From b718629a4bdfd7297d858b7e2193169db18542b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jordan=20Fr=C3=A9ry?= Date: Mon, 3 Jun 2024 12:42:07 +0200 Subject: [PATCH] feat: add FHE training deployment (#665) --- .../LogisticRegressionTraining.ipynb | 511 ++++++++++++++---- pyproject.toml | 4 + script/make_utils/check_pytest_determinism.sh | 38 +- .../ml/deployment/fhe_client_server.py | 178 ++++-- src/concrete/ml/pytest/utils.py | 19 +- .../ml/quantization/quantized_module.py | 8 +- src/concrete/ml/sklearn/linear_model.py | 6 - src/concrete/ml/torch/hybrid_model.py | 2 +- .../test_pbs_error_probability_settings.py | 8 +- tests/common/test_serialization.py | 6 +- tests/common/test_skearn_model_lists.py | 4 +- tests/deployment/test_client_server.py | 83 ++- .../test_p_error_binary_search.py | 29 +- tests/seeding/test_seeding.py | 10 +- tests/sklearn/test_common.py | 20 +- tests/sklearn/test_dump_onnx.py | 7 +- tests/sklearn/test_fhe_training.py | 259 +++------ tests/sklearn/test_sklearn_models.py | 221 +++----- 18 files changed, 858 insertions(+), 555 deletions(-) diff --git a/docs/advanced_examples/LogisticRegressionTraining.ipynb b/docs/advanced_examples/LogisticRegressionTraining.ipynb index e346332f8..4ab0bd34c 100644 --- a/docs/advanced_examples/LogisticRegressionTraining.ipynb +++ b/docs/advanced_examples/LogisticRegressionTraining.ipynb @@ -35,15 +35,32 @@ "from concrete.ml.sklearn import SGDClassifier\n", "\n", "\n", - "def plot_decision_boundary(clf, X, y, n_iterations, title=\"Decision Boundary\", accuracy=None):\n", + "def plot_decision_boundary(\n", + " X, y, clf=None, weights=None, bias=None, title=\"Decision Boundary\", accuracy=None\n", + "):\n", " # Create a mesh to plot the decision boundaries\n", " x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n", " y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))\n", "\n", - " # Predictions to get the decision boundary\n", - " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", - " Z = Z.reshape(xx.shape)\n", + " if clf is not None:\n", + " # Predictions to get the decision boundary\n", + " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + " learned_weights = (\n", + " f\"Learned weights: \"\n", + " f\"{clf.coef_[0][0]:.3f}, \"\n", + " f\"{clf.coef_[0][1]:.3f}, \"\n", + " f\"{clf.intercept_.reshape((-1,))[0]:.3f}\"\n", + " )\n", + " elif weights is not None and bias is not None:\n", + " # Compute the linear model for the mesh grid\n", + " linear_model = np.dot(np.c_[xx.ravel(), yy.ravel()], weights[0]) + bias[0]\n", + " Z = np.round(1 / (1 + np.exp(-linear_model)))\n", + " Z = Z.reshape(xx.shape)\n", + " learned_weights = \"\"\n", + " else:\n", + " raise ValueError(\"Either 'clf' or both 'weights' and 'bias' must be provided.\")\n", "\n", " # Define red and blue color map\n", " cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n", @@ -52,11 +69,7 @@ " plt.figure(figsize=(10, 6))\n", " plt.contourf(xx, yy, Z, alpha=0.3, cmap=cm_bright)\n", " plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\", cmap=cm_bright)\n", - " plt.title(\n", - " f\"{title} (Iterations: {n_iterations}, Accuracy: {accuracy})\\n \"\n", - " f\"Learned weights: {clf.coef_[0][0]:.3f}, {clf.coef_[0][1]:.3f}, \"\n", - " f\"{clf.intercept_.reshape((-1,))[0]:.3f} \"\n", - " )\n", + " plt.title(f\"{title} (Accuracy: {accuracy})\\n {learned_weights}\")\n", " plt.xlabel(\"Feature 1\")\n", " plt.ylabel(\"Feature 2\")\n", "\n", @@ -117,7 +130,7 @@ "outputs": [ { "data": { - "image/png": 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r17t3bwWo0aNHJ+rP1Ptl2rRpSqPRqOvXrxuW1a9fXzk6OhotSxiPUkqNGTNGWVtbq8ePHxuW3b9/X+XJk0dNmDBBKfXf74SE8Zly6NChRL/j4o0YMUIBRu/nF3333XcKSPQ7fvTo0QpQbdu2NVr+6aefqpIlSxr6LF68uGrTpo3Jvvft26cAtWzZsiSPQYiEZLqgECnQrFkz9u/fT7t27Thx4gTffPMNLVq0wMPDg3Xr1hna/f333+j1ej7//HO0WuMfr5RMawkNDaV58+acP38ef3//RCNBmUEpxapVq/Dx8UEpxYMHDwyvFi1aEBoaytGjRwGMvjXU6/U8fPiQ2NhYqlWrZmiTUOfOnQ3f4L9o4MCBRv+uV68eISEhhIWFmY318OHD3L9/n4EDBxp9e9mnTx+cnZ2N2q5YsYIKFSpQvnx5o2OKn2MfP8Uxrdcwb9683Lp1i0OHDplt86KtW7cSHR3NsGHDjPb1/vvv4+TkxIYNG4zaOzg44Ovra/i3lZUV3t7eXLlyJUX7q1WrFl5eXoZ/FytWjPbt27Np0yZ0Oh06nY7NmzfToUMHSpUqZWhXuHBh3nrrLfbs2ZPk9UjKa6+9Rr169Qz/dnV1pVy5ckax//vvv9SsWRNvb2+jdvHTnBJKOLr15MkTHjx4QL169QgPD0803cza2pq+ffsaLdNqtbz99tusW7eOJ0+eGJYvWbKE2rVrJyqQ8KKQkBDy5cuXzFEnbfXq1ej1erp162b0nixUqBBlypRJNO3W1HFA6s5FSty5c4fjx4/Tp08fo9HmypUr06xZM5MjCsn9/MaPyK1du9bk9M94/v7+6T6K1a1bNxYsWECvXr3o0KEDkydPZtOmTYSEhCQ7TSwpS5Yswc3NjUaNGgFxvx+6d+/O0qVLjaYkrlq1iipVqiQa7YnfJr6Ni4uLySJKqZ0SmdCgQYMSLUv4fnn27BkPHjygdu3aKKU4duwYAMHBwezatYt+/foZTd17MZ5evXoRFRVlVE1z2bJlxMbGGn5X9enTB6VUssWZ4qcrmioMFH/PoKkpjfHeeustnJ2d6devH1u2bOHatWvMnTuXWbNmJdo2MDCQH3/8kenTp6eoEFH8z3rC6Z1CJEeSLCFSqHr16qxevZpHjx4REBDAmDFjePLkCV26dOHs2bNA3L1bWq2W1157LU37GDZsGIcOHWLr1q2GKVRpodPpuHv3rtErOjo6RdsGBwfz+PFj5s6di6urq9Er/gNewmIfixYtonLlytjY2FCgQAFcXV3ZsGGDyfn3SX1wffEPefwftUePHpnd5vr16wCJpvxYWloaJQkAFy9e5MyZM4mOqWzZskbHlNZrOGrUKBwcHPD29qZMmTIMGTIk0VQoc/G/eP+PlZUVpUqVMqyPV6RIkUQfuPLly5fkOUrI1NSosmXLEh4eTnBwMMHBwYSHh5u8H6lChQro9fpE9wql1IvXFxLHfv36dZMxmornzJkzdOzYEWdnZ5ycnHB1dTV8qHvxvefh4WGyUEmvXr2IiIgw3FNy4cIFjhw5wjvvvJOiY3rZZODixYsopShTpkyi9+W5c+cSFdUxdxypORcpYe59CXHvgwcPHvDs2TOj5cn9/Hbv3p06derw3nvv4ebmRo8ePVi+fHmSCVdGqlu3LjVq1GDr1q1p2l6n07F06VIaNWrE1atXuXTpEpcuXaJGjRrcu3fPqIjC5cuXk51idvnyZcqVK0eePOl3F0eePHkoUqRIouU3btwwJNAODg64urrSoEED4L/3S/yXH8nFXb58eapXr250L9qSJUuoWbNmqqssxid/pu6RjYyMNGpjSqFChVi3bh1RUVE0b96ckiVLMmLECMNjVhJOjx06dCi1a9emc+fOKYot/mf9ZRJekfvIPVlCpJKVlRXVq1enevXqlC1blr59+7JixYp0qSjVvn17li5dyldffcUff/yRaCQlpW7evJkoodmxY0eKimLEf+jx9fWld+/eJttUrlwZiLthv0+fPnTo0IERI0ZQsGBBLCwsmDZtmuFm7YSS+gNprupeen2rrdfrqVSpEjNmzDC5PuH9W2lRoUIFLly4wD///MPGjRtZtWoVs2bN4vPPP2fSpEkv1Xe8jD5HqWHuw4a5ogLpGfvjx49p0KABTk5OfPHFF3h6emJjY8PRo0cZNWpUog/u5t53r732Gl5eXvj5+dGrVy/8/PywsrKiW7duycZQoECBFCe35uj1ejQaDf/73/9Mnp8X75kydRypPRcZJbnra2try65du9ixYwcbNmxg48aNLFu2jMaNG7N58+YsqbpZtGhRLly4kKZtt2/fzp07d1i6dClLly5NtH7JkiUpvgc3pVL7M2dtbZ3ob4hOp6NZs2Y8fPiQUaNGUb58eezt7bl9+zZ9+vRJ0/ulV69eDB06lFu3bhEVFcWBAwf45ZdfUt1P4cKFAUyW479z5w758+dPdtSpfv36XLlyhVOnTvHs2TOqVKlCUFAQgOELte3bt7Nx40ZWr15tVIAmNjaWiIgIrl27Rv78+XFycjKsi/9Zj7+nUoiUkCRLiJcQf4Nu/B8FT09P9Ho9Z8+eTdNUvw4dOtC8eXP69OmDo6Njih6iaOoPb6FChdiyZYvRsipVqqQoBldXVxwdHdHpdMlWTlu5ciWlSpVi9erVRnFkVgnj+KIDFy9eNCqtGxMTw9WrV42O2dPTkxMnTtCkSZMkv418mWtob29P9+7d6d69O9HR0XTq1IkpU6YwZswYkyWy4+O/cOGC0chbdHQ0V69eTfb8p9bFixcTLQsMDMTOzs4wjdPOzs7kB8/z58+j1WoNyWj8SMXjx4+NijO8OPqWGsWLFzcZ44vx+Pv7ExISwurVq6lfv75h+dWrV1O9z169ejF8+HDu3LnDn3/+SZs2bVI0DbB8+fKsWrUqRfsw937z9PREKUXJkiUNHwBTKzXnIqXfwid8X77o/PnzuLi4pKkUuFarpUmTJjRp0oQZM2YwdepUxo4dy44dO9L9vZ4SV65cMTt9OTlLliyhYMGCzJw5M9G61atXs2bNGubMmYOtrS2enp6cPn06yf48PT05ePAgMTExZp9XlvBnLqHU/MydOnWKwMBAFi1aRK9evQzLX/x7Ef/7KLm4Ia563/Dhw/nrr78Mz1vr3r17imOK5+HhgaurK4cPH060zlQhJXMsLCyM2saPVsa/x27cuAHEFaV50e3btylZsiTff/+90fPk4n+eKlSokKIYhACZLihEiuzYscPkN+7x9ybET6vp0KEDWq2WL774ItE3gin9xr5Xr1789NNPzJkzh1GjRiXb3t7ePtEfXRsbG5o2bWr0Sun9IxYWFnTu3JlVq1aZ/AMbHBxs1BaMj+3gwYPs378/Rft6WdWqVcPV1ZU5c+YYTYdcuHBhonPSrVs3bt++zbx58xL1ExERYZj+lNZrGBISYvRvKysrXnvtNZRSJsuEQ9wffSsrK3766Sejvn///XdCQ0Np06aN2f2lxf79+43ulbt58yZr166lefPmhmd4NW/enLVr1xp9w3vv3j3+/PNP6tata/h2N77aWcIS8M+ePWPRokVpjq9169YcOHCAgIAAw7Lg4OBEZbFNve+io6MN916kRs+ePdFoNAwdOpQrV64Y3fOWlFq1avHo0aMU3Q8Xn5C8+J7s1KkTFhYWTJo0KdF7SymV6D1lSmrOhb29fYqmDxYuXJiqVauyaNEio5hPnz7N5s2bad26dbJ9vOjhw4eJlsV/EE44PSw1JdxTKuHvrHj//vsvR44coWXLlqnuLyIigtWrV9O2bVu6dOmS6PXBBx/w5MkTw/26nTt35sSJEyZLncdft86dO/PgwQOTI0DxbYoXL46FhUWixy6k5n1v6v2ilOLHH380aufq6kr9+vWZP3++ISl5MZ54Li4utGrVCj8/P5YsWULLli2NRnxSU8K9c+fO/PPPP0bTkrdt20ZgYCBdu3Y1LIuJieH8+fPJPoQ6ODiYr7/+msqVKxuSrMaNG7NmzZpEL1dXV6pVq8aaNWvw8fEx6ufIkSM4Ozu/1DR+kfvISJYQKfDhhx8SHh5Ox44dKV++PNHR0ezbt49ly5ZRokQJw71KpUuXZuzYsUyePJl69erRqVMnrK2tOXToEO7u7kybNi1F+/vggw8ICwtj7NixODs7m31SPcSVlp09ezZffvklpUuXpmDBgil6YOL8+fPZuHFjouVDhw7lq6++YseOHdSoUYP333+f1157jYcPH3L06FG2bt1q+MDUtm1bVq9eTceOHWnTpg1Xr15lzpw5vPbaazx9+jRFx/oyLC0t+fLLLxkwYACNGzeme/fuXL16lQULFiS6J+udd95h+fLlDBw4kB07dlCnTh10Oh3nz59n+fLlbNq0iWrVqqX5GjZv3pxChQpRp04d3NzcOHfuHL/88gtt2rTB0dHR5Daurq6MGTOGSZMm0bJlS9q1a8eFCxeYNWsW1atXT/EH/pR6/fXXadGihVEJd8BoOuOXX35peJ7R4MGDyZMnD7/++itRUVFGz+Rq3rw5xYoV491332XEiBFYWFgwf/58XF1dE30oS6mRI0eyePFiWrZsydChQw0l3IsXL87JkycN7WrXrk2+fPno3bs3H330ERqNhsWLF6dp6qGrqystW7ZkxYoV5M2bN8WJbZs2bciTJw9bt25N8gGpgKHYyNixY+nRoweWlpb4+Pjg6enJl19+yZgxY7h27RodOnTA0dGRq1evsmbNGvr378+nn36aZN+pORdeXl4sW7aM4cOHU716dRwcHBJ9mIw3ffp0WrVqRa1atXj33XcNJdydnZ2ZOHFiis5RQl988QW7du2iTZs2FC9enPv37zNr1iyKFClC3bp1De1SU8J9/fr1nDhxAoj70H3y5Em+/PJLANq1a2eY1ly7dm3eeOMNqlWrhrOzM0ePHmX+/PkULVo00e/WPn36sGjRIq5evZqoTH28+GIp7dq1M7m+Zs2auLq6smTJErp3786IESNYuXIlXbt2pV+/fnh5efHw4UPWrVvHnDlzqFKlCr169eKPP/5g+PDhBAQEUK9ePZ49e8bWrVsZPHgw7du3x9nZma5du/Lzzz+j0Wjw9PTkn3/+SXTvXlLKly+Pp6cnn376Kbdv38bJyYlVq1aZnPr6008/UbduXd5880369+9PyZIluXbtGhs2bOD48eNGbXv16kWXLl0AmDx5stG6NWvWpKiEO8Bnn33GihUraNSoEUOHDuXp06dMnz6dSpUqGRV9uX37NhUqVKB3795Gzwdr0KABtWrVonTp0ty9e5e5c+fy9OlT/vnnH8PUyWLFipm8R3TYsGG4ubnRoUOHROu2bNmCj4+P3JMlUicTKhgKkeP973//U/369VPly5dXDg4OysrKSpUuXVp9+OGH6t69e4naz58/X73xxhvK2tpa5cuXTzVo0MBQ5leppEu4JzRy5EgFqF9++UUpZbo89t27d1WbNm2Uo6NjorLlpsT3Ye518+ZNpZRS9+7dU0OGDFFFixZVlpaWqlChQqpJkyZq7ty5hr70er2aOnWqKl68uLK2tlZvvPGG+ueff1Tv3r2NSkXHlxiePn16onjiS0C/WNrZ1LGaM2vWLFWyZEllbW2tqlWrpnbt2pXoHCsVV/L966+/VhUrVjRcGy8vLzVp0iQVGhpq1Da11/DXX39V9evXVwUKFFDW1tbK09NTjRgxwqhfc8f0yy+/qPLlyytLS0vl5uamBg0apB49emTUpkGDBiZLxL94rs0B1JAhQ5Sfn58qU6aM4Xq9WA5aKaWOHj2qWrRooRwcHJSdnZ1q1KiR2rdvX6J2R44cUTVq1FBWVlaqWLFiasaMGWZLuJsqjWzqGp08eVI1aNBA2djYKA8PDzV58mT1+++/J+pz7969qmbNmsrW1la5u7sbHqvACyWuzZ23hJYvX64A1b9//yTbvahdu3aqSZMmRsvM/SxPnjxZeXh4KK1Wm+hYVq1aperWravs7e2Vvb29Kl++vBoyZIi6cOFCio4jpefi6dOn6q233lJ58+ZVgOF9Y6oEuFJKbd26VdWpU0fZ2toqJycn5ePjo86ePWvUJqU/v9u2bVPt27dX7u7uysrKSrm7u6uePXuqwMBAo+1SU8I9vky5qVfCYxk7dqyqWrWqcnZ2VpaWlqpYsWJq0KBB6u7du4n67Ny5s7K1tU3085eQj4+PsrGxUc+ePTPbpk+fPsrS0tLwGIyQkBD1wQcfKA8PD2VlZaWKFCmievfubfSYjPDwcDV27FhVsmRJw+/cLl26GD1OITg4WHXu3FnZ2dmpfPnyqQEDBqjTp0+bLOFub29vMrazZ8+qpk2bKgcHB+Xi4qLef/99deLECZPvgdOnT6uOHTuqvHnzKhsbG1WuXDk1fvz4RH1GRUWpfPnyKWdnZxUREWG0LqUl3BPus3nz5srOzk7lzZtXvf3224muVfx79sWy/R9//LEqVaqUsra2Vq6uruqtt94yOn9JMfd76ty5cwpQW7duTVE/QsTTKCWPrxZCCJF7rV27lg4dOrBr1y6jMvPJ2b17Nw0bNuT8+fMv/VBbkT24ubnRq1evRA+0FkmLjY3F3d0dHx8ffv/996wOJ10NGzaMXbt2ceTIERnJEqkiSZYQQohcrW3btpw7d45Lly6l+kNUq1atKFKkiMl7/UTOcubMGWrVqsWVK1ekilwqxU+H9Pf3N5SDfxWEhIRQvHhxli9fnqZ7EUXuJkmWEEKIXGnp0qWcPHmSadOm8eOPP/LRRx9ldUhC5CgHDx7k5MmTTJ48GRcXF5MPoRcit5IkSwghRK6k0WhwcHCge/fuzJkzJ10fBCtEbtCnTx/8/PyoWrUqCxcuTPbhxULkJpJkCSGEEEIIIUQ6kudkCSGEEEIIIUQ6kiRLCCGEEEIIIdKRJFlCCJGJJk6cmC3KADds2JCGDRumeVu590IIIYQwT5IsIUSmu3btGhqNhm+//TarQxEZKCgoiIkTJ3L8+PEM3U94eDgzZ86kefPmFC5cGEdHR9544w1mz56NTqdLFJOvry/lypXD0dGRvHnz4u3tzaJFi0jJLcpPnz5lwoQJtGzZkvz586PRaFi4cKHJthqNxuyrWbNm6RaTKSEhIUyfPp369evj6upK3rx5qVmzJsuWLUvU1t/f32ycBw4cSNH+bt++Tbdu3cibNy9OTk60b9+eK1euJGo3e/ZsunbtSrFixdBoNPTp08dsn48fP6Z///64urpib29Po0aNMqR63Z07dxg9ejSNGjXC0dERjUaDv79/onbxv7fMvd5///1k95VRx//kyRNGjhxJyZIlsba2xsPDgy5duhAeHp7S0yCESGdSSkkIIXKhzZs3Z/g+goKCmDRpEiVKlKBq1aoZtp8rV67w4Ycf0qRJE4YPH46TkxObNm1i8ODBHDhwgEWLFhnaPnjwgFu3btGlSxeKFStGTEwMW7ZsoU+fPly4cIGpU6cmua8HDx7wxRdfUKxYMapUqWLyw3i8xYsXJ1p2+PBhfvzxR5o3b55uMZmyf/9+xo4dS+vWrRk3bhx58uRh1apV9OjRg7NnzzJp0qRE23z00UdUr17daFnp0qWT3dfTp09p1KgRoaGhfPbZZ1haWvL999/ToEEDjh8/ToECBQxtv/76a548eYK3tzd37twx26der6dNmzacOHGCESNG4OLiwqxZs2jYsCFHjhxJ14c/X7hwga+//poyZcpQqVIl9u/fb7Kdq6uryWu6ceNGlixZYnRNzcmI4w8NDaVBgwbcunWL/v37U7p0aYKDg9m9ezdRUVHY2dml4CwIIdKdEkKITHb16lUFqOnTp2dpHHq9XoWHh2fqPidMmKBy+q/eBg0aqIoVKybb7tChQwpQCxYsyNB4goOD1enTpxMt79u3rwLUxYsXk+2jbdu2yt7eXsXGxibZLjIyUt25c0cplbbje/fdd5VGo1E3b95Mt5hMuXLlirp27ZrRMr1erxo3bqysra3V06dPDct37NihALVixYpU70cppb7++msFqICAAMOyc+fOKQsLCzVmzBijtteuXVN6vV4ppZS9vb3q3bu3yT6XLVuWKKb79++rvHnzqp49e6YpTnPCwsJUSEiIUkqpFStWKEDt2LEjxds3adJEOTk5qYiIiGTbZsTxDxo0SOXNm1dduXIlxTELITKeTBcUQmRbUVFRTJgwgdKlS2NtbU3RokUZOXIkUVFRRu0WLFhA48aNKViwINbW1rz22mvMnj07UX8lSpSgbdu2bNq0iWrVqmFra8uvv/5qmC61fPlypkyZQpEiRbCxsaFJkyZcunQpUT8HDx6kZcuWODs7Y2dnR4MGDdi7d2+idnv27KF69erY2Njg6enJr7/+mqLj/umnn7CwsODx48eGZd999x0ajYbhw4cblul0OhwdHRk1apRhmV6v54cffqBixYrY2Njg5ubGgAEDePTokdE+TN2Tdf36ddq1a4e9vT0FCxbk448/ZtOmTWanT509e5ZGjRphZ2eHh4cH33zzjWGdv7+/YVSkb9++hilV8VPrLl68SOfOnSlUqBA2NjYUKVKEHj16EBoaaujjwYMHnD9/PtkpTy4uLlSsWDHR8o4dOwJw7ty5JLeHuPdGeHg40dHRSbaztramUKFCyfZnSlRUFKtWraJBgwYUKVIk3WIypWTJkhQvXtxomUajoUOHDkRFRZmcygdx085iY2NTta+VK1dSvXp1o1Gw8uXL06RJE5YvX27Utnjx4im6J3HlypW4ubnRqVMnwzJXV1e6devG2rVrE/0OeBmOjo7kz58/TdveuXOHHTt20KlTJ2xsbJJtn97H//jxYxYsWED//v0pWbIk0dHR6XpuhBBpJ0mWECJb0uv1tGvXjm+//RYfHx9+/vlnOnTowPfff0/37t2N2s6ePZvixYvz2Wef8d1331G0aFEGDx7MzJkzE/V74cIFevbsSbNmzfjxxx+NprF99dVXrFmzhk8//ZQxY8Zw4MAB3n77baPtt2/fTv369QkLC2PChAlMnTqVx48f07hxYwICAgztTp06RfPmzbl//z4TJ06kb9++TJgwgTVr1iR77PXq1UOv17Nnzx7Dst27d6PVatm9e7dh2bFjx3j69Cn169c3LBswYAAjRoygTp06/Pjjj/Tt25clS5bQokULYmJizO7z2bNnNG7cmK1bt/LRRx8xduxY9u3bZ5TAJfTo0SNatmxJlSpV+O677yhfvjyjRo3if//7HwAVKlTgiy++AKB///4sXryYxYsXU79+faKjo2nRogUHDhzgww8/ZObMmfTv358rV64YJZa//PILFSpUMDqvqXH37l0gLgl7UUREBA8ePODatWssWrSIBQsWUKtWLWxtbdO0r5T4999/efz4caL3VGbGlNQ56du3L05OTtjY2NCoUSMOHz6cbH96vZ6TJ09SrVq1ROu8vb25fPkyT548SXWcx44d480330SrNf6Y4u3tTXh4OIGBganuMyMsXboUvV5v9pqmVUqPf8+ePURGRlK6dGm6dOmCnZ0dtra21KlTJ8PvhRRCJCOrh9KEELlPSqYLLl68WGm1WrV7926j5XPmzFGA2rt3r2GZqSl/LVq0UKVKlTJaVrx4cQWojRs3Gi2Pny5VoUIFFRUVZVj+448/KkCdOnVKKRU33apMmTKqRYsWhik/8fsvWbKkatasmWFZhw4dlI2Njbp+/bph2dmzZ5WFhUWy0wV1Op1ycnJSI0eONOy3QIECqmvXrsrCwkI9efJEKaXUjBkzlFarVY8ePVJKKbV7924FqCVLlhj1t3HjxkTLGzRooBo0aGD493fffacA9ffffxuWRUREqPLlyyeaPtWgQQMFqD/++MOwLCoqShUqVEh17tzZsMzcdLpjx46laHpa/NTK1EzdShjPa6+9pkqWLKliYmISrZ82bZoCDK8mTZqoGzdupGofqZ0u2LlzZ2VtbW24XhkRU1JCQkJUwYIFVb169YyW7927V3Xu3Fn9/vvvau3atWratGmqQIECysbGRh09ejTJPoODgxWgvvjii0TrZs6cqQB1/vx5k9smNV3O3t5e9evXL9HyDRs2mPwZTi+pnS7o5eWlChcurHQ6Xar3lR7HP2PGDAWoAgUKKG9vb7VkyRI1a9Ys5ebmpvLly6eCgoJSHZcQIn3ISJYQIltasWIFFSpUoHz58jx48MDwaty4MQA7duwwtE34TX9oaCgPHjygQYMGXLlyxWj6GcRNo2rRooXJffbt2xcrKyvDv+vVqwdgmFp1/PhxLl68yFtvvUVISIghpmfPntGkSRN27dqFXq9Hp9OxadMmOnToQLFixQz9VahQwey+E9JqtdSuXZtdu3YBcdPdQkJCGD16NEopw435u3fv5vXXXydv3ryGc+bs7EyzZs2MzpmXlxcODg5G5+xFGzduxMPDg3bt2hmW2djYmK2Y5uDggK+vr+HfVlZWeHt7m52GlpCzszMAmzZtSnIq4MSJE1FKpanU/AcffMDZs2f55ZdfyJMncY2nnj17smXLFv7880/eeustIG4kKaOEhYWxYcMGWrdubbhemRlT/GjL48eP+fnnn43W1a5dm5UrV9KvXz/atWvH6NGjOXDgABqNhjFjxiTZb3x81tbWidbFT59LyzFERESke5/pLTAwkCNHjtCjR49EI04vK6XH//TpUyBuKui2bdt46623GDRoEH///TePHj0yOZovhMgcUl1QCJEtXbx4kXPnzuHq6mpy/f379w3/v3fvXiZMmMD+/fsTfWgPDQ01fKiHuCTLnIQJEUC+fPkADPczXbx4EYDevXub7SM0NJSoqCgiIiJMVkArV64c//77r9nt49WrV4+JEycSERHB7t27KVy4MG+++SZVqlRh9+7dNGvWjD179tCtWzfDNhcvXiQ0NJSCBQua7DPhOXvR9evX8fT0THS/iLnqckWKFEnUNl++fJw8eTLZYytZsiTDhw9nxowZLFmyhHr16tGuXTt8fX2NrlVaTZ8+nXnz5jF58mRat25tsk3x4sUN9yz17NmT/v3707RpUy5cuJAhUwZXrVpFZGRkktPKMjKmDz/8kI0bN/LHH39QpUqVZNuXLl2a9u3bs3r1anQ6HRYWFibbxcdl6j6gyMhIozapYWtrm659RkdH8/DhQ6Nlrq6uZo8rJZYsWQKQ7lMFIeXHH/9fHx8fHBwcDO1q1qxJyZIl2bdvX7rHJoRIGUmyhBDZkl6vp1KlSsyYMcPk+qJFiwJw+fJlmjRpQvny5ZkxYwZFixbFysqKf//9l++//x69Xm+0XVIfzsx94FLPn1UU39f06dPNliR3cHBIlxvP69atS0xMDPv372f37t2GUbV69eqxe/duzp8/T3BwsGF5fHwFCxY0fPh7kbmENS2SO1fJ+e677+jTpw9r165l8+bNfPTRR0ybNo0DBw6kqCiEOQsXLmTUqFEMHDiQcePGpXi7Ll26MG/ePHbt2pWi0cbUWrJkCc7OzrRt2zbTY5o0aRKzZs3iq6++4p133knxdkWLFiU6Oppnz57h5ORksk3+/PmxtrY2WY48fpm7u3uqYy5cuHC69rlv3z4aNWpktOzq1auUKFEi1bHF+/PPPylXrhxeXl5p7sOclB5//H/d3NwStS1YsGCigjdCiMwjSZYQIlvy9PTkxIkTNGnSJMlqXOvXrycqKop169YZjUQlNTXuZWICcHJyomnTpmbbubq6Ymtraxj5SujChQsp2pe3tzdWVlbs3r2b3bt3M2LECADq16/PvHnz2LZtm+HfCePbunUrderUSfU3/cWLF+fs2bMopYzOt6nqiimVXBW1SpUqUalSJcaNG8e+ffuoU6cOc+bM4csvv0zT/tauXct7771Hp06dUj1NKn761YvTS9NDfAW6Pn36mJwClpExzZw5k4kTJzJs2DCzRUzMuXLlCjY2NkYjJC/SarVUqlTJZJGMgwcPUqpUKRwdHVMdd9WqVdm9ezd6vd5oKt7Bgwexs7OjbNmyqeqvSpUqbNmyxWhZWqtExsdx6dIlQ3GX9JbS449P8G7fvp2oj6CgIMqXL58h8Qkhkif3ZAkhsqVu3bpx+/Zt5s2bl2hdREQEz549A/4bUUk4ghIaGsqCBQvSPSYvLy88PT359ttvDfdCJBQcHGyIqUWLFvz999/cuHHDsP7cuXNs2rQpRfuysbGhevXq/PXXX9y4ccNoJCsiIoKffvoJT09PChcubNimW7du6HQ6Jk+enKi/2NhYo8p9L2rRogW3b99m3bp1hmWRkZEmz39K2dvbAyTab1hYWKIy4ZUqVUKr1RqNAqa0hDvArl276NGjB/Xr12fJkiVm75GJv0Yv+v3339FoNLz55ptp2n9SkqtAl5qYUmPZsmV89NFHvP3222ZHhM3t/8SJE6xbt47mzZsbncsbN25w/vx5o7ZdunTh0KFDRonWhQsX2L59O127dk1T7F26dOHevXusXr3asOzBgwesWLECHx+fVCWrEDeVtWnTpkavlJRcN+fPP/8EMNw796Lw8HDOnz/PgwcP0tR/So+/XLlyVKlShbVr1xrta/Pmzdy8eZNmzZqlaf9CiJcnI1lCiCyzbds2wz0GCXXo0IF33nmH5cuXM3DgQHbs2EGdOnXQ6XScP3+e5cuXG5511bx5c6ysrPDx8WHAgAE8ffqUefPmUbBgQZPTbV6GVqvlt99+o1WrVlSsWJG+ffvi4eHB7du32bFjB05OTqxfvx6Im6K1ceNG6tWrx+DBg4mNjeXnn3+mYsWKKbpvCeISqq+++gpnZ2cqVaoExE0BKleuHBcuXKBPnz5G7Rs0aMCAAQOYNm0ax48fp3nz5lhaWnLx4kVWrFjBjz/+SJcuXUzua8CAAfzyyy/07NmToUOHUrhwYZYsWWL4IJqSZ/u8yNPTk7x58zJnzhwcHR2xt7enRo0anDhxgg8++ICuXbtStmxZYmNjWbx4MRYWFnTu3Nmw/S+//MKkSZPYsWNHksUv4p/vpdFo6NKlCytWrDBaX7lyZSpXrgzAlClT2Lt3Ly1btqRYsWI8fPiQVatWcejQIT788EOje9DM7f+XX37h8ePHBAUFAXGjqbdu3QLi7n168b6yJUuW4O7ubvYYUhOTv78/jRo1YsKECUycONHsOQkICKBXr14UKFCAJk2aJJpCWrt2bUqVKgVA9+7dsbW1pXbt2hQsWJCzZ88yd+5c7Ozs+Oqrr4y269WrFzt37jT6UmPw4MHMmzePNm3a8Omnn2JpacmMGTNwc3Pjk08+Mdp+/fr1nDhxAoCYmBhOnjxpGLls166d4Tp16dKFmjVr0rdvX86ePYuLiwuzZs1Cp9MxadIkoz779OnDokWLXmr6X3wMZ86cAWDx4sWGRyi8OO1Up9OxbNkyatasaRjdflFAQIDJ65QRx//999/TrFkz6taty4ABAwgNDWXGjBmULVuWQYMGpel8CCHSQRZWNhRC5FLxJdzNvRYvXqyUUio6Olp9/fXXqmLFisra2lrly5dPeXl5qUmTJqnQ0FBDf+vWrVOVK1dWNjY2qkSJEurrr79W8+fPV4C6evWqoV3x4sVVmzZtEsUTX8L9xZLi8XGaKkHeqVMnVaBAAWVtba2KFy+uunXrprZt22bUbufOncrLy0tZWVmpUqVKqTlz5hjKkqdEfLnmVq1aGS1/7733FKB+//13k9vNnTtXeXl5KVtbW+Xo6KgqVaqkRo4caVTO+cUS7kopdeXKFdWmTRtla2urXF1d1SeffKJWrVqlAHXgwAGjbStWrJhov71791bFixc3WrZ27Vr12muvqTx58hjO5ZUrV1S/fv2Up6ensrGxUfnz51eNGjVSW7duNdo2pSXc46+fudeECRMMbTdv3qzatm2r3N3dlaWlpXJ0dFR16tRRCxYsMCrLn9T+4x8FYOqV8P2mlFLnz59XgBo+fLjZ+FMT0/r16xWg5syZk+Q5WbBgQZLnJOF7+scff1Te3t4qf/78Kk+ePKpw4cLK19dXXbx4MVG/8eX7X3Tz5k3VpUsX5eTkpBwcHFTbtm1Nbt+7d+8UxaSUUg8fPlTvvvuuKlCggLKzs1MNGjRQhw4dStRn586dla2trdnS+CmR1Ll6UfwjEX766Sez/cW/JxO+95TKmONXSqktW7aomjVrGn6e3nnnHXXnzp1UnwchRPrRKJXCu5SFEELkOj/88AMff/wxt27dwsPDI6vDyfVGjhzJX3/9xaVLl1I9Ze5V5ebmRq9evZg+fXpWhyKEEAaSZAkhhADi7nVLWDAjMjKSN954A51OR2BgYBZGJuJVr16d999/n/79+2d1KNnCmTNnqFWrFleuXMHFxSWrwxFCCANJsoQQQgDQqlUrihUrRtWqVQkNDcXPz48zZ86wZMkSszf4CyGEECIxKXwhhBACiKsw+Ntvv7FkyRJ0Oh2vvfYaS5cupXv37lkdmhBCCJGjyEiWEEIIIYQQQqQjeU6WEEIIIYQQQqQjSbKEEEIIIYQQIh3JPVnJ0Ov1BAUF4ejomKaHcQohhBBCCCFeDUopnjx5gru7O1qt+fEqSbKSERQURNGiRbM6DCGEEEIIIUQ2cfPmTYoUKWJ2vSRZyXB0dARg/vyb2Nk5ZXE0QgghhBBCiKwSHh5Gv35FDTmCOZJkJSN+iqCdnZMkWUIIIYQQQohkbyOSwhdCCCGEEEIIkY4kyRJCCCGEEEKIdCRJlhBCCCGEEEKkI7knSwghhBBCiGxBodXGotHokCcHZT6lQCkL9Po8wMtdAEmyUmjZMqhYEby9szoSIYQQQgjxqtFqo3FyuoOtbbgkWFlIKYiIsCMsrDB6vVWa+5EkK6XuBOEf7ERgIPj6ZnUwQgghhBDi1aHHxeUqDg4W5MvnTp48VrzsSIpIC0VsbDSPHgVjZXWV+/fLkNa7qyTJSqHuLOM09QkMKoufnwdly8qolhBCCCGEeHkWFtHkyaPHxaUo1tZ2WR1OrmZlZYuFhSWRkdexsIhGp7NJUz9S+CKFWtV7xmT3OfjiB0G38fcHPz8ICMjqyIQQQgghRE4WPz1Qo0mfj+YaDWi1yLTDNIq/Di9z/mQkK6W8vKBePXwCAsDfjwC8CQwqi3+QByCjWkIIIYQQIutoNGBpCdYWMVhYWxqW66JiiNJZEhMTd7+RyBySZKWWtzc+3uDjN4f1QW8SgDf+/o0IDESmEAohhBBCiEyXJw/Y28TG/WPVali5Eh49gnz50Hbpgm3nzthawbPIPMTGZm2suYVMF0wrX198Gj5lsvscGrLDaAqhEEIIIYQQmSFPHrC308PmzWiKFEHTo0dckrVtG6xciaZHDzRFisDmzdjb6cmTRUMs+fJp2LDh76zZeRaQJOtleHuDr6/hXq34ZEvu1RJCCCGEEBlNo3k+grVxI5p27eDePdMN792LW79xI/Y2sel+r9a9e3cZOfJDqlYthZubNRUrFqVHDx927tyWvjtKI6UUU6d+TvnyhSlc2JYOHZpy+fLFDN2nJFnpIcGolhTGEEIIIYQQmcHy+a1Xmn79QKdLurFOh+bdd422Sw83blyjUSMvdu/ezhdfTGfv3lOsXLmRevUaMWLEkPTb0Uv48cdv+PXXn5gxYw5bthzEzs6ezp1bEBkZmWH7lCQrvTwf1fJp+NRoVMvfXxItIYQQQgiR/qwtYmDVKvMjWC+6exdWr47bLp188slgNBoNW7cG0K5dZ0qXLkuFChUZMmQ4W7YcMLvdhAmjqFatLO7udlStWoopU8YTE/NfXKdOncDHpxFFizpSrJgTDRt6cezYYQBu3LhOjx4+lCiRDw8Pe2rVqsjmzf+a3I9SijlzfuDTT8fRunV7Xn+9MrNn/8Hdu0EZOn1RCl+kNymMIYQQQggh0kHevMm1sIy7/yoVNCtXYtGtG3mtTa9//DjlfT169JBt2zYybtwU7O3tE613ds5rdltHR0dmzlxI4cLunDlzimHD3sfBwZGhQ0cC0L//21Su/AbffTcbCwsLTp06Tp48cUNwI0YMISYmmg0bdmFvb8/582ext3cwuZ/r169y795dGjZsmiAuZ7y8anDo0H46d+6R8gNOBUmyMoqvLz4BAfgEzmF8EIZy74GB4Oub1cEJIYQQQohXwqNHGds+CVeuXEIpRdmy5VO97aefjjP8f7FiJbh06VNWr15qSLJu377BRx+NMPTt6VnG0P7WrRu0a9eZihUrAVCiRCmz+7l37y4Arq5uRssLFnTj/v27qY47pSTJykje3uDtzeQEo1qBQWXx8/OQUS0hhBBCCJGkpEaVNBpwdgby5Utdp8/bh4a+/HOz1Et0sHr1Mn799SeuXbvMs2dPiY2NxdHRybB+8ODhfPTReyxbtpgGDZrSoUNXSpb0BGDAgI/45JNBbN++mYYNm+Lj05nXX6/8cgeTzuSerMwghTGEEEIIIUQ6UiruQcOqS5fUbdelS9x26fBgYk/PMmg0GgIDz6dqu4CA/fTv/zbNmrVm6dJ/2LnzGJ98Mpbo6GhDm9GjJ7J//xmaN2/D7t3bqVnzNf75Zw0AvXq9x7FjV+je/R3Onj1F48bVmDv3Z5P7cnMrBEBwsPF9a/fv36NgwUKpijs1JMnKLFIYQwghhBBCpKMonSV07gxubsk3BihUCDp1itsuHeTLl5/GjVvw++8zefbsWaL1oaGPTW4XELCPokWL8+mnY3njjWp4epbh5s3ridqVLl2WwYM/ZvXqzbRt24klSxYY1hUpUpR+/QayePFqhgz5hEWL5pncV/HiJXFzK2RUTj4sLIwjRw5SvXqtVB5xykmSldm8vfEZ+ZrRs7VkVEsIIYQQQqRWfDE+NX8+WFgk3djCAvX770bbpYdvv52JTqejaVNv1q1bxeXLF7lw4Ry//voTzZubTmJKlSrDrVs3WLVqKVevXubXX38yjFIBREREMGLEB+zZ48+NG9c5cGAvx44domzZCgCMGTOMbds2cf36VU6cOMqePTsoV66CyX1pNBoGDhzGt99+yb//ruPMmVMMGtSLQoXcadOmQ/qdiBfIPVlZRQpjCCGEEEKIl6AUPIvMg33Llqh16+Keg3XXRDGHQoXiEqyWLXkWrk2XqYLxSpQohb//Ub77bgrjxn3CvXt3cHFxpUoVL777brbJbVq3bsegQR8zcuQHREdH0axZG0aMGM9XX00EwMLCgocPQxg4sBfBwfcoUMCFtm07MWbMJAB0Oh0jRgwhKOgWjo5ONGnSkqlTvzcb49ChIwkPf8bHH/cnNPQxNWvWZeXKjdjY2KTfiXiBRr3MHWu5QFhYGM7OzoQuXYqTnV3G7MTP77/CGJQFdymMIYQQQgiRW+TJE0nBglcpWrQkVlap/+CfJw/Y28TG/WP1ajQrV8ZVEcyXL+6erU6dgLiELDY2PSN/NUVHR3Lz5lXu3y9JbKzx9QgPD6NHD2dCQ0NxcnIy04OMZGUPCUa11ge9iV+Qr2FUq2xZ05tIAiaEEEIIIQBiYyHsWR4sLcG6fUcsunUzrNNHxRAVnYcENSVEJpAkK7t4Xu7dJyAA/P0M5d4DgxI3DcLDsIkQQgghMp5SimvXThIW9gBX12K4u5dJfqNsTKfTcfnyESIinuLhURYXlyJp6uf27UAePLiJk5MrJUpUQqPRpHOkIqWUguhoiMYSTWRciXelQKn0KXIhUidHJVm7du1i+vTpHDlyhDt37rBmzRo6dOiQ5Db+/v4MHz6cM2fOULRoUcaNG0efPn0yJd408fbGxxt8/OaYXB0/rdB/VWUCAwvItEIhhBAigx069A8LFozh1q3ThmXlytWlf/8ZlClTPQsjS5tNm+bx119f8vDhjedLNHh5tWHAgB8pVMj8Q10TOn/+AL/99gmBgfsMy4oWrUy/fl/h5dUqA6IWqRGXXGV1FLlbjqou+OzZM6pUqcLMmTNT1P7q1au0adOGRo0acfz4cYYNG8Z7773Hpk2bMjjSdODra/IV/7ythCXg/fyyOlghhBDi1bRnzwomT27H7dvuwCbgMrCCixfDGT26IYGBOas08MqVXzFzZn8ePqwH7AEuAfM4duwMn35ah+DgG8n0AOfP7+ezzxpx6VI0sJK4c/I/bt1y5Ysv2nLgwN8ZeQhC5Ag5tvCFRqNJdiRr1KhRbNiwgdOn//vmqUePHjx+/JiNGzemaD+ZUvgiLQICWO/vIMUyhBBCiAwSGxtDnz7FCAurAyzH+LvpCLTaepQpY8v06buzKMLUefToLn37FkOvHw589cLa+2i1b9C4cUs++uj3JPv5+OMaXLkCSu0EEhYF0AGdyJfvKPPnX8XCIkdNmMpSL1v4QqSv9Ch8kaNGslJr//79NG3a1GhZixYt2L9/fxZFlI68vQ2jWr74wamT8rwtIYQQIh0dOfI/wsLuAhNJ/JHJFr1+NBcu7OHWrQuZH1wa+Pv7oVQeYLSJtQXR64fg7/8nUVHhZvu4fv00ly8HoNRnGCdYABbABB49usXx41vSL3AhcqBXOsm6e/cubi88AdvNzY2wsDAiIiJMbhMVFUVYWJjRK9vy9jZMIVxWaYrRFEJJtIQQQoiXExx8HY3GGnjdTItqhnY5wf3719FqSwN5zbSoRmxsJKGhwWb7+G86YTUzLd4ANNy/nzPOyatKowGtNu6/Imu80klWWkybNg1nZ2fDq2jRolkdUvKeJ1vxo1oN2YH/qhAZ1RJCCCFegpOTK0pFAebuU7pkaJcTODm5oNRNINJMi0toNFocHPIl2Ueci2ZaXAVUgnYis2g0YGUFtrbg7AxOTnH/tbWNWy4JV+Z6pZOsQoUKce/ePaNl9+7dw8nJCVtbW5PbjBkzhtDQUMPr5s2bmRFq+pDCGEIIIUS6qV69LdbWjsB3Jtbq0Wi+w939NUqVqprJkaVNgwZvodc/BkzdcxWBVvsL1au3w87O/H0mpUtXw82tDHHnxNRt/d9ia5uXatVap0vMImXy5AF7+7hk6u+/oWtXaNo07r9//x233N4+rp3IHK90klWrVi22bdtmtGzLli3UqlXL7DbW1tY4OTkZvXKU+FGtzicMo1oE3ZZRLSGEECKVbG0d6NlzPPATMAyI/+L1LNADpTbSp8/UHPNsKHf3MjRr9h4azTBgKhBCXKK0H42mFRYW1+jZ8/Mk+9BqtfTtOw34B3gLOP98zQ3gA2A2b701AWvrbFQs7BWXJw/Y2cHmzVCkCPToAStXwrZtcf/t0SNu+ebNce2yKtHKl0/Dhg1/Z83Os0COSrKePn3K8ePHOX78OBBXov348ePcuBE3jD9mzBh69eplaD9w4ECuXLnCyJEjOX/+PLNmzWL58uV8/PHHWRF+5pLCGEIIIcRL69jxU/r0+QZr6/lAMTQaW6Aijo67+PTTv6hZs31Wh5gqgwfPpm3bIVhYTAJcnx9PbVxcbjFp0kY8Pd9Ito/atTszfLgfDg7bgArP+yiOtfVi3n13Bu3aDc3goxDxNBqwsYGNG6FdO3hhApfBvXtx6zdujGuf3t8L3Lt3l5EjP6Rq1VK4uVlTsWJRevTwYefObclvnAnWr19Np07NKVWqAPnyaTh16niG7zNHlXD39/enUaNGiZb37t2bhQsX0qdPH65du4a/v7/RNh9//DFnz56lSJEijB8/PlUPI862JdxTIyAAAgMZHzSQQMoShAcNG0q5dyGEECKlwsOfcOjQekJDgylYsDheXq2xtLTK6rDSLDQ0mEOH/iEi4glFilSgSpUmaLWp++49JiaKw4f/JTj4Bs7OBalRox02NvYZFPGrLa0l3K2s4l5FiphPsBIqVAhu3oTo6LhXerhx4xotW9bB2TkvY8Z8wWuvVSImJobt2zexaNFcAgLiRjvz5dPg57eGNm06pM+OU2Hp0sXcuHGVQoXcGTr0fXbtOkalSlXNtk+PEu45KsnKCq9EkhXPz4/1QW8SgDf+IZVxr1RAnq0lhBBCCJHF0ppk2drG3XPVo0fK97VsGbRvD2YKbada166tOXv2JAEBF7C3N06yQ0Mf4+ycF0icZE2YMIoNG9YQFHSLggUL0bXr24wc+TmWlpYAnDp1gs8+G8bx44fRaDSUKlWG77//lTfeqMaNG9cZOfIDDhzYQ0xMNMWKlWDSpOk0b570vYA3blyjSpWSmZJkye1vuYmvLz4BAfgEzmE9b+IX5It/kAeBgeDrm9XBCSGEEEKIhPLmTb7NypWp63PlSujWDaytTa9//DjlfT169JBt2zYybtyURAkWYEiwTHF0dGTmzIUULuzOmTOnGDbsfRwcHBk6dCQA/fu/TeXKb/Ddd7OxsLDg1Knj5MkTl4CNGDGEmJhoNmzYhb29PefPn8Xe3iHlgWcCSbJyG2/vuPu1AgLA348AvAkMKoufn4eMagkhhBBC5DCPHmVs+6RcuXIJpRRly5ZP9baffjrO8P/FipXg0qVPWb16qSHJun37Bh99NMLQt6dnGUP7W7du0K5dZypWrARAiRKlXuYwMoQkWbmVtzc+PB/VCnoTv1Ot8A+qTGAgkmwJIYQQQmQDSY0qaTRxz8HKZ/6xZibFtw8NhZe9aehl7jpavXoZv/76E9euXebZs6fExsbi6Pjf9LvBg4fz0UfvsWzZYho0aEqHDl0pWdITgAEDPuKTTwaxfftmGjZsio9PZ15/vfLLHUw6y1HVBUU6e17u3afhU5ZVmmL0bC2pQCiEEEIIkX0pBVFR0KVL6rbr0iVuu/SoyuDpWQaNRkNg4PnkGycQELCf/v3fplmz1ixd+g87dx7jk0/GEp2gGsfo0RPZv/8MzZu3Yffu7dSs+Rr//LMGgF693uPYsSt07/4OZ8+eonHjasyd+/PLH1A6kiRL/Pdsrefl3huyA/9VIVLuXQghhBAiG9PpoHNncHNLWftChaBTp7jt0kO+fPlp3LgFv/8+k2fPniVaHxr62OR2AQH7KFq0OJ9+OpY33qiGp2cZbt68nqhd6dJlGTz4Y1av3kzbtp1YsmSBYV2RIkXp128gixevZsiQT1i0aF76HFQ6kSRL/Of5qNZk9zmMLPCbYVTLzy+rAxNCCCGEEC+KiYn77/z5YGGRdFsLC/j9d+Pt0sO3385Ep9PRtKk369at4vLli1y4cI5ff/2J5s1rmdymVKky3Lp1g1WrlnL16mV+/fUnwygVQEREBCNGfMCePf7cuHGdAwf2cuzYIcqWrQDAmDHD2LZtE9evX+XEiaPs2bODcuUqmI3x0aOHnDp1nPPnzwJw8eIFTp06zr17d9PvRLxAkixhLMEUwvhRLYJuy6iWEEIIIUQ2oxRERkLLlrBuXdxIlSmFCsWtb9kyrn16PsCpRIlS+PsfpW7dRowb9wm1a79Op07N2LlzG999N9vkNq1bt2PQoI8ZOfID6tevysGD+xgxYrxhvYWFBQ8fhjBwYC+qVy9Lv37daNq0FWPGTAJAp9MxYsQQatSoQJcuLfH0LMu3384yG+P//reO+vXfoHv3NgC8+24P6td/gwUL5qTfiXiBPCcrGa/Uc7JS6/lDjNcHvYlfSCuCClTG3V0KYwghhBBCpKe0Pifrv+3B5vlmq1fHlWl/9CiuyEWXLnFTBCEuwYqNTcfAX1HynCyRsRKUe/cJnML4oIEEBpXFP8jDsFoIIYQQQmSt2Fh49gwsLeMeNNyt23/roqIgOjruJTKPJFkiec+Trcl+ceXeA/DGf1VlAgMLyKiWEEIIIUQ2oNR/iVRkZFyJd6XSd2qgSDm5J0uknBTGEEIIIYTI9pQCvV4SrKwkSZZIHSmMIYQQQgghRJIkyRJp4+1tGNXyxQ9OnTSMakmyJYQQQgiRcvEjTlKPLnuIvw4vczkkyRJpl2BUa1mlKYZRLX9/SbSEEEIIIVJKr7dEr4fo6PCsDkUQdx30+rjrklZS+EK8PCmMIYQQQgiRZkpZ8PRpXh48uA+AlZUdGo0mi6PKfZRSREeH8+DBfZ4+zYtSyTzhOQmSZIn04+v7vNz7HNbzJn5BvvgHeRAYCL6+WR2cEEJkb7dunWfTpnncunUeW1sHatfuTM2aHcmTJ+3fpAp4/Pg+CxaM4PjxLej1OooUKU/fvt9Stmz1rA5NCCNPn8Y9STg29j5amWuWZfR6ePo0r+F6pJU8jDgZufphxC8jIID1/g4E4E0gZcHdQ0a1hBDCjGXLvmTJkvFotS7o9XXQau+g1wdQtGglvvhiIwUKuGd1iDnS/v1r+OqrHigVA9QFnIDtQCQtWvRnyJA5WRugECZoNDq02hhkICvzxVVltExyBCulDyOWJCsZkmS9hIAACAxkfdCb+IW0IqhAZdzdkWRLCCES2LnzL7777i3gc+AzwPr5miNote0pUcKd778/KFOHUunx4/v07l0UpcoAawHP52vCgKHAIoYMmUOLFv2zLEYhRM6T0iRLBiNFxpHCGEIIkSSlFCtXfoNG0wqYxH8JFoAXev1Crlw5xOnTO7MowpxrwYIRz0ewEiZYEDea9RtQmr/+mpwlsQkhXn2SZImM9zzZii/33pAd+K8KkXLvQohc7/Hje1y/fhyl+php0QQLCw8OH/43M8N6JZw4sRWog3GCFc8C6MvDh0GZG5QQIteQJEtknuejWpPd5zCywG+GUS0/v6wOTAghskZsbPTz/3M000IDOCRoJ1JKp9MRN2pljgMgd0wIITKGJFkicyWYQhg/qkXQbRnVEkLkSvnzu+PkVAhYb6bFBXS6C5QuXS0zw3olFClSHthB3D1YpqzF2jqpJEwIIdJOkiyRNby9DaNavvjBqZOGUS1JtoQQuYWFRR5atx6ARjMf8H9hbTgazRAcHQtSp06XLIguZ3v33W+BSOBDQPfCWj9gG40bv5XpcQkhcgdJskTWkcIYQghB165jeP31emg0TYEuwExgHFptWSwtDzB69DKsrGyyOMqcp3TparRqNRBYDJQDpgA/AU2AdyhUqAwDBvySlSEKIV5hUsI9GVLCPRP5+bE+6E0C8MY/pDLulQpIuXchRK4QExPN5s3z2LDhV4KCzmFt7UC9el3o0OGT59PeRFpt2fI7f/45iZCQIECPtbUTjRu/xYABv6CVJ74KIVJJnpOVTiTJymQJn62FL0F44O4Ovr5ZHZgQQgghhMjt5DlZImeSwhhCCCGEECKHkyRLZE9SGEMIIYQQQuRQebI6ACHM8vaOS7YCAvAJnML4oIEEBpXFP8gjyU2EEEIIIYTISpJkiezvebI12W+OoTBGoH9ZcE+cbAUGIsUyhBBCCCFElpIkS+Qcvr7PR7Xiki1InGT5BTXCP8hDki0hhBBCCJFlJMkSOUuCKYRwIfH6oNtxI10JphVKoiWEEEIIITKTJFkiZzKTOfl4g0+CaYX+/o1kVEsIIYQQQmQqqS4oXj3PS8BPdp9jKAEfX5lQCCGEEEKIjCZJlng1PX/eVnwJeHnelhBCCCGEyCySZIlXW4JRLV/8jEa1JNkSQgghhBAZQe7JEq++hMUy/P2kMIYQQgghhMhQkmSJ3MPbWwpjCCGEEEKIDCfTBUXuI4UxhBBCCCFEBpIkS+ROUhhDCCGEEEJkEEmyRO4mhTGEEEIIIUQ6k3uyhJDCGEKIbCIi4ikhIbewsXHAxaVImvrQ6XTcu3cVULi5lcTCIm1/6kNCbhMR8YQCBYpga+uQpj6ePn3Eo0d3cXQsQN68BdPUR3by+PF9njwJIV++wjg45M2yOHS62OfXWPP8GltkWSyv2jUWGSM6OpL7969jaWlNwYLF0Wg0WR1ShpMkS4h4UhhDCJFFQkOD8fMbx/btfsTEhANQunQNevYcT/XqbVLUh06nY+3aGaxd+zOPHt0EIF++IrRr9yEdOnyS4g/ihw//y59/TubSpQMAWFra0ajR2/j6fpniD9FBQRdZvHgc+/evRq+PBaBKlRb4+k6iXLkaKeojO7lw4SB+fp9z4sRmALTaPNSu3Rlf3y9xdy+daXHodLH8/fd3rF37M48f3wYgf/5itG//Ie3afZypydbt24H88cdYDhxYg1I6AKpWbck770ymTJlqmRaHyN4iIp7y118T2bTpdyIiHgNQpMjrdOs2moYN387a4DKYRimlsjqI7CwsLAxnZ2dCly7Fyc4uq8MRmSUgAAIDGR80kEDKEoQH7u7g65vVgQkhXjVhYQ/45JPaBAc/RK//CGgI3EWjmY1S/nz00XyaNu2bZB9KKb777h127foL6AN0f75mORrNQurU6cqnny5Bq036LoHt2//ghx/6oNHUR6lBgDvgj1b7Ey4uefn2273JJlq3bp1nxIi6REQ4otcPA94ELqLV/oRGc55Jk/5H5cqNkj0v2cWJE9uZOLE1SpVHrx8KlAaOotX+gK3tE6ZP30ORIuUzPA69Xs+3377N3r0rUKov0A1QwDJgIQ0avMXw4X9kygjBjRtnGTmyHpGRzs+v8RtAIFrtj2i1F/nii028/nr9DI9DZG+Rkc/47LMmXLlyBr1+MNAKCEWjmY9S6/D1nUK3bp9ldZipFh4eRo8ezoSGhuLk5GS2nSRZyZAkK5fz8zOMagVSFtw9ZFRLCJGu5sz5gI0b/0KvDwA8E6xRwHtYWi5l0aLbSU5PO3RoA5MntwX+Anq8sHYF0I2xY9dSo0Y7s308exZK794eREd3AeZjfNv2FbRab5o378rgwbOTPJ5x45pz+vQN9Pr9QL4Ea6LQaFrh4nKdefMuJpvwZQc6nY733y9DSEhJlPoXsE6w9iFabS0qVSrB5MmbMjyWAwfWMnVqB+KuZ5cX1v4FvMXnn2+gWrXWGR7LmDGNOXfuLnr9PiBvgjWRaDQtKFjwDr/+ej5HXGORcVasmIaf3ySU2gt4vbB2PDCFX3+9SOHCnia2zr5SmmTJu1+IpEhhDCFEBoqOjmTbtj+ef8v74gcNDTCF2Nhodu5ckmQ/GzfORautRuIEC6ArWq03GzfOS7KPnTv/JCYmCphK4o8HpdDrh7B9ux9RUeFm+7h79yonT25Brx+HcYIFYI1SUwkOvsKJE9uSjCW7OHlyGw8eXEWpaRgnWAD50evHcuLEZu7du5bhsfzvf3PRamuQOMEC6IFW+yb/+9/cDI8jKOgiZ87sQK8fj3GCBWCDUlO4d+8ip0/vzPBYRPb2779zUeotEidYAGPQavOyZcvvmR1WppEkS4jkPC/37tPwqVG5d39/SbSEEC/n0aO7REU9AeqZaVEIC4tyBAUFJtnPjRsX0OvN9QF6fX1u3ky6j6CgQLTa0sRNETSlPtHRT3n06K7ZPu7cufj8/8zFUgONxjLZ48kubt8ORKOxAqqbaRE3JS4o6KKZ9enn1q3AJK6xBr2+PrduZfx5/e9YzcVSB9DkmGssMkZsbAwhIdcw/z6xQ6nqr/T7RJIsIVLK2xufka8ZPVtLRrWEEC/Dzi5+qsltMy1i0OvvYmtrfkoKgIODcxJ9ANzC3j7pPuzsnFHqPhBttg8AW1vHJPuIYy6W+ygVk+C4szc7OyeUigaCzbS4ZWiX0eztk7/GmRFH8tf4DqCSfc+KV5uFRR4sLW0x/z5RaLW3Xun3iSRZQqRWgimECUe1/PyyOjAhRE7j6JifSpWaoNXOAmJNtFiGXh9C3brdkuynfv1uaDR/AzdNrL2FRrOGBg2S7qNOnS7o9Q+Ju7/nRTq02llUrNgIZ2dXs32ULl2NAgWKAz+baTETS0tbqldvm2Qs2UX16m3Jk8cGmGmmxS+4uJSkdOmMr6bXoEFXNJrVmP7QegONZm2y1zg9lCtXg3z5imD+Gv+CpaVdptwbJrIvjUZD3bpd0Wp/A0xNMfZHpzub7O+2nEySLCHS4vkUwoSjWgTdllEtIUSq9ew5HqWOAT2Bq8+XRgEL0WoHULNmZ0qUqJRkH82a9SNvXje02mbAbuKKZihgD1ptc/LmdaVZs3eT7KN48depVasrWu1g4gpfRD1fcxV4C6UO07Pn+CT7sLCw4O23JwBLgWHA/edrwoCvgSm0bz8MB4cX79fKnhwd89O+/VDgS+Ab4MnzNfeBocAy3n7780wpnd6s2Xs4O7ug1TYH9vLfNd6FVtucfPkK07RpvwyPw8IiD2+99TmwBBjOf6N8YcTdz/cVnTp98nzkTeRmnTuPxMIiGI3GBzjzfGkssBqtthtlytTijTeaZ2GEGUuqCyZDqguKZD0v974+6E388DWUe5cqhEKIlNq3bxU//vgeERGhWFiURKkQ9PpQatfuxscfL8DaOvm/P0FBF5k8uSO3b5/BwqIwoEGnC8Ld/TXGj1+Dh0fZZPuIiorghx/6snfvMrRaJzQaF3S6q9jYODF06G/UqWOq6EJia9d+z6JFn6HT6bCwKIZefwelIvHxGUrfvtOz9OG5qaXT6Zg//1P++ecnNBobtNrC6HQ3sLCwoHfvabRvPyzTYrl16wJfftmRoKBzWFi4Awqd7g5FirzOuHGrcXcvk2mxrFnzHYsXj0WnU1hYFEWnCwKi6dDhY3r3/loqCwoATp/eyVdf9SAs7C4WFiVQ6gl6fQiVKjVl1KilODkVyOoQU01KuKcTSbJEigUEsN7fwVDuPQgPGjaUREsIkTKRkc/Yu3cFt26dx8bGgdq1O1O0aIVU9aHX6zlxYhtnzsRVdqtYsT5VqjRN9QfeW7fOs2/fKiIinlCkSHnq1OmKjY19qvp48uQhu3b9RXDwDZydXalXrwcuLkVS1Ud2Ehx8kz17lhEaGoyrazHq1++Jo2P+TI8j7hpv5fTpnWg0Gl5/vSGVKzfOkqQmLCyEXbv+4sGDmzg7F6R+/R4UKOCR6XGI7C0mJpqDB//m8uVjWFnZUL16W0qXNlVxMGeQJCudSJIlUi3Bs7X8aSSjWkIIIYQQr4hX9jlZM2fOpESJEtjY2FCjRg0CkrkB5ocffqBcuXLY2tpStGhRPv74YyIjIzMpWpErSWEMIYQQQohcLUclWcuWLWP48OFMmDCBo0ePUqVKFVq0aMH9+/dNtv/zzz8ZPXo0EyZM4Ny5c/z+++8sW7aMzz77LJMjF7mOFMYQQgghhMi1ctR0wRo1alC9enV++eUXIG5ectGiRfnwww8ZPXp0ovYffPAB586dY9u2/54s/8knn3Dw4EH27NmTon3KdEHx0qQwhhBCCCHEK+GVmy4YHR3NkSNHaNq0qWGZVquladOm7N+/3+Q2tWvX5siRI4YphVeuXOHff/+ldWvzz26IiooiLCzM6CXES3k+quXT8KnRqJa/v4xqCSGEEEK8ivJkdQAp9eDBA3Q6HW5ubkbL3dzcOH/+vMlt3nrrLR48eEDdunVRShEbG8vAgQOTnC44bdo0Jk2alK6xCwGAtzc+3uDjN+e/whj+jQgMlFEtIYQQQohXSY4ZyUoLf39/pk6dyqxZszh69CirV69mw4YNTJ482ew2Y8aMITQ01PC6efNmJkYscgUpjCGEEEII8UrLMSNZLi4uWFhYcO/ePaPl9+7do1ChQia3GT9+PO+88w7vvfceAJUqVeLZs2f079+fsWPHmnymhLW1NdbW1ul/AEIk5O0N3t5MTjCqFRhUFj8/DxnVEkIIIYTI4XLMSJaVlRVeXl5GRSz0ej3btm2jVq1aJrcJDw9PlEjFP2U+B9X7EK+yBKNavvgZjWrJ/VpCCCGEEDlTjhnJAhg+fDi9e/emWrVqeHt788MPP/Ds2TP69u0LQK9evfDw8GDatGkA+Pj4MGPGDN544w1q1KjBpUuXGD9+PD4+PoZkS4gs93xUyycgAPz9DKNa/kEehtVCCCGEECLnyFFJVvfu3QkODubzzz/n7t27VK1alY0bNxqKYdy4ccNo5GrcuHFoNBrGjRvH7du3cXV1xcfHhylTpmTVIQhhnhTGEEIIIYR4JeSo52RlBXlOlsgSz5+tNf5UVwIL1DI8W8vXN6sDE0IIIYTIvV6552QJkas8f7bW5EorjJ6tJfdqCSGEEEJkf5JkCZGdSWEMIYQQQogcJ0fdkyVEriSFMYQQQgghchRJsoTIKaQwhhBCCCFEjiBJlhA5ja8vPgEB+ATOYfypEAKphX+QB4GBUhhDCCFepNfrOXp0I4cP/0tsbDSlS3vRoMHb2No6ZHosoaHBbN++iNu3L2Br60jt2l0oX74WGo0m02MRrzalFGfP7mHfvlVERT2jaNHXaNSoF05OBbI6tFxDqgsmQ6oLimzNz88wqhVIWXD3kFEtIYR4Ljj4BhMn+nDz5kksLMoAjuh0x7G1dWLkyL/w8mqZabFs3DiXuXM/QqfToNVWAu6g092icuVmjBmzAnt750yLRbzanjx5yJdfduLcuZ1YWBQDCqLXn8TCQsuQIXNo0qR3VoeYo6W0uqAkWcmQJEtke8/Lva8PehM/fA3l3k2RBEwIkVvExETxwQdVuXcvEr1+CVAL0AA30GgGY2GxjRkzAihRolKGxxIQsJ4vv2wHDAS+BAoAemA9Wm1vqlSpw6RJGzI8DvHqU0rx2WdNOHfuJHr9H0BL4urcBQOjgQVMmrSJN95olqVx5mRSwl2I3OJ5uXefhk8N5d7LEpjoFV+ZUKoSCiFyg337VnPnznn0+r+B2sQlWADFUGoVer0bf/89I1NiWbp0KhpNQ2AWcQkWxH0Ea49eP5djx/7l8uVjmRKLeLWdP7+fM2d2oNcvBFrz30d9V2AeWm1Nli+flmXx5SZyT5YQr4oEhTFMWc/zYhmrKhMYWEBGtYQQr7SDB/9Go6mJUlVMrLVGr+/D3r3fM2zYggyN49Gju1y6dAD4i/8SvYQ6odW6cODAGjw938jQWMSr78CBv7GwcEena21irRa9/n3OnOnH06ePcHDIl+nx5SaSZAnxqjFT/SK+WMZ63sQvyFeKZQghXmmRkc9QyjWJFgWJiQnPlDjimIslDxpNfqKiMj4W8eqLinoGuGB+slrB5+0iJMnKYDJdUIjcwsS0QoJuy4ONhRCvpGLFKqLV7gEiTK7XaLZQpEjFDI+jQAEPbGycga1mWlxFp7tIsWIZH4t49RUrVhGd7gwQZKbFFuztXXB2TuoLCJEeJMkSIrfx9san4VMmu8/BFz84dRJ/fyTZEkK8Ulq06I9e/xiYALxY42szSq2jTZuBGR6HlZUNzZv3RaudDZx5YW0MGs2n2No6U7dutwyPRbz6GjR4GysrG+BTQPfC2uNotb/RsuV75MljmQXR5S6SZAmRGyUY1VpWaYphVEsKYwghXhWFC3vSr9+3wHQ0mkbAQmAV0AeNpi1vvtmKZs3ezZRYevSYgIdHcbTaWsAw4G9gFlptNTSadXz88QJsbOwzJRbxarO3d2bYsPloNMvRar2BOcS93z5Aq61LiRIV6Nr1s6wNMpeQEu7JkBLuIldI8Lwt/5DKuFeSwhhCiFfDwYPrWLlyOhcu7AGgQIEStG07iPbtP87Ub/OfPQtl5cqv2LjxN549ewBo8PJqQ7duY6hQoXamxSFyh9Ond7F8+TSOH98EKBwdC9Kq1ft06jQKOzvHrA4vR5PnZKUTSbJErmHmeVtSGEMI8SoIDw8jNjYaB4f8aLVZN5FHp9Px9OlDbGzssbaWzxUiY0VGPiMqKhwHh/xYWFhkdTivBEmy0okkWSLXCQhgvb8DAXgTSFlw95BRLSGEEEII5GHEQoi0ksIYQgghhBAvRZIsIURiUhhDCCGEECLN5GHEQgjzvL3B25vJfnP+K4yxqjKBgVIYQwghhBDCHBnJEkIk7/mo1mT3OYws8JthVMvPL6sDE0IIIYTIfiTJEkKkTIIphL74GaYQyr1aQgghhBDGJMkSQqSOFMYQQgghhEiSJFlCiNSTwhhCCCGEEGZJ4QshRNpJYQwhhBBCiERkJEsI8fKkMIYQQgghhIEkWUKI9CGFMYQQQgghAEmyhBDpTQpjCCGEECKXkyRLCJH+pDCGEEIIIXIxKXwhhMg4UhhDCCGEELmQjGQJITKeFMYQQgghRC4iSZYQInNIYQwhch29Xs+zZ6HodLFp7kMpRXj4E6KjI18qlujoSMLDn6CUSnMfOl0sz56FotfrXyqW9BAVFU5UVHhWhyEykFzjnE2mCwohMpe3Nz4E4BMYN4XQ71Qr/IMqExiITCEU4hXx6NE9Vq36ii1bFhER8QhLS1vq1+9B165jcHcvk6I+dLpY/v13FuvWzeTevUAAKlVqQufOI3jzzRYpjuXo0U2sWjWdU6e2AeDmVpZ27YbQuvVgLCxS9jHo9u1AVq78il27lhITE4GtbT6aN+9Dp06jyJfPLcWxvCylFP7+fvz9949cvXoEgFKlqtOx4zDq1++JRqPJtFhExlBKsX37H/z9949cv34MAE9Pbzp2/Jh69brLNc5BNOplvtLJBcLCwnB2diZ06VKc7OyyOhwhXi0BARAYyPiggQRSliA8aNhQEi0hcrIHD24xYkRdHj16gl7/HvAmcBGt9lesrZ8ydep2PD3fSLIPnS6WadO6cujQepTqBrQFnqDVLkSvP8DAgTNp3XpwsrFs2DCTX3/9AK22Jnp9H8ARWI9GswJv73aMHr082UTr8uWjjBnTmOhoJ/T6AUBp4Cha7W/ky+fE9Om7cXEpkpJT81KUUvz228esX/8jGk3r5+dFodEsQ6mNdOz4KX37Ts/wOETGUUrx668f8u+/M9Fo2qBUV0CPRrMUpTbTufNoeveeltVh5nrh4WH06OFMaGgoTk5OZttJkpUMSbKEyAR+fv8VxgipjHslKYwhRE41ZUonDh06hF6/DyiaYE0oWm0T3N2jmDnzZJLfyG/cOJdZswYB64A2CdYoYBgazS/MnXsZN7cSZvu4e/cqAwaURqkPge+BhPv7B2jHBx/MpXnz98z2oZRi8OBK3Llji16/DUj4geoGWm0dvL29+eyzVWb7SC8nTmxj/PimwEzgxQTzR2AYU6fu5PXX62d4LCJjHD26iYkTWwJzgAEvrP0O+JSvvtrDa6/VyfzghEFKkyy5J0sIkfWkMIYQr4SQkNsEBKxFrx+HcYIF4Ixe/zW3bp3m7Nk9Sfbzzz+z0Wh8ME6wIC5RmopG48jmzfOS7GPz5nloNI7AVIwTLIC2aDRtWb9+VpJ9nD27h9u3z6DXf4NxggVQDL3+Mw4eXEtISFCS/aSHDRtmo9W+DgwysfYjtNpy/Ptv0scjsre4a1wV6G9i7cdYWJTm339nZ3JUIq0kyRJCZA9mCmN8840UxhAip7hx4yxK6YHmZlo0BvJw/fops30opbh58yRKmevDHr2+Hteume8D4Nq1k+j19QHTs1CUasHNm8n3odFYAg3NtGiBUjpu3jybZD/p4cqVk+j1LUicMAJo0OtbcPly0scjsre4a9wc09dYi07XgitX5BrnFJJkCSGyF29vw6iWL364h5w0jGpJsiVE9mZtbfv8/x6aaREKxGJlZWtmPWg0GiwtbZPoAzSaBwn2ZZqVlS1abUgSLUKe7yfpPpSKfR636T7i22W0uONN+niSOycie4u7fubf95D8+15kH5JkCSGynwSjWssqTTGMavn7S6IlRHZWpow3jo5ugLmpfL+j1Vri5dUqyX5q1myPhcVCINrE2tModYAaNdon2UeNGu2f3xd2xsTaaLTaRdSsmXQfXl6t0GotgPlmWvyGk1MhypbN+BtIa9duj1a7EnhkYu0DtNo11K6d9PGI7C3uGi/HdFJ/H41mrVzjHESSLCFE9vU82Yof1WrIDvxXhciolhDZlKWlFV26jAB+BaYD8c+2igX80GjG0rRpP/LlK5RkPx07fopSN4CewN0Ea46g1XbEza0MtWt3TrKPOnW6ULBgabTaDsDRBGvuoNH0RKO5RceOnyTZR/78hWnatB8azWfAEkD3fE0k8A0wly5dRpAnj2WS/aSHli0HYm1tiVbrA1xJsOYSWm1bbG1tadHC1L08Iqdo3XowVlaa59f4aoI1gWi1bbGzc0iyUIvIXiTJEkJkf1IYQ4gco0OH4XTo8AkwEq3WA622PhYWJYB3qFWrPQMG/JhsH56ebzB69AqsrDah0RRFq62NhUUloBpubnmYPHkTlpbWSfZhZWXDl19uws0tD+CFhUUltNraaDTFsLTczOjRKyhVqmqysQwY8BO1arUDfLGwKI5WWx+t1gMYRceOn9K+/cfJ9pEeChRw54sv/oe9/UWgNFptdSwsqgFlcHC4zhdfbMzUZ3aJ9OfiUoQvvvgfdnbnAU+0Wu/n17gcjo63+fLLTTg7u2Z1mCKFpIR7MqSEuxDZTEAA6/0dCMBbnq0lRDYWFHSRbdsWcv/+dZydXWnY0JfSpb1S1cfTp4/ZseMPLl06jKWlNdWqtaF69bYpfogwxD1z69Chfzh8eAMxMVGULl2NRo164eCQN1WxXLp0BH9/P0JDgylYsDhNmvTF3b10qvpID1FREezZs4xTp/zRaDS8/npD6tbtJvfqvEKiosLZtWspZ87sQqPRULlyY+rU6YqVlU1WhyaQ52SlG0myhMiGnj/EeH3Qm/iFtCKoQGXc3ZFnawkhhBAiQ8lzsoQQry4pjCGEEEKIbCzl4+1CCJHdeHuDtzeT/eawPuhNAvDGf1VlAgMLyKiWEEIIIbKMJFlCiJzP1xefgAB8AuewnjfxC/LFP8iDwMC4KYSmSAImhBBCiIwiSZYQ4tXwfFTLJyAA/P3iCmMElSUQj0RNg4L+20QIIYQQIr1JkiWEeLV4e+PD81GtoDch6IX17h4E4IG/fyPDSJckW0IIIYRIT5JkCSFePfGjWqbWBQTgw3rGnwohkFqGaYW+vpkdpBBCCCFeVVJdUAiRuzyvTDi50gp88TNUJvTzk8qEQgghhEgfkmQJIXKn5yXgJ7vPwRc/Qwl4SbaEEEII8bJkuqAQIvcyUyzDP8jDsFoIIYQQIrUkyRJCCG9vfLzBJ+HztqQwhhBCCCHSSKYLCiFEvARTCBuGrDSaQiiEEEIIkVI5LsmaOXMmJUqUwMbGhho1ahCQzM0Tjx8/ZsiQIRQuXBhra2vKli3Lv//+m0nRCiFyHCmMIYQQQoiXlKOSrGXLljF8+HAmTJjA0aNHqVKlCi1atOD+/fsm20dHR9OsWTOuXbvGypUruXDhAvPmzcPDI/HDSYUQwogUxhBCCCFEGmmUUiqrg0ipGjVqUL16dX755RcA9Ho9RYsW5cMPP2T06NGJ2s+ZM4fp06dz/vx5LC0t07TPsLAwnJ2dCV26FCc7u5eKXwiRQwUEsN7fIa4wBmUJwoOGDeVeLSGEECK3CQ8Po0cPZ0JDQ3FycjLbLseMZEVHR3PkyBGaNm1qWKbVamnatCn79+83uc26deuoVasWQ4YMwc3Njddff52pU6ei0+nM7icqKoqwsDCjlxAil/P2xmfka4ZRrYbskFEtIYQQQpiVY6oLPnjwAJ1Oh5ubm9FyNzc3zp8/b3KbK1eusH37dt5++23+/fdfLl26xODBg4mJiWHChAkmt5k2bRqTJk1K9/iFEK8AX198AgLwCZzD+FMhBFIL/yAPAgPB1zergxNpdfXqSQ4f3kBsbDSenm/i5dUaCwuLVPXx6NE99u5dQVhYMC4uRalTpyv29s6p6iM6OpIDB/7m1q3z2No6ULNmRwoX9kxVH0opzpzZzZkzu1BK8frr9alYsT4ajSZV/QiRGz18eId9+1YSFvYAV9fi1KnTFTs7x6wOK82Cgi5x4MAaIiOfUazYa9So0R5LS+usDivXyDHTBYOCgvDw8GDfvn3UqlXLsHzkyJHs3LmTgwcPJtqmbNmyREZGcvXqVcMfzBkzZjB9+nTu3Lljcj9RUVFERUUZ/h0WFkbRokVluqAQwpifn6HceyBlwd1Dyr3nME+fPuKbb97i+PGNaLVOaDS26HT3KFCgOKNGLaV8+ZrJ9qHX6/nzz89ZufIblNKg1bqi093FysqGvn2/pk2bISmKJSBgPd9/349nzx5gYeGOUo/R6yNo0MCXDz+ci5WVTbJ93L17lalTu3Dt2lG02vyABr0+hOLF32Ds2JUUKlQqRbEIkdvo9XoWLRrN2rXfo5QFWq0LOt0drKzseP/9GbRo8X5Wh5gqUVER/PTTe+ze/SdarT0ajTM6XRCOjgUZPnwhXl6tsjrEHO2Vmy7o4uKChYUF9+7dM1p+7949ChUqZHKbwoULU7ZsWaNvJCtUqMDdu3eJjo42uY21tTVOTk5GLyGESEQKY+RoOp2OiRPbcvLkIWApev0DdLq7wCEePfLg889bEBR0Mdl+li//kuXLp6DXf4ZSd9DpbgHXiY7uxa+/fsC2bYuS7ePs2T1MndqJ8PBawDl0utvo9cHALHbtWsEPP/RNto9nz0IZM6YxN26EApvR64Of97GFmzefMmZMY54+fZxsP0LkRn5+41iz5lv0+gkodff5z/E1oqN7MHNmf3btWprVIabKd9+9w969fwNz0euD0eluA2d4+rQakye35/z5A1kcYe6QY5IsKysrvLy82LZtm2GZXq9n27ZtRiNbCdWpU4dLly6h1+sNywIDAylcuDBWVlYZHrMQ4hX3vNy7T8OnRuXe/f0l0crujh3bRGDgPvT6FUB3IL44UjX0+o1ERzvw99/fJdlHeHgYK1d+A4wAJgL5n6/xAGYCXfHzm5jkfcAAf/01GaiEUquA8s+X2gEDUWoWe/Ys5caNs0n2sXXrAh4+vI1evwVoRtyfdw3QFL1+Cw8fBrF16/wk+xAiNwoLC+Hvv78HxgLjgLzP1xQF5gLtWLx4Ajlk4hdXrhznwIFV6PW/Au8Dts/XvIZSa4DyLFs2JesCzEVyTJIFMHz4cObNm8eiRYs4d+4cgwYN4tmzZ/TtG/ctX69evRgzZoyh/aBBg3j48CFDhw4lMDCQDRs2MHXqVIYMSdn0DSGESBEpjJHj7N69DK22EtDQxFpH9Pq++Psn/e31oUMbiI5+Bgw1sVYDfERIyDUuXjxkto+nTx9x4sRm9PrB/JfoJfQ2Wm1+9uxZlmQsO3cuQ6l2QEkTa4ujVAd27ky6DyFyo0OH1hMbGwV8aGJt3M/xvXuBXLlyPHMDS6M9e5aj1boS9+XRi6zQ6wdx5MgGwsOlsFtGyzGFLwC6d+9OcHAwn3/+OXfv3qVq1aps3LjRUAzjxo0baLX/5Y1FixZl06ZNfPzxx1SuXBkPDw+GDh3KqFGjsuoQhBCvMimMkWM8e/YYvb4YcR+iTClOZGQoer3e6O/Ki33EfVfpbraP/9qZiyPUqG1iVmg0hZPsA+DJk0eA6Vkd8f0/eXIiyT6EyI2ePn2MRmOLUgXNtEj+5zg7efbsMRqNB6a/tIG441FERDzBzk5uiclIOSrJAvjggw/44IMPTK7z9/dPtKxWrVocOCBzT4UQmcTbG7y9meznx/qgy3GFMYLK4ucnhTGyk8KFS6PV+qHXRwOmpo/vxdXV02yCFd8H6IGDgKkiGXuftzNfITBvXjesrOyJjt5D3DS/F91Hrw+kUKH+ZvsAKFKkDMHBe0kwO96IVrsXD4/UVSoUIjdwdy+NUuHAMeANEy3ifo5zSuGYwoVLo9fPB0KAAiZa7MXa2gknJ5dMjiz3yVHTBYUQIseQwhjZWvPm76HX3we+N7H2GBrNUlq3TrqiWOXKjXFxKYlGMx54sZhSGFrtFF57rQHu7mXM9mFtbUvjxu+g1c4Crr+wVgETsLCwoGHDpIdCW7Z8H70+AFhtYu1a9Pr9tGyZsyqkCZEZ3nyzJXnzeqDRjAViXlj7GK12GlWqtKBgQXOjzdlLo0bvoNUqYBJxv0MSuopWO4dmzXpLKfdMIEmWEEJkFCmMkW0VLVqBLl3GAKOBnsAm4AAwDq22ISVLVqJNG9OzJuJZWFjw0Udz0Wh2otXWBpYAh4Bf0WqrY2V1kwEDfko2lp49J5I/vyNabQ3gayAAWItG0wqYQ//+P+DomD/JPqpXb0utWl3QaLoDQ4CdwC7gAzSaLtSo0YkaNdonG4sQuY2FRZ7nP8db0GjqAX8R93M8G622GjY29+nf39SXMdmTs7Mr7703A/gZjaYtsJ643ynT0Gpr4uKSn+7dx2dtkLlEjnlOVlYJCwvD2dlZnpMlhHh5CZ6t5U8j3N2RKYRZSCnFpk3zWLnyG+7fvwyAtbUjzZr1wdf3yxTfr3D27F4WLx7PmTM7ANBotFSr1pbevadSrFjFFPXx8OEdFi0aw65dS9Hp4p7VWKxYFd5663Nq1+6Uoj50ulhWrJjG+vUzefIk7nEnjo5u+PgMpkuXMeTJY+4eDSHE6dM7Wbz4c86d2wXE/Rx7e7end+9pFClSLoujS709e1bw11+TuXnzFAB58tjQoEFPevWaRr58blkcXc6W0udkSZKVDEmyhBDpKiAAAgMZf6orgQVqEYQH7u5SGCMr6fV6goICiYmJonDh0tjY2KepnwcPbhEaGkz+/O5p/hDz9Olj7t+/ho2NA4ULe6LRmCvMYV5MTDRBQYEAuLuXxdJSHlkiREoFB98kLOwBBQp4kDevuWIYOYNSijt3LhMZ+RQ3t5LY2ztndUivBEmy0okkWUKIDBEQwHp/h7jCGJQFdymMIYQQQmR3KU2y5J4sIYTICt7eUhhDCCGEeEXluBLuQgjxynhe7t0nIAD8/Qzl3v2DPAyrhRBCCJHzSJIlhBBZzdsbH2/w8ZvzX2EM/0YEBkphDCGEECInkumCQgiRXSR4tlbDkJVGUwiFEEIIkXNIkiWEENnJ82drTa60wujZWnKvlhBCCJFzSJIlhBDZUYJRLSmMIYQQQuQsck+WEEJkV1IYQwghhMiRJMkSQojsTgpjCCGEEDmKTBcUQoicQgpjCCGEEDmCJFlCCJGTxBfG6HxCCmMIIYQQ2ZQkWUIIkRN5e0thDCGEECKbknuyhBAip5LCGEIIIUS2JEmWEELkdFIYQwghhMhWZLqgEEK8KqQwhhBCCJEtSJIlhBCvEimMIYQQQmQ5mS4ohBCvIm9vfAjAJzBuCqFfkC/+QR4yhTADPH58n3/++Zlt2/x48iSYAgWK0rx5X1q1GoSdnWOK+oiMDGfWrIHs2/c30dHhaDQWFCtWjnff/Z6qVZtk8BEYu3v3CuvW/cCuXSuJjHyCh0c5WrXqT9OmfcmTxzJTYxHZV3j4E/73v9ls3ryAkJCbODq60rTpO7Rt+yHOzq5ZHZ4QWU6jlFJZHUR2FhYWhrOzM6FLl+JkZ5fV4QghROoFBLDe3yGuMAZlCcKDhg0l0UoPd+9eYdSohoSGPkKvfxsoCxxDo1lOkSLl+eqrHTg65k+yj/Dwp/TvX5qwsPtAK6AxcBeYD4QycODPtG49KKMPBYDz5/fz+ectiY62Rq/vBbij0fij1D9UrdqC8ePXYmlplSmxiOwrLCyEMWMacetWIEp1A6oCgWi1S8ibNz9ffeVPoUIlszhKITJGeHgYPXo4ExoaipOTk9l2kmQlQ5IsIcQrw8/vv8IYNDLZxN1dRrpSY8SIuly8eA+93h/wSLDmDFptA+rWbcmnnyZ9U9z48c04cWIH8C/QPMGaJ0BrNJoA/vorGDs783/M00NMTDT9+pXkyZNS6PX/AglH4bai0bShZ8+x9OjxeYbGIbK/b799mz17Nj9/31dMsOYWWm1DypQpzPTpu7MoOiEyVkqTLLknSwghcosEhTFG8jUj3f0SvaRYRspdvnyMCxf2otdPxzjBAqiIXj+WPXuW8/jxfbN9REdHcvLkLuBdjBMsiEty5qJUNH/8MTZdYzfl4MG/CQ0NQq+fg3GCBdAUpfrxzz9z0OliMzwWkX09enSPPXuWo9ePwzjBAiiCXv8NFy7s4erVE1kRnhDZhtyTJYQQuUnCZ2txIfH6oNuG5219841MK0zKxYuHAA3Q1kyLDuj1w7l69ThvvPFiAhXn6tUTKBUNtDfTRwWgJOfP733peJMTGBiAhYUnOt2LH5zjdSAsbA4PHtzCza1EhscjsqerV4+j18di/j3rA2gIDAygZMkqmRiZENmLJFlCCJEbmcmcjIplhLTC37+yFMsww8IiD6CASMDBRItwALRa839qLS2tjdomFte/VmuR5jhTKu54Ip/vU2OiRXiCdiK3+u/6m3vPxr2H5H0icjuZLiiEEOI/z0vA+zR8yrJKUwwl4P39pQT8i6pWbYpGowUWm2nxBzY2zpQrV8NsHyVKVCZPHntgoZkWO4E71K3b7aViTYk33miBTncb2G5yvUbzBx4eFSlQ4MWpkSI3KVu2BtbWTsAfZlosRqPRUqVK08wMS4hsR5IsIYQQicU/b8t9juF5W/6rQuR5Wwm4uhajbt3uaLWjgK0J1ijgTzSaGbRtOxgbG3uzfWi1Wpo08QU2AFOBmARrTwPvYGnpQIcOn2TAERirVKkhJUt6odX2A04mWBMDfI1Sf9Oly6doNKZGuURuYWvrQNu2g9FovgP+Iu79zvP/bkGrHU3duj1wdS2adUEKkQ1IkiWEEMK8hMUyCvwmhTFeMGTIr1So4AU0Q6t9E+iJVlseeJs6dbrw9ttfJNvHoEGzqFChLjCWuAIaPYC6QCUsLEKYOnUrWm3G/7nWaDSMH/83hQrZA1XQaOoDPdBqSwCj6dr1Mxo37p3hcYjs7+23v6BOnc7AW2i1FYh733sBzalQoRoffPBrFkcoRNaTEu7JkBLuQgjxnDxvyySdTsfRo/9j+/bFPH4cTMGCRWnWrB8VK9ZP1ajPnj3LWbp0Mg8e3MHKypIaNdrTu/dXODjkzbjgTYiJiWLv3pXs2bOC8PAnFC1ajhYt+lOqVNVMjUNkb0opzpzZxZYt87l//yb58hWkUaN3ePPNllhYZPw9hEJkFXlOVjqRJEsIIRIICIDAQENhjKACleXZWkIIIXINeU6WEEKI9CeFMYQQQohkSX1NIYQQqff8eVuT/eLKvQfgjf+qygQGFpBRLSGEELmejGQJIYRIOymMIYQQQiSSqiQrIiKCPXv2cPbs2UTrIiMj+eMPc89MEEII8cpKMIUwvtw7Qbf55huZQiiEECJ3SnGSFRgYSIUKFahfvz6VKlWiQYMG3Llzx7A+NDSUvn37ZkiQQgghcgBvb8Ooli9+uIecNIxqSbIlhBAiN0lxkjVq1Chef/117t+/z4ULF3B0dKROnTrcuHEjI+MTQgiRk0hhDCGEECLlhS/27dvH1q1bcXFxwcXFhfXr1zN48GDq1avHjh07sLc3/0R7IYQQuYwUxhBCCJGLpXgkKyIigjx5/svJNBoNs2fPxsfHhwYNGhAYGJghAQohhMjBpDCGEEKIXCjFI1nly5fn8OHDVKhQwWj5L7/8AkC7du3SNzIhhBCvhuejWj4BAeDvRwDeBAaV5ZtvPGjYUEa1hBBCvHpSPJLVsWNH/vrrL5PrfvnlF3r27IlSKt0CE0II8Yp5oTBGQ3ZIYQwhhBCvJI2SzChJYWFhODs7E7p0KU52dlkdjhBCvBoCAiAwkPFBAwmkLEHIqJYQQojsLzw8jB49nAkNDcXJyclsuxRPFxRCCCHSjRTGEEII8QpL1cOIhRBCiHQlhTGEEEK8giTJEkIIkbUSPFsr/l4tgm7zzTdyr5YQQoicSaYLCiGEyB68vfEhAJ/ABFMI/RsRGIhMIRRCCJGjSJIlhBAi+0hQ7t0ncA7jgyAwqCz+QR6G1QktXz6NgIB1WFpa0bnzKKpVa53qXV65cpwNG2YSExPFG280p1Ej3/Q4klRTSnHmzG5u3TqPra0DXl6tcHDIlyWxZCf+/ks4enQTlpbWtGo1iNKl30x1HyEhQRw/voXY2GhKl66Gp+cbGRBp8uKu8S5u3bqAra3j82ucN0tiefr0EUeO/I+IiKcUKVKeihXrodFosiSW9BAScpvjx7cSGxtNmTLVKVWqalaHJHK5NFUXXLx4MXPmzOHq1avs37+f4sWL88MPP1CyZEnat2+fEXFmGakuKIQQWcjP779RrZDKuFeKK4zx7JkfP/zwLkpFJ2iswcbGkZ9/Po6bW8lkuw4Le8jIkbUICroI/Pen0NraidGjl+Ll1Sr9j8eM8+f388MP7xIUdA7QAApLS1vatfsIX98pWFhYZFos2cWxY1uYOrUrUVGhCZZqKFSoNN9+uw8nJ5dk+4iKCmf27A/YseMPlNIZlpcpU4tPPlmEu3uZDIjctLNn9/Ljj+9x5855/rvGdnToMIy33voi066xTqfDz28s69b9RExMhCEWd/cKDBs2n/Lla2ZKHOklMvIZs2YNZufOJUbXuGzZ2nzyyR8ULuyZhdGJV1FKqwum+p6s2bNnM3z4cFq3bs3jx4/R6eLe0Hnz5uWHH35Ic8BCCCFEIiYKY6xZs53vv++LUkWANUA08Bj4gcjIaAYOrEB0dHSS3er1egYPfo2goOvAd8BDIAZYR1RUISZNas+lS4cz9tieu3r1JOPGNePu3bzADiAWuENMzKesWjWd334blilxZCeXLh1l4sS2REW5AmuJuzaPgO+5e/cmgwe/jl6vT7IPpRRTp3bB338ZSn33fPsY4G8uX37IqFENePjwTkYfCgCXLx9j/Pjm3LtXANhJ3DUOIibmY1as+Ir58z/JlDgA5s0byurV3xITMwK48zyWHdy9m5dx45py9erJTIvlZen1eqZM6cSuXatQ6nvifg9EA2u4dCmYUaMa8OjR3awNUuRaqU6yfv75Z+bNm8fYsWONvnWpVq0ap06dStfghBBCiBcLY1y/2A2wAfYAHfh/e3ceV1W1/nH8c/ZBJhWcB4icTS01hzA0E9Nyil+Z3dTE1GywzLrZZINTVJplWWlZWXm9WE43S63MUnHKopwyJ3IeEGdBRBnO3r8/QAJlFA6H4ft+vc6r3GvvxbPPQjkPa+1nQTnAF3gSWIDDkcjHHw/PscslSz4gLu4Y8BXwNFCZ1BX0IcAawJtp04Y5644y+fLLcaSk+GOaPwPBpP5orgW8CrzNd99NIyZmX5HEUlx8+OGjWNalMf4/UsemEvAUMJe4uGMsWjQlxz7+/HMFmzb9gGl+lXZdpbR+7sI0Izh37kKufRSWL78ch8NRB9P8CbiV1DGuDbwGTGTJkg84fvyA0+M4enQP33//IZb1NjCe1O8zAwjGNH8iJcWfOXNedXochWXLlp/ZsmUZpjkPGEHqvwPlgLsxzQji4s6xePH7rg1Syqx8J1n79u2jVasr1zJ7eHhw/vz5QglKRETkCoGBhATHE58UBwwm9UPq5XoC17FmzZwcu/rhh4+AeqQmaZerATzEnj3O/43++fOxREYuwjSfALJakv4ohlGRVatmOz2W4mT37i3Ag0DNLFpDgAb88MP0HPuIiAjHMJqknX+5WpjmIH7+eVaBY81NfPwZ/vhjCaY5AvDK4ozHsNm8Wb36K6fHsmrVlxiGD/BIFq3lMc0n+PXXb0hIiHN6LIVh5cr/Yhg3AFkt7fXDNAfy88//LeqwRICrSLLq1avH5s2brzi+dOlSmjZtWhgxiYiIZC0wkNTlTU2yOcEGNCM5OeflgufPnwWapp2fleuAZFJSUq4uzjyKjz+T9hxJdvfjjc0WQGzscafGUZykLgNMJnUMspI6xgkJsdm0pzp79jimeR05jfG5c85/X+PiTmFZJtnfTwVsNv8iGePY2OPYbAFkndADXIdlOYiPP+P0WApD6hg3xtVjLJKVfFcXHDlyJMOHD+fixYtYlkVkZCRfffUVEyZMYMaMGc6IUUREJAM3YGM2bSawAQ+PrGYM/uHjU52zZzcBDiCrggObsNnccXNzbhFeH59q2O0eOBybgNuzOOMsprmXatUGOTWO4sQwDGw2DyxrczZnOICNuRa+qFbtGuz2ZWnPjmc9xpUrBxQs2DyoVKkGhlEO09wM3JbFGacxzQNUq+b8WKpVC8Cy9gKxpC6tu9wm7HaPPBUVKQ5Sx3gVDodJ1vMGm6hSxfnvq0hW8j2T9dBDD/Hmm2/yyiuvkJCQwP33389HH33Ee++9R79+/ZwRYybTpk2jbt26eHp60q5dOyLzuFPlnDlzsNls3H333c4NUEREnKpejcrAbCAqi9bZwEECAh7NsY977rn00P/MLFr3AjNp2rRdwQLNAy+vCtxyy78wjKnAqSzOmIzNlkxwsGvKyrtKs2Y3A/8BdmfR+l/gCL17P5tjH126DMHh2J92/uV2Yxiz6d79wYKGmitvbx86dLgXw3if1AIrl3sLm81Bp04DnB5LcHAolpVIarGXy53CMKZy66198fQs7/RYCkPXrg/icOwBvsyidRc22xy6dRtS1GGJAPlMslJSUpg1axZdu3bl77//Jj4+npiYGA4fPszQoUOdFWO6uXPnMnLkSMaOHcvGjRtp2bIl3bp14/jxnKeC9+/fz7PPPkvHjh2dHqOIiDjXNy+/TOqMVXtgKnAQ2AY8DwymnOHFDVWGM2kSZPd7uNtue4DatRuT+mzKs8Bfaf18CARhGBYjRnzm9HsBGDBgPN7eFzGM9lxKIOAPYCjwGv36jaZKlayePyu9nnrqC+x2G6ljPI1/xvg5YCg1azbgtttynt277rp2BAc/ADyUdt22tH6mYRgdqVEjgDvvHOHM20g3YMCreHnFYxgdSP1FwBHgd1KfLZzI/fePo1KlGk6Po2pVP/r1Gw2Ekfr99UdaLP/FMNrj7Z1I//7jnB5HYWnatD233no/NtsQ4AVgO3AA+ADDuJXatevSs2fORXBEnCXf+2R5e3uzY8cO6tSp46yYstWuXTtuuukmpk6dCqSu2w4ICGDEiBGMGjUqy2scDge33norDz74IGvWrOHs2bN88803ef6a2idLRKT4Wbd9O7ePGceFpET+2ePKzjUVvdnRpQsrT96curcWnfHzg8aNr9zIOCkpibFjb2fbtl9Ifc4LwEblyv6MG/cd9eq1KLL7OXx4Fx999ARbt/6cfszHpzb9+r1Er17DS/QmsVdr//6/GDeuB6dPH+GfMXajWbMgXn31Z9zd3XPtw+FwMGfOeBYt+oALF84CYLPZufnm3jz22LQiSWwuOXx4Z9oYL08/5uvrR//+r9Cjx7AiG2PLsvjuu2nMmfMGcXH/lLBv0eJ2HntsKv7+jYskjsLicKTw5ZdjWbx4Ghcvpj6nZxhuBAXdw7BhU/H1re7iCKW0yes+WflOsoKDg/n3v/9d5MvukpKS8Pb2ZsGCBZm+9qBBgzh79izffvttlteNHTuWP//8k4ULFzJ48OBck6zExEQSExPT/xwXF0dAQICSLBGRYuinTZuYtXIlXu7uvPivf1GvZlo1ushIiIpidPQwomhMNP4EB1+ZaAHExZ1k+fL/kJh4nrZte9KwYdsivYeMjh7dw5Eju/D0rECTJkG4uZVzWSzFxe7dG/njjyW4u3vRteuQq3peKDExgZ0715OSkkTdui2pWtXPCZHmTXT0bqKjo/Dyqsh1193ssjFOSUlm5871XLwYj7//dSV+096LF8+za9evpKQkUa/ejWVu9leKTl6TrHw/0fv444/zzDPPcPjwYdq0aUP58pnX7bZo4Zzf/J08eRKHw0HNmpnLudasWZOdO3dmec3atWv57LPPsqyGmJ0JEyYwfvz4goQqIiJF5PZWrbg9i21FCAyEwEDCwqezOLp16qzW/1oQFVX1ilktH59q9O5ddJvB5qR27QYl/sNuYWvYsDUNG7YuUB8eHt60bNmlkCIqGD+/hvj5NXR1GLi5leOGG251dRiFxtOzfLEZYxG4iiTrUnGLJ598Mv2YzWbDsixsNltaFR/XO3fuHAMHDuTTTz+lWrW8/9brxRdfZOTIkel/vjSTJSIiJVBoKCGRkYRETWcxrQmPDiUi2p+oKAgtW7UkRESkCOU7ydq3zzW7zlerVg273c6xY8cyHT927Bi1atW64vw9e/awf/9+QkL+2YQwde8NcHNzY9euXTRocOVvCz08PPDw8Cjk6EVExGXSZrVCIiMhIpxIAomKbsykSdkvIRQRESmIfCdZrih4AeDu7k6bNm1Yvnx5+jNZpmmyfPlynnjiiSvOb9KkCVu3bs107JVXXuHcuXO89957mp0SESlrAgMJIW1W69ISwojORGVVCZ6si2WIiIjkRb6TrFmzZuXY/sADD1x1MLkZOXIkgwYNom3btgQGBjJlyhTOnz/PkCFD0r+2v78/EyZMwNPTkxtuuCHT9ZUqVQK44riIiJQRGWa1QqKmMzoawP+K06Kiy2tZoYiIXLV8J1lPPfVUpj8nJyeTkJCAu7s73t7eTk2y+vbty4kTJxgzZgwxMTHceOONLF26NL0YxsGDBzGMfO+vLCIiZU2GwhhZWUzr9GWF4eH+mtUSEZF8yXcJ96z8/fffPPbYYzz33HN069atMOIqNrRPlohIGZRWAn5xdGvCCSUa/2z32xIRkbIjryXcC2Xap1GjRkycOPGKWS4REZESKTAwtTJhcDyhhBPMSog+QkREav4lIiKSk3wvF8y2Izc3oqOjC6s7ERER1wsMJCQQQsKvLJahWS0REclOvpOsRYsWZfqzZVkcPXqUqVOn0qFDh0ILTEREpNjIsN/W6K2niCJIhTFERCRb+X4m6/LCEjabjerVq3PbbbcxefJkateuXagBupqeyRIRkUwiI1kcUSG1MAaNwU+FMUREyoq8PpOV75msSxv6ioiIlEmX7bcVHh2aPqulZEtEROAqCl+8+uqrJCQkXHH8woULvPrqq4USlIiISLGmwhgiIpKDfC8XtNvtHD16lBo1amQ6furUKWrUqIHD4SjUAF1NywVFRCRX4eH/FMags8q9i4iUUk4r4W5ZFjab7YrjW7ZsoUqVKvntTkREpORLm9UK85tO8KkF6bNa4eGuDkxERFwhz89kVa5cGZvNhs1mo3HjxpkSLYfDQXx8PMOGDXNKkCIiIsVeYCAEBhIWGcniiD2phTGiGxMersIYIiJlTZ6TrClTpmBZFg8++CDjx4/H19c3vc3d3Z26desSFBTklCBFRKTgdhw6xLTvvuOnzduxLIvbWjZleM+eNK9b19WhlWjnL17kvytXEr58OcfPnuXaGjUY0qABY8r9wdKYtiqMISJSBuU5yRo0aBAA9erVo3379pQrV85pQYmISOH6atUqBr47BRtVSTH7AAb7jn/NJz8u47MRTzCka1dXh1giHT97li4vvcT2w4fpZbNxs2Wx6eRJQv/6ixnXX893dzaAdeHps1oR0f6AEi0RkdIu3yXcO3XqlP7/Fy9eJCkpKVN7Tg+AiYhI0dt1+DAD352CwxwAfAq4A5DieBcYztAPptK6QQNa1qvnyjBLpMHvvsvJ6Gj+BK6/VEfKNFkN9Nyxg2evvZYPn3+MkPDp/xTGiOisWS0RkVIu34UvEhISeOKJJ6hRowbly5encuXKmV4iIlK8fPjDD9ioDHzCpQQrVTngQ+y2Wnyw5DvXBFeC7Tp8mB82beIt0+T6y9puBUaZJjN//pmz8fEqjCEiUsbkO8l67rnnWLFiBR999BEeHh7MmDGD8ePH4+fnx6xZs5wRo4iIFMDyLdtJMe8GPLJodSPFvJflW7YXcVQl35rtqe/Zvdm03wdcSE7mj927Uw+k7a0V1mdLpr21wsO1t5aISGmT7+WCixcvZtasWQQHBzNkyBA6duxIw4YNqVOnDrNnz2bAgAHOiFNERJwoi505xFkCAwkhkpCo1CWEKowhIlL65Hsm6/Tp09SvXx9Iff7q9OnTANxyyy2sXr26cKMTEZEC69KyGXZjIZCYRWsKbvYFdG3ZrKjDKvE6Nkt9z+Zn0z4X8CpXjrYNG17ZmDarFRIcn2lWKyJCs1oiIqVBvpOs+vXrs2/fPgCaNGnCvHnzgNQZrkqVKhVqcCIiUnCP9+gBnAUeJnOilQw8hsOM4YlevVwRWol23TXX0LNVK54zDP66rG0VMNFmY3DXrlSqUCH7TgIDCXm+GWF+09OTrUvPainZEhEpufKdZA0ZMoQtW7YAMGrUKKZNm4anpydPP/00zz33XKEHKCIiBXPdNdcQPvJp7MaXuBkBwGPAcNyMazFsn/P5kyNoocqCV+WLp5+mhr8/LYEQm41ngM6GQTDQrlkz3n7wwbx1pMIYIiKlis2yLtWcvToHDhxgw4YNNGzYkBYtWhRWXMVGXFwcvr6+xM6Zg4+3t6vDERG5ajsPH2bad9+xbNM2LMuiS8umDO/Vixvq1HF1aCVaQmIi4StX8t8MmxE/eMcd3NuhA+Xc8v3oM0RGsjiiQureWjQGP389qyUiUkwkJMTRr58vsbGxOW5dVaAk6+LFi3h6el7t5SWCkiwRESlykZEQFZVaGINQovHHz0+FMUREXC2vSVa+lws6HA7CwsLw9/enQoUK7N27F4DRo0fz2WefXX3EIiIikkqFMURESrR8J1mvv/46M2fOZNKkSbi7/7Op5Q033MCMGTMKNTgREZEyTYUxRERKpHwnWbNmzeKTTz5hwIAB2O329OMtW7Zk586dhRqciIiIoMIYIiIlTL6TrCNHjtAwiz0/TNMkOTm5UIISERGRy6QtIQzrsyXTEkLNaomIFD/5TrKaNWvGmjVrrji+YMECWrVqVShBiYiISDYCA9NntUIJzzSrpWRLRKR4yHdt2TFjxjBo0CCOHDmCaZp8/fXX7Nq1i1mzZrFkyRJnxCgiIiIZBQamJluRkRARnlruPboxEdH+6c0iIuI6V1XCfc2aNbz66qts2bKF+Ph4WrduzZgxY7jjjjucEaNLqYS7iIgUe+HhLI5uTSSBRNBZ5d5FRJyk0PfJ2rt3L/Xq1cNmsxVakCWBkiwRESkR0vbWGr31X0RVDUrfWys01NWBiYiUHoW+T1ajRo04ceJE+p/79u3LsWPHChaliIiIFA4VxhARKTbynGRdPuH1/fffc/78+UIPSERERApAhTFERFwu34UvREREpJhTYQwREZfKc5Jls9mueB6rrD2fJSIiUqIEBhISCCHh0/8pjBHRmagoFcYQEXGmPCdZlmUxePBgPDw8ALh48SLDhg2jfPnymc77+uuvCzdCERERKZjQUEIiIwmJms7oraeIIoiIaH+iolQYQ0TEGfKcZA0aNCjTn0P1r7KIiEjJkbaEMCwyksURe9KXEIaH+2tWS0SkkF3VPllliUq4i4hIqZNW7n1xdGvCCU0v965kS0QkZ3kt4a7CFyIiZciZ+Hgio6KwLIubGjWiag4/IJxt6/79HDhxgmoVKxLYuDGGkeeCt6XW7uhodh05QgUvL9o3aUI5Nyf9mFZhDJeJjv6bI0ei8PKqSJMmQbi5lXN1SCLiBEqyRETKgITERJ79/HNm/vQTF1JSAPCw2wnt3JkpDz9MBS+vIovllx07eGr6dP7Yty/9WIPq1ZkwZAj/uuWWIoujONl1+DCPf/ghK/76K/1YLR8fXurXjyd69XJeoSkVxigyhw7t4MMPh7Nt28r0Yz4+tenf/2V69nxcxcREShklWSIipVyKw0HI+PH8tn07L5sm/QAbsMDh4LUVK9h56BDL33gDj3LO/436+p076fLyy7R0OFgEtAV2A5NPnOC+SZOYlZzMwM6dnR5HcbLn6FFuee45ql64QDjQGTgKfBQXx5OffMLpc+cY27+/c4PIoTBG48ZXnq7kK3+io//muedu4eLFmsCXQCcgmri4D/n44yc4f/4s9933soujFJHCpGeycqFnskSkpJu7Zg393nqLFaR+gM/oVyAI+OKppxjcpYvTY+nw7LMk797NGtPEI8NxCxgI/Fi+PIdnzSqShK+4GDh5MqvWrGGTaVL1srbRwETD4ODnn1O7SpWiCSgyksURFVKXENIY/PyvOCU6GoKDlWzl1Vtv3c+6db9gmpuAype1vohhTOaLLw5RuXJNV4QnIvmQ12eytABeRKSU+2LZMjoaxhUJFsDNQDebjc9//NHpcew6fJhfoqJ44bIEC1Jn1kYDJ8+fZ8nvvzs9luIi/sIF5q1dyxNZJFgAzwLuQHhERNEFFRhISHA8YX7TCSU8y1cwK4mIgPDw1Boakr2EhDjWrVuAaT7JlQkWwPNYlp1Vq2YXdWgi4kRaLigiUsodPnGC20wz2/ZWlsXcEyecH8epUwDcmE37dYCXzcbhkyedHktxcTIujiSHg1bZtPsC9Ww2DhX1e5KxMAa7rmgOYTGjo1GxjDw4e/Y4pplM9t/5lTGMupw6dbgIoxIRZ1OSJSJSytWoXJldR49CNqvDd9ps1KhUyflx+PoCqR/ZG2TRfgC4YFnp55UFVSpWxG6zsdOyuD2L9gTgkCvfk+wyp8BAwjIWy/hfC6KiqqpYRhZ8fKpisxlY1i7gtizOiMeyDuHrW6OoQxMRJ9JyQRGRUm5gly78ZFlsyqJtO7DYsnjg9qw+4heuG+rUoeW11/K2zYYji/a3AB8PD/6vXTunx1Jc+Hh7c1e7dkw1DM5n0T4dOGeaDOjUqahDy11oaPqywuerzoDoI+lLCOUfFSpUpm3bOzGMD0hNmy/3IZZ1gU6d7i/q0ETEiZRkiYiUcv1vvZVWdetyh2HwOakf8y4As4AuhkETf38eKIKKfjabjTcffJBVwF02G3+QWvBiNzAMmAaMDw2lvKen02MpTsb2788RNze62mysAEwgmtRn1J4DhvfqRb1atVwaY7YCA9OTrUvPahF9hEmT9KxWRgMGjMduP4Bh3A5E8M8ovwyM4s47n6R69WtdGaKIFDJVF8yFqguKSGlwKi6Oh95/n28jI8n4j37PVq344umni2S54CWLfvuNER99xMHTp9OPVfbyYlxoKCPuvLNM7hf0686dDJ0yhe3R0enHvMuV46m77iJswADsdrsLo8ujyEiIivpnCSGd8fPTfluX7NjxC++99xDR0TvSj7m7l+fuu//N/fe/qs24RUqIvFYXVJKVCyVZIlKa7Dl6lNXbtmFZFrc0a0Zj/yvLcxcFh8PBz1u2cOD4car5+NCjTRu8PC6vOVi2WJbFuh072HHoEBU8PenRpg2VKlRwdVj5l5ZsjY4eRhSNicZf5d7TWJbFtm1rOHJkF15eFWnTpgfly5edZxBFSgMlWYVESZaIiMhVCA//Z1brVAv8mqswhoiUfNonS0RERFxHhTFEpAxTkiUiIiLOocIYIlJGaZ8sERERca7AQEKIJCQqw95aEZ2JilJhDBEpnZRkiYiIiPMFBqYmW5GpydboaIiKbkxEtH96s4hIaaEkS0RERIpOWrIVFp5hVut/LYiKUmEMESk9StwzWdOmTaNu3bp4enrSrl07InNY1P3pp5/SsWNHKleuTOXKlenatWuO54uIiEgRUWEMESnFSlSSNXfuXEaOHMnYsWPZuHEjLVu2pFu3bhw/fjzL8yMiIujfvz8rV65k/fr1BAQEcMcdd3DkyJEijlxERESuoMIYIlJKlah9stq1a8dNN93E1KlTATBNk4CAAEaMGMGoUaNyvd7hcFC5cmWmTp3KAw88kKevqX2yREREikDaJsbpSwjpjJ+fCmOISPFS6vbJSkpKYsOGDXTt2jX9mGEYdO3alfXr1+epj4SEBJKTk6lSpUq25yQmJhIXF5fpJSIiIk6WYVYrzG96+qxWRIRmtUSk5CkxSdbJkydxOBzUrFkz0/GaNWsSExOTpz5eeOEF/Pz8MiVql5swYQK+vr7pr4CAgALFLSIiIvmQlmyF+U1PX0IY8b9ThIcr2RKRkqPEJFkFNXHiRObMmcPChQvx9PTM9rwXX3yR2NjY9NehQ4eKMEoREREBVBhDREq0ElPCvVq1atjtdo4dO5bp+LFjx6hVq1aO17799ttMnDiRn3/+mRYtWuR4roeHBx4eHgWOV0RERAoow95aRIQTSSBR0Y2ZNMmf4GA9qyUixVeJmclyd3enTZs2LF++PP2YaZosX76coKCgbK+bNGkSYWFhLF26lLZt2xZFqCIiIlKYAgPTZ7XSlxBGoCWEIlJslZiZLICRI0cyaNAg2rZtS2BgIFOmTOH8+fMMGTIEgAceeAB/f38mTJgAwJtvvsmYMWP48ssvqVu3bvqzWxUqVKBChQouuw8RERHJpwyzWiFR0xkdDVHRjYmI9k9vFhEpLkpUktW3b19OnDjBmDFjiImJ4cYbb2Tp0qXpxTAOHjyIYfwzOffRRx+RlJTEvffem6mfsWPHMm7cuKIMXURERApDWrIVFj79n3Lv/2tBVFRVlXsXkWKjRO2T5QraJ0uk7IpLSCA8IoIlkZEkJiVxY4MGPNq9O439/V0d2lX5/KefeOLjj0lMSgLAvVw53hk6lMd69sxzH4nJycxfu5b5a9dyLiGBxgEBPNKtG60bNMhzH5ZlsWbbNj7/+WcOxMRQrVIlBgQHc+dNN+Fmt+f7vgri8MmTPD1jBqu2bsV0OKjn58fEBx6gy4035rkPh8PBd3/8wX9XRnDsbBx1a1RjSNcuBDdvjs1my3M/m/fu5ZMff2TbwSP4eHtyb/sg+nbsiKe7+1XcWRmRYW+tcEKJxh8/PwgNdXVgIlJa5XWfLCVZuVCSJVI2bd2/n+6jR3MsNpYuNhuVLIvlhsFp0+SDRx9leK9erg4xX9o+/TSb9uzBDehO6gO5S4EkoElAANumTcu1jyOnTnHHyy+zPTqajjYb/pbFWrudww4Hz/XuzZuDB+eaVKQ4HAyZMoXwVatoZBi0NU12Gwa/myYdrruO78aNw7d8+UK449zNW7uW0LfewmFZdAV8gWVALND/1lv58tlnc+3jXEICPV99nbXbt2I3WuEwm+BmbCDFjOK+WzoSPvJpyrnlvGjEsixeCQ/njfnzcTNqk2J2wrBFY1qraVDrGla8Pp5rq1cvjFsuvSIjWRxRIbUwBo2JRoUxRMQ5St1mxCIiReVCYiI9x46l+rlz7AF+tCzmAodMkxHAEx9/zPItW1wcZd5NnD+fTXv2cDtwFPgWWJj2/3cCOw8d4tnPP8+xD8uyuOe11zgXE8MWYLVl8RWwz+HgbeCthQv57Kefco0lbO5cvlq1ilnALtPkSyDSNFkFbPv7bx6cMqUAd5p3J+PiGPjWWzSzLPYDPwLzSH1PngC+Wr2aKd9+m2s/D039kPU79wM/4zA3Al+SYu4EvmL+uvWMmT071z7+u3Ilb8yfD0wgxTwAfIVprQL+4sBxO3e++gb6fWguVBhDRIoZJVkiIpeZu3Yth8+cYb5pUifDcS9gCtDaMHhn4ULXBHcVxnz5JV7AfKBKhuOVgDmkzuBMWbQoxz7Wbt9O5J49fGaaZNwIww14BrgXeHvBghyTgQuJiUxdtIgngYFAxjmvW4HJpsnC335jz9Gjeb63q/Xs55+TbFl8DWTcct4LeA+4AZi0YEGOfRw4fpz569biMCcBXTK02IB+WNZIpn73I+cvXsy2D8uymLjgG2y2EGAUUC5D6/WkmDPZemAPK/78M1/3VyalbWJ8KdkKZqX21hIRl1GSJSJymR83biTIMGiURZsNGGia/LhpE6ZpFnVoV8fh4F6gYhZNXkA/wJ7Lvfy4aRO17Ha6ZtP+ALArJoYDx49n28eGPXs4nZDAwGza+5P6Q+mnzZtzjKUw/LxlC+2B+lm0GcBgICY2NscxXr5lC5ZlAtk9APQA8Rfj+W3Xrmz7iDlzhh2H92NZg7I5oxNu9mtYunFjtn3IZdKSrYyzWtFbT2lWS0SKlJIsEZHLpDgceOUwI+MNOCyrxCzhspGaTGUnL0+bpjgceJJ59ikjrwzn5dRHxnMv5w7YbbYc+ygspmnmeN9egJV2XnZS47QBntmckfoVUnLt49JXzIoNG55F8p6UOhlmtZ6vOkOzWiJSpJRkiYhcJrBxY9YBJ7NpX2iz0bZePexFXAnvaiUD3wApWbSZwIJs2jIKbNSI/Q4H2S1a+waoWbEidWrUyLaP5nXq4GG3k92TTkuBJMsisHHjXKIpuBZ167IKOJ1N+9eAr4cHbjkUrUiN0wIWZ3PGQtwMN26sVy/bPvyqVKFmpWqkvoNZ2U6yYzftiuA9KZUyLCG8NKtF9BEmTdKslog4l5IsEZHLDOnaFbubG4+SWn0vo1nAUsviif/7PxdEdnXu69CBGFKf+Mk492YB44ADQNdWrXLsIyQwkIDKlRlmGMRe1rYCmGGz8WivXjlW0qvq48OA4GAmGgaXlw05CjxjGAQ2aMBNjbJaqFm4Jg8dSgowjNQkNKMvgOXA/bfdlmMfN9avT9B11+NmPAscvqz1L+zG69x3SwdqVKqUbR92u50Rd3bHsM0ktfxGRucwjEep7luVe4KCcr0nyYEKY4hIEVMJ91yohLtI2bTot9/418SJ1AJCHQ58ge8Mg9WmydCuXfl0xIh87YHkar79+hGXkEAT4H5Sf8M2B/gL8HZ353wuRR4AIqOi6DZ6NG6JiQw0TfyA1TYbSyyL21u2ZNGYMXiUK5djH2fj4+ny0ktsPXCAPpZFW2A3MNswqFixIqvefJOGfn4Fvd08eWHmTN76+mv8gEGkFgD5FvgFaOrvz1/TpmXa4D4r+2Ji6PDCyxyPTcBhDgAaAxsxbAtoGnANqyeEUaViVk/D/SMpOZm7X5/IDxv/wLB1x7RuA45iN2bhWe4Cy14dQ/umTQvjlgXS99YaHT1M5d5FJN+0T1YhUZIlUnb9uW8f7y1ezOJffyUxOZlW9evzeEgI/+rQoUQlWJd0evFF1m7blr6EwQTaNmrEb5Mn57mPfTExvL9kCfNXrybuwgUa+/nxSI8eDOnaNdf9oC45f/Ein/z4I58tXcqBEyeoVrEioV268ESvXtSsXDn/N1YA89au5eVZszgQE4MF+Hh7M/j223lryJBcE6xLTsTGMvW77/j85whOxcXiX7U6j3bvwqPdulExjz83klNSmLViBdO+X8auI4cp7+FFv1uDeCokhAa1axfgDiVb4eEsjm5NJIFEnGqBX/OqNG6sZEtEcqYkq5AoyRIRESml0ma1Fke3JpxQovHHzw9CsysYKSJlnjYjFhEREcmJCmOIiJPkbW2HiIiISGkVGEgIkYRETf9nCWFEZ6Ki0BJCEbkqmskSERERyTCrFeY3PX1WS3tricjVUJIlIiIicklaspWx3Hv01lMq9y4i+aIkS0RERORyGWa1nq86Q7NaIpIvSrJEREREsqLCGCJylVT4QkRERCQnORTGyIqKZYiIkiwRERGR3AQGpiZbkanJ1uhoAP8rTouKLk9EtD9RUdpvS6QsU5IlIiIikldpyVZY5AqymspaTOpMV1R0Y8LD/TWrJVJGKckSERERya+0ZOtyl2a6Fke3JnxrDyKiW2i/LZEySEmWiIiISGHJsKyQiB+I5BRR0Y2JiPZPbxaR0k9JloiIiEhhCwwkJBBCwq8slqFZLZHSTyXcRURERJwlw35bwacWaL8tkTJCSZaIiIiIM6XttxXWZ0um/bbCw7XflkhppSRLREREpCgEBqbPaoUSDlv/TJ/VUrIlUrromSwRKVSWZbE3JobYhATq1qhBlYoVXR1SqXHg2DF+2bmTKhUrcvuNN2IYrvk9mWVZ7Dl6lHMXLlCnAGO87eBBNu/dS/1atQhq0uSq+jh/8SJ/R0fj7ubGdf7+2O32q+pHSjeHw8GuI0dISkmhkZ8f5T09XReMCmOIlAlKskSk0CyOjGRceDgb9+8HoJxhcG+HDkwcPJhrq1d3bXAl2J/79nHPG2+w79gxzLRj5d3ceLhnT9596KEijeWbX39l/OzZbD5wAEgd43/dcgtvDh7MNdWq5amPJb//ziPvv09MbCxW2rFKnp6MHTCAf991V576iL9wgVfCw/li2TLiEhMBqFu1Ks/eey+P9+yJzWbL971J6WNZFh/98AMTF3zDoZMxAHh7ePPQHV14bcAAKnp7uy44FcYQKdVslmVZuZ9WdsXFxeHr60vsnDn4uPIfY5Fi7j/LlzP4vffoYrPxhGVxDbAGmGwY4OPD+smTCVCilW/bDh6k7ZNPUt40eR7oDBwDPgR+AP7VoQPzXnihSGL5/KefGPrBB9xuszHcsvAHVpM6xnZfX9a/8w7+Vavm2Me3v/3Gva+/TgDwPHATsBt4B/gdGNu/P2P798+xjwuJidz20kv8tWcPT5omIcB5YCYQDoy86y4mDx1awLuV0uD5L77grYULgQHAYKAisBi78R431vNj9cTX8fbwcGmMQOpawagoRm/9F1FVg4jGHz8/CA11dWAicrmEhDj69fMlNjYWHx+fbM9TkpULJVkiuTuXkID/oEHck5jIF0DGOYSjQFvDoMuttzJr5EgXRVhytRgxgkMHDrAJqJvhuAU8SWqy9de0aTQNCHBqHLHnz+M/aBD9kpL4lMxjfITUMe7RuTOfP/VUjv1U79+fyufPEwlUynA8Bfg/YLnNRuz8+Xi6u2fbxzvffMOoL75gnWVx02Vt7wIjgc3vvUfLevXyfH9S+vy5bx8tn3oKeAt49rLWDRi29rw5eADP9u7tguiyERnJ4ogKRBJIFI3Bz1+zWiLFTF6TLBW+EJECm7t2LecTE3mNzB++AWoDT5km89asIS4hwQXRlVxxCQlsP3CA4WROsCD1fR4L2IEXZ81yeixfrV5NYnIyr3LlGPsDI0yTr1at4lwOY7z6r784ef48r5A5wYLUteuvAUmWxcQFC3KM5dMffqBPFgkWwBOAn2Hw6Y8/5nxDUurN+Okn3IyaQFaJfxtM61989P1PRR1WzlQYQ6TUUJIlIgW2++hR6tjtXJNNewcg0eHgyKlTRRlWibfj0CEcpL5/WakGNAL2xsQ4PZY9MTHUMwz8smnvAFxMSeHomTPZ9vFbVFT6uVlpDbgDW9Oe98o2lmPHuCWbtnJAO9Nkz9GjOfYhpd/uozGkmDeT+l2RlQ7sP14Mv0/Syr2HBMcTWvWH9HLvERFKtERKEiVZIlJglcqX54Rlkd0cxqWPzL5acpsvtatUAeBgNu3JpC7HLIqlzJXKl+eYZXExm/a8jHHtypWB7O/nGJAEVM2lWmElb29ySsMOGAaVKlTIsQ8p/SpXKI/d2J/DGQeo6FWMv08CAwl5vln6rFYwKzWrJVKCKMkSkQL7V4cOnDdNvsiizQFMNQw6NmmCXy5FESSza6tXp4aPDx+Qmnxc7ivgDPB0HivyFcR9t9xCnGnynyzaUoBphkHn66+nZloilZV+HTviZRhMAbJ6GPgDUn8ovXjvvTnG0rdTJ2YaBmezaFsLbDRN+nXsmGMfUvr1veUWHOYWUsuzXC4WN+MLQoOzm1ctRtJmtcL8phN8akH6rFZ4uKsDE5GcKMkSkQJrULs2Q7p0YaTNxntAfNrxKKAv8JtlMXbAANcFWIK9PnAg24G7ge1pxxKAj4FHgIAqVejTvr3T42jk58egzp150mbjA1Kr+QHsAu4DNlgWY+6/P8c+3NzceKhnTxYBDwGH0o6fAV4F3gCCmjalXq1aOfYz8q67SPbw4A7D4FdSE7ZkYD7Q2zAIbNCAXjdl9cSWlCW92rblpkZNsBu9gXmkfpdYwG/YjTvwdE9g5N13uzTGPEtbQhjWZ0v6rBbRRzSrJVKMqbpgLlRdUCRvkpKTeWL6dD77+WfcgcqGwVGHg6rly/PxiBFFkgiUVq/OmcNrX35JMlAdOAdcBOrXqMHv77xDlRyqGxWmxORkhn/0EZ///DOeNhuV0sa4WvnyfPrUU9x988156ueBd97hy4gITFKfKztD6oxnYOPGrJ04ETe33Ldw3LhnD/dNmMCe48epYbdzwbI4Z5rc0aIFXz7/PFWL6D2R4u30uXP0e+sdftq8AbtRAZvNixTHCa6tXpv/jXqWto0auTrE/Esr9744ujXhp3oQXbUFfn7aW0ukqKiEeyFRkiWSPwdPnOB/v/xCXEICjfz8uCcoKMdy3JI38QkJjPnySzbt3UsFLy+evusubmvRwiWx7D92jIW//kpcQgKN/fzofRVjHH3qFK/Mns3eo0ep7uvL2H79uKFu3Xz14XA4WLZ5M7///Tfubm70aNNGZdslS3/u28f3GzaQlJJC24YN6daqFXa73dVhFcxl5d6j8Sc4WImWiLMpySokSrJERESk2AoPZ3F0ayIJJILOmtUScTLtkyUiIiJS2qkwhkixpCRLREREpCRTYQyRYkdJloiIiEhpEBiYPqsVSjhs/VN7a4m4SO4lnERERESkZAgMTE22IiMh4gciOUVUdGMiov3Tm0XE+ZRkiYiIiJQ2gYGEBEJI+PR/CmNEdCYqSoUxRIqClguKiIiIlFYqjCHiEkqyREREREozFcYQKXJKskRERETKAhXGECkySrJEREREyoq0Wa2Q4HjmNn89fVYrIkKJlkhhUuELERERkbImrQphmApjiDiFZrJEREREyioVxhBxCiVZIiIiImWZCmOIFDolWSIiIiKiwhgihUhJloiIiIikUmEMkUKhwhciIiIikpkKY4gUiJIsERfbvHcv/125kpizZ7mmalUGd+lC04AAV4flUuErVzJq1izOxMfj5e7Oo9278+r992O32/Pcx8ETJ3jms8/YuGcPdrudO9u25bXQULw9PfPcx8WkJMZ/9RVfr19PssNB8zp1eGfoUBrUrp2v+5kdEcHkb77hdHw8NStVYmy/fvRs2zZffRw9fZovfv6Z7YcOUcHLiz5BQXRp2RLDyPuChAuJicxbu5aVW7diWRa3Xn89/Tt1wtvDI899WJZFxNatzF+3jriEBK7z92dI165cU61avu5HREqI0FBCIiMJiZrO6K2niCKIiGh/oqIgNNTVwYkUXzbLsixXB5Ef06ZN46233iImJoaWLVvywQcfEJjDr1Pmz5/P6NGj2b9/P40aNeLNN9+kZ8+eef56cXFx+Pr6EjtnDj7e3oVxCyIAJKek8OB77xG+ahW17XYaWxbbgROmyeM9evDBo4/m6wN0aeBwOKgzdChHTp/GB2gB7AWiAW83N6I++QT/PHyYH//VV4R99RUAbYF4YBvgaRh8M2YM3Vq3zrWPdTt2cMdLL5HgcNAEqAT8AZjAU//3f7zz0EO59hGfkMANI0Zw4MQJagCNgO3AGaBFnTpsePdd3Nxy/13X9B9+4MmPP6Yc0AY4ZrMR5XBwc8OGLB43jmo+Prn2sWH3bkLGj+dobCxtDQMD+N00qVahAt+MHk37pk1z7eNMfDx3h4WxescOGtjt+FsWG4ELlsVbDz7I03fdlWsfIlKCRUayOKICkQQSRWPw89eslpQ5CQlx9OvnS2xsLD45/PwtUZ/g5s6dy8iRIxk7diwbN26kZcuWdOvWjePHj2d5/i+//EL//v0ZOnQomzZt4u677+buu+/mr7/+KuLIRa70/BdfMHf1aj4HDjocRJgmh02T94GPfviB1+bNc3WIRa7NyJEcOX2a14EYYA1wEJgLOFJSaPb447n2sXD9el796it6AIeAX4G/gC1AgGly16uvcjIuLsc+Ei5e5PaXXqKaw0EksANYDxwB/gVMWbSIz376KddYOrzwAkdOnGBm2rVrgaPAFGDrgQP0fPXVXPv47vffeeyjj3jENIk2TVabJjsdDpYDe/fu5Z7XXiO335UdP3uWbqNHE3DuHH+Tmlz9ZprsAZokJNBz7FgOnzyZayz93nyTv3btYinwt8PBqrSYnrIsRn72GQvWrcu1DxEpwXIojJHVS89wSVlWopKsd955h4cffpghQ4bQrFkzpk+fjre3N59//nmW57/33nt0796d5557jqZNmxIWFkbr1q2ZOnVqEUcuktnpc+eY/sMPjLEshvDPul13YATwb2DKwoVcSEx0VYhFLv7CBbbt28cg4CXAK+24HbgPeBs4d/Eii377Lcd+Xpg5k1rAAiDjor4WwHdAkmny/MyZOfbxSng4FxwOvgFuynC8BhAONATGf/lljn3sOnKEvw4c4FVgEP+MsQfwFPAEELF5M2fj43PsZ+K8edxqs/EB4Jt2zAbcBsw0Tdbs3MkvO3bk2Meny5aRkJDAEtOkYYbj9YBFpomZlMT0pUtz7GPD7t0s27KFT02TbmkxAFQkdWy62Wy8MWdOrgmfiJRwWRTGaEzUFS/ttyVlXYlJspKSktiwYQNdu3ZNP2YYBl27dmX9+vVZXrN+/fpM5wN069Yt2/MBEhMTiYuLy/QSKWzLNm3iYkoK2S04ewg4c+ECa7ZvL8qwXOqtr78mBXg4m/ZBpP6DFTZ3bo797D96lAdJTWYu1wjoCPzwxx859rHw11+5EWiVRZsbqeNz+NQpTNPMto/3Fi3CTDs3Kw8ByZDjjNipuDjW7trFQ5aVntRk1A24xm7n21wSz29/+YW7LYvqWbRVAu4zTb7NZRZqUWQk1ex2sloQaAMesiw2HTjAkVOncuxHREqJS3tr+U0njNFXvC7ttxW99ZRmtaRMKjGFL06ePInD4aBmzZqZjtesWZOdO3dmeU1MTEyW58fExGT7dSZMmMD48eMLHrBIDi4kJQFQJZv2qpfOK0MzWWfPnwf+uffLVQDKkVqMIicm2b+vANWB7SkpOfaRlJKSbRyQGqMFpKSk4O7unuU55y9eBKByDn0AnLtwIduvk9v3iZHW/4Vc3pMLiYk5vidVyf177UJiIr6kzixm1wd5iEVESplsql9cKpaxmNaER4eqWIaUOSVmJquovPjii8TGxqa/Dh065OqQpBRqWa8eAD9m035p4VbzunWLIpxioU+HDtj4594vtxa4CAQ1aZJjPxU8PPghm7YLwM9AnVq1cuzjOn9/fgGym8f+ntRCHNklWADBzZsDsCyb9kv3eXurrObLUtWqXJkaFSpk+31yENjmcNAil++TFg0asMwwyGrezQJ+MAxaNGiQcx9167LH4eDvbNqXApW8vAhQlUERgUzLCi/NahF9hEmTNKslZUOJSbKqVauG3W7n2LFjmY4fO3aMWtl8YKpVq1a+zgfw8PDAx8cn00uksLVu0IDABg14yTA4fVnbUWC8YdCtZUvq55IMlCa3Xn893u7uvEFqRcGMzgEjSZ3JmvrIIzn2c1+nTvwEfH3ZcQsYA8QCEwYOzLGPSUOGcBF4Dq5ITH4EvgF65lJOa9Btt1GxXDmeJ7WaYEZHgHFATR8fOuRQ1c/Nbufhnj35zDD4/bK2ZOBpoIKnJ/1vvTXHWB7r2ZO/TZN3s2ibDmw1TYblUnX13g4dqFa+PP+22bh8zmsL8JFhMOSOO/DMIfEUkTLosmIZwaxMf1ZLyZaUZiUmyXJ3d6dNmzYsX748/ZhpmixfvpygoKAsrwkKCsp0PsBPP/2U7fkiRenzp5/mqJcXNxgGY4E5wItAC8MgyceHj4YPd3GERW/R6NGcBVoCz5D6nrwONAE2AS/cd1+Os0eQmoRdW7Uq9wL3klqo4mMgiNQCDXe3a5fj7BFA24YNGdi5M5+QWgL+Q2A20B/oBVT38eE///53jn0YhsEXzzzDrrT4x6fdzyjgBuCEzcb/Xn45xz4AXrz3Xlo2aMCtNhsPA18B7wI3GgaLDINZI0dSwcsrxz5uadaMF/r04VngDpuNz4GZQE+bjceBJ++8k9tvvDHHPjzd3Ql/7jmWGwYtDYPJabE8BrQ3DBpdey3j+vfP9X5EpAzKMKsV5jc9fVZLhTGkNCtR+2TNnTuXQYMG8fHHHxMYGMiUKVOYN28eO3fupGbNmjzwwAP4+/szYcIEILWEe6dOnZg4cSK9evVizpw5vPHGG2zcuJEbbrghT19T+2SJM+2LieHNr78mfMUKzicl4evpyaDbb+eFe+7Br2pOTwWVXr/u2kXvN97g5JkzpJD6m6Dynp5MHjqUh7t1y1MfSUlJPDBlCovXryfB4QCgqrc3I+66i7H5SATe+vpr3v7f/zh+7hwAXoZBt5tuYvYzz+R5U+OIrVt57KOP+PvwYRykPgjbvH59Pn/ySW6sXz9PfSQkJvLut9/y8XffcejMGew2G3e1a8fzffrQ7rrr8tSHZVnMW7uWdxcu5LfduwFoU68eT919N6HBwdhsWZXWuNLGPXt4c8ECvl6/nhTTxM/Xl4d79OCZu++mov6NFJG8CA9ncXRrIgkk4lQL/JpX1X5bUmLkdZ+sEpVkAUydOjV9M+Ibb7yR999/n3bt2gEQHBxM3bp1mZmhPPP8+fN55ZVX0jcjnjRpkjYjlmLH4XBwPjGRCp6eZW4D4uwkJSWx//hxAqpWxSuXmZrsmKbJybg4PN3dC/T3Nz4hgYSkJKr5+Fz1+CQlJXHy3Dlq+PrmaQPirFiWRfyFC3i6u1PuKvuAf4pceHlkVYMxb1IcDi4kJlLByyvPCZqISLrISIiKYnF0a8IJJRp//PxUGEOKv1KbZBU1JVkiIiIiThIZyeKICkQSSBSNicaf4GDNaknxldckq8SUcBcRERGRUiYwkBDSyr1fWkIY0ZmoKLSEUEo0rUsSEREREddRYQwphZRkiYiIiIjrpSVbGcu9R289pXLvUiIpyRIRERGR4iPDrNbzVWdoVktKJCVZIiIiIlK8ZFhCeGlWi+gjTJqkWS0pGVT4QkRERESKJxXGkBJKM1kiIiIiUnypMIaUQEqyRERERKT4U2EMKUGUZImIiIhIyaHCGFICKMkSERERkZJFhTGkmFPhCxEREREpmVQYQ4opzWSJiIiISMmlwhhSDCnJEhEREZGSL4vCGEQfUWEMcQklWSIiIiJSemSY1QolXLNa4hJKskSk0J2/eJFjZ86Q4nBcdR9JyckcO3OGC4mJhRjZ1YlLSOBEbCymabo0jhSHg+NnzxJ/4YJL4ygsxWmMRaSUUWEMcTEVvhCRQvPrzp28MW8e323YgGlZVPH2Zsgdd/DyffdRuUKFPPURc+YMr82dy6zlyzmXmIibYdD75pt5pW9fWtSr5+Q7yOy733/nzfnzWbNzJwDXVK7MsF69eLZ3bzzKlSuyOM4lJDBhwQJmLF3Kifh4ALq1bMmLffvS6YYbiiyOwhJ96hSvzZvHf5cvJz4pCTfDoE/79rzSty831Knj6vBEpDRRYQxxEZtlWZargyjO4uLi8PX1JXbOHHy8vV0djkixteT337nn9ddpAjxqmlwDrAFmGAb+tWqxetIkqvr45NjH4ZMnueW55zh/5gzDTJObgD3AR4bBEbudH8PCuKVZM+ffDDB1yRJGfPIJHQ2DIaZJJeB7YJbNxq033MCSceOKJNGKS0ig86hRRB08yIOmyW1ADPCpYbDZspj97LP07djR6XEUloMnTnDLs89yMTaWYaZJW2A3qWN81M2Nn157jaAmTVwdpoiURpGREBXF6OhhRNGYaPzx84PQUFcHJiVJQkIc/fr5Ehsbi08On2uUZOVCSZZI7i4mJXHNoEF0SEhggWWRMfXYBbQ3DPp268aHjz2WYz/3TpjAb7/9xvq0JO2SBKC7zcaRatX4+9NPMQznrnQ+eOIE9R96iCcsi3cBW4a2COB2m423HnyQf991l1PjAHhh5kw+/OYb1pomLTMcdwADgcXu7hyZNavE/PvU+7XX2PjHH6w3TfwyHD8PdDMMjlevzs6PP3b6GItIGRYe/s+s1qkW+DWvqlktybO8Jln6KSYiBbZg3TpOnT/P5MsSLIDrgBGmyX+XL+f8xYvZ9hFz5gzf/Poroy5LsAC8gUmWxd4TJ/hp8+bCDT4LM5Yto7zNxmtkTrAAgoF7LYvp333n9DiSU1L47McfeeSyBAvADrwNXEhKYnZEhNNjKQxHTp1i0e+/89JlCRZAeWCiafL3sWOs3LrVFeGJSFmRoTDG81VnqDCGOIWSLBEpsG0HD1LXbqdhNu1dgfikJA6dPJltH1FHjuCwLLpk094OKG8YbD90qIDR5m7bwYPcbFlk9xRZV2BXTAyOAhT2yItjZ89y6vz5bN8TP+B6u71I3pPCsOvwYcwcxrgD4GmzlZj7EZESTIUxxMmUZIlIgZX39OSMZZGUTfvxtP96e3jk2EfGcy8XB1w0zRz7KCzlPT05brt8DusfxwEPNzenL2m7dK/ZvScmcJKc39fiJLcxPgskWhbe7u5FFZKIlHWBgZnKvQezMn1WS8mWFISSLBEpsN5BQcSaJvOyaLOAj2022tSrR0C1atn2cWO9etSpWpWPs2n/HMBmI+SmmwoecC5633wzm02TrH6+JgGfGwb3BAVhyyERKwxVKlYkuFkzPjUMsioevwSIdji4JyjIqXEUljYNGxJQuXK2YzwDcDMMehXBGIuIpMswqxXmNz19VktLCKUglGSJSIFdf+219A4M5HHDYAGpRRkATgNPAsssi5f79csxKbHb7bzUty9fAq+QOnMFkAx8Abxos/Fg1674Va3qvBtJExIYSIuAAPoYBqtITRQBDgH32WwctNl49p57nB4HwIt9+7LeNHmIf2aATFITrCGGQZcbbiCwceMiiaWg3Ox2Xuzbl1nAWOBc2vEk4DPgFZuNh+64g1qVK7ssRhEpw9KSrYyzWkQf0ayWXBVVF8yFqguK5E38hQv0e/NNvtu4ET+7HT/gL9PENAzeffhhHu/ZM9c+LMvi9XnzGPfll3jZbDSx2TgIHHc4uP/WW/n8qaeKbH+q6FOnCBk/no3791PfbscX2OJwUMHTk9nPPcedRTjb8p/lyxk2bRqmw0Fzw+A4cMjhoPP11/O/l1/O8x5kxYFlWbw6Zw6vzpmDd9oYHwBOOByEdurEZ08+iXsR7kEmIpKltHLvi6NbE06oyr1LOpVwLyRKskTy5/e//2bumjXEJSTQyM+PQbfdRo1KlfLVx+GTJ/nPihXsP36cqhUrMqBTJ5rXreuUeHNimibLt2xh8e+/k5icTKv69RnQqRMVXfBvwam4OP67ciXbDx2igpcXfYKCaN+0qdOXLDrLoRMn+M+KFRw4cYJqFSsyIDhYGxGLSPETGcniiApEEpi+t1ZwsMq9l2VKsgqJkiwRERGRMizDrFYkgUTQGT8/tLdWGaV9skRERERECkqFMeQqKMkSEREREcmNCmNIPijJEhERERHJqwyzWqGEa1ZLsqQkS0REREQkPzIsIcw4qzVpkma1JJWbqwMQERERESmRAgMJIZKQqOn/FMaI6ExUlApjlHVKskRERERErlZgYGqyFZmabI2OhqjoxkRE+6cnW1ldIqWbkiwRERERkYJKS7bCwv+Z1YqKbkwU/lecqpmu0k9JloiIiIhIYQkNTZ/VWhzdGrJIssK3tiAiuoWSrVJMSZaIiIiISGHKsIQQdl3RHMJiRkcPS19WeOkSKT2UZImIiIiIOEN2mdNlywpVLKP0UQl3EREREZGilmG/reBTC7TfVimjJEtERERExBXS9tsK67Ml035b4eHab6ukU5IlIiIiIuJKgYHps1qhhMPWP9NntZRslUxKskREREREXC1tViskOJ65zV9Pn9WKiFCiVRKp8IWIiIiISHGRxX5bKoxR8ijJKgMuJCay4Jdf+OvAAbw9PLj75ptpWa+eq8OSYsY0TVb8+Scrt27Fsiw6NG1K99atsdvt+epn1+HDLPjlF2LPn6exvz99b7mFit7eTopaRESklMqw39boraeIIoiIaH+ioiA01NXBSW5slmVZrg6iOIuLi8PX15fYOXPwKYEfFJf8/juDJk/mdEIC9e12zlgWZ0yTkDZtCH/uuRJ5T1L49sbEcHdYGFsPHaK23Y4dOOxw0KhmTRaOHs31116bax8Xk5J46P33mb16NT6GQXWbjX0OB+U9PPho+HAGBAc7/T5ERERKpchIFkdUIJJAomgMfv6a1XKRhIQ4+vXzJTY2Fh8fn2zP0zNZpdhvu3Zxz+uvc8uFC/wN7HE4OGaafAms2rSJ+yZMQDm2xCUk0PWll7h45AgRwBGHg4MOB+sBrxMn6PrSSxw/ezbXfh7+4AP+t2YNnwDHTJPdDgf7gbsSExn4zjss3bDBmbchIiJSeqkwRomjJKsUe2PePK4DFlgWDdOOlQP6AzNNkx+3bOG3XVfuQi5ly6wVKzh48iQ/miadAFva62ZgmWkSFx/Px0uX5thH1JEjhK9axfuWxcOAZ9rxAOA/QEebjbCvvnLiXYiIiJRyKoxRoijJKqUSEhNZ8scfPGqalMui/S7gGrudeevWFXVoUszMW72aXkBWT+nVBP5lmsxbtSrHPhasW0dFw2BgFm0G8Lhl8UtUFEdOnSp4wCIiImXZpb210ma1glmpWa1iSElWKXX+4kVMy8I/m3YD8CN1qZiUbXHnz2f7fQJwDbl/n8RduEA1my19BiurPshDPyIiIpJHabNaYX7TCT61IH1WKzzc1YEJKMkqtapUqEDV8uVZk037aeBP06Sxn19RhiXFUOOAANYYBtk9nbfKMGh8zTXZtKa6zt+fAw4HB7LrA/B0c+OaqlULEqqIiIhkdGlWq8+W9Fktoo9oVqsYUJJVStntdoZ268YMw2D7ZW0WMAYwDYPBXbq4IDopTh7p3p2/TJP/ZNH2LbDWNHm0Z88c+7jvlluo6OnJKMBxWdtB4H3D4P7gYJVyFxERcQYVxih2lGSVYi/eey91/f3pYBi8QupswnzgDpuNacC7Dz9MjUqVXBqjuF6Xli0Z2rUrDwKDgB+AZcAjwL02G31uvpneN9+cYx/lPT35eMQI5tls3GoYzAZWA68BNxkG3lWq8PrArJ7YEhERkUKhwhjFivbJykVJ3yfrTHw8Y2bP5j8//8y5xEQA2tavz0t9+9I7KMjF0UlxYZom7y9ezHvffMP+tOIU/pUqMTwkhOfuuQe3PG5I/NOmTYR99RVrdu4EwKtcOfp36sRroaHUrlLFafGLiIjIZcLDWRzdmkgCiaAzfn5ob61CkNd9spRk5aKkJ1mXJCQmcvjkSbw9PLimWjVXhyPFlMPhYP/x41iWRd2aNfOcXF3u6OnTxCUk4F+1KhW8vAo5ShEREcmTyEiIimL01n8RVTWIaPzx84PQUFcHVnIpySokpSXJEhEREZEyKjKSxREViCSQKBqDn79mta5SXpOsEvNM1unTpxkwYAA+Pj5UqlSJoUOHEh8fn+P5I0aM4LrrrsPLy4trr72WJ598ktjY2CKMWkRERETExVQYo8iVmCRrwIABbNu2jZ9++oklS5awevVqHnnkkWzPj46OJjo6mrfffpu//vqLmTNnsnTpUoYOHVqEUYuIiIiIFAMqjFGkSsRywR07dtCsWTN+//132rZtC8DSpUvp2bMnhw8fxi+Pez3Nnz+f0NBQzp8/j5ubW56u0XJBERERESl1VBjjqpSq5YLr16+nUqVK6QkWQNeuXTEMg99++y3P/Vx6M3JKsBITE4mLi8v0EhEREREpVdJmtcL8phN8akH6rFZ4uKsDKx1KRJIVExNDjRo1Mh1zc3OjSpUqxMTE5KmPkydPEhYWluMSQ4AJEybg6+ub/goICLjquEVEREREiq20JYRhfbYQSnj6EkI9q1VwLk2yRo0ahc1my/G1M22/nYKIi4ujV69eNGvWjHHjxuV47osvvkhsbGz669ChQwX++iIiIiIixZYKYxS6vD2Y5CTPPPMMgwcPzvGc+vXrU6tWLY4fP57peEpKCqdPn6ZWrVo5Xn/u3Dm6d+9OxYoVWbhwIeXKlcvxfA8PDzw8PPIUv4iIiIhIqRAYmJpsRUYSEvU6o6OHERXdmIho//RmyTuXJlnVq1enevXquZ4XFBTE2bNn2bBhA23atAFgxYoVmKZJu3btsr0uLi6Obt264eHhwaJFi/D09Cy02EVERERESp20ZCssfPo/hTEiOhMVpcIY+VEinslq2rQp3bt35+GHHyYyMpJ169bxxBNP0K9fv/TKgkeOHKFJkyZEps1pxsXFcccdd3D+/Hk+++wz4uLiiImJISYmBofD4crbEREREREp3lQYo0BcOpOVH7Nnz+aJJ56gS5cuGIZBnz59eP/999Pbk5OT2bVrFwkJCQBs3LgxvfJgw4YNM/W1b98+6tatW2Sxi4iIiIiUOJdmtSIjWRyxh0gCiYpuTHi4v2a1clEi9slyJe2TJSIiIiJlXmQkREWxOLo14ad6EF21RZncW6tU7ZMlIiIiIiIulFbuPSQ4nrnNX08v9x4RoQqEWSkxywVFRERERMTFVBgjTzSTJSIiIiIi+aPCGDlSkiUiIiIiIvmXtoQwrM8WQglPX0KoTYy1XFBKmOhTp3jw/fdZtWULF02TckCzevWYNmwYHZo2LdJYwleu5JXwcA6fOIEJVHB3p0/Hjnz82GO4u7sXWRzJKSnMWLaM6d99x7bDhynv4cE97dvzTO/e3FCnTpHFUVjiEhKYumQJny1dyv5Tp6havjz3d+7MM3ffTUAe9tUDsCyLOatXM3XxYn7fvRs3u53urVvzTO/edGjWzMl3ICIiUsYEBhJCJCFRqUsIw7f2ICK6RZleQqjqgrlQdcHiY19MDC2GDycpOZn+QDtgHzADOAd89fzz3HvLLUUSy3Off87kb76hDjAUqAx8BywFAqpW5e+PPy6SRCspOZm7wsL4acsW7gK6WBbHgZmGwTHDYNHo0dzeqpXT4ygsp8+do/OoUUQdPkx/yyIQ2AN8YRgY3t6smDAh18TRsiyGTZvGJ8uW0dVm4/8si/NAuGGw3TSZMWIED95+e1HcjoiISNmTVoVwdPQwomhMNP4EB2d9aklMvvJaXVBJVi6UZBUfTYYN42h0NGuB5hmOnwY6A7vtds79738YhnNXwe46coRmjz3GvUA4UC5D29fAvUD/Tp2Y/cwzTo0DYOKCBYz573/53rLomuH4ReAem41fvbw4NHMm5T09nR5LYRgyZQqLIiJYbZpcn+H4KeA2w8Dy82PLtGnYbLZs+5i3di19J03iC2BwhuMm8Bjwmc3G7k8+oW7Nms64BREREQEID08vjBHl1znb00raTJdKuEupcuDYMf6OjuYFMidYAFWA94AEh4MpixY5PZaRn32GHZhG5gQL4B4gBPh23Tqnx2GaJh8uXszAyxIsAE9gmmVxNiGBOatXOz2WwnD63Dm+WrWK5y9LsACqApNNk62HD7N2+/Yc+5m2eDHBhpEpwYLUf+zeASrYbHzy44+FFreIiIhkIUNhjNDoNwkl/IpXaS6WoSRLSoQfN2/GJDWByUonwBtY/uefTo9l6/79BAHVsmm/CzifkkJ8QoJT4zgRG8uhM2eyfU/qAS3sdv7YvdupcRSWbQcPkuhwZHs/XQAvmy3X+9mwZw8hppllW3ngNtPkj7//LlCsIiIikgcZ9tYKabzrilfGYhmTJpWuYhkqfCElgnfa803nsmlPBJIBz3KXzy0VvnJ2O3E5tF+K0d3NuX+93NPuNbv3xEprcy+C96QwXHq/srufC6SOcW7vq7vdnm0fAOdstiL5PhEREZE02awHzFgso7Ttt6WZLCkR7gkKwt1mY2Y27XNJ/QA+rHt3p8fS66ab2AxszaLNBL4Aavj4OL3wReUKFbi5YUNm2mxk9WDlWmCvw0Gvtm2dGkdhaVW/PjUrVsx2jL8CHJZF99atc+ynZ2Ag/7XbScmi7SCwwrLoddNNBQtWRERECi7DTFeY3/T0Wa3SsIRQSZaUCN6entzepg2fAlMh/QO0BfwMjAD8K1Uqkkp6b4SG4mkY3APszHA8HhgObAGe79PH6XEAPP+vf7HCsniR1JmeSzYBoYbBjXXq0LVlyyKJpaDcy5Xj6Xvu4WPgQzKP8Y/ASMOgT1AQDWrXzrGfp++6i/2myWDgbIbj+4DehkENHx8GdOpU6PGLiIjIVbq035bf9FKz35aSLCkxvnnpJZrXqcMI4BpSn326AbgdcPPy4pfJk4skjgre3iwaO5ZDhkFToD1wJ1AT+BgYGBzMM717F0ksvYOCeGvIECYB/obB/wHtDIPWQIVatVg0ZozTqy0Wpud692ZYjx4MB+rZ7dwNNDcMugNtmjbls6eeyrWPNg0bMvvZZ5lvt+Nvs9ELCLbZaAAcrViRpWFhVFSlUBERkeInY7GMEl4YQyXcc6ES7sXPV6tWMWHBAmLOnKGilxdDunZlVJ8+uDn5GajLnY6LY9SsWXy/YQMpKSk08vNj0pAhBDVpUqRxAOyOjubTZcvYdvAg5T09uScoiN4331xinse63MY9e/jsp5/Yf+wYVdNmnm6/8cZ8JYxHT59mxrJl/P7335Rzc6N769bc36lTiSlnLyIiUqZFRrI4okJqCfgM+225+lkt7ZNVSJRkiYiIiIi4QNrGxumFMeiMn59rC2NonywRERERESm5SnBhDCVZIiIiIiJSfJXAwhhKskREREREpPgrQYUxlGSJiIiIiEjJkGEJYcZZrUmTitesVtGWYxMRERERESmowEBCiCQkavo/hTEiOhMV5drCGJdoJktEREREREqeYlwYQ0mWiIiIiIiUXMWwMIaSLBERERERKfmKUWEMJVkiIiIiIlI6FJPCGCp8ISIiIiIipYuLC2NoJktEREREREofFxbGUJIlIiIiIiKllwsKYyjJEhERERGR0q8IC2MoyRIRERERkbKhiApjqPCFiIiIiIiULU4ujKGZLBERERERKXucWBhDSZaIiIiIiJRdTiiMoSRLRERERESkEAtjKMkSERERERGBQiuMocIXIiIiIiIiGWVTGGPbtrxdriRLRERERETkcoGBqclWZGqyNToath2tnadLtVxQREREREQkOxkKY/Rlbp4u0UyWiIiIiIhIbkJD6ZGQAP1yP1UzWSIiIiIiIoVISZaIiIiIiEgh0nLBXFiWBUBcQoKLIxEREREREVe6lBNcyhGyY7NyO6OMO3z4MAEBAa4OQ0REREREiolDhw5xzTXXZNuuJCsXpmkSHR1NxYoVsdlsOZ4bFxdHQEAAhw4dwsfHp4gilNxoXIonjUvxpHEpnjQuxZPGpXjSuBRPpWVcLMvi3Llz+Pn5YRjZP3ml5YK5MAwjxyw1Kz4+PiX6m6e00rgUTxqX4knjUjxpXIonjUvxpHEpnkrDuPj6+uZ6jgpfiIiIiIiIFCIlWSIiIiIiIoVISVYh8vDwYOzYsXh4eLg6FMlA41I8aVyKJ41L8aRxKZ40LsWTxqV4KmvjosIXIiIiIiIihUgzWSIiIiIiIoVISZaIiIiIiEghUpIlIiIiIiJSiJRkiYiIiIiIFCIlWQV0+vRpBgwYgI+PD5UqVWLo0KHEx8fn6VrLsujRowc2m41vvvnGuYGWMfkdl9OnTzNixAiuu+46vLy8uPbaa3nyySeJjY0twqhLn2nTplG3bl08PT1p164dkZGROZ4/f/58mjRpgqenJ82bN+f7778vokjLlvyMy6effkrHjh2pXLkylStXpmvXrrmOo1yd/P59uWTOnDnYbDbuvvtu5wZYRuV3XM6ePcvw4cOpXbs2Hh4eNG7cWP+WOUF+x2XKlCnpP+MDAgJ4+umnuXjxYhFFW/qtXr2akJAQ/Pz88vy5NiIigtatW+Ph4UHDhg2ZOXOm0+MsUpYUSPfu3a2WLVtav/76q7VmzRqrYcOGVv/+/fN07TvvvGP16NHDAqyFCxc6N9AyJr/jsnXrVuuee+6xFi1aZO3evdtavny51ahRI6tPnz5FGHXpMmfOHMvd3d36/PPPrW3btlkPP/ywValSJevYsWNZnr9u3TrLbrdbkyZNsrZv32698sorVrly5aytW7cWceSlW37H5f7777emTZtmbdq0ydqxY4c1ePBgy9fX1zp8+HARR1665XdcLtm3b5/l7+9vdezY0brrrruKJtgyJL/jkpiYaLVt29bq2bOntXbtWmvfvn1WRESEtXnz5iKOvHTL77jMnj3b8vDwsGbPnm3t27fP+vHHH63atWtbTz/9dBFHXnp9//331ssvv2x9/fXXefpcu3fvXsvb29saOXKktX37duuDDz6w7Ha7tXTp0qIJuAgoySqA7du3W4D1+++/px/74YcfLJvNZh05ciTHazdt2mT5+/tbR48eVZJVyAoyLhnNmzfPcnd3t5KTk50RZqkXGBhoDR8+PP3PDofD8vPzsyZMmJDl+ffdd5/Vq1evTMfatWtnPfroo06Ns6zJ77hcLiUlxapYsaL1n//8x1khlklXMy4pKSlW+/btrRkzZliDBg1SkuUE+R2Xjz76yKpfv76VlJRUVCGWSfkdl+HDh1u33XZbpmMjR460OnTo4NQ4y6q8fK59/vnnreuvvz7Tsb59+1rdunVzYmRFS8sFC2D9+vVUqlSJtm3bph/r2rUrhmHw22+/ZXtdQkIC999/P9OmTaNWrVpFEWqZcrXjcrnY2Fh8fHxwc3NzRpilWlJSEhs2bKBr167pxwzDoGvXrqxfvz7La9avX5/pfIBu3bple77k39WMy+USEhJITk6mSpUqzgqzzLnacXn11VepUaMGQ4cOLYowy5yrGZdFixYRFBTE8OHDqVmzJjfccANvvPEGDoejqMIu9a5mXNq3b8+GDRvSlxTu3buX77//np49exZJzHKlsvAzX58eCyAmJoYaNWpkOubm5kaVKlWIiYnJ9rqnn36a9u3bc9dddzk7xDLpasclo5MnTxIWFsYjjzzijBBLvZMnT+JwOKhZs2am4zVr1mTnzp1ZXhMTE5Pl+XkdM8nd1YzL5V544QX8/Pyu+OEoV+9qxmXt2rV89tlnbN68uQgiLJuuZlz27t3LihUrGDBgAN9//z27d+/m8ccfJzk5mbFjxxZF2KXe1YzL/fffz8mTJ7nllluwLIuUlBSGDRvGSy+9VBQhSxay+5kfFxfHhQsX8PLyclFkhUczWVkYNWoUNpstx1deP5BcbtGiRaxYsYIpU6YUbtBlgDPHJaO4uDh69epFs2bNGDduXMEDFyklJk6cyJw5c1i4cCGenp6uDqfMOnfuHAMHDuTTTz+lWrVqrg5HMjBNkxo1avDJJ5/Qpk0b+vbty8svv8z06dNdHVqZFhERwRtvvMGHH37Ixo0b+frrr/nuu+8ICwtzdWhSimkmKwvPPPMMgwcPzvGc+vXrU6tWLY4fP57peEpKCqdPn852GeCKFSvYs2cPlSpVynS8T58+dOzYkYiIiAJEXro5c1wuOXfuHN27d6dixYosXLiQcuXKFTTsMqlatWrY7XaOHTuW6fixY8eyHYNatWrl63zJv6sZl0vefvttJk6cyM8//0yLFi2cGWaZk99x2bNnD/v37yckJCT9mGmaQOqs/a5du2jQoIFzgy4DrubvS+3atSlXrhx2uz39WNOmTYmJiSEpKQl3d3enxlwWXM24jB49moEDB/LQQw8B0Lx5c86fP88jjzzCyy+/jGFozqGoZfcz38fHp1TMYoFmsrJUvXp1mjRpkuPL3d2doKAgzp49y4YNG9KvXbFiBaZp0q5duyz7HjVqFH/++SebN29OfwG8++67fPHFF0VxeyWWM8cFUmew7rjjDtzd3Vm0aJF+U18A7u7utGnThuXLl6cfM02T5cuXExQUlOU1QUFBmc4H+Omnn7I9X/LvasYFYNKkSYSFhbF06dJMzzpK4cjvuDRp0oStW7dm+jnyf//3f3Tu3JnNmzcTEBBQlOGXWlfz96VDhw7s3r07PekFiIqKonbt2kqwCsnVjEtCQsIVidSlRNiyLOcFK9kqEz/zXV15o6Tr3r271apVK+u3336z1q5dazVq1ChTqfDDhw9b1113nfXbb79l2weqLljo8jsusbGxVrt27azmzZtbu3fvto4ePZr+SklJcdVtlGhz5syxPDw8rJkzZ1rbt2+3HnnkEatSpUpWTEyMZVmWNXDgQGvUqFHp569bt85yc3Oz3n77bWvHjh3W2LFjVcLdCfI7LhMnTrTc3d2tBQsWZPp7ce7cOVfdQqmU33G5nKoLOkd+x+XgwYNWxYoVrSeeeMLatWuXtWTJEqtGjRrWa6+95qpbKJXyOy5jx461KlasaH311VfW3r17rWXLllkNGjSw7rvvPlfdQqlz7tw5a9OmTdamTZsswHrnnXesTZs2WQcOHLAsy7JGjRplDRw4MP38SyXcn3vuOWvHjh3WtGnTVMJdMjt16pTVv39/q0KFCpaPj481ZMiQTB8+9u3bZwHWypUrs+1DSVbhy++4rFy50gKyfO3bt881N1EKfPDBB9a1115rubu7W4GBgdavv/6a3tapUydr0KBBmc6fN2+e1bhxY8vd3d26/vrrre+++66IIy4b8jMuderUyfLvxdixY4s+8FIuv39fMlKS5Tz5HZdffvnFateuneXh4WHVr1/fev311/XLOifIz7gkJydb48aNsxo0aGB5enpaAQEB1uOPP26dOXOm6AMvpbL7HHVpHAYNGmR16tTpimtuvPFGy93d3apfv771xRdfFHnczmSzLM2TioiIiIiIFBY9kyUiIiIiIlKIlGSJiIiIiIgUIiVZIiIiIiIihUhJloiIiIiISCFSkiUiIiIiIlKIlGSJiIiIiIgUIiVZIiIiIiIihUhJloiIiIiISCFSkiUiIiXC4MGDsdlsV7x2795dKP3PnDmTSpUqFUpfV2v16tWEhITg5+eHzWbjm2++cWk8IiJydZRkiYhIidG9e3eOHj2a6VWvXj1Xh3WF5OTkq7ru/PnztGzZkmnTphVyRCIiUpSUZImISInh4eFBrVq1Mr3sdjsA3377La1bt8bT05P69eszfvx4UlJS0q995513aN68OeXLlycgIIDHH3+c+Ph4ACIiIhgyZAixsbHpM2Tjxo0DyHJGqVKlSsycOROA/fv3Y7PZmDt3Lp06dcLT05PZs2cDMGPGDJo2bYqnpydNmjThww8/zPH+evTowWuvvUbv3r0L4d0SERFXcXN1ACIiIgW1Zs0aHnjgAd5//306duzInj17eOSRRwAYO3YsAIZh8P7771OvXj327t3L448/zvPPP8+HH35I+/btmTJlCmPGjGHXrl0AVKhQIV8xjBo1ismTJ9OqVav0RGvMmDFMnTqVVq1asWnTJh5++GHKly/PoEGDCvcNEBGRYkVJloiIlBhLlizJlPz06NGD+fPnM378eEaNGpWevNSvX5+wsDCef/759CTr3//+d/p1devW5bXXXmPYsGF8+OGHuLu74+vri81mo1atWlcV27///W/uueee9D+PHTuWyZMnpx+rV68e27dv5+OPP1aSJSJSyinJEhGREqNz58589NFH6X8uX748AFu2bGHdunW8/vrr6W0Oh4OLFy+SkJCAt7c3P//8MxMmTGDnzp3ExcWRkpKSqb2g2rZtm/7/58+fZ8+ePQwdOpSHH344/XhKSgq+vr4F/loiIlK8KckSEZESo3z58jRs2PCK4/Hx8YwfPz7TTNIlnp6e7N+/nzvvvJPHHnuM119/nSpVqrB27VqGDh1KUlJSjkmWzWbDsqxMx7IqbHEp4bsUD8Cnn35Ku3btMp136RkyEREpvZRkiYhIide6dWt27dqVZQIGsGHDBkzTZPLkyRhGas2nefPmZTrH3d0dh8NxxbXVq1fn6NGj6X/++++/SUhIyDGemjVr4ufnx969exkwYEB+b0dEREo4JVkiIlLijRkzhjvvvJNrr72We++9F8Mw2LJlC3/99RevvfYaDRs2JDk5mQ8++ICQkBDWrVvH9OnTM/VRt25d4uPjWb58OS1btsTb2xtvb29uu+02pk6dSlBQEA6HgxdeeIFy5crlGtP48eN58skn8fX1pXv37iQmJvLHH39w5swZRo4cmeU18fHxmfb92rdvH5s3b6ZKlSpce+21BXuTRESkyKiEu4iIlHjdunVjyZIlLFu2jJtuuombb76Zd999lzp16gDQsmVL3nnnHd58801uuOEGZs+ezYQJEzL10b59e4YNG0bfvn2pXr06kyZNAmDy5MkEBATQsWNH7r//fp599tk8PcP10EMPMWPGDL744guaN29Op06dmDlzZo77ev3xxx+0atWKVq1aATBy5EhatWrFmDFjrvatERERF7BZly80FxERERERkaummSwREREREZFCpCRLRERERESkECnJEhERERERKURKskRERERERAqRkiwREREREZFCpCRLRERERESkECnJEhERERERKURKskRERERERAqRkiwREREREZFCpCRLRERERESkECnJEhERERERKURKskRERERERArR/wNBYnr6bCxx3QAAAABJRU5ErkJggg==", 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", 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" ] @@ -136,10 +149,9 @@ "y_pred = sgd_clf_binary.predict(X_binary)\n", "accuracy = (y_pred == y_binary).mean()\n", "plot_decision_boundary(\n", - " sgd_clf_binary,\n", " X_binary,\n", " y_binary,\n", - " n_iterations=N_ITERATIONS,\n", + " clf=sgd_clf_binary,\n", " accuracy=accuracy,\n", " title=\"Scikit-Learn decision boundary\",\n", ")" @@ -163,103 +175,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Compiling training circuit ...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compilation took 2.1670 seconds.\n", - "Key Generation...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Key generation took 2.6752 seconds.\n", - "Training on encrypted data...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 0 took 3.3890 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 1 took 2.7277 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 2 took 2.5810 seconds.\n", - "Iteration 3 took 2.6149 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 4 took 2.0795 seconds.\n", - "Iteration 5 took 1.9905 seconds.\n", - "Iteration 6 took 2.1283 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 7 took 2.0645 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 8 took 3.8554 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 9 took 2.4222 seconds.\n", - "Iteration 10 took 2.0885 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 11 took 2.0956 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 12 took 2.1080 seconds.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 13 took 2.1329 seconds.\n", - "Iteration 14 took 3.2754 seconds.\n" + "Compiling training circuit ...\n", + "Compilation took 1.5863 seconds.\n", + "Key Generation...\n", + "Key generation took 2.9771 seconds.\n", + "Training on encrypted data...\n", + "Iteration 0 took 3.3669 seconds.\n", + "Iteration 1 took 3.0545 seconds.\n", + "Iteration 2 took 2.7963 seconds.\n", + "Iteration 3 took 2.8256 seconds.\n", + "Iteration 4 took 2.5419 seconds.\n", + "Iteration 5 took 2.2143 seconds.\n", + "Iteration 6 took 2.2999 seconds.\n", + "Iteration 7 took 2.1388 seconds.\n", + "Iteration 8 took 2.1676 seconds.\n", + "Iteration 9 took 2.4134 seconds.\n", + "Iteration 10 took 2.2311 seconds.\n", + "Iteration 11 took 2.1930 seconds.\n", + "Iteration 12 took 2.2134 seconds.\n", + "Iteration 13 took 2.2318 seconds.\n", + "Iteration 14 took 2.6019 seconds.\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -298,10 +238,9 @@ "accuracy = (y_pred == y_binary).mean()\n", "\n", "plot_decision_boundary(\n", - " sgd_clf_binary_fhe,\n", " X_binary,\n", " y_binary,\n", - " n_iterations=N_ITERATIONS,\n", + " clf=sgd_clf_binary_fhe,\n", " accuracy=accuracy,\n", " title=\"Concrete ML (training on encrypted data with FHE) decision boundary\",\n", ")" @@ -358,14 +297,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Sklearn clear accuracy: 99.42%\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Full encrypted fit (simulated) accuracy 90.06%\n" + "Sklearn clear accuracy: 95.32%\n", + "Full encrypted fit (simulated) accuracy 83.63%\n" ] } ], @@ -408,7 +341,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -462,6 +395,352 @@ "plt.grid(True)\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deployment\n", + "\n", + "In this section, we will prepare a model for deployment. This involves initializing the model with the necessary parameters and compiling it with sample data to ensure it is ready for production." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
SGDClassifier(fit_encrypted=True, max_iter=15, parameters_range=(-1.0, 1.0),\n",
+       "              random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "SGDClassifier(fit_encrypted=True, max_iter=15, parameters_range=(-1.0, 1.0),\n", + " random_state=42)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initialize the model with parameters\n", + "parameters_range = (-1.0, 1.0)\n", + "batch_size = 8\n", + "\n", + "sgd_clf_binary_fhe = SGDClassifier(\n", + " random_state=RANDOM_STATE,\n", + " max_iter=N_ITERATIONS,\n", + " fit_encrypted=True,\n", + " parameters_range=parameters_range,\n", + ")\n", + "\n", + "# Generate the min and max values for X_binary and y_binary\n", + "x_min, x_max = X_binary.min(axis=0), X_binary.max(axis=0)\n", + "y_min, y_max = y_binary.min(), y_binary.max()\n", + "\n", + "# Create a dataset with the min and max values for each feature, repeated to fill the batch size\n", + "x_compile_set = np.vstack([x_min, x_max] * (batch_size // 2))\n", + "\n", + "# Create a dataset with the min and max values for y, repeated to fill the batch size\n", + "y_compile_set = np.array([y_min, y_max] * (batch_size // 2))\n", + "\n", + "# Fit the model with the created dataset to compile it for production\n", + "# This step ensures the model knows the number of features, targets and features distribution\n", + "sgd_clf_binary_fhe.fit(x_compile_set, y_compile_set, fhe=\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from concrete.ml.deployment import FHEModelDev\n", + "\n", + "# Save the training FHE circuit for production\n", + "fhe_dev = FHEModelDev(\"fhe_training_sgd\", sgd_clf_binary_fhe)\n", + "fhe_dev.save(mode=\"training\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from concrete.ml.deployment import FHEModelClient\n", + "\n", + "# On the client side, load the circuit.zip with the information to create\n", + "# - the key\n", + "# - the pre and post processing functions\n", + "\n", + "fhe_client = FHEModelClient(\"fhe_training_sgd\")\n", + "fhe_client.load()\n", + "serialized_evaluation_key = fhe_client.get_serialized_evaluation_keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from concrete.ml.deployment import FHEModelServer\n", + "\n", + "# On the server side, we load the server.zip which contain the training model\n", + "fhe_server = FHEModelServer(\"fhe_training_sgd\")\n", + "fhe_server.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Define utils function to evaluate the model, print it's accuracy\n", + "\n", + "\n", + "def model_inference(weights, bias, X):\n", + " # Compute the linear model\n", + " linear_model = np.dot(X, weights[0]) + bias[0]\n", + "\n", + " # Apply the sigmoid function\n", + " sigmoid = 1 / (1 + np.exp(-linear_model))\n", + "\n", + " # Compute the prediction\n", + " prediction = np.round(sigmoid)\n", + "\n", + " return prediction\n", + "\n", + "\n", + "def compute_model_accuracy(weights, bias, X, y):\n", + " # Compute the prediction\n", + " prediction = model_inference(weights, bias, X).squeeze()\n", + "\n", + " # Compute the accuracy\n", + " return np.mean(prediction == y)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1 completed. Accuracy: 0.94\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "batch_size = sgd_clf_binary_fhe.batch_size\n", + "\n", + "# Initialize the weight and bias randomly\n", + "# They are going to be updated using FHE training.\n", + "weights = np.random.rand(1, 2, 1)\n", + "bias = np.random.rand(1, 1, 1)\n", + "\n", + "# Shuffle X_binary and y_binary\n", + "perm = np.random.permutation(X_binary.shape[0])\n", + "X_binary = X_binary[perm, ::]\n", + "y_binary = y_binary[perm]\n", + "\n", + "# Plot the decision boundaries before starting\n", + "plot_decision_boundary(\n", + " X_binary,\n", + " y_binary,\n", + " weights=weights,\n", + " bias=bias,\n", + " title=\"Decision Boundary before training\",\n", + " accuracy=compute_model_accuracy(weights, bias, X_binary, y_binary),\n", + ")\n", + "\n", + "\n", + "def train_fhe_client_server(\n", + " X_binary,\n", + " y_binary,\n", + " batch_size,\n", + " fhe_client,\n", + " fhe_server,\n", + " serialized_evaluation_key,\n", + " weights,\n", + " bias,\n", + " n_epochs=1,\n", + "):\n", + " acc_history = []\n", + "\n", + " for epoch in range(n_epochs):\n", + " # Shuffle X_binary and y_binary\n", + " perm = np.random.permutation(X_binary.shape[0])\n", + " X_binary = X_binary[perm, ::]\n", + " y_binary = y_binary[perm]\n", + "\n", + " for idx in range(X_binary.shape[0] // batch_size):\n", + " batch_range = range(idx * batch_size, (idx + 1) * batch_size)\n", + "\n", + " # Make the data X (1, batch_size, n_features)\n", + " # and y (1, batch_size, n_targets)\n", + " x_batch = X_binary[batch_range, :].reshape(1, -1, 2)\n", + " y_batch = y_binary[batch_range].reshape(1, -1, 1)\n", + "\n", + " # Encrypt the batch\n", + " x_batch_enc = fhe_client.quantize_encrypt_serialize((x_batch, y_batch, weights, bias))\n", + "\n", + " # Run the circuit on the server\n", + " results = fhe_server.run(x_batch_enc, serialized_evaluation_key)\n", + "\n", + " # Back on the client, we deserialize the result\n", + " weights, bias = fhe_client.deserialize_decrypt_dequantize(results)\n", + "\n", + " # Compute and store accuracy\n", + " acc = compute_model_accuracy(weights, bias, X_binary, y_binary)\n", + " acc_history.append(acc)\n", + "\n", + " print(f\"Epoch {epoch + 1}/{n_epochs} completed. Accuracy: {acc_history[-1]}\")\n", + "\n", + " return weights, bias, acc_history\n", + "\n", + "\n", + "weights, bias, acc_history = train_fhe_client_server(\n", + " X_binary, y_binary, batch_size, fhe_client, fhe_server, serialized_evaluation_key, weights, bias\n", + ")\n", + "\n", + "# Plot the decision final model boundary\n", + "plot_decision_boundary(\n", + " X_binary,\n", + " y_binary,\n", + " weights=weights,\n", + " bias=bias,\n", + " title=\"Decision Boundary after training\",\n", + " accuracy=acc_history[-1],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fine-Tuning on a Rotated Dataset\n", + "\n", + "The encrypted training FHE circuit created allows learning parameters for different dataset as long as it has the same number of features and the floating point distribution of the input remains similar. In this section, the dataset is changed to see if the model can adapt to a task that could be seen as fine-tuning on a slightly different dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2 completed. Accuracy: 0.89\n", + "Epoch 2/2 completed. Accuracy: 0.79\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's rotate the dataset 90 degrees and see\n", + "# if the model can learn the new dataset\n", + "\n", + "# Define the 90-degree rotation matrix\n", + "rotation_matrix = np.array([[0, -1], [1, 0]])\n", + "\n", + "# Apply the rotation matrix to X_binary\n", + "X_binary_pivoted = X_binary @ rotation_matrix\n", + "\n", + "# Plot before training\n", + "plot_decision_boundary(\n", + " X_binary_pivoted,\n", + " y_binary,\n", + " weights=weights,\n", + " bias=bias,\n", + " title=\"Pivoted Dataset\",\n", + " accuracy=compute_model_accuracy(weights, bias, X_binary_pivoted, y_binary),\n", + ")\n", + "\n", + "# Train the model again with the pivoted dataset\n", + "weights_pivoted, bias_pivoted, acc_history_pivoted = train_fhe_client_server(\n", + " X_binary_pivoted,\n", + " y_binary,\n", + " batch_size,\n", + " fhe_client,\n", + " fhe_server,\n", + " serialized_evaluation_key,\n", + " weights,\n", + " bias,\n", + " n_epochs=2,\n", + ")\n", + "\n", + "# Plot the decision boundary for the pivoted dataset\n", + "plot_decision_boundary(\n", + " X_binary_pivoted,\n", + " y_binary,\n", + " weights=weights_pivoted,\n", + " bias=bias_pivoted,\n", + " title=\"Decision Boundary after training on pivoted dataset\",\n", + " accuracy=acc_history_pivoted[-1],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook, we have trained a logistic regression model in FHE. We have also seen how to evaluate the accuracy of the model on encrypted data and how to deploy the model for production.\n", + "\n", + "Disclaimer: FHE training is a very experimental feature (e.g. only learning rate = 1 supported) and can exhibit a lot of variance during training.\n" + ] } ], "metadata": { diff --git a/pyproject.toml b/pyproject.toml index 5ba1d6038..99a6e6c17 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -136,6 +136,10 @@ filterwarnings = [ "ignore:The --rsyncdir command line argument and rsyncdirs config variable are deprecated.:DeprecationWarning", "ignore:Converting a tensor to a NumPy array might cause the trace to be incorrect.", "ignore:torch.from_numpy results are registered as constants in the trace.", + "ignore:ONNX Preprocess - Removing mutation from node aten*:UserWarning", + "ignore:Liblinear failed to converge,*:sklearn.exceptions.ConvergenceWarning", + "ignore:lbfgs failed to converge,*:sklearn.exceptions.ConvergenceWarning", + "ignore:Maximum number of iteration reached before convergence.*:sklearn.exceptions.ConvergenceWarning", ] [tool.semantic_release] diff --git a/script/make_utils/check_pytest_determinism.sh b/script/make_utils/check_pytest_determinism.sh index 00adf5e1e..680c4b530 100755 --- a/script/make_utils/check_pytest_determinism.sh +++ b/script/make_utils/check_pytest_determinism.sh @@ -33,12 +33,35 @@ then exit 255 fi -set -e - # Exceptions: -# passed in: since it is related to timings -diff "${OUTPUT_DIRECTORY}/one.txt" "${OUTPUT_DIRECTORY}/two.txt" -I "passed in" -echo "Successful determinism check" +# in X.Xs: since it is related to timings +diff_output=$(diff "${OUTPUT_DIRECTORY}/one.txt" "${OUTPUT_DIRECTORY}/two.txt" -I "in [0-9]*\.[0-9]*s") + +# If a diff is present, we need to print the tests that failed to be reproduced for debugging. +if [ -n "$diff_output" ]; then + echo "Differences found:" + echo "$diff_output" + + # Extract line numbers of differences + diff_lines=$(echo "$diff_output" | grep -E '^[0-9]+(,[0-9]+)?[acd][0-9]+(,[0-9]+)?' | sed -E 's/([0-9]+).*/\1/') + + for line in $diff_lines; do + + # Find the first line number before the diff that starts with 'tests/seeding/' + start_line_num=$(awk 'NR<'"$line"' && /^tests\/seeding\// {print NR}' "${OUTPUT_DIRECTORY}/one.txt" | tail -n 1) + + # Print lines from start_line_num to the diff line + if [ -n "$start_line_num" ]; then + sed -n "${start_line_num},${line}p" "${OUTPUT_DIRECTORY}/one.txt" + fi + done + + exit 255 +else + echo "Successful determinism check" +fi + +set -e # Now, check that one can reproduce conditions of a bug in a single file # and test without having to relaunch the full pytest @@ -58,14 +81,13 @@ do # SC2086 is about double quote to prevent globbing and word splitting, but here, it makes that we have # an empty arg in pytest, which is considered as "do pytest for all files" # shellcheck disable=SC2086 - poetry run pytest "$x" -xsvv $EXTRA_OPTION --randomly-dont-reset-seed | sed -n -e '/collecting/,$p' | grep -v collecting | grep -v "collected" | grep -v "passed in" | grep -v "PASSED" >> "${OUTPUT_DIRECTORY}/three.txt" + poetry run pytest "$x" -xsvv $EXTRA_OPTION --randomly-dont-reset-seed | sed -n -e '/collecting/,$p' | grep -v collecting | grep -v "collected" | grep -v "PASSED" | grep -v "SKIPPED" | grep -v "in [0-9]*\.[0-9]*s" >> "${OUTPUT_DIRECTORY}/three.txt" ((WHICH+=1)) done # Clean a bit one.txt -sed -n -e '/collecting/,$p' "${OUTPUT_DIRECTORY}/one.txt" | grep -v collecting | grep -v "collected" | grep -v "passed in" | grep -v "PASSED" | grep -v "Leaving directory" > "${OUTPUT_DIRECTORY}/one.modified.txt" - +sed -n -e '/collecting/,$p' "${OUTPUT_DIRECTORY}/one.txt" | grep -v collecting | grep -v "collected" | grep -v "PASSED" | grep -v "SKIPPED" |grep -v "Leaving directory" | grep -v "in [0-9]*\.[0-9]*s" > "${OUTPUT_DIRECTORY}/one.modified.txt" echo "" echo "diff:" echo "" diff --git a/src/concrete/ml/deployment/fhe_client_server.py b/src/concrete/ml/deployment/fhe_client_server.py index 50abc7034..61387df65 100644 --- a/src/concrete/ml/deployment/fhe_client_server.py +++ b/src/concrete/ml/deployment/fhe_client_server.py @@ -3,8 +3,9 @@ import json import sys import zipfile +from enum import Enum from pathlib import Path -from typing import Any, Optional +from typing import Any, Optional, Tuple, Union import numpy @@ -13,6 +14,7 @@ from ..common.debugging.custom_assert import assert_true from ..common.serialization.dumpers import dump from ..common.serialization.loaders import load +from ..common.utils import to_tuple from ..version import __version__ as CML_VERSION try: @@ -25,6 +27,25 @@ from importlib_metadata import version +class DeploymentMode(Enum): + """Mode for the FHE API.""" + + INFERENCE = "inference" + TRAINING = "training" + + @staticmethod + def is_valid(mode: Union["DeploymentMode", str]) -> bool: + """Indicate if the given name is a supported mode. + + Args: + mode (Union[Mode, str]): The mode to check. + + Returns: + bool: Whether the mode is supported or not. + """ + return mode in {member.value for member in DeploymentMode.__members__.values()} + + def check_concrete_versions(zip_path: Path): """Check that current versions match the ones used in development. @@ -105,30 +126,35 @@ def load(self): def run( self, - serialized_encrypted_quantized_data: bytes, + serialized_encrypted_quantized_data: Union[bytes, Tuple[bytes, ...]], serialized_evaluation_keys: bytes, - ) -> bytes: + ) -> Union[bytes, Tuple[bytes, ...]]: """Run the model on the server over encrypted data. Args: - serialized_encrypted_quantized_data (bytes): the encrypted, quantized - and serialized data + serialized_encrypted_quantized_data (Union[bytes, Tuple[bytes, ...]]): the encrypted, + quantized and serialized data serialized_evaluation_keys (bytes): the serialized evaluation keys Returns: - bytes: the result of the model + Union[bytes, Tuple[bytes, ...]]: the result of the model """ assert_true(self.server is not None, "Model has not been loaded.") - deserialized_encrypted_quantized_data = fhe.Value.deserialize( - serialized_encrypted_quantized_data + serialized_encrypted_quantized_data = to_tuple(serialized_encrypted_quantized_data) + + deserialized_data = tuple( + fhe.Value.deserialize(data) for data in serialized_encrypted_quantized_data ) - deserialized_evaluation_keys = fhe.EvaluationKeys.deserialize(serialized_evaluation_keys) - result = self.server.run( - deserialized_encrypted_quantized_data, evaluation_keys=deserialized_evaluation_keys + deserialized_keys = fhe.EvaluationKeys.deserialize(serialized_evaluation_keys) + + result = self.server.run(*deserialized_data, evaluation_keys=deserialized_keys) + + return ( + tuple(res.serialize() for res in result) + if isinstance(result, tuple) + else result.serialize() ) - serialized_result = result.serialize() - return serialized_result class FHEModelDev: @@ -149,17 +175,22 @@ def __init__(self, path_dir: str, model: Any = None): Path(self.path_dir).mkdir(parents=True, exist_ok=True) - def _export_model_to_json(self) -> Path: + def _export_model_to_json(self, is_training: bool = False) -> Path: """Export the quantizers to a json file. + Args: + is_training (bool): If True, we export the training circuit. + Returns: Path: the path to the json file """ + module_to_export = self.model.training_quantized_module if is_training else self.model serialized_processing = { - "model_type": self.model.__class__, - "model_post_processing_params": self.model.post_processing_params, - "input_quantizers": self.model.input_quantizers, - "output_quantizers": self.model.output_quantizers, + "model_type": module_to_export.__class__, + "model_post_processing_params": module_to_export.post_processing_params, + "input_quantizers": module_to_export.input_quantizers, + "output_quantizers": module_to_export.output_quantizers, + "is_training": is_training, } # Export the `is_fitted` attribute for built-in models @@ -173,35 +204,58 @@ def _export_model_to_json(self) -> Path: return json_path - def save(self, via_mlir: bool = False): + def save(self, mode: DeploymentMode = DeploymentMode.INFERENCE, via_mlir: bool = False): """Export all needed artifacts for the client and server. Arguments: + mode (DeploymentMode): the mode to save the FHE circuit, + either "inference" or "training". via_mlir (bool): serialize with `via_mlir` option from Concrete-Python. - For more details on the topic please refer to Concrete-Python's documentation. Raises: - Exception: path_dir is not empty + Exception: path_dir is not empty or training module does not exist + ValueError: if mode is not "inference" or "training" """ + + if isinstance(mode, str): + mode_lower = mode.lower() + if not DeploymentMode.is_valid(mode_lower): + raise ValueError("Mode must be either 'inference' or 'training'") + mode = DeploymentMode(mode_lower) + + # Get fhe_circuit based on the mode + if mode == DeploymentMode.TRAINING: + + # Check that training FHE circuit exists + assert_true( + hasattr(self.model, "training_quantized_module") + and (self.model.training_quantized_module), + "Training FHE circuit does not exist.", + ) + self.model.training_quantized_module.check_model_is_compiled() + fhe_circuit = self.model.training_quantized_module.fhe_circuit + else: + self.model.check_model_is_compiled() + fhe_circuit = self.model.fhe_circuit + # Check if the path_dir is empty with pathlib listdir = list(Path(self.path_dir).glob("**/*")) if len(listdir) > 0: raise Exception( - f"path_dir: {self.path_dir} is not empty." + f"path_dir: {self.path_dir} is not empty. " "Please delete it before saving a new model." ) - self.model.check_model_is_compiled() # Export the quantizers - json_path = self._export_model_to_json() + json_path = self._export_model_to_json(is_training=(mode == DeploymentMode.TRAINING)) - # First save the circuit for the server + # Save the circuit for the server path_circuit_server = Path(self.path_dir).joinpath("server.zip") - self.model.fhe_circuit.server.save(path_circuit_server, via_mlir=via_mlir) + fhe_circuit.server.save(path_circuit_server, via_mlir=via_mlir) # Save the circuit for the client path_circuit_client = Path(self.path_dir).joinpath("client.zip") - self.model.fhe_circuit.client.save(path_circuit_client) + fhe_circuit.client.save(path_circuit_client) with zipfile.ZipFile(path_circuit_client, "a") as zip_file: zip_file.write(filename=json_path, arcname="serialized_processing.json") @@ -265,6 +319,8 @@ def load(self): # pylint: disable=no-value-for-parameter # Initialize the model self.model = serialized_processing["model_type"]() + + # Load the quantizers self.model.input_quantizers = serialized_processing["input_quantizers"] self.model.output_quantizers = serialized_processing["output_quantizers"] @@ -300,72 +356,98 @@ def get_serialized_evaluation_keys(self) -> bytes: return self.client.evaluation_keys.serialize() - def quantize_encrypt_serialize(self, x: numpy.ndarray) -> bytes: + def quantize_encrypt_serialize( + self, x: Union[numpy.ndarray, Tuple[numpy.ndarray, ...]] + ) -> Union[bytes, Tuple[bytes, ...]]: """Quantize, encrypt and serialize the values. Args: - x (numpy.ndarray): the values to quantize, encrypt and serialize + x (Union[numpy.ndarray, Tuple[numpy.ndarray, ...]]): the values to quantize, + encrypt and serialize Returns: - bytes: the quantized, encrypted and serialized values + Union[bytes, Tuple[bytes, ...]]: the quantized, encrypted and serialized values """ + + x = to_tuple(x) + # Quantize the values - quantized_x = self.model.quantize_input(x) + quantized_x = self.model.quantize_input(*x) + + quantized_x = to_tuple(quantized_x) # Encrypt the values - enc_qx = self.client.encrypt(quantized_x) + enc_qx = self.client.encrypt(*quantized_x) + + enc_qx = to_tuple(enc_qx) # Serialize the encrypted values to be sent to the server - serialized_enc_qx = enc_qx.serialize() - return serialized_enc_qx + serialized_enc_qx = tuple(e.serialize() for e in enc_qx) - def deserialize_decrypt(self, serialized_encrypted_quantized_result: bytes) -> numpy.ndarray: + # Return a single value if the original input was a single value + return serialized_enc_qx[0] if len(serialized_enc_qx) == 1 else serialized_enc_qx + + def deserialize_decrypt( + self, serialized_encrypted_quantized_result: Union[bytes, Tuple[bytes, ...]] + ) -> Union[Any, Tuple[Any, ...]]: """Deserialize and decrypt the values. Args: - serialized_encrypted_quantized_result (bytes): the serialized, encrypted - and quantized result + serialized_encrypted_quantized_result (Union[bytes, Tuple[bytes, ...]]): the + serialized, encrypted and quantized result Returns: - numpy.ndarray: the decrypted and deserialized values + Union[Any, Tuple[Any, ...]]: the decrypted and deserialized values """ + + serialized_encrypted_quantized_result = to_tuple(serialized_encrypted_quantized_result) + # Deserialize the encrypted values - deserialized_encrypted_quantized_result = fhe.Value.deserialize( - serialized_encrypted_quantized_result + deserialized_encrypted_quantized_result = tuple( + fhe.Value.deserialize(data) for data in serialized_encrypted_quantized_result ) # Decrypt the values deserialized_decrypted_quantized_result = self.client.decrypt( - deserialized_encrypted_quantized_result + *deserialized_encrypted_quantized_result ) - assert isinstance(deserialized_decrypted_quantized_result, numpy.ndarray) + return deserialized_decrypted_quantized_result def deserialize_decrypt_dequantize( - self, serialized_encrypted_quantized_result: bytes + self, serialized_encrypted_quantized_result: Union[bytes, Tuple[bytes, ...]] ) -> numpy.ndarray: """Deserialize, decrypt and de-quantize the values. Args: - serialized_encrypted_quantized_result (bytes): the serialized, encrypted - and quantized result + serialized_encrypted_quantized_result (Union[bytes, Tuple[bytes, ...]]): the + serialized, encrypted and quantized result Returns: numpy.ndarray: the decrypted (de-quantized) values """ + # Ensure the input is a tuple + serialized_encrypted_quantized_result = to_tuple(serialized_encrypted_quantized_result) + # Decrypt and deserialize the values deserialized_decrypted_quantized_result = self.deserialize_decrypt( serialized_encrypted_quantized_result ) + deserialized_decrypted_quantized_result = to_tuple(deserialized_decrypted_quantized_result) + # De-quantize the values deserialized_decrypted_dequantized_result = self.model.dequantize_output( - deserialized_decrypted_quantized_result + *deserialized_decrypted_quantized_result ) - # Apply post-processing the to de-quantized values - deserialized_decrypted_dequantized_result = self.model.post_processing( + deserialized_decrypted_dequantized_result = to_tuple( deserialized_decrypted_dequantized_result ) + # Apply post-processing to the de-quantized values + deserialized_decrypted_dequantized_result = self.model.post_processing( + *deserialized_decrypted_dequantized_result + ) + return deserialized_decrypted_dequantized_result diff --git a/src/concrete/ml/pytest/utils.py b/src/concrete/ml/pytest/utils.py index f97a34719..573a5ac82 100644 --- a/src/concrete/ml/pytest/utils.py +++ b/src/concrete/ml/pytest/utils.py @@ -13,6 +13,8 @@ from numpy.random import RandomState from torch import nn +from concrete.ml.sklearn.linear_model import SGDClassifier + from ..common.serialization.dumpers import dump, dumps from ..common.serialization.loaders import load, loads from ..common.utils import ( @@ -127,7 +129,13 @@ def _get_sklearn_models_and_datasets(model_classes: List, unique_models: bool = # the test execution timings n_classes_to_test = ( [2] - if unique_models or get_model_class(model_class) == KNeighborsClassifier + if unique_models + or get_model_class(model_class) == KNeighborsClassifier + or ( + isinstance(model_class, partial) + and model_class.func == SGDClassifier + and model_class.keywords.get("fit_encrypted", False) + ) else [2, 4] ) @@ -184,6 +192,15 @@ def get_sklearn_linear_models_and_datasets( partial(TweedieRegressor, link="identity", power=0.0), ] + # If the linear model is SGDClassifier, + # we need to handle the training parameters + if is_model_class_in_a_list(SGDClassifier, linear_classes): + linear_classes += [ + partial(SGDClassifier, fit_encrypted=False), + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4460 + # partial(SGDClassifier, fit_encrypted=True, parameters_range=(-1, 1)), + ] + return _get_sklearn_models_and_datasets(linear_classes, unique_models=unique_models) diff --git a/src/concrete/ml/quantization/quantized_module.py b/src/concrete/ml/quantization/quantized_module.py index 97172911c..bc89ca718 100644 --- a/src/concrete/ml/quantization/quantized_module.py +++ b/src/concrete/ml/quantization/quantized_module.py @@ -252,7 +252,9 @@ def post_processing_params(self, post_processing_params: Dict[str, Any]): self._post_processing_params = post_processing_params # pylint: disable-next=no-self-use - def post_processing(self, values: numpy.ndarray) -> numpy.ndarray: + def post_processing( + self, *values: numpy.ndarray + ) -> Union[numpy.ndarray, Tuple[numpy.ndarray, ...]]: """Apply post-processing to the de-quantized values. For quantized modules, there is no post-processing step but the method is kept to make the @@ -262,9 +264,9 @@ def post_processing(self, values: numpy.ndarray) -> numpy.ndarray: values (numpy.ndarray): The de-quantized values to post-process. Returns: - numpy.ndarray: The post-processed values. + Union[numpy.ndarray, Tuple[numpy.ndarray, ...]]: The post-processed values. """ - return values + return values[0] if len(values) == 1 else values def _set_output_quantizers(self) -> List[UniformQuantizer]: """Get the output quantizers. diff --git a/src/concrete/ml/sklearn/linear_model.py b/src/concrete/ml/sklearn/linear_model.py index f75d3f5cd..82e7aea29 100644 --- a/src/concrete/ml/sklearn/linear_model.py +++ b/src/concrete/ml/sklearn/linear_model.py @@ -237,12 +237,6 @@ def __init__( if self.fit_encrypted: self.classes_: Optional[numpy.ndarray] = None - warnings.warn( - "FHE training is an experimental feature. Please be aware that the API might " - "change in future versions.", - stacklevel=2, - ) - # Check the presence of mandatory attributes if self.loss != "log_loss": raise ValueError( diff --git a/src/concrete/ml/torch/hybrid_model.py b/src/concrete/ml/torch/hybrid_model.py index 277afc9ea..b6ee537bc 100644 --- a/src/concrete/ml/torch/hybrid_model.py +++ b/src/concrete/ml/torch/hybrid_model.py @@ -289,7 +289,7 @@ def remote_call(self, x: torch.Tensor) -> torch.Tensor: # pragma:no cover # We need to iterate over elements in the batch since # we don't support batch inference - inferences = [] + inferences: List[torch.Tensor] = [] for index in range(len(x)): # Manage tensor, tensor shape, and encrypt tensor clear_input = x[[index], :].detach().numpy() diff --git a/tests/common/test_pbs_error_probability_settings.py b/tests/common/test_pbs_error_probability_settings.py index 85e4128bd..c511f64da 100644 --- a/tests/common/test_pbs_error_probability_settings.py +++ b/tests/common/test_pbs_error_probability_settings.py @@ -1,11 +1,9 @@ """Tests for the sklearn linear models.""" -import warnings from inspect import signature import numpy import pytest -from sklearn.exceptions import ConvergenceWarning from torch import nn from concrete.ml.pytest.torch_models import FCSmall @@ -34,10 +32,8 @@ def test_config_sklearn(model_class, parameters, kwargs, load_data): model = model_class() - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - # Fit the model - model.fit(x, y) + # Fit the model + model.fit(x, y) if kwargs.get("p_error", None) is not None and kwargs.get("global_p_error", None) is not None: with pytest.raises(ValueError) as excinfo: diff --git a/tests/common/test_serialization.py b/tests/common/test_serialization.py index a39adfc03..ba8a7f7c9 100644 --- a/tests/common/test_serialization.py +++ b/tests/common/test_serialization.py @@ -6,7 +6,6 @@ import inspect import io -import warnings from functools import partial import numpy @@ -18,7 +17,6 @@ from concrete.fhe.compilation import Circuit from numpy.random import RandomState from sklearn.datasets import make_regression -from sklearn.exceptions import ConvergenceWarning from skops.io.exceptions import UntrustedTypesFoundException from skorch.dataset import ValidSplit from torch import nn @@ -123,9 +121,7 @@ def test_serialize_sklearn_model(concrete_model_class, load_data): # Instantiate and fit a Concrete model to recover its underlying Scikit Learn model concrete_model = concrete_model_class() - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - _, sklearn_model = concrete_model.fit_benchmark(x, y) + _, sklearn_model = concrete_model.fit_benchmark(x, y) # Both JSON string are not compared as scikit-learn models are serialized using Skops or pickle, # which does not make string comparison possible diff --git a/tests/common/test_skearn_model_lists.py b/tests/common/test_skearn_model_lists.py index 7edaf77d0..ff424aa33 100644 --- a/tests/common/test_skearn_model_lists.py +++ b/tests/common/test_skearn_model_lists.py @@ -110,5 +110,5 @@ def test_get_sklearn_models(): def test_models_and_datasets(): """Check that the tested model's configuration lists remain fixed.""" - assert len(MODELS_AND_DATASETS) == 32 - assert len(UNIQUE_MODELS_AND_DATASETS) == 21 + assert len(MODELS_AND_DATASETS) == 34 + assert len(UNIQUE_MODELS_AND_DATASETS) == 22 diff --git a/tests/deployment/test_client_server.py b/tests/deployment/test_client_server.py index c93ca7911..e36a88f8b 100644 --- a/tests/deployment/test_client_server.py +++ b/tests/deployment/test_client_server.py @@ -5,23 +5,40 @@ import tempfile import warnings import zipfile +from functools import partial from pathlib import Path from shutil import copyfile import numpy import pytest -from sklearn.exceptions import ConvergenceWarning from torch import nn -from concrete.ml.deployment.fhe_client_server import FHEModelClient, FHEModelDev, FHEModelServer +from concrete.ml.deployment.fhe_client_server import ( + DeploymentMode, + FHEModelClient, + FHEModelDev, + FHEModelServer, +) from concrete.ml.pytest.torch_models import FCSmall from concrete.ml.pytest.utils import MODELS_AND_DATASETS, get_model_name, instantiate_model_generic from concrete.ml.quantization.quantized_module import QuantizedModule +from concrete.ml.sklearn.linear_model import SGDClassifier from concrete.ml.torch.compile import compile_torch_model # pylint: disable=too-many-statements,too-many-locals +# Add encrypted training with SGDClassifier manually +# FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4460 +MODELS_AND_DATASETS = MODELS_AND_DATASETS + [ + pytest.param( + partial(SGDClassifier, fit_encrypted=True, parameters_range=(-1, 1)), + {"n_samples": 100, "n_features": 10, "n_classes": 2, "n_informative": 10, "n_redundant": 0}, + id="SGDClassifier_Encrypted_Training", + ) +] + + class OnDiskNetwork: """A network interaction on disk.""" @@ -96,11 +113,12 @@ def test_client_server_sklearn( y_train = y[:-1] x_test = x[-1:] + # Instantiate the model model = instantiate_model_generic(model_class, n_bits=n_bits) # Fit the model with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) + warnings.simplefilter("ignore", category=UserWarning) model.fit(x_train, y_train) key_dir = default_configuration.insecure_key_cache_location @@ -203,7 +221,7 @@ def check_client_server_files(model): with pytest.raises( Exception, match=( - f"path_dir: {disk_network.dev_dir.name} is not empty." + f"path_dir: {disk_network.dev_dir.name} is not empty. " "Please delete it before saving a new model." ), ): @@ -337,3 +355,60 @@ def check_input_compression(model, fhe_circuit_compressed, is_torch, **compilati "Compressed input ciphertext's is not smaller than the uncompressed input ciphertext. Got " f"{compressed_size} bytes (compressed) and {uncompressed_size} bytes (uncompressed)." ) + + +ERROR_MSG_BAD_MODE = "Mode must be either 'inference' or 'training'" +ERROR_MSG_NO_FHE_CIRCUIT = "Training FHE circuit does not exist." + + +@pytest.mark.parametrize("n_bits", [2]) +@pytest.mark.parametrize( + "mode, fit_encrypted, error_message", + [ + ("invalid_mode", True, ERROR_MSG_BAD_MODE), + ("INVALID_MODE", True, ERROR_MSG_BAD_MODE), + ("train", True, ERROR_MSG_BAD_MODE), + ("", True, ERROR_MSG_BAD_MODE), + (None, True, None), + ("inference", False, None), + ("inference", True, None), + ("training", False, ERROR_MSG_NO_FHE_CIRCUIT), + ("training", True, None), + (DeploymentMode.INFERENCE, False, None), + (DeploymentMode.TRAINING, False, ERROR_MSG_NO_FHE_CIRCUIT), + (DeploymentMode.TRAINING, True, None), + ], +) +def test_save_mode_handling(n_bits, fit_encrypted, mode, error_message): + """Test that the save method handles valid and invalid modes correctly.""" + + # Generate random data + x, y = numpy.random.rand(20, 2), numpy.random.randint(0, 2, 20) + + x_train = x[:-1] + y_train = y[:-1] + + # Instantiate the model + parameters_range = [-1, 1] if fit_encrypted else None + model = instantiate_model_generic( + partial(SGDClassifier, fit_encrypted=fit_encrypted, parameters_range=parameters_range), + n_bits=n_bits, + ) + + # Fit the model + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) + model.fit(x_train, y_train) + + # Compile + model.compile(X=x_train) + + # Create FHEModelDev instance + with tempfile.TemporaryDirectory() as temp_dir: + model_dev = FHEModelDev(path_dir=temp_dir, model=model) + + if error_message: + with pytest.raises((AssertionError, ValueError), match=error_message): + model_dev.save(mode=mode) + else: + model_dev.save(mode=mode) diff --git a/tests/parameter_search/test_p_error_binary_search.py b/tests/parameter_search/test_p_error_binary_search.py index 57686f29e..350b72a9c 100644 --- a/tests/parameter_search/test_p_error_binary_search.py +++ b/tests/parameter_search/test_p_error_binary_search.py @@ -1,14 +1,12 @@ """Test binary search class.""" import os -import warnings from pathlib import Path import numpy import pytest import torch from sklearn.datasets import make_classification -from sklearn.exceptions import ConvergenceWarning from sklearn.metrics import r2_score, top_k_accuracy_score from tensorflow import keras @@ -126,9 +124,8 @@ def test_update_valid_attr_method(attr, value, model_name, quant_type, metric, l predict="predict", n_simulation=1, ) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - search.run(x=x_calib, ground_truth=y, strategy=all, **{attr: value}) + + search.run(x=x_calib, ground_truth=y, strategy=all, **{attr: value}) assert getattr(search, attr) == value @@ -167,10 +164,7 @@ def test_non_convergence_for_built_in_models(model_class, parameters, load_data, max_metric_loss=-10, is_qat=False, ) - - warnings.simplefilter("always") - with pytest.warns(UserWarning, match="ConvergenceWarning: .*"): - search.run(x=x_calib, ground_truth=y, strategy=all) + search.run(x=x_calib, ground_truth=y, strategy=all) @pytest.mark.parametrize("model_name, quant_type", [("CustomModel", "qat")]) @@ -205,9 +199,7 @@ def test_non_convergence_for_custom_models(model_name, quant_type): labels=numpy.arange(MODELS_ARGS[model_name]["dataset"]["n_classes"]), ) - warnings.simplefilter("always") - with pytest.warns(UserWarning, match="ConvergenceWarning: .*"): - search.run(x=x_calib, ground_truth=y, strategy=all) + search.run(x=x_calib, ground_truth=y, strategy=all) @pytest.mark.parametrize( @@ -279,9 +271,8 @@ def test_binary_search_for_custom_models(model_name, quant_type, threshold): k=1, labels=numpy.arange(MODELS_ARGS[model_name]["dataset"]["n_classes"]), ) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all) + + largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all) assert 1.0 > largest_perror > 0.0 assert ( @@ -337,9 +328,7 @@ def test_binary_search_for_built_in_models(model_class, parameters, threshold, p # The model does not have `predict` return - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all) + largest_perror = search.run(x=x_calib, ground_truth=y, strategy=all) assert 1.0 > largest_perror > 0.0 assert ( @@ -475,9 +464,7 @@ def test_success_save_option(model_name, quant_type, metric, directory, log_file # When instantiating the class, if the file exists, it is deleted, to avoid overwriting it assert not path.exists() - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - search.run(x=x_calib, ground_truth=y) + search.run(x=x_calib, ground_truth=y) # Check that the file has been properly created assert path.exists() diff --git a/tests/seeding/test_seeding.py b/tests/seeding/test_seeding.py index 49450b276..be44ba5be 100644 --- a/tests/seeding/test_seeding.py +++ b/tests/seeding/test_seeding.py @@ -2,11 +2,9 @@ import inspect import random -import warnings import numpy import pytest -from sklearn.exceptions import ConvergenceWarning from sklearn.tree import plot_tree from concrete.ml.pytest.utils import MODELS_AND_DATASETS @@ -88,16 +86,14 @@ def test_seed_sklearn(model_class, parameters, load_data, default_configuration) # Force "random_state": if it was there, it is overwritten; if it was not there, it is added model_params = {} - if "random_state" in inspect.getfullargspec(model_class).args: + if "random_state" in inspect.signature(model_class).parameters: model_params["random_state"] = numpy.random.randint(0, 2**15) # First case: user gives his own random_state model = model_class(**model_params) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - # Fit the model - model, sklearn_model = model.fit_benchmark(x, y) + # Fit the model + model, sklearn_model = model.fit_benchmark(x, y) lpvoid_ptr_plot_tree = getattr(model, "plot_tree", None) if callable(lpvoid_ptr_plot_tree): diff --git a/tests/sklearn/test_common.py b/tests/sklearn/test_common.py index 4f7889878..78409db6a 100644 --- a/tests/sklearn/test_common.py +++ b/tests/sklearn/test_common.py @@ -1,11 +1,9 @@ """Tests common to all sklearn models.""" import inspect -import warnings import numpy import pytest -from sklearn.exceptions import ConvergenceWarning from concrete.ml.common.utils import get_model_class from concrete.ml.pytest.utils import MODELS_AND_DATASETS @@ -44,10 +42,8 @@ def test_seed_sklearn(model_class, parameters, load_data): # First case: user gives his own random_state model = model_class(random_state=random_state_constructor) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - # Fit the model - model, sklearn_model = model.fit_benchmark(x, y, random_state=random_state_user) + # Fit the model + model, sklearn_model = model.fit_benchmark(x, y, random_state=random_state_user) assert ( model.random_state == random_state_user and sklearn_model.random_state == random_state_user @@ -56,10 +52,8 @@ def test_seed_sklearn(model_class, parameters, load_data): # Second case: user does not give random_state but seeds the constructor model = model_class(random_state=random_state_constructor) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - # Fit the model - model, sklearn_model = model.fit_benchmark(x, y) + # Fit the model + model, sklearn_model = model.fit_benchmark(x, y) assert (model.random_state == random_state_constructor) and ( sklearn_model.random_state == random_state_constructor @@ -69,10 +63,8 @@ def test_seed_sklearn(model_class, parameters, load_data): model = model_class(random_state=None) assert model.random_state is None - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - # Fit the model - model, sklearn_model = model.fit_benchmark(x, y) + # Fit the model + model, sklearn_model = model.fit_benchmark(x, y) # model.random_state and sklearn_model.random_state should now be seeded with the same value assert model.random_state is not None and sklearn_model.random_state is not None diff --git a/tests/sklearn/test_dump_onnx.py b/tests/sklearn/test_dump_onnx.py index 34e14d242..923796c86 100644 --- a/tests/sklearn/test_dump_onnx.py +++ b/tests/sklearn/test_dump_onnx.py @@ -6,7 +6,6 @@ import numpy import onnx import pytest -from sklearn.exceptions import ConvergenceWarning from concrete.ml.common.utils import is_model_class_in_a_list from concrete.ml.pytest.utils import UNIQUE_MODELS_AND_DATASETS, get_model_name @@ -420,11 +419,7 @@ def check_onnx_file_dump( # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3979 model.n_bits = 2 - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - - model.fit(x, y) + model.fit(x, y) with warnings.catch_warnings(): # Use FHE simulation to not have issues with precision diff --git a/tests/sklearn/test_fhe_training.py b/tests/sklearn/test_fhe_training.py index 55b654617..07afab79d 100644 --- a/tests/sklearn/test_fhe_training.py +++ b/tests/sklearn/test_fhe_training.py @@ -1,7 +1,6 @@ """Tests training in FHE.""" import re -import warnings import numpy import pytest @@ -42,59 +41,39 @@ def test_init_error_raises(n_bits, parameter_min_max): random_state = numpy.random.randint(0, 2**15) parameters_range = (-parameter_min_max, parameter_min_max) - with pytest.warns( - UserWarning, - match=( - "FHE training is an experimental feature. Please be aware that the API might change " - "in future versions." + with pytest.raises( + ValueError, + match=re.escape( + "Only 'log_loss' is currently supported if FHE training is enabled" + " (fit_encrypted=True). Got loss='perceptron'" ), ): SGDClassifier( n_bits=n_bits, fit_encrypted=True, + loss="perceptron", random_state=random_state, parameters_range=parameters_range, ) - with warnings.catch_warnings(): - - # FHE training is an experimental feature and a warning is raised each time `fit_encrypted` - # is set to True - warnings.filterwarnings("ignore", message="FHE training is an experimental feature.*") - - with pytest.raises( - ValueError, - match=re.escape( - "Only 'log_loss' is currently supported if FHE training is enabled" - " (fit_encrypted=True). Got loss='perceptron'" - ), - ): - SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - loss="perceptron", - random_state=random_state, - parameters_range=parameters_range, - ) - - with pytest.raises( - ValueError, match="Setting 'parameter_range' is mandatory if FHE training is enabled." - ): - SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=None, - fit_intercept=True, - ) + with pytest.raises( + ValueError, match="Setting 'parameter_range' is mandatory if FHE training is enabled." + ): + SGDClassifier( + n_bits=n_bits, + fit_encrypted=True, + random_state=random_state, + parameters_range=None, + fit_intercept=True, + ) - SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=parameters_range, - fit_intercept=False, - ) + SGDClassifier( + n_bits=n_bits, + fit_encrypted=True, + random_state=random_state, + parameters_range=parameters_range, + fit_intercept=False, + ) @pytest.mark.parametrize("n_classes", [1, 3]) @@ -109,19 +88,13 @@ def test_fit_error_if_non_binary_targets(n_classes, n_bits, max_iter, parameter_ # Generate a data-set with three target classes x, y = get_blob_data(n_classes=n_classes) - with warnings.catch_warnings(): - - # FHE training is an experimental feature and a warning is raised each time `fit_encrypted` - # is set to True - warnings.filterwarnings("ignore", message="FHE training is an experimental feature.*") - - model = SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=parameters_range, - max_iter=max_iter, - ) + model = SGDClassifier( + n_bits=n_bits, + fit_encrypted=True, + random_state=random_state, + parameters_range=parameters_range, + max_iter=max_iter, + ) with pytest.raises( NotImplementedError, @@ -148,19 +121,13 @@ def test_fit_single_target_class(n_bits, max_iter, parameter_min_max, use_partia # Generate a data-set with a single target class x, y = get_blob_data(n_classes=2) - with warnings.catch_warnings(): - - # FHE training is an experimental feature and a warning is raised each time `fit_encrypted` - # is set to True - warnings.filterwarnings("ignore", message="FHE training is an experimental feature.*") - - model = SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=parameters_range, - max_iter=max_iter, - ) + model = SGDClassifier( + n_bits=n_bits, + fit_encrypted=True, + random_state=random_state, + parameters_range=parameters_range, + max_iter=max_iter, + ) if use_partial: with pytest.raises( @@ -219,19 +186,13 @@ def test_encrypted_fit_warning_error_raises(n_bits, max_iter, parameter_min_max) # Generate a data-set with binary target classes x, y = get_blob_data(scale_input=True, parameters_range=parameters_range) - with warnings.catch_warnings(): - - # FHE training is an experimental feature and a warning is raised each time `fit_encrypted` - # is set to True - warnings.filterwarnings("ignore", message="FHE training is an experimental feature.*") - - model = SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=parameters_range, - max_iter=max_iter, - ) + model = SGDClassifier( + n_bits=n_bits, + fit_encrypted=True, + random_state=random_state, + parameters_range=parameters_range, + max_iter=max_iter, + ) with pytest.warns( UserWarning, @@ -269,20 +230,6 @@ def test_encrypted_fit_warning_error_raises(n_bits, max_iter, parameter_min_max) with pytest.raises(NotImplementedError, match="Target values must be 1D.*"): model.partial_fit(x, y_2d, fhe="disable") - with pytest.warns( - UserWarning, - match="FHE training is an experimental feature. " - "Please be aware that the API might change in future versions.", - ): - model = SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=parameters_range, - max_iter=max_iter, - loss="log_loss", - ) - with pytest.warns(UserWarning, match="ONNX Preprocess - Removing mutation from node .*"): model.fit(x, y, fhe="disable") @@ -290,20 +237,6 @@ def test_encrypted_fit_warning_error_raises(n_bits, max_iter, parameter_min_max) model.loss = "random" model.predict_proba(x) - with pytest.warns( - UserWarning, - match="FHE training is an experimental feature. " - "Please be aware that the API might change in future versions.", - ): - model = SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=parameters_range, - max_iter=max_iter, - loss="log_loss", - ) - assert isinstance(y, numpy.ndarray) with pytest.raises( @@ -388,73 +321,61 @@ def check_encrypted_fit( fit_kwargs = {} # Initialize the model - with warnings.catch_warnings(): + model = SGDClassifier( + n_bits=n_bits, + fit_encrypted=True, + random_state=random_state, + parameters_range=parameters_range, + max_iter=max_iter, + fit_intercept=fit_intercept, + verbose=True, + **init_kwargs, + ) - # FHE training is an experimental feature and a warning is raised each time `fit_encrypted` - # is set to True - warnings.filterwarnings("ignore", message="FHE training is an experimental feature.*") + # If a RNG instance if provided, use it to set the new model's one + if random_number_generator is not None: + model.random_number_generator = random_number_generator - model = SGDClassifier( - n_bits=n_bits, - fit_encrypted=True, - random_state=random_state, - parameters_range=parameters_range, - max_iter=max_iter, - fit_intercept=fit_intercept, - verbose=True, - **init_kwargs, - ) + # We need to lower the p-error to make sure that the test passes + model.training_p_error = 1e-15 - # If a RNG instance if provided, use it to set the new model's one - if random_number_generator is not None: - model.random_number_generator = random_number_generator + if partial_fit: + # Check that we can swap between disable and simulation modes without any impact on the + # final training performance + for index in range(max_iter): + if index % 2 == 0: + model.partial_fit(x, y, fhe="disable", classes=numpy.unique(y)) + else: - # We need to lower the p-error to make sure that the test passes - model.training_p_error = 1e-15 + # We don't need to provide `classes` if index>=1 + model.partial_fit(x, y, fhe="simulate") - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", - message="ONNX Preprocess - Removing mutation from node aten::sub_ on block input.*", - ) + # Check that we raise an error if we call `partial_fit` with a different classes. + with pytest.raises( + ValueError, + match="classes=.* is not the same as on last call to partial_fit, was: .*", + ): + model.partial_fit( + x, y, fhe="disable", classes=numpy.array(numpy.unique(y).tolist() + [len(y)]) + ) - if partial_fit: - # Check that we can swap between disable and simulation modes without any impact on the - # final training performance - for index in range(max_iter): - if index % 2 == 0: - model.partial_fit(x, y, fhe="disable", classes=numpy.unique(y)) - else: - - # We don't need to provide `classes` if index>=1 - model.partial_fit(x, y, fhe="simulate") - - # Check that we raise an error if we call `partial_fit` with a different classes. - with pytest.raises( - ValueError, - match="classes=.* is not the same as on last call to partial_fit, was: .*", - ): - model.partial_fit( - x, y, fhe="disable", classes=numpy.array(numpy.unique(y).tolist() + [len(y)]) - ) - - elif warm_fit: - - # Check that we can swap between disable and simulation modes without any impact on the - # final training performance - half_iter = max_iter // 2 - model.max_iter = half_iter - model.fit(x, y, fhe="disable") + elif warm_fit: + + # Check that we can swap between disable and simulation modes without any impact on the + # final training performance + half_iter = max_iter // 2 + model.max_iter = half_iter + model.fit(x, y, fhe="disable") - other_iter = max_iter - half_iter - model.max_iter = other_iter - model.fit(x, y, fhe="simulate") + other_iter = max_iter - half_iter + model.max_iter = other_iter + model.fit(x, y, fhe="simulate") - assert half_iter + other_iter == max_iter + assert half_iter + other_iter == max_iter - else: - # Fit the model - model.fit(x, y, fhe=fhe, **fit_kwargs) + else: + # Fit the model + model.fit(x, y, fhe=fhe, **fit_kwargs) y_pred_class = model.predict(x) y_pred_proba = model.predict_proba(x) diff --git a/tests/sklearn/test_sklearn_models.py b/tests/sklearn/test_sklearn_models.py index b9cb33e84..34f3d0d02 100644 --- a/tests/sklearn/test_sklearn_models.py +++ b/tests/sklearn/test_sklearn_models.py @@ -38,7 +38,7 @@ import pytest import torch from sklearn.decomposition import PCA -from sklearn.exceptions import ConvergenceWarning, UndefinedMetricWarning +from sklearn.exceptions import UndefinedMetricWarning from sklearn.metrics import make_scorer, matthews_corrcoef, top_k_accuracy_score from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline @@ -145,10 +145,7 @@ def preamble(model_class, parameters, n_bits, load_data, is_weekly_option): # Get the data-set. The data generation is seeded in load_data. model = instantiate_model_generic(model_class, n_bits=n_bits) x, y = get_dataset(model_class, parameters, n_bits, load_data, is_weekly_option) - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) return model, x @@ -169,10 +166,7 @@ def get_n_bits_non_correctness(model_class): def fit_and_compile(model, x, y): """Fit the model and compile it.""" - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) model.compile(x) @@ -194,10 +188,7 @@ def check_correctness_with_sklearn( model = instantiate_model_generic(model_class, n_bits=n_bits, **hyper_parameters) - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model, sklearn_model = model.fit_benchmark(x, y) + model, sklearn_model = model.fit_benchmark(x, y) model_name = get_model_name(model_class) acceptance_r2score = 0.9 @@ -270,88 +261,83 @@ def check_double_fit(model_class, n_bits, x_1, x_2, y_1, y_2): model = instantiate_model_generic(model_class, n_bits=n_bits) - # Sometimes, we miss convergence, which is not a problem for our test - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - - # Set the torch seed manually before fitting a neural network - if is_model_class_in_a_list(model_class, _get_sklearn_neural_net_models()): + # Set the torch seed manually before fitting a neural network + if is_model_class_in_a_list(model_class, _get_sklearn_neural_net_models()): - # Generate a seed for PyTorch - main_seed = numpy.random.randint(0, 2**63) - torch.manual_seed(main_seed) + # Generate a seed for PyTorch + main_seed = numpy.random.randint(0, 2**63) + torch.manual_seed(main_seed) - # Fit and predict on the first dataset - model.fit(x_1, y_1) - y_pred_1 = model.predict(x_1) + # Fit and predict on the first dataset + model.fit(x_1, y_1) + y_pred_1 = model.predict(x_1) - # Store the input and output quantizers - input_quantizers_1 = copy.copy(model.input_quantizers) - output_quantizers_1 = copy.copy(model.output_quantizers) + # Store the input and output quantizers + input_quantizers_1 = copy.copy(model.input_quantizers) + output_quantizers_1 = copy.copy(model.output_quantizers) - # Set the same torch seed manually before re-fitting the neural network - if is_model_class_in_a_list(model_class, _get_sklearn_neural_net_models()): - torch.manual_seed(main_seed) + # Set the same torch seed manually before re-fitting the neural network + if is_model_class_in_a_list(model_class, _get_sklearn_neural_net_models()): + torch.manual_seed(main_seed) - # Re-fit on the second dataset - model.fit(x_2, y_2) + # Re-fit on the second dataset + model.fit(x_2, y_2) - # Check that predictions are different - y_pred_2 = model.predict(x_2) - assert not numpy.array_equal(y_pred_1, y_pred_2) + # Check that predictions are different + y_pred_2 = model.predict(x_2) + assert not numpy.array_equal(y_pred_1, y_pred_2) - # Store the new input and output quantizers - input_quantizers_2 = copy.copy(model.input_quantizers) - output_quantizers_2 = copy.copy(model.output_quantizers) + # Store the new input and output quantizers + input_quantizers_2 = copy.copy(model.input_quantizers) + output_quantizers_2 = copy.copy(model.output_quantizers) - # Random forest and decision tree classifiers can have identical output_quantizers - # This is because targets are integers, while these models have a fixed output - # precision, which leads the output scale to be the same between models with similar target - # classes range - if is_model_class_in_a_list( - model_class, - _get_sklearn_tree_models(classifier=True, select=["RandomForest", "DecisionTree"]), - ): - quantizers_1 = input_quantizers_1 - quantizers_2 = input_quantizers_2 - else: - quantizers_1 = input_quantizers_1 + output_quantizers_1 - quantizers_2 = input_quantizers_2 + output_quantizers_2 + # Random forest and decision tree classifiers can have identical output_quantizers + # This is because targets are integers, while these models have a fixed output + # precision, which leads the output scale to be the same between models with similar target + # classes range + if is_model_class_in_a_list( + model_class, + _get_sklearn_tree_models(classifier=True, select=["RandomForest", "DecisionTree"]), + ): + quantizers_1 = input_quantizers_1 + quantizers_2 = input_quantizers_2 + else: + quantizers_1 = input_quantizers_1 + output_quantizers_1 + quantizers_2 = input_quantizers_2 + output_quantizers_2 - # Check that the new quantizers are different from the first ones. This is because we - # currently expect all quantizers to be re-computed when re-fitting a model + # Check that the new quantizers are different from the first ones. This is because we + # currently expect all quantizers to be re-computed when re-fitting a model - assert all( - quantizer_1 != quantizer_2 - for (quantizer_1, quantizer_2) in zip(quantizers_1, quantizers_2) - ) + assert all( + quantizer_1 != quantizer_2 for (quantizer_1, quantizer_2) in zip(quantizers_1, quantizers_2) + ) - # Set the same torch seed manually before re-fitting the neural network - if is_model_class_in_a_list(model_class, _get_sklearn_neural_net_models()): - torch.manual_seed(main_seed) + # Set the same torch seed manually before re-fitting the neural network + if is_model_class_in_a_list(model_class, _get_sklearn_neural_net_models()): + torch.manual_seed(main_seed) - # Re-fit on the first dataset again - model.fit(x_1, y_1) + # Re-fit on the first dataset again + model.fit(x_1, y_1) - # Check that predictions are identical to the first ones - y_pred_3 = model.predict(x_1) - assert numpy.array_equal(y_pred_1, y_pred_3) + # Check that predictions are identical to the first ones + y_pred_3 = model.predict(x_1) + assert numpy.array_equal(y_pred_1, y_pred_3) - # Store the new input and output quantizers - input_quantizers_3 = copy.copy(model.input_quantizers) - output_quantizers_3 = copy.copy(model.output_quantizers) + # Store the new input and output quantizers + input_quantizers_3 = copy.copy(model.input_quantizers) + output_quantizers_3 = copy.copy(model.output_quantizers) - # Check that the new quantizers are identical from the first ones. Again, we expect the - # quantizers to be re-computed when re-fitting. Since we used the same dataset as the first - # fit, we also expect these quantizers to be the same. + # Check that the new quantizers are identical from the first ones. Again, we expect the + # quantizers to be re-computed when re-fitting. Since we used the same dataset as the first + # fit, we also expect these quantizers to be the same. - assert all( - quantizer_1 == quantizer_3 - for (quantizer_1, quantizer_3) in zip( - input_quantizers_1 + output_quantizers_1, - input_quantizers_3 + output_quantizers_3, - ) + assert all( + quantizer_1 == quantizer_3 + for (quantizer_1, quantizer_3) in zip( + input_quantizers_1 + output_quantizers_1, + input_quantizers_3 + output_quantizers_3, ) + ) def check_serialization(model, x, use_dump_method): @@ -487,18 +473,14 @@ def check_offset(model_class, n_bits, x, y): """Check offset.""" model = instantiate_model_generic(model_class, n_bits=n_bits) - # Sometimes, we miss convergence, which is not a problem for our test - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) + # Add the offset: here, we really need to fit, we can't reuse an already fitted model + y += 3 + model.fit(x, y) + model.predict(x[:1]) - # Add the offset: here, we really need to fit, we can't reuse an already fitted model - y += 3 - model.fit(x, y) - model.predict(x[:1]) - - # Another offset: here, we really need to fit, we can't reuse an already fitted model - y -= 2 - model.fit(x, y) + # Another offset: here, we really need to fit, we can't reuse an already fitted model + y -= 2 + model.fit(x, y) def check_inference_methods(model, model_class, x, check_float_array_equal): @@ -701,10 +683,7 @@ def cast_input(x, y, input_type): model = instantiate_model_generic(model_class, n_bits=n_bits) x, y = cast_input(x, y, input_type=input_type) - # Sometimes, we miss convergence, which is not a problem for our test - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) # Make sure `predict` is working when FHE is disabled model.predict(x) @@ -770,11 +749,7 @@ def check_pipeline(model_class, x, y): # We need a small number of splits, especially for the KNN model, which has a small data-set grid_search = GridSearchCV(pipe_cv, param_grid, error_score="raise", cv=2) - # Sometimes, we miss convergence, which is not a problem for our test - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=ConvergenceWarning) - - grid_search.fit(x, y) + grid_search.fit(x, y) def check_grid_search(model_class, x, y, scoring): @@ -804,8 +779,6 @@ def check_grid_search(model_class, x, y, scoring): } with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) warnings.simplefilter("ignore", category=UndefinedMetricWarning) # KNeighborsClassifier does not provide a predict_proba method for now @@ -895,12 +868,8 @@ def check_hyper_parameters( model = instantiate_model_generic(model_class, n_bits=n_bits, **hyper_parameters) # Also fit with these hyper parameters to check it works fine - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - - # Here, we really need to fit, to take into account hyper parameters - model.fit(x, y) + # Here, we really need to fit, to take into account hyper parameters + model.fit(x, y) # Check correctness with sklearn check_correctness_with_sklearn( @@ -966,10 +935,7 @@ def check_fitted_compiled_error_raises(model_class, n_bits, x, y): with pytest.raises(AttributeError, match=".* model is not fitted.*"): model.decision_function(x) - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) # Predicting in FHE using a trained model that is not compiled should not be possible with pytest.raises(AttributeError, match=".* model is not compiled.*"): @@ -994,10 +960,7 @@ def check_class_mapping(model, x, y): assert numpy.array_equal(numpy.arange(len(classes)), classes) # Fit the model - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) # Compute the predictions y_pred = model.predict(x) @@ -1009,10 +972,7 @@ def check_class_mapping(model, x, y): new_y = classes[y] # Fit the model using these new targets - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, new_y) + model.fit(x, new_y) # Compute the predictions y_pred_shuffled = model.predict(x) @@ -1034,10 +994,7 @@ def check_exposition_of_sklearn_attributes(model, x, y): ): getattr(model, training_attribute) - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) for name in vars(model.sklearn_model): if name.endswith("_") and not name.endswith("__"): @@ -1092,10 +1049,7 @@ def check_exposition_structural_methods_decision_trees(model, x, y): ): model.get_depth() - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) # Get the number of leaves from both the scikit-learn and Concrete ML models concrete_value = model.get_n_leaves() @@ -1126,10 +1080,7 @@ def check_load_fitted_sklearn_linear_models(model_class, n_bits, x, y, check_flo model = instantiate_model_generic(model_class, n_bits=n_bits) # Fit the model and retrieve both the Concrete ML and the scikit-learn models - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - concrete_model, sklearn_model = model.fit_benchmark(x, y) + concrete_model, sklearn_model = model.fit_benchmark(x, y) # This step is needed in order to handle partial classes model_class = get_model_class(model_class) @@ -2002,10 +1953,7 @@ def test_valid_n_bits_setting( model = instantiate_model_generic(model_class, n_bits=n_bits) - with warnings.catch_warnings(): - # Sometimes, we miss convergence, which is not a problem for our test - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) # A type error will be raised for NeuralNetworks, which is tested in test_failure_bad_data_types @@ -2068,10 +2016,7 @@ def test_initialization_variables_and_defaults_match( model = instantiate_model_generic(model_class, n_bits=n_bits) # Fit the model to create the equivalent sklearn model - with warnings.catch_warnings(): - # Ignore convergence warnings - warnings.simplefilter("ignore", category=ConvergenceWarning) - model.fit(x, y) + model.fit(x, y) # Assert the sklearn model has been created assert hasattr(model, "sklearn_model"), "Sklearn model not found"