kap10-embeddings.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DJCordhose/buch-machine-learning-notebooks/blob/master/kap10-embeddings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "912d7d82",
      "metadata": {
        "id": "912d7d82"
      },
      "source": [
        "# Embeddings\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "BhKtZIx7BC8S",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BhKtZIx7BC8S",
        "outputId": "57782a45-dde0-4c45-fa92-8ea305eddc4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import sys\n",
        "IN_COLAB = 'google.colab' in sys.modules # True if running in Google Colab.\n",
        "IN_COLAB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "NnkgKmBYxFhH"
      },
      "outputs": [],
      "source": [
        "if IN_COLAB:\n",
        "    !pip install -q openai\n",
        "    !pip install -q umap-learn"
      ],
      "id": "NnkgKmBYxFhH"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNr6IVnwxFhI"
      },
      "source": [
        "# OpenAI Client"
      ],
      "id": "iNr6IVnwxFhI"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "LIhBdwOXxFhI"
      },
      "outputs": [],
      "source": [
        "# TODO: hier https://platform.openai.com/api-keys den eigenen Key erzeugen und eintragen\n",
        "OPENAI_API_KEY = '...'\n",
        "\n",
        "assert OPENAI_API_KEY != '...', \"Bitte tragen Sie Ihren OpenAI API Key ein!\""
      ],
      "id": "LIhBdwOXxFhI"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "IS-E6uzlxFhI"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "client = OpenAI(\n",
        "  api_key=OPENAI_API_KEY\n",
        ")\n",
        "models = client.models.list()"
      ],
      "id": "IS-E6uzlxFhI"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvxM3JP6xFhJ"
      },
      "source": [
        "# Embeddings\n",
        "\n",
        "Einführung\n",
        "* https://qdrant.tech/documentation/concepts/search/\n",
        "* https://openai.com/blog/new-embedding-models-and-api-updates\n",
        "\n",
        "Links\n",
        "* https://openai.com/blog/introducing-text-and-code-embeddings\n",
        "* https://platform.openai.com/docs/guides/embeddings\n",
        "* https://platform.openai.com/docs/quickstart?context=python&lang=Embeddings\n",
        "* https://platform.openai.com/docs/guides/embeddings/what-are-embeddings\n",
        "\n"
      ],
      "id": "bvxM3JP6xFhJ"
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "_4GmcIEjxFhK",
        "outputId": "0706d14e-6fee-468e-e9e3-79638fe6ad6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 1536)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "texts=[\n",
        "    \"Das Essen war toll und die Bedienung war super!\",\n",
        "    # \"Uns hat das Restaurant sehr gefallen, sowohl die Speisen als auch der Service!\",\n",
        "    # \"Was für ein übles Erlebnis, meine Nudeln waren viel zu weich und es hat ewig gedauert, bis wir unser bedient wurden.\",\n",
        "    \"Top Restaurant\",\n",
        "    # \"Elendiger Fraß\",\n",
        "    \"Ich hatte nach dem Essen Salmonellen\",\n",
        "    # \"Es war alles super nachdem ich mir den Magen hab auspumpen lassen\",\n",
        "    \"Ach, was für ein schöner Abend (not)\",\n",
        "    \"Hunde mögen gern Knochen.\",\n",
        "    \"Katzen lieben Wollknäuel\",\n",
        "    \"Wie hat es euch gefallen?\",\n",
        "    \"Was mögen Tiere?\"\n",
        "]\n",
        "\n",
        "response = client.embeddings.create(\n",
        "    # https://openai.com/blog/new-embedding-models-and-api-updates\n",
        "  # model=\"text-embedding-ada-002\",  # Klassiker\n",
        "  model=\"text-embedding-3-small\",  # Das Neue in klein\n",
        "  # model=\"text-embedding-3-large\",  # Das Neue in groß\n",
        "  input=texts\n",
        ")\n",
        "\n",
        "embeddings = np.array([embedding.embedding for embedding in response.data])\n",
        "embeddings.shape"
      ],
      "id": "_4GmcIEjxFhK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ähnlichkeit und Nähe von Embeddings\n",
        "\n",
        "https://qdrant.tech/documentation/concepts/search/"
      ],
      "metadata": {
        "id": "d0IvBtq47Zpz"
      },
      "id": "d0IvBtq47Zpz"
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "jRHIkChNxFhK",
        "outputId": "c44951a8-bbfb-481c-a30c-e5b35fe23732",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.  , 0.41, 0.45, 0.41, 0.3 , 0.23, 0.42, 0.29],\n",
              "       [0.41, 1.  , 0.26, 0.24, 0.14, 0.08, 0.15, 0.14],\n",
              "       [0.45, 0.26, 1.  , 0.33, 0.27, 0.2 , 0.31, 0.22],\n",
              "       [0.41, 0.24, 0.33, 1.  , 0.24, 0.27, 0.44, 0.3 ],\n",
              "       [0.3 , 0.14, 0.27, 0.24, 1.  , 0.57, 0.28, 0.5 ],\n",
              "       [0.23, 0.08, 0.2 , 0.27, 0.57, 1.  , 0.3 , 0.48],\n",
              "       [0.42, 0.15, 0.31, 0.44, 0.28, 0.3 , 1.  , 0.44],\n",
              "       [0.29, 0.14, 0.22, 0.3 , 0.5 , 0.48, 0.44, 1.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "cos_sim_matrix = cosine_similarity(embeddings)\n",
        "cos_sim_matrix = np.round(cos_sim_matrix, 2)\n",
        "cos_sim_matrix"
      ],
      "id": "jRHIkChNxFhK"
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "fATk5G8yxFhK",
        "outputId": "d90e2555-99a9-4f56-8a45-3b0d642c8d84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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lwoULsnHjRqlQoYKEhIQUeDzy4jUzMxN/f385d+6chIaGikqlkt27dyv1Fi9eLL///rukpqbKvn37pHbt2jJy5EhlfVxcnBgaGsrIkSPlww8/lJo1a8r8+fPl1KlTcvr0abGzsxMAMmPGjEL7eN7ChQvFyspKNm7cKGfOnJFhw4aJgYGBmJubK3U++eQTqVOnjuzcuVNSUlIkODhYDA0NJSIiQmP/WrZsKQcOHJDjx4+Ls7OzeHl5SceOHeX48eOyf/9+sba2lgULFhQay/Pn9XlZWVliYWEhU6ZMkfPnz8uZM2ckJCRELl68KCIi9erVkwEDBkhiYqKcO3dOfvrpJ4mPjxcRkfPnz4upqaksXrxYzp07J9HR0dK4cWMZMmSI0r6jo6NUqFBBli9fLsnJyRIYGCg6Ojpy9uzZQuNRq9UyYcIEefTokVL++PFjjXovOq/BwcFiYWGhLPv5+YlarZa3335bTp8+LVu3bhUDAwPx9fWVsWPHytmzZ2XNmjUCQA4fPiwiIjk5OdKoUSNp3bq1HDt2TA4fPixNmjQRLy+vfO2+9dZbcvLkSdm/f7/Y2dnJzJkzReTpd7VOnTrFOtd55+j52Pfv3698v1NSUmT37t3i5OQk/v7+Sp0XfV+JiIiItFWJE+C8ROtZ+/btEwBKkiUiEhQUJPXr1xcTExOpWrWqjBw5Uu7du6esT0tLk65du4qlpaWYmJhI3bp1JSwsrNC+vby8ZPz48YWuf/z4sYwePVrs7OzE0NBQqlWrJp9++qmIPE0W/fz8xMHBQQwMDMTe3l7Gjh2rbPvo0SOZPHmyVK5cWUxMTKR58+YSHh6urM/7A3Tnzp1Sp04dMTU1FV9fX/nrr78KjadJkyby+eefK8vdu3cXPT095RhcvnxZAEhycrKIiKxdu1aaNGkiarVabG1tpV+/fnL16lVl+7w/irdv3y7u7u6ir6+vxPjseQGg8cn74zwnJ0cCAgLE1tZW+eN4x44dIiJy+vRppX5RCfDs2bOVePK2+eqrr5SyjIwMAaDE1aNHDyW5bdKkiejq6oqVlZVGspOXkFtZWYlarZb33ntPpk+fLgYGBvL9999rHNN58+aJh4eHiIiEhYVJrVq1xMjISLy9vSU4OFhJEJ89Z82aNZPp06eLiMjmzZulcePGYmhoKNWrVxd/f3/ZsGGDWFtbK33069dPPD09ReRpMuPm5qYRQ8uWLQWAHD16VClzd3eXhg0bSsWKFcXMzEx8fHyUpFBExN7eXjp37iyVKlUStVotQ4YMETMzMyUBfvTokejp6UmbNm2UbXJycqRp06ZiamoqRkZGUqNGDeVYPnt+AEj9+vXF2NhYPDw8pG/fvuLr65vvusjTq1cvASCmpqZiamoqOjo6oq+vL9bW1mJlZSU2NjYCQEnGREQSExPF09NTDA0NRUdHR6ZOnSoA5Ndff9Voe9iwYfLBBx/Ib7/9JhYWFpKdnS1RUVGio6MjAGT69Oni6OgoAwYMkGHDhkn//v0lNzdXbGxspGnTplK5cmUxNjaW+vXra5z7X375RfT09ERXV1fs7e3F2NhYmjZtKgUJDw+XZs2aiYGBgahUKmnVqpWkpaVJcHCw6OvrK8fDz89PTExMZOTIkcp3xNfXV4yMjGTUqFEyevRoMTc3Fx0dHfHx8ZHc3FzZvXu36OrqSnJysvL7wsjISADIihUrlHbz/nNjy5Yt4urqKiqVSho1aiQiIgMGDBBdXV05ePBgvmPXr18/jXNbWALcvn175XdbnnXr1om9vb2y/Pz3NTMzUwAo33kiIiIibVUqzwC3a9cObm5u2LRpk1Kmo6ODL7/8EqdPn0ZoaCh+//13jVs/R48ejcePH2P//v04efIkFi5cCLVa/dIxfPnll9i6dSt++uknJCUlYf369XBycgIAbNy4EYsXL8bKlSuRnJyMzZs3o0GDBsq2Y8aMwaFDh7BhwwacOHECvXv3RqdOnZCcnKzUefDgAb744gusW7cO+/fvx6VLlzBlypRC4/Hy8kJERAQAQEQQFRUFS0tLHDhwAAAQGRmJKlWqwNnZGQCQlZWFefPmISEhAZs3b0ZaWhqGDBmSr90ZM2ZgwYIFSExMRMOGDfOtDw4OBgCsW7cO6enpyjlZunQpgoKCMGvWLACAj48P3nzzTSQnJ8Pe3r5Yx/jZ/kxMTAAAt27dUspWrVoFAOjRowfUajW2bdsGAJg1axaCgoLQtWtX6Ojo4L333lO2yaszduxYHDt2DPb29li+fDmePHmCYcOGQa1WK59PPvkEKSkpuHz5Mt566y1069YN8fHxGD58OGbMmAEAqFevnkbM9vb2uHbtGqKiojBo0CB06NAB7u7uuHv3LgICAjBgwADcvHlTuW04Pj4e7du3L3D/Hz9+jCtXrsDAwAC1a9dWyi9duoSMjAzs2LEDsbGxcHd3R/v27XHr1i3cuXMH6enp2Lt3Lz799FMcO3YMVapUwcOHD5Xtz58/j+zsbBw8eFDZV2NjYxw7dgxVq1bF6dOn0bt3bwDId1uvjo4OvvrqKxw7dgx6enqIjo7GtWvXXnguo6KiEB8fj6ZNm8LQ0BADBw7EkSNH8MUXXwAA3njjDXTr1g2LFy9G165dYWJigiNHjmDw4MFKnY0bNyIlJUVpMyEhASEhIXjnnXdw584dmJmZwdfXF7m5ubC0tFS+Dw0bNkRkZKTyWEPFihVRoUIFhIWF4dSpU/jggw8wcOBAHD16FMDTZ4A9PDygr6+PGjVqoHbt2oiLi0NISIjS9969e9GuXTu0a9cO8fHxUKlUEBEMHjwYKpWqwGPg5OQEAwMDZdnW1hYmJiZYu3Yt9PT0cPToUbi4uCAqKgqrV69GYmIiHBwcsHDhQuX3xalTp2BsbIyxY8cqvy8qVqyIhw8fYuHChVi9ejWmT5+uPJt+9+5d5OTk4I033tC4tteuXatxLIuSkJCAjz/+WGP7999/H+np6Rq3vz/7fTU1NYW5uXmxrg0iIiKi11pJsuXCRoBFRPr27Suurq6Fbvvzzz9rjLQ1aNBA45a9F/Hy8hJ9fX1l5Crv89///ldERMaOHSvt2rWT3NzcfNsGBQWJi4uLPHnyJN+6ixcviq6urvz5558a5e3bt1duW8wbXTx//ryyfvny5WJra1tovFu3blVGweLj48XOzk7Gjx+vjEYOHz5c3n333UK3j4mJEQDKiHHeqNDmzZvz1X32vOzYsUMAyLZt2zTqVK5cWebPn6+MuMbFxUmzZs1k1KhRysgtXjAC/OyIX147AQEBIiLyww8/KKNh3377rSQnJ0vXrl01Ri0HDx6sjKA+fPhQREQaN26s0a/I0xFVAPLf//5XkpOTNT4XLlyQmTNnSt26dTX2b/r06QJARowYISL/GzXr3r27DB48WNq3by9Tp04VQ0NDmTBhghw6dEg+++wzsbCw0NhPd3d3mTt3rog8Hc3T0dFRrjWVSiW6uroa34GoqCjR09OTAQMGaMRTs2ZNWblypdy+fVsASI8ePTTWW1lZKSPAhw8fFgDi6ekpycnJcvr0aTEyMpKffvpJLl26pHE+evXqpbFsamqqtBkWFiYApGHDhvmuizx5I8B5++vl5SWtW7fWqNOsWTMZMmSIfPrpp+Lq6prvesr7Pri5uYmBgYFs2rRJRETq1KkjY8eOleTkZKlbt65Mnz5dkpOTpUOHDhIQECAGBgZSrVo18ff3FwBy7tw5ERFxc3MTPz8/jRj+85//yOTJk5VlLy8vady4sbI8bNgwqVatmog8vRYNDQ1lxIgRAkDWrVsn3333Xb5R1OdHgN3c3GT8+PHKCPDgwYPF2tpaXF1dld8jXl5e0qRJE3F1dZWlS5dK1apV8/2+sLS0lLp168rMmTOVO00AKHcBLF68WBwdHUVEpEuXLsoI+/PX9vPnurARYCMjI1m4cGG+7ZOTkyUnJ0dE8n9fRUQsLCwkODhYiIiIiLRZqc0CLSIaIy179+5F+/btUaVKFZiZmWHgwIEaI23jxo3DJ598Ak9PT/j5+eHEiRMv7KN///6Ij4/X+ORNhDNkyBDEx8ejdu3aGDduHHbv3q1s17t3bzx8+BA1atTA+++/j19//VWZMObkyZPIycmBi4uLxohKZGSkxoiMiYkJatasqSznjSwWpk2bNrh37x7i4uIQGRkJLy8veHt7K6NgeSNgeWJjY9GtWzdUq1YNZmZm8PLyAvB0dPFZTZs2LfIY5U0qdeTIEaXs7t27+Ouvv+Dp6alR19PTE4mJiUW2V1zR0dFo3rw5AKBWrVpwdnbGX3/9BeB/I1EGBgbQ19cHAOXY5R3jnJwcpa22bdtCT08PFy5cgLOzs8anevXqSExMRIsWLTT69/DwKDK+hIQELFmyBI8fP8a3336LDh06YO7cubhz545GvYYNG2Lfvn3Kcu3atZVrLTY2FpUrV8b27duViZ8SEhKQnZ2NDRs2aFw/qampSElJgYWFBVQqFQwNDZU2s7OzNSYsqlu3LnR0dPDw4UM4OztDRPDo0SMMHToUrq6uUKvV6Ny5MwAgNTVVI15dXV3l57yR/GfbLo7n7ySwt7eHSqXCzJkz8eGHH8LQ0BC7du1S1r/11lsAnk749NZbbyl3Hbi7u+PMmTNwdnaGr68vTp06hZo1ayIuLg59+vSBq6srHj16hPPnz6Ny5cqoVauW0mZkZCQaNGiAChUqQK1WY9euXfmu/SZNmmgcs7zJtWJjY5Gbm4vly5djyJAhGD58OD777DMAeKlXO7Vs2VLjd5m9vT2Sk5Ph4uKCv/76S+P3hYmJCW7fvo2kpCSN3xcGBgYF3qFhaWkJHR0dXLp0Kd+17eDgUKz43N3dkZSUlG97Z2dnZSIzIiIiIipYqf21lJiYqCRfaWlp6Nq1Kxo2bIiNGzciNjYWy5cvBwA8efIEADB8+HBcuHABAwcOxMmTJ9G0aVMsW7asyD4sLCzy/cFnZmYG4OkfhampqZg3bx4ePnyIPn364O233wYAODg4ICkpCV9//TWMjY0xatQotG3bFllZWcjMzISuri5iY2M1EuvExESNWYzzErc8ebdYFsbS0hJubm6IiIhQkt22bdsiLi4O586dQ3JyspLk3r9/H76+vjA3N8f69esRExODX3/9VeN45TE1NS3yGOUlWkuXLlVuq4yJiSlym5eR94d23jGoVasW4uLiAACXL1/GnDlzkJSUBOB/x87JyUm5TfTGjRvIysqCSqWCgYEBdu7ciatXryoJqZ2dHQIDA/Hll1/i3LlzOHnyJIKDg7Fo0aKXijczMxOjRo0CAEycOBFbt27FvHnzYGtrq1Fv5syZiImJwahRo3D16lXk5uZiz549sLS0ROPGjVGjRg2o1WosWbJEadfIyEi5HTvvk5SUhKlTpwJ4ek62bduGzZs34+zZsxg1ahQeP36s9GlmZoZ69erh1KlTCA0NxdmzZwEAw4YNw9y5cxEfH4/Vq1cDAEJDQwvdx7ykLe+c6Ojo5LtG85Lja9eu4cqVK3jy5AmePHmCK1euICsrC6mpqUhMTMSVK1dw8eJFnD17FllZWXB1dcXDhw8xZswYREVFAXj6nY+JiYGrqyuApzNwHzx4EGPGjEG1atUQGRmJJUuW4OHDh6hTpw68vb3x6NEjpKSkKNd+XixHjhzB9OnTER4ejvj4ePj6+uLJkye4efMm2rVrh6tXr+LBgwdITU3Fzz//jM8++wzdu3cHADg7OyMrKwvLli3DnDlzMGvWLKSnpwN4+h9Ghw8fVo7P88cjKyur0OP5vA4dOqBatWoAgLVr1yI0NBQ1a9ZE8+bNcfbsWY3fF8bGxgXeeq2vrw9nZ2dMnDgRoaGhSElJwfHjx7Fs2bIiz+2z5s6di7Vr1yIgIACnT59GYmIiNmzYgNmzZxd7X4iIiIi0VakkwL///jtOnjyJXr16AfjfiExQUBBatmypjJw8z8HBASNGjMCmTZswefJk5RnSl2Vubo6+ffti1apV+PHHH7Fx40blGVVjY2N069YNX375JSIiInDo0CGcPHkSjRs3Rk5ODq5du5Yvubazs/tb8Xh5eSE8PBz79++Ht7c3KlSoAFdXV8yfPx/29vZwcXEBAJw9exY3b97EggUL0KZNG9SpU+eln9XLe6axf//+mDt3LlxdXZVXzkRHR2vUjY6ORt26dV+qHxsbGwBQnm388MMPlVHYUaNG4ebNm0qCkuf9999Xnstu0qSJ0n+rVq2wcuVKVK5cGd27d8fhw4dhbW2N1atXIzg4GA0aNICXlxdCQkJQvXp1uLq6Ks+H5slLcgrj7u6Oe/fuYdGiRVizZg26du2KvXv34vPPP9eo5+Ligt27dyMhIQGrVq3C+fPnsWXLFujp6Sl1VCqV8gyvu7s7Hj16BB0dnXzXT8WKFQEAjRo1Qs2aNTF48GB4eHgorxB6VuPGjeHi4oLAwED069cPABAeHo6WLVvC2dkZVapUAQBUrVq1yP18lo2NjZII5jl//jyApyPb9vb2OHToEFavXg17e3vExsbCxMQE9+7dQ0REBFxcXLBp0yaICHr06AFdXV3cvHkTQ4cOBQB88cUX6Ny5MwICAgD879nec+fOYc6cOcjMzMS8efOUOyfyEuDz589r3P1w//591K5dGwMGDICbmxtq1KiBc+fOAQDUajVatGiBP/74A7/88gvq16+POXPm4P3338dXX30FAHBzc8OiRYuwcOFC1K9fH9HR0co6V1dXfP/998p5e/545L33+lnP3j0BPB1FrlWrFvT09JTR7gEDBigj9Fu3bi3274uQkBCcPXsWc+bMQWBgIFxdXdGpUyeEhYUp/4H4Ir6+vti2bRt2796NZs2aoWXLlli8eDEcHR2LtT0RERGRVivJ/dJFvQapa9eukp2dLSIi8fHxAkCWLFkiKSkpsnbtWuU1O3nPtY0fP1527twpFy5ckNjYWGnRooX06dOn0L4Lew3SrVu3ROTpc77ff/+9JCYmSlJSkgwbNkzs7OwkJydHgoODZfXq1XLy5ElJSUmR2bNni7Gxsdy4cUNERPr37y9OTk7KK3eOHDkin376qfLc4/PP4ImI/Prrr/Kiw7d582bR1dUVOzs7pWz8+PGiq6sr77zzjlJ27do1MTAwkKlTp0pKSops2bJFXFxcinwmtzBZWVlibGwsn3zyiVy5ckVu374tIk+fQzQ3N5cNGzbI2bNnZfr06aKvr688h/nss8HF7a9ly5bSpk0bOXPmjEREREjz5s01ZoEuqI24uDgBIKmpqSIismHDBjEyMpI1a9ZIUlKSzJ07V8zMzPLNvvysixcvioGBgUyZMkXOnj0r69evV15PVNhzkzt37hQ9PT3x9/eXU6dOyZkzZ+SHH36QWbNmFdqPn5+f1KtXT7nWzp07J/PmzRMAEhoaKiJPZxhv3bq1uLm5ya5duyQ1NVWio6Plo48+kpiYmGLv4/PP686aNUusra2VV1nFxsbKl19+qbwGqjjHdufOnaJSqSQ0NFTOnTsnc+fOFXNzc43X9hQ0u3rec9MiItnZ2VK7dm3x9fWVhIQEOXDggPIcd0HPoz+rUaNGoqurq8yQfPPmTdHX1xcAGjOBT5w4URwcHCQ6OlrOnDkjw4cPF3Nzc43j8aJZ4EVELly4IDNmzJCDBw9KWlqa7Nq1S6ytreXrr78u0fFQq9UyceJEOXv2rHz//fdiamoq33zzjVLnZX5f5Bk4cKCMHj26yP0gIiIiorJT4gQY/z9Zkp6entjY2EiHDh1kzZo1yuQreRYtWqS8ssTX11fWrl2r8Qf7mDFjpGbNmmJoaCg2NjYycOBAJSEtiJeXV75X/ABQXvny7bffSqNGjcTU1FTMzc2lffv2cvz4cRF5mqy2aNFCzM3NxdTUVFq2bKlMzCTyv3fYOjk5ib6+vtjb20vPnj3lxIkTIvLyCfDNmzdFpVJJ375982337B/UIiLff/+9ODk5iaGhoXh4eMjWrVtfKgEWEVm1apU4ODiIjo6OxmuQ/P39pUqVKqKvry9ubm4ar0R5mQT4zJkz4uHhIcbGxtKoUSPZvXt3iRNgEZH58+dLxYoVRa1Wy+DBg2XatGlFJsAiIr/99ps4OzuLoaGhtGnTRnlna2EJsMjTBKhVq1ZibGws5ubm0rx5c/n2228L7cPPz0/jWjMxMZEGDRooCV2eu3fvytixY6Vy5cqir68vDg4O0r9/f2VSo+Ls4/MJcG5urixZskRq164t+vr6YmNjI76+vhIZGVmiYzt37lyxtbUVCwsLmThxoowZM6ZECbDI/16DZGBgIHXq1JHffvtNAMjOnTsLPXYiT/+zB4AkJiYqZW5ubhr/ISTy9HvSvXt3UavVUqlSJZk9e7YMGjSoxAnwlStXpEePHmJvby8GBgbi6Ogoc+fO1fjdVJzjMWrUKBkxYoSYm5uLlZWVfPTRRxqT673M74tn2y9sIkEiIiIiKnsqkSIeZCUiek50dDRat26N8+fPa0wM9zrw9vZGo0aNlGe8iYiIiOj1ovfiKkSkzX799Veo1WrUqlUL58+fx/jx4+Hp6fnaJb9ERERE9PpjAkxERbp37x6mT5+OS5cuoWLFiujQoQOCgoLKOywiIiIiohLjLdBERERERESkFUrtPcBERERERERE/2RMgImIiIiIiEgrMAEmIiIiIiIircBJsIheIzm5OTh+7TiuP7gOGxMbuFdyh66ObnmHRURERET0j8AEmOg1sffiXiw4ugBXH1xVymxNbDGj+Qx0cOxQjpEREREREf0zcBZoem3k5AqOpt7CtXuPUMnMCM2rV4Cujqq8w3ol9l7ci0kRkyDQ/Dqr8HT/F3kvYhJMRERERFqPCTC9FnaeSkfAb2eQfueRUmZvYQS/bnXRqb59OUZW9nJyc+C70Vdj5PdZKqhga2KLnb128nZoIiIiItJqnASL/vV2nkrHyP8e10h+AeDKnUcY+d/j2HkqvZwiezWOXzteaPILAALBlQdXcPza8VcYFRERERHRPw8TYPpXy8kVBPx2BgXdxpBXFvDbGeTkvr43Olx/cL1U6xERERERva6YANO/2tHUW/lGfp8lANLvPMLR1FuvLqhXzMbEplTrERERERG9rpgA07/atXuFJ78vU+/fyL2SO2xNbJUJr56nggp2JnZwr+T+iiMjIiIiIvpnYQJM/2qVzIxKtd6/ka6OLmY0nwEA+ZLgvOXpzadzAiwiIiIi0npMgOlfrXn1CrC3MCpk7BNQ4els0M2rV3iVYb1yHRw7YJH3IlQyqaRRbmtiy1cgERERERH9P74Gif718maBBqAxGVZeUrxigPtr/yqkPDm5OTh+7TiuP7gOGxMbuFdy58gvEREREdH/YwJMrwVtfg8wEREREREVDxNgem3k5AqOpt7CtXuPUMns6W3PujqF3RxNRERERETahgkwERERERERaQVOgkVERERERERagQkwERERERERaQUmwERERERERKQVmAATERERERGRVmACTERERERERFqBCTARERERERFpBSbAREREREREpBWYABMREREREZFWYAJMREREREREWoEJMBEREREREWkFJsBERERERESkFZgAExERERERkVZgAkxERERERERagQkwERERERERaQUmwERERERERKQVmAATERERERGRVmACTERERERERFqBCTARERERERFpBSbAREREREREpBWYABMREREREZFWYAJMREREREREWoEJMBEREREREWkFJsBERERERESkFZgAExERERERkVZgAkxERERERERagQkwERERERERaQUmwERERERERKQVmAATERERERGRVmACTERERERERFqBCTARERERERFpBSbAREREREREpBWYABMREREREZFWYAJMREREREREWoEJMBEREREREWkFJsBERERERESkFZgAExERERERkVZgAkxERERERERagQkwERERERERaQUmwP9ATk5OWLJkSXmH8bc8ePAAvXr1grm5OVQqFW7fvg3gf/sWHx8PlUqFtLS0Munf398fjRo1KpO2i8Pb2xsTJkx4YT2VSoXNmzcjJCQElpaWZR7X3zVkyBD06NGjXPou7jmdM2cOPvjggzKP58mTJ3BycsKxY8fKvC8iIiIiKh1MgMvIoUOHoKuri//85z/lHUq5CA0NRVRUFA4ePIj09HRYWFgAAGJiYvDBBx+gfv36SE9Ph4ODQ5n0P2XKFOzbt69M2i5N6enp6Ny5M/r27Ytz586VdzgAgA8//BC6urr4+eefyzuUErty5QqWLl2KWbNmlWq7BSXfBgYGmDJlCqZPn16qfRERERFR2WECXEa+++47jB07Fvv378dff/1V3uG8cikpKXB1dUX9+vVhZ2cHlUoFALCxsYGJiQn09PRgZ2cHXV3dItvJysp6qf7VajWsra1fattXyc7ODoaGhjA2NkalSpXKvD8RQXZ2dqHrHzx4gA0bNmDatGlYs2ZNmcdT2lavXo1WrVrB0dHxlfTXv39/HDhwAKdPn34l/RERERHR38MEuAxkZmbixx9/xMiRI/Gf//wHISEh+er89ttvaNasGYyMjFCxYkX07NlTY/2DBw/w3nvvwczMDNWqVcO3335b7P5FBDY2Nvjll1+UskaNGsHe3l5ZPnDgAAwNDfHgwQMAwKJFi9CgQQOYmprCwcEBo0aNQmZmplL/4sWL6NatG6ysrGBqaop69eph+/btBfbv7e2NoKAg7N+/HyqVCt7e3gD+d7vvsywtLZXjk5aWBpVKhR9//BFeXl4wMjLC+vXrC+zj9u3bGD58OGxsbGBubo527dohISFBWf/8iF3erbtffPEF7O3tYW1tjdGjRxeZYCckJMDHxwdmZmYwNzdHkyZNNG53jY6Ohre3N0xMTGBlZQVfX19kZGQo63NzczFt2jRUqFABdnZ28Pf312j/0qVL6N69O9RqNczNzdGnTx9cvXo13z6sW7cOTk5OsLCwwDvvvIN79+5p9BEYGIjq1avD2NgYbm5uGuc9IiICKpUKO3bsQJMmTWBoaIgDBw4Uus8///wz6tatixkzZmD//v24fPlygfUCAgKUYz9ixAg8efKkxDHt27cPTZs2hYmJCVq1aoWkpCSNPhYsWABbW1uYmZlh2LBhePToUaFx59mwYQO6deumUebt7Y1x48a99LkICQlBQEAAEhISoFKpoFKplGvWysoKnp6e2LBhwwtjIyIiIqLyxwS4DPz000+oU6cOateujQEDBmDNmjUQEWV9WFgYevbsiS5duiAuLg779u1D8+bNNdoICgpC06ZNERcXh1GjRmHkyJH5EoTCqFQqtG3bFhEREQCAjIwMJCYm4uHDhzh79iwAIDIyEs2aNYOJiQkAQEdHB19++SVOnz6N0NBQ/P7775g2bZrS5ujRo/H48WPs378fJ0+exMKFC6FWqwvsf9OmTXj//ffh4eGB9PR0bNq0qdjHDgBmzJiB8ePHIzExEb6+vgXW6d27N65du4YdO3YgNjYW7u7uaN++PW7dulVou+Hh4UhJSUF4eDhCQ0MREhJS4H9O5Onfvz+qVq2KmJgYxMbGYsaMGdDX1wcAxMfHo3379qhbty4OHTqEAwcOoFu3bsjJyVG2Dw0NhampKY4cOYLPPvsMH3/8Mfbs2QPgaZLYvXt33Lp1C5GRkdizZw8uXLiAvn37asSQkpKCzZs3Y9u2bdi2bRsiIyOxYMECZX1gYCDWrl2Lb775BqdPn8bEiRMxYMAAREZG5jumCxYsQGJiIho2bFjoPn/33XcYMGAALCws0Llz5wKPz759+5CYmIiIiAj88MMP2LRpEwICAkoc06xZsxAUFIRjx45BT08P7733nrLup59+gr+/Pz799FMcO3YM9vb2+PrrrwuNGwBu3bqFM2fOoGnTpvnW/Z1z0bdvX0yePBn16tVDeno60tPTNc5T8+bNERUVVWRsRERERPQPIVTqWrVqJUuWLBERkaysLKlYsaKEh4cr6z08PKR///6Fbu/o6CgDBgxQlnNzc6VSpUqyYsWKYsfw5ZdfSr169UREZPPmzdKiRQvp3r270kaHDh3ko48+KnT7n3/+WaytrZXlBg0aiL+/f7H7Hz9+vHh5eWmUAZBff/1Vo8zCwkKCg4NFRCQ1NVUAKMeuMFFRUWJubi6PHj3SKK9Zs6asXLlSRET8/PzEzc1NWTd48GBxdHSU7Oxspax3797St2/fQvsxMzOTkJCQAtf169dPPD09C93Wy8tLWrdurVHWrFkzmT59uoiI7N69W3R1deXSpUvK+tOnTwsAOXr0qLIPJiYmcvfuXaXO1KlTpUWLFiIi8ujRIzExMZGDBw9q9DNs2DDp16+fiIiEh4cLANm8eXOhseY5d+6c6Ovry/Xr10VE5Ndff5Xq1atLbm6uUmfw4MFSoUIFuX//vlK2YsUKUavVkpOTU6KY9u7dq6wPCwsTAPLw4UMRefodGTVqlEYbLVq00Dinz4uLixMAGsdUpPTORWF9L126VJycnAqNi4iIiIj+OTgCXMqSkpJw9OhR9OvXDwCgp6eHvn374rvvvlPq5I0eFuXZUTqVSgU7Oztcu3at2HF4eXnhzJkzuH79OiIjI+Ht7Q1vb29EREQgKysLBw8eVG5NBoC9e/eiffv2qFKlCszMzDBw4EDcvHlTuUV63Lhx+OSTT+Dp6Qk/Pz+cOHGi2LGUVEEjeM9KSEhAZmYmrK2toVarlU9qaipSUlIK3a5evXoazxzb29sXeUwnTZqE4cOHo0OHDliwYIFG2yU9h8/3l5iYCAcHB41JwOrWrQtLS0skJiYqZU5OTjAzMyuwjfPnz+PBgwd44403NI7D2rVr8x2HFx1TAFizZg18fX1RsWJFAECXLl1w584d/P777xr13NzclDsHAMDDwwOZmZm4fPlyiWJ69vjk3Z7/7PFp0aKFRn0PD48i43/48CEAwMjIKN+60jgXhTE2Nla+J0RERET0z6ZX3gG8br777jtkZ2ejcuXKSpmIwNDQEF999RUsLCxgbGz8wnbybrXNo1KpkJubW+w4GjRogAoVKiAyMhKRkZGYP38+7OzssHDhQsTExCArKwutWrUC8PTZ265du2LkyJGYP38+KlSogAMHDmDYsGF48uQJTExMMHz4cPj6+iIsLAy7d+9GYGAggoKCMHbs2GLHpFKpNG4FBwqe5MrU1LTIdjIzM2Fvb6/c4v2sol4lVNJj6u/vj3fffRdhYWHYsWMH/Pz8sGHDBvTs2fOVnMMXtZH3jHZYWBiqVKmiUc/Q0FBj+UXHNCcnB6Ghobhy5Qr09PQ0ytesWfPCZD9PSWJ6dt/yJkkr6fF5Vl7inpGRARsbm0L7yuvv7/T1rFu3buXrj4iIiIj+mTgCXIqys7Oxdu1aBAUFIT4+XvkkJCSgcuXK+OGHHwA8HY0q61f0qFQqtGnTBlu2bMHp06fRunVrNGzYEI8fP8bKlSvRtGlTJSmKjY1Fbm4ugoKC0LJlS7i4uBQ4c7WDgwNGjBiBTZs2YfLkyVi1alWJYrKxsUF6erqynJyc/FIjZ+7u7kqi5uzsrPHJS4JKi4uLCyZOnIjdu3fjrbfeQnBwMIC/fw5dXV1x+fJljUmmzpw5g9u3b6Nu3brFaqNu3bowNDTEpUuX8h2Hkr5eavv27bh37x7i4uI0rt28Z3zz3uMMPB2BzxttBYDDhw9DrVbDwcGh1GJydXXFkSNHNMoOHz5c5DY1a9aEubk5zpw5U+x+8vp60bkwMDDQeL77WadOnULjxo1L1CcRERERlQ8mwKVo27ZtyMjIwLBhw1C/fn2NT69evZTboP38/PDDDz/Az88PiYmJyqRSpc3b2xs//PADGjVqBLVaDR0dHbRt2xbr16+Hl5eXUs/Z2RlZWVlYtmwZLly4gHXr1uGbb77RaGvChAnYtWsXUlNTcfz4cYSHh8PV1bVE8bRr1w5fffUV4uLicOzYMYwYMSLfyFxxdOjQAR4eHujRowd2796NtLQ0HDx4ELNmzdKYpfnvePjwIcaMGYOIiAhcvHgR0dHRiImJUfZ55syZiImJwahRo3DixAmcPXsWK1aswI0bN4q9Dw0aNED//v1x/PhxHD16FIMGDYKXl1exblcGADMzM0yZMgUTJ05EaGgoUlJScPz4cSxbtgyhoaEl2t/vvvsO//nPf+Dm5qZx3fbp0weWlpYas3E/efIEw4YNw5kzZ7B9+3b4+flhzJgx0NHRKbWYxo8fjzVr1iA4OBjnzp2Dn5/fC181pKOjgw4dOhQ5y3VBinMunJyckJqaivj4eNy4cQOPHz9Wto+KikLHjh1L1CcRERERlQ8mwKXou+++Q4cOHWBhYZFvXa9evXDs2DGcOHEC3t7e+Pnnn7F161Y0atQI7dq1w9GjR0vUl7e3N4YMGVJkHS8vL+Tk5Gg86+vt7Z2vzM3NDYsWLcLChQtRv359rF+/HoGBgRpt5eTkYPTo0XB1dUWnTp3g4uLywll5nxcUFAQHBwe0adMG7777LqZMmaLxLGlxqVQqbN++HW3btsXQoUPh4uKCd955BxcvXoStrW2J2yuIrq4ubt68iUGDBsHFxQV9+vRB586dldmOXVxcsHv3biQkJKB58+bw8PDAli1bNG4fftE+bNmyBVZWVmjbti06dOiAGjVq4McffyxRnPPmzcOcOXMQGBionJuwsDBUr1692G1cvXoVYWFh6NWrV751Ojo66Nmzp8Yz7O3bt0etWrXQtm1b9O3bF2+++abGa4VKI6a+fftizpw5mDZtGpo0aYKLFy9i5MiRL9xu+PDh2LBhQ4luby7OuejVqxc6deoEHx8f2NjYKHdzHDp0CHfu3MHbb79d7P6IiIiIqPyo5PmHMulfwdHREQEBAS9Mgom0iYigRYsWmDhxojIRXVnq27cv3Nzc8NFHH5V5X0RERET093EE+F/o9OnTsLCwwKBBg8o7FKJ/FJVKhW+//RbZ2dll3teTJ0/QoEEDTJw4scz7IiIiIqLSwRFgIiIiIiIi0gocAS5FERERUKlUGjPmlpS/vz8aNWpUajG9bkJCQop81VFZevDgAXr16gVzc/Nin+fnr4nyjJ+IiIiISNsxAS7AN998AzMzM43bKDMzM6Gvr68xeRTwvwQnJSUFrVq1Qnp6eoGTYL1KaWlpUKlUiI+PL9c4XjehoaGIiorCwYMH/xHnOc+JEyfQs2dPVKpUCZaWlnj77beLPRs1EREREZE2YQJcAB8fH2RmZmq8UicqKgp2dnY4cuQIHj16pJSHh4ejWrVqqFmzJgwMDGBnZweVSlUeYVMZS0lJgaurK+rXr/+POs9RUVHw9PREeHg4du3ahZMnT2Lq1KnlHRYRERER0T8OE+AC1K5dG/b29oiIiFDKIiIi0L17d1SvXh2HDx/WKPfx8VF+fv7W2AMHDqBNmzYwNjaGg4MDxo0bh/v3778whnXr1sHJyQkWFhZ45513cO/ePWXdzp070bp1a1haWsLa2hpdu3ZFSkqKsj7vdTONGzeGSqXKN2r9rFOnTqFz585Qq9WwtbXFwIEDNUYPf/nlFzRo0ADGxsawtrZGhw4dioz/Re05OTlhyZIlGts0atRI4zU6t2/fxocffghbW1sYGRmhfv362LZtm8Y2u3btgqurK9RqNTp16oT09PRCYwKArVu3olatWjAyMoKPjw9CQ0NLdK68vb0RFBSE/fv3axzTdevWoWnTpjAzM4OdnR3effddXLt2rchYnrdlyxa4u7vDyMgINWrUQEBAgMbdByqVCqtXr0bPnj1hYmKCWrVqYevWrcr60aNHY8qUKahXrx5atGgBX19fXL58uUQxEBERERFpAybAhfDx8UF4eLiyHB4eDm9vb3h5eSnlDx8+xJEjR5QE+HkpKSno1KkTevXqhRMnTuDHH3/EgQMHMGbMmCL7TklJwebNm7Ft2zZs27YNkZGRWLBggbL+/v37mDRpEo4dO4Z9+/Yp72rNe/dp3juF9+7di/T0dGzatKnAfm7fvo127dqhcePGOHbsGHbu3ImrV6+iT58+AID09HT069cP7733HhITExEREYG33noLhc2b9qL2iiM3NxedO3dGdHQ0/vvf/+LMmTNYsGABdHV1lToPHjzAF198gXXr1mH//v24dOkSpkyZUmibqampePvtt9GjRw8kJCTgww8/xKxZszTqvOhcbdq0Ce+//z48PDw0jmlWVhbmzZuHhIQEbN68GWlpaSV6NVVUVBQGDRqE8ePH48yZM1i5ciVCQkIwf/58jXoBAQHo06cPTpw4gS5duqB///64detWvvYSEhKwdu1avPfee8WOgYiIiIhIawgVaNWqVWJqaipZWVly9+5d0dPTk2vXrsn3338vbdu2FRGRffv2CQC5ePGiiIiEh4cLAMnIyBARkWHDhskHH3yg0W5UVJTo6OjIw4cPC+zXz89PTExM5O7du0rZ1KlTpUWLFoXGev36dQEgJ0+eFBGR1NRUASBxcXFF7uO8efOkY8eOGmWXL18WAJKUlCSxsbECQNLS0opsp7jtiYg4OjrK4sWLNeq4ubmJn5+fiIjs2rVLdHR0lPrPCw4OFgBy/vx5pWz58uVia2tbaFzTp0+X+vXra5TNmjWrxOdq/Pjx4uXlVWAfudnZknn4iIQvWiwA5O7t2yKS/5oIDg4WCwsLZbv27dvLp59+qtHWunXrxN7eXlkGILNnz1aWMzMzBYDs2LFDY7uEhASxsrKSzz//vNBjQURERESkzfTKJev+F/D29sb9+/cRExODjIwMuLi4wMbGBl5eXhg6dCgePXqEiIgI1KhRA9WqVSuwjYSEBJw4cQLr169XykQEubm5SE1Nhaura4HbOTk5wczMTFm2t7fXuK02OTkZc+fOxZEjR3Djxg1l5PfSpUuoX79+sfcxISEB4eHhUKvV+dalpKSgY8eOaN++PRo0aABfX1907NgRb7/9NqysrF6qPRcXlxfGFB8fj6pVqxZZ18TEBDVr1lSWnz8+z0tKSkKzZs00ypo3b54v9pc5V7GxsZg9ejQS4uNx50kWBE9Hxw90eAOe8z8BDAwK39n/7zc6OlpjxDcnJwePHj3CgwcPYGJiAgBo2LChst7U1BTm5ub59nnmzJnw9fUtcjSciIiIiEibMQEuhLOzM6pWrYrw8HBkZGTAy8sLAFC5cmU4ODjg4MGDCA8PR7t27QptIzMzEx9++CHGjRuXb11hSTMA6OvrayyrVColyQWAbt26wdHREatWrULlypWRm5uL+vXr48mTJyXax8zMTHTr1g0LFy7Mt87e3h66urrYs2cPDh48iN27d2PZsmWYNWsWjhw5ojxnXJL2AEBHRyffLdRZWVnKz8bGxi+Mu6Dj83ybJfUy5+r+/fvo2K4dPAAsrGSLCrq6SM/Oxvt/XMbDGzfw5/gJuP/e0Bf2GxAQgLfeeivfOiMjI+XnF10TAPDXX3/lS/SJiIiIiOh/mAAXwcfHBxEREcjIyNCYVbdt27bYsWMHjh49ipEjRxa6vbu7O86cOQNnZ+dSi+nmzZtISkrCqlWr0KZNGwBPJ296lsH/jzrm5OQU2Za7uzs2btwIJycn6OkVfCmoVCp4enrC09MTc+fOhaOjI3799VdMmjTppdqzsbHRmLDq7t27SE1NVZYbNmyIP/74A+fOnSvWiHFx1K5dG9u3b9coi4mJyRd7Sc9V4unTuHX3LibVqAn7/09QT+XNEP7/CXnGMyPKBXF3d0dSUlKpXCPr16/nO4aJiIiIiIrASbCK4OPjgwMHDiA+Pl4ZAQYALy8vrFy5Ek+ePCl0AiwAmD59Og4ePIgxY8YgPj4eycnJ2LJlywsnwSqKlZUVrK2t8e233+L8+fP4/fff8yWjlSpVgrGxsTIJ1Z07dwpsa/To0bh16xb69euHmJgYpKSkYNeuXRg6dChycnJw5MgRfPrppzh27BguXbqETZs24fr164XeDvyi9gCgXbt2WLduHaKionDy5EkMHjxYY4IrLy8vtG3bFr169cKePXuQmpqKHTt2YOfOnS99zD788EOcPXsW06dPx7lz5/DTTz8hJCQEAJRXGb3Muap4KwP6KhXWZ2Tg8pMn+D3zHr65efN/FUSQU8BEVc+aO3cu1q5di4CAAJw+fRqJiYnYsGEDZs+eXeL9HDt2bKETnhERERERERPgIvn4+ODhw4dwdnaGra2tUu7l5YV79+4pr0sqTMOGDREZGYlz586hTZs2aNy4MebOnYvKlSu/dEw6OjrYsGEDYmNjUb9+fUycOBGff/65Rh09PT18+eWXWLlyJSpXrozu3bsX2FblypURHR2NnJwcdOzYEQ0aNMCECRNgaWkJHR0dmJubY//+/ejSpQtcXFwwe/ZsBAUFoXPnzi/VHvD0OVUvLy907doV//nPf9CjRw+N53kBYOPGjWjWrBn69euHunXrYtq0aS8czS5K9erV8csvv2DTpk1o2LAhVqxYocwCbWhoCODlzpVVdjY+tbPHrsx76JaWitU3b2FqJZsSxebr64tt27Zh9+7daNasGVq2bInFixfD0dGxxPuZkpKi8copIiIiIiLSpJK/+/Ak0b/Q/Pnz8c033/yt9+XeP3IUlwYPfmG9aqGhMG3R/IX1iIiIiIiobPEZYNIKX3/9NZo1awZra2tER0fj888//1u3ogOASdMm0LOzQ/bVq8ozvxpUKujZ2sKkaZO/1Q8REREREZUO3gJNWiE5ORndu3dH3bp1MW/ePEyePBn+/v5/q02Vri5sP5r5/wuq51Y+Xbb9aCZUzzzjTERERERE5Ye3QBP9TXd378bVTwORfeWKUqZnZwfbj2bCvGPHcoyMiIiIiIiexQSYqBRITg4eHItF9vXr0LOxgUnTJhz5JSIiIiL6h2ECTERERERERFqBzwATERERERGRVmACTERERERERFqBCTARERERERFpBb4HmF4pyRU8Tr2D3HtPoGNmAMPqFlDpqF68IRERERER0d/EBJhemYenbuD2bynIufNEKdO1MIBlt5owrl+xHCMjIiIiIiJtwFmg6ZV4eOoGbv43sdD11gNcmQQTEREREVGZ4jPAVOYkV3D7t5Qi69z+7QIkl/8XQ0REREREZYcJMJW5x6l3NG57LkjOncd4nHrnFUVERERERETaiAkwlbnce0UnvyWtR0RERERE9DKYAFOZ0zEzKNV6REREREREL4MJMJU5w+oW0LUoOrnVtTCEYXWLVxQRERERERFpIybAVOZUOipYdqtZZB3LbjX4PmAiIiIiIipTfA0SvTIFvwfYEJbdavAVSEREREREVOaYANMrJbmCx6l3kHvvCXTMDGBY3YIjv0RERERE9EowASYiIiIiIiKtwGeAiYiIiIiISCswASYiIiIiIiKtwASYiIiIiIiItAITYCIiIiIiItIKTICJiIiIiIhIKzABJiIiIiIiIq3ABJiIiIiIiIi0AhNgIiIiIiIi0gpMgImIiIiIiEgrMAEmIiIiIiIircAEmIiIiIiIiLQCE2AiIiIiIiLSCkyAiYiIiIiISCswASYiIiIiIiKtwASYiIiIiIiItAITYCIiIiIiItIKTICJiIiIiIhIKzABJiIiIiIiIq3ABJiIiIiIiIi0AhNgIiIiIiIi0gpMgImIiIiIiEgrMAEmIiIiIiIircAEmIiIiIiIiLQCE2AiIiIiIiLSCkyAiYiIiIiISCswASYiIiIiIiKtwASYiIiIiIiItAITYCIiIiIiItIKTICJiIiIiIhIKzABJiIiIiIiIq3ABJiIiIiIiIi0AhNgIiIiIiIi0gpMgImIiIiIiEgrMAEmIiIiIiIircAEmIiIiIiIiLQCE2AiIiIiIiLSCkyAiYiIiIiISCswASYiIiIiIiKtwASYiIiIiIiItAITYCIiIiIiItIKTICJiIiIiIhIKzABJiIiIiIiIq3ABJiIiIiIiIi0AhNgIiIiIiIi0gpMgImIiIiIiEgrMAEmIiIiIiIircAEmIiIiIiIiLQCE2AiIiIiIiLSCkyAiYiIiIiISCswASYiIiIiIiKtwASYiIiIiIhKJCIiAiqVCrdv38aQIUPQo0eP8g6pWNLS0qBSqRAfH1/eoVA5YQJMRERERPSKffPNNzAzM0N2drZSlpmZCX19fXh7e2vUzUs2U1JSXnGUhWvVqhXS09NhYWGBpUuXIiQkpLxDgr+/P1QqVZEfBwcHpKeno379+mUez4IFC1CvXj2YmJjAxcUF33//fZn3SS/GBJiIiIiI6BXz8fFBZmYmjh07ppRFRUXBzs4OR44cwaNHj5Ty8PBwVKtWDTVr1iyPUAtkYGAAOzs7qFQqWFhYwNLSsrxDwpQpU5Cenq58qlatio8//lijTFdXF3Z2dtDT03vpfp48eVKselFRUVi8eDFOnTqFAQMGYNCgQbhw4cJL90ulgwkwEREREdErVrt2bdjb2yMiIkIpi4iIQPfu3VG9enUcPnxYo9zHxwcAsG7dOjRt2hRmZmaws7PDu+++i2vXril1MzIy0L9/f9jY2MDY2Bi1atVCcHBwoXF4e3tj7NixmDBhAqysrGBra4tVq1bh/v37GDp0KMzMzODs7IwdO3ZobBcZGYnmzZvD0NAQ9vb2mDFjhsZo9r1799C/f3+YmprC3t4eixcvhre3NyZMmKDUefz4MaZMmYIqVarA1NQULVq00DgeISEhsLS0xK5du+Dq6gq1Wo1OnTohPT29wH1Rq9Wws7NTPrq6uspxyvsUdAv0qVOn0LlzZ6jVatja2mLgwIG4ceOGxjEaM2YMJkyYgIoVK8LX17dY24WFhaFjx46oUaMGxowZg5ycHPz111+Fngt6NZgAExERERGVAx8fH4SHhyvL4eHh8Pb2hpeXl1L+8OFDHDlyREmAs7KyMG/ePCQkJGDz5s1IS0vDkCFDlDbmzJmDM2fOYMeOHUhMTMSKFStQsWLFIuMIDQ1FxYoVcfToUYwdOxYjR45E79690apVKxw/fhwdO3bEwIED8eDBAwDAn3/+iS5duqBZs2ZISEjAihUr8N133+GTTz5R2pw0aRKio6OxdetW7NmzB1FRUTh+/LhGv2PGjMGhQ4ewYcMGnDhxAr1790anTp2QnJys1Hnw4AG++OILrFu3Dvv378elS5cwZcqUlzvgBbh9+zbatWuHxo0b49ixY9i5cyeuXr2KPn365DtGBgYGiI6OxjfffFPs7QBARDB58mTUr18fzZs3L7XY6SUJERERERG9cqtWrRJTU1PJysqSu3fvip6enly7dk2+//57adu2rYiI7Nu3TwDIxYsXC2wjJiZGAMi9e/dERKRbt24ydOjQYsfg5eUlrVu3Vpazs7PF1NRUBg4cqJSlp6cLADl06JCIiHz00UdSu3Ztyc3NVeosX75c1Gq15OTkyN27d0VfX19+/vlnZf3t27fFxMRExo8fLyIiFy9eFF1dXfnzzz814mnfvr3MnDlTRESCg4MFgJw/f16jH1tb22Ltm6OjoyxevFijLDU1VQBIXFyciIjMmzdPOnbsqFHn8uXLAkCSkpKUY9S4cWONOsXZLs97770nLi4u8scffxQrbipbL3/zOxERERERvTRvb2/cv38fMTExyMjIgIuLC2xsbODl5YWhQ4fi0aNHiIiIQI0aNVCtWjUAQGxsLPz9/ZGQkICMjAzk5uYCAC5duoS6deti5MiR6NWrlzJy26NHD7Rq1arIOBo2bKj8rKurC2trazRo0EAps7W1BQDlVuvExER4eHhApVIpdTw9PZGZmYk//vgDGRkZyMrK0hjttLCwQO3atZXlkydPIicnBy4uLhqxPH78GNbW1sqyiYmJxrPP9vb2Grd8/10JCQkIDw+HWq3Oty4lJUWJr0mTJi+1XUxMDNasWYOzZ8+iSpUqpRY3vTwmwERERERE5cDZ2RlVq1ZFeHg4MjIy4OXlBQCoXLkyHBwccPDgQYSHh6Ndu3YAgPv378PX1xe+vr5Yv349bGxscOnSJfj6+ioTM3Xu3BkXL17E9u3bsWfPHrRv3x6jR4/GF198UWgc+vr6GssqlUqjLC/RzUu2S0NmZiZ0dXURGxsLXV1djXXPJpUFxSYipRpHt27dsHDhwnzr7O3tlZ9NTU1faru8Z36fTf6pfDEBJiIiIiIqJz4+PoiIiEBGRgamTp2qlLdt2xY7duzA0aNHMXLkSADA2bNncfPmTSxYsAAODg4AoDGLdB4bGxsMHjwYgwcPRps2bTB16tQiE+CScnV1xcaNGyEiSnIcHR0NMzMzVK1aFVZWVtDX10dMTIwycn3nzh2cO3cObdu2BQA0btwYOTk5uHbtGtq0aVNqsZWUu7s7Nm7cCCcnpxLNDF3c7by8vBATE1MaoVIp4SRYRERERETlxMfHBwcOHEB8fLwyAgw8TZxWrlyJJ0+eKBNgVatWDQYGBli2bBkuXLiArVu3Yt68eRrtzZ07F1u2bMH58+dx+vRpbNu2Da6urqUa86hRo3D58mWMHTsWZ8+exZYtW+Dn54dJkyZBR0cHZmZmGDx4MKZOnYrw8HCcPn0aw4YNg46OjpIwu7i4oH///hg0aBA2bdqE1NRUHD16FIGBgQgLCyvVeIsyevRo3Lp1C/369UNMTAxSUlKwa9cuDB06FDk5OX97u/DwcAwYMOBV7AoVExNgIiIiIqJy4uPjg4cPH8LZ2Vl51hZ4mgDfu3dPeV0S8HRkNyQkBD///DPq1q2LBQsW5BvZNTAwwMyZM9GwYUO0bdsWurq62LBhQ6nGXKVKFWzfvh1Hjx6Fm5sbRowYgWHDhmH27NlKnUWLFsHDwwNdu3ZFhw4d4OnpCVdXVxgZGSl1goODMWjQIEyePBm1a9dGjx49NEaNX4XKlSsjOjoaOTk56NixIxo0aIAJEybA0tISOjqFp0rF3e7OnTtISkp6FbtCxaSS0ryJnoiIiIiI6Dn3799HlSpVEBQUhGHDhpV3OKTF+AwwERERERGVqri4OJw9exbNmzfHnTt38PHHHwMAunfvXs6RkbbjLdBERERERFTqvvjiC7i5uaFDhw64f/8+oqKiULFixXKJxcnJCUuWLFGWVSoVNm/eXOzthwwZgh49epR6XOXl+f3x9vbGhAkTlOXnj9fLiI+Ph0qlQlpaGvz9/dGoUaO/1V5B0tLSoFKpEB8fX+xtmAATEREREZGGghK+X375BUZGRggKCnrh9o0bN8bYsWOhp6eHW7duYc+ePRrvFi5v6enp6Ny5c3mH8UItW7bEiBEjNMq++eYbqFQqhISEaJQPGTKkXGfUfl79+vWRnp4OBwcHTJkyBfv27SvvkAAwASYiIiIiohdYvXo1+vfvjxUrVmDy5MnlHc7fZmdnB0NDw/IO44XyXpP1rPDwcDg4OOQrj4iIUN4Z/U+gp6cHOzs76OrqQq1Ww9raurxDAsAEmIiIiIiIivDZZ59h7Nix2LBhA4YOHaqUL1q0CA0aNICpqSkcHBwwatQoZGZmAniajA0dOhR37tyBSqWCSqWCv78/IiIilOVnP0OGDFHa3bJlC9zd3WFkZIQaNWogICAA2dnZynqVSoXVq1ejZ8+eMDExQa1atbB169YS7dPzt0BfvnwZffr0gaWlJSpUqIDu3bsjLS0t33YBAQGwsbGBubk5RowYgSdPnijrcnNzERgYiOrVq8PY2Bhubm745ZdflPV5+75v3z40bdoUJiYmaNWqVZGzRPv4+CApKQlXrlxRyiIjIzFjxgyNBDg1NRUXL15UXpl18uRJtGvXDsbGxrC2tsYHH3ygnJuXsXr1alhaWiqjuN7e3hg3bhymTZuGChUqwM7ODv7+/hrbLFy4EPXr14eJiQkcHBwwevRojRgKui16yZIlcHJyytd33gziderUwddff/3S+wEwASYiIiIiokJMnz4d8+bNw7Zt29CzZ0+NdTo6Ovjyyy9x+vRphIaG4vfff8e0adMAAK1atcKSJUtgbm6O9PR0pKenY8qUKWjVqpWynJ6ejt9//x1GRkZo27YtACAqKgqDBg3C+PHjcebMGaxcuRIhISGYP3++Rt8BAQHo06cPTpw4gS5duqB///64devWS+1jVlYWfH19YWZmhqioKERHR0OtVqNTp04aCe6+ffuQmJiIiIgI/PDDD9i0aRMCAgKU9YGBgVi7di2++eYbnD59GhMnTsSAAQMQGRmp0d+sWbMQFBSEY8eOQU9PD++9916hsXl6ekJfXx/h4eEAgDNnzuDhw4cYNmwYbt68idTUVABPR4WNjIzg4eGB+/fvw9fXF1ZWVoiJicHPP/+MvXv3YsyYMS91fD777DPMmDEDu3fvRvv27ZXy0NBQmJqa4siRI/jss8/w8ccfY8+ePcp6PT09fPXVVzhz5gxCQkKwb98+5foorvXr12Pu3LmYP38+EhMT8emnn2LOnDkIDQ19qX0BAAgREREREdEzBg8eLAYGBgJA9u3bV6xtfv75Z7G2tlaWg4ODxcLCotD6N27ckBo1asioUaOUsvbt28unn36qUW/dunVib2+vLAOQ2bNnK8uZmZkCQHbs2FFoX46OjrJ48WKNNn799Vel/dq1a0tubq6y/vHjx2JsbCy7du0SkafHo0KFCnL//n2lzooVK0StVktOTo48evRITExM5ODBgxr9Dhs2TPr16yciIuHh4QJA9u7dq6wPCwsTAPLw4cNCY/f09JQPPvhARESWL18uXbp0ERGRjh07ypo1a0REZODAgeLj4yMiIt9++61YWVlJZmamRj86Ojpy5coVZX+6d++urPfy8pLx48fnO17Tpk0Te3t7OXXqlEZMXl5e0rp1a42yZs2ayfTp0wvdj+evDz8/P3Fzc9Oos3jxYnF0dFSWa9asKd9//71GnXnz5omHh4eIiKSmpgoAiYuLK7Tf5/E1SERERERElE/Dhg1x48YN+Pn5oXnz5lCr1Rrr9+7di8DAQJw9exZ3795FdnY2Hj16hAcPHsDExKTItrOystCrVy84Ojpi6dKlSnlCQgKio6M1RnxzcnLytduwYUNlvampKczNzXHt2rWX2s+EhAScP38eZmZmGuWPHj1CSkqKsuzm5qaxXx4eHsjMzMTly5eRmZmJBw8e4I033tBo48mTJ2jcuLFG2bOx29vbAwCuXbuGatWqFRift7c3fv75ZwBPb6P29vYGAHh5eSm3mkdEROD9998HACQmJsLNzQ2mpqZKG56ensjNzUVSUhJsbW2LdVyCgoJw//59HDt2DDVq1Mi3/tn9yNuXZ8/B37k+gKfvjk5JScGwYcOUfQOA7OxsWFhYFGsfCsIEmIiIiIiI8qlSpQp++eUX+Pj4oFOnTtixY4eSJKalpaFr164YOXIk5s+fjwoVKuDAgQMYNmwYnjx5UmCCk5ubi4sXLyIzMxOffPIJLl++jKNHj0JP738pSWZmJgICAvDWW2/l297IyEj5WV9fX2OdSqVCbm7uS+1nZmYmmjRpgvXr1+dbZ2NjU+w2ACAsLAxVqlTRWPf8ZFvPxq5SqQCgyNh9fHwwf/58/Pnnn4iIiMCUKVMAPE2AV65ciZSUFFy+fLnUJ8Bq06YNwsLC8NNPP2HGjBn51hd1Dopzfejo6EBENNrIyspSfs47pqtWrUKLFi006unq6r70fjEBJiIiIiKiAjk6OiIyMlJJgnfu3AkzMzPExsYiNzcXQUFB0NF5Oq3QTz/9pLGtgYEBcnJyADx9dnXnzp24e/cuDh06hIiICIwbNw5Xr17VmB3Y3d0dSUlJcHZ2fmX76O7ujh9//BGVKlWCubl5ofUSEhLw8OFDGBsbAwAOHz4MtVoNBwcHVKhQAYaGhrh06RK8vLxKNb5WrVrBwMAAX3/9NR49eoQmTZoAAJo1a4br169jzZo1MDU1RfPmzQEArq6uCAkJwf3795VR4OjoaOjo6KB27drF7rd58+YYM2YMOnXqBD09PSXxLo7iXB82Nja4cuUKRET5j4Bn3+dra2uLypUr48KFC+jfv3+x+34RToJFRERERESFynvlzrVr1+Dr64u7d+/C2dkZWVlZWLZsGS5cuIB169bhm2++0djOyckJmZmZWLNmDUJCQnDz5k1cuHABe/bswRtvvAERwZo1axAZGYk7d+4AAObOnYu1a9ciICAAp0+fRmJiIjZs2IDZs2eX2f71798fFStWRPfu3REVFYXU1FQlQf/jjz+Uek+ePMGwYcNw5swZbN++HX5+fhgzZgx0dHRgZmaGKVOmYOLEiQgNDUVKSgqOHz+OZcuW/b0JmwAYGxujZcuWWLZsGTw9PZXRTwMDA43yvBHZ/v37w8jICIMHD8apU6cQHh6OsWPHYuDAgcW+/TlPq1atsH37dgQEBGDJkiXF3q4414e3tzeuX7+Ozz77DCkpKVi+fDl27NihUScgIACBgYH48ssvce7cOZw8eRLBwcFYtGhRifbjWUyAiYiIiIioSFWrVkVERARu3LgBX19fVK9eHYsWLVJedbN+/XoEBgZqbNOqVSt8+OGHGDt2LD7//HNER0fj0qVLEBGEhYUhKCgIQUFByit1AMDX1xfbtm3D7t270axZM7Rs2RKLFy+Go6Njme2biYkJ9u/fj2rVquGtt96Cq6srhg0bhkePHmmMCLdv3x61atVC27Zt0bdvX7z55psar/6ZN28e5syZg8DAQLi6uqJTp04ICwtD9erV/3aMPj4+uHfvnvL8bx4vLy/cu3dPef1R3v7s2rULt27dQrNmzfD222+jffv2+Oqrr16q79atWyMsLAyzZ8/GsmXLirWNm5vbC68PV1dXfP3111i+fDnc3Nxw9OjRfKPMw4cPx+rVqxEcHIwGDRrAy8sLISEhf+uYquT5G6+JiIiIiIhKQWpqarFGQAcPHlwqiSLRi3AEmIiIiIiIykTeREalVY/o72ICTEREREREZeL5Vyf93XpEfxcTYCIiIiIiKhOOjo5FzqwMAObm5mX6jC/Rs5gAExERERFRmdDR0UGnTp2KrNOpUyflVTlEZY2TYBERERERUZl69j3AeczNzdGpUyfUrVu3HCMjbcMEmIiIiIiIylxubi4uXryIzMxMqNVqODo6cuSXXjkmwERERERERKQV9Mo7ACIiIiIiotdZbm4O/kw8jczbGVBbWqGKaz3o6OiWd1haiQkwERERERFRGUk+chC/h3yLzFs3lDJ1hYpoN+QD1GrRqhwj0068BZqIiIiIiKgMJB85iK2LPi10/ZuTPmIS/IrxqXMiIiIiIqJSlpubg99Dvi2yTnjot8jNzXlFERHABJiIiIiIiKjU/Zl4WuO254Lcu3kDfyaefkUREcAEmIiIiIiIqNRl3s4o1XpUOpgAExERERERlTK1pVWp1qPSwQSYiIiIiIiolFVxrQd1hYpF1jGzrogqrvVeUUQEMAEmIiIiIiIqdTo6umg35IMi6/gM/oDvA37F+BokIiIiIiKiMlLQe4DNrCvCZzDfA1wemAATERERERGVodzcnKezQt/OgNrSClVc63Hkt5wwASYiIiIiIiKtwGeAiYiIiIiISCswASYiIiIieo04OTlhyZIl5dL3kCFD0KNHDwCASqXC5s2byyUOKh5vb29MmDChvMN4pZgAExERERGVkmcTwGdFRERApVLh9u3brzymV2np0qUICQkBAKSnp6Nz587lG9A/lL+/Pxo1aqRRFhUVBUtLS0yYMAF8SrXsMAEmIiIiIqJSYWFhAUtLSwCAnZ0dDA0NyzegMiQiyM7OLpW2wsLC4Ovri0mTJmHJkiVQqVSl0i7lxwSYiIiIiOgVK2gEcMmSJXByclKW80aTv/jiC9jb28Pa2hqjR49GVlaWUufatWvo1q0bjI2NUb16daxfvz5fX7dv38bw4cNhY2MDc3NztGvXDgkJCYXGlpaWBpVKhZ9++glt2rSBsbExmjVrhnPnziEmJgZNmzaFWq1G586dcf36dWW73NxcfPzxx6hatSoMDQ3RqFEj7Ny5U6PtgwcPolGjRjAyMkLTpk2xefNmqFQqxMfHK3VOnTqFzp07Q61Ww9bWFgMHDsSNG/97hZC3tzfGjRuHadOmoUKFCrCzs4O/v3+Rxzs7Oxvjxo2DpaUlrK2tMX36dAwePFhjtD43NxeBgYGoXr06jI2N4ebmhl9++UVZnzeKv2PHDjRp0gSGhoY4cODAS8XzrO+//x5vvfUWPvvsM8ydO1cpL875z8jIwKBBg2BlZQUTExN07twZycnJGu1HR0fD29sbJiYmsLKygq+vLzIyMjT2u6jYX3T95F3L69atg5OTEywsLPDOO+/g3r17xT4GrxITYCIiIiKif6jw8HCkpKQgPDwcoaGhCAkJUW4xBp4mSZcvX0Z4eDh++eUXfP3117h27ZpGG71798a1a9ewY8cOxMbGwt3dHe3bt8etW7eK7NvPzw+zZ8/G8ePHoaenh3fffRfTpk3D0qVLERUVhfPnz2skbEuXLkVQUBC++OILnDhxAr6+vnjzzTeVhOzu3bvo1q0bGjRogOPHj2PevHmYPn26Rp+3b99Gu3bt0LhxYxw7dgw7d+7E1atX0adPH416oaGhMDU1xZEjR/DZZ5/h448/xp49ewrdl4ULF2L9+vUIDg5GdHQ07t69m+/55MDAQKxduxbffPMNTp8+jYkTJ2LAgAGIjIzUqDdjxgwsWLAAiYmJaNiw4UvFk2f58uUYOnQo1qxZgzFjxuRbX5zzf+zYMWzduhWHDh2CiKBLly5KkhwfH4/27dujbt26OHToEA4cOIBu3bohJyen2MeyONdPSkoKNm/ejG3btmHbtm2IjIzEggULXrj/5UKIiIiIiKhUDB48WHR1dcXU1FTjY2RkJAAkIyNDRET8/PzEzc1NY9vFixeLo6OjRluOjo6SnZ2tlPXu3Vv69u0rIiJJSUkCQI4ePaqsT0xMFACyePFiERGJiooSc3NzefTokUZfNWvWlJUrVxa4D6mpqQJAVq9erZT98MMPAkD27dunlAUGBkrt2rWV5cqVK8v8+fM12mrWrJmMGjVKRERWrFgh1tbW8vDhQ2X9qlWrBIDExcWJiMi8efOkY8eOGm1cvnxZAEhSUpKIiHh5eUnr1q3z9TN9+vQC90dExNbWVj7//HNlOTs7W6pVqybdu3cXEZFHjx6JiYmJHDx4UGO7YcOGSb9+/UREJDw8XADI5s2bNeq8TDx+fn5iYGAgAOS7774rsM6Lzv+5c+cEgERHRyvrb9y4IcbGxvLTTz+JiEi/fv3E09Oz0DheFHtxrh8/Pz8xMTGRu3fvKuunTp0qLVq0KLTf8qRXXok3EREREdHryMfHBytWrNAoO3LkCAYMGFDiturVqwddXV1l2d7eHidPngQAJCYmQk9PD02aNFHW16lTR3kGFwASEhKQmZkJa2trjXYfPnyIlJSUIvvOG90EAFtbWwBAgwYNNMryRpvv3r2Lv/76C56enhpteHp6KrfLJiUloWHDhjAyMlLWN2/eXKN+QkICwsPDoVar88WTkpICFxeXfLEBT4/L8yPfee7cuYOrV69q9KWrq4smTZogNzcXAHD+/Hk8ePAAb7zxhsa2T548QePGjTXKmjZtmq+PksSTp2rVqrC0tMTnn3+Ozp07w97ePl+d4pz/Fi1aKOutra1Ru3ZtJCYmAng6Aty7d+8i4ygq9uJeP05OTjAzMyvR/pcXJsBERERERKXI1NQUzs7OGmV//PGHxrKOjk6+mX6ffbYzj76+vsaySqVSkrbiyMzMhL29PSIiIvKtezZRLsizfedNyvR8WUliKY7MzEx069YNCxcuzLfu2QTx7x6XgvoFnk5GVaVKFY11z0/kZWpqmm/7l4nHzMwMe/fuxRtvvAEfHx+Eh4fnS4L/7n4aGxu/sE5RfRT3+int81GW+AwwEREREdErZmNjgytXrmgkwc9OBFUcderUQXZ2NmJjY5WypKQkjVctubu748qVK9DT04Ozs7PGp2LFin93NxTm5uaoXLkyoqOjNcqjo6NRt25dAEDt2rVx8uRJPH78WFkfExOjUd/d3R2nT5+Gk5NTvngLSjyLw8LCAra2thp95eTk4Pjx48py3bp1YWhoiEuXLuXr18HB4aX6LQ4rKyvs3bsX5ubm8Pb2xl9//VXsbV1dXZGdnY0jR44oZTdv3kRSUpJyzBs2bIh9+/a9dHyv6vp5lZgAExERERG9Yt7e3rh+/To+++wzpKSkYPny5dixY0eJ2qhduzY6deqEDz/8EEeOHEFsbCyGDx+uMerXoUMHeHh4oEePHti9ezfS0tJw8OBBzJo1C8eOHSvVfZo6dSoWLlyIH3/8EUlJSZgxYwbi4+Mxfvx4AMC7776L3NxcfPDBB0hMTMSuXbvwxRdfAPjfCPPo0aNx69Yt9OvXDzExMUhJScGuXbswdOhQjYmbSmrs2LEIDAzEli1bkJSUhPHjxyMjI0Pp18zMDFOmTMHEiRMRGhqKlJQUHD9+HMuWLUNoaOjfPDJFs7S0xJ49e2BlZVWiJLhWrVro3r073n//fRw4cAAJCQkYMGAAqlSpgu7duwMAZs6ciZiYGIwaNQonTpzA2bNnsWLFCo1ZtYtSWtfPV199hfbt2xe7flliAkxERERE9Iq5urri66+/xvLly+Hm5oajR49iypQpJW4nODgYlStXhpeXF9566y188MEHqFSpkrJepVJh+/btaNu2LYYOHQoXFxe88847uHjxovJcb2kZN24cJk2ahMmTJ6NBgwbYuXMntm7dilq1agF4Okr822+/IT4+Ho0aNcKsWbOUWaTzngvOG0XOyclBx44d0aBBA0yYMAGWlpbQ0Xn51GX69Ono168fBg0aBA8PD6jVavj6+mo8jzxv3jzMmTMHgYGBcHV1RadOnRAWFobq1av/jaNSPBYWFti9ezcqVqwILy8v/Pnnn8XaLjg4GE2aNEHXrl3h4eEBEcH27duVW5JdXFywe/duJCQkoHnz5vDw8MCWLVugp1e8J2FL6/q5cePGC585f1VU8vzDB0RERERERK/A+vXrMXToUNy5c6dYz6uWltzcXLi6uqJPnz6YN2/eK+uXyh8nwSIiIiIioldi7dq1qFGjBqpUqYKEhARMnz4dffr0KfPk9+LFi9i9eze8vLzw+PFjfPXVV0hNTcW7775bpv3SPw8TYCIiIiIieiWuXLmCuXPn4sqVK7C3t0fv3r0xf/78Mu9XR0cHISEhmDJlCkQE9evXx969e+Hq6lrmfdM/C2+BJiIiIiIiIq3ASbCIiIiIiIhIKzABJiIiIiIiIq3ABJiIiIiIiIi0AhNgIiIiIiIi0gpMgImIiIiIiEgr8DVIRERERET02snNFaQn38b9u49ham4I+1qW0NFRlXdYVM6YABMRERER0WslJe4aon5Mxv3bj5UyU0tDtOlbCzUbVyrHyKi88T3ARERERET02kiJu4adK08Vur7Th/WZBGsxPgNMRERERESvhdxcQdSPyUXWOfBTMnJzOQaorZgAExERERHRayE9+bbGbc8Fycx4jPTk268mIPrHYQJMRERERESvhft3i05+S1qPXj9MgImIiIiI6LVgam5YqvXo9cMEmIiIiIiIXgv2tSxhall0cqu2evpKJNJOTICJiIiIiOi1oKOjQpu+tYqs07pPLb4PWIvxNUhERERERPRaKeg9wGorQ7Tuw/cAazsmwERERERE9NrJzZWns0LffQxT86e3PXPkl5gAExERERERkVbgM8BERERERESkFZgAExERERERkVZgAkxERERERERagQkwERERERERaQUmwERERERERKQVmAATERERERGRVmACTERERERERFqBCTARERERERFpBSbAREREREREpBWYABMREREREZFWYAJMREREREREWoEJMBEREREREWkFJsBERERERESkFZgAExERERERkVZgAkxERERERERagQkwERERERERaYX/AxWoNX7CVKRcAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# from sklearn.manifold import TSNE\n",
        "# tsne = TSNE(n_components=2, random_state=42, perplexity=3)\n",
        "# reduced_embeddings = tsne.fit_transform(embeddings)\n",
        "\n",
        "# bestes Ergebnis bei etwas mehr Beispielen\n",
        "from umap import UMAP\n",
        "umap = UMAP(n_components=2, random_state=21, n_neighbors=3)\n",
        "reduced_embeddings = umap.fit_transform(embeddings)\n",
        "\n",
        "# macht als einziges Sinn bei ganz wenigen Beispielen\n",
        "# from sklearn.decomposition import PCA\n",
        "# pca = PCA(n_components=2, random_state=42)\n",
        "# reduced_embeddings = pca.fit_transform(embeddings)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i, label in enumerate(texts):\n",
        "    x, y = reduced_embeddings[i, :]\n",
        "    plt.scatter(x, y)\n",
        "    plt.annotate(label, (x, y), textcoords=\"offset points\", xytext=(0,10), ha='center')\n",
        "\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "plt.gca().spines['right'].set_visible(False)\n",
        "plt.gca().spines['top'].set_visible(False)\n",
        "plt.gca().spines['left'].set_visible(False)\n",
        "plt.gca().spines['bottom'].set_visible(False)\n",
        "\n",
        "# plt.xlabel('1. Hauptkomponente')\n",
        "# plt.ylabel('2. Hauptkomponente')\n",
        "\n",
        "# add space between title and plot\n",
        "plt.subplots_adjust(top=1.5)\n",
        "\n",
        "# plt.title('2D Darstellung der Text-Embeddings', fontsize=16)\n",
        "\n",
        "# compress distance between points\n",
        "plt.gca().set_aspect('equal', 'box')\n",
        "\n",
        "\n",
        "plt.show();"
      ],
      "id": "fATk5G8yxFhK"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dPh0zjzqSNyN"
      },
      "id": "dPh0zjzqSNyN",
      "execution_count": 65,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}