QRISE_UF_Challange_2024.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "KnY2cZgp_FCp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install mitiq"
      ],
      "metadata": {
        "id": "XWz6Nw1b_Gpc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import partial\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import cirq\n",
        "import mitiq\n",
        "from mitiq import cdr, ddd, pec, rem, zne\n",
        "from mitiq.benchmarks import generate_rb_circuits\n",
        "from mitiq import MeasurementResult, Observable, PauliString, raw"
      ],
      "metadata": {
        "id": "zA_Y8Eri_wSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0UQj6IVJ_AbW"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiments"
      ],
      "metadata": {
        "id": "y-lqa4WcaIAV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Executor"
      ],
      "metadata": {
        "id": "BP7eO67UEhlI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def execute(circuit: cirq.Circuit, noise_level: float = 0.005, p0: float = 0.05, p1: float = 0.05) -> MeasurementResult:\n",
        "    \"\"\"Execute a circuit with depolarizing noise of strength ``noise_level`` and readout errors ...\n",
        "    \"\"\"\n",
        "    measurements = circuit[-1]\n",
        "    circuit =  circuit[:-1]\n",
        "    circuit = circuit.with_noise(cirq.depolarize(noise_level))\n",
        "    circuit.append(cirq.bit_flip(p0).on_each(circuit.all_qubits()))\n",
        "    circuit.append(cirq.phase_flip(p1).on_each(circuit.all_qubits()))\n",
        "    circuit.append(measurements)\n",
        "\n",
        "    simulator = cirq.DensityMatrixSimulator()\n",
        "\n",
        "    result = simulator.run(circuit, repetitions=10000)\n",
        "    bitstrings = np.column_stack(list(result.measurements.values()))\n",
        "    return MeasurementResult(bitstrings)"
      ],
      "metadata": {
        "id": "HtgbU2mPEhlR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "observable = Observable(PauliString(\"ZI\"), PauliString(\"IZ\"))"
      ],
      "metadata": {
        "id": "6dM3GTBbEhlR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Utility functions"
      ],
      "metadata": {
        "id": "_WFnK3u_Ek1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "def plot_surface_interactive(X, Y, Z):\n",
        "    fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y)])\n",
        "\n",
        "    fig.update_layout(scene=dict(\n",
        "                    xaxis_title='X',\n",
        "                    yaxis_title='Y',\n",
        "                    zaxis_title='Z'),\n",
        "                    autosize=False,\n",
        "                    width=400, height=400)\n",
        "\n",
        "    fig.show()\n"
      ],
      "metadata": {
        "id": "1n76U6zm0PJy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from matplotlib import cm\n",
        "from matplotlib.ticker import LinearLocator\n",
        "\n",
        "\n",
        "def plot_surface(X, Y, Z, x_labels=None, y_labels=None):\n",
        "    fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
        "\n",
        "    # Plot the surface.\n",
        "    surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n",
        "                        linewidth=0, antialiased=False)\n",
        "\n",
        "    # Customize the z axis.\n",
        "    ax.set_zlim(-1.01, 1.01)\n",
        "    ax.zaxis.set_major_locator(LinearLocator(10))\n",
        "    # A StrMethodFormatter is used automatically\n",
        "    ax.zaxis.set_major_formatter('{x:.02f}')\n",
        "\n",
        "    if x_labels:\n",
        "        ax.set_xticklabels([0] + x_labels)\n",
        "    if y_labels:\n",
        "        ax.set_yticklabels([0] + y_labels)\n",
        "\n",
        "    # Add a color bar which maps values to colors.\n",
        "    fig.colorbar(surf, shrink=0.5, aspect=5)\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "c6Qh8aTDkDhh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Experiment setup"
      ],
      "metadata": {
        "id": "vXfpqG1TEqz-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mitiq.benchmarks import generate_rb_circuits, generate_ghz_circuit, generate_mirror_circuit, generate_w_circuit\n",
        "from mitiq.ddd.rules import xx, yy, xyxy\n",
        "from mitiq.zne.inference import ExpFactory, LinearFactory, PolyFactory, RichardsonFactory\n",
        "from mitiq.zne.scaling.folding import fold_gates_at_random, fold_all, fold_global\n",
        "\n",
        "\n",
        "rb_circuits = {\n",
        "    \"rb5\": generate_rb_circuits(2, 5)[0],\n",
        "    \"rb10\": generate_rb_circuits(2, 10)[0],\n",
        "    \"rb20\": generate_rb_circuits(2, 20)[0],\n",
        "}\n",
        "circuits = {\n",
        "    \"rb5\": generate_rb_circuits(2, 5)[0],\n",
        "    \"rb10\": generate_rb_circuits(2, 10)[0],\n",
        "    \"rb20\": generate_rb_circuits(2, 20)[0],\n",
        "    \"ghz5\": generate_ghz_circuit(5),\n",
        "    \"ghz10\": generate_ghz_circuit(10),\n",
        "    \"w5\": generate_w_circuit(5),\n",
        "    \"w10\": generate_w_circuit(10)\n",
        "}\n",
        "folds = {\n",
        "    \"local\": partial(fold_gates_at_random, random_state=42),\n",
        "    \"all\": partial(fold_all),\n",
        "    \"global\": partial(fold_global)\n",
        "}\n",
        "factories = {\n",
        "    \"lin\": LinearFactory(scale_factors=[1, 3]),\n",
        "    \"rich\": RichardsonFactory(scale_factors=[1.0, 2.0, 3.0]),\n",
        "    \"poly\": PolyFactory(scale_factors=[1, 1.5, 2, 2.5, 3], order=2),\n",
        "    \"exp\": ExpFactory(scale_factors=[1, 3, 5])\n",
        "}\n",
        "rules = {\"xx\": xx, \"yy\": yy, \"xyxy\": xyxy}\n",
        "probs = [0.1, 0.01, 0.05, 0.001, 0.005]"
      ],
      "metadata": {
        "id": "WHuHz9NNJPaZ"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DDD"
      ],
      "metadata": {
        "id": "HY6BlDKVyXk7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ddd_results = {}\n",
        "\n",
        "print(\"\\n================ DDD ================\\n\")\n",
        "for i, (circuit_name, circuit) in enumerate(rb_circuits.items()):\n",
        "    for j, (rule_name, rule) in enumerate(rules.items()):\n",
        "        print(f\"({i}, {j}) => Circuit: {circuit_name}, Rule: {rule_name}\")\n",
        "\n",
        "        executor = ddd.mitigate_executor(\n",
        "            execute,\n",
        "            observable=observable,\n",
        "            rule=rule\n",
        "        )\n",
        "\n",
        "        result = executor(circuit)\n",
        "\n",
        "        ideal_value = raw.execute(circuit, partial(execute, noise_level=0, p0=0, p1=0), observable).real\n",
        "        noisy_value = raw.execute(circuit, execute, observable).real\n",
        "        mitiq_value = result.real\n",
        "        error = abs((ideal_value - mitiq_value) / ideal_value)\n",
        "        rem_value = abs((ideal_value - mitiq_value) / (ideal_value - noisy_value))\n",
        "        ddd_results[(i, j)] = [ideal_value, mitiq_value, error, rem_value]\n",
        "\n",
        "        print(f\"Mitigated value: {mitiq_value:.3f}\")\n",
        "        print(f\"Error w Mitiq: {error:.3f}\")\n",
        "        print(f\"REM w  Mitiq: {rem_value:.3f}\")\n",
        "        print(\"\\n\")"
      ],
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          "text": [
            "\n",
            "================ DDD ================\n",
            "\n",
            "(0, 0) => Circuit: rb5, Rule: xx\n",
            "Mitigated value: 1.227\n",
            "Error w Mitiq: 0.386\n",
            "REM w  Mitiq: 1.018\n",
            "\n",
            "\n",
            "(0, 1) => Circuit: rb5, Rule: yy\n",
            "Mitigated value: 1.239\n",
            "Error w Mitiq: 0.381\n",
            "REM w  Mitiq: 0.972\n",
            "\n",
            "\n",
            "(0, 2) => Circuit: rb5, Rule: xyxy\n",
            "Mitigated value: 1.243\n",
            "Error w Mitiq: 0.379\n",
            "REM w  Mitiq: 0.984\n",
            "\n",
            "\n",
            "(1, 0) => Circuit: rb10, Rule: xx\n",
            "Mitigated value: 0.812\n",
            "Error w Mitiq: 0.594\n",
            "REM w  Mitiq: 0.976\n",
            "\n",
            "\n",
            "(1, 1) => Circuit: rb10, Rule: yy\n",
            "Mitigated value: 0.822\n",
            "Error w Mitiq: 0.589\n",
            "REM w  Mitiq: 1.023\n",
            "\n",
            "\n",
            "(1, 2) => Circuit: rb10, Rule: xyxy\n",
            "Mitigated value: 0.813\n",
            "Error w Mitiq: 0.594\n",
            "REM w  Mitiq: 1.016\n",
            "\n",
            "\n",
            "(2, 0) => Circuit: rb20, Rule: xx\n",
            "Mitigated value: 0.458\n",
            "Error w Mitiq: 0.771\n",
            "REM w  Mitiq: 0.992\n",
            "\n",
            "\n",
            "(2, 1) => Circuit: rb20, Rule: yy\n",
            "Mitigated value: 0.446\n",
            "Error w Mitiq: 0.777\n",
            "REM w  Mitiq: 1.019\n",
            "\n",
            "\n",
            "(2, 2) => Circuit: rb20, Rule: xyxy\n",
            "Mitigated value: 0.466\n",
            "Error w Mitiq: 0.767\n",
            "REM w  Mitiq: 0.997\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(0, len(rb_circuits), 1)\n",
        "Y = np.arange(0, len(rules), 1)\n",
        "X, Y = np.meshgrid(X, Y)\n",
        "Z = np.array([[ddd_results[(i, j)][2] for i in X[0]] for j in Y[:, 0]])\n",
        "\n",
        "plot_surface(X, Y, Z, x_labels=list(rb_circuits.keys()), y_labels=list(rules.keys()))"
      ],
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            "<ipython-input-7-80b21e089ec6>:22: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n",
            "<ipython-input-7-80b21e089ec6>:24: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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vkTXJAGxsbEChUGBzcxPx8fEuTVue6r6hdnHmz3i7C5f7KkzyIdWMd6DsVLqevw4UgOh1oA6HA3a73UW8+b7modgnQKEEChXrEIBEG6TbW6lUwmw2Q6vVQqFQ4NixY4iOjhZ0LCHpaaPRCI1Gg7i4uG0NakLxdPE3GAzQarVISkrCwYMHufQ9qfvOzs5y3t5kq1ZsbCx3LDlH1nw8nacn4SJjYlNTU+jr63OZ8U5KStq1VaC7VVuXYh0oEe/JyUk4nU7k5+dT8aa8JaBiLXM8pb1nZ2fR19eHvLw8lJeXB3Rx8tUNzrIsxsfHMTw8jLKyMhQWFoq+iPNvCliW5XZlV1RUIC8vj3NX8+XtPTY2xjUskaUTodK05Q+VSoWUlBSuUc99m1ZPTw83452cnOzi6S01e/UzDQsLc/kZ8OfcJycnt93AEJMapVLJZZgUCgUXeQOe3dWoeFP2A1SsZYqn2Wmn04m+vj4sLy+jpqaG27wUCN4ia5vNhu7ubqyvr6OpqQmJiYlBnT+5+NtsNnR1dWFjY8Ol8U3IqBC/YWl6ehqrq6vQ6/Vc1L2XqWN/BCp+nrZpkZuWoaGhbeUC4iwmBXK5AQpkHajFYuHq3e6RN3+jmEKhoOJN2RdQsZYhfMtQYOvCbzKZoNVqERUV5dWIRAieImuSnk5MTERra6sk6VdizHL+/HkkJCSgpaUl4OPyG5ZIp3BqaioMBgOXOubvcg6kWU3uuM9488fESLmAP+MdFxcnWoTkItbu+FoHSm7cjEbjNoc5vniTG14SebuLN+k2p1Dkzv64su0jyMWFRL8KhQITExMYGhpCSUkJiouLg7q48LvB+elpb3PZYmBZFouLi9jY2EBVVZUkxyUjPfyap91u5wRMp9PBYrFwDmPJycmSRp+BsBO1dfdyAd8WdGpqipvx5juLCf2Zkz4IucNfB+p0OhEeHs4tIPG3DhTwvMubL978bnMKRW5QsZYJ/AsJ6fa22+3o7u7G2toaGhsbA+7I9gSp+1qtVnR1dcFisaCpqYlblRgsJJ2+urqKqKgoFBQUSHJcYLsIqtVql13OfIcx/lIKIt5xcXH74kLsyRbU3VnMkzmJt9cu18jaFwzDIDw8HJmZmcjMzAQgbB0o/+bNk3gTAxd+w1qo/Wwo+xMq1jLAUxOZwWBAV1cXEhMTcezYMck6g8kWrpGRESQlJaG2tlay1DG/i/zAgQMYHh72+thAL4BCHu/uMMaPPicnJwFg10xKdvMC78lZzJM5ibcZ71AVa/dzFrIOlL9RTKh403WgFDlAxXqPcbcMZVkWw8PDmJiY4Dqnpbo4kG7rpaUlHDhwALm5uZKlvYl5SmlpKQoLC2EwGPZ0Raan6HNtbW2bSQm/WU1sH4DccDcncTqd3B5v9/nm5ORkwXajcoJkn3zhaR2ov7o/f5c3eR6bzebirkbFm7IXULHeI8js9NTUFBYWFlBTU4PNzU1otVo4HA4cPXoUcXFxkj3f5uYmurq6sLm5ifz8fOTl5UlyXLLda3V11cU8xZ/5SqBCHuzKTXeTEvdd1gMDA4iKipJkzllu8+Aqlcplxps/3zwxMcH5vqekpHCNWrs14y0WIWLNx5M9rKe6P1+8yTpQgIo3Ze+hYr0H8NPe5M2/sLCAnp4eZGZmoqqqStLGKGJHmpqaivDwcFEmJ55YXV2FVqtFdHT0NvMUqfdZA9KKoCeDDn7Nt6enx6VZzdc6zFDDfb755ZdfRk5ODiwWC3Q6HTY2NlxWgSYmJsrutQcq1u6IXQfKF2/yYbVaYbPZAHie86biTZECKta7DHEiIxcb4kbW09ODgwcPcs0yUsAwDIaHhzE5OYkDBw4gJycHPT09QS/Y4K/KLC4u9tihLrVY7/QFLywszGVMiO+s5r4OkzSr+RKLULtAp6SkICYmBoDra+c3avmzhN1NghVrd6RaB0rE29tSErpRjCIWKta7hPvsNBHpoaEhOBwOHD9+XLBlqBAsFguXUm9paUFsbCyA4EXU4XCgp6cHKysrqK+v5yJTd+QeWfsjIiKC6zTm1zvJjDfLsi4p85iYmJC9CLs3mPFfO+B7xjs5ORmxsbG7Lt47PW4m1TpQlmW5Xd7kPaFSqRAVFUXXgVICgor1LuA+Ow0A09PTGBgYQFpaGkwmk6RCvbi4iO7ubmRkZGxLqYvdZw0Aa2tr6Ozs5IxZfLmHhVpk7e+53eud/Au3Tqdz6bYmXf2hgr9ucF8z3pOTk3ty48Lf4b4beFsH6t6w6GmjGF+8p6enYTAYUF1d7XGjGBVvijeoWO8gnmanHQ4Hent7YTAYUFtbC6VSia6uLkmej2EYDA0NYWpqyusWLjEiyrIsZmZm0N/fj6KiIpSUlPi9oIR6ZO0LT1EXf5uU1WpFX18fUlNTkZyczHlayxHyMw1k9am3GW+DwcBFnO5jYlIL0F4bufA77gF4XQfKX0oSFRUFlmWhVqsRFhbGXR/4kTcVb4o3qFjvEJ5mp0lDVkxMDI4dO4aIiAgYjUZJRGhjYwNarRYMw6C1tZWrP7pDzFaE4nA4OD/y2tpazrfZH/7EWq/Xw263Izk5WVDn8U6Iv1S4b9Q6f/48srOzYbfbMT4+jvX19V1byhEogYq1O95mvA0GwzbRknJETuqadbAIXQdKRNhqtXI/B37kzTAMrFYrNjc3uZ6WsLAwjI+PIyMjg2sKpLz1kMcVY5/hPjsNAGNjYxgZGdm2zSqYtDRhfn4ePT09yM7ORkVFhc/O3UBEb319HRqNBmq1OmA/cm/PwzAMBgYGMDs7i/DwcPT29iIuLo6bd94vXdekpgu8uZTDYDC4NGyR1xwfH79nwhOsWLvDjziLioo8jsiRNZjkQ0zWQW5i7Y63daCjo6PY2NjA+fPnuVFB/jpQAhFvp9MJp9OJT3ziE/jUpz6Fm266aa9eEmWPoWItIeTN5XA4uIsJf+uUp21WSqVSdI3T6XRicHAQs7OzgjvJhd4czM7Oore3FwUFBSgtLQ34wuhJrC0WCzQaDViWRXNzM9RqNex2OwwGA1ZWVtDX18fts+Y3LxEPZ7lG1v7wtpTDYDBgeno6KF/vYJFarN3xJFpkGcfExAR6e3tFLWMJNSMXMi63vLyM5ORkFBQUCF4HCgBms5lrEhXK3/72Nzz88MNob2/H3Nwcfve73+Gaa67x+T0vv/wyTp8+jd7eXuTl5eGee+7Bxz72saCOSZEGKtYS4Sntrdfr0dXVheTkZK/brMRG1mazGVqtFgDQ2toquEHNn+g5nU709/dzRi1i1nB6ep6lpSV0dXUhMzMTlZWVALYMVSIiIrZZhBLxHh8f59KLwWYf5ISnHd7kNRNfb3LRTk5OdrEGlRryO9qtKNV9DSZ/kxaZ8XYfE3PPtJCoMxQzME6nk0uHC1kHOjg4CJVKBYfDEbBYm81mHDlyBDfffDPe//73+3382NgY3v3ud+NTn/oU/uu//gvnzp3DLbfcgqysLJw8eVLUMSnSQcVaAtxnp1mWxdDQECYnJ1FVVYWcnByvUQC5SAaS1pubm0NPTw9yc3NRUVER0IXW182B2WyGRqOBSqVCa2trUCJBXi/DMNDpdBgfH+dmvQF4zCbwm5f4+6wNBgPm5uawubmJtrY2F4tQuTptBdKwReZ7Sc2XWIOSmi+xBiXiLWWz2k5H1v7gb9ICXJexuM+3k5LBXp9zMDidTo83Gd7Wgf75z3/GM888g6mpKdx22214z3veg7e//e249NJLud3w3jh16hROnTol+NzOnj2LoqIifO973wMAVFVV4dVXX8UjjzzCiXWgx6RIBxXrIODPTpPuVDLfzDCMy3yzNwIRa37Ue+TIEe4CFwjeIuu5uTn09vYiNzcX5eXlQUda5EJ68eJFbG5uirJP5dc/VSoVjEYjsrOzt7mMya3eHUy6nszuJiYmcjVf93RpTEwM95rJDudgz1Uuwue+jIU/4z09PQ2n08l1YJvNZqjVatmcuxCEjpyRm5h7770X99xzD3Jzc/Hxj38co6OjuOOOO9DQ0IBf/OIXkp5bW1sbTpw44fK5kydP4vOf/7ykz0MRBxVrkTAMA4fD4ZL2np+fR29vr6BGLwJfrH1Bmr3CwsKCinrdI2t+w9fhw4dF3QB4wmQyAdiKGOrq6rYJCkllBoJCoXBJHRKnLYPBwNW7ExISuBrpbtZ+dwqVSuViDcrf4U12OLvfsARyoyXnPgBP8+1kEc3Kygq6uro8uorJ+XfuLbL2BRmP++AHP4iSkhIAW3/7UjM/P8/1VRAyMjJgMplgsVh2tBxD8Q8V6wDhz04TMwmGYdDX14eFhQUcOnRo2x+8L4SI9czMDPr6+pCfn4+ysrKgPZHJBXpjYwMajQYKhSKgurcvyAauoaEhAMDBgwcFRX6nPtwh8BnefNz/PV3n4jLmrd69G7Vfd3ZKMDzt8CavmbiL8cXL3w5v8jcsZ4EjkJKBSqXC2NgYLrnkkm3mNPylJTs14x0MDMMELNYWiwUsy7pk6XwZElH2J1SsA8C9iUyhUHARb3h4uKiIl5geeBJrMuO8tLQUVLOX+/MxDIOFhQV0d3cjJycn4Lq3NxwOB7q7u7G6uoq6ujpcvHhR0PcJF+pAvo+k3G0AFgAs4L7PWTnhlnO9OxAiIyORnZ3t4i7Gv2FRKBTbblj44hVqXdXAmyUjT65i7vV+/ox3cnLynoucGOc1s9kMAAE3mAVKZmYmFhYWXD63sLCA+Ph4GlXLACrWAvE0O02WWRQWFqKkpES04Hlq+lpbW+NuAo4dOybZrmWSSuzu7pZ0cYi7Fan7akFviBVqMXztRxEANv7xMe3zsf/3dJ3o59mr1LK3Br2VlRXOElOtVruIlz+rUTnizb3MV71/enoa/f39HsejdhMxafD19XWoVKod37fe0tKCP/3pTy6fe+GFF9DS0rKjz0sRBhVrP3ianSbLLIxGI+rq6oJ2FeKLNfEPHhgYCPomwB2yAtHhcODYsWNeXc4ChaTpCwsLUVpa6pIp8CZcJ68TFnXvFYHeRAQj7jsFv0HPfYc3Ea/IyEgwDIOlpaWQ2GMNCJ+c8FTvdx+PIs5yUjTrCUGMWG9sbHAbvgJhfX0dIyMj3L/Hxsag0WiQnJyM/Px83H333ZiZmcF//Md/AAA+9alP4cc//jG+/OUv4+abb8aLL76IX/3qV3j22WcFH5Oyc1Cx9oGn2Wmj0QitVou4uDgcO3ZMkjtzItbEN1yv1wdk7SkEstyDNCBJIdS+ZrJJtOZJrN/5/y4E/dxyw1XcowHofD5+L8Sdb1BSUlICu92O2dlZjI2Nueyxllt3vTti3cs8jUd5atbzNeMtxbmLiazFvF8vXryIt7/97dy/T58+DQC46aab8OSTT2Jubg6Tk5Pc14uKivDss8/ijjvuwA9+8APk5ubi3//937mxLSHHpOwcVKy9wDAMbDaby4VhdHQUOp0O5eXlKCgokNSicW1tDV1dXYiKiuJ8w6WAv9O6uroa4eHh6O/vD/q47s1p3mpa7mK9H4VaDIFE7jsl7Gq1mosmjx49CqvVytW7+/v7Ybfbt+3wlkPKXCqrUXdnOfcZb7vdzhm0CNlh7g/SnBqoWBODlEC57LLLfJZkPInrZZddhs7OTtHH3NzchM1mC+g8d5rw8PAdLyHsBlSs3SBpb9LtrVQqYbVa0dXVhc3NTTQ3N3NznlI+X39/P0pKSlBcXCzZBXFzcxNarRZ2u52b+dbr9UHXVBcXF9HV1YXs7GxUVlZ6vIC5W4RSkRbPTqbk+TVrT25y/FWYAFysYKOjo/dEvHfKF9zfjHewtrD8DF0gELGWw42SLzY3N5EdFYsVyGtFbGZmJsbGxkJesKlY82BZFiaTCQqFAmq1GkqlEktLS+ju7kZaWprHeeFgsNvt6Onpgd1uR2lpKTdDKQXLy8vQarVIT0/HgQMHuLv5YBaH8KP0gwcPIisry+/3sCxLhXqXcRf3fzn3SY+Pu2ThDa8NZp5WYZJmteXlZZcd3iRtvlsXw93oYPc2403m+sfGxlw67ZOSkvzevBCx3q3Ierex2WxYgRNPRRYjGvJYsrIBBjfNj8Jms1Gx3g/wZ6c7OztRUFCAzMxMDA4OYmpqysUmUypWV1eh0WgQExOD2NhYyd6MLMtiZGRkm70nQexCDKvVCo1GA5vNJsiZDdi6MXjvTd0BPxdFOrwJNQC8ktH45v97+PolC29w/69QKLaNSa2ursJgMLhs09oNK9i92LjFt4X11mlPbl74M958nE4nFAqFqMh6p8e2pCQ2So0YhTx6HZSsE9jc67OQhre8WPMtQ4Gtu16LxYILF7aiQV+7ocU+HzENKS0tRVFREV5//XXRm7f4WK1WaLVaWK1Wr/aeYiJrg8EArVaL5ORk1NfXC8oubEXTgdmLUqTFl1ALgS/m3iCCTrZpkaiTWMHyO62lataSw3pM9057cvPivr+aL95i6tWA+AazvUKpUkCplEfKXsnI4zyk4C0t1iSaJuKlVCpht9uh0+mQl5cnmVkIwWazobu7G2tra2hsbOT2HUux01qv10Or1SIlJcVnut6bAYsnWJblOoUrKiqQl5cnKP1I0957T7BCLRRvgq7GVkBjf/k/MDAw4LKQI9hmLTmItTt8tzzgzf3VKysrmJqaQl9fHzcmt7i4GFDmYWNjI6Qia4VaAYVMxFpBxTq04ae9yRvf6XSip6cH6+vryM7ORlVVlaTPubKyAq1Wi/j4eLS2trqMfAUj1izLQqfTYWxsDJWVlcjNzfUpqGQrmD/sdju6u7thMpnQ1NQkuKmOCvXes1tCLYTly24EsHWhMf/jg29HU3jxtwF7estRrN0h+6v5M95TU1OYmprymHlISEjweoMdKjVrgipKCZVMfj8qRr7e94HylhNrT7PTxC0sMjIS6enpklrr8aPTsrIyjyNfYsXaZrNBq9XCYrGgubnZ78o8QFjN2mQyobOzEzExMdtuLHxBhXpvkZNIC2W84f0Y9/MYfu0cCA2xdketViMuLg5RUVFoamriltCsrKxgcHAQVqvVZY93fHw8lzJfX18Xbbz06KOP4uGHH8b8/DyOHDmCH/3oR2hqavL42Msuuwx//etft33+yiuv5IxRFhYWcOedd+LPf/4zjEYjLr30UvzoRz9CWVkZ93ilSgGlSh4RrdIpj/OQgreUWHuyDJ2YmMDw8DCKiopQUlKCvr6+oFPSBJvNhq6uLpjNZp/RaTB15KSkJNTW1gruUieRtacuYL57WnFxseAxMirSe08oCrVQvKXaSVOcu5jLFX7NOiIigltCA8BlTIwsZCF761dXV5Gbmxvw8z3zzDM4ffo0zp49i+bmZpw5cwYnT57E4OCgx+16v/3tb11mpPV6PY4cOYIPfvCDALauD9dccw3UajX+8Ic/ID4+Ht///vdx4sQJ9PX1cd+nUCmgkIlYKyCP85CCt4RYe5qdJmnetbU1NDQ0cLUmlUolSbMXEdPExES0trb6rE+pVCpRdeTy8nLk5+cHNMbCdxbjf5/T6eSWhgRioUqFeu/Zz0ItBH+NcHIRc19LPKKiohAVFeWykOWll15Cd3c33njjDTz77LPo7OzEO97xDrzjHe/AkSNH/D7f97//fdx66634+Mc/DgA4e/Ysnn32WTzxxBO46667tj0+OTnZ5d+//OUvER0dzYn18PAwLly4gJ6eHlRXVwMAHnvsMWRmZuIXv/gFPvShDwEAVGolVCp5ZD5USpoGDxk8pb1J/TghIcFj/TgYBx5+DVloU5bQyJo0qK2vrwdUR3Z/LsA1lWg2m6HRaKBSqdDa2ip4HpEK9d7zVhdqIchFzIX6gpMZ9/e85z14z3veg/e+971oaWlBSkoK/vKXv+AXv/gFXn/9dZ/HsNlsaG9vx9133819TqlU4sSJE2hraxN0vo8//jiuu+46rl5Odmjzrw9KpRIRERF49dVXObFWqJRQyESsFaBiHRKQaJoIE38G2ZuQBhLlukOczgKpIQPCOrSNRiM0Gg3XoCZ2hpW8XvJ88/Pz6OnpQW5uLsrLywXVAqlIywMq1NIQyIhaMIhZ4gFsdYNXVVXhhhtuwBe+8AVBDaLLy8twOp2clSohIyMDAwMDfr//9ddfR09PDx5//HHuc5WVldwCkJ/85CeIiYnBI488gunpaczNzXGPU4UpoQqTh1irFFSsZY377LRSqcTm5ia6urpgs9l8CqnYNPjy8jK6urqQkpISUA2ZPKfdbvf6Wkhd3VuDWiAQMSYWpzMzMwGtyqRCvfdQkd59pIjOxc5Zu5ui7Ibt6OOPP45Dhw65NKOp1Wr89re/xSc+8QkkJydDpVLhxIkTOHXqlMsNhEIF2TSY7SOt3n9i7T47rVAosLi4iJ6eHmRkZPg19SBjXIE8n06nw/j4OKqqqpCTkxPwm8lbGpzYka6urqKxsRGJiYkBHdcT5Nw6OzvBMAxaWloEj4VQod57qFDLE3cx9yTeYiJrUr8OdM46NTUVKpUKCwsLLp9fWFjwe2NuNpvxy1/+El/72te2fa2+vh4ajQarq6uw2WxIS0tDc3MzGhoauMfIqsGMlcd5SMG+EWtPs9MMw2BgYACzs7Oorq4W5GUdSBqcLMqw2WxeHcOE4EmsiR1pbGxsQONT/jAYDAC2GloOHz4s6OJBRVoeUKEODbxF2U6nU1T5SozdaHh4OOrr63Hu3Dlcc801ALYCi3PnzuGzn/2sz+/99a9/DavVio9+9KNeH0P6ZYaHh3Hx4kV8/etf576mDFNBGSYTu1Fas5YXnprIzGYztFotlEolWltbER0dLehYQtPgS0tL6OrqQnp6umALTm/wxZplWUxOTmJoaAglJSUoKiqSJO3FsixGR0cxOjoKhUKB0tJSKtQhBBXq0MBXOtzpdIpaJiHWG/z06dO46aab0NDQgKamJpw5cwZms5nrDr/xxhuRk5ODhx56yOX7Hn/8cVxzzTUeJ0J+/etfIy0tDfn5+eju7sbtt9+Oa665Bu9617tgMpkAyGzOmkbW8sHT7PTMzAz6+/uRn5+PsrKygAwU/KXB+ZunpFrwQcTa4XCgp6cHKysrqK+v3zZKIRZ+F3lzc7PfTlICFWp5QIU6NPBXtxZTs2YYRrTd6LXXXoulpSXcd999mJ+fR01NDZ577jmu6WxycnLbtXFwcBCvvvoq/vznP3s85tzcHE6fPo2FhQVkZWXhxhtvxL333uvyGKVKRpF1kOuA5UTIijWZnXY4HC6Wob29vdDr9aipqUFaWlrAx/WVBrdYLNBoNHA6nYI3TwmB7Mw+f/48oqKi0NraioiICEmOvbq6is7OTpcuciHd51So9x4q0qGDkAYzMTXrjY0NsCwr+lrz2c9+1mva++WXX972uYqKCp/d5rfddhtuu+02n8+pUMrIG1wm5yEFISnWntLeJpMJWq0WUVFROHbsmGix85YGX1hYQE9PDzIzM1FZWSnZBiGWZbGysgKTycTttJYq7T01NYXBwcFt6XRf/uBUpOUBFerQQehYly9TFG+YzWYACKlFHrJKg9NFHnuH++w0AIyPj2NkZESSGq+7WDMMg8HBQczMzAhuUhOKw+FAX18fFhcXER0djdLSUsmO29vbC4PB4DGd7i2ypkItD6hQhw7qP/wIg4ODgnZ4i4mszWYz1Gq1ZJm23UBWDWY0Db778GeniWUoqcWazWbJRpv4zV4bGxvQaDQAENCIkxDI8pCIiAhUVVVhbGxMkuOur69Do9FArVajpaXFY0OLp8iaCrU8oEIdOhydPg+j0YiVlRWMjo5iY2OD26SVnJyMhIQEF3EWU7M2m82Ijo7eldlqqaBp8J0hJMSaNF/x094GgwFdXV1ISkoKytHLHVKznpubQ29vL7Kzs1FZWSnplp+ZmRn09fWhsLAQpaWl0Ov1kiwPIcb//hrr+DckVKTlAxXq0ICf9k5LS+N6Y8gmLYPBgP7+fm6Hd3JyMpKSkuBwOAIW6/X19ZBajwlQsd4pZC3W/NlpsniCZVkMDw9jYmJC0P5mMc8JAL29vQE5ewmBLMtYXFx0aYALZp81AJd58iNHjnjcqMOH/BypUMsHKtShga/6NH+TFsuysFgsMBgMWFlZweTkJBwOB3Q6HdLS0gTv8BY7trWXKMOUMkqDS7NBUQ7IVqzdm8gUCgUsFgu6urrgcDiCMiHxhtlsRmdnJwCgsbFR1KIMb/DT08eOHXNJTwfjR0461FmWFTxPrlQqcf0/j4p6Poq0UJEOHQLxB1coFIiOjkZ0dDRyc3PBsixeeuklxMfHQ6/XQ6fTISwsjKt1JyUlISoqattxNjY2BIm6nFAo5dNgpthH+6zl4bbuBsMwWF5exvDwMBQKBZRKJRYWFnD+/HnExcWhpaVFcqGenZ3F+fPnkZqaCgCSOYaRY7e1tSEtLQ2NjY3b6shiI+ulpSXuZ9Lc3CzY+OWe7+2fP+BQhgp16BDsIg+SscvLy0NNTQ0uvfRSVFdXIyoqCrOzs7hw4QLa2towMDCAxcVFbvNfMGnwRx99FIWFhYiMjPTrr/Dkk09CoVC4fHjqd+nv78d73/teJCQkICYmBo2NjZicnHR5DGkwk8vHfkFWkTV/dnpjYwPz8/MoKirCwMAA5ubmJE9LA28utFhYWOBSyJOTk5LstHY6nRgYGMD8/LzP9HSgYs3fHhaIMQtNe8sHKtShgxQbt8j7m9SslUolF1EXFxfD4XBwzWpjY2OYmprCgw8+iNLSUjidTpjN5oBE+5lnnsHp06dx9uxZNDc348yZMzh58iQGBwe9Xofi4+MxODjI/ds9mtfpdDh+/Dg+8YlP4Ktf/Sri4+PR29u7TdRpzXpnkI1Yu6e91Wo17HY72traEBYWFpBlqFBIapocn6ShgklLE4jdqUKhcDm2J4hYk7q8L2w2G7RaLSwWS0ClACrU8oEKdegg1a5rfnOsJ8LCwpCamspl9srKymA0GvG73/2Oa6RtaWnBO9/5Ttx+++1+S3Tf//73ceutt3LWomfPnsWzzz6LJ554AnfddZfH71EoFD6Doa985Su48sor8Z3vfIf7XElJyfbjULHeEWSRBmcYBlarFQ6Hg0vB6PV6WK1WpKeno6mpSVKhZlkW09PTaGtr447PF1OxazIJ8/PzaGtrQ1JSEpqbm30KNfDmG9jfntqVlRWcP3+eu7mgQh16UKEOHaQSamBLrElJTwjJycm45ZZb0NLSgg984AMYGBjARz/6UQwMDPidubbZbGhvb8eJEye4zymVSpw4cQJtbW1ev299fR0FBQXIy8vD1Vdfjd7eXu5rDMPg2WefRXl5OU6ePIn09HQ0Nzfj97///bbj7HXae7+mwfdUrMnstNVq5UxOHA4HtFotpqamoFQqUV5eLunYlMPhQHd3N4aGhlBTU+Px+IGuySQwDIO+vj709PTg4MGDqKqqEnTu5DHeonmWZTE+Po6LFy+isLAQNTU1ghaHvPP/XaBCLRP+5dwnqVCHEFIKNSDOEAUAl/4uLi7GrbfeiqefftrvMpDl5WU4nU7OA5yQkZGB+fl5j99TUVGBJ554An/4wx/w85//HAzDoLW1FdPT0wCAxcVFrK+v41vf+hauuOIK/PnPf8b73vc+vP/978df//pXl2MplEpZfYghkHo/ABiNRnzmM59BVlYWIiIiUF5ejj/96U/c1x944IFtPQGVlZUBndOepcE9WYby10I2NDSgra1NUGpYKHwjktbWVq9/9GLS4BsbG9BqtQF1ZRN8iTV/uUdDQwOSkpIEHZOKtHygIh06SC3SBDGGKMCWWIvZcRAoLS0taGlp4f7d2tqKqqoq/OQnP8HXv/517tp09dVX44477gAA1NTU4Pz58zh79ize9ra3cd+rUCqhUMkiaStKrAOt99tsNlx++eVIT0/Hb37zG+Tk5GBiYmKbSVd1dTX+8pe/cP8OdFPjnog1y7Kw2WxgGIYT4rGxMeh0OpSWlqKwsBAOhwPA1h1pMOsnyfMRn2xiROLrBiDQNPji4iK6u7uRlZWFioqKgN+U3sSa3FxERkYGtNyDCrV8oEIdOuyUUAPBRdbFxcUBfU9qaipUKhUWFhZcPr+wsCC4QVetVqO2thYjIyPcMcPCwnDgwAGXx1VVVeHVV191+VwwEa3UiDmPQOv9TzzxBAwGA86fP8+ZcxUWFm57XFhYWFAN0nvyEyW1G4VCAZvNhosXL2J6ehpNTU2ctzf5ww62K5uk1XU6Herq6lBWVuY3UheaBidmJFqtFgcOHMCBAwdEvSFJWoT/nGScIzMzEw0NDYKEmqa95QUV6tDh+LywtbFiEbPEA9jK2AXarxMeHo76+nqcO3eO+xzDMDh37pxL9OwLp9PJBSDkmI2NjS7d4gAwNDSEgoICl8+RBjO5fACAyWRy+bBarR5ft5h6/x//+Ee0tLTgM5/5DDIyMnDw4EE8+OCD2zRkeHgY2dnZKC4uxkc+8pFtI2/+2LM0uEKhwPLyMrq7u5GSkoLa2lqXCJqIeTBivbq6ym3iCiQyFZIGt1gs0Gq1cDqdaG1tDdoSkHSE88e9AlnzSUVaXlChDh2czzyM8+fPc57eSUlJki/OEBtZr6+vi3IwO336NG666SY0NDSgqakJZ86cgdls5qLFG2+8ETk5OXjooYcAAF/72tdw9OhRlJaWwmg04uGHH8bExARuueUW7phf+tKXcO211+LSSy/F29/+djz33HP4n//5n22rNmXlYPYP/cjLy3P5/P33348HHnhg2+N91fsHBgY8Psfo6ChefPFFfOQjH8Gf/vQnjIyM4J//+Z9ht9tx//33AwCam5vx5JNPoqKiAnNzc/jqV7+KSy65BD09PYIbhfdMrHU6HYaHh1FVVYWcnByP0W5YWBiXDg8ElmUxOTmJoaEhFBcXo7i4OKC6t780+NLSErq6upCRkYGqqipJ1mUqlUpsbGygu7tb0LgXHyrU8oGKdGhxycIbcDqdWF1dxcrKCqamptDX14eYmBhOuBMTE4MuxQWTBhdjAHXttddiaWkJ9913H3fj/9xzz3EiNDk56RLpr6ys4NZbb8X8/DySkpJQX1+P8+fPu6S93/e+9+Hs2bN46KGHcNttt6GiogL//d//jePHj7s8txxHt6amphAfH899XsqbMYZhkJ6ejn/7t3+DSqVCfX09ZmZm8PDDD3NiferUKe7xhw8fRnNzMwoKCvCrX/0Kn/jEJwQ9z56JNZkp9HXXKGaEym63o6enB0aj0eN6SCF4MylhGAYjIyOYmJgIyIxECCzLoqurCzk5OQEtDqFCLR+oUIcWpEatUqmQnJyM5ORklJSUwG63cws5hoeHsbm5ifj4eO4xcXFxAae0xTaYEbtRMXz2s5/FZz/7WY9fc4+GH3nkETzyyCN+j3nzzTfj5ptv9v0gpXLrQw784zzi4+NdxNobYur9WVlZUKvVLr/fqqoqzM/Pw2azeXTDTExMRHl5OdcTIIQ9E+vExES/UXOgYm00GqHRaBAXF4djx46Jtgz19Lybm5vQarWw2+1oaWmRzFyf3AA4HA4UFxejvLxc0Pddft3WhYafbmIcwbuuUcRBhTq08NVMplarkZ6eznX+8hdyTE9Pg2EYzn0sOTlZ0ApLMTVrlmVDc5GHSgWlBNlGKQj0PPj1/muuuQbAm/V+bzc+x44dw9NPP82NHwNbtfysrCyvGrS+vg6dTocbbrhB8LnJxsHMEyqVSlAanMwhDw8Po6ysDIWFhUGNe7k3mOn1emi1WqSmpqK+vj7olBjBarVCq9XCarUiOjpa8FgWEept5y2wTkRFXVqoUIcWgXZ9R0VFIScnBzk5OWBZFuvr6zAYDFheXoZOp4NarfZb7w4mDR5qYi3HNHggBFrv//SnP40f//jHuP322/G5z30Ow8PDePDBB3Hbbbdxx/ziF7+Iq666CgUFBZidncX9998PlUqF66+/XvB5yVqsw8LC/EbWNpsN3d3dWFtbQ1NT07bZNjGoVCrYbDYXD25ftXUxGAwGaLVaJCUloa6uDq+//rqg2W5vQh0IQkSdCrowqFCHDlKMZikUCsTFxSEuLg4FBQWC691vJbGGSgWFTBrM4Aj8PAKt9+fl5eH555/HHXfcgcOHDyMnJwe333477rzzTu4x09PTuP7666HX65GWlobjx4/jwoULAc3QK1h/Hpc7BNlT7Yv29nakpaUhPz/f49dXVlag1WoRHx+PQ4cOcTNuwaLT6WAymeBwOGCxWFBbWyvZli+SBRgZGUF5eTny8/OhUCjQ1taGwsJCblTCHSlEeid4q4o6FenQYidnqPmQejepeZN6N8MwiImJCagfxeFwIDk5GWNjYx7nduWGyWRCQkICxu+5GfGR0m0tDAbTpg2F33gCq6urgmrWckbWkbW3mjXLshgdHYVOp0N5eTkKCgok3fdqtVqxtLSE9PT0bSNlwWC329Hd3Q2TyYTGxkaXLICvcTG5CjXw1ozSqVCHFrsl1IDnejfZpGU2m7G0tCS43m02mwFA8nXAO45SsfUhB+RyHhIgk5Y9z3ga3bJarZyJSnNzc9D1aT4sy0Kn02FqagrR0dE4cuSIZEJtMpnQ1tbGee66p+u9daDLWaiFsp8M96lQhxa7KdSeiIqKQnZ2NuLi4lBcXIy6ujokJCRAr9fjjTfewN///nf09fVhbm5um1EHEWsxafBAvK1/+9vfoqGhAYmJiYiJiUFNTQ3+8z//0+vjP/WpT0GhUODMmTMev77XiztC8boihD01RfGHe2St1+u5dXGtra2Spb2Brdp3V1cXzGYzSkpKYDAYJLsJmJ6eRn9/P4qKilBSUuLxuO5ivR9EOlDkHqVToQ4t9lqo+RDbZPd6t8lkgsFg4K4RMTExSEpKwtTUFJKTkxERERHwdS5Qb+vk5GR85StfQWVlJcLDw/G///u/+PjHP4709HScPHnS5bG/+93vcOHCBWRnZ3s/AYUCUMgkDpQw47rXyD4NbrfbuYh3bGwMFRUVyMvLkzTtTWrfCQkJaG1thV6vx9LSUtDHdTqd6Ovrw+LiImpra7ldtZ7gi/VbUaiFslcd71SoQws5CTXgec5apVJxKXH3+e4vfelLGBsbQ1xcHL72ta/h8ssvR1NTkyDhDtTb+rLLLnP59+23346nnnoKr776qotYz8zM4HOf+xyef/55vPvd7/b6/Iow+TSYyeU8pEAmtz+eUalUsFqteOONNzA7O4vm5mauIUsKWJbF2NiYy+pJtVotekUmH7PZjAsXLsBsNuPYsWM+hRp4U6ypUEuDlOkxKtShhdyEGhA2ukXq3ZWVlejo6MBPf/pTREREYHBwENdccw2Sk5NdtjZ5QuwuawLLsjh37hwGBwdx6aWXcp9nGAY33HADvvSlL6G6utrnMdxXQe71x35B1mnwzc1NLC0tISsrC3V1dZLVjwHxzV5CWFhYQHd3N3JyclBRUSF4p/WnvmIQ/ZyUwPEn2Hf+7z9sANX75w2/36kZeVHStbpSIcYUJS4uDklJSfjFL34BhmHQ1dW1bWmGO2K8rYGtPQo5OTmwWq1QqVT413/9V1x++eXc17/97W8jLCzMZXbYKwoZOZjJJR0vAbJMgxNXr5mZGURHR+PQoUOSvvn4Tmetra3bXGbE2JwCW+c9NDSEqakpHDp0SPA6NBpNyw9OqAGwdt/TjQoq5ntOUfvvYDAY0NHRAaVSyXVbJycne91bv5uImbPmb9xSKpWoqanZgTPbIi4uDhqNBuvr6zh37hxOnz6N4uJiXHbZZWhvb8cPfvADdHR0CLoOK1QqKGTiYCaX85CCPRVrhUIB9zFvss3K4XCgrKwM8/Pzkqa9JyYmMDw8zO3NFtLsJQSxdqRUqOUHX6iFQMV8byFp79zcXDAMg7W1NRgMBszNzWFwcBBRUVGcSUlSUpKkGTqhiPEGF7NxS+wua6VSidLSUgBATU0N+vv78dBDD+Gyyy7DK6+8gsXFRRe/C6fTiS984Qs4c+YMxsfH3Q5GR7d2AllF1ouLi+ju7kZ6ejoOHDgAvV4fdO2YwF/w0dDQ4NPaM9DIWqwdKRVq+RGoUAuBivnO4V6fViqVSEhIQEJCAoqKiuBwOLimLZ1OB4vFEvRSDjGIiazNZnPASzzEeFt7gmEYbpTshhtucKmBA8DJkydxww03cE1sfBQKJRQyST/L5TykQBZizU8fV1dXc2MBQuxGhUD2WkdHRwta8EFq1v5qX8ScZXR0FJWVlcjNzRWUBaAiLT92QqSFQsVcHEIaycLCwpCWlsbZOm5ubsJgMHDjUizLupiUREVFSV7vJteSQG8KNjY2dmWX9UMPPYSGhgaUlJTAarXiT3/6E/7zP/8Tjz32GAAgJSUFKSkpLs+hVquRmZmJioqKbc9Pu8F3hj1Pg5vNZmi1WjAMsy19LLZ2TGBZFlNTUxgcHAxorzV5U/lKXdntdnR1dWF9fR1NTU1ISEgQdE5UqOXHXgq1EKiYb0dsx3dkZCSys7ORnZ0NlmW5lPni4iKGh4cRERHBRd1JSUmSeDmQa5iYNLiY9ZiBelubzWb88z//M6anpxEVFYXKykr8/Oc/x7XXXhvwcwP4x5y1TP4m5XIeErCnYj0/P4+uri5kZWWhsrLS4xyiWLF2OBzo7e2FwWBAXV3dtjtDX5Dz8Ja6Wl1dhUajQWxsbEDmLFSo5YfchVoI/sQc2F+CXtzxe0mOo1AouD3HhYWFcDqdMBqNMBgMGBsbQ09PD+Li4jjxTkhIEJUyJ/0vu5EGJwSyy/ob3/gGvvGNbwR0/G11aj4qJSCXxi4VTYNLgl6vR3V1tdflFSQNHugoxtraGjo7OxEZGYnW1laPK+t8Qd6Q7jcK/Ei9pKQERUVFNO0dwuwHoRbKfonOY5/7tx2rMatUKpeUr9Vq5VLmvb29cDqdSExM5MRbyB5r4M2xrUDT62az2eu1Uc4olEooZDK6JZfzkII9FeuDBw/6jJz5Ea7Qpi1i21dYWIjS0lJR9SeFQrHNGMXhcKCvrw/Ly8sBRepUqOXJW0mohRAKYn7Jwhtob2/flYYwAIiIiEBWVhaysrLAsizMZjMMBgP0ej10Oh3CwsI44U5OTvbaCyNmxhrYqlmLjaz3FIVSPvPNcjkPCZBFg5k3AhFrvpj6s/YU+twkfbW+vg6NRgO1Wo3W1lbBc5tUqOUHFWlx7KWY8+vTDMPsmljzUSgUiI2NRWxsLPLz8z3usY6NjXVJmfsrp/kjJHdZA1AoZTRnrZTHeUiBrMWapI4cDofPVLZYMfUFqZfPzc2hp6cH+fn5KCsrE3ShoCItT6hQ7xw7JebujWR7JdbuqFQqTphLSkpgs9m4EbH+/n7Y7XYkJCQgOTkZKpVK1DmLmbOWBXTOekfY825wf/gb35qdnUVvby8KCgpQWloq2RtZoVBgbGwMBoMBhw8f3mbf5w0q1PKECvXeIqYJzlPHN8MwsrMSBbbmmzMyMpCRkQGWZbGxscGJt8FgAMuy6O3t5UbEhAQUZrM59HZZA4BStfUhB+RyHhIg68ga8N4R7nQ60d/fj4WFBdTU1HBzlFJgsVg4Q4DW1lbO8s8fVKjlCRXq0IAv6JcaLnp8jBgnsN1GoVAgJiYGMTExyM3NxdzcHCYnJxEVFYXZ2VkXV7Xk5GQkJiZ6LPPx7UZDCqWMvMHlch4SIPtXolKp4HA4XD5HNlqtr6+jtbVVUqFeXl7G+fPnoVKpUFpaSoU6xKFCHXo4//thtLe3Y2xsDKurqy6WxHKNrH3BMAwiIiJQXFyMhoYGHD9+HCUlJWBZFsPDw3jllVfQ0dHh8npJQ5uYyPrRRx9FYWEhIiMj0dzcjNdff93rY3t7e/GBD3yAs14+c+bMtsf87W9/w1VXXYXs7GwoFAr8/ve/930CpMFMLh/7hJBLg8/NzaG3txe5ubkoLy+XLO3N35ldVVWF2dlZQd9HRVqeUJEOTS41XHRxGZuamgIALn0strN6L3FvMFOr1S6uahaLxeX1Pvnkk1haWkJ0dDSMRmNAz/XMM8/g9OnTOHv2LJqbm3HmzBmcPHkSg4ODSE9P3/b4jY0NFBcX44Mf/CDuuOMOj8c0m804cuQIbr75Zrz//e/3fxJ0znpHCJk0OMMwGBgYwOzsLA4dOiS4hiwEm82Grq4ubGxsoLm5GfHx8VhYWPBryEKFWp5QoQ5NSOrbk8uYXq/H/Pw8tyoyNTXVZwpZTvjrBo+KikJOTg5ycnLAsizCw8PxP//zP+ju7saHPvQh5Ofn4/LLL8dHP/pRHD9+3Odzff/738ett97KWYuePXsWzz77LJ544gncdddd2x7f2NiIxsZGAPD4dQA4deoUTp06JfTlUgezHULef+XYEmuLxYILFy4ACKyGLASyLjMhIQEtLS2cG5n7nLU7VKjlCRXq0MRbjdrdZeyll15CYWEh1tbWMDw8jM3NTa7rmizmkFuaPJA6u0KhwNGjR1FfX48f/ehH6Ovrw9jYGP785z9jbGzMp1jbbDa0t7fj7rvv5j6nVCpx4sQJtLW1Bf06BENr1juC7MXabrdjdHQUubm5qKyslDTtPTk5iaGhIY/rMvlz1nyoSMsXKtShiTehdofUrlNTU7llP/yu64mJCSiVShejkkDdC3cCMan79fV1AEBmZiaqqqpw5ZVX+v2e5eVlOJ3ObVnHjIwMDAwMBPT8QUG7wXcE2dasGYbB4OAgVldXuZWZUuFwONDT04OVlRWv6zI9daFToZYvVKhDE6FCDbzpsc0XvujoaERHRyMnJwcMw8BkMsFgMGBmZgb9/f2IiYlx6brei05yp9MZ8E2D2WwGgNCcs1bIKLKmDWY7i8VigUajAcMwyMrK8rvSMhDW1tag0WgQERHh0zfcPQ1OhVqeUJEOTQIRaYInseajVCqRmJiIxMREFBcXw263c1H3wMAAZ1SSkpKC5ORkxMTE7ErKXIyD2cbGBiIjIwP6vtTUVKhUKiwsLLh8fmFhAZmZmQE9f1DQyHpHkJ1YLy4uoru7GxkZGaiqqoJOp4PNZpPk2HwDlbKyMp9vVJVKBbvdTkVaxlChDk3ECDXwplgLFVi1Wo309HSkp6dzRiWk63psbMzFhcyXt3ewiJkNJ+sxA7mZCA8PR319Pc6dO4drrrmGe+5z58553cC1I9AGsx1BNmlwhmEwPDyMyclJVFdXczWpYHdak2P39/djfn4eR44c8TjC4I5KpcJn7lsN6nkpOwcV6tBErFADb1qNil3OQ4xK8vLywDAMVldXuXEpd2/vxMREyfpjxNasxSzxOH36NG666SY0NDSgqakJZ86cgdls5rrDb7zxRuTk5OChhx4CsNWU1tfXx/3/zMwMt/63tLSUO5eRkRHuOcbGxqDRaJCcnIz8/PztJ0EbzHaEPY+sFQoFLBYLtFot7HY7WlpaXOo0/uxG/bGxsQGNRgMAaGlpEdxJ/qmvGEQ/J2VnoUIdmgQj1IC0vuBKpRJJSUlISkrivL1J1N3X1weHw8HNdgeyDtMTYtPgsbGxAT/ntddei6WlJdx3332Yn59HTU0NnnvuOa7pbHJy0uVnODs7i9raWu7f3/3ud/Hd734Xb3vb27i91xcvXsTb3/527jGnT58GANx000148sknt50Dq1SClUn6maViLR1LS0vQarVcE5n7H3UwkfXS0hK6urq4jkqhb3Sa+pYnVKRDl2CFGthZ97Lw8HBkZmYiMzPT4zpMtVrtkjInI55CECPWZrNZ9IjqZz/7Wa9pbyLAhMLCQheHOE9cdtllfh/jgpycw+RyHhKwp2JNjE4qKiqQm5vr8TGe7Eb9QWz8JiYmXFLq/qAiLV+oUIcuUgg1sHsbt7ytwyTjYb29vYiLi3NZh+nrvMTWrEOyExwAq1TJKLKWx3lIwZ6KtVKpxLFjx3zeLQeaBrdardBqtbBarTh69Khgb10q1PKFCnXoktH2X1haWkJSUlLQTmN7tR6T34gGbF1jSMq8p6cHDMO4pMyjoqJcrmliI2sxNWtZQBvMdoQ9T4N7Mx/hf12oWK+srECj0SApKQl1dXWCLw5UqOULFerQ5cDQczAYDNDpdLBYLEE7jclll3VERASysrKQlZUFlmWxvr4Og8GApaUlDA8PIyIignudSUlJohrMNjY2QlesaYPZjrDnYu0PIWLNsizGx8cxMjKC8vJy5OfnC7oQUJGWN1SoQxeS+k5NTQXguqzC3WksJSVF0NgUy7KyEGs+CoUCcXFxiIuLQ0FBAZxOJ4xGI/R6PUZHR2GxWMCyLGZnZ5GRkYG4uDhBr8FsNtM0uATI5TykICTE2lfN2m63o6enB6urq2hsbERiYqKg41Khli9UpEMbTzVq/rIK4jSm1+sxPT2N/v5+xMbGcmYl3mrAobBxS6VSISUlBSkpKQC2IuQLFy5gc3MTWq0WALalzD0R0jVrhRKsTBq75HIeUrDnr8RfBBwWFgaGYTx2I66traGtrQ0OhwOtra1UqPcBVKhDGyHNZMRprKSkBI2NjTh+/Djy8/NhtVrR29uLV155BV1dXZiensbGxgb3fXKMrP1Busarq6txySWXoKamBnFxcZifn8eFCxfQ1taGoaEhLC8vuwQlYmvWLMvivvvuQ1ZWFqKionDixAkMDw/7/B4h+6p/+9vf4l3vehdSUlKgUCi4cViPkJq1XD72CbL/yyeNGe6p8OnpaVy4cAHZ2dloaGgQlEa7/Lo3qFDLGCrUoculhouiu77J2NSBAwdw7Ngx1NXVISEhAYuLi3jttdfQ1taGwcFBmEwm2W3U8ge5bhEzF7I9rL6+HpdccglKS0u56ZVXXnkFv/zlL/GVr3wF6+vrXqNuX3znO9/BD3/4Q5w9exavvfYaYmJicPLkSWxubnr9HrKv+tFHH/X5mOPHj+Pb3/6233NgFSouFb7nHwpxafBHH30UhYWFiIyMRHNzM15//XWfjzcajfjMZz6DrKwsREREoLy8HH/605+COqY7IZEGB7aWb5DO8P7+fiwsLKCmpoZb4O4PKtLyhgp16CLVaBawvQbscDi4GvDi4iLsdjs6Ojq4lLkY45DdhKTuPZ1jWFgY0tLSuGuYxWLBSy+9hK6uLrz++ut44YUX0NfXh3e96124/PLLvY63EliWxZkzZ3DPPffg6quvBgD8x3/8BzIyMvD73/8e1113ncfvE7Kv+oYbbgAAjI+P+3vJ/4hoZRIHivjbeOaZZ3D69GmcPXsWzc3NOHPmDE6ePInBwUGP7pc2mw2XX3450tPT8Zvf/AY5OTmYmJhwyfQGekxP7LlY+3ujkT90p9PJuZEpFAq0trYKvvOkQi1vqFCHLlIKtSfCwsKQmpqK1NRUREZGYmVlBampqTAYDBgfH981f2+xBDJjHRUVhSuvvBJXXnklTp06hUsvvRTh4eH46U9/iieffBJ//etffX7/2NgY5ufnceLECe5zCQkJaG5uRltbm1exlppQbzD7/ve/j1tvvZWzaD179iyeffZZPPHEE7jrrru2Pf6JJ56AwWDA+fPnubJHYWFhUMf0xJ6LtRDCwsKwtLSEkZER5OTkoKKiQlDtioq0vKEiHdrstFC7wzAMwsPDkZubi9zcXBd/78nJSfT19XFmJSkpKYiPj9/zGreYGWtgK8quqanBhz70ITzwwAOCjKHm5+cBwOM+a/K13YCFAizkke0I9DxsNhva29tx9913c59TKpU4ceIE2traPH7PH//4R7S0tOAzn/kM/vCHPyAtLQ0f/vCHceedd0KlUok6pidkL9YMw4BhGAwNDeHQoUPIysoS9H1UqOUNFerQZreFGtg+Z+3N31uv16O7u5szKyEpczE14GAR28G+vr7uYjfqyTPiv/7rv/DJT36S+/ezzz4r7iQlRo7d4CaTyeXzERERHtcjLy8vw+l0erzhGRgY8Pgco6OjePHFF/GRj3wEf/rTnzAyMoJ//ud/ht1ux/333y/qmJ7Yc7H2lQYn4w4Mw+DAgQOChZoib6hQhzZ7IdSAf1MUd3/v9fV16PV6LCwsYGhoCFFRUS5mJWIi3kARE1kTb3J/7ovvfe970dzczP3barUC2Npfzb9Wkv6e3UKOafC8vDyXz99///144IEHJHkOhmGQnp6Of/u3f4NKpUJ9fT1mZmbw8MMP4/7775fkOQAZiLU3DAYDNBoNUlNT4XQ6AzLOB4AXftno8fM2mw3vvlErxSlSRECFOrTZK6EGAqv/8hvVCgsL4XA4sLKyAoPBgKGhIVitViQmJnJRd6C7o4UiNg0uZHSLvD4Cy7LIzMzEuXPnOHE2mUx47bXX8OlPfzrgcxCLHCPrqakpxMfHc5/3FFUDWyY+KpUKCwsLLp9fWFhAZmamx+/JysqCWq12+T1XVVVhfn4eNptN1DE9ITuxZlkWY2Nj0Ol0qKioQF5eHt54442gd1oDW+31Go0G37kzAYcOHfJpR0rT6NJDhTq0Ke35I6xWq9cL3U7DMEzAN+0Efuc1y7KcoxpxGgsLC+Nq3YFu1fJ3zmLEmqzIDASFQoHPf/7z+MY3voGysjIUFRXh3nvvRXZ2Nq655hruce985zvxvve9j9vMJWRfNekLmJ2dBQAMDg4CAJfJcEFGkTX+cR7x8fEuYu2N8PBw1NfX49y5c9zPjGEYnDt3zusms2PHjuHpp592yfwMDQ0hKyuLa3gM9Jie2HOx5t/N2u12dHd3w2QyoampCQkJCQCCW5MJbN0ATE1NYXBwEKWlpSgsLPR7F/3CLxvxt7/9DdXV1ZwbER8q5sKhIh365LX/GrOzsxgcHERMTAwnbP42TkmJVKYoCoUC0dHRiI6O5hrVjEajy1at+Ph47jUKtQj1hJiatc1mg81mE7yEiM+Xv/xlmM1m/NM//ROMRiOOHz+O5557DpGRkdxjdDodlpeXuX8L2Vf9xz/+ketkBsB1lntKJ4dygxmw9fpvuukmNDQ0oKmpCWfOnIHZbOZe/4033oicnBw89NBDAIBPf/rT+PGPf4zbb78dn/vc5zA8PIwHH3wQt912m+BjCmHPxZqwuroKjUaD2NhYtLa2uoxgiFmTSXA6nejt7cXy8jLq6+u5zTlC8HWT8OuzZejs7ERycjKqq6u3RelUzLegQh36kNR3UVER7HY7F5H29vbC6XTuWhPXTi3y4PuUA65btbq6usCyLGcRmpKS4iJ8/hC7cQuAKLtRhUKBr33ta/ja177m9THus9JC9lV/7GMfw8c+9jFB5yDHNHggXHvttVhaWsJ9992H+fl51NTU4LnnnuMaxCYnJ13+DvPy8vD888/jjjvuwOHDh5GTk4Pbb78dd955p+BjCkHBBrRVXHoYhsHo6CgGBgZQXFyM4uLibVFvT08PIiIiUFZWFtCxNzY20NnZCZVKhZqamoDeZADQ1taGoqKibWmeqakpDAwMoKysDAUFBaJqXW8FMadCHdr4q0/zm7gMBgNWV1cRFRXFCXdiYqKkTVxdXV1ITk72aw4iJSzLYm1tjXuNJpMpoNc4MjICp9OJiooKwc85NTWF6upq2O32oNeK7iYmkwkJCQkYee0lxMnE13xtfR2lzW/H6uqqoDS4nNnzv4TNzU2Mjo6irq7OY7oZEJcGX1xcRFdXV0Bz2f6el2EY9PX1YXFx0ef5CoE0wFmtVmg0GjgcDqSnp2NtbQ11dXUAQlvQqVCHNkIaybw1cen1egwMDMBut3NNXCkpKdv2PAfKXqzIJBah8fHxXGaBNKoNDg7CZrMhMTGRi8zdG9XE1KzNZjOio6P3fEZcLKxCAVYmrnJyOQ8p2HOxjo6OxiWXXOLzD5MMlguBZVmMjIxgfHwc1dXVyM7OFn1ufLG2WCyceX1ra2vAUbonVldX0dnZicTERNTX12Nubg5Go5H7ureOdoIcxZyKdOgjtuPbvYlrY2MDer0ey8vLGBkZQUREBCfciYmJAUeNDMPsubWoWq1Geno60tPTuddIUuajo6NQq9Vcupzssg70da6vr+9Yd/puwChUYER6ckuNXM5DCvZcrIEtUfSVjQ8LC4PFYvF7HJvNhq6uLmxsbODo0aOiGjT4KJVKOJ1O6PV6aLVapKeno6qqSpLU3szMDPr6+lwa3pRKJRiGEXwMvpgvLCygq6sLhYWFKC0thUKh2HUxp0Id+kg1mqVQKBATE4OYmBjk5+fD6XRyEenw8DA2Nze5iDQlJUWQOIntrN4p+K8xLy8PTqcTq6ur0Ov1GBsbQ29vL1QqFeLi4rg0rBABFrtxSy6Ees1arshCrP0hJA1OotT4+Hi0tLRIMnqhVCqh1+sxMjKCysrKbYP1YmAYBoODg5idnUVtbS1SU1Ndni8QsQa2Mgk6nQ5jY2M4dOiQS33dV2Q+MTGBW+5cDPwFeIEKdeizkzPUKpWK8/gGwEWkRNjcI1JP7185RNa+4PuUA2+aOtntdm6XNfl6SkqK1xE4kgaX82v1BU2D7wz7QqxJw1dJSQmKiook+SN3OBxYXV2F1WpFY2Oj4F3ZvrDZbNBoNLDZbGhpaXGxEwQCF2uHw8GNugWaSVAoFPjWl5Sor6/nPkeifX7jnJDonAp16LPbZif80SlPEWl8fDzXxBUXFweFQrEnNetgiIyMhFqtRlZWFjIzM2EymWAwGLgRuOjoaJcROJI1MJvNojrBga2b9/vvvx8//elPYTQacezYMTz22GM+m3Mfe+wxPPbYY1yXeHV1Ne677z6Pm7hYlsWVV16J5557Dr/73e9c5rcJNA2+M8hCrBUKhd80uKfRLafTib6+PiwtLQXd8MVnY2MDHR0dcDqdyMrKkkSo+fXpuro6j3WsQMSanGN4eDhaWloC3jbEfy6WZTE0NISpqalt0X4o1s0pgbGXrmSA54iUdF9PTExAqVQiJSUFdrs94MzTXkNS9wqFAgkJCUhISHBpVNPr9ejv74fdbkdcXBxeeOEFqFSqbTfyQiH7rJ966inOFOXkyZPo6+vz2meTm5uLb33rWygrKwPLsnjqqadw9dVXo7OzE9XV1S6PPXPmjN9gKNTnrOWKLMTaH54ia/d1mVI0fAHA0tIStFotcnJyoFQqRc9385mdnUVvb6/fyF+oWOv1emg0GmRnZ4vudCeRisPhgFarhdlsRktLS8C1si0x7wKw1RjT0dGBuLg4HDx4EH9Nqvf9zZQ9p7jr93t9CtuIjIxETk4OcnJyXDZrkUwS37BEaB14r/BmiuKpUW1wcBAvvPACNBoN1Go1PvnJT+LkyZN45zvfyRlE+ULsPuurrrrK5d/f/OY38dhjj+HChQsuYq3RaPC9730PFy9e9LmngYWMataQx3lIQUiK9dLSErq6upCVlYXKykpJUmP82i/pItfpdJw5vhjItrDp6WnU1NRwS+a94U+sWZbF5OQkhoaGUFVVFdS8KbkRuXDhAiIjI4Ou8y8vL0Oj0SA/Px+lpaVgWRaXGi5yvxuz2Yzl5WUsLy/DaDRC9YEviX4uijQkvPS4rIUOcN2sNTs7iwMHDsBms0Gv12N6ehoAXGxC98oK1RtCTFFIo1pdXR3OnTuHBx98EG1tbYiOjsY999yDD3/4w9Dr9X5vpKXYZ+10OvHrX/+au3knbGxs4MMf/jAeffRRv37WjEIpm/QzI5ObBikIGbF2OBwugnrgwAHk5ORIcnxic7q2tobm5mZueF5MwxfBZrNBq9XCarUKjlh9PR9/xruhoQFJSUmizouwvr6O9fV15Ofni47OCRMTExgaGkJ1dTWysrLgdDq5+iIRg9jYWMTGxqKwsBB2ux36wf/jxJtlWaSkpCA1NRWDVe8O6nVRhFHc9XssLCzIXqz5MAyDyMhIpKSkICsrCyzLwmQyQa/XY2ZmBv39/YiNjeWEezetUL0hxsHMarWirKwMjzzyCICtSQ8h149g9ll3d3ejpaUFm5ubiI2Nxe9+9zscOHCA+/odd9yB1tZWLmL3BU2D7wyyEGt/Fwwi1h0dHTCbzZKMZRFI6jY6Onpb7VesJ7nJZOI602trawXPWapUKo9ibbVa0dnZCYZhJEn5T05OYmxsDBEREaiqqhJ9HIZhMDAwgPn5eTQ0NCAhIcGjULujVqtdVhmaTCYsLy9jfHwcjt98B2q1Grm5uUhPT+eaiwDgpYRa0edKeZNCzW+xuLiI1dVVWCwWmM3mHXEckxr3BjN+Hbi4uJjbZ20wGNDT0wOGYVy6r6UqlQV6zoH+TNfX110azLxZUkq5z7qiogIajQarq6v4zW9+g5tuugl//etfceDAAfzxj3/Eiy++iM7OTkHHYqAEI5P0s1zOQwpkIdb+sFgsXAOaVGNZwNadaHd3t8tsMh8xYk3q096sU33hKbJeXV1FR0cHUlJSUF1dHdTFlGEY9Pf3Y2FhAWVlZZiZmRF9LLvdznW2Hz16FJGRkdzPypdQu0MuuA6HA5OTk8jLy0NsbCz0ej0uXrzIjfykpaXhEv0bXm98qJD7h99Ilp+fj4sXLyI+Ph5Op5NzHCM+38RxTC6wLOt3kYe3fdbz8/MYGhry2n29k+csZpHHxsYG0tPT/T5Oyn3W4eHhKC0tBbC1IeqNN97AD37wA/zkJz/Biy++CJ1Ot63R9gMf+AAuueQSvPzyy25HU8qoViyX8wge2Yv19PQ0+vr6AACHDx+WRKgZhsHw8DCmpqZw+PBhr3euxBRF6DFJffrIkSOC3myeno9clBQKBSf8QjeF+YKMjdntdrS0tGBjYwNTU1OijmU2m9He3o7Y2Fg0NTVBoVC4CHWgTE9PY3BwEFVVVZzjHNmGtLKyguXlZQwPD8NisSApKYmb1+WnBt++6v2unwq5947vhIQEZGRkgGVZmM1m6PV6LC4uYnh4eEd9vgOF3MQK/fvyZIVK5rpJ9zV/AYnY7msh5yzGblRI2nsn91kzDMOJ/1133YVbbrnF5euHDh3CI488sq05DaBp8J1CFmLtSYRIjXZhYQG1tbVob2+H0+kMWqxJLXlzcxNHjx71Oc/oLS3t65hiOqoJ5ELkdDoxMjIiuDHNH2tra+jo6EB8fDw3NmaxWETV40knem5uLjfqQaKHQG8myMjY3Nwc6urqttXhychOSkoKKioqsLGxwdW5iZgQ4U5KSvJ6IX+rC7k3oSY3hcDWe5D0FRQUFHj0+d7LqDtQsXYnLCzMpfvabDbDYDBgaWkJw8PDXC08OTkZSUlJktyYkBtYMWItpswndp/13XffjVOnTiE/Px9ra2t4+umn8fLLL+P5558H4GVnNbayM0VFRds+T9PgO4MsxNod4sPNsixaW1sRFRUVUJTrDTLrnJCQgJaWFr+1ZCFpcCKEcXFxgo7pC3Ih6ujoCKgxzRdkoUlBQYFLqp9E8YEwOTnJRcA5OTmC6tPecDgc6OnpgdlsRlNTk6DIJjo6Gvn5+cjPz+cipeXlZfT29sLhcHBCkpqaKrg+6U3I94uI+5qh5ou1O+4+396ibuLzvdONXESspWiI49+YkL8lo9EIvV6PoaEhWK1WlwUkYt3ExN5gBGM3Kmaf9eLiIm688UbMzc0hISEBhw8fxvPPP4/LL79c1DnQyHpnkJ1YLy8vQ6vVIjMzE1VVVdwfuthmLwJx5wrE5cxfN/jc3Bx6enpQVFSEkpKSoC8kGxsbALYuJsEKP8uyGBsbg06nw8GDB7fNRZI5ayHwLVIDaSTzxubmJjdL2tTUJCpb4h4pra+vY2lpCbOzsxgYGEBsbCwXdSckJAR8jvshGhey4lLIz8VT1M1PKTscDpd9zzsRdROr0Z24KQgLC+P+VliWhcVi4UxZyHIOItxJSUmC35ekEzzQv71gHMzE7LN+/PHHA34eXzf6DKsEw8ojopXLeUiBLMSaOJiNjo5idHTU4wyxWLEmHctzc3Pb3Ln84e05+Y5fYuvT7pAIGAAOHDgQlFA7nU709vbCYDCgqanJo6GC0Mia+BqTskFUVJSoRjIC6ZRPS0uTbEaeX58kXcF6vR5LS0vo7OyEQqHgLsYpKSlBl1L4Qs4wDFcKYBgGbRktPr5z9xDiSiZUrN3xlFLe6ah7t3zBFQoFZ4VKlnMYjUYYDAbodDpYLBYkJCRwNyaxsbFez0tMcxkxSAnpRR40st4RZCHWdrsdHR0dWF9fd5lz5uPNctQXJIJjGMajF7c/PKXeiXhZLBa/NW8h8G9SDh48iO7u7oDT03w2Nze5EYuWlhavJhFCIuuNjQ20t7cjOjoazc3NLo1kCoUi4IvnwsIC1ylPvMd3gvDwcGRlZSErK4tzwFpeXsbY2Bh6enqQkJCAtLQ0rkktmPMgF2OFQoG+vj6E/e8PcejQIURHR3NCTriQ2Rr0axOCUPtQsWLNx1fU3dfXB6fT6dLIJTbq3itfcJVKxd14lJWVwWKxcK9vYmKCs0olr49/IyhmxhrYGt2SajR1L6BivTPIQqzX19c521BvUU+gkfXKygo0Gk1QI0+kwYxc1NbW1tDZ2YmYmBgcPXo06AiN1G2NRiN3k9Lb2yvaiCWQMS9/kbXBYEBnZydycnJQXl4OhmFEp71ZlsXExAR3QyJFJkIofAcscrElTWo6nQ7h4eFc1J2cnCzq78ThcKCrq4tb+kLqg+5R99H58y7nRW54lEol/pbcIMnrDcTnWwqxdsdb1L2wsIChoSHRUbe/sa3dIioqapsVKhFu9wUkYsU61CNrJ5RwyiT97KQNZtKSnJyMuro6n48RKtYsy2JqagqDg4MoLy9Hfn6+6AsSeaMxDIOlpSWfM9mBYrFY0NHRgbCwMJcIWKxrGqmfCx3zch8T4zM9PY3+/n5UVlZyW5HECjWZ7dbr9WhoaPCYNdlNoqKikJeXx6U4yWjYwMAAbDYbkpOTOfEWEgUSwxq1Wo3GxkaX8gW/3wLY+lmQnyXLsi5NU8eWXuOEm6Re9Xo9lpeX4XjPbYJeW6ALOXZaAKWMusWklHca/o0gsPW3QGrdU1NT3Htrbm4OKSkpgpbtkBucYDN2ewmNrHcGWYi1EIiLmS/IFq7l5WVJLDnJRXZoaAgzMzM+Z7IDwWAwQKPRICMjw6WJDghcrFmWxcjICCYmJgKqnxPR5Ys1y7IYHBzEzMwMN0oVjFCTkoHD4UBTU9OeOEj5gr9juaKigvMvX1hY4FYYknS5J+vK9fV1dHZ2IikpCQcOHPArJkqlknuMe9TNvxHdlg2Y/hsn3EajkYtO55qv575HzOasnYisfeGpKZAfdfNNS9yjbrlE1r6IiIhAdnY2srOzwTAM59VNbn7j4uJcFpB4ej1WqxUOhyO00+CsAiwrD5GUy3lIQciIdVhYmM/I2mKxoLOzE0qlEi0tLZIIA3m+xcVFSerTwJvjTxUVFcjPz9/29UDEmmwhWltbC/j8yEWaCDHZvrWxsYHm5mZER0cH1UhmNpuh0WgQExOD2tpaWdtYAq5RIOdf/g+B1Gq1Lv7lqamp3OvLy8sTNQngL+omN6YKhQIRERHIyclBXl6ey/yz6n9+wHViz8zMBGypudtizcfdtIS/MtI96k5JSQm5XdZKpRIRERGIjY3FkSNHOCtUvV7P9aXwswrk92Y2mwFA1LVGzC7rBx54AF/96lddPldRUYGBgQGXz7W1teErX/kKXnvtNahUKtTU1OD555/3mA1hWIVsurAZKtbSIuSC4SsN7m3cKxiIZzgA1NTUBC3UfKvP+vp6bnevO0LFemNjg0u/Hj16VNQ+a+DN7tOOjg5ERkaiubnZ5WctppHMYDCgq6sL2dnZKCsrC6llEQRP/uVLS0uYnJxEb28vACAtLU2y+rt71M3/cI+6U1JSXOafl5eXOUvNmJgYTuC8RW+EvRRrd9xXRrpbhYaHh4NlWRgMhl2Z65YCvi+4uxXq2toa9Ho95ubmuCzO3//+d6SmpiIsLEyUo5qYXdYAUF1djb/85S/cv90nUdra2nDFFVfg7rvvxo9+9COEhYVBq9V6/R3QNPjOIAuxFoKnNDh/ljjYlZF8FhYW0NXVhcLCQkxMTAR9QbNardBoNHA6nWhpafFZmxPimkaav4JZEUq+x2AwoLe3F1lZWSgvL+dSs2JnWmdnZ9Hf34+KigrJfh97DX9hhFqthtlsRk5ODqxWKy5evOgyp5ucnBzU2B3gOV1OhNs96o6KikJ+fj4XnbpHbyTt6qlmKiex5uMp6h4bG8Pi4qLHqFtu5RWCtzq7QqFAfHw84uPjUVRUxGUV/v3f/x0vvPACWJbFNddcg1OnTuGKK65AcXGx3+cSu8sa2BJnX2sv77jjDtx222246667uM9VVFR4fTzDKuCUSURLI+sdgMxaeyMsLMxltzRJAa+urnqdJQ4UUv8dHx/HoUOHkJmZienpadHd2cDWXHFHRwcSExNx6NAhv+lgf5E1qX95S6MLhVyktVotKisruYarYDq+iUVqbW2t18xBqMK3RiXGMAAC8i8Xg7d0ORFw96g7LS2N8/peW1vD8vKyS82UpPLj4uJkK9buqNVqxMbGwmw2o6amxuOCDiLccliLSRDaDU6yCj/96U9x4cIFfPjDH8bx48fxm9/8Bp///Ofx7LPP+nUTC2aX9fDwMLKzs7m99g899BB3bVlcXMRrr72Gj3zkI2htbYVOp0NlZSW++c1v4vjx4x6PR2vWO4NsxNof/NSs2WxGZ2cnIiIi0NraGnAK2BN2ux1dXV3bVnAG45xGOrSlcE3ju4jV1dUhJSVF1DkBbwoPsJUCy87ODkqonU4nenp6sLa2hqamppAeO/EEMZkxmUzbrFG9+ZcTz2mh/uVC8dakxo++ga2bsZiYGMTGxroYxRBvdzJnr9froVarJdtkt1OQlLK3WjexnZVT1C1mdGtzcxPx8fH48pe/jDvvvBNra2uCrm9id1k3NzfjySefREVFBebm5vDVr34Vl1xyCXp6ehAXF4fR0VEAW7Xt7373u6ipqcF//Md/4J3vfCd6eno81sNpGnxnCDmxJk5feXl5KCsrk6w+3dnZiaioqG0rOMV4krMsi+HhYUxOTgbscOZJrMk6SuIXHsyGIDITTGbbyYpEsUJNUvxKpRJNTU2S3DjJCfKzZ1lW0OsT4l9OxNubYY1QhEbdpKSRkZHhYhTT2dmJmZkZjIyMcPPBUhjF7ATeHMz81br3Mup2Op0Bvx/W19ddvMi9dYVLtcv61KlT3P8fPnwYzc3NKCgowK9+9St84hOf4K5Fn/zkJ/Hxj38cAFBbW4tz587hiSeewEMPPbTtmNRudGeQjVj7S4MrlUqYTCZotVouRS0FfPEvLy/3uNM6kDS4e4QeaGOau1iTRjdixBJMPZTMdoeHh+Po0aP461//CrPZjMjISFFCvba2Bo1GI3h0KdQgEwbR0dGCShjuuI8qkbT0zMwM+vv7Of/ytLQ0xMfHBy2QvkbD+FG3UqnkRKC2thYsy3JR98TEBMLCwkR5Ye8kQrrBPUXdpIZPou7k5GSujr/TUTe/wUwoQmespdxlzScxMRHl5eUYGRkBAO5YBw4ccHlcVVUVJicnPR6DYbc+5IBczkMK9v5dKAC73Y6pqSnYbDa0tLRIMoPIsix0Oh3GxsZ8in8gaXCz2YyOjg6PEbpQ+GK9vLzMjQd5upEIhJWVFXR2diIjIwOVlZVgWRZpaWnQarWIiori5omFdtoSk5iioqKgd23LEbJNLT09HZWVlZLYcpKmInf/8o6ODsn9y/2NhlksFgBbmZbIyEhkZWVxrlzEkIV4YScmJnLnFRUVtSe/azGjW2q1GhkZGVwNf7ejbjFGLuvr63u6y3p9fR06nQ433HADAKCwsBDZ2dkYHBx0edzQ0JBLVM6HRtY7g+zFmlh8qtVqREVFSSLUJBVM5pN9HVOoWC8tLUGr1QYtrGTmeXx8HMPDw1xNORhmZ2fR29vLObqRi/bBgwdRVVXF7fXt7u4GwzCcaKSmpm4TDZZlMTk5CZ1Oh+rqaklMYuSGXq/npgF26kZkN/3LAdeoe319HT09PcjMzIRard7WpJaYmIjk5GSUlZVhY2ODi7p1Oh0iIiJc7EJ3a34+2DlrX1F3T08PGIZx6ZwPtkQBiKtZi7UaFbvL+otf/CKuuuoqFBQUYHZ2Fvfffz9UKhWuv/567rhf+tKXcP/99+PIkSOoqanBU089hYGBAfzmN7/xeC4su/UhB+RyHlIgG7H2dDHir6BMSkpCd3d30M9Dol/S+eivpuSvO5tlWYyPj2NkZEQSYVUoFFhcXITdbkdjYyMSExNFH4tfO6+trUVKSgpX0yRpb/dULZknHh8fR29vLxdVpaWlISoqCoODg1hcXER9fb0kHfhyg4yeHThwYNta0Z1iN/zLCSRjkJWVxTUH8dPlvgxZiD2rXq/HwMAA7Ha7SzPXTqzGJEjtYOYedXuaew426hYj1sFYjYrZZT09PY3rr78eer0eaWlpOH78OC5cuIC0tDTuMZ///OexubmJO+64AwaDAUeOHMELL7yAkpISj+fBQAFGJo1dcjkPKVCwwax4khCHw8Hd4TMMg6GhIUxPT+Pw4cNIT0/H6uoqLl68iHe+852in0NMc1pXVxdiYmI8/mGSLmiDwYC6urqgxctqteL8+fNgWRatra1B1dT47ma1tbWIiYkJeBkHEY2lpSUYDAYoFAqoVCpUVFQgIyNjX9Woycz+xMQEDh8+HFS3vZQ4nU6uSW15eVmUfzmBLLfxlTFwN2ThXx5IdE585cmSDr1ej9XV1R1NKw8ODiIsLMyrQEgJP+rW6/Wio+7XXnsNpaWlAf0t3XPPPdjc3MRPfvITsae/Z5hMJiQkJOB3ry4iJnZvdwAQzOsmvO/4ln7s9V6CYJFNZE2wWq3QarVcfZqkhPzZjfrCfQ1lIBGTt27wzc1NdHR0QKlUorW1Nei0GZnHDgsLQ2JiYlBCTc5NpVKhubnZ5WcXSCMZWXqRmprKHS82NhZDQ0MYGBjwmS4PJcjOc+IpLydfZpVKhbS0tG2OZcS/PCYmhvsd+BJIUuYoLy/3aVYTqCFLXl7etiUd/GYuqdLKu2k36i3qnp2d5X7m5LX5+pmLqVmbzeaQz1jR0a2dQVZibTQaodFokJiYiLq6OpcuVPd1lUIhEabJZPK6K9sXnrrBSbNWenq6JF3QxDGtuLgYTqfTxfwlUIxGIzo7O5GWloaqqiqX7U5izpP8TjIzM1FeXs5FVb7S5aE0Z+10OtHV1QWLxSLLZSN8AvUvJyUekto/ePBgQD0GgRqypKamuoxQLS8vcwIXGxvrYoMqZnvbXmRyPLmNCa11i61ZB1tK22toN/jOIBuxnpubg1ar9brikfzRO51OwaMkfPMUIfVpT6hUKtjtdu7fxA0q2PWbgGtHOtnopdPpRDumkRp/WVkZ8vPzuYuqmLEscrz+/n6UlZUhLy+P+zzffrO0tNQlXT4yMiKqu3wvsNls6OzshEqlQmNjY8hlB3z5l/f19SE+Ph5qtZqrM6ampgb1fIGMhkVHR6OgoABFRUUuSyy0Wi0UCoWLwAn5uctlkYevqHtgYIC7KUlOTobD4RBVsw6lm11PsIwCDCOPiJaVyXlIgWzEOiYmxqczF/mjdzgcgsSadGfn5uZyEaEYVCoVNjc3uVTp3Nxc0A5iwNZNR3d3N4xGo0tHuph91vw1mTU1NUhNTQ3aOnR0dBSTk5M4fPiw34s8f0c0SYcK7S7fK8iNXHx8PA4ePCgLIQgG9xuozc1Nbo+4QqFAX1+f5P7lgLBd3SqVCunp6cjMzATDMNy8+eTkJPr7+zlDlpSUFMTGxnqtpcttPNBT1E32Wff09HDvy7S0NMGlgFDfZQ3QBrOdQjZinZCQ4POOktzV+6tb85d7SNGdTUapLl68yNXRg3EQA1xryu52qYGKNRH91dVVNDc3IyYmJmjr0L6+PhiNRjQ2NgZ84Qiku3yvIgiS2s/JyUFpaansRCBYyHuAjCZGR0d79C8n2Y9g/56BwHZ1kxGqkpISWK1WF0MWlUrFCTf/pkIukbUv+JkOh8OBv/3tb4iJidkWdfvaiLaxsRH6Yi2jyFou5yEFsvnrD3ZNJgBuJ/Pk5CSampokqf3Y7XYsLy9zqyiDvbAZjUa0tbUhPj4ejY2N21LzgTimbW5u4rXXXoPVasXRo0c5oeaPZgWCzWZDe3s7LBYLmpubg75okGivtLQULS0tOHbsGDIyMmAwGNDW1oa///3vGBwchMFgCGpZSiAsLi6io6MDxcXFIbu+0xcMw6C7uxsGg4G72SL+5RUVFTh27BhaW1uRlpaGpaUlnD9/nvs9kM7nYFEqlVCpVAgPD0dkZCQiIiIQFhbG9Ts4HA44HA7Y7XZu49OhQ4dwySWX4MCBAwgLC8Po6CheeeUVdHR0YHJyEna7PaR+V+TnWFRUhIaGBhw/fhz5+fnY3NxEd3c3Xn31VXR3d2N2dtalR0VsGpxlWdx3333IyspCVFQUTpw4geHhYZ/fQ8qN7h+f+cxnuMfodDq8733v41z2PvShD2FhYcH3a2fl9SGGRx99FIWFhdza4Ndff93rY5988sltP0P33pePfexj2x5zxRVXBHROsomsheBpTSaB7GQODw+XbLnH/Pw8xsfHERERgZqamqAvFjMzM+jr60NZWRkKCgo8Hk9oZL26uoqOjg6kpqbiwIEDXCRDjhEoxB89ISEB1dXVO2J2sdfp8qmpKQwPD+PgwYOS7aGWE06nk5uk8HQjSNhN/3IgsF3dfEMWi8XCRd3r6+sYGRmByWTibFB3y5BFDO7vRW/7rEnU/frrr2N+fh5KpVLUvLqYXdZvvPGGy8+/p6cHl19+OT74wQ8C2LpxeNe73oUjR47gxRdfBADce++9uOqqq3DhwgXv+6xD3BTlmWeewenTp3H27Fk0NzfjzJkzOHnyJAYHB71eN+Lj411c3jxd26+44gr87Gc/4/4d6HsrpMTa2/jW0tISurq6kJ2djYqKiqDTZfxVmfn5+TAajUE3kg0NDWFqagq1tbU+a8BCxHp+fh7d3d0oLS1FQUFB0I1kxLErPz8fxcXFuxLB7Ga6nPw+Z2ZmUFdXF5TRjFyx2+3o7OyEUqlEQ0OD4Jq0HPzLvY2GhYeHIzs7G7m5ubhw4QLS09PhcDgwNDQEm822a4YsYiCd4J5+Xu61bpvNhtXVVbS3t2NgYADXXXcdrrzySpw6dQqnTp3y28Evdpc13/gEAL71rW+hpKQEb3vb2wAAf//73zE+Ps71dgDAU089haSkJLz44osu6zhdXjvks8/aKaJm/f3vfx+33nort7jk7NmzePbZZ/HEE0+47PTmo1Ao/O6riIiICGqnhWzEWkwanF+fPnDgAHJycoI+D3crUnJ3H8zxtFotNjY2XObGveFLrPnz4keOHEFaWlpQ9WlgK9ocGhraVccud4R0lxPBCLS7nGEY9Pb2YnV1FY2NjSHfaesJ0gMhduEIwZN/OTFj2S3/cm9RN8uyiI+PR2pqqosNKn8VKd8Gda/r24Es8QgPD8f73vc+XHPNNcjJycFjjz0GnU6Hs2fPYnx8HA888IDP7w9mlzXBZrPh5z//OU6fPs1dR6xWK+diRyBLf1599VWvYh3KkTUpBd59993c55RKJU6cOIG2tjav37e+vo6CggIwDIO6ujo8+OCDqK6udnnMyy+/jPT0dCQlJeEd73gHvvGNbwTUqCwbsRYCPw3ucDjQ09MDo9GIpqYmSYwESCqdP+pltVpFm7HwF3scPXpU0MXNm1gTt7SVlRWX+nQwHd9DQ0OYm5tDfX29rKJNqdLlZAMasW6VIqUrN8jfWHJyMqqqqiQVKRLZZmdne/QvJ9mPnfAv5zepGQwGWK1WhIWFwWazcTXB3NxcLpVPGuj6+vokN2QRgxhDFJZlsbGxgaNHj+KjH/0o7r//fp+bCAlid1nz+f3vfw+j0YiPfexj3OfIdebOO+/Egw8+CJZlcdddd8HpdGJubs7rsZyMAk6ZNHaR8zCZTC6fj4iI8Ph3sby8DKfT6fFnOTAw4PE5Kioq8MQTT+Dw4cNYXV3Fd7/7XbS2tqK3t5czILriiivw/ve/H0VFRdDpdPiXf/kXnDp1Cm1tbYJv6kJOrJ1OJzY2NtDZ2YmwsDC0tLRI8mYkxhLuqfRAV2QS9Ho9NBpNwKl5T2JttVq5yObo0aMIDw8X5UhGIEYxxAhEim7gnUJsunxzc5ObsQ8kLRxKmEwmdHZ2cj7fO1m+2E3/cvJ8AGAwGNDX14eqqirEx8d7NWRJSUnhXN7Idi3i8x0TE+O3C1tqxBiiWCwWMAzj4qDn6Xcq1S5rPo8//jhOnTrl0pSblpaGX//61/j0pz+NH/7wh1Aqlbj++utRV1fn82cox8ia7xMBAPfff7/fjIVQWlpa0NLSwv27tbUVVVVV+MlPfoKvf/3rAOCS3Th06BAOHz6MkpISvPzyy4IttGVzBRNyoQkLC4PJZMLIyAiysrJQWVkpSX16YmICw8PDqKqq2mbFGMiKTMLk5CQGBwc9Hs8f7mJNbEiTk5O5nbLkfEhXYSBYLBZoNBqEh4eHnBGI0HR5bGwsRkZGkJqaKnm0KRf4Pt9FRUW7/vz87Affv3xgYCAo/3I+CwsL6Onp2ea8JsSQJSYmxmW71vLyMrq7u8GyrEvULUUjqifE7LLe2NgAAL9TGFLvsp6YmMBf/vIX/Pa3v932tXe9613c8g9ihZyZmYni4mKvx5OjWE9NTbm4V3oL8FJTU6FSqbZ1vC8sLAiuN6vVatTW1nI7wT1RXFyM1NRUjIyMhJ5Y+4OkiEwmE6qrqwMWQU+QmeLl5WWvG64CmXtmGAb9/f1YWFhAQ0MDkpKSAj4n/vPxbUiLioq4C5TY+vTq6io0Gg3S09MlacTbazyly6enpzE5OcnN5C8sLMjKjEUKhPp87xbe/Mvn5+cD8i/nQ9zzDh8+vK0RKhBDFqVSibS0NM5xzGQyQa/Xc06EfEOWuLg4ybITYiLr9fV1KJVKv5a3Uu+y/tnPfob09HS8+93v9voY0hT74osvYnFxEe9973u9PpaRURqczFmTXgx/hIeHo76+HufOneNWizIMg3PnznFrRf1BvC+uvPJKr4+Znp6GXq8PqE9IVmKtUCg81mhIvXZ9fR0ZGRmSXKBImhTYSmN4e4OQyNqfJ7nNZoNGo4HdbkdLS4voaIKIzOjoKHQ6Hbd1LNhGsoWFBfT29qKkpCRom1Q5QiYFVlZWUFVVhbi4ONmZsUiBWJ/v3UKofzlx9fIU2c7MzGBwcBBHjhwR1IAjxpCluLgYVquVs0GdnJyESqXiMgJJSUlB3eCJXeIhpvYvdpc1sPXz+tnPfoabbrrJY6noZz/7GaqqqpCWloa2tjbcfvvtuOOOO1BRUeH1fOQYWQfC6dOncdNNN6GhoQFNTU04c+YMzGYz1x1+4403IicnBw899BAA4Gtf+xqOHj2K0tJSGI1GPPzww5iYmMAtt9wCYOsm7Ktf/So+8IEPIDMzEzqdDl/+8pdRWlqKkydPCj4vWYm1JywWC7eNKjc3V3SzFx+y7CIlJcXvTDH/7t3b48iO4Pj4+G0LSMTAMAwmJyc5Y5JgG8nGx8cxNjaGQ4cObYtS9gOklEG65EkU4CldrtPpEBkZKbq7fC+ZmJiATqdDTU2NbFZ4+sObf/nExAR6e3u5Du+0tDTExsZienoaw8PDqKmpQXJycsDP5y/q5o+GhYWFISMjA1lZWVwDnV6vx9jYGHp7e5GQkMBF3YGKqNjI2pvdqj/E7LIGgL/85S+YnJzEzTff7PG4g4ODuPvuu2EwGFBYWIivfOUruOOOO3yeS6gv8rj22muxtLSE++67D/Pz86ipqcFzzz3H3RyTzB1hZWUFt956K+bn55GUlIT6+nqcP3+eK1uqVCp0dXXhqaeegtFoRHZ2Nt71rnfh61//ekD9VrLZZw1sRaf80yFNWqQ+PT4+jrW1NRw5ckT0cwgxJuHjcDjwl7/8Be94xzs8RgFkR3ZBQUHQ1pVWqxXt7e0wmUy49NJLERkZGVR9mmEY9PX1wWAwoLa2VlarH6WCZVkMDg5iYWEBtbW1flNd/O7y5eVlMAzDRXpyTZeThS/T09Oora0N+RWKhM3NTS7qJuORDMOgpKQEeXl5kjcFCt3VzT834vWtVqs5sxghhiyjo6OwWq2oqqoSfH4vvvgivvCFL2B4eDgkM19kn/WP/7iKqBh57I62mE347HsT6D5rqSFpcG9NX74czPzBMAwGBwcxOzvr15iED/8OnQ9/xvvQoUNBDbsDW9F5e3s74uPjYTKZXBrbxAi1zWaDVqsFwzBobm7el2NL/PJIU1OToNJDKHiX82FZFgMDA1haWkJDQ0PI+0bziYyMRE5ODnJycqDT6TAxMYGMjAzMzs5Cp9Ptin+5N0MWtVqNrKws5OTkwOl0wmg0Qq/XY3h4GJubmy6GLJ7OTUyDmdg0uNwI9charshKrIGtC3Bvby/0ev22pi8xndnAm8LF99AWCmlS4T8vOUeDwSDJjPfi4iK0Wi2KioqQn5+PF198EQaDASkpKaJGYMhGqbi4OBw8eFDWtoxiIT0CCoUCTU1NoiJiX93lckiXMwyDnp4erK2tobGxUXYuXVJAsgYzMzNobGzksj+kSW1paQlDQ0Ncpz+JbIP9XQS6q5uIMwDOkEWv12NkZASRkZGccJNzCyYNHuo4nVsfckAu5yEFshJri8WC9vZ2KJVKj01f3uxGfbG2tobOzk7Exsbi6NGjolJr/Flr98a0YCJWUk8eGRnBoUOHkJGRAYfDgZycHPT19QHY6sJMT08XLNwGg4FbDbofN0oBb/YxxMbGSnoz4s+MZTfT5cRJz5/PdyhDjHnI9AT/JjomJgYxMTEoKCjYE/9y0qRGMn38qDsiIgI5OTnc38nKygr0ej0GBgZgt9uRnJwMm80W8E38fliPCYR+g5lckZVY9/X1ISEhwetsbKCRtVT1ZHKnTJZnCGlM8wexwVxeXkZTUxPi4+O5i0NVVRWqqqpgNBo5O8Xu7m4kJydzIzKeLlAzMzMYGBhAZWWlJNarcoSMn2VkZKCiomLHbkb2Ml1OsgaB+nyHEiS9v7y8jIaGBp9pbl/+5X19fYiLi5PcvxwQHnXzDVnMZjPXXW4ymbgMWUpKit+xNbPZLGuDIqFQsd4ZZHUVqK2t9fl1oTVrvoe2FPVklUqFxcVFTExMoLS0lFstJxabzYbOzk44nU4cPXoUERER3Ovid3wTx6jy8nKYzWYsLi5yW3ri4+M54Y6OjsbIyAhXjxfTRRsKLC8vo6ura9fHz3YzXS6Vz7ecYVkWfX19WFlZQUNDQ0Dp/d32Lwd8j4a5G7KQ7IzRaERSUhIiIyOxvLyMnp4ev4Ys+yWydrIsnDIpFjv3kVrLSqz9Rc5C0uDEStNkMqG5uTnoDkCWZWG32zE+Po7a2tqgR5/W19e5RrJDhw5BoVAIaiSLiYlBUVERioqKYLVasby8jMXFRYyOjnLfV1FRISuPbykhs7cHDhwI+uYrWHYqXb6TPt9ygWSU1tbW0NDQ4NcAxB974V8O+DdkIbu6SRmLvxaTbDSLi4vj6txxcXGixfq3v/0tzp49i/b2dhgMBnR2dgpyLvv1r3+Ne++9F+Pj4ygrK8O3v/1tFyMPscelkfXOICux9vdG8ifmxDNcrVZziziCgQi/0+lEeXl50EK9tLQErVaLgoIClJSUbLsrFwqpmaWkpKCzsxMsyyIuLg5DQ0MYGhoKuM4tZ0iWZHJyUvTs7U4iVbqc2MpmZ2fvuM/3XsEwDLq7u7GxsYGGhgbJ6/BC/MvJTZRU+7A9Rd0LCwtYX19HZGSkS8YsNjbWZS0maVL705/+hG9961uoqKhARkYGDAZDQH/nZrMZx48fx4c+9CHceuutgr7n/PnzuP766/HQQw/hPe95D55++mlcc8016OjowMGDB0UfFwAYGTWYMTI5DymQ1Zy10+n0mebe3NzEyy+/jHe9613bxM19JjvYqIQv/AzDIC8vT3QdmD+KVl1djaysrKAdyUwmEzQaDVJSUrgojGVZrK6uYnFxEUtLS9jc3PRb55YzxL6VzImHWoqQny5fWVnxmi4nPt9FRUUoLCzc25PeIZxOJ7q6umC1WlFXV7frDXN8//Ll5WXJ/MvdWVxcRHd3Nw4dOoTU1FSXqJtcasmECfmvzWbDyy+/jDNnzmB4eBjLy8s4evQoPvjBD+L2228X/Nzj4+MoKioSFAFfe+21MJvN+N///V/uc0ePHkVNTQ3Onj0r6rhkzvqbTxsRGS2PmebNDRO+8uFEOme925BGG76VH8uymJqawuDgICorK7dtVxEDSfkQ4e/o6BDtnEaMSZaWltDY2IiEhISghXpxcRE9PT3cxZ0cQ6FQIDExEYmJiVyde2lpyWOdW+7znKQb2mq1orGxMeh06V4gJF0eERGB6elpVFRUyMLneydwOp3QaDRwOp2or6/fE+OZnfAvd4f8bg8dOoT09HQAcIm6ve3qJvuSn3zySXzhC1/A9ddfj+eee44zitkJ2tracPr0aZfPnTx5Er///e+DPjZNg+8MshJrIWlwYOvNTyLevr4+LC4uil6c4c7U1BQGBgZQUVGB/Px87nnFrMnk+4U3NzdzjmTBWIcSW83q6mq/3tBk/KWwsJCrcy8tLWF0dBQRERFIS0tDenq66IvTTmG1WrmsRmNj477ohvaULifpfWBrcYXD4ZCNGYtUOBwOdHZ2QqFQSGLFKwVS+Je7s7S0hK6uLhw8eJATaj5CDFkGBgZw+PBh5Obmcr7SO8X8/HxQ+6994XSycDrloZJyOQ8p2Pt3TgDwDUrIBZ1hmKAWZxD4Dmd1dXUu3stizFjW19fR0dGBuLg41NbWuhiriBFqhmE4J6v6+vqAZzhJnZs4Mun1eq6GDoBLz6akpOzpBZU0WSUlJeHAgQOyuomQCoVCAaPRiJWVFdTX1yMqKkpWZixSYbfbub3zR44ckW3/hLt/OWlS8+Zf7v7eJes3hdxAA56b1M6cOYOZmRlUVlb6/F73Xdb/93//h0suuSTQl7yj0Mh6ZwgpsQa2/sCNRiOGh4eRlJQkiSmG3W6HRqOB1WpFS0vLtlnHQNZkAltvXo1Gg/z8fJSWlopuJOOfHzHIIBF6MKhUKpcoj9S5R0ZG0NPTs2d17pWVFc7QpaSkRNZperHwfb75N11yMWORCpvNho6ODkRERODw4cOyFWp3+KWk0tJSF//y8fFxrsM7NTUVycnJWF1dRVdXl+gpBZZl8a//+q/43ve+h5dffhlNTU0+H+++y1psH01mZmZQO5t9wTAsGJmMbsnlPKQg5MQaAHp7e1FWVhb0vDPwZgQcExPj1eEskMh6YmICQ0NDOHDgALKzs4OuT29sbECj0SAqKmpHUsJyqXOTFZ5y2dG8E7Asi/7+fm5/uqd0t7/u8oSEBJffhxyxWq0us+KhmhkAXP3LGYbBysoKlpeXMTw8DIvFwu2SFtO8xLIsfvrTn+Kb3/wm/u///s+vUAPbd1mLpaWlBefOncPnP/957nMvvPACWlpagj42w2x9yAG5nIcUyEqsfQkBsSa02+0oLS1FUVFR0M9H0sB5eXkoLy/3+vwqlQp2u93nsUiaen5+Hg0NDUhMTORcj8QKNYk0s7KyfJ6flOxFnXtycpKzXN2PKzwBcT7f7mYsm5ubWFpaknW6fHNzE+3t7UhISNh3ZQziVkYyHZ2dncjIyIDNZsP58+cD8i9nWRZPPfUU7rvvPvzv//4vWltbRZ+XwWDA5OQkZmdnAWyttQTApfaB7TuYb7/9drztbW/D9773Pbz73e/GL3/5S1y8eBH/9m//FtBxPcGwLBiZ5J/lch5SICux9obdbodWq4XFYuEaQ4LBfZQqOzvb5+PdF3l4Oj9+Gj0iIiLoiHp2dhb9/f172iW803VulmUxPDyM2dlZUXX4UMHhcECr1cLhcATl8x0ZGSnrdDnx9iemLvuxjAFsiZhGo0FVVRV37eD/Pnp6euB0Or36l7Msi6effhp33nkn/vCHP+DSSy8N6nz++Mc/4uMf/zj37+uuuw4AcP/99+OBBx4AsH0Hc2trK55++mncc889+Jd/+ReUlZXh97//PTdjLfS4nmAYFoxMGrv2UxpcVnPWLMvCZrO5fI6fpj58+DA6OzuRnZ0tWsD4nty1tbWCHL/GxsawurrqccbQbDajvb2dOz9+fVvMaktS05yamsLhw4ddGt3kAqlzLy0tYXFxUdQ8N4k0TSYT6urq9oUnsifIRIBKpcKRI0d2pHmPny5fWlqC2Wze9XT5xsYG2tvbkZaWtqOe7XvNysoKOjs7UVFR4bVezPcvX15ehslkQmxsLN544w1UV1djcXERt912G37zm9/giiuu2OVXsHOQOes7zy4jIkoeM81Wiwnf/lQqnbOWGvc3uKc0tZjNWwT3DnKhjVreatbEiCU3NxdlZWVBN5KR1ZsmkwmNjY2yNQHh17nLysq4Ovfc3BwGBgYQFxeH9PR0r3VukilxOp1oamralxulgDd9vmNiYnZ0Velep8uJhW5WVta+dV8DAKPRiM7OTpSXl/ts7PLmX/6DH/wAX//612G323H8+HGYTCYYjcZ9ZxHMN4DZa+RyHlIgK7EmsCyLsbEx6HS6bWlqsTutiZ1jYmJiwAsSPHWDT05OYnBwEFVVVVzzSTBpb6vVCq1Wy+1nDiUB49e5bTYbJxT8OjcRCnLDFBUVhdra2pDpEg6UvfT55qfL+eWLnUiXr62tob29HXl5eSguLn5LCHWgWT3iX/6Rj3wE586dw7333ou1tTV8/etfx0c+8hGcP38ejY2NO3Tmuw+ds94ZZCfWxD94ZWUFTU1N2+qYQjdv8VlYWEBXVxeKi4tFXVD4Nwj8eez6+nokJSUFXZ9eW1uDRqNBYmIiqqurQ7opJzw83GOdu6urizOCIK9zvwo1uTHMycnZ853i7mN6UnaXr66uorOzEwUFBZI0fMoV8jpLS0tFl9+ef/553HzzzXj88cdx7bXXAgC+/e1vY2JiAllZWVKe7p5D56x3BlmJNcMweO2116BUKrlGLXcCiaxJ/XdsbAyHDx8WZFjgCfKcJH27ubnJpdGDFWpiqEAuePspMuELBVlvGR8fj83NTbzyyish7VvuDYPBAK1WK0ufbynT5STSLC4uRkFBwS6+it2F7LAvKSkRbWX80ksv4YYbbsDZs2fxoQ99yOVr+/Fn53QycDrlMTMll/OQAlmJtVKpRGlpKVJSUrxeKMLCwmC1Wv0ey+l0oru7G0ajEUePHg1qNlGpVMLhcODChQuIiopCc3Ozy02DWKEmI0tyWPu4k5DOdn73bKB17lCAeLb7aj6SE2LT5aQbuqysTBIvfrlCMiRkf7oYXnnlFVx33XX44Q9/iI985CMh+XcdKCyz9SEH5HIeUiArsQa2/Gl9uYUJSYOTxh6VSoXW1tag679msxkWiwUFBQWoqKhwMeIXk7JmGAZDQ0NYWFhAXV3dvmswIbAsi/HxcYyPj6Ompsalsz2QOncolAXIDQl/iUMo4S1dTiw3Sbo8LCyM69XwN/IYyphMJrS3t6O4uFi0ULe1teGDH/wgvvOd7+DjH//4W0KoATpnvVPITqz94S8NbjQa0dHRgfT0dElMGaanpzE4OAiVSoXKysqgG8nINqnNzU00NTVJtppPbpDa/tLSEhoaGnxmNnzVuQH5+JZ7gyxXqa2tld2+bTF4S5fPzMxgbW0N4eHhWFtbg8FgCJmbqUBYW1tDR0cHioqKRKep33jjDXzgAx/AN77xDXzqU596ywg1QNPgO4X8rnx+8DW6NTMzg76+PpSXlyM/Pz+oNwjLshgcHMTMzAwOHDiA/v7+oOvTFosFnZ2diIyMRFNTkyyFRwrI7mKLxSLYrYvgybd8aWlpz33LPcGyLEZGRjAzM4O6urp9a+oSGRmJ8PBwmM1mbgRNTmYsUkK62wsKCkT3HHR2duKaa67BPffcg8997nNvKaEGaIPZTiE7tRCyJtNdrIkV6dTUFGpra5GamhrUORDHqY2NDRw9ehQKhQJOpxMjIyPIyMgQVf82Go3QarXIyMhAeXn5votGCDabDZ2dnVCpVGhsbAzq4h3sPPdOQny+9Xq9V5/v/cLc3Bz6+/tx+PBhzg7WX7o8LS0N0dHRISVUZF48Pz9fdHd7d3c33vve9+JLX/oSvvCFL4TU65cKlmXBysQ5jM5Z7yHuYs0X1paWlqAvmhsbG9y2oObmZi6SP3ToEBYWFnDx4kWEh4dzHtmJiYl+35Dz8/Po6+tDaWmp6PpXKEB+dvHx8Th48KDkNyRyqXOT8UKz2YzGxsagt6DJmenpaQwNDeHIkSPb3PT8dZeHUu/B+vo6Ll68yM2Li6Gvrw9XXXUVPve5z+Huu+9+Swo1ADAySoMzMjkPKQg5sQ4LC+MazIjxRFRUFI4ePRp0Cm5lZQUdHR3IyspCRUUFWJblbgwyMjKQmZkJp9MJg8GAxcVFziObCHdycrLL7DAxd5mYmNjXSyqAN2dRs7Ozd8XFyr3OTX4n3d3dYFl2x+rcfJ/vhoaGkDKvCZTJyUnodDrU1tYiKSnJ7+N304xFSohlcF5eHkpKSkQdY2hoCFdddRU+8YlP4P7773/LCjUAsIyMImuZnIcUyE6shabBidVnTk6OJGllUu+uqKjgLjae6tMqlYqLFhiG4XZBDwwMwG63IzU1lRPuoaEhrKys+G2wCnXIyNJeZQ74vxN+nVun06G7uxvJyclIT09HampqUFEwSfGHhYWhvr5+3/YcAMD4+DjGxsZE1+KFdpfvdbrcbDbj4sWLyMnJER1R63Q6vOc978GHP/xhfPOb33xLCzVAHcx2Clkt8gC2mpN8jWaZzWa8+uqrUCqVqKqqCnojFb/eTcaLxDSSEfP+xcVFLC4uwmw2Q6VSobi4GFlZWXveDLVTTE1NcdvLxJrO7CSkzr20tITV1VXExcVxmZBA6tx8n+9Q39HsC5INmpycRF1d3Y4sP+Cny1dWVvYsXU6EOjs7W7TT3Pj4OE6dOoWrrroKP/zhD/ft34UQyCKPj90/hvBIeSzNsG2a8ORXi+gij92GYRjodDqwLIu6urqgN1KRMar19XUcPXoU0dHRoju+iXm/UqnE/Pw8UlJSkJSUhMXFRYyMjCA+Pp6LNPbDhin3Tmi5zop7q3OPjY0JFglSbklJSdnXqx/J73R2dhYNDQ07tkhGDulysiUsKytLtFDPzMzg3e9+N6644oq3vFDzYRj5rKb0YdkRcshOrL29aciqQbJCM9i7JIvFgo6ODqjVajQ3N0OtVgftSKbX69HV1cXVvhQKBYqKimC1Wrl1kiMjI4iJieG6mOPi4kLu4s8wDPr6+rCyshJSndBi6txy8vneSUiGaWFhAQ0NDbv2O92LdPnGxgYuXryIjIwM0f0V8/PzuPLKK/H2t78d//qv/0qFmgfjZGTT2CWX85AC2Ym1J4hJQXx8PGpra3Hu3Dk4nU7Rd91kJ21GRgYqKysBgBNqMTuogTfNUzw5O0VERCA3Nxe5ubmw2+1YXl7mLkhqtZq7WAnpLN9rSIOV3W5HU1NTyKb3hdS5Y2JiMDMzg+LiYtn5fEsJfwytoaFhzzI/3rrLl5eXJesut1gsaG9v50YoxbzfFhcX8e53vxtNTU346U9/um8X0oiFNpjtDLIXa9J1XVhYyEU2xKtbDLOzs+jt7UV5eTny8vI4RzJy3EAhEcnc3Bzq6ur8ds2q1WpkZWUhKysroM5yObC5uYnOzk5ERESgoaFh3zRYeZrnJnVbYGtrG8MwAde5QwGWZV2yJHIaQ5M6XW6xWHDx4kWkpaWJFurl5WVcddVVOHToEJ566inZvUflgJORz+iWcx/lwWV3tSVvIP5O60OHDrksuhCz05plWQwPD2NychI1NTVITU0N2pHM4XCgp6cHZrMZTU1NAUck7tGd0Wjc1lkul1GX9fV1dHZ27sl+5t1mdXUVCwsLOHLkCBITE0XVuUMBhmHQ09OD9fV1NDY2yjpLEmy6nETUqampqKioEPV+X1lZwdVXX42SkhL8/Oc/3zc3q1LDsqxszEjkch5SIMu/NqfTid7eXhgMBo87rX1ZjnrC4XCgu7sbJpMJR48eRUxMTNBCvbm5CY1GA7VajaampqDFVKFQICkpCUlJSSgvL8f6+joWFxe5vcNk/GgvbDbJ2sf8/HxR+8BDCTKyxPf5dq9z86M7IhBy9S33BjF22djYCLl58UDT5TabDe3t7UhOTkZlZaWov9/V1VVcffXVyM7OxjPPPBNSP6/dhmVY2TSY0TT4DrK5uYnXX38dACTZac3fwEWMU4IVapPJhM7OTqSlpaGyslLy6EqhUCAuLg5xcXEoKSnBxsYGFhcXOZvN3ewsJ+5robL2USz87vb6+nqPDYxC6tzk63JKJ7vjdDq5voOGhoY9z9oEi690OblOxMfHi24QXFtbw/ve9z4kJyfjv//7v2WdgZADtMFsZ5BdDi8sLAypqak+m5eEirXRaERbWxvi4+O5GqvT6QTLsqKFmliOFhQU7Fo6ODo6GoWFhWhsbMQll1yC7OxsGAwGnD9/Hm1tbRgZGYHJZJI05UPWW/b19eHw4cP7Xqj7+/sxPz+PxsZGQZMGpM5dVlaG1tZWtLS0IDk5GfPz83j11Vfx2muvYXR0FGtra7JKxTmdTmg0GjgcDtTV1YW8ULtD0uXV1dWcXXBUVBTsdjv+9re/4Y033sD4+DjMZrOg34vZbMb/+3//D1FRUfjd734n65swucAyjKw+xPDoo4+isLAQkZGRaG5u5gJITzz55JNcYzL5cP87YVkW9913H7KyshAVFYUTJ05geHg4oHOSXWStVqtRVlbm8zF8y1FvzM3NoaenB2VlZcjPzwfDMNybU2wjGUmRHjx4cM92FvM7yx0OB5aXl7G4uIiLFy9yneVpaWlISkoSna4mG8fIGE+omwn4Qiqfb/d5bvJ7kVOd2+FwoLOzEwqFAnV1dSGVtg8Uq9XKpb6rq6uhUCgC7i63WCz40Ic+BAD4n//5n5AZUdxrGBmlwcWcxzPPPIPTp0/j7NmzaG5uxpkzZ3Dy5EkMDg56ve7Hx8djcHCQ+7f7tfc73/kOfvjDH+Kpp55CUVER7r33Xpw8eRJ9fX2CrzmyczADtmaqfZ0WMajwtGuWpDMnJia4LUEkmhY7lsUwDDfaUlNTI0vx4tdTFxcXAYjrLHc6nVzTUV1d3b7dtw24+nzX1tbuSB2S/3tZWlraszq33W7nfAWOHDmyr7uYiVDHx8dzQu0OP12+vLzMdZc7nU7k5OQgPj4e1113HdbW1vDcc8/t2/WnUkIczK7+tBbqCHnYK9uta/jDY0cCcjBrbm5GY2MjfvzjHwPYuv7n5eXhc5/7HO66665tj3/yySfx+c9/Hkaj0ePxWJZFdnY2vvCFL+CLX/wigK0eiIyMDDz55JO47rrrBJ1XSN5ae0uDO51OdHd3Y3V1Fc3NzYiNjQ1aqO12O3dBb2pqkm0ajF9Praqq2tZZnpKSwvlje0t92mw2aLVasCyLxsbGfd1Es1s+33Koc9tsNnR0dCAyMhKHDx8O6Q52f5Bmsri4OK9CDXjvLv/+97+P//zP/0RBQQFsNhv++7//W5Y353JGjnPWJpPJ5fMREREey6zk7+fuu+/mPqdUKnHixAm0tbV5fZ719XUUFBSAYRjU1dXhwQcfRHV1NQBgbGwM8/PzOHHiBPf4hIQENDc3o62tbf+LtXsanMwAK5VKyRrJzGYzNBoNYmJiUFtbGzLRiJDOchJ1kz9Y4ugWGxuLgwcPhsxrFcNe+Xx72889Pz+PwcFBzrc8LS0NsbGxknTdkyiT/F7fCkIdGxvrU6jd4XeX/+AHP4DBYIBOp0NJSQne9ra3IS8vD1/84hfxyU9+codfwf7AyTBQBjhau1OQOeu8vDyXz99///144IEHtj1+eXkZTqdz256DjIwMDAwMeHyOiooKPPHEEzh8+DBWV1fx3e9+F62trejt7UVubi7m5+e5Y7gfk3xNCLIUa4VC4TMN7h5Zr66ucqlxcjcTrHWowWBAV1fXrq183Ck8dZbzBSI+Ph7x8fGYn59HZmam6BnUUEFOPt87Xefe3NxEe3s7EhIScODAgbeEUMfExIi+KXE4HLjlllswOjqKl19+GWlpaTCbzfjLX/5C69UBIMfIempqyiVDImVHf0tLC1paWrh/t7a2oqqqCj/5yU/w9a9/XbLnkaVY+yMsLAxWqxXA1mhRd3c3SkpKUFhYGHQjGbDlctbf34+Kioqgt3rJjejoaBQUFKCgoABWqxVjY2OYmpqCQqHgIor09PSQ9Cz3h5x9vsPDw5GdnY3s7GxJ5rmJCQgxsZHTa5UaUo+Pjo4WLdROpxOf/vSn0dPTg5deeonbPR8TE4Orr75a6lPe18jRFIUEJf5ITU2FSqXCwsKCy+cXFhZcjLl8oVarUVtbi5GREQDgvm9hYQFZWVkux6ypqRF0TCBExVqlUsFut0On02F0dBSHDx9Genp60Glv0pw2PT3tYoqxX1leXsbMzAwOHTqE1NTUbZ3lJFUe6k5dwJvGLkVFRbL3+Q62zm02m9He3o709PR9nymx2+1ob29HZGSk6JKG0+nE5z73Obz++ut4+eWXBV+UKZ5xOpxQKmWSBncEdh7h4eGor6/HuXPncM011wDYajA7d+4cPvvZzwp7zn/0Tl155ZUAgKKiImRmZuLcuXOcOJtMJrz22mv49Kc/LfjcQlKsFQoFjEYj10gWFxcXtFCTLui1tTU0NTXt67QXy7IYHR3F5OSky01JZmYmMjMzwTDMto1U/Mgu1OrZi4uL6OnpCUljl0Dr3ESog9nRHCqQiDqYxjmGYXD69Gn87W9/w0svvRRyfx9yhGUZsKw8zEjEnMfp06dx0003oaGhAU1NTThz5gzMZjM+/vGPAwBuvPFG5OTk4KGHHgIAfO1rX8PRo0dRWloKo9GIhx9+GBMTE7jlllsAbL2HP//5z+Mb3/gGysrKuNGt7Oxs7oZACLIUa18XGKvVisnJSTidTlxyySWSNJJZrVZoNBoolUo0NTXt6y5o/hhaY2Ojx53FSqUSqampSE1NdfEsHxoags1mE9RZLhdmZmYwODi4p7PxUuKpzk18y9VqNex2OzIyMva9LSwR6vDw8KCE+s4778Tzzz+Pl19+2eMoKCVwWAcDRikTsXYEfh7XXnstlpaWcN9992F+fh41NTV47rnnuAaxyclJl7+3lZUV3HrrrZifn0dSUhLq6+tx/vx5HDhwgHvMl7/8ZZjNZvzTP/0TjEYjjh8/jueeey6gCRBZzlk7HA6Po1mk5hgVFQWHw4GWlpagV1uura1Bo9EgKSlp3zfhOBwOdHV1wWq1ora2NuBRIZZluc7yxcVFmM1mJCUlcUYschtrIyY2R44c2fcljZWVFa6bf3NzEwzDIDU1Fenp6SHnW+4Ph8OBjo4OhIWFoaamRrRQ33vvvfjVr36Fl19+2a8RE8U/ZM76Hde9grDw7UHAXuCwrePFX14S0Jy1XAmZd/DCwgK6urpQXFyM2NhYDA4OwuFwiF5tCYBr4CF1zP0ciVitVnR2dkKtVov2gxbSWU6Eey/LCEJ8vvcTKysr0Gg0nFtfKPuW+4Mv1EeOHBHtRviNb3wDv/jFL/DSSy9RoZYYOXaD7wdkKdZ80XRflZmRkQGz2QybzYbz588jIyMD6enpSEhIECy2LMticnISOp0O1dXV2+bf9htkXCkxMRHV1dWSZQ/4neU2mw2Li4tYWlrCyMgIoqOjOdOJ3ewsJz7fJM2/n3sPgK3GOY1Gg/Lycm5yYS/muXcDYpeqUqlEu7CxLItvf/vbePzxx/Hiiy+iqqpqB870rQ3jdIIJsLFrp2BkMu8tBbJMgzudTjgcDm7frl6vR11dHeLj47n6NMuyWFlZ4VKyfEciX93LDMNgcHAQi4uLqKmp2fc2gkajERqNZlfHlfie5cvLy7vWWc73+a6rqwvpCFIIy8vL6OrqQmVlJbKzswV9D7/Ovby8jPDwcC4bIueuf6fTiY6ODiiVStTU1IgW6kceeQSPPPKIS2cuRRpIGvzS959DmFomaXD7Ov7223fuizS4bMXabDajs7MTLMtyvs0Mw3hsJGMYBisrK1hYWMDS0hJYluWEOzk5mbsA2e12dHV1wWazoabm/7d333FN3esfwD9BAVH2VhyADBHZ4MBdBypIolete+DsbW3ttL213rb+bq9XW3/ttY7W+kNbbWsFnIAbtC1UKxsVq2LYJJHIHiHJ+f3h65yCosWQkBCe9+vVPwySfI8pfHLO9znP46/Xfa+BP6ug3d3dn+jg01laVpaz740mKss7o8+3LmEr9b29vVW+1aitvuW6uM+tUCiQkZEBACp3EmQYBl9++SW2bt2Kc+fOISQkRN3L7PbYsB4juIiehrpxRUveXIdfjlNYa0xlZSWuXr0KS0tLrslBewvJ2OplkUgEsVgMhULBnTUUFBSgd+/e8PHx0ZlfRJpSWFiIu3fv6lQVNLuXyl4NaWpq4sKhI5XlLft8+/n56f17W15ejhs3bsDHx0dt723LfW6JRIL6+nqd2OdmR3qyPZdVDep9+/bhww8/RGJiYqtuU0R92LAOjTinU2GdcnoqhbWmPHz4EGVlZXBxcWnVkex5K77ZBv2FhYUoLy8Hj8fjzrhtbW318pc6wzC4c+cOSktL4e/vD0tLS20vqU0tK8slEglqa2tVqizXVp9vbSktLUVeXh58fX1ha2ursddhiwfFYjGqqqpgamrKvTedtc/dMqgDAgJU+nllGAYHDx7Eu+++i9OnT2PcuHEaWCkB/gzrUeFndCqsU+OnUVhrilKphEwmg1KphEKhUPn+aeDRXOtbt27Bzc0NVlZW3Bl3Q0MDd7+wnZ2dzt8v3B7sHn91dTUCAgK6VHFVQ0MDd8bN/mCx+9xPOw5d6vPdGYqLi/HHH3/A39+/U29Fa7nPXVFR0Sk1CAqFgtvWUHX2NsMwOHz4MN58802cPHkSEydOVPs6yZ/YsA6e9BN69NSN3z0KeR2uX5xHYa0pd+/ehampKfr06dOh1qFsly62nWZLLe8Xrq2thbW1NXfW3RX3O9lRngqFosvv2cpkMu6srqKigqsst7Ozg7m5OXg8HqqqqpCRkaGTfb41gb17ISAgQKtXSzpjn1upVCIrKwvNzc0dCuqjR4/ilVdeQWxsLMLCwjq8LvJsjY2NcHFxea5JUp3B0dER9+/f7/IFpzoZ1qtWrcLhw4cxadIk8Pl8hIeHP9etWQqFAjdv3kRlZSUCAgLa7NLVUn19PRfc1dXVsLS0hIODQ5e5J5W9FGxiYgJfX98u1w70WeRyOSoqKrjK8p49e8LMzAwVFRVwdXWFi4uLtpeocffv34dQKERgYKBO3b3AbjOxWxnq2Odmg1omkyEwMFDlK17Hjh3DmjVrcOTIEURERKj0HOT5NTY2QiaTaXsZrRgZGXWJ3+N/RSfDmmEY5ObmIiYmBseOHcPt27cxYcIECAQCREREwNra+qnBLZPJkJmZCQDw9/d/7jPMxsZGLrgrKythbm7O3cuti9XjNTU1yMjIgK2tLYYMGaLXe7ZKpRL5+fkQCoXo0aMHeDxeq7M6ffqQAvx5daioqAhBQUEwMzPT9pKeqaP73Eqlkuuw15GgPn36NFasWIFDhw5h1qxZKj0HIbpGJ8O6JYZhcPv2bcTGxiIuLg7Z2dkYO3YsBAIBZs6cCXt7e+6XgFgsxu3bt2FpaYmhQ4d2+Jd3U1MT98tHKpXC1NSUC25d2A+uqKhAdnY2nJ2d9b4DG9C6z7ednV2bleXsWV1Xr0Fgu7CVlpYiKCjoL68O6Zrn3edm75FvaGhAUFCQyu/fmTNnsGTJEkRHR2PevHnqOBRCdILOh3VL7JkGG9zXr19HaGgo+Hw+evXqhU2bNuHo0aMYNWqU2oOrubkZEokEIpEIUqkUJiYmXHBrowtUWVkZbt68CS8vr3Y3xOjKntXnm2EY1NXVtapB0OWe5X+F/YAqFosRFBSkEx8MO+Jp+9x2dnawtbWFgYGBWoL60qVLmD9/Pvbu3YtFixbp/YdX0r10qbBuiW0ZGhcXh127dkEoFGLMmDEICwuDQCDAwIEDNfbDynboEolEePDgAYyNjbngZgugNIVhGAiFQgiFQvj6+sLGxkZjr6ULWvb5ZrvY/RW2slwikaCyshJmZmZc8aCuBx/bLlUqlSIoKEgnt146oq19bkNDQ/B4PAQEBKh8qf/KlSuYO3cu/vvf/2L58uUU1ETvdNmwBh59Yn/zzTdx+PBh7Nu3D8XFxYiLi8PPP/8MX19fCAQC8Pl8DB48WGM/vAqFAhUVFVxw9+zZs1XbU3W+LsMwyMvLg1gsRmBgoM7vYXYUO85TKpUiMDBQpaBtWVnOXhFhL8dq+oPV81Iqlbh58yaqqqoQFBTU5a4IPC+lUonMzEzU1NTAxMQE1dXVKt3PnZKSgtmzZ2Pbtm1Yu3atTr2nhKhLlw7r3NxcLFu2DEePHoWrqyuAR4EmFotx/PhxxMXFISkpCUOGDIFAIIBAIICnp6fGfpiVSiVXuSyRSFo1YbGysupQ8ZdCoUBOTg7q6+sREBCgd2dcj1MoFMjNzVVrn+/HK8vZfvJ2dnYdfn86ir1Hvra2FkFBQTA2NtbaWjoDW0RaU1OD4OBgGBkZqXQ/9++//w4+n48tW7bglVdeoaAmeqtLhzUArld4W9hhHydOnEBsbCwuXLgAV1dX8Pl8zJo1S6Pzq9l+5ew+KtsTm61cfp7XZSvceTwe/P39u3zx1F9h+3wrFAqVKvrb4/Ge5UqlstX705mV5WwVdGNjIwIDA7v0PfLtwTAMbty4gerq6qd+MHnWPnevXr1gaWmJjIwMREREYNOmTXjjjTcoqIle6/Jh/TwqKytx6tQpxMXF4ezZs3BycuIulas6xL492H7lbHDL5XLY2trCwcHhL4Ohvr4e6enpMDc3h7e3t97dnvQ4bfT5bqtneWd1t2M7dbENQPT9gxjDMFwPhODg4HZdQWD3uSUSCXc1zdfXF0VFRYiKisK///1vCmqi97pVWLdUU1ODhIQExMbGIjExEba2toiMjIRAIEBISIhGg5stsBGLxWhsbOSC+/F+5WyXrr59+8LDw0PvfyE1NjYiLS0NZmZm3ACXztaZleVyuRyZmZncZDl97FXfkipB3ZaEhAR88sknePjwIYqKiuDn5wc+n4/XXntNp5rGEKJO3TasW6qvr8eZM2cQGxuL+Ph4mJmZYebMmRAIBBg1apTGzmZbDrMQi8VcBygHBwfweDzcunULgwcPxqBBgzTy+rpEV/t8NzQ0cAVq6qwsb25uRmZmZofmM3clLavcg4ODVf7Qc/v2bUyfPh1RUVH417/+BalUivj4eCQkJCA6OlrvazlI90Vh/ZjGxkacP38ecXFxOHHiBIyMjDBz5kzMmjULo0eP1uhlSvaMrri4GI2NjTA1NcWAAQNgZ2en1wVH7BWE/v37a7Ryv6Meryzv1asXF9zPU1ne3NyM9PR0GBoaws/Pr1sEdV5eHioqKjoU1Pfu3cO0adMwf/58bN++Xa+79RHyOArrZ5DJZEhKSkJsbCyOHz8OpVKJiIgIzJo1C+PHj1d7IRDDMLh37x6Kiorg5eWFpqYmiEQibpoNey+3Pt3SI5VKkZmZ2eWuIKhaWS6TyZCWlsb1cdf3wFFXUAuFQkyfPh2RkZH44osv9P7fjZDHUVi3k1wux5UrVxATE4Pjx4+joaEB4eHhEAgEeOGFFzocoOw9tg8fPnxi+EhjYyPXPY3tV86e0fXu3bujh6Y1IpEIubm5Xb4L2+OV/0+rLG9qakJaWhpMTU21tiffmdhObBKJBMHBwSpfoi4uLkZYWBjCwsKwe/duvf93I6QtFNYqUCgU+PXXXxEbG4tjx46hqqoK06ZNA5/Px9SpU587QNlblZqbmxEQEPDMS94ymYwLBbZfORvcXal/dMs+3/b29tpejtqwleXs5fLGxkbY2NjA0tISRUVFsLKygre3t85e6lcXhmHwxx9/QCwWdyioy8rKMG3aNIwdOxb79u3T+y0DQp6GwrqDlEolrl27xk0IE4lEmDJlCgQCAaZNm/aXXcYaGxuRkZEBY2Nj+Pr6PldFMNuvnJ37bGJiAnt7ezg4OGilX3l7PavPtz5hK8tLSkpQVFQEhmG4ynJ9285oqWVQBwUFqXz1RyQSYfr06QgODsbBgwcpqEm3RmGtRkqlEhkZGYiJiUFcXBwKCwsxefJk8Pl8zJgx44mZ3LW1tcjIyIC1tTW8vLw6dHmP7VfO7qEaGRlxwa0rbTUZhsGdO3dQVlaGgICAdvX57urq6uqQlpYGBwcHDBw4sM3Kcra1pj5g3+Py8nIEBwerHNQPHjzAjBkz4O3tjcOHD+v9bW2E/BUKaw1pOZM7Li4Of/zxByZOnAiBQIDw8HBcvXoVX331FT777DO1V0Cz/crZ7lxs8RPb9lQbwa2OPt9dTW1tLdLS0tCvXz+4ubm1+ndnW2uyV0VUrSzXJezQlbKysg5NC5NKpQgPD4erqyt++uknvW8UQ0h7UFh3gpYzuWNjY5Gbmwsej4cFCxbgww8/hJ2dnUb7lbNtNcViMXg8Huzs7ODg4NBp/bA10edb11VXVyM9PR0DBw6Ei4vLM99fhULB9cRmP1yxBWra7lneXuydDCUlJQgODlY5qKuqqjBz5kw4OjoiNjZWr29ZJOR5UFh3sh07dmDz5s2YN28ebty4gfT0dIwaNQoCgQCRkZHo27evRoO7ZdtThULBBbe1tbVG9gQ7o8+3rqmqqkJ6ejpcXFzg7Oz8XN/7tMpydvazru7b3rt3D8XFxR0K6pqaGvD5fFhYWODEiRPd4kMdIe1FYd2JDhw4gI0bNyI+Ph7BwcHcTO7Y2FjExcXht99+w/Dhw7m2pwMGDNBYcLfshy0SidDc3Nyq7ak6QkEbfb617eHDh9x94wMHDuzQc7XVmpbtWW5ra6szH3zYoA4KClJ5772urg6zZ8+GoaEhTp8+3aVvSSREEyisO1F9fT3EYnGbZ1sMw6C0tBRxcXGIi4vDL7/8Aj8/P27QiKurq0aDu6amhgtutl85Gwqq7Bk2NDQgPT1dq32+O1tFRQWysrLg4eGB/v37q/W52cpytkCtpqYGlpaWWq8sz8/PR2FhIYKDg1UO6vr6esydOxcKhQIJCQl6U2xHiDpRWOsghmEgEom4mdzJycnw8vLigluTM7nZUBCJRBCLxairq2s1gao9Z3O1tbVIT0+Hra2tTvX51iSJRIKcnBx4eXmhb9++Gn+9xsZG7oy7srKy1f32ffr06ZR/8/v376OgoABBQUF/eYvi0zQ2NuLFF19EbW0tzp492y3uECBEFRTWOo5hGEil0lYzud3c3MDn8yEQCDQ6kxt4dNbDBndNTU2r+4TbKv7pKn2+1UksFiMnJwfDhg2Dg4NDp7/+0yrL7ezsnrhdUF2EQiGEQmGHgrqpqQmLFi2CRCLBuXPnYGVlpeZVEqI/KKy7EHaf+eTJk4iLi8O5c+fQv39/Lrj9/Pw0GtwNDQ3c2VxVVRUsLCy44DYxMeEuA3e1Pt8dUVZWhlu3bulMJ7bOqCwvKChAfn4+goKCVD4Tbm5uxtKlS1FQUICLFy/Cxsamw+siRJ9RWHdhNTU1iI+P52Zy29nZITIyErNmzUJwcLBGg7upqYkL7ocPH6JXr15obGzE4MGD4eLiorHX1SWlpaXIy8uDr68vbG1ttb2cJ7SsLJdIJFAoFK1qEVQpIlRHUMvlckRFRSEvLw9JSUmws7NT6XkI6U50Kqx5PB6OHTsGgUCg7aV0OXV1da1mcltYWHAzuUeOHKnRW36EQiHu3r0LMzMz1NTUoE+fPlz3tM7aP+1sRUVFuHPnDvz9/btEy9SnVZazt4W1pxahsLAQ9+7dQ2BgICwsLFRah0KhwNq1a5GZmYlLly7B0dFRpechpLvpcmHt7OyMgoKCVo/9+9//xrvvvqvh1XUdDQ0N3EzukydPwtjYuNVMbnXeQsX2+fb394eVlRWam5tbtT1l908dHBxgZmamF8HNnl0GBATA0tJS28tRSW1tbZuV5XZ2dm0O3SgqKsLdu3c7HNTr169HSkoKkpKS4OTk1NHDIKTb0JmwlslkMDY2bldYr1y5EqtXr+YeMzMz6xbtK1Uhk8lw6dIlbiY3AEREREAgEHRoJnd7+nyz+6fsZVhDQ0MuuDVV+KRp9+/fh1Ao7FBo6Rq2slwikeDhw4dPVJaXlJTgzp07HfpwolQqsWHDBly6dAnJyckdvgedkO5Ga2E9YcIEDBs2DD179sShQ4fg4+OD5ORk7N69GydPnkRycjL69u2Lbdu2Yc6cOdz3OTs7Y8OGDdiwYYM2lt2lsTO5jx49iuPHj6OxsRERERHg8/nPNZNblT7fCoWiVdvTlv3KLS0tdf4+bLadJtv8Q9UKaF33eGV5z5490dzcjCFDhsDJyUmlD1hKpRLvvPMO4uPjkZSUBFdXVw2snBD9ptXfkAcPHoSRkRF+/fVX7N27FwDwwQcf4G9/+xuysrKwaNEizJ8/H7du3Wr1fVu3boWNjQ0CAgKwfft2yOVybSy/y+nZsydeeOEF7NmzB8XFxThx4gSsrKzw+uuvw8XFBVFRUThx4gTq6+uf+hwKhQLZ2dmoqqpCSEhIu69osFXJ3t7eGD9+PLy9vaFUKpGTk4MrV67g5s2bePDgAZRKpboOV23Yqwhs32t9DWoAMDIyQr9+/eDv7w8PDw/I5XJYW1vjzp07uHLlCm7duoWKiop2v09KpRKbNm3CyZMnceHCBbUHtUQigaOjIz755BPusZSUFBgZGWH//v0wMDDA9evXW33P559/jkGDBkGpVOLjjz9Gv379UFFRwX09PDwcEydOhFKpRFRUFCIiIlp9f3NzM+zt7bF//361Hgshz6LVM2t22AG3GB4P69atw549e7jHRo4cicDAQOzevRvAo97agYGBsLa2RkpKCt577z2sWLECO3bs6PRj0BdKpRJXr17lZnKLxWJMnTqVm8nNdpSSSqXIyspCnz59EBAQoJZpSAzDoLKykruXm+1Xbm9vDxsbG633wmaHsEgkkg7NZu5q2Ep3toCOrSxn97lbVpbb2Ni0WQfBMAw+/vhjHDx4EMnJyRgyZIhG1pqQkACBQICUlBR4enrC398ffD4fO3bswNSpU+Hu7o5du3Zxf5/tDPjRRx9BoVBg7NixcHBwwLFjx7Br1y5s2rQJWVlZGDhwIFJSUjBu3DgUFRVxzW6OHTuGJUuWoLy8nLqtkU6j1bB2d3fHvn37/lwMj4eDBw9i6dKl3GOvv/46MjMzkZSU1Obz/N///R/Wrl2L2tpamtCjBkqlEunp6dxoz+LiYkyaNAmTJ0/Grl27EBISgr1792okRNmKZTa4ZTJZq1uNOru3OMMw3OX+oKCgNguv9NHjQf24lpXlEokEDQ0NsLa25t4nY2NjMAyDrVu3Yu/evUhKSsKwYcM0uuaXX34ZFy5cQHBwMHJycvD777/D2NgYP/30E9atW4eysjIYGxsjPT0dwcHByM/P59r+5ufnw9/fH3//+9/x3//+F9988w0WLlzIPbe3tzeWLVuGd955BwAQGRkJGxsbREdHa/SYCGlJq5fB1VEUNmLECMjlcgiFwo4viMDAwADBwcHYunUr8vLy8Ntvv8HV1RXvvvsu5HI5pFIpDh8+DKlUCnV/zuPxeLCwsICHhwdGjx6NkJAQ9O7dG/n5+bh8+TIyMzNRWlqK5uZmtb5uW5RKJXJzc/Hw4UMEBwd3m6AuKytDXl4e/Pz8nnpLGvs+ubu7IzQ0FCNHjoSlpSWKi4uxYMEChIaGYtmyZdi5cyfOnz+v8aAGgE8//RRyuRxHjx7F4cOHuQ/uAoEAPXr0wLFjxwA8GqYzceLEVv35XV1d8emnn+I///kPIiMjWwU1AKxatYoLZpFIhMTERERFRWn8mAhpSeeqen777bcn/uzl5fXUv5+ZmQkDAwOd6B6lbwwMDGBoaIi4uDgsX74cJ0+eRGhoKL766iu4uLiAz+dj//79EIvFGgluMzMzuLm5ITQ0FCNGjIC5uTkKCwtx+fJlpKeno7i4GDKZTK2vC4DbS6+trUVwcHC3GdVYXl6OW7duwc/P77k6ivXp0wcuLi4YMWIEtm/fDi8vL1y+fBm1tbVYuXIltmzZ8kTdibrdu3cPpaWlUCqVrT64GxkZYenSpYiOjoZMJsP333/fZtBeuXIFPXr0gFAofKIGZunSpcjPz0dqaioOHToEFxcXjB07VqPHQ8jjdG5m4dGjRxEcHIwxY8bg8OHDuHbtGlfIkZqaiqtXr2LixIkwMzNDamoqXn/9dSxevJj6CmvIN998gxUrVuCjjz4Cj8fDsGHDsGnTJty9exexsbH47rvv8MYbbyA0NBR8Pl9jM7lNTU1hamoKV1dXbnoZe7lWndOn2AK6pqYmBAUF6cwYSk0TiUS4efMmfH19VW79yTAMLly4gMTERCQmJsLT0xPx8fE4duwYpFIp/vd//1fNq35EJpNh8eLFePHFF+Hp6YlVq1YhJyeH+wC/atUqDBs2DLt374ZcLsfs2bNbff+RI0e4gTnz5s3Dli1b8NFHH3Fft7GxgUAgQHR0NFJTU7FixQqNHAchz6LVPWt/f398/vnnfy6Gx8OuXbtw/PhxXLlyBX379sV//vMfzJs3DwCQnp6Ov//978jLy0NTUxNcXFywZMkSvPHGG7RfrSEMwzwzeBmGQUFBATeT++rVqxg+fDj4fD74fL5GZ3IDT06fMjc35+7lft5L1wqFAllZWZDL5WoroOsKRCIRcnNz4evrq3LrT4ZhcODAAbz33ns4ffo0xo0bp+ZVPt3bb7+NmJgYZGVlwdTUFOPHj4eFhQVOnz7N/Z3Ro0fj+vXriIqKalXAWlxcDF9fX3z00UdYv349zp49i4iICPz8888YOXIk9/fOnz+PiIgIKBQKFBYWol+/fp12fIQAOtQUhXR9DMOgpKSEm8n966+/wt/fnxvt6eLiotHglslkXHBLpVKYmprCwcGBa+7xLHK5HJmZmWAYBgEBAZ1ezKYt7MSwjgb1oUOH8NZbb+HkyZOYOHGimlf5dMnJyZgyZQqSkpIwZswYAI+66vn5+WHr1q146aWXADwqRF25ciWuXbuGkJAQbt1TpkxBz549kZiYyP2/+eqrryIhIQGZmZlctTfDMHBxcYG3tzfi4+M77fgIYVFYE41gZ3IfO3aMu8To7e3NBbeHh4dGg7u5uRkSiQQikQhSqRQmJiZccJuamrZ67ebmZmRkZKBHjx7w9/fX+u1inYUNah8fH5VrPhiGwU8//YT169cjNjYWYWFhal6lemzZsgVHjx5Fdna2St9fW1sLJycnREdHP3EZnZDOQGFNNI5hGFRUVHAzuS9evAh3d3dutKeXl5dGO5jJ5XI8ePAAIpEIDx48gLGxMRfcvXr1QkZGBoyNjeHr69ttgloikSA7O7tDQQ0AcXFxWLt2LX766SeEh4ercYXqUVtbC6FQiEmTJuF//ud/WrUpbg+lUokHDx7gs88+w48//oh79+51m6suRLdQWJNO1XImd2xsLM6dO4eBAwdyoz19fX01GtwKhQIVFRUQiUSQSCRQKpXo1asXvLy8YG1t3SX7lT8vNqiHDRsGBwcHlZ/n9OnTWLFiBQ4fPqyzk/KWL1+OH374AQKBAN9///1zfxgTCoVwcXFB//79ceDAAUyaNElDKyXk2Sis24nGd2pGdXU1N5P7zJkzsLe354I7KChIY8Hd2NiItLQ0GBsbw8TEBBKJBDwej6sqt7Ky0vl+5ap48OABsrOzMXTo0A6Npzxz5gyWLFmCAwcOYO7cuWpcISGkLfr320iL/vWvfyE0NBS9e/d+6nSiwsJChIeHo3fv3rC3t8fbb7/drXubm5ubY8GCBYiJiYFIJMK2bdsgEokQGRmJoUOH4p133kFKSgoUCoXaXrOhoQHXr1+HpaUlgoKC4O3tjXHjxsHHxwcAkJubiytXruDGjRvc2bc+qKioUEtQX7p0CUuXLsXXX3/dasgOIURzaPOlHdrbeEMmk2Hu3LkYNWpUm03+FQoFwsPD4ejoiJSUFJSVlWHp0qUwNDRsNYigu+rTpw/mzJmDOXPmoKGhAefOnUNcXBzmzZuHXr16cTO5Q0NDVd43rK+vR1paGmxtbTFkyBDusreBgQGsra1hbW2NIUOGoKqqCiKRCHl5eZDL5a3annbFfe2KigpkZWXBy8urQ0F95coVLFiwADt37sTChQu7xbYBIbqALoO3QdXxnawDBw5gw4YNqKysbPV4YmIiIiIiUFpayu0V7t27Fxs3boREIuk2DTiel0wmw4ULFxAXF4cTJ06Ax+NxM7nHjRvX7n+3uro6pKWlwcHBod3V6C37YIvFYjQ2NnLBbWdn1yWKjaRSKTIzMzFkyJAO3R+ckpKC2bNn49NPP8Xq1aspqAnpRHQZ/ClUHd/5LKmpqfDx8WlV1BMWFobq6mrcuHFD7cegL4yMjDBjxgx88803KC0txQ8//AAjIyOsWbMGrq6uWLduHRITE9HU1PTU56ipqcH169fRr1+/57pt7PE+2MOHD4epqSmEQiGSk5ORkZHRaf3KVaGuoL527RrmzJmDTz75hIKaEC2gM+s2qDq+k/W0M+s1a9agoKAAZ8+e5R6rr69Hnz59kJCQgOnTp2vmgPSUQqHAL7/8gpiYGBw/fhw1NTWYNm0aBAIBJk+ezI2zzMjIgFQqhYuLi1rnKdfV1UEsFkMkEqG2thZWVlZwcHCAnZ2dTnTUe/jwITIyMuDp6QknJyeVnyc9PR0zZ87EBx98gNdff52CmhAtoDPrpwgKCnrisVGjRj3xZ00PKCBP16NHD4wfPx47d+5EQUEBEhIS0K9fP/zjH//gWtFu374dYWFhKCkpUWtQA38OsBg5ciRGjx4NW1tblJaW4ueff8bvv/+OwsJCNDY2qvU126uyshIZGRnw8PDoUFBnZ2eDz+dj48aNFNSEaBGF9VOoY3zn4xwdHSESiVo9xv65I0U/5FGBWGhoKHbs2IG7d+/i0qVLMDY2xpYtW+Ds7IyEhAT8+OOPqKqq0sjrm5iYYNCgQRg+fDjGjBkDR0dHiMVi/PLLL7h69SqEQiHq6+s18tqPY4Pa3d0d/fv3V/l5bt68iZkzZ+LVV1/Fxo0bKagJ0SIK6+fwvOM7Hzdq1Cjk5ORALBZzj50/fx7m5uYYOnSo2tbZ3RkYGKCyshKnTp3Cnj17cOjQIfj5+eGzzz6Ds7Mz5syZg++++04jM7kBoFevXhgwYACCg4Mxbtw4ODk5QSqVIiUlBampqcjPz0dtba3aXxcAqqqqkJGRATc3NwwYMEDl57l9+zYiIiKwZs0abN68mYKaEC3T/VJWHfKs8Z3Ao3uopVIpCgsLoVAokJmZCQBwc3ODqakppk6diqFDh2LJkiXYtm0bysvLsWnTJrz88ss6scepLxiGwc6dO7F3714sWrQIAODv74+PP/4Yt27dQkxMDPbs2YP169dj/Pjx4PP5mDlzJmxtbdUeSkZGRujfvz/69+/P9SsXi8W4f/8+TExMuCYsZmZmHX7tqqoqpKenY/DgwR0K6rt37yIiIgKLFy/Gli1bKKgJ0QFUYNYGVcZ3Ao9aGx48ePCJ50tKSsKECRMAAAUFBXjppZeQnJyMPn36YNmyZdi6dWuXuAWoK2nPaM+7d+8iJiYGcXFxyMzMxOjRo7mZ3I6OjhoNKbZfuVgsxoMHD2BkZMQFt4WFxXO/dnV1NdLS0uDq6opBgwapvC6hUMgV6X3++ed62cWNkK6Iwpp0ewzDQCgUcjO5r127hhEjRnAzufv376/R4Gb7lYvFYkgkEvTo0aNV29O/em02qF1cXODs7KzyOoqKihAWFoZp06Zh9+7dFNSE6BAKa0JaYBgGxcXFrWZyBwYGcqM9nZ2dNRrcSqUSUqmUa8LC4/FgZ2cHBweHNvuV19TUIC0tDc7Ozh0K6rKyMoSFhWHcuHHYt29fl+zSRog+o7Am5CkYhkF5eTk3k/vy5csYNmwYF9zu7u4aD+7KykouuBUKBRfc1tbWXOvUQYMGwcXFReXXEYlEmD59OkJCQnDgwAEKakJ0EIU1Ie3QciZ3TEwMLl26BA8Pj1YzuTUZ3OxoUbYJi0wmA8MwsLe3h7e3t8oB++DBA8yYMQPe3t44fPgw1U4QoqNoU0qP8Xg8HD9+XNvL0As8Hg+2trZYuXIlEhISUF5ejrfeegvZ2dkYO3YsgoKC8OGHHyIrK0sjU7p4PB4sLS3h4eEBf39/GBgYwMLCAjU1NUhOTkZmZibKysqeq+2pVCrFzJkz4e7ujkOHDmklqL/99lvY2Ng80SpWIBBgyZIlMDAwwPXr11t97fPPP8egQYP0ZhoaIe1BYd2NCYVCrFy5Ei4uLjAxMcHgwYPxz3/+84kpY2wgsfcPb9u2TUsr1g08Hg9WVlZYtmwZTp48CZFIhM2bN+PevXuYPHky/Pz88P777+P69etqD5S6ujqkp6dj4MCBCAkJQWhoKEaMGAEzMzMIhUJcvnwZ6enpKCkpeea0uMrKSvD5fAwYMABHjhyBoaGhWtfZXnPnzoVCocDJkye5x8RiMeLj4xEVFYXJkycjOjq61fdER0dj+fLlVABHuhW65qWn2jPWMy8vD0qlEl999RXc3NyQm5uL1atXo66uDp9++imAR5XGU6dOxeTJk7F3717k5OQgKioKlpaWWLNmjaYPo0swNzfHwoULsXDhQtTW1iIxMRGxsbGIiIiAlZUVIiMjwefzMWLEiA7tB9fV1eH69etwcnLiWqfyeDyYmprC1NQUgwcPRn19PUQiEYqLi3Hr1i1YWVlxleXsvfzV1dWYPXs2bG1tERMTo9VpbyYmJli4cCGio6Mxd+5cAMChQ4cwcOBATJgwARKJBOvWrcOOHTtgbGyM9PR05OTk4MSJE1pbMyHaQHvWeqKjYz1Z27dvx549e5Cfnw8A2LNnD95//32Ul5dzv9TfffddHD9+HHl5eZ1ybF1VQ0MDzp49i7i4OJw6dQomJiaIjIyEQCB47pnc7HjPvn37ws3NrV374w0NDZBIJBCJRBAKhdixYwemTJmC1NRUGBsbIz4+HiYmJh05RLXIyMhASEgICgoK4OTkBF9fX8ydOxcffPABZDIZnJycsHPnTsyfPx+vvvoqbty4gYsXL2p72YR0KrqOpEfUMdazqqoK1tbW3J9TU1OfmBkdFhaG27dv4+HDh5o7GD1gYmICgUCAb7/9FmVlZdi3bx+am5uxePFiuLm5Yf369bh48eJf7jOzVd+Ojo7tDmr29dnL5RMmTEBkZCROnTqF1NRUVFVV4YsvvsCdO3fUcagdEhAQAD8/P3z77bdIS0vDjRs3sHz5cgCPOsAtXboU0dHRkMlk+P777xEVFaXdBROiBRTWesTd3R3btm2Dp6cnPD09ATzaE1y1ahU8PDywZcsWBAcHY+fOnW1+/927d7Fz506sXbuWe6y8vLzV/G0A3J/Ly8s1dCT6p1evXggPD8f+/ftRVlaG77//HoaGhli9ejVcXV3x0ksv4cyZM08UWrENTxwcHDp0q5iZmRl+++032NjYID8/H6+88gp++eUXDBs2rNUoWG1ZtWoVDhw4gOjoaEyePLlVu9RVq1bhwoUL2L17N+RyOWbPnq3FlRKiHRTWeqQjYz1LSkowbdo0zJ07F6tXr9bYGglgaGjI1QAUFxcjNjYWZmZmePXVV+Hi4oKVK1fi1KlTyM7OxvDhw1FXVwcPDw+Vg7qpqQmLFy9GVVUVEhIS4OzsjBUrVuD06dOQSCTw9/dX7wGqYOHChSguLsa+ffueOHP28vLCyJEjsXHjRixYsEAnLt0T0tkorPWIqmM9S0tLMXHiRISGhuLrr79u9TUa66lZPXv2xIQJE/Dll1+ioKAA8fHxcHR0xFtvvYVJkyZhwIABqKurQ11dnUrPL5PJsGzZMpSVleHMmTOwtLRs9XVzc3OdqKq2sLDA3/72N5iamkIgEDzx9ZUrV0Imk9ElcNJtaf+nlGjUX431LCkpwYQJExAUFITo6OgnfnGPGjUKV65cabWvev78eXh6esLKykqzi+9mevTogdGjR+ONN96AoaEhpk+fjtDQUHz88cdwdnbGggUL8OOPP6K6urpdzyeXy7Fq1Srcv38f586da1WLoItKSkqwaNGiNifQlZSUwMfHByEhIVpYGSE6gCF6Yfz48cxrr73W6jEAjK2tLbN//37m9u3bzObNmxkDAwPmxo0bDMMwTHFxMePm5sZMmjSJKS4uZsrKyrj/WJWVlYyDgwOzZMkSJjc3l/nxxx+Z3r17M1999VVnHl63IZfLGR8fH2bt2rWMQqFgGIZhFAoFk56ezrz//vuMl5cXY2xszMyYMYPZu3cvU1xczNTW1jJ1dXWt/quqqmLmz5/PeHl5MeXl5Vo+qmeTSqVMXFwcY2BgwOTl5bX6Wk1NDZOTk8PY29szX3/9tZZWSIj2UVjriaeF9a5du5gpU6YwxsbGjLOzM3PkyBHu69HR0QyANv9rKSsrixkzZgxjbGzMODk5MVu3bu2MQ+q2bty4wQX145RKJZObm8t8+OGHjK+vL2NoaMhMmTKF+fLLLxmhUMjU1tYy1dXVzNKlSxl3d3empKSkk1f//AYNGsSYm5sz27dvf+Jry5YtY4yMjJh58+YxcrlcC6sjRDfQfdaEdFEMw+DOnTvcTO6srCyEhoZCLpejtLQUly9fxsCBA7W9TEKIGlBYE6IHGIbB/fv38d133+GLL77Azz//DG9vb20vixCiJhTWhBBCiI6janBCCCFEx1FYE0IIITqOwpoQQgjRcRTWhBBCiI6jsCaEEEJ0HIU1IYQQouMorAkhhBAdR2FNCCGE6DgKa0IIIUTHUVgTQgghOo7CmhBCCNFxFNaEEEKIjqOwJoS0SSKRwNHREZ988gn3WEpKCoyMjHDx4kVMnjwZYWFhYGcBSaVS9O/fH5s3b9bWkgnRWzR1ixDyVAkJCRAIBEhJSYGnpyf8/f3B5/OxY8cOlJSUwMfHB//85z/x2muvYd68eSgoKMCvv/6Knj17anvphOgVCmtCyDO9/PLLuHDhAoKDg5GTk4Pff/8dxsbGAICjR49i6dKl2LBhA3bu3ImMjAy4u7trecWE6B8Ka0LIMzU0NGDYsGEoKipCWloafHx8Wn194cKF+OGHH7Bnzx6sW7dOS6skRL/RnjUh5Jnu3buH0tJSKJVKCIXCVl+rr69HWloaevTogTt37mhngYR0A7SxRAh5KplMhsWLF+PFF1+Ep6cnVq1ahZycHNjb2wMA3nzzTRgYGCAxMREzZsxAeHg4XnjhBS2vmhD9Q5fBCSFP9fbbbyMmJgZZWVkwNTXF+PHjYWFhgdOnTyM+Ph6zZ89GamoqAgMD8Y9//APfffcdsrOzYWVlpe2lE6JXKKwJIW1KTk7GlClTkJSUhDFjxgAAhEIh/Pz88NZbb2HXrl147bXX8N577wEAmpubMWrUKAwePBhHjhzR5tIJ0TsU1oQQQoiOowIzQgghRMdRWBNCCCE6jsKaEEII0XEU1oQQQoiOo7AmhBBCdByFNSGEEKLjKKwJIYQQHUdhTQghhOg4CmtCCCFEx1FYE0IIITqOwpoQQgjRcRTWhBBCiI77f0y+dKE/mpfNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_surface_interactive(X, Y, Z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "540zch8v1UI2",
        "outputId": "60536dc6-13f7-4b30-8b95-5ce740ecd3c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Max Mitigated Value: {max(list(ddd_results.values()), key=lambda x: x[1])} \")\n",
        "print(f\"Min Error %: {min(list(ddd_results.values()), key=lambda x: x[2])[2]}\")\n",
        "print(f\"Min REM: {min(list(ddd_results.values()), key=lambda x: x[3])[3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9tHWxFsGL0s",
        "outputId": "78ca13cc-63df-4123-8c1b-0730cf58896c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Mitigated Value: [2.0, 1.2429999999999999, 0.37850000000000006, 0.984139365574623] \n",
            "Min Error %: 0.37850000000000006\n",
            "Min REM: 0.9721583652618138\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Min Error %: {min(list(ddd_results.values()), key=lambda x: x[2])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ec5GPeZTNDBx",
        "outputId": "b350815c-a26b-4492-cb8f-825bd458ea5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min Error %: [2.0, 1.2429999999999999, 0.37850000000000006, 0.984139365574623]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ZNE"
      ],
      "metadata": {
        "id": "43x4TZJ2yhsU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "zne_results = {}\n",
        "\n",
        "print(\"\\n================ ZNE ================\\n\")\n",
        "for i, (circuit_name, circuit) in enumerate(rb_circuits.items()):\n",
        "    for j, (fold_name, scale_noise) in enumerate(folds.items()):\n",
        "        print(f\"({i}, {j}) => Circuit: {circuit_name}, Fold: {fold_name}\")\n",
        "\n",
        "        executor = zne.mitigate_executor(\n",
        "            execute,\n",
        "            observable=observable,\n",
        "            scale_noise=scale_noise\n",
        "        )\n",
        "\n",
        "        result = executor(circuit)\n",
        "\n",
        "        ideal_value = raw.execute(circuit, partial(execute, noise_level=0, p0=0, p1=0), observable).real\n",
        "        noisy_value = raw.execute(circuit, execute, observable).real\n",
        "        mitiq_value = result.real\n",
        "        error = abs((ideal_value - mitiq_value) / ideal_value)\n",
        "        rem_value = abs((ideal_value - mitiq_value) / (ideal_value - noisy_value))\n",
        "        zne_results[(i, j)] = [ideal_value, mitiq_value, error, rem_value]\n",
        "\n",
        "        print(f\"Mitigated value: {mitiq_value:.3f}\")\n",
        "        print(f\"Error w Mitiq: {error:.3f}\")\n",
        "        print(f\"REM w  Mitiq: {rem_value:.3f}\")\n",
        "        print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nsthr9uJtBEd",
        "outputId": "dd724f41-24e6-4a9c-819a-a583ff260afc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================ ZNE ================\n",
            "\n",
            "(0, 0) => Circuit: rb5, Fold: local\n",
            "Mitigated value: 1.899\n",
            "Error w Mitiq: 0.050\n",
            "REM w  Mitiq: 0.132\n",
            "\n",
            "\n",
            "(0, 1) => Circuit: rb5, Fold: all\n",
            "Mitigated value: 0.631\n",
            "Error w Mitiq: 0.685\n",
            "REM w  Mitiq: 1.778\n",
            "\n",
            "\n",
            "(0, 2) => Circuit: rb5, Fold: global\n",
            "Mitigated value: 1.939\n",
            "Error w Mitiq: 0.031\n",
            "REM w  Mitiq: 0.078\n",
            "\n",
            "\n",
            "(1, 0) => Circuit: rb10, Fold: local\n",
            "Mitigated value: 1.710\n",
            "Error w Mitiq: 0.145\n",
            "REM w  Mitiq: 0.242\n",
            "\n",
            "\n",
            "(1, 1) => Circuit: rb10, Fold: all\n",
            "Mitigated value: 0.167\n",
            "Error w Mitiq: 0.916\n",
            "REM w  Mitiq: 1.598\n",
            "\n",
            "\n",
            "(1, 2) => Circuit: rb10, Fold: global\n",
            "Mitigated value: 1.470\n",
            "Error w Mitiq: 0.265\n",
            "REM w  Mitiq: 0.448\n",
            "\n",
            "\n",
            "(2, 0) => Circuit: rb20, Fold: local\n",
            "Mitigated value: 1.070\n",
            "Error w Mitiq: 0.465\n",
            "REM w  Mitiq: 0.594\n",
            "\n",
            "\n",
            "(2, 1) => Circuit: rb20, Fold: all\n",
            "Mitigated value: 0.124\n",
            "Error w Mitiq: 0.938\n",
            "REM w  Mitiq: 1.229\n",
            "\n",
            "\n",
            "(2, 2) => Circuit: rb20, Fold: global\n",
            "Mitigated value: 1.021\n",
            "Error w Mitiq: 0.489\n",
            "REM w  Mitiq: 0.637\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(0, len(rb_circuits), 1)\n",
        "Y = np.arange(0, len(folds), 1)\n",
        "X, Y = np.meshgrid(X, Y)\n",
        "Z = np.array([[zne_results[(i, j)][2] for i in X[0]] for j in Y[:, 0]])\n",
        "\n",
        "plot_surface(X, Y, Z, x_labels=list(rb_circuits.keys()), y_labels=list(folds.keys()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "NBypXDyStgG9",
        "outputId": "af101cc1-7962-40fb-bf81-7fdc73f412c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-80b21e089ec6>:22: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n",
            "<ipython-input-7-80b21e089ec6>:24: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_surface_interactive(X, Y, Z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "t4EyTiKl1yj6",
        "outputId": "aca83060-46e8-4624-dc9e-ffb7c1764355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Max Mitigated Value: {max(list(zne_results.values()), key=lambda x: x[1])} \")\n",
        "print(f\"Min Error %: {min(list(zne_results.values()), key=lambda x: x[2])[2]}\")\n",
        "print(f\"Min REM: {min(list(zne_results.values()), key=lambda x: x[3])[3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GyZh8-2G6IK",
        "outputId": "640ba169-5567-46d5-ae38-19290816a8c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Mitigated Value: [2.0, 1.9386000000000019, 0.03069999999999906, 0.07780030410542083] \n",
            "Min Error %: 0.03069999999999906\n",
            "Min REM: 0.07780030410542083\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Min Error %: {min(list(zne_results.values()), key=lambda x: x[2])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDk5PlOLM8_f",
        "outputId": "3eab47f0-8618-42d3-b97d-ba46d7d49036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min Error %: [2.0, 1.9386000000000019, 0.03069999999999906, 0.07780030410542083]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### REM"
      ],
      "metadata": {
        "id": "Kx9ofwhBykxY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mitiq import rem\n",
        "\n",
        "rem_results = {}\n",
        "\n",
        "print(\"\\n================ REM ================\\n\")\n",
        "for i, (circuit_name, circuit) in enumerate(rb_circuits.items()):\n",
        "    for j, prob in enumerate(probs):\n",
        "        print(f\"({i}, {j}) => Circuit: {circuit_name}, Probability: {prob}\")\n",
        "\n",
        "        p0 = p1 = prob\n",
        "        icm = rem.generate_inverse_confusion_matrix(2, p0, p1)\n",
        "        executor = rem.mitigate_executor(\n",
        "            execute,\n",
        "            inverse_confusion_matrix=icm\n",
        "        )\n",
        "\n",
        "        result = observable.expectation(circuit, executor)\n",
        "\n",
        "        ideal_value = raw.execute(circuit, partial(execute, noise_level=0, p0=0, p1=0), observable).real\n",
        "        noisy_value = raw.execute(circuit, execute, observable).real\n",
        "        mitiq_value = result.real\n",
        "        error = abs((ideal_value - mitiq_value) / ideal_value)\n",
        "        rem_value = abs((ideal_value - mitiq_value) / (ideal_value - noisy_value))\n",
        "        rem_results[(i, j)] = [ideal_value, mitiq_value, error, rem_value]\n",
        "\n",
        "        print(f\"Mitigated value: {mitiq_value:.3f}\")\n",
        "        print(f\"Error w Mitiq: {error:.3f}\")\n",
        "        print(f\"REM w  Mitiq: {rem_value:.3f}\")\n",
        "        print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUzbVKDUyJph",
        "outputId": "0bc28ca1-6a78-4b12-8468-70f0a3ad2a12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================ REM ================\n",
            "\n",
            "(0, 0) => Circuit: rb5, Probability: 0.1\n",
            "Mitigated value: 1.524\n",
            "Error w Mitiq: 0.238\n",
            "REM w  Mitiq: 0.609\n",
            "\n",
            "\n",
            "(0, 1) => Circuit: rb5, Probability: 0.01\n",
            "Mitigated value: 1.276\n",
            "Error w Mitiq: 0.362\n",
            "REM w  Mitiq: 0.915\n",
            "\n",
            "\n",
            "(0, 2) => Circuit: rb5, Probability: 0.05\n",
            "Mitigated value: 1.375\n",
            "Error w Mitiq: 0.312\n",
            "REM w  Mitiq: 0.802\n",
            "\n",
            "\n",
            "(0, 3) => Circuit: rb5, Probability: 0.001\n",
            "Mitigated value: 1.212\n",
            "Error w Mitiq: 0.394\n",
            "REM w  Mitiq: 1.025\n",
            "\n",
            "\n",
            "(0, 4) => Circuit: rb5, Probability: 0.005\n",
            "Mitigated value: 1.234\n",
            "Error w Mitiq: 0.383\n",
            "REM w  Mitiq: 0.981\n",
            "\n",
            "\n",
            "(1, 0) => Circuit: rb10, Probability: 0.1\n",
            "Mitigated value: 1.029\n",
            "Error w Mitiq: 0.486\n",
            "REM w  Mitiq: 0.803\n",
            "\n",
            "\n",
            "(1, 1) => Circuit: rb10, Probability: 0.01\n",
            "Mitigated value: 0.850\n",
            "Error w Mitiq: 0.575\n",
            "REM w  Mitiq: 0.971\n",
            "\n",
            "\n",
            "(1, 2) => Circuit: rb10, Probability: 0.05\n",
            "Mitigated value: 0.894\n",
            "Error w Mitiq: 0.553\n",
            "REM w  Mitiq: 0.943\n",
            "\n",
            "\n",
            "(1, 3) => Circuit: rb10, Probability: 0.001\n",
            "Mitigated value: 0.834\n",
            "Error w Mitiq: 0.583\n",
            "REM w  Mitiq: 1.007\n",
            "\n",
            "\n",
            "(1, 4) => Circuit: rb10, Probability: 0.005\n",
            "Mitigated value: 0.796\n",
            "Error w Mitiq: 0.602\n",
            "REM w  Mitiq: 1.009\n",
            "\n",
            "\n",
            "(2, 0) => Circuit: rb20, Probability: 0.1\n",
            "Mitigated value: 0.588\n",
            "Error w Mitiq: 0.706\n",
            "REM w  Mitiq: 0.933\n",
            "\n",
            "\n",
            "(2, 1) => Circuit: rb20, Probability: 0.01\n",
            "Mitigated value: 0.516\n",
            "Error w Mitiq: 0.742\n",
            "REM w  Mitiq: 0.985\n",
            "\n",
            "\n",
            "(2, 2) => Circuit: rb20, Probability: 0.05\n",
            "Mitigated value: 0.538\n",
            "Error w Mitiq: 0.731\n",
            "REM w  Mitiq: 0.962\n",
            "\n",
            "\n",
            "(2, 3) => Circuit: rb20, Probability: 0.001\n",
            "Mitigated value: 0.499\n",
            "Error w Mitiq: 0.750\n",
            "REM w  Mitiq: 0.984\n",
            "\n",
            "\n",
            "(2, 4) => Circuit: rb20, Probability: 0.005\n",
            "Mitigated value: 0.498\n",
            "Error w Mitiq: 0.751\n",
            "REM w  Mitiq: 0.979\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(0, len(rb_circuits), 1)\n",
        "Y = np.arange(0, len(probs), 1)\n",
        "X, Y = np.meshgrid(X, Y)\n",
        "Z = np.array([[rem_results[(i, j)][2] for i in X[0]] for j in Y[:, 0]])\n",
        "\n",
        "plot_surface(X, Y, Z, x_labels=list(rb_circuits.keys()), y_labels=probs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "LRcCmJf7yqUu",
        "outputId": "37b76c9c-74da-4e10-f8dc-2f18b7728dc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-80b21e089ec6>:22: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n",
            "<ipython-input-7-80b21e089ec6>:24: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_surface_interactive(X, Y, Z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "LOFew5M32YYB",
        "outputId": "0cee5172-f9fc-446e-dea5-34ad716d71c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Max Mitigated Value: {max(list(rem_results.values()), key=lambda x: x[1])} \")\n",
        "print(f\"Min Error %: {min(list(rem_results.values()), key=lambda x: x[2])[2]}\")\n",
        "print(f\"Min REM: {min(list(rem_results.values()), key=lambda x: x[3])[3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbyPkv65HA_P",
        "outputId": "2d865d68-2c61-421d-f9ab-a90cf586916e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Mitigated Value: [2.0, 1.524, 0.238, 0.6088513686364798] \n",
            "Min Error %: 0.238\n",
            "Min REM: 0.6088513686364798\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Min Error %: {min(list(rem_results.values()), key=lambda x: x[2])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKuplmEzNGJk",
        "outputId": "a2a06a3d-7e36-4163-ca90-a8b444ffa88c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min Error %: [2.0, 1.524, 0.238, 0.6088513686364798]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### REM + ZNE (Local)"
      ],
      "metadata": {
        "id": "aWBujYuX2ypZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rem_zne_local_results = {}\n",
        "\n",
        "print(\"\\n================ REM + ZNE (Local) ================\\n\")\n",
        "for i, (circuit_name, circuit) in enumerate(rb_circuits.items()):\n",
        "    for j, prob in enumerate(probs):\n",
        "        print(f\"({i}, {j}) => Circuit: {circuit_name}, Probability: {prob}\")\n",
        "\n",
        "        p0 = p1 = prob\n",
        "        icm = rem.generate_inverse_confusion_matrix(2, p0, p1)\n",
        "        executor = rem.mitigate_executor(\n",
        "            execute,\n",
        "            inverse_confusion_matrix=icm\n",
        "        )\n",
        "        # result = observable.expectation(circuit, executor)\n",
        "\n",
        "        executor = zne.mitigate_executor(\n",
        "            executor,\n",
        "            observable=observable,\n",
        "            scale_noise=folds[\"local\"]\n",
        "        )\n",
        "\n",
        "        result = executor(circuit)\n",
        "\n",
        "        ideal_value = raw.execute(circuit, partial(execute, noise_level=0, p0=0, p1=0), observable).real\n",
        "        noisy_value = raw.execute(circuit, execute, observable).real\n",
        "        mitiq_value = result.real\n",
        "        error = abs((ideal_value - mitiq_value) / ideal_value)\n",
        "        rem_value = abs((ideal_value - mitiq_value) / (ideal_value - noisy_value))\n",
        "        rem_zne_local_results[(i, j)] = [ideal_value, mitiq_value, error, rem_value]\n",
        "\n",
        "        print(f\"Mitigated value: {mitiq_value:.3f}\")\n",
        "        print(f\"Error w Mitiq: {error:.3f}\")\n",
        "        print(f\"REM w  Mitiq: {rem_value:.3f}\")\n",
        "        print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAwAHBgA23Eq",
        "outputId": "ffec932f-0f8b-48e6-a3cf-39d687242932"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================ REM + ZNE (Local) ================\n",
            "\n",
            "(0, 0) => Circuit: rb5, Probability: 0.1\n",
            "Mitigated value: 2.297\n",
            "Error w Mitiq: 0.149\n",
            "REM w  Mitiq: 0.383\n",
            "\n",
            "\n",
            "(0, 1) => Circuit: rb5, Probability: 0.01\n",
            "Mitigated value: 1.688\n",
            "Error w Mitiq: 0.156\n",
            "REM w  Mitiq: 0.396\n",
            "\n",
            "\n",
            "(0, 2) => Circuit: rb5, Probability: 0.05\n",
            "Mitigated value: 2.129\n",
            "Error w Mitiq: 0.065\n",
            "REM w  Mitiq: 0.168\n",
            "\n",
            "\n",
            "(0, 3) => Circuit: rb5, Probability: 0.001\n",
            "Mitigated value: 1.877\n",
            "Error w Mitiq: 0.062\n",
            "REM w  Mitiq: 0.161\n",
            "\n",
            "\n",
            "(0, 4) => Circuit: rb5, Probability: 0.005\n",
            "Mitigated value: 1.680\n",
            "Error w Mitiq: 0.160\n",
            "REM w  Mitiq: 0.408\n",
            "\n",
            "\n",
            "(1, 0) => Circuit: rb10, Probability: 0.1\n",
            "Mitigated value: 2.070\n",
            "Error w Mitiq: 0.035\n",
            "REM w  Mitiq: 0.059\n",
            "\n",
            "\n",
            "(1, 1) => Circuit: rb10, Probability: 0.01\n",
            "Mitigated value: 1.759\n",
            "Error w Mitiq: 0.120\n",
            "REM w  Mitiq: 0.209\n",
            "\n",
            "\n",
            "(1, 2) => Circuit: rb10, Probability: 0.05\n",
            "Mitigated value: 1.826\n",
            "Error w Mitiq: 0.087\n",
            "REM w  Mitiq: 0.148\n",
            "\n",
            "\n",
            "(1, 3) => Circuit: rb10, Probability: 0.001\n",
            "Mitigated value: 1.634\n",
            "Error w Mitiq: 0.183\n",
            "REM w  Mitiq: 0.317\n",
            "\n",
            "\n",
            "(1, 4) => Circuit: rb10, Probability: 0.005\n",
            "Mitigated value: 1.641\n",
            "Error w Mitiq: 0.180\n",
            "REM w  Mitiq: 0.308\n",
            "\n",
            "\n",
            "(2, 0) => Circuit: rb20, Probability: 0.1\n",
            "Mitigated value: 1.342\n",
            "Error w Mitiq: 0.329\n",
            "REM w  Mitiq: 0.439\n",
            "\n",
            "\n",
            "(2, 1) => Circuit: rb20, Probability: 0.01\n",
            "Mitigated value: 1.281\n",
            "Error w Mitiq: 0.359\n",
            "REM w  Mitiq: 0.476\n",
            "\n",
            "\n",
            "(2, 2) => Circuit: rb20, Probability: 0.05\n",
            "Mitigated value: 1.200\n",
            "Error w Mitiq: 0.400\n",
            "REM w  Mitiq: 0.519\n",
            "\n",
            "\n",
            "(2, 3) => Circuit: rb20, Probability: 0.001\n",
            "Mitigated value: 1.141\n",
            "Error w Mitiq: 0.430\n",
            "REM w  Mitiq: 0.561\n",
            "\n",
            "\n",
            "(2, 4) => Circuit: rb20, Probability: 0.005\n",
            "Mitigated value: 1.106\n",
            "Error w Mitiq: 0.447\n",
            "REM w  Mitiq: 0.577\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(0, len(rb_circuits), 1)\n",
        "Y = np.arange(0, len(probs), 1)\n",
        "X, Y = np.meshgrid(X, Y)\n",
        "Z = np.array([[rem_zne_local_results[(i, j)][2] for i in X[0]] for j in Y[:, 0]])\n",
        "\n",
        "plot_surface(X, Y, Z, x_labels=list(rb_circuits.keys()), y_labels=probs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "1IVGBm4z3F0A",
        "outputId": "7ade056f-8741-492f-c9b8-94849a13d796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-80b21e089ec6>:22: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n",
            "<ipython-input-7-80b21e089ec6>:24: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_surface_interactive(X, Y, Z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "LZ94ou883F4b",
        "outputId": "7536a242-c6f3-48a1-cd30-b9760f79bc15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Max Mitigated Value: {max(list(rem_zne_local_results.values()), key=lambda x: x[1])} \")\n",
        "print(f\"Min Error %: {min(list(rem_zne_local_results.values()), key=lambda x: x[2])[2]}\")\n",
        "print(f\"Min REM: {min(list(rem_zne_local_results.values()), key=lambda x: x[3])[3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rDWLauRHIXO",
        "outputId": "33e4de0f-d87a-4005-874b-fd29103d5ee6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Mitigated Value: [2.0, 2.297200000000002, 0.14860000000000095, 0.3825952626158623] \n",
            "Min Error %: 0.035000000000001474\n",
            "Min REM: 0.05887300252313116\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Min Error %: {min(list(rem_zne_local_results.values()), key=lambda x: x[2])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljlxgsZbMl32",
        "outputId": "7c7e713b-0316-4ed0-d667-e52933186dce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min Error %: [2.0, 2.070000000000003, 0.035000000000001474, 0.05887300252313116]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### REM + ZNE (Global)"
      ],
      "metadata": {
        "id": "0lAa96zj5sCU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rem_zne_global_results = {}\n",
        "\n",
        "print(\"\\n================ REM + ZNE (Global) ================\\n\")\n",
        "for i, (circuit_name, circuit) in enumerate(rb_circuits.items()):\n",
        "    for j, prob in enumerate(probs):\n",
        "        print(f\"({i}, {j}) => Circuit: {circuit_name}, Probability: {prob}\")\n",
        "\n",
        "        p0 = p1 = prob\n",
        "        icm = rem.generate_inverse_confusion_matrix(2, p0, p1)\n",
        "        executor = rem.mitigate_executor(\n",
        "            execute,\n",
        "            inverse_confusion_matrix=icm\n",
        "        )\n",
        "        # result = observable.expectation(circuit, executor)\n",
        "\n",
        "        executor = zne.mitigate_executor(\n",
        "            executor,\n",
        "            observable=observable,\n",
        "            scale_noise=folds[\"global\"]\n",
        "        )\n",
        "\n",
        "        result = executor(circuit)\n",
        "\n",
        "        ideal_value = raw.execute(circuit, partial(execute, noise_level=0, p0=0, p1=0), observable).real\n",
        "        noisy_value = raw.execute(circuit, execute, observable).real\n",
        "        mitiq_value = result.real\n",
        "        error = abs((ideal_value - mitiq_value) / ideal_value)\n",
        "        rem_value = abs((ideal_value - mitiq_value) / (ideal_value - noisy_value))\n",
        "        rem_zne_global_results[(i, j)] = [ideal_value, mitiq_value, error, rem_value]\n",
        "\n",
        "        print(f\"Mitigated value: {mitiq_value:.3f}\")\n",
        "        print(f\"Error w Mitiq: {error:.3f}\")\n",
        "        print(f\"REM w  Mitiq: {rem_value:.3f}\")\n",
        "        print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgGpMr2-5vUR",
        "outputId": "45578498-33e7-49a7-bf43-80b6605333dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================ REM + ZNE (Global) ================\n",
            "\n",
            "(0, 0) => Circuit: rb5, Probability: 0.1\n",
            "Mitigated value: 2.189\n",
            "Error w Mitiq: 0.095\n",
            "REM w  Mitiq: 0.252\n",
            "\n",
            "\n",
            "(0, 1) => Circuit: rb5, Probability: 0.01\n",
            "Mitigated value: 1.899\n",
            "Error w Mitiq: 0.051\n",
            "REM w  Mitiq: 0.134\n",
            "\n",
            "\n",
            "(0, 2) => Circuit: rb5, Probability: 0.05\n",
            "Mitigated value: 1.976\n",
            "Error w Mitiq: 0.012\n",
            "REM w  Mitiq: 0.031\n",
            "\n",
            "\n",
            "(0, 3) => Circuit: rb5, Probability: 0.001\n",
            "Mitigated value: 1.858\n",
            "Error w Mitiq: 0.071\n",
            "REM w  Mitiq: 0.184\n",
            "\n",
            "\n",
            "(0, 4) => Circuit: rb5, Probability: 0.005\n",
            "Mitigated value: 1.702\n",
            "Error w Mitiq: 0.149\n",
            "REM w  Mitiq: 0.382\n",
            "\n",
            "\n",
            "(1, 0) => Circuit: rb10, Probability: 0.1\n",
            "Mitigated value: 2.025\n",
            "Error w Mitiq: 0.012\n",
            "REM w  Mitiq: 0.021\n",
            "\n",
            "\n",
            "(1, 1) => Circuit: rb10, Probability: 0.01\n",
            "Mitigated value: 1.563\n",
            "Error w Mitiq: 0.218\n",
            "REM w  Mitiq: 0.368\n",
            "\n",
            "\n",
            "(1, 2) => Circuit: rb10, Probability: 0.05\n",
            "Mitigated value: 1.566\n",
            "Error w Mitiq: 0.217\n",
            "REM w  Mitiq: 0.367\n",
            "\n",
            "\n",
            "(1, 3) => Circuit: rb10, Probability: 0.001\n",
            "Mitigated value: 1.372\n",
            "Error w Mitiq: 0.314\n",
            "REM w  Mitiq: 0.528\n",
            "\n",
            "\n",
            "(1, 4) => Circuit: rb10, Probability: 0.005\n",
            "Mitigated value: 1.442\n",
            "Error w Mitiq: 0.279\n",
            "REM w  Mitiq: 0.478\n",
            "\n",
            "\n",
            "(2, 0) => Circuit: rb20, Probability: 0.1\n",
            "Mitigated value: 1.359\n",
            "Error w Mitiq: 0.320\n",
            "REM w  Mitiq: 0.415\n",
            "\n",
            "\n",
            "(2, 1) => Circuit: rb20, Probability: 0.01\n",
            "Mitigated value: 0.980\n",
            "Error w Mitiq: 0.510\n",
            "REM w  Mitiq: 0.659\n",
            "\n",
            "\n",
            "(2, 2) => Circuit: rb20, Probability: 0.05\n",
            "Mitigated value: 1.191\n",
            "Error w Mitiq: 0.405\n",
            "REM w  Mitiq: 0.539\n",
            "\n",
            "\n",
            "(2, 3) => Circuit: rb20, Probability: 0.001\n",
            "Mitigated value: 1.025\n",
            "Error w Mitiq: 0.487\n",
            "REM w  Mitiq: 0.648\n",
            "\n",
            "\n",
            "(2, 4) => Circuit: rb20, Probability: 0.005\n",
            "Mitigated value: 1.126\n",
            "Error w Mitiq: 0.437\n",
            "REM w  Mitiq: 0.566\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(0, len(rb_circuits), 1)\n",
        "Y = np.arange(0, len(probs), 1)\n",
        "X, Y = np.meshgrid(X, Y)\n",
        "Z = np.array([[rem_zne_global_results[(i, j)][2] for i in X[0]] for j in Y[:, 0]])\n",
        "\n",
        "plot_surface(X, Y, Z, x_labels=list(rb_circuits.keys()), y_labels=probs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "5fE97zem5wds",
        "outputId": "0fdacf9d-7398-42f5-c325-51426d1caf2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-80b21e089ec6>:22: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n",
            "<ipython-input-7-80b21e089ec6>:24: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_surface_interactive(X, Y, Z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "g5Un6pQR5wmZ",
        "outputId": "5bd5f6ff-9347-429b-b4a3-8bebddf80088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Max Mitigated Value: {max(list(rem_zne_global_results.values()), key=lambda x: x[1])} \")\n",
        "print(f\"Min Error %: {min(list(rem_zne_global_results.values()), key=lambda x: x[2])[2]}\")\n",
        "print(f\"Min REM: {min(list(rem_zne_global_results.values()), key=lambda x: x[3])[3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72pUwuxpHQDW",
        "outputId": "e48355a9-7b1f-4cbe-93f2-7d2a367a0374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Mitigated Value: [2.0, 2.1894000000000022, 0.09470000000000112, 0.2515272244355939] \n",
            "Min Error %: 0.011899999999999467\n",
            "Min REM: 0.02072800808897948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Min Error %: {min(list(rem_zne_global_results.values()), key=lambda x: x[2])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVRTZu9MNVFS",
        "outputId": "e41f283b-98d0-45f6-abef-638063fbf623"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min Error %: [2.0, 1.976200000000001, 0.011899999999999467, 0.030781169167096393]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### REM + DDD"
      ],
      "metadata": {
        "id": "Z7B6_VIb7nGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rem_ddd_results = {}\n",
        "\n",
        "print(\"\\n================ REM + DDD ================\\n\")\n",
        "for i, (circuit_name, circuit) in enumerate(rb_circuits.items()):\n",
        "    for j, prob in enumerate(probs):\n",
        "        print(f\"({i}, {j}) => Circuit: {circuit_name}, Probability: {prob}\")\n",
        "\n",
        "        p0 = p1 = prob\n",
        "        icm = rem.generate_inverse_confusion_matrix(2, p0, p1)\n",
        "        executor = rem.mitigate_executor(\n",
        "            execute,\n",
        "            inverse_confusion_matrix=icm\n",
        "        )\n",
        "        # result = observable.expectation(circuit, executor)\n",
        "\n",
        "        executor = ddd.mitigate_executor(\n",
        "            executor,\n",
        "            observable=observable,\n",
        "            rule=xx\n",
        "        )\n",
        "\n",
        "        result = executor(circuit)\n",
        "\n",
        "        ideal_value = raw.execute(circuit, partial(execute, noise_level=0, p0=0, p1=0), observable).real\n",
        "        noisy_value = raw.execute(circuit, execute, observable).real\n",
        "        mitiq_value = result.real\n",
        "        error = abs((ideal_value - mitiq_value) / ideal_value)\n",
        "        rem_value = abs((ideal_value - mitiq_value) / (ideal_value - noisy_value))\n",
        "        rem_ddd_results[(i, j)] = [ideal_value, mitiq_value, error, rem_value]\n",
        "\n",
        "        print(f\"Mitigated value: {mitiq_value:.3f}\")\n",
        "        print(f\"Error w Mitiq: {error:.3f}\")\n",
        "        print(f\"REM w  Mitiq: {rem_value:.3f}\")\n",
        "        print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfxwifJ-7pV1",
        "outputId": "5b8f60be-5b8e-4614-e788-61a7c706add5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================ REM + DDD ================\n",
            "\n",
            "(0, 0) => Circuit: rb5, Probability: 0.1\n",
            "Mitigated value: 1.552\n",
            "Error w Mitiq: 0.224\n",
            "REM w  Mitiq: 0.590\n",
            "\n",
            "\n",
            "(0, 1) => Circuit: rb5, Probability: 0.01\n",
            "Mitigated value: 1.255\n",
            "Error w Mitiq: 0.373\n",
            "REM w  Mitiq: 0.961\n",
            "\n",
            "\n",
            "(0, 2) => Circuit: rb5, Probability: 0.05\n",
            "Mitigated value: 1.388\n",
            "Error w Mitiq: 0.306\n",
            "REM w  Mitiq: 0.789\n",
            "\n",
            "\n",
            "(0, 3) => Circuit: rb5, Probability: 0.001\n",
            "Mitigated value: 1.220\n",
            "Error w Mitiq: 0.390\n",
            "REM w  Mitiq: 0.991\n",
            "\n",
            "\n",
            "(0, 4) => Circuit: rb5, Probability: 0.005\n",
            "Mitigated value: 1.259\n",
            "Error w Mitiq: 0.371\n",
            "REM w  Mitiq: 0.963\n",
            "\n",
            "\n",
            "(1, 0) => Circuit: rb10, Probability: 0.1\n",
            "Mitigated value: 1.041\n",
            "Error w Mitiq: 0.480\n",
            "REM w  Mitiq: 0.811\n",
            "\n",
            "\n",
            "(1, 1) => Circuit: rb10, Probability: 0.01\n",
            "Mitigated value: 0.862\n",
            "Error w Mitiq: 0.569\n",
            "REM w  Mitiq: 0.955\n",
            "\n",
            "\n",
            "(1, 2) => Circuit: rb10, Probability: 0.05\n",
            "Mitigated value: 0.903\n",
            "Error w Mitiq: 0.548\n",
            "REM w  Mitiq: 0.950\n",
            "\n",
            "\n",
            "(1, 3) => Circuit: rb10, Probability: 0.001\n",
            "Mitigated value: 0.885\n",
            "Error w Mitiq: 0.557\n",
            "REM w  Mitiq: 0.968\n",
            "\n",
            "\n",
            "(1, 4) => Circuit: rb10, Probability: 0.005\n",
            "Mitigated value: 0.789\n",
            "Error w Mitiq: 0.606\n",
            "REM w  Mitiq: 1.052\n",
            "\n",
            "\n",
            "(2, 0) => Circuit: rb20, Probability: 0.1\n",
            "Mitigated value: 0.575\n",
            "Error w Mitiq: 0.713\n",
            "REM w  Mitiq: 0.928\n",
            "\n",
            "\n",
            "(2, 1) => Circuit: rb20, Probability: 0.01\n",
            "Mitigated value: 0.425\n",
            "Error w Mitiq: 0.787\n",
            "REM w  Mitiq: 1.041\n",
            "\n",
            "\n",
            "(2, 2) => Circuit: rb20, Probability: 0.05\n",
            "Mitigated value: 0.517\n",
            "Error w Mitiq: 0.742\n",
            "REM w  Mitiq: 0.969\n",
            "\n",
            "\n",
            "(2, 3) => Circuit: rb20, Probability: 0.001\n",
            "Mitigated value: 0.444\n",
            "Error w Mitiq: 0.778\n",
            "REM w  Mitiq: 1.008\n",
            "\n",
            "\n",
            "(2, 4) => Circuit: rb20, Probability: 0.005\n",
            "Mitigated value: 0.472\n",
            "Error w Mitiq: 0.764\n",
            "REM w  Mitiq: 1.008\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.arange(0, len(rb_circuits), 1)\n",
        "Y = np.arange(0, len(probs), 1)\n",
        "X, Y = np.meshgrid(X, Y)\n",
        "Z = np.array([[rem_ddd_results[(i, j)][2] for i in X[0]] for j in Y[:, 0]])\n",
        "\n",
        "plot_surface(X, Y, Z, x_labels=list(rb_circuits.keys()), y_labels=probs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "QZEbIF8X7151",
        "outputId": "25f5c252-198f-4b52-e0a9-1d54033ae79a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-80b21e089ec6>:22: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n",
            "<ipython-input-7-80b21e089ec6>:24: UserWarning:\n",
            "\n",
            "FixedFormatter should only be used together with FixedLocator\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_surface_interactive(X, Y, Z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "GeGf76_N718e",
        "outputId": "f8f19788-2434-41ed-85d7-e02486cbc04c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Max Mitigated Value: {max(list(rem_ddd_results.values()), key=lambda x: x[1])} \")\n",
        "print(f\"Min Error %: {min(list(rem_ddd_results.values()), key=lambda x: x[2])[2]}\")\n",
        "print(f\"Min REM: {min(list(rem_ddd_results.values()), key=lambda x: x[3])[3]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDa3X2k7HoHX",
        "outputId": "0b4d145e-016e-4b5b-e851-197a0e168d4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Mitigated Value: [2.0, 1.552, 0.22399999999999998, 0.5896288496972887] \n",
            "Min Error %: 0.22399999999999998\n",
            "Min REM: 0.5896288496972887\n"
          ]
        }
      ]
    }
  ]
}