diff --git a/notebooks/string_kernel_example.ipynb b/notebooks/string_kernel_example.ipynb index 4a4b786..f81a162 100644 --- a/notebooks/string_kernel_example.ipynb +++ b/notebooks/string_kernel_example.ipynb @@ -4,14 +4,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Imports and Utils" + "# Using a string kernel on the Photoswitch dataset using the SMILES representation\n", + "\n", + "\n", + " \"Open\n", + "" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -19,18 +32,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/adeshwall_dg/anaconda3/envs/mbo/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], + "source": [ + "!pip install gauche --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import and Utils\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], "source": [ "import torch\n", "import numpy as np\n", @@ -41,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -58,18 +78,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "from gauche.dataloader import DataLoaderMP\n", + "from gauche.dataloader import MolPropLoader\n", "from gauche.dataloader.data_utils import transform_data\n", "from gauche.kernels.string_kernels.sskkernel import pad, encode_string, build_one_hot, SubsequenceStringKernel" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -112,6 +132,7 @@ } ], "source": [ + "# Use GPU if available or default to CPU\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "tkwargs = {\"dtype\": torch.float, \"device\": device}\n", "print(tkwargs)" @@ -126,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -137,20 +158,23 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Found 13 invalid labels [nan nan nan nan nan nan nan nan nan nan nan nan nan] at indices [41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 158]\n", + "To turn validation off, use dataloader.read_csv(..., validate=False).\n", "len(smiles) 392 | len(targets) 392\n" ] } ], "source": [ - "loader = DataLoaderMP()\n", - "loader.load_benchmark(\"Photoswitch\", \"data/property_prediction/photoswitches.csv\")\n", + "dataset = 'Photoswitch'\n", + "loader = MolPropLoader()\n", + "loader.load_benchmark(\"Photoswitch\")\n", "smiles = loader.features\n", "targets = loader.labels\n", "print(f'len(smiles) {len(smiles)} | len(targets) {len(targets)}')" @@ -158,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -166,9 +190,9 @@ "output_type": "stream", "text": [ "alphabet \n", - " ['F', 'S', '5', '7', '%', '@', '2', 'B', 'N', 'I', '[', '8', 'c', 'l', '=', '\\\\', '/', '1', '(', '9', '0', 'O', '-', '4', 'r', '#', 'n', 's', ')', 'H', ']', '3', '6', 'C', '+'] \n", - " length of alphabet 35\n", - "maxlen 122\n" + " ['n', 'O', 'o', 'B', '[', 'C', '#', '(', ']', '-', 'N', 'H', 'I', 'S', '3', 'F', 's', '+', '2', '1', '4', 'r', '=', 'l', ')', 'c'] \n", + " length of alphabet 26\n", + "maxlen 79\n" ] } ], @@ -189,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -245,9 +269,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\AntObi\\anaconda3\\envs\\gauche\\lib\\site-packages\\botorch\\models\\gp_regression.py:161: UserWarning: The model inputs are of type torch.float32. It is strongly recommended to use double precision in BoTorch, as this improves both precision and stability and can help avoid numerical errors. See https://github.com/pytorch/botorch/discussions/1444\n", + " self._validate_tensor_args(X=transformed_X, Y=train_Y, Yvar=train_Yvar)\n", + "c:\\Users\\AntObi\\anaconda3\\envs\\gauche\\lib\\site-packages\\botorch\\models\\utils\\assorted.py:174: InputDataWarning: Input data is not contained to the unit cube. Please consider min-max scaling the input data.\n", + " warnings.warn(msg, InputDataWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "ExactMarginalLogLikelihood(\n", + " (likelihood): GaussianLikelihood(\n", + " (noise_covar): HomoskedasticNoise(\n", + " (noise_prior): GammaPrior()\n", + " (raw_noise_constraint): GreaterThan(1.000E-04)\n", + " )\n", + " )\n", + " (model): SingleTaskGP(\n", + " (likelihood): GaussianLikelihood(\n", + " (noise_covar): HomoskedasticNoise(\n", + " (noise_prior): GammaPrior()\n", + " (raw_noise_constraint): GreaterThan(1.000E-04)\n", + " )\n", + " )\n", + " (mean_module): ConstantMean()\n", + " (covar_module): ScaleKernel(\n", + " (base_kernel): SubsequenceStringKernel(\n", + " (raw_gap_decay_constraint): Interval(0.000E+00, 1.000E+00)\n", + " (raw_match_decay_constraint): Interval(0.000E+00, 1.000E+00)\n", + " (raw_order_coefs_constraint): Interval(0.000E+00, 1.000E+00)\n", + " )\n", + " (raw_outputscale_constraint): Positive()\n", + " )\n", + " (outcome_transform): Standardize()\n", + " )\n", + ")" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mll, gp_model = get_gp_model(X_train, y_train)\n", "fit_gpytorch_model(mll)" @@ -255,24 +325,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/adeshwall_dg/anaconda3/envs/mbo/lib/python3.7/site-packages/gpytorch/lazy/lazy_tensor.py:1811: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n", - "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n", - "X = torch.triangular_solve(B, A).solution\n", - "should be replaced with\n", - "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /opt/conda/conda-bld/pytorch_1659484772347/work/aten/src/ATen/native/BatchLinearAlgebra.cpp:2189.)\n", - " Linv = torch.triangular_solve(Eye, L, upper=False).solution\n" - ] - }, { "data": { - "image/png": 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mk2HcuHFQKpUYNGgQ1q5dC6lUau62hBBCnIBEAnz5ZXXa0yeecL0ELEKIMngDwMiRIzFy5EiL+xmGQVJSEpKSkiwe4+7ujqVLl2Lp0qX1UEJCCCGOcvs20KrVvdcSCTBmTKMVp9GJMsMaIYSQpmPZMqBTJ+DkycYuifOg4E0IIcRpcetx37oFDBoEFBY2domcAwVvQgghTokL3JyXXwb+NxO4yaPgTQghxOnUDNzcqPKmODjNHArehBBCnAoF7tpR8CaEEOI0KHALQ8GbEEKIU6DALRwFb0IIIU7BMEsaBW7rRJukhRBCiGvh8pXfvEmBuzYUvAkhhDgNwwVHiGXUbU4IIaRRLF8OfPddY5dCnCh4E0IIaXDLlgGvvgqMHUsBvC4oeBNCCGlQhqPKtVrgxInGLY8YUfAmhBDSYMxNB7Oy+COxgII3IYSQBkHzuB2HgjchhJB6R4HbsSh4E0IIqVcUuB2PgjchhJB6c+0aMHPmvdcUuB2DgjchhJB6ExxcPRVMoaDA7UiUYY0QQki9GjYMOHUKCA2lwO0o1PImhBDiUMePm27r1IkCtyNR8CaEEOIwy5YBUVHAwoWNXRLXRsGbEEKIQxiOKp8zBzhwoHHL48ooeBNCCLGbuelgjz3WeOVxdRS8CSGE2IXmcTc8Ct6EEELqjAJ346DgTQghpE4ocDceCt6EEEJs9vXXFLgbEwVvQgghNouOBtq0qf43Be6GRxnWCCGE2CwsDNi7F9i6FXjrLQrcDY2CNyGEEEFY1jhIh4VV/5CGR93mhBBCarVsGTBpEqDTNXZJCEAtb0IIIbUwHFXOssD69YCEmn6Nit5+QgghFtWcDnb//fR82xlQ8CaEEGIWzeN2XhS8CSGEmKDA7dwoeBNCCDFCgdv5OSx47927F5MmTUJoaCi8vb0hk8nw+++/Gx3zyy+/YPny5UhLS3PUbQkhhDgQBW5xsHu0eWVlJSZPnozNmzcDAFiWBQAwZj5pqVSK1157DQzDoGfPnggNDbX39oQQQhxErQa++ureawrczsvulvf48eOxefNmsCyLHj16YNasWRaPjY6ORteuXQEAmzZtsvfWhBBCHEguB3bvBrp1o8Dt7OwK3lu2bMGPP/4IAFi1ahUOHz6MxYsXWz0nNjYWLMti//799tyaEEJIPfDzA3JyKHA7O7uC97p16wAA8fHxeP755wWdExUVBQDIz8+359aEEEIc4JtvgLIy421eXhS4nZ1dwfvo0aNgGAbjx48XfE5gYCAA4ObNm/bcmhBCiJ2WLQPGjQOGDjUN4MS52RW8b926BQBo27atzefq9Xp7bk0IIcQOhqPKDx8GsrIatzzENnYFb29vbwBAmQ1f2S5dugQA8PX1tefWhBBC6sjcdLAXXmi88hDb2RW8ualeR44cEXwON8o8IiLCnlsTQgipA5rH7RrsCt4jRowAy7JYvnw5qqqqaj1+586d2LRpExiGwciRI+t836SkJDAMY/QTEBDA73/22WdN9vfq1cvoGiqVCq+//jr8/Pzg5eWF0aNH46+//qpzmQghxNmtWCGhwO0i7Arer732Glq0aIHLly8jNjaWfwZeU1VVFT799FPExsZCr9cjICAAU6ZMsefW6NKlCwoLC/mf06dPG+0fNmyY0f7t27cb7U9MTMSWLVuQlZWFgwcPory8HCNHjoSOFqslhLig7dvbY/p0Kf+aAre42ZVhrUWLFkhLS8MTTzyBn376Cffddx/69evH7//www9RUlKCX3/9FRUVFWBZFm5ubkhPT4e7u7t9BZfJjFrbNSkUCov7S0tLsXr1aqSmpmLw4MEAgLS0NAQHB2P37t0YOnSoXWUjhBBn8uOPDFat6sa/psAtfnanRx0xYgS2b9+OhIQEFBUVYefOnXxq1I0bNwK4lzLVz88PmZmZ6N+/v723xcWLFxEUFASFQoGePXtiwYIFeOCBB/j9+/btQ5s2bdCiRQv069cP8+fPR5s2bQAAubm50Gg0iImJ4Y8PCgpCeHg4cnJyLAZvlUoFlUrFv+YG6mk0Gmg0GsFl54615RyxoTq6Bqqja+jfX4NHHrmJI0cC8fbbOrz3nh5abWOXyrFc5XMUWn6G5SKrnSorK5GSkoLvv/8ex44dQ0lJCQDA09MTkZGRGD16NF566SV+hLo9duzYgcrKSnTq1Ak3btzARx99hHPnzuHs2bPw9fXFhg0b0KxZM4SEhKCgoADvvvsutFotcnNzoVAokJGRgSlTphgFYgCIiYlB+/bt8eWXX5q9b1JSEubNm2eyPSMjA56ennbXixBC6otGwyAnpy369v2LWtxOrLKyEs888wxKS0vh4+Nj8TiHBe+atFotdDodFApFfVzeSEVFBTp06IA333wTM2bMMNlfWFiIkJAQZGVlITY21mLwHjJkCDp06ICVK1eavY+5lndwcDCKi4utvsk1aTQaZGdnY8iQIXBzcxN8nphQHV0D1VG8ysuBZs2q/+2qdTTkKnUsKyuDn59frcHb7m5zixeWySCT1dvljXh5eaFr1664ePGi2f2BgYEICQnh9wcEBECtVuPOnTto2bIlf1xRURGio6Mt3kehUJj9MuLm5lanX5a6nicmVEfXQHUUl2XLgMWLgb17AYOniS5VR0vEXkehZXfYet6NSaVSIT8/n0+9WtOtW7dw7do1fn9UVBTc3NyQnZ3NH1NYWIgzZ85YDd6EEOLsuHncV68CAwYAt283dolIfRBl8J41axb279+PgoIC/Pbbb3j66adRVlaGyZMno7y8HLNmzcKhQ4dw+fJl7Nu3D6NGjYKfnx+efPJJAEDz5s0xdepUzJw5Ez///DNOnDiB+Ph4dO3alR99TgghYlMzAUtCAmDQuUhciF392oaju23FMAyfKtVWf/31F+Li4lBcXIzWrVujV69eOHz4MEJCQqBUKnH69GmsX78eJSUlCAwMxIABA7BhwwajwXJLliyBTCbDuHHjoFQqMWjQIKxduxZSqdTKnQkhxDlR5rSmxa7gffnyZcHHMgwDw7FxjB2/UVlWMuh7eHjgp59+qvUa7u7uWLp0KZYuXVrnchBCiDOgwN302BW8J0+eXOsxFRUVOH/+PE6fPg2GYRAZGYmuXbvac1tCCCH/Q4G7abIreKekpAg+9syZM3juuedw9uxZzJ49G0899ZQ9tyaEkCaPAnfT1WAD1sLDw7Fnzx4EBgZi0qRJOHfuXEPdmhBCXNK1a/f+TYG7aWnQ0ebNmjXDjBkzoFQq8cknnzTkrQkhxOUsXAi89RYF7qaoYbKoGOjRowcA4Oeff27oWxNCiEthmOoAzv2bNB0NPs+bG3F+48aNhr41IYSI2sqVwC+/GG9jGArcTVGDB29uGlfz5s0b+taEECJay5YBL78MDB9uGsBJ09OgwTsrKwsLFy4EwzB49NFHG/LWhBAiWoajyisqqnOWk6bNrmfezz33XK3H6PV63LlzB8ePH8c///wDlmUhk8nw9ttv23NrQghpEsxNB3v33cYrD3EOdgXvtWvXCs6Uxj3r9vHxwddff43u3bvbc2tCCHF5NI+bWGJX8L7vvvtqDd4SiQTe3t5o3749+vXrh/j4ePj5+dlzW0IIcXkUuIk1DZbbnBBCiDAUuEltRLkkKCGEuKoLF4A33rj3mgI3MYeCNyGEOJFOnYC1a6uDNQVuYkmDZ1gjhBBiXUICEB4OPPwwBW5inqDgffXq1Xq5+X333Vcv1yWEEDE5dw546CHjbZGRjVMWIg6Cgnf79u0dfmOGYaDVah1+XUIIEZNly6qfcX/9NTBlSmOXhoiFoGfeLMvWyw8hhDRl3KhyvR6YOhU4fryxS0TEQlDLOyUlpb7LQQghTUrN6WBz5lBXORFOUPCePHlyfZeDEEKaDJrHTexFU8UIIaQBUeAmjkBTxQghpI4qKytRWVkp+PjMTE+88YYn/1pI4Lb1Hp6envD09Kz9QCJqFLwJIaSO8vPzkZuba7RNqVSCZVkwDAMPDw9++82bwJYtUQCiAAhvcdtyDwCIiopCVFRU3StFRMFhwZtlWZw8eRJ5eXkoLi7mf7msee+99xx1e0IIaXCdO3dGSEiI0bZt27ZBqVTCw8MDo0aN4refOAH897/VLWJbusptuQcAanU3EQ4J3uvWrcO8efNw5coVm86j4E0IETNzXdRyuRwajQZyudxoBcUhQ4DvvgN+/hl4553qwF3XLnFL9yBNh93Be+7cufj4448FzdtmGIbmdxNCmqzo6OofDnWJk7qyK3j/9ttvWLhwIRiGwZAhQ/DJJ59Ar9fjX//6F59B7c6dOzh27BhWrFiB77//Ho8++ii++eYb+Pv7O6oOhBDidK5cAf7v/4DFiy13j1OXOKkru4L3ihUrAAAhISH48ccfIZPJcPbsWX4/wzBo1aoVYmJiEBMTgxUrVuDVV1/FsGHD8Ntvv0Eul9tXekIIcUI3bwL5+UB6OsCywCefmA/gtnS7E2LIrnneOTk5YBgGb7zxBmSy2r8HvPzyy3jqqadw6tQpLF++3J5bE0KIU7pyBTBcy8ndvfHKQlyXXcG7sLAQANClS5d7F5Tcu6RGozE5JyEhASzLYsOGDfbcmhBCnM6yZcDvv997TQlYSH2xK3hzwblNmzb8tmbNmvH/vnnzpsk5wcHBAIA//vjDnlsTQohTqZk5rUMHCtyk/tgVvFu3bg0AKCsr47f5+/tDKpUCqB5JWRPXWr979649tyaEEKdRM3AHBACdOlHgJvXHruDNdZefO3eO3yaXy/nt5rrG09PTAQBBQUH23JoQQpyCuRZ327aNVx7SNNgVvB977DGwLIu9e/cabR8/fjxYlsWaNWvw3nvv4ezZszh69Chee+01ZGZmgmEYDB8+3K6CE0JIY6uoAP7zn3uv586tbnHbQ6/XQ6VSQa/X23ch4tLsCt5jxowBAPzwww9GXefTp0/H/fffD71ej/nz56Nbt27o1asXP7WsZcuWmD17tj23JoSQRuflBezdC9x//73BaXWVl5eHKVOm4LnnnsMbb7yB5557DlOmTEFeXp7JsRTgiV3zvLt06YK9e/dCq9VCq9Xy2z09PbF3717Ex8fj119/NTonPDwcqampaNeunT23JoQQp3D//cDx40CLFnV/xp2ZmYmEhAQ+uRUAaLVapKWlITU1FampqYiLi0NeXh6Sk5ORlpYGrVYLmUyGPXv2IDExEREREQ6rE3F+dqdH7devn9ntISEh+OWXX3D+/HmcPXsWWq0WoaGhiIyMtPeWhBDSaLZtA2JiAIXi3raWLet+vby8PCQkJECn05ns4wJ5QkICrly5gnfeeafWAE+ahnpfEvTBBx/Egw8+WN+3IYSQescNTnv8cWDTJuMAXlfJyclgBDTZ58yZY3ZtCMMAHxYWRi3wJsKuZ970vIUQ0lQYjir/8Udg40b7r6nX65GZmWn02NEcnU5X66JODMMgOTnZ/kIRUbAreLdt2xb//ve/cfToUUeVhxBCnE7N6WBz5wLx8fZfV6lUQqVS2X8hVLfAMzMzaeXGJsKu4H3jxg18/vnn6NWrFzp16oQPPvgAFy9edFTZCCGk0ZkL3I7KnObh4QGFI/re/0elUkGpVDrsesR52RW8hw0bBqlUCpZl8ccff2DevHl46KGH0LNnTyxduhQ3btxwVDkJIaTB1SVw2zKNSyKRIC4uTtDCTkIoFAqTNcCJa7IreG/fvh2FhYX44osvEB0dDZZlwbIsjh07hsTERLRr1w7Dhg1DamoqysvLHVVmQgipd7YGblvmaRtKTEwU1NXNpZ22RCaTIS4uTtDgNyJ+dgVvAPD19cUrr7yCgwcPoqCgAPPnz0fnzp3Bsix0Oh2ys7Px7LPPwt/fH3Fxcfjhhx9qHZxBCCGN6dtvbQvcmZmZiIqK4udfA/emcUVFRSEzM9PivSIiIpCamgqpVGrSApfJZJBKpVi4cGGtZWZZFomJibUeR1yD3cHbUEhICGbPno0zZ87gxIkT+L//+z+0bdsWLMtCqVRi48aNeOKJJxAYGIhXXnnFkbcmhBCHGToU6NOn+t9CWtzcPO2aDROtVgudToeEhASrLfC4uDjk5uYiPj6eD+AymQzx8fHIzc3F22+/XWuAT01NpWliTUi9zfOOiIhAREQEFi1ahP379yM9PR2bNm3CnTt3cOvWLXz55ZdYvnx5fd2ekCarsrISlZWVgo/39PSEp6dnPZZIfLy9gR07gKws4PnnrT/jFjJPm5vGlZKSYvGYiIgIpKSkYODAgSgpKUHLli0RbzCkPS4uDmFhYSYZ1uLj4ynDWhNU70lagOosbJGRkYiMjMTcuXNRUlJi1/WSkpIwb948o23+/v64fv06gOruo3nz5mHVqlW4c+cOevbsiWXLlvGrnQHVozJnzZqFzMxMKJVKDBo0CMuXL6e0rUT08vPzkZuba7RNqVSCZVkwDGMyoCkqKgpRUVENWUSnpFIBbm73Xnt7Ay+8YP0cofO0uWlca9asqTXQSyQSKBQKs8fVFuBJ01GvwVutVuOHH35ARkYGtm/f7rD5jEB1XvXdu3fzrw0HcyxevBifffYZ1q5di06dOuGjjz7CkCFDcP78eXh7ewOoHiSybds2ZGVlwdfXFzNnzsTIkSORm5tb68AQQpxZ586dERISYrRt27ZtUCqV8PDwwKhRo4z2Uasb2L69Pd55R4affwbatBF+ni3ztLlpXI54v60FeNI01Evw3rNnD9LT07F582Z+tTFuNGXHjh3xzDPPYOLEiXbdQyaTISAgwGQ7y7JITk7G3LlzERsbCwBYt24d/P39kZGRgWnTpqG0tBSrV69GamoqBg8eDABIS0tDcHAwdu/ejaFDh9pVNkIak7lucLlcDo1GA7lcDj8/v0YqWf2q6+OCFSskWLWqGwBg0CDg0CGgWTNh1+DmaQsJ4DSNiziSw4L38ePHkZ6ejg0bNqCwsBDAvYDdunVrjB8/HhMnTkTPnj0dcr+LFy8iKCgICoUCPXv2xIIFC/DAAw+goKAA169fR0xMDH+sQqFAv379kJOTg2nTpiE3NxcajcbomKCgIISHhyMnJ8di8FapVEb/k3JfTDQaDTQajeCyc8faco7YUB2dCzeNk2VZl/1d5QbKGrL2uCAyMhKHD0dh+vR7PW0jR+ogl+thS3XHjx+PjIwMq13nMpkMEyZMEDTTRshnZevnKabPsa5cpY5Cy29X8L506RIyMjKQnp7OZ1bjAraXlxeeeOIJxMfHY8iQIQ7tiu7ZsyfWr1+PTp064caNG/joo48QHR2Ns2fP8s+9/f39jc7x9/fHlStXAADXr1+HXC5HyxpLARk+Nzdn4cKFJs/aAWDXrl116grLzs62+RyxoTo6h6KiImi1WlRUVGD79u02ny+GOmq1WrRo0cJoW1lZGXQ6HaRSKQIDA432paQwWLny3t+lsWPPo2fPc9ixw7b7RkZGIi0tzeoxer0eDz/8sKD3XshnVdfPUwyfo73EXkehvUd2Be/Q0FAwDMMHbJlMhiFDhmDixIkYM2ZMvT1LGz58OP/vrl27onfv3ujQoQPWrVuHXr16AYDJsyDu27c1tR0ze/ZszJgxg39dVlaG4OBgxMTEwMfHR3D5NRoNsrOzMWTIELgZjpBxIVRH57JhwwZUVFTAy8sLI0aMEHyemOpojmG9ucdoALBihcQkcKekBEMuf6BO9/H19cWUKVOMlusEqv8msiyLlJQUTJgwweYyW/qsbP08xf45CuEqdeR6dGtjd7c5y7Lo2bMnJk6ciPHjx6N169b2XtJmXl5e6Nq1Ky5evIgxY8YAqG5dG37TLioq4lvjAQEBUKvVuHPnjlHru6ioCNHR0Rbvo1AozOYhdnNzq9MvS13PExOqo3NgGIb/aUq/q+bqvWwZMH36vWPefluHnj3PQS5/oM51TEhIQLdu3RwyjUvIZ1XXz1Osn6MtxF5HoWW3K3gnJSUhPj4eDzxQt2+rjqJSqZCfn4/HHnsM7du3R0BAALKzsxEZGQmgetT7/v37sWjRIgDVU2Pc3NyQnZ2NcePGAQAKCwtx5swZLF68uNHqQQipX+ZSnr73nt7mrnJz6jKNy9wgO7VaDa1WC7VajeLiYqN9NCefcOwK3u+9956jymGTWbNmYdSoUbjvvvtQVFSEjz76CGVlZZg8eTIYhkFiYiIWLFiA0NBQhIaGYsGCBfD09MQzzzwDAGjevDmmTp2KmTNnwtfXF61atcKsWbPQtWtXfvQ5IcS1sCxw7Ni911zmNEdna7ZlGpe1OfkajQabN2822kdz8gmnQZK0ONpff/2FuLg4FBcXo3Xr1ujVqxcOHz7Mz2198803oVQq8corr/BJWnbt2sXP8QaAJUuWQCaTYdy4cXySlrVr19Icb0JcFMMAX39dHcTbtXPcsp72MDcn3xpqdROOKIN3VlaW1f0MwyApKQlJSUkWj3F3d8fSpUuxdOlSB5eOEOKspFJgzZrqoN3YgRsQ1g1u2LXO/Zu61okogzchhAhx7Rrg4QF4ed3bJnHockz1j7rWiTkUvAkhLmnZMuDMGaB5c8DMJBHRoK51Yg4Fb0KIy+FGlU+cWD0graiosUtUd9QNTswRWQcSIYRYV3M6WEAA0KFD45WHkPpAwZsQ4jJqBu4HHtDDz08FvV7feIUipB5QtzkhxCUYB+48dOuWjIUL72U727Nnj83ZzghxVtTyJoSInnHgzgTDROH339P4PONarRZpaWmIiopCZmZmo5WTEEehljchhFczXadGo0FVVRWKi4vN5lx2hsFUJ04Yt7gZJgEsqzPJnMYF8oSEBISFhVELnIiaoOB99erVern5fffdVy/XJYTUTc05xSzL4vbt2/jnn38gkUhM1sR2hjnFkZHA4sXAm28C3bol4/ffGaspTxmGQXJyMlJSUhqukIQ4mKDg3b59e4ffuObSeYSQxldzTrFGo0FaWhrkcjk8PT0xatQoo+Mbu9XN+b//Ax55RI+hQzNr/bui1WqRmZmJNWvWCMo/TogzEhS8ufW6CSGurWY3uEajgVQqhUwmg1wuh5+fXyOW7p6rV4GaHXfduyuhUqkEna9SqaBUKp3mywchthIUvGvrXlq+fDmOHj0KNzc3xMTE4JFHHoG/vz9YlkVRURGOHj2KXbt2QaPRoEePHnj55ZcdUnhCSNOzbBkwcyawaRPw+OP3tnt4eEChUAgK4AqFwuQRACFiIih4T5482eK+559/HseOHUNMTAxWr16Ntm3bmj3u77//xgsvvICffvoJXbt2xVdffVW3EhNCmizDUeWxsUBeHvDQQ9WvJRIJ4uLikJaWZrXrXCaTIS4ujrrMiajZNVXs22+/xZo1a9C9e3f8+OOPFgM3ALRt2xbbtm1DVFQU1qxZg40bN9pza0JIE1MzAcv//R/w4IPGxyQmJtb6mI9lWSQmJjq+gIQ0ILuC95dffgmGYTBjxgxB62BLpVLMnDkTLMti1apV9tyaENKE1Azcc+eaX487IiICqamp/HN6QzKZDFKpFKmpqQ6bJlZZWYni4mKjn5rLdRr+GE7DI8Qeds3zPnXqFACgU6dOgs/hjj19+rQ9tyaENBFCAzcnLi4OYWFhSE5O5rvQZTIZ4uPjHZ5hjZbrJI3FruB99+5dAECRDUv2cMdy5xJCiCW2Bm5OREQEUlJSMHDgQJSUlKBly5aIj493ePlouU7SWOwK3iEhIbhw4QLWr1+PoUOHCjpn/fr1AChBCyHEuuXL6xa4DUkkEigUinobnOYMGeZI02TXM+8nnngCLMsiKysLixcvrvX4//znP8jMzATDMHjyySftuTUhxMUFBwNcRta6BG5CXJldLe+3334b69evx40bNzB79mxkZmZi8uTJ6NGjB9q0aQOGYXDjxg0cPXoUqampOHnyJAAgICAAb731liPKTwhxUaNGVc/lPnYMSEqiwE2IIbuCd4sWLbB7924MHToUf//9N06dOoWZM2daPJ5lWbRr1w47d+5EixYt7Lk1IaQJGDWq+ocQYszuJUHDwsJw9uxZ/Pvf/0aLFi3AsqzZnxYtWmDGjBk4c+YMwsLCHFF2QohAer0eKpUKer2+Qc+1xbJlwGef1estCHEZDlkS1MfHB59++ikWLlyI3NxcnD59Gnfu3AHLsmjVqhW6du2KqKgoyOVyR9yOECJQXl6eyZSpPXv2CJoylZeXh88++wwZGRk2n2urmqPKZ8xw6OUJcTkOXc9bLpejd+/e6N27tyMvSwipg8zMTCQkJBit4KfVapGWlobU1FSkpqYiLi7O4efaqmbgvn3bIZclxKXZ3W1OCHE+eXl5SEhIgE6nM8nzrdVqodPpkJCQgLy8PIeeaytL87gJIdY5NHj/+eefSE9Px6effooPP/wQxcXFjrw8IUSg5OTkWuc2MwyD5ORkh55ri7omYCGEOCh4nzhxAv369UNoaCgmTZqEN998E0lJSSaZ15YtW4Y2bdogNDQUGo3GEbcmhNSg1+uRmZlpdWUtoLoVnZmZabSQhz3n2oICNyH2sTt4//jjj4iOjsbBgweNRpebM3nyZCiVSvz555/44Ycf7L01IcQMpVIpaE1rAFCpVFAqlQ45VygK3ITYz67gff36dcTFxUGlUiEsLAw7duywmrO8WbNmGDNmDABgx44d9tyaEGKBh4cHFAqFoGMVCgU8PDwccq4Qd+4A8+bde+3IwE0rfJGmxK7R5kuWLEF5eTlCQkLwyy+/CEq80r9/f6Snp5usxEMIcQyJRIK4uDh+epglMpkMcXFxRs+37TlXiJYtgd27gUGDgGnTHNviphW+SFNiV/D+6aefwDAMZs6cKThj2oMPPggAuHz5sj23JoRYkZiYiNTUVKvHsCyLxMREh54rRLduwOnTgL+/Y7vKaYUv0pTY1W1eUFAAAHjkkUcEn+Pt7Q0AKC8vt+fWhBArIiIikJqaCqlUCpnM+Du6TCaDVCpFamqq2WQr9pxrzs8/AzWTswUEOP4Zt6enJ/z8/AT/UPAmYmZX8OZGjLtxS/8IUFJSAgDw8vKy59aEkFrExcUhNzcX8fHxfBCWyWSIj49Hbm6u1SQr3LkTJ060+VxDy5YBgwcDzz9vGsAJIXVnV7d5QEAArly5goKCAkRGRgo659ChQwCAdu3a2XNrQlxOZWWlTYOohKwlHRERgZSUFAwcOBAlJSVo2bIl4uPjBZWjbdu2WLhwITp37gw3Nze0aNECo0ePBgCjHA6WymE4qjwlBRg5EoiNFVw9QogVdgXvPn364MqVK9iyZQtiBfxfWVlZiZUrV4JhGPTt29eeWxPicqwNuGIYxmRkty0DriQSCRQKhaABZoblYFkWarUaGo0GSqUS6enpgsphbjrYk08KKiohRAC7gvfkyZORnp7O50GOiYmxeGx5eTkmTJiAq1evgmEYTJ061Z5bk0YkpIWo0WhQVVWF4uJiNG/enJ4vCmBuwNW2bdugVCrh4eGBUTXWxqyv99SwHBqNBmlpafwjMh8fn1rLQfO4Cal/dgXvwYMHY8yYMfjuu+8wevRovP766xg7diy///bt2/jtt9+wa9curFy5EtevXwfDMJg0aZLgbnbifIS0EFmWRWlpKbZu3Yru3bvTlBwBzHU/y+VyaDQayOVy+Pn5NXg5NBoNpFIpdDodXx5r5aDATUjDsHtVsbS0NIwcORL79u3DZ599hs8++4zvmuvXrx9/HJd1bdCgQVi5cqW9tyWNSEgLUaPR4MCBA+jbty+aN2/eSCUlDYkCNyENx+7g7enpid27d2PJkiX47LPPUFhYaPa4Vq1aYdasWXjzzTchkdBiZmImpIWo0Wjg7u4OPz8/m2YjNEXWHkPUzBAGCBuo1tDS02sG7kpMn16JW7eEne+MdSLEmTlkPW+JRIKZM2di+vTpOHLkCI4dO4aioiLodDr4+voiMjISjz76qOC0i4Q0JdYeQ1RUVIBhGJSVlfEZwpwxM9iQIUBYGPD779Ut7jFj8rFlS/0MviOEOCh48xeTyRAdHY3o6GhHXpYQl2btMYRKpYK3t7fRgDVnbKG2aQPs3Qts2FDdAlcqO+P++xt/8B0hrsqu4H3gwAEAQI8ePQQvUFBVVYUjR44AAE0XIwTWH0NIJBLIZLIGHbAmlE4HSKX3XrdpA7z+evW/nWXwHSGuyq7g3b9/f0gkEpw6dQphYWGCzvn777/582pbM5gQ4pyWLQM2bgR+/BFo1kzY9EHD5/eVlZXU2ibEDnZ3m1tau7u+ziOEWGYpiJob+AbUbaDY0qWV+OCD6ns8/TSwZg1w8eJZnD171ui4qqoqsCwLiUQCLy8v3L59G3q9HmVlZcjPz6dn3ITYwaHPvIXQ/y/BsdSwv40Q4hCWBr+Vl5eDZVnIZDKjpTFtHSh25QqweXM+YmOr7xEQAPzwA1BRUQG9Xg+GYeDu7g4AUKlU0Ov18PHxQWxsrNEz786dOzugtoQ0XQ0evLmlQGnuLyGOZ2nwGzdq3d/f32iwWM1Wd82Wu0ajgU6ng16vh0YDXL6sxo0bbbBnzwCMHw9MneoOT08Ps4PRuG3NmjWDn5+f0TNv6jInxD42Be+rV6+a3V5YWIhmzZpZPVelUuHSpUt49913wTAMunTpYsutLVq4cCHmzJmD6dOnIzk5GQDw7LPPYt26dUbH9ezZE4cPHzYqz6xZs5CZmQmlUolBgwZh+fLltGAKETVLA8W43Aq1DRar2XJnWRZarRYaDQutlkVAwA2MGLEDAQFA27bAzZvVLXdzg9G4bdTLRojj2RS827dvb7KNZVmrOc0tmTRpks3n1HT06FGsWrUK3bp1M9k3bNgwpKSk8K/lcrnR/sTERGzbtg1ZWVnw9fXFzJkzMXLkSOTm5tIfG9Jk1Wy5azQafPTRn6isvABPTyUqKz3Qtu0ovPZadeY0akET0jhsCt6WBpnZMvjM3d0db7zxBp577jlbbm2ivLwcEydOxFdffYWPPvrIZL9CoUBAQIDZc0tLS7F69WqkpqZi8ODBAKrTvAYHB2P37t0YOnSoXWUjRKxqttw//1yH5csDMXHiZWi1Gtx3nxxJSX6U8pSQRmZT8DZsyQLAlClTwDAMPvzwQ7Rt29biedwglsDAQERGRtbaxS7Eq6++iscffxyDBw82G7z37duHNm3aoEWLFujXrx/mz5+PNm3aAAByc3Oh0WiMegyCgoIQHh6OnJwci8FbpVJBpVLxr8vKygBUt064VZeE4I615Rxnx7Is/2P4frhSHWuqzzpy76Xhv+t6n7peR68Htm+/l8o4IADo2JGFVmt6fs3P39w2c8c4A/pddQ2uUkeh5WdYO+ZsSSQSMAyD06dPC57n7QhZWVmYP38+jh49Cnd3d/Tv3x8PP/ww/8x7w4YNaNasGUJCQlBQUIB3330XWq0Wubm5UCgUyMjIwJQpU4wCMQDExMSgffv2+PLLL83eNykpCfPmzTPZnpGR0eS7Dy9dugStVguZTIYOHTo0dnFEj3s/1Wo15HK5yfuq1WrN5knQarX8CmCcwsJCVFVV8WlJ77vvPn6fTCaDTGb5O7xaLcGiRT3Qv/9WtGlTYvHzNff519xGvyOE1K6yshLPPPMMSktL4ePjY/E4u0ab7927F4D5Z+H15dq1a5g+fTp27drFT0mpafz48fy/w8PD0b17d4SEhODHH39EbGysxWtzeZctmT17NmbMmMG/LisrQ3BwMGJiYqy+yTVpNBpkZ2djyJAhLrNox4YNG1BRUQEvLy+MGDHCJetYU33WkXs/y8rK4OPjw7+vnBMnTuDEiRNG53DpVDUajdHvMRfkWZaFTqdDSUkJvy8yMtLq8rzVA852o7zcHUplc5Ny1Cyv4f6a2zIzM1FSUoIWLVqYvUZjod9V1+AqdeR6dGtjV/A2XPKzoeTm5qKoqMhobqpOp8OBAwfwxRdfQKVSmQw4CwwMREhICC5evAgACAgIgFqtxp07d9CyZUv+uKKiIqt52RUKhdnFVdzc3Or0y1LX85wRwzD8j2GdXKmOltRHHbn30vDfhvcIDw/HAw88YHQONyVMLpdjwIAB/PZvv/0WarXa4lQxw+uuWwcMGAAYNM4hlbJ8L1vNctQsr+F+7vXVq1fx4osvIi0tjW957927F4mJiYiIiLDvjXIg+l11DWKvo9Cy2xW8dTodfv31VwBARERErXO3S0pKcOrUKQB1z2s+aNAgnD592mjblClT8NBDD+Gtt94yO1L81q1buHbtGgIDAwFUJ6Zwc3NDdnY2xo0bB6C6a/HMmTNYvHhxncpFSEOyljvcy8sLoaGhRnO21Wq12T8KhsdkZnrijTc80b49sG+fcQCvqyNHjiAlJcUoHbJWq0VaWhpSU1ORmpqKuLg4+29ESBNjV/DevXs3hg8fDl9fX4tzwA3J5XI89dRTuH37Nnbv3m3UOhDK29sb4eHhRtu8vLzg6+uL8PBwlJeXIykpCU899RQCAwNx+fJlzJkzB35+fnjyyScBVCeImTp1KmbOnAlfX19+rfGuXbvyo88JEbvvv/8eS5cuxZEjR6DT6SCVStGtWzf8/vvvaNeundFiQjdvAlu2RAGIQkEB8O23gMETojq5cuUKUlJSoNfr+cyKHC6QJyQkICwszKla4ISIgaT2QyzbuHEjAGDs2LGCVhXz9PTE+PHjwbIsNmzYYM+tLZJKpTh9+jSeeOIJdOrUCZMnT0anTp1w6NAheHt788ctWbIEY8aMwbhx49CnTx94enpi27ZtNMebuITMzEwkJCTwgRuo7inLy8vDp59+it9//x2xsbGIjY2FUhmL5ORY5OdXpyydOxf497/tL8POnTtrPYZhGH6gKSFEOLta3rm5uWAYBgMHDhR8zoABA7B8+XJ+WVBH2LdvH/9vDw8P/PTTT7We4+7ujqVLl2Lp0qUOKwchzuDKlSt47733TEadA/fWFvj666/xyiuv4ODBCCQm3ts/dy7w4YeweR63Xq+HSqXiv8Tr9Xrk5OSYtLhr0mq1yMzMxJo1a6wOFiWEGLMreP/1118AbBttfv/99wOoXhqUkKaqtiU0uVXA9Hq92elf1uzcuVNQIHzppWQcPnwvd0NdAndeXh6Sk5ONBqN9++23NpVZpVJBqVQ2+emWhNjCruDN/fGxZao4d6zQ4fCEuCJLq39x0xWVSiX0er3J1C5zuC8CarUaarUahw4dMjsH3JBer8fhw5kA1gBg6hS4ua55hmGMBqNt3bpV+EVQPYtDyGM3Qsg9dgXv1q1b46+//sK5c+fQvXt3QeecO3cOAODr62vPrQkRNUurf3Erc/n4+ECpVOLu3bvw9va2mpWQ+yJw+/ZtVFVV1Rq471EBUGLuXM86tbgTEhJs6hEwRyaTIS4ujrrMCbGRXQPWevToAZZlsX79esHnrF27FgzD4F//+pc9tyZE1Dw9PeHn52f0w2VSk8vl/L8lEglkMpnVgZSdO3dGbGws/P394efnZzVjmjEF5szxqNMz7uTkZIcEXJZlkWj40J0QIohdwfvpp58GAPz888/49NNPaz3+008/xZ49ewBUj1AnRAwqKytRXFxs9qeqqspkm7Vn2fWB+yLABf3evXvzS4BaIpFIMGRIHD76iKnT4LTMzEwbWvimpFIppFIpUlNTaZoYIXVgV7f5+PHjsWjRIuTl5eHNN99ETk4OEhMT0atXLz4hhFarxaFDh7BkyRJ8//33YBgG4eHhiI+Pd0gFCKlv+fn5OHr0qNHI6aqqKrAsC5VKheLiYqNWaJcuXdCjR49GG4A1bNgwPnmSNZ98klin1cG4NKz2iIuLw6xZsyhwE1JHdgVvhmGwZcsW9OnTB4WFhfjuu+/w3Xffwc3NDa1atQLDMLh165bRKkNBQUF8ECdEDDp37ozy8nKcPXuW31ZVVQWdTge9Xo+qqiqjAVf5+flo1qyZUQrfhhQSEoKXXnoJK1euBACjLx3c/3fTpk2rc+D08PCAQqGocwCXyWRYv349/Q0gxA52BW+geurXiRMnMG3aNGzduhUsy0KtVuP69etGxzEMg9jYWCxfvpxfmpMQMfD09ESPHj3QpUsXftu2bdtQWFgIlmXRsmVLjB492uQcWxnOla6t29va+XK5HN27d8dbb72Fn346gLy8w0YDy1iWxVdffYU7d+5g2rRpeOSRR2wqr0QiQVxcHD89zBYSiQTR0dEUuAmxk93BGwDatGmDLVu24Pz589i+fTtOnDiB4uJiAICfnx/+9a9/4fHHH0doaKgjbkdIg6uZS1wul/OLdcjlcvj5+dX52ubmSvfu3Rv9+vUTtFqdufO7du2Knj2jMXToCNx3nz++/34LGIbhW+FarRYbN27Exo0b8cEHH2Du3Lk2lTkxMRGpqal1qu/QoUPrdB4h5B6HBG/Ogw8+iAcffNCRlyRElGpLwsLZvHkzXnnlFZO50r/++it+/fVXjB8/Hv3797d4vqW51nl5eTh58iT69h2CAweywbKsST4GLpC///77GDlypE3d6BEREUhNTTW5tyUymQx6vR5TpkwxmSJHCLGdQ4M3IaRabUlYPDw8cO3aNSxYsMBsClFuW1ZWFjp06ICHHnrI5Bhrc6258/fv3wWGkQCwnEiJyy+ekpJi8Rhz4uLiEBYWZtLqf/zxx8GyLLZv385vi4+PR2hoqF09FISQeyh4E1IPakvCMmrUKLz++uu1PvtlGAZ79uwxG7yFzrVm2frLLx4REYGUlBQMHDgQJSUlaNmyJT+TJDU11Whbeno6KioqbLo+IcQ8QcHbcLnP+wwW+RWyDKg19zliwWBCnJC19bblcjlatWqFLVu21JqhTK/X48iRI3j55ZdNtts719qQvfnFJRIJFAqFUfA3t40Q4hiCgje38EjNZ1u2LEhSk5DnZIS4KlvmSmu1WqjV6jqfLwTlFydEXAQFb0sLj9iyIAkh5B5b5kpzKVPrer6Q61N+cULERVDwtjSQxdYBLoSQakLnSkskEjzyyCMmgVXo+QzD1Polm/KLEyI+goL35MmTbdpOCKmdkLnSer0ejz76qNkR6QEBidDrrZ/PMAzGjh2Lb7/9lr8eRyaTgWVZyi9OiAjRaHPiEIbZwVxRzXnbarUaer2ezyjIJSXimBuwVpO1udJci1kikWDx4sWQyWTYs2cPEhMTERERgWXLgI8/jgDDpEIiSYBEYnw+l6HtpZdeQnR0NDp16oQDBw4gJyfHaPoWdz1H49YX557XFxcXm7w2JOT9IoTcQ8Gb2MVcdq89e/bg1VdfbeyiOVTNedu3b9/mc/YXFhZi8+bNRsdHRUUJym1ubq60RCKBXq/n/wtUD1pLS0v7X7BPxdq1cQAAlo3D88+HQaMx/gyio6PRt29ffopZcHAwXnzxRTz//PMmU7rqQ35+Pm7cuAG9Xo+ysjJs3ryZn+eu0Wjq/H4RQqpR8CZ1Zim7Fxdkpk+fjhEjRjRyKR2j5rztbdu24dq1a2BZFjKZDLGxsUbH29KKNJwrffbsWXzyyScAYNJVzr3Ha9cmAAgDEIG5c4EPP4wAwxjPtWYYxuyc6oaavtW5c2ecOnXKaF67NdTqJsQ2goL3Bx98UC83f++99+rluqT+WcvuxQWZ5ORkxMXFoXv37g1dPEGEpjDlGHbtyuVyo65tR2QOk0gkOHDggIAjGQDJmDs3BR9+CH5Zz/oMzLY+FvH09DSa106Z1QhxLEHBOykpqV7+IFDwFi8h2b0YhsEXX3yBtWvXNkyhbCQkhamh+u7a1ev1JuuGm6eFVJqJDz6wPSOarfLy8vD555/jwIEDRo9F6utZOSFEGMHd5rVNNxEyJUXIMcQ5WGuV6vV6ZGRk1JpkR6fTISsrCykpKU45h1hIClND9d21yw3oEkKnU6Gqqu4Z0YTgHouwLGv22fvy5cv5xwXmBqNx22rLIkcIsZ2g4G2tJXD58mWMHz8eR48exfDhw/Hcc8/hkUcegb+/PwDgxo0bOHr0KFavXo0dO3agR48e2LhxI60s5OSstUrVarVJxi9L7E27WZ9qS2Fan129lr4cyWQyQQG8vjOiCXks8vLLL+PatWsIDg42Oxjt9u3bAnoRCCF1YdeAtdLSUsTExKCgoADr1683O3o1ODgYwcHBiI2NRXp6OiZPnozBgwfj2LFjaN68uT23J/XIWqvU29sbcrlcUABv7LSbtj7XbqhWoqUvR5GRkcjNzbUa9IRkRDN8Rs1NG7OF0Mcily5dwvTp06FUKlFVVWW0f+/evaiqqoJcLqepYYQ4mF3Be8mSJfjjjz/w0ksvCZp2MnHiRBw8eBBffvklPv3003obCEfsZ61V6ubmhtjYWHzzzTdWg51EIsGYMWMateVt63Nt7rlufTP35QioHnk+ePBgq+day4h27do17N+/H4cOHeLr0rNnT/Tt2xehoaEAUOtca6GLnuh0OmzevBnp6ek4fvy4xfe5oqKCpoYR4mB2/ZXatGkTn8FJqHHjxuHLL7/E5s2bKXiLVHl5OTp27Cgo7WZoaCjy8/Mb7Q+1rc+1t2/fbtKCtBfX+jd8LmypNyA8PBxz5nyN+fOnArAtI1pOTg5WrlxpdJ5Wq8WhQ4eQk5ODcePGoXfv3rXOtbZl0RPusYilLyOWUKubEPvYFbwvX74MADZ1f3PHXrlyxZ5bk0bUrFkzTJ8+HcHBwXjllVcAGHc3S6VSAMArr7yCV155pVEfj9j6XJsruxBarRaVlZUmi4bUxLX+uWfAZWVlSE9PN9v6v3kT2L49Cs8//yHOnPkRR44cEZQRLS8vDytXrjTb3c5t++abb/DSSy8hPDzcbDm598mWRU+4xyIMw1BAJqQB2RW83dzcAACnT5/Gv/71L0HnnD592uhcIj5SqRR+fn548cUX0bNnT5MMa/Hx8Xj11Vfx999/w8/Pz+U+640bN2LGjBkoKirit61evRoLFizAuHHjTI7nWqWGLX4uiHt7e+OJJ54AAKSnA8nJQGWlJx566ByefHIyRo8eDU9Pz1ozoiUnJwsq+7p162pdUEjooidiX42M6xHRaDSoqqpCcXGx1d9Vek5PnIldwTsiIgL79+/HokWLMHbs2Fp/sSsrK7Fo0SIwDINu3brZc2viJLjsYA899BD/bPvNN9+ERqPB33//3djFs5lOpzN5Jmw4GOuDDz5AWlqayXmXLl3C+PHjsWvXLnz++edG/y9wf/Tlcjn+/PNPk2fSBw8eREBAIj7++F6L+oEH9FAoVKisRK2JV7hn1LWN7OaOW7Om9vnhQhZNEftqZFyPCMuyKC0txdatW1FVVdVo8/wJsYVdwfv555/H/v37cf78efTv3x+rVq3Cww8/bPbYvLw8vPjiizh37hwYhsGLL75oz62Jk5FIJHzWMTErLy9HSUkJn48bAEpKSqBWq3H69Gls3LjR6vmrV69Gx44d8fbbb5vss/RMev36tP+tDpYKIAzduiVj4cJ7PRk9evTAyJEjLd6zLs+obVk0BTB+LOIqq5FxPSIajQYHDhxA3759sXPnzkab50+ILewK3hMnTsSWLVuwefNm5ObmIioqCl27dkWPHj3Qpk0bMAzDz/PmussBIDY2Fs8884zdhSf1y9xKWpZGKnMrbIlds2bNAMDoj/d3332HyspK7N27V9A1vv76a5Pgbf2ZNNc1Xd0t/vvvEqNc8b/99ht+++03tG/fHnFxcSbn1+UZtRBxcXEIDQ3FW2+9ZZRhrT5XI2tIXI+IRqOBu7s7/Pz8KKUrEQ2758Rs2LABiYmJWLFiBfR6PU6dOmUUqDlcV9Rrr72Gzz77zN7bkgZgbiUtrVYLhmGQnp5uFATUarXoW91A9fN8mUxm9Mfbw8MDarUaN2/eFHSNS5cu8auCAdWB+6mnnhKQsIRrjRsfx52XkJCAsLAwREREmHyxevLJJ7Fx40ar9+CeZdvyOUVEROCNN97A5MmTUVpaWu+rkRFChLE7eEulUixduhQvvvgiVq5cid27d+OPP/4waoWFhoZi8ODBmDZtGj3rFhFzK2kVFhYCAHx8fIy6FVevXi04tacY2ZLoBQAuXrwIX19fbN68GS+//LJDMo0xDIPk5GSkpKSYfLESMnUPQJ2fUTfUamSEEGEclo2ia9euWLZsGYDq52olJSVgWRYtW7aEQqFw1G1IA6o5ulYul/OtyZrdihKJxGX/sOv1epumkAFAdnY2Ll++jM8++8xhjxO0Wi0/4MzcvGpLU/e4z+yll14SfVc3IaRavaSSUigUfG5zQsQqLy/PaBqcUK1bt4ZEIsHPP//s8MV4DAec1RxAZWnqXnR0NPr27YuHHnrIYeUghDSu+s8DSYgI5eTkYPLkyWAYxubHAS+//DIOHDiAkydPOrxccrkcFRUVFrvx27Zti2XLlmHgwIEoKSlBy5YtwTAMKioqHF4WQkjjcVjw/uOPP7B+/XocOnQI169fh1KpxM6dO9GxY0f+mDNnzuDq1avw8vJCv379HHVrQhzq2rVrFkeG1yYmJgbz58+vh1JVd393794dW7ZsqXXdcXpGTYhrszt46/V6vPXWW0hOTjaaLsQwjMmqU9euXcPIkSMhk8lQUFCAtm3b2nt7QhyGyz+enZ1t87kdO3bECy+8gDlz5tRpZTKJRAKWZa12sTMMg/nz5yM8PLzWdcfPnTtncxkIIeJh+1qBNUybNg2fffYZdDodgoKC8PTTT1s8dvjw4XjggQeg0+nw7bff2ntrQhwqPz8fhYWFOHbsmKBWt0QiwcyZM/Hxxx/j4sWLyM/Pr1NLt2PHjjh+/DjS09MhlUpNlvCUSCSQSCRITU1F//79+fnIhlPaDH8omQghrs+u4L1v3z6sXr0aADBnzhxcvny51gxUY8eOBcuyghNeENJQOnfujFatWgluOev1eigUCkgkEsHLaNbEMAy++uortG3bFkOGDMHu3bvRp08ffnS7TCZDr1698OGHH5pN0CK0nCqVyiHT1QghzsGubnMu1eOIESPw0UcfCTrnkUceAQCcPXvWnlsT4nCenp7w8vKCTCYTFISlUikfZG1JUQrc6yaPj4/HhQsXcOHCBX7fhAkTMGzYMGi1Wri7u6NFixbw8vKyuT7m1vbes2ePS2RHI6Spsyt4Hzp0CAzDYOrUqYLPadeuHQDg+vXr9tyakHrzr3/9S1DXuU6nw4IFC/Dwww9j8ODBglOUAtXr2k+bNs3s8pzbt29HWVmZXbniLeVRT0tLQ2pqKlJTU+vckieEND67gje3JGL79u2F31BWfUuNRmPPrYmD1EyzaY1areZHOLuaus7p1ul0OHHiBHr06IE+ffrg4MEcg1zlphiGwaOPPorMzEyLx9iaEKYma3nUuboZplolhIiPXcGby/lsS+rIq1evAgBatmxpz62Jg9RMswnA4jSk27dv1ynbmLPLzMxEQkJCneZ0A/datgcPHkRtj5UZhsGwYcPqUkyTe6pUKrOLjAhZ29sw1SohRHzsCt7t27fHyZMnceLECfTu3VvQOT/88AMAICwszJ5bEwcxl2bT0jSkbdu2udzjjry8PCQkJNRpeldNEokEvXtH49dff4VEwhi1wGUyGfR6PaZMmWLyftta3poZ1AyfYwtd29sw1aor9qQQ4ursGm0eExMDlmWxatUqQSNZc3NzkZqa6rDWBwAsXLgQDMMYLbjAsiySkpIQFBQEDw8P9O/f32SAnEqlwuuvvw4/Pz94eXlh9OjR+OuvvxxSJjHx9PQ0mWpkaRqSK6zXXVNycnKtdRJaZ61Wi2PHfsPhw8cwaVI830MhlUoRHx+PDz/8kB+wWReZmZmIiooy6trnnmNHRUUhMzOzTmt7E0LEx67g/dprr8HDwwOnT5/GCy+8YPU59qZNmzBs2DCo1Wr4+PjgxRdftOfWAICjR49i1apVJiuVLV68GJ999hm++OILHD16FAEBARgyZAju3r3LH5OYmIgtW7YgKysLBw8eRHl5OUaOHOmQFhgRB6HTu2zJTa5SqXD16kWTdKRVVVXQaDT8eui2rlJ25coVvoegZnm1Wi10Oh0SEhJw4cIFwQsB2bK2NyHEudgVvNu2bYvPP/8cLMti7dq1eOCBB/hVjYDqZSJffvllhIaGYty4cbh16xYYhsGqVavQvHlzuwpeXl6OiRMn4quvvjJ6fs6yLJKTkzF37lzExsYiPDwc69atQ2VlJTIyMgAApaWlWL16NT799FMMHjwYkZGRSEtLw+nTp7F79267ykXEQ61W2zS9SwiGYTB+/Hhs2rSJ/yKo0+mwceNGzJs3D/v378eNGzeQn59v03V37twpqIfg888/R1xcnEmil5pkMpnNa3sTQpyH3elRp06dCoZh8MYbb+Dvv//Gl19+yf9B4AbOcC0XhUKBlStXYuzYsfbeFq+++ioef/xxDB482GiOeUFBAa5fv46YmBh+m0KhQL9+/ZCTk4Np06YhNzcXGo3G6JigoCCEh4cjJycHQ4cONXtPlUpl9Me+rKwMQPXIeVtGz3PHOuuIey5NJ8uyRmXkPkdz+wz3G74fzlhHrvxubm6Cp3dxv9PWWuGGKU5rHsc9Vvrmm28QFhaGjh07WnxvdDod1Go1P49cp9Pxc7Wt4Z5jHzx4EOvXr7d6LMuyePXVV2v9fLj9XOpjc5+72Bn+rlr63Rc7Z/7/0VFcpY5Cy++QhUmee+45xMTEIDk5GVu3bsUff/xhtL9t27YYPXo0/u///g/333+/3ffLysrC8ePHcfToUZN93ICqmkuS+vv748qVK/wxcrncZMS7v7+/1QFZCxcuxLx580y279q1q04pKeuSQ7shFBUVQavVoqKiAtu3bzfazv1iFRUVGe3jMnipVCqj7c5YR8P69enTB/v27bM6ZkMikSA0NNQokYo5er2eD+CWMAyDH3/8EQ8//LDJvoKCAmzbtg0HDhyAVquFVCpFt27d0Lt3b8Gj4FUqFf7880/ExcUhIyMDDMMY1U0qlYJlWUyfPh1///03/v77b0HXLS4uNvs74Uqys7Mt/u67Cmf8/9HRxF5HoY/UHLaqWLt27fCf//wH//nPf1BWVoaioiLodDr4+vrCz8/PUbfBtWvXMH36dOzatQvu7u4Wj6vZHShkfnJtx8yePRszZszgX5eVlSE4OBgxMTHw8fERWIPqb1bZ2dkYMmQI3NzcBJ/XUDZs2ICKigp4eXlhxIgRRtv/+usvsCyLNm3aGO27cOEC1Go15HI5RowY4dR1zMzMRElJCVq0aIFFixahV69eVo9nWRYDBw7EI488grS09P9tuxcQuS5qiURSa5DV6/U4ceIEhg8fbvS7lpWVhVmzZhlNV9PpdMjLy8PJkyf5FKy1USgUGDNmDNRqNdq3b2+SYW3ixIl47bXXBM/v5j5HPz8/KJVKk98JV2D4u3r37l2zv/ti58z/PzqKq9SR69GtjV3Be+DAgQCqEz5MmTKF3+7j42NTMLNFbm4uioqKEBUVxW/T6XQ4cOAAvvjiC5w/fx5Ades6MDCQP6aoqIhvjQcEBECtVuPOnTtGre+ioiJER0dbvLdCoTA7GMjNza1Ovyx1Pa++MQzD/xiWjws25vYZ7jfc7kx1NDfNKj4+Hh999BHeeecdk3neXCv66aefRkhICNzdp2LbtuegVK6CVrsROp0OUqkU0dHRWLx4seDpklqtFlqtlu+tycvLw5QpU8wOluQCNsuyfBe6JdxzbG5WQHBwMKZNm4YXXniBX9s7Pj7elreMJ5FILH7ursLNzc3i776rcKb/H+uL2OsotOx2Be9ffvkFer0e7777rj2XscmgQYNw+vRpo21TpkzBQw89hLfeegsPPPAAAgICkJ2djcjISADVA5P279+PRYsWAahe79jNzQ3Z2dkYN24cAKCwsBBnzpzB4sWLG6wupOGYS8TCTbNiWRYfffQRzp8/zwd2qVSKPn36IDIyEi1atIBSCbz9th+A/gD64733IqDT3YVMJkPHjh3xyCOPCH5+LpPJjEZ5C5muJpFIap0JwbKs0ZRJw3NpbW9CXItdwbtNmza4fv06WrRo4aDi1M7b29skH7SXlxd8fX357YmJiViwYAFCQ0MRGhqKBQsWwNPTE8888wwAoHnz5pg6dSpmzpwJX19ftGrVCrNmzULXrl0xePDgBqsLaRjWErFwgfydd95Bbm4uBg4ciIKCAhQXF+PkyZNYtmzZ/4K5DMA5AImYOzcCnp7A3bv3BrBJJBLExcXVml5VIpEgOjqaD6RCp6vp9Xo+0UvN7nmZTAaWZZGamkrpTglpIuwK3hEREbh+/TouXLjAt3KdwZtvvgmlUolXXnkFd+7cQc+ePbFr1y54e3vzxyxZsgQymQzjxo2DUqnEoEGDsHbtWpdL/UmEJ2JJTk7G4MGD8fvvv2Pjxo1Gg710Oi2ANADrceDAozh0KIdvoUdHR6Nr165ITExEampqreUxnM1gS1IVrVaLQ4cO4csvvzTp+qeVwghpWuwK3s8//zx27tyJlStXYvz48Y4qk8327dtn9JphGCQlJSEpKcniOe7u7li6dCmWLl1av4UjjUpoy1ar1SIjIwP3338/Nm7caHa6F1B9jV9+OcBv0el0+PXXXxEVFcWv1mUuT7ql9KgeHh6Cu9sVCgV69uyJXr16YeDAgXY/xyaEiJddSVpiY2MRHx+P/fv347nnnjPJKkVIQ6isrERxcbHZn2vXrglu2arVanz33Xc2PxvW6/V8hrOwsDDk5uYiPj6eX0GPax2bS4/Kdbdzx1rCHceVzfA5trn6q9VqPpub4b+Li4ttzu5GCHE+drW8169fj0GDBuHUqVNYt24dvv/+e4waNQrdunVDy5Yta+2CnjRpkj23JwSA9ZXRWJaFTCYTNE/azc0NZ86cETQly5JFixbh888/x7Jly0xax+np6Wa/4Artbjc3GA2wXn+NRgOlUgm9Xo+ysjJs3rwZUVFRRrM1CCHiY1fwfvbZZ41aKXfu3BH0Rwio7tqm4C0uer0earW61lZiQ6ttZbSnn34a33zzTa3TrMaNG4f09PQ6l0On0+Gbb75Bv3790L17d8GjvCMiIix2t3NzyF966SWLz7TN1d9QzVXi6pJQqKmwttQqIc7E7r/CNZ8L2rKIAxGHmvOjuUFa4eHhTjFIytPT0yQgyeVyaDQayOVyvP322/jmm2+sXoNlWbRrNxMSSRb0+rovTqPVajF8+HD4+fnh3Llzgs+Li4tDWFiYyfscGRmJYcOG4aGHHrJ4rrn6GzJ8LxyZMMmVFBQU4Pnnn0dGRobZpVYJcTZ2Be+CggJHlYM4KXPzo2sO0oqLi2vkUlpnrWXLTbNKSEjFokWS/w0sq3vwVigUCA4OrtOc6oiICKSkpPDT1dzc3CCTyeDt7c0/r+bUfI5tqLZgToxlZWVh5syZRlPwuBwA3CBEZ/8dJ02PXcHbWlcdEb8rV67gvffes5r5ixuk5ezMtWy5gWQBAYn4+OPfAURBq6174JZKpYiNjYVSqbQreEokEsjlcv51VVUVSkpKsHnzZn6b4TNtw+0A6Jm2Dbjsdnq93mSsAxfIud9xaoETZ+JcDy+JUxG6DGVycrIoArhhy5YbSFZaGo/XXssDkADAvrXcWZZFhw4dkJ+f79Dg6e7ujubNm2PUqFGCjqdWt3C25ABISUlpoFIRUjubg7dKpcLq1auxY8cOXLlyBTqdDkFBQRgwYACmTZsGX1/f+ignaWB6vd6mZSg/+OCDBiqZ/biBZFevMpg7FwCSAdQ9dSg3q2L58uWIjY2Fp6cnKisrTbq269rVzbXE6Xm1Y9mSAyAzMxNr1qyhFLPEadgUvC9evIjhw4ebPOs+d+4c9uzZg08++QSbN2/GgAEDHFpI0vA0Go1Ny1CqVCp+5avi4mJoNBpUVVWhuLgYzZs3d8rWYHX19AAywSVgEcJwbW9upa5///vfRt2qubm5uHHjhtEULerqdi62ZLdTqVR2Pw4hxJEEB2+VSoXRo0fjzz//tHhMaWkpYmNjcfr0abRr184hBSTVKisrbUquYe+gJW6wlJAALpPJwDAMWJaFVqvF5s2bwbIsSktLsXXrVnTv3t0pA1OHDsB77ynxwQfC/oBzJBIJkpKS4OPjA41GgzfeeMNkJaDOnTvj1KlTRlO0rOFa61yLnXv+yg2wo4FpjmdrdjuaPkacieDgnZaWhvPnz4NhGPTo0QPz589Hr169IJPJcOrUKcyfPx9bt25FWVkZPv30UyxZsqQ+y93kWEvEwTCMyR8We1tyEokEvXv3rrXrXCqVYuzYsWjbtq1RoNJoNDhw4AD69u2L5s2b17kc9e399z2waJGwP+BA9fuSmpoKvV6P8vJyi++Np6enzVO0cnNzkZubi9u3b/PXZVkWarWaWuv1QOhiMtxSq9RlTpyJ4OD93XffAahuUezfv99oXesePXrgu+++w+jRo/HDDz9gy5YtFLwdrLZEJDVbdo5okQ0bNgw5OTm1HvfWW2/hzJkzRoFKo9HA3d0dfn5+TrO27ooV1a1tQ0L/gANAx44d8e233yIiIsIkmYu5nhFbn3Fzn/G2bdtQWFgIAAgMDLTYaqdWt/2EZLeztNQqIY1JcPDOy8sDwzBITEw0CtyG5syZgx9++AHXrl1DaWmpU7e4xKa2RCT1MZgpJCSEnx8NwGTKGMMw+OijjxAREYEzZ844/P6A4x4XLFsGvPYaoFAAixYBhh0VQv6AMwzDB25zaktRKqTVzJVdLpfzmdVooFr94mYgPPvss7TUKhEVwcGbazk8/PDDFo8x3Hfr1i0K3i4gLi4OV65cwezZs032SSQSvPPOO/U6398Rjwu4wA0AKhVw545x8DZM4sKyrNF8X4lEApZlMWHCBKt/wGtLUVoTtZqdx4QJE3Dr1i2cPHkS6enptNQqEQXBwbuqqgoMw1j9o+Pu7m50PHEOtrZeDVvYeXl5eOedd6wel5CQgA8++KBeWoj2Pi64cgX/mw5Wbe5cIDQUqLk+CJfE5Y033sCvv/4KnU4HqVSKbt26oU+fPrjvvvuslpMGj4lb+/bt8eqrr2LQoEG01CoRBUrS0gTY2nrlWh6A8CQWP/30EyZOnFjnMtr6BUPI3OebN4H8/Huv584FPvwQyMgwf3xERARefPFFDB06lJ8WxOUYJ02D0MVkCGlsFLybAFtbr9u3b0dVVZVNSSx+/fVXPPXUU3WeTmPLF4zbt2/D3d0d3t7eFq935Qpw9eq911zgFvI3mfti0FT/gBt+keLm61MudUKci83Be/ny5WjTpo1DjnvvvfdsvT2pA1sHuzEMA5VKxf9XCJ1Oh+nTp/OrMb366qs2ldGWLxjbtm2zWq5ly4Dffwe8vKpfWwrcOp3OJBBxc6y5Z9/cmuB6vR6VlZUOD1KWRqlzz91rBsuGCJSGX6S4+fpcrnWaskaIc7A5eK9YscLqfq61UttxAAVvZ1Nz6U+ZTMZnTRPKcDWm6dOnY8SIEYLOs+ULBrfdnIIC4N//BsaNq37doQOQlGS+xV1eXm4SiLjWPpdwhmVZ6HQ66PV6h+csB6z3OLAsixs3bhiVsSECpeEXKcP5+pam/FGrm5CGZ1PwduRa3U21S9JZmVv6U6vV1ulz4s5PTk5GXFwcunfv7tCyWtO+PZCVBWzZAgQEAJ06We4qb9asmdkvF4bzrLln3jKZDJ07d3Z4eS2NUrfU69AQgdLwi5QzztcnhNgQvPfu3Vuf5SCNKC8vDwkJCWaX/rTnCxvDMPjiiy+wdu1aO0pnu9jY6sFqtX3vkEqlZge8Gc6zZhgGDMNAIpHUS+C01A1e33P4CSHiJjh49+vXrz7LQRqRkBHldaHT6ZCVlYWUlBSHXN8w97fh4KkLF6pb2IbkcjVUKuHd/YQQIiY02ryJEzqiHLjXCrXlGbgjV2Ping/fvn2bX61rxYp03L7NolUrBv7+to1Ir6m4uBh37txBRUUF3wuh1+v5RwkXL17kj62oqBC86hohhDgaBe8mTq1WCx5RzrIsP8hw/vz5goKXI1djMsz9rVQqceeOB/78E/D0VOLPPz3Qu/cocEn+ahuRbs6BAwdw7tw5fqAahxt1/s033/DbtFotFAoFPQcmhDQKCt5NnFwuF7wsokwm4xNYCF1xbMKECQ7rkjfM/f3PPxrk51dPX9JqNbjvPjkGDfLjn3NbG5FuSd++fdG1a1fs3bsXN2/eBAC0bt3a7Pr0e/fuhUajsfkLAiGEOIKksQtAGhe3qhaXUc0SmUyG6OhoPhAPGzas1sFsLMviNS6puAPVTMBS26hyofz8/BAaGgovLy9IpVJIpVJ4eXkhNDTU5MfLy6vW94wQQuoLBW+CxMREQYF46NCh/GtuxTFuGpUhmUwGqVRaL4s6LFsGnD2rh0ajAsvq0aED0Lat6XF6vR4qlcqm5/OEECIW1HQQMS5A2ftM2XBVLcN53oDxsoh6vR7Xrl3j93GLedRM7DJ27Fg899xzuHXrFoqLi80+F65LprDZs/Pw8cfJkEjSoNdrIZXKEB3dG/369cNDDz0EwHyimT179lj8IuGMGc4IIaQ2FLxFyNYAJYSlQGy4LGJ6errJedx6yFFRUSguLoZCoYCvry8uXbqE27dv459//oFEIhG0dKc1zz+fidWrEwAw0Ourv1zodNU51X/99Ve89NJLkEgkZhPNcBnfUlNTERcXZ3RdZ8xwRgghtaHgLTKWMqFZC1BCcYF44MCBNi+L6OPjwwfpUaNGQaPRIC0tDXK5HJ6enrUu3WnNlStXkJLyHgDTJDJcC3nFihVYuXKl2W5y7n1KSEhAWFiY0T5nzHBGCCG1oeAtItYyodUMUPY8a67Lsojcs28uI5hGozHZZivuscCOHTsgkTCw9vhaSCY4hmGQnJyMwYMH89sowxkhRIwoeIuI0LW1k5OTkZKS0kClcryajwWEqi2Aa7VaZGRk4LHHHnOq5S0tPXd3pjISQpwLBW+RsGVt7czMTKxZs0aUi79wjwVY9t6zbUdSq9W4desW3NzcnGZ5S2vP3Z2ljIQQ50LBWySUSqXghCCOTEnakKw9FnAUuVyOZ5991uIXm8Z4zyw9d7dEbJ8rIcTxKHiLhIeHh+BMaOZSktbsmrXWLWs4VaohJScng2Xr3lvAMIzVrnOZTIZnnnkGrVu3rvM96gN1gxNCbEXBWyS4TGi1PQeWyWSIi4szaVnW7Jq9ffs2v153enq6UbCvy6Ie9tLr9UhLy6xzVzm3aIq14M2yLBITE+tYQkIIcR4UvEUkMTERqampVo+xFKBqds1u27YNhYWFAKqneRlOh6rLoh72Sk5WQqu1/Z7cl5SXXnoJjz32WK2JZhyd8Y0QQhoDpUcVES4TmrWUpJYClKenJ/z8/PgfuVwOiUQCiUTCT4equa++VVZWori4GP/9bzEWLKgAILfpfC5wsyyLr776Crt27UJGRgbi4+P594dLNJObm1vn+e+EEOJsqOUtMkIyoYlFfn4+du7MxdWrwFNPAfv2ReGPP36z+rxdJpOhQ4cOuHDhgtHa4jUT1dQl0QwhhIgFBW8RsicTWl1wg92s5fw2HABXWVkJNzc3PsmKpcFY7dt3xvffh6CgoPr1mDGd8Mcfg6yWRa/X48KFC3z6UkOGiWo++OAD+Pn5iXK6HCGE1IaCt4jVJRNaXXCD3bhBbgDwzz//IC0tDQzDwMPDA7dv34Zer0dZWRm+//577Ny5ExkZGVZzr7dq5Ynvv/fEgAHA008DH37YH3L5S1i5ciUAGLXAuefWffr0wcGDB60OTGMYBj/99BMmTpxYT+8IIYQ0LnrmTWrVuXNnxMbGwt/fHzKZjP+Ry+Xw8fHh97Vo0QIFBQVISEjgAzdwr0s7KioKmZmZRtcODASOHAE+/LB6Pe7o6GjMmTMHjz76qMlz66NHj+K336x3q3P3y8nJEZQylRBCxIha3qRW3Dxkw4FsEonEKG+5XC7Hn3/+ia+//trq4iDx8Qlo3z4MvXrda4H7+BgfGxwcjBdffBHPP/+80WOBiooKwaPgtVotNBpNHWtMCCHOjYJ3E2KYqEXI82tbM539/PPPtR6j1zN48slkXLqUgtryktR8LGBLohqZTGZ2HXFCCHEFFLybEMNELcXFxaisrIRUKsU///xjlKiFe35tC71ej6NHjwo4T4vr1zORmbkGU6fa9qyeS1Szfv36WkekR0dH2zUWgBtsVzNTHXEd3JdZjUaDqqoqFBcX04IwRDQoeDchnTt3xt27d/Hll1/i22+/hVarhVQqRbdu3fDEE0/wA7y4taybNWsm+NrcHz1hVJgwQQnA9j+EiYmJWL9+vdVjWJbF0KFDbb42YLqimaXBdkT8uC+zLMuitLQUW7duRVVVFS0IQ0RBlMF7xYoVWLFiBS5fvgwA6NKlC9577z0MHz4cAPDss89i3bp1Ruf07NkThw8f5l+rVCrMmjULmZmZUCqVGDRoEJYvX4527do1WD0a2vfff2+SgUyn0yEvLw95eXnw8fHByJEjodFooNVqUVVVhYsXL8Ld3R0eHh5GXe01k7jI5XLIZDJBAVyhUMDTs24t2oiICLz0kvUR6ampqdDr9aioqLDp2tyKZobvT83545ToxXVwWQc1Gg0OHDiAvn37Wn3UQq1u4kxEGbzbtWuHjz/+GB07dgQArFu3Dk888QROnDiBLl26AACGDRtmtKa1XG6cvSsxMRHbtm1DVlYWfH19MXPmTIwcORK5ubmQSqUNV5kGYm3FLi4Azpw5E3/88Qf8/f0BVLdgN2zYALlcjhYtWhhNFavZJS2RSNCjR49aR4Nbyr1ui+joaLRq1QoHDhxATk6O2UQ16enpNl3T2vtjOH88LCyMWuAugusG12g0cHd3h5+fH42TIKIhyuBtmIcbAObPn48VK1bg8OHDfPBWKBQICAgwe35paSlWr16N1NRUDB48GACQlpaG4OBg7N69u85drs4sOTm51oDJMAxOnjyJyZMno6qqChUVFfDy8oKnpyfGjBljlA/d3BecQYMG4bfffrN6D0ctDmJpRHpdCX1/kpOTjb4UOpuaq8cB1leQo+e4hIiTKIO3IZ1Oh2+++QYVFRXo3bs3v33fvn1o06YNWrRogX79+mH+/Plo06YNACA3NxcajQYxMTH88UFBQQgPD0dOTo7F4K1SqYxGOpeVlQEANBqNTdOSuGPtncrEZRnjntGZU1lZifLycqN515bo9XocOXIEL730EnQ6HSQSCaRSKSQSCTQajdG8ae6eKpUKhYWF0Gq1aNeu3f+6tL+EXs8AMF0cJCUlBWFhYRbLa1gnw9eGxxsewzAM36ti6Zja3me9Xo/MzMxa3x+tVovMzEx8+eWXRmV0pilpZ86cwYkTJ4y2KZVKsCwLtVqNTZs2Ge2LjIxEZGSkxes56nfVmVEdXYOr1FFo+RlWpJksTp8+jd69e6OqqgrNmjVDRkYGRowYAQDYsGEDmjVrhpCQEBQUFODdd9+FVqtFbm4uFAoFMjIyMGXKFJMpRzExMWjfvj2+/PJLs/dMSkrCvHnzTLZnZGQ0Suvl0qVLfJdxhw4d+O1arZYPRCUlJSgqKsJ7770n+LoLFy6EXq+HRqPhl9pUKBTQ6XT8LxYXOLl9er2en/ut1crw8ceHUFr6LfT66vL17dsXo0aNQvv27QXXiatLzfrZeozhdnOqqqowYcIEwe/Phg0b8Ndffwm+fkMy/OyF4BLuEEKcQ2VlJZ555hmUlpbCp2YSDAOi/b/2wQcfxMmTJ1FSUoJNmzZh8uTJ2L9/P8LCwjB+/Hj+uPDwcHTv3h0hISH48ccfERsba/GaXECyZPbs2ZgxYwb/uqysDMHBwYiJibH6Jtek0WiQnZ2NIUOG1PkZW2VlJW7dugWlUgkPDw888sgj/L78/Hzk5+cDqG5VNmvWTPBgMolEgrZt26K8vBxlZWVo1qwZPDw8+MGAO3bsgFKpRHl5udG+Xbt2oaqqCl5eXhg/fjymTn0en376KPz9tWjVqpXg4Lhhwwa+ux4A7t69C4VCYVQ/w3oDMHoPuG5gw+twX+os0ev1guePKxQKjBkzBhs3bkR5eTkqKyvt+hydnSN+V50d1dE1uEoduR7d2og2eMvlcn7AWvfu3XH06FH897//NdtqDgwMREhICC5evAgACAgIgFqtxp07d9CyZUv+uKKiIkRHR1u8p0KhgEKhMNnu5uZWp1+Wup4HAH/88QeKioqg1+tx9+5dfnoX153LlVMqlUIqleJf//oXjh49WmvKUL1ej2vXrsHPzw8SiQRubm5QKBQIDAwEUP0eaLVak30SiYxvibu5ucHHB/DzU8Hd3Ys/VgjuGtyXKJVKhdLSUmzbto0/hqunVquFUqk0eg+46TyG1xFy77i4OH56mCXcYDu5XG5URns+R7GgOroGqqPzE1p20QbvmliWtdhyunXrFq5du8YHoKioKLi5uSE7Oxvjxo0DABQWFuLMmTNYvHhxg5XZHp07d8apU6f4VueoUaP4AM69NnT79m0cOXKk1usaLuohNFHJsmVAXh5QH4Ow3d3d0bx5c5P6cGrWua6PLxITE5Gammr1GEcNtiOEEHuJMnjPmTMHw4cPR3BwMO7evYusrCzs27cPO3fuRHl5OZKSkvDUU08hMDAQly9fxpw5c+Dn54cnn3wSANC8eXNMnToVM2fOhK+vL1q1aoVZs2aha9eu/OhzZ8flGtdoNEb5xQ1fGwoJCYFEIqk1AxrLsjh48CDUajWOHTtmkqikpitXgLlzgYkTgfPnAXtzWBh+YZBIJJBIJGbrw7FWZ1tEREQgNTXVZJ43YDx/nKaJEUKcgSiD940bN5CQkIDCwkI0b94c3bp1w86dOzFkyBAolUqcPn0a69evR0lJCQIDAzFgwABs2LAB3t7e/DWWLFkCmUyGcePG8Ula1q5d65JzvAHjXOa10ev1RgltDBOVTJs2jQ9gN28C/3u0DgBo1Qqo69inw4cP47///S+f+U0mk6Fnz57o27cvQkNDG2SKU1xcHMLCwkwyrBnOHyeEEGcgyuC9evVqi/s8PDzw008/1XoNd3d3LF26FEuXLnVk0RyOm7erVCpRVVVltK+iogJVVVXQ6XQ4duwYSktLoVarAQDXrl0z6e4WOmjNHO68lStXYs6cOXBz88Hff9/b36EDEBRUp0sjMzOTn6PNfcHQarU4dOgQcnJyEB8fbzJ9or5SVUZERCAlJQUDBw7k54/HxsaisrLS4uItxcXFRs+paO40IaS+iTJ4NyVc/mXD5BtcgONGx+v1emzfvp0fjFZWVoaMjAzIZNWDyNzd3VFSUoLw8HCcPHnS7jL9+ONuDBkyBjKZFm5uarzzTjHuv18Nlcq2xUyAe5nNzPUKcNsyMjLw3HPPITw8nN9X38HRcEUzwwVdONxAOa1Wi61btxrNUqAc2ISQ+kbB28lx+ZcNW9579+5FVVUV3N3dMWDAAFRVVUGlUuHIkSNQq9Xw9PTkp3NJJBK4u7uDZVk8/PDDdgdvvV6PU6eOYfjwx9GiRQlaty5DUNBm3LhxG+7u7kaPJoQQmtls3bp1jZbZjPsMarKUE5ta3YSQ+kbB28mZ64LlRo17eXkhNDSU337+/Hl+brO7uzvc3Nxw8+ZN/PLLL/yzZEfQ6bS4etUPHTroERJyb6S7kHnShmzNbLZmzRq7cqLXlaVucMqJTQhpLBS8XRTDMPjtt9+QmpoKqVTqsMBdTYH27ZshKKjSZKS7LZRKpeCAr1KpcOvWLZPtlvJ22zJAjxBCxIaCt4u5du0a9u/fj5ycHH6FLEcGbolEgq5dY/DggwwMV9zU6XRGQVSj0ZhsM+Tp6QkPDw+bMptdvnwZx48fN9rOPXuuqqrCxo0b+e2lpaWQy+VQKBS0GAchxOVQ8HYhOTk5Zte5diSGYbBy5RxcunSJ31ZZWYmSkhLcvXsXJSUl2LhxI78gCNe6Ngys3PKhUVFRNmU2CwsLw/3332+y/+zZszh79qzRNi7DXFlZGTZv3my0jwaUEULEjoK3i8jLy8PKlSvrLWhLpVKwLIvly5ejY8eOOHv2LCoqKiCVSpGXl4eK/zXDDdOasizLD5gz1LlzZ3Tu3BmAbZnNLLWYe/TowS8FKwS1ugkhYkfB20UkJyfX27UlEgmioqLQt29f/PHHHxg9ejSOHDkCnU4HqVSKoUOH4pFHHkHr1q35NKVbt26FWq1GixYtMHr0aKPrGQZhR2Q2o25wQkhTQ8FbBAzneAOmg7SOHj2K9evX11ur++23v0DHju745ZdfsG7dOjAMwz9P1+l02LVrF3bu3IkpU6ZgwIAB/AC22lKbciizGSGE2IaCtwjUTBJy+/ZtaLVaMAyDGTNm1NrtbA+JRILOnb1x9epVrFu3zuwXBK61nJKSggceeKBO9zGX2YzLukYIIcQYBW8RqJkkZNu2bSgsLERhYSHS09Pr9d4sy2LVqlUoLy8XdPxPP/2EOXPm1Pl+hpnNCCGEmEfBWwRqPtPluqRzcnLq/d4sy+LXX38V1CWv1+uRk5MDlmWh1+tprjUhhNQTCt4ixM1rPnnyZIMER1vuodVqMWnSJGRlZfHPrvfu3UvPrgkhxIEkjV0AIlxeXh6mTJmC5557DvPmzXPaVi0XuIF7y4lGRUUhMzOzkUtGCCGugVreIpGZmWl2OpVQDMPwq47Vt5rl414nJCQgLCyMWuCEEGInanmLALdsJpdutC4aKnBbwzBMvc5HJ4SQpoKCtwgIWTazMUmlUkHHcauDOcMXCUIIETMK3k5O6LKZjUUqlSI2Nlbw8SqVCkqlsh5LRAghro+Ct5OzZdnMhsYwDCIiIjBw4EDIZMKGTygUCnh4eNRzyQghxLVR8HZy3LKZzohlWZw6dQpPPfUUnn766Vq7z7nVwZz5EQAhhIgBBW8nJ5FIEBcXJ7hl29C0Wi28vLzw9ttv13ostzoYIYQQ+1DwFoHExERBg7wkkto+TikAx7Z6ZTIZPDw8+NXBpFKpyRcNmUwGqVRa6+pghBBChKHgLQLWAqNEIoFEIsGYMWNqDfAMowfguJHeEokE0dHRfDd4XFwccnNzER8fz5eTWx0sNzcXcXFxDrs3IYQ0Zc7ZF0tMmFs2UyqVok+fPli0aBE6duyINm3a4KuvvgLDMDWyr0nBMCzmzUvChx9+CI1G47ByDR061Og1tzrYgAEDcPXqVYSEhCAhIcHsuTWXOgVMlzs1ROt2E0JINQreTsZcQOO0bdsWn3zyCR599FFcu3YNbm5uCA4ORq9evQAAffv2RZs2bXDgwAH8+msOdLrqAN+1a08MHhwNqVSK8PBw5OXl2ZValWEYMAyDKVOmGK12Zohby9va4LSaS50C1aPrWZaFRqPB5s2bjfZFRUUhKiqqzuUmhBBXQcHbyZgLaBUVFdDr9WAYBu7u7igtLYVEIoFOp0NpaSnfQlWr1QgMDMTUqVMxYsQIlJcrIZV64NVXp6KqSomqqiqEh4djzJgxdpVxxIgRiI6Ohp+fn13XqbnUaW2o1U0IIdUoeDsZcwEtIyMDlZWVfPBmWZZ/vl1aWsq3UG/fvs23qCUSCTw85JDJGDBM9ZQzDw8PREdHY9y4cdiwYYPFMnAta8PWuUQiAcuyGDduHP7v//4P586dQ0VFhV11pW5wQgipGwreTsZcQGvRogU/qnvUqFHYtm0brly5Ao1GAy8vLwwYMADbtgEq1V60bFkFuVyO0tJSqFQq6PV6bNy4EXq9HjqdDizLQqfTWVyohGEYDB8+HLdv38axY8f4ZT179uyJvn37IjQ0FJ07d8a5c+f4cyw9u+bW9KZn14QQ4lgUvEWAG2Uul8vx999/IyUlBTk5OdDpdJBKpfj2220IDR2ADh288fvvf+PIkRycOnWK3x8VFYXevXujVatWuH79OjZt2mRxZDrLstixYwdefvllTJgwAXq9Hs2bNwfDMFCpVJDL5SaB11xXf2VlJfR6PcrKyujZNSGEOBgFbxHJycnB5MmTwbIs36Wt0+lw4sRRnDz5GyIje+LEid+Murx1Oh2OHTuGo0eP4qmnnsIff/xR6/KgDMPgyJEjaNeuHViW5dOzuru7w9vb2+R4c139Go0GBw4cQN++feHm5ma0j1rdhBBiHwreInHt2jWsXLnSwihxHVgWOH78MADT5T+5czZt2gSZTFbrSHO9Xo9Tp07h5ZdfRlVVFZ+L3FKOdXPd4BqNBu7u7vDz8zMJ3oQQQuxDSVpEQKfTITs72+7rSCQSwXO81Wq1yepfhs+wDedjW5raRgghpH5Qy1sEysrKcOzYMbvmZgPVXwKEkkqluH37NiQSCUpKSvhR7lVVVdi4cSNKS0vBsizKysqQn59Pz7AJIaQBUfAWAYVCYVPgrY1EIrH6RUAikaB79+58Vzg3Rc1Qs2bNIJVK4eHhgc6dOzusbIQQQmpHwdvJVVZWQiKRQCaTQavV2n09Nze3Wq/DMAw+/vhjhIeHWzxm+/btqKqqMjv6nBBCSP2i4O3k8vPzcfPmTXTo0AHnz5+361pSqRT33XcfLl++bLYlL5PJwLIsUlNT0b9//1qvRQghpHHQgDUn17lzZ/j7+0Mul9t9LZ1OZzFwSyQSWv2LEEJEgoK3kztwwBNSqQz5+fkOuZ6lZ+d6vR63b992yD0IIYTULwreTmzZMmD4cCA3V+2Q59212bZtG6KiopCZmVnv9yKEEFJ3FLyd1LJlwGuvVf+7uNj60pqOwuU9T0hIQF5eXr3fjxBCSN1Q8HZChoEbAB54QG81namjMQyD5OTkBrsfIYQQ29BocydjHLjz0K1bMj7+OK1By6DVapGZmYk1a9aAYRiLq4ZxGdbMrRpGKVEJIaT+UPB2IsaBOxMMk4Dff2ca5Hl3TSqVCkqlEp6enmZXDVMqlWBZFhqNxuyqYd26dWvI4hJCSJNCwdtJ1GxxM0wCWFaHRojbAKqzunELkphbNcwasSVtqUvPgtjqSAhxLRS8nUBWlvEz7m7dkv/X4nbcPRiG4ZcCre35uUwmQ1xcHD9Iri7BSugCKM6gLj0LlMudENKYKHg7gaFDgagoIDcXmDtXj//8J9OmrnKpVMrnKjdcyxuoTr4CAFOmTEFgYCC2bt2KU6dOWb0ey7JITEy0vSIi5eo9C4QQ1yPK0eYrVqxAt27d4OPjAx8fH/Tu3Rs7duzg97Msi6SkJAQFBcHDwwP9+/fH2bNnja6hUqnw+uuvw8/PD15eXhg9ejT++uuvhq4KAKBlSyA7G1i+HHj7baXFdbPNkUgkeOaZZ7Bnzx7s2bMH48ePh0xW/Z1MJpNh/Pjx+Pnnn/Hxxx9j+vTp+PnnnzFt2jS+JW5IJpNBKpUiNTUVERERDq2jM/P09ISfn5/gHwrehJDGJsqWd7t27fDxxx+jY8eOAIB169bhiSeewIkTJ9ClSxcsXrwYn332GdauXYtOnTrho48+wpAhQ3D+/Hl4e3sDABITE7Ft2zZkZWXB19cXM2fOxMiRI5Gbm9soebtbtgRefhnQ6z2gUCgEBXCpVIp3330X77//Pr+tf//+GD58OEpKStCyZUvEx8ebnPfYY4+hdevWOHDgAHJycqDVaiGTyRAfH4/ExMQmFbgJIUSMRBm8R40aZfR6/vz5WLFiBQ4fPoywsDAkJydj7ty5iI2NBVAd3P39/ZGRkYFp06ahtLQUq1evRmpqKgYPHgwASEtLQ3BwMHbv3o2hQ4c2eJ04EokEcXFxSEtLs9p1LpFI0LVrVzAMYzKgimVZKBQKq4ldgoOD8eKLL2LEiBH8qPI333zTYfUghBBSf0QZvA3pdDp88803qKioQO/evVFQUIDr168jJiaGP0ahUKBfv37IycnBtGnTkJubC41GY3RMUFAQwsPDkZOTYzF4q1QqoxZxWVkZgOrBWbYM0OKOtXTOq6++itTUVKvXYFkWvXr1AgBs2rTJaJ9Op4NUKuUHXJk7l/uRSCRwc3MDwzAOHWRWWx1dAdXRNVAdXYOr1FFo+UUbvE+fPo3evXujqqoKzZo1w5YtWxAWFoacnBwAgL+/v9Hx/v7+uHLlCgDg+vXrkMvlaNmypckx169ft3jPhQsXYt68eSbbd+3aVafnoNnZ2Rb3TZ8+HcnJyWAYxmgxEW4AWmxsLAICAgCAn9LFuXHjBliWRUVFBbZv325y7aKiImi1WlRUVEClUkGv10OlUpk91l7W6ugqqI6ugeroGsRex5rTVi0RbfB+8MEHcfLkSZSUlGDTpk2YPHky9u/fz++v2WXMsmyt+cFrO2b27NmYMWMG/7qsrAzBwcGIiYmBj4+P4LJrNBpkZ2djyJAhFjORjRgxAnFxcfjiiy+Qnp4OrVYLqVSKRx55BIMGDYK7uzv0ej0kEgmUSqXRuV5eXpBKpfDy8sKIESNMrr1hwwZUVFTAy8sLhYWFUKvVkMvlZo+tKyF1FDuqo2ugOroGV6kj16NbG9EGb7lczg9Y6969O44ePYr//ve/eOuttwBUt64DAwP544uKivjWeEBAANRqNe7cuWPU+i4qKkJ0dLTFeyoUCigUCpPtbm5udfplqe287t27Y+3atejfvz/u3LmD5s2bY/To0QCqVwBTKpXw8PAwGQOwfft2VFVVgWEYs9fnRppzX1S4/9bHL3xd3xsxoTq6BqqjaxB7HYWWXZRTxcxhWRYqlQrt27dHQECAUdeJWq3G/v37+cAcFRUFNzc3o2MKCwtx5swZq8G7sbi5ucHLywsKhYKfriSXy/mpXTXpdDqj7GCGP0K7ZAghhDgvUba858yZg+HDhyM4OBh3795FVlYW9u3bh507d4JhGCQmJmLBggUIDQ1FaGgoFixYAE9PTzzzzDMAgObNm2Pq1KmYOXMmfH190apVK8yaNQtdu3blR5+LRXl5uUkGMMPsYN9++61R0pYuXboYpf7U66tXLNPr9SguLqbUn4QQIgKiDN43btxAQkICCgsL0bx5c3Tr1g07d+7EkCFDAABvvvkmlEolXnnlFdy5cwc9e/bErl27+DneALBkyRLIZDKMGzcOSqUSgwYNwtq1axtljrc9mjVrZvVZ9dmzZ/kENXq9HqdPn4ZarQbLsqiqqoJKpeLX8d64cSO6dOmCLl26AKAc3oQQ4qxEGbxXr15tdT/DMEhKSkJSUpLFY9zd3bF06VIsXbrUwaVzvJrd4MC9hTMMR6JzDINujx49+GDMBXJ3d3f+2MrKSqOBevn5+Th//jwAyuFNCCHOSpTBu6kpLy9HSUkJysrK+C5yoQtnGAbyLl26ICgoyOjYvXv3oqqqCu7u7hgwYIDRvppT6QghhDgHCt4i0KxZMwAwO7LcHEtd3VeuXLG4epZSqcTevXuN9kVFRcHPz6+OpSaEEFJfKHiLgFQqhUwmg1wutyuY0upZhBDiGih4NyE0AI0QQlyDy8zzJoQQQpoKCt6EEEKIyFDwJoQQQkSGgjchhBAiMhS8CSGEEJGh0eZOprKy0mTxEMNc5FyGNQ6NICeEkKaHgreTyc/Pt5hIpbZsaoQQQpoGCt5OhhKpEEIIqQ0FbydD3eCEEEJqQwPWCCGEEJGh4E0IIYSIDAVvQgghRGQoeBNCCCEiQ8GbEEIIERkK3oQQQojIUPAmhBBCRIaCNyGEECIyFLwJIYQQkaHgTQghhIgMBW9CCCFEZCh4E0IIISJDwZsQQggRGQrehBBCiMhQ8CaEEEJEhoI3IYQQIjIUvAkhhBCRkTV2AcSMZVkAQFlZmU3naTQaVFZWoqysDG5ubvVRtEZHdXQNVEfXQHUUDy6ecPHFEgredrh79y4AIDg4uJFLQgghxJXcvXsXzZs3t7ifYWsL78QivV6Pf/75B97e3mAYRvB5ZWVlCA4OxrVr1+Dj41OPJWw8VEfXQHV0DVRH8WBZFnfv3kVQUBAkEstPtqnlbQeJRIJ27drV+XwfHx9R/5IJQXV0DVRH10B1FAdrLW4ODVgjhBBCRIaCNyGEECIyFLwbgUKhwPvvvw+FQtHYRak3VEfXQHV0DVRH10MD1gghhBCRoZY3IYQQIjIUvAkhhBCRoeBNCCGEiAwFb0IIIURkKHg7yIoVK9CtWzc+QUDv3r2xY8cOfj/LskhKSkJQUBA8PDzQv39/nD171ugaKpUKr7/+Ovz8/ODl5YXRo0fjr7/+auiqWFRbHZ999lkwDGP006tXL6NrOHsdDS1cuBAMwyAxMZHf5gqfoyFzdXSFzzEpKcmkDgEBAfx+sX+OtdXPFT5DAPj7778RHx8PX19feHp64uGHH0Zubi6/X+yfo11Y4hBbt25lf/zxR/b8+fPs+fPn2Tlz5rBubm7smTNnWJZl2Y8//pj19vZmN23axJ4+fZodP348GxgYyJaVlfHXeOmll9i2bduy2dnZ7PHjx9kBAwawERERrFarbaxqGamtjpMnT2aHDRvGFhYW8j+3bt0yuoaz15Fz5MgR9v7772e7devGTp8+nd/uCp8jx1IdXeFzfP/999kuXboY1aGoqIjfL/bPsbb6ucJnePv2bTYkJIR99tln2d9++40tKChgd+/ezf7xxx/8MWL/HO1BwbsetWzZkv36669ZvV7PBgQEsB9//DG/r6qqim3evDm7cuVKlmVZtqSkhHVzc2OzsrL4Y/7++29WIpGwO3fubPCyC8XVkWWr/2A88cQTFo8VSx3v3r3LhoaGstnZ2Wy/fv34wOZKn6OlOrKsa3yO77//PhsREWF2nyt8jtbqx7Ku8Rm+9dZb7KOPPmpxvyt8jvagbvN6oNPpkJWVhYqKCvTu3RsFBQW4fv06YmJi+GMUCgX69euHnJwcAEBubi40Go3RMUFBQQgPD+ePcSY168jZt28f2rRpg06dOuGFF15AUVERv08sdXz11Vfx+OOPY/DgwUbbXelztFRHjit8jhcvXkRQUBDat2+PCRMm4M8//wTgOp+jpfpxxP4Zbt26Fd27d8fYsWPRpk0bREZG4quvvuL3u8rnWFe0MIkDnT59Gr1790ZVVRWaNWuGLVu2ICwsjP8l8ff3Nzre398fV65cAQBcv34dcrkcLVu2NDnm+vXrDVMBASzVEQCGDx+OsWPHIiQkBAUFBXj33XcxcOBA5ObmQqFQiKKOWVlZOH78OI4ePWqyjyuj2D9Ha3UEXONz7NmzJ9avX49OnTrhxo0b+OijjxAdHY2zZ8+6xOdorX6+vr4u8Rn++eefWLFiBWbMmIE5c+bgyJEjeOONN6BQKDBp0iSX+BztQcHbgR588EGcPHkSJSUl2LRpEyZPnoz9+/fz+2suG8qybK1LiQo5piFZqmNYWBjGjx/PHxceHo7u3bsjJCQEP/74I2JjYy1e01nqeO3aNUyfPh27du2Cu7u7xePE/DkKqaPYP0eg+gsIp2vXrujduzc6dOiAdevW8QO3xPw5WqvfjBkzXOIz1Ov16N69OxYsWAAAiIyMxNmzZ7FixQpMmjSJP07Mn6M9qNvcgeRyOTp27Iju3btj4cKFiIiIwH//+19+FGjNb3pFRUX8t8aAgACo1WrcuXPH4jHOwFIdzQkMDERISAguXrwIwPnrmJubi6KiIkRFRUEmk0Emk2H//v34/PPPIZPJ+DKK+XOsrY46nc7kHLF9juZ4eXmha9euuHjxokv9/8gxrJ85YvwMAwMD+V49TufOnXH16lUAcMnP0RYUvOsRy7JQqVRo3749AgICkJ2dze9Tq9XYv38/oqOjAQBRUVFwc3MzOqawsBBnzpzhj3FGXB3NuXXrFq5du4bAwEAAzl/HQYMG4fTp0zh58iT/0717d0ycOBEnT57EAw88IPrPsbY6SqVSk3PE9jmao1KpkJ+fj8DAQJf8/9GwfuaI8TPs06cPzp8/b7TtwoULCAkJAQCX/Bxt0ijD5FzQ7Nmz2QMHDrAFBQXsqVOn2Dlz5rASiYTdtWsXy7LVUxqaN2/Obt68mT19+jQbFxdndkpDu3bt2N27d7PHjx9nBw4c6FRTGqzV8e7du+zMmTPZnJwctqCggN27dy/bu3dvtm3btqKqY001R2K7wudYk2EdXeVznDlzJrtv3z72zz//ZA8fPsyOHDmS9fb2Zi9fvsyyrPg/R2v1c5XP8MiRI6xMJmPnz5/PXrx4kU1PT2c9PT3ZtLQ0/hixf472oODtIM899xwbEhLCyuVytnXr1uygQYP4wM2y1dMa3n//fTYgIIBVKBRs37592dOnTxtdQ6lUsq+99hrbqlUr1sPDgx05ciR79erVhq6KRdbqWFlZycbExLCtW7dm3dzc2Pvuu4+dPHmySfmdvY411QzervA51mRYR1f5HLn5vm5ubmxQUBAbGxvLnj17lt8v9s/RWv1c5TNkWZbdtm0bGx4ezioUCvahhx5iV61aZbRf7J+jPWhJUEIIIURk6Jk3IYQQIjIUvAkhhBCRoeBNCCGEiAwFb0IIIURkKHgTQgghIkPBmxBCCBEZCt6EEEKIyFDwJoQ0CQzDgGEYJCUlNXZRCLEbBW9CHOTy5ct8gLDnhxBCakPBmxBCCBEZWs+bEAdp27YtTp8+bXH/0KFD8c8//yAoKAg//fRTA5aMEOJqKHgT4iBubm4IDw+3ul/IcYQQUhvqNieEEEJEhoI3IU6gf//+YBgG/fv3BwBcvHgRr732GkJDQ+Hp6QmGYXD58mUAwNq1a/nBbdw2cwwH0K1du9bq/bOzsxEfH4/27dvDw8MDPj4+iIiIwJtvvonCwsI61eny5cuQSCRgGAbvvPNOrcdv3LiRL++2bduM9t25cwcpKSmIj49HWFgYmjVrBrlcjoCAAAwdOhSrVq2CWq2uUzkBICkpSdCAwX379vHH7du3z+JxLMvi22+/xVNPPYXg4GC4u7ujZcuWeOSRR/Dhhx+ipKSkzmUlBABoPW9CGkhISAgLgA0JCTHZ169fPxYA269fP/a7775jvby8WABGPwUFBSzLsmxKSorJNnMKCgr441JSUsweU15ezj755JMm9zL8adasGbtt27Y61fnRRx9lAbDt27ev9dgnnniCBcC2atWKVavVRvu4987aT2RkJFtYWGjx+txx77//vsm+999/n99vzd69e/nj9u7da/aYoqIitk+fPlbL6u/vzx4+fLjW94QQS+iZNyFO5OrVq4iPj4enpyfeffddPPbYY5BKpTh69CiaNWvm0HvpdDqMGjUKe/fuBcMwmDBhAmJjY9G+fXtoNBocOXIEn376Ka5evYqnnnoKOTk5iIqKsukeEydOxMGDB1FQUICcnBxER0ebPe7OnTvYsWMHAGDcuHH8+ADDsvbs2RMjR45EZGQk/P39oVarUVBQgLS0NOzcuRMnTpzAhAkTrLaI61tFRQX69euH/Px8yOVyTJkyBSNGjEBwcDAqKipw4MABfPbZZ7hx4waGDx+OEydOICQkpNHKS0Sssb89ENJUCGl5A2CDgoLYK1euWLyOo1re//nPf1gArJubG7t9+3az17h9+zbbpUsXFgD76KOP1lZFE7du3WLd3NxYAOyrr75q8bhVq1bxZf3ll19M9l+4cMHqfdasWcOfv3v3brPHoAFa3q+99hoLgG3evDl79OhRs9e4fPkyGxgYyAJg4+Pjrd6PEEvomTchTubjjz/GfffdV6/30Gg0+PTTTwEAr732GoYPH272uJYtW+KTTz4BABw8eBB//PGHTfdp1aoVhg0bBqD6mbZWqzV7XHp6OgAgJCQEffr0MdkfGhpq9T5TpkxBZGQkAOC7776zqYyOUlxcjK+//hoA8MEHH6B79+5mjwsJCcG7774LANiwYQMqKysbrIzEdVDwJsSJyOVyjB07tt7vc+TIEX4g2rhx46we27dvX/7fhw4dsvle8fHxAICbN28iOzvbZP9ff/2FX375BQDwzDPP1DpojGVZXL9+HRcuXMCZM2f4n6CgIABAXl6ezWV0hJ9++glVVVUAhL+nGo0Gubm59V424nromTchTiQ0NBTu7u71fp9jx47x/+7du7fg865fv27zvUaNGgVvb2/cvXsX6enpJq38zMxM6PV6ANXB25Iff/wRK1aswIEDB3D37l2LxxUXF9tcRkcwfE8DAwMFn1eX95QQankT4kRatmzZIPcpKiqq03l16eL18PBAbGwsgOou7ZrX4LrMIyIizCavYVkWzz//PEaOHIkff/zRauAGAKVSaXMZHaEh31NCqOVNiBORSqUNch+dTsf/e9++ffD19RV0Xps2bep0v4kTJ2LdunWoqKjA999/j7i4OADA77//zndzT5w40ey5a9aswerVqwEADz/8MBITE9GzZ0+0bdsWnp6e/Hs2adIkpKamgmXZOpXRXtx7KpfLbeoKb9euXX0VibgwCt6EiIxEcq/DjOtuNqeiosLiPsNgLZfL6z1d66BBgxAYGIjCwkKkp6fzwZtrdTMMw2+r6auvvgIAdOjQATk5OfDw8DB73J07d+pcvprvqeFrQ0LeU7VaDV9fX5u6zgmxFXWbEyIy3t7e/L+tBazz589b3MeNzAaAXbt2OaZgVkgkEkyYMIG/H/dcOjMzEwDQr18/iy3Qs2fPAgCeeOIJi4GbZVkcP368zuUT43tKmjYK3oSITPv27fl/Gw6SqikjI8PivkcffRStWrUCAKxcuRJlZWWOK6AFXLe4RqPBN998g5ycHBQUFBjtM4ebXmbt2fDWrVvxzz//1LlsQt9T7suGOcOHD+eTyyxZssTitDhCHIGCNyEiEx4ezgfeL774AiqVyuSYzMxMbNq0yeI13N3dMWvWLADVo50nTJhgtUv47t27+OKLL+wqd1RUFB566CEA1d3lXJe5XC7H008/bfE8bo73tm3bzLaKL126hFdeecWusvXp0wcyWfVTxCVLlph9bv7xxx9bDext27bFlClTAFRPV5s2bZrVAF5UVMTPCyfEZo2aIoaQJkRobnMh3n77bT7TV3R0NPvdd9+xx48fZ7dv385OmTKFlUgkbO/eva1mWNNqteygQYP4Y+677z52wYIF7N69e9kTJ06wBw4cYL/66it24sSJrJeXF+vr62vfG8Cy7IcffsgCYBmGYX18fFgA7JNPPmn1nE8++YQv40MPPcSuWbOG/e2339j9+/ez77//Ptu8eXPW3d2d/de//mXx/WVZ6xnWWJZlJ0yYwB8zcuRIdseOHezx48fZ7777js//bviemsuwdvfuXTY8PJw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+ "image/png": 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", "text/plain": [ "
" ] @@ -292,15 +350,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.8345390628906045\n", - "16.316833\n" + "0.8807364374893456\n", + "12.612753\n" ] } ], @@ -318,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -328,29 +386,28 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 13.2220, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + "tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.5073, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", + " 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", " device='cuda:0', grad_fn=)\n" ] } ], "source": [ - "qmc_sampler = SobolQMCNormalSampler(num_samples=32)\n", + "qmc_sampler = SobolQMCNormalSampler(sample_shape=32)\n", "qEI = qExpectedImprovement(\n", " model=gp_model, best_f=y_train.max(),\n", " sampler=qmc_sampler)\n", @@ -377,7 +434,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.10.14" } }, "nbformat": 4,