKR6Qe1rSmZmZla6VfItdcmufbaVVevf+PGjfnkk0/o06cPurq62g7nkdTVa18fyLUvq6ioiMTERI4dO4a7uzvu7u5cuHCh2u8hkGtfdSpFUZQHNXBxcSEpKQkHBwdMTExQFAWVSsXRo0cfqcO3336bZcuWoaenR25uLnfv3mXw4MH89NNPFR7j5+dHXFzcI/X3OGJiYggNDdV4v0KuvTbJtdeuunr9FUVh+fLlODg40LlzZ22H80jq6rWvD+Tal3bhwgU2bNiApaUlvXv3pnHjxjXWl1z70h5Uc1Y40nzx4kVatWrFn3/+Wa3B/Oc//+E///kPUPJCzZkz54EFsxBCiNpPpVLRt29fFi1ahIuLC02aNNF2SELUOVlZWWzZsoVz587Rq1cvXFxcZAm5WqTCGeQDBw4EoHXr1kybNo3WrVuX+hJCCCHuZ2lpSbdu3Vi3bh2VfIgphLiPoigcPHiQ+fPnY2xszJQpU3B1dZWCuZapcKT5/h94586dq5HOQ0ND5SMBIYSoR/z9/Tl27BhxcXHVdi+MEPXZ1atXiYqKQlEUnn32WZo3b67tkEQFKiya7//tRn7TEUIIURU6Ojr079+fH374gfbt22NhYaHtkISolfLy8oiJieHo0aM88cQT+Pj4SL1Vy1VYNB85cgRzc3MURSEnJwdzc3MA9Y2Ad+/e1ViQQggh6g4bGxsCAwPZsGEDI0aMkEJAiPsoisKJEyfYtGkTjo6OTJ48GRMTE22HJaqgwqK5qKhIk3EIIYSoR0JCQli0aBEJCQl4enpqOxwhaoXbt2+zceNG7ty5Q0REhNwjVsdUuk6zEEII8bB0dXUZMGAAy5cvp23btjKSJhq0wsJC9uzZQ2xsLMHBwQwbNqzOrmfekEnRLIQQokbY2tri7e3NH3/8wZAhQ7QdjhBacf78eaKiorCysiIyMhJLS0tthyQekRTNQgghakxoaCgLFizg5MmTuLi4aDscITQmMzOTzZs3c+HCBXr16oWzs7PM76/jpGgWQghRY/T19enfvz+rV6/GwcEBIyMjbYckRI26t+bytm3b8Pb2ZsqUKRgYGGg7LFENpGgWQghRoxwcHGjfvj1btmyhX79+2g5HiBpz5coVNmzYgK6uLqNHj6ZZs2baDklUIymahRBC1Lgnn3yS+fPnc/78edq0aaPtcISoVnl5eWzbto2EhASefPJJvL29ZSpGPVThY7SFEEKI6mJkZESfPn1Yv349BQUF2g5HiGqhKArHjx9n3rx55OXlMWXKFDp27CgFcz0lI81CCCE0wtnZmWPHjrFt2zbCw8O1HY4Qj+XWrVts3LiRu3fvMmTIEFq1aqXtkEQNk5FmIYQQGvPUU09x9OhRUlNTtR2KEI+ksLCQ7du3891339GmTRsmTJggBXMDIUWzEEIIjTExMaFnz56sW7dOnjwr6pxz586xYMECrly5woQJEwgJCZGHlDQgMj1DCCGERnXo0IGEhAR27dpFt27dtB2OEJXKzMzkzz//JCUlRb3msmh4ZKRZCCGERqlUKvr06cO+ffu4fv26tsMRokLFxcXs37+f+fPnY2FhweTJk6VgbsBkpFkIIYTGWVhY8MQTT7B27VrGjh2Ljo6M4Yja5fLly2zYsAF9fX3GjBlD06ZNtR2S0DIpmoUQQmiFr68vCQkJ7N+/n8DAQG2HIwQAubm5/PXXXyQmJvLkk0/i5eUlS8gJQKZnCCGE0BKVSkX//v3ZsWMHt2/f1nY4ooFTFIVjx44xb948ioqKmDx5sjykRJQiI81CCCG0xtramuDgYDZs2MCoUaOkQBFacfPmTaKiosjKymLo0KG0bNlS2yGJWkiKZiGEEFoVHBzM8ePHOXLkCN7e3toORzQghYWF7Nq1i/3799OlSxcCAgJkfr2okBTNQgghtEpHR4cBAwawbNky2rVrh6mpqbZDEg3A2bNniYqKolmzZkyYMAELCwtthyRqOSmahRBCaF3z5s3x8fFh48aNDB06VNvhiHosIyODTZs2cfnyZXr37o2Tk5O2QxJ1hHwGIYQQolbo1q0b165dIzExUduhiHqouLiYffv2sWDBAqysrJg8ebIUzOKhyEizEEKIWkFPT4/+/fvz22+/0aZNG4yNjbUdkqgnUlNT2bBhA4aGhjz//PPY2NhoOyRRB0nRLIQQotZo1aoVrq6ubN68mQEDBmg7HFHH5ebmEh0dzYkTJwgLC8PT01NWaBGPTKZnCCGEqFV69OjBuXPnOHv2rLZDEXWUoigcPXqUefPmoSgKU6ZMkYeUiMcmI81CCCFqFUNDQ/r27cv69euZPHkyBgYG2g5J1CE3btwgKiqKnJwcnnnmGezt7bUdkqgnpGgWQghR6zg5OdG6dWv++usvnnrqKW2HI+qAgoICdu7cSVxcHF27dqVTp06y5rKoVvJuEkIIUSv17NmTY8eOkZKSou1QRC2XlJTE/PnzuXnzJhMnTiQwMFAKZlHtZKRZCCFErdSoUSN69erFunXrmDBhAnp68l+WKO3u3bts2rSJK1eu0KdPH9q1a6ftkMRjyM/P58yZM5w6dQpDQ0N69+6t7ZBKkZ9AQgghai03NzcSEhLYuXMn3bt313Y4opa4t+byzp078ff3Z9CgQejr62s7LFEFiqJw7do1Tp06xalTpzh58iTnD58j5/gtTK7l44ABbVDIsrSi920pmoUQQogqUalU9OnThwULFuDm5kazZs20HZLQskuXLrFhwwYaNWrE2LFjadKkibZDEuXIy8vjzJkznDx5khOHT3Bl7wXyT12nUVoGLQsLaUMevtxhCFdpTHqpY2/TmATL2lUwgxTNQgghajkzMzOefPJJ1q5dy/jx42WuagOVk5PD1q1bOX36NOHh4XTo0EGWkNMyRVFIS0vj+NHjnIw+xrXYcxSfvYbZjdu0yMukDXcJ4hoRpJU6LgcjLum15qa5A0dte6A4OGLk2obGvo40D2pD41YWdNVSTg8iRbMQQohar2PHjiQkJBAbG0twcLC2wxEadG/N5S1btuDm5saUKVMwMjLSdlgNSk52Doe3HOT0n0dIjz+HKvkKlrdvYJt7Cweu04Ur9KBA3b4IHVJVdlwxbkmidQcSHdtj7OaEuVcbmga0oalnc5x0VNS1h5hL0SyEEKLWU6lU9OvXj++++w4XFxesrKy0HZLQgOvXrxMVFUVeXh7Dhw/Hzs5O2yHVW5lpmRzfcJDkv46QffwsBqmpWN9Nwy7/Gg5cJphM7v919RpNSNG354ypB8ea98HQxQmbTu40DWxHi04tadVIn1Zay6ZmSNEshBCiTrCysqJz586sW7eO5557Tj6ar8cKCgrYsWMHBw8epFu3bvj7+8u0nMdUkF3A5diLXNl7imuxx8g/fQaTtBRsMlK5VnyFptwgAAj4X/sMTElW2XHZqDnHLb0ptG+JmVd7HHt0pE13V5o2N6WpNhPSAimahRBC1BmBgYEcP36cQ4cO4evrq+1wRA04ffo0GzduxN7enkmTJmFmZqbtkOoEpVjh2tGrpO09x90j58hMOInqwlksbl3ENjcFO+UyrSmm9f/aF6DHBWy5oLJhcyM/MprYQBs7rP3a49rbF7eu7njo6eKh1axqFymahRBC1Bk6Ojr079+fpUuX4uTkhLm5ubZDEtUkPT2dTZs2kZaWRr9+/Wjbtq22Q6p1CnMLOf/HSW7uPU3uifNw7gyGV87SJOMC9oUXaUYu968vk0ozztOUHSoHrpl4kdXMGn1nW1oEOdGhpxcu7i5cOnCAUaGh2kqpTpGiWQghRJ3SrFkz/P39iYqKYtiwYTJNo44rKiri2LFj7N+/n06dOhERESEPsgHy7uZxbt0xrm2Ko2D/AZqkHME59xhO5KpvoLuNBedpzlEas5YWpDUyIqe5JY3cm9Oyc1vcfN1wcXEhxNZW/p1UA3lXCiGEqHO6dOnCN998w/Hjx+nQoYO2wxGPKCUlhQ0bNnD58mVee+01rK2ttR2SVmSmZXJoyXbSNu7D+ORxWt86hUvhKVwpxBVIx5xDODKfThzTV3HXzhgzz6a09WuHs7MzLi4u9HZywtjYWNup1GtSNAshhKhz9PT0GDBgACtWrMDR0ZFGjRppOyTxELKzs9m6dStJSUn07NmT69ev1/uCWVEUbt68yZHt8ZxddYjiuNM0u3wRl5xzOCvn6IoCwHWsOYgD0cZhpNlZo+NnS5vuTji7OjOqfXuaNm0qo8ZaIkWzEEKIOsne3p4OHTrw559/MmjQIG2HI6pAURTi4+OJjo7G3d1dveZyTEyMtkN7LMXFcPcu3L6tcP78HU7Fn+PG4UTyzpxH7/IlzG6l4ZB1A28lhR6k0ON/x6XQguMG7dhlE0ShhyMt+njSsa8f4a1a8pSsFlLrSNEshBCiznriiSdYsGABSUlJODnVtUclNCzXrl1jw4YNFBUVMWLECGxtbbUdUin5+XDnDty+XfJV3t/v3IE7t4rIv3QJ/SvnML9zCZucVGwLr9CSFFpxkQ5c5Amulzn/eVVrkizcOeQ4BPNQXzyfC6Wllx0tNZyneHRSNAshhKizDAwM6NevH2vXrmXy5MkYGhpqOyTxN/n5+Wzfvp3Dhw/TvXt3fH19a2TNZUWBzMyKi93KtmVnl5zHhEx1AXz/VxDJtOICLUnFgMJSfWdiwBUDa26b2pBo48OJVg6YurWnqW8HLDzbYdrejjbGhrSp9qyFJknRLIQQok5zdHTE0dGR6Ohoevfure1wxH1OnTrFH3/8QatWrZg8eTKmpqYPbF9Y+L/R3DtVL3bv/3tR0YPjaWxeRHuzK7g0uoi/XjK2hedoqnuGJo3OY0MKTfPSsCjKLnVMEZCKiuvGRlyysiK5hTf6jo6YubvTxMeH5p06YWpjg5PMM673pGgWQghR54WHhzN//nzc3d1p3bp15QeIGnXnzh3++OMPbty4Qf/+/XF0dCzT5vp1+O23kq/ExEByciAj48Hn1deHxo3B0rLkTysraNv2/7c1M0rHrugizfMv0iT7Ima3z6Obegrd1HMYXruMyZ3b6N5VSp3zNnARSNLR4ZClJfnNHdBt0wYTNzeadOxIy4AA7Fq3ppWubnVdHlFHSdEshBCizjM2NqZ3796sW7eOSZMmyTq/WlJUVERsbCy7d+8mMDCQp59+utRrkZEB//0v/PwzbNlSMjLs7g4dO97BxaU5jRuXLorv/dm4MViaFNDodiqqlItw8f+/ii5coODgWXRTU9HPLj1KXACkUFIUpwBX9fXJb94cVevWNHJxwdrbm9YeHrRr1w7PFi1kVQrxQPJTRQghRL3g6upKQkIC27dvp0ePHpUfIKrVhQsXiIqKwtzcnPHjx2NlZQVAXh788UdJobx+PeTmQuvW8MYbMGIEeHhAzLYThHrqlyqGSbgIKSlw8SLFFy6gunIFlVJ6lPimjg7JxcXqwvgicLNRI1StW2PUvj02HTrQtn172rVrR8927bCxsZHCWDwyKZqFEEJoREJCAtHR0aSnp2NhYUGPHj3w8PCo1j569+7NggULcHNzo0WLFtV6blG+7OxstmzZwtmzZ3nqqadwdXWluFhFdHRJofz775CeDjY2MG4cPDvgLp0M4lHFH4Y5h+DwYbokJZVU0/cp0NUlTV+f88XFnMnPVxfFF4HMxo0xcnKipbMz7dq1o127dgS2a8eodu3UxboQ1U2KZiGEEDUuISGB9evXU1BQAEB6ejrr168HqNbC2dTUlLCwMNauXcsLL7yArsxDrTGKonD48GGio6Px8PBg8uQpHDliyKuvwsqVcPUqtDa5wT+CDjOg9WHa3T2IsvEAevPOq89xx8iIRCMjDisKSVCqMNazsaGdk5O6KHZt145+7drRtm1bLCwstJS1aMikaBZCCFHjoqOj1QXzPQUFBeqCqzp5eXmRkJDA3r176dy5c7WeW5RIS0tjw4YNKIpCp06j2LqlOe9NuIL1xUP46RxihcVe3E0O0iTrOmwtOeYCcBA49L+vIyoV+s2aqZ/o2LVrV7r+r0B2dHSsdKUNITRNimYhhBA1Lj09/aG2Pw6VSkW/fv1YtGgRrq6uVX4886pVq+rsk+ksLS155ZVXHvs8ObdySN15DqVYKXd/Xn4+2/Zv4+CxeKyuWtHqjEJW9mqmcIh/kQaUPB0v6TZsAY7p63PN3p5cV1eaubri6OhIl7Ztec7RkdatW2NgYABATEwMoaGhjx2/EDVJimYhhBA1zsLCotwCuaY+Zre0tKRbt26sW7eOMWPGVOnmr8zMTL7++usaiaemzZo165GOu3nqBmeW7iZnyy6sT+7COfMg7Sgo004BTlJSCDsAYwEToBBdTupYc9DKkNuOnSj08MA4MJDWHTrQw9GRYXLjnahHpGgWQghR43r06FFqTjOAvr5+ja5y4e/vz7Fjx4iLi8Pf37/G+qkrlGKFi9vOcmH5DgpjttMqJZZ2haexBvIw4JCuG1/pdyOuIIsCiv//OF0Vd4xz0THUo2NzFwyatuS/FhY4dnfCe1QoHays6KC9tITQGCmahRBC1Lh785ZrevWM++no6NC/f39++OEH2rdv32BuHlMUhevXr3PmxBlOrjhI0fZE7C+cpmP2MVpzjdbALSzZjRPf0o093CatZTZ2bS1p06Ylbm3a0LZtW1q3bs3Nmzc5ceIEwcHBBAcHy42VokGTolkIIYRGeHh41GiRXB4bGxsCAgLYsGEDI0aMqDdTBTIzMzl//v/au/foqOp77+PvSSYXksCES+5XAgRICCSGiyIqEAIKihdQrKfSdazlVD1dXpZ92udxWR/XeWo9Vp/aVttTWk9bbQv60AsChWKDqEQQE4hEJQgmIVdCSMhAQjLX/fwREokBZiKTmRA+r7X2ysyePXv/9nfv7Hyy5zd7V1FZWUlVVRVbt26lvPgAEQe6mHLCxtXuk1zNIebSfbOPSlJ4J2wan8cnYpuZSGJBBhMzJ/Jv48fzf1JSCAkJ6TP/6upqtmzZQnR0NGvWrGH06NGBWE2RIUWhWUREhrV58+axdu1aysvLmT59eqCb4xWbzUZNTQ3V1dW9wfjc4cSJEyQSxzzGcy2h5PI5v6CEYNy4COKTkGnsSFoJ8+aQvaaQjOsm0f9G1v11dHSwfft2qqurufHGG5kyZcqw+UdD5FIpNIuIyLAWHBzM8uXL+dOf/sSECROIjIwMdJOw2+3U1tZSXV1NVVUV1dXVvUNVVRWNjY0Y59z9LiQ4hOvHzWGpKZ1c21jygj4lzV0DNNFBBI+Fx/Le7IcYedM8MldfzfTEkQzk3wPDMNi3bx87duxgxowZPPTQQ71XthCRbgrNIiIy7CUlJTFjxgy2bt3KypUrB2UZhtug8u8V1P56K+6WVux2G51dNmxdXXR1ddFl66Lr7HOb3dbnvWGYmB4WxqzwcYSHJxGeEk54eDgjQkKIPfY5U1r3EN20C4DjQXF8njCP6lmPEnP7PCatnEHCcz9k/le8gsaxY8fYvHkzJpOJ1atXExcXd6mlEBmWFJpFROSKsGDBAn75y19y6NAhJk+efEnzstvt1NXVUXmkkiN/2E9U0X7mHPuASe5KJgBuvkKXBtvZ4UtX5qsKncyBzJUEXTePlK/NI3V+BrFBl95lwmazsXPnTg4cOEBBQQF5eXnqiiFyEQrNIiJyRQgJCeGWW27hr3/9K2lpaYSHh19w2s7OTmpqajh69CjV1dUcPXq0d2iorGdKw1huI5xbOMQimrETwjtBubw2LpfGWRYSZiaTnp5Oeno648ePJzk5ud+X7bw14ezgK4ZhcPDgQbZt20ZGRgYPPvjgkOiyIjLUKTSLiMiw4rK7qNtVzfF3K+gorYDGxj6vd7V9zvMvvUP+iCRsdjt2mx273caJ1v388v8txWa343T2vcHHGEzEh4ayiiAW2I8TRRWnGElJ7EL233QTs3+wgsKMcRT6c0W/gtbWVv7+979z6tQpVqxYQVpaWqCbJHLZUGgWEZHL0umG09T+8xAnd1fgKK8grLqCcScqSLUdJg07PXHwDCNwE9T7vmkY/BonqQSTds74T7Fzr6Phwgu0gzV4DPuy7iXintvI+c58Fo4KG6S18y2n08n777/Pnj17mDt3Ltdcc42uuSwyQArNIiIyZLmdbho/rOPYO4doL6mAQxWMrKsg4VQFCe4Gss5O5ySYSlMah4Li+JtpLgcNBxVYOUQdrbQRFRVFWloaaWlppKenExERwY+bm/nWt77FhAkTiI2NpeG++4j63e8u2p4oIGmwV9rHqqqq2LJlC2PHjmXNmjVER0cHukkilyWFZhERGTIaS+r55NkNmN9/j5iWw6Tbj5DEmd6gamUUB0lhO+OpYBIVdFJBM6dj7CSmjyUtLYnU1FRy09JYnppKWloaqampjBkzpt+X3DZs2MCZM2eIj4/3/4r6QXt7O9u3b+fo0aPcdNNNTJkyJdBNErmsKTSLiIjfOJ1OGhsbqamp6f6iXfVRWt6rJ/HDY1zXUsFM42MSgFoS+IRE3mImn5ng+JgQbOPDGJsVQ1p69xnjgtRU7ktLIyUl5aJf6ruQm266iV/84hdkZ2eTmJjo+5UNELfbTWlpKTt37iQ3N1fXXBbxEYVmERHxCcMwaG1tpaamhtra2vP+bGhowHC5uYZp3MpoVnCUSRwFoCR4Or9MvJfTBZNIXTyJ9PHp3JWaSnx8PEFBQR6WPnCRkZEsWbKEjRs3smbNGp/PPxAaGxvZvHkzwcHBfOMb3yA2NjbQTRIZNhSaRUTkvDpbOznwwlt0vfkPgjs7cBtunA4nTmffwdH72NHnLnZRQBYmppvNmM0jMIdkExGRxcz2fcQY5dgJ4aOxC3h38ffJfHw5M69KZKaf1zEnJ4fy8nKKi4v9vGTfcjqdbN26lY8//phFixaRm5uray6L+JhCs4iI4HA4aGhooKL4II2/Lia5dA/XnH6fOZzhFCNpxQIY53lndzDrCWimoJ7n3a+ZOPs2R/fgNgXxWeoiDt96K9O+exOzkkcN+rpdjMlk4uabb+ZXv/oVDofD8xuGoI6ODj788ENmz57NQw89RERERKCbJDIsKTSLiAxzLpeLY8eOUVtbS21tLXV1db2Pa2trcR3p4toWC7dhZRHlBOOmlgReDZpNccwI2qYHkZyRTGpqKikpKb0/k5KSCAsb+CXXUgdhHS+FxWJhzZo1PPHEE/zvr3gr6kAyDIPrr7+e5cuXB7opIsOaQrOIyCD7wQ+gtBS2bPnq8zhxsJnPX3ufzrd2EXbsaO94AwO3y43L5cLpcuFyOXE5XWefO3G5uh+fKwlINpmYFxzMJLeVHHcFAAfNWbw5+QGi7i1kzrcX8IBlFA989SZfVqKjo7nzzjuZP39+oJsiIkOUQrOIyCD7j/8Y2PSG26Dm7c85+sd3cb7zLmk1u5ng/IxxgI1Qjgal4DJMZ/sPn6/LxNmOESYTJpMJU3B3NwmT6ZxxZ6c7FRXPzuu/xfiHb2XqwglMvYT1FBEZzhSaRUQGUWdrJ8soYhKH2XnbF+MNt5vOri66Ojvp6uqis7MTR3s7yY3VNNkrSOM4aUAr0RQzibXcwC5a+Di0hpgUSElJJiUlheTk/j/Hjh2rL4GJiPiYQrOIiI/Vl9dR9sO/EvXPfzCz5W02c6b7hY2e31tJCu+FT6cyMZHOqxKJvSGF1PRU7klJ4X8kJ5/3Jh0iIjL4FJpFRLzUcx3i+vp66uvrqaur6318qryVzIN2Fp6u5zrjAMtwUU8cvyefjXRwMKqFuIRxJCQkkJCQQHxCAgnx8SQkJJKQEE98fDyjx42h5lApd6pfrYjIkKPQLCIC2LpsHP7gMyp3fkrrR9U4K+txt1mxddmw2bqw2Wx02Wy43e4+74sCHqSJPLq/TFcRlMn6lHs4sziPjDtzKEhLYXVSElFRUd415JCPV0xERHxCoVlEhjXDMDh58iRVB6uofPcQLfuPYv+8kdBjLVisrcR2tZLoOkEqx5jGGaYNcP5uTHw8ci47r/8x6Q/fypTCSUwZlDUREZFAUmgWkQHbuxdOnYJFi/yzvK62Lj77UwnODluf8Q6HnVOnTnHq1Cms1u6fncdaCGk8yai2NmK7rCS6TpJKM/k0k/+l+R4jhvrgeKoj0ikbNQt7XBwhE5IZk5tBxvxs0nLHExR88ds3m4JMTA/XoVREZLjz+5G+traW1atX09TUhMlkYs2aNTz88MP+boaIXII5c7p/ZmXBbbfBrbfCzJkQdPF86TWn08nhvZ9R8X+3YXl7B7Na32E67QOaRwcR1AUl0hQexwejptAVk4A5I4XonAmkzssi5ZoM4qPDifdNk0VEZJjze2g2m8288MILXHXVVZw+fZr8/HwKCwvJysryd1NE5BLFx8N//ic88wwkJnaH59tug/nzITS0//Qul4vm5mYaGhpoaGigsbGx93FDQwOdn7WTezSEJbYTzKecqThpJJY/ksc2kw3XqDAsFgsWSzQWi4Xo6O5h1CgL0dHRWCzRRCfHEDcrjdETxjA5yMRkfxdFrmjl5eUUFRVhtVqxWCwUFBSQk5MT6GaJiA/4PTT3fHMcYOTIkUydOpX6+nqFZpHLhOOMg5mUEc8xNj0Cp78Je/e6KS52cuCVYH7+y2DWhjqIj28gOvownZ0ttLef5vTpdjo6OnAbfb9IZwJmhlj4N2cjVxmfAPBZ8CT+knovjpvnMOXr+dyWmsy3YmIIDg72/wqLeKm8vJxNmzbhcDgAsFqtbNq0CUDBWWQYCGhHvOrqavbv38+cns96RWRIcTqdVH1UycHf7sKxs5TEqo+YfmY/H/Zcd3g5jAQKzg697EDN2cELboeJT6KuZud1/0nqd24l86bJZPpyRUT8oKioqDcw93A4HBQVFSk0iwwDJqP7Pqx+197ezg033MATTzzBHXfc0e/1tWvXsnbtWgDq6upYv369v5tIe3u795eJEp9S7Qem61gnhsPtecKz7HYHbdY2TlmtnDzZRpu1DWtbG21tVtqbrKQdC2VGu41rXA3M4DDBuHERRBmZ7CKWD0NG0TamkbHjuhg1ahTR0dGMsnR3kYi2WLBYLERFjaK5eSROp+cbcYRPjCYyY+SllGDY0L4fOJda+8bGxgu+1vMJq5yf9vvAUe37evzxxykpKTnvawEJzQ6Hg5tvvpklS5bw2GOPeZx+5syZF1yBwbRz507m6yYDAaHaX5jb6ebIxk84tmEXwXuKSavdRbLrqM+X00EE+8NzqUnOwnl1Nul35zF+egZxcXGEnq/DsviE9v3AudTav/jii1it1n7jLRYLjzzyyFdv2BVA+33gqPZ9XSxz+r17hmEYfPOb32Tq1KleBWaRK117czv7fvU2bZuLGXuwhKxTpWTSRibQSBwfhE7h16FZtNm6cLmd/d4fZAomPDycESPCCQ8PP/t4RO/j8BEjGBEeTmhYGEGmII6fbCF79TImrZzBvIgQ/6+wyGWqoKCgT59mgJCQEAoKCi7yLhG5XPg9NBcXF/Paa6+Rk5NDbm4uAM888wxLly71d1NE/MJwGzSVNdL4dgWnPqyAgxVE1VUwtr2aEWFOwsKcBAfbcblcuJxOnC4XLpcLp9OJ0zGKZMPK9XT/Ef6ECbxBDrtw8VFkK/Ykg8SkIOLjo0lISCAlIYH4+PgvbtUcH8/o0aMxmTx3keixc+dOsubPHKxyiAxbPf2WdfUMkeHJ76F53rx5BKgbtcigsp2ycWTbJ9T9s4yusoOMOPo5sW1VZNg/J57TvdcDPk0UFaTyAYk47AlwOhiwAfWYqCd8xEkiRnSfDW4+MZNNplTMC0Yz7ubJpOaNZ0lCAqvj4hgxYkQA11ZEzicnJ0chWWSY0m2sRDxwOBwcP36cY8eO0dTURO3+Gqwf1BF8+DjRTc0ktTczwVFPBjVk4yb77PtqSOQQcbxHHlWhoTSNDuFMqpnISaOIT4gnLi6O0aOTqavLYv/+NN57bxQnTwYR5obCud3XPH7hBZgwATZvDmgJRERErngKzXLZeuABKCuDefO6h7lzISam7zRlZXDHHdDV9aU3G26SnFXM7HqP2Y5iZjt2M8ZoOfsaGPT9NMQEJAAZdLGUL77o00UYR0zpVIzI4N3R19CREk/YjBTirhtPalYq2fHxzI+JISTEc99gpxN27YK//Q02bvwiKE+cOICiiIiIyKBQaJbL1vbtcOIE7NsHzz/fPW7CBAfTprUxfnwDsbGfUVYWRlXVctKSi8nsKiG34wAzbZ8w132IZNoAsBLObjIoZiJBpiCCQ8yYzWbMwcGYzWaCzWbMwWbM5mBMYeEYEyYSPSeHlMIcEq9OZVpoMNN8sD5mc/ed9ObPh5/8BA4cgC1buv8ZEBERkcBSaJYhyzAMOjo6aGpqoqmpqbuLROMxTuxpJPTDRv5nlYP40MOEhJTT5QrH6YqGz8d2D3RfEi0TF9/iZ8yp38lIwwXAychI6jIyqM6+Efvs2YyYNYvMxESui4sjMjIycCt8DpMJZszoHkRERCTwFJrFr1wuF62trb0huOfnuY+bmpqoqanBarXi6LSTRybXEsM8bNzO58RxAoBWojlhhBFuNmG2ODGb2zCb2wk2N2AYIdjtIdi6gug0RjBi1RqY392PY3RqKqMDXAcRERG5vCg0y3kZBjQ0QEUFHDkCS5ZAenr/6TpbOzn89wM07DhA12fV2Gw27DYbNrsNu82OzW7vfm6zYbPZcTjs/foLA5gwER8WSlpoGKFhKUQ5Esh1N5HLASI5CBzkaHA6FUmFHLz2OpJXXU/GsqmMMQcNei1EREREFJqvcDYbHD4Mhw51B+SKCjh40E1FBXR09ARSg8Uz3mPeuDcJq2ph3IkWUs40M9FZRxr1TMdguk8ac3Y4DS6C+GxELiUz7id04TzGf/1a0q5KJM0XyxEREREZIIXm4cJmg337sBXtom79LqIPvs9Id//buX6ZCcg8O9zSO9boM5gIJuSjL+4010EEh4PSORA5laLR8zmTGk/o9ERiZ6cSmxhHTEwMY8eOvaTrCO8u2c2ipYuY+pXnICIiIuI7Cs2XIYfDQcuRI3Tu2IGpuJiI/fsZc+QIZqeTMMDFJLaFXMPxUAcOhwOnq/+tlXuEmIMYMeIMkZE2LBYnUVFhREZGEhER0T1ERnK8ycLJsDSir84hYcFUEmYlk2sOIncQ19EcoV1TREREhg4lkwDoauvi2Ie1tJbV0HGwBvuRKoLqqhnRWo/Z2YXhduM2DAzDwHC7MQzjnOcGY2gmi3qCAAewjzRe4wF2sYCSkA7MSZtJSztGbGwsMTExxMTE9D4+9+fo0aMJDg722N6UQa+IiIiIyNCm0OxjLoeLyj1HqNl1iLYDVTg+ryP02DFGtTUxrrOJBGcjcTSTDqSf875GYqllHKfPXiqth8lkIshkwhQURFCQCbcplGpXErVzr2fb6Vv4749voSs4kjvvtPEf/yuEadOCga/7b4VFRERErgAKzRew78dFnPrvDbw9cgsOhxOn04nT6Tj7+IufLoeDkZ3txNlPkuQ+QQqNTMLOpHPm1U4kNcRRyxj2m6fRPCIKqyWCrthIzBkWLFnjiEmO6T0r3DNER0djMpn6tOsnP4HHHgPeh8hI+LdH4dFHITk53K/1EREREbmSKDRfwEe/fp27D7/m1bTNjKXRHMvBEZnsHjWXrthxmNLiGJmdROLVGaTkpJIeF0tWRMQltysnByZNgtWr4cEHYcyYS56liIiIiHig0HwBCT+7g9WvnCQ3N5eYmBjGjRvHuHHjeh+PHj0as7m7fKlnB39YtAg++8xPCxMRERERQKH5gm688UbCw8OZP39+oJsiIiIiIgGm26mJiIiIiHigM80iEjDl5eUUFRVhtVqxWCwUFBSQk5MT6GaJiIj0o9AsIgFRXl7Opk2bcDgcAFitVjZt2gSg4CwiIkOOQrOIBERRUVFvYO7hcDgoKioKeGjWGXAREfkyhWYRCQir1Tqg8f6iM+AiInI+Cs0iEhAWi+W8AdlisQSgNV8YymfARYYCfRIjVypdPUNEAqKgoICQkJA+40JCQigoKAhQi7oN1TPgIkNBzycxPb8PPZ/ElJeXB7hlIoNPoVlEAiInJ4dbbrml98yyxWLhlltuCfgZqwud6Q70GXCRoeBin8SIDHfqniEiAZOTkxPwkPxlBQUFffo0w9A4Ay4yFOiTGLmSKTSLiJyjJ8Srz6ZIf0P1uwgi/qDQLCLyJUPxDLjIUKBPYuRKptAsIiIiXtEnMXIlU2gWERERr+mTGLlS6eoZIiIiIiIeKDSLiIiIiHig0CwiIiIi4oFCs4iIiIiIBwrNIiIiIiIeKDSLiIiIiHig0CwiIiIi4oGu0yxyBSgvL/fZzQi2bNlCaWkphmFgMpnIz89n2bJlPm7x8OHL2svQ58vtrX1neNB2HJihXC+FZpFhrry8vM9tb61WK5s2bQIY8IFoy5YtlJSU9D43DKP3uYJzf76svQx9vtze2neGB23HgRnq9VL3DJFhrqioqPcA1MPhcFBUVDTgeZWWlg5o/JXOl7WXoc+X21v7zvCg7TgwQ71eCs0iw5zVah3Q+IsxDGNA4690vqy9DH2+3N7ad4YHbceBGer1UmgWGeYsFsuAxl+MyWQa0PgrnS9rL0OfL7e39p3hQdtxYIZ6vRSaRYa5goICQkJC+owLCQmhoKBgwPPKz88f0PgrnS9rL0OfL7e39p3hQdtxYIZ6vfRFQJFhrufLE774NnLPl/109Qzv+LL2MvT5cntr3xketB0HZqjXS6FZ5AqQk5Pjs4POsmXLFJIHwJe1l6HPl9tb+87woO04MEO5XuqeISIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4oNIuIiIiIeKDQLCIiIiLigUKziIiIiIgHCs0iIiIiIh4EJDRv27aNyZMnM3HiRJ599tlANEFERERExGtmfy/Q5XLx0EMP8dZbb5GcnMysWbNYvnw5WVlZ/m7KJSsvL6eoqAir1YrFYqGgoICcnJyvNK9XX32Vqqqq3ufjx49n9erVfabZsmULpaWlGIaByWQiPz+fZcuWfaV2vfzyy5w4caL3+bhx43jooYf6zcubZfqyXT3TxMXF8eKLL16wpt4s09t19GW9vOFtvbxZprf7oDfL7NkHMzMzefrpp8+7DwI8++yz2Gy23udhYWF8//vf7zedN23ztha+3A+94W1dB+P39mL7/lDdd7zly33CG4HYJ3zJ38v0Zn/2NW+P+dItEPuhfMHvZ5r37t3LxIkTycjIIDQ0lLvvvpuNGzf6uxmXrLy8nE2bNmG1WgGwWq1s2rSJ8vLyAc/rywcqgKqqKl599dXe51u2bKGkpATDMAAwDIOSkhK2bNky4HZ9+Q8qwIkTJ3j55Zf7jPNmmb5sl7c19WaZ3q6jL+vlDW/r5c0yfVkvb/ZB6B+YAWw2W79PjLxpm7e18OV+6A1v6+rv39uhuu94y5fr6I1A7BO+5O9lensM8KVA1PVypnoFnt9Dc319PSkpKb3Pk5OTqa+v93czLllRUREOh6PPOIfDQVFR0YDn9eUD1fnGl5aWnneaL4/3pl1f/oN6ofHeLNOX7fK2pt4s09t19GW9vOFtvbxZpi/r5c0+CPQLzBca703bvK2FL/dDb3hbV3//3g7VfcdbvlxHbwRin/Alfy/T22OALwWirpcz1SvwTEbPv+F+smHDBrZt28ZvfvMbAF577TU++OADXnrppT7TrV27lrVr1wJQUVHBlClT/NlMAJqbm4mJiTnvaw0NDRd8X2Ji4oCW4828vF3ecJnXmTNniIiIGHLtuti8vHEl1P6rLNNf8/JGoNfxQvUPdLsuNp03hup2PNeFjvm+bJe3/L3MQK/jxY470m2wttHFss6VqLq6+oInH/zepzkpKYna2tre53V1dSQlJfWbbs2aNaxZs8afTetn5syZlJSUBLQNVyrVPnBU+8BS/QNHtQ8c1T5wVHvv+b17xqxZszh8+DBVVVXY7XbWr1/P8uXL/d0MERERERGv+f1Ms9ls5qWXXmLJkiW4XC7uu+8+srOz/d0MERERERGv+T00AyxdupSlS5cGYtEDEujuIVcy1T5wVPvAUv0DR7UPHNU+cFR77/n9i4AiIiIiIpcb3UZbRERERMQDheazamtrWbBgAVlZWWRnZ/PTn/4UgNbWVgoLC5k0aRKFhYWcPHkywC0dfrq6upg9ezYzZswgOzubp556Cui+PuicOXOYOHEiq1atwm63B7ilw5fL5SIvL4+bb74ZUO39JT09nZycHHJzc5k5cyagY46/tLW1sXLlSqZMmcLUqVPZvXu3au8Hhw4dIjc3t3cYNWoUL774omrvJz/5yU/Izs5m2rRpfO1rX6Orq0vH+wFQaD7LbDbzwgsv8Omnn7Jnzx5efvllPv30U5599lkKCgo4fPgwBQUF/e58JpcuLCyMHTt28NFHH1FWVsa2bdvYs2cP3/ve93j00Uc5cuQIo0eP5pVXXgl0U4etn/70p0ydOrX3uWrvP2+//TZlZWW9l3zSMcc/Hn74YW688UYqKir46KOPmDp1qmrvB5MnT6asrIyysjJKS0uJiIjg9ttvV+39oL6+np/97GeUlJTw8ccf43K5WL9+vY73A2HIeS1fvtzYvn27kZmZaTQ0NBiGYRgNDQ1GZmZmgFs2vHV0dBh5eXnGnj17jLFjxxoOh8MwDMN4//33jcWLFwe4dcNTbW2tsXDhQqOoqMhYtmyZ4Xa7VXs/SUtLM5qbm/uM0zFn8LW1tRnp6emG2+3uM161969//OMfxty5cw3DUO39oa6uzkhOTjZaWloMh8NhLFu2zNi2bZuO9wOgM83nUV1dzf79+5kzZw5NTU0kJCQAEB8fT1NTU4BbNzy5XC5yc3OJjY2lsLCQCRMmEB0djdncfYGXy/V265eDRx55hOeee46goO7DQUtLi2rvJyaTicWLF5Ofn997B1QdcwZfVVUVMTEx/Ou//it5eXncf//9dHR0qPZ+tn79er72ta8B2u/9ISkpiccff5zU1FQSEhKwWCzk5+freD8ACs1f0t7ezooVK3jxxRcZNWpUn9dMJhMmkylALRvegoODKSsro66ujr1791JRURHoJl0RNm/eTGxsLPn5+YFuyhVp165d7Nu3j61bt/Lyyy/z7rvv9nldx5zB4XQ62bdvHw888AD79+8nMjKyX3cA1X5w2e123nzzTe68885+r6n2g+PkyZNs3LiRqqoqGhoa6OjoYNu2bYFu1mVFofkcDoeDFStW8C//8i/ccccdAMTFxdHY2AhAY2MjsbGxgWzisBcdHc2CBQvYvXs3bW1tOJ1O4MK3W5dLU1xczJtvvkl6ejp33303O3bs4OGHH1bt/aSnrrGxsdx+++3s3btXxxw/SE5OJjk5mTlz5gCwcuVK9u3bp9r70datW7nqqquIi4sD9LfWH/75z38yfvx4YmJiCAkJ4Y477qC4uFjH+wFQaD7LMAy++c1vMnXqVB577LHe8cuXL+f3v/89AL///e+59dZbA9XEYau5uZm2tjYAOjs7eeutt5g6dSoLFixgw4YNgGo/WH70ox9RV1dHdXU169evZ+HChfzxj39U7f2go6OD06dP9z7evn0706ZN0zHHD+Lj40lJSeHQoUMAFBUVkZWVpdr70bp163q7ZoD+1vpDamoqe/bs4cyZMxiG0bvf63jvPd3c5Kxdu3Zx3XXXkZOT09u385lnnmHOnDncdddd1NTUkJaWxhtvvMGYMWMC3Nrh5cCBA3zjG9/A5XLhdru56667+MEPfkBlZSV33303ra2t5OXl8Yc//IGwsLBAN3fY2rlzJ88//zybN29W7f2gsrKS22+/HejuLnDPPffwxBNP0NLSomOOH5SVlXH//fdjt9vJyMjgt7/9be/xR7UfXB0dHaSmplJZWYnFYgHQfu8nTz31FK+//jpms5m8vDx+85vfUF9fr+O9lxSaRUREREQ8UPcMEREREREPFJpFRERERDxQaBYRERER8UChWURERETEA4VmEREREREPFJpFRPwsODiY3NxcsrOzmTFjBi+88AJut/u80zY0NLBy5cpBa8vf/vY3TCaT7sIpIuKBLjknIuJnUVFRtLe3A3D8+HHuuecerr32Wp5++uk+0zmdTsxms0+WeaF5rVq1ioaGBhYuXNhv+SIi8gWdaRYRCaDY2FjWrl3LSy+9hGEY/O53v2P58uUsXLiQgoICqqurmTZtGgBXX301n3zySe9758+fT0lJCR0dHdx3333Mnj2bvLw8Nm7cCNBvXl/W3t7Orl27eOWVV1i/fn3veLfbzYMPPsiUKVMoLCxk6dKlvXcMKy0t5YYbbiA/P58lS5b03vpYRGS4U2gWEQmwjIwMXC4Xx48fB2Dfvn1s2LCBd955p890q1at4o033gCgsbGRxsZGZs6cyQ9/+EMWLlzI3r17efvtt/nud79LR0fHRecFsHHjRm688UYyMzMZO3YspaWlAPzlL3+hurqaTz/9lNdee43du3cD4HA4+M53vsOGDRsoLS3lvvvu44knnhi0uoiIDCW++dxPRER8prCw8Ly3EL7rrrtYvHgxTz/9NG+88UZvX+ft27fz5ptv8vzzzwPQ1dVFTU3NRecFsG7dOh5++GEA7r77btatW0d+fj67du3izjvvJCgoiPj4eBYsWADAoUOH+PjjjyksLATA5XKRkJDg25UXERmiFJpFRAKssrKS4OBgYmNjAYiMjDzvdElJSYwdO5YDBw7w+uuv81//9V8AGIbBn//8ZyZPntxn+g8++OCC82ptbWXHjh2Ul5djMplwuVyYTCZ+/OMfX7CdhmGQnZ3de+ZZRORKou4ZIiIB1NzczLe//W3+/d//HZPJ5HH6VatW8dxzz2G1Wpk+fToAS5Ys4ec//zk93+vev3+/x/ls2LCBe++9l6NHj1JdXU1tbS3jx4/nvffe49prr+XPf/4zbrebpqYmdu7cCcDkyZNpbm7u013j3D7WIiLDmUKziIifdXZ29l5ybtGiRSxevJinnnrKq/euXLmS9evXc9ddd/WOe/LJJ3E4HEyfPp3s7GyefPJJj/NZt24dt99+e59xK1asYN26daxYsYLk5GSysrL4+te/zlVXXYXFYiE0NJQNGzbwve99jxkzZpCbm8v7778/sJUXEblM6ZJzIiLST3t7O1FRUbS0tDB79myKi4uJj48PdLNERAJGfZpFRKSfm2++mba2Nux2O08++aQCs4hc8XSmWURERETEA/VpFhERERHxQKFZRERERMQDhWYREREREQ8UmkVEREREPFBoFhERERHxQKFZRERERMSD/w8ZkrQmEnnCmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax.plot(test_data, predictions, color=\"black\", label=f\"Float clear trend line, d={dev_real:.3f}\")\n",
    "ax.plot(test_data, y_pred_fhe, color=\"blue\", label=\"FHE quantized trend line\")\n",
    "ax.scatter(df_test[\"DrivAge\"], df_test[\"Frequency\"], marker=\"o\", color=\"gray\", label=\"Test data\")\n",
    "ax.set_xlabel(\"Driver Age\")\n",
    "ax.set_ylim(0, 10)\n",
    "ax.set_title(\"Poisson Regression, float in clear vs. quantized FHE encrypted\")\n",
    "ax.set_ylabel(\"Frequency of claims\")\n",
    "ax.plot(test_data, y_pred, color=\"red\", label=f\"Quantized trend line, d={dev_q:.3f}\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.grid()\n",
    "\n",
    "axins = ax.inset_axes([0.5, 0.5, 0.47, 0.47])\n",
    "axins.plot(test_data, predictions, color=\"black\", label=f\"Float clear trend line, d={dev_real:.3f}\")\n",
    "axins.plot(test_data, y_pred, color=\"red\", label=f\"Quantized FHE trend line, d={dev_q:.3f}\")\n",
    "axins.plot(test_data, y_pred_fhe, color=\"blue\", label=\"FHE quantized trend line\")\n",
    "x1, x2, y1, y2 = 60, 65, 2.3, 2.7\n",
    "axins.set_xlim(x1, x2)\n",
    "axins.set_ylim(y1, y2)\n",
    "axins.grid()\n",
    "ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
    "\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14394b94",
   "metadata": {},
   "source": [
    "## A multi-variate model\n",
    "\n",
    "The simple single-variable model does not achieve good results (age is not a good predictor for the number of claims). So, let's train a model with all of our predictor variables. We proceed by transforming the raw features into ones that can be input into a regression model. Thus, the categorical features are transformed into one-hot encoding, but we can also reduce the resolution of vehicles and persons by binning. Transforming the data this way, we end up with a total of 57 continuous features (instead of the initial 11).\n",
    "\n",
    "Here is where we encounter one of the limitations of our framework. We perform a dot product in the prediction, in the QuantizedLinear class, but in our framework the maximum integer size is, for now, limited to 7 bits. As every multiplication doubles the number of bits of precision of the inputs performing 57 multiplication-additions of integers to compute w.x would quickly overflow 7 bits. \n",
    "\n",
    "As a workaround to the limited accumulator resolution, we perform PCA to reduce dimensionality from 57 to 14 dimensions and train our multi-variate model in this reduced dimensionality space. However, we also train a reference model on all of the original features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "759507c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import (\n",
    "    FunctionTransformer,\n",
    "    KBinsDiscretizer,\n",
    "    OneHotEncoder,\n",
    "    StandardScaler,\n",
    ")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "log_scale_transformer = make_pipeline(FunctionTransformer(np.log, validate=False), StandardScaler())\n",
    "\n",
    "linear_model_preprocessor = ColumnTransformer(\n",
    "    [\n",
    "        (\"passthrough_numeric\", \"passthrough\", [\"BonusMalus\"]),\n",
    "        (\"binned_numeric\", KBinsDiscretizer(n_bins=10), [\"VehAge\", \"DrivAge\"]),\n",
    "        (\"log_scaled_numeric\", log_scale_transformer, [\"Density\"]),\n",
    "        (\n",
    "            \"onehot_categorical\",\n",
    "            OneHotEncoder(sparse=False),\n",
    "            [\"VehBrand\", \"VehPower\", \"VehGas\", \"Region\", \"Area\"],\n",
    "        ),\n",
    "    ],\n",
    "    remainder=\"drop\",\n",
    ")\n",
    "\n",
    "poisson_glm = Pipeline(\n",
    "    [\n",
    "        (\"preprocessor\", linear_model_preprocessor),\n",
    "        (\"regressor\", PoissonRegressor(alpha=1e-12, max_iter=300)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "poisson_glm_pca = Pipeline(\n",
    "    [\n",
    "        (\"preprocessor\", linear_model_preprocessor),\n",
    "        (\"pca\", PCA(n_components=14, whiten=True)),\n",
    "        (\"regressor\", PoissonRegressor(alpha=1e-12, max_iter=300)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "poisson_glm.fit(df_train, df_train[\"Frequency\"], regressor__sample_weight=df_train[\"Exposure\"])\n",
    "\n",
    "poisson_glm_pca.fit(df_train, df_train[\"Frequency\"], regressor__sample_weight=df_train[\"Exposure\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbd0ff1",
   "metadata": {},
   "source": [
    "### Now we evaluate the new models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0ffae598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoissonRegressor evaluation: 1.3770\n",
      "PoissonRegressor+PCA evaluation: 1.4338\n"
     ]
    }
   ],
   "source": [
    "def score_estimator(y_pred, y_gt, gt_weight):\n",
    "    \"\"\"Score an estimator on the test set.\"\"\"\n",
    "    y_pred = np.squeeze(y_pred)\n",
    "    dev = mean_poisson_deviance(y_gt, y_pred, sample_weight=gt_weight)\n",
    "    return dev\n",
    "\n",
    "\n",
    "def score_sklearn_estimator(estimator, df_test):\n",
    "    \"\"\"A wrapper to score a sklearn pipeline on a dataframe\"\"\"\n",
    "    return score_estimator(estimator.predict(df_test), df_test[\"Frequency\"], df_test[\"Exposure\"])\n",
    "\n",
    "\n",
    "def score_concrete_glm_estimator(poisson_glm_pca, q_glm: QuantizedGLM, df_test):\n",
    "    \"\"\"A wrapper to score QuantizedGLM on a dataframe, transforming the dataframe using\n",
    "    a sklearn pipeline\n",
    "    \"\"\"\n",
    "    test_data = poisson_glm_pca[\"pca\"].transform(poisson_glm_pca[\"preprocessor\"].transform(df_test))\n",
    "    q_test_data = q_glm.quantize_input(test_data).qvalues\n",
    "    y_pred = q_glm.forward_and_dequant(q_test_data)\n",
    "    return score_estimator(y_pred, df_test[\"Frequency\"], df_test[\"Exposure\"])\n",
    "\n",
    "\n",
    "print(f\"PoissonRegressor evaluation: {score_sklearn_estimator(poisson_glm, df_test):.4f}\")\n",
    "print(f\"PoissonRegressor+PCA evaluation: {score_sklearn_estimator(poisson_glm_pca, df_test):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de58b9eb",
   "metadata": {},
   "source": [
    "### Test the multi-variate GLM with multiple quantization bit widths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bce8b011",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, get calibration data from the held out set\n",
    "calib_data = poisson_glm_pca[\"pca\"].transform(poisson_glm_pca[\"preprocessor\"].transform(df_calib))\n",
    "\n",
    "# Let's see how performance decreases with bit-depth.\n",
    "# This is just a test of our quantized model, not in FHE\n",
    "n_bits_test = np.asarray([28, 16, 6, 5, 4, 3, 2])\n",
    "dev_bits_test = np.zeros_like(n_bits_test, dtype=np.float32)\n",
    "for i, n_bits in enumerate(n_bits_test):\n",
    "    q_glm = QuantizedGLM(n_bits, poisson_glm_pca[\"regressor\"], calib_data)\n",
    "    dev_bits_test[i] = score_concrete_glm_estimator(poisson_glm_pca, q_glm, df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dcb5f7e",
   "metadata": {},
   "source": [
    "We plot the Poisson deviance with respect to the quantized bit width, to show how performance degrades with quantization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0e3c4858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax.plot(n_bits_test, dev_bits_test, label=\"Poisson deviance for quantized FHE GLM\")\n",
    "ax.set_xlim(2, 28)\n",
    "ax.invert_xaxis()\n",
    "ax.set_xlabel(\"Number of bits\")\n",
    "ax.set_ylabel(\"Poisson deviance\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xticks(n_bits_test)\n",
    "ax.set_xticklabels([str(k) for k in n_bits_test])\n",
    "ax.grid()\n",
    "ax.legend(loc=\"upper left\")\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e6fd06",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "While the prediction quality is mostly stable until 6 bits, we see a decrease in prediction performance in lower bit widths. For 4 bits the performance seems to improve, but this is probably just a lucky sampling of the data as this graph shows a single experiment. We expect to have a smooth increase of the deviance with lower bit-widths when running the experiment multiple times.\n",
    "\n",
    "With 14 features, we can have weights and data in, at most, 2 bits. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac216b1",
   "metadata": {},
   "source": [
    "We now choose an operating point that is compatible with FHE: 2 bit quantization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c521ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_glm = QuantizedGLM(2, poisson_glm_pca[\"regressor\"], calib_data)\n",
    "test_data = poisson_glm_pca[\"pca\"].transform(poisson_glm_pca[\"preprocessor\"].transform(df_test))\n",
    "q_test_data = q_glm.quantize_input(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f45c8c",
   "metadata": {},
   "source": [
    "### Compile the multi-variate GLM to FHE. Again, with a single line of code we compile to FHE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f89eaa07",
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = q_glm.compile(q_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa0667b",
   "metadata": {},
   "source": [
    "Finally, we evaluate the model on encrypted data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f6fe2737",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████| 100/100 [12:22<00:00,  7.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PoissonRegressor evaluation: 1.3770\n",
      "PoissonRegressor+PCA evaluation: 1.4338\n",
      "FHE Quantized deviance: 1.6757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_fhe = np.zeros((test_data.shape[0],), np.float32)\n",
    "for i, test_sample in enumerate(tqdm(q_test_data.qvalues)):\n",
    "    q_sample = np.expand_dims(test_sample, 1).transpose([1, 0]).astype(np.uint8)\n",
    "    q_pred_fhe = engine.encrypt_run_decrypt(q_sample)\n",
    "    y_pred_fhe[i] = q_glm.dequantize_output(q_pred_fhe)\n",
    "\n",
    "dev_pca_quantized_fhe = score_estimator(y_pred_fhe, df_test[\"Frequency\"], df_test[\"Exposure\"])\n",
    "\n",
    "print(f\"PoissonRegressor evaluation: {score_sklearn_estimator(poisson_glm, df_test):.4f}\")\n",
    "print(f\"PoissonRegressor+PCA evaluation: {score_sklearn_estimator(poisson_glm_pca, df_test):.4f}\")\n",
    "print(f\"FHE Quantized deviance: {dev_pca_quantized_fhe:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c18dbdd1",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we have discussed how we can use Concrete-Numpy to convert a scikit-learn based Poisson regression model to FHE. \n",
    "\n",
    "First of all, we have shown that with the proper choice of pipeline and parameters, we can do the conversion with little loss of precision. This decrease in the quality of prediction is due to quantization of model weights and input data, and some minor noise can appear due to FHE. This noise is visible on the single variable FHE trend line as minor deviations of the blue curve with respect to the red one. \n",
    "\n",
    "Finally, we have shown how conversion of a model to FHE can be done with a single line of code and how quantization is aided by the tools in Concrete-Numpy. \n"
   ]
  }
 ],
 "metadata": {
  "execution": {
   "timeout": 10800
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}